From b8458b426b114f315eb25e984b8e16c11af87481 Mon Sep 17 00:00:00 2001
From: sangcom <lkdmc0310@gmail.com>
Date: Tue, 31 Dec 2024 10:33:44 +0900
Subject: [PATCH] update
1. add more config options
2. add readme
3. footer format is updated.
---
README_sync.md | 26 +
config_sync.yml | 11 +-
content/Week_2_4/PA/PA_2_4_B_solution.ipynb | 39 +-
content/Week_2_8/WS_2_8_solution.ipynb | 30 +-
conversion_errors.log | 498 +++++++------
footer.html | 22 +
notebook_hashes.json | 178 +++++
sync_notebooks.py | 226 ++++--
synced_files/GA_1_1/Task_2_solution.html | 308 ++------
synced_files/GA_1_1/Task_2_solution.md | 84 +--
synced_files/GA_1_2/Analysis.html | 52 +-
synced_files/GA_1_2/Analysis.md | 15 +-
synced_files/GA_1_2/Analysis_solution.html | 243 +-----
synced_files/GA_1_2/Analysis_solution.md | 15 +-
synced_files/GA_1_3/Analysis.html | 94 +--
synced_files/GA_1_3/Analysis.md | 31 +-
synced_files/GA_1_3/Analysis_Solution.html | 689 +++---------------
synced_files/GA_1_3/Analysis_Solution.md | 35 +-
synced_files/GA_1_3/Warmup.html | 60 +-
synced_files/GA_1_3/Warmup.md | 10 -
synced_files/GA_1_4/Analysis.html | 94 +--
synced_files/GA_1_4/Analysis.md | 22 +-
synced_files/GA_1_4/Analysis_solution.html | 580 ++-------------
synced_files/GA_1_4/Analysis_solution.md | 22 +-
synced_files/GA_1_5/Analysis.html | 112 +--
synced_files/GA_1_5/Analysis.md | 10 -
synced_files/GA_1_5/Analysis_solution.html | 252 ++-----
synced_files/GA_1_5/Analysis_solution.md | 10 -
.../GA_1_5/central_diff_illustration.html | 88 +--
.../GA_1_5/central_diff_illustration.md | 10 -
synced_files/GA_1_6/Analysis.html | 62 +-
synced_files/GA_1_6/Analysis.md | 13 +-
synced_files/GA_1_6/Analysis_solution.html | 140 +---
synced_files/GA_1_6/Analysis_solution.md | 13 +-
.../Analysis_discharge_solution.html | 2 +-
.../Analysis_discharge_solution.ipynb | 2 +-
.../Discharge/Analysis_discharge_solution.md | 22 +-
.../Analysis_emissions_solution.html | 18 +-
.../Analysis_emissions_solution.ipynb | 18 +-
.../Emissions/Analysis_emissions_solution.md | 22 +-
.../Force/Analysis_force_solution.html | 18 +-
.../Force/Analysis_force_solution.ipynb | 18 +-
.../Solution/Force/Analysis_force_solution.md | 22 +-
.../Student/Discharge/Analysis_discharge.html | 6 +-
.../Discharge/Analysis_discharge.ipynb | 6 +-
.../Student/Discharge/Analysis_discharge.md | 22 +-
.../Student/Emissions/Analysis_emissions.html | 6 +-
.../Emissions/Analysis_emissions.ipynb | 6 +-
.../Student/Emissions/Analysis_emissions.md | 22 +-
.../GA_1_7/Student/Force/Analysis_force.html | 6 +-
.../GA_1_7/Student/Force/Analysis_force.ipynb | 6 +-
.../GA_1_7/Student/Force/Analysis_force.md | 22 +-
.../Unused/Temp/Distribution_Fitting_T.html | 14 +-
.../Unused/Temp/Distribution_Fitting_T.ipynb | 14 +-
.../Unused/Temp/Distribution_Fitting_T.md | 22 +-
synced_files/GA_1_8/Analysis.md | 19 +-
.../GA_1_8/solution/GA_1_8_solution_HT.md | 26 +-
.../GA_1_8/solution/GA_1_8_solution_hu.md | 26 +-
.../solution/GA_1_8_solution_traffic.md | 26 +-
synced_files/GA_2_1/Analysis.md | 13 +-
synced_files/GA_2_1/Analysis_solution.md | 13 +-
synced_files/GA_2_1/mesh/mesh.md | 10 -
synced_files/GA_2_1/mesh_dev.md | 10 -
synced_files/GA_2_1/mesh_tips.md | 10 -
synced_files/GA_2_2/the_big_M.md | 10 -
synced_files/GA_2_3/Analysis.md | 16 +-
synced_files/GA_2_3/Analysis_solution.md | 16 +-
synced_files/GA_2_4/GA_2_4_Beary_Icy.md | 10 -
synced_files/GA_2_4/GA_2_4_solution.md | 10 -
synced_files/GA_2_5/Analysis_GA.md | 65 +-
synced_files/GA_2_5/Analysis_GA_solution.md | 65 +-
synced_files/GA_2_5/Analysis_LP.md | 36 +-
synced_files/GA_2_5/Analysis_LP_solution.md | 36 +-
synced_files/GA_2_6/Analysis.md | 10 -
synced_files/GA_2_6/Analysis_solution.md | 10 -
synced_files/GA_2_7/rain_solution.md | 31 +-
synced_files/Week_1_2/In_Class_Activity.md | 67 +-
.../Week_1_2/In_Class_Activity_Solution.md | 82 +--
.../Week_1_2/PA_1_2_Random_Adventure.md | 10 -
synced_files/Week_1_2/PA_1_2_solution.md | 10 -
synced_files/Week_1_2/WS_1_2_Pipe_Dreams.md | 22 +-
synced_files/Week_1_2/WS_1_2_solution.md | 28 +-
.../Week_1_4/WS_1_4_Nonlinear_Rain.md | 37 +-
synced_files/Week_1_4/WS_1_4_solution.md | 38 +-
.../Week_1_6/PA/PA_1_6_Boxes_and_Bugs.md | 10 -
synced_files/Week_1_6/PA/PA_1_6_solution.md | 10 -
synced_files/Week_1_6/WS_1_6_solution.md | 10 -
synced_files/Week_1_6/WS_1_6_time_to_c_ode.md | 12 -
.../PA/PA_1_7_Classy_Distributions.md | 10 -
synced_files/Week_1_7/PA/PA_1_7_solution.md | 10 -
.../Week_1_7/WS_1_7_lets_be_concrete.md | 22 +-
synced_files/Week_1_7/WS_1_7_solution.md | 36 +-
synced_files/Week_1_7/scipy_stats.md | 10 -
.../PA/PA_1_8_Equations_Done_Symply.md | 10 -
synced_files/Week_1_8/PA/PA_1_8_solution.md | 10 -
.../Week_1_8/WS/WS_1_8_Thingamajig.md | 24 +-
synced_files/Week_1_8/WS/WS_1_8_solution.md | 29 +-
synced_files/Week_1_8/WS/linearity_or_not.md | 10 -
synced_files/Week_1_8/data.md | 10 -
synced_files/Week_1_8/intersection.md | 10 -
synced_files/Week_1_8/test_bivariate.md | 10 -
.../Week_2_1/PA/PA_2_1_classy_city.md | 10 -
synced_files/Week_2_1/PA/PA_2_1_solution.md | 10 -
synced_files/Week_2_1/PA/PA_dev.md | 10 -
synced_files/Week_2_1/PA/U.md | 10 -
synced_files/Week_2_1/WS_2_1_solution.md | 19 +-
synced_files/Week_2_1/WS_2_1_wiggle.md | 13 +-
.../Week_2_1/WS_2_1_wiggle_test_ci.md | 13 +-
.../Week_2_2/PA/PA_2_2_love_is_sparse.md | 10 -
synced_files/Week_2_2/PA/PA_2_2_solution.md | 10 -
.../Week_2_2/PA_2_2_love_is_sparse.md | 10 -
synced_files/Week_2_2/PA_2_2_solution.md | 10 -
synced_files/Week_2_2/WS_2_2_more_support.md | 10 -
synced_files/Week_2_2/WS_2_2_solution.md | 10 -
synced_files/Week_2_2/old/PA_2_1_solution.md | 10 -
.../Week_2_2/old/PA_2_1_solution_sympy.html | 18 +-
.../Week_2_2/old/PA_2_1_solution_sympy.ipynb | 18 +-
.../Week_2_2/old/PA_2_1_solution_sympy.md | 10 -
.../Week_2_2/old/WS_2_2_more_support.md | 10 -
.../Week_2_2/old/old_PA10_Love_is_Sparse.md | 10 -
.../Week_2_2/old/old_PA10_solution_sympy.html | 18 +-
.../old/old_PA10_solution_sympy.ipynb | 18 +-
.../Week_2_2/old/old_PA10_solution_sympy.md | 10 -
.../Week_2_3/PA/PA_2_3_iter_remoto.md | 10 -
synced_files/Week_2_3/PA/PA_2_3_solution.md | 10 -
.../Week_2_3/WS_2_3_DFT_you_try_meow.md | 13 +-
synced_files/Week_2_3/WS_2_3_solution.md | 13 +-
.../Week_2_4/PA/PA_2_4_A_gurobilicious.md | 10 -
.../Week_2_4/PA/PA_2_4_B_axis_of_awesome.md | 10 -
.../Week_2_4/PA/PA_2_4_B_solution.html | 31 +-
.../Week_2_4/PA/PA_2_4_B_solution.ipynb | 39 +-
synced_files/Week_2_4/PA/PA_2_4_B_solution.md | 49 +-
synced_files/Week_2_4/PA/PA_2_4_B_solution.py | 39 +-
.../Week_2_4/WS_2_4_Feel_the_Pressure.md | 10 -
synced_files/Week_2_4/WS_2_4_solution.md | 10 -
.../Week_2_5/PA/PA_2_5_data_framework.md | 10 -
synced_files/Week_2_5/PA/PA_2_5_solution.md | 10 -
.../Week_2_5/WS_2_5_Profit_vs_Planet.md | 10 -
synced_files/Week_2_5/WS_2_5_solution.md | 10 -
.../Week_2_6/PA/PA_2_6_3_way_split.md | 10 -
synced_files/Week_2_6/PA/PA_2_6_solution.md | 10 -
synced_files/Week_2_6/WS_2_6_be_a_NN.md | 10 -
synced_files/Week_2_6/WS_2_6_solution.md | 10 -
.../Week_2_7/PA/PA_2_7_Times_Tables.md | 10 -
synced_files/Week_2_7/PA/PA_2_7_solution.md | 10 -
synced_files/Week_2_7/WS_2_7_solution.html | 14 +-
synced_files/Week_2_7/WS_2_7_solution.ipynb | 14 +-
synced_files/Week_2_7/WS_2_7_solution.md | 20 +-
synced_files/Week_2_7/WS_2_7_student.html | 6 +-
synced_files/Week_2_7/WS_2_7_student.ipynb | 6 +-
synced_files/Week_2_7/WS_2_7_student.md | 16 +-
synced_files/Week_2_8/WS_2_8_dirty_water.html | 47 +-
.../Week_2_8/WS_2_8_dirty_water.ipynb | 12 +-
synced_files/Week_2_8/WS_2_8_dirty_water.md | 22 +-
synced_files/Week_2_8/WS_2_8_dirty_water.py | 12 +-
synced_files/Week_2_8/WS_2_8_solution.html | 162 ++--
synced_files/Week_2_8/WS_2_8_solution.ipynb | 34 +-
synced_files/Week_2_8/WS_2_8_solution.md | 44 +-
synced_files/Week_2_8/WS_2_8_solution.py | 34 +-
synced_files/tutorials/Week_1_3/Analysis.md | 10 -
synced_files/tutorials/Week_1_5/Tutorial.md | 10 -
synced_files/tutorials/Week_1_6/Tutorial.md | 10 -
.../week_1_1/PA_1_1_Catch_Them_All.md | 10 -
...Data_Cleaning_and_Boosting_Productivity.md | 10 -
synced_files/week_1_3/PA_1_3_solution.md | 10 -
synced_files/week_1_3/WS_1_3_Moving_Ice.md | 10 -
synced_files/week_1_3/WS_1_3_solution.md | 10 -
synced_files/week_1_5/PA/PA_1_5_solution.md | 10 -
.../week_1_5/PA/PA_1_5_useful_tricks.md | 10 -
synced_files/week_1_5/PA_ice/PA_plots.md | 10 -
.../week_1_5/WS_1_5_dont_integr_hate.md | 10 -
synced_files/week_1_5/WS_1_5_solution.md | 10 -
172 files changed, 1813 insertions(+), 4764 deletions(-)
create mode 100644 README_sync.md
create mode 100644 footer.html
create mode 100644 notebook_hashes.json
diff --git a/README_sync.md b/README_sync.md
new file mode 100644
index 00000000..932b6570
--- /dev/null
+++ b/README_sync.md
@@ -0,0 +1,26 @@
+# Notebook Sync Tool Documentation
+
+## Overview
+This tool synchronizes Jupyter notebooks to various formats including Markdown, Python, and HTML.
+
+## Technical Decisions
+
+### Markdown2 Package
+Selected for:
+- Robust HTML conversion capabilities
+- Built-in support for code syntax highlighting
+- Extended Markdown feature support including tables and footnotes
+- quick solution for rendering Markdown
+
+
+### Architecture Decisions
+- Uses Jupytext for reliable notebook conversion
+- Implements file hash comparison for change detection
+- Supports selective file updates with force update option
+- Preserves essential metadata while cleaning notebooks
+
+## Configuration
+See `config_sync.yml` for available options including:
+- Sync options (replace_all, force_update)
+- Format selection
+- Specific notebook processing
\ No newline at end of file
diff --git a/config_sync.yml b/config_sync.yml
index 9b3ecdf8..6d0a5f20 100644
--- a/config_sync.yml
+++ b/config_sync.yml
@@ -1,3 +1,6 @@
+sync_options:
+ replace_all: false # When false, only update specified files
+
content_dir: "content"
output_dir: "synced_files"
formats:
@@ -9,7 +12,11 @@ html_options:
template: "basic"
include_code: true
include_outputs: true
+
# Uncomment and modify if you want to process specific notebooks only
# specific_notebooks:
-# - "Week_2_8/WS_2_8_dirty_water.ipynb"
-# - "Week_2_8/WS_2_8_solution.ipynb"
\ No newline at end of file
+# options:
+# force_update: false # Force update even without changes
+# files:
+# - "Week_2_8/WS_2_8_dirty_water.ipynb"
+# - "Week_2_8/WS_2_8_solution.ipynb"
\ No newline at end of file
diff --git a/content/Week_2_4/PA/PA_2_4_B_solution.ipynb b/content/Week_2_4/PA/PA_2_4_B_solution.ipynb
index 9b597997..dca087c2 100644
--- a/content/Week_2_4/PA/PA_2_4_B_solution.ipynb
+++ b/content/Week_2_4/PA/PA_2_4_B_solution.ipynb
@@ -359,24 +359,39 @@
"**End of notebook.**\n",
"<h2 style=\"height: 60px\">\n",
"</h2>\n",
- "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n",
+ "<h3 style=\"position: relative; display: flex; flex-direction: row-reverse; margin: 20px 50px; border: 0\">\n",
" <style>\n",
" .markdown {width:100%; position: relative}\n",
" article { position: relative }\n",
+ " .footer-links {\n",
+ " display: flex;\n",
+ " flex-direction: row-reverse;\n",
+ " align-items: center;\n",
+ " gap: 20px;\n",
+ " margin-bottom: 20px;\n",
+ " }\n",
+ " .footer-links img {\n",
+ " height: auto;\n",
+ " max-width: 100px;\n",
+ " }\n",
" </style>\n",
- " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n",
- " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n",
- " </a>\n",
- " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n",
- " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n",
- " </a>\n",
- " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
- " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n",
- " </a>\n",
- " \n",
+ " <div class=\"footer-links\">\n",
+ " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">\n",
+ " <img alt=\"Creative Commons License\" style=\"width:88px; padding-top:10px\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" />\n",
+ " </a>\n",
+ " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n",
+ " <img alt=\"TU Delft\" style=\"width:100px;\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" />\n",
+ " </a>\n",
+ " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
+ " <img alt=\"MUDE\" style=\"width:100px;\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" />\n",
+ " </a>\n",
+ " </div>\n",
"</h3>\n",
"<span style=\"font-size: 75%\">\n",
- "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>."
+ "© Copyright 2024 <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">MUDE</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">CC BY 4.0 License</a>.\n",
+ "</span>\n",
+ "\n",
+ "<userStyle>Normal</userStyle>"
]
}
],
diff --git a/content/Week_2_8/WS_2_8_solution.ipynb b/content/Week_2_8/WS_2_8_solution.ipynb
index 35d0c93a..66325eca 100644
--- a/content/Week_2_8/WS_2_8_solution.ipynb
+++ b/content/Week_2_8/WS_2_8_solution.ipynb
@@ -282,26 +282,24 @@
"metadata": {},
"source": [
"**End of notebook.**\n",
- "<h2 style=\"height: 60px\">\n",
- "</h2>\n",
- "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n",
- " <style>\n",
- " .markdown {width:100%; position: relative}\n",
- " article { position: relative }\n",
- " </style>\n",
- " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">\n",
- " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" />\n",
+ "\n",
+ "<div style=\"margin-top: 50px; padding-top: 20px; border-top: 1px solid #ccc;\">\n",
+ " <div style=\"display: flex; justify-content: flex-end; gap: 20px; align-items: center;\">\n",
+ " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
+ " <img alt=\"MUDE\" style=\"width:100px; height:auto;\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" />\n",
" </a>\n",
" <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n",
- " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" />\n",
+ " <img alt=\"TU Delft\" style=\"width:100px; height:auto;\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" />\n",
" </a>\n",
- " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
- " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" />\n",
+ " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">\n",
+ " <img alt=\"Creative Commons License\" style=\"width:88px; height:auto;\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" />\n",
" </a>\n",
- " \n",
- "</h3>\n",
- "<span style=\"font-size: 75%\">\n",
- "© Copyright 2024 <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">MUDE</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">CC BY 4.0 License</a>."
+ " </div>\n",
+ " <div style=\"font-size: 75%; margin-top: 10px; text-align: right;\">\n",
+ " © Copyright 2024 <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">MUDE</a> TU Delft. \n",
+ " This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">CC BY 4.0 License</a>.\n",
+ " </div>\n",
+ "</div>"
]
}
],
diff --git a/conversion_errors.log b/conversion_errors.log
index e7a224f3..80f77fc7 100644
--- a/conversion_errors.log
+++ b/conversion_errors.log
@@ -1,244 +1,256 @@
-INFO: === Starting synchronization at 2024-12-30 14:45:41 ===
-INFO: Searching in: C:\Users\rlanzafame\code\MUDE\2024-files\content
+INFO: === Starting synchronization at 2024-12-31 00:44:51 ===
+INFO: Searching in: C:\Users\ncore\Desktop\2024-files\content
INFO: Found 120 notebooks
-INFO: [1/120] Processing GA_1_1\Task_2_solution.ipynb
-INFO: No changes detected
-INFO: [2/120] Processing GA_1_2\Analysis.ipynb
-INFO: No changes detected
-INFO: [3/120] Processing GA_1_2\Analysis_solution.ipynb
-INFO: No changes detected
-INFO: [4/120] Processing GA_1_3\Analysis.ipynb
-INFO: No changes detected
-INFO: [5/120] Processing GA_1_3\Analysis_Solution.ipynb
-INFO: No changes detected
-INFO: [6/120] Processing GA_1_3\Warmup.ipynb
-INFO: No changes detected
-INFO: [7/120] Processing GA_1_4\Analysis.ipynb
-INFO: No changes detected
-INFO: [8/120] Processing GA_1_4\Analysis_solution.ipynb
-INFO: No changes detected
-INFO: [9/120] Processing GA_1_5\Analysis.ipynb
-INFO: No changes detected
-INFO: [10/120] Processing GA_1_5\Analysis_solution.ipynb
-INFO: No changes detected
-INFO: [11/120] Processing GA_1_5\central_diff_illustration.ipynb
-INFO: No changes detected
-INFO: [12/120] Processing GA_1_6\Analysis.ipynb
-INFO: No changes detected
-INFO: [13/120] Processing GA_1_6\Analysis_solution.ipynb
-INFO: No changes detected
-INFO: [14/120] Processing GA_1_7\Solution\Discharge\Analysis_discharge_solution.ipynb
-INFO: No changes detected
-INFO: [15/120] Processing GA_1_7\Solution\Emissions\Analysis_emissions_solution.ipynb
-INFO: No changes detected
-INFO: [16/120] Processing GA_1_7\Solution\Force\Analysis_force_solution.ipynb
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-INFO: [17/120] Processing GA_1_7\Student\Discharge\Analysis_discharge.ipynb
-INFO: No changes detected
-INFO: [18/120] Processing GA_1_7\Student\Emissions\Analysis_emissions.ipynb
-INFO: No changes detected
-INFO: [19/120] Processing GA_1_7\Student\Force\Analysis_force.ipynb
-INFO: No changes detected
-INFO: [20/120] Processing GA_1_7\Unused\Temp\Distribution_Fitting_T.ipynb
-INFO: No changes detected
-INFO: [21/120] Processing GA_1_8\Analysis.ipynb
-INFO: No changes detected
-INFO: [22/120] Processing GA_1_8\solution\GA_1_8_solution_HT.ipynb
-INFO: No changes detected
-INFO: [23/120] Processing GA_1_8\solution\GA_1_8_solution_hu.ipynb
-INFO: No changes detected
-INFO: [24/120] Processing GA_1_8\solution\GA_1_8_solution_traffic.ipynb
-INFO: No changes detected
-INFO: [25/120] Processing GA_2_1\Analysis.ipynb
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-INFO: [30/120] Processing GA_2_2\the_big_M.ipynb
-INFO: No changes detected
-INFO: [31/120] Processing GA_2_3\Analysis.ipynb
-INFO: No changes detected
-INFO: [32/120] Processing GA_2_3\Analysis_solution.ipynb
-INFO: No changes detected
-INFO: [33/120] Processing GA_2_4\GA_2_4_Beary_Icy.ipynb
-INFO: No changes detected
-INFO: [34/120] Processing GA_2_4\GA_2_4_solution.ipynb
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-INFO: [35/120] Processing GA_2_5\Analysis_GA.ipynb
-INFO: No changes detected
-INFO: [36/120] Processing GA_2_5\Analysis_GA_solution.ipynb
-INFO: No changes detected
-INFO: [37/120] Processing GA_2_5\Analysis_LP.ipynb
-INFO: No changes detected
-INFO: [38/120] Processing GA_2_5\Analysis_LP_solution.ipynb
-INFO: No changes detected
-INFO: [39/120] Processing GA_2_6\Analysis.ipynb
-INFO: No changes detected
-INFO: [40/120] Processing GA_2_6\Analysis_solution.ipynb
-INFO: No changes detected
-INFO: [41/120] Processing GA_2_7\rain_solution.ipynb
-INFO: No changes detected
-INFO: [42/120] Processing tutorials\Week_1_3\Analysis.ipynb
-INFO: No changes detected
-INFO: [43/120] Processing tutorials\Week_1_5\Tutorial.ipynb
-INFO: No changes detected
-INFO: [44/120] Processing tutorials\Week_1_6\Tutorial.ipynb
-INFO: No changes detected
-INFO: [45/120] Processing Week_1_1\PA_1_1_Catch_Them_All.ipynb
-INFO: No changes detected
-INFO: [46/120] Processing Week_1_2\In_Class_Activity.ipynb
-INFO: No changes detected
-INFO: [47/120] Processing Week_1_2\In_Class_Activity_Solution.ipynb
-INFO: No changes detected
-INFO: [48/120] Processing Week_1_2\PA_1_2_Random_Adventure.ipynb
-INFO: No changes detected
-INFO: [49/120] Processing Week_1_2\PA_1_2_solution.ipynb
-INFO: No changes detected
-INFO: [50/120] Processing Week_1_2\WS_1_2_Pipe_Dreams.ipynb
-INFO: No changes detected
-INFO: [51/120] Processing Week_1_2\WS_1_2_solution.ipynb
-INFO: No changes detected
-INFO: [52/120] Processing week_1_3\PA_1_3_Data_Cleaning_and_Boosting_Productivity.ipynb
-INFO: No changes detected
-INFO: [53/120] Processing week_1_3\PA_1_3_solution.ipynb
-INFO: No changes detected
-INFO: [54/120] Processing week_1_3\WS_1_3_Moving_Ice.ipynb
-INFO: No changes detected
-INFO: [55/120] Processing week_1_3\WS_1_3_solution.ipynb
-INFO: No changes detected
-INFO: [56/120] Processing Week_1_4\WS_1_4_Nonlinear_Rain.ipynb
-INFO: No changes detected
-INFO: [57/120] Processing Week_1_4\WS_1_4_solution.ipynb
-INFO: No changes detected
-INFO: [58/120] Processing week_1_5\WS_1_5_dont_integr_hate.ipynb
-INFO: No changes detected
-INFO: [59/120] Processing week_1_5\WS_1_5_solution.ipynb
-INFO: No changes detected
-INFO: [60/120] Processing week_1_5\PA\PA_1_5_solution.ipynb
-INFO: No changes detected
-INFO: [61/120] Processing week_1_5\PA\PA_1_5_useful_tricks.ipynb
-INFO: No changes detected
-INFO: [62/120] Processing week_1_5\PA_ice\PA_plots.ipynb
-INFO: No changes detected
-INFO: [63/120] Processing Week_1_6\WS_1_6_solution.ipynb
-INFO: No changes detected
-INFO: [64/120] Processing Week_1_6\WS_1_6_time_to_c_ode.ipynb
-INFO: No changes detected
-INFO: [65/120] Processing Week_1_6\PA\PA_1_6_Boxes_and_Bugs.ipynb
-INFO: No changes detected
-INFO: [66/120] Processing Week_1_6\PA\PA_1_6_solution.ipynb
-INFO: No changes detected
-INFO: [67/120] Processing Week_1_7\scipy_stats.ipynb
-INFO: No changes detected
-INFO: [68/120] Processing Week_1_7\WS_1_7_lets_be_concrete.ipynb
-INFO: No changes detected
-INFO: [69/120] Processing Week_1_7\WS_1_7_solution.ipynb
-INFO: No changes detected
-INFO: [70/120] Processing Week_1_7\PA\PA_1_7_Classy_Distributions.ipynb
-INFO: No changes detected
-INFO: [71/120] Processing Week_1_7\PA\PA_1_7_solution.ipynb
-INFO: No changes detected
-INFO: [72/120] Processing Week_1_8\data.ipynb
-INFO: No changes detected
-INFO: [73/120] Processing Week_1_8\intersection.ipynb
-INFO: No changes detected
-INFO: [74/120] Processing Week_1_8\test_bivariate.ipynb
-INFO: No changes detected
-INFO: [75/120] Processing Week_1_8\PA\PA_1_8_Equations_Done_Symply.ipynb
-INFO: No changes detected
-INFO: [76/120] Processing Week_1_8\PA\PA_1_8_solution.ipynb
-INFO: No changes detected
-INFO: [77/120] Processing Week_1_8\WS\linearity_or_not.ipynb
-INFO: No changes detected
-INFO: [78/120] Processing Week_1_8\WS\WS_1_8_solution.ipynb
-INFO: No changes detected
-INFO: [79/120] Processing Week_1_8\WS\WS_1_8_Thingamajig.ipynb
-INFO: No changes detected
-INFO: [80/120] Processing Week_2_1\WS_2_1_solution.ipynb
-INFO: No changes detected
-INFO: [81/120] Processing Week_2_1\WS_2_1_wiggle.ipynb
-INFO: No changes detected
-INFO: [82/120] Processing Week_2_1\WS_2_1_wiggle_test_ci.ipynb
-INFO: No changes detected
-INFO: [83/120] Processing Week_2_1\PA\PA_2_1_classy_city.ipynb
-INFO: No changes detected
-INFO: [84/120] Processing Week_2_1\PA\PA_2_1_solution.ipynb
-INFO: No changes detected
-INFO: [85/120] Processing Week_2_1\PA\PA_dev.ipynb
-INFO: No changes detected
-INFO: [86/120] Processing Week_2_1\PA\U.ipynb
-INFO: No changes detected
-INFO: [87/120] Processing Week_2_2\PA_2_2_love_is_sparse.ipynb
-INFO: No changes detected
-INFO: [88/120] Processing Week_2_2\PA_2_2_solution.ipynb
-INFO: No changes detected
-INFO: [89/120] Processing Week_2_2\WS_2_2_more_support.ipynb
-INFO: No changes detected
-INFO: [90/120] Processing Week_2_2\WS_2_2_solution.ipynb
-INFO: No changes detected
-INFO: [91/120] Processing Week_2_2\old\old_PA10_Love_is_Sparse.ipynb
-INFO: No changes detected
-INFO: [92/120] Processing Week_2_2\old\old_PA10_solution_sympy.ipynb
-INFO: No changes detected
-INFO: [93/120] Processing Week_2_2\old\PA_2_1_solution.ipynb
-INFO: No changes detected
-INFO: [94/120] Processing Week_2_2\old\PA_2_1_solution_sympy.ipynb
-INFO: No changes detected
-INFO: [95/120] Processing Week_2_2\old\WS_2_2_more_support.ipynb
-INFO: No changes detected
-INFO: [96/120] Processing Week_2_2\PA\PA_2_2_love_is_sparse.ipynb
-INFO: No changes detected
-INFO: [97/120] Processing Week_2_2\PA\PA_2_2_solution.ipynb
-INFO: No changes detected
-INFO: [98/120] Processing Week_2_3\WS_2_3_DFT_you_try_meow.ipynb
-INFO: No changes detected
-INFO: [99/120] Processing Week_2_3\WS_2_3_solution.ipynb
-INFO: No changes detected
-INFO: [100/120] Processing Week_2_3\PA\PA_2_3_iter_remoto.ipynb
-INFO: No changes detected
-INFO: [101/120] Processing Week_2_3\PA\PA_2_3_solution.ipynb
-INFO: No changes detected
-INFO: [102/120] Processing Week_2_4\WS_2_4_Feel_the_Pressure.ipynb
-INFO: No changes detected
-INFO: [103/120] Processing Week_2_4\WS_2_4_solution.ipynb
-INFO: No changes detected
-INFO: [104/120] Processing Week_2_4\PA\PA_2_4_A_gurobilicious.ipynb
-INFO: No changes detected
-INFO: [105/120] Processing Week_2_4\PA\PA_2_4_B_axis_of_awesome.ipynb
-INFO: No changes detected
-INFO: [106/120] Processing Week_2_4\PA\PA_2_4_B_solution.ipynb
-INFO: No changes detected
-INFO: [107/120] Processing Week_2_5\WS_2_5_Profit_vs_Planet.ipynb
-INFO: No changes detected
-INFO: [108/120] Processing Week_2_5\WS_2_5_solution.ipynb
-INFO: No changes detected
-INFO: [109/120] Processing Week_2_5\PA\PA_2_5_data_framework.ipynb
-INFO: No changes detected
-INFO: [110/120] Processing Week_2_5\PA\PA_2_5_solution.ipynb
-INFO: No changes detected
-INFO: [111/120] Processing Week_2_6\WS_2_6_be_a_NN.ipynb
-INFO: No changes detected
-INFO: [112/120] Processing Week_2_6\WS_2_6_solution.ipynb
-INFO: No changes detected
-INFO: [113/120] Processing Week_2_6\PA\PA_2_6_3_way_split.ipynb
-INFO: No changes detected
-INFO: [114/120] Processing Week_2_6\PA\PA_2_6_solution.ipynb
-INFO: No changes detected
-INFO: [115/120] Processing Week_2_7\WS_2_7_solution.ipynb
-INFO: No changes detected
-INFO: [116/120] Processing Week_2_7\WS_2_7_student.ipynb
-INFO: No changes detected
-INFO: [117/120] Processing Week_2_7\PA\PA_2_7_solution.ipynb
-INFO: No changes detected
-INFO: [118/120] Processing Week_2_7\PA\PA_2_7_Times_Tables.ipynb
-INFO: No changes detected
-INFO: [119/120] Processing Week_2_8\WS_2_8_dirty_water.ipynb
-INFO: No changes detected
-INFO: [120/120] Processing Week_2_8\WS_2_8_solution.ipynb
-INFO: No changes detected
-INFO: === Completed at 2024-12-30 14:45:42 (Duration: 0:00:01.171471) ===
+INFO: [1/120] Checking GA_1_1\Task_2_solution.ipynb
+INFO: No changes detected for GA_1_1\Task_2_solution.ipynb
+INFO: [2/120] Checking GA_1_2\Analysis.ipynb
+INFO: No changes detected for GA_1_2\Analysis.ipynb
+INFO: [3/120] Checking GA_1_2\Analysis_solution.ipynb
+INFO: No changes detected for GA_1_2\Analysis_solution.ipynb
+INFO: [4/120] Checking GA_1_3\Analysis.ipynb
+INFO: No changes detected for GA_1_3\Analysis.ipynb
+INFO: [5/120] Checking GA_1_3\Analysis_Solution.ipynb
+INFO: No changes detected for GA_1_3\Analysis_Solution.ipynb
+INFO: [6/120] Checking GA_1_3\Warmup.ipynb
+INFO: No changes detected for GA_1_3\Warmup.ipynb
+INFO: [7/120] Checking GA_1_4\Analysis.ipynb
+INFO: No changes detected for GA_1_4\Analysis.ipynb
+INFO: [8/120] Checking GA_1_4\Analysis_solution.ipynb
+INFO: No changes detected for GA_1_4\Analysis_solution.ipynb
+INFO: [9/120] Checking GA_1_5\Analysis.ipynb
+INFO: No changes detected for GA_1_5\Analysis.ipynb
+INFO: [10/120] Checking GA_1_5\Analysis_solution.ipynb
+INFO: No changes detected for GA_1_5\Analysis_solution.ipynb
+INFO: [11/120] Checking GA_1_5\central_diff_illustration.ipynb
+INFO: No changes detected for GA_1_5\central_diff_illustration.ipynb
+INFO: [12/120] Checking GA_1_6\Analysis.ipynb
+INFO: No changes detected for GA_1_6\Analysis.ipynb
+INFO: [13/120] Checking GA_1_6\Analysis_solution.ipynb
+INFO: No changes detected for GA_1_6\Analysis_solution.ipynb
+INFO: [14/120] Checking GA_1_7\Solution\Discharge\Analysis_discharge_solution.ipynb
+INFO: No changes detected for GA_1_7\Solution\Discharge\Analysis_discharge_solution.ipynb
+INFO: [15/120] Checking GA_1_7\Solution\Emissions\Analysis_emissions_solution.ipynb
+INFO: No changes detected for GA_1_7\Solution\Emissions\Analysis_emissions_solution.ipynb
+INFO: [16/120] Checking GA_1_7\Solution\Force\Analysis_force_solution.ipynb
+INFO: No changes detected for GA_1_7\Solution\Force\Analysis_force_solution.ipynb
+INFO: [17/120] Checking GA_1_7\Student\Discharge\Analysis_discharge.ipynb
+INFO: No changes detected for GA_1_7\Student\Discharge\Analysis_discharge.ipynb
+INFO: [18/120] Checking GA_1_7\Student\Emissions\Analysis_emissions.ipynb
+INFO: No changes detected for GA_1_7\Student\Emissions\Analysis_emissions.ipynb
+INFO: [19/120] Checking GA_1_7\Student\Force\Analysis_force.ipynb
+INFO: No changes detected for GA_1_7\Student\Force\Analysis_force.ipynb
+INFO: [20/120] Checking GA_1_7\Unused\Temp\Distribution_Fitting_T.ipynb
+INFO: No changes detected for GA_1_7\Unused\Temp\Distribution_Fitting_T.ipynb
+INFO: [21/120] Checking GA_1_8\Analysis.ipynb
+INFO: No changes detected for GA_1_8\Analysis.ipynb
+INFO: [22/120] Checking GA_1_8\solution\GA_1_8_solution_HT.ipynb
+INFO: No changes detected for GA_1_8\solution\GA_1_8_solution_HT.ipynb
+INFO: [23/120] Checking GA_1_8\solution\GA_1_8_solution_hu.ipynb
+INFO: No changes detected for GA_1_8\solution\GA_1_8_solution_hu.ipynb
+INFO: [24/120] Checking GA_1_8\solution\GA_1_8_solution_traffic.ipynb
+INFO: No changes detected for GA_1_8\solution\GA_1_8_solution_traffic.ipynb
+INFO: [25/120] Checking GA_2_1\Analysis.ipynb
+INFO: No changes detected for GA_2_1\Analysis.ipynb
+INFO: [26/120] Checking GA_2_1\Analysis_solution.ipynb
+INFO: No changes detected for GA_2_1\Analysis_solution.ipynb
+INFO: [27/120] Checking GA_2_1\mesh_dev.ipynb
+INFO: No changes detected for GA_2_1\mesh_dev.ipynb
+INFO: [28/120] Checking GA_2_1\mesh_tips.ipynb
+INFO: No changes detected for GA_2_1\mesh_tips.ipynb
+INFO: [29/120] Checking GA_2_1\mesh\mesh.ipynb
+INFO: No changes detected for GA_2_1\mesh\mesh.ipynb
+INFO: [30/120] Checking GA_2_2\the_big_M.ipynb
+INFO: No changes detected for GA_2_2\the_big_M.ipynb
+INFO: [31/120] Checking GA_2_3\Analysis.ipynb
+INFO: No changes detected for GA_2_3\Analysis.ipynb
+INFO: [32/120] Checking GA_2_3\Analysis_solution.ipynb
+INFO: No changes detected for GA_2_3\Analysis_solution.ipynb
+INFO: [33/120] Checking GA_2_4\GA_2_4_Beary_Icy.ipynb
+INFO: No changes detected for GA_2_4\GA_2_4_Beary_Icy.ipynb
+INFO: [34/120] Checking GA_2_4\GA_2_4_solution.ipynb
+INFO: No changes detected for GA_2_4\GA_2_4_solution.ipynb
+INFO: [35/120] Checking GA_2_5\Analysis_GA.ipynb
+INFO: No changes detected for GA_2_5\Analysis_GA.ipynb
+INFO: [36/120] Checking GA_2_5\Analysis_GA_solution.ipynb
+INFO: No changes detected for GA_2_5\Analysis_GA_solution.ipynb
+INFO: [37/120] Checking GA_2_5\Analysis_LP.ipynb
+INFO: No changes detected for GA_2_5\Analysis_LP.ipynb
+INFO: [38/120] Checking GA_2_5\Analysis_LP_solution.ipynb
+INFO: No changes detected for GA_2_5\Analysis_LP_solution.ipynb
+INFO: [39/120] Checking GA_2_6\Analysis.ipynb
+INFO: No changes detected for GA_2_6\Analysis.ipynb
+INFO: [40/120] Checking GA_2_6\Analysis_solution.ipynb
+INFO: No changes detected for GA_2_6\Analysis_solution.ipynb
+INFO: [41/120] Checking GA_2_7\rain_solution.ipynb
+INFO: No changes detected for GA_2_7\rain_solution.ipynb
+INFO: [42/120] Checking tutorials\Week_1_3\Analysis.ipynb
+INFO: No changes detected for tutorials\Week_1_3\Analysis.ipynb
+INFO: [43/120] Checking tutorials\Week_1_5\Tutorial.ipynb
+INFO: No changes detected for tutorials\Week_1_5\Tutorial.ipynb
+INFO: [44/120] Checking tutorials\Week_1_6\Tutorial.ipynb
+INFO: No changes detected for tutorials\Week_1_6\Tutorial.ipynb
+INFO: [45/120] Checking week_1_1\PA_1_1_Catch_Them_All.ipynb
+INFO: No changes detected for week_1_1\PA_1_1_Catch_Them_All.ipynb
+INFO: [46/120] Checking Week_1_2\In_Class_Activity.ipynb
+INFO: No changes detected for Week_1_2\In_Class_Activity.ipynb
+INFO: [47/120] Checking Week_1_2\In_Class_Activity_Solution.ipynb
+INFO: No changes detected for Week_1_2\In_Class_Activity_Solution.ipynb
+INFO: [48/120] Checking Week_1_2\PA_1_2_Random_Adventure.ipynb
+INFO: No changes detected for Week_1_2\PA_1_2_Random_Adventure.ipynb
+INFO: [49/120] Checking Week_1_2\PA_1_2_solution.ipynb
+INFO: No changes detected for Week_1_2\PA_1_2_solution.ipynb
+INFO: [50/120] Checking Week_1_2\WS_1_2_Pipe_Dreams.ipynb
+INFO: No changes detected for Week_1_2\WS_1_2_Pipe_Dreams.ipynb
+INFO: [51/120] Checking Week_1_2\WS_1_2_solution.ipynb
+INFO: No changes detected for Week_1_2\WS_1_2_solution.ipynb
+INFO: [52/120] Checking week_1_3\PA_1_3_Data_Cleaning_and_Boosting_Productivity.ipynb
+INFO: No changes detected for week_1_3\PA_1_3_Data_Cleaning_and_Boosting_Productivity.ipynb
+INFO: [53/120] Checking week_1_3\PA_1_3_solution.ipynb
+INFO: No changes detected for week_1_3\PA_1_3_solution.ipynb
+INFO: [54/120] Checking week_1_3\WS_1_3_Moving_Ice.ipynb
+INFO: No changes detected for week_1_3\WS_1_3_Moving_Ice.ipynb
+INFO: [55/120] Checking week_1_3\WS_1_3_solution.ipynb
+INFO: No changes detected for week_1_3\WS_1_3_solution.ipynb
+INFO: [56/120] Checking Week_1_4\WS_1_4_Nonlinear_Rain.ipynb
+INFO: No changes detected for Week_1_4\WS_1_4_Nonlinear_Rain.ipynb
+INFO: [57/120] Checking Week_1_4\WS_1_4_solution.ipynb
+INFO: No changes detected for Week_1_4\WS_1_4_solution.ipynb
+INFO: [58/120] Checking week_1_5\WS_1_5_dont_integr_hate.ipynb
+INFO: No changes detected for week_1_5\WS_1_5_dont_integr_hate.ipynb
+INFO: [59/120] Checking week_1_5\WS_1_5_solution.ipynb
+INFO: No changes detected for week_1_5\WS_1_5_solution.ipynb
+INFO: [60/120] Checking week_1_5\PA\PA_1_5_solution.ipynb
+INFO: No changes detected for week_1_5\PA\PA_1_5_solution.ipynb
+INFO: [61/120] Checking week_1_5\PA\PA_1_5_useful_tricks.ipynb
+INFO: No changes detected for week_1_5\PA\PA_1_5_useful_tricks.ipynb
+INFO: [62/120] Checking week_1_5\PA_ice\PA_plots.ipynb
+INFO: No changes detected for week_1_5\PA_ice\PA_plots.ipynb
+INFO: [63/120] Checking Week_1_6\WS_1_6_solution.ipynb
+INFO: No changes detected for Week_1_6\WS_1_6_solution.ipynb
+INFO: [64/120] Checking Week_1_6\WS_1_6_time_to_c_ode.ipynb
+INFO: No changes detected for Week_1_6\WS_1_6_time_to_c_ode.ipynb
+INFO: [65/120] Checking Week_1_6\PA\PA_1_6_Boxes_and_Bugs.ipynb
+INFO: No changes detected for Week_1_6\PA\PA_1_6_Boxes_and_Bugs.ipynb
+INFO: [66/120] Checking Week_1_6\PA\PA_1_6_solution.ipynb
+INFO: No changes detected for Week_1_6\PA\PA_1_6_solution.ipynb
+INFO: [67/120] Checking Week_1_7\scipy_stats.ipynb
+INFO: No changes detected for Week_1_7\scipy_stats.ipynb
+INFO: [68/120] Checking Week_1_7\WS_1_7_lets_be_concrete.ipynb
+INFO: No changes detected for Week_1_7\WS_1_7_lets_be_concrete.ipynb
+INFO: [69/120] Checking Week_1_7\WS_1_7_solution.ipynb
+INFO: No changes detected for Week_1_7\WS_1_7_solution.ipynb
+INFO: [70/120] Checking Week_1_7\PA\PA_1_7_Classy_Distributions.ipynb
+INFO: No changes detected for Week_1_7\PA\PA_1_7_Classy_Distributions.ipynb
+INFO: [71/120] Checking Week_1_7\PA\PA_1_7_solution.ipynb
+INFO: No changes detected for Week_1_7\PA\PA_1_7_solution.ipynb
+INFO: [72/120] Checking Week_1_8\data.ipynb
+INFO: No changes detected for Week_1_8\data.ipynb
+INFO: [73/120] Checking Week_1_8\intersection.ipynb
+INFO: No changes detected for Week_1_8\intersection.ipynb
+INFO: [74/120] Checking Week_1_8\test_bivariate.ipynb
+INFO: No changes detected for Week_1_8\test_bivariate.ipynb
+INFO: [75/120] Checking Week_1_8\PA\PA_1_8_Equations_Done_Symply.ipynb
+INFO: No changes detected for Week_1_8\PA\PA_1_8_Equations_Done_Symply.ipynb
+INFO: [76/120] Checking Week_1_8\PA\PA_1_8_solution.ipynb
+INFO: No changes detected for Week_1_8\PA\PA_1_8_solution.ipynb
+INFO: [77/120] Checking Week_1_8\WS\linearity_or_not.ipynb
+INFO: No changes detected for Week_1_8\WS\linearity_or_not.ipynb
+INFO: [78/120] Checking Week_1_8\WS\WS_1_8_solution.ipynb
+INFO: No changes detected for Week_1_8\WS\WS_1_8_solution.ipynb
+INFO: [79/120] Checking Week_1_8\WS\WS_1_8_Thingamajig.ipynb
+INFO: No changes detected for Week_1_8\WS\WS_1_8_Thingamajig.ipynb
+INFO: [80/120] Checking Week_2_1\WS_2_1_solution.ipynb
+INFO: No changes detected for Week_2_1\WS_2_1_solution.ipynb
+INFO: [81/120] Checking Week_2_1\WS_2_1_wiggle.ipynb
+INFO: No changes detected for Week_2_1\WS_2_1_wiggle.ipynb
+INFO: [82/120] Checking Week_2_1\WS_2_1_wiggle_test_ci.ipynb
+INFO: No changes detected for Week_2_1\WS_2_1_wiggle_test_ci.ipynb
+INFO: [83/120] Checking Week_2_1\PA\PA_2_1_classy_city.ipynb
+INFO: No changes detected for Week_2_1\PA\PA_2_1_classy_city.ipynb
+INFO: [84/120] Checking Week_2_1\PA\PA_2_1_solution.ipynb
+INFO: No changes detected for Week_2_1\PA\PA_2_1_solution.ipynb
+INFO: [85/120] Checking Week_2_1\PA\PA_dev.ipynb
+INFO: No changes detected for Week_2_1\PA\PA_dev.ipynb
+INFO: [86/120] Checking Week_2_1\PA\U.ipynb
+INFO: No changes detected for Week_2_1\PA\U.ipynb
+INFO: [87/120] Checking Week_2_2\PA_2_2_love_is_sparse.ipynb
+INFO: No changes detected for Week_2_2\PA_2_2_love_is_sparse.ipynb
+INFO: [88/120] Checking Week_2_2\PA_2_2_solution.ipynb
+INFO: No changes detected for Week_2_2\PA_2_2_solution.ipynb
+INFO: [89/120] Checking Week_2_2\WS_2_2_more_support.ipynb
+INFO: No changes detected for Week_2_2\WS_2_2_more_support.ipynb
+INFO: [90/120] Checking Week_2_2\WS_2_2_solution.ipynb
+INFO: No changes detected for Week_2_2\WS_2_2_solution.ipynb
+INFO: [91/120] Checking Week_2_2\old\old_PA10_Love_is_Sparse.ipynb
+INFO: No changes detected for Week_2_2\old\old_PA10_Love_is_Sparse.ipynb
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+INFO: No changes detected for Week_2_2\old\old_PA10_solution_sympy.ipynb
+INFO: [93/120] Checking Week_2_2\old\PA_2_1_solution.ipynb
+INFO: No changes detected for Week_2_2\old\PA_2_1_solution.ipynb
+INFO: [94/120] Checking Week_2_2\old\PA_2_1_solution_sympy.ipynb
+INFO: No changes detected for Week_2_2\old\PA_2_1_solution_sympy.ipynb
+INFO: [95/120] Checking Week_2_2\old\WS_2_2_more_support.ipynb
+INFO: No changes detected for Week_2_2\old\WS_2_2_more_support.ipynb
+INFO: [96/120] Checking Week_2_2\PA\PA_2_2_love_is_sparse.ipynb
+INFO: No changes detected for Week_2_2\PA\PA_2_2_love_is_sparse.ipynb
+INFO: [97/120] Checking Week_2_2\PA\PA_2_2_solution.ipynb
+INFO: No changes detected for Week_2_2\PA\PA_2_2_solution.ipynb
+INFO: [98/120] Checking Week_2_3\WS_2_3_DFT_you_try_meow.ipynb
+INFO: No changes detected for Week_2_3\WS_2_3_DFT_you_try_meow.ipynb
+INFO: [99/120] Checking Week_2_3\WS_2_3_solution.ipynb
+INFO: No changes detected for Week_2_3\WS_2_3_solution.ipynb
+INFO: [100/120] Checking Week_2_3\PA\PA_2_3_iter_remoto.ipynb
+INFO: No changes detected for Week_2_3\PA\PA_2_3_iter_remoto.ipynb
+INFO: [101/120] Checking Week_2_3\PA\PA_2_3_solution.ipynb
+INFO: No changes detected for Week_2_3\PA\PA_2_3_solution.ipynb
+INFO: [102/120] Checking Week_2_4\WS_2_4_Feel_the_Pressure.ipynb
+INFO: No changes detected for Week_2_4\WS_2_4_Feel_the_Pressure.ipynb
+INFO: [103/120] Checking Week_2_4\WS_2_4_solution.ipynb
+INFO: No changes detected for Week_2_4\WS_2_4_solution.ipynb
+INFO: [104/120] Checking Week_2_4\PA\PA_2_4_A_gurobilicious.ipynb
+INFO: No changes detected for Week_2_4\PA\PA_2_4_A_gurobilicious.ipynb
+INFO: [105/120] Checking Week_2_4\PA\PA_2_4_B_axis_of_awesome.ipynb
+INFO: No changes detected for Week_2_4\PA\PA_2_4_B_axis_of_awesome.ipynb
+INFO: [106/120] Checking Week_2_4\PA\PA_2_4_B_solution.ipynb
+INFO: No changes detected for Week_2_4\PA\PA_2_4_B_solution.ipynb
+INFO: [107/120] Checking Week_2_5\WS_2_5_Profit_vs_Planet.ipynb
+INFO: No changes detected for Week_2_5\WS_2_5_Profit_vs_Planet.ipynb
+INFO: [108/120] Checking Week_2_5\WS_2_5_solution.ipynb
+INFO: No changes detected for Week_2_5\WS_2_5_solution.ipynb
+INFO: [109/120] Checking Week_2_5\PA\PA_2_5_data_framework.ipynb
+INFO: No changes detected for Week_2_5\PA\PA_2_5_data_framework.ipynb
+INFO: [110/120] Checking Week_2_5\PA\PA_2_5_solution.ipynb
+INFO: No changes detected for Week_2_5\PA\PA_2_5_solution.ipynb
+INFO: [111/120] Checking Week_2_6\WS_2_6_be_a_NN.ipynb
+INFO: No changes detected for Week_2_6\WS_2_6_be_a_NN.ipynb
+INFO: [112/120] Checking Week_2_6\WS_2_6_solution.ipynb
+INFO: No changes detected for Week_2_6\WS_2_6_solution.ipynb
+INFO: [113/120] Checking Week_2_6\PA\PA_2_6_3_way_split.ipynb
+INFO: No changes detected for Week_2_6\PA\PA_2_6_3_way_split.ipynb
+INFO: [114/120] Checking Week_2_6\PA\PA_2_6_solution.ipynb
+INFO: No changes detected for Week_2_6\PA\PA_2_6_solution.ipynb
+INFO: [115/120] Checking Week_2_7\WS_2_7_solution.ipynb
+INFO: No changes detected for Week_2_7\WS_2_7_solution.ipynb
+INFO: [116/120] Checking Week_2_7\WS_2_7_student.ipynb
+INFO: No changes detected for Week_2_7\WS_2_7_student.ipynb
+INFO: [117/120] Checking Week_2_7\PA\PA_2_7_solution.ipynb
+INFO: No changes detected for Week_2_7\PA\PA_2_7_solution.ipynb
+INFO: [118/120] Checking Week_2_7\PA\PA_2_7_Times_Tables.ipynb
+INFO: No changes detected for Week_2_7\PA\PA_2_7_Times_Tables.ipynb
+INFO: [119/120] Checking Week_2_8\WS_2_8_dirty_water.ipynb
+INFO: No changes detected for Week_2_8\WS_2_8_dirty_water.ipynb
+INFO: [120/120] Checking Week_2_8\WS_2_8_solution.ipynb
+INFO: Content changed for C:\Users\ncore\Desktop\2024-files\content\Week_2_8\WS_2_8_solution.ipynb (hash mismatch)
+INFO: Processing Week_2_8\WS_2_8_solution.ipynb
+INFO: Stats: 14 cells (2 code, 12 markdown), 193 lines
+INFO: Successfully processed markdown format
+INFO: Successfully processed python format
+INFO: Successfully processed clean_python format
+INFO: Converting C:\Users\ncore\Desktop\2024-files\synced_files\Week_2_8\WS_2_8_solution.ipynb to HTML at C:\Users\ncore\Desktop\2024-files\synced_files\Week_2_8\WS_2_8_solution.html
+WARNING: Alternative text is missing on 2 image(s).
+INFO: HTML conversion successful
+INFO: Successfully processed html format
+INFO: Successfully processed formats: ['markdown', 'python', 'clean_python', 'html']
+INFO: Completed in 3.32 seconds
+INFO: Skipping cleanup due to replace_all=False
+INFO: === Completed at 2024-12-31 00:44:54 (Duration: 0:00:03.520648) ===
diff --git a/footer.html b/footer.html
new file mode 100644
index 00000000..dd041a85
--- /dev/null
+++ b/footer.html
@@ -0,0 +1,22 @@
+**End of notebook.**
+
+<div style="margin-top: 50px; padding-top: 20px; border-top: 1px solid #ccc;">
+ <div style="display: flex; justify-content: flex-end; gap: 20px; align-items: center;">
+ <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
+ <img alt="MUDE" style="width:100px; height:auto;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" />
+ </a>
+ <a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
+ <img alt="TU Delft" style="width:100px; height:auto;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" />
+ </a>
+ <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">
+ <img alt="Creative Commons License" style="width:88px; height:auto;" src="https://i.creativecommons.org/l/by/4.0/88x31.png" />
+ </a>
+ </div>
+ <div style="font-size: 75%; margin-top: 10px; text-align: right;">
+ © Copyright 2024 <a rel="MUDE" href="http://mude.citg.tudelft.nl/">MUDE</a> TU Delft.
+ This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 License</a>.
+ </div>
+</div>
+
+
+<!--tested with WS_2_8_solution.ipynb-->
\ No newline at end of file
diff --git a/notebook_hashes.json b/notebook_hashes.json
new file mode 100644
index 00000000..db18c8f1
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+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_1_8\\WS\\linearity_or_not.ipynb": "8a0c3ce71dde044c0301a8d0806d5c4b",
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+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_1\\PA\\PA_2_1_classy_city.ipynb": "bca21b5374b348afe895762e372859e7",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_1\\PA\\PA_2_1_solution.ipynb": "56c84891870b58e21850ef992757fe17",
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+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_2\\PA_2_2_solution.ipynb": "d2b1f254b52a295ae6623ada29a0d70b",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_2\\WS_2_2_more_support.ipynb": "562a649ca5bfe5effec35777884ef83f",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_2\\WS_2_2_solution.ipynb": "665b30958fd57c25de0421233f9cb9f6",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_2\\old\\old_PA10_Love_is_Sparse.ipynb": "b7db0f1a339af8bb06420adae22988a8",
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+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_2\\old\\PA_2_1_solution_sympy.ipynb": "3e9ec0151bed05b03f679f20a7a7682b",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_2\\old\\WS_2_2_more_support.ipynb": "fb7eae2117715591833d8d52216ba91e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_2\\PA\\PA_2_2_love_is_sparse.ipynb": "bcbe1857daec9f229e7a846f4fe07208",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_2\\PA\\PA_2_2_solution.ipynb": "4ac8e338d2deb4d4d914c559995a52b4",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_3\\WS_2_3_DFT_you_try_meow.ipynb": "c6dfb00fd39e1966e7e30868c90cc006",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_3\\WS_2_3_solution.ipynb": "f43494c19f7a4b95405411c6db5ba305",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_3\\PA\\PA_2_3_iter_remoto.ipynb": "d3186fc527ce933da61eabcf37017648",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_3\\PA\\PA_2_3_solution.ipynb": "8f6da19e82a21ec8891b6176972a076c",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_4\\WS_2_4_Feel_the_Pressure.ipynb": "03541daba628b93961fbc6b08aa95524",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_4\\WS_2_4_solution.ipynb": "47317a603d22640ef67101671a7dcd93",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_4\\PA\\PA_2_4_A_gurobilicious.ipynb": "1b34df3aee1ab82d2fd3492d8d9978fe",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_4\\PA\\PA_2_4_B_axis_of_awesome.ipynb": "6b85234a33f5a84a1e9a9987ecdc80cd",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_4\\PA\\PA_2_4_B_solution.ipynb": "a6dc08788c41eed19518f50afdc4e6ec",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_5\\WS_2_5_Profit_vs_Planet.ipynb": "e0d01e331733b7fb991b90194c48deee",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_5\\WS_2_5_solution.ipynb": "71023b88600ee60cc8cbfe149aafef4f",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_5\\PA\\PA_2_5_data_framework.ipynb": "2d194497da39a12494546624d14a380e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_5\\PA\\PA_2_5_solution.ipynb": "25176f422715d5bbd8c71fccf7a0e675",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_6\\WS_2_6_be_a_NN.ipynb": "4594d16ce940cc875ce0ad0b6cb474b7",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_6\\WS_2_6_solution.ipynb": "8ea3e550f4679392f89f59363476ca90",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_6\\PA\\PA_2_6_3_way_split.ipynb": "27de0a19179060cee2deb4d4624228b8",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_6\\PA\\PA_2_6_solution.ipynb": "833397156266c8c553a8343a08bad8e4",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_7\\WS_2_7_solution.ipynb": "32ac0909fbc763bfeaa2fcb4242d52a9",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_7\\WS_2_7_student.ipynb": "6c72a8f73a08f721552a4b9310288d66",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_7\\PA\\PA_2_7_solution.ipynb": "82df0edd9c61003a9e926de7f17bb325",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_7\\PA\\PA_2_7_Times_Tables.ipynb": "8edce2d5c6816f161cf051fa504d75a1",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_8\\WS_2_8_dirty_water.ipynb": "ac802cd6b62c130b6777a575c187e04f",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\Week_2_8\\WS_2_8_solution.ipynb": "7b4a07edfb48607a73d321166a139d2f",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_1\\Task_2_solution.ipynb_markdown": "5ef36adc38d3ef09603c31e52b16725c",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_1\\Task_2_solution.ipynb_python": "5ef36adc38d3ef09603c31e52b16725c",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_1\\Task_2_solution.ipynb_clean_python": "5ef36adc38d3ef09603c31e52b16725c",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_1\\Task_2_solution.ipynb_html": "5ef36adc38d3ef09603c31e52b16725c",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_2\\Analysis.ipynb_markdown": "dbae012b7965e1bfe125239dc8534727",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_2\\Analysis.ipynb_python": "dbae012b7965e1bfe125239dc8534727",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_2\\Analysis.ipynb_clean_python": "dbae012b7965e1bfe125239dc8534727",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_2\\Analysis.ipynb_html": "dbae012b7965e1bfe125239dc8534727",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_2\\Analysis_solution.ipynb_markdown": "0d44b860d7a7b543115e17d3d1bc7bf8",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_2\\Analysis_solution.ipynb_python": "0d44b860d7a7b543115e17d3d1bc7bf8",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_2\\Analysis_solution.ipynb_clean_python": "0d44b860d7a7b543115e17d3d1bc7bf8",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_2\\Analysis_solution.ipynb_html": "0d44b860d7a7b543115e17d3d1bc7bf8",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Analysis.ipynb_markdown": "b902ca516780be395aa95e4d894cf3bf",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Analysis.ipynb_python": "b902ca516780be395aa95e4d894cf3bf",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Analysis.ipynb_clean_python": "b902ca516780be395aa95e4d894cf3bf",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Analysis.ipynb_html": "b902ca516780be395aa95e4d894cf3bf",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Analysis_Solution.ipynb_markdown": "9282cce4dc409329154d8618676f2f4e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Analysis_Solution.ipynb_python": "9282cce4dc409329154d8618676f2f4e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Analysis_Solution.ipynb_clean_python": "9282cce4dc409329154d8618676f2f4e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Analysis_Solution.ipynb_html": "9282cce4dc409329154d8618676f2f4e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Warmup.ipynb_markdown": "5dda4ff9f47bd9df0fecbe68d2ce01e5",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Warmup.ipynb_python": "5dda4ff9f47bd9df0fecbe68d2ce01e5",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Warmup.ipynb_clean_python": "5dda4ff9f47bd9df0fecbe68d2ce01e5",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_3\\Warmup.ipynb_html": "5dda4ff9f47bd9df0fecbe68d2ce01e5",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_4\\Analysis.ipynb_markdown": "ed8ee7827b7ff7ecccda94b166761961",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_4\\Analysis.ipynb_python": "ed8ee7827b7ff7ecccda94b166761961",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_4\\Analysis.ipynb_clean_python": "ed8ee7827b7ff7ecccda94b166761961",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_4\\Analysis.ipynb_html": "ed8ee7827b7ff7ecccda94b166761961",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_4\\Analysis_solution.ipynb_markdown": "70c40a8feb7d130ef44a0f5a08916dfc",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_4\\Analysis_solution.ipynb_python": "70c40a8feb7d130ef44a0f5a08916dfc",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_4\\Analysis_solution.ipynb_clean_python": "70c40a8feb7d130ef44a0f5a08916dfc",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_4\\Analysis_solution.ipynb_html": "70c40a8feb7d130ef44a0f5a08916dfc",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\Analysis.ipynb_markdown": "dceccae507ed3530857e958b0f38f519",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\Analysis.ipynb_python": "dceccae507ed3530857e958b0f38f519",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\Analysis.ipynb_clean_python": "dceccae507ed3530857e958b0f38f519",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\Analysis.ipynb_html": "dceccae507ed3530857e958b0f38f519",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\Analysis_solution.ipynb_markdown": "e941bddc519025d4ebeef4b3303fb8bc",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\Analysis_solution.ipynb_python": "e941bddc519025d4ebeef4b3303fb8bc",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\Analysis_solution.ipynb_clean_python": "e941bddc519025d4ebeef4b3303fb8bc",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\Analysis_solution.ipynb_html": "e941bddc519025d4ebeef4b3303fb8bc",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\central_diff_illustration.ipynb_markdown": "b22032a79aaec478e740f9569715a3f3",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\central_diff_illustration.ipynb_python": "b22032a79aaec478e740f9569715a3f3",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\central_diff_illustration.ipynb_clean_python": "b22032a79aaec478e740f9569715a3f3",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_5\\central_diff_illustration.ipynb_html": "b22032a79aaec478e740f9569715a3f3",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_6\\Analysis.ipynb_markdown": "f5ec7ff0563eed9a29eea0beb4bcae3e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_6\\Analysis.ipynb_python": "f5ec7ff0563eed9a29eea0beb4bcae3e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_6\\Analysis.ipynb_clean_python": "f5ec7ff0563eed9a29eea0beb4bcae3e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_6\\Analysis.ipynb_html": "f5ec7ff0563eed9a29eea0beb4bcae3e",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_6\\Analysis_solution.ipynb_markdown": "b1d9c5f09465e1d93eeb813e980bbb02",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_6\\Analysis_solution.ipynb_python": "b1d9c5f09465e1d93eeb813e980bbb02",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_6\\Analysis_solution.ipynb_clean_python": "b1d9c5f09465e1d93eeb813e980bbb02",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_6\\Analysis_solution.ipynb_html": "b1d9c5f09465e1d93eeb813e980bbb02",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_7\\Solution\\Discharge\\Analysis_discharge_solution.ipynb_markdown": "dfad23da3961ba933d0fa0d9a18d0f36",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_7\\Solution\\Discharge\\Analysis_discharge_solution.ipynb_python": "dfad23da3961ba933d0fa0d9a18d0f36",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_7\\Solution\\Discharge\\Analysis_discharge_solution.ipynb_clean_python": "dfad23da3961ba933d0fa0d9a18d0f36",
+ "C:\\Users\\ncore\\Desktop\\2024-files\\content\\GA_1_7\\Solution\\Discharge\\Analysis_discharge_solution.ipynb_html": "dfad23da3961ba933d0fa0d9a18d0f36"
+}
\ No newline at end of file
diff --git a/sync_notebooks.py b/sync_notebooks.py
index d09e18ff..f293692d 100644
--- a/sync_notebooks.py
+++ b/sync_notebooks.py
@@ -28,7 +28,9 @@ def get_file_hash(filepath: Path) -> str:
return hasher.hexdigest()
except Exception as e:
logging.error(f"Error calculating hash for {filepath}: {e}")
- return None
+ return "" # Return empty string instead of None for better comparison
+
+
class NotebookSyncer:
def __init__(self, config_file: Optional[str] = None):
@@ -57,6 +59,8 @@ class NotebookSyncer:
self.output_dir = Path(self.config['output_dir']).absolute()
self.error_log = "conversion_errors.log"
self.start_time = None
+ self.hash_cache_file = Path("notebook_hashes.json")
+ self.hash_cache = self._load_hash_cache()
# Clear existing log file
if os.path.exists(self.error_log):
@@ -103,21 +107,69 @@ class NotebookSyncer:
}
return stats
+ def _load_hash_cache(self) -> Dict[str, str]:
+ """Load the hash cache from file."""
+ if self.hash_cache_file.exists():
+ try:
+ with open(self.hash_cache_file, 'r') as f:
+ return json.load(f)
+ except:
+ return {}
+ return {}
+
+ def _save_hash_cache(self):
+ """Save the hash cache to file."""
+ with open(self.hash_cache_file, 'w') as f:
+ json.dump(self.hash_cache, f, indent=2)
+
+ def should_process_file(self, source_path: Path, output_path: Path, force: bool = False, fmt: str = None) -> bool:
+ """Determine if a file needs processing based on hash comparison."""
+ if force:
+ self.logger.info(f"Force update enabled for {source_path}")
+ return True
+
+ if not output_path.exists():
+ self.logger.info(f"Output file does not exist: {output_path}")
+ return True
+
+ # For all formats, check against source hash
+ source_hash = get_file_hash(source_path)
+ cached_hash = self.hash_cache.get(str(source_path))
+
+ if not source_hash:
+ self.logger.warning(f"Could not calculate hash for {source_path}")
+ return True
+
+ if cached_hash is None: # First time seeing this file
+ self.logger.info(f"First time processing {source_path}")
+ self.hash_cache[str(source_path)] = source_hash
+ return True
+
+ if source_hash != cached_hash:
+ self.logger.info(f"Content changed for {source_path} (hash mismatch)")
+ self.hash_cache[str(source_path)] = source_hash
+ return True
+
+ return False
+
def find_notebooks(self) -> List[Path]:
"""Find all Jupyter notebooks in the content directory."""
if not self.content_dir.exists():
self.logger.error(f"Content directory {self.content_dir} does not exist")
return []
-
+
notebooks = []
self.logger.info(f"Searching in: {self.content_dir}")
# Use specific notebooks if configured
- if self.config['specific_notebooks']:
- for nb_path in self.config['specific_notebooks']:
+ specific_notebooks = self.config.get('specific_notebooks', {})
+ if isinstance(specific_notebooks, dict) and 'files' in specific_notebooks:
+ for nb_path in specific_notebooks['files']:
full_path = self.content_dir / nb_path
if full_path.exists() and full_path.suffix == '.ipynb':
notebooks.append(full_path)
+ else:
+ self.logger.warning(f"Specified notebook not found: {nb_path}")
return notebooks
# Otherwise find all notebooks
@@ -126,31 +178,26 @@ class NotebookSyncer:
if file.endswith('.ipynb'):
notebook_path = Path(root) / file
notebooks.append(notebook_path)
-
+
self.logger.info(f"Found {len(notebooks)} notebooks")
return notebooks
def convert_to_html(self, input_path: Path, output_path: Path):
"""Convert file to HTML."""
try:
+ self.logger.info(f"Converting {input_path} to HTML at {output_path}")
if input_path.suffix == '.ipynb':
nb = self.read_notebook_safely(input_path)
html_exporter = nbconvert.HTMLExporter()
html, _ = html_exporter.from_notebook_node(nb)
- elif input_path.suffix in ['.md', '.markdown']:
- with open(input_path, 'r', encoding='utf-8') as f:
- content = f.read()
- html = markdown2.markdown(content)
- elif input_path.suffix == '.py':
- with open(input_path, 'r', encoding='utf-8') as f:
- content = f.read()
- formatter = HtmlFormatter(style='monokai', full=True)
- html = pygments.highlight(content, PythonLexer(), formatter)
-
- output_path.parent.mkdir(parents=True, exist_ok=True)
- with open(output_path, 'w', encoding='utf-8') as f:
- f.write(html)
- return True
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ with open(output_path, 'w', encoding='utf-8') as f:
+ f.write(html)
+ self.logger.info("HTML conversion successful")
+ return True
+ else:
+ self.logger.error(f"Unexpected input file type: {input_path.suffix}")
+ return False
except Exception as e:
self.logger.error(f"Error converting to HTML: {e}")
return False
@@ -183,75 +230,72 @@ class NotebookSyncer:
return None
def convert_notebook(self, notebook_path: Path, file_index: int, total_files: int):
- """Convert notebook to specified formats."""
try:
+ start_time = time.time()
+
# Calculate relative and output paths first
relative_path = notebook_path.relative_to(self.content_dir)
output_base = self.output_dir / relative_path.parent / relative_path.stem
stripped_path = output_base.with_suffix('.ipynb')
- # Log first
- self.logger.info(f"[{file_index}/{total_files}] Processing {relative_path}")
- start_time = time.time()
-
- # Force processing each time for HTML
- need_processing = False
+ self.logger.info(f"[{file_index}/{total_files}] Checking {relative_path}")
- # Check each output format
- for fmt in self.config['formats']:
- if fmt == 'html':
- output_path = output_base.with_suffix('.html')
- if not output_path.exists():
- need_processing = True
- elif fmt == 'markdown':
- output_path = output_base.with_suffix('.md')
- if not output_path.exists():
- need_processing = True
- elif fmt == 'python':
- output_path = output_base.with_suffix('.py')
- if not output_path.exists():
- need_processing = True
- elif fmt == 'clean_python':
- output_path = Path(str(output_base) + '_clean.py')
- if not output_path.exists():
- need_processing = True
+ # Get force_update option
+ force_update = False
+ specific_notebooks = self.config.get('specific_notebooks', {})
+ if isinstance(specific_notebooks, dict):
+ options = specific_notebooks.get('options', {})
+ if isinstance(options, dict):
+ force_update = options.get('force_update', False)
- # Always process if any format is missing
- if not need_processing:
- self.logger.info(f"No changes detected")
+ # Check if any format needs processing
+ needs_processing = self.should_process_file(notebook_path, stripped_path, force_update)
+
+ if not needs_processing:
+ self.logger.info(f"No changes detected for {relative_path}")
return True
+
+ self.logger.info(f"Processing {relative_path}")
- output_base.parent.mkdir(parents=True, exist_ok=True)
-
- # Strip metadata and get notebook object
+ # Strip metadata and get notebook object first
nb = self.strip_metadata(notebook_path, stripped_path)
if nb is None: # If stripping failed
return False
-
+
# Log stats
stats = self.get_notebook_stats(nb)
self.logger.info(f"Stats: {stats['cells']} cells ({stats['code_cells']} code, "
f"{stats['markdown_cells']} markdown), {stats['total_lines']} lines")
# Process all formats
+ processed_formats = []
for fmt in self.config['formats']:
try:
+ processed = False
if fmt == 'html':
output_path = output_base.with_suffix('.html')
- self.convert_to_html(notebook_path, output_path)
+ processed = self.convert_to_html(stripped_path, output_path)
elif fmt == 'markdown':
output_path = output_base.with_suffix('.md')
- cmd = ['jupytext', '--to', 'markdown', str(stripped_path), '-o', str(output_path)]
- if subprocess.run(cmd, check=True, capture_output=True, text=True, encoding='utf-8'):
+ cmd = ['jupytext', '--to', 'markdown',
+ '--opt', 'notebook_metadata_filter=-all',
+ '--opt', 'cell_metadata_filter=-all',
+ str(stripped_path), '-o', str(output_path)]
+ processed = subprocess.run(cmd, check=True, capture_output=True, text=True, encoding='utf-8').returncode == 0
+ if processed:
with open(output_path, 'r+', encoding='utf-8') as f:
content = f.read()
+ if content.startswith('---'):
+ end_idx = content.find('---', 3)
+ if end_idx != -1:
+ content = content[end_idx + 3:].lstrip()
f.seek(0)
f.write("<userStyle>Normal</userStyle>\n\n" + content)
f.truncate()
elif fmt == 'python':
output_path = output_base.with_suffix('.py')
cmd = ['jupytext', '--to', 'py:percent', str(stripped_path), '-o', str(output_path)]
- subprocess.run(cmd, check=True, capture_output=True, text=True, encoding='utf-8')
+ processed = subprocess.run(cmd, check=True, capture_output=True, text=True, encoding='utf-8').returncode == 0
elif fmt == 'clean_python':
output_path = Path(str(output_base) + '_clean.py')
cmd = [
@@ -261,20 +305,26 @@ class NotebookSyncer:
'--opt', 'cell_metadata_filter=-all',
str(stripped_path), '-o', str(output_path)
]
- subprocess.run(cmd, check=True, capture_output=True, text=True, encoding='utf-8')
-
- if output_path.exists():
+ processed = subprocess.run(cmd, check=True, capture_output=True, text=True, encoding='utf-8').returncode == 0
+ if processed and output_path.exists():
with open(output_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
clean_lines = [line for line in lines if not line.strip().startswith('#') and line.strip()]
with open(output_path, 'w', encoding='utf-8') as f:
f.writelines(clean_lines)
-
+
+ if processed:
+ processed_formats.append(fmt)
+ self.logger.info(f"Successfully processed {fmt} format")
+ else:
+ self.logger.warning(f"Failed to process {fmt} format")
+
except subprocess.CalledProcessError as e:
self.logger.error(f"Error converting to {fmt}: {e.stderr}")
return False
-
+
end_time = time.time()
+ self.logger.info(f"Successfully processed formats: {processed_formats}")
self.logger.info(f"Completed in {end_time - start_time:.2f} seconds")
return True
except Exception as e:
@@ -287,6 +337,12 @@ class NotebookSyncer:
if not self.output_dir.exists():
return
+ # Check replace_all option
+ replace_all = self.config.get('sync_options', {}).get('replace_all', True)
+ if not replace_all:
+ self.logger.info("Skipping cleanup due to replace_all=False")
+ return
+
valid_stems = {nb.relative_to(self.content_dir).stem for nb in processed_notebooks}
for output_file in self.output_dir.glob('**/*'):
@@ -315,29 +371,37 @@ class NotebookSyncer:
def sync(self):
"""Main synchronization process."""
- self.start_time = datetime.now()
- self.logger.info(f"=== Starting synchronization at {self.start_time.strftime('%Y-%m-%d %H:%M:%S')} ===")
-
- self.output_dir.mkdir(parents=True, exist_ok=True)
-
- notebooks = self.find_notebooks()
- if not notebooks:
- self.logger.info("No notebooks found")
- return True
+ try:
+ self.start_time = datetime.now()
+ self.logger.info(f"=== Starting synchronization at {self.start_time.strftime('%Y-%m-%d %H:%M:%S')} ===")
- processed_notebooks = []
- total_notebooks = len(notebooks)
-
- for idx, notebook in enumerate(notebooks, 1):
- if self.convert_notebook(notebook, idx, total_notebooks):
- processed_notebooks.append(notebook)
+ self.output_dir.mkdir(parents=True, exist_ok=True)
+
+ notebooks = self.find_notebooks()
+ if not notebooks:
+ self.logger.info("No notebooks found")
+ return True
- self.cleanup_output_dir(processed_notebooks)
-
- end_time = datetime.now()
- duration = end_time - self.start_time
- self.logger.info(f"=== Completed at {end_time.strftime('%Y-%m-%d %H:%M:%S')} (Duration: {duration}) ===")
- return len(processed_notebooks) > 0
+ processed_notebooks = []
+ total_notebooks = len(notebooks)
+
+ for idx, notebook in enumerate(notebooks, 1):
+ if self.convert_notebook(notebook, idx, total_notebooks):
+ processed_notebooks.append(notebook)
+
+ self.cleanup_output_dir(processed_notebooks)
+
+ # Save hash cache after processing
+ self._save_hash_cache()
+
+ end_time = datetime.now()
+ duration = end_time - self.start_time
+ self.logger.info(f"=== Completed at {end_time.strftime('%Y-%m-%d %H:%M:%S')} (Duration: {duration}) ===")
+ return len(processed_notebooks) > 0
+ except KeyboardInterrupt:
+ self.logger.info("Process interrupted by user")
+ self._save_hash_cache() # Save cache even on interrupt
+ return False
if __name__ == "__main__":
syncer = NotebookSyncer("config_sync.yml")
diff --git a/synced_files/GA_1_1/Task_2_solution.html b/synced_files/GA_1_1/Task_2_solution.html
index feefab33..6b7ed7a9 100644
--- a/synced_files/GA_1_1/Task_2_solution.html
+++ b/synced_files/GA_1_1/Task_2_solution.html
@@ -7558,10 +7558,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">sci</span>
<span class="kn">import</span> <span class="nn">scipy.optimize</span> <span class="k">as</span> <span class="nn">opt</span>
@@ -7604,15 +7604,15 @@ a.anchor-link {
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">'data/days.csv'</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">';'</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">'data/days.csv'</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">';'</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s1">','</span><span class="p">,</span> <span class="s1">'.'</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
@@ -7622,27 +7622,6 @@ a.anchor-link {
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[2]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>array([[1917. , 119.4791667],
- [1918. , 130.3979167],
- [1919. , 122.60625 ],
- [1920. , 131.4486111],
- [1921. , 130.2791667],
- [1922. , 131.5555556],
- [1923. , 128.0833333],
- [1924. , 131.6319444],
- [1925. , 126.7722222],
- [1926. , 115.66875 ]])</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7665,32 +7644,20 @@ a.anchor-link {
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>(103, 2)</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7713,15 +7680,15 @@ a.anchor-link {
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
<span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Mean: </span><span class="si">{</span><span class="n">mean</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="se">\n\</span>
@@ -7731,20 +7698,6 @@ a.anchor-link {
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Mean: 123.647
-Standard deviation: 6.516
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7756,15 +7709,15 @@ Standard deviation: 6.516
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Measured data'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Measured data'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Year [-]'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Number of days/year [-]'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Number of days per year between </span><span class="si">{</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s1">.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s1">.0f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
@@ -7774,18 +7727,6 @@ Standard deviation: 6.516
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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"/>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7828,10 +7769,10 @@ Standard deviation: 6.516
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">regression</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">regression</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Determine linear regression</span>
@@ -7857,35 +7798,20 @@ Standard deviation: 6.516
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">r_sq</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">m</span> <span class="o">=</span> <span class="n">regression</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">r_sq</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">m</span> <span class="o">=</span> <span class="n">regression</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Coefficient of determination R^2 = 0.151
-Intercept q = 291.239
-Slope m = -0.085
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7938,10 +7864,10 @@ Slope m = -0.085
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</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">calculate_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">q</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">calculate_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">q</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Determine y values from linear regression</span>
@@ -7965,24 +7891,24 @@ Slope m = -0.085
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="n">line</span> <span class="o">=</span> <span class="n">calculate_line</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">m</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">line</span> <span class="o">=</span> <span class="n">calculate_line</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">m</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">'Observations'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">line</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Fitted line'</span><span class="p">)</span>
@@ -8005,24 +7931,6 @@ Slope m = -0.085
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-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
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-<pre>Text(0.5, 1.0, '(b) Observed and predicted number of days')</pre>
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"/>
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-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8075,10 +7983,10 @@ Slope m = -0.085
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">RMSE</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fit_data</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">RMSE</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fit_data</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Compute the RMSE</span>
@@ -8101,33 +8009,20 @@ Slope m = -0.085
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">RMSE_line</span> <span class="o">=</span> <span class="n">RMSE</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">RMSE_line</span> <span class="o">=</span> <span class="n">RMSE</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>RMSE = 6.003
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8181,10 +8076,10 @@ Slope m = -0.085
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">rbias</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fit_data</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">rbias</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fit_data</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Compute the relative bias</span>
@@ -8204,33 +8099,20 @@ Slope m = -0.085
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">rbias_line</span> <span class="o">=</span> <span class="n">rbias</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">rbias_line</span> <span class="o">=</span> <span class="n">rbias</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>rbias = 0.002
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8345,10 +8227,10 @@ The confidence intervals as computed here and later in part 5 are based on a sim
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conf_int</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">conf_int</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Compute the confidence intervals</span>
@@ -8367,15 +8249,15 @@ The confidence intervals as computed here and later in part 5 are based on a sim
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">k</span> <span class="o">=</span> <span class="n">conf_int</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">k</span> <span class="o">=</span> <span class="n">conf_int</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
<span class="n">ci_low</span> <span class="o">=</span> <span class="n">line</span> <span class="o">-</span> <span class="n">k</span>
<span class="n">ci_up</span> <span class="o">=</span> <span class="n">line</span> <span class="o">+</span> <span class="n">k</span>
@@ -8394,24 +8276,6 @@ The confidence intervals as computed here and later in part 5 are based on a sim
</div>
</div>
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-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>Text(0.5, 1.0, 'Number of days as function of the year')</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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"/>
-</div>
-</div>
-</div>
-</div>
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@@ -8485,10 +8349,10 @@ If you consider that you need to place a bet with not only the day but also the
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">parabola</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">parabola</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Compute the quadratic model</span>
@@ -8507,15 +8371,15 @@ If you consider that you need to place a bet with not only the day but also the
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">popt_parabola</span><span class="p">,</span> <span class="n">pcov_parabola</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">curve_fit</span><span class="p">(</span><span class="n">parabola</span><span class="p">,</span> <span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">popt_parabola</span><span class="p">,</span> <span class="n">pcov_parabola</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">curve_fit</span><span class="p">(</span><span class="n">parabola</span><span class="p">,</span> <span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Optimal estimation for parameters:</span><span class="se">\n\</span>
<span class="s1">a = </span><span class="si">{</span><span class="n">popt_parabola</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s1">.3e</span><span class="si">}</span><span class="s1">, b = </span><span class="si">{</span><span class="n">popt_parabola</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">, c = </span><span class="si">{</span><span class="n">popt_parabola</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
@@ -8527,25 +8391,6 @@ If you consider that you need to place a bet with not only the day but also the
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-<pre>Optimal estimation for parameters:
-a = -1.277e-03, b = 4.942, c = -4654.244
-
-Covariance matrix for parameters:
-Sigma = [[ 5.60366689e-07 -2.20560328e-03 2.16981823e+00]
- [-2.20560328e-03 8.68165065e+00 -8.54118418e+03]
- [ 2.16981823e+00 -8.54118418e+03 8.40337452e+06]]
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@@ -8563,10 +8408,10 @@ Sigma = [[ 5.60366689e-07 -2.20560328e-03 2.16981823e+00]
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="n">fitted_parabola</span> <span class="o">=</span> <span class="n">parabola</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="o">*</span><span class="n">popt_parabola</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">fitted_parabola</span> <span class="o">=</span> <span class="n">parabola</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="o">*</span><span class="n">popt_parabola</span><span class="p">)</span>
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@@ -8583,15 +8428,15 @@ Sigma = [[ 5.60366689e-07 -2.20560328e-03 2.16981823e+00]
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-<div class="highlight hl-ipython3"><pre><span></span><span class="n">k</span> <span class="o">=</span> <span class="n">conf_int</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">fitted_parabola</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">k</span> <span class="o">=</span> <span class="n">conf_int</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">fitted_parabola</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
<span class="n">ci_low_2</span> <span class="o">=</span> <span class="n">fitted_parabola</span> <span class="o">-</span> <span class="n">k</span>
<span class="n">ci_up_2</span> <span class="o">=</span> <span class="n">fitted_parabola</span> <span class="o">+</span> <span class="n">k</span>
@@ -8610,24 +8455,6 @@ Sigma = [[ 5.60366689e-07 -2.20560328e-03 2.16981823e+00]
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-<pre>Text(0.5, 1.0, 'Number of days as function of the year')</pre>
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"/>
-</div>
-</div>
-</div>
-</div>
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@@ -8639,15 +8466,15 @@ Sigma = [[ 5.60366689e-07 -2.20560328e-03 2.16981823e+00]
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</div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">RMSE_parabola</span> <span class="o">=</span> <span class="n">RMSE</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">fitted_parabola</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">RMSE_parabola</span> <span class="o">=</span> <span class="n">RMSE</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">fitted_parabola</span><span class="p">)</span>
<span class="n">R2_parabola</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="p">((</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">fitted_parabola</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">/</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">var</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Coefficient of determination = </span><span class="si">{</span><span class="n">R2_parabola</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">rbias_parabola</span> <span class="o">=</span> <span class="n">rbias</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">fitted_parabola</span><span class="p">)</span>
@@ -8656,21 +8483,6 @@ Sigma = [[ 5.60366689e-07 -2.20560328e-03 2.16981823e+00]
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>RMSE = 5.918
-Coefficient of determination = 0.175
-rbias = 0.002
-</pre>
-</div>
-</div>
-</div>
-</div>
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@@ -8760,14 +8572,14 @@ No right answer, that depends on the risk you want to take ;)
article { position: relative }
</style>
<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">
-<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px">
-</img></a>
+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
+</a>
<a href="https://www.tudelft.nl/en/ceg" rel="TU Delft">
-<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
-</img></a>
+<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
<a href="http://mude.citg.tudelft.nl/" rel="MUDE">
-<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
-</img></a>
+<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
</h3>
<span style="font-size: 75%">
© Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
diff --git a/synced_files/GA_1_1/Task_2_solution.md b/synced_files/GA_1_1/Task_2_solution.md
index 0553e474..4f3f444c 100644
--- a/synced_files/GA_1_1/Task_2_solution.md
+++ b/synced_files/GA_1_1/Task_2_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.1, Part 2: Data-driven approach: Which model is better?
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.1, Friday, Sep 6, 2024. This assignment does not need to be turned in.*
-<!-- #region cell_id="21f9833788f64e78a35bc8cac535e76d" deepnote_cell_type="markdown" -->
+
## Overview
In this assignment we will fit two models to observations of ice break-up date and reflect on their performance.
@@ -43,7 +33,6 @@ We will follow these steps:
5. Interpret confidence intervals;
6. Compare the linear model with a non-linear one;
7. And finally...choose a model to make a bet in the Ice Classic!
-<!-- #endregion -->
```python
import numpy as np
@@ -54,9 +43,8 @@ import scipy.optimize as opt
%matplotlib inline
```
-<!-- #region cell_id="b1d3e3d2f92c4de29aba4aa61c525867" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
## 1. Import data
-<!-- #endregion -->
+
We will import the dataset by using the `numpy` function `loadtxt`. If you open the *data-days.csv* file, you will notice that the comma is used as decimal separator for the number of recorded days. For this reason, we will import the data in the following steps:
@@ -78,25 +66,19 @@ data = data.astype(float)
data[0:10]
```
-<!-- #region jp-MarkdownHeadingCollapsed=true -->
## 2. Preliminary analysis
-<!-- #endregion -->
-<!-- #region cell_id="1b15837c64e748b89fafad1f8007a399" deepnote_cell_type="markdown" -->
+
One of the first steps when getting familiar with new data is to see the dimensions of the data. To this end, we can use the `numpy` function `shape`.
-<!-- #endregion -->
```python
np.shape(data)
```
-<!-- #region cell_id="e901697b36064391b4a62d78c955dd6b" deepnote_cell_type="markdown" -->
The result is a (103, 2) array, i.e., a matrix with 103 rows and 2 columns. The first column contains the year of record, while the second one contains the measured data.
-<!-- #endregion -->
-<!-- #region cell_id="43139b45e34f4252a6a270336ca401ba" deepnote_cell_type="markdown" -->
+
We can also compute the mean and the standard deviation of the variable of interest (second column) to get a sense of how the variable behaves.
-<!-- #endregion -->
```python
mean = np.mean(data[:,1])
@@ -106,9 +88,7 @@ print(f'Mean: {mean:.3f}\n\
Standard deviation: {std:.3f}')
```
-<!-- #region cell_id="2a1ddf21c02141dc8dfebee83c602a73" deepnote_cell_type="markdown" -->
We can also quickly plot them to see the scatter of the data and the evolution in time.
-<!-- #endregion -->
```python
plt.scatter(data[:, 0], data[:, 1], label='Measured data')
@@ -118,15 +98,13 @@ plt.title(f'Number of days per year between {data[0,0]:.0f}-{data[-1,0]:.0f}')
plt.grid()
```
-<!-- #region cell_id="37edb558a10b47d88b3e4f683da56221" deepnote_cell_type="markdown" -->
In the figure above, we have plotted the year of the measurement in the x-axis and the number of days until the ice broke during that year in the y-axis. We can see that there is a significant scatter. Also, there seems to be a trend over time: as we go ahead in time (higher values in the x-axis), the number of days until the ice broke seems to decrease.
We have identifid a trend but **can we model it**?
-<!-- #endregion -->
-<!-- #region cell_id="a05297ff6298401192d49f8e257ff9ec" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## 3. Fit a linear regression model: is it a good model?
-<!-- #endregion -->
+
We are going to create a model which allows us to predict the number of days until the ice broke as function of the year. For that, we are going to assume a linear relationship between the variables (a linear model) and we will fit it using linear regression. This is, we will fit a regression model $days=m\cdot year+q$, where $m$ represents the slope of the line, and $q$ is the intercept.
@@ -176,7 +154,7 @@ r_sq, q, m = regression(data[:,0], data[:,1])
</p>
</div>
-<!-- #region cell_id="9a082db4cf644c9c9854af0a458c0b8a" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Solution:</b>
@@ -184,11 +162,9 @@ r_sq, q, m = regression(data[:,0], data[:,1])
<li> 2. Based on the answer to the previous question, the linear model is not an accurate model. Whether this low level of accuracy is good enough or not, depends on the use we want to give to the model. Would you bet 3\$? And 1,000\$? </li>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="143e5d1bf8324d9f80fca4af9a0d162c" deepnote_cell_type="markdown" -->
+
We can also plot the data and the fitted model to see how the fit looks. To do so, we can make computations using the previous equation $days=m\cdot year+q$ with the fitted intercept $q$ and slope $m$. We have already defined a function which does it for you.
-<!-- #endregion -->
```python
def calculate_line(x, m, q):
@@ -232,7 +208,6 @@ axes[1].grid()
axes[1].set_title('(b) Observed and predicted number of days')
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 2:</b>
@@ -242,9 +217,8 @@ axes[1].set_title('(b) Observed and predicted number of days')
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="9a082db4cf644c9c9854af0a458c0b8a" deepnote_cell_type="markdown" -->
+<!-- #region -->
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Solution:</b>
@@ -257,11 +231,9 @@ In the plot (b), we compare the observations with the predictions of the model.
</div>
<!-- #endregion -->
-<!-- #region cell_id="67e44fda81f24273b8d28edd35a50d87" deepnote_cell_type="markdown" -->
We can also assess the scatter using the Root Mean Square Error ($RMSE$). Don't you remember it? Go back to the [book](https://mude.citg.tudelft.nl/book/modelling/gof.html)!
Let's see how our model performs for this metric.
-<!-- #endregion -->
```python
def RMSE(data, fit_data):
@@ -288,7 +260,6 @@ def RMSE(data, fit_data):
RMSE_line = RMSE(data[:,1], line)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3:</b>
@@ -300,9 +271,8 @@ RMSE_line = RMSE(data[:,1], line)
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="604f11f41ac54d59b11422bac89e6411" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Solution:</b>
@@ -310,7 +280,7 @@ RMSE_line = RMSE(data[:,1], line)
<li> 2. Based on the previous interpretation, the linear model is not accurate. Whether this low level of accuracy is good enough or not, depends on the use we want to give to the model. Would you bet $3? And $1,000? </li>
</p>
</div>
-<!-- #endregion -->
+
Finally, we can compute the bias of our model using $rbias$.
@@ -336,7 +306,6 @@ def rbias(data, fit_data):
rbias_line = rbias(data[:,1], line)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 4:</b>
@@ -346,18 +315,16 @@ rbias_line = rbias(data[:,1], line)
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="604f11f41ac54d59b11422bac89e6411" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Solution:</b>
<li> 1. $rbias$ provides an standardized measure of the systematic tendency of our model to under- or over-prediction. It is very low for our model, so it does not have a clear tendency to under- or overestimate and, thus, does not seem to be biased. </li>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 5:</b>
@@ -369,7 +336,7 @@ rbias_line = rbias(data[:,1], line)
</ol>
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -378,9 +345,9 @@ No right answer, that depends on the risk you want to take ;)
</p>
</div>
-<!-- #region cell_id="19496dbefd3247f09c2226579c7b665f" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## 4. Confidence Intervals
-<!-- #endregion -->
+
One way of assessing the uncertainty around the predictions of a model are confidence intervals. They give us insight into the precision of their predictions by transforming them into probabilities. In short, the 95% confidence interval (significance $\alpha=0.05$) shows the range of values within which my observation would be with a probability of 95%. Here, we want you to focus on their interpretation. In the following weeks (1.3), you will learn more about how to compute them.
@@ -426,7 +393,6 @@ plt.legend()
plt.title('Number of days as function of the year')
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 7:</b>
@@ -435,28 +401,25 @@ plt.title('Number of days as function of the year')
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="604f11f41ac54d59b11422bac89e6411" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Solution:</b>
If you consider that you need to place a bet with not only the day but also the hour and minute at which the ice would break, the model is not accurate enough. You can see that the confidence interval spans almost 20 days!
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="a19fb5b7e1cd4c32b435b8b967933700" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## 5. Non-linear models
-<!-- #endregion -->
+
As we have seen, the data-driven linear model is not really a good choice for representing the data we have. Let's try with one which is slightly more complicated: a non-linear model.
In this section, we will analyze the fitting of a quadratic model as $days = A year^2 + B year + C$. The steps are the same as in the previous section, so we will go fast through the code to focus on the interpretation and comparison between the two models.
-<!-- #region cell_id="ca8a6b9d68234ae69a799a3f4f3866a2" deepnote_cell_type="markdown" -->
+
You do not need to worry about this right now, but in case you are curious: we will make use of the `scipy.optimize` library, which contains the `curve_fit` function. For further info on the function see [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html).
-<!-- #endregion -->
```python
def parabola(x, a, b, c):
@@ -485,19 +448,15 @@ print(f'Covariance matrix for parameters:\n\
Sigma = {pcov_parabola}')
```
-<!-- #region cell_id="10efd81771064ce0ac4d50095be06e23" deepnote_cell_type="markdown" -->
Therefore, our parabola now looks like $days = -1.277 \cdot 10^{-3} \cdot year^2 + 4.942 \cdot year - 4654.244$.
Now that we have fitted it, we can use it to compute predictions.
-<!-- #endregion -->
```python
fitted_parabola = parabola(data[:,0], *popt_parabola)
```
-<!-- #region cell_id="6cc6a36c668f4ee2b26d41b27d335691" deepnote_cell_type="markdown" -->
We can also determine the confidence intervals for this fit and see how it looks!
-<!-- #endregion -->
```python
k = conf_int(data[:,1], fitted_parabola, 0.05)
@@ -525,7 +484,6 @@ print(f'Coefficient of determination = {R2_parabola:.3f}')
rbias_parabola = rbias(data[:,1], fitted_parabola)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 8:</b>
@@ -534,7 +492,7 @@ rbias_parabola = rbias(data[:,1], fitted_parabola)
</ol>
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
diff --git a/synced_files/GA_1_2/Analysis.html b/synced_files/GA_1_2/Analysis.html
index 4fbf1784..30ac9087 100644
--- a/synced_files/GA_1_2/Analysis.html
+++ b/synced_files/GA_1_2/Analysis.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
- var scriptElement = document.createElement('script');
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
-
- var widgetState;
-
- // Fallback for older version:
- try {
- widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
-
- if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) {
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-js-widgets@*/dist/embed.js';
-
- }
- } catch(e) {}
-
- scriptElement.src = widgetRendererSrc;
- document.body.appendChild(scriptElement);
- }
-
- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7596,10 +7569,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
@@ -7627,10 +7600,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">stefan</span><span class="p">(</span><span class="n">constant</span><span class="p">,</span> <span class="n">H0</span><span class="p">,</span> <span class="n">Ts</span><span class="p">,</span> <span class="n">Tfr</span><span class="p">,</span> <span class="n">time</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">stefan</span><span class="p">(</span><span class="n">constant</span><span class="p">,</span> <span class="n">H0</span><span class="p">,</span> <span class="n">Ts</span><span class="p">,</span> <span class="n">Tfr</span><span class="p">,</span> <span class="n">time</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">constant</span><span class="o">*</span><span class="n">time</span><span class="o">*</span><span class="nb">abs</span><span class="p">(</span><span class="n">Ts</span><span class="o">-</span><span class="n">Tfr</span><span class="p">)</span> <span class="o">+</span> <span class="n">H0</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
@@ -7645,7 +7618,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Ice thickness: '</span> <span class="o">+</span>
+<div class="highlight hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Ice thickness: '</span> <span class="o">+</span>
<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">stefan</span><span class="p">(</span><span class="mf">1.44</span><span class="o">*</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">),</span><span class="w"> </span><span class="mf">0.15</span><span class="p">,</span><span class="w"> </span><span class="mi">261</span><span class="p">,</span><span class="w"> </span><span class="mi">273</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="o">*</span><span class="mi">24</span><span class="o">*</span><span class="mi">3600</span><span class="p">)</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> m'</span><span class="p">)</span>
</pre></div>
</div>
@@ -7687,10 +7660,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">H_taylor</span><span class="p">(</span><span class="n">mu_H0</span><span class="p">,</span> <span class="n">mu_iT</span><span class="p">,</span> <span class="n">sigma_H0</span><span class="p">,</span> <span class="n">sigma_iT</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">H_taylor</span><span class="p">(</span><span class="n">mu_H0</span><span class="p">,</span> <span class="n">mu_iT</span><span class="p">,</span> <span class="n">sigma_H0</span><span class="p">,</span> <span class="n">sigma_iT</span><span class="p">):</span>
<span class="w"> </span><span class="sd">""" Taylor series approximation of mean and std of H"""</span>
<span class="c1"># Write your own preliminary variables here</span>
@@ -7811,7 +7784,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">mu_iT</span> <span class="o">=</span> <span class="mi">10</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">mu_iT</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">sigma_iT</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">mu_H0</span> <span class="o">=</span> <span class="mf">0.20</span>
<span class="n">sigma_H0</span> <span class="o">=</span> <span class="mf">0.03</span>
@@ -7892,7 +7865,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">N</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="mi">50000</span><span class="p">]:</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">for</span> <span class="n">N</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="mi">50000</span><span class="p">]:</span>
<span class="n">mu_H_simulated</span><span class="p">,</span> <span class="n">sigma_H_simulated</span><span class="p">,</span> <span class="n">h_samp</span> <span class="o">=</span> <span class="n">samples_plot</span><span class="p">(</span><span class="n">N</span><span class="p">,</span>
<span class="n">mu_H0</span><span class="p">,</span>
<span class="n">mu_iT</span><span class="p">,</span>
@@ -7930,7 +7903,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mi">10</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'for an ice thickness of </span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s1">5.2f</span><span class="si">}</span><span class="s1"> m --> '</span> <span class="o">+</span>
@@ -7987,7 +7960,4 @@ $$</p>
</div>
</main>
</body>
-<script type="application/vnd.jupyter.widget-state+json">
-{"state": {}, "version_major": 2, "version_minor": 0}
-</script>
</html>
diff --git a/synced_files/GA_1_2/Analysis.md b/synced_files/GA_1_2/Analysis.md
index e8f3f382..62afae88 100644
--- a/synced_files/GA_1_2/Analysis.md
+++ b/synced_files/GA_1_2/Analysis.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.2: Analysis Notebook
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.2, Friday, Sep 13, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+<!-- #region -->
_This assignment does not need to be turned in._
## Let's account for the latest news!
@@ -98,7 +88,7 @@ print('Ice thickness: ' +
2. Find the distribution of `H_ice` numerically with a simulation, then compare this to the Normal distribution defined by the mean and standard deviation of the linearized function of random variables
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -107,7 +97,6 @@ $\textbf{Task 1}$
Complete the two functions in the cell below, and support your work by including an image showing the analytic equations.
</p>
</div>
-<!-- #endregion -->
```python
def H_taylor(mu_H0, mu_iT, sigma_H0, sigma_iT):
diff --git a/synced_files/GA_1_2/Analysis_solution.html b/synced_files/GA_1_2/Analysis_solution.html
index bc14398f..fe61988c 100644
--- a/synced_files/GA_1_2/Analysis_solution.html
+++ b/synced_files/GA_1_2/Analysis_solution.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
- var scriptElement = document.createElement('script');
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
-
- var widgetState;
-
- // Fallback for older version:
- try {
- widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
-
- if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) {
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-js-widgets@*/dist/embed.js';
-
- }
- } catch(e) {}
-
- scriptElement.src = widgetRendererSrc;
- document.body.appendChild(scriptElement);
- }
-
- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7596,10 +7569,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
@@ -7627,44 +7600,31 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">stefan</span><span class="p">(</span><span class="n">constant</span><span class="p">,</span> <span class="n">H0</span><span class="p">,</span> <span class="n">Ts</span><span class="p">,</span> <span class="n">Tfr</span><span class="p">,</span> <span class="n">time</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">stefan</span><span class="p">(</span><span class="n">constant</span><span class="p">,</span> <span class="n">H0</span><span class="p">,</span> <span class="n">Ts</span><span class="p">,</span> <span class="n">Tfr</span><span class="p">,</span> <span class="n">time</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">constant</span><span class="o">*</span><span class="n">time</span><span class="o">*</span><span class="nb">abs</span><span class="p">(</span><span class="n">Ts</span><span class="o">-</span><span class="n">Tfr</span><span class="p">)</span> <span class="o">+</span> <span class="n">H0</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=3a8c482a">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=3a8c482a">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Ice thickness: '</span> <span class="o">+</span>
+<div class="highlight hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Ice thickness: '</span> <span class="o">+</span>
<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">stefan</span><span class="p">(</span><span class="mf">1.44</span><span class="o">*</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">),</span><span class="w"> </span><span class="mf">0.15</span><span class="p">,</span><span class="w"> </span><span class="mi">261</span><span class="p">,</span><span class="w"> </span><span class="mi">273</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="o">*</span><span class="mi">24</span><span class="o">*</span><span class="mi">3600</span><span class="p">)</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> m'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Ice thickness: 0.259 m
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1ee42f39">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7700,10 +7660,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">H_taylor</span><span class="p">(</span><span class="n">mu_H0</span><span class="p">,</span> <span class="n">mu_iT</span><span class="p">,</span> <span class="n">sigma_H0</span><span class="p">,</span> <span class="n">sigma_iT</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">H_taylor</span><span class="p">(</span><span class="n">mu_H0</span><span class="p">,</span> <span class="n">mu_iT</span><span class="p">,</span> <span class="n">sigma_H0</span><span class="p">,</span> <span class="n">sigma_iT</span><span class="p">):</span>
<span class="w"> </span><span class="sd">""" Taylor series approximation of mean and std of H"""</span>
<span class="c1"># # Write your own preliminary variables here</span>
@@ -7855,15 +7815,15 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=55ff8dd6-86ef-401a-9a56-02551c348698">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=55ff8dd6-86ef-401a-9a56-02551c348698">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">mu_iT</span> <span class="o">=</span> <span class="mi">10</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">mu_iT</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">sigma_iT</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">mu_H0</span> <span class="o">=</span> <span class="mf">0.20</span>
<span class="n">sigma_H0</span> <span class="o">=</span> <span class="mf">0.03</span>
@@ -7900,45 +7860,6 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Comparison of propagated and simulated distributions:
-
-Number of iT samples adjusted to 0: 80 (0.8% of N)
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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iTxRbSw4oINmpqaUl+eGGOsf//+XN09e/ZILV+zZg0DwJo2barwPkVKE2woTps2bWR+QS5LcPLDhw9MX1+fAWBLliyRWU8UDPPz85MoFx0jAPb3339L1Xvx4gUXiLh9+7ZiB8kYCwkJYQCYr6+vRHmDBg2YpqYmW7FiBQMgEXiKi4tjAJihoSHLz8+XqFcewcnyPkZZFA1ONm7cmAFg06dPl7vO2rVrGQDm6OgoUa6jo8MAsPXr18utO23aNAaA9ejRgyt78+YNdx6OHz8ut67oPTVp0iSu7Nq1a1zdBw8eyK0rChwtW7ZM7jriXr58yT3nJUHXi+KvF7t372YAWN26daWCnyI9evRgANiiRYu4ssTERO46fuTIEak6iYmJXJC3tMFJdXV1mc8PY4x16dKFAWAzZsyQuTwpKYnp6OgwPp/Pnj9/rvC+N27cyACwCRMmSC0rr+CkKMhoZ2fHXr16pXDbGBP+MMPj8aTqlSU4mZCQwD2Xe/fulaqXm5vLateuzQCwyZMnSywTXVsBsKioKKm6omsCj8dj6enpXHloaKjMzxpCCCGEEEIIY5RzshJMmjQJV65cwaBBg6CnpwdAOMz7+PHj+OWXX1CvXj2MHDkSHz9+LPG2O3fuDBsbG6lyFxcXAICRkRH69+8vtVw05FGUE7My5Ofn49ChQ5g0aRK6deuGtm3bwsPDAx4eHnj8+DEA4ObNm+W2v+joaGRmZkJbWxsTJkyQuY5oopNTp07JHI5oYGCAoUOHSpVbWFhwQ3uLyk9XWLt27QAAFy5c4HL8vXz5EnFxcWjdujW6dOkC4L9h3OL/9/LygopK+b9ly/sYy+Lz588AIDGUtTDRkM3C+RtLW1dUr7LrFsXMzAyamppIT0+Xyq1ZFnS9APbu3QsAGDp0KHR1dWWu07dvXwCS78MTJ06goKAAderUQbdu3aTq2Nraonfv3iVujzhfX1+Zz09mZiY3U/y4ceNk1rW2toabmxvy8/MRGRkptfz+/fsIDg5G37594ePjw53LlStXAijfa6+4JUuWYOPGjTAwMMDRo0dhbm4utU52djZ27tyJcePGoXPnzvD09OTal5WVBcaY3PzApSF6Lu3s7LjnWpyqqiomT54MQDipnSxOTk7w9PSUKndxcYGGhgYYYxLvF9HzeunSJe71SwghhBBCCBGinJOVxNXVFaGhocjPz8fdu3dx8+ZNhIeH4+jRo3j37h22bt2K1NTUEk9OIC+HZY0aNQAIJwcpavmHDx9KtL/SEs/xVpS3b9+W2z4fPnwIQJhLTEdHR+Y6olxlnz59QlJSEuzt7SWW16tXT+5kCubm5nj06FGJzqG1tTXq1KmD+Ph4XLp0Cd7e3lwApF27dqhfvz6srKwQGRmJgoICqKiolCjfZGmU9zGWhZaWFgAgJydH7jqiST+0tbWl6n78+LHEdUX7LM1+y1K3KHw+Hz///DMWLFiAXr16oVGjRujQoQPc3d3h7e2NWrVqKbSdwuh6Ady+fRuAMNfhmTNnZK6Tnp4OAHj+/DlXFhcXBwBo1KiR3G03btyYC36Whrxt3717F/n5+eDxePjmm2/k1n/06BEAyXYDwIwZM7B48WIur60s5XntFTlw4ABmzJgBVVVV7Nu3T+bxPXjwAN26dZOaaKoi2yf6bBDlXZVF9Nnw5MkT5Ofng8/nSyyvX7++zHo8Hg81atRAcnKyxPulZcuW8PT0RHR0NBo0aAAPDw94eXmhRYsWaNu2LffDJSGEEEIIIV8j6jlZyfh8PpydnTFixAhs27YNT58+5XrbHD16FJcvXy7R9uQF3URfuIpbXtSX1fI0YsQIxMTEwN7eHqGhoXj27Bmys7PBhKkFuJmZc3Nzy22fWVlZAICaNWvKXUd8BnXR+uLknT8AXC/GgoKCErVL1HtSFHQUD04CwiBkeno6bt68CcYYN7OtaHl5q4hjLC0jIyMARQci3r17J7FuWesaGBhwx1nSuuL/L02bizJ//nxs2LABTk5OuH//PlauXImhQ4fCysoKHTp0kDm5SHHoeiGc1AYQBqguXLgg8+/evXsAJHu6iq4Psnr+iRS1TBHyzr+ozYwxuW2+cOECN7O1eLt37dqF33//HTweD4GBgbh9+zYyMzORn58PxhjOnj0LoHyvvQBw48YNfPPNNygoKMD69evh6+srtU5BQQH69u2LhIQENG/eHGFhYUhJSYFAIOCea1HvRGV9NjDGZAblS3rdVFFRwdGjR/HLL7/AwsICUVFRmD9/Prp3744aNWpg1KhRFRIgJoQQQgghpDqg4KSSGRgYYOvWrdyXmZIGJyuSvEBESYefv3r1CidPngQAHD58GIMGDYKNjQ00NDS4dSriS5moJ8qrV6/krvPy5Uup9SuaqAekKCgZHh4OPT09bmZXURAyPDwcsbGxSEtLg5mZGZo0aVIp7VMmBwcHAEUPIxcNlRStW9a6ampqCg1fl1XXzs6Oex3Lq5uTk4Pk5GSZbS4Kj8fD2LFjcfv2baSmpmL//v2YNGkSzMzMcObMGbRr1w4vXrxQeHsVrbpcL0RDuQ8ePMgFwOT9JSYmcvXEU3LIU9SyshC12dDQsNg2M8YQFBTE1Q0JCQEA/PzzzwgKCoKTkxP09PQUCsiXVnJyMnr06IFPnz5hxowZGD16tMz1rl69igcPHkBLSwsnT55Ejx49UKtWLYkUCcr+bODxeHKH/5dmvwsXLsTz58/x+PFjbN26FYMHDwYAbNmyBX5+fpX2QxAhhBBCCCFVCQUnqwADAwOYmZkBKHpoaGUR9QiR90VbNHRQUaLhesbGxjKH9eXl5eHatWsy68obcqeIBg0aAACePn0qN9ff3bt3AQiH28rK9VYRRMHHq1ev4u7du0hISICnpydUVYVZFtq3bw9AGLwUBTC9vb1LdC7Kct6UqVWrVgCAa9eucUOhC4uKipJYt3Dd6OhomfWys7Nx9erVIuuKtl1YUlISF6gSr8vn87mgsry6ly9fRk5ODrS0tNC0aVOZ6xTHzMwMvXv3xqpVq/D48WPY2dnh7du32LNnT6m2V56q2/VCNFz3woULJWqX6Hry4MEDueuIelyWt8aNG4PH4yE9PR33798vUV3R+ZSVHxEALl68WOb2icvKykL37t3x8uVL9O/fHwsXLiy2bQ0bNoSpqanU8rdv33JDsAsrj8+Ge/fuyQ2qiz4b6tWrJzWkuzzUrVsXw4cPx86dO3H58mXweDycP3++VD2iCSGEEEIIqe4oOFnB0tLSiu0J8fDhQ6SmpgKQn8eqMtWrVw+AMHF/YRkZGQgNDS3R9kR59jIzM2X2ogoJCeGGI8qrKz7xiKI8PDygr6+PT58+YcOGDTLXWbZsGQCgU6dOXHCwopmbm6Nhw4bIzc1FcHAwAMkh26K8lNHR0VwPspLmmyzLeVOmXr16QUNDAx8/fsTff/8ttfzUqVN4+vQpNDU10atXL4lloolc4uPjcfr0aam627Ztw6dPn2BhYYG2bdtKLBswYAAAICIigsstKO7PP/8EALi5uXG9LAvX3bt3r8xeXqK63bt3VzjnZFH09fXh7OwMAFWi52R1u16Inq9NmzZJ9JwuTqdOnaCiooInT57g+PHjUsuTkpJw8OBBhbdXEqamptw1Yv78+SWqKzonsl4rqamp+Ouvv8rewH/l5+dj0KBBuHPnDlq0aIFt27YVGUQUte3Vq1cyg4R//PEH8vPzi6xbmmtc586doaKigoSEBJnPWV5eHjdRkKzJj8qbs7MzDAwMAFSN9zQhhBBCCCGVjYKTFWzXrl1o3LgxVq5cKTVJAWMMJ0+eRK9evcAYg7W1NTp16qSklv6nZ8+eAISzrIr34nj16hUGDx7MTRahqMaNG8PU1BR5eXmYOHGixJfJPXv24IcffuBmMy6sTp064PF4SE1NLXGvJB0dHfz0008AgLlz50rMeiwQCBAQEIDIyEioqqpi5syZJdp2WYkCDf/884/EY/HlHz9+5IKTJc03KZr45OrVq6WaBV5ZTE1N8f333wMAAgICJHoj3r17lxse+uOPP8LY2FiirpOTEzfz7ujRo7meT4CwV2NAQAAA4WuhcE+o7t27w9XVFQUFBRg0aBA3DBsA9u3bh6VLlwIA5s2bJ9Xm0aNHw9raGllZWRg0aBCXX5IxhtWrVyM0NBSqqqqYO3euwufh/v37GDVqFM6fPy/148bp06e5PIGiXpvKVN2uF4MHD0bLli3x/v17tGvXDufPn5da58GDB5g7dy4OHz7MldnZ2WHQoEEAgPHjx3MT6wBASkoKt6yiLF68GJqamti1axdGjx4tNSQ5JycHJ0+elJpt3cvLCwCwcOFCicD706dP0a1bt3L9AWPy5Mk4duwYateujbCwMLnPk0irVq2gpqaGFy9eYNasWVwgsqCgAKtXr8aiRYvkbkN0jbt3716Jh9Pb2tri22+/BQB89913Er1Hs7KyMHz4cMTHx0NfX5+btbustm/fjrlz50r9+JGbm4slS5YgPT0dqqqqpe5dTQghhBBCSLXGSIVas2YNA8D9WVhYMBcXF+bk5MSMjIy4cnNzc3b9+nWp+qLlCQkJEuX+/v4MAAsMDJS5361btzIAzMvLS+byhIQEbtuFpaenM3t7ewaAqaioMAcHB+bs7MxUVVWZjY0N+/XXX+Vu29bWlgFg4eHhEuWbN2/m9mdgYMBcXFxYrVq1GADWqVMn9s0338g9nu7duzMATF1dnTVv3px5eXkxLy8vFhMTU+x5ys3NZX379uWW29jYMDc3N2ZgYMAAMD6fzzZu3Fji88cYY15eXgwA27p1q9x15Nm3bx/XJmNjY1ZQUCCxPDQ0lFteq1YtuduRd74/fPjAzM3NGQBmaGjIWrRowZ23yjjG0NBQZmJiwv1pamoyAExDQ0OiPDQ0VKpudnY2a9euHXf8Dg4OzNHRkamoqDAAzNfXlwkEApn7fffuHWvSpAn32nV0dGQODg7ctvz9/aXOtUhCQgKztLRkAJiamhpr2rQps7Oz4+rOnTtX7vFeu3aNe01pa2uz5s2bc69vFRUVtmnTphKdv5iYGG6/2trazMnJibm5uXHbBMD8/PykjoWuF4pdL16/fs1at24tcf11d3dnzZo1Y8bGxlx54dd9Wloa9/oCwBo2bMiaNm3KVFVVmYmJCZszZ06x7ylZint+RI4ePcq9zlRUVFiDBg1Yy5YtWcOGDZmamprM5yglJYW7FqiqqrJGjRpx7ydDQ0O2evVqBoDZ2tpK7U/e+z88PFxmHdH+7e3tWZs2beT+3bx5k6sjOmcAmKmpKXN1dWWmpqYMABs9erTcNhQUFDBHR0cGgOno6DA3NzfuuX758iVjrOjXbWZmJvPw8OCW161bl7m6ujJtbW0GgGlpabGjR49K1QsMDOSuJfLIel3/8ccf3L5MTExY8+bNWbNmzZihoSFXvnz5crnbJIQQQggh5EtGPScr2Lhx4xAVFYU5c+bA29sb2traiIuLQ1xcHNTV1eHj44OlS5fi4cOHcHFxUXZzAQhzYF64cAGjR4+Gubk5nj59infv3mHcuHG4efMmLC0tS7zNkSNH4sCBA2jVqhVycnIQFxcHMzMzLFmyBEeOHCkyp9e2bdvw/fffw8rKCnfv3kVkZCQiIyMV6pGlqqqKvXv3YseOHWjXrh2ysrJw69Yt6OjoYPDgwbh69arcyRoqko+PDzfcUVY+yXbt2nFlJR3SDQh7jZ49exZ9+/aFpqYmbty4wZ23ypCdnY23b99yf6L8kQKBQGa5OA0NDZw6dQorV66Ei4sLUlJSkJCQgGbNmmHVqlU4ceKExIQZ4oyMjHD16lXMmzcPDRs2xJMnT/Dq1St4eHjg77//RkhIiNxhpnZ2drhz5w6mTp0KW1tbPHjwAJmZmejYsSOOHj3KDcGXxdXVFbGxsRg3bhxMTU1x9+5d5Obmws/PD+fPn8eoUaNKdP7q16+PzZs3Y8iQIbCxsUFycjJiYmKQk5MDX19fbNu2Df/880+VyC1aHa8XNWrUQGRkJP7++2906dIFjDHExMTg+fPnsLGxwciRI3Ho0CGp3pAmJia4ePEiZsyYgdq1ayM+Ph6vX7/G0KFDcfPmTdjb25f4WEuia9euiIuLw8yZM+Hs7IwXL14gJiYGnz59QsuWLREYGIiYmBiJOrVq1cKVK1cwdOhQGBkZ4fHjx0hPT4e/vz9iYmIqZKKtp0+fFjmreEZGBrfuvHnzsGnTJjRt2hRZWVl49OgR6tati02bNmHjxo1y98Hj8XDs2DH4+/vD2NgYt27d4p5reflqxenp6eHcuXNYu3YtWrdujdTUVNy5cwdmZmYYPXo07ty5g65du5bL+QCAvn37YunSpejWrRv09fXx8OFD3Lt3D3p6ehg4cCCioqIwZcqUctsfIYQQQggh1QmPMTnZ4AkhhBBCCCGEEEIIIaQCUc9JQgghhBBCCCGEEEKIUlBwkhBCCCGEEEIIIYQQohQUnCSEEEIIIYQQQgghhCgFBScJIYQQQgghhBBCCCFKQcFJQgghhBBCCCGEEEKIUlBwkhBCCCGEEEIIIYQQohQUnCSEEEIIIYQQQgghhCgFBScJIYQQQgghhBBCCCFKQcFJQgj5CkRERIDH48HOzk7ZTVGIt7c3eDweQkJClN0UQgghhBCZ7OzswOPxEBERUWn7LMs9kry6Rd0nfkn3ZDweDzweD4mJicpuCiGkEApOkq9aTk4Ozp49i59++gkuLi4wMDCAuro6atasie7du+Off/4p1XZFH/CK/I0YMULmNl68eIGpU6fC0dEROjo6UFdXh6WlJfr06YNTp04Vuf/8/HysWbMGbm5u0NfXh76+Ptzc3LB27VoUFBSU6pjKQ0pKCiZMmAA7OztoamrC3Nwcffv2xeXLl+XWSU9Px549exAQEIB27drB0NCQO3ek6klMTERQUBBWrFih7KZUuB07dmDMmDFwdXVFrVq1oKGhAT09PTg5OWHq1Kl4/vx5qbYbEhJS7HVj0KBBMus+fPgQy5cvR6dOnWBhYQF1dXUYGBjAzc0N8+bNw/v37xVqw+nTpzFw4EBYW1tDQ0MDZmZmcHFxwZQpU/Dq1atSHVdZZGdnY8GCBdz10MjICG3btsXOnTvl1hFdO/z9/dGgQQNoa2tDU1MT9vb28Pf3x/Xr1yvxCAghhJSVKEgm/sfn82FsbIzWrVtj8eLF+Pjxo7Kb+dW4desWgoKClBK0lHVvpK6uDgsLC3Tt2hWhoaFgjFV4O5R5Dgj54jBCvmKzZ89mABgApqqqyho0aMCaNWvG9PT0uPI+ffqwnJycEm335s2brE2bNnL/mjVrxm1/48aNUvWvXr3KDA0NGQDG5/NZnTp1WNOmTZm+vj5Xb9q0aTL3LRAIWIcOHbj1HBwcmKOjI+PxeAwA69SpU4mPpzzcvHmTOyZtbW3WvHlzVqtWLe4Yt2zZIrPegQMHuGMp/EcUFx4ezgAwW1vbarEfLy8vBoBt3bq1XNpVEerUqcMAMA0NDWZnZ8dcXV2Zra0tU1FRYQCYnp4eO3v2bIm3u3XrVgaA6evry72GBAYGStV78uSJxPujVq1azNXVlVlaWnJlFhYW7M6dO3L3nZuby7799ltu/Zo1azI3NzdWv359pqWlxQCw6OjoEh9TWbx//545OzszAExFRYU5OjoyBwcHro0jR46UWc/Dw4NbR0dHhzk5ObFGjRoxdXV1blu///57pR4LIYSQ0hPdG1hbW3Ofh+7u7szU1JS73terV4+lpKRUWptsbW0ZABYeHl5p+yzLPdKwYcOYg4MD279/v0R5Ufdv8uqI7le8vLxK3I6yEj3fTZo04V4LTZs2ZTo6OtyyXr16sdzcXJn1EhISyqUdyjwHhHxp6Ns9+arNmjWLtWnThu3cuZNlZWVx5Tk5Oey3337jPsBmzpxZrvv9888/uSBdZmamxLKCggJWv359BoA1b96cPXr0iFuWnZ3N5syZw7UrIiJCatsBAQEMADM0NGSRkZFceWxsLLO2tq6Q4ynO58+fmY2NDQPAfH192du3bxljwmNdtWoVFxy+d++eVN0TJ04wT09PNnnyZLZ9+3a2a9cuCk6WAgUny9/atWtZVFSUVLD/0aNHXGDMzMyMffz4sUTbLe2N7uPHj1mNGjXYvHnzWHx8vMSy8+fPc1+g7O3tWXZ2tsxt+Pv7czf7Fy5ckFiWm5vLzp07V6lf+hhjbMCAAQwAs7GxYbGxsVx5ZGQkMzAwYADY//73P6l6bdu2ZYMHD2bnzp2T+HKSlpbGBg0axF1HTp06VSnHQQghpGxE9wayfqDbt28fF5jq2bNnpbWpugUn5SnN/VtVCE4WPu/Z2dls7ty53PLly5fLrEfBSUKqHvp2T75qaWlpRS4fM2YMA8BMTExYfn5+ue3X3d2dAWDffvut1LJ79+5xH5w3b96UWb9ly5YMAPv5558lyt+8ecM0NTXlflk/efIkA8C0tLS4AGFlWL16NdeTTNY5Hzx4MAPABg4cWOy2rl27RsHJUqDgZOV6+fIl9zo9fvx4ieqW9kb38+fP7MOHD3KXX7hwgWvToUOHpJYfPXqU63FZ3LWxssTGxhYZRNywYQPX5ry8PIllRR1DTk4Oa9SoEQPAevfuXe7tJoQQUv6KCk4yxriOBSoqKpV2n0vByaoVnBTp3LkzA8CaNm0qsx4FJwmpeijnZCUTT5r88OFDDBkyBDVr1oS2tjacnZ3x119/cetmZmZi5syZqFevHjQ1NWFtbY1p06bh06dPcrcvEAiwZs0aeHp6wtjYGBoaGrCzs8Po0aPx5MkTmXWeP3+OlStXonPnzqhTpw60tLSgr68PFxcXLFiwAB8+fJBZLygoCDweD8OHD0d+fj7++OMPODk5QUtLC0ZGRujevTtu3LhRthNWwUxMTIpc3qVLFwDA27dv8ebNm3LZ5/3793H16lUAwMiRI6WWiz+/9erVk7mN+vXrAwByc3MlysPCwpCdnQ0dHR0MGzZMql7Hjh1hb2+Pz58/IywsTOa2U1NT8csvv8DJyQl6enrQ1tZGkyZNMHfuXGRkZCh2kIXs2bMHADBgwACZ53z8+PFc+4t6fZdURb/f5FHWeyo3NxeLFy9G48aNuZye/fr1Q2xsbImPQdzBgwfRpUsX1KhRA2pqajAxMUHDhg3xzTff4ODBg9x63t7e8PHxAQA8e/ZMKhdQ4WTxt2/fRu/evWFiYgJtbW04Ojpi6dKlyM/PL1N7q4KaNWvC2NgYACot/5WmpiZ0dHTkLm/dujUMDAwAAA8ePJBavmTJEgDA9OnTi702ylKaz5/iiK4dderUQYcOHaSWf/vtt9DW1saLFy8QHR0tsayoY1BTU4Ovry8A2eeiKIUnDQgNDUWrVq2gr68PU1NT+Pn54f79+9z6N27cQJ8+fWBubg4tLS24uLhg//79JdonIYSQ4rVv3x4AUFBQgPj4eADS1+y///4bHh4eMDIyAo/Hw61bt7j6ycnJmDRpEurXrw8tLS0YGBjA3d0dy5cvR3Z2drH7v3v3LgYMGICaNWtCU1MTDRo0wPz58+XWjYmJwdy5c9GmTRtYWVlBXV0dJiYmaNeuHf7++2+FciYmJydj5MiRsLKygoaGBmrXro2pU6ciPT1d5vqlmdxGVh07Ozsub35kZKTUPV9iYiKWLFkCHo+HNm3aFLn9IUOGgMfj4ccff1S4TcURvRYePXqkcB3GGEJDQ9GhQweYmJhAXV0dVlZWGDp0KGJiYqTWV+QcEEJKQNnR0a+N6Ne1pUuXMl1dXaarq8tcXFyYubm5RPfztLQ01qhRI8bn85mTkxOzt7fncgZ27dpV5rZfvHjB5TLk8XjMysqKOTs7M21tbQaA6erqsjNnzkjV+/nnn7nedLVr12Zubm7M3t6e8fl8bnjf+/fvpeoFBgYyAGzo0KGsY8eODACrW7cuc3Z2ZhoaGtw2r169Wt6nsdLs3LmTe14yMjLKZZtTp05lAFidOnVYQUGB1PKsrCzuOTt27JjUcoFAwA373rlzp8SyUaNGMQDMx8dH7v6HDx/OALAxY8ZILTt//jwzMTFhAJiamhqrX78+a9CgAVNVVeXy+CQnJ5foePPy8rjXw19//SVzHYFAwOWBKzyUtLCS9JysyPdbUZTxnsrOzma+vr7ccdnb2zMXFxemqanJtLS02KJFi0rVo1HUJvw7RLl58+asYcOGXP7TNm3acOt+//33rEmTJgwQ5mEsnCdRvCfwsWPHuOdcR0eHubi4cM9Xnz59qn3PSVGPPz6fz54+fVqiuqJf4WvXrs2GDx/O2rVrx7p168amTJlSpp4ZeXl53LVl1apVEsvevXvHveafPXvGbty4wSZNmsQ6dOjAunXrxqZPn87u3r0rd9ul/fwpTvv27RkANmLECLnreHt7MwBswYIFJdr22LFjufQZJSHeu+SXX37hhpw3bdqU67lubGzMHj9+zA4cOMA0NDSYkZERc3FxYUZGRtw52r17d4n2SwghX7viek5evXqVu2cR3SuJX7MnTZrEgP/yKZubm7OYmBjGmGSqEHV1ddasWTOJ/MYuLi4ye+SL7l0WLVrEtLS0mIaGBmvevDmrW7cuV7dVq1YyRza4uLgwAMzAwIA1aNCAubq6cvnYAbBvvvmmyPMwZ84cZmJiwvh8PnN2dmaNGjXiPsvr16/PXr58Kbdu4furonpOyqrTr18/Vq9ePQbIzpH98uVL9ubNG+5e7/79+zKP5d27d9xnp3jqluKIzpG8+6LFixczQJhCS1a9wj0nc3NzWb9+/bjlVlZWzNXVlXtN8Pl8tmnTJok6ipwDQojiKDhZyUQfYGpqamz8+PESuchE+TH09PRYx44dWevWrSUCQcePH+eCRIW/5OXn57NWrVoxAKx9+/bs4cOH3DKBQMBmzJjBAOHw5MIfrGfOnGERERFSQ+KSkpJYz549GQD23XffSR2LKGihpqbG7Ozs2PXr17llqamp3NDjtm3blvg89evXr8gJZeT9ff/99yXeV1G6devGADBnZ+dy2V5ubi6rWbMmA8Dmz58vd73ff/+dAWDm5uZs+/bt7PXr1+zjx4/s2rVrXJvat28vNdTc09NTbuBRZP78+TKfl+TkZGZsbMwAsB9++EEiePby5UvWpUuXUg1biI+P5z7oiwo8iiYXkTcxjkhpgpPl/X4rjjLeU6LJnQwMDCTa++7dO9a9e3empqZW4uDkmzdvmKqqKlNVVWV79uyRCqZfv35d6kZNkWFBqamp3Gtt0KBBEnlXDx06xLS0tLj2ljQ4uWDBglJdO8SDrKVVUFDAXr58yXbt2sXs7OwYULr8rqLgpLy/Ll26sHfv3pV4u3v37uW2IfoyJiJK+aCvr89+//13blIf8T8VFRUWFBQktd2yfP4UR5Qnt6jAo+hHGVlpMuT58OED9yPFjz/+WKI2iV7jqqqqTFdXl4WFhXHLUlNTWfPmzRkA1qFDB2ZgYMDmzZvH5bzMzc3l8nra2NjI/IGKEEKIbCUZ1i36nBRds/l8PtPU1GShoaHc+vn5+UwgELA3b94wMzMzBoB1795d4rPqxo0b3GdRr169pPYpfq/ZrVs3ieHk0dHR3GQ9EydOlKq7Y8cOmQG5q1evckGvPXv2yD0PampqzN3dnSUlJXHL7t69y91Td+vWTW7dsgYnGVNsSLMox/NPP/0kc/nKlSsZANayZUu525CluOCkaFh3s2bNZNYrHJwMCgrigpniE/9kZ2ezKVOmcJ/7165dk6hHw7oJKT8UnKxkog8wR0dHqcBSbm4u92uZpqYme/bsmVR90S86kydPlijfvXs3A4S9rMQndhHXo0cP7pc9RX38+JGpqakxXV1dqUCLeI+qqKgoqbqiIBKPx2Pp6ekK75Ox/85TSf/K84NB/Iu8rBuD0ggLC+NumsRvJGTZvn07a9q0qdQxmpiYsOXLl8uccbtx48YMAJs+fbrc7a5du5Z7DYqbMGECFyySJTMzk5v199KlSwocrZB4MPHBgwdy13Nzc2MA2LJlyxTeXnEq6v1WFhXxnvrw4QPXk3HNmjVS9bKysrgeWyUJTl66dIkB0vl6iqJIcHLevHkMALO0tGQCgUBq+YIFC7jzUNLgpCjwU5q/0vrjjz+ktuXk5FTq68aBAwfYhAkTWHh4OEtJSWECgYA9ffqU/fbbb1xPxLZt25YoD+7bt2+5Salk9QYW3VyLAvItW7ZkV69eZQKBgCUkJLBx48Zxx1b4B4SK+vxhjHGTG6xfv17uOtOmTWMAWI8ePRTe7sSJExkg7Iks671fFNFrHABbvHix1HJR7k555zotLY3rCX3r1q0S7ZsQQr5mpZkQR/ya/dtvv8ncrui+RN4kdmfOnOG2Ufi6LbrXNDY2lvkZuGPHDgYIe2O+fv1a4WM9ffo0A4Q/SBYmOg+qqqosMTFRarl4junC7a3s4KRou2ZmZjLv+ZycnBgAtnnzZrnbkEVecFIgEJR4Qhzx++glS5bI3J+oA4ifn59EOQUnCSk/lHNSSUaNGgUVFcnTr6qqCicnJwBA586dYWNjI1XP1dUVALg8KiJ79+4FAAwdOhS6uroy99m3b18AwLlz56SWZWZmYuPGjRgxYgQ6deoET09PeHh4oGPHjlBRUcGHDx/w+PFjmdt1cnKCp6enVLmLiws0NDTAGJNqb3ESExPBhMHzEv0VzmlXWrdv3+ZyiAwZMgT9+/cvl+1u3boVAODr6wtra2u56+Xn5+Pp06dIS0uDiooKbG1t4ezsDF1dXbx9+xZ//fUXrl27JlXv8+fPAAB1dXW529bU1AQAqVyK+/btAwCMGzdOZj09PT0u55us15A8ojaVtl3lobzfb4qozPfU+fPnkZmZCV1dXZl5THV1dTF69OgSH4PonDx69AhXrlwpcX15jh07BkCYa1TWa2LixIlQVVUt1bZDQkJKde1gCuR1ksfS0hJt2rRBy5YtYWlpCR6Ph/v37+Pvv//GixcvSrw9Pz8/rFu3Dt7e3qhVqxbU1dVRu3ZtTJ8+HSdPngSfz0dUVBR2796t0PZyc3PRv39/JCUlwczMDBs3bpRaR5QHNS8vDyYmJjhx4gTc3Nygrq4OOzs7/Pnnn+jWrRsAYM6cORJ5Qcv6+VOUslzT5Nm8eTPWrl0LAFi5cqXM976ixo4dK1Xm4uJS5HITExPUrl0bQOmuLYQQ8rXbsmULPDw84OHhgRYtWsDMzAz9+vXDx48fUa9ePaxfv15mPdG9fWFHjx4FAHz33XfQ1taWWt6+fXs0a9ZMYt3CRo0aJfMzcODAgbCwsEBOTg7Onj0rtfzZs2f4/fffMXDgQLRv3547rl9++QUAcPPmTZn7A4DevXvD1tZWqrx169Zwc3MD8N89l7J4e3vDwcEBb968waFDhySWXb16FXfu3IGenh4GDhxYqu1PmjSJO2fNmjWDiYkJ5s2bBwDo1asXJk2aVOw2oqOjkZmZCW1tbUyYMEHmOlOnTgUAnDp1Cnl5eaVqKyGkaKX79kfKrG7dujLLa9SoodDywhNq3L59GwCwa9cunDlzRmZdUWLk58+fS5RHRUWhf//+SE1NLbLNb9++lVkumpylMB6Phxo1aiA5OVnuBCBV0ePHj9G5c2d8+PABXl5e2LRpU7ls982bNzhy5AgA2RPhiOvbty8OHToEFxcXnDx5Eo0aNQIgDDKsWLECAQEBaN++Pc6fPy/xRVhLSwsAkJOTI3fboqTc4jdfL1684Cb8mT59OtTU1GTWffbsGQDp11BRRG0qTbvKS3m/34pT2e+puLg4AEDt2rUlzre4xo0bK9J0CbVq1cLQoUOxY8cOtGzZEq6urmjXrh3c3d3h7e1dqklTxNsrel0XZmBgACsrq2qTSLx///4SP2A8fvwYP//8Mw4fPoxbt27h3r170NPTK5d9eXh4oG/fvtizZw/27duHwYMHF7l+fn4+vvnmG5w7dw56eno4fPgwatWqJbWe+Otm7Nix3MQ54qZNm4ajR48iJSUFt2/fRvPmzQGU7fOnOFpaWvj48WO5XTsOHjzITcA1bdo0jBkzpkTtEWdqairzPImuG0DR15a4uLhq9dlICCFVRXJyMpKTkwEAKioq0NfXR6tWreDn54eJEyfKnBzO1NRU4vos7uHDhwAAR0dHuft0dHRETEwMdw9TWJMmTWSW8/l8ODg44OXLl1ITsK1evRpTp04t8jNO3r1iUfsEhPd9165dK/GkbxVh7Nix+Pnnn7F582aJ+6XNmzcDAAYPHlzkhH5FuXv3Lvd/VVVVmJiYwMPDA8OGDcPgwYPB4/GK3Ybo+be3t5fbDtFr49OnT0hKSoK9vX2p2ksIkY+Ck0oi78InuoAWt7ygoECi/P379wCEF1fRBVYe8d4lmZmZ6NevH968eYP27dtjxowZcHJygpGRERegsrGxQXJystTM0MUdCwCut1rh9lZViYmJaN++PV69eoXWrVvjyJEjcoM9JbV9+3bk5ubCyMgIfn5+ctc7evQoDh06BFVVVezdu5frYQMIZ5idNm0aHjx4gK1bt2LOnDkSv4gaGRkBKPpG5t27dxLrAv+9fgBwM4kXRfw15OHhIXOdffv2oWbNmhL7KWm7ykt5v9+Kooz3VFZWFgDA3Nxcbr2ilhVly5YtcHJywqZNm3D9+nVcv34dgPAGsGfPnli+fLnMX+2Lomh7q0twsrB69erhwIEDcHJywv3797F69WrMnDmz3Lbfpk0b7NmzR27PW5GCggKMHDkSe/bsgY6ODo4ePYoWLVrIXFc0szggP2gsXv706VMuOFnaz59JkybJnP1y1qxZ6NKlCwDh9eDjx4/lcu04fvw4Bg4ciLy8PPzwww9YvHhxkesXp7jrhiLrVJfPRkIIqUoCAwMRFBRUojpF3VuJ7ktq1qwpdx0LCwuJdQtT5B5MvO6lS5fwww8/ABCOGPH390e9evWgp6cHPp+Pp0+fok6dOkX20ivpPpXF398fM2fOxOnTp5GUlAQbGxt8+vQJu3btAoAy/VAYHh4Ob2/vMrWvJM+/+PqEkPJFwckvhK6uLtLS0nDw4EH06tVL4XrHjh3DmzdvYG1tjSNHjnDD40QYY9wXv8rUv39/vHz5ssT1mjVrhtWrV5dqn8nJyfDx8UFycjJatGiB48ePyx2iWBohISEAhMPENTQ05K4XFRUFQNh7TjwwKa5z587YunWrVCDRwcEB58+fx5MnT+RuXzSM0MHBgSsTP87379/D0NCwyGMRd+HCBZnlot5MdnZ20NDQgEAgwJMnT9CmTRupdXNycrhfwMXbVR0p4z0l6pX3+vVruesUtawo6urqCAgIQEBAAJ4/f47z58/jzJkz2Lt3L/bv34/Y2FjExMSU6BdvPT09pKenV0h7Fy5cWOohTOfPny9VPVn4fD66dOmC+/fvcwHd8iIa4iwvuA0IX2fjxo3Dtm3boK2tjSNHjshMFSDSoEED7v/yrk/ir2XxYd2l/fyJjY2Vef0Qf+4dHBzw/PnzEl/TCjtz5gz69OmDnJwcfPfdd1i5cqXC7SSEEPJlE92XvHr1Su46ou8l8kZCKHJPI173r7/+AiD8zrNmzRqpOkX9KFfafSqLiYkJ+vXrhx07dmDLli0ICgrC7t27kZmZiaZNm3JplJRFdI4Uef7F1yeElC8KTn4hHB0dkZiYiAsXLpToy2FCQgIAwM3NTSqIAgB37tzBx48fy62dirp27Ro3hLgkSpunLiUlBT4+PkhMTISbmxtOnjwJfX39Um1Llhs3buDOnTsAih/SnZmZWez2RPnxRAFAkVatWmHz5s24du0asrOzZT6nouBnq1atuDIrKysYGhoiPT0dFy9eRNeuXYttQ+G2yMPn8+Hm5obz588jKioK/v7+UutcvnwZOTk50NLSQtOmTRXed1WkjPeUKLCUkJAg93m/d+9emfdjZWWFQYMGYdCgQQgKCkLjxo3x+PFjnD59musNrMjwmQYNGuDy5cu4f/8++vTpI7U8IyOjxMN/RR49eiQ3YF7ZRL0dxAN55SE2NhYAisxbO3HiRGzatAlaWloICwsrtldB/fr1YWZmhjdv3sjNgygeILSysuL+X9rPH0VyBLdq1Qpnz55FdHS0zOXZ2dncjzTi1zRx4eHh6NmzJ7KzszFu3DiZXwIJIYR8vUT3JXfv3uVyJBcmGj7csGFDmcvl3Wfl5+dzowrE64ruF+X9cHjx4sVi213UvZ1ombz2lgdF7vlExo0bhx07dmDr1q2YO3culzarNDnRy5voPvrp06f49OmTzDQxoudfW1tbIld1Sc4BIaRoNCHOF2LAgAEAgE2bNpWox6Ho4itv0oYlS5aUvXGlUJkT4rx69Qrt2rVDfHw8XFxccOrUKZl5xMpCNBGOk5MTNxRSHlHvn0ePHnE3LoWdOHECgPQNR69evaChoYGPHz/i77//lqp36tQpPH36FJqamhJBBD6fz92MLVq0qNyDKaLX5969e2X+Evznn38CALp3714hOScrkzLeUx4eHtDT08OHDx+415q4jx8/cnl9youVlRXXs1f8WEXHLz4RUmGiIbsbNmyQ2ftv3bp1pU42rowJcWQRCARcjllREv3y8Pz5c2zfvh0A0KlTJ5nr/PDDD1i/fj00NTVx6NAhtG/fvtjt8vl87n0aEhIi8xog+iJhaGjIJdoHSv/5owhRbqr4+HicPn1aavm2bdvw6dMnWFhYoG3btlLLo6Oj0aNHD3z+/BljxozB+vXr6YsEIYQQCaIJ39avXy/z/iU8PJybmEbeD/ibNm2S+ePznj178PLlS6irq0t8Hhd1v/j582eFfkg7cOAAkpKSpMovXbrETZwpuueqCIrc84l4enqiUaNGSEpKwsqVK3Hx4kVoaWlh6NChFdY+RXl4eEBfXx+fPn3Chg0bZK6zbNkyAMJ7L/HOMCU5B4SQolFw8gsxePBgtGzZEu/fv0e7du1kDk988OAB5s6di8OHD3NlXl5eAIQ918RnthMIBJg5cyZ27txZ5Cyp1Z0oL+CjR4/QvHlznD59ukRDmu3s7GBnZ8fNdC2LQCBAaGgogOJ7TQLCL+NaWlrIy8tD//79JRJZ5+bmYsmSJdwQ8cKzDpqamuL7778HAAQEBHC9JAHhL36iXyd//PFHiRxzADB37lyYmJjg/Pnz6N27N54+fSqxPD8/H9HR0Rg1ahRSUlKKPQ5xo0ePhrW1NbKysjBo0CBuWDNjDKtXr0ZoaChUVVUxd+7cEm23KlLGe0pHR4ebjXDmzJkIDw/nlqWnp2PIkCGlyo9z5swZTJ48GTdv3pQI3jHGsGPHDu5XZPHhOHXq1AGPx0NqaqrcX/THjx8PQ0NDPH/+HCNGjJCYFOTIkSP49ddf5U7KVFUcPXoUixcvlvml4MGDB+jevTvi4+Ohp6cnM5fSoEGDYGdnx83+KJKZmYkBAwYgOjpaKh/hxYsX0b59e2RmZsLKykrmTNABAQFYvXo1F5js0KGDwsc0c+ZM6Orq4uHDh5g0aZJEz+wdO3Zws3xPnz5d4jVc2s8fRTg5OXE/nIwePVoi8X1UVBQCAgIACK9ffD5fou6VK1fQrVs3fPz4EaNGjcKGDRsoMEkIIUTK+PHjYWZmhtevX2PIkCES6XdiYmK4+20/Pz84OzvL3EZWVhaGDBkikcf94sWLmDx5MgDhbN7iE/KI7hfXrVuHy5cvc+Wpqano27evwiNIBg0aJLHugwcPuFFKXbp0qdARSaJJ3+7du6dQOh7RfYvos7t///4l+t5VUXR0dPDTTz8BEN5PhIWFccsEAgECAgIQGRkJVVVVqRziJT0HhJAiMFKpbG1tGQAWHh4uc7m/vz8DwAIDA2Uu37p1KwPAvLy8pJa9fv2atW7dmgFgAJi5uTlzd3dnzZo1Y8bGxlz51q1bJeoNGzaMW1arVi3m6urKDAwMGAD266+/ym1zYGAgA8D8/f1LfbzKNnbsWO7YmzRpwtq0aSP37+XLl1L15Z1Tcbt372YAmLq6Onvz5o1C7QoNDWXq6uoMAFNRUWF2dnasadOmTE9Pj9vn4MGDWX5+vlTd7Oxs1q5dO249BwcH5ujoyFRUVBgA5uvrywQCgcz9XrlyhdWqVYurW6dOHdayZUvWpEkTpqWlxZUnJCQodBzirl27xr2utLW1WfPmzbl9qaiosE2bNsmta2Jiwv2JtgFAorxnz55S9Sry/VYUZbynPn/+zHx8fCSeOxcXF6apqcm0tLTYokWLGABma2ur8HEcOHCA256+vj5r2rQpc3FxYWZmZlz5jz/+KFWve/fu3Gu+efPmzMvLi3l5ebGYmBhuncOHDzM1NTUGgOno6DBXV1dmZ2fHALDevXszLy+vYt9byiR6bQBgNWvWZC4uLszd3Z1ZWVlx5aampnJfe6LjK/xcv3//nquvq6vLnJycWIsWLZilpSVXbmtry+7cuSO1zYsXL3Lr1KhRo8jr2ebNm2W26/jx49x73cDAQOqYhg4dKvO6U9rPH0W8e/eONWnShLtWODo6MgcHB26b/v7+rKCgQKpe/fr1GQDG4/FY69at5Z6Lfv36lag94eHhxb6XirtWVvXXNyGEVEWia6e8+zZZFLlmM8ZYREQE09fXZwCYhoYGa968OWvQoAF3PW/evDlLS0uTqie6L1u0aBHT0tJimpqazMXFhdWrV4+r6+7uzjIzMyXqZWVlsYYNG3KfU/Xq1WPNmjVjampqTENDg23atImrL+88zJkzh5mYmDBVVVXWtGlT1rhxY8bj8bj7wJSUFLl1C3/+FHWe5NUpKChgjo6O3L2cm5sbd88n63vTu3fvJL5PREdHy3gmFCPaRkm/Y8r7fM7NzWV9+/blltvY2DA3Nzfu/p3P57ONGzdKba+k54AQIh/lnPyC1KhRA5GRkdi1axd27tyJGzduICYmBoaGhrCxsYGfnx969eqFjh07StTbunUrmjRpgi1btuDp06f4/Pkzmjdvjh9++AF+fn5cb5kvkUAg4P4v3iNHlsL5HRUlGmbbo0cPmJqaKlRn0KBBcHZ2xsqVKxEREYGkpCSkpKTAxMQEbdu2xYgRI+TmxNHQ0MCpU6ewdu1abNu2jctz06xZM/j7++O7776T6mEk4u7ujvv372P9+vUICwvDgwcPkJSUBHNzczg7O8PT0xN+fn4lnp0ZEPaui42NxYIFC3D8+HHcvXsXBgYG8PPzQ0BAgNx8cYD8pODi5RkZGSVuU0VRxntKU1MTJ06cwPLly/HXX38hISEBmZmZ6Nq1K4KCghRKrF6Yp6cn1q5di7NnzyI2NpbLxWNmZoYePXpgzJgx6NGjh1S9bdu2Ye7cuTh27Bju3r2LnJwcAMJenCLdu3fHlStXEBwcjKioKNy9exd16tTBkiVLMGXKFIWGIitThw4dsGTJEkRGRuLBgwd49OgRsrOzYWhoiLZt26JLly4YO3asVA/l4ujo6GDJkiVc7qvk5GRkZWVBX18fnp6e6NWrF8aMGSMzJ6749Sw1NRWpqaly9+Pr6yuzvHPnzoiNjcVvv/2GU6dO4datW9DW1ka7du0wduxYDBw4UGa90n7+KMLIyAhXr17F0qVLsXv3bjx58gTq6urw8PDAuHHj8M0338isJzofjLEic3eV5npGCCHky+Ll5YXY2FgsWbIEx48fx71796CmpgZXV1cMGjQIEydOlJnTW6Rly5a4evUqd1+Tnp6O+vXrY8iQIQgICICWlpbE+rq6uoiOjsacOXNw6NAhJCYmwsTEBL1798asWbMUyn1vb2+PmJgYBAYG4uTJk0hLS4ONjQ369OmDOXPmwMjIqMznpSg8Hg/Hjh3D7Nmzce7cOdy6dYtL1yPre5ORkRH69u2L7du3o0GDBvDw8KjQ9pWEqqoq9u7di9DQUGzevBkxMTF4+fIlzMzM0LVrV0ydOlVmaq6SngNCiHw8xso50RYhhBBCCCGEEEKImI4dO+L06dNYunQpfv75Z2U3hxBShVBwkhBCCCGEEEIIIRXmyZMnqF+/PtTV1fH8+XOFR5QRQr4ONCEOIYQQQgghhBBCKkReXh6mTZsGxhiGDh1KgUlCiBTqOUkIIYQQQgghhJByFRISgq1btyI+Ph4pKSnQ09PD3bt3YWNjo+ymEUKqGOo5SQghhBBCCCGEkHKVmJiIqKgoZGRkoG3btjhz5gwFJgkhMlHPSUIIIYQQQgghhBBCiFKoKrsBVU1BQQFevHgBPT098Hg8ZTeHEEIIIaTEGGPIyspCrVq1oKJCA2WqG7ofJYQQQkh1V5L7UQpOFvLixQtYW1sruxmEEEIIIWWWnJwMKysrZTeDlBDdjxJCCCHkS6HI/SgFJwvR09MDIDx5+vr6Sm4NIYQQQkjJZWZmwtramruvIdUL3Y8SQgghpLoryf0oBScLEQ2d0dfXp5tBQgghhFRrNCS4eqL7UUIIIYR8KRS5H6UkRIQQQgghhBBCCCGEEKWg4CQhhBBCCCGEEEIIIUQpKDhJCCGEEEIIIYQQQghRCgpOEkIIIYQQQgghhBBClKJKBie3b9+OcePGwdXVFRoaGuDxeAgJCZFaLzc3F//88w+GDx+Ohg0bQkdHB3p6emjRogXWrVuH/Pz8ym88IYQQQgghhBBCCCFEIVVytu7Zs2fj2bNnMDU1hYWFBZ49eyZzvfj4ePTr1w96enpo164devbsiYyMDBw+fBgTJ07EiRMncOjQIZqpkhBCCCGEEEIIIYSQKqhKBic3bdqEevXqwdbWFr/99ht++eUXmevp6elh3bp18Pf3h7a2Nle+bNkyeHt74/Dhw9i3bx/69+9f4W3Ozc2lnpqEELn4fD7U1NSU3QxCCCFfMLofJeTrpKamBj6fr+xmEEJIqVXJ4KSvr69C61laWmLChAlS5To6Ovjpp58wZMgQREZGVmhwMjMzE2lpaRAIBBW2D0LIl0FDQwOmpqbQ19dXdlMIIYR8Qeh+lJCvG4/Hg4GBAWrWrEmjBgkh1VKVDE6WB1EPJVXVijvEzMxMpKSkQFdXF6amplBTU6MPA0KIFMYYcnNzkZGRgZSUFACgACUhhJByQfejhHzdGGP4+PEj3rx5Ay0tLRgaGiq7SYQQUmJfbHByy5YtAICOHTsWuZ5AIJD4lTkzM1PhfaSlpUFXVxdWVlZ0E0gIKZKWlhb09PTw/PlzpKWlUXCSEEJIuaD7UUKIlpYWBAIBUlNTYWBgQNcCQki1UyVn6y6r//3vfzh+/DjatWuHrl27FrnuokWLYGBgwP1ZW1srtI/c3FwIBAK6+BNCFCYaciMQCJCbm6vs5hBCCKnm6H6UECKir6+P/Px8yjtLCKmWvrjg5NGjR/H999/D1tYW27dvL3b9X375BRkZGdxfcnKyQvsRXfRpggtCSEmIrhl040gIIaSs6H6UECIiSmeWl5en5JYQQkjJfVHDuk+ePIm+ffvC3Nwc586dg4WFRbF1NDQ0oKGhUep90q/UhJCSoGsGIYSQ8kafLYQQug4QQqqzL6bn5IkTJ+Dn5wdTU1OEh4fD3t5e2U0ihBBCCCGEEEIIIYQU4YsITooCk0ZGRggPD0fdunWV3SRCCCGEEEIIIYQQQkgxqn1wsnBgsl69espuEiGEEEIIIYR8FRITE8Hj8TB8+HBlN0VpQkJCwOPxEBISouymEEKIYhhTdgskVMmck5s2bcL58+cBALGxsVxZREQEAMDPzw9+fn6Ii4uDn58fBAIBvL29ERoaKrUtOzu7r/qDkhBCCCGEkC/drVu38OeffyIqKgrPnz9HdnY2TExM4OjoiK5du2LYsGEwMTFRdjMrlZ2dHQBh8JAQQgjhPH4M+PsDW7cCDg7Kbg2AKhqcPH/+PP766y+JsgsXLuDChQsAhB+0fn5+ePXqFQQCAQBg165dMrfl5eWllOBkz549ER8fX+n7VUSdOnUQFham7GZUqsTERNSuXRv+/v4K/6JZmjplUdn7qw5ycnIwb948hIaGIjk5Gbm5uQgPD4e3t7eym0YIIYSQKqCgoAABAQFYtmwZVFVV0bZtW3Ts2BHa2tpITU3FxYsXMWXKFMydOxdPnz6FqampsptMCCGEKM+xY8CQIUBGBuDnB1y5AujrK7tVVTM4GRISolBwxtvbG6yKdUUViY+Px/2Hj6BmWEvZTZGQm/6i3LYlCqYVxdnZGbdu3Sq3fZL/yDr/ampqMDc3h6enJ2bMmAEnJ6ci19fS0oKhoSEaNmyINm3awN/fH3Xq1FFoX4VVxHtx6dKlWLBgAby9vTF48GCoqqpyvQCK4uTkhNjYWCQnJ8PKykrmOsbGxvj06ROysrKgpqZWzi2vOor7oeRr/LGCEEJIyeTnA9HRwMuXgIUF4OkJ8PnKbpXQrFmzsGzZMri6umLXrl0y72OuXbuGgIAAZGdnK6GFhBBCSBXAGLBoETB79n9DuuPigGnTgA0blNs2VNHg5JdCzbAWao1ep+xmSHix6bty32adOnXwzTffyFxWs2bNct9faVhaWuLBgwcwMDCo0DrKIH7+P3z4gMuXLyM0NBT79+/HuXPn0Lp1a7nrCwQCpKam4urVq5g/fz4WLlyIgIAALFiwADwer8h9VYZjx45BV1cXp06dUjiAmJ2djQcPHsDCwkJuYPLx48d4//49XFxcvujAJFD0DyXl+WMFIYSQL9P+/cCPPwLPn/9XZmUFrFwJ9OmjvHYBws/zJUuWoEaNGjh+/LjcXpFubm44d+4cCgoKpJbduXMHCxcuRGRkJN6+fQsLCwv07NkTQUFBEsPAxUe4zJ07FwEBATh79ixycnLQqlUrLFu2DM7OzlLbT01NxaJFi3D48GEkJydDT08PXl5eCA4ORpMmTSTWFf0Ae+vWLcydOxcHDhzAy5cvsWnTJgwfPhw3btzA1q1bERERgeTkZOTk5KBu3boYOnQofv75Z+6epvCPyuL3dIGBgQgKCuIeR0VFYcmSJbh06RKysrJgY2ODgQMHYubMmdDW1pZoX35+PpYuXYqNGzfi+fPnsLKywqhRozBw4EA5z5BsL1++xG+//YZjx47h+fPn0NLSgqWlJTw9PfHbb79B/98ePI8ePcKmTZtw5swZPHv2DB8/foSNjQ369OmD2bNnQ1dXV2K73t7eiIyMRHZ2NoKDg7F9+3a8efMGjRo1wu+//w5fX19kZWVh1qxZ+Oeff/D27Vs0a9YMq1evhqurq8znIiYmBgEBAQgLC0NmZiYcHR0xe/Zs9OzZU+HjTUhIwIIFC3Dq1Cm8fv0axsbG6NSpE4KDg2Frayux7s2bN7Fw4UJcvXoVr1+/hqGhIezt7dGrVy/MmDGjROeZEEI4Hz4Aw4cD//wjWd62LTB/vlKaVBgFJ0mZ1a1bV+ImpypSU1NDgwYNKryOMsg6/7Nnz8aCBQswa9YshIeHF7s+AERHR+Pbb7/FokWLwOfzMV/GRaqyn+sXL17AxMSkRAHE27dvIy8vDy1atJC7ztWrVwEAzZs3L3MbqwN5P5RUxI8VhBBCvhz79wP9+knnzE9JEZbv26fcAGVISAjy8/Mxbty4Yodr83g88At19wwLC8OAAQPA5/PRs2dPWFtb4/79+1izZg1OnjyJK1euwMjISKJOYmIiWrRogUaNGmHkyJGIj4/HoUOH4OPjgwcPHsDc3JxbNz4+Ht7e3khJSUHHjh3h5+eH1NRU/PPPPzh58iTOnj0rdb8iEAjQrl07ZGVloUePHlBXV+e2uXHjRhw+fBht27ZF165d8enTJ0REROCXX37BtWvX8M+/XzoNDQ0RGBiIFStWAAAmT57MbV88Nc6ff/6J7777DkZGRujRowfMzMxw7do1LFiwAOHh4QgPD4e6ujq3/tixY7FlyxbUrl0bEydORHZ2NpYvX46LFy8W/USJ+fTpE9q0aYPExER07NgRvXv3Rk5ODp4+fYqQkBAEBARwwcn9+/dj8+bN8PHxgbe3NwoKCnD58mX8/vvviIyMRFRUlMx7xIEDByI2NhY9e/bE58+fsWPHDnTv3h0XL17EuHHjkJ2djX79+uHNmzfYvXs3OnXqhISEBG6/Ijk5OfD19cXnz5/h7++P9PR07Nq1C35+fvj7778xdOjQYo/3ypUr6NSpEz5+/IgePXqgbt26SExMxI4dO3D8+HFcunQJ9vb2AIRB6datW4PP56NXr16wtbVFeno67t27h40bN1JwkhBSOvHxwuHbd+9Kln//PbB8OVBVOuswIiEjI4MBYBkZGUWu9/nzZ3b//n32+fNnmcsbNWrE1ExsmO30I1XqT83EhjVq1KhczlVCQgIDwDp16qTQ+uHh4QwACwwMZBcuXGDe3t5MV1eXmZqasgkTJrBPnz4xxhg7fvw4a926NdPW1mY1atRgAQEBLC8vT+62IiMjWdu2bZmOjg4zMjJigwcPZsnJyTLb6u/vL3MbFy9eZB07dmQGBgZM9LaQVUdcVFQU8/PzYzVq1GDq6urMysqK9e7dm0VHR3PrCAQCtmrVKtaxY0dmZWXF1NXVmZmZGevduze7efNmsW0sSlHn/9WrVwwA09HRUWh9kYcPHzINDQ2mrq7OkpKSSlRXUSEhIaxFixZMR0eH6ejosBYtWrCQkBCJdQIDAxkAqT8vL69it79u3ToGgC1cuFDuOj/++CMDwP7888+yHk6JFXftKG9FXYvK83pACKlaFL2fIVVTed2PlkVeHmNWVowJQ5PSfzweY9bWwvWUxcfHhwFg586dK3HdtLQ0pq+vz6ysrNizZ88klu3cuZMBYN9//z1XJroXAsB+++03ifVnz57NALBFixZJlLdu3ZqpqqqyU6dOSZQ/fPiQ6enpMUdHR4lyW1tbBoB17NiRuy8Wl5iYKHVPXFBQwEaOHMkAsPPnz0ttz9bWVubx37t3j6mqqrJmzZqxt2/fSixbtGgRA8CWLl3KlYnum52dndmHDx+48ufPnzNTU1OF72HDwsIYADZlyhSpZZmZmUwgEEhsW/yxSHBwMAPAtm/fLlHu5eXFALA2bdpItHHXrl0MADM0NGT9+/dnubm53LLff/+dAWDLly+X2JbouWjXrh3Lycnhyh88eMC0tLSYoaEhy8zM5Mq3bt3KALCtW7dyZTk5OczOzo7p6emxW7duSWw/Ojqa8fl81r17d67sp59+YgDYoUOHpI45LS1Nqqywyr7HJIRUAydOMGZoKPkBrq7O2JYtlbL7ktyPqlRY1JMQOa5cuYL27dvDwMAA48aNg42NDdavX48xY8Zg79696NOnD6ytrTFu3DgYGhpi8eLF+O2332Ru6/Lly+jQoQNMTEzwww8/wN3dHaGhoWjdujVev36tUHsuXrwILy8vAMJfhBUZmrJ27Vp4eXnh1KlT6NChA37++We0a9cOt2/fxr59+7j13r17h8mTJ0MgEKBr166YMmUKvL29cezYMbRu3RrXrl1TqI0lJWtItiLq16+PgQMHIicnBwcPHizfRgGYMmUKhg8fjufPn2PUqFEYPXo0UlJSMHz4cPz000/cet7e3ggMDISBgQEMDAwQGBiIwMBAhSa3unHjBgBQz0lCCCGkDKKjJYdyF8YYkJwsXE9ZXr16BQCoVUs6dcm5c+cQFBQk8Xf+/Hlu+bZt25CZmYlFixbBxsZGou7gwYPRvHlzmRNu1q5dG9OmTZMoGzVqFABI3NfFxMTg4sWL8Pf3R4cOHSTWr1+/PsaMGYPY2FjcLdyTBcCSJUugpaUlVW5rayvV+5PH42HixIkAgDNnzkjVkWfDhg3Iy8vDqlWrYGxsLLEsICAAZmZmCA0N5cq2bdsGAJg7dy50dHS4cktLS/z4448K71dE1vHp6elJ9NS0tLSUeCzy/fffA5B/vAsWLJBoY79+/aCmpob09HQsXboUqqr/DR4cPHgwAOHIG1nmz58v0TuzQYMGGDlyJNLT03Ho0KGiDhFHjhxBYmIiAgICpIb8e3h4oFevXjh27BgyMzMllsk6N1/bTPOEkDJiDPj9d6BrVyA9/b9yS0vhB/eIEUprmjw0rJuU2ZMnT+QO9W3ZsiU6d+4sUXbixAkcPHgQvXr1AgDk5ubC1dUVO3fuxMmTJxEZGQk3NzcAQHBwMOrWrYs//vgD06dPl7iZAICTJ09i06ZN3E0hAMybNw+BgYGYOXMmNm/eXGz7T58+jc2bN2PkyJEKHW9sbCx+/PFHWFhY4MKFCxITtDDG8PLlS+6xkZERkpKSYGlpKbGNe/fuoWXLlpg5cyZOnz6t0H5LYtWqVQDAnceS8PLywrZt22QGTuU91507d0bLli2L3G50dDRWrFiBhg0b4tKlS1wuz+DgYLRs2RJ//PEH+vTpAw8PD3h7e8Pb25ubGKskQ8lv3rwJAFizZg22bNkidx1VVVWJCYO+BqwgH58eXUL+h3fQadJO2c0hhBBShYndzpTLehWBFTEZ37lz57BgwQKJMk1NTXh4eAAQ/sAt+vfJkydS9bOzs5GWloa0tDSJIePOzs5QUZHs3yHKcZ0u9gVQtP1Xr17JvI+Ji4vj/hXPPampqQlHR0eZx5STk4M1a9Zg165diIuLw4cPHyTOwYsXiueSFrXvxIkTMoN8ampqXBuB/4J3np6eUuvKKpOnbdu2qFmzJhYtWoRbt26hW7du8PDwgKOjo9SP64wxbN26FSEhIbh79y4yMjIk8obKO95mzZpJPObz+ahRowaXs1KchYUFACAlJUVqO2pqajLvbz09PbF27VrcunWryFzsonMcFxcn8zXw6tUrFBQU4NGjR3B1dUW/fv2wYsUK+Pn5YcCAAejQoQM8PDyk2kwIIUX6+BEYORLYs0ey3MMD2LsXqCLzghRGwUlSZvHx8QgODpa57Mcff5QKTnp7e3OBSUD4wd+vXz/cuXMHPXr0kAio6enpoXv37tiyZQueP38uNVOzg4ODVFBx2rRpWLNmDUJDQ7F+/XqZv7iKa9asmcKBSUCYnyc/Px+//vqrVHt4PJ7Er/caGhpSgUkAaNy4MXx8fHDy5Enk5uaWaVIW8YChaEKcCxcuQFNTEwsXLizx9kTtT0tLk1om77k2NDQsNjgpHmgUn2RI1DNy8ODBCAkJ4b40lEZOTg7XA+HAgQNFruvs7AwNDQ2JsqFDh8LIyAhr1qwpdRuqqryst0g7shSCpFgAQNb1QwCPOs8TQgiR7d+YTbmtVxHMzc0RFxeHlJQUODg4SCz79ddf8euvvwIQ3oOMKNRL5N27dwCEo2GK8vHjR4ngpKyJEkU/nufn50tt/+jRozh69GiR2xdXo0YNuSNg+vXrh8OHD3MjXWrUqMH1CFy5ciUEAkGRxyJO1L7CAVx5MjIyoKKiIjO3p3iezeIYGBjg0qVLCAwMxOHDh3Hs2DEAwgDvL7/8gu+++y8f9g8//IA1a9bA2toaPXv2hIWFBXfvFhwcLPd4C+eOBITPUVHPXW5urtQyExMTqUA08N/xZmRkFHmsonO8Y8eOItcTvQZatWqFc+fOYdGiRQgNDeXunV1cXLBkyRL4+PgUuR1CCMHTp0Dv3sCdO5LlEyYAK1YAxcRGlImCk6TMOnXqhBMnTii8fuFfM4H/frVs2rSp3GUpKSlSwcA2bdpI3cBpaWnBxcUFJ06cwKNHj6RmQizM3d1d4bYD/w0J7tixo0Lr37p1C4sXL8b58+fx6tUrqZuftLQ07hhLQzxgqKamBnNzcwwZMgQzZsyQ+8t7UYrqhVDS51pcTEwMAMlE7CKislu3bpVq2yJ37txBbm4u+vXrh71798pc5++//8a3334rc0j3qlWrZA6lqe4Kcj7jZcgPKPj03010XsZrgKeCT4YaRdQkhBDytfL0FM7KnZIiPSEOAPB4wuUl6DRX7lq3bo3IyEiEh4ejXbuSjQgQBbBiY2OLvVcsDdH2V69ezQ1DVoS8wOS1a9dw+PBhdOrUCUePHpUY3n358mWsXLmyVO3LzMyEnp5esesbGBigoKAAaWlpMDMzk1imaColETs7O/z111/Iz89HbGwsTp06hVWrVmHixIkwMjLC4MGDkZqairVr18LJyQmXLl2SmDn81atXcjtGlKe3b9+ioKBAKkApOl5ZwU5xonN8+PBhdO/eXaF9enl5wcvLC58/f8aVK1dw+PBhrFu3Dt26dUNsbCzq1KlTiiMhhHwVTp8GBg4E3r//r0xdHVi3DhAbaVpVUbcZUunk/ZpZ3DJZv2jWqFFD5j4U/UVTfF1Fpaeng8fjKRRQvHjxIlq2bIn9+/ejadOmmDRpEubOnYvAwEAu90xJfuWWpVOnTmCMgTGGnJwcJCcnY8eOHaUKTALghqUXvvEsq8zMTKioqMjcrrm5OVRUVBR6vooiGtItK8gtIgqAygpOmpiYSNz8Vnd5eXlITU1FftYbicAkhxXg2bNnxeZMIoQQ8vXh8wFRvKtwvEz0eMUK4XrK4u/vDxUVFfzvf/+TOeKjKKLc1JcuXaqIppX79uPj4wEA3bp1k8o7GS0n8Sefz5fozSmrfaKhx8UR3bfK2pe8/ReHz+ejadOmCAgI4PJbhoWFAQCePn0Kxhh8fX2l7s1Ku7+Sys3NlXl+RPsv6n4TKNtrQEtLC97e3li2bBlmzpyJz58/lyinKCHkK8IYsHQp0LmzZGCyVi0gMrJaBCYBCk6Sai41NVVmuaK/aAIlnzzG0NBQKrekPAsWLIBAIMDZs2cRFhaGZcuWITg4GEFBQahZRXM9REREAChdvsqi6Ovro6CgAG/evJFalpqaioKCApnB6ZIQTYZT1M2iqAdn4eBkXFwceDwely/qxYsXGDZsGExMTKCjo4OWLVtK5KR6+vQp+vTpA319fdSoUQOTJk1CTk5Omdpfnp4/fw4fHx+pL2tqNeyhYdmQe8wYg5+fHywsLNC4cWOpv549e1Z20wkhhFQRffoA+/YJ8+eLs7ISlvfpo5x2iTg4OOCnn35CamoqunTpwgXwChPPBSkyYsQI6OnpYdasWbh3757U8k+fPikcuJPF3d0dLVq0QGhoKHbv3i21vKCgAJGRkQpvz9bWFgAkJvUBhHnMFy1aJLOOsbEx0tLSkJ2dLbXsu+++g6qqKiZNmoTk5GSp5enp6dw9EwB8++23AIS53cWHoqekpJSo1+bdu3fx7NkzqXLRvbtoBIvoeC9evCiRZ/L58+eYMWOGwvsrqzlz5kh0kIiLi8OWLVtgYGAgkaZKll69esHGxgbLly9HVFSU1PLc3FyJ5zM6OlpqchxA+twQQgjn0ydg6FBg2jRA7FqJ1q2B69eBYlKvVSU0rJtUaxcuXABjTCLA+PnzZ9y4cQNaWlqoX79+ue/T3d0d169fx6lTp6TyFxUWHx8PY2NjtGnTRqL806dPXC+/quTRo0fYs2cPNDQ00Lt373LddrNmzRATE4OIiAgMGDBAYpno5ry4X6CLo0jPydu3b3O/1Iu7c+cObGxsYGhoiCdPnqBly5bo2bMnTp48CV1dXRw5coQLnj548ACenp6YNm0afv/9d7x+/RrDhw+HtbU1AgICynQM5SE9PR0eHh5SN/96zbvByGcUGGNIO7IUnx/990v+q1evkJr+CSraBtz7KTdd8cT6hBBCvkx9+gC9egkn93z5Uphj0tNTuT0mxf3222/Izc3FypUr4eDgAC8vLzg5OUFbWxupqam4desWrl+/Dn19fYmJ8ESzUffv3x/Ozs7o3LkzGjRogOzsbDx79gyRkZFo3bp1qdPZAEBoaCh8fHwwaNAgrFixAi4uLtDU1ERSUhIuXbqEN2/eyAwcyuLu7g53d3fs2bMHL1++RMuWLZGUlISwsDB069YN+/btk6rTrl07XL9+HT169ICnpyfU1dXh4eEBDw8PNGnSBOvWrcOECRPg4OCArl27ok6dOsjMzMTTp08RGRmJ4cOH488//wQgTMEzYsQIbN26FY6OjujduzcEAgF2796Nli1b4siRIwodx5kzZ/Dzzz+jTZs2aNCgAUxMTPD06VOEhYVBS0uLGwJvYWGBvn374p9//oGrqyvat2+P169f48iRI2jXrh2ePn2q4LNQehYWFkhPT0fTpk3RrVs3ZGRkIDQ0FNnZ2di4cWOxw+E1NDSwb98+dOnSBV5eXmjfvj2XQiApKQnR0dEwMTHhJh5atmwZTp8+DR8fH9jb20NTUxM3b97E2bNnUbdu3XK/NyeEVHOJicL8koVTo40bB6xaVaXzS8pCwUlSrT18+BBbtmyRmK17yZIlePPmDUaOHFnsZDilMX78eGzYsAGzZ89Gu3btuF92AWEvtFevXnFDvm1tbfHo0SPcu3cPjRs3BiBMlj516lSZPQiV6fz58xg2bBgEAgGCgoJkTuRTFv7+/tiyZQuCg4PRuXNniVxHorxB/v7+pd5+bm4uYmNjYWpqKrftz549w7t379CoUSOpIUK3b9/mvrSMGTMGXbp0kZjtu0GDBtz/x40bh+nTp2PatGkAgHr16mHMmDGIiIioEsHJ//3vf5KBSR4Ppj2nQ6eBcLIhHgCzXjOQtLQ3wP77ha0gOxOG3sOh5yzMp/pi03cghBBC+HxARsroKoHP52PFihUYNmwY/vzzT0RFReHKlSvIycmBsbExHB0dsXz5cgwbNkxqMpdu3bohJiYGS5YswZkzZ3D69Gno6OjAysoKI0aMKHImZkXUrl0bMTExWL58OQ4ePIgtW7aAz+fDwsICbdu2Rb9+/Up0nEeOHMGMGTNw4sQJXLt2DfXq1cPSpUvRpUsXmcHJOXPm4P379zhy5AjOnTuHgoICBAYGcpMPjhkzBk2bNuV69oWFhcHAwAA2NjaYMmWK1H3Zxo0bUb9+fWzcuBFr1qyBlZUVfvrpJwwYMEDh4GSnTp2QmJiIqKgo7N+/Hx8+fIClpSUGDRqEgIAANGz43+iOkJAQ2NnZ4Z9//sHq1athY2ODn376CdOnT6+Qe/zC1NXVcfr0aUyfPh1//fUXMjIy4OjoiDlz5ig8ssTNzQ23b9/GkiVLcOzYMZw/f56bMNPPzw+DBw/m1p0wYQIMDAxw5coVREVFgTEGGxsbzJ49G5MnT1YoNygh5Ctx9qwwv+Tbt/+VqakBa9YAY8cqr11lQMFJUmbis0XLUtSysurYsSO+++47HD16FA0aNMDNmzdx8uRJWFtbl2qmakU4OjpixYoV+OGHH9C4cWP4+fnB1tYWr169QlRUFLp164YVK1YAACZNmoRTp07Bw8MDAwYMgKamJiIiIpCSkgJvb29uCHVlEn++cnJykJqaiitXruDu3bvg8/mYPXs25s6dW+77bdu2LSZNmoTVq1ejSZMm6Nu3Lxhj2L9/P5KTk/HDDz+gbdu2pd7+vXv3IBAI4FlEZv6i8k3euXMHzs7OePLkCSIiIvD48WOZ23j8+DGio6Nx/fp1iWTsubm56NSpU6nbX17y8vIkZh5VVVUFdGtwgUkRngof4KlARUsfBZ/SufKsaweg69ShxOkOCCGEEGVycXHBxo0bS1zPwcEBmzZtKnY9Ozu7IicNlLfMyMgI8+fPx/z584vdR2JiYpHLzczMsHnzZoX3r6uri//9739FbtPNzY3L91gcPp+PGTNmyBxWXdS5EdewYUPuPrk4urq6WLp0KZYuXarQ/oq6ry7q3BbVdmNjY2zcuLHY19bw4cMxfPhwmcssLS2xYsWKYo+7U6dOVeJekhBShTEmTPg8darkMO6aNYF//hEO566mKDhZgXLTX1S5nke56S8A8/Id6iw+W7QsFRmcbNWqFWbNmoXZs2dj5cqVUFdXx6BBg7B48eIST3RTEt9//z2aNGmCZcuW4fjx4/jw4QNq1KiBFi1aSAxZ7t69O/bt24eFCxdi+/bt0NbWRrt27XDgwAHMmzevwtpXFPHnS0tLC4aGhmjQoAHmzJkDf3//Cp0FcNWqVWjWrBnWr1/P3Sw3btwYwcHBxQ6RL05Z8k0Cwp6Tw4YNw+3bt2FsbIy6devK3MadO3dgaWkp8wa4rDkzy0NYWBiSkpK4x8bGxnifL+dSz+OBr6UPXSdfZF4W9rjIfZsMwfN70LQu/5lLCSGksly7dg2BgYG4dOkScnJy0LhxY0yePBlDhgwp1fZyc3O5HlAODg7cMMyK3i8hhBBCCADg82dgzBhgxw7J8hYtgP37hRPgVGM8pujPXF+JzMxMGBgYICMjo8hAQ3Z2NhISElC7dm1oampKLe/Zs6fcpNzKVqdOHW4mvOoqIiICPj4+CAwMrNDgJ/k6pKenw8jICHFxcXj48CEGDhyIrKwsbqZ4cWFhYRg8eDDev39fqiFFxV07ysrb25vL4amlpQUbGxs8TfuMWqPXSa37bGlvqBlaoMaAYKT8OYob4q3d0AtmPafhxabvUM9cV+ZEAYSQqk3R+5kvUUREBDp16sT9YGlgYID9+/cjISEBCxYswMyZM0u8zblz52L58uX4+PGj3OBkee63vO5HCSGlY2dnB6D43qxVCV0PCPmCJSUJ80sWnrdi1Chg7VpAQ0M57SpGSe5HqedkBanuwT9Cvia3b9+GlpYW6tWrByMjI6irq2PSpEmYPHkycnJycOrUKfj7+8PU1BStWrWCuro6Ro0axeU8iouLw82bN5UeKL9z547EzJ9Dhw7FxYsXi62nqm8GrTpu+PzkCgDg08MLyP84psLaSQghFSUvLw+jR48Gj8dDVFQUmjVrBgAIDAxEq1atEBgYiP79+6NevXoKb/PmzZtYtGgRli9fjh9++KHS9ksIIYQQgogIoH9/IC3tvzJVVeGkN+PHA19IOi4VZTeAEEKU7c6dO2jcuDFUVFRQo0YNHDp0CDdu3EDz5s3h4+ODixcvwsTEBIAw39ORI0eQmJiIVq1awd3dHYsWLaqQmeFLavXq1RKPJ02apHBdvaZd/ntQkIcPd8+UV7MIIaTSnDt3DvHx8RgyZAgXIAQAPT09zJkzB3l5edi6davC28vJycHw4cPRsmVLbhbhytgvIUS5EhMTq1WvSULIF4gxYQDS11cyMFmjBnDuHDBhwhcTmASo5yQhhGDSpEkSgTxvb29cvXpV7vpt2rRBdHR0ZTRNYW/fvsX27du5x97e3tzs44rQrN0MfANz5Ge8BgB8uHUCUFEr93YSQkhFEuUD7tixo9QyUZl4D/PiBAUF4fHjx7h9+3aRE4WV934JIYQQ8hX7/FnYK3LbNslyNzdhfkkrK+W0qwJRcJJUS97e3grPCkjI12DTpk3Izs7mHssbeigPT4UPPedOSI8SfgDmpb8CX88MgF55NpMQQirU48ePAUDm8GkjIyOYmppy6xTn2rVrWLx4MRYuXFhs7/iy7lcgEEAgEHCPMzMzFWojIYQQQr4wycnC/JL/TvjKGTECWLcOKGNO2fx8IDoaePkSsLAAPD0BPr9MmywXNKybEEKquby8PKxdu5Z7bGtrix49epR4O7pOHQCV/z6ZCgQfyqV9hBBSWTIyMgAABgYGMpfr6+tz6xRFIBBg+PDhaNasGX7++ecK3++iRYtgYGDA/VlbWxe7T0IIIYR8YaKiABcXycCkqiqwZg2weXOZA5P79wN2doCPDzBkiPBfOzthubJRcJIQQqq5Q4cOITk5mXs8ceJEmTONF4evYwTt+q25xyznM3Jzc8uljYQQUp3MmTMHjx8/xpYtW8CvhO4Ev/zyCzIyMrg/8Ws6IYQQQr5wjAkDkO3bA2/e/FduZgacPQtMnFjm/JL79wP9+gHPn0uWp6QIy5UdoKTgJCGEVHOrVq3i/q+lpYVRo0aVelsSE+MASE9PL/W2CCGksol6LsrrpZiZmSm3d6PIzZs3sXz5csyaNQuOjo6Vsl8NDQ3o6+tL/BFCCCHkK5CdDYwaBUyaBOTl/Vcu6kHZtq1Ulfx84STeoaHCf/Pzi95Ffj7w44/CGGhhorLJk4vfTkWi4CQhhFRjt27dQlRUFPd42LBhMDY2LvX2NGwcoWpsyT1+//498sQ/JAkhpAoT5XyUld/x/fv3SEtLk5kXUtydO3eQn5+PoKAg8Hg8iT8AePjwIXg8HgwNDct1v4QQQgj5yjx/Dnh5AVu3SpZ/+60wMaSMNC+lGZodHS3dY1IcY8JUl8qc85WCk4QQUo2tXr1a4rH4rOOlwePxJHpP5uXl4dixY2XaJiGEVBYvLy8AwKlTp6SWicpE68hTv359jBo1SuYfIOwlOWrUKHz77bflul9CCCGEfEXOnwdcXYGrV/8r4/OBFSuAkBBAS0uqSmmHZr98qViTFF2vItBs3YQQUk0JBALs3r2be9yuXTs0adKkzNvVadIe6VHbwPJyAAB//vknevbsWebtEkJIRWvfvj3s7e2xc+dO/PDDD2jatCkAICsrC/Pnz4eqqiqGDx/OrZ+Wloa0tDSYmprC1NQUANC6dWu0bt1axtaBzZs3o2bNmti0aVOZ9ksIIYSQrxRjwIYN0sO4TU2BPXsAHx+ZM2oDRQ/N5vGEQ7N79ZKefdvCQrGmKbpeRaCek4QQUk1dvnwZHz9+5B6PGDGiXLbL19KDdgNP7vGJEyfw7Nmzctk2IYRUJFVVVWzatAkFBQXw9PTE2LFjMXXqVDg7O+PevXsICgpC/fr1ufXXrFmDhg0bYs2aNZW6X0IIIYR8hQQCYOxYYMIEycBks2bA9euAj4/cYdsLFpR+aLanJ2BlJX9OHR5POILc01P28spAwUlCCKmmzpw5I/G4Q4cO5bZtvaaduf8zxnD8+PFy2zYhhFQkHx8fnD9/Hh4eHtizZw/WrVsHExMTbN++HbNmzfri9ku+bomJieDxeCXqmfvw4UP06tUL5ubm4PF4sLOzAwAMHz4cPB4PiYmJFdJWQgj5qr14AXh7A4VGX2DoUOEQb1vbIodtBwYqthtZQ7P5fGDlSuH/CwcoRY9XrJDucVmZaFg3IYRUU2fPnuX+7+joCHNz83LbtnotB4nH06dPl8hvmZSUBACwsbGRWb9OnToICwsrt/YQQkhJuLu7K/SjSlBQEIKCghTeLpM1lqoU+yXlIzExEbVr1wYAdOvWDUeOHJFaJyIiAj4+Phg3bhz+/PPPym5ilZOfn4/evXvj6dOnGDZsGCwtLSUmdypMdP4CAwNL9F4hhBAi5tIloE8f4NWr/8pUVIAlS4ApUwAeT6EZtRUhb2h2nz7Avn3CfYgHP62shIHJPn0U30dFoOAkIYRUQxkZGbgqljzZ19e3XLfP46kIf0b795MwM+sDHr3K4marzf3wAeCr4vHrD1J1c9NflGtbCCGEkOIcPXoUUVFRaNu2rbKbUqUlJCTgwYMHMoO1ixYtwowZM2Bpaamk1hFCyBfof/8Dvv8eyM39r8zEBNi9G/ne7REdKezt+Pp10cO2i8PjCQONRQ3N7tNHmJOycD5LZfaYFKHgJCGEVEORkZHIz8/nHpd3cFKIB+Dfn+lYAcx6BUC9hrCHyrOlvaFmaIFao9dJ1Xqx6bsKaAshhBAim52dHZKSkjB9+nRcunRJ2c2p0l68EP6AWLNmTallFhYWsFDmbAiEEPIlyckBfvhBOPmNOGdn4OBB7L9phx/tSheQFOtDwj0GFBuazecLR5dXNZRzkpBSKk2OH0LKi3i+SVVV1YrpKcKT/IjIfnan/PdBCCGElJGDgwOGDRuGy5cvY//+/QrXS0pKwqhRo2BpaQl1dXVYWVlh1KhRSE5OllrX29sbPB4PAoEAc+fORd26daGmpsYNdebxePD29kZKSgqGDBkCU1NT6OnpoVu3bnj69CkAYa7H3r17w9jYGHp6eujfvz9SU1Ol9rVlyxb06tULdnZ20NTUhLGxMTp16oTw8PDSnaB/2dnZwcvLCwAQHBwMHo8HHo+HkJAQANI5J4OCguDj4yO1PuWlJISQYrx8KZzJpnBgctAg4OJF7L9pJzO3pCKCg4HCHdytrIRDtpU9NLssKDhJSkUUmOPxeOjevbvMdSIiIsDj8TB+/PhKbl3VFBQUJHFTx+PxoKOjAycnJwQFBUnMugwIbyDF19XQ0ICZmRnc3d0xceJEnD9/Xu6+Cu+n8B/dUFZ/4sHJVq1aQVdXt2J2pPLfT2/ZSbcrZh+EEEJIGc2bNw8aGhqYOXOmxMgCeR4/fgw3Nzds2bIFLi4u+Pnnn9G8eXNs2bIFrq6uePLkicx6ffr0wZYtW+Dl5YXJkyfD3t6eW/b+/Xt4eHggISEB/v7+8Pb2xrFjx9ChQwfcu3cPrVq1QlZWFkaOHAlXV1fs27cPQ4cOldrHxIkT8fr1a/j6+mLKlCno3r07Ll26BF9fXxw6dKjU52jy5Mnw9/cHAHh5eSEwMBCBgYFo2rSpzPW9vb1lrh8YGFhknkpCCPmqXb4MuLoCFy/+VybKL7lzJ/I1tOXmliyKaEbtWbOAxEQgPBzYuVP4b0JC9Q5MAjSsm5QDyvFTMn379kWTJk0AAC9fvkRYWBiCg4Nx5MgRXLx4Eerq6ty6fD4fs2fPBgDk5eXh/fv3iI2NxYYNG7Bu3Tr06NEDf/31F4yMjKT2Y2Jigu+//15mG+iGsnpLSUnBgwcPuMcVM6QbAI8HHl8dLOcTACA76S5YQT54KlUgKQkhhJCiMQZ8+qTsVhRPW1t66tBSsLGxwcSJE7F8+XJs3rwZY8eOLXL98ePHIzU1FRs2bJBY93//+x/GjRuH8ePHS/wQKPLixQvcuXMHxsbGUsvu3LmDKVOmYPny5VzZhAkT8Oeff8LDwwNBQUH48ccfAQgnV+revTuOHTuGmJgYNGvWjKtz//59bqIfkZcvX8LV1RXTpk1Dr169FDsphUyePBkRERH466+/4O3tXewEN97/jvtTdH1CCPnqbd4MfPedcEi3iJERsHs30KEDAGG+x5L2mJQ1bLsqDs0uCwpOkjKhHD8l169fPwwaNIh7vHTpUri7u+PGjRsIDQ3lfqEGhMN1Zd0IPnv2DKNGjcLhw4fRu3dvnDt3Dioqkh2hTU1N6SbyCyU+SzcAnDp1Crt375a5bnx8PKBb+lm8VdQ0kf9vcJLlfELO63hoWNQv9fYIIYRUkk+fgIrqVV+ePnwAdHTKZVOzZs3C5s2bERwcjG+++Qba2toy10tOTsa5c+fQqFEjjBkzRmLZmDFjsGLFCpw9exbJycmwtraWWB4cHCwzMAkAurq6mD9/vkTZkCFD8Oeff8LExAQ//PADV87j8TBo0CAcO3YMt2/flghOFg5MAsJ8kH379sXq1avx7Nkz2NraFn0yCCGEVJ6cHOGs2+sK5eN3dAQOHgTEetm/fFnyzVeVGbUrEg3rJmVCOX7KTk9Pj8tbee3aNYXq2Nra4vDhw2jUqBEiIyOxb9++CmsfqXrEe3Lo6enh3bt3uP/wER6//iD1JxAIyrQvnpqmxGPKO0kIIaSqMjY2xvTp0/HixQusWLFC7noxMTEAhEOVeYV6bfJ4PG400O3b0ulM3N3d5W63Xr160CkUaBVNMOPk5CS1L9GylJQUifKnT59izJgxqFOnDjQ1Nbm0PKtXrwbw36Q2hBBCqoDXr4H27aUDk/37A5cuSQQmAeEM2Yr4448va9h2cajnJCmzefPmYdeuXZg5cyZ69eoFfjHTQz1+/BgeHh5ITU1Fjx490LhxY9y7dw9btmzBkSNHcOHCBdStW1eqXp8+fXD79m106tQJxsbGMnP81KxZE/7+/nj06BGOHDmCuLg4hIWFwdPTE82bN8fIkSNx48YN7Nu3D+np6Th9+rTEPiZOnAhnZ2f4+vrCzMwMKSkpOHjwIHx9fbF///5SD6OpCFpaWpg6dSpGjhyJ3bt3Y8CAAcpuEqkEjDGJ4KS3tzfi4+OhZlhL5szZz5b2LtP+eHxVqBpaIC9d+BNf9rPbMGjZr0zbJIQQUgm0tYW9Eqs6Ob0bS2vy5MlYs2YNFi9ejHHjxslcJzMzEwBgbi57ZIFoJuuMjAypZfLqAIC+vr5UmaqqarHLcnNzubInT57A3d0dmZmZ8PHxQY8ePaCvrw8VFRVEREQgMjKyzD88EkIIKSfXrgG9ewPiPzLxeMCiRUBAgMy0JZ6ewp6QKSmy807yeMLlkyYVP/P2l4SCkxUgIyMDsbGxym5GsRwdHWFgYFDm7VCOn7LJysriZkl0c3MrUV3RjIuyelympaXJHNbdsmVLdO7cucTtJFXDgwcP8FJsLICvr69w6HYF0rR1wod/g5OC5/fB8nOLqUEIIUTpeLxyGy5dnWhpaSEoKAhjx47FwoUL0aNHD6l1RIHC169fy9yGqFxWQLFw78fy9scff+D9+/fYvn271GQ548ePR2RkZIXunxBCiIJCQoDx4wHxH4wMDYHQUKCI79t8PrByJdCvn/CjWjxAKSu35NeCgpMVIDY2Fp6enspuRrGio6Ph4eFRLtuiHD+K27dvH+Li4gAAr169wqFDh/Dq1Su4urpi8ODBJdpWrVq1AAgDkYW9ffsWwcHBUuU//vgjBSerscKBe19fX2zYsKFC96lp44QPt08CAFieAIIXDyt0f4QQQkhZjBw5EsuXL8fatWvh7OwstVw0O3VUVBQYYxIBR8YYoqOjJdarTKIfHHv27ClRXlBQgAsXLlR6e0QjohSZAZ0QQr4KubnAzz8D/6ba4DRuLMwvKWMUaGF9+gD79gE//ig5Oc7XkFtSHso5ScoF5fhR3D///IPg4GAEBwfj77//Ro0aNRAUFISIiAiJmboVwWT1A/+Xg4MDGGNSf0U9P6TqEw9OWlhYoGHDhhW+T01bJ4nHlHeSEEJIVcbn87Fw4UIIBALMmzdParmNjQ18fHy4tELitmzZgnv37qFdu3ZSP5RXBtGP4OfPn5co//3333H37t1Kb4+oY8Dzkk4tSwghX6LUVOGs24UDk337CvNLFhOYzM8HIiKEnSuNjYH4eGFOya8pt6Q81HOSlBvK8aOY0NBQidm6y0I0vNfMzKxctkeqttzcXERERHCPfX19K3x4GQDwdYygZmqD3LQkAEB2EgUnCSGEVG29e/dGq1atcOnSJZnL169fDw8PD4wZM4abZPD+/fsICwuDmZkZ1q9fX8ktFho/fjy2bt2KPn36YODAgTAxMcHly5dx8+ZNdOvWDUePHq3U9jRo0AC1atXCrl27oK2tDSsrK/B4PEyYMKFc0kMRQki1ceOGML+k+CS+PB7w66/AL7/IzC8pbv9+2T0lV64ESjiA8otEwckK4OjoyA0HqcocHR3LdXuU46fyiQJVJc1VSaqna9euISsri3vs6+tbafvWtHXmgpOClLhK2y8hhBBSWr///js3KqcwBwcHXL9+HcHBwThx4gSOHj0KMzMzDB8+HIGBgWVO41NazZo1w6lTpzB79mzs378ffD4frVu3xoULFxAWFlbpwUk+n4/9+/dj+vTp+Pvvv7n7kEGDBlFwkhDy9di2DRg7VjK/pIGBsMtj167FVt+/X5hjsvDAx5QUYfm+fV9vj0kRCk5WAAMDg3LL5VjdUI6fyvP582csW7YMAEqcq5JUT4XzTbZv377S9q1p44SsG4eFDwryAJWvLEMzIYSQKsfOzq7IFDeenp5FLre1tZUa1i2P+MgFWeTtp6g2ent7y1zm7e0tNawbAJo3by412WFx50DRfQJASEgIN0mjuBYtWhR7/IQQ8kXKzQWmTRN2bxTXsCFw6BBQr16xm8jPF/aYlHXpZUzY4XLyZKBXr69vEhxxlHOSlCvK8VM5nj17hh49euD+/fvw8fFBn6/9Z5avhHhwsmHDhrC0tKy0fWvYOAIQ671cgi9ChBBCCCGEEFKtvHkDdOokHZj08wOuXFEoMAkA0dGSQ7kLY0w4UrwaDL6tUNRzkpQ7yvFTfvLy8rhfyPPz8/H+/XvExsbiwoULyM/PR69evRASElIpeQeJcn348EHiPVWZQ7oBgK+pC/WadZDz6omwgIKThBBCCCGEkC9RTIwwCJmUJFk+bx4waxagolg/v/x84OxZxXb573QSXy0KTpIKQTl+ykd+fj6Cg4MBAOrq6tDX10ft2rUxbtw4DBkyBG3atKn0NhHliIqKQl5eHve4soOTgHBoNxecBAMrKKj0NhBCCCGEEEJIhdm5Exg9Gvj8+b8yfX1g+3ZAxrwa8siaAKcoFhYlbOcXhoKTpFQox0/Jc/wEBQVJbaMoiYmJCq9bWEnaRaqHs2I/ufH5fHh5eVV6GzRtnJB5dT/3mOVlV3obCCGEEEIIIaTc5eUBM2YA/87rwHFwEOaXdHCQWzU/Xzgs++VLoEYN4f//7WNULB5POGu3p2cZ2v4FoOAkIYRUA5cvX+b+7+rqqpQZMjWsGgknwinIBwCwXEExNQghhBBCCCGkinv7Fhg4UHoMdo8ewN9/C2fmlqOkPSTFibKzrVjxdU+GA9CEOIQQUuXl5eUhJiaGe+zu7q6UdqhoaEPDoj73uCCXek4SQgghhBBCqrHbtwFXV+nAZGAgcPCgzMBkfj4QESGcZbtv39IFJgFhj8l9+wCa35Z6ThJCSJUXFxeHz2I5T1xcXJTWFg0bJwhSHggf5OeiQPAJKhraSmsPIYQQQgghhJTK7t3AiBGS+SX19IBt2wA/P+Fw7QggJUU4ebeJCRAeLhzl/e5d2XY9ezYQFEQ9JkUoOEkIIVXc9evXJR67uroqqSWARq36Eo9zUp9C07qJklpDCCEEoFzThBC6DhBSIvn5wMyZwOLFkuX16wt7SzZsWKbh2opo354Ck+JoWDchhFRxN27c4P6vra2NBg0aKK0t6uZ1JB7nvIpXUksIIYSoqamBx+Ph48ePym4KIUTJPn36BEB4XSCEFOHdO6BrV+nAZLduwNWrXGCyX7+KCUzyeIC1NU2AUxj1nCSEkCpOvOdks2bNwFfiT2x8XROo6Bii4GM6ACDn9ROltYUQQr52fD4fBgYGePPmDQQCAfT19aGqqgqeKMM+IeSLxxjDp0+fkJqaCkNDQ6XeJxJS5cXGAn5+wNOnkuWzZwun11ZRQX6+sMdkRXZGpglwpFFwkhBCqrC8vDzcunWLe6zMfJMAwOPxoG5eB9lPhb05qeckIYQoV82aNaGlpYXU1FRkZmYquzmEECUxNDREzZo1ld0MQqquvXuB4cOBf3sZAwB0dYG//pKYkSYiouKGcltZAStX0gQ4slBwsowotwchpCRKes24f/8+srP/mxVbmfkmRTTM63LBydx3z1GQkw0VdU0lt4oQQr5OPB4PhoaGMDAwQH5+PvLy8pTdJEJIJVNTU6Mek4TIk58v7Bn522+S5XXrCvNLNm7MFe3bB4wcWTHNCA4GZs2iHpPyUHCylMRz/GhpaSm7OYSQauLjx4/g8XgK5wMSzzcJKL/nJACo1xTLO8kKkJOaAE2rhsprECGEEPB4PKiqqkJVlW7vCSGEEADA+/fAkCHAiROS5V26ADt2AEZGXNG0acDSpeXfBGtr4TBu6i1ZNLp7KSXK8UMIURRjDHl5ecjMzERmZmaJ8gGJ55vU0dGBg4NDRTVTYeo160o8znn9hIKThBBCCCGEkKrj3j2gVy8gvlAaql9+AebPl+jCOHUqsGxZ+e06MBBwcAAsLIQT31BvyeJRcLIMKMcPIaQk+Hw+LCwsYGBgoHAd8Z6Typ4MR4SvZybxmPJOEkIIIYQQQqqM/fuBb78FPn78r0xbGwgJAfr3l1h1797yC0xSL8nSo+BkGVCOH0KIolRVVcHn80vUuzo3Nxe3b9/mHleFId0A/j0GHgBh/kyasZsQQgghhBCidAUFwNy5wIIFkuX29sL8ko6OEsU5OcDo0aXfnZGRsHOmry9gaUm9JMuCgpPlgHL8EEIqQlWcDIfD4wH/Tu6Tm5aEglwBVNQ0lNwoQgghhBBCyFcpPR345hvg6FHJ8o4dgdBQwNhYonj/fmDECKA0g2BnzgQ6dKBgZHmiaBohhFRRVXEyHA6PJ+o4CbAC5L5JhEYt5efDJIQQQgghhHxlHjwQdmF8/FiyPCAAWLhQKoK4fz/Qt2/Jd8PjAVZWwLx5FJQsbyrKbgAhhBDZxCfD0dXVRf369ZXYmsIkh6fnvKa8k4QQQgghhJBKdugQ0KKFZGBSWxvYtQv4/XepKGJODjB8eOl3t2IFBSYrQpUMTm7fvh3jxo2Dq6srNDQ0wOPxEBISInf9zMxM/PTTT7C1tYWGhgZsbW3x008/0SQ1hJBqrSpOhiOB999HSM4ryjtJCCGEEEIIqSQFBcJpsf38gKys/8rt7ICLF4GBA6Wq7NsH6OlJrq4oa2thfZrspmJUyWHds2fPxrNnz2BqagoLCws8e/ZM7rofP36El5cXbt26hQ4dOmDw4MG4ffs2/vjjD4SHh+P8+fPQ0dGpxNYTQkjZFZ4Mp0rlmwQAHg88vhpYrgAA9ZwkhBBCCCGEVJKMDGDYMODwYclyX19hj0kTE64oJwdYswZYtw6IL8VXlq5dgWnTKL9kRauSPSc3bdqExMREvHnzBuPHjy9y3cWLF+PWrVsICAjAqVOn8Ntvv+H48eOYO3cubt26hcWLF1dSqwkhpPzcu3cPAoGAe1yl8k3+i8dX5/6f8+YZWF6uEltDCCGEEEII+eLFxQmHcRcOTE6dChw/zgUm8/OFnSc1NICffy5dYDIwUDi/jrc3BSYrWpUMTvr6+sLW1rbY9Rhj2LRpE3R1dTF37lyJZb/88guMjIywefNmMMbkbIEQQqom8XyTQBUNTqr+F5xEQR5y0uT3cieEEEIIIYSQMjl8GHB3Bx4+/K9MSwvYsQNYsgRQFQ4O3r8f0NUF9uwp/a4sLYE5c8rYXqKwKhmcVNTjx4/x4sULtGnTRmrotqamJtq2bYuUlBQ8eUK50Agh1Yt4vsmqNxmOkERwEpR3khBCCCGEEFIBCgqEU2T37CmZMNLWFrhwARgyhCvat084E3d2dtl2uWoV9ZasTFUy56SiHv87G1O9evVkLheVP378WO46AoFAYugkTaJDCKkKxHtONm/eHCoqVfC3JBVV8NS1wXI+AQByXguDk3mZbxD/4TUaN24st2qdOnUQFhZWKc0khBBCCCGEVFOZmYC/P3DwoGS5j4+wa6SpKQBhbsnRo4G//y7b7ng84WZp4pvKVa2DkxkZGQAAAwMDmcv19fUl1pNl0aJFCA4OLv/GEUJIKeXk5ODOnTvc4yo3Gc6/eDwe1M3tIUi+CwDIeSVM5MIK8iAoYHj8+oPMernpLyqtjYQQQgghhJBq6tEj4WzcDx5Ilk+ezA3jzs8XdpwsyxBucaGhQL9+5bMtorhqHZwsD7/88gt++ukn7nFmZiasra2V2CJCyNfu7t27yMnJ4R5XxXyTIuo16/4XnHyTAJYvnBRHzbAWao1eJ7POi03fVVr7CCGEEEIIIdXQ0aPA0KHCmblFNDWB//1POFM3hEO4hwwBcstpXs5p04ST6JDKV62Dk6Iek/J6RoqGaMvrWQkAGhoa0NDQKP/GEUJIKYnnmwSqbs9JAFA3r/Pfg/w85KYlK68xhBBCCCGEkOqNMWDhQuFsNOKTG1v/n737jo6q2ts4/j2ZVBICoXeQjlhAehMQBS8iRUFAEGl27xXEclGqoCg2fO2KoqCIdLGgXMWAICCogApSQu8kQEJISJ33j5HJnDTSz5Tns1bWzd7nzD4/wAuZZ3apCcuWQYsWJCdD9+6wZk3RPDIkBD7+GAYMKJrxJP/ccBOzvHPdUzI7l9uTUkTEHbmGk6VLl6Z+/foWVpO7oCrm2pJ0KI6IiIiIiBREfLwjIZwwwRxMdu5M2qYtrIppwZVXQlBQ0QWTd9zhOGNHwaS1PHrmZIMGDahWrRrr16/nwoULphO7L168yNq1a6lWrZpbv7EXEcnM9TCcFi1auOdhOP/wj6iGERCMPcVxHF7yySiLKxIREREREY+zd69jf8m//jL3/+c/LOvwEsMaBhCf/Zb2BRIeDrNnK5R0F+77jjcPDMNg9OjRxMfH88wzz5iuzZgxg7NnzzJ69GgMw7CoQhGR/ElKSjIdhuPO+00CGH42AivXdbaTNXNSRERERETy49tvoVUrczAZFAQffcTCDq9x28CiCyb79oXvv4czZxRMuhO3nDk5e/Zs1q1bB8Aff/zh7IuMjASgb9++9O3bF4AnnniCFStWMHPmTH7//XdatGjBtm3bWLlyJc2aNeOJJ56w4pcgIlIgf/75JykuOzq7836TlwRWrkfSkR0ApJzeb16CISIiIiIikh27HV54AZ56yvweokYNWLqUsfNbMWtW0T1uwQIdeOOu3DKcXLduHR9//LGpb/369axfvx6AOnXqOMPJ0NBQIiMjmTp1KosXLyYyMpIqVaowduxYJk+ebFrqLSLi7rZu3WpqX3fdddYUkg+BLvtO2lOTwc8t/2kRERERERF3ER8PI0fCokXm/k6dSFuwiCu7Vmb37qJ7nE7idm9u+Q7yo48+4qOPPsrz/WXKlOGVV17hlVdeKb6iRERKwJ9//un8PiQkxCP2zDWd2A2aOSkiIiIiIjnbt8+xvvqflbJODz3E4vavMKB6YJE9qnRp+OADLeF2d24ZToqI+Kq/XPZZufLKK/Hz86N3795EReV80ExUVBSEVS6J8rIVUL4mhn8Q9tSkf3oUToqIiIiISDZWrYJBg+Ds2Yy+wEB4+21uXTaSr94smse0bg3PPQdduoDNVjRjSvFROCki4kZcZ042bdoUcISPO3btJqBstWxfk5KUREBYiZSXLcPPRkDFOiQf3+Xo0MxJERERERFxZbfDSy/Bf/8L6ekZ/dWqwdKl1LuzDfv2Fc2jxo1zPEo8h8JJERE3cfbsWY4fP+5sX3XVVc7vA8pWo9rot7J93cGX+hV7bZcTWLF2RjipmZMiIiIiInLJhQswerTjRBpX7duTtnAJ1VtU4eTJwj8mOBjmztUSbk/kZ3UBIiLi4LqkGzJmTnqCgAq1TG17eppFlYiIiIiIiNvYvx86dMgSTKbfdz+Tr/8R/xqFDyY7dHCsFo+PVzDpqTRzUkTETbgu6QbzzEl3lyWcTEuxqBIREREREXELP/wAd9wBZ85k9AUE8OvIN2kz+x7SimA+w8KFCiS9gWZOioi4CdeZk6VLl6ZmzZoWVpM/WcLJVIWTIiIiIiI+yW6HV16B7t3NwWTVqvzf7Wto+W7hg8mwMFiyRMGkt9DMSRERN5H5MBzDMCysJn9sYeUxgkKxJ11wdGjmpIiIiIiI70lIgHvvhU8/NXXb27aj18XFfLMg+0M+8yo0FJYtgxtu0Cnc3kThpIiIm3CdOelJ+00CGIZBYIVaJB3dCWhZt4iIiIiIzzl4EPr1g99/N3VHdbuHK394nWSCCjX8ddfBr78WaghxU1rWLSLiBk6dOsXp06edbU/ab/IS16Xd9rQU7Had2i0iIiIi4hN+/BFatjQFk/aAAJ6v8w71f3iv0MHkI48omPRmmjkpIuIGPPmk7ktM+07a00m/cA5bWIR1BYmIiIiISPGy2+G11+Cxx3DdSDKxTGVujF3Czwc6FPoRCxbAwIGFHkbcmGZOioi4AU8+qfuSgAq1Te3k6IMWVSIiIiIiIsUuMRHuvhvGjjUFk3+FtqZ+7K/8TOGDyYULFUz6AoWTIiJuwHXmZEREBFWqVLGwmoLJfGJ3SvQhiyoREREREZFidegQdOoE8+aZuj9gJC0urOEY1Qs1vGHoNG5fonBSRMQNuM6cvOqqqzzqpO5LbKER+AWHOdsKJ0VEREREvNCaNY79JV02gUzFn4d4g9HMJongQg1/++2QkgK33VbYQsVTaM9JERGL2e12jz6p+xLDMAioUIukIzsAhZMiIiIiIl7Fboc333Qs405NdXafNipxu30RP3F9oYavVg3274fAwMIWKp5GMydFRCx2/Phxzp0752x74n6Tl7gu7U6JPqgTu0XEEps3b6Znz55EREQQGhpK69atmT9/fp5fHxkZyZ133kmTJk0oW7YspUqVolGjRowcOZJdu3Zl+5o6depgGEa2X/fff39R/dJERESscfEijBwJ//63KZjcTEuus28pdDDZogUcPapg0ldp5qSIiMUyH4bjqTMnwXwoTnrSBdLiz+BfuryFFYmIr4mMjKRHjx4EBgYyaNAgypQpw9KlSxkyZAgHDhzgqaeeuuwY33//PevWraNNmzbOsXbu3MncuXOZP38+K1eupGvXrlleV6ZMGcaMGZOlv2XLlkXxSxMREbHGkSOONdabN5u6P+JuHuBtLhJSqOHHjoVXXinUEOLhFE6KiFjMdUk3eHo4melQnJjDCidFpMSkpqYyevRoDMNg7dq1NG/eHIDJkyfTrl07Jk+ezIABA2jQoEGu40yYMIHp06dn6f/hhx+48cYbeeKJJ9ic6Q0aQNmyZZkyZUqR/FpERETcwrp1jk0gT51ydqViYyyv8gYPAwXfK1/LuOUSLesWEbGY68zJSpUqUbFiRQurKZzALCd2H7SoEhHxRatXryYqKoo777zTGUwClC5dmokTJ5KamsqcOXMuO05wcPYb+Xfr1o2IiAj27t1bZDWLiIi4Jbsd3noLe9eupmDyNBW4ke95g39TmGBy7Fgt45YMmjkpImIxbzgM5xK/UmVNbR2KIyIlKTIyEoDu3btnuXapb82aNQUef8OGDZw9e5aOHTtmez0pKYmPP/6Yo0ePEhERQfv27bn22msL/DwRERFLJCXBQw/BBx+Y4sdfuY5+LOMwtXJ86eWUKgVnzyqUFDOFkyIiFsp8UrcnH4YDjhO7HZ+gOg7CUTgpIiVpz549ANku246IiKBChQrOe/IiMjKSyMhIkpKS2LNnD1999RUVKlTg1Vdfzfb+EydOMHz4cFPfzTffzLx586hQoUKOz0lKSiIpKcnZjouLy3ONIiIiReroUccy7k2bTN3zGMq9vFeo/SX//W/4v/8rbIHijRROiohY6NChQ8THxzvbnj5zEgDDcCwDAZKjD2G32/8JLUVEildsbCzgOJgmO+Hh4Rw5ciTP40VGRjJ16lRnu379+ixYsIAWLVpkuXfkyJF07tyZpk2bEhQUxI4dO5g6dSorV66kd+/erF+/Pse/C2fMmGF6joiIiCXWr8fevz/GiRPOrlRsPMZLvMYjFHQZt2ZLyuVoz0kREQtlPqnb02dOZmZPukBafIzVZYiIFMiUKVOw2+3Ex8fzyy+/0LhxYzp06MD8+fOz3Dtp0iQ6d+5MhQoVKF26NG3atOGrr76iY8eObNiwgW+++SbH54wfP57Y2Fjn1+HDh4vzlyUiIpLVu++S1rmrKZiMpjzdWcVrjKGgwWSzZnDhgoJJyZ3CSRERC3nTSd1OmWYGaWm3iJSUSzMmL82gzCwuLi7HWZW5CQ0NpVWrVixbtozGjRtz7733cvr06cu+zs/PjxEjRgCwfv36HO8LCgoiPDzc9CUiIlIikpLgvvvg/vuxpaU4u3+nGS3Zwo/cUOChb70Vfv+9KIoUb6dwUkTEQq4zJ6tXr07ZsmWtK6bIKJwUEWtc2msyu30lz549S3R0dLb7UeaVv78/Xbt25cKFC2zZsiVPr7m012RCQkKBnysiIlIsjh8n7fqu8N57pu75DKYD6zlInQIPvWABrFhRyPrEZyicFBGxkDed1O1kGGBk/POicFJESkrnzp0BWLVqVZZrl/ou3VNQx44dAxxBZV5s+udAgTp16hTquSIiIkVq40ZO12qB7ZcNzq40/BjHSwzhUxIpVaBh+/aF1FQYOLCI6hSfoHBSRMQiaWlp7Ny509n2pv0mDVuA8/vk6IMWViIivqRbt27UrVuX+fPns3XrVmf/+fPnmTZtGv7+/qbTtKOjo/n777+Jjo42jbN27Vrs/xzs5WrVqlUsW7aMMmXK0L59e2f/jh07OHfuXJb7161bxyuvvEJQUBC33XZboX99IiIihZWYCO+2mk1Su85UTD3u7I+hHDfzLa8wjvzuL2kY8NxzjhXiy5aBzVbERYvX02ndIiIW2b9/P4mJic6218ycxBFO2lOTAEiJPqwTu0WkRPj7+zN79mx69OhBp06dGDx4MOHh4SxdupT9+/czffp0GjZs6Lz/jTfeYOrUqUyePJkpU6Y4+3v37k2FChVo1aoVNWvWJDExke3bt7N27VoCAgKYPXs2oaGhzvsXLlzIzJkz6datG3Xq1CEoKIg///yTVatW4efnxzvvvEOtWrVK8rdCREQki363JHPTN2N4kLdN/du4hr4s5wBX5Gu8UqXg2DEowHbOIiYKJ0VELJL5MJzp06fz8ssvZ7kvKioKwiqXVFlFwz8AHNkk9uQE0s5H4x9e0dqaRMQndO3alXXr1jF58mQWLlxIcnIyTZs2Zdq0aQwZMiRPY0ydOpVvv/2WdevWcfr0aQzDoGbNmowePZoxY8Zk+TCpa9eu7Ny5k99++401a9Zw8eJFKleuzMCBAxk7diytW7cujl+qiIhInl1V4QRvxwygE+tM/QsYyCg+IIHQHF6ZvfPnISysKCsUX6ZwUkTEIq6H4QDsP3iIgIjqWe5LSUoiwMP+4Xdd1g2OfScVTopISWndujUrV6687H1TpkwxzZi85JFHHuGRRx7J8/M6d+5c6L0sRUREikuHgF/4NvU2anDU2ZeGH+OZwYs8Tn6WcYeGQnx8MRQpPk3hpIiIRVxnTgYEBEB4VaqNfivLfQdf6leSZRWJrOHkQULqtrCoGhERERER3zTCmMNq7ieIZGffWcoyiAWsoke+xrrlFvjqq6KuUEThpIiIZVxnTgYFBV1aBe0VDD8bfqFlSb9wDoBkndgtIiIiIlJi0i6m8HbIWObwpqn/D66iL8vZR718jZeQACEhRVmhSAad1i0iYoHU1FR27drlbAcFBVlYTfEIrJBx+EOKwkkRERERkRLx9ZxTrAu5kYczBZOL6E87NuQrmLzmGrDbFUxK8VI4KSJigf3795OcnLG0whvDyYAKtZ3fp8Q4TuwWEREREZHi878ZW7hmZAs6s9bZl47BeJ7jDhZygbxvZj9/PmzbVhxViphpWbeIiAX+/vtvUzswMBCS0i2qpngEuMyctCcnkhZ3Gv8ylSysSERERETEe33QeS5D1t5LsMuGUecow2A+41v+la+xUlPBZivqCkWyp5mTIiIWcF3SDd46c7KWqZ0SfdCiSkREREREvFhKCrOMMYxae7cpmPyLK2nF5nwFk82aOZZxK5iUkqRwUkTEAq4zJytXrozNC//1DyifKZyMOWJRJSIiIiIi3inx0Gl+DOzOGF4z9S+lH23ZyF4a5GmcHj3g/Hn4/ffiqFIkdwonRUQs4DpzsnHjxhZWUnxsIaXxCwl3tlPOKJwUERERESkqj3T6jVO1W9KVSGdfOgYTmEZ/FhNP6TyNY7fDt99CWN63oxQpUgonRUQs4DpzslGjRhZWUrwCytdwfp9y5qiFlYiIiIiIeI/RpT7l+XUdqM0hZ18s4dzKlzzLBOx5iHvKlnUEkyJWUzgpIlLCYmJiiI6Odra9deYkQEA513BSMydFRERERAojOSGVl41xzE4cSggXnf07aUxrfuEbbsnTODExcPZscVUpkj8KJ0VESljmw3C8eeakf7nqzu/TL5wj/WK8hdWIiIiIiHiuCfdHszb0Zsbxiql/OX1owyZ2k7f3FQkJUK5ccVQoUjD+VhcgIuJrMoeTvjJzErS0W0RERESkIHpW28pbx/tSh4Om/slMYRoT87SMG6BPHwgJKY4KRQpO4aSISAlz3W8yKCiI2rVrW1hN8XLdcxK0tFtEREREJL+G+H3GYvsoSpHo7IujNEP5hC/pnedx+vSB5cuLoUCRQlI4KSJSwlxnTjZo0ACbzWZhNcXLv0xl8LNBehqgmZMiIiIiInmVlpTKK8Hj+ZSXTP27aEgfvmAXeV+BlZCgGZPivrTnpIhICfOVk7oBDJs//mWrOtupMZo5KSIiIiJyOYvePcMPwT15PFMw+SW9aM0veQ4mb7rJcSK3gklxZwonRURKUEpKClFRUc62N+83eYnr0m4t6xYRERERyV33KttpcX9LuvM/U/8zTKQPXxBHmTyNM24crFpVHBWKFC0t6xYRKUH79u0jNTXV2fb2mZMAAeWqO3fHSTl7HP8yVSytR0RERETEHZ0+DQ9VWsgyRhBKgrP/PGEMYy7L6ZfnsZKSIDCwOKoUKXoKJ0VESpAvndR9SUC56hmNtBTn/pMiIiIiIuJQJiyN8ReeZiEvmPr3UJ8+fMFOrszzWHZ7UVcnUry0rFtEpAS57jcJvjFz0r+c+cRue1qKRZWIiIiIiLiXtDSIMM7y+YVb+G+mYPJretKKzXkOJi/tLyniaRROioiUINeZk1WrViU8PNzCakqG656ToHBSRERERATg3XfhWv8/2UwrbuY707XpPE1vVhBL2TyNlZCg/SXFc2lZt4hICfKlk7ovsYWE4xcSTnpinKMjLTX3F4iIiIiIeLG0NAgIgH72JWzkbsK44LwWTyh38zFLuT3P42m2pHg6zZwUESlBrjMnfWG/yUtc953UzEkRERER8VXvvw+B/mlMsz/NEvqbgsm91KMtGxVMis/RzEkRkRISHR1NTEyMs+0rMycB/MtVJ+noTkDhpIiIiIj4Jn9/CEs7x5fcSU9Wmq59Sw8G8xnniMjTWNWqwdGjxVGlSMnTzEkRkRLiiyd1X2Lad9KeTlqaTuwWEREREd+wYwcYBjRM28EvtM4STM7gv9zC13kOJs+dUzAp3kUzJ0VESogvntR9SUCmE7uTk5MtqkREREREpOQYhuN/+7KMuQyjNPHOaxcoxQjmsIg78jyelnGLN9LMSRGREuI6czI4OJhatWpZWE3Jct1zEiApKcmiSkREREREit+hQ45g0iCdqUxiGbeZgsl9XEE7NuQ5mJw1S8GkeC/NnBQRKSGuMycbNGiAzWazsJqS5V+2KvjZIN2xnFszJ0VERETEW/n7O07kDieWTxjKrXxlur6KmxjMZ5yhfJ7GS00FH3rrID5IMydFREqIr57UDWDY/PEvW8XZ1sxJEREREfFGhuEIJhvxN5tokyWYfJHH6Mk3eQomu3VzzJZUMCneTuGkiEgJSE5OJioqytn2pf0mL3Fd2q2ZkyIiIiLiTVasyNhf8lZW8AutaUzG5IQEQhjMfJ7gRdLysIg1IQG+/764qhVxL1rWLSJSAvbt22c6odrXZk6C41CcRH4BHOFkWlqaTy1tFxERERHvExsLZcs6vjdIZyLTmMoU0z37qUM/lrGNZnkaU3tLiq/RzEkRkRKQ+aTuiRMn0rRpU+eX66xKb+XvcmK33W7n0KFDFlYjIiIiIlI4tWplBJOliWMpt2UJJn/gBlqxOU/B5L59CibFN3lFOGm321m6dCldu3alatWqlCpVikaNGnHfffexb98+q8sTETHtNwlwODaFPSfjnV++sAdjQHnzid2Zf09ERERERDyFYcDhw47vG7CbTbShL1+Y7nmZR+nBd8RQ4bLj2e1wxRXFUamI+/OKZd2PPfYYr7zyClWrVqVv376Eh4ezbds23n//fT777DN+/vlnrrrqKqvLFBEf5jpz0hZWjur3vGO6fvClfiVdUokLcJk5CY5w8uabb7aoGhERERGRgrm0tyTALXzFpwyhDHHOvkSCGc1s5jPksmM1bgw7dxZHlSKew+PDyRMnTjBr1izq1KnDtm3bCA8Pd16bNWsWY8eO5ZVXXuHDDz+0sEoR8XWuswQDytfI5U7v5RcSjl9wGOkX44GsS91FxLfFxcWxadMmQkJC6NChA4brOz8RERE3kJwMQUGO7w3SeYrneIZJ+JGxFvsgtejHMn7nusuOd/48hIUVV7UinsPjl3UfOHCA9PR0OnToYAomAW655RYATp06ZUVpIiKAY+sJ1yDOv5xvhpOGYZhmT2pZt4hv+uCDD+jWrRtnz5519m3bto1GjRpx880307lzZzp37kxiYqKFVYqIiJjddVdGMBnGeRbTn+lMNAWTP9KFlmy5bDA5apRjGbeCSREHj5852aBBAwIDA1m/fj3nz5+ndOnSzmvffPMNADfccEOOr09KSjLt9RYXF5fjvSIiBREdHW16E555ebMv8S9Xg6RjjqBW4aSIb/rkk09ISEggIiLC2ffoo49y+vRpRowYwcmTJ/nmm294++23efTRRy2sVERExMF1Mn999rCcvjRlh+meWTzC47xIKgG5jpWUBIGBxVGliOfy+JmT5cuX59lnn+XAgQM0adKEBx98kCeffJKePXvy+OOPc++99/Lvf/87x9fPmDGDMmXKOL9q1qxZgtWLiC/IvHw5oFz1HO70fq6H4hw7dozz589bWI2IWGH37t00a9bM2T59+jSRkZGMHj2a2bNn8+WXX9KqVSs+/fRT64oUERH5h2sweTMr2UwrUzB5kSCG8TFjmXXZYNJuVzApkh2PDyfBcSDOp59+SmxsLG+//TYzZ85k5cqVtGrViqFDhxIQkPNfEOPHjyc2Ntb5dfjScVsiIkUk8wzBgPK++yFIdofiiIhviYmJoWLFis72Tz/9BMBtt93m7OvYsSP79+8v8dpEREQu2bXLNZi0819m8DW3UJZY5z2HqUFH1jGPYbmO1ayZI5gUkex5RTg5ffp0hg8fzvjx4zl8+DDx8fGsW7eO1NRUunbtytKlS3N8bVBQEOHh4aYvEZGiZJ45aWALr2BZLVZTOCki5cuX5/jx48726tWrsdlstG/f3tlnt9tJSUmxojwREREMw3GKNkAo8SzkDmbwlGl/ybV0oiVb+JWWOY7TurXj0Jvffy/uikU8m8eHk6tXr2bixIk8/PDDPPXUU9SoUYPQ0FA6dOjAV199RUhICGPHjrW6TBHxYaYAzuaPYXj8X70F5h9RxdRWOCnie6655hq++OIL/vrrL6Kiovjss89o3749YS6nAhw4cICqVataWKWIiPgq12XcdYliA+0YwGLTPa/zMN34gVNUznEcux02bdKhNyJ54fHvkL/++msAunbtmuVaxYoVufrqqzl06BDR0dElXZqICODYX+0Sw5b7PjTezrAFgF/GWWwKJ0V8zxNPPMHZs2e55ppraNiwIefOnWPMmDHO60lJSURGRtKiRQvrihQREZ8TG2sOJrvzHZtpxdX86exLIpARfMh/eD3X/SW1hFskfzz+tO7k5GTAsZl6di71BwUFlVhNIiKXpKSksG/fPmfbsHn8X7uFZtj8saenAlkPCxIR79e1a1dWrFjBnDlzALjjjjvo27ev8/r69eupVauWaQ9KERGR4lStGmTsOGLncV5kBuOxke685wjVuY2lbKZ1rmMpmBTJP49/l9yhQwfeeOMNXnnlFW6//XbKlCnjvPbxxx+zd+9eWrRoQenSpS2sUkR81YEDB0hNTXW2FU46Zk/aUy4Cjlml6enp+Pl5/ER+EcmHW265hVtuuSXbazfccAO/a3MuEREpIa6zJUtxgQ8YxSA+N93zEx0ZwCJOUoWcXHMNbNtWXFWKeDePf5c8YMAA3n33XSIjI2nQoAG9e/cmIiKCbdu28b///Y+goCBmzZpldZki4qNcl3QD4Ofby7rBvLT94sWLHDlyhFq1allYkYhY6cyZM1y4cIGaNWtaXYqIiPgY12CyDvtZTl+uZbvpnrd4gDHMIoXAHMc5f157S4oUhsdPVbHZbHz77be88MIL1KxZk88++4xZs2axY8cO7rzzTrZs2ULHjh2tLlNEfFTmcFIzJ4FMvwdZAlwR8XqxsbE88sgjVK5cmYoVK3LFFVc4r23atImePXvy66+/WlihiIh4s127zMFkN75nCy1NwWQSgYzmfR7irVyDSbtdwaRIYXnFu+SgoCCeeOIJnnjiCatLERExcQ3ebDYbhp/NwmrcQ+ZDgXbv3s2NN95oUTUiUtLOnDlD+/bt2b17N9dddx0VK1Zk586dzuvXXHMN69ev59NPP9WhOCIiUuRcQ0mwM46XeYEnTftLHqMqt7GUTbTNcRybDVx2bxKRQvD4mZMiIu7MNZwMDMz5E1efYviZ9pjUzEkR3zJlyhR2797NZ599xpYtWxgwYIDpekhICJ07d2b16tUWVSgiIt7KNZgMIYFPGcJLPG4KJn+mHS34Nddg8tQpBZMiRUnhpIhIMVI4mZVhGKbfC4WTIr5lxYoV9OrVi4EDB+Z4T+3atTly5EgJViUiIt7ONZiszQHW04E7+cx0z7vcS1d+5ARVcxzHboeKFYurShHfpHBSRKSYXLhwwfTmWuFkBtffiz179lhYiYiUtOPHj3PllVfmek9wcDAXLlwooYpERMTbuQaTXVnNFlrSnK3OvmQCuI93uJ93SSYo2zE6dnQEkyJS9Lxiz0kREXe0d+9eUzsoKAgSLSrGzbiGk/v37yc5OVnhrYiPKF++PIcPH871nr///puqVXOetSIiIpIX8fFQuvSllp1HeI2XeAx/0pz3HKcK/VnMz3TIcZyEBAgJKd5aRXyZZk6KiBSTzDMCFb5lcP29SEtLY//+/RZWIyIl6frrr2fFihUcPXo02+s7duzg22+/1UFZIiJSKE2aZASTwSQyl2HMYqwpmNxIG1rwa67BpN2uYFKkuCmcFBEpJpn3UlQ4mSEoyLxcRvtOiviOp59+mtTUVDp06MD8+fOJjo4GYOfOnXzwwQfccMMNBAUF8fjjj1tcqYiIeCrDgL//dnxfk0OsoyN38YnpntmMojNrOE61HMfRMm6RkqFl3SIixcQ1cKtZs6bphGpflzmoVTgp4juuvvpqPv/8c4YNG8Zdd90FgN1u56qrrsJut1O6dGkWLlxIgwYNLK5UREQ8TVoa+LukHNezhkUMoBKnnX0p+PMf/o93uB8wsg4C3HUXzJ1bzMWKiJPCSRGRYuIauDVo0IATJ05YWI17sdlsVKxYkdOnHT8oKpwU8S29e/dm3759fPzxx2zatIkzZ84QHh5OmzZtGDFiBBUqVLC6RBER8TDTpsGkSZdadh7mDV7NtIz7JJXoz2LW0SnHcZKSQAueREqWwkkRkWLiGrg1bNhQ4WQmDRs2VDgp4sPKlSvH2LFjrS5DRES8gOtp3EFc5B3uZzgfm+75hVbcxlKOUiPHcbSMW8QaWmMoIlIMYmJiiImJcbYbNmxoYTXuyfX3ROGkiIiIiOTXhg3mYLIGh/mJTlmCyTkM53rWKpgUcVOaOSkiUgwyn9StcDIr19+TY8eOER8fT1hYmIUViUhJmJuPTbyGDRtWjJWIiIgnMzJtF9mJtSxiAJU55exLxcYYZvEmD5HT/pKgYFLEagonRUSKQeaZgAons8p82MXevXtp1qyZNcWISIkZPnw4RuZ3lJnY7XYMw1A4KSIiWezaBY0bu/bYeZC3mMUYAkh19p6iIgNYxFo65zqegkkR6ymcFBEpBq7hpL+/P3Xq1LGuGDeVObDdvXu3wkkRHzBnzpxs+2NjY/ntt9+YP38+vXv35tZbby3hykRExN1l/mwriIu8yUOM4kNT/xZacBtLOUytXMdTMCniHhROiogUA9dwsm7dugQEBFhYjXuqX7++qa19J0V8w913353r9fvuu49u3brxwAMPlFBFIiLiCTIHk9U4ylJuow2/mPrnchf38S4XCclxrIMHoVbuuaWIlCAdiCMiUgxc95zUku7shYSEUMvlp0KFkyIC0K5dO2699VYmTZpkdSkiIuIGvv02azDZnvX8SgtTMJmKjUeYxd18nGswabcrmBRxNwonRUSKmN1uNwVtCidzphO7RSQ7tWvXZtu2bQV+/ebNm+nZsycRERGEhobSunVr5s+fn+fXR0ZGcuedd9KkSRPKli1LqVKlaNSoESNHjmTXrl3F9lwRETEzDPjXv8x99/IuP9KVKpx09p2mAjfxP/6PR9DBNyKeR+GkiEgRO3bsGAkJCc62wsmcuf7e7Nq1C7t+YhTxeXa7nbVr1xISkvOsl9xERkbSsWNHfvrpJ/r3788DDzxAdHQ0Q4YM4bnnnsvTGN9//z3r1q3jqquuYvjw4Tz88MM0bNiQuXPncu211/Ljjz8Wy3NFRMRh796ssyUDSeJd7uVd7ieQFGf/bzSnJVuIpGuO423frmBSxJ1pz0kRkSKmk7rzzvX35ty5c8TExFChQgULKxKR4rZ27dps+1NTUzl69Chz585l8+bN3HXXXfkeOzU1ldGjR2MYBmvXrqV58+YATJ48mXbt2jF58mQGDBhAgwYNch1nwoQJTJ8+PUv/Dz/8wI033sgTTzzB5s2bi/y5IiKSNZQEqMoxFtOf9mww9X/CEO7lPRIpleN4CiVF3J/CSRGRIpY5nNSb0Zxl/r3Zs2ePwkkRL9elSxeM7N55/sNut9OuXTteeeWVfI+9evVqoqKiGDFihDMgBChdujQTJ05k0KBBzJkz57IzGYODg7Pt79atGxEREezdu7dYnisi4ssWL4YBA7L2t2UDS7idahx39qXhx+O8yKuMRcu4RTyfwkkRkSLmGk6WKlWKatWqWViNe8s8q3T37t20a9fOompEpCRMmjQp23DSz8+PiIgIWrZsSdu2bQs0dmRkJADdu3fPcu1S35o1awo0NsCGDRs4e/YsHTt2LNHnioh4u5w+sxrN+7zJQ6Zl3DGUYyCf8wM35jjeqVNQsWJRVykixUXhpIhIEXMNJxs0aICfn7b3zUmdOnXw9/cnNTUV0KE4Ir5gypQpxTb2nj17gOxnrEdERFChQgXnPXkRGRlJZGQkSUlJ7Nmzh6+++ooKFSrw6quvFulzk5KSSEpKcrbj4uLyXKOIiCc7cQKqVs3aH0Ay/8d/uJ93Tf1buZZ+LOMAV+Q4pmZLingehZMiIkVMJ3XnLjXuNFHxJ2natCmAKbx9/fXX+eOPP1ixYoVV5YmIB4uNjQWgTJky2V4PDw/nyJEjeR4vMjKSqVOnOtv169dnwYIFtGjRokifO2PGDNNzRER8QWAgpKRk7a/MCRbTn46sN/UvYCCj+IAEQrMdb98+uCLnzFJE3Jim84iIFKGUlBT27dvnbCuczMqenkpSahp7Tsaz52Q8KXab89r58+eJioqysDoRkQxTpkzBbrcTHx/PL7/8QuPGjenQoQPz588v0ueMHz+e2NhY59fhw4eLdHwREXfy66+OZdzZBZOt2cSvtDAFk479JWcymM9yDCbtdgWTIp5MMydFRIrQgQMHnEuUQeFkTgLKVqPa6LcAOLv6A+I2L/vnioFda3FEvIqfn1+uB+DkxDAM09+neXFp5uKlmYyZxcXF5Ti7MTehoaG0atWKZcuW0bJlS+69915uuukmKv6zoVlhnxsUFERQUFC+6xIR8TS5/XMwgg95mwcIItnZd4YIBrGA/5F1T1+AcuUgJqaoqxSRkqZwUkSkCGXeU0zh5OX5l6vu0rLnO4wQEfd2/fXXFyicLIhLez7u2bMny9Lrs2fPEh0dTfv27Qs8vr+/P127dmXbtm1s2bKFf/3rXyXyXBERb5DTPwX+pPAqY3mYN03927mafixjH/Wyfd25c1CAz5tExA0pnBQRKUKZD3RROHl5ARHm08xdD4UQEc936STrktC5c2dmzJjBqlWrGDRokOnaqlWrnPcUxrFjxwBHUFmSzxUR8VRpaeCfQ/JQiZMsYgDX85OpfyEDGMmHXCAsy2tCQyE+vjgqFRGrFGrPyejo6KKqQ0TEK7iGk+XLl6dcuXIWVuMZ/MuZw8nk5OQc7hQRyV23bt2oW7cu8+fPZ+vWrc7+8+fPM23aNPz9/Rk+fLizPzo6mr///jvLz7Rr167NdouJVatWsWzZMsqUKWOaCZnf54qI+Ip583IOJluymS20NAWT6Rg8yfMM5PNsg8lz5xRMinijQs2crFGjBn369GH06NHcdNNNRVWTiIjH0knd+WcLK48REIQ9xTFjUuGkiBSUv78/s2fPpkePHnTq1InBgwcTHh7O0qVL2b9/P9OnTzf93fzGG28wdepUJk+ezJQpU5z9vXv3pkKFCrRq1YqaNWuSmJjI9u3bWbt2LQEBAcyePZvQ0NACP1dExBdUqJDzfpDD+Jh3uY9gMlbMnKUsg/mM77g529doW3IR71WocPKaa65h0aJFLF68mFq1ajFq1ChGjBhB9erVL/9iEREvpHAy/wzDwD+iOimnHKecK5wU8Q0bNmzg+++/59ixY9lu52AYBh988EG+x+3atSvr1q1j8uTJLFy4kOTkZJo2bcq0adMYMmRInsaYOnUq3377LevWreP06dMYhkHNmjUZPXo0Y8aMoWnTpsXyXBERb5Hb/pIv8RiP8H+m/j9pSl+WE0X9LK/Zt08ncYt4u0KFk7/88gt//vkn77//Pp9++imTJk1i6tSp/Otf/2L06NH06tULP79CrRwXEfEYCQkJHD582Nm+dECCXF5AOYWTIr4iNTWVwYMHs3TpUux2O4ZhmJZQX2oXNJwEaN26NStXrrzsfVOmTDHNmLzkkUce4ZFHHim254qIeLOcgskKnGYRA+jCGlP/Em5jOB8RT+ksr9FsSRHfUOjk8KqrruK1117j2LFjzJ8/n86dO/P111/Tr18/atasydNPP82+ffuKolYREbe2d+9eU1szJ/PO9VCc5ORkUlJSLKxGRIrTyy+/zJIlSxgxYgRbtmzBbrczZswYNmzYwAsvvEDZsmUZMGAAUVFRVpcqIiL5kJaWczB5Hb/yKy1MwWQ6Bk8znf4szhJMbtqkYFLElxTZtMbAwEAGDRrE999/T1RUFE8//TRpaWk8//zzNGzYkJtuuoklS5Zku7m4iIg30EndBedfzrwdyP79+y2qRESK26effspVV13F7Nmzue666wAoW7Ysbdq04fHHH2ft2rV89dVXfPfddxZXKiIiefXCCzkffDOUeayjI7XIWGF0jjLcypc8x9OAOdG026F162IsVkTcTpGvubbb7fz5559s376dmJgY7HY7VatWZc2aNdxxxx00a9aMPXv2FPVjRUQslzmcrF8/6545kj3XmZOQ9fdSRLzH3r176dKli7NtGIZptnTTpk259dZbefvtty2oTkRE8ssw4L//zdpvI5VXGMs8hhHCRWf/DprQml/4hltM9+/Zo9mSIr6qyMLJ/fv3M2HCBGrWrEmfPn1YuXIlffv2ZdWqVRw+fJiDBw8ybtw4duzYwQMPPFBUjxURcRuugVqNGjVMJ7lK7jLPnFQ4KeK9AgMDKVWqlLMdFhbGqVOnTPfUrl1bH2aLiLi53JZxlyea7+jBWGaZ+pfRl7ZsZA/mFUZ2O+hzfRHfVagDcVJSUliyZAmzZ88mMjKS9PR0rrjiCp599llGjhxJpUqVnPdWrVqVmTNncv78eebNm1fowkVE3I1O6i44W0hp/ELCSU+MA1AoIeLFatasaTo8rHHjxqxdu9Z5CA7Axo0bKVeunFUliojIZUydCtmcJwZAM35nGf2ow0FT/ySmMp0J2F3mSFWrBkePFmOhIuIRChVOVqtWjTNnzmCz2ejbty/33XcfN910U66vqV27NgkJCYV5rIiIW3INJxs1amRhJZ4pIKIaSf+Ek7t27bK4GhEpLp07d+aLL75whpEDBw7kscceo1evXvTs2ZN169axbt06Ro4caXWpIiKSjZxmSwIM4jM+YBSlSHT2xRLOUD7hK2413Xv+PISFFVeVIuJJChVOhoWF8eijjzJy5EgqV66cp9c8+OCDDB48uDCPFRFxOzExMcTExDjbmjmZf/7lqpN07G9Ay7pFvNnIkSNJS0vjyJEj1KxZk3//+99ERkby1VdfsXLlSgBat27N888/b3GlIiKSWU7BpI1Unue/PMbLpv6/aUQfvmA35g/utbekiLgqVDi5b98+5/KbvAoPDyc8PLwwjxURcTuZlyErnMy/AJd9J48ePUp8fDxh+jhdxCv07duX0aNH07NnT6677jrTYTcBAQGsWLGCLVu2EBUVRe3atWndujV+fkV+bqOIiBRQcjIEBWV/rRwxLGAQN/G9qX8Ft3IX84ijjKlfwaSIZFaon/rq1avH66+/nus977zzDnXr1i3MY0RE3F7mZcgKJ/PPP9OJ3Xv37rWoEhEpaitWrKBPnz7UqFGDp556Ktv/f7ds2ZKBAwfStm1bBZMiIm7k9ttzDiavYRubaZUlmJzCZPqyXMGkiORJoX7yO3DgAGfPns31ntjYWA4ePJjrPSIins51GbK/vz916tSxrhgPFVC+hqmtpd0i3uOLL76gd+/exMTE8Pzzz9OoUSO6dOnCvHnzSExMvPwAIiJiCcOApUuzvzaAhfxMe+qy39l3njD6soypTDEdfAMKJkUkZ8X+sXRsbCxBOX3MIiLiJVyDtHr16uHvX6hdM3ySf9mqprbCSRHvceutt7Js2TKOHDnCiy++SJMmTVi7di3Dhw+natWq3H///fzyyy9WlykiIv/YsSPn/SX9SGMG/2UhAwkl47Db3TSgDZv4gr6m+/ftUzApIrnL97vntWvXmtoHDhzI0gc4NzqfN2+eljeKiNdzDdL0d17B+AUEgZ8N0tMAndgt4o0qVqzIuHHjGDduHJs3b+aDDz7g888/57333uP999+nadOmjBo1iqFDh1K+fHmryxUR8Um5HSsRwRnmcyc3852p/ytuYSifEEtZU79CSRHJi3yHk126dHEegmMYBh9//DEff/xxtvfa7XYMw+C5554rXJUiIm4sPT3ddCCOwsmCM2wB2P8JJzVzUsS7tWrVilatWjFr1iwWL17MnDlziIyM5NFHH+XJJ5+kd+/eLFy40OoyRUR8Sm7B5FX8wXL6Uo99pv5pTGAyU7WMW0QKLN/h5KRJkzAMA7vdzjPPPEPnzp3p0qVLlvtsNhvlypWja9euNGnSpChqFRFxS0ePHjXtmdaoUSMLq/Fshp8/l36O3b17t/NDLhHxXsHBwQwdOpShQ4eyb98+7r77btavX8+SJUusLk1ExKfk9iPX7SzmI4YTxgVnXzyhDGMuy7jNdO+iRdC/f3FVKSLeKN/h5JQpU5zfr1mzhhEjRjBs2LCirElExKNknuGnmZOFYAtwfnvu3Dmio6OpWLGihQWJSEnYt28fc+bMYe7cuRw5cgSA2rVrW1yViIhvOHECqlbN/pofaUxjIk8xw9S/l3r0ZTl/cZWpX7MlRaQgCnViw48//lhUdYiIeKzMeyMqnCy49KQLpnbr1q0pVaqUs12vXj1WrFhR0mWJSDFITExk4cKFzJkzh59++gm73U5QUBADBw5k1KhRdOvWzeoSRUS8XlAQJCdnf60sZ/mUIfRkpal/JTdzJ/M5R4SpX8GkiBSUjpMVESkk15mTYWFhVKlSxcJqPJw93dQ8fDoOv2BHX8q5Y1ZUJCJF7Oeff+bDDz9k0aJFxMfHY7fbufbaaxk1ahRDhgwhIiLi8oOIiEih5baM+0r+Yjl9acBeU/9zjGci00jH5ux76SUYN664qhQRX5CvcLJu3boYhsH333/PFVdcQd26dfP0OsMwiIqKKlCBIiLuLvNJ3dojseiEXnUDEV2GA3Bs9oPWFiMiBXb8+HHmzp3LnDlz2LNnD3a7nbJly3L//fczatQorrvuOqtLFBHxKbn9uNqPpXzM3ZQm3tl3gVIM5yMWM8B0b2oq2GyZRxARyZ98hZPp6emmN92Z2zmxa363iHixzOGkFIJhOPadTEsBIOXsUYsLEpGiUKtWLdLTHbOgu3TpwqhRo7j99tsJCgqyuDIREd/yyy/Qpk321wzSmcpkJjLd1B9FXfqynD+52tSvt/kiUlTyFU4eOHAg17aIiK9JTk5m//79zrbCycIz/Pyx/xNOpp5ROCniDSpXrsyIESMYOXIkV1xxhdXliIj4pNzmFYUTyycM5Va+MvV/R3cG8xlnKWfqVzApIkVJe06KiBTCvn37nLOBQOFkUTBs/tgd2SQpZ49jT0/D8NN6IRFPdujQIfz8/KwuQ0TEJx06BLVr53y9MTtZTl8asdvU/wJP8BTPmfaXPH4ctL26iBS1Ygkn4+Li2LRpEyEhIXTo0EH7r4mI13Jd0g0wceJEnnvuuWzvjYqKgrDKJVGWRzNsARmNtBRS404TUFY/BYt4MgWTIiLWsNkgPT3n67eygk8YSjjnnX0JhDCSD/mcQaZ7NVtSRIpLocLJDz74gPnz57N48WLnyYrbtm3j5ptv5tSpUwB06NCB7777jpCQkMJXKyLiZnbt2mVq7z94iICI6tnem5KUREBYSVTl4Wzmf5pSzxxVOCkiIiKST7nNETJIZxLPMIWppv791KEvy9nOtaZ+BZMiUpwKFU5+8sknJCQkOINJgEcffZTTp08zYsQITp48yTfffMPbb7/No48+WuhiRUTcjevMSZvNhl/Z6lQb/Va29x58qV9JleXRTDMngZSzxwihhUXViIiIiHie3ILJ0sQxj7vowwpT//d0YyCfc4byzr7bb4fFi4urShERh0Ktsdm9ezfNmjVztk+fPk1kZCSjR49m9uzZfPnll7Rq1YpPP/20sHWKiLgl13BSp84WEcMPIyjU2dShOCIiIiJ5l1sw2ZBdbKJNlmDyJcZxM9+agsmkJAWTIlIyChVOxsTEULFiRWf7p59+AuC2225z9nXs2NF0kq2IiDdxDScDAwMtrMR7GIZBQLlqznaKwkkRERGRy/rjj9yDyV58yS+0pgl/O/sSCeZOPuVxXiLNZWGl3Q760VZESkqhwsny5ctz/PhxZ3v16tXYbDbat2/v7LPb7aSkpBTmMSIibikuLo4TJ0442woni47rvp0KJ0VERERyZxhwzTU5XCOdCUzjS3pThjhn/wFq056f+Yw7Tfdrf0kRKWmFCievueYavvjiC/766y+ioqL47LPPaN++PWFhGSc+HDhwgKpVqxa6UBERd7Nnzx5TW+Fk0fEvlxFOpsWdJj0lycJqRERERNxXbrMlwzjPEm5nGpNM/avpSku2sJXmzr5+/RRMiog1CnUgzhNPPMGNN97INS4f0YwZM8b5fVJSEpGRkXTv3r0wjxERcUuuS7rhnz0nEzVTvCgElHM98dxO6rnjOd4rIu6tbt26BXqdYRhERUUVcTUiIt5j1y5o3Djn6/XZw3L60pQdpv5XGcPjvGhaxp2UpGXcImKdQoWTXbt2ZcWKFcyZMweAO+64g759+zqvr1+/nlq1apn2oCxOy5Yt46233uK3334jISGBKlWq0LZtW2bOnEnNmjVLpAYR8R27du1yfu/n50dAQACgcLIomMNJSD1zzKJKRKSw0tPTMTJN60lOTnZuDeTv70/58uWJiYkhNTUVgKpVq2o2uohILnKbLQnwL75hPndSllhnXyLB3Mt7fMJdpns1W1JErFaocBLglltu4ZZbbsn22g033MDvv/9e2Edclt1u5/777+e9996jXr16DBo0iNKlS3Ps2DHWrFnDwYMHFU6KSJFznTlZp04d/PwKtVOGuPCPqGZqp5w5YlElIlJYBw4cMLXPnTvHjTfeSIMGDXj22Wdp164dfn5+pKen8/PPPzNhwgQuXLjA999/b03BIiJuLvdg0s54ZjCdCfiRkToeoib9WMZvtHD2RUZC587FVqaISJ4VOpx0B6+//jrvvfceDz30EK+99ho2m810/dKn8CIiRck1nGzYsCGHDh2ysBrv4hcYgi2sHGnxZwBI0cxJEa/x5JNPcvHiRTZt2mT6mc3Pz4+OHTvy/fffc+211/Lkk0/yzjvvWFipiIj7yS2YDCWejxhOf5aY+iPpzB0s5DSVnH2aLSki7qRIwslffvmFzZs3c+7cOdLS0rJcNwyDiRMnFsWjskhMTGTq1KnUrVuXWbNmZQkmwbFcSESkKNntdoWTxcy/XA1nOJl6Vid2i3iLL774guHDh2f7Mxs4fm7r1asXc+fOVTgpIuIit2CyLlEspy9X86ep///4N+N4mVQCnH0KJkXE3RQqtTtz5gx9+/Zl/fr12HP5G644w8n//e9/nDlzhuHDh5OWlsaKFSvYvXs3ZcuW5cYbb6R+/frF8lwR8W0nT57k/PnzznbDhg21BLGIBZSrRtKh7QCknDmKrVRZawsSkSIRFxdHbGxsrvfExsZe9h4REV8RHw+lS+d8vQff8hmDieCcs+8iQdzPO3zMcNO9CiZFxB0VKpx89NFHWbduHV26dOHuu++mRo0aJT5LccuWLYDjU/Zrr702ywEVY8eO5aWXXsrx9UlJSSQlJTnbcXFxxVesiHiNzCd1N2zY0KJKvFdARMahOOmJcfgF5/JTuYh4jKZNm7JgwQIee+wx6tWrl+X6nj17WLBgAVdddZUF1YmIuJcmTeDvv3O6audJXuA5njLtL3mE6vRjGVtoldF3BKpXz24MERHrFSpJ/Oqrr2jdujU//PBDllMYS8qpU6cAePnll7nuuuv45ZdfaNKkCb///jv33nsvL7/8MvXq1eOBBx7I9vUzZsxg6tSpJVmyiHgBhZPFzz/Tid32NO0fLOINJkyYQL9+/WjevDmjRo2iY8eOVKpUiVOnTvHTTz/x4YcfcuHCBSZMmGB1qSIilsrtLXYpLvAhIxnIQlP/T3SkP4s5RWVnn2ZLioi7K9TRshcvXuT666+3LJgESE9PByAwMJDly5fTqlUrwsLC6NSpE4sXL8bPz4+XX345x9ePHz/euXQoNjaWw4cPl1TpIuLBXMPJ4OBgatasaWE13ikgUzhJeoo1hYhIkerTpw8fffQRhmHw2muvcccdd9ClSxfuuOMO/u///g8/Pz/mzJlD7969rS5VRMQyub3FvoJ9bKBdlmDyTR6kGz8omBQRj1OomZPNmzfnwIEDRVRKwZQpUwaAli1bUq1aNdO1pk2bUrduXfbu3cu5c+coW7ZsltcHBQURFBRUEqWKiBdx3UKiQYMG+PkV6rMeyYZ/mcrgZ4N0x0Frmjkp4j2GDRtGv379WL58Odu2bSM2NpYyZcpw7bXX0qdPH8LDw60uUUTEMrkFkzfyPz5nIOU46+xLIpAHeYsPGeXs69sXli0rxiJFRIpQocLJKVOmcMstt7Bx40batm1bVDXlS6NGjQCyDR5d+xMTE3O8R0QkvzKf1C1Fz7D541+2CqlnHCd1K5wU8S6lS5fmrrvu4q677rK6FBERt5CYCKVK5XTVzjhe5gWexEa6s/co1biNpfxCG2dfUhIEBhZvrSIiRalQ4eTRo0fp1asXnTt3ZsiQITRv3tw5kzGzYcOGFeZROeratSsAO3fuzHItJSWFvXv3EhoaSsWKFYvl+SLie1JTU4mKinK2FU4Wn4CIai7hpJZ1i3ib+Ph4du/ezYULF+jUqZPV5YiIWObGG+GHH7K/FkICsxnNnXxm6l9Pe25nCSep4uzTMm4R8USFCieHDx+OYRjY7XY++ugj5/5Brux2O4ZhFFs4Wa9ePbp3786qVauYPXs2o0ePdl57/vnnOXfuHEOHDi3xU8RFxHsdOHCAlJSMoKxBgwYWVuPd/MtVh6jNjkZaKnb9xC3iFQ4cOMAjjzzCN998Q3p6OoZhkJrqmB29fv167rnnHt566y26dOlibaEiIiUgt2XctTnAMvrRnK2m/re5n0d4jRQypkjqxyQR8VSFSuzmzJlTVHUUyltvvUX79u255557WL58OY0bN+b3339n9erV1K5dmxdffNHqEkXEi7juNwnQuHFjiyrxfgHlari07M7wQkQ816FDh2jbti0xMTH06dOHEydOsGHDBuf1Nm3aEB0dzWeffaZwUkS8Xm7B5A38wOcMpAIxzr5kAniIN5nNPaZ7FUyKiCcrVDh59913F1UdhVKvXj22bNnCpEmT+Pbbb1m1ahVVqlThoYceYtKkSVSqVMnqEkXEi/z999+m9qW9b6XoBZQzH3SWlJRkUSUiUlQmT57M2bNnWbNmDe3bt2fq1KmmcNLf359OnTqxfv16C6sUESl+OQeTdsYwi5d4zLS/5HGqcDtL2EB7Z9/s2TBqVHZjiIh4Dq9Z61yzZk23mckpIt7NNZysVKkS5cqVs7Aa7+YfUd3UTk5OtqgSESkq3333Hf369aN9+/Y53lOrVi1Wr15dglWJiJScX36BNm2yvxZMIu9zD0P51NS/gbbczhKOk/HBbWoq2GzFWamISMnwK4pBli1bxh133ME111xD/fr1nf1///03M2fO5OjRo0XxGBERt+AaTmpJd/GyhZXDCAxxthVOini+M2fOUKdOncvep5nSIuKNDCPnYLIWB1lPhyzB5PuMpguRpmDSblcwKSLeo1AzJ9PT0xk8eDCLFy8GICQkhMTEROf1iIgInn76adLS0hg/fnzhKhURcRMKJ0uOYRgElKtB8ok9gMIKEW9QuXJl9u7dm+s9f/75J7Vq1SqhikRESkZu+0t24UcWcgcViXb2peDPv3mdd7kPyHix9pcUEW9TqJmTr776KosWLeK+++7j7NmzPPbYY6brlStXplOnTnz99deFKlJExF3ExMQQHZ3xQ6PCyeIXUD7jUByFkyKe76abbuLLL7/kzz//zPb6Tz/9xA8//EDPnj1LuDIRkeKT2/6S/+E1/sdNpmDyBJXpyo+8y/0omBQRb1eocPKjjz6iZcuWvPXWW4SHh2Nk8zdu/fr12b9/f2EeIyLiNjKf1K3DcIqf64ndqampxMfHW1iNiBTWhAkTCAkJoWPHjjz33HPOWZQrV65k4sSJ3HzzzVSoUIHHH3/c4kpFRIpGTsFkMIl8xHBeYwz+pDn7N9GalmxhPR2dfVddpWBSRLxXoZZ17927l4ceeijXe8qXL09MTExhHiMi4jYyn9StmZPFz99l5iTA7t27ue666yyqRkQKq06dOnz33XcMGjSICRMmYBgGdrudXr16YbfbqVWrFosXL6Zq1apWlyoiUmg5BZM1OMwy+tGSX039HzKCB3mLJIKdfefPQ1hYcVYpImKtQoWTISEhxMXF5XrPwYMHKVu2bGEeIyLiNlzDyaCgIGrXrm1hNb4hoHxNU/vvv/9WOCni4dq0acOePXv48ssv2bRpE2fOnCE8PJw2bdrQp08fAgMDrS5RRKTQcgomO7GWxfSnEqedfSn4M4ZZvMWDaBm3iPiaQoWTzZs357vvviMpKYmgoKAs18+cOcO3337L9ddfX5jHiIi4DddwsmHDhth0TGKxCyhbFQw/sKcDWWeviohneeaZZ6hbty5Dhw6lX79+9OvXz+qSRESKXPbBpJ2HeJNXGUsAqc7ek1RiAIv4CfP7ZgWTIuIrCrXn5H/+8x8OHz5M//79OXr0qOlaVFQU/fr1IzY2lv/85z+FKlJExF3opO6SZ/gH4F+2irOtcFLEs02fPp0//vjD6jJERIpFbGz2wWQQF/mAUbzBv03B5GZa0pItCiZFxKcVauZknz59+O9//8vzzz9PrVq1CA0NBaBSpUrExMRgt9uZOHEiN9xwQ5EUKyJipeTkZPbt2+dsK5wsOQHla5B69higcFLE09WuXZszZ85YXYaISJGrUQMyzdkBoDpHWMLttOEXU/9H3M0DvM1FQpx9mzZB69bFXamIiHsp1MxJgOeee47vvvuOXr16UapUKWw2G+np6dx8882sXLmSqVOnFkWdIiKWi4qKIi0t4yRFhZMlx/XE7t27d5v+HETEswwePJjvvvuO2NhYq0sRESkyhpF9MNmBdWyhpSmYTMXGf3iNEcwxBZN2u4JJEfFNhZo5eclNN93ETTfdVBRDiYi4LZ3UbZ0AlxO7k5KSOHjwIHXr1rWwIhEpqAkTJvDbb79xww038Mwzz9CqVSsqVapkdVkiIgWydSs0b57dFTv38S6vZ1rGfZoKDGARa+hivlvLuEXEhxUqnDx69CjLly9n8+bNREdHA44l3a1ataJv375UrVq1SIoUEXEHmcPJIUOG4OeXMQE9KioKwiqXdFk+wb+c+cTuLl26ULp0aVNfvXr1WLFiRUmWJSIFEBLimCVkt9vp3bt3jvcZhkFqamqO10VErJbTadyBJPEGD3MPs039v9GcfizjELVN/QomRcTXFTicnDx5MjNnziQ5ORl7pr9NP/74Y8aNG8dTTz3FhAkTCl2kiIg7MIWTfjaiTieYrqckJREQVsJF+YiA8tVN7aMx57ElZLwjSDl3rKRLEpEC6tSpE0ZO7+hFRDxETn+NVeUYS7iddmw09c9jKPfynmkZNyiYFBGBAoaTTz/9NDNmzCAoKIi77rqLzp07U61aNex2O8ePH+fHH39k0aJFTJ48mdTUVKZMmVLEZYuIlDzXcDK45tVUHjTddP3gS/1KuiSfYQsJN7VL1W9N+X/9x9k+NvvBki5JRAooMjLS6hJERAolp2CyHT+zhNupyglnXyo2HudFZjEGML9QwaSIiEO+w8l9+/Yxc+ZMrrjiCr799lsaNGiQ5Z4RI0YwYcIEevTowXPPPcfdd9/NFVdcUSQFi4hYwW63m8JJ1z0QpaQYgOOn+JQzR6wtRURERHzOmTNQvnz21+7hPd7gYQJJcfZFU547WMiP3JDlfgWTIiIZ8n1a98cff0x6ejpz587NNpi8pGHDhsybN4/U1FTmzp1bqCJFRKx28uRJ4uLinG2FkxZwmWyQEqNwUsTTJScn88033/DKK68wbdo0Z//Fixc5deoU6enpFlYnImJWvnz2wWQAybzN/bzHfaZg8nea0ZItCiZFRPIg3+Hk+vXrueqqq+jQocNl7+3YsSNXXXUVP/30U4GKExFxF5kPw/Evp3Cy5GWkk+mJcaQlxFpYi4gUxooVK6hVqxa33norjz32mGkLoO3bt1O1alUWLFhgXYEiIi4MwzFrMrMqHOdHunI/75r65zOYDqznIHVM/b//rmBSRCQ7+Q4nd+7cSevWrfN8f5s2bbK8qRcR8TSZ/x7TzEkLZNrgKeXMUYsKEZHCWL9+Pf379ycoKIjXXnuNO++803S9devW1K9fnyVLllhUoYhIhpz2l2zDRrbQkg787OxLw49xvMQQPiWRUqb77XZo1qwYCxUR8WD5DifPnTtHpUqV8nx/pUqVOHfuXH4fIyLiVszhpIEtLIcNh6QYZQontbRbxCNNnz6dsmXLsmXLFh5++OFstwlq0aIF27ZtK/AzNm/eTM+ePYmIiCA0NJTWrVszf/78PL9+3bp1jBs3jhYtWlC+fHmCg4Np3LgxTz75ZI4/19apUwfDMLL9uv/++wv8axER6+QUTI7kA9bQmeocc/bFUI6b+ZZXGIcOvhERyZ98H4iTmJhIUFBQnu8PDAwkMTExv48REXErruGkYQvAyOmnVSlmGYfipOpQHBGPtHHjRvr370/FihVzvKdmzZqsWLGiQONHRkbSo0cPAgMDGTRoEGXKlGHp0qUMGTKEAwcO8NRTT112jP79+xMdHU3Hjh0ZNmwYhmEQGRnJzJkzWbJkCT///HO2H9aXKVOGMWPGZOlv2bJlgX4tImKNDRugffus/QEk8ypjeYi3TP3buZq+LGc/dbO8RsGkiMjl5TucFBHxRaaZkzb91WkJw3D83qc5NptPiTlscUEiUhBJSUmUKVMm13tiY2Px88v3Ah9SU1MZPXo0hmGwdu1amjdvDsDkyZNp164dkydPZsCAAbke6ggwduxYhg0bRtWqVZ19drudhx56iLfffpupU6fy5ptvZnld2bJlTftniojnyenz50qcZDH96cQ6U//n3MFIPiSBUFP/kSNQvXpxVSki4l0K9A77k08+YePGjXm6d+/evQV5hIiI20hISODgwYPOtmELsLAa32bYArBfCic1c1LEI9WtW5ctW7bkes+GDRto3LhxvsdevXo1UVFRjBgxwhlMApQuXZqJEycyaNAg5syZw3PPPZfrOE8++WSWPsMwmDhxIm+//TZr1qzJd20i4v5yCiZbspll9KMGGftdp2MwnhnM5Am0jFtEpHAKFE7u3bs3X6Gjlj+KiCfbs2ePqa1w0jqGzZ9LP++nnjuJPTUZwz/Q0ppEJH9uv/12pk+fzty5cxk2bFiW6y+99BJ//vknM2fOzPfYkZGRAHTv3j3LtUt9hQkWAwIcf//7+2f/I3RSUhIff/wxR48eJSIigvbt23PttdcW+HkiUnJyest6Nx/xDvcTTJKz7yxlGcQCVtEjy/0KJkVE8i/f4eT+/fuLow4REbeV+aRuQ8u6LWMKhu3ppJw9RmDFOpbVIyL59/jjj7NkyRJGjBjBJ598wsWLFwF44okn2LBhAz///DPNmjXj4YcfzvfYlz5Mym7ZdkREBBUqVMjygVN+fPjhh0D24SfAiRMnGD58uKnv5ptvZt68eVSoUCHHcZOSkkhKygg+4uLiClyjiORfdsGkPym8wqP8mzdM/X/SlL4sJ4r6WV6jYFJEpGDy/Q67du3axVGHiIjbyhxOopmTlsk8azUl5ojCSREPExYWxk8//cTDDz/MwoULSUtLAxwzJg3D4I477uCtt97K1wGMl8TGxgLkuKdleHg4R44UbEuIrVu3MnXqVCpVqsQTTzyR5frIkSPp3LkzTZs2JSgoiB07djB16lRWrlxJ7969Wb9+fY6riWbMmMHUqVMLVJeIFE52/7esyCkWMYDOrDX1L+Z2hvMRFwgz9f/8M7RrV5xVioh4N03/ERG5DNdwMiAgIOd1P1L8Ms1a1b6TIp4pIiKCTz/9lP/7v/9j8+bNnDlzhvDwcFq1akXlypWtLi+L/fv306tXL9LS0liwYEG2syAnTZpkardp04avvvqKzp07s27dOr755htuueWWbMcfP348jz76qLMdFxdHzZo1i/YXISJZZPcj3XX8yjL6UYuMg/fSMZjAdGYwHu0vKSJS9BROiohchms4GRQU5LLjkJQ0w/DDFl6RtLjTAKTGKJwU8WTly5fn5ptvLrLxLs2YvDSDMrO4uLjLnhSe2cGDB+natSunT59myZIldO3aNc+v9fPzY8SIEaxbt47169fnGE4GBQUVaKaoiBRcdsHkXczlPe417S95jjIM5jO+5V9Z7lcwKSJSNPysLkBExJ2lp6eza9cuZzswUIevWC2gfMZsIs2cFBFXl/aazG5fybNnzxIdHZ3tfpQ5OXDgAF26dOHYsWMsXLiQXr165bumS7MsExIS8v1aESkemYNJf1J4lTHM5W5TMPkXV9KKzVmCyZtvVjApIlKUNHNSRCQXR44cITEx0dkOCgrifLKFBQkB5Wpwcf9vgGPPSbveHYi4tRtuuKFArzMMgx9++CFfr+ncuTMzZsxg1apVDBo0yHRt1apVznvywjWY/Pzzz+nTp0++arlk06ZNANSpU6dArxeRopOYCKVKmfsqcJrPGcgN/GjqX0o/7uZj4ilt6l+wAAYOLO5KRUR8i8JJEZFcZD4MJzAwEJIVhlkpoHwN5/f2lIuknY+xsBoRuZzIyMhs+w3DyPbDhUv9OR0ek5tu3bpRt25d5s+fz3/+8x+aNWsGwPnz55k2bRr+/v6m07Sjo6OJjo6mQoUKpn0kLwWTR48e5fPPP6dfv365PnfHjh1Uq1aNsmXLmvrXrVvHK6+8QlBQELfddlu+fz0iUnT+9S/49ltzXzN+Zzl9qc0hZ186BpN4hud4CnumhYapqWCzlUS1IiK+ReGkiEguMoeTQUFBEH/RomoEzOEkaGm3iLtLT083tZOSkhgwYAB79uxhwoQJdOrUicqVK3Py5EnWrl3Ls88+S4MGDVi0aFG+n+Xv78/s2bPp0aMHnTp1YvDgwYSHh7N06VL279/P9OnTadiwofP+N954g6lTpzJ58mSmTJni7O/SpQsHDx6kbdu2bN++ne3bt2d5luv9CxcuZObMmXTr1o06deoQFBTEn3/+yapVq/Dz8+Odd96hVq1a+f71iEjRCAhwBIuuBjOf2YymFBkrZGIJZwif8jVZt3DQQg0RkeKjcFJEJBeu4WS5cuWw6eNyywWUM59gmxJzOIc7RcQdTZ48mT/++IM//viDsLAwZ3+tWrUYOnQovXv35pprrmHy5Mk8//zz+R6/a9eurFu3jsmTJ7Nw4UKSk5Np2rQp06ZNY8iQIXka4+DBgwBs3LiRjRs3ZnuPazjZtWtXdu7cyW+//caaNWu4ePEilStXZuDAgYwdO5bWrVvn+9chIkUj8yRsG6k8z395jJdN/TtpTF+Ws5tGWcZQMCkiUrwUToqI5MI1nGzcuDHnzp2zrhgBwC+0LEZQKPakC4Bj30kR8Rzz58/njjvuMAWTrsLDw7n99tv57LPPChROArRu3ZqVK1de9r4pU6aYQsZL8ruXbefOnfO8l6WIlJzMwWQ5YvicgdyIeT/bL+jNXczjPOFZxlAwKSJS/HRat4hILnbs2OH8vlGjrJ+kS8kzDIOAchlLu1PPaOakiCc5ffo0KSkpud6TmprKqVOnSqgiEfFGmYPJa9jGFlpmCSYnM4V+LFMwKSJiIYWTIiI5iI6O5uTJk872VVddZWE14sp130nNnBTxLPXq1WPRokXExGR/mNXp06dZuHAh9evXL+HKRMRbZA4mB7KADbTjCg44++IoTR+W8wyTsxx8AwomRURKksJJEZEc/PXXX6Z206ZNLapEMnMNJ9Piz2DPdOCGiLivMWPGcOLECa677jpee+01fv31Vw4fPsyvv/7KrFmzaNGiBadOnWLs2LFWlyoiHubECXMw6UcaL/AECxhsOvhmFw1pwyZW0CfbcRRMioiULO05KSKSA4WT7st1WTeAPS33JaIi4j5Gjx7N8ePHmTZtGo8++qjpmt1ux2azMWXKFEaOHGlRhSLiiUqVgsSM/JEIzrCAQXTnf6b7vqQXQ/mEOMpkO46CSRGRkqdwUkQkk969exMVFcXx48edfX5+fnTv3p19+/ZBWGULqxOAgIq1zR0KJ0U8ysSJE7nzzjv59NNP2b59O7GxsZQpU4Zrr72WO++8k3r16lldooh4kMzLuK/iD5bTl3rsM/U/w0SmMCXbZdxly8LZs8VYpIiI5EjhpIhIJlFRUezYtRvDsDn77H7+7D11gZSkJAKyP2BWSpB/mcoY/kHYU5MAzZwU8SRz586lcuXK9OjRg0mTJlldjoh4uMzBZH8W8RHDCSXB2XeeMIYxl+X0y3aMmBgoV644qxQRkdxoz0kRkWz4l6mKERDkbIc27Uq10W+BLcDCquQSw89m2ndS4aSI5xg1ahTfffed1WWIiBfIvL/kc4xnEXeYgsk91KcNm3IMJu12BZMiIlZTOCkikh17OumJcc5mQIVaFhYj2XFd2m1PVTgp4imqVq1KcnKy1WWIiIdzDSbLcpav6MV4njfd8w3/ohWb2cmV2Y6h/SVFRNyDwkkRkWxknokXUKF2DneKVUyBsT2NtLQ064oRkTzr27cv//vf/0hKSrK6FBHxUK7B5JX8xWZa8S++Nd3zLE9xK18SS9lsx1AwKSLiPhROiohkI/NMPM2cdD+BmQLjixcvWlSJiOTHtGnTCAsLo1+/fvz1119WlyMiHiQ21hxM9mMpm2hDfaKcffGE0p9FTOBZ0rFlM4qCSRERd6MDcUREsuMyc9IvuDS20AgLi5HsZJ7NqllYIp6hefPmJCUlsXXrVr777juCg4OpVKkSRqZTLQzDICoqKodRRMTX1KsH+/45fNsgnWeYxASeNd0TRV368AV/cVW2Y4SHOwJOERFxLwonRUSy4bqsO6BCrSxvmsV6tvCKGIEh2JMTAYWTIp4iPT2dwMBAatUyz0i3Z5rKlLktIr6rVClIdPxzTxnO8SlDuIVvTPd8R3cG8xlnyf50m7lz4a67irtSEREpCIWTIiKZ2O127GkZhzVoSbd7MgyDgAq1SD62C1A4KeIpDhw4YHUJIuJBQkLg0s4tTdjBcvrSkD2me57nSZ7OZRl3airYsr8kIiJuQHtOiohkkpqaatqMSOGk+3LddzIpKUkzrURERLyIYWQEk31YzibamILJC5RiIAsYz/O57i+pYFJExL1p5qSISCaZZ+AFVtRJ3e7Kdd/JtLQ0Tp06ReXKlS2sSETy6ujRoxw/fhzDMKhSpQrVq1e3uiQRcSOXdtQxSGcyU5nMM6br+7iCviznD67JcQx9Ziki4hk0c1JEJJPM4WTmg1fEfWSe1aqTf0XcW3x8PFOmTKFWrVrUqlWLNm3a0Lp1a2d76tSpxMfHW12miFjsUjAZTizL6ZslmFzFTbRis4JJEREvoXBSRCQT13DSr1QZbKXKWFiN5CYg06zWP//806JKRORyoqKiaNGiBdOmTePIkSNUrVqV1q1b06pVK6pWrcqRI0d45plnaNGiBfv377e6XBGxQHx8RjDZiL/ZRBt686Xpnhd5jJ58wxnK5ziOgkkREc+icFJEJBPXcFL7Tbo3W2gEfsGlnW3NnBRxT0lJSdxyyy3s2bOHwYMHs3PnTo4cOcKGDRvYuHEjR44cYefOndx5553s2bOHnj176pArER/TvDmU/uef9F58yS+0pjG7nNcTCGEw83mCF0nLZXcyBZMiIp5H4aSIiAu73W56QxyocNKtXTqx+xLNnBRxT2+//Ta7d+9m8uTJfPLJJzRq1CjLPY0aNWLevHlMnTqVXbt28c4771hQqYhYwc8Ptm517C85kWf4kt6Ec955/QC1ac/PLGBwruMomBQR8UwKJ0VEXBw+fJj09HRnW/tNuj/Xpd1//fWXTuwWcUNLliyhfv36TJo06bL3TpgwgQYNGrBo0aISqExErGYYjlCxNHEs4XaeYbLp+g/cQEu2sI1mOY5x8KCCSRERT6ZwUkTEReZlwVrW7f5cZ7fGxsZy9OhRC6sRkezs2LGD7t27Y1zaTC4XhmHQvXt3du7cWQKViYiVLv2V0IDdbKQt/Vhuuv4KY+nBd8RQIccx7HaopR/XREQ8msJJEREXWcNJzZx0d5n/jLS0W8T9XLhwgTJl8n64WHh4OBcuXCjGikTEapeCyZ58zS+05koyPpBIJJihzGMcr2h/SRERH6BwUkTEhWs4aQuNwBZSOpe7xR1knt2qQ3FE3E+lSpXYu3dvnu+PioqiYsWKxViRiFjJEUzaeYpn+ZJbKUus89ohatKB9XzK0FzHUDApIuI9FE6KiLhwnXWnWZOewVaqDBgZ/5xp5qSI+2nXrh0rV67kxIkTl733xIkTfP3113To0KEEKhORkmYYEMZ5FtOfZ5mAHxkpYySdackWfue6XMdQMCki4l0UToqI/CM9PZ0dO3Y429pv0nMY/gHO7zVzUsT93H///cTHx9OvXz+io6NzvC8mJoZ+/fqRkJDAvffeW4IVikhJMAyox1420pbbWWq69hr/4Sb+x2kq5TqGgkkREe+T8wYeIiI+5uDBgyQkJDjbCic9h2ELwJ6SBDjCyfT0dPz89PmbiLvo2rUr99xzD++//z5NmjThvvvu44YbbqBmzZoAHD58mB9++IH333+f6OhoRo0axQ033GBx1SJSlAwDevAtnzGYCM45+y8SxL28xzyG5fr6Jk3A5TNkERHxIgonRUT+ocNwPJdhC3R+n5CQwMGDB7niiissrEhEMnvrrbcIDw/n1VdfZcaMGcyYMcN03W634+fnx9ixY5k5c6ZFVYpIUTt6FGrUsPMkL/AcT5mWcR+mBv1Yxq+0zHWM8+chLKy4KxUREat4ZTg5c+ZMnnzySQA2bNhA27ZtLa5IRDxB5r0KAyvUtKgSya+0i/GmdufOnSld2nyYUb169VixYkVJliUiLmw2Gy+++CL33Xcfc+bMYcOGDc49KKtUqUL79u25++67adCggcWVikhRCQyEwJR4Pmckd7DIdG0tnRjAIk5ROdcxtIxbRMT7eV04uXPnTiZNmkRoaCgXLlywuhwR8SCmmZN+NvyC9RG9x7Cnm5pHY+KwJRjOdsq5YyVdkYjkoH79+jz77LNWlyEixSgtDfz94Qr2sZy+XMMfpuuv8zCP8gqpBOQwgoOCSRER3+BVG3KlpaVx9913c+2119KvXz+ryxERD+MaThq23H9YFjdjGOBnczZD6jSn2ui3nF8BZatZWJyIiIjv+PRTRzB5E6vYQktTMJlEICP4kP/wuoJJERFx8qpw8oUXXmDbtm18+OGH2Gy2y79AROQfaWlp7Ny509lWOOl5XP/MkqMPWliJiIiIb6pXD4YOtfMYL7KSf1GOs85rR6nG9azlI0ZcdhwFkyIivsVrlnX/+eefTJ06lQkTJtC0aVOryxERD7Nv3z4uXrzobCuc9DyOE7sdf4YpMUewp6dh+OmDKhERkZIQHg5p5y8wn9EMZoHp2jo60J/FnKTKZcdRMCki4nu8IpxMTU1l+PDhNGnShP/+97/5em1SUhJJSUnOdlxcXFGXJyIeIPNJ3Sic9DimQDkthdSzxwkoX8O6gkRERHyEYUAd9rOMfjRjm+na29zPI7xGCoGXHUfBpIiIb/KKZd3PPfecczl3QED+AoUZM2ZQpkwZ51fNmjqdV8QXZT6p2/BXOOlxMv2ZaWm3iIhI8TMM6Mb3bKGlKZhMJoB7eI8HeVvBpIiI5Mrjw8lt27Yxffp0HnvsMa677rp8v378+PHExsY6vw4fPlwMVYqIu9u2LeOH6YCAAAzD4/969DmOmZMuJ3RHH7KuGBERER9gGHYe5WW+owflOePsP0ZVOrOG2dxz2TGqV1cwKSLi6zz+3ffdd99NvXr1mDJlSoFeHxQURHh4uOlLRHzP1q1bnd8HBwdbV4gUmGH44V+2srOdclozJ0VERIpL2cAEPmEoL/MYNtKd/T/Tjhb8ykbaXXaMRx6BI0eKs0oREfEEHr/n5KXZTjmFCe3aOf5RXLZsGX379i2pskTEg8TFxbF3715nOzg4mIvpubxA3FZAhdqknjsBQPLpA9YWIyIi4qXu7HCQH1P60pytpv53uZf/8H8kE3TZMZKSIPDyq71FRMQHeHw4OWrUqGz7165dy549e+jduzcVK1akTp06JVuYiHiM7du3m9rBwcGQYFExUiiBFeuQuHcTAKlnjpKefBG/QM2EFRERKSo9An/kk5Q7qEi0sy+ZAP7N67zHfZd9/cGDUKtWcVYoIiKexuPDydmzZ2fbP3z4cPbs2cP48eNp27ZtCVclIp7EdUk3XAonk6wpRgolsHI9l5adlNMHCKre2LJ6REREvIbdzljba3xtfwx/0pzdx6lCfxbzMx3yMoSIiEgWHr/npIhIYf3+++/O78uXL4+/v8d/buOzAirXNbWTT+2zqBIREREvkpjIguC7edU+1hRMbqQNLdmiYFJERApF4aSI+DzXmZPNmjXDMIycbxa35l+mMn5Boc528skoC6sRERHxAocOcah2JwYlzzN1f8BIOrOGY1S/7BAKJkVEJDdeG05+9NFH2O12LekWkVylpKTw559/OtvNmze3sBopLMMwTLMnk09q5qSIiEiBrVnDxataUuv0r86uFPx5kDcZzew8HXyjYFJERC7Ha8NJEZG82LlzJ8nJyc52s2bNrCtGikRgJZdw8vQB7GmpFlYjIiLigex2eOMNUrrcSPD5087uk1TiBlbzNg8Cl19pomBSRETyQhuriYhPy3wYjsJJz2c6FCcthZQzR6wrRkRExNNcvAgPPAAffUSAS/dmWnIbSzlCzTwNo2BSRETySuGkiPg018NwgoODadSokYXVSFEIzHwojvadFBERyZsjR+C222DzZlP3R9zN/bxDEsF5GkbBpIiI5IeWdYuIT3OdOXn11VfrpG4vEFC+JoZ/oLOtfSdFRETy4KefoEULUzCZio1/83+MYI6CSRERKTYKJ0XEJ/Xu3Zsrr7yStWvXOvv27NlD06ZNiYrSTDtPZvjZCKhY29nWzEkREZFc2O3w1ltwww1w6pSz+xQV6cYPvMG/ycv+kpeGEhERyS+FkyLik6Kioti5azfp6enOvrgUgz0n40lKSrKwMikKgZUy9p1MPrUfu94tiYiIZJWUBPfcAw89BKkZB8htoQUt2cJaOud5KP1TKyIiBaVwUkR8li00wtSu1O8pqo1+C2wBObxCPIXrvpP2pAuQnmZhNSIiIm7o6FHo3Bk++MDUPZe76MRPHKZWnodSMCkiIoWhcFJEfJY9NdmlZRBQ8QrLapGiZTqxm8x/1iIiIj5u/Xpo2RI2bXJ2pWLjEWZxNx9zkZA8D6VgUkRECksnP4iIz7Knpji/9y9XHb/AvG30Lu4voGJtMPzA7li2b09TOCkiIgLAu+/Cv/8NKRk/B0VTngEsIpKu+RpKwaSIiBQFzZwUEZ/lGlgFVtKsSW/iFxBMQLkazrZmToqIiM9LSoL77oP77zcFk7/TjJZsUTApIiKWUTgpIj4pNTXVtA9h5mXA4vlM+066zJIVERHxOcePQ9eu8N57pu5PuZMOrOcgdfI1nIJJEREpSgonRcQnZT6RWzMnvY9rOIk9zRFIi4iI+JqNG6FFC9iwwdmVhh/jeImhfEIipfI1nIJJEREpatpzUkR80sWLF01tU5AlXiEg02zYzH/mIiIiXm/2bHjoIUjO2N4khnIM5HN+4MZ8D6dgUkREioPCSRHxSa5BlS2sHLbQCAurkeIQWMkcOCucFBERn5GcDI88Au+8Y+rexjX0ZTkHyP+KEQWTIiJSXLSsW0R8kmtQFaAl3V7JFlIaW3hFZ1vhpIiI+IQTJ+CGG7IEkwsYSHt+VjApIiJuR+GkiPicxMRE056TOgzHe7n+2SqcFBERr/fLL9CyJaxf7+xKw4/HmclgPiOB0HwPqWBSRESKm8JJEfE5f/31l6mdefmveA/XP9vk5GTOnz9vYTUiIiLFaM4c6NQJjh51diWFRvAvVvISjwNGvodUMCkiIiVB4aSI+JytW7ea2jqp23tlnhW7bds2iyoREREpJikp8PDDMHKk6eAb+9VXc+WFzfyP7gUaVsGkiIiUFIWTIuJzfv/9d+f3RmAI/hFVLaxGilPmU9hd/+xFREQ83qlTcOON8Oab5v7+/Sn9x8/so2Bb1yiYFBGRkqRwUkR8juvMycCKV2AY+qvQW9lKV8AvJNzZVjgp4hs2b95Mz549iYiIIDQ0lNatWzN//vw8v37dunWMGzeOFi1aUL58eYKDg2ncuDFPPvkk586dK7bniuTLli3QogWsXZvRZxgwYwbG4oVcIKxAwyqYFBGRkuZvdQEiIiUpPT3dtLQ388w68S6GYRBYqS4XD24FFE6K+ILIyEh69OhBYGAggwYNokyZMixdupQhQ4Zw4MABnnrqqcuO0b9/f6Kjo+nYsSPDhg3DMAwiIyOZOXMmS5Ys4eeff6ZSpUpF/lyRPJs7F+69F1wO+KNsWZg/n5Db/lXgYRVMioiIFRROiohP2bt3LxcuXHC2A3QYjtcLrJwRTv71118kJycTGBhobVEiUixSU1MZPXo0hmGwdu1amjdvDsDkyZNp164dkydPZsCAATRo0CDXccaOHcuwYcOoWjVj2w+73c5DDz3E22+/zdSpU3nTZRltUT1X5LJSUuDxx+G118z9TZvC8uX4Naxf4IBRwaSIiFhFaxlFxKds2rTJ1A6qUrC9mMRzuB6Kk5KSwo4dOyysRkSK0+rVq4mKiuLOO+90BoQApUuXZuLEiaSmpjJnzpzLjvPkk0+agklwzMSeOHEiAGvWrCmW54rk6vRp6N49azB5222wYQNGAwWTIiLimRROiohP2bhxo0vLIKBiHatKkRKiQ3FEfEdkZCQA3btnPZ34Ul/mYDE/AgICAPD3Ny8+Ku7nivDbb9CyJfzz3xrg2F9y+nRYtAgjvHSBh1YwKSIiVlM4KSI+xXXmpOEfiOFns7AaKQn+EdUAw9nevHmzdcWISLHas2cPQLbLpyMiIqhQoYLznoL48MMPgawhZGGfm5SURFxcnOlLxOmTT6BDBzh0KKMvPBy+/JLER5/GsBX8LV1qahHUJyIiUkgKJ0XEZyQkJJgOwzH8te+gLzD8bKY/a/PsWRHxJrGxsQCUKVMm2+vh4eHOe/Jr69atTJ06lUqVKvHEE08U6XNnzJhBmTJlnF81a9YsUI3iZVJT4dFH4a674OLFjP4mTWDzZm595xZKlSr48J9/DjZ9RisiIm5A4aSI+IzffvuNVJcpAoZ/kIXVSEly/bPevn276VAkEZHL2b9/P7169SItLY0FCxZQoUKFIh1//PjxxMbGOr8OHz5cpOOLB4qOhh494NVXzf19+8LGjVS5viFffVXw4W+5Be64o1AVioiIFBmFkyLiMzLPmDMCNHPSV7j+WaelpWlpt4iXujRzMadZinFxcTnObszJwYMH6dq1K6dPn2bx4sV07dq1yJ8bFBREeHi46Ut82Natjv0lV68290+dCkuW0KJrOCdPFnz4ihUpVLApIiJS1BROiojPcA0n/f39Mfz8c7lbvEnmWbIbNmywqBIRKU6X9nzMbn/Hs2fPEh0dne2+kDk5cOAAXbp04dixYyxcuJBevXqVyHPFh332GbRvDwcPZvSVLg1ffAGTJvHpZ3789lvBhw8OhlOnCl+miIhIUVI4KSI+wzWcLFWYTZrE4xh+Nucpu6B9J0W8VefOnQFYtWpVlmuX+i7dczmXgsmjR4/y+eef06dPnxJ5rvio1FR4/HG4805ITMzob9QIfvkFevdmwQIYOrTgj6hd2zy0iIiIu1A4KSI+4ciRIxw9etTZDgkJsbAasYLrn/mGDRuw2+0WViMixaFbt27UrVuX+fPns3XrVmf/+fPnmTZtGv7+/gwfPtzZHx0dzd9//010dLRpHNdgcsGCBfTr169InyticuYM9OwJL71k7u/VCzZtgsaN6d0bBg8u+CNuvhkOHChUlSIiIsVGaxpFxCds2rTJ1A4JCYHz6RZVI1YoVaoUcXFxAJw+fZp9+/ZRr149i6sSkaLk7+/P7Nmz6dGjB506dWLw4MGEh4ezdOlS9u/fz/Tp02nYsKHz/jfeeIOpU6cyefJkpkyZ4uzv0qULBw8epG3btmzfvp3t27dneZbr/fl9rojT9u2OQ2727zf3T5oEkyeDnx+tWsGWLQV/RN26sHJloaoUEREpVgonRcQnuC7jDQgIIDg4GM4nWFiRlLTMs2U3btyocFLEC3Xt2pV169YxefJkFi5cSHJyMk2bNmXatGkMGTIkT2Mc/Ge/v40bN+a4DYRrOFlUzxUfs3AhjBgBCS4/j4SFwbx5jsASeOSRwgWTt94KK1YUrkwREZHipnBSRHyC65vLZs2aceHCBQurESsEBwcTEhJC4j8bbm3YsEGBgYiXat26NSvzMFVsypQpWUJGoMDbPuT1ueLj0tLg6afhhRfM/Q0awPLlcOWVADz8MLz5ZsEfk5AA2sVGREQ8gfacFBGvl5KSwhaXaQdt27a1sBqximEYtGzZ0tnWid0iIlLizpyBW27JGkz27Ok4+OafYLJy5cIFk3a7gkkREfEcCidFxOtt376dixcvOttt2rSxsBqxUrt27Zzfb9u2jYQELe0XEZES8scf0KoVfPeduf/ppx1rr8uWJTERDANOnSr4Y86fL1yZIiIiJU3hpIh4vcz7hWnmpO9y/bNPS0szzagVEREpNosXQ7t2sG9fRl9oKCxZAtOng81Gr15QqlThHtOqlWPbShEREU+icFJEvJ5rOFmhQgXq1q1rYTViJdeZk6Cl3SIiUswu7S85YAC47nddrx5s3Ai33QZA2bLw9deFe1SrVo6V4SIiIp5GB+KIiNfbtGmT8/u2bdtiGIaF1YiVqlSpQp06dThw4ACQdVatiIhIkTl3Du68EzIfknTzzTB/PkREAFC6NMTHF+5R589rxqSIiHguzZwUEa8WExPDnj17nG0t6fZNqXGniYqKomnTppw5c8bZ/+WXX3LllVfSu3dvC6sTERGvs2MHtG6dNZj873/hq6+cwWSFCoULJiMiHIffKJgUERFPppmTIuLVXGdNgsJJX2VPTyUp3c6ek/Gkpdqc/WlpaezctVuzaUVEpOgsWwbDhplTx1Kl4KOPHMu7/9GiBcTEFPwxQUGOw79FREQ8nWZOiohXc122axgGrVq1srAasVJA2WpUG/0WlfpPNPXbSpW1piAREfEu6ekwaZJjH0nXYPKKK2DDBlMw+dln8NtvBX9UUBBcvFiIWkVERNyIwkkR8Wqu4WTTpk0JDw+3sBpxB4GVrsDwD3S27alJFlYjIiJeITYW+vSBadPM/TfdBFu2wDXXOLvS0hxbURZUs2YKJkVExLsonBQRr5Wenp7lMBwRwxZAYOX6zrY9JdnCakRExOP9/Te0aePYS9LV44/DN99AuXKm7o4dC/6o+fPh998L/noRERF3pD0nRcRr/f3338TFxTnbCiflkqDqjUk6ugMAe1oy6enpFlckIiIeacUKGDrUcVz2JSEh8OGHMGhQlts//xxcFnXkWaVKcOwY2GyXv1dERMTTaOakiHitjZl++m/Tpo1FlYi7CazWyNS+qPVxIiKSH+npMHWqYym3azBZpw78/HO2wWRaGgwfnv9HzZ0LJ08qmBQREe+lmZMi4rVcl3SXLl2aJk2aWFiNuJOgao1N7YSEBIsqERERjxMXB3fd5Zg16apbN8fUyPLls33Z1Kn53ytywQIYOLCAdYqIiHgIhZMi4pV69+7NqlWrnO309HSucdmMPioqCsIqW1GauAH/0uWxhVckLe40AImJiRZXJCIiHmHXLujb17HPpKtx4+D558E/+7dX48bBK6/k71FjxiiYFBER36BwUkS80q5du0hKyjiFOTHdxp6T8c52SlISAWFWVCbuIqhaYxJcwkm73Y5hGBZXJSIibuurr2DIEMfMyUuCg+GDD3I9frtXL/j66/w9qnp1ePXVAtYpIiLiYRROiohXyrxMt2Kf/xJcO2Pm5MGX+pV0SeJmgqo1JuHvnwBITU3l4MGD1KlTx9qiRETE/aSnw3PPwaRJYLdn9NeqBcuWwXXXmW5PS4NVq+DFFyEy0vySvNq3r3Ali4iIeBIdiCMiXunChQvO7w3/QIKqN87lbvFFmf+biIyMtKYQERFxX+fPQ//+MHGiOWXs2hW2bMkSTC5cXbw5WAAAS/VJREFU6JhM2bMn/PhjwYLJO+6AwMBC1i0iIuJBFE6KiFdyDSeDqjfB8NdP+WIWWKU+RmApZ/v777+3sBoREXE7e/ZA27aO2ZGuxoxxTI2sWNHZlZwMTZs69ohMTS34I202mD+/4K8XERHxRAonRcTrHD58mOTkZGc7uPa1FlYj7srwsxFc62pn+4cffsBekCkuIiLifVauhFatYMeOjL6gIJg717EZpL+/c/l2kyaOS663FtS8eY6AUkRExJconBQRr7N69WpTO7jWNTncKb4uuE4z5/cnTpxgR1G8sxQREc9lt8OMGXDLLRAbm9FfowasWwd33QXA4sUQFgY9emQ9uLugWrSAwYOLZiwRERFPonBSRLyOazhpBIYQWLWBhdWIO8s8q/aHH36wqBIREbFcfLxjw8ennjJvFnn99fDrr9CyJQBPPAEDBsDFi0X36EqVHFtYioiI+CKFkyLiVex2uylgCq55FYaf1kdJ9gLK1wQj478P7TspIuKjoqKgXTvHlEhXDz8M33/vSA+BRYscp3AXpTp14OTJoh1TRETEkyicFBGvsmfPHo4ePepsB9duZl0x4vYMw8AICHK2IyMjSS3MSQYiIuJ5vvvOsb/kn39m9AUFwZw58PrrEBAAQFoaDBlStI/u1Qv27y/aMUVERDyNwkkR8SpZ9pusrf0mJXd+AcHO78+fP8/mzZstrEZEREqM3Q4vvAA9e8LZsxn91avDTz/B8OHOrrQ0qF0bUlKK7vELFsCXXxbdeCIiIp5K4aSIeBXTnoGGHwEVa1tXjHgEwyWcBO07KSLiEy5cgEGD4L//hfT0jP6OHR37S7Zq5exauBD8/cFlYUah3H47pKbCwIFFM56IiIinUzgpIl4jPT2dH3/80dk2AoIxDP01J7kzbP4EBgY629p3UkTEy+3bB+3bO1JHVw8+CD/8AJUrO7v69CmaELFcOcckzaQkx7aWNm2HLSIi4uTx79qPHj3KrFmz6N69O7Vq1SIwMJAqVapw++23s2nTJqvLE5EStH37dmJiYpxt170ERXITGhrq/H7Dhg1cuHDBwmpERKTYfP+9Y1bk9u0ZfYGBMHs2vPkmBAaSlubIKOvXhxUrCve44GBHBhoT4zjl2+WzMBEREfmHx4eTr7/+OmPHjmXfvn3cdNNNjBs3jo4dO/LFF1/Qvn17Fmb+RFREvFbm/Sb9Mi3XFcmJaziZnJzMunXrLKxGRESKnN0OL70EPXrAmTMZ/dWqwZo1MGoU4AgSw8PhxhsdB3gXVOPGsGoVxMfDgAGFrF1ERMTL+VtdQGG1bt2atWvX0qlTJ1P/Tz/9RLdu3XjggQfo06cPQUGaQSXi7Vz3CvT39wc/j/8rTkpIaGgofn5+pP+z79gPP/xAjx49LK5KRESKREICjB4Nn31m7m/f3rHGumpVwLGEu7AzJTt1ckzO1AxJERGRvPP4mZO33XZblmASoFOnTnTt2pUzZ87wxx9/WFCZiJSklJQU1q5d62yHhoZiGIaFFYknsdlstGjRwtnWvpMiIl7iwAHo0CFrMHnfffDjj85gsnfvwgeTvXrB2rUKJkVERPLL48PJ3AQEBAD/zKASEa+2efNm4uPjnW3XZboiedGtWzfn91u3biU6OtrCakREpNBWr4aWLWHr1oy+gAB491145x1nivjoo/Dll4V7VIsWhR9DRETEV3ltOHno0CG+//57qlSpwtVXX53jfUlJScTFxZm+RMTzZN5vUuGk5NeNN97o/N5ut5tOfhcREQ9it8OsWdC9u+MkmkuqVIHISLj3XmfXokXw6quFe1yLFrBlS+HGEBER8WVeGU6mpKRw1113kZSUxMyZM7HZbDneO2PGDMqUKeP8qlmzZglWKiJFxXW/yUaNGjlnTovkVfv27U37E7v+NyUiIh4iMRGGDYOxYyEtLaO/bVv49VfHPpP/SEuDBx8s3OPGjlUwKSIiUlheF06mp6czcuRI1q5dyz333MNdd92V6/3jx48nNjbW+XX48OESqlREikpiYiI///yzs33DDTdYWI14qpCQEDp27Ohsa99JEREPc+gQdOwIn3xi7h892jFjslo1Z1dammNyZUF38GjSBJKS4JVXClytiIiI/MOrNmO02+3cc889fPLJJwwdOpR33nnnsq8JCgrSSd4iHqp3795ERUURHx9PcnKys//LL7/k9OnTEFbZwurEU6TGnSYq/iRNmzY17TMZFRVFgwYNaNKkCSsKe0qCiIgUr8hIGDDAnDb6+8PrrzsOv/nnkLy0NJg2DV54AS5eLNijbr218IfniIiISAavCSfT09MZPXo0c+bMYfDgwXz00Uf4+XndxFARcREVFcWOXbvxCyhl6j9xwU5qUhIBYRYVJh7Fnp5KUrqdPSfjSU81n/C+N2ofgTp2VUTEfdntjgDy0UfNy7grV4bFix0zKckIJZ97DlJSCvYow3A85qWXiqBuERERcfKK9M41mBw4cCDz5s3LdZ9JEfEeAWWr4Vcq3NkOrFyP6ve+CzbtOSl5F1C2GtVGv0X1e9/DLyjjMCUjINjCqkREJFcXL8KIEfDII+ZgsnVrx0aQ/wSTixdD6dIwdWrBgskqVRyB5MWLCiZFRESKg8eHk+np6YwaNYo5c+YwYMAAPvnkEwWTIj7EnppC6pkjznZIg7YWViOezvCzEVT7GmfbnnIRu91uYUUiIpKtw4ehUyf4+GNz/4gRsGYNaVVrEBkJvXo5VnsnJhbsMf37w/HjMG4caCK9iIhI8fD4Zd3PPPMMH330EWFhYTRs2JDp06dnuadv3740a9as5IsTkWKXnpxgapdq2M6iSsRbhFzRgsTdGxwNezoJCQm5v0BERErW2rWOxPHUqYw+f3/HCTcPPsjiJQYPPginTxf+UbfdVvgxREREJHceH04eOHAAgPj4eJ599tls76lTp47CSREvZU/OmArhH1GVgAq1LaxGvEGpBm04892bgGPGZFxcnLUFiYiIg90Ob70FY8ZAampGf8WKjrXb11/PE0/Aiy8W3SOrVi26sURERCR7Hr+s+6OPPsJut+f6NXz4cKvLFJFikJKSgj0t45TuUg3aYRhGLq8QuTxbaARBNZs62+fPnyc9Pd3CikREhIsXYfRoePhhczDZogX8+itcfz2LFhVtMFmjhmPluIiIiBQvjw8nRcR3nT9/3tTWkm4pKqUatnd+n5qayqZNmyysRkTExx09Cp07w4cfmvuHDSMt8icio2oyb55ju8mi9NproK3sRUREip/CSRHxWK7LbW1h5Qis1sjCasSbuIaTAEuWLLGoEhERH7d+vWN25C+/ZPTZbKS/Motn6n5EpdohdO0Kw4bBhQtF88jy5WHJEu03KSIiUlIUToqIR4qOjjYdVBLSoC2Gob/SpGj4h1cgsGpG2L1kyRKd2i0iUtLefRe6doWTJzP6KlRg7cT/UenZR5g8xeDMmcI/xs/PsXz7qafg++8dj1MwKSIiUnI8/kAcEfFNX375paldqoGWdEvRKtWoHcnHdwGOw9e2bt1K8+bNLa5KRMQHJCXBv/8N779v7m/enG/uWcYtDxbN4XcBAY5AcuJELd8WERGxkqYZiYhHWrZsmfN7v6BQgmtdbWE14o1KNexgamtpt4hICTh2DLp0yRpMDh3KknHr6f3vogkm77gDEhNhyhQFkyIiIlZTOCkiHic+Pp5Vq1Y52yH1W2PYNBFcilZARFWwBTjbCidFRIrZhg2O/SU3bszos9lIf+kVnqk/l/5DQ0hLK9wjKlaEhQvh888VSoqIiLgLvZsXEY/z7bffkpSU5GzrlG4pNi77TP7999/Ur1+foKAg0y316tVjxYoVJV2ZiIh3ef99eOghSElxdtnLl+eTXp/zyLPdOHu2cMPfdptjpXinTgolRURE3I3CSRHxOEuXLnVpGQRfcZ1ltYhv2X8sGlupMs52yrljFlYjIuIFkpPhP/9xHH7j4lyda+l6bjlbP65T6EdUr+6YLalQUkRExD0pnBQRj5KcnMzXX3/tbBsBwfgFBFtYkXg9P39ITwXAVro81Ub8n/PSsdkPWlWViIjnO3EC+veH9etN3Yc7DKLR+g9IpFSRPOb//k/BpIiIiDvTnpMi4lFWr15NXFycs+0XFGJhNeL1DAO/oIw3xymn9pFy7oSFBYmIeIlNmxz7S7oGk35+pL/wIu0PzC+SYLJ8eViyxLGkW0RERNyXZk6KiEdxPaUbwAhQOCnFyy+wFOmJGYF4wq6fKdNG73RFRArsww/hgQccS7oviYiAzz9nbcBNHDlasGHvugtq1gQ/P8eB3126aMakiIiIJ1A4KSIeIy0tjS+++MLZDg0NJdlP7zqkmNkCsJWpTFrsSQASdq9XOCkiUhApKTB2LLz5prn/6qth+XKoW5fjnxVs6IULYcCAQlcoIiIiFtCybhHxGD/99BMnT550tkuXLm1hNeIrDMMwnQiffGwXqXHRFlYkIuKBTp6Ebt2yBpMDBsCGDVC3LgBVq+Zv2Bo1HEu3FUyKiIh4LoWTIuLWevfuTdOmTWnatCl9+/Y1XTtz5ow1RYnPKdWwg6mdsPtniyoREfFAmzdDy5bw008ZfYYBzz8Pn38OoaHO7k6dHIGjYVx+2KlT4cAB7SkpIiLi6bSsW0TcWlRUFDt27cY/vDKpsbHOfiMgmOTkiwRYWJv4jqDqjbCFlSMt3hGIx//xP0q3uNXiqkREPMDHH8N990FSUkZf2bKwYAH06EFamiOzPH4cKlVyXO7fH2bNcgSUdnvWIcuXh/feUygpIiLiLRROiojbCyhbjbBre3B29fvOvgq9H+f08uctrEp8iWH4Edr0BuI2LQYg5dR+ko/tsrgqERE3lpIC48bB66+bui/Uacrq/yyndFB9ohc7tqA8ciT7Ifz8IC0to12uHDzyCDz9tA66ERER8SYKJ0XE7dntduK3fets28LKE1K3pYUViS8Ka3YzcZuWAI5pPOe3rrS2IBERd3XqFNxxB6xZY+r+OuR2Bh74iAuPhuVpmEvB5Jgx0KePY8m3QkkRERHvoz0nRcTt2VOTSIk57GyHXdMdQ6d0SwkLKFuF4Cuuc7YT/v4Je3paLq8QEfFBv/6KvWVLUzBpNwye5ll6JS7iAnkLJi8xDMeBNwomRUREvJfCSRFxe+kX411aBmHX3mRZLeLbSjf/l/N7e2oy6UkXLKxGRMTNzJtHWvuOGIczPlA8Rxl6G1/xHE8BeTjlJhO7HQ4fNp+lIyIiIt5F4aSIuLW0tDTsyQnOdkjdFviHV7KwIvFlIfVaYQsr72ynX4zHnt1pDSIiviQ11bF55LBh2JIvOrt30IRWbOar9J6FfsTx44UeQkRERNyU9pwUEbd27tw5Uzus2c3WFCICGH42wq7tQez6+Y6O9FQSEhJyf5GIiJdxPWG7Vqlo2r82EOPH1aZ7ltGXYcwlntJF8syqVYtkGBEREXFDmjkpIm7LbrebwklbWDlC6rWyriARIOza7mBk/PN59uxZC6sRkexs3ryZnj17EhERQWhoKK1bt2b+/Pl5fv2pU6eYMWMG/fv354orrsAwDAwj9yXJderUcd6X+ev+++8v7C/JbSxdCnXqQNeuMPPO36net2WWYHIiz3A7S4okmDQMqFnTseekiIiIeCfNnBQRt/Xzzz+TlJTkbIddfZMOwhHL+ZeuQEj91iTu2QhAXFwcJ0+epHLlyhZXJiIAkZGR9OjRg8DAQAYNGkSZMmVYunQpQ4YM4cCBAzz11FOXHWPHjh089dRTGIZBgwYNKFWqVJ5mSZcpU4YxY8Zk6W/ZsmVBfiluZ+lS6N/fsQ/kID7jA0ZRikTn9VjCGconfMWtRfK8S3nwrFk6DEdERMSbKZwUEUv17t2bqKiobK8dPXrUpWUQdm2PkilK5DJKN/uXM5wE+PDDDxk/fryFFYkIQGpqKqNHj8YwDNauXUvz5s0BmDx5Mu3atWPy5MkMGDCABg0a5DpOkyZNWLNmDc2bN6d06dI0btyYXbt2Xfb5ZcuWZcqUKUXxS3E7aWnwyCPgZ0/lef7LY7xsur6TxvRlObtpVGTPrFHDEUzedluRDSkiIiJuSOGkiFgqKiqKHbt2E1C2mqnfnp5Oamycsx1c9//bu/O4qOr9f+CvwwAj+yoqKqCGoJj7ehOBNFFzyzDEMnG7XbtZ2eJSGZjmlnWt/HUtvaJpqKWmZlYuCIi7qajlggSEuyiIgAIz8/n9wXdGxhkUZYYDzOv5eMwD5nM+c857zmdgzrzns3SEtQsXwqGaoV6zDrB2bQhV3hUAwDfffIMpU6ZAwa49RLJKSEhAeno6xowZo0tMAoCTkxNmzJiBESNGIC4uDnPmzHngfho0aMDe0PfZswcounADv2AEnsFOvW2bMRijsAq34fzAfSgUZUlOraZNgU8/BerXL5u/0uv/3uavXSubYzI4mD0miYiILAGTk0QkOxtXb3iP/0qvLP/3n5C782vdfad2XAiHag5JsoJj+37IS1wBAMjMzMT27dvRv39/eQMjsnCJiYkAgL59+xps05YlJSWZ7fjFxcVYuXIlLl68CDc3N/zjH/9Au3btzHa86nTnQCoO4zk0R4ZeeQxiMQszIMpNZS9JZUO/y98HgDVr7iUimXwkIiIiLSYniajGEUKDgmO/3CuQFFwIh2ocxyef0SUnAWDJkiVMThLJLC0tDQCMDtt2c3ODp6enro45XLlyBdHR0Xpl/fr1w6pVq+Dp6Vnh44qLi/XmWM7Pz6+wrizWrUPf2DFQlJtfMh9OGIVV2IIhelVnzgSWLgUuXLhXxuHZRERE9CBcrZuIapyiM3tReuNv3X2reg6QFPwuhWoWhb0LJFt73f2tW7ciMzNTvoCICLdu3QJQtjCNMc7Ozro6pjZ27FgkJibi+vXryM/Px4EDB9C/f3/8+uuvGDx4MET5roT3mTt3LlxcXHS3pk2bmiXGR6ZWA1OnAiNGQFF8LzF5Fi3RDQf1EpPaVbXffx/IzAR27wbi48t+ZmQwMUlEREQVY3KSiGoUoVEjL+U7vTKrek4yRUP0YFb1HHW/azQafPTRRzJGQ0Ry+vDDDxESEgJPT084OTmhW7du2Lp1K3r27In9+/dj27ZtFT52+vTpuHXrlu6WnZ1djZFX4OZNYMAAYMECveKteBbdcAhn0EpXdv+q2goFEBoKREWV/eTQbSIiInoQJieJqEYp/DMJqpvlxoJJVpCs+KmGaibJWgk7Ozvd/ZUrV+L06dMyRkRk2bQ9JivqHZmfn19hr0pzsLKywpgxYwAAe/furbCeUqmEs7Oz3k1WJ08CXboA27frl3/wAUp+2AKnJvrnsEkTYP169o4kIiKix8PkJBHVGEKtwq298br7kk09QOK/Kaq5JEmCl9e9VeQ1Gg0+/PBDGSMismzauSaNzSuZm5uLnJwco/NRmpN2rsmioqJqPe5jW78e6NED+Ouve2WOjsCGDcCsWRgWYcVh20RERGRS/NRPRGY3ePBgBAUFGb2lp6fr6hWc2gVV3hXdfafOQ+6NFSOqoRwcHPDMM8/o7q9fvx6///67jBERWa6QkBAAwPb7e/yVK9PWqS4HDx4EAPj5+VXrcR+ZWg289x4wfDhQWHiv/IkngAMH9LKPHLZNREREpsTkJBGZXXp6Ov48ew5pVwsMbtrVSYWqFLf2rtU9RlI6wLnrc3KFTFQpqvzrSE9Px/nz5/XKQ0JCEBQUhMGDB8sUGZFl6t27N5o3b474+HgcP35cV3779m3MmjUL1tbWeqtp5+Tk4MyZM8jJyanScf/880/k5eUZlKekpOCzzz6DUqnEsJrctTA3Fxg0CJg7V7+8f3/g0CEgKEieuIiIiMgicPlbIqoWNq7e8B7/lUF51sKyBGTBid+gvn1dV+7cZSgU5RYbIaqJhEaFYo3AhXw1JFs7iJKy1WwLCwvx55kzMkdHZHmsra2xbNkyhIeHIzg4GFFRUXB2dsbGjRuRkZGB2bNno2XLlrr6ixcvxsyZMxETE4PY2Fi9fZVPYl6+fNmgbOHChboh299//z0WLFiA3r17w8/PD0qlEqdOncL27dthZWWFJUuWwMfHx2zPu0r++AMYOhS470sWTJ8OzJrFbpFERERkdkxOEpHshNDg1v7vdfet6jnBufMQGSMiqjxt4r0k529cXv4aIDQAAMnKGkIImaMjsjxhYWFISUlBTEwMvv/+e5SUlCAoKAizZs3Ciy++WOn9rFy58oFlsbGxuuRkWFgYTp8+jaNHjyIpKQl3795FgwYNEBkZicmTJ6Nr165Vf2LmsHEjMHo0UFBwr8zBAVixAoiIkC0sIiIisixMThKR7DR3C6ApytPdd+7+PKyU9vIFRPQYbD194BAUhsJTuwAAQlWCgvIf+Imo2nTt2hW//PLLQ+vFxsYa9JjUepQvF0JCQqp9Lssq0WiAmBhg9mz98ubNgc2bgTZt5ImLiIiILBLnnCQieQkBzZ183V0re1c4dRgoY0BEj8+150jA6t73fteuXYNGo5ExIiIiI06eBObN0y/r2xc4fJiJSSIiIqp2TE4SkbyERjcMFgBcegyHlW09GQMienzWLg3g1L6f7n5xcTHWrl37gEcQEcmgXTvgP/+5d3/qVGDbNsDdXb6YiIiIyGIxOUlEsim5+pdeYlLh6AGn9v1ljIio6lx6REKyUeruv/fee8jPz3/AI4iIZPDvf5fd1q4t60XJhW+IiIhIJkxOEpEshKoUOT9/plfmGhoNydpWpoiITEPh6AanToN197OysvDmm2/KFxARkTGSBCxeDERGyh0JERERWTgmJ4lIFnl741F6PVN3365lDzi0DpUtHiJTcuk+HLC61wspLi4OGzdulDEiIiJ9ajWQmAisWVP2U62WOyIiIiKyVExOElG1K754BvkHN9wrkKzg0fffkCRJvqCITMhKaQ+Fo6de2T//+U9cuXJFpoiIiO7ZuBHw8wPCwoCRI8t++vmVlRMRERFVNyYniahaaUrvImfbf/TnmnRwh8LBVb6giMzAykYJDw8P3f0bN25g3LhxEELIGBURWbqNG4GICODCBf3yixfLypmgJCIiourG5CQRVau8pG+hunnxXoEkwUppL19ARGbk5eWFDh066O5v27YNX3/9tYwREZElU6uBN94AjH1Hoi17800O8SYiIqLqZS13AERUOwwePBjp6ekPrNOiRQts2bKlwu13s07g9u/3tisc3aEu4irGVHdJkoTVq1ejY8eOKC4uBgC8/fbbePrpp9GyZUuZoyMiS7Nnj2GPyfKEALKzy+qFhlZbWERERGTh2HOSiColPT0df549h7SrBUZvf54998DkpdCokfPL53plHv3fKFstlKgOa926NebPn6+7X1RUhFGjRqG0tFTGqIjIEl2+bNp6RERERKbAnpNEVGk2rt7wHv+V0W2Xlr1a4eM0Gg3Ut69DqEp0ZY7t+sGueSeTx0hUU6jyryO94CqCgoIghICDgwMKCwsBAIcOHULLli3x119/cSEoIqo2jRqZth4RERGRKbDnJBGZVUlJCbKzs/USk9auDeEWNlbGqIjMT2hUKFapkXa1AOevFaLYxkWvp3BmZiY++OADGSMkIksTHAw0aVLxoAVJApo2LatHREREVF3Yc5KIzEatVuPll1/W9RYDACt7F3gNn8lFcMgi3N/buCjtAK7/OEe3Wv2cOXPg5OSEadOmyRUiEVkQhQL4/POyVbklSX9hHG3CctGisnpERERE1YU9J4nILIQQeP3117Fu3TpdmWRrB6/hM2Hj3ljGyIjkY+/fHR79X9crmz59Ov7f//t/MkVERJZm2DBg/Xqg8X1vxU2alJUPGyZPXERERGS52HOSiEyi/Px6AHDt2jXk5OTo1fF6fgaUDZ+QIzyiGsPxyT7ITVwJTVGuruy1116Do6MjRo8eLWNkRGQphg0DhgwpW5X78uWyOSaDg9ljkoiIiOTB5CQRmYTQqFCsETh35TY0d29DU5Snt13h5Il6Pm3lCY6ohlHYOcHDwRrXr1/XlY0dOxYODg6IiIiQMTIishQKBRAaKncURERERBzWTUQmZO3SEMqGLQwSk5AUsLLlHJNE5dWvX19vrkmNRoMRI0bgk08+gSg/ERwREREREVEdxuQkEZmGEFDduorCPxP1il1DxwBW/FdDZMycOXPw73//W3dfrVZjypQpGDJkCG7evCljZERERERERNWDGQMiqrKitIOARgWoS+8VWing1ucVuHR7Xr7AiGo4SZLwxRdf4JVXXtEr/+mnn9CxY0ccOnRIpsiIiIiIiIiqB+ecJKLHJtQq5O1dg/z96/TKFY7u8BwyHfWatJIpMqKa7f4FpIQQaNSoEa5cuaIb0p2VlYWePXti4cKFmDRpEiRJkjNkIiIiIiIis2Bykoh0Bg8ejPT0dKPb0tPTAccGAMoSKXfSDiA3aSVUNy/o1VM2bYP6g6dC4ehm9niJaivtAlJpVwvKldpA4dwAqts5ZT2RAZSWluKNN97ATz/9hHnz5qFTp07yBExERERERGQmTE4SWZiHJSCLVWrYuHobbCstLoaNI1B88TRyd8eh+OKfBnWs6jmhwYiPIVkpTB43UV1j4+oN7/FfGZRriotw4atoiJIiXdnOnTvRuXNnDB8+HLNmzUJAQEB1hkpERERERGQ2TE4SWZj09HT8efZcxQlIDx+jCZOsT4ZCdfs6rqx+12CbldIBmtK7UDi4MTFJVEVWSnsoHD3gaeuMmzdvoqSkRLfthx9+wMaNGzF27Fh8+OGHaNKkiYyREhERERERVR0XxCGyQNoeW/ffoLDRq6cpLsLt1O248t0UQKOCKLmjvyOFNZy7PAfvV5YBEv+dEJmKJElwd3dHamoqhg0bprdNrVZj6dKlaNGiBSIiIrB582a9BCYREREREVFtUmeyCYcPH8aAAQPg5uYGBwcHdO3aFfHx8XKHRVTrCCFwJ/M4crZ+iguLR+Hmr1+g+ILhEG771iFoPOFruD09Dgo7JxkiJar7AgMDsWHDBhw8eBBhYWF620pKSrBhwwYMHToU9vb2cHd3R7NmzTBo0CCZoiUiIiIiInp0dWJYd2JiIsLDw2Fra4sRI0bAxcUFGzduxIsvvojMzEy89957codIVGMJjRolV9Nx9++TgFoFVe4FXFv3QYX1lT5t4RY2FsqGT1RjlESWrWvXrti1axd27tyJwYMH4+7du3rb1Wo1cnNzkZubiwsXLmDkyJHo1asXgoOD0apVK1hZ1ZnvIomIiIiIqI6p9clJlUqF8ePHQ5IkJCcno0OHDgCAmJgY9OjRAzExMRg+fDj8/f1ljpRIfvn5+SgqKoLmbgFydy9HyfVMFF88o7fwBoTh46zqOUFTXAhrZy80jJpTfQETWShV/nWkF1xFUFCQwTaNRgOFU30oGweiKO0AoC7Vf6xKhTVr1mDNmjUAAA8PD/Ts2RPt2rVDYGAgAgMDERAQAHt7+2p5LkRERERERA9S65OTCQkJSE9Px5gxY3SJSQBwcnLCjBkzMGLECMTFxWHOHCZUqO64f8VtjUYDtVoNjUaDS5cuQaPRwNXVFaWlpVCpVFCpVCgpKYFKpdI9Jv/QxgcfRLKCXfNOcHiyD+xbdMXfi16AZG1rrqdEROUIjQrFGoG0qwUG20pLSmDjZIf6Q6ZCU1yIwjN7UfhHAoqzTxnd140bN7B582Zs3rxZr9zX1xf+/v5o3LgxvL290ahRI3h7e8Pb2xuenp5wdXWFq6srbGxsjO6XiIiIiIjIFGp9cjIxMREA0LdvX4Nt2rKkpKRH3u/WrVvZq6SWEMJIVz8T7KN8mbHfhRC62/33779pNBqDm1qt1iUOtb+r1WqUlJSguLgYJSUlutvdu3dRVFSkux05cqRsWKdkBQgBY90dr169+ljnQlI6wL33P2HXrCMUjm6PtQ8iqjrtwlX3y1r4nO53K6UDnNr1hVO7vlDduobL374FB4Uatra2uHHjxgP3n5WVhaysrIfGYW9vD1dXV7i4uMDBwQH29vawt7eHnZ2d7qetrS1sbW1hY2Oj+93a2hoKhcLgp5WVlcFNkiSDm7YcQIU/H/R7eRWVPwpT7KM6FRUVPbwSEREREVENIAlTZHZkNHz4cKxfvx5HjhxBp06dDLbXr18fkiTh2rVrRh9fXFyM4uJi3f1bt27Bx8fHbPES1ShW1oC1LayslZBslFDfugJYKWDt0sigqir3QoXbHrb9cbfVtv3WpedS2/Zbl55LlfZ76zJsrRXw8/NDaWkpCgsLcefOHdy5c0ev5zRZjry8PLi4uMgdBj2iW7duwdXVFdnZ2XB2dpY7HCIiIqJHlp+fj6ZNm1bqerTWJyf79u2LHTt2IC0tDU88YbhAR4sWLXDhwgW9BGR5sbGxmDlzprnDJCIiIqp26enpaN68udxh0CO6cOECmjZtKncYRERERFWWnZ2NJk2aPLBOrR/WXVXTp0/HW2+9pbufl5cHX19f/P333+xpUEtps/PsbVA7sf1qP7Zh7cc2rP20I0Hc3d3lDoUeg7e3N7Kzs+Hk5FTpKQX4d6uP5+Menot7eC7u4bm4h+dCH8/HPTwX9zzOuRBC4Pbt2/D29n5o3VqfnNQmEG/dumV0e35+/gOTjEqlEkql0uh+Lf3FV9s5OzuzDWsxtl/txzas/diGtZ+VlZXcIdBjsLKyemgPg4rw71Yfz8c9PBf38Fzcw3NxD8+FPp6Pe3gu7nnUc1HZTn+1/orV398fAJCWlmawLTc3Fzk5Obo6REREREREREREVHPU+uRkSEgIAGD79u0G27Rl2jpERERERERERERUc9T65GTv3r3RvHlzxMfH4/jx47ry27dvY9asWbC2tkZ0dHSl96dUKhETE2N0qDfVDmzD2o3tV/uxDWs/tmHtxza0PGxzfTwf9/Bc3MNzcQ/PxT08F/p4Pu7hubjH3Oei1q/WDQC7d+9GeHg4lEoloqKi4OzsjI0bNyIjIwOzZ8/G+++/L3eIREREREREREREdJ86kZwEgEOHDiEmJgb79+9HSUkJgoKC8Oabb+LFF1+UOzQiIiIiIiIiIiIyos4kJ4mIiIiIiIiIiKh2qfVzThIREREREREREVHtxOQkERERERERERERyYLJSSIiIiIiIiIiIpKFRSQnDx8+jAEDBsDNzQ0ODg7o2rUr4uPjH2kfGo0GixcvRtu2bWFnZ4f69evjhRdeQFpampmipvKq2obXrl3D3LlzERERgWbNmkGSJEiSZMaIqbyqtl9KSgrefvttdOrUCR4eHqhXrx4CAwMxdepU5OXlmS9w0qlqGyYmJmLkyJFo1aoVXF1dYW9vj4CAAIwdOxZnz541Y+SkZYr3wvJKS0vRvn17SJKEwMBAE0ZKxpjib1D73mfsduDAATNGTzXBgQMHoFAoIEkS5s2bJ3c41WrevHno27cvmjZtCjs7O3h4eKBz58747LPPUFRUJHd41aqwsBCrV6/GCy+8gJYtW8LOzg6urq4ICQnBmjVr5A6v2iUnJ+Odd95BWFgYXFxcIEkSoqOj5Q7LrEx9PVBbrV69Gq+88go6d+4MpVIJSZKwYsUKucOSxcWLF7Fo0SL07dsXPj4+sLW1RcOGDfH888/j4MGDcodXrfLy8vD666+jR48eaNiwIZRKJRo3boynn34aGzZsgKUv2bJgwQKzXTtam3RvNVBiYiLCw8Nha2uLESNGwMXFBRs3bsSLL76IzMxMvPfee5Xaz7/+9S8sXboUrVu3xqRJk3D16lWsW7cO27dvx759+9C6dWszPxPLZYo2/PPPP/Hee+9BkiT4+/vD3t7e4i5G5WKK9ouIiEBOTg569uyJl19+GZIkITExEQsWLMCGDRuwb98+eHl5VcOzsUymaMOdO3ciJSUF3bp10+3r9OnT+PbbbxEfH49ffvkFYWFh1fBsLJOp3gvLmzVrFs6fP2+GaOl+pmy/kJAQhIaGGpQ3adLEhBFTTXPnzh1ER0fDzs4OhYWFcodT7b7++mt4enrimWeegZeXFwoKCpCYmIi3334b3377Lfbt2wd7e3u5w6wWe/bswahRo+Dh4YHevXvj+eefx7Vr17Bx40aMHDkS+/btw5dffil3mNVm+fLlWLlyJezt7eHj44P8/Hy5QzIrc1wP1FYffPABsrKy4OnpiUaNGiErK0vukGTz5ZdfYv78+WjRooXu/2RaWho2bdqETZs2Yc2aNXjhhRfkDrNa5OTkYPny5ejevTuGDh0Kd3d3XLt2DT/99BMiIiIwYcIEfPPNN3KHKYvTp0/jww8/hIODg3muJUQdVlpaKlq0aCGUSqU4evSorjw/P18EBQUJa2trce7cuYfuJyEhQQAQwcHB4u7du7rynTt3CkmSRK9evcwSP5muDa9cuSKSkpJEfn6+EEKIgIAAUcdf/jWCqdpv3rx54tKlS3plGo1GTJw4UQAQr776qsljpzKmasM7d+4YLd+5c6cAIDp37myymEmfqdqwvN9//11YW1uLL774QgAQAQEBpg6b/o+p2m/37t0CgIiJiTFjtFRTTZ48WTg7O4tZs2YJAGLu3Llyh1StKnoPGjVqlAAgFi9eXM0Ryef48ePiu+++EyUlJXrlV65cEb6+vgKAOHTokEzRVb/Dhw+LU6dOCZVKJfbv3y8AiNGjR8sdllmY43qgNtuxY4fIzMwUQggxd+5cAUDExcXJG5RMNmzYIJKTkw3Kk5OThY2NjXB3d9fLg9RlKpVKlJaWGpTn5+eL1q1bCwDi1KlTMkQmL5VKJbp06SK6du0qXnrpJQFA7N+/36THqNPDuhMSEpCeno6RI0eiQ4cOunInJyfMmDEDKpUKcXFxD93P0qVLAQCzZ8+GUqnUlffu3Rvh4eFITk7GuXPnTP8EyGRt2KBBA/Tq1QtOTk7mDJfuY6r2mzp1Kho1aqRXJkkSZsyYAQBISkoybeCkY6o2rFevntHy3r17w83NjT3wzMhUbahVUlKC6OhodO/eHa+99po5QqZyTN1+ZHn27t2Lzz//HAsXLrTYHrIVvQdFREQAgEW9B7Vr1w4jR46EjY2NXnmDBg3wyiuvALCs66rOnTsjKCgICoVC7lDMju8n+vr06QNfX1+5w6gRhg0bhuDgYIPy4OBghIWF4ebNmzh58qQMkVU/hUIBa2vDAcZOTk4IDw8HYFnvGVrz589Hamoqli9fbrb/l3U6OZmYmAgA6Nu3r8E2bVll3nwTExPh4OCAp556ymCb9gVqSW/i1clUbUjyMHf7aS+sjb2BkGmYuw3379+P3NxctGnT5rH3QQ9m6jaMjY1FWloa/ve//3Hu3mpg6vZLS0vDF198gXnz5mHNmjXIyckxSZxUMxUVFSE6OhqhoaGYMGGC3OHUOD///DMA8D3o//C6qm7j5yp6HPy/UObu3btISEiAJEkWN6XfqVOnMHPmTHzwwQcICgoy23Hq9CtMu1iNv7+/wTY3Nzd4eno+dEGbwsJCXL58GW3atDGaIdbumwvjmIcp2pDkY+72W758OQDjF1lkGqZuw8TERCQmJqK4uBhpaWnYunUrPD098Z///MdkMZM+U7bh4cOHsWDBAsyZMwctW7Y0aZxknKn/BuPj4/UWPrCzs8PMmTPx7rvvVj1YqnGmTZuGy5cvY/v27XKHUiMsWrQIeXl5yMvLw969e3HkyBH07dsXL7/8styhyU6tVuPbb7+FJEno06eP3OGQGfBzFT2qv//+Gzt37kTDhg3x5JNPyh1OtcrLy8OiRYug0Whw7do1bNu2DdnZ2YiJiTH6N1RXqVQqREdHo1WrVpg2bZpZj1Wnk5O3bt0CALi4uBjd7uzsjAsXLlR5H+XrkWmZog1JPuZsv+PHj2PmzJnw8vLClClTHjtGejBTt2FiYiJmzpypu//EE09g7dq16NSpU9UCpQqZqg2Li4sRHR2NDh064O233zZpjFQxU7Vf/fr18cknn2DgwIHw8fFBXl4edu/ejalTp2LKlClwdnbWDemkuiEpKQmLFy/GokWL0KxZM7nDqREWLVqkt+jFSy+9hP/+978GQ5wt0YwZM3Dy5EmMHTuWPUnrKH6uokdRWlqKUaNGobi4GAsWLLCIqQ/Ky8vL0/vMYmNjg08++cTiroHnzJmD1NRUHDx40OzvlXV6WDcR1U0ZGRkYOHAg1Go11q5dC09PT7lDokqKjY2FEAIFBQU4dOgQAgMD8dRTT+n15KKaacaMGUhLSzPrXDNkPkFBQXjnnXcQGBgIe3t7eHt748UXX8Svv/4KW1tbxMTEQKPRyB0m3cfT0xOSJFX6ph22WVhYiLFjx6JHjx51Zm7Yxz0X5WVmZkIIgcuXLyM+Ph6JiYno1q1brUzImOJ8aH3zzTeYO3cuOnTogM8//7z6noSJmPJcEBGg0WgwduxYJCcnY8KECRg1apTcIVU7Pz8/CCGgUqmQkZGBjz76CO+//z6ef/55qFQqucOrFqmpqZg9ezbeeecddOzY0ezHq9M9J7XfClXUqzE/P7/Cb44eZR/l65FpmaINST7maL+srCyEhYXh+vXr2LBhA8LCwqocJ1XMXH+DDg4O6NKlC3788Ud07twZ//znP/HMM8+gfv36VYqXDJmiDY8ePYrPPvsMM2bMsLhhPXIz9/tgmzZt0K1bN+zZswfnz5/ncP0aJioqCrdv3650/YYNGwIA3n//fVy6dAnbtm2DlVXd6IvwuOeiom1RUVF44okn0LVrV7z99ttYt26dKcKsNqY6H3FxcfjXv/6FJ598Ejt27ICjo6OpQqw2pnxt1GX8XEWVIYTAhAkTsHr1arz00ktYsmSJ3CHJSqFQwM/PD9OmTYNCocCUKVOwdOlSTJw4Ue7QzG706NFo0aIFYmNjq+V4dTo5WX4+yPuHDObm5iInJwf/+Mc/HrgPBwcHNGrUCBkZGVCr1Qa9RR40dwdVnSnakORj6vbLzMxEWFgYLl26hB9++AEDBw40abxkyNx/g9bW1ggLC0NqaiqOHDmC/v37VyleMmSKNjxx4gTUajViY2ONXqCcPXsWkiTBxcUFeXl5pgqdUD3vg9re50VFRVXaD5nel19++ViPO378OO7evYvAwECj26dPn47p06fjjTfewKJFi6oQYfV53HPxIF26dIGbm1ut7ElnivOxfPlyTJgwAa1bt8auXbvg4eFhgsiqnzleG3URP1fRw2g0GowfPx5xcXGIiorCihUr6swXXKbQt29fTJkyBYmJiRaRnExNTQUA1KtXz+j2Hj16AAB+/PFHDB06tMrHq9OvtJCQEAAwOgm4tkxb52H7KSwsxN69ew22/fbbb5XeDz06U7UhycOU7ZeZmYnQ0FBcvHgR69atw5AhQ0wXKFWoOv4GL126BICrAJqLKdqwZcuWGDdunNEbUNYbY9y4cVxUwgzM/TeoUqlw9OhRSJIEHx+fx94P1SzPPvus0b/XXr16AShLyo0bN073wcJSFRQU4NatWxb5/rN8+XKMHz8egYGBSEhI4MgFC8DPVfQg5ROTkZGRWLVqFafxuY+lfWap6Npf+0XH4MGDMW7cOPj5+ZnmgKIOKy0tFc2bNxdKpVIcO3ZMV56fny+CgoKEtbW1OHv2rK78+vXr4vTp0+L69et6+0lISBAARHBwsCguLtaV79y5U0iSJHr16mX252KpTNWG9wsICBB1/OVfI5iq/TIyMoSvr6+wtrYWGzZsqK7wSZiuDZOSkoRGozHY/2+//SZsbGyEi4uLKCgoMNvzsGTm+j+qBUAEBASYOmz6P6Zqv3379hn8DZaWloo333xTABD9+vUz6/OgmiEuLk4AEHPnzpU7lGqTmZkpMjIyDMpLSkrEuHHjBAAxbty46g9MRsuWLROSJIlWrVqJK1euyB1OjbF//34BQIwePVruUMziUd9PLMncuXMFABEXFyd3KLJQq9UiOjpaABDDhw8XpaWlcockm2PHjom8vDyD8hs3boj27dsLAGLVqlUyRFZzjB49WgAQ+/fvN+l+63TK19raGsuWLUN4eDiCg4MRFRUFZ2dnbNy4ERkZGZg9e7be3EqLFy/GzJkzERMTozdsLSwsDOPHj8eyZcvQoUMHPPvss7h69SrWrVsHZ2dn/Pe//5Xh2VkGU7UhAERHR+t+v3z5skHZwoULubCKiZmq/UJDQ5GVlYXu3bvjxIkTOHHihMGxqmsuDEtjqjYcPHgwPD090aVLFzRt2hR37tzBiRMnkJycDBsbGyxbtgwODg4yPMO6z5T/R6n6mar9oqKiIEkS/vGPf6Bx48bIy8tDcnIyzp49Cx8fH4ufU4rqrmPHjuH5559HcHAw/P394enpiatXr2Lnzp3Izs5GQEAAPv74Y7nDrDYJCQmYMGEChBDo1auX0c8x7du3N8kQvdogJSUFy5YtAwBcv35dV6b9jBAYGIhp06bJFZ5JPer7SV23bNkypKSkAABOnjypK9NO8zB06FCL+Tv46KOPsGLFCjg6OqJly5aYPXu2QZ2hQ4eiffv21R9cNVuxYgWWLVuGsLAw+Pr6wsHBAVlZWfj5559RUFCA559/HiNHjpQ7zLrJpKnOGurgwYOiX79+wsXFRdjZ2YnOnTuL1atXG9SLiYkRAERMTIzBNrVaLb744gsRFBQklEql8PDwEBERERb77VJ1M0UbAnjgzdi36mQaVW2/h7Wdhfwrk1VV23DRokWiX79+okmTJkKpVIp69eoJf39/MX78eHHq1KlqehaWzRT/R40Be05Wi6q237x580RoaKjw9vYWtra2wt7eXrRt21a8//774ubNm9X0LEhulthzMisrS0yePFl06tRJeHh4CIVCIVxcXET37t3F/PnzLa7XvvY18KBbXe05aMzDzkdISIjcIZpcZd9P6jpt76+KbpW9DqoLHnYuYEG9Svfs2SOio6NFYGCgcHZ2FtbW1sLLy0v069dPxMfHGx0JZmnM1XNSEkII06U6iYiIiIiIiIiIiCqnTi+IQ0RERERERERERDUXk5NEREREREREREQkCyYniYiIiIiIiIiISBZMThIREREREREREZEsmJwkIiIiIiIiIiIiWTA5SURERERERERERLJgcpKIiIiIiIiIiIhkweQkERERERERERERyYLJSSIiIiIiIqpREhMTIUkSYmNj5Q5FFrGxsZAkCYmJidVyvBUrVkCSJKxYsaJajlfTVXQ+/Pz84OfnJ0tMRHUZk5NERERERERkNpIkPdLNElh68lVumZmZkCQJ0dHRcodCRACs5Q6AiMhSrVixAmPGjNHdj4yMxNq1a81yrPPnz8Pf319339fXF5mZmWY5FhEREVF5MTExBmUzZ86Ei4sL3nzzzeoPqBZ47bXXMGLECPj4+MgdikV67rnn0L17dzRq1EjuUIgsApOTRGSx2rZti5MnTyI7OxtNmjQxWsfd3R1FRUW4ffs2bGxszBLHkCFD0L59e7Rp08Ys+wfKnof2g8GiRYvMdhwiIiKi+xnrHThz5ky4urqy52AFPD094enpKXcYFsvFxQUuLi5yh0FkMTism4gs0t27d3H69Gk0atSowsRkWloacnNz0aZNG7MlJgFg6NChiI2NRUREhNmO4e7ujtjYWMTGxsLV1dVsxyEiIiIytaNHjyI8PBxOTk5wcXHBc889V+EIkIyMDIwfPx4+Pj5QKpVo1KgRoqOjkZWVZbT+vn378Oyzz8Ld3R316tVDYGAgYmNjUVRUZFBXkiSEhobi4sWLiI6ORsOGDWFlZaU3L2RycjIGDRoET09PKJVK+Pv744MPPtDbX2xsLMLCwgCUJWnLD2nXPq8HzTl54sQJvPTSS2jSpInuOfbr1w8//fSTrs6tW7cwf/58hISEwNvbG7a2tvD29sbLL7+M9PT0h5zxyklJSUFISAgcHBzg4eGByMhIZGdnIzQ01GB4fnR0tN7zK8/Ycy0pKcGXX36J8PBwNG3aFEqlEl5eXhg2bBiOHTtmsI/yc0Tu2rULPXv21MU1evRo3LhxQ69us2bNAAArV67UO//aGB51Dk4hBJYvX46nnnoKzs7OsLe3R+fOnbF8+XKDunfv3sWnn36Kdu3awcXFBY6OjmjRogWioqJw8uTJSh2PqK5hz0kiskipqalQqVTo1q1bhXUOHToEAOjYsWN1hUVERERE5Rw5cgSffPIJQkND8corr+DYsWPYtGkTTp48iVOnTqFevXq6ugcPHkR4eDgKCwsxaNAgPPHEE8jMzMR3332HX375Bfv370fz5s119Tds2IARI0bA1tYWkZGR8PLyws6dOzFz5kxs374du3fvhlKp1Ivnxo0b6NGjB9zd3REZGYmSkhI4OzsDAJYsWYJXX30Vbm5uGDRoEOrXr4/Dhw/j448/xu7du7F7927Y2toiNDQUmZmZWLlyJUJCQhAaGqrb/8O+RP7xxx8RFRUFjUaDQYMGISAgANeuXcPBgwfxv//9D4MGDQIAnD59Gh9++CHCwsLw3HPPwcHBAWfOnEF8fDx+/vlnHD16FL6+vo/dLrt27UL//v1hZWWFyMhIeHt7Y9euXXjqqafg5ub22PvVunnzJt58800EBwdjwIABcHNzw19//YUtW7bgl19+QXJyMrp06WLwuJ9++glbt27FoEGDMHHiRCQnJ+Pbb79Feno6UlJSAADt27fHG2+8gc8//xzt2rXD0KFDdY9/nMVuhBB46aWXEB8fj5YtW2LkyJGwtbXFjh07MG7cOPz5559YuHChrv7o0aPx/fffo23bthgzZgyUSiX+/vtv7N69G+Hh4XjyyScfOQaiWk8QEVmgr776SgAQc+bMqbDOG2+8IQCIJUuWmCWGuLg4AUDExcVVWOf7778XAMSXX34ptm3bJkJCQoSjo6No0KCBmDp1qlCr1UIIIeLj40W3bt2Eg4OD8PX1FZ9//nmF+/T19RW+vr4mfjZERERElQfggdcju3fvFgAEALF27Vq9baNGjRIAxJo1a3RlJSUlws/PTzg5OYnjx4/r1d+zZ49QKBRi4MCBurL8/Hzh6uoqlEqlSE1N1ZVrNBoxcuRIAUDMmjXLIGYAYsyYMUKlUult++OPP4S1tbXo0KGDuHHjht62uXPnCgBi4cKFBs8vJibG6POPiYkRAMTu3bt1ZVevXhWOjo7CwcFBHD161OAx2dnZut/z8vIM4hBCiISEBGFlZSXGjx+vV16Z61IttVotmjdvLiRJEnv27NGVlz9396caRo8eLQCIjIyMSj3Xu3fvigsXLhjUPXXqlHB0dBR9+vQxGr+1tbVISUnRlatUKhEaGioAiP379+vKMzIyBAAxevRoo8+xovNh7Dr6m2++EQDEuHHjRGlpqa68uLhYDBo0SAAQR44cEUKUtYskSaJz584GryGVSiVyc3ONxkNU13FYNxFZpN9//x0AanzPyePHjwMAduzYgaioKDRt2hTjx4+HRqPB/PnzsWTJEkycOBFvvfUW2rRpoxu28sYbbyAhIUG2uImIiIhMoVevXoiMjNQrGzt2LADg8OHDurKtW7ciMzMTU6ZMQbt27fTq9+zZE0OGDMG2bduQn58PANi0aRPy8vIwduxYtG3bVldXkiTMmzcP1tbWRof02traYsGCBVAoFHrlX3/9NVQqFb744gu4u7vrbZsyZQrq16+PNWvWPPoJKGflypUoKCjA22+/jQ4dOhhsLz9VkYuLi0EcABAWFoagoCDs3LnzseNISUnBX3/9hYEDB6Jnz566ckmSMGfOHINz8ziUSiUaN25sUB4UFISwsDAkJyejtLTUYPvIkSPx1FNP6e4rFAqMHj0agP7rxZQWL14MBwcHLF68GNbW9wan2tra4uOPPwYAXdtLkgQhBJRKpcF5UigUnH6JLBaHdRORRTp69CiAsosJY3PBaOtYW1vrXbBWN21y8vz58zh58iSaNm0KABg2bBh69eqFadOmoUOHDvjjjz90F6A9evTAqFGjsGPHDjz99NNyhU5ERERUZca+JNYm4fLy8nRlBw4cAACcOXPG6CI7V65cgUajwblz59C5c2fdvIXlh1RrNW3aFC1atMDZs2dx+/ZtODk56bY1a9bM6EI12uP/+uuvRhN/NjY2OHPmTMVPtBK0X5z37du3UvUTExOxaNEiHDx4EDk5OVCpVLpttra2jx1HamoqACA4ONhgm6+vL5o2bVrhnKCP4vjx41iwYAFSUlJw5coVg2RkTk6OwWralX29mEpRURFOnjwJb29vzJs3z2C7NmZt2zs7O6Nfv3749ddf0bFjR0RERCA4OBjdunWrUpsQ1XZMThKRxSkpKcGpU6cAlM3b8yDt2rXTm2voxRdfhJubGxYvXmzWGLWOHTsGKysrfP/997rEJABdwlQIgbVr1+p9M66dp6agoKBaYiQiIiIyF2MrJmt7p6nVal3ZzZs3AQDffffdA/dXWFgIALoelA0aNDBar2HDhjh79izy8/P1kpMV1dceX9tTzhy0yTVjPQrv98MPPyAyMhKOjo4IDw+Hn58f7O3tdYu8VLRAUGXcunULAODl5WV0e4MGDaqcnNy3b5/uS/a+ffvC398fjo6OkCQJmzZtQmpqKoqLiw0eV9nXi6nk5uZCCIGLFy9i5syZFdbTvu4AYP369ZgzZw7WrFmD999/HwDg5OSEsWPHYs6cObC3tzd5nEQ1HZOTRGRxTpw4gdLSUkREROCHH34wWmfVqlV4+eWXDb59/eKLL2BnZ1cdYeL69eu4fPkygoODERQUpLdNe0E5aNAgg2+ML1y4AAB6yUwiIiKiuky7KM1PP/2EgQMHVrr+1atXjW7Xlmvrad2/CvX9+7s/mWlK2iG/Fy9efOjCLbGxsahXrx5+//13+Pv7621bu3ZtleLQJgCvXbtmdLuxc2plVTajXPnem1raZGd5H3/8MYqLi5GSkqI3TBso66Wq7b0pN227d+rUCUeOHKnUYxwcHPDxxx/j448/RkZGBnbv3o0lS5bg888/x507d/D111+bM2SiGolzThKRxdEO6W7fvn2FdbTDqe9PTnp4eFTbt5na4UZ9+vR5pG3ai7X751siIiIiqqu084jv37+/UvW1czYmJiYabLt48SLS09PRvHnzSicatcfXDu9+GO18g4/Sm69r164AgO3btz+0bnp6Olq1amWQmLx06RLS09MrfUxjtNeYe/bsMdiWlZWF7Oxsg3LtCt4XL1402Ka9ri0vPT0d7u7uBonJoqIi3bV8VTzO+TfGyckJrVq1wunTpx9r2HizZs0wduxYJCUlwdHREVu2bKlSPES1FZOTRGRxtIvhPCg5qb1IKp+cPHPmDCRJ0rvwuHTpEkaNGgUPDw84ODige/fuOH/+PADgr7/+wrBhw+Ds7AwvLy9MmjQJJSUllY5TG4OxCc+1F2XG5tUxFjsRERFRXTZkyBD4+Pjgs88+Q3JyssH20tJSpKSk6NV3cXFBXFwc/vjjD125EALTp09HaWkpoqOjK338V199FdbW1pg0aZLR5FxeXp5eEk47JY92xEtljB49Go6Ojvj00091X6SXVz7x5+vri/Pnz+v1Yrx79y4mTpxotPfio+jZsyeaNWuGrVu36p1TIQTee+89owm/zp07A4DBIkPr169HUlKSQX1fX1/k5ubqtY1arcY777yD69evVyl+oCxZKknSI53/irz++usoKirChAkT9IZva2VkZOiGuV+/fl03d2h5ubm5KC4urrYRWkQ1DYd1E5HFqUzPydTUVCgUCr06J06cgI+Pj25Izfnz59G9e3cMHjwYv/32GxwdHbF161Y4Ozvj9OnTCA4Oxrvvvov58+fj6tWriI6ORtOmTTFlypRKxam96DSWnDx27BiUSqXBcG/ttsaNG6N+/fqVOg4RERFRbadUKrF+/Xr0798fISEh6N27N9q0aQMA+Pvvv7Fnzx54eHjoLUyydOlSREVFoVu3boiMjET9+vWxa9cuHDlyBF27dsW7775b6eO3adMGX331FSZOnIiAgAAMGDAALVq0QH5+Pv766y8kJSUhOjoaS5YsAQAEBgbC29sba9euhb29PZo0aQJJkjBx4kSj8yYCZXM8fvvttxgxYgS6du2KwYMHIyAgADk5OTh48CD8/PywadMmAMCkSZMwadIkdOjQAREREVCpVNixYweEEGjXrl2VhkVbWVnhm2++wYABA9CnTx9ERkbC29sbCQkJuHz5Mtq2bYsTJ07oPWbo0KFo1qwZVqxYgezsbHTo0AGnT59GQkICBgwYgG3btunVnzRpErZv346ePXvihRdeQL169ZCYmIiLFy8iNDTUaI/XR+Ho6IguXbogOTkZY8aMgb+/P6ysrDBy5Ej4+Pg80r5eeeUVHDhwACtXrsTevXvRp08feHt74+rVqzhz5gwOHjyI+Ph4+Pn54eLFi+jWrRuCgoLQsWNHNG7cGDdu3MDmzZtRWlpa6c8JRHUNk5NEZFFKS0tx8uRJeHp6VjiZeFZWFm7evInWrVvrDeFOTU3VW7l7woQJ6N+/v95q34GBgQCAiIgITJ06VXdR6+/vjwkTJiAxMfGRkpP169fXrTCoJYTA8ePH0aZNG9jY2Oht014AP/vss5U6BhEREVFd0aVLF6SmpuKTTz7Btm3bkJKSAqVSicaNG2Po0KGIiorSqz98+HA0bNgQc+fOxcaNG1FUVAQ/Pz/MmDEDU6dORb169R7p+BMmTED79u11vTe3bNkCFxcX+Pj4YPLkyRg9erSurkKhwMaNGzF16lSsWrUKt2/fBgCMGDGiwuQkADz33HM4ePAg5s6di6SkJGzZsgWenp5o3749JkyYoKv373//GzY2Nvjyyy+xdOlSuLq64tlnn8WcOXPwwgsvPNLzMqZPnz7YtWsXPvjgA/zwww+ws7ND79698cMPP+Dll182qG9nZ4ddu3Zh8uTJSEhIwIEDB9C9e3ckJydj69atBsnJgQMH6haOWb16Nezt7fH000/jxx9/xEcffVTl+IGyOeYnT56MTZs24datWxBCoHv37o+cnNQuMjRgwAAsXboUW7duRUFBAby8vODv74+FCxfqpmLy8/NDbGwsEhISsHPnTty4cQOenp7o2LEjJk+eXOmV2InqGkkIIeQOgoiouhw/fhwdOnRAnz59sGPHDqN1Nm/ejKFDh+Kll17CqlWrdOWDBg1Cu3btMHv2bJw/fx7+/v5IS0vDE088off4tLQ0tGzZEnZ2drrJv4GyxGh4eLhuLpkVK1ZgzJgxiIuLMxg2VFRUBCcnJ/Tp0we//fab3rZz584hICAAEyZMwDfffKO3LTk5GSEhIfjwww8rXDFQO4F6VVdRJCIiIiK6X2hoKJKSksBUAxFVFntOEpFFedz5JoGynpOjRo3S/e7u7m6QmATKhn83btzY6HCT+1d8rMiJEyeg0WgqHNJtLL7y24w9joiIiIiIiKimYc9JIqJKyMvLg5ubG86cOYOAgABs2bIFkZGRuH37Nqyt9b/n2bJlC6KiopCbmwtbW9sK9/mgnpPmxJ6TRERERGQu7DlJRI+Kq3UTEVVCamoq7Ozs4O/vDwDo3r07bG1tMWnSJJw9exYnT57Ep59+ipycHPTo0QO2trYYN24cTp06hXPnzmHLli2IjY01uu8xY8ZAkiSMGDHCbPGfP38ekiRBkiRkZWWZ7ThEREREREREj4LDuomIKuHEiRMICgrSzSHp5eWFzZs3Y8qUKejYsSPs7OwQEhKCt956C5IkYevWrZg2bRp69OgBhUKBVq1aYdKkSXr7bN++PWJiYnT3tStKmoO7u7vesbQrjhMRERERmVJVV9ImIsvDYd1EREREREREREQkCw7rJiIiIiIiIiIiIlkwOUlERERERERERESyYHKSiIiIiIiIiIiIZMHkJBEREREREREREcmCyUkiIiIiIiIiIiKSBZOTREREREREREREJAsmJ4mIiIiIiIiIiEgWTE4SERERERERERGRLJicJCIiIiIiIiIiIlkwOUlERERERERERESyYHKSiIiIiIiIiIiIZPH/ARdC5GdQP7ssAAAAAElFTkSuQmCC"/>
-</div>
-</div>
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>
-
-Mean and standard deviation of linearized function:
- μ ₕ= 0.278 m
- σ ₕ= 0.034 m
-
-
-Mean and standard deviation of simulated distribution:
- μ ₕ = 0.278 m
- σ ₕ= 0.035 m
-
-
-</pre>
-</div>
-</div>
-</div>
-</div>
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@@ -7978,15 +7899,15 @@ Mean and standard deviation of simulated distribution:
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=8c336566">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">N</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="mi">50000</span><span class="p">]:</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">for</span> <span class="n">N</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="mi">50000</span><span class="p">]:</span>
<span class="n">mu_H_simulated</span><span class="p">,</span> <span class="n">sigma_H_simulated</span><span class="p">,</span> <span class="n">h_samp</span> <span class="o">=</span> <span class="n">samples_plot</span><span class="p">(</span><span class="n">N</span><span class="p">,</span>
<span class="n">mu_H0</span><span class="p">,</span>
<span class="n">mu_iT</span><span class="p">,</span>
@@ -8000,95 +7921,6 @@ Mean and standard deviation of simulated distribution:
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"/>
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-<pre>For N = 5 samples:
- mean = 0.254 m
- std = 0.033 m
-
-</pre>
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"/>
-</div>
-</div>
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-<pre>For N = 50 samples:
- mean = 0.283 m
- std = 0.032 m
-
-Number of iT samples adjusted to 0: 2 (0.4% of N)
-</pre>
-</div>
-</div>
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"/>
-</div>
-</div>
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-<pre>For N = 500 samples:
- mean = 0.279 m
- std = 0.034 m
-
-Number of iT samples adjusted to 0: 19 (0.4% of N)
-</pre>
-</div>
-</div>
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"/>
-</div>
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-<pre>For N = 5000 samples:
- mean = 0.278 m
- std = 0.034 m
-
-Number of iT samples adjusted to 0: 300 (0.6% of N)
-</pre>
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"/>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>For N = 50000 samples:
- mean = 0.277 m
- std = 0.035 m
-
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=7f4ce674">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8105,15 +7937,15 @@ Number of iT samples adjusted to 0: 300 (0.6% of N)
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=fb0f562b">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=fb0f562b">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mi">10</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'for an ice thickness of </span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s1">5.2f</span><span class="si">}</span><span class="s1"> m --> '</span> <span class="o">+</span>
@@ -8124,28 +7956,6 @@ Number of iT samples adjusted to 0: 300 (0.6% of N)
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>for an ice thickness of 0.10 m --> 0.0000% of samples, 0.0000% of distribution
-for an ice thickness of 0.13 m --> 0.0160% of samples, 0.0014% of distribution
-for an ice thickness of 0.17 m --> 0.2320% of samples, 0.0642% of distribution
-for an ice thickness of 0.20 m --> 1.8920% of samples, 1.2165% of distribution
-for an ice thickness of 0.23 m --> 10.5720% of samples, 9.9560% of distribution
-for an ice thickness of 0.27 m --> 36.1440% of samples, 37.5909% of distribution
-for an ice thickness of 0.30 m --> 73.6660% of samples, 74.2661% of distribution
-for an ice thickness of 0.33 m --> 95.3340% of samples, 94.7318% of distribution
-for an ice thickness of 0.37 m --> 99.7340% of samples, 99.5162% of distribution
-for an ice thickness of 0.40 m --> 99.9960% of samples, 99.9811% of distribution
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=dae9ee19">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8173,14 +7983,14 @@ for an ice thickness of 0.40 m --> 99.9960% of samples, 99.9811% of distri
article { position: relative }
</style>
<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">
-<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px">
-</img></a>
+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
+</a>
<a href="https://www.tudelft.nl/en/ceg" rel="TU Delft">
-<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
-</img></a>
+<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
<a href="http://mude.citg.tudelft.nl/" rel="MUDE">
-<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
-</img></a>
+<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
</h3>
<span style="font-size: 75%">
© Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
@@ -8192,7 +8002,4 @@ for an ice thickness of 0.40 m --> 99.9960% of samples, 99.9811% of distri
</div>
</main>
</body>
-<script type="application/vnd.jupyter.widget-state+json">
-{"state": {}, "version_major": 2, "version_minor": 0}
-</script>
</html>
diff --git a/synced_files/GA_1_2/Analysis_solution.md b/synced_files/GA_1_2/Analysis_solution.md
index 0a242021..d7cf86be 100644
--- a/synced_files/GA_1_2/Analysis_solution.md
+++ b/synced_files/GA_1_2/Analysis_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.2: Analysis Notebook
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.2, Friday, Sep 13, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+<!-- #region -->
_This assignment does not need to be turned in._
## Let's account for the latest news!
@@ -98,7 +88,7 @@ print('Ice thickness: ' +
2. Find the distribution of `H_ice` numerically with a simulation, then compare this to the Normal distribution defined by the mean and standard deviation of the linearized function of random variables
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -107,7 +97,6 @@ $\textbf{Task 1}$
Complete the two functions in the cell below, and support your work by including an image showing the analytic equations.
</p>
</div>
-<!-- #endregion -->
```python
def H_taylor(mu_H0, mu_iT, sigma_H0, sigma_iT):
diff --git a/synced_files/GA_1_3/Analysis.html b/synced_files/GA_1_3/Analysis.html
index 18283d49..966040be 100644
--- a/synced_files/GA_1_3/Analysis.html
+++ b/synced_files/GA_1_3/Analysis.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
- var scriptElement = document.createElement('script');
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
-
- var widgetState;
-
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- widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
-
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-
- scriptElement.src = widgetRendererSrc;
- document.body.appendChild(scriptElement);
- }
-
- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7578,10 +7551,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interpolate</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -7634,7 +7607,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">my_dictionary</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'key1'</span><span class="p">:</span> <span class="s1">'value1'</span><span class="p">,</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">my_dictionary</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'key1'</span><span class="p">:</span> <span class="s1">'value1'</span><span class="p">,</span>
<span class="s1">'key2'</span><span class="p">:</span> <span class="s1">'value2'</span><span class="p">,</span>
<span class="s1">'name'</span><span class="p">:</span> <span class="s1">'Dictionary Example'</span><span class="p">,</span>
<span class="s1">'a_list'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
@@ -7685,7 +7658,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
<span class="n">function_that_uses_my_dictionary</span><span class="p">(</span><span class="n">my_dictionary</span><span class="p">)</span>
</pre></div>
</div>
@@ -7719,10 +7692,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">gnss</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'./data/gnss_observations.csv'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">gnss</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'./data/gnss_observations.csv'</span><span class="p">)</span>
<span class="n">times_gnss</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">gnss</span><span class="p">[</span><span class="s1">'times'</span><span class="p">])</span>
<span class="n">y_gnss</span> <span class="o">=</span> <span class="p">(</span><span class="n">gnss</span><span class="p">[</span><span class="s1">'observations[m]'</span><span class="p">])</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">*</span><span class="mi">1000</span>
@@ -7763,7 +7736,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
</pre></div>
</div>
</div>
@@ -7791,10 +7764,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">to_days_years</span><span class="p">(</span><span class="n">times</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">to_days_years</span><span class="p">(</span><span class="n">times</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''Convert the observation times to days and years.'''</span>
<span class="n">times_datetime</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">times</span><span class="p">)</span>
@@ -7815,10 +7788,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">days_gnss</span><span class="p">,</span> <span class="n">years_gnss</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times_gnss</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">days_gnss</span><span class="p">,</span> <span class="n">years_gnss</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times_gnss</span><span class="p">)</span>
<span class="n">days_insar</span><span class="p">,</span> <span class="n">years_insar</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times_insar</span><span class="p">)</span>
<span class="n">days_gw</span><span class="p">,</span> <span class="n">years_gw</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times_gw</span><span class="p">)</span>
@@ -7859,7 +7832,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
</pre></div>
</div>
</div>
@@ -7900,7 +7873,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">,</span> <span class="n">YOUR_CODE_HERE</span><span class="p">,</span>
<span class="s1">'o'</span><span class="p">,</span> <span class="n">mec</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">'GNSS'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">,</span> <span class="n">YOUR_CODE_HERE</span><span class="p">,</span>
@@ -7963,10 +7936,10 @@ Describe the datasets based on the figure above and your observations from the p
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'data_type'</span><span class="p">:</span> <span class="s1">'InSAR'</span><span class="p">,</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'data_type'</span><span class="p">:</span> <span class="s1">'InSAR'</span><span class="p">,</span>
<span class="s1">'y'</span><span class="p">:</span><span class="n">y_insar</span><span class="p">,</span>
<span class="s1">'times'</span><span class="p">:</span><span class="n">times_insar</span><span class="p">,</span>
<span class="s1">'groundwater'</span><span class="p">:</span> <span class="n">GW_at_InSAR_times</span>
@@ -8031,7 +8004,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
</pre></div>
</div>
</div>
@@ -8082,7 +8055,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_insar</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_insar</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">model_gnss</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
</pre></div>
</div>
@@ -8140,7 +8113,7 @@ where $\Sigma_{Y}$ is the covariance matrix of the observables' vector.</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
</pre></div>
</div>
</div>
@@ -8200,10 +8173,10 @@ where $\Sigma_{Y}$ is the covariance matrix of the observables' vector.</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_insar</span><span class="p">[</span><span class="s1">'Sigma_Y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_insar</span><span class="p">[</span><span class="s1">'Sigma_Y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">model_gnss</span><span class="p">[</span><span class="s1">'Sigma_Y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
</pre></div>
</div>
@@ -8255,10 +8228,10 @@ _1</code>). To make this assignment easier for you to implement we have split th
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">BLUE</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">BLUE</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Calculate the Best Linear Unbiased Estimator</span>
<span class="sd"> </span>
<span class="sd"> Uses dict as input/output:</span>
@@ -8322,7 +8295,7 @@ _1</code>). To make this assignment easier for you to implement we have split th
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="n">BLUE</span><span class="p">(</span><span class="n">model_insar</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="n">BLUE</span><span class="p">(</span><span class="n">model_insar</span><span class="p">)</span>
<span class="n">x_hat_insar</span> <span class="o">=</span> <span class="n">model_insar</span><span class="p">[</span><span class="s1">'x_hat'</span><span class="p">]</span>
<span class="n">YOUR_CODE_HERE</span>
@@ -8390,7 +8363,7 @@ Do the values that you just estimated make sense? Explain, using quantitative re
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">Sigma_X_hat_insar</span> <span class="o">=</span> <span class="n">model_insar</span><span class="p">[</span><span class="s1">'Sigma_X_hat'</span><span class="p">]</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">Sigma_X_hat_insar</span> <span class="o">=</span> <span class="n">model_insar</span><span class="p">[</span><span class="s1">'Sigma_X_hat'</span><span class="p">]</span>
<span class="n">YOUR_CODE_HERE</span>
@@ -8438,7 +8411,7 @@ Do the values that you just estimated make sense? Explain, using quantitative re
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_CI</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">get_CI</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Compute the confidence intervals.</span>
<span class="sd"> </span>
<span class="sd"> Uses dict as input/output:</span>
@@ -8468,10 +8441,10 @@ Do the values that you just estimated make sense? Explain, using quantitative re
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">model_gnss</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
</pre></div>
</div>
@@ -8497,7 +8470,7 @@ Do the values that you just estimated make sense? Explain, using quantitative re
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Keys and Values (type) for model_insar:"</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Keys and Values (type) for model_insar:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">model_insar</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">key</span><span class="si">:</span><span class="s2">16s</span><span class="si">}</span><span class="s2"> --> </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Keys and Values (type) for model_gnss:"</span><span class="p">)</span>
@@ -8544,7 +8517,7 @@ Do the values that you just estimated make sense? Explain, using quantitative re
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_model</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_model</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
</pre></div>
</div>
</div>
@@ -8558,7 +8531,7 @@ Do the values that you just estimated make sense? Explain, using quantitative re
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_residual</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_residual</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
</pre></div>
</div>
</div>
@@ -8572,7 +8545,7 @@ Do the values that you just estimated make sense? Explain, using quantitative re
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_residual_histogram</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_residual_histogram</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
</pre></div>
</div>
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@@ -8613,7 +8586,4 @@ Do the values that you just estimated make sense? Explain, using quantitative re
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diff --git a/synced_files/GA_1_3/Analysis.md b/synced_files/GA_1_3/Analysis.md
index b9848be4..fbf24c15 100644
--- a/synced_files/GA_1_3/Analysis.md
+++ b/synced_files/GA_1_3/Analysis.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
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- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.3: Modelling Road Deformation using Non-Linear Least-Squares
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -106,11 +96,10 @@ YOUR_CODE_HERE
function_that_uses_my_dictionary(my_dictionary)
```
-<!-- #region id="160d6250" -->
## Task 1: Preparing the data
Within this assignment you will work with two types of data: InSAR data and GNSS data. The cell below will load the data and visualize the observed displacements time. In this task we use the package `pandas`, which is really useful for handling time series. We will learn how to use it later in the quarter; for now, you only need to recognize that it imports the data as a `dataframe` object, which we then convert into a numpy array using the code below.
-<!-- #endregion -->
+
<div style="background-color:#facb8e; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Tip: note that we have converted all observations to millimeters.</p></div>
@@ -253,7 +242,7 @@ model_gnss = {'data_type': 'GNSS',
}
```
-<!-- #region id="76c9115b" -->
+<!-- #region -->
## Task 2: Set-up linear functional model
We want to investigate how we could model the observed displacements of the road. Because the road is built in the Green Heart we expect that the observed displacements are related to the groundwater level. Furthermore, we assume that the displacements can be modeled using a constant velocity. The model is defined as
@@ -319,7 +308,6 @@ model_insar['A'] = YOUR_CODE_HERE
model_gnss['A'] = YOUR_CODE_HERE
```
-<!-- #region id="9325d32b" -->
## 3. Set-up stochastic model
We will use the Best Linear Unbiased Estimator (BLUE) to solve for the unknown parameters. Therefore we also need a stochastic model, which is defined as
@@ -330,7 +318,7 @@ where $\Sigma_{Y}$ is the covariance matrix of the observables' vector.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -389,11 +377,10 @@ model_insar['Sigma_Y'] = YOUR_CODE_HERE
model_gnss['Sigma_Y'] = YOUR_CODE_HERE
```
-<!-- #region id="09e965bf" -->
## 4. Apply best linear unbiased estimation
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -482,11 +469,11 @@ Do the values that you just estimated make sense? Explain, using quantitative re
-<!-- #region id="65e42a43" -->
+
## 5. Evaluate the precision
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -510,9 +497,8 @@ Sigma_X_hat_gnss = model_gnss['Sigma_X_hat']
YOUR_CODE_HERE
```
-<!-- #region id="886efe26" -->
## 6. Present and reflect on estimation results
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -575,9 +561,8 @@ Use the functions provided to visualize the results of our two models.
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p><strong>Note</strong>: remember that you will have to use the same function to look at <em>both</em> models when writing your interpretation in the Report.</p></div>
-<!-- #endregion -->
```python
_, _ = plot_model(YOUR_CODE_HERE)
diff --git a/synced_files/GA_1_3/Analysis_Solution.html b/synced_files/GA_1_3/Analysis_Solution.html
index d7e2e809..30c2bcf3 100644
--- a/synced_files/GA_1_3/Analysis_Solution.html
+++ b/synced_files/GA_1_3/Analysis_Solution.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
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<style type="text/css">
pre { line-height: 125%; }
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</div>
</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interpolate</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
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<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight hl-ipython3"><pre><span></span><span class="n">my_dictionary</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'key1'</span><span class="p">:</span> <span class="s1">'value1'</span><span class="p">,</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">my_dictionary</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'key1'</span><span class="p">:</span> <span class="s1">'value1'</span><span class="p">,</span>
<span class="s1">'key2'</span><span class="p">:</span> <span class="s1">'value2'</span><span class="p">,</span>
<span class="s1">'name'</span><span class="p">:</span> <span class="s1">'Dictionary Example'</span><span class="p">,</span>
<span class="s1">'a_list'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
@@ -7668,23 +7629,6 @@ a.anchor-link {
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-<pre>value1
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-[1, 2, 3]
-[1 2 3]
-hello
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
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<span class="c1"># function_that_uses_my_dictionary(my_dictionary)</span>
<span class="c1"># SOLUTION:</span>
@@ -7720,24 +7664,6 @@ hello
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-[1, 2, 3]
-[1 2 3]
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<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">gnss</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'./data/gnss_observations.csv'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">gnss</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'./data/gnss_observations.csv'</span><span class="p">)</span>
<span class="n">times_gnss</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">gnss</span><span class="p">[</span><span class="s1">'times'</span><span class="p">])</span>
<span class="n">y_gnss</span> <span class="o">=</span> <span class="p">(</span><span class="n">gnss</span><span class="p">[</span><span class="s1">'observations[m]'</span><span class="p">])</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">*</span><span class="mi">1000</span>
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@@ -7847,60 +7773,15 @@ new_key exists and has value: new_value
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-------------------------------------------------
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-
-
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-------------------------------------------------
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-
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-------------------------------------------------
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-First value = -109.698
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># SOLUTION:</span>
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<span class="n">times_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">data_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> <span class="n">times_gnss</span><span class="p">,</span>
<span class="n">data_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="n">times_insar</span><span class="p">,</span>
<span class="n">data_list</span><span class="p">[</span><span class="mi">2</span><span class="p">]:</span> <span class="n">times_gw</span><span class="p">}</span>
@@ -7922,36 +7803,6 @@ Last value = -117.268
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"/>
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-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a9c02e8f-81f9-41a3-b894-c23dd9617207">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7990,10 +7841,10 @@ Last value = -117.268
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">to_days_years</span><span class="p">(</span><span class="n">times</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">to_days_years</span><span class="p">(</span><span class="n">times</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''Convert the observation times to days and years.'''</span>
<span class="n">times_datetime</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">times</span><span class="p">)</span>
@@ -8014,10 +7865,10 @@ Last value = -117.268
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">days_gnss</span><span class="p">,</span> <span class="n">years_gnss</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times_gnss</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">days_gnss</span><span class="p">,</span> <span class="n">years_gnss</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times_gnss</span><span class="p">)</span>
<span class="n">days_insar</span><span class="p">,</span> <span class="n">years_insar</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times_insar</span><span class="p">)</span>
<span class="n">days_gw</span><span class="p">,</span> <span class="n">years_gw</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times_gw</span><span class="p">)</span>
@@ -8050,15 +7901,15 @@ Last value = -117.268
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=6cdfb46b-1324-4c2b-8148-5a6a102ede2e">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=6cdfb46b-1324-4c2b-8148-5a6a102ede2e">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'array size of GW_at_GNSS_times'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">GW_at_GNSS_times</span><span class="p">))</span>
@@ -8078,37 +7929,6 @@ Last value = -117.268
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>array size of GW_at_GNSS_times 730
-array size of GW_at_InSAR_times 61
-array size of GW before interpolation 25
-
-First values of times_gw:
-0 2017-01-01
-1 2017-02-01
-Name: times, dtype: datetime64[ns]
-
-First values of y_gw:
-[-109.698 -102.044]
-
-First values of times_gnss:
-0 2017-01-01
-1 2017-01-02
-Name: times, dtype: datetime64[ns]
-
-First values of GW_at_GNSS_times:
-[-109.698 -109.451]
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3b8c68eb-9774-4c3c-91da-f29f035b178c">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8155,15 +7975,15 @@ First values of GW_at_GNSS_times:
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=e868e488">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=e868e488">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># plt.figure(figsize=(15,5))</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># plt.figure(figsize=(15,5))</span>
<span class="c1"># plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE,</span>
<span class="c1"># 'o', mec='black', label = 'GNSS')</span>
<span class="c1"># plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE,</span>
@@ -8186,18 +8006,6 @@ First values of GW_at_GNSS_times:
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"/>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c9b45b8e">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8263,10 +8071,10 @@ Describe the datasets based on the figure above and your observations from the p
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'data_type'</span><span class="p">:</span> <span class="s1">'InSAR'</span><span class="p">,</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'data_type'</span><span class="p">:</span> <span class="s1">'InSAR'</span><span class="p">,</span>
<span class="s1">'y'</span><span class="p">:</span><span class="n">y_insar</span><span class="p">,</span>
<span class="s1">'times'</span><span class="p">:</span><span class="n">times_insar</span><span class="p">,</span>
<span class="s1">'groundwater'</span><span class="p">:</span> <span class="n">GW_at_InSAR_times</span>
@@ -8323,15 +8131,15 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=3a3eb1a1">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=3a3eb1a1">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="n">A_insar</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">times_insar</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
@@ -8352,36 +8160,15 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The first 5 rows of the A matrix (InSAR) are:
-[[ 1. 0. -109.698]
- [ 1. 12. -106.735]
- [ 1. 24. -103.772]
- [ 1. 36. -106.536]
- [ 1. 48. -117.316]]
-The first 5 observations [mm] of y_insar are:
-[ -3.792 -5.999 -11.403 -9.92 -11.283]
-m = 61 and n = 3
-</pre>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=4bcd395d">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=4bcd395d">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="n">A_gnss</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">times_gnss</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
@@ -8402,28 +8189,6 @@ m = 61 and n = 3
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The first 5 rows of the A matrix (GNSS) are:
-[[ 1. 0. -109.698]
- [ 1. 1. -109.451]
- [ 1. 2. -109.204]
- [ 1. 3. -108.957]
- [ 1. 4. -108.71 ]]
-
-The first 5 observations [mm] of y_gnss are:
-[-13.981 10.392 -17.091 -7.924 -14.729]
-m = 730 and n = 3
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d390f466">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8488,15 +8253,15 @@ m = 730 and n = 3
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=396ac3a5">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=396ac3a5">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># model_insar['A'] = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># model_insar['A'] = YOUR_CODE_HERE</span>
<span class="c1"># model_gnss['A'] = YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
@@ -8514,31 +8279,6 @@ m = 730 and n = 3
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Keys and Values (type) for model_insar:
-data_type --> <class 'str'>
-y --> <class 'numpy.ndarray'>
-times --> <class 'pandas.core.series.Series'>
-groundwater --> <class 'numpy.ndarray'>
-A --> <class 'numpy.ndarray'>
-
-Keys and Values (type) for model_gnss:
-data_type --> <class 'str'>
-y --> <class 'numpy.ndarray'>
-times --> <class 'pandas.core.series.Series'>
-groundwater --> <class 'numpy.ndarray'>
-A --> <class 'numpy.ndarray'>
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=9325d32b">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8582,15 +8322,15 @@ where $\Sigma_{Y}$ is the covariance matrix of the observables' vector.</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=163acdb3">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=163acdb3">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="n">std_insar</span> <span class="o">=</span> <span class="mi">2</span> <span class="c1">#mm</span>
@@ -8604,35 +8344,15 @@ where $\Sigma_{Y}$ is the covariance matrix of the observables' vector.</p>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Sigma_Y (InSAR) is defined as:
-[[4. 0. 0. ... 0. 0. 0.]
- [0. 4. 0. ... 0. 0. 0.]
- [0. 0. 4. ... 0. 0. 0.]
- ...
- [0. 0. 0. ... 4. 0. 0.]
- [0. 0. 0. ... 0. 4. 0.]
- [0. 0. 0. ... 0. 0. 4.]]
-</pre>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=5d583bd8">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=5d583bd8">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="n">std_gnss</span> <span class="o">=</span> <span class="mi">15</span> <span class="c1">#mm (corrected from original value of 5 mm)</span>
@@ -8646,27 +8366,6 @@ where $\Sigma_{Y}$ is the covariance matrix of the observables' vector.</p>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>
-Sigma_Y (GNSS) is defined as:
-[[225. 0. 0. ... 0. 0. 0.]
- [ 0. 225. 0. ... 0. 0. 0.]
- [ 0. 0. 225. ... 0. 0. 0.]
- ...
- [ 0. 0. 0. ... 225. 0. 0.]
- [ 0. 0. 0. ... 0. 225. 0.]
- [ 0. 0. 0. ... 0. 0. 225.]]
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b2665071">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8742,10 +8441,10 @@ Sigma_Y (GNSS) is defined as:
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># model_insar['Sigma_Y] = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># model_insar['Sigma_Y] = YOUR_CODE_HERE</span>
<span class="c1"># model_gnss['Sigma_Y'] = YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
@@ -8801,10 +8500,10 @@ _1</code>). To make this assignment easier for you to implement we have split th
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">BLUE</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">BLUE</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Calculate the Best Linear Unbiased Estimator</span>
<span class="sd"> </span>
<span class="sd"> Uses dict as input/output:</span>
@@ -8874,15 +8573,15 @@ _1</code>). To make this assignment easier for you to implement we have split th
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=4a592ac1">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=4a592ac1">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="n">BLUE</span><span class="p">(</span><span class="n">model_insar</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_insar</span> <span class="o">=</span> <span class="n">BLUE</span><span class="p">(</span><span class="n">model_insar</span><span class="p">)</span>
<span class="n">x_hat_insar</span> <span class="o">=</span> <span class="n">model_insar</span><span class="p">[</span><span class="s1">'x_hat'</span><span class="p">]</span>
<span class="c1"># YOUR_CODE_HERE</span>
@@ -8908,27 +8607,6 @@ _1</code>). To make this assignment easier for you to implement we have split th
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The InSAR-estimated offset is 9.174 mm
-The InSAR-estimated velocity is -0.0243 mm/day
-The InSAR-estimated velocity is -8.8667 mm/year
-The InSAR-estimated GW factor is 0.202 [-]
-
-The GNSS-estimated offset is 1.181 mm
-The GNSS-estimated velocity is -0.0209 mm/day
-The GNSS-estimated velocity is -7.615 mm/year
-The GNSS-estimated GW factor is 0.16 [-]
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=bef2f3be">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8991,15 +8669,15 @@ Do the values that you just estimated make sense? Explain, using quantitative re
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=835eefc8">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=835eefc8">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">Sigma_X_hat_insar</span> <span class="o">=</span> <span class="n">model_insar</span><span class="p">[</span><span class="s1">'Sigma_X_hat'</span><span class="p">]</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">Sigma_X_hat_insar</span> <span class="o">=</span> <span class="n">model_insar</span><span class="p">[</span><span class="s1">'Sigma_X_hat'</span><span class="p">]</span>
<span class="c1"># YOUR_CODE_HERE</span>
@@ -9031,35 +8709,6 @@ Do the values that you just estimated make sense? Explain, using quantitative re
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Covariance matrix of estimated parameters (InSAR):
-[[ 4.530e+00 -4.173e-04 3.363e-02]
- [-4.173e-04 1.472e-06 8.776e-07]
- [ 3.363e-02 8.776e-07 2.646e-04]]
-
-The standard deviation for the InSAR-estimated offset is 2.128 mm
-The standard deviation for the InSAR-estimated velocity is 0.0012 mm/day
-The standard deviation for the InSAR-estimated GW factor is 0.016 [-]
-
-Covariance matrix of estimated parameters (GNSS):
-[[ 2.160e+01 -2.244e-03 1.595e-01]
- [-2.244e-03 6.945e-06 2.238e-06]
- [ 1.595e-01 2.238e-06 1.249e-03]]
-
-The standard deviation for the GNSS-estimated offset is 4.647 mm
-The standard deviation for the GNSS-estimated velocity is 0.0026 mm/day
-The standard deviation for the GNSS-estimated GW factor is 0.035 [-]
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=8e62592d">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -9110,10 +8759,10 @@ The standard deviation for the GNSS-estimated GW factor is 0.035 [-]
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_CI</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">get_CI</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Compute the confidence intervals.</span>
<span class="sd"> </span>
<span class="sd"> Uses dict as input/output:</span>
@@ -9150,10 +8799,10 @@ The standard deviation for the GNSS-estimated GW factor is 0.035 [-]
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># model_insar = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># model_insar = YOUR_CODE_HERE</span>
<span class="c1"># model_gnss = YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
@@ -9175,15 +8824,15 @@ The standard deviation for the GNSS-estimated GW factor is 0.035 [-]
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b3bb808e">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b3bb808e">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Keys and Values (type) for model_insar:"</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Keys and Values (type) for model_insar:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">model_insar</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">key</span><span class="si">:</span><span class="s2">16s</span><span class="si">}</span><span class="s2"> --> </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Keys and Values (type) for model_gnss:"</span><span class="p">)</span>
@@ -9194,57 +8843,6 @@ The standard deviation for the GNSS-estimated GW factor is 0.035 [-]
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Keys and Values (type) for model_insar:
-data_type --> <class 'str'>
-y --> <class 'numpy.ndarray'>
-times --> <class 'pandas.core.series.Series'>
-groundwater --> <class 'numpy.ndarray'>
-A --> <class 'numpy.ndarray'>
-Sigma_Y --> <class 'numpy.ndarray'>
-Sigma_X_hat --> <class 'numpy.ndarray'>
-x_hat --> <class 'numpy.ndarray'>
-y_hat --> <class 'numpy.ndarray'>
-e_hat --> <class 'numpy.ndarray'>
-Sigma_Y_hat --> <class 'numpy.ndarray'>
-std_y --> <class 'numpy.ndarray'>
-Sigma_e_hat --> <class 'numpy.ndarray'>
-std_e_hat --> <class 'numpy.ndarray'>
-alpha --> <class 'float'>
-CI_y --> <class 'numpy.ndarray'>
-CI_res --> <class 'numpy.ndarray'>
-CI_Y_hat --> <class 'numpy.ndarray'>
-
-Keys and Values (type) for model_gnss:
-data_type --> <class 'str'>
-y --> <class 'numpy.ndarray'>
-times --> <class 'pandas.core.series.Series'>
-groundwater --> <class 'numpy.ndarray'>
-A --> <class 'numpy.ndarray'>
-Sigma_Y --> <class 'numpy.ndarray'>
-Sigma_X_hat --> <class 'numpy.ndarray'>
-x_hat --> <class 'numpy.ndarray'>
-y_hat --> <class 'numpy.ndarray'>
-e_hat --> <class 'numpy.ndarray'>
-Sigma_Y_hat --> <class 'numpy.ndarray'>
-std_y --> <class 'numpy.ndarray'>
-Sigma_e_hat --> <class 'numpy.ndarray'>
-std_e_hat --> <class 'numpy.ndarray'>
-alpha --> <class 'float'>
-CI_y --> <class 'numpy.ndarray'>
-CI_res --> <class 'numpy.ndarray'>
-CI_Y_hat --> <class 'numpy.ndarray'>
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=aaf72e41">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -9273,15 +8871,15 @@ CI_Y_hat --> <class 'numpy.ndarray'>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=ec7c8bef">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=ec7c8bef">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># _, _ = plot_model(YOUR_CODE_HERE)</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># _, _ = plot_model(YOUR_CODE_HERE)</span>
<span class="c1"># SOLUTION:</span>
<span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_model</span><span class="p">(</span><span class="n">model_insar</span><span class="p">)</span>
@@ -9291,33 +8889,15 @@ CI_Y_hat --> <class 'numpy.ndarray'>
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"/>
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"/>
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-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=104d155d">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=104d155d">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [25]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># _, _ = plot_residual(YOUR_CODE_HERE)</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># _, _ = plot_residual(YOUR_CODE_HERE)</span>
<span class="c1"># SOLUTION:</span>
<span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_residual</span><span class="p">(</span><span class="n">model_insar</span><span class="p">)</span>
@@ -9327,33 +8907,15 @@ CI_Y_hat --> <class 'numpy.ndarray'>
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</div>
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"/>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=1dc93ce9">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=1dc93ce9">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [26]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># _, _ = plot_residual_histogram(YOUR_CODE_HERE)</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># _, _ = plot_residual_histogram(YOUR_CODE_HERE)</span>
<span class="c1"># SOLUTION:</span>
<span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plot_residual_histogram</span><span class="p">(</span><span class="n">model_insar</span><span class="p">)</span>
@@ -9363,34 +8925,6 @@ CI_Y_hat --> <class 'numpy.ndarray'>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The mean value of the InSAR residuals is 0.0 mm
-The standard deviation of the InSAR residuals is 3.115 mm
-The mean value of the GNSS residuals is -0.0 mm
-The standard deviation of the GNSS residuals is 15.393 mm
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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"/>
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"/>
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@@ -9407,15 +8941,15 @@ The standard deviation of the GNSS residuals is 15.393 mm
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=113f3809">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=113f3809">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">k_true</span> <span class="o">=</span> <span class="mf">0.15</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">k_true</span> <span class="o">=</span> <span class="mf">0.15</span>
<span class="n">R_true</span> <span class="o">=</span> <span class="o">-</span><span class="mi">22</span>
<span class="n">a_true</span> <span class="o">=</span> <span class="mi">180</span>
<span class="n">d0_true</span> <span class="o">=</span> <span class="mi">10</span>
@@ -9432,33 +8966,15 @@ The standard deviation of the GNSS residuals is 15.393 mm
</div>
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</div>
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"/>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=aa877dd5">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=aa877dd5">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">ipywidgets</span> <span class="k">as</span> <span class="nn">widgets</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">ipywidgets</span> <span class="k">as</span> <span class="nn">widgets</span>
<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interact</span>
<span class="c1"># Function to update the plot based on slider values</span>
@@ -9494,50 +9010,20 @@ The standard deviation of the GNSS residuals is 15.393 mm
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(FloatSlider(value=0.0, description='x0', max=10.0, min=-10.0), FloatSlider(value=0.0, de…</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[28]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre><function __main__.update_plot(x0, x1, x2)></pre>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=d8b31540">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=d8b31540">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">xhat_slider_plot</span><span class="p">(</span><span class="n">model_gnss</span><span class="p">[</span><span class="s1">'A'</span><span class="p">],</span> <span class="n">model_gnss</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span> <span class="n">model_gnss</span><span class="p">[</span><span class="s1">'times'</span><span class="p">],</span> <span class="n">model_gnss</span><span class="p">[</span><span class="s1">'Sigma_Y'</span><span class="p">])</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">xhat_slider_plot</span><span class="p">(</span><span class="n">model_gnss</span><span class="p">[</span><span class="s1">'A'</span><span class="p">],</span> <span class="n">model_gnss</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span> <span class="n">model_gnss</span><span class="p">[</span><span class="s1">'times'</span><span class="p">],</span> <span class="n">model_gnss</span><span class="p">[</span><span class="s1">'Sigma_Y'</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(FloatSlider(value=1.1806136803794327, description='xhat_0', max=15.122548861077993, min=…</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3203d779">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -9554,14 +9040,14 @@ The standard deviation of the GNSS residuals is 15.393 mm
article { position: relative }
</style>
<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">
-<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px">
-</img></a>
+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
+</a>
<a href="https://www.tudelft.nl/en/ceg" rel="TU Delft">
-<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
-</img></a>
+<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
<a href="http://mude.citg.tudelft.nl/" rel="MUDE">
-<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
-</img></a>
+<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
</h3>
<span style="font-size: 75%">
© Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
@@ -9573,7 +9059,4 @@ The standard deviation of the GNSS residuals is 15.393 mm
</div>
</main>
</body>
-<script type="application/vnd.jupyter.widget-state+json">
-{"state": {}, "version_major": 2, "version_minor": 0}
-</script>
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diff --git a/synced_files/GA_1_3/Analysis_Solution.md b/synced_files/GA_1_3/Analysis_Solution.md
index ac55c108..e40bc539 100644
--- a/synced_files/GA_1_3/Analysis_Solution.md
+++ b/synced_files/GA_1_3/Analysis_Solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.3: Modelling Road Deformation using Non-Linear Least-Squares
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -106,11 +96,10 @@ my_dictionary['new_key'] = 'new_value'
function_that_uses_my_dictionary(my_dictionary)
```
-<!-- #region id="160d6250" -->
## Task 1: Preparing the data
Within this assignment you will work with two types of data: InSAR data and GNSS data. The cell below will load the data and visualize the observed displacements time. In this task we use the package `pandas`, which is really useful for handling time series. We will learn how to use it later in the quarter; for now, you only need to recognize that it imports the data as a `dataframe` object, which we then convert into a numpy array using the code below.
