diff --git a/0_prepare_workload.ipynb b/0_prepare_workload.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1eaf0ff09227b1a7df19d48d54c84d9c7e0557e3
--- /dev/null
+++ b/0_prepare_workload.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "forced-resolution",
+ "metadata": {},
+ "source": [
+ "# Downloading and preparing the workload and platform\n",
+ "## Workload\n",
+ "We use the reconverted log `METACENTRUM-2013-3.swf` available on [Parallel Workload Archive](https://www.cs.huji.ac.il/labs/parallel/workload/l_metacentrum2/index.html)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f66eb756",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2023-04-25 14:12:14-- https://www.cs.huji.ac.il/labs/parallel/workload/l_metacentrum2/METACENTRUM-2013-3.swf.gz\r\n",
+ "Resolving www.cs.huji.ac.il (www.cs.huji.ac.il)... 132.65.118.16\r\n",
+ "Connecting to www.cs.huji.ac.il (www.cs.huji.ac.il)|132.65.118.16|:443... connected.\r\n",
+ "HTTP request sent, awaiting response... 200 OK\r\n",
+ "Length: 57685899 (55M) [application/x-gzip]\r\n",
+ "Saving to: 'workload/METACENTRUM-2013-3.swf.gz'\r\n",
+ "\r\n",
+ "METACENTRUM-2013-3. 100%[===================>] 55.01M 7.66MB/s in 7.0s \r\n",
+ "\r\n",
+ "2023-04-25 14:12:23 (7.87 MB/s) - 'workload/METACENTRUM-2013-3.swf.gz' saved [57685899/57685899]\r\n",
+ "\r\n",
+ "File 'workload/METACENTRUM-2013-3.swf.gz' already there; not retrieving.\r\n",
+ "\r\n",
+ "FINISHED --2023-04-25 14:12:23--\r\n",
+ "Total wall clock time: 8.3s\r\n",
+ "Downloaded: 1 files, 55M in 7.0s (7.87 MB/s)\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download the workload (548.3 MB unzipped)\n",
+ "!wget https://www.cs.huji.ac.il/labs/parallel/workload/l_metacentrum2/METACENTRUM-2013-3.swf.gz \\\n",
+ " --no-check-certificate -nc -P workload workload/METACENTRUM-2013-3.swf.gz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "bound-harvey",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Unzip the workload\n",
+ "!gunzip workload/METACENTRUM-2013-3.swf.gz"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "graphic-rabbit",
+ "metadata": {},
+ "source": [
+ "It is a 2-year-long trace from MetaCentrum, the national grid of the Czech republic. As mentionned in the [original paper releasing the log](https://www.cs.huji.ac.il/~feit/parsched/jsspp15/p5-klusacek.pdf), the platform is **very heterogeneous** and underwent majors changes during the logging period. For the purpose of our study, we perform the following selection.\n",
+ "\n",
+ "First:\n",
+ "- we remove from the workload all the clusters whose nodes have **more than 16 cores**\n",
+ "- we truncate the workload to keep only 6 month (June to November 2014) where no major change was performed in the infrastructure (no cluster < 16 cores added nor removed, no reconfiguration in the scheduling system)\n",
+ "\n",
+ "Second:\n",
+ "- we remove from the workload the jobs with an **execution time greater than one day**\n",
+ "- we remove from the workload the jobs with a **number of requested cores greater than 16**\n",
+ "\n",
+ "To do so, we use a the home-made SWF parser `swf_moulinette.py`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ff40dcdd",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unix Time Jun 1st 2014: 1401573600\n",
+ "Unix Time Nov 30th 2014: 1417388399\n",
+ "We should keep all the jobs submitted between 44578794 and 60393593\n",
+ "Processing swf line 100000\r\n",
+ "Processing swf line 200000\r\n",
+ "Processing swf line 300000\r\n",
+ "Processing swf line 400000\r\n",
+ "Processing swf line 500000\r\n",
+ "Processing swf line 600000\r\n",
+ "Processing swf line 700000\r\n",
+ "Processing swf line 800000\r\n",
+ "Processing swf line 900000\r\n",
+ "Processing swf line 1000000\r\n",
+ "Processing swf line 1100000\r\n",
+ "Processing swf line 1200000\r\n",
+ "Processing swf line 1300000\r\n",
+ "Processing swf line 1400000\r\n",
+ "Processing swf line 1500000\r\n",
+ "Processing swf line 1600000\r\n",
+ "Processing swf line 1700000\r\n",
+ "Processing swf line 1800000\r\n",
+ "Processing swf line 1900000\r\n",
+ "Processing swf line 2000000\r\n",
+ "Processing swf line 2100000\r\n",
+ "Processing swf line 2200000\r\n",
+ "Processing swf line 2300000\r\n",
+ "Processing swf line 2400000\r\n",
+ "Processing swf line 2500000\r\n",
+ "Processing swf line 2600000\r\n",
+ "Processing swf line 2700000\r\n",
+ "Processing swf line 2800000\r\n",
+ "Processing swf line 2900000\r\n",
+ "Processing swf line 3000000\r\n",
+ "Processing swf line 3100000\r\n",
+ "Processing swf line 3200000\r\n",
+ "Processing swf line 3300000\r\n",
+ "Processing swf line 3400000\r\n",
+ "Processing swf line 3500000\r\n",
+ "Processing swf line 3600000\r\n",
+ "Processing swf line 3700000\r\n",
+ "Processing swf line 3800000\r\n",
+ "Processing swf line 3900000\r\n",
+ "Processing swf line 4000000\r\n",
+ "Processing swf line 4100000\r\n",
+ "Processing swf line 4200000\r\n",
+ "Processing swf line 4300000\r\n",
+ "Processing swf line 4400000\r\n",
+ "Processing swf line 4500000\r\n",
+ "Processing swf line 4600000\r\n",
+ "Processing swf line 4700000\r\n",
+ "Processing swf line 4800000\r\n",
+ "Processing swf line 4900000\r\n",
+ "Processing swf line 5000000\r\n",
+ "Processing swf line 5100000\r\n",
+ "Processing swf line 5200000\r\n",
+ "Processing swf line 5300000\r\n",
+ "Processing swf line 5400000\r\n",
+ "Processing swf line 5500000\r\n",
+ "Processing swf line 5600000\r\n",
+ "Processing swf line 5700000\r\n",
+ "-------------------\r\n",
+ "End parsing\r\n",
+ "Total 1649029 jobs and 556 users have been created.\r\n",
+ "Total number of core-hours: 18222722\r\n",
+ "4075060 valid jobs were not selected (keep_only) for 75784902 core-hour\r\n",
+ "Jobs not selected: 71.2% in number, 80.6% in core-hour\r\n",
+ "7119 out of 5731209 lines in the file did not match the swf format\r\n",
+ "30 jobs were not valid\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "# First selection\n",
+ "# Create a swf with only the selected clusters and the 6 selected months \n",
+ "from time import *\n",
+ "begin_trace = 1356994806 # according to original SWF header\n",
+ "jun1_unix_time, nov30_unix_time = mktime(strptime('Sun Jun 1 00:00:00 2014')), mktime(strptime('Sun Nov 30 23:59:59 2014'))\n",
+ "jun1, nov30 = (int) (jun1_unix_time - begin_trace), (int) (nov30_unix_time - begin_trace)\n",
+ "print(\"Unix Time Jun 1st 2014: {:.0f}\".format( jun1_unix_time ))\n",
+ "print(\"Unix Time Nov 30th 2014: {:.0f}\".format( nov30_unix_time ))\n",
+ "print(\"We should keep all the jobs submitted between {:d} and {:d}\".format(jun1, nov30))\n",
+ "\n",
+ "! ./scripts/swf_moulinette.py workload/METACENTRUM-2013-3.swf \\\n",
+ " -o workload/METACENTRUM_6months.swf \\\n",
+ " --keep_only=\"submit_time >= {jun1} and submit_time <= {nov30}\" \\\n",
+ " --partitions_to_select 1 2 3 5 7 8 9 10 11 12 14 15 18 19 20 21 22 23 25 26 31"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "6ec15ee8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing swf line 100000\r\n",
+ "Processing swf line 200000\r\n",
+ "Processing swf line 300000\r\n",
+ "Processing swf line 400000\r\n",
+ "Processing swf line 500000\r\n",
+ "Processing swf line 600000\r\n",
+ "Processing swf line 700000\r\n",
+ "Processing swf line 800000\r\n",
+ "Processing swf line 900000\r\n",
+ "Processing swf line 1000000\r\n",
+ "Processing swf line 1100000\r\n",
+ "Processing swf line 1200000\r\n",
+ "Processing swf line 1300000\r\n",
+ "Processing swf line 1400000\r\n",
+ "Processing swf line 1500000\r\n",
+ "Processing swf line 1600000\r\n",
+ "-------------------\r\n",
+ "End parsing\r\n",
+ "Total 1641191 jobs and 551 users have been created.\r\n",
+ "Total number of core-hours: 11723935\r\n",
+ "7838 valid jobs were not selected (keep_only) for 6498787 core-hour\r\n",
+ "Jobs not selected: 0.5% in number, 35.7% in core-hour\r\n",
+ "0 out of 1649030 lines in the file did not match the swf format\r\n",
+ "1 jobs were not valid\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Second selection\n",
+ "# Keep only the selected jobs\n",
+ "! ./scripts/swf_moulinette.py workload/METACENTRUM_6months.swf \\\n",
+ " -o workload/MC_selection_article.swf \\\n",
+ " --keep_only=\"nb_res <= 16 and run_time <= 7*24*3600\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "afde35e8",
+ "metadata": {},
+ "source": [
+ "## Platform\n",
+ "According to the system specifications given in the [corresponding page in Parallel Workload Archive](https://www.cs.huji.ac.il/labs/parallel/workload/l_metacentrum2/index.html): from June 1st 2014 to Nov 30th 2014 there is no change in the platform for the clusters considered in our study (<16 cores). There is a total of **6304 cores**.(1)\n",
+ "\n",
+ "We build a platform file adapted to the remaining workload. We see above that the second selection cuts 35.7\\% of core-hours from the original workload. We choose to make an homogeneous cluster with 16-core nodes. To have a coherent number of nodes, we count:\n",
+ "\n",
+ "$\\#nodes = \\frac{\\#cores_{total} * \\%kept_{core.hour}}{\\#corePerNode} = 6304 * 0.643 / 16 = 253$\n",
+ "\n",
+ "In SimGrid platform language, this corresponds to such a cluster:\n",
+ "```xml\n",
+ "<cluster id=\"cluster_MC\" prefix=\"MC_\" suffix=\"\" radical=\"0-252\" core=\"16\">\n",
+ "```\n",
+ "\n",
+ "The corresponding SimGrid platform file can be found in `platform/average_metacentrum.xml`.\n",
+ "\n",
+ "(1) clusters decomissionned before or comissionned after the 6-month period have been removed: $8+480+160+1792+256+576+88+416+108+168+752+112+588+48+152+160+192+24+224 = 6304$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index 09859c89a5e0f60e5573aafe1dcfdba6161e00d9..eb4753845a86876614728e824413a008c574088b 100644
--- a/README.md
+++ b/README.md
@@ -1,30 +1,22 @@
# Multi-behavior experiment
-This directory contains the necessary script for the experiment
-with batmen multi-behavior class using metacentrum dataset.
-It is a fork from demand-response-user
-(https://gitlab.irit.fr/sepia-pub/open-science/demand-response-user).
+This directory contains the necessary script for the experiment with batmen multi-behavior class using metacentrum dataset.
+It is a fork from demand-response-user (https://gitlab.irit.fr/sepia-pub/open-science/demand-response-user).
## Description of the main files
-- `scripts/prepare_workload.sh` shell script to prepare the workload
-- `scripts/run_expe.sh` shell script which launch experiments when workload is prepared.
-- `scripts/compute_stat.sh` shell script which compute stats when the experiments finished.
-- `default.nix` nix file given all the necessary dependencies.
-- `campaign3.py`: Python script preparing and launching in parrallel the experiments.
-Each experiment corresponds to one instance of `instance3.py`.
+- `scripts/prepare_workload` shell script to prepare the workload
+- `campaign3.py`: Python script preparing and launching in parrallel the experiments. Each experiment corresponds to one instance of `instance3.py`.
- `analyse_campaign3.ipynb`: Jupyter notebook analysing the results.
-## Steps to reproduce the experiments
+## Steps to reproduce
+
+### 1. Install
+For the sake reproductibility, all the dependancies for these experiments and their version (release tag or commit number) are managed with the package manager Nix. If you don't have it on your machine, the following command should install it. Otherwise, please refer to [their documentation](https://nixos.org/download.html).
-The version used for the experiments is the version tagged `experiments-version`.
-Once the repository clone you can type this command to switch to the tagged version :
```bash
-git checkout tags/experiments-version
+curl -L https://nixos.org/nix/install | sh
```
-To reproduce the following steps make sure to be on this version.
-
-### 1. Install dependencies
The main software used (and configured in the file `default.nix`) are:
- [Batsim](https://batsim.org/) and [SimGrid](https://simgrid.org/) for the infrastructure simulation
@@ -32,23 +24,10 @@ The main software used (and configured in the file `default.nix`) are:
- [Batmen-tools](https://gitlab.irit.fr/sepia-pub/mael/batmen-tools/-/tree/main/) : a set of tools to manipulate swf files
- python3, pandas, jupyter, matplotlib etc. for the data analysis
-
-All the dependencies of the project are given in the default.nix file to
-install nix you can launch the command
-```bash
-scripts/install_nix.sh
-```
-It might be required to type other commands for the nix command to be
-available in the current shell. In this case it will be indicated by the
-prompt of the nix installation.
-
-
-To go into the experiments environment you can now type :
+Enter a shell with all dependencies managed. This will take some time to download and compile the first time you launch it, but then all the environment is cached for future use.
```bash
nix-shell -A exp_env --pure
```
-This will compile and install all the dependencies needed for the experiments
-it can take some times (in our case, it took 6 minutes)
### 2. Prepare input workload
Inside the nix-shell use the following command inside project directory :
```bash
@@ -56,70 +35,13 @@ scripts/prepare_workload.sh
```
### 3. Launch the campaign
-By default the run_expe scripts only run one seed by experiment
-to do the 30 experiments you have to modify `--nb_replicat ` argument
-it should look like this :
-```bash
-python3 campaign3.py --nb-replicat 30 --expe-range -1 --window-mode 8 --nb-days 164 \
---json-behavior behavior_file/big_effort.json behavior_file/low_effort.json behavior_file/max_effort.json behavior_file/medium_effort.json \
---compress-mode --production-file data_energy/energy_trace_sizing_solar.csv data_energy/energy_trace_sizing_solar.csv
-```
-As every experiments can take up to 20 GB of RAM,
-you might be limited by the memory of your system.
-When you are running the experiments you can limit
-the number of parallel run using `--threads n` command-line argument
-with `n` the maximum of experiments to run in parallel.
-By default, it uses every physical cores available.
-
-Once you have done all the previous step,
-launch the bash script in the nix-shell :
+#### Quick start
+Still inside the nix shell, launch the bash script
```bash
scripts/run_expe.sh
```
-If you see this line :
-```
-Rigid finished, Now monolithic behavior
-```
-It means that the program has finished computing the simulation
-and is now computing stat on the obtained value.
-You can stop the program now if you only want raw simulation results
-### 4. Generate the metrics
-The tagged `experiments-version` version forget some metrics in computation.
-After the experiments, to compute the metrics we have to switch to tag
-`metrics-compute-version`, and then compute the desired metrics
-using the command :
-```bash
-scripts/compute_stat.sh
-```
-The stat will be computed and place in the shell directory with the names
-`campaign3_metrics.csv` and `campaign3_metrics_relative.csv`.
-It will likely differ from the one provided in result_big_expe.
-In our reproduction we noticed a relative difference of 0.5% in energy related metrics and
-2% in user behaviors related metrics
-
-### 5. Generate the graph
-To generate the graph you can launch the notebook `analyse_campaign3.ipynb`.
-You will have to change the variable `RAW_DATA_DIR` and `OUT_DIR` to match with your setup.
-
-
-### Tips
-With the experiments, there are various scripts_file provided
-to help you manage the data of experiments :
-- `scripts/compress_out.sh out/ out.tar.zst`
-allow you to compress the out_dir in a tar file for archive purpose.
-In our case, we divided by 7 the space used by the experiments result.
-- `scripts/sync_expe_out.sh out/ path_to_backup/` allow you to do
-backup of simulation data in a backup directory.
-It uses rsync so it will only write changes if you do twice this command.
-## Energy Data
-The provided example_trace_occitanie_2019.csv comes from a modification of the Open Data Réseaux Electrique
-energy production dataset for Occitanie in 2019 ( the original file can be directly downloaded
-[here](https://odre.opendatasoft.com/explore/dataset/eco2mix-regional-cons-def/information/?disjunctive.libelle_region&disjunctive.nature)).
-The provided energy_trace_sizing_solar.csv
-is the energy trace of the energy produced by DataZero2 sizing algorithm
-
-## Advanced options
-Inside the nix shell exp_env, launch the commands :
+#### Advanced start
+Still inside the nix shell, launch the commands :
```bash
python3 campaign3.py --help
```
@@ -128,49 +50,14 @@ You will have to at least give the following arguments :
- `--expe-range` to provide the expe to do for all available
experiment type `--expe-range -1` else provide the list of experiment
with experiments `[0,1]` it will be `--expe-range 0 1`
-## Information about experiments
-The experiments took 7 hours to do on two Intel Xeon Gold 6130 and the output took 55 GB to store,
-once compress using `scripts/compress-out.sh` it takes 7.8 GB.
-As each experiments took approximately 20 GB of RAM and we had 188GB available we weren't able to exploit at full capacity the CPU,
-so with more memory we could have better speed.
+### 4. Analyse the results
+```bash
+jupyter notebook analyse_campaign3.ipynb
+```
+
+## Energy Data
+The provided example_trace_occitanie_2019.csv comes from a modification of the Open Data Réseaux Electrique energy production dataset for Occitanie in 2019 ( the original file can be directly downloaded [here](https://odre.opendatasoft.com/explore/dataset/eco2mix-regional-cons-def/information/?disjunctive.libelle_region&disjunctive.nature)).
+## More information about these experiments
+Here we give more details about how the simulation works.
-### List of metrics
-The metrics computed for the experiments are the following ( available in result-big-expe) :
-- `XP`,`dir`, `behavior`, `seed`,
-the experiment number in our case it is always 0, the directory from which the data where computed,
-the behavior the user have (probability distribution can be found in behavior_file),
-the seed the random generator used for computation.
-- `#jobs` the number of jobs submitted, renounced jobs are not accounted in this total.
-- `Duration_red (seconds)`, `Duration_yellow (seconds)`, `Duration_total (seconds)`
-the duration in which the red state occured, yellow state occured, the total measures were done
-- `NRJ_red (Joules)`, `NRJ_yellow (Joules)`, `NRJ_total (Joules)`
-the energy consumed in red state, yellow state and during the whole duration of the experiments
-- `mean_waiting_time`, `max_waiting_time`, `mean_slowdown`, `max_slowdown`.
-The slowdown and waiting time metrics computed by batsim it doesn't take into account the user behavior
-- `energy overproduced (Joules)`, `energy underproduced (Joules)`
-Computation of the energy differences between consumption and production.
-When the energy produced is higher than the energy consumed,
-the absolute difference is added to overproduced energy.
-When the energy produced is lower than the energy consumed,
-the absolute difference is added to overproduced energy.
-- `energy balance (Joules)`,
-it is the computation of the difference between the energy produced and energy consumed.
-It has been computed by two ways : using overproduction and underproduced (1),
-compute the total energy produced - total energy consumed (2).
-Relative Accuracy is the difference between both computation normalized.
-- `true_rigid_jobs` is the number of jobs that was unmodified.
-- `mean_delay`, `max_delay`
-the amount of time the jobs have been delayed by a see_you_later behavior
-- `renonce_jobs`,`reconfig_jobs`,`degrad_jobs`,`rigid_jobs`
-the number of jobs that was renounced, reconfigured, degraded,
-submitted rigidly (might have been delayed by see_you_later)
-- `number_of_see_you_later`,`C_you_later_jobs`,
-the number of see_you_later (a jobs can be subject to more than one see_you_later),
-the number of jobs that get a see_you_later.
-It was computed using two ways, only looking for see_you_later in logged behavior (1),
-check if the original submission time is the real submission time in the jobs batsim record (2).
-- `mean_corrected_wtime`, `max_corrected_wtime`, `mean_corrected_sdown`, `max_corrected_sdown`.
-Compute the waiting time and slowdown while taking into account of behaviors
-(submission time is the original one without see you later, reconfig jobs execution time is the one before reconfiguring)
-It has been computed by using behaviors stat and jobs data (1), or by crossing the data with the rigid case (2).
-As the difference (sanity) are quite big we are not sure of the accuracy of these data computed.
\ No newline at end of file
+TODO... (add the picture of the system?)
diff --git a/analyse_campaign3.ipynb b/analyse_campaign3.ipynb
index 1e6ce1ce69a4709257cb848f867fc3d176bc4a9d..4c26d1caa33351c638941c7e7c8d8e7e7afc2f16 100644
--- a/analyse_campaign3.ipynb
+++ b/analyse_campaign3.ipynb
@@ -14,56 +14,55 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 34,
"id": "yellow-parking",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1401573600.0\n",
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- ]
- }
- ],
+ "outputs": [],
"source": [
- "from datetime import datetime,timedelta\n",
+ "import datetime\n",
+ "import importlib\n",
+ "import instance3\n",
"import pandas as pd, json, numpy as np\n",
- "from dateutil import parser\n",
- "from scripts.util import *\n",
- "from scripts.plot_library import *\n",
+ "from compute_metrics_campaign3 import compute_metrics_all_expe_parr,compute_metrics_all_expe_seq\n",
+ "import os, random\n",
+ "from scripts.util import * \n",
"import matplotlib.pyplot as plt\n",
- "import math\n",
- "from scipy.stats import linregress\n",
- "\n",
- "#RAW_DATA_DIR is the directory where the raw simulation data are, OUT_DIR is the directory where the computed stats are\n",
- "RAW_DATA_DIR = f\"{ROOT_DIR}/out/\"\n",
- "OUT_DIR = f\"{ROOT_DIR}/result-big-expe\"\n",
- "FIG_DIR = f\"{ROOT_DIR}/../article/fig\"\n",
"#%matplotlib inline\n",
"#plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
"\n",
"# time used by the experiments\n",
- "nb_days=164\n",
+ "nb_days=8\n",
"save_number=0\n",
- "nb_replicat = 30\n",
- "\n",
- "expe_start_time = parser.parse('Sun Jun 1 00:00:00 CEST 2014').timestamp()\n",
- "print(expe_start_time)\n",
+ "OUT_DIR = f\"{ROOT_DIR}/out\"\n",
"with open(f\"{OUT_DIR}/expe_done_dict_{save_number}.json\") as expe_done_file :\n",
" expe_done_dict =json.load(expe_done_file)\n",
+ "expe_done_dict={int(k): v for k,v in expe_done_dict.items()}\n",
"with open(f\"{OUT_DIR}/windows_dict_{save_number}.json\") as windows_dict_file :\n",
" windows_dict = json.load(windows_dict_file)\n",
" red_windows_dict = windows_dict[\"red_windows\"]\n",
" yellow_windows_dict = windows_dict[\"yellow_windows\"]\n",
"red_windows_dict = {int(k): v for k,v in red_windows_dict.items()}\n",
- "yellow_windows_dict = {int(k) : v for k,v in yellow_windows_dict.items()}\n",
- "print(red_windows_dict,yellow_windows_dict)\n",
- "\n",
- "def confi_interval(group):\n",
- " \"\"\"Confidence interval 99.7%\"\"\"\n",
- " return 3 * group.std() / math.sqrt(nb_replicat)"
+ "yellow_windows_dict = {int(k) : v for k,v in yellow_windows_dict.items()}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "51bac175",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Check if all XP succeded\n",
+ "for xp in expe_done_dict.keys():\n",
+ " expe_dir = f\"{OUT_DIR}/expe{xp}/replay_user_rigid_0\"\n",
+ " if not os.path.exists(expe_dir):\n",
+ " print(f\"{expe_dir} does not exist\")\n",
+ " for window_mode in expe_done_dict[xp].keys() :\n",
+ " for seed in expe_done_dict[xp][window_mode] :\n",
+ " for behavior in [\"dm_user_multi_behavior\"]:\n",
+ " expe_dir = f\"{OUT_DIR}/expe{xp}/{behavior}{window_mode}_{seed}\"\n",
+ " if not os.path.exists(expe_dir):\n",
+ " print(f\"{expe_dir} does not exist\")\n"
]
},
{
@@ -71,94 +70,44 @@
"id": "b20d17c8",
"metadata": {},
"source": [
- "## Compare loads with graph\n",
- "Let's compute some load graph with 4 scenario only rigid, low_effort, max_effort with yellow state, renounce only in red state. We will also compute the queue size."
+ "## Compare loads with graph"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 36,
"id": "impressed-disclosure",
"metadata": {
"scrolled": true
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/mael/.local/lib/python3.10/site-packages/evalys/metrics.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " event_df = start_event_df.append(\n",
- "/home/mael/.local/lib/python3.10/site-packages/evalys/metrics.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " event_df = start_event_df.append(\n",
- "/home/mael/.local/lib/python3.10/site-packages/evalys/metrics.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " event_df = start_event_df.append(\n",
- "/home/mael/.local/lib/python3.10/site-packages/evalys/metrics.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " event_df = start_event_df.append(\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Line2D(rigid only) black\n",
- "Line2D(low effort) blue\n",
- "Line2D(max effort yellow) darkred\n",
- "Line2D(renounce) gray\n"
+ "Line2D(rigid only) blue\n",
+ "Line2D(multi behavior) black\n",
+ "Line2D(multi behavior yellow) orange\n"
]
},
{
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1vXrpe0nS5JJqmUwNVwL3tMbqhNKO99lcKPNaKNrLXNiR5jV/+HktekoqO958BADN6ZC/v65cuVJTpkwxtmnv3LmzZs2adUAvECxatEgbN9b9gx7IVveSd9v/+qqqqhq0+VNVVWWU/e1KEKlx9xcbG6vY2NgG7VZJvBsXeSarJFtwv3hYLCa5Te3nh26L1SyLrf18PYGyeEyymYP8pdNq6piTfpgwrwWPOSo0zGtBYF47IMxrwWNeCw3zWhCY1w4I81rwmNdC0xHmNWtFoaQ0ySPja62d17Ky6maq0lLJ5ud/Q0ZbO5/Xrs3pp8fy1kmSLAr/HNTYvPaLTjXKlYW7lNq5i1GfMuowffLjMuVkZHTIOTGUeS0U7W0u7Ajzmj/8vBY9HXE+AoDmhO1f3r///e/GnxUrVoQ0xhtvvGGMESnr1q3TpEmTVFxcLElKT0/Xl19+qYEDBx7QuK+88opRTk5O1plnnhnQfUn1zoqSpMrKyoDuq6ioaHSMSI4LAAAAAACA9s1trklB2cv12N+e8rlWf9H2tm11ZY8n8ueAtzaWeknaKrcras+9amK+Uc5bvdLn2qhefSVJbg+nZgMAALRmYXvp67777jO2VnrkkUf06quvBpyorjVz5kzNnj1bJpNJ99xzT7hCM2zatEkTJkzQrl27JHmT0F988YWGDx9+QONWVVXpnXfeMeq///3vA1q1LklZWVk+9fz8fGVmNn8OTkFB3TlB/vpHalwAAAAAAAC0b6UlNmVKKnXY9MyDL2jKeZN18EHebe+Li+uS0Skpa1R7OLOnAyaFbfXeWFhTVqwxaVlN9A6fmHorxS023/Wp8TExkiS7g62lAQAAWrOw72Hj8XhUUVGhc845Rw888EC4hw/Ztm3bNH78eG2recU3Pj5en376qY444ogDHvujjz4yVuZLgW91L0mDBg3yqW/ZsiWg+7Zu3WqUBw8eHLVxAQAAAAAA0Pa57NWqKtjt91pCojcR7/ZYJEl5G+o+Lxo+vG5hytq1Perd5U1Yd6RcvclkUobNmxSfs2enXt2+KSrP3VCQpAqlS5LMFovPtRird01WpaM6KrEAAAAgNBE7aMbtduu+++7T+eef73POeUvYuXOnJkyYoE2bvD8ox8bG6qOPPtK4cePCMn79re779u2rY445JuB7BwwYIKu1bkODQI8K+Omnn4zykCFDojYuAABAR+NwmnT9yyP0wdJuLR0KAABA2MzqM0Wfdz9BZb9ta3DNnOj9yDC5T4okyV5lN66ZTCbt3u1dMV5/i/uOlJyv75j0TkZ5c1WF3ivY2kTv8BjavVS5GitJys/b4XPNVpO0X7E5L+JxAAAAIHRhT9KPGzdO55xzjlF/5513NHbsWOXn5zdxV+QUFRVp4sSJWrdunSTJZrPpnXfe0aRJk8Iyfn5+vr766iujfvHFFxvb/gciJibGZzX/woULm72noKBAGzduNOpjx46N2rgAAAAdzXtLe+iZr/vr908c1dKhAAAAhI195x5J0uLTb2pwLXZgnCTJEuvdSn3rpu0+11NSSiRJw4b9bLTVJuk7WrL+kOQ0/aXXQKO+trxEC/YWRvSZfzv9FyXJe5znuq2+n0OWVlVKkvp36RrRGAAAAHBgwp6kj4+P11tvvaX77rvPaFu2bJlGjx6t5cuXh/txTSopKdHkyZO1evVqSZLFYtHrr7+uU089NWzPeO211+RyebcAM5lMuvjii4Me47TTTjPKc+bM0a5du5rs//rrrxvltLS0RncEiNS4AAAAHUlxRd05nw5n4C9jAgAAtAWlv+Q2aHPke7dKt++rkCRZbVaf6zExdVupu90RDK6NSLba9Od6ifp5RbuUV1kesefFx7i1QyMkSYvWJPhc65XVWZK0vagoYs8HAADAgYvYdvf33HOP3n33XSUkJMhkMmn79u0aO3as3nnnnUg90kdFRYVOPvlkLVu2TJJkNpv10ksv6fe//31Yn1N/q/uxY8eqT58+QY9x/vnnKzY2VpLkcDj08MMPN9q3rKxMTzzxhFG/8MILfba1j8a4AAAAHUm39Eqj/GNeuu59f6jW5Se1YEQAAAAHxlNvuXtct04Nrrv2OSVJ+9btlCSt//m3RseqrvZur15enlgzdsd8qTHVatN1PQcY9R+K90T0eXYlS5JiVObTbrN6/z627yVJDwAA0JpFLEkvSWeeeaYWLFigHj16SJIqKyt1/vnn6957743kY2W323XaaacZW7ybTCY9//zzuuiii8L6nOXLl2vNmjVG/ZJLLglpnB49eujqq6826tOnT9f777/foJ/D4dBll12mLVu2SPLuWnDXXXdFfVwAAICOxOWu+6D5qPuP19SPhmrorSe0YEQAAADBW/mXx/Tl4DNVva9Uyy6+x2hP6J3doK/Z6X1J0eLx7h6ZkZXmcz07O8Yo793rTQrHx3tX3TsciWGNuy3JsMVoeHKaJMlqiujHriqW9/PWYfrApz0hxrtgp3bbewAAALROEV8qPWLECC1dulRnnHGGFi9eLI/Ho6lTp+qXX37Rq6++qvj4+LA/c/r06ZozZ45RT0tL07vvvqt33303oPsnTpyov/71r832q7+KPiEhQWeffXbwwda477779MUXX2jDhg1yuVw655xzdMEFF+j0009XRkaG1q1bp2eeeUarVq0y7nn00UeVnd3wF6lojAsAANBROF3+V4NZLjpbJS98qMQ4V5QjAgAACN5vT74lSdr479e19Y0vjPai71Y16Ju47xc5dIy6arckKX/rTp/rkyal6ZVXvMcqvvxyrm6+uZtsNu/PRDExZZJ8t2DvSLJj47SyVCqwRzZJfmr3j6XtDdszk7w7Prk4hwAAAKBVi8p+5p07d9a8efN01VVXGYntDz74QLm5ufr444+NlfbhUlFR4VPfu3evZs+eHfD9Xbt2bbaPw+HQm2++adTPOussJScnBx7kftLT0/Xpp59qwoQJ2rp1q9xut1577TW99tprfvvfdtttuu6661psXAAAgI7C4Wp8FdSR9x2v1Q99FcVoAAAADsyvU/+fvfsOj6po+zj+3d30nkCA0Hvv2EAFFKSIKD5WsAH2hmLB8ihi7/WxF5qKvIKKgAKKCDYUVKoU6R0CpNet7x9nc5IlPdkU4Pe5rlzMmTNnziTocnLumXs+9DkOb1XEezmLd5KiNy1+SnKaz+mEhECf45UrM7DbjdeMMTFZnMxBers3OB5YxSvpG595Ofs//wEwti+weP/O4rxB+rwV9SIiIiJSO1Xt02IBgYGBTJkyhRdeeMF8aFy1ahWnnXYaK1asqK5h+M0333zDkSNHzOOKprovqG3btqxdu5brr7++2AwDHTt2ZN68eTz33HM13q+IiIjIyWB/cvGZn1rXzyj2nIiIiMjx6ojV2Ft9j8cI4EdE+qawz3u3lyc52WmWk5JO3nT3ALGBxlYANkvR2Zj8pUnLhmbZmZtrlkO8989x2Kv0/iIiIiJSOdWykr6g++67j44dOzJq1CjS09M5ePAg/fv35/333/fbPSZNmsSkSZP81l9RRowYgcc7m9ifYmJi+PDDD3n11VdZsmQJe/bsITMzk4SEBLp06UKPHj1qVb8iIiIiJ7qY8OJfcB5M8f/WTSIiIiL+lnPoaLHnQhrUKVTn8IQA4CQGgJU/ryqx/927c/F4rznZBXlX0Luq4L1hQbHR+a91c7IyCQwxfv4hgUaWA4fLhcvtwma1Vek4RERERKRiqj1ID3D++eezfPlyhg8fzvbt28nJyeG6664jPPzknmlbUGRkJBdddNFx06+IiIjIicrhNF60XnzKXjxYWLimATkO42Xniu1x/LM3ik6N00rqQkRERKRmlbCqu6hFKKHeuH1gWDoALds3K7H7Vq1CWFt4a/uTUt4K+oP2nCq9T5M6+f0nHsogMs74Swv1rqQHyLbbiQjRpFIRERGR2qja0t0fq0OHDqxcuZL+/fubdZmZmTU1HBERERGRIjm9e9IfSEnk6ctmc8UZA2kQfZV5/qVv2tbU0EREREQqLem3tRxatNynLrx9NAANuhsp1V1OV6Hr5s69wCynpbnytq+niheQ13qB1vzXrWvSkqvsPpGh+VsM7NqVapZDg/KD9DkOR5XdX0REREQqx69B+vKmf4+NjeX777/nlltuqZLU8SIiIiIilZWeY7wA/X3rP3R64G6m/byUg6kzgMUA/LMvquYGJyIiIlIWRbx3i7z0GrP86/l34nbmB30dh7MAOLzxAAAul7vQ9Xv3NjbLhw45TvrgfJ6Gwfkr11emJVXLPTf/+oNZthaYJHAkPb1a7i8iIiIi5ee3dPdTpkwBoFGjRuW6zmaz8fbbb9OlSxc+//xzfw1HRERERMQvvluX7S0duxJpGTCQ6AKrmERERERqpQIR9JheHQiKi+aZ2SO5gY/N+q2vf0bbe6/B7YasPXai48NI22+sBC9qJX1iYv38PmMCyc11eG91ckfrrRYLw+MbMu/wfg7kVm3K+zzrs3oXWZ9lz62W+4uIiIhI+fktSH/ddddV6vpbb72VW2+91U+jERERERHxjyZxeS83Wx1zZhsASzbUI9dhJTiw8AozERERkVrFYuHcFUZg3mlL9Tnl8Qbiv/vYSR3LDpz0wOIJNto6C09KfP/9n3j99Uu57LLZ7N6dS0hIGFD0qvuTTYPgELPs8rixWapmx9GV1ps51f0eLeuk+NS3qlefbYmHyLEr3b2IiIhIbVVje9KLiIiIiNR2R9ODmL2yr/do1TFn8x+lJ87uVG1jEhERESkvR1qmUSiwyj2Jlr6NvOe2PfC4WdXKswcoeiV9XFxuge5CzXJgoM0vYz6e1Q/KD9JnuQr/7PylabwxmdSetNOnPiTQ2Jf+UFpKld1bRERERCpHQXoRERERkQI27Y/k+3X18Hhg1NunY3cGec9Yjmm5wiz9tCm+2sYnIiIiUl6HFvzqc/zgDc3IoJ5PXfq/uwFwxLUGi/HcE+CxA7B9865CfYaFOUlNjQEgK8tmBulP8mz3AFgsFgK9P0NnFf5AnHbj78cVGOtT/88+Y3JFUIDfkqiKiIiIiJ8pSC8iIiIi4nUwJZhODwxmyAt9Cbj2Upb8U/Dl9V0kxMRybsfO/P74s1gtW4G5AKzYHofdeWwQX0RERKR2iGjbzOd41pS6OAjzqds9bR4AqckBeLwB5oJPN8fuNR8Xl4vTaQSBQ0Mz/Tzi41+gN8X9/tzsKrtHQJyRDSHo6HKf+jPbtgPAXsQ2BSIiIiJSO5RrOuUTTzzhczxx4sRiz1VWwb5FRERERKrDvuRQn2O3J//VdIeG97H++fcLnPMATwIXAvDxL824vv/OahiliIiISPl4vPvEx57a0Wel+/24GRb9Av1THwTAnpzG1sSWtLEYK+ezCqbHP5JKg7j8a0NDnbhcRmp7i8Vt9mvRvEUAstxGmvuVqUl0ioiukntEhxjp7jM9dXzqg2zGK9+9R49WyX1FREREpPLKFaSfNGkSlgJP2gUD6ceeqywF6UVERESkujldxSWamkhCzCLgDLNm/n0Pc8FLz5jHC9ce5vr+VTo8ERERkQqxH0kxClYrvourLayJvdsM0v96/p0EMAZPTAwAGaHBZsukIykQh4/c3GCkaI2DQ9mbm82enCw8Ho9f35vmSQ5sTwIQnbOWhWvrM6TrIQD2JicB4EF7D4iIiIjUVuVOd+/xeAqltzr2XGW/RERERERqgsttvDxtVS/jmDP/YLPZfGqGduuBc/rn5vGXK4eyYd/+qh6iiIiISLkFRIUDkLr6X4Z17+hzzmrLfxeXvOIfWof+irtpUwCCunQxzyUdSfW5rl27VJ8gvdVqrNbXqz1Dv7j8bZNSnY4quUeT+vmB/9un9DTL3Zo2B2D30SNVcl8RERERqbxyraS/7rrrKnROREREROR44PQG6QNs7mPOfMmuwwmF2lssFhJi9nAgpQkAXR4cR+ZHXxISdOz1IiIiIjXIbTybbMk9g22bfLf32b0thEaXnce+Wd8D0LrLPux0AyAsNj+N+pvPfsyF824yjy0WuO66reZxUJAr70xVfAfHnZZhEWZ5TXqKT9DeX0Jj65PuLe88EmbW5y3az7bb/X5PEREREfGPcgXpp0yZUqFzIiIiIiLHg+TMQMB3L9VGsXvZlwyN4+oUec22V1YSNraJeRx+/X9wTp+t/VhFRESk1vC4jeXtniKSatap56DL8+PMIL2refMi+4jwrsYvKDAwf9l8UJCRR99i0VL6POE2G5kuF+vSU6skSG8Jzt/rPooDZjk61AjYBwcE+v2eIiIiIuIf5U53LyIiIiJyogoJNFaZbdofxRvXriI6zM6oPtMAiAkv/GIaIDjQTeLbc33qDqSEVO1ARURERMrB411JXzBIb/OmuT+aGEhQfGx+/bZtRfbxzewfC/frgfR0Y8V4SEjeZveaqZinfXgUAMnOqlnR3rBu/oSIFvxslhNijL9Pu9NZ6BoRERERqR0UpBcRERER8UrKyPKW/iImbCpH351Lw1gjjWugrfgkVHUi7Xw+7j7zeMGaDVU5TBEREZHyOWYlvdXq5q3/20BoqPHs0y7yTLDZjLYF0gGlpKSU2G3v3onYbEaa+8BAY991l8tV0iUnlc4R+SvdPR7/Zxjo2jTVLF/NlWY577nV4VKQXkRERKS2UpBeRERERMRrX3Lei04Xry+aj8UCTpex8izAVvKj8yWnnmGWb586pKqGKCIiIlJuHm/gPC9IP378qxxcP5sHHniR9u03Go3ygusFgvSeIN8gr8v7XJSnVas0jh41tgQKDjbqUlOz/T3841ajkFCzfNTh/9X09aJyzLKDYLMcGGBMuNi4f6/f7ykiIiIi/lFjQfrs7Gx+/PFHZs6cyXfffUdaWlpNDUVERERExMu7ggwnf+3YjsfjITkzA4AAq634y47hcHUlO7dq0pqKiIiIlFfBPenvnrSfyMgM89yVV34OwG5OMyoKBOnTcpJ8+snMzPU5DgtzkJwc61Pncmnbnzw2i5UA78/zQK7/Jy8EBXj4m6sA+Jm7zfq859fosKK3axIRERGRmufXIP3s2bP5/PPPmTVrVokpnF5//XUSEhIYOHAgV111FUOHDqV+/frcf//9OBwOfw5JRERERKTMHGZ2VqPQ6YG7Sc7MBCCnDM+pX9/znVk+84mW/h6eiIiISIXYc433dG5sDP5PUpFtpjIHAE+AkSp93bp1YPFwyXXDzTZut+/7vhdffLOInrKKqDt5Ob3vSLPdVbMNQItmRiA+wbbFrGsUa2Q3CLSVfZKpiIiIiFQvvwXp//jjDy6//HJGjhzJRx99hKXArNuCXnnlFe655x7S0tLweDzmV25uLq+88gqXX365v4YkIiIiIlIuzmOC9JsP7CckKBAAu7P0PT0v6JGfHWrN7jN4bm47fw9RREREpNxyjDmHeLDSpIVvED0tLRKAdBLYSn8y2hkr6i0WCxaLhafefdhse2y6+xkzlrF/f4JP3fDhOUi+1mERACxLOlwl/fdsfADw/bsJ8+494HBVzcQAEREREak8vwXpFy5caJbHjh1bZJuDBw8yceJEADOIHx0dTVhYGAAej4e5c+cyZcoUfw1LRERERKTMnN53m8EB+Y/Jry6YD0DLevXK1MeYvq+a5f/O6uK/wYmIiIhUlMd4yPFgJSc985iT+zj17F689ul23uNH/k07y2jr8WCxWAgMDDBbHhukN9ql+Bx37hxbqM3JLNNlTPTMqaKV9FFxxs+7EX+Tl9g0bwW9gvQiIiIitZffgvQrVqwAjOD7kCFDimzz4YcfkpVlzNatW7cuP/74I8nJySQlJTFhwgTA+AXgxRdf9NewRERERETKzOFdLB9QRGZQC0VnijrWBzc09uOIRERERCqv4J70brdvoN1qtbLy57/pN2Q3P21fR/+hRjr8PXv28Pnkr7FYLFitxivEooL06ekHfI5PPTWiKr6F49bgOvmZBlwlbA9aUbH1jNT2sezG4TKeVwNtxsSKrFxlNRARERGprfwWpN+yxdj3qHnz5kRFRRXZZtasWWb56aefpl+/fgAEBQXx3HPP0bt3bwA2b97M5s2b/TU0EREREZEySfSme7VYoFcL3z3lX/p2bpn6sFgsnNPxCvO4Ct7FioiIiJSPO38l/bcf/uJzKiIigoceeoifFi2nTt10ju7d773ETXpqhlmGooP0MTFHfI6t1rJNbDxZJASHmOW/05L83n/dRg3Ncq7DmGlq806qWLt7t9/vJyIiIiL+4bcg/aFDh7BYLCQkJBR5PjExkXXr1gEQEhLCVVddVajNyJEjzfKqVav8NTQRERERkTIJDcoGwOFswl87tvuc++yO8WXuJ9eRYpavfPN0v4xNREREpKJy9hiBd08xrwKDg4NxZLk4tOuoWXf48GEuv36ET7s2bR6ga9dHSExMAyAkJJBt2940zw8aFOPfgZ8AAqxWgizGz31DRprf+4+rX8csJ6cZ6e2d3tT67RIaFnmNiIiIiNQ8vwXp89LY5+0vf6yff/4ZMFYW9e3bl9DQ0EJtOnToYJYPHjzor6GJiIiIiJQq227l7cUXAeDxBPqcu/asflx+ep8y93VW2/xn4tkrmvhngCIiIiIVFFivLgANWI/Fu9J969YtPm3efW4a1kBvWnu3i127dtG0ZSOfNm63h82bD/LYY18BEBwcwKFDe8zz7dsXft8n0C0yBoDdOVl+7zs8Mn97gbSUTADiI40sp7lOh9/vJyIiIiL+4bcgfUiIkbopPT29yPM//fSTWc5Lc3+sgoH7zMxMfw1NRERERKRYu46EsW5PFHP+yn8JbXf57it/9VlFP78W5/IzfAP6OxKLnsgqIiIiUh3sRrIgnBcMMfen//77xT5tzjnnHJbOXAmAzWqkTbdYik5dP3nyz7hcblJTjY6HDQti7Nh6hIfbqmL4x70eUbFmOdPp9GvfBf+OsrLsAAQHGhNO1+1RunsRERGR2spvQfp69erh8XiK3Ut+4cKFZrl///5FtklNTTXLRa20FxERERHxt5bjz6f7w4P4bm19s+6UFn192gzo1KVcfXZu3ISCj9rzVinVqIiIiNSc5MM2HGecQcwp0Wady+XyadOoUSPANyhvKWF/+U2bDpjlmBgb9esH+WewJ6D6BfalX5We7Pf+czD+XrfsMFbqx3gznRY3yUJEREREap7fgvTdu3cHICUlxScgD/Dbb7+xZYuRQisiIoJTTz21yD527Nhhlhs0aOCvoYmIiIiIlGr6L829pb8IDTrCeV26ATBu8Pnl7iswIADwAEkAjP+kuz+GKCIiIlIxHjf2IUN8qiwWC2+99RYBQYVXv2/atAkAq7W4PewD+Pnnf83jyMiQIttJvrxwucPt9nvfVoy09ukpGQAkxBgr9z0eDy63q9jrRERERKTm+C1If/HFF5vlG2+8kaVLl2K32/nzzz8ZM2YMYDz8jxgxAput6NRXK1euNMtt2rTx19BERERERMqhFzarlXfH3MRTl41k4sWXVaiXzS+9ATxlHtuuuZSpPzXz0xhFREREys5TRGD4yJEjPPzKXYx8YCjp1sPMmzfPPPf7778Dxa/Ezs11Yi2wyj4mRlv7lCZvX/q/0vy/kt4ebGRtcuUYQfoQb7p7gFyHf9Pri4iIiIh/BPiro8svv5wnn3ySrVu3sn//fgYMGFCojdVq5b777ivyepfLxTfffANAUFAQ3bp189fQRERERESKVPRCpgnYrFaax9fjoQv/U+G+W9dP4NBbEdS/Pb/u+g9Oxem2cEP/nRXuV0RERKS8PC43ZGdDaCjvvPMOhw4dAqDvkN4ARMdFsm3bNrN9dHR0kf0UNGvWCgAuueQUpVUvgxzvg2d2Faxs9wRGQy5k2Y31WMEB+UH6HIedsOBgv99TRERERCrHbyvpg4KC+Pzzz4mNjcXj8RT6Anjqqafo0qXo/TwXLFjAkSNHsFgsnH766QQFaR8rEREREalaLndRL5Rf99uL5rpRkfzv2huAN826mz86hcNpetYVERGRauTxQGgoAO4CsxTzsl326N2FlJQUsz47OxuAd5+bAsC8vz4t1GV0tLF63uXyf/r2E1HPqFiznOPyb6A+J6gpANlH9wEQUCCLaa5TK+lFREREaiO/BekBunXrxvr167n99ttp2bIlQUFBREVFcc455zBnzhweeOCBYq994YUXAGOvpOHDh/tzWCIiIiIiRTo2SB8alAXYsRWz/2pF3HbeEOBO4BmzrsHtF+KdxyoiIiJS5bKzcsxywYUxIWHGXvJ169cB4OOPP2bdhjX8+6+x33zy0VQAomOjfPpr0SKeefNWAzBkSNELcsRXy9Bws7wxM82vfVvtRwEIDjH+bi0WixmodyhILyIiIlIr+S3dfZ4GDRrwv//9r9zXffzxx2a5fv36/hySiIiIiEiRnAWC9Efe/ZpZK5Zw62T8GqQHWPPMy3R7+F5gLNAAgH3JoTSOy/brfURERESK4iF/duDBgwfNcmCg8WqwXkJdALZt28auXTsLXe84Zl/zgID8Z6Xmzev6c6gnLIvFQpDFit3jJtvPK+kDYltAxk+QtB44DzD2pc9wuXD4+V4iIiIi4h/+fftYCc2aNTO/QkJCimzzww8/MH36dKZPn17NoxMRERGRE1HBlfTBAXYWrlkOgNXP+6p2btKUl0ddBySYdc3uGubXe4iIiIgUx+02gvQelwdXMUHbuHgjHbvTmX++/9AzAWjeuolP2y1bDpnl009v6dexnsgaBhtbDvj5URMbdgAyHWFmXWDeSnoF6UVERERqpVoTpC+Ll156iTFjxjB27NiaHoqIiIiInAAKBum//msFX/+1EgCrxf+PyTcPOK9Q3ekTz/X7fURERESOZV36f0bBXfz+8e4i9pbPC+4XJyQkkLCw4EqN7WQSExgIwFF7rl/7zQw3thxwO+xmXXJmJgDr9uz2671ERERExD+OqyA9GHvWe7SBp4iIiIgUsDcplE9/bcKf22PLdV3BIP2GfTvNcpM6dfw1NFNoUN4L7Clm3Z874nhjUWu/30tEREQkT+7hZPBu5WNx56etf3v2iz7tuvTqUOjann26lth3+/YJJZ4XX3nTIFalp/i13zoxxrYFcRk/FTr36OzP/HovEREREfGP4y5ILyIiIiJyrCHPn821757O6Y8N4Nm57ct8XcEg/bJN68zy69dUTeamG/oPAAJ86j77rWmV3EtEREQEYPs7s8wgfcGV9L3O7ObTbsCFfX2Ozx7Um5vuv9Y87nZK4Wes1au1Srs86gYGmWV/LkIKPvpbseeaxPl/8qmIiIiIVJ6C9CIiIiJy3DuUlp9m9ZFZnal36/AyXef25AXpnfz672YAEmJisfh7o1CvsOBgYI5P3YrtceQ69FguIiIiVSO6a1szSG8nhH8yfuGPA99Rt16cT7vup3fxOZ664H8EB+cHlT+a8yzPPHOpT5v4+MgqGvWJqVdU/s/8sB9T3kc0KpwFIc9Z7Yo/JyIiIiI1R28DRUREROS453b7BtWPZgRz1dunlXpd/kp6l1l3ICXZn0PzcWbb9sCXQDMuPmWZWf/md0p5LyIiIlXD43bjjjOCw0GhFkJCQwoF6AGat25ilkNCC+8zX69BHe69d4hP3bvvXufn0Z7YQmw2s7wjO9Nv/dbt0Nssu93Gc23bBsZWBNGhYX67j4iIiIj4j4L0IiIiInLcS8kyVnmte26RWTdzeVM27S95dZfTjM3nB+k/u2O8v4dnuvS0vBeou/nqz/vM+gkzu5KZYyv6IhEREZHKcLshIKDUZhFR4Wb5kVfuLbbdkCH5K+5PP71V5cZ2EgqyGK9jM1xOv/VpDQwxyy67A4DuzVoA4MF/afVFRERExH8UpBcRERGRE0ZooIvgwPyA+8YyB+nz92cd3qNXFYwsX5M6efuC/sl95y8361fvjqnS+4qIiMjJyeP2QK6RWv3QoYhi21ksFgIDjWB+nwHFZyTaufOIWY6N1Srt8uoSGQ3Awdxsv/WZmpM/wSIny1ihHxIYCIDL7S7yGhERERGpWQrSi4iIiMhxz2Y1Xj4GBbgJKRCk/+TXZsVe43ZD+wnDvEf5L6ytlqp9RO7QsLFZrhv5tlnu++Q5VXpfEREROTnt+OBLsBhb/LhcrhLbrjj4PT9s/opmrRoX22bo0K5mOSBAmYAqyp8r6ft3yt+uac+GDQAEeFPrO10K0ouIiIjURgrSi4iIiMhxzeMBl9t4rA2webiw537z3Jw/GxV73e6jhVd+XdjzVIK9q46qSpcmTc3yg//3CRZLfgpSh9NSpfcWERGRk8/hJSvNsqeUzOdRMZE+e9MXJShIgfnKaBZqrHpPtOf6rc+6Efl9eYJjgQJBenfJEzNEREREpGYoSC8iIiIixzV3gZfNAVYPU276s0zX5TgKvmDuR9oHH/PV+An+HVwRbjznPJ/jDS+8b5a/WFn8pAIRERGRighrlmCupLdaK/8qcPTos+nbty1PPvmfSvd1MooLDDLLuX4KoIcEudnDKQBk5Rgr523e7FCbD+wv9joRERERqTkK0ouIiIjIcc3pyn+ktVk9WCww5eYVpV53ztP9Chz9hNVaPavY2zRIIHvKDPN42cZVZjktu2pX8YuIiMjJJ6pzazNIb/5ZCS1bxvP99xOYMOH8Svd1MmoQFGKWd2Vn+a1fJ8EAbD9o/LkvOQmAqNBQv91DRERERPxHQXoREREROa653PkvmwNsxsqh7k1TzTqnq+iX0YlpIT7HNj+sLCuroID8YPwtk9/jop47ALA79XguIiIi/uVxuczgvNsVUkprqWqWAhMlcvyYit6FsUJ/yz7jObNtgwSgep9xRURERKTs9JQmIiIiIse1gkF6m9XIfd+hYZpZt2l/ZJHXBXoD+vCa99qaezT+bcs6AF5Z0LbGxiAiIiInJk+BvYGs1uwaHInkaR8eBcC/mel+7zshyJj8GRFiTMhwuLQnvYiIiEhtpCC9iIiIiBzXnAVX0nuD9IEB+S+jNx8sOkjvMNPkfwqA1VK9j8YFJwUcTjdW/qdmKd29iIiI+Jnb7Zc09+JPxrPqIXuO33qsH+zdez4gzPjDZgPA4XT67R4iIiIi4j8K0ouIiIjIcc3h9N2TPk9cRC4AWw9GFLpm3t8JBY6MfTst1fzyesIFFxU4egeAlKwgcux6RBcRERH/8bjdeMLDAbAUeFaSmtMkxAikB/lxkmiwMxGA7B2/AhDoDdI73e5irxERERGRmqM3gCIiIiJyXLO78h9pV+3aZpZb18sE4OHPu/i0T8sOYMSrZxao+ZWggIAqHWNR7h5yQYGjlWYp9uaLCjcWERERqSCP240lx1ixHR2VW8OjEYCE4FAADvpxJX2QKxkA+94/AAiwGkH6fw/s99s9RERERMR/TuggfUpKCl999RXjxo2jb9++NGjQgODgYCIiImjatCnDhw/ntddeIzk5ucx9ZmVlsWDBAu6//34GDhxI48aNCQ0NJSwsjEaNGjFo0CCefvpp9u8v3wOwxWIp99e7775brnts376diRMn0qtXL+Lj4wkNDaVVq1ZcfPHFzJ49G5f2qBIREZHj0EdLW3hLGZw28UGzvnWD/D0+7c78VfIvfdOuUB81sR993cgoXB/P8h7lpyG1O23VPhYRERE5gbk9Zrr7o0mhNTwYAQgq8Ozp8NNK94N1rgTAGWNMUE3JMiasxnizKIiIiIhI7VL9S4Yq4bzzzqNBgwalttu0aRP3338/3333HXa7vdB5u91OZmYme/bsYf78+fz3v//l6aef5q677io2zemhQ4cYN24c8+fPJysrq8g22dnZ7N+/n++//57HH3+c++67j0mTJhEUFFS+b7QKvPbaazz44IPk5vrOmN6+fTvbt29nzpw59O7dm08//ZQWLVoU04uIiIhI7ZJtt/LEVx29R0Zae6fLRYDNxgfX/8WM35oBkJIZRL1o4zno6a87FOihk7efws+M1SVr8gzCxo4CLgOMoH3nBwax/vnvamxMIiIicmLweDy4snPwWAO9xzU8IAGgXlCIWd6UmUaXyJhK97nd2YsGzCTA4gAgPioKoEYyRomIiIhI6Y6rp7R77rmnTO3Wr1/P/PnzfepsNhutW7emfv36uFwuNm7cSFJSEmCsjh8/fjzr16/ngw8+KDJQv2fPHj7//HOfOovFQsuWLWnQoAE2m40tW7Zw4MABABwOB88++yyrV69mzpw55QrU9+3bl9DQ0mc2N23atEz9Pfnkk0ycONE8tlqtdOzYkbi4OJ8xL1++nL59+7Jy5coyTYYQERERqWlZuYUfZ9fu2UXP5i0JCXITGeIgPSeQX7fU4eJTjExHQ7sdYMGavD3p/63G0RYtODDQW/rGrNu4P4odiWG0qFf05FARERGRslhyylWkrv4XevQAwEPRi1OketkKvHtMczr80mez+h5IBU+AsXI+IsR4t5jr8E//IiIiIuJfVRqk//7771myZAl///03R44cITU1FY/Hw7Zt2wq13bJlCx7vdN62bdv65f4BAQFccMEFjB49mnPOOYco7wxSMGYSz507l9tvv519+/YB8NFHH9GrVy9uvfXWYvu0WCyce+65jB07lkGDBlG3bl2f88uWLeO2225jw4YNACxYsIBHH32U559/vszjnjZtGs2bNy/Hd1q8RYsW8dhjj5nHvXv3ZurUqebP2O128/nnn3PjjTeSkZHB3r17ufTSS/nll1/8cn8RERGRqrT1UESBozcAmPbTUno2bwlAeo4RAH9sdiczSJ8foP+Qgmnma9Ifjz/H6Y896FN39yfd+fqe32poRCIiInK8yd6XSFDdGGzBxkKRwz/+aQTowUx3j1bS1xqdIqL4JyONJUmJnBkbX+n+LMGRALhdRlA+OMB4Dt5z9Eil+xYRERER/6uSzTfnzZtHu3btGDJkCC+88AKLFy9m9erVbN++nZ07dxZ5zZ133kmHDh3o0KEDP/30U6XuHxgYyA033MC2bdv46quvuOiii3wC9GAE2y+66CKWL1/us2p84sSJOIqYYWq1WrnkkktYv349ixcvZtSoUYUC9AD9+vXjt99+o2PHjmbda6+9xqFDhyr1PVWEx+PhgQceMCc/tGvXjsWLF/tMgrBarVx55ZV89dVXZt2vv/7qcywiIiJSW036smOBo7sAmP7LUrPm6jN3ARAS5Cri6k1VN7By6tk8b7uhBLNu/qqGNTMYEREROe4cmPcTC5qez7y4cwA4vOwvfh54S34D7x7oSndfe9gL7EXv8cNfjC3Am8Uz1VgcFRhgA2Dj/n2V7ltERERE/M/vQfoJEyYwYsQItm7disfj8fkqyb333mu2mT59eqXGcNFFF/HBBx+UKR18kyZNePzxx83jI0eOFDlJoGfPnsyePdsn+F6c6OhoXn31VfPYbrcXSr9fHRYuXMiaNWvM49dff52wsLAi2w4cOJArrrjCPH7uueeqfHwiIiIildW5cZq3tMusS8vONsvXnb0TgGy7zayLCs31luaZda3q1a+qIZaJ1Wpl6s13AAeBIWb9lJ+a1diYRERE5PixfISxRaQ7JxeP283P597s28C7kt7lch97qdSQC+s1Msu57sr/veR41xxluowV9eHBwQBEF/MuUERERERqll+D9C+++CIvvfSSGZTv2LEjTz31FHPmzOHUU08t8dqBAwdSv359PB4PCxYs8OewSjV8+HCf402bKr+qasCAAT77yvujz/L64osvzHKLFi0YNGhQie1vvjn/F7iVK1eyd+/eKhubiIiISHHWrYvlmmv6c911/ViyJKHEtq8syMsQ5BvMzpv8GepdQb9hX7R5Ltuet6reSHV/fveerH/+VWraNWf185a+N+sem92mZgYjIiIix41jF8Yc+XlVoTZ/xNwNgNutpfS1RZgtfxfSPTlZle7PFmIE56M4CEDLeGMSqj9W6YuIiIiI//ktSL9nzx5z73OLxcLTTz/N+vXrefjhh7nwwguJi4sr8XqLxcKQIcaqoYMHD7JlyxZ/Da1Ux44tLS2tmJZlZ7PZiI7Ofxnsjz7L65tvvjHLgwcPxpK3/1gxzj77bMLDwwHjAf7bb7+t0vGJiIiIFOWUU/7D55+3YubM1gwdej7FLSxa+EOjok8Ah73PXkEBhS/2ePIegY0g/ZzxEwjy7tlZ07a/+jbjBg8B3gZgX3JMjY5HREREaj+Pw+lzXGgVPZDuMgK2FkudahmTlE+my1l6o1Kc1jD/Xao9J5vgQOP5NtdZ+b5FRERExP/8FqT/4IMPyMnJwWKxMG7cOB566KFy99GrVy+zvGHDBn8NrVS7du3yOa5Xr16l+8zOziYxMdGvfZZHYmIiBw8eNI979+5d6jUBAQE+GQ8KpsoXERERqSnLluWvpn/55S4MHjyUPXvCueiq4rMErdq1A4Amcfmp74+mG/t0Ot3GI3BIoI20Dz7GZrUV7qCGNKsbz6tXj6FN/WVm3Zj3TqnBEYmIiEht53G5ij13sf0P7sdDTk4IADGxu6trWFIGHcOjAPjh6KFK9xXRtIdZzkrPJCjAWKmf63Dg9kM6fRERERHxL78F6RctWgQYgd68FfXl1aJFC7NcnanWv/zyS5/jM844o9J9fv311z4PwOXp8/7776dTp05ERUURGhpK48aNOeecc5g0aRI7duwoUx8bN270OW7VqlWZrivY7tg+RERERGrCkSPGS2WXy8LDD5/G0qUNad36ymNaTfU5enT2ZwDUi8416/Yk5W1FZATrcxxZhIeEVMWQK+3yM/Jftk//pTn3zehag6MRERGR2sxTwj7zFpsxGdFi8aY8tx2tjiFJGQVarT5/VkauNcYsHziUQ1RI/jacyZmZle5fRERERPzLb0H6nTt3YrFY6Ny5MzExMRXqoybSw6empvL666+bx127dqVTp06V6tPpdPLMM8+Yx/Xq1WPAgAFlvn727Nls2LCB9PR0cnJy2LdvH0uXLuXxxx+nbdu23HLLLWRnZ5fYx7HZAZo2bVqmexdst3PnzjKPWURERKSqXH31uQA4HPmPrvXr5+/b+e6YH4HbADilhTHh8K8d20nOzACgZT3jz5SsIHLs+X1Muen2Kh13ZTx+yRVAa/P41QVtSUwNrrkBiYiISK1VXJA+/tz8bIlWqxGkL2UnRKlmPaJiAUh1Oirdl82Sv/d8To6LsOD8Z0eHH9Lpi4iIiIh/Bfiro9TUVKDw/u7lkZubv9IpOLh6XkLee++9Pmnhn3rqqUr3+dxzz7Fu3Trz+JFHHiGkHKu04uPjadmyJREREaSmprJp0yYyMoyXy06nk/fee48VK1bw448/+kxsKOjYSQ7FtTtWVFSUWU5PTy+1fW5urs/fW959nUDlf72Q0jidgKN8v2G73B5cluM/zZnL6fb582Rj9XhwuMr5dsUJntJbyTGcx/wpZXcyf0ZVhD7X8j/XQkKc5OT4PqY6HBZycvID7IcOhQHQq0USg3psgSnZ2KxWZt3zAC3uvAmAureMIefjWaRnG30dSAsmzZGf2r5r85ja+7xisXBupzCW/NMH+A2Ajg8M5sC7cyvdtT7XKk6fa+WjzzU9r1UXfa5VnD7Xyqe2fq45cwo/0fSa9iQN/zMAl8MYq6VAADevrrz0ueZ/0YFBZjnd5SLEm/mgIp9rzetn8BsdacAG0rMdOC0WAm02HC4XKbm51PHfsGu1inyuVcSJ8llYWz/Xqos+16pPrf3dW0SkBvktSB8XF8ehQ4dISkqqcB8FV3/XrVvXH8Mq0eTJk/noo4/M4yuuuILhw4dXqs/vv/+eSZMmmcd9+vThtttuK/W6jh07ctNNNzF8+HBatmzpc87pdLJo0SIefvhh1q5dC8CqVau48sorWbBgQZH9ZR6TxqqskwRCQ/NTYR3bR1GeffZZHn/88UL1SyMCCQsLLNM9pRL+rMikmGTv14lh8+KDpTc6YVXgV+xI/4/iZLE4Up9p5abPqArR5xo4nYWTPS1eXIfMzMKPrn/vjKXluJsJBQLcbtY0qedzfn6oBULckA5rw6zkBuY9E7lZHmVjTy3+f9sRFwP8THBwMrm5sYRE2Vnox/Hqc60C9LlWIfpcKyc9r1WYPtcqQJ9rFVLbPtec3oUzeQLi40mL7kLaD4lmXV6QPjfDwYaF+ytxN32u+Vf+59YnGUdo3Lixz9lyfa51zKYextaVKxMPkRrZAYfL2ELpxxALW06Wz8gKfa5VxIn1WVjbPteqlz7XqkOW7ST5DBIRKQe/BembNm3KwYMH+eeff8jOzvYJ9pbVkiVLzHJlU86X5ueff/YJnrdo0YL33nuvUn1u2rSJK6+8Epf3ATg2NpYZM2Zgs9lKuRL++eefYs8FBAQwbNgwBgwYwKWXXso333wDwMKFC5k3b16REwscDkehPsqiYDu73V5q+4ceeoh77rnHPE5LS6NJkyb0z3AQ59L8uKrmPCcdYso3d/OwrTUOS/n//6xtXE43mxcfpN3ABtgC/LZzx3Ej0JNNvGtr+S5KsRDwo36LKC8nxouRgekO//2jeZI4mT+jKkKfa/mfa5GuPbThe1YyFo93d6aBA4+SlBRU6Lr2DRPZuA+e8B4POHxMJqBfV9Cx7tksOxxG86NW+qTk7fVuZbDTSpP02vu88vwh48X6yDOeYeqyFzlwIILBaY5Kp6nV51rF6XOtfPS5pue16qLPtYrT51r51NbPtZyDQewAsFg4+6cphDWtT0h936BTXpA+JDKQjkMaVug++lyrGqu9f3pS0xgSbTybVvRzbYt3fW/oho0MST+TBtExHExN4bSUbLpG197nXn+qyOdaRZwon4W19XOtuuhzrfqkZZ8cn0EiIuXht99fBw0axIoVK3A4HEydOpVbb721XNfv2bOH2bNnA0Zwu1evXv4aWiFr1qxh+PDhZpr2evXqsXDhwjKnhC/Knj17GDRokJlJICwsjPnz59OsWTO/jBmM1fCfffYZbdq04dChQwD873//KzJIHxYW5nOck5NTqK4oOTk5Zjk8PLzU9sHBwUVuTRBAwbnAUlUsAUBg+X7xsNksuC0nzkO3LcCKLfDE+X7KyuaxEGgt5y+dARa9tKwEfa6Vnz6jKuZk/1zzeOBhT3MALuNGpjOL+HN7ExjoweVyE0waIaRyz+Mf8edvo4nN+pDN+/L7Wffdt9SJiORohhGsP5ySgstl/Dyfm9OBvYfznm+SCbPZavX/1+0aNGT5v5uZuuwD4EUAktOCqR+dW/KFZaTPtfLT51rFnOyfa+Wi57VK0eda+elzrWJq2+ea1TsUl8fGuP/+h89+/NfnfLPWOVgseWnvLRUeuz7XqsaQug1YeOQgWS5Xoc+w8n6uJdKeemwiMLoRgUCQdzGO2+U8aT4fK/K5VhEn2mdhbftcqy76XKs+J8tnkIhIefjtX96RI0eaK8YffvhhNm/eXOZrc3JyGDlyJHa7HYvFwpgxY/w1rEI2b97MoEGDSPWmAouNjeW7776jbdu2Fe7z0KFDDBw4kD179gBG4HrOnDn06dPHL2MuKDIy0mcCxM8//+wTWM8TERHhc5ydnV2m/