From cda74166c3adf992c9943625c6b7fd16731f5c2b Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Tue, 20 Feb 2024 20:19:53 +0000 Subject: [PATCH 01/52] added .gitignore and .github/workflows --- .github/workflows/run-tests.yml | 44 ++++++++++ .gitignore | 142 ++++++++++++++++++++++++++++++++ 2 files changed, 186 insertions(+) create mode 100644 .github/workflows/run-tests.yml create mode 100644 .gitignore diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml new file mode 100644 index 0000000..1b680dc --- /dev/null +++ b/.github/workflows/run-tests.yml @@ -0,0 +1,44 @@ +name: Pytest unit/integration + +on: + pull_request: + push: + branches: + - main + +# Use bash by default in all jobs +defaults: + run: + shell: bash + +jobs: + build-test: + name: Test Run (${{ matrix.python-version }}, ${{ matrix.os }}) + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: ["ubuntu-latest"] +# os: ["ubuntu-latest", "macos-latest", "windows-latest"] + python-version: ["3.10", "3.11"] + + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install nox + - name: List installed packages + run: pip list + - name: Run tests with pytest & nox + run: | + nox -s test-${{ matrix.python-version }} +# Codecov is only free for open source / public repos. Not really needed anyway +# - name: Upload coverage to Codecov +# if: ${{ matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'}} +# uses: codecov/codecov-action@v3 + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7bd967b --- /dev/null +++ b/.gitignore @@ -0,0 +1,142 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# R +.Rproj.user +.Rhistory +.RData +.Ruserdata + +# cache +venv/ +*.gz +*.zip + +manuscript/*.pdf + + From cc924e038670a997f5438fe90d930f64af628887 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Tue, 20 Feb 2024 23:12:59 +0000 Subject: [PATCH 02/52] hyperpars --- ...{ppo-caribou-v0-1.yml => ppo-greencrab-v0-1.yml} | 3 ++- ...{tqc-caribou-v0-1.yml => tqc-greencrab-v0-1.yml} | 3 ++- src/rl4greencrab/util.py | 13 +++++++++++++ 3 files changed, 17 insertions(+), 2 deletions(-) rename hyperpars/{ppo-caribou-v0-1.yml => ppo-greencrab-v0-1.yml} (73%) rename hyperpars/{tqc-caribou-v0-1.yml => tqc-greencrab-v0-1.yml} (73%) diff --git a/hyperpars/ppo-caribou-v0-1.yml b/hyperpars/ppo-greencrab-v0-1.yml similarity index 73% rename from hyperpars/ppo-caribou-v0-1.yml rename to hyperpars/ppo-greencrab-v0-1.yml index 0f13a68..f897f51 100644 --- a/hyperpars/ppo-caribou-v0-1.yml +++ b/hyperpars/ppo-greencrab-v0-1.yml @@ -1,7 +1,7 @@ # stable-baselines3 configuration algo: "PPO" -env_id: "Caribou-v0" +env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" total_timesteps: 6000000 @@ -9,3 +9,4 @@ config: {} use_sde: True id: "1" repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/hyperpars/tqc-caribou-v0-1.yml b/hyperpars/tqc-greencrab-v0-1.yml similarity index 73% rename from hyperpars/tqc-caribou-v0-1.yml rename to hyperpars/tqc-greencrab-v0-1.yml index dba7db2..f57f01b 100644 --- a/hyperpars/tqc-caribou-v0-1.yml +++ b/hyperpars/tqc-greencrab-v0-1.yml @@ -1,7 +1,7 @@ # stable-baselines3 configuration algo: "TQC" -env_id: "Caribou-v0" +env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" total_timesteps: 6000000 @@ -9,3 +9,4 @@ config: {} use_sde: True id: "1" repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/src/rl4greencrab/util.py b/src/rl4greencrab/util.py index 087c112..8687b75 100644 --- a/src/rl4greencrab/util.py +++ b/src/rl4greencrab/util.py @@ -1,4 +1,5 @@ import yaml +import os from stable_baselines3.common.env_util import make_vec_env from stable_baselines3 import PPO, A2C, DQN, SAC, TD3 from sb3_contrib import TQC, ARS @@ -31,6 +32,7 @@ def sb3_train(config_file): ) ALGO = algorithm(options["algo"]) model_id = options["algo"] + "-" + options["env_id"] + "-" + options["id"] + save_id = os.path.join(options["save_path"], model_id) model = ALGO( "MlpPolicy", @@ -40,6 +42,17 @@ def sb3_train(config_file): use_sde=options["use_sde"], ) + model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id) + + os.mkdirs(options["save_path"], exist_ok=True) + model.save(save_id) + try: + full_path = save_id + ".zip" + deploy_model(full_path, "sb3/"+path, repo=options["repo"]) + deploy_model(config_file, "sb3/"+config_file, repo=options["repo"]) + except: + print("Could not deploy model to hugging face :(.") + def sb3_train_v2(options = dict): vec_env = make_vec_env( options["env_id"], options["n_envs"], env_kwargs={"config": options["config"]} From 3f27ead5a37ea50267c85f9e9645ec5c4009d94f Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Tue, 20 Feb 2024 23:35:13 +0000 Subject: [PATCH 03/52] erased broken time-series model --- src/rl4greencrab/ts_model.py | 73 ------------------------------------ 1 file changed, 73 deletions(-) diff --git a/src/rl4greencrab/ts_model.py b/src/rl4greencrab/ts_model.py index eba2b43..5bb4441 100644 --- a/src/rl4greencrab/ts_model.py +++ b/src/rl4greencrab/ts_model.py @@ -79,80 +79,7 @@ def state_to_pop(self, state): return self.base_env.state_to_pop(state) -class ts_env_v2(gym.Env): - """ - UNDER CONSTRUCTION - BROKEN - - takes an environment env and produces an new environemtn ts_env - whose observations are timeseries of the env environment. - - v2: the timeseries includes past observations AND past actions - """ - def __init__(self, config = {}): - self.N_mem = config.get('N_mem', 5) - if 'base_env' not in config: - raise Warning( - "ts_env initializer needs to have a base environment " - "out of whose dynamics the time-series will be built! " - "Try: ts_env(config = {'base_env': <>, ...})." - ) - self.base_env = config['base_env'] - self.action_space = spaces.Box( - np.float32([-1]), - np.float32([+1]), - ) - self.observation_space = spaces.Box( - np.float32([ [-1] * self.N_mem ] * 2), - np.float32([ [+1] * self.N_mem ] * 2), - ) - # - # [[obs t, obs t-1, obs t-2, ..., obs t - (N_mem-1)] - # [act t, act t-1, act t-2, ..., act t - (N_mem-1)]] - # - # here 'obs t' is the observation at the end of last timestep - # and 'act t' is the action taken during the last timestep - # - _ = self.reset() - - def reset(self, *, seed=42, options=None): - init_state, init_info = self.base_env.reset(seed=seed, options=options) - empty_heap = np.float32([-1] * self.N_heap) - obs_heap = np.insert( - empty_heap[0:-1], - 0, - init_state, - ) - act_heap = empty_heap.copy() - self.heap = np.array( - [obs_heap, act_heap] - ) - return self.heap, init_info - - def step(self, action): - new_state, reward, terminated, truncated, info = self.base_env.step(action) - - # update the heap timeseries - [obs_heap, act_heap] = self.heap - obs_heap = np.insert( - obs_heap[0:-1], - 0, - new_state, - ) - act_heap = np.insert( - act_heap[0:-1], - 0, - action, - ) - self.heap = np.float32([obs_heap, act_heap]) - - return self.heap, reward, terminated, truncated, info - - def pop_to_state(self, pop): - return self.base_env.pop_to_state(pop) - - def state_to_pop(self, state): - return self.base_env.state_to_pop(state) From 9100c3cc792d45a3945cb788caed50f020562fb7 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Wed, 21 Feb 2024 01:15:41 +0000 Subject: [PATCH 04/52] training metaenvs (such as time-series envs) from yaml files --- hyperpars/ppo-greencrab-ts-v2-1.yml | 14 ++++++ hyperpars/ppo-greencrab-v0-1.yml | 2 +- hyperpars/tqc-greencrab-ts-v2-1.yml | 14 ++++++ hyperpars/tqc-greencrab-v0-1.yml | 4 +- scripts/train.py | 2 +- scripts/train_metaenv.py | 10 +++++ src/rl4greencrab/__init__.py | 7 +-- src/rl4greencrab/ts_model.py | 66 +++++++++++++++++++++++++++++ src/rl4greencrab/util.py | 44 ++++++++++++++++++- 9 files changed, 154 insertions(+), 9 deletions(-) create mode 100644 hyperpars/ppo-greencrab-ts-v2-1.yml create mode 100644 hyperpars/tqc-greencrab-ts-v2-1.yml create mode 100644 scripts/train_metaenv.py diff --git a/hyperpars/ppo-greencrab-ts-v2-1.yml b/hyperpars/ppo-greencrab-ts-v2-1.yml new file mode 100644 index 0000000..936f3b0 --- /dev/null +++ b/hyperpars/ppo-greencrab-ts-v2-1.yml @@ -0,0 +1,14 @@ +# stable-baselines3 configuration + +algo: "PPO" +meta_env_id: "TimeSeries-v2" +meta_env_kwargs: {N_mem: 3} +base_env_id: "GreenCrab-v2" +base_env_cfg: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 6000000 +use_sde: True +id: "N_mem_3" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/hyperpars/ppo-greencrab-v0-1.yml b/hyperpars/ppo-greencrab-v0-1.yml index f897f51..00b1201 100644 --- a/hyperpars/ppo-greencrab-v0-1.yml +++ b/hyperpars/ppo-greencrab-v0-1.yml @@ -5,7 +5,7 @@ env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" total_timesteps: 6000000 -config: {} +config: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} use_sde: True id: "1" repo: "cboettig/rl-ecology" diff --git a/hyperpars/tqc-greencrab-ts-v2-1.yml b/hyperpars/tqc-greencrab-ts-v2-1.yml new file mode 100644 index 0000000..a865b68 --- /dev/null +++ b/hyperpars/tqc-greencrab-ts-v2-1.yml @@ -0,0 +1,14 @@ +# stable-baselines3 configuration + +algo: "TQC" +meta_env_id: "TimeSeries-v2" +meta_env_kwargs: {N_mem: 3} +base_env_id: "GreenCrab-v2" +base_env_cfg: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 6000000 +use_sde: True +id: "N_mem_3" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/hyperpars/tqc-greencrab-v0-1.yml b/hyperpars/tqc-greencrab-v0-1.yml index f57f01b..a32fd37 100644 --- a/hyperpars/tqc-greencrab-v0-1.yml +++ b/hyperpars/tqc-greencrab-v0-1.yml @@ -4,8 +4,8 @@ algo: "TQC" env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" -total_timesteps: 6000000 -config: {} +total_timesteps: 1000000 +config: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} use_sde: True id: "1" repo: "cboettig/rl-ecology" diff --git a/scripts/train.py b/scripts/train.py index 45cecc2..3270a65 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -5,6 +5,6 @@ args = parser.parse_args() import rl4greencrab -from rl4greencrab.utils import sb3_train +from rl4greencrab import sb3_train sb3_train(args.file) diff --git a/scripts/train_metaenv.py b/scripts/train_metaenv.py new file mode 100644 index 0000000..d062f38 --- /dev/null +++ b/scripts/train_metaenv.py @@ -0,0 +1,10 @@ +#!/opt/venv/bin/python +import argparse +parser = argparse.ArgumentParser() +parser.add_argument("-f", "--file", help="Path config file", type=str) +args = parser.parse_args() + +import rl4greencrab +from rl4greencrab import sb3_train_metaenv + +sb3_train_metaenv(args.file) diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py index 33011d3..a755c03 100644 --- a/src/rl4greencrab/__init__.py +++ b/src/rl4greencrab/__init__.py @@ -1,8 +1,9 @@ from rl4greencrab.invasive_ipm import invasive_IPM, invasive_IPM_v2 -from rl4greencrab.ts_model import ts_env_v1 -from rl4greencrab.util import sb3_train, sb3_train_v2 +from rl4greencrab.ts_model import ts_env_v1, ts_env_v2 +from rl4greencrab.util import sb3_train, sb3_train_v2, sb3_train_metaenv from gymnasium.envs.registration import register register(id="GreenCrab-v1", entry_point="rl4greencrab.invasive_ipm:invasive_IPM") register(id="GreenCrab-v2", entry_point="rl4greencrab.invasive_ipm:invasive_IPM_v2") -register(id="TimeSeries-v1", entry_point="rl4greencrab.ts_model:ts_env_v1") \ No newline at end of file +register(id="TimeSeries-v1", entry_point="rl4greencrab.ts_model:ts_env_v1") +register(id="TimeSeries-v2", entry_point="rl4greencrab.ts_model:ts_env_v2") \ No newline at end of file diff --git a/src/rl4greencrab/ts_model.py b/src/rl4greencrab/ts_model.py index 5bb4441..53ffd5f 100644 --- a/src/rl4greencrab/ts_model.py +++ b/src/rl4greencrab/ts_model.py @@ -80,6 +80,72 @@ def state_to_pop(self, state): +class ts_env_v2(gym.Env): + """ + takes an environment env and produces an new environemtn ts_env + whose observations are timeseries of the env environment. + + v2: same as v1, but base_env is input rather than base_env_cls + base_env_cfg. + """ + def __init__(self, config = {}): + self.N_mem = config.get('N_mem', 5) + if 'base_env' not in config: + raise Warning( + "ts_env initializer needs to have a base environment " + "out of whose dynamics the time-series will be built! " + "Try: ts_env(config = {'base_env': <>, ...}). \n\n" + "(Here, <> should be a class, not an instance!)" + ) + self.base_env = config['base_env'] + + self.action_space = self.base_env.action_space + self.observation_space = spaces.Box( + np.array( + [ + - np.ones(shape=self.base_env.observation_space.shape, dtype=np.float32) + for _ in range(self.N_mem) + ] + ), + np.array( + [ + + np.ones(shape=self.base_env.observation_space.shape, dtype=np.float32) + for _ in range(self.N_mem) + ] + ), + ) + # + # [[state t], [state t-1], [state t-2], ..., [state t - (N_mem-1)]] + # where each [state i] is a vector + # + _ = self.reset() + + def reset(self, *, seed=42, options=None): + init_state, init_info = self.base_env.reset(seed=seed, options=options) + empty_heap = - np.ones(shape = self.observation_space.shape, dtype=np.float32) + self.heap = np.insert( + empty_heap[0:-1], + 0, + init_state, + axis=0, + ) + return self.heap, init_info + + def step(self, action): + new_state, reward, terminated, truncated, info = self.base_env.step(action) + self.heap = np.insert( + self.heap[0:-1], + 0, + new_state, + axis=0, + ) + return self.heap, reward, terminated, truncated, info + + def pop_to_state(self, pop): + return self.base_env.pop_to_state(pop) + + def state_to_pop(self, state): + return self.base_env.state_to_pop(state) + diff --git a/src/rl4greencrab/util.py b/src/rl4greencrab/util.py index 8687b75..5eef6dc 100644 --- a/src/rl4greencrab/util.py +++ b/src/rl4greencrab/util.py @@ -1,5 +1,7 @@ import yaml import os + +import gymnasium as gym from stable_baselines3.common.env_util import make_vec_env from stable_baselines3 import PPO, A2C, DQN, SAC, TD3 from sb3_contrib import TQC, ARS @@ -42,9 +44,9 @@ def sb3_train(config_file): use_sde=options["use_sde"], ) - model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id) + model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=True) - os.mkdirs(options["save_path"], exist_ok=True) + os.makedirs(options["save_path"], exist_ok=True) model.save(save_id) try: full_path = save_id + ".zip" @@ -72,4 +74,42 @@ def sb3_train_v2(options = dict): model.save(model_id) path = model_id + ".zip" # deploy_model(path, "sb3/"+path, repo=options["repo"]) + # deploy_model(config_file, "sb3/"+config_file, repo=options["repo"]) + +def sb3_train_metaenv(config_file): + with open(config_file, "r") as stream: + options = yaml.safe_load(stream) + + vec_env = make_vec_env( + options["meta_env_id"], + options["n_envs"], + env_kwargs={ + "config": + { + "base_env": gym.make(options["base_env_id"], config=options["base_env_cfg"]), + **options["meta_env_kwargs"], + } + } + ) + ALGO = algorithm(options["algo"]) + model_id = "{}-Meta_{}-Base_{}-{}".format( + options["algo"], + options["meta_env_id"], + options["base_env_id"], + options["id"], + ) + save_id = os.path.join(options["save_path"], model_id) + + model = ALGO( + "MlpPolicy", + vec_env, + verbose=0, + tensorboard_log=options["tensorboard"], + use_sde=options["use_sde"], + ) + model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=True) + + model.save(save_id) + # path = model_id + ".zip" + # deploy_model(path, "sb3/"+path, repo=options["repo"]) # deploy_model(config_file, "sb3/"+config_file, repo=options["repo"]) \ No newline at end of file From 3845b5cba45e81dfe8e4eafd2d8e0ee034f4c06b Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Wed, 21 Feb 2024 02:05:34 +0000 Subject: [PATCH 05/52] testing N_mem=2 in yaml files --- hyperpars/ppo-greencrab-ts-v2-1.yml | 4 ++-- hyperpars/tqc-greencrab-ts-v2-1.yml | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/hyperpars/ppo-greencrab-ts-v2-1.yml b/hyperpars/ppo-greencrab-ts-v2-1.yml index 936f3b0..7870869 100644 --- a/hyperpars/ppo-greencrab-ts-v2-1.yml +++ b/hyperpars/ppo-greencrab-ts-v2-1.yml @@ -2,13 +2,13 @@ algo: "PPO" meta_env_id: "TimeSeries-v2" -meta_env_kwargs: {N_mem: 3} +meta_env_kwargs: {N_mem: 2} base_env_id: "GreenCrab-v2" base_env_cfg: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} n_envs: 12 tensorboard: "/home/rstudio/logs" total_timesteps: 6000000 use_sde: True -id: "N_mem_3" +id: "N_mem_2" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" diff --git a/hyperpars/tqc-greencrab-ts-v2-1.yml b/hyperpars/tqc-greencrab-ts-v2-1.yml index a865b68..0c1ec67 100644 --- a/hyperpars/tqc-greencrab-ts-v2-1.yml +++ b/hyperpars/tqc-greencrab-ts-v2-1.yml @@ -2,13 +2,13 @@ algo: "TQC" meta_env_id: "TimeSeries-v2" -meta_env_kwargs: {N_mem: 3} +meta_env_kwargs: {N_mem: 2} base_env_id: "GreenCrab-v2" base_env_cfg: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} n_envs: 12 tensorboard: "/home/rstudio/logs" total_timesteps: 6000000 use_sde: True -id: "N_mem_3" +id: "N_mem_2" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" From 967e1206fa192c999c02454b00b5c41b318aa4ef Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Wed, 21 Feb 2024 02:39:53 +0000 Subject: [PATCH 06/52] added systematic exploration of training script --- .../TQC-Nmem_1/1-tqc_nmem-1_bmk.yml | 12 ++++++++++++ .../TQC-Nmem_1/2-tqc_nmem-1_bmk.yml | 12 ++++++++++++ .../TQC-Nmem_1/3-tqc_nmem-1_bmk.yml | 12 ++++++++++++ .../TQC-Nmem_1/4-tqc_nmem-1_bmk.yml | 12 ++++++++++++ .../TQC-Nmem_1/5-tqc_nmem-1_bmk.yml | 12 ++++++++++++ scripts/train_benchmarks.sh | 11 +++++++++++ 6 files changed, 71 insertions(+) create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml create mode 100644 scripts/train_benchmarks.sh diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..7872f4a --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml @@ -0,0 +1,12 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 6000000 +config: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-1" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..727515a --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml @@ -0,0 +1,12 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 6000000 +config: {r: 0.5, imm: 1000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-2" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..555fae4 --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml @@ -0,0 +1,12 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 6000000 +config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-3" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..e03c924 --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml @@ -0,0 +1,12 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 6000000 +config: {r: 0.5, imm: 250, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-4" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..feab44a --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml @@ -0,0 +1,12 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 6000000 +config: {r: 0.5, imm: 125, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-5" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" diff --git a/scripts/train_benchmarks.sh b/scripts/train_benchmarks.sh new file mode 100644 index 0000000..b3653da --- /dev/null +++ b/scripts/train_benchmarks.