-<!-- #endregion -->
+
<div style="background-color:#facb8e; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Tip: note that we have converted all observations to millimeters.</p></div>
@@ -139,9 +128,8 @@ Once you have used the cell above to import the data, investigate the data sets
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>The code below gives some examples of the quantitative and qualitative ways you could have looked at the data. It is more than you were expected to do; the important thing is that you showed the ability to learn something about the data and describe aspects that are relevant to our problem. We use a dictionary to easily access the different data series using their names, which are entered as the dictionary keys (also not expected of you, but it's hopefully fun to learn useful tricks).</div>
-<!-- #endregion -->
```python
# YOUR_CODE_HERE
@@ -353,7 +341,7 @@ model_gnss = {'data_type': 'GNSS',
}
```
-<!-- #region id="76c9115b" -->
+<!-- #region -->
## Task 2: Set-up linear functional model
We want to investigate how we could model the observed displacements of the road. Because the road is built in the Green Heart we expect that the observed displacements are related to the groundwater level. Furthermore, we assume that the displacements can be modeled using a constant velocity. The model is defined as
@@ -486,7 +474,6 @@ for key, value in model_gnss.items():
print(f"{key:16s} --> {type(value)}")
```
-<!-- #region id="9325d32b" -->
## 3. Set-up stochastic model
We will use the Best Linear Unbiased Estimator (BLUE) to solve for the unknown parameters. Therefore we also need a stochastic model, which is defined as
@@ -497,7 +484,7 @@ where $\Sigma_{Y}$ is the covariance matrix of the observables' vector.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -593,11 +580,10 @@ model_insar['Sigma_Y'] = Sigma_Y_insar
model_gnss['Sigma_Y'] = Sigma_Y_gnss
```
-<!-- #region id="09e965bf" -->
## 4. Apply best linear unbiased estimation
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -723,11 +709,11 @@ As long as the velocity is negative and around -0.02 mm/day or -10 mm/yr it make
</p>
</div>
-<!-- #region id="65e42a43" -->
+
## 5. Evaluate the precision
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -783,9 +769,9 @@ The off-diagonal elements show the covariances between the estimated parameters,
</p>
</div>
-<!-- #region id="886efe26" -->
+
## 6. Present and reflect on estimation results
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -859,9 +845,8 @@ Use the functions provided to visualize the results of our two models.
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p><strong>Note</strong>: remember that you will have to use the same function to look at <em>both</em> models when writing your interpretation in the Report.</p></div>
-<!-- #endregion -->
```python
# _, _ = plot_model(YOUR_CODE_HERE)
diff --git a/synced_files/GA_1_3/Warmup.html b/synced_files/GA_1_3/Warmup.html
index d8ed212f..aba30698 100644
--- a/synced_files/GA_1_3/Warmup.html
+++ b/synced_files/GA_1_3/Warmup.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
- var scriptElement = document.createElement('script');
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
-
- var widgetState;
-
- // Fallback for older version:
- try {
- widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
-
- if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) {
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-js-widgets@*/dist/embed.js';
-
- }
- } catch(e) {}
-
- scriptElement.src = widgetRendererSrc;
- document.body.appendChild(scriptElement);
- }
-
- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7574,10 +7547,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interpolate</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -7625,7 +7598,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">my_dictionary</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'key1'</span><span class="p">:</span> <span class="s1">'value1'</span><span class="p">,</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">my_dictionary</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'key1'</span><span class="p">:</span> <span class="s1">'value1'</span><span class="p">,</span>
<span class="s1">'key2'</span><span class="p">:</span> <span class="s1">'value2'</span><span class="p">,</span>
<span class="s1">'name'</span><span class="p">:</span> <span class="s1">'Dictionary Example'</span><span class="p">,</span>
<span class="s1">'a_list'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
@@ -7672,7 +7645,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
<span class="n">function_that_uses_my_dictionary</span><span class="p">(</span><span class="n">my_dictionary</span><span class="p">)</span>
</pre></div>
</div>
@@ -7722,7 +7695,7 @@ new_key exists and has value: new_value
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Keys and Values (type):"</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Keys and Values (type):"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">my_dictionary</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">key</span><span class="si">:</span><span class="s2">16s</span><span class="si">}</span><span class="s2"> --> </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
@@ -7762,10 +7735,10 @@ new_key exists and has value: new_value
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">warmup</span> <span class="kn">import</span> <span class="o">*</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">warmup</span> <span class="kn">import</span> <span class="o">*</span>
</pre></div>
</div>
</div>
@@ -7779,7 +7752,7 @@ new_key exists and has value: new_value
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
</pre></div>
</div>
</div>
@@ -7801,10 +7774,10 @@ new_key exists and has value: new_value
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">dataset1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'./data_warmup/dataset1.csv'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">dataset1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'./data_warmup/dataset1.csv'</span><span class="p">)</span>
<span class="n">times1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">dataset1</span><span class="p">[</span><span class="s1">'times'</span><span class="p">])</span>
<span class="n">obs1</span> <span class="o">=</span> <span class="p">(</span><span class="n">dataset1</span><span class="p">[</span><span class="s1">'observations[m]'</span><span class="p">])</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">*</span><span class="mi">1000</span>
@@ -7824,7 +7797,7 @@ new_key exists and has value: new_value
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset1</span><span class="p">),</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span>
+<div class="highlight hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset1</span><span class="p">),</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span>
<span class="nb">type</span><span class="p">(</span><span class="n">dataset2</span><span class="p">),</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span>
<span class="nb">type</span><span class="p">(</span><span class="n">times1</span><span class="p">),</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span>
<span class="nb">type</span><span class="p">(</span><span class="n">times2</span><span class="p">),</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span>
@@ -7866,10 +7839,10 @@ new_key exists and has value: new_value
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">to_days_years</span><span class="p">(</span><span class="n">times</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">to_days_years</span><span class="p">(</span><span class="n">times</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''Convert the observation times to days and years.'''</span>
<span class="n">times_datetime</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">times</span><span class="p">)</span>
@@ -7893,7 +7866,7 @@ new_key exists and has value: new_value
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">days1</span><span class="p">,</span> <span class="n">years1</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times1</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">days1</span><span class="p">,</span> <span class="n">years1</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times1</span><span class="p">)</span>
<span class="n">days2</span><span class="p">,</span> <span class="n">years2</span> <span class="o">=</span> <span class="n">to_days_years</span><span class="p">(</span><span class="n">times2</span><span class="p">)</span>
<span class="n">interp</span> <span class="o">=</span> <span class="n">interpolate</span><span class="o">.</span><span class="n">interp1d</span><span class="p">(</span><span class="n">days2</span><span class="p">,</span> <span class="n">obs2</span><span class="p">)</span>
@@ -7943,7 +7916,4 @@ new_key exists and has value: new_value
</div>
</main>
</body>
-<script type="application/vnd.jupyter.widget-state+json">
-{"state": {}, "version_major": 2, "version_minor": 0}
-</script>
</html>
diff --git a/synced_files/GA_1_3/Warmup.md b/synced_files/GA_1_3/Warmup.md
index 3ece66b2..e68cef59 100644
--- a/synced_files/GA_1_3/Warmup.md
+++ b/synced_files/GA_1_3/Warmup.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.3: Warmup Notebook
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_1_4/Analysis.html b/synced_files/GA_1_4/Analysis.html
index b7642fcc..dad5a13a 100644
--- a/synced_files/GA_1_4/Analysis.html
+++ b/synced_files/GA_1_4/Analysis.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
- var scriptElement = document.createElement('script');
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
-
- var widgetState;
-
- // Fallback for older version:
- try {
- widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
-
- if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) {
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-js-widgets@*/dist/embed.js';
-
- }
- } catch(e) {}
-
- scriptElement.src = widgetRendererSrc;
- document.body.appendChild(scriptElement);
- }
-
- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7584,10 +7557,10 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interpolate</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -7638,10 +7611,10 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">m1_blue</span> <span class="o">=</span> <span class="n">load_pickle_file</span><span class="p">(</span><span class="s1">'m1_blue.pickle'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">m1_blue</span> <span class="o">=</span> <span class="n">load_pickle_file</span><span class="p">(</span><span class="s1">'m1_blue.pickle'</span><span class="p">)</span>
<span class="n">m2_blue</span> <span class="o">=</span> <span class="n">load_pickle_file</span><span class="p">(</span><span class="s1">'m2_blue.pickle'</span><span class="p">)</span>
</pre></div>
</div>
@@ -7667,7 +7640,7 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_summary</span><span class="p">(</span><span class="n">m1_blue</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_summary</span><span class="p">(</span><span class="n">m1_blue</span><span class="p">)</span>
<span class="n">model_summary</span><span class="p">(</span><span class="n">m2_blue</span><span class="p">)</span>
</pre></div>
</div>
@@ -7693,7 +7666,7 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">m1_blue</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">m1_blue</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
</pre></div>
</div>
@@ -7724,7 +7697,7 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">x0_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x0'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">x0_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x0'</span><span class="p">)</span>
<span class="n">x1_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mf">0.1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x1'</span><span class="p">)</span>
<span class="n">x2_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x2'</span><span class="p">)</span>
@@ -7768,10 +7741,10 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize_new_dict</span><span class="p">(</span><span class="n">d_old</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">initialize_new_dict</span><span class="p">(</span><span class="n">d_old</span><span class="p">):</span>
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">d</span><span class="p">[</span><span class="s1">'data_type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">d_old</span><span class="p">[</span><span class="s1">'data_type'</span><span class="p">]</span>
<span class="n">d</span><span class="p">[</span><span class="s1">'model_type'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'Non-Linear Least Squares'</span>
@@ -7816,7 +7789,7 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
</pre></div>
</div>
</div>
@@ -7872,10 +7845,10 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">compute_y</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">compute_y</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Model, q: ground surface displacement.</span>
<span class="sd"> Inputs:</span>
@@ -7935,7 +7908,7 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">x0_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">40</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x0'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">x0_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">40</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x0'</span><span class="p">)</span>
<span class="n">x1_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">50</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x1'</span><span class="p">)</span>
<span class="n">x2_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x2'</span><span class="p">)</span>
<span class="n">x3_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x3'</span><span class="p">)</span>
@@ -7969,10 +7942,10 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">d_init</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">d_init</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">R_init</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">a_init</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">k_init</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
@@ -8004,10 +7977,10 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">jacobian</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">jacobian</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Compute Jacobian of the model.</span>
<span class="sd"> Model, q: ground surface displacement.</span>
@@ -8069,7 +8042,7 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">'The first 5 rows of the Jacobian matrix (InSAR):'</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="n">test_J</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">,:])</span>
@@ -8147,10 +8120,10 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">gauss_newton_iteration</span><span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">gauss_newton_iteration</span><span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Use Gauss-Newton iteration to find non-linear parameters.</span>
<span class="sd"> </span>
<span class="sd"> Inputs:</span>
@@ -8263,7 +8236,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">m1</span> <span class="o">=</span> <span class="n">gauss_newton_iteration</span><span class="p">(</span><span class="n">initial_guess</span><span class="p">,</span> <span class="n">m1</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">m1</span> <span class="o">=</span> <span class="n">gauss_newton_iteration</span><span class="p">(</span><span class="n">initial_guess</span><span class="p">,</span> <span class="n">m1</span><span class="p">)</span>
<span class="n">m2</span> <span class="o">=</span> <span class="n">gauss_newton_iteration</span><span class="p">(</span><span class="n">initial_guess</span><span class="p">,</span> <span class="n">m2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1"> InSAR Reults for each iteration (Iterations completed ='</span><span class="p">,</span>
@@ -8317,7 +8290,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_fit_iteration</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">plot_fit_iteration</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Plot value of each parameter, each iteration."""</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
@@ -8372,10 +8345,10 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># initial_guess_alternative = initial_guess</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># initial_guess_alternative = initial_guess</span>
<span class="c1"># print(initial_guess_alternative)</span>
<span class="c1"># plot_convergence_interactive(gauss_newton_iteration(initial_guess_alternative, m1))</span>
<span class="c1"># plot_convergence_interactive(gauss_newton_iteration(initial_guess_alternative, m2))</span>
@@ -8421,7 +8394,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">show_std</span><span class="p">(</span><span class="n">Sigma_X_hat</span><span class="p">,</span> <span class="n">data_type</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">show_std</span><span class="p">(</span><span class="n">Sigma_X_hat</span><span class="p">,</span> <span class="n">data_type</span><span class="p">):</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">'The standard deviation for'</span><span class="p">,</span>
<span class="n">data_type</span> <span class="o">+</span> <span class="s1">'-offset is'</span><span class="p">,</span>
<span class="n">YOUR_CODE_HERE</span><span class="p">,</span> <span class="s1">'UNITS'</span><span class="p">)</span>
@@ -8493,7 +8466,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="n">get_CI</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="n">get_CI</span><span class="p">)</span>
</pre></div>
</div>
</div>
@@ -8507,7 +8480,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">m1</span><span class="p">[</span><span class="s1">'Y_hat'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">m1</span><span class="p">[</span><span class="s1">'Y_hat'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">m1</span> <span class="o">=</span> <span class="n">get_CI</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
<span class="n">plot_model</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
<span class="n">plot_residual</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
@@ -8525,7 +8498,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">m1</span><span class="p">[</span><span class="s1">'Y_hat'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">m1</span><span class="p">[</span><span class="s1">'Y_hat'</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">m1</span> <span class="o">=</span> <span class="n">get_CI</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
<span class="n">plot_model</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
<span class="n">plot_residual</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">)</span>
@@ -8575,7 +8548,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span> <span class="p">(</span><span class="n">probably</span> <span class="n">will</span> <span class="n">be</span> <span class="n">more</span> <span class="n">than</span> <span class="n">one</span> <span class="n">line</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span> <span class="p">(</span><span class="n">probably</span> <span class="n">will</span> <span class="n">be</span> <span class="n">more</span> <span class="n">than</span> <span class="n">one</span> <span class="n">line</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'The critical value is </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
</pre></div>
</div>
@@ -8606,7 +8579,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">t1_insar</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">t1_insar</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">t2_insar</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">t_insar</span> <span class="o">=</span> <span class="n">t1_insar</span> <span class="o">-</span> <span class="n">t2_insar</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'The test statistic for InSAR data is </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">t_insar</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
@@ -8639,7 +8612,7 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">t1_gnss</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">t1_gnss</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">t2_gnss</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">t_gnss</span> <span class="o">=</span> <span class="n">t1_gnss</span> <span class="o">-</span> <span class="n">t2_gnss</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'The test statistic for GNSS data is </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">t_gnss</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
@@ -8683,7 +8656,4 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
</div>
</main>
</body>
-<script type="application/vnd.jupyter.widget-state+json">
-{"state": {}, "version_major": 2, "version_minor": 0}
-</script>
</html>
diff --git a/synced_files/GA_1_4/Analysis.md b/synced_files/GA_1_4/Analysis.md
index 9a2903be..030ed71f 100644
--- a/synced_files/GA_1_4/Analysis.md
+++ b/synced_files/GA_1_4/Analysis.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.4: Modelling Road Deformation using Non-Linear Least-Squares
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -158,7 +148,6 @@ Confirm that the stochastic model is transferred properly by printing the approp
YOUR_CODE_HERE
```
-<!-- #region id="80a9b60f" -->
## Part 1: Set-up Non-Linear Model
In the model we fitted using BLUE, we only considered a linear velocity and a dependency on the groundwater level. However, when a heavy construction is built on 'soft' soil layers, compaction of the upper layers will be observed. This compaction is relatively large in the beginning but decreases when time passes. We can approach the behavior with a simplified model, assuming an exponential decay.
@@ -188,7 +177,7 @@ y_comp = compute_y(x, <auxiliary_arguments>)
```
Where `<auxiliary_arguments>` will be different in type and/or number on a case-by-case basis. Your code will generally be more compact and adaptable to other cases if the parameters are specified in a list, array or tuple.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -344,14 +333,13 @@ print(f'The redundancy (InSAR) is {m1["y"].shape[0] - n_2}')
print(f'The redundancy (GNSS) is {m2["y"].shape[0] - n_2}')
```
-<!-- #region id="b4633d84" -->
## Part 2: Gauss-Newton Iteration
This is an essential step of the algorithm for creating a non-linear least square model. We will use a function to easily repeat the analysis for InSAR and GNSS models. The `while`-loop and its contents are very similar to WS 1.4 earlier this week, with two notable exceptions:
1. the loop is "wrapped" in a function, and
2. the model dictionaries are used, which makes it easier to use existing functions (e.g., `BLUE()`) to find a solution
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -549,9 +537,8 @@ It is set up to visualize the model on each iteration. You can gain insight into
# plot_convergence_interactive(gauss_newton_iteration(initial_guess_alternative, m2))
```
-<!-- #region id="b9c40713" -->
## Part 3: Assessing Results
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -635,7 +622,6 @@ plot_residual(YOUR_CODE_HERE)
plot_residual_histogram(YOUR_CODE_HERE);
```
-<!-- #region id="af211a2b" -->
## Part 4: Hypothesis Test
In GA 1.3 and GA 1.4 we used two different models:
@@ -643,7 +629,7 @@ In GA 1.3 and GA 1.4 we used two different models:
* GA 1.4: A model with linear and power components
Now we are going to test which model fits the data better. We will do this with the Generalized Likelihood Ratio (GLR) test for both the GNSS and InSAR observations.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
diff --git a/synced_files/GA_1_4/Analysis_solution.html b/synced_files/GA_1_4/Analysis_solution.html
index 40856dfa..d2efea6f 100644
--- a/synced_files/GA_1_4/Analysis_solution.html
+++ b/synced_files/GA_1_4/Analysis_solution.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
- var scriptElement = document.createElement('script');
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
-
- var widgetState;
-
- // Fallback for older version:
- try {
- widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
-
- if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) {
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-js-widgets@*/dist/embed.js';
-
- }
- } catch(e) {}
-
- scriptElement.src = widgetRendererSrc;
- document.body.appendChild(scriptElement);
- }
-
- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7584,10 +7557,10 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interpolate</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -7638,10 +7611,10 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">m1_blue</span> <span class="o">=</span> <span class="n">load_pickle_file</span><span class="p">(</span><span class="s1">'m1_blue.pickle'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">m1_blue</span> <span class="o">=</span> <span class="n">load_pickle_file</span><span class="p">(</span><span class="s1">'m1_blue.pickle'</span><span class="p">)</span>
<span class="n">m2_blue</span> <span class="o">=</span> <span class="n">load_pickle_file</span><span class="p">(</span><span class="s1">'m2_blue.pickle'</span><span class="p">)</span>
</pre></div>
</div>
@@ -7659,55 +7632,21 @@ where $d$ is the displacement, $t$ is time and $\textrm{GW}$ is the groundwater
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=e02d5566">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=e02d5566">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_summary</span><span class="p">(</span><span class="n">m1_blue</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">model_summary</span><span class="p">(</span><span class="n">m1_blue</span><span class="p">)</span>
<span class="n">model_summary</span><span class="p">(</span><span class="n">m2_blue</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Summary of Model
-----------------
- Data type: InSAR
- Model type: BLUE
- Number of observations: 61
- Model parameters:
- X_hat_0 = 9.174 +/- 2.128 (c.o.v. 0.232)
- X_hat_1 = -0.024 +/- 0.001 (c.o.v. -0.050)
- X_hat_2 = 0.202 +/- 0.016 (c.o.v. 0.081)
-----------------
-
-Summary of Model
-----------------
- Data type: GNSS
- Model type: BLUE
- Number of observations: 730
- Model parameters:
- X_hat_0 = 1.181 +/- 4.647 (c.o.v. 3.936)
- X_hat_1 = -0.021 +/- 0.003 (c.o.v. -0.126)
- X_hat_2 = 0.160 +/- 0.035 (c.o.v. 0.220)
-----------------
-
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=78308267">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7719,55 +7658,21 @@ Summary of Model
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=cabfa723">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=cabfa723">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">m1_blue</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">m1_blue</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>data_type
-model_type
-times
-y
-days
-groundwater
-groundwater_data
-A
-std_Y
-Sigma_Y
-Sigma_X_hat
-X_hat
-Y_hat
-e_hat
-Sigma_Y_hat
-Sigma_e_hat
-std_e_hat
-k
-CI_Y
-CI_res
-CI_Y_hat
-alpha
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0b4e08a3">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7784,15 +7689,15 @@ alpha
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=73af3940">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=73af3940">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">x0_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x0'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">x0_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x0'</span><span class="p">)</span>
<span class="n">x1_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mf">0.1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x1'</span><span class="p">)</span>
<span class="n">x2_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x2'</span><span class="p">)</span>
@@ -7804,18 +7709,6 @@ alpha
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(FloatSlider(value=0.0, description='x0', max=10.0, min=-10.0), FloatSlider(value=0.0, de…</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=36948058">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7848,10 +7741,10 @@ alpha
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize_new_dict</span><span class="p">(</span><span class="n">d_old</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">initialize_new_dict</span><span class="p">(</span><span class="n">d_old</span><span class="p">):</span>
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">d</span><span class="p">[</span><span class="s1">'data_type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">d_old</span><span class="p">[</span><span class="s1">'data_type'</span><span class="p">]</span>
<span class="n">d</span><span class="p">[</span><span class="s1">'model_type'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'Non-Linear Least Squares'</span>
@@ -7888,15 +7781,15 @@ alpha
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=9aeda35f">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=9aeda35f">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="c1"># First uncomment and run this to quickly view the keys:</span>
@@ -7910,32 +7803,6 @@ alpha
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>[[4. 0. 0. ... 0. 0. 0.]
- [0. 4. 0. ... 0. 0. 0.]
- [0. 0. 4. ... 0. 0. 0.]
- ...
- [0. 0. 0. ... 4. 0. 0.]
- [0. 0. 0. ... 0. 4. 0.]
- [0. 0. 0. ... 0. 0. 4.]]
-[[225. 0. 0. ... 0. 0. 0.]
- [ 0. 225. 0. ... 0. 0. 0.]
- [ 0. 0. 225. ... 0. 0. 0.]
- ...
- [ 0. 0. 0. ... 225. 0. 0.]
- [ 0. 0. 0. ... 0. 225. 0.]
- [ 0. 0. 0. ... 0. 0. 225.]]
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=80a9b60f">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7986,10 +7853,10 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">compute_y</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">compute_y</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Model, q: ground surface displacement.</span>
<span class="sd"> Inputs:</span>
@@ -8063,15 +7930,15 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b20c1e61">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b20c1e61">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">x0_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">40</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x0'</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">x0_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">40</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x0'</span><span class="p">)</span>
<span class="n">x1_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">50</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x1'</span><span class="p">)</span>
<span class="n">x2_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x2'</span><span class="p">)</span>
<span class="n">x3_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'x3'</span><span class="p">)</span>
@@ -8084,18 +7951,6 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(FloatSlider(value=0.0, description='x0', max=40.0, min=-40.0, step=0.5), FloatSlider(val…</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c9bfc4d7">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8117,10 +7972,10 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># d_init = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># d_init = YOUR_CODE_HERE</span>
<span class="c1"># R_init = YOUR_CODE_HERE</span>
<span class="c1"># a_init = YOUR_CODE_HERE</span>
<span class="c1"># k_init = YOUR_CODE_HERE</span>
@@ -8158,10 +8013,10 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">jacobian</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">jacobian</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Compute Jacobian of the model.</span>
<span class="sd"> Model, q: ground surface displacement.</span>
@@ -8222,15 +8077,15 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=c57e5e26">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=c57e5e26">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="n">test_J</span> <span class="o">=</span> <span class="n">jacobian</span><span class="p">((</span><span class="n">d_init</span><span class="p">,</span> <span class="n">R_init</span><span class="p">,</span> <span class="n">a_init</span><span class="p">,</span> <span class="n">k_init</span><span class="p">),</span> <span class="n">m1</span><span class="p">)</span>
@@ -8247,28 +8102,6 @@ where $d$ is the displacement, $t$ is the time and $\textrm{GW}$ is the groundwa
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The first 5 rows of the Jacobian matrix (InSAR):
-[[ 1.000e+00 0.000e+00 0.000e+00 -1.097e+02]
- [ 1.000e+00 3.921e-02 3.203e-03 -1.067e+02]
- [ 1.000e+00 7.688e-02 6.154e-03 -1.038e+02]
- [ 1.000e+00 1.131e-01 8.869e-03 -1.065e+02]
- [ 1.000e+00 1.479e-01 1.136e-02 -1.173e+02]]
-
-The number of unknowns is 4
-The redundancy (InSAR) is 57
-The redundancy (GNSS) is 726
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b4633d84">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8333,10 +8166,10 @@ The redundancy (GNSS) is 726
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">gauss_newton_iteration</span><span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">gauss_newton_iteration</span><span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Use Gauss-Newton iteration to find non-linear parameters.</span>
<span class="sd"> </span>
<span class="sd"> Inputs:</span>
@@ -8469,15 +8302,15 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="n">m1</span> <span class="o">=</span> <span class="n">gauss_newton_iteration</span><span class="p">(</span><span class="n">initial_guess</span><span class="p">,</span> <span class="n">m1</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">m1</span> <span class="o">=</span> <span class="n">gauss_newton_iteration</span><span class="p">(</span><span class="n">initial_guess</span><span class="p">,</span> <span class="n">m1</span><span class="p">)</span>
<span class="n">m2</span> <span class="o">=</span> <span class="n">gauss_newton_iteration</span><span class="p">(</span><span class="n">initial_guess</span><span class="p">,</span> <span class="n">m2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1"> InSAR Reults for each iteration (Iterations completed ='</span><span class="p">,</span>
@@ -8492,41 +8325,6 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
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-<pre>
- InSAR Reults for each iteration (Iterations completed = 7 )
-[[ 9.000e+00 -2.500e+01 3.000e+02 1.500e-01]
- [ 1.273e+01 -1.982e+01 1.407e+02 1.726e-01]
- [ 1.296e+01 -2.162e+01 1.768e+02 1.719e-01]
- [ 1.297e+01 -2.192e+01 1.793e+02 1.713e-01]
- [ 1.297e+01 -2.192e+01 1.794e+02 1.713e-01]
- [ 1.297e+01 -2.192e+01 1.794e+02 1.713e-01]
- [ 1.297e+01 -2.192e+01 1.794e+02 1.713e-01]
- [ 1.297e+01 -2.192e+01 1.794e+02 1.713e-01]]
-
- GNSS Reults for each iteration (Iterations completed = 10 )
-[[ 9.000e+00 -2.500e+01 3.000e+02 1.500e-01]
- [ 3.747e+00 -1.777e+01 2.435e+02 1.425e-01]
- [ 3.895e+00 -1.812e+01 2.269e+02 1.418e-01]
- [ 3.939e+00 -1.815e+01 2.247e+02 1.416e-01]
- [ 3.945e+00 -1.815e+01 2.242e+02 1.415e-01]
- [ 3.946e+00 -1.815e+01 2.241e+02 1.415e-01]
- [ 3.946e+00 -1.815e+01 2.241e+02 1.415e-01]
- [ 3.946e+00 -1.815e+01 2.241e+02 1.415e-01]
- [ 3.946e+00 -1.815e+01 2.241e+02 1.415e-01]
- [ 3.946e+00 -1.815e+01 2.241e+02 1.415e-01]
- [ 3.946e+00 -1.815e+01 2.241e+02 1.415e-01]]
-</pre>
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@@ -8558,15 +8356,15 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># def plot_fit_iteration(d):</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># def plot_fit_iteration(d):</span>
<span class="c1"># """Plot value of each parameter, each iteration."""</span>
<span class="c1"># plt.figure(figsize = (15,4))</span>
<span class="c1"># plt.subplots_adjust(top = 2)</span>
@@ -8635,24 +8433,6 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
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"/>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b5439535">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8667,15 +8447,15 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=0701ba54">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=0701ba54">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">initial_guess_alternative</span> <span class="o">=</span> <span class="n">initial_guess</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">initial_guess_alternative</span> <span class="o">=</span> <span class="n">initial_guess</span>
<span class="nb">print</span><span class="p">(</span><span class="n">initial_guess_alternative</span><span class="p">)</span>
<span class="n">plot_convergence_interactive</span><span class="p">(</span><span class="n">gauss_newton_iteration</span><span class="p">(</span><span class="n">initial_guess_alternative</span><span class="p">,</span> <span class="n">m1</span><span class="p">))</span>
<span class="n">plot_convergence_interactive</span><span class="p">(</span><span class="n">gauss_newton_iteration</span><span class="p">(</span><span class="n">initial_guess_alternative</span><span class="p">,</span> <span class="n">m2</span><span class="p">))</span>
@@ -8684,43 +8464,6 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>(9, -25, 300, 0.15)
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(IntSlider(value=0, description='iteration', max=7), Output()), _dom_classes=('widget-int…</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(IntSlider(value=0, description='iteration', max=7), Output()), _dom_classes=('widget-int…</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(IntSlider(value=0, description='iteration', max=10), Output()), _dom_classes=('widget-in…</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(IntSlider(value=0, description='iteration', max=10), Output()), _dom_classes=('widget-in…</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b9c40713">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8750,15 +8493,15 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=5af9e513">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=5af9e513">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># def show_std(Sigma_X_hat, data_type):</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># def show_std(Sigma_X_hat, data_type):</span>
<span class="c1"># print ('The standard deviation for',</span>
<span class="c1"># data_type + '-offset is',</span>
<span class="c1"># YOUR_CODE_HERE, 'UNITS')</span>
@@ -8817,64 +8560,6 @@ print(f'On iteration {iteration} X_hat_i is:', X_hat_i[iteration+1,:])
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Covariance matrix of estimated parameters (InSAR):
-[[ 4.771e+00 -5.522e-01 -7.605e+00 3.183e-02]
- [-5.522e-01 1.017e+00 4.726e+00 2.657e-03]
- [-7.605e+00 4.726e+00 4.461e+02 6.328e-02]
- [ 3.183e-02 2.657e-03 6.328e-02 2.771e-04]]
-
-The standard deviation for InSAR-offset is 2.18 mm
-The standard deviation for InSAR-R is 1.01 mm
-The standard deviation for InSAR-a is 21.12 days
-The standard deviation for InSAR-the ground water factor 0.017 [-]
-
-Summary of Model
-----------------
- Data type: InSAR
- Model type: Non-Linear Least Squares
- Number of observations: 61
- Model parameters:
- X_hat_0 = 12.971 +/- 2.184 (c.o.v. 0.168)
- X_hat_1 = -21.916 +/- 1.009 (c.o.v. -0.046)
- X_hat_2 = 179.353 +/- 21.120 (c.o.v. 0.118)
- X_hat_3 = 0.171 +/- 0.017 (c.o.v. 0.097)
-----------------
-
-Covariance matrix of estimated parameters (GNSS):
-[[ 2.306e+01 -2.474e+00 -7.557e+01 1.514e-01]
- [-2.474e+00 4.674e+00 2.167e+00 6.718e-03]
- [-7.557e+01 2.167e+00 6.317e+03 4.459e-01]
- [ 1.514e-01 6.718e-03 4.459e-01 1.289e-03]]
-
-The standard deviation for GNSS-offset is 4.8 mm
-The standard deviation for GNSS-R is 2.16 mm
-The standard deviation for GNSS-a is 79.48 days
-The standard deviation for GNSS-the ground water factor 0.036 [-]
-
-Summary of Model
-----------------
- Data type: GNSS
- Model type: Non-Linear Least Squares
- Number of observations: 730
- Model parameters:
- X_hat_0 = 3.946 +/- 4.802 (c.o.v. 1.217)
- X_hat_1 = -18.147 +/- 2.162 (c.o.v. -0.119)
- X_hat_2 = 224.095 +/- 79.482 (c.o.v. 0.355)
- X_hat_3 = 0.142 +/- 0.036 (c.o.v. 0.254)
-----------------
-
-</pre>
-</div>
-</div>
-</div>
-</div>
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@@ -8908,50 +8593,29 @@ Summary of Model
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="n">get_CI</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="n">get_CI</span><span class="p">)</span>
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-<pre>Help on function get_CI in module functions:
-
-get_CI(d, alpha)
- Compute the confidence intervals.
-
- Uses dict as input/output:
- - inputs defined from existing values in dict
- - outputs defined as new values in dict
-
-</pre>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=pDJfjxgs6veD">
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># m1['Y_hat'] = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># m1['Y_hat'] = YOUR_CODE_HERE</span>
<span class="c1"># m1 = get_CI(YOUR_CODE_HERE)</span>
<span class="c1"># plot_model(YOUR_CODE_HERE)</span>
<span class="c1"># plot_residual(YOUR_CODE_HERE)</span>
@@ -8968,47 +8632,15 @@ get_CI(d, alpha)
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-<pre>The mean value of the InSAR residuals is 0.0 mm
-The standard deviation of the InSAR residuals is 1.852 mm
-</pre>
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"/>
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"/>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=ngZMBQM87QMr">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=ngZMBQM87QMr">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># m1['Y_hat'] = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># m1['Y_hat'] = YOUR_CODE_HERE</span>
<span class="c1"># m1 = get_CI(YOUR_CODE_HERE)</span>
<span class="c1"># plot_model(YOUR_CODE_HERE)</span>
<span class="c1"># plot_residual(YOUR_CODE_HERE)</span>
@@ -9025,38 +8657,6 @@ The standard deviation of the InSAR residuals is 1.852 mm
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The mean value of the GNSS residuals is -0.0 mm
-The standard deviation of the GNSS residuals is 15.315 mm
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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-<img alt="No description has been provided for this image" class="" 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o0ahfj4eCQlJekDl9UHkNepX7++wf+mWgkBwGOPPYbp06djzZo1mD9/PtatW4ehQ4catHpbv349mjRpgs2bNxusp7S01NwuqpVuW1UqldFrV69ehVQqNWp5Z4na9pspO3bsQGFhIbZt22bQ6uno0aNWf79OvXr19L9rVaaWuZOl+ahevXo15l1r9ejRA61atcJrr72Gfv36GQWjdarmk4YNGxq8dvXqVYP0mUtPdna2wUQP1h47RERE3ogt2YiIiDzYSy+9BLVajaeeesqqSQaqmzBhAtq0aYMXX3zRaDbHv//+22gZoB08/o8//kBwcDDq1KkDwHRwB4C++6OpWU6rmjNnDm7evImZM2cavVZYWIhp06YhODjYYGB+nQ0bNhj8v3HjRgDQz8RZVWBgIHr16oW33noLgLZbIwA89NBDOHfuHOrVq2fU0qlLly4GQYWa1K1bF0OHDsXatWvx2WefITs726irqEQigUwmMwiwZWdnG80uqktv9ZZfprRq1QoNGjTAxo0bDQaaLywsxNatW/UzjtrK3H4zRbddupZxgLar76pVq2z+/j59+uDEiRM4duyYwXLdb+0M1Vv2VX/N1HJL81GfPn0AmM+7tnj55ZcxZMgQzJgxw+x7+vbtCwAGk0oAwJEjR3Dq1Cncf//9AIB77rkHQUFBRuk7ePCgUQtaRx07REREnowt2YiIiDxYjx498N577+GZZ55BQkICJk2ahHbt2kEqlUKlUmHr1q0AgPDw8BrX4+fnhwULFmDYsGEAtGNh6axbtw4ZGRkYNWoUunbtCoVCgcuXL+PDDz/EiRMn8Oqrr0ImkwEABgwYgIYNG2LIkCFo3bo1NBoNjh49iiVLliA0NNRk19KqHnvsMWRlZWHx4sW4cOECJkyYgOjoaJw5cwZvv/02zp07h40bN6Jp06YGn5PJZFiyZAkKCgrQtWtXHDx4EPPmzcOgQYNw7733AgBeffVVXL58Gffffz8aNmyIW7duYfny5QgICECvXr0AaLvObt26FT179sTzzz+Pjh07QqPR4OLFi9izZw9mzJiBbt26WfTbTJgwAZs3b8bTTz+Nhg0b4oEHHjB4/aGHHsK2bdswZcoU/POf/8SlS5fw+uuvQ6lU4o8//jB4b4cOHbB//37s2rULSqUSYWFhaNWqldF3SqVSLFy4EKNHj8ZDDz2E1NRUlJaWYtGiRbh16xbefPNNi9JelSX7zZR+/fpBJpPhsccewwsvvICSkhKsWLHCqLuqNZ577jl89NFHePDBBzFv3jxER0djw4YN+m65ztChQwcAwPLlyzF+/HgEBASgVatWCAsLQ4cOHfDxxx9j8+bNaNq0KYKCgtChQweL81H//v3Rs2dPvPDCCygsLESXLl3www8/YN26dTand8yYMRgzZkyN72nVqhUmTZqEd955B1KpFIMGDcKFCxfwyiuvIC4uTh/Erlu3LmbOnIl58+bhySefxCOPPIJLly4hPT3dqLuoI48dIiIij+XOWReIiIjIMY4ePSo88cQTQpMmTYTAwEAhKChIaN68uTBu3Djhm2++MXhv1dlFq+vevbsAwGB20ZMnTwozZswQunTpIkRGRgr+/v5C3bp1hV69egnr1q0z+PzmzZuFUaNGCS1atBBCQ0OFgIAAoVGjRsLYsWOFkydPWrw9X3zxhTB48GChXr16QkBAgNCgQQNh7NixwokTJ4zeO378eCEkJEQ4fvy40Lt3b0EulwsRERHC5MmThYKCAv37PvvsM2HQoEFCgwYNBJlMJkRFRQmDBw8WvvvuO4P1FRQUCC+//LLQqlUrQSaTCQqFQujQoYPw/PPPC9nZ2fr3ARCmTp1qdhvUarUQFxcnABDmzJlj8j1vvvmmEB8fLwQGBgpt2rQRVq1apf99qjp69KjQo0cPITg42GBWx+qzi+rs2LFD6NatmxAUFCSEhIQI999/v/DDDz8YvMdcPtDNwKmbTdPS/WbKrl27hLvuuksICgoSGjRoIMyaNUs/o2bVNPfq1Uto166d0efHjx8vNG7c2GDZyZMnhX79+glBQUFCRESEMHHiROG///2vVbOLVp8BU8fU7KKCIAgvvfSSEBsbK0ilUoPvuXDhgtC/f38hLCxMAGCQVkvz0a1bt4QJEyYIderUEYKDg4V+/foJp0+ftnp20ZpUn11UELT586233hJatmwpBAQECPXr1xfGjBkjXLp0yeB9Go1GeOONN4S4uDhBJpMJHTt2FHbt2iX06tXLaGZVS7eZs4sSEZG3kghClb4EREREREREREREZDWOyUZERERERERERGQnBtmIiIiIiIiIiIjsxCAbERERERERERGRnRhkIyIiIiIiIiIishODbERERERERERERHZikI2IiIiIiIiIiMhO/u5OgNhoNBpcvXoVYWFhkEgk7k4OERERERERERG5iSAIuH37NmJjYyGV1txWjUG2aq5evYq4uDh3J4OIiIiIiIiIiETi0qVLaNiwYY3vYZCtmrCwMADanRceHu7m1BARERERERERkbvk5+cjLi5OHy+qCYNs1ei6iIaHhzPIRkREREREREREFg0pxokPiIiIiIiIiIiI7MQgGxERERERERERkZ0YZCMiIiIiIiIiIrITx2QjIiIiIiIichO1Wo3y8nJ3J4PIZ/n5+cHf39+iMddqwyAbERERERERkRsUFBTg8uXLEATB3Ukh8mnBwcFQKpWQyWR2rYdBNiIiIiIiIiIXU6vVuHz5MoKDgxEZGemQVjREZB1BEFBWVobc3FycP38eLVq0gFRq+8hqDLIRERERERERuVh5eTkEQUBkZCTkcrm7k0Pks+RyOQICAvDXX3+hrKwMQUFBNq+LEx8QERERERERuQlbsBG5nz2t1wzW45C1EBERERERERER+TAG2YiIiIiIiIiIiOzEIBtZTaVSIT09HSqVyt1JISIiIiIiIpGKj4/HsmXL3J0Mh9m/fz8kEglu3brl7qSQSDHIRlZTqVSYO3cug2xEREREREQi4I6GEJcuXcLEiRMRGxsLmUyGxo0b49lnn8X169ddlgZn6t27N5577jmDZd27d4dKpYJCoXBPokj0GGQjIiIiIiIi8mCubgjx559/okuXLvj999+xadMmnD17Fh988AG++eYbJCUl4caNGy5JR3VqtRoajcZp65fJZIiJieFkFWQWg2xkEZVKhaysLP0DgMH/bNVGRERERETkG6ZOnQqZTIY9e/agV69eaNSoEQYNGoT//e9/uHLlCubMmaN/7+3btzFq1CiEhoYiNjYW77zzjsG60tPT0ahRIwQGBiI2NhbTpk3Tv1ZWVoYXXngBDRo0QEhICLp164b9+/frX1+zZg3q1KmDzz77DG3btkVgYCBWrVqFoKAgoy6d06ZNQ69evQAA169fx2OPPYaGDRsiODgYHTp0wKZNm/Tvffzxx3HgwAEsX74cEokEEokEFy5cMNlddOvWrWjXrh0CAwMRHx+PJUuWGHxvfHw8FixYgAkTJiAsLAyNGjXCypUrDbbx6aefhlKpRFBQEOLj4/HGG29Y/ZuQODDIRhbJyMhAYmIiEhMTkZKSAgBISUnRL8vIyHBzComIiIiIiHyHuxpC3LhxA1999RWmTJkCuVxu8FpMTAxGjx6NzZs3QxAEAMCiRYvQsWNHZGVl4aWXXsLzzz+Pr7/+GgDw6aef4u2330ZGRgb++OMP7NixAx06dNCv74knnsAPP/yAjz/+GMePH8cjjzyCgQMH4o8//tC/p6ioCG+88QY+/PBDnDhxAmPGjEGdOnWwdetW/XvUajU++eQTjB49GgBQUlKCxMREfPbZZ/jtt98wadIkjB07FocPHwYALF++HElJSUhJSYFKpYJKpUJcXJzRvsjMzMSIESPw6KOP4tdff0V6ejpeeeUVrFmzxuB9S5YsQZcuXfDLL79gypQpmDx5Mk6fPg0A+Ne//oWdO3fik08+wZkzZ7B+/XrEx8fb+OuQu/m7OwHkGVJTU5GcnAxAe+JOSUnBqlWrkJCQAABQKpXuTB4REREREZFPycjIwNy5cw2W6RpEAEBaWhrS09Md/r1//PEHBEFAmzZtTL7epk0b3Lx5E7m5uQCAHj164MUXXwQAtGzZEj/88APefvtt9OvXDxcvXkRMTAweeOABBAQEoFGjRrj77rsBAOfOncOmTZtw+fJlxMbGAgBmzpyJ3bt3Y/Xq1ViwYAEAoLy8HO+//z7uuusufRpGjhyJjRs3YuLEiQCAb775Bjdv3sQjjzwCAGjQoAFmzpypf/8zzzyD3bt3Y8uWLejWrRsUCgVkMhmCg4MRExNjdl8sXboU999/P1555RX99p08eRKLFi3C448/rn/f4MGDMWXKFADA7Nmz8fbbb2P//v1o3bo1Ll68iBYtWuDee++FRCJB48aNLfwlSIwYZCOLKJVKo0BaQkKCPshGREREREREriPWhhC6Fmy6ccuSkpIMXk9KStLPOPrII49g2bJlaNq0KQYOHIjBgwdjyJAh8Pf3R1ZWFgRBQMuWLQ0+X1painr16un/l8lk6Nixo8F7Ro8ejaSkJFy9ehWxsbHYsGEDBg8ejLp16wLQtmx78803sXnzZly5cgWlpaUoLS1FSEiIVdt66tQpPPzwwwbLevTogWXLlkGtVsPPzw8ADNInkUgQExODnJwcANquqf369UOrVq0wcOBAPPTQQ+jfv79V6SDxYJCNiIiIiIiIyMO4qyFE8+bNIZFIcPLkSQwdOtTo9dOnT6Nu3bqoX7++2XXoAnBxcXE4c+YMvv76a/zvf//DlClTsGjRIhw4cAAajQZ+fn7IzMzUB6t0QkND9c/lcrnRRAR33303mjVrho8//hiTJ0/G9u3bsXr1av3rS5Yswdtvv41ly5ahQ4cOCAkJwXPPPYeysjKr9oUgCEbfrQsyVhUQEGC0/boJGhISEnD+/Hl8+eWX+N///ocRI0bggQcewKeffmpVWkgcGGQjqymVSqSlpbGLKBERERERkY+pV68e+vXrh/fffx/PP/+8wbhs2dnZ2LBhA8aNG6cPPv34448Gn//xxx/RunVr/f9yuRzJyclITk7G1KlT0bp1a/z666/o3Lkz1Go1cnJycN9991mdzlGjRmHDhg1o2LAhpFIpHnzwQf1r3333HR5++GGMGTMGAKDRaPDHH38YdIGVyWRQq9U1fkfbtm3x/fffGyw7ePAgWrZsaRQYrEl4eDhGjhyJkSNH4p///CcGDhyIGzduICIiwuJ1kDgwyEZWUyqVTunbT0RERERERNZzdUOId999F927d8eAAQMwb948NGnSBCdOnMCsWbPQoEEDzJ8/X//eH374AQsXLsTQoUPx9ddfY8uWLfj8888BaGcHVavV6NatG4KDg7Fu3TrI5XI0btwY9erVw+jRozFu3DgsWbIEnTt3xrVr17B371506NABgwcPrjGNo0ePxty5czF//nz885//RFBQkP615s2bY+vWrTh48CDq1q2LpUuXIjs72yDIFh8fj8OHD+PChQsIDQ01GfCaMWMGunbtitdffx0jR47EoUOH8O677+L999+3eF++/fbbUCqV6NSpE6RSKbZs2YKYmBjUqVPH4nWQeHB2USIiIiIiIiIPpmsI4aogW4sWLfDzzz+jWbNmGDlyJJo1a4ZJkyahT58+OHTokEFAasaMGcjMzETnzp3x+uuvY8mSJRgwYAAAoE6dOli1ahV69OiBjh074ptvvsGuXbv0Y66tXr0a48aNw4wZM9CqVSskJyfj8OHDJmf6NJXGrl274vjx4/pZRXVeeeUVJCQkYMCAAejduzdiYmKMur7OnDkTfn5+aNu2LSIjI3Hx4kWj70hISMAnn3yCjz/+GO3bt8err76K1157zWDSg9qEhobirbfeQpcuXdC1a1dcuHABX3zxBaRShms8kUQw1WHYh+Xn50OhUCAvLw/h4eHuTg4RERERERF5oZKSEpw/fx5NmjQxaGVFRK5X0/FoTZyIoVEiIiIiIiIiIiI7MchGRERERERERERkJwbZiIiIiIiIiIiI7MQgGxERERERERERkZ0YZCMiIiIiIiIiIrITg2xERERERERERER2YpCNiIiIiIiIiIjITgyyERERERERERER2YlBNiIiIiIiIiIiIjsxyEZEREREREREduvduzeee+45l33fmjVrUKdOHZd9X00uXLgAiUSCo0ePWvwZV+8vcj4G2YgAqFQqpKenQ6VSuTspREREREREovX4449DIpEYPc6ePYtt27bh9ddf1783Pj4ey5YtM/i8mAJjRI7GIBsRtEG2uXPnMshGRERERERUi4EDB0KlUhk8mjRpgoiICISFhbk7eURuwyAbERERERERkZsJgoCisgq3PARBsCqtgYGBiImJMXj4+fkZdH/s3bs3/vrrLzz//PP61m779+/HE088gby8PP2y9PR0AEBZWRleeOEFNGjQACEhIejWrRv2799v8L1r1qxBo0aNEBwcjGHDhuH69es1plPXhfOTTz7BfffdB7lcjq5du+L333/HkSNH0KVLF4SGhmLgwIHIzc3Vf06j0eC1115Dw4YNERgYiE6dOmH37t0G6/7pp5/QuXNnBAUFoUuXLvjll1+Mvv/kyZMYPHgwQkNDER0djbFjx+LatWtW7WvyLP7uTgB5F5VKhYyMDKSmpkKpVLo7OTXS3XEBgKysLIO/AKBUKkW/DURERERE5B2Ky9Vo++pXbvnuk68NQLDMseGBbdu24a677sKkSZOQkpICAIiIiMCyZcvw6quv4syZMwCA0NBQAMATTzyBCxcu4OOPP0ZsbCy2b9+OgQMH4tdff0WLFi1w+PBhTJgwAQsWLMDw4cOxe/dupKWlWZSWtLQ0LFu2DI0aNcKECRPw2GOPITw8HMuXL0dwcDBGjBiBV199FStWrAAALF++HEuWLEFGRgY6d+6Mjz76CMnJyThx4gRatGiBwsJCPPTQQ+jbty/Wr1+P8+fP49lnnzX4TpVKhV69eiElJQVLly5FcXExZs+ejREjRmDv3r2O2s0kMgyykUPpul0mJyeLPkCVkZGBuXPnGizTnfwB7YlYd1elNp4UXCQiIiIiIrLHZ599pg+OAcCgQYOwZcsWg/dERETAz88PYWFhiImJ0S9XKBSQSCQGy86dO4dNmzbh8uXLiI2NBQDMnDkTu3fvxurVq7FgwQIsX74cAwYMwIsvvggAaNmyJQ4ePGjUwsyUmTNnYsCAAQCAZ599Fo899hi++eYb9OjRAwAwceJErFmzRv/+xYsXY/bs2Xj00UcBAG+99Rb27duHZcuW4b333sOGDRugVqvx0UcfITg4GO3atcPly5cxefJk/TpWrFiBhIQELFiwQL/so48+QlxcHH7//Xe0bNmy1nST52GQjXxWamoqkpOTAWhbsKWkpGDVqlVISEgAAKuCZZ4UXCQiIiIiIvGRB/jh5GsD3Pbd1ujTp4++1RcAhISE2PX9WVlZEATBKPBUWlqKevXqAQBOnTqFYcOGGbyelJRkUZCtY8eO+ufR0dEAgA4dOhgsy8nJAQDk5+fj6tWr+gCcTo8ePXDs2DF9Wu666y4EBwcbpKWqzMxM7Nu3zyAYqXPu3DkG2bwUg2xkN0/tdmkqXQkJCfogGxERERERkatIJBKHd9l0lpCQEDRv3txh69NoNPDz80NmZib8/AwDfroglbXjxlUVEBCgfy6RSEwu02g0Bp/RvU9HEAT9MkvSotFoMGTIELz11ltGr4mxfkyO4RlHMImaI7tdehJPDS4SERERERG5gkwmg1qtrnVZ586doVarkZOTg/vuu8/kutq2bYsff/zRYFn1/x0hPDwcsbGx+P7779GzZ0/98oMHD+Luu+/Wp2XdunUoLi6GXC43mZaEhARs3boV8fHx8Pdn6MVXcHZRsltqaioyMzORmZmJVatWAQBWrVqlX5aamurmFNZOqVQiLS3NqqBYRkYGEhMTkZiYqA8qpqSk6JdlZGQ4K7lERERERESiFx8fj2+//RZXrlzRz6oZHx+PgoICfPPNN7h27RqKiorQsmVLjB49GuPGjcO2bdtw/vx5HDlyBG+99Ra++OILAMC0adOwe/duLFy4EL///jveffddi7qK2mLWrFl46623sHnzZpw5cwYvvvgijh49qp/cYNSoUZBKpZg4cSJOnjyJL774AosXLzZYx9SpU3Hjxg089thj+Omnn/Dnn39iz549mDBhglGQkbyHxwTZ3njjDXTt2hVhYWGIiorC0KFD9bOR6AiCgPT0dMTGxkIul6N37944ceKEm1LsO5RKpb6bpa6rZdX/PaE1l1KpRHp6ulVp9YbgIhERERERkbO89tpruHDhApo1a4bIyEgAQPfu3fHUU09h5MiRiIyMxMKFCwEAq1evxrhx4zBjxgy0atUKycnJOHz4MOLi4gAA99xzDz788EO888476NSpE/bs2YOXX37ZKemeNm0aZsyYgRkzZqBDhw7YvXs3du7ciRYtWgDQdmHdtWsXTp48ic6dO2POnDlG3UJjY2Pxww8/QK1WY8CAAWjfvj2effZZKBQKSKUeE4ohK0kEezo2u9DAgQPx6KOPomvXrqioqMCcOXPw66+/4uTJk/pBFt966y3Mnz8fa9asQcuWLTFv3jx8++23OHPmDMLCwiz6nvz8fCgUCuTl5SE8PNyZm+SVsrKykJiYiMzMTJ8a28xXt5uIiIiIiGxTUlKC8+fPo0mTJggKCnJ3coh8Wk3HozVxIo/pGFy9Gejq1asRFRWFzMxM9OzZE4IgYNmyZZgzZw6GDx8OAPjPf/6D6OhobNy4ka2KXMSWbpdERERERERERJ7OY9so5uXlAQAiIiIAAOfPn0d2djb69++vf09gYCB69eqFgwcPml1PaWkp8vPzDR5kO1u6XXoDBheJiIiIiIiIfJtHBtkEQcD06dNx7733on379gCA7OxsAEB0dLTBe6Ojo/WvmfLGG29AoVDoH7r+3kTW8NXgIhERERERERFpeWSQ7emnn8bx48exadMmo9ckEonB/4IgGC2r6qWXXkJeXp7+cenSJYenl4iIiIiIiIiIvJvHBdmeeeYZ7Ny5E/v27UPDhg31y2NiYgDAqNVaTk6OUeu2qgIDAxEeHm7wIPuoVCqkp6dDpVK5OylERERERERERC7hMUE2QRDw9NNPY9u2bdi7dy+aNGli8HqTJk0QExODr7/+Wr+srKwMBw4cQPfu3V2dXJ+mUqkwd+5cBtmIiIiIiIiIyGd4zOyiU6dOxcaNG/Hf//4XYWFh+hZrCoUCcrkcEokEzz33HBYsWIAWLVqgRYsWWLBgAYKDgzFq1Cg3p56IiIiIiIiIiLyZxwTZVqxYAQDo3bu3wfLVq1fj8ccfBwC88MILKC4uxpQpU3Dz5k1069YNe/bsQVhYmItT63tUKpW+5VpWVpbBX0A7MQAnBSAiIiIiIiIibyURBEFwdyLEJD8/HwqFAnl5eRyfzQrp6emYO3eu2dfT0tKQnp7uugQRERERERGJWElJCc6fP48mTZogKCjI3ckh8mk1HY/WxIk8piUbiVtqaiqSk5MBaFuwpaSkYNWqVUhISAAAtmIjIiIiIiKyxNmVrv2+5pNc+30+5ocffsBTTz2F06dP48EHH8Rzzz2HPn364ObNm6hTp47Jz6xZswbPPfccbt265dK0itX+/ftr3Wdi4TETH5C4KZVKJCQk6B8ADP5nkI2IiIiIiMjz3b59G8899xwaN24MuVyO7t2748iRI0bvO3XqFJKTk6FQKBAWFoZ77rkHFy9erHHdW7duRdu2bREYGIi2bdti+/btBq9v2LABcXFxiIiIwKxZswxeu3DhAlq2bIn8/HyLtmPr1q3o3bs3FAoFQkND0bFjR7z22mu4ceOGRZ+31PTp09GpUyecP38ea9asQffu3aFSqaBQKBz6Pe7w+OOPQyKRQCKRwN/fH40aNcLkyZNx8+ZNh36PJ+0zBtmIiIiIiIiIyCJPPvkkvv76a6xbtw6//vor+vfvjwceeABXrlzRv+fcuXO499570bp1a+zfvx/Hjh3DK6+8UmO32EOHDmHkyJEYO3Ysjh07hrFjx2LEiBE4fPgwAODatWt48sknsXjxYnz11Vf4z3/+g88//1z/+cmTJ+PNN9+0aNinOXPmYOTIkejatSu+/PJL/Pbbb1iyZAmOHTuGdevW2bF3jJ07dw59+/ZFw4YNUadOHchkMsTExEAikTj0e9xl4MCBUKlUuHDhAj788EPs2rULU6ZMceh3eNI+Y5CNHE6pVCItLY2t14iIiIiIiLxIcXExtm7dioULF6Jnz55o3rw50tPT0aRJE/1khYA2iDV48GAsXLgQnTt3RtOmTfHggw8iKirK7LqXLVuGfv364aWXXkLr1q3x0ksv4f7778eyZcsAAH/++ScUCoU+ONanTx+cPHkSALBx40bIZDIMHz681m346aefsGDBAixZsgSLFi1C9+7dER8fj379+mHr1q0YP368/r0rVqxAs2bNIJPJ0KpVK6MAnEQiwYcffohhw4YhODgYLVq0wM6dOwFoW9ZJJBJcv34dEyZMgEQiwZo1a7B//35IJBKDrqBr1qxBo0aNEBwcjGHDhuH69etG6d61axcSExMRFBSEpk2bYu7cuaioqLAoLTonTpzAgw8+iPDwcISFheG+++7DuXPn9K+vXr0abdq0QVBQEFq3bo3333+/1v0ZGBiImJgYNGzYEP3798fIkSOxZ88eg/fUtt6DBw+iU6dOCAoKQpcuXbBjxw5IJBIcPXoUAEzus61bt6Jdu3YIDAxEfHw8lixZYrDO+Ph4LFiwABMmTEBYWBgaNWqElSud3xWbQTZyOKVSifT0dAbZiIhqoVKpkJ6erp+dmYiIiEjMKioqoFarjVqkyeVyfP/99wAAjUaDzz//HC1btsSAAQMQFRWFbt26YceOHTWu+9ChQ+jfv7/BsgEDBuDgwYMAgBYtWqCoqAi//PILbty4gSNHjqBjx464ceMGXn31Vbz77rsWbcOGDRsQGhpqtrWVbsyv7du349lnn8WMGTPw22+/ITU1FU888QT27dtn8P65c+dixIgROH78OAYPHozRo0fjxo0biIuLg0qlQnh4OJYtWwaVSoWRI0cafd/hw4cxYcIETJkyBUePHkWfPn0wb948g/d89dVXGDNmDKZNm4aTJ08iIyMDa9aswfz58y1KCwBcuXIFPXv2RFBQEPbu3YvMzExMmDBBH6hbtWoV5syZg/nz5+PUqVNYsGABXnnlFfznP/+xaL8C2kDo7t27ERAQoF9W23pv376NIUOGoEOHDsjKysLrr7+O2bNn1/g9mZmZGDFiBB599FH8+uuvSE9PxyuvvII1a9YYvG/JkiXo0qULfvnlF0yZMgWTJ0/G6dOnLd4emwhkIC8vTwAg5OXluTspRETk5TIzMwUAQmZmpruTQkRERC5WXFwsnDx5UiguLjZ84Y8M1z6slJSUJPTq1Uu4cuWKUFFRIaxbt06QSCRCy5YtBUEQBJVKJQAQgoODhaVLlwq//PKL8MYbbwgSiUTYv3+/2fUGBAQIGzZsMFi2YcMGQSaT6f/ftm2b0L59e6FZs2ZCWlqaIAiC8MQTTwjLli0TDhw4IHTq1Elo166dsGXLFrPfM2jQIKFjx461bmf37t2FlJQUg2WPPPKIMHjwYP3/AISXX35Z/39BQYEgkUiEL7/8Ur9MoVAIq1ev1v+/b98+AYBw8+ZNQRAE4bHHHhMGDhxo8D0jR44UFAqF/v/77rtPWLBggcF71q1bJyiVSovT8tJLLwlNmjQRysrKTG5vXFycsHHjRoNlr7/+upCUlGTy/YIgCOPHjxf8/PyEkJAQISgoSAAgABCWLl1q8XpXrFgh1KtXz+A4WLVqlQBA+OWXXwRBMN5no0aNEvr162ewzlmzZglt27bV/9+4cWNhzJgx+v81Go0QFRUlrFixwuS2mD0eBeviRJxdlIiIiIiIiIgssm7dOkyYMAENGjSAn58fEhISMGrUKGRlZQHQtmQDgIcffhjPP/88AKBTp044ePAgPvjgA/Tq1cvsuquPuSUIgsGyYcOGYdiwYfr/9+/fj19//RXvvvsumjdvjk2bNiEmJgZ33303evbsabJ7avV1mnPq1ClMmmQ482qPHj2wfPlyg2UdO3bUPw8JCUFYWBhycnJqXX/V76m6TQCQlJSE3bt36//PzMzEkSNHDFquqdVqlJSUoKioCMHBwbWm5ejRo7jvvvsMWpnp5Obm4tKlS5g4cSJSUlL0yysqKmqdbKBPnz5YsWIFioqK8OGHH+L333/HM888Y/F6z5w5g44dOxq0jrz77rtr/M5Tp07h4YcfNljWo0cPLFu2DGq1Gn5+fkb7QyKRICYmxqrfxhYMshEREbmQSqXSdw/VFUZ1fwFtl3t2tyciIiKxatasGQ4cOIDCwkLk5+dDqVRi5MiRaNKkCQCgfv368Pf3R9u2bQ0+16ZNG32XUlNiYmKQnZ1tsCwnJwfR0dEm319aWoopU6Zg/fr1OHv2LCoqKvQBvJYtW+Lw4cMYMmSI0edatmyJ77//HuXl5SYDTlXVFvQDYLQOiUSiDzRaQhCEWt+j0Wgwd+5ck2POVQ1O1ZQWuVxe4/oBbdfObt26GbymC1iZExISgubNmwMA/vWvf6FPnz6YO3cuXn/9dYvWa2qf1rZPLP2Mvb+NLTgmGxERkQtlZGQgMTERiYmJ+jt6KSkp+mUZGRluTiERERFR7UJCQqBUKnHz5k189dVX+pZFMpkMXbt2xZkzZwze//vvv6Nx48Zm15eUlISvv/7aYNmePXvQvXt3k+9//fXXMWjQICQkJECtVhtMAlBeXg61Wm3yc6NGjUJBQYHZQf11g+ubCgoePHgQbdq0MbsNtmjbti1+/PFHg2XV/09ISMCZM2fQvHlzo4dUallYp2PHjvjuu+9QXl5u9Fp0dDQaNGiAP//802j9uuCppdLS0rB48WJcvXrVovW2bt0ax48fR2lpqX4dP//8c43f0bZtW5O/TcuWLWsNCjobW7IRERG5UGpqKpKTkwFoW7ClpKRg1apVSEhIAAC2YiMiIiJR++qrryAIAlq1aoWzZ89i1qxZaNWqFZ544gn9e2bNmoWRI0eiZ8+e6NOnD3bv3o1du3Zh//79+veMGzcODRo0wBtvvAEAePbZZ9GzZ0+89dZbePjhh/Hf//4X//vf/0y2fjtx4gQ2b96sn32ydevWkEql+Pe//42YmBicPn0aXbt2NZn+bt264YUXXsCMGTNw5coVDBs2DLGxsTh79iw++OAD3HvvvXj22Wcxa9YsjBgxAgkJCbj//vuxa9cubNu2Df/73/8ctzMBTJs2Dd27d8fChQsxdOhQ7Nmzx6CrKAC8+uqreOihhxAXF4dHHnkEUqkUx48fx6+//mo0SYI5Tz/9NN555x08+uijeOmll6BQKPDjjz/i7rvvRqtWrZCeno5p06YhPDwcgwYNQmlpKX7++WfcvHkT06dPt3h7evfujXbt2mHBggV49913a13vqFGjMGfOHEyaNAkvvvgiLl68iMWLFwMwbkmoM2PGDHTt2hWvv/46Ro4ciUOHDuHdd9+1aDZUp6t11DYfw4kPiIjIVTjxARERke+qaaB1Mdu8ebPQtGlTQSaTCTExMcLUqVOFW7duGb3v3//+t9C8eXMhKChIuOuuu4QdO3YYvN6rVy9h/PjxBsu2bNkitGrVSggICBBat24tbN261Wi9Go1G6N69u7Br1y6D5bt27RIaNWokREdHC6tWrbJoO3r27CmEhYUJISEhQseOHYXXXntNP7i+IAjC+++/LzRt2lQICAgQWrZsKaxdu9ZgHQCE7du3GyyrPtFBbRMfCIJ2XzVs2FCQy+XCkCFDhMWLFxtMfCAIgrB7926he/fuglwuF8LDw4W7775bWLlypVVpOXbsmNC/f38hODhYCAsLE+677z7h3Llz+tc3bNggdOrUSZDJZELdunWFnj17Ctu2bTO7D8ePHy88/PDDRst1E1ZcvHjRovX+8MMPQseOHQWZTCYkJiYKGzduFAAIp0+fNrvPPv30U6Ft27ZCQECA0KhRI2HRokUGaWjcuLHw9ttvGyy766679BNmVOeoiQ8kgmBBB2Afkp+fD4VCgby8PISHh7s7OURE5MWysrKQmJiIzMxMfUs2IiIi8g0lJSU4f/48mjRpYjCuFpGv27BhA5544gnk5eXVOJacI9V0PFoTJ2J3USIiIjdRKpVIS0tjF1EiIiIi8llr165F06ZN0aBBAxw7dgyzZ8/GiBEjXBZgcyQG2YiIiNxEqVQiPT3d3ckgIiIiInKb7OxsvPrqq8jOzoZSqcQjjzyC+fPnuztZNmGQjYiIiIiIiIiI3OKFF17ACy+84O5kOIRlc70SERERERERERGRWQyyEREREREREbkJ5yIkcj9HHYcMshERERERERG5mJ+fHwCgrKzMzSkhoqKiIgBAQECAXevhmGxEIqZSqZCRkYHU1FTOPkhERERE5EX8/f0RHByM3NxcBAQEQCplGxgiVxMEAUVFRcjJyUGdOnX0wW9bMchGJGIqlQpz585FcnIyg2xERERERF5EIpFAqVTi/Pnz+Ouvv9ydHCKfVqdOHcTExNi9HgbZfARbRBEREREREYmLTCZDixYt2GWUyI0CAgLsbsGmwyCbj2CLKM+hUqmgUqkAAFlZWQZ/AUCpVPI3JCIi0eKNPSIi60ilUgQFBbk7GUTkAOz0TSQyGRkZSExMRGJiIlJSUgAAKSkp+mUZGRluTqF7qFQqpKen6wOQREQkTrobezxfExERka9hSzYvxhZRnik1NRXJyckAtL9XSkoKVq1ahYSEBADw2d+MrTGJiIiIiIhIzBhk82IZGRmYO3euwTJdyygASEtLQ3p6uotTRbUxFfxMSEjQB9mIiIjEhjf2iIiIiBhk82qWtIjiuCkkZqy0ERF5Bt7YIyIiIgIkgiAI7k6EmOTn50OhUCAvLw/h4eHuTo7DZGVlITExEZmZmQYtoswtJ3Hw9SBoenq6UaWtKlbaiIjEofpNEVM39nzxOkZERESez5o4EVuyEbmArcEypVLp00Ekjk9HjuTrQWsiZ+JQB0REREQMsvkMpVKJtLQ0fRdRMXbB8+YKMAfttw0rbeRIPA59hzdfT4iIiIhIvKTuTgC5hq5FlFKpREZGBhITE5GYmKgfLyUlJUW/LCMjwy1p1FWAdQFAIiIiW/B64l5Vb+wRERER+RK2ZPNBtnTBY6sA64m1xaCnYqWNbMHjkMj1fH2oAyIiIvJdDLL5IFu64Dmrm5U3V4A505pjsdJGtuBx6Du8+XpCRERERJ6BQTZyK2+uAHPQfiL343HoO7z5ekJEREREnoFBNh9XUxc8V7QK8OYKMAftJ3I/Hoe+w5uvJ+T9OCwHERGRd2CQzcfV1AXPFa0CWAEmIiJH4PWEPBlnPyZPwqAwEZF5DLKRWWwV4DgctJ/I/XgcEhER2Y9BYSIi8xhkI7Nc3SrAmyvAHLSfyP14HPoOb76ekPfgZB1ERETeh0E2Eg1WgImIyBF4PSFPwMk6yJMwKExEZBkG2cgibBVgGY5RQURERJbgsBzkSRgUJiKyjEQQBMHdiRCT/Px8KBQK5OXlITw83N3JIQ+TlZWFxMREZGZmcrBtIiIisgjLDyR21VuymQoKMzBMRN7KmjiR1EVpIrKLSqVCenq6/uJOhrh/SKyYN4mIiBzHXddVpVKpH5tZF1ir+j8DbEREWgyykUfQzWIkxoq6SqVCVlaW/gHA4H9XpFnM+4d8G/MmEVHtOCwHWYrXVSIicWOQjchOGRkZSExMRGJion5sipSUFP2yjIwMN6eQiIiIxEw3WQeDbOQJGBQmciz2/PAunPiARMtTZjFy18DFnrJ/yPcwbxIRETmO2K6rnMGZyDbmJsnTtVBNTk5mGdkLMMhGouVJsxjt3LkTqampRmNUOJMn7R/yLcybREREjsPrKpF3YDDNNzDIRqLlKVPbVz1ZOvM7qt/18JT9Q76HeZOIiMhxeF0lsTHXIossJ7YWquQ4DLKRaJk6sbiihZg9nDVGham7Hp64f8iz2FqAYt4kIiJyHF5XSWzYIsty5oJpR44cwcqVKw3eyxaq3oETH5BHcvfgkOZmFFWpVE5t0UbkSpzBzLXcfV4zR6zpIiIiIhI7c5Pk6QJskyZNwqpVqwAAq1atQmZmJjIzM5Gamuq2NJN92JKNPEL1FmLuvnviirExrGlCzFmeSKyYNy3n7vOaOWJNFxGRL+J1ldyF3RttY0l3b91+ZQtV72BRkG348OFWr/iDDz5AVFSU1Z8jMkVssxi5YmwMawJ5Yts/5LkcXYBi3iQiInIcXlfJXTgBh20s6e7N3gLexaIg244dOzBixAjI5XKLVrpx40YUFBQwyEZ2qzomlO5/wP13T1wxNgYHuSV3YAHKtcR6V1is6SIiIiL3YN3EedhC1btIBEEQanuTVCpFdna2xUGzsLAwHDt2DE2bNrU7ga6Wn58PhUKBvLw8hIeHuzs5Pi8rKwuJiYnIzMzEzp07jSr/Vbmr8l81jc5q3uuK7yACjIMrpgpQLAA4Tnp6uijPa2JNFxEREbkf6ya24aysnsuaOJFFLdn27duHiIgIixPw5ZdfokGDBha/n8gS9t49cdZJjXceyJtwBjPXEutdYbGmi4iIiMhTsbu3b7AoyNarVy+rVnrvvffalBgiwHw3paqVO91zayr/zhq82xUnSwbyiLyTWIOaYk0XERERuR/rJkTm2Ty7aE5ODnJycqDRaAyWd+zY0e5E2ev999/HokWLoFKp0K5dOyxbtgz33Xefu5NFFrJkTChdCwtfwbse5A4sQBERERFRdaybeDZ2W3Uuq4NsmZmZGD9+PE6dOgXdcG4SiQSCIEAikUCtVjs8kdbYvHkznnvuObz//vvo0aMHMjIyMGjQIJw8eRKNGjVya9rIMpZ2U7Kk8s/Bu4lsxwKUa4k1qCnWdBERERGR9ZzVw4u0LJr4oKqOHTuiefPmmD17NqKjoyGRSAxeb9y4sUMTaK1u3bohISEBK1as0C9r06YNhg4dijfeeKPWz3PiA3Gxd1BNDt5NREREREREpMWJK6zn8IkPqjp//jy2bduG5s2b25xAZykrK0NmZiZefPFFg+X9+/fHwYMHTX6mtLQUpaWl+v/z8/OdmkZyLQ7eTURiwab5REREjsFrKpF12MPLdaTWfuD+++/HsWPHnJEWu127dg1qtRrR0dEGy6Ojo5GdnW3yM2+88QYUCoX+ERcX54qkkoXs7aakmySh6oDdVf/3tROJSqVCenq6/gRLRK6ja5rP44+IiMg+vKYSWScjIwOJiYlITEzUj3eekpKiX5aRkeHmFHoPq1uyffjhhxg/fjx+++03tG/fHgEBAQavi2FA+updWHXjxZny0ksvYfr06fr/8/PzGWgTEY4J5Vjsf09ERERERORb2MPLdawOsh08eBDff/89vvzyS6PX3D3xQf369eHn52fUai0nJ8eodZtOYGAgAgMDXZE8cjNdqzipVIr09HQ2Lycip2PTfCIiIsfgNZXIdqaOj6q9vchxrO4uOm3aNIwdOxYqlQoajcbg4e6ZRWUyGRITE/H1118bLP/666/RvXt3N6WKxELXKk6j0fhU83KVSoWsrCz9A4DB/76yH4jcgU3ziYiIHIPXVCLyBFa3ZLt+/Tqef/55sy3D3G369OkYO3YsunTpgqSkJKxcuRIXL17EU0895e6keRQOJuo9MjIyjGZY1RVMAM6w6irefEx587bZi03ziYiIHIPXVCLHsHfcc6qZ1UG24cOHY9++fWjWrJkz0mO3kSNH4vr163jttdegUqnQvn17fPHFF2jcuLG7k+ZRTI3d5ciKtKsr5b7cvJwFEnHw5vHwvHnb7MWm+URERI7BayqRY3Dcc+eyOsjWsmVLvPTSS/j+++/RoUMHo4kPpk2b5rDE2WrKlCmYMmWKu5PhdRxZkXZ1pdyXW3OxQEJERERERETkfDbNLhoaGooDBw7gwIEDBq9JJBJRBNnINrW19srNzXVLuhyBrbnIHby5BaU3b5uz+ErTfHYfJiIiZ3PWNZXXMCKyl0QQBMHdiRCT/Px8KBQK5OXlITw83N3Jcan09HSj1l5VDRkyBLt27TIKTll6AapeKTcV6HLFxSwrKwuJiYnIzMz0+tZc1QsK1hYcWNCwT23HlCe3oPTmbSP7+NI5lnwDr4VEvoPXMPN4LiRfZk2cyOrZRcl7paamIjMzE5mZmVi1ahUAbWBNZ9euXQBsn8WHMwK5nq5bri64qet/b01g1JdmYnU0U8fUqlWr9MtSU1PdnELbefO2EZH3UqlUSE9Pt+q6prsWWvs5IiJvwnoBkWWs7i4qCAI+/fRT7Nu3Dzk5OdBoNAavb9u2zWGJI9cy1ZJs6tSp+tYo9nazdFWXzdrusvhKly0y5I67b948Hp43bxtZj92HxYWtDcyzZ0zYlStXcp8SeTBz50Zew4jIkawOsj377LNYuXIl+vTpg+joaEgkEmeki0QiMjLSqNJsa0XaVZXy2grQ3j6bir0FBW8taHAGTCLnceTkMgwQ2c8Xz3eOzjemroUAcOrUKQCeey0k8mXmzo2+PEFabby1XkDkTFYH2davX49t27Zh8ODBzkgPiQRbe3kuewsKLGg4R9VjytuCCDxfkCNbKvtigIjsV1O+saWSaOpaCABjxowBAEyaNMmq4ReInMnbyhWuxgnSzGO9gMh6VgfZFAoFmjZt6oy0kIiYau3lyIq0bl1SqRTp6ek2D8yvw7ssd9hbUPCmgoY1+cLZBdSqx1RWVpZXBRG8vXUo1Y7dh93P3ddBMVfybakkpqamQqVSYeXKlSbXuXLlSp77SDQ86eaEq88Vlp4beQ0zzZvqBUQuI1hpzZo1wqOPPioUFRVZ+1GPkJeXJwAQ8vLy3J0Un5CZmSkAEDIzM03+b6m0tDQBgNlHWlqaE1IvfrbuT0d93t2syReu3FZP2q9Xr14V0tLShKtXr7o7KeQhbMnfV69eFTIzM4XMzExh1apVAgBh1apV+mXMf1o1HY/uvg6647xmab6xNX/pPrd+/Xr9fmS+JDHypHKFq9Nq7bnRk/alq3HfkC+zJk5kdUu2Rx55BJs2bUJUVBTi4+MREBBg8HrVOwNErsK7LGRKTfkiNzcXe/bsgUqlEtWdVLHxpLvjJA62tHpmdxTL1HQ8+uJ10NJ8Y2srFbZuITHz1HKFq1l7buQQGOIh5hbSRDWxOsj2+OOPIzMzE2PGjOHEB2QTU4WC7du349SpUzh9+rTBcsCyQgILwqbZW1BwREHDnRfImvJFVlYWli5dioSEBLRp08bpBVR3BxFYUCFXsaULnS8GiBzNHddBd1fyXZVvlEolJk2aZLbrKJE7uLtcYQ13niusPTeyG7h5rg5A8kYveSxrm8kFBwcL3333nU1N7DwBu4s6X23Ntqs/rO3iwqbM4iKW38Nc12RH5r2auLs7nDW/g7vTSr5J1xVy9+7dojhniIUtx6Orzrvu7qJalaXbbGsXeE/oOu8JaSTH8aRrtVjOFWIpk5JlvOX34rnZOzi1u2hcXBzCw8Ot/RiRnqk7zy+//DJat26N06dPY968eXbdiRZrM2+2JHIvpVKJ6dOnIzc3F1lZWfo7qI7Me7V9v6e0tvSku+PkPXR3rNevX+/upIiKLcejq66DntgC0dZWKp7QuoWtPnyLJ5UrxHKuEGsdge5wdwtpZ+C52fdYHWRbsmQJXnjhBXzwwQeIj493QpLI25k6OQ4bNkzfhW/evHl2FRLEWhD2pROsGC+QSqUSYWFhGDhwoMHyefPmGfwv1gKqLWz9HcRSGCbfVL9+fVaCqrDleHTVdVBMlXxWnonESyznCrHWEegO3uglb2B1kG3MmDEoKipCs2bNEBwcbDTxwY0bNxyWOCLyTGK9QNZUWT116hTGjBnjknS4qjJo6+8glsIweT9TgeBLly4hOTlZv9zXgyY8Hi3jq5VnU8dQeno6pk6disjISI9s9UHWY5CZvIW33OgVY4MDch2rg2zLli1zQjLIV1UvFHhbIcFXT7BivUDWVFl1Zd5zVWVQrL8DiZM7urSLNSBP1vO267enMHUM7dq1C7t27QLAY8hXeFKQmecKqom33Fhi+ca3SQRBENydCDHJz8+HQqFAXl6e94w9V5IDaMqBwEjAT8axwVwoPT3d6ARblRhPsI7OH1lZWUhMTERmZqaoLpBiTZez2Lq9PF94Dnt/K2ccE7WlqfqNCFOBYOa7O3g8UnWmjiEAWL9+Pdq0acNjiIg8lieX1Vm+8T7WxIksasmWn59vVcDp9u3bCAsLs/j95GSFF4Gbla2nAupA9fst7dhgA+6FMjoKkPq5N31u5OwKiye2JPKVseOsvZPqq5VbT7o77uuccezam+9rS5O33LF2FR6PVJ3u+FGpVJDL5frlxcXF+uVV30dE5Ck8udUjyze+TWrJm+rWrYucnByLV9qgQQP8+eefNieKnKj8FlB0Ufs8Zz/w52rg0jYg51sg7xRQkgto1O5MYa1UKhXS09P1BUd71zV37lyHrMsUpVKpP6HqTqpV//fEi4a1xHqB1FVWrQmyOTOvOJtYfwdHceR5wZeoVCr9bLtVu7TrHsePHzfI99zPROKTkZGBxMREg3FFU1JSkJiYiMTERGRkZLgxdUREtrG2rE4kFha1ZBMEAR9++CFCQ0MtWml5ebldiSLHU+XkQZWbBwDIOnHR4C9wAcpIBZRRCqhy8pDx8XdIHTsEykYtgMB6QGB9QFYP8JO5KfWGqreM8NUWRo7kzLHjxNTywpfziph+B2fwlRaYValUKixevBj9+/dHZGSkTcdubWOGTJo0yeg7a9vPtp5PvD0QTOQsulbzubm5eO+997Br1y7Rt5onIvIVLN/4HouCbI0aNcKqVassXmlMTIzRrKPkXhkff4u573xmsCxlzjr987RnHkL6tCFQ5eZh7ju7kHx/RyjrCsDtKh/wD9UG2wLrAYERgCwCCFAAEkmt3181uAHAoYEOayvX7pqMQMwnWF8ZnNNT8gqRJVQqFZYuXYqlS5caLLfm2DXVpX3RokUGXdB0rwHAqVOnak2XPbPaesN5hsjVql6LIiMjsWvXLnZLIiISCZZvfI9FQbYLFy44ORnkbKmP9kTy/XcB0LZgS5mzDqvmj0VCu0YAAGWkovaVVBRoH0V/3Vkm8QMC6gCyupWBt7rah3+YQfCtanADgNUtTmoKdlhS6avKXQElMZ9gPXHsOFfwleCjp3JVEFTsLSB1A5zXduya2g5T++iPP/7ArFmzDJZVzfdAzfuZ5xMicRH7OYyIiMibWBRkI8+njNJ2B60qoV0jJLRrpO9KqsrNM9GVFPqupCYJaqDsuvZRUGW5xE/byk1WF5DVAQova5drKgBpzdnOVGGwtmAHYHnlmhVAY948OKc9gRhn5xVWfOzjqiComLqimsrPugHOdYOemzt2Ld2O4cOH61sdV52tsKqa9rM3n0+IxM5Uq3kxncOIPAnLaWQpc3mFecg3MchGFncltYqghuryeahyjwIA9h46DQDYvnouINV2Jd6780MgvxfgHwJlXDMo41oAUn+ThUFTwY7qLK1cswLoW+wJxDg7r7DiYx9fDJhbcsPBVrrKeceOHY32naWt5YjEwJcrNWJuNe/tfDnfeSuW08hS5vIK85BvYpDNBykjFUh75iF9F1GHdCU1wVTwbt77X+ifz5q7Api7AkCVQJ5fCPB35Uy2FSV30mwi2OHqSp+vFJ7EPHacLXwxEOMrnBkEFet4fDXl59zcXOzZs8eocGfpdtRUOW/Tpo3BfrV0P3vb+YQ8Ays14j2HeTPmOyLyVL5Sz3UVBtl8kDJKYdAyrbaupBkff4vUR3ua7zJqRtXg3d5DpzHrra14ecpgQALMe+8LLJr9D/RNag0AkEokRl1Vs75ZBRRdBMKaQxnbwOiAt7XSV5U1FUBLC0+efpLytrvgjgrEOCpYwIqPZ3D3eHzmziO15ecBAwYYvGbLdlT/bnvyvbedT4g8hbvPYUSeiuU0spS5vJKbm4tr166hfv36uHTpksHrgDjzEG8SOJbVQbaLFy8iLi4OkmozSgqCgEuXLqFRo0YOSxy5n3a20c+QfP9dVgfZTAXvhvXvDEAbZOub1FrfWi79X7uMu6z+3xoAawAAaS9NQ/qC5dr1OrBlhDMqgDxJeSdH5RVWfJzD0S2m3N0C0lHnEVu2o+p3JyQkGI23xpZpJEasGBty9znMVzDfOYaYblCznEaWsmYID+Yh32J1kK1JkyZQqVSIiooyWH7jxg00adIEarXaYYkj16veldRVLOqyenknUD/JKNjhzEofC0/eRQwBAlZ8HKd6odyRBRZPGLvRkvzs6O1gyzQSK1aMDXnCOcwbMN85hphuULOcRpYyl1fi4uIMWrKJNQ+xnus8VgfZBEEwasUGAAUFBQgKCnJIosh9lFEKpD7a0/bZRqvRdTcd+kAng+Bd9UBeTV1W9UqygcvbgbAWQERXICBU+1knVvosLTzxJOVelt4BFUOAQOwVHzHdTa6NowrlYtlma88jjszP3ngOE8vvSq4htoox859vEFu+8yRVjxExEXs5jcTDkryiK0uJMQ/xJoHzWBxkmz59OgBAIpHglVdeQXBwsP41tVqNw4cPo1OnTg5PILmeI2cbrdrdtOpnrJ6ttKrbfwAFfwKKDkDdToCfzPZ11cLSwhNPUu4lpjugns4X92Vt2+yqFpDOPo/UtB3eeA7zxbzsy8RWMRZT/hNDK25vJbZ850mOHz+OuXPnokWLFiguLgbg+Td3yDxvufHgLdsB8CaBM1kcZPvll18AaFuy/frrr5DJ7gQ2ZDIZ7rrrLsycOdPxKSS7FJRWQFomILj2t+o5a7ZRS1nUZVVQA7eOAvmngYhEQNEGkEgdn5bKC7xKpcKRI0cAmC48+cpJyh0XFm+6mFWnq/hIpVKkp6d75TY6mjtaXFnaYszevOrs80hN2+Er5zBf5c3nUaqdGFpxk7i54xyxbds2AMCYMWP0y8R2c4cBascR040He5jbDnN5Rcx5iDcJnMfiINu+ffsAAE888QSWL1+O8PBwpyWKHGflgXP4194chAYoERWsRqRcg6hgNaKCNYgOVlc+VyNKroGm6CY2bN2Ppx7tadhNEya6btZAlaPtagrApu6m1Wc/rZGmBLj2A5D3G1DvbiC0iWWfs5JKpcLKlSvNvu4rJyl3XCDNfac3dG/TVXyysrLcWvDwpH3pqBZXzthme48Pd55HvOUc5kl52ZW8pXJjKXdVao4ePYoVK1Zg+PDhHjOjHDmOmCvTtXHVOaLqObpFixYAgJdffhkAMG/ePCxatAh9+/YFII6bOwxQ208XwE1KSnJ3UpzKXF5hHvJNVo/Jtnr1amekg5zkRlEZAKCgXIqCPCn+zKvp3dHQBDTB7q8ENFRIEB2iAYqlCL/nEezNicRtRSCi5NoAXd0gDaTGQ/MBcGx3U4uV5wHZXwOBUUD9ewB5jGPXX2nSpEkOv+izhYFtvLF7m7t40r50VIurxYsXY+nSpQbLxLjNubm5bOVoBUfmZZ6bPZcjKjW2/P4rVqzAypUrDW7KifG8Qs7BynTtTJ2j582bp3/+xx9/sGeUC7jq+qZSqbB3717MnTtXH0x1540HW7fbF27gefJNAjGyOshWWFiIN998E9988w1ycnKg0WgMXv/zzz8dljiy3+sPt8fsu4uRc/U35BRJkVPsh9wiP+3zIj/8rXte7IfbZVJIAwLxdynwd45uDXLU7TUe754DcO7Oev0kAiKCNKgvV6O+XIN6lc/ryTVo0H0IVib0giKgHJfO/4WZ6R9h1WuPuqa7aWkOcGUnENxI27ItMMLmVZk6oXbt2lW/3NwJ1dqTlCvuHtpzMXXHhcWS7/T07m1iumB70r50VIur/v37Y+nSpVi/fj2Ki4tt3maVSoXjx49j27Zt+rvyjvgddecRAC5vgSTmglZt5zJH5mVPb/0lpnOMJ7Ll9x8+fDhWrlxp93nF2RhAJsA95whz52i5XI4xY8Zg+PDhDv0+X1XbMe6q61vVoKoumOrOGw+2brcn3Yy2lauGRvEVVgfZnnzySRw4cABjx46FUqk0OdMoiYdEIkFYoBRhdSrQrI7x61W7dh7+7QqmLfoS/zd9FCIbNsTNMhnK/MNQJJEjp6gyOFcsxY0SP6gFCXKL/ZBb7GfiWxUAGlQ+74jGM4dgaWEFok5AG4i7qkE9edUAnTY4Vy9I20KubqAGfvYOr1Z0ESi6VDkTaRf9TKTWsPWEKsY7mfZcTN1xYbH0Oz25e5uYLtje0lXQFHOFgcjISABAmzZt9Mts2WZn/Y5VuxK7mhjPYTqWTFLhrXnZWmI6x3izqoEKXRfR4uJiyOVyAEBcXJzo8p+nB5DJMdxxjjB3jtbd3OnYsaNDv89XufsY150Xk5KS8PLLL2PevHkYN24c1q5di5dffhn33nsvIiMjPeb840k3o53N3XnLU1gdZPvyyy/x+eefo0ePHs5ID7mYqa6dr6W9rX+e9sxDWFKta2eZGrhRIsW1Yj9cL5HiWrEU14v9cL1YitwS7d/rxdrXrxVLUCFIUaT2x4V84EJ+7VlOAgGRwRokNy3C+HaFiAtT27RtqpxbyPjXEqQ+2gvKFt2Bup0Bf7nFn3fECdVcBV+sLQxMpdcdFxZfuJjZu428k2RZi6uqhQHd/4DhcaerDOfm5lqdhtTUVLRo0QJjxozRFyTtzatiPT/4Am/a975wHnU0W37/2gIV27Ztw4ABA5yVZCKbiekcIeabO97Cldc3U+fFtWvXAtC2aHPlTR5HbDdv4JG1rA6y1a1bFxERtnfBI9dSqVTIeHslUoe2MDnZgC0zicr8gJgQDWJCNEavVXf17zy8s/lHDH2oJyTBiirBNymul2iDc7rnN0r8cKtUCgES5BT54cPfwvDRiVAMaFyCie0LkBhdBmsaTqpy8zD3nc+QfP9dUEb9pp2JtE4HoM5dgJ+s1s874oRqLtrvinGDarqo5ObmYs+ePZg5c6bRZ6qn19kXFlPpt/Y7xdy9zRx796uz7iR50r60tlBe23G3Z88eiyvDVY+v4uJiAMCBAwcAAHK53K7CKlsgGbK1gGxLXq5t30+fPh1hYWE2B7ddGRy35RyjS9/QoUOxY8cOnwviW3rsVf0dzQUq4uLisG3bNkyePNml22CONwWQyTHcHTjwpPKGJ6jtGN+wYYPLxqI1dV7U3Yhcv369fnILV2CZyn68fljP6iDb66+/jldffRX/+c9/EBwc7Iw0kQOpVCrMXbQKyT3mmAyyKaOMZ/q0ZibR2sRGK/DGNF2ltazW91dogJslUvx6LQCrT4TiuytB+PKCHF9ekKNj/TJMbF+AQU2KITPVS7U2QgVw8xcg7yRQp6M24Ca1+hBwCFeMG1TbRQUARo8e7bKTYk3BQHuDRbwD6jjesC/NFQaSkpKwfv161K9fH5cuXbLruDN1fH333XcAgDFjxthVaHNl6wJPaBHpyq77te373NxcDBw40Obzldi7WejS16JFC1Gn01ksPfaq/o66rm5V6QIV7mzBVv3YZkWTHMVR1w1vKG+IiSU3iTIzMwE4v2xhKuhy7733Yvr06cjKynJpkM3RZSpfDA7z+mE9qyMMS5Yswblz5xAdHY34+HgEBAQYvO6O8WPIeqqcPGR8/C1SH+1pMvjmLv5SIDJYg76NStG3USnO3PDH6hOh2HY2GMevyfDs/ggs+EmNcW0LMKp1EeoGGbamqzrGXNaJiwZ/AW3LPGWUArhxBMj7TduqTdEOkNYctbPmhGpptN/Zdw9ruqicOnUKY8aMsSq9uue2XliqVy4tndLbFy5mlm6jmO4kiTk4Y0lhQHds2HrcmTq+AGDYsGFITU21a1wZV7YuEHvQB3Bt0LG2fe+pZRxfOI86grtb9jhS9WNbTF0DSXysLeeK/brhiyw5xl1ZttCVEXUiIyMxevRoJCYmuvQmv6O321HBYTGXo6vj9cN6VgfZhg4d6oRkkCOZrIhXCzQZdqVU6JenPfOQc2f/tFKriAq8ed8tzOqSj42nQ7D2ZAj+LvLDop8VeOeXMAxrUYyJ7QrQvG4FANNjzKXMWad/nvbMQ0jXjTGnLgau/wjcOg7U7QSEtzEbbLPmhOqKaL+tgTzd2FO67m1ZWVk4cuQIVq5caVF6HXlhsXRKb2+602nugmrpNorpTpKYC9mWFAZ0x4+tqrZo0R1XADB48GBERkbq1y+2feOJ3B34yM3NNTrPWhPcFkNwvKZzjC59ubm5+P777wFou04DwPbt25Gbm6sfoNqX87Olv6NYg5nuPo5czZMqsGLgTWUtT+HoPCqmY7x6GVF3XrS37OVNxFyOrk5MectTWB1kS0tLc0Y6yIFMVsSrBZp047BVpYxS3AlAiUw9uQbPdL6N1I638dmfcvz7t1CcuC7DptMh2HQ6BL0almBC+wJMGmn9GHNQFwHXDgI3j1UG21rX2rKtJtZG+50xbpC5YIuu9Zqpz0yaNAldu3atsXuMtYUBc5WSqoG9mqb09rZCsr0XVE+/k+Sq39PSwoC9lWFXBD2dUWkXQ9DH1WzJe7p9v2fPHrvGsRFTcNyU2gao1nF3Ol2t+rFn6e/ozn3ki8e2OZ5UgXU1R5bndHwpbzmKO/Ooq28IJCcnQ6VSuT3viPlGCHkXiSAIgrUfunXrFj799FOcO3cOs2bNQkREBLKyshAdHY0GDRo4I50uk5+fD4VCgby8PISHh7s7OTapfiFMSUnBqvljEaesi2s3C1C/biguqW6aDECJqetoTQQB+Clbho9+C8Wev4IgQDsjQvM65ZjQvgDDmhfj1Jm/kDh0PjJ3zLFujDm/4FpbtlkqKysLiYmJyMzMdGi03+RvbCLYUrWp9uLFi9G/f39ERkbW2LrHXHpt2Zb09HSjSkl15qb0ViqVTtt/7uLI7XHHvrE231UvxLgjzc78zqotgN577z3s2rWr1q4ZYlDbcSnmYIqtgVp78oEl+d6almzWft7ZqrdkmzdvXo3nZW9jaZ4S++8IWH5se9sNLFO8rfzgSM4oz4n5uiFWzi6fuPoYN3eONNVjpipHTPbmKTzhOlIbT/8N7GFNnMjqlmzHjx/HAw88AIVCgQsXLiAlJQURERHYvn07/vrrL/3dT3Ifk6042jXCzm+OWd6VUuQkEqCbsgzdlDdwMd8Pa06G4pMzwTh7KwD/931dLDoSjr71NfALrWf9yvUt245qJ0hQtHXbBAnmWNJSJysry2Bg5iVLlhitp/pnHN2M29y4VVW5a0pvV3H13V9nXvwsHetMTK0HnHnXsupvFxkZiV27dnlE83lHT7ziysKWI7o0WZtme7tJiL2bRfV8PG/ePPTv3x9r167FsGHDnD5ej7vPE5a2JhH77whYfmx7a9dAtrZyHk9vSS8Wrsqj7jjGaysj1tZjxhLubqFq77VL7C3bLeGt1w9HszpyMH36dDz++ONYuHAhwsLC9MsHDRqEUaNGOTRx5Fipj9rQldIDNApX49V78vB8Qj4++T0Ya06E4tJtf2y90hBxUz5CxsXbmBpVho6R5datWF2kHbPt5lHtTKSKdoCfzKpVeFqz5OrptbcwYOr19evXo02bNman9Pa2QrKjLqjVL+zm8pYzCyC2jHVm6e/prEo3CwPGHBkscFR+c8bvby7vyeVyzJ07F0lJSR51LvEm7q4oeStPCAQ6k1gqsGIKIus4ozwnlrwlxv1tjljyqDPoyog1te7X5UGx5B1r+fqwL2Q5q4NsR44cQUZGhtHyBg0aIDs72yGJIsdRKpVIm5Wi7wpavTtoQrtG1nWlFLEwmYCJ7QvxeNtCfH0xCB/9FoqfsgPx1ZU6+OoK0CW6FBPbF6B/4xL4Sa1YsaZEOxvprWNAeFttwM1fXvvn4JoKftVgizUTIpgK0FRPrzMKA23atDG4sN57771IS0tD37599d/vTQUQR11Qq1/Y3RE8MlfIrpr3que7jIwMiybW8PRKt6cF1MXGGb9/beevbdu2YcCAAVat097fWez5RJe+du3aiTqd9nJEwMGb94/YWBpEEUsFVozXM28O7ohxf5sjljzqDLrzZlZWFnbt2gXAMT1mvOnmu5iD1eRYVgfZgoKCkJ+fb7T8zJkziIyMdEiiyHGUSiXSZ08CbmbV/maRU+XkIePjb5H6aM8ax47zkwID40swML4Ev14LwOrfQrHrTzl+/jsQP/8diIahFRjXthAjWxVCEWjFkISaMuDWUSDvVyCslTbYJnN/67+qwRZLA1SWBmgcWRgwVymJjIw0SIunFkBqmjnU2RdUdxdALOkikJqa6lG/p7U8tcWcLcGCo0ePYsWKFRg+fDguXboEQJwF3qrnkr1792LWrFn62YznzZuHFi1a6NNtaZrt/Z3Fnk+qpq9Tp04OXbe7z1NV2RtwEPvvCHhXINCbuvS6iyvKc1Q7X8+jtuQda8/Xjm7ZKKZrF3kOq4NsDz/8MF577TV88sknAACJRIKLFy/ixRdfxD/+8Q+HJ5AcxyBIFalA2jMPeVQXUVVuHua+8xmS77/L4gkaOtQvx9LeNzG7ax7WnwrBhtMhuFzgjwU/KfB2VhiGNi/G420L0CqiwvKECGog/6T2EdwIqNMeCG5o41Y5lqMDVI4sDFSvlNTUms4TCyDOuJNq6YXdHXeoq/5+luS7qvtE93vqtk8MM075KluCBStWrMDKlSsNWija2gXamQXXqp/XtcCvOlvmrFmzrE6zr7On8iKmljSeejPHGp4QCPQmYq+IO7M85w6O3t+e1OVUjEz9HkOGDEFubi6ysrIMetJYm3esPV87ujzurGsXg9Xezeog2+LFizF48GBERUWhuLgYvXr1QnZ2NpKSkjB//nxnpJHsFdwAkPhBdfm0Nkg1fDQS7mqL9LkDtDMIAAAkNa5Cq7LVlyBUPjfxvyAAgqbyedW/msr3abRBKkFz56FfpluuBtSl2m6aDhIdosGMLrcxtdNt/PdcMNacCMGpGzJsOh2CTadDkKQsxfh2BXigUQn8relKWnRR+whQaMdsC2tp9bhtjuRJASoxFNJczdoLqqUXdndUGE0FTauyJN+JqdJNlhs+fDhWrlyJ9evXo7i42Ob8Vtvv36tXL2zatMkh+ddRafZ19lRexBTYEsu1khV78zytSy+vZ67l6P3tji6n3hRkMfV77Nq1S99t1J787+7ztbOuXb5YD/IlVgfZwsPD8f3332Pv3r3IysqCRqNBQkICHnjgAWekjxxBrtQ+wiuDYuEtgbod3ZsmS5TkQHX2Z6j+Og5UFCDrxEUA0P8FoB9rzpzqXUyD/IGRrYowomURfsqW4T8nQ/HVhSAcUgXikCoQDUIrMLZNIUa2KkLdII3laS3P085Iev0nIKy5NuAWaMPMpiLmjsKA2Asg1lYCrL2gWjNTnBgqjLWp/nuKqdLtap5Wua6a13VdRIuLiyGXa8enjIuLszq/1fT7nzp1CmPGjIFKpXLI/unYsaN+7EdPH3jZ0ezJizV91tRkLZ5wnnIlTxpLytU8rUuvM65nzpwQSMxlK0t4Q/nBm4Is7v49nNmS1Ny1S9eTJDU11cZUkzezOsim07dvX/Tt29eRaSEnEHvz9RoFRSHj05+MC1lz1umfpz3zENKnDTG7CnNdTCUSoJuyDN2UN3C1wA/rT4Vg0+lgXCnwx5tHFHg7KxxDmxVhfLsCtK1nTVfSCiD/tPYRGAUo2gKhTQGp9YeavYUrRxei3FEYsPY7XR24cNad66rbUb0CKvZKaU35zlTrN1+tdHta5dpZkwi46vevmvd018Tc3Fx9S1BP+A2cQaVSIT09HStXrjSbF2sqR5w6dcogH1c9d3lKHveGgIM3cnel3VrOOJ856xjy1OCOqcB9Vdbub4+uI4mMq67n5s7X7mhJ6inXOHIPm4JsP/30E/bv34+cnBxoNIatfZYuXeqQhJFjeHrzdZOFrA/+hYRW9YGiK1DWsWLiAjNiQ9V4oWs+pnXOx84/g/HhsSD8nifH5t9DsPn3ENwdU4rH22lnJbWqK2lpDpCTA1w7BIS1AMJbA4ERFn/c3pO3OwpR7m6d4+oLnrMqAfZsh7srjJ5aeK/O3XlZbMzl9bi4OGzbtg2TJ092yPecOnVK/x1V/wKOq/DojhEAPl9AVqlURrP/VldbOaL6+nT7tCaOOk854jh19TnLloq9L56PfPkmjLdwxgD0njDWFjmPufO1q4LyVa9dtsyUSr7D6iDbggUL8PLLL6NVq1aIjo6GRHJnLK+qz0kcPO1OYHUmC1lde9wpZJUXAAV/ah+lOQC0XURVuXkAYFUX0yB/YETLIjQrO40eT2/D8Gdn4/CNevgpOxA/ZQciNqQCo9sU4rHWRYiwpiupphTI+037CIzSBtvCmgHSAGt2hUfwtbs6rm6JY0ml1FODXO4ODlbnzoksxKimvG5tCzZz6+/VqxfGjBljsNwZFR7dMVJ13zub2IIkuryoC2oC5vOiqXLEyy+/jNatW+P06dOYN2+e/rMHDx4EoA2WFhcXm12vo85TnnjNsaVi74nbKQbuOu7suZ7VdJ3Izc3Fnj17MHPmTI/IBzXlW0f+Nrbub0+vI4mVO8pzriyPJycnc8IuqpXVQbbly5fjo48+wuOPP+6E5JCjef2dwIBQ7fhydTtqA26F55Hx/nzMfXuzwdus6WIqkQClV05hfP0sNLj8N0LuGohdFyNwtdAfi35WYPkv4Xi4WRHGty1E+/rl1qW3NAfIrWzdFtpEG3CTx+hf9uRKuLt4yz6zZDvMVUrFVoG3hW77dN3XPHlbzOFdc/OUSiU2bdpkcAw4o8LjrvOFmIIkVbuIVmUuL5raJ1Vnaa3+WQAGwVLmcUOs2FvP1kq7u447e4LIlrQcHT16tMfnE0t/G0vP2bbO7ujVdSQrODroKabzvCO3jeU4spTVQTapVIoePXo4Iy1ENaq1kBUQCtTpgNRZbyP50aeBoivI+ulbpMx+H6vmj0VCu0ba9UQat2Iz1frt+5/PYvH7X2D9khBsvb8NMguiseZEKH69JsOW30Ow5fcQdIkuxajWhRjUpARyfyu6rgrlwO3ftQ//cO1kFGEtPfLk7e4gl1j2mb137mxt3ZCRkYGkpCTRVODt5c5ghLPzsrdUrp11l9oVFZ7ajrPp06cjLCzMK4O8OhkZGTV2EZ00aVKtAzmvX78ebdq00efjmgwZMsQgYGcvd19z7GVpPrdkOwF4/A0WS4it0u5MlkwII2aOPj7FUsbzdmK6EWSv6mUUR26bt5TjyPmsDrI9//zzeO+997Bs2TInJIecSWzdsaxlaSHL4AIe3gLA+0joPhgJzWRA6TWTn8n4+FvMfeczg2Xz3v8CADBmxkf61m/DmxcjK0eG/5wMwRd/yvHz34H4+e9ApB3S4OFmRXi0VZH1rdsq8oEbPwM3fkZqclMk990KBDdE1tHjHnHydncBSCwXPHsrAbZsh67gsH79epu/l+5wdl72lrvmnlzhre04y83NxcCBAx1SGLe2sumqFqmm9gFwJ3BWUyVYV47o27evwXuqB91WrVoFuVyOMWPGYOrUqS4NlHpLpduS7UxOTvaairGjeGMQVjeLc01dsMWitnyrC+Jb+tu4Y6wt8mxVyyi666oj1+3Ocpy95QRv6PniKawOss2cORMPPvggmjVrhrZt2yIgwHBcqW3btjksceRYzq4YifrADW8JxCUAZbeAgnPA7bNAeZ7+5dRHeyL5/ruQe+M2vv/5LOa9/wXGDb0Ha3f8iJenDkZS56bIOnERykgFEqMVSIwuw5y787D59xB8ciYYlwv8sf5UKNafCkXbemUY2bIIQ5sXQRFo3cQMyvASKMNLAMktoJF2VtOEzp1EXQl3R5DL0TNMiYG121F1PKXTp08DEHfBuyZiqRQ5cyILZ50bxXLedXQ63NVazpHjtC1evNhoMqiagkHV77ZX3aeA41osmTue2rRpU+t501w5ovpnqz6PjIy0PbEmiOXGiiPUlM9rm2xk6NChRpOPeRNbzyneGIR1xViVjlLT8alrRVu1JW1t2+KqMp4n3zyylVjKXs5QddxRXX4T47ZZe56zt1WeN7VYFDurg2zPPPMM9u3bhz59+qBevXoumezgwoULeP3117F3715kZ2cjNjYWY8aMwZw5cyCTyfTvu3jxIqZOnYq9e/dCLpdj1KhRWLx4scF7yHnEeOAaFWBldYCIRO2j5BpQcBa4fRbKKEAZpUD6v3bpW7Ct3fEjAGDee1/o11d1PLfoEA2mdb6NpzvdxqGrgfj4TDC+uiDHyesypB2SYf5PCgyKL8bIVoW4R1kGqTWHilABFF/WPr/yBRBXop0sQX6n6bMYKtaAe+7qiDGvuYqu4FC1y5dufCQxF7xrIpZKkbPysqn86qggkliOBUenw5UVntzcXKMKhrWFcVPn5P79+2Pp0qVYv349iouLrQ4GVZ+p05m/86RJk2xab0352JO7FbtKbfl8586dSE1N1W9bQkICcnNzsXLlSvTs2dMjWjbZytZzircFYadPn47+/fsjMjLSI7bHVP47cuQIHnzwQf14q4Dn/zbeQCxlL2dwxbY54honljIcOZ7VQba1a9di69atePDBB52RHpNOnz4NjUaDjIwMNG/eHL/99htSUlJQWFiIxYsXAwDUajUefPBBREZG4vvvv8f169cxfvx4CIKAd955x2VpJcdxRCCpxgJsUH3to143oFgF3P4DqaOK0CI+CmNmfISXpw7GvPe+qHU8N6kE6NGgFD0alOJmSR52nJVj85kQnL4ZgP+eC8Z/zwWjUVgFRrQsxD9bFkEovImMj79F6qM9Tc5yqk97pAJpzzwEZX05kH9S+/ALAcKaQXWhgCdlExwZuHBXEFOlUmHDhg2YPn26ye82VXCoStcVw5PyhadUirx5YGBfpDtf7Nmzx6oWZ6aYKijrWnC1adNG/77qwaCaWhJUnfnTWXT7wNY8XT0fVz0HM4/bp3qQVUfXY4STS5jmbUHYJUuWGC0X2/bUdm1cuXKlPlhsz2/DLp3Wq+m38ZSyl7VUKhWSkpKwfv16/QzYAPSzYtevXx8dO3a0+3tcdY2zt8WhN7dYFDOrg2wRERFo1qyZM9Ji1sCBAzFw4ED9/02bNsWZM2ewYsUKfZBtz549OHnyJC5duoTY2FgAwJIlS/D4449j/vz5CA8PN7nu0tJSlJaW6v/Pz8934pZ4H2ceuC6L7kskQHAsEBwLZWQP9K13BGlXSpDUvj4AIKFdI32QrTZ1gzR4on0hHm9XiOPXArD5TAh2nZPj4m1/LM5UYGlWODorgvDl7msY3Ce/5iBblMJ4FlR1IXDrOJCjnZwBN38FSuKAINu744hhGnVLOHOGKVPf5c7B95cuXYrMzEyLuxC9/PLLmDdvHtavX280VpInEGOlyFRetiZfOPvcKIYCk1jSYSvd+UKlUmH06NEA7K9omGsVpxtTKTc31+D9lswkuH37dv3zvXv3GqTfVcFeS68Trg6s+UqlWyqVYtKkScjNzUWLFi0AaCuMgLYl86JFi9C3b18AnlsxBjz/nOKrzF0blUolJk2aVONEK9bwlcC9I8vlNZVbxFj2cgRzN6N1wba0tDQMGDDA1cnSs/Y8Z2+rPG9usShqgpU++ugjYcSIEUJhYaG1H3WoOXPmCImJifr/X3nlFaFjx44G77lx44YAQNi7d6/Z9aSlpQkAjB55eXlOS7s3Mbf/dI+0tDSb152ZmSkAEDIzMx2XYGu+//AP2u//7C1B+CPD5kfhyZXCls/XCY+8vVVoPPsz/eOul7cJC9ZvEs4e+bdF67n6w0Ihc8ccIXPHHGHV/LECAGHV/LHaZbvmC1d/3SUIxX9bv51u3s+WcmZeu3r1qpCWliZcvXpVEATH7ZPq67WENd+te+/69esFAMKkSZOs+i4xEnN+tCZtzsyvzly3J6bDkaz5ja9evSpkZmYKmZmZwqpVqwQAwpAhQ2rcJ9OnT691HZY+XLl/xXxcehNT+WHVqlXCpEmTaswLkyZNcnfSHcLR5xRbrsFiJtbtqX5+MJePdcuuXr0q2m0RA0eeby1dlzed481dV9evX6/Pf+5k7XnOkuOpJvZ+nu7Iy8uzOE5kdUu2f/3rXzh37hyio6MRHx9vNPGBIwcNNufcuXN45513DJpQZ2dnIzo62uB9devWhUwmQ3Z2ttl1vfTSS5g+fbr+//z8fMTFxTk+0V7K0U2NxXQXUxnXRHuHPGEsUDcAuP27dsIETYlV6wkOENCjjgpNm+ThSkwQNp4Mwvc3YnArtC4yfpUh49cw3BVRhLHtS5EQmo21nx4w2ZXU1AyoKXPW6Z9rx4u7qu1SGhoPhDTRjuHmgnETXcGZzdp1d/patGihnyFP9z06tuQ9S1s+2Zvv69evr79b7GldRasTW8sUW38bSwYtt5VYuniIJR3uYuru8K5du/TPhwwZgl27dtW4T0zlH91MnXv37sWsWbO8ssUSmWbJzIxdu3Y1msF1+PDhrk6qUzj6nOJtLZ/EtD01XRurjhmrY6rljFi2xdvYUm4RW9nLHqa2b9KkSUY9Pdw1NIy15zl7Wxx6a4tFsbM6yGZPxaC69PT0GscWArSDZXbp0kX//9WrVzFw4EA88sgjePLJJw3ea2oSBkEQapycITAwEIGBgVamnHQcfeCKqUmrUWEmqD5Q/x6g8AKQdxoovgLtTYfaGQXIpH6QN+uK0I79IW+aiGM3gnHs22DI/cKQc+YC2pyvwIhIw/iYbgZUAMg6cREpc9aZHi9OXQjkndA+pEFASCMgJB4IbghItYe8mIKZltKlJyMjA0lJSQAcf5Fw1wxetuZ7XaGoY8eOiIyMdFiXDHdyZJdfU4UnawtV9vw2ps6NwJ3xaWwllgKTWNLhSNZUNFJTU5GUlIRt27ahRYsWmDVrlkFBOTc3F7t27UJcXJx+AHtL1lt9ps5hw4YB0AbZ+vbt67L964nXCUcRc+VL95voxrfSXQO8gTeeU7yVJQHh1NRUr70B4+hzhCPPt7aUW8QUwHUGU7+Tu4aG4XnON1gdZEtLS3PYlz/99NN49NFHa3xPfHy8/vnVq1fRp08fJCUlGVUmY2JicPjwYYNlN2/eRHl5uVELNxIv0beMkEiB0KbaR0WRtmVbwVmg9FqNHzMVIPvX4y2Q0E6N66VH8XNRHL68EoGLt/0R1nkQZv8KrL5cjhGtCjG0eTEigjRQRimMWrfVOl6cpqSyBd7vgMQfkNUFAsKQ8fZmzF30ocFbPaF/vu6CuH79eoesq3qBRjcoqm6gVFvyni0FJXvyfXJyMlQqlc9VgmtjrvBkbaFK9OckD+Os4IWj1mtNRUMX8Fi5cqX+nFS1oKw7Fq9du2ZRnhNbSwIx3fRyNTFXvnTXF937vfU3sJWYZl93BTEHhKumx9uCCI4+RzjyfMtyyx3WXFc94dxhbzlBbOUMb2Z1kA0Abt26hU8//RTnzp3DrFmzEBERgaysLERHR6NBgwYWr6d+/fqoX7++Re+9cuUK+vTpg8TERKxevRpSqdTg9aSkJMyfPx8qlUqfcfbs2YPAwEAkJiZavnFkM0ccuB4V3fcPBup21D7KbgL5f2gDbhUFRm+tKUCmyslDvdxz6FfnHHYcL8EHRwBFu544fTMAr/1YBwsOK/BA4xI80rIIvRqWwF9qtHrLCBVAaS5QmovUoS2R3GMOgCqt4t6YgISObQD/EChj44BbvwH+oUBAmPavn3hafNavX9/uvGaqQKMbFFXHVOWmtguwrXcQbcn37q4Ee0KBxF6OOCdVHbT80qVLABwXDDV33nX1b2Pp+d+Wiokl2+LOyUrM0e0TS8s51QMm1fepqwvGrKiJk69UkmzdTjGeC5xJzAFhspwjz7e2/jbeWKarfl2tbUZvVx9L1p7n7L2xwhszrmN1kO348eN44IEHoFAocOHCBaSkpCAiIgLbt2/HX3/9hbVr1zo8kVevXkXv3r3RqFEjLF682GB2rpiYGABA//790bZtW4wdOxaLFi3CjRs3MHPmTKSkpJidWZQcy6cPXFldoP7d2kfRVW3LsYI/tYGtWpgaa+3m1x8guG1vhHZ4AFC2wO4Lcuy+IEekXI3hLYrQu14k0p556E4XUSuZDPq1USKhpe5YuQpcu2r4IYm/NtjmHwoEhAL+IZX/h9xZLrUpbl8jUxfES5cu6VtwAbZV9moq0Jw6dcqo66guLbVdgJ1WMdVUaPNTlb+p44YhuV83QKhA1i/HkTLtZaxaloaEji0ATQWUUeFA9l5AUw4I5YCmTPtc9wiM0I7dFxoPBFh/nhRbZcZc4en06dPYunUr/vnPf6KwsNDgdcD6IFdGRgbS09Mt/syOHTuwcuVKgxbYjgqGmjvvuvq3ceb53xPymUqlwqRJk3D69Gls2LABY8eOhUajQXJyss2tTKvvU2d2ozb3/b5UiRZb91hzlS9fKWs5aju9MXDgSbwpKOzMc4QYzrdiu9Y6gyUzeruSr5zPfZHVNeLp06fj8ccfx8KFCxEWFqZfPmjQIIwaNcqhidPZs2cPzp49i7Nnz6Jhw4YGrwmCdkwsPz8/fP7555gyZQp69OgBuVyOUaNGYfHixU5JEzmfx16Yg2O1j8h7tYG2/DNASZXuHZEKgwCZqa6kGa8Or+wGeg2FgVLsvxaN7WflyC32Q8bxMGQgDJ3jG2HfjUI8VKcY4TLLxoazi1ABlN/SPorNvEcqqxZ8C9FOxKB77h8M+AVZ9bXOaq1VU4Gm1rynqQDKCyqDVpUPdQmgKYMySANlEykgCECeXLveJgFIiCsBBA2AP4C/TwOCWvu/oDZ4KNXXkPbsP6AsPwj8+at2vwtq09sAQKnrDd9I+6MkxFcgIa6wcmEuUJBr6qNaJX9rH9d/BGQRQGgT7fh9gfUs2ofOZEvFqLa8sm3bNpPLa8pD1dNhywQTvt4iyFkVE3cFRUzls1mzZgGAPpBaUFBg0cDfruYLlShbubtlcHWsfFlObK1TnM1TAsK617wlH4vtHGEJj61LOYmp8lj1oWI47Ao5gkTQRakspFAokJWVhWbNmiEsLAzHjh1D06ZN8ddff6FVq1YoKbFu5kWxyc/Ph0KhQF5eHlvAkeOU5wP5lWOjmehOqpN14iISh85H5o45RmOtXczOQ9rWiyhv1AOHckKhFrSzIgT5aTAwXtudNCm2FFIrJxNV5eQh4+NvTc5o6hQSv8rAW3BlEC7Y+LlfMOAn06avWmHSIEAhaKCMiYFSGVMZwNJUBq40AASzgSz9o7JVWNbRX5HYZxQy/7cGCR2aVbb6qgA05VBl/w1Vdi4glCPr17NI+b81RhNOmNtvNf2etqjpt3Lod/mHASGNtQE3eYx2LEJdGmr6PeC4AklWVhYSExORmZlp8k6uqSCcubSVlJTgmWeewTvvvIOgoCCr0lw9HVX/VyqVVgcCa9sue7jqt7FWbZMcmaqYWLItpio8ta3XEWpKm64V7O7duxEZGVlj+t3xW9ia/2oKentLSyGxHj9UO0smUnPGOdce9hw3tpxTyX6OOEdYOvyBq86pvnze010Pa8JjiaqyJk5kdUu2oKAg5OfnGy0/c+aMvkBJvsVbCtjWsHqbA8KBel2AiETtrKT5p4GCCwA0Fn/ntet5WLPsXWTuUKDh/fHYcTYYW34Pxh+3ArDjXDB2nAtGg9AK/LNFEf7ZsghxYaZbP1WnjFIgfdoQi9NhN0ENVORrHzWR+AGQQAkBSl08KfIvAEBC5AUkhFfuu2IAf9qXJKUsT9u6UJ4D5JcavJbxn11G3XlT5qzTP0975iGz+696q0V7qXLzMPedz5B8/11GQTaHflfFbSDvN+1DGggEx2mDbsFxormTa6pFTtXCoG5YAblcrv9MUFCQ/v+4uDirKlynTp0CYNhqQC6XY+7cuUhKShLFuU8svw1geI60pSWfJdvirhaCpiodunxVXKxtUXrp0iV9mSguLg6AdV1/HHlddUSrl5pao7ijdZwzyh1i6K5Fthk6dChUKhWGDx+OS5cueUTrFHuOG19vHe0ujjhHWPK7u7L1n5jKDe6yfv16tGnThscSOZTVQbaHH34Yr732Gj755BMAgEQiwcWLF/Hiiy/iH//4h8MTSOLni91PbN5miQQIbqh9qEu0rdvyT2u7YMLyQElUsAaTOhYgpUMBjuYGYMvvIdj1pxxXCvyx/JdwLP8lHEnKUoxoWYiBTUog93dBd1JHM9lF0jnbUVOg0VR33uot2WxZr6M57bs0pdoJPQrOApAiNbkpknt/DMhjkPXrH3YVSKpXlB0RDNCt8+zZswBgMLZe9a6jAwYMMLuO6umoPkafpeuqzpldN8RU8ap6jtR1wa6qtoqJpTPXiSUoUlP+mDRpktXrc+R11RsrUZ5c7vC2G5Ni2B6NRqPvxq8LblefyMhVeb7q/gDglH0jpnOfqzkqv4kh34qBmMoNrqYrj/Xt29dgO33lWHIkHk/GrA6yLV68GIMHD0ZUVBSKi4vRq1cvZGdn62f3JCIL+QXdmZ20OBvIPwWl5E+DQIkqJw+q3DwA2gBP1b+ANsDTOUqBzlG38Oo9t/DVBTm2/B6MH64G4pBK+3j1oAYPNS3GP1sWISGqDBIru5OKiaNbhln0nTXMDOsKpvLAz79dQqlainJBgnp1FagToUCZGihTS1CmkaC0QoIStQSlaglK1UBphe551QfQMFSNXnElUIZY2qJSA2V4CZThJQDygJjrAICENg2Q0OkuQOpn3bZVqyjXFgyYPn06Ro8erd0XZoJwunVu2LABISEhBi0bVq1ahbi4OGzbtg2TJ082m67auiECwMsvvwxAW5Fr0aKFPh21BQJtuUNtaeHFmypeFm2LIADqosru3+65kaBUKjF9+nT0798fkZGRJispUqnUrS1nnFGJEtuYUI7mzGC4JwcITRHr9rirdUrV/QHAYN/UdtxIpVLs2LGDFdUaOCq/WbMeU9dga84RR48exYoVK/TlEUA850tvKjdYy5vGC3Q3sV4H3MnqIFt4eDi+//577N27F1lZWdBoNEhISMADDzzgjPSRSHl6AdtcpbW2cWects3yGO2jfnftuG15p4DyWyZnHjXXVTHIH3i4eTEebl6My7f9sK2yO+ml2/7YdCYEm86EIFymQVSwGpFyDSJ1f+Vqg+f15RpEBGngJ4XouLxrqwXUGuB2mQS3SqW4VSpFXpn27+0yiTboVRn4Kq8MgpVqtMvKNbjzuhoo01R7b+X/ubfCkV+kBPwDIPHrjEaz/okFt/2Anxy3Da3rlqN3XAn6xJUgIboMAZb+9urKCRau/QCcVwHyWCA4Dqo8f2Ss3mR1RaG2YMCGDRuMxs+o3jpB9/nWrVvrJ+PRHae6gmNtrc5qSse8efOwfft2g1YSukHvdWlwdKHNUwovlp4jLaqYaCqAikLto6DyxsLNY4Dq+p3l6iLoWrcqy24j7bl/Qik9A9Vv2cjY8AVSU1KgbNzK6slWrKFUKrFkyRKj5dUrKZ06dap1XZbuP2vvGDujEuWO1nGuLHdYU/niHXzXUKlU+snMZs6cqV+Wm5uL9957D4A2P8TFxWHSpElo166dwXEnhsBBbceNbmIda871zgwIV+XL+dzc8BSWniNWrFjhtBnGyXF0x5JUKkV6erpP5nVyHKuDbDp9+/ZF3759HZkW8iBi7X5iaSHAXKW1psqsS7bZLxCo00H7KLqK1IkKJN/fCYBgVVfFhmFqTOt8G093uo3DKhm2/BGCL88HIb9MivwyKc7eqjkZUomAekHVA3F3gnBV/w+XCR7dOk6nVA3klUqRVxks0wfNSqXIK9UG0bLz2yJp5grMOVUfhb8FIK9MivxSCQQ4cQdIw+AXWvNbZFIBMj8BAVIBQf4CAv2AQD8BgX66/6s+tK/5SwX8dl2GozkBOH1T+/jgeBjCAjS4r2EpeseVoHfDEkQFa8xOuGDQslCoAIouAkUXoTpxEXPnzkfyvXFQht2vDb6Zmcii6l/AuKJctWKkVCoNWrJVbZ127do11K9f3+Q6deOzWaqmoERqaiq2b9+O9evXo7i4+E4ArnMnoKIQynpy4MYvQNlNbTfwslvaFUgDtcEev8q/0qrPda/pngdq/9pxYNlb8XLG7K5paWlIf/VlpM+ZDlQUaWdfrijUPlcXGT7XlN3ZFn/dmInXgMJyk9+tjAxD+tR+AG4j68QJzH3zPSTfUwdKTSPtvgxQALI6lX8VQEAdICAcqr9zRVNxtPQaI4agqzu6GIm53FHb7+HpNyarc8f2qFQqLF26FAAwevRo7Ny5s8b8oFQqLQpuOyptuv2xd+9eAMD27dv1r+uWJSUl6SdFMXXc5ObmGs1KXBtXtcZx53nHUfnNXcfh8OHDsXLlSuNyg8i6ZDojYOtJwVndsZSVleX2a6wn8LbrmqNZHWSbNm0amjdvjmnTphksf/fdd3H27FksW7bMUWkjERNrH35nFgJcvs3BsVDeNRLKdkO047ZJvwBgXVdFqQRIii1DUmwZ5veQ4NJtP+QW+eFasRTncsuw92g2GjaJR6EQhNwiP+QWS3GjRAqNIEFusR9yi2vv/ifzE7RBN7kG9Sv/1pOrUS9Ig3qVy3TP6wY6t4WcIABFFZLKQJkEf/5djE/3ncE9d3eAIAvRB80Mg2jaAFpxhYUJ81Pg6i3jxSEBGtQJ1EARqIFCJiBMpkGgnzb4JZNq95M2EHbnuX65tIbXKoNnMj/gj3NXMOTJZfjf6mfQpW0D/WfsCXLeKJHiu8uB2HcpCN9eCcSNEj98cV6OL85rB3JvV68MbYIEvLvtHB7sazjhQq0tCwvPA9l7AEiBoEhA3gAZ736MuQsMW/5YWlE2F/yqrbI1ffp0hxUcO7ZtjrT/m4G+XeKgunxemwblNSQojkI/kckNEx9UV9xp+WcBVU4eVNeKAKkMWScvAwCy9m/UtuaSyqBUNoCyQdydgJxfYGXgThvMtLfiZdW5VNAA6hKkjv8nkvvdDahLkfXLL0h57nWsWjwdCW3jAE0JlPWCgD8/sjwNVYK7drVg1ZQCpTnaR/XvOJ2r3c5746CU360NvskUgH+oway6lrKnkuKKa0yN6VOXaQOc6mLto6JI+9CUaPOYfygQEAr4hUAZVdchreOsqYDp9o9uBlcAoih3WEKsAUJbiWF7LM0PrmjpZWp/WNraWTcxCgDRdSV0l+rnBUflN2vWY28Aoerndb9rcXGxzZMvuYIzArZiuCnkTJ4URHQ0MVwHxMzqINvWrVuxc+dOo+Xdu3fHm2++ySCbj/DEPvzmLpi5ubn6VjA1FXDcts3+wUBEAhCrAfAKEFjfptXI/QW0rFuBlnUrAABZpRcxY+l8ZO6YYxC0K9cAN4qllUG2yr9Fhn+vFUuRW+SH2+VSlKkluFLgjysFtadBAgERlQE3beBNjfpBGkTINagXpNYG5SqXh8sEFJZLjFqV3flfYrLVWbmmWsQpMB77j1m2j6QSAQqZBopAAYpAbdBM91BUPrT/C9plsjvLZdYNR2aT/KBSqAtuIDygAqEyx4xBFRGk0XczVmuA49cCsO9SEA5cDsKxXBlOXJfhBBoiZswiPHGkAr1zytAnrgS9GpaivvzOWG61jx94E8qov5H6YDSS704Dguoj6/Q1pDw31+6KkaWD5FtE0GhbVJXnA+W3oZRlI236GCjVWcC5Y1AK5Uh/oiWA01DpujFWFACIsGz9FjLZVXzGneCk+VltJXeCbtVbxvkFAVKZ+eVVo7XqyhZjJde0MyGrS+4EX6o+Koq1QRgASgDK6MrP39TmjYTmciS0CAYQbPU+qGk2XaP3WjB+pcl1qLWzgaLwPHCt6mQrUiAgrEoLuHB96zcEmG9aak8lpaZrjO76pVKprKvwqUvvBMw0JVDKi5D+zIOA+g/g6vHK37EysGZyspkaSPwB/xAgJ1v7/62TQF6wdlllME4X9DXHmgqYmMod1lbAxXpj0lau2h6VSoXjx4/j2rVrOH36tH752rVrUb9+fdSpUwclJSX65boARtX974qWXlX3x969ezFr1iyDcTsXLVqk7/1Tfd9s27bNqPWaWCqq7mqpUv284Kj8Zs167A0g1PZ5ayZMIuezNa97exCxJt52XXM0q4Ns169fh0JhXFANDw/HtWvXHJIoImtYemKs7YJnbrlYIvHK2AbaoMNdI4F6QcCtE9rx24QKh35PgBSIDtEg2oLB8IsrJNqAW2XQLafIDzdKpLheIsX1YimuFfvpn98s9YMACa6X+OF6iXMjUgFSbRAsCKX44/fzuK+jEo3rB0JRGRyrUyVgpgjUtrBTBGoQJhMgFXHXV2dP/OAnBTpHlaNzVDkei7uMU1cLcfRWHfzvggy/3o5AgTwMn/3pj8/+1AZMOtYv04/ltv3j7/D6O7sM1mdq/ECDiSRKtHk3IeoKEpSNtN1K5YHaba2sGKlUKpNjY1QNwllV+daotS3KyguAitvaAFl51b+F0LdIA6AMAtIn36ddJhgeE878PWyf1VbQBr00JUB5nlXfqbpWBFVuAaApRdaJv7TfvX89kHvnO2sLdrmLNeNXWhyQK8/TPoouwoDEvzLYprjzV6bQ/vW3PphoiVorfLMmIn3G6DtBz4rKIChqP4/bTKgAyvOgDC/THgdBKiD3W8P3SGXaoJtfZeDNP0TbIk73V2PZ9cvUdR4ATp06hdzcXOzZswczZ850WaHe2gq4mAKEjuCq7TE3Cc3y5ctNvl/Xos3V5TZT+2PYsGEAtEG2vn37Gu0b3TVs6NCh+plIxVZRFUtLFUflN2vWY28AwdznLZl8ydN5YjdCseR1T+Jt1zVHszrI1rx5c+zevRtPP/20wfIvv/wSTZs2dVjCyHO4oil+TSw9MdZ0wavaks2SC6k7ttnobmzUvUC9u4HbZ7QBt4p8ADA7fpbuNZtae5gh9xcQF6ZGXJgagOmxknQqNNquiTdKpLhe7IdrlcG365WBuGvFUm0ArlgbpCssl0LurwuECUatyvzKi3D06Gk81KMp4qOC9C3MSvJv4uaNW5BIKoMTG9dhQLuxSKgv/kBBbVw58YNR0EIiRWBsS8ibdkFQ0y4IjGmO49dkOH5Nhn/9Eg5FnYkYu2g4EurcgjT7Nzz7yr8tDAoBEMqBwgvaB6Dt9ihXAnIlVH/lmLxLaLJ1groEKKsMLBVc0HbbrCjSBtDURdrAmuZOq4eajhVLOPP3qG1WW1VOHtL/tcvmtJuSsfEbiwNVtbE1AGntOapMDagFiVVBSWsCcibT+Pd1ZHy83fS+l/jfCbr5h1cG38JNB+AEQZtnNaWVLQUrWwtqSqGUXUHajHFQSk4Bly4gdXAkkju/CkBtfvtu/1HTrnWaGo8DTRlQVgbgJlDZaNDkb/z1SiCnLeAfDGVsHJQNGuuDcqqcfDw2ZjwOHDhgtHpdUAXQjtPlqmsy7+C7RmpqKpKSkvQt2XRdMJ999lmDlmyzZs3CkCFDMHXqVERGRnrE/q+phZ07K6pVu7/Zms9t6UIntsCMvQGEmj7v7S3YPDFgZU1e5yzBZAmrg2zTp0/H008/jdzcXH3T52+++QZLlixhV1Ef5apBV82x9MRoyQWz+iyE5rh7m/X8ZNpJEhTtgaJLwK1focq9aLaLlb2VS3v4S4GoYA2igjUAam+9UKHRfsacrBMXMXvZIszsPQcJsXe6u8545yss/eh/Bu911TZ6suoBJ1NBi3endK2s1OciIEyDUyWROHApEN9eCUJemT++vRaJb69FQoLmiBnTBmfDQ3B3VCDa1y832ULQbCBGU6Ltvld4Hvi7MsCSexC4KQUkfnda6VQdO6qy1Y5SUzlIvv854EbNraut6Y5oiTI1cLnAH3/l++GvfH/8le+PS7f9IJWgcqy+O12RFTINwquM4afrdmzpzK6OTjtgT+s5Y9YGICs0QH6ZFAs3H8P7n/4Mv6AwSOVhCEtMxswv1ZDuuw4/eRhatApDRFQkblZ2ES8s1+6wOoExiA2tQINQNQKC4xF+dwGKozsDkXXQMFRt0LXZ3u2scd8LFUDZde2jOom/tguqRq0NrGlKzX6HMghIf6oHgEKgtBDKiAAoIxoYvMea8TnFxOR16MUM/fPq52jViYs4cOAA1i97Gm1aNUfWyctImbEYq/41FwkJiTj1xyWMecK1rULsqYC7+8akoymVSkyfPh0bNmxweCCk6vqysrL0QbZx48bp97Wu3Jaeni6KFhTVf19P+62rdn9LSEiwKZ/b0oXO0sCMo44fbzsOxcQTb0JYc053xizBno7HkzGrg2wTJkxAaWkp5s+fj9dffx0AEB8fjxUrVmDcuHEOTyBRbXy1uarRncKQRtrH5RAA8wEYd8l0ZCXa2WoKsNWk/71tsfSj/2H9kgkoLil33TZKK2eHlPhVPqTmn0NaZbm02nKp8XNzr0Fi+NfUMpN/Jdq/5XlA0RWg+ApU1w4aBA5qa0kFAB1QhBEti1CuAbL+lmH/5SDsuxSE0zcCENigNT6+DHx8GagfpEbPuFL0aViCng1LoAjUjidnLhBjsqVL5k9AiXbsp5paIzq7tV9huQR/5fvhYr4//rrtjwv5/rhYGVS7WugHjWBff+OQAF0ATtCP+RegCcbg6fOx+6YSv52UQSHTIPdmHchiW+FKcRAaFUutCtCZY8lvXpuyarP03jQYT1FiZnxFKW6XVSZe/hiUYx8zu/6rAK6aiF9p1yPDyesAIEfdPhOx5A9gSWXjrgCpAGWIGrGhajQIrYMGoWrEhqghbaCAf0QDtGsdj4R2DS3eTpsIFdpZZ32cqesQAKxfMgFtminNnqPbxCuQ0FwOlGq7lMvL/wKulaH4auU5Yvdy4GorwE8OZWys9rooCQCkAYDU3/RziX/lMt1zf5smvLCGI27SiWmwbaVSO+tzYmKiS1sTilX139ea39qXK6rW3DB3xE1ua9Zj7+9i6vNiOoYdzdvrZbXlVVtmCXY0Z+Uvc+sVTeMTEbE6yAYAkydPxuTJk5Gbmwu5XI7QUPODABOJkbkLpisLOPaeAKveKdT9DwBZv57V/v07BlCEAbfPQVkv0OLAiSvY2k3Pkq5kkRFhAIA2ze7sU9u3UQL4BWu7efmH3Hlu8FcO+MmdXjGzhsV5S1ZH+6jTDmhQD8BcILwtIA8HirNh6XhOAVKgm7IM3ZRlmN01H8f+KsRbn11FQOMEZF4PxbUSP2z7Ixjb/giGVCIgIaoMvRqWondcCdrVM27l5swWl7Xloej6CkhCInDxth8u3vbHpXztX93za7WMJyj316BxmBqNwyvQOLyisis1kF8m0Qef8sp0f7XL8kuluF3ZIquwXNs666rBRKRyIKAuTpyuuiwCyrFL8MxRAEcr3+WnQZ0gbcu4O63kDFvPKQI1CJdpEF75Ht1zub9gMO9BmUYCv9AI/FUkR7lKZhAQu2UicJZXrWWZrcJkd7qFS8uLcPDH4xjeuyVaKINRJ1CDukHaMRTrVP6tG6iBRAKoCv1wtcAPVwr88XtOBX44fROhUQ1wrVSG7CI/lGsk+t8RCKzyjXXRICUDj/0ERBzXBt60gTjtIza0ArEhagSU3ERx/g1IJY7pam8PR4wFKAhAiVqC22US5JdJkV8mwe0ybbDzznPtawXlUoQHVE5YUzlbdP0qM0eHBAhWzXBs6joEaM/ZVbtDmztOT53TXuvGzDCcqTZlzlr9c7taLEv87gTfqv+V+hstU8pvIm32FChDi4GC83de0wXtDP53zHikvjjYtq7FnO551eXeEphyV0XV0q6aNe1ne7t7ijkwY+/vYurzvngMi4YgaIcy0A3VAACyCO35GrWfU0zlVbHNEuys/MV8azmbgmw6kZGRjkoHkUNYWtgyd8F0ZQHHkScqk02XU6fon6e9+AzSpzQHSnPt+h5HsbWrW23Bl0kj70PXjvEAtBUyeVAAACD3xm3jlUn8DAfj9gupHIy7ysNPDqtqjyJhad4yKBT/8ov277kiQNEa0ERBWVcKZXg5lLGwqlJ/V+MQbJzaAsBtlKlv4+e/Zdh/KQj7Lwfh95sB+PnvQPz8dyCWZIajXpAaPRuWonfDEtzXsBQRQRqntrjM+PhbvPbB/+BfJwb+dWIQ1nUoXtgXgoBfAuBfJxqBEbHQSGquCNcNVKNRuBrxlYG0RmHa543CKxAp19iUZSo0wO0yKfJKJXeCcNUCcXt/uYyj525AGhgKaVAI/IJCIa18AECxWoriQilUhbV8mQl+EgHhMu0suXmlEpSoG6Dh1Hvw/DEAFs7OqyOBoB9H0dQsvYb/C6gbpH0eLtMYtGDNOnERif+3EKkT5tQaJFcEVqB1RAWAUqAtgN7BALStxio0wN9FuiCc9nG1wA9XC/3x1y3gUr4U5RIZbpT44UaJH34z0VIOiIJQUYaK/GuoyA9GvUHPYuYXuaj4+Ceo83Pw1MPt8ew/O2HNlgMOHSfPFGWUAnOeHoLbZVL8la8NhN0ukyC/VIr88jvPbxs8l1QG0LTLbpeZmInZRoF+gj7gVq9ypmhtEO7OrNH15BqoC2/h0x37MfnRewForwO6gBlgGLTcsPNwjd3+7+nUBOnThiAyIsxh5wijmz81dOetShkApD95F4ATQPaJWt4tuRN8C6gDhLUAQpvWOgurGLl6DC2lUoklS5aYXO4pLSjE2HpJpVLhscceMxrz0FRXTXtm0xTjOFzkfE4NgmvU2uFF1KXaoJm6pMrf0srl1f9WvseIFAiMAAKjoAyJRPr/Pas9R1tIzLMEk3vYFGT79NNP8cknn+DixYsoKzPMqFUvsESu5kmFLVuYK9QmJSVh/fr1NU/eoFQajAWklF5E2ksFULYZCNRXGF+kql+YzLRqsnfweGuYC74cOX4BKzd/p38AhhWyPUdyMWBY98pZ7UK1gTW/IKem1RNYPAZKZBLSO48Gii7feVhYAZX5Ad1jy9A9tgz/1y0fl2/74cDlQBy4HIQfrgbieokftp8NxvazwZBAQMfIcvRqGIZeDSPRKfLO9cWa1ogaAfi7SIq/8v2NWqNdqPMkGk1/yvxnAfhLBDQMUyMurAKNwirQKFyNxmEViAvXLguXCRalwxr+UmhbaQUBgNrkewaF5WHF5e8xfEBnXFLd1Of/u9o2QlGFP4IVdRAYqqjSWu5OwC6/cll+qa7lkrbFUn6pFBWCBGpBgpulhsFFP0n1QFnNgTNdsMxRs/Q6avZWfyn0LdO6mnhdELQtDXWBt6sFfrisC8QV+OFqoR/+LvSDxl+GgIhYBETEGq1jO4DtnwNqIQbffROAFpF++pZwDULUUFZ2T61XOTZcQXll8KtMgtvl0mrPKwNiuufllS3M9M8lKK5wTMtZCQSEyQSEybS/m7Zl453nYTJtS7WCMql+gpqqs0YXVUhRqpbgSoE/rhTU9m1RQPD/t3fn8VHU9//AX7ubZHPf1wYCEUFOARMvtICiolau0tYLVL61JJ5UUVsP2oSKvVD8tV5NaLXaWLVVtKCUoqIi9SgmKBQDKqIEsoFw5b42O78/Znezm+xuZndndmZ2X8/HYx9JNruzn5n9zGc+n/d8jtOwbmMvTD1tOHywD30duci85Cb0dTTjzn8cRd+zX8He3ozrvzsBW1/+BRJNfdjxufcgmty9spWY53AwQVzopa9XnEeyywo0bROnekgZDSSOcPWmGJQ+FSaG9xcUYlAlMM6VsrU2V5PVahXnPKyuxvjx44OeQ0vOebgiqXeiO60t7hAOQ7bLBMFPQMztp7fgmeC9rhQcO9B9VHy0OJ4yxALxOYA5B4jPFX/Geo7ec+ZVLawSrFT+isZ8K4eAg2x/+MMf8MADD+CGG27AP//5T/zf//0f9u3bh+3bt+PWW29VIo1EESPUgkpKpdZZyfHazd5oAoyJABJhGZWBil/9P+mJt/cOuOiJgTjrwU/Fhsm8BbAkF3m/MEKQZWVTj2FGRjFIVnzmebhi/jUou/knQEwiand9haU3Lxt8cUuP7AtAMHkroEpxTBKQOlZ8CILYK7KjHmivd/SQlBZ4Gp7Sh0XjO7BofAd6+oCaw3F472A83jtoRt3xOHzWJD7+sENcLGBishlJky7C8Z5Yj+209xpQ32pyLC4QIw7vbBF/HmyNQc8QvXQyzH3IiunEZ5/U4rqLR+HsU5IwwtErLT+pb8g5AcMZXHayCwKqXnofZdfMcA2LHhxY8L/K70CCAHTaDK4hgt19BlfQLDnAYYByC9dqugYDHENqbRif5X1Rll470OgYkvrB3pOo+PMHuPKHl6EnLs3VQ67DZoQpKQP72oF9PnoTxhgE9AmAAHkObGKMHfEGGxqtVpxelIn89FhXkCwlrn84cIpb8Cwlzo4D3xzEZdf9Bv99aTnOnBR8YKqj19C/OrSXlaKdzx/tMuJ4pxF2GNBmiwWMGYgfkeFzu5sBbN4OxBkFpMQUI/+GErxhzMLnR83Iju9DVp8dWSfFIatNbUkwpWTJ1jMv/Oz9qysbYoHkIiD5VCBxuMc0BGoEtfz1itbj5OZqslqtqs/T5M/48eM96ozBDNVcv349ysrKXO8LdrhnpN4wj4rAdPdxcTV3j+CYn8CZ115lwZG9Xib0Ap0N4sPJlOAIuonBN0tOjmZWCVYqf0VFvlVAwEG2J598ElVVVbjmmmvw7LPP4qc//SlGjRqFX/ziFzh+/LgSaSSFabH7eqSSUlCVlZX5/D6kVGqdgRbZOSeHxoA5GFMdrcnMM4B8HxeTvh5UPluOlat+4