rOysortQ0RERKSqud2+y8Sv5TKzLjfXwz105WGakb7jB1Yt78inPz/t88v+rvVrqRuZv6rh1inv43QbM/Z3Hw3nmbkdvGdiCShDtqOa1LlxE28pP4XtN6sTamYwIiIiUms5UjMAsOFkxU+ReI6JN8XVTTFX0h89XPFtKqVq1A8ytmMKtlb+NW1O3BkA2I/uACDQ+7yblJFR6b5FRERExL/8NumrQ4cOjBkzhg8//JC0tDTOOussXn/9dUaOHImlhJycy5Yt46677mLdunVYLBaio6OZMGGCv4blY8eOHQwcOJDERCN1aEREBAsWLKBbt24V7jMpKYnzzjuPf/81ZikHBATwf//3f5x33nl+GXNR+vfvb5ZzcnLYs2cPbdq08WlTt25dn+MDBw5Qp07p++scPJi//1BZ2ouIiIj4k8tV+Llx3z4jG1BOpos4dgGweuov3OU9/1eBtns3/sOcF16nw4S7zLrft/YBIwmsjwBr7Q7S3znofO6bMR0AmzUTlzucGz88hbH9dtbswERERKRW2frWLJ/jjDQrkdFu8zg1OZO4cCNIn3jgSLWOTUoX5H0mdR47u6ICQl3GO093SH0AtiUamThr++RUEREAl8tVaBtfEZHjjc1mIyAgoMTYeB6/ZmZ57bXXWLNmDStXriQpKYlrrrmGe++9l759+7Jp0yaz3bhx40hMTOT33383V597PB5sNhufffYZ8fHx/hwWAHv37mXAgAHs3bsXgNDQUObPn8/pp59e4T7T0tIYPHgw69atA8BqtfLxxx9z0UUX+WXMxUlI8F1BdeTIkUJB+nbt2vkc7969m86dO5fad97fB0D79u0rMUoRERGR8nO5LNgJJYj8LED79hkp6g/sy18zf1qBa47dJKlJRARXnXk2n/76s7dmZ5H3CqzlLysLvkx1ud8EHgBg95FQmtYtW5YkEREROfHZk9LxWK04Bgygzc5/WfhFLJeNPWqeDwgIxOpN5+zxQyBY/CvA+wI3w+WsdF+ZoR2IT/2W9Ezj77lXi5b8tWM7dmfl+xYRqUoZGRns3btX/06JyAkhLCyMhIQEgoKCSmzn1yB9WFgYCxYs4Oqrr2bhwoUAJCYmmnvN580aeOutt8xr8j50IyMjmTZtGoMHD/bnkID8dPQ7dhgrqPLS0ffr16/CfWZmZnL++efz559/Asb39uGHH3LllVf6ZcwlKZiSHgrvPw/Qpk0bAgICcHofwlevXs35559fat+rVq0yyx06dCihpYiIiIj/OZ1WrLh86urXNwLSQQH5W/zEltDHkumTmX773Tx16UhajL8NgJbxt7D98LsFWk3BZg3217CrzDf3P8ywF58BHiQvSH/BS2ex9rnva3RcIiIiUntEduuAc/MBHGeeyVVnfsZT9z7iE6R3Om0EBtoBaNaqcU0NU4pRMM292+PBWoZVV8XJdBip8+1243k6yGa8+lWQXkRqM5fLxd69ewkLCyM+Pr5Mq09FRGojj8eD3W7n8OHD7NixgzZt2mAtYUsjvwbpAeLi4vj222+ZMmUKL730Ehs3biyxvc1m44orruDxxx+nVatW/h6OmY5+8+bNAAQGBvL5558zaNCgCveZk5PDhRdeyK+//mrWvfXWW4wZM6bS4y2LDRs2+BzXq1evUJugoCBOP/10c4y//PJLqf0ePHiQrVu3msd9+/at5EhFREREysfptGA5JkjfZsfLQFNyc9xFX3SMzb//yrDb76Zp3XiGduvBgjWr2H54MlAwSD+WwICZfht3VRnStUeBo7VAV/7ZF83M5Y25svfemhqWiIiI1CLbX56Cp2N+9sSMNBtpKTaiYlzY7Ra2b27EtSM/BKBjFy3IqG3CbPmvZw/kZtMopPBinDL3FWq8BA7KNN7vBQUYfSdlak96Eam9HA4HHo+H+Ph4QkNDa3o4IiKVEhoaSmBgILt27cJutxMSElJs2+LD95U0ZswY/vnnH1auXMkrr7zC2LFjueiiixg4cCCXXHIJt912G9OnT2f37t188sknVRKgPzYdvc1m49NPP+XCCy+scJ8Oh4NLL72UJUuWmHWvvPIKt956a6XHW1YzZ+a/UG7evHmh9Pd5CqbdX7x4MYmJiSX2++mnn5rlmJiYSmUaEBEREakIl8uC7Zgg/Xk8AUBudn7aO09AADlXXIF94ECzbuSkZ8xy8sEDADx92ShvTcF97W4GwGqpskdhv3px5LXeUn5WpKvePoNN+yNrZkAiIiJSqziSUvBERJjHzZrt5I9lxvHMD+rStOlu81yAze/rdaSSbAVWjB5x2CvVV1xwqlHwdnkkPR3QSnoROT5oBb2InChKWj1fUJU/mffq1YtevY7dKbTqZWVlMWzYMDMdvdVqZcqUKVx22WUV7tPlcjFq1Ci++eYbs+7pp59m/PjxlR5vWc2bN4958+aZxyNGjCi27ciRI3n00UfJzc3F4XDw/PPP8/LLLxfZNiMjgzfeeMM8vuqqqwgI0C9uIiIiUr2cjqL3n9u7N4zMjGTs/frh6NOHyFW/4+rQARcQtX0nw0dfT0KL1mb7tT9+T7+R19K1aTOz7ou7JtCtaRta3/s+ATbbcfMC4J7zh3P/Z9OBfcBLwH0AdHpgMK6PZ9fk0ERERKSG2ZPTAHCecYZZ16rVdpb/2InzLkrl8XFNad9+k3nunCtPrfYxSulah0WwNSuDdekpdIuMqXA/YZ7DAISkrjaOg419UANttsoOUURERET87PhYPlROubm5XHTRRWaKd4vFwvvvv88111xT4T49Hg/XX389s2fnvwidOHEiDz/8cKXGmpqayiWXXMJff/1VatvPPvuMkSNHmsdhYWE88MADxbZv3LgxN998s3n8+uuv88UXXxRq53A4GDNmDLt3GzOrQ0ND+e9//1ueb0NERETEL5zFLB5q1WokO7aH4zjnHAgOJv2M/Iw/Vz4yiSbeAH10vfoAbP79N8B4DmzbID/rkM1qrCIKKOOM1trivC7dvKX7aV4306wPvPYSn3Y7EsPIytVLWBERkZOFIyW9UF379puY9r/6dI/r5lOflpZGaETx6Tal5mS5jExSO7IzS2lZMmtMC5/jhrFxADhcrqKai4iIiEgNOiGXSr/++ussXrzYPI6JiWHWrFnMmjWrTNefd9553HvvvT51s2bNYtq0aeZxSEgIf/zxB0OGDClTn127duWFF14oVO/xePjyyy/58ssvad++PYMHD6Z79+4kJCQQHh5Oeno669atY/bs2axcudK8zmKxMGXKFBo0aFDifSdNmsSCBQvYsmULLpeLyy+/nFGjRjFixAji4uLYvHkz77zzDmvXrjWvefHFF4tNoS8iIiJSlRz2/JX0PwADgKMYLxvffOV0bv3vzkLX/JGSxNB449mlw5l9+f2rWWQkHTXP142M4t+DB1ize6e5sj7gOFtN9Omtd1HvtrEA/PjfxbQYb2xr5PZYeGNRa8YN3sqaXdH0fOQ8GsVmsfuNb2tyuCIiIlJN3HYHnmMmH9arZ6ymTk81XvtZLMbzVUhEUPUOTsqse2QM+3OzK92PLa4tAC5LMJC/J73TpXT3IiLVxWKx8NVXX5WYBbmgpUuXcs4555CcnExMTEyRbaZOncrdd99NSkqK38ZZlObNm3P33Xdz9913V+l9RMRwQgbps7KyfI6Tk5NZtGhRma8vKvB9bJ85OTnl6jMnJ6fUNps2bWLTpk2ltouMjOS9997j8ssvL7VtbGws8+fPZ+DAgezZswe3280nn3zCJ598UmT7Bx54gNtvv73UfkVERET8zeWy8NQT3clb85Xo/TOK/QAE2IpeZr81Kx0wgvSd+57D718ZEzMP79lFfJNmJGdmAPDO4kWM7H2W0Zf1+ArSBwbkjzc+KoOsyV8SNvY/AIz/pDvjBm9l7t8NAdiXHFYjYxQREZHq57Y7oYgtfIKDc8nNNQK1eUH6oGAF6WurtuGRfHvkAAAOtxsqmPVpb0okjQGbJxeAQJvx6vdQaqpfxikiIqU7cOAAsbGxZW7fp08fDhw4QHR0dBWOSkRqI78F6X/66Sd/dQVA3759/dpfbRUaGsqYMWNYsmQJu3btKrFtdHQ01113Hffeey9NmzYt8z3atm3L2rVrue+++5gxYwbZ2YVn5nbs2JHnn3+eCy64oNzfg4iIiIg/fPllc2Z/3oxux9Qn0gGAQGvRQfoUpwOPx4PFYiGqbrxZv/GXn4gfeQ2tGySwcf8+6kZGkmU3XlimZFUulWh1CwsKNsvzV/3NZaf35pv7f2bYi2cD8M/eKGxWT3GXi4iIyAnq8NI/iwzoduq0nr//7gXkB+mLiOVLLRFhy39Fm+lyEm6t2IQKT0AEANkYgZ5U76Iju1Mr6UVEqprdbicoKKjU7MfHqsg1InJi8FuQvn///lj89LRvsVhwVuLhcdKkSUyaNMkvY8kzevRoRo8e7dc+AYKDg5k8eTIAiYmJrF27lt27d3PkyBFyc3OJiIggLi6Orl270rVrV2wVTM0aExPDhx9+yKuvvsqSJUvYs2cPmZmZJCQk0KVLF3r06OHPb0tERESk3I4cCcGCsV+mB0j21jdiNQCB3pX0HrebBx9qwvIlGfy00lgVtCM7k5ZhxkvJuk2acWTPLnKzjZeS44dcwLy//2Tj/n1YOD7fThdMz3/lm69wfrePGdL1kFl31/TunNflUFGXioiIyAls8zOTiwzSt2+/2QzSt2691aj0HJ/PQSeDgu9UD9pzaBVYsSD9JX2SWPE7hJJKalYAVqvRb0RIiF/GKSJSHTweT6HMxtUlLCyszHGu/v3707lzZwICAvjkk0/o0qULP/74Y6F097/99hu33XYbmzZtonPnzjzyyCNcfPHFrFq1iu7duxeZ7n7q1KlMnDiRI0eOMHjwYM4666xSx7Nu3Truuusuli9fTlhYGJdccgmvvPIKERHGu5LRo0eTkpLCWWedxcsvv4zdbufKK6/ktddeIzAwsFB/Y8eOJTExkfnz55t1DoeDRo0a8eyzz3L99deX6eckIsXze7p7j0creCqqXr16DBw4sErvERkZyUUXXVSl9xARERGpiKAgN1ac5A4divP00wl4/XVINkL1FtwEBhrbB1msVqxWC2f2ijaD9IftuWaQ3pFjZA1a9+Nizht7M1Gh+enfez5yf3V+S37Vo1kLVu3aAUDUjddw8K0PqReVQ2JaCD9urMeQbgdreIQiIiJS3aK7tiHnlzXm8YYNG+jYsSNt225h0qTHmTL1a3r0MN4DeRwK0tdmARYLTo+HjEosXAoNy8++9MhnrWgcWwcAp8tV6fGJiFSXrKwsM7Bc3TIyMggPDy9z+2nTpnHrrbfy66+/Fnk+LS2N4cOHc/755zNjxgx27dpV6n7vf/zxB9dffz3PPvssI0aMYOHChTz22GMlXpOZmcngwYPp3bs3K1euJDExkRtuuIE77riDqVOnmu1+/PFHEhIS+PHHH9m6dStXXHEF3bt358YbbyzU5w033EDfvn05cOAACQnGFoPz588nKyuLK664ouQfjIiUid+C9E2bNi3zDCOXy0VKSgoZGcb+oHnXNWzYkIAAv88bEBEREZFazu2GyZPbYcWJ8/TTAeh/6aXwwQcANOdXwq3Jha5rHBzK3txsPORPFE09nGiWs9JSaRQbV8Wjrx5/PvUCtmsuM4/7PTWRz8e1ov9T/QE4nKYX7yIiIicbuzPQZyV9yy6NoUA8dszoi8jIzCAiPAKClfK8NmsdFsmmzDT+TE2iW3TFnl/DIkLN8uGlr1L/POO/DZfb7ZcxioiIrzZt2vDCCy8Ue37GjBlYLBY++OADQkJC6NixI/v27SsyKJ7n9ddfZ8iQIUyYMAEwtjP+7bffWLhwYYn3ycnJYfr06eYkgzfffJPhw4fz/PPPU79+fQBiY2N58803sdlstG/fnmHDhvHDDz8UOZ4+ffrQrl07Pv74Y3MsU6ZM4bLLLquxSRQiJxq/RcR37txZ7mt2797NnDlzeOmll9i7dy9t27bl888/p06dOv4aloiIiIgcB1b8HM3ff8YwkKcA42Vio0aNzPOnRDzOAWsdoCN4J3oC1AkKZm9uNq4C2Zzqt2zFoe3bAEg+eIBGbdsXut9Lo66tmm+kiiW+PZl6t40FwOFycXa7I+a5KT81Nsvr9uyiS5Nm1T4+ERERqV5uuwNPgSD9yNtG8NX/lvi02bd3H+3atSPLnlndw5NySLQbWaOOOHIr3EdoUH4wPoP6NLQak1cVpBeR40lYWJi5wLMm7l0evXr1KvH85s2b6dq1KyEFth057bTTSrxm48aNXHzxxT51vXv3LjFIv3HjRrp16+aTBeDMM8/E7XazefNmM0jfqVMnny2VExISWLduXbH93nDDDbz//vtMmDCBQ4cOsWDBApYsWVJsexEpn8KbVlWjpk2bMm7cONavX0///v1ZunQp/fv3r7H9RkRERESkah367ncWtR3B6juex5Ga/0t3SnIA3fic83jCp/1R75+nZfzABdafAHB78l8y2rwZmQoG6S97KD8NXPpRo4ferduadQM7d2X80OH++YaqWZ3ISGaNuw+A7Ym+e9AfzYg1y90fvq9axyUiIiI1w5VrB+/zkNvtJiKmcHChbt26AGSk1kzAQ8rmrJh4ACqzkWjjuGyz3Cx0OzbvBI6M3JzKDE1EpFpZLBbCw8Nr5Kus2aLzlCc1fm1w7N7zFosFdwkTua699lq2b9/O8uXL+eSTT2jRogVnn312VQ9T5KRRo0H6PFFRUcyZM4f4+Hg2bNjA+PHja3pIIiIiIuJnbruDX4feQea2vWx/ZxYrRj0MwKRxTbjp0o4EUXh1186CB97Z3lZX/i+QVoxfoJclH+bHJCNoHRQSSnR8PQByM42X0f8dcal5zeL1a/31LdWIxnH56U9Xe/eoL0qO3V4dwxEREZEalPnXKjPdfd5L9nOu7kVmVoHMQ96MlSFhIYU7kFqjcUh+qnqnp2Ir3wNs+SH+yKBMftuyCYC3vi9+9aWIiFSddu3asW7dOnJz87OkrFy5ssRrOnTowB9//OFT9/vvv5d6zZo1a8jMzH+v8uuvv2K1WmnXrl0FRm6oU6cOI0aMYMqUKUydOpUxY8ZUuC8RKaxWBOnBCNTfdNNNeDwePv74Y5KTC+85KiIiIiLHL1e2b+rOQwt/A+Djt+oRzmEu5eZC19h69DDLHm96OEuBWd5xgUFm+Zfk/NTv4bHGqnKPd4X90G75/QQWSO12PDq1ZWuz3OuRCXx863tFtvtn357qGpKIiIjUkGSa+KykB2jcIoFbnr2yUNvMdKW7r80KPtcerMTK95To/gAExTSgc+OmlR2WiIhUwqhRo3C73dx0001s3LiRRYsW8dJLLwEUu2p/3LhxLFy4kJdeeoktW7bw5ptvlpjqHuCqq64iJCSE6667jvXr1/Pjjz9y5513cs0115ip7ivqhhtuYNq0aWzcuJHrrruuUn2JiK9aE6QHY18NgNzcXH788ccaHo2IiIiI+JPb4SxUd2D+zwD04NMir2lz0UVm2eINuHsKrCQ/PaYOIxPyXz5mOo17RNYx0roeLRConnfvQ5zdrgOH3v6oot9CrWCxWHj8kivM42veuYW2DdIKtVvyz/rqHJaIiIjUgFj24PaulPd4fBOl5zh8g/IBgQHVNi4pv4LBmmyXq8L9RMREA+ByWTmnYxcAzu3YuXKDExGRComKimLevHmsXr2a7t2789///peJEycC+OxTX9AZZ5zBBx98wOuvv063bt347rvveOSRR0q8T1hYGIsWLSIpKYlTTz2VSy+9lAEDBvDmm29W+nsYOHAgCQkJDB48mIYNG1a6PxHJV6ueziMiIszy7t27a3AkIiIiIuJvuVmFXzYuv2g8AdxCCs2Aovfg9AAWwONN5RrmSPc53zos0iy7vT38+7uxSn/ND9/Rd9S1BAYFc373npzfvWflv5Fa4JERl+Jyu3niq1kA/HvwWeBZnzYP/t8n3H/BRUVcLSIiIicCZ5Z3tXWokSY9ODjY57zNGuRzHN+gTrWMSyqueWg4O7MzSXbklt64GAERxopJW/IaAmxnAeCsRNBfRESKtnTp0iLrj50016dPH9asWWMef/rppwQGBtK0qbHgoH///oWuGTt2LGPHjvWpu/fee0scT5cuXViyZEmx56dOnVqo7rXXXvM53rlzZ6E2mZmZJCcnc/3115d4fxEpv1q1kn7r1q1m2aWHRxEREZETSm5G0XtrhpCKBe+zXxHp3tIeeQRPYCCeusbq+JhmcYXa5F31+q5/AQiLijbP/b3wm0qMuvZ67D+XFzgq+rH+2F/0RURE5MThSPFOXPQ+PyW0rOtzPtAW6HPcZ8Bp1TIuqbg0pwOAFIej4p3YUwFIpSkBVmObJ6e7Ynvci4hI5U2fPp1ffvmFHTt2MGfOHB544AEuv/xyQr2T7Gort9tNYmIiTz75JDExMVx44YU1PSSRE06tCdK73W7effdd87hJkyY1OBoRERER8TdHdn66+385zyw343eu5TLjwFr48TQgIIDUG27APmQIAPv3F35pmRBs/HLrAQ7kZnPT/943zyXu3OGP4ddKuVNnekszjjljvKz/bcvmah2PiIiIVB93rh0AR5MWAFisRe9tm8dCyeel5kUHGBMrDuVmV7gPa2xLAHJz7QTYjCC9w1V42ykREakeBw8e5Oqrr6ZDhw6MHz+eyy67jPfff7/0C2vY7t27qV+/PjNmzGDy5MkEBNSqxNwiJ4RaEaTfv38/F198MStXrgTAZrMxYMCAGh6ViIiIiPiTMzd/Bc8HfGeWRzMiv1ERK+kBAuvXL7Hv6xu3NMsf7t2OxWKhVc9TjGuPSf16Igmw2Uh8ezKwE0gpcMZ4zN98YH/1D0pERESqRdbug7ijonCfZmznk3bUdw/6qDoRPsc71u+rtrFJxeRNPE1y2CvcR2ioEehv7P6DAJvxTLhy+7bKD05ERCpkwoQJ7Ny5k5ycHHbs2MGrr75KWFhYTQ+rVM2bN8fj8bBnzx7F60SqiN+mvjzxxBPlau9wOEhOTmbt2rX8/vvvZnp7i8XCDTfcQJ062idLRERE5ETidBip13MJByCn8amE7F3p02aJ1coZFew/zGojy208U7o9Hpp07MK2v//EVZl0oceBOpGR1I+O5lBqM8BIbzqk66ksXPs7SzeuZ2y/c2t2gCIiIuJ3+3cH8uudM8m55x6zrkWnRj5thl5/Jv/3wiLzuHmnhtU2PqmYht4gfba74tuAxgWnkgmkBrTB4TT6ad+wUckXiYiIiEi181uQftKkSViKWflUGo/HY1571lln8fLLL/trWCIiIiJSS7hyjZeEdm+Q/si5bWk83TdIv7RAkL5v3yh++inN53zz5sH06RNZZP93N2/HM9s3ALA/N5uAoCAANq9YzjDG++vbqJXiI6M4lJpsHqdl5wLgdmtPehERkRPR2S268iI/wmX9zLpOZ7byaRMUHMjwm/txaPdRXE43LTorUFvbxQflZ4DyeCr2HBcWWxcAqzubOhHGc7PDqXT3IiIiIrWNX9PdezyeCn0B1KtXj2eeeYbFixcTGhrqz2GJiIiISC1gzzbS3bsJAB7i/6Z/6nP+X8BaYE/6ooLxI0bUoVmzkCL7txWYMLovJxt7tncvT48He07F9/U8HvTv0BnIMo/7tR8CwGfLf6mhEYmIiEjVKhzADQwqvBYnpl4k7U5pTsczWmK11YpdL6UEeXvSA2bW0fIKDjH+O6jv/IuQIKO/bYmHKj84EREREfErv62kv+6668rVPigoiMjISJo2bUqPHj3o06cPNpvNX8MRERERkVrG4023aQTpnyP9mPPTgKACQXqLxcIll9Thiy+OmnXWUt4tNwoOZV9uNkkOO2ed0YefPpsOwN6NG2jZo5cfvova6cVR1/Dm9wswAvVhpOZ0Ms+lZmUSHRZeY2MTERER/xvMozU9BKkCgQUedp1OJ1D+d6V5E1eTLS0ILPCuNdfhIDgwsLjLRERERKSa+S1IP2XKFH91JSIiIiInIGduXpDeeHGYCUwGQoANgYFce911NG7cGMgPxjdqFOTTR0BAydsrxQUGsS83m0R7DpGNEggOCyM3K4vUI4n+/FZqnaAA3xeub39/FXA1AHE3j8b18awaGJWIiIhUlbZ8V9NDkCpis1hweTzk5uaCNazc18fEGhlKrR4HQQH5r37fX/I9dw4+32/jFBEREZHKUZ4rEREREakWKb8uB8DlXRF04cghnHbnlbS/6T+8O+NlM0AP+UH68HAbp58eAcA118Rjs5UcpA+zGS8ig70dxCYYe69mpaT47fuorf588gWiQ5cUqGlrllKzMqt/QCIiIlJlIjixJyCezMKtxrNybm5uha4PCTGut2KnUUwds75FvfqVH5yIiIiI+I2C9CIiIiJSLXIPHQYgln0APPH2g0x87T6efPsh7BnuY1rnB+PPPTeGhx5qTOPGwaXeo36wsV/9lqwMACJi4wDwFLFva3F2/7OO7av/KnP72qJH8xYcfDv/ZW6b+nPM8pNzZtfAiERERKSqbOVcXA0bmsed+rSqwdGIPwV7g/TZ2dkVuj7cWEhPJInYbPkr8WPCyr8qX0REapfmzZvz2muv1fQwxI+uueYannnmmWq/77vvvsvw4cOr/b7iS0F6EREREakWztQ0AH7kFpZtm0tklLFCftuaPRzYleLb1ln2oHpBkbb8lJ4HcrOJqd8AgIPbtpbp+qP79jL7uSeY8/JzrF3yvVmfnZHO/n83V2hM1SkowENUqAOAU1vlv4gN0f6jIiIiJ5Ro9pFz003mcbMOCTU4GvGnGO9zm8vlqtD1keH5z8OZOTY6NDQySx1OT6v84ERExDR69GgsFgu33HJLoXO33347FouF0aNH+/WeK1eu5KYC//7L8W3NmjV8++23jBs3zqyzWCxFfr344otmm+bNmxc6/9xzz/n07fF4eOmll2jbti3BwcE0atSIp59+2jw/duxY/v77b37++eeq/0alWFUepN+8eTOfffYZ//vf/3jqqad44oknqvqWIiIiIlIL5S5ZAIATG42bGyu/PB4Py+et9ds9WoSGm+Vd2Zk4cnMAI8heFtkFXl4unvK+OcZ3bh3LzCcf4YepH/ptrFXl6cvWA2B3Wrl7yDAAZq/4vSaHJCIiIn4WZTvscxwSXnrGITk+tAg1JrKmpVUsqB4elT9RMy3dydZDBwGwWbVWS0TE35o0acLMmTN9sp/k5OQwY8YMmjZt6vf7xcfHE3YcZ0ax2+01PYQi1dS4/ve//3HZZZcRERFh1h04cMDna/LkyVgsFi655BKfa5944gmfdnfeeafP+bvuuosPP/yQl156iU2bNjF37lxOO+0083xQUBCjRo3ijTfeqNpvUkpUJU9n6enpPP300zRp0oSOHTty9dVXc/fdd/PYY4/x+OOPF3nNlVdeSZ8+fTjzzDPZt29fVQxLRERERGqQMzASgCS6kZOZy/Z1e1k45dci215xRd0K3cNisdA6zPjlxunxUKdREwAO79qJ0+Eo8pqUQwdJ3LXDuOaYNvbsbDJTks3jNT8sqtC4qlNggLF1wOwVTcyXsc3qxtfkkERERMTPHKG+/7aHR4fW0EjE30JttkpdH+jd/gkgLS2Ls9q2ByApI6NS/YqISGE9e/akSZMmfPnll2bdl19+SdOmTenRo4dP24ULF3LWWWcRExNDnTp1uOCCC9i2bZt5fvr06URERLBlyxaz7rbbbqN9+/ZkZWUBhdPdWywW3nvvPS644ALCwsLo0KEDy5cvZ+vWrfTv35/w8HD69Onjc5/Ro0czYsQIn7Hdfffd9O/f3zzu378/d955J3fffTexsbHUr1+fDz74gMzMTMaMGUNkZCStW7dmwYIFJf58mjdvzpNPPsm1115LVFSUmQXgl19+4eyzzyY0NJQmTZowbtw4MjMzzevefvtt2rRpQ0hICPXr1+fSSy81z+Xm5jJu3Djq1atHSEgIZ511FitXrjTPT506lZiYGJ9xzJkzB4slf1vFSZMm0b17dz788ENatGhBSIjxb2dKSgo333wz9evXJyQkhM6dOzN//nzzusqM+1gul4vZs2cXSjnfoEEDn6+vv/6ac845h5YtW/q0i4yM9GkXHp6/aGXjxo288847fP3111x44YW0aNGCXr16cd555/n0MXz4cObOnVvhLXak8gJKb1I+f/zxByNHjmTXrl2AsfKooIL/IxTUp08fPv/8cywWC9OnT+ehhx7y99BEREREpAZ5XEbwOCNuP7Ne2VFi25YtQ0o8X5LoACNFqN3tpnmXbmb9no3radG1R6H2k+8zZhtf8eiTZCQd9TmXkZJE4s6dPnWvjxnJ2JffJDgsjKCQ2vdCPC/dPUDdyDOBebi8P3sRERE5MVhCin6/Jse/5gUyQ7k9HijmXWpxLBYLbmxYcZGb68LucgIK0ovIcea9fpCRWP33jagHNy8r1yVjx45lypQpXHXVVQBMnjyZMWPGsHTpUp92mZmZ3HPPPXTt2pWMjAwmTpzIxRdfzOrVq7FarVx77bXMnz+fq666it9++41Fixbx4Ycfsnz58hJXzz/55JO88sorvPLKKzzwwAOMGjWKli1b8tBDD9G0aVPGjh3LHXfcUWpA/VjTpk1jwoQJrFixgv/7v//j1ltv5auvvuLiiy/m4Ycf5tVXX+Waa65h9+7dJY7vpZdeYuLEiTz22GMAbNu2jSFDhvDUU08xefJkDh8+zB133MEdd9zBlClT+PPPPxk3bhwff/wxffr0ISkpyScl+4QJE/jiiy+YNm0azZo144UXXmDw4MFs3bqVuLi4Mn9/W7du5YsvvuDLL7/EZrPhdrsZOnQo6enpfPLJJ7Rq1YoNGzZg806eq+y4j7V27VpSU1M55ZRTim1z6NAhvvnmG6ZNm1bo3HPPPceTTz5J06ZNGTVqFOPHjycgwAj5zps3j5YtWzJ//nyGDBmCx+Nh4MCBvPDCCz4/o1NOOQWn08kff/zhM0lDqo9fg/R///03gwYNIiMjwwzOt27dms6dO7NixQr2799f7LXXXXcd999/P06nky+++EJBehEREZETjMVjvCDsM6joFe3+YvO+yNyZncm5jVsaLzY9HnOSQHH+78lHC9Wt+eE76rdo5VPncjr54C5jz7k7Pphe6wL1l562l1FvGeWfN50OnM5Pm78n4vpQMj6aU5NDExERET8JaJyfFrXngPY1OBLxt3Bb/uvaDJeTYO8E1PLItcQS6jlCdmYuTu8zcFCA39dqiYhUnYxESC8+nlSbXH311Tz00EPmwtVff/2VmTNnFgrSH5uufPLkycTHx7NhwwY6d+4MwHvvvUfXrl0ZN24cX375JZMmTaJXr14l3n/MmDFcfvnlADzwwAP07t2bRx99lMGDBwNG2vMxY8aU+/vq1q0bjzzyCAAPPfQQzz33HHXr1uXGG28EYOLEibzzzjusXbuWM844o9h+zj33XO69917z+IYbbuCqq67i7rvvBqBNmza88cYb9OvXj3feeYfdu3cTHh7OBRdcQGRkJM2aNTOzEmRmZvLOO+8wdepUhg4dCsAHH3zA999/z0cffcT9999f5u/Pbrczffp04uON7ETfffcdK1asYOPGjbRt2xbAZ/X6s88+W+FxF2XXrl3YbDbq1atXbJtp06YRGRnJf/7zH5/6cePG0bNnT+Li4vjtt9946KGHOHDgAK+88goA27dvZ9euXcyaNYvp06fjcrkYP348l156KUuWLDH7CQsLIzo62vxvV6qf357OnE4nI0eOJD09HYvFQvfu3Xn33XfNPQ6GDh1aYpA+Ojqac845h++++47Vq1eTlJRUrlkvIiIiIlK7BXhycdevT6v2+cHyC27qy/z3fwLg9NMj2Lgxm0GDYip1nxyXC4B9uUa6rgYtW3Nw2xaO7N1Nyx6+v9y6vW2Ls2rRtwwce3Ox5/du2kDL7iX/wlzdbFbo0+YIv22py7dr+gNX4nJHkm2HNbui6dgstYZHKCIiIpVmyX+e6nB6yxIayvHGVmDlfJI9lzoVCNKHeo4Y16c6ad+wEX9s28LG/Xv9NkYRkSoXUXzgsrbdNz4+nmHDhjF16lQ8Hg/Dhg2jbt3CW/ht2bKFiRMn8scff3DkyBHcbuPf8t27d5tB+tjYWD766CMGDx5Mnz59ePDBB0u9f9euXc1y/fr1AejSpYtPXU5ODmlpaURFRZX5+yrYr81mo06dOoX6BUhMLDnjwbErxdesWcPatWv59NNPzTqPx4Pb7WbHjh2cd955NGvWjJYtWzJkyBCGDBnCxRdfTFhYGNu2bcPhcHDmmWea1wYGBnLaaaexcePGMn9vAM2aNTMD9ACrV6+mcePGZoD+WJUZd1Gys7MJDg4uNvs4GBM5rrrqKjMdf5577rnHLHft2pWgoCBuvvlmnn32WYKDg3G73eTm5jJ9+nTz+/noo4/o1asXmzdvpl27dub1oaGh5nYKUv38FqT/+OOP2bJlCxaLhR49evDTTz+VmOKiKL179+a7777D4/Gwdu1apVcQEREROUF4vL98Ok491aw76+IexNbP/wWxZcsQzj03ptL3ahUWydoMIxCdmJtDRnISAL98PoPThl9stsvNymTag/cU2UdByQeMiaZNOnZmz4b1PudSD9dA+r0yOKudEaQ33G3W700OVZBeRETkBBB6SlMA9m/ei9VmreHRSFVZfOQgbZq2Lvd1R2lJHbbjceSw17udU1x4RClXiYjUIuVMOV/T8lLKA7z11ltFthk+fDjNmjXjgw8+oGHDhrjdbjp37ozdbvdp99NPP2Gz2Thw4ACZmZlERkaWeO/AwPzJXHkB36Lq8iYFWK3WQttUOxyFMx4W7COvn5L6LU7BvdIBMjIyuPnmmxk3blyhtk2bNiUoKIi///6bpUuX8t133zFx4kQmTZrks+98Scr6/R07rtDQkrMkVmbcMTExha6pW7cuWVlZ2O12goKCCp3/+eef2bx5M//3f/9X4rgATj/9dJxOJzt37qRdu3YkJCQQEBDgM+GgQ4cOgDEppGCQPikpyWeyglQvvz3Fz5kzxyy/++675Q7QA+ZsITD2gxARERGRE4MrOxePzYbTO4O6QfM6tOjcCIB+l/XilHNb0axZsF/u1SkiP/C/JyeLrucMNI+zM9JJTzrKt++8wVs3jzYD+AVd89SL3D11Zv416WnG9+B0MG7yDL+Msao9cMHmIuuz7bZqHomIiIhUhaDD+wCIb1ZDKw2lWmR595MvLztG4OHQoXQ6NW4CgNNdcgYpERGpuCFDhmC323E4HGaa+YKOHj3K5s2beeSRRxgwYAAdOnQgOTm5ULvffvuN559/nnnz5hEREWEG/v0pPj6eAwcO+NStXr3a7/cpTs+ePdmwYQOtW7cu9JUXrA4ICDD3UF+7di07d+5kyZIltGrViqCgIH799VezP4fDwcqVK+nYsaP5/aWnp5OZmVmu769r167s3buXf//91+/jLkr37t0B2LBhQ5Hn81a+d+vWrdSxr169GqvVaqbOP/PMM3E6nWzbts1sk/d9NWvWzKzbtm0bOTk5Jabll6rltyB93n/kzZo1K5S+oqwKprdPSUnxw6hEREREpDbwuFxQYB/MngM6mOWm7RPo2qdZiSm+ysNisdAx3AjUOz0ezhhxqXlu4XtvMnXCXWz67edC110y4RHGTZ5BfLPmWG35wey81fJxDRsTEBhIhzPPNs/9OH2yX8bsbzHhDupE5Baq37C37KntREREpPZyBhqroi0pSk96Ihoa3xCArAoG1oNJB2DGb80IDTQCB7mOigX8RUSkdDabjY0bN7JhwwZstsKT42NjY6lTpw7vv/8+W7duZcmSJT4pywHS09O55pprGDduHEOHDuXTTz/l//7v/5g9e7Zfx3ruuefy559/Mn36dLZs2cJjjz3G+vXrS7/QTx544AF+++037rjjDlavXs2WLVv4+uuvzQkJ8+fP54033mD16tXs2rWL6dOn43a7adeuHeHh4dx6663cf//9LFy4kA0bNnDjjTeSlZXF9ddfDxirysPCwnj44YfZtm0bM2bMYOrUqaWOq1+/fvTt25dLLrmE77//nh07drBgwQIWLlxY6XEXJT4+np49e/LLL78UOpeWlsasWbO44YYbCp1bvnw5r732GmvWrGH79u18+umnjB8/nquvvprY2FgABg4cSM+ePRk7diyrVq3ir7/+4uabb+a8887zWV3/888/07JlS1q1alXqz0eqht+C9IcPH8ZisdCiRYsK9xFQ4MWt06kHRxEREZEThdvhAmv+o2dsg+gqvV+g914HvfvS59mx+m8cuYWD1wDNunQjoEDqtpAI4+X3vs3GvmY277Pqedff6nNdafva15T9b84vVBcWXDvHKiIiImWXkmSDesZCF3eAsuSciOKDjAxTwdaKvbrdT3cA+jT4iyDvM+w/+/b4ZWwiIlK0qKioYvd8t1qtzJw5k7/++ovOnTszfvx4XnzxRZ82d911F+Hh4TzzzDOAsa/8M888w80338y+ffv8Ns7Bgwfz6KOPMmHCBE499VTS09O59tpr/dZ/abp27cqyZcv4999/Ofvss+nRowcTJ06kYUNjglpMTAxffvkl5557Lh06dODdd9/ls88+o1OnTgA899xzXHLJJVxzzTX07NmTrVu3smjRIjNAHRcXxyeffMK3335Lly5d+Oyzz5g0aVKZxvbFF19w6qmnMnLkSDp27MiECRNwed/5VHbcRbnhhht89rjPM3PmTDweDyNHjix0Ljg4mJkzZ9KvXz86derE008/zfjx43n//ffNNlarlXnz5lG3bl369u3LsGHD6NChAzNnzvTp67PPPuPGG28s089Gqobf9qQPCQkhNzeX3GJeepbFkSNHzHLBVfUiIiIicnzLOpDsE6T306L5Yjm8e6KtzUjlovqNCY2MMtPWF3TZw5NY+ulU2p9xVqFzORkZRR4HBAZy15QZvD5mFAAr5s3hjBGX+PtbqLQAm4dTWmzgzx0dzboHZnblrmFFp24TERGR48PlZ7djZK8MiI8iIFj70Z+Iwm3GK1vXMXvqllW76G2QCqERYRxKSwWgQXSMv4YnIiJQ6ursgltEg7G6+djU5gX3Tp88uXCmvnvuucdnxf3OnTuLvR6gefPmher69+9fqO7xxx/n8ccfL3bsS5cuLVR37L2Lun9ZrgE49dRT+e6774o8d9ZZZxV5/zwhISG88cYbvPHGG8W2GTFiBCNGjPCpKxiMnjRpUpGB+7i4uCL/Hvwx7qKMHj2aZ599luXLl9O7d2+z/qabbuKmm24q8pqePXvy+++/l9p3w4YN+eKLL4o9/88//7B69Wo+//zzco1Z/MtvT/L169fH4/GwZcuWCvfxxx9/mOUmTZr4Y1giIiIiUgscXb4OjzdI7/F4/JbavjiRBTI0HbHncuvbHxVqc+XEp2jSoRPXPPUip15wUal91m2c/3xqC8hfcf/bFzNxOR2VHHHVuPyMvwrVbU8Mr4GRiIiIiL9s3xyCxWo8SwUFaiX9icjmfVZ2VjBI7wmrD4Alcy8t4uv5bVwiIiLiP6GhoUyfPt1nAXN1OXDgANOnTyc6umozXUrJ/Bakz5vlceTIkSL3UCiN0+nks88+A4z9O846q/BqJhERERE5PoW2ap6/kr6KV9EDDKjTwCy/s2crAPd8PMtnckB8k2bl6vPUC0b4HNctcP3rY0axbMa0Coy0arVLOFSoruO9Q2tgJCIiIuIvVivmGz1bkFbSn4gCLPl/r9kV2FopMPFXAML3fWXuSW/X1qIiIiK1Tv/+/Rk+fHi133fgwIEMHjy42u8rvvz2JH/RRfmrjwru01BWzzzzDHv27MFisTBgwAAiIyP9NTQRERERKQO3G/5YFkF6qv9f9roJBO/qdks1ROltFgtRtsI7O7XvY0wEDYuKJjAkpMQ+bn9/Gv2vGk3fK6/hzo8+wWrzXal27TMv+Rz/tWA++7fWrlTydSJygQ2F6ufMaUWOXS/1RUREjkcul8VcSW8LqIbZj1LtQgo8d27PziihZdEy659vFKwBBHj7clQg2C8iIiIiVcevQfru3bsDRtr6Sy+9lLS0wvt+Hsvj8fDMM8/wxBNPmHWPPvqov4YlIiIiImXUJrAXo85tx7WD2/q97033PeP3PkszpnFLs5zmTUc/5OY7uWTCI4x6/NlSrw8ODaPnkGGcMuxCAoOCy3TPPf+sq9hgq4jNagU6AS2B/EmwU6d2pudDg2pqWCIiIlJJFquRBt0WqEl3J7ptWeUP0sfUiQIgLfwUAr0TZf/YVrsmk4qIiIic7Pz6JP/BBx8QFhYGwNy5c2ndujUTJkxgwYIFpKenm+1WrVrFwoULmTRpEm3btuXRRx/F7XZjsVi4/fbb6dOnjz+HJSIiIiKlaGXrZZbXrix9z/K0jTvY+/n3ONLK9tIwfc0GM919YHDhFe5VIarAvvGv7/oXp/d5s1mXbkTVjffLPa5+6gWf4y1//uGXfv3FlrfFADsA37+r7YkR1T4eERER8Q9LrDH5LgBHDY9Eqkp4uPFMnusu/wr4oHhj0m14xhrSsrIAaF2vQUmXiIiIiEg18+sb0l69ejFr1iwuv/xyMjIyOHr0KC+//DIvv/yy2cbj8XDKKaf4XOfxGLN///Of//Daa6/5c0giIiIiUord24PK1d6elMrizpcBYLHZuNheemA6IDaGXG/AOC89a3XbmZ1J63D/bqlUr1kL6jRuwtG9ewBI3LnDr/1XVn6QPs8s4DLzaPeRUJrWza7WMYmIiEjlnDUwDYvbjQcICLaV2l6OT5GRkWRmZrIpM730xsfIe97OIZLWdesC4PHr6ERERESksvyeE2vIkCH8/fffnH322Xg8HvMLwGKxYLFYfOo9Hg8RERE899xzzJo1C2uhF4kiIiIiUpXSUnxf7iY0sZfYPudQkln2uFzkHkkp9R6uzCxzJb3VVn1B+gdatDfLTk/VvJq87tlXuPy/j5vHuzesr5L7VITVcuyz9Zs+R32fOqf6BiMiIiJ+4fEA2cYkO2tUWM0ORqpMaGgoACHW8k/EsEYYq+aj3LsJtBlrtH7evNF/gxMRERGRSquSiHjr1q1ZtmwZv//+O7fffjtdu3bFarX6BOzDwsI499xzef7559m1axcTJkyoiqGIiIiISCmcjvygeUuWcf2e5nxpO4W50X1Z1OYiMrbu8Wm/d/ten+Mlva4qsX9XTi4euz0/SF+NkzKDrDaahBgvr39NOVJl92ncvqNZ/v2rWVV2n/IKCjg2cdbPPkd7jurFvoiIyPFm5Q9W87kqNDK0hkcjVSUkJAQAl8dd7mvrxBjPgA5LOBbvo35IYGAJV4iIiIhIdavSN6SnnXYa//vf/1i9ejUOh4Pk5GT27t1Leno6GRkZLF68mPvvv5/Y2NiqHIaIiIiIlCAvSH8673Mr/YnmgFGfkUXm9n0cWfaXT/v0ZN+Um9l7D+F2OovtP2un0Z+7gbGix1rN6e6DvKvJ9+dmk+0qfpyV1axLNwD2btrAK9dcxo41q3Dk5FTZ/cqibYOEY2o8wF00bGjsT9+8bma1j0lEREQq7sDeQEZzUf7kxyAFXk9UFm903VGBbFDBocZETJsnh7YNGhp1CtKLiEgB/fv35+677y7XNRaLhTlz5hR7fufOnVgsFlavXl2psZVm9OjRjBgxotL9fPTRRwwaNKjyA6olNmzYQOPGjcnM1Lue40W15paPjo6mYcOGhIeHV+dtRURERKQETqfxAvBSbjbrMkLqYw0JBmDTM5M59P3vHFz4GwAuh6NQH7mHU4rtPzvZCFS769c3jjNy/TLusrqkQWOzvCI1qYSWldNj0Pk+x1+99Azv3H5Dld2vLKxWK1edeTYAAzp18da+wbhxvwPgdFfvhAkRERGpnMT9gbRhCXnLowPDQmp4RFJVbLb8NPd/pBwt17XBIcZKeisuAvKC/U6X/wYnIiKMHj0ai8XCLbfcUujc7bffjsViYfTo0dU/sDL68ssvefLJJ8t1zYEDBxg6dGgVjah65eTk8Oijj/LYY4/51KekpHD77beTkJBAcHAwbdu25dtvvzXPN2/e3Nzau+DX7bff7tPP8uXLOffccwkPDycqKoq+ffuS7d2uqChl6bd///6Fzhf8769jx46cccYZvPLKK5X98Ug10QbwIiIiIic5R46bmxhoHq9kNC/aPsPjXR2ftXM/vw65g9+GjSP5zw0EFpGu3p1beB/7HX8cZfr5s9mwzFi1nfcyuVW3xoXaVqVgq41I716c6zNSq+w+Lbv3pEX3nj51TnsujtzqnZRwrA9vuJWljzzB3HseNOsyMlIA2JukdPciIiLHk68+rsMGLjBX0tuCj93aRk4UBYP0u3LKuSLOFmwWrS4jOO+owoxSIiInqyZNmjBz5kyf4GtOTg4zZsygadOmNTiy4tntxvubuLg4IiMjy3VtgwYNCA4OLr3hcWD27NlERUVx5plnmnV2u53zzjuPnTt3Mnv2bDZv3swHH3xAo0aNzDYrV67kwIED5tf3338PwGWXXWa2Wb58OUOGDGHQoEGsWLGClStXcscdd5S4/WNZ+gW48cYbfdq98MILPufHjBnDO++8g7OEjJdSeyhILyIiInKS27dyC234wTxexn1kZZ5Du1cfK9T2ret240grnMI96fd1PseZSTl8N+hRIhY9x7YnXwfA0bMXAEGhQf4cfpm0DTd+8UxyFJ5M4E8X3/sQlz/yhE/d+p+WVOk9SxMUEMjZ7ToQEhREaJDxs//116/N80fTq//vQ0RE5GQ155M4nrmvMRXIYG6wwBFam0H6gAKBXDnxnBkbD8DO7PIF6etE55c9dmPCqMPlwlPh//BERKQoPXv2pEmTJnz55Zdm3ZdffknTpk3p0aOHT1u3282zzz5LixYtCA0NpVu3bsyePds873K5uP76683z7dq14/XXX/fpIy/N+0svvURCQgJ16tTh9ttvx1FExsM8kyZNonv37nz44Ye0aNGCkBAjC8+x6e4PHDjAsGHDCA0NpUWLFsyYMYPmzZvz2muvmW2OTXe/YsUKevToQUhICKeccgqrVq0q9WeWnJzMtddeS2xsLGFhYQwdOpQtW7aY56dOnUpMTAyLFi2iQ4cOREREMGTIEA4cOFBkf9OnT6dOnTrkHrNAYsSIEVxzzTXFjmPmzJkMHz7cp27y5MkkJSUxZ84czjzzTJo3b06/fv3o1q2b2SY+Pp4GDRqYX/Pnz6dVq1b069fPbDN+/HjGjRvHgw8+SKdOnWjXrh2XX355iRMcytIvQFhYmE+7qKgon/PnnXceSUlJLFu2rNh7Se2hIL2IiIjISc6Z6vuLziE6ATD8zkcLtW276QkO7DH2sywY7k78YYVZXvV7OB/E30N8hlEXm7MRAIvNePS05xT/y2NVOSUqziwftlftyvbG7Tpwz8ezzOMfp0+u0vuVR7Z3xnxkZLpZN2tF9WY2EBEROZnde10LPnq1Pr8uLt/KtTxdT8nEhgOP94VsSSuy5PgXF2hMpgyzli9jQliw2yynHdhvll1ud1HNRUSkEsaOHcuUKVPM48mTJzNmzJhC7Z599lmmT5/Ou+++yz///MP48eO5+uqrzWCq2+2mcePGzJo1iw0bNjBx4kQefvhhPv/8c59+fvzxR7Zt28aPP/7ItGnTmDp1KlOnTi1xjFu3buWLL77gyy+/LHa/+GuvvZb9+/ezdOlSvvjiC95//30SExOL7TMjI4MLLriAjh078tdffzFp0iTuu+++EscBxkSDP//8k7lz57J8+XI8Hg/nn3++z0SDrKwsXnrpJT7++GN++ukndu/eXWzfl112GS6Xi7lz55p1iYmJfPPNN4wdO7bYcfzyyy+ccsopPnVz586ld+/e3H777dSvX5/OnTvzzDPP4HIVvWWM3W7nk08+YezYsVi82SMTExP5448/qFevHn369KF+/fr069ePX375pdSfTUn95vn000+pW7cunTt35qGHHiIrK8vnfFBQEN27d+fnn38u8/2k5vgtJ1bLli391RUWi4Vt27b5rT8RERERKZ5r/x6z3GfX79As/9zauDvpmvQ/8zgAO6mr9hENHATykrclDDvbbDP5zCmcQ37Q/litujXx08jLrl5w/n6t7+7ZyqOtOlX5PTv3H8D6pUaGArfbhdVa8yvd7j1/OC9/Ow+Hw058ZA6H00P49Nem3DJge00PTURE5KRy5FBgha5zOS205TvgKgDSk4rf21SOfzHeIH2ys3zZoCJD8lPcpuzLD9I7XE5lXxCR48bhrMMczj7sUxcVFEXjyMbkunLZllI4htSxTkcAdqTuINvp+29ko4hGRAdHk5STxMHMgz7nwgPDaRbVjIq4+uqreeihh9i1axcAv/76KzNnzmTp0qVmm9zcXJ555hkWL15M7969ASOm9ssvv/Dee+/Rr18/AgMDefzxx81rWrRowfLly/n888+5/PLLzfrY2FjefPNNbDYb7du3Z9iwYfzwww/ceOONxY7Rbrczffp04uPjizy/adMmFi9ezMqVK83A9YcffkibNm2K7XPGjBm43W4++ugjQkJC6NSpE3v37uXWW28t9potW7Ywd+5cfv31V/r06QMYQecmTZowZ84cM7W7w+Hg3XffpVWrVgDccccdPPHEE0X2GRoayqhRo5gyZYp5/SeffELTpk3p379/kdekpKSQmppKw4YNfeq3b9/OkiVLuOqqq/j222/ZunUrt912Gw6Ho9De9QBz5swhJSWF0aNH+/QBRgaDl156ie7duzN9+nQGDBjA+vXrS/yZltQvwKhRo2jWrBkNGzZk7dq1PPDAA2zevNknkwNAw4YNzf8epXbzW5B+586dhWZ0lObYFEsWiwWPx1PufkRERESk4jKDookE7ITQoHEARpR+GtCfb5PG0ZX/+bSPXmLMEA8G9gKNgSx7/ovDc/DdDyuPx+nCEmAjNLxm9i9rFhLGrhxjhvHcxH1cWK9RKVdUzsDRN5pB+sWT32fQDcX/olpd4rxp/xcsWEC/Dqks2xhCRIj2KRMREakJbjd89XEcnXtm0a5L4e2EimLPtXCENuZEyaDQigX75fgQbjNe3ZY3X0JcuJ0kmhHHLiIb5AedHC4XoX4cn4hIVZr17yzeWfOOT92wlsN47uznOJR5iCvmX1HomnXXGVvxPfLrI6w9vNbn3DNnPcPwVsNZtHMRz/zxjM+5Pg378N5571VonPHx8QwbNoypU6fi8XgYNmwYdevW9WmzdetWsrKyOO+883zq7Xa7T1r8t956i8mTJ7N7926ys7Ox2+10797d55pOnTphKzDhKiEhgXXrfLcgPFazZs2KDdADbN68mYCAAHr27GnWtW7dmtjY2GKv2bhxI127djXT5wPmBISSrgkICOD000836+rUqUO7du3YuHGjWRcWFmYG6MH4Hkta1X/jjTdy6qmnsm/fPho1asTUqVMZPXp0sbHG7GxjAkfBsYORzaBevXq8//772Gw2evXqxb59+3jxxReLDNJ/9NFHDB061CfY7/Zmrbn55pvNjAo9evTghx9+YPLkyTz77LPFfh8l9Qtw0003meUuXbqQkJDAgAED2LZtm8/PKzQ0tNAKe6md/Bakh8JB97LK+x9F+yKJiIiIVD+P03gGO0oDb81uYAKwgqO05ml2kkUdnsY3LetBi4W6CQl4Dh3i8N5EWpd0D8ASYPwSabXVTFrWaxu14Mlt/wCwJj2lyoP01gK/NK9ftqRWBOntzvyA/LKNtwJf8t26BsVfICIiIlXmp0VRTBjbAoBtrr9Kbe9ywTez47iEb8niNADCI8OqdIxSs4K92xm4AbfHg7WMC5uCA90k04I4dpGbm//s7XAWna5XRKQ2uqztZfRv0t+nLirI2O6lfnh9/u+C/yv22qfOfKrIlfQAg5sPplt8N59z4YHhlRrr2LFjueOOOwAj0H6sjIwMAL755hsaNfJ9F5G3T/nMmTO57777ePnll+nduzeRkZG8+OKL/PHHHz7tAwN9J+hZLBYzMFyc8PDKfX/VrajvsaT4YY8ePejWrRvTp09n0KBB/PPPP3zzzTfFtq9Tpw4Wi4Xk5GSf+oSEBAIDA30mQXTo0IGDBw9it9sJCgoy63ft2sXixYsLrWJPSEgAoGPHjj71HTp0YPfu3cWOqbR+i5I32WHr1q0+QfqkpCSfY6m9/Bak//HHH8vc1uVykZKSwvr16/n6669ZtWoVFouFa665psQ9IkRERETE/1wO78s6q/FL0AVXDGL+//0ATAbGkuLNf/8DVzKAmeZ1aWecQevBg8ndvJmcnUYazWJ/LyzwC47VVnNZk65r2Jxp+3cCkONyEVLF6T77Xz2GpZ8YmQfev+sWbnr93Sq9X2nqx8QUOMo0S1m5NsKC9dJWRESkuhxJDODff8q3prljWE9w5nBxgS0naypDkVSPQEt+gP2wPZf6wSEltM5nsYAL49n+6J6dZr3Trec9ETl+xIfFEx9W9OrvYFuwmdq+KC2iWxR7Li4kjriQuEqPr6AhQ4Zgt9uxWCwMHjy40PmOHTsSHBzM7t276devX5F95KWAv+2228y66toWul27djidTlatWkWvXr0AI/B7bBC7oA4dOvDxxx+Tk5Njrkj//fffS7xPhw4dcDqd/PHHH2a6+6NHj7J58+ZCQe3yuuGGG3jttdfYt28fAwcOpEmT4rdaDAoKomPHjmzYsIFBgwaZ9WeeeaaZxt/qnSj377//kpCQ4BOgB5gyZQr16tVj2LBhPvXNmzenYcOGbN682af+33//ZejQoaV+H8X1W5TVq1cD+RMD8qxfv55LL7201Oul5vltGVO/fv3K/HXuuefyn//8h4kTJ/LXX38xa9YsoqKi+OSTT4zUm8V8SImIiIiI/9mOHgAgwb0FgImv38+Tb93PfU//jZH6/iEgjO+4nIMYvzR9C/T0/uLpatcOj3d1vMOR3++3FEjhVSAYbguouX0wm4Tkrzabk7i3yu/Xc/D5Zjkj6WiNZ45q26DgL26LzdKY90+p/sGIiIicxJ69vwnvvVD2bDbpaVacTgsRHMbVLD99eUT08bUyTsonwJr/6nZPTvnS1sZZ9gDgsDsJ9D6LO1wK0ouIVAWbzcbGjRvZsGGDzyrsPJGRkdx3332MHz+eadOmsW3bNv7++2/+97//MW3aNADatGnDn3/+yaJFi/j333959NFHWblyZbWMv3379gwcOJCbbrqJFStWsGrVKm666SZCQ0OLTRk/atQoLBYLN954Ixs2bODbb7/lpZdeKvE+bdq04aKLLuLGG2/kl19+Yc2aNVx99dU0atSIiy66qFLfw6hRo9i7dy8ffPBBmRYDDx48mF9++cWn7tZbbyUpKYm77rqLf//9l2+++YZnnnmG22+/3aed2+1mypQpXHfddQQE+K6Ftlgs3H///bzxxhvMnj2brVu38uijj7Jp0yauv/56s92AAQN48803y9zvtm3bePLJJ/nrr7/YuXMnc+fO5dprr6Vv37507drVbLdz505zooLUfjWTa/QYl1xyCXPnzsVisfDiiy8yY8aMmh6SiIiIyEmj3k/v4WrShNyhQ8nJslMnPpZRt1zKTfdfi5H6/jkgGzceXuYfHrC+Sfd77/bpw5nr8P6Z/+LvnCFJ+Q0KvGC0WmtuJX3BXy7TnNWzF/sVjzxhll+99vIaDdQHBxRMGZef9mD2iuJnmIuIiIh/lJKJtkQZacYL/768jLvAPrex9aMqOyyp5SJtFUuEuttjrIQ8khZgBuczc3P8Ni4REfEVFRVFVFTx/y4/+eSTPProozz77LN06NCBIUOG8M0339CihbHq/+abb+Y///kPV1xxBaeffjpHjx71WVVf1aZPn079+vXp27cvF198MTfeeCORkZGF9m3PExERwbx581i3bh09evTgv//9L88//3yp95kyZQq9evXiggsuoHfv3ng8Hr799ttCKe7LKzo6mksuuYSIiAhGjBhRavvrr7+eb7/9ltTUVLOuSZMmLFq0iJUrV9K1a1fGjRvHXXfdxYMPPuhz7eLFi9m9e3exkwHuvvtuHnroIcaPH0+3bt344Ycf+P77731S0G/bto0jR46Uud+goCAWL17MoEGDaN++Pffeey+XXHIJ8+bN82n32WefMWjQIJoVmNQptZdf96SvjLPPPpsrrriCzz77jIcffpiRI0cWO0NHRERERPzHExhMrnc279qf/uW0IZ0BYyb42LtHMfk1YwJli7b92PEv3HRvf6LDfX+RyMy0A+DMyQ/SZ4eGYCZxLRCkt9RgkB5gSN0GLDxykEP26nlJ2ahdB5/jnMwMQiMiq+Xex+rYqPExNZcBswB4+Zu23Dvs32ofk4iIyMnCYa/4M5DLaVwbTDrW5GRcgMvtxGarFetvpAo1CQljQ2Ya+3KzKU/uo4MYz/Rb/tlp1mlPehER/5k6dWqJ5+fMmeNzbLFYuOuuu7jrrruKbB8cHMyUKVOYMmWKT/2zz+ZnKSzqnq+99lqJ45g0aRKTJk0qVL906VKf44SEBL799lvzeO/evSQmJtK6dWuz7thFB2eccYaZcr24NseKjY1l+vTpxZ4fPXo0o0eP9qkbMWKET7/F/ez37dvHVVddRXBw6dsBdezYkWHDhvH222/z0EMPmfW9e/cuNW3/oEGDSv0+H3zwwULB/YJ27txZrn6bNGnCsmXLSryn3W7n3Xff1ULo40itepIfPnw4AHv27OGnn37yS5+HDx9mwYIFPPHEE1x44YUkJCRgsVjMr9I+SIuTk5PDxx9/zGWXXUbr1q2JiooiKCiIunXrcsopp3D77bezfPnyCvV98OBBnn/+eXr37k1CQgIhISE0b96cIUOGMG3aNLKzs2tVvyIiInJ8O9grf5+qgzuP+px7+KXxfP7zR6xJWcagEcZqeU8Rq8BcexIBcGbnr07/6qsFZtnjDdJbLNT4RMxmoflpYX9KSqyWe1549wSz7Cq4J0A1iwkPZ3jPU2nYsKG3ZrZ5bk9S+fbFFRERkfKxVyJI7/QG6XdypvlcleMpX/pzOT5luozn6xSHvVzXhWE810fVjadJnToA5NTgc6iIiNRuS5YsYe7cuezYsYPffvuNK6+8kubNm9O3b9+aHlqpkpOT+eqrr1i6dGmh1PQlefHFF4mIiKjCkVWv3bt38/DDD3PmmWfW9FCkjGrNSnqARo0ameV///23UnvTHzx4kDPOOINdu3b5Y2g+Fi9ezNixY9mzZ0+hc0ePHuXo0aP89ddfvP322wwbNoyPPvqI+vXrl6nvmTNncsstt/ik2ADYtWsXu3btYtGiRTz33HPMmDGDHj16lHnMVdWviIiIHP9ygqLNcurhdNKOZhBVx/glxWKx0KtPNwACAo3ZvE6H8ZLYarPidnkj9jFGH47s/Bd/dtLzb+Ldk81irfk5ovWC8lO1LUs+zJmx8diqeOJA616nEhgcjCM3F0dubpXeqzSzxk9gYWRggfRvLwL38+b3rXntmjU1ODIREZETW94zVJHnnBBQwlu63OwC13qfp2pyCyGpPnWCgtmVk8W+nPItromMbwiHISYkk+BcI4VwYlpqKVeJiMjJyuFw8PDDD7N9+3YiIyPp06cPn376aaXT0FeHHj16kJyczPPPP0+7du3KfF3z5s258847q3Bk1at169Y+mQ+k9qv5t6QFFAwgJyUlldCydDk5OVUSoJ8/fz5Dhw71CdBHRkZyxhlncO6559KpUyesBV4+f/PNN/Tr16/Q3hJF+fjjjxk5cqTPz6Ft27b069fPZ/+ITZs20b9/fzZs2FCmMVdVvyIiInJisB4ToP767aVsW7MXe05+wN3j8RAYaKTHdDqMYH12dph5Pi3CWJ3jyjWC9m6s7KTAhMZa9jL50vr5e7B/un9ntdzT5TRWQR3eXT33K81X9+alXcsEwOOxsGZXdPEXiIiISIVs2RDC1o0hOOwWWrKMnnxSqE1KUtnW0VzODbi9GXHsueVbWS3Hp1ahxuTZMO+k17KKDDeeu50H/2broYNA7XkWxj6jRgABAABJREFUFxGR2mfw4MGsX7+erKwsDh06xFdffXXc7Gu+c+dOUlNTue+++2p6KCLlUquC9HPnzjXLdbxpmPwhPj6eIUOG8Mgjj/D1119XuJ+UlBTGjh2L0/uCNTIyko8++oijR4+yfPlyfvjhB9avX8+uXbsYNWqUed3mzZtL/XBYt24dN954o3ncrl07/vzzTzZv3szSpUvZuXMn3333nbkiPy0tjeHDh5OTU/JeqlXVr4iIiJw4grKPFqr7be5qfv9mHQCOXCefPPUN0UylVatt/DAvCoCszPyXyemHjT7s2XlB+gDcZLGas7w3CQLA6agd+2B2iIgyy7tysshyOUto7R/BYcakhqT9+6r8XmUxuGte9qTXzbqej5xXM4MRERE5QeXmWBjSpRODO3di/V/h3Ep/RnINDVjn0+7+0c1xF7GlUJ68dPcAeFOWx9erWxVDllomPsjYVzfd5Sx1/9uCAvBO4ohsTu/WbQHIsSvdvYiIiEhtUWuC9LNmzWLKlCnm8WmnnVap/uLi4pg1axY7d+4kMTGRBQsW8OSTT3LhhRdWuM+ZM2dy+PBhn+OxY8cWSvfRuHFjPv300wIpRGHGjBmkpKQU2/d///tfcr2pT+vWrcuyZcvo1auXT5vzzjuPH374geBg4+F8+/btvPPOOyWOuar6FRERkRNHurNekfW7Nuwn+VAaOzfsB8CCi5Ytt2GxGC8H3W6r+TI5fO9eAFx2Iwjvwng+snkfNz01vA99UW5pkp8CbHo1rKaPiDMmoe77d1OV36ssrFYrnRo1AVKAaWa901X7/q5ERESOV6nJ+aufb7wo/9mjDtt82v20KJo3nkgotp+CqfIt3gewoLDyrayW41NUQP57x6Pl2JfeEerNHHV0PaHeCbN7jpae6VNEREREqoffgvS7d+8u19e2bdv4888/mTx5MkOHDuXKK6/E5XJhsVjo2bMnXbt2rdR4oqKiuPTSS/2ajuPnn382y507d+b8888vsf1///tfs+xwOFi5cmWR7TZs2MC8efPM46eeeqrYPew7derE3XffbR6/8MILuIuZal1V/YqIiMiJJTgkCwBXloPOZ7UmIiY/jf1v89bw+/y15nFUVDph4caqc7fbSuou43EyKMq7J32OEaR3Y+PWh8YQnrdKzLuKPDwmtGq/mXKIDwomzGq83D5sz2V3dmaV3i+hVRsAdq2rPfu+T73lDm/pDrPunk+71cxgRERETkQeNy9i4UUsQP4q6IZNCu8v/r8nGxbbTcGV9K5GjQCwBSpIfzIItFoJshjP3NmusmelyrAbz932wPps2GdMqI0MrT3P4iIiIiInO78F6Zs3b06LFi3K/NW2bVtOP/10brzxRr777jszXVNERATvv/++v4blVwVX0Xfu3LnU9se2KXh9QV9++aVZjoiI4Kqrriqx35tuusksHzx4kOXLl1drvyIiInKC8T6HOdNy6HFOe4aOPZPQCCPDTtKBVJ+mrVptIyjISJNptbqxu40XfRnZUXg8YE83Xji7sRETF42DZAByrr0WgMyUwi+ka9K1jZqb5Wn7d5JUjtVJ5dX2tN5m+f+efLTK7lMePZu39JYyzLq3vm9ddGMREREpt7Q/8yfnRXLILE94Zle5+nF5g/TbrH3MyY/REbF+GKEcD+weYyGNw1P2BTUhkcb2TjZPDqe2Mp7vHOUI8ouIHJcOHIBJk4w/RURqOb+nu/d4PBX6AujYsSM//PADPXr0KOUuNSMiIsIs2+2lv8DNSzOfJza26F+evvnmG7N81lln+dynKC1btqRdu3ZFXl8d/YqIiMiJxWM1XvblbXEZEh7MOVecWmTbsLBsLvnPTADq1EnC4zZeGNfhIBv/cOGwG52Ek4zT4WR11Q690uKDQugRmf+M9tbuLVV2r6adupjlff9u4pVrLmPjrz+XcEX16NXCCNRf2ftTsy4xNbimhiMiInJCCWzQwCw7CTLLW9Zt4q/DS8rcT26O8cxlC8wP0lpttWYXS6li9bz70pdnQmn7xkZbmzOVQJuRdcGpIL2InOgOHIDHH1eQXkSOC359ms8LtpdFYGAgcXFxdO/enTFjxjB37lzWrFnDKaec4s8h+dVpp51mlpcvX47T6Syx/bJly8xyYGCgz/V5PB4P69atM4979+5dqE1RCrZbs6ZwytSq6ldEREROPBaXd1/Tuvlp7uMSoul+TrviLgHA4QikQRtjBbardWt+emUt9iwH9nPOIXPSJBo3a8ifwGtAdraxgn7o9WdVxbdQKcPiE6gbmB+Utrur7uXlDa++7XO84N03yvUMXRWa160HwJltfjTrJi9rXkOjERERObF4rAFmOYD8AGtWYjKzpswF9vi0//HbKKa/FV+on6wMI8jazJa/DdHpQ7sUaicnJod3S0p7ObamDA7O38s+0LvFk8NV8rtMEREREak+fgvSu93ucn3l5uZy5MgR/v77bz766CMuuOACbLbavZfWddddR5g3pdiBAwd4+umni22bkpLCQw89ZB6PGTOGOnXqFGq3e/duMjPz9z9t1apVmcZSsN3GjRurrV8RERE58YQ6jZT0uYfynx0sFgtdzmrDlROGMOja3lz9yDAC6l7sc92XX44wy64uXbCv+5ucDAeOfv0ASNlmvIjeB4R6978MCgmktrFYLFzdsJl5/EvykSq7V1Tdwi/dv3379Sq7X1kEBnhXVrldRIQYWxm880PZnh1FRESkZG5H/mS8AAps+xMUSE52LtAFmGRW3zC8DY+Pa8ralfmTJwH+n737Do+q6AI4/NuSTe8EEiAQOqFXFRAI0kUEFVBBBUFABRWkKKiIqCCI9VNRaQEFQRFBEVBQOihNaugtiKGnl822749NbrLJpmeTkJz3eXi4Ze7cSQiTu/fMnElJiacd87A4WZ+lzCZTmXyuEo4R7OoOwNXU/C8dlWJxV7Z1KmsmhvjklOJtmBBCCOEgp06dIjAwkPj4+FzLDRs2jP79+xeo7pCQED7++ONcy6hUKtasWVOgegtq+vTptGjRwqH3KGmvvvoqL7zwQmk3444hebEKICgoiIULF+KU9oFo+vTpPProo2zfvp24uDiMRiNXrlwhPDyc1q1bExERAUDXrl2ZO3eu3TovXbJdg6xGjRr5akvmcpcuXco2A8tR9QohhBCi/DForEviaEzZ02c6OWupUtMflUpFYM2M2eZnz9YhxRSs7KtiYrCc3suKLysrxzRO1uBv5iV3NGU0LaunNuMl9xV9/l9+FsbL3/zA0Pc+VPYjjx/NpbTjadNnVhlNPNzmCgD/3nbL7RIhhBBC5JPJmDHzuSZ/ZTphQqvVALHAh9mu27nZdtBgYryeh3kec1r6fHUZn+giilf6DHq3TJkZ8lLJN+OdntlgHYh5LS6mWNslhBAV2bBhw1CpVKhUKpycnKhVqxaTJ08mJaXkBkQVJkBdmsLCwhg3bly+yk6ZMoUXXngBT0/PXMt98sknhIeHF6gd+/btY9SoUQW6RlgdOXKEjh074uLiQnBwMHPmzLE5P3HiRJYsWcL58+dLqYV3lvw/2QkAHnvsMQIDA3n++ec5ceIE33//Pd9//73dsgEBAYwaNYo333xTCexnFRcXZ7Pv7e2dr3Z4eXkp22azmaSkJNzdM0bIOqrerPR6PXq9Ptt9jYAhX3cURWE0AgZVga4xmS2YVPlPj1ZWpb/oyPzCoyJRWywYTAX7t8cIMuym4IxZ/hb5V5H7qMIozX7NZLauj2pUOWEy5Hx/H18Tz0x/0+aYk4cKQ4IF9dWrBHOAfacvZpxztj5quri4KMecXXV271EW+rXW3n4ciL3NxeREhz/HeFULpsvTo9iy+GuS4+NK9Lkpa7+Wns1q0/EjfPjkSZbuDAHgzA03QgKSSrBlZZ/0awUjz2ul369VFPK8VnjSrxVMYfo1Y0rGMjr384qybdl7HE299Ew+2QcxXv03AZMh4xnKeCMtaK+2ljUYDbk+tzmC9GslJ2u/VsXZhZOJceyLu03XgKB81eHhDjFp23EJ1lmIfp5e5f59XWH6tcIoL32hPK9Jv1ZSymvf06tXLxYvXozBYODAgQMMHToUlUrF7NmzS7tpd7TIyEjWrVvH//73vxzLmEwmVCpVvmNfmQUEZM9yKPIWFxdHjx496NatG19++SVHjx5l+PDh+Pj4KIMeKlWqRM+ePZk3bx7vv/9+Kbe47JMgfSGEhYWxfv16XnjhBdatW2e3jJOTE8OGDWPUqFE5BugBm5T0YPsSOzfpKWMz15M5mO6oerOaNWsWb731VrbjWz2ccHOTtGsOt9+vEBdFp/0pH05tvlraTShF2ZfQyFPuAw9FLjZ7Sp9WYNJHFUqp9GsW68tjk1FNxMb/cix27Yo70EjZ12pNpMQa0Wg0qNKePR5iDElpKVuT46wD+TIv+XPit6hcGlK6/Vq00RNibwOwwUOLSuXYl3uJNTJesG5wAVUuz4yOkN6vnUpLm7rp6GHO1ssY8T92fWPGjj1Uom0q86RfKxR5XisgeV4rNHleKwTp1wqlIP3a5X9ilW3fTOvPGy/f4Mbp9PStsWSVEBVHxMaMLEfn912jGWBJm1yhT9Hn+tzmONKvlaT0fu1GivVvtVrNxnz2dQd0Wnxwx5lEdFUC4NQJTmos+b7+jlWofq0wyldfKM9rBST9WoElacpn3+Ps7ExgWpab4OBgunXrxqZNm5QgvdlsZvbs2Xz99ddcvXqV+vXr88YbbzBgwAAAoqOjGTt2LL///jsJCQlUr16dqVOn8vTTTwNw+fJlJkyYwO+//45araZjx4588sknhISEMH36dJYsWQKgvL/YsmULYWFh2dqZWzvMZjM1atTgtdde47nnnlOu+eeff2jdujUXLlygZs2axMTEMHHiRNauXYter6dNmzZ89NFHNG/eHLBmnl6zZg0TJkzgjTfeIDo6mt69ezN//nw8PT0ZNmwY27ZtY9u2bXzyiXXZvwsXLhASEpKtvd9//z3NmzenWrVqyrHw8HDGjRvH0qVLefXVVzl9+jRnz55l+vTpxMTEKKnp4+PjefbZZ1mzZg1eXl5MnjyZtWvX0qJFCyXFfUhICOPGjVNm9Z85c4YRI0awd+9eateurbQvN3q9nkmTJrFixQri4uKU70fbtm0B2Lp1K126dGHz5s288sorRERE0KJFCxYvXkyDBg2y1bd9+3a6du3K5cuXlZ8pgHHjxnHgwAF27NiRZ5scbdmyZaSmprJo0SJ0Oh2NGzfm0KFDfPjhhzaZCfr27ctrr70mQfp8kCB9ASUnJzNp0iS++uorjEbreFY3NzeaNGmCh4cHN27cICIiAoPBwPvvv8/HH3/M9OnTmTp1qt36DAbbMWRabf7+SbKWS021TU/rqHqzmjJlCi+//LKyHxcXR3BwMGEJBvxM5XV8XNlh7BIPPgUbu3lDUxeDyjXvgmWcyWjm1OarNOgWiEZbNlMnO5KTJZkA09mCXRSjQrtFPkUUlBHri5Fu8Qb5pVlAFbmPKozS7Nf+/NA6a0Klhka9quZYrhHw6MHrrFxoTWm/8JczuGnqc3z3OWVWl/J3moFP9uevrQfS9iw51l8W+jWTxo30jw81o27RyLPgo7ELIrVabb5K2/b5ZjntBg526P3SZe3XrjVtxV9/WdPvbp73NdDfur25JuuG7iuRNt0ppF8rGHleK/1+raKQ57XCk36tYArTr1ksKuz1BKZmdTC4JOR4ncqjKqm+/syeEszU9yPZsqUmzQBDqvUlvIeXe67PbY4g/VrJydqv3Va78jXWIEvPuNR8DSZ11iaxCy+cSSREb32HWTPZQK/48v2+rjD9WmGUl75QntekXyspccnlu+8BOHbsGLt376ZmzZrKsVmzZvHtt9/y5ZdfUq9ePbZv384TTzxBQEAAnTt35o033iAiIoINGzZQqVIlzp49S3KydSC9wWCgZ8+etGvXjh07dqDVannnnXfo1asXR44cYeLEiZw4cYK4uDgWL14MgJ+f/YFKebXj8ccfZ/ny5TZB+mXLltGhQwfl6xk4cCCurq5s2LABb29vvvrqK7p27crp06eV+547d441a9awbt06oqOjGTRoEO+99x7vvvsun3zyCadPn6ZJkybMmDEDyHlG+44dO2jTpk2240lJScyePZsFCxbg7+9P5cqVs5V5+eWX2bVrFz///DNVqlRh2rRpHDx4MMe1381mMw8//DBVqlTh77//JjY2Nl8p+SdPnsyPP/7IkiVLqFmzJnPmzKFnz56cPXvW5t/htdde44MPPiAgIIBnn32W4cOHs2vXrmz1derUidq1a/PNN98wadIkwPozsGzZsmwp5TPr3bt3rgH8mjVrcvz4ccCaoaBRo0Y5lgWYOnVqjvHMPXv20KlTJ3Q6nXKsZ8+ezJ49m+joaHx9fQG46667+Pfff7l48aLdQRgiQ7F9fq1du3ZxVZUrlUrFuXPnSuReWaWmptKnTx+2bNkCgI+PDx988AFPPPGEzQ/lrVu3mD17NnPnzsVgMPDaa6+RmJjIu+++m61ONzfb9T7zu15J1nJZZ7s7qt6snJ2dcXZ2znZcC5TPsXFli0oLOBXsg4dGo8KsKj8P3RqtGo1T+fl68ktjUeGkLuCHTq1KXloWgfRrBSd9VOGURr+mcrWmO1dryPPeM7++zGOjbhF7W0OH7omc+Nv6HGBJXxc1S5C+cZ0WOFvS16RX5Vh/WejXnDL97B2IvUVzBwfpnVwzntf2//wTnUooSJ8uvV97plMXRs//AoDPf9/AtIcimPGT9UNbXLwOf8/cB21WJNKvFY48rxWAPK8ViTyvFZz0a4VTkH7NkGq/3MHtB1i9/QBtgCtA60ffZ93KScr5Ncs6sna5BYtFxSMdGlOJ0wBYqlkD82pNzs9VjiL9WslL79cqZcq4FG9IxV+X/V1cVp46E95Ys1jpTNYgvcVsKvf9ZGH6tcIob32hPK8VgPRrhVJe+55169bh4eGB0WhEr9ejVqv57LPPAOtM65kzZ7J582batWsHWONoO3fu5KuvvqJz585ERkbSsmVLJSCdOaC5cuVKzGYzCxYsUAZnLV68GB8fH7Zu3UqPHj1wdXVFr9fbzLzOKj/tGDJkCB988AGRkZHUqFEDs9nMihUreP311wHYuXMne/fu5fr160o8aO7cuaxZs4ZVq1Yps6jNZjPh4eHKOvJPPvkkf/zxB++++y7e3t7odDrc3NxybS/ApUuX7AbpDQYDX3zxhTJ7P6v4+HiWLFnC8uXL6dq1q/I9q1o154GNmzdv5uTJk/z2229KuZkzZ9K7d+8cr0lMTGTevHmEh4cr5ebPn8+mTZtYuHChEmQHePfdd+ncuTMAr776Kn369CElJcVu9usRI0awePFi5fpffvmFlJQUBg0alGNbFixYoAzssCdzpu+qVaty6NChHMtCzgM9AK5evUqtWrVsjlWpUkU5lx6kT/8+Xrp0SYL0eSi23ycXL15UOgqLJfsvtqwjPO2VsVcu6zWOTjuam3feeUcJ0Lu4uPDHH3/QqlWrbOX8/f2ZM2cONWrU4IUXXgCsI5X69evHXXfdZVPWw8PDZj+3/0yZJSXZrg+atR5H1SuEEEKIcijZ+tJO5aLJV/FmbTKeF9Rq67PZZbWaegB2svfUrVu3yE0sKbVd3TmfnMgVff6enYqq+4hn2bTwSwD2rlvDXQ/0L5H7ZvXrpKn0eX8mAA2CFgIfAPD6qsbMe/qfUmmTEEIIUR788d5B7D0JpQINgUexrhv+7srJwCSbMhZLxjswJ9KeTdo0A8Bc/ickikw0mYLBKWZTvq5xcTKTiB/u3MbJZM2clajXO6R9QghRUXXp0oV58+aRmJjIRx99hFar5ZFHHgHg7NmzJCUl0b17d5trUlNTadmyJQDPPfccjzzyCAcPHqRHjx7079+f9u3bA3D48GHOnj2rBLzTpaSkFGgia37a0aJFC0JDQ1m+fDmvvvoq27Zt4/r16wwcOFBpS0JCgs1yhmCNO2VuS0hIiE17g4KCuH79er7bmrlee0FsnU5Hs2bNcrzu/PnzGAwGmzict7e33fTy6U6cOEFwcLBNID99MENOzp07h8FgoEOHDsoxJycn7rrrLk6cOGFTNnN7g4Ksyx5ev36dGjVqZKt32LBhvP766/z111/cc889hIeHM2jQoFwn02ZeEiAvWq22RN7RpS+rnTXeKLIr1kFfmQPvmYPpFoslX0H53MqVtpSUFJt1KEaPHm03QJ/ZmDFjmD9/PkeOHMFisfC///2Pb775xqZMpUqVbPajonJbpzXD1asZ6wR5enpmW/feUfUKIYQQovxJf+dnTjQW4lrrs5xnzZqs1Wl5vH1zcnoEd/Yo+/MN2vtU4nxyIgDxRgOeWsc+CzUN66oE6XeuXFZqQfpezVoq2zN+WoGrbjbJqVp2nqqUy1VCCCGEyIv3jb/sHtcAwWnbPiXVGHFHc1VrSDabOJOUQDUXtzzLa9QW4qiGO7dJD/GfuZq/94NCCCHyx93dXQl6Llq0iObNm7Nw4UJGjBhBQoJ1WZtff/01WyA1fTZ67969uXTpEuvXr2fTpk107dqVMWPGMHfuXBISEmjdujXLli3Ldt+c0sTbk592AAwZMkQJ0i9fvpxevXopQfmEhASCgoLYunVrtvp9fHyU7azxJJVKhdlszndb01WqVIno6Ohsx11dXUt1Im9hZP6epLc9p+9J5cqV6du3L4sXL6ZWrVps2LDB7vc8s5JMdx8YGMi1a9dsjqXvZ86OcPv2baBgP6cVVbG9KU2fYf7vv/8yfvx4bt26hcVioWnTpvTu3ZvGjRvj7++Ps7Mz8fHxXLhwgb179/Lrr7+SkJCASqXiscceY/To0cXVpGK1d+9e4uLilP0HH3wwz2tUKhUPPvggR44cAWD79u3ZytSvX99mPzIyMl/tuXz5srLdsGHDEqtXCCGEEOWPu+U2EFKoaw1p61vGxMSwM9XI+BnPcWPzRbtlvf28CtfAElTLLSOL0OIrF3ixZv1cShePu/o+xN5ffgLAbDahVucvo0Fx6960OZuOHuZGfBxzHjvKC0tbEnHFsSn/hRBCiPLO5Gz/d+m9wPoC1KPG+sxlvngRdUgITl531gtqUXTJaTPoE4z5G1jr7mwkmpoEcRR9qnUG/c34uDyuEkIIUVhqtZqpU6fy8ssvM3jwYBo1aoSzszORkZFKunN7AgICGDp0KEOHDqVjx45MmjSJuXPn0qpVK1auXEnlypXx8rL/PkWn02Ey5Z5hJb/tGDx4MK+//joHDhxg1apVfPnll8q5Vq1acfXqVbRabZHSl+envQAtW7YkIiKiwPXXrl0bJycn9u3bp8xUj42N5fTp03Tq1MnuNaGhoVy+fJmoqChlpvtff9kfZJmuTp066HQ6du3aRc2aNQFrKv59+/blaz373DzzzDM8/vjjVK9enTp16tjM1renJNPdt2vXjtdeew2DwaDUu2nTJho0aKCkugc4duwYTk5ONG7cONd7iWIM0nfu3Jl//vmHcePGcfv2bUJDQ5k3bx4dO3bM9brExERmzpzJ7NmzWblyJc7OzixevLi4mlVsrly5YrMfHBycQ0lbmctlnqWezsPDg+DgYCU4ntd/kHT//JORdjQ0NLTE6hVCCCFE+ZOi8cEJUBsLvva4l59tyq0fFv1MwxrNMBqNtOoSypEdZ5RzWqfSCT4XVqzRwFeXzzI62LGpwNo9PFAJ0uuTknD18MzjCscY2aUbm44e5nZCAl0aZaSj23oigLDQG6XSJiGEEOJOlBodx5mPllFjcG8sucwey3qmxT2xHPrLflBfgzW/vSXV+rzWvJ28s6loMpZlyl/qWJ3WTAM2AuCZaM0UVcXbx1HNE0IIAQwcOJBJkybx+eefM3HiRCZOnMj48eMxm83ce++9xMbGsmvXLry8vBg6dCjTpk2jdevWNG7cGL1ez7p165S4zJAhQ3j//ffp168fM2bMoHr16ly6dInVq1czefJkqlevTkhICL/99hunTp3C398fb2/vbLPZPT0982wHWFPVt2/fnhEjRmAymWwmqnbr1o127drRv39/5syZQ/369fnvv//49ddfeeihh+yuH29PSEgIf//9NxcvXsTDwwM/Pz/UanW2cj179uSZZ57BZDKh0eT/XZKnpydDhw5l0qRJ+Pn5UblyZd58803UanWOM/C7detG/fr1GTp0KO+//z5xcXG89tprud7H3d2d5557TrlPjRo1mDNnDklJSYwYMSLf7bWnZ8+eeHl58c477zBjxow8y5dkuvvBgwfz1ltvMWLECF555RWOHTvGJ598wkcffWRTbseOHXTs2FFJey9ylv2nv5BiY2N5+OGHuXXrFq1atWLPnj15BujB+sP87rvv8s0332CxWFi6dCkffvhhcTWr2GRO+wGFW+M9px/IzCN4du7cmWedBoOBv//+2+71JVGvEEIIIcoXMzoATFrnPEpml75QUfrAxAN7rBmEzGYzqam2s3zSU+OXdWNr1FO2r6fquZSW/t5RNJlS6u9bt9ah98pNo6rVle2GVTNmWU1Z0bQ0miOEEELcsY6Mm8updxeyufmjuQbpMz8pBQAdu88H3kdN9gXn02fSk/ai2tlVV3wNFncEPyfrv7mnJn/LMem0ZjRpPzfON6ypaI35mL0ohBCi8LRaLWPHjmXOnDkkJiby9ttv88YbbzBr1ixCQ0Pp1asXv/76K7Vq1QKsM8unTJlCs2bN6NSpExqNhhUrVgDg5ubG9u3bqVGjBg8//DChoaGMGDGClJQUZWb9yJEjadCgAW3atCEgIIBdu3bZbVde7Ug3ZMgQDh8+zEMPPWQTz1KpVKxfv55OnTrx9NNPU79+fR577DEuXbpElSpV8v39mThxIhqNhkaNGhEQEJBjBujevXuj1WrZvHlzvutO9+GHH9KuXTseeOABunXrRocOHQgNDbW7xj1YMyD89NNPJCcnc9ddd/HMM8/w7rvv5nmf9957j0ceeYQnn3ySVq1acfbsWX777TebGeWFoVarGTZsGCaTiaeeeqpIdRU3b29vfv/9dy5cuEDr1q2ZMGEC06ZNY9SoUTblVqxYwciRI0uplXeWYgvSh4eHc+nSJVQqFYsWLcLTs2AzgB5//HEeeughLBYL7733HsZ8pm4qKVWrVrXZ379/f76uO3DggLKd04iWfv36KdsnTpywmc1uz88//0x8fDxg/Q/bt2/fEq1XCCGEEOWLk7/1RZ/ZXPAkS67utoH929ej0+oy4+3vYXMuOV5fyBaWLF8nHZNrZSz7E2fM/qLcUfb/WnpB+jqZPlhHxUTTtrZ1DbG953NOdSaEEEKI7P5bZ50oYTGawGwd0riBDnwMZE6e+lhoxovxycD/3p7ECCbzOtVxwnaQYHqQXp2WhlWjKbZXeuIOUdPVmsEqOp/Zr3RaM4d4FABtJetz3p8RxxzTOCGEqIDCw8NZs2ZNtuOvvvoq169fx93dHZVKxUsvvcTJkydJTU3l+vXrbNy4UZkg+frrrxMREUFSUhK3bt1izZo1NoHzwMBAlixZwo0bN0hJSeHcuXN8/fXXSpA+ICCA33//nfj4eCwWC2FhYXbbmlc70j333HNYLBaWLFmSrQ5PT08+/fRTrly5QmpqKpGRkXz77bfKpI3p06dny+g8btw4Ll68qOzXr1+fPXv2kJSUhMViyTF1vlarZerUqTYTeocNG0ZMTEy2sln/HTw9PVm2bBmJiYlERUUxatQoTp06ZTOD/OLFizZp6evXr8+OHTvQ6/WcOnWKnj17YrFY6N+/v932Abi4uPDpp58q/zY7d+6kbdu2yvmwsDAsFgs+Pj7KsRYtWth83fa+Z2DN7H3//fcr6ffLkmbNmrFjxw5SUlL4999/eeWVV2zOb9iwAbVazYABA0qphXeWYnui//777wGoV68eTZsWbrbNwIEDAbh16xZ//vlncTWtWLRq1Qp394x0rp9//nmea2dcvnyZVatWKfs5rfdx//33ExAQoOy/8847OdZpMpl47733lP3evXtTuXLlEq1XCCGEEOWLOdGaIUjnUfCZ7j6VMwZmurm5KWnVLBYLGq3to2bt5tW5UzirNdROexGaaHL84NHm3Xo6/B550WWa0X/22lUm9jkFgEZtxmLJ6SohhBBCZJWalGn2fNpMeiMargB7M5VLOXEh27UNAU+u04Df8OQqPXgTd67jxm0sKhUqNzcA1BKkr3BUWJ/VbxvyF6R31pqVZRLOJcoMeiGEEHeW0aNH06lTJ2ViaX79888/fPfdd5w7d46DBw8yZMgQwHZSa1kVGxvLzp07Wb58OS+88EJpN6dQEhMTWbx4MVptsa22Xq4V2xP9+fPnUalUBVr/IKvM1164kP2DSmnS6XQMHjxY2T948CAjR47EYLA/s+q///6jX79+pKSkKMeGDx9ut6y7uztTpkxR9levXs3HH3+crZzFYmHixInKLH6VSsXbb7+dY5sdVa8QQgghypf0B8LUhILPdNe5ZAR2M49KtrfMTw7Lf5V5m25dw+LgKHWb+zPWevv31AmH3is/jl2OpGdTa1pUk1lNQop8uBJCCCHyK9mcKbukxRqkN6ctEvTou2PyVYc7N3mFunRnBk/xCEbexpBpdlyVmv7F1l5xZ3AvwJq8AK46E1dpAkANvxqOaJIQQgjhMFqtltdee63AWbsB5s6dS/PmzenWrRuJiYns2LGDSpUqOaCVxatfv3706NGDZ599lu7du5d2cwplwIAB3H333aXdjDtGsQXp09NMXL9+vdB1ZL42Nja2qE1i5MiRuLi4ZPtT0DLppk+fbjO7fPHixTRq1IiZM2eyceNGdu7cyU8//cT48eNp1KiRTXr5Z555hjZt2uTY1jFjxtChQwdlf/z48fTt25cVK1awdetWwsPD6dSpk02QfcKECbRs2TLX74Gj6hVCCCFEOZIWPU+fBV9YDz/8sJKyy83LBTcv20B93C3Hru1e3Ly1GWu97o656dB7uXl7K9vfvzPNoffKTetatQE4cPE8Hi4ZGQTWHqya0yVCCCGEyMKgyXiZbEkL0qcP93vu1afzVccARuOclvK+NjvxdT6BIVOGxswDJUXFUMU5432l3pz3zHiVCrzcrD95ZpN1iaoa/mU/QCGEEEIURcuWLTlw4AAJCQncvn2bTZs2FTr7d0nbunUrSUlJfPTRR6XdFFFCim1KTFBQEBcvXiQiIoLz589Tu3btAtexdm3GGpyBgYFFbpPBYECvz31GmNFoxGjMXwrTqlWrsmHDBvr27ct///0HwNmzZ3nttddyvW7QoEF88cUXuZbR6XT89NNPdO3alaNHjwKwbt061q1bZ7f84MGDmT17dp5tdlS9QgghhCg/VOnDNothpnuvXr0AawrWrC+Pb/wbXfQblKAHKlfln3hrm3fH3KSDb0AeVxSek87ZYXUXhI+bNcW/m05nk/lga0QAT3SILKVWCSGEEHeaTL9E09akTw/S37gSjcsjD5Dyo/33Mjnp7+5Octq2f1XvXMuK8slZnTGTPtZooLIu75n10clpyyNgDeob8li6UwghhBBClJxim0l/7733KtujRo3KMQ18TjZv3szy5cuV/cyzv8uSVq1acezYMSZMmICfn1+uZVu3bs2KFStYuXJlvmamBQQEsHfvXiZNmoS3t/0PXCEhISxcuJBly5ahVufvn89R9QohhBCifHBWJQGgKuQzQP+xXbId0yem4h9k+9yhdSpYis6y4F4f62yjFLPZ4Snv+094Vdle88F7Dr1XTjo1bARkvMAdcNdlAA5c8C2V9gghhBB3poxnBm38vxjatqV6G2tWmk1L93CraRtMQUHZrur1UPZnKoU5Y537zgNzztQoKoYrKcl5FwKq+qWtX59qLR8Vc2cNmhVCCCGEKM+KbSb9yJEj+fbbbwHYsmUL3bp1Y/78+dSvXz/X68xmM/PmzWPSpEmYzWZUKhVhYWHUqVOnyG0KDw8nPDy8yPVk5evry9y5c5k1axZHjhzhyJEj3Lp1C71ej5eXF9WqVaNt27YEBwcXuG4XFxfmzJnDjBkz2Lp1KxcvXiQ6OpoqVaoQGhrKPffcg6oQC7o6ql4hhBBC3PksabNy1JbCzazx9HXnkXHd+PHjzcoxQ6o1U5FarcKcNoMsqNadl17zbh9/dqalur+WmkKgs2seVxReSNMWyvb5Qwccdp/cOGutA0v1aQNuW9eKYdXeYI5c9imV9gghhBB3IrMlY+BjZfNJkvq8SiPgly+XcWDdGQBa/vwZR9o+YnPd/5bPZI1rO/uVpg+mTE3F3ctxzyOibHNWq9Gbzfx64z9aeuU9iDJ9EK4l/ioAni7ysyOEKOeCguDNN61/CyFEGVdsQfqOHTsycuRI5s+fj0qlYufOnTRp0oQuXbrQs2dPmjRpgr+/Pzqdjvj4eC5evMjevXtZvXo1V65cUWYmubm58eWXXxZXsxzKycmJ1q1b07p162Kv28XFRUkXeyfUK4QQQog7WGBlAFTawj8aunm62D3eqlso+3+PAKBRu6IPwixpbpqM78maa1d4tkZdh91LrbHNNLD123DCnhjmsPvZo0v7Gdh7/iwAd9W5XaL3F0IIIe5Uqbdj+W/tNqoP7IbGmKQct2R6vqrdoJYSpFdrNYTtCef8vFVELrWmvs8xQA+QVo8Kx2b2EWVbqLsXh+Jj8v1ToFGlDcJNiQUg1ZS/JT+FEOKOFRQE06eXdiuEECJfii1ID/DFF18QHx/PihUrUKlUGI1GNm/ezObNm3O8xmKxKDO4vby8WLduHfXq1SvOZgkhhBBCiFyYbiagCfRB5Vy0dPSZZ9O3vK8hAA3vqkXlYD+8KnnckenuAaq7uPJvSjI3DHqSTEabwH1xe3HRcj4dPhiAg7/9WuJB+pikRADqVA4EoEFQvHLOYFThpJXAgBBCCGHP7w0fJvVWLDf+2Iu3OVI5rsq8XE6WpXP87mqC311NlCB9flh0zkVuq7hztfepxKH4GACu6pPzzPJkcLJmslK5+UNKNHqDweZdrBBCCCGEKD3Fuvi4RqNh+fLlzJ8/H39/f8AahE+fJZ++nXk//e8+ffpw+PBhm7XthRBCCCGE46nU1jVOLcaiPRq6ebrwxOt9GDSxB006WGecq1Qq/Kv64KRzXGDb0R4IqKZsf3DxlEPvpXVyYuDU6cr+7agrDr1fVnWrWFMCphis65cGeOqVc//elvSoQgghRE5Sb1lnKl/+bmOOZdKXACoMS1raclcPCdJXZP6ZBmkcjY/Ns7zR2fpsZ06OyzhmKtwSV0IIIYQQongVa5A+3YgRI7h8+TLffvstAwYMoGbNmkpAPp2Liwvt2rVj8uTJHD16lF9++YWaNWs6ojlCCCGEECIX6RNpVJqiv7BTqVQ4u+qKXE9ZElDCM9aCQxsr2+v+92GJ3ttVZ/23233GOhhBq8l4hr98261E2yKEEEKUB+nBdYDYmwmFryitHrXGIa/yxB3ER+sEwF+xt9h6+3quZaOT3QFQJWeUS0rV51RcCCFEAYSFhTFu3LjSboaNkydPcs899+Di4kKLFi1yPCaEKBscNqXJ2dmZwYMHM3iwNV2n0WgkJiaG1NRUvLy88PDwcNSthRBCCCFEAajU1ii91unOne3uaONq1ufjS6cBOJMYTz13T4fez9Pfn/hbt/CrWi3vwsUo1Whdp7RBUNVs5/QGCQoIIYQQ9vy5zjvnk5nSimde+ifzZJb++r9IOHMZY1wiB0e/Q+uFb7Llridt69FYr1WrJU15RdfBtxK/3ogCYEf0DcL8KudY1qS2DrJUOXuD3jrz3mgyO76RQgghSsWbb76Ju7s7p06dUmJw9o4VVkhICOPGjStzgxOEuFOV2Js2rVZLpUqVqFq1qgTohRBCCCHKkvQgvU6CsDnxTJuxBLDiamQuJYtH2z79Abgccdzh98qsspcXAAZjRlaFu+vcAiApVWP3GiGEEKIi06eoGNmvrs0xs4cH+n79MAUH2wTpo87fULbjo5OUbbVWi1doLfzubkK3QyvwbR3KnKw3SptJr1LL81pF18rLj6FVQ5T9OKMhx7K+PtZnWJMhRVmH3miWdPdCCFFenTt3jnvvvZeaNWsqS1LbO1ZQqampxdlMIUQaebIXQgghhKjg0mfSa7TyaJibRh5eynZsLi9Di4M6bbZccnwc21d869B7ZabTWrMp6DN9fS5O1tlWF2+4l1g7hBBCiDvBj0v8aOTeKttxQ5cuGFu2RD9woBJcBzi87bSyfXz3OVJTDPy2ZDcHNkVkq8OtbjAn0ra1nu7Ufdk6s15m0guAGq4Zz2WnE+NzLHfmhjUYozbrcU4P0sua9EII4RDR0dE89dRT+Pr64ubmRu/evTlz5gxgzaATEBDAqlWrlPItWrQgKChI2d+5cyfOzs4kJSVlqzvdggULCA0NxcXFhYYNG/LFF18o51QqFQcOHGDGjBmoVCqmT59u9xjA0aNHue+++3B1dcXf359Ro0aRkJCxLM+wYcPo378/7777LlWrVqVBgwaEhYVx6dIlxo8fj0qlUgZ/CSEKT97ECiGEEEJUcFqVdV1KWeM0d/0qZ6Sen3/5nEPvVa/t3cr2/l/XYkhJcej90jk7WWdbnb12VTl2Pi047+5iLJE2CCGEEHeKycNr2T1uCg4GwOLlxULVertlAkP8OXMwkuuRt4n463y28xuPfs9z//3G/f/9xoMx2/Dran02MKTK72NhpUkLjiSacv6ZGNIxStmuQ3MAjGZJdy+EKNssFkhMLJ0/mVajKbBhw4axf/9+fv75Z/bs2YPFYuH+++/HYDCgUqno1KkTW7duBawB/RMnTpCcnMzJkycB2LZtG23btsXNzc1u/cuWLWPatGm8++67nDhxgpkzZ/LGG2+wZMkSAKKiomjcuDETJkwgKiqKiRMn2j2WmJhIz5498fX1Zd++ffzwww9s3ryZsWPH2tzvjz/+4NSpU2zatIl169axevVqqlevzowZM4iKiiIqKspeM4UQBSALjwohhBBCVHDmtHXPNZoifBqtALQqNbVc3bmQnEiy2cStVD3+OmeH3MvV0wufKoHEpAXLv585nSEz3nPIvTLz9/BUtk1mExq1hrtq3+byLTdSDZLuXgghhMiPVIs76Qvl1A/7DKiXrYxPZU/M5pyfvXQ6J7wr+XLrSgw6kxlN2mBKCdKLdE08vDkcH8P26Bvc6xugBO0zG9PnOp//aN2uY9FwHJlJL4Qo+5KSoLRWTE5IAPdCJJE7c+YMP//8M7t27aJ9+/aANageHBzMmjVrGDhwIGFhYXz11VcAbN++nZYtWxIYGMjWrVtp2LAhW7dupXPnzjne48033+SDDz7g4YcfBqBWrVpERETw1VdfMXToUAIDA9FqtXh4eBAYGAiAh4dHtmPz588nJSWFpUuX4p72xX722Wf07duX2bNnU6VKFQDc3d1ZsGABOp1OaYNGo8HT01OpSwhRNDJdSgghhBCigrPciAZApXPKo6QYGBisbH9/9bJD7/X0+58q29cunCM66j+H3g8gyMdH2U7UWzMsODtZX+Reuml/NL8QQghR0fkQabOvJiOQ3qZZ9gA9WGfq5bW8/N71R/ltyW72/Xacw9tOAUhqWaFo7OGtbEcb7K8V7Jzp8f4/tfU5Ni452aHtEkKIiujEiRNotVruvjsjK56/vz8NGjTgxAnrAjadO3cmIiKCGzdusG3bNsLCwggLC2Pr1q0YDAZ2795NWFiY3foTExM5d+4cI0aMwMPDQ/nzzjvvcO5cwTL9nThxgubNmysBeoAOHTpgNps5deqUcqxp06Y2AXohRPGTmfRCCCGEEBWcSmWdxeXiKjOl8+Ks1qBTqUm1mHNNLVocVCoVw+Z8TPjkcQD8+vnHPPHOHIfe01mb8SZXbzCAK9xKsGYLcNJKalQhhBDClgUdibxGTZujJouGvJ6qTvx1nqYdMwL4qSkGdC5OHN99jpgb8bR/sDlnD1kHBJ4+cEkpp0+yH4wVFU8dt4xpppEpiVTKIcPTLefW+OsPYDBZ1z02OPgZVgghisrNzTqjvbTu7ShNmzbFz8+Pbdu2sW3bNt59910CAwOZPXs2+/btw2AwKLPws0pfL37+/Pk2AwHAOrvdEdwLk1JACFEgEqQXQgghhKjALBYgfUKWxOjzpUelQNbd+I9ks4lr+hSqOLs47F5+QdUIqluPqLNnuH7pAsbUVLQOHMmuVqvRabWkGo0kpKQQ4OVNgKd1Rr1aJu4JIYQQNgbyDHexCACzry/GVq1w+usv9BYvdOQ9uE2d6Zdr7M0EAqr7cvAP62y72k2rOabRolwJ0DlzI1XPkfhYWnn52S3jTDwAfrp4SIWElJSSbKIQQhSYSlW4lPOlKTQ0FKPRyN9//60E2m/dusWpU6do1KgRYB2I37FjR9auXcvx48e59957cXNzQ6/X89VXX9GmTZscA+NVqlShatWqnD9/niFDhhS5reHh4SQmJir327VrF2q1mgYNGuR6rU6nwyTLpghRbCTdvRBCCCFEBWYyYf0EDOhcJY1ZftR3y1i3fVv0dYffr9/4V5TtI1s2Ofx+qUbr7KqkVOtMvcpe+rR9GcUhhBBCpLt41lkJ0AMkv/QSho4duf7UJFJ1GS/Yjx07lmMdbl4ZA/3+/G6vzblUvcx2FvlgTYiFWy6zKF3dXAHwNMcAcC0u1tGtEkKICqdevXr069ePkSNHsnPnTg4fPswTTzxBtWrV6Nevn1IuLCyM7777jhYtWuDh4YFaraZTp04sW7Ys1/XoAd566y1mzZrFp59+yunTpzl69CiLFy/mww8/LFBbhwwZgouLC0OHDuXYsWNs2bKFF154gSeffFJZjz4nISEhbN++nStXrnDz5s0C3VcIkZ0E6YUQQgghKjCTSYUqbRaX1lmSLOWHu1ZLgJM1neipxHiH38/NK2O90X2/rHH4/QK9fQDQGw0AaDXWmYBHIr1zukQIIYSocLo2aGL3uEcVMJkygu+BgYF2y7m4O6PWZLyWS00x2JzPKYFNUO2AgjVUlGt3+/gDEJmclGOZ9KWtEk0hALg4OeVYVgghROEtXryY1q1b88ADD9CuXTssFgvr16/HKVO/27lzZ0wmk83a82FhYdmO2fPMM8+wYMECFi9eTNOmTencuTPh4eHUqlWrQO10c3Pjt99+4/bt27Rt25YBAwbQtWtXPvvsszyvnTFjBhcvXqROnToEBMgziRBFJW9ihRBCCCEqMLMJ5S2wViczpfPrPv8qrLwaCcDJxDgauns59H7Nu/bk8B+/kRgbg8ViQaVyXO55d2frAITYJOvL3pgk6wuFYP+cX/4KIYQQFUVMjI5PZlQFwFi3LoZOnXD57jubMpUr31C2PTw8sEelAovZkuN9cppJ375vs4I2WZRj6cM8Usw5px5Odq2LO0dwUmnBAkZT3ksxCCGEyNvWrVtt9n19fVm6dGmu17Ro0QKLxfb3/7hx4xg3bly+7jl48GAGDx6c4/lDhw7l61jTpk35888/c6wnPDzc7vF77rmHw4cP59VMIUQ+yUx6IYQQQogKzGiwgNr6SOjkIuM386u+e0bK+x+uXnb4/e59NOND+JXTJx16r6iYGAAS9dY096FVrdkC/jxe2aH3FUIIIe4Ew4b15vOZ1vXi9U88gblGDVK7drUpEx+vV7bTB9Y1vNt2lltygp6Y67YZeTK/tE+Izj44rnr9Krh5uRbtCxDlil9adicLZAv6pPP1tj7jW8zWDA8GkyylIIQQQghRFkiQXgghhBCiAjPpzRlr0ku6+wLp4pcRtP4u6pJD7+Xs6qZsf//ONIfeK9jfmjbVZLbOskrSWzMs1K2S6ND7CiGEEHeSUXRTti0uLjbnrl3LGMCnc9YB4OHjRod+LQjNFKyP+Ou8zXWZY6y3omKy3TPuVkJRmizKoUDnjJ+9U0n2l2Hy8bD+YKkxAx4YzTKTXgghhBCiLJAgvRBCCCFEBaZPNoHWGpx3cpUgfUF08KmkbJ9NcvxLc8+04DnA0a1/OOw+VX18AYhJsgblG6TNpE8xyEcHIYQQAkBHAvXI9Ls4yzI0vr6+GafS1hVSqaB2s+q0vK9hjvVmnln/37kb2c77VvEubJNFOeWkzng+SzbZT3lfzd8AQFUOAy9hzKGcEEIIIYQoWfKmTQghhBCiAjPqDVh8fADQ6iRIXxAqlYrBQTWVfVMOKUaLyzMffqFsb1r4JRYHzYJKMVhf5EYnWAceuDhZ77P3vJ9D7ieEEELcaTrysc2+qXFjm33/TAPrstJoNTmei7kel+t9L0X8l3fjRIXTyN0LgKv6ZLvnUxNjAYghGNBx+dbNkmqaEEIIIYTIhQTphRBCCCEqsJREA6p466wtnYuulFtz56nt6q5sX0nJvnZscVKp1fQb/4qyf/7QQYfcJzVtnVJnJycAzGljD6r62H/xK4QQQlQkoayjF28U+LprF2/lWWbX2kOFaJGo6BLSnt2u6lPsng8Itg4qrcufQCg+bu52ywkhhBBCiJIlQXohhBBCiAosJUGPJS1NppuXWx6lRVaqTOltD8fHOPx+dVq1UbZvRF5yyD0aBFUD4FTUFQACPPVAtky+QgghRIUxa3I1ejRuREKcmuH0xQIkvfhigeowpBqL3I72DzYvch2i/Elfl/5ffTLxRkO288a0Y9GEAAMl3b0QQgghRBkhQXohhBBCiAosNUEPaUF6nYtTKbfmzuSc9v07VAJBeoAqtesAEHvjmkPqv3LbOtPP08UVAGetNd19iiHn9LxCCCFEebbgg0DOnXRl7XfWNPamevWw+BV+GZguj7YFQKVW0SKsgd0ylWv40eOpdvhU9lSO1WkeXOh7ivLrbu+M5RX+Tcme+cgvsCoAHlifHa/HeWYrI4QQQgghSp4E6YUQQgghKrCEA0eUIL1GI0HYwgjJlPJ+2X8XHX4/74DKAJzd/7dD6m8ZUgsAfdqsKxeddbbVtVgXh9xPCCGEuFPcvGYhtX179EOGFPjau3s3Vbar16/Ck288wBOv9aFpx3rUb13TpmzXwXfTc2h7qtT0p3q9KgA2wXohMvNx0lHTxZoRa/2N/7KdV2u1APhzDoAdp5qUXOOEEEIIIUSOJEgvhBBCCFGBmd29wNkZsM7mEgXXN6Cqsn0+ORGTxezQ+6WvK6pPSsJiLv57pc+g33fuLABOGuui9M5OkhpVCCFExfb5zFoYevTIV9nAWpVs9j39cl4HvFmnejb7QbUzrm1yb13a9W1G2KA2WS8TQqFLG3SbZDZx25Bqcy59IG4M1kwMp6IeKNnGCSGEEEIIuyRIL4QQQghRgaW6ZKRqTUnUl2JL7lyuGi1ja2S8XJ95/gSJxqKvO5uTxp26KNu7Vq0o9vpvJyYAUN3fGiBwTZtJbzDKRwchhBAiJ1XrBijbHr5uNmns67Wqkeu1rh4uNLyrFvVa1eDJNx5ApcoYOOmk01K3RQ08fXMO8gvxcJXqynZkcqLNOWd3DwAqcxqA2OSqCCGEEEKI0idv2oQQQgghKjBjqkHZdvWQdOaF5euks9n/8NIph93LwzdjYMXx7VuKvf4m1a2BhJ2nTgDgpLHO1jdbVJgcmyRACCGEuGN1ffxuZdtsMhNQ3ZfHJvfi0ck9uadPszyvb9uzcb7KCWGPTq3BPW3GvMlisTmnSUt3b8E6+MPL9WrJNk4IIUSxCQkJ4eOPPy7tZohi9OSTTzJz5swSv++XX35J3759S/y+wpYE6YUQQgghKrDUpIx0mFqdrElfFK/XbmSzH5Ml1Whxuu+pEQAkxsYUe92paVkAalayzgjUaTMi8zKbXgghREUUxmxGcx9aku2e37Vrl82+yWj93enkrEXn7OTw9gkBUNvVOmP+TFK8zXFnN+t69RaV9TkuJdWrZBsmhBDl1LBhw1CpVDz77LPZzo0ZMwaVSsWwYcOK9Z779u1j1KhRxVqnKD2HDx9m/fr1vPjii8oxlUpl98/777+vlAkJCcl2/r333rOp22KxMHfuXOrXr4+zszPVqlXj3XffVc4PHz6cgwcPsmPHDsd/oSJH8pZNCCGEEKICMxszArCyJn3RqFQq3qjTWNmPSIhz2L3q391O2Y6+FlWsddeqXBmAizevAxkz6QFSTfLxQQghRMXTh1epyxZastzu+efffsJmv3q9yiXRLCFspFqsz2xZ16TXpmV8UltMgAW90bOkmyaEEOVWcHAwK1asIDk5YyBfSkoKy5cvp0aN3Je7KYyAgADc0gZf3YlSUx03maEoSqtd//vf/xg4cCAeHh7KsaioKJs/ixYtQqVS8cgjj9hcO2PGDJtyL7zwgs35l156iQULFjB37lxOnjzJzz//zF133aWc1+l0DB48mE8//dSxX6TIlbxlE0IIIYSowIzxGWtWZl7/VBSem9qakcCR3043L29lO2LHtmKtW5vW/muxsQA4Z5pJn5wq2RaEEEJUPBa1GouLC+7csnu+WVvrIL02PRrhF+RN215NSrJ5QgAZM+n1Ztv1iTROWmVbix5AljASQohi0qpVK4KDg1m9erVybPXq1dSoUYOWLVvalN24cSP33nsvPj4++Pv788ADD3Du3Dnl/NKlS/Hw8ODMmTPKseeff56GDRuSlJQEZE93r1Kp+Oqrr3jggQdwc3MjNDSUPXv2cPbsWcLCwnB3d6d9+/Y29xk2bBj9+/e3adu4ceMICwtT9sPCwnjhhRcYN24cvr6+VKlShfnz55OYmMjTTz+Np6cndevWZcOGDbl+f0JCQnj77bd56qmn8PLyUrIA7Ny5k44dO+Lq6kpwcDAvvvgiiYkZ76e++OIL6tWrh4uLC1WqVGHAgAHKOb1ez4svvkjlypVxcXHh3nvvZd++fcr58PBwfHx8bNqxZs0am3de06dPp0WLFixYsIBatWrh4mJd/jEmJobRo0dTpUoVXFxcaNKkCevWrVOuK0q7szKZTKxatSpbyvnAwECbP2vXrqVLly7Url3bppynp6dNOXd3d+XciRMnmDdvHmvXruXBBx+kVq1atG7dmu7du9vU0bdvX37++WebQSaiZGnzLiKEEEIIIcorkyVjzKZaZtIXiwbunvwTH0OyyeTQ+3j6+xN/6xZ/r/2RDgMeK7Z6vVxdAajibR0IoFaDSmXBYlFJkF4IIUSFE31Lg/6xxzDVqkWNTzcB99qcb3lfQ2U79O7ahN5dGyFKg4/WurRCgsmIxWJRghEabcaSCxpSMeJCbJITfh6GUmmnEELkx/cz95EUV/Kzm928dAya2rZA1wwfPpzFixczZMgQABYtWsTTTz/N1q1bbcolJiby8ssv06xZMxISEpg2bRoPPfQQhw4dQq1W89RTT7Fu3TqGDBnC7t27+e2331iwYAF79uzJdfb822+/zYcffsiHH37IK6+8wuDBg6lduzZTpkyhRo0aDB8+nLFjx+YZUM9qyZIlTJ48mb1797Jy5Uqee+45fvrpJx566CGmTp3KRx99xJNPPklkZGSu7Zs7dy7Tpk3jzTffBODcuXP06tWLd955h0WLFnHjxg3Gjh3L2LFjWbx4Mfv37+fFF1/km2++oX379ty+fdsmJfvkyZP58ccfWbJkCTVr1mTOnDn07NmTs2fP4ufnl++v7+zZs/z444+sXr0ajUaD2Wymd+/exMfH8+2331KnTh0iIiLQaDTF0u6sjhw5QmxsLG3atMmxzLVr1/j1119ZsmRJtnPvvfceb7/9NjVq1GDw4MGMHz8erdYa8v3ll1+oXbs269ato1evXlgsFrp168acOXNsvkdt2rTBaDTy999/2wzSECVHgvRCCCGEEBWYMTUjkCwz6YuL9fu4K+YmYX6VUTvo+3pX34f5I3w+AMkJ8bh6FE/qUndn6wjy9LXpATycjcSnOHErQUdIQFKx3EcIIYS4E9wV2Jxp0+oD0LBJIpnDBQ8+2xnvAEkdLsqGKmnPcAAXkxOp5WadWa/WZgyy1GANzMenSJBeCFG2JcWlkhijL+1m5MsTTzzBlClTuHTpEgC7du1ixYoV2YL0WdOVL1q0iICAACIiImjSxJqF56uvvqJZs2a8+OKLrF69munTp9O6detc7//0008zaNAgAF555RXatWvHG2+8Qc+ePQFr2vOnn366wF9X8+bNef311wGYMmUK7733HpUqVWLkyJEATJs2jXnz5nHkyBHuueeeHOu57777mDBhgrL/zDPPMGTIEMaNGwdAvXr1+PTTT+ncuTPz5s0jMjISd3d3HnjgATw9PalZs6aSlSAxMZF58+YRHh5O7969AZg/fz6bNm1i4cKFTJo0Kd9fX2pqKkuXLiUgIACA33//nb1793LixAnq17c++2WevT5r1qxCt9ueS5cuodFoqFw552WSlixZgqenJw8//LDN8RdffJFWrVrh5+fH7t27mTJlClFRUXz44YcAnD9/nkuXLvHDDz+wdOlSTCYT48ePZ8CAAfz5559KPW5ubnh7eys/u6LkSZBeCCGEEKICsxgyXs7JmvTFI9DZBeKt2+eTEqjr7piX983u664E6X/+aA6PvvF2sdTrnJYSNUmfEYaIT7HOwDLImvRCCCEqCH2Kik9nOGE2ZzwfWdS2vwclQC/KEndNxmveb6Mu8UYd6zIMKlWmzFncAvzZfKwyI8IulnALhRAi/9y8dHfMfQMCAujTpw/h4eFYLBb69OlDpUqVspU7c+YM06ZN4++//+bmzZuY05YniYyMVIL0vr6+LFy4kJ49e9K+fXteffXVPO/frFkzZbtKlSoANG3a1OZYSkoKcXFxeHl55fvrylyvRqPB398/W70A169fz7WerDPFDx8+zJEjR1i2bJlyzGKxYDabuXDhAt27d6dmzZrUrl2bXr160atXLx566CHc3Nw4d+4cBoOBDh06KNc6OTlx1113ceLEiXx/bQA1a9ZUAvQAhw4donr16kqAPquitNue5ORknJ2dc50ws2jRIoYMGaKk40/38ssvK9vNmjVDp9MxevRoZs2ahbOzM2azGb1ez9KlS5WvZ+HChbRu3ZpTp07RoEED5XpXV1dlOQVR8iRIL4QQQghRgSXGqXAPsm7LTPri0cbbjw03owD48/Z1hwXpM/97XTl9kuT4eFw9i34vXdoL3viUjDXJGgTFcSrKC71BgvRCCCHKP32Kig416hN9y8PmuLFFC2W7y6MFS4UrhKOpVSq6+FVmy+3reGQK2KtUKtQaDWaTCQ3WQMG8zXUkSC+EKNMKmnK+tKWnlAf4/PPP7Zbp27cvNWvWZP78+VStWhWz2UyTJk1ITbVN6799+3Y0Gg1RUVEkJibimcfnfCenjGVN0t8T2DuWPihArVZjsVhs6jAYsmdXyVxHej251ZuTzGulAyQkJDB69GhefPHFbGVr1KiBTqfj4MGDbN26ld9//51p06Yxffp0m3Xnc5Pfry9ru1zTlv7LSVHa7ePjk+2aSpUqkZSURGpqKjpd9sEhO3bs4NSpU6xcuTLXdgHcfffdGI1GLl68SIMGDQgKCkKr1doMOAgNDQWsg0IyB+lv375tM1hBlCx5yyaEEEIIUYE5ae6M9HF3mmAX6wvQa6kpDr3PQ5OmKtvznh9eLHV6ZBqhfeX2LQCctdYP3SkGWZNeCCFE+bf9N322AD2AJdMLzMBa2WfICVHaGrpbZ0gmmIw2x9Vp6+k6cRSAfy75lmzDhBCinOvVqxepqakYDAYlzXxmt27d4tSpU7z++ut07dqV0NBQoqOjs5XbvXs3s2fP5pdffsHDw0MJ/BengIAAoqKibI4dOnSo2O+Tk1atWhEREUHdunWz/UkPVmu1WmUN9SNHjnDx4kX+/PNP6tSpg06nY9euXUp9BoOBffv20ahRI+Xri4+PJzExsUBfX7Nmzfj33385ffp0sbfbnhZpgz8jIiLsnk+f+d68efM8237o0CHUarWSOr9Dhw4YjUbOnTunlEn/umrWrKkcO3fuHCkpKbmm5ReOJUF6IYQQQogKzBRzq7SbUC71qhSkbCcajbmULJpazTI+SKW/fC2qAC9vZXvn6ZMAmC3WEfJXY13sXiOEEEKUJzev5/18pHWSgWui7HHKlGkp8yxCY9osTV8+BiDYX9LaCiFEcdJoNJw4cYKIiAg0dj6b+/r64u/vz9dff83Zs2f5888/bVKWA8THx/Pkk0/y4osv0rt3b5YtW8bKlStZtWpVsbb1vvvuY//+/SxdupQzZ87w5ptvcuzYsWK9R25eeeUVdu/ezdixYzl06BBnzpxh7dq1yoCEdevW8emnn3Lo0CEuXbrE0qVLMZvNNGjQAHd3d5577jkmTZrExo0biYiIYOTIkSQlJTFixAjAOqvczc2NqVOncu7cOZYvX054eHie7ercuTOdOnXikUceYdOmTVy4cIENGzawcePGIrfbnoCAAFq1asXOnTuznYuLi+OHH37gmWeeyXZuz549fPzxxxw+fJjz58+zbNkyxo8fzxNPPIGvr3UQXrdu3WjVqhXDhw/nn3/+4cCBA4wePZru3bvbzK7fsWMHtWvXpk6dOnl+f4RjSJBeCCGEEKICs7hYZ4lZkh0747uiCXTOCGbfNDg2W0Gv0dYPhGaTqdjrXvfPAQBuJVhHhTtri/8eQgghRFmjzhTodCY+2/m6LYJLsjlC5JtLpsBQsjn7c5sL1tl6l2/ZXx9XCCFE4Xl5eeW45rtarWbFihUcOHCAJk2aMH78eN5//32bMi+99BLu7u7MnDkTsK4rP3PmTEaPHs2VK1eKrZ09e/bkjTfeYPLkybRt25b4+HieeuqpYqs/L82aNWPbtm2cPn2ajh070rJlS6ZNm0bVqlUB8PHxYfXq1dx3332Ehoby5Zdf8t1339G4cWMA3nvvPR555BGefPJJWrVqxdmzZ/ntt9+UALWfnx/ffvst69evp2nTpnz33XdMnz49X2378ccfadu2LY8//jiNGjVi8uTJmNLetRS13fY888wzNmvcp1uxYgUWi4XHH3882zlnZ2dWrFhB586dady4Me+++y7jx4/n66+/Vsqo1Wp++eUXKlWqRKdOnejTpw+hoaGsWLHCpq7vvvuOkSNH5ut7IxxDZcm6OIMQRRAXF4e3tzfXv16Cv6s88DuasXs8+Oa+5ktW1zT1Maju/H8bk8FMxMb/aNSrKhqnijfeyMmSRBWT/dQ7OYpWo93kmHWRyzMDsNHTiV7xBpzyLC0yq8h9VGGUVr82b8AKPJp7YEpKZdish0vsvlmVx35t1vkIjBYL91cKorW3n8PukxATzdcvjAKg0+NP0eb+vrmWz0+/Vn/CWM5dv0b/Nnfx40uTeGBuBzYcDuLjJw7xQs+zxfsF3EGkXysYeV4rf/1aWSXPa4Un/Zp93y+6ypSRfQB4hFE0nV7N5vzAl3vg4p597dDyTvq1klPYfs1isfDOeWsgvod/IHf7+AOw6r0ZRB4/ynf4cpDbACQuXI2LrmD//+8EhenXCqO89IXyvCb9WkmJS07Cd9RQYmNjswWyU1JSuHDhArVq1cLFRbK3iYojOTmZBg0asHLlStq1a1ei9z5+/Dj33Xcfp0+fxtvbO+8LRIHkt1+reL95hRBCCCGEwmxMe4FllnGbxc1TowUgxmhw6H08fDLWFN2x8ttiqfOJDp0BWLN/LwDJqdZZWbHJEv4SQghR/lkn0lsAC/cwP9t5J522pJskRL6oVCol5X2qJSNQ7eTsDEAAGesfbzgSWLKNE0IIIYQNV1dXli5dys2bN0v83lFRUSxdulQC9KVMgvRCCCGEEBWY2ZQWnJfkSsXOz8n6MlSVR7ni0Ohea1DdYjbz15qir1fXJLiGsh2dmIA67YtYtbdaDlcIIYQQ5YfGYuRlmvM+asx+jsuGI4Qj1HWzzvA9FJcRkI++GgVAcqZy3+ysWZLNEkIIIYQdYWFh9O2be0ZER+jWrRs9e/Ys8fsKWxKkF0IIIYSowMxpM+gt5S/TZalLX5d+V4zjR0R3GJixTtnuH1diMRftH/ShNncp2xsO/0NIQCIARy/7sGhbSJHqFkIIIco6y6VzBHEUAP2AATbn0tc7FaKsik/L4pQ5m1NQ3foANKgSCPwIwN5zMgBFCCGEEKI0SZBeCCGEEKICS4hL25CZ9MXupkGvbKeYTA69l6efv83+irffKFJ9KlXG/P+FW/9Ap80I+o9c0KZIdQshhBBlXWpKxu9Bc9WqAOhcnAgb2Ibg4ODSapYQ+XKXT8ZzoSXtGV+jtS7RkJqaCuwHICrGVT4CCCGEEEKUIgnSCyGEEEJUYB6e1uCr2VwSSdkrlv6VM1LDH0+ILdF7R509XeQ6ejRtDkCAlxeJek2R6xNCCCHuFJcvWgOaxjp1lGPtH2xO1TqVUavlVZoo22q7uivbtwypAKg11mc560z6Dcr5zccql2jbhBBCCCFEBvlkIYQQQghRgVkM1hneOj+XUm5J+aNTa3BJe5G//maUw+83+K1ZNvv/nowoUn0Pt70HgB0nT/B6/xM255JT5WOEEEKI8uvEYQPJTz+N/sknlWPV61cpxRYJkX+uGq2ynZ76Pn0mfQMXV+Cwcn7/BUl5L4QQQghRWuTtmhBCCCFEBabVW2d4p15PKOWWlE9Bzq7Ktt7s2JT3gbXr8vI3Pyj75/7ZX6T60lPeX42NoW6VRB69J1I5t/mYBCqEEEKUXyonX8w1a9oeU0nWIXHncFJZX/n+dP0KAAkx0QB4+ldKK3EIgNd/aFLSTRNCCCGEEGkkSC+EEEIIUYEZnazpMDWm1FJuSfn0aGANZfuvmFslcs+guvUA0GqdilRPm1q1bfaXj9mrbP/v97pFqlsIIYQoqywWiI+rZnMsbFCbUmqNEIXjmTZzPtFkBCCgRggAt65cxtPFFfhdKRudWLRnRiGEEEIIUTgSpBdCCCGEqMAsabPCjBZtHiVFYTip1binrQG6PfpGidwzqE59AMxmc5HqqVM5UNlOSEkGoH5gPAAeLsYi1S2EEEKURVs3eFFX2xoLtrPmq9WTdbvFnSXzQNGbqXrMRuuzm0arpbqfH/Cqcv7ABd+Sbp4QQgghhECC9EIIIYQQFZoKCwBmi6RwdZSWnhkvPo2WogXO80Oltj7iW4oYpPdwcVG2r0TfBmB879MArD1Qze41QgghxJ1sxAPWbDQai8HmuFotr8/EncXfSads/xMXjZu3DwA3Ll/C190D0j4DAEz/sXEJt04IIURJmj59Oi1atCjtZthISkrikUcewcvLC5VKRUxMjN1jJeHWrVtUrlyZixcvlsj9HC01NZWQkBD27y/aEoiiZMinDCGEEEKICsyscbZuWHIvJwqvg2+Asv3j1X8dfj8lSF/EAQGZ196NvHkTABedqUh1CiGEEHcCb64o267uulxKClE2qVQqarq4AXBFn4wmLf29u7cPTYPTZ9n/B8Ces/6l0UQhhLjjDRs2DJVKxbPPPpvt3JgxY1CpVAwbNqzkG3YHWLJkCTt27GD37t1ERUXh7e1t91hhDRs2jP79++er7Lvvvku/fv0ICQmxOR4eHk6zZs1wcXGhcuXKjBkzRjk3ffp0VCpVtj/u7u42dfzwww80bNgQFxcXmjZtyvr163Nty+rVq+nevTsBAQF4eXnRrl07fvvtN5sys2bNom3btnh6elK5cmX69+/PqVOnlPM6nY6JEyfyyiuv5OvrF6VLgvRCCCGEEBWYyqgHwMnPtZRbUn7pMs2+O50Uz/ob/zn0fumz/cymogfUndJS9UcnJQLQu9lV5VxyqnyUEEIIUT5VtkQo2z5VCv+CWIjSVN/dE4Br+hQ8/ayBeLPZzNzBQ9NKZLy8v3hD1qUXQojCCA4OZsWKFSQnJyvHUlJSWL58OTVq1Mjlyort3LlzhIaG0qRJEwIDA1GpVHaPFZTJZCrQ0n9JSUksXLiQESNG2Bz/8MMPee2113j11Vc5fvw4mzdvpmfPnsr5iRMnEhUVZfOnUaNGDBw4UCmze/duHn/8cUaMGME///xD//796d+/P8eOHcuxPdu3b6d79+6sX7+eAwcO0KVLF/r27cs///yjlNm2bRtjxozhr7/+YtOmTRgMBnr06EFiYqJSZsiQIezcuZPjx4/n+3shSoe8WRNCCCGEqMA0FmuQXn89qZRbUr6Nq1lf2T4QF0280ZBL6aJRaayP+BE7txe5rja16gDw321ruvtKnqnKucu33IpcvxBCCFEWeWWaSS/phsSdykdrzQKRajFjdrIG4W/9exk3Z2f2vz0H+FYpO/DTw6XRRCGEuOO1atWK4OBgVq9erRxbvXo1NWrUoGXLljZlzWYzs2bNolatWri6utK8eXNWrVqlnDeZTIwYMUI536BBAz755BObOtJniM+dO5egoCD8/f0ZM2YMBkP+3zGYzWZmzJhB9erVcXZ2pkWLFmzcuFE5P2DAAMaOHavsjxs3DpVKxcmTJwFrOnV3d3c2b96c4z127txJx44dcXV1JTg4mBdffFEJIoeFhfHBBx+wfft2VCoVYWFhdo8BREdH89RTT+Hr64ubmxu9e/fmzJkzyn3Cw8Px8fHh559/plGjRjg7OzN8+HCWLFnC2rVrlRnuW7dutdvO9evX4+zszD333KMci46O5vXXX2fp0qUMHjyYOnXq0KxZMx588EGljIeHB4GBgcqfa9euERERYRPs/+STT+jVqxeTJk0iNDSUt99+m1atWvHZZ5/l+H37+OOPmTx5Mm3btqVevXrMnDmTevXq8csvvyhlNm7cyLBhw2jcuDHNmzcnPDycyMhIDhw4oJTx9fWlQ4cOrFixIsd7ibKh3Afpb9y4wYYNG5gxYwYPPvggQUFBNuknwsPDC1TfyZMnWbhwIWPHjqVLly7Url0bT09PtFot3t7e1KtXj0GDBhEeHk5SUv5fdoeEhNhNj5Hbn1dffbVAbb969SqzZ8+mXbt2BAUF4eLiQkhICL169WLJkiU2o72EEEIIUTFoU2PTthy/VnpF5ql14sVMgfqPL5122L2MqdZAuj4pMY+SeVOrrSPXL9y4DkDmgexnrnkUuX4hhBCirLBYoDtvMZFG1FLvUY6bTRKkF3emmq4ZKXf3aKw/x85u1mMtQ2rZlD14cVrJNUwIIfIpIfo2186ftfkTe92a3c2Ymprt3LXzZ5Vrb//3b7ZzyQnxACTFxWY7Fx11xW4b8mP48OEsXrxY2V+0aBFPP/10tnKzZs1i6dKlfPnllxw/fpzx48fzxBNPsG3bNsAaPK9evTo//PADERERTJs2jalTp/L999/b1LNlyxbOnTvHli1bWLJkCeHh4QWKc33yySd88MEHzJ07lyNHjtCzZ08efPBBJfjduXNnm6D2tm3bqFSpknJs3759GAwG2rdvb7f+c+fO0atXLx555BGOHDnCypUr2blzpxL4X716NSNHjqRdu3ZERUWxevVqu8fAOihh//79/Pzzz+zZsweLxcL9999vMyghKSmJ2bNns2DBAo4fP86nn37KoEGD6NWrlzLLPae27tixg9atW9sc27RpE2azmStXrhAaGkr16tUZNGgQly9fzvF7umDBAurXr0/Hjh2VY3v27KFbt2425Xr27MmePXuyXp4js9lMfHw8fn5+OZaJjbW+18ta5q677mLHjh35vpcoHdrSboCjXL16lXvuuYdLly4VW52//vorDzzwQI7n4+LiiIuL4+zZs/zwww+88sorfPrppzz66KPF1obCWrFiBc8++6zyHzbdpUuXuHTpEr/99hvvvfcey5cvzzbCSwghhBDll0HnC4DKkJpHSVFU3lonNKgwpc3Is1gshUrflpdazVtxYL11lPWNy5cICK5Z6Lo809YyvXjzunLMxy2VmCQdF2+453SZEEIIcccxm6EH0wEw+jRRjnfo16J0GiREEblqNHhotCSYjGi11pn0ZpNROf/eo0/w6sqM8nHJWrxcjVmrEUKIUnNk8wb2rPrO5ljovWHc/8JE4m/f5Nsp47JdM2HlOgA2fvERUWdO2ZzrPXYCjTp24dSeHfy56EubczWbtWTAa28Xqp1PPPEEU6ZMUWJRu3btYsWKFTaBbr1ez8yZM9m8eTPt2rUDoHbt2uzcuZOvvvqKzp074+TkxFtvvaVcU6tWLfbs2cP333/PoEGDlOO+vr589tlnaDQaGjZsSJ8+ffjjjz8YOXJkvto7d+5cXnnlFR577DEAZs+ezZYtW/j444/5/PPPCQsL46WXXuLGjRtotVoiIiJ444032Lp1K88++yxbt26lbdu2uLnZz643a9YshgwZwrhx4wCoV68en376KZ07d2bevHn4+fnh5uaGTqcjMDBQuS7rsTNnzvDzzz+za9cuJci+bNkygoODWbNmjZJa3mAw8MUXX9C8eXOlLldXV/R6vU399ly6dImqVavaHDt//jxms5mZM2fyySef4O3tzeuvv0737t05cuQIOp3OpnxKSgrLli3LNqn26tWrVKlSxeZYlSpVuHr1Kvk1d+5cEhISbP79MzObzYwbN44OHTrQpEkTm3NVq1Yt1viocIxyG6RPSUkp9h9Ai8V29LSTkxO1a9cmICAAZ2dnbt++zYkTJ0hJSQHg+vXrPPbYY1y9epWXXnop3/dp27ZtriNj0jVo0CBf9X3zzTc89dRTNsfq169PUFAQFy9eVL5PJ0+eJCwsjD179tCoUaN8t1cIIYQQdy5VWsDYrHUp5ZZUDONC6vPBReuLgvU3o+gTUDWPKwquWv2MZ8Rvpk7k5W9+KHRdVbyt6/D6uGUE5D1cjMQk6dBqJPuCEEKI8iM11YJFpQKNBlNIiHLc3du19BolRBHd61uJjTevctKoxx0wpGYMzB3e+T5eXfkwYJ2teCTSm3sb3CqdhgohhB3NuvWmTuu7bY65eFgzunn6VeKJWR/neG2v58djSIvTpPOqbA2YNmjXkar1Gtqc07kW/vd9QEAAffr0ITw8HIvFQp8+fahUqZJNmbNnz5KUlET37t1tjqemptpMmvz8889ZtGgRkZGRJCcnk5qaSosWLWyuady4MRqNRtkPCgri6NGj+WprXFwc//33Hx06dLA53qFDBw4fti590qRJE/z8/Ni2bRs6nY6WLVvywAMP8PnnnwPWmfXp6ejtOXz4MEeOHGHZsmXKMYvFgtls5sKFC4SGhuarrSdOnECr1XL33Rk/A/7+/jRo0IATJ04ox3Q6Hc2aNctXnVklJyfj4mL7PsxsNmMwGPj000/p0aMHAN999x2BgYFs2bLFZm16gJ9++on4+HiGDh1aqDbkZPny5bz11lusXbuWypUr2y0zZswYjh07xs6dO7Odc3V1LVC2b1E6ym2QPrOAgABat25NmzZtaNu2Lf369StUPWq1mnvuuYeHHnqIsLAwWrVqhVZr+y1MSUlh+fLlvPLKK9y8eROACRMm0KlTp3zPUJ8zZ06unVxBHD161GYEVYMGDVi2bJlNCo9Nmzbx5JNPcu3aNeLi4ujbty/Hjx/P1jkJIYQQojySFK4lyU2T8ex4MC7aIUF6jdaJSsE1uHk5EijajP1WIbVZsmMrKZlSybWrd4sf/nbDYCz3K2cJIYSoQAypFlKGDsVcqRKkBQC8/GVpF1HOWCyYzSbUag3+np7AT8qps9dU3Ju/+UBCCFEiPHz98PC1P5lRq9NRpXbdHK/1q1o9x3NuXt64eXkXuX2ZDR8+XEnnnh7MziwhIQGwZmuuVq2azTlnZ2fAmg154sSJfPDBB7Rr1w5PT0/ef/99/v77b5vyTk5ONvsqlQqzufgG0atUKjp16sTWrVtxdnYmLCyMZs2aodfrOXbsGLt372bixIk5Xp+QkMDo0aN58cUXs52rUaNGsbUznaura6HfeVSqVIno6GibY0FBQQA2E1kDAgKoVKkSkZGR2epYsGABDzzwQLZZ8+lr1Wd27dq1PGf3g/Vn4ZlnnuGHH37IljI/3dixY1m3bh3bt2+nevXsP++3b98mICAgz3uJ0lVu36z5+fnxww8/cPHiRa5fv86GDRt4++23efDBBwtd5/3338+ePXuYPHkyd911V7YAPYCLiwvDhw9ny5YtSpDbZDLx8ccfF/q+RfHaa6+h1+sBa4ezbdu2bGtsdO/enT/++EP5ZXD+/HnmzZtX4m0VQgghRGko/nTrIncDqwQr21kzNRXbPaZOV7ZP7Npe6Hpc0tK4Hfs344OoTmv98J8qQXohhBDlQPQtDf9FOpGUYMYcEqIE6AEMekPOFwpxB2jg7qVsm32ty1xlnln6/YsTAGtq6BHzw0qyaUIIUa706tWL1NRUDAZDtpnWYA34Ojs7ExkZSd26dW3+BAdb3xGkp3V//vnnadmyJXXr1uXcuXPF2k4vLy+qVq3Krl27bI7v2rXLJiidvi791q1bCQsLQ61W06lTJ95//330en22mfiZtWrVioiIiGxfZ926dbOlis9NaGgoRqPRZpDCrVu3OHXqVJ6ZoHU6HSaTKc97tGzZkoiICJtj6V/bqVMZyyXcvn2bmzdvUrOm7XKCFy5cYMuWLYwYMSJb3e3ateOPP/6wObZp0yZluYOcfPfddzz99NN899139OnTJ9t5i8XC2LFj+emnn/jzzz+pVauW3XqOHTsmS1vfAcrtmzUvLy8GDBiQ7T9NSWnSpAmDBw9W9jOvP1JSIiIi+OWXX5T9d955J9tonnSNGzdW1ggB62z+4hx9JYQQQoiyyWSyjsC2WCRYX1JqZ0odH21MzaVk4bl6eCrbETu3FboeU9rzYOafDl1amvuYpPx/uBZCCCHKIovFwsuNTzGx7n42/+yV7Xzr7rIUoLizeWbK4pT80ktY1Gr0mVLfPtzmbiBG2Tea5DOBEEIUhkaj4cSJE0RERNikok/n6enJxIkTGT9+PEuWLOHcuXMcPHiQ//3vfyxZsgSwrt2+f/9+fvvtN06fPs0bb7zBvn37ir2tkyZNYvbs2axcuZJTp07x6quvcujQIZslm8PCwoiIiOD48ePce++9yrFly5bRpk0b3N3dc6qeV155hd27dzN27FgOHTrEmTNnWLt2rZJpIL/q1atHv379GDlyJDt37uTw4cM88cQTVKtWLc9s2SEhIRw5coRTp05x8+ZNDAb7Ay979uzJ8ePHbWbT169fn379+vHSSy+xe/dujh07xtChQ2nYsCFdunSxuX7RokUEBQXRu3fvbHW/9NJLbNy4kQ8++ICTJ08yffp09u/fb/N9mDJlis1S1cuXL+epp57igw8+4O677+bq1atcvXqV2NhYpcyYMWP49ttvWb58OZ6enkqZ5ORkm/vv2LFDSdcvyq5yG6QvCzLPWL969WqJ33/16tXKtoeHB0OGDMm1/KhRo5Ttq1evsmfPHoe1TQghhBBlg5OnNeBqNstjYUnRqTM+sH8eedZh93H3sc6Wijyev7Xp7KmWllrwxH9XlGPpWeQu35I1eoUQQtzZ/mjxGH1vPMYA0wi2hl/Jdr5Wk2p2rhLizqFSqajtmhFISZo2jZRMM+lVKhW9my1S9pNTsweWhBBC5I+XlxdeXtkH/aV7++23eeONN5g1axahoaH06tWLX3/9VZkJPXr0aB5++GEeffRR7r77bm7dusXzzz9f7O188cUXefnll5kwYQJNmzZl48aN/Pzzz9SrV08p07RpU3x8fGjRogUeaVmGwsLCMJlMeS7V3KxZM7Zt28bp06fp2LEjLVu2ZNq0aVStWvDl/hYvXkzr1q154IEHaNeuHRaLhfXr12dL+Z/VyJEjadCgAW3atCEgICBb5oDMX2erVq34/vvvbY4vXbqUu+++mz59+tC5c2ecnJzYuHGjzX3NZjPh4eEMGzbM7sCM9u3bs3z5cr7++muaN2/OqlWrWLNmDU2aNFHKREVF2aTQ//rrrzEajYwZM4agoCDlT+YBFPPmzSM2NpawsDCbMitXrlTK7Nmzh9jYWAYMGJDr90mUvgqxJn1pMRqNynZunbOj/Prrr8r2vffeq3SmOalduzYNGjRQ0nj8+uuvuaYtEUIIIUQ5YLCm/3KSJVdLlE6lJtViHSBxMjGOhu7F/6xYtV59zuyzpoVLSUzEJZeR7jnxT3t+rJqWHhUyZlhdulnw+oQQQoiywqRPJe5YRgrZ8/vd6PxAKTZICAd5PKgmX10+x02DdTnMeIOeypnOP9S2IRuOWLf/OO5P/zbXslcihBAim/Dw8FzPr1mzxmZfpVLx0ksv2QRcM3N2dmbx4sUsXrzY5visWbNyvWdeSy1Pnz6d6dOnK/tqtZo333yTN998M8dr1Go1t2/ftjnWokWLfC/Z17ZtW37//fccz9trs71jvr6+LF26NMd6hg0bxrBhw7IdDwgIyPX+mU2bNo1JkyYxcuRI1GrrBBYvLy8WLlzIwoULc7xOrVZz+fLlXOseOHAgAwcOzPF81n/P/GTkzs+/wccff8ykSZNwdZXJFWWdTJlyoG3bMlKL5rXORHGzWCwcPZoxayq/989c7vDhw8XeLiGEEEKUMWbrw71Z75i10YV940PqK9s3UvUOuUefseOV7b/W/FCoOnzdrUH65NSMtPyVvaztjU2W8b5CCCHuXKZE25SgGpXtEjRtekiqe1E+qFUqnqtRF1Xa81xCQrzN+c6hocr2I5/8w+iFXxKTmFiibRRCCCFKS58+fRg1ahRXrmTPqnQnSk1NpWnTpowfPz7vwqLUSZDeQdatW8dPP/2k7L/88sv5vnbu3Lm0bNkSHx8fnJ2dCQoKon379rz66qs2gffcREZGkpjpgbpOnTr5ui5zuRMnTuS7zUIIIYS4M2lU1nW5LCYJ0pcknVpDWy9rKnlD2rrvxU2dKa3+wY2/5lIyZy5pqdyiMz1X1q6SAIDFImuWCiGEKDs+nxlIr2aNSE7K+/fTuc9Xsi6gq82xYSrbtU2r1KxUrO0TorRZ0tbjNWP7f6SmfwDuzjvS9l5kwdY/qPrCyBJunRBCCFF6xo0bR3BwcGk3o1jodDpef/11mUV/h5DpL8XEbDYTGxvL0aNHWbZsGQsXLlTSTkyfPj3PdToyy5ymHqzrw6evET9nzhwefvhhvv76a/z8/HKs49KlSzb7NWrUyNe9M5e7dOkSFosFlSrnD7h6vR69PmP2V1xcHABGwJCvO4qiMBoBQ8FekJvMFkwqxwQDSpLJaLb5u6JRWywYTAUMjhhBQnAFZ8zyt8i/itxHFUZp9Ws6VdrvcbMZk6H0vvcVsV/TpKVRO5+cSEcH3aPhvZ05uXMb1UIbK89mBenXtDqdsp1iNqFRawjwtq5jqtGYK9zznvRrBSPPaxWvXyst8rxWeOWpX/vwDev68b+u9OGhJ27lWvbwi+8r2xaNBrRaXExJJGUq4+7lmu3ZSPo16ddKiiP6NQ1gAq6R5RlOq+WVB72ZpiRemoDeMP+Ofc4rTL9WGGW1Lywo6dekXyspd2qfIoQQjiRB+iIYMGAAP/74Y47nGzZsyMyZM3nooYcKVK+3tzf169fHy8uLhIQEzpw5o6z/YbFY+PHHH9m/fz87duzIcXRPerA8c5354eWVsR6q2WwmKSkJ91zWL501axZvvfVWtuNbPZxwc3PK1z1FEezPeaBGzqLT/pQPpzZfLe0mlCL/gl/iWfytqCg2e0qfVmDSRxVKSfdrerUHOsCSYiBi438leu/sKla/9m+s9e94Jw0bHdTHxLZoCju3cTM1Jds98tOvJWs9lO11LiqcnZ04mfbPdPCCn8PaXWZJv1Yo8rxWQHdwv1ba5HmtEMphv3b27wQiKuX/mSblmWcw+/jg+sUXNsdP/5HzutzSrxWQ9GuFVpz9mint/d4xlRlDlnprPnAFfmiWtjcX6MVGzzs05X2h+rXCKNt9YUFJv1ZA0q8VWJJGntOEECIrCdI7SI0aNXjxxRfp2bNnvsqHhIQwcuRI+vfvT+PGjW3OWSwWdu7cyeuvv8727dsB6yz3vn37snfvXnSZZjilS8yydpSLi0u+2pE1BUZiYmKuQfopU6bYpPKPi4sjODiYsAQDfiYZH+doxi7x4FOwsZs3NHUxqO78VCcmo5lTm6/SoFsgGm3FW7nDyZJMgOlswS6KUaHdIp8iCsqI9cVIt3iD/NIsoIrcRxVGafVru+ZbZw2oXF1o1Ktqid03q4rYr53SuPITkJCQQM+41FyzFxXWSQNsAvTnztEr3vpsVpB+zWTOaNM9N+Ko4u2DV9p6pj5uqUqdFYX0awUjz2sVr18rLfK8VnjlsV+r3MAnz2eaM5m2zUFBABhbt7YpY68O6dekXyspjujXTpw9i75uXZJdXekRl4o687On2sBIbTKpxvT/293oFb+qmO5csgrTrxVGWe8L80v6NenXSkpccsX67CiEEPkhn1+LoGXLliQkWNfkNBqN3L59mxMnTpCSkkJkZCTPP/88b7/9Nt988w1du3bNta6tW7fmeE6lUtGxY0e2bNnCs88+y/z58wE4fPgwX331FS+88EK2awwG2196Wm3+/qmzlktNewmbE2dnZ5ydnbPXA8jYOMdTaQGngn3w0GhUmFXl56Fbo1WjcSo/X09+aSwqnNQF/NCpVUmnXwTSrxWc9FGFU9L9mtYn7cWSWlWq/WlF7NcqZxpo+b+Lp5hQq2Hx36N6RtalrH1Yfvo1p0zr2q8/sI+n7u1MsI813X1Mkq7C9YvSrxWOPK8VwB3er5U2eV4ruPLYrxlNZqXPMZlgwypfWrVPoGpw7sEBQ6ZlCu99qGWu/Zb0awUg/VqRFGe/Vuu/q5ysWxeAZKMBHyfbST9bpv5AhxlPKft3an9amH6tMMp6X1hQ0q8VgPRrhVJifUpUFHz1FYweDWkD8YQQoqyqeL95i9Frr73Gxo0b2bhxI5s3b+bgwYPExsayYsUKQkJCAIiKiqJPnz5s27atyPdTq9V88cUXNG3aVDn2v//9z25ZNzc3m/2UlJR83SNrudxm0QshhBDizmeKTQZA46zJo6QobpV1GZmOkswmrunz97xWEN4BVZTtmGtFS2H53OKvcR8xhEOXjijHDEbHr/cphBBCFMT8uRnL/a34uhIvDa5N1/pN7Ja1F5YJruNPrSbVHNQ6IUqPZ6rBOnIFiLLz3HlPPTdcnE6VdLOEEKJ4RUXBW29Z/xZCiDJOgvTFTKfT8eijj3Lw4EGaNLF+CNTr9Tz99NMYjcYi16/VapkwYYKyf+bMGS5dupStnIeHh81+cnJyvupPSkrKtR4hhBBClC8WizU4b9KbSrklFdMrmWbPb719vdjrd8k04PLsgX2FqmP6w4Ns9gd++payrTfKxwkhhBCl7+LZjAx/t2/4Kts7NnkBkJqaw+8rdfbjySnyTCTKp9gb10FjffaPNtjPnNm54TVlOyElf+8ShRBCCCFE4chbNQfx9fXl66+/VvYvXLjAhg0biqXusEwp2ABOnz6drUylSpVs9qPyOXLs6tWMGVaenp44Od2pya2EEEIIkR9qtXUQodEgM+lLg06toZarNZB+OimeFJPjAgORx48W6rr6QVnX5NUrWynycyOEEKIM+O5r23cgy+ZVYs8WD8ymPDK+qLKfb9A2pBhbJkTZUaVWbbQHDgBwRZ9kt0z/NgnKduPJ75dIu4QQQgghKioJ0jtQu3btqF69urK/Z8+eYqk3KMtaKjdv3sxWpn79+jb7kZGR+ar78uXLynbDhsW/LqoQQgghyhaV2vpyWuNU9Iw/onCae/oo259GZh98WVwuHvmnUNcNurt9liNmNCrrYIIkvQTphRBClL61y/xt9qeNrckT3RoQfdv+qsHG0FD0DzyAMdNygukk1b0orzRaLZa0LEvxOWT7fKRtxvF/o4/w99kzJdI2IYQQIqtTp04RGBhIfHw8AOHh4fj4+JRuo7KwWCyMGjUKPz8/VCoVhw4dsnusoomIiKB69eokJiaWdlPKPAnSO1hwcLCybS+YXhhZU9JnXX8erGnqM987vx3BP/9kvLwNDQ0tXAOFEEIIcedIm0CmdbL/Els4XtNMQfpKTs45FywlKpWKxaPH2hwzpS2ToDdKkF4IIUTpc3W3n4nm4mn7v1f1jz6KsU0bUvv3tzk+eOr9qOzMrheiPEhJSECdlmnzij4Zo8WcrYy/ZyqtQjLeX07+TlLeCyFEToYNG4ZKpUKlUuHk5EStWrWYPHkyKSkpJdqG/lmeZ8qysLAwxo0bl6+yU6ZM4YUXXsDT09OxjSqCjRs3Eh4ezrp164iKiqJJkyZ2jxXW9OnTadGiRfE1uJikpKQwZswY/P398fDw4JFHHuHatYwlcxo1asQ999zDhx9+WIqtvDNIkN7BYmNjle3iGuUTERFhs1+5cmW75Tp16qRs79y5M896DQYDf//9t93rhRBCCFE+uauszyryPrp0PRpYA4CbBn0eJQuuV5YAe2E8dW9nhnTomOmI9cNXcqoE6YUQQpQ+nc7+7886ofl/SR7coAoajbwmE+WXf/Vg1P/+q+x/ePGU3XKbp2S8Q9x5uiaGHGbdCyGEgF69ehEVFcX58+f56KOP+Oqrr3jzzTdLu1l3vMjISNatW8ewYcNKuym5OnfuHEFBQbRv357AwEC0Wq3dYwVlsVgwluHfv+PHj+eXX37hhx9+YNu2bfz33388/PDDNmWefvpp5s2bV6a/jrJAPn040I0bNzh58qSyX6dOnWKpd8WKFcq2q6srLVu2tFuuX79+yvaJEydsZsnb8/PPPyupQ9RqNX379i2G1gohhBCiLEtRewGgtjhuLXSRN1d12sx0sxmTnVlNReETmLZUUhFHYix99kVM3/yQtmfN7HTmmkeR6hRCCCGKg8lkP9i4b0f2mVeG2AQ7JaFd3+bF2iYhyhqtkw7NxYuoE63PcXqz2e5sem83I/c1Sv8/NYIf9rqUYCuFEOLO4uzsTGBgIMHBwfTv359u3bqxadMm5bzZbGbWrFnUqlULV1dXmjdvzqpVq5Tz0dHRDBkyhICAAFxdXalXrx6LFy9Wzl++fJlBgwbh4+ODn58f/fr14+LFi4B1lvWSJUtYu3atMqN/69atdtuZWzvMZjPVq1dn3rx5Ntf8888/qNVqLl26BEBMTAzPPPMMAQEBeHl5cd9993H48GGlfPqs72+++YaQkBC8vb157LHHlJjTsGHD2LZtG5988onS3vSvJavvv/+e5s2bU61a7ssQzZs3jzp16qDT6WjQoAHffPONcm7ixIk88MADyv7HH3+MSqVi48aNyrG6deuyYMGCHOs/duwYvXv3xsPDgypVqvDkk08qGbOHDRvGCy+8QGRkJCqVipCQELvHAPR6PS+++CKVK1fGxcWFe++9l3379in32bp1KyqVig0bNtC6dWucnZ359ttveeuttzh8+LDy/QoPD8/1+1ESYmNjWbhwIR9++CH33XcfrVu3ZvHixezevZu//vpLKde9e3du377Ntm3bSrG1ZZ8E6R1o+vTpmM0ZD7v3339/kes8cOAAX3/9tbLfs2dPXFzsPyzff//9BAQEKPvvvPNOjvWaTCbee+89Zb937945ztAXQgghRDmSHrjVlb006xVJ1UzPc1tuXy/Wur0D0p7pLBYs5uIaAFALgEGftium+oQQQojC+y/ydp5lDu5x590J1Tnx4fd2zzu76oq7WUKUKakpKahMJhps/E05ti/W/v+dKQ9eVbafnNcfo0nSbgkhRF6OHTvG7t270ekynilmzZrF0qVL+fLLLzl+/Djjx4/niSeeUAKXb7zxBhEREWzYsIETJ04wb948KlWqBFgzH/fs2RNPT0927NjBrl278PDwoFevXqSmpjJx4kQGDRqkzOaPioqiffv2dtuWWzvUajWPP/44y5cvt7lm2bJldOjQgZo1awIwcOBArl+/zoYNGzhw4ACtWrWia9eu3L6d8bvk3LlzrFmzhnXr1rFu3Tq2bdumxJ0++eQT2rVrx8iRI5X2Zl6yObMdO3bQpk2bXL/fP/30Ey+99BITJkzg2LFjjB49mqeffpotW7YA0LlzZ3bu3InJZJ2Usm3bNipVqqQMZLhy5Qrnzp0jLCzMbv0xMTHcd999tGzZkv3797Nx40auXbvGoEGDlK9nxowZVK9enaioKPbt22f3GMDkyZP58ccfWbJkCQcPHqRu3br07NnT5nsH8Oqrr/Lee+9x4sQJunfvzoQJE2jcuLHy/Xr00UfttnXZsmV4eHjk+mfHjh1K+fSBBzn9ady4cY7f9wMHDmAwGOjWrZtyrGHDhtSoUYM9e/Yox3Q6HS1atLC5r8hOFh/Np7i4OEaMGMHUqVNznLmeLiUlhWnTpvHFF18ox4YMGZJjhzNgwADGjBlDWFhYrmuf/fHHHzz++OPo9dY0biqVKtfUKe7u7kyZMoWXX34ZgNWrV/Pxxx9nW/PDYrEwceJE9u/fr9T79ttv5/o1CiGEEKJ8UDlZZ3CrJL1rqdKoMr7/pxPj6eYfWGx1O7u6Kdv65GQ07u5Fqk8fvgLnYduAztZ9gxpnp+Kd/S+EEEIUiCr9ZbgBcLJbZPC9wVTmJH4dfPHonuXclN4ObZ4QZYGXvzXoY0pNVY4diouhnU+lbGXva3wDGA4sAmDYV2349vl92coJIURFt27dOjw8PDAajej1etRqNZ999hlgnT09c+ZMNm/eTLt21gHutWvXZufOnXz11Vd07tyZyMhIWrZsqQSk02deA6xcuRKz2cyCBQuUuNHixYvx8fFh69at9OjRA1dXV/R6PYGBOb9DyE87hgwZwgcffEBkZCQ1atTAbDazYsUKXn/9dcC6nPLevXu5fv06zs7WSR5z585lzZo1rFq1ilGjRgHWWfnh4eHKOvJPPvkkf/zxB++++y7e3t7odDrc3NxybS/ApUuX8gzSz507l2HDhvH8888D8PLLL/PXX38xd+5cunTpQseOHYmPj+eff/6hdevWbN++nUmTJrFmzRrAOnu9WrVq1K1b1279n332GS1btmTmzJnKsUWLFhEcHMzp06epX78+np6eaDQam68n67HExETmzZtHeHg4vXtbnznnz5/Ppk2bWLhwIZMmTVKunTFjBt27Zzyoenh4oNVq8/x+Pfjgg9x99925lsmclWDBggUkJyfnWNbJyf7zNMDVq1fR6XTZlveuUqUKV69etTlWtWpVJRODsK9cB+lHjhxpk94ipzLPPvtstuMpKbbrlpnNZlatWsWqVato0KAB3bt3p0WLFlStWhUvLy+Sk5OJiopi7969rFq1yuaHsU6dOnzwwQc5tmHz5s38+OOP1KhRg/vvv5+WLVsSHByMp6cniYmJnDp1irVr1/Lnn3/aXDd79mxatGiR69c3ZswYfvzxR3bt2gVY14r4448/GDJkCIGBgVy8eJGFCxfarFk/YcKEPAciCCGEEKJ8UPta05WrJUhf6jr5BrA9+ga3DKmkmk3o1MWz3rs20yh+gz6lyEF6rUYD9AGs6YLdhj+M6ZtVuV4jRHkRc/g0/36/iQavDsPJs2j/l4QQxcegvyttaygwFWiSrcyz3EcN9sIuSOw+XTm+a1dtntQWz+9cIcoyjZP1NfCNSxdp4+XH/rjb3DTosVgsdicN/f5qMD3Skm5+t6cmS5/dh1o+MgghhI0uXbowb948EhMT+eijj9BqtTzyyCMAnD17lqSkJJugK0BqaqoSf3nuued45JFHOHjwID169KB///7KbPjDhw9z9uxZJeCdLiUlhXPnzuW7jflpR4sWLQgNDWX58uW8+uqrbNu2jevXrzNw4EClLQkJCfj7+9vUkZycbNOWkJAQm/YGBQVx/XrBswUmJyfnmEE63YkTJ5TBAek6dOjAJ598AoCPjw/Nmzdn69at6HQ6dDodo0aN4s033yQhIYFt27bRuXPnHOs/fPgwW7ZswcMj+zJ/586do379+vn6Ws6dO4fBYKBDhw7KMScnJ+666y5OnDhhUzavgQk58fT0zPZzkpu8lhEoLq6uriQlJZXIve5U5TpIbzAYlFnnOTEajRiNxgLVe+rUKU6dsr/eWVbt2rVj5cqVVKlSJc+ykZGRfPnll3mW0+l0zJo1S5khn1fZn376ia5du3L06FEAJdWIPYMHD2b27Nl51iuEEEKI8sFyOx4q+4AE6UtdSy9ftkffAGBFVCRPVatVbHU7u7mhT0oiJTEBFz//vC/Ig4eLiYSUGMAHgPWHArm/xdXcLhGiXPiz1WAAjHEJtPjfK6XcGiFEOi+fSGJuNwLcgYyAu5ZkujCbCPpaA/R2VK4aVzKNFKKUpS/J6erpSSffAPbHWVPsJppMeGizvyLu2rgpUBc4C8DtRB2VPFOzlRNCiIrM3d1dmYm9aNEimjdvzsKFCxkxYgQJCdaB7b/++mu2oGj6bPTevXtz6dIl1q9fz6ZNm+jatStjxoxh7ty5JCQk0Lp1a5YtW5btvpmXOc5LftoB1mzQ6UH65cuX06tXLyUon5CQQFBQkN017zPPqM46A1ulUtksCZ1flSpVIjo6usDXZRUWFsbWrVtxdnamc+fO+Pn5ERoays6dO9m2bRsTJkzI8dqEhAT69u1rN14WFBRU5LbZ417ISRXLli1j9OjRuZbZsGEDHTt2BKw/d7mloa9ZsybHjx+3ey4wMJDU1FRiYmJs/u2vXbuWbcb/7du3qVOnTj6/ioqpXAfpi5O7uzvvvvsu69evZ9++faSm5v5Q2qFDB0aNGsUTTzyBOo9hpsOHD2f9+vV5Bv5dXV159NFHmTRpEo0aNcp32wMCAti7dy/Tpk3j66+/JjY2NluZkJAQ3njjDYYPH57veoUQQghRDqgtAFjMllJuiPDSOuGq1pBsNnEppXhHGuvTRi6bDIZiqW9K34d57YfKgPWZeNXe6hKkFxVKzD/5G7QthHAcgwGmjqpJrfp6EuLTZw5dAX4FQgHowmx68BY9eAtTlSqYGjTAafdum3qCgs8B95Rk04UoFe5ePtYNlQr3TEH5XTE36Fkpp2DDBWVrzxl/+raKclwDhRDiDqdWq5k6dSovv/wygwcPplGjRjg7OxMZGZnrjO2AgACGDh3K0KFD6dixI5MmTWLu3Lm0atWKlStXUrlyZby8vOxeq9PplDXXc5LfdgwePJjXX3+dAwcOsGrVKpsJpa1ateLq1atotVqblPwFlZ/2ArRs2ZKIiIhcy4SGhrJr1y6GDh2qHNu1a5dN7Kxz584sWrQIrVZLr169AGvg/rvvvuP06dM5rkcP1q/5xx9/JCQkBK2dwWz5VadOHXQ6Hbt27aJmzZqAdYLxvn37si1NnVV+v18lme6+devWODk58ccffyhZI06dOkVkZKSynEK6Y8eOMWDAgDzbX5GV6yB9eHg44eHhxVKXk5MTU6dOZerUqej1eo4ePcqZM2e4evUqiYmJODs74+3tTe3atWnVqhV+fn75rvvDDz/kww8/JDo6miNHjnDx4kVu3LhBcnIybm5u+Pr60qhRI1q1aoUuU7rSgnBxcWHOnDnMmDGDrVu3cvHiRaKjo6lSpQqhoaHcc889dlNbCSGEEKJ8S//176STmfRlQf8q1fguKhKABKPR7qymwvAOqEzsjeuYjXl/uMuP57r14LUflgNrgX7otLImvRBCiJK1ea0Pq5dmXUvbALwDTASgKoeVMynPPWfdyPK7VaORdyGiYnBKmy0Ze/2azfG9sbcJ86uMs52llibe/wBz198G/JjxU6gE6YUQIg8DBw5k0qRJfP7550ycOJGJEycyfvx4zGYz9957L7GxsezatQsvLy+GDh3KtGnTaN26NY0bN0av17Nu3TpCQ62DDYcMGcL7779Pv379mDFjBtWrV+fSpUusXr2ayZMnU716dUJCQvjtt984deoU/v7+eHt7Zwuwenp65tkOsE7kbN++PSNGjMBkMvHggw8qdXTr1o127drRv39/5syZQ/369fnvv//49ddfeeihh/Kdpj0kJIS///6bixcv4uHhgZ+fn91Jrj179uSZZ57BZDKh0dhflmjSpEkMGjSIli1b0q1bN3755RdWr17N5s2blTKdOnUiPj6edevW8d571jVcwsLCGDBgAEFBQbmmrB8zZgzz58/n8ccfZ/Lkyfj5+XH27FlWrFjBggULcmxXVu7u7jz33HNMmjQJPz8/atSowZw5c0hKSmLEiBG5XhsSEsKFCxc4dOgQ1atXx9PT0yb7QbqSTHfv7e3NiBEjePnll/Hz88PLy4sXXniBdu3acc89GQNfL168yJUrV+jWrVuh71URlOsgvaM4OzvTpk2bQq8PkRNfX186d+6c62imonJxcVFGDAkhhBBCaNUGzIDGSdZiLQtqu2asdXZFn0QDrf3R8gWlSfuQbjIVbJmnnHi7udOyZi3+ubQN6Mf8LbX5cvjBYqlbiDuBJR+zGYQQjnUm4hqQNX2mAYhl4br/karvzaFhySQ/NhLtvn1KCWPjxjZXqO0EJoUoj9KfB9OD9fdXCmL9TWvQ/UxiAk08vbNd82DrtsxdHwv4EXEl+5q8QhSHlKs3ubphNwFd2uAeUrW0myNEkWi1WsaOHcucOXN47rnnePvttwkICGDWrFmcP38eHx8fWrVqxdSpUwHrTOkpU6Zw8eJFXF1d6dixIytWrADAzc2N7du388orr/Dwww8THx9PtWrV6Nq1qzKzfuTIkWzdupU2bdqQkJDAli1b7M4Oz6sd6YYMGcLzzz/PU089haurq3JcpVKxfv16XnvtNZ5++mlu3LhBYGAgnTp1ytdSz+kmTpzI0KFDadSoEcnJyVy4cMHuzPzevXuj1WrZvHkzPXv2tFtX//79+eSTT5g7dy4vvfQStWrVYvHixTZfv6+vL02bNuXatWs0bNgQsAbuzWZznnG4qlWrsmvXLl555RV69OiBXq+nZs2a9OrVK8/s2Vm99957mM1mnnzySeLj42nTpg2//fYbvr6+uV73yCOPsHr1arp06UJMTAyLFy9m2LBhBbq3I3z00Ueo1WoeeeQR9Ho9PXv25IsvvrAp891339GjRw8le4CwT2WxWCS3qSg2cXFxeHt7c/3rJfi7upV2c8o9Y/d48C3YzLVrmvoYVHf+v43JYCZi43806lUVjVPFm/3pZEmiiul0wS6KVqPdlP8RdcLKAGz0dKJXvIGcE/0IeypyH1UYpdWvffP0F1C7Bk4mHY9N71Fi981K+rUM4VcucDkt3f1rtRuhLoZsR0unTuTm5Uv0GTue2ne3L5Z+LSElGe+R24AFAJz5YAO1KycWua1lmfRrBVMen9dWa6wDtd3rVKfn6TW5lpV+reTI81rh3cn92tTRZ1i54LEsRzsAuxk3fTQvvDGSRY/PxqlRlqB8VBTmTOuIxpquM3Z6/pb+K4/9WkFIv1ZyHNGvJUTf5usXR6NSqRi3ZCUAM89HYAba+fjTzT/Q7nWaJ+cBfwBgXLqKsp6IszD9WmGUlb6wqMpCv7aj27Pc2LIf97rB9Dz1U4neW/q1khOXnITvqKHExsZmS92ekpLChQsXqFWrFi4uLkW70cGD0Lo1HDgArVoVrS5Rqj7//HN+/vlnfvvtt9Juiiig1NRU6tWrx/Lly+nQoUNpN6dU5Ldfq3ifKIQQQgghRAaV9XFQrZMES2VFlD5jXbAfr10uljqjr/4HgIrie6vq4eJKDf+flf0hn+e+/pkQ5UnSRUn3K0Rpc3HNnuoTrIPcPp7+FQA6VfbXXpkD9AB+gcWTtUaIsk6btoSmxWLBZDSiUqmo5mINMh+Oi8nxukoel5TtLzbXcmgbRcV0c/s/ACSeLZ7PPkKI8mH06NFKunpxZ4mMjGTq1KkVNkBfEBKkF0IIIYSoyNKeBtUaeSwsKybVaqhsn0wsng+jVes1ACAlMaFY6ks3vHMX4DsA9p73K9a6hSjLJN29EKVPq7X3//CozZ7RJ3v67sw+/PBDBo7oX3yNEqIMc3LOmMVl0KcA4Ku1ztNPMptIzuF327HZU5Tt+VtqOLCFoqIy1KxJysCBGNPW4RZCCLAuHfDaa68VaK11UTbUrVuX0aNHl3Yz7gjyNlYIIYQQoiJLy1ep1spjYVmhVal5Isi6ZpeTnRmAhZK2wlVibEzx1JcmOTUVeFvZvxWvK9b6hRBCiJy4uBmUbZUqBvCmRu0s6bpzWeExMjKSuLg4nJwkm5CoGDTajJ/12OvXALjXN0A5diDutt3rAry8gc8AOHo5wG4ZIYoi9YEHMDVujP7RR0u7KUIIIUSJkrexQgghhBAVWTXry2yNtowvLlnB+DhZg90GSzGtp+mgxUMbV68BnFD2Ry+SNf+EEEKUDGeXjFnyFosPKuJ4MtOs+AunL6GulHNA0SQZMUQFptZoAPDXOeOTNpt+y+3rpOTw/8JJ87uy/e9tV8c3UFQoFh+f0m6CEEIIUSokSC+EEEIIUZHdvAWASi1B+rJEmymofiguusj1+QVVBeB21JUi15WZp2t62lTr+tw/7a9erPULIYQQOVGpbGfJDwe8Z8zHI22/W+gjmK78m+P1P/zwA8G1qjmugUKUQR5+/gBYzBkDQbv6V1G2jyfE2r2uZ7MoZfviDTcHtU4IIYpBUBC8+ab1byGEKOMkSC+EEEIIUYGlh4KdnCXVa1nimTajCWBn9I0i15ccb13b3t3Lp8h1Zda2dt20rYeUY/LiVlQUhtgEdnR/jt/q9yfh7OXSbo4QFY4+JTnTXhANVCowmWmS6ag6JcXutf/9l0BSUhIL133i0DYKUdakp7w3GY3KsUYe3rhrrMf3xtpPef/oPe2BfwA48Z+XYxsphBBFERQE06dLkF4IcUeQIL0QQgghREWWvia9kzwWljVhfpUBiDYaMOeypm5++KbNpDebize1b5CPb9rW38qxhVtrFes9hChL9LUbkDJwIBZ3d37xC+PGn/tIPPcvu/u+VNpNE6LC0emc07YOAFfRP/ooSRMm4OyWMVjMyZR92Zhbt1xYtOhjAHz8vbOdF6I8Sw/S65OTbI77py21lJOGVasB1ln4X/9Z2yFtE0IIIYSoaORtrBBCCCFEBWWxAGlp7p2cJN19WXOPt7+yfTQ+pkh1qdXWdUfNJbD+7s2E3F/yCnEnMz71OKbGjdH37m1zPOF0ZCm1SIiKK+NXmjUQb2rYEFxdCQwNzfW6//3vVYxps4j9A3xzLStEeRN38yYAxtRUm+N3pT133jTo7V7nrHUCtgGQqJfXyUIIIYQQxUGeqoQQQgghKiij0aLMpNe5Sbr7ssZJnfGo/uuNqFxK5k2tdVyQXpc2I6t9vb0AaNVFm/UvxJ3A4uNT2k0QosIzmdN/39jOlm+uVvN4epnGjW3OGTOl+J6/9iMHtk6Issk3Lf2zJUuWicwz6fV2Mi/5e3gAnwMQk+T4QZ9CCCGK39dff01wcDBqtZqPP/44x2Nl3bBhw+jfv7+yHxYWxrhx40qtPUIUhQTphRBCCCEqKEOKGdICwTqdPBaWRaHu1jU/TRQt8K3WWIP0Ny5fKnKbsurSyLr6r057FgC9UX6WRPln9ve32df5ScpsIUqaJctMeoVaTSvgfVAGI6Z7//33Afhxdzj3PdDRwS0Uouxx87L+voq5dtXmeGVnF2U7xZx9mYhAH18gBYBrsX6Oa6AQQgiHiIuLY+zYsbzyyitcuXKFUaNG2T1WWFu3bkWlUhETE1N8jRaiApA3aEIIIYQQFVRqojEjSO/hnEdpURq6+1dRtq/qUwpdT0pCAgDuXj5FbVI2SXprWlSNypo29dd/gor9HkKUBYa4hIwdV1ebc6m3Y0u4NUIIcw4z6S1Z/n+mu3z5Mnq9nqlzx9Hi7iYObp0QZVPsjesA6NzccizzX0qy3eNta2U849mJ4wshhCjDIiMjMRgM9OnTh6CgINzc3OweKwyDwVDMrRWi4pAgvRBCCCFEBWVMNmaku3d3KuXWCHu8M6Ue3R97u/D1VLYG+y2W4n+j2qhadQBuJVjbdzXWfnBEiDvduc9WlnYThBCZZKzgYqFViwbKcUNYGBatFova9pXXho0bqBxUiUEj+pdYG4UoawJr1QHAlEtA5XYO69J7umYMSJPMSUIIYU2z/sILLzBu3Dh8fX2pUqUK8+fPJzExkaeffhpPT0/q1q3Lhg0blGtMJhMjRoygVq1auLq60qBBAz755BPlfEpKCo0bN7aZ1X7u3Dk8PT1ZtGhRjm2JiYnhmWeeISAgAC8vL+677z4OHz4MQHh4OE2bNgWgdu3aqFQqu8cuXrwIwLx586hTpw46nY4GDRrwzTff2NxLpVIxb948HnzwQdzd3Rk5ciRdunQBwNfXF5VKxbBhw7K1MTExES8vL1atWmVzfM2aNbi7uxMfHw9YB1YOGjQIHx8f/Pz86Nevn9K2/IiOjuapp57C19cXNzc3evfuzZkzZwCwWCwEBATYtKFFixYEBWUMRNu5cyfOzs4kJSXl+55CFJY8UQkhhBBCVFD6eAMWL2s6dZ2rrElfVqWvEfpPfHSh69C5WFOYXjl9sljalJk+bX3f+kERyrHbCTLoQ5Q/CacjS7sJQohMzOb0VPZmPpk5xuacsVUrLJlmg9VqUo3tl9ayK3I9nl4eJdhKIcoWjZP1GS0pLibbuRAXdwCup9oP0jesmrHUS4pBU/yNE0KINBaLhdTExFL5Y7EUbKm5JUuWUKlSJfbu3csLL7zAc889x8CBA2nfvj0HDx6kR48ePPnkk0rA12w2U716dX744QciIiKYNm0aU6dO5fvvvwfAxcWFZcuWsWTJEtauXYvJZOKJJ56ge/fuDB8+PMd2DBw4kOvXr7NhwwYOHDhAq1b/Z+++w5uq3gCOf7O7d2kLFMose4MsAREFBRVUFAWVpfxURFHALeDCAQ7cihRQERURFMGFgoCIbER2gZZCGW3pbtOs3x9J094m3S2l5f08Dw/JnSdpcnLvec95TxeuvvpqUlJSuP322/ntt98A+Oeff0hMTGTkyJEuyyIjI/nuu+94+OGHeeyxx9i3bx+TJk1i3Lhx/PHHH4rzzZo1ixEjRvDvv/8ye/Zsvv32WwAOHTpEYmKiouNBPm9vb0aNGkVMTIxieUxMDLfeeiu+vr6YTCYGDx6Mr68vGzduZPPmzfj4+DBkyBDy8vLK9DcZO3Ys27dv5/vvv2fLli3YbDauv/56TCYTKpWKfv36sX79esAe0D9w4AA5OTkcPGhvL9mwYQPdu3evcGYBIcpDWmOFEEIIIS5TuelGyMkBT0+0Wmlou1S19fHnzwvnAcixWPDUlP9vZXbczAZFNKjSsgFER9QHwEN31Lnsyy2NePCa2Co/lxA1qd7VPThwoqDRLmTNYZKub1mDJRLi8ma12L+Pvv5emNOyFOvyrr8edZcuAKhV0HdEZ8A+8kuIy5nF0bkyL8c1pX2QXs+J3Cw0xXxPzqYlAxZAQ1q2jkBvSW8shKgepuxs5vvUTKe6KZmZ6L29y7x9x44deeaZZwB48skneeWVVwgJCeHee+8F4LnnnuODDz5g79699OzZE51Ox+zZs537N2nShC1btvD1119z2223AfaR3S+++CITJ05k1KhRxMXFsXr16mLLsGnTJv755x/OnTuHwWCfynDu3LmsXLmS5cuXc9999xEcbO9oFRoaSnh4OIDbZXPnzmXs2LE88MADADz66KP8/fffzJ071zlaHuDOO+9k3LhxzufHjx8HoF69egQEBBRb1okTJ9K7d28SExOJiIjg3LlzrFmzxtlh4KuvvsJqtbJgwQLndVtMTAwBAQGsX7+ea6+9tvg/BnDkyBG+//57Nm/eTO/evQH44osviIyMZOXKlYwcOZIBAwbw0UcfAfDnn3/SuXNnwsPDWb9+Pa1atWL9+vX079+/xPMIUVVkJL0QQgghxGXKmF3QsKbTy8jnS1W/wFDn40WnjlfoGN4BgQBYC3IDVxkPx4isrbFHnMsuZOmL21yIWstQL0jx/JN5YTVUEiEEFIykV6lsxP30j+t6R2OztXwD4oS4LGgdQZzCQvX2ZSdz3af37dS4MWDvLJqeI/cOQggB0KFDB+djjUZDcHCwM408QFiY/Z7h3LlzzmXvvfceXbt2JTQ0FB8fHz7++GPi45VZux577DFatmzJu+++y8KFC50BdXf27NlDZmYmwcHB+Pj4OP8dP36c2NjydZ4/cOAAffr0USzr06cPBw4cUCzr1q1buY6br0ePHrRt25bFixcD8Pnnn9O4cWP69evnfC1Hjx7F19fX+TqCgoLIzc0t02s5cOAAWq2WK664wrksODiY6Oho52vo378/+/fv5/z582zYsIEBAwYwYMAA1q9fj8lk4q+//mLAgAEVen1ClJeMpBdCCCGEuEwZs0zOOek1OhlJf6lSqVR4qTVkWy0kmYzYbLZyjwTUaO2X/RaLucrLl+uY0zQlM5NJA2P56Pdm7In3r/LzCFHTiqa+7HtNOj7xjcg8Eo9PdOMaKpUQly+LYyQ9KhsWv+J/d8KbhFykEglx6fOvVw+AlNOnXNblWa0A+GndB+A99XogFmhGfLIXHRqlud1OCCEqS+flxZTMzBo7d7m21ynrTJVKpViWf+9uddSxy5YtY9q0acybN49evXrh6+vL66+/ztatWxXHOXfuHIcPH0aj0XDkyBGGDBlSbBkyMzOJiIhwpnAvrKRR7ZXhXY5sA0VNnDiR9957jyeeeIKYmBjGjRvnfJ8yMzPp2rUrX3zxhct+oaGhLssqon379gQFBbFhwwY2bNjASy+9RHh4OK+++irbtm3DZDI5R+ELUd0kSC+EEEIIcZnKyzGD2p5YSW+Qkc+XsvENm/JuvH2kekJuDpGe5Ws4UDtS5FvNVR+kjwy2Bz889XrUKnvA5L8Evyo/jxA1rsho3PQLKhqNG87+p+aj8XAdkSiEqF42xxB5FVZSUnKgmAFmPYd2cL9CiMuQ2Wifb16rc732D3IssxYzH7PJYgGaAWCxVk/5hBAC7IHt8qScr03y07Dnp5MH3I4QHz9+PO3bt2fChAnce++9DBo0iNatW7s9ZpcuXThz5gxarZaoqKhKla9169Zs3ryZe+65R1HmNm3alLifXm//DbGUIXvfmDFjmDFjBvPnz2f//v2Kc3Xp0oWvvvqKevXq4edX/naF1q1bYzab2bp1qzPQnpyczKFDh5yvQaVSceWVV7Jq1Sr+++8/+vbti5eXF0ajkY8++ohu3bpVqhOCEOUh6e6FEEIIIS5TmWlm50h6nYykv6QFFmpI/ebsyXLvnx+kv3AmscrKlK9pqD19X3xyEk1C7elR6/kZq/w8QtS0fzZ6Q26u8/l3izSs/Kc+2VOmcE7ftAZLJsTlqXC6e01wYLHb+QaWr2ObEHWZt7/9u3LuxDGXdVqVvZk42ZTndt9W9RsAfwCQa5J7B1GF1BKiEJePFi1asH37dn7++WcOHz7Ms88+y7Zt2xTbvPfee2zZsoXFixczevRohg8fzujRo8nLc18/Dxo0iF69ejF8+HB++eUXTpw4wV9//cXTTz/N9u3by1W+6dOns2jRIj744AOOHDnCG2+8wYoVK5g2bVqJ+zVu3BiVSsXq1as5f/48mSVkQggMDOTmm29m+vTpXHvttTRs2NC5bvTo0YSEhHDTTTexceNGjh8/zvr165kyZQoJCQmllr9FixbcdNNN3HvvvWzatIk9e/YwZswYGjRowE033eTcbsCAAXz55Zd06tQJHx8f1Go1/fr144svvpD56MVFJb+AQgghhBCXKaspzxmkV0vDyCUvRGcfqZtVgZT1+enuPX19q7RMACGFjtk20p72dOOhqklDJ8SlxJitRlUoSH///R/RoFMutqAg8vp1qrmCCXGZSk32cTxSY9R7AhDpoxzeG9FUUt0LoeC49k87f85lVf7dgNHqfhSkVq0BcgCIPevjdhshKit9v2sHEiHqkkmTJnHzzTdz++23c8UVV5CcnKwYVX/w4EGmT5/O+++/T2RkJADvv/8+SUlJPPvss26PqVKpWLNmDf369WPcuHG0bNmSUaNGERcXR1hYWLnKN3z4cN5++23mzp1L27Zt+eijj4iJiSl1jvYGDRowe/ZsnnjiCcLCwpg8eXKJ20+YMIG8vDzGjx+vWO7l5cWff/5Jo0aNuPnmm2ndujUTJkwgNze3zCPrY2Ji6Nq1K8OGDaNXr17YbDbWrFmjmIagf//+WCwWxesaMGCAyzIhqpukuxdCCCGEuEzlZhqdoxbKOcW5qAFj6jfmrbjDAKSZTfgXM1+oOx7e9obUnIyMKi9XmH/BPMB5JnsAs0lozcwfKER1CgwxcSbVfWWp8pRbayEuNovVHmTMyW6ExpgLPv7YinQ6NBmrfpoXIWqzgLBwAAxu0vj6ODp1motJd2+/5msAQFp22a9DhSiPnIRz+LWRDEWidnA3B/yJEydcltkK1asGg4GYmBhiYmIU28yZMweAVq1akZ2drVgXEBBAfHx8iWXx9fVl/vz5zJ8/3+36Tp06KcpR3DKA+++/n/vvv7/Yc7nbB+DZZ58ttiNBUadOnSI4OFgxuj1feHg4ixcvLnbfRYsWKZ4X/TsEBgayZMmSEs/v7rU/8sgjPPLIIyXuJ0RVkyFTQgghhBCXKVNGjjM6r1JLlP5S51soKL8r/UK59tXqC9Ll26xVO4moj4en87FafR6A4+dldJWoo4rJOqLSSpBeiIvN4JGfxn4HeSnpAHga1Bi8Cn7zmnWMrIGSCXHpCq5vD7K7ux70UBeksHcXgDFodcARABasb1I9BRSXPWOOTJslRF2WnZ1NbGwsr7zyCpMmTXLOZS/E5UqC9EIIIYQQlymzhYIgvQylrxUiDB4AbLxwvlz76Rz7AVjMpiotU2HZxtRqO7YQNc5mw1ZMXWkzup8fUghRfSxm+/cxIMgbrb+jc1ieibBGQc5tWnRpVBNFE+KSpXGk+rWYXbNMeGoKgvTuRtM3CgkB7MsbBqVXTwHFZW/jBvlsCVGXvfbaa7Rq1Yrw8HCefPLJmi6OEDVOgvRCCCGEEJcpc17BCBoZSV87tPMpSC1/1phbwpZKhUfSm/OqPpjY3JE69UJWonNZFQ/YF+LSUMxIeopJ+SiEqD5mR5BerbGS5xcAgCHQl55DOxDWOIjeN3aUTohCFOEM0ptMWIvMPa8p9H2xuPld8/XwBBYBsC8hpNrKKC5vuTlyEyFEXTZr1ixMJhPr1q3Dx0cy8AkhQXohhBBCiMuUObegYU4asWuHngEFDaIpprIH29UajfNvnJeTU+XlupCVBYCHvqBMWUZJ/y3qFpsN8PJyu07lYbi4hRFCkD8QWK22oXb8tllV9nT3197dW1LdC+GGp6+v83FORoZinYaC+4FMi+tIe5VKRctwe0BFo852WS9EVdCcO13TRRBCCCEuGgnSCyGEEEJU0vkNO9gxYTYXdh6s6aKUS3ZWwWONVi4La4smnt4AmG3lG2WSP7dodYyk79GsOQDxSXHOZZkSpBd1TEZuRukbCSEumpwse+cYjcbqTN/i4Sm/PUKURK3WoHJkhbFalCPpC3faNRYZZZ/v7r7NALDIYGdRXeS2VAghxGVEfvaEEEIIISpp68gZxC36gT+6jyH3XEpNF6fMtGqj87FaI5eFtUV+KlJrOdNr+wYHA2AyGkvZsvzOpacBkJmbg7fBPvIqLsn9iGMhaqu03LQS18ct+sHZGUYIUf1ys+1BeqvVA6vJ/tuj0UmQXojSaLT274nVzbz0BkcAP9Vscrtvg2APxyMvmdpIVAurTTK8CSGEuHxIa6wQQgghRCXlJRcEbjZedV8NlqR88kfP2CxWSXdfi+QH6benXyjXfjarPXiYm5le5WVqH9kYgLPpac4092nZuio/jxA1ylpQT7733nt89dVXitU7Jsxmx9iZF7tUQly2crITAdCZjdgcgUWdt2dNFkmIWkGt0QBgzHZNWe+htq9bl3zW7b5NQwvS5WfnaaqhdOJyl4l3TRdBCCGEuGgkSC+EEEIIUUmekWHOx9lxiTVYkvKxpCYBoJJR9LWKyjFf6Glj+eaWz83KrI7iAOCp0wOg12ppH5kKyEh6UfdYswtS//5x7DtuHHuNyzbxn68h4ZvfLmaxhLhsefv4ATAweSY2H/s82T7N6tdkkYSoFfJycwFIPHrYZZ2nI0ifbXGf7r6eX0G2iqxcuYcQVU9lcc3wIIQQQtRVcjUlhBBCCFFJhnpBzsf5o5VrBQ8ZpVAbdfELdD7elpZc5v3qNW4CQEZSUpWXqWVEBABbjhwiPcc+gl6vlRyoom4yJ6bi6eXBDaMGs2DBOedym7e9Tv1n1BMkbdxVU8UT4rJhttg7rUXn/g1e9o5haq2kuxeiNCENIwFISjjpsu76UPs1naaYLFsRAQGAfeqkPfHuU+ILUSmS4E0IIcRlRIL0QgghhBCVVHgOYlsxo04uRVaLvdyWrLwaLokoj6aeBZ0rfko6U+b98kfS7/3tpyovk8ls/9yfSUulc1QqALvjAqr8PEJcSiIahpGQkOV8bvXzcz5O/y+2JookxGXFYrZHcnK69HQuCyiUilsI4Z7OYADAw9u1w66Pxt7RJdfq/p7G19MTsO+/M06mNhJCiIstKiqKt956q6aLIarQXXfdxcsvv3zRz/vhhx9yww03XPTzCiUJ0gshhBBCVNLJY6ecj22W2jN62GZ1lNVWi0b/C1QqFT39g53PbWX8+1kdHUiST8ZjNVXtyKfGoaEA+Hl4kpZlb7BNSJF5gUXd12vgtaSm+gNgCwoqZWshRFWymNX4kojthsHOZTqDjKQXojRhTZoDYMP1GlKnLmgqthR7jWm/h8g21p7OyaI2kaH0ovYYO3YsKpWK//3vfy7rHnzwQVQqFWPHjq3Sc27bto377ruvSo8pas6ePXtYs2YNU6ZMcS5TqVRu/73++uvObaKiolzWv/LKK4pj22w25s6dS8uWLTEYDDRo0ICXXnrJuX78+PHs3LmTjRs3Vv8LFcWSIL0QQgghRCWlpRoKnthsZQ6a1rT8IH2tStEvALgyMNT5OKGMc9P3HXmn83HKF19UaXma1QsH4ETSeeoH2ssT7CMZGkTd4q5uP3msAwEBaQBY69VzLjfmya22ENXNdFbFc9QHs33+4kbRYTVcIiFqh/xM9vs3bnBZp1cV/H6Zbe47H+u19v3OpWdWfeGEcNN5RIhLWWRkJMuWLSMnp+C+PDc3l6VLl9KoUaMqP19oaChejml+aqO8vEuznaCmyvXOO+8wcuRIfHx8nMsSExMV/xYuXIhKpeKWW25R7Pv8888rtnvooYcU6x9++GEWLFjA3LlzOXjwIN9//z09evRwrtfr9dx5553Mnz+/el+kKJG0HAghhBBCVJKKIjde1toxmr5gJH3NlkOUn4dG43y86NTxMu3TokdBOuALK1dWaXmahBYEJ5vWSwTgUKKkHBZ1VKE6s0vvH52PTX37Oh//u90HIUT1ap63FpunJzjmoe/QP7qGSyRE7WB13ANkXkhxWVd4LvrsYqbxUqvsy2PPhldD6cRlT+5NRS3TpUsXIiMjWbFihXPZihUraNSoEZ07d1Zs+9NPP9G3b18CAgIIDg5m2LBhxMYWTJO1ZMkSfHx8OHLkiHPZAw88QKtWrcjOzgZc092rVCo++ugjhg0bhpeXF61bt2bLli0cPXqUAQMG4O3tTe/evRXnGTt2LMOHD1eU7ZFHHmHAgAHO5wMGDOChhx7ikUceITAwkLCwMD755BOysrIYN24cvr6+NG/enLVr15b4/kRFRfHCCy9w99134+fn58wCsGnTJq688ko8PT2JjIxkypQpZGUVTCX2/vvv06JFCzw8PAgLC+PWW291rjMajUyZMoV69erh4eFB37592bZtm3P9okWLCAgIUJRj5cqVqAr9xs2aNYtOnTqxYMECmjRpgoeHBwCpqalMmjSJsLAwPDw8aNeuHatXr3buV5lyF2WxWFi+fLlLyvnw8HDFv1WrVnHVVVfRtGlTxXa+vr6K7bwLTWNz4MABPvjgA1atWsWNN95IkyZN6Nq1K9dcc43iGDfccAPff/+9opOJuLgkSC+EEEIIUcVqy8j0XEda8tpSXqHkpy3fPKAqlYpu19/ofG4xV13K+4BCN4MpmbkAmC1yqyHqPmPOCXJz7dlUtLt3O5ebzZKqVYjqlqfXkf34487nXr4eNVgaIWqP9v2vdj7e9PVSxbrCAYz0Yq4V1arGAJgt56qhdOKyJ5dQohYaP348MTExzucLFy5k3LhxLttlZWXx6KOPsn37dtatW4darWbEiBHOzlN33303119/PaNHj8ZsNvPjjz+yYMECvvjiixJHz+cHwXfv3k2rVq248847mTRpEk8++STbt2/HZrMxefLkcr+uxYsXExISwj///MNDDz3E/fffz8iRI+nduzc7d+7k2muv5a677nJ2ICjO3Llz6dixI7t27eLZZ58lNjaWIUOGcMstt7B3716++uorNm3a5Czj9u3bmTJlCs8//zyHDh3ip59+ol+/fs7jzZgxg2+//ZbFixezc+dOmjdvzuDBg0lJce18VpKjR4/y7bffsmLFCnbv3o3VauW6665j8+bNfP755+zfv59XXnkFjWOQRGXLXdTevXtJS0ujW7duxW5z9uxZfvzxRyZMmOCy7pVXXiE4OJjOnTvz+uuvY3ZklwL44YcfaNq0KatXr6ZJkyZERUUxceJEl/eoW7dumM1mtm7dWq73TlQdmaxLCCGEEKKK2SwW0F36l1kqMgFfbFZpCamNxkQ05v2TRwEwWi0Y1JpS9oArbx/N9jXfA5Cw/z+ad+hUZeXx9/IiLTsbG0kAnE6VYImo+xo3D2P//tZ06bIbc9eu6H/7DVVODiaTdFIRorqFRSsbqw1e+hoqiRC1S2jjKOfjf374jmZduhPRvIXLdnnFZAcL9N5Bdl4LjOZQIKOaSimEuNx99NFHZGZe/Gk1fHx8mDRpUrn2GTNmDE8++SRxcXEAbN68mWXLlrF+/XrFdkXTlS9cuJDQ0FD2799Pu3btAPvr7tChA1OmTGHFihXMmjWLrl27lnj+cePGcdtttwHw+OOP06tXL5599lkGDx4M2NOeu+s0UJqOHTvyzDPPAPDkk0/yyiuvEBISwr333gvAc889xwcffMDevXvp2bNnsccZOHAgjz32mPP5xIkTGT16NI888ggALVq0YP78+fTv358PPviA+Ph4vL29GTZsGL6+vjRu3NiZlSArK4sPPviARYsWcd111wHwySef8Ouvv/Lpp58yffr0Mr++vLw8lixZQmiofTrBX375hX/++YcDBw7QsmVLAMXo9Tlz5lS43O7ExcWh0WioV2jatKIWL16Mr68vN998s2L5lClT6NKlC0FBQfz11188+eSTJCYm8sYbbwBw7Ngx4uLi+Oabb1iyZAkWi4WpU6dy66238vvvvzuP4+Xlhb+/v/OzKy6+S7/1WAghhBDiEqdW5SrS8tWWkene2FNyqco3IFtcIoJ0BcGIY9lZtPbxK3UflbogcPjrh+/Q/P1Pq6w82UYjAF56e5o0owQpRR1jczNDiFptoUuX3c7n2Y8/jvesWeg9asfvgBC1WVZqAJAKgOr8+ZosihC1ikqlYsIb7/Hpow8CsPX7bxn+6BPO9WF6D87m5WItJu+4j0cAACeT5VpPCFF9MjMzycioHR2BQkNDGTp0KIsWLcJmszF06FBCQkJctjty5AjPPfccW7duJSkpyTmCPj4+3hmkDwwM5NNPP2Xw4MH07t2bJ554wuU4RXXo0MH5OCwsDID27dsrluXm5pKeno6fX+ntBu6Oq9FoCA4OdjkuwLlzJWdWKTpSfM+ePezdu5cvvvjCucxms2G1Wjl+/DjXXHMNjRs3pmnTpgwZMoQhQ4YwYsQIvLy8iI2NxWQy0adPH+e+Op2OHj16cODAgTK/NoDGjRs7A/QAu3fvpmHDhs4AfVGVKbc7OTk5GAwGRRabohYuXMjo0aOd6fjzPfroo87HHTp0QK/XM2nSJObMmYPBYMBqtWI0GlmyZInz9Xz66ad07dqVQ4cOER1dME2Up6dnqdkQRPWRIL0QQgghRKUpG7BsxczfeKkxqr3xBbReEqWvjQrfyO1ITylTkL6wgIiIKi3PldGt+X3/PrwN9kDJmTTPKj2+EJciH780Th7rSGRkgmK52uZ+9KEQouqk50WQH6T3CvWv0bIIUdv4h9YjrGkzzh6L5diuHYp1ekenzuL6HRtN9qCZpyG3WssohLi8+fj41Krzjh8/3pn2/L333nO7zQ033EDjxo355JNPqF+/PlarlXbt2pGXl6fY7s8//0Sj0ZCYmEhWVha+vr4lnlunK2jTyW8ncLcsv1OAWq3GZlNW8iaT6xQnhY+Rf5ySjlucwnOlg70DxqRJk5gyZYrLto0aNUKv17Nz507Wr1/PL7/8wnPPPcesWbMU886XpKyvr2i5PD1LbsOoTLkDAgJc9gkJCSE7O5u8vDz0eteMUBs3buTQoUN89dVXJZYL4IorrsBsNnPixAmio6OJiIhAq9UqOhy0bt0asHcKKRykT0lJUXRWEBeXBOmFEEIIIaqYJceIzte79A1rmuNGypgkDWy1VWMPL+Jys0nIzSnzPl2uv5Gda77nwunTVVoWrWOetqSMeAA8dLWjs4oQZeeu8cnKV1/dzrRp8xRLbXrppCJEdVOpChpfs5BU90KUV79Rd/HNy7MAuHA2kcAwewdOjWNScIvNfZS+XUMrJ5LAYtUCxotRVCHEZai8Kedr2pAhQ8jLy0OlUjnTzBeWnJzMoUOH+OSTT7jyyisB2LRpk8t2f/31F6+++io//PADjz/+OJMnT2bx4sVVWtbQ0FD27dunWLZ7926XoHx16dKlC/v376d58+bFbqPVahk0aBCDBg1i5syZBAQE8PvvvzN48GD0ej2bN2+mcePGgD0Av23bNmca+tDQUDIyMsjKynIG4nfv3l1quTp06EBCQgKHDx92O5q+MuUumq4eoFOnTgDs37/f+biw/JHvHTt2LLXsu3fvRq1WO1Pn9+nTB7PZTGxsLM2aNQPg8OHDAM73DSA2Npbc3NwS0/KL6iV5iYQQQgghqpjNZK7pIpSJLdseRC2m/U3UAp38AgEw2axYyjhyNyiyEQC5mVWbOjAhJRmA+oH2D1SuSYPJXHzaNiFqrUKVZrsurcnMbOuyydENZy9miYS4LFmzjjsfN2wZVoMlEaJ2imxd8PsVM61gVKDacfl23uS+I6+Ho09McoZrKmchKkvuHkRtpdFoOHDgAPv370fj6MBeWGBgIMHBwXz88cccPXqU33//XZGyHCAjI4O77rqLKVOmcN111/HFF1/w1VdfsXz58iot68CBA9m+fTtLlizhyJEjzJw50yVoX50ef/xx/vrrLyZPnszu3bs5cuQIq1atcmYiWL16NfPnz2f37t3ExcWxZMkSrFYr0dHReHt7c//99zN9+nR++ukn9u/fz7333kt2djYTJkwA7KPKvby8eOqpp4iNjWXp0qUsWrSo1HL179+ffv36ccstt/Drr79y/Phx1q5dy08//VTpcrsTGhpKly5d3HbWSE9P55tvvmHixIku67Zs2cJbb73Fnj17OHbsGF988QVTp05lzJgxBAba24gGDRpEly5dGD9+PLt27WLHjh1MmjSJa665RtEBYePGjTRt2tQZyBcXnwTphRBCCCEqSVUk3b3FVDtGEHto0gCwWaQppLZq412Q4j7ZlFfClgUCw+2jpDx9y5cevzRdopoCoFEXBP8zciVxl6hDiu0HE8/nn69QLDGkV30jl8Vq4f6Yj7npjVc4lHiqyo8vRG1jUxdkrOg/slsJWwohyiPFcU3pqXYNMtnZ7x1y8updpBKJy4v0IBe1l5+fX7FzvqvVapYtW8aOHTto164dU6dO5fXXX1ds8/DDD+Pt7c3LL78M2OeVf/nll5k0aRKnTlXd9f/gwYN59tlnmTFjBt27dycjI4O77767yo5fmg4dOrBhwwYOHz7MlVdeSefOnXnuueeoX78+AAEBAaxYsYKBAwfSunVrPvzwQ7788kvatrV3LnvllVe45ZZbuOuuu+jSpQtHjx7l559/dgaog4KC+Pzzz1mzZg3t27fnyy+/ZNasWWUq27fffkv37t254447aNOmDTNmzMDimNKysuV2Z+LEiYo57vMtW7YMm83GHXfc4bLOYDCwbNky