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +# move to script directory for normalized relative paths. +scriptdir="$(dirname "$0")" +cd "$scriptdir" + +python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml & +python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml & +python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml & +python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml & +python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml & \ No newline at end of file From 8a0d04fb3cd225377c1cad7ec1f50caac77507e9 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora <34276401+felimomo@users.noreply.github.com> Date: Tue, 20 Feb 2024 19:44:11 -0800 Subject: [PATCH 07/52] Update README.md --- README.md | 38 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 36 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 75b1f0f..3841e15 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,37 @@ -European green crabs are prolific predators known to cause considerable perturbations to the ecosystems they colonize. This has had a damaging economic effect, particularly for shellfisheries in the western american coast. +# Deep Reinforcement Learning for Invasive Green Crab Management -This project approaches this management problem from the point of view of Reinforcement Learning (RL). RL allows us to optimize management responses in highly complex quantitative models of the system. \ No newline at end of file +European green crabs are prolific generalist predators known to cause considerable perturbations to the ecosystems they colonize. +This has had a damaging economic effect, particularly for shellfisheries in the western american coast. +To counter this, there is a growing investment in catching these crabs and removing them from estuaries and bays they are in the process of colonizing. +A question that naturally appears here is *how to best allocate limited resources in a dynamically evolving colonizing event?* + +Here we approach this question from the point of view of deep reinforcement learning (RL). + +## RL in environmental management + +RL is a broad class of machine learning algorithms that are aimed at solving *adaptive control/management problems*: problems in which an agent takes action on an environment based on its observations of the enviornment. +These algorithms have been used for, e.g., teaching computers how to [become prolific](https://www.science.org/doi/full/10.1126/science.aar6404?casa_token=Gq_WicEszrcAAAAA%3Ax2KMhvk4p7mdPuAgnA2MBW6xpzH63x6jWsSDJs9oGZtJ5geNZn_1BCHQ4Amk0ErXfEqqcjPss9FGpw) at board games like Chess and Go. +Here, the environment would be the position of the Chess pieces in the chess board, which the agent observes and uses that observation to decide its next move. +Other classic uses of RL are for playing [atari games](https://arxiv.org/abs/1312.5602) and for solving [physics-based](https://journals.aps.org/prx/abstract/10.1103/PhysRevX.8.031086) optimal control problems. + +This project is part of a wider research program in which we take a step to extend RL out of its usual ‘‘comfort zone:’’ using it for adaptive management problems that arise in environmental science. +Our focus on this project is to leverage the tools developed in RL to the problem of resource allocation for invasive green crab management. + +## Why try RL out here? + +Our project starts with the question: *How to best use the data we collect on green crab populations in order to make policy decisions?* +Traditional methods used in adaptive management problems like this one, e.g. optimal control theory and Bayesian decision theory, have a hard time using high dimensional observations in their decision processes. +However, the data collected in our problem is naturally high dimensional: each month a *catch per unit effort* is recorded, and several such observations are needed in order to have enough information to make an informed opinion---for example, a sequence observations are needed in order to distinguish whether a green crab population is has a firm foothold within a bay or estuary. + +So that is our setting: how do we efficiently use these high dimensional observations? + +Here RL offers an edge over traditional methods: RL is naturally suited for problems with high dimensional observations. +(Think of chess, for instance, where an observation has 32 components---the location of each individual piece on the board.) + +# Installation + +1. `git clone https://github.com/boettiger-lab/rl4greencrab.git` +2. `cd rl4greencrab` +3. `pip install .` + +(Coming soon: publishing our tools on PyPI in order to provide an easier installation!) From 1ccd0a4bf74197283710099eac61f06d1ec4250a Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora <34276401+felimomo@users.noreply.github.com> Date: Tue, 20 Feb 2024 19:49:39 -0800 Subject: [PATCH 08/52] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 3841e15..2401d6d 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,9 @@ However, the data collected in our problem is naturally high dimensional: each m So that is our setting: how do we efficiently use these high dimensional observations? Here RL offers an edge over traditional methods: RL is naturally suited for problems with high dimensional observations. -(Think of chess, for instance, where an observation has 32 components---the location of each individual piece on the board.) +(Think of chess, for instance, where an observation has 32 components---the location of each individual piece on the board.) + +Our project aims to use this advantage in order to generate new, more responsive, quantitative policy rules that could complement traditional management approaches. # Installation From 4574b1d5a734f7c06bf2f6d887b9fa70e44c87b4 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Wed, 21 Feb 2024 18:03:13 +0000 Subject: [PATCH 09/52] relative paths --- scripts/train_benchmarks.sh | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/scripts/train_benchmarks.sh b/scripts/train_benchmarks.sh index b3653da..26884bf 100644 --- a/scripts/train_benchmarks.sh +++ b/scripts/train_benchmarks.sh @@ -4,8 +4,8 @@ scriptdir="$(dirname "$0")" cd "$scriptdir" -python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml & -python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml & -python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml & -python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml & -python scripts/train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml & \ No newline at end of file +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml & \ No newline at end of file From 85db29877edfc7dcdbbf2adcfbe3b848f8f83e21 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Wed, 21 Feb 2024 19:13:55 +0000 Subject: [PATCH 10/52] benchmarking stochasticity. progress bar optional --- .../TQC-Nmem_1/1-tqc_nmem-1_bmk.yml | 5 +++-- .../TQC-Nmem_1/2-tqc_nmem-1_bmk.yml | 5 +++-- .../TQC-Nmem_1/3-tqc_nmem-1_bmk.yml | 5 +++-- .../TQC-Nmem_1/4-tqc_nmem-1_bmk.yml | 5 +++-- .../TQC-Nmem_1/5-tqc_nmem-1_bmk.yml | 5 +++-- src/rl4greencrab/util.py | 9 ++++++--- 6 files changed, 21 insertions(+), 13 deletions(-) diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml index 7872f4a..965e591 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml @@ -4,9 +4,10 @@ algo: "TQC" env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" -total_timesteps: 6000000 -config: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +total_timesteps: 1000000 +config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.05} use_sde: True id: "bmk-1" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml index 727515a..2e48455 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml @@ -4,9 +4,10 @@ algo: "TQC" env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" -total_timesteps: 6000000 -config: {r: 0.5, imm: 1000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +total_timesteps: 1000000 +config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} use_sde: True id: "bmk-2" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml index 555fae4..9caa66a 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml @@ -4,9 +4,10 @@ algo: "TQC" env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" -total_timesteps: 6000000 -config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +total_timesteps: 1000000 +config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.15} use_sde: True id: "bmk-3" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml index e03c924..c8fef01 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml @@ -4,9 +4,10 @@ algo: "TQC" env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" -total_timesteps: 6000000 -config: {r: 0.5, imm: 250, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +total_timesteps: 1000000 +config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.2} use_sde: True id: "bmk-4" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml index feab44a..5bc846d 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml @@ -4,9 +4,10 @@ algo: "TQC" env_id: "GreenCrab-v2" n_envs: 12 tensorboard: "/home/rstudio/logs" -total_timesteps: 6000000 -config: {r: 0.5, imm: 125, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +total_timesteps: 1000000 +config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.25} use_sde: True id: "bmk-5" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/src/rl4greencrab/util.py b/src/rl4greencrab/util.py index 5eef6dc..4c0f76c 100644 --- a/src/rl4greencrab/util.py +++ b/src/rl4greencrab/util.py @@ -44,7 +44,8 @@ def sb3_train(config_file): use_sde=options["use_sde"], ) - model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=True) + progress_bar = options.get("progress_bar", False) + model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=progress_bar) os.makedirs(options["save_path"], exist_ok=True) model.save(save_id) @@ -69,7 +70,8 @@ def sb3_train_v2(options = dict): tensorboard_log=options["tensorboard"], use_sde=options["use_sde"], ) - model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=True) + progress_bar = options.get("progress_bar", False) + model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=progress_bar) model.save(model_id) path = model_id + ".zip" @@ -107,7 +109,8 @@ def sb3_train_metaenv(config_file): tensorboard_log=options["tensorboard"], use_sde=options["use_sde"], ) - model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=True) + progress_bar = options.get("progress_bar", False) + model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=progress_bar) model.save(save_id) # path = model_id + ".zip" From 7e52e68e2550ecc604234639fc680f3673ad117a Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Wed, 21 Feb 2024 20:05:01 +0000 Subject: [PATCH 11/52] more benchmarks --- .../TQC-Nmem_1/6-tqc_nmem-1_bmk.yml | 13 +++++++++++++ .../TQC-Nmem_1/7-tqc_nmem-1_bmk.yml | 13 +++++++++++++ .../TQC-Nmem_1/8-tqc_nmem-1_bmk.yml | 13 +++++++++++++ .../TQC-Nmem_1/9-tqc_nmem-1_bmk.yml | 13 +++++++++++++ scripts/train_benchmarks.sh | 9 ++++----- 5 files changed, 56 insertions(+), 5 deletions(-) create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml create mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..860b05f --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml @@ -0,0 +1,13 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 1000000 +config: {r: 0.7, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-1" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..9f1fdce --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml @@ -0,0 +1,13 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 1000000 +config: {r: 0.9, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-1" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..32bc5ca --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml @@ -0,0 +1,13 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 1000000 +config: {r: 1.1, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-1" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml new file mode 100644 index 0000000..f0d28f0 --- /dev/null +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml @@ -0,0 +1,13 @@ +# stable-baselines3 configuration + +algo: "TQC" +env_id: "GreenCrab-v2" +n_envs: 12 +tensorboard: "/home/rstudio/logs" +total_timesteps: 1000000 +config: {r: 1.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} +use_sde: True +id: "bmk-1" +repo: "cboettig/rl-ecology" +save_path: "/home/rstudio/saved_agents" +progress_bar: False diff --git a/scripts/train_benchmarks.sh b/scripts/train_benchmarks.sh index 26884bf..9e1634f 100644 --- a/scripts/train_benchmarks.sh +++ b/scripts/train_benchmarks.sh @@ -4,8 +4,7 @@ scriptdir="$(dirname "$0")" cd "$scriptdir" -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml & -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml & -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml & -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml & -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml & \ No newline at end of file +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml & From 203cffddc4c69b4dd4dac506941be9f936334fee Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Wed, 21 Feb 2024 20:07:38 +0000 Subject: [PATCH 12/52] yaml --- hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml | 2 +- hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml | 2 +- hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml | 2 +- hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml index 860b05f..3fecc5f 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml @@ -7,7 +7,7 @@ tensorboard: "/home/rstudio/logs" total_timesteps: 1000000 config: {r: 0.7, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} use_sde: True -id: "bmk-1" +id: "bmk-6" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml index 9f1fdce..f984d5e 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml @@ -7,7 +7,7 @@ tensorboard: "/home/rstudio/logs" total_timesteps: 1000000 config: {r: 0.9, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} use_sde: True -id: "bmk-1" +id: "bmk-7" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml index 32bc5ca..55cb983 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml @@ -7,7 +7,7 @@ tensorboard: "/home/rstudio/logs" total_timesteps: 1000000 config: {r: 1.1, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} use_sde: True -id: "bmk-1" +id: "bmk-8" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" progress_bar: False diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml index f0d28f0..47a84bb 100644 --- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml +++ b/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml @@ -7,7 +7,7 @@ tensorboard: "/home/rstudio/logs" total_timesteps: 1000000 config: {r: 1.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1} use_sde: True -id: "bmk-1" +id: "bmk-9" repo: "cboettig/rl-ecology" save_path: "/home/rstudio/saved_agents" progress_bar: False From 7b21c8ff78ef1b5bb5f43ca6fc69e5f9504e06d9 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Thu, 22 Feb 2024 23:33:26 +0000 Subject: [PATCH 13/52] benchmarks script --- scripts/train_benchmarks.sh | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/scripts/train_benchmarks.sh b/scripts/train_benchmarks.sh index 9e1634f..9f43b24 100644 --- a/scripts/train_benchmarks.sh +++ b/scripts/train_benchmarks.sh @@ -4,7 +4,8 @@ scriptdir="$(dirname "$0")" cd "$scriptdir" -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml & -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml & -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml & -python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/10-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/11-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/12-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/13-tqc_nmem-1_bmk.yml & +python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/14-tqc_nmem-1_bmk.yml & \ No newline at end of file From 2f5b1d9c0ee22c470b2fc4cc73e25210812691f3 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Fri, 23 Feb 2024 00:02:36 +0000 Subject: [PATCH 14/52] naming, defaulting --- src/rl4greencrab/__init__.py | 20 ++- .../{invasive_ipm.py => green_crab_ipm.py} | 6 +- src/rl4greencrab/time_series.py | 60 +++++++ src/rl4greencrab/ts_model.py | 151 ------------------ 4 files changed, 77 insertions(+), 160 deletions(-) rename src/rl4greencrab/{invasive_ipm.py => green_crab_ipm.py} (98%) create mode 100644 src/rl4greencrab/time_series.py delete mode 100644 src/rl4greencrab/ts_model.py diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py index a755c03..9dc4851 100644 --- a/src/rl4greencrab/__init__.py +++ b/src/rl4greencrab/__init__.py @@ -1,9 +1,17 @@ -from rl4greencrab.invasive_ipm import invasive_IPM, invasive_IPM_v2 -from rl4greencrab.ts_model import ts_env_v1, ts_env_v2 +from rl4greencrab.green_crab_ipm import greenCrabEnv, greenCrabSimplifiedEnv +from rl4greencrab.time_series import timeSeriesEnv from rl4greencrab.util import sb3_train, sb3_train_v2, sb3_train_metaenv from gymnasium.envs.registration import register -register(id="GreenCrab-v1", entry_point="rl4greencrab.invasive_ipm:invasive_IPM") -register(id="GreenCrab-v2", entry_point="rl4greencrab.invasive_ipm:invasive_IPM_v2") -register(id="TimeSeries-v1", entry_point="rl4greencrab.ts_model:ts_env_v1") -register(id="TimeSeries-v2", entry_point="rl4greencrab.ts_model:ts_env_v2") \ No newline at end of file +register( + id="GreenCrab", + entry_point="rl4greencrab.green_crab_ipm:greenCrabEnv", +) +register( + id="GreenCrabSimpl", + entry_point="rl4greencrab.green_crab_ipm:greenCrabSimplifiedEnv" +) +register( + id="TimeSeries", + entry_point="rl4greencrab.time_series:TimeSeriesEnv", +) diff --git a/src/rl4greencrab/invasive_ipm.py b/src/rl4greencrab/green_crab_ipm.py similarity index 98% rename from src/rl4greencrab/invasive_ipm.py rename to src/rl4greencrab/green_crab_ipm.py index c85d649..40bd902 100644 --- a/src/rl4greencrab/invasive_ipm.py +++ b/src/rl4greencrab/green_crab_ipm.py @@ -12,7 +12,7 @@ with ts_model.py """ -class invasive_IPM(gym.Env): +class greenCrabEnv(gym.Env): metadata = {"render.modes": ["human"]} def __init__( @@ -257,8 +257,8 @@ def reward_func(self,action): return reward -class invasive_IPM_v2(invasive_IPM): - """ like invasive_IPM but with simplified observations. """ +class greenCrabSimplifiedEnv(greenCrabEnv): + """ like invasive_IPM but with simplified observations and normalized to -1, 1 space. """ def __init__(self, config={}): super().