/G0xzxbd12Hijuv6r8ACzZHr7MUICZV/BmbAsSkiM+3fAbg50D22bBMLIbrSCVIWyFWL6Sen8FcBIOeA8VgEO/qxecCmSXi3cWOg0D7AUcvty5J+xZnAqYV9GBaQQ/uPRs43G7Ee4fi8V69Ge8fikdztxEfdGcj+4o78eMaYPz+HsTHQNLcaN56o41IEedIG5Eq9kar3X0AJb/4HRb/6AEUjwss0BCeXi/KMxiAxFgBibEC8pOkzcEXjWKNQGFKHwpT+hB7/ChaPvoHflBqweZtn+Pa70xA9rgUfLDbirv+8A7uuOVKdJhS8Pzb+3DRxeehLyEL1jYTDneIvQad4ozCoICY99+dPc0cc+m5/S/G6BhWu+AhvP7a0MNqnTqsPRB6OkPucSjmnT7H3IODA7zuN1dq/ncANz30T6y67zoUjByB5t5YwJyCAyf68Mwbu3DRhefAFpvkCtC19RrRYzfgWI8Z5vzR2HES2HHSWyqyMfyWZ3HVx0DKDjuy4+3IjO9DUpyAeJM436DzpzlG/Ol8rruzA53tbTAb7fi2vhvmYRPwxs5WHOxoQpypD4U5ySjKT0G8SYApHNNuCr1A65fiw2gGkoqAlFOBhALNBbW0PIeWljhvgNXV1bmeU7sXiL+bcu7pDHSb7vME0+D6m9bOYdn0nABavxIfNi/Ts4RJWOplfZ1AxwHx4WRKcgTdssUAnE1aHVhuSuWviM23Cgs4yHbgwAGcd955AICEhAS0toon03XXXYdzzz0Xjz/+uLwpJMWFMjcYA3SBkRok8/V9SK3UKt3NPuBeU6Y4lN2yDPO+90PXa30OaZVIq8MJ5D4npJ6fql4ETfHiUKeU0Y4Z1Y+IFZD2A0CP1wmuvMpLsuPK08QVS2124LOmOLx30Iw398ei7kQ86o57zluUbrY7AmhiDzTn74UpfbBI6I0m13BEr0yJ/XP7OYdDuN/RFbz3mBrIVw/QQksGSq+aDqMO5wyUkxo9Cp35BgDWPP3WoLnDVlf81vV7Vt5XqFy1GIDYG+5Ih0nsnRVnR3wIs+JajzRjf4A9g+XoTRwob/No3v+LJ1y/l98+B9deNAWP/PsJ3HtzukeQsMsGHOsyYW9DB57fvAfnnjsFtthkHHP0lDva2d9r7ninGMB0Lt6wv0XqwU13+30c8hdfgD+3w+s8hHHGwUG6eLcAXnxM/0+P55yvc3s+IUacx+60DJvv3qL2bqB1r/gwxsOSNBKWsaOAxGGulygR1OLQIPm4DxF1p3YvkKFuys2cOVPSd+xe33Gn1fpZuA2sv0VUYLq3DWjbJwbWAqjnRaS+dqC9XeyNDMBia0b5su/BYtgDHBccwbdssT6oIKXyV0Tl2zAKuIqXn5+PY8eOYeTIkRg5ciQ++ugjTJkyBfv374cgyD9PDWkbVxkJjJSCKtSeaOHoZh/WXlM++Fu8Qs0VYtU6J0I9vrIdN4MBSMgTH1lniRUxZ8Ct85DkOTRijEBJXg9K8npwQfwBnHXNY/h/j1Vg9IgsR48iG9LMoV1zAh2O2B+kMKC27ggAoHZfD5AqrjxrGVYEy4hTxZ6WQ602K9j7h0r09QwIwvW4HpV/rMLKR571eKt7D1BLbhqmTigcuHVtc6206FytccBPj9UY3VZ09PK39dhurHzsp5h39W0A0lD5p+dQdt08WHISAVuneNfZ+ZAY2ByKM984A1TVj/wI40+1YMuHe3DPb1/Bilu+CxiAVU9sxJiiXNfrLDlpGCZTECuQFXidgcjW9i6/iwks/9HFSEmKlzVg6ZxHs+l4K56ofhcbtuz0ukCBt2B3fIxjLr3TzJh12hTHs94nfrMLQEu3AUcdveCOdxnRYTOg02ZAl/PRJ85l12UTFwnptgEnOvrQ0mlHj92Ikx19OHS0HZlZGRBMsejpM6FH6D+Pe+wG9PQY0Crf6CZkJ/RhxrBuTB/Whe8M60ZOoo/epPYut4BbHHDU0VPCLk+edhfs9Z1BlcEqKyv9DhEtLS0dFKAKByk35aR8lzt37sTKlSsxZswYdHZ2urZXXFzMHm2RyNYBtH0tProa1U4NAHVuHg3FkpuGitsvA9AGHK/p/4cx3hFwy+r/GZumywXWPHis1uqszzp+TywAYlPVTqEqAg6yzZo1Cxs2bEBxcTFuvPFG3HnnnXj55ZfxySefYOHChUqkkRTAO5XaEsz3oWalVstdhwcG35TsbankEtnhPj8VC87GJgNpE8SH3eYYVvqtGHjr65S8GXtnC87PPobiU5S9E+hiShArBrGp4hDl2FRUPlOFlb/yXN1u6V2rXb87G5+S8pzBKPYANMUDsd5fAgBld52CedcuA+DlXLPbYMnLAvIyHRUb56PXI1Dn+/leCYEngyPoFSuugGiI7R867nzeGOf2d4zjdTFu/3f7nzEW1sZG+c7JWHFuOsTnwNoCrPztU5h35Y9hyfUSXLY7JpsfGHxzPmwd/b/7mCfGW4W+s0scJvnBjn0AgFVPbnS9/p7fvuL63T3wNXCbgfbGC2QFXucQmk1PL8Oieef4fE/T8VZc9qM/yDrUxjmPZu3uA9iwZScA7wsUhDr3ntEApMcLSI+3YXR6cNtwDrvd4Dbs1i4A3X1ikK7TFajrD9x5e871t+Nnt4/XfdtqwtFOE9Z9lYh1XyUCACZk9WD6sG7MGNaNM/O7YfY2Kt7eA0tyqxiY7N0GNBwXh5UmjQBiEoPbeTfBXN8jdQ6tUHk7lgBciwyoVdeW66bnunXrAACLFy92PReuXnoDr7NK1PWC2abU+ptuAtO2TqB9vxhY67RC6vy74RLIDSdvBEHco8NNYegVb+8COg+KDydDDBCX6Qi8ZQFxWeIqp0Y/FUMJAspfriCZ88Zv/7zbA2/+WgwNKL/zalj6aoBv9jim9/EzF3D+bAbZpKqqqoLdLt5pu+mmm5CZmYlt27Zh7ty5uOkm3yu2kbaEMokhA3TycC8Ag71zrFalVjO9pjB0JSgcPcuampoGnQuhnBOhTjKq2cqbMcYxqXdR/7DS9m/FLva9Jz1eGpa7k8Z4IC5dvJMYl+YIqKWKv3up4JTddhfmff9aMS1+Gp9y5jnFu+kLdrfgW6/Y09A4IHAWIm+NoVCOj69rUEJCAgDxfPTKuT9SKnyC3UtArgOVlY9h5cNPe7zUvUIPiD3bOrt6/Qa+PPYniHlkPBaBcfC+uqYRzqpeTk4uik8fDRhMQGwGgL+iuPhMFE8ZBxhMqN0lBgmRfCqQPlFcKMfgeDjzyKDKt9vvMja+1BgG7I3RANfwTt/LNASnuw+oPRyHrYfi8f5BM/53LA6fOx6VO1MQb7LjXEsPpg/vwsxh3Tg1vX9oqUcvXOf8QE0Q5wNKGiGuUhqfE1S6pJY5nC5kaL6u/QMXGdAT9/J3zJgxAIAVK1YAAFatWoXVq1dj1qxZAJS94TrwOqJEXc/bNofK95JXbddyYLq3TayXte139FjTRmCtp09cqKm914iOXgM6bAacdfFlqCyeia4+E/bWn8RfX/8UC684F5nZmejqM6EpNh63vm1Gh82I9l6D630dvUa02wzo7BULVUt8BvZ8exTdO5Jx4bh4jM3oRWFKnywrpfsl2IDuI+LDwXqkGZV//whlS34AS+EYMegWl+m715u9b8AN1G5YUnpQcefVgL0JOHbI+zXbeU33FyQbwJICVNxyIQABsA25pHhUCzjIZjQaYTT2d5+/8sorceWVV8qaKFJeKD2RuMqIPNwvsFruGaYEOSsX4R6e6a2B/8QTT2DDhg0erwvlnAg1P2i68ubkPqw0+2ygp7m/Utd9JOS7k/2MjgBaultALV382xTn/60DROS8FAYjYDKLD4XIfY4OdQ164oknkJMjBhiCvuljMPbPqeembPkqzLtGXEnddW5WVaF46iQ0HT6EzZvfxKy5N8DacBDAX1F81nQUTyx0C9Q5hvkZYvqHvca2iM+Zc8UeSYYYMbDl/L/R8bfB298moOlzAA8B+ZcAI0sAYwysjUdgbWwCDAbUOmKOtY15QIHYKG4yiT0AmwxjUWvNFv//rVi3q93fB2TEOI5fnvTj51qBWqy8WxvqYT10CBB6UPtNPQBg7uxpaGpPQu1XHbBkp8CSneS2wlz/UEk1FhZRdI5GL8xuC7/87CzgaKcR/zlkxtZD4sIvRzpMePdgPN49GI8HARQk2TB9WDemD+/G+QXdyIj3MrS0u0l8HK8Re+MmDgcShos/YxK8piPYYJna04XoNchXWlqqqfQGelPOW/m7atUq1+9ffvkl7r77btnSp7Xveah8r9v6fM8JoO0bsR7W7eNGlUS9dngEszpsBrT3GjwCZO1egl3tbq/3eI1NfI+UFaQzLz4b73YDOBRYmus7E5E0fgZeqAdeEC9XSIix47QMG07L6MXYDBvGZvRibGYvchLsio7wtDY1Y+Xv12HeheNhSXZbSMJgAuIyABg9b3ZJnIaFwktSkG3nzp2SNzh58uSgE0PhE0pjUbcXEA3Tc+Ndi72mlOxt6a2C6R5gmzt3LjZs2BDSOaHn/BC0uDQgbgqQMQWwdaDs1rGY991LgO4m1O7+duheQYYYMXgWlyE+Yh2/x6YMPT+aDMLRw1eL51ognKvWhXp83K9BFRUVgwLcGzZscD0n900fr+dmSYnr3Lx0zg8AANZWx75llQAFg89bj/zSIAbZag8mArnZ/Z+TJ+14WIoMYr4YcZo4PBtA5dqn/QYily9fjvLycmzevBlr1qzx+bqAjt+Ano+Vf/3j4LJy84fYsPlD79t27y135BMADwGZZwHZoz0bFL7mMYSXoFMAAp2jUW7ZCXbMH92J+aM7IQjA3hMxeP9gPLYeMuO/jWY0tMfgpS9i8NIXSTBAwOScXswY1oXpw7txRm4PYgcWc32d/SuVAmJPiMThQMIwICHf9V0NtdiSVssctYN8gXIeS60Ei5wCvSnnqw2QkJCAxYsXyz510MBVSwdeZ1999VXU1dVhz549Hs8DwV13/V3Lm5qa8Ne//tXne319pibrb4Id6Gx0jCb4FrC1BLUZuwB8fiwW7x00472D8djZFIuuPmXrXHFGAQmxApJi7EiMFZAUK/Y2tnW1473/7MK8C8ZhRE4SkmLF/yfGiCuoO1+fGCO+p72lBfu/OYSN732OtMJT8dw7BzFj9oVoi8vBwY5EdNqM+KwpDp81ed6QzTCLi9aMzewVA28ZNozJ6A15nuAhCX1A91FlP4NkIynINnXqVBgMhiEXNjAYDOjrYzQ1Uvi6e6SbCwiFRTjnQHP/DH8Bjeeff16+huMAQwWZm5qaXPNW8pwIUkwiLGNnwjJ2ptiAztgI4K8onniK2CsoLl1sMMZlOLrRZ4jzpoVx8tiBjc9w9PDVRQ9FB2/nqPu8PUDwx8f9GnTrrbdiw4YNqK6uRmdnp2Zu+gwVnJAzv3jLF1InNrdarVi0aJHf1wUr0Bty1sNH+/PM544h4nubgKRCx+tH+k+P3eY5+bLQJw7FEfo8H3b353z93+75P7sNgN3zvSEG9fwxGIBxmTaMy2zD0slt6LIB/2004/1DZmw9GI+9J2Jdjb/HPgWSY+2YViDO5TZjeBdGpnqpi/ccFx8ndwIwisNJ4y1Ap++VAb1d3zldSHD0VH47eavP+WoDOMs8JTtbDNWLDgj9ujtU2eykxXw/ZP3b1gF01DseB33OPTqUY51GvH9IDKq9f9CMo13eJo8ETAYxmCUGueyuYJj4nN3tf4IYEHP8nhgjwGo9jGW/eA7P/foaFI/Nc/0/IUZAnPePE4dZHqpD2bk5sOQOvShMxd/eGjRi4vX/ivMMwmDEHbdfg9lzZmPviVjsPR6LvSdi8E1LDE50m/BxowkfN3qOAChIsnkE307LsGF0eq+klcT9TZHSdLwVm7d9jrtvnK3q9AkUGElBtv379yudDlKRr8aA3u4SRgot3zmWIhz5ZqhK0PLly1FTI67oI3fDcaggs3vFSw56zw8hM5mB5JHi78OuAE49Myw904YysMEUjT18/VXovZ2jA8lxfJzDQsePH+96LlwBbn/n5lANamd+qaurcwUf5cwvUm+GKXnTLNBthxx4NDqG0iL0yf8lEYTBAbpBQTxfD5s431/PMbFnwhCN3fgYYMbwbswY3o0HzmnB4XYj3j8k9nLbdsiM410mvPltAt78VhwSOiLFhunDujBjeDemFXQjNc7zJrn1yAlYm75xrfoKALVv/Qk4cTZgzoRl5ERYCk8dlA61pwtRIsinteGIWhJIfW6oMi+Q4+zre542bRqqq6uRnZ2N+vp6LF26FCtWrMC4ceOwZ88erFq1KuRy1Nu13DlCwZ2UfB/u+tug78vZW63joBhY6/EdUPen1w7sOBKHrY7eav87GgsB/Tc1E2PsOK+gGzMd5U1WvB2JsXbEGYO/91nbdRLd9bswOnkeknqOofI5cY7OND9BpkB7I5ddPQNjinKx+K6nseLW72LVExsHjZiw5Hbh8lO6XO/psgFfnYzFFydiXMG3L07EoKG9//HuwXjX640GAUWpNnG4qbPnW6YNI1NsMLlVZYeaIgUAFs07B5bcNEXmLNXKPKiRRFKQbeTIkUqng1QUyt21qA8A+BFsxU2PdzvDLZjl57XQ8A52e9GeH1zHdFihJgJs3kRjD19/DTB/56gzsCTH8XE/35yNMiX46tERyrUz2vLLUHQXqDYYxGHqgU9vPFhvC9DVBOu3dah8+m8o++FZsOT4Xkk5L8mOH5zWgR+c1uExXOv9Q/GoORyHA60xeH5PMp7fkwyTQcAZuT2YMbwb04d1IUc4isXL1+K9/37psc2lP3sKwFMAHHNeLr8GiM9zPHKBuCzVvyMlgny8oRy8QOo7gRxnKd+zMx9+73vfQ3FxMWpra7Fq1Srf5ajd1r+iudHsc05Wb2Xz7NmzcdVVV7kCeQBcwb3s7GyfvffCXn9zjjhr+RI41CguWjDkCuLeHWw1Yauj5+x/DpnR2utZ9xqf2YOZw7sxc3gXSvJ6fPYuC4Sv3lwJ8bFY+djrmHbGKFkCQO6f41wd3Lm2Q0J8rN+FteJjgEnZvZiU3Qug0/V8c7cBX54Qe7v193yLxcluI75ujsXXzbH41zf982KaTQJGp4sBt7EZvZh84Xfx7++chay4Huz43HP177p9Viy+q3/BJSXmLJW6TUEAuvoMrkUkxHnzjGhz/d0/B197rwHtcVZ8/5wMTBoWfYG7oGoGe/fuxWOPPYa6ujoYDAaMGzcOt99+O8aOHSt3+ijMAr1LyACAb9FUcQv3EBKtNFC9VTClnhNSgrC8wy5iOaM/4TpHB+YNpW76yFmeeysvgf4565QoL6UcFyVvmknZtlbKdVXEpgKxqbD2NGPloy9g3qK7YBkxCuhqEldh7josDvX0ssqf0dDf6Lt1ahvaew34yGrG+wfFRRS+bo7FJ4fN+OSwGWtqUpFsysRhy0L8ZFUSxsQdxm0/fQIABs95aWsD2tqAtn3OT4IlLh2WggxxyH672Iu0eOrksH1HSgb5KisrXas+Riqp9Q6p9Tmlrs1Dfs95ubAe+lZ8cWcT0PY10Pq1+PeJz4DGk+IiM/Yu8Wdfl5dgk8Exj6Rj4R/XzzjHw+za5u233z4ojc5gW3l5OS699FLZj4EkggDrN7thPbAb6DqK2lrHCI4P3gCa3XtjDR3g6LIBHzeKPdW2HjTjq5OeK4tnmPswfXj/kPTcRPmHyw/Vm2vdv3fg0ukTFfmcVU9uBAAsvutprwtrDdXTK80s4Mz8HpyZ398rWRCApk6jK+C290QMvjgh9nzrtBmx+1gcdh9zBnvTAIxAapwdw8wTkTk7BZ/ZC2BuicHJzkYA/YHHun3SbigKgriKdZvHYhPG/gCYW0BsX0MhMi5aiif2jULC4RRxQQq3xSdcC1bYDLALgXRPPI4pp7QxyCbFyy+/jGuuuQZnnnkmpk2bBgD46KOPMGnSJPztb3/DD3/4Q9kTSeGj9lAA0ict5xulG47B7peURns0BWq1JNTgZiT38JUjoK7U8dFLMNbXUFrnsFElFmuQsj0lj59evhvNMBj6F3FJPU18zt7rCLodFnuodB32Osw0KVbARSO6cNEIcYhTfasJ2xwrlm47ZEZLTwySxk3Ha45F6wp+PAmd+3fgW0M2JvQZcUp+io9Gub1/XjcAOC42+HBoPZD9lWOxmTS3FZxTZZ8rU65A7KeffoqnnnoKCxcuRH29uJRgVVUVZsyYgfHjx2tmji25SalTyFmfG/J6kZ8HS26mYx7FLsfPbljiu2AZaRefOyr2NCouOInizD3ic502wNYsrgZs2Ak0fgtLrOPvhKNAW6+E1An9C6nYWr2+whLbjOU/uhizvzMBOZkpqN19wBX0qX7kRmRnZmDzf7bD+smfYbHkuwXo4gYE7Nyfd/s90J759j6g2xF077QCXYdR+f9eCWoldkEA9p2MwXuOudU+tprR3dd/rhoNAs7I6cHMwm7MGNaF07N7PYY3KjG8sOzqGZh30RQAwJYP9+Ce376CFbd8FzAAq57YiDFFuajdfSDkOcrcP8f5na7+2ffx5TdHsPDSMzB57PBB7wmm95jBAOQm2pGbKK4K7WQXxHLZfa63vcdj8XVzDFp6jGjpSUXKGd/FhjZgw24AmIRht8zAA599i963voXd1oOMWT/GLz/JRdIXZnT1mWA3xcFmiO0PngUcEEtD6pnD8fYRAEekvSPRbeEJ5xx7SQP+TkwvwujcZIlpiCwBB9l++tOf4r777sMvf/lLj+fLy8vxs5/9jEE2nVN7KIDeReukwGrmm6Ea7HLOE0LRIdTgZiQHFIJpgA08R/VyfJQqz3mdHVokB6oHkpzPjLFAYoH4AMRWcs8JR2PbKv7s6xy0/cKUPlyQacXYvmb8Xx5w79OfouZYCuJPKYa5YCxiswoRm1WIvzYDz33YCwuO4PpzkzFjWDcmZPXC6KONZslJE4Mazl5vtjag8+CAVxldvfTE4Fta/98xyaoN/3/qqadQVVWFqqoqj+eVCnTrScDlk93mCo71r/7bDdi7Ufn/HsPK33keY4/rhY8AkJP1SDMqn3bMhdZ7ErCluv43cP4tJVYHtuSm4ZH7BrdrS6+ajlnnjoW1qRlr/rQRi+acAUt6EAv/GUzeA3HGWM+/bW1ij9buoxi44ErZ1TMw7YxRWPfvHRhTlIt7fvuKz5XYW3oM+OCQGe8dEnurHWrzDANYkmyYMawbMwu7cH5Bt2u1TOuRZjz4uGdATYkhi5bc/l53lS9sBdDfwwwA7vntKx6vd85RFsrnOM2aNg53/3h2wNsKhtEAjEztw8jUPswe2T/fW3cfsL85Bh9904sXtjUgLucUHLal4Eh3PGJSshCTkoWEU/pvKHzaC0DiNHveAmLWhsP44ssDEHo7Ye/pgtDTBXtPB4TeLth7uvD9iyZgybwz0NXWho1vbcd1V5yBovwk1wIUJinFd34ekBx9vdiAIIJsjY2NuP766wc9v3jxYqxevVqWRJF6onq4hgy03KNLSWrPgRbKMQ1nbzEpjSnn6/y9JhoanqRNwQSI9BJUG0ip8jwSrrNK35zQa54JRtD5zGAQV1c2ZwJwDKHqOQF0NACdjodd7D3hbXhU8wcvwmBOQvzIycg//TxkjD8Xx5CARgzD77YDv9sOZMX34TuO4WHTh3V7DBGTFtSwi8GR3pNe/mcUA22xqUBsilvwzfG7jzmzXJ8fQiB24cKFqKqqwoMPPoju7m6v82xZrdaIuNYGMw3MoPJp6mQUTxwp5q+eg0DDTvF3Wxv8rbJbNn805p33gPiZuz3nmQI8A0Be097UjKqX3kfpVdOHfK3rPWGYwL3sGnHbznm9gib0iYFxL8FxqZzpqHrpfVQ/8iMAQPHEESieOAJ2Adh9LBav7BB7q9UeiUOfW8+mOKOAcyzdrsDamHSb146nSgTUfDMCBiMWXnaWuE+P3ozOLhuW3rcWa39zE4pPPxV1+xqweNmjgDkX1tZkVFb/C2XXz4ElP9fxfpMYwDe4/25ybdv1HIxAozhNA9ImAqnDxPkxe1sAWzusR076XPETkD4UVyqzybmqtAFLiocB6AFwDP/ZeRCzbv87br5lEWzJ+Wg82o63t36K+ReMxamWFMSb+pCfHo/h2fFIjHH2JutfvdVXQMx6pA3WJhOAZNTuPu52fp7mtn/dqN1txeN/eBb/N2s4chMTBm+IvAo4yHbBBRfg/fffx+jRoz2e37ZtG6ZPny5bwoj0SK89FNibKzykNKYARGWgVm1K9VrS2rklx1BYtQNE4Tqmei3Pw4FD2eUjaz5zDjFNn+iYkOco0HEQZTeYMe+iqQAEj2BHQnwsFt/1NJ669yzM/s5xfNtiwlZHL5cPrWYc6zLhn/sS8c994oqtY9J7cVpKO47t24XSi4fhO6eaQ5jw3A7YWsSHtxiDMd4RfEsBYlLdfk8BYpIDDsS6l/HOIaI///nPPV7jDLYBkXOtDSiI29cF9JwUH70ngaZPxOcPrQfSRwT82d56DDkDQIFwBrWksDY1Y+XjGzFj+ploicnCsS4jjneZcKzTiGNdRhgNQFa8HZnxfchKsDt+tyPdbB+yZ44lJw3Lf3Qxmo63onb3gbAEXgJlTEzDe03ZeO6dDLx/SDyH3Y1K68UMx4IF5+T3IDF28DyPvjj3GxgQdDLEAHHpsAwfA8uIMeIqz96CWu7BrYGBMBgBY39aJydaUV6egllXlYnn7X1rkTD8O0D+eHQ2OOpoBxORcEzAyjXVmDZ7MSxTLgz4eFlOTRaD9WMvAHLdylrBjspn78fKVb/1eL2UobhySzDZ0dOwF9dP6kbxRAG1u4/ilVXPYtnyB1A8Mcvtld1e3+8r8CzX+UneBRxkmzdvHn72s5+hpqYG5557LgBxTrZ//OMfWLlyJdavX+/xWtKvaBquIRctNECDIWeDSQ/5Ru6AysBGv68ggNTGFBv24adUryWtBSO0lp5ghGsfwlGe66G8HMhZvulJsIHZcAR0FctnBgMQnwPE58CSeQYsZ/QCHYeApLcA/NWjIZWTmQKDAShK60NRWjuun9COXjtQezgO7zvmc9vZFIsvT8biy5PpQNx0fLQViNsmYHxWL6bk9GBytvjz1HSbzyGmAbF3Ad1dQHeT9/+bEvuDbq6fya4gnHuDHfA9F6K7SLzWeq13PPkoik8fBfS2wZJlBupfA3qbXT0fnSwpnf1DgsPE2wqTn/yvHid6YtHSG4uYpDQgPtURPDPiWKcjiOb4/Uh7Lkbc8xp+VGMEaqR/rtEgIMMsBtzE4Fuf2+/OoFwc+pIt+O6tf4G9qw0QxF58agRenMfJZjdgw65OpM+4Hv/vyAwU3r4Qv/+q/3VJsXacVyAG1WYO70ZhirRhrd6+hyeq38WGLTs9Xuex7+XlqKi4IsQ9E7kH0Z31dedwbtdnu9XR1q1bF9QCFD6D9QYjym75CeZ970oAA+rjZ5wB9HXAkpUEZMcDPc3i+dPbDPS2wl/vzmB4DM0PQjA9EX2t9OqeJjWDyXoQcJDtlltuAQA8+eSTePLJJ73+DwAMBgP6+oIYn06aEU3DNUg+esg3cgdUBjb6fQUBpDam9Bio1Tv2WgqcHgNEWqNkeRlqgMjbzQOr1Yq6ujrXXFZ6GcoebGA2EoLSLsZYILkIyHRcS/JmwZLai/I7rvLaeIs1AudYenCOpQd3n9mK411G7DgSh027O/Hc+43IPW0KWm2x+KwpDp819Q/tTI61Y1J2L6Zk92BKrhh8G5bcJ+f6B6K+DvGBw97/b0oETPHiw2hG2fenYN70ZwGTGbW7vsTSZT/H2iceRuHIU1D1p2ex7rX1kXOt7etxDHlrgcXcDMuwVvHvPHGVwuKCoyjOT3S8uM1XBxhZ5zlzDxT02oETXgJkx7uM+Pf2k9jxtR2mxHSYEs/D8GWX41etqQEFzJx5LTmmF2mxvchJEJCfakRmvB0C4Pqs411GHO00oaXHCLtgwLEuE451mfDlST8bT7gKhcuughEC4g3dONl0GGPy4jEs3YTUGBtMaSb89fNYZDqCdNkJ0nvKSVXfasJPX27C29/EIX7kZBjNiUibBjjD0d2NX+HMrBY8sKAQxbk9QfU29TbE3D3ANnfuXGzYsCEs9SSLxYLly5dj9uzZyMnJwZYtW3DPPfdgxYoVAMQeqGPGjHFdj+S6FgV1A0Swi4E2Z9Ct52T/z76O4NIxcP7BEINuXj9jwDaHWuk1XMFkPQs4yGa3y79kL1Ek0noDVMuLNCjde4ABFRpIzt4koZ5bcud/pc71cAbU1S6vtF6ee+MrQCQ1fw18f7TOORpO4chnrs8YKa6iWfHopYCtA2g/ALR/A3QeEueKcuPs1ZABYGzrARz5x1+xatV1GDZ6DL5qS4a1Lwv7OpKx62gs2nqN+MhqxkdWs+v9WfF9mJLTi8k5Pa5eb1kJCrcnXEE4x34nAEgQh01NO2MUAKB42AkUj01BzpLTse619cDB9UDmHkdgLt6xQqRZ/OkI1nk8Z3Q8H+4FHAQBsLU7AmmOAFpva/98Uvb+ydTdh4q5Py+nnj5H0KxrcNDsWJcJxzuNONaVjeOWMXj13yY0d/s5XrETkTR28NMGCEiJsSEz3o7cZHHIZ1aCGMj6+OPP8Pq/PoC9oxl9joe9s8XV08wZEHAei5UDhs05g35HvQT9+tPf/3dztxF2GNAhxCMueyS+7QO+dU5AfxjAF4PTb4SADMfQVDH41uf2u91vUK7TZsBH1jhsPRiP9w6a8XVzLBCXj0THwsOJhi40/e8jXFMSj/kTY5AR1+voaeS5+rCk+epiUoEEC8pum4B5i+8BYpK91pObmpqwYcMGVz1JjnqLr21YLBY88sgjrr+dPandh3ffc889rt9VvRYZjOICL3Fejm9fj1vg7aRjaPYJ8ZwNoPeblOB3oD3RBm7T2wqsgcynSEEE2bw5efIk0tPT5dgUUcTQeo8uNRpMwTbu5CZHQMVbo//VV19FXV0d9uzZ4/G8t8+U0piSo8GltTnBokGo55bc+X+o9CxfvtyjAqtFagd4tF6eByKY/GW1WjFt2jRUV1djz549XieKnzx5spLJDliwgVklArpSy+Fw9Wwc9BkxiUDaOPFh73UE3PaLPwWb114NpSs8ezX8fdlc2OzAVydjsLMpDp81ib3c9hyPxbEuE7bUm7ClPt71nuHJNkzJ6XEF307P7kVSAPNDBcM5bMo5QbyTqxdHdlL/SqkIYBJ9Q4yfINyAnx7P+VncwdbpCKC19v90BtKGWHDA2z7Pu2hKyD1guvuAXU1x+LgxDp8cNuOb5hgc6zKipSfwIKMBgivI5G2IZma8HccbrfjxXU9g01P/h5mTC3z2BLMWZuFnM2cA8B8Q8DVsLtYI5CbaHQt72IZMuzMot+1/R7Do/r/jNz+/AalZuWJwbkBQ7kibAW19MbCjv6ecFEaDgHSzHRlmO+rbYtDT198V1GQQcEZuD2Y65lbrObwPZ/3mYSz58QMonuinbj1g/61HmlH5949RduMiWIpOBxIsQEySeMzygIFbcq8nu5eJgDz1FqnbcC5cUl1djc7OzrDcKJflBogpDjCJQ/g9CHbxvHYG3ZyP3pODbnhIFWpPNHG+tgxxfj2jeMOkeMoEFJ8+xrEqrmPePYNJLP+cvw983pwZVPojQcBBtt/+9rcoKirCVVddBQD44Q9/iFdeeQUWiwUbN27ElClTZE8kEclPjd5ckTT0xluj3/2uGuA/CCClMSVHgytcxzyUYJ7WAoGhVqa01lPSV3oSEhKwePFizJ4dnmXrQ6G1Y6pVUlcwDvT927dvdw0Rdecs88rLy4OaD0dJwQZmlQjoytUADXX4r680DNp2yqniw24TF064MQPzLioBYHMFMQCg+pEfYfypFlcQI8boXB3PhisdvZG6bEDdcTHgtrMpDp82xeLr5lgcbIvBwbYYvLFffJ3RIGB0ug2Ts8XA25ScHozL7A1hYQXfsjOSPYJNvnqGSJ7LSLA5Al9tAabEMCAAFyP2KrS1idsMkfVIMypf2Or6O9Dhn209BtQcicP2xjj8t9GMT5viPII97owGAZlmOzIdAbIsx8IC4u/9vc4CWWigtrMFvUcPID3W5ve14Z7A3RmUm1YUi5/NK8SV422w5LZ7fW3t7gMoWfgbvPnizzFsRCGOd5lwtMuI446eckcdQbnjjp6Arp5yggHHu0w47gjKFSTZMHO4uNLveQXdSDP3B6RrjwS4AwnDgdyZsB5vwsrf/xTzrr8flpTRQ7/PjZq9uydPnozy8nLMmjXLdb1Seqi3ojfaDEYgLl18oKj/eUEQA+zdxz2Db4KtP7DlCmp5/l1WOgzTpl+Kda+/gzGji3DPL9Zg7R9WoXjqJDQdb8Hmt9+H1XgOLAUF3rdjMPWPuz5ZC+AXgGU2MCIChtOHScBBtsrKSlRXVwMA3nzzTbz11lvYtGkT/v73v+Oee+7B5s2bZU8kEclP7uFxcnQTV2M4WLAVBW+NfmevDmdPj2gKAoTSiNRa8DXUylQw55aS+d9XepxycnIGviUoSgZLw7EIQSQYKkBUWlqKs846C4D3/CX1/c7nqqurMX78eNmGNcuZf4INzGo1oKtkOelz28YYILkIlilFsJy+UBxKmvwOADHINv5Uy5CBjPgY4IzcXpyR2wtADES09Bjwv6Ox+NQReNvZFIuG9hh8cSIWX5yIxctfiu+NM3ourDA1pwejAlxYwduwqXrrCcy7aIrrefUm8BbEIZwyD+N07nPdPiuqXnofgLRJy491GsWA2mEztjfGYfexWNgFz4MdL3RgWoEN00faMSGrF9nxYmAt3WyXZ8ELN3LMPaXkBO4+g7MDP9Peh2++3IfMuF4kADg7Jw2WU31/5sA563IT+zA63TZoXkNnT8sFF0/1eZysR1pgbY4BEvJQ2yCuRFl7IAbIbkfdl996/fyBZbG3erLFYkFZWZmr7uK8nlRUVODWW29FTk6OpHpLMHUf9zqa870RyWAAYlPFh3vwTQJLDmDtqUXVc7e74jbF51+O4uJi1NbWYs1j12PRkptgGTV0/c85J97zzz+v6blXtSbgIJvVakVhYSEA4PXXX8eVV16J2bNno6ioCOecc47sCSQi7RvqDrmUC6haw8GCDah4u9B873vfc13AVq1apVoQQO35qyhw4cr/TU3i1Mh1dXXo7OwEIF/e0FqwNBRa610pla8AkbMnmvMBeM9fUgJMzrKltLQUs2bNku34yJ1/gg3MyhXQ1UI5LFcarFYrHn74YcyePRtHj/Y3HWrrk4AkI9DVBEtmjORgRWqcgPMKenBeQf+cUUc6jP3DTI+KwbeT3UbfCyu4ze/mb2GFQIZNWY804+E/b8bs70wAIK6mCGhzVT1/Q1ml7vPBVhP+2xiH7Y1m/LcxDvuaYwd9TmGKDWfl9+DsvG4kt32Dudf8HH977QHFeom5C3jhBWM8LAUjUH7nNbAUTQbShqNy7bNY+TvPXrgex+Ku61Fx5w/FOe5sHYDQG3K6Qx2qJ3X4qntPS4/tmRKAxEIgaQQqn3sGKx/0PcoCGFwmDCyLfdWTvdVbNmzYgA0bNoj7KaHeEmrdR4/zpeqNxWLBokWLUFJSgkWLFvFYSxRwkC0jIwP19fUoLCzEpk2bXEMFBEHgaqJEOqXkRUrqBVSrvQf0KFwBm1AacFpogIaD1HMrXPnf2dt88eLFruf0Nnl9OCrV7o0MAJoNuHnrceAtQHTFFVegrKwMgP/8JSXA5DxvtXg8tESOcjjUclJKz8SysjKv2zYajXjttddcvVXWrFmDNWvWeG7r9vv792fFvai4ew7QcRDobADsPpas9CE30Y6LR3bh4pFiry5BAA60mhzDTMXhpv875n1hhez4Pkx2W1hhSk4vMuPFOcsCmcDb2tSMNU+/hTVPv+W5nxpcVc/XUFbrkWZMO2MUqh/5Efbsa8SqJzcCAB649bvIHDEGR2OHoSFmGM57IQkN7YObgWMzenFWfrcYWMvvhiWpf+632t3KLJzgl9EszhloSnCsGJvQ/7fr+QTXAhSWUUDFWaWut5fdcQrmXeW97GtqasLmzZthNZbAMlI8j6wHv0Vl5VMoW3IlLDkpQJ8j+GZr73/0dcLfnHi+8lyhJQPr/r0DCy6eGvTh8FjIwl1cJpA0UnzE5/an5eZbMG/B9zz2f6CB5ZLzujcUb/UWILAezqHWfSJpvlQ5eLtmWK1WLF68GB988AGamppQX1/v8X8gcurcWhJwkG3hwoW49tprMWbMGBw7dgyXX345AODTTz/F6NGBjecmIm0I5iIltfIv9QKqp+FgQ3WlV/vOWrgCNqE0ItWeyD5cpJ5b4cr/d999NxYtWgRAnryhRrA03JVqLffQk5o2OfOXnOVbuPJPsGkOZV/lKIdDLSf9paGystKjZ+PAbZeWlqKqqsqjwe1sPPvcnzQLkDZejJB1N4kBt46DQNcRBLJ6HiCOlBqZ2oeRqZ2Yd6rY69a5sIKzd9tnTbHYezwWR30srHBKmg3DktMxLLkPw5JtMBemwJSagykT/M/X5VwYYfFdTwOAKyjXdLwVm7d9DuuRZk30ZvPG1YvKaEJc3qlIOet7iB8+Ac8aJ8B03DPNMQYBk7J7cbYjqHZmXg8y4j2/JzmHW/YHiGaKc0GZEh2BskQvgTPHcyGu3Oqv7KutrcWaNWs8eudYjxzDylW/xbzvXQnLqWO8b1QQxECbK+jmGYSzDEuHJa/do1ecM79VvfQ+yq6Z4X27EuzcexArH3sdY4py0dmXAACobcxHU10K1q37E26++WZMndofZPO2/97O48LCQhw9ehTZ2dkBDdsExHI8ISHB9byzl7yzbPdW1rnXowdeh7Ra9w+3YHrUe7tmOFdedQ4bdRrqWhItN8OVEnCQ7dFHH0VRURHq6+vxu9/9DsnJyQDEL+KWW26RPYFEpE1SK/96Cp5JNVRXerXvrDnTZLVasX37dgDKHPNQGpHsuagOuc/HSAqW+qpQOhsPzqG2ehFqMMzX++Us38KVf0KZFiDYz5fjXJOjl4evNDh7kPva9p49e1BVVeUxtNz503lO+Nwfg0HsTROfC2QWiyuWdhxyBN3qxcm8g+C+sMJVYzsAiAsrfH48Fp8dicPOo2LgzX1hBU+ZGH7zM7jqIwH5u/owLLkPmTGdSEYbcszdOPR1C2Iyh+N/+44ixtA/TC8hvn8Y5Zqn38KieeeoEmTzF/Dq7jPiWEweYs5YiLm/uhpftKagy+65akScsQ+nZ3bhO4V9ODu/B2fk9iBxiBVdAxr6aDANCJwleQTRrEe/xMrHfop5S1bCUqTfOiAMBnG/YhIB+JnTyt4LHPsAwENARomjBxyA+HzAnO3WK04qI9a9vRdAfwAYAJbedLvHqyorK/1uZfz48R7nbXFxMdavXx/WhWK0fPNKK4I5Rr6uGe5B1Pr6eknXEinfbVlZmWZ7+qst4CBbbGws7r777kHP33HHHXKkhyKIXue0IWmUDJKo3RMsUlitVq8rAsol1Ebk+vXrPe5i6j346i6U8k9P+T+SgqVDVSifeOIJ1yIRat7BlXp32VeASGr+CsfNgkjKP0pQ8iaVt20751wGgBdffBGA76HlATHGAslF4gMAeluA9nox4NbZ4LGSpr+5xryJjwGKc3tR7LawQnO3AZ8fEwNth9pMrse3J42wtptgN5jQ0B7jGC5pBpDu2NqpGLb0UrwAoK/9JPKvvxi2libc/kYTbM27MGtyNmJzR6G1NwaCAJ/zwCnFPeBlNCch4dSzcM9mE8x16TDnj4bB5DmnWrLJhiN7ajA1qwP3zCvCzDFxAa/W6nXo469vRPGZ04C4DFiGjQSGn+IIpsX731hcoMtgystiESdwb2pqQm1travc3LJlC+rq6gBgUNnqfF9QZZExFkZzGkpLS9HUmYj6+mPituvjgZwicdv5eeKQZVvbgCGp/b9bj9thbU0AEgswZupJ4Pl/Y8WKFQDEFZ5Xr14Ni8WCxYsXY+HChX7331fZH+pCMU1NTXjiiSewYcOGoMvwQOs+bGcOJuWa4czbQ11LpM7RymCpd5KCbOvXr8fll1+O2NhYrF+/3u9rpY7jpsjHE086PV4ogqn8a6lxFyg9d5suLS3VXNoGznkVaUIp/8KV/+UI5kVST1X3CmVFRYVr8manQCd0VoocE0VrpXyVO/9o9VqqhcC5lDSsW7fO742ZuXPnoqKiwjWXVdD7E5sKpE8UH/Y+oKtRDLh11MPadMDrXGOBSDMLmFbQA6Bn0P/sAtDUYXQF4PY29mLfcQFN3WbsPwkc7TbDaE6EKSkdpqR0mC2nud77KYCC/1uIGz4B4mv7kGPuRmFKH0ZlGTAsuQ8FyX0Y7hiampso72qbh9uNOHX6HNx0ygJ83pqCbzsSAXh+QE58L84t6MXZ+T04K78bbQ1f46xfPYTfvPYAisfFed/wECy5juGgselAylgAf0Xx7Fskn6NaqjtZLBakpKTgsssu83jeOZzOnVw9al977TW/w7Jd245N9rmNyr9UDCrznXOiA8CHH37omr6pvr7edXwHHltvoy6cZUKwZbH7+3JycrBhwwaf75OSFwI5zpHYztTa+TJUnojo1V1DJCnItmDBAjQ2NiI3NxcLFizw+TqDwcDFD4iCEIkXCm+01LgLlF6GxXm7QJ911lmu55W6QIfSiNRCAzQa6fl8VIL7uXHrrbdiw4YNqK6uRmdnp6Z6WLH3l29Sr6XhDsbJca7JMfzXVxqc216wYIHXIaQJCQlYvHgxbr31Vlc+u/TSS4NKxyBGE5A4THzgXKDRAuAhIN4CGGK9rvYYaG83j48zAHlJduQl9aAkD8Cpzv90oHb3AZQseAj/d83FOG/6OVi25i3MuGAa/ruvFabUHMSk5iImLQempAx02U2o70xEfSfwgZcOWrFGAZakPtd8cMOS+zAspQ/DHb9bkvp89ioTBOCbFpNr1c/th834tmVwk6332EFcNj4Wl4+Lw9n5YsDPvXed1ZaG8tvneCzuEBBzDpB8CpBUBMSlA8dqh3rHIFqrO3krP509wQCxbLjnnntkK1PlKK99bWPjxo149dVXsW7dOqxbtw6Atm+4qJ0XtHoTxp2cx8jflA+hXEukBAIB7S4YFS6Sgmx2u93r70QDaSkCT+ETDUESvTRs1arESKms+SofiouLI6JHm57LPzkqn5FUDjiHhY4fP971nFZ66EVS70F34cw/eryxpWSD2N+23fOV87yQm0fZuXOP+LPeDGSfDnQdgyXNBktqJ9BzXHy9j5U15fLMC2/hojNHoPOrj3Hn/ecgZ06+mKbdO7D0gb/iyVVLMPzUU9HUbUZPXDraDMk45DY0tbHdhF67AQdaY3Cg1Tkk1ZMBAnIS7R5BuHSzHf87Gov/NprR1Gka9PrxWWIvtbPzu2E+uR8XX1mBW197AMWneV/EwZKbFuBKqAYgPg9IHiUG1gb0rgrmHNVa3clb+Tlr1ixXeqQOpQvl8wLdtq9tnHnmmcjJycHChQslz7Ml9fOUWChGjrwQ6qr2Wi/35TxffJXrwVxL3L9bKe2MefPmaf5YKy3gOdmI/FH7LoWeKNEgV+suTTT0iNFLw1ZLFdqB+THSywc9758clc9IKgfcK5QcDhEeweYff9dS57DGu+++Oyor+nLVCZQOgEouO23tQPsBIH6LIumw5KSh9KrpqHrpfWRnJKP89jmYPHb4oEDeOZOGoXiicwVHAYDnQg42O3C4wzEXXKtzTrgYHHQE4RraTOjqM+JIhwlHOkzYcWTwUM44o4DJOT2uoZ8leT1IjetfpMB6JDG0XmouBiDB0h9Yi0n0+cpgG+d6qDvp0dSpU12LHMgZHAzkex5YxgzVcy7UvKC3elagZbBWzxf371bqXG3RLqAgm91ux1/+8hesW7cO33zzDQwGA0455RT84Ac/wHXXXQdDuGcAJc3RUgNf65S4UOjhLg0pS0sX6IH5UWr5oIcu/d5otfzT6/FU08DGglZ76EVS78FgDXUtBYBFixYBGDypeTA3tuQ+n5Q8P0OdG9J9viYlG65SG22u769e7BlWeygNSDYDXVZYMkxB92pzX7XzrMlFqHrpfdRbT2DeRVNczwey7RgjHD3U+oD8wf8XBOB4l9FtUQYxAHes04ixGb04K78HU3J6EO+nlRZ4LzV3BiChwC2wlhDkdvTNW/mpZJkqx7bDXeYPVT6Fu90RaD1L7REGkdgu89XOcC+n9TaaQwmSg2yCIGDevHnYuHEjpkyZgtNPPx2CIKCurg5LlizBunXr8NprrymYVNIDLTXwtU6rDXIampoNWyUaZIFsM9RVM6WUD3qtlGi1/PN1PNWufOqFlnvoaTlt4eLvWlpXV+daHVOuG1tyl09ybE+J60I485aUsrOiYvDk70tv/anr9/IV96Liru8CHQccK5ZKnyPafdVO17Yf+Gv/tm+fg4plc2HJGTzHWTDzwxkMQFaCHVkJdkzOGTznnDKcPdZOFedZG2olUAVo7aaAtzyu1rDsULeh1LFVqj4WypDUQOpZeuv55i7YY6TWjVU9H2slSA6y/eUvf8HWrVvx9ttv48ILL/T435YtW7BgwQI899xzuP7662VPJFEkkqtBHgkNZb31tFGzYRtIhUfqBTqQbQ712kjIj9GCFSKKBN7KlIQEsWdOZ2cnALEMmjZtGqqrq5GdnS3r/EVqs1qtqKioQFVVlSvYGIllsKTebp1AZdU/ULb0RljSAXQfBXpOiHO59Tb7DLyVXT0D8y6aIm579wEsfeCvWPvQdSieKM515gyqees9pvT8cKFxHwp6iuo91nhTQDnhPLZy1PPClV41OjTIVQ8OZQqFcN2odm9nlJWVYcyYMVi8eDFWrFiBVatWRcQ1NliSg2wvvPAC7r///kEBNkCcMPLee+/F888/zyAbuWjtjlWkioSGspZ6Lukt4OePGhVaqflxYPkQacE5tcs/KceTvWkpUjl7rzl5m5QZkH5jS+7V1OQs76xWK6qqqlx/a6VOMPBaKvXa6qvslHJjsra2tr8uMawYSCrsf7FgB3pb+oNuPSeA7uNAbwssuWmDAmTFE0e4gmy6E5/f32PNzxxrehJJdTOtG6p8ev7557FmzRqP96hRxkipZ6kxwkArZXA4DGxnOBeLGjduHABtjOZQi+Qg286dO/G73/3O5/8vv/xy/OEPf5AlURQZpDbweeEMrUHOhnLg/OU5LQX8nJRaJEPqNgN5rdT8OLB8iLRKSbgDnMEuMqHF4a1EwbJYLFi+fDlmz56NnJwc2SZllmM1NfdzdKjtzZw5Ey+88ILfct1ZLtfV1bme01KPvYHXUqnXVsXKToMRiEsXHzil/3nBDvSc7A+8xfcMuSn3Odxqdx/w+AmIPd/C3qvNnOMIrI0atCpoJNBi3SxSDVU+LV++HDU1NQDUbXdotWeknnvPyfXZe/aIq0SvWrUKv/jFLzB16lRFPlvTBIliY2OFhoYGn/8/dOiQEBcXJ3VzmtXc3CwAEJqbm9VOStSoqakRAAg1NTVqJ0WShoYGoby83O/5oBY9HcuGhgahpqZGqKmpEdauXSsAENauXet6Tsnj6+84afEYlpeXCxCXMPP6KC8vV3SbwX5+IMdSan5Q8vzT8rk9lIHH2nk8N23aJMydO3fI80uL+Z4oVL7ydaDnupTyaahzyP3//rZXXV095LnY0NAglJaWDlkuq3leD/xsOdPi/v3JXZdwbfvgAUHobBKE5r2C0PShIBzaKAhfVwvCl5VC+e1z/B/72+cIwpeVyj++/bsgHKsVhJ7Ib7MEm3/0fF1XS0NDg7Bp0yahtLRUWL16td9zSk91BzXyQriOjxLtBLk+u7S0VLHPDrdA4kSSe7L19fUhJsb3y00mE2w2m9TNEekW76bJQ0s9l7Q+VFGJu2KBbDMcd+WkdulX8vzTw7kdyJAri8WC2tpabNiwAYD/XmpqD2+lyBVsb3Ule7kH2gPCV/nk7KHlazU1o9GI1157DWVlZZK2J7UXaWVlpccQ0YFKS0tRVlYWcI+9UHm7lr766quoq6tz9WyQ49rq/v15XRAhhLrEoLwRn+35gr4elN1xLuZ9fwlgaxGviT993OscboqISQVSThV7rZkzlfscDZCjbqaH67o/aoz2cZZrVVVVqK6uBhAZvdy12vNNDmqOavL12QkJCVi8eDEWLlyo2GdrWUCriy5ZsgRms9nr/7u7u2VLFEU+uYMaHHIq0lNDWckLgrf84C/PeWuwaGmoohJzSgSyzWA/X0/5US/cGwzOvwH/80RJEcmVT1JXsI1cORrHSpdBQ90sWrx4MaqrqzFmzBiPRRjc0+fkHPYppU7k7foJANXV1Rg/frzH6wPZ/1DrUt6Ox6pVqzz+lvvaGvbGpSkOllGTYRk1Wfw7YwqAx1F8yc0onjjSc863nuOAfejhp0N/ZqIYVEs5FYjPDX17OqGlm7Fq0XqQkPU8/8J1fNSYe87bZzc1NQEQFx9yXvPq6+td1zO1Oy2Ek+Qg2w033DDka7joAUkl14XTWSGcNm2aohchrfd0ck+HXiocSl4QvFVKhspzzjv/7pX0wsJCrFu3DgsWLAg5TdEo2Pw4sFKi5Pmnl3PbGyl5+qyzzgIAzJ07F01NTaitrdX0PqlFjzdq9JjmYAW7r0pcE93Lp6ECPM7Jwd0XYhhY1ykrK8PMmTP9LtawfPlyPPLIIx5p8HYcxo8f73ENDXT/Q23QezseK1aswLhx47Bnzx5FVptTunEpOe+ZzEBigfhwf3/9PlT+8QmULb4ClkyTI/h2AhCGGP1jNIsLFySPFlcINRhk2Bt9CTaAqufrupq8HTer1YrS0lI0NTXBarUOOm56aneoIdjjo9fr+7p16wD4v+ZFTX5RfvSqvLq6uoQpU6YIAIQdO3Z4/O/bb78V5syZIyQmJgpZWVnC7bffLnR3dwe0fc7JFh5yzaHhHOsuZQ6TUKg51j0ayD1ngbftSc1z7u/V4lwTSswpEcg21ZjTQsnzTw/ntq+8u2nTJqG6ulrYtGmTx/NS5moiT85zvbS0VDdz92ixfHIK9hrv633Oa/ymTZvCvCfSOb+P559/XigtLRU2bdrkms9oxYoVwooVKwQAwurVq73ONRnsfsuZd5XYlhJzskn9zHBsc6hros/39zQLQts34pxq1rcF4dt/CMK+vwiC9S3xeXufbPsQCq3MaRbId6uH67o/as1ZrPfjFklCKcvUPGd37Njhuv6Fe77tcAgkTqS7INuyZcuEyy+/fFCQzWazCZMmTRIuvPBCoba2VnjzzTeFgoIC4bbbbgto+wyyKcfXSR/KZKbOCqiz8qrUiazmJP1aJWchLse2AvmOpC58oOVGbDRR8vzTw7ktpeIrdVJ1reyT1jiPn57Ody2XT8E21vQ8gbLz+/je974X0j4Eeg0K9frprbxw3rwMpbxQI8imROMy1HRr+TyVQivpDyQder8GqhXs0vtxiyRaOe9CEQn7MJAiCx9owb/+9S9s3rwZr7zyCv71r395/G/z5s34/PPPUV9fj4ICsav2I488giVLluChhx5CamqqGkkmN3LNK+Dszuw+j5Zzzg+luqSqOdZdq+ScJ0KO7uZyDUE2Go2urvH19fUAOMxATsF0gVfy/NPDuS1lyIz7BOd62Cct8DY0BuifG0uL57pehkEFO8zL/X1btmzBPffcgxUrVgAQr/NjxozR7NwuzmGkp512Gl599VVUV1ejs7Mz4EmgnXPa1NXV+ZzHzX2/Q71+ert2Av3DfYKtSw0c9h+OuYkCORb+rkXezrMtW7agsrISCxcuxOTJk/3uh17OUz0JJP/o/Rqo1iT2ej9uesdyI8KEIegni8bGRmHYsGHC9u3bhf379w/qyfbzn/9cmDx5ssd7jh8/LgAQtmzZ4nO7XV1dQnNzs+tRX1/PnmwK8RXRDvTOo5Q73UrecYnEyHwwtHYcArkD5y/Psbu8srTcK0BredqbQMtRPeyTWqRcS7R2516N8inU3kHB5kE9DXuWMtxT6nFcvnx5WPe7oaHBNfTcOSoAjhECzuHoWjsP5OAvX4Z6num9HhFKjyatDC910ts1cODxUyv9ejtukUDJckON81JrZYEcIm64qN1uFy677DLhwQcfFARB8BpkW7p0qXDJJZcMem9cXJzwt7/9zee2fWVoBtnkoUTXY2/bdFYMq6urFT+ZI7HQkEovXclDnctAD/uoV6FW3Lydf3Kdk3o4twM9fnrYJ7U0NDToKpAjCOqUT2oFxjdt2uS6rmu9LJZzmKsa37Heg0LB8JcvQ61n6r0eEUp+0FJwpqGhQVi+fLmwfPlyzR9zJzWGWXvDukP4hVpu+PvOtHRe6pluhotWVFR47aLubvv27fjggw/Q0tKC++67z+9rDV5W3hEEwevzTvfddx+WL1/u+rulpQWFhYVDpJykUmL5bW/dZceNGwcArqXrlRTNK+lEw3Lq7C4vPzm7wHs7/+QauqyHczvQIVd62Ce1OI9NWVkZ6urqXMPjlB6WE8qqYXosn4IdJjh58mSUl5dj1qxZrvIjkH0N5+psvoZ3OVeovvnmmyVvS43v2Jn+cJ4HapB6LXI+rFYrEhISPLbR2dnp2o6va5cez1N3ag1XlJvVasWaNWtQU1OjmzQ7NTU1oaKiAgsWLFB8mLU3rDuEX6jlhpzT+FDoJAfZvvrqKzQ3N6OkpMT13Ntvv41Vq1ahvb0dCxYswP333x/Qh9922224+uqr/b6mqKgIq1atwkcffQSz2ezxvzPPPBOLFi3Cs88+i/z8fHz88cce/z9x4gR6e3uRl5fnc/tms3nQdkk+4bpQZ2dnq3IRijZ6qXiFY+4Xki4agrPhwoqvvNRoDOuhIqx0YDzQ97nPOShVOI+zv3x06aWXKvrZcnAPLJWWlqKqqkpXQSGpAr0Wub9e6bl/tSTQcpFzSYXG2/Hbtm2bax5KOW8UhPPmAw0WzuPP81JlUrvHLViwQFixYoXr76+//lpISEgQZs+eLSxbtkxITk4WHn300WB63g3p22+/FXbt2uV6/Pvf/xYACC+//LJQX18vCIIgbNy4UTAajR5dJF988UXBbDYHNPSTq4sqR4muquzOrJ5o6HrM/CWPcA0b19NwHNIW96GjSpdpcpWdSpZPWhs+GMy+RsJcRmpcgyL52h7odcM5X11paamwevXqoK4zeqxHuKdZSn4ItbyQe7V6NesGweyL3OUthw2Gn9TvXerxl7q9HTt2CKWlpcKmTZsG5Xe9TYehB4rMyTZ8+HDhgw8+cP394IMPClOmTHH9/ac//cnjbyV5m5PNZrMJkyZNEi666CKhtrZWeOutt4Thw4cLt912W0DbZpBNOSzYIwu/TwqGXPlGa0EA0j8lG8NqN/wCpVZ6Q/0OtHCc9RhUcSd3wEMrxyLUCeWjqc7jvq9SvsNQzzs5j63adYNg9sV5/DZt2uSa++/66693zQW4adOmgMovf2mIpnwcTlKPq9zHf6hAmnMxQD3UO/RAkTnZjh49iuHDh7v+fueddzB37lzX3xdccAHuuusuqZuTnclkwhtvvIFbbrkF559/PhISEnDttdfi4YcfVi1N5IlD+CILv09Sk16GLpN+KDkUV29DptWaUyrUIZ5aOM56H9ItZ/q1NDRaS2nREyn5QUtz0OmxbuA8fhUVFa5hyc899xyA/mHKQPDlF4cNqkvJ479w4UJUVVWhuroanZ2dXvO7+7YjcRoArZIcZMvMzITVakVhYSHsdjs++eQT3Hnnna7/9/T0QBAERRI5UFFRkdfPGjFiBF5//fWwpIECp/eKJ3ni90nBkCs4q6VKPdFQ9Njw0yMeZ3VZrVbXze27775b5dT4F8wiMpF8YzHcgRilPk+teTbl2JeysjKMGTMGixcvxooVK7Bq1SrJ5Ze/NFRWVqKqqsrj9Vq+yaMXUr93uW/+uH9ufX09AHFBFuciLYWFhawLa4HU7nHXXHONMGfOHOHAgQPCI488IiQnJwttbW2u/7/88svC5MmTg+l5pykcLkokHy0NFSH985WfOPzBE887bdNbflU6Pyk1xFNvxzkSOI85AKG6ulr1IUpaGD6sF3INs5RaXoRjWGe4ygA598V5/DZt2hRQ2odKQ7QNGwxHPUjq9y53OSTlux7I1/FgfTEwiszJ9vXXXwunnnqqYDQahZiYGOHJJ5/0+P/8+fOFO+64I/DUagyDbETyYSOH5OQrP7GS4Innnbbx+/GkVGObxzn83INsSgZPpFJ7fq5gqHU9C3dAMhyf534swz3n5ty5cwOeS81doOWX1OMZLeViOPYzmDwcbLoG5mVvn+tcpMV9zvqhREt+kIsic7KdcsopqKurw+eff46cnBwUFBR4/H/lypUec7YREZF2RdIy7hy6HJ30mocjfdhZoJQa4snjHB5WqxU7d+7E0aNHsWfPHtfzN954IwDgz3/+M1avXo1Zs2YBCO+QXT0OH1Zr7rhwD7MMx+e51w1qa2sVO67e9mXDhg2oqKgIen+CGc7s63i6XyspcL7qGuE8Z9zLheLiYp+fe+mll8r2eXqsX2mJ5CAbAMTGxmLKlCkez9lsNnR1dQ16noiiEydY1TbnhXPatGm6mASa+UkaPR6nUCtxep3InEFhT0o1VHicw8PbfEOAGFxz+vLLL1WZo41zd5JeKbkASaTefFCqHiRnXUMLx1/KcdJr/UpLJAfZNm7ciGPHjuG6665zPffQQw/hwQcfhM1mw6xZs/DSSy8hIyNDkYQSkT5oYXU38s154ayurlY7KZIwP0mjx+PEStxgvHtMelNWVoZp06a5erI5V0NcsWIFzGYzfv7zn2PhwoUqp1Lb5AwOyFGGhDsQoNTnhfPmk/OzmpqaMHfuXGzYsEG1G13+jmck33xQsx4kNQ8Hcvyl5t9Azx0px8nZA5hCIHUM6oUXXig8/vjjrr//85//CEajUVi1apXwyiuvCOPGjRPuvPPO4Aa4agjnZKNIFa55PjjRsHY1NDQI1dXVAgBhxYoVuvhumJ+k0eNxCmYuED3uZyCifX4Uzq+ob+5zsjnPR618n1pKy0Byzh0X7WWIu3DOyae1+f8i/VrpjZz7rIXjp1Se8jenW3V1tbBp06aoyTOBUmThg5ycHKG2ttb195133ilceumlrr/feOMNYfTo0QEmVXsYZKNIpUbFi5U9bXBeUEtLSzVVCQwU85M0Wj5OoVZctdaQkZuWvzuioQwMspE0cjboWYb0C2egJJTPUiIAHOnXyqH4Ow+kHG8tHL9w5F/346SFfdY6RRY+aG1tRVZWluvvbdu24Qc/+IHr74kTJ6KhoUHq5oiIyI2Sw8R8zZnjVFpaGlHD0zjkTrtCHc4RzETmWs8PepxPj8gbi8WC5cuXu34PlNbPVaWEOnccyxDvwjknXyifpcTUCXpc9CNcpBxvLRy/cM8pqYV9jiSSg2wFBQWoq6vDiBEj0NbWhs8++wyPPvqo6//Hjh1DYmKiIokkouCoXfHSwgSfapPaaFByfipvF84VK1Zg1apVqK6uxqxZs3TxHUnNT9E+15eWz7tQK3HBVDq1nh/0OJ9epIjWoI5SLBYLHnnkkaDfr/VzVatYhtBA0b7oR6j1oHAfP7WuRe7HKdrzjNwkB9l+8IMf4I477sD999+PjRs3Ij8/H+eee67r/5988gnGjh2rSCKJQhWtFWm1K16RPMGqVFpoNHi7cI4bNw4AMH78eN2cE8xP0mj5OLESNxjvHqtHC+UzkbtgggMsQ4YWzptPUj5L7ZvgWqBk22xgPSjcxzvQfRvqWjRUngr2WGq5vqh3koNs5eXlaGhowLJly5Cfn4/q6mqYTCbX/1944QXMnTtXkUQShSpaK9KseClHjsqBmpWs7OxsLF++HM8//3xEVOZYYY0+/iqdesgP7mXIwCBjtAceKXro4VwNp2Aavbx5MbRwBhOkfFYwN8FDCaTIEWCUOygWzrZZKJ0Ogjl+cu/bUHlKic+LpDaCKsIwR5yucOGDyMSJYHkM5DbUpKpSJitVY5JR9wlfIylPcMJW/VFytT895Adv518knZNapYVV46ifHs5VPWEZog/BlENqf7dyf3449yfc5b6UfdP6gidq5zctUmThAyK94d1RUovUO2Zq9DR0vxvmPD8iAXtt6o+SPQv0mh+0PJ9epFB7GgXypNdzNdyk9iLyVYZE67QpWhWtvQ/VapsFe7wDOW8C3bdQr0VKHEv3/Q3ktSxTBpMcZLvwwgthMBj8vsZgMODtt98OOVFEcmBF2hMbb6GTekGT2mhQo5IVqcHnaK2wkndazQ9Szr9oui4FQq4KPYM62rN+/XqPYdNaOFe1RupwMF9lSLROm6J3atfZ5P58vbXNAjlvAt23UK9FShzLnTt3YuXKlRgzZgw6OztdaXNy/75ZpvgnOcg2depUn/9raWnBCy+8gO7ubjnSRCQLVqQ9sfEWOqkXNK028AH9VXCIIgnPv+DJVaHXQvmsxx4ASqXZ/XslUoPa56O/m+BqXzPk/nwpbTOlvw+lOh0E2u4M9VqkRDt33bp1AIDFixe7nmMdJTiSg2yPPvrooOdsNhueeOIJPPTQQxg2bBgefPBBWRNHFAotVKQpsigZuA1XT0MtVHCUxl6b5E5L+YE3fwjQZw+AcKRZS+eqFoTai0jtXlB6ofb56O8muNrXDLk/X0rbrLa2NuDvI5B6q5RFBII5b8Ld7pTr89z3d8yYMQCAFStWAABWrVqF1atXY9asWQAAo9E46JiwTPEh2InfqqurhVGjRgkWi0V44oknhN7e3mA3pSlc+CAycfLG8FNyUnMtkJqntHwcfO0Dz5fIpuU8GU14ng1N6cmq1ToX9Pjdy5lmLj4hTaiLQnBRCWn0cj6qnc5wLXwQzOfImTY5zptA0xPqtSiU/R9qf0tLSyW/NtLLFEUXPti0aRPuvfde7N+/H3fffTeWL1+OpKSkQDdDFFa8Oxp+at8Z1AoO0yWt4blJeqH0UKlwls967FWkVJrVHgKnJf564ITai0jtXlBapsfzMdK4t8209H3Icd4E2u4M9VoUSjvX1/4mJCRg8eLFWLhw4ZCvZZnihdTI3ccffyxccMEFQnx8vHDHHXcITU1NIUUCtYo92YjkofYdN6VFQm8g931gz4LoEennpl5EQhmitEgql/TYA0CpNHv7XgEI1dXVuvteQyW1PA613Ga570mP56Pa1wwlPz+Y7yMc14doO2/c93eo7zvajo0gKNST7dxzz0VCQgJuvvlmFBUV4W9/+5vX1y1btkzqJokowmjpTpTSIqGHmvs+VFRUsGdBBIumc1MvIqEMUVokza2qxx4ASqXZV3kzfvx4XX63pD96PB/VvmYo+fnBfB/sEasstfOb3kkOso0YMQIGgwGvvvqqz9cYDAYG2YiiGC94+qXHCidJx3OTSF16DBgqmWZn4L+urs71XLQE/oO56RHqtCf+3q/3xY6CocfzUSla+P6D+T7CUW+NtumGAtnfaDs2gTIIgiConQgtaWlpQVpaGpqbm5Gamqp2cigMtHBxiRQDK47eLng8xtpXW1uLkpIS1NTURGWFM1zCWfbw3CS9i6RrtR7LWLnT7K33tLtIDvwPte/Lly9HSkpK2PK6HvOjnLj/2tr/YNKjtX2gyBRInCjghQ+IIg0nAZcP7wySN5HUOJZTOMsetc9N5gEKVTiGroQrn+qxB4DcaV6wYAGsVisWLlyI+vr6qOo9PVQPnKamJlx22WWsl4aJHs/HSMbvgyKB5CBbZ2cn3n77bcyZMwcAcN9996G7u9v1f5PJhAcffBDx8fHyp5JIJWwYUjSSu4LDQDYxD5AehCuf6nGuGznTbLVa8dRTT6GqqgplZWXIyckBED035Ya66eE+dFQpnKeznx7Px1Bp7fsf2N4K9PtgYI60RnKQ7bnnnsPrr7/uCrI9/vjjmDhxIhISEgAAe/bsQUFBAe68805lUkokI6kXFzYMg8cLnn5FY4UzXLRQsdXSuckbGUTRx2q1oqqqSu1kaEpTU9Oga4LUa0Mw5Sjn6YxuWvv+Q21vsd5KWiM5yPb8888PCqD97W9/w6hRowAA1dXVeOKJJxhkI13Q2sUlEvGCF920EEzSIi2UPeE6N6XkAd7IGFqkBSK1tj8sq8LH12IHhYWFKC0thdFoVDF10smZh503PTZv3ow1a9Z4/E/qtSGYcpSLHemHEmUmv38iZUkOsn3xxRc47bTTXH/Hx8d7XAzPPvts3HrrrfKmjkgh/i4uTU1NrudZ4SYKjhaCSVoUTRVbKXnAeSzIt0gLRGptf1hWhYfVakVFRcWgHmzux9pisWDq1KlhTlng5MzDzpseVqsVixYtAhCea4Pa83SSdEqUmVr4/nmDgyKZ5CBbc3MzYmL6X+4MRDjZ7XaPOdqItMzfxcXbqk+scBMFJpqCSYHQQsU2XHzlgcLCQhw9ehTZ2dmsWJPqWFaFR2Vlpd8hoqWlpSgrKwtjirQl0GsDAxSkd7zBQZFMcpBt+PDh+N///oexY8d6/f/OnTsxfPhw2RJGpBZWuMmd1oY26UU0BZPIO195YP369axYDyHSGtBa3h+WVeHhrW4FiNPNjB8/XvN5Wmt5WM4AhZbm6SRROPObWt8/21sU0QSJli1bJkyYMEHo7Owc9L+Ojg5hwoQJwrJly6RuTrOam5sFAEJzc7PaSaEwaWhoEMrLy4WGhoZB/6upqREACDU1NSqkjLSAeSB0PIbe+St7Io17HmhoaBBqamqEmpoaYe3atQIAYe3ata7nouF4DKW8vFwA4PNRXl6udhIDopf9YVkVHs7jPNSx1lIZOVQenjlzpmzplLLfLEcjm17KTLmw7CU9CCROJLkn2/3334+///3vGDt2LG677TacdtppMBgM2LNnDx5//HHYbDbcf//9wcT5iFTFCfopWqjVK493yb2LprLHPQ+w59DQIu0Ov172h2VVeJWWlvo91lqav89fHq6rq8PixYthtVplSaeUawPL0cimlzKTiLyTHGTLy8vDBx98gJtvvhn33nsvBEEAABgMBlxyySV48sknkZeXp1hCidTACnd0DpfU2rAQuajVYImmYBJ5xzwQmEhrQOtlf5hPleWsTyxYsADl5eW6qlcomYejsZ5F/umlzJQL21sUaSQH2QDglFNOwaZNm3D8+HF89dVXAIDRo0cjMzNTkcQRqY0Vbm3dSQ4XTsZKFB6sWBNFD/f6hK9rqJ5uctXV1QEIPZ2h1rNYjpJeuQeYWa+mSBJQkM0pMzMTZ599ttxpISLShEjqpq+nBgtFH97IGFqkNaAjbX9IXnq4yWWxWDBz5kwsXrzY43m10slyNLJFcpkZjTfyKToEFWQjosgW7YGZSOqmr4cGCxH5FmkN6EjbH/Iv0PqEHm5yWSwWvPDCCx77FWg6o72eRdKxzCTSHwbZiGgQBmYihx4aLEREFJkCrU/o5SZXqOlkPcs7zk8X+RhgpmjAIBsRDcLATD+9d9PXS4OFyB82vIj0ifUJ73hcvOPwwcjHADNFAwbZiGgQBmb6sZs+kfrY8CLSp1DqE3q5yRVMOlnPihy8CRQYBpgpGjDIRkQUJfTSYCEiosjhDEJMmzYtoPfp5SaXXtLpTkuBIb0PH+RNoMAwwCwPLZ3DNJhR7QQQkbYxMBM5nA0BfpekB1arFbW1ta4HAI+/nY0yItI2ZxACAOsTXqhRz3J+J1ooRysrK1FSUoKSkhLXsMGlS5e6nqusrFQ5hUTao6VzmAZjTzYi8kuPd2iJSH7hvmvKeVuIIktOTg7PWQwuS6O9nqXH4YN6732nFbyRT5GKQTYiIiIaUriHxOix4UVEIgYhfFNreKFWvxM9Dh/kTSB5RHuAOVBaPYdpMAbZiIiISHP02PAiIhGDENrD70Q+vAlEauA5rB8MshFRwDjZJlF04F1TIgoGgxCetFCW6uE70cvwQd4ECj+2PfRxDpOIQTYiChhXUiKKDlq5a6qXhhcRiRiE8KSFslQP3wmHD5IvbHvo4xwmEYNsREQkCe8iRh+t3DVlw4uI9EwrZSnJjzeBIg/ruxQqBtmISJJwDHXgRU3beBcx+kTaXVOWMUThxyCE9spSfify4U0g5ag1zFoP9V2ew9pmVDsBRKQPlZWVKCkpQUlJiWuIw9KlS13PVVZWhvwZzoua84JKRCQnljFE4ecMQrAxqB38TkgPwtH20Cuew9rGnmxEJAmHOkQnLUzWTNrAu6ZERKFjWUqhipZe2eFse7C+S3JikI2IJFFqqAMvatqmhcmaSRv0OiSGZQwpJVoauiQvvZalpB16GM4oh3AOs2Z9l+TEIBsRqYoXNW1jD0bSO5YxpJRoaegSEUU61ndJTgyyEVHA5BzqwIuatmltsmaiQLGMISIivYv2XtlKD7NmfZfkxCAbEQVMzqEOvKgRkZJYxpCcor2hS0TqiPZe2RxmTXrCIBsREUnCyZqJKNpFe0OXiAYLx/yM7JUdPqzvUqgMgiAIaidCqjfeeAO//OUvsXPnTiQlJWHGjBlYt26d6/8HDhzArbfeii1btiAhIQHXXnstHn74YcTFxUn+jJaWFqSlpaG5uRmpqalK7AYR+cBJpIlISSxjKFQDe7J5a+gybxFFl9raWpSUlKCmpiYsvaTD/XlEFFicSDc92V555RUsXboUv/rVrzBr1iwIgoBdu3a5/t/X14crrrgCOTk52LZtG44dO4YbbrgBgiDgscceUzHlkYkNFVICu4ITkZJYxkQvueotHH5MRHJju4oosugiyGaz2fCTn/wEq1evxo033uh6fuzYsa7fN2/ejM8//xz19fUoKCgAADzyyCNYsmQJHnroIfZKkxlX1CIiIiK9YL2FiOQk5/yMgZZPHM5IpG1GtRMgRW1tLQ4dOgSj0YgzzjgDFosFl19+OXbv3u16zYcffohJkya5AmwAcOmll6K7uxs1NTU+t93d3Y2WlhaPBxERERGRP2zoEkWvyspKlJSUoKSkxDUv49KlS13PVVZWKvbZzl7ZLHuItEkXPdm+/vprAEBFRQXWrFmDoqIiPPLII5g5cya++OILZGZmorGxEXl5eR7vy8jIQFxcHBobG31u+9e//vWgCWzJO66oRURERHqhdL2Fw4+JoleoCxGwXUUUuVTtyVZRUQGDweD38cknn8ButwMAHnjgAXz/+99HSUkJnnnmGRgMBvzjH/9wbc9gMAz6DEEQvD7vdN9996G5udn1qK+vl39HI4Sad2yIiIiIAsF6C1H0sVqtqKiocAWwlGKxWFzzMToDa+5/DxUgY/lEFLlU7cl222234eqrr/b7mqKiIrS2tgIAJkyY4HrebDZj1KhROHDgAAAgPz8fH3/8scd7T5w4gd7e3kE93NyZzWaYzeZgdyGqcOloIiIi0gvWW0jvOCF+4PQy/yLLJ6LIpWqQLTs7G9nZ2UO+rqSkBGazGXv37sV3vvMdAEBvby+++eYbjBw5EgAwbdo0PPTQQ7Bara5CafPmzTCbzSgpKVFuJ6IIV9QiokjFhgxR5GG9hfROLwGjaBfM/Iwsn4gily7mZEtNTcVNN92E8vJyFBYWYuTIkVi9ejUA4Ic//CEAYPbs2ZgwYQKuu+46rF69GsePH8fdd9+NpUuXcmVRIiLyiw0ZIiKSijdmtEXt+c04PyMRudNFkA0AVq9ejZiYGFx33XXo7OzEOeecgy1btiAjIwMAYDKZ8MYbb+CWW27B+eefj4SEBFx77bV4+OGHVU55ZOKKWkRERKQXrLeQnJS8MaN2wEiPKisrBy1k55znDADKy8s1HQRj+UQUWQyCIAhqJ0JLWlpakJaWhubmZvaAIyKKYAMbMt7mQ2GFl4iIBqqtrUVJSQlqampkH95XswK0sQAAF35JREFUUVExKGDkTusBIzXwek5ESgskTqSbnmxERERy0vudbyIiCp9w9TDjhPiB4/xmRKQlDLIREVFUYkOGiIikCteNGQaMiIj0jUE2IhVwwlwi9bEhQ5GE1xUiZfHGjD5wfjMiUhuDbEQq4EqGREQkJ15XiJSlxo0ZBowCx5U+iUhtDLIREVHUY0OGiIi0hgEjIiL9YZCNKEiBDs3hkuxE2sWGDOkRrytE6uCNGSIi8sUgCIKgdiK0JJClWSm6Bbp8O5dkJyIiOfG6QkRERKS8QOJE7MlGFCacMJeIiOTE6woRERGRtjDIRhSAUIbmcCVDIiKSE68rRERERNrCIBtRACorKwcNzVm6dKnrdw7NISIiIiIiIopODLIRBUCuoTmcMJeIiOTE6woRERGR+rjwwQBc+ICkCnThA70JdPVUIiIiIiIiokgTSJzIGKY0EZHOWK1WrFy50jUHHRERERERERH5xiAbUZA4NIeIiIiIiIiimdVqRUVFBTtnOHBONqIgWSyWiFvkIJTVU4mIiIiIiCi6OEdAzZs3j21FMMhGRG64eioRERERERFRcBhkIyIXuVZPJSIiIiIiosjEEVC+MchGRC7eCsPi4uKIXD2ViIiIiIiIAscRUL4xyEZERERERERERJJwBJRvDLIRkVdcPZWIiIiIiIgG4ggo34xqJ4CIpAvn8sjO1VMZZCMiIiIiIiIaGoNsRDriXB45HEE2IiIiIiIiIn84AsoTh4sSEREREREREVHAnCOgSMQgG5HGcXlkIiIiIiIiIu1jkI1I47g8MhEREREREZH2MchGpHFcHpmIiIiIiIhI+xhkI9I4Lo9MREREREREpH1cXZSIiEhlVqsVFRUVXDmYiIiIiEjHGGQj0hEuj0wUmaxWK1auXMkgGxERERGRjnG4KJGOcHlkIiIiIiIiIm1ikI2IiEgFVqvV1XOttrbW4yfgfT5GIiIiIiLSLgbZiIiIVFBZWYmVK1d6PLd06VLX7+Xl5ey5SkRERESkIwyyERERqaCsrAzz5s0DIPZgW7p0KdauXetaOZi92IiIiIiI9IVBNiIiIhV4Gw5aXFzsCrIREREREZG+cHVRIiIiIiIiIiKiEDHIRkREpDKLxYLy8nIOESUiIiIi0jEG2YhIU6xWKyoqKlyrLhJFA4vFgoqKCgbZKCQsP4mIiIjUxSAbEWmK1WrFypUr2UgkIgoQy08iIiIidTHIRkREREREREREFCKuLkpEqrNara6eF7W1tR4/Ae+rMBIREctPIiIiIi0xCIIgqJ0ILWlpaUFaWhqam5uRmpqqdnKIokJFRQVWrlzp8//l5eWoqKgIX4KIiHSC5ScRERGRsgKJEzHINgCDbEThN7AnxtKlS7F27VoUFxcDYE8MIiJfWH4SERERKSuQOBGHixKR6rw1AouLi12NRCIi8o7lJxEREZF2cOEDIiIiIiIiIiKiEDHIRkSaYrFYUF5ezuFNREQBYvlJREREpC7OyTYA52QjIiIiIiIiIiIgsDgRe7IRERERERERERGFiEE2IiIiIiKiCGC1WlFRUeFadZiIiMKLQTYiIiIiIqIIYLVasXLlSgbZiIhUwiAbERERERERERFRiGLUTgAREREREREFx2q1unqu1dbWevwExJWHueowEVF4MMhGRERERESkU5WVlVi5cqXHc0uXLnX9Xl5ejoqKijCniogoOjHIRkREREREpFNlZWWYN28eALEH29KlS7F27VoUFxcDAHuxERGFEYNsREREREREOuVtOGhxcbEryEZEROGjm4UPvvjiC8yfPx/Z2dlITU3F+eefj3feecfjNQcOHMDcuXORlJSE7OxsLFu2DD09PSqlmIiIiIiIiIiIooVugmxXXHEFbDYbtmzZgpqaGkydOhVz5sxBY2MjAKCvrw9XXHEF2tvbsW3bNrz44ot45ZVXcNddd6mcciIiIiIiIuVZLBaUl5dziCgRkUoMgiAIaidiKEePHkVOTg62bt2K6dOnAwBaW1uRmpqKt956CxdddBH+9a9/Yc6cOaivr0dBQQEA4MUXX8SSJUtw5MgRpKamSvqslpYWpKWlobm5WfJ7iIiIiIiIiIgo8gQSJ9JFT7asrCyMHz8ezz33HNrb22Gz2VBZWYm8vDyUlJQAAD788ENMmjTJFWADgEsvvRTd3d2oqanxue3u7m60tLR4PIiIiIiIiIiIiAKhi4UPDAYD3nzzTcyfPx8pKSkwGo3Iy8vDpk2bkJ6eDgBobGxEXl6ex/syMjIQFxfnGlLqza9//etBS14TEREREREREREFQtWebBUVFTAYDH4fn3zyCQRBwC233ILc3Fy8//77+O9//4v58+djzpw5sFqtru0ZDIZBnyEIgtfnne677z40Nze7HvX19YrsKxERERERERERRS5Ve7LddtttuPrqq/2+pqioCFu2bMHrr7+OEydOuMa/Pvnkk3jzzTfx7LPP4t5770V+fj4+/vhjj/eeOHECvb29g3q4uTObzTCbzaHvDBERERERERERRS1Vg2zZ2dnIzs4e8nUdHR0AAKPRs+Od0WiE3W4HAEybNg0PPfQQrFarazWdzZs3w2w2u+ZtIyIiIiIiIiIiUoIuFj6YNm0aMjIycMMNN+Czzz7DF198gXvuuQf79+/HFVdcAQCYPXs2JkyYgOuuuw47duzA22+/jbvvvhtLly7lKqFERERERERERKQoXQTZsrOzsWnTJrS1tWHWrFk488wzsW3bNvzzn//ElClTAAAmkwlvvPEG4uPjcf755+PKK6/EggUL8PDDD6uceiIiIiIiIiIiinQGQRAEtROhJS0tLUhLS0NzczN7wBERERERERERRbFA4kS66MlGRERERERERESkZQyyERERERERERERhYhBNiIiIiIiIiIiohDFqJ0ArXFOUdfS0qJySoiIiIiIiIiISE3O+JCUJQ0YZBugtbUVAFBYWKhySoiIiIiIiIiISAtaW1uRlpbm9zVcXXQAu92OhoYGpKSkwGAwqJ2ckLW0tKCwsBD19fVcLTXKMS+QO+YHGoh5grxhviCA+YA8MT/QQMwT5E0k5QtBENDa2oqCggIYjf5nXWNPtgGMRiOGDx+udjJkl5qaqvuMTfJgXiB3zA80EPMEecN8QQDzAXlifqCBmCfIm0jJF0P1YHPiwgdEREREREREREQhYpCNiIiIiIiIiIgoRAyyRTiz2Yzy8nKYzWa1k0IqY14gd8wPNBDzBHnDfEEA8wF5Yn6ggZgnyJtozRdc+ICIiIiIiIiIiChE7MlGREREREREREQUIgbZiIiIiIiIiIiIQsQgGxERERERERERUYgYZCMiIiIiIiIiIgoRg2wq+PWvf42zzjoLKSkpyM3NxYIFC7B3716P1wiCgIqKChQUFCAhIQEXXHABdu/e7fGaqqoqXHDBBUhNTYXBYMDJkyc9/v/uu+/CYDB4fWzfvt1vGnft2oWZM2ciISEBw4YNwy9/+Uu4r5FhtVpx7bXXYuzYsTAajbjjjjtCOibRKhLygrv//Oc/iImJwdSpUwM+FhQZ+WHJkiVetztx4sTQDk6U0nqe6OrqwpIlS3D66acjJiYGCxYs8Pq69957DyUlJYiPj8eoUaPwxz/+MajjQaJw5QsA+OKLLzB//nxkZ2cjNTUV559/Pt55550h08h6hPIiIR+4Yx0iNJGQH1iHkJfW8wTrEOoIZ76ora3FJZdcgvT0dGRlZaG0tBRtbW1DplHvdQgG2VTw3nvv4dZbb8VHH32EN998EzabDbNnz0Z7e7vrNb/73e+wZs0aPP7449i+fTvy8/NxySWXoLW11fWajo4OXHbZZbj//vu9fs55550Hq9Xq8fjxj3+MoqIinHnmmT7T19LSgksuuQQFBQXYvn07HnvsMTz88MNYs2aN6zXd3d3IycnBAw88gClTpshwVKJTJOQFp+bmZlx//fW46KKLQjgi0S0S8sPvf/97j+3W19cjMzMTP/zhD2U4QtFH63mir68PCQkJWLZsGS6++GKvr9m/fz+++93vYvr06dixYwfuv/9+LFu2DK+88kqQR4XClS8A4IorroDNZsOWLVtQU1ODqVOnYs6cOWhsbPT5HtYjwiMS8oET6xChi4T8wDqEvLSeJ1iHUEe48kVDQwMuvvhijB49Gh9//DE2bdqE3bt3Y8mSJX7TFxF1CIFUd+TIEQGA8N577wmCIAh2u13Iz88XfvOb37he09XVJaSlpQl//OMfB73/nXfeEQAIJ06c8Ps5PT09Qm5urvDLX/7S7+uefPJJIS0tTejq6nI99+tf/1ooKCgQ7Hb7oNfPnDlT+MlPfuJ3mySNnvPCVVddJaxYsUIoLy8XpkyZMsSekhR6zg9Or776qmAwGIRvvvnG77ZJGq3lCXc33HCDMH/+/EHP//SnPxXGjRvn8VxZWZlw7rnnSt42+adUvmhqahIACFu3bnU919LSIgAQ3nrrLZ/pYT1CHXrOB6xDyE/P+cGJdQh5aS1PuGMdQj1K5YvKykohNzdX6Ovrcz23Y8cOAYDw5Zdf+kxPJNQh2JNNA5qbmwEAmZmZAMSIfWNjI2bPnu16jdlsxsyZM/HBBx8E/Tnr16/H0aNHh4wef/jhh5g5cybMZrPruUsvvRQNDQ345ptvgv58Gppe88IzzzyDffv2oby8POg00WB6zQ/u/vznP+Piiy/GyJEjg04f9dNanpDiww8/9EgfIOabTz75BL29vSFvn5TLF1lZWRg/fjyee+45tLe3w2azobKyEnl5eSgpKfH5PtYj1KHXfMA6hDL0mh/csQ4hL63lCSlYh1CeUvmiu7sbcXFxMBr7Q04JCQkAgG3btvl8XyTUIRhkU5kgCFi+fDm+853vYNKkSQDg6labl5fn8dq8vDy/XW6H8uc//xmXXnopCgsL/b6usbHR62e7p43kp9e88OWXX+Lee+/F888/j5iYmKDTRJ70mh/cWa1W/Otf/8KPf/zjoNNG/bSYJ6TwlW9sNhuOHj0a8vajnZL5wmAw4M0338SOHTuQkpKC+Ph4PProo9i0aRPS09N9vo/1iPDTaz5gHUIZes0P7liHkJcW84QUrEMoS8l8MWvWLDQ2NmL16tXo6enBiRMnXENLrVarz/dFQh2CQTaV3Xbbbdi5cydeeOGFQf8zGAwefwuCMOg5qQ4ePIh///vfuPHGGz2enzhxIpKTk5GcnIzLL7/c72d7e57ko8e80NfXh2uvvRYrV67EaaedFlR6yDs95oeB/vKXvyA9Pd3nRLYUGK3mCSl4TVGOkvlCEATccsstyM3Nxfvvv4///ve/mD9/PubMmeOqILMeoQ16zAesQyhHj/lhINYh5KXVPCEFryfKUTJfTJw4Ec8++yweeeQRJCYmIj8/H6NGjUJeXh5MJpPrNZFYh+AtIxXdfvvtWL9+PbZu3Yrhw4e7ns/PzwcgRmotFovr+SNHjgyK6kr1zDPPICsrC/PmzfN4fuPGja6uts7um/n5+YOixEeOHAEwOKJN8tBrXmhtbcUnn3yCHTt24LbbbgMA2O12CIKAmJgYbN68GbNmzQoqndFMr/nBnSAIePrpp3HdddchLi4uqLRRP63mCSl85ZuYmBhkZWUFlUYSKZ0vtmzZgtdffx0nTpxAamoqAODJJ5/Em2++iWeffRb33nsv6xEaoNd8wDqEMvSaH9yxDiEvreYJKViHUE446pbXXnstrr32Whw+fBhJSUkwGAxYs2YNTjnlFACRG4tgTzYVCIKA2267DevWrcOWLVtcmczplFNOQX5+Pt58803Xcz09PXjvvfdw3nnnBfV5zzzzDK6//nrExsZ6/G/kyJEYPXo0Ro8ejWHDhgEApk2bhq1bt6Knp8f1us2bN6OgoABFRUUBfz75pve8kJqail27duHTTz91PW666SaMHTsWn376Kc4555yA0xjN9J4f3L333nv46quvBvWGosBoPU9IMW3aNI/0AWK+OfPMMwd9BkkTrnzR0dEBAB7zqTj/ttvtAFiPUJPe8wHrEPLSe35wxzqEPLSeJ6RgHUJ+4a5bAmJwLDk5GS+99BLi4+NxySWXAIjgOoT8aynQUG6++WYhLS1NePfddwWr1ep6dHR0uF7zm9/8RkhLSxPWrVsn7Nq1S7jmmmsEi8UitLS0uF5jtVqFHTt2CGvXrnWt6LJjxw7h2LFjHp/31ltvCQCEzz//XFL6Tp48KeTl5QnXXHONsGvXLmHdunVCamqq8PDDD3u8bseOHcKOHTuEkpIS4dprrxV27Ngh7N69O4QjE30iJS+448pgwYuk/LB48WLhnHPOCfJIkJPW84QgCMLu3buFHTt2CHPnzhUuuOAC17XB6euvvxYSExOFO++8U/j888+FP//5z0JsbKzw8ssvB39goly48kVTU5OQlZUlLFy4UPj000+FvXv3CnfffbcQGxsrfPrppz7Tx3pEeERKPnDHOkTwIik/sA4hD63nCUFgHUIN4axbPvbYY0JNTY2wd+9e4fHHHxcSEhKE3//+937TFwl1CAbZVADA6+OZZ55xvcZutwvl5eVCfn6+YDabhRkzZgi7du3y2E55efmQ2xEEQbjmmmuE8847L6A07ty5U5g+fbpgNpuF/Px8oaKiYtCSud4+e+TIkQF9TrSLlLwwMC2sIAcnUvLDyZMnhYSEBKGqqiqgbdNgesgTI0eO9Lptd++++65wxhlnCHFxcUJRUZHw1FNPBfQZ5Cmc+WL79u3C7NmzhczMTCElJUU499xzhY0bNw6ZRtYjlBcp+WBgWliHCE6k5AfWIeSjhzzBOkT4hTNfXHfddUJmZqYQFxcnTJ48WXjuueckpVHvdQiDIDhmkSMiIiIiIiIiIqKgcE42IiIiIiIiIiKiEDHIRkREREREREREFCIG2YiIiIiIiIiIiELEIBsREREREREREVGIGGQjIiIiIiIiIiIKEYNsREREREREREREIWKQjYiIiIiIiIiIKEQMshEREREREREREYWIQTYiIiKiKFVRUYGpU6eqnQwiIiKiiGAQBEFQOxFEREREJC+DweD3/zfccAMef/xxdHd3IysrK0ypIiIiIopcDLIRERERRaDGxkbX7y+99BJ+8YtfYO/eva7nEhISkJaWpkbSiIiIiCISh4sSERERRaD8/HzXIy0tDQaDYdBzA4eLLlmyBAsWLMCvfvUr5OXlIT09HStXroTNZsM999yDzMxMDB8+HE8//bTHZx06dAhXXXUVMjIykJWVhfnz5+Obb74J7w4TERERqYxBNiIiIiJy2bJlCxoaGrB161asWbMGFRUVmDNnDjIyMvDxxx/jpptuwk033YT6+noAQEdHBy688EIkJydj69at2LZtG5KTk3HZZZehp6dH5b0hIiIiCh8G2YiIiIjIJTMzE3/4wx8wduxY/OhHP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EEQhELIyEYQBEEQBEEQBEEQBEEQCiEjG0EQBEEQBEEQBEEQBEEohIxsBEEQBEEQBEEQBEEQBKEQMrIRBEEQBEEQBEEQBEEQhEIG6V0BwlwwDIOqqiqcOHECI0aMwMyZM2G1WvWuFkEQBEEQBEEQBEEQhK6QkY0QjdPpRF5+AWqOHvG+ljo6DSXFG5CZmaljzQiCIAiCIAiCIAiCIPSFrosSonA6ncjOzkaTzYHk3HVIzS9Hcu46NNkcyM7OhtPp1LuKBEEQBEEQBEEQBEEQumFxu91uvSthJFpbWxEXF4eWlhYMGzZM7+oYAoZhMHZcOppsDiRmroDF0mebdbt70egsQkJPAw5XH6KrowRBECKh6/cEQRAEQRAEYXyk2InIk40ISlVVFWqOHsGwaTk+BjYAsFgiMGzafNQc+Q5VVVU61ZAgCMJcOJ1OjB2XjtmzZ2PRokWYPXs2xo5LJ69ggiAIgiAIgjAxFJONCMqJEycAAJH2NN73IxPTfMoNNMgbhSAIKbDX76PTpyI5dzEi7WnocR1B074yZGdnY8eOHRTnkiAIgiAIgiBMCHmyEUEZMWIEAKDHdYT3/Z7GIz7lBhLkjUIQhBQYhkFefgGi06ciMXMFolImIcIWjaiUSUjMXIHo9KnIK1gKhmH0ripBEARBEARBEBIhIxsRlJkzZyJ1dBpa95XB7e71ec/t7kXrvnKkpo3BzJkzdaqhPlAyCIIgpELX7wmCIAiCIAgifDGtke3xxx+HxWJBXl6e9zW3241Vq1Zh5MiRiI6OxqxZs/DFF1/oV8kwwWq1oqR4Azqq96PRWYSu2q/Q29WOrtqv0OgsQkf1fpRsWG/aK5IMw6CyshJbt25FZWWlKA8S8kYhCEIOdP2eIAiCkIscnZUgCIIILaY0su3fvx/PPPMMLrnkEp/X16xZgw0bNuDJJ5/E/v37kZycjKuvvhqnT5/WqabhQ2ZmJnbs2IGE7nrUbSlETUkO6rYUIqGnwdTxg+Re9yRvFIIg5EDX7wmCIAg5UIgSgiAIc2A6I1tbWxtuuukmPPvsszjvvPO8r7vdbpSUlGD58uXIzMzERRddhBdffBHt7e0oLS0V/Lyuri60trb6/CP4yczMxOFvq1FRUYHS0lJUVFTgcPUhUxvY5F73JG8UgiDkQNfvCYIgCKkYKUQJedMRBEEExnRGtt/+9re49tprMXfuXJ/XDx8+jLq6OlxzzTXe16KionDVVVdhz549gp/3+OOPIy4uzvsvNTVVs7qHA1arFbNmzcLChQsxa9YsU18RVXLdk7xRCIKQQ7hfvycIgiDUxUghSsibjiAIIjimMrJt27YNH3/8MR5//PF+79XV1QEAHA6Hz+sOh8P7Hh/3338/WlpavP9qamrUrTRhSJRe9yRvFIIg5BKu1+8JgiAI9TFKiBIjedMRBEEYmUF6V0AsNTU1WLJkCd58800MHjxYsJzFYvH53e1293uNS1RUFKKiolSrJ2EOlF73ZL1RsrOz0egswrBp8xGZmIaexiNo3Vfu8UbZsYO8UQiC4CUzMxMZGRmoqqrCiRMnMGLECMycOZNkBkEQBOGDEUKU+HvTscY+1puu0VmEvIKlyMjIoHWMIIgBj2k82T766CM0NDRg8uTJGDRoEAYNGoR3330XTzzxBAYNGuT1YPP3WmtoaOjn3UYQalz3JG8UgiCUEC7X7wmCIAjtMEKIEqN40xEEQZgB0xjZ5syZg//973/49NNPvf+mTJmCm266CZ9++inGjRuH5ORkvPXWW96/6e7uxrvvvosZM2boWHPCiKh13TPckkEQBEEQBEEQxsEIIUqM4E1HEARhFkxzXTQ2NhYXXXSRz2tDhgxBQkKC9/W8vDw89thjmDBhAiZMmIDHHnsMMTExWLRokR5VJgyMmtc9WW8UgiAIgiAIglATI4Qo4XrTRaVM6vc+JfwiCILowzRGNjHce++96OjowN13342TJ0/ihz/8Id58803ExsbqXTXCgLDXPfPyC1CzpdD7emraGJTQdU+CIAiCIAjCAOits7LedE37ynxisgGU8IsgCMIfi9vtdutdCSPR2tqKuLg4tLS0YNiwYXpXhwgBDMNQ8HGCIAiCIAjC0Oips7LZRaPTp/J601E8YoIgwhkpdiIysvlBRjaCIAiCIAiCIAhfnE6nx5vuaF8ShtS0MSjZsJ4MbARBhDVkZFMAGdkIgiAIgiDkQd7hBBHe0BwnCGIgIsVOFFYx2QiCIAiCIAh94PVyGZ2GkuIN5OVCEGECJfwiCIIITETwIgRBEARBEAQhDBuvqcnmQHLuOqTmlyM5dx2abA5kZ2fD6XTqXUWCIAiCIAjNoeuiftB1UYIgCIIgCPEwDIOx49LRZHPwZh5sdBYhoacBh6sP0bUygiAIgiBMhxQ7EXmyEQRBEARBELKpqqpCzdEjGDYtx8fABgAWSwSGTZuPmiPfoaqqSqcaEgRBEARBhAaKyUYQBEGEPRSomSC048SJEwCASHsa7/uRiWk+5QiCIAiCIMIV8mQjCIIgwhqn04mx49Ixe/ZsLFq0CLNnz8bYcekUI4ogVGLEiBEAgB7XEd73exqP+JQjCIIgCIIIVygmmx8Uk40gjAd5IRFyYYOxR6dPxbBpOYi0p6HHdQSt+8rQUb0fO3bsoKyHBKEQismmDFrjCIIgCMLYSLETkZHNDzKyEYSxcDqdyMsvQM3RPg+J1NFpKCneQMYRIiC08SeI0OFr0J6PyMQ09DQeQeu+cjJoB4DWOIIgCIIwPmRkUwAZ2eRDJ7GE2pAXEqGEyspKzJ49G8m56xCVMqnf+121X6FuSyEqKiowa9as0FeQIMIMXoNR2hiUbFhPspoHWuMIgiAI2kObAzKyKYCMbPKgk1hCbcgLiVDK1q1bsWjRIqTmlyPCFt3v/d6udtSU5KC0tBQLFy7UoYYEEX7QZkEctMYRBEEQtIc2D1LsRJRdlFAM9yQ2OXex9yS2aV8ZsrOz6SSWkEVVVRVqjh5Bcu5in80HAFgsERg2bT5qthSiqqqKvJAIXrjB2Pk82SgYe3hCRh59sVqtJJNFQGscQRDEwMJfP2lsbEROTg7tocMQyi5KKIJhGOTlFyA6fSoSM1cgKmUSImzRiEqZhMTMFYhOn4q8gqVgGEbvqhIm48SJEwCASHsa7/uRiWk+5QjCn5kzZyJ1dBpa95XB7e71ec/t7kXrvnKkpo3BzJkzdaohoTaUSZYwC7TGEQRBDBz49JMbF91Ee+gwhYxshCLYk9hh03KET2KPfIeqqiqdakiYFa4XEh/khUQEw2q1oqR4Azqq96PRWYSu2q/Q29WOrtqv0OgsQkf1fpRsWE9eTmEC61XdZHMgOXcdUvPLkZy7Dk02B7Kzs8nQRhgKWuMIgiAGBnz6yXlz7wLT00176DCFjGyEIugkltAK8kIi1CAzMxM7duxAQnc96rYUoqYkB3VbCpHQ00Bu+GEEeVUTZoPWOIIgiPBHSD+xRscCoD10uEJGNkIRdBKrHQzDoLKyElu3bkVlZeWA2xySFxKhFpmZmTj8bTUqKipQWlqKiooKHK4+RAa2MIK8qgmzQWscQRBE+COkn1iHngeA9tDhCiU+IBTBnsQ27SvjzY5FJ7HyoEwzHlgvpLz8AtRsKfS+npo2BiXkhURIgIKxhzfkVU2YEVrjCIIwI5RgSDxC+knUqAthjXOgZW8Z7Fm0hw43yMhGKII9ic3OzkajswjDps1HZGIaehqPoHVfueckdscOErwSoGytvmRmZiIjI4MWc4IgBKFMsoRZoTWOIAgzQY4A0hDSTywRVsTPvg2uVx6Ha+cjiJueQ3voMMLidrvdelfCSLS2tiIuLg4tLS0YNmyY3tUxDbwCN20MSjasJ4ErAYZhMHZcOppsDl7PwEZnERJ6GnC4+hAJXYIgiHOQ7CQIgiAIbeE6AgybluN1BGjdV4aO6v0DzhFADMH0k7qXCsA0HQXT0+19nfbQxkSKnYiMbH6QkU0+5DqsnMrKSsyePRvJuet4vTG6ar9C3ZZCVFRU0NU3giAIDr7Kf3+valL+CYIgCEIedJgln2D6yfbt22G322kPbXCk2InouiihGhTzSDkUV4ggCEIeFN+KIAiCILSBDeCfnLtYOMHQlkJUVVXRftAP0k8GHmRkIwgDQXGFCIIg5EPxrQiCIMIfuj0TesgRQBmknwwsyMhGEAaCsrUSBEEog7yqCYIgwhcKvK8P5AigHNJPBg4Uk80PismmDDpZUg7FFSIIgiAIgiAIXyjwvn5QTLbwgfbr8qDEBwogI5t86GRJPShbK0EQBEEQBBGMgbJhJiOP/pAjgPmh/bp8yMimADKyyYNOltSHOXs2uNJksehTObXhE0PcZ+tl2ILcP+r7MSKy72em+9x7nPe5nz8ouu/nsx0Aev2+n/NzZCyn7Bmg96xwHWzn9f3ccxro7eYU83u+qIS+5+tpBZhO4ToMTgJYRbL7FHC2XbgOg5OBiHNRALpPeurBLcP9jpiUvnbragZ6TgnXN2YUYI3qK9vVyP/9ABCTCgyKAQAw7S589P4/4XK5YLfbMXnKZFgjrL5lI4f2PVtHgDgeMSlA5DmZ3N0CdNQKl40eCdiGe37uOQ201wQoO6Kv786eAc4cES472OHpO8DTD2e+Ey4bZQcG2z0/M53A6eoAZROBaMe5st3A6YMBysZ76gwAvT1A6zfCZW3DPe0GeOZQ69fCZSOHAUNSPT+73UDrV8JlBw0Fhozu+73lK/QbB96yQ4AhnPgtrQcAdy9/WWs0MHRM3++nD52bc3xlo4ChY/t+b/vW0x58REQCQ8f1/X7mCMB0CZQd5Fe2xtN/fFgigNj0vt/bawGmg78sAMSO7/u5o84z3oQYOq5PRnQ2AGfbhMvGpAHsvOpsBM6eDlA2tU9GdDV55I8Q0SmA1XaubLNHpgh+rr+MaApclpXD3SeBTte5N3jGUMwozxgCzsmIOs6b/rLHT0a0HwtQhxSOjGj1nff95F9K37zvOe0Za0J1iB7pkdkA0NMGnA4wP6NH9M3ls+1Ay5cBPneEpy0Az3g8+Zlw2cGOvnnEdAMnPxZ+tsFJfWO49yzQ9B/h+kYlAsMm9n1O414AlnPr07n/LRYAEYAtzncetbDyhFv+3M/W6L52ADzrgNvt+7ls+YjIvj4GOHOI/W7/+vgGZw9HJG+Y3b2Amzn3/7mf0dv3u224r87R0+pbHhz5PWRsn4zodHF0Ax6dY+i4vnnf2QB01vuV4ZZN79MNOhs8shXAhx99iNtvvwOJ1+bDZh8DADjSPQJtvR6dI6bhQ0S+vQrPPfcspk6Z2v/Zh4zxjE322c4c7V/GWza1TzfoaQ1cNnqkZ20GPPM+kB4RnSxfj2gLVDaBU7YbOHNYuKztvD451XsWaAtQNnJYn37i7gXOfIfXX38djxQ9iuPHj3u7b0TKKCx/8BFcl3XLubJujp5m8f3fYgGsg3315o76vvf8/yYisq/fAE/fwc3/uRZr35oB+OnMFt+ysPStW4CfDmHp/zNXf/XfD/jLVnZeAOf2Jb3CZbn7Eqbz3JwUKMvOC8DzbO6zAcoO62vPs+1Abw/+/o+/4xc334zosZMRO/UGDEoYjZ7Gozjx/i50VH/o2a9nXMv5XJ52sA7u+9zes7719WlfAJZB4bNXBRnZFBG2Rja3+9wCedbzr/esR5i4z3oEM7vZ7nQBnXXnJs3Zvv/d58onTO0Tiq0HgKYP0ct04777lqEjchiGXfYzWC1uWNGLt1qn4Vh3IhqdRZiZWIN/PvMbRMDtt6gznn9jbwaGX+z53Kb9QPXznEWd+z8DnH8PkPQjT9nGfcDnj/CXRS/wvUJgVEZf2Q/u8P0s7t9c9CAw/tfnyn4AVPzEt47cshevBi5+0FP25GfAvy4VbvsL/g/4/uOen09XA/8YL1z2/MXAlCc8P3ecAP42UrjsuFuBac97fu5pBcrjhMum3QhcsdXzcy8DbIv0fZ8rAFOuB658pe/37UOAXu6GlFPW8WPgx2/0/b4zCehuBu/CkzgNuGZP3+t/GyVsKBl+MfD/OJuIf0wU3qwMTQd+fqjv939dBpz8hL/s4GQgk2PIeetHgOt9/rKDYoEczgb0nZ8AdW/yl7VYgYWche69TODY3/jLAsCCzr6FfU8u8N1fhctmNfUpbx/cCRx6RrjsvJq+jdhHBcCBYuGy133dt2H67wrgi0eFy/70IyD+Ms/PXzwO/PcB4bJzq4CkH8HpdOK/5b/G6usDbMxnvQ6M/Inn5+rngf/8WrjszJ1A6rkNw3dbgT2LhMtOfwkY+wvPz8f+DryXIVx26mZgwp2en+veBt6ZK1z2B+uB7xV4fnbtBd6aIVz24odNLiMChGxNuR646u99v2+L8jUqc3H8GJjzdt/vO+KFjTUJ04Cf7O37/ZVUYUOJJBkxDvg5x8gpSUbMBFz/5i8rSUZEAAs5iqgkGfEL4LstwmWlyIiMo33GVEky4kHgiyLhspJkxHtA0rm4ogeeAD5aIlyWKyMOPQd8cLtwWUky4mVgbK7nZ0kyYjfwztXCZaXIiEseAS5a4flZkow4BPxjgnBZKTIi/Tbgh895fpYkI8721yO4KJER5ef5HvpwSfgh8JN9fb9L0SNevcBzgGHhbLTZ/2PTgWu/6Cv71o+Ak/9Fvw054DGSXMc5sKi89pxxkvOZrE41KNZXP3l/EdDwrm8ZduMfMchXTu2/Gzjxhl9d3X2GrusPwvnKP5CdnY2nl9iRdWkbIiIsiHAzsPR2I8Ldi8GDbbBGAMis7zMq/+d2oPo5/jYDgHnH+g5nPsoDDmwULnvdAWDY+Z6fg+oRHwPxP/D8/MVjwH+XC5c9p0cAAL7eCHycJ1j05m9X4722yQCAnNh/YM3Yp4U/d6YTSL3B87MkGbELeG+ecFkpMuKyDcCkfM/PkmTEf4F/fV+4rCQZ8Ttgyrl+lSIjuluAHcOFy6YtBK4o9fwcVEb8HLhqV9/v22zCB2WOOcCc3X2/B5IRidMl7DUuAf7ff/t+/8f5woec/fYaPwBOfspfNnoEcMPxvt8D7TUihwHzW/p+f+caoO4t/rL99ho3AMde4S8LAAu6+ox9QfYaF39eim/LS5DQ04Dvts1FxLeBZAR3r5EPHCgRLsuVEWGAFDsRJT4YCHz2kMcQJQR34at+VvzCd+IN4KMliACwNgsAmgE85S16uCsFtT1JGDZtPlJqChHxv4eEPzdxep+R7fQh4FCARTLlWgDn6tDZABz/p3DZMbl9P589A5z6n3DZHo6QQ6+wAA8L/GzrXFu7v5eJ2/+Uwu890WXJnh8cjgGT7+THYvWcCvV7n6dsxCDPaZPQ53N/jrD5nvz1Ox3keANERAGRcf3Lek+trF7P1rtz0tDc3eWpt5tBb0830HsWsbGxiIqy+XogRkT1ne7yEWHz/Tlg2SjxZbltFBHp8dYQgnvaGDEoSNmYvp8t1iBlOW1viRBfFhGek24huJ6YsAQp66csBKpDv7IJwhto/7K288A7XvnKRsYBNgFvr35lhwGRw8WVHTTUbwwHKjuk/2ve92L9fo/p/xqLv0eNdbCnHmKwRvn1ewAibIA1Rvh9rtyw+s/7foV9P1fo2QAeGeHfZlzZZvUrO1z4cyM4aqo1CrDF+32sXx25ZfuNYYvv+9w6DHYI18Ea7VuW63Xl/7lcLwOrzeMNKFRfbntGRAIxoyEId7xGRHo8coTgtpHF6utR5l9fH5lgAYb6Gfi59R2c7Pve0HHn1vVz/9y95/5395e5UQnnvEe55c79z21fwNPnFiunjEDdAQh60PKVZQ+M+f7E37P17Blh71H/+va0CHtuDvLzbO1qBDqO85flzgvAY/zw8Zr0hTnbg7z8AkSnT0VCegzibZU8pbo9DjTcdgzm3cctG2E7t0ZGcDwDud5AnM8aFOPX7/56BOf5rEP85pyfHsHVDSKHejzFAHR1daOxsRERMcNgsXpkQ7e7r2xby2kcPwkkJCR4dAx/uOu9dXD/MS1UNiKqz/OLtyxnTFgiA6+1Vn89IpB+wv1ca3/5J1g2wtdLzB+uLgOL8HrINz8HDUV/T8Vz/0f4tXmETbjsAPAyNRMWSwSGTZuPmi2FqKs7gQBmV0Ik5MnmR1h6sn22Cvh8tcCbFuAn//F4qAHAgU2e02pL5DlFZ1Df/5ZBntOMhCmesjWvAAf/iON1Lnz08X9hGzsV7ohBYNwRcCMCTzVk44vOdPR2tWP0P3Pwl5WzMX78hHOGAis8i7bV437L9WQ79TlQs7OvnMV6bnE/93Py1UDc9zxlzxz1eJ/4l2F/Pu/7fVeKupo91ya4380tG5Pa5xJ9tgNoP9r/c1lFY9DQPmW6tyfwNRprDKcsc87TS6hstISyg/s20e5evyt8fkRE9blau919bvq8ZW19XhEA0C6gELJlB3M2Mh118NVguRugSF9lorMRfe7TfsafCKuvgtB9qk/x8zcuWSJ8N3Q9bcKfa7H4uZF3oL/Gzd0EcZSL3p7+hkKL3/N5yzL8ZYIZ0cIAiplCEARBqAZryGP/5xpefUI+9HLK9p4zNAzvK9vZcM5Txu234Xef0wFH9ZVtP8YxvPmVt1h9r4m3fQcw7f0/k73yOvyivrKnD3GuaPvXA326OOC5LdLVzHnfDa6xq/Kz05j94zlIzl2HtDGJGBLRATcs6HVbwMCKzrpq1Dsfx/btZZg+O7PvqtvZM552sERwPs/q97sx9RPSLwYIfFeIuUY57lXNfmUtvlc1vXo+z3yDxfdghL0a3e9zLf3lSU+r8CGAxdL/WnugAwNuHZjOwGV9rpay4XEEykZE9s3l3h5s37YVN//yl0j9XSmsNt9D+C53JHq7OlBTkoNtf30RC3KyfT/bJ+xOTF9/MJ19h6x8/TYo1vearckhTzbCl+8tBc6/u7/BLGJQ/5OEiYs9/8SQOg9InYdvKivx85tmIzl3gWBK5/e/AY6NfAjjL58V/HOHX+SrlARiyGgg/VfiykbFA8kBroJxGRTdd00mGBGRgU+4fMpa+2ImqFnWEiG+DhaL57qDWGIknGdI+dzBATxl/OEubMGIFOkdAvid5gUhIoDbe7+y4bOgSKWqqgo1R48gOXexjwIM+J6UVVVVURpzgiAIIjDeWGs870lZw8XqSICvwS0Y3DiSwYgdH7wMSxAd9ESd5+pupD0NdT3926F3yDDUNAHfNZzFdK5OItYr1oBYrVaUFG9AdnY2Gp1FvIH3S3bsIAOb2ekXl82PCAnmCykyQshrXWlZKXOu3w2UQGV5vDWFiIiEY+RodJ8FzjQcF9yvA4Bj5Gjf2xgB6zBYWp0HEOSrORCIjPUoF1HxHqEwKMYzMVVy1Z05cyZSR6ehdV8Z3H7Wd7e7F637ypGaNgYzZ85U5fsIgjAuJ0544llF2tN4349MTPMpRxAEQRCENEaM8Fxb7nEd4X2f3TCz5cKFzMxM7NixAwnd9ajbUoiakhzUbSlEQk+DKknWGIZBZWUltm7disrKSjCMQAgUgjAZtF8PLWRkIxTDnix1VO9Ho7MIXbVfoberHV21X6HRWeQ5Wdqwnk6WCGIAMFAV/3CFNhwEQRDGYyBvmDMzM3H422pUVFSgtLQUFRUVOFx9SLGBzel0Yuy4dMyePRuLFi3C7NmzMXZcOpxOp0o1Jwj9oP16aKGYbH6EZUy2EMGbRjxtDEo2rFe88JkZhmFQVVWFEydOYMSIEZg5cyYJMCJsoZgp8jCinOCV6aPTUFK8YUDLdIIgCCPAJhmKTp/Ke3VSDc+ugYJvW+Yg0p6GHtcRtO4ro7Ykwgrar8tHip2IjGx+kJFNGUbcKOoJbVKJgQgp/tIwopygDQdBEITxoQ2zcuhwkBho0H5dHmRkUwAZ2Qi1oE0qMZAhxV8cRpQTtOEgCIIwD7RhVkZlZSVmz56N5Nx1vAHhu2q/Qt2WQlRUVFDCJoIYwJCRTQFkZCPUgDapBEGKfzCMKidow0EQBEFogZ56gdB3b926FYsWLUJqfjkibDyZWrvaUVOSg9LSUixcuDAkdSUIwnhIsRNR4gOC0ICqqirUHD2CYdNyfDbOAGCxRGDYtPmoOfIdqqqqdKohQWiP1WrFrFmzsHDhQsyaNYsMbH4YVU5QhliCIAhCbfRMLBDouylhE0EQakNGNoLQgFBuUin7H0GYE6Mas2jDQRAEQagJGxqhyeZAcu46pOaXIzl3HZpsDmRnZ2tqaAv23S6Xa8BmaiUIQhvIyEYQGhCqTSqlGye0goy32mNUY9bMmTNpw0EQBEGoAsMwyMsvQHT6VCRmrkBUyiRE2KIRlTIJiZkrEJ0+FXkFSzXRM8R899LCe7Fh/Tp0VO9Ho7MIXbVfoberHV21X6HRWYSO6v0o2bCevPEJghANGdkIQgNCsUnV81SQCG/IeBsajGrMslqtKCneQBsOgiAIQjF6hkYQ+92JiYnYsWMHErrrUbelEDUlOajbUoiEngZKVHYOOnwlCPGQkY0gNEDrTaqep4JEeEPG29BhZGNWZmYmbTgIgiAIxegZGkHKd2dmZuLwt9WoqKhAaWkpKioqcLj6EK13oMNXgpAKZRf1g7KLEmridDqRl1+AmqN918FS08agZMN6RYs2Zf8jtMCo2S7DHa3khBpQhliCIAhCCXrqrKQvK4c9fI1On4ph03IQaU9Dj+sIWveVoaN6Px28EQMGKXYiMrL5QUY2Qm202KRSunFCC0gZ1Q8yZhEEQZgXkuHC6HmAR4eHyqD2I4g+pNiJBoWoTgQxYLFaraobJLgB0/mMIZT9j5CDUbNdDgS0kBMEQRCE9vB6I49OQ0nxBvLwQV9ohOzsbDQ6izBs2nxEJqahp/EIWveVe0Ij7NihiZFGz+8OB9iYdsm5i4Vj2m0pRFVVFekwBMGBYrIRhAkxasB0wtwYNdslQRAEQRgRimMqDj3jfFKMUfnQ4StByIOui/pB10UJs+AbI6H/yRwpDoRU6FqAdtBVIoIgiPCC1kzp6LkW0josHQojQhB9UEw2BZCRjTAagZQCIwdMJ8wJGW/Vh64SEQRBhB9kgCDCHTIkE0QfFJONIMKEYJvzzMxMZGRk0MkcoRrstYq8/ALUbCn0vp6aNgYlZGCTDNdomZy72JuVq2lfGbKzs8loSRAEYVLoKh0R7lBMO4KQB3my+UGebIRc1HZDp5TZhJ7QtQrl0AkwQZgfkoWEEOTJRgwU6OYMQdB1UUWQkY2Qg9rXwWhzThDmhzZgBGEspBrM+NZ2e5IDf3xyE+bPnx+KKhMGhnQ1YiBBBw7EQEeKnYiyixKEQrTILMWmzB42LUc4ZfaR71BVVaXWYxAEoTJ0lYggjIPT6cTYcemYPXs2Fi1ahNmzZ2PsuHTBNZpd2+u7I2EdGu993dVQj5wbF+Lee+8NVdUJg8Jepeuo3o9GZxG6ar9Cb1c7umq/QqOzyHOVbsN6MkQQYYHVasWsWbOwcOFCzJo1i8a1SBiGQWVlJbZu3YrKykowDKN3lYgQQEY2glAAwzDIyy9AdPpUJGauQFTKJETYohGVMgmJmSsQnT4VeQVLJQtU2pwThPkZMWIEAKDHdYT3/Z7GIz7lCILQBqmHYezaHulIR3ddNWzJ433+LnrsZVi7di127Nih0xP1hzZy+sDGMU3orkfdlkLUlOSgbkshEnoaKKwHQQxwpB7uEOEDXRf1g66LElLQ6joYXTMjCPNDV4kIQn/kzEN2DbYOjYcteTzsPH/n2vkIYtuO4Xhtje7zlzIY6w9dpSMIdTH7nKLY2uEHXRcliBChlcfZzJkzkTo6Da37yuB29/q853b3onVfOVLTxmDmzJkyak0Q6kHeE8IMxKtENB4IPQg07uSEX2DXbKatGXECfxc3PQcN9Sd0D9ugRcgKQjp0lY4g1MPsHmBa3XQizAMZ2QhCAVpdB9Nic06bX0JtzK4EhYKBdJWIxgOhB8HGnZzDMO6abeSwDf4bOduICeiuO4izLfUYOiUDg8dNoY0cQciE9GZ9CIeDA4qtTQzSuwKE9pjd3dbIsB5nTfvKeK+hKPE4YzfnefkFqNlS6H09NW0MSiRuzukqCaE2XDf45NzFXjf4pn1lyM7ODjsDkhIyMzORkZER1nKYxoNxGEhrvphxxz0M4wu/wHcYNnPmTNiTHHA11Ev6u1DDbuSScxej45t9aK54HkxLvfd969B41LQ1o6qqikJLEIQESG/WB/+DA3ZfxXqANTqLkFewFBkZGYZe1yi2NkEx2fwIt5hstEhoC8MwePTRR7Fy1SrEpE/FsGnzEZmYhp7GI2jdV67KnXulGyaKCUCoDcUaI7jQeDAOA2nNFzvuDn1zAOMnnC95fJaXlyPnxoWIHnsZ7FkPGnJcb926FYsWLULCdcvQ9Op6RI+fijjOOt+ytwwd1R8gLy8PxcXFutSRIMwG6c36ES4xqcPlOQhfpNiJyMjmRzgZ2WiR0JZ+mxlLBMCJn5aaNgYlG9br2sa0+SW0gJQHgguNB2Mw0NZ8KeOuubmZ0zbiD8PuvfderF27FtHpUxE3PUf1QzSlmC1BA0EYHdKb9YU9OEjNL0eELbrf+71d7agpyUFpaSkWLlyoQw3FQeMoPKHEBwQFXNQYvngBjoW/R9RIj6K/evVqHK4+pPuGhmICGINwi+tBbvAEFzXHQ7jNFTUJ1DYDcc2XMu7kxkZcs2YNysvLMbStxpAxFdlrrWZI0EAYF73lrt7fz4X0Zn3RKtZ1qBmIia8IX8jIFqbQIqEdQpuZwakXwJG7BjHjL8dzL/xZ72oCIGOIEQjHYPDhogQR6qDWeAjHuaIWwdpG7zVfj02y1HGXmZmJw99Wo6KiAqWlpaioqBB1GJadnY0Ttcck/10osFqtuGmRx5uD1nlCDnrLXb2/3x/Sm/WFjXXduq8Mbs7tIEB5rOtQM5ASXxH9ISNbmGK2RcJIp1jB0HszIwUyhuhLOGRI4iOclCBCOWqMh3CdK2ogpm30XPP12iTLGXdWqxWzZs3CwoULMWvWLNFeBHL/LhRkZGQAEF7nuxsOAwC+/PJLw+tXRGjRU+4yDIOHH34YWQaT+6Q360u4eYDJPdwhzA/FZPMjXGKyiY1VUlxcDIfDoWsGMrMFajZTvACKCaAf4d72vvGf1E/4QZgLJeMh3OeKEsS2zZ+ffw5z584NeVw8vePAkRwKPEbOHPg3ml4rgbun0/uakfUrInToKXedTieW5OXj2LFjiB432VCJRWg9Mga8e0MDxLomBjYUk40IesLbvPtpWCNtyM/P19U924zeC2Y65VJyImQm70IjYiaPRzmQGzzBRcl4CPe5ogSxbQMg5N6lRogDR3JIeJ1v2VuGxld+j8GjLzaNfkWEDr3kLqv3u5gYwN2LuOkLDCX3w82TyqyEiwcY7aUGLoP0rgChDewikZ2djUZnkc8Jb/Pup9Fddwgx4y/3OXlu2leG7OzskCmm/go6u8iyCnqjswh5BUuRkZFhqMWMNWA27SvjPeUy2lU5dhOSl1+Ami2F3tdT08agRKCvzeZdGAyGYVBVVYUTJ06EzGvTbFe25ZCZmYmMjIyQty1hTOSOh4EwV+Qitm0aGhoE13zWq6tkxw5V5ya7SU/OXSy8Sd5SiKqqKk2zypIcEljnLRGITp/q4yVkdP2KCB16yF2u3h89aSa6jn9tSLkvR28m1Ie9pm9WzLSX0mOfFO6YxpPt8ccfx9SpUxEbG4ukpCTMmzcPBw4c8CnjdruxatUqjBw5EtHR0Zg1axa++OILnWqsP0InvEzTUcSMv1z3DGRm9V4w4ymXlBMhM3oXBkKveEFm8nhUgpFjFRGhR854GChzRQ5S2ibUXl1GMo6SHPJd51esWGFILyHCOOghd7l6/6DYhJB/vxTCxZOK0Acz7aWMlnwkXDBNTLaf/vSnuPHGGzF16lScPXsWy5cvx//+9z98+eWXGDJkCADgD3/4Ax599FH85S9/wfnnn4+ioiK89957OHDgAGJjY0V9T7jEZOPCtU7X19cjPz9fUtwWrazbZoptxofR4wXI6bdwi0WhZ7ygcGtLgtAKmivCyGmbUJ1Ii439qnYcOCI4ZtevzIxZPEL0kLvccWkZZEPtM3fAZk+DneQ+YXK48z4pKQm33Hobmk2g0+gdV9VshGVMttdffx233HILLrzwQlx66aX485//jKNHj+Kjjz4C4PFiKykpwfLly5GZmYmLLroIL774Itrb21FaWqpz7fWFe8LrcDgAiD951tK6bXbvBSOfcsntN7N6F/IhJV6QFjETzOjxSBB6YLVasWH9OporPMiRI6Hy6qIsw8bF7PqVUvSKg2QmjxA9dBTuuLREWBE/+zZ0HNoPF8l9wsT4z/u5c+fimAn2UkaIqxrOmMbI5k9LSwsAID4+HgBw+PBh1NXV4ZprrvGWiYqKwlVXXYU9e/YIfk5XVxdaW1t9/oUzUhQvrV1dw0FBN+IVFSX9ZqTrP0oRazB89NFHNVOKha5vDTl9BEuWLEF8fLyhFy8K2Dpw0LOvnU4nCpYug9vtRnv1hwM2gL0QRg3uTwcJxiUc9Cu56GXoMtP1MJZQyxb/cRkzcQbs8+5Ht+uI7/d3k9wnzAHfvI+74iYAxt9LhZNjhRExzXVRLm63GxkZGTh58qS34/fs2YMrrrgCtbW1GDlypLfsHXfcgSNHjuCNN97g/axVq1Zh9erV/V4Pp+uiXMS6hx/65gDGTzhfczdyXzfV/oGaaZGVhlL3/3C6/hPouoy7l0HHtx/DtXM1AAtixmvrJs26ke/atQt/3boNrvo673tGDYJqpoCthDL07Gv/qwqDEkbhzBeVOPO/t9BdX42ysjLMnz9f0zqYBaNeQzN66ISBykDUr/S6+mSmK+98cgSAaNmiVA7xjcvuhsM4VflndB3/GqtXr8by5ct1byeCCIbQvO88+hnqtz5g+L0UhRWQjpTroqY0sv32t7/Fa6+9hn//+98YNWoUgD4j2/Hjx33c32+//XbU1NTg9ddf5/2srq4udHV1eX9vbW1Fampq2BrZAHGKV3x8fMiMLaSgq4dSI5mZFMVgCLVF+4E9aK54HkxLvSf72rjJPtnXAG2e1UxxD8xUV8C4xgczQHELCTWgOWhMBpJ+pac8McsBpdIDFbUOZAbSuNQDksehQWjeu3sZT7zBxDTYs4yr25hFbhmJsIzJxrJ48WL8/e9/R0VFhdfABgDJyckAgLq6Op/yDQ0N3jhkfERFRWHYsGE+/8IdMe7hobw2aOTYZmZDab+pff1HzytofNdl2g/sgeuVx2FLTMN5c+8KWfY1M8U9MFNdAXPFwDEawfp68LgpuOvuu/HXv/5V1vwNNv/pqkL4YMTQCcTA0q/0lCda6Mxq609Kr7OqeR12II3LUCNGJ6JQIOogNO+98Qar98O18xHDhlIYyGEFQsEgvSsgFrfbjcWLF+Nvf/sbKisrMXbsWJ/3x44di+TkZLz11lv4wQ9+AADo7u7Gu+++iz/84Q96VNnQZGZmIiMjQ/Ckgxu7jc+6rXbQXFZBJ5ShRr+xRti8/ALUbCn0vp6aNgYlErxa9L5uyBoMs7Oz0egsQuzlWWh+51lEp0+FPWsF2r/yKNqhMCSzyn9y7mJh5X9LIaqqqnSfB2aqK9cLKzl3sdcLq2lfGbKzsw3ncaclck6uA/V1xzf70F1fDVdbM3JzcwGo7/EQTjEgCcKoDBT9Sk95orbOrLb+5H+gwsp79kCl0VmEvIKlyMjI4F03lP49HwNlXIYSMToRAAoFohKB5n3MxBkYfuUvcKpqCzqq93tfl7qX0hL/fRLf7baSHTt0NwaaFdN4sv32t7/Fli1bUFpaitjYWNTV1aGurg4dHR0AAIvFgry8PDz22GP429/+hs8//xy33HILYmJisGjRIp1rb0wCnTwbybpNJy7iUavflJ4yGiUAMNdrs770PjCtLsRN95xyW4eeByA02dfMZEwwS13N5nGnJXK9+YT62uvx6RjvM38bI5OQlZWF/Pz8gLJY7Pwf6BkQCYJQDz3liZo6sxb6k1IvP/I6Nj5idKI77rrLELp5uBBs3ncf/xqjUlOxe/duw3psGjWxUjhgGiPbU089hZaWFsyaNQsjRozw/tu+fbu3zL333ou8vDzcfffdmDJlCmpra/Hmm28iNjZWx5qbE6NkDaOrYNJQs9/kXv8xmvGDNRiuWLECQJ9BIWrUhbDGOdASAkOy3sYEKYZqvesqFlL6PSjZkPH1tbuXQXPF84ge7/H4ZOcv09aM7nN9X1JSIiiLpcx/Ix3mEOEDHcwFJlzbR095opbupZX+pPTwzCyHb2ZC7XkYTCeKvTwLTY1NhtHNzQq336qqqrBh/bqA835j8QbMmTPH0KEUpDhWhOv6oQWmMbK53W7ef7fccou3jMViwapVq3DixAl0dnbi3XffxUUXXaRfpU2O3tZto3hDmQ29+82Ixg+r1Yo5c+YA6DMoeGMmHNoPl8aGZD2Vf6mGarMYPga60s8wDN5++23cceddspVmvr7uOvYFmJZ6xHHmr9ezzT4mqCyWMv+NcphDhA90MBcYM7aP2E2d3vJEDd1LK/1J6eGZWQ7fzIIW8zCYTtTb1Q64e3XTzcPBOMPXbwVLl2HZsmWm9wQT41hhxvVDT0xjZCP0Qa/gpEbzhjIbegaVNarxg8+gEDNxBuzz7ke36ztNF0e9lH85hmq9NypiGchKP6vozJ07F02NLlFKM5+Cy9fXPSc9yYPY+evj2SZCFkud/3ofChDhAx3MBcaM7SN1U6e3PFGqe2mlPyk9PDPL4ZsZ0GoeBtOJuusOAtBHNw8H40ygflu3bh3Wr1sb1ok8zLh+6I3F7Xa79a6EkZCSmpXQDkorbF6M3HfcoLDcAJ8te8vQUb0feXl5yMjI0CzdeSjT1jMMg7Hj0tFkc/gEKgbEpRAPZV3loPT5pHyP1IQCWsIdw5GOCWh5/69IzS9HhC26X9nernbUlOQgLy8PO51/Ewx0zNfX7PztPPoZ6rc+IHo+y53/RmtnwlyESh6YFTO2j+96neMN4t66z7NeBzKamVWeBJKf7l4Gpz/5F07u3ozi4mIsXrxY0jMJ6T9sgPNgRkilf09oOw+DfXb9lnvRdfzrkOvmO3bsQE5Ojqx5bBTMKD/VZKA/PxcpdiIysvlBRjZjsHXrVixatIh38+juZdDx7cdw7VyNFStWYNWqVaLiXJhR4TIjRhfGehuPQjUW1TB2Gn3eaK30650h1x//udVV87koA5jFYgmq4LJ9XVtbi/yly3AmdjQSMz1ZeBv/sTaoIa+0tBQLFy40/PwnwhMjH+4YAbO1z0CVI0LP3X5gD5orngPT0uAtK2ctUqr/6K0/mR2t52Egnaj90AdIsCehI25MyOZUeXk5Ft6UC9voS2HPetC089hs8lNtBvrzc5FiJxoUojoRhCSE0iJ7FI3nwbTUAwCKiorw4ksvB1Q0jLZRNjJqGFWMnhI6MzMTGRkZuhmPQpW2Xo1rJ6Gqq1zYq0F5+QWo2VLofV2NFOlcZTU5d7HXONW0rwzZ2dm6nL6y8XqScxfDYonwSd5h51GaW/aWwRppQ1Ta932UavaqZ6OzCHkFS5GRkeHT19HR0d75GzXmMgD8KeqB/tdyjT7/ifDEaGEKjHZAYbT2CYa/rOPivQq/pRBVVVWGXqOkwic/e07VoenVDYhOn4q46+9VtBYp1X/01p/MjtbzMJBO9PLOnQAQsrXZ6XQiJycHABA3fYGp57HZ5KfaDPTnlwsZ2QhDwsZ/aNpX5t0cssG3PYpGoShFw4gbZaOipjFSS+OHGkg1HhltwyQGIUM1i9ljlrF90tXVhT+/8DwAoKGhQZX+8Y8JGcw4FSr8FR02eYfrlcfhchYhjkdpBhA4ZhuPguszf3dvBiwRaNm7nfckmi8Wj9HnPxF+GEneGfFgz0jtIwY1NnVmXLcBHvlpiUB0+hTYs9RZi5Qenhn98M3IhGIeBjOEhmJtZnUomyMd3fXVpjfOmE1+qs1Af3650HVRP+i6qHHgGshiL89C46vrYLOP9VE0AGGX44F63UAOSmKfBMKsSi4XI26YxBDO41/rPjGqa7xQvfw9fAGP0px1wzyUlJSIvuoJ+M7ZpKQkAMCrr76KjRs3Sr6WGw7znzAHRpF3Wq2lUvGfezNmzMD4Cefr3j5iUSqDzbpuc2EYBps2bUJ+fr7h1iIjYaZ1xihySus2Y+fveXPvwsndmw0zfuU+txb9RuPWnEixE1F2UcKwcLNE1ZfeB6bVhbjp4lNPa5UKPdzQMpOrmJTQoUJO+nAzZ9MxS5ZQqYSiT4zqGi+U4S1m4gyMvH0zokZOQnxiInbv3o3D1YeQkZEBQHwGVv8MYHPnzsWvbr0NM2fOlJWxz0jznwhvjCDvjJIVnS+T3/gJ5+PGBTmmWQ+UZLM087rNxWq1wuFwADDeWmQUzJa10ghyiq2HlmszOx6HXDjLG9JC76y0SsaK2v1G43ZgQJ5sfpAnm/FgGAarVq1CUVGRJI+MQMkThP5mIGJUrx01kXOqHS4nN+EUqDhUp4lVVVW6zolAJ5xSkj1Iaa9du3YF9cChWDyE0dFT3r399tuYO3eurmtpME+6ZcuWYdv2MlOsB3IS24TLus0yEPQzuRjFa1QO4aSX8cEdt0xbsyfUz/ipPiEtWvaWofPbD0PST2qNFTX6jcatuaHsogogI5tx4G406+vrJbvMk3IijnA3Rspd0MJp/JjJLT0QaveJkPF1/bq1WLqsUJeNmhiDsBRFR8xGNSMjI6w2psTARg15J/UznE4nbr/jTjQ3Neq2loo1MB365gD27NljivVA6qYunNZtIPyMhmqhdrvooSOFi17Gh3//dHyzr19IiwjrIJT+dQsWLFgQ0rroOVbCYT6H87gVA2UXJUwPn2JljbShlZMIgUXI5ZgveUKwvxmIhHNASyUB7I16ZVAO4RKoWM0+CZQUZcGCBVi2bBnWrVsX0gyZYhO1SMnwJiYJQWVl5YDM5EeEJ0rlnVTPZ3be2kZMBNCo21oqNiPnnj17TDOPpWazDKd1G6BszUKomX1Wr/h94aKX8cE3bodfcRNOVjyH3o5WAEAvcxaF996HyMhITdtZ7UzFSvotHLImh/O4VRsyshGGQ2ij2bx7M9oPfSBa0eAT8oPiU3Hmy0qc+d9b6K6vxl/KygaccuJPOBsjlSxo4Wx8NCtq9YkY4+u2snKUlZWhYOmykGTIlGoQlqLoBNuohtvGlCDkIjUjOXfeJsy7H8efvQst+8pg12EtDdd5LEXWheO6bdZszVp6vKg11qXOd0IcDMMgPj4eS5YswV+3bkPduXEbnT4VcdMXhLSdjSQXjVQXQnso8QFhKAIFDk6+eQNsyePRdeRT0cG3uckT6rYU4timRTi5ezO666sBAEuXFRo20GSoCOeAlkoWNCWBlwltUKtPxCZFSUxMxOFvq1FRUYHS0lJUVFTgcPUhTZRBrRO1BAp0zN2Y8mHGjamcRCfhCrWFOOQkLuDO2whrJOJn34aOQ/vh0mEtDcd5LJVwXbczMzNDthapgRaB3blyrL7ec+1QyVg3SqKScIPb9yUlJXDV1yFiUCSi0y+HPevBkLezkeSikepCaA8Z2QhDEWyjGT/3TjA93SguLhataGRmZmLD+nWwWCyISZ9i6mxTWuFvjBSbPdDoKFnQtDY+0sZXOmr1SW1tLQBxxtdQZcjU84Qz3DamZsvcpSXUFuKRY+j2n7cxE2fAPu9+dLuO+Kyl0S3fab6Whts8lkM4HxqaJVuzmOyuUvUffzmWn5/vDSEjd6xrfbA1EOHr+/Pm3oXesz2Im65POxtJLhqpLoT20HVRwlCI3Wg6HA7RgYMZhkHB0mWy4nINJKTGPjEDSq/CanVNQ68YIOGAlD7hu66ya9cu5BcsBaDulSKlV2P0vOYUTnF/1Lr+Ew7BfekqlDTE6h+1tbWorKz0JmUCfOdtzMQZiJ7wQ3Qd+wKdRz9Hy/ul2L61FHPmzNG0/uE0j5XMP7NerwwHxIQ9uOOuu7AkvwDHJMY8VBpCxh+6uqcuQn1vjY4FoF87G0kuGqkuhPZQdlE/KLuovmiRGcoI2abCYcNmVsRkVwymdKvZf2ZO320kgvUJnyEzIdGOpqZGRI+bgu76atgc42HPUp7hSQ2jqRGyTpk9PbtabRgORnAjjCezIVZXsDuS4aqv875ujbQhKu37hmlns89jteYf6V2hJ9gcatlbhlPvvYSY8ZeL0n+CybG6lwrANB0F09PtfV3sWDfC3iCcEGrPzqOfoX7rA6q0s5I5bSS5aKS6ENKQYiciI5sfZGTTFy02Blu3bsWiRYuQml+OCFt0v/d7u9pRU5KD0tJS0d5xUgiHDZuREbPoGmVBo41vaOAzZHbXH0ZD+UMYPPpi2LMeRMc3++B65XFEj5+KOJnGV6Hvkms09TcI+ydqKSsrw/z585U2T0DMvDFVY9OkpRE8lG1LG0jpiNnQd9cd6mcgaN69mfO6fFmi9rOYcR7TIZQ0jNbPgfRtdy+D2qd/DZt9DOxZD4rSf8TKseLiYjgcDkltQPqYugj1vbuXQe0zd8CWmKboUFOtw0wl80XN+Wa0uUuIQ4qdiK6LEoZCC1daPa9h0XUdbRG76BrlKmw4pO82OkJXFuA+C3dPJ+KmL4DFEuGNndRc8bw38xUAJDlG4GUJVwqXSMgIGox+15wsEQAnbsfSZYWwWq2aygwzp2dXev1HaoZXKYT6sIWuQkknkP7RsrcM3Q2HETP+8n5jI/nmDah7qcCTlOnQB97P0/N6ohnnsZbzLxwx4gFuIH2769gXYFpdiPv5faL1Hy1CyLDQ1T11Eep7S4QV8bNvg+uVx+Ha+QjipudIbme19lJK5KLa882MMpqQhigj29///nfJH3z11VcjOrq/1xBBBEPteBpK43LJhRRG5QQ66ZG66BphQTP6xjccTtaEDJlM20kAvm3PjZ3Uc7IOza8/gQ3r14qWMY8++iiOqWw0zczMRG9vL3JychCdPsXHm4OM84FReqCilRFcj8MWPQ+XzIyQ/mF3ONDRywRMyiTXo4bwMJAOoZSutUY9wA2kb5893QRAmv6jtRyj+H3qMXPmTCTYk9Cyd3s/T8Xo86fBlpyO7qP/RV31fu/rYtrZCHspo843wtiIui4aESEtCanFYsHBgwcxbtw42RXTC7ouahy0i4MVmuscdF1HGYFOjTIyMkzp5m/kMWHEU3E5CF1ZUDMuCOBpr6ysLABQ9So6XWERJtiaoLTtgoUWYDracOyJG3HPPfcgKytL1JqkV3/K+V62fWtra+FyuWC325GSkjIgjUX+Y622tha5ubm6hZ0YCKgR2sMMB0VK11qjrRH+bd7Y2HjukGgqYi/PQm9XO7rrDqL96yr0NNVIWoND9axmGDdGh6sTRadfjrjpvp7AHd9+iLLt22G32yW1s956s9HmG6EvmlwXraurQ1JSkqiysbGxYj+WIARR0/NIj9Mqo3stGZlgp0arVq0y5Ym3Xl6VwQinUzqhk++oURfCGudAy94y3rggUtqePVmNGjkJXce/VvWUPZTeHGbaWIjZmCq9/hPIa6L9wB40794MAHjyySfx5JNPitoY6+WdI7UtfNrX75qyGY3tYhGaA/76R2VlJQDyDNQSpV5LZjgoCrTWZmVlYfXq1ZgwYUJAeWwkjz+hNl+2bBle+PNfUL/1fh9ZEjEoEq0S9J9QXek0wk0HFjOtyyysThQz/nLEXPRjnKz4s08YDostGgmJicjMzJT8LFL3Umq3n5HmG2EuRBnZfvnLX0q6+pmbm0teYIThCHVcrnC7rhOqhV+Ma/jGTZsAmM+AacQYIEZwxVcTIUOmJcKK82b/Co2v/F52XBAWVulyLFqDxtfWq2K4YwmVcd4MG1IWKUZgJQcqQmOn/cAeT5KM9KmImyft+q6ehy1i24Jt30hHOgALosdN8cwPExvbxSBlDhj1gCScUNLGQjKice92ZGVlIS8vDxkZGZobLLh6EuuY0NDQgBEjRmDGjBmCa23MRT9Gx5HPsHLlSu9nCY1FoxzgBpLLa9euBSwWxPglsGjevRnthz6QpP8MpCudeq3LSvV7riEqKmUSYiZM98TgazsJ69DzAIsV9aX3yTJESdlLadF+QvPN3ct4Q40AQG1trazPJ8IXyi7qB10XJdQinFyMQ7nwi3UNB2DIa5di4GvP+MRELFm8GMuXLw/peNDbFV8LAl0Pbz/0ARLsSWhyNXjLS800y73W1PL+VrR+4PQYYDiGO/Z6xE6Jm4BQ9IeZMvjJlaNyNw18GV5PvHA3bI7xsjKjGWF+BWoLtn0bI5PQ3XjEk/nP5OuVGOTMAT3CTgw05LSxkIxoP7AHzRXPgWnhyHoNDRb91nU/j1B7kgOuhvp+ssDHiD89+Fg0ikwRksu9TA+ObVzozeTNl6WXaToKpqfb+7qYNdiMHl5S0GtdVkO/V+OqtxBidYAN69Z6rymr2X58880jW54H01LvLWd3JGPzn/5Ia0CYI8VOREY2P8jIRqhJOCjloV74xS7WCYl2dAwfa1oDJsMwePTRR/HEpifR1Ojyvh5qbyItlSM94VUczynySj1aWaVr+JW/xKn3XoItOR1MWzOYtua+QhYrFuRkY9u2bZLqrbVx3mzGfz02lHxjR+73G7292fY9b+5dOLl7c1gZ24VQ0ieB5IrR13KzILWNhTbBrlceR/T4qYgLgd7C1ZNsI7+HU++91M9o1vzWU+iur/ZZa929DGqfuQM2e5po47YRZEoguSw2/iklCelDqz4NZphUS7/Xep0Otpfavn07li4r1GRO+PdNxzf7Qipb/OuiRHcNd0N1KJBiJ5KW0QBAZ2cn1q5di//3//4fpkyZgssuu8znH0HoCcMwqKysxNatW1FZWQmGYXStD+vmntBdj7othagpyUHdlkIk9DSYwsDmf5UwKmUSImzR3quE0elTkVewVNV25rqG88G6hv9u8T3oqN6PRmcRumq/Qm9XO7pqv0Kjs8hz7WDDekMvHrt27cKqVavQMXwsknPXITW/HMm569BkcyA7OxtOpzMk9RDb3ma51sySmZmJw99Wo6KiAqWlpaioqMDh6kPemCCzZs3CwoULMWvWLMnjZObMmRiVOhote7d7rsrcvAEpv/kzHAsfQ+L1hUi68VEMHvsD7Nn7H8lzg71SrNXYZq91CGVJHDZtPmqOfIeqqipZn682elyN4o6de+65R9H3a92fSmHrbYmMAhCadtZ7nVYyBwLJFUIdpLaxv4xw9zJornge0eOnwh4CvYWrJyXMux+n//u657uzfL97+OxbAfiutV3HvgDTUo84CWPRCDIlkFzmy+TNhZUlDodD9hocbmixLjudTowdl47Zs2dj0aJFmD17NsaOS/fqlmrq9+xV79Z9ZXBzvDcBda7TB9tL2e12zfQa7nxz7XwEzbs3Izo9NLKFS7D+1PrvCemITnzAcuutt+Ktt95CdnY2Lr/8clgsFi3qRRCSMWqMoVDHglMTrQN+8p2qiI3Lsnz5clx00UWmjNNhpDhooYo1pMcJmlbBjK1WK27/9W1YuXIl4qafU+oswODRl3jLRAyyyZ4bWsagMUo8H7GoEdtSztjjjp0nn3xS0fcbOaYQW293TxcA7WOIGmGdVjoHjBQkPVyR0sZs7DN27HoNV9cXhiRQOVdP6q79SvC7B6deBOvQeLTs3e69RinWIOU/FvWWKYHksnXoeYLvAeY9uNMStddlMXFM4+PjVdPvQxFvONBeauvWrQC002vY+XbXb+6Gq60ZcfMCGPM0SIKgNDlZOCU3MxOSjWyvvfYa/vnPf+KKK67Qoj4EIQujCxCzKuVabsgDbbbELtZmNWAaKVuRHOVIqtEi2MbajC7sEyZMAKCtUqfF2FYzIUso+k2pEVipUUctI7RRZRX7fI2HP4I1Lgkt+8p4r62pYWw3yjptlKREYuePGeVjqHA6nViSlw9YIrzGK7mGK7lw9aSOQx8Ifrclworhs36FplfXe9daiy0GgLyxqKdMCSQXbSnfgyVysI8xkSVckoSoPSfVXpfFHOI+/mgRAPXmSSgMv0J7qVDI9MzMTHR0dCA3N1e1NhMzjpQeyhvpUH+gIfm6aEpKCmJjY7WoCzFAUPuqiB5XGs2E2PbmK6fWVUL/z96xYweys7PRZHPwXpUEIPqardKrf3pgNG8iKdeapbqcsxtrob6+9957TenCHoprtlqMbbWudYTq6oGSq1F8Y8+xaA0azkYjKysLDz/8cNB1Qc2rWUaUVezzdX77IazRw9BxaD9cO9W/gmakdVrrq01iEDt/6IqPMOz8bo5KxvCZv0BH9Ydw7SwC03EaQOhCIHDXAq4XFx+Rw5MBAENOH0XdlkK4dq72GgjljEW9ZEogudj0yuNw93Si49sPDXlFXilazEk1ZZLYq6culycWsJrzRK/r9KGS6SkpKQDUaTOx40jpVWKzhQgJJyQnPvjXv/6FJ554Aps3b0ZaGv8G0cxQ4gNt0eKqiBEyLRkVse0tVG79urWKg4nyfbY10gZb2veDBvoFEJYn+ErHrFaeDWoHyQ0WzLfupQJ01x1CzPjLDZ/l0h8jBJ+Wi9KELHpkQZMaDJ2vf/gygo0anYaNItafcA947/N8flkR1XhOo63TeiYlEjt/gpXbvn077HZ72K2PYgg6vy0RiB43mdeTSm3ZzK1Lwrz7cfzZu4ImMjj0zQHs2bMHJ06cwMGDB7Fq1SpTJsgKJBcBhJ3M1HLtU0smiU1mtWXLFtz/wHJT6jB8hEKmq6X3SRlHSpOThWtyM73QNLuoy+VCTk4O3nvvPcTExCAyMtLn/ebmZoG/NAdkZNNuA6/V4kQChB+h9m7Zux0d1fuRl5eHjIwMNDY2Bkx7vWzZMqxbt07WwsVXh+a3n8OZz94IutnavXs3rFZrWG4gVM9uF4KYRmLqHN9dj7+88DwaGhowYsQIMAyDuXPn8va1u5dB7VO/gs2RHpKNkBaYOXuwXKORFsZFLa7N+Rt11Mg2GO7X9tjnq62thcvlgt1uR0pKiirPacR1Wg/Dqdj5c+ibAxg/4fyABxRM01EwPd19dTdADNpQIWS0dfcy6Dr2BU5/8i+0f1117gBHe9nsm110Ek699/K57KLivtvMRvxAcjGcZGYoDtbUGAdSDjSam5tNq8Pwwdd+o0an4fbbbsWECRNUGYNK9T6p40jpAZXRDrjMjqZGtrlz5+Lo0aO47bbb4HA4+iU++OUvfym9xgZioBvZAnk0KTkx1XJxIgHSH6H29pz0PgempcFb1hppQ1Ta9wP2y4Z1a1GwdJmkhV/opNn1ymOevw+y2UpItKOp0dX3fWG2gZCzUOvhQcQSbJ617C3DqaotPh4w8QmJaG5q5O3rzqOfoX7rA6aft+G6ORLi7bffFjScAtL7TSujMdeoYxlkQ+0zdwT1LjGyQdfsGHWdDrURQGw7FBcXIz8/n7ec12CcPgVx0xeYygNYCdy++vLLL1FUVCRdj9BQNveTZRI9QsPJIBWOqCHDxMbgUjIOpO63zKzD8MFtv4MHD+LZ51/AMZX1CyVtJnUcKd0/m/nWhRGRYieSnPhgz5492Lt3Ly699FLZFSSMiVBQ4vrdm5Fz40Kgty9WilQhpWWg91BlRzQTfO3t48lx/b2ItKeh7X9v4+TuzYHv6m8pRGJiIg5/Wy1p4fevg7uXQXPF87CNnITu418LBig9/fGrAICO4WOR/NP7DJfEQi2kBonVO3hpoDhy7Qf2nDu59930nXznOQCNvH0d6uDUWmHUgPZikJqQxel04vY77gSgTr9pGQifGyfJzXSHNNtguKJk82fUdTrUSYnExuOsrq7mLdd7thvNbz2F6PSpsGcNnCDWO3bswN33LIarvs7n9WCBzrdv2xoyj3j/tYDNesp6dgf7brMmyBooKI2lK/ZASek4kJrMysw6DB9s+zmdTu81bLX1CyVtJnUcKc3cGorMrwQ/ko1skyZNQkdHhxZ1IXREaAPPtDWju676nNt7jmwhpWWgdxIg/fFvb9bAFT1+qo8nhzU61qecP2y/1NbWSl5M/OvQdewLMC31SLx2KRpfW8+bxa6X6UHL3rIBkwVHykKtd0ZSoexN3rHFs+lLWvQ4jm1cyJtlLGLIcN7PYwlVhj81GAibI9YgZhsxEUKGU0BaMhQtjcZco070JI/hxuwGXT3hvYaTOhq3//o23ms4fAY5tddpM3r+iM2Cl56e3q9c+4E9aHrrKfSeOYm46YEPxsLJYHzvvfdi7dq1ns3y1cswKGEUjj/3G7i7O9Cyt8xn3QE8RtuWvWVIHT0m5AlGwnUtMONcUxslGSxDnVlZ6iFuuI1bhmFwx12/weBxUzTbS8htMznjSGnm1lBkfiX6Izm76O9//3ssXboUlZWVaGpqQmtrq88/wpzwZR/xMcxkKcsGpnUmPinZEbVE7cypcvFvb9bAFefnsRYsExbbL/lLl0nOpORfB9ZzyeYYi/jZt3my2Pllnqov/T+4ezoRN31B2GXBERobYrOD6Z2RVCh7k3ds8Wz6IqyRiJuew5uB7PT+V4AIq64Z/ghxcA1iSYsehzXOgRaF/aZ1xitu9ru2j18DELpsg2ZCzJrFl6V1+JW/RG1dA1auXOldF8aMHYeHH34Y+fn5GJEyqt+aAYjPGh0Ms2bdFJsF784774Tdkewtx3qiW4fGAxg4BuPy8nKsXb8B0emXw571IKJSJqGn4dtzhsYF6Kjur0e4dnoyWP76tl8ZyhBkFP1QKmada2ojN4OlXpmV9cr0aQQeffRRNLkaDLmXkDuOlPbnQB4PeiHZk+2nP/0pAGDOnDk+r7vdblgsFtMsGoQvfBt47+ZZhSs2obgqorfLsx4B6YVOF/3bW+hqXtSoCz0bZoHT4ObdTwMAzsSORvLVyySdvvnXgWvQi5k4A/Z596O54nnUcU5VAAtvPVnMuoFQY2woOUVVAyGP0c6jnwMQ7rPYy67DqfdeQvSpwz59nZo2BjcuLcC6devIA9XgcL0oI6yRiJ99G1yvPA6XswhxMvstkNHY3cuA6WgD4IkBJ1eOs4cvS/LyccwSwetRGQqDrlG9QPiu4PnLJT6PQ8/18Jd8PNxPf/Qqavdux8qVKwHA63XEt2ZIDT3gT6i9QtREjOf9jcuWYeKk73n6pb4ODTseRk/Dt4hOn4rYqRlo2LbcMB7AWo5thmHw23sWA72MzyEOq8/ETr4OkfEp/fQI6zDPNc0JEyaoUg81EDPXjIiZ55rayL01U1lZqdstBCneVkZdp6TCMAw2PrEJgDH3EkpuX6lxlTicPBaNjuTEB++++27A96+66ipFFdKbgZr4gC8Q45kv30XjP9aqlg3MaJn41FxQ9AhIH8xww61T1JjLcHL3ZhFBlHO8/dKytwwdhz9GzLjJsoNlcusQe3kWGl9dB5t9rNegx2YCO3u6CW0fv4bYsyfR2FBvuODYSlBrbBgleCnfuAMgO1tsuAXdFYPZlFm+zJCeJCrPg2mp95ZLsCfhmc1Pieo3oeC/fJ+rdDPKMAweffRRrFy1CjHn1p9B8ak482UlzvzvLXTXV6OsrAzz58+X9fmB0CsbcDC4V/ACBc/37yd3L9MviQS7hgweNwU9DdWwOcbzHtqoIaOMIgeVIiT3bsyZz8nmnYOeU3U4uftp9HaeRnLuOthGTDBMEg+txzY79gDfREn+SXNYPYJpO+k5zLNYUV96n2H0BLFzzWgYca6ptXZK+Rz/so2NjaITgbFxTIUSQAH6ZFb2x6jrlBy4csPIe4mBqPuGA5pmFw13BqqRjW8x1SL7XyChEkovNDUXFCWKiFyFQazhxuc5LRGIHjeZ15Oj7qUCME1HwfR0e1+3Oxxw1Ss3ePG1tdcDws/Qun37dixdVmgopU4JaiupwQzV27dvV5QFWMpzcYM733LrbWhW8Izs59XW1sLlcsFutyMlJUU345OWRjAzKrNCBjF2c9t59HO0vF+K3bt39/NyF0I4+/Dj/WKAqrUZ9ZeHPpn/NOgDPbMBB6K8vBw5Ny5E9NjJQY1hZWVlPgZWPgMHa/SJneLxstJyY2PULKVy8JczM2bMwPgJ5/dbL9q+qEDTq+u9feCTxEinA8tQjG3WuA/4bpb5DL0sRtMTpMw1vevqj9Hmmlprp5TPESq7ft3aoLoWN45p1/GvDdOO/hh1nZILKzesw5JgSxrDKyNcOx9BTOsR1J84ruu8M9uBKxECI1tnZyc+++wzNDQ0oLfX9z7xz3/+c6kfZygGqpEN6L+BHxSfihMv3A2bI53XMCNXOeDbUFdXV2uSZjn4c4pbUAIJQrmKiFyFQYzhJr67Hn954Xk0NDR4M1y9+uqr2Lhxo2gDTW1tLXJzc1U5fQuaUptzemM0j0clqKWkimm/G3PmY9v2Ml2MN2r0mVGMT1rWw6zKrFYeDcE8XdX4Dn927NiBnJwczfvAiF4gbL1GjEyBS6THMAAfGebv4c41up1tqVfV+50PPq9Ktb9DL4TWC74DTz6PzyTHCDz1pyc1lyGhGttse1iHxsOWPN5nsyzkgW8kPUHqXDOCUZira3z55ZcoKioyxFxTa+2U8jlKvpM7RxLm3Y/jz95lSKOwUdcpJbByY/iVv/SENfA7jGjZW46O6g+wevVqPPTQQ3pXlzAZmhrZXn/9ddx8881obGzs/2FhEJNtIBvZAOGrYDHjL1fV4CHs3aStK72cBSXYpluO0q9k8Q5muGnZW4ZTVVt4vTQAiHZP1vIUM9jpTbi4UauxIRST1a+xsTEkhoNAKOkzoxiftKyH2ZVZrYzf/uNGy81oKPtAqvwM1Ym20BU8Lly5lJOT49NmXTWf+xh8uEa37rqDqnu/C9XfLEYLKQitF17PrcQ0HwO0u5dBZ83nOFXxAuLQhmNHj8Jms2lez1D1ATtf67sjPVnu/TbLzW9tRnfDYV9dx0B6gtS5prdRWG4oCLXnmlgPT0Ca3JYi/wEoWiv854gRvE/5CEd5yu3nmIt+jJMVf/Y5jLDYohEfNxTHjx3Dnj17yIuMkIQUO5Hk7KL33HMP5s+fjxMnTqC3t9fnn9kNbAR/9pHy8nJVs3ZyM5U5Fq2BdViST+YoLbPuSM1ox5dVLTl3HZpsDmRnZ8PpdErOnKo001CggOGegNQvI3rcZN76AhCdXUZuBhwxBMuoabYsOEJZw5Rm1RUaf81RyVi1ahWioqIwc+ZMFCxdFvLMVf7I7TO9Mm+Fuh5aZ9PUGq0yOLPjZsWKFQC0DVQcrA9iL89CzZHvsGrVKsXZ/2praz31FvE8oczex20/MXKJm6W10VkEWKywDrOjZa9nXeAmtPEm0tEwY7CW65LeCK0XlgirJyN39X64dj7izaTZfeIbtH24Cz0N32Lzn/4UEgMbELpM1+zY66mvhi05Hd11h3xkT7frOywtyDesniB1rukJn67hWLQGlsjBaNm7PWRzjU8WjkodrcraKWUNVrpe+88RNtlXt+uIzxiObvlOV69LvbPWawF3zWr//B0kXlsAe9ZKxF2xCFEjJ8Hd3YFbb7kF4yecP+Az5hLaItnI1tDQgIKCAjgcDi3qQxgAfwNIdna2agYP/40s3GfBtDb4ZI5i0WLjKWVBEbvpnjFjhiSlX+niLaSIu3sZNFc8j+j0qQENlgACGrhY/DdXrGLfVfsVGp1Fngw4G9ZrdvITzBBnFAJtkJVsCMWOPzZzlRGMN3L6zCjGJ63rEQ7KrFbGb6vV6o3lpuVmNNgBReOr6wAARUVFipRup9OJ/HOyNtjzHDx4kNeQ3hiZhKysLOTn5+Ptt9/G22+/3c+ILwe2/axD4wWNYS17y5DkGOGVS1wDa33pfWBaXeio/gCunY/4GN1gsXiMQYf2w6XRmqH3uqQlgdaL6POneQxNR/+rqpFbDkoPj6TAjj2HrQdMW7P3dbvDgbJtW7Fu3TrD6gly5poeCOkag1MvQMK1ef2Mu8HmmtChYzCEDhVbLEMBKF87pazBStdrvjkSM3EGUu54Bo6FjyHuCk+swe1bS3U1CodyLocS/zXLtXM1Wt4vRVJkJwoLC7Fu3bqAa67SdZYgAGCQ1D/Izs5GZWUl0tPTtagPYVDUSvvLbmTZVNZsKvZQbTy5CwqfazR3QfGvKxdu2u09e/ZISsesdPFmFfGmfWU+buxdx74A01KPuOsLVUsTzi5UefkFqNlS6H09NW0MSgwQ84SLHgFExaS3l5uqW+z4q6ysBGBe441RjE9a10OK7DEyWqWAF5JrgHpeE0J94JNw4ef38c7jYHEyuTETV61ahcHjpsDa3oOWvWW8MeZa95Vj1Og0PPv8C97NLVuGaWtG97nxUFJSgpKNT6iWpIFt5/ruSK8xzDdejedq9Evl5T5yKTMz0yc5kTc2ZOl9AICOVhdcOx9B3PQcJFxXgFOVf0adRmuGmdYlKbAGRKH1oqe+OmTJbQIRirnKxX/smeVql9y5FkoYhsGmTZsEdY0hE3+Es1fejFNVW9BRvd/7utBcUxJrmGvoY+sRlTIJw2ffioZtyxWvnXLWYLnfKTRHLBFWRKVehNP7X0Fq2hjdr2CGei6HEj65wV49DrrmlpQYPiGVkaAEDvxINrI9+eSTmD9/PqqqqnDxxRcjMjLS5/3f/e53qlWOCD/8N7I+V01CsPGUsqCUlZX51NUf7qZ74cKFopV+pZttIUW88+jnousrBTMouHoEzQ+kFCZmrkCjswh5BUtxuPqQrA2hWKMPi1mNN0YxPmldDy2UWaMoNmrUI5iBIZBBWix8feDrASw8jzMyMni/u5/ssUQgetwU2LMeRMc3++B65fF+m2v2eW5ftQorV6702dxyY/dEXfozT+DmcVN8sq2KNf4Fa2fuFTwvEVYUFhZ6wwv4/y13U7h8+fL+RjfOZ9kdDty0cCEyMjJUH5tmWJfkYAYDYijmKt936m2QkIqSuRYK/GWXkK4Re9l1OPXeS1ixYgUuuOCCoNk0Ax06Co3fQIeKg1Mv8ngD7t3Om4RN7NopdQ1Wsl7rMUfkYJZ6ysVfbrA3P4TW3Ljr71VlnR1IGCVpmRGRnPjgueeew1133YXo6GgkJCTAYrH0fZjFgm+//Vb1SoaSgZ74QGv8g2zqkYpdbABvOQFBxWw21Qq+bZQgtSx6bfj1CpqvdWBzsZ+/e/du/OrW20wbUN8oCQFCUQ81kwcYRbFRux5aJz3x7wOmow2unatlyU1/2cN0tsG1Y5XoLJBdXV0+ge6562Gixhnp+NrZ7nDgj5s2Yf78+ZI/DzCO0VcpRngOI9QhGFqMoXDEiO3ElV1RYy7Dyd2bFemOStfPYEmi2r6oQNOr6xUnYZOyBmuWNT1tDNavXaO7R6qYeholiYha+I8zPfag4YRRkpaFEk2ziyYnJ+N3v/sd/u///g8REZJDuhkeMrJpC99C7HNVZ7q0hUyuIipmQVGqNASqm1qbbe53JCUl4ZZbb0OzDsYKvTb8ehpo1Mgcygfbp7W1tchfugxnYkcHfbZdu3ZpkvkxVGiVuVLNerQf8qR8ZzO6ipU1/nKgsbERBUuXKVJmjaLYaFUPrQ0MvPJK4jzmkz3cTJv+2SG7jn2BnpN1aH79CWzZsgU33XRTP0N659HPvNk53Uy35pk6zWDICTVGMV6bhR07duDuexbDVV/nfY3aqz9Gmmv+sgtut2JDg9IslWL/3u5I9h1rMgxBUgxKahifROkABpgzRhqjWhFozQ2X7KqhwigH5KFGUyNbfHw89u/fH7Yx2cjIpj18G9nTH7/qyVbW0+ktF2whU6oMi1lQ7r33Xqxdu/acATBH9OZfTN20ODnSw1ih54Zfz/TjWny3kHeimNNbs59EGqX+fPVIsNsBWNDkauirmwhZIyQH1q9bK/sU2yiKjVHqIRdW/r/99tsoKiqSPI/55r9Uhd2/Ddu/qvIa6ToOfcBrsGORa8SXw0DYfAHGMV6bBWovc8Inu3yuzMnQHZUeOopdTw59cwB79uxRLIukyDQ15R/NGX0JtObqvc6aDT33X3qiqZEtPz8fdrsdDzzwgKJKGhUysoUGvs3nqNFpuP22WwN6irCL3a5du7Bx40ZNFyp2MYx0pINpa/bJbIUIKwqXFmDNmjWCfyemblpsXkJprNB7o62VN5kY1H52oXHTvHszuhsOA719mY6E+tPsm2G16q/0c/gC2UuVNVop00ZRbIxSD6XIncd8sofv6gnrxXb2dBPaPn4N9kEd+O7bal6vZu61rVB4solhoHh26b2WmQ1qL/MipDfxXW0XqzuqsR4YxaNdK2jOGAO1r0oPVPTcf+mJpka23/3ud3jppZdw6aWX4pJLLumX+GDDhg3Sa2wgyMgWOqRuhH2UfUsEosdN5g2CqsZCxedO33XsCzBtJxExZDhO738FiWdd/b7DKItoqIwtemy0uc9WX1+P/Px83RZINa/9Bho3rp2PYGjbURSvX4+UlBTTGc9CSSCjgNRA6XLns5ZywCiKjVHqoQZy5rGQ7OOGP4hKmYTTn/4LTKvL+36CPQnPbH7K5/P41rbEGx7QNCab9HYJb6+LcDEahwpqL/MSqO/cvQxOf/IvnNy9GcXFxVi8eLEo+dLd3Y1Ro9NEhbeQGmvYTB75gaA5YxxCuZ8MVwbqeJZiJ5IcVO1///sffvCDHyAiIgKff/45PvnkE++/Tz/9VG6diQEIm/Vl4cKFmDVrVtCFNzs7G002B86bexfg7kXc9AX9shBZLBEYNm0+ao58h6qqKtl1YzMdDZuWA4slApYIKwaPvgRDLrgK0WmXIm56Du93+P+dFnUTg5S2VYLYDJhSM5oK4XQ6MXZcOmbPno1FixYhPz8f1kgbWveVwe3u9SkbivTjbCa4hO561G0pRE1JDuq2FCKhp0HSBjTYuImbngNXfT1SUlL69SfDMKisrMTWrVtRWVkJhmH8P37AwJUTybnrkJpfjuTcdWiyOZCVlQXHiJHesTN79myMHZcOp9Mp+Hly57OWcoCbBZUPPbKx6lkPNZAzj10uF6yRNrTs3e4je2ImzkDivPvQ8d0nOPXeS7DZx/iMxY64McjOzvYZd5mZmTj8bTUqKiqQt+R36Pz2QzS98jhiL/0JOg7th2tnEbpqv0JvVzu6ar9Co7PIk/Vtw3rNZLt/9uSolEmIsEV7s65Gp09FXsHSsJE3oV7LzA61l3lhs2zy6U2wWND13cdITRsj2sDmdDoxfsL5cNXXof3QB3DtfES2vOLKwtLSUlRUVOBw9SHTG9gAmjNGgm/NbXSGfp01M4HkSCj2X2ZgkNQ/qKio0KIeBCGIv7Lf/pVnY6rlQiV3MRxoiyh3o813kqHmRlsoPXzz7s1oP/SBbunHMzMzJXtI+SNn3DAMg0cffRRPbHoSTY19njLheJVLDP5ygjVuRaVMQsxFP0b7oQ/QPiwNyT8Rn6LdiHKAVWya9pXxegyESrExSj3UQso8djqdWLBgASId6eio/hCunUU+SXvO/O9toJdB9PjLfbzQWANVo7MIeQVLkZGR4f189mBk1qxZmDlzpueU/b0PAAAd336IjuoPvN+fmjYGJRp7kbGG4uTcxcKG4i2FqKqqCotT6lCuZeEAtZd5sVqtKCnegOzsbEV6E6uDrFy1CjHn9LKeU3U4VfkC6rYUestJlVesLAw3aM4YC941V+K4NXuIFiWoJUfCGclGNoIINf7KvnXoeQC0XajkLoYDbREN1UY7kAEl+eYNqHupAF1HPkXdodBuRFmUKoVSx43T6cQdd96FpkaXx+j40/tEG47MQDDFhe99IaNA79lunHzraUSnXw57ljiDB4vacoCNz9V59HMAQFJSkuS2MYpiY5R6qImYeewvizq+2Yfmiud9NpWIiPB4WwfyZAxgoPI3+LHjpKGhIWSK/EA7MAo3o7HW6N1eRonfaVZY7105hgXAo4MsycvHsWPHED1uincMRKVMwpDvzURnzec4VfEC4tCGQ98cgM1m0/qRDIf/2JoxY8aAkzFmmV/cNbe2thYulwt2ux3x8fFgGEbwsM2o8UpD1e5K5Ui4IyomW2ZmJv7yl7+IjlF20003obi4WNYGQm/CPSabWQQeF//YP3zBpVm0islmhFhMRiUUwWrF3v0vLi6Gw+EwzdhmkTJudu3ahaysLFgiB2Pw6Et8DEd85c3SBizBFBeh97Myb0BJSYlPjLD2A3vQ9NZT6D1zUlbcCDXlAF9Q6VGj07BRpkIWKHaNUs9KteoRbok5AH5Z5O5l0LJ3O05//E/0tp/yljVavDop7T8Q462EYi0z0xwIVle9AtWrtbk18iY5VMgZj2y/20ZMRNfxrweUjBCL0Ni6cUEO1q1bF7bJHbiYcX6JrbOR45Vq2e5C8sJM65pSVE98YLVa8c0338Butwf9crfbjdTUVHz66acYN26c+FobhHA2shlB4MmZiFqkGxeDXAUy3DMk8aF1sNpQBlnXa7EQM24yMjIwdlw6Gs5Gh6VyG0xxWbZsGUdB9X2//ZwXI9smrIyIdIxDT3217LGjhhywjZyEU++9jOj0qYibrp5CxjdWd+3aFXI5L2bOGGH9UQM+WeS7HuWA6WyDa8cqQ81Pqe1v5AMjLWW0lmuZmeaAlM1mKAPVq7W5NfIm2chw5UL0pJloenW94Q4T9EaMHrNte1lYJndgMeP8EltnIycq07LdzbR+aYnqRraIiAhYLBZJlTh48CAZ2QyEngKPVYh37dqFv27dBld9nfc9MRNUSKB5PEOeA9PS0Pd5Ki9UchVIpYqnGU8FtKxzqLwq9F5EduzYgbvvWew7Rzjjhm2HuCtuQsv7fw0r5VaM4tJ99L+ISvt+0PcT5t3vzcwYOyUDDduWKxo7fOPC7nDgj5s2Yf78+YLP5HulRvvsVUZVbLWuVyjlpb8s4vOs9r6WmGYIT1O57W/EA6NQyGgtxpNR5yYfUusaqvmnluHXPxOm2OzxAw2hsAys/HMz3ajf+oChDhP0RuwYPfTNAezZs8dUOr5YjHxAI4SUOnPngP+4bz+wB827N4Npa/a+Fqo9hJbtbqb1S2tUN7K9++67kisxbdo0REVFSf47vQlHI5ueQt9fIfZ4ciwIeELAp6wJKfstez1/n5eXh4yMDE0WKrkKpNy/09vQY0QYhsGYcelo1mDx4BqBN27cqNsiwmvISXLgj0/2GXJYLxp79irDecooJZghtfWjV3Fy9+agzwyLBVGcayy2ERNkXy/nzuEDBw7gT09thquh77qnmHn59ttvY+7cuZr3lVEVW6X1EnNlLZTy0v95umo+591oer3b0qf6JEUQY6BS02ihtP2DHRiF0sBpREVfzPMbdW7yYeS6qnHY5nQ6cddv7oaroR7JuevAtDX3u8ZvHRoPpq3ZVOun2ogJy2AZZNM8dIsQoZA7at284WJG3UwqZmwDKXU+ceIE780a3zU/9OuTVu1u5DVBD6TYiUQlPrjqqqtUqRihD2KzhI0anSbZyywQrEI8eNwUWIeehs0xPmDg8d7eXhQsXSa4WRIMrrhzp6aCS25Qezl/J5RBU6tg9mbxmNu1axc62tvRftSTHj5ueo4qQdZ9FElLhE8AX0BccHw1EOr31n1lWLBgAaxWKzIzM70B9SNsMbDGOdCyr4xXuTVjAN1ggdYtg2wB3+85VQdYPAHnu44f8Ja1RFgRP/s2uF55HC5nEe/1cr6xw7fJiE6fiuRrChFpT0N3/WE0VL6ArKwsrF69GsuXL+cdGw0NDQHrrVYAeaNmg1RSLzHx+UIpL4H+CR8iHeMB9O/fmIkzYJ93P5rfeVZSpj21jYZKx0WgrKuhNHAGSn4TChnNh9jnN+rc5MPIdVWajIOVF5FJ4wDUo+dUHZpeXe+55n19oVd+tOwtQ0fbB9i1a5fu/aEmYvW9QHK1pKQEQF9SHzlrq1L45t2o1NG4/de3YcKECaroskJze/26tbDb7YJtONASxvBhxjaQUme+xFbuXgbNFc8jevxU0RnF9XwGKRh5TTA6EcGLEGYn2MTrOeUxrJ2JHY3k3HVIzS9Hcu46NNkcyM7OhtPplPydXIU4duo8MG3NiJseINPake+Qk5ODJptDsA6ZmZk4/G01KioqUFpaioqKChyuPhQ23l3+m4iolEmIsEV7hXR0+lTkFSwFwzCqfJ/T6cTYcemYPXs2Fi1ahNmzZ2PsuHRZ/a0lrMLXMXwshl/5S3S7jqBuSyFqSnJQt6UQMS3fydpMs5/bZHPgvLl3ebIBTl8QcIxWVVWJ/nyGYVBZWYmtW7eisrJSsN+k9Dub0e30Bztw3uxfoePQfricReiq/Qq9Xe3oqv0Krp2PeJTbDes13WyKfT6xf88myulxHeEt7z7bLfh++4E9aHp1A6LHTUZy7jokZq3wKcsaPPzHztDTNbxjhzs2HIvWwDos6Vx20gcRlTIJnYc/QeNr69F1/GsAwMqVKzFGYO5wFTI+1Mo4bFTFVm69uH3AtyaUl5eHVF5yYQ99Errr0fJ+KQD+/o2ZOAOJ1y0DAKxYsSLomhXsmeXIZjXGBXtgtHDhQsyaNcvHu1zNugaCVfSHBcrYKlFGK0HK86s9N5XK3kCIrWttba1mdRBCiSzlrrPDZ98KADhV+YJ3U8yVH/Ysj/wo3bo9JM8VCsTqe2L0EWukDa37yuB29wqurQndDaolCuGOsx07dvSbd8Ov/CVq6xqwcuVKVXRZobld3x2JnBsXBmzDUK33RsaMbSClzqwezs4BAJ7r5i31gTOKa7w+adXuRtUtzQAZ2QYAgSaeu5fBqco/q75R4SrEvWdOARCeoIPiUz1eRCLqwKfshwuh3ESEeoMkF3+FL276fKTc+SwcCx9DwnVLETVyEgZHRyMjI0PR51qjYwGot4hIMWBK6XfWi6ajej/aP38Hw6/8BbobDvsot5GNB7F9+3bNr7YqMdDy/f0tv7oVCfYkH8WFxe3uRefhj3yUe+97vQyaK55DdPpUrxEsZtwUj6ff3r6yMRNnIOWOZ5B046OwOdJhdzhQc/Q73gyY3LEB91kwrQ3eQwL2SoDNnuYzd5oF5g6fQsZ9LrW8Do2k2HI3RvX19ZLrJWaj99vFi3U1urCHPrt370ZCol2wf09/sBOpaWOwatWqgGuWVocsWoyLUB8IAcZS9KU+v5p9oPXhmNi65i9dFvIDOiWylLvODk69CBFDzvMc/grIj7jpOWioPxEyo62WSNH3gukjcdNzwPR0o6N6PxrPHfANHvN9JF5bgKiRHq+e1atX4/C3yg/A+cb6jYtu8pl3nYc/wan3XsLg0ZeoossKzW2mrRndddWIHjs54PeEar03MmZsAyl15urh7BzoOelxVtFzfdKq3Y2kW5oNMrINAAJNvM6az895mannwQP4KsTWoecBEJ6gZ76sBNy9hjmh1otQnSDrsUGSC5/CZ4mwYvDoSzD0wtk478e34djRI5LHhv/nBhujUjdBUgyYUjePXC+aU++9BKbV5bkmeY7WllNYuqxQsw2PUgOt0N83RyWjydWAdo7iwnrnNTqL0PnthyjIW+Kj2PR2teP0J/8C09Lg4ynLXhHtqN4P186+st0nvkHbh7vQ0/AtNv/pT7DZbP3q5z82mLaTADz9438lQMzc4VPIuM+lltehURRb/41Rfn4+r3E0UL3EGJ5d54x3eiq1VqsVc+bMwTNPb1bcv2KN7ZWVlZLkvxbjQg+vMiMp+lKfX60+CMXhWLC6Nu9+GoC6Nx/EokSWctdZS4QVQ753pfd3PsLFO0OqvidWH1myZAkSuuu9B3z1pfchKbITO3fuxEMPPcQbl1CK3OIb6+fNvQtMT7d33vmsx1nq6LJ8c9v7PenBvydU672RMWMbSK0zVw+v21KI5tefAKDv+qRVuxtFtzQjZGQbAASaeKcqXgCgvqLhc2d91IXe+FF8E/TM/97SpA5mI1QnyEa7dhMIrbwX/D832BgVu4jIMWDK2TyyXjSrV68GLBbEpE8JyYZHqYE22N/HjL8cCYmJiOco73VbCpHQ47l6smbNGh/FpqYkByd3bwYgHBOr23WY97OETtn9xwbXACv3SoC/Qia2LmybidmcqKlgyb2OJmQEsCaMRvuhD0TXS+y8B4xhdFHSvyxin3lBkOtK/miheKsll6WMM5fLBURY0bJ3u+6KvtTnV6MPQnU4Fqiurp2PoLvhMGLGX67bAZ3cuea/zsZM+KHP7/6Ei3eGVH1PrD6SkZEhOnyLVO9LobHuf+NAiyt6fHPb+z1BQt6w36PGemB2zNgGUuvMDWG0ZcsW2B3JuhuitGh3MxpNjYKoxAeE+RFKHGB3OOCCbwBHFncvg7bP3wEA1NfXe09pxMBavpv2lSExc0XA4Kjd9dWAQB0A+ddZzBDQn4t/m/kHs/c5Qb56mewg30a6dhMMvgCjXOQqwv6fKyY4/vrt24OOKTkBQoP1e6DF+bnnX0BMCAOBKw2AKubv67YUYvfu3bBarbxt7R+Ivb6+Hvn5+bxjJGbiDETExKG+9D6sWLECc+bMCSoL/McG1wAbM8nTB3LmTqAA8kJIDSwfMEGMSPkgN5h9oMD0yTdvQN1LBeg68inqDn0QtF5i5z2r1EqdN1ogp3+5BHvm0x+/CgDoGD4WyT+9T5L8V2NcSKmrGLksZZwxDIOlywphSxqLjuoPfWR0d8NhnKx8Ad3HD+DeVaskPYdc5Dy/0j4IZfDpQPpiRy8T2GATggDYcuaa/zrrlet7y3wScgHh5Z0hVd+Too+ISe4lJzmN0FjnHnhFpUzy8TIX82xi4Jvb7Qf/I/l7lK4HcjDavkePNlCK1Dpz50B0dLQ3IdKwECUBUeMZxH6mmjrEQMHidrvdwQr94Ac/gMViEfWBH3/8seJK6YmU1KxmxF8Iz5gxA+MnnN8vNW/7gT1orngOTEuD92+lZg3jLq7Dps1Hz6k6nKr8M5i25r7PTBuD9WvXYOmyQtXSA4cy45na+LcZK6Rb9pah4/DHiBk3WXEbaZXmWQu0Sh0t9Lm84z5tDG7MmY9t28uCjqmtW7fypvZm6e1qR01JDkpLS7Fw4ULv60L9zi7OfIqoHv0o9/nk/r0YpVHtMcL3ed44bCMnovv41yFpc98xIS0dvFxlW8l3ih2PxcXFcDgcAesltk83rFuLnJwcSfNGjXbSgkDP3Mv04NjGhRg8+mLYsx6UPcbVel6lc07qOOOOLaatGc0Vz4Np8VwXZjMKs4RirVfy/HL7QKnslYN/XWtra5GbmxvSOqjJjh07fOSFJ7voBkSnT+HNWL59+/aAmSTNwNtvv425c+dKWrPk6CN8yJ0nQmPd3cug9pk7YEtMgz1rBbpqPkf91gdUXY/96wy3G8f+dAt6z5w0tM5s5n1POMHbD2ljULJhfVj0g5F0Jr2QYicSZWRbvXq16C9fuXKl6LJGJNyNbHzwGcM8isdUj+IhYaPFhWEYPProo3hi05NoanR5X7c7HLhp4UJkZGR4J6hai7qSTaJR4BPSdocDrvp6VRZ5rQxXWqHW2BD7uS17PWMlLy8PGRkZaGxs5CjmgceUEsOX1MVZj02XUkOKlPZpbm4WrTSqPUb4Pu/0x6/i1J7tANOD6HGTFRk7gqHHHFX6nWqPR7F9KiQv/dcYvs832qZE6JlPvvM8ukJk3FVa12BzTuo4YxgGq1atQlFRkXdsuXsZtOzdjpZ/b1Wsp4T6+eWi1aGKlE2TmQ7o/PGZ736GWWukDUxPt/d3KQdrRsbpdGJJXj6OHTsmec1Sw1ggd7wE+jv2wCs6fQqG/TAbja+ug80+ltcbUe46yZ3bUWMuw8ndm2EdGg9b8njYeWSWa+cjiG07huO1NbrozOGw7wknlBqiyJBlbFQ3sg0kBqKRDeivgCjdRPIt0PEJiVjyu8VYvny5+E2PhEXdbMajQGh9ghzqDYJStDodCva5cjaESsag0Tc8wZ6v7qUCME1HfTcsnI2JPA8lcUqj2mOE7/NGjU7DFdOnYXtZGWI0nDt69K3S79SizmL7lJ03u3btwl+3boOrvq6vPM/G2MibEt61MzERzY2NhvMgkjPnlBja2b/xerTY03g3vaFa60PptaCFfiPV0GxWHct/vg9KGIUzX1TizP/eQnd9NbZt2waHw+Fdd6UcrBkV7jPbRn4Pp9576ZxBWppBXMlmX+7Bixw9w2tsV/EQljs3Eq5bhqZX1yN6/FSfcCIte8vRUf0B8vLyUFxcLPl7lGLWOakF4WCcMuLhH+ELGdkUMFCNbIBHQG3atAn5+fmKNkp6XHMCzH3KGgw9N7BGQasFNNDnyml3rQyYYq96A9oqV0LP17z7aXTXHULM+MsDzvtg7bN9+3bZ18eljBGxV1H5ymg9d/TwUlT6nVpc262qqkJtbS1cLhfsdjtSUlICeqaJWXfMsCnxH3cMw0i+8qVXXYPJZf9x5u5lPIHF207COvQ8RNrH4dgTNyIvLw8bN25EdPpUxF7OeqyM0eyamFxCubFTc22Rq6eZ7YAu1AdloSLQuGMYBo4RI9E+LM17WO4JhcG5Zg0gdfQYlBRrp+8p9ewPpiewV3kPHjyIZ59/AcdUXo/990T9rqoDiBg8DL2drSguLsbixYtDPibCed8jhXAwThn58I/oQ1MjG8MwKC4uRllZGY4ePYru7m6f95ubmwX+0hwMZCMbYLyNVijrbmS0jE1m9pMfLVESY01rr6rU0Wm4cUEO1q1bF/IND199rJE2RKV9X9T4DNQ+8fHxmiuNaihkWs4dM3qyAeptwLX0sKmqqlL0nHrITLNs/MUQML4aAOvQeDBtzbA7knEmdnS/uIjR6VNhSx6PlvdLVV3rzbIWqrG2qBFTzywHdFLk2syZM1U5aNaaYPLx4YcfxsqVK/s9A2vQ7jz6OVreL8Xu3bsxZ84czeqpZJw5nU7cceddaGpq8rnam2hPwtObn+JNjqLVIax/jLauY1+g/eB/cOar99B75qS3rB5GnXDe94glHIxTwWKyNpTejyHdjSjbtg2zZs0y5No0UJBiJ4oI+C4Pq1evxoYNG5CTk4OWlhYUFBQgMzMTERERWBWizE6EdohN3y2UNUxqunA1UVp3I6NVCmU2M87ChQtJcPMgd0xxU3sHS28fDFaBaLI5kJy7Dqn55UjOXYcmmwPr1q3DsmXLQp4m3f/5iouLwfR0i573gdpH6+y3gdozOzsbTqdT1OdoOXfYDG9C6eBb9pbB7nCgtrYWlZWVYBhG8+8Uk21PjfTxcvpHyrqjZHw5nU6MHZeO2bNnY9GiRZg9ezbGjksXPWbkopX81wN2nDXv3uxJJmJP8+lnmyMdAOCqr/Ppz5iJM2Cfdz+6G4+g5f1SAOqt9Xr1qxzUWFuU6mlqrm9aI3a+79q1C2PHpSM/P1+wvLuXAdPRBsCTUEANuSuVYPKxvLwcG5/YBKD/M1girBg8+hIMmzoPANDQ0OD/8aoiV26xz9gxfCwcC38Pe/YqxF1xE6JGTkKji7/O7Hqck5MDACgrK1NlbfR/hu4T3+Ds6Sac/vDviEqeoEiHUIODBw8CCM99jxj8s5pHpUxChC0aUSmTkJi5AtHpU5FXsFSXuSoFIZncfmAPjj97F7qOf43mxkbMnTsXY8aOw8MPP4ytW7eqpv8xDIPKykpVP5OQ4cmWnp6OJ554Atdeey1iY2Px6aefel/bt28fSktLtaqraP70pz9h7dq1OHHiBC688EKUlJSITsM90D3ZjBb8OpR1DwXBXPyDncSZ6QQ5HNB7TIn9/kPfHMCePXt088JQc95r6cWld39KIeC13IbDQG+fEqTWCbqaWeXkeBWonY2OhTv+RowYIWt8BTstD0UmwnCR/+Xl5ci5cSGix07mDVhe92I+uuurefvT3cugvfpDNP7tUUSnT1Ecky0cvCCkMpC8X8SuJxaLBdHpU2GJOQ9nPnujX3n+65ah9VwSIx+HnD4CV72njkbxxpMit5R6v2l1ZVDtuNVq4HQ6kZWVBUvkYAwefYmqyR/MQrhcl+WTyV7v7fFTEXdubTr90ato2bsd7p5O798qHePhcNU2lGjqyVZXV4eLL74YADB06FC0tLQAAK677jq89tprMqqrLtu3b0deXh6WL1+OTz75BDNnzsTPfvYzHD16VO+qGRrWil1WVoZf33ar7BNzPb3JjH7aH+i0XOxJuplOkPVGjZMZvceUWI+DPXv2aOZVJaYd1Zz3anhUCaGnp61UhLzCuusOIWbcZE1O0NXwRAPke/nJ7R8p40/O+Ap2Wh7pSMfCm3I194Qys/znypHa2lqgl0HcdP5+HnLx1QD4+9MSYcWgmGGAuxedCuVyuHhBSIFhGLz55psA5Mlrs3k8iPEKtkbaEJ0+FQnz7kfHd5/AEjkYLXv7yrObXVtimq6eS2LkI2tgsw5LQkuAZ06wJ8laQ+UgRW7JXQPU8lAP9gzFxcWAuxdx0xfoqkOwsitm/OVIuDYPHdX74fKTha6dj+i+79EatW4+6C3X/HUYdy+D5ornET1+Kuzn1qbOw5/g1HsvYfDoS1Qb41rPm4GOZCPbqFGjvIN1/Pjx3sV6//79iIqKUrd2MtiwYQNuu+02/PrXv8b3vvc9lJSUIDU1FU899ZTeVTMs/gaelStXIj4hEdEt30neaGm5QRaDWptEtQkkyLKyspAlQcjRFc/gqHn9R88xpfXVyWCIbUc1572Whk2921Mq3M3Jli1bYHckI2b85ZoaBPQ05MjtHynjT874CrTx6/hmH7rrqmEbfalXfjsWrUHD2WhkZWXh4YcfVlVh95f/AAxv9PCXI4Gu5AHAkAtmAZaIoP1ZVlamSC6byeiuBk6nE2PGjsNf/vJiP0MSC2uESR3dX16b6Voti5j5zoY66K79Cr2tDYibvsBrtOis+QLN7zyL6PSpsGfpa4gVKx8BIPb7P0PHIT7Di+eZf3fPb0OqO4rVW+WsAaEyllutVjgcDkn108p4w5VdQyb+yHOV3nXERxZ2fPsxVq1aZYpDGLmoccBrBLnmr8N0HfsCTEs94s6tTT5GN5Xk0EA8ZAo1ko1sN9xwA95++20AwJIlS/Dggw9iwoQJuPnmm3HrrbeqXkEpdHd346OPPsI111zj8/o111yDPXv28P5NV1cXWltbff4NJISMPx3Dx6LJ1YDVq1dL2mjp7fkDKNskarEgBhJkCfPuhyVyMKLHTSEhpxJanMzoZXjQ0zNUSjuqPe+FDJtDTh/B9u3bZbe7GeM2spuTlJSUfnGqWNQ2COhlyJfbP1LHn1TDudDGz6v4pk+FPetB72lz42vr0XX8awDAypUrMUYjhV3u5iCUp/Z8cuS8uXcBEO7ns801gLs3aH9mZ2crkstmM7orge0HFxMDwO1jSOIzwvz6tl/5zHszezwEmu95eXkAPGOAafMEsY+dfJ3XaFFfeh+YVpeg12UoDbFi5aPdkYzu418hcd59/QwvnTX/Q4LdjuXLl2teXznIWQNCaSyXUj8tjTf+sitm4gyk3PEMHAsfQ+L1hbBnrQTcDCZMmKD4u/RA7Bql9IDXKHLNX4fpPPo5gL7+9Te6cZE7xgfaIZMeSDay/f73v8cDDzwAAMjOzkZVVRV+85vfoLy8HL///e9Vr6AUGhsbPamrz500sDgcDtTV1fH+zeOPP464uDjvv9TU1FBU1RAEs2LHjL8cz73wZ+Tk5EjaaBnBm0zOJlGrBTGQIOuu/Qrunk7dXc/NhtACrOXJjB6GB708Q+W0o9rzPjMzExvWr4Pdkex9zVVfj6XLCmXPSb09bZUwEAwCSvpH6viTYjgX2lh5Fd9zG3DvtTK/YP7NGijscjcHoTy1F5IjsT/4GaxxSZ7YMgo91ZTIZTMa3eXA7Yehl10LwNeQ5HMd3fUdAPhszsPB40FovmdkZADwjAHr0PO8P7NGi2HTFwAwhtwVKx//9OQmdFTvR/vn7yDx2gLYs1Yi7opFiBo5Ce7uDjyzebMs/SUUxnk5a0Ao10ax9WtsbNTUeMMnu9jEFkMuuArW6KE+5cyElDVKyQGv0eQaV4fxT+7DHgCoOcbFzhs2wZaRveWNimQjmz/Tpk1DQUEBfv7zn6tRH1WwWCw+v7vd7n6vsdx///1oaWnx/qupqQlFFQ2BllZss8WO8d+wjFqyDefNvQv1XYOQlZWF8vJy2Z8dSJCJFZxqCzm94w8oIdACHG4nM3p5hsptR7Wzqubk5OBM7GjVlFQjeNrKZSAYBJT2j9TxJ9ZAw7excvcy6PjuvwA88psvhopWCrvczUGoT+2F5Iglwor42b9GR/WHcO18RDNPtWCY2eguBW4/DIpNAOBrSGK9XxwLH0PidUsB6OcppCV88507Bs62nwKskV7jryXCiugxlwIwhtwVKx+zs7O9m/X60vvg2rkaLe+XIimyEzt37pS9HofCOC9nDRC7NiYlJYUkTu/6tWtQsHSZpsabcJVdctYouQe8RpRrrA6ze/duJCTavf3LPQDgQ44cEjtv8pcuM1WIACMxSOofvPTSSwHfv/nmm2VXRimJiYmwWq39vNYaGhr6ebexREVFGSKWnB5offrDKjRGx3/D0vHNPrj+sdYni9TCm3JhsViQnZ0t+fO5gsw/+w1XcPJlxuEKOVd937hWkvnFzJlkuJngknMXezPBNe0rQ3Z2NhYvXgzAGKfOasEqEHn5BajZUuh9PTVtDEo08gxVIhvUmPf+c5JVgFgltdFZhLyCpcjIyJBsEFOjPeVmz1TCjBkzYHcko3VfGW/WNbMq1f4o7R8t1h12Y5WdnY1GZxFsIyfh9Kf/AtPqAuCR326m2+PVdn2hsMK+pRBVVVWK68duDpJzF4v+Li3nlBCB5EjMxBlIuK4ATa8Vo6N6v/d1/37WUo/w71e+jLolO3YY0uguBW4/WAbZYI1zoGVfmScz6znvF6AvG2EwTyF3L+Px4mw7CevQ8xBpH+dTzkywYyArKwvthz6AzTH+nPG3CHHT5yPSPg7WofFo2budN5tkqOWuWPmYmZmJjIwMVdapYHqX2jdUpK4BrMGpKcDamGC345Zbb8MxFfTeYPWLj4+XLJ+lEkrZxafvAFBdBxJao2wjJmDolAycPd2Eu+6+G9dddx1sNpvP38oZ77t27QJgvP2C1WrFnDlz8MzTm739G3t5FqzD7GjZW8abRTaQHBLSV4PNm+bdTwOA56D76mWaz/twRLKRbcmSJT6/9/T0oL29HTabDTExMboa2Ww2GyZPnoy33noLN9xwg/f1t956y+sSTvQRyPgDmN8zQuxGmLth6fhmX1/K5OsLvUKlZe925OTkyBIqgQSZLeV75wIQ8ytwagu5UCtLfMg1UATbJNa9VIAn//gnAOE3ptVUmMWgt2yQY0SQgpL21MNIzX6nq74OqK+Da+cjiJueE5YGASD0411snXbs2IE77rwLTYc+QHT6VAy7rhCNr65Dy94yxHzPo9xKVdjlyEM5RnCt5xQfweRI5PBkwN2L4uJiOBwOXfpZj0OMUOPfD/Gzb4PrlcfhchYhTsTmnPv3TFszmiue9zmEtA6N9ylnNjIyMpBgT0L7sDTYsx5Exzf70FzxPOo446Gjrdkwhlix8tHoB16BkLIGBDM4tR/6AO0WC2JU1HsD1W/r1q0AtDfeZGRkYNWqVXhi05M+Y1VIdslZa/j0nYREO2CxoMnV0PedKuhAfGtU+4E9PvLGBWDU6DRs/tMf+32XlPHudDpRUlICQJyeq8fBqs/aVHofAKCj1SVJ/wumrwrNm5a9ZehuOOxNtBWqeR9uWNxut1vphxw8eBC/+c1vUFhYiJ/85Cdq1Es227dvxy9+8Qts3rwZ06dPxzPPPINnn30WX3zxBdLS+AUel9bWVsTFxaGlpQXDhg0LQY21I5hQYBgGY8elo8nm4LViNzqLkNDTgMPVh0w3iaRshLdu3YpFixZh1JJtOPGXJbDZ0zwnvCq2B9e4xacA4JwC4C/kOg5/jJhxk1XpHyP0txIDRWVlJWbPno3k3HX9FkQ2FtLgcZPR0/AtbI7xvKc9Zh7ToUTvscLOydT8ckTYovu939vVjpqSHJSWlmLhwoWqf78QvvM4x6ust+4rQ0f1fk2M1P7f2XOqDqcqXwDT1uwtk5o2BiUb1oeFQcDIMAyDMePS0cyZF944bCMnovv417zyCQC6ar9C3ZZCVFRUeDcCcuVhIFno/10zZ85EVVUVdu7ciSeffDKkc4phGDhGjPQaL8TKET02NHp8Z6jgk+f+m1cASB09BiXF/eUI+/f13ZHorqv2HEJy5F/L3u3oqN6P8vJyWd7+esM3n7jeekzHaZzcvRl2R7LvjYK0MVi/dg3sdntYjhtAmqzR++YKnzwdNToNHR0d6IgbEzJdRqjN2DHVefRztLxfit27d2POnDmSPpuVU7t27cJft27zGY/xCYlY8rvFWL58uWRjCx98+s7pj17FqfdeRHT6VMRNX6CqDuSv97Frq7+8UfpdrDxrjExCd+MR2OxjAu75Nqxbi4Kly3S7/cNdmw4ePIhnn3/B1yNTQP8Tq6/yjQ27wwFXfb0p5n2okWInUsXIBgAffvghcnNz8fXXX6vxcYr405/+hDVr1uDEiRO46KKLUFxcjCuvvFLU34aLkU2sQA1k/NFq06g1UjfC7IJ43ty7cHL3Zs2ECm+fnBOOADQXcnorS0oNFEKGF3cvg9pn7oAtMQ32rBW+3ohhMqa1hm+DuWvXLt1kg95jlQ89DI9C3+nuZdBZ8zlOVbyAOLTh2NGj/a5PEOojNC7bD+xB0zvPofd0I6LHTRZlUFIiD8WORb7NgdCc6qz5AvWl92HFihWYM2eOKsYCp9OJrKwsAEB0+uWe63fcQ6RvP8ROv+c0czgDpWhp6OPT9bobDuNU5Z/RdfxrrF69mndzzlJeXo6cGxcieuxkTQ+wxLSB2u0k9lBny5YtSElJ8X5vY2OjrpvvUKDmgVcoDNn+38EwDObOnRtSXUKsUXvU6DRslDBW/GWjWEOX0FrDGsfz8vKQkZHh0x98z+Cva6stA7jrq23EBM93aeD0wP0epq2Zd7/QsrcMnd9+iGXLlmHdunUhPVgNhlgZKUVf9f/M2tpa5ObmGu6g2whIsRMpTnzAYrVacfz4cbU+ThF33303vvvuO3R1deGjjz4SbWALF4IFjiwvL/cG/4yPjxeVwcssyAkIzV7nPPO/twBo5+IdKCg333vF69erWh89sxOqkcVHbIa/mIkzeLOmDT1dY8oxrTVCAY0B6JYl2IhBffUIkhsocHx02qWIv/ouuOrrsWfPHtW+00yEOoGLkAyNmTgDo+58FsN+mI2O6v1wBQnYrVQeigm+fWPOfOTk5Pgk82FjS/nPqTMH/o2G8pUAgKKiIlWCG7PPGDP+ciTO+z90N/rK486az5GQmOgTyiPUiRmMhNaB5fmCg9eX3ucNhv/QQw8JblYZhkFtbS3Qy3jXWS5qyT8xbaBFO4kNAJ6SkuJNnNDc3Owzv8J1rKqVcCeUiRO4yS0aGjxXGsXqvWqsKf7yuWVvmcfbOVF+1mlWNjZGJsE6NB7R6ZfDnvVg0LVDaK1h2prRfa7vSkpK+vUHn+7hr2tzUUMGcPW+zprPPd/Fo2/B7UbUmMtQc+Q7bNq0SXIfcddxof1Cx7cfY/Hixdi2vcww2UdZxCRskqqv+n9mSkoKAGMkfDEzkmOy/f3vf/f53e1248SJE3jyySdxxRVXqFYxQh5i4lYtvCkXTE+3929SR6dh/bq1hnZ5lxNfTWzcGW7gW0DbOFSBYgb4v1dZWSm5PoHaSYs4W1r2iz9Cse34MrTGTJyB6Ak/RNexL9Bzsg7Nrz+BDevXkoHNDzEx+g5/Wx3ya1RGDEiuh5FaT8O4VqjlzaCHx1MgGWqJsCJm/FS07ivD0NNHA8bIUUMeBooltn77dixdVthPD4ife5cnFhcnpsvpj1/Fqfde6ucVoTROJ/cZo1ImIWbCdJ9g+bBYUV96n/cZ9Yr9ZARCESuVYRjEx8fj8ccehcvlgt1uR0pKivegorKykndO+s8zrWSRmDYAoEk7iQmczz3UGUhjVWrb8KFnLOBAMtvdy6Dt83cAAPX19dixY4dqnomsfF6Sl49jVR8iOn2Kj/eXlLHCHW9Dp2SgYdtyxM3zNZ6wV1EjHeNR8/4HqKysxJw5cwTjnPXFnr6Xtz+6uroA+M53Pl2bixrJ8li97+zpJt7v8vcIzM/Px4biEkl95D8muPsF7vXwsWPHhjyOqVoo1R3VmPeEDE+2efPm+fzLzMzEqlWrcMkll+CFF17Qoo6EBAJZrzu+2YfuumrYRl/a7+RtwYIFaG5uDmgZ1wspJ2ByBUtmZibKyspgjbTxnvTrIVSkevMEaye1vYNC0S9chLw3mI7TAPqfuLBZ02yJqQDgPZkJd8SexIr1pgEQ9NRMC+SmZdcKsSf6SUlJqnlXqeVFoCfc8fjwww9jjAreDHweT45Fa9BwNhpZWVl4+OGHNTldFitDjx09wuuxzKKW8VTIO9put/PqAd5T+/pq75w6VbUF0elTRXlFSMH/GVl5POSCqzxyOWmsTzk9PEWNgBpe3sHgrtW5ubnIz8/H/Q8sR3NzM3bt2iW4jnPn2Xlz7wKgjSwS0wZL8guwRKN2EuMZynqhAgNrrEptG39CMb4DISSz2w/sQe0zt+Pk7s0APMaa+fPnq+qZmJmZib/8+QXA3Yu46QtkjxXueOs9cwqA79rheZY7UL/1AbS8XwoAWHDjQjidTt7MwM0VzyN6/FTYA/RHUlISAN/5bh16Xr/XuKihj7B6X5y7rd93eeOf2tMUrft8Y4Jdn2K+NxNd332M1LQxsNvtAMx5yHnw4EEA/H3lb1zmazOl857wINnI1tvb6/OPYRjU1dWhtLTU0Ir+QEFIefcKVg2UaS2Ren1EyaZ0/vz52Fb6V3R++6EhhIoUISemndQUmqHsFy58hpeTuzfDGmkz1NVCvZBi+DTDRiHQFetQI8bAkmC345Zbb1PtSowRr81KwX88rly5Ek2RSYo2MXybts7Dn6DxtfXoOu6JCbty5UqM0egqkhgZarPZAhqm1TSe8l0dCWTEi5k4AyNu9WRhnjdvnuINoBBSnzEcvTbFoLUcDrRWZ2VlISvAe7+67dfeeRb7g5/BGudAiwaySEwbHDt6BMc0bCcphzoDbawqOfDSW8/gk9ltX1Scu745xucqfXT65aobAqVeV+WDO978DV18hqfk3HXoGD4W2dnZ/Ywt3iufQfoDQD/dI2rUhR4ZsFdbfSQzMxPHao7C7kj2fj+fcVDuui92HTfrlUmn04mVK1fCEjm4X1/xGZeFdFSjHXSbEdVishHGQGzcKi5G2VD7oyS+mtxNKesqbRShIkbISWknNYSmHv3i3yb+hpdtpX8d8CcuUg2fZtkoiIk/Eap6BFLM2g99gKbGRjSreBJu5tNE7nh0LFoD67Ak0XFkAuG/aRPaZEiJdyMFNWSoy+UCIqyaeU0HM3Cdba4BAFx00UUAtJEBUmV+OHhtykFLORxorU6Ydz8skYMRPW5Kv/diLvoxYI1E66mT3nlmibAifvZt6DgUPOagVm0gpszbb78t24tY7KGOlLEa6riRWiH3wMsIeoa/zG56rdh7fTMqZRJ6Gr4F09asyf5IDbnmc72RNXTtK0Mv0xPUK+3Z51/AKI4cFnvls7KyElmZN/joHu6eLsRe+hN0VH8A185HfGSAa+cjaD/0AbJumIeqqirF49xms2Hzn/7o/f7Tn/zLxziodN0Xs46b8ZCTGws14do8nxix/sZlMToqO+93796NFStWYMWKFfjz88/5xFIlhBGVXbSgoED0B27YsEFRhfTG7NlFhTKKnPnyXTT+Y62pMoXIzTCoRsbUUGRBkkKg+shpJyXPp2e/BCJQ9tZwP3GRk/nSiBk8zQDfOBs1Og0dHR3oiBujSeZRs41t//HYVfM56rc+oMpY42a7swyyaZaBTMwzypGhbNvUd0eiu66aN6tZR/V+lJeXIzs7W3bdxMiDPz//nKaZ96TIfD2y9xoBLeVwoM/uPPoZ75xkN6+RSePQ01DdT2fky5KoVBaJbQNAODtuy94ynKraAnA2w1rFaFSS3Tfcso8Gw0h6BsMw2LRpE/Lz833qo+X+SA255v8ZHd/s8xhLRk5E9/Gvg7bt6tWrsWrVKkSnT0XUmMtwcvdmSfPIGmnzieOdYLcDsKDJ1SBYRq1x7q/7qL3uB1vHtd63qI3/fPOR15YI0dnPuQzkjN98SLETiUp88Mknn/j8/tFHH4FhGEycOBEA8M0338BqtWLy5Mkyq0yohVDAcG7cKq2C+quNkvhqQgGhS0QKxEAJCvTAvz7s6eiJEyfw5ZdfApDWTkqeT89+CURmZiYyMjJ0NY6qbZzVMrEEBTaVB984YxjGY6z4yb2aBMg1wtj2J9DY9B+PagZM5p7qu5luz+n29YWaBiYWelY5n9nXNuvAtDWjueJ5nyQJ1qEJAIDExETZ9RWbOGTWrFmaygApMt+IyU5CgZZyONBazTcnuVeyYs8FWPfXGdkg4ac/+RdO7t6M4uJiLF68WFG/iGmDUaM99WzmKXPmwL81Sd4hhJixeuOyZcjJydEl2L+RMJKeYbVa4XA4APiOe+4VTLX3R2rINb7PSLiuAM1vbe73LFzYdXXChAl9cnj3ZsASgZa92/sZW4TmUcve7eio3o+8vDxkZGR4+6qqqgq7du3Cxo0bEZX2fQyblqNonPOts6zuwxpH1V73g63jWu9bAiFnP+Ev873y+qN/4OQ7zwUODcHTZnomLQkHRBnZKioqvD9v2LABsbGxePHFF3HeeR7BdPLkSfzqV7+izZhBEBIKbNwqvRc6sSjJhmnETala8J0qAKEzoBqpX/gWISFjpNZjQO3THimfJ8fwOVA3tWrgr5ht3boVgLZXYoxk+A82Nv3Ho5qbGO6mLXrSTJ/v8UeNdld7XnPbJiplkk9WM+vQ8xBpH4djT9yo+PqU2M2B1jJAiszXc0OjF1rK4UBrNd+c9IYVub4QthETvNfS/L1FYLF4g4MrNbAB4tpgIye7KLdMd8NhNL1W4o03rHamT6GNrpzsvuGYfTQYRtMz+OYE9womn2dUKA8bpH6G/7P4vM5ZV2fNmuWVw6xhTOw8smc9iEZnEXb+7RWsW7fO21czZ87Ezb+8RZVxHmydXbx4MTYUl4Rs3eeix35Srt7BN74tEVZYh3jkvZQ2G0hZlLVC1HVRLikpKXjzzTdx4YUX+rz++eef45prrsHx48dVrWCoMft1US7+ykFjY6P3ZM0Mbq8D9fpIIHxdlz2nRt31h9FQ/hAGj75YshuwHIzSL8EWIa1dnLnz6+DBg153fO5pXuu+Mllzi6+fA32ekisZZruKaESMdCVGa8SMzfj4eJ/2cPcyql7rZOtgGzERXSKuy6hz3VH5vAZCP1bEnIYbTQYYLVxDKAjWB3LaJNBa3cv04NjGhT56g/+1OfbqqP+VZq10RjHjUOiQUYv5JEaH4OuXqqqqAbMeiMUoMkZoTnjHevoUxE3P0WSsqyHXuJ+RlJSEW269Dc0ydHE15pFaa5nYdTZU677eKNE7hMa3UHgAFi37N9yQYieSbGSLjY3Frl278OMf/9jn9XfeeQcZGRk4ffq09BobiHAysnFhBfOuXbvw163b4Kqv875n5A21Ee/D67UBCKQwnznwbzS+8vtzbt7aKAhc9O6XYIvQsmXLsG7dOlU3x/7f76OgyIx1wIccI6ZSw+dA3NQKofZmNpwOBMQ+56FvDmD8hPN9yvVtYqYibrpymeF0OrEkLx/Hjh1Tbe7JeVapn23UsUIyoD+hbhOh71NyYBRorW4/9AFgsSDm3HtMRxtcO1f7bKr4YrAlOUbgqT89qckaL6bNuWW+/PJLFBUVqR5PS8lGlxs30iwxkEOBUWSM0Jxo3v00uhsOA719QfuNvD8ClOniSueR/zh39zK8XtmBxrnU9VDrdV9v1NAP+MZEd8NhNJSvlOSMIVaOrVixAhdccMGA0Rs0NbLdfPPNePfdd7F+/XpMmzYNALBv3z4UFhbiyiuvxIsvvii/5gYgHI1sfAqaPcmBmxYt9N6vN/KkMMoJmGBdQhQAMtipAm/gXw3bSa9+CbYIuXY+gu6j/0VU2vc1C0LPVb6Zzja4dqxS7bTHqIklBgJabWbDpf2ljM3m5uZ+7XH641c9KeV7Or1/o0RmMAyDRx99FCtXrfIaCtRqdy1PcQfCWDEScjb3Rgn2rIY3ZaC1GkDQAyN3L4POms9xquIFxKENx44ehc1m0+aBJaLFPOXTMVgDwtnTTWj7+DXYB3Xgu2+reccReYAYH6E5sX7tGtjtdt0NgVJQQxeXM2a5f8PGF+Ua461D48G0NQcc53K+V8t1X2/U9A70HxMJdjuaGhtFt5msPecASIigeuIDLps3b8ayZcuQm5uLnp4ez4cMGoTbbrsNa9eulVdjQjOEgha27ivDxo0bTbGAGCW+mt4BIIPF3Yq97Dqceu+lkJ0q6NUvwYL8Dx47GR3V+zFsWoB07DKDofPFKDjz5bsA1IsPYdTEEuGO0vk9ENpfythcuHAhb3uMGp2G22+7FRMmTFAsM6xWKx566CFcdNFFqre73HkohoEwVoyCHGOZ2mu9kky0asTECbZWc99jQx/4x89q+3AXehq+xeYdO2QZ2LTyYtIisL6/jsHnzXfMEoFHH30UDz30UEjqRKiLUfYVaqDGs8gZs+zf1O/e3Jcp+/pC34QJbc1obGwU/F658YS1Wvf1Ri29Q2hM7Nq1S3SbBRoToU42Y1Yke7KxnDlzBtXV1XC73Rg/fjyGDBmidt10IZw82Yx6LcWMGKEtzXA6GorrAMFcmE//9000v/6EJlc1+PpATqwDqd8h5fOMciXDTKg5v8O5/eWeOoeiPdT+nlDIW7OOFbPUW44XmNprvRKPOL3WfDU8Y/xjlj77/As4ppFXoBzP0EBjmKtjdB7+hBOXLsfXgPDth9gpsJkkb1V1MYvMMTNyxmx5eTlyblyI6LGTYc+SLi9J3/XFaHqHWldPwwlNr4uGO+FkZDODUcYsGKEtjWDoC0SortcE64vWj17Fyd2bNekrPgOf2kHdjd7P4YgR5ncoUKqQDqSxOZCeVQo7duzA3fcs9o3rasArInL7T01ZoPSqp56xvZTICj5dwN/jQa34qIG+U8gwGExXYceAY9EaNL62XvbabqRQJ2bGKFe39SDURiSpY1auvGSfq7a2FvlLl+FM7GhaZ2FMvUNukozi4mI4HI6wMH5yUf26aGZmJv7yl79g2LBhomI/EMZAy+suAw2121LOwmm0VOhcQnmVNphbe+fhj2CNtKFVg6saQumx42ffBtcrj8O1s4g3qLuUfjFyP4crA0FWqrFRGUhjcyA9q1juvfderF271iPnr15m6CsiwcIKCIUNUEsWqHHVk2+94dLTeMSnnJpYrVbFGXkdi36LxlfXwWYf4+PlIvW6qxjEXpkTo6tkZGQgdXQaGipfANNSj7jrC2WFnginK4l6oXeYFj3Rw7godczKkZe8Rpv6OlpnYUy9w39MsEkyhPq851QdYIlAfn6+97WBYhT3R5SRLS4uDhaLxfszYQ70VNDCDTXbUsnCacRYPmrFjRFLsEWo89sPvdlF1V6khAx8MRNnIHHefWh6rQQd1R94y8vtFyP2czijlaw0ylUGNTcqA2lsmvlZ1R575eXlWLt+A6LTL9fcYKIGco1laskCuUY+Lkpje+mRHZWrC3TVfA6mtQFxP79X9fiofAQzDErRVUqKNyArKwuAMoOrXGOlUdBzDQu1bmkk9DQuShmzUuWl0HM1796M9m8/8mQ8PkewddYo+pXaKNU7tGgX7piorKxEUVERb5+3H9iDplc3IDp9CsVqA10X7Uc4XRc1otupWVGrLYNdH9m+fbuozEZGWlyMGjdGq6sageJWtB/6AKtXr1YlqDtgrH4OZ7SQlUa54qLVOqDX2NTje802DwONPbFeCtxnTkpKwo2LbkJjQ71prlQrucakxnxR66qn3Nheesgf/zY/8+W7aPzHWl2uu4qpnz/+Y+Lhhx/GypUrTTPm1UbvNUwN3dJsshsw1t4tWPtJqSuAgGVdOx/B0LajKF6/HikpKUFjhRlBv9ISo2bFFupzT+ic22FLHCMrPp9Z0DS7aEdHB9xuN2JiYgAAR44cwd/+9jdccMEFuOaaa+TVmNCEULqdqr2QGW1hVKMtg53K1b1UgIU35YLp6fb+jZBwNNLpqF5X7YK5tYtxe5czzgKdMr28c6eohUzs9xqpn7XCCHNdbVlppCsuQh417l4GXce+QKRjPGre/wCVlZWYM2eO6M/VY2zqpVibaR4GGntZWVlIsCehydXgLc/XfkIxWADzXKmW6wWmlixQyyNOjleDXvLHXxewDj0PgHFuU0jVVZYvX45nn38BzQMwS6gR1jCluqVZDTFqeMGqgZj2kyIvKysrAz5X3PQc1G0pREpKSsDnMsLYFIsS/Vaq3hGqdhHq87bP3wHT0oC460PjuWwGJHuyXXPNNcjMzMRdd92FU6dOYeLEibDZbGhsbMSGDRvwm9/8Rqu6hoRw8mRj0Tr4qtoLmV4LoxhhqKQtA53KtR/Y48lg5ediq3ZwYC0wa9B4peNM7uIp9nuNYHzSGqMpwWpl1VNyCq12v/N51LQf2IPmiufBtNR7yyUk2vHM05sNK2eUBpEfCAQae2cO/BuNr/w+aAB6vnZu/eAVtLz/VwDBgx3LlfNayDslGR6VygK1vVHEto+eXjD+uoDaSYHUrp8/fGN4IGYJNYonlRLd0szrhZ4JT1jEth8rl3bt2oW/bt3mmxDHT16q8VxGGZtiCKV+q0e7CB3GGcVzWSs0zS6amJiId999FxdeeCGee+45bNq0CZ988gl27tyJhx56CF999ZWiyutNOBrZAO027GovZHotjFKEody2FFpgvIpoYpopXWzNtOix6DnOxHyv0YxPWmBUJViprFS6MVC73/3r4zXoj5+KOAO1eyDMKGP0QGjsiV1jDn1zAOMnnN+vnTuPfob6rQ/AOjQetuTxvAYT185HENt2DMdrayT3gZbyTomxTKks0MNAo+ehF9887TtAnMqbFChU8oZhGFRWVmLBjQvRMXysJDkSyiyhRjhcM8rBqVy5b/b1Qu/2F9t+G9atRcHSZT7zwp7kwE2LFiIjI6Pf2FXjuZR8RijnVqj1W73GDNfIWlJSAkC7gzijoKmRLSYmBl9//TVGjx6NnJwcXHjhhVi5ciVqamowceJEtLe3K6q83oSrkU0LtDipDcXC6C9oGxsbkZOTo7kwFBKC7AbGzILJTKe9eilgUhSXUIxHPTG7EhwIuae1Will3LZOmHc/jj97l2E8S8Si96bDLAiNPbFrTHFxMfLz8wWNdNbBseiurz5noO2T8y17PWO0vLwc2dnZkuocis2InoaLUBhouM/HZn7Ty5uATxc4/fGraNlbBndPp7ecVkYqoTr594HH6JcjWlcJxRgyyuGaEo8jtdtJjm5p9vVCb/1IbPtZLBZJcluN51KiX4WzV5me3o/s8zZGJqG78Ygnm7SJ9EupaBqTbfz48XjllVdwww034I033vCmaG1oaCCj1ABD7bgBoYhDwCdorZG2kGQvEooRw7SdBGCeWDd8mCkLn17xLsR+7933LA77bFpGiTmiBXJiMWmZRe3/t3fn4U1Uex/Av2noCrTQ0g3ozo4isglotRXcrnqLpYD0ouL6uoGA4r0ICgiIVwtWwR2EKwiy5bpdF0RbrUIVFFwAUUrZW7oALdDSlmTeP0LSJE3SLJPMTPL9PA8PbXI6OTM5c+ac38w5x3T+jIo1M6CtOYGIW6eLftw92Qn19ryPlpP+A/o2jtyGbVsec0NeLcueo9eYkpISq+lUAWpEZt6Lyg8WIig2FY3l+1FuUs8jQI3p06c7HWDz1uqBUs6p58jcoO6wNWTHlXnQxDiHbbUFuiYm4f577xFtUSBHWZun6MxPn6Bm2zrUl2w3pmutreLpMiSneaZcnU/QE4EMV9qWUs0TLAbDOTg6+za8/PLLHp9P25rWjl+byARAFYDQtEFO1dtizHfpStn09rklRftWrDlAXWG6v9qzJ1H5wUJUauYjfMho6Brq0Fj+F+pLf0Lj8X1YtWmTbNpP3uB0kO2ZZ55Bbm4upk6dimuvvRbDhg0DAGzevBmXX3656Bkk+RL7QubpC6O1ivbsb1/h1JY3ED50rMcrQ1sXGG39GQDSTQ4sVudYrM6Ep+8YS9UAc/RzK0+UI+66J3wu+GRKyY3g1rgy4bqnG2WGjsr9D/wfGiD+cff0XWJvNiBb7IsqABB0xvflMmzb2jHvmpCIqOgY1FqUPUcnoE9LS7OZLqzncHS4+g6cLlptdjyiY2Px6pIlGDNmjNP7IHa5l8NQO2s8FaCx1qZpPFGKig3PoGbbOkSPftrhyfrFPIc9HVh0lK0gbsSwMWg/ZBQq1sxA28YqrH//fWRkZEhWVrwVbHbU8OHDER0b16IeAWyXIU8GMpwtT1IGHNxh7RxsOLQL5ft/NP7ujRvXrR2/c3sKAUHnUr/J3RvyzravpDi3pGjfurrQjxhM9ze4Sy9Ej5qB6s2v4sTaGWZthXbhEaJ/ttwFtJ7EXE5ODg4fPowdO3bgiy++ML4+YsQIvPTSS6JmjuTNtCK2xtkLmdjbM2VZ0QZ36YWAoFCoQ9sD8F5laLjARDWeQPnq6TiSPxantrwBdWAQaovXQzCpkADPV44ajQYpqWnIzMxEbm4uMjMzkZKaBo1G49L2DJ2J8ePHt2i0GuZEWbt2LQoLC6HVaj2eH2vELmeW+9XY2Gh1Px39XEB5wSdHvltTnjzXpWYIpteXbEeVZj4aju2FrqEODcf2okozX3+3dvEis3PDG42y7OxsrF/3PgBxj7uhc1UdFIu4CXlImLoBcRPyUB0Ui5ycHFHOXUMD0tN1pOm+dLj6LgAqhKYO8th+iZFP07ydDI5DdWUF6izKHlRqqAJDULNtnd3j9/DDD9s9zo3H/0DXhARs2bIFa9asQUFBAcqOHXMpwAaIW+69ce2QE61WiwcefAghqYPM2jQhCX0QdfMU1JdsR+WmeQ7VP6blKTb3BUTnzEHElf9AxYVQjB492qVjaK8t4C2GIK61YECAOhAdr70XJ6uqoFarJQ3G2sunMWhx6CCKioo8nheNRoNu3Xug8kQ56vb/6FAZstW+NgQyQtMGY8q0x1ttF9jjTHny1vVCTJZ1etfH3kfHkQ8ioGNXAMDkyZNRUFCA0pL9Hr/BU1lZCXVgkM3rxbnfvgTger2dnZ2N0gMlKCgoMF5HHN0vZ9tXUpxbUrRvXWl3isXa/urqalu0nXQxPSVvO3mb03OyGezfvx8lJSW4+uqrERoaCkEQoFKpxM6f13FONscpaU42uc2HZn9eOO/MaebNiTkduUvurfyIWc5sDT/WNjW22M+srKxWP7ftmUOoPHFCUXOJuPIEhNRzjniDM3MxeWsOGSXV2ZY8Pe+jUuauc+SYh9aUIjQ0DEdNyl5UdDSqq6oQ1srx8+b8mmKVe2evHXJ64s3VvDz77LOYPXu2zWNXs219i6cOrdU/puUp7JJrcapghdmqw6rAEER2aI8TZWWKq4vlsEqjI+SST8vzqOl0OU4XvgPt2ZPGNNbKkBznQFPyPMH1fxa3WP1bHRiE99e85/SQfGcZjltgbBoay0usLlhSd/HJOim/b0fbV1KcW1K2b725SIuBUtpOYvHowgfV1dUYO3YsCgoKoFKp8NdffyE1NRX33nsvOnTogEWLFrmVeakxyOYcsS9knrowKmFlT2+vYCVN59h6B8iRAJSY34MY5cxag7T6k0XNEypb2U8Adj933bp1ePyJ6YoJPrkaGNVqtViwYAFmz5nTaqdfyRztQCs1WOXtzpUn60jTfRG0jbJdjMbRY75lyxao1Wqzsvfhhx86dPy8dS0So9zb24ZO29RiSKDVYyDREGBXh2hqtVrExMbhZHVVq53HWbNmoU+fPjbrH0N56nD1XTj97bstVh3WL2rxI+bOnYtnnnlGvJ33AjkGf6yRQz5tnUeCTovzR37H6YJ3EIGzOHr4MIKCgsz+Vi5BQksbN27Ew49OQuWJcuNr3lxww1Gm379hTquW5+E6nD+ww6PtIkeDfe+tehfTn/yn5O1UR9pXUp1b7raz3LkRJMVNJMP+BsX3RMPxP2Rf57rDo0G2O++8ExUVFVi2bBl69+6NX375Bampqdi8eTOmTp2K3bt3u5V5qTHI5jyxG+SeaODbq2ibl5gf5NRqU57grcpRbk/OrFi+DCNHjvRqxexOObPcLwiCPljrwN2b1jq6SrkD62oHudU5r2TYCPYGb37vYtWxUt0l9kQdabov9ft/RNXHL8qu02iZT1fy5kzg1xvXInfLva1rWd2+rS06iFGdolFdbXiaz7WnpcU6Lu48uW3YZ8D9p0kM5UkdHoOgGOurwlVumoew2kM4UXZcFjd3HKWUJ6bFzKer5dOdNqG77UlP1DXWrnHRMbF4dalr80c6ypV9MZyDXR97H2UrH5PsKSBr36Og06Lh6G5oz56Ctv4MTm15AwUFBTh58qRPt1PF4Go7Sy6rDDtLo9Hg/gf+z6GbP1I/PewOj64uunnzZnzxxRfo2rWr2evdu3fHoUPWxx+TbxN7kltXt2fv4mZvUsjQHkMRFJeGxsO/oNyJ1aY8wVuroIk5F4694+7oxNaFhYWi5cdR7pRby/06f+RXh1dtbO1zlbJSqyuTltuaqPt04TtoOP4H5s6di5kzZyqqIycWb37vYtXZUkww7ak60nRfHF0oQIo5A9095o4eP29di9wt99auZcYbZ90GI+LW6WYLAoSmOrcinimxOj/uTsZt2Gd1eAxqitdb7ZDXbFuPqOiYVuedMpQTbW0FIv7+pNW6PGLYWJS7sPCK1MNyxVjNUEn5dKd8utMmdGfSdU8EFGwtwlBbvB7jxo2DWq32SDvK1X0xnIPndhd6bPVvR1grA6oANUIS+wHQB0hObXkDZWVlGD9+vCLaqVLWAa60s+S0yrCzsrOzERERgZEjR8qy7SQFp4Ns586dQ1hYWIvXq6qqEBwcLEqmSHnEbpA7u73WLm6tVbRNJ0qwbt06REdHy2KeFk8Tq3Pc2nF3tOFm/FwvV8yullvL/dKePWX2uyXLBmprn6uElVqdbZTb6liGJPRB7IQXUKWZj2XvrMDMmTNFyZ+UXD3u3lyVz7QMuppfKVe0EpvpvkSNmgF1RKzNAIaU++VLx9zAnXJveS0TdFqcLFiO0G6Dzb874QKEpvOIGDbOpQ6smJ0fezcoIAgITh6AI1vewJIlSzBp0qQWx8Gwz+3734TT376LSs18RJi0aWq26TuP/5o7t9VjmJ6ejsioTjhZXSXqTS65PI2hlJtW7ubT3fLpTpvQ1UCGJwIKUq3U6s6+GOr0E24uKOAuZ8uAXFYRbo2UdYAzfQxPll1v3fDIyMjwufaJO5weLnrzzTdjwIABmDdvHtq3b49ff/0VSUlJuP3226HT6YzzDikVh4sqjzPDLqSYFFKOxHiE2pHjHhkZ6fD8QXffc6/sh3UYWD5WL9UCGvZ4upPj7BARd+aSksN37ii5dC4d5W5+lTK82RGm+xLUuRdOf7vK6sTPUu+XLx1zd1leyxqO/G61Lj635xuXhwCLPeTI1pBfa0NcrZ2LrS5WEBSKyIh2Di9W0NoiCs5ev7y5qJKjpH6qzlGu5NPTcxs60yZ0tH3tqWF8UszBJdaxGz16NAD3h4C7WtaVMrzaVXKvAzxVdr3dJvX19olH52Tbs2cPMjIyMHDgQHz99df4+9//jt27d+PkyZP4/vvvkZaW5lbmpcYgm7K4clGQe0XrLe5UhI4e9/1/7kO37j0c+n4+/PBDxVTM7szJ5o2y5o1OjrPnnqNzSUV1ikZ1VaXxdTkHqCzJsXNpj1j59aWbF0qZM9CXjrm7TMtxYGw31Hy/pkU9486NEEc7Py+99BJiY2OttitM2x0nTpzA1KlTzbZnNsTVgXPRdJ/bDxkNXUMdGsv/wvnSn9Fw/A9s2rTJ4XKg1WoRG98ZdeFJiB79tNtzgvlyR12OxOqci9E5drR97amAghTzhIq1Lxs2bMD4f0xAUOJlLp+HvGmmXJ6Yb9W8X+W9Nqkvt088GmQDgPLycrz++uv46aefoNPpMGDAADzyyCM+McaWQTbnSB2wksOqTErmakXozHF3ZoJUJVXMlo0R/eqiiyVfQEOuK1W2VmZqtq3H6W/fRVi3IYoIUFmSa+fSVh0tdn6lvhaIyXRfYmJiAAAVFRWy2y9fOubusrx2WNYz7qwk3lrn5+zuAlT/7yXzYKxJx9badU0dGITgpP52b9K0trKjmNdLsQIs+fn5eOKJJ9gm8yIxA0veaoN5KhgmRZ9AzH3ZuHEjxo4d69J56MhNM0eGdyqpHe5L3Cm71r6zrgmJqD9/HvURyZK0SX21feKxIFtTUxOuv/56vPnmm+jRo4fbGZUjBtkcJ4dhUXJdOlxJ3FkNydHj7uwwAjlXzKb5++uvv/D28ndw1KLzpG1qNP7u7caJtxuZtr7bRS++YDbH4fDhw20+1ajTNuHoy+MRknip209SSEWOAX97dbSjQ7nZGSal0Gq1KCwsxLjbx6O+Q0qLeubcvu9Q9cHzF4cAO34jxN65bb46+Tizjm3d/h8xbtw4rFu/vsWKpie3vIHG8v0I6zYEwckDcGrLGy2ebLMcOhodG4c3Xnu1xdBRsa6X7nSuNRoNHvi/B1FdVQVAYJvMi8S+9nijDeap66UUN7ss98V0RU51u46ASo0Ta/7p1HBrZ89DR/Y7tKYUoaFhZu1VW302ubfDfZGrZddWcPXU18vQcPwPtvFE5rHVRQMDA/H7779DpVK5lUFSPrmsgCLF6naOUNIFypXJ/z05Qaq3VrVzha27RXPnzkX37t2NwaStW7dK9t2LuXKsI6x9t1VVVZj2+BMtgju3jxuLvLy8FpMjn/p6uUOTkhcWFsp2vjZvH/fWtFZHP/bYY7LKL5G71Go1RowYgbfefMPqJOx1v38NAAirPYRyJybAtrXYhH6RhWUITRts9nRccJdeCLvkWtQd/AXr1m+wuqJp3J2LUf7uNDQc2oW6/T8CaD4Xra2OagjcWbavxLxeujqRuel8UkGde6Hx+B+ya5Mpnb02pdiLoXijDeapBVykWE3SdF+szpEYGIKo6GiH98WV87C11d6DOvdC9f4fEdZtiEN9Njm3w32VK2W3sbERDz78iNXFEtoNuBkNx/9gG09CTg8XffzxxxEYGIjnn3/eU3mSFJ9ka52chkXJKS8GcnjCz9PkeNw9TSnzbUn9RFVrx+mJJ57A++vWm50fkZ064WRVlaLna5P6uJty5Pxse+YQKk+ckEV+icRm72kQVwNJlsMpz/7+dYsn0IDmIFlQ555odOBJgkceeQSvvvoq4ibkISi+u6zm92yNVqtFckoqjpVXICSxHzrdNgPH335QMflXAkfalEqcS8uTefb2kEfTQHNo2hD9k7IX2z4129ah/sAObPLgd2BvdImg0+LYm/chKDpZsSMF/ImjZVej0eDBhx5GZYX1dpwcF2TzBR6dk23SpEl499130a1bNwwaNAht27Y1e3/x4sXO51hGGGRrnZw6k4C8GhdKCcSIQU7H3dOUFFSUMq/OLIhh+rSfVqvFyJEjFT1fm5zKiKN1dHRsHM61T5Q8v0SeIPYT5dY6PwDMOrbGud+ikxDWKx3Vnyxq9ebB6tWrMeOpmagOikW7QVmoeH+mbNpXrTHUNUDzPHjmizj4dtvA05xpUypxLi1P5tmbc2uKuXiIK+xd8xlskQ9Hr0mtpTPUC4ExqWg8UcLgqhd5bLgoAPz+++8YMGAAAODPP/80e4/DSP2D3IZFZWdnY+PGjfoLtRNDQMSm1WoxZeo0q4/tdsqehSrNfEyZ9jiysrJ8okKTy3H3htYexTcMZywqKpK8kaJWq3H7uLF48cUXUblpntW5h8QeLmHg6HHaunWr2XHSarU2h47otE2o2bZe9ueVFMNUbHG0jv7H+Nvx8ssvS55fIktiBMjEHvJkOYzLsEqo6dDIhqO7oa05gYhbp0PQ6ufmbG3oZJcuXYx1x4Uz1QDk075qjWk+DHkO6zkc0aNm4GTBcrNhuVHRMVjVyqrlSplmwxucbVO6OtxXSp7Ms+H812g0uPueez06uqSoqAjVlRWIu+FJSdqI9obfKq1O8VXOjHKyd+0yrRcMN2WsXWNUAWq0738TTn/7Ltt4EnE6yFZQUOCJfJCCyHEeNDk0LpQUiBGLHI67N8gtsGyPRqNBXl4eguK6ofFEiVknBwFqTH/iCY8FQF09TvYCVI7O1+bueSVGB0+qwLNl3g137Vuro7OyspCenu4XgXKShivnlZynXDDt/Gi1Wix+Kd+sY2vaoVW1CYI6IhY1xeutDp00nXdKrVZj48aN+uE/kFf7yh7TfJjmOazncIR2vwINR3fj/OHfUfP9GqxbuwYjRoywuh05f+eeZO/8cKVNqcS5tDyZZ2/NH+1OG1GsGwq22lBnf/4fAOXUKb5IzHJoWi8ExXe3e41pPP4HoqKjEdp4AkfZxvM6p4NsRJ6asNRdUjculBSIEZPUx90b5BhYtsbyzjcEwbjKVUDbDjiz/QO8v34DFi5c6FIgtLXGoDvHyVaAKrJTJzTAs+eVmB08bweebS3G0T6iA2q2rbM6TMCyc+8PgXLyPlfOK2udkcYTpagofAejR4/G3LlzMXPmTFmUT8uObVDnXqjd8SGA5jowMvNeVH6wEJWa+VaHTpo+SZCdnY1bbrkFXROTUCuz9pUt6enp6JqQiGPlFajZtt5sAQhVgBrBCZeg9sf/IiEx2WY7QS4LaXlba+eHmG1Kf3xK0JujS1xt+4jd9rDWhuqSkIgzHToqpk5RInvnlxjl0HT7e/bsAXDxRk6AutVrzKqNG2228fyxXvAmp+dk83Wck80x/jQfl6PkNlcdiUdO823Z48ky6EhjUIzjZHnRb22+NnfPKyXPo2gr7ye3vIHG8v0ADJMwN9fRNdvWe3wSZvI8uTeOXTmvrNUfdfu24mTBcrPV+romJuFlGT3hpNFo8MD/PYjqqkqEpA5CU8UBBMV2MwacrO1DQmIy8l+yPu+U0tpXLSd9d7y+Ucq1VWyOnB+RkZGiXM/99SlBb7bJXSnHnmp7mF4b/vrrL7y9/B0cvfjdh6YNtjqFiNzqFCVp7fxytxzamgvUdHvWrjExsfF4/bWldm9o+WO94C6PLnzg6xhkc5wSJ1n1JH9tLLpL7p1FAyV0fOytMAU0T7S9Zs0ajB8/3uHtOjv5spjHyZPnldLOWcuJnCfecy9OWuRdP/H6/QjqlIy2l16LUwUrzBpeqqBQREa0w4myMlnsEzlP7o1jV88ry86I+QT68g2Aa7VaJKemGc/F+j+LW0z831hRitOFK9Bw/A+HnsZTWvvKGGisrgYEnfH1TtExePON123m2R9vTjqzQFC37j3cuj4p+SaSuzzVHrLFmbaPN9oelt990+lynC58B9qzJ41p5FynKIEj51dDQ4PL5dDa9htPlKJiwzMISbzUbKSCoNPi/JHfcbrgHUTgLI4ePoygoCCX880yYR2DbG5gkM05SgmQeIsSAjFyIvfOoiW5d3w80WFx9Q6tmMfJU+eVkjp4jtzNBFquJCbotMYhw+p2HQGVGifW/FMW+yQXSrqOKaFx7Op5ZdopVrUJMq7SaW2uGTkFwK3trytPFlhSUrkE9PktLCxEYWEhACAjIwMZGRl28+ztQIgcOHN+nDx50uVrn9JuIolNiuu7o20fT+fN1nfvTCCG7HP0/FqxfJlLozHsbf/cvu9Q9cHzLj2Z6Ei+IxtPYOU7yz22Gq+SeXR1USJT/jAflzP8acVNdylxHha5L/TgynyJrXXkXJl8Wezj5KnzSinzKFo7V2p//AA137/XIu/as6cANO+TKkCNkMR+xvd1DXUApN8nubDWKYqOicWrS5dgzJgxEuasJaWsYO3qeWU6r5GgbTSu0in3hYSs7a/pxP9Np8px8vNXsHjRi07VVUprX6nVaowYMcLm4gbWKGW+UzG1dn60iUwAAGzatAmjR4/G+vXrMe3xJ5y+9vnjYlympJg/2tG2jyt1pDNBd1vfvSpAjdCkyxBw3YMot7LSOznO0fMLgEvl0N722/a8CheuvhOni1ajvmS78XUx6oWgzr1wtGgHRo4c2bxdGT/8IGcOB9n279+PmpoaDBw40PjaV199hfnz5+PcuXMYNWoUnnrqKY9kkkhJpArEKOmut5SdxdYmKG3tGErZ8Wktf/ZWmLI20bYjTxK6s2KomMfJ1nkF6O8Ku1LuldDBs3WuhCT2Rc33LfOubtcRgLz3SS4MwcvA2DSo20Uah9FUVpzA2NvHY/r27XjhhRckzmUzpXSaXT2vTDvFob3057arAXBvXg9t7a8hwK1SBwIAunTp4pHPVzK5LqTlSfbOj7p9W3FyyxsAgKVLl2Lp0qVISEzCorwXER0d7VR5VspNJE9xtj0k5ue2Vv86W0c6O+rD3797b3D0GFdUVLhUDlvbfvsBt+D0t+9i1qxZ6NOnjyj1Qt2+rTj97SqEpg1CxLBxinj4Qc4CWk+iN336dHzwwQfG30tLS3HrrbciKCgIw4YNw8KFC5Gfn++BLBIpj+EiO378+FaHS4hBo9EgJTUNmZmZyM3NRWZmJlJS06DRaDz6ua4ydBbDh4613Vk8dBBFRUWifq694yT3Y+ho/gxPfUU1nkD56uk4kj8W5aunI6qpwurcadVBsYibkIeEqRsQNyEP1UGxyMnJMW7XtDFojTeDNpbn1YcffujWd2bo4NUWr4dgMo8QIJ8Onq1zJbhrX/3S7dvM865/PQY129bJdp/kwBC8DIxNQ2N5CYLiupmdB6EpA/Diiy9i48aNUmfVSCkdJ1fPK0OnuL5kO87+/D8ArtU73q7LlVCPyJXpd16lmY+GY3uha6hDw7G9qNLM13dAFy+S7Q1DV9gqL4Y5CINiu7W4Jo8bNw4nT550qk0pp2u3VBxtD3mbM3WGo201U/zuPc+ZY+xKOXR0+yNGjBClXhB0WpwsWI7QtMGIHv00grv0QkBQqPHhh9C0wZgy7XFotdpWP4P0HJ6TLSEhAevXr8ewYcMAAPPnz8fGjRuxa9cuAMDy5cuxZMkS4+9KxTnZSGmUMEePJSnmYbF3nOr2/wioVAiT6TF0daU+e0/sOTpXCwDR5nUR8+kSscq93OdRtHeuGCeGTxtkNi/HyS1vorF8P8K6DZHlPsmBYU4cdbtIBMV1szrvV+WmeWh/9iiOHzsiWiffnXNAijmGXM2vO+eVRqPBY1Om4ujRowhNHWg2uTNgv96R6noo93pErgzl68MPP8R7a99H5Yly43tymu9UbJblpU1kAsreedhsVVoDV+dP8/c52UzJcaSHI3VGVlaWS9+hv3/33vi+XTnGzuTLU9+hre1azudrSU5zFEvJIwsfhIaG4s8//0RCgn6ugBEjRmD48OGYN28eAKCkpAQDBw7E6dOn3cu9xBhkIyVR6oXUk51FaxcxwHagSKdtwtGXx7dYqQeQxzH0xHfs7PEXowMp5iIXYh8TOS9o0dp3VbNtPU4XrTZb0S8hKRm3jx2D99etl+U+yYEheAm0XDzCQOxGpbvngLfre3fz6855pdVqsWDBAsyeM+fizY/W6x2pr4dyrkfkyNZ8iP/IHY+srCxZBEI8ydr+i10XMfgrb63VGe60lf31uxd7QTV7gTFPH2NPbd/admu3f4Ca79f41SI0rnAmTuTwcNHIyEjjEASdTocdO3bgiiuuML7f2NgILlRK5F1SDbt0l6eG19gaJrRgwQKbx6nx2F4ITecRMWycLI+hJ75jZ4eduTvkwpXhDvaIfUyys7NReqAEBQUFWLNmDQoKClBasl8WDdDWzpXG43+ga0ICtmzZYpb3F154Qbb7JAemw2S8MfxSjHPAm0PrxMivO+eVWq3GM888g01O1DtSXw/lXI/Ija3ydS48CS+//DJOnjzp0wE2wLy8PProowDEr4vkOlyS9FqrM9yZIsAfv3ux25qtTT3g6WPsqe1b227N92sAcIixmBxe+OCaa67BvHnz8Nprr2HDhg3Q6XTIzMw0vr9nzx4kJyd7Io9EPs2dx5qVMkePJU9MSGtvtdLZs2cDsH6cLFdjtCT1MfTEd+zKxOSuLujhiUUuPHFM5LqSnyPnyssbN1pd0U+u+yQH6enpiI6JRWXFCY8vEiHmOeCNFazFzK+7ZdCZekcO10Oec61Tyiq53mBaXpYuXeqRukjuq6L7O3t1hruLM/nTdy92vWKvT2G6CICnj7Ej23elH2m53ZiYGEy8516c9KNFaDzN4SDbggULcN111yE5ORkBAQF45ZVX0LZtW+P7q1atwrXXXuuRTBL5Kncfa1bC6oi2iNlZbO3iemL1k2g4/ofV4yT31Rg98R27uqKbKx1IT6yIqORy7wpvBFb8jVqtxqtLl2Ds7eNRs22d1aHiYjUqxT4HPN2ol2oVU1sdBUfrHX+rF5TKsnwJOi0aju6G9uwpqNt1RPsho3FkzT8lXyXXm9xZZdXdVdHlOF8Z6Ymx+q6/BP7FvG45G7Bz9xi3dg7a2747/UjL7b4swWq8vszhIFtKSgr27t2LPXv2IDo6Gp07dzZ7f+7cuejatavoGSTyVY7eJbFHjAuwK8RqlInVWWzt4toh426cWDsDtVaOU1CX3lAFhrToaAs6Lc4f+R2nC95BdGwshg8f7vT+icET37E3l7YX6+kS0zIXExODrolJfnXHzZ/uSHvLmDFjMH37drz44ouo3DTPbPEIMc8DpT15KcUTYWLMoyPV9dAfiBmIMS1fdfu24mTBcmhrThjfV4dHm6WTG08EpVy9Jntk3kQ35q9SIjkHGb3ZVlM6Ma9b3rzR5M45KEY/0hRv6IrL4SAbAAQGBuKyyy4ze+3ChQs4f/58i9dJGdy5uMj5wiR3Yj3WLMUFWOxGmRidxdYurkExKYCgQ93FuYwsj5PQdB71B3YY32s6XY7ThSugPXsSAFAJoFv3HpI0PD31HXvrYirG0yXWylxUp2jUHbH+ffpqw9Nf7kh70wsvvIAhQ4bg4UcnodxD54HSnrDydn7F6iiwQ+oZYl/zDeXmzE+f4PS37yK022BE3Drd+L3XbFuP+tpK/PXXX6Ltg1g8GZRy9prs7nkjdgddiZQQZGTgwzFiXre8daPJnXPQU8PuDTd0CwsLUVhYCADIyMhg29MFDq8u+umnn6K6uhp33HGH8bUFCxZg3rx5uHDhAq699lqsW7cOHTt29FhmvcGfVhd1N3ou9wuTnIm9uqa3VjUzX5FmrPGCUFu8XtLVihw9nnPnzsWy5e9YPU4AzI5haNpgRAwbJ5t99NR37Olgubsr/tkrc3X7f0RUdAyqKyuM6bmaH7nCk+eB1KteOsub+fXEZ3GVT/F44pqv1WqRnJKKY+UVCEnsh+jRLb/3yk3z0KmpEqUH5HFOAN5r/zhSF7l73iitTvIEubZnbeGDDfaJWabF7qN5Ir+ezCP7+LY5EydyOMh27bXXYvTo0XjkkUcAAFu3bkV6ejqeffZZ9O7dGzNnzsRNN92ExYsXu78HEvKXIJs7FxelXZjkaO3atcjNzRV1qWS5B0s8yZm8AbB5nBobG9E1MQnn2ifKbh8B5TayXF2G3JHvNbLxBFa+sxwVFRWKOibkX1w9B6Tirfx6qqOg1LpSTjx5zX/22Wcxe/Zsj3ZixSS39o+75403gghyJrfvUwmUUKc6ct1yZNoNMYLYrX2Gu+egp/qRCxYswOw5cxDWSh9fCeXBE5yJEwXYfdfE77//bjYn0caNG3Hddddh5syZyM7OxqJFi/Dxxx+7nmvyGstHTIO79EJAUKjxEdPQtMGYMu1xaLVaUf+Wmpk+1myNK8NxDEPJxo8fj4yMDNErO8McBeFDx9qeo+DQQRQVFYn6uY4wDBOqvzgctOHYXuga6tBwbC+qNPP1w4QWLzKboNTacdq6dSsqT5TLch8Bz3/HnuLqMuSOlLmjhw9BrVYr7piQf3H1HJCKt/LrqWE5Sq0r5cST1/zu3bsDkO+q3pbsHQsIAoKTB+DIoYNYsmSJ0+1frVaLwsJCrF27FoWFhQ79vbvnjRxW4pWSnNuzcqTRaJCSmobMzEzk5uYiMzMTKalp0Gg0Lm/TlXLfmtauWwAc2g9n+hSWHD1W7p6DYvcjNRoNklNSMXvOXISmDrLbx9+4caPo5cEXORxkO3PmDKKiooy/f/fdd2arifbt2xfHjx8XN3fkEe5cXHhhEodhguba4vUQBJ3Ze3KdoFnujTIxOoVy30cly87ORumBEhQUFGDNmjUoKChAacl+u98Lvw/neaLhSuJw5RyQkjfy64kbTiQOT9a/MTExAJTzvds6FnX7tuLYWw/g1JY3AABTp051qrPpavDC3fPG3887ti0cZ3g6rDooFnET8pAwdQPiJuShOigWOTk5LgVWPBG0M7B13QLg1H640qdw5li5ew6K2Y805LtSGwYIOkQMG2e3jz927FhRy4OvcjjI1rlzZ+zduxcAcPbsWfzyyy+48sorje9XV1cjLCxM/ByS6Ny5uPDCJA537pJIRQmNMnc7hUrYRyVz9ukSfh/O8WTDlcShtCesPJ1fJd5w8heeqn81Gg0m3n0PoApAzbZ1ivjerR2Lun1bUfnBQgRFJ7nU2XQneOHueePv5x3bFo7xxOglTwTtLFletwC4tB/O9CmcPVbunoNi9SNN891uwM0AbPfx20QmAKoAjmZzkMNBtpycHEyZMgWrVq3C/fffj7i4OAwdOtT4/o4dO9CzZ0+PZJLE5c7FhRcm8Sht+JBSGmXudAqVso/+gt+H47zRcCUSmxJvOPkLT9S/hnrqZHAcOqTfgfqSHajcJP/v3fJYCDotThYsR2i3wYh2obPpbvDC3fPG3887ti0cI/boJammHHJnPxztUzj7GWKcg2L0I03z3aa9fsSirT7+uT2FgKDjaDYHORxkmz17NgYNGoTJkydj165dWL16tdkXv3btWtx6660eySSJy52LCy9M4lLS8CF/aJT5wz4qCb8Px3CuTN/gr0N9lXbDyV+IXf9qtVo88OBDCLk430/EsDGIHjUDjVWHzL/3Rvl975bH4szOz6CtOYEIFzubYgQv3D1v/Pm8Y9vCMWKPXpJqyiFvjMJy5TPEOAfd7Uea5ju4a1+oI2JRY6OPf+63L53eR3/WxtGEYWFhWLVqlc33CwoKRMkQeZ7h4pKTk4MqzXyrK7AsWrfO6qohjvxt/saNfn9hcobhLokSGC4IU6ZOw5HV042vJyQlI99HGmX+sI9Kwu+jdYaGa9yESbYbrquno6ioSDF1jb/RaDT6Mn64+Q5yQmIS8l9a7BdlPDs726FV38Tkr6ujOUPM+nfBggWorqxA3A1PGuupsJ7DEdr9CjQc3Y3zh39HzfdrsOKdZRgxYoTo++Ius2NxcQ42qRcecPe8keK8kwu2LVpnOnrJ2gqYzo5ekmrKIVf3w5lrhKufIcY56E4/0jLfkZn3ovKDhajUzEeERR+/8USJS/vor1SCIAhSZ0JOnFmaVemsNuqTknH72DF4f916u419W3+bv3gRL0x+wB86J/6wj0rC78M2Tyzl7mnufp++VB4MQ+hC0wYjfOhYBEYnoanyEGqL16O+ZLvPP1UiBX8PajpLjPM1JjYOJ6urFFVPWaPVarFkyRJMnToVcRPyrHY2G47tRfnq6SgoKLDa+S0sLERmZqbLf0/ikfu1RMr8abVapKSmoTooFp2yZ5ndxBMEHao08xHVVIHSkv0O5Umqcu/Kfjh7jRD7WHmLtXzX7duKkwXLoa05YUyXkJiMRXkv4PEnpituH8XkTJzI4SBbZmYmVCqV/Y2pVPjqq68cz6kM+VOQDWhZeVdVVWHs2LEONfblfmEiIv/jj/WS0jps7gY4fClAotSGuZIxqOl9hjoKgMP1lJzrcnfPW5735AhvXOtaO8/M68uWo5ecqS8dKfeRjSew8p3lqKioEPW8d2Y/XL1GiHmsvMlavhsrSnG6cAUajv+BuXPnYubMmVCr1YrdR7F4JMg2depUux+4du1aNDQ0KH4OEX8LspniRZ/EJOcGMvkmXwq+OENJdbe7AQ5fC5AoLUCqdEo6V3yJ4WlbdXgMgmKSEW3l2Fdumoew2kM4UXYcH374oezrcnc7m/7eWSX7vHGtc7TNJOboJXvlvm7/j4iKjkF1ZYXd/LjKkf1w9xqh1JFezuRbqfsoBo8E2ay5cOECXn31VSxYsAARERGYN28ebr/9dlc3Jwv+HGRjY5/E4q/BDpKOrwVfnKWEDhuf/mhJiUN9lYztHGkYjnuHq+/C6W/fRWi3wWbz/dRs24D6kh8xd+5cXHLJJYqpy93tbPpzZ5Vs88a1ztk2k5g3zq2V+6joaFRXVSHMhfPemby1llaMa4RSHzIQ8zj6Kq8E2d577z0888wzqK+vx6xZs/DAAw+gTRuH11GQLX8OsrGxT2Lw92AHeZ8vBl9cIfcOm7uNV18KkBgaqF999RXmz5/vE/ukBGznSMO0jg675FqcKlhhNt+PKigUkRHtcPzoUXTr3kNRdTnnlySxefpaJ4c2k2m5j4mJwcR77sVJF/Ij9k19XiPIHmfiRAF237Xi888/R//+/fHwww9j4sSJ+Ouvv/Dwww/7RIDN35muMGINVw2h1mi1WkyZOg2haYPRKXsWgrv0QkBQKIK79EKn7FkITRuMKdMeV/ywcpIXqZaFlxt3l3L3NHdXFnPl77VaLQoLC7F27VoUFhbKou7RaDRISU1DZmYm5s+fD6gCULNtHQRBZ5ZOEHSoLd6AhKRkpKenS5Rb3yKHdo4cy6SnGVamry/Zjrrfv0anm6chevRsRFyZi+DOvSA01uOtN97A1q1bFVeXG1b2Gz9+PDIyMloNSlh+/wCc+nvyfZ5ehVMObSbT80atVuOoC/kx3NSvDopF3IQ8JEzdgLgJeagOikVOTg40Go3T+ZLDNYJ8g8NBth9//BGZmZm47bbbkJmZiZKSEjz99NNo27atJ/MHADh48CDuvfdepKSkIDQ0FGlpaZg9ezYaGxvN0h0+fBi33nor2rZti06dOmHy5Mkt0pBt6enpSEhMQm3xejb2ySVyuHCT/5FqWXg5crbD503uNl6d/XvTYFZubi4yMzORkprmUsNbLNY6BR3S70B9yXZUbpqHhmN7oWuoQ8OxvajSzEd9yXbkL14kq+9RyaRu58ixTHpLVlYW5syZg9DTpTix5p+o3DQXNd+vQUzgeWzatAnZ2dk+X5f78/dPjvN0oEdu55mrN9A8cVNf6msE+Q6HHz8bOnQoQkND8dBDDyE5ORlr1qyxmm7y5MmiZc7gjz/+gE6nw5tvvolu3brh999/x/33349z584hLy8PgP5ku/nmmxEdHY3vvvsO1dXVuOuuuyAIApYsWSJ6nnyR4U5jTk4OqjTzrc7rk79xIxv7ZJPcLtzkH0wbpNaGVvDOozwYGq/VxeutDglprfHqzN+bDluPmzDJOGy9ung9cnJyJBm2btkpMOQ/YtgYtImMR/X/8lFfst2YPiEpGfkcXg9AvCF1UrZz5FgmvcXakK7IqE54bPIk46p1gG/X5f78/ZNz3L1WtkZu55kr+THc1I+bMMn2Tf3V01FUVOTUkFr2hUksDs/JlpycDJVKZX9jKhUOHDggSsZa8+KLL+L11183ft5nn32GW265BUeOHEHnzp0BAO+//z4mTpyIiooKh+dX8+c52QzkPq8PyZcvzZnky3xtDhg5zC9CjvHGinxZWVmyLA+t1Y/nj+zGiTX/xKxZszBixAjFn5di8cRCOt5u5/hzHeXMPK2+epx8db/Iczy5mJHcyqMr+fH03GnsC5M1XltdVEqzZs3C559/jh07dgAAnnnmGXz44Yf45ZdfjGlOnTqFyMhIfP3118jMzLS6nYaGBjQ0NBh/r62tRUJCgl8H2QDf64SLhcfFPrlduMmcVqvFggUL8MqSpaiuqjS+7gsrvyphdU3S8/SKfHIN9nNCZed5ciEdb17P5VomPc2VNoEv1uX++v2TezwZ6JHbeeZsfrxxTrHPR5acCbIpcrWCkpISLFmyBIsWLTK+Vl5ejtjYWLN0HTt2RFBQEMrLy21ua+HChZg7d67H8qpUhnl9qJkn7qZ7i7cuFHzMWr40Gg0e+L8HUV1VqR+ucuM/fWq4SnZ2NjZu3Kg/R1dPN77OIXfyk52djaysLJfrpNb+Xq7D1uU2REfubA2vNcy5U6WZjynTHkdWVpbLQ0e91c6Ra5n0NFeGdPliXS6375/BA2Vw91rZ2rbldJ45mx9PD6kF2Bcm9zgcZKuvr8dXX32FW265BQAwY8YMsyfA1Go15s2bh5CQEIc/fM6cOa0GuLZv345BgwYZfz9+/DhuvPFGjBkzBvfdd59ZWmvDWQVBsDvMdcaMGZg2bZrxd8OTbESmlDyXhreDg3K7cJO+DIwePRqqwBCEpg1B9GjxO6xy4MkGKYnL3carvb+XazDLG50CX+KpOXekINcy6WmuBpd8rS6X0/ev5BvG/siTgR65nWfO5Ic39UnuHB4u+uabb+KTTz7Bxx9/DABo3749+vbti9BQ/ZCHP/74A08++SSmTp3q8IdXVVWhqqrKbprk5GRj4O748ePIzMzEFVdcgZUrVyIgoLnR5epwUUuck40sKXkIpCeH2rSGd0rlwVB+Ky6EouH4HxyuQj5PznW23IboyJkvDa+Vc5n0JA6T1JPL9y9lm5DIFnf6C5w7jbzJI3OyXX311Zg6dSpuu+02APog2y+//ILU1FQAwOrVq/Hqq69i27ZtbmbfumPHjiEzMxMDBw7E6tWrW5x8hoUPjh49arwTtG7dOtx1111c+IDcotRGolwadf7M0YaDJwOShvIbceU/UPP9ez7RYSXX+UvwW87BLHYKHKPUa68tci6TnsJ2SDOpv38lfBf+cn2iZmI8WclyQ97iTJwowO67Jv7880/06NHD+HtISIjZk2RDhgzBnj17XMhu644fP46MjAwkJCQgLy8PlZWVKC8vN5tr7frrr0efPn1wxx13YOfOnfjqq6/wxBNP4P7772ewjNwit7k0HGUYahM+dKztoTaHDqKoqEiiHPo2jUaDlNQ0ZGZmIjc3F5mZmUhJTYNGo3EpnasM5TIovjsA/XAVa3x1uBI183RZkxPDsPWoxhMoXz0dR/LHonz1dEQ1VUgezMjOzkbpgRIUFBRgzZo1KCgoQGnJfp8LsLjLMLy2tng9BEFn9p4Sh9fKuUx6imFIV33JdlRp5qPh2F7oGurQcGwvqjTz9UO6Fi/yiw6x1N+/3NuE/nR9Ij1D4Lk6KBZxE/KQMHUD4ibkoTooFjk5OQ5/94YhtePHj0dGRoZf1Cckfw7PyVZTU4M2bZqTV1ZWmr2v0+nM5mgT0+bNm7F//37s378fXbt2NXvP8CCeWq3G//73Pzz88MO48sorERoaitzcXOTl5XkkT+Q/5DSXhjOUGhz0BY7O4eeNuf4M5TIgKAzqiFjUFK9HNOeD8jtKnlfSVXKbb8YUJ1RunS/OuSPnMukpWVlZmDNnDl5ZshTlfj5Pq5Tfv5zbhEq+PvEpKtd4emEbIqk5PFy0e/fueP755zF69Gir769fvx5PPfUU9u/fL2oGvY3DRcmSEh6xt8bXhtrIib1GlaPlZf+f+9Ctew+PlyvT/IRdci2qPvg3QrsNRoRJh7Vm23qcP7BD1g1Zcp1S6zAiwHvDa9lZFp+17y4yqhMemzwJM2fO5PH1Irm2CZV8feIiEq6Ta3kksscjw0X/9re/4ZlnnsH58+dbvFdfX4+5c+fi5ptvdj63RDKn1OEOvjbURi5aG9Lg6JCM1157zStDN0zLb93vX6PD1XegsaLUbLhKYNVfWLduHRuFPkruw4SI7PHG8FoOVROfraFg5zumYs6cOfjwww+lzqJfkWubUKnXJ7GGOvorOT9ZSSQGh4NsTz31FE6ePImePXvixRdfxIcffoiPPvoIL7zwAnr27IlTp07hqaee8mReiSQj9VwarlBqcFDOHGlUOdpwKCkpcSidGA0M0/J7+tt3oa2tBEwas7U1p/H4E9PZKPRRbMyS0nlyzh12lsVnORQsuEsvBASFGoeChaYNxpRpj0Or1UqdVb8h1zahEq9PLN/uM52Kxxq5TsVD5CiHg2yxsbHYunUrevfujX/961+47bbbMGrUKMyYMQN9+vTBd999h9jYWE/mlRREq9WisLAQa9euRWFhoU9caJQ4WbUSg4Ny5WijKiYmBkDrDYe0tDSH0onVwDCU37lz5wIqFcLSBrFD6SfYmCWyjp1lz1Dq00m+To5tQiVen1i+3SfXJyuJxOLwwgcAkJKSgs8//xwnT540zr3WrVs3REZGeiRzpEy+PEeBEierlmKiXV+c28bQqIqbMMl2o+ripM4JiUmoLl5vdX4RQ8Ph4YcfxuKX8ltNJ3YDY9nydxBmZ6LZx6ZOQ0REBCoqKnzmu/N3hsast8sakdw5Wq8XFRUp7tovJSU+neQv5Lb4hhKvTyzf7vPFhW2ITDkVZDOIjIzEkCFDxM4L+QAlrxDky7wZHPTVIKujjaqKigqHGg5BQUFeb2C01qEM6twLR4t2YOTIkcbXfeG7kyNvBqLZmCWyjp1lz1Dqquz+Qk43jJV4fWL5FofhycopU6cZb1ID/rnyMPkeh1cX9RdcXdR1Sl4hiMRhGmQNHzrWGGStLV6P+pLtig6yOrsSkqMr4nlr5TwAWLt2LXJzc5EwdQMCgkLN3qvbtxWVHyxEaNogRAwbJ9p354tPNbpLqkC0N8uaKZYB1/nasZPb/nCFO89ge5CcJdX1yRUs3+IwXA+OHTuGyspKREdHo0uXLpJfF5RAbtdSf+FMnIhBNgsMsrmOjVX/5uuNDlf2z9GLoLculrbOUUGnxbG3HkBQpyREjxbvu/PVpxrdIXUg2tsNM5YB1/nasZPj/vj6dUtK5nVdy6eTlHzTjTxDSYEDlm/3yPF6oBQ8dtJhkM0NDLK5zt5TMgCga6jDkfyxWLNmDcaPHy/a5yrpouzL/CHIqvRGla0O5fnDv+LE2qdE/e6kDibJkb916FkGXOdrx07O+6P0el3OlPR0EpGzWL5dI+frgdzx2EmLQTY3MMjmOimCLIzmy4dUQVZvU3qjylqHsnb7B6j5fo1o352/BZMc5Q+BaANHykBk4wmsfGc5F9mw4GvnjxL2R+n1upzxRij5MpZv5yjheiBXPHbScyZOFGD3XSIneHs55o0bNyInJwfVQbGIm5CHhKkbEDchD9VBscjJyYFGoxHlc8gxSlyG3RXZ2dkoPVCCgoICrFmzBgUFBSgt2a+YjphhotmoxhMoXz0dR/LHoub7NQDE++64vL11/jTJemtlIKhzLxw9cgQjR45Ebm4uMjMzkZKaxnobvnf+KGF/lF6vy5lhkv3x48cjIyODnT/yKSzfzlHC9UCueOyUxaXVRYmscWWFIFfvAG3YsAHj/zEBIamDzKL5wV16oVP2LFRp5mPKtMeRlZXFC56XKHEZdlfJaWUuV2RnZyMrK8t47sXExGDiPffipEjfnT8Fk5zhTyuS2SsDdfu24vS3q1osssFVqPV87fxRyv4ovV4nIpI7pVwP5IjHTln4JBuJytpTMuWrpyOqqaJFx0mj0SAlNQ2ZmZlOPcmg0WgwduxYaJsaETFsHKP5MmEIstaXbEeVZj4aju2FrqEODcf2okozXx9kXbyIQU+ZML37OmLECLws4nfnL081OsvbT/tKyVYZEHRanCxYjtC0wYge/TSCu/RCQFCo8QZJaNpgTJn2OLRarRTZlgVfO398bX+IiKSk1WpRWFiItWvXorCwUFHXS14PXMdjpyyck80C52QTR2tPqLk6caNhPPqJhjZoPFHi8/N/KRHntlEusb47zhthm79Msu7NRTZ8ja+dP762P0REUlH6XNS8HriOx056XPjADQyyeZ47lYRh4vCOIx/EqS1vsKMmU5wIVrnE+u78JZjkCn8JRHtjkQ1f5Wvnj6/tD5E3sC1FpnxlZUleD1zHYyctBtncwCCb57mzwp5hBcuuj72PspWPISg6CdGM5hPJkr8Ek1zhL50na2UAAG+QOMDXzh9f2x8iT1L6E0skLl97ionXA9fx2EmHQTY3MMjmeYZAmStPMpgG6LRnT6Lyg4UI7TYYESbR/Jpt63H+wA5G84lkwF+CSWSbaRkwLrLhIx0FT/O188fX9of8k6fLsa88sUTicecBBbni9cB1PHbSYJDNDQyyeZ47FwrLOzn1fxbjZMFyaGtOGNOoA4Pw/pr3kJOT4+ldISIiJ3G4AxEplaefMPO1J5b8gTcCHu48oEBE4nAmTsTVRcnr3Flhz3IFS3W7joi/62V0HPkggmLTAABr31vNABsRkUw5swo1EZFcGG4QVAfFIm5CHhKmbkDchDxUB8UiJycHGo3G7c8oKirCkcOHED50rFmADQBUqgCEDx2DI4cOoqioyO3PIvdpNBqkpKYhMzMTubm5yMzMREpqmlNlwZHVQrmyJJGy8Ek2C3ySzTMs7/JUVVVh7NixLj/JwPHoRETKxuEORKQU3nrCjE8sKYcYw3odfTLS359wZHuB5IDDRd3AIJv4bF1Abh83Fu+vW+9yoIwVLnkayxgRERF5a04sX5x7yxeJEfRyNkjnr1MtcBEQkgsG2dzAIJu4WruArFu3DtHR0QxikOzwok5ERESA954w8/cnlpTC3WCoq9+zv43k4SIgJCfOxInaeClP5Ie0Wi2mTJ2G0LTBZheQ4C690Cl7Fqo08/H49CfZUCDZMb2ox02YZLyoVxevR05ODi/qREREfsR0TixrQRWx5sQyzD2ck5ODKs18q08s5W/cyHazxMrKygAAgdFJVt8P7JRkls6SYe69uAmTbM+9t3o6ioqKzIJ02dnZyMrK8otRFo70I6dMexxZWVmi7j9HsZAYuPABeQwnbyUlsryoB3fphYCgUONFPTRtMKZMe9zqxLRERETke9xZtMtZXBxG/txdiMCdIJ1arUZGRgbGjx+PjIwMnw0ASdGPFGMhCyKAQTbyIHfv8pD0HFnxyNcwOEwkL/5YDxGRvFiubt9wbC90DXVoOLYXVZr5+ifMFi8SLeCRnZ2N0gMlKCgowJo1a1BQUIDSkv0MsMmEu0FXrhbaOm/3I72xejD5Dw4XJY/x1qP15Bn+OieZrwaH+fg7uUOq8uOv9RARyY/hCbMpU6fhyOrpxtcTkpKR74EnzAxPLJH8uDus1xCkqy5eb3VONjGfjFQqb/YjpRqaSr6LT7KRx3jz0XoSlz/fzfHFu4t8/J3cIVX58ed6iIjkiU+YkYE7w3q9/WSkEnmzH8lRLCQ2ri5qgauListfl5tWMn9f2crX9p8rM5E7pCo/vnYeEhGR50nx1LU7n+lvq4U6y1v9SG+tHkzK5kyciEE2CwyyiY8XEGVxd1lyX+ArwWEGKsgdUpYf1kNEROQMpU4vwOk87PNGP5JtDnKEM3EizslGHudPy037Al+dk8wZ3p53xVNcXSKeCJC2/LAeIiIiR5neHI2bMMn41HV18Xrk5OTI+uYo596zzxv9SM6RR2JjkI28QsoLCO8QOYcLVuj5QnCYgQpyh5Tlh/UQERE5gpPW+z5P9yPdXciCyBIXPiCfxgnfnccFK5oZLurjx49HRkaG4i6uvriIA3mPlOWH9RDJnVarRWFhIdauXYvCwkJotVqps0TklzhpPYnBnYUsiCwxyEY+iyvTuYYrHvkOBirIHVKWH9ZDJGe8gUckH3xqn8TC1YNJLFz4wIJPLnxwYCVQugpQtQEC2lz8P7D590vnAu3T9GnLvwKOfWL+vun/SbcDbRP1aWv2AFU/XEyrvvgvoPn/qKFAaKw+bd1xoPYP6+lUaqBdKhDUQZ+26QxQXw4EqJvTI6D558D2gDpYn1anBYQmk3QqQKXihO8i4IIVvsFXFnEgaUhdflgPkdxwxWYieeGk9UTkDVxd1A0+GWT75Wlg93zb79+4A4gcqP959/PALzNspx35DRBztf7nfUuAnybbTpvxKdD5Jv3PJcuBH+6znfaqjUDiaP3PB98HttpZHnnof4DUO/U/H/0Y+Pbv5u+rAqATAtDQeAFP78vBRmEiAGBw2O94PXkhdEIAtFotms7WIjo2DqGhYfoAXZ9/AWn36Ldx+jdg20SLgKDJz6l3AykT9GnPHgB+eMB2fpPHA2n36n+uOw4U32U7bddRQI9H9D83VAPfWx4Hk9M1/kag9+P6ny+cA77Jsr3d2Ezgkpn6n3VaoOA622k7DQMuWwBAPxym5r9XoKmxDsFBQYiIiIBKpWpO23EAMHBx8++FtwIXzlz8xSSdSgWE9wEGL21+7bvbgYaq5vebEwPtUoAhbza/9MN9QN3RltuFCgiNB4Yub35px2PA2RKLnbr4N0EdgeHvNr+8awZQu9d6WnUYcOV7zS//Ng84vct6HlQBwFXrml/auwio/rFlWsN+DntXH5wGgD9fAyq/N3/f9O8Gvw4EttP/fGAlcKLAen4BYMBiIDhS//PB94HyzfofDx1CcfEPOHfuHAw1/uvbumLmsy/rO4NHPgCOf9oyn4Zt932qObh+/Avg2IdWPv/i/72mNgftT3wDHNkEm7o/BET01v9cuQ049L7ttGn3AB0v0/988ifgwLu206ZMAKIG638+/Ruwf5nttEljgegr9T/X/gn8+arttAmj9OcSAJwtBfa9bDtt/E1A5xv0P9cdB/a+YDtt3Eigyy36n89X2a+vo9Ob68rGGuC32bbTRl2hr38AQHteX96NLC79HQc016uCDvhpqvGt/SX78e23RThzRn9u/34U+OKASaDrp2mAcMF6Htr3AHo+2vz7zn8C2jrradsmN9dpgP7a1XgKOkFA2fHjOFd3Dm3D2iK+c2cEtO2iL5cGvz0LnD9hfbvBnYB+c5t/370QqDtiPW1gOND/+ebf9y4Czuy3nlYdAgx8qfn3fa/obz5ZpQKGvN78619vAKd22kgLYNDS5jqi5B2g+gfbaQcsBtq01f9cugqosDMkqv/zzXXEofVA+Rbbafs9C4TG6X8++qF5HQHArO4xqyM+16e3pffjQPtu+p9PFACHN9pO2+MRIKKP/ufKrcChtbbzkHav/TrCtH5NngBEDdL/fPp3/TG2JTEHiB4OANCe/gPL/nUFzqvDENp9qPk2BQGbPvkDe/ef19/AO3/USn1ikj7+eiBuhP7n+jIr9YlJ2phrgM436n9uqAb+WAybOg1rrk+azgB7LOoey2t4wij9z9rz+nPDVh46XKI/FoC+HfHbHOjrEQH6C4vu4v8CEN4bSLu7+W9/mqa/IWqW7uL/Eb311w2D7Q8DTWcvbldn/n/77sBlJvVj8T36doThcw3pIABtU8zPua13AHWHm9MCzXkP6wJctd4k7Z3AmX0Xk1ikD+4EZH5ukoe7gZM/m2/P8HObdsANxc1pf7gPqPyu5XYFQX9j+ZY/mtP++CBQ9kXztgAY2xsIAG7+vfmG8y8zgWP/M7+BjQD9NhEAXPMxEBShT7vvFeD4Zy3TGn4e9CoQ0kmf1tCOMEujgrFcXPJM8830Y5+Y5Bdo0TboMx0I66r/uXwLcMxOm6PHI/p2IABUfGfR5rCQdh+0bbshJTUNKSlt8Y9RPVqcl/V/FSNEV4f75n8DdVR//esnfwZKV9vebnKuRR2x3HbaxDHGOgK1+4C/XredtusoIDZD//PZUuCPl2yn7XyzSTviGLDnedtp464Dul7sD52vvHh+2hCboc8zoG9H2Or3CYJ+v1Lu0P9+oR74eYp5GTf9P2oI0O1if0inBX68r2Vaw++Rl5uf99vuAnRN1tN2uAS4ZJZJ2onAhbPW04b3BPqb1GPF9wKN1S3PNwBolwwMWtKc9of7TfoaFm2k0M7AUJPrxI5JwBmLvoah3AVFAsNXNb++80mTtoFFn0AdBlxl0vb9dTZwapf17arUQLpJm3rPv/UPu1hLCwBXvt/cjvjjZaDyW5Pvw2Ifh61q7mv8+SpQttl6OgC44p3mOmL/2ybXe4vjCwBXvNV83vsAri5K5pLG6hupwgVAd0H/v3BBX5npLgBhCc1pOw3VB5tM0+pM0oeYzL3TNgno/DeTbeoAQQtAp69cgyKb0wZ2ACL66t8XtM1pDf/ahDWnDVDrOzrW0gIXn1gzMB/CBAAQdAiADqFBQJv2kUCt/uXggCZ0alNzMT8AQgBoywFDPd1U07yNpjPAqZ9hU0yGSdqzwImvbKc1dPYBQFtvv1MT3rv5Z10jUP6l7bRhic0/C1r7eQiJNv+9RaDGRJt2xh/VajUiL/wGoBFoBFBpkdbsuwBQ9T3QeMr6di/Um/9e+R1Qf8x62g6Xmv9eUQSc+dN62nZpFtstst15DYlrmdYQ4LLUpn3L/JZvtp7W8jhUfg8c/a/1tAAwdGXzz1VbgUNrbKcdaNLxqtoGlNoJLl02H8DF8676R+DACgBAMoDkweZJ737hC6g7Xuy8ntwBlLxte7vdHmjuQJ/62X4DMun25iDb6V+AP5fYTtv55uYgW+0e4M9XbKeNvaa5A137p/20UYOaz7uzpfbTRvRpDrLVHbGftm1Sc5Ctvtx+kC0oqrlx3FBlP6061KRTXGM/raBrDrJp6+ynTb2nOcimawT25dtOm3S7SZBNMDsO3QB0u7I5aVXwcLwx6tvmp4D/elW/fWtirzUPspW8bbuOiBpqHmQrXQnUHUUAgC6G1+oA7Ie+jjANsh18z04dkQr0m2tcBKff8bcQiYPW04bEmQfZjn7Q3Cm21Ka9eZDt2P/s1BEB5h3+ss3264iBJuXwRAFw0E5n8LKFzUG2yu/sn8uXPN0cZKsqtp+29+PNQbbqHcD+t2ynNasjdgL737CdNjm3Och2+jfgr9dsp+3y9+YgW80e4M+lttPGZjheR0QOau5Anz0A7LPT0Q3vaexA//7D//B/V9dC37D4oEXS48NuweavPtGvuNs3GNj7ou3tBrZvDrKdr9R3mOwxBNkaTwG7n7Odrsek5vrkwln7Qfu0e5uDbLpG4PdnbadNur05yAbB/na73GoeZLNbR4ww72wfXAs0nbaeNmqoeZCtbLPj7YjqHx1vR9T81rKja2DZjjjzF3D6V+tpLdsR5w7rgzDWWLYjzp8Azh20nlb/B80/nj2ov97aZNJWPv07UPa57aQDFjX/XP2DsR1hVc/HmoNsVcX2z8/Uic2d7eof7Z9zXUc1B9lO/gTszbOdNnYk1OE9kf/SYny2dDQeiLFyoyPm4v91pYAhyFa7z34eIgeY1BEl9q+f4b2ag2x1R+xfl9smNQfZ6svtt5FCYszbEfaOb5t2zUG2phr79aqqTXOQTVtnv00nNDUH2XSN9q8DF+qag2wQ9DeGbWmqMT/vD601CbJZaKgyD7Id/dB2HdFpmPnvZV/YqSP6mf9e8Y3+fLamRV/jO9t1RKjFPLVVW233NQItAjVV22z3/Vr0uYr1bRRbTINdJ7cDR+xMZyCYHPtTvwDHPrKdVne++eeaPcDx/9lOe8HGTVU/wCCbP+hwacvGhi2xGc2Vf2u6/r25Qm9N4ujmjmGracc0V/6WLOYFQue/ATmnzYNx0GHb1u8wbtxYBN2SCFxsD/1c1wvX71uKAJUAbWUJTn2WjzffeA0DL+8PQKd/isIgorf+STyzYKDJz6bHs20iMNxOkCTCJHAWEgMMf8922vY9mn8O7AAMs9Oxam9S4atD7eehrUlATqXS392wJbSz+e/D18BqMBMAgmPMf7/iHZOGtMWdD9OgK6B/UkNbb57OcEEw3HE1uPwFoKnW+h0Yk6AgAODS2UDDSYvPv/izOtQ8be8ngZQT1vNguPtj0HNSc2fENB1g8QQagLT79MEFa3kAzC+UyRP0HT5rebDMc8Jo/Z18W3eiAk2OW5eb9eXNWjoA6rDY5l/iRuqfyrH2+YB5gyH6KuDSORbpTNK3NQnaRw4G+s6ETe1Sm3/ucJn9tO17Nv8c0dd+WtPzs313+2k7Xt78c9tk88CNpaghzT+HdbGfNuaq5p9DYuynjb66+eegDq3kYWjzz23aAn3sPHlseEIZAAKC7Kc1bWyqVHbz0Cm8F2A6zL7Pv5pvglgy/Y4BoPcTLQPuBqZlB9B34JpqracNiTX/vccj+id8rAnqaDbs9IFrgfgOQEREBG684Qb07m1SRwda1Cep9+jPD2sCgsx/T7mjOWDbgkUdkTzevOy1SG5yfBNG6wM9tpjWEV2yzG/AWDKtWzvfBARH2U4b3Kn557gRLffXlFkdka6fhsLIoj4xvR5FXQFcMtt6OsD8Ohd5uT5IaNysRXqzOqKP/fOowyUmf9dNX4Zt6djf+OPhahU+/xgIH5QFlbpNizz/jn4APtHP/TR4uL6828pv1BXNPwd3AnpOhU2m5SowQn9u2NpujEl9og7TB92aE5unNe2QqgKB7g/bzoNpfaIKALo/cvHaF2D+vypA/+S6qT4z9HWEWfqLaU3bXoD+CUpdQ8vtQtUc9DXo/299kEAVoH/f+KSXSv/kuqmB+ReffjF5Estw7bZsRwzIOZMoowAANY1JREFU19c9Zk9YGZ5yDzZPe/niix1+020afrboZl2eZ5724hQnzZ9hsW99/mW+XeMTgDr9NC4Gff8FpN5l0k7VNacTdOb7l3q3vjwZ02ib0wk6fdvToMstF+tanXkaA9N2XWyGyT5YaRuY1tmdhpmcc1aefgnr0vxz5ADz88hSu2QA+rm0Ouqex+tfPofa2ubrRnh4OK699lr07NFD3x4wiOgN9Pmn7e1G9G3+uX13+9dPt9oRs2ynjTZtR8Sa138t0prMjRrUUf+Uoc08mNQ9bdo1t+msMd03dTDQb97FXyzOIaiab4gA+vOw/79tp7UMWl2ed7FsWUlr+STU5S9c7GtYSWvZNjDUEYb3TdNa1hH9n9c/ZNFiu7DS13gWaDxp8oKNdjugL+vnK6ynVVn2NaZY9IFN62yLOqLb/wHxN9hIC/N2ROrd5uXJcnumeU65w+QBEYsn7wDz45Z0e8v2o+nfWX4ffoTDRS345HBRP8Q52YiICOAcWiQezv1EJG+GJ5bLysoQHx+P9PR0tvOJSBSck80NDLL5Dqkn7PZVnmrAsGFERGLjDRcSE8sTERGRf3ImThRg910iBcvOzsbGjRsR1XgC5aun40j+WJSvno6opgoG2Fyk0WiQkpqGzMxM5ObmIjMzEympadBo7Izzl3C7JB6tVovCwkKsXbsWhYWF0GptDA8kkpGioiIcOXwI4UPHmgVEAEClCkD40DE4cuggiorsLBZAiidW/aVWq5H/0mLUl2xHlWY+Go7tha6hDg3H9qJKMx/1JduRv3gRA2xERER+jHOykU/Lzs5GVlaWS09I8ckqc6ZPBsZNmGQcclVdvB45OTkuBy49tV0Sj+l8VgYJiUnIf2kxvxuStbKyMgBAYHSS1fcDOyWZpSPfI3b9ZbiBN2XqNBxZPb15m0nJyOf1ioiIyO9xuKgFDhf1L7YCaQwqmPPUEBkOvZE/zmdFSsY5tPybJ+sv3ogjIiLyH5yTzQ0MsvkPW4G028eNRV5eHoMKJjzVUWUHWN4YBCWlYxn2X/zuiYiISCyck42oFYa729VBsYibkIeEqRsQNyEPVYExeHHRYoSmDUan7FkI7tILAUGhCO7SC52yZyE0bTCmTHvc7+aj8tSQKw7lkjfOZ0VKxzm0/BfrLyIiIpICg2zkd7RaLaZMnWY1kNZ+8ChAp2Wj3EJ8fDwAoKnykNX3m6oOmaWTerskDgZByRdwERz/xPqLiIiIpMAgG/kde3e3dedOA2Cj3FJ6ejoSEpNQW7wegqAze08QdKgt3oCEpGSkp6fLYrskDgZByVdkZ2ej9EAJCgoKsGbNGhQUFKC0ZD8DbD6M9RcRERFJgUE28jv27m6r23UEwEa5JU8NueJQLnljEJR8iVqtRkZGBsaPH4+MjAzWKz6O9RcRERFJgUE28jv27m4Hd+0LdbtI1Gxbx0a5BU8NueJQLvliEJSIlIr1FxEREUmBq4ta4Oqivq+1FcfK352GxvL9COs2BOFDxyCwUxKaqg6htniD364uakqr1aKoqAhlZWWIj49Henq6KJ0UT22X3Gd1Jd6kZOQvXuTX5wIRyR/rLyIiInKXM3EiBtksMMjmHwyri4amDbYaSHviiSfw/rr1bJQTXeTLQVBf3jci4jlORERE7mGQzQ0MsvmP1u5us1FO5Pus1gOJSch/aTED6kRERERExCCbOxhk8y8MpBH5L/MnWsciMDoJTZWHUFu8nkPDiYiIiIgIAINsbmGQjYjI97U2N2OVZj6imipQWrKfgXciIiIiIj/mTJyIq4sSEZHfKSoqwpHDhxA+dKxZgA0AVKoAhA8dgyOHDqKoqEiiHBIRERERkdIwyEZERH6nrKwMABAYnWT1/cBOSWbpiIiIiIiIWtNG6gwQERF5W3x8PACgqfIQgrv0avF+U9Uhs3RERERERGSOc5y3xCfZiIjI76SnpyMhMQm1xeshCDqz9wRBh9riDUhISkZ6erpEOSQiIiIiki+NRoOU1DRkZmYiNzcXmZmZSElNg0ajkTprkmKQjYiI/I5arUb+S4tRX7IdVZr5aDi2F7qGOjQc24sqzXzUl2xH/uJFfn8njpRBq9WisLAQa9euRWFhIbRardRZIiIiIh+m0WiQk5OD6qBYxE3IQ8LUDYibkIfqoFjk5OT4daCNq4ta4OqiRET+Q6PRYMrUaThy+JDxtYSkZOQvXoTs7GwJc0bkGKtlODEJ+S8tZhkmIiIi0Wm1WqSkpqE6KBadsmeZLSImCDpUaeYjqqkCpSX7feaGtTNxIgbZLDDIRkTkXziXBCmV4S5yaNpghA8di8DoJDRVHkJt8XrUl2zHxo0bGWgjIiIiURUWFiIzMxNxE/Kszm3ccGwvyldPR0FBATIyMryfQQ9wJk7EhQ+IiMivqdVqn2kAkP/QarWYMnUaQtMGm91FDu7SC52yZ6FKMx9Tpj2OrKwsBo2JiIhINGVlZQCAwOgkq+8HdkoyS+dvOCcbERERkcIUFRXhyOFDCB861myYBgCoVAEIHzoGRw4dRFFRkUQ5JCIiIl8UHx8PAGiqPGT1/aaqQ2bp/A2DbEREREQKw7vIREREJIX09HQkJCahtng9BEFn9p4g6FBbvAEJSclIT0+XKIfSYpCNiIiISGF4F5mIiIikoFarkf/SYtSXbEeVZj4aju2FrqEODcf2okozH/Ul25G/eJHfTlfBhQ8scOEDIiIikjt/XNmLiIiI5MPqCudJychfvMjnFl7i6qJuYJCNiIiIlMB8ddExCOyUhKaqQ6gt3sDVRYmIiMjjtFotioqKUFZWhvj4eKSnp/vkzT0G2dzAIBsREREphT/dRSYiIiKSgjNxIsXNydbQ0ID+/ftDpVJh165dZu8dPnwYt956K9q2bYtOnTph8uTJaGxslCajRERERB6WnZ2N0gMlKCgowJo1a1BQUIDSkv0MsBERERFJoI3UGXDWk08+ic6dO+OXX34xe12r1eLmm29GdHQ0vvvuO1RXV+Ouu+6CIAhYsmSJRLklIiIi8iy1Wo2MjAyps0FERETk9xT1JNtnn32GzZs3Iy8vr8V7mzdvxp49e7B69WpcfvnlGDlyJBYtWoS3334btbW1EuSWiIiIiIiIiIj8hWKCbCdOnMD999+PVatWISwsrMX727ZtwyWXXILOnTsbX7vhhhvQ0NCAn376yeZ2GxoaUFtba/aPiIiIiIiIiIjIGYoIsgmCgIkTJ+LBBx/EoEGDrKYpLy9HbGys2WsdO3ZEUFAQysvLbW574cKFiIiIMP5LSEgQNe9EREREREREROT7JJ2Tbc6cOZg7d67dNNu3b8fWrVtRW1uLGTNm2E2rUqlavCYIgtXXDWbMmIFp06YZf6+trWWgjYiIiIiIiLxCp9NxwT4iCQUGBkKtVouyLUmDbI8++ihuv/12u2mSk5Mxf/58FBcXIzg42Oy9QYMG4R//+Af+85//IC4uDj/88IPZ+6dOnUJTU1OLJ9xMBQcHt9guERERERERkac1NjaitLQUOp1O6qwQ+bUOHTogLi7O7kNajlAJgiCIlCePOXz4sNlcacePH8cNN9yAjRs34oorrkDXrl3x2Wef4ZZbbsHRo0cRHx8PAFi3bh3uuusuVFRUIDw83KHPqq2tRUREBGpqahz+GyIiIiIiIiJnCIKAw4cPo6mpCZ07d0ZAgCJmcyLyKYIgoK6uDhUVFejQoYMxnmTKmTiRpE+yOSoxMdHs93bt2gEA0tLS0LVrVwDA9ddfjz59+uCOO+7Aiy++iJMnT+KJJ57A/fffz2AZERERERERycqFCxdQV1eHzp07W13cj4i8IzQ0FABQUVGBmJgYt4aO+kyoXK1W43//+x9CQkJw5ZVXYuzYsRg1ahTy8vKkzhoRERERERGRGa1WCwAICgqSOCdEZAh0NzU1ubUdRTzJZik5ORnWRrkmJibik08+kSBHRL5Fq9WiqKgIZWVliI+PR3p6umgTQRIRERERUTN354AiIveJdR4qMshGRJ6j0WgwZeo0HDl8yPhaQmIS8l9ajOzsbAlzRkRERES28CYpEZH0fGa4KBG5T6PRICcnB9VBsYibkIeEqRsQNyEP1UGxyMnJgUajkTqLRERERGRBo9EgJTUNmZmZyM3NRWZmJlJS09h28xNarRaFhYVYu3YtCgsLjcNQlUqlUuGDDz6w+f7BgwehUqmwa9cuUT83OTkZ+fn5om6T/A+DbEQEQH9xnjJ1GkLTBqNT9iwEd+mFgKBQBHfphU7ZsxCaNhhTpj2u+Is2ERERkS/hTVL/JkWAdeLEiVCpVFCpVGjTpg0SExPx0EMP4dSpU6Jsv6ysDDfddJMo2yLyNgbZiLxMrneaioqKcOTwIYQPHQuVyrxqUKkCED50DI4cOoiioiKJckhEREREpniT1L9JGWC98cYbUVZWhoMHD2LZsmX4+OOP8fDDD4uy7bi4OAQHB4uyLSJvY5CNyIvk/Ch/WVkZACAwOsnq+4GdkszSEREREZG0eJPUf0kdYA0ODkZcXBy6du2K66+/HuPGjcPmzZuN769YsQK9e/dGSEgIevXqhddee834XmNjIx599FHEx8cjJCQEycnJWLhwofF9y+GiP/74Iy6//HKEhIRg0KBB2Llzp1leVq5ciQ4dOpi99sEHH5hNZF9SUoKsrCzExsaiXbt2GDx4MLZs2SLS0SBqxiAbkZfI/VH++Ph4AEBT5SGr7zdVHTJLR0RERETS4k1S/yWnAOuBAwfw+eefIzAwEADw9ttvY+bMmViwYAH27t2L5557Dk8//TT+85//AABeeeUVfPTRR1i/fj327duH1atXIzk52eq2z507h1tuuQU9e/bETz/9hDlz5uCJJ55wOo9nz57F3/72N2zZsgU7d+7EDTfcgFtvvRWHDx92eb+JrOHqokReYHmnyXAhNNxpqtLMx5RpjyMrK0uyVaDS09ORkJiE6uL1ZnkEAEHQobZ4AxKSkpGeni5J/oiIiIjInOlN0uAuvVq8z5ukvkvqAOsnn3yCdu3aQavV4vz58wCAxYsXAwDmzZuHRYsWITs7GwCQkpKCPXv24M0338Rdd92Fw4cPo3v37rjqqqugUqmQlGR9HwDgvffeg1arxTvvvIOwsDD07dsXR48exUMPPeRUfi+77DJcdtllxt/nz5+P//73v/joo4/w6KOPOrv7RDbxSTYiL5DTnSZb1Go18l9ajPqS7ajSzEfDsb3QNdSh4dheVGnmo75kO/IXL+JS8EREREQyYbhJWlu8HoKgM3uPN0l9m9SjUDIzM7Fr1y788MMPmDRpEm644QZMmjQJlZWVOHLkCO699160a9fO+G/+/PkoKSkBoF84YdeuXejZsycmT55sNszU0t69e3HZZZchLCzM+NqwYcOczu+5c+fw5JNPok+fPujQoQPatWuHP/74g0+ykegYZCPyAqnvNDkqOzsbGzduRFTjCZSvno4j+WNRvno6opoqsHHjRuPdKCIiIiKSHm+S+i+pA6xt27ZFt27d0K9fP7zyyitoaGjA3LlzodPp8/L2229j165dxn+///47iouLAQADBgxAaWkp5s2bh/r6eowdOxY5OTlWP0cQhFbzEhAQ0CJdU1OT2e/Tp0/Hpk2bsGDBAhQVFWHXrl249NJL0djY6MruE9nE4aJEXqCkR/mzs7ORlZWFoqIilJWVIT4+Hunp6WycEREREcmQ4SbplKnTcGT1dOPrCUnJyOdNUp9lCLDm5OSgSjMf4UPHILBTEpqqDqG2eIM+wLpxo9fa8LNnz8ZNN92Ehx56CF26dMGBAwfwj3/8w2b68PBwjBs3DuPGjUNOTg5uvPFGnDx5EpGRkWbp+vTpg1WrVqG+vh6hoaEAYAzWGURHR+PMmTM4d+4c2rZtCwDYtWuXWZqioiJMnDgRt912GwD9HG0HDx50c6+JWmKQjcgLlDbfmVqtRkZGhtTZIPILWq2WQW0iInILb5L6JzkFWDMyMtC3b18899xzmDNnDiZPnozw8HDcdNNNaGhowI4dO3Dq1ClMmzYNL730EuLj49G/f38EBARgw4YNiIuLa7FCKADk5uZi5syZuPfeezFr1iwcPHgQeXl5ZmmuuOIKhIWF4amnnsKkSZPw448/YuXKlWZpunXrBo1Gg1tvvRUqlQpPP/208ak7IjFxuCiRF/BRfiKyRqPRICU1DZmZmcjNzUVmZiZSUtMkX22YiIiUx3CTdPz48cjIyGC70k9kZ2ej9EAJCgoKsGbNGhQUFKC0ZL8kTzBOmzYNb7/9Nm644QYsW7YMK1euxKWXXoprrrkGK1euREpKCgCgXbt2+Pe//41BgwZh8ODBOHjwID799FMEBLQMT7Rr1w4ff/wx9uzZg8svvxwzZ87Ev//9b7M0kZGRWL16NT799FNceumlWLt2LebMmWOW5qWXXkLHjh0xfPhw3HrrrbjhhhswYMAAjx0L8l8qwZFBzn6ktrYWERERqKmpQXh4uNTZIR+j0Wj0d5oON09QmpCUjPzFi/goP5Gf0Wg0yMnJQWjaYIQPHYvA6CQ0VR5CbfF61Jds5zyIREREPu78+fMoLS1FSkoKQkJCpM4OkV+zdz46EydikM0Cg2zkCHeGd3FoGBFptVqkpKahOijW6hDyKs18RDVVoLRkP+sHIiIiH8UgG5F8iBVk45xsRE6y+jRaYhLyX1rs0FMnnO+MiIqKinDk8CHETZhkFmADAJUqAOFDx+DI6ukoKipifUFEREREpBCck43ICYbhXdVBsYibkIeEqRsQNyEP1UGxyMnJ4TxKROSQsrIyAEBgdJLV9wM7JZmlIyIiIiIi+WOQjchBWq0WU6ZOQ2jaYHTKnoXgLr0QEBSK4C690Cl7FkLTBmPKtMeh1WqlzioRyVx8fDwAoKnykNX3m6oOmaUjIiIiIiL5Y5CNyEGG4V3hQ8faHt516CCKioo88vlarRaFhYVYu3YtCgsLGcwjUrD09HQkJCahtng9BMF8+XhB0KG2eAMSkpKRnp4uUQ6JiIiIiMhZnJONyEFSDu9ydx44IpIXtVqN/JcWIycnB1Wa+QgfOgaBnZLQVHUItcUbUF+yHfkbN3LRA3IYF9UhIiIikh6DbOQ0f23Imw7vCu7Sq8X7nhreZZgHLjRtMOImTEJgdBKaKg+hung9cnJysHHjRgbaiBQoOzsbGzdu1AfQV083vp6QlIx8ntfkBN6IISIiIpIHlSAIgtSZkBNnlmb1R/7ckNdqtUhJTUN1UCw6Zc8yGzIqCDpUaeYjqqkCpSX7RQs6SvGZRORd/nrjgsRheiMmfOhY442Y2uL1qC/ZzhsxREQydv78eZSWliIlJQUhISFSZ4fIr9k7H52JEzHIZoFBNtvYkLc8Bi2Hd4l9DAoLC5GZmYm4CXlWn55rOLYX5auno6CgABkZGaJ9LhERyR9vxBARKRuDbETyIVaQjQsfkEO4sqaeYXhXVOMJlK+ejiP5Y1G+ejqimio8EmSUch44IiKSN6kX5CEiIqLWff/997j00ksRGBiIUaNGobCwECqVCqdPn7b5NytXrkSHDh28lke5c+SYyQWDbOQQNuSbZWdno/RACQoKCrBmzRoUFBSgtGS/R57iM50HzhpPzQNHRETyxxsxREQkhTNnzmDKlClISkpCaGgohg8fju3bt7dIt3fvXvz9739HREQE2rdvj6FDh+Lw4cN2t71p0yb06dMHwcHB6NOnD/773/+avf/ee+8hISEBkZGRmD59utl7Bw8eRI8ePVBbW+vQfmzatAkZGRmIiIhAu3bt0K9fPzz77LM4efKkQ3/vqGnTpqF///4oLS3FypUrMXz4cJSVlSEiIkLUz5HCxIkToVKpoFKp0KZNGyQmJuKhhx7CqVOnRP0cJR0zBtnIIWzIm1Or1cjIyMD48eORkZHhsWE46enpSEhMQm3xegiCzuw9QdChtngDEpKSkZ6e7pHPJyIi+eKNGCIiksJ9992HL7/8EqtWrcJvv/2G66+/HiNHjsSxY8eMaUpKSnDVVVehV69eKCwsxC+//IKnn37a7rDYbdu2Ydy4cbjjjjvwyy+/4I477sDYsWPxww8/AACqqqpw3333IS8vD1988QX+85//4H//+5/x7x966CE8//zzDk37NHPmTIwbNw6DBw/GZ599ht9//x2LFi3CL7/8glWrVrlxdFoqKSnBtddei65du6JDhw4ICgpCXFwcVCqVqJ8jlRtvvBFlZWU4ePAgli1bho8//hgPP/ywqJ+hpGPGIBs5hA15aajVauS/tBj1JdtRpZmPhmN7oWuoQ8OxvajSzEd9yXbkL17kM3PtaLVaFBYWYu3atSgsLPT54cdERO7gjRgiIh914Zztf9rzjqe9UO9YWifU19dj06ZNeOGFF3D11VejW7dumDNnDlJSUvD6668b082cORN/+9vf8MILL+Dyyy9Hamoqbr75ZsTExNjcdn5+Pq677jrMmDEDvXr1wowZMzBixAjk5+cDAA4cOICIiAhjcCwzMxN79uwBAKxZswZBQUEOjS768ccf8dxzz2HRokV48cUXMXz4cCQnJ+O6667Dpk2bcNdddxnTvv7660hLS0NQUBB69uzZIgCnUqmwbNky3HbbbQgLC0P37t3x0UcfAdA/WadSqVBdXY177rkHKpUKK1eutDr0ceXKlUhMTERYWBhuu+02VFdXt8j3xx9/jIEDByIkJASpqamYO3cuLly44FBeDHbv3o2bb74Z4eHhaN++PdLT01FSUmJ8f8WKFejduzdCQkLQq1cvvPbaa60ez+DgYMTFxaFr1664/vrrMW7cOGzevNksTWvb3bp1K/r374+QkBAMGjQIH3zwAVQqFXbt2gXA+nDRTZs2oW/fvggODkZycjIWLVpkts3k5GQ899xzuOeee9C+fXskJibirbfeanV/3CaQmZqaGgGAUFNTI3VWZOXChQtCQmKSENZtiJD45EdC0j8/Mf5LfPIjIazbECEhKVm4cOGC1Fn1SZs2bRISEpMEAMZ/CUnJwqZNm6TOmmis7mNikk/tIxGR2DZt2iSoVCohrNsQIW7Ci0LClPVC3IQXhbBuQwSVSsU6lIhIxurr64U9e/YI9fX15m+8B9v/Cv5mnvb9MNtpv7zGPO3GTtbTOaG2tlYAIGzZssXs9aFDhwrXXKP/PK1WK7Rr10549tlnheuvv16Ijo4WhgwZIvz3v/+1u+2EhARh8eLFZq8tXrxYSExMFARBEE6ePCm0b99e+Pnnn4Xq6mohJSVF+Pzzz4Xq6mohLS1NOHz4sEP7MHnyZKFdu3ZCY2Oj3XQajUYIDAwUXn31VWHfvn3CokWLBLVaLXz99dfGNACErl27CmvWrBH++usv47arq6uFCxcuCGVlZUJ4eLiQn58vlJWVCXV1dUJBQYEAQDh16pQgCIJQXFwsqFQqYeHChcK+ffuEl19+WejQoYMQERFh/JzPP/9cCA8PF1auXCmUlJQImzdvFpKTk4U5c+Y4lBdBEISjR48KkZGRQnZ2trB9+3Zh3759wjvvvCP88ccfgiAIwltvvSXEx8cLmzZtEg4cOCBs2rRJiIyMFFauXGnzGN11111CVlaW8feSkhKhT58+QmxsrPG11rZbW1srREZGChMmTBB2794tfPrpp0KPHj0EAMLOnTsFQRBaHLMdO3YIAQEBwrPPPivs27dPWLFihRAaGiqsWLHC+LlJSUlCZGSk8Oqrrwp//fWXsHDhQiEgIEDYu3ev1X2xeT4KzsWJGGSzwCCbbWzIS+vChQtCQUGBsGbNGqGgoMCnAprmZStPSJi6QYibkMeyRUTkAH+4EUNE5IuUGGQTBEEYNmyYcM011wjHjh0TLly4IKxatUpQqVRCjx49BEEQhLKyMgGAEBYWJixevFjYuXOnsHDhQkGlUgmFhYU2txsYGCi899575ofivfeEoKAg4+8ajUa45JJLhLS0NGH27NmCIAjC3XffLeTn5wvffPON0L9/f6Fv377Chg0bbH7OTTfdJPTr16/V/Rw+fLhw//33m702ZswY4W9/a/4OAAizZs0y/n727FlBpVIJn332mfG1iIgIs+CPZcBo/Pjxwo033mj2OePGjTMLsqWnpwvPPfecWZpVq1YJ8fHxDudlxowZQkpKis3gYkJCgrBmzRqz1+bNmycMGzbManpB0AfZ1Gq10LZtWyEkJMTYDjENlra23ddff12IiooyOw/efvttu0G23Nxc4brrrjPb5vTp04U+ffoYf09KShImTJhg/F2n0wkxMTHC66+/bnVfxAqytfH8s3LkKwwra06ZOg1HVjdPMpmQlIx8D6ysSeYM88D5GsuVaw0LaxhWrq3SzMeUaY8jKyvLZ4bFEhGJKTs7G1lZWSgqKkJZWRni4+ORnp7OOpOISKnGnrX9nsqibh9dYWdDFrNDZR10NUdmVq1ahXvuuQddunSBWq3GgAEDkJubi59//hkAoNPppzDIysrC1KlTAQD9+/fH1q1b8cYbb+Caa66xuW3LObcEQTB77bbbbsNtt91m/L2wsBC//fYbli5dim7dumHt2rWIi4vDkCFDcPXVV1sdnmq5TVv27t2LBx54wOy1K6+8Ei+//LLZa/369TP+3LZtW7Rv3x4VFfa+l5afY7pPADBs2DB8/vnnxt9/+uknbN++HQsWLDC+ptVqcf78edTV1SEsLKzVvOzatQvp6ekIDAxskYfKykocOXIE9957L+6//37j6xcuXGh1sYHMzEy8/vrrqKurw7Jly/Dnn39i0qRJDm9337596Nevn9l8fUOGDLH7mXv37kVWVpbZa1deeSXy8/Oh1WqNbSDT46FSqRAXF+fUd+MKBtnIKWzIk9gMK9fGTZhke+Xa1dNRVFTkk0FGIiIx+OqNGCIiv9SmrfRp7UhLS8M333yDc+fOoba2FvHx8Rg3bhxSUlIAAJ06dUKbNm3Qp08fs7/r3bs3vvvuO5vbjYuLQ3l5udlrFRUViI2NtZq+oaEBDz/8MFavXo39+/fjwoULxgBejx498MMPP+DWW29t8Xc9evTAd999h6amJqsBJ1OtBf0AtNiGSqUyBhodIQhCq2l0Oh3mzp1r9cEW0+CUvbyEhoba3T4AvP3227jiiivM3mutr9+2bVt069YNAPDKK68gMzMTc+fOxbx58xzarrVj2toxcfRv3P1uXMGFD8hp3lpZk/wDV64lIiIiIlKetm3bIj4+HqdOncIXX3xhfLIoKCgIgwcPxr59+8zS//nnn0hKst7mB/RPb3355Zdmr23evBnDhw+3mn7evHm46aabMGDAAGi1WrNFAJqammwuopabm4uzZ8/anNTfMLm+taDg1q1b0bt3b5v74Io+ffqguLjY7DXL3wcMGIB9+/ahW7duLf4FBDgW1unXrx+KiorQ1NTU4r3Y2Fh06dIFBw4caLF9Q/DUUbNnz0ZeXh6OHz/u0HZ79eqFX3/9FQ0NDcZt7Nixw+5n9OnTx+p306NHD8njE3ySjYgkZbpybXCXXi3e58q1RERERETy8cUXX0AQBPTs2RP79+/H9OnT0bNnT9x9993GNNOnT8e4ceNw9dVXIzMzE59//jk+/vhjFBYWGtPceeed6NKlCxYuXAgAeOyxx3D11Vfj3//+N7KysvDhhx9iy5YtVp9+2717N9atW2dcfbJXr14ICAjA8uXLERcXhz/++AODBw+2mv8rrrgCTz75JB5//HEcO3YMt912Gzp37oz9+/fjjTfewFVXXYXHHnsM06dPx9ixYzFgwACMGDECH3/8MTQaDbZs2SLewQQwefJkDB8+HC+88AJGjRqFzZs3mw0VBYBnnnkGt9xyCxISEjBmzBgEBATg119/xW+//Yb58+c79DmPPvoolixZgttvvx0zZsxAREQEiouLMWTIEPTs2RNz5szB5MmTER4ejptuugkNDQ3YsWMHTp06hWnTpjm8PxkZGejbty+ee+45LF26tNXt5ubmYubMmXjggQfwr3/9C4cPH0ZeXh6Alk8SGjz++OMYPHgw5s2bh3HjxmHbtm1YunSpQ6uhehqfZCMiSaWnpyMhMQm1xeshCOaP7gqCDrXFG5CQlIz09HSJckhERERERAY1NTV45JFH0KtXL9x555246qqrsHnzZrOhebfddhveeOMNvPDCC7j00kuxbNkybNq0CVdddZUxzeHDh81GqwwfPhzvv/8+VqxYgX79+mHlypVYt25di2GGgiDggQcewEsvvYS2bfVDYENDQ7Fy5Uo8++yzuPfee7F06VJ06dLF5j78+9//xpo1a/DDDz/ghhtuQN++fTFt2jT069cPd911FwBg1KhRePnll/Hiiy+ib9++ePPNN7FixQrRp2cYOnQoli1bhiVLlqB///7YvHkzZs2aZZbmhhtuwCeffIIvv/wSgwcPxtChQ7F48WK7TwZaioqKwtdff42zZ8/immuuwcCBA/H2228bv7f77rsPy5Ytw8qVK3HppZfimmuuwcqVK51+kg0Apk2bhrfffhtHjhxpdbvh4eH4+OOPsWvXLvTv3x8zZ87EM888A8B8KKypAQMGYP369Xj//fdxySWX4JlnnsGzzz6LiRMnOp1XsakERwYA+5Ha2lpERESgpqYG4eHhUmeHyC9oNBrk5OQgNG0wwoeOQWCnJDRVHUJt8QbUl2zHRi6sQUREREQ+5vz58ygtLUVKSorNYAKRP3rvvfdw9913o6amxu5ccmKydz46EyficFEikhxXriUiIiIiIvJP7777LlJTU9GlSxf88ssv+Oc//4mxY8d6LcAmJgbZiEgWuHItERERERGR/ykvL8czzzyD8vJyxMfHY8yYMViwYIHU2XIJh4ta4HBRIiIiIiIi8jQOFyWSD7GGi3LhAyIiIiIiIiIiIjcxyEZEREREREQkEQ4uI5KeWOchg2xEREREREREXmaYe7ixsVHinBBRXV0dACAwMNCt7XDhAyIiIiIiIiIva9OmDcLCwlBZWYnAwEAEBPAZGCJvEwQBdXV1qKioQIcOHdxeeI9BNiIiIiIiIiIvU6lUiI+PR2lpKQ4dOiR1doj8WocOHRAXF+f2dhhkIyIiIiIiIpJAUFAQunfvziGjRBIKDAx0+wk2AwbZiIiIiIiIiCQSEBCAkJAQqbNBRCLgoG8iIiIiIiIiIiI3MchGRERERERERETkJgbZiIiIiIiIiIiI3MQ52SwIggAAqK2tlTgnREREREREREQkJUN8yBAvsodBNgtnzpwBACQkJEicEyIiIiIiIiIikoMzZ84gIiLCbhqV4Egozo/odDocP34c7du3h0qlkjo7bqutrUVCQgKOHDmC8PBwqbNDEmJZIFMsD2SJZYKsYbkggOWAzLE8kCWWCbLGl8qFIAg4c+YMOnfujIAA+7Ou8Uk2CwEBAejatavU2RBdeHi44gs2iYNlgUyxPJAllgmyhuWCAJYDMsfyQJZYJsgaXykXrT3BZsCFD4iIiIiIiIiIiNzEIBsREREREREREZGbGGTzccHBwZg9ezaCg4OlzgpJjGWBTLE8kCWWCbKG5YIAlgMyx/JAllgmyBp/LRdc+ICIiIiIiIiIiMhNfJKNiIiIiIiIiIjITQyyERERERERERERuYlBNiIiIiIiIiIiIjcxyEZEREREREREROQmBtkksHDhQgwePBjt27dHTEwMRo0ahX379pmlEQQBc+bMQefOnREaGoqMjAzs3r3bLM1bb72FjIwMhIeHQ6VS4fTp02bvFxYWQqVSWf23fft2u3n87bffcM011yA0NBRdunTBs88+C9M1MsrKypCbm4uePXsiICAAU6ZMceuY+CtfKAumvv/+e7Rp0wb9+/d3+liQb5SHiRMnWt1u37593Ts4fkruZeL8+fOYOHEiLr30UrRp0wajRo2ymu6bb77BwIEDERISgtTUVLzxxhsuHQ/S81a5AIA///wTWVlZ6NSpE8LDw3HllVeioKCg1TyyHeF5vlAOTLEN4R5fKA9sQ4hL7mWCbQhpeLNc/Pzzz7juuuvQoUMHREVF4YEHHsDZs2dbzaPS2xAMskngm2++wSOPPILi4mJ8+eWXuHDhAq6//nqcO3fOmOaFF17A4sWLsXTpUmzfvh1xcXG47rrrcObMGWOauro63HjjjXjqqaesfs7w4cNRVlZm9u++++5DcnIyBg0aZDN/tbW1uO6669C5c2ds374dS5YsQV5eHhYvXmxM09DQgOjoaMycOROXXXaZCEfFP/lCWTCoqanBnXfeiREjRrhxRPybL5SHl19+2Wy7R44cQWRkJMaMGSPCEfI/ci8TWq0WoaGhmDx5MkaOHGk1TWlpKf72t78hPT0dO3fuxFNPPYXJkydj06ZNLh4V8la5AICbb74ZFy5cwNdff42ffvoJ/fv3xy233ILy8nKbf8N2hHf4QjkwYBvCfb5QHtiGEJfcywTbENLwVrk4fvw4Ro4ciW7duuGHH37A559/jt27d2PixIl28+cTbQiBJFdRUSEAEL755htBEARBp9MJcXFxwvPPP29Mc/78eSEiIkJ44403Wvx9QUGBAEA4deqU3c9pbGwUYmJihGeffdZuutdee02IiIgQzp8/b3xt4cKFQufOnQWdTtci/TXXXCM89thjdrdJjlFyWRg3bpwwa9YsYfbs2cJll13Wyp6SI5RcHgz++9//CiqVSjh48KDdbZNj5FYmTN11111CVlZWi9effPJJoVevXmav/d///Z8wdOhQh7dN9nmqXFRWVgoAhG+//db4Wm1trQBA2LJli838sB0hDSWXA7YhxKfk8mDANoS45FYmTLENIR1PlYs333xTiImJEbRarfG1nTt3CgCEv/76y2Z+fKENwSfZZKCmpgYAEBkZCUAfsS8vL8f1119vTBMcHIxrrrkGW7dudflzPvroI1RVVbUaPd62bRuuueYaBAcHG1+74YYbcPz4cRw8eNDlz6fWKbUsrFixAiUlJZg9e7bLeaKWlFoeTC1fvhwjR45EUlKSy/mjZnIrE47Ytm2bWf4AfbnZsWMHmpqa3N4+ea5cREVFoXfv3nj33Xdx7tw5XLhwAW+++SZiY2MxcOBAm3/HdoQ0lFoO2IbwDKWWB1NsQ4hLbmXCEWxDeJ6nykVDQwOCgoIQENAccgoNDQUAfPfddzb/zhfaEAyySUwQBEybNg1XXXUVLrnkEgAwPlYbGxtrljY2NtbuI7etWb58OW644QYkJCTYTVdeXm71s03zRuJTaln466+/8K9//Qvvvfce2rRp43KeyJxSy4OpsrIyfPbZZ7jvvvtczhs1k2OZcIStcnPhwgVUVVW5vX1/58lyoVKp8OWXX2Lnzp1o3749QkJC8NJLL+Hzzz9Hhw4dbP4d2xHep9RywDaEZyi1PJhiG0JcciwTjmAbwrM8WS6uvfZalJeX48UXX0RjYyNOnTplHFpaVlZm8+98oQ3BIJvEHn30Ufz6669Yu3Zti/dUKpXZ74IgtHjNUUePHsUXX3yBe++91+z1vn37ol27dmjXrh1uuukmu59t7XUSjxLLglarRW5uLubOnYsePXq4lB+yTonlwdLKlSvRoUMHmxPZknPkWiYcwWuK53iyXAiCgIcffhgxMTEoKirCjz/+iKysLNxyyy3GBjLbEfKgxHLANoTnKLE8WGIbQlxyLROO4PXEczxZLvr27Yv//Oc/WLRoEcLCwhAXF4fU1FTExsZCrVYb0/hiG4K3jCQ0adIkfPTRR/j222/RtWtX4+txcXEA9JHa+Ph44+sVFRUtorqOWrFiBaKiovD3v//d7PVPP/3U+Kit4fHNuLi4FlHiiooKAC0j2iQOpZaFM2fOYMeOHdi5cyceffRRAIBOp4MgCGjTpg02b96Ma6+91qV8+jOllgdTgiDgnXfewR133IGgoCCX8kbN5FomHGGr3LRp0wZRUVEu5ZH0PF0uvv76a3zyySc4deoUwsPDAQCvvfYavvzyS/znP//Bv/71L7YjZECp5YBtCM9QankwxTaEuORaJhzBNoTneKNtmZubi9zcXJw4cQJt27aFSqXC4sWLkZKSAsB3YxF8kk0CgiDg0UcfhUajwddff20sZAYpKSmIi4vDl19+aXytsbER33zzDYYPH+7S561YsQJ33nknAgMDzd5LSkpCt27d0K1bN3Tp0gUAMGzYMHz77bdobGw0ptu8eTM6d+6M5ORkpz+fbFN6WQgPD8dvv/2GXbt2Gf89+OCD6NmzJ3bt2oUrrrjC6Tz6M6WXB1PffPMN9u/f3+JpKHKO3MuEI4YNG2aWP0BfbgYNGtTiM8gx3ioXdXV1AGA2n4rhd51OB4DtCCkpvRywDSEupZcHU2xDiEPuZcIRbEOIz9ttS0AfHGvXrh3WrVuHkJAQXHfddQB8uA0h/loK1JqHHnpIiIiIEAoLC4WysjLjv7q6OmOa559/XoiIiBA0Go3w22+/CePHjxfi4+OF2tpaY5qysjJh586dwttvv21c0WXnzp1CdXW12edt2bJFACDs2bPHofydPn1aiI2NFcaPHy/89ttvgkajEcLDw4W8vDyzdDt37hR27twpDBw4UMjNzRV27twp7N69240j4398pSyY4spgrvOl8jBhwgThiiuucPFIkIHcy4QgCMLu3buFnTt3CrfeequQkZFhvDYYHDhwQAgLCxOmTp0q7NmzR1i+fLkQGBgobNy40fUD4+e8VS4qKyuFqKgoITs7W9i1a5ewb98+4YknnhACAwOFXbt22cwf2xHe4SvlwBTbEK7zpfLANoQ45F4mBIFtCCl4s225ZMkS4aeffhL27dsnLF26VAgNDRVefvllu/nzhTYEg2wSAGD134oVK4xpdDqdMHv2bCEuLk4IDg4Wrr76auG3334z287s2bNb3Y4gCML48eOF4cOHO5XHX3/9VUhPTxeCg4OFuLg4Yc6cOS2WzLX22UlJSU59jr/zlbJgmRc2kF3jK+Xh9OnTQmhoqPDWW285tW1qSQllIikpyeq2TRUWFgqXX365EBQUJCQnJwuvv/66U59B5rxZLrZv3y5cf/31QmRkpNC+fXth6NChwqefftpqHtmO8DxfKQeWeWEbwjW+Uh7YhhCPEsoE2xDe581ycccddwiRkZFCUFCQ0K9fP+Hdd991KI9Kb0OoBOHiLHJERERERERERETkEs7JRkRERERERERE5CYG2YiIiIiIiIiIiNzEIBsREREREREREZGbGGQjIiIiIiIiIiJyE4NsREREREREREREbmKQjYiIiIiIiIiIyE0MshEREREREREREbmJQTYiIiIiIiIiIiI3MchGRERE5KfmzJmD/v37S50NIiIiIp+gEgRBkDoTRERERCQulUpl9/277roLS5cuRUNDA6KioryUKyIiIiLfxSAbERERkQ8qLy83/rxu3To888wz2Ldvn/G10NBQRERESJE1IiIiIp/E4aJEREREPiguLs74LyIiAiqVqsVrlsNFJ06ciFGjRuG5555DbGwsOnTogLlz5+LChQuYPn06IiMj0bVrV7zzzjtmn3Xs2DGMGzcOHTt2RFRUFLKysnDw4EHv7jARERGRxBhkIyIiIiKjr7/+GsePH8e3336LxYsXY86cObjlllvQsWNH/PDDD3jwwQfx4IMP4siRIwCAuro6ZGZmol27dvj222/x3XffoV27drjxxhvR2Ngo8d4QEREReQ+DbERERERkFBkZiVdeeQU9e/bEPffcg549e6Kurg5PPfUUunfvjhkzZiAoKAjff/89AOD9999HQEAAli1bhksvvRS9e/fGihUrcPjwYRQWFkq7M0RERERe1EbqDBARERGRfPTt2xcBAc33YWNjY3HJJZcYf1er1YiKikJFRQUA4KeffsL+/fvRvn17s+2cP38eJSUl3sk0ERERkQwwyEZERERERoGBgWa/q1Qqq6/pdDoAgE6nw8CBA/Hee++12FZ0dLTnMkpEREQkMwyyEREREZHLBgwYgHXr1iEmJgbh4eFSZ4eIiIhIMpyTjYiIiIhc9o9//AOdOnVCVlYWioqKUFpaim+++QaPPfYYjh49KnX2iIiIiLyGQTYiIiIicllYWBi+/fZbJCYmIjs7G71798Y999yD+vp6PtlGREREfkUlCIIgdSaIiIiIiIiIiIiUjE+yERERERERERERuYlBNiIiIiIiIiIiIjcxyEZEREREREREROQmBtmIiIiIiIiIiIjcxCAbERERERERERGRmxhkIyIiIiIiIiIichODbERERERERERERG5ikI2IiIiIiIiIiMhNDLIRERERERERERG5iUE2IiIiIiIiIiIiNzHIRkRERERERERE5Kb/B2Nj4dQDXYfVAAAAAElFTkSuQmCC"/>
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174+OOP8fTpU8ydOxf37t174fc/fvx4XL9+Ha1atUKJEiVw//59/PDDDybXk+WXzZs3Y86cOXj11VdRunRpiKKI9evX4/79+2jTpo1xXrVq1bB37178+eefKFasGAoVKoQKFSpgwoQJ2Lx5M1q0aIHx48fD29sbq1atwpYtWzB9+nR4eXkByLo8YPHixWjfvj0mTZoEf39//PLLL7h48SIAy5bSadiwIYoUKYIhQ4ZgwoQJ0Gq1WLVqFU6fPp0/fzhEtsq693MQUV44deqU+M4774ghISGis7Oz6OLiIpYtW1bs06eP5I7C/94t+7yGDRuKAEzulr1w4YL44YcfimFhYaKfn5+o0WjEIkWKiM2aNRNXrFhh8v61a9eKvXv3FsuVKyd6eHiIWq1WDAoKEt9++23xwoULL/w+sst269Yt0cPDQ2zRooWxtm/fPrFjx46it7e3qNVqxeLFi4sdO3YUf/vtN1EURfHp06fikCFDxOrVq4uenp6iq6urWKFCBXHChAni48ePjduRu+P1/v37Yv/+/cXChQuLbm5uYps2bcSLFy9K7pYVRVGMjIwUa9asKbq6uoqlS5cWf/rpJ9m7Zf/880+xRo0aoouLi1i8eHHxo48+Mt6Z+9+7QZ/Ps3nzZrF9+/Zi8eLFRScnJ7Fo0aJihw4dxAMHDrzwz1Tu7udnjh8//sK7ZS9evCj26tVLLFOmjOjq6ip6eXmJdevWFZcuXWqyrVOnTomNGjUS3dzcRABis2bNjGNnz54VO3fuLHp5eYlOTk5ijRo1TPb5zLlz58TWrVuLLi4uore3tzhgwABx2bJlkjtdmzVrJlapUkX2ezp8+LDYoEED0c3NTfTz8xMHDhwoxsTESL7Pvn37iu7u7pL3m9t2dn+ORLZGEEVRtFJfSURElK3Bgwdj9erVSElJMbtQNxGZ4mlZIiKyCZMmTUJgYCBKly6NR48eYfPmzVi4cCE+//xzNnZEOcDmjoiIbIJWq8W3336L69evQ6fToVy5cpg5c2aur9UkKqh4WpaIiIjIgXApFCIiIiIHwuaOiIiIyIGwuSMiIiJyILyhIpcMBgNu3ryJQoUKyT4Gh4iIiCgviaKIhw8fIjAwMNuFvdnc5dLNmzdRsmRJa8cgIiKiAiYhIQElSpQwO87mLpcKFSoEIOsP+EWPdiIiIiJ6WampqShZsqSxBzGHzV0uPTsV6+npyeaOiIiIFPOiy8F4QwURERGRA2FzR0RERORA2NwRERERORA2d0REREQOhM0dERERkQNhc0dERETkQNjcERERETkQNndEREREDoTNHREREZEDYXNHRERE5EDY3BERERE5EDZ3RERERA6EzR0RERGRA2FzR0RERORANNYOQERU0MTHxyM5OdnaMV6ar68vgoKCrB2DiJ7D5o6ISEHx8fGoULESnqY9sXaUl+bi6oZLF2PZ4BHZGDZ3REQKSk5OxtO0J/Dp9CG0PiWtHSfXMlMSkLL5OyQnJ7O5I7IxbO6IiKxA61MSzgFlrR2DiBwQb6ggIiIiciBs7oiIiIgcCJs7IiIiIgfC5o6IiIjIgbC5IyIiInIgbO6IiIiIHAibOyIiIiIHwuaOiIiIyIGwuSMiIiJyIGzuiIiIiBwImzsiIiIiB8LmjoiIiMiBsLkjIiIiciBs7oiIiIgcCJs7IiIiIgfC5o6IiIjIgbC5IyIiInIgbO6IiIiIHAibOyIiIiIHwuaOiIiIyIGwuSMiIiJyIGzuiIiIiBwImzsiIiIiB8LmjoiIiMiBsLkjIiIiciAaawcgIqLcU0MPXzxAUeE+fIRUpInOiBMDcBuFAQjWjkdEVsDmjojIboioIlxFZ/URNFCdR6CQDB88hEoQJTMfiS64KgYgTgzABUMw/jTUx3WxqBUyE5HS2NwREdk0ERWEBHRSH0EnVRRCVLcsepeH8BRVhauoiqxm8BOswUF9FazVt8B2QxjS4ZTPuYnIWtjcERHZqJrCP/hMuxJ1VH/nyfYaq8+jsfo87oke2KBvjAW6jkiET55sm4hsB5s7IiIb44d7+ES7Ft3V+/Nl+0WER+iv2Yoe6j34TvcGlunDoYc6X/ZFRMrj3bJERDbCCZl4T70Je5w/zLfG7r/chXSM167ARqcvUEWIy/f9EZEyeOSOiMgGVBDiMUf7A8qoEi2a/0R0xiFDFdwQfXFHLIzbKIxk0QveeIhgVRJChESUFpJQTrgOraDPdlvVVFexyelzLNG3w0zd63gCl7z4lojIStjcERFZWVvVMczUzoW7kJ7tvKeiFnsMNbFZ3wC7DTWRZq4JM/zv//rgAbqqD6Kneg/Kqm6a3bZaEDFQ8xeaq07jncyPkCD65+ZbISIbwOaOiMhKBBgwQrMeIzXrs533WHTGbF0XLNeH4xHccrSPFHhhob4jFuo7IFT4G300O9BFfdjs/LKqm9jgNAGDM0YjRiyfo30RkW3gNXdERFbgLqRjnjbihY3dOn1jtEz/DnP0r+a4sTMl4IRYASMyh6Fnxue4YihmdqavkIrVTlPQUXXkJfZHRNbC5o6ISGGBhQRsLrYAbdXRZuecMYTgtfSJ+DBzKG7BO0/3f8RQGe0zvsH3md2QLsqfwHEWMjHbaRbeU28CIF0kmYhsF5s7IiIFaZ7exa4+bqjodNvsnF90LdEt48t8PS2aAS1+0HdD+4xvcNYQbHbeJ9o1mKJZDOG/F/IRkU1jc0dEpJTHKSgXNQYVfeXXlMsU1fgssz8+1Q1EpkKXRP8rBuKNjPHYoQ81O+dNzS58rlkFHsEjsg9Wb+7mzJmDkJAQuLi4IDQ0FAcOHMh2/r59+xAaGgoXFxeULl0a8+bNMxlfsGABmjRpgiJFiqBIkSJo3bo1jh07ZjJn4sSJEATB5BUQEJDn3xsRkVHafWBlV7g+lF9PLkUshDczPsUqfWtlcwFIgwvezRyFRbr2ZucM0PyFYeqNyoUiolyzanO3du1ajBw5Ep999hlOnjyJJk2aoH379oiPj5edHxcXhw4dOqBJkyY4efIkPv30UwwfPhzr1q0zztm7dy969eqFPXv2ICoqCkFBQQgPD8eNGzdMtlWlShUkJiYaX2fPns3X75WICrD0h8Cq7kDiadnhS4YSeCX9KxwTKykc7H8MUGGy7m2Mz+wLvSjIzhmj/Q1vqXconIyIckoQRdFqx9nr1auH2rVrY+7cucZapUqV8Oqrr2Lq1KmS+Z988gk2bdqE2NhYY23IkCE4ffo0oqKiZPeh1+tRpEgR/PTTT+jTpw+ArCN3GzduxKlTp3KdPTU1FV5eXnjw4AE8PT1zvR0icnAZT4BVrwPXDsoOXzEUQ4+M8UiGl8LBzGupisFcbQScBZ1kzCAKGJH5Pn6/WRRJy0bixIkTqF27thVSEhU8lvYeVjtyl5GRgRMnTiA8PNykHh4ejsOH5ddgioqKksxv27YtoqOjkZmZKfueJ0+eIDMzE97epnebXb58GYGBgQgJCUHPnj3x77//Zps3PT0dqampJi8iomyJIvDH+2Ybu2uGouid8ZlNNXYAsNtQG8Mzh8kewVMJImZq56KF62UrJCMiS1ituUtOToZer4e/v+kq6P7+/khKSpJ9T1JSkux8nU6H5ORk2feMHTsWxYsXR+vW/7uOpV69eli+fDm2bduGBQsWICkpCQ0bNkRKSorZvFOnToWXl5fxVbJkSUu/VSIqqA7PAs7Lr2N3Q+eF3hmf5fkyJ3llm6EuPtUNlB3TCnos9FuDqkWtftk2Ecmw+t9MQTD9zVAURUntRfPl6gAwffp0rF69GuvXr4eLy/8e09O+fXt069YN1apVQ+vWrbFlyxYAwLJly8zud9y4cXjw4IHxlZCQ8OJvjogKriu7gZ0TZYduPjSge1I/3ICfsplyaK2+BaZm9pIdc1NlYt0brlBlPlY4FRG9iNWaO19fX6jVaslRutu3b0uOzj0TEBAgO1+j0cDHx8ekPmPGDHz99dfYvn07qlevnm0Wd3d3VKtWDZcvmz/N4OzsDE9PT5MXEZGsu3HAb+8AonRtOJ22EFotf4KrOh+ZN9qe+frOmKfrJDtW3keNUqemZZ1+JiKbYbXmzsnJCaGhodixw/TOqx07dqBhw4ay72nQoIFk/vbt2xEWFgatVmusffvtt5g8eTK2bt2KsLCwF2ZJT09HbGwsihUz/zgeIiKLZDwG1r4FPL0vHRNUiAsdj4vJ9rUg8De6XvhV10x2rEjiASBqtsKJiCg7Vj0tO3r0aCxcuBCLFy9GbGwsRo0ahfj4eAwZMgRA1qnQZ3e4All3xl67dg2jR49GbGwsFi9ejEWLFmHMmDHGOdOnT8fnn3+OxYsXIzg4GElJSUhKSsKjR4+Mc8aMGYN9+/YhLi4OR48eRffu3ZGamoq+ffsq980TkeMRReCPYcCtc/LjrSbgYdEX/8JpewR8ruuPk4ay8sM7xgPX5G+EIyLlWbW569GjByIiIjBp0iTUrFkT+/fvR2RkJEqVKgUASExMNFnzLiQkBJGRkdi7dy9q1qyJyZMnY9asWejWrZtxzpw5c5CRkYHu3bujWLFixteMGTOMc65fv45evXqhQoUKeO211+Dk5IQjR44Y90tElCtH55u9gQJVXgMajVA2Tx7KgBZDM0bgrughHRT1wG/9gIe3FM9FRFJWXefOnnGdOyIycecSML8poHsqHfOvCgzYDji5IyYmBqGhoQjoGwHnADNHwmxYY9VZLNd+A5Ug86OjVCOgzyZArcyj04gKGptf546IyGHoM4H1g+UbO5fCQI+VgJO74rHyw0FDNczUdZcfvHYIiPpR2UBEJMHmjojoZe3/Fkg8JT/WfTHgHaJonPw2W98FO56Ulx/c8zVwO1Z+jIgUweaOiOhlXD8B7J8hP1b/faBsK2XzKECECh8kd0PcPZm7fvUZwMb3AL300WVEpAw2d0REuZXxBNgwOOuGguf5VgBafaF8JoU8MLii9/o0iHI/Rm6eBA5FKJ6JiLKwuSMiyq2dE4GUf6R1lQZ4bT6gdVU8kpKOXNfjdpnX5Qf3fgPcOq9sICICwOaOiCh3/t0LHJsvP9bsEyCwlqJxrOVmxXcAX5nr7wyZwIYhWTebEJGi2NwREeVU5lPgz5HyY8VDgcajFY1jTaLaGXh1LiDI/DhJOgMcmKl8KKICjs0dEVFOHYoA7sVJ6xpXoOvPBW+dtxJhQMPh8mP7pwO3Liibh6iAY3NHRJQTKVfMH41q8yXga38LE+eJ5uMAv4rSukEHRH6U9Wg2IlIEmzsiIkuJYlajok+XjhUPBeoMUj6TrdC6AK/OAQS1dOzaQeDcOuUzERVQbO6IiCx1YSNwZZe0LqiAjjMBVQH/J7V4KNBwmPzY9s+B9IfK5iEqoAr4v0RERBZKfwhsHSc/VmcQEFhT0Tg2q+nHQKFAaf1hIrBvmvJ5iAogNndERJbYMzWrQXmehz/Q8jPl89gqZw+g7RT5sSNzgdsXlc1DVACxuSMiepGkc8DRefJjbb8GXLyUzWPrqnQFQppK6wYd8BdvriDKb2zuiIiyI4rA1rHyjxgLaQZU7aZ8JlsnCECHGVlP6nhe3H7g/AblMxEVIGzuiIiy8/c24OoBaV2lBTp+l9XIkJRfBaD+e/Jj2z4D0h8pm4eoAGFzR0Rkjl4H7PhCfqzRcMC3nLJ57E2zT4BCxaT1hzeBqNnK5yEqINjcERGZE7MMSP5bWvfwL1CPGMs150JA+FfyY4dnAY9uK5uHqIBgc0dEJCf9IbB3qvxYi8+y7gqlF6vaDSjVWFrPeMSlUYjyCZs7IiI5h34AHt+R1otWBmq9pXweeyUIQPgk+bETS7Me50ZEeYrNHRHR8x7cAA7/JD/WZhKgknnEFplXPBSo8pq0btABu75UPg+Rg2NzR0T0vD1TAF2atF66OVC2teJxHEKrL7LuMH7ehT+AhOPK5yFyYGzuiIj+K/EMcOoXmQEBaDOZS5/klndpoM4A+bEd47mwMVEeYnNHRPRfuyYBkGk0avYGilVXPI5DafoR4FRIWo8/DFz6S/k8RA6KzR0R0TMJx4B/dkjrGtesO2Tp5bj7Ao1HyI/tnJi1riARvTQ2d0REz+w2syZbg6GAV3Flsziq+u/LL2ycfAk4s1b5PEQOiM0dEREAXD0IxO2T1l28gIbDlc/jqJzcgObj5Mf2Twf0mcrmIXJAbO6IiEQR2D1FfqzBB4BrYUXjOLyabwK+FaT1e1eB02sUj0PkaDTWDkBEZHX/7sm6qP95rkWAeu8qn8eOxMbG5up9RUq9gZDkyZJ6+s4pOG+oAKiU+fHk6+uLoKAgRfZFpBQ2d0RUsGV31K7RCMDFU9k8dkL/6B4gCHjrrdw9rUMAcOY9d1QtarogtPOTRMwZ3BiLTipzetbF1Q2XLsaywSOHwuaOiAq2y9uBG9HSursfUHew8nnshCH9ESCK8On0IbQ+JXO1jR9wDgvwq6Q+oUMxbK8+HJn5/CMqMyUBKZu/Q3JyMps7cihs7oio4BLFrKdRyGk8CnByVzaPHdL6lIRzQNlcvXcnSiPWEIVKqgSTeknNfbxZ/AZW61vlRUSiAoc3VBBRwXVxC5B4WlovVAwI6698ngJGhAo/6LrJjr2v+QNacN07otxgc0dEBZMoAvumyY81+RDQuiqbp4DaZghDrEF6SrSEkIw31HsVz0PkCNjcEVHB9M9OIOmMtO5ZAqjdR/k8BZQIFSLMHr3bCCdw3TuinGJzR0QF04Hv5OtNPwQ0zspmKeC2GcJw3lBKUg8U7uI19QErJCKyb2zuiKjguXoIiI+S1gsVy1pglxQmmD16N0T9J9TQK5yHyL6xuSOigufADPl6ww941M5KdhhCZY/eBatuoYPqqBUSEdkvNndEVLDciAGu7JbWXb2B0H6Kx6FnBMzRdZEdeU/zJwBR2ThEdozNHREVLOautas/lOvaWdlfhrr41xAgqVdWXUNz1SnlAxHZKTZ3RFRw3I4FLm6W1p0KAXUHKZ+HTBigwjx9Z9mxoZpNCqchsl9s7oio4Dj4vXy97kDAtbCiUUjeBn0TJIreknpd1SXUES5aIRGR/WFzR0QFw9044Ozv0rrGFaj/vvJ5SFYmNFio6yA7NlTzh8JpiOwTmzsiKhgOzwJEmSU1QvsCHn7K5yGzVutb4p7oIam3UJ9GZeGq8oGI7IzG2gGIiHIiPj4eycnJOXqPJv0eqsaslPw2axA0OO/ZApkxMXkX8AViY2MV25e9egIXLNG1w2it9Ejre5pN+CBzuBVSEdkPNndEZDfi4+NRoWIlPE17kqP3TWjmjOrNpevXLYl5goET2+dVPMpDy/ThGKzZDA/hqUm9g+ooZghJuCZK76oloixs7ojIbiQnJ+Np2hP4dPoQWp+SFr3HVcjAByVmApA2hMuKfoiAvsqekk37NxoPDqxUdJ/26AE88Iu+FQZrtpjU1YKIAeq/MF73jpWSEdk+NndEZHe0PiXhHFDWorm91Tvgo5Y2djv0tRHv0wBKP48iMyVB4T3ar4W6Duir3gZnQWdSf129DzN13XEfhayUjMi28YYKInJYKhgwQB0pO7ZA11HhNJRTt1EEG/WNJXVXIQNvqndZIRGRfWBzR0QOq43qBEJUtyT1U4YyOCZWtEIiyqmFevllUfpptsEZGQqnIbIPbO6IyGEN1sg8jQLAz7qOAARlw1CuXBZLYK++hqTuJzzAK+rDVkhEZPvY3BGRQ6ot/I1Q1WVJPd7gh22GOlZIRLn1s17+FPog9RYAorJhiOwAmzsickjvmjlqt1DfAXqoFU5DL+OwoQouGEpJ6uVVN9BcddoKiYhsm9Wbuzlz5iAkJAQuLi4IDQ3FgQMHsp2/b98+hIaGwsXFBaVLl8a8efNMxhcsWIAmTZqgSJEiKFKkCFq3bo1jx4699H6JyH6ECIloozohqd8TPfCbvpkVEtHLEbDAzCPJBqq3yNaJCjKrNndr167FyJEj8dlnn+HkyZNo0qQJ2rdvj/j4eNn5cXFx6NChA5o0aYKTJ0/i008/xfDhw7Fu3TrjnL1796JXr17Ys2cPoqKiEBQUhPDwcNy4cSPX+yUi+9Jf/RdUgvR03Qp9a6TBxQqJ6GVtNjRAougtqTdWn+cjyYieY9XmbubMmRgwYAAGDhyISpUqISIiAiVLlsTcuXNl58+bNw9BQUGIiIhApUqVMHDgQPTv3x8zZswwzlm1ahWGDh2KmjVromLFiliwYAEMBgN27frfbfM53S8R2Q8vPEI3tfRIfLqowXJdWyskoryQCQ2Wmvn8Bmrkl7shKqis1txlZGTgxIkTCA8PN6mHh4fj8GH5O6CioqIk89u2bYvo6GhkZmbKvufJkyfIzMyEt7d3rvcLAOnp6UhNTTV5EZHt6aneAzchXVLfoG+MZHhZIRHlldX6lngkSo+8dlZFIQApVkhEZJus1twlJydDr9fD39/fpO7v74+kpCTZ9yQlJcnO1+l0Zh8kPnbsWBQvXhytW7fO9X4BYOrUqfDy8jK+Spa07NFHRKQcNfToo9kuO7bIzHppZD9S4Y61+haSulbQ423NDiskIrJNVr+hQhBM15oSRVFSe9F8uToATJ8+HatXr8b69evh4mL6215O9ztu3Dg8ePDA+EpI4COEiGxNW9VxFBekR3AO6KvisljCCokory3Rt4NelP5b3Uu9m4saE/0/qzV3vr6+UKvVkqNlt2/flhxVeyYgIEB2vkajgY+Pj0l9xowZ+Prrr7F9+3ZUr179pfYLAM7OzvD09DR5EZFt6a/ZKltfrG+vcBLKL9dF+XUKvYVHeFV9yAqJiGyP1Zo7JycnhIaGYscO00PpO3bsQMOGDWXf06BBA8n87du3IywsDFqt1lj79ttvMXnyZGzduhVhYWEvvV8isn01hH8QpvpbUv/XEIC9BukTDsh+LdG1k62/o94KLmpMZOXTsqNHj8bChQuxePFixMbGYtSoUYiPj8eQIUMAZJ0K7dOnj3H+kCFDcO3aNYwePRqxsbFYvHgxFi1ahDFjxhjnTJ8+HZ9//jkWL16M4OBgJCUlISkpCY8ePbJ4v0Rkf94xc9Ruib4dROtfgUJ56LhYAecMwZJ6RVUCGqguKB+IyMZorLnzHj16ICUlBZMmTUJiYiKqVq2KyMhIlCqVtRJ5YmKiydpzISEhiIyMxKhRozB79mwEBgZi1qxZ6Natm3HOnDlzkJGRge7du5vsa8KECZg4caJF+yUi+1IU99BRdVRSTxXdsE7f1AqJKH8JWKJrh++c5klG+qu3IspQxQqZiGyHVZs7ABg6dCiGDh0qO7Z06VJJrVmzZoiJiTG7vatXr770fonIvryt2QGtoJfU1+hb4AkXLXZIfxoaYKz4C/wE02WpWqliUFK4hQTR/DXURI6O5yqIyK45IwNvqndK6npRwHJ9uMw7yBFkQItf9K0ldZUgoq9afjkcooKCzR0R2bVX1YfgLTyS1LcZ6uC66GeFRKSUlbrWyBDVkvob6r1wR5rygYhsBJs7IrJjIvqpt8mOLDZzRyU5jjsojM2GBpK6p5CGbur9VkhEZBssuuaudu3aOdqoIAjYtGkTihcvnqtQRESWqCNcQiVVvKR+1hCMaLGCFRKR0pbo2uE19UFJvZ96G1bo2/BOaSqQLGruTp06hQ8//BAeHh4vnCuKIr755hukp0uf7UhElJf6auSP2i3VtQNg/okz5DjOiqURbSgvWeOwtCoJzVSnsddQy0rJiKzH4rtlP/roIxQtWtSiud99912uAxERWcIfd9FOdVxSTxELYbOhvhUSkbUs0bVDmJN0Aes+6h1s7qhAsuh4dVxcHPz8LL8w+cKFC1wzjojyVW/NLmgEg6S+Rt8C6XCyQiKylq2GOrgpekvqzVWnESTcskIiIuuyqLkrVaoUBMHyUxwlS5aEWi29g4mIKC84QYfe6l2Sul4UsEonXR6DHJseatnPXSWIeEtmmRwiR/dSV5pWq1YNCQkJeZWFiMgindzPSxavBYDthjDchK8VEpG1rdG3QLoovdKoh3oPXMBrwKlgeanm7urVq8jMzMyrLEREFnmnkPRRYwC4aHEBlgIvRBrqSepewhN0UR+2QiIi6+E94kRkV0KLqRDmcl1Sv2QogShDZSskIluxXCff3PdRbwcgKhuGyIpeqrlr0qQJXF1d8yoLEdELDasrf7NE1lE7Ln9SkJ0Uy+KMIURSr6K6hlBBejctkaN6qeYuMjISxYoVy6ssRETZ0qTfR8+qWkk9VXTFBn1jKyQi22L+ecJ9NXzeLBUcFjV3mzZtytG1dZGRkUhL43P9iChv+cRvgYtGenTud30zPIGLFRKRrflT3wB3RemC++1Vx+CHe1ZIRKQ8i5q7rl274v79+xZvtGfPnkhMTMxtJiIiKYMevlf/lB1arm+jcBiyVelwwq/6FpK6VtCjl3qPFRIRKc+iJ1SIooh+/frB2dnZoo0+ffr0pUIREUlc3g7nNOmCtPv01XFV5OUh9D8r9a0xWL0ZKsH0Joo3NTsxR/8KdJY/nInILln0X3jfvn1ztNE333wTnp6euQpERCTr+ELZMo/a0fOui37YZaiNNuoTJnV/4T7CVdGI5OPpyMFZ1NwtWbIkv3MQEZl391/gH+mTBq6LvtjDZ4eSjGX6cElzBwBvqXeyuSOHx3XuiMj2RS+WLa/StYaB/4yRjEOGKrhikJ6ub6i+gDLCDSskIlIO/1UkItuWmQacXCkpp4sa/KpvZoVAZA9EqPCLvpXsGJ83S46OzR0R2bbzG4A06RIWkYZ6SIGXFQKRvfhN3xRponTR627q/XAFb/wjx8Xmjohsm5kbKVboeCMFZS8VHtikbyipewppfN4sObQcN3dxcXH5kYOISOpGDHBDelH8uYwAxIjlrBCI7M0KfWvZ+tvqHeDzZslR5bi5K1u2LFq0aIGVK1dyPTsiyl/HF8mWl6XWBZ8jS5Y4J5bGKUNpSb2K6hpCnROskIgo/+W4uTt9+jRq1aqFDz/8EAEBAXj33Xdx7Nix/MhGRAXZk7vAud8l5QdPRax/XM0KgcherTSzFmLfQscVTkKkjBw3d1WrVsXMmTNx48YNLFmyBElJSWjcuDGqVKmCmTNn4s6dO/mRk4gKmlO/ADrp2YFlpzPwRLTsaTlEQNbzZu+L7pL6K+7n4OPKI8DkeHJ9Q4VGo0HXrl3x66+/Ytq0abhy5QrGjBmDEiVKoE+fPny2LBHlnsFgdm27udGZCoche5f1vNnmkrqzoEf/WlrlAxHls1w3d9HR0Rg6dCiKFSuGmTNnYsyYMbhy5Qp2796NGzduoEuXLnmZk4gKkqv7gbtXJOWHPjVxMdlghUBk737Rt5StDwlzAkT+N0WOJcfN3cyZM1GtWjU0bNgQN2/exPLly3Ht2jV89dVXCAkJQaNGjTB//nzExMTkR14iKgjM3EhxJ4S/NFLuXBWLYb9eeq1m6SIqeN7mtXfkWHLc3M2dOxe9e/dGfHw8Nm7ciE6dOkGlMt1MUFAQFi2S/8eZiChbqYnAxS3SuntRPAhopHwechgrzNxY4Xttk8JJiPKXJqdv2LFjB4KCgiQNnSiKSEhIQFBQEJycnNC3b988C0lEBcjJFYCol9Zr94Go4vVRlHu7DbVwU/RGoHDXpO6VdAS4nwAULmmlZER5K8dH7sqUKYPk5GRJ/e7duwgJCcmTUERUQOl1wIml0rqgAkL7KZ2GHIweaqzRSa+9E2AAYpZbIRFR/shxcyeK8it6P3r0CC4uLi8diIgKsMvbgdQb0nq5cB5VoTyxRt8COlHmR1/MckDPO7HJMVh8Wnb06NEAAEEQMH78eLi5uRnH9Ho9jh49ipo1a+Z5QCIqQKLNXKsbNkDZHOSwbqMIdhhC0V793E0Uj5KAS5FAZd60Q/bP4ubu5MmTALKO3J09exZOTk7GMScnJ9SoUQNjxozJ+4REVDDcjQP+2SWtFw4CyrZSPg85rJX61tLmDsi6S5vNHTkAi5u7PXv2AADeeecd/PDDD/D09My3UERUAJ1YCtkHuYf2A1RqhcOQIztsqII4gz9CVLdMB+L2Acn/AL5lrROMKI/k+Jq7JUuWsLEjorylS8+6S/Z5Ki1Q623l85BDE6HCKn1r+cETS5QNQ5QPLDpy99prr2Hp0qXw9PTEa6+9lu3c9evX50kwIipALmwCnqRI65U6Ax5Flc9DDm+dvgnGaNbCRdCZDpxaBbT8HNC6WicYUR6wqLnz8vKCIAjG/09ElKfMPEcWdXgjBeWPe/DE5sdV0N3jtOlA2j3g/EagZi+r5CLKCxY1d0uWLJH9/0REL+12LBB/WFr3rQCU4hMpKP8se1hH2twBWb9ssLkjO5bja+7S0tLw5MkT49fXrl1DREQEtm/fnqfBiKiAiDbzC2NYf+D/zxgQ5Yfo9JI4c0vmaSjXjwGJZ5QPRJRHctzcdenSBcuXZ63kff/+fdStWxffffcdunTpgrlz5+Z5QCJyYBmPgdNrpHWNK1Cjp/J5qIARMDc6Q36IN1aQHctxcxcTE4MmTZoAAH7//XcEBATg2rVrWL58OWbNmpXnAYnIgZ1bD6Q/kNardQNcCysehwqeVWcyoVfLPF3pzK9A+kPlAxHlgRw3d0+ePEGhQoUAANu3b8drr70GlUqF+vXr49q1a3kekIgcmNknUvRXNgcVWA8zgLslZJZFyXgEnP1N+UBEeSDHzV3ZsmWxceNGJCQkYNu2bQgPDwcA3L59m+vfEZHlbsQAN09K68VqAIG1lc9DBVZy8CvyA8cXA2aep05ky3Lc3I0fPx5jxoxBcHAw6tWrhwYNGgDIOopXq1atPA9IRA7K3DVNvJGCFJbmVQ4oHioduHUWuHFC+UBEL8nix4890717dzRu3BiJiYmoUaOGsd6qVSt07do1T8MRkYN6+gA4+7u07uwJVO2ufB6isAHyjdzxRUCJMOXzEL2EHB+5A4CAgADUqlULKtX/3l63bl1UrFgxz4IRkQM78yuQ+URar94DcPZQPg9Rla6Ai8wi/efXA0/uKp+H6CXk+Mjd48eP8c0332DXrl24ffs2DAaDyfi///6bZ+GIyAGJYtbREDlh7yibhegZJzegRm/g6HNLeumeZi3X02CodXIR5UKOm7uBAwdi3759ePvtt1GsWDHjY8mIiCwSfwS4Eyutl6wP+FdRPg/RM2HvSJs7IOuJFfXf47WgZDdy3Nz99ddf2LJlCxo14mOBiCgXzD1HlsufkLX5VQBKNQauHTStp1wGrh4EQppYJxdRDuX4mrsiRYrA29s7P7IQkaN7nAJc2Citu3oDlbsoHodIwtylAeZ+KSGyQTlu7iZPnozx48ebPF+WiMgip1YCepnHPdXsDWhlnhJApLRKnQE3X2k99k/g0W3l8xDlQo5Py3733Xe4cuUK/P39ERwcDK1WazIeExOTZ+GIyIEYDEB0NmvbEdkCjTNQ6y3gUIRp3ZAJnFwBNPnQKrGIciLHR+5effVVfPjhhxgzZgy6d++OLl26mLxyas6cOQgJCYGLiwtCQ0Nx4MCBbOfv27cPoaGhcHFxQenSpTFv3jyT8fPnz6Nbt24IDg6GIAiIiIiQbGPixIkQBMHkFRAQkOPsRJQDcXuBe3HSeunmgE8ZpdMQmRfaT74evRQw6JVMQpQrOT5yN2HChDzb+dq1azFy5EjMmTMHjRo1wvz589G+fXtcuHABQUFBkvlxcXHo0KEDBg0ahJUrV+LQoUMYOnQo/Pz80K1bNwBZz74tXbo0Xn/9dYwaNcrsvqtUqYKdO3cav1ar1Xn2fRGRDLM3UgxQNgfRi3iHAGVaAVd2mdYfxAP/7ALKh1snF5GFcrWI8f3797Fw4UKMGzcOd+9mLe4YExODGzdu5Gg7M2fOxIABAzBw4EBUqlQJERERKFmyJObOlbkVHcC8efMQFBSEiIgIVKpUCQMHDkT//v0xY8YM45w6derg22+/Rc+ePeHs7Gx23xqNBgEBAcaXn59fjrITUQ6k3gQuRkrrHgFAhfbK5yF6kTpmfungjRVkB3Lc3J05cwbly5fHtGnTMGPGDNy/fx8AsGHDBowbN87i7WRkZODEiRMIDzf9DSg8PByHDx+WfU9UVJRkftu2bREdHY3MzMwcfR+XL19GYGAgQkJC0LNnzxcuvpyeno7U1FSTFxFZKGYFIMqczqrdB1BrpXUiayvXFvAsLq1f3gbcT1A+D1EO5Li5Gz16NPr164fLly/DxeV/d7e1b98e+/fvt3g7ycnJ0Ov18Pf3N6n7+/sjKSlJ9j1JSUmy83U6HZKTky3ed7169bB8+XJs27YNCxYsQFJSEho2bIiUlBSz75k6dSq8vLyMr5IlS1q8P6ICTa8DYpZJ64IKCO2rfB4iS6g1QG2Z/z5Fg/x/z0Q2JMfN3fHjx/Huu+9K6sWLFzfblGXn+SdciKKY7VMv5ObL1bPTvn17dOvWDdWqVUPr1q2xZcsWAMCyZeb/wo4bNw4PHjwwvhIS+JsbkUUubwNSZS7ZKN8O8CqhfB4iS9V+GxBkrseOWQ7oc3a2iEhJOW7uXFxcZE9JXrp0KUfXrfn6+kKtVksawtu3b0uOzj0TEBAgO1+j0cDHx8fifT/P3d0d1apVw+XLl83OcXZ2hqenp8mLiCzAJ1KQvfIMBCp2kNYf3QIublE+D5GFctzcdenSBZMmTTJe4yYIAuLj4zF27FjjHauWcHJyQmhoKHbs2GFS37FjBxo2bCj7ngYNGkjmb9++HWFhYZL19nIiPT0dsbGxKFasWK63QUQy7sZl3V34vMJBQJmWyuchyilzv4REL1I2B1EO5Li5mzFjBu7cuYOiRYsiLS0NzZo1Q9myZVGoUCFMmTIlR9saPXo0Fi5ciMWLFyM2NhajRo1CfHw8hgwZAiDrVGifPn2M84cMGYJr165h9OjRiI2NxeLFi7Fo0SKMGTPGOCcjIwOnTp3CqVOnkJGRgRs3buDUqVP4559/jHPGjBmDffv2IS4uDkePHkX37t2RmpqKvn15/Q9RnjqxFIAorYf2A1RcfojsQEhzwLu0tB63H0g2f7aHyJpyvM6dp6cnDh48iN27dyMmJgYGgwG1a9dG69atc7zzHj16ICUlBZMmTUJiYiKqVq2KyMhIlCpVCgCQmJiI+Ph44/yQkBBERkZi1KhRmD17NgIDAzFr1iyTI4Y3b95ErVq1jF/PmDEDM2bMQLNmzbB3714AwPXr19GrVy8kJyfDz88P9evXx5EjR4z7JaI8oEvPWtH/eSotUOtt5fMQ5YZKBYS+A+z4Qjp2YinQNmcHNYiUkOPm7pmWLVuiZcuXP60ydOhQDB06VHZs6dKlklqzZs2yfcRZcHCw8SYLc9asWZOjjESUCxf+AJ7I3IFeqTPgUVT5PES5VfNNYPdXgD7dtH5yJdDyc0Drap1cRGbk6LSswWDA4sWL0alTJ1StWhXVqlXDK6+8guXLl7+woSKiAua4mWuSzC0OS2Sr3H2AKq9K60/vA+c3KJ2G6IUsbu5EUcQrr7yCgQMH4saNG6hWrRqqVKmCa9euoV+/fujatWt+5iQie5J0Dkg4Iq37VQRKNVI+D9HLMveYvOMLlc1BZAGLT8suXboU+/fvx65du9CiRQuTsd27d+PVV1/F8uXLTW6AIKICytydhGEDgBysSUlkM0rWBfyrArfOmdZvnABungQCa8m/j8gKLD5yt3r1anz66aeSxg7Iuv5u7NixWLVqVZ6GIyI79DQVOPOrtK51A2r0UD4PUV4QBPOXFJi7BIHISixu7s6cOYN27dqZHW/fvj1Onz6dJ6GIyI6dWQtkPJLWq78BuHgpn4cor1R7A3AqJK2f/R1Iu694HCJzLG7u7t69a/bJEUDWM17v3buXJ6GIyE6JYjZPpOCNFGTnnD2AGj2ldV0acHq18nmIzLC4udPr9dBozF+ip1arodPp8iQUEdmp+CPA7QvSeom6QLHqyuchymvZnZrlqhFkIyy+oUIURfTr1w/Ozs6y4+np6bJ1IipAzN05yOVPyFEUrZR1x/e1Q6b1lMtZT60o3cw6uYj+w+LmzpJHc/FOWaIC7NGdrIWLn+fqDVR+VfE4RPmmzgBpcwdk/XLD5o5sgMXN3ZIlS/IzBxHZu5PLAUOmtF7rLUDronweovxSsTPgXhR4fNu0fnELkJoIeBazTi6i/5ejJ1QQEcky6IFoM78Ahr2jbBai/KZxAmrLnKkS9UDMMuXzED2HzR0Rvby/twIPEqT1Mq0A79LK5yHKb6H9AEHmR+iJpYBe5gg2kYLY3BHRyzu2QL5ed5CyOYiUUrgkUF5m7deHicClSOXzEP0HmzsiejnJ/wD/7pHWvYKAcuHK5yFSirm7wM39skOkEDZ3RPRyzC5/0h9QqZXNQqSk0i3lLzu4egC4Hat8HqL/Z3Fz988//+DEiRMmtV27dqFFixaoW7cuvv766zwPR0Q2LuMxcOoXaV3tDNTi0kjk4FQqoM5A+TEevSMrsri5++ijj7Bx40bj13FxcejcuTOcnJzQoEEDTJ06FREREfkQkYhs1tnfgPQH0nrV1wB3H+XzECmtZm9A4yqtn14DPJX5u0GkAIubu+joaHTo0MH49apVq1C+fHls27YNP/zwAyIiIrB06dL8yEhEtkgUgWPmTsnyRgoqIFyLANXfkNYzH2c1eERWYHFzl5ycjBIlShi/3rNnDzp37mz8unnz5rh69WqehiMiG5ZwFLh1VloPrAWUCFU+D5G1mLsr/NgCPm+WrMLi5s7b2xuJiYkAAIPBgOjoaNSrV884npGRAZH/ERMVHOauKeJROypoAqoBJetL6ymXgX/3Kh6HyOLmrlmzZpg8eTISEhIQEREBg8GAFi1aGMcvXLiA4ODg/MhIRLbm0W0zz5EtknW9HVFBY+7onbm7yYnykcXPlp0yZQratGmD4OBgqFQqzJo1C+7u7sbxFStWoGXLlvkSkohszImlZp4j+zaglbm4nMjRVXpF/nmzlyKB+wlZix4TKcTi5i4kJASxsbG4cOEC/Pz8EBgYaDL+5ZdfmlyTR0QOSp8JRC+WGRDML+pK5Og0TlmPJNs/3bQuGrL+vrSeYJVYVDDlaBFjrVaLGjVqmDR2Op0Ojx49Qo0aNeDjw6UPiBxe7KasRyw9r1w4UCRY8ThENiPsHUCQWbg7ZhmQ+VT5PFRgWdzcRUZGYsWKFSa1KVOmwMPDA4ULF0Z4eDju3buX5wGJyMYcnS9fr/eusjmIbI1nIFCpk7T+JAU4v175PFRgWXxadsaMGejWrZvx68OHD2P8+PGYNGkSKlWqhM8++wyTJ0/GzJkz8yUoEdmAm6eylkB5nk85oHQLaZ3IDsTG5t2jwjwKN0d5SG82erJnJi7qKwCCkGf7esbX1xdBQUF5vl2yXxY3d+fOncN3331n/Pr3339HmzZt8NlnnwEAXFxcMGLECDZ3RI7s2M/y9bqDsx7FRGRH9I/uAYKAt956K0+3e3qIO6r7m56edXtwGR90rYfDCfo83RcAuLi64dLFWDZ4ZGRxc/fw4UOTa+oOHjyI7t27G7+uUqUKbt68mbfpiMh2PE4Gzv4urTsVAmr2Uj4P0UsypD8CRBE+nT6E1ifv7mZdpj6B72SO3n3Uox7euyPzNIuXkJmSgJTN3yE5OZnNHRlZ3NwFBgYiNjbrN4NHjx7h9OnT+P77743jKSkpcHNzy5eQRGQDTiwF9OnSeq03AedCischyitan5JwDiibZ9vbgpL4XNyFIsIjk3ontwv4JqAIksCbDyl/WXwepXv37hg5ciRWrFiBQYMGISAgAPXr/29F7ujoaFSoUCFfQhKRlZld/gR8IgXRc57CGWv00mtQNYIBb2p2WSERFTQWN3cTJkxAWFgYhg8fjlOnTmHlypVQq/93TcHq1atNnjVLRA7k4mYg9Ya0XrYN4Jt3RzyIHMUKXRvoRenNE73Vu+CMDCskooLE4tOybm5ukqVQ/mvPnj15EoiIbNBRMzdScPkTIlk34YtthjrooD5mUvcRHqKzOgq/65tZKRkVBLy9jYiyl3gGiD8srXuXAcq0Uj4PkZ1YqmsrW++n3gZAVDYMFSgWH7lr0aIFhBeszyMIAnbt4vUERA7l6Dz5Opc/IcrWMbEiYg1BqKSKN6lXVV1FmHAJ0WJFKyUjR2dxc1ezZk2zY6mpqVi9ejXS02XupCMi+/XoNnD2N2ndyQOo2Vv5PER2RcASfVtMVy2QjPTTbEd0Jps7yh8WN3f/XfbkGZ1Oh9mzZ2PKlCkoXrw4Jk+enKfhiMjKohcDepmLv2v2Blw8lc9DZGf+0DfCOM1qybIo7VTHEIhk3ISvlZKRI7O4uXveqlWrMH78eKSlpWHixIkYPHgwNJpcb46IFBAfH4/k5GSL5gr6DFQ9PA/a5+oiBFzwaIr0mJi8D/gCefmYKCIlpMMJq/UtMVSzyaSuEQzoo9mOb3Q8Ak55L8fd2NatWzF27FjExcVhzJgxGD16NNzd3fMjGxHlofj4eFSoWAlP055YNL9vDS2WvuoqqW+6mIFXv3wlr+MROawVujYYrN4MjWAwqfdW78Ys3Wt4AhcrJSNHZXFzd+zYMXzyySc4cuQIhgwZgp07d8LXl4eTiexFcnIynqY9sfBRSyI+CpwD4JZkZHnhQQjoWzpfMr5I2r/ReHBgpVX2TZRbifDBX4a66Kw+YlL3FJ6gu3ofluvl76olyi2Lm7v69evD1dUV7733HoKDg/HLL7/Izhs+fHiehSOivGfJo5YaqM6jipO0sYs1BOFE4TZwRvZ3zueXzJQEq+yX6GUt0nWQNHcA8I56K1bo20DkymSUhyxu7oKCgiAIAjZs2GB2jiAIbO6IHEB/9V+y9UX69oCVGjsie3ZKLIsThnIIVV02qYeobqGV6iR2GkKtlIwckcXN3dWrV/MxBhHZimAhEa1UJyX1O6In/tQ3sEIiIsewUNcBoU4/SOoDNZHYmcHmjvIOjwMTkYl+6m1QCdLV81fq2iAdTlZIROQYthvCcF2UXqteXxWLKsJV5QORw7L4yF1aWhp27dqFTp06AQDGjRtnsmixWq3G5MmT4eLCu36I7JUnHuN19T5JPV3UYJW+tRUSETkOPdRYomuLL7SrJGP9NZH4MHOoFVKRI7L4yN3y5csxf/5849c//fQTDh8+jJMnT+LkyZNYuXIl5s6dmy8hiUgZPdW74S5InzSzSd8QyfCyQiIix/KrvgUeidKDIJ1VUfDDPSskIkdkcXO3atUq9O/f36T2yy+/YM+ePdizZw++/fZb/Prrr3kekIiUoYUO72i2yY4t1rdXOA2RY3oIN/yqby6pOwl69NHsUD4QOSSLm7u///4b5cuXN37t4uIC1X8eGl63bl1cuHAhb9MRkWI6qaJQTLgrqR/WV0asWMoKiYgc0xJ9W+hF6V3nb6l3wgV8Rju9PIubuwcPHpg8XuzOnTsIDg42fm0wGEyuwSMieyJisGaL7MjP+k4KZyFybAmiP7YbwiT1IsIj2WteiXLK4uauRIkSOHfunNnxM2fOoESJEnkSioiU1UR1FpVU8ZL634bi2GuoYYVERI5tkU7+UoeB6kioYJAdI7KUxc1dhw4dMH78eDx9+lQylpaWhi+//BIdO3bM03BEpIxBavmjdgv0HcFFi4nyXrRYAScN0ifFlFLdRjvVMSskIkdicXP36aef4u7du6hQoQK+/fZb/PHHH9i0aROmT5+OChUq4N69e/j000/zMysR5YNKwjU0VZ+V1G+JhfGHvpEVEhEVBALm6+QveRis2QxAutYkkaUsXufO398fhw8fxnvvvYexY8dCFLP+wxMEAW3atMGcOXPg7++fb0GJKH8MMnOt3VJdO2RAq3AaooJjuyEMcQZ/hKhMn+NcU/Uv6qticcRQ2UrJyN5Z3NwBQEhICLZu3Yq7d+/in3/+AQCULVsW3t7e+RKOiPJXMaSgsypKUn8sOmOVvqUVEhEVHAaosFDfEVNUiyVjg9Wb2dxRruXq8WPe3t6oW7cu6tat+9KN3Zw5cxASEgIXFxeEhobiwIED2c7ft28fQkND4eLigtKlS2PevHkm4+fPn0e3bt0QHBwMQRAQERGRJ/slckT9NFuhFfSS+hp9S6TCwwqJiAqW3/VNkSx6Suot1adQTrhuhUTkCKz6bNm1a9di5MiR+Oyzz3Dy5Ek0adIE7du3R3y89K49AIiLi0OHDh3QpEkTnDx5Ep9++imGDx+OdevWGec8efIEpUuXxjfffIOAgIA82S+RIyqEJ+it3i2p60QVFuvaWSERUcGTDics14XLjg1Wb1Y4DTkKqzZ3M2fOxIABAzBw4EBUqlQJERERKFmypNnHmM2bNw9BQUGIiIhApUqVMHDgQPTv3x8zZswwzqlTpw6+/fZb9OzZE87OznmyXyJH1Fu9C4WENEl9i6E+bsDPComICqbl+jZ4Ikp/XnVRH4I/pAuLE72I1Zq7jIwMnDhxAuHhpr+xhIeH4/Dhw7LviYqKksxv27YtoqOjkZmZmW/7BYD09HSkpqaavIjslTMyMEDzl+zYAl0HhdMQFWz3UQi/6ptJ6k6CHu9otlohEdk7qzV3ycnJ0Ov1kjts/f39kZSUJPuepKQk2fk6nQ7Jycn5tl8AmDp1Kry8vIyvkiVLWrQ/IlvUXb0fRYX7kvphfWWcE0srH4iogFuo7yD7SLLe6l0ohCdWSET2zKqnZYGspVT+SxRFSe1F8+Xqeb3fcePG4cGDB8ZXQkJCjvZHZCvU0ONd9Z+yY3P0XRROQ0QAcF0sir8M9SR1TyENb6t3WCER2TOrNXe+vr5Qq9WSo2W3b982u15eQECA7HyNRgMfH5982y8AODs7w9PT0+RFZI9ecT+PINUdSf2MIQQHDVWtkIiIAGCemUWN+2v+ggv47HaynNWaOycnJ4SGhmLHDtPfSHbs2IGGDRvKvqdBgwaS+du3b0dYWBi0WssWW83NfokcyTAv+WV/5ui6gI8aI7Kec2Jp7NdXk9R9hVT0UO9VPA/ZL6uelh09ejQWLlyIxYsXIzY2FqNGjUJ8fDyGDBkCIOtUaJ8+fYzzhwwZgmvXrmH06NGIjY3F4sWLsWjRIowZM8Y4JyMjA6dOncKpU6eQkZGBGzdu4NSpU8ZFly3ZL5Gj6lhOg8pOtyT1K4Zi2GYIs0IiIvovc5dGDNZshhY6hdOQvcrREyryWo8ePZCSkoJJkyYhMTERVatWRWRkJEqVKgUASExMNFl7LiQkBJGRkRg1ahRmz56NwMBAzJo1C926dTPOuXnzJmrVqmX8esaMGZgxYwaaNWuGvXv3WrRfIkc1trGTbH2evjNE61+CS1TgHTFUwglDOYSqLpvUiwspeFV9EL/pm1snGNkVqzZ3ADB06FAMHTpUdmzp0qWSWrNmzRATE2N2e8HBwcabLHK7XyJH5J5yBrWDpH/lb4re2KhvbIVERCQlYLauCxY7zZCMDFH/iXX6pjDwFzF6Af4XQlRABFz+Rba+QNcRmdb/PY+I/t9uQy3EGoIk9TKqRLRTHbNCIrI3bO6ICoLEM/C6fVRSvit6YI2+hRUCEZF5AuboXpEdeV/zB4AXn52igo3NHVFBsH+6bHmJrh3S4KJwGCJ6kS2G+ogzSJfnqqK6huaq01ZIRPaEzR2Ro0s6B8RKFy1+JLpgmV7+geVEZF0GqDBPb+7o3Ubw6B1lh80dkaPb/61seaW+DVLhoXAYIrLUBn1jJIreknod1d9ooLpghURkL9jcETmy27HAhT8k5SeiMxboOlghEBFZKgNaLNB1lB0bqVkHHr0jc9jcETmy/d9C7gfASn1rpMBL+TxElCOr9S2QLEofd1lPdZFH78gsNndEjurOJeDcekk5zaDBz2aeYUlEtiUNLphv5u8rj96ROWzuiByVmaN2Kx7WQTKP2hHZjZX61maP3jV0ibNCIrJ1bO6IHFHyZeDcOkn5qU7E7FQ+jYLInmR39G5M4T0KpyF7wOaOyBHtnwGIBkn55xMZuK0vZIVARPQyzB29a+ByDc2D1VZIRLaMzR2Ro0m5Apz9VVI2qLSYdijDCoGI6GWlwQU/m7lzdmIzZ4XTkK1jc0fkaPZOlT1qlxLUATcf8uJrInu1Qt8GKaL0yHuzYA08kk8pH4hsFps7Ikdy6wJw9ndpXaVFUrneyuchojyT3bV3xS4tAUT+8kZZ2NwROZI9UyC7NELtPsh0Lap4HCLKW+aO3hVKOQNc2W2FRGSL2NwROYobJ4CLm6V1jQvQ9CPl8xBRnsu69s7MOpW7JvHoHQFgc0fkOHZ/JV+vOwjwLKZsFiLKNyv0bXBH5s5ZJJ4CYjcpnodsD5s7Ikdw9ZD8KRmnQkCjUcrnIaJ88wQumK17VX5w91eAQa9oHrI9bO6I7J0oArsny481GAq4+yibh4jy3S/6Vrgu+koHkv8GTq9RPhDZFDZ3RPbun11AfJS07lIYaPC+4nGIKP9lQIsfdK/JD+79BtClKxuIbAqbOyJ7lt1Ru8YjARc+Q5bIUa3XN8HlDJmjdw/igRNLFc9DtoPNHZE9u/BH1kXUz3MvCtQdrHgcIlKOHmpMu99KfnD/t0DGY2UDkc1gc0dkr3QZwK4v5ceajgGc3JXNQ0SK2/KkMqJvytxA8fgOcGSu8oHIJrC5I7JXJ5YAd/+V1r1KAqH9FI9DRNYg4NNdT+WHDv0APE5RNg7ZBDZ3RPbo6YOsi6bltPwc0PBB4kQFxY5/9XjoU0M6kJ4K7JumfCCyOjZ3RPboYASQdldaD6gGVHtD8ThEZF03Kw2SH4heBCT/o2wYsjo2d0T25sF14Mgc+bE2kwEV/1oTFTSPvasAlTpLBww6YOcE5QORVfGnAJG92T0F0MlcY1O2NVCmhfJ5iMg2tP4SUGmk9Yubs55iQwUGmzsie5J0Fji9WmZAyPqHnYgKLp8yQB0zp2e3fwYYDMrmIathc0dkT3aMByBK6zXfBAKqKh6HiGxMs4/lFy+/eRI4t075PGQVbO6I7MXlHcCV3dK6xhVo8anyeYjI9rh5A00/kh/b9SWQaWbZFHIobO6I7IEuA9g6Vn6swVDAq7iyeYjIdtUdDBQOktYfJABHubBxQcDmjsgeHJsPpMgsZ+DmAzQaoXweIrJdGmeg9UT5sf3fAQ+TFI1DymNzR2TrHt4C9ppZiLTl5/LX1xBRwVblNaB4mLSe8RDYOVHxOKQsNndEtm7XpKx/kJ8XUA2o3Vf5PERk+wQBaDtFfuz0aiD+qLJ5SFFs7ohs2fUTwKmV8mPtpwMqtbJ5iMh+BNUHqr0uPxY5BjDolc1DimFzR2SrDAbgr4/lx6p2A0o1VDYPEdmfNpMArbu0nnQGiFmmfB5SBJs7Ilt1Zi1wI1pa17hm/YNNRPQinoFZa9/J2TUJeCLzjGqye2zuiGzR01Tzz4Ns8iHgVULZPERkv+oPBXzKSutp94DdXymfh/IdmzsiW7RnCvDolrReOAhoOEz5PERkvzROQHszd9yfWAIknlE2D+U7NndEtuZGDHB0vvxY268BrauyeYjI/pVtDVToKK2LBmDLh3zurINhc0dkS/Q6YPNIyD4/tnRzoGInhQMRkcNoOwVQO0vr149lHcEjh8HmjsiWHPsZSDwtraudgY4zs9auIiLKDe8QoPFI+bGdE4HURCXTUD5ic0dkKx7cyLrWTk7TMYBPGWXzEJHjaTQSKFxKWk9PBbZ+ongcyh9s7ohsxV8fAxmPpHWfcnx+LBHlDSc3oNP38mMX/gAuRiqbh/IFmzsiW3AxEri4WX6sc0TWg8CJiPJC2VZA9R7yY5FjgHSZxx2SXWFzR2Rt6Q+ByI/kx2q+CQQ3VjYPETm+tl8DrkWk9dQbXPvOAbC5I7K27V8AqdeldVdvoM1k5fMQkeNz9wXCzVzje3R+1nOtyW6xuSOypit7zC9B0HYK4O6jbB4iKjhq9gaCm8gMiMCmYYAuXfFIlDfY3BFZy9NUYNMH8mPBTYAavZTNQ0QFiyAAnX+QX/vu9gVg7zfKZ6I8weaOyFp2fAE8SJDWte7AKz9yTTsiyn8+ZYBmZq75PRQBJBxXNA7lDTZ3RNZwZTdwYqn8WJsvsxYbJSJSQsMRgH81aV00ABuHABlPlM9EL4XNHZHSnqYCf2RzOjZsgLJ5iKhg0zgBXecBKq10LOUfYDdv7LI3GmsHmDNnDr799lskJiaiSpUqiIiIQJMmchd4Ztm3bx9Gjx6N8+fPIzAwEB9//DGGDBliMmfdunX44osvcOXKFZQpUwZTpkxB165djeMTJ07El19+afIef39/JCUl5e03Rw4jPj4eycnJebKtoNMz4Ctzd6xe7YLYMu8h49SpPNnP82JjY/Nlu0TkAAKqAs3HyjdyR+YAFTtyWSY7YtXmbu3atRg5ciTmzJmDRo0aYf78+Wjfvj0uXLiAoKAgyfy4uDh06NABgwYNwsqVK3Ho0CEMHToUfn5+6NatGwAgKioKPXr0wOTJk9G1a1ds2LABb7zxBg4ePIh69eoZt1WlShXs3LnT+LVarc7/b5jsUnx8PCpUrISnaS9/aqJTeQ3+7OUmO/bBpnuYO77TS++DiChXGo0ELkUCN2SWQdk4FHjvEOBcSPFYlHNWbe5mzpyJAQMGYODAgQCAiIgIbNu2DXPnzsXUqVMl8+fNm4egoCBEREQAACpVqoTo6GjMmDHD2NxFRESgTZs2GDduHABg3Lhx2LdvHyIiIrB69WrjtjQaDQICAvL5OyRHkJycjKdpT+DT6UNofUrmejv+6lQsC5wDQNokHkgLwcYqfRFQJf+ulEj7NxoPDqzMt+0TkZ1Ta4BX5wHzmwC6p6Zj968B2z7NutmLbJ7VmruMjAycOHECY8eONamHh4fj8OHDsu+JiopCeHi4Sa1t27ZYtGgRMjMzodVqERUVhVGjRknmPGsIn7l8+TICAwPh7OyMevXq4euvv0bp0qXN5k1PT0d6+v/W/ElNTbXk2yQHovUpCeeAsrl6rwADftJOhbda2tg9El0wTjUSTgF+LxsxW5kpMnfmEhH9l195oNX4rEbueTHLgTItgSpdpWNkU6x2Q0VycjL0ej38/f1N6tld+5aUlCQ7X6fTGa+HMjfnv9usV68eli9fjm3btmHBggVISkpCw4YNkZKSYjbv1KlT4eXlZXyVLJn7IzhU8AxWb0Fj9XnZscm6t3FdzN/GjojIYvXeA0o1kh/bNAK4d1XROJRzVr9bVnhuLS9RFCW1F81/vv6ibbZv3x7dunVDtWrV0Lp1a2zZsgUAsGzZMrP7HTduHB48eGB8JSTwKAhZpprwL8ZofpUd26Kvi7X65soGIiLKjkoFdJkNOHlIx9IfAL8PAPSZyucii1mtufP19YVarZYcpbt9+7bkyNszAQEBsvM1Gg18fHyynWNumwDg7u6OatWq4fLly2bnODs7w9PT0+RF9CLuSMMs7Y/QCnrJ2A3RB+MyBwLgYsVEZGO8Q4COM+XHbkQDe8w8l5ZsgtWaOycnJ4SGhmLHjh0m9R07dqBhw4ay72nQoIFk/vbt2xEWFgatVpvtHHPbBLKup4uNjUWxYsVy860QmTVRswwhqluSul4UMDLjfaRC5jdjIiJbUKOH+ccgHvw+azF2sklWPS07evRoLFy4EIsXL0ZsbCxGjRqF+Ph447p148aNQ58+fYzzhwwZgmvXrmH06NGIjY3F4sWLsWjRIowZM8Y4Z8SIEdi+fTumTZuGixcvYtq0adi5cydGjhxpnDNmzBjs27cPcXFxOHr0KLp3747U1FT07dtXse+dHN8b6j14XbNfduwn/as4LlZUOBERUQ51mAF4l5EfW/8u8Oi2snnIIlZt7nr06IGIiAhMmjQJNWvWxP79+xEZGYlSpUoBABITExEfH2+cHxISgsjISOzduxc1a9bE5MmTMWvWLOMyKADQsGFDrFmzBkuWLEH16tWxdOlSrF271mSNu+vXr6NXr16oUKECXnvtNTg5OeHIkSPG/RK9rKrCv5isWSo7dsJQDrN0rykbiIgoN5w9gNeXAGon6djj28C6AYBep3wuypbVn1AxdOhQDB06VHZs6dKlklqzZs0QExOT7Ta7d++O7t27mx1fs2ZNjjIS5URhPMQ8pwg4C9ILjlNFV4zIfB96cNFsIrITxWoAbSYDWz+RjsXtB3ZNBMK/UjwWmWf1u2WJHIkKBvygnY0Sgvyjyj7JHIzrYlGFUxERvaR67wLl28uPHf4ROLdO2TyULTZ3RHlohGY9mqnPyI7N13XEX4Z6smNERDZNELKWR/EsIT/+xzDglvxanqQ8NndEeaSlKgYjNOtlx6L0lTFd11PhREREecjdB+ixAlA7S8cynwBr3gTS7imfiyTY3BHlgTLCDURo58iOJYlF8EHmB7zOjojsX/HaQCcz69/diwPWDwYMBmUzkQSbO6KX5I1ULNFOh6cgfW5spqjG0IwRSIaXFZIREeWDWm8BYQPkxy5vB3ZPVjYPSbC5I3oJTsjEfKeZCFLdkR2frHsLMWJ5hVMREeWzdt8AJc1cQ3xwJhCzQtk8ZILNHVGuiZim/Rl1VH/Ljq7XN8ZyfbjCmYiIFKBxAl5fBniYebTn5pF8goUVsbkjyqXh6g3oqj4kOxZtKM/nxhKRY/MsBryxAlBppWMGHfBrX+DWBeVzEZs7otx4RXUYo7W/y47FG/wwOGM00iGzojsRkSMJqge8Mkt+LD0VWPU68DBJ2UzE5o4opxqrzuJb7TzZsVTRFf0zP8JdeCqciojISmr2BprJPL0CAFKvA7+8AaQ/UjZTAcfmjigHagr/YL52JpwF6bMUdaIKQzNH4h/RzCKfRESOqvk4oHoP+bHE08CvbwO6dGUzFWBs7ogsVF57G0udpsFdkP8HaoKuHw4aqimciojIBggC8MqPQKnG8uNXdgO/9wf00l+MKe9prB2AyB4EFxawxn8ZCguPZccX6Dpglb61wqmIiLLExsZaOwIAQF3pY1RIuQaXRwnSwYubkbKkJ67VGgsI0mNLvr6+CAoKUiCl42NzR/QCmqd3seNtdxTTPJQdX6dvgq91vRVORUQE6B/dAwQBb731lrWjGIUUFhA1wB3+HtIGzuf6DqzdsAXvRz6VjLm4uuHSxVg2eHmAzR1Rdh7dQbmoMXD1lr+CYYc+FB9nDobIKxyIyAoM6Y8AUYRPpw+h9Slp7TgAgDQAvR8kYZ3rYhRWS5u4oXWcYCjfElPutcGz5aIyUxKQsvk7JCcns7nLA2zuiMx5mAQsewWuD+Nkh6P0lTGMz4wlIhug9SkJ54Cy1o5hdAVl0V9XFCtUU2WvUx7mdRAGd1/M1L0Orgea93i4gUhO6k1gaUcg+ZLs8BlDCAZlci07IiJzYsTyGJT5IdJFmUWOAQzXbMSnml8AiMoGKwDY3BE978F1YEkHIOUf2eErhmLol/EJHsFN4WBERPblsKEqhmYOh06UbzcGa7ZgkmYpBBgUTubY2NwR/df9+KzG7p78qdgrhmLonfEZFykmIrLQLkMoRmcOhUGUP/3aR7MD3/n8ARXPzuYZXnNH9Myt88DK7sDDm7LDf2f44U3D57iDIgoHIyKyb5sMDaHO1GOGdh7UgvQ0bK9CJ4GurlnPpKWXxiN3RAAQtx9Y3M5sY3f2lh7dkt5hY0dElEsbDE3wQeYHyBTlb0LrVU2LMkc/BZ6mKpzM8bC5Izr7O7CyW9ZDrmU88SyNlsufINngoXAwIiLHEmmojyGZI5Euyp849LpzHFjSHnhwQ+FkjoXNHRVsh38C1g0A9Bny4wHVcbnhTCQ/4d1cRER5YZchFAMyP0KaaGa1gVvngIWtgaRzygZzIGzuqGDSZwKRHwHbPzM/J7gJ0G8z9E5eyuUiIioADhqqoW/GJ3gkushPeHgz61KZf3YpG8xBsLmjgufRHWD5q8Cxn83PqdoNeGsd4MLGjogoPxwTK6FHxhe4LRaWn5DxEPjlDeDIPEDk2ZOcYHNHBcvNk8DPzYFrB83PafgB8NpCQOOsWCwiooLovBiCrulf4lKGn/wEgw7Y+gmwfhCQ8VjZcHaMzR0VHKfXZh3mT71uZoIAtPsGCP8KUPGvBhGREm7AD68kDcTuuGyWQTn7G7CwDZByRblgdow/wcjxZaYBWz4ENgwGdNKHWAMANK7A60uA+u8pm42IiJBqcEW7lU+QUqKN+Um3zwM/twAubVUumJ3iIsbk2G5dyLob9vYF83O8goCeq4Bi1ZXLRUREJjINwFaXV9GiQgkUu7QUgtwzZ9MfAKt74Fbp7rhZaRBEtW0939vX1xdBQUHWjsHmjhyUKALHFwLbPgP06ebnBTcBXl8GuPsol42IiEzoH90DBAFvvf02AKB9WQ1WveaKIq7yzyTz//d33I5ai97r03Dutu08l9bF1Q2XLsZavcFjc0eO59Ed4M/hwKXI7OfVHwq0mQyo+deAiMiaDOmPAFGET6cPofUpiZMA2qfcxcKia1DVKUn2PdX81Yge4oWv77XBgtT6EK18pVlmSgJSNn+H5ORkNndEeUYUgTNrga1jgbR75udp3YBO3wM1eiqXjYiIXkjrUxLOAWUBAEkAuhtq4Gv9Irymll/hwFnQ40vvrWjjlYBxuoFIEP0VTGu7eEMFOYb78cCq7sCGd7Nv7AKqAYP3sbEjIrIDT+GM0Znv4fPMd/BU1Jqd11h9HtudPsG76j+hQTZ33RYQbO7Ivhn0wNH5wOz6wD87s5/bYBgwcBfgV16ZbERElAcErNS3QaeMKThvKGV2lquQgXHa1fjT6XPUEP5RMJ/tYXNH9uvqQWB+M+Cvj4HMbBa3dPcD3lwHtJ3ChYmJiOzUP2IJdM2YhHm6TjCI8jdaAEAlVTw2OE3Al5olKIyHCia0HWzuyP7cuwqsfRtY2hG4dTb7uZU6A+8dBsq1ViQaERHlnwxo8Y2uN97M/BQ3RW+z81SCiL6aHdjnPAoD1FvghEwFU1ofmzuyH2n3gV2TgJ/qArGbsp/r4Q+8sQLosRLwKKpIPCIiUkaUoQrapk/HCl3rbI/ieQlP8IV2FbY7fYx2qmOA3Np5Doh3y1K+i4+PR3Jycq7fr9I9QdF/16PolbXQZD564fzkoA64UXkI9E8LATExud7vM7GxsS+9DSIiylsP4YYvdP2xQd8YU7ULUUFl7tGSQLDqFuY5ReCEoRy+13XHQUNVAOabQnvH5o7yVXx8PCpUrISnaU9y/F43LfB+HSd83MgJvm4vPsh8KVmPoZFPsTtuDYA1uUhLRET2JkYsj04ZX2OQejNGaDbAWTB/CjZUdRkrnabihKEcInTdcMBQDY7Y5LG5s3Eve9TL2mJjY/E07YlxYUpLeKseo2+hY+jveQy+6mxulPh/DwwumHm/OZY8qovMphoENH3Z1KbS/o3GgwMr83ajRESUZzKhwRz9q9hiqI9xmtVopz6e7fxQ1WWscPoGMYay+En3KvYYalp9EeS8xObOhr3MUS9b89+FKc0JERIxQB2JbuoDcBUyXrhNvShgjb4lvtO9jrtunlC5AflxL2xmSkI+bJWIiPLaNTEAQzJHoZ4uFp9rV6Ca6mq282ur/sFipxm4YiiGJfp2WKdvgjS4KBM2H7G5s2HJyck5Pupla1501EsFA5qqTuNN9S60Up2ESrDsYtf9+mqYquuNWNH8mkdERFQwHRUr4ZWMr9BVdRAfaX9FMeFutvPLqBLxlWoJPtKsxWp9K6zUt8Z10U+htHmPzZ0dsOSol60yd9SrGFLwhnov3tDsRXEhxeLtHdJXwfe6bogWK+ZRQiIickQiVFhvaIrI9Hp4U70TQzSb4Sc8yPY9XsITDNH8icHqzThkqILf9M2xzRCGdDgplDpvsLkjxbgjDeGqaLyiPoymqjNQW3iUDgCOGSpgpu51HDFUzseERETkaJ7CGYv0HbFK3xpvqnfhXc1mFBXuZ/selSCiifocmqjP4b7ojo36Rtigb4zTYhnYww0YbO4oXzkLOnStqME7fmvRxvlvuGRzF5Oc3fqaWKDviChDZdjDXygiIrJNWU1eB6zUt0Zv9S4M1ERadOaosPAY/TTb0U+zHddFX2zW18dmfX2cE0Ngqz+X2NxRnvPBA7RUn0Qr1Uk0rnESHrXdAJy3+P3pogbr9U2wSN8e/4gl8i8oEREVOOlwwhJ9e6zQt0FbVTQGaCJRW2XZs2hLCMkYotmMIZrNuGrwxw5DKHYbaiHaUAHp+Zw7J9jc0UtTQ4/qwr9opDqH5urTqC1ctvjGiP+6KXpjra4FVulbIxle+ZCUiIgoiw4abDHUx5aM+qglXEZ/zV9opzoOraC36P3BqlsYpIrEIEQiVXTFPr/SWFdDC036vXxO/mJs7ijHVDCggpCAeqpYNFKdQz1VLDyFtFxtSyeqsNtQC6v1LbHPUAMGB1pniIiI7MNJsRw+yCwHXzxAV/UB9FDvRVnVTYvf7ymkobP7eXR+1RXitm7AudpA++lAibD8C50NNnf0Qp54hBqqfxGq+huhwt+opfoHHsLTl9rmJUMJ/KFviHX6prgF8w9/JiIiUkoyvLBA3wkL9B1RW7iMN9R70VF9FIVycABDgAjcOAG4Fsm/oC/A5o7+Q4Q/7qGiKgFVhDhUVV1FVSEOQao7ebL1eIMf/jQ0wCZ9Q1wSg/Jkm0RERHlPQIxYHjG68pig64dmqtPopD6CVqoYuAsWXF3nXQbwKZP/Mc1gc1cAqaFHcSEZIUISQoRElBeuo5zqOsoL1+El5O3TMC6kFcGG6CQcKjMMF7ybw1bvLCIiIpKTDidsN9TBdkMduCAdLVSn0EF9FE1VZ8z/zCzfVtmQz2Fz54AEGOCDhygu3EFxIRkl/v9/Swp3UEq4hSDhtsUXjOZUuqjFMUMF7DLUxk5DbVy8cB4pu79DQMmScGZjR0REduwpnPGXoR7+MtSDGnqECn+jpfoUWqpiUF51438Ty4VbLyTY3NkREYWQhsLCQ/giFT5CKnyFB/BB1v/6C/cQINxFUeE+iuIenPKpeXueXhRwVgzBIUNVHDJUxQlD+edW8rZ8CRQiIiJ7oYcax8RKOKarhG/QC0WTj6POma8xpX9rFC7VyKrZ2NzZmkd3gF0TgSf3UP5OPM4PdYef9zQUVj3Nt6NtOfFEdMYpQxlEi+URYyiPGEM5pMLd2rGIiIisKkFXBMePZ2LAvK9QW2Pdx5VZfd2JOXPmICQkBC4uLggNDcWBAweynb9v3z6EhobCxcUFpUuXxrx58yRz1q1bh8qVK8PZ2RmVK1fGhg0bXnq/yhGBkyuBS1vgcfcsKvup4ad+bJXGLl3U4LShNH7RtcSnmQPQKf0rVEtfiN6Zn2Om7g3sNdRkY0dERGRjrHrkbu3atRg5ciTmzJmDRo0aYf78+Wjfvj0uXLiAoCDp3ZRxcXHo0KEDBg0ahJUrV+LQoUMYOnQo/Pz80K1bNwBAVFQUevTogcmTJ6Nr167YsGED3njjDRw8eBD16tXL1X4VZaVbpxMMfrgklsBlsQT+NpTAJbEk/hZLQMeDu0RERHbFqj+5Z86ciQEDBmDgwIEAgIiICGzbtg1z587F1KlTJfPnzZuHoKAgREREAAAqVaqE6OhozJgxw9jcRUREoE2bNhg3bhwAYNy4cdi3bx8iIiKwevXqXO1XUWot4OwJpKfm+aZviYWRIBbFVTEAcYYAXBWzXnFiAJ7AJc/3R0RERMqzWnOXkZGBEydOYOzYsSb18PBwHD58WPY9UVFRCA83vQOlbdu2WLRoETIzM6HVahEVFYVRo0ZJ5jxrCHOzXwBIT09Hevr/1rZ58OABACA1Ne+bMKi8gPQHOXrLXb0rkvSFcEtXCLf1hXBL74EkvSdu6Arjus4LSTpPZMh+3I8AWPZMvdzITEkAAKQn/QNDxsstfGwtjvA9AI7xffB7sB2O8H3we7AdjvB9ZN69DgB49OhR/vQG+F/PIYoveMSnaCU3btwQAYiHDh0yqU+ZMkUsX7687HvKlSsnTpkyxaR26NAhEYB48+ZNURRFUavViqtWrTKZs2rVKtHJySnX+xVFUZwwYYIIgC+++OKLL7744suqr4SEhGx7LKtfUCUIpmufiaIoqb1o/vN1S7aZ0/2OGzcOo0ePNn5tMBhw9+5d+Pj4ZPs++p/U1FSULFkSCQkJ8PT0tHYcegF+XvaFn5d94edlX2zl8xJFEQ8fPkRgYGC286zW3Pn6+kKtViMpKcmkfvv2bfj7+8u+JyAgQHa+RqOBj49PtnOebTM3+wUAZ2dnODs7m9QKFy5s/hskszw9PfmPmR3h52Vf+HnZF35e9sUWPi8vL68XzrHaUihOTk4IDQ3Fjh07TOo7duxAw4YNZd/ToEEDyfzt27cjLCwMWq022znPtpmb/RIRERHZC6uelh09ejTefvtthIWFoUGDBvj5558RHx+PIUOGAMg6FXrjxg0sX74cADBkyBD89NNPGD16NAYNGoSoqCgsWrTIeBcsAIwYMQJNmzbFtGnT0KVLF/zxxx/YuXMnDh48aPF+iYiIiOyVVZu7Hj16ICUlBZMmTUJiYiKqVq2KyMhIlCpVCgCQmJiI+Ph44/yQkBBERkZi1KhRmD17NgIDAzFr1izjMigA0LBhQ6xZswaff/45vvjiC5QpUwZr1641rnFnyX4pfzg7O2PChAmS09tkm/h52Rd+XvaFn5d9sbfPSxDFF91PS0RERET2wuqPHyMiIiKivMPmjoiIiMiBsLkjIiIiciBs7oiIiIgcCJs7UlR6ejpq1qwJQRBw6tQpk7H4+Hh07twZ7u7u8PX1xfDhw5GRkWGdoAXY1atXMWDAAISEhMDV1RVlypTBhAkTJJ8FPy/bMmfOHISEhMDFxQWhoaE4cOCAtSMVeFOnTkWdOnVQqFAhFC1aFK+++iouXbpkMkcURUycOBGBgYFwdXVF8+bNcf78eSslpv+aOnUqBEHAyJEjjTV7+bzY3JGiPv74Y9nHpuj1enTs2BGPHz/GwYMHsWbNGqxbtw4ffvihFVIWbBcvXoTBYMD8+fNx/vx5fP/995g3bx4+/fRT4xx+XrZl7dq1GDlyJD777DOcPHkSTZo0Qfv27U2WkiLl7du3D++//z6OHDmCHTt2QKfTITw8HI8fPzbOmT59OmbOnImffvoJx48fR0BAANq0aYOHDx9aMTkdP34cP//8M6pXr25St5vPK9snzxLlocjISLFixYri+fPnRQDiyZMnTcZUKpV448YNY2316tWis7Oz+ODBAyukpf+aPn26GBISYvyan5dtqVu3rjhkyBCTWsWKFcWxY8daKRHJuX37tghA3LdvnyiKomgwGMSAgADxm2++Mc55+vSp6OXlJc6bN89aMQu8hw8fiuXKlRN37NghNmvWTBwxYoQoivb1efHIHSni1q1bGDRoEFasWAE3NzfJeFRUFKpWrWpyVK9t27ZIT0/HiRMnlIxKMh48eABvb2/j1/y8bEdGRgZOnDiB8PBwk3p4eDgOHz5spVQk58GDBwBg/LsUFxeHpKQkk8/O2dkZzZo142dnRe+//z46duyI1q1bm9Tt6fOy6hMqqGAQRRH9+vXDkCFDEBYWhqtXr0rmJCUlwd/f36RWpEgRODk5ISkpSaGkJOfKlSv48ccf8d133xlr/LxsR3JyMvR6veTz8Pf352dhQ0RRxOjRo9G4cWNUrVoVAIyfj9xnd+3aNcUzErBmzRrExMTg+PHjkjF7+rx45I5ybeLEiRAEIdtXdHQ0fvzxR6SmpmLcuHHZbk8QBElNFEXZOuWcpZ/Xf928eRPt2rXD66+/joEDB5qM8fOyLc//ufOzsC3Dhg3DmTNnTJ6F/gw/O9uQkJCAESNGYOXKlXBxcTE7zx4+Lx65o1wbNmwYevbsme2c4OBgfPXVVzhy5IjkmXxhYWF48803sWzZMgQEBODo0aMm4/fu3UNmZqbktyTKHUs/r2du3ryJFi1aoEGDBvj5559N5vHzsh2+vr5Qq9WSo3S3b9/mZ2EjPvjgA2zatAn79+9HiRIljPWAgAAAWUeEihUrZqzzs7OOEydO4Pbt2wgNDTXW9Ho99u/fj59++sl4p7NdfF5WvN6PCohr166JZ8+eNb62bdsmAhB///13MSEhQRTF/12gf/PmTeP71qxZwwv0reT69etiuXLlxJ49e4o6nU4yzs/LttStW1d87733TGqVKlXiDRVWZjAYxPfff18MDAwU//77b9nxgIAAcdq0acZaenq6TV6gXxCkpqaa/Kw6e/asGBYWJr711lvi2bNn7erzYnNHiouLi5PcLavT6cSqVauKrVq1EmNiYsSdO3eKJUqUEIcNG2a9oAXUjRs3xLJly4otW7YUr1+/LiYmJhpfz/Dzsi1r1qwRtVqtuGjRIvHChQviyJEjRXd3d/Hq1avWjlagvffee6KXl5e4d+9ek79HT548Mc755ptvRC8vL3H9+vXi2bNnxV69eonFihUTU1NTrZicnvnv3bKiaD+fF5s7UpxccyeKWUf4OnbsKLq6uore3t7isGHDxKdPn1onZAG2ZMkSEYDs67/4edmW2bNni6VKlRKdnJzE2rVrG5fbIOsx9/doyZIlxjkGg0GcMGGCGBAQIDo7O4tNmzYVz549a73QZOL55s5ePi9BFEXRCmeDiYiIiCgf8G5ZIiIiIgfC5o6IiIjIgbC5IyIiInIgbO6IiIiIHAibOyIiIiIHwuaOiIiIyIGwuSMiIiJyIGzuiIiIiBwImzsiKvAEQcDGjRvNjl+9ehWCIODUqVN5ut/g4GBERETk6r0TJ06EIAgQBCHX23hZwcHBxgz379+3SgYikmJzR0Q2rV+/fsYGQqPRICgoCO+99x7u3buXZ/tITExE+/bt82x7SqlSpQoSExMxePBgq+z/+PHjWLdunVX2TUTmaawdgIjoRdq1a4clS5ZAp9PhwoUL6N+/P+7fv4/Vq1fnyfYDAgLyZDtK02g0Vs3u5+cHb29vq+2fiOTxyB0R2TxnZ2cEBASgRIkSCA8PR48ePbB9+3aTOUuWLEGlSpXg4uKCihUrYs6cOcaxjIwMDBs2DMWKFYOLiwuCg4MxdepU4/jzp2WPHTuGWrVqwcXFBWFhYTh58qTJvpYuXYrChQub1DZu3AhBEIxfX7lyBV26dIG/vz88PDxQp04d7Ny5M9vvc+LEiQgKCoKzszMCAwMxfPhwS/+ITL6X+fPno1OnTnBzc0OlSpUQFRWFf/75B82bN4e7uzsaNGiAK1eumOy3Zs2aWLx4MYKCguDh4YH33nsPer0e06dPR0BAAIoWLYopU6bkOA8RKY9H7ojIrvz777/YunUrtFqtsbZgwQJMmDABP/30E2rVqoWTJ09i0KBBcHd3R9++fTFr1ixs2rQJv/76K4KCgpCQkICEhATZ7T9+/BidOnVCy5YtsXLlSsTFxWHEiBE5zvno0SN06NABX331FVxcXLBs2TJ07twZly5dQlBQkGT+77//ju+//x5r1qxBlSpVkJSUhNOnT+d4vwAwefJkzJw5EzNnzsQnn3yC3r17o3Tp0hg3bhyCgoLQv39/DBs2DH/99ZfxPVeuXMFff/2FrVu34sqVK+jevTvi4uJQvnx57Nu3D4cPH0b//v3RqlUr1K9fP1e5iEgZbO6IyOZt3rwZHh4e0Ov1ePr0KQBg5syZxvHJkyfju+++w2uvvQYACAkJwYULFzB//nz07dsX8fHxKFeuHBo3bgxBEFCqVCmz+1q1ahX0ej0WL14MNzc3VKlSBdevX8d7772Xo8w1atRAjRo1jF9/9dVX2LBhAzZt2oRhw4ZJ5sfHxyMgIACtW7eGVqtFUFAQ6tatm6N9PvPOO+/gjTfeAAB88sknaNCgAb744gu0bdsWADBixAi88847Ju8xGAxYvHgxChUqhMqVK6NFixa4dOkSIiMjoVKpUKFCBUybNg179+5lc0dk43halohsXosWLXDq1CkcPXoUH3zwAdq2bYsPPvgAAHDnzh0kJCRgwIAB8PDwML6++uor46nHfv364dSpU6hQoQKGDx8uOaX7X7GxsahRowbc3NyMtQYNGuQ48+PHj/Hxxx+jcuXKKFy4MDw8PHDx4kXEx8fLzn/99deRlpaG0qVLY9CgQdiwYQN0Ol2O9wsA1atXN/5/f39/AEC1atVMak+fPkVqaqqxFhwcjEKFCpnMqVy5MlQqlUnt9u3bucpERMphc0dENs/d3R1ly5ZF9erVMWvWLKSnp+PLL78EkHXECcg6NXvq1Cnj69y5czhy5AgAoHbt2oiLi8PkyZORlpaGN954A927d5fdlyiKL8yjUqkk8zIzM02+/uijj7Bu3TpMmTIFBw4cwKlTp1CtWjVkZGTIbrNkyZK4dOkSZs+eDVdXVwwdOhRNmzaVbNcS/z1l/ew6QLnasz+758efzZGr/fc9RGSbeFqWiOzOhAkT0L59e7z33nsIDAxE8eLF8e+//+LNN980+x5PT0/06NEDPXr0QPfu3dGuXTvcvXtXcrdn5cqVsWLFCqSlpcHV1RUAjE3iM35+fnj48CEeP34Md3d3AJCsgXfgwAH069cPXbt2BZB1Dd7Vq1ez/b5cXV3xyiuv4JVXXsH777+PihUr4uzZs6hdu7YlfyxERADY3BGRHWrevDmqVKmCr7/+Gj/99BMmTpyI4cOHw9PTE+3bt0d6ejqio6Nx7949jB49Gt9//z2KFSuGmjVrQqVS4bfffkNAQIDkjlcA6N27Nz777DMMGDAAn3/+Oa5evYoZM2aYzKlXrx7c3Nzw6aef4oMPPsCxY8ewdOlSkzlly5bF+vXr0blzZwiCgC+++CLbo15Lly6FXq83bnvFihVwdXXN9vpAIiI5PC1LRHZp9OjRWLBgARISEjBw4EAsXLgQS5cuRbVq1dCsWTMsXboUISEhAAAPDw9MmzYNYWFhqFOnDq5evWq8UeB5Hh4e+PPPP3HhwgXUqlULn332GaZNm2Yyx9vbGytXrkRkZCSqVauG1atXY+LEiSZzvv/+exQpUgQNGzZE586d0bZt22yPwBUuXBgLFixAo0aNUL16dezatQt//vknfHx8Xv4Pi4gKFEG05AITIiKyKRMnTsTGjRvz/JFoObV37160aNEC9+7dkz0SSkTK45E7IiI7dfbsWXh4eJgs2KykKlWq2OVj24gcHY/cERHZobt37+Lu3bsAsm7w8PLyUjzDtWvXjHfzli5dWvY0NxEpj80dERERkQPhr1lEREREDoTNHREREZEDYXNHRERE5EDY3BERERE5EDZ3RERERA6EzR0RERGRA2FzR0RERORA2NwREREROZD/AzwB3rVproCpAAAAAElFTkSuQmCC"/>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=af211a2b">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -9089,15 +8689,15 @@ The standard deviation of the GNSS residuals is 15.315 mm
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=d431d4f5">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=d431d4f5">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE (probably will be more than one line)</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE (probably will be more than one line)</span>
<span class="c1"># print(f'The critical value is {np.round(k, 3)}')</span>
<span class="c1"># SOLUTION</span>
@@ -9110,19 +8710,6 @@ The standard deviation of the GNSS residuals is 15.315 mm
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The critical value is 7.879
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=89fe3313">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -9139,15 +8726,15 @@ The standard deviation of the GNSS residuals is 15.315 mm
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=92b5b797-093d-46fb-a2c0-2b9ccdae4052">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=92b5b797-093d-46fb-a2c0-2b9ccdae4052">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE (probably will be more than one line)</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE (probably will be more than one line)</span>
<span class="c1"># print(f'The test statistic for InSAR data is {np.round(t_insar, 3)}')</span>
<span class="c1"># SOLUTION</span>
@@ -9164,19 +8751,6 @@ The standard deviation of the GNSS residuals is 15.315 mm
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The test statistic for InSAR data is 95.616
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=8a76e05b">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -9193,15 +8767,15 @@ The standard deviation of the GNSS residuals is 15.315 mm
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=ae6038da">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=ae6038da">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE (probably will be more than one line)</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># YOUR_CODE_HERE (probably will be more than one line)</span>
<span class="c1"># print(f'The test statistic for GNSS data is {np.round(t_gnss, 3)}')</span>
<span class="c1"># SOLUTION</span>
@@ -9218,19 +8792,6 @@ The standard deviation of the GNSS residuals is 15.315 mm
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>The test statistic for GNSS data is 7.788
-</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4ff38f63">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -9247,14 +8808,14 @@ The standard deviation of the GNSS residuals is 15.315 mm
article { position: relative }
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+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
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+<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
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-<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
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+<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
</h3>
<span style="font-size: 75%">
© Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
@@ -9266,7 +8827,4 @@ The standard deviation of the GNSS residuals is 15.315 mm
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diff --git a/synced_files/GA_1_4/Analysis_solution.md b/synced_files/GA_1_4/Analysis_solution.md
index 508404f9..a50ea68d 100644
--- a/synced_files/GA_1_4/Analysis_solution.md
+++ b/synced_files/GA_1_4/Analysis_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.4: Modelling Road Deformation using Non-Linear Least-Squares
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -167,7 +157,6 @@ print(m2['Sigma_Y'])
```
-<!-- #region id="80a9b60f" -->
## Part 1: Set-up Non-Linear Model
In the model we fitted using BLUE, we only considered a linear velocity and a dependency on the groundwater level. However, when a heavy construction is built on 'soft' soil layers, compaction of the upper layers will be observed. This compaction is relatively large in the beginning but decreases when time passes. We can approach the behavior with a simplified model, assuming an exponential decay.