+vfvT9u2bXnppZeYOnUqH3/8sXMbtVrNDz/8QEhICP369WPo0KG0bt2aZcuWKY715Zdfcu+995bpvRHVQ1rNhBBCCCGqmNVcO4L0Jq19XjMNZQvuikuPVl3Q59ZUyjxw+Tx87H93i6VqMz7otfZbC7PFiEplw2ZTkWeWPsGi7ihoMi7o2OTp5QHAmTPHCrbTaIjI3Frl598TH8fHv/8KQIfIxrww0rXBRojLicHL8V28kIpaLR0OhaiIoPoNSDl9ikZt2zuXNfL0JjUjtdh09/UD7fcOVpvHRSmjuLzYZCy9qEVKG529cuVKxfNBgwaxf/9+xbLCc6cvXLjQ5RiPPvqoYsT9iRMnit0fICoqymXZgAEDXJbNnj2b2bNnF1v29evXuywrem535y/LPgDdu3fnl19+cbuub9++bs+fz8PDg/nz5zN//vxitxk+fDjDhw9XLCscjJ41a5bbwH1QUJDbv0NVlNudsWPHMmfOHLZs2UKvXr2cy++77z7uu+8+t/t06dKFv//+u9Rj169fn2+//bbY9f/99x+7d+/m66+/LleZRdWSVjMhhBBCiCqWl1s7gvQ2g310jIXiRsmI2iBYZ885ai7jvAVqR+o9i8lUpeXQOY575Oxp9Fp7hwEJ0ou6xVbkfzA5pjfJyclxLssbNAivvPNVfvadJwpSexvNVfv9FaI2ysuxj5YiMKBGyyFEbdaqV18A4v/717ks/86guO6fDQLtv31WW2g1lkxcvmQkvRDi8uHp6cmSJUtISkq66OdOTExkyZIl+Pv7X/RziwIykl4IIYQQotKUDQmm3FoSPMmzdybQB8komNpMo7KPNjmXl0tjT+9St1c7RrxbTCZsVisqddUE0lOzswE4lHgKncaK0aQhPUcH5JS8oxC1TsEIryv6d+Geh0ax+J2CtIHmXr2wHj9T5Wf9Yec252NLGTNnCFGXqdWO+bKTUmq2IELUYlpHZ8/C1I5rS2sxHUAbO6eiT66mUonLm4ykF0JcXgYMGFAj5x00aFCNnFcoydAWIYQQQogqZjbVkuCJowHOmCRB1NosxzE/2vGcrDJt7+nr63ycnZ5WZeXwNhgACPXzJzNXB8CJpNI7DQhRWxTEKgqCFmq1mufemuZMe59PG2AgO6tqb7dX79rhfGy21JLfGSGqlf13TyWjLoWosJZXFKTWzclIBwqC9Mkmo9t9Qnzyf99CMVZxZiYhpE4XQghxOZEgvRBCCCFEFTPnVu1c39VO2kFqtVC9PTh+KCujTNtrtDrn49yszCorx5Wt2gBgNJnw1Nu/A0kZrqOzhKiLPlr5hnKBzcbR/6ovcV2mMbfaji1EbaHVOEZbynWMEBXmF1KQsv7E3j1AQQdQTTEjmjs08nE+PnhaOvsKIYQQQlSUBOmFEEIIIapYZnotCdKr8hu3pXW7NusZ4Mw5SlwZR9OrHaPpTUb3I6QqwtORLnX78ViuaXcWgP8SZG4zUZcUX1f2ubqHyzKrMa9Kzx5WaK7APXEnqvTYQgghLl/eAYEAbF7+JQDBjmu688WMpA/ysTgf7z9Vtb91QgghhBCXEwnSCyGEEEJUUtGUfMbs2pH20WSuvlGe4uJp5lUwmmn5mZNl2kettze+ZqVeqLJy2BzfgwtZWZxPt6f+DvCShltRh9jKN0fqgX+q9vRn0wqmp9gVd5z4pPNVewIhahm1ynH9JZ0NhagUn8AgANKL/K7oVe6bjVUqFWCfduXYuYDqLJoQQgghRJ0mQfpKSEpKYs6cOQwcOJD69etjMBioV68eXbp04YknnmD//v0VOu6xY8d47rnn6Nq1K6GhoXh6etKsWTNGjBjB8uXLsVgspR/kIh5XCCGEEEq1Jd29Vmtv1DZLHLXW6+pnHwGVbbVgLUOwwppjT01alXGNyKBgxzFtdG+WAsDpVM+qO4EQNc6m+K80GUdOVV9RgP4vPkdadtmyZwhRF6lUVsf/NVwQIWq5wfc+4HxstVoI1JU+XZFOY8+adDJFpjYSVUzqdCGEEJcRCdJX0DvvvEOTJk146qmn+OOPP0hMTCQvL4/z58+za9cuXn31VTp06MAzzzyDyVT20XRvvfUWbdq04YUXXmDnzp0kJSWRm5vLsWPHWLlyJSNHjuTKK6/k+PHj5SpvdR1XCCGEEKBFmQoy70JaMVteWlQae+O2VgbU13pXB4c5H/+afKbU7Q0tWgCQklh1QcRGwQVp9y32jxbJmdJwK+qQUmL0vg0MygVnjlVDIRrh62GfPzg+OYkV26t4uL4QtYiqnB1nhBDuBUZEOB8bs7PRq+3NxXG52SXsZe+Quflwi+osmrgcSZ0uhBDiMiJB+gp47LHHmDJlCpmZmc5ljRs3ZsCAAfTo0QMPD3t6T4vFwksvvcTEiRPLdNwXXniBqVOnYnTMDapWq2nXrh39+vUjotAF85YtW+jXrx9nzpTeAFudxxVCCCGEXYgtTvHcoqodUW+tX35ASVpCajuDWuN8/E9aSqnbm8/aRz/p9IZStiy7EF8/5+OcPHs2iQCv2jH1gxBloUuxd2opboDXVTf2cj62+fuTs/mPKju3xWoBmgJxWKwJzuXPfvMleWb5nonLk0ol1y9CVAWNVodaY7+WzM3KROdIc++jKf6eRq2yb2+2xFd/AcXlRUbSCyGEuIxIkL6cli5dyhtvvOF83rZtWzZv3syJEyf4448/2Lp1K0lJScycORO1o+fpkiVLePPNN0s87s8//8zMmTOdz3v16sWBAwf4999/2bBhAwkJCXz55Zf4+NjnHE1ISODWW28ttbzVdVwhhBBCFDijUo4gseRZa6gk5WM12gOpGi9dDZdEVIUbQus7H+eWMo2RR+vW9u2yMkvcrjwMOh0ax/VvkLe9o0CeWW43RB2itdeVatx/vwxehTJH5OYSnL2ryk6dkZMLvA9Adp6e8f0HApCYeoHf9++rsvMIUZvInPRCVB2r49rxzNGjBOjsv3eZluKn8GoQuBKAw2eGYrZIVFVUktTjQohCNm/eTPv27dHpdAwfPrzYZZe6RYsWERAQ4Hw+a9YsOnXqdNHLcdddd/Hyyy9f9PNWlw8//JAbbrihpotRZaTVrBzy8vJ48sknnc8bN27Mxo0b6d27t2I7b29vZs2axVtvveVc9vzzz5OS4n5Uk81m4/HHH8fmuCCJjo7mt99+o2XLls5t1Go1o0aN4rvvvnMu27x5s+L5xTquEEIIIUpmMdWOIH0+S6ZMSl8XdPQNcD7ekV7yaHqVI5iekZxUpWWwWK2O/3MBMEqQXtQhhnP2qcFy8XG73sNLT35mEltQEDpjcpWdOz45CRjsfD7r5tucj//4T4L04vIkI+mFqHq/LHhfkaHJYnN/X3Nlq4L7h5g/o6q7WEIIcUkaO3YsKpWK//3vfy7rHnzwQVQqFWPHjr34BavlHn30UTp16sTx48dZtGhRscsqSqVSsXLlykqXszbYs2cPa9asYcqUKc5l+Z/bwv+GDBniXL9+/XqX9fn/tm3b5tzu559/pmfPnvj6+hIaGsott9zCiRMnSizPjTfeSKNGjfDw8CAiIoK77rqL06dPK7ax2WzMnTuXli1bYjAYaNCgAS+99JJz/fjx49m5cycbN26s5LtzaZBWs3L49ddfiY8vSOP06quvEhgYWOz2Dz30EB07dgQgNTWV9957z+12P/30E3v27HE+f/vtt/Hy8nK77aBBg7j99tudz1955ZViz19dxxVCCCGEkkoF1nr1sKnso0iyZj6FKa3qRihXF5XaXl6bWRq56wKVSoXakR9yWykp763Z9jlGDd7eVVqGVvUbAHAh+wIAfx0OrtLjC1GTNFn2z7WK4jtiqQz2+tTcpQsANmvVdNrKyVN2pmoQFExksP37NXfN9zy46JMqOY8QtYmXKs3xSK5jhKispp27AmAxm9GrCpqLU0zuO/NGBu91Pl6zO7x6CyfqPlVBNgat1ViDBRGi/CIjI1m2bBk5OTnOZbm5uSxdupRGjRrVYMlqr9jYWAYOHEjDhg2dI9HdLSuvvLzLb4DKO++8w8iRI52ZtPMNGTKExMRE578vv/zSua53796KdYmJiUycOJEmTZrQrVs3AI4fP85NN93EwIED2b17Nz///DNJSUncfPPNJZbnqquu4uuvv+bQoUN8++23xMbGumT2fvjhh1mwYAFz587l4MGDfP/99/To0cO5Xq/Xc+eddzJ//vzKvj2XBAnSl8MffxTMKWgwGBgxYkSp+4waNcr5+JtvvnG7zbfffut83KRJE6699toSjzlp0iTn423btpGQkOB2u+o6rhBCCCGUDMN7k/PAAxgLXYz+EDQAi/HSvgFQaRxBeqs0btcV3f3tHUgzLGbOGnOL3U7fsCEAaefOVen5E1LsI4czcuyfKZmTXtQlFq8AAMwqfbHb5JkLvnc2nY6041WTrWLH8VjFc5sNHhhUMNrhw3W/YDTJ901cXow4OprlFv97J4Qom06FflN06oLm4gvF/LY0D4sANgDw/c4G1Vo2cXnxzz1JdvyZmi6GEGXWpUsXIiMjWbFihXPZihUraNSoEZ07d1Zsa7VamTNnDk2aNMHT05OOHTuyfPly53qLxcKECROc66Ojo3n77bcVxxg7dizDhw9n7ty5REREEBwczIMPPoiphHuB/DTrCxcupFGjRvj4+PDAAw9gsVh47bXXCA8Pp169eorRygBvvPEG7du3x9vbm8jISB544AEyMwsGpIwfP54OHTpgNNo71+Tl5dG5c2fuvvvuYstS0ntw4sQJVCoVycnJjB8/HpVKxaJFi9wuA9iwYQM9evTAYDAQERHBE088gdlcMFXLgAEDmDx5Mo888gghISEMHjyYqKgoAEaMGIFKpXI+L2rgwIFMnjxZsez8+fPo9XrWrVsHgNFoZNq0aTRo0ABvb2+uuOIK1q9fX+xrd/dePP/88zRs2BCDwUCnTp346aefnOtvvfVWRRkeeeQRVCoVBw8eBOzvt7e3N7/99pvb41ssFpYvX+42NbzBYCA8PNz5r/BgZL1er1gXHBzMqlWrGDduHCpHp6odO3ZgsVh48cUXadasGV26dGHatGns3r27xM/i1KlT6dmzJ40bN6Z379488cQT/P333859Dhw4wAcffMCqVau48cYbadKkCV27duWaa65RHOeGG27g+++/V3SOqa0kSF8OhVM1REdHo9cX3ziTr0OHDs7H//77L8ePH3fZ5scff3Q+Hjx4sPODXpwrr7wSb8eoJ5vNxpo1a9xuV13HFUIIIYSSrkMTACzt2yuWn1l9aade0od6AjinxhG13xX+BSPXV5wtvsOlzTHvqNVc/FyjFTp/sxYAaNUnADiZ4j6LkxC1jc1qRZdu79SSZ/MtdrvBo/sV7OPjQ9K/J6vk/HtPxrksmz70Jra98KrzuamEuYOFqIs8fByZKjw8a7YgQtQBQfULAu3mvDzqG+zfq3Sz+4b2zo2bAB87n8eerdrsTOIyplKRdUwGjonaZfz48cTExDifL1y4kHHjxrlsN2fOHJYsWcKHH37If//9x9SpUxkzZgwbNtg7PVmtVho2bMg333zD/v37ee6553jqqaf4+uuvFcf5448/iI2N5Y8//mDx4sUsWrSo1BTwsbGxrF27lp9++okvv/ySTz/9lKFDh5KQkMCGDRt49dVXeeaZZ9i6datzH7Vazfz58/nvv/9YvHgxv//+OzNmzHCunz9/PllZWTzxxBMAPP3006SmpvLuu+8WW46S3oPIyEgSExPx8/PjrbfeIjExkZEjR7osu/322zl16hTXX3893bt3Z8+ePXzwwQd8+umnvPjii4rzLV68GL1ez+bNm/nwww+d6dpjYmJITExUpG8vbOLEiSxdutTZAQHg888/p0GDBgwcOBCAyZMns2XLFpYtW8bevXsZOXIkQ4YM4ciRIyX+LfK9/fbbzJs3j7lz57J3714GDx7MjTfe6Ny/f//+iqD/hg0bCAkJcS7btm0bJpPJZTrufHv37iUtLc05+r2w9evXU69ePaKjo7n//vtJTi5+qrbvv/+e5ORkxWe6a9euqNVqYmJisFgspKWl8dlnnzFo0CB0Ol2ZXn9KSgpffPEFvXv3du7zww8/0LRpU1avXk2TJk2Iiopi4sSJLlOJd+vWDbPZrPi81lYSpC+HtLQ052Nf3+IbZgrz8/NTPN+9e7fi+blz5zhzpqB3YK9evUo9plarpXv37s7nhVPaV/dxhRBCCFEyS4OCBi5TakYNlqR0pgv2mw2tV9kuoMWlz1+np4OPPwBJpuJTRaoMBgC0htI7nZaH2jHySq+zp9P385SRvaL2M6VnkrRhp/N5ns39nPQADRqHk5lpz6Ji8/Iied+pKimDukiH6xdXtubDdc1oGdbEuexgkbn8hKjrrEZ7kN7mW/x3UghRNr7BIc7HibFHyHZ0/DIVMyd924aRQEFq3FdXR1dr+cTlw9yhAxbJ9CYcjMZzpGfsU/zLybF3grVYjC7r0jP2OffNyjrmss5kSgUgLy/ZZV12tuvgyrIaM2YMmzZtIi4ujri4ODZv3syYMWOKvBYjL7/8MgsXLmTw4ME0bdqUsWPHMmbMGD766CMAdDods2fPplu3bjRp0oTRo0czbtw4lyB9YGAg7777Lq1atWLYsGEMHTrUObq7OFarlYULF9KmTRtuuOEGrrrqKg4dOsRbb71FdHQ048aNIzo6WpFN+pFHHuGqq64iKiqKgQMH8uKLLyrK4uPjw+eff857773Hc889x1tvvcVnn33mEhMr63ug0WgIDw9HpVLh7+9PeHg43t7eLss8PT15//33iYyMdL4Pw4cPZ/bs2cybNw9roSnHWrRowWuvvUZ0dDTR0dGEhoYCEBAQQHh4uPN5Uflp21etWuVctmjRIud87vHx8cTExPDNN99w5ZVX0qxZM6ZNm0bfvn0VHTZKMnfuXB5//HFGjRpFdHQ0r776Kp06deKtt94C7JkA9u/fz/nz57lw4QL79+/n4Ycfdgbp169fT/fu3Yud4jouLg6NRkO9evUUy4cMGcKSJUtYt24dr776Khs2bOC6667D4hjMUdSnn37K4MGDaejIyAj2zN2//PILTz31FAaDgYCAABISElw+q+48/vjjeHt7ExwcTHx8vOI9PnbsGHFxcXzzzTcsWbKERYsWsWPHDpeU+F5eXvj7+xMX59qZvbbR1nQBapPCgfmMjLI1uqenpyue79+/X5Em/8CBA4r1zZo1K9NxmzVr5vwyFj1GdR5XCCGEEK5sFgsqjQYA4+234/XGG47lVTMXcXXJD/mYsySQWpf0DAhhb6a9c2me1Ype7dovVxsUBEDq2apNJRkdXp9f/93DmbSjAMQlyagqUbud/fVvNg+ZTHCfjs5lFgwl7qM3OH4PRozAqEnBZrOVmtWsNKYiDSazVrQFwNezYPT8mA/e5uDrdWNePiHKQqWyB3HUmVk1XBIhaj+VSoVao8FqsZByKoGQtq1INZuKnZNepVLRKDiYeMfAu0/XN+XjCTvdbitEuXh7s2VDCiMG1nRBxKXg1KkvOX5CeX0bHnYTbdu+gdGYyLZtN7nsc/VA+zRR+w/MID19l2JdmzbziAgfztlzazh8eJZiXVDQlXTutKhC5QwNDWXo0KEsWrQIm83G0KFDCQkJUWxz9OhRsrOzXdJ256eIz/fee++xcOFC4uPjycnJIS8vj06dOin2adu2LRpHGxRAREQE//77b4lljIqKUsS3wsLC0Gg0zo72+cvOFZoS77fffmPOnDkcPHiQ9PR0zGYzubm5ZGdnOwPDvXr1Ytq0abzwwgs8/vjj9O3bt9gylPU9KIsDBw7Qq1cvxX1Wnz59yMzMJCEhgUaNGgH2Ed8V4eHhwV133cXChQu57bbb2LlzJ/v27eP7778H7FmzLRYLLVu2VOxnNBoJDg52d0iF9PR0Tp8+TZ8+fRTL+/Tp4xw8265dO4KCgtiwYQN6vZ7OnTszbNgw3nvvPcA+sn7AgAHFniMnJweDweByL1p4iu727dvToUMHZ1zw6quvVmybkJDAzz//7BJ8P3PmDPfeey/33HMPd9xxBxkZGTz33HPceuut/PrrryXe/06fPp0JEyYQFxfH7Nmzufvuu1m9ejUqlQqr1YrRaGTJkiXO9/bTTz+la9euHDp0iOjogk6Bnp6eZGdnF3ue2kKC9OVQuKfI4cOHycvLKzXlfdHKsWi6+6I9PfIrj9IU3q5wGv7qPq4QQgghXFnPpqKpb78It3kWpFy1WiwkfPMbhnqBhPav2I1BdZLxCXVTPX1BAHFvRird/INcN3L0LLdUcbr7tBz7DZIK+7xgOs2l3VFFiNL8+5i901Xy5j3QqhUAWl1KSbtg8NBjs1iwhYSwmRAOf3qWiRPDK1WOVTt2OB/f1PUU+0/5ceSML+fTDQzv1oOV2//hyJlE0nOy8fOUaSbE5UGttgAakGl7hKgSXn7+ZF5IwZSXh15lD9rsTL/A0ND6brePT04CrgPWAnA+XU+on/ugvhDFMZtdR25mpLsfzSkuPw0a3EFIqDJoqNPaM8cZDBF0777K3W4AtGn9GharMoDn6WGP74TVux5/f2VQWKupXAfz8ePHO+cPzw+iFpY/l/uPP/5Ig0IZGME+PzjAsmXLmDZtGvPmzaNXr174+vry+uuvu6T0LppOPD+4WRJ3+5R0nBMnTjBs2DDuv/9+XnrpJYKCgti0aRMTJkwgLy/PGaS3Wq1s3rwZjUbD0aNHSyxDWd6DqpY/vXNFTJw4kU6dOpGQkEBMTAwDBw6kcePGgP21aDQaduzYoegwAfYMA1VBpVLRr18/1q9fj8FgYMCAAXTo0AGj0ci+ffv466+/mDZtWrH7h4SEkJ2dXWocs2nTpoSEhHD06FGXIH1MTAzBwcHceOONiuXvvfce/v7+vPbaa85ln3/+OZGRkWzdupWePXuWWK6QkBBatmxJ69atiYyM5O+//6ZXr15ERESg1WoVnR9at24NQHx8vCJIn5KSUmwmhNpEgvTl0LdvX9555x0AcnNzWbVqFSNHjixxn2XLlimeFx2BX3Skvb+/f5nKUjhliLtR/dV13KKMRqNiXo7885oBGZNX/cxmwFS+UTkWqw2LqvY3mFvMVsX/lxu1zYbJUs4RWWYJyFWEucj/ouwu1zrKpirohbxncsFcwUOTN6D1Ln7O1Jqo15x/HZsNi6lm33ep16pQoR7Lv6ecpWORIL0ZUHl4AKD39KrSa7bIEPsNUnquPe22yaImy6xCr60bf6nLtV6rqLpwvWbOynVZplKZSqwzu17Xgu2rDzqfnz9vxlTWz00x9VpyZsGVyIJJ25j+RUeOnPFl2tKObJr1NCu320cR7ToZT++Wrcp2rjpErtcqrjbXazqVEfACGxftOqYu1GuVIddrF09N1GshjZuQeSGFcyfjaNmvP/uz7G18i04dZ1T9xmhUyuxMix94mHvef9v5/K/YYK7vnHgRS+xeReq1irhU6sLKqul6rfB5bVnZqLy90OzfgcU05KKcX+q1i6ci950GQz0Mhnpu12k0Bvx82xW7r7d302LX6fXB6PWlj3YujyFDhpCXl4dKpWLw4MEu69u0aYPBYCA+Pp7+/fu7PcbmzZvp3bs3DzzwgHNZbGxslZazrHbs2IHVamXevHnO0fbuUpm//vrrHDx4kA0bNjB48GBiYmIUc5cXVpb3oKxat27Nt99+q8hatnnzZnx9fRWDbd3R6XTFpnYvrH379nTr1o1PPvmEpUuX8u677zrXde7cGYvFwrlz57jyyivLXX4/Pz/q16/P5s2bFe/F5s2b6dGjh/N5//79+eSTTzAYDLz00kuo1Wr69evH66+/jtFodBmJX1h+Bob9+/e7ZGMoLCEhgeTkZCIiIhTLbTYbMTEx3H333S4dOrKzsxVZGABnZ4XSOowUlr9tfoyxT58+mM1mYmNjndnBDx8+DODsIAH270Vubm65MzBciiRIXw7XX389gYGBXLhwAYAnnniCa665hoCAALfbv//++y5z0BcNfGdlKdOyeTgaTEvjWWiUXtFjVOdxi5ozZw6zZ892Wb7eR4eXzG9b/ba7GRlXqguOf3XDod+qNk1v7VKBi0nf0jcR7v3mK3VauV2mdZRVpSOWfjTjT8Xy/d8fQxsYWOr+F7NeszlGnpnzrOz/6VKYy1jqtari7+9PWloaJuAnN/WXxtGzOyk70+36ikrws/emN/kV3PCuUHkR4FtHRlVdpvVaZdXm6zWrpz/gqB/zO8DYTCXWmWfPpmE1W1FrCxotfvutHPWb23qthfPRn8EqzJE5zuc3vH0t4A+k8fGR/0jv2r7s56pj5HqtAmpxvWZQ5wBeWHJL/k5Wh9pcr1WeXK9dTBezXkvytV/HJfp4YokIhrP2eZ9P5mbzLUb8fJVzDPtfexUUCtJ/eTIEdb+ki1beYlWoXquIS6MurCo1Va8VHkmfHyrXxB+9yPW61GsXQ7ambl+naTQa5/TBRUdWg30q5WnTpjF16lSsVit9+/YlLS2NzZs34+fnxz333EOLFi1YsmQJP//8M02aNOGzzz5j27ZtNGnS5GK/HJo3b47JZOKdd97hhhtuYPPmzXz44YeKbXbt2sVzzz3H8uXL6dOnD2+88QYPP/ww/fv3p2lT104SZXkPyuqBBx7grbfe4qGHHmLy5MkcOnSImTNn8uijj7oEj4uKiopi3bp19OnTB4PBQGAJbXUTJ05k8uTJeHt7K6axbtmyJaNHj+buu+9m3rx5dO7cmfPnz7Nu3To6dOjA0KFDS30N06dPZ+bMmTRr1oxOnToRExPD7t27+eKLL5zbDBgwgKlTp6LX651TCQwYMIBp06bRvXv3EjMFhIaG0qVLFzZt2uQM0mdmZjJ79mxuueUWwsPDiY2NZcaMGTRv3tylc8nvv//O8ePHmThxosuxhw4dyptvvsnzzz/vTHf/1FNP0bhxY2fg/J9//uHuu+9m3bp1NGjQgK1bt7Jt2zb69u1LYGAgsbGxPPvsszRr1oxevXoBMGjQILp06cL48eN56623sFqtPPjgg1xzzTWK0fUbN26kadOmZZ7m+1ImQfpy8PHxYcaMGTz55JMAHDt2jH79+vHxxx8r0jdkZ2czb948Zs2a5XIMk8lU4nOttmx/ksLb5eW5NnpW13GLevLJJ3n00Uedz9PT04mMjGRApokgi4ylr27mqzIgoHx9N89rmmNSFT+Ss7awmK0c+u0M0YPC0WhL/uGti3S2HEItJacQcpGqQvuH3EWUlxl7w8igDJP8aJbT5VRH7VhT8FittrKPm12C9M17B+HVOILi1ES9tnGxvRlEq9fQZoj7NJYXi9RrVSveO5ClaWlYrVaGZCivyczA945e0KZTp1zWV0a6TxBLgKMH9qHVWDFb1LQ9paW9pm7MF3w51WtVoS5cr515Oo+iY+k1OmOJdWbQKS2HZu7Dr3lBg++gQcllO6Gbei0hJRl4zvl8aLaJa68+wMG/wtl+LIgLFzzQaZ7BZJnOsmXL+PS64WjUrg2DdZlcr1Vcba7XVr1uDyhqVZaLdh1TF+q1ypDrtYunJuq1HfUb8xcbUe3ew5AME/2iopl/4hAAPudTGFLs934VcBOpB/2r9LqyoipSr1XEpVIXVlZN12t5RhP79tmni821+eCBFXQX7/5U6rWLJz2n5uuH6lY4S7E7L7zwAqGhocyZM4djx44REBBAly5deOqppwCYNGkSu3bt4vbbb0elUnHHHXfwwAMPsHbt2otRfIWOHTvyxhtv8Oqrr/Lkk0/Sr18/5syZw9133w3YM02PGTOGsWPHcsMNNwBw33338eOPP3LXXXfx559/uu2sUNp7UFYNGjRgzZo1TJ8+nY4dOxIUFMSECRN45plnSt133rx5PProo3zyySc0aNCgxGmf77jjDh555BHuuOMOl4GwMTExvPjiizz22GOcOnWKkJAQevbsybBhw8r0GqZMmUJaWhqPPfYY586do02bNnz//fe0aFHQQbt9+/YEBATQsmVLZxr9AQMGYLFYSpyPPt/EiRNZsmSJcyoGjUbD3r17Wbx4MampqdSvX59rr72WF154wWXKgU8//ZTevXvTqpVrpraBAweydOlSXnvtNV577TW8vLzo1asXP/30k3MgcHZ2NocOHXLGKr28vFixYgUzZ84kKyuLiIgIhgwZwjPPPOM8t1qt5ocffuChhx6iX79+eHt7c9111zFv3jzF+b/88kvuvffeMr3Plzq5fy2nGTNmsGnTJn788UfAPud8r169iIqKokmTJmRnZ7N3715ycuwjG/r160dmZiY7d+4EXNPO58/dkS83N9dlmTu5uQVNRe56y1TXcYsyGAxu5wvRAnW7b9ylQaUFdOW78dBoVFhVdacxQaNVo9HVnddTVhqbCp26nDedWpVU+pUg9Vr5Xa51lEoFJtxksLGay1Rf1Ui9pqLG61Kp16pWmK7g+sxqtWAoErDTFGo8OLxxPW2vHFAl51U7Ej+G+wdwKNH+mcrN1daZ+vNyrdcqqzZfr2m9C9XnjkYmg95S4usxeOnRFJnzT1fWz42bei329ClgUsGxAJ3WxrIH/6b5Y9cD4KkfiylnOgDzvl/JUzfd7Ez7eDmR67Xyq831mmewvQwqT8+LXsfU5nqtMuR67eK7mPWazWQfrOMfEooO8NdoaezhRVxuNjkWi9tyDGzTjt/3HwBuYm98wCVRB1ekXquIS6UurCo1Va9prAXntDnG0qu4ePenUq9dPJdC/VDVFi1aVOL6lStXKp6rVCoefvhhHn74YbfbGwwGYmJiiImJUSyfM2dOied86623SizHrFmzXAaUujvO+vXrFc+nTp3K1KlTFcvuuusu5+P//vvP5RirVq0qsSylvQcAqampZVrWv39//vnnn2KPU/T15LvhhhucHQtKk5SURG5uLhMmTHBZp9PpmD17tttM0wBjx45l7NixzudF/w5qtZqZM2cyc+bMYs+vVqtJSUlRLOvUqZMzM2Zpxo4dy5w5c9iyZQu9evXC09OTn3/+uUz7Ll26tMT1o0aNYtSoUcWuHzBggKKc7du35/fffy/1vPXr1+fbb78tdv1///3H7t273U6/UBvVnSuJi0StVvPdd9/x4IMPKtJmnDhxgj/++IOtW7c6A/TDhg1j5cqVijnbi6bGz+/9ki9/39JkZ2cXe4zqPK4QQgghXBUNg5hUrh3jTGmZF6cwFXH5xXHqPO9CPdaT8owu6w1RUc7HP3/8XpWdt0mofb7AQ4mnaR+ZCsCpC2WbdkmIS1GWpiADSp4j/Z+fb8mdnz29PDhzOLXKypCTlwf84bK8Sb1spg+1j3BMzwkB7J1znvt2GWv27Kyy8wtxqTKl22frtpZxej8hRMlCGjYCICWxIM14Y0/7AJ7jOe7vZXq3jAbsafHPpsl3UVSSTW5MhRCXBpPJxJkzZ3jmmWfo2bMnXbp0qekiVYinpydLliwhKekSmI6miiQmJrJkyRKXAdG1lQTpK0Cn0/Huu+/y77//8uijj9KpUycCAwPR6/VERkYyfPhwVq5cyQ8//EBgYKDiC9CoUSPFsUJCQhTPExMTy1SGM2cK5gkKDnadN6e6jiuEEEKI0p1Xu6aCuiRHNOYXqfoHmoiLTKVSoXV85nIsFrfbXHHzbc7HJ/burpLzeurso4f9vbw4ds7e4bOU6eCEuKTtPtnB+djmW7a0pmq1mr1bDldZGdJysoE0AF6+7V/FuqvbnnU+jgwuSO1447xXsFqtVVYGIS5FarX9M67Jde2MJoQoP7Wjk2dGckE7ZrjBHni3FDNiLzqiARAPgL9X6dNmClGUxex6vWLxLD0brBBCVKfNmzcTERHBtm3b+PDDD2u6OJUyYMCAMmcOqA0GDRrEYEcH+rpAmswqoU2bNsybN49du3aRkpKC0WgkPj6e7777jptuugmAlJQUzp4taDjp3r274hjR0dGK5/Hx8WU698mTJ52P3c0JUV3HFUIIIYQbReLvNpXrvFs2s/tAaU1SuTwQdYm3xp6Esbg+GD1GjHQ+XvH6S2VOl1aSQMd0Sek5OfSNtjfwHk6U+RpFLVaBr4VaoybNklZlRdgWexToCtinVCns6rbnnI9PJj8DhaZbSc7MqLIyCHEpUjvTE8uFjBBVwS8kFABDoekvA7T2Dpg2wGh1vZ/JM5uBI4B8E0XlqTT2et189YCaLYgQ4rKXn6r90KFDtG/fvqaLI+owCdJXs23btjkfq9VqlyB9ixYt0GoLZrHZvXt3mY67a9cu5+PWrVu7rK+u4wohhBCidJEtTrgsO/DiAhK++uXiF6ZE0rhdl/lp7bP+mcsYfN+2emWlz+npmIfbZrORkGIPFuo1MppX1F5qlT0gYSs0hURWXnqJ+xgMejI5iWH5cueyynSC8fHwAOwdv0N8lSOG1Wr4/rFNhZYUpPwzFZNFQ4i6wktt7wxTh6aEFqJGaR3XccasLOeyQF3BLNLrks+67HN12/aA/bcpNVuPJHER5WUpdL2SlD/GzGSumcIIIYQQF5ncylSz5YUaZq655hqXNPR6vZ4rrrjC+XzTpk2U5syZMxw9etT5vF+/fi7bVNdxhRBCCFG6wbf/i6VpU8Wys2s388+dT9VQiYojwfm6TOP4++bZim8tHTTuPufjTV8vxWKuXIOYT6F5gbs1sY/w3R1fN+YJE5enrGw1xuuu41zXW5zLklPPl7pfUJumaArdW1UmaPH7f/8C3QBoWi/LZf3QTmcw6PIbuPXO5XmV/D4LcamzqA32B5LuXogq4eHt43ycfMqeaVOvLuiklpTn+l2zZ1EqmMM+JUvvso0QZRUeKvenQgghLi8SpK9GCQkJLF261Pn83nvvdbtdfmp8gN9++41z58653S7fF1984XwcEBBA//79L+pxhRBCCFG63Lvvdrs843DcRS6JuNydNeYUu67DwGvoPmy48/m+Db9X6lwGbcFoqzOp9uBJqK/MTypqr2bD1JivuAKf69s4l2n0rlOaFHX04AHIK/jsX7hQ8YD5oTMFx6kf4P77bNDaewFc024wcDegJjXbNaAvRJ2idXwXC3UQE0JUnKdvwRRFaz98x/m4l38wAHG52S77+Hh4AgW/U2aLBFlFxamKzusjhBBC1HESpK8mFouF//3vf2Rn2y9ge/TowYgRI9xue8cdd2Aw2BsxTSYTr776arHHzczMZP78+c7no0ePVqS1vxjHFUIIIUTFnf7uj5ouQgGVI/2ytIXUSRkWEwCaUvIAX3n7aOfjdYs+qdQ51Wo1Okda8LYN7R1Ec0xyyyFqL6u/wWWZ3rv0IP09D92EqtDw+czMiqeeT8su+A61jMh0u43KUZ//uu8TYDHwD1lGGV0s6jZDqP37qSqUjlsIUTXSkwqyxgTq7KPjg3TuR8mH+PqSH6g3WeS6T5SP1VzoZtTx8ZFYvaiUxESYNcv+vxBCXOLkyqmcMjIyWL58uWK+nKKSk5O57bbb+PHHHwHQ6XR88sknqNXu3+6GDRsyadIk5/O3336bb7/91mU7k8nEuHHjiI+3T9Dj6enJ008/XWw5quu4QgghhKg4leZSuvxytH5UfKpkcQmL9PACwFKGubDbDbja+bgyc2cDWB37q1W5AGw9Glyp4wlRk0wZyuc5OTl069Op1P3q1Q+1P3AEynfscB9cL01uXh4QAUCgV/FZMdKyiwZOupKQkkxCSnKFzitEbWBOtf/O2LSld5wRQpRNeNPmALQvdG0YbrBnq8i2uM8Kk5SRQf50KyeSvKu3gKJOKwjOS5ReVEJiIsyeLUF6IUStIEOlyykrK4uRI0cSFhbGjTfeSM+ePWncuDEajYazZ8+yfv16vv76a1JSUgDQaDR8/vnndOjQocTjzpo1i7Vr13LkyBEsFgu33XYbd955J8OHDycoKIhDhw7xwQcfsHfvXuc+r7/+OhERETVyXCGEEEJUzKUVpBd1ma8j9fyZEtLd5+t/593sW78OgPh9e2ncvmOFz2txjB5OzbbfajQOkZTbovbyCFE+9/T0pN/gXqXu16p9C5IAHJ1WdLqK1f3/nToJBAFwIduz2O3+d3UsH65rplh253tvAZDwzsdEBARW6PxCXMq81alACJpKdi4TQhSo3yKaM8eOkn6+YCS9xhE5zS2UIaawAa3bsv6ABdCQkSNNzaJ8rJaCz5VzgJvE6IUQQlwmpJW4gs6ePcsnn3zChAkTGDRoEFdddRWjRo3iww8/dAboAwMD+eqrr7jttttKPV5gYCCrV68mMjISAKvVyueff86tt97KwIEDuf/++xWB9Mcff5wHH3ywxo4rhBBCiELcNSI45iLOw4tnSMPU42YAck6evYgFK5kMVKjbjFZ75idfbelpgA2eXs7H3772YqXOGxViH0Fcz+8MAHvjAyp1PCFqku1kxUbg9L3mCvahRr9hAwCpSXkc/3EP313/BnmZuWU+TnZeHmD/fvZtmVTsdu/es8vN0kYAPPXVF2U+nxC1iVHjD4Aqt+zfKSFEybLSUwHw8PFxLvPRFATe3WVcsmdtSQAgLsnLZb0QZaaWG1MhRIFDhw4RHh5ORkZG6Rtfgg4ePEjPnj3x8PCgU6dOxS671K1fvx6VSkVqaioAixYtIiAgoEbLVNN69uzpNmt5RUiQvpy8vb255ppr0Ovdz8MEEBAQwH333cfBgwe55ZZbynzsli1bsnfvXiZMmICnp/tREm3atOGHH37glVdeqfHjCiGEEEJJZdTi7e/4rXXMy21FjRE/dvxjH4559O0vyT2XUlNFVFIp/hN1TIjOPlfv+byyzUvdc8RI5+OstNQKn/dsepr9GI7zGnQVn4tbiJqWk6usIX/77bcy7adWq/kGKzimSTt11sz3t7zLyXX7WTPqgzKff/+pBKAvAJ4G92mGwZ4e9rYrThZZ6gvAkk0bynw+IWoTbZA9BbeqUDBRCFE5AWHhgDIYr1cXTCmRa3W9ruvZvCVgD6Bo1JLZQlScWiajF7XM2LFjUalUqFQqdDodTZo0YcaMGeRexA6EY8eOZfjw4RftfJU1YMAAHnnkkTJt++STT/LQQw/h6+tbvYWqJjNnzsTb25tDhw6xbt26YpdVVFRUFG+99VYVlFQU9c0339CqVSs8PDxo3749a9asUax/5plneOKJJ7AWk2WoPCQHUTn5+vryyy+/kJuby65du4iPj+fs2bNkZ2cTFhZGVFQUffv2RacrfcSSOwEBASxYsIA333yT33//nZMnT5KVlUVERATt27enc+fOl9RxhRBCCFFAZdZw3fg+LH/zN9BosAEe2Ochbsbvzu3WRFzLlb9/RGj/rjVUUiWbhOnrJAv2RlJNGRu7eo0Yyd/ffQPAvvXruOKmsnc2LaxxSCgHT58iwDMVgEOJfhU6jhCXAs+sFPJHpANkZpZ9bvlsQBMX53xuadUKm7c3cet2KraLj/dm0aKWdOlynmF9TivWadRq8gMfKZnFdxQHmHvnHhJTPdh4yJ7N4tYeV7L8n/8AyMjJwbeYDttC1FY2syNYqJbxJ0JUFZ1j/vmU06ecy/SFvmPn8ow09lQ2J1/IygL+A9qxL8H/YhRT1CGF0907hxNKsF7UIkOGDCEmJgaTycSOHTu45557UKlUvPrqqzVdtFotPj6e1atX884779R0USosNjaWoUOH0rhx4xKXlVdeXl6Jg4hF5fz111/ccccdzJkzh2HDhrF06VKGDx/Ozp07adeuHQDXXXcdEydOZO3atQwdOrRS55M7mQry8PCgV69e3H777UyZMoUnnniCcePGcdVVV1U4QF+Yr68vN910E5MnT+bxxx/n7rvvrpJAenUdVwghhLi8FTQiqAs3FGsKRp3s42bFHhsHTqr2UpWVtIHUTR6OUU+6Mv6BVSoVPkHBAMTu2lHh80YG27NGnM+wp7sP98+p8LGEqEkp/+zDy5quWDbgxp7lOsbr5wpGRxhvv528YcMw9SqY0z4vT82tt17Dyy934uef41i2+rxi/+PnzgLTALi67bkSz9UgKJf1z2ygnp995M53298DXgLg7g9rb+OWEMWxGe3ZJdSSHlmIKmMxmQAwO/4vyuhmxFiPZs2BJgD4ehSf9UWI0qhUEqoQtY/BYCA8PJzIyEiGDx/OoEGD+PXXX53rrVYrc+bMoUmTJnh6etKxY0eWL1/uXH/hwgVGjx5NaGgonp6etGjRgpiYGOf6kydPcttttxEQEEBQUBA33XQTJ06cAGDWrFksXryYVatWOUf0r1+/3m05SyqH1WqlYcOGfPCBMuPXrl27UKvVxDk6HqempjJx4kRCQ0Px8/Nj4MCB7Nmzx7n9rFmz6NSpE5999hlRUVH4+/szatQoZ7r6sWPHsmHDBt5++21nefNfS1Fff/01HTt2pEGDBs5l+WnWV69eTXR0NF5eXtx6661kZ2ezePFioqKiCAwMZMqUKVgsBZlfPvvsM7p164avry/h4eHceeednDtXcG/1/PPPU79+fZKTk53Lhg4dylVXXVXiSOkFCxbQunVrPDw8aNWqFe+//75znUqlYseOHTz//POoVCpmzZrldhnAv//+y8CBA/H09CQ4OJj77rtP0Tk8P1vCSy+9RP369YmOjmbAgAHExcUxdepU53vpzvjx4xk2bJhimclkol69enz66adA6Z/Rsvjggw9o1qwZer2e6OhoPvvsM+e6adOmKcrw1ltvoVKp+Omnn5zLmjdvzoIFC8p1zury9ttvM2TIEKZPn07r1q154YUX6NKlC++++65zG41Gw/XXX8+yZcsqfT755RNCCCGEqCoq0BoKjSwp1HHvJ1zn+baaargRK/8aXrJS1kn5o54sbuYOLU6zzvbsDmdij1T4vMfO2oPzQd72m+I8i9xyiNrp2PvfuIzQ1RnKl4wuydbWZZnpmmucj3v2vIk9e4KpV+889esncjwhR5FieO/JOMA+dcXWo8FlOue5dPsoSItVDTwFtOP7ndvKVW4hagOVIzivkpH0QlQZn8AgALRFRuiF6e2/LVkW1/uXLGMusAkAs0U6zYiKU2kc9bl8jEQttW/fPv766y/FKOc5c+awZMkSPvzwQ/777z+mTp3KmDFj2LDBPiXVs88+y/79+1m7di0HDhzggw8+ICTE3vHdZDIxePBgfH192bhxI5s3b8bHx4chQ4aQl5fHtGnTuO222xgyZAiJiYkkJibSu3dvt2UrqRxqtZo77riDpUuXKvb54osv6NOnj3PU98iRIzl37hxr165lx44ddOnShauvvpqUlIIpHWNjY1m5ciWrV69m9erVbNiwwTnF8ttvv02vXr249957neWNjIx0W96NGzfSrVs3l+XZ2dnMnz+fZcuW8dNPP7F+/XpGjBjBmjVrWLNmDZ999hkfffSRIshsMpl44YUX2LNnDytXruTEiROMHTvWuf7pp58mKiqKiRMnAvDee+/x119/sXjxYuVAnCLvzXPPPcdLL73EgQMHePnll3n22WdZvHgxAImJibRt25bHHnuMxMREpk2b5nZZVlYWgwcPJjAwkG3btvHNN9/w22+/MXnyZMX51q1bx6FDh/j1119ZvXo1K1asoGHDhjz//PPO99KdiRMn8tNPPynWr169muzsbG6//Xag9M9oab777jsefvhhHnvsMfbt28ekSZMYN24cf/zxBwD9+/dn06ZNzo4TGzZsICQkxNmh5NSpU8TGxjJgwIBiz+Hj41Piv//973+Kv01p22/cuLHYc23ZsoVBgwYplg0ePJgtW7YolvXo0aPE45SVpLsXQgghhKgqKuVoLpuPD8dyrwDAio36MbM5PW6mc33GwRP4t29+0YvpQhpB6iSN4w9rLkeQvnH7TuxZ9wsANqu1QoGPXi2iiT13loQL9pvAlExDuY8hxMV08suf+Pfx+fRc/hpBPdo5l8d/9iMUCqinpKTgHVneW+iTJPzrQ8P27tPkX7iQS9++GwkISHMus2BD6/j+rt1d8B1sEZ5RznPnewqt5q4K7ivEpSt/wKVKRtILUWU8fe3TFJ06dECx3GSzjyQ021xHFLYIrw/k2beTzpminKzmgs9UwUhQqddF7bF69Wp8fHwwm80YjUbUarVzxK3RaOTll1/mt99+o5cjm1bTpk3ZtGkTH330Ef379yc+Pp7OnTs7A9JRUVHOY3/11VdYrVYWLFjg/H7ExMQQEBDA+vXrufbaa/H09MRoNBIeHl5sGctSjtGjRzNv3jzi4+Np1KgRVquVZcuW8cwzzwCwadMm/vnnH86dO4fBYL/Hnzt3LitXrmT58uXcd999gH1U9qJFi5zzyN91112sW7eOl156CX9/f/R6PV5eXiWWFyAuLs5tkN5kMjlHbQPceuutfPbZZ5w9exYfHx/atGnDVVddxR9//OEMQo8fP965f9OmTZk/fz7du3cnMzMTHx8fNBoNn3/+OZ06deKJJ55g/vz5LFiwgEaNGrmcP9/MmTOZN28eN99sz5rZpEkT9u/fz0cffcQ999xDeHg4Wq0WHx8f52v18fFxWfbJJ5+Qm5vLkiVL8Pb2BuDdd9/lhhtu4NVXXyUsLAwAb29vFixYoOgAotFonNkBitO7d2/nyPYZM2YA9s/QyJEj8fHxKdNnozRz585l7NixPPDAAwA8+uij/P3338ydO5errrqKK6+8koyMDHbt2kXXrl35888/mT59OitXrgRg/fr1NGjQgObNi28f3b17d4ll8PMrmGbxxhtv5Iorrihx+8IZGoo6c+aM833PFxYWxpkzZxTL6tevz8mTJ7FarcV25igLCdILIYQQQlSSOjzI8UiZYupCj2EsWvOW45meD5euJca8je+03QHYOnIGfX56F++o+he1vK6kEaQu0jo+i2fycsu8T1SHTs7HaefPERBW8o2zOxm59vT2KgrSpNpsMq2CuHRtG2NvePr75mlcn/CTYp3NcbO9f7+VVas+Ym962UYTFPDmdHoLGrLL7doJEz5Fo1EGPMxWG1qNvYHLxr3O5W+M3lN0d7d+eGwT933alcTU/Dno78Bsmcwz3yzlxZF3lrP8Qly6ZCS9ENXA0bkzoJ6ycdpfqyPFlEeqmzT4GrUaHNd9fx8NclkvRFk5q3O5bxC1yFVXXcUHH3xAVlYWb775JlqtlltuuQWAo0ePkp2dzTWFOv6CfU7x/CmI77//fm655RZ27tzJtddey/Dhw52j4ffs2cPRo0edAe98ubm5xMbGlrmMZSlHp06daN26NUuXLuWJJ55gw4YNnDt3jpEjRzrLkpmZSXCwMrtXTk6OoixRUVGK8kZERChSy5dVTk4OHh4eLsu9vLycAXqwB0+joqLw8fFRLCt8zh07djBr1iz27NnDhQsXnCns4+PjadOmDWAPTM+dO5dJkyZx++23c+edxd83ZWVlERsby4QJE7j33oL7NbPZjL+/f7le54EDB+jYsaMzQA/Qp08frFYrhw4dcgaL27dvX+F56CdOnMjHH3/MjBkzOHv2LGvXruX3338HyvbZKMtryO+kUfg1vP322wAEBATQsWNH1q9fj16vR6/Xc9999zFz5kwyMzPZsGFDqZ0BSgrgF+Xr6+vynakOnp6eWK1WjEYjnp6epe9QDAnSCyGEEEJUkvV8KurQAFDZG7UMPv4YM9M4qL+JXPIv0LPY+OvfpKakUW/QFZz7bSuZR+LZetvjXLV1SY2VHZB093WU2hEV99WU/ZJfW2iKhjPHjlYoSN/AkSZVp8l2LsszqzHoip/LTYiacmbtZsytW5N3zTVY3M275/gepVzQ8tbSl9Bqy3sLbeSvbf3o0cd9kL5ogB7A5qiUNxzcD1wFQOOQJLw9LC7bunN9pzMkvPMjH/zWlMmLuziWTmPO90/x6HU3EORT/Q0WQlwMelU2VoJApanpoghRZ3gHBAKuc9Lnx0x1bjrF2IP09uu/IJ+86iyeqIOslkI3o2qV3JuKWsfb29sZQFy4cCEdO3bk008/ZcKECc55xX/88UeXkbv5o9Gvu+464uLiWLNmDb/++itXX301Dz74IHPnziUzM5OuXbvyxRdfuJw3NDS0zGUsSzkARo8e7QzSL126lCFDhjiD8pmZmURERLid8z4gIMD5WFeoTQHsGTJKmte9OCEhIVy4cMFlubvjl3TO/HTygwcP5osvviA0NJT4+HgGDx5MXp7yN+vPP/9Eo9Fw4sQJzGZzsfd++e/nJ5984jJiW6OpnuvSwkH88rr77rt54okn2LJlC3/99RdNmjThyiuvBMr+2aisAQMGsH79egwGA/379ycoKIjWrVuzadMmNmzYwGOPPVbi/oU7YbgzZswYPvzwQ8Ce7n7SpEklbr927Vrne1BUeHg4Z8+eVSw7e/asS8aClJQUvL29KxWgBwnSCyGEEEJUnuPiX4W90co3LAJjZhoNGyYU2sgbGEm3eoPYuWcZm655AOO5FFJ3HGDHhOfp/NGzF73YzgEKKmkJqYu8HcH5DDdzh5bE4OWFMTubtPPl7+0OEOFo3N11oiBNqlGC9OISZLXCX8MexjhrFgDG64dhs9nYuGInJqPZ3kbsCNLXiwjh2uE9ynX85+ZN5vnH9pKaOthl3ck/DhB5VWv35XL8b+9oEwHAyCtOl+vcAJMGHuOxpR0xmjTAk8BTvP/bzzwz/NZyH0uIS5FZ7YkaUNtcR/YKISpGo3NcPyYnKZaH6j04lpPFGWOO6z5qNbAPgLV7Iqq9jKLuUqmBsvVJFOKSpFareeqpp3j00Ue58847adOmDQaDgfj4+BJHCoeGhnLPPfdwzz33cOWVVzJ9+nTmzp1Lly5d+Oqrr6hXr54inXdher3eOdd3ccpajjvvvJNnnnmGHTt2sHz5cmfQE6BLly6cOXMGrVarSMlfXmUpL0Dnzp3Zv39/hc+T7+DBgyQnJ/PKK68QGRkJwPbt2122++qrr1ixYgXr16/ntttu44UXXmD27NlujxkWFkb9+vU5duwYo0ePrlT5WrduzaJFi8jKynIG4jdv3oxarSY6OrrEfcv6XgYHBzN8+HBiYmLYsmUL48aNc64r62ejtNewefNm7rnnHueyzZs3O7MUgH1e+oULF6LVahkyZAhgD9x/+eWXHD58uMT56OHiprvv1asX69at45FHHnEu+/XXX53TAeTbt29fmbMNlESC9EIIIYQQlZWfxzs/2G21XyRrNBYMhlyiow9x7FhTMjO/BlQcvpDBwO2fs7bR9QDEL/4Bj/AQ6HXTRS22hy0DqCd5yOsovWOkU3n/uiENG3Hq8EFSTp+q0HlTs7MAaBTiz7bj9mXZRg1+nuXrLCBEdfvs/VAKjym3BQaw8qWVZNrsIzE8GjVy5l01eBjLffyJD9/G8499B7gG6Tc8uQJdUChceb3LOqsj1bBW4we0BKBJaPm/P2o1LPnfP9z+Tn5jwnxmfjuFx4cNR1fujABCXHpU/vaGTLVH1Y3yEeJyp/f0AkBdZCRglqPTp0HtOkJQq9YAYS7LhSgvtUZjD9KrVBjPX8AQGljTRRKi3EaOHMn06dN57733mDZtGtOmTWPq1KlYrVb69u1LWloamzdvxs/Pj3vuuYfnnnuOrl270rZtW4xGI6tXr6Z1a3tn3tGjR/P6669z00038fzzz9OwYUPi4uJYsWIFM2bMoGHDhkRFRfHzzz9z6NAhgoOD8ff3dxlZ7uvrW2o5wJ6qvnfv3kyYMAGLxcKNN97oPMagQYPo1asXw4cP57XXXqNly5acPn2aH3/8kREjRridP96dqKgotm7dyokTJ/Dx8SEoKMjtfN6DBw9m4sSJWCyWSo1Ob9SoEXq9nnfeeYf//e9/7Nu3jxdeeEGxTUJCAvfffz+vvvoqffv2JSYmhmHDhnHdddfRs2dPt8edPXs2U6ZMwd/fnyFDhmA0Gtm+fTsXLlzg0UcfLXP5Ro8ezcyZM7nnnnuYNWsW58+f56GHHuKuu+5ymRe9qKioKP78809GjRqFwWAgJCSk2G0nTpzIsGHDsFgsimB6WT8bJZk+fTq33XYbnTt3ZtCgQfzwww+sWLGC3377zblNv379yMjIYPXq1bzyyiuAPUh/6623EhERQcuWLUs8x8VMd//www/Tv39/5s2bx9ChQ1m2bBnbt2/n448/Vmy3ceNGrr322gqfJ59M3CWEEEIIUWnKkejefvbG4sDAVJ588lVuvnklt9yywrG2P6MGTGbEkMm0XP22c5/Dr8ZgcaSZuliManu6KE2e64gYUfvpHJ0vbBQE/coiP81pXk52KVu617qBvXf6Hwf2OpedSXOdS06ImmY5cUi5wNvLGaAHMA4bhtWRRtJmq9it87hx9nHxmzcre92fvOkejrfq7nzu8eGH4BgFkf9t/Se2oHd/w6CK1dPDuxYegf8QAEfPnnEuWfTnHwx6eZbz34sr3aT8F+JSpbM32KqqKa2oEJcjvWP+X6vFgq1QeuIwffHXciaLGdgIgKdeOmWK8imc7l5VaC7njIMnaqA0QlSeVqtl8uTJvPbaa2RlZfHCCy/w7LPPMmfOHFq3bs2QIUP48ccfadKkCWAfDf3kk0/SoUMH+vXrh0ajYdmyZYB9/vU///yTRo0acfPNN9O6dWsmTJhAbm6uc+TwvffeS3R0NN26dSM0NJTNmze7LVdp5cg3evRo9uzZw4gRIxRpvFUqFWvWrKFfv36MGzeOli1bMmrUKOLi4koNJhc2bdo0NBoNbdq0caaed+e6665Dq9UqAr0VERoayqJFi/jmm29o06YNr7zyCnPnznWut9lsjB07lh49ejB58mTA3kHg/vvvZ8yYMc508EVNnDiRBQsWEBMTQ/v27enfvz+LFi1yeT9L4+Xlxc8//0xKSgrdu3fn1ltv5eqrr+bdd98tdd/nn3+eEydO0KxZs1KnPxg0aBAREREMHjyY+vXrK9aV9bNRnOHDh/P2228zd+5c2rZty0cffURMTIxidHxgYCDt27cnNDSUVq1aAfbAvdVqrfAI/urSu3dvli5dyscff0zHjh1Zvnw5K1eupF27ds5tTp06xV9//aXISlBRKputHC12QpQiPT0df39/npr+NB4GaYytLqvM0WTbZPSLEELUGTZHA5hKBTabI8/fRTkx5R9nLeo8mw3nZ0OyLIi6LP+zftHq3BLYrJdGOYQQQgjnvYn8LglRVl4qMzdpD5W+4WUs15jLy6+/RFpamkvq9tzcXI4fP06TJk3w8KhkTGHnTujaFXbsgC5dKncsUaPee+89vv/+e37++eeaLkqtl5mZSYMGDYiJieHmm2+u6eLUeo8//jgXLlxwGV1fWFnrNYnyiWph1ViwaKT3bHXJNmnJRl/TxRBCCFFVVMU8FqImyGdQXC4upc/6pVQWIYQQlzf5TRKi/GxIW3gprJrS584WorBJkyaRmppKRkZGpdKXX86sVitJSUnMmzePgIAAxRQGouLq1atXrmkNSiJBelEt1BYNGot8vKqLl8pcNLOyEEKI2sxmdbPwYmz7tP4AAQAASURBVIxilpH0wh2bY4SxjKQXddylNHr9UiqLEEKIy5uMpBei3LxUZmkLL4XaIp0YRPlotVqefvrpmi5GrRYfH0+TJk1o2LAhixYtQquVeqoqPPbYY1V2LPmLiGrxSIuWBHt61XQx6iz7T5MF8zUZEOgusFO8s5qWmFS1/29jMVnZ/9Np2gypj0Z3+d046mzZhFkOl2+nC2q0v0qvw/IyAT/56hiSYUJX6taisMupjlr80Geo6wWiM3kx6vmBHD+Qzablv7tsN3v2s27nNe5Na0Zw0GV5mxcfoNWT46ulzACf3PEJHm0iyDiSyQNLRlXbecpC6rXq8ULsfwBMbtSCQJ09C09p9dqx3TtYOe8Vwpo0ZfTzr5b7nEfPJhI9bQoAvVuc568jISx/+C9GdDtdyp6XvsupXqsKl8r1WmaGmo4BnQFYu/c/GjU10sWnBTO7X03e0KEu22t37MDctatimcpgZcyM8o06yK/X3n77F2bM+Jrw8JmcOfMc/v6JTJ26wLndup/2cOPf35E1a5Zzme+5Lqz+Yy1/HHgfvTaWnJhd5Tp3Uf1fGMCmwyH4e+4lLaejc3mzemHsnfMGHno93Z6Zwa644wB0b9qMbcdiAYh94z2iQutV6vzVTa7XKq4212uLpy5FHeRHg2BfBj5wceayvFTqtZoi12sXT03Wa4uffJTkhJPcNHUGzbp0B+BodgZfJsYTrvfg3shmiu3jks7TdOpcIBYfDxNpn6y6yCVWqki9VhGXSl1YWTVdr506kszvy7YAENCwJakJ9jqmh28G0Y/cUe3nr7p6TQW0qqpi1UnpOdm8XNOFEOIyExUVhcx4fmm7/O4ohBBCCCGqWNHBxiqN+9HH/v7pAGh1VgaPiHcuP8J3brff/8z7VVPAYsgg6brPS60BwOQ2W4N7Wkcw/+zxYxU7p97gfKxS2W8GTybX/gZMUXtZLQWVnSlPxfWd2tCL97G0aOF2e90//7gs0/pXvLH/4YevJSfnY55+OhbwIS2tiWJ9asYp+zl27nQuu+D/H38csHeysVgrP+poxg32jmBpOR2If/tLUj9eQurHSzjw+tt46O3f+e+mznBunx+gBzBZJDWpEEJcTnKzMgHITk93LtM4sm+dyct12V6rVgP23yqzRZqaRfnYLAXXWGENC+bs3ftXYk0URwghhLio5MpJCCGEEKKKqBxR7+KyR9099juatzzPmt37eX/5eSY/Yx9ZfL5Qj/uztFbss2PCbNL/i6V6OHrTSrS+zsr/TOaWJ8jm2MfT169C5wwpNFfc6Qv2gL1eW/2jmYQojrXQx89mA+PRIwzlCaxBQS7besTEOKZ7KJCXl4dNVbnPsFqt5p57+gA52MdHFjhzJh61ToP+11/R/forAGYb5Ce+M2g1lTo3gIeuoPxTP++Lr6cnvp6eaNQFxzZo3Y/VNEuQXlyi8q9eVGq5jhGiKvkE2n8fs1JTnct0ansTsqfa9TdJq9EA9t+KXFPlf7PE5atRs4L7CLNKcuMIIYSo+yRIL4QQQghRxTQa95dYQQHxjLnzff5e8R1L56whzONTZs56kaeffonUp1/g0MBneY9NrORt5z5xi37g8LzPq6egjkCUTYL0dVZWBeb983QE2XMy0kvZ0j19oUBf6wZnADh4WtLcippjKTSS3mZTcZfHOGxqNbaQEJdtNXFx7Bu+UbFszpw5jLp3RKXLYTDomDx5EABZWVnO5f2vasRtm55GlZODdv9+ANRqK7AOAL228nV0nxZJ+HjYOwd8u62houNCvnr+/swYdpPLchlJLy5ZjusXtQTphahSOkdWJJMxx7nMW2PvOJZjdf1N0Ko1QLbzebZRAvWi4ozJeQBkGmWWXlFBEREwc6b9fyGEuMRJkF4IIYQQoso4Gos1Jc/3ZLVYsZjt/1RY0OnM6HQWPNuGkkMQf/EgK/0WU3/4Vfbtc43VVFxHKnRT5VMpi0tTqKOR1VKOOcj0hoI0kzZ3kbxySM6wHyvAy1TKlkJUn8LxBJsVAowHi015or7/DZ54s6CDyqxZs7DZbM6sFJXVvbs91f2qVQXz9U6dei31OjVi6NcPODtP5U8VAZCarZz7tyI89FZ2vPib8/nBRGXHmX9P+vFATGe06jk0D1Om4z+YeKrS5xdCCFF75HfYPBN71LlMpypoQi56Xenn6QmkOZ/n5EmQXpRd0b6AhmD7NDwyg7KosIgImDVLgvRCiFpBgvRCCCGEEFUkv+1Kpy/5EuvqO3tw85Srnf/a9benuw8OTqF16zuxoWZz+kAW/vwXAAlf/cIKTTd2THy+asvrSN+s1sh84XVV/vyhlnI0c3kHBDof52ZnlbBl8RoGBQMQGRwPwOEzPhU6jhBVofBI+scnRnHG1kYxzceBQ/a53602C8Pf7YeHh4rf//qZ55+317l6g77KyjJq1BV07RrF4cOHee2113j++efRaOxliejV3JmbX6cr6DylVVfNSPbmYQXf59W76pOWreW3ffVY9189rn65Px/93oyXv29NWvZDFCQSh6///qtKzi9EdZF090JULb2n/d7Ap9C0MJ6FMoWZinTi1Gm1gJX8eekzcmUEtKgYtUZF5vHMmi6GEEIIcdFIkF4IIYQQorKKtA17lBDz1mjVhDUOxtvf0/mvfuOCoOjtt0czcOAfgJoTOcoR9HEx31dhoUFvswds9HpJZVxXqR2ByAxz2bMlaPUFAcm87OwStixeQkoyAFmOdKcyo4KoSVYL3MFo7mEEh/d54E8COf/7n3P9qu9+YNWqVbwx703nsj9/2YLVEYT46s8FVVqejz66B4Ds7GysViteXvaMF1ovPSpjQb3v4WFPMxwdUXWN1Y1D7PX+vDUt6ffCVQx+tR/XvtKP5EyDc5vzGVOBp5zP/9i/r8rOL0SVcvy2qFXStCVEVaoXZc+oci7uhHOZutANj7vOnwadDrAH51Oyqq5zm6j7rEWH0jtuHIwG6eQrhBCi7pM7GSGEEEKIquJou9IUyfAY3T2KYZP6cccT13HbtMFotMoNgiL8Fc8DAlIJDA7iowPfsrhx9aVos6jsDWgqm6S7r6vSzPY5HTXljJJ7+dk/k1lpqRU6b/9WbQAwW87az6+WhJWi5pgyc+jCUtqxkgDiMbQLxxZYKGNEbi67du3CanPfYalDtzZVWp727SMVzxs1so9U1Pt4KIL0Ot0YmoQe4YPxO6vs3Lf2SAAgKcPAvgT/ErZ80fkoNTsLWzmmzBBCCFG7WRxTYZkL/SapVCpnmL7oSHoAo8kE2H9jzBbpnSnKzmYtuMbQGjRovHT2JyaZLksIIUTdJ0F6IYQQQohKszdE5cdBi85d3LF/SwLr+aHVadDqXOdo1Om1jJx6LYGOoFHbtv9xIdmAyRRNnq8Xix3bGUIDXfatDKtjTnr0MtqlrmpgsKd1OJ9nLGVLpex0+7yi5ry8Cp33RNJ5AIJ87COB/z4SXKHjCFEVLKaCYIKWPCwjhjmf79tXMErcw8vDZd/FP71bLWWKiZngfGwwFKQFDuvcBByZL+Lm38rRN/bQp2VylZ130sBjaDWuwZWkD1fx3Ij9zueRwdnsnfOG8/mxc2errAxCVDVJdy9E1fILDQUg7fw5xfL8UGqmpbgOvvbrPpNFmptF2dksyo6AeYlpNVQSIYQQ4uKTSYKEEEIIIapaobZivYcOg2fpQXCdQUt6ejoAGo2V+vUTmTO9IWq1miTncau2EVrlGBmplsbtOivNbB+B4ql27RxSknpRTTh34jipZ8/QqG37cp+3R7PmxCWdJynd/hmLCq3Y3PZCVIXlCwNppteDWo0uN0eR7uTEiRPOxynnL9BM002xb6sOLaqlTHfe2YuOHRvRqFEwanVBMEOt04Cjbq6O0evNwrI4+faPJKYWdEhoFJJNoLeJ50bs5/QFDxasb8qV0edp27BgxP/T3yzl9TvuJjI4pMrLJESFOa6LVOX7iRNClMI3yH3nyiCdnhRTnttplNpHNuLfk/brzqQM6QAsys5aKEiv1hRcE8kdqqiN8vLy0MsgCCFEOUiQXlSLw7YzBNg8nc998aChKhCjzcwxzrts31plT+V7wpZEDsp0RvUJwF/lSYoti7OkK9Z5oaexKhiLzcphXEd3NKceOpWGk7YUMlGOIKuHL8EqH9JtOZwiVbHOgJamKnvP4QO2RJfjNiEED5WO07ZU0hw9hfMF4U2Yyo8sm5F4UhTrtKhpoQoD4IjtLGaUo1gaEYS3ysBZWzopKBuz/fGkviqAXJuJ445wjTktC2w2VECroHoAHE9LIbdIr+YGPn746T1IzsniqDEes6qgUc5L70WEbz3MVjNxFxJcXmtUYCM0ajWn08+QY8pVrAvxDsLfw48MYybnMpMU6wxaAw397X/X2OQTLseNDKiPXqPnbMZ5MvOUrzXQM4AgrwCy83JIzFD+XXUaHY0CGgBwxnIGQ4oJjbbg0r2+XzieOg+SslJIy1V+Xvw8fAn1DsZoNpKQpvy7qlQqmgY1BiA+9RQmi/JzGOYbio/emws5qaRkpyrWeeu9CPeth8liJj7V9T1sEtQItUrNqfQz5BZ5D0O9g/Hz8CU9N4PzWcpRUh46Dxr4hWO1WTmeEq9Yp7XlEuRrQafRkJCZRkaREZKhnt6EeHqTkZdLQqbjfUhXobVlokdLM8fn+6DtDLYi88lFEYKnSkeiLY1UlHMR53++s215xKEsrwY1LR2f76O2c5hQpoyNJAgflYHztgySUM6teqnXEafN5zloMzt/NGtLHVFY/nt4zHYeI0XqCALwU3mSbMvkHBmKdT4YiFQFYbJZOIpyJAVAS8LQqNTE2ZLJRjniNiRPTSAepBlzOZ2l/Nt4arVE+dnT+x5IKThuitoDs8qjSuqIExdOYrEqP4fVVUfkM1pNxCafwGIqOG+e0b5taXXE6fQzWArNx6fXG0lP90atVqHyUkO2lazMTD4eN43ud1xLvZ4diq0jABoFNESn0XIm4xxZeUW+y14BBHoGoDLYUwnmqc3Ourrwe3gsJc4lSNTQPwKD1sD5rGTSc5WfF38PP0K8g8gx5XI6/YxinUatISowstj3sJGPL2jgfE4WSTnKv7mf3kADH3+MFjPH0gp9bxz1Wk3XEZfydYSnwQZ5kGqznyfXZuIISZw2axX1WtE6IjPHXk8fSzpOlC2n3HVEWoARj8YeaLSxAKSbrS7vRRh+BKm8SbPlcLrIe+iJjihVSLHvYVNCMai0JNgukEGR6xN8CFX5kmkzcrLIe6hDQ3OV/ZrpsO0sliLvYWOC8VLp3dazAXgRipocs4kT6RcU69QqiA60H/dYWjLGQt/lFLUHwX6Rl9R1BJStjsjMy+JshvJ3uTx1xIXMdE6bzzuv1ypTR0T4huGl9yQlO5ULOcr30EfvTZhvKHmWPE6mnnZ5rZ+/dyUzHn4Am8HA1De6kXP+PmyOUYI4MqpqfDXognSK/ay5VnR6HRarlRMXXN/DxoEN0aq1JGacI7vIexjsFUioh470vFxOZSrrHg+Nlib+QbRt24CDKeewpRSsz8XqDNIfMCYTUOSOvSrqCJvfefR+BZ/vM0COzX4dEd3Q/tlONudxwJaIXzMf8kxmvtm6hW+2bmHprEfp0DRKcdzquo4oTx1hBkW9VlN1RITKnxybiRNFrsVUqGilCgcg1naePJdrsUD8VB4k2TI5X+Q9zL9OLu49jCYctUrFCVsyOUXew3D8CVR5ccGWzRmUIxM90ROlCsZqs3EoxfW4zf2Di73XwCcMXy+vStcRVXUdkZJzgey8nArXEc2CowBISEvEaC7ye+8Tgq/Bh7TcdJKyUrCYbc56zcfTk/p+4RWuIwI8/ck0ZnE2U/ke6rV6Iv3rl/Ae1seg1XMuM4kMo/K+KsDTn2CvQLJNOSSmK69tCr+H7q6TI/zC8NJ5kpx9gdQc5efF1+BDPZ8QjOY8DlxQfl5KbY8wBxCEb43WEUXVhjqicL3W+CLXETkBBd8vk8nEUa29vPnTKCXYLtDc5o1OpXG+h9qGOjhpnx4mzWb/HqXbcjmF8prpYrRHxGYkkVdkOq9IX398dIby3Ws4tHZ8vk+kp5BTqINCitqDIN+GijqiME+dR62oI/LrNe9UK01DG9lfayXqiIQ0ZT1b+F76ZNpp8szKz2GWseDzdjI9AZvK/nqsKhuJGeeqvc3yaHI8KVbld6epfxAGjZZTmWmkF/kNDPH0JpSaryMKq/HrCJS/2VBwL124ns205bhsV9sNGDCAdu3aodVq+fzzz2nfvj3vvPMO06dPZ+PGjXh7e3Pttdfy5ptvEhIS4tynQ4cOeHh4sGDBAvR6Pf/73/+YNWuW87jx8fE89NBDrFu3DrVazZAhQ3jnnXcIC7PXc2PHjiU1NZWVK1c693nkkUfYvXs369evL/N5UlNTefzxx1m5ciVpaWk0b96cV155hWHD7JnHNm3axJNPPsn27dsJCQlhxIgRzJkzB29v72p9X4W4nEiQXlSLe62L0FgLurNfr2rPHNXNnCWdUdaPXbbfo5kJwLPWVexFedH1kmoEw1Qd+MX2H3NsaxXretGMDzVjyMHk9rh/qKcRhDevW39mA4cV6x5TXcvdql78zTGmW5cr1rUinK80kwC4y/qpS7DxW/X9NKceH9v+5DvbLsW68ao+PKwaxH4SmWhdrFhXD19+1TwKwAPWL1xuUBeo76E7USyz/cNC22bFuhGqzsxS3UgCFwpe6xb7fzq1hr9vfwiAp7es5dAF5QX8K32u55pGLVkbd4g3d/2pWNe9YSeeueoRsvKyeXTNLIr68vYP8NJ78tE/n7M7cZ9i3X09xjA0ehA7Tu3lzc3K9z86pBmvXfcsgNvjfnjTq0T4hfHFnhVsOL5FsW5Uh5u4o+MIDiYdZfa6eYp14b71+Gj4awAszFxI9i/KG5lXhzxDq9DmrDrwM98f+Fmx7rqWA/nfFXeTkJboUiZPnQfLRn0IwGt/vsfJIjcVTw14mCsiO7Pu6CY+2638vPRu1I3H+08mLTfd7WtdfucnqDVq3v87hn1nDynWPdhzHNe26M/fJ3fy3t8xinXtwqJ56donsVgtbo+75qYJhHn5Mn/3JtadPKI8bofejG/bg53nTvHoxh8U65oSyneaBwAYb40hq8hF+DL1fbQmghjbJr6ybVesG6PqyXTVYI5wlrutCxXrAvFivWY6AI9Yl3GyyM34++rR9KE5y207+NC2QbHuUq4j/uEY72cu5/1C62pNHeGgQ8N2zTMAPGldwcEiN1Cvq2/lWtryo+1f5tl+UazrT0vma+4gg1y37+Fm9RP4YGCOdS1biFWsezxxILeFdWDz6eM8+7fy+9g+OJxF144CYMzPS12OWxV1xLO/vka6UfkeVlcdcYtqEABnjOd4dM0XqK1qbuZmAC60cDRqlVJHfLBtMa1oR/5sj1dc8Q9ffXU7cIjGLa6DIz+hzc4jZMl6ji9Zz/K1t5RYR3x68xuEeAexeOfX/BWv/C7f1elWbm0/DJWvvUNdgq3gNUf61+fdG18G4Klf5rg0drxx/SyaBUfx7b4fWXv4d8W6G1sPZkK3O4hLTeDxn15UrPMz+PLZbe8A8NL6tzmToWwgeGHgZJqEaVlxdC8f79uqWHdd41a82HsI57Iz3X5e5DrCzl0d0ZnONKMZ22wnuJ5IErjAGOvHkImzXnNXRwz1yiQEWK7fgxd9yl9H3AHNaY7H+/ZGtp3Hg1ze4ydV1zFK1YONtiM8bftOsa4DDflMY08H7u5v84P6IRoRxHu2P1hj+1ex7n+q/tyvGsAeTvKA9QvFukgCWa2ZAsB91iVcKNL4u0Q9no5EssS2hc9tfyvW3a7qxgz6cSL9gsvn0Fur58+R9t/Wxzf9yLF0ZYPdpXgdUZY64r+zh3l5/duKdRWqIxwfm8rUETOvfowu9dvz85E/WLZ3lWJd/ya9eLTvJJKzLrh9rTrdWmwBAQDYAgIKAvRAULMg2AL+Pfypf1d9xX4Z/2bg4WnAaDa6Pe6SkfPx9/Dj0+1L2ZawW7FufNdR3Nq6H1vPxPPE5jWKddGBoSwdMhqAsb9+halQI/g1WWfx19k7C/yanMi3nsrruOq+jsjU2Bvzf/6nBX/M0BJ5/wYM9Q+w/157KvyPgjeRbFXWA9V1HVHuOqJQvVZTdcRTqqGcIMnltXqj5y/NkwBMs37j0in1bfUoBhDNKtsu5tuUv63X0Ia5mpGkkOX2Pdymfho9Wl6w/sB24hTrZqpu4GZVF/6wHWS2TflZ6kZjPtWMxWyzMOYX19/Wku417ul0Ize3v7nq6giH8tYRtzAEgLVxf1A/KbLCdcSquxYBMP+vBRxKUn4Op/a5jwFNe7Mp7h8+/ufzghW/QKeIdsweNK3CdcRNbYaw58x/vPbn+4p1TYMa8+bQ2QBMX/sCZqsyGPPODS/RKKABX/37Pb8dVd7f39J2KHd3GUls8gme+fVVxbpgr0AW3vImAM///gbJ2cr7tReveZz24a358eBvfPvfj4p1g5r346Fe4zmTmcT9RX4DS22P6DSM6wit+TqikFpTRzjqtYtdR6h8bNzteJyTm8UoL/tx+xn6US+3Hm9b19GBWwnDj7esv/Er+2GSJ57n9pAT25G/LSe4Gz92EsfD1mWKc16M9ohHd60iIVsZUHxnwHB6R0RV6F5jxx2PADDr71/4N1l5L11sHUHtqyOCNgQSc2vl6oizmeddXqtWreXb0QsAeGPTRxxLUX4O7w4c43z87B+vMUDTEwCTxsSn25dWe5vllLWvuBx35bCxRPoG8MHeLayNO6g8brsrmNSwz6VRRzjU6HUEFrfH/UU9VVlHgEvHj5LYbDZMJveDI6qbTqdzmcKwJIsXL+b+++9n8+bNpKamMnDgQCZOnMibb75JTk4Ojz/+OLfddhu///67Yp9HH32UrVu3smXLFsaOHUufPn245pprsFqt3HTTTfj4+LBhwwbMZjMPPvggt99+uzMAX56ylXSe6667joyMDD7//HOaNWvG/v370TiyjsXGxjJkyBBefPFFFi5cyPnz55k8eTKTJ08mJiamlDMLIcpKZauOHHrispWeno6/vz9rX3sGP8+C0dqBOm+ivRuSZTbyb+YJl/16BkQD8G/GCbIsyh6KTT3DqGcI4HRuCvG5yosQX40HbX0bY7Ka2ZGuvGED6OTbFA+NjgOZJ0kzKy+MGnoE09AjhPPGNGJzlBfZnmodHf2aArA19RBFvyTtfBrho/XkSNZpkk3Kxq8wfQBNvMK4YMrkUNYpxTqdSkNX/+YA7Eg7ismmvDiJ9m5AoM6H49lnOZuXqlgXrPOlhXd9Ms057Mu098C1tMsBbxsqFfToYC/v3kMnyclVXsQ0bRRKaKAvCWdTOHDOA7OqIO2Oj68/TZq1IC/PyKH9yptTgNbtOqHVajl25CBZWcoeuOH1GxJaL5yU5POcOqm8kPPw9KZFdGsA/t2tvLECaB7dBk9PL+KOx5KeprzwD6kXTkT9hqSnpRJ3/KhinV5vILpNeywmK79+sZ6Idn6oC3U3imrWEl9fP06djCMlWfl5CQgKIbJRFNlZmcQeUV5kq1Vq2nbsAsChA/vIMyobkxo2bkJgYDBnzpzi/BllT3xfP3+imrbAaDRy+IDre9imfWc0Gg1Hjxwkp8h7GNEgkpDQMJLOnyXx1EnFOk9vH5q3aIXFYmH/v8ogjtaWx5UtdXgYdBw6fobUdOXnu35YAJHhQSSlZhAb53gfslRo9nniodbRyfH5/if1MNYin/A2Po3w03pyNDuRpDzlyK8wvT9NvMJJM2VxIEsZCNOq1HTzt6eE3ZUei7HIjWJLrwYE6X2IyzlHolH5N7+U64jTxjRW2c7TIddCftej2lJH5FMBVzjewz3px8ixKuuIZp7hhBr8SchNIiFXOSLBX+tFa59Ici0mdmcco6iufs3QqbX8lxFHhkX5vWlwtScRzfw5l5zO8QRlT3BvLz3tWjQEYOueguOmq8Mxq/SVriMADvy7G3ORkTzVVUfsWnQQVXAAapOKDjeHYbXY2LvGPjKhVZ8Iug/sWmodcWT/fnavPUFudkFv+9defoTsPH+6Nf2M24/drdgvbuYY9FpfbDYrHt42ul/ZSZEesGXr9hgMBk4cO0JGurKRKjQ8gvDwBiy66wM0LSPJTcqg5zj791dv8CC6dTsA/tuzE6tN2fu/WYtWeHn7cDL+BKkpyr9rUHAoDSIbk5GRzolYZVBbq9HSun0n+3u4/1/yioxIaBZVn5Y+54hPTCHxXKpiXaC/Fy2jwsnKMbLvcKHvjaNek+sIO3d1xMEsCydzbbTx9uCW8GZkmnPYkxnPXg+Ns15zV0cc+/oHzmzcSsMhA7jq1jHlriO+2foXq3Zso4XuPn7Z+yLtIg/x0QxlcLqRRyj1PYI4Z0zlWI5ytJ+3xkB73ygA/k5VBqYB2vtE4a01cCgrgQsm5SiUCEMgjT3rkZKXyeFs5XtoUGvp7NcMgO1pRzAX+Xy39m6Iv86b49lnOJun/N6E6P2IusmHDH02+48qO+qo1dC9vf1vvudgPLnGgronXR1OeFT0JXUdAWWrIy5cSCYh7rhiXXnqiJRzSZzee4H6HQJRaytXRzRu0hw//wASTyeQdE75nfPzD6Rxk2bk5GRz9NB+inrhniu54dZvAND9/jumgQOd6+a9/SoZF3LwC/YguJ6vYr/cHBObYn/DbDZzYN9ul+NGt2mPXm/geOwRMjOU72FYeH0ahAWgTtrBsXhl3ePpoaNDtH006z97j1H4jvzEyz9zMjMQU9++qKxWAg0a2vqo8XBMS1Ld1xE/7vXl/k+aOZc3aRDPKcvtHD9or+9vGNiFK9u0pk/LVs5U49V1HVGeOsICinqtpuqI5l4RpJtz2F/kWkyNih4BLQHYnX6M3KLXYl4RhOr9OJmTxCmj8j0M0HrTyqchuZY8dmcov48A3f1aoFGr+Tcjjqwi72Fjz3pEGAJJNF4gLkfZ8cVb40F738YYB6ax/ZTr36Zjq8hi7zUM9TsSEtGs0nVEpa4jjEZ2LT2Kys+HAO9c+k8YVOE6on0n+zQXRw4dILfICNsGkY0JCg7l/LkznDmdgNWMs17z9fehaYtWFa4j6oXX50JKMgnxyvfQ4OFJy1ZtAdi3Zye2ou9hy9Z4eXkTH3eMtAv/Z++8w6Qo8gb8dk9OmzOwS0ZAogTBACIq6OmZPcWcOD3OcGb9ULzzDvUMp56KeiKgIpwJz4hZERBJCyI5x2VzmtnJ/f3Rk3pndtld2F1Cvc+zz/Z0dVfX1HT/uuqXSusUlp6ZTV6HTlRXV7Jjq3acrNcb6H38AADWrVmF36+9Dwu6dicpKYW9e3ZRVqJ95pJT08gv6IrPWUrxeq3R74D6iORscn/Ja1cZUZ8jQUbEyrWe7SAjFv1ZdeC8/NGp7EhT3ycrqwOU+hS6WCQuzu6OWWdkfe1uKv1O7nxzOvt2PEbQM5QHL9vAH0+uo8RbzRaXdtzTFvqIZV3W4DFq78MeXbJIS7I3b64RYvgAtb1rNu3G6Yo6FVTLOWTn99DIiFhstiNDRoTlWv4JmfQdMBBouYxwuZxs2bhOUyZJMseH5tIb1/+Gx62NpnYXGVm/VH0m+pyWzi/P/4q9Xx41q3Yz/onTW11nuW7lIpKC2vfG8T07YLOY2Li9iIoq7TswNyuFfHMG1Z9KYhzhKCAQDLK0WuvMBzDQ0UUjIwCcdW7OvPcxqqqqSEpK0hzvdrvZtm0bXbp0wWw24/V6mTp1aly9bcEDDzzQ5JT1o0ePprq6mhUrVgDw2GOPsWDBAubPjwaF7N69m06dOrFhwwZ69uzJ6NGjCQQCLFiwIHLMsGHDGDNmDI8//jhfffUV48ePZ9u2bXTqpM4Z1q5dS9++ffnll18YOnRokyPpG7vOl19+yfjx41m3bh09e/aM+2433ngjOp2OV155JbLvp59+YtSoUTidTsxmc9w5AoEgSn251hAikl7QKixf/AtmQzRd5HEdutJrcEd8Hg+LFyyOO/7Ec1XF8G+rfmNfPa/r5EGnkNUxheJ9+1lcz9u1IDOPvicWEAwEE9bb/8wC0BnYuHYjW/drFZen9hlKx24ZVJRVsHi59tyspDQGjAop1xcuIRDUDqq6j87C7rCwffM21uzUDkSGdu9Hl97Z1FbVsHixtl672coJZ6iKsxW/rKDWrR3odRhxFqkZdvbs2M3SzdrB5/H5PegxIA93nTv6XUPvWFkvM+wNtb1r39xEyQ7tQC9t0jAyhzsoWVHG+tlaz9Kcwb3ock8PFLefzU9ovSIBjnv9eNDr2ffeLxSv1howbNedTeaZOdSu3cvml7TnpvboSI+/qgPeRPV2ebYLWKyUfFrIroWrNWX6i0aTe3FH6raXxp1ry06j179UA5zn+yVsna8dQOY9eiM4kij7fh1bPtP2f5czhtLp+s54i6vj6tVbTPSdrk4adr3+PTV7tPdhxl2Xw5B0qn7ewua532ivObwPne/oQbDWnfC79pk1EHSw753FlK7brilz3HQeGWOyqf11N5tf056b0bsz3R8+DoJKwnpHPDcOTAa2fryDzUu1xoIRl/Sh03lpVG+t5ZdnV2nK0uwpDDxNvV+WLl6Kt97Eq8spGSSlWNi5ZQertmsNlYO79qFL3xycNU4WL9T2r8VoYshZISP90kKqXFrDU87wM0jLsrN35x5+3qht0+EsI6rKKnAv/4lfYsqOGBkRQifLDD9H7cM1K9dQXC/CM+2E0WTmJbN/z34Wr12qKeua3YnewzoR9AcS9uGgcV1Ahg2/bWBHifY+HJU9kNxuyZSvr+SXadrfPKd7Ksc/ohrpf3kqtkzdPhQyYusL8/HW1HNgaSUZIWWrv5upqozNT6h3iz6vC0GzFd+qzTDmhAPLiHd/RrdhJ/q8LviT05F8Xob2/J5FG8YzqLsT6uktc57+gN21Lj6SJO665Gy2LtEqCLr/uweYTOz/3wr2LtEqpI2XnU7O+R2QdapSLKlsO5ufUJ93R4dMej2lKte3PvcF/jqtoazTP/KwdrFT9vUatn2lvV+6nT2CDlcV4N1XFfddjQ4rvV8dCMCOV7/FuV97H+bddwkcD/sW7+eXD7Wyp9fITvS8JQdfta/e/aIixhEqiWSEJ6sjZOSyt6oGcsBd5+aX0HcNy7VEMkLZryphdq9eS8VpFc2WER2R+FO/YcxdphpZRnR5i8ULtIZi8/HDyeuSRllJGYtXar9rbmom/U7uDJDwtzluTC7oTWzdsJX19YxLJ/YcQEGvLKorq1i8RHtustXBoNNDirOfl1FXzxCcf9LZJKfZ2LltJyu2ap+bAZ2PozN9qStxx92HBrOeoa+FFLgzNlK+J/YduIqUu9IOu3FEU2SEe0sJm5/WntsSGbH1K7XsYGRE1v1XkTQghcpFm9j8/vfaa57Un4JJ3QhU1SX8rklJwyLbsQZ6gB6ONFZU7OH41Gwu7He8pmxDkSpPFG/icXLPV3qD0UTRB0spWqE1ApmvPIsOZw+kcmM1v/xbe79kFiTT/zFV4bb02V8J+qMyQtFnoV+xBN/JJ6PIMuU+hSXrt6OvLkfy+RjW/fhWHUcM77KTO898QVMmSSfyVt0uVu5YRTedFbm4ksXF0eiv1hpHtERGhOVae8mI7v1yqat1xX1Xo97AsPGq8nPV8tWU11ZqyjOHjiEzJ4l9u/eyeP0KTVmP3AKOG9IRn9eXsA+HnK3+5ut/XcvusnqGyv4jyS1IpbSohMWrted2TM+m38gCCCoJ3619n8ttcK7R77JkMs7vdshkRJjmygi5Yy8UwL1wFXWnD2yxjOj3jmqk3/P2T1Rs0hrZkm+9kLRTMqkp3MHmN6JZMbZ+BVn9u9H1geNaLCOyzsnDtbGIzc9pz03unEvPqaoBbstTnxD0ax1uCp7MB6uN0vm/suM77f0in3cyeZd3wrO7Iq5N5rQker+oGum3v/w17nKtATRn8rXQJ4WKH9ez+X8/aa952mDyb+6Kr9wZd78cUB9x3XByyTssZESYI0lG/ALktIOMCLNu4Y+sSlEdgZ3djgeThT3bduBLLcBsMbJ5/SY27dvBpZ17Q+e3gbehbgyQT3V5JYuXauttC33E6tnrqCrVOl/k3DOStP72Fs01hr+ptnfd7M0UbY51IF+F7VZbQhkBR56M2LnMQd+XBgItlxH+MmdcvbJex/FvqnPp3TMXULVd67iRM3Z0ZHv7C5+TYrTjJ480fQVFHyxtdZ3l+ie1GfYAej2VBRYT2z/fzYZF2nngsAuOI/+0jMNGRkD7jiMURUlYb9+xHTUyAsDdTpHxrc0JJ5wQ2V61ahXfffcddrs97rgtW7ZEjOH9+/fXlOXm5lJcrM7B161bR6dOnSIGeoA+ffqQkpLCunXrGDp0aJPb1th1CgsL6dixY0IDffi7rF69mrffjmaMUBSFYDDItm3b6N27d5PbIRAIGkZE0gsOKeFI+t+efJZUS3RNerPRSLLVgT/gp6yedyhAdko6AOW1Vfj82qjHZKsds9GEy+Ompp5Xu1FvINWeRFAJUlIv0hIgIykVnSxT6azGU28gYLdYsZksuL0eqlxab0u9Tke6IwWA4qoy6j8l6Y5k9Do91a7auAGXzWTGbrHh9fuoqLf+pE6WyUhKBaC0uiJOaZ9qT8KoN1Bb58RZL0rTYjSRZLVr+jBwohOS1DXpMzur7S3fU43fp603KdOK2WbEVeVmZ1WWZk16vdWMPTuNoD9A9a749XiTC3KQZJnafWX43drvaklPxpRkw1vrwlVSqf2uJiOOPHWtncpt8ev+OTpkojMacBZX4HNqvWjNKQ7MqQ58LnecglbW60nqlEXAF2TlO7/SZUSGZk16e246erOJurIqPNX17heHFWtGCn6Pl9q99dbsliRSOqvrUVXvLibo096H1qxUjDYL7soa3BVa47PBasaWnUbQ76d6V/z6TuE+rNlXSsCtTeUW7kNPtZO6Mu2zoTMbceRmoASDVO3QevXqFTc98irR6WWqip14XNr725ZixpZixuP0UlUSUtBWS+h+th3w/k6zJ2PQJ76/rSYzjgbub1mWyAytMV5aU0EgoL0PU2wOTAYjtW4Xznqe04ezjKjxevhCcjPSGV27+UiREWEkCbKS1T4sq6nEH9BO4sN96PTUUVtXL1LKYCDFlkQgGKS0Or4PM5NTkSWZitrqOAWLdZyCJd+Au9ZLdam2XoNJR2quGrFYvL0ysr9czscvmQ9aRgBU7ShCqdeHrSUj/vfct0jpKVgCJsZe01f7XZsoI6p2lrDxuz2k9bKy6Kto9JjHY2Tmyxdzt7c/AZc77vyFdgtL9Qbu+cckTjnzxMj+pE5ZyHo9zv3l+OqdZ051YE5xMPPaacjdOkKll3MnqefKBj1JHdU+rNy+j/o3uD0vA73JiKu0Ms4JwpRkw5KejN/toXaf1oNfkmWSC9R19Kp3FROs9yynZFnoaNqBs9KNs1LbXpPVQHKWDb83QPneGBkckmtiHKGSSEZ8W1PJZq+bflY75+cW4A/42V9TxSKbPiLXEsmIH159id2/rsaensHVT/6r2TLi2c8/ZvaiBQzs/Ds+LXybDPseVk/9RHOuw2LDajIn7EODXk+aPRmA/ZXaeym2D6tcNbi92nerzWzBbrbi8XmpdGrf2TqdTIZD7cOS6nKCQe2PE+7DmjonrgRy1vp7BZ/VS8U+bXslCTILUoD4sVi5nI8xO/ewGkdA02SE11mHq1j7bDRHRtRVONm6qISuIzPR6aWDkhG27DQMVjPuihrclfX60GbBlpVKwOuLc6ICePRiF4PPXx+3v7qyknNvOpnT+l6MzWQkxaL1bvf4/Syp+rGRPsxG1uuo3V+OP64Pk3AkyyRXr6G6RPvc6A0yaR3U6KGS7ZXaGMJqCc97Fbz/2kt4LriAYIxyLkWn49qsjjis9lYdR/y8McAVLw0H4PwhezhnYBHnPXMxfTt+xr6KKzCEsrYsmPw3zEZTq40jmiMj/KCRa+0lI5Ksdnx+P+W1zRyL2eyYDSacbhe19cbJJoORFJuDQDBAaXVlXB9mJachSRIVtVV46z03SRYbFpOZOo+b6rh3oJ5UezK+06spqYn/bdI7JjU413Cl90GfmnXQMuJgxhE+j4/PXlkASXa6ZJgYfs0pLZYRKV3UpS5q9pYS8GjvF2tmCka7NSJnA34lItdMdjP23PQWywhzih1vbR2uknp9aDSQ1EFdlqNy2z6oF2kcHie7Sirx1tbrw2Q7lrQkfHUenEX1+lCnIzlfXcu2aud+lHr3oS0nHYPFRF15NZ4q7TNntFuxZqYgeaow7NI63R1QH2GwY1+Y3q4yoj5HgoyIlWvp7SAjPv7bI1QXqzqiCS+oy359X1vJRo+bYVY7Y7M7opN1VDpr8Pi83D17Jj+suwEYxY2nlfHwhbtw+zxUOZs3Tj4U+oj9x+8hYK0318i2YbQYmjfXCPdh6P6u2FeDzxN9bsrlfIxZORoZofmuZtMRISPCcq37KdmkdlXnvC2VEYnlrERKl9Bcek8JQa/2Wd65qZZVS9T571mX9ueD/5uPrX8uzsI9XPnSJa2us6zdupW0oDZyPS3Pgd6oa1jfplgJfGYS4wh7MoqiUFyl1csAZCSlaGQEQE1dHb3uvbNJkfRHSrr70aNHM3DgQP71r38BMH78eKxWK0888UTcsbm5udhstrhzAM4//3xSUlKYMWMGzz//PM8++yzbtmkzH6SmpvLcc89x9dVXc/3111NWVsZHH0WX+PnTn/7Eb7/9pomkb+w6L7zwAk899RQ7dmgzTYTp3bs3Z5xxBrfddltcWX5+fpOzDQgExyoikl7QrmSmpJFuscbt1+v0EUV6IsKTqERYTWaspsQ3syzJjdabYktqsMxsNGE2mhosDw+CEpFktZNkjfeMA1Xx31ibwgq0RNgtNuwWW8Ky2D70dzJCqnYCHFb4JcKabCY1LQefFP/byHpdRDmRsE25DX8Xo92K0R5fZ5jG6rVlpQKJ+8JgNTd6rik1nZTOuegMclyZJT0ZS3ri+0lvMjZab1iplAhziqq0ToSs1zdaryM3o8EyU5INU1Li31yS5bh6DYoLXUCdlCZnJT4PwGQzkmULDZoqZPTrtW1vtfvb0cj9bbZiNye+Xw5bGeGwk2XwYUhQfjjLiESEFSKJsJks2EyWhGU6ufE+TLXH96HfUQMEMduNmO0ND97DSg8ARaeVUQcjI8KK3ES0loyQjKYGzz2QjLDnpmNO89Cxfzapq4uoLncS8AUwmbzk95U44813ue/sPyOv344HuPp3p1D0yQLyHDaq95Xy4K3/YMaC1zlh5ABNvbbstAavGU5V7NclbnfYMSER1owUrBkpCcv05ob7AYg4UsRiUFwQiDoZJazXqNPcL/XlmhhHqMTKiAJFYXO5G0VWF+zQ6/RkpaRjdhgSyrWwjDCHZEFtWWnk+zVHRuRn5LCnsppBkurwUlrbgXRHBnpdvH/wgfqwsX5ItjpIbmAIYjIYGz03rMhNhMNiw5FAzvqpwWDSa+/DetQfi6lyTe23w2UcEUtjMsJos2Dskvg3hwPLCFNyEub1Stx4rSUyIow5VXXUSoTOaEhY78mbRuHinrj9r7z2Gn96+koKK77nu09+IinVwejxJ/HQH//O9s27mDX/ReDAfWhvqA8VF2abEbOt4XdgZv17qUJGf3w+aUYTJcuWQcdOBGUJv6JQGQhgNKu/R2uOI07urWfcQJnXv+/Ki19358Wv1bJVO89leLcOLNmiRvt1vet2zh08hJeuvYk8o+mQjyPCNEVG+CChXGtrGQGqcbDFYzGzFVsD42SdrDtAHzb8DrSYzFgaeAdKstSoTEs019ivs+Pj4GXEwYwjfL4giiQhAaa0FAxW9fu1REaECRtsEhGWswFfME6utVhGAEa7BaO9kT7s0kgfZqZgzUxJWGawNN6HYWN9IixpSVjSEj+TeqOhWe9AACrUfmpPGdEQh7OMSCTX2lJGDDvn93z9hrrOdIY9CZ3egM3rBo8bq9mKLjS2TLGpz5s3AHsqTwG6k+FQDZdmgwlzStuPk9Nzk+J0ZWGaNdeoR9jJPEzs3PVgxmLtLSPCci2pU1QutFRGHEjOhp0LYtm1fXtkO61bB/yKaq7wK4ZI37SmzjK1Sw5ZgeqEZQ3q2yraX0Ykol3GEZJ0gLl09LmxmFwNHpeo3iPRCDx48GDef/99OnfujF7fMtNb79692bVrF7t27dKku6+srKRPnz4AZGZmsmaNNltuYWEhBkMizWVi+vfvz+7du9m4cWPCaPrBgwezdu1aunfv3qLvIRAImka8ZUsgEAgEAoFA0Dya6GXdFPQGHb+7+VSuuH88Ros6odXrKvn25z70vvI8PgH2Dj4Oa4GqkOmyr5SHgUeBH27/Z7OuJUuq8kpnEGuJHa3oQvdmUb1IjQPRc9gIAJIy4hVpTcEYUg4s2xqNtqtyNV1hIBAcShQ5fto7depUnE41IsiRZOe8K8YxevxJAPx92kO8/fU0dDpdm7YzFlmWMaxaxRU+uLfLcZH9bZUHb87i/IT7xx5/u+bzxyuW8dHypQmPFQhaE0104qEbhgkEghC9ThwZ2f553vsAyKFxpbteFC/ArrJSQE2zHQiKh1LQdILBxA4VAsGRyJ/+9CfKy8u5/PLLWbp0KVu2bGH+/Plcd911BBLIzkSMHTuWfv36MWHCBFasWMEvv/zC1VdfzahRoxgyRF2iZ8yYMSxbtoxZs2axadMmHnnkkTij/YEYNWoUp556KhdddBFfffUV27Zt4/PPP+eLL74A4L777mPRokVMmjSJwsJCNm3axEcffcSkSZOa1ykCgaBRhJFeIBAIBAKB4LBFVVicevKX3H5FVzoUTOKtr6cx84t/k33mCPQOG8QYn8x7ips88QOQJfVYg0EoRo5WvIr626Y2w6MewGRTIzCqS+PTAjfpuqHUhDZT9P70BYTCVtA+1Bnio8E8Hg/PzPpbO7SmaUgh2a4Eg0gxFsggbWOlP6t/fFpegLk//w7QATJ9O6iRPfVT5wsEbY18CJ0lBQKBiskajfJd8tH71FaUEwx5ilX6vXHH9+nQEVBTZfuFkV7QQvR6Xf3s/QLBEUVeXh4LFy4kEAhw5pln0q9fP+644w5SUlKQEzgOJ0KSJD766CNSU1M59dRTGTt2LF27dmXu3LmRY8466ywmT57Mvffey9ChQ6mpqeHqq69udnvff/99hg4dyuWXX06fPn249957Izql/v3788MPP7Bx40ZOOeUUBg0axMMPP0xeXsNZLQQCQfMR6e4FAoFAIBAIDhGS7tAqpPqOLGDlN2sJBtVozooyE7+/QvWc5nencF7lDwC8+Yf7sb37NeX7yzln4OV8snJ2k1KrBSXVcCsb2i9aVNC6JOnU+yDYTGWXsYF0hk2lIFONwC+rrcWoD+D16/D6hX+woO0o3a/nw7fSufDqMqrNXYlNyrp69WqMRgPnXn5Wu7XvQISVeMFAQBMk3FaR9C9es5IPlnakZ04N/TpV8f7SjgBs3t8J8JNsKWN496v4bc8uHnr3He4+5/dt0zCBIEQwSCSTkXyIx18CgUDl5Msm8NPctwFY+sk