__init__(config=config) self.observation_space = spaces.Box( diff --git a/src/rl4greencrab/time_series.py b/src/rl4greencrab/time_series.py new file mode 100644 index 0000000..b1b483a --- /dev/null +++ b/src/rl4greencrab/time_series.py @@ -0,0 +1,60 @@ +import gymnasium as gym +from gymnasium import spaces +import numpy as np + + +class timeSeriesEnv(gym.Env): + """ + takes an environment env and produces an new environemtn timeSeriesEnv(env) + whose observations are timeseries of the env environment. + """ + def __init__(self, config = {}): + self.N_mem = config.get('N_mem', 3) + if 'base_env' in config: + self.base_env = config['base_env'] + else: + from rl4greencrab import greenCrabSimplifiedEnv + self.base_env = greenCrabSimplifiedEnv() + + self.action_space = self.base_env.action_space + ones_shape = np.ones( + shape = (self.N_mem, self.base_env.observation_space.shape), + dtype=np.float32, + ) + self.observation_space = spaces.Box(-ones_shape, +ones_shape) + # + # [[state t], [state t-1], [state t-2], ..., [state t - (N_mem-1)]] + # where each [state i] is a vector + # + _ = self.reset() + + def reset(self, *, seed=42, options=None): + init_state, init_info = self.base_env.reset(seed=seed, options=options) + empty_heap = - np.ones(shape = self.observation_space.shape, dtype=np.float32) + self.heap = np.insert( + empty_heap[0:-1], + 0, + init_state, + axis=0, + ) + return self.heap, init_info + + def step(self, action): + new_state, reward, terminated, truncated, info = self.base_env.step(action) + self.heap = np.insert( + self.heap[0:-1], + 0, + new_state, + axis=0, + ) + return self.heap, reward, terminated, truncated, info + + def pop_to_state(self, pop): + return self.base_env.pop_to_state(pop) + + def state_to_pop(self, state): + return self.base_env.state_to_pop(state) + + + + diff --git a/src/rl4greencrab/ts_model.py b/src/rl4greencrab/ts_model.py deleted file mode 100644 index 53ffd5f..0000000 --- a/src/rl4greencrab/ts_model.py +++ /dev/null @@ -1,151 +0,0 @@ -import gymnasium as gym -import numpy as np - -from gymnasium import spaces - -class ts_env_v1(gym.Env): - """ - takes an environment env and produces an new environemtn ts_env - whose observations are timeseries of the env environment. - - v1: the timeseries includes only past observations of base_env, - not past actions. - """ - def __init__(self, config = {}): - self.N_mem = config.get('N_mem', 5) - if 'base_env_cls' not in config: - raise Warning( - "ts_env initializer needs to have a base environment " - "out of whose dynamics the time-series will be built! " - "Try: ts_env(config = {'base_env': <>, ...}). \n\n" - "(Here, <> should be a class, not an instance!)" - ) - if 'base_env_cfg' not in config: - raise Warning( - "ts_env initializer needs to have a base environment " - "config!" - "Try: ts_env(config = {'base_env_cfg': <>, ...}). \n\n" - ) - self.base_env_cls = config['base_env_cls'] - self.base_env_cfg = config['base_env_cfg'] - self.base_env = self.base_env_cls(config=self.base_env_cfg) - - self.action_space = self.base_env.action_space - self.observation_space = spaces.Box( - np.array( - [ - - np.ones(shape=self.base_env.observation_space.shape, dtype=np.float32) - for _ in range(self.N_mem) - ] - ), - np.array( - [ - + np.ones(shape=self.base_env.observation_space.shape, dtype=np.float32) - for _ in range(self.N_mem) - ] - ), - ) - # - # [[state t], [state t-1], [state t-2], ..., [state t - (N_mem-1)]] - # where each [state i] is a vector - # - _ = self.reset() - - def reset(self, *, seed=42, options=None): - init_state, init_info = self.base_env.reset(seed=seed, options=options) - empty_heap = - np.ones(shape = self.observation_space.shape, dtype=np.float32) - self.heap = np.insert( - empty_heap[0:-1], - 0, - init_state, - axis=0, - ) - return self.heap, init_info - - def step(self, action): - new_state, reward, terminated, truncated, info = self.base_env.step(action) - self.heap = np.insert( - self.heap[0:-1], - 0, - new_state, - axis=0, - ) - return self.heap, reward, terminated, truncated, info - - def pop_to_state(self, pop): - return self.base_env.pop_to_state(pop) - - def state_to_pop(self, state): - return self.base_env.state_to_pop(state) - - - -class ts_env_v2(gym.Env): - """ - takes an environment env and produces an new environemtn ts_env - whose observations are timeseries of the env environment. - - v2: same as v1, but base_env is input rather than base_env_cls + base_env_cfg. - """ - def __init__(self, config = {}): - self.N_mem = config.get('N_mem', 5) - if 'base_env' not in config: - raise Warning( - "ts_env initializer needs to have a base environment " - "out of whose dynamics the time-series will be built! " - "Try: ts_env(config = {'base_env': <>, ...}). \n\n" - "(Here, <> should be a class, not an instance!)" - ) - self.base_env = config['base_env'] - - self.action_space = self.base_env.action_space - self.observation_space = spaces.Box( - np.array( - [ - - np.ones(shape=self.base_env.observation_space.shape, dtype=np.float32) - for _ in range(self.N_mem) - ] - ), - np.array( - [ - + np.ones(shape=self.base_env.observation_space.shape, dtype=np.float32) - for _ in range(self.N_mem) - ] - ), - ) - # - # [[state t], [state t-1], [state t-2], ..., [state t - (N_mem-1)]] - # where each [state i] is a vector - # - _ = self.reset() - - def reset(self, *, seed=42, options=None): - init_state, init_info = self.base_env.reset(seed=seed, options=options) - empty_heap = - np.ones(shape = self.observation_space.shape, dtype=np.float32) - self.heap = np.insert( - empty_heap[0:-1], - 0, - init_state, - axis=0, - ) - return self.heap, init_info - - def step(self, action): - new_state, reward, terminated, truncated, info = self.base_env.step(action) - self.heap = np.insert( - self.heap[0:-1], - 0, - new_state, - axis=0, - ) - return self.heap, reward, terminated, truncated, info - - def pop_to_state(self, pop): - return self.base_env.pop_to_state(pop) - - def state_to_pop(self, state): - return self.base_env.state_to_pop(state) - - - - From 159d12d9e50b1b6cbf27d25e1e76c69f8987eaa6 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Fri, 23 Feb 2024 00:12:28 +0000 Subject: [PATCH 15/52] GreenCrab.__init__ --- src/rl4greencrab/green_crab_ipm.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/rl4greencrab/green_crab_ipm.py b/src/rl4greencrab/green_crab_ipm.py index 40bd902..b47e946 100644 --- a/src/rl4greencrab/green_crab_ipm.py +++ b/src/rl4greencrab/green_crab_ipm.py @@ -57,6 +57,9 @@ def __init__( self.K = config.get("K", 25000) #carrying capacity self.imm = config.get("imm", 2) #colonization/immigration rate self.r = config.get("r", 2) #intrinsic rate of growth + + self.max_action = config.get("max_action", 2000) + self.max_obs = config.get("max_obs", 2000) self.area = config.get("area", 4000) self.loss_a = config.get("loss_a", 0.265) @@ -74,7 +77,7 @@ def __init__( self.config = config # Preserve these for reset - self.observations = np.array([0,0,0,0,0,0,0,0,0], dtype=np.float32) + self.observations = np.zeros(shape=9, dtype=np.float32) self.reward = 0 self.years_passed = 0 self.Tmax = config.get("Tmax", 100) @@ -92,14 +95,14 @@ def __init__( # action -- # traps per month self.action_space = spaces.Box( np.array([0], dtype=np.float32), - np.array([2000], dtype=np.float32), + np.array([self.max_action], dtype=np.float32), dtype=np.float32, ) # Observation space self.observation_space = spaces.Box( - np.array([0,0,0,0,0,0,0,0,0], dtype=np.float32), - np.array([2000,2000,2000,2000,2000,2000,2000,2000,2000], dtype=np.float32), + np.zeros(shape=9, dtype=np.float32), + self.max_obs * np.ones(shape=9, dtype=np.float32), dtype=np.float32, ) From 48f22bbe00c7b0b79bca27206e0135737fdf1bec Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Fri, 23 Feb 2024 00:13:13 +0000 Subject: [PATCH 16/52] GreenCrab mini --- src/rl4greencrab/green_crab_ipm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/rl4greencrab/green_crab_ipm.py b/src/rl4greencrab/green_crab_ipm.py index b47e946..093b63f 100644 --- a/src/rl4greencrab/green_crab_ipm.py +++ b/src/rl4greencrab/green_crab_ipm.py @@ -256,7 +256,7 @@ def dd_growth(self,popsize): # 1. impact on environment (function of crab density) # 2. penalty for how much effort we expended (function of action) def reward_func(self,action): - reward = -self.loss_a/(1+np.exp(-self.loss_b*(np.sum(self.state)/self.area-self.loss_c)))-self.action_reward_scale*action/2000 + reward = -self.loss_a/(1+np.exp(-self.loss_b*(np.sum(self.state)/self.area-self.loss_c)))-self.action_reward_scale*action/self.max_action return reward From 0acb2d4835670ac5831d6b98d26300cf01d002ba Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Fri, 23 Feb 2024 00:26:42 +0000 Subject: [PATCH 17/52] GreenCrabSimplifiedEnv --- src/rl4greencrab/green_crab_ipm.py | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/src/rl4greencrab/green_crab_ipm.py b/src/rl4greencrab/green_crab_ipm.py index 093b63f..9089c4a 100644 --- a/src/rl4greencrab/green_crab_ipm.py +++ b/src/rl4greencrab/green_crab_ipm.py @@ -55,8 +55,8 @@ def __init__( self.w_mort_scale = config.get("w_mort_scale", 5) self.K = config.get("K", 25000) #carrying capacity - self.imm = config.get("imm", 2) #colonization/immigration rate - self.r = config.get("r", 2) #intrinsic rate of growth + self.imm = config.get("imm", 1000) #colonization/immigration rate + self.r = config.get("r", 0.5) #intrinsic rate of growth self.max_action = config.get("max_action", 2000) self.max_obs = config.get("max_obs", 2000) @@ -73,7 +73,7 @@ def __init__( self.delta_t = config.get("delta_t", 1/12) self.env_stoch = config.get("env_stoch", 0.1) - self.action_reward_scale = config.get("action_reward_scale", 0.001) + self.action_reward_scale = config.get("action_reward_scale", 0.5) self.config = config # Preserve these for reset @@ -274,17 +274,15 @@ def __init__(self, config={}): np.float32([1]), dtype=np.float32, ) - # self.r = 0.5 - # self.imm = 1 - # self.K=25_000 - self.problem_scale = config.get('problem_scale', 50) # ad hoc based on previous values + self.max_action = config.get('max_action', 2000) # ad hoc based on previous values + self.cpue_normalization = config.get('cpue_normalization', 100) def step(self, action): - scaled_action = np.maximum( self.problem_scale * (1 + action)/2 , 0.) + action_natural_units = np.maximum( self.max_action * (1 + action)/2 , 0.) obs, rew, term, trunc, info = super().step( - np.float32(scaled_action) + np.float32(action_natural_units) ) - normalized_cpue = 2 * self.cpue_2(obs, scaled_action) - 1 + normalized_cpue = 2 * self.cpue_2(obs, action_natural_units) - 1 observation = np.float32(np.append(normalized_cpue, action)) return observation, rew, term, trunc, info @@ -294,12 +292,12 @@ def reset(self, seed=42, options=None): # completely new obs return - np.ones(shape=self.observation_space.shape, dtype=np.float32), info - def cpue_2(self, obs, scaled_action): + def cpue_2(self, obs, action_natural_units): if any(scaled_action <= 0): return np.float32([0,0]) cpue_2 = np.float32([ - np.sum(obs[0:5]) / (100 * scaled_action[0]), - np.sum(obs[5:]) / (100 * scaled_action[0]) + np.sum(obs[0:5]) / (self.cpue_normalization * action_natural_units[0]), + np.sum(obs[5:]) / (self.cpue_normalization * action_natural_units[0]) ]) return cpue_2 From 17a27d8f614791bccfd3959be75dbc5adb8bbd98 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Fri, 23 Feb 2024 00:40:22 +0000 Subject: [PATCH 18/52] tests --- tests/test_greencrab.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tests/test_greencrab.py b/tests/test_greencrab.py index 783543d..0e48991 100644 --- a/tests/test_greencrab.py +++ b/tests/test_greencrab.py @@ -1,7 +1,8 @@ # Confirm environment is correctly defined: from stable_baselines3.common.env_checker import check_env -from rl4greencrab import invasive_IPM_v2 +from rl4greencrab import greenCrabEnv, greenCrabSimplifiedEnv, TimeSeriesEnv def test_GC(): - check_env(invasive_IPM_v2(), warn=True) - + check_env(greenCrabEnv(), warn=True) + check_env(greenCrabSimplifiedEnv(), warn=True) + check_env(TimeSeriesEnv(), warn=True) From e6e81641f07b05f75333241ac00f7c3670a4f8d6 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Fri, 23 Feb 2024 00:46:36 +0000 Subject: [PATCH 19/52] indentation --- src/rl4greencrab/time_series.py | 82 ++++++++++++++++----------------- 1 file changed, 41 insertions(+), 41 deletions(-) diff --git a/src/rl4greencrab/time_series.py b/src/rl4greencrab/time_series.py index b1b483a..27a8f96 100644 --- a/src/rl4greencrab/time_series.py +++ b/src/rl4greencrab/time_series.py @@ -4,56 +4,56 @@ class timeSeriesEnv(gym.Env): - """ - takes an environment env and produces an new environemtn timeSeriesEnv(env) - whose observations are timeseries of the env environment. - """ - def __init__(self, config = {}): - self.N_mem = config.get('N_mem', 3) + """ + takes an environment env and produces an new environemtn timeSeriesEnv(env) + whose observations are timeseries of the env environment. + """ + def __init__(self, config = {}): + self.N_mem = config.get('N_mem', 3) if 'base_env' in config: self.base_env = config['base_env'] else: from rl4greencrab import greenCrabSimplifiedEnv self.base_env = greenCrabSimplifiedEnv() - - self.action_space = self.base_env.action_space + + self.action_space = self.base_env.action_space ones_shape = np.ones( shape = (self.N_mem, self.base_env.observation_space.shape), dtype=np.float32, ) - self.observation_space = spaces.Box(-ones_shape, +ones_shape) - # - # [[state t], [state t-1], [state t-2], ..., [state t - (N_mem-1)]] - # where each [state i] is a vector - # - _ = self.reset() - - def reset(self, *, seed=42, options=None): - init_state, init_info = self.base_env.reset(seed=seed, options=options) - empty_heap = - np.ones(shape = self.observation_space.shape, dtype=np.float32) - self.heap = np.insert( - empty_heap[0:-1], - 0, - init_state, - axis=0, - ) - return self.heap, init_info - - def step(self, action): - new_state, reward, terminated, truncated, info = self.base_env.step(action) - self.heap = np.insert( - self.heap[0:-1], - 0, - new_state, - axis=0, - ) - return self.heap, reward, terminated, truncated, info - - def pop_to_state(self, pop): - return self.base_env.pop_to_state(pop) - - def state_to_pop(self, state): - return self.base_env.state_to_pop(state) + self.observation_space = spaces.Box(-ones_shape, +ones_shape) + # + # [[state t], [state t-1], [state t-2], ..., [state t - (N_mem-1)]] + # where each [state i] is a vector + # + _ = self.reset() + + def reset(self, *, seed=42, options=None): + init_state, init_info = self.base_env.reset(seed=seed, options=options) + empty_heap = - np.ones(shape = self.observation_space.shape, dtype=np.float32) + self.heap = np.insert( + empty_heap[0:-1], + 0, + init_state, + axis=0, + ) + return self.heap, init_info + + def step(self, action): + new_state, reward, terminated, truncated, info = self.base_env.step(action) + self.heap = np.insert( + self.heap[0:-1], + 0, + new_state, + axis=0, + ) + return self.heap, reward, terminated, truncated, info + + def pop_to_state(self, pop): + return self.base_env.pop_to_state(pop) + + def state_to_pop(self, state): + return self.base_env.state_to_pop(state) From e234a0c8ead7245e98556b82de160c0b994faa42 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Fri, 23 Feb 2024 00:50:20 +0000 Subject: [PATCH 20/52] typo --- tests/test_greencrab.