@@ -197,7 +186,7 @@ y_comp = compute_y(x, <auxiliary_arguments>)
```
Where `<auxiliary_arguments>` will be different in type and/or number on a case-by-case basis. Your code will generally be more compact and adaptable to other cases if the parameters are specified in a list, array or tuple.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -385,14 +374,13 @@ print(f'The redundancy (InSAR) is {m1["y"].shape[0] - n_2}')
print(f'The redundancy (GNSS) is {m2["y"].shape[0] - n_2}')
```
-<!-- #region id="b4633d84" -->
## Part 2: Gauss-Newton Iteration
This is an essential step of the algorithm for creating a non-linear least square model. We will use a function to easily repeat the analysis for InSAR and GNSS models. The `while`-loop and its contents are very similar to WS 1.4 earlier this week, with two notable exceptions:
1. the loop is "wrapped" in a function, and
2. the model dictionaries are used, which makes it easier to use existing functions (e.g., `BLUE()`) to find a solution
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -651,9 +639,8 @@ plot_convergence_interactive(gauss_newton_iteration(initial_guess_alternative, m
plot_convergence_interactive(gauss_newton_iteration(initial_guess_alternative, m2))
```
-<!-- #region id="b9c40713" -->
## Part 3: Assessing Results
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -779,7 +766,6 @@ plot_residual(m2)
plot_residual_histogram(m2);
```
-<!-- #region id="af211a2b" -->
## Part 4: Hypothesis Test
In GA 1.3 and GA 1.4 we used two different models:
@@ -787,7 +773,7 @@ In GA 1.3 and GA 1.4 we used two different models:
* GA 1.4: A model with linear and power components
Now we are going to test which model fits the data better. We will do this with the Generalized Likelihood Ratio (GLR) test for both the GNSS and InSAR observations.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
diff --git a/synced_files/GA_1_5/Analysis.html b/synced_files/GA_1_5/Analysis.html
index ed75d4d1..5ce2cce9 100644
--- a/synced_files/GA_1_5/Analysis.html
+++ b/synced_files/GA_1_5/Analysis.html
@@ -3,34 +3,7 @@
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+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
</pre></div>
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<div class="cm-editor cm-s-jupyter">
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+<div class="highlight hl-python"><pre><span></span><span class="n">data</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath_or_buffer</span><span class="o">=</span><span class="s1">'justIce.csv'</span><span class="p">,</span><span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
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"/>
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@@ -7697,15 +7658,15 @@ a.anchor-link {
</div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b07eb42c">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b07eb42c">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">data_2021</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'2021'</span><span class="p">]</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">data_2021</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'2021'</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data_2021</span><span class="o">.</span><span class="n">index</span><span class="p">,</span><span class="n">data_2021</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'green'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'x'</span><span class="p">)</span>
@@ -7717,18 +7678,6 @@ a.anchor-link {
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"/>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f2ebf27a">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7772,10 +7721,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [47]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">h_ice</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_2021</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">())</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">h_ice</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_2021</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">())</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">t_days</span> <span class="o">=</span> <span class="p">((</span><span class="n">data_2021</span><span class="o">.</span><span class="n">index</span> <span class="o">-</span> <span class="n">data_2021</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">days</span><span class="p">)</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
<span class="n">dh_dt_FD</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
@@ -7819,7 +7768,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">,</span> <span class="n">dh_dt_FD</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'dh_dt_FD Forward Difference'</span><span class="p">)</span>\
@@ -7869,7 +7818,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">dh_dt_BD</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">dh_dt_BD</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
@@ -7924,7 +7873,7 @@ a.anchor-link {
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">dh_dt_CD</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">dh_dt_CD</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
@@ -8073,7 +8022,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">5</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">5</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">YOUR_CODE_HERE</span>
@@ -8110,10 +8059,10 @@ $$</p>
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</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [36]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">f_1</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">f_1</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">YOUR_CODE_HERE</span>
<span class="k">def</span> <span class="nf">f_2</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
@@ -8151,10 +8100,10 @@ $$</p>
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</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [39]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">x0</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">x0</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">taylor_1</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">taylor_2</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">taylor_3</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
@@ -8188,7 +8137,7 @@ $$</p>
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<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">,</span> <span class="n">YOUR_CODE_HERE</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'$f(x) = 2cos(x) + sin(x)$'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
@@ -8256,7 +8205,7 @@ where $T_n$ refers to the TSE computed using $n$ number of terms (or derivatives
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">error_1</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">error_1</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">error_2</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">error_3</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">error_4</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
@@ -8366,10 +8315,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [44]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">f2D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">f2D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">x0</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
@@ -8416,7 +8365,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Create a meshgrid of x and y values</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># Create a meshgrid of x and y values</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="o">+</span><span class="n">x0</span><span class="p">,</span> <span class="mi">2</span><span class="o">+</span><span class="n">x0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="o">+</span><span class="n">y0</span><span class="p">,</span> <span class="mi">2</span><span class="o">+</span><span class="n">y0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
@@ -8481,7 +8430,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">error_2d</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">error_2d</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">9</span><span class="p">))</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">'3d'</span><span class="p">)</span>
@@ -8512,11 +8461,11 @@ $$</p>
article { position: relative }
</style>
<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">
-<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px">
-</img></a>
+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
+</a>
<a href="https://www.tudelft.nl/en/ceg" rel="TU Delft">
-<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
-</img></a>
+<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
<a href="http://mude.citg.tudelft.nl/" rel="MUDE">
<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
</a>
@@ -8531,7 +8480,4 @@ $$</p>
</div>
</main>
</body>
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-{"state": {}, "version_major": 2, "version_minor": 0}
-</script>
</html>
diff --git a/synced_files/GA_1_5/Analysis.md b/synced_files/GA_1_5/Analysis.md
index 2ce52d1f..e980d65b 100644
--- a/synced_files/GA_1_5/Analysis.md
+++ b/synced_files/GA_1_5/Analysis.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
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- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Project 3: Numerical Derivatives and Taylor Series Approximations
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_1_5/Analysis_solution.html b/synced_files/GA_1_5/Analysis_solution.html
index 2551b04e..a608511e 100644
--- a/synced_files/GA_1_5/Analysis_solution.html
+++ b/synced_files/GA_1_5/Analysis_solution.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
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- document.body.appendChild(scriptElement);
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- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7596,10 +7569,10 @@ a.anchor-link {
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</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
</pre></div>
@@ -7634,15 +7607,15 @@ a.anchor-link {
</div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=87dd870e">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=87dd870e">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath_or_buffer</span><span class="o">=</span><span class="s1">'justIce.csv'</span><span class="p">,</span><span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">data</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath_or_buffer</span><span class="o">=</span><span class="s1">'justIce.csv'</span><span class="p">,</span><span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
@@ -7655,18 +7628,6 @@ a.anchor-link {
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
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"/>
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@@ -7697,15 +7658,15 @@ a.anchor-link {
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b07eb42c">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">data_2021</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'2021'</span><span class="p">]</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">data_2021</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'2021'</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data_2021</span><span class="o">.</span><span class="n">index</span><span class="p">,</span><span class="n">data_2021</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'green'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'x'</span><span class="p">)</span>
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-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f2ebf27a">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7772,10 +7721,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">h_ice</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_2021</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">())</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">h_ice</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_2021</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">())</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">t_days</span> <span class="o">=</span> <span class="p">((</span><span class="n">data_2021</span><span class="o">.</span><span class="n">index</span> <span class="o">-</span> <span class="n">data_2021</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">days</span><span class="p">)</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
<span class="c1"># dh_dt_FD = YOUR_CODE_HERE</span>
@@ -7814,15 +7763,15 @@ a.anchor-link {
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=25592875">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=25592875">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="c1"># ax1.scatter(YOUR_CODE_HERE, dh_dt_FE,</span>
<span class="c1"># color='blue', marker='o', label='dh_dt_FE Forward Euler')</span>
@@ -7853,18 +7802,6 @@ a.anchor-link {
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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@@ -7880,15 +7817,15 @@ a.anchor-link {
</div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=108dea5f">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=108dea5f">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># dh_dt_BE = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># dh_dt_BE = YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="n">dh_dt_BD</span> <span class="o">=</span> <span class="p">(</span><span class="n">h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">h_ice</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">t_days</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">t_days</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
@@ -7928,18 +7865,6 @@ a.anchor-link {
</div>
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-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=680f937d">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7957,15 +7882,15 @@ a.anchor-link {
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=8c2a379e">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8c2a379e">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># dh_dt_CD = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># dh_dt_CD = YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="n">dh_dt_CD</span> <span class="o">=</span> <span class="p">(</span><span class="n">h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">h_ice</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">t_days</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">t_days</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
@@ -8006,18 +7931,6 @@ a.anchor-link {
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=72be2ce6-601c-4976-94bf-cfad7af4607d">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># x = np.linspace(-3*np.pi, 5*np.pi, 400)</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># x = np.linspace(-3*np.pi, 5*np.pi, 400)</span>
<span class="c1"># def f(x):</span>
<span class="c1"># return YOUR_CODE_HERE</span>
@@ -8227,18 +8140,6 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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"/>
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<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f8641d16-274a-43cf-ad20-d31ad9b41ab2">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8262,10 +8163,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># def f_1(x):</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># def f_1(x):</span>
<span class="c1"># return YOUR_CODE_HERE</span>
<span class="c1"># def f_2(x):</span>
@@ -8316,10 +8217,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># x0 = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># x0 = YOUR_CODE_HERE</span>
<span class="c1"># taylor_1 = YOUR_CODE_HERE</span>
<span class="c1"># taylor_2 = YOUR_CODE_HERE</span>
<span class="c1"># taylor_3 = YOUR_CODE_HERE</span>
@@ -8351,15 +8252,15 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=5b571bb0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=5b571bb0">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="c1"># plt.plot(YOUR_CODE_HERE, YOUR_CODE_HERE,</span>
<span class="c1"># label='$f(x) = 2cos(x) + sin(x)$', color='b', linewidth=2)</span>
@@ -8402,18 +8303,6 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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9YsANLT0ylfvnyWy9y5c4dy5cple76mpaURExNjUaze3t7ZHjtQ6zF27tyZ2NhYduzYQUBAgNnr//77L3fv3qVRo0bZbuPcuXOkp6dnmUhkNGxRq1YtQL1pcPbsWX7++Wd+++03pk6dysyZM9m4caPpaVpux9GSYwi5H8eMJ433mzBhAn5+frzyyitm87N70mjJ+wkPDycoKAg3NzfTtJ+fX6bk6ejRo9jY2JiOVVEeCyHKKknIhBCFavXq1QwaNMiscndKSgqxsbGZlu3Xrx/jx49n5cqVJCcnY2tryzPPPJPttr29vSlXrhxnzpzJ9Nrp06exsrKiYsWKBfI+7te4cWMAoqKi8Pb2xsXFBYPBkGWrdvmV1+36+/szcuRIRo4cyY0bN2jYsCGzZs0q0IQsPDycmTNnMmTIEI4cOcKwYcM4duyY6QdehgcTtfPnz2M0Gs36jcvLeZEXeTkORXX+FNZ+MoqgPZjw16xZE1Bbxsvq6dl7773H4sWL+fjjj3nllVeYNWsWixcvzrRcREQEoaGh2e4/o58+S+TUb2BKSgrdunXj7NmzbN682SwRyJDRumdOT1YyXssovni/JUuWEBwcbNY4j7OzMwMHDmTgwIGcOXOGunXr8uOPP5oSmJyOo6XHMGP9nI5jxpPGB+f5+/vn6Tslt/cTHh5u9j6OHTtGWFhYpu2Eh4cTEhJidhyL6lgIUVZJkUUhRKGytrbO9PTgo48+wmAwZFrWy8uLzp078+233/Ldd9/l2jGxtbU1TzzxBL/88otZcbjo6GhWrFhBq1atsi3eZImtW7dm+eQjoy5UjRo1sLa25umnn+ann37i+PHjmZa9efNmvvZt6XYNBkOmIn4+Pj4EBASQmpqar31nRa/XM3jwYAICAvjggw9YtmwZ0dHRZs1aZ/jkk0/Mpj/66CMAs6QoL+eFJfJzHAr7/Cns/Rw9ehTInJBltEh64MCBTOusXbuW1157jbfeeotRo0bxwgsv8M033xAREZFp2UOHDtGiRYts918QdcgMBgPPPPMMu3fvZtWqVdm2pppRbHnz5s2ZXtPr9YBaNNbGxiZTK34//vgj27dvZ9KkSeh0ukwtigKm7gPufzKX3XHMyzGE3I/jw7Lk/RgMBk6ePGk6VwwGA6dOncoyIcuqGGxJORZClFTyhEwIUaiefPJJli9fjpubG7Vq1WL37t1s3rzZ1KT6gwYOHEjv3r0BeOutt3Ld/ttvv82mTZto1aoVI0eOxMbGhs8//5zU1FTmzp37ULGPGTOGu3fv0qtXL2rWrElaWhq7du3ihx9+oFKlSqa6XO+88w5bt26lWbNmDB8+nFq1ahETE8OhQ4fYvHmzxcW6HmTJdhMSEggMDKR3796EhYXh7OzM5s2b2b9/f6Ymp7OzYcMGU72R+7Vo0cLUCMXbb7/NkSNH2LJlCy4uLtSrV49p06bxxhtv0Lt3b7p06WJaLyIigu7du9OpUyd2797Nt99+y4ABA8x+/OX1vMhNfo9DYZ4/hb2f8PBwKlSokKmIWJUqVahTpw6bN2826x/q4MGDPPvsszz77LNMmTIFgFdffZXPPvss01ONgwcPEhMTQ48ePbLdf1ZPdvJqwoQJ/Prrr3Tr1o2YmBi+/fZbs9czGvRxdXVl8ODBLFu2jNTUVNq0aUNCQgJbt26la9eujBo1CicnJ1588UU+++wzbGxsqFevHnv37uWrr77iueeeY8SIEYD6uT5+/Djdu3encuXKREVF8cUXXxAYGMjw4cNzPI55OYaWHseHZcn7OXfuHCkpKabirRnTDyZkycnJnD9/PlMx6ZJyLIQosbRp3FEIUZrk1Oz9nTt3lCFDhiheXl6Ks7Oz0rFjR+X06dNKcHBwls06p6amKh4eHoqbm1umpq0VJeumyw8dOqR07NhRcXZ2VsqVK6e0bdtW2bVrl9l6Gc2W37x50+L3tWHDBuX5559XatasqTg7Oyt2dnZKtWrVlDFjxijR0dFmy0ZHRyujRo1SKlasqNja2ip+fn5Ku3btlC+++MKiGLJ6X5ZsNzU1VXnllVeUsLAwxcXFRXFyclLCwsJMTVvnJKdm7wFl6dKliqIoysGDBxUbGxtlzJgxZuunp6crTZo0UQICApQ7d+6Y3t/JkyeV3r17Ky4uLoqHh4cyevToTP9LS8+L7I7Zg8fLkuOQ3TF+mPMnu21mxZL95KXZ+6ZNmyqdO3fO8rUFCxYozs7Opu4FIiMjFX9/f6Vly5amJuIzjBgxQrG1tVX+/fdf07xJkyYpQUFBitFozDWOh9G6descz8H7JSUlKVOmTFGqV6+u2NvbK/7+/srTTz+tnD9/3rTM3bt3ldGjRyteXl6Ko6OjEhYWpixatMjsfSxZskR5/PHHFW9vb8Xe3l6pVq2a8vLLLys3btzIFN/9xzGvx1BR8n8c89LsvSXv58cff1QA5cSJE2bTx48fN9vWvn37FEBZt25dpv1odSyEKAt0iqJBTWQhhMhGenq6qaPSJUuWaB2OyIMZM2bw5ptvcvPmzRyLmorCFxcXR5UqVZg7dy5Dhw7N07qpqalUqlSJ1157jbFjxxZShCWDHMd75FgIUXikDpkQolhZu3YtN2/eZODAgVqHIkSJ5ebmxquvvsp7772Xbd9V2Vm6dCm2trZ56rOvtJLjeI8cCyEKjzwhE0IUC3v37iU8PJy33noLLy+vHDs4FcWTPCETQggh8k6ekAkhioVFixYxYsQIfHx8+Oabb7QORwghhBCiSJTIhGz79u1069aNgIAAdDoda9euNXtdURSmTZuGv78/jo6OtG/fnnPnzuW63U8++YRKlSrh4OBAs2bN2LdvXyG9AyHEg5YtW0Z6ejoHDhww6ytIlBwzZsxAURR5OiaEEELkQYlMyJKSkggLC8vU102GuXPn8uGHH/LZZ5+xd+9enJyc6NixIykpKdlu84cffmD8+PFMnz6dQ4cOERYWRseOHblx40ZhvQ0hhBBCCCFEGVfi65DpdDrWrFlDz549AfXpWEBAABMmTGDixImA2jKQr68vy5Yto1+/fllup1mzZjRp0oSPP/4YAKPRSMWKFRkzZgyvvfZakbwXIYQQQgghRNlS6jqGjoiI4Pr162adVbq5udGsWTN2796dZUKWlpbGwYMHmTx5smmelZUV7du3Z/fu3dnuKzU1ldTUVNO00WgkJiYGT09PdDpdAb0jIYQQQgghREmjKAoJCQkEBARgZZV9wcRSl5Bdv34dAF9fX7P5vr6+ptcedOvWLQwGQ5brnD59Ott9zZkzhzfffPMhIxZCCCGEEEKUVpGRkQQGBmb7eqlLyIrS5MmTGT9+vGk6Li6OoKAgIiIicHFx0TCykkOv17N161batm2Lra1tjsvq1q5FqVcPqlQpouhEaZKXc02IhyHnmigqhXGuJZ1I4ljrYwC4tHSh9i+1C2S7JYqiYNOmDboTJwBIX70apU0bbWPSmHyv5U9CQgKVK1fONS8odQmZn58fANHR0fj7+5vmR0dHU79+/SzX8fLywtramujoaLP50dHRpu1lxd7eHnt7+0zzy5cvj6uraz6iL3v0ej3lypXD09Mz9w/40KFFE5QolfJ0rgnxEORcE0WlMM61+C3xOOEEQNX+VfH09CyQ7ZYov/8O/yVjNGkCTz0FZbwqinyv5U/GscqtKlOJbGUxJ5UrV8bPz48tW7aY5sXHx7N3716aN2+e5Tp2dnY0atTIbB2j0ciWLVuyXUcIIYQQorSx9bbFsZojAF69ymgXFo6OkHETf8qUMp+MicJXIhOyxMREjhw5wpEjRwC1IY8jR45w+fJldDod48aN4+233+bXX3/l2LFjDBw4kICAAFNLjADt2rUztagIMH78eL788ku+/vprTp06xYgRI0hKSmLIkCFF/O6ERdLSYPFiiI3VOhIhhBCi1AgcHUjTs01perYp9gGZSwGVCY8/DocOwebN0K2b1tGIMqBEFlk8cOAAbdu2NU1n1OMaNGgQy5Yt49VXXyUpKYkXXniB2NhYWrVqxcaNG3FwcDCtc+HCBW7dumWafuaZZ7h58ybTpk3j+vXr1K9fn40bN2Zq6EMUA1u3wqBBEBkJUVEwdarWEQkhhBClhk6no1z1clqHoS2dDtq10zoKUUaUyISsTZs25NR9mk6nY+bMmcycOTPbZS5evJhp3ujRoxk9enRBhCgKU1AQXLumjr//PowbB9KIiihhFEUhPT0dg8GgdSiigOn1emxsbEhJSZH/bylkbW2NjY2NdG8jhCgwJTIhE2Vc1aowYAAsXw4xMfDppzBpktZRCWGxtLQ0oqKiuHv3rtahiEKgKAp+fn5ERkbKj/ZSqly5cvj7+2NnZ6d1KAUm7UYahgQDjlUdtQ5FO3PmQJs2IO0HiCImCZkomaZMgW+/BUWB+fNh9GhwctI6KiFyZTQaiYiIwNramoCAAOzs7ORHeyljNBpJTEzE2dk5x45ARcmjKAppaWncvHmTiIgIqlevXmr+x1GLo4iYEoFzI2dCPg3BtWkZay362DF4/XV1/Nln1d8YQhQRSchEyVSjBjzzDHz/Pdy8CZ9/Dvf1CSdEcZWWlobRaKRixYqUK1fG62iUUkajkbS0NBwcHErNj3Vxj6OjI7a2tly6dMn0fy4NbvxwA4DEg4nY+ZaeJ38Wmz373njjxtrFIcokuVKIkmvKlHvj770HycnaxSJEHskPdSFKrtL2+U06nURSeBIALs1ccAguHUmmxU6dgh9+UMe9vGD4cG3jEWVO6fpGEWVLnTpqZ40A16/DkiXaxiOEEEKUQDd/vGka93nGR8NINPL222oVCIBXX5UqEKLISUImSrb7m7x/911ITdUuFiGEEKIEuvHjDdO4d29vDSPRwOnTsHKlOu7lBSNHahuPKJMkIRMlW/369zptjI6GPXs0DUcIIYQoSZJOJHH3hNriq2tLVxwqlrHiivc/HZs4UZ6OCU1Iox6i5Js6FSpWVJu+DwrSOhohhBCixLj/6ZhP3zJWXPHMmXtPxzw9YdQobeMRZZY8IRMlX5Mm8MknkowJUYQ++ugjgoODsbGxYciQIfj4+HDx4kWL1+/Xrx/z588vvAA1FhMTg5+fn8XHRMvj8eKLL/Lss8/maZ3bt2/n6X9e2v/fJZWiKKbWFdGVweKKb78NRqM6PnEiODtrG48osyQhE0IIkSdHjx5l/PjxLFq0iMjISDw8POjRoweVKlWyeBtvvPEGs2bNIi4urkBjmzNnDk2aNMHFxQUfHx969uzJmTNnCnQflpg/fz7du3e3+JgU1vGwxJw5c/jiiy/ytM6sWbPy9D/X8v2J7CUdSyL5jNpCsdujbtgH2GscURHS6+HSJXW8fHl5OiY0JQmZKH2Sk9VKukKIQrFu3TqaNm1Kly5dcHNzY8mSJQwdOjRP26hTpw5Vq1bl2wLufPXvv/9m1KhR7Nmzh02bNqHX63niiSdISkoq0P3k5O7duyxfvpznn3/e4nXyejzatGnDsmXL8hmhufLly+OUh3ozd+/ezfP/vLD+3+LhKAYFzyc90dnqyl5xRVtb+Ptv2LQJPvoIXFy0jkiUYZKQidLDYIB586ByZejVS50WQhSoatWq8cYbb7Br1y50Oh1eXl7Y29vzyCOPmC23cuVKHB0diYqKMs0bMmQI9erVMz0l6datG99//32Bxrdx40YGDx5M7dq1CQsLY9myZVy+fJmDBw+aLXf58mUGDRqEr68vjo6OhIWF8c8//5heP378OF26dMHV1RU/Pz8mTJhAWloaoHb8PHv2bKpXr46DgwO+vr4MHjzYtO769eszHROtjkdu8V68eBGdTmcqenjhwgV0Oh3r1q2jXbt2lCtXjho1arB3795i+/5E/rk0cKHub3VpEd0C34G+WodT9HQ6aN8eBgzQOhJRxklCJkoPKyv45Re1tcXTp+Gnn7SOSAiLNW4MgYFFPzRunLc4d+3aRZUqVXjvvfeIioqif//+NGrUKNNy/fr1IyQkhNmzZwMwffp0Nm/ezIYNG3BzcwOgadOm7Nu3j9QsuquYPXs2zs7OOQ6XL1/ONd6MZKB8+fKmeZcuXaJp06YkJyfz66+/Eh4ezujRo3F1dQXg8OHDtGjRgoYNG3Lo0CG+//57Vq5cybvvvguoRfy+//57vvjiC86cOcOaNWt47LHHTNv/559/CAsLK9Dj8TByivfo0aO4u7ubih4ePXoUnU7HggULmDp1KkePHiUoKIjXXnvNtL0dO3Zk+p9r+f7Ew7P1sMXGRdp5E0Ir8ukTpYdOp7a42LGjOv3WW9C7t5qoCVHMXb8OV69qHUXunJ2duXjxIq1atcLPz4/bt28TEBCQaTmdTsesWbPo3bs3fn5+fPTRR+zYsYMKFSqYlgkICCAtLY3r168THBxstv5LL71E3759c4wlq/3ez2g0Mm7cOFq2bEmdOnVM80eMGMEjjzzCjz/+aJpXvXp10/jw4cN57rnnePvttwH1qeCQIUNYt24dU6dO5Y8//qBbt260bdsWgODgYFq0aGFa/9KlS/j7+xfo8XgYOcV75MgR6tWrZ1o2I0H74Ycf8PZWG3jo3r07n3/+udn7e/DYa/n+hMiTiAi4cAHatVN/NwhRDEhCJkqXDh2gWTPYuxeOH4fVqyGXH3VCFAd+fiVjv+Hh4QDUrVsXgOTkZBwcsu636Mknn6RWrVrMnDmTP//8k9q1a5u97ujoCKh1kh5Uvnx5s6da+TFq1CiOHz9uVhTx0qVLbNiwgcOHD2e5zunTpzl48GCmuk52dnamJzvdu3dn0qRJHDhwgD59+vD000/j4eFhWjY5OdmUzNzvYY7H7NmzTU+fMvaxZ88eRo8ebZp38uRJgrJobTaneI8ePUr9+vVNyx49epQePXqYxR8REUG1atXM9p3V//xh3p8oWoqicGfzHdxbu2NlV8ZuWs6cCcuWQYsW6t/7bsYIoRVJyETpotPBm29Cp07q9IwZ8PTTYG2taVhC5ObAAa0jsMyRI0eoVq2aqREILy8v7ty5k+WyGzdu5PTp0xgMBnx9M9dPiYmJAcgyeXkwAclKdgkIwOjRo1m3bh3bt28nMDDQLH47OzuzJOR+J06cwNbWlpCQkEz7ykhCJ06cSPfu3Vm7di0LFy40JTuVK1cG1GMSGxubadsPczwefGL47LPP8vTTT/PUU0+Z5mX3xDCneI8cOcKTTz5pWvbo0aNMnjzZbP0jR46YFcnM7n/+MO9PFK2EfQmEPxGOjYcNlaZXInBsYO4rlQYXLsDy5er4yZPgU8YaMhHFVhm7LSLKhCeeUO98AZw6BfcVSxJCPJwjR46Y1Y9q0KABJ0+ezLTcoUOH6Nu3L0uWLKFdu3ZMnTo10zLHjx8nMDAQLy+vTK+99NJLHDlyJMchqwREURRGjx7NmjVr+Ouvv0xJUgZbW1vS09OzfUrj4uKCwWBAr9eb5kVERLBmzRqzvrpCQkJ49dVXOXjwIAkJCWbHoEGDBpma2n/Y41G+fHmqVatmGhwdHfHx8TGbZ2OT/T3WrOKNj4/n4sWLpuQ0Li6Oixcv0qBBA7N1jxw5YpbAZvU/f9j3J4pW9MpoANLvpGPtWoZuWM6efa/Br//7P/ivfqMQWpMnZKL00enUIgnt26vTM2ZAnz6Qw48VIYRljhw5Qvfu3U3THTt2ZPLkydy5c8dUDO7ixYt07dqV119/nf79+1OlShWaN2/OoUOHaNiwoWndHTt28MQTT2S5n/wWWRw1ahQrVqzgl19+wcXFhevXrwPg5uaGo6MjzZo1w83NjREjRvDaa6+hKArbt2+nXbt2VK9enWbNmuHu7s5rr73GmDFjuHjxIqNHj6Zfv3506tSJuXPn4ufnR5MmTbCysuLzzz/H09PTrA7ZE088weuvv86dO3fw9PQskOORXznFe/ToUaytrU1FC8PDw7GxsTE9CQS1iOedO3fMErIH/+davj+Rd4pB4eYPNwHQ2enw6lVGEuR//4Wvv1bH3dzg5Ze1jUeI+8gTMlE6Pf44ZBSxOXsWVq7UNh4hSgGj0cixY8fMnpDVrVuXhg0bmhrIiImJoVOnTvTo0cPUMl+zZs3o3Lkzr7/+umm9lJQU1q5dy/Dhwws0xkWLFhEXF0ebNm3w9/c3DT/88AMAnp6e/Pbbb5w7d44mTZrQqlUrfv31V3z+K7rk5ubG2rVr2b59O7Vr12b48OEMHDiQpUuXmuKeNWsWDRs2pFWrVvz777/89ddfZnXI6tatS1hYGD/++KPmxyOneI8ePUrNmjWxt1c7Az569Cg1atQwqx92+PBhs1YYM95fxv9c6/cn8i52Wyxp19UuHDy7emLrbqtxREXk/qdj48aBu7uW0QhhRqcoiqJ1EKVFfHw8bm5uxMXFmZpPFjnT6/WsX7+eLl26YGtbwBeFv/+GNm3U8bAwOHxYWlQqwwr1XMuDlJQUIiIiqFy5craNYZQ0v//+O6+88grHjx/HysJWTRctWsSaNWv4888/Czm6omc0Glm1ahVvvvmmxcekpB2PvP7PS9r7y01x+Rzn53vt9LDTXF+iPjmu9WMtfPqUgXpUEREQEgLp6erTsYsXJSHLo+JyDS1pLM0NpAyXKL1at4auXaFmTXj1VUnGhCgkXbt25dy5c1y9epWKFStatI6trS0fffRRIUemnY4dO3Lt2jWLj0lJOx55/Z+XtPdXWhlTjdxcrRZXtHa2xvNJT40jKiKzZ6vJGMDYsZKMiWJHEjJRuv32myRiQhSBcePG5Wn5YcOGFU4gxcjYsWMtfmJYEo9HXv7nJfH9lUYxG2MwxKnF9rx6eWHtWAYa9Lh4UW3eHsDVVS2uKEQxI3XIROkmyZgQQggB3GtdEcCnfxkoqgjwyy/mT8fuq+8pRHEhCZkoW+Li4L/OXYUQQoiyIj0xndu/3gbAxtMGj/ZlJDEZOxZ27oTu3eXpmCi2JCETZUNCAsyaBZUqwZIlWkcjhBBCFCljipEKoypgV8EOnz4+WNmWoZ+ALVqoT8ry0ZWGEEWhDH0aRZl29iy88QbExqqJWUqK1hEJIYQQRcbOy46q71Wl+eXmVHm3itbhCCHuIwmZKBsaNYIePdTxa9fgiy+0jUcIIYTQgM5Kh41rGWjTbdcuMBq1jkIIi0hCJsqOGTPujc+ZA3fvahaKEEIIIQrJoUPQsiU0bgybN2sdjRC5koRMlB3168PTT6vj16/DZ59pGo4QQghRFKJXRpNypQwV1Z86Vf17+DCcPq1tLEJYQBIyUbbMmHGvKfx33oGkJE3DEaK0aNOmTZ77IitOSnr8QmQn5XIKpwacYk/QHk4/XwaSk127YP16dTwoCIYP1zYeISwgCZkoW+rUgb591fGbN+GTT7SNR4gSZPDgweh0ukzD+fPn+fnnn3nrrbceavs6nY61a9datOy6deto3bo1Li4ulCtXjiZNmrAso/NXIYRJ9Ir/+h5TwKGyg7bBFIWMp2MA06aBvb12sQhhIUnIRNkzffq9p2Rz56pN4gshLNKpUyeioqLMhsqVK1O+fHlcXFyyXS8tLa3AYvjoo4/o0aMHLVu2ZO/evYSHh9OvXz9eeuklJk6cmO16iqKQntFBbAEzGAwYpQEBUcwoikL08nudQfs+66thNEXgr7/UAaBaNRg4UNt4hLCQJGSi7AkNhQED1PHbt+HTT7WNR4gSxN7eHj8/P7PB2to6U5G/SpUq8dZbbzFw4EBcXV154YUXSEtLY/To0fj7++Pg4EBwcDBz5swxLQ/Qq1cvdDqdafpBkZGRTJgwgXHjxjF79mxq1apFtWrVmDBhAu+99x7z589n7969AGzbtg2dTseGDRto1KgR9vb2/PPPPyQlJTFw4ECcnZ3x9/dn/vz5mfaTmprKxIkTqVChAk5OTjRr1oxt27aZXl+2bBnu7u78+uuv1KpVC3t7ey5fvlwgx1iIgpJ4JJG7J9UGrFxbuuJYxVHjiAqRoqjd22SYMQNsbTULR4i8KAPtngqRhWnTYN06ePlleOklraMRAoAFuxewYPeCXJdr6N+QX/v/ajav+8ruHIo6lOu645uPZ3zz8fmOMS/mzZvHtGnTmD59OgAffvghv/76Kz/++CNBQUFERkYSGRkJwP79+/Hx8WHp0qV06tQJa2vrLLe5evVq9Hp9lk/CXnzxRV5//XVWrlxJs2bNTPNfe+015s2bR5UqVfDw8OCVV17h77//5pdffsHHx4fXX3+dQ4cOUb9+fdM6o0eP5uTJk3z//fcEBASwZs0aOnXqxLFjx6hevToAd+/e5d1332Xx4sV4enri4+NTUIdOiAIR/e29p2N+z/lpGEkR2LABdu9Wx2vXhn79tI1HiDyQhEyUTSEhcPUqODlpHYkQJvGp8VxNuJrrchXdKmaad/PuTYvWjU+Nz1dsGdatW4ezs7NpunPnzqxatSrLZR9//HEmTJhgmr58+TLVq1enVatW6HQ6goODTa95e3sD4O7ujp9f9j8cz549i5ubG/7+/ples7Ozo0qVKpw9e9Zs/syZM+nQoQMAiYmJLFmyhG+//ZZ27doB8PXXXxMYGGgW59KlS7l8+TIBAQEATJw4kY0bN7J06VJmz54NgF6v59NPPyUsLCzbeIXQimJQuLHiBgA6Wx3efbw1jqgQPfh0bOZMyOamjhDFkSRkouySZEwUM672rlRwqZDrct7lMv+w8i7nbdG6rvau+YotQ9u2bVm0aJFp2imHz1Hjxo3NpgcPHkyHDh2oUaMGnTp14sknn+SJJ554qHgscX8cFy5cIC0tzewJWvny5alRo4Zp+tixYxgMBkJCQsy2k5qaiqenp2nazs6OevXqFWLkQuTfnb/ukHZdrbvp2dUT2/KluPje3r1qE/cADRpAr17axiNEHklCJkSGpCRJ0oSmHqY44YNFGAuLk5MT1apVs3jZ+zVs2JCIiAg2bNjA5s2b6du3L+3bt2f16tUW7z8kJIS4uDiuXbtmenqVIS0tjQsXLtC2bdsc48hNYmIi1tbWHDx4MFPRyfufDjo6OqLLaCBIiGLGrDGP50p5Yx6PPKImZNOmqdUQ5HMpShhp1EOIO3dg8mSoUAEuXNA6GiFKNVdXV5555hm+/PJLfvjhB3766SdiYmIAsLW1xWAw5Lj+008/ja2tbZYNcXz22WckJSXRv3//bNevWrUqtra2poY/AO7cuWNWzLFBgwYYDAZu3LhBtWrVzIacilMKUVwYkgzc/PkmADbuNnh29cxljVKgfn349Vfo0kXrSITIM3lCJsTHH6udRIN6d+2777SNR4hSasGCBfj7+9OgQQOsrKxYtWoVfn5+uLu7A2pLi1u2bKFly5bY29vj4eGRaRtBQUHMnTuXCRMm4ODgwHPPPYetrS2//PILr7/+OhMmTDArjvggZ2dnhg4dyiuvvGJqiGPKlClYWd27PxkSEsKzzz7LwIEDmT9/Pg0aNODmzZts2bKFevXq0bVr1wI/NkIUKCuo/mF1opdH41jDESt7uf8uRHEmn1Ahxo6FjHohK1dCeLi28QhRSrm4uDB37lwaN25MkyZNuHjxIuvXrzclQ/Pnz2fTpk1UrFiRBg0aZLudcePGsWbNGnbs2EHjxo2pU6cOK1asYNGiRcybNy/XON577z0effRRunXrRvv27WnVqhWNGjUyW2bp0qUMHDiQCRMmUKNGDXr27Mn+/fsJCgp6uIMgRBGwdrTG/3l/6m+tT8inIbmvUFKdPw+5PFUXoiTQKYqiaB1EaREfH4+bmxtxcXG4uj5cxfmyQq/Xs379erp06YKtlv2FLFgAGa3BPfkk/PabdrGIQlFczrWUlBQiIiKoXLkyDg4OmsUhCo/RaCQ+Ph5XV1ezJ2+i9Cgun+Pi8r2midRUtcXkcuXgzTehTx+pO1aIyvS59hAszQ3kSiEEwMiRkNHs9bp1sHOntvEIIYQQInuLFsHly3D6NHz9tSRjokSThEwIAAcH+K/zWkBt5EMeHgshhChhIqZFcGfrHRRjKb6GxcXB22/fm541S7tYhCgAkpAJkWHwYLX4A8COHbBxo6bhCCGEEHmReCyRS29d4ujjRzn+1HGtwyk88+bB7dvq+IABaguLQpRgkpAJkcHGxvyO2+uvg9GoXTxCCCFEHlz/+rpp3KN95lZKS4Xr19V63wC2tvDWW9rGI0QBkIRMiPs9/TQ0bKiOHzkCP/2kaThCCCGEJYzpRqK/VTuD1tnq8O1fSjuDnjkT7t5Vx196CapU0TYeIQqAJGRC3M/KCubMAQ8PmDtXbXFRCCGEKObu/HEHfbQeAM9unth6lsKW8M6dgy++UMedneGNN7SNR4gCUioTskqVKqHT6TINo0aNynL5ZcuWZVpWmqMuwzp0gEuX4JVXwNFR62iEEEKIXN1fXNFvsJ+GkRSiN9641+/YxIng46NtPEIUEButAygM+/fvx3BfR4HHjx+nQ4cO9OnTJ9t1XF1dOXPmjGlaJ82nll06Hbi4aB2FEEIIYRF9jJ5bv9wCwNbblvKdymscUSG4cgXWrFHHfXxg/Hht4xGiAJXKhMzb29ts+p133qFq1aq0bt0623V0Oh1+fnm7o5SamkpqaqppOj4+HlA7z9Pr9XnaVlmVcZyK/fFKTpanZSVccTnX9Ho9iqJgNBoxSqMxpZLyX5cZGf9nUfoYjUYURUGv12Ntba1ZHBnfZ9HfR6Okqeed9wBvDBgw6A05rVry+PrCkSNYT5+O8uijGB0coLj/dihFiss1tKSx9HiVyoTsfmlpaXz77beMHz8+x6deiYmJBAcHYzQaadiwIbNnz6Z27do5bnvOnDm8+eabmeZve2kb9mH2GEIMYP/Qb6FM2LRpk9YhZMk+NpaQH38kYPdutnzyCenlymkdknhIWp9rNjY2+Pn5kZiYSFpamqaxlCUjR44kLi6O7777rsj2mZCQgIeHB99++y1du3Ytsv2KwpeWlkZycjLbt28nPT1d63C48MkFbP77SXem0hlOrT+lcUSF6H//U/sJXb9e60jKJK2voSXN3YwGaHJR6hOytWvXEhsby+DBg7NdpkaNGnz11VfUq1ePuLg45s2bR4sWLThx4gSBgYHZrjd58mTG3/fIPD4+nooVK+Kw2gGn1U7obHU4N3LGtZUrbm3dcG3lirWjdnfSiiO9Xs+mTZvo0KEDtrbFrwKy9fDhWP33pd8pPBzjzJkaRyTyq7icaykpKURGRuLs7Fzi6qrevHmT6dOns379eqKjo/Hw8KBevXpMnTqVli1bah1ejmxtbbGxscHV1TXbZQwGAx999BFLly7l3LlzODo60qxZM6ZMmZKn96coCgkJCbj8V/TZ0dExx/2KkiclJQVHR0cee+wxTT/Her2eLcu2YHNO/TnnFOZEy1HF+7MoSqbicg0taTJKz+Wm1CdkS5YsoXPnzgQEBGS7TPPmzWnevLlpukWLFoSGhvL555/zVg79W9jb22Nvn/0jMEWvkLAngYQ9CVyddxUrByvc27hT8dWKeLQtpf2D5JOtrW3x/IBPmwYrVoBej/UHH2A9ejRUqKB1VOIhaH2uGQwGdDodVlZWWFmVrHaV+vTpQ1paGl9//TVVqlQhOjqaLVu2cOfOnWL/XjIabMouTkVRGDBgAJs3b+a9996jXbt2xMfH88knn/D444+zatUqevbsmeW6aWlp2NnZmaYziilmlMp4mP/1g9sWxYOVlRU6nU7z7xMAY4CRun/X5dZ3t3Bp7KJ5PAXu9GmoVAlK2A2s0qo4nPMliaXHqnhfQR/SpUuX2Lx5M8OGDcvTera2tjRo0IDz58/na78hn4XgP8wfxxDzOkfGFCMxG2MwJJmX6zamG1GMSr72JQpZlSqQ0TpncrKaoAlRBsXGxrJjxw7effdd2rZtS3BwME2bNmXy5Ml0797dbLlhw4bh7e2Nq6srjz/+OEePHjXb1m+//UaTJk1wcHDAy8uLXr16mV67c+cOAwcOxMPDg3LlytG5c2fOnTtnen3ZsmW4u7vzxx9/EBoairOzM506dSIqKsq0jMFgYPz48bi7u+Pp6cmrr75qqteVnR9//JHVq1fzzTffMGzYMCpXrkxYWBhffPEF3bt3Z9iwYSQlJQEwY8YM6tevz+LFi6lcubLpCcm5c+d47LHHKFeuHI888kiWRXsiIyPp27cv7u7ulC9fnh49enDx4kXT64MHD6Znz57MmjWLgIAAatSoYcF/R5RpOnBt7kqNz2sQMDz7m88lUloadO0KNWrA11+rRRWFKIVKdUK2dOlSfHx88lx232AwcOzYMfz9/fO1X9/+vtT4sgbNzjSjeVRzan1fC7/n/bCrYIfOVod7G3ez5W+uvsnuCrs589IZ7vx1B8UgXzjFyhtvgJubOr50KRw7pm08ovRasAACA3Mf7kuATLp3t2zdBQvyFZqzszPOzs6sXbvWrDGjB/Xp04cbN26wYcMGDh48SMOGDWnXrh0xMTEA/P777/Tq1YsuXbpw+PBhtmzZQtOmTU3rDx48mAMHDvDrr7+ye/duFEWhS5cuZhWj7969y7x581i+fDnbt2/n8uXLTJw40fT6/PnzWbZsGV999RX//PMPMTExrMlonS0bK1asICQkhG7dumV6bcKECdy+fdsswTp//jw//fQTP//8M0eOHMFoNPLUU09hZ2fH7t27mT9/PpMnTzbbjl6vp2PHjri4uLBjxw527txpSijvr0+4ZcsWzpw5w6ZNm1i3bl2OcQtRqn3xBfz7L1y+DN9+q7aCLERppJRSBoNBCQoKUiZNmpTpteeee0557bXXTNNvvvmm8scffygXLlxQDh48qPTr109xcHBQTpw4kad9xsXFKYASFxeX5etGo1G5++/dTPOP9z2ubGWradgZsFM5N/6cEn8gXjEajXmKoaRJS0tT1q5dq6SlpWkdSs7mzlUU9d6conTqpHU0Ih+Ky7mWnJysnDx5UklOTs784vTp986znIZHHsm87iOPWLbu9On5jn316tWKh4eH4uDgoLRo0UKZPHmycvToUdPrO3bsUFxdXZWUlBSz9apWrap8/vnniqIoSvPmzZVnn302y+2fPXtWAZSdO3ea5t26dUtxdHRUfvzxR0VRFGXp0qUKoJw/f960zCeffKL4+vqapv39/ZW5c+eapvV6vRIYGKj06NEj2/dWs2bNbF+PiYlRAOXdd99VFEVRpk+frtja2io3btwwLfPHH38oNjY2ytWrVxWDwaDcuXNH+f333xVAWbNmjaIoirJ8+XKlRo0aZt/rqampiqOjo/LHH38oiqIogwYNUnx9fZXU1NRsYxXay/FzXISKy/daoYiPVxRv73vfXQcOaB1RmVaqz7VClFtukKHU1iHbvHkzly9f5vnnn8/02uXLl83K89+5c4fhw4dz/fp1PDw8aNSoEbt27aJWrVoFGpNOp8Oxcuam063srLBysMKYotY7SLuWxpUFV7iy4AqONRzxG+SH32A/7P2lyUbNjBkDH3+s3qXbuBE2b4b27bWOSpQ2rq6W1VF8oGsP0zxL1n2IxiWefvppunbtyo4dO9izZw8bNmxg7ty5LF68mMGDB3P06FESExPx9PQ0Wy85OZkLFy4AcOTIEYYPH57l9k+dOoWNjQ3NmjUzzfP09KRGjRqcOnWv1bhy5cpRtWpV07S/vz83btwAIC4ujqioKLNt2NjY0Lhx41yLLeb2+v2Cg4PNulg5deoUFStWJCAgwFSH7P66yQBHjx7l/PnzpsY+MqSkpJiOD0DdunWl3pjIlf62nvBu4diF2ZHeMh1br1JWr2fBArh5Ux1/5hlo1EjbeIQoRKU2IXviiSeyvbhu27bNbHrhwoUsXLiwCKLKWujyUEI+C+H2uttEr4gmZkMMil6NPflMMhGvRxAxNYKQRSGlr3x4SeHgALNmwXPPqdOvvAIHD0Ixb8hAlDDjx+e/s9Nffy3YWLLh4OBAhw4d6NChA1OnTmXYsGFMnz6dwYMHk5iYiL+/f6bvWAB3d3dAbXHwYT1YSVqn0+UpmcpKSEiIWdJ3v4z5ISEhpnlOTk553kdiYiKNGjXKsun9+5O7/GxblD3R30WTsDsBx92ORDpEErIwJPeVSoobN2DePHXcxgbeflvbeIQoZPJrspiwdrLG5xkf6v5SlxbXWxDyRQhurd3uLWAA10ek2WRNDRgADRuq40eOwE8/aRqOEMVBrVq1TI1dNGzYkOvXr2NjY0O1atXMBi8vLwDq1avHli1bstxWaGgo6enp7N271zTv9u3bnDlzxuISC25ubvj7+5ttIz09nYMHD+a4Xr9+/Th37hy//fZbptfmz5+Pp6cnHTp0yHb90NBQIiMjzRoX2bNnj9kyDRs25Ny5c/j4+GQ6Pm5ubg9uUohsKYpC1JJ755rPQB8NoykEb78NiYnq+PDhUK2atvEIUcgkISuGbMvbEjA8gAbbGtDsQjOC3wjGq6cXznWdzZa79vk1TjxzgtgdsQ99d1hYwMoK3nsPfHxg0SK4r2U4IUq727dv8/jjj/Ptt98SHh5OREQEq1atYu7cufTo0QOA9u3b07x5c3r27Mmff/7JxYsX2bVrF1OmTOHAgQMATJ8+nZUrVzJ9+nROnTrFsWPHePfddwGoXr06PXr0YPjw4fzzzz8cPXqU//3vf1SoUMG0D0uMHTuWd955h7Vr13L69GlGjhxJbGxsjuv069ePXr16MWjQIJYsWcLFixcJDw/nxRdf5Ndff2Xx4sU5Prlq3749ISEhDBo0iKNHj7Jr1y6mTp1qtsyzzz6Ll5cXPXr0YMeOHURERLBt2zZefvllrly5YvH7EyLxUCJJ4eqNkPTq6TjVKUVPVc+eVa+xAOXKSevGokwotUUWSwvHKo5UfqtypvmKohC5MJLkM8nc/PEmTmFOVBxfEZ9+PljZSZ5daB5/HC5ehAIodiVESeLs7EyzZs1YuHAhFy5cQK/XU7FiRYYPH87rr78OqEUH169fz5QpUxgyZAg3b97Ez8+Pxx57DF9fXwDatGnDqlWreOutt3jnnXdwdXXlscceM+1n6dKljB07lieffJK0tDQee+wx1q9fn6d+byZMmEBUVBSDBg3CysqK559/nl69ehEXF5ftOjqdjh9//JH333+fhQsXMnLkSBwcHGjevDnbtm3LtWNoKysr1qxZw9ChQ3nkkUcICgriww8/pEuXLqZlypUrx/bt25k0aRJPPfUUCQkJVKhQgXbt2knH0SJP7n86ltY+LYclS6BJkyA9XR2fOBH8/LSNR4gioFPk0UqBiY+Px83Njbi4uEK/uCZHJHPokUPob+jN5ttVsCNwbCABLwRg41b88229Xs/69evp0qWLdDQoClVxOddSUlKIiIgw679KlC5Go5H4+HhcXV2LfYfZIn+0/Bwbkg3s8t+FIc6AVTkr7iy+Q5fepeQaGhkJNWvC3bvg768+LXN2zn09UeiKyzW0pLE0N5ArRQnlWNmR5pebE/ptKC7N7rXYlXY1jX9f/ZfdFXdzfuJ5UqOy7y9IFJDoaK0jEEIIUUbc/OkmhjgDAF5Pe0E5jQMqSBUrwpkzMGgQzJ4tyZgoMyQhK8Gs7K3wfdaXRnsa0eCfBnj19IL/+kw0JBi4Mv8K+0L2kR6Xrm2gpVVkJAweDMHBcP681tEIIYQoA64vuW4a9xlSyhrzALUD+2XL1OurEGWEJGSlhFtLN+qsqUPTU03xf9Efnb2amXn39i4RRRdLpMWL4euvITUV/qtDI4QQQhSW5AvJxG6LBcAxxBHXllL3UIjSQBKyUqZcjXLU+KwGzS81p+IrFQmaHGT2ujHNyIVJF0i5nKJRhKXIxIlqi4sAq1bBA01cCyGEEAUpbnccOhv1hqv/8/7odDqNIyogW7dCfLzWUQihGUnISik7Xzuqzq1KuRDzwuVRS6KInBvJ3up7OffyOVKvSx2zfHNxgRkz7k1PnAjSRo4QQohC4vc/P5pfaU6VuVXwHeirdTgF4/p16NYNqleHJUu0jkYITUhCVoYoisLVj66q42nq+N4qe7nw2gX0sfpc1hZZGjYMatRQx3fuhLVrNQ1HCCFE6Wbna0fQK0HY+9trHUrBmDoVkpLgxg3IpQN3IUorScjKEJ1OR4MdDQh6LQircuq/3phsJPLdSPZW3UvkwkiMqUaNoyxhbG3hv05tAbX/FL0kt0IIIUSujh2Dr75Sx11dzUudCFGGSEJWxth62lJlThUe+fcRAscForNTy5+nx6RzYfwF9tXcR/TKaBSjFL2zWPfu8Oij6vi5c/Dpp9rGI4QQolRJT0wvnTdMJ04E43/v6/XX79XLFqKMkYSsjLLztaPawmo0O9sM3+d8Tc3lp1xM4fTA06RckkY/LKbTwYIF96ZnzIBbtzQLRwghROly5f0r7Kqwq3T1L7pxI/z5pzoeHAxjx2objxAakoSsjHMIdiD0m1AaHWyER3sPAAJGBOBY2VHjyEqYxo3VjiwBYmOlYrIok7Zt24ZOpyM2NjbbZWbMmEH9+vXzvO2LFy+i0+k4cuRIvuPLj/zGK0RBUQwKUYujSL+dzpUFV1DSSkEJlvR0mDDh3vQ774CDg3bxCKExScgEAC4NXAjbFEa9P+oRPDXY7DVjqpHI+ZEY7ho0iq6EmD1bbeDj22/hlVe0jkaIAqXT6XIcZlhY92PixIls2bKl0OI8ceIEffv2xdvbG3t7e0JCQpg2bRp3794ttH0KUZhi/owh9ZL6VKx8p/I4BJeCxGXJEjh5Uh1v1gyeeUbbeITQmCRkwkz5J8pj521nNu/KB1e4MPEC+2rt49avUhQvWwEB6gXm2WfBSj5aonSJiooyDe+//z6urq5m8yZOnGjRdpydnfH09Mz29bS0tHzHuGfPHpo1a0ZaWhq///47Z8+eZdasWSxbtowOHTrkuG19ITbGU5jbFqVf1BdRpvGAFwM0jKSAxMfDtGn3phcsUIv+C1GGya9GkSNDsoHL714GIPVSKsd7HOdYz2PSsXR2JBETpZSfn59pcHNzQ6fTmc1zdnY2LXvw4EEaN25MuXLlaNGiBWfOnDG99mARwMGDB9OzZ09mzZpFQEAANf7rRmLfvn00aNAABwcHGjduzOHDh3OMT1EUhg4dSmhoKD///DNNmzYlODiYPn368Ntvv7F7924WLlxoWl6n07Fo0SK6d++Ok5MTs2bNAuCdd97B19cXFxcXhg4dSkpK5u+6xYsXExoaioODAzVr1uTT+xryuXjxItbW1vz888+0bdsWBwcHvvvuu7wdbCH+k3otlVu/qTdC7QLsKN+1vMYRFYCjRyHjc9WnD7RooW08QhQDNloHIIo3a0drGuxswPkx57mz+Q4At3+5zZ1Nd6g0vRKB/xeIla0kIdnauROaNAE7u9yXFWVe5IJIIhdE5rqcS0MX6v5a12zese7HSDiUkOu6FcdXpOL4ivmO0RJTpkxh/vz5eHt789JLL/H888+zc+fObJffsmULrq6ubNq0CYDExESefPJJOnTowLfffktERARjc6nwf+TIEU6ePMmKFSuweuDGSFhYGO3bt2flypVMmjTJNH/GjBm88847vP/++9jY2PDjjz8yY8YMPvnkE1q1asXy5cv58MMPqVKlimmd7777jmnTpvHxxx/ToEEDDh8+zPDhw3FycmJQRj1S4M0332T+/PksW7YMB6kbI/Ip6qso+K+2gP9Qf6xsSsH19tFH4fx5mDkT/u//tI5GiGJBEjKRK6eaTtT7sx43vr/BhfEXSLuehvGukX8n/Uv08miqL6qOeyt3rcMsXi5fVissr14N8+aZV14WIhvp8emkXc29yJ6+YuYicPqbeovWTY9Pz1dseTFr1ixat24NwGuvvUbXrl1JSUnJNjFxcnJi8eLF2P134+KLL77AaDSyZMkSHBwcqF27NleuXGHEiBHZ7vPs2bMAhIaGZvl6aGgo//zzj9m8AQMGMGTIENN0v379GDp0KEOHDgXg7bffZvPmzWZPyaZPn878+fN56qmnAKhcuTInT57k888/N0vIRowYwVNPPZUpORTCUopBIerL/4or6tSErNTw9oaPPtI6CiGKDblSCIvodDp8+/vS9HRTKoyuYGomP+l4EkcePcKZF8+gKKWg5aeCcusW/PSTOj5zJty8qW08okSwcbXBroJdroOtt22mdW29bS1a18a18O/D1atXzzTu76/+iLxx40a2y9etW9eUjAGcOnWKevXqmSVwzZs3t2jfefkeaty4sdn0qVOnaNasmdm8+/eblJTEhQsXGDp0KM7Ozqbh7bff5sKFC2brScuM4mHF/BlD6uVS1piHECJL8oRM5ImNmw3VP6qO7yBfzr50lsSDiep8dxt0Uin3noYNYcgQ+OortQLz1Knw2WdaRyWKuYcpTvhgEUYt2dreSxgzvheMxuw7tXVycnrofYaEhABqUtWgQYNMr586dcq0TH73m5ioft99+eWXmRI3a2vrh9q2EA+69vk103iJb8zDYFBbIh4xAry8tI5GiGJHnpCJfHFt7EqjvY2o/nF1nOo5UWlaJa1DKn5mzQIXF3X8yy8hPFzbeIQoIUJDQwkPDzcrKrhnz54c16lfvz41a9Zk4cKFmZK/o0ePsnnzZvr375/rfvfu3Ws27/79+vr6EhAQwL///ku1atXMhsqVK1v69oTIlaIoONdzxtbXtnQ05rF4sdqyYvXq8OOPWkcjRLEjCZnIN521jgqjKtD4cGOsnczvDl/99CpXPrqCYizDxRj9/GDKFHXcaIRx40CKdQqRqwEDBqDT6Rg+fDgnT55k/fr1zJs3L8d1dDodS5Ys4eTJkzz99NPs27ePy5cvs2rVKrp160bz5s0ZN25cjtsYO3YsX331FUuXLuXs2bNMnz6dEydOmC3z5ptvMmfOHD788EPOnj3LsWPHWLp0KQsWLHjYty2EiU6no/LMyjSPbE7Y5rCS3ZjHnTv3roWxsWoXMUIIMyX4Ey6KC52VeVHFu+fucmHCBc6/fJ4jbY9w93wZ7pB13DjIaKFt61b45RdNwxGiJHB2dua3337j2LFjNGjQgClTpvDuu+/mul6LFi3Ys2cP1tbWdO7cmWrVqjF58mQGDRrEpk2bsLe3z3H9Z555hqlTp/Lqq6/SqFEjLl26lKkhkWHDhrF48WKWLl1K3bp1ad26NcuWLZMnZKJQWNla4RRawou/Tp8Ot2+r4/37Q6tW2sYjRDGkU6QlhgITHx+Pm5sbcXFxuLq6ah2OZq58dIXzL583TVs5WlFlThUqjKmQKXnT6/WsX7+eLl26mNU7KVXWrIH/WmSjalU4cQJy+WEoCl5xOddSUlKIiIigcuXK0hx6KWU0GomPj8fV1VVaWSylisvnuLh8r2Xr+HGoX1+tQ1auHJw5A4GBWkcl8qHYn2vFlKW5gVwpRIELHBNI2F9hOFRWL1LGZCPnx53n8GOHuXu2DD4t69kT2rZVxy9cgA8+0DQcIYQQxVfUV1GkXMncIXmJoygwdqyajAG8/rokY0JkQxIyUSg82nrQOLyx2kT+f+J3xnMg7ACRCyJRDGXowaxOBwsXQsad8l27pC6ZEEKITJJOJ3Fm6Bn2VNrD+fHnc1+hOPv5Z/jrL3W8cmXpj1OIHEhCJgqNjbPaRH79bfVxqPLf07IUIxcmXODwo4dJiSwFdwAtFRYGM2aoF6g1a9QkTQghhLjPtUX/NXVvAPuKJbhoe3KyeQK2YAFIEW0hsiUJmSh07q3daRLehApj73UonXottUg6qC1Wpk6FXr0kGRNCCJGJIcnA9WXXAbXutd9gP40jegjvvQeXLqnjHTpAjx7axiNEMScJmSgS1k7WVH+/OvX/ro9jiCOhy0OxcStjCZkQQgiRjejvojHEq/WtfAb4YOtRghtOqFQJvL3B2href19uRAqRC0nIRJFyf9SdJiea4P6ou9n81MhUYjbHaBOUVtatgx9+0DoKIYQQGlMUhaufXDVNVxhZIYelS4CBA+HsWVi9GmrV0joaIYo9SchEkcvUwaURzg09R3iHcM6PP48hxaBNYEVFr4fu3aFbNxg58l7/LEIIIcqk+N3xJIUnAeDSzAWXhi4aR1QA3N3VVoaFELmShExoznanLXHb4gC4svAKh5oeIvF4osZRFSJbW3D572IbE6M2BSyEEKLMKlVPx4QQeSYJmdCcvqWeyvMqo7NTy5gnHUviYOODXPnwCqW23/L33ruXlH35Jezfr208QgghNJF2I42bq24CYONpg3dfb40jyqdFi+C55+DaNa0jEaLEkYRMaM8KAl4OoNH+RjjVcQJASVU4P/Y8x7ocI+1GmsYBFoKAALUZfFD7JBs1CoxGTUMSoqRp06YN48aN0zoMtm3bhk6nIzY2VutQRAl0fel1FL1689H/eX+sHaw1jigfbt+GKVPg22+hZk2IjtY6IiFKFEnIRLHhXM+Zhvsbqs3j/ydmYwwH6h8g9u9Y7QIrLGPGQO3a6vj+/bBkibbxCJGLwYMHo9PpMg3nzxduB7YFnfDExMQwbtw4goODsbOzIyAggOeff57Lly8XyPaFyIsKYytQY2kNnBs5EzAyQOtw8uf11+HOHXW8Z0/w9dU0HCFKGknIRLFi7aA2j19vYz1sfdUmf9Oi0jjy+BGSTiZpHF0Bs7WFjz++N/3aa9LAhyj2OnXqRFRUlNlQuXLlQtufXq8v0O3FxMTwyCOPsHnzZj777DPOnz/P999/z/nz52nSpAn//vtvtuumpRXe0/rC3LYo3qwdrPEf7E/jA41xrOSodTh5t2cPfPGFOu7sDO+8o208QpRAkpCJYql8x/I0PtIY93buAPgP9ceplpO2QRWGNm2gf391PCZGLfIhRDFmb2+Pn5+f2WBtrRax+vvvv2natCn29vb4+/vz2muvkZ6eblq3UqVKvP/++2bbq1+/PjMyiu8COp2ORYsW0b17d5ycnBg+fDht27YFwMPDA51Ox+DBg03LG41GXn31VcqXL4+fn5/ZtrIyZcoUrl27xubNm+ncuTNBQUE89thj/PHHH9ja2jJq1CjTsm3atGH06NGMGzcOLy8vOnbsCMD69esJCQnB0dGRtm3bcvHixUz72b17N61bt8bR0ZGKFSvy8ssvk5R076ZSpUqVeOuttxg4cCCurq688MILOcYtRLGUng4jRtybfusttUi+ECJPpGdeUWzZ+9kT9kcY1z67ht/zflqHU3jmzYPffoPERPUu47Bh0Lix1lEJDURGLiAyckGuy7m4NKRu3V/N5h071p2EhEO5rlux4ngqVhyf7xizc/XqVbp06cLgwYP55ptvOH36NMOHD8fBwSHXJOlBM2bM4J133uH999/H2tqa7t278/TTT3PmzBlcXV1xdLz3FOHrr79m/Pjx7N27l927dzN48GBatmxJhw4dMm3XaDTy/fff8+yzz+LnZ/6d4ujoyMiRI3njjTeIiYmhfPnypu2PGDGCnTt3AhAZGclTTz3FqFGjeOGFFzhw4AATJkww29aFCxfo06cPb731Fl999RU3b95k9OjRjB49mqVLl5qWmzdvHtOmTWP69Ol5Oj6idFAUBV1J7zD500/hyBF1PCwMRo/WNBwhSipJyESxprPWUWFU5iaAo7+PJvlMMsFvBKOzLuEXtIwGPiZOVPsm8/LSOiKhkfT0eNLSrua6nF5fMYt5Ny1aNz09Pl+xZVi3bh3Ozs6m6c6dO7Nq1So+/fRTKlasyMcff4xOp6NmzZpcu3aNSZMmMW3aNKysLC+QMWDAAIYMGWKajoiIAMDHxwd3d3ezZevVq2dKaKpXr87HH3/Mli1bskzIbt68SWxsLKGhoVnuNzQ0FEVROH/+PE2bNjVtc+7cuaZlXn/9dapWrcr8+fMBqFGjBseOHePdd981LfPOO+/Qu3dvxo4di5WVFdWrV+fDDz+kdevWLFq0CAcHBwAef/zxTMmcKDvODDuDMdlIhZcr4PaIm9bh5N21a/DGG/emFy0CG/lZKUR+yCdHlDh3z97l7PCzGBINxG6PJfS7UOz97LUO6+G8/LJ6d7F9e60jERqysXHFzi73PohsbTM3i21r623RujY2rvmKLUPbtm1ZtGiRadrJSS1KfOrUKZo3b252x79ly5YkJiZy5coVgoKCLN5H4zw8Ia5Xr57ZtL+/Pzdu3Mhxnbx0p9GoUSOz6VOnTtGsWTOzec2bNzebDg8PJzw8nNWrV5vt02g0EhERYUoI8/I+RemSej2V6OXRKHqFmD9iaH61eclrXXHCBEhIUMeHD4cHPgdCCMtJQiZKnLidcRjuGgCI/SuWA2EHqLWiFh7tPDSO7CHY2koyJh6qOOGDRRgLi5OTE9WqVcvXulZWVpmSoawa7chI8ixha2trNq3T6TBm04WEt7c37u7unDp1KsvXT506hU6nM3t/eYklQ2JiIoMHD2bChAmZngzen5jmZ9uidIj6PMrU1H3ACwElLxnbvx++/14d9/SEOXO0jUeIEk4a9RAljv8Qf+pvrY9dgB0A+ht6jj5xlEvvXCpdHUnf1xiCEMVdaGgou3fvNvsM7ty5ExcXFwIDAwE1IYqKijK9Hh8fbyqOmBM7O/WzbjAYHipGKysr+vbty4oVK7h+/brZa8nJyXz66ad07NjRVH8sK6Ghoezbt89s3p49e8ymGzRowJkzZ6hWrVqmIeO9iLLLmGbk6qL/ihdbQ8CIEtgIRuPGsHIl+PnB3LlqUiaEyDdJyESJ5P6YO42PNKZ8p/9+OBkhYnIEJ546QXpcCU9kFAVWr4Zq1eDAAa2jEcIiI0eOJDIykjFjxnD69Gl++eUXpk+fzvjx401PiR5//HGWL1/Ojh07OHbsGIMGDTK10JiT4OBgdDod69at4+bNmyQmJuY7ztmzZ+Pn50eHDh3YsGEDkZGRbN++nY4dO6LX6/nkk09yXP+ll17i3LlzvPLKK5w5c4YVK1awbNkys2VeffVV9u3bx5gxYzhy5Ajnzp3jl19+YbQ0eCCAm6tuoo9Wnwx79/LGIchB44jyQaeDfv3gzBm4r9VTIUT+SEImSiw7bzvq/l6XSm9Wgv+qrdxae4uDTQ6SeDz/P9g098MP0KcPXLqkNif8kE8FhCgKFSpUYP369ezbt4+wsDBeeuklhg4dyhv3VfqfPHkyrVu35sknn6Rr16707NmTqlWrWrTtN998k9deew1fX9+HSmw8PT3Zs2cPbdu25cUXX6Rq1ar07duXqlWrsn//fqpUqZLj+kFBQfz000+sXbuWsLAwPvvsM2bPnm22TL169Vi3bh1nz57l0UcfpUGDBkybNo0AaQ5cAFc+vGIar/By7vU+izVXV8hDgz1CiKzplFJVxktb8fHxuLm5ERcXh6vrw1WcLyv0ej3r16+nS5cumeqC5MXtDbc59ewp0u+oT8fsg+1pdq4ZVrYl8EKh10ODBnDihDr9wQdqox/ioRTUufawUlJSiIiIoHLlyqbW9kTpYjQaiY+Px9XVNU+tS4qSI7+f47jdcRxucRgA5/rONDrU6KGavi/S7zVFUeuO/dcCqShbiss1tKSxNDcolVeKGTNmoNPpzIaaNWvmuM6qVauoWbMmDg4O1K1bl/Xr1xdRtKIgeHb2pNHBRjg3cAYrqLmkZslMxkBt4OPzz+9NT5kCkZHaxSOEEKJARM6/911eYWyFktUP2c8/Q7Nm0Ls3XLmS+/JCCIuV0F+suatduzZRUVGm4Z9//sl22V27dtG/f3+GDh3K4cOH6dmzJz179uT48eNFGLF4WI6VHWmwswF1f6tbsltcBGjZEl54QR1PTIQxY7SNRwghxENJ/jeZW2tuAWDnZ4dvf1+NI8qDhAQYO1Yd/+kn2LtX23iEKGVKbUJmY2ODn5+fafDKobPdDz74gE6dOvHKK68QGhrKW2+9RcOGDfn444+LMGJREKwdrfHsYt7ak6IonB15lth/YrUJKr/eeQd8/7tg//ILrF2raThCCCHyT39Tj1MdtauDCmMqYGVfgn6CvfkmXP2vZcjOneGpp7SNR4hSptT2Q3bu3DkCAgJwcHCgefPmzJkzJ9uOSXfv3s348eZ9/3Ts2JG1ufwATk1NJTU11TQdHx8PqOVss+pbR2SWcZwK83hdmX+Fa4uuEfVlFJXeq4T/SP+SUUzE2RndvHnYPPccAMro0aQ/+qhaiVrkWVGca5bGkdFJcHb9ZYmSLaNqdsb/WZQ+RqMRRVHQ6/UWtRQK4NjQkbD9YcRtjcMpzKlAvouK5Hvt2DFs3n8fHaA4OJC+YIF0y1IGFZdraElj6fEqlQlZs2bNWLZsGTVq1CAqKoo333yTRx99lOPHj+Pi4pJp+evXr+Pra150wNfXN1M/NQ+aM2cOb775Zqb5f/75J+XKlXu4N1HGbNq0qXA2bIRyP5TDFluUdIWI/4vgzLozJL+UDCWhTqqzM480bIjvoUPorl4lctAgjg0frnVUJVqhnWsWynh6n5iYSFpamqaxiMKVkJCgdQiikKSlpZGcnMz27dtJz09yUsAl/grvGmqk1ZQpeP7X2u/pXr04e+aM2ty9KJO0voaWNHfv3rVouTLRymJsbCzBwcEsWLCAoUOHZnrdzs6Or7/+mv79+5vmffrpp7z55ptER0dnu92snpBVrFiRW7duSSuLFtLr9WzatIkOHToUWqs9SrrCpamXuDr/qmmeyyMu1PyhJnb+JaCT1ogIbOrXR5ecjOLuTvrp05BDx7Uia0VxrlkiJSWFyMhIKlWqJK0sllKKopCQkICLi0vJeBov8iwlJYWLFy9SsWJFTT/Hhf29pluyBJsRIwBQqlUj/fBhsLcv8P2I4q+4XENLmvj4eLy8vHJtZbFUPiF7kLu7OyEhIZw/fz7L1/38/DIlXtHR0fj5+eW4XXt7e+yz+GKytbWVkzWPCvWY2UL1edVxbeTKmaFnMCYbSdiTwNEWR6mzpg6uTYp58hwSAjNmwL596D78EFvfElQRvBjS+vNpMBjQ6XRYWVlJk+ilVEYxxYz/syh9rKys0Ol0Fn2fxG6PxcreCtdmhXetKZTvtWvX4LXXTJO6RYuwdXYu2H2IEkfra2hJY+mxKhNXisTERC5cuIC/v3+Wrzdv3pwtW7aYzdu0aRPNmzcvivBEEfHt70uDfxpgX1FNotOupnH40cNcX55z0dRi4ZVXYPVqkI5lhRCixFAUhfPjznPokUMcanUIfWwJqn8zZgzExanjgwdD+/aahiNEaVYqE7KJEyfy999/c/HiRXbt2kWvXr2wtrY2FUkcOHAgkydPNi0/duxYNm7cyPz58zl9+jQzZszgwIEDjB49Wqu3IAqJS0MXGu1vhFsrNwCUVIXTA08TtTRK48hyIcWehBCixIn9O5bEw4mAer2xcSshBZNSUu413OHjA/PmaRuPEKVcqUzIrly5Qv/+/alRowZ9+/bF09OTPXv24O3tDcDly5eJirr3A7xFixasWLGCL774grCwMFavXs3atWupU6eOVm9BFCI7XzvCtoTh/4L6xNSxhiNevbLvFqFYio2FTz6B0l8FVAghSqwr8+91oBw4PrDk1Cl0cFC7Wlm1Cr74Ajw9c11FCJF/JeRWTd58//33Ob6+bdu2TPP69OlDnz59CikiUdxY2VlR4/MauDRywb21O7buJag89G+/wfDhEB2t3rmU81YIIYqdpFNJ3F53GwD7ivZ49/bWOKI80umgd2+toxCiTCiVT8iEsFTACwGUq2HeRUHq9VRub7itUUQWSEtTkzGAl1++V8ZfCGGxNm3aMG7cOK3DyFVhx3n79m18fHy4ePFioe2jpOnXrx/z589/6O1Ezo00jQeOC8TKVn5yCSGyJt8OQtzHkGLgRK8THOt6jMj5kRTLXiGeegq6dVPHr1+H++pDirLFYDCwbds2Vq5cybZt2zD811dQYRo8eDA6nS7T0KlTp0Lfd0H6+eefeeuttwp1H0OGDDEdHzs7O6pVq8bMmTPz1G9VfuLMSxI3a9YsevToQaVKlfK0j4exaNEi6tWrh6urK66urjRv3pwNGzbkut727dvp1q0bAQEB6HQ61q5dm+2y77zzDjqdLtNxmDNnDk2aNMHFxQUfHx969uzJmQf61HrjjTeYNWsWcQ9xsyvlSgrR36k3zmzcbfAfnnWjYsVKSgq0bQtr1mgdiRBljiRkQtzn+pLrxO+JBwUuTLzAmWFnMKYZtQ7LnE4HH38MTk7q9Gefwa5d2sYkitzPP/9MpUqVaNu2LQMGDKBt27ZUqlSJn3/+udD33alTJ6KiosyGlStXFvp+C1L58uVxcXEp9P1kHKtz584xYcIEZsyYwXvvvWfx+oUZ5927d1myZEmW/XMWpsDAQN555x0OHjzIgQMHePzxx+nRowcnTpzIcb2kpCTCwsL45JNPclxu//79fP7559SrVy/Ta3///TejRo1iz549bNq0Cb1ezxNPPEFSUpJpmTp16lC1alW+/fbb/L1B4MrCKyh69YZewKgAbFxKQA2Rt96CbdvUm37Tp2sdjRBliiRkQtwnYGQAwdODTdPXv7rO0Q5HSbuVpmFUWQgKUi+eoDbsMXSoendTlAk///wzvXv35sqVK2bzr169Su/evQs9KbO3t8fPz89s8PDw4ObNm/j5+TF79mzTsrt27cLOzs7UtUibNm0YPXo0o0ePxs3NDS8vL6ZOnWr2NHrjxo20atUKd3d3PD09efLJJ7lw4YJZDG3atOHll1/m1VdfpXz58vj5+TFjxgyzZVavXk3dunVxdHTE09OT9u3bm354P/gUKTU1lZdffhkfHx8cHBxo1aoV+/fvz9P+cjpWwcHBjBgxgvbt2/Prr7/mab/3x5lbHIMHD+bvv//mgw8+MD2dy6444vr167G3t+eRRx4xzVu5ciWOjo5mDV8NGTKEevXqPdQTo/t169aNLl26UL16dUJCQpg1axbOzs7s2bMnx/U6d+7M22+/Ta9evbJdJjExkWeffZYvv/wSDw+PTK9v3LiRwYMHU7t2bcLCwli2bBmXL1/m4MGDmWLMrT56dvQxeqK+UI+flYMVgS8H5ms7ReroUZg7Vx23tYVnntE2HiHKGEnIhLiPTqej8ozKhK4MxcpB/XjEbY/jULNDJJ1MymXtIvbyy9C0qTp++rTaebQo9QwGA2PHjs2yOG3GvHHjxhVJ8cUHeXt789VXX5m6DklISOC5555j9OjRtGvXzrTc119/jY2NDfv27eODDz5gwYIFLF682PR6UlIS48eP58CBA2zZsgUrKyt69epl6nD5/u04OTmxd+9e5s6dy8yZM9m0aRMAUVFR9O/fn+eff55Tp06xbds2nnrqqWyLIb/66qv89NNPfP311xw6dIhq1arRsWNHYmJiLNqfpRwdHUlLu3eDx5L9PiinOD744AOaN2/O8OHDTU8vK1asmOV2duzYQaNGjczm9evXj5CQEFNSPX36dDZv3syGDRtwc3MzW3b27Nk4OzvnOFy+fDnH42EwGPj+++9JSkoqkL4/R40aRdeuXWlvYZ9ZGUlm+fLlzeY3bdqUffv2kZqamucYbq6+iSFR/fz5DfHDzscuz9soUgYDDBt2r5n7KVOgVi1tYxKirFFEgYmLi1MAJS4uTutQSoy0tDRl7dq1SlpamtahZBK3J07Z6bdT2cpWZStble2u25XbG29rHZa548cVxc5OUUBRrKwUZd8+rSMqtorLuZacnKycPHlSSU5Oztf6W7duVYBch61btxZs4P8ZNGiQYm1trTg5OZkNs2bNMi0zcuRIJSQkRBkwYIBSt25dJSUlxfRa69atldDQUMVoNJrmTZo0SQkNDc12nzdv3lQA5dixY2bbadWqldlyTZo0USZNmqQoiqIcPHhQAZSLFy9muc3WrVsrY8eOVRRFURITExVbW1vlu+++M72elpamBAQEKHPnzrVof/czGAzKnTt3lIEDByo9evRQFEVRjEajsmnTJsXe3l6ZOHFinvabEaelcTy4TnZ69OihPP/885nm//bbb4q9vb3y9ttvKx4eHsrx48ezXP/27dvKuXPnchz0en2W64aHhytOTk6KtbW14ubmpvz++++5xns/QFmzZo3ZvJUrVyp16tQxfbZyOw4Gg0Hp2rWr0rJly0yvHT16NMfzJ6fPsdFoVGL+ilGOdjqq3D1/1/I3lQ8F8r22YIF6DQFFqVVLUe77vAqRobhcQ0saS3ODElCoWQhtuDZzpeG+hhzvfpzEI4kY4g2Edwmn2vvVCBxTTIqg1K4N06bBG2+A0QjPPw8HD4JdMb8jK/Lt/qJkBbFcfrRt25ZFixaZzbv/CcO8efOoU6cOq1at4uDBg9jb25st+8gjj5j1x9S8eXPmz5+PwWDA2tqac+fOMW3aNPbu3cutW7dMT8YuX75s1j/kg3WE/P39uXHjBgBhYWG0a9eOunXr0rFjR5544gl69+6dZTG2CxcuoNfradmypWmera0tTZs25dSpUxbtLzvr1q3D2dkZvV6P0WhkwIABpiKGlu73QfmJIyvJyck4ODhkmv/kk09Sq1YtZs6cyZ9//knt2rWzXL98+fKZnixZqkaNGhw5coS4uDhWr17NoEGD+Pvvv6mVzyczkZGRjB07lk2bNmX5nrIyatQojh8/zj///JPpNUdHR0CtZ5dXOp0Oj7YeeLTNfK4VOxER6vUD1PrJixfDA59XIUThkyKLQuTAoaID9XfUx6vnfx1HG+HumbxfoAvVq69Cgwbg6grjxqnl/0Wp5e9vWWttli6XH05OTlSrVs1suP+H+YULF7h27RpGozFfzal369aNmJgYvvzyS/bu3cvevXsBzIr6gZq83E+n05mSN2trazZt2sSGDRuoVasWH330ETVq1CAiIiLP8Viyv+y0bduWI0eOcO7cOZKTk03FDR9GfuLIipeXF3fu3Mk0f+PGjZw+fRqDwYCvr2+26z9MkcWMVicbNWrEnDlzCAsL44MPPsjze8hw8OBBbty4QcOGDbGxscHGxoa///6bDz/8EBsbm0xFeEePHs26devYunUrgYGZb7BlFBn19i5hfYflhaLASy9BRtI5ahQUQLFRIUTeyRMyIXJh42xD7Z9qEzElgoRDCVR7v5rWIZmztYWVK9VWF7P4YSFKl0cffZTAwECuXr2aZX0onU5HYGAgjz76qAbRqUnT//73P5555hlq1KjBsGHDOHbsGD4+PqZlMhKsDHv27KF69epYW1tz+/Ztzpw5w5dffml6D1k9wbCETqejZcuWtGzZkmnTphEcHMyaNWsYP3682XJVq1bFzs6OnTt3EhysNuqj1+vZv3//Q/cBlpG8ZqWw9mtnZ2dRHcIGDRpkaknw0KFD9O3blyVLlrBs2TKmTp3KqlWrslz/pZdeom/fvjnuIyAgwKKYjUZjvuprZWjXrh3Hjh0zmzdkyBBq1qzJpEmTsLa2BtR6lmPGjGHNmjVs27aNypUrZ7m948ePExgYiJeXl8Ux6O/osXG3MXv6W6x9+y38+ac6HhgI9zXGI4QoWpKQCWEBnZWOKnOqYNQbsbIxf7BsTM88r8jVqKHt/kWRsba25oMPPqB3797odDqzpCzjh+D7779v+gFaGFJTU7l+/brZPBsbG7y8vJgyZQpxcXF8+OGHODs7s379ep5//nnWrVtnWvby5cuMHz+eF198kUOHDvHRRx+ZOuL18PDA09OTL774An9/fy5fvsxrr72W5xj37t3Lli1beOKJJ/Dx8WHv3r3cvHmT0NDQTMs6OTkxYsQIXnnlFcqXL09QUBBz587l7t27hdokfGHtt1KlSuzdu5eLFy/i7OxM+fLlsbLK/B3VsWNHJk+ezJ07d/Dw8ODixYt07dqV119/nf79+1OlShWaN2/OoUOHaNiwYab181tkcfLkyXTu3JmgoCASEhJYsWIF27Zt448//jAt8/HHH7NmzRpT65ygtqB4/vx503RERARHjhwxHbv7i7OCenw9PT3N5o8aNYoVK1bwyy+/4OLiYjqP3dzcTMUUQW3w5IknnrD4PSmKwtF2R7Gyt6LipIp49fAq3omZwQAzZ96b/uwzKIJuIIQQWZMii0LkgZWt+Ucm8Wgi+0P3E78/XqOIcnDpktYRiELy1FNPsXr1aipUqGA2PzAwkNWrV/PUU08V6v43btyIv7+/2dCqVSu2bdvG+++/z/Lly3F1dcXKyorly5ezY8cOszpnAwcOJDk5maZNmzJq1CjGjh3LCy+8AICVlRXff/89Bw8epE6dOvzf//1fnvrtyuDq6sr27dvp0qULISEhvPHGG8yfP5/OnTtnufw777zD008/zXPPPUfDhg05f/48f/zxR5Z1zgpSYex34sSJWFtbU6tWLby9vbMtNli3bl0aNmzIjz/+SExMDJ06daJHjx6mBLhZs2Z07tyZ119/Pd+xZOXGjRsMHDiQGjVq0K5dO/bv388ff/xBhw4dTMvcunUrU1cHBw4coEGDBjRo0ACA8ePH06BBA6ZNm2bxvhctWkRcXBxt2rQxO39/+OEH0zIpKSmsXbuW4cOHW7zdmI0xJB5OJH5PPJdn5dyyZLFgbQ1//w1dukC/ftC1q9YRCVGm6ZSsyryIfImPj8fNzY24uDhcXV21DqdE0Ov1rF+/ni5dumSqF1HcpV5N5WCzg6RdTcPK0YrQ70Lx7lUM6hvExsKECWpxlEOH1IY/RLE511JSUoiIiKBy5coWNz6QHYPBwI4dO4iKisLf359HH320UJ+MFYQ2bdpQv3593n//fa1DKTRGo5H4+HhTUlqc/f7777zyyiscP3682MdaVBYtWsSaNWv4M6M4Xxbu/xzb29tzuOVh4nerN+Zqr6mNd8+iuRY89Peaoqh9WN73dFCIrBSXa2hJY2luIEUWhcgnK0crHKs6knY1DWOykRNPn6DqvKoE/l+gtkVVPv4YvvpKHX/+edi5E2zko14aWVtb06ZNG63DECVY165dOXfuHFevXs22v7KyxtbWlo8++sji5WO3xZqSMac6Tnh1t7zemeZ0OknGhCgG5HaYEPlkW96WsD/D8Hn2v8YKFLgw4QLnRp/DmJ73Fs8KzMSJULOmOr5vHyxcqF0sQohib9y4cZKM3WfYsGHUyEO93Etv3yseHvR6EDqrYlp3TFFgwQLIomVNIYS2JCET4iFY2VsRujyU4GnBpnnXPr3G8Z7HSU9M1yYoBwf1CVnGU7qpU+HMGW1iEeIBGfXMhCgNEg4nEPtXLACO1R3x6euT8wpaWrpULc5epw5s2qR1NEKI+0hCJsRD0ul0VH6zMjWX1URnqyZBMb/HcOTRI6RezX8zzg+leXP4v/9Tx1NTYehQtVUtIYQQBebaZ9dM40GTg9BZF9OnY5cvq/1UAly7Bg/06SeE0JYkZEIUEL9BftT7ox7WbmqjColHEjnU8hCGZI0Sobfegoz+j3buVOuWCSGEKBDGNCNxf8cBYB9kj+//su9EW1OKot6US0hQp4cMkVYVhShmJCETogB5tPWg4e6GOFRSW88Lei0Ia0eNWr0rVw6WLLk3PXkynDunTSxCCFHKpMfdK5Ye9FpQpm5Rio3PP4fNm9XxwECpVyxEMVRMvz2EKLmcQp1ouKch1T+pToWXKuS+QmF67DEYNUodT06GgQMhXaO6bcKM0ahhwy9CiIdiNBqxcbXB7TE37ALs8Bvip3VIWYuIUBt6yrBkCbi5aRePECJL0ha2EIXAzteOCiMzJ2N3tt3B/TH3om2F65134I8/4Px5cHZWi60Ucme3Int2dnZYWVlx7do1vL29sbOz07abBFHgjEYjaWlppKSkSN9epYyiKKSlpXHz5k1sHG0IXRaKIc6AtUMx7P/PaFS7PklKUqdfeAGeeELbmIQQWZKETIgicmP1DU72OYnX016ELg8tuqKMzs6wfDns368+LZMfiJqysrKicuXKREVFce3atdxXECWOoigkJyfj6OgoyXYpVa5cOYKCgrCyssLKo5h+p37yCWzbpo4HB8O8eZqGI4TIniRkQhQB/W09Z4aoTc/f+ukWR64coe5vdbHztiuaAB55RB1EsWBnZ0dQUBDp6ekYpPXLUkev17N9+3Yee+wxbG1ttQ5HFDArKytsbW2Ld7IdGQmTJt2b/uorcHHRLh4hRI4kIROiCNh62lLrx1qc7HsSQ6KBhL0JHG55mHob6uFY1VGboFJTwd5em30LdDodtra28oO9FLK2tiY9PR0HBwf5/5YySSeSODbgGMFTg/F+yrv4dgIdGAgffADjx8OgQfD441pHJITIQTF9zi5E6ePZ2ZP6O+pj568+FUs+l8yhFoeI3x9f9MFs3Qo1asCWLUW/byGEKKEuvnmRpPAkTvY5SdRXUVqHkz2dDoYPh+PH1XrEQohiTRIyIYqQS30XGu5pSLla5QDQ39BzpM0Rbq+/XXRBbN+u3i29dAkGD4bY2KLbtxBClFCJxxK5ueomALa+tvgOKKb9jt0vOFitRyyEKNYkIROiiDkEOdDgnwa4PaY2PWy8a+RY92NcW1xEDTy0agXt2qnjV67A6NFFs18hhCjBLr550TQeNCkI63LFrGXF5GTYtEnrKIQQ+SAJmRAasPWwpd4f9fDu663OMMC/r/xL2q20wt+5lRUsXXqvL5rvvoNVqwp/v0IIUUIlHk3k1k+3ALDzsyPgpQCNI8rCa6+pzdqPGAF372odjRAiDyQhE0Ij1g7W1FpZi8DxgejsdNRZWwc7ryJqdbFiRbVJ5AwvvQTSBLsQQmTp4oyLpvGg14KKrtsSC+n++AM+/FCdWLZMLZIuhCgxJCETQkM6Kx3V5lejyfEmuLd2L9qdDxgAffqo4zExageiilK0MQghRDGXcDiBW2v/ezrmb4f/C/4aR2TOLi4O62HD7s147z0IDdUuICFEnklCJkQxUK56ObNpRVG4NOsSqddTC2+nOh0sWgT+//24+OMP+OyzwtufEEKUQBenXzSNB00uZk/HFIX6H3+MLjpane7SBUaN0jYmIUSeSUImRDF0ccZFIt6I4HDzw9w9U4h1ATw91Q5DM0yYAGfPFt7+hBCiBInbHcft39RWcO0q2OE/vHg9HbNavBj//fvVCW9v9fu8OHdYLYTIkiRkQhQz6XHpXF92HYCUiykcanGIuJ1xhbfDTp3USuCgXshPnCi8fQkhRAli52Nnanyp0vRKWDsUo6djp09jNXHivemvvgLfEtAUvxAiE0nIhChmbNxsaLi7IU5hTgCkx6RztP1Rbv58s/B2+t570L8/HD4MvXoV3n6EEKIEcazqSO0fatPocCP8BvtpHc49aWnw7LPokpMBMLz4Ijz5pMZBCSHySxIyIYoh+wB7GmxvgEd7DwCMKUZO9D7BlY+uFM4OnZxgxQoICSmc7QshRAnmUt8FK9ti9JPp7bfh0CEAEgIDMb77rsYBCSEeRjH6dhFC3M/G1Ya6v9fFd+B/RVAUOP/yeS68egHFWEStIer1RbMfIYQQlhs2DFq3RrG15eD48VCuXO7rCCGKLUnIhCjGrOysqLmsJkGvB5nmRb4Xyan/ncKYaiy8HScnw8iR0K0bGAtxP0IIUcwY042Edw7n+tfXUQzFtCuQoCDYsgXD1q3EVamidTRCiIckCZkQxZxOp6PKrCpUX1Td9IlNOJSAIclQeDvt2VNtEv+PP2DBgsLbjxBCFDPRy6OJ2RjD6cGnOT30tNbhZM/aGqVpU62jEEIUAEnIhCghKrxUgTpr6+BQxYF6G+thW9628HZ2f8tdkydDRrPKQghRihlTjVyccdE07T+0GDVz/9dfcOeO1lEIIQqBJGRClCBe3bxoeropjpUcC3dHHTrApEnqeHo69OsH8fGFu08hhNDYtc+ukXo5FYDyncvj/qi7tgFlOHFCbUWxYUPYt0/raIQQBUwSMiFKmAdb+jLcNRDeJZw72wr4zulbb0GzZur4v/+qfZUpxbQ+hRBCPCR9rJ6Lb100TVd+u7J2wdwvKQn69lXr9l68qPY3JoQoVSQhE6IEM6YbOdn/JDEbYgjvGM6NH24U3MZtbWHlSnB1VadXrICvvy647QshRDFy+Z3LpN9OB8BngA8uDV00jug/Y8bAyZPqeN26sHChtvEIIQqcJGRClGCKXjG1AqakKZzsd5LIhZEFt4PKleHLL+9NjxoFZ84U3PaFEKIYSLmcwpX31X4edXY6Ks8qJk/Hvv4ali5Vx52c4McfwbGQi6wLIYqcJGRClGDWjtbUWVsHv6F+pnkXxl/g/ITzBddXWd++ap83AHfvqvXJUlIKZttCCFEMREyNQElVvzMDXw4s/Hq6ljh5Uu1+JMPnn0PNmtrFI4QoNJKQCVHCWdlYUePLGgRPDzbNu7LgCicHnCy4vso++ABCQ9VxNzdISCiY7QohhMYSjiQQvTwaABsPG7N+HzVz9656M+zuXXV66FB49lltYxJCFBpJyIQoBXQ6HZVnVCbkyxCwVufd/OEm4Z3C0cfqH34H5crBDz/AzJmwZQt4ez/8NoUQohiw87VTm7e3guA3grH1KMQuRSw1ZozasiJAnTrw4YfaxiOEKFQ2WgcghCg4AcMCsPe350TfExjvGondFsuRR48Q9lcYdt52D7fxunXVQQghShF7f3tqfFmDwHGBOFYrBkUVt22715KikxOsWqXeFBNClFryhEyIUsazqyf1t9bH1ku9y2sfZI+NRyHde4mJgdTUwtm2EEIUIafaTljZF4OfRa1bwyefgJ0dLFok9caEKAOKwTdPwZszZw5NmjTBxcUFHx8fevbsyZlcWoZbtmwZOp3ObHBwcCiiiIUoWK5NXWmwqwHez3hT+8faWNkUwkd93z5o0AAmTiz4bQshRCErsIaPCppOpzbmcfYsPPec1tEIIYpAqUzI/v77b0aNGsWePXvYtGkTer2eJ554gqSkpBzXc3V1JSoqyjRcunSpiCIWouCVq16O2t/XxtrJ2my+Icnw8BuPjYUOHeDyZfj4Y7V+mRBClBDpCensr7ufKx9dwagvoMaPClpwcO7LCCFKhVJZh2zjxo1m08uWLcPHx4eDBw/y2GOPZbueTqfDz88v29eFKOlSIlM43PIwFSdWJPDlwPxvyN0d5s2DF15Qp4cNg/r1oUaNgghTCCEK1eXZl7l78i7nXz5P8vlkqn9QXduAVq1S64t16aJtHEIITZTKhOxBcXFxAJQvXz7H5RITEwkODsZoNNKwYUNmz55N7dq1s10+NTWV1Pvqz8THxwOg1+vR6wugZbsyIOM4yfEqfIZEA+GdwkmNTOX82PMkX0omeHYwOitd/jY4aBDWf/+N1XffQWIiylNPkb5zp/qjohiSc00UFTnXireUf1OIXBAJqJ1A+4300/Z/deoUNkOGoEtKwjBhAsZZs8DKsgJMcq6JoiLnWv5Yerx0iqIU00LUBcNoNNK9e3diY2P5559/sl1u9+7dnDt3jnr16hEXF8e8efPYvn07J06cIDAw6ycJM2bM4M0338w0f8WKFZSTFpFEcaOA/Up7HH68Vzcy7bE0ksckQz5bebZOSeGxV1/F9fJlACJbt+bQuHFqHQghhCiGyr1TDts96pdeylMppA7UrmEim+RkHnvlFVyuXAHgUrt2HBkzRrN4hBAF6+7duwwYMIC4uDhcXV2zXa7UJ2QjRoxgw4YN/PPPP9kmVlnR6/WEhobSv39/3nrrrSyXyeoJWcWKFbl161aOB13co9fr2bRpEx06dMDWthj0/VIGXP/yOhfGXID/qk24Pe5GzR9qYuOWzwfmp09j07w5uv/qaBrefx/jyJEFFG3BkXNNFBU514qvuL/jON7hOAC2vrY0PNkQGxeNCgspCtb9+mG1Zo06Wbcu6Tt25KmJeznXRFGRcy1/4uPj8fLyyjUhK9VFFkePHs26devYvn17npIxAFtbWxo0aMD58+ezXcbe3h57e/ss15WTNW/kmBWdiiMr4hjoyMl+JzEmG4n7K47j7Y5Tb0M97AMyn8+5qlsXli6Fvn0BsJ44EesmTaBFiwKOvGDIuSaKipxrxYtiUIiYGGGarjK7Co7lNex3bN48+C8Zw80N3Zo12Lq55WtTcq6JoiLnWt5YeqxKZSuLiqIwevRo1qxZw19//UXlypXzvA2DwcCxY8fw9/cvhAiF0JZXdy/CtoRh46nek0kKT+JQ80Mkncy5JdJs9ekDEyao4+np0Ls3/Fd3UwghioOoJVEkHVW/45wbOuM3WMNGvLZuhUmT7k1/+y1UrapdPEIITZXKJ2SjRo1ixYoV/PLLL7i4uHD9+nUA3NzccHRU74YNHDiQChUqMGfOHABmzpzJI488QrVq1YiNjeW9997j0qVLDBs2TLP3IQqeokBiotpqe2wsJCRAWhro9fcGo1Htj9PW9t7fcuXUhgU9PMDV1eL61sWaW3M3Gu5qSHincFIiUki9nMrhVodpcqIJ9v75eFL2zjtw8CDs2gVvvgn5vNMrhBAFTR+rJ+KNe0/Hqr1fLf8NGj2sK1fgmWfUiw3A1Knw5JPaxFJMpKer1+OMIT4ekpLAYFAHo1H9C+DgYD44OUH58uo1ujRcm0XZVCoTskWLFgHQpk0bs/lLly5l8ODBAFy+fBmr+z65d+7cYfjw4Vy/fh0PDw8aNWrErl27qFWrVlGFLR5SejpcugTnzsG//6rXvGvX4OpV9e/162oSZnzILmd0OjXX8PaGgACoUEEdAgKgcmUICYEqVSCL0qzFTrmQcjTY1YBjXY+ReCgR/xf885eMAdjYqP2RXboETZoUbKBCCPEQIt+NRH9Tbe3Mu6837o+6axNIaqpaouDmTXW6Y0eYPl2bWIqIXq9eFi5cUIeICIiKUq/JGcPt2w+/Hysr9aappyf4+kLFihAUdO9v5cpQrVrJuDaLsqdUJmSWtFOybds2s+mFCxeycOHCQopIFCSjEc6fh6NH4cgRCA+Hs2fVL/miaI1VUe49YTt3LutlrKzUC0BICNSpo3bRVb8+1KypPnErTuz97Km/rT5RX0YROO4h+iYD8PFRByGEKEaCXgvCmGIk6qsoqs7VsGjg2bPqAGrHz999B9bW2sVTgNLT1bd27Ni94fhxuHjx4W+EWsJoVBO727fvHeIHWVurN0xr1lSHOnWgYUN13KZU/iIWJYWcfoVg6C9D6VW/F52qdaK8Y859n4ncRUWppeB27YI9e9RELCmPVZ0cHcHf/16xhozBxSVz8USdTk3sMooypqbC3bv3krA7d9QhOlotVpEVo1G9CF28CH/+eW++nZ16AWjWTG3zokUL9a6d1q3E27jYUHF8xUzzE48l4lTb6eGK9qxapdaNaNjwISIUQoj8s3GzodrCagRPC8bWQ8O7YnXrwv79MGAAfPqp+jinBFIUuHwZ9u5Vr8t798KhQ5CSkrftODio12YfH/Wa7OqqXpddXMDZWU2SrKzURCqjUFNKivmQkHAvEcsYEhKy3p/BoN5IPXcOfvvt3nxHRwgLUy9TzZrBo49CpUraX5tF2SEJWSFYfXI1q/9djbXOmpZBLXmy+pM8GfIkNb1qopNPd64uX4YtW9Rh5041qcmNo6NaFKF6dXWoVk19QpVRlNDdvXC+WBMT1SKRV6+qRSQvXFC/6M+eVf8+mLClpakXrUOH4L+Stfj4qInZ449D+/bqnbricJrE7YzjaPujeD3tRc2vamJll8fC+QaDWjdizhz1n7F/vzw9E0JoStNkLEOVKrB7d/H4oreQoqilULZuhb/+gm3b1KoAuXFygho11HtyVarc+xsYqCZiLi6FcxiSkiAyUh0uX1aH8+fh9Gl1uHvXfPnkZDWx3LNHzZNB/e3w6KPq8PjjxefaLEonScgKkUExsP3SdrZf2s6rm1+lqkdVngx5kp41e9KmUhutwys2kpOtWbtWx9atsHlz9sUAM1SqdK8IYP366l2toCBtKvM6O6sXmxo1Mr+mKGrZ+PBwtWhlRhHLM2fMi2/cuAFr16oDqBeBdu3U5KxzZ7WuWlFLT0jneK/jGFOM3PjuBvpoPbV/qo2Nax6+MtLT1as3qFfDPn3Uf3BxK7MphCiV9LF6DIkGHAIdtA0kLk599HP/r/kS8Ms+IQE2bYL169W/ly/nvHzVqtCokfoQMGOoVEmba7OT071iiQ8yGtWbqCdPqtfkgwfVm6QXLpgvd+2aWi36hx/U6YoV4Ykn1Gp/7dqpJW6EKCilvmPoohQfH4+bmxvrwtfx17W/WHduHWdvZy7I3LV6V9YNWKdBhMXHlStqcYFffjGyZYtCenrWZegdHaFpU2jeXH2K9Mgj2iQoBSkhAfbtu1cMc/fu7FuI1+nU9929uzrUqFF01/Gba29yqv8pjClq9ugU5kS99Xnsq+zaNWjcWC13CjBqFHz8cSFEmzu9Xs/69evp0qWL9KEiCpWca8XDuTHniFoaRaVplQgcF5j3p/wFITlZfcRSvTosWZKnTp8tUdDn2r//wq+/wu+/w99/Z18v28lJvS43b64W8WvatORfm2Nj1eRs507YsUO9NmdXPcLKClq2hJ491aFKlSIMVCPyvZY/GblBbh1DS0JWgLI66Gdvn2Xd2XWsO7uOHZd3kG5MZ1HXRbzU+CXTenqDni4rutC2UlueDHmSuj51S2XRxosX4ccf1eHgwayXsbFRv+Dbt1fvQDVtWvofqBiN6lO0LVvUB0jbt2cuTpEhJER90PTMM2pdtMI+TeJ2xXGs2zHSY9IBsA+2p97GejjVdLJ8I3v3wmOPqeU1ARYvhqFDCyHanMnFRBQVOde0l3A4gYOND4IRrMpZ0fR0UxwqFvGTMkWBQYNg+XJ1ulcv+PnnAt1FQZxr58+rVX1Xr1afFGXFwUG9Odi2rVp8r0mT0n9tTk9XS7Zs26bWBd++Pfs6cnXrqonZM89A7dpFGWXRke+1/JGETAO5HfTYlFj+vPAnjwU/hp/zvQ4pt0Zs5fFvHjdNB7kFmeqdta3cFgcbjYtbPIT7H/nv3Zv1Mp6eyfTubUe3bta0bq0WAyzL0tLUO3MbNqh3Kk+dynq50FD1y79fv6yLTBaUpNNJhHcKJ/VSKgA25W2o+2td3FrmoZ+xpUvh+efVcVtbtShjy5aFEG325GIiioqca9pSjAqHWx0mfrdaibfKO1UImhRU9IHMnXuv8+dy5dQv9nr1CnQX+T3Xrl5VG3hcuVIttpeVSpWga1d1aNNGLbFSliUnq0/O/vxTfYJ4+nTWy9Wtq7bZ0q+fegxLC/leyx9JyDRg6UF/0Jwdc3j9r9ezfK2cbTk6VOnAkyFP0rV6V/xd/Asq3EKTkgK//ALLlqlfXFk1d9uggXo3qVMnPdeuradrV/mAZyejNahffoF//sn6eDZrBoMHqwmah0fBx5AalcqxLsdIPJIIgJWDFaErQ/HumYcyKi+/DB99pI57e6uNfAQHF3yw2ZCLiSgqcq5p69oX1zj7olpdoFzNcjQ+2rjoiyv+9hv06KE+JQO1aEifPgW+m7yca4mJ6gO65cvVEhlZ/fpr1Ah691aLyIeGloiqbpo5fVq9Lq9Zk/0N5xYt7l2b8/CzsFiS77X8kYRMA/lNyAAi7kSoRRvPrWPbxW2kGdIyLVPJvRL/vvxvsS3OuH+/WkT++++zrhNVr576pdS3r9oKIsgHPK+uX1eLlfzwg5qcPcjeXk10hwyBDh0KtjJ1enw6J54+wZ3NdwA1KWsW0Qx7PwvrlOn1aislW7ao03XrqoX1XVwKLsgcdy/nmigacq5pJzUqlX2h+zDEGQAI2xKGx+OFcJcqJ8eOqb/EE9UbWMyYUWidP+d2rimKWmf588/VnDCrOlFNm6pJWO/eajcsIu+uXYOffoIVK9SWGh/k6Kjm40OHqlUKi+nPuBzJ91r+WJobSCuLxURlj8qMaTaGMc3GkJCawOZ/N/Pb2d/4/dzv3Ei6AaiNgTyYjM3aPot6vvVoV6Ud5WwLtrKwJZKS1CIPn32Wdb2wSpVg4EDo3z/r1o5E3vj5wejR6nD1qnqB/fprtZw7qH2mZRQRrVIFXnxRLSno5fXw+7ZxtaHu73U5M/QM0d9GU2NpDcuTMVCLKq5apT7OO3dOzS4jIgq8CI8Qouw6P+68KRnzHeRb9MnYzZvq46WMZKxvX5g2rWhjQL0p+u238MUXah1lAGtrPX5+V/D1vUTdupepXbsNffoEUfW/frLv3j3D4cMvoCjpWFs7YW3tbBpsbMrj6FgFR8dqODpWxc7OH51Og0ZSiqmAABgzRh3+/Ve9Mb1iBZw4ob6enAzffKMO1arBiBHqjdPCKNEiSiZ5QlaAHuYJWXaMipED1w6w7uw6OlXrRIuKLUyvXU+8jv98tQijg40Dj1d+3FT3rKJb5k5+C9KZM/DJJ+qXy4NPw5yc1DttgwerbTnk9JRG7rgUjCNH1CKi330Ht26Zv2Znp96ZGzVKbaXyYe/MKUaFuH/icH/MPX8bOHMGXnhBzSSLsIC9nGuiqMi5po3bv9/m2JPHALDxtKHp6abYedkVXQBpaWprVBnFFxo1UluCKOCWFe/34Ll24gR8+CH8/HMcFSocoXr1Q1Svfpjq1Q8RFHQKa+t7Zd5DQ1fg69vfNJ2YeIwDByy7Qdao0SFcXBqYphVFKbald7SiKGojKUuWqMnZg7+VHB3VumajRqnVOIo7+V7LH0tzA7m9UcxZ6axoWqEpM9vONEvGANafW28aT0lPYf259YxcP5Kg94Oo/1l93vjrDfZc2YNRyaLSUT4oitoXSZcu6tOujz4y/4Jp1EhtQO/6dTU5aNNGm/5HyqL69eH999WnZj/9pBZXzJCWpiZqGd0GfP999k0ZW0JnpcsyGYteEU16QnruG6hRQ222qjTVdhZCaCo9MZ2zI+91M1NtYbWiTcYA5sy5l4z5+6sVjAoxGctgNMLvv+vo0AHq1TMQFvYIP/zgwfvvt2HUqPE88cRyKlc+YZaMAaSkXDKbtra2PFZHx2pm01FRi9m7N4SzZ0dw5842FMWQ/zdUSuh06u+iTz9Ve3757ju1lcoMyclqstawodrG1U8/gUEOW5klRRZLsH51+uHj5MNvZ35j3bl1XEu4ZnrtaPRRjkYfZdaOWQS4BPDvy/9ib5OH4mX3SU1Vv0gWLoTjx81fc3RUWxIaMUJtBldoy84OnnpKHc6dU+sNLF0KMTH/z959h0dRbgEc/u1uNr0XSICEQAoJoYTeBOk1iIAKdlBR7ICADUXELhZUEEXBioqKKKE36R1CJwFCCIT03rPt/jGQsDcgLcmmnPd5fO7u7Mzs2dxhZ8/M+c6nvL57t1I+2qiRUlrx+OPg6nrr75v4bSLRj0Urc5Utb4VNw2sca/9/JdVoVGblDAq69WCEEHVOUVwRXPxace3jSv0H6ld9EJMmKV+yGzbA0qXQsGGlvl1W1nn++usU06b14cKFSz/nNBgMVqjV5sVPKpUV9vZh2NkFYmvbGFtbP1xcbjdbx9a2CT16FAEajMYCDIa8i//lU1KSRGHhaQoLT6HTpWBlZT72Nzt7M4WFJyksPMmFC/Owtm5AvXr3UK/eaJycOtb5u2eX7obdd5/SDGTuXKVIJEdpBFo6L2nTpjBhglLOWNc7Ttc1UrJYgSqjZPF6mUwmopKiWBazjMiYSPZc2FP62u2Nb+ffMf+arb/29FoC3ANo6nb12QxzcpQf9J98Ujav7yV+fkrTvEceubUaaLkFXvkKC5UxZZ98UjaW4BInJxg/HiZOVC7o3gx9rp5dAbvQpSq33awbWtNqRSscW13n2aSgAB54QJmFdMcOZbK1SiDHmqgqcqxZhiHfQNyMOHwe98E+sOrHVAPK5FVRUdC+faXsvrDwDPHxfxIT8ydOTjtJSvLj3nvjuJSNBgTAW2/NoEmTv3Bz64qjY1ucnNri4BCGWn1zF2Wvx9Gjo0hL+wuTqXz5hZ1dM3x9X6B+/QfRaGruND4VLS9Pudj9xRflL3a7uSnn5gkToF49i4RXjnyv3RzpsmgBlkzI/l9SXhLLY5YTeTKSfk378VSHp0pfM5qM+HzkQ0p+Cs29mpeOO+vi2wUrtRWpqTB7tjJGLCvLfL9duig/3ocPVyZxvlXyD7zqmEzK9F+ffAKRkeav2dgoY/6mTlWu0N2o/BP5HB58mKIzyqyZGicNYb+H4T7A/dobv/ACfPyx8jgwUEnKKqILyf+RY01UFTnWREUyGApITf2Ds2cXUFi4qdzr48fvpkGDdkycqGbwYFCrLTOeS6/PIz19GSkpv5KRsbJccqbV1qdZs6/w9BxW5bFVZ5eGg3z0kTJV0OXs7GDcOJgyRalssST5Xrs5kpBZQHVKyP7L7oTddPqmU7nlbjbu1M8dxOlVEeiODYQiV0CpLhsxQvlC6FR+s1si/8AtIyZGyYEWLlTGmF2iVislFdOm3fhk0yUpJRweepjc3bnKAg0EfxlMg3EN/nvDnBy47TalVTQoxfTr1oFtxV5JlWNNVBU51uqIM2eUVrYLFlTKr+WcnD0kJn5DUtIvmEy55V6PjW1JWtoI7OzCGD/+zmp1rOl0maSlLSUp6TuyszeXLm/Xbh9OTm0tGFn1dviwcm7++Wfzsd5aLTz8MLz0EqVdMauafK/dHGnqIa6qoVND3un9Dl19u6Ki7CpaZnEGJ6x/RnfHvTDVE9WYXox+NIVjx5S5ryo6GROWExysTFUQF6ck2pdq1Y1GpVVy8+bw4INK4na9rOtZE74xHM/hF+9uGSDm8RhiX4nFZPyP6z7OzsotO29v5fm2bUot7JVmwBZCiIvyj+VzaNAhCmMLq/7NMzOVDldr1yonx2PHKnT3Ol0WBw70IDHxa7NkLD6+Gd999zYrVkRz++2HmDjxVfz8qrh5yXXQat3w8RlLmzabaNt2N15e9+Dm1r9cMlZcnCQNQC7TsqVyofTMGaVc0c5OWa7TKU3TmjVT5jI7c8aiYYpKIAlZHdTQuSEvd3+ZvyK28URuMtrIH+Do3VB0WeauNuAecpSf53uazR+WkJOAznALLfpEteLjAx98APHxMHMmeHgoyy8lZqGhyjxyp05d3/409kqpYqNJZVeL49+N59h9xzAU/cdJ189PScoudST75ReLzN0jhKgZjHojJ8aeIGNVBnta7iF7W/a1N6ooxcUwcqTSnQGUi0o3Owj3IqOxrFQhJQUmT3Zl+fIHASgsdGDFikeYPHkbhw8f5/33X+GDD4ItdqfkRjk7dyAs7DdatlxuttxkMnLkyFD27WtPVtbmq2xdNzVsqAwvOHsWXnlFOcRA6cK4YIFyUfWJJ5Rzt6gdJCGrg7Kzld+6AQEw7yMvdHsfhN8XY/NZKiPz1vN4y4kEugcyNGQI6v+b+PHBvx7E80NPRv0xih8P/khaQdpV3kXUJG5uSpnimTNK52b3i0O/jEb48UclMXv6aUhOvva+VBoVgR8FEvh5YOk3TPam7NKmH1fVrp2SiF0a+/D22zB//s1/KCFErXX+4/Ol5dG2frY4tquilnRGo9ICb+NG5bmXFyxfftPdrYqLL3Dq1CR27vQnKyuXN95Qzs2ffQa//jqJDz/8hoceSkKj+ZaVK7vy4Ycq6luggWRFUKvNB54nJf1Abu5e8vKiiIq6nePHH0Sny7JMcNWUl5dyKjx7FmbMABcXZbler0z6HRiodExOSbFsnOLWSdv7OqSwUGnU8e67ZW3QQRmq8+STMHWqNd7evYHemEwfUaQvMts+qyiLLfFb0Bv1LD66mMVHF6NWqenSqAsRwUpjkDCvsDrf3rYy6PV5FBQcpbAwFp0ulZKSFHS6FHS6VAyGfFq1WoXqsuQ5NXUp6emRaLWeWFt7odV6YWPji719CNbW3lf9/8jJSalRf/pppfPTrFnKsaLXl7XpnTxZ6cPh5HTFXZRq9EwjbBvbEv1YNC0jW2Lrex1jwu64Q5lQ7fnnlefjxytXniMirvMvJYSo7fJP5HPm9Ys1W2potrAZGltN1bz5Sy8pF45AqSdbtuymOiEp3RLfJylpISaTcnfs2We/56efnildJy0thNatQ/jhh7LqhdrE3j4YR8c25OUdACA5+SeysjYREvIDbm49LRpbdePqqlxIf+455c7ZJ59Abq5SyvjFF8q5+cUXlTJHBwdLRytuhiRkdcCl8rNXXlEmDr7EykqZh2ratPLVFiqVCjutndmynOIcRoWNYsXJFWQWZSr7NhnZdm4b285t4+X1L+Pv6k9EUARTu03F18W3sj9arZaTs4v4+PfIyztMUVEscP39d3JydpCU9O0VX9NonLC3b4a9fQjOzl1o2PCpcus4OcHLLyuJ2ccfK4lZfr7y34wZ8OWXyslh3Dhl7rOr8RzqidsZNzT2N/Bj6bnnlDqMjz5SDt4PP4QhQ8rPXSaEqHNMBhPRY6MxFSvfh40mNsKls0vVvPnnnyvfR6B0QPrttxseXF1YGMfZszNISvoRKCvjLiqyQ6/PB5Rz87hx8Nprt1wJWa25uHSlXbs9JCYu5PTpyRgM2RQXn+Pgwd74+k6hSZM3K7VVf03k6qqcg59/Xjk3f/qpcl7OzVV+y82ZA2++qXRNrohO2KLqSMliLbdpkzJh88MPlyVjKpUy7VN0tPKP93q/8P1c/PhpxE+kTElh85jNTOk6hVDPULN14rLi+GLPF1j9X2mC0SQNGq7GZDKRl3cYnS7DbLnBUEha2lKKik5zI8kYgE539VJSgyGX3Ny9JCf/RErK4nKv5+UdRK9XSoGcneGNN5Q5m596quwLPiUFnnlGaf6xeLHStvdq/j8ZMxlMHB19lJTF/1Fj8cEHyozjgwYp5UCSjAkhgPOfnidnpzKbrl2wHU1mNqmaN16ypOzOPSglA0OHXvfmxcVJxMQ8w+7dwSQlfcelZCw/34mff36Je++N49dfX2T0aDh+XNl9bU7GLlGpNDRo8BgdOhzC1bXnxaUmzp37gH37OpGff9yS4VVb7u7w1lvK+O7x40Fz8TSbmKgk861bK8OypY96zSH5cy116pQyp9Rff5kvj4iAd95ROvncLCu1Fd0bd6d74+580O8DTmecJjImksiTkWyK20Rr79b4OJmfSSasmsCuhF2lc56Fe4fX+dLG4uILJCX9QFLSdxQWRtOs2Tf4+Dxa+rqjo/J/klptj4NDCxwcWpaWHFpb10OrrYe1tRcajRNg/rcMCPiARo2eR6dLKy1xLCw8TWFhNAUF0RQVxQEmXFy6mG1nMpk4dGgIOl0KLi634e4+CHf3QdSrF8acOSomTFCuwi2+mMedPg2jRikXjd9/H3r3vvbnPjXpFKm/pZL6WypFcUX4TvEtfyyo1fDdd8r/SntdIQSQfzSf2FdjlScqCFkQgsauCkoVo6Ph/vvLft2+8orSUeE6paUt49ixURiNZd0gc3Nd+eOPiSxZ8ix5eW4MGKAMJ2jTpqKDrxlsbf1o3Xo95859zJkzr2Ay6cjPP8j+/Z1p3/4gdnb+lg6xWvL2VipWnn9eqWpZulRZfuyYcr3g9tuV83OHDhYNU1wHSchqmcxMpVveF1+Yz2ERHq5UgF3PD+YbFeAewPOdn+f5zs+TU5xDQk6C2esmk4llMcuIy4pjd8JuXv/3dRo6NSQiOIKBTQdScll3qdrOaNSRlvY3SUkLychYBZTdOczIWG2WkGm1HnTqdBpbW3+z8WHXQ6v1QKu9+qADg6GQwsIYNBrzUp/8/MOUlCj//2VlbSQrayOxsVOxswvEy2sUPj6j+e23FkyerNSrXxrXvncv9OmjDAH7+OOrz5NiMpgw5JSV6cS+GEvRmSICPw9EbfV/n9HmCqUqGRnKbbpqPM+fEKLiGUuMHH/geFmp4vONcOlWRaWKwcHK4Jz33lPazr711g1t7uTUoTSXKyx04PffJ7J48Qvk57vSsaOy2169Kj7smkalUuPnNxk3t74cP34/BQXHqFdvFLa2jS0dWrUXEqJcgN+2TZnKZscOZfmmTdCxo3Lh9N13oUkV3VAWN05KFmsJnU7pyhQYqAz2vJSMeXsrLVL37q2cZOz/Ods4E+plXsaYUZiBs435D+iE3AS+2vcVw38fzoOHH+TOxXfy1d6vSM1PrfwgLcBoLOHCha/ZvTuYY8fuJiNjBZcnYy4ut+PuPqDcdnZ2TW84GbseGo0djo6ty111VKvtaNDgKWxtzb+1CwtPER//Nnv3tmT37jC8vN5k9eosVq6EVq3K1vvnH6WM8dVXIS+v/PuqNCqaLWiG/5tl73th3gWO3HEEfY7+v4NOSIDu3WH4cCgq+u91hRC1SsbqDPKilC8V++b2NHmnCn9ZqlTKr9nFi5XOr/9R3WEymSgqKutFXlICc+Z4s2jRVP7443nuv/80CxfOpEEDV/78E3bulGTs/zk5hdOu3W78/WcQFPRFna+muRHduilJ2Z9/QlBQ2fLfflO6Jb/+OhQUWC4+cXUqk0kqTCvK9c7GXdEiI5Wud5dP4mtnp1wluXzSX0s7m3WW5SeXExkTyYYzGyg2FJdbZ92D6+jTtI8Foqs8GRlriY5+lOLic2bLbWx88fYeg7f3w9jZVa8JZUwmE4WFMWRkrCIt7W+ysv7l8nFsGo0jXbokYmXlWNo05qWXlPr1Sxo0UEol7r33yr9fkn5KIvqRaEw6Zb8OLRyUboyNr9CN0WSC9u1h/37l+YgRyo8jzfWXK+l0OlasWMHgwYPRShmkqERyrFWOjDUZxDwRQ9iSMJzaXKPN660ymW547Gpu7n5OnZpIQcFxOnU6xZo1zkycaH5udnaG6dOVMbj/1RDpetW1Yy07exv29qFote6WDqXa0+mU6wdvvAGpl13r9vNTKqZGjryxQ7yuHWsV5XpzA7lDVoPFxio1wkOHmn/hP/igUvI+Y0b1ScYAGrs25qkOT7Hi/hWkTU1j6ailPBL+CG5WyvwtzjbOdG/c3WybXw7/wqN/P8rSE0vJK7nCLZcawMamAcXF50ufu7sPpFWrNXTufIYmTd6sdskYKF027e2b0ajR84SHb6BLlwQCAz/D2bkbAPXqjcbKSjm41GqlimfHjl+ZMeN06Y+MCxeUYRfdu8OBA+Xfw/sBb1qvbY2Vm1I5nX8kn30d95G98woTvKpUyij3S/18lyxRRjLL9SQh6gz3/u50jOlY+clYTg707Qtbt17X6jpdOtHR49m3rz3Z2ZvR6VKZNetdhgwpOzerVPDYY8rzSZMqJhmra/LyjnDo0CCionpSUiITb12LVqs04zp1Srk4f6kpV3w83H039OunjDUT1YMkZDVQUZGSbDVvrtwdu6R7d9izB374AXyrecd5R2tHhoUMY97geXwb9i07xu5g/tD5WGvMz1KLjixiQdQChv82HI8PPBj400Dm7J7D2ayzFor82oxG89I7B4cwvLzuwsMjgrZtd9Gq1Urc3fuhUlXRvDkVwMbGh0aNnqVt26107hxP48avmb2u12dz9uwj9OgRxKZNdzJ+/L9cuqO2bZsy5/MTT5hfpQNwvd2VtjvbYhekTLGgS9ER1TOKrM1Z5YPo1ElJxC5dmfvmG2VwvRCizlBrK/lnS1ER3HknbNig/GJdteqqq5pMBhISvmTXriASE7/i0nfe+fNB/PNP19L1brtNGTYwfz41dlJnSzOZTMTEjMNgyCU//zBRUT0pLk689oYCZ2elcfHhw9C/f9ny9euVIQcTJ0L2Fa6DiqolCVkNs2IFhIUpt6CLL1b8NWgAv/6qDN5s396i4d0UtUpNO5923BN2j9lyvVHPjnM7Sp+XGEpYfXo1z6x8Bv/Z/rT6shWvrH+F7ee2YzAa/n+3VU6vz+P06akcONC1XFIWGvozLVsuw9m5o4Wiqzi2tr7Y2vqZLUtO/uViBzETRUV/M2pUL9avb8tDD32PlVUJJhN8/bUyNv6zz5SJpi+xD7an7c62uPZyBcCxpSNO7a9yBbx/f/jxx7I6i/feU2ovhBC1Tto/aSR8mUCVjazQ6ZTpNi51K7K3h8ZXbiiRnb2Tffvac/LkU+j1yrychYWOzJ07i7Fjj7Bjx1AaNVLmkN68Gdq2rZqPUFupVCpCQn7AxqYRAAUFxy8mZQnX2FJcEhKiXF/46y/w91eWGQzKXGbBwbBwoTL1p7AMSchqiLg45aLdkCFKqSIot58nT4YTJ5QOOrVt3KuV2opzE8+x/L7lPNn+SXydzW/7HU45zLtb36Xbgm58te8rC0WpyMxcz549zTl37kNyc/dw4cJcs9fV6tpdb+3ldRdNmryLtXXD0mVqdRRjx45h+fJARo/+HGvrQrKylPa8HTsqd3Mv0bprabWqFb5TfWnxT4v/nkh61ChlAr1LJk9Wsj0hRK1RdL6IE2NPcPKpkxwechhDQSVfdDMYlNl0//5beW5vr1wBDTVvUqXX5xAT8wwHDnQlLy+qdPmaNQ/wwAMx/P77C1hZWTN9ujJ0YPTo2nduthR7+yDCwzdjY6MkyYWFMRw40IOioupbMVPdqFTKb8ljx5RKK9uLQ7ZTUuCRR6Br17Kh2qJqSUJWzRUXw9tvK+WJl84ToMwtERWlNE1wquRyekuy09oxOGgwc4fM5eyEs0Q9EcVbvd6ic6POqC6be2tg4ECz7Q4lH+LTnZ9yKuNUpcZnNOqJjZ3GwYP9Spt2qFQ2GOtQK38Aa2tPGjd+ic6dzxAa+gtOTh0ve+0cTzzxHEuX+nPHHV8CypiyTp2Uge2XSiXU1moC3g/Axse83X1hXCHFSf/XAObJJ5X5HS4ZP17pLCKEqPGMeiPH7zuOPkO5la62VaO2q8SfKyaT8p2yaJHy3MZGaRnbqVO5VfX6HJKTf+BSeeLp06157rnNvPvuj2Rk+HDnncpF0jfeUHI6UbHs7JrQps0mbG2bAlBUFMuBA7dTWBhn2cBqGDs7pePiiRNKc49Ldu1S5iybNOnKnZJF5ZGErBpbu1aZwHnaNCi8OJ+ktzf8/LNSUREWZtn4qppKpaK1d2te7fEqOx7dQeILiSwctpDnOj5HU7emZusuOryIiasnEvR5ECFfhDBlzRQ2xW1Cb7xGa/UbUFQUT1RUT+Lj3+bSydnVtQ8dOhzBz29yhb1PTaJWa6lffzTt2u2iTZtteHhElL5mZ5fC5MkZpW3yTSblRldoqNIw8UpVSfpsPYeHHGZ/p/3kHfq/s8Orryqzn1/a2bZtlfSphBBV6exbZ8neolypsfG1odk3zSqv9bnJpLQpnj9feW5lBb//rkyseAW2to0oLn6H4mJ75s79iMcf38vhw91p3FjJ4f7666pVjqKC2No2Jjx8E3Z2wQAUF5/l0KEBlJSkWTiymqdxY/jjD1i3TrnwD0rZ4ieflL8RICqXJGTVUGqq0imxf384eVJZptEo81JGR8N990kJBEB9x/qMCR/D7EGzy70WGVPW7SQ6PZpZO2bR8/ueeH3oxX1/3seiw4vIKMy46fdOTV3K3r3h5OQoSYBKZUXTph/QuvUa7O0Db3q/tYmLS1datlxG+/ZReHndg5WVK507P83evcqdXXt7sLPLJT29kFGjYPDgsnLcS05NPEXBsQKK44s50O0AaZGXnXBVKmUM2VNPKf845pqXiQohap7MfzM5O/NiCZoGmv/SHK17JZZ8v/GG8usTlO+UH39UWhejNO24cOEr9HolOUxMVKbxGDDgSR588AS//z4JtdqKF1+Eo0dLNxNVwNa2EeHh/2Jn1wxQyhePHLmjzlWnVJQ+fZTKlXfeKStjPHdOKW8cPlx5LCqXJGTViMmkdEgMDTWvvrrtNqWm95NPlG454toW372Y9/u+T3e/7qgvm1g5qyiLX478wv1L7sfrQy8+2PbBDe3XZDJx5sx0jh4dXjqQ29bWn/DwLfj5TamUSZxrOkfH1oSF/UanTmfQal3RapVhX0oN+1v8/HMAw4bNZe1aHWFhygmh5OI5tcnbTXDqoNTkGvIMHLnjCPHvx5cN8lep4PPP4eOP5SqFEDVcSWoJx+8/DhcbCzR5swku3Vwq7w2PHYO33ip7Pn++MugLyMs7xP793YiJGc/p0y/zxRdKU4RffwWjUUNqqi+33ab8iH3vvbIZOUTVsbHxoVWrVVhbewPg6Tkclap2j9euTNbW8PLLcOSIeTfGpUuVu2Wff67GYPn+abWW/HqsJk6fVv4BPPwwpKcry1xdlc7emzZRWuYlrk9zr+ZM7TaVzWM3kzI5hZ+G/8ToFqNxsSk7uRtNRkI9zQdsZxVlsfb0WkoMV7vKZqKw8GTpMy+vu2jX7gAuLp0r42PUKlqtq9lzb+8kOnX6HE/PRCZMeJqFC8Po2PFPXn3VRHg4bNkCNj42hG8Kx+seL2UjE8S+FMvxB45jKLx4ZlCryydj+/YpOxBC1Agmk4kTY05QckH57nXt44rfi37X2OoWNW+ujBvTaJRWc48+isFQSGzsK+zb147c3F0AJCR8xTvvxJKTo2zm4QELFijn5hYtKjdE8d/s7Pxp2XIFoaE/X7woKhfmblVAgNKN8ZdfyqZpyMuDF17QMHXq7dL0o5JIQmZhOh28/77ypb5uXdnyUaPg+HF49FHl96a4eR72Htzf6n5+GfkLqVNS2fjwRl7o8gKt6reiT1PzcQLLopfR/6f+eH7gyV2L7+L7qO9JyS+bgFKlUhMSshBX154EBHxE8+aLyyUa4vqYTMW4u5c1Y/H1PcmMGXfxxRdd0Wi20qOHMs4+X6eh+S/N8Z/hX7puyqIUonpEUZxQXH7He/Yok7oOGiTjyoSoIc5/cp6MFUoZudZLS+iPoag0VfDjetQopd7w+efJzt7J3r1tiI9/F5NJGW8cHx/ChAn/kpiojFN+9FFl6MDYsXJuri6cnNpQv/59lg6jVlGplJvFx48rc4hecvq0K127WjFhgjT9qGjydWJBe/Yo3WxeekmZixLAz0+Z7PnXX5UGHqJiaTVaevr3ZFb/WRwcfxB7rXkbrMiTytiz3JJc/jz+J2P+HoPPrPp0+bYL72x5h0PJh1CprGndeh2+vpPkatwtsLVtTIsWS2jbdhcuLj1Kl4eF7eTzz7vz5pvD+fvvWGUC9OUq/F/3J2xJGGoH5Wsrd28u+9rvI3vn/81o+fHHkJUF+fkwcCDs2IEQovoy6owkLiib5Dfkh5By3VYrzIkT5RYZAhtfnEOyG4WF0QDo9dYsXPgGjz0WxeHD3QkOVuYT++Yb5Q6ZqN7S01eSkvKHpcOo8dzcYN485dpmWJgyVMBoVDF7ttJ07vIbCeLWSEJmAXl5Sg+Czp3h4EFlmVqtLDt6VJlrTFjG/S3v54FWD+Bu5w6Avz180x7Ope/k1Q2v0npea/xn+zN71+cWjrT2cHbuSHj4v7RosQx7++aly7t3X8p33zXn9tvf5Y47lMH0pm5etN3eFlt/ZdRxSVIJWeuzzHe4cGFZAXxeHgwYoPTyFUJUS2qtmrbb2+I50hO/l/zwGFhJGc+XXyplipfNW5iTs4t9+9pw7tyHXBq8duJEBx577AA//DAdg8GGKVOUaWa6d6+csETFSkxcyOHDQzlxYgz5+UctHU6t0LUr7N6t56GHjmJnpyRmcXHQr59y1zgry6Lh1QqSkFWxdeuUqwqzZ5fNiN66NezcqTTtcHS0bHx13R3N7uDH4T+SMjmFLff/wLz29jRxgPdbgsvFscLx2fEU6YvMtjOZTCTmJl5hj+J6qFQqPD0jaN/+IM2afVM6SNvauqwk8ddfld9Sfx10pO3utrjc7oLX3V74vfJ/40xsbZVRyH37Ks9zc6F/f1SXz0QthKhWrJytCPs9jCZvNamcN/jyS6Ujq8mk1GBdvEiTnb2DggLlrpleb83XX7/L009v5+zZ5jRvrtxg/+ADZd4mUTNkZW0CDBiN+Rw5MgK9PsfSIdUKWi2MGHGKffv09OxZtnzBAmmRXxEkIasi2dnw+OPK1YS4OGWZra0yfuxS6aKoPooKo1ElT8ZGVQBAE/dWvNvnPQYEDMBaY01EcITZ+lFJUTT4uAEd5ndgxr8z2HdhX1knQHHd1GorfHwepWPHGHx9p2JvH8rAgRNxV25Ykp4ODz1kYthD1rh905qQ70PKlY2aDCbl19Pff0Pv3srCnBw0AwfidoVyJSGEZZiM5t+RKpWqcsaNffaZkoxd8tJL0FGZvN7T8zkyMroTHd2eceP288svL6FSWTFtmtLduGPHq+xTVFvBwV/i6BgOKO3wT5wYI+fjChQYCOvXK6WMTkoTZBITlRb5o0crUzeJGycJWRVYsUKZxPnSvJMAPXvC4cPKvLZa6dJareTnnyAqqjc6ndLMw8mpPR3abuLJTi+y6oFVpE9NJ8zLfFbuZTHLANh7YS9vbHqD9vPb0+iTRjy+7HH+if6H/JL8Kv8cNZmVlRMBAe/Tvn0UDz5oy/Hjyth7gCeeeJHQ0PF07ZHBl99qSu80A2RuzGRP6z3kn8hXJjr75x8uXcpT5ebS9Y03UG3eXPUfSAhhJuWPFPZ33U/RuaJrr3wrPvoInn8eAKMW0mfdpcytoVKxYwe0bavmkUf+5KmndhAXF0Z4uHKRdOZMsKmkYWyicmk0doSF/YmVlSsAaWl/XSxJFRVFrVZuNB89qswheslvvylTNy1apNyMFtdPErJKlJGhtLEfMgQSEpRljo5K5cT69cpVBlG9FBREc/BgL3S6ZAAcHdvRqtUas06KjtaO5e7KNHBqQBvvNmbLLuReYP7++Qz7dRgeH3gw+OfB/Hjwx0r/DLWJWm0NQL16SsnismUnuOuuT7jjjq/46qsgNmz4nJ499URHQ2FsIUfvPkrB0QL2d9xP6tJUZXKg5cuVW9OAVVERmmHDICnJkh9LiDqtILqA6Eeiyd2Vy762+yovKXv3XWXSQyA3CPYu8eJwuz9JTtnKpEnQrZvS4yM72wuNxoqZM2H3bmjT5hr7FdWenV1TQkPLJnSNjX2ZzMwNFoyodvL1VRrR/fgjZpUs998Pd9xR9ttXXNstJWQ6nY5z584RHR1NRkZGRcVUK/z1l1JT+8MPZcv691cm3Bs/XtrlVkdFRWeJiupFSYnyY93RsQ2tW69Bq3W75raPtX2M/U/s59zEc8wbMo+I4AhsrWxLXy82FLPy1EpWn15dblsppbh+nTodw9pa+bs6OWXx3HPP8fDD7Rg9eidfzwfri53ZDLkGjg4/Suy0WEw2dvDPPxgHDQLA+MEH0sJUCAvRZek4fMdhDLnKPIJu/d2waVTBt6JMJnjzTXjlFYxqiHsA9s9TU+CYCphYt248n35qLL2C36GDUp44bZpUrNQmHh5DaNz49YvPjBw7NoriYskQKppKBQ88oMyzfvfdZcsjI5XfwfPny92y63HDaUFubi5ffvklt99+O87Ozvj7+xMaGoqXlxeNGzdm3Lhx7Knjg+fHjIERIyBZucmCi4sy6HHVKmjc2KKhiavQ6bI4dGgIJSVKYw5Hx3Bat16HVut+Q/tp5NyIJ9o/wbJ7l5E+NZ1l9y7jiXZP0NCpIUC5sWe5xbn4ferH2L/H8uexP8kplsHH/8XLawSdOsVQv/7DpcsCAg7x0UddiSuaxCvOTbAZ6FX6Wvzb8RyOOIyuQINh8WJ2vvoqxnHjLBG6EHWeyWDi2OhjFMYUAuDQwoFmXzer+OlD3nkHpk+noCFEfQZxj4JJrdQ2x8S04bXXfsVkUmNjozTs2L5dJniurfz9Xy+d71KnS+PEiUfkImglqV8fFi+GJUvKrnnm5Cj9E/r2hdhYy8ZX3d1QQvbxxx/j7+/PwoUL6du3L0uXLiUqKoqYmBh27NjB9OnT0ev19O/fn4EDB3Ly5MnKirta++uvsscREUqN7dixylUEUT0lJX1LQYHSHtfOLohWrdbecDL2/+y19kQERzAvYh7nJp5j/+P7GRJkPqfB2ti1nM85z3dR33HX73fh+YEn/X7sx2e7PiM2U769rsTGxofQ0O9o02YH9vbhAKjVJoYNm8dTL7RguuEgsQObgkZZP2NVBvva7yP/hJ7kK3XPOX266oIXog47PfU0maszAbDysKLFPy3QOGgq/H1MgwaRMMqWvfMh5+JwX6NRzY8/vsrTT+/kzJmWdOumTDszZQpYWVV4CKKaUKk0hIb+hLV1AwCysv4lLy/KskHVcsOHK3fLxowpW7Zhg9Jh/NNPwWCwVGTV2w0lZHv27GHz5s3s3r2b1157jQEDBtCyZUsCAwPp2LEjjzzyCAsXLiQpKYk777yTLVu2VFbc1Z67O/z8s9JToGFDS0cjrqVRo0n4+7+BVutJy5YrsLb2rND9q1Qq2vi0wcnGyWx5Qk4CDlqH0uc6o451set4ftXzBHwWQNjcMF5c+yJb47dWaDy1gYtLZ9q330NAwMeA8jd0d0/mlVfuZ5FuK3P8W6NyU+qPis4UcajHIbSb/q8e6bffoFkz+OKLKo5eiLol8btEzn98HgCVlYqwP8Kwa1LxveSLiy9w2OpVTo4vwnhx9+fPB/Lss1tZsOAtrK2tmT0bNm1S/umL2k+r9SAkZAEODq1o124PTk4ySLCyubkpU4KuWgV+F2emKSiAiROV+fyk4XF5KtNN3rvNzc3Fycnp2ivWITk5Obi4uDBsWDZffeVM/fqWjqj60+l0rFixgsGDB6OtBsX7Ol06Wm0lTUp6FUX6IjbFbSIyJpJlMcs4m3223Dqt67cmanxUlcZVkxQVnSM6+jkyM5dy5kwY48YdwGDQ4qMpYl79ozhfyAXAZGui48mOODRyUAZ0tmkDer2yk5kz4dVX5Va2uGXV7XvN0rJ3ZBPVMwpTifJzI+jLIBqOr8ArlUVFyuAvjYaDBweQmbmm9KW//x7PvHmzKCpyoFcv+OYbaNq04t7a0uRYu34mkwGVquLvyNYVN3us5ebCyy/DnDlly2xslGGekybV/jvUl3KD7OxsnJ2dr7reTbeW6N69O0nSqeyKfvgBScZqAIOhoNyyqk7GAGytbBkQOIDPB3/OmefPcPjJw7zb5126+XZDrVL+iQ4NHlpuuwf/epCPtn9EdFp0VYdc7dja+tK69V+0aPE3zZt/Q/Pmyski0WDLXRfC2Ruk1EgUPFeAdX2lcyNhYfDii2U7ee01pSObjC8QosKUpJZwZPiR0mSswZMNKjYZy8lR+m4/8wyYTHh5fYpeb0t6ujcvvriCTz/9Eq3WgXnzYN262pWMiRsjyZhlODkpRSibN0NQkLKsuFg5/XbtqlwbFbeQkLVp04ZOnTpx4v/uO0ZFRTH48kkJLGjOnDn4+/tja2tLp06d2L1793+u//vvvxMSEoKtrS0tW7ZkxYoVVRSpqGq5ufvYtSuAzMx/LR2KGZVKRYt6LXjptpfY+shWkicn88OdP3B/q/vN1otJj+GnQz8xee1kQuaEEPx5MJNWT2LDmQ3oDDoLRW95np530KlTZ/buhenTlStvfgFHCJ87kO1Tv+HHs/UoLLy4skqFaeZM+PCy+Wk+/lgpfNfV3b+hEBVJ66HF51EfAFx7uhI4uwLne0lKwtC3O2zcCPPmcfye6bRtG8qrry7hkUeOsHv3IAYMUH7wPfGEdDcWZYzGEmJjp5GTU7eb0FWl7t0hKgpeeKHs3+KePdC2Lbz1lpx2b/rraeHChYwZM4bbbruNrVu3EhMTwz333EO7du3QaCx/FeK3335j0qRJTJ8+nf3799O6dWsGDBhASkrKFdffvn079957L48++igHDhzgzjvv5M477+SIpO61jk6XydGjd1NSksShQ/3Jzt5u6ZCuytPekwdbP0iIZ4jZ8o1nNpo9P5lxkk92fkKfH/rg+aEno/4YxY8HfyStIK0qw602rK3hjTdgzx4j06aNx8pKT9dBP3PPPRE8+eQfbN2qXK2PGR9DPPdg+np+WaniDz8oE6jk5VnuAwhRS6jUKpq+3ZTQn0Jp/ntz1NqKyYoM0Yc4+UUz9o8/hEELudbujPljCMnJsHv3INRqDxYuhJUry8awCAHKOMP9+zsTH/82x48/iMFQeO2NRIWwt4dZs2DbNgi5+LNGp1MKVDp1Uhrt1FW39M04Y8YMJk2aRL9+/WjRogW5ubns2LGDZcuWVVR8N+3jjz9m3LhxjB07lubNmzNv3jzs7e1ZsGDBFdefPXs2AwcOZMqUKYSGhjJz5kzatm3LFzLYv1YxmUycODGGoqIzADg6tsXJqb2Fo7pxT7R/ghNPn2BWv1n09O+J5rJSjJziHBYfXcxDSx8i4LOAOn3HrHVr6N37IfR6pW7b3T2FcePuY8uWoXx+1x4Sv04kdkosRyI7ofvuT6WwHZSRyL17Q2qqBaMXovaof399rD2tK2RfObu+Z+/etiT0zSG/KRwa50SHkm3sphOgXE85elS52S1DQsX/02q9SssXCwujiY9/x8IR1T2dO8OBA/DSS2V3yw4cgPbtlYupJSUWDc8ibnooXXJyMu+88w7z58+nefPmnDhxgjFjxtCxY8eKjO+mlJSUsG/fPl5++eXSZWq1mr59+7Jjx44rbrNjxw4mTZpktmzAgAEsXbr0qu9TXFxMcXFx6fOcHGUOKZ1Oh66u33u9Tpf+TlX19zp//iPS0/8BwMrKnWbNfsZgUGGogUlLU5emPNfhOZ7r8ByZhZmsiV3DilMrWHV6FZlFSmvpno17glHp3njJtwe+xdfFl9v9bsfGqoInZK2GfHwew919EFFRz6PXK//fd+myHF34RmAs/DWc9H/S2XvQm5CPV+D6yghU2dmwZw/GiAgMW7bIrzpxQ6r6e626SfgkAaeOTjh3u/oA9pthNOpI2PQ48Zqf4dIwtGIVsy9MJZoQPDxMfPKJgVGjTKhUdaMEqq4fazcrMPAboqI6YDLpiI9/H3f3u7G3D7V0WNVaRR9rGo3S2GPYMBWPPabh6FEVej3MmAFLlpiYP19P27YV8lYWdb1/r5vusmhvb0+zZs146623GDJkCKtWrWLUqFFMmzaNKVOm3MwuK8yFCxdo2LAh27dvp0uXLqXLp06dyqZNm9i1a1e5baytrfn++++59957S5fNnTuXGTNmkHxphuf/88YbbzBjxoxyyxctWoS9vX0FfBJRkTSaozg4vIZKpUwQmp//Gnp9OwtHVfEMJgMn8k+wN2cvzeyb0dm1c+lrJcYSHjryEEXGImzVtoQ7hdPeuT3tnNvhpnWzYNRVQ63egUazAHv7sjtf+uOhWH04Gc40xWRlwjT0Ap03PY9NXi7bZ8wgI1RO0kJcL+0GLfaf2WPSmiiYUIC+m75C9qtWn8dV9yYG98uGHUQ78vTb6zl2riNduybw+OOHcHWtg5fWxU2xsfkZW9vfAdDrw8jPfwuQi2+WoNOp+f33YP78MwiDQbllplYbGT78FKNHR6PVGi0c4c0rKCjgvvvuu2aXxZu+Q7ZgwQJGjx5d+nzgwIFs3LiRiIgI4uLimHN5f8ta6uWXXza7q5aTk4Ovry/9+/f/zz+6KKPT6Vi7di39+vWr1Ja9JSXJREU9iU6n/KNu1OhlGjd+rdLez9KGUr4rI8Ca2DUUHSoCoMhYxM7snezM3glAhwYdGBw4mMGBgwmvH46qlt0VUo41uP325zh27A2KiuYDYBV6nMKXPsDuiS9R6VWo/mrI6QHLCXokjc7D+1s4alETVdX3WnWTuTaT43OPY8KESqcizCOMhoNvraOiyWQkMXEuZ0+/iEF98UqzAQp/asbdP+7GwcOJX3/VM2JEPaDvrX+IGqauHmsVwWDoRVTUPoqKYrGyOkr79unUq/eQpcOqtir7WBs2DCZPNvDYYyoOHVJhNKr5889gjh4N4ptvDHTsWDM7IF+qnruWm07ILk/GLmnbti3bt29n0KBBN7vbCuHp6YlGoyl3Zys5ORlvb+8rbuPt7X1D6wPY2NhgY1O+5Eur1coX4w2qzL+ZyWTk2LGx6HSJALi69iYgYGadbIHbw78Hf9z9B5EnI1kes5zUgrI7RXsu7GHPhT3M2DyDhk4NiRofhad9xU6QXR3Y2XnRufPXZGY+yK5dj2NtHc2UTxdyG/UYzTkA0lcXkn/Kg7CgIpzaXJxv0WiEFStgyBApYRTXpS6dC7J3ZHPi7hOY9Bfb2z/VgMZTG9/ShR2Tycjhw3eQkbGqdMS7XTwcfHcUT574mfse0PDpp+DhUcsnMroOdelYqyharZagoDkcPqz8Zo2Le4l69e60yPQ3NUllHmsdOsDevfDee8rUoDodnDihokcPKyZNUkoc7Sp+PvlKdb1/qwpvAuvv78/27ZbtWmdtbU27du1Yv3596TKj0cj69evNShgv16VLF7P1AdauXXvV9UXNkZAwh8zMdQBYW/vQvPmiOpmMATjZODGy+UgWDltI4guJ7Hh0B692f5VW9VuZrWevtS+XjO1J2MP5nPNVGW6lcnPrTv/+UXh7R1LfpwtfEcCrtCAXK/C5QFFyKnGvx5Vt8OqrMHQoPPdc2WTSQgjyDudxePBhjAVKBYLnCE8CZwfe8l12lUqNwVB2Dl7758P88PhsZuT8yt/LNPz4I3jIb2dxCzw8BuLldQ8AOl0ap0+/eI0tRGXTapWui/v2QbuLo0qMRqU7Y3i40qGxNrqhhCw+Pv661nNzU8aiJCQk3HhEFWTSpEnMnz+f77//nuPHj/Pkk0+Sn5/P2LFjAXjooYfMmn48//zzrFq1io8++ogTJ07wxhtvsHfvXp555hlLfQRRQdRqW9RqZUxfaOiPWFvLrN0AGrWGzo0681bvtzg4/iBnJ5xl7uC5DA4azF3N7yq3/uORj+P7iS9tv2rL6xtfZ3fCboymmlvXDaBW29C8+WDWrYOvv4bDTp48rg6n8LW3MX43lr+aHyUtzaScGd57T9noiy/gzjuVCWmFqOMKYws51P8Q+izlIoVrH1eaL2qO2urWrvcaj53g27eT6NHjFTZvHsELL6zlnS++o/iB5zh6FCIiKiJ6ISAw8BM0GmWYSVLSt9V6Kpy6pGVL2LkT3n1XmcoGICZGmc9swgTIz7doeBXuhr4xO3TowBNPPMGePVefSC87O5v58+fTokUL/vzzz1sO8GaNGjWKWbNm8frrrxMeHk5UVBSrVq2ifn3lx3h8fDyJiYml63ft2pVFixbx9ddf07p1a/744w+WLl1KixYtLPURRAVp0GAc7dsfJDDwM9zc+lg6nGrLz8WPJzs8yfL7lvNOH/M2wOdzzhOVFAXAgaQDzNw8k07fdKLBRw149O9HWXpiKXklNXfeLpUKxo1TWmU/+vIC7EKPofZIp8ege1m48E6WnK0PCxag11wsX1y+HLp2hdhYywYuhAUVJxZzsN9BSpKURhpOHZ1o8VcL1DY3l4ylpUVy/vxnnP9mFQWtOhE27U50eXqmT/+TtLS+rFkD33wDrq4V+CFEnWdj04AmTd5GpdLi5/cKjo7hlg5JXGRlpbTGj4pS5ikDMJlg9mxlWptNmywaXoW6oS6LjzzyCG5ubnz77bfY2trSrl07GjRogK2tLZmZmRw7doyjR4/Stm1bXnvtNQYPHlyZsVc7OTk5uLi4XLOTiiij0+lYsWIFgwcPlvr3aiw5L5l5e+cReTKSvRf2XnEda401vfx78eWQL2ni1qSKI7y26z3WCgvPsXHj09jbl82nmJ/vxMGdM+nxZQsa5P2Ff/E81BjA3R3++AN69aqKjyBqiLrwvWYymtjXcR95+5QLMfbN7WmzuQ1ajxv/vHp9LqdPTyIx8RtMBjVtnjLhGqP8NJnJNJKemsl774GTU4V+hFqhLhxrVcFkMlBYeBp7+2BLh1JtWfpYMxjg009h2jQoKipb/tRT8P774OhY5SFdl+vNDW7oMtZPP/3E1KlTuXDhArm5ufj4+JCWlsbJkycBuP/++9m3bx87duyoc8mYqF4MhqJrrySuW33H+kzvOZ094/aQMCmB+UPnc0ezO7CzKhtdW2IoYWPcxnJjzzILMzEYDVUd8k2zs/Nl0KC/adBgMfn5yh11B4dcuvaZgP6N54n3accB2/kU0gAyMqB/f/jySwtHLUTVUqlV+L/uj0qrwqaxDa3XtL6pZCwrawt797YmMfEbZb8aI6n9lWRstcNweq54kTlzJBkTlUul0kgyVs1pNPDCC3DwINx2W9nyuXOhRQtYt85ysVWEG2pN1KBBA6KiohgwYACFhYW888471KtXr7JiE+KmGI06Dhy4DReXrjRt+i4ajYOlQ6pVGjg14LG2j/FY28co1BXyb9y/LItZRmRMJKFeoTjZmP9yem7Vc6w8uZLBQYMZGjyU/gH9cbF1sVD010elUhEcfDf+/n1Zt24q9vbKj0VaHIWvHyf3l3vZ/cs8mhV9Tn39WlRPPQWHDil1FJeK3YWo5Tzv8KTF3y2wC7TDpuGNTTJvNBZz5sxrnDs3C1ASME0BBMwBnxWwptNrdF/3BvaOFd57TIjrYjAUotHUsJZ+dUBwsFKq+MUXSjljYSGcPQv9+ilDDz78EFyq90+MK7qhb7oXXniBoUOH0r17d1QqFT///DN79uyhsLCwsuIT4oadPfs2eXn7SEj4nGPH7rN0OLWandaOQUGDmDtkLmcnnOW3u34ze11v1LPi5ArSC9P58dCP3PPHPXh+6EmfH/rwyY5POJVxykKRXx9razcGD55P48YbycoKUhZq9fDAT5j8LnCCVzjONPQ4KL16jTW7yYkQ/8WoK398ewzywD7I/ob2k5t7gH372nPu3IdcSsZcDkH7x8B9pS0nZ/xC/51vSjImLMJgyCcubgY7dvhSVHR9zexE1VKrlYbHhw/D7beXLZ8/X7lbtmqV5WK7WTf0bffss8+yd+9eBg4ciMlkYs6cOXTp0gVnZ2dCQ0MZPXo07733HitXrqyseIX4TwUF0cTHX2pIocHf/3WLxlOXqFQqXG1dzZZlFWXRy78XjtZlxd16o54NZzYwac0kgj4PIuSLECavmUxsZvVtkNGkSU+GDj1EYeEr6PVWxP31AMQ0AyCFPuzRLCT7pZ/B1taygQpRSfTZeg50P8C5T87d9D6MxhLOnHmd/fs7kp9/BABVCTSdB+EToTjfF9WWLQS/Xn6eUyGqSnz8h8TFvYFen05s7CuWDkf8h4AA2LAB5swBh4vFUOfPw6BBMHYsZGZaNr4bccOXn1q1asWrr75KQEAAO3fuJDc3l61btzJhwgTc3Nz4+++/ueeeeyojViH+k8lkIibmaUwmHQB+flNxcmpn4ajqNk97T/645w/SpqSx9sG1PNfxOZq4mjf8iE6P5qMdH5FdlG2hKK+PRmPLoEFvExq6n835c3iT5uShzGdXbHJn/3v/cGZmXNkGGRlKOyghajhdlo6DAw6SuyuX05NOkzD35qe0iYtbismktMgvOOVFuyfB7zfIa98b11P7sOnWvqLCFuKmNGo0ASsrZYK7lJSfycm5emdxYXlqtdLY48gR6Nu3bPl330FYGCxbdtVNq5Wbrgc4efIknp6e2NnZ0alTJ5544gm+/PJLduzYQY7MzyMsICXlN7KylMm9bWwa07jxNAtHJC6xsbKhb9O+zB40m9PPnebYU8d4v+/7dPfrjlqlpoFTA8K9w822mbV9Fj0W9uCDbR9wLPUYN9AQtlI1bNiSBQucmLSsHq/W68BhnGHEElQfTmG709PExFyA3Fxl1PG998p8ZaJGK0kpIapnFLm7cgHQempx6XHjAzQyM2HcOGueeOI7iors+O676dw3IY50VS+MkybjtG01eHlVdPhC3DCt1hV//+mlz0+fnlxtzj/i6vz9Yc0apWzxUhOgxES44w544AFITbVoeNdUKQXaKpWqMnYrxFXp9TmcPj2p9HlQ0OdoNDc2rkFUDZVKRahXKFO7TWXz2M2kTE5hyT1Lyn1v/HXiL7bEb+HFdS8SNjeMwM8DeX7l86yLXUeJocRC0ZeJiIBN0bacfMob3SMLAWgYvoKTJ0M58FJPTCeOw2+/Qfv2yiQqQtQwReeKOND9APkHlRlYtfW0tN7QGscW19dfOjd3H/n5R/nrL2jeHP5dcJqTJ9syevRZ4uLeYM8BexofX436ow+VCYeEqCYaNBiPnZ0ybjg7ezNpaX9bOCJxPVQqeOwxZU7RgQPLlv/8M4SEwMKF1bdwRUbMilohLm46JSXKRN8eHkPx9Bxq4YjE9fKw96BTo05my4r1xWQWmhd/x2bG8tnuz+j3Yz88P/DkrsV38X3U96QVpFVluGZcXWH2Fz6otAvIyVGu7js45JB99372zdYQ43sneSeN0LkzzJtXfc8EQvyfglMFHLjtAIUxStMuG18b2mxpg2PLaydjRmMxsbHT2LevE5GRD/HA3blMTxrPMZrT3X4f77/vxYYNEBQEyNxZohpSq7U0bfpB6fPY2BcxGvUWjEjcCF9fWLFCKVu8NJF8RgY88gj07g0xMZaM7sokIRM1Xl7eQc6f/wwAtdqOwMDZFo5I3CobKxuOPX2M6Gei+bj/x/Ru0hsrddkV9NySXP48/idj/h7DypOWbSKkUqno23c03bsfJy5uTOnyvJYGLnyznL0PdeOMYSTGJ5+B0aOlhFFUe3lH8ojqHkVxfDEAdoF2tNnaBvvga1cd5OTsZe/edsTHvw0YqF9/P+sHN2c8X2FDCRs87+aJB/JRy68PUc15eg7DxaUHAIWFMaSk/GzhiMSNUKng4Yfh+HHl1HvJv/9Cy5bw5ptQXGyx8MqRr0RR450//zmgtGNu3HgadnZN/nsDUWMEewQzsctE1j+0nrQpafx212882OpBPOyUAddqlZpBQYPMttlwZgNPL3+alSdXUqSvugnC3dw8GDNmIbCOlJQAZaG1Dsb+wNn529gTNon8xbuhTRvYsaPK4hLiRmRvyyaqexQlSUpZsENLB8K3hGPr998dRA2GfE6dmsz+/Z0oKDiqLNNpaLhAS8cV5wEw2dpiNX0a2Es5uaj+VCoVTZq8Vfo8Lm6m3CWrgby94ZdfYOVKZZwZQEkJTJ+unI63bLFoeKUkIRM1XnDwPAICPsHJqQO+vi9YOhxRSVxsXbgn7B5+GP4DyZOT2fbINj4f9Dme9p5m6/125Dfm7p3L4EWD8fjAg2G/DuOb/d9wIfdClcTZs2cfBg06xJnTUzEalC6M+J+lsGcse/mKs7FdMN7WC959t0riEeJGWPtYo7JWxnM6dXIi/N9wbLz/e9Ln9PSV7NoVxvnzH3Hp4pg+xpVO4w0E/ahDbQBCQlDt3q3UDMk4c1FDuLp2x9W1DwBFRadJTv7JwhGJmzVwoNKJccoU0Fw8NR8/Dj16wOOPW75FviRkosZTq63w9Z1A27Y7Uav/+4eDqB00ag1dfbvyVIenzJabTCbWnVlX+rxAV8A/0f8wbtk4/D/354XoF3hz85vsvbAXo6nyJnF2crJn7KPv4+G5l5T4NuiTvOHbRzFhzRkeY6/xSzITPa+9IyGqmF1TO1oub4nXXV60XtcarfvVx3iVlCRz9Oi9HD48mJKSswDoS7S4f+NM76eycLw0teATT8C+fUqdkBA1TJMmM9BoXPD3n4GX13BLhyNugYMDfPCB8nXUsWPZ8vnzITQUFi2y3FBvSchEraFSyeFc16lUKg6OP8iSe5bwSPgj1Heob/b66cLTvLX1LTrM78Cbm96s9Hhatw5n5P27SSzYzFKCMVxcXkATDh6PY+sT/2DUVV5iKMS1mAwmDEUGs2XO7Z0J+z0MK8f/7nwYG5tAcvLi0ucp+4Po8oiOVj/nKHfF3N1hyRKloY2UKYoaysWlG126nMff/3WsrG58ygdR/bRuDdu3w+efl7XIT06G+++HXr2UO2lVTX7BihqppCSZ/Pyjlg5DVEOO1o4MDx3Ot8O+5cILF9j12C5e6/Ea4fXDzdbr27Sv2fNz2eeYu2cu8dnxFRqPRmPFg48EMe1cACuHtuMETlA/CSbPQnfPCBb+MInMzGz45htl0hQhqog+W8/hYYc5fv9xTIbrvyxcWAgzZkCbNm35/feJZGd78O6737P178XYp1y8o9a7Nxw6BMPljoKo+aysrm+qB1FzaDTwzDNw7Jj519SmTRAeDhMnQnZ21cUjCZmokc6ceZ09e1oRHT2OkpJqPtufsBi1Sk3Hhh15s9eb7H50N980/4Y5A+dwT9g9dG7U2Wzdv078xdMrnqbxp41pPa81r65/lZ3nd2IwGq6y9xvj6Qmz/nEidENbTo1dCTYlqDQGAgJms/3fJiT9Pg5jWHNlwhRpjy8qWcHJAvZ33k/G8gzSlqRxeurp/1zfaCzm/Pk5LFmiIzQU3ngDiorgu+9m8NJLxxk//iFm/xuO6p13YNYsWLsWGjasmg8jRBUzmSrmvCAsr1Ej5UZ+ZCQEXOzHZTDAp59Cs2bw449Vc0qWhEzUOPn5x0hM/AYwkpKy+JrrC3GJp7Un49qO47e7fjNrow8QGRNZ+vhQ8iHe2foOXb7tgs9HPoz9eyx/HvuTnOJbb1l/ey8VD0z/inNxMykqsgPAwS2TEy/D3hkGTk7/EeOdd0FS0i2/lxBXkrE6g/0d91NwogAAKzcrPIZ4XH39jDVs29aaU6eeYfHiTzl7Ftqzh0Wq+3n2CS27d3sxfPjFXh2TJ8MLLyB97UVtVFKSwunTU9izpyVGY4mlwxEVaMgQpVRx5kywU07NJCfDQw9B9+5w8GDlvr98Y4oaJzb2RS518vLzewlray/LBiRqhQ/6fcCbPd+kY8OOZstTC1L5Luo77vr9Ljw/8OS1Da/d8nvZ2try4JhpBAYe59SpEaXLC1rmkvDVWrY3ziI1fKhSxih3y0QFMZlMnPvoHIcGH0KfpbTvtg+zp92edrj1diu3fmFhLPv338mhQwMwGKIBePCBt/jE/ml20pl7TYv4wGlm6RgMIWq7mJgnOXduFgUFx0lMXGDpcEQFs7WFadOU7osjyk7NbNsGbdvCs89WXjdGSchEjZKZ+S/p6cqdDBubRjRqNMGyAYlaI9w7nNduf41dj+0i8YVEvr3jW4aHDMdB61C6js6oo6GzeRlWiaGELWe3oL+J+WkCAxvz2GN/YjSuIjupqbJQY0Q/YgNHv47m0IfbKOx8B0RH39JnE0Kfq+f4fcc5Pfn0petZeAzzoO2OttgF2JmtazAUEBv7Ojt2NCcn5+/S5eePhNLsBUcmFMxFc2knK1eCTldVH0MIi/Lze6n0cXz8uxiNcuzXRo0bw59/wurVEBysLDMa4YsvlDLGr78GfQVPSScJmagxTCYjsbFTSp/7+89Eo7H7jy2EuDnejt480uYRloxaQvrUdFY/sJpnOjyDv6s/EcERZutujd9Kj+96UO/Dety/5H5+PfIrmYU3dgmtd+8BDBpxjPQTr2MsvDgBb54jGWfuZe/uJ4lrPhPD9LeVwnYhblDewTz2tdtHyq8ppcsav96YFktaYOVUVrprMplISfmdLVtCiI+fiVpdDEBmej0y3+vK/c8eJzDm4nx+trbw3nvKJOfaq7fGF6I2cXbugLv7YACKi+NJTZVhE7VZ//5w+LDyVedw8dpsaqoyk0fr1rBiRcUVsUhCJmqMlJTF5ObuBcDBoRXe3g9aOCJRF9hY2dA/oD+fD/6c2OdiaeTcyOz1S2PPMosyWXR4Eff+eS9eH3rR87uezNo+i+i0aEzX8Y1tbW3DyPEzCG0ZTfqBuyj+ehzorDFgT5zxMba9FcKueUeua19CXC7hywQKTxYCoHHWEPZnGE1mNEGlLpug2WQysXPnEI4duwc4B4BOpyX61z70faSQ4au3U7p2z55KB8UXX5RkTNQ5fn4vlj6Oj/9QvpNrOWtr5avuxAm4556y5ceOKePO+vWDAwdu/X0kIRM1gslkIC5ueunzgIAPUKk0FoxI1EUqlarcst5NenNP2D042ziXLjOYDGw6u4kpa6cQMieE4C+CeW/re9f1Hj6N/Bg58XfqvzmN/T5emFBO9saG+RQGt+fPj0dy8mTFtuYXtVvgx4HYh9nj2M6R9vvb4zXCfNxtUhI89ZSKBQvalC7btWsA7k+H8cRX63HKyVUWuroq4xo3bICgoCr8BEJUHy4u3XFyUsYa5+cfJDNzrYUjElWhUSP47TdlPFnny5o0r18P7drBww/DuXM3v39JyESNkJLyK4WFMQC4uNyOu/sAC0ckhCIiOILf7vqNtClprH9oPRM7TyTQPdBsnVMZp0jISSi3bVZR1lX3G95Dy8SEMLI+aM9pa3t44ivQ6vFs9xdnzgTzx4ejyHn9daX3uBCXKUk17/6msdfQamUr2m4rGy9mNBaTnV3A9OkQGKjM3fzTTy9z8GAPZs36Gze3lYTfd9nkPI88AjEx8OijF9spClE3qVQq/Pymlj4/d+5DC0YjqlrXrsqk0osXQ9OLQ79NJvjhB2W82auvQs5NNGSWhEzUCCkpv5Y+9vd/w3KBCHEVWo2W3k168/GAjzn57ElOPH2CWf1m0dO/JxqVptzYs6S8JDw/8KTbgm68u+VdjqSUL0dUqWD4FCfuzWxPmiaCohxXAKyti/HssJjDbWZy5NFwcub8IN0YBYYiA6ennmZX010UxBSYvWbra4vaRo3JZOLChd9Yty6U1157mzffhPx8sKMAVyvIzd3Eb7/dweOPq9C8NFVpNbZ9O3z7LXhJR1shADw978TOTrnwlpm5jtzc/RaOSFQllQruvlspW/z4Y3C72KS2qAjeeUe5yDV37o31O5KETNQIYWF/ERLyA97eY3Fz62npcIS4pmaezXih6wtsfHgjqVNS6dWkl9nry2OWYzAZ2H5uO69seIWWX7akyewmPLPiGVafWk2RvuzOl729mrteepkO3WKJi5uMvkQZt6Nzg7Rx0ez3msTeUYMoXL2pSj+jqD5yo3LZ32E/5z48hyHPwPGHjmPUG83WycjYyLp1nYiJGY219RmGDPkYL894HlL/RIJjM04/+AbTppUNXsfWVmk11qVL1X8gIaoxlUpDo0YvlD6Xu2R1k40NTJwIp0/DpEllQ2pTU+HppyEkBH755fr2JQmZqBHUaiu8vR8kJETm/RA1j5udG9Yaa7Nl1hprmns1N1t2Nvssc/bMYeDPA/H8wJPhvw3nh4M/lL7u4eHGmDEfEtTsBKknB5ZtWC+dvKdWsyvlXg62n4Au6kSlfh5RfRiKDJx5/Qz7O+4n/0g+ACprFV4jvUrHPGZn72bdun4cOtQbrXZP6bYXjoSwxWsQ3xsfxC3vPPbffKb8shBCXJO398NotV5otfVwdAy3dDjCgtzc4KOPyjf+iI2F8eOvbx+SkAkhhAU82PpBjj51lFPPnmL2wNn0a9oPrbqsY12+Lp+lJ5ayMGphuW0bN27K3eNW4s5OivddNrpYZ0vmgaHsbRPFwWbvoouOq4JPIiwlc2Mme1vv5ezMs5h0SsmqQ2sH2u1th98UP/IKjrBu3Z0cONAJK6t1pdslxTbF/s2WjJkcRbPjx8p22L+/jA8T4jppNHa0arWazp3PmnVeFHVX06ZK44+dO6Fv3xvbVhIyUW2ZTAby849de0UharAA9wCe6/Qcax5cQ9rUNP64+w/GhI/By14ZrzM0eKjZ+kaTkXZft2N85HjiG6TS/bn1WKeuRn+4BSx4BIwaivEmM6YLm0MPs2zaYkoKZfLS2kSXruPE2BMc7H2Qwhilnb3KSoXfq36029UOhxaOrFnzLHv2tMbKqmxi57SERvBJG0aNi6XjxsNlbezDw5UZUCMjy0apCyGuycmpDRqNraXDENVMp06wdi1s3Kg8vh5W115FCMtITf2DY8dG4+V1F/7+M3FwCLF0SEJUKmcbZ0Y2H8nI5iMxmozsSdhDY9fGZuvsSdjD/sT97E/cz1f7vsLOyo4+TfswpPPT+OR1Q7P/FI6FyghjTbPzOPV9itVL/SlQv8IddzyEnZ2NJT6aqEDH7jtG5pqyyceduzoT/FUwdqGOLP4d3n0XgoOb8vTTyl2z1NSG2CwJZMTvm1AbzpftKCgIZs5URqer5fqsEEJUpJ49lWtdrq7XXle+gUW1pMw79iagJGbFxeevsYUQtYtapaZTo054O3qbLY9Oj8ZGU5ZUFeoLiYyJ5MkVT3JnSSte/2QmaydtI9k5Gx5Sxp85+cRRv/7jbFpZn50vdibr4OEq/SyiYjV+VUnSNc4aAr8MpNGSfH7fk0pICNx7rzJn87JlT3D6dEv++usjrKxOEnHbKNSGizto2BC+/hqOHoVRoyQZE6ICFBWdIy7uLYxGqUgQZa63ClzukIlqKTX1TwoKlHJFZ+euuLn1sXBEQlQPD7V+iJGhI1l/Zj2RMZFExkSSmJdY+vqBpAMccD5Avdfq8YvHN6RHv49Xs20A2LpnUzRoFwfPtUe1sAe+HSbS5P7Blvoo4joUxBRgLDbi2NKxdJlrD1cCZjehoNsu9idMxfn4To4ceZxTp75ChZHBrMC+WRD16h3kk09USr5V/Aj8+D3cfz889hjY2VnuQwlRy8THzyI29iXAgL19MPXq3XPNbYS4nCRkotoxmUzEx79f+tzff3pptzAhBDhYO3BHszu4o9kdGE1GDiQeUJKzk5HsvbAXgCFBQ+g9bCgwlB07dnP2wIt4h/wLajA5lmC6cx1nDRuIf6MNTrpxtJk5DpXcKak2is4WETczjqTvknBq50TbHW1RqVUYDPlERf1Egv9HOOeexNlZWX/AgO8pXtiMqcXz8Ss4gSn8IVRDvy/boY2NMtJcCFHhnJzaAcot6ISELyQhEzdMEjJR7WRlbSIvT5lk0dGxDW5u/SwckRDVl1qlpl2DdrRr0I7pPadzIfcCK06uIMwrrHSdLl06EtrmL27/0J3pGi/c22eCtQ40Rkw995Ez4QF2vPsDpttsaPPzEOx9nS34ieq24oRizr59lsRvEks7J+buzuXsr3s47rYY+BY7u6zSRAwg/3Q9QpcU8lnuC6j1yjLVokXKDKUNG1b9hxCijnF17Ym9fXMKCo6Rnb2FvLxDODq2snRYogaRhExUO+fOzSp97Os7Re6OCXEDGjg14LG2j5VbvuHMBg4ZTYw0phCy3pkZ+lDqdTsL6V5wsDUlqGAL7PXbTPydRbQaH0SrAa0t8AnqpsK4Qs5/fJ4LX1/AVGwqXa5qmEf+a58Q670RO7XJbBv9AU/Cf03HY3cKZt+SPXrA5Mng41M1wQtRx6lUKho2fJqTJ58GICFhDs2afWXhqERNIvUpolrJzz9GRsZyAGxs/PDyusvCEQlROwwPGc7B8Qd5u/fbuAY3Z7TzYYYeyGfzEm/Qnipdz4gjjfq/RoZNOAsX9mbJkp/JTc/8jz2LW5GzN4ejo4+yK2AXCZ8nlCZjJlsNOwIbMyyxHxkO8agvJmMlJTY4rHai3RPQd1IanrtNSjJmZaXMSLprF2zaBEOHSrMOIapQ/foPotE4AZCc/BM6XZZlAxI1itwhE9XKuXMflz5u1GgC6ssmyhVC3DyVSkWr+q1oVb8Vr3R/hZT8FFaeXMmylsv4sPsk+u3yZ8zWodh5NUQbegKAJk02Ahs5sAv0BxpiZ5hIhxefw8qm/L9Lg8HAli1bSExMxMfHh+7du6PRaKr4U9Y85z86T+pvqWBTBF23Y2x+gn8WvMvCQl9yTlkDJpYufZq77/6YU6eeomOHR2gf2QdVzBFlBw0awOOPw7hxymMhhEVYWTnh7f0wCQlfYDQWkJT0Hb6+EywdlqghJCET1UZxcRLJyT8CoNG44ONTvuxKCFEx6jnU4+Hwh3k4/GGK9cVsid/CsuhlBOhK8Pt3GiUhv1LPW7lzZrQHdbcEipnM1iXvo9sThkPD0XR6/jE0VhqWLFnC888/z/nzZdNTNGrUiNmzZzNixAhLfcRqpzCuEK2nFitH5dRrNOopvPcENJmH6batqOwLUQORy1+ha9x6RvEbQTbxrAlbSZcuzzBq1MVT9iOPwLJl8NRTMGwYaOXClRDVQYMGT5GQ8AUAFy7MpVGj51Cp5E61uDZJyES1odV6Ehr6A/HxH+Lm1hcrKydLhyREnWBjZUPfpn3p27SvsuAOMBrfZOvWreTuHI9T2DGMDhdX9klFe8e/lJRsY7uLFamO8Swo2MT5PPO5AhMSErjrrrv4448/6nRSZsg3kB6ZTtJ3SWSsziBwrj8pHWOIjv4LR8c/cXJOhf6YjQH7p2cXmn9XpDwphm4jToDfZeP5JkyAiROr8mMIIa6Dg0Morq69ycraQGHhSTIz1+Hu3t/SYYkaQBIyUW2o1VbUqzcKL697MJlkYkUhLEmtVtGjR3dyOu1g6e5FsPgXfP2TUIWfAo0RdnXCUBCAe0EAk+nFBKtUjgxdxZ7EC/yxfyMlJcWoVComTJjAsGHD6lT5orHIiNVOK6J/jiZjeQbGAqPywoRPiGm0AXVOXrl+G5o88NoE9deDa1RR2QtOTsoEzq0vS8ik0ZEQ1VbDhs+QlbUBgKSk7yQhE9dFEjJR7ahUKlQqa0uHIYQAnG2ceaj7eOg+Hp1Bx9q/f6Fo3c+47Q9EhQFQEi0rlQvhj/1CuH0hDxbZcehAW+wOp7I8ysimDRvp3a+vZT9IJdOl60hfnk76inTSV6fi4JRO2jkHs3UKnfOxc8wrfa4qAc/tUG89eOwC9aXrUPb2cMcdMGoUDBwItrZV+EmEELfCw2MoHh5D8fIaiZeXzEcmro8kZEIIIa6LVqNl8IiHYMRDAHz66ofovjpO27wwTK10WNkXAmBrW0jHLvuhC7QEyBzJhplN0CeH4tUsgrCxd2HtaGO5D1LB9PoSDixaR8GWSGh5GO49BPYFcMc/FBTZkU4yOcSzZMujTOywlzNnInB2vpPhSxfhvPQvZSceHjB4sJKIDR6sJGVCiBpHrbaiZct/LB2GqGEkIRMWZ2W1g+PHv8XPbyouLt1k3jEhaojwfh3o9c5UAFwOu3HXtKF4dsmkQ+cYPDxSylZ0y0Hd/SDWHCSbX9nhq0aVn4e+ngarLr54jm9FcA9PrLTVs6zRZDJRnFBM3v48srZmknzoMLkDUkh2P41KtZf69Xdh07LwYvZZxjd8Ek12nkSNAZ3aGo8HkunSJYWIiIsVAJ52EBSoJGFdukAdKusUQghRRhIyYXE2NsvJyDhCRsYy2rTZhotLV0uHJIS4Dt27d6dRo0YkJCSQXZTJt9uWwTbltduDGzKiVz5hAfZoQrPAsUB5IaEBpiwfTIA6AYx/QErTMSSVbOJ8amtKSlrRKFOPo2Mgbj6daNy9E44eDlcLocKVpJZQEF1AfkwOF46cIOdoHqZt9bDK1ysraEtgxT3YWhlofJV9WOWA6wHwzDtROtmn1ljCaL+94HFZ6eaQIcp/QohazWQyycVm8Z8kIRMWVVBwDCsrZT4dO7tmODt3sXBEQojrpdFomD17NnfddRcqlQqTyVT62uaTF9h8Ev7441tatA5l57zZOGYdxinDGTtVMnpT/bIdBZxGbVeIn99OYCcAeiAVSD2oghRPSHPDuKo3ur09wcMOVUNnjM28sW52HPvAerg0roeTkxNarRUaoxpTugErKyswQmF2AbpcB0qyTZTkGClI1ZOfFU2x6Qg57bUUF2dQUpKIyngWb+NJ7NySwT8dmprQuN0Ga2aWxaqzhrONISC2dJFNErgeApcj4HIY7M+CygSmkBB4tBt06wZdu0JwcKX+/yGEqD5MJhNZWf+SmPgtJpOesLBfLR2SqMYkIRMWlZj4Venjhg2flCtIQtQwI0aM4I8//rjiPGSffvppacv74LfnAZBXkse66evY8scPOKy6gHVBKK3TAlE5F+Hofa78G6hN4J0K3qmo/+2HTXJ9SAaOAYei4Nd7KQKKkiE5+T8CHf0nZLqXPX/oe2zGfofXtT6g7zmsSceB0zhyiihrX47EPEtDUz716rUlNLQNgWO7oLK2hvBweKIN+hYtWJOZSb/Ro9HKHGFC1FFGjh9/kJKSBEBDcXEiNjY+19xK1E2SkAmL0etzSU39CQC12p769R+2cERCiJsxYsQIhg0bxpYtW0hMTMTHx4fu3btfsdW9o7Ujd4bcyZ3T7sT4qpGkvCQaODUAIDMzhxP7drN33V14arOprwlDUz8PU4MMcMmFDPf/21leuf1flXWJ+fMc5yuvZwR1hgPaVGtsk8A5NpkA7gLAZGdHwD33MPL978y3iY4G67LOsCadDt2KFdcfmxCi1lGpNHh7jyE+/m3AQHLyj/j5TbV0WKKakoRMWExy8k8YDLkAeHndi1bratmAhBA3TaPR0LNnzxvaRq1SlyZjAG5uzoTf3o13UruzLnYdRfqjkAKaRGicqybIZw5N+/5G86wAGuc0JS2sDy6bnyTXQ4/OpgDPohM0zjuISW1FodpHmS9NZUKlU+OuX4MWKzQUY1LrWeP6CK773aG+D7aOngTtXUeDPftxcGiA1qseeHqCtze084NJvuDnh8rd/cpzgFnLNB1CiPJ8fMZeTMggMfFbfH2nSCWQuCJJyIRFmEwmLlyYW/rc2/sJC0YjhKgu7LR2LLt3GQW6Ajac2UBkTCSRMZHEahKIdc2A4AwgBoBl9w4kInjulXdkMEBRERQXK0nUuIs/glQqsLLiCQcHoE/Z+sNGVOrnEkLUPXZ2Abi69iQr618KC2PIydmOi0s3S4clqiH1tVepWeLi4nj00Udp0qQJdnZ2BAQEMH36dEpKSv5zu549e16ckLjsv/Hjx1dR1HVPdvYW8vOVZh56fQiOjuGWDUgIUa3Ya+2JCI5gXsQ8zk08x4EnDjCz10w6NuwIgK2VLb2b9Dbb5pfDv9Dvx37M3jmb2Jyz4OAA7u7g5gaursp/Li7KciGEqALe3o+WPk5M/NaCkYjqrNbdITtx4gRGo5GvvvqKwMBAjhw5wrhx48jPz2fWrFn/ue24ceN48803S5/by8SclSYhoeyqdknJIAtGIoSo7lQqFeHe4YR7hzOtxzSS85I5mHwQe635d/RfJ/5iXew61sWuY8LqCYR6hhIRHEFEcARdfbtipa51pzwhRDXn5TWCkyefxmDIISVlMYGBs7GycrJ0WKKaqXVnp4EDBzJw4MDS502bNiU6Opovv/zymgmZvb093t7e1/1excXFFBcXlz7PyckBQKfTodPpbjDyusNk0lNYqLSMtrLyRKfrKn8vUekuHWNyrNV87jbu9PLrZfb/pclkIjot2my942nHOZ52nA+3f4ibrRsDAgYwOHAwA5oOwM3OrdLik2NNVBU51moCLV5eo0lK+hqjMZ+kpF+oX3+spYO6YXKs3Zzr/XupTJdPHFNLTZs2jVWrVrF3796rrtOzZ0+OHj2KyWTC29uboUOH8tprr/3nXbI33niDGTNmlFu+aNEiubt2TSY0mhhUqjT0eqmnFkLcOpPJxLmic+zN2cuenD1E50djxFhuvQd9HmRk/ZEWiFAIURdpNCdxdJwCgF7fjPz89y0ckagqBQUF3HfffWRnZ+PsfJXuvtSBhOzUqVO0a9eOWbNmMW7cuKuu9/XXX9O4cWMaNGjAoUOHePHFF+nYsSNLliy56jZXukPm6+tLWlraf/7RRRmdTsfatWvp16+fzNcjKpUca3VPWkEaq2NXs/zkctbEriGnWKliODDuAGFeYaXrHUk5wncHv2Nw4GBu87sNa82tdU2UY01UFTnWagaTyURUVDsKCo5gZxdCq1ZbsbKqWb8T5Vi7OTk5OXh6el4zIasxJYsvvfQS77//31cUjh8/TkhISOnzhIQEBg4cyN133/2fyRjA448/Xvq4ZcuW+Pj40KdPH06fPk1AQMAVt7GxscHGxqbccq1WKwfrDZK/magqcqzVHT4uPoxpM4YxbcagM+jYGr+VTWc30dqntVnr6WWnlvHZns/4bM9nONs4MyBgABHBEQwOGoynvedNv78ca6KqyLFW/QUGfoJG44Czc+ca3fpejrUbc71/qxqTkL3wwguMGTPmP9dp2rRp6eMLFy7Qq1cvunbtytdff33D79epUydAucN2tYRM3LiiorPY2ja2dBhCiDpGq9HSq0kvejXpVe61FSfLJnHOKc7h92O/8/ux31GhootvFyKCIhjabChhXmE1+oeUEMJy3N37WjoEUY3VmITMy8sLLy+v61o3ISGBXr160a5dOxYuXIhafePd/aOiogDw8fG54W3FlRUXJ7FzZwCOjuH4+k6kfv37LR2SEEKw6oFVrDq1isiYSFacXEFmUSYAJkxsP7ed7ee288qGV3i247N8NugzC0crhBCitql185AlJCTQs2dP/Pz8mDVrFqmpqSQlJZGUlGS2TkhICLt37wbg9OnTzJw5k3379hEXF8c///zDQw89RI8ePWjVqpWlPkqtk5z8E2AgL28f+flHLR2OEEIA4GrryugWo/lpxE+kTElhy9gtTO06leZezc3W6+Zr3oAotziXb/d/S1JeEkIIcSN0unRLhyCqkRpzh+x6rV27llOnTnHq1CkaNWpk9tql/iU6nY7o6GgKCgoAsLa2Zt26dXz66afk5+fj6+vLyJEjmTZtWpXHX1uZTCaSkhaWPvf2rnktX4UQtZ+V2orb/G7jNr/beL/f+8RmxrI8ZjnLTy5nQOAAs3XXnF7DY8seA6BDgw4MDR5KRHAEYR5hV9q1EEJw4cJ8EhPnU1h4iq5dE1Gry/ciEHVPrUvIxowZc82xZv7+/lzeXNLX15dNmzZVcmR1W27ubgoKjgHg4tIde/sgC0ckhBDX1tStKc92epZnOz1b7rXIk5Glj/dc2MOeC3t4/d/XaejUkBbWLTCdNDEgaAB2WruqDFkIUY1lZW0iN3cPABkZq/D0HGbhiER1UOtKFkX1lJi4oPSxt/cjFoxECCEqxrMdn2Va92m0rt/abHlCbgKr01cz/PfheHzgwQurX7BQhEKI6uby8fPJyT9bMBJRnUhCJiqdwVBASsqvAKjVDnh53WXhiIQQ4ta19WnLzN4ziRofRfyEeOYOnsvgoMHYaMpKkAr1hThYO5htZzKZOJB4AKOp/KTVQojazc2tH1qt0qQuPX0Zen2OhSMS1YEkZKLSpaYuwWBQvnDq1RuFlZWjhSMSQoiK5eviy5MdnmT5fctJmpjEK01e4dHwR/Fx9CEiOMJs3Zj0GNp+3ZYGHzXg0b8f5a/jf5FXkmehyIUQVUmttqJevVEAGI1FpKYusXBEojqodWPIRPWTlFRWrujjI+WKQojazcHagY4uHRk8eDAaKw0qzOcui4xRxp4l5yezIGoBC6IWYK2xpqd/z9LGIP6u/haIXAhRFerVu4+EhC8ASEn5GR+fMZYNSFic3CETlaqwMJasrI0A2NkF4+zc1cIRCSFE1VGr1OUmkw71CmVYs2HYa+1Ll5UYSlhzeg3PrnyWJrOb0GJuC97a/FZVhyuEqALOzp2xtW0KQGbmBoqLEy0ckbA0SchEpTIaS/D0HIFKZYW399hyP0yEEKKuGRw0mKWjl5I+NZ0V963gqfZP4evsa7bO0dSjHEg6UG7bIn1RVYUphKgkKpWK+vXvu/jMSErKbxaNR1ieJGSiUjk4hNCixZ906ZJAgwZPWDocIYSoNmytbBkUNIg5Q+ZwdsJZDo4/yNu936Zzo86oUBERZD72rEhfhM9HPvT+vjef7PiEk+knLRS5EOJW1atX1m0xJUW6LdZ1MoZMVAlr63qWDkEIIaotlUpFq/qtaFW/Fa90f4WU/BTsrMznL9sUt4msoiw2xm1kY9xGJq2ZRLBHcOm4s26+3dBqtBb6BEKIG+HgEIKjY1vy8w+h1dbHYChCo7G1dFjCQiQhE0IIIaqZeg7lL2LlleTR1K0psZmxpcti0mP4aMdHfLTjI1xtXRkYOJCIoAjubXkvapUUwQhRnYWELMDauiHW1p6WDkVYmHxbi0phNBaTlvYPRmOJpUMRQohaYWTzkZx69hTHnjrGB30/oEfjHmZJV1ZRFr8e+ZU3N78pyZgQNYCjY2tJxgQgd8hEJUlPX8nRo8OxsnIjIOAjfHzGWjokIYSo8VQqFaFeoYR6hTKl2xQyCjNYdWoVkTGRrDy1kqyirHJjzwCG/ToMfxd/IoIj6NG4BzZWNlfYuxBCCEuQhExUipSURQDo9ZlYW/tYOBohhKid3O3cua/lfdzX8j70Rj3b4rfh42T+nRufHc8/0f8A8Nnuz3C0dmRAwAAigiMYHDT4iuWRQoiqZTTqKSlJxNbW99ori1pHEjJR4fT6HNLTlwGg1Xrh5tbHwhEJIUTtZ6W24nb/28st35+4Hyu1FXqjHlDGov15/E/+PP4nKlR0atSJiKAIIoIjaFW/lUxPIkQVMhr1nDz5JKmpf2Fr60/79nstHZKwACkyFxUuLW0pRqMyV46X1z2o1dL1SwghLOXOkDtJm5LG4rsW81Drh/Cw8yh9zYSJned3Mm3jNDp+05F8Xb4FIxWi7lGrrcjLi0KvTycvbx+FhWcsHZKwAEnIRIVLTl5U+rhs4kMhhBCW4mLrwt1hd/P9nd+TPDmZbY9s4+XbXqZFvRal6/Ty74WjtaPZdp/s+IT5++ZzIfdCVYcsRJ3h5XVX6ePU1D8tGImwFClZFBWqpCSZzMy1ANja+uPs3MXCEQkhhLicRq2hq29Xuvp25Z0+7xCXFcfymOX4ufiZrac36pm5eSaZRZkAtPNpR0SwUtrY1qetdHIUooJ4eo4kNvYlAFJT/8DPb7KFIxJVTRIyUaFSUhYDRgDq1btPxiIIIUQ15+/qz9Mdny63fOf5naXJGMC+xH3sS9zHjE0z8HH0YUjQECKCI+jbtC8O1g5VGbIQtYq9fSCOjuHk5UWRm7uLoqJ4bG39rr2hqDXk8paoUJe6K4KUKwohRE3W1bcrux/bzes9XqeNdxuz1xLzEvnmwDfc+dudeHzgYTZZtRDixpmXLS6xYCTCEiQhExWmsDCWnJydADg4tMLBIczCEQkhhLhZapWaDg07MKPXDPY/sZ/zE8/zVcRXDA0eip2VXel6nvaeNHFtYrbtprhN7Di3A4PRUNVhC1EjmSdkf1gwEmEJUrIoKoxabYOv71RSUn6Ru2NCCFHLNHRuyOPtHufxdo9TqCtkw5kNRMZE4uXgVa48/eX1L7Pj/A687L0YHDSYiOAI+gf0x9nG2ULRC1G92ds3w94+jIKCo+TkbKO4OAEbm4aWDktUEUnIRIWxsWlIQMD7NG36LkZjiaXDEUIIUUnstHYMCR7CkOAh5V5LzU9l53mlWiK1IJXvD37P9we/R6vW0qNxD4YGDyUiOIIA94CqDluIas3L6y7Onj0KQGrqXzRq9IyFIxJVRUoWRYVTqdRoNLaWDkMIIYQF2Gnt+PaObxkROsKsjb7OqGP9mfVMWD2BwM8DCZ0Tyv7E/RaMVIjq5VLZorW1j4UjEVVN7pAJIYQQosI4Wjsyts1YxrYZS7G+mE1nNxEZE8mymGXEZcWVrheTHlOu1X5CTgL2Wnvc7NyqOGohLM/BIYy2bXfh5NQelUwrUadIQiYqRFLS97i69sbW1tfSoQghhKgmbKxs6B/Qn/4B/Zk9cDbHUo+VJmcatQZPe0+z9WdsmsGCAwvo5tettLSxmUczmUJF1AkqlQpn546WDkNYgCRk4pYVFMRw4sQYAHx8xtGs2deWDUgIIUS1o1KpCKsXRli9MF687UX0Rr3Z6yaTiciYSAwmA5vPbmbz2c1MWTuFALeA0uSse+PuWGusLfQJhBCicsj9UHHLUlP/LH1sb9/MgpEIIYSoKazU5teEC3QF3NviXoLcg8yWn848zae7PqXvj33x+tCLe36/h30X9lVlqEJYRElJGgZDkaXDEFVAEjJxyy6fL8PTc4QFIxFCCFFTOVg78NGAj4h5NoboZ6L5qP9H9PTviUalKV0npziH34/9TpHe/EeqzqDDZDJVdchCVIqMjLUcONCD7dvrk5m52tLhiCogCZm4JYWFZ8jLU7pkOTq2w86uyTW2EEIIIf5bsEcwk7pMYuPDG0mbmsavI3/lgVYP4G7njoedB50bdTZb/8u9X9JkdhOeWfEMq06tKpewCVGTmEwlZGdvAYykpf1j6XBEFZAxZOKWpKUtKX3s5TXSgpEIIYSojVxtXRnVYhSjWoxCb9RzJvMMGrXGbJ1lMcs4m32WOXvmMGfPHBy0DvQL6EdEUASDgwbj4yRtxEXN4eraG7XaDqOxkPT0SEwmo3RdrOUkIRO35PJyRUnIhBBCVCYrtRVBHuZjzEwmE7ZWtmjVWnRGHQD5unyWnljK0hNLAejQoAMRwRGMbjGaYI/gqg5biBui0djh5taf9PS/0elSyMnZjYtL52tvKGosScjETSsqOk9Ozk4AHBxaYm8vJzkhhBBVS6VSsezeZeQW57I2di2RMZEsP7mclPyU0nX2XNjDngt78Hb0loRM1AienneQnv43AOnp/0hCVsvJ/U9x06RcUQghRHXhZOPEiNARLBi2gMQXEtn56E5e7f4qreu3Ll1nSNAQs202xW1iyKIhzNs7j3PZ56o6ZCGuysNjCKDMvyfjyGo/uUMmbtrl7e69vO6yYCRCCCFEGbVKTadGnejUqBNv9X6Lc9nn2HZuGw2dG5qtt/TEUlacXMGKkysACPcOJyIogojgCDo07IBaxu0IC7G2ro+zc2dycnZQUHCUwsJY7OyaWjosUUkkIRM3rX79B1GrbSgpScTevrmlwxFCCCGuyNfFl9Euo8st35doPp9ZVFIUUUlRvLXlLeo51GNI0BAigiPo17QfTjZOVRWuEAB4eAwlJ2cHAOnpy2jU6HkLRyQqi1z6ETetQYPHaN16De3a7UelUlk6HCGEEOKGbBqziX2P7+ON29+gfYP2Zq+l5KewMGohIxeP5NUNr1ooQlGXeXreUfpYyhZrN0nIxC1Tq7WWDkEIIYS4YSqVirY+bZneczp7xu3hwqQLzB86n2HNhmGvtS9dLyI4wmy7pLwkXlr3Elvjt2IwGqo6bFFH2Ns3x9a2KaDCZNJjMsmxVltJyaIQQgghBODj5MNjbR/jsbaPUaQvYuOZjaw8tZLbG99utt7ymOW8v+193t/2Pu527gwOGkxEUAQDAwfiYutioehFbaNSqWje/Bdsbf2xtq5n6XBEJZKETNyw3NwDGAy5ODt3Ra2WQ0gIIUTtY2tly6CgQQwKGlTutciTkaWPMwoz+OnQT/x06Ces1FZ09+vO0OChRARHlJszTYgb5ezc0dIhiCogJYvihp079yFRUbezfXt98vOPWTocIYQQokp9HfE13w37jrua34WTdVmzD71Rz8a4jUxaM4ngL4J5eOnDFoxSCFFTSEImbojRqCMjYyUAJpMBOzu5+ieEEKJu8XLw4uHwh/n97t9Jm5rG2gfX8nyn52nqZt6WPLx+uNlzo8nIL4d/Ib0gvQqjFbWFyWTCYCiydBiiEki9mbgh2dnb0OuzAPDwGCQNPYQQQtRp1hpr+jbtS9+mfflkwCdEp0ezLHoZkScjyzUD2Z2wm/uW3Idapaarb9fS0sZQz1DpViyuqqjoHGfPvkVGxirq1buXgID3LB2SqGCSkIkbkp5e1nbVw2OoBSMRQgghqheVSkWIZwghniFM6Tal3OvLopcByp2yrfFb2Rq/lRfXvUgT1yZEBEcwNHgoPRr3wMbKpqpDF9WYWm1LYuJ8wERGxipJyGohKVkU181kMpGevuziMw3u7uUHOgshhBDiyoaHDmdyl8mEeIaYLT+TdYbPd39O/5/64/mhJ09GPmmhCEV1ZG3thZNTOwDy8w9SXHzBwhGJiiYJmbhuBQXRFBaeAsDVtTtarZuFIxJCCCFqjvYN2vNh/w85/vRxTj57kk8GfEKfJn2wuqxjcV5JHiWGknLbnso4hclkqspwRTVy+UXwjIzVFoxEVAZJyMR1My9XvOM/1hRCCCHEfwl0D2RC5wmse2gdaVPSWHzXYh5q/RCe9p7lxp5lFGYQ8kUIfp/68WTkk6w4uYJCXaGFIheW4O4+sPRxRsYqC0YiKoOMIRPXraxcUcaPCSGEEBXFxdaFu8Pu5u6wuzEYDZgwvxO2+tRqDCYD53POM2/fPObtm4edlR29m/SmcVFjwnPDaeze2ELRi6rg5NQRKytX9PosMjPXYDTqZS7YWqRW3iHz9/dHpVKZ/ffee/89ALKoqIinn34aDw8PHB0dGTlyJMnJyVUUcfVXUpJGdvZ2AOztQ7G3D7RwREIIIUTto1FrzEoYATztPRkYOBBrjXXpskJ9IctPLmfuubn4f+5Pu6/bMePfGVLWWEup1Va4ufUHQK/PIjd3t4UjEhWpViZkAG+++SaJiYml/z377LP/uf7EiRNZtmwZv//+O5s2beLChQuMGDGiiqKt/ozGIho0eBxr6wZyd0wIIYSoQv0C+rHy/pWkT03nr1F/8WibR6nvUN9snf2J+1lxakW59vkGo6EqQxWVSMoWa69ae6/TyckJb2/v61o3Ozubb7/9lkWLFtG7d28AFi5cSGhoKDt37qRz586VGWqNYGvbiODgLwkKmovRKHXrQgghRFVztHbkzpA7uTPkTowmI7vidzF75WxiVDEcSDrA0GDzC6Ymk4nmc5sT4BZARHAEQ4KG0NhVShtrKvOEbCVNmrxpwWhERaq1Cdl7773HzJkz8fPz47777mPixIlYWV354+7btw+dTkffvn1Ll4WEhODn58eOHTuumpAVFxdTXFxc+jwnJwcAnU6HTqerwE9T3WgxGivm8136O9Xuv5eoDuRYE1VFjjVRVVp7teZen3vp168fKUUpaNVas+PucMphYtJjiEmPYeWplTzN07TwasHgoMFEBEbQoUEHNGqNBT+BuBFqtScODq3Izz9EQcEpiooy0Wgcq+S95Xvt5lzv36tWJmTPPfccbdu2xd3dne3bt/Pyyy+TmJjIxx9/fMX1k5KSsLa2xtXV1Wx5/fr1SUpKuur7vPvuu8yYMaPc8jVr1mBvb39Ln6GuWbt2raVDEHWEHGuiqsixJqrK1Y61/Tn7dtD/MwAAKVJJREFU8dB6kK5LL112JPUIR1KP8MH2D3DWONPOuR3tXdrT0bkjWrW2qkIWN8nKajgm0ygMhmasXr25yt9fvtduTEFBwXWtpzLVkNGfL730Eu+///5/rnP8+HFCQkLKLV+wYAFPPPEEeXl52NjYlHt90aJFjB071uxuF0DHjh3p1avXVd/3SnfIfH19SUtLw9nZ+Xo+Vo2Qm7sbW9smaLVeFb5vnU7H2rVr6devH1qtnAhE5ZFjTVQVOdZEVbmeY81kMnEo5RDLTy5nxakV7Lmwp1wXRxcbFy5MuIBWI8eruDL5Xrs5OTk5eHp6kp2d/Z+5QY25Q/bCCy8wZsyY/1ynadOmV1zeqVMn9Ho9cXFxNGvWrNzr3t7elJSUkJWVZXaXLDk5+T/HodnY2FwxwdNqtbXmYDWZTMTEPEBR0VlcXW+ndev1qFQV3wumNv3NRPUmx5qoKnKsiapyrWOtfaP2tG/Unum9ppOcl8yKkyuIPBnJmtNryCvJY2DgQOxtzSt7nl7+NPZae4Y2G0pX367lOj+Kukm+127M9f6tasy/Li8vL7y8bu4OTVRUFGq1mnr16l3x9Xbt2qHValm/fj0jR44EIDo6mvj4eLp06XLTMdcGhYUnKSqKu/hMXSnJmBBCCCGqRn3H+oxtM5axbcZSrC9m09lNuNq6mq2TX5LPtwe+pdhQzKwds3C1dWVQ4CCGBg9lYOBA3OzcLBO8ELVUjUnIrteOHTvYtWsXvXr1wsnJiR07djBx4kQeeOAB3NyUL5CEhAT69OnDDz/8QMeOHXFxceHRRx9l0qRJuLu74+zszLPPPkuXLl3qfIfFy9uqXt7dRwghhBA1m42VDf0D+pdbvi9xH7rLmndlFWXxy5Ff+OXIL2hUGrr5dWNo8FAigiNo5tGsXKt9Ubl0ugySk38iI2MVrq698fObbOmQxC2qdQmZjY0Nv/76K2+88QbFxcU0adKEiRMnMmnSpNJ1dDod0dHRZgPtPvnkE9RqNSNHjqS4uJgBAwYwd+5cS3yEakUSMiGEEKJu6dG4B8mTk1l1ahWRMZGsOrWK7OJsAAwmA5vPbmbz2c1MWTuFsxPO4ufiZ+GI6xaDIZdTp54HQK/PloSsFqh1CVnbtm3ZuXPnf67j7+9fbiZ7W1tb5syZw5w5cyozvBrFYCgkK+tfAKytG+Dg0MKyAQkhhBCiSnjae/JAqwd4oNUD6Aw6tsZvJTImksiTkcSkxwDQol6LcsnYz4d+xmAyMChwEF4OFd8MTICtbWPs7UMpKDhOTs5OdLostFpXS4clbkGtS8hExcnO3lI6CbS7+0ApSRBCCCHqIK1GS68mvejVpBcfDfiImPQYImMiy409A3h7y9scTzuOChWdG3UuLW1sUa+F/I6oQG5u/SkoOA4Yycr6Fy+vOy0dkrgFkpCJq5JyRSGEEEL8v2CPYCZ1mVRu+emM0xxPOw6ACRM7zu9gx/kdvLLhFfxc/IgIimBos6H09O+JrZVtVYddq7i59SEhYTYAWVnrJSGr4aRlnriqsoRMjZtbX4vGIoQQQojqzd/Vn61jt/JitxcJ8wozey0+O565e+cy6OdBeHzgwd4Ley0UZe3g6no7oAEgM3O9ZYMRt0wSMnFFRUVnL94KB2fnzmi10uJWCCGEEFenUSsdGN/r+x5HnjrC6edO89nAz+gf0B+tumw+JpPJVC5hi0qKYt+FfeXG+Isrs7Jyxtm5IwAFBccpLk6wcETiVkjJorginS4dZ+eu5OTslHJFIYQQQtywpm5NebbTszzb6Vlyi3NZG7uWyJhIVKiw09qZrfv2lrf549gfNHBqwJCgIQwNHkqfpn2w19pfZe/Cza0vOTk7AMjM3IC394MWjkjcLEnIxBU5ObWlbdtt6HSZgNHS4QghhBCiBnOycWJE6AhGhI4o91qJoYTVp1YDcCH3AvP3z2f+/vnYWtnSu0lvhgYPZUjQEHxdfKs67GrNza0PZ8/OBCAzc50kZDWYlCyK/6TVuqHVelg6DCGEEELUUgajgQ/6fcCQoCFmzT6K9EWsOLmCJ5c/id+nfrT5qg1bzm6xYKTVi7NzZ9Rq5Q5iZuZ6KfesweQOmRBCCCGEsBg7rR3j249nfPvx5Jfks+HMBpbFLCMyJpLEvMTS9aKSovCwN79InFGYgVatxcnGqarDtji12obGjV9Fq/WQ5ms1nCRkopySklS0Wk+ZL0QIIYQQVcrB2oGhzYYytNlQTCYTB5IOsCx6GZEnI8kozCDUM9Rs/c93fc47W9+hp39PIoIiiAiOoIlbEwtFX/UaN37F0iGICiAJmSjn0KEBlJQk4+4+kGbNvkal0lg6JCGEEELUMSqVirY+bWnr05bpPadToCsod7E48mQkJYYS1pxew5rTa3hu1XOEeYURERzB0OChdG7UGY1afseI6k0SMmGmuDiJvLwDAOTlRUkyJoQQQohq4f87LhpNRjo17ERKfgrx2fGly4+mHuVo6lHe3/Y+HnYeDAoaxNMdnqZzo85VHbIQ10WaeggzmZlrSh9Lu3shhBBCVFdqlZovBn9B3PNxHBp/iLd7v02XRl1QUXYXLb0wnZ8O/WSWsAG1qgGGwVBARsZaTp9+kby8I5YOR9wEuUMmzGRkrCp9LAmZEEIIIao7lUpFy/otaVm/Ja90f4XU/FRWnlpJZEwkq06tolBfyICAAWbbLDm+hFc2vFI67uw2v9vQarRXeYfqLTn5J2JingDAysoVR8cWFo5I3ChJyEQpk8lEZuZ6ADQaR5yd5da+EEIIIWoWLwcvHmr9EA+1fogSQwmHkw/jYutits6ymGXEpMfwcfrHfLzzY1xsXBgYOJCI4AgGBQ4q182xOnNz61P6ODNzPY0bv2zBaMTNkIRMlMrPP4JOlwKAq2tP1OqaeaVICCGEEALAWmNNuwbtyi3PK8lDo9JgMBkAyC7O5rejv/Hb0d9Qq9R09e1KRFAEI0JHEOQRVNVh3xBb26bY2DSmuPgs2dlbMRgK0WjsLB2WuAEyhkyUunR3DMDVtc9/rCmEEEIIUXP9cc8fpExJYdGIRdzb4l5cbV1LXzOajGyN38pL61/ip0M/WS7I66RSqUrnITOZisnJ2W7hiMSNkoRMlMrKKkvILr/9LYQQQghR27jbuXNvy3tZNHIRqVNS2TRmE5O7TCbEM6R0nYjgCLNtotOiGbl4JAsPLCQ5L7mqQ74q87LFdRaMRNwMKVkUABiNOrKy/gVAq62Hg4MMCBVCCCFE3WCltqJH4x70aNyDD/t/yKmMU6w+tbpcueM/0f+w5PgSlhxfggoVHRt2JCJYaQzSun7rcvOkVRU3t96lj5WE7F2LxCFujiRkAoCiolhAmXPMza2Pxb5QhBBCCCEsLdA9kMCOgeWWb47fXPrYhIldCbvYlbCL1za+RiPnRqVdG3s36Y2dturGcVlb18fBoSX5+YfJzd2PXp+NlZXLtTcU1YKULAoA7O2b0a1bGm3b7sLP70VLhyOEEEIIUe0sHbWU7Y9s5+XbXqZlvZZmr53POc+8ffOI+CWCMX+PqfLYXF1vv/jISHb2tip/f3HzJCETpdRqK5ydO+Lo2NrSoQghhBBCVDsatYYuvl14p887HHryEHHPxzFn8BwGBg7EWmNdut7AAPO5XIv0Rcz4dwZ7L+zFaDJWSmwuLreXPs7K2lQp7yEqh5QsCiGEEEIIcRMauzbmqQ5P8VSHp8gryWN97HoiYyIZHDTYbL2NZzbyxqY3eGPTG3g7ejMkaAgRwRH0a9oPB2uHConF1bUHDg4tcXW9HXf3gdfeQFQbkpAJIYQQQghxixytHRkWMoxhIcPKvRYZE1n6OCkviW8PfMu3B77FRmNDrya9SseeNXZtfNPvb21djw4dDt309sJypGRREBc3g4MHBxAf/wE6XbqlwxFCCCGEqFVe7fEqX0d8zdDgodhZlTX7KDYUs+rUKp5Z+Qz+s/0Z/ttwC0YpLEUSMkFa2jIyM9cQG/sipkqqaxZCCCGEqKsaODVgXLtx/HPvP6RPTWf5fcsZ3248jZwbma0X4BZQbtuVJ1eSXZRdVaEKC5CSxTpOp8sgL28/AA4OrbC29rJwREIIIYQQtZed1o7BQYMZHDSYuaa5HEo+xLKYZUTGRDI0eKjZuvHZ8QxeNBgrtRW3N769dM6zQPfyLfkvV1ycSH7+Edzd+1XmRxEVRBKyOk6ZDNoEmM/yLoQQQgghKpdKpaK1d2tae7dmWo9p5V6/NPZMb9Sz/sx61p9Zz8TVEwnxDCkdd9bNrxtW6rKf9AcPDiQzczUqlTW33ZaFRlN186GJmyMli3VcZub60seSkAkhhBBCVB+dGnbi2Y7P4u/qb7b8RNoJZu2YRc/ve+L1oRdj/x6LyaRcYLe19QXAZCohJ2dnFUcsboYkZHXcpYRMpbLCxaWHhaMRQgghhBCXtGvQjs8GfUbsc7Ecfeoo7/V5j9v8bkOtKvsJn1WURWJuIiqVCvj/+cj+reqQxU2QksU6rKjoPIWF0QA4OXXCysrJwhEJIYQQQoj/p1KpaO7VnOZezXnxthdJL0hn1alVLItZxqpTq8zGnrm6liVkq49+wInobIYGD6V74+5mk1eL6kMSsjosK0vKFYUQQgghahoPew/ub3U/97e6H51Bh96oL33N1tYXrLxBn0QTuyKe2zab2btm42TtxIDAAUQERTA4aDBeDtLIrbqQksU6LDNzY+ljSciEEEIIIWoerUaLnfb/GnfYhgNgo4EQZ2VRbkkufxz7gzF/j6H+rPp0/bYr72x5h2J9cdUGLMqRhKwOy87eBIBabYuzcycLRyOEEEIIISpCs4b3lD5+p+s9PNDqAdzt3EuXmTCx4/wO5u2dV66M8VJzEFF1pGSxDmvdej1ZWZspKUlCrbaxdDhCCCGEEKICXD6OrJF1Oj8OX4fBaGDH+R1ExkQSGRPJ0dSjRARHlDYDuaT/T/1x0DoQERzBkKAh+Dj5VHX4dY4kZHWYnV1T7OyaWjoMIYQQQghRgWxtm2Bj04ji4vPk5GzHaCxBo7bmNr/buM3vNt7r+x5nMs+U2y41P5X1sesxYeLv6L8BaN+gPYMCBuFa4Cp3zyqJJGRCCCGEEELUIiqVCheX20lN/QMnp47odKnY2DQ0W6eJW5Ny28Wkx+Dl4EVKfkrpsr0X9rL3wl4APvr8I4YED2Fo8FD6B/THxkoqrCqCJGRCCCGEEELUMoGBH9Gs2TdoNLbXvU03v24kvpDI3gt7WRa9jMiTkUQlRZW+fiHvAvP3z+fbA9+SOiVVErIKIglZHVRcnMT58x/j6no7Li63YWXlYumQhBBCCCFEBbK2rn9T26lVajo27EjHhh2Z2Xsm57LPsezEMhZuX8iRgiMU6Yvo5tvNrEkIwDtb3qFAV8DQ4KH8r717j46qPPg9/pvJdUIyQchNEDDhagQMEBIJvsJbQBSC2sOyR0tb0BZXXUHFWI7YJV4LFGl5KdBa6esBUVjV5eUoKSoQFFAuSYNBwMhFUBAI4SKZ3G8z5w9eBsIlEkzmmez5ftZirZk9e2d+GZ+48suz97MHdx7c6ObVaBqFLACVlq7XoUNzdejQXHXt+qSSkmaZjgQAAAA/1CW6iyYPnKzOxZ01fNRwbfxuo0LsIY32cXvcWpS3SEfLj2rmxpmKaxenMT3HaFyvcRqVNEpRYVGG0rcNFLIAdPr0eu/j81fhAQAAgPV4PB55PPWyX1CkmisiJEKZvTIv2v7Via90tPyo93lJRYmWFi7V0sKlCg0K1fDrhyuzZ6Yye2Ve8tq1QMdcYgA6V8iC5HRmGM0CAACA1lFWVqCdO3+qTZsSdPToP1rtfZJjk3X08aN65c5XdHefu9UupJ33tdqGWq3+erUe+fARJS1I8i4QgnMoZAGmtva4Kiu/lCRFRQ1UcDBTyAAAAFbk8TToxIn/p7q6EpWWftaq75UQmaAHBjygd//3uzrxf07owwkfKmtwlrpGd/XuExMRowEJAxod99G+j/TGzjd0uvp0q+bzZ5yyGGBKSzd6H3O6IgAAgHVFRg6Q3e6Q210ll2uTz943PDhco3uM1ugeo7XwjoXadXyXVu5eKY88CrIHNdp37qa5yj2Qq2B7sP6j638os9eZUxt7dezls7ymUcgCzPnXj0VHU8gAAACsym4PUVRUmkpL16u6+hvV1BxRWFgnn2aw2WzqG9dXfeP6XvSaq8al9d+e+d203l2vj7/5WB9/87EeX/24enboqXG9ximzV6Zu6XqLQoJ+3PVv/oxTFgPMuUJmU3T0LUazAAAAoHVFRw/1Pm7t0xabKyIkQh9O+FBT06eq+zXdG72299RezdsyTz9Z9hPFzI3R2v1rDaVsfRSyAFJX970qKr6QJEVG3qSQkPZmAwEAAKBV+XMhC7YHa0TSCP3X7f+lvQ/vVVFWkeaOmqth3YYpyHbu1EZXjUs3xNzQ6Nj93+/XrpJd8ng8vo7d4jhlMYCUln4q6cyg5XRFAAAA63M6b/Y+drn8q5Cdz2azqU9MH/WJ6aPfZfxOp6pO6aN9Hylnb46OlR9TZ2fnRvsv2LpAf9n6FyW2T/Redzas2zCFBYcZ+g6unuVmyD755BPZbLZL/svPz7/sccOHD79o/9/+9rc+TN76wsOv13XXPabIyIG65pr/NB0HAAAArSwkpIMiIpIlSWVln6uhocJwoivTwdFB9/W7T8v/13Kt/VXj0xU9Ho9W7lkpSTpw+oAW5i3U6NdHK2ZujMa/OV5LPl+iY+XHTMS+KpabIcvIyNDRo0cbbZsxY4Zyc3OVmpra5LGTJ0/W888/730eERHRKhlNiYzspx495pmOAQAAAB+Kjh76P7c9apDLldfm/zDf4GnQI2mPaOWelVr/7XrVu+slSeW15Xqn6B29U/SObLIprXOanhv+nEb3GG04cdMsV8hCQ0OVkJDgfV5XV6f33ntPDz/8sGw2W5PHRkRENDoWAAAAaOuio4d6bwztcm1p84Us2B6sR29+VI/e/KhKq0u1Zv8a5ezJ0b/2/ksnKk9IkjzyaOvhrbLbGp8QWFlXKZtscoQ4TES/JMsVsgu9//77OnnypO6///4f3Hf58uV6/fXXlZCQoHHjxmnGjBlNzpLV1NSopqbG+9zlckk6UwLr6up+fPgAcPZz4vNCa2OswVcYa/AVxhquVGTkMCUlLZLTOUQREcnNHjP+PNYigiJ0V8+7dFfPu9TgblD+kXz9a9+/tGrfKn1z+hsN6TSkUe5lny9T9pps/STxJxrbY6zu6HGHOkd1buIdrt6Vfl42jxWWJmnCmDFjJEmrVq1qcr/FixerW7du6tSpk7744gs98cQTSktL0zvvvHPZY5599lk999xzF21fsWKF353uaLd/K4+nnTyeGNNRAAAAgFZXVl+mqOCoRttm7Z+lPFdeo21JjiQNdg5WanSquju6XzSrdrUqKyv185//XKWlpXI6nZfdr80UsunTp2vOnDlN7lNUVKQ+ffp4n3/33Xfq1q2b3nzzTY0fP75Z77du3TqNGDFC+/btU/fu3S+5z6VmyLp06aITJ040+aGbsGPHSLlcGxQW1lUpKQUKDo42HUnSmb8crFmzRqNGjVJIiHVv+AfzGGvwFcYafIWxBl+x0ljLXp2tt4reUnFF8SVfj28Xrzt63KGJ/SdqaJehl9znSrlcLsXExPxgIWszpyw+/vjjmjRpUpP7JCUlNXq+ZMkSdezYUXfeeWez3y89PV2SmixkYWFhCgu7eGnNkJAQvxqsbnedysvPrjBpk8Phf7Nk/vaZwboYa/AVxhp8hbEGX7HCWFs4dqH+MuYv2nZ0m3L25ChnT44KjhZ4Xz9WcUxLty9V//j+Gp40/Ee915V+Vm2mkMXGxio2NvaK9/d4PFqyZIl+9atfXdXAKSwslCRde+21zT7W35SXF8rtrpLU+OaAAAAACAxud51On14vl+sz2e3t1LXr70xHMsZusyu1U6pSO6Xq2eHP6rDrsFbtXaWVe1Zq7f61qqqv0rje4xods/nQZj2Y86DG9RqnzF6ZSu+criB70GXeoXnaTCFrrnXr1unAgQP6zW9+c9Frhw8f1ogRI7Rs2TKlpaXp66+/1ooVKzRmzBh17NhRX3zxhR577DHdeuut6t+/v4H0Lev8u7JTyAAAAAKRWzt2ZMrjqVF4eFJAF7ILdXZ21uRBkzV50GRV1VVp06FN6tGhR6N9cvbkaGfJTu0s2anZn85WTESMxvQco8yembqt+22KDr/6y4EsW8heeeUVZWRkNLqm7Ky6ujrt3r1blZWVks4slb927VrNnz9fFRUV6tKli8aPH6+nnnrK17Fbhcu1yfvY6cwwmAQAAAAm2O1hcjrTVFq6UdXV+1VTU6ywMG73dCFHiEMjkkZctP27su9kk00enVl+40TlCS3bvkzLti9TsD1Yw7oNU2avTI3rNU7dO1z6cqfLsWwhW7FixWVfu/7663X+WiZdunTR+vXrfRHL5zwej3eGLCgoSpGR/QwnAgAAgAnR0UNVWrpRkuRyfabY2OYtehfIXr37Vb048kV9sO8DrdyzUqu/Xq3y2nJJUr27XrkHcpV7IFc7S3bqv+/872Z97ZZZ0xF+q7r6W9XWHpEkOZ03y2ZrmXNdAQAA0LY4necuXTn/khZcmfjIeE1KmaS3f/a2Tkw7odW/WK1H0h5RYvtE7z6ZvTKb/XUtO0OGM1yucz9snK4IAAAQuKKjz/0u6HJtNpik7QsLDtOo7qM0qvsozb99vopOFGnl7pUamTSy2V+LQmZxpaXnrh9jQQ8AAIDAFRLSQQ5HT1VV7VVZ2Ta53TWy2y++hROax2azKTk2WcmxyVd1PKcsWpzHUy+73SHJLqfzZtNxAAAAYNDZ3wc9nlqVl283nAYShczyevd+WbfcUqrU1O0KDo4yHQcAAAAGOZ3p3scu11aDSXAWhSwA2O0hiozsazoGAAAADIuKopD5G64hAwAAAAJEZGR/RUWlKTJygDp0GG06DkQhAwAAAAKG3R6qQYOYGfMnnLJoUW53vbZsSdKOHXfqyJHFpuMAAAAAuARmyCyqomKHqqsPqLr6gOz2cHXq9KDpSAAAAAAuwAyZRZ1/93XuPwYAAIAL1dYeZ2EPP8AMmUW5XOduCO10UsgAAABwzrZtGXK5Nis4uL2GDj0pm415GlP45C3q7AyZ3R6hyMibDKcBAACAPwkJiZUk1defVlXVXsNpAhuFzIKqq79TTc1BSZLTmSa7PcRwIgAAAPgTbhDtPyhkFsTpigAAAGgKhcx/UMgs6PwfqujoIQaTAAAAwB9FRQ2WZJMkuVxbzIYJcBQyCzq/kEVFpRlMAgAAAH8UHOxURESyJKmi4gs1NFQZThS4KGQW43bXqby8QJIUHp6k0NBYw4kAAADgj86etujx1Ku8fJvhNIGLZe8txmYL0sCBef8zS+YxHQcAAAB+yulMV3Hx/5V05gwr7l1rBoXMYmw2uyIj+ykysp/pKAAAAPBjTufN3sdcR2YOpywCAAAAAahduxtlt7eTzRYij6fOdJyAxQwZAAAAEIBstiANGrRV4eHdFRQUbjpOwGKGzELq6k7rwIFndfLkh6qrO2U6DgAAAPxcu3Y3UsYMY4bMQsrK8vTtt89Jkjp3flg9ey4wnAgAAABAU5ghs5Dz7z92/t3XAQAAAPgnZsgspPENoSlkAAAA+GFHjrys77/PVVXVPg0aVCCbzWY6UkChkFmEx+PxLlcaHNxRDkd3w4kAAADQFpw8maOTJ3MkSdXV+/k90sc4ZdEiqqv3q77+pCTJ6UzjLxsAAAC4IlFRqd7HZWX/NpgkMFHILILrxwAAAHA1KGRmUcgsguvHAAAAcDUiIwd5H1PIfI9CZhGNZ8jSDCYBAABAWxIWlqCwsOskSWVlBfJ43IYTBRYKmQW43TUqL/9ckuRw9FRISAfDiQAAANCWnD1tsaGhTFVVew2nCSwUMgtoaKjQtdf+WpGRAxUdfYvpOAAAAGhjuI7MHJa9t4CQkA7q1etvks4sfw8AAAA0x4WFLD5+gsE0gYUZMothuXsAAAA0V+OFPQoMJgk8zJABAAAAAS40NEbXXfeYHI7urNjtYxSyNq6hoVoNDWUKDY01HQUAAABtWI8e80xHCEicstjGlZau16ZNcdqyJUnFxctMxwEAAADQDBSyNu7sKjjV1QdkszHhCQAAALQlFLI27vxlSaOiBjWxJwAAANC0+nqXvv/+ExUXv246SsBgSqWNO1vIgoKi5HD0NJwGAAAAbVlBwSBVVe2T3R6uuLh7ZbdTF1obM2RtWE1NsWpqvpN0ZnbMZuM/JwAAAK7e2eXv3e5qVVZ+aThNYOA3+DasvPzcPSLOv5kfAAAAcDUuvEE0Wh+FrA1rfP0YhQwAAAA/DoXM9yhkbdj5d1GnkAEAAODHiooa6H1MIfMNClkbdvaHJDi4vcLDkwynAQAAQFsXHOyUw9FbklRevl1ud63hRNZHIWujamuPq67uuKQzF1/abDbDiQAAAGAFZ8+88nhqVVGx03Aa62tzhWzmzJnKyMhQRESE2rdvf8l9Dh48qLFjxyoiIkJxcXGaNm2a6uvrm/y6p06d0oQJE+R0OtW+fXv9+te/Vnl5eSt8By0jNDRWt9xSpoEDtyox8QXTcQAAAGARXEfmW22ukNXW1uqee+7RQw89dMnXGxoaNHbsWNXW1mrTpk169dVXtXTpUj399NNNft0JEyZo165dWrNmjXJycrRhwwY9+OCDrfEttJigoHA5nWmKjh5iOgoAAAAs4vzryMrLPzeYJDC0uTu9Pffcc5KkpUuXXvL11atX68svv9TatWsVHx+vlJQUvfDCC3riiSf07LPPKjQ09KJjioqK9OGHHyo/P1+pqWf+IrBw4UKNGTNGf/rTn9SpU6dW+34AAAAAfxIZmSKbLVgORy+FhiaYjmN5ba6Q/ZDNmzerX79+io+P924bPXq0HnroIe3atUsDBgy45DHt27f3ljFJGjlypOx2u7Zu3aqf/vSnl3yvmpoa1dTUeJ+XlpZKOnP6Y11dXUt9S5ZWV1enyspKnTx5UiEhIabjwMIYa/AVxhp8hbGG1pSc/I3s9nBJ0smTJxlrV6GsrEyS5PF4mtzPcoWsuLi4URmT5H1eXFx82WPi4uIabQsODlaHDh0ue4wkzZ492ztjd77ExMTmxgYAAABgQWVlZYqOjr7s635RyKZPn645c+Y0uU9RUZH69Onjo0RX5sknn1R2drb3udvt1qlTp9SxY0dWPbxCLpdLXbp00aFDh+R0Ok3HgYUx1uArjDX4CmMNvsJYuzoej0dlZWU/ePmTXxSyxx9/XJMmTWpyn6SkK7vPVkJCgvLy8hptO3bsmPe1yx1TUlLSaFt9fb1OnTp12WMkKSwsTGFhYY22XW7lRzTN6XTyAw6fYKzBVxhr8BXGGnyFsdZ8Tc2MneUXhSw2NlaxsbEt8rWGDBmimTNnqqSkxHsa4po1a+R0OpWcnHzZY06fPq2CggINGjRIkrRu3Tq53W6lp6e3SC4AAAAAuFCbW/b+4MGDKiws1MGDB9XQ0KDCwkIVFhZ67xl22223KTk5Wb/85S+1fft2ffTRR3rqqaeUlZXlnc3Ky8tTnz59dPjwYUnSDTfcoNtvv12TJ09WXl6ePvvsM02ZMkX33nsvKywCAAAAaDV+MUPWHE8//bReffVV7/OzqyZ+/PHHGj58uIKCgpSTk6OHHnpIQ4YMUbt27TRx4kQ9//zz3mMqKyu1e/fuRishLl++XFOmTNGIESNkt9s1fvx4LViwwHffWIAKCwvTM888c9Gpn0BLY6zBVxhr8BXGGnyFsda6bJ4fWocRAAAAANAq2twpiwAAAABgFRQyAAAAADCEQgYAAAAAhlDIAAAAAMAQChmM+utf/6rrr79e4eHhSk9Pv+im3sCPNXv2bA0ePFhRUVGKi4vT3Xffrd27d5uOBYv74x//KJvNpqlTp5qOAos6fPiwfvGLX6hjx45yOBzq16+f/v3vf5uOBYtpaGjQjBkzlJiYKIfDoe7du+uFF14QawK2LAoZjHnjjTeUnZ2tZ555Rtu2bdNNN92k0aNHq6SkxHQ0WMj69euVlZWlLVu2aM2aNaqrq9Ntt92miooK09FgUfn5+Xr55ZfVv39/01FgUd9//72GDh2qkJAQffDBB/ryyy/15z//Wddcc43paLCYOXPm6KWXXtKiRYtUVFSkOXPm6MUXX9TChQtNR7MUlr2HMenp6Ro8eLAWLVokSXK73erSpYsefvhhTZ8+3XA6WNXx48cVFxen9evX69ZbbzUdBxZTXl6ugQMH6m9/+5v+8Ic/KCUlRfPnzzcdCxYzffp0ffbZZ9q4caPpKLC4zMxMxcfH65VXXvFuGz9+vBwOh15//XWDyayFGTIYUVtbq4KCAo0cOdK7zW63a+TIkdq8ebPBZLC60tJSSVKHDh0MJ4EVZWVlaezYsY3+3wa0tPfff1+pqam65557FBcXpwEDBugf//iH6ViwoIyMDOXm5mrPnj2SpO3bt+vTTz/VHXfcYTiZtQSbDoDAdOLECTU0NCg+Pr7R9vj4eH311VeGUsHq3G63pk6dqqFDh6pv376m48Bi/vnPf2rbtm3Kz883HQUWt3//fr300kvKzs7W73//e+Xn5+uRRx5RaGioJk6caDoeLGT69OlyuVzq06ePgoKC1NDQoJkzZ2rChAmmo1kKhQxAwMjKytLOnTv16aefmo4Cizl06JAeffRRrVmzRuHh4abjwOLcbrdSU1M1a9YsSdKAAQO0c+dO/f3vf6eQoUW9+eabWr58uVasWKEbb7xRhYWFmjp1qjp16sRYa0EUMhgRExOjoKAgHTt2rNH2Y8eOKSEhwVAqWNmUKVOUk5OjDRs26LrrrjMdBxZTUFCgkpISDRw40LutoaFBGzZs0KJFi1RTU6OgoCCDCWEl1157rZKTkxttu+GGG/T2228bSgSrmjZtmqZPn657771XktSvXz99++23mj17NoWsBXENGYwIDQ3VoEGDlJub693mdruVm5urIUOGGEwGq/F4PJoyZYreffddrVu3TomJiaYjwYJGjBihHTt2qLCw0PsvNTVVEyZMUGFhIWUMLWro0KEX3b5jz5496tatm6FEsKrKykrZ7Y3rQlBQkNxut6FE1sQMGYzJzs7WxIkTlZqaqrS0NM2fP18VFRW6//77TUeDhWRlZWnFihV67733FBUVpeLiYklSdHS0HA6H4XSwiqioqIuuS2zXrp06duzI9YpocY899pgyMjI0a9Ys/exnP1NeXp4WL16sxYsXm44Gixk3bpxmzpyprl276sYbb9Tnn3+uefPm6YEHHjAdzVJY9h5GLVq0SHPnzlVxcbFSUlK0YMECpaenm44FC7HZbJfcvmTJEk2aNMm3YRBQhg8fzrL3aDU5OTl68skntXfvXiUmJio7O1uTJ082HQsWU1ZWphkzZujdd99VSUmJOnXqpPvuu09PP/20QkNDTcezDAoZAAAAABjCNWQAAAAAYAiFDAAAAAAMoZABAAAAgCEUMgAAAAAwhEIGAAAAAIZQyAAAAADAEAoZAAAAABhCIQMAAAAAQyhkAAAAAGAIhQwAAAAADKGQAQAAAIAhFDIAAH6k48ePKyEhQbNmzfJu27Rpk0JDQ5Wbm2swGQDA39k8Ho/HdAgAANq6VatW6e6779amTZvUu3dvpaSk6K677tK8efNMRwMA+DEKGQAALSQrK0tr165VamqqduzYofz8fIWFhZmOBQDwYxQyAABaSFVVlfr27atDhw6poKBA/fr1Mx0JAODnuIYMAIAW8vXXX+vIkSNyu9365ptvTMcBALQBzJABANACamtrlZaWppSUFPXu3Vvz58/Xjh07FBcXZzoaAMCPUcgAAGgB06ZN01tvvaXt27crMjJSw4YNU3R0tHJyckxHAwD4MU5ZBADgR/rkk080f/58vfbaa3I6nbLb7Xrttde0ceNGvfTSS6bjAQD8GDNkAAAAAGAIM2QAAAAAYAiFDAAAAAAMoZABAAAAgCEUMgAAAAAwhEIGAAAAAIZQyAAAAADAEAoZAAAAABhCIQMAAAAAQyhkAAAAAGAIhQwAAAAADKGQAQAAAIAh/x9SCSaL2c6rEwAAAABJRU5ErkJggg=="/>
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@@ -8443,15 +8332,15 @@ where $T_n$ refers to the TSE computed using $n$ number of terms (or derivatives
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=68f0967f-0dd2-4971-88e8-e87d52cdf332">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=68f0967f-0dd2-4971-88e8-e87d52cdf332">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># error_1 = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># error_1 = YOUR_CODE_HERE</span>
<span class="c1"># error_2 = YOUR_CODE_HERE</span>
<span class="c1"># error_3 = YOUR_CODE_HERE</span>
<span class="c1"># error_4 = YOUR_CODE_HERE</span>
@@ -8486,18 +8375,6 @@ where $T_n$ refers to the TSE computed using $n$ number of terms (or derivatives
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"/>
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@@ -8621,10 +8498,10 @@ $$</p>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># def f2D(x, y):</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># def f2D(x, y):</span>
<span class="c1"># return YOUR_CODE_HERE</span>
<span class="c1"># x0 = YOUR_CODE_HERE</span>
@@ -8683,15 +8560,15 @@ $$</p>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Create a meshgrid of x and y values</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># Create a meshgrid of x and y values</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="o">+</span><span class="n">x0</span><span class="p">,</span> <span class="mi">2</span><span class="o">+</span><span class="n">x0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="o">+</span><span class="n">y0</span><span class="p">,</span> <span class="mi">2</span><span class="o">+</span><span class="n">y0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
@@ -8722,26 +8599,6 @@ $$</p>
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-<pre>c:\Users\beren\anaconda3\envs\TAMude\Lib\site-packages\IPython\core\pylabtools.py:170: UserWarning: Creating legend with loc="best" can be slow with large amounts of data.