8gmep702rLn6eYTebiRjphWOmoBkoIpJecAQTXv89lh49evDBBx8065x58+ZpPufn52vWm0/Eo48+yqOPPnpQ10lLS2P69OkN1jF06FC+/PLLRtshEAgODmGkFwgEAoFAIDhMyT8um5XfrMVgUD2Zgw0ovDr3KKAENdvrprVb2bdrP1l5qpHUaDQgNRRhFtot64WR/mgl/NsrzQxJcaRH1+VVFKXhe6gBUkNriCZbrfiDQbx+Hb6AMNIL2o4/XtiNlT/b+ezdVM7umxPZ/9hjj+H3+1lZ9l2To1naAzmUaj8YDGqev7aKpM9K9hB4873I57V7HIx6bDTlterawlV16WwruRr4HH8gQKXTSYqtgXVbBYJWQGPTEZH0AkGrMPSc30eM9FUlxWQa1PWhAwk8xgw6PWEjvUh3L2gOSiB0P7WVJ6JAIBAIBIcRh69WQiAQCAQCgeAIo7mGzAMhy1KoXlUT3VCQQX4PNeVweGA3uvvv6WMdSR/rSC4+6bqG1/kL1a8ziCHh0Yoc8sRobnyK2W6PbHtczmZfV42mArfPi0GnKtyEkV7Qlqz8Wb2HVy+1YT6pa2S/368aEHSHuXOSJKnPS01ZKRCV7+2lvu7ToYaiFz/GYfZF9n239g+E/f7T/3gt/1sh1qYXtA+HevwlEAhUJEnC4lBz0SiKgi70rLkSLK9l0OkAdb8/KMZ8gqYTDAjjvEAgEAiOXcSoSSAQCAQCgeBgaSXdcNhIL8uqwivQQCR9eO3i1PTkuLLCJWuoqqhOfF44kl4nhoRHK6FbKLKGaFMxmi2R7fJ9e5t9XbNBXUphf1VVZF91nUjiJTg8uPSG87HZre3djEapKS8DoPCrz3E7a6NZMdoxykwng9WkNcwY9bdEti949sm2bpLgGEYJKpFxjCQLI71A0Fr0P20sAHs2rIs4iiVak17N2qQ6wm3ZLzKrCFpAaIyjtNbkWiAQCASCwxChkRUIBAKBQCA4TIm1xeh0fpQGwqGlkJG9b/8eFFZ8T2HF96ws+y5SXl1Zm7j+1BQA9CKS/qhFCim5drpdzT7X4nAAUFm0r9nnmkJGer1OR4UzlBpVpD4VtDIz/53Jq09lA9CB5dzEmeSxEu/OcgB2rdnNlsAypr76f+3ZzCaR270HAGV7djP/1RcjE/fnd27iia3r2OZKLNdbm8cuWcPgzhWRz17/88R6qnl8vgRnCQSHnmBQIXzvSWIYIxC0Gukd1Yxd9tS0yNwkSWeIO87t8wFZABj1Yo1xQdNR5XkUKeQOkuHcyOq/PN0eTRIIBAKBoM0QUxmBQCAQCASCQ4R8iNOtGi1RBZhOFzxgJL2iKDiS7OpfcjRd+b5dRYkvEEpVKevFkPBoxaKLpvSu9MVHPTWGzxM6vgX3td2kprv3BwL07VgJwN5KSyNnCAQHz19vz+eJ+zqyZoWFWxhFT77iT5yM4ldlnSlw5GRzGP77iyLbW1YsI98cjUr0KkG21jV/GYpDwfWjtrP0b9/w5i1LYvYOiGxZr7+i7RslODaJ8WQU6e4FgtYjOUt1fivfuweHXn2PBhMsvtI9OwfYBoAsi/Tlgqaj1FuazWVIA8B38sms+3ZdezRJIBAIBII2Q2hkBQKBQCAQCA5T5Jj0rZKkEGzISB+KpFcCwZjjJbr37gLERyeAatCX6uoA0BkP77WZBS2nwBxN6f1teXGzzu14XB8AggnWHT0Q4XT3ADtL1Tb4/MKIIlDZv9dA0Z74KLxDxe+H9sGEasQ24kKSVBkX67x0uJPduSuXPDgFAKPFwuW5+fyloBeDk1IBCDSUWqWNuGLkrsj2n894U1P28+aNbd0cwTGIokiRJA7CSC8QtB56ozGyrYTGhImWUarzegF1qZbqutZ7xwuOYkL3VY05L7LLe9H57dQYgUAgEAjaBmGkFwgEAoFAIDhUHGolsRRrpA8SbMgmE4qkr3+A3qBGu9SPTogcG6pf1gsj/dGKJElkGU0A/FZbdYCjtejC0VJ+f7OvazWZItu989RU476AmHoIwOeDkZ36c1J+fzyeQyczm7JUe+AIiqQHNbUwgLeuDkmSsOn1yCGr5EZnTXs2DYChXdVn+8PlXTX7T3r0Idze5mXuEAiaS6wDojDSCwStR1pu1GC6e+2vABR53XHH9crtQHhN+pXbU9qiaYKjhPipqpDpAoFAIDh2EJoygUAgEAgEgkPFIR5ZSZI2kr7BdPehSHq/s07bnFAkfiAQb6RXAsGIcd9gOrIMV4LmMTw5PbKdKPKpIeRQqvyWRNJLkoQhdL7DoipyPX4x9RCAsyZmCYayQyd7Gr1NQ6KzpsrWyEGHH0ZLdIkIV5XqZKMPvRdSDcaE57Qlp/VRs3PsLrcyZ9JMTdmW4v3t0STBMUSsUUesSS8QtB46vSESTV9RrMp9Y4KHzm42A+pxnTNdbdY+wZFPMDxXDc1ThpyeoSkv+X5ZWzdJIBAIBII2Q2hkBa2CU78Po8Ec+awPWrAE0gjgw2WIT7Xq8HUAwKUrISBroy7M/lQMihWvXItHp40A0wVNWAMZKASpNeyLq9fmy0FGR52uDL+s9fQ1BZIwBh34JBdufYWmTFb02Pzquls1+j1xTpxWXxY6DLh1Ffhk7eTDGLBjCibjlzzU6Us1ZZIiY/fnAlCr34ciaY0mFn8GesWER67Cq6vVlBmCVsyBVE0fBuqcYFAHsXZ7DgAuVynBoDbizWROxqC34PXW4vZvxy9FfxtZb8VgyUEJ+vE6d8b1odHeGUmS8bn2EQxojT96UwY6YxIBXy1+t/Z3lXRmjFbV49pTszWuXoO1I7LOiK+umKBf+111xlT0plSCfhe+Ou06ypJswGjrpLZd3ou31oOsj/5ABksest6M311GwFfvfjEkoTdnEAx48Ln21GuRjMnRGQCvczdKUHsf6s3Z6Aw2/J5KAt5y7Zl6GwZLdhP6cC/BgPY+jPShtxq/R3u/yDozBmseihLEW7tdUxZQ3ATNAWRZR527goDfo72m0Y7RaMfvd+N2V6o76yR0hup69/dekLQGG6s/E51iTHh/GwI2zMGUA97fTn0RQUmrLbf409ErZjxyNV6dNvrrcJYRfsmFLNdQa/BHXppHioyIoIDDr/ahU7+foKSVEdE+rMGjq9aU6YNmLIF0ggRwGuLXNbf7cpGQcelKCcja+1Dn12HEgs9Xh8ej/W1k2YDVqhoOa2uj9bplI37JfEhkhLd2B4qivQ9bS0ZIIWO4TlcXJ/OaLCPq9iLLuzVyTWeMGle7dluL3+Xm/f+42bPThN9vobo2H4vVz7l9y7D0MOJ1bo9c32jLR9bpyM3Vo1eKNO3SGdNQ/GbQqdcxm8oj5ZJsxGjrCICnZjugvQ8N1g7IOhN+dykBn/Z+0RmS0ZvTCfrd+Or2avtI0mG0F4T6cBdK0KftJ3MySOD11uL1an9zvd6M2ZxCMOjH5Yp5bkJyTYwjVBqSEfmpCpSo2ytqSumW7kWW9VG51oCM0HUKYqk0UlWrGtuaKyP69bFT+FsVetlLhw7b2B/cT40h+myZAskYg/aEfagLGrEGMtU+NNR/HqN9WKcrxy9rxyfGgANTMAm/5KZOX6YpkxUdNn/OAfvQLVfi02nX/DYErejREwj4qKvT1gsSdrv6mztdJSjBqOxxy0awFBxW4whQZYQk6/HV7Sfo135XnTENvSmFgM+J3601tjZHRvjqqpDlkohc0xmSgSzMZicdO23D59yCp0a93w4kIwyWHGS9Fb+ngoC33jOntxOUs/mj4TTsneFz/oGFqBE7fHRBz91xMlpvzkJnsDfQhxYM1twm9GERQb/2Wdab0jAYjPj8dXjc9d+BeqxWVQEd+w4EoE7CgRkdBnRpCpYe6vfYUbKEvLTjSDfroQq21jnjno22HkdcOraSDzb4KdrXiW9+O5m6t6fTb8ofAbjtw3/y9IRr6JXZucXjiObICH+oX8Nyrb1khDmQSkDy4tKX1OtDCYc/L64Pw5j9aRgUCx65Bm+DctaP0xDv/BDtw/h3oCmQgjFowys78egqE/ahoig469+HgNWa2eBcw2/JApP1oGXEwYwjAnWuyDgGSgj6XY3KCIMli2DAi8+1O+67mhxqNgivay9KfTlbT0YE/UpErulN1oOSETpjSuK5tKYPt0G99b8j42R3CUGfdl6lM6agN6UR9Nfhq9OObSRJj9Ger37X2p0oivY+NFhykfUW/J5yAt5KTZlscGAwZxIMeOPlFgfQRwRS0eM45HONI3Ec0RwZESvX7O0pIwhSMLYXezdtwJasPh81AT9B/MjoI+NkQ0oV3XtsAdZgMKsOeT6pDre+3rinLfQR7hKUWu04wmxORa83NW+uEe7DyP1dRjBmfKKO8fJbbRzRVjIiItecfiwpnYGDkxHxclbC5FCXYUs4lw5Gn0FPzVbGnQ8fPu3BpzMhV5Sw4PQpXOD7udV0lu6a7dQGtfLHas1AlvW43ZX4/dr3gtFox0j7y4hY2nUc0eBcOlsjIwBqfPFZOAQCgeBYRxjpBa3C6oxp2GxRz9ps1yD6VP4Bj66KZZnPxx1/2t4nAFiX+l+qjdpBV++Ky8ipG0yxeTWbUj7SlKW6ezCw/EYCkjdhvScVTcYYtLMp+WPKzOs0Zd2qziHfeSoVps38lva2pszuzWNo6e0ALM98EaWesXFY8Z3Y/Dlst3/DPttSTVl+zWi61YynxrCbwoxXNWWmQBIj9z+k9lH69LgJ6sDSm0n1dmO3bRE7Hd9rynKdQzmu6mLc+rLodw3pGCVJx8kn3QfA+g0f4XRqB13HHXcBmRm9KS75jZ3bvtGUWdNPILvffQT9LvYuv5/6FJw8A0lvpWzTdOoqVmnK0ntcT1KHcdSVraBk/b+13zWpB3mD/w6QsN6Ow55HtuZQsW0OzuKfNGUpBReT2uVS3NUb2b/6H5oyvTmbTie+AIDF8jJFq7SD2txBj2FO7knV7k+o3v2ppsyRdyYZPW/E59oT1yZJZ6HzKWoEUPFvz8RNKrKOvxdbxhBqi76jYts7mjJr5olk9/0LAW9Vwu/a+dS3QZIp3fAq7qq1mrKMnhNx5J2Oq3QppRtf0ZSZk/uQO2gKKIGE9eYMnYTJlMT2bd9RWrZee82C0XTqNJKqqp2sXfdetCBTVUgML7kLgJUZ0+KUHUNKbsPh68BO+w/ssS3WlHWsPZke1efi1BexIvMlTZkhYOPk/Q8D8GvarLjJSv+y60n39GKvbQnbHV9ryg5nGVFp2ozV9jaFMcF3R4yMCCEpOkbvU5+ltSlzqDVqlZ59yyeQ5e5PkWUlW5K1z026uzf9y6/FL9cl7MNT9j2KXjGzMXkeFeZNmrJuVePIyxxMRcUWNmz8n6bM4ejAwAHXALCycHpcvYdCRuxb9dc4pURryQjZeD5BwJG0hb3Ltc9yU2VE+ebXsNrWURQjajN6Toxs//Of12A0qpPawd1hVeFw7npiNgaDh0u/mMtx01RFTfganU58GVmWuPGmNHLN77B3eVR2pXa5HFPSWcgmHQHAylz2LleVSgZrRzoOe0btw8JHUOopO/JOeByToyuVO+dRs/dLTVlSx3NI734NXudO9q38P20fGRwUnPQ6APt/fTJOqd+h312QbGLfvhXs3KX9zTMz+3Jcr9/j8VTH3y+ZYhwRpjEZwcarAdjgqqDmuNexQkSuNSQjLH+C4+iIfr6qrGmujHjyX+mcd241W0uS+PMd9zNk6AJi42B6VP6ejq6RlJnXsy51rubcJG8+J5T+CSDhbzN8/z1YAxlsc3zJfutKTVnnmrF0qTmDKuMOVqdr7xeLP50Ti+8FoDD9tTjl2OCSW0n2FbDLvoDddu192ME5gq6cTl1dWdx9qNMZGTnibgDWr/8wTsF7OI4jOp34MnpzOuVb38ZV8rOmLLXL5aQUXIC7ah3Fa57UlDVXRlhtRORaUsdzWL7mNrp03cALL1yCbw/sDdlODiQjsvs/iDVtIDV7v6Jyx3uaMlvWyVxz+TT+lPkTfaflM44rCQQKqKrKITm5iH2hLu/Qe2dcX2QeNwl7zqk4SxZRtqne/ZI6gJwBD6EE3An7MH/kf9AZkyjfPBNX2XJNWVq3qzF3HENl5XbWr/9Q215bNoMH3QBA4aqZcQ5lw/RRGRGW7UV8QxHf4Ks7B1AduOo/G20+jsiEadPgr4++wKodJ1JkWcm0UHsBKvgvmw5iHNFcGREr19pLRvSsOh+XviTuu+qCJk4t+isAa1LfinOE6Fd2DRmePhRZl7E16QtNWWZdP46vuBKvXJuwD0ft/TsSMhtSPqDSpHVC6VV5EXmuYZSaf2NDyvuashRPVwaVTURRAgnHYsMamWtkdLkYR8Glh0RGxNLccYTOdhkBPyiB2bir0xqVEVl9biPgKU/4LHcZ/V8ASte/iKdaex8mkhFhuXawMiK50+9wV/xK8dpnNWVGexc6DFHHNntXPAT1DGUdhj6N0daJyu3vU1v0raYsOf980rpegadmK0WrHtWU6Yxp5I+cBkDR6n/EOY3lDHgES2pfqvd8QdXOeZoye84YMo/7Iz53Sdz9ckB9RMcLsZB1yOcaR+I4orkyIizX2lVGECTpLidJdMTp3AMrTgDAK9diDqawJelzSiy/qu+EIQC/54v3bwUKqDJu49d0baaVttBHrN31X9z1nHWO7/sHUlO7Nn+uAZxy8oMAbNz0CTU1WqeP1hxHtKWMsNqgZO3Bywi/e3/8d5X0dBk1G4CSdS/grd2mKdbrLwNMIEfnsscNyuLX1eMwpIaX32o9neXOFVOob/4fcsIfsVjS2L7jB0pKftOU5Xc6mYKk0YeFjAjT3jIiUb0jih7QygjA6Wxo/T5Bc+jcuTN33HEHd9xxR3s3RXCIuOqqq+jduzcPPvhgm1532rRpfPrpp3z88cdtel2BFklRmpHzUiA4ANXV1SQnJ7Nt+uOkWkUkfWtGyUq+GpTexWBTH2GrNQPFbMFlVAh63ejKo9c2Gu3o9WY8aSns86chV1Qi+9SJkKQ3ozel40vKwB2sROeqwVATHYjrrXkoJht1Fh1BnxNzya5o/xtTkA026lKz8PvKMVQWo/PUhQsxmDMJODJwKZXIbifGqmib9OYsMNlx2S0E/bWYincihcSRbEhCZ3TgScnE669AX12Gvi7UH7IegyUbrymF3xZvp/sQG+aa4ph6M8FgpS45mYCvClPpbqRQ/lNZb0NnSsGTnIk3UIG+tgK9M6y8lDHYcglaknAZ/OCtxVQWvad0pjRkvRVXWiYBbznGsr3Ifl+oDy3oTWn4kjJxByvQOasw1FZq+9Bsp84sEfTWYi6NTuqifZiJ31eBoWI/utD6bpLOiN6cic+RgVupRHbXYqxSDd86vGQb3UgmC3U2AwG/B11x1PPXYLBiMFjxpSTj9juRqyqRyj3Iv1qQ0WHxp4M+mRqzC4J1yDGTL3MgFR0W3FYrPtmF7C4CRb1X9UELpqADvymFOmMtkq8KKRR9JyFj9WeAzo7T4iOo1CHHeJKbA8noMOOxpODV1SB7iiEUZaFXTJgCyQQMKbjMtUj+GqQY46rNnwU6Ky4zBCQXcowXtCmQhF4x47Wk4tHXqNcMeWbrFCPmQAqKIYVacy2S34kUEz1t8acjy1bqLAY1Yt4d7UNj0IYhaKPOnMq3yXWcXFmMPhRZE+3DFGrMtaE+jMoRsz8NnWzBbbGE+nBfJHWbIWjFGLTjN6VRZ6xG8lUihbzmI32od1Br9qAodcieqLODOZAS6sMkvLraen1oxhRIImBMxWVS+0/yx/eh0xIM/TZRz+xIH1rT8OiqNX2oV4yYAikEDak4zTVxfWj1ZyDJVlwWHQGpTtOHukEyhlQz3pQUPP5a5MoKJK8qe2RZj9mcgmJPphYnktuNXF1JlZxLAONBy4iALZU62YXkcWKsiCoKW0tGfPnqr2C3400pYOx1XVokI2r1Clt/3EHvHh5kXTiSPoUvZq2kKJjBex/+jhP77Gb/VvV5PP1cF8t/7sbn33fnukvn0OOXe3DYJLrceAGZo05Ab8nhtmv/zpJ1m/nHtLsZdVw0Kl82JOGt0fPfmYX4k9IZegL07mkLF2KwZBGwp+OiBtnjxFgZK2ezwGilLslBwFeNqWQXUijnrKy3ozMlR/uwphy9K3QfSjIGay4BazJ1ei94ajGVR+8Xk8lOplyMJzUZr7cWuawUKRCK6NaZMJkcBJKScQWdSM5aZGctOCXkXy3tKiN85lTchhokbznSYSwjvi/ysNftxWLO4ZwuySw3uBhWXUk48XgiGbHqm/ns2bieXn1H0X/kmc2WEROnT2PjlloGd5nHZ9uP5+7zCrlhWNQYbAzaMQSt+MxpuA3V6rso1Ic6xYA5kKr2oaUWAon7sM5ixi/XNdCHqdQZazR9KCNj8WeAPolasxslWIfsrd+HFtwWOz6dE9m9H0IGVL1iRj9Uh79jMnXBWqTaGmRXWPEmYbWmo1isOA0BFK8bXblab5WcC6acw2ocAaC35IDJhstmIuh3Yt6/I1IWlrPulEx8/goMVSXo3K5wYZNlhK+uipKvV9Opf7Iq13R2Ljr5RHY680jNKWbEwJ08+ljI8eAAMkJvTkfSW3GlphPwVkT6cPkiO68+3YXS0myurx2MlGvAagZOHI03rxv6kj24/Xb8uVkEqWbcbWO1fWhKQ9ZbqEvLwu8tx1hRhBx6V0k6E3pzRvS3qavBUB0zTrbkgNmOy2og6KvFXBw7Tk7CqDfgSK7C4wuNxTwhGRF5ByZRiwvJ40auCv3mTgnryg4RGbH02w8pWvVtZD5y4rV38cX+CkpJ47oe6diV6nYdR7y7pAPTvxyMy2Vn0dMr6Tflj3RxSFj00CevIw+fd3mLxxHNkRGBQB1rdNX0rwugo/1khCmQRMCUistYg+SrRopkA5Kw+TNBZ8NpCSQYiyWjx4THkoZXV62WhaI1o3I2Bae5FslfixQTgW71ZyDpbLjMUqgPo+MeY9CBIWjBa0nDo69Wv0vAo+lD3+kGnMk1SO465Orob2M2pzY416g19UAxZh60jDiYcUTF3kp+nfUTisFI3/4WCoYcn1BGAEh6K3pTKt6kDDzByjg5a7B1IGi24zKBUl/O1pMR+pJ97FteRIf+qeiM5oOSEbLBEZXflSXoPPX60JGBS6kKzaWj94s6TrbhctgI+mrqjZMd6IxJicfJkg6DNSc0Tq5T5axmnJyBZLDiSkkl4K3EWLoHOTQWC/dhMMmO3bc6OhYL34eN6SO8SZh+STvkc40jcRzRHBmheEpYbdHRvy6AtR1lhGJIYenyz1jz7SdkFeSwb+g5AFzfsQCdbFPHybKb8uL13PHWauAvVFWm8e09K/CZUnEba5C8FUghh5220EdUjalCMbvQVUR/G6MxCb3B1Ly5Ruz9bbLgMkHQ50ZXpj6P0TFe64wj2kpGyLU17FldQYcBGZgcuQclI6Jz6Ur0zvCzLGGw5RE0O3AZA+B1YiqLBg6sXVjF7h1lFAXSuPTuARjLi/jpzdW4gwbkmipMs+dwysYvWk1n6Q0WkeHaiFxdGb0PzSlIRgtuuwm/342ueD/hjAUGgxVDnQ1lZceIjAAdisEhIuljSBRJX+1yk3/9/VRVVZGUlKQ53u12s23bNrp06YLZbI6r73Dl2muvZebMmUycOJFp06Zpyv70pz/x0ksvcc011zBjxoxDds2SkhJsNhtWq/WQ1SloP1atWsWYMWPYsWMHdrsd0C59GcuTTz7JPffcA6jOGjt27NCUT506lfvvjzo0KYrC008/zauvvsqOHTvIyMjg1ltv5aGHVMdur9dLly5dmDNnDqecckprfL1jmqbKNRFJL2gVbP5cHL74F4UOQ8QgnwhrIBMaWE/SGLRjDNoTlknIjdZrCaQ3WK9BsWJI0NYw4dSOiTAHUtWJXwL0iqnRNoWN9YkwBZMxBZMTloX7UCr7COljbWRqoO9grL+/EtwlGOfMijtXefAZzJbOZL57L+Y92kis4vMfQOl/Bknr55HxudYD0tV1CEVXPonkcdJl9s1x9W6/6wN0jq5kf/o6to1aT+eyM24hMOISbLu+J/u9v2nKPDnd2XPzq0AWXZ6/BSmgTUe264+vI2V1JeP7D0ha+ZmmrHzEHwgGz8ZRU0zH2dp6/Y4Mdt75X/TmdPL/cz/6Gq2zxN6rn0HqPJDUX14jdaE2mq160Nn4z70bg3MbnerVq+gMbHtoPnpTCh3eeRxT0WZN+f6LH0bpM5rkNe+S/tXLmjJnzxHs/8PfkZVKOs/+Y1wfbrvvY3SOruR8NA3rVu16W6Xjb0MZej72bV+RNW+qpizYoQDLNWq0pvF5bRQLgPLHB7Cn5aD75mt0v63QlqWeh0P/e3CuQd6nHUgqhizMBVMxB1KRdjyJFKyXDq7DAzik7kglC5CqvtKem3waNsOV4N6BvLtevZIZU7cXMQWTkHZPQ/JqI7rJ/TMO3UAo/xS5/APtubYTsObeCv5y5O1/jfuuxm7TMPqSkPa+jeTeoO2nzGtw6E6Fqh+RS7Se/Iq5F5aO94KShLx9Sly9+s7/JBjsSFLR++icWu/2YNqFOPTngLMwvg+NeZjz/6b24fZ/ICn1Umd2fBiHVIBU8h1S1Xfac5PPwJ75B6jbjLynXr2yHVPX5zAFk5F2/RvJV8/xKfdOHPLxqoyo0MoIxX4itpybwLsfeae2XgBj99cxBh1Ie2YgeeotU5F9Iw7dCKj8FrlUGzWsWPpi7fAXVcES24fbQ+W3/xWDPQf9558ib9J6oftPPw/78NFIuwoxfDiLWMl3MDKi4qTLCZx+E+bSQvLaQEZMtIMfHf/2/xWTo2uLZITnor+h81bTbe4kTdnNwFT+xI7tPXl6wD84Y3woGsIPDIE/FT/F7DdGMK2fwjnjzbDnM5it9sej/UwM+NZPnS+dLvX6ASBJvpRyoP+exXRcppXfFadeTWD0tVj2LyW33rm+1Dx2/fkt9OYMCl65C51L60S357oXkDr1JW3RF6Qs0UazVQ35PYGzb8dYvTFOfitGE8rdU1XnstkzkUvrKREuvh57z+ORV3+N/nvtb95eMsLQ+Z8Y6IBU9BHSYSwjzg2Vz3ZfhN13NvkVH5NSNE97bj0ZcXI3oJsJ+Bl2/9xsGTF7DDDGzoriZ3lt4be4iktxbI+XPYYu/1L7cN/7SC5tBEww/VIc+rOgdilyUb3vasrH0ukRCIC07a9IaJVV+k5/xSF1QCr+Cql6gfbclLOxZ1wErvXIe+vVq0vF3OUpzMEUpJ3PIgVinDl3ABNuxV7QHd3SZegWa6OTAgOGYzvnMiRnEYbQWCw5Uu/hN45wd+jD3hvUyKIuz94YV+/OSW8ip3Ul8+s5OH7VZsFpioyQdGmM2PQXTOuixt4VE2HE61/x86Zh3NJlNl1mv6g5tzEZETRa2X7/J+hNqXR8628YS3bQBbj4IrX8w3d0bNnopd/JRk7N+xn4GdRsx/xGDz73DsUqp1KQoA+3PvgFOkcKuR+8gGWH9j4s+d1dKIPPwbHpUzI/eVpTVlcwgH3XPAsGb8JxsnfSwxiSctB/OR95vbZe/+izsY8ci7R3DYb3tGP3sIw4ZdT1KLm/oJND78D9/2Yi8AoTeH2zkXvsyzHX/KA9tw3HEddnw/VXqeXXv1LI5k0eZl1/OiM6LQNKYEdoXtGScUSIpsiIQO1STj0cZARA3j04pOOQSn9Gqqz3rko6BZvhWvDsQd5Vr170mLq/ginoQNrzHyRPvbi+nD/i0A2FivnIZf/VnmsdgDXvNgjUIG/XRk8DGLv+G6PSAWnvXKQ67VhMt/Ui7KechLx9Gfr/zdaUNTTXCMu1g5URBzOOsLmrudnwrrpztfqXSEbEUnTZ36DXSaSsmk3at//RlNX2PhX/JVPQ+UsalRE57z9Pj52rISbBzsHIiB13zEWX1JWsL2ZhX/ejpqx8zI0ETr4C696F5MzV9qE3s4Ddt7wB5kw6v3gbslcbPLD7pmlIuT1JX/Axycu0mYQqh19M4KxbMVX8Rod6v03AmsyOuz9Eb0qj04yHMVRo52slVzyKvXMOuhVfoPtJmwnhQPqIQz7XCHFEjSOgWTLi1NiydpIRwYwJ+HfryJUVzhlaCYR+n92gmLpi6fQQBCC57GE+Gg9wfaTckP8PDFIHpP2fIdVqs/a0qj7C/Hskz87IWCxSdhBzjeBxA7BeeA1UV2Kcoz7nsXPX1hpHtJWM6AYRuXYwMmLfFU8gdR9K6vIZpP6o7f+afmPxX/Ages8e8mPq7RL6/6jyZ0yOruT99xmuCYZ0lg5gop2iDUtQBo9vFZ2l68RzsO/4BcOH2vYGsztgvkHN+mB48emIc3+kPEZGoEtFSf+9prwxva85mII5mJKwTKcYG9Unh4PKEmEKOjAFHQnLZPQHoZO3YQzaEpY1Ryev1FvW4WihU6dOzJkzh2effRaLxQKoxrnZs2eTn59/yK+XmZl5yOtsS7xeL0aj8cAHtjHt1a4XXniBSy65JGKgB9i3T+v48vnnn3PDDTdw0UUXafb/9a9/5aabbop8dji0z//tt9/Ol19+yVNPPUW/fv0oLy+nvDzq6GQ0Grniiit4/vnnhZG+HRGR9IJDSjiSvvjVmaRbhDdXq+KvJDBkLyRFH2HFbIGUdPD7kOpPNgAlpyP7dT2hrBSp3jpA/pQcgpYkZGcl+mqtsi5otOJP7wjBAMb9W+Lq9WZ1BZ0effkeZI/WEzTgyCBgT0Ouq0FfqX3BKHoTvkx1XUHjvk3UX0fLl1GAYjChryxCrtN6gnpNKfy60Evf01Iw12gnBYqsx5etrito2L8Vqd6aeL60jigmK7rqEnRO7YQ5aEnCn5KD5PNgKNUqdUDCm9tDrbdkB1L9tRlTcglaHOhqy9HVM/oFTTb8aR0g4MdYXE8hAXizu4GsQ1+2O27y5E/KImhLQa6rRl+pRg7oFTfpwZ0oBiOkZ6mtK4pfX1HJyAa9ASrLkIrd6H6OGVTrk0GfAsE68NZT0Ep6MIUG2Z5dEc/1CMZskM3gL4eYKCsAdHYwpEPQA9566wpKEphCA1Tv3oj3fwRDJuis4K8Cf2W9eq1queIHT/y6gpg6gSSr1wxqfxsMaaBzQKAGfNqUbcgmMOao39Gzi/r4TB34IsnCuPK9GAL1JhT6FLUfAy7w1fN0lg1gVD2d8eyMRGZEMOao1/aVQUCrdEDvAH0aBN3grfcsS7L6XUHth3qp7TBmgWxR+8+vVXhG+jDoU/u/Pmb1eUzch+nqb5uwD83qPVGvDwMnOiFJQcnMBZ0OKkojUYRhFHsy2B1Q50KqKqdMzscvqd6FByMjArZUAkmZSB4XhnLts9EaMuLTZ74Ahx13cm/OvX1Ei2SEx5HLus93MnBQdE16gC/eWMhefzrPPncnGXIZDmMNA4c5+etLO/n2k2Tu+sswSl3pzH/qLoqeeg6AIbP+RlKfLtxxw995f/5v/Hvu4/RJ8fOfZ97kshsvYPCI/lTurGHevA0ErEmMGtOB7l21k+6APZ2AIx3J48RQrn3mFJ0BX5aqTjHu3wJB7Wzel94JxWhJ2IcBazKB5GwknxtDaVS5qFfcpCu7ULJDsqekKBLdErluSjqYLVBbjVRbDdVSVK61k4zA1EGVmb4SVRbEchjJiK11tXxTth+PLoOJXfrzndnJmMpSrcduPRmx7POPWb9oAX1PGc2gM89utoy48Lkn2VlayoDObzLj5yu4aMgO/jtRa2zR9mEx1EuLHO1DJ/jqrRGq6cMd9UUEGHNBNqrnBbTjE/RJoE9t4B2oA1MoZbdndyQCDlS5phSkg8kMNZVITu1vo1iskJwGPi9SmVpvVK4dPuOISHsNZnwZ6nvZuG9jXL2+zM4oeiP6in3Ibu37vikyIuALsvvDn+k2Ih1ZL7FisZ0pf+7ExrLuOH12Ojj2kGWLPhv/W7auQRkBgCTjzeke6sPt7NsCf7yoGwAyfq6pGIrHA1abhO7uqJFNLirClVOA3+vlgkevwlisTbMKqPVKMoayXUhe7X3oT84maE1GdlWhr9I+c4rRgi+9EyhBjPUcL/SKm7R0L+j0UFGG5NHWq9iTwJ4E7jqkylC0X1iuxdzfzqJVfPi0mm4+OSsH95jT2JzcGb9k4LIMGz1N9SIt2nAc4fVLjHx0DAAriwYC0DN9EzaDE/iJa0+Zx1WnnEpyUn6zxxERmiAjfAEni42VjHT6o3KtHWSEtg8rwK8dn6CzgSFDjSL21os6kwBTWM4mGidnqOcnHCdbwJDVhHfgfvW+iME/1gh5VqhzIlVp39kNzTXCcu1gZQS0fBxRvKOSVa/PBb2e/kPy6TSoIE5GSH5tRKA/NY+g2Z5Yzprt+FPzIOBrVEbIRTvZ/sNOuo7MjIzXWiojALxZXUBnQF+xF9mtfadE5tLuWvT1DGGK3ogvs7Pah0Wb4+Zrvox8FIMZXdX+OCeIyDjZW4ehrN4zJ+vUdwpgKN4W5yRLahpZ+t3RsVhsmxrSR4Tl2iGea0Q4gsYRar1NkxF+7z4W2fRRudZOMgJDGhuWruab1/9FUopM3UR1Oa5Lc/JJNtrVMSuAewf//vJzZix4CoBP7/6J7Mx0tZ8TjpNbTx/hP9sIdndkLBatV2reXCO2zGSB1HQI+JFK1PFU7Ny1NcYR0DYyQqquZOuikohcOxgZ4UvrgGKyoaspQ1erXQIxaHbgT81F8nsxlGyP7F/x+Sb27S6iyJ/GVX+9AEPpTuZPX00glPVBLilmyKWnkXzKia2is5RtZrJrC5GqtLJH0RsgQzWIx+nbqiV0K7pHZYSkV+9nQaNU17lIvfmaoy6SvrKyki1btnD//fczYcIEAGbPns0TTzxBly5dSElJiUTSf/HFFzz22GOsWbMGnU7HiBEjeO655+jWTX2uZs2axa233srKlSvp0UOdO9566618++23rFixAqvVGpfuXpIkpk2bxscff8y3335LQUEB06dPJzMzkxtvvJGlS5cyYMAA3nzzzch1wu2eN29e5LvccccdFBYW8v333wMwevRo+vXrh06nY+bMmRiNRh577DGuuOIKJk2axHvvvUd2djYvvPAC48ePb7CPOnfuzA033MCmTZuYN28eF154ITNmzOCnn37igQceYNmyZWRkZHDBBRcwdepUbDZVx/PSSy/x7LPPsmvXLpKTkznllFN47z1Vn+DxeLjnnnuYM2cO1dXVDBkyhGeffZahQ4cCMGPGDO644w4qKysj7Zg3bx4XXHABYXPolClTmDdvHpMmTeLvf/87O3bsIBgMUllZyX333ce8efOoqqqie/fuPP744/zud78DOKh21ycQCJCens7bb7/NOeec02Afnn/++dTU1PDNN9FljA+07MG6devo378/a9asoVevXg3W/eOPP3LGGWdQWVkZcTIRHBpEJL1AcLSjT0HJ1EFqgvV89AaUnI7x+0OElbGJCNpS8NpSEhfKOry5PRs815/WsOdk0OLAa0nszQlElNYJ603JgZQczb6ALwjsRTFZ8dobblPYEJeIQFImgaTE3oeKwdTodw0bDhPWa08jYE9LXKjTN96H6Q3/bkFLEl6LOohVFBdKQPvbN/abk5KOoshgTvAbyJaowiQRYUVuIvRp6l8iZFPj9YYVIgnrTVb/EiHpD1BvTsNlOof6l7BeufF6DZlgaKheK+ga68NGPGcN6epfImTzAX6bhp85VSGV0kC9hjbpQyWzRiujUjPidF8RLFYUixWfrjs+Kd7Jq7kyItIGk7XxZ/kQyYgiZTlIDlLC9bZERviCKJIeT04+OoMc2b1f3oIiBfjrP98jr8/JSJKJ3gODeE09GXaNROkfMwDIuu5qNr/4X1zb9uCy5WHO7clujzoIDAYVbn9oNr8u28DHP/+bwvLvqSotIWBVDQS+1Gy8uYmfScVka7QPw0qahN+10T40a+qNk2uZOQ3fL/Yk1bhlSiDXhIyIqTcqIxyyh6JyINTFHkMKijlxNERYRrg8KRQXBTGvLWHQeTHXaWIfLt8bYHd5EIP1eAA6pHsP0IdZjfShTf1rCFNj9Waof4k44DtQ+25VMmvAFOpERwqKI6WBaxoj7+VEcq29xxGJaLTe1FwgcTTQgWREtSUfT04eOoPM7y85QVO2p6YDe2qi96k3N2rIqC8j6uPL7MzUuzuzskh9PvS4ccsyGHS4nD6cUjTKSA74CEop2NkPOkPj9aY3PO4JWpPxWhsan8hx9SqKCwIh54fU9IZlmtkSHcclkGuyrQvFRep9V1y0F1a/jX/KFABcUjKYE2f2aotxhBEo8/VgZ1n0+dxYFn5nD2Tlu7dTFczkofP7qLsOSs42LiOqrCko9Q2KYdpIRmjQp6p/Ces1ttM4OUH0naUGCILFhmJpWM7GzjXqy7WDkRGJxhGlu6vYtmYfQ8d1bXAc4ZNNFBnUe7hrcn7cNcLGqUQ0LmcPJCM6UmWVI3ItlubKiFj8qQ3/5kGzvfE+DDkmJCKQnE0gOXHUpWK0NP5ds7rE7TMoLjUiMjwWS0R9fUR9udZqY7HDfxyh4QAyQjEXUGU1JJZrbSkjAIPZjNsNOncyTn0HnAE/C1wWfmeP+S3NBQzoNYqV73YGUqhWisiWQ0blxsbJraKPqNGMxRLSlLlGInT6xsd4h3AcEUuryghrJlVWW5xca4mMiNTrUB21EqHojZp6y4x1FEkKKKpjlS8jn+IkL76wL09qGn6dajhqDZ2lrLgi+oiGiLuXTLL6LoeG5YPgmOL666/njTfeiBjpp0+fznXXXRcxeIdxOp385S9/oX///tTW1vLwww9zwQUXUFhYiCzLXH311XzyySdMmDCBRYsWMX/+fP7zn/+wePHiRtPb/+1vf+OZZ57hmWee4b777uOKK66ga9euPPDAA+Tn53P99dczadIkPv/882Z9r5kzZ3Lvvffyyy+/MHfuXG655RY+/PBDLrjgAh588EGeffZZrrrqKnbu3Nlo+5566ikefvhhHnnkEQC2bNnCuHHjeOyxx5g+fTolJSVMmjSJSZMm8cYbb7Bs2TJuu+023nzzTUaOHEl5eTkLFkSz2tx77728//77zJw5k4KCAp588knOOussNm/eTFpaA2O8BGzevJn333+fDz74AJ1ORzAYZPz48dTU1PDWW2/RrVs31q5di06nOyTtrs/q1aupqqpiyJAhDR6zf/9+Pv30U2bOnBlX9vjjj/O3v/2N/Px8rrjiCu688070etXk+/HHH9O1a1c++eQTxo0bh6IojB07lieffFLTR0OGDMHv97NkyRJGjx7d5L4THDqEkV4gEAgEAoHgUCHJBz6mmZhsRvyVdZTv2cPZ19Vq1qaS5ahqKRAA2aBOHIK+0NqAoWN379jLukI1xXtNlaos87kDUFcHFgtGQ+L1rgRHD1adLrIdaGIirboaNYJIklt2X/fu0JHd5WV0SNsFW/vj8R3650Nw7OLzwby3osrf6ePHsj3vOoJpaVife05zbLCjqliVOzWcHvRwxmx30HP4CHasWY3HqUaApVTXUJnkwBNM4LDbxrx96y+c8rfTGij9HxWu+GUuBIKG8Hp8fPrPT5Azkln46V7ueOHkxAcqgNcLRiOyToxjBILWxJ6qKvOdlRWRCMSVNZX8Lktr9NTLMqBaVktrjPRoxB9DIAgTDMTPTZzVfiJJp/3++KxjgmOCc1/4iZIaz4EPPMRkOkx8/OcGxh8NcOWVV/LAAw9E1ghfuHAhc+bMiTPS109XHo54X7t2Lccfrzq3v/LKK/Tv35/bbruNDz74gClTpnDCCVpn5/pcd911XHrppQDcd999jBgxgsmTJ3PWWWcBatrz6667rlnfCWDAgAH83//9HwAPPPAAjz/+OBkZGZEU6w8//DAvv/wyq1ev5sQTT2ywnjFjxnDXXXdFPt94441MmDAhEgXeo0cPnn/+eUaNGsXLL7/Mzp07sdls/O53v8PhcFBQUMCgQYMA1dHh5ZdfZsaMGZEI/tdee42vvvqK119/PbJme1Pwer3MmjUrsoTAl19+yS+//MK6devo2VN1+unaNRrcM3Xq1Ba3OxE7duxAp9ORlZXV4DEzZ87E4XBw4YUXavbfdtttDB48mLS0NBYtWsQDDzzAvn37eOaZZwDYunUrO3bs4N1332XWrFkEAgHuvPNOLr74Yr79Nrpsn9VqJTk5OW59e0HbIYz0AoFAIBAIBIcISTr0yoMzrjyRef9W1ySvrXRhsZvRh4zxMXZXgkEJKeQxu/KWf9BnykQ2/aamxU5NS9YY9wHcNV4IGV/1Rh2CoxuLHP2NXQE/RNVeDZKSrWpWPa6WrR1oNqghU7XuSgB2lomlkI51zBYf7joD8D/gRWB+i+taMD8aiXb8oEr2f76Q4JQz1B29E0dRSZYjJ3VmLJIk8btJfwHg+7dmsGL+p7i9HsDBmi0bcbh9HDfiJGS5fWT5yJ5lFGQ42VGqRqk+M6GQpz7rxd4KC3AOz37+Kk9d0S5NExyBVJe4kDPU59tbWt7gcbGZm1vqTCYQCJqGIy0aKXx5Zh6v71dToCuKopljpNntgOoQ5w+K51LQNIJhA3zMVLq8yENOONGExSJs9McoJTUeiqrdBz7wMCAzM5NzzjmHGTNmoCgK55xzDhkZ8VkWNm3axMMPP8ySJUsoLS0lGHK43blzZ8RIn5qayuuvv85ZZ53FyJEjuf/++w94/f79+0e2s7NVOdyvXz/NPrfbTXV1ddxSA02tV6fTkZ6eHlcvQHFxcdy5sdSPFF+1ahWrV6/m7bffjuxTFIVgMMi2bds444wzKCgooGvXrowbN45x48ZxwQUXYLVa2bJlCz6fj5NOOilyrsFgYNiwYaxbt67J3w2goKAgYqAHKCwspGPHjhEDfX0Opt2JqKurw2QyxenrYpk+fToTJkyIS5f+l7/8JbLdv39/jEYjEydOZOrUqZhMJoLBIB6Ph1mzZkW+z+uvv84JJ5zAhg0bNCnwLRYLrhbqfQQHjzDSCwQCgUAgEBwElYUbkAg2nC7xIDGYorkh5/37O0wWA+dPGoPRbCB2HB8MgK1LB2rWbqVm3TY2P/8OA4f15ZtPFrDgqyX4fNo1FwO+AOEKdDphpD/akWNulrpAoJEjo5htdgAMJlOLrrmtWF1HUi+rk0mXR0w9jmWCQUIGeoDXgC8Pqr6qClVu6anjmpXadMFrBvUm0WImjeg+jhj0RtXBxldeDhkZFK/7jS/mz8ealEznfgParV23nbmZu2YP4JLhu7h93GYuO3EXHf58bqh0Ot+tfYHT+hzfbu0THJnIir/BsmBQiTzUIpJeIGhdzLboUgFGbzT9fhCInUXYTWbgV6AfXr8w0gtazqnnZLNx0a7I57JiNyKp/LFHpqNl89D2um44pTzAiy++mPCYc889l4KCAl577TXy8vIIBoMcf/zxeL1ezXE//vgjOp2Offv24XQ6cTgaXkIWVCN1mLDBN9G+sFOALMuRzChhfL745VVi6wjX01i9DWGzaZecqa2tZeLEidx2221xx+bn52M0GlmxYgXff/89X375JQ8//DBTpkxh6dKljV4nTFO/X/12HWhN9oNpd0pKStw5GRkZuFwuvF4vRmN8IMWCBQvYsGEDc+fObbRdAMOHD8fv97N9+3Z69epFbm4uer1e43DQu3dvQHUKiTXSl5eXa5wVBG2L0JQJBAKBQCAQHAQ/nnYz3HAzADKHXklsshjI65bJ/h1lBPxBPHU+aivrSMtRJ0Y6nUIgIBEMSgyb/Xc2/Ws26x6ZRtDnx2RWJ5efvftVXL1eb9RIrzeKIeGxhDvYNCO9NVmNZHQ7a1t0nePyOrJm9y5kWTWyJFu9BzhDcDQTNqoDSKhKnJY6N9W5JO6+Vl0HdSrxUQldO/dNeF5jEQpHCv3HnIHXXcdWo5l9gH/ECEzz5/PT3Lfb10h/1iZG9iylX6cqAHJSPNx/7nIe//gEIJ2xU/9K+StvkGxtZE1ogaAeNl9pg2XBYFSCyPKR/2wLBIczsdkqgjGGpKCioIt5t+p1OkA1gmzeb+P0xK9jgUCDEkl3H5Xrw0/PYe2POvR6dd6yemU1vS5McLLgqKa5Kefbm3HjxuH1epEkKZJmPpaysjI2bNjAa6+9ximnnALATz/9FHfcokWLeOKJJ/j444+57777mDRpUsL1yA+GzMxM1qxZo9lXWFgYZ5RvLQYPHszatWvp3r17g8fo9XrGjh3L2LFjeeSRR0hJSeHbb7/lrLPOwmg0snDhQgoKCgDVAL906dJIGvrMzExqampwOp0RQ3xhYeEB29W/f392797Nxo0bE0bTH0y766erBxg4cCAAa9eujWzHEo58HzDgwPO8wsJCZFmOpM4/6aST8Pv9bNmyhW7dugGwceNGgEi/AWzZsgW3291oWn5B6yLcGgUCgUAgEAgOAn+1s1XDMyVJ4vQrhnPFA2djTVIjkmM9gnU6dTsQAL3dSsqg49Rjggq3PHAdl1x3Hjkd4te38rkDkXT3Or0YEh4LpOpVz+waf7wHeSJ0enWCXr5nd4uuZw15gluMqtFORFQd2+zZod4PEkH+wmfcRdRYH+aVJ5u2Zvz8D9TI+Rx+jexTmiCHjwYjfVJGJmOuvoHjTxga2ec57zz279rBvs2b2q1dsgzDulVgMUZ/00ln7os5wsBvu1smSwTHFrFrEwelpjkRikh6gaD1sTjU9MixRnpvvchJnSwDqiGgrObASysJBBDjdFXPe3Pv3pTIttvVeJSuQHA4oNPpWLduHWvXrk2YrTA1NZX09HReffVVNm/ezLfffqtJWQ5QU1PDVVddxW233cb48eN5++23mTt3Lu+9994hbeuYMWNYtmwZs2bNYtOmTTzyyCNxRvvW5L777mPRokVMmjSJwsJCNm3axEcffRTJRPDJJ5/w/PPPU1hYyI4dO5g1axbBYJBevXphs9m45ZZbuOeee/jiiy9Yu3YtN910Ey6XixtuuAFQo8qtVisPPvggW7ZsYfbs2cyYMeOA7Ro1ahSnnnoqF110EV999RXbtm3j888/54svvjjodiciMzOTwYMHJ3TWqK6u5t133+XGG2+MK1u8eDH/+te/WLVqFVu3buXtt9/mzjvv5MorryQ1VZ0rjx07lsGDB3P99dezcuVKli9fzsSJEznjjDM0DggLFiyga9euEUO+oO0RmrJDzPbt27Hb7UiSFPmbMmXKAc+JPb6pf2Hh0FR+/fVX/vKXv9C/f3/S0tKw2+306tWLCRMmNLsugUAgEAgEIWIiS+T01k3CFzEwxRjppdDllaBaJunUHUogQJ+BvXj8Pw/Td1D8hMAfG0mvF5H0xwLe0AK+ngOkogsTViyElbLNxRC6ryQ8gEh3fyxTWy3z+6F9AOjBV+SgrlhrR7t24ZMPdGxSfbu2q4r/GxkX3dkEA7xsOnoUvH3syZFt/+DB+Pv354d3Dm2EzcGSZI51CHqDRZu2tFtbBEcfSpBounsRSS8QtDrheYirojyyr352Jr2sA74FQCc3LXOTQBBFa6XvMTSrwTKB4HAlKSmpwTXfZVlmzpw5LF++nOOPP54777yTf/7zn5pjbr/9dmw2G//4xz8AdV35f/zjH0ycOJE9e/YcsnaeddZZTJ48mXvvvZehQ4dSU1PD1VdffcjqPxD9+/fnhx9+YOPGjZxyyikMGjSIhx9+mLy8PABSUlL44IMPGDNmDL1792batGm888479O2rpmh5/PHHueiii7jqqqsYPHgwmzdvZv78+REDdVpaGm+99RafffYZ/fr145133jmgjS7M+++/z9ChQ7n88svp06cP9957L4HQkoEH2+5E3HjjjZo17sPMmTMHRVG4/PLL48pMJhNz5sxh1KhR9O3bl7///e/ceeedvPrqq5FjZFnm448/JiMjg1NPPZVzzjmH3r17M2fOHE1d77zzDjfddFOT+kbQOghN2SFm4sSJOJ3O9m6GBr/fz8MPP8wTTzwRtz7Ixo0b2bhxI7Nnz+Z3v/sd06dPF+tPCAQCgUDQDMJGcQDJ2LqpwSLrfTUQSQ8x6Shj0sBm5sY7D/i90Uh6WRZ+m8cCGQYjzoC/yenuLSHlQm2MMrY5GEJGfgU13f2ybamNHS44innp8fAaigo3xRjWZS4Hvmt2fe/PyCCLdSSzV61Vkhhc+AE/fVCoOS63ewbWFANblu1jyZIlXDP66MmTmqQ3cFPHrry2eysA3vPPZ28TFU9thdkYIN3uoazWBFzBi18t4K6zlaMio4GgbWjMJBOr2hB3lEDQ+tTV1gAQ9PuxyBbqggHqu77pdTJQDcDCjenA5jZto+AIJZhY2l96czdm3vYzcmYySgPHCATtzYGis+fNm6f5PHbsWNauXavZF5spcfr06XF1/OUvf9FE3G/fvr3B8wE6d+4ct2/06NFx+x599FEeffTRBtv+/fffx+2rf+1E12/KOQBDhw7lyy+/TFh28sknJ7x+GLPZzPPPP8/zzz/f4DHnn38+559/vmZfrDF6ypQpCQ33aWlpCX+HQ9HuRFx77bVMnTqVxYsXM2LEiMj+m2++mZtvvjnhOYMHD+bnn38+YN15eXm8//77DZb/9ttvFBYW8t///rdZbRYcWoSR/hDy1ltvNfiANodEa5YkoqnG9IkTJ2oEi8FgoE+fPtjtdtavX09ZWRmgpuM444wzWLhwYWStDoFAIBAIBI3j8/nb7FqRQHrNOqzq/2CCSPowD/zzDipKK5n/4XcMHzUYAJ8nGuEo7CXHBuE1Q5vqkmE0WwDQG1uWrlSNpoLdZZUA9O5Q3aJ6BEc+b760F+hJJhs1+xW+ByQcXEINqmKgziVhsTau6LHaA2SyLPLZ9cgjcQZ6gNEXD0Vv0LF9x3v0Gp7P0FOOrnX2ckwWzs7I5bNSNa28wWxu5xZp0cmwaMq39Lp7PAA7y+5l0aa3Oannce3cMsHhTGy6+0aPixkLxTpMCgSC1iG7c1eKtm6mbM8u5HQ1O06wnmFGTXefAkBx9eH1ThIcviihbF+NeWYdwAYoEAgERywWi4VZs2ZRWlra5tfet28fs2bNIjk5+cAHC1oNYaQ/RJSWlnLnnXcC0Lt3b6qqqti7d2+L6jqUqedfffVVjYH+vPPO46WXXqJDhw4A+Hw+XnnlFe688078fj+rVq3i5ptvTphiQyAQCAQCQSKiwylZbl3tQTTdfXSfHIqkr3OqCuqIkT5GeW132Bh/8Vjmf/hdxCIf8EZjXySRJvaYIMVghDongSZqucJGer/Xi6I0P/p1e6mayjw3NZTtISjus2MVj1t1Ltbh1ezXAb8HTuZdfuEG3uV15vwnk+tuK46vJAa/L3ovpZ11EonymA05qy96g+oocsUfLz6Y5h/W9LQ5IkZ6d0oK5Xv3kJbXoZ1bFaV7tpOrTl7Jmz8NApzsCjmICwSHhLDzmTDSCwStjqfOBYDBZEIO5a8I1rOqGvV6oBA4F5PBh0DQFJRGknwdKEJXIBAIjgZGjx7dLtcdO3Zsu1xXoEXMZA4Rd955Z8TbZdq0aRgMrZvutim4XC4eeeSRyOfRo0fzwQcfRAz0oEbVT5o0iWnTpkX2vfPOO6xYsaJN2yoQCAQCwZFI0OdHj7/tQtFD14lVVlRXqk4Cfn8okl6Oj6SH6PriS39cCYDXG80AINIHHhuEI+n3e91NOt5gMkW2ve66Zl/v+I75oS31XvMHxNTjWMVsUT3zr+Nczf5LgZND28NQHYsfu7MTwUaWjq+pktmy3oIUMgzs6dQz7pgr/+8ceg/rctDtPhKw6aKOYsFOnfj1+6/bsTWJufbUcBaN4/m0cHm7tkVw9BA7dJFFSiCBoNXJ7dYDgIDfH3HcrG8/1ck6YH3CMoHgYFAaGxwKBAKBQHAEIzRlh4Avv/ySt956C4DrrruOU089tZ1bpDJz5kyKiooANfLu5Zdfjijo63PDDTcwfPhwQFX8P/HEE23WToFAIBAIjlTKfirUfM7IaVla8KYSjniPNdLndPSG9oWO0cWvSQ/gcqpG1t4DVYOWqyZqxBcRaMcGrpDjhq+JSi59jJHe7/E0+3omveq06g+qkVQikv7YxV33KB1YTho7NPu7N3B8RVnDCd8e+mMBdvbzB64BINixQFP+7LPPHlNrnsuSRC+bA4BAp05sWbHsAGe0PXZT1Cls9qIF7K0ob8fWCA53AoEmGmJixznH0DMvELQXsl59N/s8nogyuX4kvYo651i4Ma9tGiY44onI/USeHUq9/wKBQCAQHGUIjexB4nK5+OMf/whARkYG//znP9u5RVHef//9yPaoUaM47rjG1/6bOHFiZPuzzz7D0wJlrEAgEAgExxKBOm1EckFXW6teL/Ga9EpoX2SH+rleJH1mTnroZPV4WYoqwY3m9s8AJGh9kkNGc53UtCmAJEmR9ejramqafT2DXnUODQSEkf5YZ1RmNXcwpMnHF+1uWCYV7zNwDRcAoBijjlGFhYW89tprJKXZW97QI5QqX+gZ69+f8oCfutrmP6+tSdes2phPOk756/+1W1sERxmhgZGw0QsErU/4MSvasjliL60LxOcplyW1tHNmVds0THDEs23RdnWjEcdxkZlBIBAIBEcrwkh/kEyePJlt27YB8NRTT5Gent7OLVKpra3lxx9/jHweN27cAc8ZP3685vwffvihVdomEAgEAsHRghLy+g/QNkZuKUG6+5BNPi6S3rl1j+ZcOXRgMGTgD4bS3StNjVgTHPGEjfRKM0JR/F41U4PX07QU+bEYQ2m4f9q4Rq1LGOmPWQbVRqO7nVOm4JwyBc+ZZzZ4fElRwzJ16QIHnVkMgOuOOyL7P/roI9y+Or7f/NHBN/gIo58jObLtHTWKl2+5njl//T+CCYwn7YFOjpU5SewRkfSCQ0BQE0nffu0QCI4V/CGHMFtKClV+dTv8PxZJ2guA1ydUzoImEp7IxmTxSnBQmzRFIBAIBIK2RoyYDoLly5fz3HPPAep679dcc007tyjK2rVr8fmig+URI0Yc8JycnBw6d+4c+bxq1arWaJpAIBAIBEcNAb9f87nVI7lCCgxXTTTbTdhI/8if89m+2RRphDEjRXOqXC9VftCjGl8lRRjpjxWMoZsl0IxQlOSsbACcFRXNvl4wdJ2c5FAqbmGkP2bRpaspbxWLJbLPP3Ikitmc8Hi/78D3SjA1FazWyOdfir7il31fHmRLj0wGJ6WRblCzCgR69QJg76YNVJUUt2ezIjjMse/KofgOE+cBweFJU50Hg7HvFBFKLxC0OlkFnQEo3b0TQ+iZSzymVOcpdV7xXAqah3tTSfzOSLp7YaQXCAQCwdGJMNK3EL/fz4033kggEMBoNPLyyy8fsrqvvvpqevTogc1mw2azkZ+fz7hx43jyyScpLm6aomXdunWaz926dWvSebHH1a9DIBAIBAJBFG9FNes//QkApY1CuKrLnQAYjLrIvuQ01fixZrmN15/JRm8PGa3qKTKkkIF2/epN/N8t/yAgh+po4vrkgiMffUihutvtavI57lDa7PD90xyGdlNXHPf61Sj8HaWtuxyE4PDF4twPQDApSbM/Nl09QEpaIQCb1lpIxLZN0QirQJcumrK0eo5JxxJGWWZYciijm9GI67778I4de9gotGUZDLrwu6YfANsPEwcCwZGLElRi0t0LY6BA0NqEI+llWUdfu5rBZa+nLu64QFAdZ1a4jr3lZwStQWhpt8NjSCMQCAQCwSFHGOlbyNNPP01hYSEA99133wHXe28Ob775Jps3b8blcuFyudi1axfz58/nvvvuo6CggMmTJxM4QPTBjh07Itt6vZ7c3NwmXTs/Pz+yvX379ha1XyAQCASCY4HvTryG6jf+p35oI91wRocUAJZ++RvlRdUA/PON7aSEDPV1LjmaEj+o1WQkpzoi2++8+gGle8vU48Rw8JghfEfom7gmPUBmfmcAAgnSmR4Is0FNWb69tKjZ5wqOLpIrNgGg1DPS18dXrjpyrFiU2KGjzikzjNfUDzEORs24pY9aulpt6EIvI8ViwXfyyc1a2qK1ia5LfzoATo+n4YMFgqYijPMCQZuRlJEJQOX+fdSEsolZdLq444Z17RDZFoZVQdNo7EaJX+5NIBAIBIKjCX17N+BIZMuWLTz66KMA9OjRgwcffPCQ1p+bm0vnzp2xWCxUVFSwbt063G41AsntdvPYY4+xdOlSPv74YwyGxOs1VldXR7YdDkdkHdoDkRSjOKupqTng8R6PB0+MgiV8XT/QfFWuoLn4/UAT0oHGEggqBKQjP2oy4A9q/h9ryIqCL9BMpZRfrOLVEvz1/guaztEuo5xbdgNQRB8MWNERIOgPEvC1rP1NkWtVJaqRo67Gw4pv1nHapUPp2sPFxHv28cQDnfjwzXQcdXlqnKKiaNrSq28Pxp43iq//9wMAlWXVpGBDMupb3OZDiZBrrY9Dr0Ytu4MBFEVpklyTQ2PNqvKyZo/tDIZwlHQ0daXTL2HUH7m/2tEu1w41YXm2k6HksxTPhAnaA8JORZKEpCh0YSZreIzsPG9CufTpf4u5hJvVc1JSIvtPvXDIYSHH6tOWcs1hMDGpc0+KPG7m7lMdtktcLhwHOK+t6Jhex4Z9ScB44Fl+3rqJnh07HbL6xXit5Rxucs3n0dbb0LPt9UQDF4IBpc1kgJiHivFaW3HYybXQmFBvNJFrtrClrhZvMBg3PjQZvZFtV6B9xn0tkWst4WgZ47W3XIvY35V4mR82zkt1zlaT80KutR3CViA4HJk8eTL79+/n1Vdfbe+mHBK++OIL7r//flasWNFkm6CgfRFG+hYwceJE6urUlE4vvfQS5gbWUmwqkiQxbNgwbrrpJs4555y4qHePx8MHH3zAgw8+GIlunz9/PrfddluDafadTmdkuznts8SsExlbR0NMnTo14rAQy/d2A1ZrYgcCwSFkWVoLTqoI/R0dbPj6WI7OS2/+KYeLpvYI5GuHkGnN5iiWUYqiRLQJ0/iOW6XpQIAtC0uwWGobP/kANCbXvO6omq56v4u1X+wFwOAMAKqx49P3MukH+CprIuVhhvQZETHSV5eo7/mgyxN3XPsh5FprErTpYI+6XVxczNfZ2Qc8p6imCoD1Zj37mykHq/PD9Ufvr7l+B5mp8alRjxiOYrnWmmyWx9DRGr+UlnfcOHTr1+O94AJMc+cir/sNgDn/yeSycxbHHT/tid/zTyCYkYFv9GhAXYasarOfqs2HixyrT1vKNQNggZCRfoniYeNhMn454ewdfLMmLBNu4a+fdyfrnDMP+XXEeK0FHGZyrWRXZWRbCSgNjlG2rY/qK7YuLMW0tvFsg4caMQ9tJmK81mIOF7lWl63KCrfZxGaTGkG/qqYSqVtnzXG/FUcze35iMmO1toObQYvkWks4usZ47SXXvC71HlGIl/lG3AQBd3H83PbQIuRaW+DSHR7y7FBy7bXXMnPmTCZOnMi0adM0ZX/605946aWXuOaaa5gxY0b7NFDQKEVFRTz33HP8+uuvkX1TpkyJs3f16tWL9evXa/YtXryYhx56iCVLlqDT6Rg4cCDz58/HYrHw/fffc9pppyW85i+//MLQoUMTlk2cOJGvv/6avXv3YrfbGTlyJE888YQmi/dtt93GwoULWbNmDb17945k+w4zbtw4Jk+ezNtvv81VV13VnO4QtBPCSN9M3njjDb755hsAJkyYwNixYw+6zoKCApYsWdJguclk4vLLL2fcuHGcccYZLF++HIBXXnmFW265hf79+8ed4/NFfdP0+qb/zLHHer3eRo5UeeCBB/jLX/4S+VxdXU2nTp0YXesjLSD841ob/2k1kNI8380SXXd8UuJ1Po8kAv4gG74uotfYHHT6Y88rzKDUkRnY3LyTKiX034lZRHPxoypGxtb4xEuzmRzNMkoJBgk/gbHp4rudnEVKZsues6bItd/WrSEYijBzOp10HJpEUrqdPuNg2O/XcN7Q4/HEaAu6nWDBlJka+azr2B8eV7cd6aG164MKfcbltajNhxIh19qG1aH/1dXVTLCmHVCufZKWwbbt2+lV52dwTfPGdv6gkauB2DiTEyuC9DAfuWPEo1mutQZhuUbHXOquvzu+vHdvAr17A+C57DLkKaphPjk1kFAupbEVgGBqVK7p9frDQoYlor3k2iqPB8VkovOeYrobNpGSk4vBZDqoOg+Wcf12ctb/OTn9sdMAHbt3p9F9k5vuOfGpkluCGK+1nMNNru1ca2DP/7YDIOmkBp9vd1I5639Ul9LodnIWSZ0yW6U99RHzUDFeaysON7m2z63wHmD1+enhDVKEmoh8XL3x4ZjunXnvF3V7RJlCbjvoBlsi11rC0TLGa2+59v2b6h0uES/zV3+m/o7p2xfQZ9xNrXJ9Idfajuq6I3ce2BidOnVizpw5PPvss5EASLfbzezZszVLCx9OeL1ejEbjgQ88yvnPf/7DyJEjKSgo0Ozv27cvX3/9deRzffva4sWLGTduHA888AAvvPACer2eVatWRSLXR44cyb59+zTnTJ48mW+++YYhQ4Y02J4TTjiBCRMmkJ+fT3l5OVOmTOHMM89k27Zt6GKWmLn++utZsmQJq1evTljPtddey/PPPy+M9EcIh8M474ihuLiYu+9WlUupqak888wzbXr91NRUPvjgA3r16oXb7UZRFP79738nTMVhtVoj2+FU+U0h9libLfFakLGYTCZMCRQ+etQ4DkHrIukBQ/MmHjqdRPAoWrhTp5fRGY6e79NUdIqEQW7mpFMvCaF/EAi51nyOZhmlBKLfK9ZIL+ulg5ZJjcm1zn3z2LRiZ+Tzvm0lpOaoS9UU9FAnvLVEo6MVr0dTV59BPUlKcVBdWYNO1oe/wGEhR4VcaxsGJ6WyoroCp9PZJLlmS0oGoHLPrmbLQL0sI0kSiqKQZq+jvNaCEpCPaFl6NMu11mL/fisFw11NOlbPKgDqXInloJnQ0loDB0b29R3Z7bCQYYloL7kmobrG/DLvXZaVlpKam8e1T/wLqZ3X7+6ZFY581gOr6H+vn7VPfk2PnIPLQBOLGK81n8NOrknRtkg0PEaRY/w7ZGPbzwnFPLQZiPHaQXG4yDVDyPCgBIMcZ3PwQ3kxJjl+XGeKeS58Hl27tL0lcq0lHG1jvHaTazHDk4auLwUDrdY2IdfajsNBlrUGgwcPZsuWLXzwwQdMCC3v9cEHH5Cfn0+XLl00xwaDQZ544gleffVVioqK6NmzJ5MnT+biiy8GIBAIcPPNN/Ptt99SVFREfn4+t956K7fffnukjmuvvZbKykpOPvlknn76abxeL3/4wx/417/+1eDSyFOmTGHevHlMmjSJv//97+zYsYNgMEhlZSV33303H330ER6PhyFDhvDss88yYMAAzXl33XUXkydPpqKigvHjx/Paa6/hcKiOKh6Ph3vuuYc5c+ZQXV0dqSMcLT5jxgzuuOMOKisrI+2ZN28eF1xwQWRJi6ZcJxgM8tRTT/Hqq6+ya9cusrOzmThxIg899BAAu3bt4q677uLLL79ElmVOOeUUnnvuOTp37tzgbzdnzhxuueWWuP16vZ6cnJwGz7vzzju57bbbuP/++yP7evXqFdk2Go2a830+Hx999BF//vOfG52T3XzzzZHtzp0789hjjzFgwAC2b99Ot27dAHj++ecBKCkpadBIf+655zJp0iS2bNkSOU9w+HL0jCTagNtuu43y8nIAHn/8cbKystq8Dfn5+fzhD3+IfP7qq68SHme32yPb4dT8TcHliirQYusQCAQCgUAQZfvMT2I+Sci6tjF8DBvfj7NvPIXMjmoUqaJElQk6XXRbtqhL3Sw69w78Lq2zXn63jgD4Pbq4OgRHP4Mc0QjkGv+BIxlqy8sAMLdgXChJEsaQx7leVjNANHu9R8ERz913n0pG9wNn6AK4DDXNqsctE4xZdrTOJVFbIxPOyuDPiEbMDhpzHAItSigqRkpT0/1W7NtLMNC2qcATkZvi5vwhe1CXwPARVPRsKhJzToEWJRjrCNnIcTHvE7mdHVAEgmMBWaeqkP0+L4aQYTqQ4CHVyzpAfe/vKrfGHyAQNICSQOr7Q2Zdb4cebd0cgaBZXH/99bzxxhuRz9OnT+e6666LO27q1KnMmjWLadOm8dtvv3HnnXdy5ZVX8sMP6rKEwWCQjh078u6777J27VoefvhhHnzwQf773/9q6vnuu+/YsmUL3333HTNnzmTGjBkHTKm/efNm3n//fT744INIivRLLrmE4uJiPv/8c5YvX87gwYM5/fTTIzYwgC1btjBv3jw++eQTPvnkE3744Qcef/zxSPm9997L+++/z8yZM1mxYgXdu3fnrLPO0tTRFA50nQceeIDHH3+cyZMns3btWmbPnk12aAk/n8/HWWedhcPhYMGCBSxcuBC73c64ceMazBZdXl7O2rVrE0a2b9q0iby8PLp27cqECRPYuTMaLFNcXMySJUvIyspi5MiRZGdnM2rUKH766acGv9v//vc/ysrKEt4TDeF0OnnjjTfo0qULnTp1avJ5oNoQs7OzWbBgQbPOE7QPwkjfRBYvXszcuXMBGDFiBDfd1DopdprC6ND6iwDbt29PKGgyMjIi27W1tdTWNi06oagouv5QenoL1uMRCAQCgeAYYPurH0S2PdjRG1TlsETrKollWSI9N5mkDNWoEWvEkmNGdZYCNU1gzdqtlP/8a2wV6EIKNlkKjR+Ejf6YIs8cTcm5z3NgR87M/M4ALTbw+UPn6WT1vy8gph/HGjU1JnT1otnGX39Sg8dfxES68gNrC9V71eOWOK1HPwakDMJIKBI7JGrHXD6s3aPDD0eUUJ+ce8c9kX37t25pr+ZEkCR4//bFPHfVZAhlTXhsno6/fdid/y7p2L6NExyeNDJGCcY6GQoxIBC0OlJosuGqqkIXes/4lGDccTpZBlRnsTrvoVnSRHCsEC/MXagOxkGjcPg4Zqkpgr2F2r+K7WqZzx1ftrcwem7ppvgyV8hw7CyNLytr+Xj5yiuv5KeffmLHjh3s2LGDhQsXcuWVV2qO8Xg8/OMf/2D69OmcddZZdO3alWuvvZYrr7ySV155BQCDwcCjjz7KkCFD6NKlCxMmTOC6666LM9Knpqby73//m+OOO47f/e53nHPOOZElmhvC6/Uya9YsBg0aRP/+/fnpp5/45ZdfePfddxkyZAg9evTgqaeeIiUlhffeey9yXjAYZMaMGRx//PGccsopXHXVVZFrOZ1OXn75Zf75z38yfvx4+vTpw2uvvYbFYuH1119vVh82dp2amhqee+45nnzySa655hq6devGySefzI033gjA3LlzCQaD/Oc//6Ffv3707t2bN954g507d/L9998nvN7OnTtRFIW8PO0yG8OHD2fGjBl88cUXvPzyy2zbto1TTjmFmpoaALZuVZdgmzJlCjfddBNffPFFxLlh06ZNCa/1+uuvc9ZZZ9Gx44HnPC+99BJ2ux273c7nn3/OV1991aKlCfLy8tixY0ezzxO0PSIzSxPZv39/ZHvx4sWR9SWawqOPPsqjjz4a+bxt27ZG02wciNzcXM3nsrKyuH2x6TVAFTp9+vQ5YN27du2KbB93nIhKEQgEAoEgEe5idWI3l+k8MWMd/v0BmmDvPGREjFIxCmo5JpJ+yGcz+KHrqeoh9Yyr4TGM5HbG1SE4NtBLEn5FYZurlr62pMaPDU0GwxH1zSUQ8iSRJfU+rKkT049jjaysPZT+JpHZL7ovo0Nqg8efyKsM4m3eemET//e8h+oKHSVFahRVD1QlTdhQoDcK5X8iOpgs7PHUsdcXdeZe9e2X5PXs1chZbYfZaATUl+aSLSNYEtKHHt9xPn061LRfwwSHBUqi0NxEx8V6KgorvUDQ6pht0cwn+phnrtbvxx6zVu+einJgA9CLX3clM35AEQJBozQi9pXwvZbAIURwjLDsDfjhce2+fpfCRa9B9R54dVT8OVOq1P/zboHdS7VlF7wKAy6D3z6Ez+7WlnUbA1d92KJmZmZmcs455zBjxgwUReGcc87RBFGCGsnucrk444wzNPu9Xi+DBg2KfH7xxReZPn06O3fupK6uDq/Xy8CY5b5AXTM9do3y3Nxcfv1VG6BRn4KCAjIzoxnJVq1aRW1tbVygZl1dHVu2RB0WOnfuHEk5H75WcXExoEa/+3w+Tjop6oRtMBgYNmwY69ata7Q99WnsOuvWrcPj8XD66acnPHfVqlVs3rxZcz6oSzvHfpf63xPAbDZr9o8fPz6y3b9/f4YPH05BQQH//e9/ueGGGwiGxqATJ06MRMYPGjSIb775hunTpzN16lRNfbt372b+/PlxjhYNMWHCBM444wz27dvHU089xaWXXsrChQvj2nkgLBaLJmu24PBFaMmOQOo/XLHrz4fp3bu35nNhYeEBjfQ+n481a9Y0WIdAIBAIBALY+dZn1O3YB0AtWZwwsool80KFbaQjjtroY9PdR8tlu4PkgT2pKtwIQa3WI5yqMiCpRi9ZOXDKc8HRRY7Jwm63ix11zgMeG77HPC2c3B2X14H1e/ewv1qdLG8tsTGGkhbVJTgysdtLyXZsJ0hnAIafrVrruw/KZ/PKnQnPMeHko1kOLElQUrQ+sj+AAX+vXhBSeDXHcfpYoiq0lMUet4sOPY9jz8b1rFv4I+P/+Od2bpnKxUNP5Mf1n/DR8m3Uur2YDRNw+yxUOpsfISI4hol1MpSFkV4gaG2MlqjuUe/3R7a3uGoZkJQS+Ty0azfmr1bLjTphWBUcHI48dZKrTxGOmccsQ66DXuO1+ywp6v+kDnDzDw2fe/7L4K03503JV//3vQA6DtWWmbQG3uZy/fXXM2nSJEA1tNcnnOn4008/pUOHDtpLm0yAukb63XffzdNPP82IESNwOBz885//ZMmSJZrj6689L0lSxHjcEDabLa49ubm5CSPNU1JSDupasciyHLfMos8Xr4dq7DoWiyXu+Fhqa2s54YQTePvtt+PKYh0TYgk7UVRUVDR4DKh90bNnTzZv3gxEA2jr29p69+6tSYsf5o033iA9PZ3zzjuv0e8QJjk5meTkZHr06MGJJ55IamoqH374IZdffnmTzg9TXl7e6PcSHD4II30TMZlMzUr/XlFRoREisYb0WC+nlrB27VpNu5KTk+OO6dq1Kx07dmT37t0A/PTTT1xxxRWN1rt8+XLN+vWnnnrqQbVTIBAIBIKjkYrlUW/gXQwjOXV3m7chHEkfO9GJzfgcCEiRSFOl3uQpPA5RCK8lKRQexxoFFhu73S7KfQdeJ1wXiozy1rUsVcT6vXsA8AfUeoQd5djD7w+i27mTYCiTWM8TCgA44YzecUZ65+TJ2P72NwBk/Mz6dy4QzRg2noeoO/GayGdrUvOiCY4VetuSWFpdzk63i/NGjWHPRtXRYfV3X9P/tLHt3DpIsdmY9cehXPL8U3ywdAlu32igO7fOGESSxc+YPsVMuWjtgaoRHKUEmxhJH/RHxzfi1SIQtD6mGL2mx+Ukw2Ci1Oehwq8dT/r8AWAx0Be3TzjTCZpDw/I/0LdvG7ZDcFjhyFH/EmEwQ97Ahs/N6NFwmS1D/TuEhNc/lySJs846K668T58+mEwmdu7cyahRCTIAAAsXLmTkyJHceuutkX0NRYIfLIMHD6aoqAi9Xt/irM/dunXDaDSycOFCCgrUeZ7P52Pp0qXccccdgGokr6mpwel0RhwFCgsLm3WdHj16YLFY+OabbyIp7ut/l7lz55KVlUVSUuPZAmPbnpSUxNq1a+nZs2eDx9XW1rJlyxauuuoqQI34z8vLY8OGDZrjNm7cqInCB1Vn98Ybb3D11VfHOSE0BUVRUBQFj8fTrPPCGQRiMzQIDl/EaKmJjB8/ntLS0ib/derUKXLuvffe22BZc1EUhblz50Y+jxgxosFjY71z3n333YRr18cS62nUt29funXr1uJ2CgQCgUBwtOL3qZEh3/AATjKx2pU2TxkvhSydsVn/JAlkWYnujxjplcTnhrLgK4pQbR9r9HOkRLY3OxtPLW1NUp1B3a4DR90n4tReamamQZ1XA+ALiPvtWKOkJAW/UxU4GZlRBb/RZOC8P9ZTTul0BO1qOl0ZPzaKuZJLOA01xWUdSQSzsgAYMKontqTGIyqOVXJNar/4FQVvzBJmiz9oWorFtqJDalpoS3Xm+XVXCgs3ZvC3eX1wuoUDmaBxgsFYR0XxbhEIWhtJkjCEUu0GfD5MobnGXrfWkXN4956AG4AfN4gIPsGhQfGI7G+Cwx+dTse6detYu3ZtwiBNh8PB3XffzZ133snMmTPZsmULK1as4IUXXmDmzJmAaoxetmwZ8+fPZ+PGjUyePJmlS5fG1XUoGDt2LCNGjOD888/nyy+/ZPv27SxatIiHHnqIZcuWNakOm83GLbfcwj333MMXX3zB2rVruemmm3C5XNxwww2Ausa71WrlwQcfZMuWLcyePZsZM2Y0q61ms5n77ruPe++9l1mzZrFlyxZ+/vnnyLr3EyZMICMjg9///vcsWLCAbdu28f3333PbbbdFgljrI8syY8eO5aefftLsv/vuu/nhhx8i/XHBBReg0+kikeySJHHPPffw/PPP895777F582YmT57M+vXrI985zLfffsu2bdsSOhbs2bOH4447jl9++QVQ17qfOnUqy5cvZ+fOnSxatIhLLrkEi8XC2WefHTlv8+bNFBYWUlRURF1dHYWFhRQWFmpsfz///DMmk6lR26Hg8EEY6Y8wXnzxRVavXh35fP755zd47LXXXhvZLi0t5ZVXXmnw2N27d0deBvXPFQgEAoFAEGVtYTT18lW3/ojRGKMkbqM2JEp3DxG7fCiSPnRQA5H0AV8orbBYkv6YI8UQTSn9S1V5o8eaQqlNTQmWV2oKTq/q8a0o6n9/QEw/jjXq6rogS6pzk8Vm0pQlZzoYcW5/zb6gXY180OFjIHMYwHuczQN0YgkWqiEUfZGUbkeQmO4x6wYXSXD2nfeimEx4ggGqnbUN/tW5nHjqXJq/gL/1lOJTL5sQ2rqAO8e/ztu3/hwpCwoHsmOWQKAF6bHFq0UgaBN0ejUK0O/14ghtW+oZooZ36w6oKaNX7Ti41NECwa4N6rxFqahu55YIBE0jKSmp0Ujuv/3tb0yePJmpU6fSu3dvxo0bx6effkqXLl0AdZ3zCy+8kMsuu4zhw4dTVlamiao/lEiSxGeffcapp57KddddR8+ePfnDH/7Ajh07yM7ObnI9jz/+OBdddBFXXXUVgwcPZvPmzcyfP5/U1FQA0tLSeOutt/jss8/o168f77zzDlOmTGl2eydPnsxdd93Fww8/TO/evbnssssia9ZbrVZ+/PFH8vPzufDCC+nduzc33HADbre70d/jxhtvZM6cOZr0/bt37+byyy+nV69eXHrppaSnp/Pzzz9rUsffcccdPPDAA9x5550MGDCAb775hq+++iou6PX1119n5MiRHBfjOB3G5/OxYcOGyNLWZrOZBQsWcPbZZ9O9e3cuu+wyHA4HixYtIivkqB5u86BBg3jllVfYuHEjgwYNYtCgQezduzdyzDvvvMOECRMSLpMtOPwQ6e7bmd9++41nnnmGe++9l169ejV4nKIoPP/889x1112RfXl5edx8880NnjN06FDOO+88/ve//wHw4IMPMnjwYE466STNcdXV1VxxxRXU1KiRVLm5ufzpT386mK8lEAgEAsFRS/G+FLqEtkeengm0bK3ugyFRunsAWaeAX8JdJzeY7l4OnWs0h42nwhByLGKxWKirq2NLXS2KojQYhRg2zgd8/oTlTcUQWpPU6xeWlGMNEzXopCABQNLF//7dB+bTsUc27z7zFQCeP96MNG0ajqIiDESj827jRJSYFIFpOU1LY3gsYtPpGZmSwaLKUpZVl7Ms2QoPPADAc0U7GjxPqqzE8uKLSDFrRBrMZi576K9kde7S4HktxWI0cWqv3vy4YR3Pfn4j+1+cDZwIwDuLO3HzmG2H/JqCI48lP9hZ8oODK/5YQkZW9F2kaIY3YiwjELQF7lpVb+itq6NbcibrndWsqa3iguyOkWNyUlKB74BrUBSxJr3gIAlnTalqPPuXQNBeHCgifN68eZrPkiRx++23c/vttyc83mQy8cYbb/DGG29o9k+dOrXRa/7rX/9qtB1TpkxJaBh3OBw8//zzPP/8800+74477oiksgfVuNxYHaAGmtYPNr3pppuadR1ZlnnooYd46KGHEl4jJydHE4TaFMaNG0deXh5z586NRMrPmTOnSefef//93H///Y0eM3v27AbLOnfurNHp5eXl8dlnnx3wut9//32j5aWlpbz33ntNzoYgaH+Ekb6d8fl8TJ8+nenTp3PCCScwZswYBgwYQFZWFhaLhYqKClauXMk777zD+vXRyD2TycTcuXOxWBpP8fjcc8+xePFiSkpKqK2t5fTTT+eGG27gzDPPxG63s3r1al544QW2bVMVILIs88orrxywXoFAIBAIjlVctamhLYkBw1RDRlsHozdkpPd6VAOY3ydFjGH1093LoWgXd62ahtKYKTxrj0Wys7PZvn07AK/u3sLETt0THqcLGUVryktbdJ0umdks37YVWVKflQ37RETVsYTXIzGSFwnk5QHxMiuMuV6EvX/oUOSPfySbdZr9wZjoBXuqkF2NUWC28osk4W/GcixKSgrBtDR0+/dH9vncboq2bm4VIz3Amf0H8uMG9XfeUxGN/ijckdIq1xMcAdS7Z68YowYzVJTpeeS5XZH9gdjxjUh3LxC0CSarDY/LiaIEceiijnP1HT7t5mXUusHlFSm7BM0gwe1iCg0Rpa4d4wsFAoHgIJEkiVdffZVff/21vZtyyNi+fTsvvfRSJDuD4PBHGOkPI5YvX87y5csPeFxOTg5vvvkmJ5988gGP7dy5M/PmzePcc8+lvLwcj8fDSy+9xEsvvRR3rE6n47nnnuPcc89tUfsFAoFAIDiWyM7bTHZevTTAbaQkDivByou0af/SMn2UlxgIBIjmvq8XSb9p7RYAklHXGPcUiaiEY5GUlJTIdnEoJX0iwveax9myNemNenW6UVqTDkBuirtF9QiOTGprdAzjPwT6XQXArq2NL68Qxn/CCdz+8R1sYoxmv29M9LMsi6wMjdHd5uC+Lr0JhjTeiqIQ8DecEeOlPdtwBgOc9MDDDLAnYZJ1fPrvZ9iyYhk71/5Kv9GnRzK0HEoeOO9Cnv38E8pqa1AI8vAFa/nrh30orjYd+GTBUUlQ41wYHVft3KK9J6rKowZCWRjpBYI2wZ6ahsflJOD308kcdZYr9XnJNEaf0Vq36txZ405CUYQfjaDlRF4JB5nVSyAQCBpi4MCBDBw4sL2bccgYMmQIQ4YMae9mCJqB0Gy0M7m5uVx++eXk5OQc8Njs7Gz+7//+j19//ZWxY8c2+RojR45k9erVXHTRRej1if0yhg0bxoIFC0Sae4FAIBAImooUo0Ru4yARZ7WaAtpRL5I0bD9RgtE16etH0g86UV3/2aVT1yzWBb2t2VTBYcytBT0j27UNGO8MZjMA1uTkFl1DH7opzUbVM31HqYh+PpaordYh0wKlamhdvu7SdwQ6doykuQ90T5zxQZAYWZLQSzJ6ScYg6zAbTQ3+6UIWlG8qS/i6qgy9wRBZe3jjksVsW7Wy1dppDv2+QUXBF1Db8b8Vea12PcHhTnTc4vFEs698/3myJsi+cFFa9IMwAAoEbYLOoOoUPXUuzDFr0fvqOQWf2iu6dq7Lo12zXiCIp+HJdOduqvOHZNDx4iXTefnS11nywW9t1TCBQCAQCFodEUnfSoTThx6I7OzsyNoUu3bt4rfffmPnzp1UVFTg8/lwOBxkZGQwaNAgevfu3eB6oQeiQ4cOvPfee5SUlPDjjz+ye/duvF4veXl5DB06lJ49ex64EoFAIBAIBHH89OFKvG7fgQ88hKRkqkrr+qmjdTr1cyBANOKx/pr0IeO9FFoj0o+xNZsqOIxx6KJTgTW1lZyYkhF3jMGkGuldVVUtusa2kmIAbEbVKaTWLaYfxxK/FRrZZxhM59Dnwaf3bvDY4Wf3Y8lnoTSDVtWZwz94MN5zz0XetStOlgkOLaPTslhUWUapz0OtX32nDTrrbDb+shiA2sqKVru2LvS+CgSDHJenZnfpktmy7B2CIx8l2LDOY9c2I/ldVedCr29ptECE6QoEbUJdjZrFy+9Vn8M0g5Fyn5ddbhd55uiymWZj1EHv89U5XDxsT9s2VHDUoNPJEAAMBpL6q84fS99cwvAL+7ZvwwQCgUAgOEQILdlhRKdOnejUqVOrXiMzM5OLLrqoVa8hEAgEAsExQUghvG1N2yudolHy9fer/4NBKRJWr9QzbOlCUS/h/fUj7QXHDpIkYZZ1uIMBfqutTmik1xujThzBYABZbl401MCCzizYsA4lFE2dbG1bhxZB+1Jb7cGUY4587tKvQ4PH9jyhgMyOqXzy6o8AKJKEf9AgAIKtPEcSwICkVCRJ4qPiPWytc7KksozhPY+j2+AhbFmxjDU/fMueDWsBKDh+AH1OHnXIrh1OVf7I+3M5obMOGEagEUOt4NglGHNfJKfGZAISRnqBoE0wmq1AWXTpLZ/6HP5YUczwlPTIcecNHsiXIb+7mQs6CyO9oHEamY4mFu9i/ioQCASCoweR7l4gEAgEAoHgEGE0t43/Y1hZ0VAkfTAQXUtcCWiN9GEDv06nDgODilByHMv0sqlZGZyBxCnJrUlJkW1vXfPXkw9HyFqNahSuPyAMKccS1RUBCtLXRT5bHeZGjgZ7SsxyCFYrkjvxPXf+n047JO0TaLHEOOF8WVZErd+H2aZmwSjasol1CxewbuECvnp9GsFg4JBdN8Ohypn5qwt54pP3AYSR/hgmGGh4XPL8X7MBeO3pbL7//LzI/pZmHBQIBM0jKUN16AxH0p+epj6T7npOwYM7dwU+AmDl9pYtmSQQAEi6RA7CYv4qEAgEgqMHYaQXCAQCgUAgOAjkkLH7xN/1x2Jv3AB1qIgY4OsZ2MMZ7gMBKVJWt3u/5phwJD2KJVSXGA4ey/S1q4rTKn/iCHed3oAcumc8ruann9aHjH4KqkEvoAhDyrHEEw8MxmNKP/CBIfTGqCLW9MQUsNsTHhd2NhIcWrpZ7ZyfFc124FcUTr70Ck676npOvfxqRl50GQABv/+Q6sffvOU2/nHpFWrdQW/ov/iNBQDad1PxXnXM8vi9Hdm9LTWyX9wtAkHbEB4TVu4vAqC7NfqedgeizluDOncG1OVS9lVaET7Bgv9v777joyjzP4B/Zmt6T0ioofcqUuQEERWwF8SC/sACnuVs53kWBCynp2dBz4KeCoooWFFAQUVAeu+9pEAChJCebN/5/TG7s7vZ3WR3k2xJPu/XixezU5/dnTw7M9/n+T6B8nyrylqf6nH6NDBrlvQ/EVGY41NZIiIiIn94ecjUulN68MrgLUhv70lvBaqO5gMANGnJruvYglsaoUxa1+Jf+nJqXto6jR9qT1lam9X20NVq9tzbvi4qufeLtC170rc0IpTdWgMA2mUn17Ou1AApPkXqTV9aXA1LZqbbOl0GtkNsYrTbfGo4hSCgb3wS1LbfmN2VZYhNSsbAK8Zj8JXXYMDl4+R196z6vdGO2y2rNR6U9y3VNwWlMdAZ+biiRXK5tnFtaLhxVaq8WHC+IGPDHaKgqCg+BwCISZAaeaZptPKyg9UV8rRGpYZSsVJ+vepAEO+TKOKY4f1+1Gq2epjLVh9Uj9OngeefZ5CeiCIC73qJiIiI/CI9FBBqt+AP4vNhb2PS23vSWy0CkgZ0l164BfKlhyBaQeqdphA4RnhLpnVKb33eaPC4TnS8lBLfYvE/vbU93b0o2oP0vP1oKQx6AVEoh2BLiWv2sZKsLKnxumzUzRdg+NX9mdq6iZlsvxun9K7fhcapUc+RzRsa9ZjRGg26tMoEkCvP23uSKZJbItdheNyHvNi3Q2rII8BT4IaImlJauw4AHA04FYKAeKU03Ne28hKXdS3WbfL01hMpQSohRSKlrYGeKiPBbdnZI+fc5vEqkIiImhM+JSMiIiJqAKsl+A+JFY5B6V3mK5160su97WuNEWnvSW+KlYIf9t731HLZH66eMXoe/1thWx5QT3q5EYDUGITp7luOU7kaqKGX66L2vdrUs0XdUrIS0b5HVmMUjepxTbqU/aDQoIPOqXGOQqnE2GkPAgCMOh1OHzuK08eOQldZ4XE//lAqlNj18uu4sFM6gPMAAJ2RjchaJKdrm5Q0NYBMAK3leaXF0m+SIDiubwQFH20RBYO9sa/V6behS4zUmDO61tjhQzp1ASAF6p/5ui+zKZFX9jPDXOR+PdGqeyt5umJfMQAgtfoojr39ZTCKRkQhdvjwYWRmZqKysjLURSE/3HrrrXjjjTdCXYyIwTsZIiIiogBVnK+Sp4Pas1OO0XsOsBefVTvGbHYL5NsertVIvVsVcUwb3dJpbMGNGovnILz9gazFy/K62BuFFJZKD9U2H2NPqpbinRdaIx2HYOnUCYDv48i37+k5ED/urhGNVjaqm70hmN5qxfv5R2F2StuitqU2LsrLwVfPP4Ovnn8Gnzz+EIw6XYOPG63RomfrtgCqAQAT3vY8BAc1b87XNhqtBlLK+9MAdgIA7rqyq31NeT1f6xciahj7/U7hscPyvM62celzdNUumTCOnj0N4Bv59b5T7r2kiQCn2tzDre0N/xyBqqN6qA3RsDo19Njz+JtBKRtRfaZMmQJBECAIAtRqNTp27Ignn3wSer3nBvBNVYbrr78+aMdrqEsuuQSPPvqoT+s+/fTT+Nvf/oZ4W3Y/Cg+rV6/GoEGDoNVq0aVLF8ybN89l+fTp0/Gvf/0L5eXloSlghGGQnoiIiChAlaWOVMDRcdo61mxc9ofRVqvrk4xjB6WAe1S01eu49fa2BAqlNFFzzntqaWoZWmul88bbmPT2IL2xxv9z5UxZGQAgOU4ai7RLq6o61qbmZOmiFMSgFNBoAADFBWU+bTdqwgW49LYhLvMyO6ZBqeSta7B0jI5FW1u9UGO1wOCUMaZtj15o3a07EtMzkJieAQAw6nWoLi9rlGPfdtHFUCulBkEVev4+tUTOly2u7R/jXNZzWcYgPVFQ2DOnxKekyfNinHrQry8rlqcnDBkO4DX59deb2jV9AalZuv/zCbj1pTFQilKGHWtGBkQOfURhZNy4cTh9+jROnDiBt956Cx9++CFmzpwZ6mJFvPz8fCxduhRTpkzxuo7FYoHVyiGQgiknJwdXXXUVRo8ejV27duHRRx/FvffeixUrVsjr9OnTB507d8YXX3wRwpJGDj7pICIiIvKH08NjewA8tXViUHvS24907lSpy/xBF0kBUItFcJSnVpD+9MmzAACFQhrTValyTU1JLU+ULSW9SvB8a1BedDbgfXfLknpFR6vzAQDrj6TVtTo1M0qFo+FHRjvfsyi06ZKBEdcPQHS81PhpxLX9G71s5F28So272naSXzv/isQkJuLW517CPW++h3vefA+aaCmYv2PFMmz4bpH8b/fKX2EJYIiMK/r2xxNXnQIAmC23Yk9+XoPeC0UglwaIztdWz7msxjHpiYIvs1MXAMCpQ/vlee2jYuTpvZVl8vQbt0922fbVpT2atnAUsQSrb9cLJoV0zWHp3h2Gm29uyiIR+UWr1SIzMxPt2rXD9ddfj8suuwy//fabvNxqteKVV15Bx44dER0djf79++Pbb7+Vl5eWlmLSpElIT09HdHQ0unbtirlz58rLT548iYkTJyIpKQkpKSm47rrrkJubCwCYNWsWPvvsM/z4449yj/7Vq1d7LGdd5bBarWjbti0++OADl2127twJhUKBvDzpmrysrAz33nsv0tPTkZCQgEsvvRS7d++W1581axYGDBiA+fPnIzs7G4mJibj11lvldPVTpkzBmjVr8Pbbb8vltb+X2r7++mv0798fbdo4hk2bN28ekpKS8NNPP6FXr17QarXIz8+HwWDAE088gTZt2iA2NhZDhw51+Rzs261YsQI9e/ZEXFyc3LjC+fN54YUX0LZtW2i1WgwYMADLly+Xl69evRqCIKDM1hEBAHbt2uXyHnw5DgB8+umn6N27N7RaLbKysvDQQw/Jy+r7jENtzpw56NixI9544w307NkTDz30ECZMmIC33nrLZb1rrrkGCxcuDFEpIwuD9EREREQBEm0PkoOa6h6AySiNA5mc4Zryyz4mvWgFYB+ftVaQvmufzgAAhUJKTWy1qJqwpBQJktUa25Tn4RNS27QFAFhF/wMiGpUaAFBWLd2Ud2ZP+hYlSutI89ixb+s61nTXqW9bTHj0ctz53NWISeCwHKF0zug9XadaGwUA2P37Cmxa/K38b+W8/yFv356AjpfkiPfgzg/WBbQPilzOGYAULj3kXRuMCYLTeuxRSRQUBtvQJrFJyfI8QRAwJkUaN/y8U1am2KgovHbrnQBekOcdLGC6YnKnsAXpFbae8t6URzkCdZZevTyuc/y7P3Hks18ar3BEftq3bx82bNgAjUYjz3vllVfw+eefY86cOdi/fz8ee+wx3HHHHVizZg0A4LnnnsOBAwfwyy+/4ODBg/jggw+QliY1bjeZTBg7dizi4+Oxdu1arF+/Xg78Go1GPPHEE5g4caIcCD59+jQuuugij2WrqxwKhQK33XYbvvzyS5dtFixYgBEjRqBDhw4AgJtvvhlFRUX45ZdfsH37dgwaNAhjxoxBSUmJvM3x48exePFiLF26FEuXLsWaNWvw73//GwDw9ttvY/jw4Zg6dapc3nbtPGdaWbt2LQYPHuw2v6amBq+++io+/vhj7N+/HxkZGXjooYewceNGLFy4EHv27MHNN9+McePG4ejRoy7bvf7665g/fz7+/PNP5Ofn44knnpCXv/3223jjjTfw+uuvY8+ePRg7diyuvfZal334or7jfPDBB3jwwQcxbdo07N27Fz/99BO6dOkiL/flM66td+/eiIuL8/pv/PjxLp9rXevGxcVhwYIFXo+1ceNGXHbZZS7zxo4di40bN7rMGzJkCLZs2QKDweDzZ9dS8aksERERUSAER/w72EH6mHgpKFI7lb09Li/1pJemRavnMekTFGW2bSxNV1CKCFrbiZOv85xaWh0lBUitAfSKtQdODhTuAgDoTczc0FLExP2EDoqtANIB0QqFgu3DI5VF9NyABwDGTnsQx3dsdZl3fPtWVJWWwOilTqnP1NEl+Ket00WFTh3QPihyObcHi0uIxaOz7sPiBb8g9+gBACUApKwcgnPDMqa7JwqKzE5SY9/8/Xtd5neLjcfKEqkhjVUU5eu/v191LRZueho7cmcAAFYdyEDPNpVBLDFFAotKurdViHXfl1qU0QC8N/gtKSjDhl3FgNGIzCP5SOjWvjGLSeTV0qVLERcXB7PZDIPBAIVCgXfffRcAYDAY8PLLL+P333/H8OHDAQCdOnXCunXr8OGHH2LUqFHIz8/HwIED5YB0dna2vO9FixbBarXi448/lp87zZ07F0lJSVi9ejWuuOIKREdHw2AwIDMz02sZfSnHpEmT8MYbbyA/Px/t27eH1WrFwoULMX36dADAunXrsGXLFhQVFUGrlTKevf7661i8eDG+/fZbTJs2DYDUI33evHnyOPJ33nknVq5ciX/9619ITEyERqNBTExMneUFgLy8PI9BepPJhPfffx/9+0vZ1vLz8zF37lzk5+ejdWupYfgTTzyB5cuXY+7cuXj55Zfl7ebMmYPOnaXfsoceeggvvOBoSPb666/jn//8J2699VYAwKuvvopVq1Zh9uzZeO+99+osa+3y1XWcl156CX//+9/xyCOPyPMuvPBCAL5/xrX9/PPPMJm8N3SKjnY0eh88eDB27dpV53to1aqV12VnzpxxW96qVStUVFRAp9PJx2rdujWMRiPOnDkjN/IgzxikJyIiIvKL9FA4cUAadvx+AEDwg/T2Melrd2xW2HrSW+voSW8f19mYnAktAIXF8zjk1HKYbedIotpzMEypkm4ZLCb/g/StEpMAAK2To3DyPHDyfEzdG1Czoauugqqrrcecl6EUKLy11kaj0KCrM6l4dt/+yO7rOhxBSWEBqkpL8PP7b0MTHY1OAy7w67iJMWZ0TD+LnHOtkH/+a2w7sRKDO5XWvyE1O4KgwN+em4q/PTcVPy74BY//X2sAetsypzOTdQxRUAhODe5Eq1V+neR0DXmgqgJ94hPl1xOHDsOOXB2AaPxzYV88cPnxoJWXIoV0L2IStHWuFZMcVefyLT/nARoNoNGgvLCMQXoKmtGjR+ODDz5AdXU13nrrLahUKtx0000AgGPHjqGmpgaXX365yzZGoxEDBw4EANx///246aabsGPHDlxxxRW4/vrr5d7wu3fvxrFjx+SAt51er8fx477Xp76UY8CAAejZsye+/PJLPPXUU1izZg2Kiopws214id27d6Oqqgqpqaku+9DpdC5lyc7OdilvVlYWioqKfC6r836jotz/7jUaDfr16ye/3rt3LywWC7p16+aynsFgcClrTEyMHDivXa6KigoUFhZixIgRLvsYMWKE36nm6zpOUVERCgsLMWbMGI/b+voZ1+ZPEDw6Otql535TsQfra2oCa7jdkjBIT0REROQXKUCeNCANlaXSxWZMYt0PDBq9BLZGAW496W1tBaxOY9KLVtfwiv1hmrVGamWrSIptyqJSBLA/WPXWWdZ+zlSWnPd731lJSQAAs0UKqsRq/Q/0U2QSxTPoq/wRBtwS6qJQgOyhGKuXoTC8SUhNk6d3/bbc7yA9ALRJrkbOOWn6s7XtGKRvQUQv51tyWhIAz+kyFexJTxQU7Xr2kaeNBj200VLjS5VTQ5kfik65BOnvGjUaTy36AMDjqDGqcL5Sg9R4NhImJz5eZtzxSGcseuUs0tsaYS2pcFuuK9fJ0/rqulPnEzWm2NhYOej56aefon///vjkk09wzz33oKpKyv6wbNkyl7HVAcg9pcePH4+8vDz8/PPP+O233zBmzBg8+OCDeP3111FVVYULLrjAY/rx9PR0n8voSzkAYNKkSXKQ/ssvv8S4cePkgHFVVRWysrI8jnmfZLvvBwB1rcb