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_greencrab.py b/tests/test_greencrab.py index 0e48991..171d38b 100644 --- a/tests/test_greencrab.py +++ b/tests/test_greencrab.py @@ -1,8 +1,8 @@ # Confirm environment is correctly defined: from stable_baselines3.common.env_checker import check_env -from rl4greencrab import greenCrabEnv, greenCrabSimplifiedEnv, TimeSeriesEnv +from rl4greencrab import greenCrabEnv, greenCrabSimplifiedEnv, timeSeriesEnv def test_GC(): check_env(greenCrabEnv(), warn=True) check_env(greenCrabSimplifiedEnv(), warn=True) - check_env(TimeSeriesEnv(), warn=True) + check_env(timeSeriesEnv(), warn=True) From 5650e73ac0a001b1a39fb9eb4724218dbd4cd446 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 00:22:57 +0000 Subject: [PATCH 21/52] output type --- src/rl4greencrab/green_crab_ipm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/rl4greencrab/green_crab_ipm.py b/src/rl4greencrab/green_crab_ipm.py index 9089c4a..d35a95c 100644 --- a/src/rl4greencrab/green_crab_ipm.py +++ b/src/rl4greencrab/green_crab_ipm.py @@ -197,7 +197,7 @@ def reset(self, seed=42, options=None): self.reward = 0 # self.observations = np.zeros(shape=self.ntime) - self.observations = np.random.randint(0,100, size=self.ntime) + self.observations = np.random.randint(0,100, size=self.ntime, dtype = np.float32) return self.observations, {} From 7df844984d6632572adf7954848f06c776564cb9 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 00:29:26 +0000 Subject: [PATCH 22/52] mini --- src/rl4greencrab/green_crab_ipm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/rl4greencrab/green_crab_ipm.py b/src/rl4greencrab/green_crab_ipm.py index d35a95c..c81976e 100644 --- a/src/rl4greencrab/green_crab_ipm.py +++ b/src/rl4greencrab/green_crab_ipm.py @@ -189,7 +189,7 @@ def step(self,action): return self.observations, self.reward, done, done, {} - def reset(self, seed=42, options=None): + def reset(self, *, seed=42, options=None): self.state = self.init_state() self.years_passed = 0 @@ -286,7 +286,7 @@ def step(self, action): observation = np.float32(np.append(normalized_cpue, action)) return observation, rew, term, trunc, info - def reset(self, seed=42, options=None): + def reset(self, *, seed=42, options=None): _, info = super().reset(seed=seed, options=options) # completely new obs From 63ec421fd184b662e0524b6dab8f2585315463c2 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 00:39:21 +0000 Subject: [PATCH 23/52] float32 --- src/rl4greencrab/green_crab_ipm.py | 2 +- src/rl4greencrab/util.py | 12 ++++++++++++ 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/src/rl4greencrab/green_crab_ipm.py b/src/rl4greencrab/green_crab_ipm.py index c81976e..46d89d5 100644 --- a/src/rl4greencrab/green_crab_ipm.py +++ b/src/rl4greencrab/green_crab_ipm.py @@ -197,7 +197,7 @@ def reset(self, *, seed=42, options=None): self.reward = 0 # self.observations = np.zeros(shape=self.ntime) - self.observations = np.random.randint(0,100, size=self.ntime, dtype = np.float32) + self.observations = np.float32(np.random.randint(0,100, size=self.ntime)) return self.observations, {} diff --git a/src/rl4greencrab/util.py b/src/rl4greencrab/util.py index 4c0f76c..69d3141 100644 --- a/src/rl4greencrab/util.py +++ b/src/rl4greencrab/util.py @@ -56,6 +56,18 @@ def sb3_train(config_file): except: print("Could not deploy model to hugging face :(.") +def deploy_model(path, path_in_repo, repo, clean=False): + api = HfApi() + if path_in_repo is None: + path_in_repo = basename(path) + api.upload_file( + path_or_fileobj=path, + path_in_repo=path_in_repo, + repo_id=repo, + repo_type="model") + if clean: + pathlib.Path(path).unlink() + def sb3_train_v2(options = dict): vec_env = make_vec_env( options["env_id"], options["n_envs"], env_kwargs={"config": options["config"]} From 17b6da8696abe5335f3b908a8cd969eeaa5eefcd Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 00:48:56 +0000 Subject: [PATCH 24/52] envs dir, float32 obs --- src/rl4greencrab/{ => envs}/green_crab_ipm.py | 2 +- src/rl4greencrab/{ => envs}/time_series.py | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename src/rl4greencrab/{ => envs}/green_crab_ipm.py (99%) rename src/rl4greencrab/{ => envs}/time_series.py (100%) diff --git a/src/rl4greencrab/green_crab_ipm.py b/src/rl4greencrab/envs/green_crab_ipm.py similarity index 99% rename from src/rl4greencrab/green_crab_ipm.py rename to src/rl4greencrab/envs/green_crab_ipm.py index 46d89d5..f86d176 100644 --- a/src/rl4greencrab/green_crab_ipm.py +++ b/src/rl4greencrab/envs/green_crab_ipm.py @@ -144,7 +144,7 @@ def step(self,action): size_freq[:,j+1] = [np.random.binomial(n=n_j[k], p=self.pmort) for k in range(self.nsize)] removed[:,j+1] = [np.random.binomial(size_freq[k,j+1], harvest_rate[k]) for k in range(self.nsize)] - self.observations = np.array([np.sum(removed[:,j]) for j in range(self.ntime)]) + self.observations = np.array([np.sum(removed[:,j]) for j in range(self.ntime)], dtype = np.float32) # for k in range(21): # #project to next size frequency diff --git a/src/rl4greencrab/time_series.py b/src/rl4greencrab/envs/time_series.py similarity index 100% rename from src/rl4greencrab/time_series.py rename to src/rl4greencrab/envs/time_series.py From fc0f3397bec1519748dbdaea0874fd67c7529846 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 00:54:57 +0000 Subject: [PATCH 25/52] imports --- src/rl4greencrab/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py index 9dc4851..c5e35e2 100644 --- a/src/rl4greencrab/__init__.py +++ b/src/rl4greencrab/__init__.py @@ -1,6 +1,6 @@ -from rl4greencrab.green_crab_ipm import greenCrabEnv, greenCrabSimplifiedEnv -from rl4greencrab.time_series import timeSeriesEnv -from rl4greencrab.util import sb3_train, sb3_train_v2, sb3_train_metaenv +from envs.green_crab_ipm import greenCrabEnv, greenCrabSimplifiedEnv +from envs.time_series import timeSeriesEnv +# from envs.util import sb3_train, sb3_train_v2, sb3_train_metaenv from gymnasium.envs.registration import register register( From 1dbddf1c8325fed5aa77bb003be364cf3742854c Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 00:59:54 +0000 Subject: [PATCH 26/52] imports --- src/rl4greencrab/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py index c5e35e2..024f904 100644 --- a/src/rl4greencrab/__init__.py +++ b/src/rl4greencrab/__init__.py @@ -1,5 +1,5 @@ -from envs.green_crab_ipm import greenCrabEnv, greenCrabSimplifiedEnv -from envs.time_series import timeSeriesEnv +from rl4greencrab.envs.green_crab_ipm import greenCrabEnv, greenCrabSimplifiedEnv +from rl4greencrab.envs.time_series import timeSeriesEnv # from envs.util import sb3_train, sb3_train_v2, sb3_train_metaenv from gymnasium.envs.registration import register From 91c61336dc4a05070ef8b0aa7452e795412548bc Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 01:03:51 +0000 Subject: [PATCH 27/52] var rename --- src/rl4greencrab/envs/green_crab_ipm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/rl4greencrab/envs/green_crab_ipm.py b/src/rl4greencrab/envs/green_crab_ipm.py index f86d176..d21dd15 100644 --- a/src/rl4greencrab/envs/green_crab_ipm.py +++ b/src/rl4greencrab/envs/green_crab_ipm.py @@ -293,7 +293,7 @@ def reset(self, *, seed=42, options=None): return - np.ones(shape=self.observation_space.shape, dtype=np.float32), info def cpue_2(self, obs, action_natural_units): - if any(scaled_action <= 0): + if any(action_natural_units <= 0): return np.float32([0,0]) cpue_2 = np.float32([ np.sum(obs[0:5]) / (self.cpue_normalization * action_natural_units[0]), From 9844a8a857f56474befbb6af0bb3f2583de2b213 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 01:31:27 +0000 Subject: [PATCH 28/52] time-series env obs shape --- src/rl4greencrab/envs/time_series.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/rl4greencrab/envs/time_series.py b/src/rl4greencrab/envs/time_series.py index 27a8f96..dc72e11 100644 --- a/src/rl4greencrab/envs/time_series.py +++ b/src/rl4greencrab/envs/time_series.py @@ -18,7 +18,7 @@ def __init__(self, config = {}): self.action_space = self.base_env.action_space ones_shape = np.ones( - shape = (self.N_mem, self.base_env.observation_space.shape), + shape = (self.N_mem, *self.base_env.observation_space.shape), dtype=np.float32, ) self.observation_space = spaces.Box(-ones_shape, +ones_shape) From bc1357c67a089999e100c49c03fd6198102c93ae Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 01:31:47 +0000 Subject: [PATCH 29/52] agent imports --- src/rl4greencrab/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py index 024f904..6e2eb57 100644 --- a/src/rl4greencrab/__init__.py +++ b/src/rl4greencrab/__init__.py @@ -1,5 +1,7 @@ from rl4greencrab.envs.green_crab_ipm import greenCrabEnv, greenCrabSimplifiedEnv from rl4greencrab.envs.time_series import timeSeriesEnv +from rl4greengrab.agents.const_action import constAction +from rl4greengrab.agents.const_escapement import constEsc # from envs.util import sb3_train, sb3_train_v2, sb3_train_metaenv from gymnasium.envs.registration import register From 9937bc3ab57b0d3503612c59e373b1c14cc12425 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 01:33:26 +0000 Subject: [PATCH 30/52] added agents --- src/rl4greencrab/agents/__init__.py | 0 src/rl4greencrab/agents/const_action.py | 11 ++++++++++ src/rl4greencrab/agents/const_escapement.py | 23 +++++++++++++++++++++ 3 files changed, 34 insertions(+) create mode 100644 src/rl4greencrab/agents/__init__.py create mode 100644 src/rl4greencrab/agents/const_action.py create mode 100644 src/rl4greencrab/agents/const_escapement.py diff --git a/src/rl4greencrab/agents/__init__.py b/src/rl4greencrab/agents/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/rl4greencrab/agents/const_action.py b/src/rl4greencrab/agents/const_action.py new file mode 100644 index 0000000..6695c1b --- /dev/null +++ b/src/rl4greencrab/agents/const_action.py @@ -0,0 +1,11 @@ +class constAction: + def __init__(self, mortality=0, env = None, **kwargs): + self.mortality = mortality + self.action = 2 * self.mortality - 1 + self.env = env + + def predict(self, observation): + return self.action + + + \ No newline at end of file diff --git a/src/rl4greencrab/agents/const_escapement.py b/src/rl4greencrab/agents/const_escapement.py new file mode 100644 index 0000000..60ecdc4 --- /dev/null +++ b/src/rl4greencrab/agents/const_escapement.py @@ -0,0 +1,23 @@ +class constEsc: + def __init__(self, escapement, env = None): + self.escapement = escapement + self.env = env + self.bound = 1 + if self.env is not None: + self.bound = self.env.bound + + def predict(self, observation): + obs_nat_units = self.bound * self.to_01(observation) + if obs_nat_units <= self.escapement or obs_nat_units =< 0: + return -1 + mortality = (obs_nat_units - self.escapement) / self.escapement + return self.to_pm1(mortality) + + def to_01(self, val): + return (val + 1 ) / 2 + + def to_pm1(self, val): + return 2 * val - 1 + + + \ No newline at end of file From 5a5a7af81b53988258a465d74a1f0deb7375f4f0 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 01:36:45 +0000 Subject: [PATCH 31/52] typo --- src/rl4greencrab/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py index 6e2eb57..c3e9d71 100644 --- a/src/rl4greencrab/__init__.py +++ b/src/rl4greencrab/__init__.py @@ -1,7 +1,7 @@ from rl4greencrab.envs.green_crab_ipm import greenCrabEnv, greenCrabSimplifiedEnv from rl4greencrab.envs.time_series import timeSeriesEnv -from rl4greengrab.agents.const_action import constAction -from rl4greengrab.agents.const_escapement import constEsc +from rl4greencrab.agents.const_action import constAction +from rl4greencrab.agents.const_escapement import constEsc # from envs.util import sb3_train, sb3_train_v2, sb3_train_metaenv from gymnasium.envs.registration import register From ada7881cc55083f03acb20f7a4d774269361efc0 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 01:37:47 +0000 Subject: [PATCH 32/52] utils folder with contents --- src/rl4greencrab/utils/__init__.py | 0 src/rl4greencrab/utils/hugging_face.py | 17 ++++++++ src/rl4greencrab/utils/sb3.py | 54 ++++++++++++++++++++++++++ 3 files changed, 71 insertions(+) create mode 100644 src/rl4greencrab/utils/__init__.py create mode 100644 src/rl4greencrab/utils/hugging_face.py create mode 100644 src/rl4greencrab/utils/sb3.py diff --git a/src/rl4greencrab/utils/__init__.py b/src/rl4greencrab/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/rl4greencrab/utils/hugging_face.py b/src/rl4greencrab/utils/hugging_face.py new file mode 100644 index 0000000..88665c1 --- /dev/null +++ b/src/rl4greencrab/utils/hugging_face.py @@ -0,0 +1,17 @@ +from huggingface_sb3 import load_from_hub, package_to_hub +from huggingface_hub import HfApi + +from os.path import basename +import pathlib + +def upload_to_hf(path, path_in_repo, repo, clean=False): + api = HfApi() + if path_in_repo is None: + path_in_repo = basename(path) + api.upload_file( + path_or_fileobj=path, + path_in_repo=path_in_repo, + repo_id=repo, + repo_type="model") + if clean: + pathlib.Path(path).unlink() \ No newline at end of file diff --git a/src/rl4greencrab/utils/sb3.py b/src/rl4greencrab/utils/sb3.py new file mode 100644 index 0000000..e05921e --- /dev/null +++ b/src/rl4greencrab/utils/sb3.py @@ -0,0 +1,54 @@ +import yaml +import os + +import gymnasium as gym +from stable_baselines3.common.env_util import make_vec_env +from stable_baselines3 import PPO, A2C, DQN, SAC, TD3 +from sb3_contrib import TQC, ARS + +def algorithm(algo): + algos = { + "PPO": PPO, + "ARS": ARS, + "TQC": TQC, + "A2C": A2C, + "SAC": SAC, + "DQN": DQN, + "TD3": TD3, + "ppo": PPO, + "ars": ARS, + "tqc": TQC, + "a2c": A2C, + "sac": SAC, + "dqn": DQN, + "td3": TD3, + } + return algos[algo] + +def sb3_train(config_file): + with open(config_file, "r") as stream: + options = yaml.safe_load(stream) + + vec_env = make_vec_env( + options["env_id"], options["n_envs"], env_kwargs={"config": options["config"]} + ) + ALGO = algorithm(options["algo"]) + model_id = options["algo"] + "-" + options["env_id"] + "-" + options["id"] + save_id = os.path.join(options["save_path"], model_id) + + model = ALGO( + "MlpPolicy", + vec_env, + verbose=0, + tensorboard_log=options["tensorboard"], + use_sde=options["use_sde"], + ) + + progress_bar = options.get("progress_bar", False) + model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=progress_bar) + + os.makedirs(options["save_path"], exist_ok=True) + model.save(save_id) + print(f"Saved {options['algo']} model at {save_id}") + + return model \ No newline at end of file From 5c545af1179abeaab4785584cecd4e5295405560 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 01:39:36 +0000 Subject: [PATCH 33/52] typo --- src/rl4greencrab/agents/const_escapement.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/rl4greencrab/agents/const_escapement.py b/src/rl4greencrab/agents/const_escapement.py index 60ecdc4..4254bb9 100644 --- a/src/rl4greencrab/agents/const_escapement.py +++ b/src/rl4greencrab/agents/const_escapement.py @@ -8,7 +8,7 @@ def __init__(self, escapement, env = None): def predict(self, observation): obs_nat_units = self.bound * self.to_01(observation) - if obs_nat_units <= self.escapement or obs_nat_units =< 0: + if obs_nat_units <= self.escapement or obs_nat_units <= 0: return -1 mortality = (obs_nat_units - self.escapement) / self.escapement return self.to_pm1(mortality) From 4ca3b7473a62b68f7987191bc84f6ab4e4f97418 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 07:38:32 +0000 Subject: [PATCH 34/52] added intro notebook --- notebooks/intro.ipynb | 254 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 254 insertions(+) create mode 100644 notebooks/intro.ipynb diff --git a/notebooks/intro.ipynb b/notebooks/intro.ipynb new file mode 100644 index 0000000..3a8afe1 --- /dev/null +++ b/notebooks/intro.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9c5f89cf-cee3-498d-8a83-4a05e1bd53b8", + "metadata": {}, + "source": [ + "# Intro to this package\n", + "---\n", + "\n", + "Here we introduce the functionalities of our package and the dynamical models it uses." + ] + }, + { + "cell_type": "markdown", + "id": "146823b9-fb59-437b-899c-b487291afa24", + "metadata": {}, + "source": [ + "# 1. Setup\n", + "---\n", + "Uncomment the following line to install `rl4greencrab`. \n", + "After installation you need to restart the jupyter kernel in order to use the package and run this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9059df7d-6045-4834-a9cf-fcf32edac655", + "metadata": {}, + "outputs": [], + "source": [ + "# %pip install -e .." + ] + }, + { + "cell_type": "markdown", + "id": "b73ea14c-9712-4eba-bf44-5dfba45005be", + "metadata": {}, + "source": [ + "# 2. Integral projection model\n", + "---\n", + "\n", + "Here we conceptually describe our integral projection model (IPM) of the crab population dynamics.\n", + "\n", + "Our model describes the process in which an agent observes green crab counts by laying traps to catch the crabs. \n", + "Each time-step corresponds to a year's worth of data, and has two components: \n", + "First, for 9 months crabs are caught using traps laid by the agent.\n", + "Second, for the last 3 months of the year, the crabs undergo a gestation period and no crabs are caught during this time.\n", + "This is the timeline of a time-step:\n", + "1. The agent receives an observation (nine months' worth of catch data).\n", + "2. The agent decides a density of traps to lay for the next year.\n", + "3. The population dynamics model evolves somatically for nine months, each month producing an overall catch count observation. This observation is sampled from a distribution that depends on the size-structure of the crab population.\n", + "5. New crabs are spawned and grow for 3 months. The number of new-borns is determined by a logistic function (plus a random term). During this timeline, too, new crabs immigrate with a fixed immigration rate.\n", + "\n", + "The following code block shows the input and output of this model.\n", + "Our model is encoded as a `gymnasium env` class in order to leverage existing RL algorithms." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cab72c67-653f-4aee-8bfe-f13fbebd02c9", + "metadata": {}, + "outputs": [], + "source": [ + "from rl4greencrab import greenCrabEnv\n", + "\n", + "gce = greenCrabEnv()" + ] + }, + { + "cell_type": "markdown", + "id": "f353ac81-20b4-4083-879a-44ecaa04c3e4", + "metadata": {}, + "source": [ + "After declaring `gce`, the `.reset()` function sets the state of the crab population to its initial value, and produces the following output:\n", + "\n", + "`initial observation`, `info`.