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"/>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=047fe3a1">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8768,15 +8625,15 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=9cb06eac">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=9cb06eac">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># error_2d = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># error_2d = YOUR_CODE_HERE</span>
<span class="c1"># SOLUTION:</span>
<span class="n">error_2d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Z_orig</span> <span class="o">-</span> <span class="n">Z_approx</span><span class="p">)</span>
@@ -8794,18 +8651,6 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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-<img alt="No description has been provided for this image" class="" 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+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
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+</a>
</h3>
<span style="font-size: 75%">
© Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
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diff --git a/synced_files/GA_1_5/Analysis_solution.md b/synced_files/GA_1_5/Analysis_solution.md
index 27089f5c..c8adc513 100644
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# Project 3: Numerical Derivatives and Taylor Series Approximations
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<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="cm-editor cm-s-jupyter">
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<span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<span class="n">y</span> <span class="o">=</span> <span class="n">h_ice</span>
<span class="c1"># fitting polynomila</span>
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<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># SOLUTION:</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># SOLUTION:</span>
<span class="n">dh_dt_FD</span> <span class="o">=</span> <span class="p">(</span><span class="n">h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">h_ice</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">t_days</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">t_days</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">dh_dt_BD</span> <span class="o">=</span> <span class="p">(</span><span class="n">h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">h_ice</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">t_days</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">t_days</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">dh_dt_CD</span> <span class="o">=</span> <span class="p">(</span><span class="n">h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">h_ice</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">t_days</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">t_days</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
@@ -7642,15 +7630,15 @@ a.anchor-link {
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="c1">#left axis</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_fit</span><span class="p">,</span> <span class="n">y_derivative</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Derivative'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'magenta'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'growth rate [m/day]'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'magenta'</span><span class="p">)</span>
@@ -7681,18 +7669,6 @@ a.anchor-link {
</div>
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@@ -7715,15 +7691,15 @@ a.anchor-link {
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">num_samples</span> <span class="o">=</span> <span class="mi">6</span> <span class="c1"># </span>
+<div class="highlight hl-python"><pre><span></span><span class="n">num_samples</span> <span class="o">=</span> <span class="mi">6</span> <span class="c1"># </span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span> <span class="c1">#setting seed</span>
@@ -7748,18 +7724,6 @@ a.anchor-link {
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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@@ -7776,10 +7740,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># SOLUTION:</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># SOLUTION:</span>
<span class="n">dh_dt_FD_sampled_from_fit</span> <span class="o">=</span> <span class="p">(</span><span class="n">sampled_h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">sampled_h_ice</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">sampled_t_days</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">sampled_t_days</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">dh_dt_BD_sampled_from_fit</span> <span class="o">=</span> <span class="p">(</span><span class="n">sampled_h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">sampled_h_ice</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">sampled_t_days</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">sampled_t_days</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">dh_dt_CD_sampled_from_fit</span> <span class="o">=</span> <span class="p">(</span><span class="n">sampled_h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">sampled_h_ice</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">sampled_t_days</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">-</span><span class="n">sampled_t_days</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
@@ -7788,15 +7752,15 @@ a.anchor-link {
</div>
</div>
</div>
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<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="c1">#left axis</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_fit</span><span class="p">,</span> <span class="n">y_derivative</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Derivative'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'magenta'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'growth rate [m/days]'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'magenta'</span><span class="p">)</span>
@@ -7831,18 +7795,6 @@ a.anchor-link {
</div>
</div>
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-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7870,10 +7822,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># first point</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># first point</span>
<span class="n">estimated_h_ice_FD</span> <span class="o">=</span> <span class="p">[</span><span class="n">h_ice</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
<span class="n">estimated_h_ice_BD</span> <span class="o">=</span> <span class="p">[</span><span class="n">h_ice</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
<span class="n">estimated_h_ice_CD</span> <span class="o">=</span> <span class="p">[</span><span class="n">h_ice</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
diff --git a/synced_files/GA_1_5/central_diff_illustration.md b/synced_files/GA_1_5/central_diff_illustration.md
index 84590a9d..06d310e4 100644
--- a/synced_files/GA_1_5/central_diff_illustration.md
+++ b/synced_files/GA_1_5/central_diff_illustration.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.5: Illustration of Central Differences Issue
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_1_6/Analysis.html b/synced_files/GA_1_6/Analysis.html
index 234c975c..7c46cb47 100644
--- a/synced_files/GA_1_6/Analysis.html
+++ b/synced_files/GA_1_6/Analysis.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
- var scriptElement = document.createElement('script');
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
-
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-
- // Fallback for older version:
- try {
- widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
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-
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- } catch(e) {}
-
- scriptElement.src = widgetRendererSrc;
- document.body.appendChild(scriptElement);
- }
-
- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7573,10 +7546,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">ipywidgets</span> <span class="k">as</span> <span class="nn">widgets</span>
<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interact</span>
@@ -7675,7 +7648,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">g</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">g</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">YOUR_CODE_HERE</span>
<span class="k">def</span> <span class="nf">g_der</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
@@ -7867,7 +7840,7 @@ where $g(z_j) = 0$ and $z_j$ is a guess and $z_{j+1}$ is the improved guess.</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">g</span><span class="p">(</span><span class="n">y_iplus1</span><span class="p">,</span> <span class="n">y_i</span><span class="p">,</span> <span class="n">t_iplus1</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">g</span><span class="p">(</span><span class="n">y_iplus1</span><span class="p">,</span> <span class="n">y_i</span><span class="p">,</span> <span class="n">t_iplus1</span><span class="p">):</span>
<span class="k">return</span> <span class="n">YOUR_CODE_HERE</span>
<span class="k">def</span> <span class="nf">g_der</span><span class="p">(</span><span class="n">y_iplus1</span><span class="p">):</span>
@@ -8103,10 +8076,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Initial conditions</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># Initial conditions</span>
<span class="n">T_left</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span> <span class="c1"># Temperature at left boundary</span>
<span class="n">T_right</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span> <span class="c1"># Temperature at right boundary</span>
@@ -8157,10 +8130,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">T</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">T</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">T</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">T</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
<span class="n">T</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">YOUR_CODE_HERE</span>
@@ -8203,10 +8176,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Note: you may want to use extra lines, depending on</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># Note: you may want to use extra lines, depending on</span>
<span class="c1"># how you define your A, T and b arrays</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
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@@ -8239,10 +8212,10 @@ $$</p>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_T</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
+<div class="highlight hl-python"><pre><span></span><span class="k">def</span> <span class="nf">plot_T</span><span class="p">(</span><span class="n">T</span><span class="p">):</span>
<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Function to plot the temperature profile at different time steps.</span>
<span class="sd"> '''</span>
@@ -8285,7 +8258,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">plot_T</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">plot_T</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
</pre></div>
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@@ -8347,7 +8320,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
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@@ -8501,7 +8474,7 @@ $$</p>
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
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\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TyWTNM6K2RapNExdkMhnOnTtHPB7nrrvuIhjULyrrIXmjPrE8J9VMPMCWSarXd675P2dyTODae\/8BpeShyEwt5dwGRIFUZxCPzYF7fxfRsVnsJY1wYp0HZ3uDIaE4djSCrJAantY95sLqHL2+hjWEUoiz4SmOB\/UjlJQscSW2wJ03IqHdvpzCLSalGY0tklVkAnYXgiCwx6f\/xRiJLVPv9LDLQCE3EJ7hYKBVV9AgqwqXksuGSryYlGY8ES4SV2iw3TA4nRkNk\/7rf+HiQz9hwpXFcbiLA7\/0anafPKp73Wqg9RBtdh9NYVoukUjgcrkIBoO6aTm\/31\/1PYpiDJf7WgWVqdkJvOZee+RcuOzvkyZUbR6PB7vdTldXFydOnGDXrl38xV\/8BWfPnuX222+ntbWV1772tfz5n\/+5YYRoNPZ6586dPP\/883z4wx\/m9ttvZ2Vlhfe\/\/\/384i\/+Is89p+9IAhAIBBgaGir63XoGE26LiMdms5FKpXjyySepr6+nv7+\/qMhWDeI3iGfmeoJdh2tvdltfuqx237n1TUGtfMeqKhB6dIWpT\/1Y\/yCvi6zPgXsyjEqu2mUH7O0NOFvqURJplGwWJZkhdW1G9zLu\/V1kJuZRkvr58Up9MxqhGNVQVpUMS6kox8r08NTZXRwNtDMSWybocJNWJK5kVpBTGfb4GnEX1HcuJ5fZ7QnqigjM1HwScoaRWIijBtHSfCpKWpENU3TDsSVaXD7qHbnv8CHZCedCyOf+D49nHyLU7Kb55bdx2y+9lmBLbTssjXi22iS0NC2XSqXWNLFWo5YTbau4XCN5peb6YXL42tMLZX9vto9H82qz2Wy87GUv4x\/\/8R\/p7u7mT\/\/0T3niiSd46qmnKr53o7HXv\/7rv85jjxX35X3+85\/nrrvuYmJigu5ufWsoQRCqGpVdCVtOPKqqsry8TCQS4eDBg3R3d9f8IGTTEtl0btEfubDCmV9YT5d17Q9jNptYx5iD9U1BNYIq25n8s4usfG9I9xi5uQ4hlcWxuLaeI82tIM2t4DnYjbwYxtHemHOnngshLRWrhbxHd+WUbToy1IwM81nRcBFfTMeJSumyhKJhJLZMo9tnGMUMhGc54G8uIhmckJYlLkXnSUhZUopEf2OPfkSlSFyNLla835iUNiSdaSmGR7TR5tZXtA2uznDArx9Rtdt9zIxN075qZ+pb53mSBNk9LfTedzdHXn+P4eyZQmwH4iknp3a73XR0dKxJy5lRy9lsyzhdYyaFRWY3eWYUbXDuJ+VFNWmTzgWlJqHxeJy2tjY8Hg\/33nsv9957r7nbLbheubHXhVhdXUUQhIo+mbFYLD\/76NixY3zsYx\/j+PHjVd1PIbaUeDKZDC+88AKxWAyPx7Nuo0StvgNw6WeLwOYUaEvhdApFvTHVYD1ybMHgAZFiDq697wekx1Z0j8nurMc+Z1DP4Qah3EityZGbKiFHWwOO1nrkVBrRbjdMvyXtdqIpiS63\/uuMJlcI2Fz5lFk5DKzOcLCu1TBCOR9f4Fh9+TkjLpudXd5Ghm8MmVtIx1gRJNLJJLt8jfhvpP6W0nGyDsFwbPf1+DIBu5tdBvc7uDrLfn8LboP6RSXRQ1qRuFJAgE7RRh9+GE3BX\/6Qs1\/4J0brZBpP7Ofwm15Dx\/7duq+1HYinkpy6MC3X29trqJbr6hJxeyKm1axm1aNmjsuk7aSTOuICk15t5YinFlWb0djrovtKpfiv\/\/W\/8va3v51AQN\/78MCBAzz00EMcPXqUSCTCZz\/7Wc6cOcPg4CB9fX1V3x9sMfFcuXIFh8PBkSNHuHjx4rqvl4jdVK5cfGa+5sUfzM1W1z83N62wNtNOyPXV1FBf0rnnxLDKtfd9CzWt855EgfQO4\/4cxWXD1dmiSyjZ+RVQFHDYySwt4u7rRLYJJGeWsBdsCGzdLdhnQrQapIdfCE9zxGAiqZGztIacKm3JcICcFqFohNLqqst1fznrkRSZ4dgS86kYfruLo079KObc6ix9dc14dGTZUJlQMorMhBo3fE+r2SQL6bjhe4pnUnSEbbQ\/NUX0yYf4vhwjusNP+yuOcexN9+Lx31zItgvxVFO\/KZ+WW8brWyQQlAmHZerrK0d8qiqaGkFi9rjVJf11ptpUm4ZbMfZaQzab5d\/+23+Loij8xV\/8heH1Tp8+zenTp\/M\/nzlzhhMnTvD5z3+ez33uc1XfH2wx8Rw5ciQfSm\/EILhE5OZCn03LZNIiLnet112npLpm007t3FrOzBY5H8iySug7IWY++6T+KT4XWa8D17R+f46jo4lULE72ur6bgXtvB5nZEEo8dx1NTGAHHC1BHG0NCG4H8YvjOKTyf5ObKi\/9onxKlblWIeWlWdsYRSizShKngG6EYhdtJOUsdzR04rE5iCoZRiJLqCp0e4M0OXMLwmBsnqOBdl1CySoyFyLGVjyr2RRzqahhz9F0MpcS6TOYYVQq3RYFgR67HxaB\/zPA8MPPctGbom5XB\/t+4eX03nU7sPWptvW8vtvtorc3i92R+055PHWY6cHL9ctVfj5V1YlgQmQ0O6Z\/jBmTUFVV15iE1trHU2nsdTab5a1vfSujo6P84Ac\/MIx2ykEURe68807DUdmVsKXEo3m0bcQgOChOtQHMTcXo2VtbnefFKKkWBPVGpJVBytgY+v89g\/Qzg2bOFj9CMlO2nqPBc6CL9Ng8tpT+rs17ZBeJS+O5iKcMsqEojrYG4s8Ng8NGuqmOqbE4QYdK843IJ9fDs2pIOrOpCKLTYegsPRxboqmCtc3g6gwHAm24nPo749IIxS8687UmRVUZji0xk4rQ5qpDVhVEYe21YkqW6XjYUN02k4qgqqoh6QzHlmh11RF06IeJA6szHPK34dSRbgNcjMxz0rYTrkThynd4Lvu\/mXFm+OHgBMd++fU07dy44rFZrK+BVMHpuo7dfnPTZNdrEitBNiuYcptWVXP1stGL+hs3M6k2beNdGvEUDmerFYVjrzXSGR4e5oc\/\/CFNTdULU1RVZWBggKNHa1dXbrm4AHIEpDH+eqSd8Wgx8STC65lEyjol1VsDVbUjRVSG\/\/NjSDOl9iA3kdlZj8OonqM1fBoIBHDa8eztIHFBv54jBrw4mgI3LXSyMq7lGHtuPE+rWbgcmwRV4UhAP0IZii7S7vYTFPUX3xfCMxwykDmbUaWlFYmh+JLhMVEphaKqvKI5Vz9JqhJDkTkyskyHJzcraCYZweV2VSSUQuVaOVQSGkCOJI3uNxd1za85ptXho1X1wQ9HGX30z3imTiDbUkfXa+7itn\/zKhwuc03b60Htz7yEyz2CzVayaTK5UYzH4rhcJkhFNbcJvPjMWqscDelkFkVREUX9ayk3Nm2lNZ5qm2uNxl5LksT999\/P888\/zz\/+4z8iyzJzc7ksRmNjY75Jv3Ts9Uc\/+lFOnz5NX18fkUiEz33ucwwMDPCFL3yhqnsrxLYgHu3DlmV5XcSzNF9cOJ8bS3HwZO2WE+tyqV7HCO10OkGtY0+iZyNMfvJx5JW1DgSAqXqO4HXh6jRu+LQ3BRC9LpKXJnSPce5sRkmmSY\/qp+hWM3B7sAOPaCOryAzFFolJ6VzU4s2lwZ5fmeZoUL8R00zNJyVnuRozJpSVTIKlTILb\/Po7\/6nkKqIgFBGKR7AXRWHPrkyiAq3OeryIZWs\/ZgnFqC4kKTKjFepCMSnNZHLV0ABVk3fvS9fDlARfeYpzX\/ohU24J59EeDr\/lXrpvO6B7\/npQk0mokMXtGka0rU2pmbXACdb7MTXy3QSRqSq88Lh+WwHkyMfj0ydySZIQBCH\/WaiqSjwerzriMRp7PTY2xre\/\/W0Ajh07VnTeD3\/4Q175ylcCa8deh8NhHnjgAebm5ggGgxw\/fpwf\/\/jH3HXXXVXdG0Bvby\/vf\/\/7t76BFG4SjyRJNfXv5MdfXyzOOY6cW+FV96\/H62gdue+a\/NZunCpIVb+2qoosfGOWub\/8KYgCjp3NxJAI2JykJxZyfak+N1mP3bies6MRFNWw4dO1ux1pKUJmUj+i8hzsJjViYKEDjEQFdtWp+TSVQ7Sxv+7mgr6ipDkfmqHe4SYpZ8sST6VpopCbbBpKxQ1rPhOJcE4hZlBDmVTiBO2uNfY3hXghPMPtwY58yiutSFyOLpCQs7Q4fXR76ysSil6EUgiNUA4aOD2Eskmi2ZThMWOJEHU2F93e4gUuYHdxSHLBC0u88PiDjNXXsxS00XzmNo7f\/zr8jfW616wG1ZqECkLqhhvBWoLJpcbN1XTNiodkOUkldbqUtRMLGxNeKp4xJJ7S+g7Upmr70pe+pPtvvb29pgYP\/uhHPyr6+TOf+Qyf+cxnqrqPStgWEY8gCOsaf33x4kVCoRBN9W3A9fy\/XfjpAlB51vmtQabmEQcul1DVuUrWwdhHzxJ9euzGL1SyU0tow7BFv4dMWx2qpOBcSuh6I5hp+PQc6SV5ZRJ0BAKFnmx6UG0CYbuXPehEZeQUXDGbkp\/VI6kKw7ElVrMpOoNNtAlu5tNRshWmiV6PL9PsDRjKsi9G5unx1hs6Jzwfnua24A7sjvK7cz1zU5dozy\/8GUXmZyuTOASRkewqbYJ7DYklVInxeMgwQllIxUgq2YqE0uCuo8ehX+u6HFmg21ufn7RaDi+EZzgaaMOu2NixAvzjJca\/fY5xMYm8p41db+jnyL0vq1kgUE2qTRATuN3DuqRhVgiQe7YqR0aqCg5H5YU6Eqp8TKU6T6miDWpXtb0YsC2IB2obfx2NRhkYGMDtdtPf38\/1x39Y9O9Dzy2iKLlxA7VgYyTV1TeEVnNuJmRn+Lf+GclAIJAKunCMLiPICqoo4OppxRb0Ia3EyEzmctOVGj5VUcCxdwdJg3EHZlJ0Yr2PtKjSENInnYlEGIdoo8txcxduF8SiaGQgPIOsKthFkWDWRbBMnWRwdZb9dc24BWOZ87H6DuwG0u2B8Kzh9NOULHEtGaowoiGnXCs0Ny0k0waHB6\/dgc3pNCSU0XgIv91Fj1efUC5F5unxNuATjQnlSKBNN30J8Fx4qux7col29uFHuhbj3B99heRn\/5HpOqg70cdtv3wfbbv1O+BLYTbVJooRXO4RQ7GPWSGAqjp0\/duKj3OVNRctxex4ZbJLJY1fr5R4ZFkmmUxa7tS3GtVGPNPT01y6dIne3l727t2LIAjEI8VfElVVScYFfP7aenlUNVPzaIXc+Q5TO6vy59oNz1VViL6QYfT3\/sGALCCzI1hcz1FU0uM3rT3srfW4ulpQEmlEnwsltvYhsgV9ZFwi0lX9PLajrQEEjFN0Pa1kw3EcIf3c+oXVOXb5Gg134YPROQ4XLJqyqjASXyaiZPGLDnq9DQyEZyrWRy7EFirb38RDhqQTyiQIZRIcqdMnCz3lWiGZjsSWySoKkWySmWSILk89Ta7iwvKV+BJdrkCFCGWao4F23UmroE8oGiRV4dzqrOExKTnLcHw5P6qiIQk8OUn4if\/FOTlGbGeAHa86yfE3vgaXV79gaSbVZrOt4HSNVu7JMykEMDvzSlXtmOnFO\/e0\/ndeQ7URTyyW20huhKptO0EUxdwonK28iWqnkELuD3Tp0iUWFhY4fvx4vpEMbhqEFiK8LOPz1xbyiKKMLFMxx2twhVpPxMjAUFVtzH11goWvntU\/ps5N1iUa13NaG8AmED97ozYmirh627D5vUgrUTJTS7i6W5GiCWwLUd3ruPs6yUwvoST0H1LvoR6Sw9OoWf0ospJvm97ANZsg5i1z0orMQHgGhygyuDpLj7eeRmfxAr6aTTGTinDMQEW3IqdYTSeLxlyXQqsL7TWoC00rCXw2h6Fy7fyNuUB5EYKrAUVVGUussJyJU2dz5ayDDCIzqKxu06axGhFKWpUZji4aDs\/TGlnLfTaiINBr9zM\/FiH7lR9y+W+eZMKRQTy0kwO\/+Cr2nLq96LmvlGqz2ZZwusbNbQDNtj6oZp9Lc0Q2PVSZnMwQT+HnEI\/nMgIvtYinpaWF2dnZ7RPxmEm1xWIxBgYGsNvtnDlzZo07aqmcGmBxKk1nb+2ebaLgpiYXAWBd4gSdc+W0g9EPPUP8ef1dltzqR4incS7r37d7304yU4vFZKEopMfm8z96j+2BrIzodZFIpBDKOB94j+4icXEMFJ3dqCDgPWw8BjurwIIoVJAwp5lMhI1HWKfjpB3F11FUldF4iIzbhpjM4hLt2ATBMJ01ElumxRegt0I6q8tbn7fVKYfB1RkOBtpwOvQ3EWdvpPpKyVYUBHq9DXR76nlhdZqD\/lam1ATLq2E63AF2uG82\/UmqwvlV4ybVhJxhNL5iSCjhTJKYaFwzm0tFkFTVUIQxlljBb3fmm2wPKk64EEa98AhPZP+GpSYnjS87ym2\/9FqDVJuK3TGLzVaNBc76ewGLYY7Inv+Rfq+chpHhMToP+WloaCgroCrnWuByuTZkPMR2wqtf\/Woeeuih7UM8lSKe2dlZLly4QHd3N319fWW\/rKUNpABTw3GOvWxrXKqNvNMqn7v2S5+et3PtPd9FWtHvzM7srMcxG0GQK\/itGfXniALeQz0kBkYKbkgg21xHXWsjaiRJZj6EZ29n5Tk8O5oMSSeahbgEnR79NMpMMoKCyiEDN+fReIg6u4sOsTi6EQUh705wRV3AIYospuPMp2N0e+ppdhUXb\/P2NwZ1oedXprktWCGdVUkKrSoMpUPcUaGn6Ep0MU+2Ppz01ud2wNPJCHPpKA5EHE6HoRhhORNnNZs2dMOeTUVQVNjp1O9in5UTOEUb7U79gvdQdJEOT0CXkNsdPhYmZ2n5ziXm\/vEiMSnCP+15gl2v6+foG16B3WEHVBzOSRyORWTZ3I7frGAAMD0m3szzK2XtrMxXdkqQMjKjo6NcvHgRv99fNIlVFMWyqTafz7elrhK3Ah\/84Ae5fv369km16UU8iqJw5coVZmZm8nMp9JAok2obOR8G9HdnJu5yHaeuYwdWcK6qQuSnScb+22P6x9tE0u1+4\/4ctwN3T7tx8b\/Og7OtYQ1ZCKqKYylGeimGvdGPu7cdRAHPgS5SEwuoJWk2R3sDqBiOTVhIg1uEdoN9wbXUCi12j2HXvhmvtCuZFfb4mnCItqJoYVaKMx1dwW93sZrNDYjTIwtZVbgQX+SEQc0no8g5h4AK0cf1eMgwsljNJpk38GXr9ARwCCJJJUu728\/VxDKRdJJWl4\/ugkhtUUkhK4qhqm8ktkyj00uDU\/8PcTm6QG9dEx6DvPPg6iwH\/C2GvUlnw9McC+7IRXgCHHA2wKQE\/+vHvPD\/PspsEF72iXvZeUR7D+bqs2YFA2C+10cwISyIhk1dimBdI6dOnSSdTudHPpw\/fx5FUWhoaMhbB2n\/qxHPSw2BQICHH354XUWIDUW5iCeRSPDMM88QDofp7+83JB0oTzwXnyk\/I8M81jPTI6MbVJg5F0BVbMz8z3FD0lH9brINngr1nHrsjQGSQ5O6xzg7mxHdTlIj+mTh6m1HvTHYLXlxnOSVSdR0FteudrxHd+HsbM4ZhEYSOeNQvXfXGaTRCQGDtq2z4Wl6nQFD0nluZYojgXZd0pFVhefCUxxwNpRVcO2w+7gtuIOEnOVwoI2L0XmeD08znyquaSXkDJeiC9xepx81xJQsoxWk0EvpOPOpmCHpLMlJYlKGfRXSWYIg0ONtyKnMvE2cbNhJt7eBxXScF8LTPBOaQBQE2g1GMFyMzLPDEzAknYHVGfb4mvCUsQXKHxOd40igzbghNjzFHfWdujW8YJ2Xe\/7g7gLSgei0cWOmhpxgwMxxNlO9PoriMJXiW5wyR2LpG6o2l8vFjh07OHz4MC972cs4ceIEwWCQWCzG0tISTz31FL\/5m7\/JD37wA4LBYFURzxe\/+EVuu+22vJv33XffzT\/90z\/l\/11VVT7ykY\/Q0dGBx+Phla98pSlz5m9+85scOnQIl8vFoUOHeOSRR0zfkx62PNVWOIW0MOJZWFjg3LlzdHR05EfAVkKiTKpt4soq2ayKw1Gr\/1ntDgSCoCJJInZ7DSMOBAUp5mL0D35C4qJ+57\/U6keMpXEsGdRz+jrJzCyjxPWP8RzsInV9DjWtv2sU97aTGVtELe3hkZW8O4H36C4yM8u4d7WjSArpiXnUgr4gFUh3BnFPr+oGk1oR3NAsVJaYkGMVmixvNJcaypyTLCvpPFkUFsznpQST0RAOQcRndxoKDWZSEZwuY4ucscQKXpvDcHTCtdgSza46mj3603cvRxbo8gZ1+45aXD5mUxGOB9uwY8tLtuudHnZ5G\/IL\/0BkjiN1LYYpw0qTX6GyqEG+Yf5q9HdQ\/Da6Pn4nzYeLN5d1DSbT5CYFA6riQLCZma9jTvk2fkVfdFOIcuICQRDw+\/34\/X5SqRSCIOTTa9\/4xjeYnJzk1KlT3Hfffdx3332cPn3asMHeaPro4cOH+dSnPsWf\/dmf8dBDD7Fv3z7+6I\/+iHvvvZehoSFd9dzTTz\/N2972Nj72sY\/xpje9iUceeYS3vvWtPPHEE5w6dcrUey8HQTXTynoLkclkUFWVq1evks1mOXjwIFevXmVqaorDhw+zY4e+8qgQsqzwxo6PlP23v73yWppaa\/Nty8maa0+Zra7KBIPVy+JS0yLjH\/sposeNHI6RnlxcE3yZqee4DnWTvjJpUPyv3PCJTUTpakQcW9I\/xmHD09e51kLHbsPV3YrqshOfX8bucyNMhnQvoynOjIr\/y+kEK9kke+v0DQ7nU1FSimTY76LZ33S49esaWu3IZ3cyGg+RUiR2BZtpFG4u+mY81y5F5+nxNBhKoc+tzrK\/rkV3vhDAsLTKLrGuYn1JTx24mk0xllghnE1wW0MnDbby0aReQ2whJEXmfGTO0AQ1o8hciS4YukZITXb6Pt1PoKe++PerGexBc35xsuzDZtPvDav6OKkOm12\/N07D5\/\/Ldb7zZf3Bihp+4T+e4tf+UH+Q26VLl\/B4POzalWuY\/upXv8rf\/M3f8J\/+03\/i0Ucf5bHHHuOpp55iz549FV+rEI2NjXz605\/m3\/\/7f09HRwfvf\/\/7+f3f\/30A0uk0bW1tfPKTn+Q3fuM3yp7\/tre9jUgkUhQ5vf71r6ehoYGvfe1rVd1LIbY84tFgs9mIx+P87Gc\/Q5Zl7r777qpynOWk1BoW51I1E896Xap9XpOeUDegqhB+PMrEx4qbYW0BL86dzaCqJKcWSQdcxvUcl4N0kxcMvNRMNXz6vThagqSv6yt3bA112P3e8r5tkkz6+iyS34nT7UaMZYh3BAnW1ZEYmcVWQJorWUjKmYoNlD6705B0ppUEXtFuOOXzcnSBnZ6goSptVI7R5vbjvZHGKyzO54r7EZJSbvyCkY3OYHSOwz7jyOK58BQn1hlZKKrKC6vGnnU+mwNZVXhFc24BG0+ssJiOU2d3stvXhFO0kVUVLkXmDUknKWe5Hg8Zkk5USjOTihqSTqbTyZE\/vQdP69pnPbucMk08q9cnaOwz4bRsstfHLM7+oHIPD9xMtemh3BC4xsZG3vWud\/Gud72rajPV0umjo6OjzM3Ncd999+WPcblcvOIVr+Cpp57SJZ6nn36aD3zgA0W\/e93rXseDDz5o+l7KYcuJR0u1pVIpFhYW6Ozs5ODBg6bH92ooV9\/RMDee5MBt62nEqnEwGyAa7F5LkU4pzH5hmNXvrt1ByZEEyUsTqH43ksuGT3TgPLqL7HKE7Mxy0bH2liCi0446vbzmOhryxX+Dhk9nZzNKOmNIOq6eVqTVRM4PTgeZljpcSQl1MYIMuFcgzSqiKDKdgJQsACodXmhw6Ecf51bn2FvXlCeCchhYneFQoA2nwciD58PT3FahyfLsyhTHDVRpnZ4As6kI\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\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"text\/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {}, "output_type": "display_data"}]}}, "29a880f5e6de435795b50f46a66a942e": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "29adfb5bcde241fc8e8984961ab42ea5": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "2c2ed815b0f545a9948793c1bc4b5fdf": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": {"_dom_classes": ["widget-interact"], "children": ["IPY_MODEL_68fb9a92340e434d85fbcc8de67c1970", "IPY_MODEL_47b1a57cdbeb4a11abc8e9166acedb90"], "layout": "IPY_MODEL_6373a42bb1db4fb4aa1cf6ffaac1075e"}}, "2df005db5f0d49dda9dd0c4e6e958c48": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "2f094b8f56ff4ac3b16dacdf8948dda6": {"model_module": "@jupyter-widgets\/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_66636bdd455c4d54a22c62b30195f3fb", "outputs": [{"data": {"image\/png": 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\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\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"text\/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {}, "output_type": "display_data"}]}}, "3012cdca2fe2499d8a50dee485f62217": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "32b1f33a1556467aaba78f0af3df1e4d": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "3500924f544141ef8070a2456337d727": {"model_module": "@jupyter-widgets\/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_e4081c6091bb48838c726ff8e86ca60b", "outputs": [{"data": {"image\/png": 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\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"text\/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {}, "output_type": "display_data"}]}}, "85007844bdc54cdf992dc43ba5b83a41": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "851af3a043f1495ea899768d977eda65": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "86d909bc42c141caa23af6d60020a0db": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", "model_name": "IntSliderModel", "state": {"behavior": "drag-tap", "layout": "IPY_MODEL_246b67cae6d04400856fb146b0a764d3", "max": 199, "style": "IPY_MODEL_2df005db5f0d49dda9dd0c4e6e958c48"}}, "87d61c25a76c41e1a826e0e1bcdaf617": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "88035a6b469f4a4891e35659867ff0c6": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", 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", "text\/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {}, "output_type": "display_data"}]}}, "8d43b8903d1c4542ac1d493441e99717": {"model_module": "@jupyter-widgets\/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_7cc9130632e24968ac7630fa1302ec66", "outputs": [{"data": {"image\/png": 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diff --git a/synced_files/GA_1_6/Analysis.md b/synced_files/GA_1_6/Analysis.md
index d35f4290..a319aedc 100644
--- a/synced_files/GA_1_6/Analysis.md
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<userStyle>Normal</userStyle>
----
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- extension: .md
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----
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-<!-- #region id="9adbf457-797f-45b7-8f8b-0e46e0e2f5ff" -->
# GA 1.6: An ODE to Probably Doing Enough (PDE)
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
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</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.6. For: 11 October, 2024.*
-<!-- #endregion -->
+
# Overview
diff --git a/synced_files/GA_1_6/Analysis_solution.html b/synced_files/GA_1_6/Analysis_solution.html
index e943ce73..a0a9cb2b 100644
--- a/synced_files/GA_1_6/Analysis_solution.html
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<head><meta charset="utf-8"/>
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
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<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># def g(x):</span>
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<span class="c1"># return YOUR_CODE_HERE</span>
<span class="c1"># def g_der(x):</span>
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</div>
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<span class="c1"># return YOUR_CODE_HERE</span>
<span class="c1"># def g_der(y_iplus1):</span>
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"/>
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<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6b6d9964">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8244,10 +8191,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># # Initial conditions</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># # Initial conditions</span>
<span class="c1"># T_left = YOUR_CODE_HERE # Temperature at left boundary</span>
<span class="c1"># T_right = YOUR_CODE_HERE # Temperature at right boundary</span>
@@ -8311,10 +8258,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># T = YOUR_CODE_HERE</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># T = YOUR_CODE_HERE</span>
<span class="c1"># T[0, :] = YOUR_CODE_HERE</span>
<span class="c1"># T[:, 0] = YOUR_CODE_HERE</span>
<span class="c1"># T[:, -1] = YOUR_CODE_HERE</span>
@@ -8394,10 +8341,10 @@ $$</p>
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># # Note: you may want to use extra lines, depending on</span>
+<div class="highlight hl-python"><pre><span></span><span class="c1"># # Note: you may want to use extra lines, depending on</span>
<span class="c1"># # how you define your A, T and b arrays</span>
<span class="c1"># for j in range(m-1):</span>
<span class="c1"># A = YOUR_CODE_HERE</span>
@@ -8432,15 +8379,15 @@ $$</p>
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<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Function to plot the temperature profile at different time steps.</span>
<span class="sd"> '''</span>
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<span class="c1"># SOLUTION</span>
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\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lzJkz5Obmij40AQG+vLr8JeJPxXD614MsX\/4SLq49Jffh6+sl681TXi691sfXmxs3pGV\/5Ib1TEzkZ2h8fHxkhSDlU4deknM0IK\/N5unZm9xc6dSiXArO2tpadk9SFwRvL08aGjSnFN1cpUVbDQ2kW5TLJARAezpKB059CR92IzPpi3BZfRmVlZUEBQV1iF7a\/SYnJ4f09HQCAwMxNW1NLZaUlDBx4kR8fX3ZsmXLXYdM\/1tiYmKYO3cufn5+9OvXj02bNpGdnU3SvztDBUHggw8+YPny5UyZMgV\/f3++\/vprampq2Lp1K\/Af99D333+fRx99lAEDBvDtt9+SnJzM4cOHO3T\/fwSUwCOBjo4Ofn5+xMfH07t3b0xNTZk5cybbtm2jqKiI1atXU1hYyIQJE\/Dz8+OVV14RGw\/c3d0ZNGgQQ4cOxdLSkvz8fI4dO0ZiYiI5OTli+srLy4MlSxeyadN6zp37hbfeeo3g4MB2dRcLC3PJPVpaWsp2jck9V1dX9671GznTL7mTUs+ejrJpNhsbzc0X0Jr+kxMMtZOwCYbW9J5cS7paLR2UdHRkPJtkUjh6etIniJpqzS3Renq6FORqDsyW5nIpOM0\/D0sr6Q6\/Fj2h3YT\/g0xubi7Xrl1jwIABotNpWVkZERERuLm58f3334tCoJ3J7e6hGRkZFBYWtnMG1dPTY8SIEfzyyy\/A3d1DH3aUwCOBtrY2b7zxhsaCs5GREU8++STff\/89RUVFfPTRR1RUVDB9+nS8vb15+eWXOXbsGDo6Ori5uTFw4ECGDRuGra0thYWFnDhxgoSEBLKyssRW5V693Pjb3yI5cmQvly4lsGrVmwwZEirrrePl1Vu2aywzM1NyLaCvv2wXUFOTdNOAtbW1bNC6mwS8XGDpG+Avm2aTcyn18OgluWZjY0NamvT7ZmVKv27xDelWdinjN5VKRXam5lOdq7MTzU2af266KmkPKClVanNz6ZOVqb0Z5eXlD3x6Jy8vj6tXr9K\/f3+xkaOiooLJkydjZ2fHjz\/+KGqydSaa3EPblKZvT+ve6gzand1D7wfdboC0szEwMCAiIoKIiAgaGho4fPgw27dvZ9asWWhpaREeHs7kyZMZPnw4Li4uuLi4UF9fLyppp6WlYWJiIippGxoa0rOnIwsWzGfBgvmo1Wr27NlPdPRejh\/\/pV16S87R09OzN2lXpVuw5dIVpqbybdLe3l6cPCl916ZWS6f3PDx6cf265loKtM7SSNGzp6NsZ6Cc9ULv3r0pLtZsn+Dm5kpOtuYhT2NjI0n\/HTnjN1dXJ4ryNJ8KrSytUF\/X7CLbVKs5IFnZGNMgoWSt10OF1Cc3sTPj6tWr1NfXiy6+1tbWXXIRlyI\/P5\/U1FT69+8vXqgrKyuZOnUqpqam7Nixo8tObFLuoXBnR+i9OIN2B\/fQ+4Fy4rmP6Orq8vjjj\/Pll19SUFDA1q1b0dXV5dlnn6VXr14899xzxMTEAK06dEFBQQwfPhwnJydKS0v55ZdfiI+PJz09XTyN2NraMm\/eXHbv3sa1axf55JMPGDv2UUxNTWRPHXa20vUMHR0d2RRdnz6+Yk1KEzdlBl1dXJxlmyHkZkd0dXVldd3c3aVPNEZGhrKfSa4eJaezZu9gJ3mq7N1builEToNOR0v6oi+l0SbXSq0lsT9tXW38g\/wZOnQooaGhmJqakp2dLaZ9s7OzZYeDO4PCwkKuXLlCv379xFRWdXU106ZNQ0dHh+jo6C6bN2pzD42NjW3nHmr\/73Tv7ScXtVotnoJudQ+VeszDjBJ4OggdHR0effRRNm7cSF5eHtu3b8fMzIwXX3wRd3d35s2bx549e2hubqZnz54EBgYyYsQI3NzcuHnzJqdPn+aXX37h2rVrVFZWIggCVlaW\/OlPM\/n668\/48stP+H\/\/bynh4Y9r\/MOUm+0JCPCXDR5yQcfW1lY2OLi6am4Zb6O0VLNgZuu+AmTTf8Ul0s9tlVnRfCLQ0dHh8mXpYFhXJ\/15zc2kW7dNTaSDQQ9t6eBSLVGrMTTUo6xI88yVqal0LalFQifO8N+1H5VKhbGxMb169WLQoEFi2vfGjRucPHmSU6dOkZ6eLv6edRZFRUWkpKTQt29fMaVdW1vLjBkzaGlpYe\/evZ0iXHo7d3MPdXd3x97evp0zaENDA0ePHhWdQbuze+j9QEm1dQLa2tqMHDmSkSNH8sEHH4ieQn\/\/+98pLi5m3LhxREREMG7cOBwcHHBwcKCpqYni4mLUajW\/\/vorenp6orPq1atX8fT0ZNy4cahUKqqrq\/npp8NER+\/hp58OYm1lJatkrSvRlgt3T7N5enrK1p0KCqS71by8PGWL\/7oyhWN7e3tSU6WDh4lMEPD29iQlRfNzdXX1uHJFuvYjJYUDUCmh3QZQVqo5gGqptMjP1hxAnRztqLiq+WSmpyP9p9pQofnUYijhPKqvry+mfdu8q27cuEFGRgZ6enqiYZu5uXmHpYXUajUXL16kb9++YrdhXV0dM2fOpLq6moMHD963uZzfyt3cQ1UqFYsWLWLlypV4enri6enJypUrMTQ0ZObMmeJju6t76P1AJSjjvl1GS0sLSUlJbNu2jZ07d5Kbm8uYMWOIiIhg\/PjxYmdPm4dJVlYW5eXl6Ojo4ODggJ2dHWZmZu0uDnV1dRw9epxtP27jwIEYysvbD0X26NEDQyMDKio0n3gGDx5EfLy0i6mfXx\/JE4+bmyuZmdL6asOGDeXEiZMa13R0dDAyNJTc19BhQzl5UvO+tLW1MTExlXyuv78fKSma6139+vbl4kXNwdDczIyqqgaNpwCdHj0w1DfXKKOjr6eHrrYZjY13BhFnJ0cq8zRfzAcFB5Dxq+Zut7BBvck5f2dRWktbRR9zFYIGB9heozx5bE2ExtfTxK1eOTdu3EClUomGbZaWlvdtdqbNiycgIABbW1ug9cQwa9YsCgoKOHTokJh26wqkgm2beyi0noreeOMNPv30U8rKyggNDeWTTz4RGxCg9W9x6dKlbN26VXQPXb9+Pc7O0vNdDwtK4HlAaGlp4cKFC2zbto0dO3aQnp7OI488QkREBOHh4Xz22WekpKSwevVqtLW1xeaEW33vLSws2v3RNDY2Ehd7lOjo3ezZu4+S4hIGDOjPGRmn0QED+nP27DmNa\/b29hQVFUmmY8LChnH8+J1F2DYcHR3Jz9dcVA8MHMDZM5rfFyCgbz\/Jk1jfvn1JTpaud7m6uks6uw4bFkb8L5otvUOCAzlzRnOQ9fXxIv2a5tOdr68Xmdc0t24PDBrA1TOaT4zDQ4NJjdf87xPSx5kbGXd215la6eLcojnVFjB9AGHLRmtcuxstLS1UVFSIv2eNjY3tmhN+b1tzcXEx58+fx9\/fX6x1NDY2MnfuXNLT0\/n5559l560UugdKqu0BQUtLi\/79+9O\/f3\/eeustLl26xLZt21i\/fj0vv\/wyKpWKhQsXoq2tLTo2+vr6UlZWRlFRkejm2GbnYGFhgY6ODmPGPsqYsY\/y4UfrOH78BCdOnCS\/oEBjS6e5udldDOE8ZFtB5QY7fXx8uHJFerZHrmvJwtJCtnHA1FQ6JePi4iRrJ56ZKb2mKzOjY21tLRl4LC0tyURz4NHXlW59lupoAyRVqU3MdKBMosYjYQB3L2hpaWFhYYGFhQVeXl5UVVWhVqvJysoiJSUFCwsLMSV3r2oIJSUlXLhwgT59+ohBp6mpib\/85S9cvXqV2NhYJeg8JCiB5wFEpVLh5+eHl5cXmZmZFBcXM336dH7++Wfef\/99hg4dKnoK2dnZYWVlJQYhtVpNSkoKzc3N4knIysrq33WmEYwcOYJXX\/07p06dZteu3ezevUcU6\/T19ZVNs8nJ3PTq1Yv0dOk2aWsZE7UePXpwWcam2sfbh\/hTv0quy0nouLi4kpOjOVhaW1tRWCBdr5Jba6iX7pITmqXrIg010vM05RIdbRZWxtTXaA4uNrZmNErptEnUeH4rKpUKExMTTExM8PDwoLa2FrVaTVFREampqaKmmY2NjaQyeGlpKefPn8fHxwcHh1bh3ObmZiIjIzl37hxxcXFi2k2h+6MEngeY5cuXc\/bsWRISEnB0dEQQBDIzM9m+fTv\/+te\/WLJkCYMGDRLniHr27ImlpSXe3t5UVFRQVFTElStXaGpqwtraWgxS2traDBkymCFDBrN69bskJSWxc2e0bLfa3dQIHB0dZAOP3Jq\/vx\/nz12QXK+X6bLr3duD69czJdelrKpbn+tJwq\/nNa6ZmpqQKTFUqlKpJOd3AAoLpAN0UX65xu\/r6+tSKuHPY2dvQl265jUDXW2kRn2lmgv+WwwMDHB1dcXV1fUOwzZ9fX0xCLXVH8vLyzl37hze3t6i+2tLSwsvvvgip06dIjY2VgxGCg8HSo3nAaa4uBgdHR2xyeBWBEEgNzeXHTt2sGPHDk6ePElQUJAYhNzc3FCpVAiCwM2bN8U71Pr6emxsbMRc\/e2DpBcuJBMdvZvo6N3tAo1cYwC0ziVJ2Rz4+vrIBq0hQwYT\/4vmk5axsTGNTS2S8j3Dhg3j5Ml4jWtGRoY0NUm3hw\/oH8iFC5pTeHL1HTdXFwryNKsZWFiYU1eleUrBysqC5lLNqTbP3i7clOhoGzioF+rzmk9fYcOdKE3WHARnRM3Bqre0PNH9pq05Qa1Wc+PGDbS0tDAzM6OkpAQvLy+xqN7S0sLixYs5ePAgsbGxuLm5ddoeFR4MumyOZ\/369bi7u6Ovr09QUBDHjx\/vqq08sFhbW2sMOtB61+3s7Mzf\/vY30VNozpw5\/Pzzz\/Tv35+wsDDWrFlDWloapqamoq\/9wIEDMTIyIj09naNHj3Lu3Ll2Stp9+wbwj38sJzHxNGfOJPD666\/Rt2+ArBqBu7ubrLeOnM+NlpYWqRKOoAB9\/PxkNeOKi6VPF76+0oOwKpWKzCxpmRwdXemak4mM0Zybm3THkktPaTFYSwnXUQA9HeluMkFD51wbRtYdc+KRoq3Rxd\/fnxEjRtCrVy+Ki4vR0tIiLS2N2bNn88UXX7B06VL279\/P4cOHlaDzkNIlgeeHH35g0aJFYiopLCyM8ePHk50tnatXkEalUuHg4EBkZCSHDx8mPz+fyMhI4uPjGThwIIMGDWLlypVcvnwZY2NjPDw8RDuHtmn2NiXtvLw88WLt7e3FsmVLiY8\/wY4d\/+Ltt98kJCT4jnbTuw353S3NJjdUKoedna38XJBM8Ojp6MjNCmmDvLxc6SYKfZlJemMj6UYHuTW9HtIFekHCxRSgqVKzrp2Wjjb65l3nMFpVVcW1a9fw9PRk5MiR+Pv7Y2VlxZo1a\/j0009xd3fn8OHDim7ZQ0qXBJ61a9cyb9485s+fj6+vLx988AHOzs5s2LChK7bTrWibvZg\/fz4HDhygsLCQxYsXc+HCBYYNG0ZQUBBvvPEGFy5cwNDQUJxmHzJkCJaWluTm5nLs2DGSkpLIyckRTxvu7u689NLfiIv7mdTUFFavfo+goEC0tLTukAW5FV9fX1kLBBNj6Ytxq4SOdDfb3Tx\/rl+XHqKVU1hwsLcjN1d6z8U3pA3jykqlg1lLg3TTQbNMR1uFWvr9aks0NyQY\/Rcdbf8tlZWVnDlzBjc3N1xdXcXfSWtraxoaGti7dy8TJ07km2++wcnJiWPHjnXZXhW6hk4PPA0NDSQlJbWTCwcYO3asIhd+n1GpVFhaWjJ37lx2795NUVER\/\/jHP7h27RqjR4+mX79+vPbaayQmJqKvr4+bmxuhoaEMHToUa2trCgsLOX78OAkJCWRnZ4t2Do6Ojowd+yhvvvk6Z84ksGzZEkaOHKFReFRuELDV5E76xOIf4C9rv6BpeLMNLy9P2fRgdY30c93cpIOSja01+XmaBUUBigqlg3DpDWk5oHK1dBNEXbnm9gETC32a6jSn2v6bVur\/hqqqKpKSknBxcRGlZgRBYM2aNXz22WccOnSIxx9\/nCVLlnDixAny8vIIDQ29r3s4duwYEyZMwNHREZVKxa5du9qtz507F5VK1e5r0KBB7R6juId2LJ3e1VZcXExzc7OspLhCx2BmZsYzzzzDM888Q1VVFQcOHGD79u2Eh4djYWHBxIkTmTRpEgMHDhS7ltqUtIuKirh69SqmpqYIgkBdXR0hISEYGRnh6dmb+fPnUVJSyt69+9i1K5q4uKM0NDTIWiD4+\/txUWbwU87rxtjYSPY0ZGtrR5qEa6iBgQGpV+SsxqXvx9xdXTlfqrkm5eTkSEmR5gCir69LUZ7mlKKennRHm4mZHo21mluwLa0NoVhzAO3s+g60insmJSXh7OxMr16tgq6CIPDhhx\/y0UcfcejQIfr27dvuOR0hmFldXU2\/fv3485\/\/zNSpUzU+5rHHHmPTpk3i\/9+u1r1o0SL27NlDVFQUVlZWLF68mPDwcJKSkjrU\/fRhocuaC36PpLjC\/cPY2Jhp06YRFRVFYWEhH374IeXl5Tz55JP4+PiInkLa2to4OzsTHBzMwIEDaWhooLq6msbGRpKTk8nIyBBPJVZWlsyZM5udO7eRmXmNr7\/+ipAQabtlM1Np0zMtLW2uyDQd+Pr2kRUzvVv6T0pQFCA7W7pVWi4YOjpKtwQ7OTrQokHWBsDJyU5yzaGn9InR1Fy6htXZJ56amhqSkpJwdHRsF3TWr1\/PmjVrOHDgAEFBQZ2yl\/Hjx\/P2228zZcoUycfo6elhb28vft16MlfcQzueTg881tbWaGtry0qKK3QuhoaGTJo0iW+++YaCggI+++wzUTvL09OTF154gT179vDEE0+wadMmhg0bxogRI3BxcaG8vJxTp04RHx\/P9evXqaqqQhAEzMzMePLJqURFfUdW1nW2bNnE1KmTxQFDFSquXbsmuSc\/fz\/Z4NGjh\/Rdp4WFBVdkTjSGBtIXZWfnnhQWSqfoSorLJdf0dKWDkp2N9O+2sYH088zMpBsEDPWlExb3a3j0XqitrSUpKQl7e3t69+4ttvF\/+eWXvP322+zdu\/e+p9P+W9oGVr28vHj22WfbCd8q7qEdT6cHHl1dXYKCgtrJhQMcOnRIkQt\/ANDX1+eJJ57gq6++oqCggG+\/\/Zampibmzp1LSUkJOjo6xMbG0tLSgqOjIwMGDBDtHKqqqtrZOdy8eRNBEDA2Nmbq1Cls2bKZrKzr\/PDDVhZE\/lWsGWlCTplYW1tb1ubA29tL1pm1oEC6RuPsJO3NY2RkRGaG9Gmooly6HqWNtLaZVov0n6G+jGGfTJd1pwWe2tpaEhMTsbGxwdPTUww6W7Zs4bXXXiM6OpqhQ4d2yl7ulfHjx\/Pdd99x5MgR3n\/\/fRISEnjkkUfERhrFPbTj6RLlgpdffpnZs2cTHBzM4MGD+eyzz8jOzua5557riu0oSKCjo4Ovry8JCQk88cQT\/OUvf2H37t288MILVFVV8fjjjzNp0iRGjx4t2jk0NzeLdg6JiYno6uqK0j1mZmbo6+sTHv4E4eFP8M47b4kipnv37aekbSZHpZJ1Ke3Tpw8XL0qrLMgFHQcHe1l9Npmn4uXZi0sXMzWu6ej0IDtT+qJ0s1TacM1QxxjQXONplvDaAVA1ScvvdEaqra6ujqSkJKytrfH29haDzvfff8\/SpUuJjo5m5MiRHb6P38qMGTPE\/\/b39yc4OBhXV1f27dsnm55TygH3jy4JPDNmzKCkpIQ333yTgoIC\/P392b9\/\/11NxBQ6n3fffZewsDDWr1+PtrY2Y8eO5cMPPyQ+Pp7t27ezbNkySkpKeOyxx0RPITs7O+zs7Ghubqa0tJSioiLOnj0rDhja2dlhbm6Orq4uY8eNYey4MXzU\/AHHj58gOnoPly9d5oSEBQIgOVQLrbWhS5ekLb\/d3d1RF52TXE\/PkA5Kcp4\/7u6u5GhQjwbooa1Nfo70rFK5WvqkdLNY2rCvuVq6M6+jTzxtQcfS0hIfHx\/xgrx9+3YWLVrEjz\/+yOjRv08Zu7NxcHDA1dWVtLTW9Oyt7qG3nnrUarWSlblPdFlzQWRkJJmZmdTX15OUlMTw4cPv+3usWLHijrbJNttaaL2DWbFiBY6OjhgYGDBy5EhSUqS7rB5G1q1bx8aNG9t18mhrazNs2DDWrVsnStm7u7vzxhtv4ObmxsyZM\/nhhx+orq7GxsZGnGTv06cPLS0tnD9\/nmPHjnH58mVKSkpoaWkRRUzXrfsn+w\/s4eDBfTz\/\/HM4Od057Z+VJScK6kx1tXRrcrP0oD\/u7m6UFEsHiMqb0q9rYy0tTePi0pOGes0nF11dHUoKpGd\/mqqk55TqSqX305Ennvr6es6cOYOZmRm+vr5i0ImOjmbBggVs3bqV8ePHd9j7329KSkrIyckR9eIU99COp9tbX\/v5+VFQUCB+JScni2urV69m7dq1fPzxxyQkJGBvb8+YMWOorJS+EDxs6OrqyqYXtLS0GDhwIKtXryY1NZUTJ07g5+fHmjVrcHNzY9q0aXz77bdUVFRgZWVFnz59GD58OAEBAQBcvHiRY8eOkZKSQnFxMS0tLWhpaTFkyCBWrXqHy5fPExd3kJdeeoFevdzp1auXrP2CnDyPtpY2aWnSKTy5rjSdHj3ISJee4xAE6X8jWyvpoGRlaaLRxA3AzMKQuirNnXs9dFXU35RWLTAwl7Zf+G9om8MzMTHBz89P\/N3Yt28f8+fPZ8uWLUycOLFD3vteqaqq4ty5c5w7dw6AjIwMzp07R3Z2NlVVVSxZsoT4+HgyMzOJi4tjwoQJWFtbM3nyZKC9e+jPP\/\/M2bNnmTVrluIeeh\/p1iKhK1asYNeuXeIv4K0IgoCjoyOLFi3ilVdeAVrv5Ozs7Fi1ahV\/\/etfO3m33QtBEEhJSRHdVS9fvszIkSOZNGkS4eHhWFlZiTWB8vJy0XCsqalJFDFtU9K+lZSUy+zYEU109F6NVti9e3tKKhb08fUlNTVTcs8hISGcSUrWuObj40WGhP8OgJeHLznZmpsWRgweyvlTmt83wNcDdYrm+o+XrwMNmZpvghzdzLCQcFw1tjfhT3vv\/+9vW9AxMjLC398fLa3W+9aDBw8ya9YsvvjiC5566qn7\/r6\/lbi4OEaNGnXH9+fMmcOGDRuYNGkSZ8+epby8HAcHB0aNGsVbb73VzhlUcQ\/tWLp94FmzZg1mZmbo6ekRGhrKypUrRe8YDw8Pzpw5w4ABA8TnREREYG5uztdff92FO+9eCILA1atX2b59Ozt27OD8+fMMGzaMSZMmMWHCBOzs7DQqaTc0NIh2Dm1t+Ldy5cpVoqJ+ZMeOaDIyMnFwcJBthQ4bFsYvEm6jWlpaWJhbU16u+WIeNmwIv57SHJRMTIxprteTlO8J8R9IWormU9rw0CBS4zUHtJBBvbghoUrt298OrSzNa\/ouhvRbHio2dciZ7N0rjY2NJCUlYWBgQEBAgBh0YmNjmTFjBuvXr2f27NlK8V3hnujWqbbQ0FC2bNnCTz\/9xOeff05hYSFDhgyhpKREbItUFBQ6HpVKhbe3N6+++ioJCQmkpqby+OOPExUVhZeXF4899hjr168nLy+vnZJ2SEgIhoaGXL9+nbi4OM6fP09BQYGopG1lZcHw4YM5cGAn58+f5pVXXiIoaIDkxU8qqAB4ePSSXZczfnN3d5HVjCvMlZ5HapHuD0BfV2ZOx0i6PdvWzb6d5NGvv\/5KZmYmNTXSNSE5GhsbOXPmDHp6eu2CzvHjx3nqqaf48MMPlaCj8Jvo1iee26mursbDw4Nly5YxaNAghg4dSn5+fjsTqmeffZacnBxiYmK6cKcPB4IgkJOTI3oK\/fLLLwQHB4ueQm0Ck9Caty8qKkKtVlNdXY2xsTFVVVX4+PjgdNvsTW5uHrt3H2D37n2cOpVAS0sLpqYm1NUKNDdrbkEeNnQI8fFnJPdqb9OTsjLp01Diac1+Q\/Z2NtSpdTWuAQT09EWdo1kENGxwb3LOab4JGjrCmfILmmtO\/tP6M\/yV1lpEfX09N27cQK1WU1paipGRkdhZaGRkdNdg0dTUxJkzZ+jRowf9+\/cXg058fDyTJ0\/mvffeY8GCBUrQUfhNdOsTz+0YGRkREBBAWlqa2N2mKCh0HSqVChcXFxYtWsTRo0fJzs5m9uzZHD58mH79+jF8+HD++c9\/kpaWhpGRER4eHgwePBgzMzOqqqrQ19fnypUrJCUlkZubK0roODn1JDJyPjExO0lNPcO6de8xKWKi7MWxVkZw1NXVWTLoANTLnIZ6OjhKruno9KA4X+Z1K6UlgbRkBo4Mb9Fp09PTw8nJicDAwDsGfU+ePElaWhoVFRUaT2zNzc1iG3y\/fv3EoJOYmMjUqVN56623lKCj8Lt4qKyv6+vruXz5MmFhYbi7u2Nvb8+hQ4fEGk9DQwNHjx5l1apVXbzThw+VSoWjoyPPP\/88kZGRFBcXs3PnTnbs2MHbb7+Nj48PERER1NTU8PnnnxMfH4+7uzu1tbWo1Wry8\/O5cuUK5ubmYm1DX18fOztb5s37E8yDN996lf37DrN7TwxH434RA5W2dg+uSQiKQqt3T2FeueR6fq50XcnIwAgo1rjm1NOWmnTphEOlzHxPi8xgqZGN5lZqHR2ddoO+bW6hZ86cEWesbG1tMTc3RxAEzp49i0qlon\/\/\/mJ97dy5c0RERLB8+XJefPFFJego\/C66daptyZIlTJgwARcXF9RqNW+\/\/TZHjx4lOTkZV1dXVq1axbvvvsumTZvw9PRk5cqVxMXFkZqaKivZotB5CIJAWVkZ0dHR4ulnwIABjBo1ikmTJrXrrqqrqxO748rLyzE1NcXOzg5bW1sMbjNvq6ioJCbmZ\/bsjiE3t4CLydK6cUOHDCbx14sa1+ztbakokT7xhPYdSKqENXVIoB\/ZiZrrP2bmhljUSyckQrxNqFFrtlkI\/7+puAx2l3zu7bS0tFBWVib+27VN6Ovo6BAcHCwqN1+8eJHHH3+cl156iVdffVUJOgq\/m2594snNzeXpp5+muLgYGxsbBg0axKlTp0SFhGXLllFbW0tkZCRlZWWEhoZy8OBBJeg8QKhUKiwsLMjJyaGoqIhDhw6JdaE2qZ6IiAgmTZrEgAEDcHFxwcXFRaxtFBUVkZaWhomJiXhHb2RkhJmZCTNmTGLGjElUV9dw6OAx9uw5xOHDx6iual+Ez8+TPtE4OfWkoiRLcr0wV3og1UDHANAceGwdTGnM1BxYtLRV1JXIDY\/+NtUCLS0trKyssLKywsvLi8TEROrq6mhubmbNmjWcPn2aYcOG8fHHHxMZGakEndvYsmULL730Evn5+e06CKdOnYqRkRFbtmzpwt09mHTrE49C9yA7O5sxY8awfft2\/P39xe9XVVWxf\/9+tm\/fzv79+7GyshI9hUJCQsT0UENDg1hgLykpaVdgb1PLbqOurp4jR06yZ\/dBfoqJQ09fj5ul0vWfsGFDSTyt2RfI0NAA3VppW4Oh\/YO4dkaqldqdG+c1BzxreyPs6qUDz58PR\/6uAdI2VYmGhgYCAwPp0aMHycnJrF+\/npiYGEpLSxk3bhxTp05l4sSJWFtb\/+b36I7U1tbi4ODA559\/zrRp04BW37GePXsSExOjcaboYeehai7oDO7mfngvMj2K+2F7XFxcSElJaRd0oNVTaPr06fzwww8UFRWxbt06SktLmTp1Kr6+vixevJjjx4+jpaVFz549ZZW0KysrEQQBfX09Hn\/8ETZsfI8rV4\/x6aerePqZCVhYataHq7wpLf7pLKOEAFBZLP1cfR3pZIS5lbRVgpaONvoyVgpStLS0kJycTH19PYGBgejo6KBSqTA2NubIkSM888wzXLx4keHDh\/Ppp58yYMAA2RbyhwkDAwNmzpzZzljuu+++w8nJ6YEUSX0QUALPfabN\/fDjjz\/WuH4vMj2LFi1i586dREVFceLECaqqqggPD5dsBX4Y0GSrfSuGhoZMnjyZb7\/9loKCAjZu3EhdXR0zZ87E09OTF198kdjYWKBVFLJfv36MGDECDw8PampqSEhIuKPLS1dXl+EjBvHPdcs5n7KfqG3\/x5\/mTsHWtlWWR1tbm+ws6ZkvW2tbybW7dbQh86M2MZFuzza0MvzNabCWlhYuXrxITU2NGHQAsrKyeOKJJ4iIiOD999\/Hx8eHV155hdOnT3Pp0qX7nm77I9+0Pfvssxw8eJC8vNZ63qZNm0SLbYU7UVJtHYhKpWLnzp1MmjQJuDeZnoqKCmxsbPjmm29E+fb8\/HycnZ3Zv38\/48aN66qP84eksbGR2NhYtm3bRnR0NM3NzYSHhxMREcHIkSPFnPytXV43btygR48eYjrOzMys3QWkpaWFxF8vEBf7K3t2nKQgX3NKLCxkKMmJmRrX3FwdqZVupCM0wJWiayUa1wYPd+ZmsuaLqZ2\/A1M3PyP9wrchCAIXL16ksrKyXSNBXl4e48aN49FHH2Xjxo1iA0dHcuDAAU6ePElgYCBTp05t97cDsGrVKt555x02b96Ml5cXb7\/9NseOHWvXDLRgwQL27NnD5s2bRcvq0tLSTrGsDgoK4sknn2TcuHGEhISQmZmpSOxIoASeDuT2wHMvMj1Hjhxh9OjRlJaWtpNk79evH5MmTeKNN97o7I\/RbWhqauL48eNs27aNXbt2UV1dLd7Rjx49Wux8a2lpaReEVCpVOzuH2y\/C58+mEr3jZw7sPcENdbn4\/YBe\/cnJ0ByUggf0ISepXOMagK+dFbU3NdeWwoY7USrRKec+ypPxayJk\/hX+Q5ue3s2bNwkKChKDcGFhIY899hhDhgzhyy+\/7PALtib+iDdtGzZsYN26dYwdO5a0tDR++umnDn2\/PzJKqq0TuReZHsX9sOPo0aMHo0aN4pNPPiE7O5s9e\/ZgY2PD0qVLcXd3Z+7cuezcuZPa2lpsbGzw8\/Nj+PDhYm0pOTmZY8eOcenSJVFJG8DFzZZho3z4cc9q9hz8mIWLnsanjzv5OZpPLACGutJ1GFNzA8mgAyA0SrdvG92jHYIgCFy+fJmKiop2QUetVvPEE08QEhLCF1980SVBRxMZGRkUFha2s6PW09NjxIgRoh11V1tWP\/PMM+Tl5fH555\/zP\/\/zPx3+fn9kunU79YPK7Xnfe3E2VNwP7y\/a2tqEhYURFhbG2rVrSUhIYNu2bbz++uv85S9\/YezYsURERDB+\/Hix1djHx4fy8nKKioq4dOkSzc3NmJmZUVZWhpeXV6t0jxP4+vVi0dLZZKQV8PP+JI7sP8PVlPYGc3Iabbb2pjRlSQ+PNlVKW4bfSyu1IAhcuXKF0tJSgoODxaBTXFzMhAkT8PPzY\/PmzXetq3UmcjdtWVlZ4mO68qbN1NSUqVOnsm\/fvnYpQoU7UU48nci9yPTc6n4o9RiF+4uWlhahoaGsWbOGq1evcvz4cXx9fVm1ahVubm5Mnz6d7777joqKCszNzfHx8SEsLAwHBwdKS0vR1tYmLS2N5ORkioqKxCYQd08H5v8tnK0\/\/S+7TrzDC69Oxa9\/62BnZYl08DA3k2+FrpUxgJNSLWhDEARSU1MpLi4mKCgIfX19AMrKyoiIiKBXr15s3bpVbDB40HjQb9oKCgp45pln7osieHdGCTydyK0yPW20yfS0ORsq7oddi5aWFoGBgaxcuZJLly7x66+\/EhQUxP\/93\/\/h5ubGlClT+Prrr\/nuu+8YPXo09vb2jBgxguDgYPT19bl27ZqopF1YWEhTU2tazMnNljmRj\/H13lfZ++sqJs4fhN9AF7S07rwgGuhKX\/RNLPRpqpWRy5E58QiCQFpaGmq1mqCgILGmVVFRwaRJk3BwcOBf\/\/qX2GDwIPGg37SVlpYSFRXFkSNHeP755zv0vboDD85ZuptQVVXFtWv\/kV9pcz+0tLQUBTFXrlyJp6enKNNjaGjIzJkzgfbuh1ZWVlhaWrJkyRLF\/bALUKlUBAQEEBAQwIoVK0hNTWX79u28\/\/77ZGVlMWLECOLj40XjOlNTU3r37k1VVRVqtZr09HRSUlKwtLTEzs4OGxsbdHR0sHe0JGLeICLmDaJUXUn8gcuc3HeZ5PhMWppboFm638fS2hCKpfN0hjaaA48gCFy7do2CggLRbgKgsrKSqVOnYm5uzvbt2x\/YO\/V70Va89aZt+vTpwH9u2lavXt2h+wsMDKSsrIxVq1bh7e3doe\/VHVACz30mMTGx3aTyyy+\/DLS6H27evPmeZHrWrVtHjx49mD59uuh+uHnz5gem0PswolKp8PHxoX\/\/\/hQUFPD+++9TV1fH999\/z+LFixkyZAgTJ04kIiICR0dHTExM8PDwoLq6GrVaTXZ2NpcuXcLS0lKU7tHV1cXS1oQn5gzkiTkDqSip5tRPV8hPKqQgRU1Tw53DPKbmerRo1hwFpJsL0tPTyc\/PJzg4WAw61dXVTJs2DV1dXXbt2nWHnl1n80e+acvMzOzQ1+9uKO3UCgr3SHl5OZ6enmzYsIEnn3wSaD1JZGdni55C8fHxhISEiNI9Li4uYn2hpqZGFOK8efMmFhYWki6htZX1JP98jXMxV7l0LJPGf1svhIY5U3VR8wyPVg8t\/hr\/0h31jPT0dLKzswkODhYlgmpra5k2bRoNDQ0cOHDggdAnlLOs3rx5M4Ig8MYbb\/Dpp5+KN22ffPJJO0ULxbL6j4ESeLohx44dY82aNSQlJVFQUHDHIN7cuXPvsPYODQ3l1KlT4v\/X19ezZMkSvv\/++3Z\/wLebrj1slJSUYGVlpXFNEATy8\/NFO4fjx4\/Tt29fJk2aREREBB4eHmJQaFPSLioqoqKiAjMzMzEI3X7yqKtuICUunXMxVzFpqKM4KVvj+xvbm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\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\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"text\/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {}, "output_type": "display_data"}]}}, "29a880f5e6de435795b50f46a66a942e": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "29adfb5bcde241fc8e8984961ab42ea5": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "2c2ed815b0f545a9948793c1bc4b5fdf": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": {"_dom_classes": ["widget-interact"], "children": ["IPY_MODEL_68fb9a92340e434d85fbcc8de67c1970", "IPY_MODEL_47b1a57cdbeb4a11abc8e9166acedb90"], "layout": "IPY_MODEL_6373a42bb1db4fb4aa1cf6ffaac1075e"}}, "2df005db5f0d49dda9dd0c4e6e958c48": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "2f094b8f56ff4ac3b16dacdf8948dda6": {"model_module": "@jupyter-widgets\/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_66636bdd455c4d54a22c62b30195f3fb", "outputs": [{"data": {"image\/png": 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\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\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rjXcx6BfEIxQKMXr0aISHh6OgoMCvaoSFvjpTC+TsnTeVV3URvTbU9fb0fqpoqMNvv\/g3AGD61CkYIFBi3oBRSGgXgavzTUU5w6WQRNDO0hdm8QQYZ9DDEdsfpHF6OJqs4Ah1m9f+pvN8wd5Z28w7aAsVMkjjDIAIEEql6DjeO\/XRHcEo16BVwm13wFHGFlII5FJIY3SBJdyBBAJuNyAWwXa6DFynHWKtEhKTBu4uh4dUz18nHw1REZpYCKdRBXEw1kUBIjRpvAHOhjZwVvbi6lZIIFQpfFSHIocbZohhDtODA9CqEKCorQGRUgVcnBsigRAuU\/CeYC6rI2gptaPe1mvyJwstnTYE2uMX7SvC2Fsew4jRo\/0ep6aHajQav8RDkYhGEwU\/vEO6VLuDGI\/QM9Xmb+z1pZ4+KhKJkJ2djY8++gjNzc0wm824\/vrr8dlnn\/k4JPScPjpp0iSsXbsWzz\/\/PF544QUMHDgQn332GcaPD95QloV+QTyAx8LC7Xb3eRhcXyOedmcz81hXF\/tc1E1K7U6oprPm1jZ8cOIAPtj3JQDg+mFjMH\/QGIySaKEQiCDrcoOroZorwyBWK8lZPBwA2dDYgP1AiqGxsJ2rJA1TJWYNOIeLueC6bV3oKqn2pA5zCyGNN0KsUsBR3wpHD+uZoJRrA8wetwZCTSeIDIM4XIEuInUYtGihRzTkbGiD83xTrlegIAyXw9nY5pPu6n0iKZxKGcQVzfT7BaGUkyVFw15K2\/uItEoIAbiJe0UgFkEfa4LqLAeIAYdUiFJ7KxSm4Ae6ORo6g06JdTV1Bk08kUSmAQDytuchbcETsNnZm8aKigpmSk0u9\/8ZGxrYTiCsBtOqqirm+3S4moAAFNoz1dbTENlb4+kLAmWIFAoFvvrqq4Dn6Tl9FABuu+023HbbbX26nmDQb4jHi74Og+trxNNgZRfdqTkdDQ3sHVV9PfsY1XncU4Wz+\/Qx7D59DAAwdex1mBU7DFMS4xBZ3Q6uR41EGqOH29ZJyq4F4TI4IqQQUK7ROG+8GcADLRjDUJFGCaHiwkRRe0kNvFct1qogMUXB3eWAQCYNggTiYTtbTjaYcnolXB1d4Ag1XdBy6QDfAWd3gnO70ZlXBrfN7hEoaJVwtdlgL6nlCVSgDoMDHMS17KgRQgEUQ2L7TIT+ILFo4O6ww93MTsFxEhEEhkh0ne0msLC7kYgI2KODyA+dh8saeLyzF3aqptcDLAscADiZno3xS56G4\/ya4E+FBngW3+hoC0pKev+d3W7\/91BHRwfTpbp7FNLzfcxmM8rKev+m6toqIUIS87MA\/lVt3dP4P4SRCEA\/Ip6LHQbXV+eC8jp2SoqlTxeJRKiu9r\/ASyQSpgpGJpMxj4lEInIuyLG809if+S0AIEwmx72TZ+Lm+BGIaRVArlejs6iaNrk0qAGhAFw1W3YNqUetFtB4M5haTawertYOOBju2s6GVrg67ZAao9BVUAF5coxnCmtFPdwtvqnKoImwoh4CYoETqMMhDpPTcmkvCQR4P\/mwOHR2c1rwcVCIUHjmDHEudFY3QtJM1KtkEsjjDJekb0iaYISzroWs2QkiFBAq5XBX+N\/dK2OCr\/G02ToDpsS8kImC7w3yZ4EDAIc\/OYop9z7rs6Nn\/Q4BQKfT+SWetjZ2tsJgMDDGI7DXIFYtuLyuCPGBiIfhXODFD2H6KNCPiMeLvkY8HdbgIx6BACiqzPN7jLXzATyyQn+2G4AnF8xytLVYzH5zy55jFr+7Jn\/X0tHVib\/v+gJ\/xxcQi8WYlZKGJcMnYDiiIKpo7pWukg80w17TzM+48QdhZBhEyjB0kcXxbsabBOTJMegqpq2ExDoVBCIRL\/Pmi\/ICAaRxeohU4XC0WCGJCAuCCOM9MmgiEnDrlOBsXeAqiRSptzb0HUnAbbXB3mqFs74VEoeLV\/05G9rgqLpAxMIIBcQaJTl0L5j3AwB5UjS6SmpIGbtIo4RQIoaD6PnSxge\/u5YG6TQNBC+ldjR0+hUsfPvpcUy+59e9Hq+rq0N4eLjf1DcrpVZTwyYrlcq\/lxyVqWARQ3HlWSQoZzPtqwD\/DaTd6yo\/hFk8QD8xCe2Ovqfago94FJFidNn9E5XJxNamazTsuSaUfXlPy4ru0GrZx3Q69jRDi8WCrccO4cf\/Xo3r\/v1bLMvbiB2aDrQnaiCQSXi3a4p0JNE6QCiAo5wu2AfbONl5toL2r4s3wt3lgKPGzwLIcbCX1qGruAYikQj26kYohsdDlhQN+Fno+NpQgDlC4vYuiNoJIUlkGCQ6FV0bCpJ4Ea+Hq7oZwi4n4ObQVVQN26liOKoaINaqoBgeD\/nQWIgiw+jakEgIeZDKvM5AvVNmz\/3l9zs\/D5dMDE108FJqhSr454qigpxQ2tj797t59Q7c9cJfma+Jjo72+zgrpVZXV8eMUlgu1VVV1Uwlq1zuX2DhcNoRHkVHeoEaSDs6On4QxNPvIp6+ptr6UuOREka5ajW7T4fS7rMaygBank3lcaljWq2Wt1IHgLzKMvxyw\/sAgGHJybjeMQST1bEYaJdBbO298MoHx6CzuJqcXirSeobSkTtzkRCK5MB1imCUa2KDGgKAFy3YvEPtxEK4o6MQHqmEo7YZUn1kcLWhvADRUFQYnA4n3ATxQiqGPMEUUCnHJRqA4loIGJtcZ0MrBHIp3FYb3LYuyJOiIZBKYK9q8PHGE8glkEbrebUg8\/MFk4KL08PZaCU3H4IwGdwD9BBJgt97ButK7Xa4IdUGKaW2+d6Hn\/7uC9zz4p8hFoshFAr9\/vZYv9XWVnadKyYmGvn553o9bid8Fc1mk9\/UHSXDDosSw9rA3vAEaiBtb2+\/JA2a\/R39hni6TyENNuKx2+1obiCKuD3A+RmH4IVUyv5RUbMxLtY+grLlYBU2ASA8nH0tUoUcf92Vjr+ev675qZMwb8BoDHYoENbcBVe8Frb8cuYiCXiku67mdjga2Go8QZgMUpOGVNMBwU0LlSYY4axv9btICpxuiCqa0NVig9SghqvVBsWIBLha2mEv650KCaY2JE00wVnbDDcRDQki5JBqI8n+KgBwJeogKiIiGPQ2DfVxUIjWQqxRwtXR6VELEs7gQJAquEEW2MvrLnjY+YFQFQaxMgzNruAFAH2RUnfUWhERrGih273xz1\/9Fw+94XGFdzqdiI2N9ZuOZkUplIcYy3GalV4HPJs8f8TT1MSOIuWR9HrQ1tLhM2+HZZlzraPfEI8XXjl1IE+gtrY2ZGVlwd6Hme52AaH84dg7ZKq\/h5rrQe2MqKiOIl7qO3E6L1wLx3FIz\/wa6Zme8d5zpk3HhGozxqnN0Le6IHT1JoNg7G\/E+kiPKICw5Al2WmhQ0RBfGzq\/qFR4ohRRZDik0VpwTjc6y+ugSDBekvdzKWXgBABHqAUhFMAZHQVxEe0MHqgx1FHRAK7LDoFQBFdHF+RD4wCOQ1cPW6JgVXDBNKKKdUpAKIK9oh6C5MAd7vy19kFKLWgLPlUulInAuTisfvxjPP3Xj32O6fU6v8RjZ8iqGxoamMafrLQZRVZyuf+orbKSfe\/bBW2gKhi29i4cPnwYcrkcWq0WDofD5zf9QxEX9MsaD0AvvlVVVTh06BDMZgvsnX2oBznYuv32dnYTaGsre\/ff1sYms8ZG9vtRIT7VM0SRWXg4+4atbGnEi1+txc0b38aPvv0QH4vLUGwQw6nw7D3cAwzozCsjSUeaYIS70w4HoZQThMshizcGVbDvzCsnSUAapwdnd\/qtU7ha2mE7XYquslrIo3Xg7E4ohid4FtaLfb8YHaRCMcRU+lYmgdOghJjh99fr\/Sg3AosWnJODo7b5vINCKTpzy8DZ7JAlmqAYkQBxtA7ygZbA3+fweHSepd9PbNaAc3F8w6\/EHLyirS9Saq6rD8QTIcFbj\/+nF+kAgELhXyxA\/a5YQ9xYG7329naiFus\/Uu\/q6uKnH\/dEs42OgN0OYOrUqRg0aBDcbjecTieOHz+Ojz\/+GH\/4wx\/gcDj6FPEEmj7qcDjwy1\/+EikpKQgPD4fFYsG9995LetgBwJo1a3pNLBUIBOT60xf0m4ine6oN8Nwo3WWGgGcXf\/bsWZSVlWHUqFEIkyn7ZK\/TYGXvbqibmZJwUq+rqmK\/HyWlbvRjCRLMMdZOEAAE3aZXNrS1YPWOz7EaHkPTH9+8EJNa3RisUkDR4j8dqRgah85zFYGjE7GIjoaEAiiGBC6gywfHoKuYHrjmdZfuWYuSmDUQa1VwWW2wl9Z66j5BpajqwXWyv0NXmARuuQSSasLsFEHWYRJNcNY0+e+LcrvRVVQNQbgcEl0knI1tUAxPgLvL7uOg0Kf381P3ieiDOWiwg9oAgDSl6wZnpxMvPPUv7M73n9Jsamr2+3h5Obv2yEqpUZtHk8no93dMOZIYDHq\/yrfq5jKowR621tXhgFgshl6vh06nQ2VlJUaOHInq6mp89dVXOH78OPLy8nD8+HHMmTMH06dPJ2vF3umj48aNg9PpxHPPPYdZs2bh9OnTHnPijg5kZWXhhRdewKhRo9DU1ISVK1di\/vz5OHr0KPO8gKepv+cIbZZysK\/oN8TjhVAohFAo7BXxdDcRnTBhAiIiIlBb1tync9c0+7\/BBQIBM3xWKpXMnK5Op2VaaBgMemYPj1arYXZNy2QyModMERZl58FaDVxuN840VeNfX28AAKQNGII7UiZhlESDqEY7BG4OGGQkZ9oA58dBN7XBSYyDFoTJIDUHURsKYuCaJFoLd0eX31EMjqpGj1OCVAzF0DjA5fakoUpr\/c4UCkaQINQq4bTbIWkk7JyCTYklx3hSmtSQO3UEhAopP2PIeb7mJpBJIBtsgkAiQVdVA2QWbWBSHWiGvaKhF6mq++RKHXwtU6QKvDi5bE6UHnVj1k8egurQYbjdbpw8me37HMbfo6urC2azye\/Grudm1QsqPcZSplK\/J9ZriiryMAbjmK+zdznhcrohEl8QTiiVStx555244447kJqaimXLlqG2thY\/+9nPcMMNN+CDDz5gni\/Q9NHIyEhkZGT4POcvf\/kLrrvuOpSWliIuLo55boFA0KeJpX1BvyMeoLfAoLW1FceOHYNSqcTEiRP5m6svdjkSmQg1df6JR6fTMXX7JpOR2S1tMBiYN6deb2ASj9FoZBKPyWT0W9D0vB+bzDxNruwIi3JXsHUbSna0MBdHCz0uqia1BvfdeAsmtkpgkYkgYqRQgqkNiTRKCGUSupkzyBEDsoFmOCrpKZ\/CCAUk2h5TXMUiyAaYIAyTw3HeYy4YQYLQEgVHoxViomDvFgvh0kWQg\/eA8z1IZ0ppUjWqwTndvWyGAIDrcngaWUVCyAfHwNnU5hFctHagq7S21\/ZCnny+rtUjBccB0McHn2oThfVhWJyKXlacVgfKTophHjsBJo7DhAnj8eijj6Cysgo7d+7Cjh07sWfPXnKTxRL8sKL+lpYWZq8em6wqmWInlgCooaUWihgpbG2Esq3DjnCVnD9vT3HBrFmzMGXKFHAc1yffSoA9fbTncwQCATM69MJqtSI+Ph4ulwujR4\/Gyy+\/jDFjxvTpeljoNzWe7gW27pLqyspKHD58GDExMRgzZoxvl28fRiKoDeHMtBxVzKNk1kolW7kTFsYOj1Uq9uvUanbPEDWN0Gw2MYUOgUiJlS6sbm7EnvI8LPzvW5i84x282ZmDHD3QqbxQqBUmmQPXhmL1gMv\/QnrhSWLIk4LxU4tHV3ENSTpinQqicHlvOyGnC12Fnh4bZ10LwkYPBDhAlmgCGLb6gjgdnHUtEFEqMWUY5GZt4BRcyvkeJIJ0pHF6uDq6+AjH\/5PEkA8wo\/NMKRwVDbCdKvb0BykkkA2JhXxILARhMk\/dJ99\/3adTJYdMEXxDqMQQHEm5u1yQatgRj6PFjqw9bTjd2IZjx46hvLwcDocDcrkcCQnxWL78Xnz00QfIzz+DNWv+Dw8\/\/BCSknq7AWi1\/nvdKP811ggAh8M\/SbjdbmbNiEprR+rpiM\/r1+Ylnu7ipe6WOQKBoE\/1Htb00e7o7OzEr371Kyxbtoxch4YMGYI1a9Zg06ZN+PTTTyGXyzF58mRyfk9f0G8jHqfTidzcXJSXl2PUqFF+F92+TB9VqNk\/MqPRgKIi\/1Y6VE5TKmXvAm02ooGTIQcN9H6snRngidpYuW+LxYyyMnaakdpZendQDpcTnxzahU8O7QIATElOwS2jJ+C6xkZEgp3Wlw+ORlcJbXIpVIVBHBkeUL4clFw6Tg9XczucRE7f06Nj9HHPFobLIY3VewqoZbWeuTyJBrhL6sjhbWKdCgKh0K+82wsOgDNWAwSyAWKkxLrDW\/fx12MlsDnQdX5onSIlAW5rJxTD4uFs6G3Sajf2wZXa5oI4IrilwlFngyzG\/7ntDZ2oLonCqJumoKOjA\/X19aivr0d+fj7kcjlf94iKioJEIsFNN92A66+fjt\/+dhWKioqxY8cO7NixEwcOfAOx2P\/vubycfQ9FRvpfaFm1JMBDcP5+Oy0t7PtLGkBJ7m0i9UqpvZtujuO+k6qNNX3UC4fDgTvuuANutxvvvvsuea4JEyZgwoQJ\/P9PnjwZqamp+Mtf\/oI\/\/\/nPF3V93dEviUcgEODs2bMAPMOIWKzfF9cCoYIYjkYQASVe6DnyoDsoAumLM0N3UI2s1PvpdDom8ZhMJpJ4WMcO5GWjPUyEXx87jjidEfeMux4TlNEwNrsgsns+nzg5Gp35lXRayawB53CSC3fQ8uykaE8dhyI5ZRjEURGedFU3uNs7L0waFQrgSjbDZeuEQqeCq9Z\/r4ckWge31QYnsQhBLIJ8gMnHoNPvtQchhRZFhUOokPN1HxYUI3oTtFingsToMWntKq6BcCDbHaMnHLUdEMUT3dfd4Gy1w1+3T1etDXU1RhiHDQHgSZXFxcUhLi4OTqcTjY2NqK+vR05ODpxOJzQaDXQ6HXQ6HaRSKZKTB2PQoIH46U8fRHt7Ow4ePIQhQ4YgI2Onj+TabrdDr\/df+Ge1RVD1H1ZKj8oguMX0hth2voes50iEzs5OuFwu0g2FhUDTRx0OB26\/\/XYUFRVh165dZLTjD0KhEOPGjbv2Ih4v67e2tvKsf91115G7\/A5r8MTTybEbTamemu71j56wWtnnpCIeVs0o0OsodQvVkEo1wOr1Oia5REWpyd2g1127tL4Gv\/9yLQBALpXhzutmYFxcEkZX1EFGkI4s0eSRElMml8G6SwdhZCrWRwICAW1bIwBcsRqI8qogAuA6\/zqJMcoz9qHYM+yuZ2Oo31OFySA1RQUkHWe81uNdR2xyxAY14Hb7FVNceEOBx9HaT7rSWd\/KCz\/kg2PQaQze\/qZPUmo\/6rfOynY0NMdCnzTI72vEYjEMBgMMBgM4joPVakV9fT2qqqqQm5uL8PBwnoQiIyOhVqsxe\/YszJx5E1wuF\/Ly8pCRsRM7duzEoUOHYTIZ\/RIPKw3X3t4OrVbbyy2eQmtrK1QqJVpbe\/+Wrc5GAOyoxZtq6zkEzlvP6UvEE8z0US\/p5OfnY\/fu3cw0ZaD3OX78OFJSUvr8Wn\/oN8QDeOo5OTk5UCgUMJvNJOkAfUu1Ufp6SmpJ3YzU8ClqbjtlQNjczFa0UddJuR1QTafUTW4ymZjEIxKJ\/O4UO+1d+ODAVzgyohynTuXghuGpuDU5DUM5JSKb7bxrgnRwNOyF1QF2+B65NClIQPByYldzO1ytRLFWIoJTr+w17dVZ1wJnnSfqESqkUKQkwt3eBTDSPcB5E9YIxYXGVwZkw+OAHJpUJTE6uFs76GsXCSEfFB3Q4keeHIOugirIbwlerdQnKXWPWllHqRUtnQOgG5AQ1MsFAgGUSiWUSiUSExPhcDjQ0NCA+vp6nDhxAhzHQavVQq\/XQ6vVQiaTISUlBcOGDcOjjz6ClpZWHDhwANu2fYUdO3b6GPiyvNwAj3DH32+d2niaTCa\/xNPQVgkRBjNf50219Yx4rFYrBAIBucHsiUDTR51OJ2677TZkZWVh8+bNcLlc\/HM0Gg3fWNtz+uhLL72ECRMmICkpCa2trfjzn\/+M48eP469\/ZXvo9QX9hngcDgcKCgowevRoVFVVkWklL\/ri01ZU4d+VGqAJhNWLI5VKUVvrn8zkcjmTXGQyGdPNWiAQkD1DlMsu1XRKRVGUK4NKxRZWUHUjAPx3sysnC7tysgAAAwwW3DtuBgaqjRhSVA0h1VxJyKV5nFd2XYphaoJwGRxhEogrm+lzDbSg\/chZT3QiFEAab4BIGQZnUxs\/EsITnXDMERGeNzwv4w5AFG5TJBz1LQAhbvD4vOnQmRdg7tKw867ebjciY4NXtPVJSh3WTfxT2AqrYCg0cf5NPYOBRCKByWSCyWQCx3FoaWlBfX09SkpKcOrUKURGRvLRkFKphE6nxbx5czF37i1wOp04eTIbGRk7kJGxA3l5Z5nvwwo2qT69yEj\/v4+yukIkUMTT7lvj8cKb6emLDVeg6aPl5eXYtGkTAGB0j+mtu3fv5l\/Xc\/poc3MzfvrTn6K6uhqRkZEYM2YM9u3bh+uuuy7oa6PQb4hHIpFg6tSpADyLVjB1kL7IqSsb\/f\/AKZIwmYxMIjCbzSgp8X9O1rx2zzH2qASz2czsKFYqI8jmUaoTmeoLoqauSiS0mIFFPGFhYX4jvsLaSqza8gmGDx+GkoIi3DX+BlxvHIT4dhGk7RcWVukAE5xVTUGksTSBTTW7LbbMc0VFwOF2QVzHTlcCfiIrN+cZAnceYq0S0gQTuC6H5z1ZEAkhTwocnUgGmuEoqQGcxLVHyCHRqIKLCrtNfNUnBK+WkkQFT1Li8yMO2s62oFM+CmpCidlXeCXAarUagwYNQmdnJx8NFRcXQyQS8SSk1Wohl8sxblwaUlPH4Omnn0RdXR127dqDjIwd2Llzl08GgeVAX1FRDoFA4LfWy7LhKa7Mx4DIOXD7saUCLoxG8DcSITw8vE\/EE6iBPiEhIagm+57TR99++228\/fbbQV9HX9FviAcA\/wcO1qE6WOKRh0vQVOk\/qjGbTUwi0On0TOKh5NKsee2eY\/7nvwMeBQ2LQEwmM9ra\/Bf2WMVUL6jiKUVmDgf7b0DVjSwWC86d6+0E7EV1dTWsnTa8t3cL3jv\/2JyR12FBUipMMiViims8jasM8Ck4yiEBwaXghEY1HG0dEHewxSfBNoaK1BGw5ZSA67RDIJdANsAMgcTji+Zq9kSkArmnZtVJERPOTx\/NLScJk1PKIVTI6JoVeqsBu8IkCI8Mvi9HGB5c14WrwwGJWoaWnGY4NGlQRbF7SS4F5HI5oqOjER0dDbfbjebmZtTX16OgoADZ2dmIioriiSgsLAzR0dFYtuwO3H77bThx4iTOnj2LvLyzyMjYwZRUO50uZsM3a3PsdDmgNoSjscr\/Rqa7qq17OaG7eei1jn5JPCKRiNTJexFsqi3KFA4wAoKoKDYRKJXs+gdVf6IUZlT+ljIqjYpSM48ZjQYm8RiNRjJFV1XFjpRaW9nOvRQiIti7aZVK5fdH\/OXJb\/HlyW+RkjICjsY23D1mGtIURmibHBB22\/GLLRpwNjudgguSKISxWjhrmnkVnl\/IJJDF6gOLG4bGwpZfyU8o5TodF+ThAgGksXqItUpwbg62k+wpuECQhKlVwu10wc1Q3Pmcq4fCzW7og5T6PJkEA0ddJ1qb2wDzRCiJNO3lgFAohEajgUajweDBg33k2ufOnYNMJoNOp4NGo0F5eTkEAuD225dAKBTihReeQ3l5BXbu3IWMjB3Yt2+fj3ejxRLt956lMgnhGhEaGfui7qm27xrxfF\/Rr4jHi2BHI3QE2UBKzeGhIheKXFg9Ad8F1GwfCpT80mg0MIknUKRE1ZuouhEVrZrNJlIkUVNTg9raOjxf4TGNjAyLwD0TbsAM\/QBoIUNEfQuEl4AokKCHq6zer0u3Fx7pdTg9LA6901i9wHFw2zrhqHTAUdsMUVQEpGYtOKcTncU1PrORgulVkkTr4G7rACixgQCQJPkf5OcyBJ9mc9R3QhQXXHTUUm2HbNA0KPqBrX93ubbL5UJjYyPq6uqQnZ0Nt9sNrVaLuro66HQ6KBQKDBiQiPj45Vi+\/F50dnbi4MFDyMjYiYyMHcyNFCUgEsjZv4HuqTZ\/NZ4fAvol8QSbagu2j4eaw0OBSjV1dbEjMmpRptxdRSL2n+Pi5\/ewFwGj0b\/sFPBEJtSOjvKxotIFlE1HeHh4rx9zS4cV7+zahHcApKaOQZwgDHMTRyHZGYawZt+\/q7cRNRBRcAMMQGFve5nu4BtDS4n+IgQXnUgsGrg77HA2NwMAXE1WftidQCqGbJAFkEkAiQi244wc7XlIE4yeeUL+zEX5ixfCbVLDcdb\/9yCODp4YXB3B9ZzVHa6HIvkGyC6RieSlhEgkgkajQWlpKSIiIpCcnIzm5mZUV1cjLy+vl1zb07x6I2644Xr8\/ve\/RUFBIXbu3ImMjJ34+utv+LpoU1MTZDKp37XA6mgA4P+7CCQu+CGgXxFPX4fBBVvj6XA1M49RxXWqT4fqb6GUMJSlR3Mz+5ydnezrpAiSIiUqlUhFJkKhkGw6pfylWAVZwCO88Dcl0ova2jpklZfj8\/MzhkYnDMKdI6dgjEwHpUMAkdsNN9WICsCVoIOokK6JSGJ0cLcFaAwNMp0nTTDCWdfC7FXi7E50ltRAnmhGZ06JZzhcVASstY0Q1rb5kKMsKRr2khrSnshTQ9KQMm6FpQ8RCRc47VPzTT3CRtxIDlO8mnC5XDh+\/DjcbjfGjh0LsVgMtVqNhIQEply7e\/PqkCHJGDw4CT\/72U9htVqxb98B7NixAxkZOyEWi\/2KjKqbyyBFb6sfwLfG0z3L0d7eHqrxXE0ELy4ILtXWaGUvkhSBsOTSAN25fLHjEKqrL24xp2oxFLFSpER51FksZtKanvreKJKMimL71InF4l7f3fHiczhe7CGqm6ZNw0ipDlONCbC0cBB39ngfkRBOcyTExfTwNk9jaAPcNqLGeN5yJ1A6Tz44Gl3FAYhCIfWo887XgxwVDXBUNEAETwQnjdZ5BAYikUcuTTloRyggjooI2Dukiu+D\/1eA32Hl\/npEjpkJsTh4scKVhHfeDYBeXo9Ab7l2a2sr6uvrUVZWhpycHKhUKp6EVCoVoqKiMG\/eLbjlljlwuVw4ffoMduzwpOS+\/fYIv2FudzSCtcVqbbLC5XKFIp7+hksd8VQ0sIu5LALxyKz9L1IaTRRTDeapqfhfeKljLOdcwPPjoBZzigSo0b6UgEMiYUcmlC8cS0rtBUWSVI0rJiYaxcVs+XFpdTV2nN2Ht+CZMXRb2jTMiR+BQXY5ZB0OONVhkJazU4dAcLY1ggg5JNrIXpY7PRGMjFuoCoNYFYauIv\/3oLu1A52tpZ6hcmfLIUswQqiQwVHd2Gv8BD9GIUDEpxiRAG0feni69+X0RPmeBmjGzSJTxFcTTqcTx44dg0AgwJgxY8iNFuDJuERGRiIyMhIDBw5EV1cXGhoaUFdXx\/e5eElIo9FAJpNh9OhRSEkZgZUrH0VzczN27dqNHTt2IvPwCbC2UQ11zdi\/fz9\/PTabDQqFAlar9QdDPP3GnRroW6qtvrYRLqK\/ofs5Cyv8N45FRamZtRO1Ws3UvxuNRub76fXsvgXKqsJsZneSWyxmZkOtSqUiLXio6Ku5mU0ClA8dVTeyWCzMY4Guh4pyA9l8dJehu9xufPbtHiz\/3zuYkv5H\/MaahX0d5WjSycAxXKiDmRgqioqAWBkWnFfa6RLavkenhEguhb2cjsC8NSTO7kRXQZXHWbu+FRJTFBQjEiBNMEJsVEMgFtLu3+fP1Xq2DFGG4FNi3r6cnijb3QTtdbP7PekIhcKgSMcfZDIZLBYLRo0ahenTpyMlJQUSiQQFBQXYu3cvsrKyUFZWBrvdDqlUCr1ej9tuW4y\/\/e2v+ObbPfjdhrtx22OTMXCUGd2FagpZOMaNGweRSASr1Yo1a9ZgxIgROHnyJBobG4NS9AKBp48Cnj6fVatWwWKxQKFQYMaMGcjJyQl47vXr12PYsGGQyWQYNmwYNm7c2KfvLhD65V0TKNVWV1eHb\/Z+G9S5VDoFbOf8d\/UbjWxLGJ1Oy4yGKIM9qm5CNXKxuqABQKPRMmf0ULUY1vx5L6h0YVsbu75B\/TAo2XcgwQKldqNk6IF8tkpbG\/DLg\/sBABaNDveOuwETI2NhbnFB2OUCBhgCCwTMGnBdjqAW98Dn0sLd2QVnffNFn8tR3QRHdRMk0VoIhEKINCqIdZHsYXfDPVNYOy3BqzFdHc5eUmrOzaF0bwsM42eSrhdXEw6HA8eOHYNYLMaoUaMuinR6oqdc22az8XLtgoICSKVSn2hILBZj2LgEJKfGYsljU9BcZ8WJfUU4tqcQHW1dkMvlkEgkiI+PR3JyMsLDw7FmzRp8+eWX0Ol0mDlzJhYtWoS7776beU2Bpo8CwOuvv4633noLa9asweDBg\/G73\/0OM2fORF5eHlMNe\/DgQSxduhQvv\/wyFi1ahI0bN+L222\/HgQMHMH78+O\/8XQL9lHi8EQ\/HcT6ado7jUFRUhIKCAsRYEgAcDngupVYKMOrVajU1F0fNPEYVyGkJNptcqMU8PJy96FK1GLPZzCSeQAagrJQgQIsgqHSZxWImyYV0\/CWiB7PZRBJP9++gsrEef\/jqv55rFUvwk5sXYbIVGKCUQsYY3iWNN8DZ0OYzNroXghUbxBvgrG8ljVEhFECe3Ldz8YQoEvLD7py1LXDUN3vOdd4lwW0KPpXjqLdBFHdhceJcbpzeVos2TSyclZXQ6XSXbBTypYLD4UBWVhYkEsklIx1\/UCgUiI2NRWxsLC\/XbmhoQG5uLux2u4+7tkKhgCFaihtuj8SM20bC7XbD4XDAbreD4zioVCrcfffd+Oqrr7B48WLMmjULW7duxYkTJ0jiCTR9lOM4rF69Gs899xxuvfVWAMCHH34Io9GITz75BD\/72c\/8nnf16tWYOXMmfv3rXwMAfv3rX2Pv3r1YvXo1Pv3000vy\/fVL4vEu3t07e10uF7Kzs9Hc3Izx48ejKj+45kZxOGHJT9QxKFBpQNYwNg+oEQvU69jKIuozsGxAAA8JsognPDycnFhKSclZ44o976lmHmM1lnpBedGpVOw+JrFYzHRu6HI6cKK+DH\/7xkNE04eOwuIh12GEUA1VY6fH0DROB0dVIykQgFQMeXxgsUEwnnFe4UIgKyDZIAvsZXW9z+VyXxAXiEUIG+6xTJENMHnGIZj6IKVuu3But8ONkq9tMF53PcQNDT7O0d45OpGRkVe1+dHhcCAzMxMymQyjRo26YhGZSCSCXq+HXq9HcnIy2tvbUV9fj5qaGuTl5SEsLIwnIbVaDbfbjZycHIjFYqhUKn49KSgoQFpaGlJTU5Gamtrn6+g5fbSoqAjV1dWYNWsW\/xyZTIbp06fjm2++YRLPwYMH8fjjj\/s8Nnv2bKxevbrP18RCvyKe7jUe4ALx2Gw2ZGVlQSwWY+LEiZDJZDjXRktivXCK2AsWtYum+m2onhoqtdXSwj5GkQvlkEst9FT0FRUVxSzWm81mpuWNx8j04lJ01OA8s5mOhqgITChk72qjoy3MNCUAWK0X7o+9Z05g75kTAIB4vQl3T5mJCZ1SmDiOWQwVhMshZQxm646ezgZ+zxUmg9QYFVC44HWYJkUQcgmkFh06si8Ia4RhMigGBO8o4B1x4O5yoeSwE+bxM3j3aK8U2Ztu8hbxdTod7xwdyF3+UsJutyMrKwtyuRwjR468amlAgUCAiIgIRERE8N+Rd9ZQdnY2XC4XJBIJ3G43UlNTERERAbfbjY8\/\/hjnzp0LOI6aBX\/TR72\/0541aaPRyPSZ9L7O32uo331f0a+IxwuhUAiBQACn04n29nYcO3YMJpMJQ4cO5W+oYBVtVjt7F00pvqg0FBUNUPY0tbXsY5RDdlMT+zNQJEjVySiBgFxO99pUVLAbNGkpNXunT6UM5XI5+b1SmwStVkcSD+t6S+qqsbeuEL8\/8DHCZHLcNf4G3GBKQkKHGFLr+YhPpYAozM+I7R4I6GyA8wo3JVvhxp8rGNPTcDkkOlUvLzt3RxciDH2I8kUCuDqcKD8ugOW6qb0OSyQSmM1mmM0e8YvXObqnV5per7+s\/Sl2ux2ZmZkICwtDSkpKv6o9SSQSGI1GGI1GuN1unDhxAi0tLZDL5fj973+P7du3Y9iwYfjyyy+xadMmzJ49+6Leh5o+2jMK7VnC8IeLeU1f0C+JB\/BEPeXl5SgtLcWQIUMQGxvrczzYWTy1rezdI7WYsXpqxGIxYRwaxuzoj4iIYKaSpFIpeS2UySdleUNFEBQpUY2AOp2OSTyBpNQU0VN1s+joaBQUFDCPUw27lChBoVCQ37s3Iu7o6sQ\/923FP88\/PjtlHOYmj0WyWAFNdRPpgBCM2ECkVUEoEsJeEZzCjYIwMgyicIWPa3Z3aPrQwwORAOXZUphS0wI+VSgUIioqClFRUUhKSuo12lqhUPAkpFarLxk59GfS6Q6O45Cbm4v29nZMmDABcrkc8fHxcLlc+OKLLyASiXDPPfdgzpw5WLZsWZ8IiDV91GTyKGWrq6thNpv5x2tra0llrslk6hXdBHpNX9Gv\/kpeRnW73eA4DmVlZUhLS+tFOgDQHmTzaFmt\/wVLIBAwoxqtVuNjEtgd3t2dP+h0OuZ1eG8Cf7BYLKR0m7Wj98z2ubj5PRQpyeVs4qHSJ99FSk1FQxqNmnlMIBCQfUwAOzKIjo4mlYasaPKr7CN4\/9xBzFr7Ou4+m44tEc2o1cvgFvv+nLxKMgoSs8YzWbS2mXyeIiVIApNJmQaqLpEQWktwYgBHqx0NHSaYRgUmHX\/weqWlpqZixowZSEpKgtPpRHZ2Nvbu3YuTJ0+isrIyaOmwP3R1deHo0aMIDw\/\/XpBOY2Mj0tLSeEHGkSNHsGbNGvz5z39Gc3Mz1q9fD7PZHJTc2XveRx55BBs2bMCuXbt6TR9NTEyEyWRCRkYG\/5jdbsfevXsxadIk5nknTpzo8xoA2L59O\/mavqLfRTxdXV04duwYOI7DsGHDmN3sHUH4tIklQpRU+a9VULNvjEYjMzrR6bQ+M967Q6Fg\/6gpU1GNRoPi4mK\/xwwGPZNAoqMtKCz03xyrVCrJYj1lAEqNSqCEFZSUWq2OJNVwLYQ9jUzG\/l41Gk0ARRs7FUmJLwBaZedt9MutLMVvKi8Ymt49\/gbM0A+ERiIHAszbkcYa4GwKoJbD+bHegYxDjWq4HS44CQLr1IZByOhj6g57QydqyjQwna8VfFf0HG3tdQcoLS3F6dOnoVKpeIFCsIPQurq6kJmZCaVSieHDh\/dr0jl79izq6+t9SGfr1q24\/\/77sWbNGixcuBAAMHXqVH4mWTAINH1UIBBg5cqVeOWVV5CUlISkpCS88sorCAsLw7Jly\/jz9Jw++thjj2HatGl47bXXsGDBAqSnp2PHjh1+03gXi35FPDabDd988w20Wm2vsbA9EUyNR20Ih7ueFZ2wZ98olWySoHLVVJMjtaOn0kEREWzFloeU\/ROPyWRiCh1UKhWZnqJIifqbUD9+s9lMNqxSVkKUgEKv9z+y2AtKlECl9\/wZlnaHv0ippcOKv+7ehE81UWhqasbc0RMwb8BoJDvDENHiu7OXDTTDXtEArpOeA+SRQhezn4MgR2MDwIjAk0C7amyorzPBMCQ54HMvBj3dATo7O\/mUXGFhYa9+GH\/3W2dnJzIzMxEZGYnhw4f32zECHMchPz8fNTU1SEtL43\/nO3bswPLly\/HPf\/4TS5YsuejzB5o+CgDPPPMMbDYbVqxYgaamJowfPx7bt2\/36eHpOX100qRJWLt2LZ5\/\/nm88MILGDhwID777LNL1sMD9DPiUSgUGDZsGAwGA44ePUrWIYKZxRMWxf541IJOqa+oxZX6AVALNvW7EYvZ7yeXswkrKopdrDeZjMxUm1QqJWsxFGFRQgeqhylQNEQp5bRa9rAxT82JTTzU\/RUdbcHZs\/4H7wG0vNtstqCxsQlfHDuIL44dBACkxA3AstFTkSrTI1wkBVdcDQExkgESEeSJpoCy6qDcqs+P2W4T0b5rtop2NLXGQzdoAPm8Swm5XI6YmBjExMTA5XKhqakJ9fX1Pv0w3mhILpfzpKNWqzFs2LB+TToFBQWoqqpCWloav2Hdu3cvli1bhr\/+9a+48847v\/N7BIJAIMCqVauwatUq5nN6Th8FgNtuuw233Xbbd7g6Gv2KeAQCAV\/ACmSbE0zEI1SwowyRiL2gU+9LmW5SRp7UPdLRwU612O0X1xdECQQoyWZMTDQzfScQCEihA0XK1DGLxUJGQ1QERi08gSahUgSrVtNpOIrQ\/PUVZZcW4telnpEHI4cMxQ0xyZhmSER0GyC2+RLCBeNQWlbN7OXpDqEAiuQY2HJKIElLYT6to6QNrY4kaBPjyPe8nOg+utrbD1NXV8f3DIWFhaGrqwtqtRpDhw7tt6QDAIWFhaioqEBaWhqvID1w4ABuv\/12vP3227j33nv79fVfbvQr4gF8p5BSBBBMxNNgvbhiNrVzp2TWlMKsoYGSS7NrKtTiSBEW1aNEpZg0Gg2TeEwmE5kSo8QMVKREWRBFRESQ8nWWCAQIXMOhSFQmY39HcrmcFEpQC4pEIsHpc\/k4mXsGq+ExNF00dgpujk\/BYIcC0i4XBBHSgLJqeXIsugpoQ1OIRZAPMMN2xlOTVMb6V7RZC1rRIRqOqBiz3+NXA937YRITE9Ha2so7EjQ3e0w2vSR1pXuGAqGoqIgXRnlJ5\/Dhw1iyZAleffVVPPDAAz9o0gH6IfF4EcivLZiIp8XG3pVSqi5qoaus9L8LFQgEzHqCUCgkd+30qAT264K1iekJitApCxS9Xse81kBSairio5VyZjLlVV\/Pfk+aYNkO4wBtYRQTE0NGUtSQvJiYaJ9R6y63G+uO7MO6I\/sAALfPvgXX2Y0YrdNC3dgFgbv3FxdMLw9kEshidBfGbwOI8uNK3ZrbAnvEaETq9OxzXWXYbDacOHECRqMRQ4YMAcdxaG5u7tUz5E3JXc2ZNsXFxSgpKcHYsWN5AUpmZiZuvfVWrFq1Cg8\/\/PAPnnSAfkw8gVNtgSMeq5O9sLBSJVSfTkREBDMaMhioEdM6Jin508x7oVKpmLWPQMPYqIZUipSoSIkyQA2U1qJmDVFET6UFKTscINAIbjNJPFTNKVAk1dHBrv9otTof4umJM5Ul+G\/2FgCASa3BvdfdiEnqWFha3BB1ueBO1MN2poTKsnocEPRqdBVc+G7cAkAX57sgt5xqgkM7Dsoodp3saqOjowOZmZm8HY1AIIBAIPAx7PT2DNXV1eHs2bO9LGqulOKtpKQERUVFGDt2LF+8P3HiBObPn49f\/epXWLlyZYh0zqPfEU\/3VBvVkR7M2OuSav87ZYVCztyVms1mplxap9MyiUer1TKJR6VSMYlHr9cxiYdynjabTcxGzkD9PVSaqLuFTE8IBOwfMCWlDhRdUKo1qqYWyA6HSlNS6T0ApDsDZYQqEolIJR3VIwX4\/m2qmxvx+vb\/AQAkIjF+\/KP5mNruMTSVt\/qPyITK8zN+ergpdKoVkEgv\/P0ajzdCYJkEZYDv4Wqio6MDR48ehdFoxODBg5mLtrdnKC4uDk6nk58omp2dDbfbDa1Wy9v4UFHwd0FZWRkKCwuRmprK31s5OTmYN28ennjiCTzzzDMh0umG\/il+h2c3+10iHnm4BDUN\/hcPvZ6dVqBUUgYDu3OX42jJLwtUaouykaGaVaOj2Q2pMpmMrLdQdSpqI0AtxlTzLED3y1ApL+o7AOgaDqUy1Ol0ZJ2Pqg9aLBbyOHVPq1QqZvTncDlxuqUG9\/z3L5i86U08VrUHB6JsaNHKL0ynVsohUEj8OiA4jRei1fqjDRDFTUNYPyad9vZ2HD16FCaTiSSdnhCLxTAajRg+fDimTZuG1NRUhIWFoaSkBPv27cORI0dQVFQEq9UalCosGJSXlyM\/Px9jxozh1Zu5ubmYO3cufv7zn+P5558PkU4P9LuIxwsq1eZ0uGDvOdq4B9SGMICxaaV2u5SHGSWzps5JhfrUYk4p0yIi2NdJ9ffodFrmbj6Qdc\/FNpaqVGwCDTRLhxpIR8vJ6bEPVCRlNpvJ9B8VSen1embE7Hktu7YYHW0J2uboQF42DuRlAwDidEbce90NGBlhRkKDFf4olTvvSr3r03KkzbkZsn42zqA7vKRjsVgwaNCgi160u\/cMDRo0iO8Zqqur43uGvHWhqKioixqhUFFRgbNnz2LMmDF8Wjg\/Px9z587Ffffdh9\/+9rch0vGDfkc83j8SJS4IRtEmj2T\/sakFnSKJYMZx+wO1yFGFUGrnTKW9qIZUk4mdorNYzOR4aVbDLUArAcVi9g\/aZKJn6VCyZauVvUibTGaSeKioj6plAXQaLiyM\/d0D9HdI9ToBbMIrra\/BJ7mH8LvCIsilMtx53QzMtCQjsUPCG5pKLeHY+Pdi7N3QhsmL+i\/pWK1WZGZmIjo6GgMHDryki7a\/nqG6ujqcOXMGdrsdWq2Wrw0FM2eoqqoKeXl5GD16NO+wUlRUhLlz52LJkiX4wx\/+0G8dFa42+h3xeEFFPMEo2jgpO0VDpcXIuhIh3W1tpcYhsBdIavdMRR8UmVEpBIqUNBotk3iMRuNF+8JRBErN0gkctbDPS1kUCQQCkjyoxSJQhEbdW4FqXYEkwdQ1a7UeGXynvQsfHPgKH+ArAMCNw1NxS+JIOA80Y3t6LYZMMqGwsBB6vT5oe5orBS\/pxMTEYMCAAZf12rr3DHEcB6vVivr6elRWViI3NxcRERF8NKRSqXpdS3V1Nc6cOYNRo0bx829KS0tx8803Y+7cuXj77bdDpEOg3xIPFfEUnC0O+Hqbu5l5jFq0qXoD1YtDO12zz0nJpak5PFTvD93Iyl4YKVIyGtmqvUBSakohRqU3zGY6aqHOS5Ed5dMHeOS77NfSEVprKzvy8zoasEDdlzqdjkz\/sfzsduZkYWdOFpaNfBEAYIrVoq2tDcXFxZBIJPwAs6ioqKu6ULa1tSEzMxOxsbEYOHDgFX1v74whpVKJxMRE2O123sbHayfjddb2egPm5ORg1KhRvE1WZWUlbrnlFsycORPvvPNOiHQCoN8RT\/dhcD0jHrfbjTNnzqDgrP\/6RXc0tl+cMzNroROJRKTMmpW68djB+I9qKLm0VCol00yUIzO1QFGqNUqj6+1J8IdAUmqqyE9NM6XqZoFGGlCLuF6vJ4mH6sMJtKBQmwwqugPoviyLha47UdJxkUiMxirPZiRuoAmjRo3ySTXl5OTA6XTy6i+dTnfZ1F\/+4CWduLg4DBhw5ex6WJBKpbBYLLBYLHC73XzPUH5+Pmw2GziOQ3R0NP+dV1dX45ZbbsHkyZPx3nvvXbZx29cS+h3xeCESiXx+THa7HcePH4fD4UBi7CAAJ8jXVzYUM4+xFhalUsnscTEY9EwZstFoZNY4jEYjk3gozzSLxcJ0rKZ2vyKRKMD8HsoFgE1K1IJLSakDpaZqatgLtUTCFnNER0eTZEelTCUS9m0vEolQUcEmdSr9o1QqSVVgIINVajPR3dTRH6joLyl2GJxNnk2cxqTkr8WbahoyZAisVitqa2tRVlaG06dPIzIykieh8PDwy5b2am1tRWZmJhISEnrZ+vcHCIVCvmcoKioKJ06cgMVigc1mw\/Tp0yEQCCCTyRAbG4t\/\/OMfIdIJEv02HvTKqTmOQ1tbGw4ePAiJRILx48fD0RVYBllUedbv41FRUcxFyWxmy34pEQA1L4ZqgKSOefPG\/kANZFKr1czamEQiIXfk1E6fiiC+i5SaStFRUmqK7AC6iE+dNzraQkYPVB0mOpqeR0SRYUxM9HeaTUORVoLxgtN0lKE3gXlTTQMHDsT48eMxZcqU82nOJhw+fBhff\/018vLy0NjYSDYY9xUtLS3IzMxEYmJivySd7mhoaEB2djZSUlIwfPhwjB07Flu3bkVcnMfb7tSpU7BYLFi2bBlyc3Ov8tX2f\/Q74umeagM8YeyhQ4dgsVgwevRoiMXigNNHVRoF2tr9Rxl6Pbv3g1IVUb041MJL7dqpdAZVb6F2vxRhRUdbmAuHWCwmnRComhKl9qOuldoEBHpPSnVkNBpJHztqVxoVoIufit6ojQRAp0AD9SRRdTsq4gYAXcSFcQjeiIeCV\/01ZswYzJgxA4MHD4bL5fIZ4lZVVUXW0QKhpaUFWVlZGDBgABISEi76PFcCjY2NOHHiBIYOHcpv+pqamnDfffchKioKOTk5qKqqwtatWzFw4EByPQjBg35HPF54F4dTp04hJSUFSUlJPCkFUrUp9ew\/PLUQUkRA7XQpFRm1Q6SO0aMS2AsnRayUjNxsNpMEcrFSaomEfa0xMfR8GErFRUUlRqOBPC+VDnO5qFqJiLwmapPhGeXOjkoC+YtRbgiBRhLLuAsbKo2Blor3hEgkgsFgwLBhw3waMouLi7F3714cPXoUJSUlJDH2RHNzM7KysjBw4EDEx8f36XquNJqamnD8+HEMGTKEHx\/d2tqKRYsWwWAw4H\/\/+x+kUilEIhEmTJiAl19++ZJEb3\/7298wcuRIqFQqqFQqTJw4EV9++SV\/nOM4rFq1ChaLBQqFAjNmzOg1ubSrqwu\/+MUv+HTp\/PnzUV5e3vOtrgr6JfG4XC6cOnUKADBy5Mhe6ZpAY68lEWwiuFi1CT0Ogb27phZlqqZC\/ZCpRZf6fJTEmNpxGwwG8jPSRX52+ojaBOh0OvL7oQQigTYXVA2MEjRYLGZyl0\/dI7GxMeTfjdqEeNyw2dccqOfE2eYhf6lcjAg13WdEwduQOWjQIEycOBGTJ08+P623Ad988w2++eYb5Ofno6mpibkZa2pqwrFjxzBo0CA+TdVf0dzcjGPHjiE5OZkf6261WnHrrbdCqVRi48aNQfX7XAxiYmLwhz\/8AUePHsXRo0dxww03YMGCBTy5vP7663jrrbfwzjvv4MiRIzCZTJg5c6ZPjXrlypXYuHEj1q5diwMHDsBqtWLu3LkX3Y94KdHviKezsxPffvstbDYbxGKx351goLHXLtGlJwKqF4caMU2lV6hCP9XgSF0L7YRAKZXYZE3tqL+LlJoiSZOJ3sVTtSqAHS7GxESTizxloBrIZ42SSgdKpVFEGhMTQ0bV7e3sexoAmqo9v4eoPkY7gaBQKBAbG4vU1FTMmDEDAwcORFdXF06cOIG9e\/fi1KlTqKmp4QnXSzpJSUmIjY29pNdyqdHS0sJfa3S0JzJvb2\/HbbfdBolEgvT0dDId\/l0xb9483HzzzRg8eDAGDx6M3\/\/+94iIiMChQ4fAcRxWr16N5557DrfeeitGjBiBDz\/8EB0dHfjkk0\/463\/\/\/ffx5ptv4qabbsKYMWPw73\/\/G9nZ2dixY8dlu+5g0e+Ih+M4qNVqXHfddcxenkDOBVYHe9Gm1Dl0g6T\/hU4gEDB3o55ivv9zelyw2eekduV0rxH7s1NRFPW9UPY83p0gC1RqiooQKOmxSqUiPyfVh0PVwABa+q3X0yk8Sg0XaGdMRTSB3LApUrLo4\/iNmsYYuL5zsfB6pI0YMQLTp0\/H6NGjIZPJUFBQgD179uDQoUO8kCAmJuayXcelgLf+NHDgQJ4gbTYb7rjjDrhcLnzxxRdke8Glhsvlwtq1a9He3o6JEyeiqKgI1dXVmDVrFv8cmUyG6dOn45tvvgHgGcXgcDh8nmOxWDBixAj+OVcT\/Y54wsLCMHToUAiFQqZ7QYeVjnjq29iLHWsHLhAImBGISqViRhkmk4mZfrFYzMzdNXXMZDIxFU4qlZIsulMLGN1xzzxEpogodVmgdBkV1QmF7NqQxUIPLKOiTIoAwsLCyO+IglarJQmPSm+o1ZEkkQbqqaFeOyhmOP\/fUZeReLpDIBBArVYjKSkJkyZNwrBhw2C1WqFQKFBQUICDBw\/i3LlzaGlpuWRGnZcKbW1tvOjBmwrs6urCXXfdhba2NmzZsiWgs\/mlQnZ2NiIiIiCTyfDzn\/8cGzduxLBhw\/iNZ89MhNFo5I9VV1dDKpXyVj7+nnM10e+IpztYEU8gVVtZbaHfxynLeo1Gw1xgqbQPVcyndtfejue+npNSThmNRmaqzTO\/h4qU2AsuRR6UgieQ99jFRkORkWrmMU8fE\/u8FAEEioba2tgprUBkSFkqBYoaqbpSeHg4Gamb1BeK9xrjldule9HQ0IDc3FwMGzYMkydPxvTp05GYmAibzYasrCzs27cPOTk5qK2tveq1B28ja0JCAi96sNvtuPfee1FbW4tt27YFVC5eSiQnJ+P48eM4dOgQHnroIdx33304ffo0f7xnloLjuID9VsE850qg3xFP9y+FFfFQs3iEIgFKqvzP4TGbTcybm0rtUOMJKDdrSqlEHaPCeKpwbjCwU0FmMzsyCzRUjuovsdvZfwuFgh1dmM1msh5FRUNUA2igPhyqhvNdxjcEIllKTRTotVTtKJAyMEJ0YYPjr4fncqK+vp6XIXvJVSKRwGQyISUlBdOnT0dKSgrEYjHOnj2LPXv24NixYygvLyfvjcsBr09cXFwcr0pzOBz4yU9+gpKSEmzfvj3gxuRSQyqVYtCgQUhLS8Orr76KUaNG4U9\/+hN\/n\/a8H2tra\/koyJs16Zkd6f6cq4l+RzyAby+P34iHULWpDeFwOP0vsFSUYTSyFx2JhJ3qoHpCqJ0FdexiXQKoWoyOGG1MkRJA1z0ogQS1oBoM9KhlKhpyONjEEqiIT6vDaBk+JcOmUkYqlZI0gw3U7V5RwSYttTpA\/cd24TMF08NzqVBXV4eTJ09i2LBhvAy5J7yuAMnJyZg8eTImTJiAqKgoVFVV4cCBAzh06BAKCgrQ2tp6WVNy7e3tPuakgCe9\/NOf\/hS5ubnIyMgIeF9dCXAch66uLiQmJsJkMiEjI4M\/ZrfbsXfvXkyaNAkAMHbsWEgkEp\/nVFVV4dSpU\/xzrib6rWUOwB4GR\/XxiMLY4ToVnVC9MVRNhYoGqF0bdYw6p9vN\/gFSC1hEBDvC0un0zIVer9eTCy6VhqM+BxXVmc1mkiCam9l\/D4WC\/TkDiRIoQrNYLMjLy2Mep76HmJgYnD59hnk8UBRGRVpU9AcA1voLG4rLKS7oDi\/pjBgxIujdtUAgQHh4OMLDw5GQkOBj1FlSUgKxWMxb+Gg0mktmTeMdrW2xWHhzUpfLhYcffhjHjh3Dnj17rkqE8Oyzz2LOnDmIjY1FW1sb1q5diz179mDbtm0QCARYuXIlXnnlFSQlJSEpKQmvvPIKwsLCsGzZMgCeTd\/999+PJ598ElqtFhqNBk899RRSUlJw0003XfHP0xP9mnhYRqE2K3tBc4nYyi0qkqB2\/NRNThuOshdI6hjL2w2g5eC05Qr7s1OkZDQamMQTSEpNRS1UxOfxxWMTDxWBUe7bFov5ov9egXL7VJ2Fqkl5Xks3h1LEQ9XClOGRaK65QIhXosZTW1uL7OzsPpGOP\/Q06vQamubm5vKzc7xEdLFOATabDZmZmTAajfzAObfbjcceewzffPMNdu\/eHbD+drlQU1ODe+65B1VVVYiMjMTIkSOxbds2zJw5EwDwzDPPwGazYcWKFWhqasL48eOxfft2n1T822+\/DbFYjNtvvx02mw033ngj1qxZ0y\/85Po18fgTF9isdjLslqnYCxpVN6B2nZQ1PyWhpRZP6hi10FDRB0VYXV1UzpxNSlRNKZArNdXDQ418oCLTQOMBqMgjEAFQogRq+qxCoSDHW4hE7O9XJBIFUNLRKSYqghscOwJct9vsckc8NTU1vNMIVW\/sK4RCIbRaLbRaLZKTk\/nZORUVFThz5gxUKhVPQsHOGLLZbDh69Cj0ej0\/WtvtduOpp57Crl27sHv37qva4Pr++++TxwUCAVatWoVVq1YxnyOXy\/GXv\/wFf\/nLXy7x1X139EviEQgE4DgOIpGo144uUA9Paxd7UaLkrqxdp1AoZBIB5WZNLZBU+kqtVjMXbJlMRi5wF9vf09l5sb5mauaxQCm6yko2YQdqLKWIh4oehEL2ghToeqnIIiYmGvn5bAIO9NqSklLmcSoSFwqF5MYnRjcI7eeJ57u6FgSCl3RGjhxJ+hp+V\/ScndPV1eV3nDU1Y6izsxOZmZnQ6XRITk7mSefXv\/41tmzZgt27d\/d709LvO\/qluMALfxFPINeCmmb2vHtWRED1b5hMpouSWVOFfqqwTtnaWCxmZrSn0USRaSQqwqIiwYuddEp9N0KhkHTCpjYIVAQWaEYPFbkGckqgXhtI7UQJMAIVrSnit1gs5N9HLb\/wmS61a0F3VFdXIycn57KTjj\/IZDJER0dj9OjRmDFjBoYMGQKO45CTk4M9e\/bgxIkTqKys5NPQXV1dyMzMRFRUFIYMGcKTzqpVq7Bu3Trs2LEDgwYNuqKf4YeIfk08\/mo8HVY64imtKfD7uFLJbrykZLQ6HVsJR+X9KQWdUskmF6o57WJHJVD9PYFcEqj0HU0QlHjg4lV0gRZiigwpQUegeTeUsoxq8Aw03yeQ7Qo1DDCQhY\/YeWHzc7mk1FVVVTh9+vRVIZ2eEIlE0Ov1GDp0KKZOnYq0tDRERESgtLQU+\/btw+HDh3Hw4EGEh4dj6NChfGbl1Vdfxccff4wdO3YgOTk58BuF8J3Rb1NtgH85dWsTWzwgU4hRWe0\/bWE0GplpMY0mCoX+e07JyIUqalILCtX7QkmQqa57lYr9OmpsdaDiNRUpUaCcB\/R6topOKBRedMpLq41Cgf99BwB6EafSe0ajkYykKBKNjragtJQdhVNNkxEREWTqUK\/X49w59gfubLpAwpdDSl1ZWYnc3FyfEdD9BQKBgHd2HjhwINra2nDs2DEIhUI0NDRg+fLlkMvlCA8Px7p167Br1y4MHz488IlDuCTo1xFPTzl1R0cHTmadYj5fbWSrs6gmUIpAqAWUMpt0OtkLCrXY0N3b7BoFJaulivXULlWn05EquoslCOp6AjWAUuRBjX3wOGyzNy1U9BY4DcdWwwWKAihHg0CD5agaulAoQkPFhb\/dpU61VVRUIDc3F6NHj+53pNMTDocDp06dglqtxpQpUzBjxgzMnTsXpaWl+PDDD+FwOPDaa69h7dq1pCAmhEuHfk083VNtjY2NOHjwIEQCYuga8du6WPtySqJMLWRtbex6C7VDphYxm40alcAmLCo9RaXEqLk2gaTU1OegVEeBG0DZ0RlFWIHIg6rDBDKEpGTjgVJplJKup89WT1Au5QMsg+F0XNgYOdBxyWxpysvLkZeXh9GjR1\/xbv6+wuFwIDMzEwqFAiNGjIBQKDxfY6zHyZMnsXv3bmRkZGDAgAF49dVX8d\/\/\/vdqX\/IPAv2SeLwLk1dcUF5ejszMTCQlJSEynP1jbOu6uM5yStpLFewppRi1KNMu2OwdPfV+1HVS0QeVYqJqUaxudC+oBZUi7LAwdjRkNpvJjQBFdhR5BPJ3o2AwGMiokIqKo6LU5DVTwwcBuhY2MNo3bRRljEB+fr6PLQ11X7BQVlaGs2fPYsyYMd8L0snKyoJMJsPIkSMhFArBcRzef\/99vPzyy9i8eTMmTpyI8ePH43e\/+x1OnDiBBx988JK896uvvopx48ZBqVTCYDBg4cKFvRqQly9fDoFA4PNvwoQJPs\/pz8Pcvgv6JfF4IRQKYbfbkZeXh9TUVMTGxsLawk6JQM6OJKhUCtUJz5IvU\/5mCoWCmYYKDw9jEohcLmeG+oH81Cgyo9IH1OJDTQ+lrPoDjZ6m5NBU38p3sdmhCDYmJpqMlqjPEiiSolJplE0TQP9tNJookrS6j7sGgGEjB\/nY0lRWVmL\/\/v349ttvUVRUBKvVGtCWpqysDOfOnUNqamrAaOxqw+l04tixY5BIJBg1ahRPOh9\/\/DGee+45pKenY8qUKb1ed6kMNPfu3YuHH34Yhw4dQkZGBpxOJ2bNmtWrz+xHP\/oRqqqq+H9bt271Od6fh7l9F\/RLcQHguXFyc3PBcRzGjx+P8PBwuFwuso+nw8VWYFFGi6z0jUqlYi7aJpOJuUs2m80oZKgV1OootLf73\/FbLBbm67RaDTMdFBERQTYhUjtjavGyWi\/OlZoSM3iiC\/b1sL4bgBZ6REZGkn5oVGOpVqsje2nq6thRaKA0HLVZoBpLATrCtVgs5D0tRySAC5GYV1zQ3ZamZw+MTCaDXq+HwWBAZGSkD1mXlpaioKAAY8aMuaIOzRcDl8uFY8eOQSQS+ZDOp59+iqeeegqff\/45ZsyYcVmvYdu2bT7\/\/8EHH8BgMCAzMxPTpk3jH5fJZExVrXeY28cff8zb3Pz73\/9GbGwsduzYgdmzZ1++D3CZ0S8jHpvNxk\/aAzx\/HJfLBbfbjYRhRkQxrD+qGkqY52Qpt3Q6HTMaomoclMya2g1STZdUFEFZd1CRgFarJRddKsVEiQcCqbFYCCQeqK9nvyclrgg0LI2SNIeFseswMpmMJEoqkvJ4w7E3BFTdUSAQkNdMuakDgLPNd0\/pT07dswdm8ODBcDqdOHHiBPbt28dPEC0qKkJBQQFSU1O\/N6QjEAgwevRovr65fv16rFy5Ev\/973+vileZd1PUMz25Z88eGAwGDB48GA8++KCPeKa\/D3P7Lui3xKPRaDBmzBj+\/zmOg1AoxI\/uScO7Xz+Eu343DhMWDIAu+kIdoqjqrN\/z6fV6Zg8HRS6UEo5aXKlxANQPl1qIKCWYRMK2c6FSQR6lF5WCvDjPOGoxpsQDEomEXOSpdCn1\/YhEIjI6oOowXq8wFqh6VWBVGptIo6Pp5lBKbQkAzdUX7nepXAxlFC1yEIlEMBgMGD58OD9BVCqV4syZMzh37hzCw8PR1tZ2UXWhKwWXy4Xjx4+D4zgf0klPT8dDDz2ETz75BDfffPMVvy6O4\/DEE09gypQpGDFiBP\/4nDlz8J\/\/\/Ae7du3Cm2++iSNHjuCGG27gv+P+Psztu6Bfptq0Wi2USiWcTieioqJw8OBBaLVaGAwGyGQyjzXHpAG4bfmPIBQKkX+8Aoe3n0HRvz9HS1vv9ITBwDa6pBoHZTI2EVCLK5Urv9hjgRbzvDz\/pBsRwf58RqOBKU\/WaKLIxZqqKVGLE+UeHRMTjaKiYuZxqjZE9T8F6qWhfsQ6nRZFRUXM45QaLlB0QEWUBoMB5eXsiIciPJM2Bu0tF4hHre+blNo7QdSbhh05ciRsNhvft+P1RtPr9QgPD+8Xg8XcbjdOnDgBl8uF1NRUXpixZcsWPPDAA\/jwww8xf\/78q3JtjzzyCE6ePIkDBw74PL506VL+v0eMGIG0tDTEx8djy5YtuPXWW5nn6y\/D3L4L+iXx1NXVQS6XQygUYuzYsbDZbHzIb7PZEBYWhoiICDgcDshkMiSNjkbS6Gjc\/cwxZGefwuefpyM9fRPOnMkFQEcnlHKI2ulS6ipqZ06lvajo42Kta9rb2ZEJ9b2YTCYm8QSSUlO1CUo8oNVqmcQjFovJaIiSqOt0OpJ4KFkyFYV6IjR2qpKSsYvF4gAybDZBA7RqMiluBNCtVHgx5qCFhYUoLS3F2LFj+c1ZX+tCVwpe0nE4HD6kk5GRgR\/\/+Mf417\/+hdtuu+2KXxcA\/OIXv8CmTZuwb98+xMTEkM81m82Ij49Hfr5nkGX3YW7do57a2tp+MVPnu6Bfptoef\/xxJCUl4dFHH8XOnTshEAjwpz\/9Cbt378bw4cMRExPDq3KOHj2K0tJSPpWWkjICL7zwHI4ePYysrCN48cXnERcXy3wvqv+F2lVShWwqr095lFGvo5RpVNqLWjiphZFyQggkpaYK6pR4gLKACVQbokQS1CIeSJhB\/Z1jY2PIWlcgc1Dq81DjHaRSKUl4psh4n\/9n1URZKCgo6EU6XgRbF6I+26WE2+1GdnY2urq6kJqayqed9+zZg7vuugvvvvsu7rjjjityLd3BcRweeeQRbNiwAbt27QrKdLShoQFlZWX876u\/D3P7LuiXEc9HH32EPXv2YN26dfjpT38Kp9MJkUiEZ599FhqNBnK5HPHx8ejs7ERdXR1qampw9uxZqFQqGAwGGI1GKBQKJCcPxjPPPA0A+M1vnkN6+hf4\/PN0ZGZm8VGC1cqWu1LmmSyZtcf7zP+iIBKJmGmdQHJpegooe\/dLkRL1+ajBeBoN25om0OAySiFGRW5arZZUnlHRA8BexKOjLcw0JUBvPrRaDQoL2Wk4apOh0+lQXMwWw1Ay7JiYGKb6EQAixFo04UKqLdiIh+M4FBQUoKKigvc5o+CtCxkMBnAch5aWFtTV1eHcuXM4deoUNBoNn5K72Jk5FNxuN06dOoWOjg5+kQaAAwcOYOnSpVi9ejXuueeeq5KWevjhh\/HJJ58gPT0dSqWS\/01ERkZCoVDAarVi1apVWLx4McxmM4qLi\/Hss89Cp9Nh0aJF\/HP78zC374J+STxisRg33XQThg0bhqNHj8LhcGDChAn44x\/\/iN\/85jeYM2cOFi5ciJtuugmxsbGIjY2F3W5HbW0tamtrce7cOURERMBoNMJgMCA8PBwDBgzA448\/hscffwzl5eVIT\/8C6embmAuHQCAgZdasnXBUVBRzQTebTcy8vdlsYi6e1IiFQKMSqE5\/KvqgUlfUImIw6JnEI5VKyetpa2OnIcPCqGF1tJdaWxubfAPVYSjyoNpehEIh2egXyEmjqopNpDqdhiQeQacc8CGewBFPd9IZO3ZsQNLp9Z7n60JqtRpJSUlob29HXV0dqqqqkJubyzdSXqq6kNeB2mq1Ii0tjTdqPXToEJYsWYI\/\/OEPuP\/++69aLeRvf\/sbAPSSbX\/wwQdYvnw5RCIRsrOz8dFHH6G5uRlmsxnXX389Pvvss+\/NMLfvgn5JPIDnxrrlllswevRo\/P3vf4dMJoPb7cahQ4ewfv16PPvss3jggQcwe\/ZsLFy4ELNnz0ZMTAxiYmLgcDj4SKigoADh4eF8JBQeHo6YmBg8\/PBDePjhh1BdXYPNmzdj48Z0HDjwNZ8iMJlMzAjEZDIynQJ0Oh2TeHQ6HZN4dDodk3iMRgOTeCwWM7MuEhnJJkiA9j27WCk1ZcETHW0hxQMUeVDO0gYDu28o0HkpZ2mPs0Az8zhV5zMajWQES32HanUkWSejPOkAwFrnu2kIFPFwHIdz586hsrISaWlppEIwWPQcY11XV9erLqTX66FWq\/tcF+I4DqdPn0Zra6sP6WRmZuLWW2\/FSy+9hBUrVlzVAnygZlyFQoGvvvoq4Hn68zC374J+SzwCgQCbNm1CTEwMfwMJhUJMmjQJkyZNwhtvvIGsrCysW7cOL7\/8Mn72s5\/hpptuwoIFC3DzzTfDbDbDYvHUBerq6lBbW4vi4mIoFAo+PaBUKmEyGfHAA\/fjgQfuR0NDIzZv3oL09E38bs0f6HEIbBsR6gdNHaOUd1FRGuZibjabmYVzlUpJpnOo4jWVvhMI2IsIJR5QKBQkEVJpQYrswsLCSBsiijwsFgtJPFR9MNDiTQkaLBYLKWWnotFwRQSaanwjR4p4OI5Dfn4+qqurLxnp9IRUKkV0dDSio6PhcrnQ0NCAuro6nDx5EoBn06XX66HVagPaBHEchzNnzqCpqQlpaWl89H3ixAksWLAAzz77LB577LHvverrWke\/JR4AiI1liwKEQiHS0tKQlpaGV155BdnZ2Vi3bh3eeustrFixAjfeeCPmz5+PuXPnwmQywWw2w+Vyob6+HjU1NTh69CikUikfCalUKmi1Gtx33z2477570Nraiq1bt+Hzz9OxY8dOH6UatUumaiPUj4Ha9VF9OlTzIyUxNpstaG3N83ss0E6fSpdRkQmVXrJYLCggZhpQaUHqu4uOtpDTQanPSQksALr5NtCsHOq1gUZ0U9ecHJ\/iM+4aAKIYxMNxHM6ePYuamhqkpaWR6cxLhe9SF+I4Drm5uWhsbERaWhp\/P506dQrz5s3DE088gaeffjpEOt8D9GviCRZCoRCjRo3CqFGj8Nvf\/hZnzpzBunXr8N577+HRRx\/FtGnTsHDhQsybN48nGu\/Oq7a2FllZWRCLxfwPQq1WQ6VS4Y47bscdd9yO9vZ2fPXVdqSnb8K2bdtJmXVXF3sHTamcqNdRO1wqpKcIi3JQMJlMzMVNLpeT\/TRUZEIptbRaDZN45HIZGQ1R8vVAnmKUOwBlaROoz4ka6BcVRfusBbLSoWpHMdpB6OhBPP4iHo7jkJeXh7q6uitGOj3Rl7pQWFgY8vPzUV9f70M6Z86cwbx58\/DQQw\/hueeeC5HO9wTXBPF0h0AgwLBhw\/Cb3\/wGL7zwAs6dO4d169bho48+wuOPP45JkyZh4cKFmD9\/PkwmEwwGA9xuNxobG1FTU4MTJ05AIBDwBKVWqxEeHo5bb12EW29dhM7OTmRk7IRSGYGvvz7YK+1EuURTkmjqGHVOqveHkrRSKQ26GTOaSRAemxf2Tv5ivd+io2PIaIgiQuq8er2erGVR0ZvZTHulUYKGyEgVSTzU+wYSUkQpTOjAhRSgRCbq5VrgjRy8i3ig0Q1XClRdyOu3NnToUP7ezc\/Px9y5c7F8+XK89NJLIdL5HqFf9vFcKggEAiQlJeHXv\/41Dh8+jPz8fMyfPx\/r1q1DcnIyZs2ahXfeeQcVFRXQarUYPnw4pk2bxttaZGdnY9++fTh9+jTq6+vhdrvBcRy02ig8\/\/yzKC4+h40b12P58nt57zZKYUZJoqlCNHXOhgb2osuauArQURRFSpQnmlarJaM6KmqhSJJ6z0Cmo3Y7+3OazbQ7NBW9BfJKoyTlgVypKTIM5IYtdvrWu3p6tHlrJA0NDf2KdHrCWxcaNWoU33ip1WrxzTffIC4uDgsWLMC8efOwaNEivPrqq1elcTWEi8cP5q8lEAiQkJCAJ598EgcOHEBxcTGWLl2KLVu2YMSIEbj++uuxevVqlJSUQKPRYOjQoZg2bRpGjRoFkUiE06dPY8+ePfjmm28gl8uRkpIChUKBWbNuwl\/\/+hcUFuZj69YvsHTpEr9us5RZp0ajYRbslUolcwH09P6wFzhq8aPUbhcrpaa8yeRyOUmglNCBek+LxUJeb0tLM\/MYJRn2yKGpNBw1XE9Jkgc1LVYkEpHvG0jm3Nnsm3rtPnnUqwbz1kj6K+l0R2FhIaqrq3Hddddh1KhRWLBgAVavXo3GxkZYrVb83\/\/9H+bOnYv33nuPFL2E0L\/wgyGe7hAIBIiJicGjjz6KPXv2oKysDD\/+8Y+xa9cujB49GlOmTMHrr7+O\/Px8qNVqJCcno7m5GV1dXVCpVOjo6MD+\/ftx8uRJ1NTUwOVyQSQSYfr0aXjrrT8iP\/8Mduz4Cg8\/vIIXSBgMbDNSahfLskwHPDt21qIbFsae+wPQERaV2qPqW9SiGBWlJutR1dUXJz3W6wNNLL04Z2mLhR46R9WVApmDBnrtxc5aEQqFaKz0FWF4xyF4Sae5udmnRtKfUVhYiLKyMowdO5ZX29XW1uLll19GamoqP0X0+uuvx9q1awNKmEPoP\/hBEk93CAQCmEwmPPTQQ8jIyEBVVRUeeeQRHD58GOPHj8eECRNw991348c\/\/jGcTifGjRuHyZMn8wXZc+fOYc+ePThx4gSqqqrgdDohFAoxceIEvP76q8jNPYV9+3bhzjuXYuDAAX6vgZr0SYkA9Hr2OATKWVutjiTlvFTqikrfUWMLoqOjmceUSuVFT1el7HA0Gg35Oan6mF7P3igA9HcU2ByUnR6lNigAnf4bGDMEji7flKXGEME3WzY3N2Ps2LHfC9IpLi7mbXu8G5rq6mrcfPPNmDp1Kv7+979DKBRi8ODBePrpp7F7926y7aAvCGZ6KMdxWLVqFSwWCxQKBWbMmIGcnByf51yr00MvBX7wxNMdAoEAOp0O999\/P7Zu3YrKykrExMRg69atsFgs+M1vfoNVq1bh5MmTiIiIwKBBgzBp0iSMHz8eERERKC4u5kcLV1ZW8tHI2LFj8eSTj+PkyWM4ePAAfvWrZzB06BD+faVStvqMkm5TSiSKlEwmttdaoKbTi23ypBa7yEg28QJ0ypBSygWq4VCihPBw9ncrFAovOpUWyFg0EClQ6cpE05Bej0UZI3Dq1Cm+2fL7QDolJSUoKipCamoqTya1tbWYO3cuxo4di\/fff\/+ydu4HMz309ddfx1tvvYV33nkHR44cgclkwsyZM302Ztfq9NBLgWtO1Xap0NXVhZ\/97GcoKCjAqVOnYDAYsHnzZmzYsAEzZ86EwWDAggULsHDhQowdOxYDBw7EwIED0d7ejtraWpSWluL06dPQaDS8TFsqlWLkyBSMHJmCF154Dnl5Z\/H55+k4deoU8zqo1BaVKqJ2f9RCbzZbmFFCYFdqtr0M9TmioqKYdQ3PMDX2Lp9SylGSZo\/bNZsAqOv12AJRYyHYKbpAox+oplSlUkmq4aIUZvT8C3Q4WtDWJsbYsWMvi1\/apUZZWRkKCwuRmprK\/\/0aGhowf\/58DB06FB999FHAJtPvikDTQzmOw+rVq\/Hcc8\/x4ws+\/PBDGI1GfPLJJ\/jZz352TU8PvRQIRTwMSCQSDB8+HAcPHsSgQYOgUqmwbNkyrFu3DjU1NXj99ddRXV2NefPmYfjw4fjlL3\/JCw8SExMxYcIETJ48GRqNBpWVldi3bx+OHj2KsrIyPjJITh6MX\/7yaXz88YfIzj6G3\/3utxg3Ls1HFkotrFSE4XazFzAqiqJJiR0pCYVCciGnUnSUfDvQZFFKKUftigO5XVPjKwLVcCgC1mrZk2sBWgwR6H2d1t6fVxIGnw7\/\/ozy8nKcO3cOY8aM4e+JpqYmLFiwAAkJCfj000\/J3rTLhZ7TQ4uKilBdXe0zGVQmk2H69On8ZNBreXropUCIeBgQiUR46aWX\/C4U4eHhuO222\/Dpp5+ipqYGf\/7zn9HS0oLbb78dycnJeOKJJ7Bv3z5IJBIkJCTguuuuw5QpU2AwGFBdXY0DBw7gyJEjKCkp4QvNXhPTPXt2Ii8vB2+88RomT55EyqWpPhKKsCiJMfXDpkjAbDaRxXgqRUcRAEVKcrn8olN\/1CRUgE5pUXWlQL1MgVJdFHkHqh1FiHrfq\/roKDQ3N\/f79E5FRQXOnj2L0aNH85+zpaUFixYtgtFoxP\/+9z9yw3S54G96qDf1azT6ioK6Twa9lqeHXgqEUm3fEQqFAgsWLMCCBQtgt9uxY8cOrF+\/HnfffTeEQiHmzp2LRYsWYdq0aYiLi0NcXBy6urp4J+38\/HwolUreSTssLAzR0dFYseLnWLHi56ipqcUXX3yBzz\/fhP37D\/gs0tQiRaXEWlvZNRyKBAI1Y7IW3PDwcPJ6qJoSlS4zGg3kqAQqRUeNJw8PDycJjR6VbSaJh0qlabVaMloKlGJqqendQ6UzR+Ls2bPo6urip\/jqdLqrsoizUFlZiby8PIwePZpfqNva2rB48WKoVCps2LDhqkVsrOmhQG8LrGAmg14L00MvBUIRzyWEVCrFzTffjPfffx9VVVX45JNPIJVK8eCDD2LAgAH4+c9\/zuePY2NjMXbsWEybNg0xMTFobGzEN998g4MHD6KwsJDvSTAaDXjggfuxeXM6Cgvz8e67dE7bcgAAM8xJREFU72D27FmIjrYwd\/QymYxcOKndPEUC1IJLmUtaLPTgOLoBlB1FUeIKai4SALjdbOltoOul0oaBVGkU6Qd6X6o516i1wNrsez9IZCKMGZeCyZMnY\/z48VCpVCgtLeXTvqWlpaS0+0qguroaubm5GDVqFJ\/Kam9vx5IlSyCRSJCenn7V+o2800N3797tMz3U2+LQM3Kpra3lo6Du00NZz\/khI0Q8lwkSiQQ33XQT\/v73v6OiogLr169HZGQkHn30USQmJuL+++\/HF198AZfLhejoaKSmpmL69OlISEhAa2srDh8+jG+++Qbnzp1DW1vbeccEj4npRx99gD\/\/eTVeeuk3mDv3ll4\/zOhoC7OnQaWi7VooUqIWXGoXR\/mlaTRRJNlRx6h6iclkIhdq6rN4F0AWqKbdQJ5nVDQUSA5MqfAGRQ\/v9ViU3iNDFggEiIiIwIABAzBhwgQ+7VtXV4evv\/4ahw4dQmFhIX+fXSnU1NQgJycHI0eO5P+WNpsNS5cuhdvtxubNmy+LW3YgBJoempiYCJPJ5DMZ1G63Y+\/evfxk0Gt5euilQCjVdgUgEokwY8YMzJgxA6tXr+ZnCv3qV79CfX09Zs+ejQULFmD27Nkwm80wm81wOp2or69HbW0tvv32W8hkMn6y6tmzZ5GUNAizZ8+CQCDoZWKq0bAnY5rNJmZfTCBSoqIoaudMOQuYTCayVkVFQ1TainLtBmiCpUhUpVKRBEAhUCqNUimKRCKatCR69EySslyp5XI5n\/b1zq6qq6tDUVERZDIZb8ypVqsvW1qotrYWp06dwsiRI\/l6W2dnJ5YtW4b29nZs3779kvXl9BWBpocKBAKsXLkSr7zyCpKSkpCUlIRXXnkFYWFhWLZsGf\/ca3V66KVAiHiuMEQiESZPnozJkyfjj3\/8IzIzM31mCs2cORMLFizAnDlzYDKZYDKZeCftkpISFBcXQyKRwOl0oqWlBZGRkb1MTPft24\/\/\/ncdvvzyy15zXajGUoqUAkmpqUmdFPFQ4oFACzXVWGo0mlBQ4H9Kp1IZQfbhUIatFouFfF\/KHNRsNpGfhxr9EB1tQWlpGfO4UqxFT\/oOZuS1RCKBxWKBxWLxmZXjNcv1jijQaDSXrHemrq4O2dnZSElJ4fvN7HY77r33XtTX1yMjI4O8Ly43Ak0PBYBnnnkGNpsNK1asQFNTE8aPH9+LLK\/V6aGXAiHiuYoQCoUYN24cxo0bh1dffRUnT57EunXr8Mc\/\/hErVqzADTfcgAULFmDu3Ln44IMPkJOTg9dffx0ikQi1tbU4duyYz3yTqKgoyOVyzJo1E7NmzYTD4cDu3XuRnp6OzZu3oL6+ARIJJaVWM4+ZzWamQ3QgKTVVT6HmFwVaqCk7HGqjHhkZSRIEJbCgiBsAamrYaThKKAEAdXVsabheryeJJ0KsQxN8NxnBjLzuju73ktvtRktLC2pra5GbmwuHw+EjTrhYWbPX5mbEiBF8PczhcODHP\/4xysrKsHPnzoCpzsuNYNKNAoEAq1atwqpVq5jPuVanh14KhIinn0AoFGL06NEYPXo0Xn75ZZw+fRrr1q3Du+++iyeeeAICgQCPPPIIRCIRP7Fx6NChaGpqQk1NDT\/N0TvOISoqChKJBLNm3YRZs27Cn\/+8Gvv3H8D+\/Qdw7lyBX0mnTMYmJY0mCqzJBNHRFpSVsa1AqMWYarakUi1qdSSZFqT6cEwmE2nESaXhKFeCiIgIctoplUqTyWRkWjFQgb2jsXfakZVqCwZCoRBRUVGIiorC4MGDYbVaUVtbi5KSEuTk5CAqKopPyQXrhtDQ0ICTJ09i2LBhfIHd6XTipz\/9Kc6ePYvdu3cHlLmHcG0gRDz9EAKBAMOHD8fgwYNRXFyM+vp63H777di5cyfefPNNTJ48mZ8pZDQaodVqeRKqra1FTk4OXC4Xv3vVarXn60zTMWPGdDz33K9x6NBhfP75Jmza9AXKyjw76Yt1pdZqdUziCeQ8QE3TpFISZrOZHA9NpQWphTLQkLbOTrZgITo6upenV3dQdbCYGHrmENWHo5CHo6m6N9EGk2oLBgKBAEqlEkqlEgMHDoTNZkNtbS1qamqQl5fnM7CNZRTb2NiIEydOYMiQIXwjssvlwooVK3D8+HHs2bMnoCIwhGsHIeLpx3juuedw7NgxHDlyBBaLR6lWXFyM9evX47\/\/\/S+eeuopTJgwge8jio6OhkajQXJyMlpaWlBTU4Pc3Fw4nU7odDqepEQiESZNmohJkybi9ddfRWZmJjZuTEdW1jHmtdCu1Gwll9lsJmsiVIqOUqVRNQCZTEY26VEEazDoSeKhiDJQgyflskBNYAVAmp0mx6XAXdM7PXSpiKcnFAoF4uPjER8f32tgm1wu50koMjISAoEAzc3NOH78OJKTk2GxeNwX3G43Hn30URw6dAi7d+8mXTFCuPYQIp5+jGeeeQbPPfccv8gKBAIkJibiqaeewpNPPony8nJs2LABGzZswK9\/\/WuMHTuWJ6GEhASo1WoMHjwYra2tqK2t5RsJ9Xo9n6sXiz0+XmPHjgUAnDyZjfT0TUhP34QzZ3L5a7lYV2qqJhJoAijlxEw1U1osFhQV+Vf1ASCJRafTgghaSHdhyuzVYw7KTqUFauikCDpWPwg2P9nBqD7WeC4G3oFt0dHRPuPkjx07BqFQiMjISDQ0NGDw4MG8Q7nb7caTTz6JPXv2YPfu3fzokBB+OLhqfTzvvvsuEhMTIZfLMXbsWOzfv\/9qXUq\/hU6nY+7sBQIBYmNj8dhjj\/Ezhe677z7s3LkTo0ePxtSpU\/HGG28gPz8fKpUKSUlJmDx5Mq677jqEh4ejsLAQe\/fuxfHjx32ctL0GpkePHkZW1hG8+OLzGDkyhdytU7021IJKzRoCLr6x1DsNlgVKliwQsH8Ser2OtOGhIrTY2BgyXUZFYRqNhlTaaRT+o4XLFfGw4BUnjBgxAtOnT8eAAQNQX18PoVCI\/Px83HPPPfjXv\/6Fp59+Glu3bsWOHTuQkJBwRa8xhP6Bq0I8n332GVauXMmnkqZOnYo5c+agtJRtfxICGwKBAGazGStWrMCOHTtQWVmJFStW4ODBg7juuuswYcIEvPLKKzhz5gwiIiIwcOBAfpyDt5t97969yMrKQkVFBb+oJycPxjPPPI2DBw9g587tfk1MAbq\/h1pQlUr2jtxkMpE1ESrlRdWjDAYDKTzo6KAkzeyZQgAdoQUyB6U+T6DxDmJn7yZLsVQElYZuZr2csFqtOHfuHJKSkjBjxgyMGDECWq0Wb7zxBt577z0kJiZix44dId+yHyiuCvG89dZbuP\/++\/HAAw9g6NChWL16NWJjY3n9fAgXD2\/vxQMPPIAvv\/wS1dXVePLJJ3Hy5ElMmTIFY8eOxUsvvYSTJ08iLCyM72afNGkSNBoNysvLsW\/fPmRmZqKsrIzfxScmJvqYmL7++h8wdmwqxGIx2QBK1SYolVegQnNFBVuVRkUWgWb\/UM2hgcZOU3LnQF5j1OcRCukmzq6W3sc1hsufZmOhra0NWVlZSEhIQHx8PH9P6nQ62O12bN68GfPnz8fHH3+MmJgY7Nu376pdawhXB1eceOx2OzIzM33swgFg1qxZIbvwSwyBQACNRoPly5dj06ZNqKmpwQsvvIBz587hxhtvxKhRo\/D888\/j6NGjkMvlSEhIwPjx4zF58mTodDpUV1dj\/\/79OHLkCEpLS\/k0k8ViwaxZN+G3v30RR48exptvvo4ZM6b7rbtQvTZUaoqKhgwGAxmZUGIGStEmkUjINBzV3xFoVDbVG2Q0GskoTCRi17M84657v\/a7SKm\/C6xWKzIzMxEXF8dbzXAchzfeeAP\/+Mc\/kJGRgZtvvhlPPfUUDhw4gIqKCowfP\/6SXsO+ffswb948WCwWCAQCfP755z7Hly9fDoFA4PNvwoQJPs8JTQ+9vLji4oL6+nq4XC7SUjyEy4PIyEjcdddduOuuu2C1WvHll19i\/fr1mDt3LqKiojB\/\/nwsXLgQ1113Ha9a8jpp19TU4OzZs1CpVOA4Dp2dnRg3bhzCw8ORlDQIDzxwPxoaGrF58xZ8\/nk69uzZi7AwBVmboNwOKMGCyWQka06UlxrVGxQdbUFxcQnzOEUOBoOBrElR6Uij0Ugep2xrEi2DYW\/rTWpXur4DeL6fzMxMxMbGYsAAz5h3juPwpz\/9CX\/+85+RkZGBkSNH+rzmchhmtre3Y9SoUfjxj3+MxYsX+33Oj370I3zwwQf8\/\/esRa5cuRJffPEF1q5dC61WiyeffBJz585FZmZmyHngEuCqqdouxlI8hEuHiIgILFmyBEuWLEFHRwe2b9+O9evX47bbbkNYWBjmzZuHhQsXYtKkSYiNjUVsbCxaW1tx8uRJ2O12uN1uZGdn8+McwsPDeRPT++67By0tLdixYyf+97\/1yMjY0asoH8g92mZjRzRUyisyUkXWWqgoS6fTkcRDkUMgM8u6OnYKj4ruPK9lK\/8SzUMBP4LDqCucauvo6EBmZiYsFosP6bz77rt44403sG3bNl45ebkxZ84czJkzh3yOTCZjiltC00MvP654qk2n00EkEpGW4iFcWYSFhWHhwoX4+OOPUVVVhX\/84x+w2+24++67kZSUhF\/84hf44osvcMstt+CDDz7AlClTMH36dMTFxaG5uRmHDh3CwYMHUVBQAKvVCo7jEBkZicWLb8Xatf9BSUkBPvroAyxevIgnDbPZTJIAFQ1RtSFvnwgL1CJOpeEUCgXpaEBBp9PxYy78gVLKyeVyMpIyKP1LkTWmKxfx2Gw2ZGZmwmQyYdCgQRAIBOA4Du+\/\/z5+97vfYfPmzZc8nfZd4W1YHTx4MB588EGfCDo0PfTy44oTj1QqxdixY33swgEgIyMjZBfeDyCXy3HLLbfg\/\/7v\/1BVVYV\/\/\/vfcDqdWL58ORoaGiCRSLB792643W5YLBaMGTOGH+dgtVp9xjm0traC4zhERERg8eJb8dFHa1BSUoDPPvsEt912K1MqLhKJAkRD7IWaaiwVi8XkIu5ysZtkY2KiyRoPRSyBVGlUj1R0dDTZvKsQ+P+8VyrVZrPZcPToUej1eiQlJfGk89FHH+H5559Heno6Jk+efEWuJVjMmTMH\/\/nPf7Br1y68+eabOHLkCG644QZ+IxSaHnr5cVVSbU888QTuuecepKWlYeLEifjHP\/6B0tJS\/PznP78alxMCAxKJBEOHDsWRI0dwyy234Kc\/\/Sk2bdqEX\/ziF7Barbj55puxcOFC3Hjjjfw4B5fLxY9zOHr0KKRSKW\/dExkZCblcjrlzb8HcubfgpZde5E1Mt2zZykc5gZyYqXHgVGNpTEw0mUqjhrQFMq6kPNpUKpoEWlvZxBPIpdnV7r9p9Uo0j3Z2diIzMxM6nQ7Jyck86Xz66ad4+umnkZ6e3svhuT9g6dKl\/H+PGDECaWlpiI+Px5YtW3DrrbcyXxcqB1w6XBXiWbp0KRoaGvDb3\/4WVVVVGDFiBLZu3Yr4+PircTkhEHj11VcxdepUvPvuuxCJRJg1axb+9Kc\/4eDBg1i\/fj2eeeYZNDQ04Ec\/+hE\/U8hoNMJoNMLlcqGxsRE1NTU+TtpGoxFqtRpSqRSzZ8\/E7Nkz4XK5sH\/\/AaSnb0JBQSGTeIRCIak8o6IhrVZLEg+1m6UaYQONyqYSC4Fea7ez05EA0FLt\/\/jljni8pKPRaDBkyBB+QV6\/fj1WrlyJ\/\/3vf7jxxhsv6zVcKpjNZsTHxyM\/Px+A7\/TQ7lFPbW1tKCtziXDVnAtWrFiB4uJidHV1ITMzE9OmTbvk77Fq1apessnuBUWO47Bq1SpYLBYoFArMmDEDOTk5l\/w6vs94++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"text\/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {}, "output_type": "display_data"}]}}, "3012cdca2fe2499d8a50dee485f62217": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "32b1f33a1556467aaba78f0af3df1e4d": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "3500924f544141ef8070a2456337d727": {"model_module": "@jupyter-widgets\/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_e4081c6091bb48838c726ff8e86ca60b", "outputs": [{"data": {"image\/png": 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ae8zjb3c7I3XX5GG6e3KqRsTHmFghACBQBKAAEYAGti+Pe\/XmzkK9k3tMFbWN\/vYr0+P0nSmp+odL3YqOCDOxQgDAhSAABYgAZA2+pmb9dX+p3thRqE1fn\/AvvRFrD9OtE5J095RUXT58EBOnASBIEIACRACyniJPnd7aeVRv7DiqgpO1\/vZRCbH6zpQU3TkpRUMddhMrBACcDwEoQAQg62ppMfS3Iyf1xo5Cvbe3SPWNrROnQ0NsumFMgr4zJVXTLx7K6vQA0A8RgAJEAIIkVdU36s+fF+mNHYXKLaj0t8fH2nXX5cN095RUjUqINa9AAEAHBKAAEYBwpoMlVXpz51G9veuoyqob\/O2XDx+ke65I1S0TkhVrZ+I0AJiJABQgAhDOprG5RR99Vao3dxTq4wMn1Nw2czoqPFS3TEjSnMuHaWr6EO4tBAAmIAAFiACE7ij11uvt3GN6Y0ehDp+o8bcnOOy6dUKybr8sWRNTWH4DAPoKAShABCB8E4ZhaFdBhd7ccVTv7S3qsChr2pBo3TYhWXdclqyMRIeJVQLAwEcAChABCBfK19SsTV+Xad2e4\/rwyxLVNTb7t41xO3T7Zcm6bUKyUuOiTawSAAYmAlCACEDoCbUNTcr+skTv7jmunK9PqLH51D+by4cP0u0Tk3XLhGTuLwQAPYQAFCACEHpaZW2D3v+iWOt2H9fWvHK1\/wsKsUlZI+N1+2XJ+tY4t1xR4eYWCgBBjAAUIAIQelOJt15\/\/rxI6\/Yc157CSn97RGiIpl88VLdflqwbxyQqKiLUvCIBIAgRgAJEAEJfyS+v0bt7jutPu4\/rYGm1vz0mIlQzxibqjsuG6eqMeO48DQDdQAAKEAEIfc0wDH1VXKV1e47r3T3HdbSizr9tcHS4Zl2apNsnJuvKEXEK4R5DANAlAlCACEAwU+tl9ZV6d89x\/fnzIpVV+\/zb3M5I3TohSbdflqxLh3GPIQA4HQEoQAQg9BdNzS367HC51u0+rvX7ilV12j2G0uNjdNvEZN0+MZk1yQBABKCAEYDQH\/mamrXxwAmt23Ncf91f4l+pXpLGJjlb7zE0MVnDBkWZWCUAmKe3v79NnY350ksvacKECXI6nXI6ncrMzNT777\/v324YhhYvXqzk5GRFRUVp+vTp2rdvn4kVAz3DHhaqb41z69f3Xa4d\/2+GXrznMt0wJkFhITZ9WeTVM+9\/paue+UjffmmLXvnsSIdTZwCAwJk6AvTuu+8qNDRUo0aNkiStXLlSzz\/\/vHJzczVu3Dg9++yzevrpp\/Xyyy9r9OjReuqpp7Rp0yYdOHBADkf3liJgBAjBpKKmQe99UaR1u4\/rb0dOdrjH0OS0wZoxNlEzxrqVHh9jbqEA0MssdwosLi5Ozz\/\/vB588EElJydrwYIFevzxxyVJPp9PiYmJevbZZ\/XQQw916\/MIQAhWxZ56\/fnz41q357g+P+rpsG1UQmxbGErUZSmDuJoMwIBjmQDU3NysN998U3PnzlVubq4iIyM1cuRI7dq1S5MmTfLvd8cdd2jQoEFauXJltz6XAISB4GhFrf66v1TZX5Zo6+FyNbWc+mcbH2vXTZckaMbYRF01Kl6R4dx0EUDw6+3v77Ae\/8RvaO\/evcrMzFR9fb1iY2O1du1ajR07Vlu2bJEkJSYmdtg\/MTFR+fn5Z\/08n88nn+\/UfAmv19s7hQN9KGVwtOZmjdDcrBHy1DVq44HWMJRz4ITKqn1avb1Qq7cXKio8VNeOjtdNlyTqxksSFRcTYXbpANAvmR6ALr74Yu3evVuVlZV66623NHfuXOXk5Pi3n3lvFMMwznm\/lGXLlmnJkiW9Vi9gNldUuO64bJjuuGyYGppatC2vXNlflij7yxIVeer1wb4SfbCvRCE2aUpanP9U2QjmDQGAX785Bdbupptu0siRI\/X4449f0CmwrkaAUlNTOQWGAc8wDO077tWGtjC0v6jj6GdG27yhm5g3BCAIDPhTYGcyDEM+n0\/p6elyu93Kzs72B6CGhgbl5OTo2WefPev77Xa77HZ7X5UL9Bs2m03jh7k0fphLC2eM1tGKWn34ZYmy95do2+GTOlharYOl1frNxr9rqOPUvKGskcwbAmA9pgagJ554QrNmzVJqaqqqqqq0evVqbdy4UevXr5fNZtOCBQu0dOlSZWRkKCMjQ0uXLlV0dLTuu+8+M8sGgkLK4GjNuypd865Kl6e2URu\/LtWGtnlDJ6p8+p+\/Fep\/\/lao6IhQXZsxVDeNTdSNYxI0mHlDACzA1ABUUlKiBx54QEVFRXK5XJowYYLWr1+vGTNmSJIee+wx1dXV6cc\/\/rEqKio0depUbdiwodv3AALQyhXdcd7Q1sOt84Y+3N86b2j9vmKt31fcOm9oRJxmts0bShvCvCEAA1O\/mwPU07gMHjg7wzD0xTGvsr8sVvb+0rPOG5oxNlETmTcEoA9Z5j5AvYUABHRf4clafbi\/dRL1tryTaj7tfkMJDrtuvCRRM8cmatpFQxQVwbwhAL2HABQgAhBwYTy1jfr4QKmy97fOG6r2nVq9PiI0RJPTBuuqUUN01ah4XTrMpbBQU5cWBDDAEIACRAACAudratbWwyeV\/WWx\/rq\/VEWe+g7bHZFhyryoNQxdNSpeI4fGnPN+XQBwPgSgABGAgJ5lGIbyymr06aEybT5Ups\/+Xi5vfVOHfdzOSGWNGqKr2wJRojPSpGoBBCsCUIAIQEDvam4x9MUxjzYfKtOWv5dp+5EKNTS1dNhnVEKsPwxNvShOzshwk6oFECwIQAEiAAF9q76xWTuOVOjTv5fp00Nl2nvMo9P\/LxMaYtOEFJc\/EE0aPkj2MCZUA+iIABQgAhBgrsraBm09XK7Nh8r06aFy5ZXVdNgeGR6iK9OH6OpRQ5Q1Ml5jk5xcbg+AABQoAhDQvxyrrNOnh8r8j7Lqhg7b42IilDmydf7Q1aPilRoXbVKlAMxEAAoQAQjovwzD0Ncl1W2jQ2XadrhcNQ3NHfZJjYvyny7LGhmvOJbqACyBABQgAhAQPBqbW7SnsNIfiHILKtXU0vF\/UWOTnLo6I15ZI4foyvQ4RUf0uzWdAfQAAlCACEBA8KrxNelveSf9geir4qoO28NDbZqYMkiXpw3W5cMHadLwwVxyDwwQBKAAEYCAgeNElU9b\/l6mLYdaJ1Ufq6zrtE+yK1KT0gZrUmprIBqX7FRkOFeZAcGGABQgAhAwMBmGofzyWu3Ir1BuQYV2FVTqQLFXZ5wxU0RoiMYmOzWpbYRoUuogpQyO4k7VQD9HAAoQAQiwjhpfkz4\/6lFuYYV25Vdqd2FFp6vMJGmow+4fIZo0fJAmpLiYSwT0MwSgABGAAOsyDENHK+q0q6BCuQWVyi2o0L7j3k4Tq0NDbBrjdrSOEqUO1uVpgzViSDSjRICJCEABIgABOF19Y7O+OOZpDURtI0XF3vpO+w2KDtek1EG6fPhgTRo+WBNTXXKwhAfQZwhAASIAATifIk+df4RoV0Gl9h7zdFrPzGaTMhJi2wJR6+mzUUNjuWs10EsIQAEiAAH4phqaWrS\/yOsPRLmFFSo82fmKM4c9TJcNH+SfT3RZ6iAN5kaNQI8gAAWIAASgJ5yo8im3oEK5ha0jRXsKPaprbO60X5IrUhe7HbrY7dAYt0Nj3E6NHBqriLAQE6oGghcBKEAEIAC9oam5RQdKqtpOnbWOEh0+UdPlvmEhNl00NEYXu50a43bo4sTWgMTl+MDZEYACRAAC0Fe89Y36urhKXxVX6atirw60Pa+qb+pyf4c9TKNPGy26OLF1xMgVzWRrgAAUIAIQADMZhqEiT70\/DB0o9uqr4ir9\/US1Gpu7\/t+v29l6Gm1MUnswcmpkQozsYdzRGtZBAAoQAQhAf9TY3KLDJ2r8I0XtAamr5T2k1nsVXRQfc2q0qO10GqfRMFARgAJEAAIQTE4\/jXYqGHnlPctptFh7mEYnxp6aX9QWkAZFczUaghsBKEAEIADBzjAMFXvrW+cWFXXvNFqi065RCbEaHhejtCHRGh7X+kgbEs0NHREUCEABIgABGKgam1uUV1Zzam5R0blPo7WLi4nwh6G0uGilxkUrbUhrUEpw2Dmlhn6BABQgAhAAq6mqb9TXJVXKK6tVQXmN8k\/WKr+8VoUna1Ve03lx2NNFhoe0jRbF+EPS8LaglDI4mvsZoc8QgAJEAAKAU6rqG1VwslYF5bX+YFRwskYFJ2t1rKJOLef4RgixSUmuqDOCUYz\/uZNTa+hBBKAAEYAAoHsam1t0rKJO+SfbRo7Ka1vDUltQ6urO16cbFB2utLhoDR8S0\/azdeQobUiMEhx21k3DN9Lb399hPf6JAICgFB4aohHxMRoRHyNpaIdthmHoRLWvdeSobfSo8GSt8stbR4\/KqhtUWduoylqP9hz1dPrsiNAQJTjtSnRGKtFpV4Ij8ozndiU4I+WMDGMOEvoEAQgAcF42m00JjkglOCI1ZURcp+3VviYVnHY6rX30KL+8Vscq69TQ3KKjFXU6WnHuCdqR4SGtwcgR2SEwJTpbf3d7W6ydry8EhiMIABCwWHuYxiY7NTa586mKxuYWFXvqVVrlU6m3XiXeepVU+VTirVept\/Vnibde3vom1Te2tI4wldee8\/fFRIS2hiJ\/SIpUguPU8\/aRpagI7p6NrhGAAAC9Kjw0RKltl9ufS31jc2sgqmoLSd7TAlNbe6nXp2pfk2oamnW4rEaHy7pegLadIzLs1CiSI1IJbc\/jYiI0KDpCg6PDNTg6QoOiwxVr5\/SblRCAAAD9QmR4qIa3XVF2LjW+JpVWnRo58o8i+UeVWgNTXWOzquqbVFVfrUOl1ef9\/WEhNg2KDvcHo9N\/DmoLSoOjw+WKitDgmFPBiTXaghMBCAAQVGLsYUq3hyk9Puas+xiGoWpf06lRpKq2UaS2wHSypkEVtW0Tt+saVN\/YoqYWQ2XVDSqrPve9ks4UHRGqQVFtgSmmLTBFnQpIg9vaXVGnRpycUeEK5ao4UxGAAAADjs1mkyMyXI7IcI1KiD3v\/vWNzaqobVBFTaMqaxtUWdfoD0gVNQ2qqG2Up671pz841TaoxZBqG5pV29Cs4576b1Cf5IwMbx1Rio6Qwx6mWHuYYiPbfp723NH2M8be8XVsZBijTwEwNQAtW7ZMb7\/9tr766itFRUUpKytLzz77rC6++GL\/PvPmzdPKlSs7vG\/q1KnaunVrX5cLABigIsNDleSKUpIrqtvvaWkxVFXfpMoOwagtRNW1BqSK2vaf7aGpUdW+JhmG5KlrlKeuUTrPhO9ziQgNUYw9tC0shbcGqdMCk+OMQNW+PcYe1nHfiDDL3afJ1ACUk5Oj+fPn64orrlBTU5OefPJJzZw5U19++aViYk4Nbd58881asWKF\/3VEBKscAwDMFRJikys6XK7ocKUN6f77GppaVFl3anTJU9eomoYmVdc3qcrXpBrfqefV9U2tk759HV\/XNrTelLKhuUUNtS2qqG2UdO5bDJxPa2gKVUxEmKIiQhUdEaroiDBFR4QqKqK1\/fTnZ+5z5vOottf99VSfqQFo\/fr1HV6vWLFCCQkJ2rlzp6699lp\/u91ul9vt7uvyAADocRFhIf57Kl2o5hbDH4yqfU2qagtGrQGpUdW+5tOen9pec\/q+bfs3ta1\/0t4m+XroL21lDws5a1A6M0zF2MMUFd76XI2BBbrz6VdzgDye1ruHxsV1vMnWxo0blZCQoEGDBum6667T008\/rYSEhC4\/w+fzyec79R\/P6\/X2XsEAAJggNMQmV1S4XFGBrb9mGIZ8TS2nhafWkFTb2Ky6hmbV+JpU19g6x6m2beSpq22d9mtsVvtCW76mFvma2kepuq\/Fd+GnBruj36wFZhiG7rjjDlVUVOiTTz7xt69Zs0axsbFKS0tTXl6efv7zn6upqUk7d+6U3W7v9DmLFy\/WkiVLOrWzFhgAAH2jPVjVtIWmrkJU7Wnb\/Ps1NKumoUl1Dc3yeLx6a8FNA38x1Pnz5+svf\/mLNm\/erJSUlLPuV1RUpLS0NK1evVpz5szptL2rEaDU1FQCEAAAQcQSi6E++uijWrdunTZt2nTO8CNJSUlJSktL08GDB7vcbrfbuxwZAgAAaGdqADIMQ48++qjWrl2rjRs3Kj09\/bzvKS8vV2FhoZKSkvqgQgAAMBCFmPnL58+fr9dee02rVq2Sw+FQcXGxiouLVVfXOvO7urpaP\/3pT\/XZZ5\/pyJEj2rhxo2677TbFx8frzjvvNLN0AAAQxEydA3S2RedWrFihefPmqa6uTrNnz1Zubq4qKyuVlJSk66+\/Xv\/2b\/+m1NTUbv2O3j6HCAAAet6AngN0vuwVFRWlDz74oI+qAQAAVmHqKTAAAAAzEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlmBqAli1bpiuuuEIOh0MJCQmaPXu2Dhw40GEfwzC0ePFiJScnKyoqStOnT9e+fftMqhgAAAwEpgagnJwczZ8\/X1u3blV2draampo0c+ZM1dTU+Pd57rnn9MILL2j58uXavn273G63ZsyYoaqqKhMrBwAAwcxmGIZhdhHtTpw4oYSEBOXk5Ojaa6+VYRhKTk7WggUL9Pjjj0uSfD6fEhMT9eyzz+qhhx4672d6vV65XC55PB45nc7e\/hMAAEAP6O3v77ALedMvf\/nLc27\/13\/91wsqxuPxSJLi4uIkSXl5eSouLtbMmTP9+9jtdl133XXasmVLlwHI5\/PJ5\/P5X3u93guqBQAADFwXFIDWrl3b4XVjY6Py8vIUFhamkSNHXlAAMgxDCxcu1NVXX63x48dLkoqLiyVJiYmJHfZNTExUfn5+l5+zbNkyLVmy5Bv\/fgAAYB0XFIByc3M7tXm9Xs2bN0933nnnBRXyyCOP6PPPP9fmzZs7bbPZbB1eG4bRqa3dokWLtHDhwg51paamXlBNAABgYOqxSdBOp1O\/\/OUv9fOf\/\/wbv\/fRRx\/VunXr9PHHHyslJcXf7na7JZ0aCWpXWlraaVSond1ul9Pp7PAAAAA4XY9eBVZZWemfx9MdhmHokUce0dtvv62PPvpI6enpHbanp6fL7XYrOzvb39bQ0KCcnBxlZWX1WN0AAMBaLugU2H\/+5392eG0YhoqKivTqq6\/q5ptv7vbnzJ8\/X6tWrdKf\/vQnORwO\/0iPy+VSVFSUbDabFixYoKVLlyojI0MZGRlaunSpoqOjdd99911I6QAAABd2GfyZIzUhISEaOnSobrjhBi1atEgOh6N7v\/ws83hWrFihefPmSWoNV0uWLNFvf\/tbVVRUaOrUqfr1r3\/tnyh9PlwGDwBA8Ont7+9+dR+g3kAAAgAg+PT29zdrgQEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMsxNQBt2rRJt912m5KTk2Wz2fTOO+902D5v3jzZbLYOj2nTpplTLAAAGDBMDUA1NTWaOHGili9fftZ9br75ZhUVFfkf7733Xh9WCAAABqIwM3\/5rFmzNGvWrHPuY7fb5Xa7+6giAABgBf1+DtDGjRuVkJCg0aNH60c\/+pFKS0vPub\/P55PX6+3wAAAAOF2\/DkCzZs3S66+\/ro8++ki\/+tWvtH37dt1www3y+Xxnfc+yZcvkcrn8j9TU1D6sGAAABAObYRiG2UVIks1m09q1azV79uyz7lNUVKS0tDStXr1ac+bM6XIfn8\/XISB5vV6lpqbK4\/HI6XT2dNkAAKAXeL1euVyuXvv+NnUO0DeVlJSktLQ0HTx48Kz72O122e32PqwKAAAEm359CuxM5eXlKiwsVFJSktmlAACAIGbqCFB1dbUOHTrkf52Xl6fdu3crLi5OcXFxWrx4se666y4lJSXpyJEjeuKJJxQfH68777zTxKoBAECwMzUA7dixQ9dff73\/9cKFCyVJc+fO1UsvvaS9e\/fqlVdeUWVlpZKSknT99ddrzZo1cjgcZpUMAAAGgH4zCbq39PYkKgAA0PN6+\/s7qOYAAQAA9AQCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBxTA9CmTZt02223KTk5WTabTe+8806H7YZhaPHixUpOTlZUVJSmT5+uffv2mVMsAAAYMEwNQDU1NZo4caKWL1\/e5fbnnntOL7zwgpYvX67t27fL7XZrxowZqqqq6uNKAQDAQBJm5i+fNWuWZs2a1eU2wzD04osv6sknn9ScOXMkSStXrlRiYqJWrVqlhx56qC9LBQAAA0i\/nQOUl5en4uJizZw5099mt9t13XXXacuWLWd9n8\/nk9fr7fAAAAA4Xb8NQMXFxZKkxMTEDu2JiYn+bV1ZtmyZXC6X\/5GamtqrdQIAgODTbwNQO5vN1uG1YRid2k63aNEieTwe\/6OwsLC3SwQAAEHG1DlA5+J2uyW1jgQlJSX520tLSzuNCp3ObrfLbrf3en0AACB49dsRoPT0dLndbmVnZ\/vbGhoalJOTo6ysLBMrAwAAwc7UEaDq6modOnTI\/zovL0+7d+9WXFychg8frgULFmjp0qXKyMhQRkaGli5dqujoaN13330mVg0AAIKdqQFox44duv766\/2vFy5cKEmaO3euXn75ZT322GOqq6vTj3\/8Y1VUVGjq1KnasGGDHA6HWSUDAIABwGYYhmF2Eb3J6\/XK5XLJ4\/HI6XSaXQ4AAOiG3v7+7rdzgAAAAHoLAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFhOvw5Aixcvls1m6\/Bwu91mlwUAAIJcmNkFnM+4ceP04Ycf+l+HhoaaWA0AABgI+n0ACgsLY9QHAAD0qH59CkySDh48qOTkZKWnp+u73\/2uDh8+bHZJAAAgyPXrEaCpU6fqlVde0ejRo1VSUqKnnnpKWVlZ2rdvn4YMGdLle3w+n3w+n\/+11+vtq3IBAECQsBmGYZhdRHfV1NRo5MiReuyx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\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"text\/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {}, "output_type": "display_data"}]}}, "85007844bdc54cdf992dc43ba5b83a41": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "851af3a043f1495ea899768d977eda65": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "86d909bc42c141caa23af6d60020a0db": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", "model_name": "IntSliderModel", "state": {"behavior": "drag-tap", "layout": "IPY_MODEL_246b67cae6d04400856fb146b0a764d3", "max": 199, "style": "IPY_MODEL_2df005db5f0d49dda9dd0c4e6e958c48"}}, "87d61c25a76c41e1a826e0e1bcdaf617": {"model_module": "@jupyter-widgets\/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {}}, "88035a6b469f4a4891e35659867ff0c6": {"model_module": "@jupyter-widgets\/controls", "model_module_version": "2.0.0", 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", "text\/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {}, "output_type": "display_data"}]}}, "8d43b8903d1c4542ac1d493441e99717": {"model_module": "@jupyter-widgets\/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_7cc9130632e24968ac7630fa1302ec66", "outputs": [{"data": {"image\/png": 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diff --git a/synced_files/GA_1_6/Analysis_solution.md b/synced_files/GA_1_6/Analysis_solution.md
index 86bfda86..a14fbd7e 100644
--- a/synced_files/GA_1_6/Analysis_solution.md
+++ b/synced_files/GA_1_6/Analysis_solution.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region id="9adbf457-797f-45b7-8f8b-0e46e0e2f5ff" -->
# GA 1.6: An ODE to Probably Doing Enough (PDE)
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +14,7 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.6. For: 11 October, 2024.*
-<!-- #endregion -->
+
# Overview
diff --git a/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.html b/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.html
index c43b8bfb..822803f2 100644
--- a/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.html
+++ b/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.html
@@ -8365,7 +8365,7 @@ The correlation between the simulations is: 0.0020271910707288735
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=5585ed73">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=eb35a2d3">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.ipynb b/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.ipynb
index 89183e36..03128b66 100644
--- a/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.ipynb
+++ b/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.ipynb
@@ -604,7 +604,7 @@
},
{
"cell_type": "markdown",
- "id": "47689f2f",
+ "id": "1c042a63",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
diff --git a/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.md b/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.md
index dfbeb8ca..a36ee458 100644
--- a/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.md
+++ b/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Group Assignment 1.7: Distribution Fitting
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 7, Friday Oct 18, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
## Case 3: Discharges on a structure
**What's the propagated uncertainty? *How large will be the discharge?***
@@ -47,11 +37,9 @@ $$
2. Fit the chosen distributions to the observations of $u$ and $h$.
3. Assuming $d$ and $h$ are independent, propagate their distributions to obtain the distribution of $q$.
4. Analyze the distribution of $q$.
-<!-- #endregion -->
-<!-- #region id="d33f1148-c72b-4c7e-bca7-45973b2570c5" -->
+
## Importing packages
-<!-- #endregion -->
```python
import numpy as np
@@ -97,7 +85,6 @@ print(stats.describe(h))
print(stats.describe(u))
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1:</b>
@@ -107,7 +94,7 @@ Describe the data based on the previous statistics:
<li>What does the skewness coefficient means? Which kind of distribution functions should we consider to fit them?</li>
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
@@ -163,7 +150,6 @@ axes[1].legend()
axes[1].grid()
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3:</b>
@@ -171,7 +157,7 @@ axes[1].grid()
Based on the results of Task 1 and the empirical PDF and CDF, select <b>one</b> distribution to fit to each variable. For $h$, select between Uniform or Gaussian distribution, while for $u$ choose between Exponential or Gumbel.