/giDAavV/6Ly0tDSUlrrfA0RHR7t0lKmqqoJSqcT27dvlzJF2cXFxdZar9jO1utgzwzlv46n3el3Hce7R7omvn3FtvXv3Rl5entflF198MX75RRoKZO3atS7p7z358MMPMWnSJI/LMjMzcfas6zBUZ8+eRUJCgsv7s6fn9+c8bakYpCciIiLygz29qqCUbgouvnEQ2nbzngqqScpgT2VfO0jv3JPesZLLOvae9Pb3Yapi0LSlU9oannhLaV1eJN2AaWP87wWvsffCt1YDAEwWBlJaChUA2B6SaIrP1rkuhSd7umK9xQKzaHUJwtTl0in3QhRFHFi3BiaDATXl5YiKj4NC4ftwF/Mf2ICOjxYBGAazlb2kW5LaWYLsBv9lAC6/7hL89uOvAK5ASop/vZqIqOGcrwXNBoMcpAeAS1My8EeJ1FNwfek5jEiWHsqnxScA+CuAxwEAJdUM0lNg2mTHId3W/kNpNaJi/3Ek9Hb0VoXZESwrPFmDrkEuHxEgBXKfeeYZPP7447j99tvRq1cvaLVa5OfnY9SoUV63S09Px+TJkzF58mRcfPHF+Mc//oHXX38dgwYNwqJFi5CRkYGEhASP22o0GlgsdQ8X4Ws5br/9dkyfPh3bt2/Ht99+izlz5sjLBg0ahDNnzkClUrmk5PeXL+UFgIEDB+LAgQM+rWexWFBUVISLL744oDIlJCSgdevWWL9+vcvns379egwZMgSAI9h8+vRpJCcnA0C9aeNri4+PR3Z2NlauXInRo0e7LQ/0Mw5muvvhw4fj559/dpn322+/ycMo2O3btw9t27ZFWloaqG682yUiIiLyk3MoMzM7FSp1cMfZltPd1zUmvZwSv/Y6tiC9bV2Tkb0MWjqlLRB3zuS5h2KrjtLDL0sdN33e2IP0RRXSQ1ujWem1xz41L5fHnIFo65UR0yYjxKWhQChsDXiWnCvEW7lHUGX2rVGXWqNFhz5SCvyCwwcx56F78dWsZ/3qqRIfZQawAgDw0R9Nn46Rwoe38yQmNhpzvn8dR02p2FuxE1+tdjzOCvawQ0QtlfPf2vmCUy7LLkpyPIT/o6QIVqe/5fapjp717yyPA5FnPtTl9iwNgoD8L1yDRKWr18nTNZVsiE6hc/PNN0OpVOK9995DfHw8nnjiCTz22GP47LPPcPz4cezYsQP//e9/8dlnnwEAZsyYgR9//BHHjh3D/v37sXTpUvTs2ROA1LM9LS0N1113HdauXYucnBysXr0aDz/8ME6dkurh7Oxs7NmzB4cPH0ZxcbHHYK0v5bDv66KLLsI999wDi8WCa6+9Vl522WWXYfjw4bj++uvx66+/Ijc3Fxs2bMCzzz6Lbdu2+fz5ZGdnY/PmzcjNzUVxcbHXXvZjx47Fxo0b6w3od+vWDZMmTcL//d//4fvvv0dOTg62bNmCV155BcuWLfO5XP/4xz/w6quvYtGiRTh8+DCeeuop7Nq1Sx47vkuXLmjXrh1mzZqFo0ePYtmyZXjjjTd83r/drFmz8MYbb+Cdd97B0aNH5e8BCPwz7tChA7p06eL1n3P2BHu6+7r+1R5ewdlf//pXnDhxAk8++SQOHTqE999/H19//TUee+wxl/XWrl2LK664wu/PpyVikJ6IiIjIDyLg6KWO0DwYdqSyd52vtPWkF+voSa+w96S3BfHVGk3TFZQigmB7IKbx0ktWqZYC7ecLC/zet0ruOevoMWVmb/oWYWTNbFgzMwEAUVlsPR+JusbGyQ8M9FYLir005PEkq0tXxCQ6gjJnc4771dAnKSYWgDTERnp8uc/bUTNQT2MOhQKIibW6NUIkouCyWFyDoIIg4K/tHL2aa5yCKsv/OR3AHgDA+yv/EpTyUTOVkCJPWg2uGRnUg7vL0xaL/6m1iRqLSqXCQw89hNdeew3V1dV48cUX8dxzz+GVV15Bz549MW7cOCxbtgwdO3YEIPUsf/rpp9GvXz+MHDkSSqUSCxcuBCCNb/7nn3+iffv2uPHGG9GzZ0/cc8890Ov1cs/6qVOnonv37hg8eDDS09Oxfv16j+Wqrxx2kyZNwu7du3HDDTe49MAWBAE///wzRo4cibvuugvdunXDrbfeiry8vDp7Xtf2xBNPQKlUolevXkhPT0d+fr7H9caPHw+VSoXff/+93n3OnTsX//d//4e///3v6N69O66//nps3boV7du397lcDz/8MB5//HH8/e9/R9++fbF8+XL89NNP6NpVysuhVqvx1Vdf4dChQ+jXrx9effVVvPTSSz7v327y5MmYPXs23n//ffTu3RtXX301jh49CqDxPuOm1LFjRyxbtgy//fYb+vfvjzfeeAMff/wxxo4dK6+j1+uxePFiTJ06NYQljRxMd09ERETkJ9FpXCtfGvw3Nq9j0tuiKUaD4HjhLd29QhonK5Cxwah5ibKlJFd6aXBSbRsHzmzyPzVpgnxT79hWZ1JCrWLvluZOAQsscuYOtg2PRMOS0jAsKQ0f5B9DscmAGovvf7dJrTLx13c/hlGnw7vT/g8AcGLXdnQZPMSntPeCIKBj+lHknAPEUPzQUsi4XrbUEYhnWhaikDp74gQ69hvoMi9dEwUB0l+u6PT32z2rDTIS/o2iii8BAGXVaiTFMpsXBUBha5SemgrrOcdwSuZqHWAxA5AaoFvOnoOpshrq+NhQlJJakHnz5nmc/9RTT+Gpp56SXz/yyCNyj+zapk+fjunTp3s9RmZmpktv99rS09Px66+/1ltWQRDqLIfd/fffj/vvv9/jsvj4eLzzzjt45513PC6fNWsWZs2a5TLv0UcfxaOPPiq/7tatGzZu3FhveVUqFZ555hm8+eabcgB4ypQpmDJlitu6arUazz//PJ5//nmP+/K03fXXX+/yTE2hUGDmzJmYOXOm1zKNGDECe/bscZnnvA9fjgMA9913H+677z6Px6jvMw4Hl1xyCXbu3Ol1+dy5czFkyBAMGzYsiKWKXHxaQkREROQHQQSMTim/FCEIPnkbk95kkhaczNU61vGa7l4KtpwtZA/Flk5tO1l0Vs9p5FLbtAUAWAII0ifKY5fq5HnVBrYTbgkUsMJ8wQUAgIQUPiCNZGZb2pYNZcV+bysoFPKP1tL/vonj231Phamy/V4xFtuy+Pp9y6vxBCEKqoQ0aUzenb96TiFsHyqldjPgR8c5GlyV69RNUjaKUH7U49ExtvsIvR6i2WLbXMSC22YD8n0HYFw0H0uSRiHvs6WNWVIiCrL77rsPI0eORGVlZaiLQn5Qq9VyCn+qH4P0RERERH4SbQ8AWnVIgVob/ICjPVW9vto19XBxkVSW1Ayz1570ZtvDDG2G9B5M5pqmLCpFAK1Tr1aTh8wKam0UAECh9P9cj9Zo5elYrRTkL6niEAstwamYC+TpopMlISwJNVTbKOn3wtuQGHVRa7UYecsd8uvq8jKft1XZqibGYFsYpy+8zu/evpAnCFFQdRoo/b6ntmnncbm3xsTZ6Rny9JHTHJeePPChPk9Jt91HREXh3Kb9AACTwQTFhX1dV7TdC2+/e1YjFpCIgk2lUuHZZ5+tc4x0Cj/33nsvunfvXv+KBIBBeiIiIiK/KBM0sLZpAwDoNbxzPWs3DfvzC321a8/m7n2k3spWq/O49a5BV4stSK8sOQcA6Ni9TVMWlSJAlFM2iGoP6axjEqRxpa0Wzz3t6xMXJQX5qw3SQzWOSd8yWAYPkKeHXdkvdAWhBusWKz0Uy9PXIE9X7ff2g6+6Fl0vHAoAKCk85fN29p70JdUJjMOSm9oBQCIKjva9pd90i9lzunp7T/qqWteUnTMyAVQBAJ7/vnfTFZAijx+3Bhqto3FxaUwyavJO49sHvnJfUauFuUsXiBo2DiYiovDGID0RERGRH+L7psvT2pjQ3PSrNNLDiZiEKJf5tqHFYbUIcjcWexpAu/TMNACAUSn1YFEJTDfZ0gmCID9QNXsIeihsJ1agQfootXSOtUqUUtSZrQzStwQpoxwNgJJbJYSwJNRQMU7ZNlaXFAW0j6pSKZtCTYXvQ6yUVufJ00fPsNdlS2G1uGd08bwie9IThYLKFvQ8m3PC43KjbYiU3FqNutLi4wFIGbw2HkttugJS5LFX5z5E64df1kqetsbHQ3+6GNVljjTYFRVS9h/9lCkw3HEHxLsnN25ZiYiIGhmD9ERERER+EJTSwwNjqQFprZNCUgal0j5Or+uDaUEhvbZaAdEWUK06dtJlHYUtVb66uzTOuBCCdP0UfqJtgXhLEwTpi23jxykV0vYmC29BWgKFkt9zc5EdHYv+8UkAPNcRvmjTrScAx/AZvhg/wPEg3mDm+USuGJsnCg17GNWeaam2ttpoj/NbJycDmCi/LizlPQj5T6lU4MiJTOmFIEC0WqHpKDWiL9lditrd8nWZzBoXaZgph4iaC1/rM97pEhEREQWgJr9KTikfbI5U9q7znXvS6wqkdPYxHbJqrSOtJERLY4Vrknk5SI6bh9qpSQFHkP7MiaMB7Ts1TkqVrRSkE5bp7okiiyAI6BErZUMoMOjwWs5BbC0/79c+omzjSB5Ytwb/vfcOfPbUY9BVVda5TeukFABnAAAXPrcOCffegdvefcv/N0CRxenaxljie+YFIgqO6ATp98CeIaW2dI10j1H7sbRGpcZVAwzy65cWxzRJ+SgSBRiUVShgNZig0kvXE1aFBgaD0mUV89m6rzUofNifUxiNxnrW9EFWFjBzpvQ/EVGI1NRIGYTU6rozmLLZIhEREVGEkYP0tVplKpx60sf3yEbRrxvduprV7t1q8X94YWqG9FYpKmKyuqcZNur1AIDkzNYB7Ts5NhbnqyrZk76FqThQhoReSaEuBjWSNI0WKkGAWRRhsFpxsKoCFyb6nq44o302BFuPN5PBgPMFp3A25wSy+/b3us2A7GwAUm85k6UDTBYDvt68AZ//9W9Qq/goo7lyvmoxFJWi4LuVaHPTGA8rMt09USioo+rOiGK/TzlnNLgt++GxR6GZfABAL3z4xxV4/65vm6KIFHECbMArCFj+1mrgwh4AgPjMJNz7zki89OAudMg8BQDQGEphKC6DNi2pcYpKTUalUiEmJgbnzp2DWq2GQtGAe8bkZOCpp6Rp270sEVGwiKKImpoaFBUVISkpSW6E5A3vbImIiIgijL0Dv1uQ3nbdZ7EIjnWstQP50s2u5UQBlJ3aAAyYEoAktRolJiNqrO496RMzMgAAhUcPB7RvtT17g60n/fkqTYClpEiirSkFkITqI/71uKbwlKLW4PHs7thTWYblxWdwSq+DVRSh8DGjTHa/Afjrex/DqNNh8Zv/xvmCU/UGV68ddCGeumYb/r1kMP7SbTTWHRkJwAKdEVDzSUaz5XxaWDp0wKb/m4mbPATpmQ6XKDSiYmLladFqhVArkGawSo0yK8wmt22VCiUSo/eiXNdL2l503NdQS+Zfoys5m50gQGdVwj7Awui/XggAmP7eAPx3ch6SuigBQcCyVpfhRsu2Ri4zNTZBEJCVlYWcnBzk5eWFujhERA2WlJSEzMzMetfjrS0RERFRhBEU9nT33nvSO0XyXdaxj2dv34dCySdjBKhs54uijp4scSm+95p1plZKtxx5xWkAgGhNYGPbU2TRiwnQAlAp3bMzUGTSKpSItf09WyDi1/NnMC7N9zSi0fEJiI5PgFIlpfsTfUhvmxgjpU1ed6QTgDUAgGGzVuPAa8V+lp4ihtN1i5iWBt20aR4DgbCyJz1RKNjrcADQ11Qj2jaskV0rTRT2owIn9TUetxcxC8AtAICz5VpkJrn3uKeWxs/7UfvqgoDEshMwoiuE4zno3OtqeRW12hHIBwB9UQmiMlIaoazUlDQaDbp27do4Ke+JiEJIrVbX24PejkF6IiIiokgjB+BdZzuPSW9/mC3WSl9un6+wP9xQ+HbRSM1bilqDIqMBZg/BjtjEJACAaAksuK60nXOdMvJx7GxHjknfApzM0UA0SeeLqGa2juYkO9rRg7LMFOADVHsV4ENs9dpBhfhpe2uU1qhxqLACQFsUVaQCYJC+pRBTUmA1W6DUuNYlosgGQEShoImOlqery8rcgvRptjHpAak3fYLKdRzWp6+9AE8vkqZPFMUxSE9wXBD4do8gX0bEx0MsKwMAKMtLXdZRwApAAdjviU3u2cIoPCkUCkTVM6wGEVFzwicmRERERD4o23UY+597H1GWilAXRU7xZ3VLdy+9PluoriOQbwvS28YH50NuAgCl7XwxeTgfVBopPX35uaKA9n34dKG0H1uPao5J3/zlH9dCoZLOKbVYHeLSUGOKUapwTXprAECBXtegfRl92L5H60qsm7kK+1/9FdNGLwYAlFb3xe78cw06NoUvq9X9d8haVyMx9qQnCjpNlBSoLy8647asW4wjaG/y8Pd844VD5ekjZ+KaoHTU3Am2WwlL9+6wduhge+H6OyHG2M4twZ6Bjve8REQUnviEjIiIiMgH6y5/AIdf/hQxltL6V25i3sabryyXesUbDYJTSnzXBxIKW5DeKkgJlWLiXHu3UMsk2PqklHroGatQNiz51vCu3WzHkB6esSd987dgTjoEe5IOft3NjtXW+qvGGlh2DUO11HDjfMEpv7ZLinX85k39mD3pWxKr2T24IvrX8ZKIGpE9M5enRjWCICDK3nvZw7YapQrAbwCALceZfpz8N/SKNHlajJUy/Ihxrg0+BNs9r3zjbGWDLiIiCk8M0hMRERHVw1hWCWNJuetMIXRPhe0BeKvF9cFYVLT0OjHZ4nVMeqPeFoS1LVdqOfoRAXpbsC3Kw/AHaq2UttTeo95fGpX9HJPSTJqtvAVpzsxmYMUPydC2lR6aMn7W/GRqpB6UMQEOl6KNkc4Ne09MXz11tSOYU6VnA7PmylOCH2ORhwaS9usbxl2Igi4juyMAoLSwsM71PDX+TE9IAJAOADhbXu62nFou0ccKffzNHWA5X2nbSNqmTJ/oulK89Fr0MgQcERFRuOATMiIiIqJ6lGzY7TavKjorBCWRKBSOsJdzD5bUDCkIarHAa5A+KdX2AKOV1ANBHcXLQQJaaaRx/woN7umnFUopEFdnuuE62IdnMFr0AICCEv8CcyTZu+4Yfv18I6rKakJdlDptWyf1ZDKfl84lQc1ganOjsv0G1Vgt+KmoAKKf6cZT27YDAPz51ecw6fU+b5cYq8XQztsBAIdP98eLP3zj13EpuBa/tg6fTvsM70/8FGZzAL8fTudVzZnz7ovlXpGM0hMFW+V5KZuJSuu5Aafedn9yyuB+zRKt0QL4QdqPnuPRkzP/m3ZalNI5aFLEuMxX2HvYp0gN/GpnoCMiIgoXfCpLREREVI+yXYfd5hk0ScEviI1S7ei96PzAQWkbk95qcU537/pAQqW29WqukHofKNS8HCRAZ+tJH69yD6gqbenurRaL38E4ADh1XgqulNe0AQDERZkDLWaLtmvVIZzNO4+jO/NDXRSvTuVqsHJJEgBAEKRzRTS696KjyBanVEFpa3yzu7IM5WaTX9snpKfL04XHjvi1rcGcapvS4LWlP/q1LQXXyU1HoW6Xivi+Gdj5yzGft7Par1vMjt+KirPuvW1N5VXSBOMuREHXpntPAMDpo57r8NZaqUHmgaoKj8u7Zkp1ef559qQnJ/7cZ9jWtSf1EWsF+O33xbA1GBEDbGxMRETU1PhUloiIiKgeRdsPAgBOYRC24/8AAApF6C6jBMG5J73jYYagsM+D1570SluvaMEiPfxWM909AcjQSCntdRb3ALpC5WgUEkhv+t62XrOtk6TgstXKBOgNYTaG70PG8f164dPZrQAAgiA9FI2rNUYoRb4YpQoPtusiv/Y3Rjr8hpsd2/rZ8OehK87ZptSoMSb5eWQKFYuHMeW9s6exFwGD1Mt21yP/wZnlG1zWOvfn9kYqHRH5q6ZCCq5HJyR6XB5ru98o8ZDuHgCKK6QGnDUGpiBvzvZ/vwVfjnsGS578uu4V5WsBP+4R7JvYzjWVxTUrg8LWyBgaDSxZWRBNbCRMREThiUF6IiIionoUL15tmxKhRwIAIK1Vqtf1m5pzkN45OuLak97z+HsKhQABCjmIr4lmkJ4Ala2Fx1mDe+ppe096ADB7edhaF6XtXFQrpYdnRjNvQRokjNs41FQ7jVFu+5pjbelGqXlJVGugtv2O/HH+rF/bKhRKpLfvAAA4l5fr17Yqhf1HbySAM3jxh65+bU8RRASglRqQ6adNw+GPXDMnxHSTxsS2r0NEwZPZSWqoZfXQuBMABiWkyNOexqXv3TYTAFClD939FDW9nSv3wjLiIpTFx9S/MgB/mv2JtYL09rT3dhanW2D9ffehYv8Jn/dNREQUTHxCRkREROSjttgpT3fsHrqeHy4xetE53b30v2tPetdtFUolFFACtsCpQhPGET8KGvtNgUF0P69VTgEQs8H/sUNVthNToZBSYu/K99zrinwTKX+xGsHW4EOIlBKTv7S2HLOVXoI0dSk9cxoAoFT711BsRLfz6JBWDUA65vojCX4fm4LD+S/favH9msnqZdzgkqxsl9dlIoPzRKGiVEl196lDBzwu7xjtaKB3vKbKbXlSrBScL9cNbYLSUbgQk/y95vf9mlGALY19ou0YCqXL8uQ0198I3Sn/GhQSEREFC4P0RERERD76FTPl6aw2/o3B25js480DrkF6wdbD0GIR5GcctXvSK5UKKKGSg/TRsXzITUCsrbe8RnC/PXDO3FBT7v/Yofae9JV6Ke25waSsa3WqT4QEvdWC1HNO8DOdOUWOcWlZAIAio3sGjvp0vuBCAEDh0cN+bdcpoxon3voFauXDAIC9J5X4dHUizJbI+Lsgf7jWHWZtlMvr+Bj+lhCFitko/cYrlJ4bWqkVCqSopZ7NZg8NQJOZZadl8PsS0PcNlKLtXlynAwCY4XouxiREu7xWaJg9joiIwhOD9EREREReWM1mHN20F6JCAVEQsF85FFfeXCotDGE8wDloKlrde9KLVsjp7muPSa9QKKSe9LZ9aGNdUwNSyxRt7+1eTwDYbPa/cYo9SJ8ce8b/ghGAWo1xQlgOT6xWoPCk2mVeLIog2r53ZQJ7OjdX9urCYLXC6mdjDPswGkc2b4SussLvY6uUFgDAmfL2mPrJ5Viyo7Xf+6Am5lxZWfxIYexjp3ut2nadY+Y4w0TBltK6LQCgsqTY6zqtNFLDmr2V7g08L+tdIE/XGNjgptmKjqp/HcDp98KfMeltvyv2YbUEi8vi0VeluLy2ckx6IiIKUwzSExEREXlwbs12LEm+BHuf/gA1M2agZuZMnIWAtIzQ9aC3cwnSOz33VgjOPemldcRaaWMVSgUEp3T32mjX4Bq1TPbgvLdAW1IraexQq8XicXldVPZzTSWlO1WrQjdURKRy+TsOsyj91Gu74OLsfli6KFme1xs/yXWQOi0tVEWjJtYuyjHGrNnPIP0F466Wp/U11X4f+z+3JyIj4SsAhQCA4gpmhWl2ap1S5iqdy2v775FSVxOsEhGRTVyy9JtvqPZef9sbgJ7xkG2le1apPJ1/3tfxyiniNGEjKoVouyexN0yv1dBYo3Ft/GE1MkhPREThiUF6IiIiIg8Ov/wpLDV61FwwQp438vLw6RFqD9Q797C1D8V3cE+0IyW+l570Ynw8AEATwyA9AUpb5NfiJdCmsD1oDSRIb+9JX1Yj9ZoymnkL4i/nMZqFMIvSr/5FGgv0kds7yfNuxlT5YalCw2wdzZXz8Biinzlt0ztkQxMlpaLVV7qPV1yf+8cMwXtTdgDYCAD490/Zfu+DwpO38eutIlB5OFfuDSn6Mc49ETWu+FRHA7zyIs9jfQ9KcDTeq90INDnOEZg/U+5jb2tqvhoyMpI9lZyHbGBnzrSSp/VnzzfgIERERE2HT8iIiIiIPNCfkdI3ik4BposuC5+x7AQ5Bu94qlFeKj2kSM80yz1vzdWuPc8USlu6exu1mikmydGT3uQlSK9USee+JYAeMTnnigAAZdVSrymThbcgDRJeMXo3ycgFAFizs0NaDmp6zs/D/exIDwAw6qXfpzM5xwI6fkZCAgDpoXtBSQp0uvD5jSbAOepibUgAxr6P9u3xW68JWBw1DABQtHq7NF9vaPjOicgviekZ8nR58TmP62RoHBlODFbXRjUpsXEApHl78hu/fNQC1Ep3L3gI0n/yyd3ytLHKPaMDERFROOATMiIiIiIPKvYdBwAoysrkeRde7Lh0CnWczN5T3jkNdlZbKRW/RmtF7ieLAQCnFq5w2U6lUkIBNWAw2NZlT3oC7IkXTF4GA1bYxo+uKS/ze98ZCVJP69R4qaeUyRzqv57IIwYSAQ2RJJwMdREoSBROv4SBnKFp7TpI+1EE1lhsRLceuGbgD/Jri4V1S7Mg13cispNcF+nuvBP6W2/FwVfm4syydbXWJ6JgSm8v1eGFRw55XK50yrait7pmYkqJiwdwEABw9Exs0xSQIo5f1XmtleUsck6sVsf1RVXe6UCLRURE1KQYpCciIiKqpXTbAXlaecAx3bGLvkHZ+BqTxSwFU00GR89mlVoqndUiIKF3Z4/bRUVroYJC7nWgiWIqanKku1d4aX5yvuAUAEAVQOryIZ27AgAsVqlhyObjqYEUsUVzDtKXnasMYUkkOzfF4q4ru+DIfvcUtUaMhDXV8R1rtRwrvLlyri3eyD2EFcX+PQBPapUJANj568+BHV8QcPWgLvLrvLz4gPZD4SutfxeX19bOnWHp0QN75/8O0XYdo00Jn6GIiFoSs9EIwLeGVp4bgUrj2R8o4L0I+X+HXSq0dnntKUh/w50/ytNRGbz/ICKi8MQgPREREVEtux99XZ4WYx29Ozxk0QsrCoUtSG8FWl0+FADQ7rZxLuv8+5+9IeAPefw+tYbpgcnR28nq5SFZ667dAAAWk//p7rVqKVuDwSw9hO3VpiKQIrZsTl9LXFKM9/UamUEv4M0ZrbFrs+sxn5iSjT9XJOLGYT3cthEAWJOS5NdRURxrtjlro5XGlRcBHKry72/b/ptqz9QRiNTYaHk6L4/B2vDiuGjyNs58nUQgo2uWx0WG226DcexYAIA2Kz2g0hFRw7Tv3Q8AcPLgPq/rxNnq9xJbQN+ZSrkTAHDsbCu3ZdRMqJrwPrN2t3sPN+pX3eK4jzcb3M9BIiKicMCnstQ0pk0OeRpgX4mwPT5QKgGLlIJL1GohGHwb205UKiHYt4OX9MeC4HYBKXbvCSEvB9Drgago6X/nfQgCxE6dIRw/BiQmAuXl0oZKlRR9Ea1Q2da1wtHixrkMzvPtMhUCTAIQbRGhA2B/rKWLiwaqdIgG5Pk6AGcs2xCzcAXS738Z5z54BjW3jnV7ezELVyDj7lkQDCaISoUjZ65KhbK7r0Pi/GVu2zrvU73nKFJenQfdRf1x7utXYclKk/Z57wuAfexblQpFH8/wePzaMi6agtjN+1A9tA+KNsxzK2td76W2uA++Qfpjr8PUIQtFi16FcUD3OvflaV7Sw68h+b2vUT20D2K37odu1GBU3XQpUp95V17PW7ns25Y+OBFl7zzpWrhvNkP9wHyY3r8TuHko8PgCqN9bBdPlPaFedxQQBZg+niIts8vLgfDFPIh3TAE6dAT27Ybw7mygplo6R7M7AyoFxJtugbB2DbB/r7ReRRmQngGUlkCcMlWal5cDYd7/gHYdIN44EUhKlo5RVgrhs0+AvbuAzl2B0hKgpgbiXx8C4hMgfPwBUFoKlJcBUdFA9x7A6UKgrBRo3xE4XSCVJTER6NELYlw8hKWLId58G3DNDfV+Z15tWgdhznuAxQxotUB6BsRJkyEcOQzx0suBQ/shfPIh0Kc/xMn3ON5PXg7+8vnHUEbHQsjPgzjtAaBPf2nZkh8gfPMV0LM3xPsfdmxj/xz++E3aty/zV65wfDdjap2b+3ZD+HgOxHv/6ji2p++zobwdp67jbVoH4dOPIN49DRj2F5d9qZ98F9ZhnYD2qbA8NhbISpKW1T53a8nsfwuibanmRUGAIIoQ1SqUTbsRifOXofzOq+R6BYDL307MwhXI+Ou/UH35UJgT4pA0bwlEhQBAgGC1omZgd8TsPCzNUyoAkwUCADOkiyIRAKK06KM3YBCAlQDyamow8KefkFlUBN0lbQGExxju0XFa6KoMLj8vCgVwHtFIXqsH1gKXA8BXy6V/AKwxUXhB1wXJKECbF6TxwXWzZuGMZVvw3wCFFftPd1xlJfDdImDMFS51lEqtQTqAdt8tBNq29avO0doe0OUUSeMHb89JgvD91xC7dYfw4/eudUpZKYTvvwZO5jl+b3wRaD1ZX73XRGIWrkDGpGchAND16QxzVhrif9ssL9f16QycOovosir5umwGAKNSiXMZGVDMfRaK8+V1XjtYo7Ww6gxQAS7XfP76LwDzv1xvHE/YJ2ps5QWggBZ/gwErAJj+Iv0eJKbHeRwjlJoHQRBwV5uOOKGrxpen81BhMaPaYkZs7aC7l2uffqMvx9Gtm1F8Mg/W88VQrvlDWgeQ1vdUR9TaX7/27QH8AOAG5OcnBHYt482+3RA+eh/o1RvirXe6Xrd528/XX0JY8gPEa24AJt7u0+dgv57061rX277qKltd7/10GZQfr4Hl3lGO67X//Az19O/lVUxTLgImDoX6jo/QRmeCvk8XRO05BigEj/d8rarO4+Ll2wFBQPmFg4FJ/dzehr2+shMVAm6Jjcen06aiSqVGalYirn9oNBa/u8ptWzFDGhPbZDD59pkRUaMy6qWLgPOFBY6Zb74KYafjvqJq1iwAgOKtVyEcOeKyfQWAKPwVYgkg3Nm4ZWvIYF4m2/by/2oAZkAtOubZ12vrZR+6Pp1R8fTdSJ/2EqwJsVCeLnZ5Vmh/vuN83wsAolqFonnPA4DjOhGBX8MBjvtcuWxe9tcFjueJOgBQKxFtskCnVgLds13KCXh//uk8f1J2Nlrn5SFaFAHbueDJWwB0PwJRAPSpn+PM+dX1vq/ENq5PO5WixW2dnv3iUPxbJS7asB4DNm1CtPI3eVldn6vzs9Taz1Xt54AVoR/+LhLxMyMicsee9NQkFJB+eCPhH+zTFotjnsHg+/bO23lbTxTdt8vNgaDXS6/t/zvvQxSB/Hxpurzcsa3FDEG0QnD6nJVO+3X+7JUeyqK2ilBbpPKonedX6eTXzv8DQNS6nVBWVCNq3U54ErVuJxQGk3R8ixUKk0X6pzMgZtVWj9s67zPmjy0QAMRs2A3l6WLHPnUGl315O35t2j1HIdj+91TWut5LbdGrt0EwWaA5dgrqAydclnnal6d5Mau2yuURrCJiVm1F9OptLut5K5d925hVW93KJvx5FEKFHsKf0vsU/jgkfZ9bcyHozBD0JnmZrOAUhEMHAFvaYhw/BqG6ynGO5h6HcOyoNH/DWgjlZRD27ISQmwNh62ZpmX3bglMQjh2FsOp3KcBuV1YKYdtmCAYDhAP7IJwuhFBeBhw/Jm2TmyPtF4Cg10HYvRNC0VkIRiOEY4el8tRUS9ut+h3Yt1f6e9i316fvzKtDB6W/H0Aq26mT0vv84Rup/IcOSn+L2za7vB9FwSmkHjkC1e6dEEpLpPdhZyubcGCf62dg/xzs+/Zl/v59EMxmYL+HngjHj0E4X+x6bMD9+2wob8ep63iHDkLQ6YBDB933dboMyh92QPn2bxDOlMuLap+7takP5znqNNu5qTCZ5frEuV6p/bcTtW4nFJU1iP9+FWLWbJe2tYpQWKV6U3PghGOeySLXmfZ6TwFAoTcgG9LDjPYAUqurceGOHWh36hS0B3MdBQ3xHaY98OWcBluhFBEPvVvdL/8u1OjRV9yHVih11P/BLjiFJaXtfIqrrIRi8bdudZShphqpABLOFfld5xhsDe7a2VKgJ0fppXrw+DH3OqWsFMKq311/b3wRaD1ZX73XRKLW7XTUP4fzEL11v+v12uE8qMuq3K7XtBYL2p4+De3W/fVeOyh1Brdru0D/1be9GkAUDBgHYLBSCWvHjgCAmgp9435wFHYEQUCs0tF47ZNTJ1x+lwB4vfYRFI7HEXsWf+NYx76+pzqi1v4UggDpkTmwfHlHHN1U4v+1jDfHj0EoLYGwfq37dZu3/ezbK10eeLpu9XYNGMi1rrd91VW2Ot67cKYcypeWuF6vrTzo+re++giELTkQSqqh0BkRs/UAFAaj13u+DF05Ltq0CRdt3Ii0o8fdjgk46iv5us8qIrmyQmowZhOXFIM2XTPQvkemx31cfudwz58RETWpDFtjn6qS846ZRw+7/E13PibVxRUJCW7XDtFwf4YVlv9MgCD6ed10OE+6TqvWQX262O092p/vON/32u957fe4te9TG+sarq79OR9TbWvMrjZZ3MpZ1/fmPL9Lbi5iPDwP9VZOBQB1WRUCYVTFus2LilEivqISF23ahBg/PgclgII2bfD2o4/ieNeuXtcJ+bkZof+IiMgVg/REREREdXHqBSpCDGTIvKZhL5ZzkJ5XdhQgb2PRy8sbkK6yTXIKAKDGII1XrVa493Sh5qE7gKHt28uvh4zrG7rCUNBkaKLQMVp6OF5u9r1Xc1aXbvJ0TVlZQMfOTs8A8JH8+nwNh1cIG04/K2IDLp4EQcCltw7BqJsHe1yekOIemCGippfaVvq9j01O9rpOZXw8AKA4ncNSUNNSRbnfqyhVCres+L5adOutKE9KwleTJsnzTrVpE2jxiIiIvOKjXCIiIqK6OAfpwyVAD3jsSR9O5aPIoqgnJXmcLdAeCHu6+4LSMwAAkzU8hoygxqcE5CGcAHjt+UrNi0IQcEMrR9LfIzWVdaztoNZqMfJWKcfxiV07Ajq2IAgY0e0UgBUAgKPFgddVFGZ8uKiJ0vKRFlGoxKdI9W11qYeMHjYxNVJK/JPt2gWlTNRyifFJbvNUqsDvj6tsDUycLbvqKpzo3DmwHRIREXnBOxoiahCrxYLyakOoi0FOyjyk+SKiwBTCdTzbivPO6fdCm6xNUNiD9I55xWeZvJ4Co6wnSK9QBh5YV9vGp05PiAEAmCy8BWkJNLBwPPoWROX0XX9/9hSsPj4V10Q3ZKRdiVJQQBrJFth00tsIwRR53M+hdsmuv0XDB7MhEFGoKJSOnssmo+dnQv127w5WcaiFOXIsw+W1Mi7ObR2VShFwFryY6mqP80+3bh3YDomIiLzgEzIiapDFT36O5VFZoS4GwXHvsacvU8sSNRYToiA6BR6Met/T+DY1e7FqKnTyvBsnn/eyNlH9ohXeA/FCA8ZSiIuSgmdmi9TDutrExiTNmr1yYmqPFkWrUOLadCkNrNmP777HRX9p8LFfuPk2AO8DAGLUxgbvjxqfaG2c/cSaXYMmmmT3no5EFByJGY4g6eI3/u1xnZ4HDwarONTC3P5YFxzNcTTUiop1v49RKASIYmANRmtiPXd+sXJ8OSIiamT8ZWlhNmzYgGnTpqFXr15ISEhAQkICevXqhWnTpmHDhg2hLh6FofkvLsUii2vayDVf7Mb8F5di/otLUZ3EscXChaER0weX29LSEbV0Clhd0t0nt0oMYWlcGfVmAEDewTPyPI2GQTEKnM7qfax4RQMeSGls6e5NFqmXlY5B+uZLEGDt0AEAoIzWhrgwFGzdYh0B088Lc6GzeK9T7DRR0Uhp3bAxXrtntgZwHABQrItp0L4ofFjhPr6wtrtrmmFFgnvPSSIKDoVT486TB/a5DMFlpzKbg1kkCoE9ffqE5Lg9B6TghU8H48ixDOTlpeL6Kdke11MoArs/Tiwr8zhfZJYoIiJqZAzStxDV1dW45557MGLECPzvf//DwYMHUVlZicrKShw8eBD/+9//MGLECNxzzz2o9pLSh1qe0tRUj/OPrdoZ5JJQsP1xYG+oi0Dk5nRR8MexjkoSYUhKkl+H0z15q/ZSAyqVmpdz1DjS1N6Dqg1Ldy9tW6WvsL2uP3BHkcnSqROMY8cCAJRRmhCXhoJNo1Ag1vb3flJfg3y9b/eVDcnUAQCJMTFIiilp0D4o/Chi3OuQ1K6uDTrUDNIThY1fPnjHbZ7Sh8ZaFNnOpYe2486Lnw3B9I+HIynF832M4N7eyydtCgrkaecwP4P0RETU2PhUtwWwWCy48cYb8emnn8rzoqOjMXjwYAwbNgwJCQny/E8//RQ33XQTLLyQJgBWLxefypgAr3IpYhjN4ZPSm8juaGk7n9c1AqgWG15XpWRVo8rWK7S2UN+fp7ZOAgCc2FtQ94pEPlLUcU5XnDsX8H7VcoBf6k1lsfLhVjCEpO+axhFUqyrT1bEiNUdKQcD97brIr33Net+QTB0AoFWrsee1Z13mnamKwdEzcTCZWd+Eg0BGvzi/u9xtXuvOrsEgQclHWkShNOW12fL0oY3r3JYLANRGDkNCkUdrMMjTBqfr29KUFE+rExERBYx3NC3Ac889h19//VV+PXXqVJw6dQpbt27Fxo0bUVhYiOnTp8vLV6xYgRkzZoSiqBQhlBlJoS4CeWC2Nl6a6+PnkxttX0SNpXqc7ylsv7jyBqzqP7DRyxBOQywLtlYCai0bTlHjKDN5b6CV1r59wPuN0dp7tkiNQK0ib0GCYcvQoSE9fqe+DUthTpEpWqlCuyjp99rXn8yG9qQHgLT4eACOxkRPLL8UPf4xDqNeuqTB+6bQUHg4g4RaLSRj4qOCVRwi8iAlqw1ufe4l+bXFanVbJ5bZOimEKgo9jy1fH4XTuVwd58jaUmWbLsrIgF7LoZ2IiKjh+ISsmSsoKMBbb70lv77zzjvx0UcfIcWp5V9sbCxefPFFl0D9W2+9hcLCwqCWlSJIDXtGNXfqmL+EughEbqx+3ASb2qYDfqTnroyOdj2Wt23DKErfpmsGAKmHClFjaB0V7XWZSh146nKtyj4GveNhl5VnbpPTxYR2bO645MAeilLks/91iz6G6ZVyHRE4qTf+fAioAQBEKaVcEjvzkhq8bwovfUZ0gUqjxKW3DYFKHfyhkIjIVVbXbvK02WhwW14ZHx/M4hC5sFgDC31YnRoQVsc6rmlVZjNOZ2XhgwcewFe3397g8hERETFI38y988470Ov1AICYmBjMnj3b67rPPfcc2rWTUgnrdDq8/fbbwSgiRSDRGHgC1eXvbcHnTy7Csm3u6QvtzGYLVtaxnBqP0eT54anRwp8Hap5OJaR6nG+o1QCgJivL43o1lXqIYRKoV9hyk1sbMYsGtWzKOgLnuqrKgPerVduzPTiGU7KAgZXmLi4ptI0EKHTsNclZo96n9fuPuUKettSR0aM+EyduRgdMAwC8OnYVAMBsUeCdFV3wzoou+PuCfvhoa/+A909+cur13phXKgMv7YHb/jkebbpkNOJeiShQgiCgQ59+AADRw31Jvz17gl0kIpkSgV1XOI89b3EK2CutVhzo2RMAkO9lWDwiIiJ/MArTzH3//ffy9MSJE1160Nem0Whw1113ya9/+OGHJi0bRZ7CIqlVtJAQeM+ocyVFEOJjUTWwo9d1lr65EcUelpeUurfKpoZ5Z12Sx/klOkc6rwrwITsFn0HVNCncT/Tp6dN6Vi/HP7ojvzGL0yAK2zisJoM5bBoOUGRTCt6D9ClZgacuVyrsAXlHkL4IGSgzSD33y3RaFFdqUFypQVl1w3vUtnQGS3jUB607p4W6CBQi9jNwXWmxT+s7Z+rI2b0z4OM6p0JXKaTMHVZRwGNfDMBjXwzA7OXd8NG2AQHvn4Krjp8kIgoz7eUgvaWeNYmCy9/b5OKkJGwdPNilJ31ZsutwkPx5IiKixsQgfTN2+PBhHDt2TH49bty4ercZP368PH306FEcOXKkScpGkamiUupBbzhxvkmPU1ZQ4XF+ZbX7+GbkzP9bhSOl2R7np0U7xvQ0wL9xtk4URWZQf1debqiLQDbvP/Agll57baiL4VW4BOoFheNvfvXX20JYEmou6kpNrWyUhjOOB7dtUYDLljwEALhs3q1o9cC1aPXAtbjs36Ma4TgtmzHUBbCJjuNY0S3VoATpYbZG8O1xQ3b/AfK0vibwsYtHjBghT8dpDHhz0i7cOjwftw7Px/WDCwLeb0tWwTTVROQDbYzUkcPTExuOSU+hpNL4/xzx56uvxp7+jsw71U5DSFkVCghsIE9ERI2IQfpmbPfu3S6vhw8fXu82gwYNgkbj6MlQex/UsultqdG1XdJ93uaPxLbydNk5327OKgs8NwIoraq/VXZlYTH2dOwEALAoXIPW6xbuw3dHpUfXVh97mR08luTTet7sXZWLVTFS2myDl0O+c9N8fJcT+BACgfpwZRVqrO08LjtV6Uj1bYZ/vRon/veiBpUrVA4WnKxzuaezL78qqUnK0tLVbqle258qqXfmmp3l+O6YIxx16tA5fH/It9S6zUF0XBQ0UdLfZ1F+CUxGM0wNGI6ESG/1/hBLoWxYevoYjRZANYZ1yW3Qfqh++jBo03j5ncNCXQQKoTZR0sNsk2jFgsJc/FZ8ps71NVHR6DLoQulFHfVQfdLTHfcoFqsVj4w7hgUPbMGCB7Zgzl3bA95vS3U4tQ2+uu02l3nVpYEF27zFMzhiD1Hz0GngBV6XpRb7llWFmoa+1jV87WHemjuxEa6L9/fpI08f79oV650aBX5xxx342anDGxERkb8YpG/GDh48KE9rNBp5vPm61F7PeR8UPgpbt8aRrl1d5gXj+cY5H4LktemyHA/Llry61uM6JrPrflVKz2NGFfhw/EM/bUZZq0wAQHGsa8+PHV9vh7mTtGzf0ap69yWKwJYTvjdI8GTNnA2oaS8d84zK0QDm5OFyAIDRYEXygGSY2/s/pmKxrYe7FQIqKy04ovBvH89929X7QsFR1jNo5bLIZGvgUO5jT6dz9a+Cuh/dNp1KXY08ba2jNfR6AAs8zF97unPjFyqCmC0CdhW39bisVK3Gsc5N8/lUdpF+p8706whzR+nvK6+gBj+/vg6mrq2b5JieRMeF9gGHQiFg3BSpUYxRb8LCV5dj4avLQ1omimytNN7PaYWyYT3plbaUkZuOdcQACLBCwO/jZgEAtv71M5g//xbmz7/FqB5FDToOAWeUoX/42qpDaqiLQCEUrVBCAQEigBO6auTqfLhmtDWutTYgaqt0CkRYawX746LYiM1fP/as3dhGxN61Rxtt/wU5p/FrR9+GISKi8BaXlIyYxETPy5x60rNdTvA5p21viUw1/r//mHqyP1icMowd79IFJ23P0Wuio/0+FhERUdMM+EphIS8vT55u27atyxh9dWnfvj2OHz8OAMjNzW2UslgRvi1CLHCUzV5OeyjYlzI7b+88Xf92osd1XffneZ19ffogJzsb3Y46HpKcyspCu9OnXdYzAXIfaOfpmrg4JFa5B6lrzxdEEaXbzqJ1ohTIFARAV22QoteCgHhtEgq2HEGmD3Fsk9kKk84MdaL7/JNH8zEI0udv0BvrbOmqq5bGpbearNDrDNBXG6BQOz4lk8ER4LcqBHl9ieOW0GgWay1zt3dbHNQTrMCntm30ZpdtTGar/L99fu15otMDQtGW8tMKoHivlJp615/ngDtcPw/nY1hsgWOL6FrequpKbEQH3AfggDUeHz2jw2W9szH08A6X92A2W2Cotj2UrFZAbbJAKwgwmCwwmdUQBAusTmnyTQCUAJSCCVZbnWEF5HWsAGqMVqgNJrzx81K8IAgwAVLvXdtnLwhml31uAnAVgNPlViQlSccHXP9WttrWqYYasTDJx1IAMEGEWhBghBVmg+cGHL5QWkT52Pb9l+gUiBcE6ExmWEUFrB7ej9VkwQZBQIZtG73ZCqttWbVZBasgSOeu0QTRqXyCyYxo2759ma+0WqXvxmqFpdb7VJitiBIEl2NL+3J8n2IDPpv6juPteN9uaYtf94zC58Jc5FcmIMNpm/91yUZyfDS6HD8OKwCdzgqr7VzUmi1Qo9b5aXO6tCSgshtMIixGM/xM/BCw+JRYdO5ff+O3phaXHIP4lFhUljCNJDVcpcXx92gVRZeujwa9DqKtvjNbrYDFv4DXuH798d3WzQAc11onS88jSRBgspohmqWsGCfPq2G21dX+1G2B1pP11Xu1mavNgJ9pM3VKA0yCay8m+/WCVEgP6WHruWw3GixQebgOAUL/ADyjfYrP9x3UPEUrlbi7bUecMxpwuLoCZbb5tesVZ4IgXYdZRAvMFgtUggCz1Sr/71Ln2JdbLPJ8MwBBVECw1VN6ixmCyZFtRyEACuFpCLgBZvh27WSvHwB4uJ7zXLeoIEIjCDBCdLtu9XYNqILVto3v17re9gUAMFqgFhQwGS3y9WxdZTZXm6HQWSAIAnQ6i3y9Zq7VOOuUNh4nTPG42EN59qe3wQXIQaXTPZbzN22xut977dlwGJlRdQ2LwXqEKJJ0vXAY8PsKt/kZZ8/K01VxcU2b/l70/AzN+ZlkUJ5PCrWu9WqRn+94qObq2i5kIrg6Nhr9H37pif/8By/MmuU2f8T69TAplVACOJ2VheTSUkTp9dg0dKh0LIUC3gZ/DOfn4kREFFoM0jdjFRWOcb0TvbRo9SQhIUGerqysrHNdg8EAg8Fxs20/Zu1kw5UAfC9BcOnhiOmcBtAGwF4ArWzT9amB472VAvC1L/NGgwEjPcw3OpXnmMkET30LUouLkZOd7TLPU8oqs0oFtVl6yHI+LQ2ZtjRjuqgoJFZVoSouDsm2oPzhnj2Rdu4cEquqpJa2VisUFgse/PYu/HnLa9I+BnTEt2/+JkXrAbTtmYFrHrsIf97+er3v93shDepMz/PTrhiPQd9/jx9uvBH7VuYjvl8GUFjotm7VQNvxnRw6XDvbgwr2RO1He/bGOqf1kwemyfs93jMb62vty5NMp1ry562ncabAsU2mkIb7bO/hjG1ftecl9UsC9kjjV5d27wps24YfbrwRxWlpGPnRR8i51PUbdt4XAPTpOQg3HczF8p6DsK9Wee0f5xZFJjJblwEessh9ruqJM+849VNvmw7MnAkAePypXwAAL2Km23bpteYvxU2uK5w6AvWD9+NFeYYJOHUEAHDtzCNu+3RpOjDT/Xhu63hjO0ZALhkh/att5AAAJuDpJz2+H/tnlgs4ltuXzWzr9F7NruVTwPZeTb7Nv+ZK6Z/z/u0G9AIGzHRf5vR9Nuizqe843o7X+gi6zwT+BQ/bOPVm+9+0aTizRg2skc7FTFVP3Ie17ucngEDzKuxs1w5xraOBwtKAtvfO9VZaqVSgy4B2GDi6J0SrlE63sVhsD2Qsfj2YEXD11JHyNnvXBdbDzWIK/cMghSjCZPHzCZA59AHJSGSu9b9duiYK5bbpT06dwBmr0xVd+yxg5kxHXZ172K9j9r5yHHpfOU5+/S/b/4vHXyFN5Evnbt8HjzrqFMD3ui3QerK+eq+2+b4Vx5V7vWa/XgCAnf0GoDI+HmNWrZKX7+w3ACnFxeiSmwuLUgm1xTWb0E9FADxchwBAn169MeSQf99PYxp9yxC5TgmsXms+WnK9lqaNRpo2GudNRjlI71avOLtqHH69SqojlsLo+LvtZ/u/dp0zcyYAo9t8zcyZjuu1fNffxBkzowD84vm6xRPn+qH2dZu3umXynY7p2vv3dg048Wbv23jjbV8A0C4dmDnDfX/eyjwfADTSsjWQr9dq29F7EPRpacDevW7LclMzcAGAn4VUx31Rz7aALZHagc7tXeopOw+3h07EsLg+qY31Wsut14LN2/VauEpq47kBc4LTc8Uvb78dZ1o3XeazzMJC3PfRR27zA+1cA0jP0lRmM9LKynzeZme/AdjpdK1Xm/35zvh+AzBk2zaXZd8L0tBu3rYNhqq4OBiiopBZXIzzaWnIz852K2dTasy6X6P0bzg8pcUCAcCk+fOx44ILcLBXL3lZn/375Q5Q/fbskedvufDCevfr73lHREQtB4P0zVi1U+vUqDpbqLuKdkrPU11PC9dXXnkFzz//vP+Fowa5YMcOFGX4nx7dX3FVVdiwvBCKIvde95aiMog1KhxYXghVnn9BMVN+MYSIudUkonChMDU8W4C/VKpoKMyO+qpVq0wUIRVHVzVdSu7Dvwc+AIS+PLBHoAeWuzeMCo0AUmTH178KefZ7vGvqiUoksAd0M6L1kDWpqanUahz+zb0Oa0i9Fvladr1WIcazXiG/CWYzNPFJYXR94o71mp+aUb0WbLWv18KVefRf0OOzjz0u63ngQJBLQ8ESX0/nrkA0Zt0f7eMwjXYq2zOHLsePo8vx4yho0wa/jBtXz1ZERESBY5C+GTM5BTNUKt+/aud1jUZjHWsCTz/9NB5//HH5dXl5Odq3bw8TgAqn9cy1XocT57JG2abbQWrh6EuZnd+b0sdtAKCrSosKs3u6dRMc6fZTlGpUWDwHpS5avtzlWMm5uW7HNpnNsG+tLi6Wl9unrVVV8rxWhw5BIYqoAGCyWmGwbd/+ogQM6K5BxUZg8Ol8HL9OSnDYrlc6omKlccsH9I1GxXbP73Pwrm2ogGNbpUaJTg+MhPah/8jzY4+eQAWAkT/9hOhLLkBV5/boPW+L2/txPr7FakXBrjK0GZAkj3Nr1/PVV1EBoMfmzTj/xN0uy7p/utmlPPXp8Z8Vcjm6JqqRfKFjjO3Ocza57cvTvIGfz0UFgLb79snv8/RfLnRZr6eXcvX7fB4qAPTbtQOWe26Q5ythRLcPpLJdVLgXZ69pj26fb0AFgN47HJ+dfRkAoFpA3KKjyPxlIc6MvxU1PfoiY+ufSF/nSElXBRWUMKP0orFovWGFPC/O1qhCD+C0bduEQ7vQ7pdvUIooFE26D2Zbw5GookJ0XvCevE97S+PCi8bCkJiMzF8WQgHAOfeDfZ0qKBFn+wsw2NYpz2iLxKJTON+mK85MnFLvd+YXYg+tAAAobklEQVRN2sqlaLVno/xaD6DE9j6PT3oQcXt3oNWejaiABqcn3e94P4f2ovUvC6Fyeh+lQ6U8GJlfz0VqwTHoAZya9AAMGY78G/bP4fikB6HPaF3v/LQli9Dq2B6c7dIPxdfc4lL25M1/ovWGFS7HBoCYQ3tdvs+G8nacuo6XsnIJkvZsQmnfi1B62VUu+2q/ZikqANwwbx6O/Gcq9J2kPBettu11OXedlSuyMPjmvyGppsa9Dti/GxUABuzaIf+9aDSCy99O14+lv8HrP/ofTO0y3fbRd/16j/W0UFbmMj/eqkcFgLjsGIxb6ahvU3snIOqiBA97aDiLWcTR1WfR9ZJWUKoCDWgkoAL1Z/2vPRxK+yZ6T/5QiXqkWU74t1G5ANXauKYpUDNmBrA6To1LqkxuNwOWFOnv5uqUTFhaufaIErdsQtRX81Fz822w9B8Y8PGFwkIo358N3cjRSPlzFc5deyMMffsBAJSnTyPp0w8BwK+6LdB6sr56rzZrfz0Q619jmHJFFizQuMyzXy8AQPs9e2COjnapg9rv2QOV0Shdl1ksqH1F3tdYDu358rqvHbZulffp/DffFOz7j+uQ5FKfNE69FrlYr0nqqlfshJMnoXnz36h64FEAQNz7s6EbMw7RK5e71TlCYSHi3p+NqgcehWjrkWkGsClWjYuXLkfcyhUoHTka1RePcjuOZu8BxP/0tU/1i71+AOB23eatbsn68kOknM1HSav2OH27az9Ib9eAmV/PQ2rBUb+udb3tCwC0hw4g/pcfUTn+Ohh6OHoAeiuztb8e2rOFaPfPT3Dy1XtgsF2vtZ71JVrtOizXI4M3rUXJZQM9Xkf1d7o2q31fBHi/9xpgu0+qbUTVGVz47h0eloQe6zXWa8FS1/VaeBJgjI1FhYfOPgPWrZPvC8UmHCNdsFo91inehoL0xL7c/r/alpGywo/9tN+zB8qsNK/PB+3Pd9q/vMfjcy/78Xwpb31MJpNLFxmTweA+zFIt1qoqqG3PC9XFxWhfUeH1vXTftq1Rnvc6f+6NeW/a91IRFVu8H8+NXu/6XKCgABM++aTO7+Pa+fNRAcBcXe31s2jqa/FIYf98RC/DHxERtUSCyFqx2br22muxZMkSAMCFF16ILVs8XJV4cPPNN+Pbb7+V9/Hjjz/6fMwTJ06gc+fO9a9IREREREREREREREQtxsmTJ9G2bdtQF4OIKCxERmNMCkhcnKOlsk6n83m7mpoaj/vwRUpKCgAgPz8fiYnhOgo9NQcVFRVo164dTp48iYSE0PcApeaL5xoFC881ChaeaxQsPNcoWHiuUbDwXKNg4blGwcJzjYJFFEVUVlaidevW9a9MRNRCMEjfjKWlpcnTp0+f9nm7M2ccY6ulpvo31pjClrYqMTGRF3YUFAkJCTzXKCh4rlGw8FyjYOG5RsHCc42ChecaBQvPNQoWnmsULDzXKBjYqY+IyFXTDQREIde9e3d5+vz58y495Oty8uRJebpHjx6NXi4iIiIiIiIiIiIiIiIiopaKQfpmrGfPni6vd+3aVe82BQUFOHfunNd9EBERERERERERERERERFR4Bikb8aGDBkCrVYrv163bl2926xdu1aejoqKwpAhQ/w6plarxcyZM12OS9QUeK5RsPBco2DhuUbBwnONgoXnGgULzzUKFp5rFCw81yhYeK4RERGFjiCKohjqQlDTueqqq/Dzzz8DAPr164fdu3fXuf4111yDpUuXytvap4mIiIiIiIiIiIiIiIiIqOHYk76ZmzJlijy9Z88eLFmyxOu6O3bswC+//OJxWyIiIiIiIiIiIiIiIiIiajj2pG/mRFHEwIED5R70WVlZ+OOPP9CjRw+X9U6fPo0xY8bg4MGDAIABAwZgx44dEAQh6GUmIiIiIiIiIiIiIiIiImquGKRvAbZt24aRI0dCp9MBABISEnD//fdj5MiRUKlU2LJlC959912cPXsWABAdHY0///wTgwcPDmWxiYiIiIiIiIiIiIiIiIiaHQbpW4gffvgBkyZNkgP13kRHR2PBggW44YYbglQyIiIiIiIiIiIiIiIiIqKWg2PStxA33HADtm/fjjFjxnhMYS8IAi677DLs2LHD7wD9hg0bMG3aNPTq1QsJCQlISEhAr169MG3aNGzYsKGx3gI1M+fOncMvv/yCF154Addeey2ysrIgCIL8b968eX7tz2g0YvXq1Xjuuecwfvx4dOjQAbGxsYiKikJWVhZGjRqFZ599FseOHWuaN0TNCus1CgTrNQpnrNcoEKzXKJyxXqNAsF6jcMZ6jQLBeo2IiCiCidTi5OfniwsXLhTfeOMN8Y033hAXLlwo5ufn+72fqqoq8e677xYB1Pnv7rvvFquqqprgnVAkOn36tNihQ4d6z5u5c+f6tD+dTifeddddYlJSUr37BCAKgiDee++9YkVFRdO+UYpIrNcoEKzXKJyxXqNAsF6jcMZ6jQLBeo3CGes1CgTrNSIiosjHdPcUEIvFgiuvvBK//vqrPC86Ohq9e/eGSqXCgQMHUFFRIS8bO3Ysli1bBqVSGYriUhjJzc1Fx44d611v7ty5mDJlSr3rFRcXIz093W1++/bt0bp1a0RFRSE3Nxe5ubkuywcNGoQ//vgDiYmJvhadmjnWaxQo1msUrlivUaBYr1G4Yr1GgWK9RuGK9RoFivUaERFR5GO6ewrIc88953IDMXXqVJw6dQpbt27Fxo0bUVhYiOnTp8vLV6xYgRkzZoSiqBTG0tPTMW7cOEyfPh0//vhjg/c3dOhQfPTRRzh16hTy8vKwceNGrFq1Cjk5OdixYwdGjBghr7tjxw5Mmzatwcek5oP1GjUG1msUTlivUWNgvUbhhPUaNQbWaxROWK9RY2C9RkREFKFC3ZWfIs+pU6fEqKgoOb3RnXfe6XXd6dOny+tFR0eLBQUFQSwphaPy8nLxm2++EXNzc92WIYB0XMXFxeKYMWPEDRs21Luu0WgUR48e7XKcHTt2+PsWqBlivUYNwXqNwhHrNWoI1msUjlivUUOwXqNwxHqNGoL1GhERUeRjT3ry2zvvvAO9Xg8AiImJwezZs72u+9xzz6Fdu3YAAJ1Oh7fffjsYRaQwlpCQgAkTJqBDhw6Nsr/U1FT8/vvvGD58eL3rqtVqzJkzx2Xe999/3yjloMjGeo0agvUahSPWa9QQrNcoHLFeo4ZgvUbhiPUaNQTrNSIiosjHID35zfmia+LEiUhJSfG6rkajwV133SW//uGHH5q0bET16datG7p16ya/PnToUAhLQ+GC9RpFMtZr5AnrNYpkrNfIE9ZrFMlYr5EnrNcokrFeIyIiajgG6ckvhw8fxrFjx+TX48aNq3eb8ePHy9NHjx7FkSNHmqRsRL5yvvGtqKgIYUkoHLBeo+aA9Ro5Y71GzQHrNXLGeo2aA9Zr5Iz1GjUHrNeIiIgahkF68svu3btdXvuSAmnQoEHQaDRe90EUbHl5efJ0RkZGCEtC4YD1GjUHrNfIGes1ag5Yr5Ez1mvUHLBeI2es16g5YL1GRETUMAzSk18OHjwoT2s0Gnk8rLrUXs95H0TBtmnTJpw+fVp+PWzYsBCWhsIB6zWKdKzXqDbWaxTpWK9RbazXKNKxXqPaWK9RpGO9RkRE1HAM0pNfnFtItm3bFoIg+LRd+/bt5enc3NzGLhaRz55//nl5OioqCjfeeGMIS0PhgPUaRTrWa1Qb6zWKdKzXqDbWaxTpWK9RbazXKNKxXiMiImo4BunJL87jCyUmJvq8XUJCgjxdWVnZqGUi8tUXX3yB5cuXy68feOABZGVlhbBEFA5Yr1EkY71GnrBeo0jGeo08Yb1GkYz1GnnCeo0iGes1IiKixsEgPfmlurpano6KivJ5u+joaI/7IAqWvXv34q9//av8ukuXLi6tfqnlYr1GkYr1GnnDeo0iFes18ob1GkUq1mvkDes1ilSs14iIiBoPg/TkF5PJJE+rVCqft3Ne12g0NmqZiOpz9uxZXH/99fINrFarxZdffom4uLgQl4zCAes1ikSs16gurNcoErFeo7qwXqNIxHqN6sJ6jSIR6zUiIqLGxSA9+SUmJkae1uv1Pm/nvG5sbGyjlomoLuXl5Rg3bhxOnDgBAFAqlViwYAEuvPDCEJeMwgXrNYo0rNeoPqzXKNKwXqP6sF6jSMN6jerDeo0iDes1IiKixscgPfnFuWWkTqfzebuamhqP+yBqStXV1bjyyiuxa9cuAIAgCPjkk09w0003hbZgFFZYr1EkYb1GvmC9RpGE9Rr5gvUaRRLWa+QL1msUSVivERERNQ0G6ckvaWlp8vTp06d93u7MmTPydGpqaqOWicgTvV6P6667Dhs2bJDnvfvuu5g8eXIIS0XhiPUaRQrWa+Qr1msUKVivka9Yr1GkYL1GvmK9RpGC9RoREVHTYZCe/NK9e3d5+vz58y4teOty8uRJebpHjx6NXi4iZyaTCRMmTMDKlSvlea+99hoeeOCBEJaKwhXrNYoErNfIH6zXKBKwXiN/sF6jSMB6jfzBeo0iAes1IiKipsUgPfmlZ8+eLq/taY7qUlBQgHPnznndB1FjslgsuO2227Bs2TJ53qxZs/CPf/wjhKWicMZ6jcId6zXyF+s1Cnes18hfrNco3LFeI3+xXqNwx3qNiIio6TFIT34ZMmQItFqt/HrdunX1brN27Vp5OioqCkOGDGmSshFZrVZMmTIF3333nTzvySefxMyZM0NYKgp3rNconLFeo0CwXqNwxnqNAsF6jcIZ6zUKBOs1Cmes14iIiIKDQXryS1xcHMaMGSO/XrBgQb3bOK8zZswYxMbGNknZiO6//3588cUX8uuHH34Yr776aghLRJGA9RqFM9ZrFAjWaxTOWK9RIFivUThjvUaBYL1G4Yz1GhERUXAwSE9+mzJlijy9Z88eLFmyxOu6O3bswC+//OJxW6LG9Pjjj+Ojjz6SX0+bNg2zZ88OXYEoorBeo3DEeo0agvUahSPWa9QQrNcoHLFeo4ZgvUbhiPUaERFR8DBIT36bMGEC+vfvL7++7777cOjQIbf1Tp8+jTvuuAMWiwUAMGDAANx0001BKye1HDNmzMBbb70lv54yZQrmzJkDQRBCWCqKJKzXKNywXqOGYr1G4Yb1GjUU6zUKN6zXqKFYr1G4Yb1GREQUXIIoimKoC0GRZ9u2bRg5ciR0Oh0AICEhAffffz9GjhwJlUqFLVu24N1338XZs2cBANHR0fjzzz8xePDgUBabwsTUqVMxf/58t/kGg0GeVqlUUCqVbuvo9XqX11u2bMHQoUPl1wqFAmPGjIFC4VsbpFatWuGzzz7ztejUjLFeo4ZgvUbhiPUaNQTrNQpHrNeoIVivUThivUYNwXqNiIgosjFITwH74YcfMGnSJPlGwpvo6GgsWLAAN9xwQ5BKRuFuypQpAV+4166yVq9ejdGjRwdclg4dOiA3Nzfg7al5Yb1GgWK9RuGK9RoFivUahSvWaxQo1msUrlivUaBYrxEREUU2prungN1www3Yvn07xowZ4zHtkSAIuOyyy7Bjxw7eQBBRRGC9RkTNDes1ImpuWK8RUXPDeo2IiIioZWJPemoUJ0+exIYNG1BQUAAAaNOmDS666CK0a9cuxCUjIgoM6zUiam5YrxFRc8N6jYiaG9ZrRERERC0Hg/RERERERERERERERERERERBwnT3REREREREREREREREREREQcIgPRERERERERERERERERERUZAwSE9ERERERERERERERERERBQkDNITEREREREREREREREREREFCYP0REREREREREREREREREREQcIgPRERERERERERERERERERUZAwSE9ERERERERERERERERERBQkDNITEREREREREREREREREREFCYP0REREREREREREREREREREQcIgPRERERERERERERERERERUZAwSE9ERERERGEhNzcXgiDI/yi08vLy8I9//AMXXHABkpKSoFQq5e9m1qxZQSvHvHnz5ONecsklQTtuOOJnQURERERERNQ8qEJdACIiIiIiIgovv//+O2644QZUVVWFuihERERERERERM0Og/RERERERNTosrOzkZeXBwBYtWoVe/1GEJ1Oh0mTJskBeqVSif79+yMtLU3OcNClS5eA9u2cISEnJwfZ2dkNLi8RERERERERUaRhkJ6IiIiIiIhkS5YsQVFREQAgLi4O27ZtQ/fu3UNcKiIiIiIiIiKi5oNBeiIiIiIiCgvZ2dkQRTHUxWjxtm/fLk/fdNNNDNATERERERERETUyRagLQEREREREROGjpKREnm7Xrl0IS0JERERERERE1DwxSE9EREREREQyk8kkTyuVyhCWhIiIiIiIiIioeWKQnoiIiIiIGkVubi4EQYAgCMjLy5Pnjx49Wp7v/O+SSy7xur0gCF6PM2XKFHmdWbNmAQCsViu++eYbXH311ejQoQO0Wi3S09Nx7bXX4o8//vC4n40bN+LOO+9EdnY2tFotUlJS8Je//AUfffQRrFarX+9dFEUsXrwYd911F3r06IHk5GRER0ejffv2uPbaa/Hpp5+6BL8bm8ViwYIFC3DzzTejU6dOiI2NRXx8PLp06YI77rgDP/zwQ51DCTh/pp999pk8//nnn3f73qZMmeJX2VavXu3xO+3YsaPH88LX/e/fvx8PPfQQevbsibi4OCQkJKBfv3548skncebMGb/KCAB5eXl4+eWXMXLkSLRt2xZarRapqakYMGAAnnjiCRw4cMDvffpjyZIluPHGG9GuXTtERUWhbdu2uPTSS/HJJ59Ap9P5vb/y8nIsWrQI9913H4YNG4b09HRoNBrEx8ejY8eOmDBhAj799FMYjcY69zNp0iT5u7nlllv8KkP37t3lbV977TW/3wMRERERERFRc8Ux6YmIiIiIKKKVlJTgtttuw6+//uoyv7i4GEuWLMGSJUvw73//G//85z8BSAH9Rx55BO+++67L+kajEevXr8f69evx9ddfY+nSpYiKiqr3+Nu3b8fUqVOxc+dOt2UnT57EyZMn5TJ89dVXuOCCCxrwbj0ff/Lkydi/f7/bsqqqKhw/fhwLFizA0KFDMX/+fHTt2rVRjx8Kr776KqZPnw6z2ewyf+/evdi7dy/mzJmDxYsX49JLL613X2azGTNmzMCbb74Jg8HgsqykpAQlJSXYvXs3Zs+ejYcffhj/+c9/GjXDQGVlJe644w789NNPLvMLCgpQUFCAVatW4b///S++//57n/f5+uuvY/r06W7vB5AyJVRVVSE3NxffffcdZs2aha+//hrDhg3zuK9p06bhyy+/BAAsXrwY58+fR2pqar1lWLt2LY4cOQIAUKvVmDx5ss/lJyIiIiIiImruGKQnIiIiIqJGER0djbFjxwIA1qxZA71eDwC48MILkZKS4rZ+v379GnxMs9mM6667DuvWrQMAdOrUCe3bt0dZWRn27Nkj94h/6qmn0KFDB9x66624//778dFHHwEA0tLS0KNHD1itVuzatQs1NTUAgJUrV+KRRx7Bhx9+WOfxly9fjgkTJqC6ulqel5aWhq5du0Kr1SInJ0fOKnD06FGMHj0aK1aswPDhwxv83gFgw4YNGD9+PCoqKuR5ycnJ6NmzJ6xWKw4cOCAv27x5My6++GKsXLkSvXv3dtlP37595e9u7969KCwsBAB07twZXbp0cVvXHykpKfK+V6xYIc8fOXIkoqOj3davb/8vvvgiZsyYAQCIj49Hr169EBUVhUOHDuHs2bMApMD3tddei/3796NDhw5e96XX6zFhwgQsW7ZMnqdQKNCrVy+kp6ejqqoKe/bsgcFggMViwVtvvYWTJ0/i66+/rjPbg68MBgOuueYarFmzRp6nVCrRp08fJCUlIScnB/n5+di9ezeuuOIKPPLIIz7t99ChQy4B+nbt2qFNmzaIjY1FVVUVDh06hPLycgBSQ5LRo0dj3bp1HhuQjBo1Ct27d8fhw4dhNBoxf/58PProo/WW4eOPP5anr776arRq1cqnshMRERERERG1CCIREREREVEj69ChgwhABCCuWrXKp21ycnLkbeq6VZk8ebK8TkpKighAHDx4sLh9+3aX9Y4dOyb269dPXrdz587iV199JQIQMzIyxG+//Va0WCzy+uXl5eItt9wir69QKMTDhw97LcfRo0fFuLg4ef0hQ4aIq1evFq1Wq8t6mzdvFgcOHCiv16FDB7GsrMynz6QupaWlYps2beT9xsfHi5988oloNBrldXQ6nfjmm2+KWq1WXq9Xr16iXq/3ul/nz3fmzJkNLqcz5+83JyfHp23mzp3r8n0LgiAmJSWJc+fOdXmvVqtVnDdvnqjRaOT177jjjjr3fd9998nrajQa8fnnnxfPnz/vsk5VVZX44osvikqlUl539uzZfr93T6ZPn+7ymdx8881iQUGByzqrV68WO3fuLAIQ09LS5HVHjRrldb9Tp04Vx48fL37xxRdicXGx23KLxSIuW7ZM7N69u7y/nj17uvw9OHv99dfl9fr06VPv+yovLxdjYmLkbZYtW1bvNkREREREREQtCcekJyIiIiKiiFVSUoL+/ftj1apVGDRokMuyzp074/vvv4darQYAHD9+HJMnT0ZcXBzWrFmDm266CQqF45YoISHBJR281WrFggULvB77nnvuQVVVFQDgmmuuwbp16zBq1Ci3HtZDhgzB2rVrMWDAAADS2OfvvPNOg9/7Cy+8gIKCAgBSOvGlS5fi7rvvlt8vAERFReGxxx7DF198Ic87cOAAZs+e3eDjh0JJSQmioqKwatUqTJkyxeW9CoKAyZMn45VXXpHnfffddy5ZDpytWrVKzpSg1Wrx66+/YsaMGW5ZH2JjYzF9+nR89tln8ryZM2fK332gCgoKXMZpnzhxIhYtWoTWrVu7rDdq1CisXr0amZmZKC4u9mnfb731Fn7++WdMmjTJY2p6hUKBK6+8Ehs2bECnTp0AAAcPHsQvv/zicX+TJ0+GVqsFAOzbtw9btmyp8/hfffWVnJWibdu2GDdunE/lJiIiIiIiImopGKQnIiIiIqKI9uGHHyIuLs7jss6dO7uMS240GjF9+nT06NHD4/pqtRp33XWX/Hr9+vUe19u8eTP+/PNPAEBqaio+//xzl4BxbbGxsZgzZ478es6cORBF0fubqkdNTQ3mzp0rv37ooYcwcuRIr+tPmDABEydOlF9/8MEHsFgsAR8/lJ5++mm5wYMn9913n5xGX6fTYceOHR7Xcw6QP/vssxg1alSdx500aRLGjx8PACgvL6+zAYcv5s2bB6PRCEBqIPLee+95TaHftm1bvPrqqz7vOzY21qf1UlJS8Oyzz8qvf/zxR4/rpaWl4cYbb5RfO6ey98R5+V133eXSGIaIiIiIiIiIGKQnIiIiIqII1rt3bwwdOrTOdYYMGSJPC4KAu+++u871nfd38OBBj+vMnz9fnp48eTKSkpLqLevQoUPl8d0LCwtx6NCherfxZs2aNSgrKwMgvaeHH3643m0ee+wxeTovLw+7du0K+PihNHXq1DqXx8bGYuDAgfJrT5/zuXPnsGLFCgBSw4wHH3zQp2NPmjRJnv7jjz982sabxYsXy9MTJ05EWlpanevfdtttbr38G4Pz+b5161av602bNk2eXrhwodcMBXv27MG2bdsA+Pb3RkRERERERNQSqUJdACIiIiIiokDVF6AHgMzMTHm6U6dOSE9P93l9eyC8trVr18rTzj3169OnTx8cO3YMALBjxw707NnT522dbd68WZ7u0aMHsrOz691m2LBhSE1Nxfnz5+V9XHDBBQEdP1Q6duzo8v1406ZNG3na03e4bt06OZNB//79fQ5+9+nTR5721kPfF0ajEXv27JFfjx07tt5t1Go1LrvsMnz99dd+HWv//v1Ys2YN9u3bh/Pnz6Oqqsoli4JOp5On7cMneHLJJZegW7duOHLkCCorK/HNN99gypQpbut98skn8vRll13m07lJRERERERE1NIwSE9ERERERBGrVatW9a4TExMjT2dkZPi1vn1cbWeiKGL//v3y65dffhn//e9/690vAOzdu1ee9nV8cU/sgX4A6Nu3r8/b9e3bF6tXr3bbR6TwJUAP1P8d7tu3T57Oz8/3ecx054B2Q76//Px8OdU94Br8r0vv3r19PsamTZvwyCOP1Dt+vLPy8vI6l0+bNg1PPPEEACmlfe0gvcFgwBdffCG/vvfee30+NhEREREREVFLwiA9ERERERFFLI1G06Tre1JeXu7SE3nDhg0B7ydQzr3DU1NTfd7OOaV6aWlpwMcPlUC+P3uPeWf2bAIAUFRUJKe+90dDvr/an72v36Gv6y1atAiTJk1yOU994dxwwJPJkyfj2WefhcFgwPr163H48GF0795dXv7DDz+gpKQEgHSuXX/99X4dn4iIiIiIiKil4Jj0REREREREfvA2Fre/rFZrwNsaDAZ52p/AtfO6zvtoaRrjO/QU/PdV7WC4r9+hVqutd53c3FxMmTJFDtCnp6fjmWeewe+//46cnBw53b0oihBFETk5OT6XOy0tDTfccIP8+uOPP3ZZ7vz6zjvvbJRGMURERERERETNEXvSExERERER+SExMdHl9datWzF48OCQlaGystLn7ZzXTUpKaswiRRTnz++qq67C0qVLg3r8hIQEl9eVlZVu55UnvnzXs2fPhl6vBwB06tQJGzZsqHNYCH/OH0BKeb9w4UIAwOeff46XX34ZarUaOTk5+OOPP+T1mOqeiIiIiIiIyDv2pCciIiIiIvJDXFycy5jnRUVFQS9Denq6PO1PT+jjx4973EdL4xy0DsX3l5GR4fI6NzfXp+18+a5/++03eXrGjBl1BugBoKCgwKdj240ePRrdunUDIH129gYOc+fOlbMLDB8+HL169fJrv0REREREREQtCYP0RERERETU6BQKx61GQ9KCh6thw4bJ05s2bQr68QcNGiRP79y5EyaTqd5tysrKcPjwYY/7CBZBEOTpUJ4Xzt/f7t275Z7nwdKqVStkZWXJr7ds2eLTdr6sl5+fL0/7kuFh48aNPh3b2dSpU+Xpjz/+GFarFXPnzpXnsRc9ERERERERUd0YpCciIiIiokYXGxsrT+t0uhCWpGmMHTtWnl6wYIE8/newXHzxxfJ0WVkZli9fXu82CxculMupVCoxfPjwJiufN+FyXgwdOlROL280GvHVV18FvQzO3+HXX39d7/o5OTk+Bel9abBhZ7FYMH/+fJ/Xt5syZYo83vyKFSvwySef4NSpUwCA+Ph43HLLLX7vk4iIiIiIiKglYZCeiIiIiIgaXWZmpjx97NixEJakadx7772Ii4sDAJw4cQKvvPJKUI/fo0cPlyD7jBkzYDabva5fWVmJF198UX59zTXXuKVcD4ZwOS80Gg0efPBB+fX06dNx9uzZoJbhzjvvlKc3b96MJUuW1Ln+9OnTfco+4NxDf/369XWu++abb/o1XIJdWloabrjhBgBSoP/hhx+Wl91yyy0ujTGIiIiIiIiIyB2D9ERERERE1OicU6nPnTsX5eXlISxN40tJScH06dPl1zNmzMDzzz9fby/msrIyvPPOO43S0/jZZ5+Vp3ft2oUpU6bAaDS6rVdRUYEbb7wRhYWFAKRe9E8//XSDjx8I5/Pigw8+gMFgCEk5AODxxx9HmzZtAACFhYW45JJLsG/fvnq327RpEyZOnOgy9nsgxo8fj379+smvp0yZgl27dnlc97XXXsOXX37p035HjRolT7/44otex5z/7LPPGnQeTJs2TZ52Hi6Aqe6JiIiIiIiI6qcKdQGIiIiIiKj5ue222/Cf//wHoihi165daNOmDQYNGoTk5GR5XPI+ffrgpZdeCnFJA/fkk09iy5Yt+P777yGKImbNmoVPPvkEt912G4YOHYq0tDSYzWaUlJRg37592LhxI1atWgWTyYShQ4c2+PhXXXUV7rrrLnks8AULFmD79u2YNm0a+vbtC6vViu3bt2POnDku45Q/+eSTGDJkSIOPH4jbb79dTu2+fPlyZGVlYcCAAUhISJDXufTSS116ZjeV1NRUfPfddxg9ejR0Oh0OHTqE/v374+qrr8a4cePQqVMnxMbGoqKiAidPnsSOHTuwYsUK5OXlAQDuvvvuBh1fqVTio48+wsUXXwyTyYSSkhIMGzYM99xzD6644gokJSUhJycH8+fPxx9//AFBEDBhwgR88803de73b3/7Gz7//HOIoohTp05hwIAB+Nvf/obhw4dDrVbj+PHj+Oqrr7By5UoAUlD9448/9rv8o0ePRteuXXH06FF5Xt++fRvl3CYiIiIiIiJq7hikJyIiIiKiRjdgwAA888wz+Ne//gUAqK6uxtq1a13WKSsrC0HJGo8gCFi0aBEeffRRvPfeewCAkydP4rXXXgtaGT788EPodDosXLgQAHDo0CE8/vjjXtf/29/+Jn8noXDdddfhjjvuwBdffAEAKC0txapVq1zWSUpKClp5hg4dij///BPXX389CgoKYLVa8dNPP+Gnn34K2vG/+OIL3H777bBYLDAYDHj//ffx/vvvu6377LPPonPnzvUG6S+44AK88MILeO655wAAxcXFmDlzpsd1J06ciGeeeSagIL0gCJg6dSqefPJJed4999zj936IiIiIiIiIWiKmuyciIiIioibx0ksv4Y8//sAdd9yB7t27Iy4uTu5F31yoVCq8++67WLt2La644goolUqv6wqCgIEDB+LFF1+sN9DqK7VajS+//BJffPEFOnfu7HW9Pn364KeffsI777wT8u9g/vz5+P777zFhwgS5t3ooyzR48GAcOHAAL7zwAjIzM+tcNzk5GRMnTsSSJUtw+eWXN8rxJ06ciPXr12PAgAEel7dt2xafffYZXnzxRZ/3OX36dHzyySfIyMjwuDwzMxOzZ8/GokWLGvTZ9+/fX57WarW48847A94XERERERERUUsiiKIohroQREREREREzUF5eTnWrVuHkydPoqSkBCqVCklJSejSpQv69euHtLS0Jj3+3r17sWPHDhQVFUEQBLRq1QrDhg1D165dm/S4zcmePXuwe/dunDt3DjU1NYiLi0ObNm3Qo0cP9O7dGwpF07V13717N7Zu3Ypz584hNTUV3bp1w8iRIwM+pl6vx9q1a7F//37odDpkZGSgS5cu+Mtf/lJngxJf3XXXXZg3bx4A4NZbb8VXX33V4H0SERERERERtQQM0hMRERERERGRX8rLy9G6dWvU1NQAAFauXIlLL700xKUiIiIiIiIiigxMd09EREREREREfvnvf/8rB+h79OjBAD0RERERERGRHxikJyIiIiIiIiKfrV+/Hv/+97/l1//4xz9CWBoiIiIiIiKiyMN090RERERERETkVVFREaZNmwZRFFFQUIAdO3bA/iihb9++2LFjB1QqVYhLSURERERERBQ5GKQnIiIiIiIiIq9yc3PRsWNHt/lJSUn4888/0bdv3xCUioiIiIiIiChyMd09EREREREREflEqVSiTZs2mDx5MrZv384APREREREREVEA2JOeiIiIiIiIiIiIiIiIiIgoSNiTnoiIiIiIiIiIiIiIiIiIKEgYpCciIiIiIiIiIiIiIiIiIgoSBumJiIiIiIiIiIiIiIiIiIiChEF6IiIiIiIiIiIiIiIiIiKiIGGQnoiIiIiIiIiIiIiIiIiIKEgYpCciIiIiIiIiIiIiIiIiIgoSBumJiIiIiIiIiIiIiIiIiIiChEF6IiIiIiIiIiIiIiIiIiKiIGGQnoiIiIiIiIiIiIiIiIiIKEj+H8uhiw9V1EwgAAAAAElFTkSuQmCC",
+ "image/png": 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\n",
"text/plain": [
- "<Figure size 2000x1000 with 1 Axes>"
+ "<Figure size 1440x720 with 1 Axes>"
]
},
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/mael/.local/lib/python3.10/site-packages/evalys/metrics.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " event_df = start_event_df.append(\n",
- "/home/mael/.local/lib/python3.10/site-packages/evalys/metrics.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " event_df = start_event_df.append(\n",
- "/home/mael/.local/lib/python3.10/site-packages/evalys/metrics.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " event_df = start_event_df.append(\n",
- "/home/mael/.local/lib/python3.10/site-packages/evalys/metrics.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " event_df = start_event_df.append(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Line2D(rigid only) black\n",
- "Line2D(low effort) blue\n",
- "Line2D(max effort yellow) darkred\n",
- "Line2D(renounce) gray\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 2000x1000 with 1 Axes>"
- ]
+ "metadata": {
+ "needs_background": "light"
},
- "metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Visualize results\n",
- "EXPE_LIST = [f\"{RAW_DATA_DIR}/expe0/replay_user_rigid_simu_solar_wind_0\",f\"{RAW_DATA_DIR}/expe0/dm_user_multi_behavior_low_effort_simu_solar_wind_65565\",f\"{RAW_DATA_DIR}/expe0/dm_user_multi_behavior_max_effort_simu_solar_wind_yellow_147465\",f\"{RAW_DATA_DIR}/expe0/dm_user_multi_behavior_renonce_simu_solar_wind_0\"]\n",
- "LABEL_LIST = [\"rigid only\", \"low effort\", \"max effort yellow\",\"renounce\"]\n",
- "COLOR_LIST=[\"black\",\"blue\",\"darkred\",\"gray\"]\n",
- "plot_queue_windows(EXPE_LIST,LABEL_LIST,COLOR_LIST,red_windows_dict[0][\"_simu_solar_wind\"],yellow_windows_dict[0][\"_simu_solar_wind\"],7*12*24,7*13*24-2*24,fontsize=25)\n",
- "plot_load_windows(EXPE_LIST,LABEL_LIST,COLOR_LIST,red_windows_dict[0][\"_simu_solar_wind\"],yellow_windows_dict[0][\"_simu_solar_wind\"],7*12*24,7*13*24-2*24,fontsize=25)\n"
+ "EXPE_LIST = [f\"{OUT_DIR}/expe0/replay_user_rigid_0\",f\"{OUT_DIR}/expe0/dm_user_multi_behavior_0\",f\"{OUT_DIR}/expe0/dm_user_multi_behavior_yellow_512\"]\n",
+ "LABEL_LIST = [\"rigid only\", \"multi behavior\", \"multi behavior yellow\"]\n",
+ "plot_load_windows(EXPE_LIST,LABEL_LIST,[\"blue\",\"black\",\"orange\"],red_windows_dict[0][\"\"],yellow_windows_dict[0][\"\"],0,96)"
]
},
{
@@ -167,334 +116,80 @@
"metadata": {},
"source": [
"## State distribution\n",
- "We can see the state distribution during a week of the experiments. We cross with the used energy data"
+ "We can see the state distribution during the experiment"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 37,
"id": "d9066775",
"metadata": {
"scrolled": true
},
"outputs": [
{
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1200x600 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "red_windows,yellow_windows = red_windows_dict[0][\"_simu_solar_wind\"],yellow_windows_dict[0][\"_simu_solar_wind\"]\n",
- "#energy datafile\n",
- "production_dataframe = pd.read_csv(f\"{ROOT_DIR}/data_energy/energy_trace_sizing_solar.csv\")\n",
- "day_offset = 7*(12)\n",
- "yellow_threshold = 42*150\n",
- "red_threshold = yellow_threshold//2\n",
- "# plot the windows and production we add a 150 days offset because our experiments start in 1st June and\n",
- "# the energy data start at 1st January\n",
- "beg = 24*3600 + day_offset*24*3600\n",
- "end = 8*24*3600 + day_offset*24*3600\n",
- "plot_window_list_extra(red_windows,yellow_windows,beg, end, red_threshold,yellow_threshold, production_dataframe,150,tick=6,width=12,height=6,fontsize=18)\n",
- "ax = plt.gca()\n",
- "ax.yaxis.set_ticks(np.arange(0, 30000, 5000), labels=np.arange(0, 30, 5))\n",
- "ax.set_xlim(beg, end)\n",
- "plt.ylabel(\"renewable production (kW)\")\n",
- "plt.legend()\n",
- "plt.savefig(f\"{FIG_DIR}/windows_distrib.pdf\", bbox_inches=\"tight\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "cannot convert float NaN to integer",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m/home/mael/git/stage-jolyne/demand-response-expe/analyse_campaign3.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/mael/git/stage-jolyne/demand-response-expe/analyse_campaign3.ipynb#X45sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m plot_window_list_extra(red_windows,yellow_windows,\u001b[39m24\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m3600\u001b[39;49m \u001b[39m+\u001b[39;49mday_offset\u001b[39m*\u001b[39;49m\u001b[39m24\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m3600\u001b[39;49m,\u001b[39m8\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m24\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m3600\u001b[39;49m \u001b[39m+\u001b[39;49m day_offset\u001b[39m*\u001b[39;49m\u001b[39m24\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m3600\u001b[39;49m, red_threshold,yellow_threshold, production_dataframe,\u001b[39m150\u001b[39;49m,tick\u001b[39m=\u001b[39;49m\u001b[39m6\u001b[39;49m,width\u001b[39m=\u001b[39;49m\u001b[39m12\u001b[39;49m,height\u001b[39m=\u001b[39;49m\u001b[39m4\u001b[39;49m,fontsize\u001b[39m=\u001b[39;49m\u001b[39m12\u001b[39;49m)\n",
- "File \u001b[0;32m~/git/stage-jolyne/demand-response-expe/scripts/plot_library.py:81\u001b[0m, in \u001b[0;36mplot_window_list_extra\u001b[0;34m(red_window_list, yellow_window_list, begin_exp, end_exp, red_threshold, yellow_threshold, energy_produced_dataframe, offset_day_production, tick, width, height, fontsize)\u001b[0m\n\u001b[1;32m 79\u001b[0m ax\u001b[39m.\u001b[39mxaxis\u001b[39m.\u001b[39mset_ticks(np\u001b[39m.\u001b[39marange(begin_exp, end_exp \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m, tick \u001b[39m*\u001b[39m \u001b[39m3600\u001b[39m))\n\u001b[1;32m 80\u001b[0m ax\u001b[39m.\u001b[39mxaxis\u001b[39m.\u001b[39mset_ticklabels(np\u001b[39m.\u001b[39marange(begin_exp \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m \u001b[39m3600\u001b[39m, end_exp \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m \u001b[39m3600\u001b[39m \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m, tick) \u001b[39m%\u001b[39m \u001b[39m24\u001b[39m,fontsize\u001b[39m=\u001b[39mfontsize)\n\u001b[0;32m---> 81\u001b[0m power_max\u001b[39m=\u001b[39m\u001b[39mint\u001b[39;49m((power_produced_max\u001b[39m*\u001b[39;49m\u001b[39m1.2\u001b[39;49m)\u001b[39m/\u001b[39;49m\u001b[39m/\u001b[39;49m\u001b[39m100\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m100\u001b[39;49m)\n\u001b[1;32m 82\u001b[0m tick_y \u001b[39m=\u001b[39m power_max \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m \u001b[39m10\u001b[39m\n\u001b[1;32m 83\u001b[0m ax\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mset_ticks(np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m,power_max,tick_y,dtype\u001b[39m=\u001b[39m\u001b[39mint\u001b[39m))\n",
- "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "No handles with labels found to put in legend.\n"
]
},
{
"data": {
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",
+ "image/png": "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\n",
"text/plain": [
- "<Figure size 1200x400 with 1 Axes>"
+ "<Figure size 432x288 with 1 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
"source": [
- "plot_window_list_extra(red_windows,yellow_windows,24*3600 +day_offset*24*3600,8*24*3600 + day_offset*24*3600, red_threshold,yellow_threshold, production_dataframe,150,tick=6,width=12,height=4,fontsize=12)"
+ "red_windows,yellow_windows = red_windows_dict[0][\"\"],yellow_windows_dict[0][\"\"]\n",
+ "plot_window_list(red_windows,yellow_windows,0,nb_days*24*3600,tick=12)\n",
+ "plt.legend()\n",
+ "plt.savefig(f\"{OUT_DIR}/expe0/comparative_energy_consumed.svg\")\n",
+ "plt.show()\n",
+ "\n",
+ "# print(\"Mean utilisation:\", js.mean_utilisation(begin, end))"
]
},
{
"cell_type": "markdown",
- "id": "673005dc",
+ "id": "attractive-inspection",
"metadata": {},
"source": [
- "Let's compute length distribution of the state"
+ "## Compute row summary all expes\n",
+ "We open all the experiment outputs, compute raw metrics such as energy in window or slowdown / waiting time, and save this as a csv file. Also compute these metrics for each experiments and each user behavior and each seed relatively to the baseline behavior."
]
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "ae2e08ba",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Distribution analysis of state length (in hours)\n",
- "Yellow state :\n",
- "1.000000 113\n",
- "0.999722 112\n",
- "1.999722 18\n",
- "2.000000 11\n",
- "2.999722 2\n",
- "3.000000 2\n",
- "5.999722 2\n",
- "dtype: int64\n",
- "Red state :\n",
- "11.999722 37\n",
- "10.999722 17\n",
- "15.999722 16\n",
- "12.000000 16\n",
- "12.999722 14\n",
- "14.999722 12\n",
- "13.999722 9\n",
- "16.999722 8\n",
- "9.999722 7\n",
- "15.000000 6\n",
- "14.000000 5\n",
- "13.000000 5\n",
- "17.999722 3\n",
- "0.999722 3\n",
- "11.000000 2\n",
- "18.999722 2\n",
- "16.000000 1\n",
- "5.000000 1\n",
- "20.999722 1\n",
- "22.999722 1\n",
- "dtype: int64\n",
- " 0\n",
- "0.10 10.999722\n",
- "0.25 11.999722\n",
- "0.75 14.999722\n",
- "0.90 15.999722\n",
- "Max length of windows (in hours) : \n",
- "Yellow state :\n",
- "0 5.999722\n",
- "dtype: float64\n",
- "Red state : \n",
- "0 22.999722\n",
- "dtype: float64\n",
- "Total Duration of windows (in hours) :\n",
- "Yellow state :\n",
- "0 306.962778\n",
- "dtype: float64\n",
- "Red state : \n",
- "0 2179.963889\n",
- "dtype: float64\n"
- ]
- }
- ],
- "source": [
- "def compute_windows_length_list(window_list) :\n",
- " window_length_list =[]\n",
- " for window in window_list :\n",
- " window_length_list.append((window[1]-window[0])/3600)\n",
- " return window_length_list\n",
- "yellow_windows_length = compute_windows_length_list(yellow_windows)\n",
- "red_windows_length= compute_windows_length_list(red_windows)\n",
- "yellow_windows_length_df= pd.DataFrame(data=yellow_windows_length)\n",
- "red_windows_length_df = pd.DataFrame(data=red_windows_length)\n",
- "print(\"Distribution analysis of state length (in hours)\")\n",
- "print(\"Yellow state :\")\n",
- "print(yellow_windows_length_df.value_counts())\n",
- "print(\"Red state :\")\n",
- "print(red_windows_length_df.value_counts())\n",
- "print(red_windows_length_df.quantile([0.1,0.25,0.75,0.9]))\n",
- "print(\"Max length of windows (in hours) : \")\n",
- "print(\"Yellow state :\")\n",
- "print(yellow_windows_length_df.max())\n",
- "print(\"Red state : \")\n",
- "print(red_windows_length_df.max())\n",
- "print(\"Total Duration of windows (in hours) :\")\n",
- "print(\"Yellow state :\")\n",
- "print(yellow_windows_length_df.sum())\n",
- "print(\"Red state : \")\n",
- "print(red_windows_length_df.sum())\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e3960438",
+ "execution_count": 16,
+ "id": "assumed-saver",
"metadata": {
- "collapsed": false
+ "scrolled": false
},
- "source": [
- "Let's compute the state distribution in function of the time of the day."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "a3b32d9b",
- "metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " hour red yellow green\n",
- "0 0 162 0 0\n",
- "1 1 162 0 0\n",
- "2 2 162 0 0\n",
- "3 3 162 0 0\n",
- "4 4 162 0 0\n",
- "5 5 137 25 0\n",
- "6 6 65 53 44\n",
- "7 7 17 43 102\n",
- "8 8 9 16 137\n",
- "9 9 2 10 150\n",
- "10 10 3 8 151\n",
- "11 11 2 6 154\n",
- "12 12 3 9 150\n",
- "13 13 3 14 145\n",
- "14 14 10 21 131\n",
- "15 15 24 22 116\n",
- "16 16 49 23 90\n",
- "17 17 77 56 29\n",
- "18 18 151 12 0\n",
- "19 19 162 1 0\n",
- "20 20 162 1 0\n",
- "21 21 162 1 0\n",
- "22 22 162 1 0\n",
- "23 23 162 1 0\n",
- "<BarContainer object of 24 artists>\n",
- "<BarContainer object of 24 artists>\n",
- "<BarContainer object of 24 artists>\n"
+ "ename": "TypeError",
+ "evalue": "compute_metrics_all_expe_parr() missing 1 required positional argument: 'out_dir'",
+ "output_type": "error",
+ "traceback": [
+ "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+ "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)",
+ "\u001B[0;32m/run/user/12374/ipykernel_143142/2460456686.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mmetrics_1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0mmetrics_relative_1\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mcompute_metrics_all_expe_parr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mexpe_done_dict\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0mnb_days\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0mred_windows_dict\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0myellow_windows_dict\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;32mTrue\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0mOUT_DIR\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 2\u001B[0m \u001B[0mmetrics_1\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mto_csv\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34mf\"{OUT_DIR}/metrics_campaign3.csv\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 3\u001B[0m \u001B[0mmetrics_relative_1\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mto_csv\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34mf\"{OUT_DIR}/metrics_relative_campaign3.csv\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+ "\u001B[0;31mTypeError\u001B[0m: compute_metrics_all_expe_parr() missing 1 required positional argument: 'out_dir'"
]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 600x300 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
}
],
"source": [
- "def compute_windows_datetime(window_list,expe_start_timestamp) :\n",
- " window_datetime_list =[]\n",
- " for window in window_list :\n",
- " begin,end = window[0] + expe_start_timestamp, window[1] + expe_start_timestamp\n",
- " window_datetime_list.append([datetime.fromtimestamp(begin),datetime.fromtimestamp(end)])\n",
- " return window_datetime_list\n",
- "def compute_windows_hourday(window_list_datetime) :\n",
- " window_hourday_list =[]\n",
- " for window in window_list_datetime :\n",
- " begin = window[0].hour\n",
- " end = window[1].hour\n",
- " window_hourday_list.append([begin,end])\n",
- " return window_hourday_list\n",
- "def cut_window(window_list,begin_exp,end_exp) :\n",
- " for window in window_list :\n",
- " window[0] = max(begin_exp,window[0])\n",
- " window[1] = min(end_exp,window[1])\n",
- "def compute_hour_list(hour_window) :\n",
- " begin_range = hour_window[0]\n",
- " end_range = hour_window[1]\n",
- " if hour_window[0] == hour_window[1] :\n",
- " end_range+=1\n",
- " if hour_window[0] > hour_window[1] :\n",
- " end_range = hour_window[1]+24\n",
- " hour_list =[]\n",
- " for hour in range(begin_range,end_range) :\n",
- " hour_list.append(hour%24)\n",
- " return hour_list\n",
- "def compute_hour_list_all(hour_window_list) :\n",
- " hour_list = []\n",
- " for hour_window in hour_window_list :\n",
- " hour_list+=compute_hour_list(hour_window)\n",
- " return hour_list\n",
- "\n",
- "begin_exp = 1*24*3600\n",
- "end_exp = 163*24*3600+ 3601\n",
- "cut_window(yellow_windows,begin_exp,end_exp)\n",
- "cut_window(red_windows,begin_exp,end_exp)\n",
- "yellow_windows_datetime = compute_windows_datetime(yellow_windows,expe_start_time)\n",
- "red_windows_datetime = compute_windows_datetime(red_windows,expe_start_time)\n",
- "yellow_windows_hourday = compute_windows_hourday(yellow_windows_datetime)\n",
- "red_windows_hourday = compute_windows_hourday(red_windows_datetime)\n",
- "yellow_hour_list = compute_hour_list_all(yellow_windows_hourday)\n",
- "yellow_hour_list = yellow_hour_list + [i for i in range(24)]\n",
- "red_hour_list = compute_hour_list_all(red_windows_hourday)\n",
- "yellow_windows_hourday_df = pd.DataFrame(data=yellow_hour_list,columns=[\"hour\"])\n",
- "red_windows_hourday_df = pd.DataFrame(data=red_hour_list,columns=[\"hour\"])\n",
- "aggregated_red = red_windows_hourday_df.value_counts()\n",
- "aggregated_red.sort_index(inplace=True)\n",
- "aggregated_red_df = pd.DataFrame(aggregated_red,columns=[\"number of windows\"])\n",
- "aggregated_red_df.reset_index(inplace=True)\n",
- "\n",
- "aggregated_yellow = yellow_windows_hourday_df.value_counts()\n",
- "aggregated_yellow.sort_index(inplace=True)\n",
- "aggregated_yellow_df = pd.DataFrame(aggregated_yellow,columns=[\"number of windows\"])\n",
- "aggregated_yellow_df.reset_index(inplace=True)\n",
- "aggregated_yellow_df[\"number of windows\"]-=1\n",
- "aggregated_green_df = aggregated_red_df.copy()\n",
- "aggregated_green_df[\"number of windows\"] = 162-(aggregated_red_df[\"number of windows\"] + aggregated_yellow_df[\"number of windows\"])\n",
- "aggregated_green_df[\"number of windows\"] = np.maximum(0,aggregated_green_df[\"number of windows\"])\n",
- "aggregated_windows = aggregated_red_df.loc[:,[\"hour\"]]\n",
- "aggregated_windows[\"red\"] = aggregated_red_df[\"number of windows\"]\n",
- "aggregated_windows[\"yellow\"] = aggregated_yellow_df[\"number of windows\"]\n",
- "aggregated_windows[\"green\"] = aggregated_green_df[\"number of windows\"]\n",
- "print(aggregated_windows)\n",
- "fig = plt.figure(figsize=(6,3))\n",
- "ax= plt.gca()\n",
- "aggregated_windows.plot.bar(x=\"hour\",rot=0,ax=ax,color={\"red\" : \"darkred\", \"yellow\" : \"orange\", \"green\" : \"green\" })\n",
- "\n",
- "# You can comment or uncomment this section to get the number on the bar\n",
- "for bars in ax.containers:\n",
- " ax.bar_label(bars,fontsize=4)\n",
- " print(bars)\n",
- "\n",
- "ax = plt.gca()\n",
- "ax.xaxis.set_ticks(np.arange(0, 24, 2))\n",
- "ax.xaxis.set_ticklabels(np.arange(0, 24, 2)%24)\n",
- "plt.legend(title=\"state:\", bbox_to_anchor=(0.5, 0., 0.5, 0.7))\n",
- "plt.xlabel(\"hour of the day\")\n",
- "plt.ylabel(\"number of days\")\n",
- "plt.savefig(f\"{FIG_DIR}/stat_windows.pdf\", bbox_inches=\"tight\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "attractive-inspection",
- "metadata": {},
- "source": [
- "## metrics of all expes\n"
+ "metrics_1,metrics_relative_1 = compute_metrics_all_expe_parr(expe_done_dict,nb_days,red_windows_dict,yellow_windows_dict, True,2,OUT_DIR)\n",
+ "metrics_1.to_csv(f\"{OUT_DIR}/metrics_campaign3.csv\")\n",
+ "metrics_relative_1.to_csv(f\"{OUT_DIR}/metrics_relative_campaign3.csv\")"
]
},
{
@@ -507,7 +202,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 22,
"id": "extended-minutes",
"metadata": {
"scrolled": true
@@ -540,12 +235,11 @@
" <th>behavior</th>\n",
" <th>#jobs</th>\n",
" <th>seed</th>\n",
- " <th>Duration_red (seconds)</th>\n",
- " <th>Duration_yellow (seconds)</th>\n",
- " <th>Duration_total (seconds)</th>\n",
- " <th>NRJ_red (Joules)</th>\n",
+ " <th>NRJ_red</th>\n",
+ " <th>NRJ_yellow</th>\n",
+ " <th>NRJ_total</th>\n",
+ " <th>mean_waiting_time</th>\n",
" <th>...</th>\n",
- " <th>mean_corrected_wtime</th>\n",
" <th>max_corrected_wtime</th>\n",
" <th>mean_corrected_sdown</th>\n",
" <th>max_corrected_sdown</th>\n",
@@ -553,130 +247,131 @@
" <th>max_corrected_wtime_2</th>\n",
" <th>mean_corrected_sdown_2</th>\n",
" <th>max_corrected_sdown_2</th>\n",
- " <th>max_diff_wtime_sanity</th>\n",
- " <th>max_diff_sdown_sanity</th>\n",
+ " <th>max_delay</th>\n",
+ " <th>mean_delay</th>\n",
+ " <th>true_rigid_jobs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
- " <td>0</td>\n",
- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/d...</td>\n",
- " <td>dm_user_multi_behavior_big_effort</td>\n",
- " <td>570005</td>\n",
- " <td>0</td>\n",
- " <td>7847870</td>\n",
- " <td>1105066</td>\n",
- " <td>13996800</td>\n",
- " <td>2.702822e+10</td>\n",
+ " <td>0.0</td>\n",
+ " <td>/home/jgatt/git/demand-response-user/out/expe0...</td>\n",
+ " <td>replay_user_rigid</td>\n",
+ " <td>24866.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>768.407097</td>\n",
+ " <td>291.628746</td>\n",
+ " <td>1832.666170</td>\n",
+ " <td>831.399778</td>\n",
" <td>...</td>\n",
- " <td>11996.698248</td>\n",
- " <td>646009.222222</td>\n",
- " <td>117.792405</td>\n",
- " <td>57064.700000</td>\n",
- " <td>11996.698089</td>\n",
- " <td>646012.222222</td>\n",
- " <td>118.494916</td>\n",
- " <td>400001.000008</td>\n",
- " <td>50.000001</td>\n",
- " <td>400000.000008</td>\n",
+ " <td>10613.000000</td>\n",
+ " <td>6.401110</td>\n",
+ " <td>1060.1</td>\n",
+ " <td>831.399778</td>\n",
+ " <td>10613.000000</td>\n",
+ " <td>6.401110</td>\n",
+ " <td>1060.1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
- " <td>0</td>\n",
- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/d...</td>\n",
- " <td>dm_user_multi_behavior_big_effort</td>\n",
- " <td>569774</td>\n",
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- " <td>13996800</td>\n",
- " <td>2.704235e+10</td>\n",
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+ " <td>/home/jgatt/git/demand-response-user/out/expe0...</td>\n",
+ " <td>dm_user_multi_behavior_degrad</td>\n",
+ " <td>24866.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>676.766214</td>\n",
+ " <td>279.672977</td>\n",
+ " <td>1711.044072</td>\n",
+ " <td>675.395893</td>\n",
" <td>...</td>\n",
- " <td>10570.916222</td>\n",
- " <td>676850.000000</td>\n",
- " <td>105.654509</td>\n",
- " <td>60437.200000</td>\n",
- " <td>10570.909743</td>\n",
- " <td>676853.000000</td>\n",
- " <td>106.356916</td>\n",
- " <td>400001.000008</td>\n",
- " <td>50.000002</td>\n",
- " <td>400000.000008</td>\n",
+ " <td>8964.000000</td>\n",
+ " <td>5.650168</td>\n",
+ " <td>896.5</td>\n",
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+ " <td>8964.000000</td>\n",
+ " <td>5.650168</td>\n",
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+ " <td>19108.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
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- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/d...</td>\n",
- " <td>dm_user_multi_behavior_big_effort</td>\n",
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- " <td>13996800</td>\n",
- " <td>2.701490e+10</td>\n",
+ " <td>0.0</td>\n",
+ " <td>/home/jgatt/git/demand-response-user/out/expe0...</td>\n",
+ " <td>dm_user_multi_behavior_reconfig</td>\n",
+ " <td>24866.0</td>\n",
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+ " <td>758.292956</td>\n",
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+ " <td>1835.112995</td>\n",
+ " <td>801.431481</td>\n",
" <td>...</td>\n",
- " <td>9431.066887</td>\n",
- " <td>689055.000000</td>\n",
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- " <td>108.606393</td>\n",
- " <td>400001.000008</td>\n",
- " <td>50.000001</td>\n",
- " <td>400000.000008</td>\n",
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+ " <td>6.518539</td>\n",
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+ " <td>0.000000</td>\n",
+ " <td>19108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
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- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/d...</td>\n",
- " <td>dm_user_multi_behavior_big_effort</td>\n",
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- " <td>13996800</td>\n",
- " <td>2.698951e+10</td>\n",
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+ " <td>/home/jgatt/git/demand-response-user/out/expe0...</td>\n",
+ " <td>dm_user_multi_behavior_renonce</td>\n",
+ " <td>19108.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>522.269570</td>\n",
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+ " <td>1504.302551</td>\n",
+ " <td>780.627909</td>\n",
" <td>...</td>\n",
- " <td>10328.101735</td>\n",
- " <td>657852.999999</td>\n",
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- " <td>400001.000008</td>\n",
- " <td>50.000000</td>\n",
- " <td>400000.000008</td>\n",
+ " <td>8164.999999</td>\n",
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+ " <td>19108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
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- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/d...</td>\n",
- " <td>dm_user_multi_behavior_big_effort</td>\n",
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- " <td>13996800</td>\n",
- " <td>2.697545e+10</td>\n",
+ " <td>0.0</td>\n",
+ " <td>/home/jgatt/git/demand-response-user/out/expe0...</td>\n",
+ " <td>dm_user_multi_behavior_see_you_later</td>\n",
+ " <td>24866.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>625.503229</td>\n",
+ " <td>379.655322</td>\n",
+ " <td>1846.771378</td>\n",
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" <td>...</td>\n",
- " <td>12648.252150</td>\n",
- " <td>642279.000003</td>\n",
- " <td>112.736757</td>\n",
- " <td>60636.400003</td>\n",
- " <td>12648.256066</td>\n",
- " <td>642283.000003</td>\n",
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- " <td>400001.000008</td>\n",
- " <td>50.000002</td>\n",
- " <td>400000.000008</td>\n",
+ " <td>48247.307692</td>\n",
+ " <td>132.629821</td>\n",
+ " <td>4453.2</td>\n",
+ " <td>8657.168148</td>\n",
+ " <td>48247.307692</td>\n",
+ " <td>132.629821</td>\n",
+ " <td>4453.2</td>\n",
+ " <td>43200.0</td>\n",
+ " <td>7514.163919</td>\n",
+ " <td>19108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -703,238 +398,225 @@
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>239</th>\n",
- " <td>239</td>\n",
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- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/d...</td>\n",
- " <td>dm_user_multi_behavior_medium_effort_yellow</td>\n",
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- " <td>1105066</td>\n",
- " <td>13996800</td>\n",
- " <td>2.816022e+10</td>\n",
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+ " <td>1635</td>\n",
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+ " <td>dm_user_multi_behavior_yellow</td>\n",
+ " <td>52223.0</td>\n",
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+ " <td>347.042050</td>\n",
" <td>...</td>\n",
- " <td>12093.643089</td>\n",
- " <td>663619.000001</td>\n",
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- " <td>57903.000000</td>\n",
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- " <td>105.473008</td>\n",
- " <td>400001.000008</td>\n",
- " <td>50.000000</td>\n",
- " <td>400000.000008</td>\n",
+ " <td>40812.687501</td>\n",
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+ " <td>221.197192</td>\n",
+ " <td>514748.000000</td>\n",
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+ " <td>21600.0</td>\n",
+ " <td>158.068284</td>\n",
+ " <td>32302.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>240</th>\n",
- " <td>240</td>\n",
- " <td>0</td>\n",
- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/d...</td>\n",
- " <td>dm_user_multi_behavior_medium_effort_yellow</td>\n",
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- " <td>7847870</td>\n",
- " <td>1105066</td>\n",
- " <td>13996800</td>\n",
- " <td>2.811065e+10</td>\n",
+ " <th>1636</th>\n",
+ " <td>1636</td>\n",
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+ " <td>/home/jgatt/git/demand-response-user/out/expe1...</td>\n",
+ " <td>dm_user_multi_behavior_yellow</td>\n",
+ " <td>52250.0</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
" <tr>\n",
- " <th>241</th>\n",
- " <td>241</td>\n",
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- " <td>1105066</td>\n",
- " <td>13996800</td>\n",
- " <td>3.040103e+10</td>\n",
+ " <th>1637</th>\n",
+ " <td>1637</td>\n",
+ " <td>1.0</td>\n",
+ " <td>/home/jgatt/git/demand-response-user/out/expe1...</td>\n",
+ " <td>dm_user_multi_behavior_yellow</td>\n",
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" <td>...</td>\n",
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- " <td>400001.000008</td>\n",
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" </tr>\n",
" <tr>\n",
- " <th>242</th>\n",
- " <td>242</td>\n",
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- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/d...</td>\n",
- " <td>dm_user_multi_behavior_renonce</td>\n",
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- " <td>13996800</td>\n",
- " <td>2.023153e+10</td>\n",
+ " <th>1638</th>\n",
+ " <td>1638</td>\n",
+ " <td>1.0</td>\n",
+ " <td>/home/jgatt/git/demand-response-user/out/expe1...</td>\n",
+ " <td>dm_user_multi_behavior_yellow</td>\n",
+ " <td>52238.0</td>\n",
+ " <td>20065.0</td>\n",
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" <td>...</td>\n",
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- " <td>50.000000</td>\n",
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+ " <td>40697.000001</td>\n",
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" </tr>\n",
" <tr>\n",
- " <th>243</th>\n",
- " <td>243</td>\n",
- " <td>0</td>\n",
- " <td>/home/jogatt/stage-jolyne/save-dir/out/expe0/r...</td>\n",
- " <td>replay_user_rigid</td>\n",
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- " <td>1105066</td>\n",
- " <td>13996800</td>\n",
- " <td>3.044820e+10</td>\n",
+ " <th>1639</th>\n",
+ " <td>1639</td>\n",
+ " <td>1.0</td>\n",
+ " <td>/home/jgatt/git/demand-response-user/out/expe1...</td>\n",
+ " <td>dm_user_multi_behavior_yellow</td>\n",
+ " <td>52249.0</td>\n",
+ " <td>20067.0</td>\n",
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+ " <td>407.208095</td>\n",
" <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
+ " <td>40609.000000</td>\n",
+ " <td>8.330266</td>\n",
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+ " <td>227.119460</td>\n",
+ " <td>514748.000000</td>\n",
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+ " <td>160.745660</td>\n",
+ " <td>32442.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>244 rows × 54 columns</p>\n",
+ "<p>1640 rows × 32 columns</p>\n",
"</div>"
],
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"\n",
- " behavior #jobs seed \\\n",
- "0 dm_user_multi_behavior_big_effort 570005 0 \n",
- "1 dm_user_multi_behavior_big_effort 569774 1 \n",
- "2 dm_user_multi_behavior_big_effort 569912 2 \n",
- "3 dm_user_multi_behavior_big_effort 569662 3 \n",
- "4 dm_user_multi_behavior_big_effort 569360 4 \n",
- ".. ... ... ... \n",
- "239 dm_user_multi_behavior_medium_effort_yellow 600961 213020 \n",
- "240 dm_user_multi_behavior_medium_effort_yellow 601277 213021 \n",
- "241 dm_user_multi_behavior_reconfig 649480 0 \n",
- "242 dm_user_multi_behavior_renonce 394094 0 \n",
- "243 replay_user_rigid 649480 0 \n",
+ " behavior #jobs seed NRJ_red \\\n",
+ "0 replay_user_rigid 24866.0 0.0 768.407097 \n",
+ "1 dm_user_multi_behavior_degrad 24866.0 0.0 676.766214 \n",
+ "2 dm_user_multi_behavior_reconfig 24866.0 0.0 758.292956 \n",
+ "3 dm_user_multi_behavior_renonce 19108.0 0.0 522.269570 \n",
+ "4 dm_user_multi_behavior_see_you_later 24866.0 0.0 625.503229 \n",
+ "... ... ... ... ... \n",
+ "1635 dm_user_multi_behavior_yellow 52223.0 20063.0 200.919433 \n",
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+ "1638 dm_user_multi_behavior_yellow 52238.0 20065.0 209.878802 \n",
+ "1639 dm_user_multi_behavior_yellow 52249.0 20067.0 205.587039 \n",
"\n",
- " Duration_red (seconds) Duration_yellow (seconds) \\\n",
- "0 7847870 1105066 \n",
- "1 7847870 1105066 \n",
- "2 7847870 1105066 \n",
- "3 7847870 1105066 \n",
- "4 7847870 1105066 \n",
- ".. ... ... \n",
- "239 7847870 1105066 \n",
- "240 7847870 1105066 \n",
- "241 7847870 1105066 \n",
- "242 7847870 1105066 \n",
- "243 7847870 1105066 \n",
+ " NRJ_yellow NRJ_total mean_waiting_time ... max_corrected_wtime \\\n",
+ "0 291.628746 1832.666170 831.399778 ... 10613.000000 \n",
+ "1 279.672977 1711.044072 675.395893 ... 8964.000000 \n",
+ "2 298.146582 1835.112995 801.431481 ... 11161.000000 \n",
+ "3 254.973372 1504.302551 780.627909 ... 8164.999999 \n",
+ "4 379.655322 1846.771378 1143.004229 ... 48247.307692 \n",
+ "... ... ... ... ... ... \n",
+ "1635 618.597897 1575.016992 347.042050 ... 40812.687501 \n",
+ "1636 627.119684 1601.034639 409.633611 ... 40686.000001 \n",
+ "1637 632.325397 1625.328261 382.712937 ... 41005.000001 \n",
+ "1638 628.755332 1605.699872 396.544137 ... 40697.000001 \n",
+ "1639 641.305850 1599.734782 407.208095 ... 40609.000000 \n",
"\n",
- " Duration_total (seconds) NRJ_red (Joules) ... mean_corrected_wtime \\\n",
- "0 13996800 2.702822e+10 ... 11996.698248 \n",
- "1 13996800 2.704235e+10 ... 10570.916222 \n",
- "2 13996800 2.701490e+10 ... 9431.066887 \n",
- "3 13996800 2.698951e+10 ... 10328.101735 \n",
- "4 13996800 2.697545e+10 ... 12648.252150 \n",
- ".. ... ... ... ... \n",
- "239 13996800 2.816022e+10 ... 12093.643089 \n",
- "240 13996800 2.811065e+10 ... 9831.591329 \n",
- "241 13996800 3.040103e+10 ... 10949.847486 \n",
- "242 13996800 2.023153e+10 ... 14796.787936 \n",
- "243 13996800 3.044820e+10 ... NaN \n",
+ " mean_corrected_sdown max_corrected_sdown mean_corrected_wtime_2 \\\n",
+ "0 6.401110 1060.1 831.399778 \n",
+ "1 5.650168 896.5 675.395893 \n",
+ "2 6.518539 1114.9 801.431481 \n",
+ "3 6.269179 805.5 780.627909 \n",
+ "4 132.629821 4453.2 8657.168148 \n",
+ "... ... ... ... \n",
+ "1635 7.628744 1801.0 221.197192 \n",
+ "1636 8.688343 1441.0 375.692407 \n",
+ "1637 8.466881 1441.0 264.912442 \n",
+ "1638 8.345642 2161.0 356.653201 \n",
+ "1639 8.330266 1081.0 227.119460 \n",
"\n",
- " max_corrected_wtime mean_corrected_sdown max_corrected_sdown \\\n",
- "0 646009.222222 117.792405 57064.700000 \n",
- "1 676850.000000 105.654509 60437.200000 \n",
- "2 689055.000000 107.903784 60362.100000 \n",
- "3 657852.999999 110.939163 58697.700000 \n",
- "4 642279.000003 112.736757 60636.400003 \n",
- ".. ... ... ... \n",
- "239 663619.000001 104.806675 57903.000000 \n",
- "240 613274.999999 114.816868 61327.700000 \n",
- "241 672882.000001 114.230228 64106.600000 \n",
- "242 604086.000000 94.857235 55251.500000 \n",
- "243 NaN NaN NaN \n",
+ " max_corrected_wtime_2 mean_corrected_sdown_2 max_corrected_sdown_2 \\\n",
+ "0 10613.000000 6.401110 1060.1 \n",
+ "1 8964.000000 5.650168 896.5 \n",
+ "2 11161.000000 6.518539 1114.9 \n",
+ "3 8164.999999 6.269179 805.5 \n",
+ "4 48247.307692 132.629821 4453.2 \n",
+ "... ... ... ... \n",
+ "1635 514748.000000 18.273100 40564.8 \n",
+ "1636 514748.000000 23.044599 41701.8 \n",
+ "1637 514748.000000 18.665927 40922.5 \n",
+ "1638 514748.000000 21.122433 40564.8 \n",
+ "1639 514748.000000 19.432718 41701.8 \n",
"\n",
- " mean_corrected_wtime_2 max_corrected_wtime_2 mean_corrected_sdown_2 \\\n",
- "0 11996.698089 646012.222222 118.494916 \n",
- "1 10570.909743 676853.000000 106.356916 \n",
- "2 9431.074915 689058.000000 108.606393 \n",
- "3 10328.103641 657855.999999 111.641875 \n",
- "4 12648.256066 642283.000003 113.440009 \n",
- ".. ... ... ... \n",
- "239 12093.651372 663615.000001 105.473008 \n",
- "240 9831.603488 613273.999999 115.482646 \n",
- "241 10949.866090 672877.000001 114.846750 \n",
- "242 14796.751217 604086.000000 95.872650 \n",
- "243 NaN NaN NaN \n",
+ " max_delay mean_delay true_rigid_jobs \n",
+ "0 NaN NaN NaN \n",
+ "1 0.0 0.000000 19108.0 \n",
+ "2 0.0 0.000000 19108.0 \n",
+ "3 0.0 0.000000 19108.0 \n",
+ "4 43200.0 7514.163919 19108.0 \n",
+ "... ... ... ... \n",
+ "1635 21600.0 158.068284 32302.0 \n",
+ "1636 25200.0 153.577033 32399.0 \n",
+ "1637 18000.0 151.750794 32331.0 \n",
+ "1638 21600.0 150.648953 32378.0 \n",
+ "1639 18000.0 160.745660 32442.0 \n",
"\n",
- " max_corrected_sdown_2 max_diff_wtime_sanity max_diff_sdown_sanity \n",
- "0 400001.000008 50.000001 400000.000008 \n",
- "1 400001.000008 50.000002 400000.000008 \n",
- "2 400001.000008 50.000001 400000.000008 \n",
- "3 400001.000008 50.000000 400000.000008 \n",
- "4 400001.000008 50.000002 400000.000008 \n",
- ".. ... ... ... \n",
- "239 400001.000008 50.000000 400000.000008 \n",
- "240 400001.000008 50.000000 400000.000008 \n",
- "241 400001.000008 50.000000 400000.000008 \n",
- "242 400001.000008 50.000000 400000.000008 \n",
- "243 NaN NaN NaN \n",
- "\n",
- "[244 rows x 54 columns]"
+ "[1640 rows x 32 columns]"
]
},
- "execution_count": 6,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "metrics = pd.read_csv(f\"{OUT_DIR}/metrics_campaign3_big.csv\")\n",
+ "metrics = pd.read_csv(f\"{OUT_DIR}/metrics_campaign3.csv\")\n",
"metrics"
]
},
{
"cell_type": "code",
- "execution_count": 129,
+ "execution_count": 6,
"id": "bottom-moore",
"metadata": {
"scrolled": true
@@ -963,16 +645,10 @@
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
- " <th>true_rigid_jobs_mean</th>\n",
- " <th>true_rigid_jobs_accuracy</th>\n",
- " <th>renonce_jobs_mean</th>\n",
- " <th>renonce_jobs_accuracy</th>\n",
- " <th>consider_degrad_jobs_mean</th>\n",
- " <th>consider_degrad_jobs_accuracy</th>\n",
- " <th>reconfig_jobs_mean</th>\n",
- " <th>reconfig_jobs_accuracy</th>\n",
- " <th>C_you_later_jobs_mean</th>\n",
- " <th>C_you_later_jobs_accuracy</th>\n",
+ " <th>NRJ_red</th>\n",
+ " <th>NRJ_yellow</th>\n",
+ " <th>renonced_jobs</th>\n",
+ " <th>true_rigid_jobs</th>\n",
" </tr>\n",
" <tr>\n",
" <th>window_type</th>\n",
@@ -982,1354 +658,599 @@
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th rowspan=\"7\" valign=\"top\">_simu_solar_wind</th>\n",
- " <th>dm_user_multi_behavior_big_effort</th>\n",
- " <th>0</th>\n",
- " <td>-29.484418</td>\n",
- " <td>0.020904</td>\n",
- " <td>12.281435</td>\n",
- " <td>0.014187</td>\n",
- " <td>12.279526</td>\n",
- " <td>0.015494</td>\n",
- " <td>1.447450</td>\n",
- " <td>0.007390</td>\n",
- " <td>9.432366</td>\n",
- " <td>0.019882</td>\n",
+ " <th rowspan=\"10\" valign=\"top\">_simu_solar_wind</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-29.011280</td>\n",
+ " <td>-3.982838</td>\n",
+ " <td>2.135366</td>\n",
+ " <td>-6.882530</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_degrad</th>\n",
- " <th>0</th>\n",
- " <td>-39.321303</td>\n",
- " <td>NaN</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-24.880413</td>\n",
+ " <td>-3.345362</td>\n",
+ " <td>5.250145</td>\n",
+ " <td>-19.093079</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_degrad</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-30.593702</td>\n",
+ " <td>-6.315975</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>39.321303</td>\n",
- " <td>NaN</td>\n",
+ " <td>-8.610150</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1.0</th>\n",
+ " <td>-22.460961</td>\n",
+ " <td>-3.103070</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>-22.977728</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_reconfig</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-11.942502</td>\n",
+ " <td>1.739137</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>-8.610150</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_low_effort</th>\n",
- " <th>0</th>\n",
- " <td>-9.815663</td>\n",
- " <td>0.017107</td>\n",
- " <td>3.395522</td>\n",
- " <td>0.010774</td>\n",
- " <td>3.409153</td>\n",
- " <td>0.013972</td>\n",
- " <td>0.418868</td>\n",
- " <td>0.004780</td>\n",
- " <td>3.143715</td>\n",
- " <td>0.011135</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-13.992448</td>\n",
+ " <td>-0.426631</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-22.977728</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_max_effort</th>\n",
- " <th>0</th>\n",
- " <td>-39.321601</td>\n",
- " <td>0.000021</td>\n",
- " <td>18.222881</td>\n",
- " <td>0.024452</td>\n",
- " <td>18.212190</td>\n",
- " <td>0.025068</td>\n",
- " <td>2.078555</td>\n",
- " <td>0.007603</td>\n",
- " <td>12.581275</td>\n",
- " <td>0.020472</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_renonce</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-72.882551</td>\n",
+ " <td>-14.171118</td>\n",
+ " <td>8.610150</td>\n",
+ " <td>-8.610150</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_medium_effort</th>\n",
- " <th>0</th>\n",
- " <td>-19.658737</td>\n",
- " <td>0.028837</td>\n",
- " <td>7.449016</td>\n",
- " <td>0.013369</td>\n",
- " <td>7.429313</td>\n",
- " <td>0.016337</td>\n",
- " <td>0.899494</td>\n",
- " <td>0.006831</td>\n",
- " <td>6.289421</td>\n",
- " <td>0.015590</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-60.223720</td>\n",
+ " <td>-9.926660</td>\n",
+ " <td>21.174913</td>\n",
+ " <td>-22.977728</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_reconfig</th>\n",
- " <th>0</th>\n",
- " <td>-6.310433</td>\n",
- " <td>NaN</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_see_you_later</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-64.374341</td>\n",
+ " <td>13.660283</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>-8.610150</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1.0</th>\n",
+ " <td>-52.078591</td>\n",
+ " <td>10.953130</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>6.310433</td>\n",
- " <td>NaN</td>\n",
+ " <td>-22.977728</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">_simu_solar_wind_yellow</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_yellow</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-29.817637</td>\n",
+ " <td>-12.899025</td>\n",
+ " <td>2.122899</td>\n",
+ " <td>-35.610673</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1.0</th>\n",
+ " <td>-25.894680</td>\n",
+ " <td>-13.250431</td>\n",
+ " <td>5.255296</td>\n",
+ " <td>-41.147780</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"10\" valign=\"top\">_solar</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-10.764310</td>\n",
+ " <td>-12.315888</td>\n",
+ " <td>5.576208</td>\n",
+ " <td>-18.302019</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1.0</th>\n",
+ " <td>-6.623759</td>\n",
+ " <td>-6.313912</td>\n",
+ " <td>6.557004</td>\n",
+ " <td>-24.360944</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_degrad</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-11.600117</td>\n",
+ " <td>-13.799824</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>-22.870586</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_renonce</th>\n",
- " <th>0</th>\n",
- " <td>-39.321611</td>\n",
- " <td>NaN</td>\n",
- " <td>39.321611</td>\n",
- " <td>NaN</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-5.332010</td>\n",
+ " <td>-6.006783</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>-29.554193</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_reconfig</th>\n",
+ " <th>0.0</th>\n",
+ " <td>0.943193</td>\n",
+ " <td>-4.982079</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>-22.870586</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1.0</th>\n",
+ " <td>-1.116959</td>\n",
+ " <td>-2.586555</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>-29.554193</td>\n",
" </tr>\n",
" <tr>\n",
- " <th rowspan=\"4\" valign=\"top\">_simu_solar_wind_yellow</th>\n",
- " <th>dm_user_multi_behavior_big_effort_yellow</th>\n",
- " <th>0</th>\n",
- " <td>-32.703460</td>\n",
- " <td>0.023745</td>\n",
- " <td>12.279100</td>\n",
- " <td>0.019891</td>\n",
- " <td>15.180195</td>\n",
- " <td>0.022675</td>\n",
- " <td>1.943996</td>\n",
- " <td>0.006087</td>\n",
- " <td>9.438228</td>\n",
- " <td>0.020445</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_renonce</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-32.623178</td>\n",
+ " <td>-31.462579</td>\n",
+ " <td>22.870586</td>\n",
+ " <td>-22.870586</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_low_effort_yellow</th>\n",
- " <th>0</th>\n",
- " <td>-10.905966</td>\n",
- " <td>0.016434</td>\n",
- " <td>3.410467</td>\n",
- " <td>0.010400</td>\n",
- " <td>4.330870</td>\n",
- " <td>0.011478</td>\n",
- " <td>0.581871</td>\n",
- " <td>0.004618</td>\n",
- " <td>3.153353</td>\n",
- " <td>0.012742</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-19.974470</td>\n",
+ " <td>-19.038110</td>\n",
+ " <td>26.855412</td>\n",
+ " <td>-29.554193</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_max_effort_yellow</th>\n",
- " <th>0</th>\n",
- " <td>-43.591468</td>\n",
- " <td>0.010214</td>\n",
- " <td>18.218416</td>\n",
- " <td>0.024264</td>\n",
- " <td>22.171568</td>\n",
- " <td>0.027180</td>\n",
- " <td>2.749045</td>\n",
- " <td>0.009365</td>\n",
- " <td>12.587224</td>\n",
- " <td>0.023856</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_see_you_later</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-14.916763</td>\n",
+ " <td>19.428120</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-22.870586</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_medium_effort_yellow</th>\n",
- " <th>0</th>\n",
- " <td>-21.805342</td>\n",
- " <td>0.019555</td>\n",
- " <td>7.450863</td>\n",
- " <td>0.014036</td>\n",
- " <td>9.318096</td>\n",
- " <td>0.020327</td>\n",
- " <td>1.228306</td>\n",
- " <td>0.005825</td>\n",
- " <td>6.293271</td>\n",
- " <td>0.013159</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " true_rigid_jobs_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 -29.484418 \n",
- " dm_user_multi_behavior_degrad 0 -39.321303 \n",
- " dm_user_multi_behavior_low_effort 0 -9.815663 \n",
- " dm_user_multi_behavior_max_effort 0 -39.321601 \n",
- " dm_user_multi_behavior_medium_effort 0 -19.658737 \n",
- " dm_user_multi_behavior_reconfig 0 -6.310433 \n",
- " dm_user_multi_behavior_renonce 0 -39.321611 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 -32.703460 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 -10.905966 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 -43.591468 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 -21.805342 \n",
- "\n",
- " true_rigid_jobs_accuracy \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 0.020904 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 0.017107 \n",
- " dm_user_multi_behavior_max_effort 0 0.000021 \n",
- " dm_user_multi_behavior_medium_effort 0 0.028837 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 0.023745 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 0.016434 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 0.010214 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 0.019555 \n",
- "\n",
- " renonce_jobs_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 12.281435 \n",
- " dm_user_multi_behavior_degrad 0 0.000000 \n",
- " dm_user_multi_behavior_low_effort 0 3.395522 \n",
- " dm_user_multi_behavior_max_effort 0 18.222881 \n",
- " dm_user_multi_behavior_medium_effort 0 7.449016 \n",
- " dm_user_multi_behavior_reconfig 0 0.000000 \n",
- " dm_user_multi_behavior_renonce 0 39.321611 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 12.279100 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 3.410467 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 18.218416 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 7.450863 \n",
- "\n",
- " renonce_jobs_accuracy \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 0.014187 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 0.010774 \n",
- " dm_user_multi_behavior_max_effort 0 0.024452 \n",
- " dm_user_multi_behavior_medium_effort 0 0.013369 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 0.019891 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 0.010400 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 0.024264 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 0.014036 \n",
- "\n",
- " consider_degrad_jobs_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 12.279526 \n",
- " dm_user_multi_behavior_degrad 0 39.321303 \n",
- " dm_user_multi_behavior_low_effort 0 3.409153 \n",
- " dm_user_multi_behavior_max_effort 0 18.212190 \n",
- " dm_user_multi_behavior_medium_effort 0 7.429313 \n",
- " dm_user_multi_behavior_reconfig 0 0.000000 \n",
- " dm_user_multi_behavior_renonce 0 0.000000 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 15.180195 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 4.330870 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 22.171568 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 9.318096 \n",
- "\n",
- " consider_degrad_jobs_accuracy \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 0.015494 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 0.013972 \n",
- " dm_user_multi_behavior_max_effort 0 0.025068 \n",
- " dm_user_multi_behavior_medium_effort 0 0.016337 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 0.022675 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 0.011478 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 0.027180 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 0.020327 \n",
- "\n",
- " reconfig_jobs_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 1.447450 \n",
- " dm_user_multi_behavior_degrad 0 0.000000 \n",
- " dm_user_multi_behavior_low_effort 0 0.418868 \n",
- " dm_user_multi_behavior_max_effort 0 2.078555 \n",
- " dm_user_multi_behavior_medium_effort 0 0.899494 \n",
- " dm_user_multi_behavior_reconfig 0 6.310433 \n",
- " dm_user_multi_behavior_renonce 0 0.000000 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 1.943996 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 0.581871 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 2.749045 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 1.228306 \n",
- "\n",
- " reconfig_jobs_accuracy \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 0.007390 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 0.004780 \n",
- " dm_user_multi_behavior_max_effort 0 0.007603 \n",
- " dm_user_multi_behavior_medium_effort 0 0.006831 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 0.006087 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 0.004618 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 0.009365 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 0.005825 \n",
- "\n",
- " C_you_later_jobs_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 9.432366 \n",
- " dm_user_multi_behavior_degrad 0 0.000000 \n",
- " dm_user_multi_behavior_low_effort 0 3.143715 \n",
- " dm_user_multi_behavior_max_effort 0 12.581275 \n",
- " dm_user_multi_behavior_medium_effort 0 6.289421 \n",
- " dm_user_multi_behavior_reconfig 0 0.000000 \n",
- " dm_user_multi_behavior_renonce 0 0.000000 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 9.438228 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 3.153353 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 12.587224 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 6.293271 \n",
- "\n",
- " C_you_later_jobs_accuracy \n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 0.019882 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 0.011135 \n",
- " dm_user_multi_behavior_max_effort 0 0.020472 \n",
- " dm_user_multi_behavior_medium_effort 0 0.015590 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 0.020445 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 0.012742 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 0.023856 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 0.013159 "
- ]
- },
- "execution_count": 129,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# analyze metrics relative to the control XP\n",
- "import math\n",
- "expe_filename = f\"{OUT_DIR}/metrics_relative_campaign3_big.csv\"\n",
- "metrics_relative = pd.read_csv(expe_filename)\n",
- "aggregated_behavior_mean = metrics_relative.groupby([\"window_type\",\"behavior\",\"XP\"]).mean()\n",
- "aggregated_behavior_mean = aggregated_behavior_mean.add_suffix(\"_mean\")\n",
- "aggregated_behavior_accuracy = 3 * metrics_relative.groupby([\"window_type\",\"behavior\",\"XP\"]).std() / math.sqrt(nb_replicat)\n",
- "aggregated_behavior_accuracy = aggregated_behavior_accuracy.add_suffix(\"_accuracy\")\n",
- "aggregated_behavior_mean_accuracy = pd.concat([aggregated_behavior_mean,aggregated_behavior_accuracy],axis=1)\n",
- "columns_list = [\"true_rigid_jobs\", \"renonce_jobs\", \"consider_degrad_jobs\",\"reconfig_jobs\",'C_you_later_jobs']\n",
- "columns_list = [name+ suffix for name in columns_list for suffix in [\"_mean\",\"_accuracy\"] ]\n",
- "aggregated_behavior_mean_accuracy.loc[:,columns_list]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "84af2d5b",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "Let's compute the metrics of energy in KwH and the accuracy using standard deviation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "02310919",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th>NRJ_red (kwh)_mean</th>\n",
- " <th>NRJ_red (kwh)_accuracy</th>\n",
- " <th>NRJ_yellow (kwh)_mean</th>\n",
- " <th>NRJ_yellow (kwh)_accuracy</th>\n",
- " <th>NRJ_total (kwh)_mean</th>\n",
- " <th>NRJ_total (kwh)_accuracy</th>\n",
- " <th>energy underproduced (kwh)_mean</th>\n",
- " <th>energy underproduced (kwh)_accuracy</th>\n",
- " <th>energy overproduced (kwh)_mean</th>\n",
- " <th>energy overproduced (kwh)_accuracy</th>\n",
+ " <th>1.0</th>\n",
+ " <td>-10.350962</td>\n",
+ " <td>5.632161</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-29.554193</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>window_type</th>\n",
- " <th>behavior</th>\n",
- " <th>XP</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
+ " <th rowspan=\"10\" valign=\"top\">_solar_wind</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-10.354258</td>\n",
+ " <td>-4.043161</td>\n",
+ " <td>3.025698</td>\n",
+ " <td>-10.224162</td>\n",
" </tr>\n",
- " </thead>\n",
- " <tbody>\n",
" <tr>\n",
- " <th rowspan=\"8\" valign=\"top\">_simu_solar_wind</th>\n",
- " <th>dm_user_multi_behavior_big_effort</th>\n",
- " <th>0</th>\n",
- " <td>7503.086017</td>\n",
- " <td>7.0</td>\n",
- " <td>1072.854073</td>\n",
- " <td>2.0</td>\n",
- " <td>13968.959981</td>\n",
- " <td>10.0</td>\n",
- " <td>7173.866329</td>\n",
- " <td>7.0</td>\n",
- " <td>16794.349403</td>\n",
- " <td>4.0</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-5.750231</td>\n",
+ " <td>-2.664631</td>\n",
+ " <td>4.439967</td>\n",
+ " <td>-17.913541</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_degrad</th>\n",
- " <th>0</th>\n",
- " <td>7213.664085</td>\n",
- " <td>NaN</td>\n",
- " <td>1036.689216</td>\n",
- " <td>NaN</td>\n",
- " <td>13523.178534</td>\n",
- " <td>NaN</td>\n",
- " <td>6915.808308</td>\n",
- " <td>NaN</td>\n",
- " <td>16971.845675</td>\n",
- " <td>NaN</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_degrad</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-10.233249</td>\n",
+ " <td>-5.866204</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-12.788547</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_low_effort</th>\n",
- " <th>0</th>\n",
- " <td>8194.074735</td>\n",
- " <td>4.0</td>\n",
- " <td>1124.334183</td>\n",
- " <td>1.0</td>\n",
- " <td>14815.742360</td>\n",
- " <td>5.0</td>\n",
- " <td>7842.702819</td>\n",
- " <td>4.0</td>\n",
- " <td>16608.920861</td>\n",
- " <td>3.0</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-5.247645</td>\n",
+ " <td>-2.888914</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-21.508633</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_max_effort</th>\n",
- " <th>0</th>\n",
- " <td>7019.031387</td>\n",
- " <td>8.0</td>\n",
- " <td>1046.528079</td>\n",
- " <td>2.0</td>\n",
- " <td>13399.565571</td>\n",
- " <td>11.0</td>\n",
- " <td>6717.154178</td>\n",
- " <td>7.0</td>\n",
- " <td>16909.727849</td>\n",
- " <td>5.0</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_reconfig</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-2.528002</td>\n",
+ " <td>1.811416</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-12.788547</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_medium_effort</th>\n",
- " <th>0</th>\n",
- " <td>7881.703283</td>\n",
- " <td>7.0</td>\n",
- " <td>1098.813792</td>\n",
- " <td>2.0</td>\n",
- " <td>14429.298778</td>\n",
- " <td>9.0</td>\n",
- " <td>7538.836037</td>\n",
- " <td>7.0</td>\n",
- " <td>16695.386260</td>\n",
- " <td>4.0</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-2.178845</td>\n",
+ " <td>-0.688434</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-21.508633</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_reconfig</th>\n",
- " <th>0</th>\n",
- " <td>8444.731088</td>\n",
- " <td>NaN</td>\n",
- " <td>1161.352545</td>\n",
- " <td>NaN</td>\n",
- " <td>15259.567423</td>\n",
- " <td>NaN</td>\n",
- " <td>8074.889661</td>\n",
- " <td>NaN</td>\n",
- " <td>16401.182241</td>\n",
- " <td>NaN</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_renonce</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-27.111331</td>\n",
+ " <td>-14.455575</td>\n",
+ " <td>12.788547</td>\n",
+ " <td>-12.788547</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_renonce</th>\n",
- " <th>0</th>\n",
- " <td>5619.868528</td>\n",
- " <td>NaN</td>\n",
- " <td>944.343888</td>\n",
- " <td>NaN</td>\n",
- " <td>11682.595201</td>\n",
- " <td>NaN</td>\n",
- " <td>5481.340914</td>\n",
- " <td>NaN</td>\n",
- " <td>17307.895791</td>\n",
- " <td>NaN</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-14.789082</td>\n",
+ " <td>-8.353933</td>\n",
+ " <td>18.606718</td>\n",
+ " <td>-21.508633</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>replay_user_rigid</th>\n",
- " <th>0</th>\n",
- " <td>8457.832285</td>\n",
- " <td>NaN</td>\n",
- " <td>1145.257863</td>\n",
- " <td>NaN</td>\n",
- " <td>15143.223636</td>\n",
- " <td>NaN</td>\n",
- " <td>8098.083744</td>\n",
- " <td>NaN</td>\n",
- " <td>16531.954350</td>\n",
- " <td>NaN</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_see_you_later</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-16.523675</td>\n",
+ " <td>6.575554</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-12.788547</td>\n",
" </tr>\n",
" <tr>\n",
- " <th rowspan=\"4\" valign=\"top\">_simu_solar_wind_yellow</th>\n",
- " <th>dm_user_multi_behavior_big_effort_yellow</th>\n",
- " <th>0</th>\n",
- " <td>7421.517262</td>\n",
- " <td>8.0</td>\n",
- " <td>1053.319755</td>\n",
- " <td>2.0</td>\n",
- " <td>13862.230387</td>\n",
- " <td>11.0</td>\n",
- " <td>7087.144892</td>\n",
- " <td>8.0</td>\n",
- " <td>16814.942815</td>\n",
- " <td>6.0</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-10.899773</td>\n",
+ " <td>2.805253</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-21.508633</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_low_effort_yellow</th>\n",
- " <th>0</th>\n",
- " <td>8165.403507</td>\n",
- " <td>6.0</td>\n",
- " <td>1119.532479</td>\n",
- " <td>2.0</td>\n",
- " <td>14779.105402</td>\n",
- " <td>8.0</td>\n",
- " <td>7812.993440</td>\n",
- " <td>6.0</td>\n",
- " <td>16616.045941</td>\n",
- " <td>4.0</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">_solar_wind_yellow</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_yellow</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-11.998930</td>\n",
+ " <td>-5.696210</td>\n",
+ " <td>3.057347</td>\n",
+ " <td>-16.524491</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_max_effort_yellow</th>\n",
- " <th>0</th>\n",
- " <td>6914.665159</td>\n",
- " <td>8.0</td>\n",
- " <td>1019.927423</td>\n",
- " <td>2.0</td>\n",
- " <td>13252.599830</td>\n",
- " <td>11.0</td>\n",
- " <td>6602.495035</td>\n",
- " <td>7.0</td>\n",
- " <td>16943.196475</td>\n",
- " <td>5.0</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-6.679314</td>\n",
+ " <td>-3.466127</td>\n",
+ " <td>4.443902</td>\n",
+ " <td>-25.337402</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>dm_user_multi_behavior_medium_effort_yellow</th>\n",
- " <th>0</th>\n",
- " <td>7826.659792</td>\n",
- " <td>7.0</td>\n",
- " <td>1088.183915</td>\n",
- " <td>2.0</td>\n",
- " <td>14360.540520</td>\n",
- " <td>8.0</td>\n",
- " <td>7479.663224</td>\n",
- " <td>6.0</td>\n",
- " <td>16705.156668</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " NRJ_red (kwh)_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 7503.086017 \n",
- " dm_user_multi_behavior_degrad 0 7213.664085 \n",
- " dm_user_multi_behavior_low_effort 0 8194.074735 \n",
- " dm_user_multi_behavior_max_effort 0 7019.031387 \n",
- " dm_user_multi_behavior_medium_effort 0 7881.703283 \n",
- " dm_user_multi_behavior_reconfig 0 8444.731088 \n",
- " dm_user_multi_behavior_renonce 0 5619.868528 \n",
- " replay_user_rigid 0 8457.832285 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 7421.517262 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 8165.403507 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 6914.665159 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 7826.659792 \n",
- "\n",
- " NRJ_red (kwh)_accuracy \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 7.0 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 4.0 \n",
- " dm_user_multi_behavior_max_effort 0 8.0 \n",
- " dm_user_multi_behavior_medium_effort 0 7.0 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- " replay_user_rigid 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 8.0 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 6.0 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 8.0 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 7.0 \n",
- "\n",
- " NRJ_yellow (kwh)_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 1072.854073 \n",
- " dm_user_multi_behavior_degrad 0 1036.689216 \n",
- " dm_user_multi_behavior_low_effort 0 1124.334183 \n",
- " dm_user_multi_behavior_max_effort 0 1046.528079 \n",
- " dm_user_multi_behavior_medium_effort 0 1098.813792 \n",
- " dm_user_multi_behavior_reconfig 0 1161.352545 \n",
- " dm_user_multi_behavior_renonce 0 944.343888 \n",
- " replay_user_rigid 0 1145.257863 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 1053.319755 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 1119.532479 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 1019.927423 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 1088.183915 \n",
- "\n",
- " NRJ_yellow (kwh)_accuracy \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 2.0 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 1.0 \n",
- " dm_user_multi_behavior_max_effort 0 2.0 \n",
- " dm_user_multi_behavior_medium_effort 0 2.0 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- " replay_user_rigid 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 2.0 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 2.0 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 2.0 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 2.0 \n",
- "\n",
- " NRJ_total (kwh)_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 13968.959981 \n",
- " dm_user_multi_behavior_degrad 0 13523.178534 \n",
- " dm_user_multi_behavior_low_effort 0 14815.742360 \n",
- " dm_user_multi_behavior_max_effort 0 13399.565571 \n",
- " dm_user_multi_behavior_medium_effort 0 14429.298778 \n",
- " dm_user_multi_behavior_reconfig 0 15259.567423 \n",
- " dm_user_multi_behavior_renonce 0 11682.595201 \n",
- " replay_user_rigid 0 15143.223636 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 13862.230387 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 14779.105402 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 13252.599830 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 14360.540520 \n",
- "\n",
- " NRJ_total (kwh)_accuracy \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 10.0 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 5.0 \n",
- " dm_user_multi_behavior_max_effort 0 11.0 \n",
- " dm_user_multi_behavior_medium_effort 0 9.0 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- " replay_user_rigid 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 11.0 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 8.0 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 11.0 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 8.0 \n",
- "\n",
- " energy underproduced (kwh)_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 7173.866329 \n",
- " dm_user_multi_behavior_degrad 0 6915.808308 \n",
- " dm_user_multi_behavior_low_effort 0 7842.702819 \n",
- " dm_user_multi_behavior_max_effort 0 6717.154178 \n",
- " dm_user_multi_behavior_medium_effort 0 7538.836037 \n",
- " dm_user_multi_behavior_reconfig 0 8074.889661 \n",
- " dm_user_multi_behavior_renonce 0 5481.340914 \n",
- " replay_user_rigid 0 8098.083744 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 7087.144892 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 7812.993440 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 6602.495035 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 7479.663224 \n",
- "\n",
- " energy underproduced (kwh)_accuracy \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 7.0 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 4.0 \n",
- " dm_user_multi_behavior_max_effort 0 7.0 \n",
- " dm_user_multi_behavior_medium_effort 0 7.0 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- " replay_user_rigid 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 8.0 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 6.0 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 7.0 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 6.0 \n",
- "\n",
- " energy overproduced (kwh)_mean \\\n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 16794.349403 \n",
- " dm_user_multi_behavior_degrad 0 16971.845675 \n",
- " dm_user_multi_behavior_low_effort 0 16608.920861 \n",
- " dm_user_multi_behavior_max_effort 0 16909.727849 \n",
- " dm_user_multi_behavior_medium_effort 0 16695.386260 \n",
- " dm_user_multi_behavior_reconfig 0 16401.182241 \n",
- " dm_user_multi_behavior_renonce 0 17307.895791 \n",
- " replay_user_rigid 0 16531.954350 \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 16814.942815 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 16616.045941 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 16943.196475 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 16705.156668 \n",
- "\n",
- " energy overproduced (kwh)_accuracy \n",
- "window_type behavior XP \n",
- "_simu_solar_wind dm_user_multi_behavior_big_effort 0 4.0 \n",
- " dm_user_multi_behavior_degrad 0 NaN \n",
- " dm_user_multi_behavior_low_effort 0 3.0 \n",
- " dm_user_multi_behavior_max_effort 0 5.0 \n",
- " dm_user_multi_behavior_medium_effort 0 4.0 \n",
- " dm_user_multi_behavior_reconfig 0 NaN \n",
- " dm_user_multi_behavior_renonce 0 NaN \n",
- " replay_user_rigid 0 NaN \n",
- "_simu_solar_wind_yellow dm_user_multi_behavior_big_effort_yellow 0 6.0 \n",
- " dm_user_multi_behavior_low_effort_yellow 0 4.0 \n",
- " dm_user_multi_behavior_max_effort_yellow 0 5.0 \n",
- " dm_user_multi_behavior_medium_effort_yellow 0 4.0 "
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metrics = pd.read_csv(f\"{OUT_DIR}/metrics_campaign3_big.csv\")\n",
- "#Convert energy to kwh for better view\n",
- "metrics[\"NRJ_red (kwh)\"] = metrics[\"NRJ_red (Joules)\"]/3600/1000\n",
- "metrics[\"NRJ_total (kwh)\"] = metrics[\"NRJ_total (Joules)\"]/3600/1000\n",
- "metrics[\"NRJ_yellow (kwh)\"] = metrics[\"NRJ_yellow (Joules)\"]/3600/1000\n",
- "metrics[\"energy underproduced (kwh)\"] = metrics[\"energy underproduced (Joules)\"]/3600/1000\n",
- "metrics[\"energy overproduced (kwh)\"]= metrics[\"energy overproduced (Joules)\"]/3600/1000\n",
- "aggregated_energy_mean = metrics.groupby([\"window_type\",\"behavior\",\"XP\"]).mean()\n",
- "aggregated_energy_mean = aggregated_energy_mean.add_suffix(\"_mean\")\n",
- "# accuracy is round up to integer to ease reading\n",
- "aggregated_energy_accuracy = (3 * metrics.groupby([\"window_type\",\"behavior\",\"XP\"]).std() / math.sqrt(nb_replicat)).apply(np.ceil)\n",
- "aggregated_energy_accuracy = aggregated_energy_accuracy.add_suffix(\"_accuracy\")\n",
- "aggregated_energy_mean_accuracy = pd.concat([aggregated_energy_mean,aggregated_energy_accuracy],axis=1)\n",
- "columns_list = [\"NRJ_red (kwh)\", \"NRJ_yellow (kwh)\", \"NRJ_total (kwh)\",\"energy underproduced (kwh)\",'energy overproduced (kwh)']\n",
- "columns_list = [name+ suffix for name in columns_list for suffix in [\"_mean\",\"_accuracy\"] ]\n",
- "aggregated_energy_mean_accuracy.loc[:,columns_list]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c48b00ec",
- "metadata": {},
- "source": [
- "Let's compute the correlation between effort probability alpha and metric underproduction."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "1854d2a3",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Pearson correlation coefficients:\n",
- "alpha/underprod: -0.9916222045338368\n",
- "alpha/red_nrj: -0.9918343217511406\n",
- "alpha/yel_nrj: -0.9654034516285591\n"
- ]
- }
- ],
- "source": [
- "def behavior_to_alpha(behavior):\n",
- " if \"rigid\" in behavior :\n",
- " return 0\n",
- " if \"low_\" in behavior :\n",
- " return .25\n",
- " if \"medium_\" in behavior :\n",
- " return .5\n",
- " if \"big_\" in behavior :\n",
- " return .75\n",
- " if \"max_\" in behavior :\n",
- " return 1\n",
- "\n",
- "metrics = pd.read_csv(f\"{OUT_DIR}/metrics_campaign3_big.csv\")\n",
- "metrics[\"alpha\"]=metrics[\"behavior\"].apply(lambda x : behavior_to_alpha(x))\n",
- "alpha = metrics[\"alpha\"]\n",
- "underprod = metrics[\"energy underproduced (Joules)\"]\n",
- "\n",
- "print(f\"Pearson correlation coefficients:\")\n",
- "print(f\"alpha/underprod: {alpha.corr(underprod)}\")\n",
- "print(f\"alpha/red_nrj: {alpha.corr(metrics['NRJ_red (Joules)'])}\")\n",
- "print(f\"alpha/yel_nrj: {alpha.corr(metrics['NRJ_yellow (Joules)'])}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ca592226",
- "metadata": {},
- "source": [
- "# Graph of energy and Effort\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ad7cbb52",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "Let's compute the underproduction in function of modified job percentage."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "id": "e1f48289",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "r-square : 0.9571896031960813\n",
- "linear regression : -37.700930875883984*X+8252.704793698493\n",
- "\n",
- "Underproduction in fonction of the number of modified jobs:\n",
- "(the linear regression doesn't take 'renounce/reconfig/degrad' points into account)\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 600x500 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "color_blind_map = [\"#882255\",\"#332288\",\"#117733\",\"#44AA99\",\"#88CCEE\",\"#827846\"]\n",
- "def color_map(behavior) :\n",
- " if \"rigid\" in behavior :\n",
- " return color_blind_map[0]\n",
- " if \"low_\" in behavior :\n",
- " return color_blind_map[1]\n",
- " if \"medium_\" in behavior :\n",
- " return color_blind_map[2]\n",
- " if \"big_\" in behavior :\n",
- " return color_blind_map[3]\n",
- " if \"max_\" in behavior :\n",
- " return color_blind_map[4]\n",
- " if \"renonce\" in behavior :\n",
- " return color_blind_map[5]\n",
- " if \"degrad\" in behavior :\n",
- " return \"tab:blue\"\n",
- " if \"reconfig\" in behavior :\n",
- " return \"tab:orange\"\n",
- " \n",
- "def marker(behavior) :\n",
- " if \"yellow\" in behavior :\n",
- " return \"_\"\n",
- " if (\"rigid\" in behavior or \"renonce\" in behavior or \n",
- " \"degrad\" in behavior or \"reconfig\" in behavior):\n",
- " return \"x\"\n",
- " return \".\"\n",
- "\n",
- "def pretty_name(behavior):\n",
- " if \"rigid\" in behavior :\n",
- " behavior_name=\"full rigid\"\n",
- " elif \"renonce\" in behavior :\n",
- " behavior_name=\"full renounce red\"\n",
- " elif \"degrad\" in behavior :\n",
- " behavior_name=\"full degrad red\"\n",
- " elif \"reconfig\" in behavior :\n",
- " behavior_name=\"full reconfig red\"\n",
- " elif \"yellow\" in behavior :\n",
- " behavior_name=\" \".join(behavior.split(\"_\")[-3:-1]) + \" (yellow)\"\n",
- " else :\n",
- " behavior_name=\" \".join(behavior.split(\"_\")[-2:])\n",
- "\n",
- " return behavior_name\n",
- "\n",
- "def sort_effort(string1) :\n",
- " string1 = string1.split(\"_\")\n",
- " effort_list = [\"rigid\",\"low\",\"medium\",\"big\",\"max\",\"degrad\",\"reconfig\",\"renounce\"]\n",
- " for index,effort_level in enumerate(effort_list) :\n",
- " if effort_level in string1 :\n",
- " if \"yellow\" in string1:\n",
- " return index + .5\n",
- " else:\n",
- " return index\n",
- " return len(effort_list)\n",
- "\n",
- "def plot_energy_fun_effort(energy_metrics_name,effort_metrics_name,metrics,legend_enabled=True,disable_renounce=False,ybot=0,figsize=(6,5)) :\n",
- " plt.figure(figsize=figsize)\n",
- " ax = plt.gca()\n",
- "\n",
- " behavior_list = list(metrics[\"behavior\"].unique())\n",
- " behavior_list.sort(key=sort_effort)\n",
- " for behavior in behavior_list :\n",
- " if disable_renounce and \"renonce\" in behavior :\n",
- " continue\n",
- " metrics_sub = metrics[metrics.behavior == behavior]\n",
- " marker_value = marker(behavior)\n",
- " color_behavior = color_map(behavior)\n",
- " behavior_name = pretty_name(behavior)\n",
- " ax.scatter(metrics_sub[effort_metrics_name],metrics_sub[energy_metrics_name],\n",
- " marker=marker_value,label=behavior_name,color=color_behavior)\n",
- "\n",
- " if legend_enabled:\n",
- " plt.legend()\n",
- " ax.set_ylim(bottom=ybot)\n",
- " return ax\n",
- "\n",
- "metrics = pd.read_csv(f\"{OUT_DIR}/metrics_campaign3_big.csv\")\n",
- "\n",
- "total_jobs = metrics[metrics.behavior==\"replay_user_rigid\"][\"#jobs\"].values[0]\n",
- "# metrics = metrics[metrics.behavior!='dm_user_multi_behavior_degrad']\n",
- "# metrics = metrics[metrics.behavior!='dm_user_multi_behavior_reconfig']\n",
- "\n",
- "# Unit conversion...\n",
- "metrics[\"energy underproduced (kwh)\"] = metrics[\"energy underproduced (Joules)\"]/3600/1000\n",
- "metrics[\"unmodified jobs percentage\"] = metrics[\"true_rigid_jobs\"]/total_jobs *100\n",
- "metrics[\"modified jobs percentage\"] = 100 - metrics[\"unmodified jobs percentage\"]\n",
- "metrics[\"modified jobs percentage\"] = metrics[\"modified jobs percentage\"].fillna(0)\n",
- "\n",
- "# Plot \n",
- "plot_energy_fun_effort(\"energy underproduced (kwh)\",\"modified jobs percentage\",metrics,\n",
- " legend_enabled=True,disable_renounce=False,ybot=5200)\n",
- "plt.xlabel(\"modified jobs (% of total)\")\n",
- "plt.ylabel(\"underproduction (kWh)\")\n",
- "\n",
- "# Computation of linear regression\n",
- "# Sort out renounce before regression\n",
- "selection_for_regression = metrics[\n",
- " (metrics.behavior!=\"dm_user_multi_behavior_degrad\") &\n",
- " (metrics.behavior!=\"dm_user_multi_behavior_reconfig\") & \n",
- " (metrics.behavior!=\"dm_user_multi_behavior_renounce\")].copy()\n",
- "X = selection_for_regression[\"modified jobs percentage\"]\n",
- "Y = selection_for_regression[\"energy underproduced (kwh)\"]\n",
- "slope, intercept, r_value, _, _ = linregress(X,Y)\n",
- "\n",
- "print(f\"r-square : {r_value**2}\")\n",
- "print(f\"linear regression : {slope}*X+{intercept}\")\n",
- "print(\"\\nUnderproduction in fonction of the number of modified jobs:\")\n",
- "print(\"(the linear regression doesn't take 'renounce/reconfig/degrad' points into account)\")\n",
- "X = np.linspace(0, max(X))\n",
- "plt.plot(X,slope*X+intercept, '--', color=\"gray\")\n",
- "plt.text(x=8,y=8000,color=\"gray\",\n",
- " s=f\"Y = {slope:.3f}X + {intercept:.0f}\\nr-squared: {r_value**2:.3f}\")\n",
- "plt.savefig(f\"{FIG_DIR}/underprod_VS_modified_jobs.pdf\", bbox_inches=\"tight\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Computation of quotient between underproduction gains (compared to rigid) and \"effort\" (percentage of modified jobs) to get an idea of the marginal cost of the gains:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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TQoiyyF+U8ugSwJo2ZqUuWHl0f70GZVtI2NG7gdacxsdhamaoLrZ5tEwIXVXrRNLd3Z0HDx5w9OhRNRm8efMmCQkJNGvWDHi4GMbHx4ft27dz7tw5unTpgpmZGdnZ2axcuZJ27doVm7zZ29vj5OTEr7/+yrBhwx471rCwMDIzM9X2oqOj0dPTw83NrcRzjYyMyvRcSE9PT86fP1+ofPTo0bRo0YLly5fz4MEDXnzxRXVfmzZt+P7773FxccHAoPzfDu7u7iQnJ5OcnKz2Sp4/f57U1FT1PahRowYtW7Zk2bJlGBoa0rRpU2rXrs2QIUPYuXNnkVML4uLiiu0pFkKIymRqZsTxpKmPfV5Z69C1vX+qPvHvVq2Htl1dXenfvz/BwcFERUURGxvLq6++Sp06dejfv796nJ+fH5s2baJ169ZYWFigp6dH165d2bBhQ4nzIwFmz57N/Pnz+eyzz7h48SJnz54lNDSUTz75pFyxDhs2DBMTE0aOHElcXBwHDx5k/PjxDB8+HHt7+xLPdXFx4ZdffuHatWslrqbu1auXOu+wIHd3dzp16sTUqVN5+eWXMTU1Vfe9+eab3Lp1i5dffpmYmBiSkpLYu3cvgYGBZUpe/f398fDwYNiwYZw8eZJjx44xYsQIfH19tRb++Pn5ad3vWrVq4e7uzjfffFPoPcjKyuLEiRP07Nmz1PaFEEIIUXmqdSIJD1cgt23blueeew4vLy8URWH37t0YGhqqx/j6+pKbm6vVw+Xn51eorCijR49m9erVhIaG4uHhga+vL2FhYTRo0KBccZqZmbF3715u3bpF+/bteemll+jevTvLlpX+bLA5c+Zw5coVGjVqVOJw77Bhwzh37pzWnMt8QUFB3L9/n1GjRmmVOzk5ER0dTW5uLj179sTDw4OQkBBq1KiBnl7p3x4ajYbt27dTs2ZNunbtir+/Pw0bNuSbb77ROq4878H27dupV68ePj4+pbYvhBBCiMqjURRFqewgRMWZMmUK6enp6qryfO+//z5btmzhzJkzlRRZ2XXq1IkJEybwyiuvlPmc9PR0rK2tSUtLw8rK6glGJ4QQQjxdHucztNr3SApt//nPf7QeNp6RkUFcXBzLli1j/PjxlRxd6W7cuMGLL77Iyy+/XNmhCCGEEKIU0iP5lAsICGDTpk0MGDCAjRs3Vtu/wV0a6ZEUQgghdPM4n6GSSIqngiSSQgghhG5kaFsIIYQQQvzjJJEUQgghhBA6kURSCCGEEELoRBJJIYQQQgihE0kkhRBCCCGETiSRrCL8/PwICQlRt11cXFiyZEmlxVOSrKwsBg4ciJWVFRqNhtTU1CLLhBBCCPF0M6jsAETRYmJiMDc3r+wwirR27VoiIyM5dOgQtra2WFtb88UXXxQq01VAQACpqals27at4oIWQgghRIWTRLKKKulvale2pKQk3N3dadGiRYll5ZWbm4tGo6mIEIUQ4l8t8/5dnGf4qNun3tmO54f9ATgU8g2dlwzRqd7kOZFk3b+L29yexe43NzLVqW5RPcnQdgn8/PwYP348ISEh1KxZE3t7e7788ksyMzMJDAzE0tKSxo0bs2fPHq3z4uLi6NOnDxYWFtjb2zN8+HBu3Lih7s/MzGTEiBFYWFjg6OjIokWLCrVdcGj7ypUraDQaTp8+re5PTU1Fo9EQHh4OQHh4OBqNhr179+Lp6YmpqSndunXj+vXr7NmzB3d3d6ysrHjllVfIysoq8bqjoqLw8fHB1NQUZ2dnJkyYQGZmpnpPFi1axC+//IJGo8HPz6/IMoDbt28zYsQIatasiZmZGX369CExMVFtJywsjBo1arBjxw6aNWuGsbExo0aNYu3atWzfvh2NRqN1jUIIIUqXef8uNzJua5Ul/X1V/fr0tXid6479PZ7fU1OK3X8j4zaZ9+/qXL+ofiSRLMXatWuxtbXl2LFjjB8/ntdff51BgwbRuXNnTp48Sc+ePRk+fLianKWmptKtWzc8PT05fvw4P/30E3/99ReDBw9W65wyZQoRERFs376dffv2ER4ezsmTJysk3lmzZrFs2TIOHTpEcnIygwcPZsmSJWzcuJFdu3axb98+li5dWuz5SUlJ9O7dm4EDB3LmzBm++eYboqKiGDduHAA//PADwcHBeHl5kZKSwg8//FBkGTwcoj5+/Dg7duzg8OHDKIpC3759ycnJUdvLyspi4cKFrF69mnPnzvHZZ58xePBgevfuTUpKCikpKXTu3LlQnNnZ2aSnp2u9hBBCgPMMHzw/fF6r7KXQ8erXb2yZpXPdz60aQ/dlI4vd7/nh81o9oeLpJ0PbpWjVqhXvvfceANOmTWPBggXY2toSHBwMwIwZM1ixYgVnzpyhU6dOLFu2DE9PTz744AO1jjVr1uDs7MzFixdxcnLiq6++4uuvv6Z79+7Aw2S1bt26FRLv3Llz8fb2BiAoKIhp06aRlJREw4YNAXjppZc4ePAgU6dOLfL8+fPnM2zYMHXhj6urK5999hm+vr6sWLGCWrVqYWZmhpGREQ4ODup5j5YlJiayY8cOoqOj1URww4YNODs7s23bNgYNGgRATk4Oy5cvp1WrVmpdpqamZGdna9VfVJyzZ8/W8S4JIYQQoiJIj2QpWrZsqX6tr6+PjY0NHh4eapm9vT0A169fByA2NpaDBw9iYWGhvpo2bQo87O1LSkri/v37dOzYUa2jVq1auLm5VXi89vb2mJmZqUlkfll+rEWJjY0lLCxMK/5evXqRl5fH5cuXyxxHfHw8BgYGWtdpY2ODm5sb8fH/G1YxMjLSirmspk2bRlpamvpKTk4udx1CCPE0Sp4Tyal3dmiVfRf4v5Go5YNm6Vz3zjGrODBubbH7T72zg+Q5kTrXL6of6ZEshaGhoda2RqPRKstfHJKXlwdARkYG/fr1Y+HChYXqcnR05NKlS+WOQU/vYb6vKIpaVnB4uLh4H401vyw/1qJkZGQwduxYJkyYUGhfvXr1yhV3WZiamuq0wMbY2BhjY+MKj0cIIao7cyNTsNAua2T3v9/freu461x3q7ruZJUwB9LWoqYstvmXkUSygrVp04bvv/8eFxcXDAwK395GjRphaGjI0aNH1cTs9u3bXLx4EV9f3yLrzF/BnZKSgqenJ4DWwpuKjv/8+fM0btz4sepxd3fnwYMHHD16VB3avnnzJgkJCTRr1qzEc42MjMjNzX2s9oUQ4t/M3MiUWwuOa5UV3H503+PWLf69ZGi7gr355pvcunWLl19+mZiYGJKSkti7dy+BgYHk5uZiYWFBUFAQU6ZM4b///S9xcXEEBASovY5FMTU1pVOnTixYsID4+HgiIiLUeZsVberUqRw6dIhx48Zx+vRpEhMT2b59u7rYpqxcXV3p378/wcHBREVFERsby6uvvkqdOnXo379/iee6uLhw5swZEhISuHHjRrG9r0IIIYSoXJJIVjAnJyeio6PJzc2lZ8+eeHh4EBISQo0aNdRk8aOPPsLHx4d+/frh7+9Ply5daNu2bYn1rlmzhgcPHtC2bVtCQkKYO3fuE4m/ZcuWREREcPHiRXx8fPD09GTGjBk4OTmVu67Q0FDatm3Lc889h5eXF4qisHv37kLD7Y8KDg7Gzc2Ndu3aYWdnR3R0tK6XI4QQQognSKMUnHgnRDWVnp6OtbU1aWlpWFlZVXY4QgghRLXxOJ+h0iMphBBCCCF0IomkEEIIIYTQiSSSQgghhBBCJ5JICiGEEEIInUgiKYQQQgghdCKJpBBCCCGE0IkkkkIIIYQQQieSSAohhBBCCJ1U2UTSz8+PkJAQddvFxYUlS5ZUWjwlycrKYuDAgVhZWaHRaEhNTS2y7J9w8+ZNateuzZUrVyqszlmzZtG6dWt1OyAggAEDBlRY/Y/q1KkT33///ROrXwghhBAVw6CyAyirmJgYzM3NKzuMIq1du5bIyEgOHTqEra0t1tbWfPHFF4XKdBUQEEBqairbtm0r9dh58+bRv39/XFxcdG6vsr333ntMmjSJF154ocS/QS6EEEKIylVtEkk7O7vKDqFYSUlJuLu706JFixLLyis3NxeNRlPm47Oysvjqq6/Yu3evzm1WBX369GH06NHs2bOHZ599trLDEUKIaiHz/l2cZ/gAkDwnEnMj0yL3VZRT72ynfq06FVqnqH7K1d3j5+fH+PHjCQkJoWbNmtjb2/Pll1+SmZlJYGAglpaWNG7cmD179midFxcXR58+fbCwsMDe3p7hw4dz48YNdX9mZiYjRozAwsICR0dHFi1aVKjtgkPbV65cQaPRcPr0aXV/amoqGo2G8PBwAMLDw9FoNOzduxdPT09MTU3p1q0b169fZ8+ePbi7u2NlZcUrr7xCVlZWidcdFRWFj48PpqamODs7M2HCBDIzM9V7smjRIn755Rc0Gg1+fn5FlgHcvn2bESNGULNmTczMzOjTpw+JiYlqO2FhYdSoUYMdO3bQrFkzjI2NGTVqFGvXrmX79u1oNBqta3zU7t27MTY2plOnTgAoikLjxo35+OOPtY47ffo0Go2GS5cuqfdu9OjR2NnZYWVlRbdu3YiNjS3xnhSUnZ3NhAkTqF27NiYmJnTp0oWYmBh1f7t27bRiGDBgAIaGhmRkZADw+++/a8Wjr69P37592bx5c5ljEEKIf7PM+3fJun9X3c66f5fMAq+C+yrKtbS/+O3WNTKfQN2i+ij3uOHatWuxtbXl2LFjjB8/ntdff51BgwbRuXNnTp48Sc+ePRk+fLianKWmptKtWzc8PT05fvw4P/30E3/99ReDBw9W65wyZQoRERFs376dffv2ER4ezsmTJyvkAmfNmsWyZcs4dOgQycnJDB48mCVLlrBx40Z27drFvn37WLp0abHnJyUl0bt3bwYOHMiZM2f45ptviIqKYty4cQD88MMPBAcH4+XlRUpKCj/88EORZfBwiPr48ePs2LGDw4cPoygKffv2JScnR20vKyuLhQsXsnr1as6dO8dnn33G4MGD6d27NykpKaSkpNC5c+ciY42MjKRt27bqtkajYdSoUYSGhmodFxoaSteuXWncuDEAgwYNUhPsEydO0KZNG7p3786tW7fKdI/feecdvv/+e9auXcvJkydp3LgxvXr1Us/39fVVk19FUYiMjKRGjRpERUUBEBERQZ06ddR4ADp06EBkZGSxbWZnZ5Oenq71EkKIfyvnGT64ze2pbrvN7YnzDB/1VXBfRXlu5Rg8P+xf4T2donopdyLZqlUr3nvvPVxdXZk2bRomJibY2toSHByMq6srM2bM4ObNm5w5cwaAZcuW4enpyQcffEDTpk3x9PRkzZo1HDx4kIsXL5KRkcFXX33Fxx9/TPfu3fHw8GDt2rU8ePCgQi5w7ty5eHt74+npSVBQEBEREaxYsQJPT098fHx46aWXOHjwYLHnz58/n2HDhhESEoKrqyudO3fms88+Y926ddy7d49atWphZmaGkZERDg4O1KpVq8iyxMREduzYwerVq/Hx8aFVq1Zs2LCBa9euac19zMnJYfny5XTu3Bk3NzesrKwwNTXF2NgYBwcHHBwcMDIyKjLW3377DScnJ62ygIAAEhISOHbsmFr/xo0bGTVqFPCwt/XYsWNs2bKFdu3a4erqyscff0yNGjX47rvvSr2/mZmZrFixgo8++og+ffrQrFkzvvzyS0xNTfnqq6+Ah722UVFR5ObmcubMGYyMjBg2bJhW77Gvr69WvU5OTiQnJ5OXl1fs+2Jtba2+nJ2dS41VCCGEEBWr3HMkW7ZsqX6tr6+PjY0NHh4eapm9vT0A169fByA2NpaDBw9iYWFRqK6kpCTu3r3L/fv36dixo1peq1Yt3NzcyhtaqfHa29tjZmZGw4YNtcryk6yixMbGcubMGTZs2KCWKYpCXl4ely9fxt3dvUxxxMfHY2BgoHWdNjY2uLm5ER8fr5YZGRlpxVwed+/excTERKvMycmJZ599ljVr1tChQwd+/PFHsrOzGTRokHp9GRkZ2NjYFKorKSmp1DaTkpLIycnB29tbLTM0NKRDhw7qdfn4+HDnzh1OnTrFoUOH8PX1xc/PjwULFgAPeySnTJmiVa+pqSl5eXlkZ2djamrKo6ZNm8bkyZPV7fT0dEkmhRD/WslzIsm6f1fteUx4bx9mBeZIFtxXUXaOXUUda3tsLWpVaL2ieil3ImloaKi1rdFotMryF4fk9yRlZGTQr18/Fi5cWKguR0dHdV5ceeSv5FUURS0rODxcXLyPxppfVlyvFzyMf+zYsUyYMKHQvnr16pUr7rIwNTUt1wKbgmxtbbl9+3ah8tGjRzN8+HAWL15MaGgoQ4YMwczMDHh4fY6OjkXOu6xRo4ZOcRRVT6tWrQgPD+fw4cP06NGDrl27MmTIEC5evEhiYmKhHslbt25hbm5eZBIJYGxsjLGxcYXEJ4QQ1V3BhTUAZkamhcoqWh1re1lsI578qu02bdrw/fff4+LigoFB4eYaNWqEoaEhR48eVROz27dvc/HixULJRb78FdwpKSl4enoCaC28qej4z58/rzV/Txfu7u48ePCAo0ePqnMcb968SUJCAs2aNSvxXCMjI3Jzc0ttw9PTk6+//rpQed++fTE3N2fFihX89NNP/PLLL+q+Nm3a8Oeff2JgYKDTI4MaNWqEkZER0dHR1K9fH3iY1MfExGg9B9TX15eDBw9y7Ngx5s2bR61atXB3d2fevHk4OjrSpEkTrXrj4uLU91YIIUTpzI1MubXgeLn3CfE4nvhD+t58801u3brFyy+/TExMDElJSezdu5fAwEByc3OxsLAgKCiIKVOm8N///pe4uDgCAgJKfH6gqakpnTp1YsGCBcTHxxMREcF77733ROKfOnUqhw4dYty4cZw+fZrExES2b9+uLrYpK1dXV/r3709wcDBRUVHExsby6quvUqdOHfr371/iuS4uLpw5c4aEhARu3LhRbO9rr169OHfuXKFeSX19fQICApg2bRqurq54eXmp+/z9/fHy8mLAgAHs27ePK1eucOjQIf7zn/9w/Hjpv3TMzc15/fXXmTJlCj/99BPnz58nODiYrKwsgoKC1OP8/PzYu3cvBgYGNG3aVC3bsGFDkf9hiIyMpGfPip8cLoQQQoiK88QTSScnJ6Kjo8nNzaVnz554eHgQEhJCjRo11GTxo48+wsfHh379+uHv70+XLl20Vh8XZc2aNTx48IC2bdsSEhLC3Llzn0j8LVu2JCIigosXL+Lj44OnpyczZswotKilLEJDQ2nbti3PPfccXl5eKIrC7t27Cw23Pyo4OBg3NzfatWuHnZ0d0dHRRR7n4eFBmzZt+PbbbwvtCwoK4v79+wQGBmqVazQadu/eTdeuXQkMDKRJkyYMHTqU3377TZ3vWpoFCxYwcOBAhg8fTps2bbh06RJ79+6lZs2a6jE+Pj7k5eVpJY1+fn7k5uaqj0fKd+3aNQ4dOlQoViGEEEJULRql4ERDUe3t2rWLKVOmEBcXp9WrGxkZSffu3UlOTi5zglhZpk6dyu3bt1m1alWZz0lPT8fa2pq0tDSsrKyeYHRCCCHE0+VxPkOrzV+2EWXz7LPPkpiYyLVr13B2diY7O5u///6bWbNmMWjQoCqfRALUrl1ba0W2EEIIIaom6ZF8yoWFhREUFETr1q3ZsWMHdeo8nSvspEdSCCGE0M3jfIZKIimeCpJICiGEELp5nM/QJ77YRgghhBBCPJ0kkRRCCCGEEDqRRFIIIYQQQuhEEkkhhBBCCKETSSSFEEIIIYROJJGsJvz8/LT+dnVRXFxcWLJkyROPJTo6Gg8PDwwNDRkwYECxZUIIIYR4uskDyZ8iMTExmJubP/F2Jk+eTOvWrdmzZw8WFhbFlulKo9GwdetWSUiFEEKIKk4SyaeInZ3dP9JOUlISr732GnXr1i2xrLzu37+PkZFRRYQohBCiirr7IIfnt4Y+Vh1f9niJ4P3fFSrf8UIgpgaGj1W3KB8Z2q5GHjx4wLhx47C2tsbW1pbp06dT8Hnyjw5tX7hwgS5dumBiYkKzZs34+eef0Wg0bNu2rdg28vLymD9/Pg0aNMDU1JRWrVrx3XcPf1ivXLmCRqPh5s2bjBo1Co1GQ1hYWJFlABEREXTo0AFjY2McHR159913efDggdqWn58f48aNIyQkBFtbW3r16oWLiwsAL7zwAhqNRt0WQghR/d19kMO9Ap8Durp4++8iy+89eMDdBzmPXb8oO+mRrEbWrl1LUFAQx44d4/jx44wZM4Z69eoRHBxc6Njc3FwGDBhAvXr1OHr0KHfu3OGtt94qtY358+fz9ddf88UXX+Dq6sovv/zCq6++ip2dHV26dCElJQU3NzfmzJnDkCFDsLS0pHfv3lpl1tbWXLt2jb59+xIQEMC6deu4cOECwcHBmJiYMGvWLK1rev3114mOjgagVq1a1K5dm9DQUHr37o2+vn6RcWZnZ5Odna1up6enl/NuCiGE+Kc9bk9kvo+ORxRZPvjH9QDsHzSmQtoRpZNEshpxdnZm8eLFaDQa3NzcOHv2LIsXLy4ykdy/fz9JSUmEh4fj4OAAwLx58+jRo0ex9WdnZ/PBBx/w888/4+XlBUDDhg2Jiopi5cqV+Pr64uDggEajwdraWq3X3Ny8UNny5ctxdnZm2bJlaDQamjZtyh9//MHUqVOZMWMGenoPO8NdXV358MMPC8VSo0YNta6izJ8/n9mzZ5fxzgkhhBDiSZCh7WqkU6dOaDQaddvLy4vExERyc3MLHZuQkICzs7NWMtahQ4cS67906RJZWVn06NEDCwsL9bVu3TqSkpLKFWt8fDxeXl5a8Xp7e5ORkcHvv/+ulrVt27Zc9eabNm0aaWlp6is5OVmneoQQQvxzdrwQyLf9hj92PVPa+RZZ/m2/4ex4IfCx6xdlJz2SQpWRkQHArl27qFOnjtY+Y2PjJ9KmrqvMjY2Nn1hMQgghnoyKWgjTpGbRi0tNDAxksc0/TBLJauTo0aNa20eOHMHV1bXIeYRubm4kJyfz119/YW9vDzx8PFBJmjVrhrGxMVevXsXXt+j/7ZWVu7s733//PYqiqL2S0dHRWFpalrqy29DQsMheViGEENWfqYFhhcxhlHmQVYMMbVcjV69eZfLkySQkJLBp0yaWLl3KxIkTizy2R48eNGrUiJEjR3LmzBmio6N57733ALSGmwuytLTk7bffZtKkSaxdu5akpCROnjzJ0qVLWbt2bblifeONN0hOTmb8+PFcuHCB7du3M3PmTCZPnqzOjyyOi4sLBw4c4M8//+T27dvlalcIIYQQ/xzpkaxGRowYwd27d+nQoQP6+vpMnDiRMWOK/h+Zvr4+27ZtY/To0bRv356GDRvy0Ucf0a9fP0xMTIpt4/3338fOzo758+fz66+/UqNGDdq0acP//d//lSvWOnXqsHv3bqZMmUKrVq2oVasWQUFBajJbkkWLFjF58mS+/PJL6tSpw5UrV8rVthBCCCH+GRql4IMIxVMtOjqaLl26cOnSJRo1alTZ4VSo9PR0rK2tSUtLw8rKqrLDEUIIIaqNx/kMlR7Jp9jWrVuxsLDA1dWVS5cuMXHiRLy9vZ+6JFIIIYQQlUMSyafYnTt3mDp1KlevXsXW1hZ/f38WLVpU2WEJIYQQ4ikhQ9viqSBD20IIIYRuHuczVFZtCyGEEEIInUgiKYQQQgghdCKJpBBCCCGE0IkkkkIIIYQQQieSSAohhBBCCJ1U20TSz8+PkJCQEo9xcXFhyZIlTzyW6OhoPDw8MDQ0ZMCAAcWW/RO++uorevbsWaF1PnofNRoN27Ztq9A28p0/f566deuSmZn5ROoXQgghRMV5qp8jGRMTg7m5+RNvZ/LkybRu3Zo9e/ZgYWFRbJmuNBoNW7duLTUhvXfvHtOnT2fLli2P1V5latasGZ06deKTTz5h+vTplR2OEEIIIUrwVCeSdnZ2/0g7SUlJvPbaa9StW7fEsvK6f/8+RkZGZT7+u+++w8rKCm9vb53brAoCAwMJDg5m2rRpGBg81d+iQgjx1Ln7IIfnt4YCsOOFQEwNDAG4fS+LwT9+/Vh1F6xPVA3Vdmgb4MGDB4wbNw5ra2tsbW2ZPn06BZ+v/uiQ7IULF+jSpQsmJiY0a9aMn3/+udRh2ry8PObPn0+DBg0wNTWlVatWfPfddwBcuXIFjUbDzZs3GTVqFBqNhrCwsCLLACIiIujQoQPGxsY4Ojry7rvv8uDBA7UtPz8/xo0bR0hICLa2tvTq1QsXFxcAXnjhBTQajbpdlM2bN9OvXz91+5dffsHQ0JA///xT67iQkBB8fHzU7aioKHx8fDA1NcXZ2ZkJEyaUa2j57NmzdOvWDVNTU2xsbBgzZgwZGRkAxMXFoaenx99//w3ArVu30NPTY+jQoer5c+fOpUuXLup2jx49uHXrFhEREWWOQQghROW7+yCHewU+1+49eMDdBzncvpdFcnrqY9efX5+oOqp1Irl27VoMDAw4duwYn376KZ988gmrV68u8tjc3FwGDBiAmZkZR48eZdWqVfznP/8ptY358+ezbt06vvjiC86dO8ekSZN49dVXiYiIwNnZmZSUFKysrFiyZAkpKSkMGjSoUNmQIUO4du0affv2pX379sTGxrJixQq++uor5s6dW+iajIyMiI6O5osvviAmJgaA0NBQUlJS1O2iREVF0a5dO3W7a9euNGzYkPXr16tlOTk5bNiwgVGjRgEPe0579+7NwIEDOXPmDN988w1RUVGMGzeu1HsDkJmZSa9evahZsyYxMTFs2bKFn3/+WT2/efPm2NjYqElhZGSk1jY8TLD9/PzUbSMjI1q3bk1kZGSx7WZnZ5Oenq71EkIIUbme3xrK4B//95kz+Mf1/7/sa96K2PnY9efXJ6qOap1IOjs7s3jxYtzc3Bg2bBjjx49n8eLFRR67f/9+kpKSWLduHa1ataJLly7MmzevxPqzs7P54IMPWLNmDb169aJhw4YEBATw6quvsnLlSvT19XFwcECj0WBtbY2DgwPm5uaFykxNTVm+fDnOzs4sW7aMpk2bMmDAAGbPns2iRYvIy8tT23R1deXDDz/Ezc0NNzc3dXi+Ro0aODg4FDtcn5qaSlpaGk5OTlrlQUFBhIb+74fuxx9/5N69ewwePBh4mCgPGzaMkJAQXF1d6dy5M5999hnr1q3j3r17pb4HGzdu5N69e6xbt44WLVrQrVs3li1bxvr16/nrr7/QaDR07dqV8PBwAMLDwwkMDCQ7O5sLFy6Qk5PDoUOH8PX11arXycmJ3377rdh258+fj7W1tfpydnYuNVYhhBBCVKxqnUh26tQJjUajbnt5eZGYmEhubm6hYxMSEnB2dsbBwUEt69ChQ4n1X7p0iaysLHr06IGFhYX6WrduHUlJSeWKNT4+Hi8vL614vb29ycjI4Pfff1fL2rZtW6568929excAExMTrfKAgAAuXbrEkSNHAAgLC2Pw4MHqIqTY2FjCwsK0rq9Xr17k5eVx+fLlMl1Xq1attBY1eXt7k5eXR0JCAgC+vr5qIhkREUG3bt3U5DImJoacnJxC8zpNTU3Jysoqtt1p06aRlpamvpKTk0uNVQghxJO144VAvu03XN3+tt/w/1/2Kot8n3vs+vPrE1WHrGQoQf48v127dlGnTh2tfcbGxk+kTV1XmdvY2KDRaLh9+7ZWee3atenXrx+hoaE0aNCAPXv2qEkdPLzGsWPHMmHChEJ11qtXT6dYHpX/qKbExETOnz9Ply5duHDhAuHh4dy+fZt27dphZmamdc6tW7do1KhRsXUaGxs/sfdACCGEbh5dCGNiYICpgWGFLZDJr09UHdU6kTx69KjW9pEjR3B1dUVfX7/QsW5ubiQnJ/PXX39hb28PUOJ8Q3j4KBpjY2OuXr1aaOi1vNzd3fn+++9RFEXtlYyOjsbS0rLUld2GhoZF9rIWZGRkRLNmzTh//nyh50iOHj2al19+mbp169KoUSOt3r82bdpw/vx5GjdurPN1hYWFkZmZqSbB0dHR6Onp4ebmBoCHhwc1a9Zk7ty5tG7dGgsLC/z8/Fi4cCG3b9/Wmh+ZLy4ujpdeekmnmIQQQlQeUwND9g8aU6i8polZkeWieqvWQ9tXr15l8uTJJCQksGnTJpYuXcrEiROLPLZHjx40atSIkSNHcubMGaKjo3nvvfcAtIabC7K0tOTtt99m0qRJrF27lqSkJE6ePMnSpUtZu3ZtuWJ94403SE5OZvz48Vy4cIHt27czc+ZMJk+ejJ5eyW+Di4sLBw4c4M8//yzU41hQr169iIqKKrLcysqKuXPnEhioPSQwdepUDh06xLhx4zh9+jSJiYls3769zItthg0bhomJCSNHjiQuLo6DBw8yfvx4hg8fribs+fMkN2zYoCaNLVu2JDs7mwMHDhRK0q9cucK1a9fw9/cvUwxCCCGEqBzVOpEcMWIEd+/epUOHDrz55ptMnDiRMWOK/t+Ovr4+27ZtIyMjg/bt2zN69Gh11faj8woLev/995k+fTrz58/H3d2d3r17s2vXLho0aFCuWOvUqcPu3bs5duwYrVq14rXXXiMoKEhNZkuyaNEi9u/fj7OzM56ensUeFxQUxO7du0lLS9Mq19PTIyAggNzcXEaMGKG1r2XLlkRERHDx4kV8fHzw9PRkxowZhRbtFMfMzIy9e/dy69Yt2rdvz0svvUT37t1ZtmyZ1nG+vr7k5uaqiaSenh5du3ZFo9EUmh+5adMmevbsSf369csUgxBCCCEqh0Yp+ODFf5no6Gi6dOnCpUuXSpyPV50MGjSINm3aMG3aNK3yoKAg/v77b3bs2FFJkZXN/fv3cXV1ZePGjeV6sHp6ejrW1takpaVhZWX1BCMUQgghni6P8xlaredIltfWrVuxsLDA1dWVS5cuMXHiRLy9vZ+aJBLgo48+4scff1S309LSOHv2LBs3bqzySSQ8nK7wf//3f9X+r/MIIYQQ/wb/qkTyzp07TJ06latXr2Jra4u/vz+LFi2q7LAqlIuLC+PHj1e3+/fvz7Fjx3jttdfo0aNHJUZWNo0bN9Z54Y8QQggh/ln/6qFt8fSQoW0hhBBCN4/zGVqtF9sIIYQQQojKI4mkEEIIIYTQiSSSQgghhBBCJ5JICiGEEEIInUgiKYQQQgghdCKJpCi3VatW4ezsjJ6eHkuWLCm2TAghhBBPN3n8jyiX9PR0bG1t+eSTTxg4cCDW1tY8ePCgUJmZmZlO9YeHh/PMM89w+/ZtatSoUa645PE/QgghRPnJX7YR/5irV6+Sk5PDs88+i6OjIwBxcXGFynSRk5NTUWEKIYQQT0TqvVxWnkvX6dxxHtYsO5sGwKTWNTDS11RkaJVChrarAD8/P8aPH09ISAg1a9bE3t6eL7/8kszMTAIDA7G0tKRx48bs2bNHPSc3N5egoCAaNGiAqakpbm5ufPrpp+r+e/fu0bx5c8aMGaOWJSUlYWlpyZo1a4qNJTU1ldGjR2NnZ4eVlRXdunUjNjYWgLCwMDw8PABo2LAhGo2myLIrV64AsGLFCho1aoSRkRFubm6sX79eqy2NRsOKFSt4/vnnMTc3Jzg4mGeeeQaAmjVrotFoCAgI0P3GCiGEEBXkfq7C31kPuJZ5X+c6snLz1K9z8hTu51b/QWEZ2q4C/Pz8OHnyJO+88w5Dhgzhm2++YdasWfTs2ZMXXngBPz8/Fi9ezLfffsvVq1cxMzMjJyeHuXPn0q9fP2xsbDh06BBjxowhNDSUwYMHA3D69Gk6duzIt99+y3PPPUeXLl1wdHTkhx9+KDaWHj16YGpqyowZM7C2tmblypWEhYVx8eJFTE1NOXToEP7+/hw7dgxnZ2csLS05cuSIVpmdnR07duxgyJAhLFmyBH9/f3bu3Mk777zD/v371WRRo9FQu3ZtFixYgK+vL/r6+pw4cYKBAweSkJCAlZUVpqamWFtbF4ozOzub7OxsdTs9PR1nZ2cZ2hZCCPFELDxx+4nUO7VtzSdSb3k8ztC2JJJVgJ+fH7m5uURGRgIPexutra158cUXWbduHQB//vknjo6OHD58mE6dOhVZz7hx4/jzzz/57rvv1LKPPvqIDz/8kKFDh/L9999z9uxZbGxsijw/KiqKZ599luvXr2NsbKyWN27cmHfeeYcxY8Zw+vRpPD09uXz5Mi4uLgBFlnl7e9O8eXNWrVql1jN48GAyMzPZtWsX8DCRDAkJYfHixeoxZZ0jOWvWLGbPnl2oXBJJIYQQT4IkkkWToe0qomXLlurX+vr62NjYqEPGAPb29gBcv35dLfv8889p27YtdnZ2WFhYsGrVKq5evapV71tvvUWTJk1YtmwZa9asKTaJBIiNjSUjIwMbGxssLCzU1+XLl0lKSirX9cTHx+Pt7a1V5u3tTXx8vFZZu3btylVvvmnTppGWlqa+kpOTdapHCCGEKItJrWswyt2S51xMdK5jVDNL9etxLa2Z1LpGBURWuWSxTRVhaGiota3RaLTKNJqHE3Lz8h7Or9i8eTNvv/02ixYtwsvLC0tLSz766COOHj2qVc/169e5ePEi+vr6JCYm0rt372JjyMjIwNHRkfDw8EL7yrOCujzMzc11Os/Y2Fir11QIIYR4koz0NdiZGWCopwHu6VSHmf7/+u8M9TRPxWIbSSSrqejoaDp37swbb7yhlhXVazhq1Cg8PDwICgoiODgYf39/3N3di6yzTZs2/PnnnxgYGKhD1Lpyd3cnOjqakSNHasXcrFmzEs8zMjICHg7vCyGEEFVNDRP9xxqOrgpD2RVJEslqytXVlXXr1rF3714aNGjA+vXriYmJoUGDBuoxn3/+OYcPH+bMmTM4Ozuza9cuhg0bxpEjR9SErSB/f3+8vLwYMGAAH374IU2aNOGPP/5g165dvPDCC+Uahp4yZQqDBw/G09MTf39/fvzxR3744Qd+/vnnEs+rX78+Go2GnTt30rdvX0xNTbGwsCj7jRFCCCHEP0bmSFZTY8eO5cUXX2TIkCF07NiRmzdvavVOXrhwgSlTprB8+XKcnZ0BWL58OTdu3GD69OlF1qnRaNi9ezddu3YlMDCQJk2aMHToUH777Td1jmZZDRgwgE8//ZSPP/6Y5s2bs3LlSkJDQ/Hz8yvxvDp16jB79mzeffdd7O3tGTduXLnaFUIIIcQ/R1Zti6eC/GUbIYQQQjeyalsIIYQQQvzjJJEUQgghhBA6kURSCCGEEELoRBJJIYQQQgihE0kkhRBCCCGETiSRFEIIIYQQOpFEUgghhBBC6EQSSSGEEEIIoRNJJCvIqlWrcHZ2Rk9PjyVLlhRb9k8YPnw4H3zwQYXVd+XKFTQaDadPnwYgPDwcjUZDampqhbVR0BdffEG/fv2eSN1CCCGEqDiSSFaA9PR0xo0bx9SpU7l27RpjxowpskxX5UncYmNj2b17NxMmTNC5vco2atQoTp48SWRkZGWHIoQQQogSGFR2AE+Dq1evkpOTw7PPPoujoyMAcXFxhcp0kZOTU67jly5dyqBBg7CwsNC5zcpmZGTEK6+8wmeffYaPj09lhyOEEOIpdj9XYfHpVADGNrdi5bl0neqZ1LoGRvqaCoyseqiyPZJ+fn6MHz+ekJAQatasib29PV9++SWZmZkEBgZiaWlJ48aN2bNnj3pObm4uQUFBNGjQAFNTU9zc3Pj000/V/ffu3aN58+ZavYNJSUlYWlqyZs2aYmNJTU1l9OjR2NnZYWVlRbdu3YiNjQUgLCwMDw8PABo2bIhGoymy7MqVKwCsWLGCRo0aYWRkhJubG+vXr9dqS6PRsGLFCp5//nnMzc0JDg7mmWeeAaBmzZpoNBoCAgKKjDM3N5fvvvtOa1h4zpw5tGjRotCxrVu3Zvr06er26tWrcXd3x8TEhKZNm7J8+fJi70dRvv/+e5o3b46xsTEuLi4sWrRI3bds2TKtGLZt24ZGo+GLL75Qy/z9/XnvvffU7X79+rFjxw7u3r1brjiEEEKIsrqfq5CTp6jbt+490LmutOxc7ucqpR/4lKmyiSTA2rVrsbW15dixY4wfP57XX3+dQYMG0blzZ06ePEnPnj0ZPnw4WVlZAOTl5VG3bl22bNnC+fPnmTFjBv/3f//Ht99+C4CJiQkbNmxg7dq1bN++ndzcXF599VV69OjBqFGjio1j0KBBXL9+nT179nDixAnatGlD9+7duXXrFkOGDOHnn38G4NixY6SkpDBo0KBCZc7OzmzdupWJEyfy1ltvERcXx9ixYwkMDOTgwYNa7c2aNYsXXniBs2fPMnv2bL7//nsAEhISSElJ0UqOCzpz5gxpaWm0a9dOLRs1ahTx8fHExMSoZadOneLMmTMEBgYCsGHDBmbMmMG8efOIj4/ngw8+YPr06axdu7ZM79OJEycYPHgwQ4cO5ezZs8yaNYvp06cTFhYGgK+vL+fPn+fvv/8GICIiAltbW8LDw4GHva6HDx/Gz89PrbNdu3Y8ePCAo0ePFtlmdnY26enpWi8hhBCiPBafTmXZmTR1e0tSls51rYm/o/Zs/qsoVZSvr6/SpUsXdfvBgweKubm5Mnz4cLUsJSVFAZTDhw8XW8+bb76pDBw4UKvsww8/VGxtbZVx48Ypjo6Oyo0bN4o9PzIyUrGyslLu3bunVd6oUSNl5cqViqIoyqlTpxRAuXz5srq/qLLOnTsrwcHBWvUMGjRI6du3r7oNKCEhIVrHHDx4UAGU27dvFxunoijK1q1bFX19fSUvL0+rvE+fPsrrr7+ubo8fP17x8/PTupaNGzdqnfP+++8rXl5eiqIoyuXLlxVAOXXqVJHxvPLKK0qPHj20zp8yZYrSrFkzRVEUJS8vT7GxsVG2bNmiKIqitG7dWpk/f77i4OCgKIqiREVFKYaGhkpmZqZWHTVr1lTCwsKKvNaZM2cqQKFXWlpaifdICCGEyLfg+K0Kf1VHaWlpOn+GVukeyZYtW6pf6+vrY2Njow4ZA9jb2wNw/fp1tezzzz+nbdu22NnZYWFhwapVq7h69apWvW+99RZNmjRh2bJlrFmzBhsbm2JjiI2NJSMjAxsbGywsLNTX5cuXSUpKKtf1xMfH4+3trVXm7e1NfHy8VlnBHsXyuHv3LsbGxmg02nM0goOD2bRpE/fu3eP+/fts3LhR7YHNzMwkKSmJoKAgreubO3duma+vuOtKTEwkNzcXjUZD165dCQ8PJzU1lfPnz/PGG2+QnZ3NhQsXiIiIoH379piZmWnVYWpqqvY2P2ratGmkpaWpr+Tk5LLeJiGEEAJ4OK9xXEtrdXtQI7MSji7ZKHdLJrWuUQFRVS9VerGNoaGh1rZGo9Eqy0+Y8vLyANi8eTNvv/02ixYtwsvLC0tLSz766KNCw6PXr1/n4sWL6Ovrk5iYSO/evYuNISMjA0dHR3UYtqAaNWroeGUlMzc31+k8W1tbsrKyuH//PkZGRmp5v379MDY2ZuvWrRgZGZGTk8NLL70EPLw+gC+//JKOHTtq1aevr6/jFRTm5+fHqlWriIyMxNPTEysrKzW5jIiIwNfXt9A5t27dws7Orsj6jI2NMTY2rrD4hBBC/Ps8ujimlonuaZG1sf6/crFNlU4kyys6OprOnTvzxhtvqGVF9aqNGjUKDw8PgoKCCA4Oxt/fH3d39yLrbNOmDX/++ScGBga4uLg8Vnzu7u5ER0czcuRIrZibNWtW4nn5SWFubm6Jx7Vu3RqA8+fPq18DGBgYMHLkSEJDQzEyMmLo0KGYmpoCD3t1nZyc+PXXXxk2bJgOV/W/6yooOjqaJk2aqMmor68vISEhbNmyRZ0L6efnx88//0x0dDRvvfWW1vlJSUncu3cPT09PnWISQgghysJIX8PUtjXV7YJfi9I9VYmkq6sr69atY+/evTRo0ID169cTExNDgwYN1GM+//xzDh8+zJkzZ3B2dmbXrl0MGzaMI0eOaPXi5fP398fLy4sBAwbw4Ycf0qRJE/744w927drFCy+8UK5h6ClTpjB48GA8PT3x9/fnxx9/5IcfflAX5hSnfv36aDQadu7cSd++fTE1NS3y8T52dna0adOGqKgorUQSYPTo0Wqy/GjSN3v2bCZMmIC1tTW9e/cmOzub48ePc/v2bSZPnlzqdb311lu0b9+e999/nyFDhnD48GGWLVumtfK7ZcuW1KxZk40bN7Jz507gYSL59ttvo9FoCg2NR0ZG0rBhQxo1alRq+0IIIYSoHFV6jmR5jR07lhdffJEhQ4bQsWNHbt68qdU7eeHCBaZMmcLy5ctxdnYGYPny5dy4cUPrUTgFaTQadu/eTdeuXQkMDKRJkyYMHTqU3377TZ2jWVYDBgzg008/5eOPP6Z58+asXLmS0NBQrdXKRalTpw6zZ8/m3Xffxd7ennHjxhV77OjRo9mwYUOhcldXVzp37kzTpk0LDWGPHj2a1atXExoaioeHB76+voSFhWkl4CVp06YN3377LZs3b6ZFixbMmDGDOXPmaD2mSKPR4OPjg0ajoUuXLsDD5NLKyop27doVGs7ftGkTwcHBZWpfCCGEEJVDoyjKv++hR0+xu3fv4ubmxjfffIOXl5darigKrq6uvPHGG2XqZaxM586do1u3bly8eBFra+vSTwDS0tKoUaMGycnJWFlZPeEIhRBCiKdHeno6zs7OpKamlvlzN99TNbQtHq50XrduHTdu3FDL/v77bzZv3syff/6pPjuyKktJSWHdunXl+ma+efMmgNrTLIQQQojyuXnzZrkTSemR/BfQaDTY2try6aef8sorr1R2OE9EamoqNWvW5OrVq+X+Ifi3yv8fqPTilo/ct/KTe6YbuW/lJ/dMN2lpadSrV4/bt2+X+4k00iP5L/Bv+L+Cnt7D6b7W1tbyy6OcrKys5J7pQO5b+ck9043ct/KTe6ab/M/Scp3zBOIQQgghhBD/ApJICiGEEEIInUgiKZ4KxsbGzJw5U/7aTTnIPdON3Lfyk3umG7lv5Sf3TDePc99ksY0QQgghhNCJ9EgKIYQQQgidSCIphBBCCCF0IomkEEIIIYTQiSSSQgghhBBCJ5JIimrv888/x8XFBRMTEzp27MixY8cqO6Qq75dffqFfv344OTmh0WjYtm1bZYdU5c2fP5/27dtjaWlJ7dq1GTBgAAkJCZUdVpW2YsUKWrZsqT4c2svLiz179lR2WNXKggUL0Gg0hISEVHYoVdqsWbPQaDRar6ZNm1Z2WNXCtWvXePXVV7GxscHU1BQPDw+OHz9e5vMlkRTV2jfffMPkyZOZOXMmJ0+epFWrVvTq1Yvr169XdmhVWmZmJq1ateLzzz+v7FCqjYiICN58802OHDnC/v37ycnJoWfPnmRmZlZ2aFVW3bp1WbBgASdOnOD48eN069aN/v37c+7cucoOrVqIiYlh5cqVtGzZsrJDqRaaN29OSkqK+oqKiqrskKq827dv4+3tjaGhIXv27OH8+fMsWrSImjVrlrkOefyPqNY6duxI+/btWbZsGQB5eXk4Ozszfvx43n333UqOrnrQaDRs3bqVAQMGVHYo1crff/9N7dq1iYiIoGvXrpUdTrVRq1YtPvroI4KCgio7lCotIyODNm3asHz5cubOnUvr1q1ZsmRJZYdVZc2aNYtt27Zx+vTpyg6lWnn33XeJjo4mMjJS5zqkR1JUW/fv3+fEiRP4+/urZXp6evj7+3P48OFKjEz8G6SlpQEPEyNRutzcXDZv3kxmZiZeXl6VHU6V9+abb/Lss89q/X4TJUtMTMTJyYmGDRsybNgwrl69WtkhVXk7duygXbt2DBo0iNq1a+Pp6cmXX35ZrjokkRTV1o0bN8jNzcXe3l6r3N7enj///LOSohL/Bnl5eYSEhODt7U2LFi0qO5wq7ezZs1hYWGBsbMxrr73G1q1badasWWWHVaVt3ryZkydPMn/+/MoOpdro2LEjYWFh/PTTT6xYsYLLly/j4+PDnTt3Kju0Ku3XX39lxYoVuLq6snfvXl5//XUmTJjA2rVry1yHwROMTwghnkpvvvkmcXFxMgerDNzc3Dh9+jRpaWl89913jBw5koiICEkmi5GcnMzEiRPZv38/JiYmlR1OtdGnTx/165YtW9KxY0fq16/Pt99+K9MoSpCXl0e7du344IMPAPD09CQuLo4vvviCkSNHlqkO6ZEU1ZatrS36+vr89ddfWuV//fUXDg4OlRSVeNqNGzeOnTt3cvDgQerWrVvZ4VR5RkZGNG7cmLZt2zJ//nxatWrFp59+WtlhVVknTpzg+vXrtGnTBgMDAwwMDIiIiOCzzz7DwMCA3Nzcyg6xWqhRowZNmjTh0qVLlR1Klebo6FjoP3Xu7u7lmhYgiaSotoyMjGjbti0HDhxQy/Ly8jhw4IDMwRIVTlEUxo0bx9atW/nvf/9LgwYNKjukaikvL4/s7OzKDqPK6t69O2fPnuX06dPqq127dgwbNozTp0+jr69f2SFWCxkZGSQlJeHo6FjZoVRp3t7ehR5jdvHiRerXr1/mOmRoW1RrkydPZuTIkbRr144OHTqwZMkSMjMzCQwMrOzQqrSMjAyt/6lfvnyZ06dPU6tWLerVq1eJkVVdb775Jhs3bmT79u1YWlqq83Ctra0xNTWt5OiqpmnTptGnTx/q1avHnTt32LhxI+Hh4ezdu7eyQ6uyLC0tC827NTc3x8bGRubjluDtt9+mX79+1K9fnz/++IOZM2eir6/Pyy+/XNmhVWmTJk2ic+fOfPDBBwwePJhjx46xatUqVq1aVfZKFCGquaVLlyr16tVTjIyMlA4dOihHjhyp7JCqvIMHDypAodfIkSMrO7Qqq6j7BSihoaGVHVqVNWrUKKV+/fqKkZGRYmdnp3Tv3l3Zt29fZYdV7fj6+ioTJ06s7DCqtCFDhiiOjo6KkZGRUqdOHWXIkCHKpUuXKjusauHHH39UWrRooRgbGytNmzZVVq1aVa7z5TmSQgghhBBCJzJHUgghhBBC6EQSSSGEEEIIoRNJJIUQQgghhE4kkRRCCCGEEDqRRFIIIYQQQuhEEkkhhBBCCKETSSSFEEIIIYROJJEU4ikRHh6ORqMhNTX1idR/5coVNBoNp0+ffiL1P66AgAAGDBhQ2WGU2z8Vt4uLC0uWLHni7TytHr1/Go2Gbdu2qdsXLlygU6dOmJiY0Lp16wr7efHz8yMkJKTY/WFhYdSoUaNcdT4ae1ndvHmT2rVrc+XKFeDJ/84pyaxZs2jdunW5zyvvtZfl53Po0KEsWrSo3LE8LSSRFOIp0blzZ1JSUrC2tn4i9Ts7O5OSkiJ/pq2KKy6xiImJYcyYMf98QE+plJQU+vTpo27PnDkTc3NzEhISOHDgwD/28zJkyBAuXrz4RNvIN2/ePPr374+LiwtQMb9zHB0dWbBggVbZu+++i0ajITw8XKvcz8+P4cOHl7uNwMBA3nvvPaDw+1YR3nvvPebNm0daWlqF1ltdSCIpxFPCyMgIBwcHNBrNE6lfX18fBwcHDAwMnkj9lU1RFB48eKDTuffv36/gaCqenZ0dZmZmlR2GTnJycio7hEIcHBwwNjZWt5OSkujSpQv169fHxsbmH/t5MTU1pXbt2k+0DYCsrCy++uorgoKC1LKK+J3j5+dXKGE8ePAgzs7OWuX37t3jyJEjdOvWrVz15+bmsnPnTp5//nmg8PtWEVq0aEGjRo34+uuvK7Te6kISSSGqgDt37jBs2DDMzc1xdHRk8eLFhYa01q9fT7t27bC0tMTBwYFXXnmF69evq/sfHWbK75nau3cv7u7uWFhY0Lt3b1JSUrTO6dChA+bm5tSoUQNvb29+++23ImN8dKguv70DBw7Qrl07zMzM6Ny5MwkJCcVeZ1FDYadPn0aj0ajDZWWJOzc3l8mTJ1OjRg1sbGx45513ePSvvebl5TF//nwaNGiAqakprVq14rvvvisUy549e2jbti3GxsZERUWpQ2YrV67E2dkZMzMzBg8erNXbkD/cNW/ePJycnHBzcwPg7NmzdOvWDVNTU2xsbBgzZgwZGRnliruoIejWrVsza9YsdTs1NZWxY8dib2+PiYkJLVq0YOfOnYSHhxMYGEhaWhoajQaNRqOe92i9V69epX///lhYWGBlZcXgwYP566+/1P3592H9+vW4uLhgbW3N0KFDuXPnTrHvb/57t23bNlxdXTExMaFXr14kJydrHbd9+3batGmDiYkJDRs2ZPbs2VpJvEajYcWKFTz//POYm5szb968IttzcXFh7ty5jBgxAgsLC+rXr8+OHTv4+++/1Wtr2bIlx48f1zrv+++/p3nz5hgbG+Pi4lJoWPL69ev069cPU1NTGjRowIYNGwq1XXCIVKPRcOLECebMmaPe86KGtuPi4ujTpw8WFhbY29szfPhwbty4oe7PzMxUr8XR0bFMw6VF9UCvWLGCRo0aYWRkhJubG+vXry90Xn7PnKmpKQ0bNtT62SjK7t27MTY2plOnTmqZLr9zHvXMM88QHR2tvv937tzh1KlTTJ06VSuRPHz4MNnZ2TzzzDNa55f2/Xno0CEMDQ1p3749UHhou7Sf2XyzZ8/Gzs4OKysrXnvttUL/eezXrx+bN28u/gY+xSSRFKIKmDx5MtHR0ezYsYP9+/cTGRnJyZMntY7Jycnh/fffJzY2lm3btnHlyhUCAgJKrDcrK4uPP/6Y9evX88svv3D16lXefvttAB48eMCAAQPw9fXlzJkzHD58mDFjxpS7d+E///kPixYt4vjx4xgYGDBq1KhynV/euAEWLVpEWFgYa9asISoqilu3brF161atOubPn8+6dev44osvOHfuHJMmTeLVV18lIiJC67h3332XBQsWEB8fT8uWLQG4dOkS3377LT/++CM//fQTp06d4o033tA678CBAyQkJLB//3527txJZmYmvXr1ombNmsTExLBlyxZ+/vlnxo0bV664S5OXl0efPn2Ijo7m66+/5vz58yxYsAB9fX06d+7MkiVLsLKyIiUlhZSUFK37VrCO/v37c+vWLSIiIti/fz+//vorQ4YM0TouKSmJbdu2sXPnTnbu3ElEREShYchHZWVlMW/ePNatW0d0dDSpqakMHTpU3R8ZGcmIESOYOHEi58+fZ+XKlYSFhRVKFmfNmsULL7zA2bNnS/yeWrx4Md7e3pw6dYpnn32W4cOHM2LECF599VVOnjxJo0aNGDFihJqwnzhxgsGDBzN06FDOnj3LrFmzmD59OmFhYWqdAQEBJCcnc/DgQb777juWL1+u9Z+2R6WkpNC8eXPeeuutYu95amoq3bp1w9PTk+PHj/PTTz/x119/MXjwYPWYKVOmEBERwfbt29m3bx/h4eGFfg+UZuvWrUycOJG33nqLuLg4xo4dS2BgIAcPHtQ6bvr06QwcOJDY2FiGDRvG0KFDiY+PL7beyMhI2rZtW2r7pf3sPuqZZ54hIyODmJgYtZ0mTZowcOBAjh49yr1794CHvZQuLi7qsDqU7ftzx44d9OvXr8jfa2X5mYWHP+vx8fGEh4ezadMmfvjhB2bPnq11TIcOHTh27BjZ2dml3qOnjiKEqFTp6emKoaGhsmXLFrUsNTVVMTMzUyZOnFjseTExMQqg3LlzR1EURTl48KACKLdv31YURVFCQ0MVQLl06ZJ6zueff67Y29sriqIoN2/eVAAlPDy8THFevnxZAZRTp05ptffzzz+rx+zatUsBlLt37xZZx6MxKoqinDp1SgGUy5cvlyluRVEUR0dH5cMPP1S3c3JylLp16yr9+/dXFEVR7t27p5iZmSmHDh3Saj8oKEh5+eWXtWLZtm2b1jEzZ85U9PX1ld9//10t27Nnj6Knp6ekpKQoiqIoI0eOVOzt7ZXs7Gz1mFWrVik1a9ZUMjIytO6Hnp6e8ueff5YpbkVRlPr16yuLFy/WiqlVq1bKzJkzFUVRlL179yp6enpKQkKCUpTQ0FDF2tq6UHnBevft26fo6+srV69eVfefO3dOAZRjx46p98HMzExJT09Xj5kyZYrSsWPHItvNbxtQjhw5opbFx8crgHL06FFFURSle/fuygcffKB13vr16xVHR0d1G1BCQkKKbafgNb366qvqdkpKigIo06dPV8sOHz6sAOp798orryg9evTQqmfKlClKs2bNFEVRlISEBK37UPAaCr4vgLJ161Z1u+B7pCiFf17ef/99pWfPnlrtJicnK4CSkJCg3LlzRzEyMlK+/fZbdf/NmzcVU1PTEn8PPPp+d+7cWQkODtY6ZtCgQUrfvn21Yn/ttde0junYsaPy+uuvF9tO//79lVGjRmmVlfd3TnHq1Kmjfk9MmTJFeeONNxRFUZQmTZoo//3vfxVFURQfHx8lMDBQPaes35+urq7Kzp07ta49/30ry8/syJEjlVq1aimZmZnqMStWrFAsLCyU3NxctSw2NlYBlCtXrpR4rU8j6ZEUopL9+uuv5OTk0KFDB7XM2tpaHS7Nd+LECfr160e9evWwtLTE19cXeDhEWRwzMzMaNWqkbjs6Oqo9K7Vq1SIgIIBevXrRr18/Pv300xKHoIqT34uXXz9QYu9NWZQUd1paGikpKXTs2FHdb2BgQLt27dTtS5cukZWVRY8ePbCwsFBf69atIykpSautguflq1evHnXq1FG3vby8yMvL0xq29/DwwMjISN2Oj4+nVatWmJubq2Xe3t7qeWWJuyxOnz5N3bp1adKkSbnOKyg+Ph5nZ2ecnZ3VsmbNmlGjRg2tXikXFxcsLS3V7YLvQ3EMDAzUYUSApk2batUbGxvLnDlztN6X4OBgUlJSyMrKUs8r630p+P1nb28PPHxvHi3Ljzs+Ph5vb2+tOry9vUlMTCQ3N5f4+HgMDAy0et/yr+FxxMbGcvDgQa3rbtq0KfCwZy0pKYn79+9rfX/UqlWr0O+B0hR3fY/2Nnp5eRXaLqlH8u7du5iYmJTafkk/u8UpOE8yPDwcPz8/AHx9fQkPD+fu3bscPXq00LB2ad+f8fHx/PHHH3Tv3r3Idkv7mc3XqlUrrfnFXl5eZGRkaE3ZMDU1BdD6Hv63eDpnzQvxlMkfgunVqxcbNmzAzs6Oq1ev0qtXrxIXehgaGmptazQarTl5oaGhTJgwgZ9++olvvvmG9957j/3792vNgypNwTbyh4/y8vKKPFZP7+H/XQvGUNRCitLiLk3+HKddu3ZpJYRAoYn2BT9EykPX80qjp6dX6FoL3qP8D6x/QlHvQ3HvbVllZGQwe/ZsXnzxxUL7CiYqZb2/RX3/led78p+SkZFBv379WLhwYaF9jo6OXLp0qRKiKjtbW1tu375d6nG6/Ow+88wzTJw4kZs3b3Lq1Cn1P8m+vr6sXLmSrl27cv/+/UILbUr7/tyxYwc9evQoUwL8uG7dugU8XNT2byM9kkJUsoYNG2JoaKjOEYKHvW4FH+lx4cIFbt68yYIFC/Dx8aFp06aP3euXz9PTk2nTpnHo0CFatGjBxo0bK6TeouT/ki3Y81ne5+xZW1vj6OjI0aNH1bIHDx5w4sQJdbtZs2YYGxtz9epVGjdurPUq2AtXnKtXr/LHH3+o20eOHEFPT6/E3iF3d3diY2PJzMxUy6Kjo9XzyhI3PLxHBe9Peno6ly9fVrdbtmzJ77//XuwjX4yMjMjNzS3x+tzd3UlOTtbqUTl//jypqak0a9asxHNL8+DBA63FLQkJCaSmpuLu7g5AmzZtSEhIKPS+NG7cWP2PxpPk7u5OdHS0Vll0dDRNmjRBX1+fpk2bFnpf8q/hcbRp04Zz587h4uJS6LrNzc1p1KgRhoaGWt8ft2/fLvejfYq7vkff1yNHjhTazn+PiuLp6cn58+fLFUtZPfPMM2RmZvLJJ5/g6uqqrkLv2rUrx44dY8+ePbi6uhb6T2Fptm/fTv/+/YvdX9rPbL7Y2Fju3r2rbh85cgQLCwut3yVxcXHUrVsXW1vbcsX4NJBEUohKZmlpyciRI5kyZQoHDx7k3LlzBAUFoaenp/am1KtXDyMjI5YuXcqvv/7Kjh07eP/99x+r3cuXLzNt2jQOHz7Mb7/9xr59+0hMTCzxw+Rx5Sdys2bNIjExkV27dun0IN+JEyeyYMECtm3bxoULF3jjjTe0PugtLS15++23mTRpEmvXriUpKYmTJ0+ydOlS1q5dW2r9JiYmjBw5ktjYWCIjI5kwYQKDBw/GwcGh2HOGDRumnhcXF8fBgwcZP348w4cPV4dXS4sboFu3bqxfv57IyEjOnj3LyJEj0dfXV/f7+vrStWtXBg4cyP79+7l8+TJ79uzhp59+Ah4O92VkZHDgwAFu3LhR5FCbv78/Hh4eDBs2jJMnT3Ls2DFGjBiBr69vuYfaH2VoaMj48eM5evQoJ06cICAggE6dOqlTN2bMmMG6deuYPXs2586dIz4+ns2bN6vP+XvS3nrrLQ4cOMD777/PxYsXWbt2LcuWLVMXhLi5udG7d2/Gjh2rXsPo0aMfuyf4zTff5NatW7z88svExMSQlJTE3r17CQwMJDc3FwsLC4KCgpgyZQr//e9/iYuLIyAgoNzJ9ZQpUwgLC2PFihUkJibyySef8MMPPxRa8LJlyxbWrFnDxYsXmTlzJseOHSu0yKSgXr16ce7cuTL1SpZXw4YNqVevHkuXLlV7I+Hhs2udnJxYtWpVoWHt0ly/fp3jx4/z3HPPFXtMWX5m4eHjvYKCgjh//jy7d+9m5syZjBs3Tuu9iYyMpGfPnuWK8WkhiaQQVcAnn3yCl5cXzz33HP7+/nh7e+Pu7q4OydjZ2REWFsaWLVto1qwZCxYs4OOPP36sNs3MzLhw4QIDBw6kSZMmjBkzhjfffJOxY8dWxCUVydDQkE2bNnHhwgVatmzJwoULmTt3brnreeuttxg+fDgjR47Ey8sLS0tLXnjhBa1j3n//faZPn878+fNxd3end+/e7Nq1iwYNGpRaf+PGjXnxxRfp27cvPXv2pGXLlixfvrzEc8zMzNi7dy+3bt2iffv2vPTSS3Tv3p1ly5aVK+5p06bh6+vLc889x7PPPsuAAQO05pzBw8fXtG/fnpdffplmzZrxzjvvqL2QnTt35rXXXmPIkCHY2dnx4YcfFopVo9Gwfft2atasSdeuXfH396dhw4Z88803pd6b0piZmTF16lReeeUVvL29sbCw0Kq3V69e7Ny5k3379tG+fXs6derE4sWLqV+//mO3XRZt2rTh22+/ZfPmzbRo0YIZM2YwZ84crScghIaG4uTkhK+vLy+++CJjxox57Gc1Ojk5ER0dTW5uLj179sTDw4OQkBBq1KihJiQfffQRPj4+9OvXD39/f7p06VKmldIFDRgwgE8//ZSPP/6Y5s2bs3LlSkJDQ9V5h/lmz57N5s2badmyJevWrWPTpk0l9kZ7eHio9+5JeOaZZ7hz506hOH19fblz5065E8kff/yRDh06lNhDWJafWYDu3bvj6upK165dGTJkCM8//7zW47ju3bvHtm3bCA4OLleMTwuNUp6JR0KIf0RmZiZ16tRh0aJFWg8AFk/erFmz2LZtW5X9U5BVWVhYGCEhIZXyJ/P+rVauXMn777/P77///sTb2rVrF1OmTCEuLu4fmYbwOJ5//nm6dOnCO++8o5ZlZ2djYmLC/v378ff3r7C2VqxYwdatW9m3b1+F1VmdyGIbIaqAU6dOceHCBTp06EBaWhpz5swBKHF+jxDi3y05OZndu3fTvHnzf6S9Z599lsTERK5du1amucaVqUuXLrz88svqdnp6Oj/88AN6enrqavmKYmhoyNKlSyu0zupEEkkhqoiPP/6YhIQEjIyMaNu2LZGRkf/KidtCiLJp06YNderU0XqY+pNW8K9tVWUFeyLh4d9C37hxIwsXLqRu3boV2tbo0aMrtL7qRoa2hRBCCCGETqr2JAchhBBCCFFlSSIphBBCCCF0IomkEEIIIYTQiSSSQgghhBBCJ5JICiGEEEIInUgiKYQQQgghdCKJpBBCCCGE0IkkkkIIIYQQQieSSAohhBBCCJ38P+ZUpuJjhX7pAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 600x200 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "metrics_rigid = metrics[metrics.behavior==\"replay_user_rigid\"]\n",