\n", + "\n", + "Currently, `initial observation` is a random sequence of numbers for simplicity's sake." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "693a9ba4-b422-47b6-9d5a-04b9006ac094", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([17., 20., 52., 45., 78., 74., 65., 25., 40.], dtype=float32)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observation, info = gce.reset()\n", + "observation" + ] + }, + { + "cell_type": "markdown", + "id": "188dedf0-864f-4133-bca0-d88dfd519374", + "metadata": {}, + "source": [ + "A dynamical step for the IPM is produced by the `.step(action)` function.\n", + "Here, the `action` argument takes values in [0, 2000] and corresponds to the number of traps laid.\n", + "The output is\n", + "\n", + "`observation`, `reward`, `terminated`, `truncated`, `info`\n", + "\n", + "The latter three outputs are beyond the scope of this notebook.\n", + "The `reward` output is used by the agent to train---the agent looks for strategies that lead to high average rewards over 100 time-steps.\n", + "\n", + "The following call of the step function returns a null observation since no traps were laid." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9d1b0d77-895f-4b19-8d3d-3da878a6704a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observation, reward, terminated, truncated, info = gce.step([0])\n", + "observation" + ] + }, + { + "cell_type": "markdown", + "id": "6c8bed72-996d-4e68-b850-67117830f3b7", + "metadata": {}, + "source": [ + "Lets try some other action values and see what observations we obtain:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "862dceed-6947-416c-9de7-0c2ba38d8fe5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 1., 0., 2., 1., 1., 0., 2., 1.], dtype=float32)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1.\n", + "_ = gce.reset()\n", + "observation, reward, terminated, truncated, info = gce.step([1])\n", + "observation" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "fd41a3d4-f50b-4f9a-bad5-29a2089c1631", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 7., 4., 11., 5., 8., 9., 7., 3., 6.], dtype=float32)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 2.\n", + "_ = gce.reset()\n", + "observation, reward, terminated, truncated, info = gce.step([10])\n", + "observation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fbffb33f-4b7f-4cc1-a7f7-824d12207a51", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([87., 76., 58., 58., 43., 49., 61., 55., 63.], dtype=float32)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 3.\n", + "_ = gce.reset()\n", + "observation, reward, terminated, truncated, info = gce.step([100])\n", + "observation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed44b534-9b72-4e4c-a7d3-6e7c069e7293", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ec376ad1e9439d35931a2d8778c729038600f9aa Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 08:04:03 +0000 Subject: [PATCH 35/52] more intro --- notebooks/intro.ipynb | 187 ++++++++++++++++++++++++++++- src/rl4greencrab/__init__.py | 1 + src/rl4greencrab/utils/simulate.py | 21 ++++ 3 files changed, 208 insertions(+), 1 deletion(-) create mode 100644 src/rl4greencrab/utils/simulate.py diff --git a/notebooks/intro.ipynb b/notebooks/intro.ipynb index 3a8afe1..5c62caa 100644 --- a/notebooks/intro.ipynb +++ b/notebooks/intro.ipynb @@ -32,6 +32,17 @@ "# %pip install -e .." ] }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c97fbb03-59d8-487a-9805-8faa9ec021d0", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from plotnine import ggplot, aes, geom_line" + ] + }, { "cell_type": "markdown", "id": "b73ea14c-9712-4eba-bf44-5dfba45005be", @@ -221,10 +232,184 @@ "observation" ] }, + { + "cell_type": "markdown", + "id": "3531f290-50f4-4529-969a-d70b9f7b470f", + "metadata": {}, + "source": [ + "## Plotting population distribution over time without traps\n", + "\n", + "The state of our model is a size-resolved population distribution, with 21 size classes." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "976afa61-4e00-4f7d-9507-f0030c4302b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gce.reset()\n", + "init_size_dist = pd.DataFrame(\n", + " {\n", + " 'pop_density': gce.state,\n", + " 'size_class': list(range(21))\n", + " }\n", + ")\n", + "init_size_dist.plot(x='size_class', title='initial distrib.')\n", + "\n", + "gce.step([0])\n", + "\n", + "size_dist_1 = pd.DataFrame(\n", + " {\n", + " 'pop_density': gce.state,\n", + " 'size_class': list(range(21))\n", + " }\n", + ")\n", + "size_dist_1.plot(x='size_class', title='distrib. at timestep 1')\n", + "\n", + "gce.step([0])\n", + "\n", + "size_dist_2 = pd.DataFrame(\n", + " {\n", + " 'pop_density': gce.state,\n", + " 'size_class': list(range(21))\n", + " }\n", + ")\n", + "size_dist_2.plot(x='size_class', title='distrib. at timestep 2')" + ] + }, + { + "cell_type": "markdown", + "id": "c1f1a77c-bf82-404e-b9e3-6fdb444675ce", + "metadata": {}, + "source": [ + "## Plotting population *with* traps" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "781f996b-dcc5-4106-810b-d3ab296cfac2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gce.reset()\n", + "init_size_dist = pd.DataFrame(\n", + " {\n", + " 'pop_density': gce.state,\n", + " 'size_class': list(range(21))\n", + " }\n", + ")\n", + "init_size_dist.plot(x='size_class', title='initial distrib.')\n", + "\n", + "gce.step([200])\n", + "\n", + "size_dist_1 = pd.DataFrame(\n", + " {\n", + " 'pop_density': gce.state,\n", + " 'size_class': list(range(21))\n", + " }\n", + ")\n", + "size_dist_1.plot(x='size_class', title='distrib. at timestep 1')\n", + "\n", + "gce.step([200])\n", + "\n", + "size_dist_2 = pd.DataFrame(\n", + " {\n", + " 'pop_density': gce.state,\n", + " 'size_class': list(range(21))\n", + " }\n", + ")\n", + "size_dist_2.plot(x='size_class', title='distrib. at timestep 2')" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "ed44b534-9b72-4e4c-a7d3-6e7c069e7293", + "id": "79f064a4-0d82-42dd-98a4-255854ab3808", "metadata": {}, "outputs": [], "source": [] diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py index c3e9d71..8465969 100644 --- a/src/rl4greencrab/__init__.py +++ b/src/rl4greencrab/__init__.py @@ -2,6 +2,7 @@ from rl4greencrab.envs.time_series import timeSeriesEnv from rl4greencrab.agents.const_action import constAction from rl4greencrab.agents.const_escapement import constEsc +from rl4greencrab.util.simulate import simulator # from envs.util import sb3_train, sb3_train_v2, sb3_train_metaenv from gymnasium.envs.registration import register diff --git a/src/rl4greencrab/utils/simulate.py b/src/rl4greencrab/utils/simulate.py new file mode 100644 index 0000000..b5b70a5 --- /dev/null +++ b/src/rl4greencrab/utils/simulate.py @@ -0,0 +1,21 @@ +class simulator: + def __init__(self, env, agent): + self.env = env + self.agent = agent + + def simulate(self, reps=10): + self.results = [] + env = self.env + agent = self.agent + for rep in range(reps): # try score as average of 100 replicates, still a noisy measure + episode_reward = 0.0 + observation, _ = env.reset() + for t in range(env.Tmax): + action, _ = agent.predict(observation, deterministic=True) + observation, reward, terminated, done, info = env.step(action) + episode_reward += reward + if terminated or done: + break + self.results.append(episode_reward) + return self.results + \ No newline at end of file From abb8b86f631c28f506aaccbf30729224e2056c95 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 08:04:23 +0000 Subject: [PATCH 36/52] envs/__init__ --- src/rl4greencrab/envs/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 src/rl4greencrab/envs/__init__.py diff --git a/src/rl4greencrab/envs/__init__.py b/src/rl4greencrab/envs/__init__.py new file mode 100644 index 0000000..e69de29 From de781d7f0e7e7d4bbeb62247ad700dfd590f2772 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 18:36:26 +0000 Subject: [PATCH 37/52] notebook, typo in __init__ --- notebooks/intro.ipynb | 102 +++++++++++++++++++++++++---------- src/rl4greencrab/__init__.py | 2 +- 2 files changed, 75 insertions(+), 29 deletions(-) diff --git a/notebooks/intro.ipynb b/notebooks/intro.ipynb index 5c62caa..9ffa193 100644 --- a/notebooks/intro.ipynb +++ b/notebooks/intro.ipynb @@ -24,23 +24,66 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "9059df7d-6045-4834-a9cf-fcf32edac655", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Obtaining file:///home/rstudio/rl4greencrab\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Checking if build backend supports build_editable ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build editable ... \u001b[?25ldone\n", + "\u001b[?25h Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: gymnasium in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (0.28.1)\n", + "Requirement already satisfied: matplotlib in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (3.8.3)\n", + "Requirement already satisfied: numpy in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (1.26.4)\n", + "Requirement already satisfied: pandas in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (2.2.0)\n", + "Requirement already satisfied: pyyaml in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (6.0.1)\n", + "Requirement already satisfied: scipy in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (1.12.0)\n", + "Collecting typing (from rl4greencrab==1.0.0)\n", + " Using cached typing-3.7.4.3-py3-none-any.whl\n", + "Requirement already satisfied: jax-jumpy>=1.0.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (1.0.0)\n", + "Requirement already satisfied: cloudpickle>=1.2.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (3.0.0)\n", + "Requirement already satisfied: typing-extensions>=4.3.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (4.9.0)\n", + "Requirement already satisfied: farama-notifications>=0.0.1 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (0.0.4)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (1.2.0)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (4.49.0)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (23.2)\n", + "Requirement already satisfied: pillow>=8 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (10.2.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/venv/lib/python3.10/site-packages (from pandas->rl4greencrab==1.0.0) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /opt/venv/lib/python3.10/site-packages (from pandas->rl4greencrab==1.0.0) (2024.1)\n", + "Requirement already satisfied: six>=1.5 in /opt/venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->rl4greencrab==1.0.0) (1.16.0)\n", + "Building wheels for collected packages: rl4greencrab\n", + " Building editable for rl4greencrab (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for rl4greencrab: filename=rl4greencrab-1.0.0-py2.py3-none-any.whl size=1069 sha256=04a2111d259dd039b329b1369e2857430f1ad1045264348157a0b65eab056871\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-ewoqtlyu/wheels/e9/7e/e6/00c4b11a2574abd59d64425d537139e25fadbde37f002c4dba\n", + "Successfully built rl4greencrab\n", + "Installing collected packages: typing, rl4greencrab\n", + "Successfully installed rl4greencrab-1.0.0 typing-3.7.4.3\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "# %pip install -e .." + "%pip install -e .." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 1, "id": "c97fbb03-59d8-487a-9805-8faa9ec021d0", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "from plotnine import ggplot, aes, geom_line" + "from plotnine import ggplot, aes, geom_line, facet_grid" ] }, { @@ -72,7 +115,20 @@ "execution_count": 2, "id": "cab72c67-653f-4aee-8bfe-f13fbebd02c9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'rl4greencrab.util.simulate'; 'rl4greencrab.util' is not a package", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrl4greencrab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m greenCrabEnv\n\u001b[1;32m 3\u001b[0m gce \u001b[38;5;241m=\u001b[39m greenCrabEnv()\n", + "File \u001b[0;32m~/rl4greencrab/src/rl4greencrab/__init__.py:5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrl4greencrab\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01magents\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconst_action\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m constAction\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrl4greencrab\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01magents\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconst_escapement\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m constEsc\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrl4greencrab\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulate\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m simulator\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# from envs.util import sb3_train, sb3_train_v2, sb3_train_metaenv\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgymnasium\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01menvs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mregistration\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m register\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rl4greencrab.util.simulate'; 'rl4greencrab.util' is not a package" + ] + } + ], "source": [ "from rl4greencrab import greenCrabEnv\n", "\n", @@ -93,21 +149,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "693a9ba4-b422-47b6-9d5a-04b9006ac094", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([17., 20., 52., 45., 78., 74., 65., 25., 40.], dtype=float32)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "observation, info = gce.reset()\n", "observation" @@ -132,19 +177,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "9d1b0d77-895f-4b19-8d3d-3da878a6704a", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'gce' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m observation, reward, terminated, truncated, info \u001b[38;5;241m=\u001b[39m \u001b[43mgce\u001b[49m\u001b[38;5;241m.\u001b[39mstep([\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 2\u001b[0m observation\n", + "\u001b[0;31mNameError\u001b[0m: name 'gce' is not defined" + ] } ], "source": [ diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py index 8465969..0d60929 100644 --- a/src/rl4greencrab/__init__.py +++ b/src/rl4greencrab/__init__.py @@ -2,7 +2,7 @@ from rl4greencrab.envs.time_series import timeSeriesEnv from rl4greencrab.agents.const_action import constAction from rl4greencrab.agents.const_escapement import constEsc -from rl4greencrab.util.simulate import simulator +from rl4greencrab.utils.simulate import simulator # from envs.util import sb3_train, sb3_train_v2, sb3_train_metaenv from gymnasium.envs.registration import register From 71bb99594495825035d2124d538ebf2929c79474 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 19:14:08 +0000 Subject: [PATCH 38/52] intro notebook --- notebooks/intro.ipynb | 237 ++++++++++++++++-------------------------- 1 file changed, 88 insertions(+), 149 deletions(-) diff --git a/notebooks/intro.ipynb b/notebooks/intro.ipynb index 9ffa193..3f5fea1 100644 --- a/notebooks/intro.ipynb +++ b/notebooks/intro.ipynb @@ -77,13 +77,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 54, "id": "c97fbb03-59d8-487a-9805-8faa9ec021d0", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "from plotnine import ggplot, aes, geom_line, facet_grid" + "from plotnine import ggplot, aes, geom_line, geom_point, facet_grid, labs" ] }, { @@ -112,23 +112,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "cab72c67-653f-4aee-8bfe-f13fbebd02c9", "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'rl4greencrab.util.simulate'; 'rl4greencrab.util' is not a package", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrl4greencrab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m greenCrabEnv\n\u001b[1;32m 3\u001b[0m gce \u001b[38;5;241m=\u001b[39m greenCrabEnv()\n", - "File \u001b[0;32m~/rl4greencrab/src/rl4greencrab/__init__.py:5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrl4greencrab\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01magents\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconst_action\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m constAction\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrl4greencrab\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01magents\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconst_escapement\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m constEsc\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrl4greencrab\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulate\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m simulator\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# from envs.util import sb3_train, sb3_train_v2, sb3_train_metaenv\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgymnasium\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01menvs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mregistration\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m register\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rl4greencrab.util.simulate'; 'rl4greencrab.util' is not a package" - ] - } - ], + "outputs": [], "source": [ "from rl4greencrab import greenCrabEnv\n", "\n", @@ -149,10 +136,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "693a9ba4-b422-47b6-9d5a-04b9006ac094", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([13., 88., 5., 40., 84., 64., 6., 29., 99.], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "observation, info = gce.reset()\n", "observation" @@ -177,20 +175,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "9d1b0d77-895f-4b19-8d3d-3da878a6704a", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'gce' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m observation, reward, terminated, truncated, info \u001b[38;5;241m=\u001b[39m \u001b[43mgce\u001b[49m\u001b[38;5;241m.