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
diff --git a/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.html b/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.html
index ef7ab411..d9061522 100644
--- a/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.html
+++ b/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.html
@@ -7751,7 +7751,7 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=99556ba2">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a6930fb4">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7886,7 +7886,7 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=554ae5cf">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c645b7cd">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8028,7 +8028,7 @@ $C$: Uniform</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=4fa83a06">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=ef6eb16e">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8129,7 +8129,7 @@ The p-value for the fitted Uniform distribution to C is: 0.0
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4f846ae6">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=70948c0d">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8259,7 +8259,7 @@ The p-value for the fitted Uniform distribution to C is: 0.0
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=95e3a51e">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4379f766">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8277,7 +8277,7 @@ The p-value for the fitted Uniform distribution to C is: 0.0
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c082a3a1">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=12f4760a">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8300,7 +8300,7 @@ The p-value for the fitted Uniform distribution to C is: 0.0
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=f4b7f7c8">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=a18aa4fc">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8332,7 +8332,7 @@ The p-value for the fitted Uniform distribution to C is: 0.0
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=7c169d02">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=a4b31aae">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8365,7 +8365,7 @@ The correlation between the simulations is: -0.013637688852272484
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=dae7f6ad">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1b17176f">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.ipynb b/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.ipynb
index db6f38ad..0d4cb0a8 100644
--- a/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.ipynb
+++ b/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.ipynb
@@ -157,7 +157,7 @@
},
{
"cell_type": "markdown",
- "id": "8750486d",
+ "id": "b7e053e1",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -260,7 +260,7 @@
},
{
"cell_type": "markdown",
- "id": "20d2b004",
+ "id": "da48396b",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -372,7 +372,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "3debb049",
+ "id": "a3dc58c3",
"metadata": {},
"outputs": [],
"source": [
@@ -435,7 +435,7 @@
},
{
"cell_type": "markdown",
- "id": "031cbec5",
+ "id": "279eb86e",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -536,7 +536,7 @@
},
{
"cell_type": "markdown",
- "id": "a770fee1",
+ "id": "c707ebc4",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -550,7 +550,7 @@
},
{
"cell_type": "markdown",
- "id": "af3e2583",
+ "id": "861e4aa2",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -571,7 +571,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "dfd1cbdd",
+ "id": "a575bb7f",
"metadata": {},
"outputs": [],
"source": [
@@ -587,7 +587,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "c515a918",
+ "id": "c8f88889",
"metadata": {},
"outputs": [],
"source": [
@@ -600,7 +600,7 @@
},
{
"cell_type": "markdown",
- "id": "640fee3c",
+ "id": "ae1ca76f",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
diff --git a/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.md b/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.md
index 8ff15176..328b968e 100644
--- a/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.md
+++ b/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Group Assignment 1.7: Distribution Fitting
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 7, Friday Oct 18, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
## Case 2: $CO_2$ emissions from traffic
**What's the propagated uncertainty? *How large will be the $CO_2$ emissions?***
@@ -41,11 +31,9 @@ $$
2. Fit the chosen distributions to the observations of $H$ and $C$.
3. Assuming $H$ and $C$ are independent, propagate their distributions to obtain the distribution of emissions of $CO_2$.
4. Analyze the distribution of emissions of $CO_2$.
-<!-- #endregion -->
-<!-- #region id="d33f1148-c72b-4c7e-bca7-45973b2570c5" -->
+
## Importing packages
-<!-- #endregion -->
```python
import numpy as np
@@ -92,7 +80,6 @@ stats.describe(H).mean
print(stats.describe(C))
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1:</b>
@@ -102,7 +89,7 @@ Describe the data based on the previous statistics:
- What does the skewness coefficient means? Which kind of distribution functions should we consider to fit them?</li>
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
@@ -158,7 +145,6 @@ axes[1].legend()
axes[1].grid()
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3:</b>
@@ -166,7 +152,7 @@ axes[1].grid()
Based on the results of Task 1 and the empirical PDF and CDF, select <b>one</b> distribution to fit to each variable. For $H$, select between Gumbel or Gaussian distribution, while for $C$ choose between Uniform or Lognormal.
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
diff --git a/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.html b/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.html
index 3994682f..079b9b49 100644
--- a/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.html
+++ b/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.html
@@ -7752,7 +7752,7 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=daa74242">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1f5d0df7">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7888,7 +7888,7 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1b57c3ac">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1807b351">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8028,7 +8028,7 @@ $T$: Gumbel</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=91071051">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=a4b14e54">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8129,7 +8129,7 @@ The p-value for the fitted Uniform distribution to d is: 0.0
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3cdacd06">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6c179a0a">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8261,7 +8261,7 @@ The p-value for the fitted Uniform distribution to d is: 0.0
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=88ca7895">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=85675ad1">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8280,7 +8280,7 @@ The p-value for the fitted Uniform distribution to d is: 0.0
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=7ef03e54">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=20a4ad80">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8303,7 +8303,7 @@ The p-value for the fitted Uniform distribution to d is: 0.0
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=ce163eea">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=d648b089">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8335,7 +8335,7 @@ The p-value for the fitted Uniform distribution to d is: 0.0
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=f14cbecc">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=9fc8a6ee">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8368,7 +8368,7 @@ The correlation between the simulations is: -0.0049550215283467956
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=1da62bf4">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=64ffc2cd">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.ipynb b/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.ipynb
index 6cb3ce75..48e980a2 100644
--- a/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.ipynb
+++ b/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.ipynb
@@ -162,7 +162,7 @@
},
{
"cell_type": "markdown",
- "id": "4033f550",
+ "id": "6d35c61a",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -267,7 +267,7 @@
},
{
"cell_type": "markdown",
- "id": "33bf2f42",
+ "id": "cc0500ec",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -380,7 +380,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "0609a128",
+ "id": "9f1e89a1",
"metadata": {},
"outputs": [],
"source": [
@@ -443,7 +443,7 @@
},
{
"cell_type": "markdown",
- "id": "09e8fac7",
+ "id": "f56243a4",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -544,7 +544,7 @@
},
{
"cell_type": "markdown",
- "id": "1334b1d7",
+ "id": "9fed8c92",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -559,7 +559,7 @@
},
{
"cell_type": "markdown",
- "id": "1f9ab8e4",
+ "id": "05704b85",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -580,7 +580,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "a5e5f708",
+ "id": "362b9a87",
"metadata": {},
"outputs": [],
"source": [
@@ -596,7 +596,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "2262f788",
+ "id": "9af0fc94",
"metadata": {},
"outputs": [],
"source": [
@@ -609,7 +609,7 @@
},
{
"cell_type": "markdown",
- "id": "cefec011",
+ "id": "efa199b3",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
diff --git a/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.md b/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.md
index 91358bf5..a98839c6 100644
--- a/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.md
+++ b/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Group Assignment 1.7: Distribution Fitting
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 7, Friday Oct 18, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
## Case 1: Wave impacts on a crest wall
**What's the propagated uncertainty? *How large will the horizontal force be?***
@@ -47,11 +37,9 @@ $$
2. Fit the chosen distributions to the observations of $H$ and $T$.
3. Assuming $H$ and $T$ are independent, propagate their distributions to obtain the distribution of $F_h$.
4. Analyze the distribution of $F_h$.
-<!-- #endregion -->
-<!-- #region id="d33f1148-c72b-4c7e-bca7-45973b2570c5" -->
+
## Importing packages
-<!-- #endregion -->
```python
import numpy as np
@@ -97,7 +85,6 @@ print(stats.describe(H))
print(stats.describe(T))
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1:</b>
@@ -107,7 +94,7 @@ Describe the data based on the previous statistics:
<li>What does the skewness coefficient means? Which kind of distribution functions should we consider to fit them?</li>
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -165,7 +152,6 @@ axes[1].legend()
axes[1].grid()
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3:</b>
@@ -173,7 +159,7 @@ axes[1].grid()
Based on the results of Task 1 and the empirical PDF and CDF, select <b>one</b> distribution to fit to each variable. For $H$, select between Exponential or Gaussian distribution, while for $T$ choose between Uniform or Gumbel.
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
diff --git a/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.html b/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.html
index 6619691f..70397bc6 100644
--- a/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.html
+++ b/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.html
@@ -7983,7 +7983,7 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=5e189212">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=5c36b94e">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8006,7 +8006,7 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=47164a61">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=f0b597e8">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8026,7 +8026,7 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=748e2372">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=3f86df0b">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.ipynb b/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.ipynb
index d1c15f48..a5649906 100644
--- a/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.ipynb
+++ b/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.ipynb
@@ -382,7 +382,7 @@
},
{
"cell_type": "markdown",
- "id": "59aabeb3",
+ "id": "9131aa84",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -403,7 +403,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "880d54ce",
+ "id": "04f237b9",
"metadata": {},
"outputs": [],
"source": [
@@ -419,7 +419,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "c5f193bd",
+ "id": "72edc495",
"metadata": {},
"outputs": [],
"source": [
diff --git a/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.md b/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.md
index 0df7007f..eb7d680d 100644
--- a/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.md
+++ b/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Group Assignment 1.7: Distribution Fitting
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 7, Friday Oct 18, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
## Case 3: Discharges on a structure
**What's the propagated uncertainty? *How large will be the discharge?***
@@ -47,11 +37,9 @@ $$
2. Fit the chosen distributions to the observations of $d$ and $h$.
3. Assuming $d$ and $h$ are independent, propagate their distributions to obtain the distribution of $q$.
4. Analyze the distribution of $q$.
-<!-- #endregion -->
-<!-- #region id="d33f1148-c72b-4c7e-bca7-45973b2570c5" -->
+
## Importing packages
-<!-- #endregion -->
```python
import numpy as np
@@ -97,7 +85,6 @@ print(stats.describe(h))
print(stats.describe(u))
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1:</b>
@@ -107,7 +94,7 @@ Describe the data based on the previous statistics:
<li>What does the skewness coefficient means? Which kind of distribution functions should we consider to fit them?</li>
</p>
</div>
-<!-- #endregion -->
+
## 2. Empirical distribution functions
@@ -133,7 +120,6 @@ def ecdf(YOUR_INPUT:
#Your plot
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3:</b>
@@ -141,7 +127,7 @@ def ecdf(YOUR_INPUT:
Based on the results of Task 1 and the empirical PDF and CDF, select <b>one</b> distribution to fit to each variable. For $h$, select between Uniform or Gaussian distribution, while for $u$ choose between Exponential or Gumbel.
</p>
</div>
-<!-- #endregion -->
+
## 3. Fitting a distribution
diff --git a/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.html b/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.html
index c221918e..2779d68c 100644
--- a/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.html
+++ b/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.html
@@ -7981,7 +7981,7 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c6b0153a">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=75ff9d50">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8004,7 +8004,7 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=c959337c">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=3400e594">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8024,7 +8024,7 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=1c43eb66">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=53a5425d">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.ipynb b/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.ipynb
index 1d8d8ec2..117e0f0e 100644
--- a/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.ipynb
+++ b/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.ipynb
@@ -376,7 +376,7 @@
},
{
"cell_type": "markdown",
- "id": "38d9e3ff",
+ "id": "2771410f",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -397,7 +397,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "7f51c7b1",
+ "id": "7580681b",
"metadata": {},
"outputs": [],
"source": [
@@ -413,7 +413,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "6f2b5809",
+ "id": "65651ae7",
"metadata": {},
"outputs": [],
"source": [
diff --git a/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.md b/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.md
index 725c106c..63907c47 100644
--- a/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.md
+++ b/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Group Assignment 1.7: Distribution Fitting
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 7, Friday Oct 18, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
## Case 2: $CO_2$ emissions from traffic
**What's the propagated uncertainty? *How large will be the $CO_2$ emissions?***
@@ -41,11 +31,9 @@ $$
2. Fit the chosen distributions to the observations of $H$ and $C$.
3. Assuming $H$ and $C$ are independent, propagate their distributions to obtain the distribution of emissions of $CO_2$.
4. Analyze the distribution of emissions of $CO_2$.
-<!-- #endregion -->
-<!-- #region id="d33f1148-c72b-4c7e-bca7-45973b2570c5" -->
+
## Importing packages
-<!-- #endregion -->
```python
import numpy as np
@@ -91,7 +79,6 @@ print(stats.describe(H))
print(stats.describe(C))
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1:</b>
@@ -101,7 +88,7 @@ Describe the data based on the previous statistics:
- What does the skewness coefficient means? Which kind of distribution functions should we consider to fit them?</li>
</p>
</div>
-<!-- #endregion -->
+
## 2. Empirical distribution functions
@@ -127,7 +114,6 @@ def ecdf(YOUR_INPUT):
#Your plot here
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3:</b>
@@ -135,7 +121,7 @@ def ecdf(YOUR_INPUT):
Based on the results of Task 1 and the empirical PDF and CDF, select <b>one</b> distribution to fit to each variable. For $H$, select between Gumbel or Gaussian distribution, while for $C$ choose between Uniform or Lognormal.
</p>
</div>
-<!-- #endregion -->
+
## 3. Fitting a distribution
diff --git a/synced_files/GA_1_7/Student/Force/Analysis_force.html b/synced_files/GA_1_7/Student/Force/Analysis_force.html
index 9ad3a002..9bc91400 100644
--- a/synced_files/GA_1_7/Student/Force/Analysis_force.html
+++ b/synced_files/GA_1_7/Student/Force/Analysis_force.html
@@ -7983,7 +7983,7 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0115ed7b">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6d9bfea3">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8006,7 +8006,7 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=26bdb48f">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a245efa5">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8026,7 +8026,7 @@ $$</p>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=a09ad624">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=11ef22e8">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/GA_1_7/Student/Force/Analysis_force.ipynb b/synced_files/GA_1_7/Student/Force/Analysis_force.ipynb
index 0b6399ae..f3e379c4 100644
--- a/synced_files/GA_1_7/Student/Force/Analysis_force.ipynb
+++ b/synced_files/GA_1_7/Student/Force/Analysis_force.ipynb
@@ -382,7 +382,7 @@
},
{
"cell_type": "markdown",
- "id": "69b71826",
+ "id": "659232ce",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -403,7 +403,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "30a79e9c",
+ "id": "cf7c4700",
"metadata": {},
"outputs": [],
"source": [
@@ -419,7 +419,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "ed5df6e0",
+ "id": "52dfe420",
"metadata": {},
"outputs": [],
"source": [
diff --git a/synced_files/GA_1_7/Student/Force/Analysis_force.md b/synced_files/GA_1_7/Student/Force/Analysis_force.md
index e5f9cc18..5ebfd852 100644
--- a/synced_files/GA_1_7/Student/Force/Analysis_force.md
+++ b/synced_files/GA_1_7/Student/Force/Analysis_force.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Group Assignment 1.7: Distribution Fitting
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 7, Friday Oct 18, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
## Case 1: Wave impacts on a crest wall
**What's the propagated uncertainty? *How large will the horizontal force be?***
@@ -47,11 +37,9 @@ $$
2. Fit the chosen distributions to the observations of $H$ and $T$.
3. Assuming $H$ and $T$ are independent, propagate their distributions to obtain the distribution of $F_h$.
4. Analyze the distribution of $F_h$.
-<!-- #endregion -->
-<!-- #region id="d33f1148-c72b-4c7e-bca7-45973b2570c5" -->
+
## Importing packages
-<!-- #endregion -->
```python
import numpy as np
@@ -97,7 +85,6 @@ print(stats.describe(H))
print(stats.describe(T))
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1:</b>
@@ -107,7 +94,7 @@ Describe the data based on the previous statistics:
<li>What does the skewness coefficient means? Which kind of distribution functions should we consider to fit them?</li>
</p>
</div>
-<!-- #endregion -->
+
## 2. Empirical distribution functions
@@ -133,7 +120,6 @@ def ecdf(YOUR_INPUTS):
# Your plot here
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3:</b>
@@ -141,7 +127,7 @@ def ecdf(YOUR_INPUTS):
Based on the results of Task 1 and the empirical PDF and CDF, select <b>one</b> distribution to fit to each variable. For $H$, select between Exponential or Gaussian distribution, while for $T$ choose between Uniform or Gumbel.
</p>
</div>
-<!-- #endregion -->
+
## 3. Fitting a distribution
diff --git a/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.html b/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.html
index 6dfc5489..34927ac2 100644
--- a/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.html
+++ b/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.html
@@ -7752,7 +7752,7 @@ Describe the data based on the previous statistics:
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=22ad7091">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d4218672">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7885,7 +7885,7 @@ Based on the results of Task 1 and the empirical PDF and CDF, select <b>one</b>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=39aba001">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3a20a50f">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8025,7 +8025,7 @@ Assess the goodness of fit of the selected distribution using:
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b2216275">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=2726878a">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8126,7 +8126,7 @@ Interpret the results of the GOF techniques. How does the selected parametric di
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6881d4c2">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=36b25266">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8254,7 +8254,7 @@ Interpret the figures above, answering the following questions:
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=9fc06728">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=34b969be">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8270,7 +8270,7 @@ Interpret the figures above, answering the following questions:
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6a9e6e1b">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=33eb48ed">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8293,7 +8293,7 @@ Interpret the figures above, answering the following questions:
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b0bf856d">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=2d3b51a3">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.ipynb b/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.ipynb
index fb579293..93b9baaa 100644
--- a/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.ipynb
+++ b/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.ipynb
@@ -161,7 +161,7 @@
},
{
"cell_type": "markdown",
- "id": "a705ade2",
+ "id": "5493d4fc",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -261,7 +261,7 @@
},
{
"cell_type": "markdown",
- "id": "8d70f4ab",
+ "id": "88a03eea",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -370,7 +370,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "5355f51e",
+ "id": "fd0e4448",
"metadata": {},
"outputs": [],
"source": [
@@ -432,7 +432,7 @@
},
{
"cell_type": "markdown",
- "id": "57228d07",
+ "id": "cae26c95",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -531,7 +531,7 @@
},
{
"cell_type": "markdown",
- "id": "d9528484",
+ "id": "2ee5b4aa",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -544,7 +544,7 @@
},
{
"cell_type": "markdown",
- "id": "e25c8090",
+ "id": "6b3dc9d3",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -565,7 +565,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "959fe8d5",
+ "id": "2c362f7d",
"metadata": {},
"outputs": [],
"source": [
diff --git a/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.md b/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.md
index 82f4d7b9..bb511677 100644
--- a/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.md
+++ b/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Group Assignment 1.7: Distribution Fitting
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 7, Friday Oct 18, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
## Case 1: Wave impacts on a crest wall
**What's the propagated uncertainty? *How large will be the horizontal force?***
@@ -47,11 +37,9 @@ $$
2. Fit the chosen distributions to the observations of $H$ and $T$.
3. Assuming $H$ and $d$ are independent, propagate their distributions to obtain the distribution of $F_h$.
4. Analyze the distribution of $F_h$.
-<!-- #endregion -->
-<!-- #region id="d33f1148-c72b-4c7e-bca7-45973b2570c5" -->
+
## Importing packages
-<!-- #endregion -->
```python
import numpy as np
@@ -97,7 +85,6 @@ print(stats.describe(T10))
print(stats.describe(T90))
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1:</b>
@@ -106,7 +93,7 @@ Describe the data based on the previous statistics:
<li>What does the skewness coefficient means? Which kind of distribution functions should we consider to fit them?</li>
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution 1: TO UPDATE</b>
@@ -160,14 +147,13 @@ axes[1].legend()
axes[1].grid()
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3: TO UPDATE</b>
Based on the results of Task 1 and the empirical PDF and CDF, select <b>one</b> distribution to fit to each variable. For $H$, select between Exponential or Gaussian distribution, while for $T$ choose between Uniform or Gumbel.
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution 3:</b>
diff --git a/synced_files/GA_1_8/Analysis.md b/synced_files/GA_1_8/Analysis.md
index 161c725b..681f6e60 100644
--- a/synced_files/GA_1_8/Analysis.md
+++ b/synced_files/GA_1_8/Analysis.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.8: Multivariate Distributions
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -129,9 +119,8 @@ Build the bivariate distribution by instantiating the <code>Bivariate</code> cla
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Use the helper function <code>plot_contour</code> in <code>helper.py</code>; it works exactle the same as in WS 1.8.</em></p></div>
-<!-- #endregion -->
```python
# plot_contour? # uncomment and run to read docstring
@@ -169,12 +158,11 @@ For each of the three cases, do the following:
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note that the optional arguments in the helper function <code>plot_contour</code> will be useful here.
Here is an example code that shows you what it can do (the values are meaningless)
</p></div>
-<!-- #endregion -->
```python
region_example = np.array([[0, 5, 12, 20, 28, 30],
@@ -325,9 +313,8 @@ axes.legend()
axes.grid()
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"><p>In case you are wondering, the data for this exercise was computed with a Clayton Copula. A Copula is a useful way of modelling non-linear dependence. If you would like to learn more about this, you should consider the 2nd year cross-over module CEGM2005 Probabilistic Modelling of real-world phenomena through ObseRvations and Elicitation (MORE).</p></div>
-<!-- #endregion -->
+
**End of notebook.**
<h2 style="height: 60px">
diff --git a/synced_files/GA_1_8/solution/GA_1_8_solution_HT.md b/synced_files/GA_1_8/solution/GA_1_8_solution_HT.md
index f2adc95a..de8d45a8 100644
--- a/synced_files/GA_1_8/solution/GA_1_8_solution_HT.md
+++ b/synced_files/GA_1_8/solution/GA_1_8_solution_HT.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.8: Multivariate Distributions
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -213,9 +203,8 @@ Build the bivariate distribution by instantiating the <code>Bivariate</code> cla
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Use the helper function <code>plot_contour</code> in <code>helper.py</code>; it works exactle the same as in WS 1.8.</em></p></div>
-<!-- #endregion -->
```python
# plot_contour? # uncomment and run to read docstring
@@ -258,12 +247,11 @@ For each of the three cases, do the following:
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note that the optional arguments in the helper function <code>plot_contour</code> will be useful here.
Here is an example code that shows you what it can do (the values are meaningless)
</p></div>
-<!-- #endregion -->
```python
region_example = np.array([[0, 5, 12, 20, 28, 30],
@@ -318,9 +306,9 @@ which is then defined in Python and included in the `plot_contours` function as
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: order of the tasks in this solution is not important.</p></div>
-<!-- #endregion -->
+
### Case 1 and 2
@@ -431,9 +419,8 @@ print(f'The c.o.v. is of p is {1/np.sqrt((N+1)*p_Z90_data):.3f}.')
print('\n')
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: the bivariate figures are an important concept for the exam, so if using the code is too difficult for you to use when studying on your own, try sketching it on paper.</p></div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -591,9 +578,8 @@ axes.legend()
axes.grid()
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"><p>In case you are wondering, the data for this exercise was computed with a Clayton Copula. A Copula is a useful way of modelling non-linear dependence. If you would like to learn more about this, you should consider the 2nd year cross-over module CEGM2005 Probabilistic Modelling of real-world phenomena through ObseRvations and Elicitation (MORE).</p></div>
-<!-- #endregion -->
+
**End of notebook.**
<h2 style="height: 60px">
diff --git a/synced_files/GA_1_8/solution/GA_1_8_solution_hu.md b/synced_files/GA_1_8/solution/GA_1_8_solution_hu.md
index 86630e89..08413648 100644
--- a/synced_files/GA_1_8/solution/GA_1_8_solution_hu.md
+++ b/synced_files/GA_1_8/solution/GA_1_8_solution_hu.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.8: Multivariate Distributions
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -216,9 +206,8 @@ Build the bivariate distribution by instantiating the <code>Bivariate</code> cla
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Use the helper function <code>plot_contour</code> in <code>helper.py</code>; it works exactle the same as in WS 1.8.</em></p></div>
-<!-- #endregion -->
```python
# plot_contour? # uncomment and run to read docstring
@@ -261,12 +250,11 @@ For each of the three cases, do the following:
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note that the optional arguments in the helper function <code>plot_contour</code> will be useful here.
Here is an example code that shows you what it can do (the values are meaningless)
</p></div>
-<!-- #endregion -->
```python
region_example = np.array([[0, 5, 12, 20, 28, 30],
@@ -321,9 +309,9 @@ which is then defined in Python and included in the `plot_contours` function as
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: order of the tasks in this solution is not important.</p></div>
-<!-- #endregion -->
+
### Case 1 and 2
@@ -434,9 +422,8 @@ print(f'The c.o.v. is of p is {1/np.sqrt((N+1)*p_Z90_data):.3f}.')
print('\n')
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: the bivariate figures are an important concept for the exam, so if using the code is too difficult for you to use when studying on your own, try sketching it on paper.</p></div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -594,9 +581,8 @@ axes.legend()
axes.grid()
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"><p>In case you are wondering, the data for this exercise was computed with a Clayton Copula. A Copula is a useful way of modelling non-linear dependence. If you would like to learn more about this, you should consider the 2nd year cross-over module CEGM2005 Probabilistic Modelling of real-world phenomena through ObseRvations and Elicitation (MORE).</p></div>
-<!-- #endregion -->
+
**End of notebook.**
<h2 style="height: 60px">
diff --git a/synced_files/GA_1_8/solution/GA_1_8_solution_traffic.md b/synced_files/GA_1_8/solution/GA_1_8_solution_traffic.md
index 0fca67b7..b5d5facc 100644
--- a/synced_files/GA_1_8/solution/GA_1_8_solution_traffic.md
+++ b/synced_files/GA_1_8/solution/GA_1_8_solution_traffic.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 1.8: Multivariate Distributions
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -212,9 +202,8 @@ Build the bivariate distribution by instantiating the <code>Bivariate</code> cla
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Use the helper function <code>plot_contour</code> in <code>helper.py</code>; it works exactle the same as in WS 1.8.</em></p></div>
-<!-- #endregion -->
```python
# plot_contour? # uncomment and run to read docstring
@@ -257,12 +246,11 @@ For each of the three cases, do the following:
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note that the optional arguments in the helper function <code>plot_contour</code> will be useful here.
Here is an example code that shows you what it can do (the values are meaningless)
</p></div>
-<!-- #endregion -->
```python
region_example = np.array([[0, 5, 12, 20, 28, 30],
@@ -317,9 +305,9 @@ which is then defined in Python and included in the `plot_contours` function as
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: order of the tasks in this solution is not important.</p></div>
-<!-- #endregion -->
+
### Case 1 and 2
@@ -430,9 +418,8 @@ print(f'The c.o.v. is of p is {1/np.sqrt((N+1)*p_Z90_data):.3f}.')
print('\n')
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: the bivariate figures are an important concept for the exam, so if using the code is too difficult for you to use when studying on your own, try sketching it on paper.</p></div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -590,9 +577,8 @@ axes.legend()
axes.grid()
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"><p>In case you are wondering, the data for this exercise was computed with a Clayton Copula. A Copula is a useful way of modelling non-linear dependence. If you would like to learn more about this, you should consider the 2nd year cross-over module CEGM2005 Probabilistic Modelling of real-world phenomena through ObseRvations and Elicitation (MORE).</p></div>
-<!-- #endregion -->
+
**End of notebook.**
<h2 style="height: 60px">
diff --git a/synced_files/GA_2_1/Analysis.md b/synced_files/GA_2_1/Analysis.md
index 0483f6f6..548b4ec3 100644
--- a/synced_files/GA_2_1/Analysis.md
+++ b/synced_files/GA_2_1/Analysis.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region id="9adbf457-797f-45b7-8f8b-0e46e0e2f5ff" -->
# GA 2.1: FVM with an Unstructured Mesh (diffusion)
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +14,7 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.1. For: 15 November, 2024.*
-<!-- #endregion -->
+
# Overview
diff --git a/synced_files/GA_2_1/Analysis_solution.md b/synced_files/GA_2_1/Analysis_solution.md
index 33b118ff..4a2082f2 100644
--- a/synced_files/GA_2_1/Analysis_solution.md
+++ b/synced_files/GA_2_1/Analysis_solution.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
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- jupytext:
- text_representation:
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- format_name: markdown
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- jupytext_version: 1.16.6
----
-
-<!-- #region id="9adbf457-797f-45b7-8f8b-0e46e0e2f5ff" -->
# GA 2.1: FVM with an Unstructured Mesh (diffusion)
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +14,7 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.1. For: 15 November, 2024.*
-<!-- #endregion -->
+
# Overview
diff --git a/synced_files/GA_2_1/mesh/mesh.md b/synced_files/GA_2_1/mesh/mesh.md
index c9d4b0fe..fc43a030 100644
--- a/synced_files/GA_2_1/mesh/mesh.md
+++ b/synced_files/GA_2_1/mesh/mesh.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
```python
length = 10
height = (length**2 - (length/2)**2)**0.5
diff --git a/synced_files/GA_2_1/mesh_dev.md b/synced_files/GA_2_1/mesh_dev.md
index 42af77ef..74917023 100644
--- a/synced_files/GA_2_1/mesh_dev.md
+++ b/synced_files/GA_2_1/mesh_dev.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
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- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
```python
length = 10
height = (length**2 - (length/2)**2)**0.5
diff --git a/synced_files/GA_2_1/mesh_tips.md b/synced_files/GA_2_1/mesh_tips.md
index 55ca7175..82302559 100644
--- a/synced_files/GA_2_1/mesh_tips.md
+++ b/synced_files/GA_2_1/mesh_tips.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
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- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 2.1: Mesh Tips
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_2_2/the_big_M.md b/synced_files/GA_2_2/the_big_M.md
index 1bc3b59d..3ddcd1ee 100644
--- a/synced_files/GA_2_2/the_big_M.md
+++ b/synced_files/GA_2_2/the_big_M.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
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- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 2.2: M is for Modelling
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_2_3/Analysis.md b/synced_files/GA_2_3/Analysis.md
index ac610343..f66e8c21 100644
--- a/synced_files/GA_2_3/Analysis.md
+++ b/synced_files/GA_2_3/Analysis.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
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- jupytext_version: 1.16.6
----
-
# GA 2.3: Beam Beats
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -34,9 +24,8 @@ As a warming up you will create and analyze some elementary signals yourself, an
Most of the Tasks in this notebook consist of both coding, producing a plot, and answering (open) questions. Typically, as you work your way through the Tasks, you can often re-use code, or part of it, from earlier Tasks and assignments. That will save you a lot of work!!
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>In many of the code blocks below, template code to create figures is provided. Note that there is a lot of code missing, and one line of <code>YOUR_CODE_HERE</code> does not imply that only one line of code is missing!</p></div>
-<!-- #endregion -->
<!-- #region -->
### Data Acquisition System
@@ -90,9 +79,8 @@ import matplotlib.pyplot as plt
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>We will give you the answers in this code cell for free!</p></div>
-<!-- #endregion -->
```python
T_meas = 5
diff --git a/synced_files/GA_2_3/Analysis_solution.md b/synced_files/GA_2_3/Analysis_solution.md
index f02e80c4..e3eb9eeb 100644
--- a/synced_files/GA_2_3/Analysis_solution.md
+++ b/synced_files/GA_2_3/Analysis_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 2.3: Beam Beats
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -34,9 +24,8 @@ As a warming up you will create and analyze some elementary signals yourself, an
Most of the Tasks in this notebook consist of both coding, producing a plot, and answering (open) questions. Typically, as you work your way through the Tasks, you can often re-use code, or part of it, from earlier Tasks and assignments. That will save you a lot of work!!
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>In many of the code blocks below, template code to create figures is provided. Note that there is a lot of code missing, and one line of <code>YOUR_CODE_HERE</code> does not imply that only one line of code is missing!</p></div>
-<!-- #endregion -->
<!-- #region -->
### Data Acquisition System
@@ -90,9 +79,8 @@ import matplotlib.pyplot as plt
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>We will give you the answers in this code cell for free!</p></div>
-<!-- #endregion -->
```python
T_meas = 5
diff --git a/synced_files/GA_2_4/GA_2_4_Beary_Icy.md b/synced_files/GA_2_4/GA_2_4_Beary_Icy.md
index c5508e03..5d6c6738 100644
--- a/synced_files/GA_2_4/GA_2_4_Beary_Icy.md
+++ b/synced_files/GA_2_4/GA_2_4_Beary_Icy.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
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- jupytext:
- text_representation:
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- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 2.4: Beary Icy
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_2_4/GA_2_4_solution.md b/synced_files/GA_2_4/GA_2_4_solution.md
index b9489ab5..16c9e9e0 100644
--- a/synced_files/GA_2_4/GA_2_4_solution.md
+++ b/synced_files/GA_2_4/GA_2_4_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
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- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 2.4: Beary Icy
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_2_5/Analysis_GA.md b/synced_files/GA_2_5/Analysis_GA.md
index 04e07c5c..02bd7b0d 100644
--- a/synced_files/GA_2_5/Analysis_GA.md
+++ b/synced_files/GA_2_5/Analysis_GA.md
@@ -1,16 +1,6 @@
<userStyle>Normal</userStyle>
----
-jupyter:
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- text_representation:
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- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region pycharm={"name": "#%% md\n"} -->
+<!-- #region -->
# Evolve and drive
@@ -29,7 +19,6 @@ jupyter:
<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
## Introduction
_Note: part of the background material for this project was already available in [Chapter 5.11 of the textbook](https://mude.citg.tudelft.nl/2023/book/optimization/project.html)._
@@ -39,13 +28,11 @@ _Note: part of the background material for this project was already available in
* Think about larger problems, like the road network of Amsterdam or Shanghai, and it will be even harder!
* Here we show how a metaheuristic such a the genetic algorithm can be used to find good (not necessarily optimal) solutions for the problem in potentially less time
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p> <b>Note:</b> You will need to select mude-week-2-5 as your kernel as it includes the required packages.</p></div>
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
## Genetic algorithm for NDP
As we discussed, it is challenging to use MILP for large-scale NDPs. Therefore, in this assignment, we’re going to use a genetic algorithm to address this problem.
@@ -61,9 +48,8 @@ Basic Components of a Genetic Algorithm:
* **Replacement**: New offspring replace some of the least fit individuals in the population.
* **Termination Criteria**: Conditions under which the algorithm stops, e.g., a maximum number of generations or satisfactory fitness level.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
### GA steps
Reminding you about the GA steps …
@@ -76,16 +62,14 @@ Reminding you about the GA steps …
* Termination (end): The algorithm stops when a termination criterion is met.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
### PyMOO
PyMOO is a Python library that provides a comprehensive and easy-to-use framework for multi-objective optimization (MOO). For this case, we are going to deal with only one objective; nevertheless, this is an useful tool if you have more objectives. In addition, PyMOO easily allows us to define our optimization problem by specifying the objectives, constraints, and decision variables.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+<!-- #region -->
## Problem definition and formulation
Here is the problem formulation as presented in the previous Jupyter notebook
@@ -109,7 +93,6 @@ So let's see how that works.
<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
### The network design sub-problem
The network desing is where we use the genetic algorithm. As explained before, GA uses a population of solutions and iteratively improves this population to evolve to new generations of populations with a better objective function value (being that minimization or maximization). In this problem, the decision variables are links for capacity expansion and the objective function value is the total system travel time that we want to minimize.
@@ -122,9 +105,8 @@ The network desing is where we use the genetic algorithm. As explained before, G
\end{align}
Where the values of $x_{ij}$ are not decision variables anymore, they will be obtained from solving the Traffic Assignment problem with Gurobi which evaluates the travel times on the network. This part of the problem will not be solved mathematically anymore, the $y_{ij}$ variables are decided by the genetic algorithm through the process you learned.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+<!-- #region -->
### The traffic assignment sub-problem
This is just part of the original NDP that assigns traffic to the network based on a set of given capacity values, which are defined based on the values of the DP decision variables (links selected for capacity expansion). The main difference (and the advantage) here is that by separating the binary decision variables, instead of a mixed integer programming problem, which are hard to solve, here we have a quadratic programming problem with continuous decision variables, which will be transformed to a linear problem that Gurobi can solve very fast.
@@ -148,11 +130,11 @@ The following is a diagram that shows what you are finally doing to solve the sa
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
## Part 1: Data preprocessing
The demand of the network is given by an **OD matrix**, which will be constructed below. The OD matrix is as table that tells you how many cars go from node i to node j in an a given timeframe. The functions for this can be found in the helper function in utils/read.py. You do not need to edit anything in this codeblock.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -199,9 +181,8 @@ from utils.read import create_nd_matrix
### Network Display
We will use the same function we used in the previous notebook to visualize the network.
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input.
-<!-- #endregion -->
```python
# define parameters
@@ -234,18 +215,16 @@ coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinate
G, pos = network_visualization(link_flow = fftts,coordinates_path= coordinates_path) # the network we create here will be used later for further visualizations!
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
Now we are ready to build our models!
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
## Part 2: Modeling and solving the traffic assignment sub-problem with Gurobi
### Defining functions
In this section we build a Gurobi model to solve the Traffic Assignment sub-problems. The decision variables, objective function, and the constraints of this problem were described before.
Here we wrap the code in a function so that we can use it later within the GA.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -310,15 +289,12 @@ def ta_qp(dvs, net_data=net_data, ods_data=ods_data, extension_factor=2.5):
return total_travel_time, capacity, link_flows, links_selected
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Modeling with PyMOO
Let's define a model in MyMOO and deal with the links selection problem with the GA.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
First, we need to define a problem class.
-<!-- #endregion -->
```python
#If you want to know more about the library that is being used: https://pymoo.org/algorithms/soo/ga.html
@@ -343,13 +319,11 @@ class NDP(ElementwiseProblem):
out["G"] = g
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
Now, let's initiate an instance of the problem based on the problem class we defined, and initiate the GA with its parameters. Note that depending on the problem size and the number of feasible links, you might need larger values for population and generation size to achieve good results or even feasible results. Of course this increases the computation times.
-<!-- #endregion -->
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> population size <code>pop_size</code> is 200 originally. If you change this, you will see different results. This is problem-dependent!</p></div>
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -374,13 +348,10 @@ method = GA(pop_size=pop_size,
crossover=HalfUniformCrossover())
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
Now we are ready to minimize the NDP problem using the GA method we defined.
-<!-- #endregion -->
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> Maximum computation time (termination criteria) is set here as a keyword argument.</p></div>
-<!-- #endregion -->
```python
@@ -405,12 +376,10 @@ print("Best solution found: %s" % opt_results.X)
#f_min: Minimum objective function value
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Convergence curve
Let's first define some functions (to use later) to get the results and plot them.
-<!-- #endregion -->
```python
def get_results(opt_results):
@@ -464,10 +433,8 @@ def plot_results(number_of_individuals, optimal_values_along_generations):
plt.show()
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
Now let's use these functions to plot the results.
-<!-- #endregion -->
```python
number_of_individuals, optimal_values_along_generations = get_results(opt_results)
diff --git a/synced_files/GA_2_5/Analysis_GA_solution.md b/synced_files/GA_2_5/Analysis_GA_solution.md
index 04e07c5c..02bd7b0d 100644
--- a/synced_files/GA_2_5/Analysis_GA_solution.md
+++ b/synced_files/GA_2_5/Analysis_GA_solution.md
@@ -1,16 +1,6 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region pycharm={"name": "#%% md\n"} -->
+<!-- #region -->
# Evolve and drive
@@ -29,7 +19,6 @@ jupyter:
<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
## Introduction
_Note: part of the background material for this project was already available in [Chapter 5.11 of the textbook](https://mude.citg.tudelft.nl/2023/book/optimization/project.html)._
@@ -39,13 +28,11 @@ _Note: part of the background material for this project was already available in
* Think about larger problems, like the road network of Amsterdam or Shanghai, and it will be even harder!
* Here we show how a metaheuristic such a the genetic algorithm can be used to find good (not necessarily optimal) solutions for the problem in potentially less time
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p> <b>Note:</b> You will need to select mude-week-2-5 as your kernel as it includes the required packages.</p></div>
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
## Genetic algorithm for NDP
As we discussed, it is challenging to use MILP for large-scale NDPs. Therefore, in this assignment, we’re going to use a genetic algorithm to address this problem.
@@ -61,9 +48,8 @@ Basic Components of a Genetic Algorithm:
* **Replacement**: New offspring replace some of the least fit individuals in the population.
* **Termination Criteria**: Conditions under which the algorithm stops, e.g., a maximum number of generations or satisfactory fitness level.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
### GA steps
Reminding you about the GA steps …
@@ -76,16 +62,14 @@ Reminding you about the GA steps …
* Termination (end): The algorithm stops when a termination criterion is met.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
### PyMOO
PyMOO is a Python library that provides a comprehensive and easy-to-use framework for multi-objective optimization (MOO). For this case, we are going to deal with only one objective; nevertheless, this is an useful tool if you have more objectives. In addition, PyMOO easily allows us to define our optimization problem by specifying the objectives, constraints, and decision variables.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+<!-- #region -->
## Problem definition and formulation
Here is the problem formulation as presented in the previous Jupyter notebook
@@ -109,7 +93,6 @@ So let's see how that works.
<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
### The network design sub-problem
The network desing is where we use the genetic algorithm. As explained before, GA uses a population of solutions and iteratively improves this population to evolve to new generations of populations with a better objective function value (being that minimization or maximization). In this problem, the decision variables are links for capacity expansion and the objective function value is the total system travel time that we want to minimize.
@@ -122,9 +105,8 @@ The network desing is where we use the genetic algorithm. As explained before, G
\end{align}
Where the values of $x_{ij}$ are not decision variables anymore, they will be obtained from solving the Traffic Assignment problem with Gurobi which evaluates the travel times on the network. This part of the problem will not be solved mathematically anymore, the $y_{ij}$ variables are decided by the genetic algorithm through the process you learned.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+<!-- #region -->
### The traffic assignment sub-problem
This is just part of the original NDP that assigns traffic to the network based on a set of given capacity values, which are defined based on the values of the DP decision variables (links selected for capacity expansion). The main difference (and the advantage) here is that by separating the binary decision variables, instead of a mixed integer programming problem, which are hard to solve, here we have a quadratic programming problem with continuous decision variables, which will be transformed to a linear problem that Gurobi can solve very fast.
@@ -148,11 +130,11 @@ The following is a diagram that shows what you are finally doing to solve the sa
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
## Part 1: Data preprocessing
The demand of the network is given by an **OD matrix**, which will be constructed below. The OD matrix is as table that tells you how many cars go from node i to node j in an a given timeframe. The functions for this can be found in the helper function in utils/read.py. You do not need to edit anything in this codeblock.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -199,9 +181,8 @@ from utils.read import create_nd_matrix
### Network Display
We will use the same function we used in the previous notebook to visualize the network.
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input.
-<!-- #endregion -->
```python
# define parameters
@@ -234,18 +215,16 @@ coordinates_path = 'input/TransportationNetworks/SiouxFalls/SiouxFallsCoordinate
G, pos = network_visualization(link_flow = fftts,coordinates_path= coordinates_path) # the network we create here will be used later for further visualizations!
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
Now we are ready to build our models!
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
## Part 2: Modeling and solving the traffic assignment sub-problem with Gurobi
### Defining functions
In this section we build a Gurobi model to solve the Traffic Assignment sub-problems. The decision variables, objective function, and the constraints of this problem were described before.
Here we wrap the code in a function so that we can use it later within the GA.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -310,15 +289,12 @@ def ta_qp(dvs, net_data=net_data, ods_data=ods_data, extension_factor=2.5):
return total_travel_time, capacity, link_flows, links_selected
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Modeling with PyMOO
Let's define a model in MyMOO and deal with the links selection problem with the GA.
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
First, we need to define a problem class.
-<!-- #endregion -->
```python
#If you want to know more about the library that is being used: https://pymoo.org/algorithms/soo/ga.html
@@ -343,13 +319,11 @@ class NDP(ElementwiseProblem):
out["G"] = g
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
Now, let's initiate an instance of the problem based on the problem class we defined, and initiate the GA with its parameters. Note that depending on the problem size and the number of feasible links, you might need larger values for population and generation size to achieve good results or even feasible results. Of course this increases the computation times.
-<!-- #endregion -->
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> population size <code>pop_size</code> is 200 originally. If you change this, you will see different results. This is problem-dependent!</p></div>
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -374,13 +348,10 @@ method = GA(pop_size=pop_size,
crossover=HalfUniformCrossover())
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
Now we are ready to minimize the NDP problem using the GA method we defined.
-<!-- #endregion -->
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> Maximum computation time (termination criteria) is set here as a keyword argument.</p></div>
-<!-- #endregion -->
```python
@@ -405,12 +376,10 @@ print("Best solution found: %s" % opt_results.X)
#f_min: Minimum objective function value
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Convergence curve
Let's first define some functions (to use later) to get the results and plot them.
-<!-- #endregion -->
```python
def get_results(opt_results):
@@ -464,10 +433,8 @@ def plot_results(number_of_individuals, optimal_values_along_generations):
plt.show()
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
Now let's use these functions to plot the results.
-<!-- #endregion -->
```python
number_of_individuals, optimal_values_along_generations = get_results(opt_results)
diff --git a/synced_files/GA_2_5/Analysis_LP.md b/synced_files/GA_2_5/Analysis_LP.md
index 332c379f..e49e09ad 100644
--- a/synced_files/GA_2_5/Analysis_LP.md
+++ b/synced_files/GA_2_5/Analysis_LP.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region pycharm={"name": "#%% md\n"} -->
# Don't do math and drive
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,9 +14,8 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. For: 13th December, 2024.*
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+<!-- #region -->
# Problem description
_Note: part of the background material for this project was already available in the textbook._
@@ -66,12 +54,12 @@ Using the simplifcations and assumptions referred to above we can formulate an N
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p> <b>Note:</b> You will need to select mude-week-2-5 as your kernel as it includes the required packages.</p></div>
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
## Part 1: Data preprocessing
The demand of the network is given by an **OD matrix** (origin-destination), which will be constructed below. The OD matrix is a table that tells you how many cars go from node i to node j in an a given timeframe. The functions for this can be found in the helper function in utils/read.py. You do not need to edit anything in this codeblock.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -187,13 +175,12 @@ plt.yticks(ticks=np.arange(od_matrix.shape[0]), labels=od_matrix.index)
plt.show()
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
## Part 2: Modeling in Gurobi
### Defining Parameters
Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -246,7 +233,6 @@ model.params.NonConvex = 2
model.params.PreQLinearize = 1
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Decision variables
We have a set of binary variables $y_{ij}$, these variables take the value 1 if link $(i,j)$ connecting node $i$ to node $j$ is selected for expansion, and 0 otherwise.
@@ -264,7 +250,6 @@ Therefore, mathematically we define the domain of the variables as follows:
\end{align}
As you will see below in the code block, we have one extra set of variables called x2 (x square). This is to help Gurobi isolate quadratic terms and perform required transformations based on MCE to keep the problem linear. This is not part of your learning goals.
-<!-- #endregion -->
```python
# decision variables:
@@ -277,7 +262,6 @@ link_flow_sqr = model.addVars(links, vtype=gp.GRB.CONTINUOUS, name='x2')
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Objective function
The objective function of the problem (in its simplest form), is the minimization of the total travel time on the network, that means that you multiply the flow of vehicles in each link by the corresponding travel time and sum over all links ($A$ is the collection of all links to simplify the notation):
@@ -314,7 +298,6 @@ Now, for gurobi (and other solvers as well), we have to keep binary variables an
Therefore, we use this equation to model our objective function in gurobi. You do not need to know fully understand this equation.
-<!-- #endregion -->
```python
# objective function (total travel time)
@@ -327,7 +310,6 @@ model.setObjective(
for (i, j) in links))
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Constraints
We have four sets of constraints for this problem. Let's go through them one by one and add them to the model.
@@ -336,25 +318,21 @@ We have four sets of constraints for this problem. Let's go through them one by
We can only extend the capacity of certain number of links based on the available budget. So first, we have to make sure to limit the number of extended links to the max number that can be expanded:
$$ \sum_{(i,j) \in A}{ y_{ij}} \leq B $$
-<!-- #endregion -->
```python
# budget constraint
c_bgt = model.addConstr(gp.quicksum(link_selected[i, j] for (i, j) in links) <= extension_max_no)
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
#### 2. Link flow conservation constraints
We have two sets of decision variables representing link flows; $x_{ij}$, representing flow on link $(i,j)$, and $x_{ijs}$, representing flow on link $(i,j)$ going to destination $s$. So we have to make sure that the sum of the flows over all destinations equals the flow on each link.
$ \sum_{s \in D}{x_{ijs}} = x_{ij} \quad \forall (i,j) \in A $
-<!-- #endregion -->
```python
# link flow conservation
c_lfc = model.addConstrs(gp.quicksum(dest_flow[i, j, s] for s in dests) == link_flow[i, j] for (i, j) in links)
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
#### 3. Node flow conservation constraints
The basic idea of this constraint set is to make sure that the incoming and outgoing flow to and from each node is the same (hence flow conservation) with the exception for origin and destination nodes of the trips where there will be extra outgoing flow (origins) or incoming flow (destinations). Think about a traffic intersection, vehicles enter and leave the intersection when they are moving in the network. This assures the continuity of the vehicle paths. $d_{is}$ here is the number of travelers from node $i$ to node $s$ with the exception of $d_{ss}$, which is all the demand that arrives at node $s$.
@@ -363,7 +341,6 @@ $ \sum_{j \in N; (i,j) \in A}{ x_{ijs}} - \sum_{j \in N; (j,i) \in A}{ x_{jis}}
The figure gives an example:
-<!-- #endregion -->
```python
# node flow conservation
@@ -374,11 +351,9 @@ c_nfc = model.addConstrs(
)
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
#### 4. Quadratic variable constraints (you do not need to fully understand this)
These are basically dummy equations to help gurobi model quadratic terms (that we defined as dummy variables earlier). So essentially instead of using $x^2_{ij}$ in the model, we define a new set of decision variables and define a set of constrains to set their value to $x^2_{ij}$. This let's Gurobi know these are quadratic terms and helps gurobi to replace it with variables and constraints required to keep the problem linear. This is not part of your learning goals!
-<!-- #endregion -->
```python
# dummy constraints for handling quadratic terms
@@ -397,11 +372,10 @@ After running the whole model once for 300s, come back and set up a new constrai
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b>
The cell below only has to be used when adding the constraint. </p></div>
-<!-- #endregion -->
```python
# constrain the vehicles to the capacity of the road:
diff --git a/synced_files/GA_2_5/Analysis_LP_solution.md b/synced_files/GA_2_5/Analysis_LP_solution.md
index c3497302..fe720371 100644
--- a/synced_files/GA_2_5/Analysis_LP_solution.md
+++ b/synced_files/GA_2_5/Analysis_LP_solution.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region pycharm={"name": "#%% md\n"} -->
# Don't do math and drive
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,9 +14,8 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. For: 13th December, 2024.*
-<!-- #endregion -->
-<!-- #region pycharm={"name": "#%% md\n"} -->
+<!-- #region -->
# Problem description
_Note: part of the background material for this project was already available in the textbook._
@@ -66,12 +54,12 @@ Using the simplifcations and assumptions referred to above we can formulate an N
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p> <b>Note:</b> You will need to select mude-week-2-5 as your kernel as it includes the required packages.</p></div>
-<!-- #region pycharm={"name": "#%% md\n"} -->
+
## Part 1: Data preprocessing
The demand of the network is given by an **OD matrix** (origin-destination), which will be constructed below. The OD matrix is a table that tells you how many cars go from node i to node j in an a given timeframe. The functions for this can be found in the helper function in utils/read.py. You do not need to edit anything in this codeblock.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -187,13 +175,12 @@ plt.yticks(ticks=np.arange(od_matrix.shape[0]), labels=od_matrix.index)
plt.show()
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
## Part 2: Modeling in Gurobi
### Defining Parameters
Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; vertical-align: middle; width:95%; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<p>
@@ -246,7 +233,6 @@ model.params.NonConvex = 2
model.params.PreQLinearize = 1
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Decision variables
We have a set of binary variables $y_{ij}$, these variables take the value 1 if link $(i,j)$ connecting node $i$ to node $j$ is selected for expansion, and 0 otherwise.
@@ -264,7 +250,6 @@ Therefore, mathematically we define the domain of the variables as follows:
\end{align}
As you will see below in the code block, we have one extra set of variables called x2 (x square). This is to help Gurobi isolate quadratic terms and perform required transformations based on MCE to keep the problem linear. This is not part of your learning goals.
-<!-- #endregion -->
```python
# decision variables:
@@ -277,7 +262,6 @@ link_flow_sqr = model.addVars(links, vtype=gp.GRB.CONTINUOUS, name='x2')
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Objective function
The objective function of the problem (in its simplest form), is the minimization of the total travel time on the network, that means that you multiply the flow of vehicles in each link by the corresponding travel time and sum over all links ($A$ is the collection of all links to simplify the notation):
@@ -314,7 +298,6 @@ Now, for gurobi (and other solvers as well), we have to keep binary variables an
Therefore, we use this equation to model our objective function in gurobi. You do not need to know fully understand this equation.
-<!-- #endregion -->
```python
# objective function (total travel time)
@@ -327,7 +310,6 @@ model.setObjective(
for (i, j) in links))
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
### Constraints
We have four sets of constraints for this problem. Let's go through them one by one and add them to the model.
@@ -336,25 +318,21 @@ We have four sets of constraints for this problem. Let's go through them one by
We can only extend the capacity of certain number of links based on the available budget. So first, we have to make sure to limit the number of extended links to the max number that can be expanded:
$$ \sum_{(i,j) \in A}{ y_{ij}} \leq B $$
-<!-- #endregion -->
```python
# budget constraint
c_bgt = model.addConstr(gp.quicksum(link_selected[i, j] for (i, j) in links) <= extension_max_no)
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
#### 2. Link flow conservation constraints
We have two sets of decision variables representing link flows; $x_{ij}$, representing flow on link $(i,j)$, and $x_{ijs}$, representing flow on link $(i,j)$ going to destination $s$. So we have to make sure that the sum of the flows over all destinations equals the flow on each link.
$ \sum_{s \in D}{x_{ijs}} = x_{ij} \quad \forall (i,j) \in A $
-<!-- #endregion -->
```python
# link flow conservation
c_lfc = model.addConstrs(gp.quicksum(dest_flow[i, j, s] for s in dests) == link_flow[i, j] for (i, j) in links)
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
#### 3. Node flow conservation constraints
The basic idea of this constraint set is to make sure that the incoming and outgoing flow to and from each node is the same (hence flow conservation) with the exception for origin and destination nodes of the trips where there will be extra outgoing flow (origins) or incoming flow (destinations). Think about a traffic intersection, vehicles enter and leave the intersection when they are moving in the network. This assures the continuity of the vehicle paths. $d_{is}$ here is the number of travelers from node $i$ to node $s$ with the exception of $d_{ss}$, which is all the demand that arrives at node $s$.
@@ -363,7 +341,6 @@ $ \sum_{j \in N; (i,j) \in A}{ x_{ijs}} - \sum_{j \in N; (j,i) \in A}{ x_{jis}}
The figure gives an example:
-<!-- #endregion -->
```python
# node flow conservation
@@ -374,11 +351,9 @@ c_nfc = model.addConstrs(
)
```
-<!-- #region pycharm={"name": "#%% md\n"} -->
#### 4. Quadratic variable constraints (you do not need to fully understand this)
These are basically dummy equations to help gurobi model quadratic terms (that we defined as dummy variables earlier). So essentially instead of using $x^2_{ij}$ in the model, we define a new set of decision variables and define a set of constrains to set their value to $x^2_{ij}$. This let's Gurobi know these are quadratic terms and helps gurobi to replace it with variables and constraints required to keep the problem linear. This is not part of your learning goals!
-<!-- #endregion -->
```python
# dummy constraints for handling quadratic terms
@@ -397,11 +372,10 @@ After running the whole model once for 300s, come back and set up a new constrai
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b>
The cell below only has to be used when adding the constraint. </p></div>
-<!-- #endregion -->
```python
# constrain the vehicles to the capacity of the road:
diff --git a/synced_files/GA_2_6/Analysis.md b/synced_files/GA_2_6/Analysis.md
index 6f2849fb..70d10674 100644
--- a/synced_files/GA_2_6/Analysis.md
+++ b/synced_files/GA_2_6/Analysis.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
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- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 2.6: A stethoscope for beams - neural networks for detecting defects on bridges
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_2_6/Analysis_solution.md b/synced_files/GA_2_6/Analysis_solution.md
index 43e2280d..1692d9fe 100644
--- a/synced_files/GA_2_6/Analysis_solution.md
+++ b/synced_files/GA_2_6/Analysis_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# GA 2.6: A stethoscope for beams - neural networks for detecting defects on bridges
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/GA_2_7/rain_solution.md b/synced_files/GA_2_7/rain_solution.md
index 7e55d38f..94393603 100644
--- a/synced_files/GA_2_7/rain_solution.md
+++ b/synced_files/GA_2_7/rain_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
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- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
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-
# Group Assignment 2.7: Extreme Value Analysis
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Extreme Value Analysis, Week 2.7, Friday, Jan 10, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
In this project, you will work on the uncertainty of precipitation in TurÃs, close to Valencia (Spain), where the past month of October an extreme flood occurred. TurÃs was the location where the highest rainfall was recorded. You have daily observations since 1999. The dataset was retrieved from the Ministry of Agriculture [here](https://servicio.mapa.gob.es/websiar/SeleccionParametrosMap.aspx?dst=1).
**The goal of this project is:**
@@ -34,7 +24,6 @@ In this project, you will work on the uncertainty of precipitation in TurÃs, cl
3. Compare the results from both methods in terms of design return levels.
_Read the instructions for this project in `README.md` before beginning the analysis._
-<!-- #endregion -->
```python
import numpy as np
@@ -117,7 +106,7 @@ plt.grid()
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p><b>Tip:</b> save the parameters of an instance of <code>rv_continuous</code> as a tuple to make it easier to use the methods of the distribution later.
<br><br>For example, define parameters like this:
<br><code>params = ... .fit( ... )</code>
@@ -125,7 +114,6 @@ plt.grid()
<br><code>param1, param2, param3 = ... .fit( ... )</code>
<br>(see WS15 solution for examples).
</p></div>
-<!-- #endregion -->
```python
#Function for the ECDF
@@ -213,9 +201,9 @@ Apply POT on the timeseries using a declustering time of 48 hours and a threshol
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">Hint: you can use the function <code>find_peaks</code> from Scipy, as done in PA15.</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<b>Solution:</b>
@@ -246,9 +234,9 @@ plt.grid()
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">Hint: you need to fit a GPD with a location parameter of 0 on the excesses. You can force the fitting of a distribution with a given value of the parameters using the keyword argument <code>floc</code>.</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<b>Solution:</b>
@@ -336,9 +324,9 @@ In this section, we are going to use the distributions found in the previous two
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>Remember you can use tuple unpacking as an argument for methods of <code>scipy.stats.rv_continuous</code>, like this: <code>*params</code> (see WS15 solution for examples).</p></div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<b>Solution:</b>
@@ -468,11 +456,10 @@ The degree of freedom is 1, and you can use a significance level of 0.05. The nu
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>Hint: while our primary objective in EVA is evaluating the distribution of the random variable $X$, the number of excesses observed in a given period (regardless of BM or POT) is also a random variable, which in the case of POT can be described using the Poisson distribution.
Recall that the Poisson distribution is a <em>discrete probability distribution,</em> which is defined using a <em>probability mass function</em> (PMF) instead of a PDF. The PMF can be described using notation $p_X(X=x)$, which produces the probability of observing the discrete value $x$ of random variable $X$. The implementation in Python is slightly different, inheriting methods from the parent class <code>rv_discrete</code> instead of <code>rv_continuous</code>.</p></div>
-<!-- #endregion -->
```python
# YOUR_CODE_HERE
diff --git a/synced_files/Week_1_2/In_Class_Activity.md b/synced_files/Week_1_2/In_Class_Activity.md
index 497d59e4..8f2caa86 100644
--- a/synced_files/Week_1_2/In_Class_Activity.md
+++ b/synced_files/Week_1_2/In_Class_Activity.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Lecture 1.2 Activity: The Best Model? You Bet!
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.2, Monday, Sep 9, 2024. This assignment does not need to be turned in.*
-<!-- #region cell_id="21f9833788f64e78a35bc8cac535e76d" deepnote_cell_type="markdown" -->
+
## Overview
In this assignment we will fit two models to observations of ice break-up date and reflect on their performance.
@@ -41,7 +31,6 @@ We will follow these steps:
3. Fit a linear regression model and reflect on its goodness of fit;
5. Apply confidence intervals to the model;
6. Fit a non-linear model and reflect on its goodness of fit
-<!-- #endregion -->
```python
import numpy as np
@@ -50,9 +39,8 @@ import scipy.stats as sci
import scipy.optimize as opt
```
-<!-- #region cell_id="b1d3e3d2f92c4de29aba4aa61c525867" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
## Part 1: Import data
-<!-- #endregion -->
+
We will import the dataset by using the `numpy` function `loadtxt`. If you open the *data-days.csv* file, you will notice that the comma is used as decimal separator for the number of recorded days. For this reason, we will import the data in the following steps:
@@ -74,25 +62,19 @@ data = data.astype(float)
data[0:10]
```
-<!-- #region jp-MarkdownHeadingCollapsed=true -->
## Part 2: Preliminary Analysis
-<!-- #endregion -->
-<!-- #region cell_id="1b15837c64e748b89fafad1f8007a399" deepnote_cell_type="markdown" -->
+
One of the first steps when getting familiar with new data is to see the dimensions of the data. To this end, we can use the `numpy` function `shape`.
-<!-- #endregion -->
```python
np.shape(data)
```
-<!-- #region cell_id="e901697b36064391b4a62d78c955dd6b" deepnote_cell_type="markdown" -->
The result is a (103, 2) array, i.e., a matrix with 103 rows and 2 columns. The first column contains the year of record, while the second one contains the measured data.
-<!-- #endregion -->
-<!-- #region cell_id="43139b45e34f4252a6a270336ca401ba" deepnote_cell_type="markdown" -->
+
We can also compute the mean and the standard deviation of the variable of interest (second column) to get a sense of how the variable behaves.
-<!-- #endregion -->
```python
mean = np.mean(data[:,1])
@@ -102,9 +84,7 @@ print(f'Mean: {mean:.3f}\n\
Standard deviation: {std:.3f}')
```
-<!-- #region cell_id="2a1ddf21c02141dc8dfebee83c602a73" deepnote_cell_type="markdown" -->
We can also quickly plot them to see the scatter of the data and the evolution in time.
-<!-- #endregion -->
```python
plt.scatter(data[:, 0], data[:, 1], label='Measured data')
@@ -114,15 +94,13 @@ plt.title(f'Number of days per year between {data[0,0]:.0f}-{data[-1,0]:.0f}')
plt.grid()
```
-<!-- #region cell_id="37edb558a10b47d88b3e4f683da56221" deepnote_cell_type="markdown" -->
In the figure above, we have plotted the year of the measurement in the x-axis and the number of days until the ice broke during that year in the y-axis. We can see that there is a significant scatter. Also, there seems to be a trend over time: as we go ahead in time (higher values in the x-axis), the number of days until the ice broke seems to decrease.
We have identifid a trend but **can we model it**?
-<!-- #endregion -->
-<!-- #region cell_id="a05297ff6298401192d49f8e257ff9ec" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## Part 3: Fit a linear regression model: is it a good model?
-<!-- #endregion -->
+
We are going to create a model which allows us to predict the number of days until the ice broke as function of the year. For that, we are going to assume a linear relationship between the variables (a linear model) and we will fit it using linear regression. This is, we will fit a regression model $days=m\cdot year+q$, where $m$ represents the slope of the line, and $q$ is the intercept.
@@ -172,9 +150,8 @@ r_sq, q, m = regression(data[:,0], data[:,1])
</p>
</div>
-<!-- #region cell_id="143e5d1bf8324d9f80fca4af9a0d162c" deepnote_cell_type="markdown" -->
+
We can also plot the data and the fitted model to see how the fit looks. To do so, we can make computations using the previous equation $days=m\cdot year+q$ with the fitted intercept $q$ and slope $m$. We have already defined a function which does it for you.
-<!-- #endregion -->
```python
def calculate_line(x, m, q):
@@ -218,7 +195,6 @@ axes[1].grid()
axes[1].set_title('(b) Observed and predicted number of days');
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.2:</b>
@@ -228,13 +204,11 @@ axes[1].set_title('(b) Observed and predicted number of days');
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="67e44fda81f24273b8d28edd35a50d87" deepnote_cell_type="markdown" -->
+
We can also assess the scatter using the Root Mean Square Error ($RMSE$). Don't you remember it? Go back to the [book](https://mude.citg.tudelft.nl/2024/book/modelling/gof.html)!
Let's see how our model performs for this metric.
-<!-- #endregion -->
```python
def RMSE(data, fit_data):
@@ -261,7 +235,6 @@ def RMSE(data, fit_data):
RMSE_line = RMSE(data[:,1], line)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.3:</b>
@@ -273,7 +246,7 @@ RMSE_line = RMSE(data[:,1], line)
</ol>
</p>
</div>
-<!-- #endregion -->
+
Finally, we can compute the bias of our model using $rbias$.
@@ -299,7 +272,6 @@ def rbias(data, fit_data):
rbias_line = rbias(data[:,1], line)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.4:</b>
@@ -309,11 +281,10 @@ rbias_line = rbias(data[:,1], line)
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="19496dbefd3247f09c2226579c7b665f" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## Part 4: Confidence Intervals
-<!-- #endregion -->
+
One way of assessing the uncertainty around the predictions of a model are confidence intervals. They give us insight into the precision of their predictions by transforming them into probabilities. In short, the 95% confidence interval (significance $\alpha=0.05$) shows the range of values within which my observation would be with a probability of 95%. Here, we want you to focus on their interpretation. In the following weeks (1.3), you will learn more about how to compute them.
@@ -351,7 +322,6 @@ plt.legend()
plt.title('Number of days as function of the year')
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 4.1:</b>
@@ -360,19 +330,17 @@ plt.title('Number of days as function of the year')
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="a19fb5b7e1cd4c32b435b8b967933700" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## Part 5: Non-linear Models
-<!-- #endregion -->
+
As we have seen, the data-driven linear model is not really a good choice for representing the data we have. Let's try with one which is slightly more complicated: a non-linear model.
In this section, we will analyze the fitting of a quadratic model as $days = A \cdot year^2 + B \cdot year + C$. The steps are the same as in the previous section, so we will go fast through the code to focus on the interpretation and comparison between the two models.
-<!-- #region cell_id="ca8a6b9d68234ae69a799a3f4f3866a2" deepnote_cell_type="markdown" -->
+
You do not need to worry about this right now, but in case you are curious: we will make use of the `scipy.optimize` library, which contains the `curve_fit` function. For further info on the function see [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html).
-<!-- #endregion -->
```python
def parabola(x, a, b, c):
@@ -401,19 +369,15 @@ print(f'Covariance matrix for parameters:\n\
Sigma = {pcov_parabola}')
```
-<!-- #region cell_id="10efd81771064ce0ac4d50095be06e23" deepnote_cell_type="markdown" -->
Therefore, our parabola now looks like $days = -1.277 \cdot 10^{-3} \cdot year^2 + 4.942 \cdot year - 4654.244$.
Now that we have fitted it, we can use it to compute predictions.
-<!-- #endregion -->
```python
fitted_parabola = parabola(data[:,0], *popt_parabola)
```
-<!-- #region cell_id="6cc6a36c668f4ee2b26d41b27d335691" deepnote_cell_type="markdown" -->
We can also determine the confidence intervals for this fit and see how it looks!
-<!-- #endregion -->
```python
k = conf_int(data[:,1], fitted_parabola, 0.05)
@@ -441,7 +405,6 @@ print(f'Coefficient of determination = {R2_parabola:.3f}')
rbias_parabola = rbias(data[:,1], fitted_parabola)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 5.1:</b>
@@ -450,7 +413,7 @@ rbias_parabola = rbias(data[:,1], fitted_parabola)
</ol>
</p>
</div>
-<!-- #endregion -->
+
**End of notebook.**
<h2 style="height: 60px">
diff --git a/synced_files/Week_1_2/In_Class_Activity_Solution.md b/synced_files/Week_1_2/In_Class_Activity_Solution.md
index 6225ccd3..715f9a4f 100644
--- a/synced_files/Week_1_2/In_Class_Activity_Solution.md
+++ b/synced_files/Week_1_2/In_Class_Activity_Solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Lecture 1.2 Activity: The Best Model? You Bet!
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.2, Monday, Sep 9, 2024. This assignment does not need to be turned in.*
-<!-- #region cell_id="21f9833788f64e78a35bc8cac535e76d" deepnote_cell_type="markdown" -->
+
## Overview
In this assignment we will fit two models to observations of ice break-up date and reflect on their performance.
@@ -41,7 +31,6 @@ We will follow these steps:
3. Fit a linear regression model and reflect on its goodness of fit;
5. Apply confidence intervals to the model;
6. Fit a non-linear model and reflect on its goodness of fit
-<!-- #endregion -->
```python
import numpy as np
@@ -50,9 +39,8 @@ import scipy.stats as sci
import scipy.optimize as opt
```
-<!-- #region cell_id="b1d3e3d2f92c4de29aba4aa61c525867" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
## Part 1: Import data
-<!-- #endregion -->
+
We will import the dataset by using the `numpy` function `loadtxt`. If you open the *data-days.csv* file, you will notice that the comma is used as decimal separator for the number of recorded days. For this reason, we will import the data in the following steps:
@@ -74,25 +62,19 @@ data = data.astype(float)
data[0:10]
```
-<!-- #region jp-MarkdownHeadingCollapsed=true -->
## Part 2: Preliminary Analysis
-<!-- #endregion -->
-<!-- #region cell_id="1b15837c64e748b89fafad1f8007a399" deepnote_cell_type="markdown" -->
+
One of the first steps when getting familiar with new data is to see the dimensions of the data. To this end, we can use the `numpy` function `shape`.
-<!-- #endregion -->
```python
np.shape(data)
```
-<!-- #region cell_id="e901697b36064391b4a62d78c955dd6b" deepnote_cell_type="markdown" -->
The result is a (103, 2) array, i.e., a matrix with 103 rows and 2 columns. The first column contains the year of record, while the second one contains the measured data.
-<!-- #endregion -->
-<!-- #region cell_id="43139b45e34f4252a6a270336ca401ba" deepnote_cell_type="markdown" -->
+
We can also compute the mean and the standard deviation of the variable of interest (second column) to get a sense of how the variable behaves.
-<!-- #endregion -->
```python
mean = np.mean(data[:,1])
@@ -102,9 +84,7 @@ print(f'Mean: {mean:.3f}\n\
Standard deviation: {std:.3f}')
```
-<!-- #region cell_id="2a1ddf21c02141dc8dfebee83c602a73" deepnote_cell_type="markdown" -->
We can also quickly plot them to see the scatter of the data and the evolution in time.
-<!-- #endregion -->
```python
plt.scatter(data[:, 0], data[:, 1], label='Measured data')
@@ -114,15 +94,13 @@ plt.title(f'Number of days per year between {data[0,0]:.0f}-{data[-1,0]:.0f}')
plt.grid()
```
-<!-- #region cell_id="37edb558a10b47d88b3e4f683da56221" deepnote_cell_type="markdown" -->
In the figure above, we have plotted the year of the measurement in the x-axis and the number of days until the ice broke during that year in the y-axis. We can see that there is a significant scatter. Also, there seems to be a trend over time: as we go ahead in time (higher values in the x-axis), the number of days until the ice broke seems to decrease.
We have identifid a trend but **can we model it**?
-<!-- #endregion -->
-<!-- #region cell_id="a05297ff6298401192d49f8e257ff9ec" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## Part 3: Fit a linear regression model: is it a good model?
-<!-- #endregion -->
+
We are going to create a model which allows us to predict the number of days until the ice broke as function of the year. For that, we are going to assume a linear relationship between the variables (a linear model) and we will fit it using linear regression. This is, we will fit a regression model $days=m\cdot year+q$, where $m$ represents the slope of the line, and $q$ is the intercept.
@@ -172,7 +150,7 @@ r_sq, q, m = regression(data[:,0], data[:,1])
</p>
</div>
-<!-- #region cell_id="9a082db4cf644c9c9854af0a458c0b8a" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -181,11 +159,9 @@ $\textbf{Solution}$
2. Based on the answer to the previous question, the linear model is not an accurate model. Whether this low level of accuracy is good enough or not, depends on the use we want to give to the model.
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="143e5d1bf8324d9f80fca4af9a0d162c" deepnote_cell_type="markdown" -->
+
We can also plot the data and the fitted model to see how the fit looks. To do so, we can make computations using the previous equation $days=m\cdot year+q$ with the fitted intercept $q$ and slope $m$. We have already defined a function which does it for you.
-<!-- #endregion -->
```python
def calculate_line(x, m, q):
@@ -229,7 +205,6 @@ axes[1].grid()
axes[1].set_title('(b) Observed and predicted number of days');
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.2:</b>
@@ -239,9 +214,8 @@ axes[1].set_title('(b) Observed and predicted number of days');
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="9a082db4cf644c9c9854af0a458c0b8a" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -252,13 +226,11 @@ In the plot (a), we observe that the observations have a high scatter around the
In the plot (b), we compare the observations with the predictions of the model. The perfect fit would correspond to the 45-degrees line in black. Thus, the model has a poor performance as we already quantified using the coefficient of determination. Both results are aligned.
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="67e44fda81f24273b8d28edd35a50d87" deepnote_cell_type="markdown" -->
+
We can also assess the scatter using the Root Mean Square Error ($RMSE$). Don't you remember it? Go back to the [book](https://mude.citg.tudelft.nl/2024/book/modelling/gof.html)!
Let's see how our model performs for this metric.
-<!-- #endregion -->
```python
def RMSE(data, fit_data):
@@ -285,7 +257,6 @@ def RMSE(data, fit_data):
RMSE_line = RMSE(data[:,1], line)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.3:</b>
@@ -297,9 +268,8 @@ RMSE_line = RMSE(data[:,1], line)
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="604f11f41ac54d59b11422bac89e6411" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -308,7 +278,7 @@ $\textbf{Solution}$
2. Based on the previous interpretation, the linear model is not accurate. Whether this low level of accuracy is good enough or not, depends on the use we want to give to the model.
</p>
</div>
-<!-- #endregion -->
+
Finally, we can compute the bias of our model using $rbias$.
@@ -334,7 +304,6 @@ def rbias(data, fit_data):
rbias_line = rbias(data[:,1], line)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.4:</b>
@@ -344,9 +313,8 @@ rbias_line = rbias(data[:,1], line)
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="604f11f41ac54d59b11422bac89e6411" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -354,11 +322,10 @@ $\textbf{Solution}$
1. <code>rbias</code> provides an standardized measure of the systematic tendency of our model to under- or over-prediction. It is very low for our model, so it does not have a clear tendency to under- or overestimate and, thus, does not seem to be biased.
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="19496dbefd3247f09c2226579c7b665f" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## Part 4: Confidence Intervals
-<!-- #endregion -->
+
One way of assessing the uncertainty around the predictions of a model are confidence intervals. They give us insight into the precision of their predictions by transforming them into probabilities. In short, the 95% confidence interval (significance $\alpha=0.05$) shows the range of values within which my observation would be with a probability of 95%. Here, we want you to focus on their interpretation. In the following weeks (1.3), you will learn more about how to compute them.
@@ -404,7 +371,6 @@ plt.legend()
plt.title('Number of days as function of the year')
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 4.1:</b>
@@ -413,28 +379,25 @@ plt.title('Number of days as function of the year')
</ol>
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="604f11f41ac54d59b11422bac89e6411" deepnote_cell_type="markdown" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Solution:</b>
If you consider that you need to place a bet with not only the day but also the hour and minute at which the ice would break, the model is not accurate enough. You can see that the confidence interval spans almost 20 days!
</p>
</div>
-<!-- #endregion -->
-<!-- #region cell_id="a19fb5b7e1cd4c32b435b8b967933700" deepnote_cell_type="markdown" jp-MarkdownHeadingCollapsed=true -->
+
## Part 5: Non-linear Models
-<!-- #endregion -->
+
As we have seen, the data-driven linear model is not really a good choice for representing the data we have. Let's try with one which is slightly more complicated: a non-linear model.
In this section, we will analyze the fitting of a quadratic model as $days = A \cdot year^2 + B \cdot year + C$. The steps are the same as in the previous section, so we will go fast through the code to focus on the interpretation and comparison between the two models.
-<!-- #region cell_id="ca8a6b9d68234ae69a799a3f4f3866a2" deepnote_cell_type="markdown" -->
+
You do not need to worry about this right now, but in case you are curious: we will make use of the `scipy.optimize` library, which contains the `curve_fit` function. For further info on the function see [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html).
-<!-- #endregion -->
```python
def parabola(x, a, b, c):
@@ -463,19 +426,15 @@ print(f'Covariance matrix for parameters:\n\
Sigma = {pcov_parabola}')
```
-<!-- #region cell_id="10efd81771064ce0ac4d50095be06e23" deepnote_cell_type="markdown" -->
Therefore, our parabola now looks like $days = -1.277 \cdot 10^{-3} \cdot year^2 + 4.942 \cdot year - 4654.244$.
Now that we have fitted it, we can use it to compute predictions.
-<!-- #endregion -->
```python
fitted_parabola = parabola(data[:,0], *popt_parabola)
```
-<!-- #region cell_id="6cc6a36c668f4ee2b26d41b27d335691" deepnote_cell_type="markdown" -->
We can also determine the confidence intervals for this fit and see how it looks!
-<!-- #endregion -->
```python
k = conf_int(data[:,1], fitted_parabola, 0.05)
@@ -503,7 +462,6 @@ print(f'Coefficient of determination = {R2_parabola:.3f}')
rbias_parabola = rbias(data[:,1], fitted_parabola)
```
-<!-- #region cell_id="b564cab0d51c40f2aa3c7ebb9affaade" deepnote_cell_type="markdown" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 5.1:</b>
@@ -512,7 +470,7 @@ rbias_parabola = rbias(data[:,1], fitted_parabola)
</ol>
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
diff --git a/synced_files/Week_1_2/PA_1_2_Random_Adventure.md b/synced_files/Week_1_2/PA_1_2_Random_Adventure.md
index 31a407ea..999373e1 100644
--- a/synced_files/Week_1_2/PA_1_2_Random_Adventure.md
+++ b/synced_files/Week_1_2/PA_1_2_Random_Adventure.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# PA 1.2: A Random Adventure
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_1_2/PA_1_2_solution.md b/synced_files/Week_1_2/PA_1_2_solution.md
index 6d639e3f..01b880f7 100644
--- a/synced_files/Week_1_2/PA_1_2_solution.md
+++ b/synced_files/Week_1_2/PA_1_2_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Programming Assignment 02: A Random Adventure
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_1_2/WS_1_2_Pipe_Dreams.md b/synced_files/Week_1_2/WS_1_2_Pipe_Dreams.md
index e7749762..8f7409f5 100644
--- a/synced_files/Week_1_2/WS_1_2_Pipe_Dreams.md
+++ b/synced_files/Week_1_2/WS_1_2_Pipe_Dreams.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region id="9adbf457-797f-45b7-8f8b-0e46e0e2f5ff" -->
# WS 1.2: Mean and Variance Propagation
**Sewer Pipe Flow Velocity**
@@ -27,9 +16,8 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.2. Wed Sep 11, 2024.*
-<!-- #endregion -->
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+<!-- #region -->
## Overview
In this notebook you will apply the propagation laws for the mean and variance for a function of two independent random variables. You will assess how well the approximations correspond with the <em>simulation-based</em> equivalents. You will also assess the distribution of the function.
@@ -128,7 +116,7 @@ $$\sigma^2_X \approx \left(\frac{\partial q(\mu_Y )}{\partial Y_1 } \right)^2 \s
We are interested to know how the uncertainty in $R$ and $S$ propagates into the uncertainty of the flow velocity $V$. We will first do this analytically and then implement it in code.
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1.1:</b>
@@ -136,7 +124,7 @@ We are interested to know how the uncertainty in $R$ and $S$ propagates into the
Use the Taylor series approximation to find the expression for $\mu_V$ and $\sigma_V$ as function of $\mu_R$, $\sigma_R$, $\mu_S$, $\sigma_S$. Write your answer on paper or using a tablet; later we will learn how to include images directly in our notebooks! For now you can skip this step, as you are not turning this notebook in.
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -219,7 +207,7 @@ Interpret the figures above, specifically looking at differences between Case 1
_You can write an answer in this cell using Markdown._
-<!-- #region id="a7e4c13f-a2ca-4c2d-a3e2-92d4630715a0" -->
+
## Part 2: Simulation-Based Propagation
We will use again the following values:
@@ -230,7 +218,7 @@ We will use again the following values:
Furthermore, it is assumed that $R$ and $S$ are independent normally distributed random variables. We will generate at least 10,000 simulated realizations each of $R$ and $S$ using a random number generator, and then you need to use these to calculate the corresponding sample values of $V$ and find the moments of that sample.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
diff --git a/synced_files/Week_1_2/WS_1_2_solution.md b/synced_files/Week_1_2/WS_1_2_solution.md
index abcd56d7..965a17a1 100644
--- a/synced_files/Week_1_2/WS_1_2_solution.md
+++ b/synced_files/Week_1_2/WS_1_2_solution.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region id="9adbf457-797f-45b7-8f8b-0e46e0e2f5ff" -->
# WS 1.2: Mean and Variance Propagation
**Sewer Pipe Flow Velocity**
@@ -27,9 +16,8 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.2. Wed Sep 11, 2024.*
-<!-- #endregion -->
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+<!-- #region -->
## Overview
In this notebook you will apply the propagation laws for the mean and variance for a function of two independent random variables. You will assess how well the approximations correspond with the <em>simulation-based</em> equivalents. You will also assess the distribution of the function.
@@ -128,7 +116,7 @@ $$\sigma^2_X \approx \left(\frac{\partial q(\mu_Y )}{\partial Y_1 } \right)^2 \s
We are interested to know how the uncertainty in $R$ and $S$ propagates into the uncertainty of the flow velocity $V$. We will first do this analytically and then implement it in code.
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1.1:</b>
@@ -136,7 +124,7 @@ We are interested to know how the uncertainty in $R$ and $S$ propagates into the
Use the Taylor series approximation to find the expression for $\mu_V$ and $\sigma_V$ as function of $\mu_R$, $\sigma_R$, $\mu_S$, $\sigma_S$. Write your answer on paper or using a tablet; later we will learn how to include images directly in our notebooks! For now you can skip this step, as you are not turning this notebook in.
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
@@ -238,16 +226,15 @@ Interpret the figures above, specifically looking at differences between Case 1
</p>
</div>
-<!-- #region id="d3bdade1-2694-4ee4-a180-3872ee17a76d" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
$\textbf{Solution:}$
The standard deviation of $V$ is a non-linear function of $\sigma_R$ and $\sigma_S$ - the left figure shows how $\sigma_V$ increases as function of $\sigma_R$ for a given value $\sigma_S$.
If $\sigma_S$ is zero, there is no uncertainty in the slope of the pipe, and the standard deviation of $V$ becomes a linear function of $\sigma_R$ (right figure). The uncertainty of $V$ is smaller now, since it only depends on the uncertainty in $R$.
</div>
-<!-- #endregion -->
-<!-- #region id="a7e4c13f-a2ca-4c2d-a3e2-92d4630715a0" -->
+
## Part 2: Simulation-Based Propagation
We will use again the following values:
@@ -258,7 +245,7 @@ We will use again the following values:
Furthermore, it is assumed that $R$ and $S$ are independent normally distributed random variables. We will generate at least 10,000 simulated realizations each of $R$ and $S$ using a random number generator, and then you need to use these to calculate the corresponding sample values of $V$ and find the moments of that sample.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -455,7 +442,6 @@ def samples_slideplot(sigma_R):
validate_distribution(50000, sigma_R);
```
-<!-- #region id="782c842e-ceb8-4e3c-b767-1f3efa4fb9b2" -->
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
$\mathbf{Solution:}$
@@ -468,7 +454,7 @@ Using a different value for $\sigma_R$ has several impacts:
The reason for this is that $V$ is a non-linear function of the normally distributed random variables $R$ and $S$, due to the non-linearity $V$ will <em>not</em> be Normally distributed.
</div>
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
diff --git a/synced_files/Week_1_4/WS_1_4_Nonlinear_Rain.md b/synced_files/Week_1_4/WS_1_4_Nonlinear_Rain.md
index 9752e96c..ae3cbe94 100644
--- a/synced_files/Week_1_4/WS_1_4_Nonlinear_Rain.md
+++ b/synced_files/Week_1_4/WS_1_4_Nonlinear_Rain.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region id="c-kJ0rgzjVsW" -->
# WS 1.4: Non-Linear Water
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,9 +14,8 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.4. Wed Sep 25, 2024.*
-<!-- #endregion -->
-<!-- #region id="taoAYUNojl6N" -->
+
In this notebook we will apply the least-squares method to a non-linear model: [non-linear least-squares estimation](https://mude.citg.tudelft.nl/2024/book/observation_theory/07_NLSQ.html). In this case our model is non-linear in the unknown parameters of interest:
$$
@@ -69,9 +57,8 @@ where $a$ [days] is a scaling parameter representing the memory of the system (d
$$
h_k(t) = p\cdot r\left(\exp\left(-\frac{t-t_e}{a}\right)-\exp\left(-\frac{t-t_0}{a}\right)\right), \;\; \text{for}\;\; t > t_{\text{end}}
$$
-<!-- #endregion -->
-<!-- #region id="aGZfDs4VT3DM" -->
+
## Functional model
For this example, we consider a single rainfall event. At $t_0=4$ days, it starts raining, and at $t_{\text{end}}=7$ days it stops raining (and we assume the amount of rainfall to be constant during these days, for the sake of the example...). The observation equations become:
@@ -91,7 +78,6 @@ $$\mathbb{E}\left[L(t_i)\right] = d + H(t_i-t_{0}) \cdot p\cdot r \left(1-\exp\l
with $H(\Delta t) = 1$ if $\Delta t \geq 0$ and $H(\Delta t) = 0$ otherwise. <b>Check yourself that this gives indeed the same observation equations as above.</b>
The functional model can be defined as follows, using <a href="https://numpy.org/doc/stable/reference/generated/numpy.heaviside.html"> NumPy's heaviside</a> function:
-<!-- #endregion -->
```python
import numpy as np
@@ -143,7 +129,6 @@ def compute_y(x, times, rain):
return h
```
-<!-- #region id="4IEjIQSAT78k" -->
### Apply the Functional Model
If we generate a vector of time steps, we can plot the (exact) response of a system with parameters using the ```rain_event``` function defined above.
@@ -151,7 +136,7 @@ If we generate a vector of time steps, we can plot the (exact) response of a sys
The known input is: $p=0.05$ m.
Choose your own values for $d$, $a$, and $r$.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -179,11 +164,9 @@ plt.xlim([0, test_n_days])
plt.ylim([0, 5]);
```
-<!-- #region id="wnp2SL3dj0on" -->
## Reading in the observations
We collected observations of the water level in the aquifer for 25 consecutive days and are stored ```data``` folder. Observations start at $t=1$.
-<!-- #endregion -->
```python
n_days = 25
@@ -196,7 +179,6 @@ plt.xlabel('Time [days]')
plt.ylabel('Waterlevel [m]');
```
-<!-- #region id="SPXDSa9AkFkY" -->
## Estimating the parameters using Gauss-Newton iteration
Using only the data of the observations we want to find the values for $d$, $a$ and $r$. This can be done with Gauss-Newton iteration.
@@ -215,12 +197,11 @@ Our stop criterion is:
$$
\Delta \hat{\mathrm{x}}_{[i]}^T \mathrm{N}_{[i]} \Delta \hat{\mathrm{x}}_{[i]} < \delta \;\; \text{with} \;\;\mathrm{N}_{[i]}=\mathrm{J}_{[i]}^T \Sigma_{Y}^{-1} \mathrm{J}_{[i]}
$$
-<!-- #endregion -->
-<!-- #region id="1-REMPFykGZi" -->
+
## Computing the partial derivatives for the Jacobian matrix
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -253,7 +234,6 @@ def jacobian(x, times, rain):
return J
```
-<!-- #region id="MfBggMEPkQyZ" -->
## Running the Gauss-Newton iteration
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
@@ -272,7 +252,6 @@ Next we will set the stochastic model, and initialize some other variables neede
Note in particular the 2 stop criteria used for the while loop. You should reach a solution within 50 iterations, otherwise you should reconsider the initial values.
</p>
</div>
-<!-- #endregion -->
```python
d_init = YOUR_CODE_HERE
@@ -363,13 +342,12 @@ plt.xlabel('Time [days]')
plt.ylabel('Water level [m]');
```
-<!-- #region id="KXE88mjeOzOn" -->
## Visualization
### Estimates vs. iteration number
Now we will consider how the estimate of the model parameter changes during the Gauss-Newton iteration.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -422,7 +400,6 @@ else:
print(f"(T = {Tq:.1f}) > (K = {k:.1f}), OMT is rejected.")
```
-<!-- #region id="MfBggMEPkQyZ" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 8:</b>
@@ -430,7 +407,7 @@ else:
Play with the value of <code>alpha</code> and see how it changes the critical value <code>k</code>. Why does it become smaller/larger? What is the impact on the probability of rejecting the null hypothesis?
</p>
</div>
-<!-- #endregion -->
+
**End of notebook.**
<h2 style="height: 60px">
diff --git a/synced_files/Week_1_4/WS_1_4_solution.md b/synced_files/Week_1_4/WS_1_4_solution.md
index 46fa5de3..3966789b 100644
--- a/synced_files/Week_1_4/WS_1_4_solution.md
+++ b/synced_files/Week_1_4/WS_1_4_solution.md
@@ -1,16 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
-<!-- #region id="c-kJ0rgzjVsW" -->
# WS 1.4: Non-Linear Water
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,9 +14,8 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.4. Wed Sep 25, 2024.*
-<!-- #endregion -->
-<!-- #region id="taoAYUNojl6N" -->
+
In this notebook we will apply the least-squares method to a non-linear model: [non-linear least-squares estimation](https://mude.citg.tudelft.nl/2024/book/observation_theory/07_NLSQ.html). In this case our model is non-linear in the unknown parameters of interest:
$$
@@ -69,9 +57,8 @@ where $a$ [days] is a scaling parameter representing the memory of the system (d
$$
h_k(t) = p\cdot r\left(\exp\left(-\frac{t-t_e}{a}\right)-\exp\left(-\frac{t-t_0}{a}\right)\right), \;\; \text{for}\;\; t > t_{\text{end}}
$$
-<!-- #endregion -->
-<!-- #region id="aGZfDs4VT3DM" -->
+
## Functional model
For this example, we consider a single rainfall event. At $t_0=4$ days, it starts raining, and at $t_{\text{end}}=7$ days it stops raining (and we assume the amount of rainfall to be constant during these days, for the sake of the example...). The observation equations become:
@@ -91,7 +78,6 @@ $$\mathbb{E}\left[L(t_i)\right] = d + H(t_i-t_{0}) \cdot p\cdot r \left(1-\exp\l
with $H(\Delta t) = 1$ if $\Delta t \geq 0$ and $H(\Delta t) = 0$ otherwise. <b>Check yourself that this gives indeed the same observation equations as above.</b>
The functional model can be defined as follows, using <a href="https://numpy.org/doc/stable/reference/generated/numpy.heaviside.html"> NumPy's heaviside</a> function:
-<!-- #endregion -->
```python
import numpy as np
@@ -152,7 +138,7 @@ As described in the docstring there are three unkown parameters of interest (d,
</p>
</div>
-<!-- #region id="4IEjIQSAT78k" -->
+
### Apply the Functional Model
If we generate a vector of time steps, we can plot the (exact) response of a system with parameters using the ```rain_event``` function defined above.
@@ -160,7 +146,7 @@ If we generate a vector of time steps, we can plot the (exact) response of a sys
The known input is: $p=0.05$ m.
Choose your own values for $d$, $a$, and $r$.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -193,11 +179,9 @@ plt.xlim([0, test_n_days])
plt.ylim([0, 5]);
```
-<!-- #region id="wnp2SL3dj0on" -->
## Reading in the observations
We collected observations of the water level in the aquifer for 25 consecutive days and are stored ```data``` folder. Observations start at $t=1$.
-<!-- #endregion -->
```python
n_days = 25
@@ -210,7 +194,6 @@ plt.xlabel('Time [days]')
plt.ylabel('Waterlevel [m]');
```
-<!-- #region id="SPXDSa9AkFkY" -->
## Estimating the parameters using Gauss-Newton iteration
Using only the data of the observations we want to find the values for $d$, $a$ and $r$. This can be done with Gauss-Newton iteration.
@@ -229,12 +212,11 @@ Our stop criterion is:
$$
\Delta \hat{\mathrm{x}}_{[i]}^T \mathrm{N}_{[i]} \Delta \hat{\mathrm{x}}_{[i]} < \delta \;\; \text{with} \;\;\mathrm{N}_{[i]}=\mathrm{J}_{[i]}^T \Sigma_{Y}^{-1} \mathrm{J}_{[i]}
$$
-<!-- #endregion -->
-<!-- #region id="1-REMPFykGZi" -->
+
## Computing the partial derivatives for the Jacobian matrix
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -285,7 +267,6 @@ def jacobian(x, times, rain):
return J
```
-<!-- #region id="MfBggMEPkQyZ" -->
## Running the Gauss-Newton iteration
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
@@ -304,7 +285,6 @@ Next we will set the stochastic model, and initialize some other variables neede
Note in particular the 2 stop criteria used for the while loop. You should reach a solution within 50 iterations, otherwise you should reconsider the initial values.
</p>
</div>
-<!-- #endregion -->
```python
# d_init = YOUR_CODE_HERE
@@ -429,13 +409,12 @@ plt.xlabel('Time [days]')
plt.ylabel('Water level [m]');
```
-<!-- #region id="KXE88mjeOzOn" -->
## Visualization
### Estimates vs. iteration number
Now we will consider how the estimate of the model parameter changes during the Gauss-Newton iteration.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -501,7 +480,6 @@ else:
print(f"(T = {Tq:.1f}) > (K = {k:.1f}), OMT is rejected.")
```
-<!-- #region id="MfBggMEPkQyZ" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 8:</b>
@@ -509,7 +487,7 @@ else:
Play with the value of <code>alpha</code> and see how it changes the critical value <code>k</code>. Why does it become smaller/larger? What is the impact on the probability of rejecting the null hypothesis?
</p>
</div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
diff --git a/synced_files/Week_1_6/PA/PA_1_6_Boxes_and_Bugs.md b/synced_files/Week_1_6/PA/PA_1_6_Boxes_and_Bugs.md
index 4c55074e..377bca19 100644
--- a/synced_files/Week_1_6/PA/PA_1_6_Boxes_and_Bugs.md
+++ b/synced_files/Week_1_6/PA/PA_1_6_Boxes_and_Bugs.md
@@ -1,15 +1,5 @@
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# PA 1.6: Boxes and Bugs
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_1_6/PA/PA_1_6_solution.md b/synced_files/Week_1_6/PA/PA_1_6_solution.md
index 4498b0b6..6917058e 100644
--- a/synced_files/Week_1_6/PA/PA_1_6_solution.md
+++ b/synced_files/Week_1_6/PA/PA_1_6_solution.md
@@ -1,15 +1,5 @@
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----
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# PA 1.6: Boxes and Bugs
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_1_6/WS_1_6_solution.md b/synced_files/Week_1_6/WS_1_6_solution.md
index ec847a68..136b3d1e 100644
--- a/synced_files/Week_1_6/WS_1_6_solution.md
+++ b/synced_files/Week_1_6/WS_1_6_solution.md
@@ -1,15 +1,5 @@
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<!-- #region -->
## Overview
diff --git a/synced_files/Week_1_6/WS_1_6_time_to_c_ode.md b/synced_files/Week_1_6/WS_1_6_time_to_c_ode.md
index 48c0fb4e..6c68fed3 100644
--- a/synced_files/Week_1_6/WS_1_6_time_to_c_ode.md
+++ b/synced_files/Week_1_6/WS_1_6_time_to_c_ode.md
@@ -1,16 +1,5 @@
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-<!-- #region id="9adbf457-797f-45b7-8f8b-0e46e0e2f5ff" -->
# WS 1.6: Understanding Ordinary Differential Equation
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -26,7 +15,6 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.6. For: 9th October, 2024.*
-<!-- #endregion -->
<!-- #region -->
## Overview
diff --git a/synced_files/Week_1_7/PA/PA_1_7_Classy_Distributions.md b/synced_files/Week_1_7/PA/PA_1_7_Classy_Distributions.md
index 7bd15114..8c84cdc0 100644
--- a/synced_files/Week_1_7/PA/PA_1_7_Classy_Distributions.md
+++ b/synced_files/Week_1_7/PA/PA_1_7_Classy_Distributions.md
@@ -1,15 +1,5 @@
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# PA 1.7: Classy Distributions
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_1_7/PA/PA_1_7_solution.md b/synced_files/Week_1_7/PA/PA_1_7_solution.md
index 0a85fccc..b145f4f8 100644
--- a/synced_files/Week_1_7/PA/PA_1_7_solution.md
+++ b/synced_files/Week_1_7/PA/PA_1_7_solution.md
@@ -1,15 +1,5 @@
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-
# PA 1.7: Classy Distributions
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_1_7/WS_1_7_lets_be_concrete.md b/synced_files/Week_1_7/WS_1_7_lets_be_concrete.md
index f252ac4c..0a5264c9 100644
--- a/synced_files/Week_1_7/WS_1_7_lets_be_concrete.md
+++ b/synced_files/Week_1_7/WS_1_7_lets_be_concrete.md
@@ -1,15 +1,5 @@
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-
# WS 1.7: Modelling Uncertain Concrete Strength
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.7. Due: October 16, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
Assessing the uncertainties in the compressive strength of the produced concrete is key for the safety of infrastructures and buildings. However, a lot of boundary conditions influence the final resistance of the concrete, such the cement content, the environmental temperature or the age of the concrete. Probabilistic tools can be applied to model this uncertainty. In this workshop, you will work with a dataset of observations of the compressive strength of concrete (you can read more about the dataset [here](https://www.kaggle.com/datasets/gauravduttakiit/compressive-strength-of-concrete)).
**The goal of this project is:**
@@ -35,7 +25,6 @@ Assessing the uncertainties in the compressive strength of the produced concrete
4. Assess the fit using goodness of fit techniques and computer code.
The project will be divided into 3 parts: 1) data analysis, 2) pen and paper stuff (math practice!), and 3) programming.
-<!-- #endregion -->
```python
import numpy as np
@@ -80,7 +69,6 @@ df_describe = pd.DataFrame(data)
df_describe.describe()
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1.1:</b>
@@ -89,7 +77,7 @@ df_describe.describe()
<li>Justiy your choice.</li>
</p>
</div>
-<!-- #endregion -->
+
_Your answer here._
@@ -139,14 +127,13 @@ You can summarize you answers in the following table (report your values with 3-
Now, let's assess the performance using further goodness of fit metrics and see whether they are consistent with the previously done analysis.
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.1:</b>
Prepare a function to compute the empirical cumulative distribution function.
</p>
</div>
-<!-- #endregion -->
```python
def ecdf(YOUR_CODE_HERE):
@@ -154,7 +141,6 @@ def ecdf(YOUR_CODE_HERE):
return YOUR_CODE_HERE
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.2:</b>
@@ -163,7 +149,7 @@ Transform the fitted parameters for the selected distribution to loc-scale-shape
</div>
Hint: the distributions are in our online textbook, but it is also critical to make sure that the formulation in the book is identical to that of the Python package we are using. You can do this by finding the page of the relevant distribution in the [Scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html) documentation.
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
diff --git a/synced_files/Week_1_7/WS_1_7_solution.md b/synced_files/Week_1_7/WS_1_7_solution.md
index 2ffb00fb..09951d64 100644
--- a/synced_files/Week_1_7/WS_1_7_solution.md
+++ b/synced_files/Week_1_7/WS_1_7_solution.md
@@ -1,15 +1,5 @@
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# WS 1.7: Modelling Uncertain Concrete Strength
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.7. Due: October 16, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
Assessing the uncertainties in the compressive strength of the produced concrete is key for the safety of infrastructures and buildings. However, a lot of boundary conditions influence the final resistance of the concrete, such the cement content, the environmental temperature or the age of the concrete. Probabilistic tools can be applied to model this uncertainty. In this workshop, you will work with a dataset of observations of the compressive strength of concrete (you can read more about the dataset [here](https://www.kaggle.com/datasets/gauravduttakiit/compressive-strength-of-concrete)).
**The goal of this project is:**
@@ -35,7 +25,6 @@ Assessing the uncertainties in the compressive strength of the produced concrete
4. Assess the fit using goodness of fit techniques and computer code.
The project will be divided into 3 parts: 1) data analysis, 2) pen and paper stuff (math practice!), and 3) programming.
-<!-- #endregion -->
```python
import numpy as np
@@ -80,7 +69,6 @@ df_describe = pd.DataFrame(data)
df_describe.describe()
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1.1:</b>
@@ -89,11 +77,11 @@ df_describe.describe()
<li>Justiy your choice.</li>
</p>
</div>
-<!-- #endregion -->
+
_Your answer here._
-<!-- #region id="d3bdade1-2694-4ee4-a180-3872ee17a76d" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
@@ -101,7 +89,7 @@ _Your answer here._
- Uniform and Gaussian distributions are symmetric so they are not appropriate to model the observations. We can see it by computing the difference between the minimum value and the P50% and between the maximum value and P50%. $d_{min, 50}= 33.87-2.33 = 31.54 < d_{max, 50} = 82.60 - 33.87 = 48.72$.
</div>
</div>
-<!-- #endregion -->
+
## Part 2: Use pen and paper!
@@ -119,7 +107,7 @@ Fit the selected distribution by moments.
_Your answer here._
-<!-- #region id="d3bdade1-2694-4ee4-a180-3872ee17a76d" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
Fitting by moments a distribution implies equating the moments of the observations to those of the parametric distribution. Applying then the expressions of the mean and variance of the Gumbel distribution we obtain:
@@ -133,7 +121,7 @@ $
$
</div>
</div>
-<!-- #endregion -->
+
We can now check the fit by computing manually some probabilities from the fitted distribution and comparing them with the empirical ones.
@@ -157,7 +145,7 @@ You can summarize you answers in the following table (report your values with 3-
|Empirical quantiles [MPa] | | | | | |
|Predicted quantiles [MPa] ||||||
-<!-- #region id="d3bdade1-2694-4ee4-a180-3872ee17a76d" -->
+<!-- #region -->
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
@@ -187,14 +175,13 @@ The values close to the central moments (P25%, P50% and P75%) are well fitted. R
Now, let's assess the performance using further goodness of fit metrics and see whether they are consistent with the previously done analysis.
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.1:</b>
Prepare a function to compute the empirical cumulative distribution function.
</p>
</div>
-<!-- #endregion -->
```python
# def ecdf(YOUR_CODE_HERE):
@@ -209,7 +196,6 @@ def ecdf(observations):
return [y, x]
```
-<!-- #region id="bfadcf3f-4578-4809-acdb-625ab3a71f27" -->
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 3.2:</b>
@@ -218,17 +204,17 @@ Transform the fitted parameters for the selected distribution to loc-scale-shape
</div>
Hint: the distributions are in our online textbook, but it is also critical to make sure that the formulation in the book is identical to that of the Python package we are using. You can do this by finding the page of the relevant distribution in the [Scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html) documentation.
-<!-- #endregion -->
+
_Your answer here._
-<!-- #region id="d3bdade1-2694-4ee4-a180-3872ee17a76d" -->
+
<div style="background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<b>Solution:</b>
The Gumbel distribution is already parameterized in terms of loc-scale-shape. You don't need to do anything!
</div>
</div>
-<!-- #endregion -->
+
<div style="background-color:#AABAB2; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
diff --git a/synced_files/Week_1_7/scipy_stats.md b/synced_files/Week_1_7/scipy_stats.md
index f3c236f6..191516cf 100644
--- a/synced_files/Week_1_7/scipy_stats.md
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@@ -1,15 +1,5 @@
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# Tips for Teachers: Week 1.7, Continuous Distributions
Book chapters [here](https://mude.citg.tudelft.nl/2024/book/probability/Reminder_intro.html).
diff --git a/synced_files/Week_1_8/PA/PA_1_8_Equations_Done_Symply.md b/synced_files/Week_1_8/PA/PA_1_8_Equations_Done_Symply.md
index 89f8859a..319b2435 100644
--- a/synced_files/Week_1_8/PA/PA_1_8_Equations_Done_Symply.md
+++ b/synced_files/Week_1_8/PA/PA_1_8_Equations_Done_Symply.md
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# PA 1.8: Equations Done Symply
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_1_8/PA/PA_1_8_solution.md b/synced_files/Week_1_8/PA/PA_1_8_solution.md
index d606b78b..7bc2986c 100644
--- a/synced_files/Week_1_8/PA/PA_1_8_solution.md
+++ b/synced_files/Week_1_8/PA/PA_1_8_solution.md
@@ -1,15 +1,5 @@
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# PA 1.8: Equations Done Symply
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_1_8/WS/WS_1_8_Thingamajig.md b/synced_files/Week_1_8/WS/WS_1_8_Thingamajig.md
index 4e620bfa..d54a23c5 100644
--- a/synced_files/Week_1_8/WS/WS_1_8_Thingamajig.md
+++ b/synced_files/Week_1_8/WS/WS_1_8_Thingamajig.md
@@ -1,16 +1,5 @@
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# WS 1.8: The Thingamajig!
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +14,6 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.8. For: 23 October, 2024.*
-<!-- #endregion -->
<!-- #region -->
## Objective
@@ -177,11 +165,10 @@ Build the bivariate distribution using <code>scipy.stats.multivariate_normal</co
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Use the helper function <code>plot_contour</code> in <code>helper.py</code>; it was already imported above. Either look in the file to read it, or view the documentation in the notebook with <code>plot_contour?</code></p>
<p><em>Hint: for this Task use the optional </em><code>data</code><em> argument!.</em></p></div>
-<!-- #endregion -->
```python
# plot_contour? # uncomment and run to read docstring
@@ -221,12 +208,11 @@ For each of the three cases, do the following:
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note that the optional arguments in the helper function <code>plot_contour</code> will be useful here--<b>also for the Project on Friday!</b>
Here is an example code that shows you what it can do (the values are meaningless)
</p></div>
-<!-- #endregion -->
```python
region_example = np.array([[0, 5, 12, 20, 28, 30],
@@ -275,9 +261,8 @@ YOUR_CODE_HERE
# probably several cells too ;)
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: the bivariate figures are an important concept for the exam, so if using the code is too difficult for you to use when studying on your own, try sketching it on paper.</p></div>
-<!-- #endregion -->
+
## Part 3: Validate Bivariate with Monte Carlo Simulation
@@ -380,9 +365,8 @@ axes.legend()
axes.grid()
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"><p>In case you are wondering, the data for this exercise was computed with a Clayton Copula. A Copula is a useful way of modelling non-linear dependence. If you would like to learn more about this, you should consider the 2nd year cross-over module CEGM2005 Probabilistic Modelling of real-world phenomena through ObseRvations and Elicitation (MORE).</p></div>
-<!-- #endregion -->
+
**End of notebook.**
<h2 style="height: 60px">
diff --git a/synced_files/Week_1_8/WS/WS_1_8_solution.md b/synced_files/Week_1_8/WS/WS_1_8_solution.md
index 4ac2710d..a62badc0 100644
--- a/synced_files/Week_1_8/WS/WS_1_8_solution.md
+++ b/synced_files/Week_1_8/WS/WS_1_8_solution.md
@@ -1,16 +1,5 @@
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-<!-- #region id="9adbf457-797f-45b7-8f8b-0e46e0e2f5ff" -->
# WS 1.8: The Thingamajig!
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +14,6 @@ jupyter:
</h2>
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 1.8. For: 23 October, 2024.*
-<!-- #endregion -->
<!-- #region -->
## Objective
@@ -223,11 +211,10 @@ Build the bivariate distribution using <code>scipy.stats.multivariate_normal</co
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Use the helper function <code>plot_contour</code> in <code>helper.py</code>; it was already imported above. Either look in the file to read it, or view the documentation in the notebook with <code>plot_contour?</code></p>
<p><em>Hint: for this Task use the optional </em><code>data</code><em> argument!.</em></p></div>
-<!-- #endregion -->
```python
# plot_contour? # uncomment and run to read docstring
@@ -304,12 +291,11 @@ For each of the three cases, do the following:
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note that the optional arguments in the helper function <code>plot_contour</code> will be useful here--<b>also for the Project on Friday!</b>
Here is an example code that shows you what it can do (the values are meaningless)
</p></div>
-<!-- #endregion -->
```python
region_example = np.array([[0, 5, 12, 20, 28, 30],
@@ -364,9 +350,9 @@ which is then defined in Python and included in the `plot_contours` function as
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: order of the tasks in this solution is not important.</p></div>
-<!-- #endregion -->
+
### Case 1 and 2
@@ -552,9 +538,9 @@ You should be able to confirm these observations by considering how the contours
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"> <p>Note: the bivariate figures are an important concept for the exam, so if using the code is too difficult for you to use when studying on your own, try sketching it on paper.</p></div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
@@ -734,9 +720,8 @@ axes.legend()
axes.grid()
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px"><p>In case you are wondering, the data for this exercise was computed with a Clayton Copula. A Copula is a useful way of modelling non-linear dependence. If you would like to learn more about this, you should consider the 2nd year cross-over module CEGM2005 Probabilistic Modelling of real-world phenomena through ObseRvations and Elicitation (MORE).</p></div>
-<!-- #endregion -->
+
**End of notebook.**
<h2 style="height: 60px">
diff --git a/synced_files/Week_1_8/WS/linearity_or_not.md b/synced_files/Week_1_8/WS/linearity_or_not.md
index 66e99562..caef2114 100644
--- a/synced_files/Week_1_8/WS/linearity_or_not.md
+++ b/synced_files/Week_1_8/WS/linearity_or_not.md
@@ -1,15 +1,5 @@
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```python
import numpy as np
import matplotlib.pyplot as plt
diff --git a/synced_files/Week_1_8/data.md b/synced_files/Week_1_8/data.md
index 789ed298..fc80c7ab 100644
--- a/synced_files/Week_1_8/data.md
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```python
import numpy as np
from scipy import stats
diff --git a/synced_files/Week_1_8/intersection.md b/synced_files/Week_1_8/intersection.md
index 10e8148c..e7489c75 100644
--- a/synced_files/Week_1_8/intersection.md
+++ b/synced_files/Week_1_8/intersection.md
@@ -1,15 +1,5 @@
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```python
import numpy as np
from scipy import stats
diff --git a/synced_files/Week_1_8/test_bivariate.md b/synced_files/Week_1_8/test_bivariate.md
index c7c94f4f..00707966 100644
--- a/synced_files/Week_1_8/test_bivariate.md
+++ b/synced_files/Week_1_8/test_bivariate.md
@@ -1,15 +1,5 @@
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# Illustration `bivariate`
diff --git a/synced_files/Week_2_1/PA/PA_2_1_classy_city.md b/synced_files/Week_2_1/PA/PA_2_1_classy_city.md
index 07a38819..803cfc39 100644
--- a/synced_files/Week_2_1/PA/PA_2_1_classy_city.md
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# PA 2.1: Classy City
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_1/PA/PA_2_1_solution.md b/synced_files/Week_2_1/PA/PA_2_1_solution.md
index 2bccb7c5..0c403723 100644
--- a/synced_files/Week_2_1/PA/PA_2_1_solution.md
+++ b/synced_files/Week_2_1/PA/PA_2_1_solution.md
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# PA 2.1: Classy City
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_1/PA/PA_dev.md b/synced_files/Week_2_1/PA/PA_dev.md
index 6dc2cbf0..126f31ce 100644
--- a/synced_files/Week_2_1/PA/PA_dev.md
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```python
%load_ext autoreload
%autoreload 2
diff --git a/synced_files/Week_2_1/PA/U.md b/synced_files/Week_2_1/PA/U.md
index 0411e592..5964207f 100644
--- a/synced_files/Week_2_1/PA/U.md
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```python
length = 10
height = (length**2 - (length/2)**2)**0.5
diff --git a/synced_files/Week_2_1/WS_2_1_solution.md b/synced_files/Week_2_1/WS_2_1_solution.md
index 0f1cabba..0349b358 100644
--- a/synced_files/Week_2_1/WS_2_1_solution.md
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# WS 2.1: Wiggles
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0">
@@ -248,13 +238,12 @@ def check_variables_1D():
print(f'CFL: {calculated_CFL:.2e}')
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Start of code to define a dictionary to help keep track of analysis "cases." This is not part of the handout.</b>
</p>
</div>
-<!-- #endregion -->
+
To help present specific cases in the solution, a dictionary is used to store the key Python variable values that define the problem of interest are stored:
```
@@ -322,13 +311,12 @@ p0, c, L, Nx, T, Nt, dx, dt, central, square = case_set(C[3])
check_variables_1D()
```
-<!-- #region id="0491cc69" -->
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>End of dictionary code.</b> Note that it is used below, for example with <code>case_set</code>
</p>
</div>
-<!-- #endregion -->
+
Variables are set below, then you should use the functions provided, for example, `check_variables_1D`, prior to running a simulation to make sure you are solving the problem you think you are!
@@ -555,13 +543,12 @@ $$
The code is set up in a very similar way to the 1D case above. Use it to explore how the advection problem works in 2D! In particular, see if you observe the effect called "numerical diffusion" --- when the numerical scheme causes the square pulse to "diffuse" into a bell shaped surface. Even though only the advection term was implmented!
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
The initial values of the variables below will result in numerical instability. See if you can fix it!
</p>
</div>
-<!-- #endregion -->
```python
p0 = 2.0
diff --git a/synced_files/Week_2_1/WS_2_1_wiggle.md b/synced_files/Week_2_1/WS_2_1_wiggle.md
index 499bba28..7bf9440e 100644
--- a/synced_files/Week_2_1/WS_2_1_wiggle.md
+++ b/synced_files/Week_2_1/WS_2_1_wiggle.md
@@ -1,15 +1,5 @@
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# WS 2.1: Wiggles
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0">
@@ -412,13 +402,12 @@ $$
The code is set up in a very similar way to the 1D case above. Use it to explore how the advection problem works in 2D! In particular, see if you observe the effect called "numerical diffusion" --- when the numerical scheme causes the square pulse to "diffuse" into a bell shaped surface. Even though only the advection term was implmented!
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
The initial values of the variables below will result in numerical instability. See if you can fix it!
</p>
</div>
-<!-- #endregion -->
```python
p0 = 2.0
diff --git a/synced_files/Week_2_1/WS_2_1_wiggle_test_ci.md b/synced_files/Week_2_1/WS_2_1_wiggle_test_ci.md
index 499bba28..7bf9440e 100644
--- a/synced_files/Week_2_1/WS_2_1_wiggle_test_ci.md
+++ b/synced_files/Week_2_1/WS_2_1_wiggle_test_ci.md
@@ -1,15 +1,5 @@
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# WS 2.1: Wiggles
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0">
@@ -412,13 +402,12 @@ $$
The code is set up in a very similar way to the 1D case above. Use it to explore how the advection problem works in 2D! In particular, see if you observe the effect called "numerical diffusion" --- when the numerical scheme causes the square pulse to "diffuse" into a bell shaped surface. Even though only the advection term was implmented!
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
The initial values of the variables below will result in numerical instability. See if you can fix it!
</p>
</div>
-<!-- #endregion -->
```python
p0 = 2.0
diff --git a/synced_files/Week_2_2/PA/PA_2_2_love_is_sparse.md b/synced_files/Week_2_2/PA/PA_2_2_love_is_sparse.md
index c3a9d680..e55b0aae 100644
--- a/synced_files/Week_2_2/PA/PA_2_2_love_is_sparse.md
+++ b/synced_files/Week_2_2/PA/PA_2_2_love_is_sparse.md
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# PA 2.2: Love is Sparse
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/PA/PA_2_2_solution.md b/synced_files/Week_2_2/PA/PA_2_2_solution.md
index d3a2fbe3..68a12a97 100644
--- a/synced_files/Week_2_2/PA/PA_2_2_solution.md
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# PA 2.2: Love is Sparse
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/PA_2_2_love_is_sparse.md b/synced_files/Week_2_2/PA_2_2_love_is_sparse.md
index 99b781fe..9d41a5f0 100644
--- a/synced_files/Week_2_2/PA_2_2_love_is_sparse.md
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# PA 2.2: Love is Sparse
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/PA_2_2_solution.md b/synced_files/Week_2_2/PA_2_2_solution.md
index a61bf58e..1718225b 100644
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# PA 2.2: Love is Sparse
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/WS_2_2_more_support.md b/synced_files/Week_2_2/WS_2_2_more_support.md
index 47edc1fe..ef2c96c2 100644
--- a/synced_files/Week_2_2/WS_2_2_more_support.md
+++ b/synced_files/Week_2_2/WS_2_2_more_support.md
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# WS 2.2: More support
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/WS_2_2_solution.md b/synced_files/Week_2_2/WS_2_2_solution.md
index a0ea3b14..caa3affc 100644
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# WS 2.2: More support
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/old/PA_2_1_solution.md b/synced_files/Week_2_2/old/PA_2_1_solution.md
index bab1794d..de3ea599 100644
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# Programming Assignment 10: Love Is Sparse
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/old/PA_2_1_solution_sympy.html b/synced_files/Week_2_2/old/PA_2_1_solution_sympy.html
index 47b10dcc..d74305fd 100644
--- a/synced_files/Week_2_2/old/PA_2_1_solution_sympy.html
+++ b/synced_files/Week_2_2/old/PA_2_1_solution_sympy.html
@@ -8031,7 +8031,7 @@ BSR: 0.0106
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=7da1f6e4">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=5b06a029">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8041,7 +8041,7 @@ BSR: 0.0106
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=094f5a0c">
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@@ -8055,7 +8055,7 @@ BSR: 0.0106
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=01665b5b">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=7fdcd51a">
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</div>
@@ -8069,7 +8069,7 @@ BSR: 0.0106
</div>
</div>
</div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=033c58ff">
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</div>
@@ -8085,7 +8085,7 @@ BSR: 0.0106
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=215ca219">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=39e2b25b">
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@@ -8100,7 +8100,7 @@ BSR: 0.0106
</div>
</div>
</div>
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@@ -8127,7 +8127,7 @@ $\displaystyle 1.63099252391669 \cdot 10^{-851}$
</div>
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@@ -8137,7 +8137,7 @@ $\displaystyle 1.63099252391669 \cdot 10^{-851}$
</div>
</div>
</div>
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8199,7 +8199,7 @@ $\displaystyle 1.63099252391669 \cdot 10^{-851}$
</div>
</div>
</div>
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diff --git a/synced_files/Week_2_2/old/PA_2_1_solution_sympy.ipynb b/synced_files/Week_2_2/old/PA_2_1_solution_sympy.ipynb
index 4a446ea1..56f04276 100644
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+++ b/synced_files/Week_2_2/old/PA_2_1_solution_sympy.ipynb
@@ -386,7 +386,7 @@
},
{
"cell_type": "markdown",
- "id": "e4e1ee66",
+ "id": "536c28de",
"metadata": {},
"source": [
"You could also solve this problem using sympy! What would be the benefit of doing this? Check below how long it will take! You won't need timeit for this one..."
@@ -395,7 +395,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "f6230b91",
+ "id": "8732437c",
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{
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{
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@@ -427,7 +427,7 @@
{
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@@ -438,7 +438,7 @@
{
"cell_type": "code",
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- "id": "461986b3",
+ "id": "57742cdd",
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"outputs": [],
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@@ -447,7 +447,7 @@
},
{
"cell_type": "markdown",
- "id": "30ac8973",
+ "id": "2a9a31d8",
"metadata": {},
"source": [
"What is the result for the 501th value using float values?"
@@ -456,7 +456,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "b9a8ccb6",
+ "id": "98d72428",
"metadata": {},
"outputs": [],
"source": [
@@ -496,7 +496,7 @@
},
{
"cell_type": "markdown",
- "id": "e3fbadc9",
+ "id": "f3646d71",
"metadata": {},
"source": []
}
diff --git a/synced_files/Week_2_2/old/PA_2_1_solution_sympy.md b/synced_files/Week_2_2/old/PA_2_1_solution_sympy.md
index f7cf5981..8d856d8f 100644
--- a/synced_files/Week_2_2/old/PA_2_1_solution_sympy.md
+++ b/synced_files/Week_2_2/old/PA_2_1_solution_sympy.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
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- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
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# Programming Assignment 10: Love Is Sparse
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/old/WS_2_2_more_support.md b/synced_files/Week_2_2/old/WS_2_2_more_support.md
index c14ba761..b054037f 100644
--- a/synced_files/Week_2_2/old/WS_2_2_more_support.md
+++ b/synced_files/Week_2_2/old/WS_2_2_more_support.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
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- extension: .md
- format_name: markdown
- format_version: '1.3'
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----
-
# WS 2.2: More support
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/old/old_PA10_Love_is_Sparse.md b/synced_files/Week_2_2/old/old_PA10_Love_is_Sparse.md
index 38f46a8f..b2aec04c 100644
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+++ b/synced_files/Week_2_2/old/old_PA10_Love_is_Sparse.md
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- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
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# Programming Assignment 10: Love Is Sparse
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_2/old/old_PA10_solution_sympy.html b/synced_files/Week_2_2/old/old_PA10_solution_sympy.html
index 097ab60b..0cec8d4a 100644
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</div>
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c37367c3">
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@@ -8041,7 +8041,7 @@ BSR: 0.0106
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@@ -8069,7 +8069,7 @@ BSR: 0.0106
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@@ -8100,7 +8100,7 @@ BSR: 0.0106
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=3e66efa8">
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@@ -8127,7 +8127,7 @@ $\displaystyle 1.63099252391669 \cdot 10^{-851}$
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ba702843">
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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@@ -8137,7 +8137,7 @@ $\displaystyle 1.63099252391669 \cdot 10^{-851}$
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8199,7 +8199,7 @@ $\displaystyle 1.63099252391669 \cdot 10^{-851}$
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=8eef908b">
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diff --git a/synced_files/Week_2_2/old/old_PA10_solution_sympy.ipynb b/synced_files/Week_2_2/old/old_PA10_solution_sympy.ipynb
index 73b1c22e..97d02b93 100644
--- a/synced_files/Week_2_2/old/old_PA10_solution_sympy.ipynb
+++ b/synced_files/Week_2_2/old/old_PA10_solution_sympy.ipynb
@@ -386,7 +386,7 @@
},
{
"cell_type": "markdown",
- "id": "2085d56f",
+ "id": "cdcefd0b",
"metadata": {},
"source": [
"You could also solve this problem using sympy! What would be the benefit of doing this? Check below how long it will take! You won't need timeit for this one..."
@@ -395,7 +395,7 @@
{
"cell_type": "code",
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- "id": "b963caf2",
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{
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{
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@@ -447,7 +447,7 @@
},
{
"cell_type": "markdown",
- "id": "8ac2ada4",
+ "id": "68fa3ccf",
"metadata": {},
"source": [
"What is the result for the 501th value using float values?"
@@ -456,7 +456,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "a434bb22",
+ "id": "d63a6b79",
"metadata": {},
"outputs": [],
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@@ -496,7 +496,7 @@
},
{
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+ "id": "fb84dd97",
"metadata": {},
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}
diff --git a/synced_files/Week_2_2/old/old_PA10_solution_sympy.md b/synced_files/Week_2_2/old/old_PA10_solution_sympy.md
index f7cf5981..8d856d8f 100644
--- a/synced_files/Week_2_2/old/old_PA10_solution_sympy.md
+++ b/synced_files/Week_2_2/old/old_PA10_solution_sympy.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
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- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
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-
# Programming Assignment 10: Love Is Sparse
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_3/PA/PA_2_3_iter_remoto.md b/synced_files/Week_2_3/PA/PA_2_3_iter_remoto.md
index 91adbdd4..5a6c1c31 100644
--- a/synced_files/Week_2_3/PA/PA_2_3_iter_remoto.md
+++ b/synced_files/Week_2_3/PA/PA_2_3_iter_remoto.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
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----
-
# PA 2.3: iTer-remoto
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_3/PA/PA_2_3_solution.md b/synced_files/Week_2_3/PA/PA_2_3_solution.md
index 4a8fba3c..4151f74b 100644
--- a/synced_files/Week_2_3/PA/PA_2_3_solution.md
+++ b/synced_files/Week_2_3/PA/PA_2_3_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
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- format_version: '1.3'
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-
# PA 2.3: iTer-remoto
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_3/WS_2_3_DFT_you_try_meow.md b/synced_files/Week_2_3/WS_2_3_DFT_you_try_meow.md
index 90496b9d..0b4eeb30 100644
--- a/synced_files/Week_2_3/WS_2_3_DFT_you_try_meow.md
+++ b/synced_files/Week_2_3/WS_2_3_DFT_you_try_meow.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
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-
# WS 2.3: Discrete Fourier Transform (DFT): You Try Meow (Miauw)
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -44,10 +34,9 @@ That's right! We convert our signal into the frequency domain.
And if you would like an additional explanation of the key frequencies, you can find it [here](https://medium.com/@kovalenko.alx/fun-with-fourier-591662576a77).
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>Note the use of <code>zip</code>, <code>stem</code>, <code>annotate</code> and the modulo operator
<code>%</code>. Refer to PA 2.3 if you do not understand these tools. Furthermore, note that the term _modulus_ is also used here (and in the textbook), which is another term for _absolute value._</p></div>
-<!-- #endregion -->
```python
import numpy as np
diff --git a/synced_files/Week_2_3/WS_2_3_solution.md b/synced_files/Week_2_3/WS_2_3_solution.md
index deab5c58..c9072595 100644
--- a/synced_files/Week_2_3/WS_2_3_solution.md
+++ b/synced_files/Week_2_3/WS_2_3_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
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----
-
# WS 2.3: Discrete Fourier Transform (DFT): You Try Meow (Miauw)
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -39,10 +29,9 @@ _Find the answer [here](https://tudelft.h5p.com/content/1292126914399042257/embe
That's right! We convert our signal into the frequency domain. And if you would like an additional explanation of the key frequencies, you can find it [here](https://medium.com/@kovalenko.alx/fun-with-fourier-591662576a77).
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>Note the use of <code>zip</code>, <code>stem</code>, <code>annotate</code> and the modulo operator
<code>%</code>. Refer to PA11 if you do not understand these tools. Furthermore, note that the term _modulus_ is also used here (and in the textbook), which is another term for _absolute value._</p></div>
-<!-- #endregion -->
```python
import numpy as np
diff --git a/synced_files/Week_2_4/PA/PA_2_4_A_gurobilicious.md b/synced_files/Week_2_4/PA/PA_2_4_A_gurobilicious.md
index 22314c00..10b86ae6 100644
--- a/synced_files/Week_2_4/PA/PA_2_4_A_gurobilicious.md
+++ b/synced_files/Week_2_4/PA/PA_2_4_A_gurobilicious.md
@@ -1,15 +1,5 @@
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# PA 2.4A: Gurobi Environment and License
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_4/PA/PA_2_4_B_axis_of_awesome.md b/synced_files/Week_2_4/PA/PA_2_4_B_axis_of_awesome.md
index c74c3a6c..b5f65398 100644
--- a/synced_files/Week_2_4/PA/PA_2_4_B_axis_of_awesome.md
+++ b/synced_files/Week_2_4/PA/PA_2_4_B_axis_of_awesome.md
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-
# PA 2.4B: [Axis of Awesome](https://youtu.be/5pidokakU4I?si=Y5ewcgPFFQ5cLmC6)
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_4/PA/PA_2_4_B_solution.html b/synced_files/Week_2_4/PA/PA_2_4_B_solution.html
index 5be84496..b9c20481 100644
--- a/synced_files/Week_2_4/PA/PA_2_4_B_solution.html
+++ b/synced_files/Week_2_4/PA/PA_2_4_B_solution.html
@@ -7977,26 +7977,39 @@ Max coins spent in any year: 290.0
<p><strong>End of notebook.</strong></p>
<h2 style="height: 60px">
</h2>
-<h3 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0">
+<h3 style="position: relative; display: flex; flex-direction: row-reverse; margin: 20px 50px; border: 0">
<style>
.markdown {width:100%; position: relative}
article { position: relative }
+ .footer-links {
+ display: flex;
+ flex-direction: row-reverse;
+ align-items: center;
+ gap: 20px;
+ margin-bottom: 20px;
+ }
+ .footer-links img {
+ height: auto;
+ max-width: 100px;
+ }
</style>
-<a href="http://creativecommons.org/licenses/by-nc-sa/4.0/" rel="license">
-<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
+<div class="footer-links">
+<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">
+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="width:88px; padding-top:10px"/>
</a>
<a href="https://www.tudelft.nl/en/ceg" rel="TU Delft">
-<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="width:100px;"/>
</a>
<a href="http://mude.citg.tudelft.nl/" rel="MUDE">
-<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="width:100px;"/>
</a>
+</div>
</h3>
<span style="font-size: 75%">
-© Copyright 2023 <a href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595" rel="MUDE Team">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/" rel="license">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
-
-
-</span></div>
+© Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
+</span>
+<p><userstyle>Normal</userstyle></p>
+</div>
</div>
</div>
</div>
diff --git a/synced_files/Week_2_4/PA/PA_2_4_B_solution.ipynb b/synced_files/Week_2_4/PA/PA_2_4_B_solution.ipynb
index b5137380..53ccf677 100644
--- a/synced_files/Week_2_4/PA/PA_2_4_B_solution.ipynb
+++ b/synced_files/Week_2_4/PA/PA_2_4_B_solution.ipynb
@@ -357,24 +357,39 @@
"**End of notebook.**\n",
"<h2 style=\"height: 60px\">\n",
"</h2>\n",
- "<h3 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0\">\n",
+ "<h3 style=\"position: relative; display: flex; flex-direction: row-reverse; margin: 20px 50px; border: 0\">\n",
" <style>\n",
" .markdown {width:100%; position: relative}\n",
" article { position: relative }\n",
+ " .footer-links {\n",
+ " display: flex;\n",
+ " flex-direction: row-reverse;\n",
+ " align-items: center;\n",
+ " gap: 20px;\n",
+ " margin-bottom: 20px;\n",
+ " }\n",
+ " .footer-links img {\n",
+ " height: auto;\n",
+ " max-width: 100px;\n",
+ " }\n",
" </style>\n",
- " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n",
- " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n",
- " </a>\n",
- " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n",
- " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n",
- " </a>\n",
- " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
- " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n",
- " </a>\n",
- " \n",
+ " <div class=\"footer-links\">\n",
+ " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">\n",
+ " <img alt=\"Creative Commons License\" style=\"width:88px; padding-top:10px\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" />\n",
+ " </a>\n",
+ " <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n",
+ " <img alt=\"TU Delft\" style=\"width:100px;\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" />\n",
+ " </a>\n",
+ " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
+ " <img alt=\"MUDE\" style=\"width:100px;\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" />\n",
+ " </a>\n",
+ " </div>\n",
"</h3>\n",
"<span style=\"font-size: 75%\">\n",
- "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>."
+ "© Copyright 2024 <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">MUDE</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">CC BY 4.0 License</a>.\n",
+ "</span>\n",
+ "\n",
+ "<userStyle>Normal</userStyle>"
]
}
],
diff --git a/synced_files/Week_2_4/PA/PA_2_4_B_solution.md b/synced_files/Week_2_4/PA/PA_2_4_B_solution.md
index 62560128..b1cff44f 100644
--- a/synced_files/Week_2_4/PA/PA_2_4_B_solution.md
+++ b/synced_files/Week_2_4/PA/PA_2_4_B_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# PA 2.4B: [Axis of Awesome](https://youtu.be/5pidokakU4I?si=Y5ewcgPFFQ5cLmC6)
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -191,21 +181,36 @@ plot_acf(strong_autocorr_positive);
**End of notebook.**
<h2 style="height: 60px">
</h2>
-<h3 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0">
+<h3 style="position: relative; display: flex; flex-direction: row-reverse; margin: 20px 50px; border: 0">
<style>
.markdown {width:100%; position: relative}
article { position: relative }
+ .footer-links {
+ display: flex;
+ flex-direction: row-reverse;
+ align-items: center;
+ gap: 20px;
+ margin-bottom: 20px;
+ }
+ .footer-links img {
+ height: auto;
+ max-width: 100px;
+ }
</style>
- <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">
- <img alt="Creative Commons License" style="border-width:; width:88px; height:auto; padding-top:10px" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" />
- </a>
- <a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
- <img alt="TU Delft" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png"/>
- </a>
- <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
- <img alt="MUDE" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png"/>
- </a>
-
+ <div class="footer-links">
+ <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">
+ <img alt="Creative Commons License" style="width:88px; padding-top:10px" src="https://i.creativecommons.org/l/by/4.0/88x31.png" />
+ </a>
+ <a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
+ <img alt="TU Delft" style="width:100px;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" />
+ </a>
+ <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
+ <img alt="MUDE" style="width:100px;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" />
+ </a>
+ </div>
</h3>
<span style="font-size: 75%">
-© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+© Copyright 2024 <a rel="MUDE" href="http://mude.citg.tudelft.nl/">MUDE</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 License</a>.
+</span>
+
+<userStyle>Normal</userStyle>
diff --git a/synced_files/Week_2_4/PA/PA_2_4_B_solution.py b/synced_files/Week_2_4/PA/PA_2_4_B_solution.py
index 48444df0..700cf58d 100644
--- a/synced_files/Week_2_4/PA/PA_2_4_B_solution.py
+++ b/synced_files/Week_2_4/PA/PA_2_4_B_solution.py
@@ -189,21 +189,36 @@ plot_acf(strong_autocorr_positive);
# **End of notebook.**
# <h2 style="height: 60px">
# </h2>
-# <h3 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0">
+# <h3 style="position: relative; display: flex; flex-direction: row-reverse; margin: 20px 50px; border: 0">
# <style>
# .markdown {width:100%; position: relative}
# article { position: relative }
+# .footer-links {
+# display: flex;
+# flex-direction: row-reverse;
+# align-items: center;
+# gap: 20px;
+# margin-bottom: 20px;
+# }
+# .footer-links img {
+# height: auto;
+# max-width: 100px;
+# }
# </style>
-# <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">
-# <img alt="Creative Commons License" style="border-width:; width:88px; height:auto; padding-top:10px" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" />
-# </a>
-# <a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
-# <img alt="TU Delft" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png"/>
-# </a>
-# <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
-# <img alt="MUDE" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png"/>
-# </a>
-#
+# <div class="footer-links">
+# <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">
+# <img alt="Creative Commons License" style="width:88px; padding-top:10px" src="https://i.creativecommons.org/l/by/4.0/88x31.png" />
+# </a>
+# <a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
+# <img alt="TU Delft" style="width:100px;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" />
+# </a>
+# <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
+# <img alt="MUDE" style="width:100px;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" />
+# </a>
+# </div>
# </h3>
# <span style="font-size: 75%">
-# © Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+# © Copyright 2024 <a rel="MUDE" href="http://mude.citg.tudelft.nl/">MUDE</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 License</a>.
+# </span>
+#
+# <userStyle>Normal</userStyle>
diff --git a/synced_files/Week_2_4/WS_2_4_Feel_the_Pressure.md b/synced_files/Week_2_4/WS_2_4_Feel_the_Pressure.md
index 0a041e87..2a13ab63 100644
--- a/synced_files/Week_2_4/WS_2_4_Feel_the_Pressure.md
+++ b/synced_files/Week_2_4/WS_2_4_Feel_the_Pressure.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# WS 2.4: Atmospheric Pressure
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_4/WS_2_4_solution.md b/synced_files/Week_2_4/WS_2_4_solution.md
index 6437cca2..cb9a0f93 100644
--- a/synced_files/Week_2_4/WS_2_4_solution.md
+++ b/synced_files/Week_2_4/WS_2_4_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# WS 2.4: Atmospheric Pressure
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_5/PA/PA_2_5_data_framework.md b/synced_files/Week_2_5/PA/PA_2_5_data_framework.md
index 44bbf96c..0e9c308d 100644
--- a/synced_files/Week_2_5/PA/PA_2_5_data_framework.md
+++ b/synced_files/Week_2_5/PA/PA_2_5_data_framework.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# PA 2.5: Data Framework
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_5/PA/PA_2_5_solution.md b/synced_files/Week_2_5/PA/PA_2_5_solution.md
index c46fe9c1..c6e5101a 100644
--- a/synced_files/Week_2_5/PA/PA_2_5_solution.md
+++ b/synced_files/Week_2_5/PA/PA_2_5_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# PA 2.5: Data Framework
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_5/WS_2_5_Profit_vs_Planet.md b/synced_files/Week_2_5/WS_2_5_Profit_vs_Planet.md
index 8123d032..aedbffd8 100644
--- a/synced_files/Week_2_5/WS_2_5_Profit_vs_Planet.md
+++ b/synced_files/Week_2_5/WS_2_5_Profit_vs_Planet.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# WS 2.5: Profit vs Planet
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_5/WS_2_5_solution.md b/synced_files/Week_2_5/WS_2_5_solution.md
index 3ed5f1df..2986169e 100644
--- a/synced_files/Week_2_5/WS_2_5_solution.md
+++ b/synced_files/Week_2_5/WS_2_5_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# WS 2.5: Profit vs Planet
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_6/PA/PA_2_6_3_way_split.md b/synced_files/Week_2_6/PA/PA_2_6_3_way_split.md
index 410620bc..07fee4c0 100644
--- a/synced_files/Week_2_6/PA/PA_2_6_3_way_split.md
+++ b/synced_files/Week_2_6/PA/PA_2_6_3_way_split.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# PA 2.6: 3-Way Split
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_6/PA/PA_2_6_solution.md b/synced_files/Week_2_6/PA/PA_2_6_solution.md
index 2c4d5ad3..1cfffd78 100644
--- a/synced_files/Week_2_6/PA/PA_2_6_solution.md
+++ b/synced_files/Week_2_6/PA/PA_2_6_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# PA 2.6: 3-Way Split
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_6/WS_2_6_be_a_NN.md b/synced_files/Week_2_6/WS_2_6_be_a_NN.md
index f6fd1017..270db5c1 100644
--- a/synced_files/Week_2_6/WS_2_6_be_a_NN.md
+++ b/synced_files/Week_2_6/WS_2_6_be_a_NN.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# WS 2.6 Be like a Neural Network
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_6/WS_2_6_solution.md b/synced_files/Week_2_6/WS_2_6_solution.md
index 35a294b3..43613849 100644
--- a/synced_files/Week_2_6/WS_2_6_solution.md
+++ b/synced_files/Week_2_6/WS_2_6_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
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- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# WS 2.6: Be like a Neural Network
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_7/PA/PA_2_7_Times_Tables.md b/synced_files/Week_2_7/PA/PA_2_7_Times_Tables.md
index b9526ea8..2779fe29 100644
--- a/synced_files/Week_2_7/PA/PA_2_7_Times_Tables.md
+++ b/synced_files/Week_2_7/PA/PA_2_7_Times_Tables.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# PA 2.7: Times Tables test
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_7/PA/PA_2_7_solution.md b/synced_files/Week_2_7/PA/PA_2_7_solution.md
index 8dd3e5df..dbf9ee09 100644
--- a/synced_files/Week_2_7/PA/PA_2_7_solution.md
+++ b/synced_files/Week_2_7/PA/PA_2_7_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# PA 2.7: Times Tables
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/Week_2_7/WS_2_7_solution.html b/synced_files/Week_2_7/WS_2_7_solution.html
index 3927c55c..5fce9b8a 100644
--- a/synced_files/Week_2_7/WS_2_7_solution.html
+++ b/synced_files/Week_2_7/WS_2_7_solution.html
@@ -7793,7 +7793,7 @@ Name: Date, Length: 3909, dtype: datetime64[ns]</pre>
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=27cb928e">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=501d379d">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7809,7 +7809,7 @@ Based on the above plots, briefly describe the data.
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=9a39108c">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=91993c04">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7836,7 +7836,7 @@ Based on the above plots, briefly describe the data.
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=aee75fd3">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=b4346b41">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7871,7 +7871,7 @@ Sample monthly maxima from the timeseries and plot them on the timeseries. Plot
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=37391f1a">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=d34bdefa">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7922,7 +7922,7 @@ Sample monthly maxima from the timeseries and plot them on the timeseries. Plot
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=7be79b5a">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ec3abd62">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7938,7 +7938,7 @@ Look at the previous plots. Are the sampled maxima independent and identically d
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ac284558">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=8e6434c7">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -8033,7 +8033,7 @@ Compute PDF and the empirical cumulative distribution function of the observatio
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=78306e6f">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=46f1f770">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/Week_2_7/WS_2_7_solution.ipynb b/synced_files/Week_2_7/WS_2_7_solution.ipynb
index 0fb415b2..bebb5600 100644
--- a/synced_files/Week_2_7/WS_2_7_solution.ipynb
+++ b/synced_files/Week_2_7/WS_2_7_solution.ipynb
@@ -130,7 +130,7 @@
},
{
"cell_type": "markdown",
- "id": "30cae0c1",
+ "id": "450bb4ab",
"metadata": {},
"source": [
"<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
@@ -143,7 +143,7 @@
},
{
"cell_type": "markdown",
- "id": "652592df",
+ "id": "df84ffa7",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
@@ -166,7 +166,7 @@
},
{
"cell_type": "markdown",
- "id": "c2905476",
+ "id": "0af017c5",
"metadata": {},
"source": [
"<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
@@ -196,7 +196,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "a0d21f36",
+ "id": "d2a2b498",
"metadata": {},
"outputs": [],
"source": [
@@ -223,7 +223,7 @@
},
{
"cell_type": "markdown",
- "id": "4ca84250",
+ "id": "900a7477",
"metadata": {},
"source": [
"<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
@@ -236,7 +236,7 @@
},
{
"cell_type": "markdown",
- "id": "66d45180",
+ "id": "0fd88d1f",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
@@ -309,7 +309,7 @@
},
{
"cell_type": "markdown",
- "id": "66b18235",
+ "id": "7084f1db",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
diff --git a/synced_files/Week_2_7/WS_2_7_solution.md b/synced_files/Week_2_7/WS_2_7_solution.md
index bce683a2..8732dd1d 100644
--- a/synced_files/Week_2_7/WS_2_7_solution.md
+++ b/synced_files/Week_2_7/WS_2_7_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Workshop 2.7: Extreme temperature
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Extreme Value Analysis, Week 2.7, Wednesday, Jan 8, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
In this session, you will work with the uncertainty of extreme temperatures in the airport of Barcelona to assess the extreme loads induced by temperature in a steel structure in the area. You have daily observations of the maximum temperature for several years. The dataset was retrieved from the Spanish Agency of Metheorology [AEMET](https://www.aemet.es/es/portada). Your goal is to design the structure for a _lifespan of 50 years_ with a _probability of failure of 0.1_ during the design life.
**The goal of this project is:**
@@ -34,7 +24,6 @@ In this session, you will work with the uncertainty of extreme temperatures in t
3. Assess the Goodness of fit of the distribution.
4. Compute the return level plot.
5. Compute the design return level.
-<!-- #endregion -->
```python
import numpy as np
@@ -228,10 +217,9 @@ Fit a distribution to the monthly maxima. Print the values of the obtained param
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>Use <a href="https://docs.scipy.org/doc/scipy/reference/stats.html" target="_blank">scipy.stats</a> built in functions (watch out with the parameter definitions!), similar to Week 1.7 and use the DataFrame created in Task 2.
</p></div>
-<!-- #endregion -->
```python
params_T = stats.genextreme.fit(max_list['T'])
@@ -330,10 +318,10 @@ Considering that you have sampled monthly maxima, compute and plot the return le
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>The three missing variables listed below are all type <code>numpy.ndarray</code>; the last is found using your <code>scipy.stats</code> distribution from Task 3.
</p></div>
-<!-- #endregion -->
+
<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
<b>Solution:</b>
diff --git a/synced_files/Week_2_7/WS_2_7_student.html b/synced_files/Week_2_7/WS_2_7_student.html
index 55b73b02..a0c99ead 100644
--- a/synced_files/Week_2_7/WS_2_7_student.html
+++ b/synced_files/Week_2_7/WS_2_7_student.html
@@ -7690,7 +7690,7 @@ a.anchor-link {
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=23be66ca">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=6562ef15">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7717,7 +7717,7 @@ Based on the above plots, briefly describe the data.
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=70c47495">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ac944347">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
@@ -7753,7 +7753,7 @@ Sample monthly maxima from the timeseries and plot them on the timeseries. Plot
</div>
</div>
</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=98171604">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a8574be0">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
diff --git a/synced_files/Week_2_7/WS_2_7_student.ipynb b/synced_files/Week_2_7/WS_2_7_student.ipynb
index a3b56c9d..aae5336d 100644
--- a/synced_files/Week_2_7/WS_2_7_student.ipynb
+++ b/synced_files/Week_2_7/WS_2_7_student.ipynb
@@ -130,7 +130,7 @@
},
{
"cell_type": "markdown",
- "id": "e5972dfb",
+ "id": "445076f7",
"metadata": {},
"source": [
"<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
@@ -151,7 +151,7 @@
},
{
"cell_type": "markdown",
- "id": "16b560ba",
+ "id": "d1494520",
"metadata": {},
"source": [
"<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
@@ -180,7 +180,7 @@
},
{
"cell_type": "markdown",
- "id": "bb31f828",
+ "id": "e47c30b2",
"metadata": {},
"source": [
"<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
diff --git a/synced_files/Week_2_7/WS_2_7_student.md b/synced_files/Week_2_7/WS_2_7_student.md
index 84c0c50a..431668e8 100644
--- a/synced_files/Week_2_7/WS_2_7_student.md
+++ b/synced_files/Week_2_7/WS_2_7_student.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Workshop 2.7: Extreme temperature
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -25,7 +15,7 @@ jupyter:
*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Extreme Value Analysis, Week 2.7, Wednesday, Jan 8, 2024.*
-<!-- #region id="1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" -->
+
In this session, you will work with the uncertainty of extreme temperatures in the airport of Barcelona to assess the extreme loads induced by temperature in a steel structure in the area. You have daily observations of the maximum temperature for several years. The dataset was retrieved from the Spanish Agency of Metheorology [AEMET](https://www.aemet.es/es/portada). Your goal is to design the structure for a _lifespan of 50 years_ with a _probability of failure of 0.1_ during the design life.
**The goal of this project is:**
@@ -34,7 +24,6 @@ In this session, you will work with the uncertainty of extreme temperatures in t
3. Assess the Goodness of fit of the distribution.
4. Compute the return level plot.
5. Compute the design return level.
-<!-- #endregion -->
```python
import numpy as np
@@ -153,10 +142,9 @@ Fit a distribution to the monthly maxima. Print the values of the obtained param
</p>
</div>
-<!-- #region id="0491cc69" -->
+
<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>Use <a href="https://docs.scipy.org/doc/scipy/reference/stats.html" target="_blank">scipy.stats</a> built in functions (watch out with the parameter definitions!), similar to Week 1.7 and use the DataFrame created in Task 2.
</p></div>
-<!-- #endregion -->
```python
params_T = #your code here
diff --git a/synced_files/Week_2_8/WS_2_8_dirty_water.html b/synced_files/Week_2_8/WS_2_8_dirty_water.html
index 41bb0bb2..4f9b8fdf 100644
--- a/synced_files/Week_2_8/WS_2_8_dirty_water.html
+++ b/synced_files/Week_2_8/WS_2_8_dirty_water.html
@@ -7358,11 +7358,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.6.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7433,7 +7434,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7448,6 +7450,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only `<br>` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
</script>
@@ -7532,7 +7566,7 @@ a.anchor-link {
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<h2 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">¶</a></h2><p>In this exercise we apply a few simple concepts from the textbook to derive a safety standard to regulate spills in a chemical factory. You are asked to consider an economic and societal standard, then make a recommendation to the city council.</p>
<h2 id="Case-Study">Case Study<a class="anchor-link" href="#Case-Study">¶</a></h2><p>A city with population 10,000 uses an aquifer for its water supply, as illustrated in the figure. The city owns a factory in the region that manufactures hazardous chemicals, and recently a chemical spill occurred that resulted in 10 residents getting sick and total damages of €7,000M*. The city is going to enforce stricter regulations on the factory, and <em>you have been hired to advise the city council on the maximum allowable probability of a spill occurring (per year)</em>. You will make a recommendation based on the more stringent criteria between economic and societal risk limits.</p>
-<p><img alt="" src="./sketch.png"/></p>
+<p><img alt="sketch of factory, houses and contamination plume" src="./sketch.png"/></p>
<p>Experts have been consulted and it appears under the current plan the probability of a spill is 1/100 per year. The city council is considering two strategies to upgrade the spill prevention system. A small upgrade would cost €25M and can reduce spill probability by a factor 10; a large upgrade with investment costs of €50M would reduce the probability by factor 100.</p>
<p>The city has also considered the regulations in a nearby region which uses a maximum allowable probability of 1 person getting sick as p_f=0.01. The city agrees with this, however, they are very much <em>risk averse</em> (that's a hint!), regarding spills with more significant consequences.</p>
<p><em>*M = million, so 7,000M is 7e9, or 7 billion. All costs in this exercise are expressed in units €M.</em></p>
@@ -7663,8 +7697,8 @@ a.anchor-link {
.markdown {width:100%; position: relative}
article { position: relative }
</style>
-<a href="http://creativecommons.org/licenses/by-nc-sa/4.0/" rel="license">
-<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
+<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">
+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
</a>
<a href="https://www.tudelft.nl/en/ceg" rel="TU Delft">
<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
@@ -7674,7 +7708,8 @@ a.anchor-link {
</a>
</h3>
<span style="font-size: 75%">
-© Copyright 2023 <a href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595" rel="MUDE Team">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/" rel="license">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+© Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
+
</span></div>
</div>
diff --git a/synced_files/Week_2_8/WS_2_8_dirty_water.ipynb b/synced_files/Week_2_8/WS_2_8_dirty_water.ipynb
index b688da6c..54e0db8a 100644
--- a/synced_files/Week_2_8/WS_2_8_dirty_water.ipynb
+++ b/synced_files/Week_2_8/WS_2_8_dirty_water.ipynb
@@ -34,7 +34,7 @@
"\n",
"A city with population 10,000 uses an aquifer for its water supply, as illustrated in the figure. The city owns a factory in the region that manufactures hazardous chemicals, and recently a chemical spill occurred that resulted in 10 residents getting sick and total damages of €7,000M*. The city is going to enforce stricter regulations on the factory, and _you have been hired to advise the city council on the maximum allowable probability of a spill occurring (per year)_. You will make a recommendation based on the more stringent criteria between economic and societal risk limits.\n",
"\n",
- "\n",
+ "\n",
"\n",
"Experts have been consulted and it appears under the current plan the probability of a spill is 1/100 per year. The city council is considering two strategies to upgrade the spill prevention system. A small upgrade would cost €25M and can reduce spill probability by a factor 10; a large upgrade with investment costs of €50M would reduce the probability by factor 100.\n",
"\n",
@@ -148,19 +148,19 @@
" .markdown {width:100%; position: relative}\n",
" article { position: relative }\n",
" </style>\n",
- " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n",
- " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n",
+ " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">\n",
+ " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" />\n",
" </a>\n",
" <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n",
- " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n",
+ " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" />\n",
" </a>\n",
" <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
- " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n",
+ " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" />\n",
" </a>\n",
" \n",
"</h3>\n",
"<span style=\"font-size: 75%\">\n",
- "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>."
+ "© Copyright 2024 <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">MUDE</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">CC BY 4.0 License</a>."
]
}
],
diff --git a/synced_files/Week_2_8/WS_2_8_dirty_water.md b/synced_files/Week_2_8/WS_2_8_dirty_water.md
index e546e358..ce501d2f 100644
--- a/synced_files/Week_2_8/WS_2_8_dirty_water.md
+++ b/synced_files/Week_2_8/WS_2_8_dirty_water.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
----
-
# Workshop 16: Dirty Water
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -34,7 +24,7 @@ In this exercise we apply a few simple concepts from the textbook to derive a sa
A city with population 10,000 uses an aquifer for its water supply, as illustrated in the figure. The city owns a factory in the region that manufactures hazardous chemicals, and recently a chemical spill occurred that resulted in 10 residents getting sick and total damages of €7,000M*. The city is going to enforce stricter regulations on the factory, and _you have been hired to advise the city council on the maximum allowable probability of a spill occurring (per year)_. You will make a recommendation based on the more stringent criteria between economic and societal risk limits.
-
+
Experts have been consulted and it appears under the current plan the probability of a spill is 1/100 per year. The city council is considering two strategies to upgrade the spill prevention system. A small upgrade would cost €25M and can reduce spill probability by a factor 10; a large upgrade with investment costs of €50M would reduce the probability by factor 100.
@@ -115,16 +105,16 @@ We added <a href="https://mude.citg.tudelft.nl/book/pd/reliability-component/con
.markdown {width:100%; position: relative}
article { position: relative }
</style>
- <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">
- <img alt="Creative Commons License" style="border-width:; width:88px; height:auto; padding-top:10px" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" />
+ <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">
+ <img alt="Creative Commons License" style="border-width:; width:88px; height:auto; padding-top:10px" src="https://i.creativecommons.org/l/by/4.0/88x31.png" />
</a>
<a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
- <img alt="TU Delft" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png"/>
+ <img alt="TU Delft" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" />
</a>
<a rel="MUDE" href="http://mude.citg.tudelft.nl/">
- <img alt="MUDE" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png"/>
+ <img alt="MUDE" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" />
</a>
</h3>
<span style="font-size: 75%">
-© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+© Copyright 2024 <a rel="MUDE" href="http://mude.citg.tudelft.nl/">MUDE</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 License</a>.
diff --git a/synced_files/Week_2_8/WS_2_8_dirty_water.py b/synced_files/Week_2_8/WS_2_8_dirty_water.py
index a165a7c0..624e7720 100644
--- a/synced_files/Week_2_8/WS_2_8_dirty_water.py
+++ b/synced_files/Week_2_8/WS_2_8_dirty_water.py
@@ -33,7 +33,7 @@
#
# A city with population 10,000 uses an aquifer for its water supply, as illustrated in the figure. The city owns a factory in the region that manufactures hazardous chemicals, and recently a chemical spill occurred that resulted in 10 residents getting sick and total damages of €7,000M*. The city is going to enforce stricter regulations on the factory, and _you have been hired to advise the city council on the maximum allowable probability of a spill occurring (per year)_. You will make a recommendation based on the more stringent criteria between economic and societal risk limits.
#
-# 
+# 
#
# Experts have been consulted and it appears under the current plan the probability of a spill is 1/100 per year. The city council is considering two strategies to upgrade the spill prevention system. A small upgrade would cost €25M and can reduce spill probability by a factor 10; a large upgrade with investment costs of €50M would reduce the probability by factor 100.
#
@@ -114,16 +114,16 @@ import matplotlib.pyplot as plt
# .markdown {width:100%; position: relative}
# article { position: relative }
# </style>
-# <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">
-# <img alt="Creative Commons License" style="border-width:; width:88px; height:auto; padding-top:10px" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" />
+# <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">
+# <img alt="Creative Commons License" style="border-width:; width:88px; height:auto; padding-top:10px" src="https://i.creativecommons.org/l/by/4.0/88x31.png" />
# </a>
# <a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
-# <img alt="TU Delft" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png"/>
+# <img alt="TU Delft" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" />
# </a>
# <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
-# <img alt="MUDE" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png"/>
+# <img alt="MUDE" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" />
# </a>
#
# </h3>
# <span style="font-size: 75%">
-# © Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+# © Copyright 2024 <a rel="MUDE" href="http://mude.citg.tudelft.nl/">MUDE</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 License</a>.
diff --git a/synced_files/Week_2_8/WS_2_8_solution.html b/synced_files/Week_2_8/WS_2_8_solution.html
index a7725b12..e5600664 100644
--- a/synced_files/Week_2_8/WS_2_8_solution.html
+++ b/synced_files/Week_2_8/WS_2_8_solution.html
@@ -3,34 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
-(function() {
- function addWidgetsRenderer() {
- var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
- var scriptElement = document.createElement('script');
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
-
- var widgetState;
-
- // Fallback for older version:
- try {
- widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
-
- if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) {
-
- var widgetRendererSrc = 'https://unpkg.com/@jupyter-js-widgets@*/dist/embed.js';
-
- }
- } catch(e) {}
-
- scriptElement.src = widgetRendererSrc;
- document.body.appendChild(scriptElement);
- }
-
- document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
-}());
-</script>
+<title>Notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7358,11 +7331,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.6.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7433,7 +7407,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7448,6 +7423,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only `<br>` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
</script>
@@ -7532,7 +7539,7 @@ a.anchor-link {
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<h2 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">¶</a></h2><p>In this exercise we apply a few simple concepts from the textbook to derive a safety standard to regulate spills in a chemical factory. You are asked to consider an economic and societal standard, then make a recommendation to the city council.</p>
<h2 id="Case-Study">Case Study<a class="anchor-link" href="#Case-Study">¶</a></h2><p>A city with population 10,000 uses an aquifer for its water supply, as illustrated in the figure. The city owns a factory in the region that manufactures hazardous chemicals, and recently a chemical spill occurred that resulted in 10 residents getting sick and total damages of €7,000M*. The city is going to enforce stricter regulations on the factory, and <em>you have been hired to advise the city council on the maximum allowable probability of a spill occurring (per year)</em>. You will make a recommendation based on the more stringent criteria between economic and societal risk limits.</p>
-<p><img alt="" src="./sketch.png"/></p>
+<p><img alt="sketch of factory, houses and contamination plume" src="./sketch.png"/></p>
<p>Experts have been consulted and it appears under the current plan the probability of a spill is 1/100 per year. The city council is considering two strategies to upgrade the spill prevention system. A small upgrade would cost €25M and can reduce spill probability by a factor 10; a large upgrade with investment costs of €50M would reduce the probability by factor 100.</p>
<p>The city has also considered the regulations in a nearby region which uses a maximum allowable probability of 1 person getting sick as p_f=0.01. The city agrees with this, however, they are very much <em>risk averse</em> (that's a hint!), regarding spills with more significant consequences.</p>
<p><em>*M = million, so 7,000M is 7e9, or 7 billion. All costs in this exercise are expressed in units €M.</em></p>
@@ -7544,10 +7551,10 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
</pre></div>
</div>
@@ -7572,12 +7579,14 @@ a.anchor-link {
<b>Solution:</b>
</p>
</div>
-$$
+<p>$$
R = p_f \cdot D
-$$<p>where $R$ is risk and $D$ is damages (€M). The total cost, $T$, for investment $I$ is:</p>
-$$
+$$</p>
+<p>where $R$ is risk and $D$ is damages (€M). The total cost, $T$, for investment $I$ is:</p>
+<p>$$
T = I + R
-$$<table>
+$$</p>
+<table>
<thead>
<tr>
<th style="text-align:center">Strategy</th>
@@ -7648,27 +7657,30 @@ $$<table>
<p>
<b>Solution:</b>
<p>The goal is to create a limit line, <a href="https://mude.citg.tudelft.nl/book/pd/risk-evaluation/safety-standards.html#limits-for-individual-and-societal-risk" target="_blank">as described here</a>. The equation is of form:</p>
-$$
+<p>$$
1 - F_N(n) \leq \frac{C}{n^\alpha}
-$$<p>From the information provided, we can create a limit line where one point is provided from the guidelines of the neighboring city $p_f=0.01$ and $N=1$; thus $C=0.01$. If the city is risk averse, $\alpha=2$. As the factory spill causes 10 people to get sick, the maximum allowable probability is</p>
-$$
+$$</p>
+<p>From the information provided, we can create a limit line where one point is provided from the guidelines of the neighboring city $p_f=0.01$ and $N=1$; thus $C=0.01$. If the city is risk averse, $\alpha=2$. As the factory spill causes 10 people to get sick, the maximum allowable probability is</p>
+<p>$$
p_f/d=10/10.000=0.001\%
-$$$$
+$$</p>
+<p>$$
1 - F_N(n) \leq \frac{C}{n^\alpha} = \frac{0.01}{10^2} = 10^{-4} \;\;\textrm{per year}
$$</p>
+</p>
</div>
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">C</span> <span class="o">=</span> <span class="mf">0.01</span><span class="o">*</span><span class="mi">1</span><span class="o">**</span><span class="mi">2</span>
+<div class="highlight hl-python"><pre><span></span><span class="n">C</span> <span class="o">=</span> <span class="mf">0.01</span><span class="o">*</span><span class="mi">1</span><span class="o">**</span><span class="mi">2</span>
<span class="n">alpha</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">pf_societal</span> <span class="o">=</span> <span class="n">C</span><span class="o">/</span><span class="mi">10</span><span class="o">**</span><span class="n">alpha</span>
@@ -7694,25 +7706,6 @@ $$</p>
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>0.0001
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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"/>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7785,9 +7778,10 @@ $$</p>
<p>
<b>Solution:</b>
<p>The two areas are a series system, since a leak happens if either fails. Under the independent assumption, this is:</p>
-$$
+<p>$$
p_f = p_A + p_B - p_A p_B
-$$<p>If one material is faulty, so is the other, implying positive correlation. Positive correlation between events increases the intersection probability (parallel), which would decrease the union probability (series). However, this is only true if $p_A$ and $p_B$ remain the same; since this new information affects these probabilities, it is hard to say for sure.</p>
+$$</p>
+<p>If one material is faulty, so is the other, implying positive correlation. Positive correlation between events increases the intersection probability (parallel), which would decrease the union probability (series). However, this is only true if $p_A$ and $p_B$ remain the same; since this new information affects these probabilities, it is hard to say for sure.</p>
<p>End result: we better study this important topic more so we can get hired by the city to do the component reliability analyses required to revise the estimates of $p_A$ and $p_B$.</p>
</p>
</div>
@@ -7817,34 +7811,28 @@ $$<p>If one material is faulty, so is the other, implying positive correlation.
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<p><strong>End of notebook.</strong></p>
-<h2 style="height: 60px">
-</h2>
-<h3 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0">
-<style>
- .markdown {width:100%; position: relative}
- article { position: relative }
- </style>
-<a href="http://creativecommons.org/licenses/by-nc-sa/4.0/" rel="license">
-<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px">
-</img></a>
+<p><strong>End of notebook.test</strong></p>
+<div style="margin-top: 50px; padding-top: 20px; border-top: 1px solid #ccc;">
+<div style="display: flex; justify-content: flex-end; gap: 20px; align-items: center;">
+<a href="http://mude.citg.tudelft.nl/" rel="MUDE">
+<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="width:100px; height:auto;"/>
+</a>
<a href="https://www.tudelft.nl/en/ceg" rel="TU Delft">
-<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="width:100px; height:auto;"/>
</a>
-<a href="http://mude.citg.tudelft.nl/" rel="MUDE">
-<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">
+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="width:88px; height:auto;"/>
</a>
-</h3>
-<span style="font-size: 75%">
-© Copyright 2023 <a href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595" rel="MUDE Team">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/" rel="license">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
-
-</span></div>
+</div>
+<div style="font-size: 75%; margin-top: 10px; text-align: right;">
+ © Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft.
+ This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
+ </div>
+</div>
+</div>
</div>
</div>
</div>
</main>
</body>
-<script type="application/vnd.jupyter.widget-state+json">
-{"state": {}, "version_major": 2, "version_minor": 0}
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diff --git a/synced_files/Week_2_8/WS_2_8_solution.ipynb b/synced_files/Week_2_8/WS_2_8_solution.ipynb
index 6abc6a00..a725a29e 100644
--- a/synced_files/Week_2_8/WS_2_8_solution.ipynb
+++ b/synced_files/Week_2_8/WS_2_8_solution.ipynb
@@ -34,7 +34,7 @@
"\n",
"A city with population 10,000 uses an aquifer for its water supply, as illustrated in the figure. The city owns a factory in the region that manufactures hazardous chemicals, and recently a chemical spill occurred that resulted in 10 residents getting sick and total damages of €7,000M*. The city is going to enforce stricter regulations on the factory, and _you have been hired to advise the city council on the maximum allowable probability of a spill occurring (per year)_. You will make a recommendation based on the more stringent criteria between economic and societal risk limits.\n",
"\n",
- "\n",
+ "\n",
"\n",
"Experts have been consulted and it appears under the current plan the probability of a spill is 1/100 per year. The city council is considering two strategies to upgrade the spill prevention system. A small upgrade would cost €25M and can reduce spill probability by a factor 10; a large upgrade with investment costs of €50M would reduce the probability by factor 100.\n",
"\n",
@@ -261,27 +261,25 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "**End of notebook.**\n",
- "<h2 style=\"height: 60px\">\n",
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- " <style>\n",
- " .markdown {width:100%; position: relative}\n",
- " article { position: relative }\n",
- " </style>\n",
- " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n",
- " <img alt=\"Creative Commons License\" style=\"border-width:; width:88px; height:auto; padding-top:10px\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n",
+ "**End of notebook.test**\n",
+ "\n",
+ "<div style=\"margin-top: 50px; padding-top: 20px; border-top: 1px solid #ccc;\">\n",
+ " <div style=\"display: flex; justify-content: flex-end; gap: 20px; align-items: center;\">\n",
+ " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
+ " <img alt=\"MUDE\" style=\"width:100px; height:auto;\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" />\n",
" </a>\n",
" <a rel=\"TU Delft\" href=\"https://www.tudelft.nl/en/ceg\">\n",
- " <img alt=\"TU Delft\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\"/>\n",
+ " <img alt=\"TU Delft\" style=\"width:100px; height:auto;\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" />\n",
" </a>\n",
- " <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">\n",
- " <img alt=\"MUDE\" style=\"border-width:0; width:100px; height:auto; padding-bottom:0px\" src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\"/>\n",
+ " <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">\n",
+ " <img alt=\"Creative Commons License\" style=\"width:88px; height:auto;\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" />\n",
" </a>\n",
- " \n",
- "</h3>\n",
- "<span style=\"font-size: 75%\">\n",
- "© Copyright 2023 <a rel=\"MUDE Team\" href=\"https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595\">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>."
+ " </div>\n",
+ " <div style=\"font-size: 75%; margin-top: 10px; text-align: right;\">\n",
+ " © Copyright 2024 <a rel=\"MUDE\" href=\"http://mude.citg.tudelft.nl/\">MUDE</a> TU Delft. \n",
+ " This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">CC BY 4.0 License</a>.\n",
+ " </div>\n",
+ "</div>"
]
}
],
diff --git a/synced_files/Week_2_8/WS_2_8_solution.md b/synced_files/Week_2_8/WS_2_8_solution.md
index b00e4b7a..64ebfcaa 100644
--- a/synced_files/Week_2_8/WS_2_8_solution.md
+++ b/synced_files/Week_2_8/WS_2_8_solution.md
@@ -1,15 +1,5 @@
<userStyle>Normal</userStyle>
----
-jupyter:
- jupytext:
- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.3'
- jupytext_version: 1.16.6
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-
# Workshop 16: Dirty Water
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
@@ -34,7 +24,7 @@ In this exercise we apply a few simple concepts from the textbook to derive a sa
A city with population 10,000 uses an aquifer for its water supply, as illustrated in the figure. The city owns a factory in the region that manufactures hazardous chemicals, and recently a chemical spill occurred that resulted in 10 residents getting sick and total damages of €7,000M*. The city is going to enforce stricter regulations on the factory, and _you have been hired to advise the city council on the maximum allowable probability of a spill occurring (per year)_. You will make a recommendation based on the more stringent criteria between economic and societal risk limits.
-
+
Experts have been consulted and it appears under the current plan the probability of a spill is 1/100 per year. The city council is considering two strategies to upgrade the spill prevention system. A small upgrade would cost €25M and can reduce spill probability by a factor 10; a large upgrade with investment costs of €50M would reduce the probability by factor 100.
@@ -210,24 +200,22 @@ We added <a href="https://mude.citg.tudelft.nl/book/pd/reliability-component/con
</div>
-**End of notebook.**
-<h2 style="height: 60px">
-</h2>
-<h3 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0">
- <style>
- .markdown {width:100%; position: relative}
- article { position: relative }
- </style>
- <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">
- <img alt="Creative Commons License" style="border-width:; width:88px; height:auto; padding-top:10px" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" />
+**End of notebook.test**
+
+<div style="margin-top: 50px; padding-top: 20px; border-top: 1px solid #ccc;">
+ <div style="display: flex; justify-content: flex-end; gap: 20px; align-items: center;">
+ <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
+ <img alt="MUDE" style="width:100px; height:auto;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" />
</a>
<a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
- <img alt="TU Delft" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png"/>
+ <img alt="TU Delft" style="width:100px; height:auto;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" />
</a>
- <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
- <img alt="MUDE" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png"/>
+ <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">
+ <img alt="Creative Commons License" style="width:88px; height:auto;" src="https://i.creativecommons.org/l/by/4.0/88x31.png" />
</a>
-
-</h3>
-<span style="font-size: 75%">
-© Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+ </div>
+ <div style="font-size: 75%; margin-top: 10px; text-align: right;">
+ © Copyright 2024 <a rel="MUDE" href="http://mude.citg.tudelft.nl/">MUDE</a> TU Delft.
+ This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 License</a>.
+ </div>
+</div>
diff --git a/synced_files/Week_2_8/WS_2_8_solution.py b/synced_files/Week_2_8/WS_2_8_solution.py
index b642da08..46a161a9 100644
--- a/synced_files/Week_2_8/WS_2_8_solution.py
+++ b/synced_files/Week_2_8/WS_2_8_solution.py
@@ -33,7 +33,7 @@
#
# A city with population 10,000 uses an aquifer for its water supply, as illustrated in the figure. The city owns a factory in the region that manufactures hazardous chemicals, and recently a chemical spill occurred that resulted in 10 residents getting sick and total damages of €7,000M*. The city is going to enforce stricter regulations on the factory, and _you have been hired to advise the city council on the maximum allowable probability of a spill occurring (per year)_. You will make a recommendation based on the more stringent criteria between economic and societal risk limits.
#
-# 
+# 
#
# Experts have been consulted and it appears under the current plan the probability of a spill is 1/100 per year. The city council is considering two strategies to upgrade the spill prevention system. A small upgrade would cost €25M and can reduce spill probability by a factor 10; a large upgrade with investment costs of €50M would reduce the probability by factor 100.
#
@@ -209,24 +209,22 @@ plt.show()
# </div>
# %% [markdown]
-# **End of notebook.**
-# <h2 style="height: 60px">
-# </h2>
-# <h3 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0">
-# <style>
-# .markdown {width:100%; position: relative}
-# article { position: relative }
-# </style>
-# <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">
-# <img alt="Creative Commons License" style="border-width:; width:88px; height:auto; padding-top:10px" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" />
+# **End of notebook.test**
+#
+# <div style="margin-top: 50px; padding-top: 20px; border-top: 1px solid #ccc;">
+# <div style="display: flex; justify-content: flex-end; gap: 20px; align-items: center;">
+# <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
+# <img alt="MUDE" style="width:100px; height:auto;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" />
# </a>
# <a rel="TU Delft" href="https://www.tudelft.nl/en/ceg">
-# <img alt="TU Delft" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png"/>
+# <img alt="TU Delft" style="width:100px; height:auto;" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" />
# </a>
-# <a rel="MUDE" href="http://mude.citg.tudelft.nl/">
-# <img alt="MUDE" style="border-width:0; width:100px; height:auto; padding-bottom:0px" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png"/>
+# <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">
+# <img alt="Creative Commons License" style="width:88px; height:auto;" src="https://i.creativecommons.org/l/by/4.0/88x31.png" />
# </a>
-#
-# </h3>
-# <span style="font-size: 75%">
-# © Copyright 2023 <a rel="MUDE Team" href="https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=65595">MUDE Teaching Team</a> TU Delft. This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+# </div>
+# <div style="font-size: 75%; margin-top: 10px; text-align: right;">
+# © Copyright 2024 <a rel="MUDE" href="http://mude.citg.tudelft.nl/">MUDE</a> TU Delft.
+# This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 License</a>.
+# </div>
+# </div>
diff --git a/synced_files/tutorials/Week_1_3/Analysis.md b/synced_files/tutorials/Week_1_3/Analysis.md
index 80abd925..70747795 100644
--- a/synced_files/tutorials/Week_1_3/Analysis.md
+++ b/synced_files/tutorials/Week_1_3/Analysis.md
@@ -1,15 +1,5 @@
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# Week 1.3: Programming Tutorial
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/tutorials/Week_1_5/Tutorial.md b/synced_files/tutorials/Week_1_5/Tutorial.md
index 16e5aaab..79f44e36 100644
--- a/synced_files/tutorials/Week_1_5/Tutorial.md
+++ b/synced_files/tutorials/Week_1_5/Tutorial.md
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# Week 1.5: Programming Tutorial
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/tutorials/Week_1_6/Tutorial.md b/synced_files/tutorials/Week_1_6/Tutorial.md
index 514955ef..71afe98c 100644
--- a/synced_files/tutorials/Week_1_6/Tutorial.md
+++ b/synced_files/tutorials/Week_1_6/Tutorial.md
@@ -1,15 +1,5 @@
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# Week 1.5: Programming Tutorial
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diff --git a/synced_files/week_1_1/PA_1_1_Catch_Them_All.md b/synced_files/week_1_1/PA_1_1_Catch_Them_All.md
index 83d32d3f..802243e9 100644
--- a/synced_files/week_1_1/PA_1_1_Catch_Them_All.md
+++ b/synced_files/week_1_1/PA_1_1_Catch_Them_All.md
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# PA 1.1: Catch Them All
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/week_1_3/PA_1_3_Data_Cleaning_and_Boosting_Productivity.md b/synced_files/week_1_3/PA_1_3_Data_Cleaning_and_Boosting_Productivity.md
index 1c41d7ba..08bfdb08 100644
--- a/synced_files/week_1_3/PA_1_3_Data_Cleaning_and_Boosting_Productivity.md
+++ b/synced_files/week_1_3/PA_1_3_Data_Cleaning_and_Boosting_Productivity.md
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# PA 1.3: Data Cleaning and Boosting Productivity
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/week_1_3/PA_1_3_solution.md b/synced_files/week_1_3/PA_1_3_solution.md
index 0a7562b7..46af01c7 100644
--- a/synced_files/week_1_3/PA_1_3_solution.md
+++ b/synced_files/week_1_3/PA_1_3_solution.md
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# PA 1.3: Data Cleaning and Boosting Productivity
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/week_1_3/WS_1_3_Moving_Ice.md b/synced_files/week_1_3/WS_1_3_Moving_Ice.md
index 1e1a7945..0f41e209 100644
--- a/synced_files/week_1_3/WS_1_3_Moving_Ice.md
+++ b/synced_files/week_1_3/WS_1_3_Moving_Ice.md
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# Workshop 2: Is it Melting?
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diff --git a/synced_files/week_1_3/WS_1_3_solution.md b/synced_files/week_1_3/WS_1_3_solution.md
index af25cece..4d1a2c61 100644
--- a/synced_files/week_1_3/WS_1_3_solution.md
+++ b/synced_files/week_1_3/WS_1_3_solution.md
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# Workshop 2: Is it Melting?
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/week_1_5/PA/PA_1_5_solution.md b/synced_files/week_1_5/PA/PA_1_5_solution.md
index 2ec7827f..a21e7a5a 100644
--- a/synced_files/week_1_5/PA/PA_1_5_solution.md
+++ b/synced_files/week_1_5/PA/PA_1_5_solution.md
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# PA 1.5: A Few Useful Tricks
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/week_1_5/PA/PA_1_5_useful_tricks.md b/synced_files/week_1_5/PA/PA_1_5_useful_tricks.md
index f4fbd564..68c95d2c 100644
--- a/synced_files/week_1_5/PA/PA_1_5_useful_tricks.md
+++ b/synced_files/week_1_5/PA/PA_1_5_useful_tricks.md
@@ -1,15 +1,5 @@
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-
# PA 1.5: A Few Useful Tricks
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/week_1_5/PA_ice/PA_plots.md b/synced_files/week_1_5/PA_ice/PA_plots.md
index 7d36956d..06096587 100644
--- a/synced_files/week_1_5/PA_ice/PA_plots.md
+++ b/synced_files/week_1_5/PA_ice/PA_plots.md
@@ -1,15 +1,5 @@
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-
```python
import pandas as pd
diff --git a/synced_files/week_1_5/WS_1_5_dont_integr_hate.md b/synced_files/week_1_5/WS_1_5_dont_integr_hate.md
index 6e36dac0..aa936397 100644
--- a/synced_files/week_1_5/WS_1_5_dont_integr_hate.md
+++ b/synced_files/week_1_5/WS_1_5_dont_integr_hate.md
@@ -1,15 +1,5 @@
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# Workshop 5: Exploring Numerical Summing Schemes
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0">
diff --git a/synced_files/week_1_5/WS_1_5_solution.md b/synced_files/week_1_5/WS_1_5_solution.md
index 113ffe5e..e395f109 100644
--- a/synced_files/week_1_5/WS_1_5_solution.md
+++ b/synced_files/week_1_5/WS_1_5_solution.md
@@ -1,15 +1,5 @@
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# WS 1.5 Exploring Numerical Summing Schemes
<h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 90px;right: 30px; margin: 0; border: 0">
--
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