- "\n",
- "# Sort out the experiments we don't need\n",
- "selection = metrics[\n",
- " (metrics.behavior!=\"replay_user_rigid\") & \n",
- " (metrics.behavior!=\"dm_user_multi_behavior_degrad\") &\n",
- " (metrics.behavior!=\"dm_user_multi_behavior_reconfig\") & \n",
- " (metrics.behavior!=\"dm_user_multi_behavior_renonce\")].copy()\n",
- "\n",
- "selection[\"pretty name\"] = selection[\"behavior\"].apply(lambda x : pretty_name(x))\n",
- "max_energy = metrics_rigid[\"energy underproduced (kwh)\"].values[0]\n",
- "\n",
- "# Gains in energy underproduced compared to rigid\n",
- "selection[\"gains underproduced\"] = max_energy - selection[\"energy underproduced (kwh)\"]\n",
- "selection[\"modified jobs\"] = total_jobs - metrics[\"true_rigid_jobs\"]\n",
- "selection[\"energy_quotient\"] = selection[\"gains underproduced\"] * 1000 / selection[\"modified jobs\"]\n",
- "\n",
- "# plot\n",
- "fig, ax = plt.subplots(figsize=(6,2))\n",
- "selection[\"sort_order\"] = selection[\"behavior\"].apply(lambda x : sort_effort(x))\n",
- "selection[\"color\"] = selection[\"behavior\"].apply(lambda x : color_map(x))\n",
- "selection.sort_values(by='sort_order', ascending=False, inplace=True)\n",
- "selection.plot.scatter(\"energy_quotient\",\"pretty name\",\n",
- " xlabel=\"gains in underproduction per modified job (in Wh/job)\",\n",
- " ylabel=\"\", marker=\"+\", color=selection[\"color\"], alpha=1, ax=ax)\n",
- "ax.set_xlim(0,6)\n",
- "\n",
- "plt.savefig(f\"{FIG_DIR}/ratio_gains_modified_jobs.pdf\", bbox_inches=\"tight\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "21229fdc",
- "metadata": {},
- "source": [
- "### Weighted effort"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b745ae6a",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "We see that we are near linear. Let's compute underproduction in function of a pondered effort so that renounce jobs\n",
- "have more weight than see_you_later one :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "id": "5ee97fe1",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Weighted effort for each expe:\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>mean</th>\n",
- " <th>confi_interval</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">_solar_yellow</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_yellow</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-11.866845</td>\n",
+ " <td>-13.684605</td>\n",
+ " <td>5.556503</td>\n",
+ " <td>-19.462358</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>pretty name</th>\n",
- " <th></th>\n",
- " <th></th>\n",
+ " <th>1.0</th>\n",
+ " <td>-7.219563</td>\n",
+ " <td>-7.051239</td>\n",
+ " <td>6.526208</td>\n",
+ " <td>-26.421812</td>\n",
" </tr>\n",
- " </thead>\n",
- " <tbody>\n",
" <tr>\n",
- " <th>big effort</th>\n",
- " <td>27.993175</td>\n",
- " <td>0.020917</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">_yellow</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_yellow</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-11.445814</td>\n",
+ " <td>-10.003396</td>\n",
+ " <td>5.771013</td>\n",
+ " <td>-25.833105</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>big effort (yellow)</th>\n",
- " <td>30.293658</td>\n",
- " <td>0.025879</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-10.516663</td>\n",
+ " <td>-5.196745</td>\n",
+ " <td>8.194102</td>\n",
+ " <td>-36.770513</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>full degrad red</th>\n",
- " <td>29.490977</td>\n",
- " <td>NaN</td>\n",
+ " <th rowspan=\"10\" valign=\"top\">basic</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-11.250033</td>\n",
+ " <td>-3.267090</td>\n",
+ " <td>5.747768</td>\n",
+ " <td>-18.504062</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>full reconfig red</th>\n",
- " <td>1.577608</td>\n",
- " <td>NaN</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-9.622616</td>\n",
+ " <td>-2.709204</td>\n",
+ " <td>8.195734</td>\n",
+ " <td>-29.505622</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>full renounce red</th>\n",
- " <td>39.321611</td>\n",
- " <td>NaN</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_degrad</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-11.926085</td>\n",
+ " <td>-4.099654</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-23.156117</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>full rigid</th>\n",
+ " <th>1.0</th>\n",
+ " <td>-8.229934</td>\n",
+ " <td>-3.138304</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>-36.001886</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>low effort</th>\n",
- " <td>7.759204</td>\n",
- " <td>0.013027</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_reconfig</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-1.316248</td>\n",
+ " <td>2.234977</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-23.156117</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>low effort (yellow)</th>\n",
- " <td>8.513093</td>\n",
- " <td>0.015404</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-3.496209</td>\n",
+ " <td>1.430192</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-36.001886</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>max effort</th>\n",
- " <td>41.509769</td>\n",
- " <td>0.020746</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_renonce</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-32.032178</td>\n",
+ " <td>-12.569191</td>\n",
+ " <td>23.156117</td>\n",
+ " <td>-23.156117</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>max effort (yellow)</th>\n",
- " <td>44.647972</td>\n",
- " <td>0.016775</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-32.140672</td>\n",
+ " <td>-11.854398</td>\n",
+ " <td>33.401045</td>\n",
+ " <td>-36.001886</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>medium effort</th>\n",
- " <td>16.966042</td>\n",
- " <td>0.025738</td>\n",
+ " <th rowspan=\"2\" valign=\"top\">dm_user_multi_behavior_see_you_later</th>\n",
+ " <th>0.0</th>\n",
+ " <td>-18.597417</td>\n",
+ " <td>30.184465</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-23.156117</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>medium effort (yellow)</th>\n",
- " <td>18.471296</td>\n",
- " <td>0.017461</td>\n",
+ " <th>1.0</th>\n",
+ " <td>-24.550706</td>\n",
+ " <td>16.142179</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-36.001886</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " mean confi_interval\n",
- "pretty name \n",
- "big effort 27.993175 0.020917\n",
- "big effort (yellow) 30.293658 0.025879\n",
- "full degrad red 29.490977 NaN\n",
- "full reconfig red 1.577608 NaN\n",
- "full renounce red 39.321611 NaN\n",
- "full rigid 0.000000 NaN\n",
- "low effort 7.759204 0.013027\n",
- "low effort (yellow) 8.513093 0.015404\n",
- "max effort 41.509769 0.020746\n",
- "max effort (yellow) 44.647972 0.016775\n",
- "medium effort 16.966042 0.025738\n",
- "medium effort (yellow) 18.471296 0.017461"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "weight = {\n",
- " \"reconfig\": 1/4,\n",
- " \"C_you_later\": 2/4,\n",
- " \"degrad\": 3/4,\n",
- " \"renounce\": 4/4\n",
- "}\n",
- "\n",
- "metrics = pd.read_csv(f\"{OUT_DIR}/metrics_campaign3_big.csv\")\n",
- "# Sort out the experiments we don't need\n",
- "# metrics = metrics[metrics.behavior!='dm_user_multi_behavior_degrad']\n",
- "# metrics = metrics[metrics.behavior!='dm_user_multi_behavior_reconfig']\n",
- "# metrics=metrics[metrics.behavior!=\"dm_user_multi_behavior_renonce\"]\n",
- "\n",
- "total_jobs = metrics[metrics.behavior==\"replay_user_rigid\"][\"#jobs\"].values[0]\n",
- "metrics[\"pretty name\"] = metrics[\"behavior\"].apply(lambda x : pretty_name(x))\n",
- "\n",
- "metrics[\"weighted_effort\"] = (metrics[\"renonce_jobs\"] * weight[\"renounce\"] + \n",
- " metrics[\"consider_degrad_jobs\"] * weight[\"degrad\"] + \n",
- " metrics[\"reconfig_jobs\"] *weight[\"reconfig\"] + \n",
- " metrics[\"number_of_see_you_later\"] * weight[\"C_you_later\"] ) / total_jobs * 100\n",
- "metrics[\"weighted_effort\"] = metrics[\"weighted_effort\"].fillna(0)\n",
- "\n",
- "aggredated = metrics[[\"pretty name\",\"weighted_effort\"]].groupby(\"pretty name\").agg([\"mean\",confi_interval])\n",
- "\n",
- "print(\"Weighted effort for each expe:\")\n",
- "display(aggredated[\"weighted_effort\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 82,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Underproduction in fonction of weighted user effort:\n",
- "r-square : 0.965461563762817\n",
- "linear regression : -33.896612958929346*X+8107.549057879646\n",
- "\n",
- "Underproduction in fonction of weighted user effort:\n",
- "(the linear regression doesn't take 'renounce/reconfig/degrad' points into account)\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 600x500 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "metrics[\"energy underproduced (kwh)\"] = metrics[\"energy underproduced (Joules)\"]/3600/1000\n",
- "\n",
- "print(\"\\nUnderproduction in fonction of weighted user effort:\")\n",
- "plot_energy_fun_effort(\"energy underproduced (kwh)\",\"weighted_effort\",metrics,\n",
- " legend_enabled=True,disable_renounce=False,ybot=5200)\n",
- "plt.xlabel(\"weighted user effort\")\n",
- "plt.ylabel(\"underproduction (kWh)\")\n",
- "\n",
- "# Do the linear regression\n",
- "# Sort out unwanted behaviors before regression:\n",
- "selection_for_regression = metrics[\n",
- " (metrics.behavior!=\"dm_user_multi_behavior_degrad\") &\n",
- " (metrics.behavior!=\"dm_user_multi_behavior_reconfig\") & \n",
- " (metrics.behavior!=\"dm_user_multi_behavior_renounce\")].copy()\n",
- "X, Y = selection_for_regression[\"weighted_effort\"], selection_for_regression[\"energy underproduced (kwh)\"]\n",
- "slope, intercept, r_value, _, _ = linregress(X,Y)\n",
- "\n",
- "print(f\"r-square : {r_value**2}\")\n",
- "print(f\"linear regression : {slope}*X+{intercept}\")\n",
- "print(\"\\nUnderproduction in fonction of weighted user effort:\")\n",
- "print(\"(the linear regression doesn't take 'renounce/reconfig/degrad' points into account)\")\n",
- "X = np.linspace(0, max(X))\n",
- "plt.plot(X,slope*X+intercept, '--', color=\"gray\")\n",
- "plt.text(x=8,y=8000,color=\"gray\",\n",
- " s=f\"Y = {slope:.3f}X + {intercept:.0f}\\nr-squared: {r_value**2:.3f}\")\n",
- "plt.savefig(f\"{FIG_DIR}/underprod_VS_weighted_effort.pdf\", bbox_inches=\"tight\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 600x200 with 1 Axes>"
+ " NRJ_red \\\n",
+ "window_type behavior XP \n",
+ "_simu_solar_wind dm_user_multi_behavior 0.0 -29.011280 \n",
+ " 1.0 -24.880413 \n",
+ " dm_user_multi_behavior_degrad 0.0 -30.593702 \n",
+ " 1.0 -22.460961 \n",
+ " dm_user_multi_behavior_reconfig 0.0 -11.942502 \n",
+ " 1.0 -13.992448 \n",
+ " dm_user_multi_behavior_renonce 0.0 -72.882551 \n",
+ " 1.0 -60.223720 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 -64.374341 \n",
+ " 1.0 -52.078591 \n",
+ "_simu_solar_wind_yellow dm_user_multi_behavior_yellow 0.0 -29.817637 \n",
+ " 1.0 -25.894680 \n",
+ "_solar dm_user_multi_behavior 0.0 -10.764310 \n",
+ " 1.0 -6.623759 \n",
+ " dm_user_multi_behavior_degrad 0.0 -11.600117 \n",
+ " 1.0 -5.332010 \n",
+ " dm_user_multi_behavior_reconfig 0.0 0.943193 \n",
+ " 1.0 -1.116959 \n",
+ " dm_user_multi_behavior_renonce 0.0 -32.623178 \n",
+ " 1.0 -19.974470 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 -14.916763 \n",
+ " 1.0 -10.350962 \n",
+ "_solar_wind dm_user_multi_behavior 0.0 -10.354258 \n",
+ " 1.0 -5.750231 \n",
+ " dm_user_multi_behavior_degrad 0.0 -10.233249 \n",
+ " 1.0 -5.247645 \n",
+ " dm_user_multi_behavior_reconfig 0.0 -2.528002 \n",
+ " 1.0 -2.178845 \n",
+ " dm_user_multi_behavior_renonce 0.0 -27.111331 \n",
+ " 1.0 -14.789082 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 -16.523675 \n",
+ " 1.0 -10.899773 \n",
+ "_solar_wind_yellow dm_user_multi_behavior_yellow 0.0 -11.998930 \n",
+ " 1.0 -6.679314 \n",
+ "_solar_yellow dm_user_multi_behavior_yellow 0.0 -11.866845 \n",
+ " 1.0 -7.219563 \n",
+ "_yellow dm_user_multi_behavior_yellow 0.0 -11.445814 \n",
+ " 1.0 -10.516663 \n",
+ "basic dm_user_multi_behavior 0.0 -11.250033 \n",
+ " 1.0 -9.622616 \n",
+ " dm_user_multi_behavior_degrad 0.0 -11.926085 \n",
+ " 1.0 -8.229934 \n",
+ " dm_user_multi_behavior_reconfig 0.0 -1.316248 \n",
+ " 1.0 -3.496209 \n",
+ " dm_user_multi_behavior_renonce 0.0 -32.032178 \n",
+ " 1.0 -32.140672 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 -18.597417 \n",
+ " 1.0 -24.550706 \n",
+ "\n",
+ " NRJ_yellow \\\n",
+ "window_type behavior XP \n",
+ "_simu_solar_wind dm_user_multi_behavior 0.0 -3.982838 \n",
+ " 1.0 -3.345362 \n",
+ " dm_user_multi_behavior_degrad 0.0 -6.315975 \n",
+ " 1.0 -3.103070 \n",
+ " dm_user_multi_behavior_reconfig 0.0 1.739137 \n",
+ " 1.0 -0.426631 \n",
+ " dm_user_multi_behavior_renonce 0.0 -14.171118 \n",
+ " 1.0 -9.926660 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 13.660283 \n",
+ " 1.0 10.953130 \n",
+ "_simu_solar_wind_yellow dm_user_multi_behavior_yellow 0.0 -12.899025 \n",
+ " 1.0 -13.250431 \n",
+ "_solar dm_user_multi_behavior 0.0 -12.315888 \n",
+ " 1.0 -6.313912 \n",
+ " dm_user_multi_behavior_degrad 0.0 -13.799824 \n",
+ " 1.0 -6.006783 \n",
+ " dm_user_multi_behavior_reconfig 0.0 -4.982079 \n",
+ " 1.0 -2.586555 \n",
+ " dm_user_multi_behavior_renonce 0.0 -31.462579 \n",
+ " 1.0 -19.038110 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 19.428120 \n",
+ " 1.0 5.632161 \n",
+ "_solar_wind dm_user_multi_behavior 0.0 -4.043161 \n",
+ " 1.0 -2.664631 \n",
+ " dm_user_multi_behavior_degrad 0.0 -5.866204 \n",
+ " 1.0 -2.888914 \n",
+ " dm_user_multi_behavior_reconfig 0.0 1.811416 \n",
+ " 1.0 -0.688434 \n",
+ " dm_user_multi_behavior_renonce 0.0 -14.455575 \n",
+ " 1.0 -8.353933 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 6.575554 \n",
+ " 1.0 2.805253 \n",
+ "_solar_wind_yellow dm_user_multi_behavior_yellow 0.0 -5.696210 \n",
+ " 1.0 -3.466127 \n",
+ "_solar_yellow dm_user_multi_behavior_yellow 0.0 -13.684605 \n",
+ " 1.0 -7.051239 \n",
+ "_yellow dm_user_multi_behavior_yellow 0.0 -10.003396 \n",
+ " 1.0 -5.196745 \n",
+ "basic dm_user_multi_behavior 0.0 -3.267090 \n",
+ " 1.0 -2.709204 \n",
+ " dm_user_multi_behavior_degrad 0.0 -4.099654 \n",
+ " 1.0 -3.138304 \n",
+ " dm_user_multi_behavior_reconfig 0.0 2.234977 \n",
+ " 1.0 1.430192 \n",
+ " dm_user_multi_behavior_renonce 0.0 -12.569191 \n",
+ " 1.0 -11.854398 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 30.184465 \n",
+ " 1.0 16.142179 \n",
+ "\n",
+ " renonced_jobs \\\n",
+ "window_type behavior XP \n",
+ "_simu_solar_wind dm_user_multi_behavior 0.0 2.135366 \n",
+ " 1.0 5.250145 \n",
+ " dm_user_multi_behavior_degrad 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ " dm_user_multi_behavior_reconfig 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ " dm_user_multi_behavior_renonce 0.0 8.610150 \n",
+ " 1.0 21.174913 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ "_simu_solar_wind_yellow dm_user_multi_behavior_yellow 0.0 2.122899 \n",
+ " 1.0 5.255296 \n",
+ "_solar dm_user_multi_behavior 0.0 5.576208 \n",
+ " 1.0 6.557004 \n",
+ " dm_user_multi_behavior_degrad 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ " dm_user_multi_behavior_reconfig 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ " dm_user_multi_behavior_renonce 0.0 22.870586 \n",
+ " 1.0 26.855412 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ "_solar_wind dm_user_multi_behavior 0.0 3.025698 \n",
+ " 1.0 4.439967 \n",
+ " dm_user_multi_behavior_degrad 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ " dm_user_multi_behavior_reconfig 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ " dm_user_multi_behavior_renonce 0.0 12.788547 \n",
+ " 1.0 18.606718 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ "_solar_wind_yellow dm_user_multi_behavior_yellow 0.0 3.057347 \n",
+ " 1.0 4.443902 \n",
+ "_solar_yellow dm_user_multi_behavior_yellow 0.0 5.556503 \n",
+ " 1.0 6.526208 \n",
+ "_yellow dm_user_multi_behavior_yellow 0.0 5.771013 \n",
+ " 1.0 8.194102 \n",
+ "basic dm_user_multi_behavior 0.0 5.747768 \n",
+ " 1.0 8.195734 \n",
+ " dm_user_multi_behavior_degrad 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ " dm_user_multi_behavior_reconfig 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ " dm_user_multi_behavior_renonce 0.0 23.156117 \n",
+ " 1.0 33.401045 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 0.000000 \n",
+ " 1.0 0.000000 \n",
+ "\n",
+ " true_rigid_jobs \n",
+ "window_type behavior XP \n",
+ "_simu_solar_wind dm_user_multi_behavior 0.0 -6.882530 \n",
+ " 1.0 -19.093079 \n",
+ " dm_user_multi_behavior_degrad 0.0 -8.610150 \n",
+ " 1.0 -22.977728 \n",
+ " dm_user_multi_behavior_reconfig 0.0 -8.610150 \n",
+ " 1.0 -22.977728 \n",
+ " dm_user_multi_behavior_renonce 0.0 -8.610150 \n",
+ " 1.0 -22.977728 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 -8.610150 \n",
+ " 1.0 -22.977728 \n",
+ "_simu_solar_wind_yellow dm_user_multi_behavior_yellow 0.0 -35.610673 \n",
+ " 1.0 -41.147780 \n",
+ "_solar dm_user_multi_behavior 0.0 -18.302019 \n",
+ " 1.0 -24.360944 \n",
+ " dm_user_multi_behavior_degrad 0.0 -22.870586 \n",
+ " 1.0 -29.554193 \n",
+ " dm_user_multi_behavior_reconfig 0.0 -22.870586 \n",
+ " 1.0 -29.554193 \n",
+ " dm_user_multi_behavior_renonce 0.0 -22.870586 \n",
+ " 1.0 -29.554193 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 -22.870586 \n",
+ " 1.0 -29.554193 \n",
+ "_solar_wind dm_user_multi_behavior 0.0 -10.224162 \n",
+ " 1.0 -17.913541 \n",
+ " dm_user_multi_behavior_degrad 0.0 -12.788547 \n",
+ " 1.0 -21.508633 \n",
+ " dm_user_multi_behavior_reconfig 0.0 -12.788547 \n",
+ " 1.0 -21.508633 \n",
+ " dm_user_multi_behavior_renonce 0.0 -12.788547 \n",
+ " 1.0 -21.508633 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 -12.788547 \n",
+ " 1.0 -21.508633 \n",
+ "_solar_wind_yellow dm_user_multi_behavior_yellow 0.0 -16.524491 \n",
+ " 1.0 -25.337402 \n",
+ "_solar_yellow dm_user_multi_behavior_yellow 0.0 -19.462358 \n",
+ " 1.0 -26.421812 \n",
+ "_yellow dm_user_multi_behavior_yellow 0.0 -25.833105 \n",
+ " 1.0 -36.770513 \n",
+ "basic dm_user_multi_behavior 0.0 -18.504062 \n",
+ " 1.0 -29.505622 \n",
+ " dm_user_multi_behavior_degrad 0.0 -23.156117 \n",
+ " 1.0 -36.001886 \n",
+ " dm_user_multi_behavior_reconfig 0.0 -23.156117 \n",
+ " 1.0 -36.001886 \n",
+ " dm_user_multi_behavior_renonce 0.0 -23.156117 \n",
+ " 1.0 -36.001886 \n",
+ " dm_user_multi_behavior_see_you_later 0.0 -23.156117 \n",
+ " 1.0 -36.001886 "
]
},
+ "execution_count": 6,
"metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "max_energy = metrics[metrics.behavior==\"replay_user_rigid\"][\"energy underproduced (kwh)\"].values[0]\n",
- "\n",
- "# Sort out the experiments we don't need\n",
- "selection = metrics[\n",
- " (metrics.behavior!=\"replay_user_rigid\") \n",
- "# & (metrics.behavior!=\"dm_user_multi_behavior_degrad\")\n",
- "# & (metrics.behavior!=\"dm_user_multi_behavior_reconfig\")\n",
- "# & (metrics.behavior!=\"dm_user_multi_behavior_renonce\")\n",
- "].copy()\n",
- "selection[\"pretty name\"] = selection[\"behavior\"].apply(lambda x : pretty_name(x))\n",
- "\n",
- "# Gains in energy underproduced compared to rigid\n",
- "selection[\"gains underproduced\"] = max_energy - selection[\"energy underproduced (kwh)\"]\n",
- "selection[\"energy_quotient\"] = selection[\"gains underproduced\"] / selection[\"weighted_effort\"]\n",
- "\n",
- "fig, ax = plt.subplots(figsize=(6,2))\n",
- "selection[\"sort_order\"] = selection[\"behavior\"].apply(lambda x : sort_effort(x))\n",
- "selection[\"color\"] = selection[\"behavior\"].apply(lambda x : color_map(x))\n",
- "selection.sort_values(by='sort_order', ascending=False, inplace=True)\n",
- "selection.plot.scatter(\"energy_quotient\",\"pretty name\",\n",
- " xlabel=\"gains in underproduction per weighted effort (in kWh/unit of effort)\",\n",
- " ylabel=\"\", marker=\"+\", c=selection[\"color\"], alpha=1, ax=ax)\n",
- "ax.set_xlim(0,70)\n",
- "\n",
- "plt.savefig(f\"{FIG_DIR}/ratio_gains_weighted_effort.pdf\", bbox_inches=\"tight\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bc6c80c7",
- "metadata": {},
- "source": [
- "Correlation between alpha and weighted effort:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "68091693",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Pearson correlation coefficients:\n",
- "alpha/w_effort: 0.9930736668431336\n"
- ]
+ "output_type": "execute_result"
}
],
"source": [
- "def behavior_to_alpha(behavior):\n",
- " if \"rigid\" in behavior :\n",
- " return 0\n",
- " if \"low_\" in behavior :\n",
- " return .25\n",
- " if \"medium_\" in behavior :\n",
- " return .5\n",
- " if \"big_\" in behavior :\n",
- " return .75\n",
- " if \"max_\" in behavior :\n",
- " return 1\n",
- "\n",
- "metrics[\"alpha\"]=metrics[\"behavior\"].apply(lambda x : behavior_to_alpha(x))\n",
- "alpha = metrics[\"alpha\"]\n",
- "w_effort = metrics[\"weighted_effort\"]\n",
- "\n",
- "print(f\"Pearson correlation coefficients:\")\n",
- "print(f\"alpha/w_effort: {alpha.corr(w_effort)}\")"
+ "# Create a similar file with metrics relative to the control XP\n",
+ "metrics_relative = pd.read_csv(f\"{OUT_DIR}/metrics_relative_campaign3.csv\")\n",
+ "aggregated_1 = metrics_relative.groupby([\"window_type\",\"behavior\",\"XP\"]).mean()\n",
+ "#aggregated\n",
+ "aggregated_1.loc[:,[\"NRJ_red\",\"NRJ_yellow\",\"renonced_jobs\",\"true_rigid_jobs\"]]"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5d394c86",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -2348,7 +1269,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.9.6"
}
},
"nbformat": 4,
diff --git a/campaign3.py b/campaign3.py
index ec4ae8ce08ad491e0c9164ee1709d564c4151b63..d3a7eee4d925f3542305a18591dd4c21b93e74ba 100755
--- a/campaign3.py
+++ b/campaign3.py
@@ -9,7 +9,7 @@ import psutil
from dateutil import parser
import concurrent.futures
-from scripts.util import WL_DIR, ROOT_DIR, ExpeDict, create_dir_rec_if_needed, generate_dict_from_json
+from scripts.util import WL_DIR, ROOT_DIR, ExpeDict, create_dir_rec_if_needed,generate_dict_from_json
from scripts.generate_file import save_dict_to_json
from instance3 import start_instance, prepare_input_data, generate_windows_dict, \
compress_expe_result, user_type_to_behavior
@@ -17,11 +17,10 @@ from scripts.stat_tools import plot_exec_time, plot_queue_load
from compute_metrics_campaign3 import compute_metrics_all_expe_parr
-def parse_argument():
- """ parsing of command-line arguments"""
+def main():
+ # parsing of command line arguments
argument_parser = argparse.ArgumentParser(
description="run instance for test with multibehavior batmen user class and metacentrum workload")
-
argument_parser.add_argument("--nb-replicat", type=int,
help="number of different seed we will have to choose for the experiment (default 1)",
default=1, required=False)
@@ -66,66 +65,18 @@ def parse_argument():
"have 1 heating days and 1 ending days and 7 day that we will do stat on )",
default=9, required=False)
argument_parser.add_argument("--json-behavior", type=str,
- help="path to the behavior json file", nargs="*",
+ help="path to the behavior json file",nargs="*",
required=True)
args = argument_parser.parse_args()
print(args)
- return args
-
-
-def get_behavior_dict(json_behavior):
- """generate behavior_prob_dict behavior_name_list and monolithic_behavior_list """
- behavior_prob_dict: Dict[str, Dict[any, any]] = {"rigid": {},
- "renonce": {"red_prob_renonce_mono_core": 1.0,
- "red_prob_renonce_multi_core": 1.0},
- "see_you_later": {"red_prob_see_you_later_mono_core": 1.0,
- "red_prob_see_you_later_multi_core": 1.0},
- "reconfig": {"red_prob_reconfig_multi_core": 1.0,
- "red_prob_rigid_mono_core": 1.0},
- "degrad": {"red_prob_degrad_multi_core": 1.0,
- "red_prob_degrad_mono_core": 1.0}}
- monolithic_behavior_list = ["rigid", "renonce", "reconfig", "degrad"]
- behavior_name_list = [path.split("/")[-1].split(".")[0] for path in json_behavior]
- for behavior_name, behavior_file in zip(behavior_name_list, json_behavior):
- behavior_prob_dict[behavior_name] = generate_dict_from_json(behavior_file)
- return behavior_prob_dict, behavior_name_list, monolithic_behavior_list
-
-
-def get_window_mode(window_mode_arg):
- """generate window_mode_list from argument given"""
- window_mode_list_option = ["", "_solar", "_solar_wind", "_simu_solar_wind", "_all_red"]
- window_mode_list = []
- modulus = 1
- for i in range(len(window_mode_list_option)):
- enabled = modulus & window_mode_arg
- if enabled:
- window_mode = window_mode_list_option[i]
- window_mode_list.append(window_mode)
- modulus = 2 * modulus
- return window_mode_list
-
-
-def get_seed_to_launch_expe(expe_num: int, window_mode_nb: int, behavior_nb: int, seed: int, color_nb: int):
- seed_to_return = seed
- # We authorize a max of 64 experiments
- seed_expe_inter = (2 ** 27)
- seed_to_return += expe_num * seed_expe_inter
- # We authorize a max of 64 window_mode by experiments
- seed_window_mode_list_inter = (2 ** 21)
- seed_to_return += seed_window_mode_list_inter * window_mode_nb
- # We authorize a max of 64 different behavior to try
- seed_behavior_inter = (2 ** 16)
- seed_to_return += behavior_nb * seed_behavior_inter
- # We authorize at most 2^14 replicats and 4 window red and yellow combinations
- color_switch_inter = (2 ** 14)
- seed_to_return += color_nb * color_switch_inter
- return seed_to_return
+ ###############################
+ # Prepare the start date sample
+ ###############################
-def compute_expe_start_time(args) :
begin_trace = 1356994806 # according to original SWF header
jun1 = parser.parse('Sun Jun 1 00:00:00 CEST 2014')
last_day_expe = parser.parse('Tue Nov 11 00:00:00 CEST 2014')
-
+ start_time_compute = datetime.datetime.now()
# We do one expe for every week beween Jun 1 and Oct 26
nb_days = args.nb_days
@@ -135,7 +86,7 @@ def compute_expe_start_time(args) :
end_time = nb_days * 24 * 3600 - ending_days * 24 * 3600
# the time between each simulation we try to keep the same alignment with weeks
- inter_day = max(((nb_days - heating_days - ending_days) // 7) * 7,7)
+ inter_day = ((nb_days - heating_days - ending_days) // 7) * 7
expe_start_time = []
day = datetime.timedelta(days=1)
betweentime = inter_day * day
@@ -150,12 +101,16 @@ def compute_expe_start_time(args) :
with open(f"{WL_DIR}/start_days_for_campaign3.txt", 'w') as f:
for date in str_start_day:
f.write(date + '\n')
- return expe_start_time,begin_time,end_time,heating_days
-
-def run_expe(args,expe_start_time,begin_time: int,end_time: int,heating_days : int):
- nb_days = args.nb_days
compress_mode = args.compress_mode
+ # We authorize a max of 64 experiments
+ seed_expe_inter = (2**27)
+ # We authorize a max of 64 window_mode by experiments
+ seed_window_mode_list_inter = (2**21)
+ # We authorize a max of 64 different behavior to try
+ seed_behavior_inter =(2**16)
+ # We authorize at most 2^14 replicats and 4 window red and yellow combinations
+ color_switch_inter = (2**14)
nb_replicat = args.nb_replicat
perf_record = args.perf_record
perf_rate = args.perf_rate
@@ -166,16 +121,33 @@ def run_expe(args,expe_start_time,begin_time: int,end_time: int,heating_days : i
expe_range = range(len(expe_start_time))
start_seed = 0
- behavior_prob_dict, behavior_name_list, monolithic_behavior_list = get_behavior_dict(args.json_behavior)
- window_mode_list = get_window_mode(args.window_mode)
- expe_done_dict = ExpeDict(behavior_name_list, expe_range, window_mode_list, ["red", "yellow_red"])
- print(
- f"Run expes {expe_range} with {nb_replicat} replicats on modes {window_mode_list} on behavior {behavior_name_list}")
+ behavior_prob_dict : Dict[str,Dict[any,any]] = {"rigid": {},
+ "renonce": {"red_prob_renonce_mono_core": 1.0, "red_prob_renonce_multi_core" : 1.0},
+ "see_you_later": {"red_prob_see_you_later_mono_core": 1.0,"red_prob_see_you_later_multi_core": 1.0},
+ "reconfig": {"red_prob_reconfig_multi_core": 1.0, "red_prob_rigid_mono_core" : 1.0},
+ "degrad": {"red_prob_degrad_multi_core": 1.0, "red_prob_degrad_mono_core" : 1.0}}
+ monolithic_behavior_list = ["rigid", "renonce", "reconfig", "degrad"]
+ behavior_name_list = [ path.split("/")[-1].split(".")[0] for path in args.json_behavior]
+ for behavior_name,behavior_file in zip(behavior_name_list,args.json_behavior) :
+ behavior_prob_dict[behavior_name] = generate_dict_from_json(behavior_file)
+
+ window_mode_list_option = ["", "_solar", "_solar_wind", "_simu_solar_wind", "_all_red"]
+ window_mode_list = []
+ modulus = 1
+ for i in range(len(window_mode_list_option)):
+ enabled = modulus & args.window_mode
+ if enabled:
+ window_mode = window_mode_list_option[i]
+ window_mode_list.append(window_mode)
+ modulus = 2 * modulus
+
+ expe_done_dict = ExpeDict(behavior_name_list,expe_range, window_mode_list, ["red", "yellow_red"])
+ print(f"Run expes {expe_range} with {nb_replicat} replicats on modes {window_mode_list} on behavior {behavior_name_list}")
production_files = args.production_files
# Number of day between Jan 1st and Jun 1st
start_day = 150
- yellow_threshold = 42 * 150
- red_threshold = yellow_threshold / 2
+ yellow_threshold = 42*150
+ red_threshold = yellow_threshold/2
red_windows_dict, yellow_windows_dict = generate_windows_dict(production_files=production_files,
expe_range=expe_range,
window_mode_list=window_mode_list,
@@ -200,6 +172,7 @@ def run_expe(args,expe_start_time,begin_time: int,end_time: int,heating_days : i
maximum_worker = args.threads
prepare_work = not args.no_prepare_workload
+
# Preparation of workload
if prepare_work:
with concurrent.futures.ProcessPoolExecutor(max_workers=maximum_worker) as executor:
@@ -214,20 +187,20 @@ def run_expe(args,expe_start_time,begin_time: int,end_time: int,heating_days : i
with concurrent.futures.ProcessPoolExecutor(max_workers=maximum_worker) as executor:
instances = []
counter_expe_done = 0
- for expe_nb in expe_range:
- print(f"Submit expe {expe_nb}")
- # start_instance(expe_num=expe_nb, start_date=expe_start_time[expe_nb],
+ for xp in expe_range:
+ print(f"Submit expe {xp}")
+ # start_instance(expe_num=xp, start_date=expe_start_time[xp],
# nb_days=nb_days, seed=0, red_windows=red_windows,
# yellow_windows=yellow_windows, out_dir=out_dir, behavior_prob={} ,
# batmen_exec="batmen", prepare_workload=True,
# clean_log=False, window_mode=None,
# perf_eval=False,perf_rate=60)
-
- for window_nb, window_mode in enumerate(window_mode_list):
- red_windows, yellow_windows = red_windows_dict[expe_nb][window_mode], yellow_windows_dict[expe_nb][window_mode]
+ seed_windows_offset = 0
+ for window_mode in window_mode_list:
+ red_windows, yellow_windows = red_windows_dict[xp][window_mode], yellow_windows_dict[xp][window_mode]
if monolithic_test:
for mono_behavior in monolithic_behavior_list:
- instances.append(executor.submit(start_instance, expe_nb, expe_start_time[expe_nb],
+ instances.append(executor.submit(start_instance, xp, expe_start_time[xp],
nb_days, 0, red_windows,
[], out_dir, behavior_prob_dict[mono_behavior],
batmen_exec, False,
@@ -235,24 +208,25 @@ def run_expe(args,expe_start_time,begin_time: int,end_time: int,heating_days : i
perf_record, perf_rate))
for j in range(start_seed, start_seed + nb_replicat):
# only red_windows expe
- for behavior_nb, behavior in enumerate(behavior_name_list):
- seed_red = get_seed_to_launch_expe(expe_nb, window_nb, behavior_nb, j, 0)
- seed_yellow = get_seed_to_launch_expe(expe_nb, window_nb, behavior_nb, j, 1)
- instances.append(executor.submit(start_instance, expe_nb, expe_start_time[expe_nb],
- nb_days, seed_red,
+ for k in range(len(behavior_name_list)) :
+ behavior = behavior_name_list[k]
+ seed_to_launch_expe = j + xp * seed_expe_inter + seed_windows_offset + k*seed_behavior_inter
+ instances.append(executor.submit(start_instance, xp, expe_start_time[xp],
+ nb_days, seed_to_launch_expe,
red_windows,
[], out_dir, behavior_prob_dict[behavior],
batmen_exec, False,
True, window_mode, behavior,
perf_record, perf_rate))
# red_windows and yellow_windows expe
- instances.append(executor.submit(start_instance, expe_nb, expe_start_time[expe_nb],
- nb_days, seed_yellow,
+ instances.append(executor.submit(start_instance, xp, expe_start_time[xp],
+ nb_days, seed_to_launch_expe + color_switch_inter,
red_windows,
yellow_windows, out_dir, behavior_prob_dict[behavior],
batmen_exec, False,
True, window_mode, behavior,
perf_record, perf_rate))
+ seed_windows_offset += seed_window_mode_list_inter
for instance in concurrent.futures.as_completed(instances):
(xp_num, seed, expe_time, expe_dir, is_yellow, user_type, window_mode) = instance.result()
@@ -271,44 +245,24 @@ def run_expe(args,expe_start_time,begin_time: int,end_time: int,heating_days : i
if is_yellow:
window_mode += "_yellow"
compress_expe_result(xp_num, behavior, seed, window_mode, red_windows, yellow_windows, begin_time,
- end_time, False, out_dir, destroy=False,energy_file=production_files[1],offset_production_day=start_day)
+ end_time, False, out_dir, destroy=False)
print(
f"\033[92m Expe {xp_num} with seed {seed} terminated took {expe_time // 60}m{expe_time % 60}s \033[0m")
# End of experiment computation of stats
expe_done_dict.to_json(json_filename=savefile)
- return expe_done_dict, red_windows_dict, yellow_windows_dict, save_num, begin_time, end_time
-
-
-def compute_metrics(args, expe_done_dict, red_windows_dict, yellow_windows_dict,
- begin_time, end_time, offset_production_data, save_num,):
- monolithic_test = not args.no_monolithic_test
- maximum_worker = args.threads
- production_files = args.production_files
- start_day = offset_production_data
- out_dir = args.out_dir
metrics, metrics_relative = compute_metrics_all_expe_parr(expe_done_dict, begin_time, end_time,
red_windows_dict, yellow_windows_dict, monolithic_test,
True,
- maximum_worker, out_dir,
- {"_simu_solar_wind": production_files[1]}, start_day)
+ maximum_worker, out_dir)
metrics_file_name = f"{out_dir}/metrics_campaign3_{save_num}.csv"
metrics.to_csv(metrics_file_name)
metrics_relative.to_csv(f"{out_dir}/metrics_relative_campaign3_{save_num}.csv")
-
- plot_exec_time(expe_done_dict, WL_DIR, out_dir, f'{out_dir}/execution_times_{save_num}.svg')
- plot_queue_load(expe_done_dict, out_dir)
-
-
-def main():
- args = parse_argument()
- expe_start_time,begin_time,end_time,heatings_days = compute_expe_start_time(args)
- start_time_compute = datetime.datetime.now()
- print(f"Starting experiment at {start_time_compute}")
- expe_done_dict, red_windows_dict, yellow_windows_dict, save_num, begin_time, end_time = run_expe(args,expe_start_time,begin_time,end_time,heatings_days)
end_time_compute = datetime.datetime.now()
+
print("Experiments took " + str(end_time_compute - start_time_compute))
- compute_metrics(args, expe_done_dict, red_windows_dict, yellow_windows_dict, begin_time, end_time, 150, save_num)
+ plot_queue_load(expe_done_dict, out_dir)
+ plot_exec_time(expe_done_dict, WL_DIR, out_dir, f'{out_dir}/execution_times_{save_num}.svg')
if __name__ == "__main__":
diff --git a/compute_metrics_campaign3.py b/compute_metrics_campaign3.py
index b6eba71ade14ec6033b959af3d4cbd3bf4d9bde5..200f31d3dcf03c592a99cfc3e798f875c89f66d2 100644
--- a/compute_metrics_campaign3.py
+++ b/compute_metrics_campaign3.py
@@ -14,12 +14,10 @@ class UndefinedColor(Exception):
def compute_metrics_behavior(xp, behavior: str, seed, window_mode: str, red_windows: List[List[int]],
yellow_windows: List[List[int]],
- begin_time: int, end_time: int,sanity_check: bool, out_dir: str, energy_file:str,
- offset_production_day,
+ begin_time: int, end_time: int,sanity_check: bool, out_dir: str,
control_exp_metrics):
current_exp_metrics = compute_metrics(xp, behavior, seed, window_mode, red_windows, yellow_windows,
- begin_time, end_time, sanity_check=sanity_check,
- out_dir=out_dir,energy_file=energy_file,offset_production_day=offset_production_day)
+ begin_time, end_time, sanity_check=sanity_check, out_dir=out_dir)
current_exp_metrics_dataframe = pd.DataFrame(current_exp_metrics, index=[0])
current_metrics_relative_dataframe = pd.DataFrame()
if not(control_exp_metrics is None):
@@ -29,12 +27,10 @@ def compute_metrics_behavior(xp, behavior: str, seed, window_mode: str, red_wind
def compute_monolithic_behavior_aux(xp, window_mode: str, red_windows: List[List[int]], yellow_windows: List[List[int]],
- begin_time: int, end_time: int, sanity_check: bool, out_dir: str,energy_file :str,
- offset_production_day):
+ begin_time: int, end_time: int, sanity_check: bool, out_dir: str):
behavior = "replay_user_rigid"
control_exp_metrics = compute_metrics(xp, behavior, 0, window_mode,
- red_windows, yellow_windows, begin_time, end_time, sanity_check,
- out_dir, energy_file,offset_production_day=offset_production_day)
+ red_windows, yellow_windows, begin_time, end_time, sanity_check, out_dir)
control_exp_metrics_dataframe = pd.DataFrame(control_exp_metrics, index=[0])
print("Rigid finished, Now monolithic behavior")
new_metrics = pd.DataFrame()
@@ -45,9 +41,7 @@ def compute_monolithic_behavior_aux(xp, window_mode: str, red_windows: List[List
current_exp_metrics, current_exp_metrics_relative = compute_metrics_behavior(xp, behavior, 0, window_mode,
red_windows, yellow_windows,
begin_time, end_time, sanity_check,
- out_dir, energy_file,offset_production_day,
- control_exp_metrics
- )
+ out_dir, control_exp_metrics)
new_metrics = pd.concat([new_metrics, current_exp_metrics], ignore_index=True)
new_metrics_relative = pd.concat([new_metrics_relative, current_exp_metrics_relative], ignore_index=True)
@@ -55,7 +49,7 @@ def compute_monolithic_behavior_aux(xp, window_mode: str, red_windows: List[List
def compute_monolithic_behavior(expe_done_dict, red_windows_dict, yellow_windows_dict,
- begin_time: int, end_time: int, sanity_check: bool, out_dir: str,energy_file_dict,offset_production_day):
+ begin_time: int, end_time: int, sanity_check: bool, out_dir: str):
_,expe_range, window_mode_list, _ = expe_done_dict.range()
metrics = pd.DataFrame()
metrics_relative = pd.DataFrame()
@@ -72,9 +66,7 @@ def compute_monolithic_behavior(expe_done_dict, red_windows_dict, yellow_windows
begin_time,
end_time,
sanity_check,
- out_dir,
- energy_file_dict[window_mode],
- offset_production_day)
+ out_dir)
control_exp_metrics_dict[xp][window_mode] = control_exp_metrics
metrics = pd.concat([metrics, new_metrics])
metrics_relative = pd.concat([metrics_relative, new_metrics_relative])
@@ -84,7 +76,7 @@ def compute_monolithic_behavior(expe_done_dict, red_windows_dict, yellow_windows
def compute_metrics_all_expe_parr(expe_done_dict, begin_time: int, end_time: int, red_windows_dict,
yellow_windows_dict, compute_relative: bool, sanity_check: bool, maximum_worker: int,
- out_dir: str,energy_file_dict,offset_production_day):
+ out_dir: str):
control_exp_metrics_dict = {}
if compute_relative:
metrics, metrics_relative, control_exp_metrics_dict = compute_monolithic_behavior(expe_done_dict,
@@ -92,21 +84,16 @@ def compute_metrics_all_expe_parr(expe_done_dict, begin_time: int, end_time: int
yellow_windows_dict,
begin_time,
end_time, sanity_check,
- out_dir,energy_file_dict,
- offset_production_day)
+ out_dir)
else:
metrics = pd.DataFrame()
metrics_relative = pd.DataFrame()
print("Monolithic behavior finished, now MultiBehavior")
- nb_expe_done = 0
- nb_expe_total = len(expe_done_dict.iter())
- progress=0
with concurrent.futures.ProcessPoolExecutor(max_workers=maximum_worker) as executor:
instances = []
for (behavior_name,xp, window_mode, color, seed) in expe_done_dict.iter():
red_windows = red_windows_dict[xp][window_mode]
yellow_windows = yellow_windows_dict[xp][window_mode]
- energy_file = energy_file_dict[window_mode]
control_exp_metrics = None
if compute_relative:
control_exp_metrics = control_exp_metrics_dict[xp][window_mode]
@@ -116,15 +103,10 @@ def compute_metrics_all_expe_parr(expe_done_dict, begin_time: int, end_time: int
behavior = "dm_user_multi_behavior_" + behavior_name + "_yellow"
instances.append(executor.submit(compute_metrics_behavior, xp, behavior,
seed, window_mode, red_windows, yellow_windows,
- begin_time, end_time,sanity_check, out_dir, energy_file,
- offset_production_day,
+ begin_time, end_time,sanity_check, out_dir,
control_exp_metrics))
for instance in concurrent.futures.as_completed(instances):
- nb_expe_done+=1
- if nb_expe_done*10//nb_expe_total > progress:
- print(f"progress {nb_expe_done}/{nb_expe_total}")
- progress+=1
current_exp_metrics, current_exp_metrics_relative = instance.result()
metrics = pd.concat([metrics, current_exp_metrics], ignore_index=True)
metrics_relative = pd.concat([metrics_relative, current_exp_metrics_relative], ignore_index=True)
@@ -219,37 +201,30 @@ def main():
help="The day the experiments ended (for example 8)", required=True)
argument_parser.add_argument("--threads", type=int,
help="number of threads (default : 1 sequential)", default=1, required=False)
- argument_parser.add_argument("--output", type=str, required=True,
+ argument_parser.add_argument("--output", type=str,
help="name of the output csv file (example : out/metrics_campaign3_2)")
argument_parser.add_argument("--merge", action="store_true",
help="merge the already computed data when using compress_mode")
- argument_parser.add_argument("--energy-file", default=None,nargs="*",
- help="provide the production file to compute the energy difference "
- "between production and consumption ")
- argument_parser.add_argument("--offset-production-day", default=0,type=int,
- help="provide the offset date for energy production needed "
- "For example, if the provided energy production start at January 1st "
- "and the experiment start the 10th you must give 10")
args = argument_parser.parse_args()
print(args)
expe_done_dict = generate_expe_dict_from_json(args.expe_done)
windows_dict = generate_dict_from_file(args.windows_dict)
- energy_file_dict ={}
- for window_mode,i in zip(expe_done_dict.window_mode_list,range(len(expe_done_dict.window_mode_list))) :
- if args.energy_file is None :
- energy_file_dict[window_mode] = None
- else :
- energy_file_dict[window_mode] = args.energy_file[i]
if not args.merge:
- metrics, metrics_relative = compute_metrics_all_expe_parr(expe_done_dict =expe_done_dict,
- begin_time=args.begin_day * 24 * 3600, end_time= args.end_day*24*3600,
+ if args.threads > 1:
+ metrics, metrics_relative = compute_metrics_all_expe_parr(expe_done_dict =expe_done_dict,
+ begin_time=args.begin_day * 24 * 3600, end_time= args.end_day,
red_windows_dict = windows_dict["red_windows"],
yellow_windows_dict=windows_dict["yellow_windows"],
sanity_check=True, maximum_worker = args.threads,
- out_dir = args.out_dir, compute_relative =True,
- energy_file_dict=energy_file_dict,offset_production_day=args.offset_production_day)
- metrics.to_csv(args.output + ".csv")
- metrics_relative.to_csv(args.output + "_relative.csv")
+ out_dir = args.out_dir, compute_relative =True)
+ else:
+ metrics, metrics_relative = compute_metrics_all_expe_seq(expe_done_dict.data, args.begin_day * 24 * 3600,
+ args.end_day * 24 * 3600,
+ windows_dict["red_windows"],
+ windows_dict["yellow_windows"], True,
+ args.out_dir)
+ metrics.to_csv(args.output + ".csv")
+ metrics_relative.to_csv(args.output + "_relative.csv")
else:
metrics = merge_data(expe_done_dict, args.out_dir)
print(metrics)
diff --git a/instance3.py b/instance3.py
index 74b509fc55840e1320e0250d6854fd5b71999363..3541215492ac0229b4fae993687f29fb7628e9ac 100755
--- a/instance3.py
+++ b/instance3.py
@@ -32,10 +32,10 @@ def prepare_input_data(expe_num: int, start_date: int, nb_days: int):
def compress_expe_result(exp_num: int, behavior: str, seed: int, window_type: str, red_windows: List[List[int]],
yellow_windows: List[List[int]], begin_time: int, end_time: int,
- sanity_check: bool, out_dir: str,energy_file,offset_production_day, destroy: bool = True,):
+ sanity_check: bool, out_dir: str, destroy: bool = True):
expe_dir = f"{out_dir}/expe{exp_num}/{behavior}{window_type}_{seed}"
out = compute_metrics(exp_num, behavior, seed, window_type, red_windows, yellow_windows, begin_time, end_time,
- sanity_check, out_dir,energy_file,offset_production_day)
+ sanity_check, out_dir)
data = pd.DataFrame(data=out, index=[0])
stat_filename = f"{expe_dir}/log/condensed_stat.csv"
diff --git a/scripts/compute_stat.sh b/scripts/compute_stat.sh
deleted file mode 100644
index 4b222c7a18c76c9b0bbea65b79cb174a4dca9ecb..0000000000000000000000000000000000000000
--- a/scripts/compute_stat.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-#Compute the metrics of experiments in out_dir
-out_dir="out/"
-python3 compute_metrics_campaign3.py --out-dir "${out_dir}/" \
---expe-done "${out_dir}/"expe_done_dict_0.json \
---windows-dict "${out_dir}"/windows_dict_0.json \
---output campaign3_metrics --begin-day 1 --end-day 163 \
---energy-file data_energy/energy_trace_sizing_solar.csv \
---offset-production-day 150 \
---threads 4
\ No newline at end of file
diff --git a/scripts/plot_library.py b/scripts/plot_library.py
deleted file mode 100644
index 3d8955396babc2bf4d55c642f645d76a629a6d61..0000000000000000000000000000000000000000
--- a/scripts/plot_library.py
+++ /dev/null
@@ -1,231 +0,0 @@
-
-
-import evalys.visu.legacy as vleg
-from matplotlib import figure, pyplot as plt
-import numpy as np
-import pandas
-from scripts.util import JobSetMulticore,intersect_windows
-def plot_load_and_details(expe_file):
- begin, end = 24 * 3600, 48 * 3600
-
- js = JobSetMulticore.from_csv(expe_file + "/_jobs.csv")
- js.df = js.df[(js.df.submission_time >= begin) & (js.df.submission_time < end)]
- fig, axe = plt.subplots(nrows=2, sharex=True, figsize=(16, 8), tight_layout=True)
- fig.suptitle(expe_file, fontsize=16)
- vleg.plot_load(js.utilisation, js.MaxProcs, time_scale=False, ax=axe[0])
- vleg.plot_job_details(js.df, js.MaxProcs, time_scale=False, ax=axe[1])
-
- for ax in axe:
- ax.xaxis.set_ticks(np.arange(begin, end, 2 * 3600))
- ax.xaxis.set_ticklabels(np.arange(24, 48, 2))
-
- plt.xlim(begin, end)
- fig.savefig(expe_file + '_viz.png')
- plt.show()
- plt.close(fig)
-
-
-def plot_energy_consumed(out_dir, red_windows, yellow_windows, label=None, tick_size=2, begin_time=0, end_time=None):
- if label is None:
- label = out_dir
- data = pandas.read_csv(out_dir + "/_consumed_energy.csv")
- for red_window in red_windows:
- plot_window(red_window, "red")
- for yellow_window in yellow_windows:
- plot_window(yellow_window, "yellow")
- ax_1 = plt.gca()
- data.plot(x="time", y="energy", ax=ax_1, xlim=(begin_time, end_time), label=label)
- ax_1.xaxis.set_ticks(np.arange(begin_time, end_time, tick_size * 3600))
- ax_1.xaxis.set_ticklabels(np.arange(begin_time // 3600, end_time // 3600, tick_size))
-
-def plot_window_list(red_window_list, yellow_window_list, begin_exp, end_exp, tick=12,width=21,height=5):
- plt.figure(figsize=(width,height))
- plot_window([begin_exp, end_exp], "green")
- for red_window in red_window_list:
- if intersect_windows([begin_exp,end_exp],red_window) :
- plot_window(red_window, "red")
- for yellow_window in yellow_window_list:
- if intersect_windows([begin_exp,end_exp],yellow_window) :
- plot_window(yellow_window, "yellow")
- ax = plt.gca()
- ax.xaxis.set_ticks(np.arange(begin_exp, end_exp + 1, tick * 3600))
- ax.xaxis.set_ticklabels(np.arange(begin_exp // 3600, end_exp // 3600 + 1, tick)%24,fontsize=12)
-
-def plot_window_list_extra(red_window_list, yellow_window_list,
- begin_exp, end_exp,red_threshold,yellow_threshold,
- energy_produced_dataframe, offset_day_production, tick=12,width=21,height=5,fontsize=12) :
-
- plt.figure(figsize=(width, height))
- offset_time_prod = offset_day_production*24*3600
- energy_produced_dataframe["time"] -= offset_time_prod
- energy_produced_dataframe_sub = energy_produced_dataframe[energy_produced_dataframe.time <= end_exp]
- energy_produced_dataframe_sub = energy_produced_dataframe_sub[energy_produced_dataframe_sub.time >= begin_exp]
- power_produced_max = energy_produced_dataframe_sub["power"].max()
- plot_window([begin_exp, end_exp], "green",label=True)
- first_window = True
- for red_window in red_window_list:
- if intersect_windows([begin_exp, end_exp], red_window):
- plot_window(red_window, "red",label=first_window)
- first_window = False
- first_window = True
- for yellow_window in yellow_window_list:
- if intersect_windows([begin_exp, end_exp], yellow_window):
- plot_window(yellow_window, "yellow",label=first_window)
- first_window=False
- ax = plt.gca()
- energy_produced_dataframe_sub.plot.scatter(x="time",y="power",ax=ax,zorder=500,label="power produced")
- plt.axhline(red_threshold,label="red threshold",color="black")
- plt.axhline(yellow_threshold, label="yellow threshold",color="black",linestyle="dashed")
- ax.xaxis.set_ticks(np.arange(begin_exp, end_exp + 1, tick * 3600))
- ax.xaxis.set_ticklabels(np.arange(begin_exp // 3600, end_exp // 3600 + 1, tick) % 24,fontsize=fontsize)
- power_max=int((power_produced_max*1.2)//100*100)
- tick_y = power_max // 10
- ax.yaxis.set_ticks(np.arange(0,power_max,tick_y,dtype=int))
- ax.yaxis.set_ticklabels(np.arange(0,power_max,tick_y,dtype=int),fontsize=fontsize)
- plt.xlabel("time (in hour of the day)",fontsize=fontsize)
- plt.ylabel("power (in Watt)",fontsize=fontsize)
-def plot_window(window, window_type, label=False):
- [begin, end] = window
- label_name = None
- if window_type == "red":
- color_fig = "#FDA3AB"
- if label:
- label_name = "red_window"
- elif window_type == "yellow":
- color_fig = "#F9F99C"
- if label:
- label_name = "yellow_window"
- elif window_type == "green":
- color_fig = "#DDFDCC"
- if label:
- label_name = "green_window"
- else:
- print("[Warning] undefined window color ")
- return
- plt.axvspan(xmin=begin, xmax=end, color=color_fig, label=label_name)
-
-
-def plot_load_windows(expe_file_list, expe_name_list, color_panel=None, red_windows=None, yellow_windows=None,
- begin_time=24, end_time=48,
- split=12, interactive=False,width=20,height=10,fontsize=10):
- if yellow_windows is None:
- yellow_windows = []
- if red_windows is None:
- red_windows = []
- nb_exp = len(expe_name_list)
- begin, end = begin_time * 3600, end_time * 3600
- fig = plt.figure(figsize=(width, height))
- plot_window([begin, end], "green")
- top=100
- for expe_file, expe_name in zip(expe_file_list, expe_name_list):
- js = JobSetMulticore.from_csv(expe_file + "/_jobs.csv")
- js.df = js.df[(js.df.submission_time >= begin) & (js.df.submission_time < end)]
- vleg.plot_load(js.utilisation, js.MaxProcs, legend_label=expe_name)
- top = max(js.MaxProcs + 100.0,top)
- ax = plt.gca()
-
- ax.xaxis.set_ticks(np.arange(begin, end, split * 3600))
- ax.xaxis.set_ticklabels(np.arange(begin_time, end_time, split)%24,fontsize=fontsize)
-
- ax.set_ylim(bottom=-10.0, top=top)
-
- for window in yellow_windows:
- plot_window(window, "yellow")
-
- if color_panel:
- for line, color in zip(plt.gca().get_lines()[::3], color_panel):
- print(line, color)
- line.set_color(color)
- for window in red_windows:
- plot_window(window, "red")
- plt.xlim(begin, end)
- for label in (ax.get_xticklabels() + ax.get_yticklabels()):
- label.set_fontsize(fontsize)
- lgd = ax.legend(bbox_to_anchor=(1, 0.5), loc="upper left")
- plt.xlabel("time (in hour of the day)",fontsize=fontsize)
- plt.ylabel("Load of the server (in number of core)",fontsize=fontsize)
- fig.savefig(expe_file_list[0] + 'comparative_viz.png', bbox_extra_artists=(lgd,), bbox_inches='tight')
- fig.savefig(expe_file_list[0] + 'comparative_viz.svg', bbox_extra_artists=(lgd,), bbox_inches='tight')
- plt.show()
- if not interactive:
- plt.close(fig)
-
-def plot_queue_windows(expe_file_list, expe_name_list, color_panel=None, red_windows=None, yellow_windows=None,
- begin_time=24, end_time=48,
- split=12, interactive=False,
- width=20,height=10,fontsize=10):
- if yellow_windows is None:
- yellow_windows = []
- if red_windows is None:
- red_windows = []
- nb_exp = len(expe_name_list)
- begin, end = begin_time * 3600, end_time * 3600
- fig = plt.figure(figsize=(width, height))
- plot_window([begin, end], "green")
- top = 100
- for expe_file, expe_name in zip(expe_file_list, expe_name_list):
- js = JobSetMulticore.from_csv(expe_file + "/_jobs.csv")
- js.df = js.df[(js.df.submission_time >= begin) & (js.df.submission_time < end)]
- vleg.plot_load(js.queue, js.MaxProcs, legend_label=expe_name)
- top=max(top,6*js.MaxProcs)
- ax = plt.gca()
-
- ax.xaxis.set_ticks(np.arange(begin, end, split * 3600))
- ax.xaxis.set_ticklabels(np.arange(begin_time, end_time, split) % 24,fontsize=fontsize)
- ax.set_ylim(bottom=-10.0,top=top)
- end_y=top//100*100
- tick = end_y//10
- ax.yaxis.set_ticks(np.arange(0,end_y,tick))
- ax.yaxis.set_ticklabels(np.arange(0,end_y,tick),fontsize=fontsize)
- plt.xlabel("time of the day",fontsize=fontsize)
- plt.ylabel("queue_size",fontsize=fontsize)
- for window in yellow_windows:
- plot_window(window, "yellow")
-
- if color_panel:
- for line, color in zip(plt.gca().get_lines()[::3], color_panel):
- print(line, color)
- line.set_color(color)
- for window in red_windows:
- plot_window(window, "red")
- plt.xlim(begin, end)
- lgd = ax.legend(bbox_to_anchor=(1, 0.5), loc="upper left")
- fig.savefig(expe_file_list[0] + 'comparative_queue_viz.png', bbox_extra_artists=(lgd,), bbox_inches='tight')
- fig.savefig(expe_file_list[0] + 'comparative_queue_viz.svg', bbox_extra_artists=(lgd,), bbox_inches='tight')
- plt.show()
- if not interactive:
- plt.close(fig)
-def plot_load_and_details_windows(expe_file, red_windows=None, yellow_windows=None, begin_time=24, end_time=48,
- tick_size=2):
- if yellow_windows is None:
- yellow_windows = []
- if red_windows is None:
- red_windows = []
- begin, end = begin_time * 3600, end_time * 3600
- js = JobSetMulticore.from_csv(expe_file + "/_jobs.csv")
- # js.df = js.df[(js.df.submission_time >= begin) & (js.df.submission_time < end)]
- fig, axe = plt.subplots(nrows=2, sharex=True, figsize=(16, 8), tight_layout=True)
- fig.suptitle(expe_file, fontsize=16)
- vleg.plot_load(js.utilisation, js.MaxProcs, time_scale=False, ax=axe[0])
- vleg.plot_job_details(js.df, js.MaxProcs, time_scale=False, ax=axe[1])
-
- for ax in axe:
- ax.xaxis.set_ticks(np.arange(begin, end, tick_size * 3600))
- ax.xaxis.set_ticklabels(np.arange(begin_time, end_time, tick_size))
-
- for window in yellow_windows:
- plt.sca(axe[0])
- plot_window(window, "yellow")
- plt.sca(axe[1])
- plot_window(window, "yellow")
-
- for window in red_windows:
- plt.sca(axe[0])
- plot_window(window, "red")
- plt.sca(axe[1])
- plot_window(window, "red")
- plt.xlim(begin, end)
- fig.savefig(expe_file + '_viz.png')
- fig.savefig(expe_file + '_viz.svg')
- plt.show()
- plt.close(fig)
diff --git a/scripts/prepare_workload.ipynb b/scripts/prepare_workload.ipynb
deleted file mode 100644
index 6002aab1474e18e9765dfe10dfd369ff2412e543..0000000000000000000000000000000000000000
--- a/scripts/prepare_workload.ipynb
+++ /dev/null
@@ -1,129 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Path to workload\n",
- "MC_path = \"/home/mael/big_files/swf_logs/metacentrum.swf\"\n",
- "jun1 = 44578794\n",
- "nov30 = 60393593"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[KProcessing swf line 5730000\n",
- "-------------------\n",
- "End parsing.\n",
- "Total 730978 jobs and 486 users have been created.\n",
- "Total number of core-hours: 12978927\n",
- "4993111 valid jobs were not selected (keep_only) for 81028697 core-hour\n",
- "Jobs not selected: 87.2% in number, 86.2% in core-hour\n",
- "7119 out of 5731208 lines in the file did not match the swf format\n",
- "0 jobs were not valid\n"
- ]
- }
- ],
- "source": [
- "# Step 1 selection\n",
- "! swfFilter {MC_path} \\\n",
- " -o /tmp/METACENTRUM_6months.swf \\\n",
- " --keep_only=\"submit_time >= {jun1} and submit_time <= {nov30}\" \\\n",
- " --partitions_to_select 7 9 11 14 15 18 19"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[KProcessing swf line 730000\n",
- "-------------------\n",
- "End parsing.\n",
- "Total 693066 jobs and 474 users have been created.\n",
- "Total number of core-hours: 1833310\n",
- "37912 valid jobs were not selected (keep_only) for 11145617 core-hour\n",
- "Jobs not selected: 5.2% in number, 85.9% in core-hour\n",
- "0 out of 730978 lines in the file did not match the swf format\n",
- "0 jobs were not valid\n"
- ]
- }
- ],
- "source": [
- "# Step 2 selection\n",
- "! swfFilter /tmp/METACENTRUM_6months.swf \\\n",
- " -o /tmp/MC_selection_article.swf \\\n",
- " --keep_only=\"nb_res <= 18 and run_time <= 15*3600\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[KProcessing swf line 690000\n",
- "-------------------\n",
- "End parsing.\n",
- "Total 533681 jobs and 311 users have been created.\n",
- "Total number of core-hours: 309170\n",
- "159385 valid jobs were not selected (keep_only) for 1524140 core-hour\n",
- "Jobs not selected: 23.0% in number, 83.1% in core-hour\n",
- "0 out of 693066 lines in the file did not match the swf format\n",
- "0 jobs were not valid\n"
- ]
- }
- ],
- "source": [
- "# Nb of jobs 1 core\n",
- "! swfFilter /tmp/MC_selection_article.swf \\\n",
- " --keep_only=\"nb_res == 1\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/scripts/run_expe_test.sh b/scripts/run_expe_test.sh
deleted file mode 100755
index b5000e2fe36a882da39de2518dc7e7bcc0e198a4..0000000000000000000000000000000000000000
--- a/scripts/run_expe_test.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-# example launch of experiments of 164 days with 1 replicat with every behavior json
-python3 campaign3.py --nb-replicat 1 --expe-range 0 --window-mode 8 --nb-days 3 --threads 2 \
---json-behavior behavior_file/low_effort.json behavior_file/max_effort.json --compress-mode \
---production-file data_energy/energy_trace_sizing_solar.csv data_energy/energy_trace_sizing_solar.csv
diff --git a/scripts/stat_tools.py b/scripts/stat_tools.py
index 2d33a5c1448277b010265514be7cde59f423995a..e498b508541a51092f55e373c0b206f71f4ee61d 100644
--- a/scripts/stat_tools.py
+++ b/scripts/stat_tools.py
@@ -259,25 +259,23 @@ def compute_energy_delta_slow(produced_energy_data: pd.DataFrame, consumed_energ
energy_total_data.loc[i, :] = [time_to_fill, power_consumed, power_produced]
return energy_total_data
-def compute_energy_delta(produced_energy_data,consumed_energy_data,start_time,end_time,offset_production_day) :
- """ Compute the positive and negative difference between power produced
- and power consumed express the balance in Joules"""
- energy_dataframe = compute_energy_delta_dataframe(produced_energy_data, consumed_energy_data, start_time, end_time, offset_production_day)
+def compute_energy_delta(produced_energy_data,consumed_energy_data,start_time,end_time) :
+ """ Compute the positive and negative difference between power produced and power consumed express the balance in Joules"""
+ energy_dataframe = compute_energy_delta_dataframe(produced_energy_data,consumed_energy_data,start_time,end_time)
positive_energy_balance = energy_dataframe[energy_dataframe.energy_delta_time > 0]["energy_delta_time"].sum()
- negative_energy_balance = - energy_dataframe[energy_dataframe.energy_delta_time < 0]["energy_delta_time"].sum()
+ negative_energy_balance = energy_dataframe[energy_dataframe.energy_delta_time < 0]["energy_delta_time"].sum()
return positive_energy_balance,negative_energy_balance
def compute_energy_delta_dataframe(produced_energy_data: pd.DataFrame, consumed_energy_data: pd.DataFrame, start_time,
- end_time,offset_production_day):
+ end_time):
new_consumed_energy_data = consumed_energy_data[consumed_energy_data.time <= end_time]
new_consumed_energy_data = new_consumed_energy_data[new_consumed_energy_data.time >= start_time]
new_consumed_energy_data = new_consumed_energy_data.rename(
columns={"power": "power_consumed", "energy": "energy_consumed"})
- new_produced_energy_data = produced_energy_data[produced_energy_data.time <= end_time + offset_production_day*24*3600]
- new_produced_energy_data = new_produced_energy_data[new_produced_energy_data.time >= start_time + offset_production_day*24*3600]
+ new_produced_energy_data = produced_energy_data[produced_energy_data.time <= end_time]
+ new_produced_energy_data = new_produced_energy_data[new_produced_energy_data.time >= start_time]
new_produced_energy_data = new_produced_energy_data.rename(
columns={"power": "power_produced", "energy": "energy_produced"})
- new_produced_energy_data["time"] -= offset_production_day*24*3600
new_energy_dataframe : pd.DataFrame = pd.concat([new_consumed_energy_data, new_produced_energy_data])
first_consumed_power_index = consumed_energy_data[consumed_energy_data.time <= start_time]["time"].idxmax()
first_power_consumed = consumed_energy_data["power"][first_consumed_power_index]
@@ -391,7 +389,7 @@ def user_behavior_related_stat(data, expe_dir, sanity_check, out):
def compute_metrics(exp_num, behavior, seed, window_type, red_windows, yellow_windows, begin_time, end_time,
- sanity_check, out_dir,energy_file,offset_production_day):
+ sanity_check, out_dir):
"""Returns a dictionnary of metrics for the given expe."""
# print(behavior)
behavior_name = behavior
@@ -408,13 +406,12 @@ def compute_metrics(exp_num, behavior, seed, window_type, red_windows, yellow_wi
energy_array_yellow = energy_consumed_in_windows_fast(yellow_windows, expe_dir)
out["Duration_red (seconds)"] = time_length_windows(red_windows)
out["Duration_yellow (seconds)"] = time_length_windows(yellow_windows)
- out["Duration_total (seconds)"] = time_length_windows([[begin_time,end_time]])
out['NRJ_red (Joules)'] = np.sum(energy_array_red)
out['NRJ_yellow (Joules)'] = np.sum(energy_array_yellow)
- out["NRJ_total (Joules)"] = energy_consumed_in([begin_time, end_time], expe_dir)
out["NRJ_red_normalized_window_length (Watts)"] = out["NRJ_red (Joules)"] / out["Duration_red (seconds)"]
out["NRJ_yellow_normalized_window_length (Watts)"] = out["NRJ_yellow (Joules)"] / out["Duration_yellow (seconds)"]
- out["NRJ_total_normalized_window_length (Watts)"] = out["NRJ_total (Joules)"] / out["Duration_total (seconds)"]
+ out["NRJ_total (Joules)"] = energy_consumed_in([begin_time, end_time], expe_dir) / time_length_windows(
+ [[begin_time, end_time]])
out["mean_waiting_time (seconds)"] = data["waiting_time"].mean()
out["max_waiting_time (seconds)"] = data["waiting_time"].max()
out["mean_slowdown"] = data["stretch"].mean()
@@ -423,26 +420,9 @@ def compute_metrics(exp_num, behavior, seed, window_type, red_windows, yellow_wi
if window_type == "":
out["window_type"] = "basic"
user_behavior_related_stat(data, expe_dir, sanity_check, out)
- if not(energy_file is None ) :
- compute_energy_delta_to_out(energy_file,begin_time,end_time,sanity_check,expe_dir,offset_production_day, out)
return out
-def compute_energy_delta_to_out(energy_file, begin_time, end_time, sanity_check, expe_dir, offset_production_day, out) :
- produced_energy_data = pd.read_csv(energy_file)
- consumed_energy_data = load_consumed_energy(f"{expe_dir}/_consumed_energy.csv")
- positive_energy,negative_energy = compute_energy_delta(produced_energy_data,consumed_energy_data,begin_time,end_time,offset_production_day)
- out["energy overproduced (Joules)"] = positive_energy
- out["energy underproduced (Joules)"] = negative_energy
- out["energy balance 1 (Joules)"] = positive_energy - negative_energy
- if sanity_check :
- out["energy balance 2 (Joules)"] = produced_energy_total(produced_energy_data,begin_time + offset_production_day*3600*24,
- end_time+ offset_production_day*3600*24) - \
- energy_consumed_in([begin_time,end_time],expe_dir)
- out["energy balance relative accuracy"] = (abs(out["energy balance 1 (Joules)"] - out["energy balance 2 ("
- "Joules)"])/
- min(out["energy balance 2 (Joules)"],out["energy balance 1 ("
- "Joules)"]))
def pct_change(a, b):
"""a + pct_change(a, b) * a = b"""
return (b / a - 1) * 100
@@ -456,7 +436,7 @@ def compute_metrics_relative(control, current):
for metric in ["#jobs", 'NRJ_red (Joules)', 'NRJ_yellow (Joules)', "NRJ_total (Joules)", "max_slowdown", "max_waiting_time (seconds)", "mean_slowdown",
"mean_waiting_time (seconds)"]:
res[metric] = pct_change(control[metric], current[metric])
- for metric in ["rigid_jobs", "renonce_jobs","C_you_later_jobs","number_of_see_you_later", "degrad_jobs", "reconfig_jobs",
+ for metric in ["rigid_jobs", "renonce_jobs", "number_of_see_you_later", "degrad_jobs", "reconfig_jobs",
"consider_degrad_jobs", "consider_reconfig_jobs"]:
res[metric] = current[metric] / control["#jobs"] * 100
# for metric in ["rigid_jobs","renonced_jobs","delayed_jobs","degraded_jobs","reconfig_jobs"] :
@@ -468,9 +448,6 @@ def compute_metrics_relative(control, current):
res["true_rigid_jobs"] = pct_change(control["#jobs"], current["true_rigid_jobs"])
res["mean_corrected_sdown_1"] = pct_change(control["mean_slowdown"], current["mean_corrected_sdown"])
res["mean_corrected_wtime_1"] = pct_change(control["mean_waiting_time (seconds)"], current["mean_corrected_wtime"])
- res["energy overproduced (Joules)"] = pct_change(control["energy overproduced (Joules)"],current["energy overproduced (Joules)"])
- res["energy underproduced (Joules)"] = pct_change(control["energy underproduced (Joules)"],
- current["energy underproduced (Joules)"])
return res
diff --git a/scripts/util.py b/scripts/util.py
index 407a04a4b5f4c60ff7443eed1f0b7e26bb88a110..08b8fa17d0cd9ec58f3d65b967a0b1a7266c3576 100644
--- a/scripts/util.py
+++ b/scripts/util.py
@@ -4,8 +4,10 @@ import os.path
import subprocess
import numpy as np
import pandas
+from matplotlib import figure, pyplot as plt
from evalys.jobset import JobSet
from evalys.metrics import compute_load
+import evalys.visu.legacy as vleg
import json
from typing import List, Tuple
@@ -187,16 +189,148 @@ class JobSetMulticore(JobSet):
return self._utilisation
+def plot_load_and_details(expe_file):
+ begin, end = 24 * 3600, 48 * 3600
+ js = JobSetMulticore.from_csv(expe_file + "/_jobs.csv")
+ # js.df = js.df[(js.df.submission_time >= begin) & (js.df.submission_time < end)]
+ fig, axe = plt.subplots(nrows=2, sharex=True, figsize=(16, 8), tight_layout=True)
+ fig.suptitle(expe_file, fontsize=16)
+ vleg.plot_load(js.utilisation, js.MaxProcs, time_scale=False, ax=axe[0])
+ vleg.plot_job_details(js.df, js.MaxProcs, time_scale=False, ax=axe[1])
-def intersect_windows(window1,window2) :
- if window1[0] <= window2[0] <= window1[1] :
- return True
- if window1[0] <= window2[1] <= window1[1]:
- return True
- if window2[0] <= window1[0] <= window1[1] <= window2[1] :
- return True
- return False
+ for ax in axe:
+ ax.xaxis.set_ticks(np.arange(begin, end, 2 * 3600))
+ ax.xaxis.set_ticklabels(np.arange(24, 48, 2))
+
+ plt.xlim(begin, end)
+ fig.savefig(expe_file + '_viz.png')
+ plt.show()
+ plt.close(fig)
+
+
+def plot_energy_consumed(out_dir, red_windows, yellow_windows, label=None, tick_size=2, begin_time=0, end_time=None):
+ if label is None:
+ label = out_dir
+ data = pandas.read_csv(out_dir + "/_consumed_energy.csv")
+ for red_window in red_windows:
+ plot_window(red_window, "red")
+ for yellow_window in yellow_windows:
+ plot_window(yellow_window, "yellow")
+ ax_1 = plt.gca()
+ data.plot(x="time", y="energy", ax=ax_1, xlim=(begin_time, end_time), label=label)
+ ax_1.xaxis.set_ticks(np.arange(begin_time, end_time, tick_size * 3600))
+ ax_1.xaxis.set_ticklabels(np.arange(begin_time // 3600, end_time // 3600, tick_size))
+
+
+def plot_window_list(red_window_list, yellow_window_list, begin_exp, end_exp, tick=12):
+ plot_window([begin_exp, end_exp], "green")
+
+ for red_window in red_window_list:
+ plot_window(red_window, "red")
+ for yellow_window in yellow_window_list:
+ plot_window(yellow_window, "yellow")
+ ax = plt.gca()
+ ax.xaxis.set_ticks(np.arange(begin_exp, end_exp + 1, tick * 3600))
+ ax.xaxis.set_ticklabels(np.arange(begin_exp // 3600, end_exp // 3600 + 1, tick))
+
+
+def plot_window(window, window_type, label=False):
+ [begin, end] = window
+ label_name = None
+ if window_type == "red":
+ color_fig = "#FDA3AB"
+ if label:
+ label_name = "red_window"
+ elif window_type == "yellow":
+ color_fig = "#F9F99C"
+ if label:
+ label_name = "yellow_window"
+ elif window_type == "green":
+ color_fig = "#DDFDCC"
+ if label:
+ label_name = "green_window"
+ else:
+ print("[Warning] undefined window color ")
+ return
+ plt.axvspan(xmin=begin, xmax=end, color=color_fig, label=label_name)
+
+
+def plot_load_windows(expe_file_list, expe_name_list, color_panel=None, red_windows=None, yellow_windows=None,
+ begin_time=24, end_time=48,
+ split=12, interactive=False):
+ if yellow_windows is None:
+ yellow_windows = []
+ if red_windows is None:
+ red_windows = []
+ nb_exp = len(expe_name_list)
+ begin, end = begin_time * 3600, end_time * 3600
+ fig = plt.figure(figsize=(20, 10))
+ plot_window([begin, end], "green")
+ for expe_file, expe_name in zip(expe_file_list, expe_name_list):
+ js = JobSetMulticore.from_csv(expe_file + "/_jobs.csv")
+ # js.df = js.df[(js.df.submission_time >= begin) & (js.df.submission_time < end)]
+ fig.suptitle(expe_file, fontsize=16)
+ vleg.plot_load(js.utilisation, js.MaxProcs, legend_label=expe_name)
+ ax = plt.gca()
+
+ ax.xaxis.set_ticks(np.arange(begin, end, split * 3600))
+ ax.xaxis.set_ticklabels(np.arange(begin_time, end_time, split))
+ top = js.MaxProcs + 100.0
+ ax.set_ylim(bottom=-10.0, top=top)
+
+ for window in yellow_windows:
+ plot_window(window, "yellow")
+
+ if color_panel:
+ for line, color in zip(plt.gca().get_lines()[::3], color_panel):
+ print(line, color)
+ line.set_color(color)
+ for window in red_windows:
+ plot_window(window, "red")
+ plt.xlim(begin, end)
+ lgd = ax.legend(bbox_to_anchor=(1, 0.5), loc="upper left")
+ fig.savefig(expe_file_list[0] + 'comparative_viz.png', bbox_extra_artists=(lgd,), bbox_inches='tight')
+ fig.savefig(expe_file_list[0] + 'comparative_viz.svg', bbox_extra_artists=(lgd,), bbox_inches='tight')
+ plt.show()
+ if not interactive:
+ plt.close(fig)
+
+
+def plot_load_and_details_windows(expe_file, red_windows=None, yellow_windows=None, begin_time=24, end_time=48,
+ tick_size=2):
+ if yellow_windows is None:
+ yellow_windows = []
+ if red_windows is None:
+ red_windows = []
+ begin, end = begin_time * 3600, end_time * 3600
+ js = JobSetMulticore.from_csv(expe_file + "/_jobs.csv")
+ # js.df = js.df[(js.df.submission_time >= begin) & (js.df.submission_time < end)]
+ fig, axe = plt.subplots(nrows=2, sharex=True, figsize=(16, 8), tight_layout=True)
+ fig.suptitle(expe_file, fontsize=16)
+ vleg.plot_load(js.utilisation, js.MaxProcs, time_scale=False, ax=axe[0])
+ vleg.plot_job_details(js.df, js.MaxProcs, time_scale=False, ax=axe[1])
+
+ for ax in axe:
+ ax.xaxis.set_ticks(np.arange(begin, end, tick_size * 3600))
+ ax.xaxis.set_ticklabels(np.arange(begin_time, end_time, tick_size))
+
+ for window in yellow_windows:
+ plt.sca(axe[0])
+ plot_window(window, "yellow")
+ plt.sca(axe[1])
+ plot_window(window, "yellow")
+
+ for window in red_windows:
+ plt.sca(axe[0])
+ plot_window(window, "red")
+ plt.sca(axe[1])
+ plot_window(window, "red")
+ plt.xlim(begin, end)
+ fig.savefig(expe_file + '_viz.png')
+ fig.savefig(expe_file + '_viz.svg')
+ plt.show()
+ plt.close(fig)
def energy_consumed_in(window, OUT_DIR):