\u001b[39mstep([\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 2\u001b[0m observation\n", - "\u001b[0;31mNameError\u001b[0m: name 'gce' is not defined" - ] + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -208,17 +205,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "862dceed-6947-416c-9de7-0c2ba38d8fe5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0., 1., 0., 2., 1., 1., 0., 2., 1.], dtype=float32)" + "array([0., 0., 2., 0., 0., 1., 0., 0., 0.], dtype=float32)" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -232,17 +229,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "id": "fd41a3d4-f50b-4f9a-bad5-29a2089c1631", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 7., 4., 11., 5., 8., 9., 7., 3., 6.], dtype=float32)" + "array([ 8., 15., 9., 9., 5., 13., 8., 6., 3.], dtype=float32)" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -256,17 +253,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "id": "fbffb33f-4b7f-4cc1-a7f7-824d12207a51", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([87., 76., 58., 58., 43., 49., 61., 55., 63.], dtype=float32)" + "array([83., 79., 55., 43., 55., 66., 50., 54., 77.], dtype=float32)" ] }, - "execution_count": 20, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -290,80 +287,51 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 67, "id": "976afa61-4e00-4f7d-9507-f0030c4302b5", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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WmF26dAls37p1a7OysrLeYz/xxBPm6aefbn722Wem3++vd7v58+ebnTp1Chy3ffv2DR43ESXA1p9F06ZNzQ8++KDOttXV1eaECROC2tGnT596S8v9fr958sknB21/9913h/xzmTNnjrnffvsFtsvJyTG///77ettt7yutdh9++OHm8uXL62y7e/du8+yzzw5qx2233RbFn1L62bNnj9mxY0dTkunz+cxFixYFfW7/swpXAvzmm28Gbf/ee+9F1IZ33nknaL+hQ4dGtN8jjzwS2GfkyJFBnyWiBLhbt26B47dp0yaiqRUAIB2RAQgASBq9evXS8OHD9e6770qqWQDhqquuqpN1kI5yc3P10UcfqVu3bmG3bdKkiX7++eeQK8PaNW/eXFOmTNHWrVsDmZVPPfWUTj/99IjaVF1drS5duui9994L+R20adNG7733no477jitXr1aUs2CLmPGjKmzcIW9tPXKK68Mm6HWpk0b3XHHHRG1MxLTpk3Tn//8Z8eOZ3fdddfpuuuui/s4tbNUIl24wefzqW3btlq/fr0kaeXKlXG3JZlVV1fr+uuvD5o2wK5Tp07617/+Ffgd27p1qz799FOddtppIbcfN25c0GrK9TnxxBM1c+ZMHXvssaqoqNCGDRv0zjvv6Jxzzon9YuJkZcm+8sorGjp0aJ3PDcPQrbfeqm3btgXKL7/44gtNnz5dw4YNq7P922+/rXnz5gXe33DDDbrrrrtCnrtPnz56++231a9fP1VXV6u8vFy33XZbRKvDVldXq3nz5vrggw/UuXPnOp8XFBTotddeU58+fbRw4UJJ0sMPP6wbbrjBkYVYFixYoIsvvjju44RyzjnnBK3k7ZTbbrtN69atkyRdc801Ou6442I+1v777x/0vnbfU5/a20XS16xbty7Qlzdt2lSPPfZYhK10xvTp07V8+fLA+3PPPdexhbEAINUQAAQAJJUJEyZo+vTpqq6uVlFRkR566KF6B/rp5Nprr40o+CdJWVlZYYN/Fp/Pp7/85S/q16+fJOnTTz+V3++Xz+eLaP/777+/wQBsixYtdP/992vUqFGSpC1btmj69Ol1Voe0z+HVtWvXiM7tpOLiYv3yyy+uHNu+am88as+1VlhYGPG+hYWFgQBguDkkU13Lli31wAMPNLjNqaeeqoMOOigQoFi4cGG9AcBoVrE+/PDDdfHFF2vixImSpA8//NDTAKAknXbaaTrrrLMa3Oaee+7RK6+8ou3bt0uSXnjhhZABwGeeeSbwunXr1rr33nsbPO4pp5yiMWPG6MUXX5RUE2xZv369OnToELbdN998c8jgn6Vx48Z69NFHAyu+VlRU6JVXXom4zLghpaWlrvUHVumyk77++ms98cQTkmrK1MN9L+Ecc8wx8vl8gZLczz77TNdee23Y/T777LOg9/XND2l39dVXB+aHvfvuuyP63XBKcXGxrrnmmsD73Nxc3XzzzQk7PwAkGxYBAQAklaOOOioQTJKkxx57LCMmgL/00ktdO3bv3r2Vk5MjqWbg++OPP0a0X2FhYUTBjXPPPTcoWPXee+/V2cY+l+P8+fMjOn+mqb2ISjSBKft8WuEWY0l1559/fkTzh9nnUPzpp58cO789e/V///ufY8eN1fjx48Nu06xZM11wwQWB9zNnzgyab1Kq6RuseQ4ladSoUWratGnYY1911VWB136/Xx9++GHYfXw+n8aOHRt2u169eunII48MvA/Vt6S7qqoqXXHFFYFsz0ceeaTOohbRKigo0Iknnhh4XztLLpSlS5fq/fffD/pZuL7mzTffDHxn3bt3j3guTieYpqmxY8cGLXhy1113hZybFAAyBQFAAEDS+ctf/qLs7GxJNYtH3HfffR63yF3NmjXTUUcd5drxfT5fUNnchg0bItpvwIABge+hIdnZ2erfv3/g/X//+9862xx77LGB1y+99JLuv//+kCuWumXMmDEyTdOV/+6++25H2lh7BdpIszwlBQK8khL65+oFe+CiIe3atQu8jmUF7PrYFxCI9O+Sm+yrQDdkyJAhgdfl5eX6/vvvgz5ftGiRqqqqAu9DlRSH8pvf/CaopNQq2W3IMcccE/HqtfZ2L1q0KOSKwdHq37+/a/3BpEmT4m6f3SOPPKJvv/1WUk225/nnn+/Ice0Zf1VVVbrooovqLA5i2blzpy666KI6K2o31Nfs3r07MDWCYRh65pln6kwN4aa//OUvevPNNwPv+/Xr59o0EACQKggAAgCSzkEHHRSU1fLcc89FPEdRKurSpUvMcxL98MMPuuOOO3Taaaepc+fOat68uXw+nwzDCPrPHqiwVu4Nx555E449gBkqk2TMmDFBAa3bbrtNbdu21cUXX6wXX3xRK1asiPhc6ap2xl9FRUXE+5aXlwdep/tKqW3atIlou/z8/MDrffv2hd2+srJS7733nsaPH6+ePXvqgAMOUF5eXp2/S/aAW6R/l9zSsWNHFRQURLRt7YcMtf+e/vzzz0Hvu3fvHnE7jj766HqPE0qsfUtRUZG2bt0a8b6pbvXq1YEHDLm5uXryyScdO/b5558f9OBm0aJFOv744zVx4kRt3LhRlZWV2rhxo1588UUdf/zxgRV/7VmhDWWI3nLLLdq4caOkmnk2rVLuRHjuuef0l7/8JfD+4IMP1tSpU5WVxdAXQGZjDkAAQFK64447NGnSJJWUlKiyslJ33XWXXnrpJa+b5YpYyrk2bdqka665RtOmTYt639qZZvWJNENHCp5UvqysTGVlZUEBrQMPPFDPP/+8xo0bF8gy2r17t6ZMmaIpU6ZIkjp06KDTTjtNl1xySdDANFPUHkzX/jNsiD0TJ5KyzVQWTWm0JVzW2MyZM/X73/8+6gcNkf5dckusf0cl1cn2qv2+9vaRHru+LDK7eNttz8JMZ1dddZVKSkok1QTUDj74YMeOnZWVpVdffVWnnnqqli5dKqkm4NhQafbNN9+sTz75JJDlXd/8sF999VVgPslWrVq5sihKff7zn/8ElaW3bdtWH330Ucb8zgBAQ3gMAgBISm3atAmaL2jy5Mn64YcfPGyRe6Iti9q0aZP69OkTMviXk5OjNm3aqEuXLjr44IMD/9nPEWkJXTSZZPY5/qTQc0Nddtll+vLLLwMLktS2fv16vfDCCxowYIBOOumkOiWK6a52JlckgRSLvcQ13vnBMs2rr76q3/72tyGDf82bN1f79u2D/i7ZS4u95uTfUXuWZKNGjSIq/7fYsy0jmYPS6b4lHb322muaOXOmpJqFk2666SbHz9GmTRvNmzdPF198cYNZ6Lm5uXrkkUd0//33By0yFGpF5srKSl1xxRWBf2ceeughtWzZ0vG2hzJjxgxdcsklgfkS99tvP3388cc66KCDEnJ+AEh2ZAACAJLWn//8Zz399NMqKipSdXW1brvtNr399tteN8tz1113XdAKlsOGDdPYsWN10kkn1Vse2blzZ61duzaq80Qzl5yVpWKpLwvtxBNP1OzZs7V8+XLNmDFDs2bN0ty5c7Vjx46g7RYsWKCTTjpJn3/+uXr06BFVu1NVly5dgt6vXbu2zs9C8fv9gVI7SQx2o7B582b97ne/CwQMCgoKdM0112jYsGE6+uij6wSfJGnWrFk69dRTE93UkJz8O2p/X1VVpcrKyoiDgPbgYSQZqG70LemkoqIiaLXjp556KmieTye1aNFCr7zyim677Ta9+eabmjdvnjZv3qyqqip16NBB/fr106WXXqp27drJNM2g6STsc7va22o9vLFWiU6EWbNm6ZxzzgksblNQUKCZM2dGVW4OAOmOACAAIGm1aNFCN910k2655RZJ0jvvvKMFCxZEvAhAOtq4cWPQxOZ33nln0FxH9YllEYRo5tratm1b4HVubm7YMs1u3bqpW7duuuGGG2Sapr755hu99dZbeuGFF7Rp0yZJNUGF8ePHa9GiRVG3vbZp06a5NgH8ddddF5jsPh6HHXZY0Ptffvml3mxJu9WrVwdNzl/7OKjfxIkTAxlNTZo00dy5c8MuyOPkgiLxivXvqKSglbtDvd+2bVvE2Y72Y9c+TihOtjsWCxYs0MUXXxz3cUI555xz4i55LSkp0ZYtWyTVlOpeeeWVUe0/bdo0ffnll4H3Dz30kEaOHNngPocffrhuv/32BrdZtWpVUAbmCSecEHIby7Jly9S1a9cGj7lz586g9/379w9krHfo0EGzZ89ucH+ppuR4+PDhgZL8Jk2aaPr06frNb34Tdl8AyCQEAAEASe26667TY489ps2bN0uqmQfps88+87hV3pk1a1agtKp58+a67bbbwu6zc+dOFRcXR32uaEqu7eW63bp1i+o8hmHo+OOP1/HHH6+bb75ZZ555ZmDQ980332jp0qU6/PDDozpmbcXFxUFZk06qPYCN1WGHHabs7OxABstXX33V4Hxclq+++irofTSLN2Q6e19y2WWXRbQadzItSLRu3ToVFxdHtBBI7ZL62n9PDznkkKD3S5YsiTgAuGTJksDrcAEfKfa+pUWLFlHNH1if0tJS1/oDK3DnlOrq6qjbumfPnqBS3Vj6/1BqB+MGDBjQ4Pbbt2/X9u3bozrHmjVrAq/tq1LX55tvvtEZZ5wRCEw2btxYb731lvr06RPVeQEgEzAHIAAgqTVp0kR33HFH4P2sWbP08ccfe9iiYPYSOauM0E32Us/DDjssaGXd+syaNSumc82ePTsQjGpIZWVl0MAwVFZIpPLz8/Xoo48G/cyaoD7dNWnSJCjj79NPP41ovkb734emTZsy8I2C/e/TMcccE9E+sf59ckuk/eFHH30UeJ2bm1sn2Hn88ccHzRX64YcfRnTc//3vf0FZepFkaC9evDjiLEB7u3v06BHziumIn7Vgk1RT3hvtwx6nLV26VKeddlogK7dRo0aaOnWqhgwZ4mm7ACBZEQAEACS98ePHB81rFknWW6LY56NyKsuiIfaAUKQrkD7xxBMxnWvnzp166623wm43bdq0oAU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" 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", 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", 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" - ] + "metadata": { + "image/png": { + "height": 480, + "width": 640 + } }, - "metadata": {}, "output_type": "display_data" } ], "source": [ "gce.reset()\n", - "init_size_dist = pd.DataFrame(\n", + "size_dist = pd.DataFrame(\n", " {\n", " 'pop_density': gce.state,\n", - " 'size_class': list(range(21))\n", + " 'size_class': list(range(21)),\n", + " 't': 21*[0],\n", " }\n", ")\n", - "init_size_dist.plot(x='size_class', title='initial distrib.')\n", "\n", - "gce.step([0])\n", - "\n", - "size_dist_1 = pd.DataFrame(\n", - " {\n", - " 'pop_density': gce.state,\n", - " 'size_class': list(range(21))\n", - " }\n", - ")\n", - "size_dist_1.plot(x='size_class', title='distrib. at timestep 1')\n", + "for t in range(1,4):\n", + " gce.step([0])\n", + " new_size_dist = pd.DataFrame(\n", + " {\n", + " 'pop_density': gce.state,\n", + " 'size_class': list(range(21)),\n", + " 't': 21*[t],\n", + " }\n", + " )\n", + " size_dist = pd.concat(\n", + " [size_dist, new_size_dist],\n", + " ignore_index=True,\n", + " )\n", + " new_total_pop = pd.DataFrame({'pop': [sum(gce.state)], 't': [t]})\n", "\n", - "gce.step([0])\n", + "ggplot(size_dist, aes(x='size_class',y='pop_density')) + geom_line() + facet_grid(cols='t') + labs(title=f\"N. traps = 0, final pop = {sum(gce.state):.0f}\")\n", "\n", - "size_dist_2 = pd.DataFrame(\n", - " {\n", - " 'pop_density': gce.state,\n", - " 'size_class': list(range(21))\n", - " }\n", - ")\n", - "size_dist_2.plot(x='size_class', title='distrib. at timestep 2')" + "\n" ] }, { @@ -376,80 +344,51 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 61, "id": "781f996b-dcc5-4106-810b-d3ab296cfac2", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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ICAA/WQ/fyVbeEIsAJIJVgAEkiwQgAKBKSLYCkHYXAMlgFWAAfqIFGECySAACAKqERB++aQEG4Aa3WoB58AaQiGQTgCwCAoAEIACgSkh2ERBudgEkgwpAAH6yxw5eRABIBAlAAECVkOj8W8wBCMANVAAC8JObFYDcEwGZiQQgAKBKSLYCsLCw0PUxAcgc1sN3MBiMe18WAQGQLDfnACQOAZmJBCAAoEpgERAAfkr0JYRE5Q2A5NnvY5KNQ9wTAZmJBCAAoEpgERAAfkqmBZgHbwDJst/H0AIMIBEkAAEAVQKLgADwU6LzkErhbcPEIgCJcLMFuKSkxJUxAahaSAACAKqERCsAWQQEgBuSSQAahsHLCABJSbYFmDkAAZAABABUCcnOAVhSUqLS0lLXxwUgMyT6EsLCdAQAkmGPHYksRmQYRmg/4hCQmUgAAgCqhGRbgO3HAIB4JbMIiEQ1MoDkJFsBKP0vDlGJDGQmEoAAgCoh2UVA7McAgHhZc2Yl+uBNCzCAZLiZAOR+CMhMJAABAFVCsi3A9mMAQLysB+ZEWu8kHrwBJCfZFmApfFoUAJmHBCAAoEpItP3OXjHIgzeARCX6EsJCBSCAZNiTdsnGIe6HgMxEAhAAUOmVlpaGFvBgDkAAfmAOQAB+sscO4hCARJAABABUevYb1XjnAKQCEIAbrMo9VgEG4Ac3WoBZBATIbCQAAQCVXjJvvatXrx76mBteAIkoKSmRaZqSEk8AWvsx9xaARLjRAkwFIJDZSAACACq9ZFa+owUYQLKSqUIuux9xCEAi3FwEhBeiQGYiAQgAqPRoAQbgp2ReQpTdjwdvAImwT0NgGEZCxyAOAZmNBCAAoNKjAhCAn9xIAFIBCCAZya5ELhGHgExHAhAAUOlRAQjAT2623hGHACTCih2JTkMgUQEIZDoSgACASo9FQAD4iRZgAH5LdiVy6X8vMHgRAWQmEoAAgEovmYdvKgABJItFQAD4zY0EIC8igMxGAhAAUOkl8/DNHIAAkkUFIAC/WfcwbswBSBwCMhMJQABApZdMCzAJQADJsj8sUwEIwA9uVgASh4DMRAIQAFDpsQgIAD+52QJM5Q2ARNACDCBZJAABAJWeWxWA3PACSAQtwAD8ZsWOZFqAWQQEyGwkAAEAlV4yD9+0AANIFi3AAPxmxQ4qAAEkigQgAKDSYxEQAH5yowWYB28AyXCjBZipCIDMRgIQAFDp2W9Uq1evHte+tAADSJYbLcBUAAJIhhstwCwCAmQ2EoAAgErPrUVACgsLXRsTgMxBBSAAv1lxyJrHLxHEISCzkQAEAFR6bs0ByA0vgES4uQgIlTcAEuFGBSCVyEBmIwEIAKj0klkFOBgMyjCMcscBAKdYBASA35gDEECySAACACq9ZNrvDMOg5QVAUmgBBuA3VgEGkCwSgACASi/Z9jsqbwAkw40WYGveLuIQgESUlJRIogUYQOJIAAIAKr1kq2+YewtAMtxoAbbikGmaKi0tdWVcADIHFYAAkkUCEABQ6SVbfUMCEEAy3GwBLns8AHDCzTkAiUFAZiIBCACo9JJZBMS+Dze8ABLhRguw/aGdWAQgXm6uAlxaWkolMpCBSAACACo962HZvqJvPGh5AZAMtysAiUUA4uVmC7D9eAAyBwlAAECll+xbb1peACSDCkAAfrMWAXGjBVjiRQSQiUgAAgAqvWTfetMCDCAZbi4CUvZ4AOCE2xWAxCEg85AABABUetZNLxWAAPxgxY5AIKBAILHbZ1rvACQj2XshiUpkINORAAQAVHrJvvWuXr162HEAIB5uTr4vEYsAxI8WYADJIgEIAKj03KoA5GYXQCKs2EHrHQC/sAgIgGSRAAQAVHrJVt8wByCAZLjx4B0MBkMfkwAEEC+3K5GJQ0DmIQEIAKj0WAQEgJ/cePCmAhBAMqhEBpAsEoAAgErPukm15vKLF4uAAEgGrXcA/GSapitxiLlIgcxGAhAAUOm5tQgIb7sBJMLtyfd58AYQDysGSe5VIhOHgMxDAhAAUOkl235HBSCAZLhdAcjLCADxsMcM+3yi8WIOQCCzkQAEAFR6bs0ByM0ugEQkuxK5RAUggMTZ719YBARAokgAAgAqvWQfvqkABJAMFgEB4Cf7/QtxCECiSAACACq9ZB++rf0KCwtdGxOAzGHFoGRa75h7C0CivGgBJg4BmYcEIACg0qMFGICf3KgA5MEbQKLcagHmRQSQ2UgAAgAqPbcqALnZBZAINxYBse9rX9ETAGKx37+4FYd4KQpkHhKAAIBKjwpAAH5iERAAfrLfv7iVACQOAZmHBCAAoNJLtgKQRUAAJMOq2EvmwZvWOwCJ8qIFmEpkIPOQAAQAVHpuVQDy0A0gEW60ALP6JoBEudUCzIsIILORAAQAVHrW6r3MAQjADywCAsBPtAADcAMJQABApefWIiDFxcUyTdO1cQHIDFQAAvCTWy3ALAICZDYSgACASs+tFmCJG14A8bPiBpU3APzgxSrAxCEg85AABABUem4tAiJxwwsgfm60ABuGEYpFvIgAEA/7gh1UIgNIFAlAAECl52YFIAlAAPFyowVYYj5SAInxYhEQEoBA5iEBCACo9KgABOAnNyoAJSkYDEoiDgGIjz1mJBOHAoGAAoFAuWMCyAwkAAEAlZ51k1q9evWE9rfvxxtvAPGy4oaVwEuU9eBub+cDgFjcagGWwhdGA5BZSAACACo10zRpAQbgKytuJFsBaMUw4hCAeLjVAixRiQxkMhKAAIBKzf7WmxZgAH5wqwWYOQABJMJeredWHKICEMg8JAABAJWaG2+9qQAEkAy3FwHhwRtAPOwxgzgEIFHJRQ9UKsnMS+Nk388++0zjx4/X/v374zr2UUcdpcceeyzsAdw6X7Jz6bgh1WPg2rl2v3k5Bi/iUGlpaejj7OzshM5hnwOwtLRUwWAwY/5Nop2Pa/ePH+fPlGtP9PjR9rMqkbOyspIav/3BuzLFIX4e/Tkn1+6fqhaH7N0Qid4LWexzkRKHMvPaK8t1+zGGTL52iQRgWmnYsGFC+wWDQUf7Pvnkk1q0aFHcx1+6dKmuvPJKDRo0qNz36tWrF/fx3OT02r3AtfuHa/fu2r2IQ/YEYP369RM6R+PGjUMf16xZM+wY6f5vEg3X7g8/r1tK/2tP5PixxmVVANarVy+p8WdnZ0s6uBJnZYlD/Dxy7X5I92t3Ow7ZX2Q2bdo0qfFbxyIO/U+mXjv3gf7x69pJAKaRnTt3xrV9vXr1FAwGVVJSory8vJjb5+bmSpJatmypnj17xtx+7969mj17tiRp8+bNYeMLBoOqV6+e8vLyfFkJL95rdxPXzrVXlWtP5JeSF3Fo27ZtoY8LCwvjPock5efnhz7esWOHdu7cWSX/TdzCtftz7X5et1Q1r93rOORkXPaFiIqLixOKQZZA4ODsO/v27fM9DvHzyLVz7c5Uhjhk//qePXviHo+dFYf27t1LHMrQa+c+sOpdu1vJQhKAaSSZH2An+xYWFkqSevTooeeeey7m9hs2bAglAPPz8yOeo6SkxJf/8cqOwa/zcu3+4dor37Er2regoCD0sfULM17Wza51PPsx0v3fJNZ5uXZ/+H1uv89fGY9f0X72rycagyz2VYArUxzy+9x+n9/Pc/t9fj/P7ff5K+PxndwLBQIB4pAH5/b7/H6dl/tA//hxfhYBgWPW22+nK08x6T4AN7AICAA/2WNGsvP12B+8AcApVgEG4AYSgHDMqgC0z0ERjX07a18AiJcbN732xCEP3gDi4eaDtxWLePAGEA97zLB3NSSCFxFA5iIBCMfirQC0JwD5BQMgUW5UANrjEQ/eAOJhj0FU3gDwgxUzsrKyZBhGUsciDgGZiwQgHIu3AtB+k0wFIIBE2eMHFYAAUs3+kJzoS4iy+xOHAMQj3kKMaKypDIhDQOYhAQjHSAAC8IMb7Xf2/XjjDSAebiYAqbwBkAgrZiQ7D6n0vzhEAhDIPCQA4ZiVxHP6AB4IBEI3yiQAASSKRUAA+MmNGFR2f+IQgHjYW4CTZR3D7xVQAaQeCUA4Zt2sOq0AtG/LjS6ARLm9CAgvJADEw4s5ALkvAhAPK2Yk+xLCfgziEJB5SADCEdM0E5p7wtqWB24AiXK7ApDWOwDxsFfJsAgIAD9YMcONBCBxCMhcJADhiP0BnApAAKmUaPyxowUYQKLsMSPZ+beovAGQCDdbgIlDQOYiAQhHEl2FkwpAAMliERAAfnIjBpXdnzgEIB5Wss6NRUCsBCBxCMg8JADhCBWAAPziRguwfT/iEYB4uLkIiPXwzoM3gHhYUxG4uQgIcQjIPCQA4QgVgAD84kb1jWEYtLwASIgXFYDEIQDxYBEQAG4gAQhHqAAE4Be3qm948AaQCHsCMNmHbypvACTCzQQgcQjIXCQA4QgVgAD8Yk/YJVN9QwIQQCLcbAGm8gZAItxsASYOAZmLBCAcSbQCkAduAMlyq/2ON94AEuHWSwj7/sQhAPHwogWYOARkHhKAcCTRCkArWUgFIIBEuVV9wxtvAImwKm+k5BOAxCEAibCSdbQAA0gGCUA4QgUgAL+4XQFIPAIQD3vMsFbxTZQVh0pKSmSaZlLHApA53EwA8iICyFwkAOGIvYIvngRgdnZ2uf0BIB5uPXxzwwsgEV6sAiwRiwA4Z8ULN+YApAIQyFwkAOEIFYAA/GLdoGZlZckwjISPw6rkABLhxSIgZY8LANFQAQjADSQA4QhzAALwi1tvvXnjDSARblYA2h/e7XMLAkA0XswByFQEQOYhAQhHqAAE4BfrBYJblTfEIwDxsCcAqQAE4Ad7N0SyiENA5iIBCEeoAATgF7duenkhASARbrYAMwcggERY8cLNFmCJrggg05AAhCNUAALwi1s3vVQAAkiEV4uA8OANwCkvWoDtxwWQGUgAwpGCgoLQx1QAAkgltyoArXjEzS6AeNACDMBvtAADcAMJQDiSbAUgCUAAiaICEICfrJgRCAQUCCR360zlDYBEWHEoGAwmfSymIgAyFwlAOJLsHID8cgGQKCt+xPPyIRKmJACQCCpvAPjNqzjEiwggs5AAhCNUAALwi1s3vdYNLze7AOLh5uT7VN4ASAQJQABuIAEIR6wEnmEYcZWe2+fcKi0t9WRsANKbWw/fvJAAkAg3H7xpAQaQCK9agIlDQGYhAQhH7C14hmE43o833QCSxSIgAPzk5uqbtAADSERJSYkkpiIAkBwSgHDEqpiJ95eOvV2YXzAAEsEiIAD85GYLsP0Y1gM9AMTCVAQA3EACEI4kOgm//RcMbXcAEuFWBaC1PxWAAOLhVQswD94AnDBN07NKZO6JgMxCAhCOuFEBSAIQQCKoAATgJ1qAAfjJXi3MIiAAkkECEI4kWgFICzCAZFmxw60KQGIRgHh41XrHgzcAJ+z3LbQAA0gGCUA4kmgFIC3AAJLldgswN7sA4uFmCzAVgADiZX9ZwIsIAMkgAQhHqAAE4Be3qm9IAAJIBBWAAPxkjxW8iACQDBKAcIQKQAB+YREQAH6y5t9iERAAfvCyBZh7IiCzkACEI9Yvnuzs7Lj2owIQQLKslwduLgJimmbS4wKQGaz7l2AwmPSxqLwBEC+3W4DtsYw4BGQWEoBwhFWAAfjF7QpA0zTDVtQDgGjcWoio7DGovAHgBHMAAnALCUA4whyAAPzi1vxbVN4ASISbi4DYK2948AbghP2ehRcRAJJBAhCOMAcgAL+4VX1jfyHBDS8Ap6x4QeUNAD+4XQHIC1Egc5EAhCNUAALwi1vVN9zwAkiEVwlA4hAAJ2gBBuAWEoBwhApAAH5xqwWYB28AiXArBklSIBBQIBAIOy4ARGNP0rnRAswLUSBzkQCEI1QAAvCLdeMbb/wpiwQggES4uQiI/ThU3gBwwn7P4nYLMHEIyCwkAOFIQUGBJCoAAaQei4AA8JO1argbD9724xCHADhBCzAAt5AAhCNuVACSAAQQL9M0Qw/fLAICwA9utgBLVAACiI/bLcDBYFCGYUjiRQSQaUgAwhHmAATgB/uNKYuAAPCDWwsRWazjEIcAOGGPFcFg0JVj8iICyEwkAOEIcwAC8IOb894wByCARLhdAUgLMIB4uF0BKBGHgExFAhCOJFoBGAwGQ6vdUQEIIF5u3vSSAASQCCsOuZ0AtKY3AIBovEwAUgEIZBYSgHAk0QpA+z48cAOIl5sVgLQAA0iE2y3AVN4AiActwADcQgIQMZmmmXAFoH0fKgABxMvNt94sAgIgESwCAsBP9mphXkQASAYJQMRUUlIi0zQlUQEIILVYBASA31gEBICf3LwXKnscXkQAmYUEIGKyV+5RAQgglbxaBIQbXgBOeTUHIAlAAE7Y71ncagEmDgGZiQQgYrL/YqACEEAqsQgIAD+ZpkkLMABfsQowALeQAERMVAAC8Is9bri5CAjxCIATzL0FwG9udkNYrHjGauRAZiEBiJioAATgFxYBAeAnLx+8iUMAnLDHCqYiAJAMEoCIyV4pk0gCkApAAIly8+GbRUAAxMteHcODNwA/eNECzIsIIDORAERMVAAC8IubK9+xCAiAeHm5+ib3RQCcsGKFYRgsAgIgKSQAEVOycwBaCUAqAAHEy8233lQAAoiXFy3A1nGYewuAE1ascCsG2Y/F/RCQWUgAIiYqAAH4xasKQOIRACdYfROA36xY4VYMsh+LFxFAZiEBiJhYBRiAX9x8+CYBCCBe9hjkVusdc28BiId1z0IFIIBkkQBETFQAAvCLm+13gUAg9ABPPALghBdzAPLgDSAeXrQA8yICyEwkABETFYAA/OL2wzc3vADiweqbAPxGBSAAt5AAREz2xB0VgABSye2Hb254AcTDi0VAWAUYQDy8mAOQ+yEgM5EARExUAALwi9sP3zx4A4iHfYJ8t1cBJg4BcMLLFmAWAQEyCwlAxOTWHIAkAAHEiwpAAH7ysgKQFmAATtACDMAtJAARExWAAPzi9sM3UxIAiAeLgADwm/WywM0WYF5EAJmJBCBiYhVgAH5hERAAfvJiERArAUjrHQAnrDgUDAZdOyYvIoDMRAIQMVEBCMAvtAAD8JM9Brn18M1cpADi4cUiIMQhIDORAERMVAAC8IsVNwKBgAKB5H9lccMLIB5etwCbpunKMQGkLy9agK04REcEkFlIACImNysAudEFEA+3b3qpAAQQDy9agO3HoQ0YQCxetgCTAAQyCwlAxGQvOzcMI+797VWD/JIBEA/rBYRbK99Z8YhYBMAJL1YBth+HlxEAYmEREABuIQGImKwH8ER/6dj3Yx5AAPGgAhCAn+wVem4lAO3xjFgEIBYrTrgVg+zHIgYBmYUEIGKyfjEkMv9f2f34JQMgHm5PfM0cgADi4UUFoD2eUX0DIBYrTriZALRXADJFE5A5SAAiJioAAfjF7bfezHkDIB5eLgJS9vgAEImXi4DYjw8g/ZEARExW0i47Ozuh/akABJAot2967YsSAUAs9hZgLxYB4b4IQCxetgBLJACBTEICEDEl24JHBSCARLndAswiIADiYU/QubUCp/04rAIMIBYrTnjRAizxIgLIJCQAEZOVtGMOQACpxiIgAPxkf1lABSAAP1ABCMAtJAARU7IVOPYEIBWAAOLh9k2vfdJrAIjFizkASQACiIfb3RBlj0UcAjIHCUDERAUgAL+4fdNLBSCAeFgvC4LBoAzDcOWYVN4AiIcXLcAsRgRkJhKAiIk5AAH4xXo4drsCkJtdAE64HYMkKm8AxMfrFmDmIgUyBwlAxEQFIAC/uF0BSAIQQDy8fvAmFgGIxe35kMseizgEZA4SgIjJSgBSAQgg1dy+6SUBCCAeXj940wIMIBYqkQG4hQQgYrJ+KVABCCDVWAQEgJ+ovAHgN1YBBuAWEoCIiQpAAH5x++GbRUAAxMOKFcFg0LVj0gIMIB68iADgFhKAiIkKQAB+8aoCkFgEwAkvHryZfB9APOyrkbvFfiwqAIHMQQIQMblZAVhQUODKmABkBhYBAeAnL1rvqLwB4JRpmsxFCsA1JAARExWAAPzi1SIgpaWlKi0tdeWYANKX1xWA3BcBiMaenKMFGECySAAiJuYABOAXK2a4VX3DgzeAeHi9+iaVNwCisccI5iIFkCwSgIiJCkAAfvGqAlAiHgGIzevVN4lDAKJJRQUgLyKAzEECEDElWwFov9GlAhBAPLxaBMR+bACoCKtvAvCTPUbwIgJAskgAIqZkKwANwwjtyy8YAPFw++GbG14A8aAFGICf7DHCqwQgcQjIHCQAEVOyFYD2fakABBAPt1cBZkoCAPGgBRiAn2gBBuAmEoCIqqSkRCUlJZISrwC078uNLoB4uJ0A5I03gHjQAgzAT7QAA3ATCUBEZf+FkEwCkApAAIlgERAAfvKiAjAYDMowDEm8iAAQnVctwNwPAZmJBCCicisBSAUggESwCAgAP1ldEG5WAEr/i2kkAAFEQwswADe59xrBZvbs2TrttNO8OLTrHnjgAX311VeSpFNOOUW33HJLhdt+8cUX+uCDD7R69WoVFBSoSZMmOvHEE3XBBReoXr16Uc+ze/duvfnmm/rqq6+0Y8cOZWdnq0OHDho8eLB69uzp5iW5yl6xxxyAAFKppKREpmlKYhEQAP6w4kQwGHT1uFlZWSoqKiIOAYjKqxZge0wjAQhkDk8SgGeccYbat2+vMWPG6IorrlCzZs28OE3SPv/881DyL5b//Oc/ev/99yVJgUBA2dnZ2rRpk6ZPn65PP/1UDz74oFq3bh1x3/Xr1+vOO+/U7t27JUk1a9bUvn379P333+v777/X0KFDNWbMGHcuymVUAALwixc3vSwCAiAebs9DaqECEIATVhWyRAswgOR51gKck5OjcePGqXXr1hoxYoQ+/vhjr06VkH379un5559X7dq11apVq6jbzpo1S++//74Mw9Cll16qqVOnaurUqXr88cfVtm1b7dq1Sw888EDE4FlUVKQHHnhAu3fvVtu2bfX444+H9r/00ktlGIZmzJihjz76yKtLTYpbFYDWQzcVgACc8qLthQpAAPHwYhEQ+/GIQwCioQIQgJs8SQCOGjVKNWrUkGmaKioq0ptvvqnTTjtNnTt31iOPPKLt27d7cdq4TJw4Ubm5ubr00kvVoEGDCrcrKirS5MmTJUmDBw/WhRdeqOzsbElSu3btdPfddys7O1ubN2/WnDlzyu0/a9Ys/fbbb8rOztY999yjdu3aSZKys7N14YUX6swzz5Qkvfbaa5Uy+FIBCMAv9njhxSIglTHmAqhcrDjh5oO3/XjEIQDReHEvJEmGYfAiAshAniQAJ0yYoE2bNunxxx/XUUcdJdM0ZZqmVq9erbFjx6pVq1YaOXKk5s2b58XpY1q2bJlmz56tTp06hRJwFfnhhx+0c+dOGYah8847r9z3mzVrpr59+0pSxOuxvta3b181bdq03PeHDx8uwzCUm5urpUuXxn8xHmMOQAB+8eKtNxWAAOLhVQKQB28ATnjVAiwRh4BM5FkLcP369XXjjTfqhx9+0Oeff67LL788VBVYWFioqVOnauDAgerSpYseffRR5ebmejWUMEVFRXrqqadkGIZ+//vfKxCI/lfwww8/SJJat24dMYEnSccff7wkacWKFcrPzw99/cCBA/rll18kSd26dYu4b9OmTUMtyEuWLInvYlLAnrCjAhBAKnnRAsycNwDi4fZK5BbreMQhANF41QIs/a8N2J5kBJDePEsA2vXq1UsTJ06MWBX4yy+/6E9/+pMOPfRQXXbZZfrss888Hcsbb7yhjRs36qyzzlKHDh1ibr9hwwZJUtu2bSvcxvqeaZrauHFj6OsbN24MrWDpZH/rXJUJFYAA/MIiIAD85tUiINbxaAEGEI1XLcD243E/BGSOlCQALWWrAi+77LJQVWBBQYEmT56s/v3768gjj9QTTzyhXbt2uXr+DRs26M0331SjRo10ySWXONrHqkxs1KhRhdvYv7dz585y+zrd375vZUEFIAC/sAgIAL9ZlTE8eAPwg5ctwFQiA5nH3SgSh169eqlXr1564okndPfdd+upp56SdLCKbvny5br11ls1btw4jRo1SuPGjdOhhx6a1PlM09TTTz+t4uJiXX311apVq5aj/ayWXmvhj0js39u/f3+5fZ3uf+DAgahjee2110ILkkRy8cUXa+TIkVGPYWe1PwcCATVs2DDq2CSpcePGFW4XS+3atSVJpaWlatiwoQzDkHQwKWxVSaaSk2v3CtfOtafztcd77GjjqlGjRujjBg0auDLuvXv3hj62HsDT/d8kkkz5eYzEz2v387qlzLn2eI4fa1zWi4jatWu7Om7r/ipT/k0i4dq59nS+drfikFvPYpFYx7bOn+7/JpFkys9jWdwHZua1Sz4mAIuLi/XWW2/p//7v/zRv3jwZhhH6B7D+u3//fv3nP//Ryy+/rEcffVRjxoxJ+HyzZs3SsmXL1L17d/Xp08eVa0i1ffv2aevWrRV+f//+/WFLujtlGEaF+9nfOtWsWTOh40v/+wVTWFgYdoxYczB6Ldq1e41r9w/X7t21J3rsSOMqLS0NfVyjRg1Xxm1PKlrxLd3/TaLh2v3h53VL6X/tbt4LWZUx2dnZro67bAtwuv+bRMO1+4NrrxpxyP4sRhzyTqZeO/eB/vHr2lOeAFy1apX+7//+Ty+//LK2b98u6X8Jv5NOOknXX3+9TjvtNE2dOlXPPfecVqxYof379+u6665TmzZtdPrpp8d9ztzcXL388suqXr26rr322rj2tR4WCwoKKtzG/j17ZaH9QbOgoKDCqkNr/5o1a0YdS+3atdWsWbMKv1+rVq24JnENBAKhxKv9QdvOXsUYDAYTniTW+gVTUFCgkpISGYahQCCg0tJS3zLvsa7dK1w7115Vrj2RX0rxxoho47LHn0Ag4Mok1fZf9tbxq9K/iVuq4s+jW/y8dj+vW6qa1+51HIo1LisBmMw9UCRW65011UpV+jdxS1X8eXQL1161rt3POGSfjsmteyELcahq/jy6gfvAqnftbiULU5IALCoqClX7ffrpp5L+l/SrVauWLr74Yv3+978PraYrSbfccotuueUWvfrqq7r++uu1f/9+PfTQQwklAF955RXt27dPF1xwgerXr1+u1db6iy8pKQl9Lzs7W4FAQI0aNdKaNWuirlJs/569jNM+719ubm6FCUBr/1gloJdeeqkuvfTSCr+/ffv2uOYRbNiwoYLBoEpLSyvcz35tBw4cSHieQuvfOz8/Xzt37lQwGFTDhg21e/duX1aecnLtXuHaufaqcu1NmjSJ+zzx/r1GG5c9/lixI1n2aRr27NkjSVXq38QtVfHn0S1+Xruf1y1VzWv3Og5FG5dpmqHKmOLiYlf/zawWJOu+syr9m7ilKv48uoVrr1rX7mccss+Jv2/fPlf/zayXosShqvXz6AbuA6vetScShyLxNAH4yy+/hKr9duzYIel/iaAuXbro+uuv1+WXX6769etXeIzLLrtMK1eu1IMPPqiffvopoXFYbbNvvPGG3njjjQq3+/TTT0MJyscee0zt27dX69at9c0332j9+vUV7md9zzAMtWrVKvT1Vq1ahbK769evD/tepP1bt24d34WlgH1S2GQWAWEVYADxYhEQAH7ycvJ9VgEG4IQ9RrAICIBkedL4/Prrr2vAgAHq0qWL/v3vf2v79u0yTVPVqlXTBRdcoE8++UTLli3TjTfeGDX5ZznppJMkKZRETKVjjjlG0sEkndWyXNbixYslSYcffnhY22/NmjXVqVMnSdJ3330Xcd/t27drw4YNkqRjjz3WtXG7xZ6wS+YBnFWAAcTLHi/cSgDaj0M8AhCNPUbw4A3AD14mAFmNHMg8nlQAXnLJJWGLerRq1UrXXHONrr76ajVv3jzu4yVTeSZJ48ePj/r9cePG6ccff9Qpp5yiW265Jex7xxxzjBo2bKidO3fq7bffLrcQybZt2zR//nxJUv/+/csdu3///lq5cqXmz5+vESNGqGnTpmHfnzZtmkzTVKNGjXT00UfHf3EeowIQgF+8SAAGg8HQ7ycqbwBE40UVctnj8eANIBov7oUsVkKR+yEgc3i69Mlpp52mt99+W2vXrtVdd92VUPJPOlgBOHfuXH3yyScujzC2rKwsjRw5UpL03nvv6c033wwt2pGTk6P7779f+fn5atGihU499dRy+59++ulq3ry58vPzdf/99ysnJ0fSwcUw3nzzTc2cOVPSwfn93H6r4wYqAAH4xauHb15IAHAiFa13PHgDiIYKQABu8iTj9Kc//UnXXnutOnTo4MrxGjZsqH79+rlyrEScfvrpysnJ0fvvv69XXnlFkyZNUnZ2dmgy+QYNGuiuu+6K+ICalZWlu+66S3feeafWrl2rm2++WbVq1VJ+fn5o8ZEhQ4Zo0KBBKb0mp9x668QDN4B4edV+l5WVpcLCQh68AUTlZQswD94AnLDuVQzDcG0VUAsvIoDM40kC8B//+IcXh/XVddddp2OPPVbvv/++1qxZE6r6O+mkk3T++edHncuwTZs2evLJJ/XWW2/pq6++0vbt21W7dm21b99eZ511lnr27JnCK4mPlbALBoNJ/dKxKgBLS0tVUlLi+i8wAOnH6wpAHrwBRONlCzAP3gCcsGKE2zFIIg4BmciTBOCVV14pSbrpppt03HHHOd7vxx9/1L///W8ZhqEXX3zRi6FFFGuOQEuvXr3Uq1evhM7RoEEDXXXVVbrqqqsS2t8v1gNydnZ2Usex/9IqLCxMel5HAOnPywrAsscHgLJSsQgID94AorHikBdTRXE/BGQeT+YAnDhxol5++WWtX78+rv1+/fVXTZw4URMnTvRiWEiAVQGY7Fsne8KPXzIAnPBq4msevAE4wdxbAPxmxSEvEoDcDwGZx9NFQFD1WTemyVbsla0ABIBYWAQEgJ9oAQbgNy9bgHkRAWSeSpUALCkpkeTNGw4khgpAAH7xqv3OOhaxCEA0LAICwG9WjPBi/nReRACZp1IlAHNyciRJ9erV83kksLhVAWjfn6obAE54VX1jxSNueAFE42UFIAlAAE6wCAgAN3laamcYhqPt9u/fr++++06PP/64DMPQEUcc4eWwEAcqAAH4hQpAAH6yPxS7XX3DgzcAJ2gBBuCmpJ+o/va3v+m+++4r93XTNHXOOeckdMxzzz03yVHBLcwBCMAv9pXvnL5QcoIbXgBOeLUQkf14xCEA0aSiBZg4BGQOV0oqTNOM6+vR9O/fX3/4wx+SHRJcUlBQIIkKQACpZ8UKJt8H4IdULALCPRGAaKw58r2sAOR+CMgcSScADzvsMPXr1y/sa59++qkMw1DXrl3VpEmTqPsHAgHVqVNH7dq106BBgzR48GAFApVqasKM5tYDOBWAAOJlrwB0E6sAA3AiFYuAmKap0tJSV48NIH14dS9kPyYvIoDMkXQkGTVqlEaNGhX2NSuB9+CDD2rYsGHJngI+sh6QWQQEQKp5Ne8Ni4AAcMIeI9x++LYfj4dvABVJRQKQ+yEgc3iyCEjfvn1lGEbM6j9Ufl5UAHKjC8AJr256eeMNwIlUtABLxCIAFaMFGICbPEkAzps3z4vDwgdUAALwi1cVgEy+D8CJVLQAlz0PANh5WQHI/RCQeZhsD1ExByAAv7AICAA/UQEIwG9WHPJ6DsBEFu8EUPWQAERUXlQAcqMLwAmvEoAsAgLACXsCMBgMunpsKgABOOFlAtAeh6xWYwDpLalIct9994U+vueeeyJ+PVH248E/VAAC8AstwAD8ZI8RXsWhsucBADuvXoZK4S826IoAMkNSCcC//vWvMgxDUnjCzv71RJEArByYAxCAX7ya94ZJrwE4QQswAL+lqgKQOARkhqQjiWmaEZN9ycwjkGzyEO5x660TLcAA4kUFIAA/sQgIAL+lMgHIMziQ/pKKJHPnzo3r66h63KoApAUYQLy8qgBkERAATtjnxPIqDkkkAAFUzKuXoVL5OJTs8x6Ayi+pu5l+/frF9XVUPdZNqZsJQG50ATjBIiAA/OTlHID2B29eRgCoiBUf3F6ISCr/fEYCEEh/rAKMqNyqAAwEAqGbXR66ATjhVdsLLcAAnLBiRDAYdL01jhejAJxgERAAbiIBiKjc/KVjJRG50QXghNcVgNzsAogmla13ABCJl3GIFxFA5nF/NtE4bN26VV9++aWKi4t17LHHqkOHDn4OB2WYpulaC7BE2x2A+Hh102s9eHOzCyCaVLbeAUAkxCEAbvIkAZibm6uJEydKks466ywdfvjh5ba5//779eCDD4YFmxEjRuill15SjRo1vBgW4uT23DdUAAKIh1eLgNgrAJNZsR5AeqMCEIDfiEMA3ORJAnDq1Kn605/+pOrVq2vUqFHlvj9p0iTde++9Mgwj7OFr6tSpKi0t1ZQpU7wYFuJkr9SjAhBAqnl108sbbwBOeDn3FnEIgBPEIQBu8mQOwLlz50qSTj75ZDVu3Ljc9++55x5JB1tMzz77bN1yyy1q3bq1TNPUG2+8oc8++8yLYSFOVAAC8JP1ssCrCkCJeASgYl5VIUvEIQDOeNkCbD8mcQjIDJ4kAFeuXCnDMNSrV69y31u4cKFycnJkGIYeeOABvf322/r3v/+tr7/+Wg0bNpQkvfrqq14MC3FyuwLQOgYVgACcoAIQgJ9KSkokeZMApPUOgBOpWgSEhdGAzOBJAnD79u2SpE6dOpX73kcffSRJys7O1s033xz6erNmzXTxxRfLNE19+eWXXgwLcbLfkLrZAsyNLgAnvGp74cEbgBNUAALwG3EIgJs8SQDu2LFDklS7du1y3/v8888lHWwPLvv9Y445RpK0fv16L4aFOBUUFIQ+drMFmApAAE54vQiI/RwAUJaXc2/ZW++ovAEQiWmaVCIDcJUnCUDDMCRJO3fuDPt6aWmpFi1aJMMwdPLJJ5fbz5ovcP/+/V4MC3GiAhCAn6yHYjfijx0JQABOpKr1jjgEIBL7ywHiEAA3eJIAbNasmSTpl19+Cfv6l19+qby8PElSz549y+23d+9eSVLNmjW9GBbiZK/UowIQQKp5VQFoPx7xCEBFvJx8nwdvALHYYwMtwADc4EkC8Pjjj5dpmpoyZUrYw9Xzzz8v6WAiqHfv3uX2W7NmjSSpZcuWXgwLcaICEICfvKq+sccz4hGAinhZAUjrHYBYrPZfyZsEIFMRAJnHkwTgBRdcIEnasGGDBg4cqP/85z8aM2aMXn75ZRmGoWHDhkWs8vvyyy9lGIaOOOIIL4aFOFEBCMBPqagA5MEbQEWYfB+An6gABOA29yOJpIsvvlhPPvmkFi1apIULF2rhwoWh72VnZ+vee+8tt8+uXbs0b948SVKPHj28GBbi5FUFIAlAALGYpulZ9Q03vACcoAIQgJ/ssYE4BMANni0CMnPmTJ1zzjkyDEOmaco0TR166KF666231LVr13L7TJw4MRR4Bg0a5MWwECevKgD5BQMgFi8nvuaGF4ATVhzyovLGMIzQcYlDACLxugWYF6JA5vGkAlCSGjVqpGnTpmnbtm1as2aNateura5duyoQiJxz7Nq1qyZMmCDDMNS9e3evhoU4UAEIwC9etr3Yb3iJRwAq4mULsHQwFhUXF/PgDSAiWoABuM2zBKCladOmatq0acztTjvtNK+HgjhRAQjAL15WALIICAAnvGwBlkQFIICovLwXklgEBMhEnrQAIz1QAQjAL16+9aYFGIATXrYAS/+7L+LBG0Ak9thABSAAN5AARIWoAATgFy8nvuaGF4ATXrcAW9U3xCEAkXjdAswLUSDzeN4CLEmbN2/Wjz/+qJ07dyo/P9/RPpdffrnHo0Is9l8E2dnZSR+PCkAATrEICAC/ed0CbB2XOAQgEq9bgK3FiJiLFMgcniYAp0yZooceekhLly6Naz/DMEgAVgJUAALwC4uAAPBbKhYBsZ8HAOzsCUD7fH1uYjEiILN4lgC86aab9PTTT0uSTNP06jTwEHMAAvALLcAA/FZSUiKJRUAA+MPLeyELUxEAmcWTBOC7776rp556KvR5jx49dOqpp6pVq1autJIiNaxEXSAQcOWtk5VELC4uVmlpadLHA5C+UlUByA0vgIpY8cHLyhv7eQDAzusWYPtxWYwIyAyeJACfe+45SQdvmCZOnKhLLrnEi9PAY9YNqRvVfxIP3QCc8/Kml1gEwAkrPlABCMAPqWgBJg4BmcWTVYC/+eYbGYahSy+9lORfFWZVALp142tPJNIGDCAaL9teWAQEgBMsAgLAT6loASYOAZnFkwTg7t27JUkDBw704vBIEbcrAO3H4ZcMgGhYBASA36wEoFeLgFB5AyCaVLYAE4eAzOBJAvCQQw6R5F2gQmpQAQjAL/abXrdeQlioAATghNcJQB68AURDCzAAt3mSAOzRo4ck6eeff/bi8EgRK0nHHIAAUs3LCkDDMHjwBhCTFR+8rgBk8n0AkaSiApA4BGQWTxKA119/vUzT1GuvvcbDVRVGBSAAv3h900sCEEA0pml6PgcglTcAovHyZWjZ4xKHgMzgSQJwwIAB+sMf/qA1a9Zo9OjRBJQqystVgEkAAojG64mvueEFEE1JSUnoY+beAuAH+8tQpiIA4AZPIsn69et12223KTc3V5MnT9Z3332n3//+9+rVq5eaNGmiQCB23rFNmzZeDA1x8LICkF8yAKLx+q23Fdd4GQEgEnsMYu4tAH5gERAAbvMkAXjYYYfJMAxJB+daWrlypW655RbH+xuGwTwElQAVgAD8QgswAD/x4A3Ab7QAA3CbN5FEB+dOQdVGBSAAv6SqApBYBCCSVDx4E4cARJOKFmAWAQEyiyeRZNSoUV4cFinmdgWg/TgFBQWuHBNAevK6+oY33gCiSeXqm8QhAJEQhwC4zZME4IQJE7w4LFLM7QpA+3H4JQMgGq8XAaHyBkA0TL4PwG9WbAgEAo7m0E8EcQjILN5EEqQFLysAmQMQQDS0AAPwE3NvAfCbtRq5VzFI4n4IyDQkAFEhLysASQACiCZVLcDEIgCRpLL1jrm3AERiJeW8TADyIgLILCQAUSEvKwD5JQMgGitGGIahYDDo+vGteEQsAhAJqwAD8JsVG7yKQfZj8yICyAzevU74/3bt2qXnn39es2bN0rJly5Sbm6vi4uJyQeaTTz7Rb7/9piZNmui0007zelhwwKqMcSsBSAUgAKe8vunljTeAaOyxwYuXEBJxCEB0qWgBtuIbcQjIDJ4mAKdOnaprr71We/bskSSZpinpYEVHWUuWLNFtt92mmjVravPmzapXr56XQ4MDVAAC8IvXbS9U3gCIhgpAAH5LRQswcQjILJ61AL/yyisaOXKk8vLyZJqmmjdvrs6dO1e4/ejRo1WtWjXl5+drxowZXg0LcWAOQAB+sR6+qQAE4AcWAQHgN6/vhezHJg4BmcGTBOCmTZt03XXXyTRNtWzZUrNnz9avv/6qhx9+uMJ9GjZsqL59+0o62A4M/7EKMAC/eN0CbB2XWAQgklRWAJaUlIS6ZADAYsUhFgEB4BZPEoBPPfWU8vPzVbNmTX388ccaNGiQo/1OOukkmaapJUuWeDEsxMntCsBgMKhA4OCPHL9kAERDCzAAP9kTgF7HIYlYBKA8WoABuM2TBODs2bNlGIZGjhypww8/3PF+HTt2lCStXbvWi2EhTm5XANqPRdUNgGish283448dN7wAokllC3DZ8wGAlJoWYGsREFYBBjKDJwnAnJwcSVKfPn3i2q9+/fqSFFo0BP4xTdP1CkD7sbjRBRANFYAA/JTKFuCy5wMA6X9xwauVyCXuh4BM40kCcN++fZKkOnXqxLXfgQMHJEk1atRwfUyIj30+GjcrcLKzsyVRAQggOhYBAeCnVCQA7S84SAACKMvr+ZDtx+Z+CMgMniQAGzduLEnasmVLXPv98ssvkqSmTZu6PibEp6CgIPQxFYAAUi1VFYC8jAAQif0+xavqG1qAAUTDIiAA3OZJArBr166SpPnz58e134wZM2QYhrp37+7FsBAH+y8B5gAEkGqpWgWYG14AkdACDMBvqUgAWnHINE2VlJR4dh4AlYMnCcAzzzxTpmlq+vTpoaq+WKZMmaLvv/9ekjR48GAvhoU42BN0XlQAkgAEEI3XLcAkAAFEwyIgAPyWykVA7OcDkL48SQBeffXVatSokQoLCzVs2LDQoiAVmTp1qsaMGSPDMNSyZUuNHDnSi2EhDl5XAHKjCyAar1uAaXkBEI39QdjrqQgkYhGA8ry+F5KIQ0Cm8SSa1KtXT88++6wuuugirVy5UkcddZTOPffc0AIQkvT0009rw4YN+vDDD7V06VKZpqlgMKiXXnrJ07cccIYKQAB+ogIQgJ9YBASA31LZAmw/H4D05Vk0ueCCC7Rr1y7deOONOnDggF5//XVJkmEYkqSbbroptK1pmqpevbqee+45nXrqqV4NCXGgAhCAn1gEBICfUtECTOUNgGhS0QLMVARAZvGkBdgyZswYff311zrnnHNkGIZM0yz3Rzo459+iRYs0atQoL4eDOFABCMBPqVwExPpdBACWVC8CwoM3gLJoAQbgNu+iyf939NFHa9q0adq9e7c+//xzrV27Vrt27VKdOnXUqlUrnXzyyWratKnXw0CcWAUYgJ+8bnux3/CWlJSEqtMBQErNHIC0AAOIxlqV18sEoP3YrAIMpD/PE4CW+vXrs7pvFUIFIAA/eV0BWLblxc0XHQCqPisGBYNBz14QMPcWgGhSUQFICzCQWTxtAUbVRQUgAD+lqgXYfi4AsDD3FgC/eX0vVPbYxCEg/ZEAREReVwDyCwZANF6/9ab1DkA0qa68IQ4BKCvVLcDEISD9JRVN1q9f79Y4ymnTpo1nx0Zs9gRddna2a8elAhCAE15X39grm4lHAMpKxYM3lTcAomEREABuSyqaHHbYYZ7Mi2IYBm8gfEYFIAA/eX3Ty9xbAKJJResdLcAAomEqAgBuS/rJyjRNN8aBSoY5AAH4ybrp9WpxDm54AUST6sobXkQAKMuKC6wCDMAtSUWTUaNGRf3+unXrNG/ePElSIBBQ165d1bFjR9WuXVv79u3TqlWr9PPPP6ukpESGYah///60/lYSVAAC8BOLgADwUyoqb4hDAKKhBRiA25KKJhMmTKjwe3PnztX555+vYDCoW2+9VX/84x/VvHnzctv99ttvevTRR/Xoo49qyZIluvvuu9W/f/9khgUXUAEIwE8sAgLAT1ZcCAaDnp2DSmQA0dACDMBtnqwCvHHjRl1wwQXatWuXpk6dqn/84x8Rk3+S1Lx5cz388MOaOnWqcnNzdeGFF+rXX3/1YliIAxWAAPzk9U2v/bi8kABQVirmAKQFGEA0qXgRQRwCMosnCcAnn3xSubm5Ouecc3Teeec52ufcc8/Vueeeqx07dujJJ5/0YliIAxWAAPyUykVAeCEBoCwqbwD4yTTN0Jx8TEUAwC2eJADfe+89GYahwYMHx7Xf4MGDZZqmZsyY4cWwEAd7gs7NB3B7ApAFZABEUlpaqtLSUkmpqQDkjTeAslIx9xZTEQCoiD0Z52UC0F5dSBwC0p8nCcANGzZIkurWrRvXftb21v7wj/VLp3r16jIMw7Xj2qsJ+SUDIBL7TS8VgAD8YFXesAowAD/YYwItwADc4kkCMBA4eNiff/45rv2WL18etj/8U1BQIMn9N07MuwUgllS89ab1DkA0qagADAQCoXte4hAAO3syjqkIALjFk0xb586dZZqmXnrpJe3bt8/RPvv27dOLL74owzDUqVMnL4aFONgrAN1kPx4JQACRpOKmlwpAANGkYhEQ+/GJQwDs7DEhVRWAxCEg/XmSADz//PMlHVwNeMiQIdq2bVvU7bdt26Zhw4aFWn9HjBjhxbAQBys5RwUggFSjBRiA36wXEV5WAEr/i0W03gGwS1UFIC3AQGbx5K7mpptu0vPPP681a9Zo/vz56tSpk0aOHKmBAweqY8eOqlWrlvbv369Vq1bpk08+0eTJk5WXlydJ6tixo2688UYvhoU4pKoC0O3jA6j6qAAE4LdUJQCt4xOHANj50QJMAhBIf57c1dSoUUOzZs3SKaecovXr12vPnj167rnn9Nxzz0Xc3loNtk2bNvrwww+VnZ3txbAQByoAAfgl1RWA3PACKMuKC7QAA/BDqlqAmQMQyCyerbbRvn17LVmyRGPGjFFWVpZM06zwT/Xq1XXNNddoyZIlateunVdDQhyYAxCAX1gEBIDfUrEIiP34vIgAYGetRC7RAgzAPZ7e1dSvX1/PPfecHnzwQc2cOVNfffWVNm3apL1796pOnTo69NBDddJJJ2nw4MFq0qSJl0NBnKgABOCXVCQAaQEGEA0VgAD8lIp7IUkyDEPBYFAlJSXEISADePta8/9r0qSJRo0apVGjRqXidHBBKioACwoKXD02gPTAIiAA/MYiIAD8lKoWYOlgHCIBCGQGz1qA3TZnzhy1b99eHTp08HsoGYEKQAB+YREQAH5LVQuw9WBPAhCAXapagCWmIgAySUoqAN2wf/9+rV27VoZh+D2UjMAcgAD8wiIgAPxGCzAAP6XiXqjs8bkfAtJflakARGpZyTm3E4BUAAKIxX7T63YMslhz3pQ9HwBIqasAJAEIIJJUJgCJQ0DmqDIVgIgtmfkhyu5r/QLIzs52dd6JGjVqhD4uLCz0fE4LJ1I9But8XLu/uPbKd2xr39LS0tDX3I5BdtacN8XFxb7/u/DzmPrz+X3tfpw/U6490ePb97Pa77Kysjwdr/XgXVJS4uu/Cz+P/pyTa/dPZY9DpmmGvublvZBEHPLr3JHGkerz+X3dfowhk69dIgGYVho2bJjQfsFgsNy+1gN47dq1Ez5uJE2bNg19XFhYqHr16rl27EREuvZU4dr9w7V7d+1uxCH7i4LGjRt7Nt7q1asrPz9fgUDAt58HiZ/HTLx2P69bSv9rT+T4ZcdltcLVrVvX0/Fa8c40Td9+Jvh55Nr9kO7XnmwcStW9kPS/bgvDMIhDPsjUeyGJa/fj2kkAppGdO3fGtX29evVCy77n5eWFfe/AgQMJHzea/fv3hz4uLCxUXl5e2CS3qRLt2r0WDAZVr149rp1rT6lErj2RX0puxCH7Mfbv3+9qDLKzWmr27t3r2Tmi4ecx867dz+uWqua1ex2HKhqXNU1JSUmJp/HBmtv6wIEDKY9D/Dxy7Vy7M37EIfv++/bt8zQ+WFVIXp8nEn4eM+9eSOLaUxWHIiEBmEaS+QEuu69141utWjVX/8ewl7kWFhaqpKTEl//x7Pw6P9fOtfvFy/O7EYcKCgpCXwsEAp6N12p5sWKRn/h59Oe8fl673+f2+/yV8fj2/awKQOsm3SvWi4iioqK0/jeJdW6/z+/nuf0+v5/n9vv8lfH41n72udK9jkP21cjT+d8k1rn9Pr9f5+U+0D9+nJ9FQBARqwAD8It9FTovV+Bk1TsAFbEnAL3E5PsAImEREABeIAGIiKzknNsP36wCDCAW+w2olwlAbngBRGKaZigB6GUMsh+fFxEA7OyVQSQAAbiFBCAiogIQgF9S9dabG14AkaSqClkKb70DAEsqKwDpiAAyBwlARORVBWC1atVCE16TAAQQSaoevkkAAojEHoOovAHgh1R1Q9iPTxwC0h8JQERkJefcrgA0DCN0TBKAACKhAhCAn/xIAFJ5A8DOjxZg4hCQ/kgAIiKvKgDtxyQBCCASFgEB4Cc/Wu94EQHALpVxiKkIgMxBAhDllJSUqLS0VJL7FYD2Y5IABBAJi4AA8BOtdwD8lsq5SIlDQOYgAYhy7Ik5KgABpJoVG4LBYGjOUC8QiwBEYm+98/rBm0pkAJEwFykAL5AARDn24E8FIIBUs256U/XGmwdvAHb2+yCrNc4rPHgDiMSKCYFAQIGAt4/svIgAMoe3rxNcdPbZZ4faUuEtKgAB+Mm66eWNNwA/0HoHwG+pehlqPwdxCEh/KUkAFhcXa8GCBfrqq6+0adMm7dmzR3Xr1lXLli3Vo0cP9enTx/M3rHCOCkAAfkp1BSA3vADsUjkHIJU3ACKxYkIqnpGJQ0Dm8DQBWFpaqkceeUSPPfaYtmzZUuF2zZs316233qo//vGPnpc4IzYqAAH4KVUVgNzwAogklXNvEYcARGLdC6WiApA4BGQOz7Jtu3btUp8+fXTHHXdoy5YtMk2zwj+bN2/W2LFjdfLJJ2vXrl1eDQkOpaoCsKCgwPVjA6j6rBjkRfyx42UEgEj8mnzfNE1PzwWg6qAFGIAXPLmrMU1TQ4YM0Zdffinp4OSlp512mgYNGqROnTqpdu3a2rdvn1atWqWPPvpIc+bMUUlJib788ksNHTpUn332mRfDgkNUAALwE4uAAPCT/SE4VRWA0sHVh70+H4CqIZUtwNwPAZnDk7uMCRMmaOHChTIMQx07dtTUqVN13HHHRdz2j3/8o5YsWaKLLrpIK1as0MKFCzVx4kSNHj3ai6HBAeYABOAnFgEB4KdUzgFoP35RUREJQACSUlsBaMUd7oeA9OdJC/Brr70mSapfv77mzp1bYfLPcuyxx+rjjz9WgwYNJEmvvPKKF8OCQ/bEnBcJQCoAAUTDIiAA/FRSUhL6OJUJQKpvAFhS9TLUfg5iEJD+PEkALl26VIZh6Morr1TLli0d7dOyZUtdddVVMk1TS5cu9WJYcMjrBCAVgACiYREQAH6yvxTwuv3OHud4GQHAYt2bpCIBSAswkDk8SQDu27dPktS9e/e49uvWrZskaf/+/a6PCc553fpCBSCAaFJdAUgsAmBnfwhOdQswAEi0AAPwhicJQKvqz95C4YS1fYsWLVwfE5yjAhCAn1JVAWjFIt54A7Dzaw5AYhEASypbgJkSBcgcniQA+/btK0lauHBhXPtZC4f069fPi2HBISoAAfjJikFeP3jzxhtAJPZEXCpXASYWAbCksgXYOkdpaalKS0s9Px8A/3iSALzhhhsUCAQ0ceJE/fzzz472+fnnnzVx4kQFg0HdcMMNXgwLDlEBCMBPLAICwE9+JQDj7ZwBkL78mAPQfl4A6cmTBGD37t31z3/+UwUFBTrllFP0/vvvR93+gw8+0MCBA1VYWKh//etfobkA4Q8qAAH4KdWLgPDGG4Cd/T7I6zjEHIAAIklVN4REJTKQSTy5q3nllVfUqFEjnXvuuZo2bZqGDh2qLl26aNCgQerUqZNq166tffv2adWqVZozZ46WL18uSTrvvPNUv359vfLKKxUe+/LLL/diyLChAhCAn1JdASgdvOHNzs729HwAqoZUzgHIgzeASKyKYCoAAbjJk4gyevRoGYYhSTIMQ6Zpavny5aFEX1mmacowDE2bNk3Tpk2r8LiGYZAATAESgAD8lOpFQKxzkgAEIIW34rIICAA/pHIREF5EAJnDs4himmbUz2NtD//YE3Ne3PiSAAQQTaoXAbGfEwDs8SAYDHp6LlqAAUSSyhZgXkQAmcOTBOCECRO8OCxShDkAAfgpVW+9efAGEIn9AZgXEQD8kMoWYOIQkDk8iSijRo3y4rBIESsxV61aNQUC7q8TY1UAlpaWsuIdgHJS9dabN94AIvFrERDiEACLXy3AxCEgvXmyCjCqNusXjhfz/0nhN7tUAQIoK1WLgPDGG0AkVgyqVq1aaE5rr1CJDCCSVN0LlT0HCUAgvZEARDlWUs6rXzhlJ94HALtUVQASiwBEYk8Aeo3KGwCRWPHA63lIJV5EAJnE+zsbmy1btmjz5s3as2eP6tatq5YtW6pZs2apHAIcSHUFYO3atT05D4CqKVVvvbnhBRBJKlvvqLwBEEkqFwGhIwLIHJ7f2axfv16PP/64pk2bpvXr15f7fps2bXT++efrpptuUuvWrb0eDhxIZQUgLcAAykrVwzc3vAAiSWXrHXEIQCS0AAPwgqctwBMmTNCRRx6pxx57TOvXr5dpmuX+rF+/Xv/+97/VtWtXTZw40cvhwKFUVgByswugLD8qALnhBWChBRiA31LZAkwcAjKHZ3c2EyZM0FVXXSXDMGSapgzD0BFHHKHOnTurTp062rt3r1auXKnly5fLNE3t27dPV111lSRp9OjRXg0LDqSyArCgoMCTcwCouvxYBZiXEQAsfrUAE4cAWPyqRCYBCKQ3T+5sNm/erBtvvDH0+XXXXae//OUvatOmTbltN2zYoIceekjPPfecSktLdeONN+qMM85Q8+bNvRgaHKACEIBfTNNM2cM3sQhAJLTeAfAbLyIAeMGTFuBnnnlG+/fvl2EYev755/XMM89ETP5JUuvWrfX000/rhRdekCTt379fzzzzjBfDgkNWBaBXCUDmAARQkZKSktDHVAAC8EMqH7yZAxBAJKmcioD7ISBzeJIAnDVrlgzD0GmnnaYrr7zS0T6jR4/WGWecIdM09eGHH3oxLDjkdftd2VWAAcBiv/FkERAAfuDBG4DfaAEG4AVPEoBr1qyRJJ1zzjlx7Xf22WeH7Q9/pLICkJtdAHb2G08WAQHgB1qAAfjNrxZg4hCQ3jxJAO7Zs0eS1KhRo7j2s7bfu3ev62OCc1QAAvBLKisAqbwBEEkqH7yDwaAMwwg7L4DMVlpaqtLSUkmsRg7AXZ4kABs3bixJysnJiWu/tWvXSoo/cQh3UQEIwC/2mOB19Q0twAAiSWUFoP089jlQAWSuVHZDSNwPAZnEkwTgUUcdJdM09eqrr4beXsRSUlKiV199VYZh6KijjvJiWHCICkAAfknlTS8vIwBEYsWhYDCYkvNZD9/EIQBSarshJDoigEziSQJw2LBhkqRly5bp97//vUzTjLq9aZq64YYb9OOPP0r631yA8AerAAPwi18twLS8ALB4/SK0LOs8PHgDkMKrgZkDEICbPEkAXn311WrVqpUk6fnnn1e3bt00adIkbd26NWy7bdu2adKkSerevbuef/55GYahVq1a6eqrr/ZiWHAolRWA3OwCsKMFGIDfUrkKsPS/WMeDNwAp9RWAzAEIZA5PIkqNGjX01ltv6ZRTTtH+/fv1ww8/6PLLL5ck1a1bV7Vr19a+fftCi4VIB6sAa9eurWnTpik7O9uLYcEhKgAB+IVFQAD4jQpAAH5K5ctQiReiQCbxpAJQkk488UR9/vnn6tq1q0zTDP3Jy8vTb7/9pry8vLCvH3300Vq4cKG6d+/u1ZDgkJWU8+oXDvNuAahIKm96SQACiMRqv6MCEIAfaAEG4BVPI8oxxxyjH374QTNnztS0adO0aNEibd68WXv27FHdunXVokUL9ejRQ8OHD9fgwYNlGIaXw4FD1oMwFYAAUi2Vi4AEg0EFAgGVlpaSAAQQYsWDVCcAiUMApNS3AAcCAe6HgAzheUQxDENDhgzRkCFDvD4VXOJ1BSBVNwAqkuq2l6ysLBUUFPDGG0CIFQ9oAQbgh1S+DLWfh/shIP151gKMqsvrCkDDMEK/zKgABGCX6pteHrwBlOVXBSAP3gCk8FhAJTIAN6UkouzYsUMzZszQV199pU2bNoVagFu2bKkePXpoyJAhaty4cSqGAge8rgCUDiYXi4qKSAACCJPqthcevAGU5dcqwDx4A5BSfy8kcT8EZApPI8qePXs0duxYTZw4UQUFBRG3ee6555Sdna0rr7xSDz30kOrUqePlkBBDaWlpKPB7VQEocbMLIDI/WoDLnhdAZkt1C7D1gG+f+B9A5vKjBdiKQyQAgfTmWQvw+vXrdfzxx+u5555Tfn5+2Iq/Zf/k5+fr2Wef1fHHH68NGzZ4NSQ4kKqHbyu5SAUgALtU3/QSiwCUxSIgAPxkvxcKBoMpOSdxCMgMntzZFBYW6owzztCaNWskSXXq1NEll1yiQYMGqVOnTqpdu7b27dunVatW6aOPPtKkSZO0Z88erV69WmeccYa+//77lL3tQDh70KcCEECq0QIMwG8sAgLAT6nuhrCfh/shIL158nT1zDPPaPny5TIMQz179tQbb7yhli1bltvumGOO0Xnnnae7775bF154oT7//HMtX75czzzzjG6++WYvhoYY7FUwVAACSDUWAQHgN7/mAOTBG4Dk3yrAEvdDQLrzpAV46tSpkqQWLVrogw8+iJj8s2vRooX+X3t3Hl9Ffe+P/3VyErKSjQQCBAKEfSfIjogsIouoVVEQt1pqe29dWvv1ttXignrbXmurrcu17c8NESouKAYQVGSRVSDsaxKSQCAJgUBWknPO74/czzBZyTIzn8+ZeT0fDx8ekpyZz+ScfM7Me97vzzs1NVX7uaVLl5oxLGoCfUDOigxABgCJSI8ZgEQkG0uAiUgmmSXAPB8isjdTAoBHjhyBy+XCj3/8Y0RGRjbpOW3btsWDDz4In8+HI0eOmDEsagKrSoCZAUhE9WETECKSTVYJMC+8iQhgExAiMo8pAUAR1BkwYECznte/f38AvBCTyaoMQAYAiag+spqA8HOHiARmABKRTDLXAOQ8RGRvpgQAExMTAQBlZWXNep74+c6dOxs+Jmoaqz5w+CFDRPWRVQLMuYiIAMDn88Hj8QDgGoBEJAdLgInILKYEAKdOnQqfz4dvvvmmWc/7+uuv4XK5cMMNN5gxLGoCZgASkUxsAkJEMnHxfSKSjfMQEZnFlADgww8/jNDQUHz44YfYuHFjk56zceNGLF26FGFhYXj44YfNGBY1ATMAiUgm/ZxgxV1v3vEmIj2rs5ABzkNEVBPnISIyiykBwN69e+Ptt99GYGAgZsyYgddff73BTK/Kykq88cYbmDlzJoKCgvD222+jV69eZgyLmoAZgEQkkzjpDQoKgsvlMn1/vBlBRHpcfJ+IZNPPBVYFADkPETmDKTPKc889B6C6FHjlypV4+OGH8fvf/x7jx49Hr169EB4ejpKSEhw/fhwbN27EhQsXAACzZs3CwYMHtefXZ+HChWYMmf6PVV2AedFNRPXRBwCtwCYgRKQnc+0tzkNEBLAEmIjMY0oA8JlnntEyN8T/z58/j5UrV9b5WZ/Pp/3MypUr6/0ZPQYAzaXPyDPzA4cZgERUH3bfJCKZZHbfZOYNEQEsASYi85g2o/h8viZ9rbGv12ZFOZjTWZUByAAgEdVHnHjywpuIZGDmDRHJJjqRA7whSkTGMmVG+fbbb83YLFmgoqJCe8wmIERkNatLgDkXEZGezMwbzkNEBDATmYjMY8qZzXXXXWfGZskCVmUABgcHA2AGIBHVJCsDkBfeRATIzbzhhTcRAVfOSdxut2UVcJyHiJzBlC7A5L+sWgOQF91EVB+uAUhEMsnMvOE8RETAlRsRVp0L6ffFeYjI3hgApBq4BiARycQuwEQkk8wSYJ/PVyMDkYicyeqboQAzAImcwrpZhfwCMwCptpycHKxcubLZr9U111yDMWPGmDQqsis2ASEimWQ2ARH7d7vdluyXiNRk9bmQfl+8NiOyNwYAqQZmAFJtCxYswM6dO5v9vKCgIOzcuROdOnUyYVRkVywBJiKZ9AFAqwJx+vmusrJSWyeZiJxJzEPMACQio7EEmGoQAbmAgABTT3z1HzJer9e0/VDrXL58Gbt3727RcysrK7Fnzx5jB0S2xwxAIpJJ5hqAAOciImIJMBGZhxmAVIP4wDEz+6/29nm3W13p6enaekT//Oc/cfPNN1/1OWVlZUhKSoLP58PRo0cxY8YMs4dJNsIMQCKSSXYJMOciIpJRAswmIETOwAxAqkFkAJr9gVM7AEhqOnr0qPa4d+/eTXpOaGgounbtCgA4duyYKeMi+7L6pFfMRVVVVfD5fJbsk4jUJbMJCMDsGyK6Mg9YuR4oMwCJnIEBQKrBqgxA/cku1wFUlwjgBQQEoEePHk1+Xq9evQDUDCASNYWsDED9vonIufRdeDkPEZEMYh5gExAiMhoDgFSDCMZZWQLMAKC6RACvW7duzSrTFtmCx44dY1YVNYvVJ7288CYiPdlrAHIeIiKZXYA9Hg/P3YlsjAFAqoEZgKQnAoBNLf8VRAZgSUkJTp8+bfi4yL5kNQHR75uInIslwEQkm8wSYP3+ich+GACkGrgGIAlerxcnTpwAcCWg11T6gCHLgKk5xBzE0jsikoFNQIhINplNQADOQ0R2xgAg1SCjCzAzANWUnZ2NsrIyAM3PAGQAkFpKVhMQgCe8RFQzAGhV9o1+vtOvQUhEzmT1esgAMwCJnIIBQKrBqgxAlgCrryUdgIXo6GjEx8cDYCdgah42ASEimWSsAcjMGyLSEwE4WQFAzkNE9sUAINUgIwOQHzJq0gfumlsCDFwJGjIDkJpD5hqAnIuIiCXARCSbzCYgAOchIjtjAJBqYAYgCSJw17FjR7Rt27bZz9d3AiZqKpldgFnyQkT6eYBNQIhIBpYAE5FZGACkGpgBSEJLOwALImuwoKAAhYWFho2L7E1mAJBzERHJDgByHiIi2SXADAAS2RcDgFSDjC7AzABUj8/n0zL3WlL+C7ARCLUMm4AQkUz6eYAZgEQkg4wAoH5fnIeI7IsBQKpBBOPMzgDk3W615efn48KFCwBangGofx7LgKmp2ASEiGTSz0Eul8uSfXIeIiI92SXAnIeI7IsBQKqBGYAE1AzYtTQAmJCQgIiICADMAKSmYxMQIpLJ4/EA4OL7RCQP5yEiMgsDgFQD1wAkoGbArqUlwC6Xi52AqdlkZgCy5IWIxBzkdrst2yfnISLSk50ByHmIyL4YAKQamAFIwJWAXXR0NOLj41u8HRE8ZAkwNYXP57P8rjfveBORntVZyLX3xXmIiNgEhIjMwgAg1WBVBiBPdtWmbwDSmjWQRAZgdnY2SkpKDBkb2Zd+LmATECKSgZk3RCSbjBsR+jmP50NE9sUAINXADEACrmQAtnT9P0H//OPHj7dqW2R/srtv8oSXiGRfeDMASES8EUFEZmEAkGpgBiBdunQJubm5AFofANSvH8gyYLoa/QmnjBJgnvASkezSO54TEZFYDoXzEBEZjQFAqsGqDEC3260tsM0MQLXoA3UtbQAiJCUlacFkNgKhq2EGIBHJxswbIpKN8xARmYUBQKrBqgxA/T540a0WfaCutRmAgYGBSE5OrrNdovrIzgDkXEREskuAOQ8RkZgHZDUjYgCQyL4YACSNz+ezLAMQuBIAZAagWkSgLjQ0FF26dGn19tgJmJpKRhMQBgCJSE9GCbDL5dL2xwtvImIJMBGZhQFA0uhPOoODg03fHzMA1SQCdcnJyQgIaP0UIbII09PT+VpTo/Q3A6w66WUXYCLSk1F6B1y5+OY8REQy5iE2IyJyBgYASaO/+LYyA7CiosL0fVHTGdUBWBDbqaqqQkZGhiHbJHuSXQLME14iklECDFy5+GYAkMjZvF4vvF4vAHklwJyHiOyLAUDS6Cd7rgHoTBUVFcjMzARgXABQ30iE6wBSY2Q0ARHNiGrvn4icSXYGIG9EEDmbfg5gExAiMhoDgKSRlQHINQDVkZ6ert11bG0HYCE5ORkulwsAA4DUOBkZgC6XixfeRKSRlQHIeYiIADk3QwFmABI5BQOApGEGIBnZAVgIDQ1FUlJSne0T1SajCYh+X5yLiEgE4PTZwVYQ++M8RORsMm6G1t4Xb0QQ2Ze19Q0S5efnY8uWLdi7dy8yMzNRWFiIwMBAxMfHY+jQobjpppuQkJDQ6Da2bNmCVatW4cSJE6ioqEBcXBxGjBiBO+64A5GRkY0+t6ioCMuXL8f27dtx7tw5BAcHIzk5GTNmzMDo0aONPNQWYwYgiQYgbrcbPXr0MGy7vXr1QmZmJgOA1CjZAUDORUQk5iFmABKRDPo5wMobEWwCQuQMjggA5ufn4yc/+Ql8Pp/2tbCwMFy+fBnZ2dnIzs7GmjVr8Nhjj2H8+PH1buPNN99EamoqACAgIADBwcE4ffo0VqxYge+++w4vvPACunTpUu9zs7Ky8OSTT6KoqAhAdUZUSUkJ9uzZgz179uCmm27CggULDD7q5mMGIIkAXbdu3Qx9D/Tu3Rtr167FsWPHtBJjotpk3/XmCS8RyS4B5jkRkbPJuhnqdrvhcrng8/k4DxHZmCMCgCLgkJKSgkmTJmHo0KGIjIyEx+PBoUOH8NZbbyEzMxMvv/wyEhMT0a1btxrPX7NmDVJTU+FyuXD33Xfj5ptvRnBwMDIyMvDyyy/j5MmTeP755/H3v/+9zkRdWVmJ559/HkVFRUhKSsKvfvUrdO/eHRUVFVixYgU++OADfPHFF+jevTumTJli1a+kXswAJKM7AAtiPcGSkhLk5OSgbdu2hm6f7EH2ujc84SUiEQC0ugmI2B9vRBA5m6yboWJ/ly9f5jxEZGOOWAMwIiICf/nLX/DMM89gwoQJWrmu2+3GwIED8eyzzyIqKgpVVVVYsWJFjedWVlZiyZIlAIAZM2Zgzpw5CA4OBgB0794dv//97xEcHIzc3FysXbu2zr7XrFmDM2fOIDg4GAsXLkT37t0BAMHBwZgzZw6mT58OAFi8eLH0yZYZgM7m8Xhw4sQJAMY1ABH0AcVDhw4Zum2yD1knvbzwJiJBVgCQNyKICJBXAgxwHiJyAkcEAMPDwxtdzywmJgbDhw8HAC0AIuzduxfnz5+Hy+XCj370ozrPbd++PSZMmAAAWL9+fZ3vi69NmDAB8fHxdb5/2223weVyobCwEPv27WvqIZmCGYDOlp2djfLycgDGZwAyAEhNwQxAIpJNzAOyAoC8EUHkbDIzAHlDlMj+HBEAbAqRFejxeGp8fe/evQCALl261BvAA4Bhw4YBAI4cOaIFUACgrKxMa6qQkpJS73Pj4+ORmJgIAEhLS2vFEbSePhDHDEDnMaMDsBAVFYX27dsDYACQGia7CQjnIiKS1QREXHhzHiJyNlk3Q/X7YwCQyL4YAPw/+/fvBwAkJSXV+Hp2dna9X9cT3/P5fMjJydG+npOTozUeacrzxb5kYQags4lgNWB8CTBwJajIACA1RHYTEF54E5G4EcwmIEQkg/5ciEsREJHRHNEE5Gq2bt2K48ePAwAmT55c43uFhYUAgNjY2Aafr//e+fPn6zy3qc/XP7c+ixcv1tYjrM/cuXMxb968RrehFxAQoP0/JiZGW9sQAOLi4hATE9PkbTWXy+XS9uf1ek3dV31qH7uVXC4XgOqsOH1naqs0dOwnT54EACQmJjbY0bo1Bg0ahE2bNuHQoUPKHbsVVH3djdbcbevHpc88NnsOAq68JiEhIdq/rXxf8P3ovGOXedyAc469OduvPS4RAAwPD7fkNZI5D/H9yGPnsZunpfNQaGhojW1YOQ+J8zC32815yAJOPRcCeOzi/zLe844PAObn5+O1114DAIwaNUpbC1AQJb364Fht+u+VlpbWeW5Tn19WVtboWEtKSpCXl9fg90tLS1u0WKzL5YLb7a5xxyk0NNT0hWf1GYBWL3IriGOXQfzxy1L72I8cOQIA6Nevnym/k/79+wMACgoKUFhYiLi4OMP30VR83c079pZu2+Vy1ViCISQkxLLXSL/2loz3Bd+Pzjt2mccN2P/YW3MuJDJf2rRpY+lrJHMe4vuRxy6D3Y+9pfOQ1+vV/m3luRDAeUgWp54LATx2Gcfu6ABgcXExFi1ahKKiIiQkJOCRRx6RPaRGhYeHa+uo1ScsLKzOGoaNCQgIgMvlgs/ng9frrRGwdLvdzdpWc7lcrhoBQDP3VZ/ax24ll8uFgIAAeL1eaXcdah+7z+fTSnP79u1ryuuhX1fw4MGDGDdunOH7uBq+7s079pZ8KDX3vaMfV0VFRY2vmz0viNdEnPBaPRfx/ei8Y5d53IB/HrvZ81DtcYkAoNnnQYJ4TUSpn5XzEN+PPHYee9NYOQ/pz4Vq3xw1S+3zocrKSs5DFnDquRDAY7dqHqqPYwOAZWVlePbZZ5GZmYnY2Fg899xzaNu2bZ2fEyUZ+sm4Nv33wsLC6jxX/Iz+e/U9X5/yXZ/58+dj/vz5DX6/oKDgqmXEejExMXC73fB6vTh//nyN55aWljZrW83ldru1AGB5ebmp+6pP7WO3kkirLyoqsjzwCdR/7Hl5edrjrl27mvI76dSpk/Z4165dWkaglfi6N+/YW5Kl2dzfq35cRUVF2teLi4tNX4NGvCaiFMDsea82vh+dd+wyjxvwz2M3ex6qPS4x71RVVVnyGtUutbPynIjvRx47j71prJ6HBKvmA/GaiIyokpISzkMWcOq5EMBjt2oeqo8jm4BUVFTgueeew5EjRxAVFYVFixYhISGh3p8V6/Pp1/OrTf89/Umcft2/pjxfRg24nv5im12AncXsBiAA0KFDBy3Iru84TCSwCQgRySbmITYBISIZVOgCzHmIyL4cFwCsqKjAokWLcODAAUREROC5555rtOGB+F5WVlaDPyO+53K5kJiYqH09MTFRyyxpyvPNaLzQHOwC7Fz6gJy+VNdILpdL2zYDgFQfMRcEBARYui6Hfs0bInI2ceErq/sm5yEiZ9NnJMkKAHIeIrIvRwUAKysr8eKLL2Lv3r0ICwvDM888g+7duzf6nMGDBwOoDtIVFBTU+zO7d+8GAPTp06dG2W9oaKiWTbVr1656n1tQUIDs7GwAwJAhQ5p3QAZjBqBziYBcTEyMqc05GACkxsjKvOEdbyICqtfDFRffnIeISAb9HMBMZCIymmMCgFVVVfjDH/6A3bt3IyQkBAsXLmxSptPgwYMRExMDn8+HTz/9tM738/PzsWHDBgDAxIkT63xffG3Dhg3Iz8+v8/1PPvkEPp8PsbGxGDRoUPMOymD6TDwr7jjpMwBlLMBJV4gS4F69emlZq2bo06cPACA7OxslJSWm7Yf8k+zMG57wEjmbPuuFGYBEJIPMEmCeDxHZnyMCgB6PBy+99BJ27NiBNm3a4KmnnmpyA4KgoCDMmzcPALBy5UosX75ca9qRkZGBRYsWoby8HB07dsTUqVPrPH/atGlISEhAeXk5Fi1ahIyMDADVpcjLly/Hl19+CaC6wYfVk3xtYrIPDg42NQgk6LMMecIrl8jIM6v8V9Bv//jx46bui/yPrAxAZiMTEcALbyKST4USYBmNEYjIGo7oAnzo0CF8//33AKrLO1566aVGf/69996r8e9p06YhIyMDqampeO+99/DBBx8gODgYpaWlAIDo6Gg89dRT9V60BgUF4amnnsKTTz6JzMxMPProowgLC0N5ebnW9nnWrFmYMmWKEYfaKiID0KqLb30A8PLly5Zf9FO1S5cuITc3F4C1AcBjx45JL3sntYgLX5beEZEMshoRAVx7i4iqsQkIEZnJEQFAfXlpZWUlLly40Oxt/OxnP8OQIUOQmpqK9PR0Letv5MiRuP322xEVFdXgc7t27Yq//e1v+Pjjj7F9+3YUFBQgPDwcPXr0wMyZMzF69OiWHJbhxGRvxfp/tfdz+fJlhIeHW7JfqsmKDsBCUlISgoODUVFRwXUAqQ5ZAUCW3hERoEYGIOchImdTYQ1AzkNE9uWIAOCgQYPw+eeft3o7Y8aMwZgxY1r03OjoaDz44IN48MEHWz0Os8jOACQ5rOgALLjdbvTu3Rv79u1jAJDqkFUCzNI7IgLkrgHIzBsiAtQoAeY8RGRfjlgDkJpGZgYgP2jkEYG4sLAwJCYmmr6/fv36AaiZeUgEyGsCwtI7IgJYAkxE8qmQiczrMiL7YgCQNMwAdCYRiEtOTkZAgPlTgggApqen8wSDamAGIBHJxAtvIpJN5o0IsT82ASGyLwYAScMMQGeyqgOwIAKAVVVVyMzMtGSf5B9kZQDywpuIAHUuvPVrVxORs8hcisDtdgPg+RCRnTEASBpmADpPRUWFFoQzuwGIIAKAALgOINUguwkIT3iJnE2FNQABzkVETib+/t1uN1wul6X7ZhMQIvtjAJA0zAB0nvT0dHi9XgDWZQD27t1bKzVmAJD0xAmnzAxAZt4QOZcKJcC1x0FEziJrORT9PjkHEdkXA4CkqaioAMAMQCexsgOwEBISgqSkJABsBEI1ycoA1F/oc90bIudSoQS49jiIyFnE378ox7USuwAT2R8DgKRhBqDziACc2+1G9+7dLduvCDYyA5D0ZDcBATgXETmZzAxAlgATEaBGBiBvhhLZFwOApOEagM4jAnDdu3e3LPALXAkAHjt2TCtBJpLVBIQ3I4gIkLsGIDMAiQiQVw2h36fK50Lbt29HSkoKXnrpJdlDIfJLDACSxuoMwODg4Dr7JmtZ3QFYEPsrLS3F6dOnLd03qUvWXW9m3hARILcEWD8PMQBI5FwyS4DFPlWeg1599VVkZ2fjlVde4TkbUQswAEgaZgA6i8fjwYkTJwBY1wFY0AccWQZMgpgHuPg+EcnAEmAikk2FEmBV56Di4mKsX78eAFBeXo6DBw/KHRCRH2IAkDRcA9BZTp48ifLycgDWZwD26dNHe8wAIAkqrAGo8l1vIjIXm4AQkWyylkMBrsxDVVVV8Pl8lu//ar7++mutaSUA7Ny5U+JoiPwTA4CkYQagsxw6dEh7bHUAMDIyEh06dADATsB0hQpdgHkzgsi5VFkDkPMQkXOJeUhGAFC/TxUbgXz55Zc1/v3DDz9IGgmR/2IAkDTMAHSWw4cPa4979uxp+f7ZCZhqYxMQIpKJJcBEJJsKJcCAevNQRUUF1q5dW+NrzAAkaj4GAEnDDEBnEQHAzp07IyIiwvL9i3UHmQFIgjjptbIjNcALbyKqxhJgIpJNZgmwys2INmzYgOLiYgDA+PHjAQAZGRk4d+6czGER+R0GAEnDDEBnESXAVjcAEUQG4Llz5/jhTQDknfTywpuIALkZgCpn3hCRdUTprewAoGrzkCj/DQ8Pxy9/+Uvt6ywDJmoeBgBJI7LwrAoAut1uuFyuGvsma/h8Pi0D0Or1/wR2AqbaVGgCotoJLxFZR+YagCpn3hCRdVRoAqIfhwqqqqqwevVqAMDUqVMxYsQI7ffDACBR8zAASBqrMwBdLpe2L5U+ZJzg7NmzuHDhAgB5AUB95iHLgAmQd9Kr8h1vIrIOS4Cv7sCBA8jMzJQ9DCLbktUQrfY+VZqHtm3bplULzZgxA6GhoRg4cCAAYNeuXTKHRuR3GAAkjdVrAOr3xQxAa+k7AMsqAe7QoQMiIyMBMAOQqsk66eVyBEQEsAnI1WzevBmTJ0/GlClTUFBQIHs4RLakSgmwSgFAUf7bpk0bTJ06FQAwfPhwANUZgF6vV9rYiPwNA4AEoPrDRkyeVi7AL/bFAKC19AFAWRmALpeLnYCpBpYAE5FMMjMAVb3w1nvppZfg8XhQVFSE7777TvZwiGyJTUBq8vl8SE1NBQBMnDhRa1x4zTXXAAAuXbrESiKiZmAAkADUDMBZedLLAKAcIgAYGxuLuLg4aeNgJ2DSU6EEWJUTXiKynv7v3+12W7pv1W9E/PDDD9i0aZP2740bN0ocDZF9yboZWnufqsxDaWlpOHXqFIDq8l9BZAACwM6dOy0fF5G/YgCQANSc5K3MABQfNKp8yDiF7A7AgsgAzMnJQXFxsdSxkFwejwc+nw8AMwCJSA79TQjRpMwqqq69Jbzyyis1/r1582ZJIyGyN/H3b/VNCEDNpQhWrlwJAAgICMCNN96ofb1bt25o164dADYCIWoOBgAJADMAnUYEAGWV/wr6/Z84cULiSEg2mWtvMQBIRIDczBsVL7yFI0eOYNWqVQCA+Ph4AEBmZiaysrJkDovIltgEpCZR/jt27Fgt4AdULyWkXweQiJqGAUACwAxAJykqKsLp06cByA8A6jMQuQ6gs+nnAGYAEpEMMtfeUnkeevXVVwFU/17EYwA1SoKJyBiqlACrEAA8evSotkyQvvxXEAHAQ4cOsZKIqIkYACQAzAB0ksOHD2uPZZcAd+3aFcHBwQAYAHQ6BgCJSDaZ3TdVu/AWsrOz8cknnwAAbr/9dkyePFnLAmQZMJHxWAJ8hSj/BeoPAIpGID6fD7t377ZsXET+jAFAAsAMQCfRBwBlZwC63W707NkTABuBOJ0qJcAqXXgTkbVU6b6p0jnRa6+9hqqqKrhcLjz88MNwuVwYN24cgOpGIGLtViIyhipLEahwPiTKf1NSUtC5c+c63x82bJi2XisbgRA1DQOABACoqKjQHjMD0N7E+n9hYWH1fphaTWQhMgPQ2WRmAKp64U1E1lJl7S2RiShbfn4+PvjgAwDV2TfipuG1114LAMjNzUV6erq08RHZEZciqJadnY20tDQA9Wf/AUDbtm3Rt29fAFwHkKipGAAkAPIyAMW+ZH/IOInIAOzbty8CAuRPAeKCIiMjg+8DB9PfBGAGIBHJoErmjSqfhf/4xz9QXl4OAHj00Ue1r48fP157zHUAiYwl5iHZmciyz4e+/PJL7fGsWbMa/LmUlBQA1QFAZiQTXZ38q39Sgqw1AMW+mAFoHX0AUAUiA7CqqgoZGRmSR0OycA1AIpJN5tpbAQEB2k05FeahS5cu4V//+hcAYMKECRg2bJj2ve7du6NTp04AqsuAicg4bAJSTZT/9u3bF8nJyQ3+nGgEUlBQgJMnT1oyNiJ/xgAgAZCXASgaQKhwsusE5eXlWrmOKgFA/TqELAN2LpkBQJfLpd315s0IIueSeeGt36/sC28AeOedd3Dx4kUANbP/gOo5U2QBbt68mVk3RAZiCXD18gNbt24F0HD5ryAagQDArl27TB0XkR0wAEgAmAHoFOnp6fB6vQCAfv36SR5NteTkZC3rgQFA55LZBARQ68KbiOSQeeGt36/sm6Ll5eV48803AQBDhw7V1vzTE18rKCio0VyMiFpHZgmwPvtZ5vnQ6tWrtRsLM2fObPRne/fujYiICABsBELUFAwAEgCuAegU+gCbKhmAwcHBSEpKAsBOwE4maw4SVLnwJiJ5ZDYB0e9X9o2IZcuWIS8vD0B19p/osqmnXweQZcBExlGlBFjm+ZBY/69Lly4YNGhQoz/rdrtrrANIRI1jAJAAMAPQKU6cOAGg+sOyZ8+ekkdzhSgDZgDQuZgBSESyie67sjIAxTwk88K7qqoKf//73wFUr9HbUPldYmIiunXrBqC6DJiIjKFKCbCs86GLFy9iw4YNAKqz/+q7AVGbWAdw3759WuMiIqofA4AEgBmATiHW/+vWrZu0DIf6dO/eHQCQmZkpdyAkjcw1AAFmABIRS4AB4PPPP9c+ix9++GFtiY76iDLgzZs3a8FTImo5r9erlb7K7gIsax5at26dtu+rlf8KYh3AyspK7Nu3z7SxEdkBA4AEgBmATiFO6lXK/gOuBACLiopw/vx5yaMhGWRnAPJmBBE5vQmIz+fDq6++CgDo3LkzbrvttkZ/XpQBFxUVYf/+/aaPj8juZJ8LqZABuHLlSgBAfHw8RowY0aTniBJggGXARFfDACABqBmAE515rcCLbmtlZGQAqG68oRJRRgRcGSM5CzMAiUg2mYvv6/crax5au3YtDhw4AAD4+c9/ftWKkHHjxmmPN23aZOrYiJxAlXMhQE4AsKysDF9//TUAYPr06TWakjQmLi5Ou5ZgIxCixjEASADkfeCIk0tmAJqvuLgY+fn5ANTNAARYBuxU+hNNmQtfMwBI5FyyS4DFPCSrnFZk/8XGxmL+/PlX/fkOHTqgT58+ANgIhMgI+nMh2V2AZZwPfffddygtLQXQ9PJfQZQBMwOQqHEMABKAmgE4K9cA5EW3dfSBNdUyABMTE7WTDmYAOpMqZS9sAkLkXLJLgGVmAG7duhXbtm0DAPz0pz9FeHh4k54nsgC3bNnCczmiVpJ9LuRyuaSeD4ny38jIyBqdxptCNALJycnBmTNnDB8bkV0wAEgAmAHoBPoAoGoZgEFBQejSpQsABgCdSpWyF17AEjmX7AxAmfPQK6+8AgAIDw/Hgw8+2OTniUYgpaWl2LNnjxlDI3IM2edC+v1aPQ9VVlZizZo1AIAbbrih2QkpIgMQYBYgUWMYACQAVwJwQUFBTWq3bhTxIeP1epl5YzJ9YE1fcqsKsXYHA4DOJPuuN9cjJSJReiv7wtvq86G0tDSsW7cOAHD//fcjOjq6yc8dO3asdt7IMmCi1pFdAqzfr9Xz0IYNG3DhwgUAzS//BYD+/fsjJCQEAAOARI1hAJAAXLnotfqkV393h1mA5hIZgImJiQgNDZU7mHqIoCTXAHQm2Xe9mQFIROLvv6kLzxtN1jz0pz/9CUD1OdnPfvazZj03NjYWAwYMAMBGIEStJftmKCDvRsRnn30GAAgJCcH111/f7Oe3adMGgwcPBsBGIESNYQCQAFwJvlm5/l/t/fHC21wis65Hjx6SR1I/EQDMy8tDcXGx5NGQ1WQHALkGIBHJuhkqyJiHTpw4gY8++ggAcOeddyIhIaHZ2xBlwDt27EB5ebmh4yNyEtnnQsCVGyBWXpd5vV6sWLECADBp0qQmr0FamygDTktL4/kcUQMYACQA8k569ftjBqC5RGadauv/Cfqy5JMnT0ocCckg+643MwCJyIlNQP7nf/4HXq8XAQEB+MUvftGibYjF+svLy1l6R9QK+qCVrExkGWsAbt++HadPnwYAzJgxo8XbEY1ASktLcejQIUPGRmQ3DAASAGYA2l1FRQVycnIAqJsBKNYABLgOoBPJvuvNjuREJC6+ZZfeWTUP5ebm4u233wYAzJ49u8XnB2PGjNGCFSwDJmo52edC+v1amUH3ySefAKiee2+44YYWb0cEAAGuA0jUEAYACQAzAO0uOzsbPp8PgLoZgElJSdpjrgPoPPq/fxkX3wwAEpEqAUCrLrz/+te/anPvI4880uLttG3bFkOHDgXARiBEraFCANDqJiA+nw+ffvopgOps4piYmBZvq1OnTtoyBlwHkKh+DAASgOoMMYAZgHaVnp6uPVY1AzA0NBQdO3YEwAxAJxJ//4GBgZZ2IhcYACQi2WsAWn3hvWTJEgDAtGnTMGjQoFZtS5QB79q1CyUlJa0eG5ETqVQCbNU8tH//fhw/fhxA68p/AcDlcmlZgMwAJKofA4AEgBmAdqfPqEtOTpY3kKtgJ2Dnkn3hzSYgRKS/ESGDlQHA4uJi5ObmAgCmTp3a6u2JAGBlZSW2b9/e6u0ROZEKGYBWNwER2X8ulwvTp09v9fZEI5Djx4/j/Pnzrd4ekd0wAEgAuAag3YmMunbt2iEqKkryaBom1gFkBqDzqHLhzXmIyLk8Hg8A+RmAVsxDWVlZ2mMjKgNGjhyp/d5YBkzUMioEAK2uiBDdf0ePHt2iLuS16dcB3LVrV6u3R2Q3DAASAHnZN/oAIDMAzSMy6vSddlUkxnfq1CmtLJ2cQcxBVt+EEJgBSERiHnJC6Z0+096Ic4OwsDAt84aNQIhaRoUSYCszkU+dOoW0tDQAwC233GLINocMGaL97lgGTFQXA4AEgBmAdicy6vSddlUkxuf1epGdnS13MGQpVRbf5zxE5Ew+n096BqCV89DJkye1x0atDSzKgNPS0nDx4kVDtknkJE7LADxw4ID2eMKECYZsMywsDAMGDADARiBE9WEAkAAwA9DOPB6PVurjLxmAAMuAnUaVNQAZACRyJhUuvK0sARYZgB06dEB4eLgh2xQBQK/Xiy1bthiyTSInETchAPk3RPVjMcuRI0e0x/369TNsu6IMeNeuXfB6vYZtl8gOGAAkAPIyAPUn2bzwNsepU6e0363qGYAMADqX7DUAGQAkcjZ9uZvsecjKEmCjsv+A6ovu0NBQAFwHkKgl9OcgTlgTWQQAu3XrhoiICMO2K5YjKCoqwokTJwzbLpEdMABIAJgBaGdGr/NjpsjISLRr1w4AOwE7jewMQDYBIXI2p114ixLg5ORkw7YZHByMkSNHAuA6gEQt4bRMZBEA7N+/v6Hb1TcCYRkwUU0MABIAeQvwcw1A8+kz6VTPAATYCdipZAcA2QSEyNlUCABaNQ95PB5tnV0jMwCBK2XABw4cwLlz5wzdNpHdqZCJbFUTEK/Xi6NHjwKAtmafUXr06IGYmBgAbARCVBsDgARAjRJgZgCaQ2TSRUREIC4uTu5gmkBkKTID0FlklwAzA5DI2VTIvLEqAJibm6udcxkdALz22mu1x5s3bzZ020R2p9KNCLPPh3JyclBaWgrA+ACgy+XSsgCZAUhUEwOABECNDEAGAM2h7wDscrkkj+bqRAbgyZMnLVmAmNQgOwNQzEVer5fvOyIH0gfdZM1DbrcbQPV86PP5TNuPvgOwkSXAADBkyBBtLS+WARM1j0o3Isw+Fzp8+LD22OgSYODKOoCHDh1CSUmJ4dsn8lcMABKAK8E3qz9s2ATEfCKTTvX1/wQxzsrKSpw+fVryaMgqqmQA6sdCRM6h/7sXgTir6c+JzLz41mfYG50BGBgYiDFjxgBgAJCouVQqATb7XEiU/wLGdgAWRAag1+vFnj17DN8+kb9iAJAAMAPQrnw+X40MQH+gHyfXAXQO2RmA+v1yHUAi51Eh88aqGxEiABgSEoKEhATDty/KgI8dO4YzZ84Yvn0iu1KhBNiqNQBFBmBSUpKhHYCFlJQUrfKJ6wASXcEAIAFgBqBd5eXlaetr+FsGIMB1AJ1EdgYg5yIiZ1PhwtuqGxGiBLhHjx4ICDD+UmDcuHHaY64DSNR0KsxDVmcAmlH+CwCRkZHo1asXAAYAifQYACQA8jIAXS6XdsLLDEDj6TPo/CUAGBcXh/DwcADMAHQS2RmALAEmcjYVSu+suhEhAoBmnRcMHDgQ0dHRAICNGzeasg8iOxLzUGBgoLR1u61oAuL1enHkyBEA5pT/CvpGIGauq0rkTxgAJADyMgD1+2QA0Hj6DDp/CQC6XC5trAwAOofsAKD+5gcDgETO46QSYH0GoBkCAgK0LEBmABI1nexqCMCaJiCnTp0yrQOwnmgEkpeXh5ycHNP2Q+RPGAAkeL1e7Y6T1RmA+n3yott4IoAWHByMjh07Sh5N04l1AFkC7ByyT3qZAUjkbCqU3llRAnzp0iWcO3cOgHkBQAAYP348gOrP8aysLNP2Q2Qnss+F9Ps281xI3wHYigxAoDoLkIgYACTIv+vNDEDziABgUlKSKev8mEWfAciUfWeQnQHIJiBEzib7XAiw5kaEVZUBohEIwG7ARE2lLwGWxYomIGZ3ABb69u2LsLAwAFwHkEjwn4gAmUYfeJOZAcgAoPHEib6/dAAWxEVJaWkp8vLyJI+GrCD7rjczAImcTbU1AM0qvxPlv4C5GYC9e/dGfHw8AJYBEzWV7Juh+n1bkQGYmJiItm3bmrYft9uNlJQUAAwAEgkMABIqKiq0xzIzAHnRbTyRAegv6/8J7ATsPLIaEQnsAkzkbKqVAPt7BqDL5dLKgDdu3MhsfqImUCkDsLKy0rS/W5EB2KdPH1O2ryfKgPfu3VvjmpfIqRgAJGYA2tT58+dx4cIFAP6XAagfLxuBOIPsDEA2ASFyNhVKgN1ut/bYrHlIZAAmJCQgNDTUlH0Iogw4NzcX6enppu6LyA5UCACanYns8/m0DsBWBABFI5DLly9j//79pu+PSHUMAFKNwBszAO3DHzsAC506dUJwcDAAZgA6heyyF5YAEzmbaiXAZq2/JT5Tk5KSTNm+nsgABLgOIFFTyL4ZWnvfZsxDp06dQklJCQBrAoCiBBhgGTARwAAggRmAdqUPnPlbBmBAQAC6du0KgBmATiE7AOgPTUBKS0tx9uxZ2cMgsiUVAoBWNgGx4rygW7du6Ny5MwAGAImaQva5UO19m3E+pO8AbEUAsH379ujYsSMAaJmHRE7GACBJzwAUAUBm3RhLlNu43W506dJF8miaT2QtMgPQGWTf9VY9AzArKwvjxo3DkCFDeAebyAQqlACbvQZgVVUVcnJyAFgTAHS5XFr53aFDh0zfH5G/U6EE2OzzIX0QzooAIAD06tULQM3uw0ROxQAgMQPQpkTgLDExUVpjhdYQFyfMAHQG2Xe9VW4CUlBQgDlz5iAnJwcejwfvv/++7CER2Y5qTUDMyLw5ffq0tl0rSoABIDk5GUD1Z7lZnY2J7EL2zdDa+zYzANi5c2dEREQYvv369OzZEwBw/PhxS/ZHpDIGAEl6BiDXADSHv3YAFsS49c1MyL5kn/Tqg+QqlQAXFxdj7ty5OHHihPa11atXKzVGIjtQIQPQ7AtvGUuDiADg5cuXcerUKUv2SeSvZN8Mrb1vM841rGwAIvTu3RtA9Q3VwsJCy/ZLpCIGAIkZgDZl5To/ZtCPm2XA9ubz+bSTTBUuvFWZiy5fvowHHngAe/bsAQAMHjwYAHDu3Dls27ZN4siI7EeFNQDNvvAWHYAB6zMAAdS4kUFEdYm/e31HcKuZ2QRE3wG4b9++hm67MaIEGACOHTtm2X6JVMQAIEkPADID0HglJSVaswB/zwAEWAZsd/oTTBVKgFXIrvN6vXj44Yexfv16AMDs2bPx6aefat2xv/zyS4mjI7IfFUqArcoADAsLQ3x8vOHbr0+PHj20xwwAEjVOtQxAo+chfQdgkZVnBQYAia5gAJCkBwDZBMR4+rv8/poB2KVLFwQEVE9RDADamxMuvJvD5/Ph97//PT755BMAwLXXXovXX38dkZGRuO666wAAqamp8Pl8ModJZCsqlACbfSNCBACTkpLgcrkM3359YmJiEBsbC4ABQKKrUSEAaGYGoL4BiJUZgAkJCdp6g1wHkJyOAUCqcdIrMwNQlbI7O9AHzPw1A7BNmzZa92KWANubEy68m+PVV1/FW2+9BQAYNGgQ3n33XS3zb+bMmQCq76KnpaVJGyOR3ejnIVnld2ZeeANXbg5afWNQlAGnp6dbul8if6NaCbDRN0T1AUArMwBdLpeWBcgMQHI6BgBJehMQZgAaTx8ws2qdHzOwE7AzqJABqEoX4CVLluD5558HUP3+X7p0Kdq2bat9f9q0adqFwcqVK6WMkciOxIV3YGCgZdlxtZmdiSwCgFafF4gyYAYAiRqnQgagmTdE9R2A9ec2VhABwKNHj1q6XyLVMABI0kuAmQFoPBEwS0hIQFhYmOTRtJzIXmQGoL2plgEoay5as2YNfvWrXwEA4uPj8e9//xvt27ev8TPt2rXDmDFjAHAdQCIj2f3C+8KFC7hw4QIA6wOAIgMwKysLFRUVlu6byJ/ob0TIYkUJsJXZf0LPnj0BVM9D5eXllu+fSBUMABIzAG3I3zsAC2L8Z86cQWlpqdzBkGlkz0G19yujBHjbtm34yU9+Ao/Hg4iICCxdurTB8n1RBnz8+HHeySYyiDgHkXnhbWYmssy1gUUGoNfrrTEOIqpJhQCgWfOQrA7AgsgA9Hq9rCwiR2MAkJgBaEPig81f1/8T9OPnRYN9qVACHBAQoDWdsfpmxIEDB3D33XejvLwcbdq0wXvvvYfBgwc3+PMzZszQHrMMmMgYKmQAmlkCrM+kl7UGIMAyYKLGqDYPGXlD9PTp0yguLgYgJwNQv0/ePCUnYwCQpGffiKAjA4DGuHz5MnJycgDYJwMQ4DqAdqZCCbB+31ZmAGZlZWHGjBkoKiqCy+XCG2+8gWuvvbbR53Tq1AkpKSkAWAZMZBQVFt83MxNZBABdLpfWYMsq+pt57ARM1DAVMpHNuhEhqwOw0K1bN21+ZydgcjIGAEl6AJAZgMbKzs6G1+sF4P8ZgPp1irgOoH2pkAGo37dVGYAFBQWYNm0aTp06BQD44x//iNmzZzfpuaIMeO/evcjOzjZtjEROIQJuMm9CuN1urQGJWSXAnTp10rqKWyU8PBydOnUCwAAgUWNUKwE28kaErA7AQps2bbTEAnYCJidjAJC0wJvb7ZZy51tkAHo8Hng8Hsv3bzf6TDl/DwCGh4ejQ4cOAJgBaGf6C10ZyxDU3rcVAcCSkhLMnj0bhw8fBgD8+te/xgMPPNDk54sAIMAsQCIjqFB6p9+/0RmAsjoAC2IdQAYAiRqmQgagWWsAivOdTp06ITIy0rDtNocIPDIASE7GACBpAUBZF95mLnrtRPpAmb+XAAPsBOwETswA/POf/4zt27cDABYsWIAnnniiWc9PTk7WSmhSU1MNHx+R06hw4a3fv90CgGIdQK4BSNQwFW5EmLUGoFh3T0b2nyA6AR8/flyrliJyGgYASQsAyvqw0QceGQBsPREoi4mJQXR0tNSxGEEEMZkBaF+qrQFoxTz0zTffAADGjBmDv//971rZX3OIZiBbt25FXl6eoeMjchoVSu/0+zdyHqqsrJS+NrAIAJ45c0ZrBEBENakwD5lRAiy7A7AgOgGXlpbi9OnT0sZBJBMDgISKigoAamQAch3A1rNLB2BBHEd2djbfHzalWgDQ7CYgJSUlOHToEADgxhtvbPHSC6IM2OfzYc2aNYaNj8iJVMkANGMeysnJ0ZZYkR0ABJgFSNQQFeYh/TmJUTcicnNzcenSJQByMwBFABBgGTA5FwOAJD3dnBmAxhIZgHYo/wWuBAC9Xi+bHdiU00qA09LStNKTUaNGtXg7gwYNQteuXQFwHUCi1pJ9LiSYMQ+J8l9A/hqAANcBJGqICvOQGUszifX/ADUyAAEGAMm5GAAkpdYAZIZX63g8Hu1E324ZgADXAbQrVTIArWoCsmvXLu3xyJEjW7wdl8ullQFv2LABFy9ebPXYiJxKhQtv/f6NzADUf3bKujnYtWtXLbOIGYBE9bNrCbBY/w8A+vTpY8g2WyIqKgrt27cHwAAgORcDgMQ1AG0kNzdXez3tkgGoPw6uA2hPqgQArcoA/OGHHwBUl8HExMS0aluiDLiyshJr165t9diInEpc6La0JN8oZmYARkREIDY21rDtNkebNm20jGUGAInqp0IJsBlNQEQGYMeOHaV1ABZEFiADgORUDACS9AxA/X6ZAdg6+gCZXTIAo6OjtSAJMwDtSZUSYKvWABQZgK3J/hNGjBiB+Ph4ACwDJmoN1TIAjQwAis/OpKSkFjUcMopYB5AlwER1eTwe+Hw+AOqUABudASgz+09gAJCcjgFAUioDkAHA1lGhzMcM7ARsb07KADxz5ozWec6IAKDb7cb06dMBAF9//TXKyspavU0iJ1Ih8wYwtwRY9nmBWAeQAUCiuvR/8zIzkY1uAuLz+bQMQJUCgHl5eSgqKpI8GiLrMQBI0jMAzVhs1qlEgCwsLExb48IORDYjMwDtSbUMQDPnIVH+CxgTAASulAGXlpZi/fr1hmyTyGlUyQA0+kaEz+fTSoBlBwBFBuCFCxdQWFgodSxEqlHlZqjL5TJ0Hjpz5ozWAVilACDALEByJgYAiRmANiICgN27d5da5mM0cdFy8uRJrXsq2YcqJ71WBgCDg4MxePBgQ7Y5fvx4bU0dlgETtYwKi+8DV+Yhj8djyPbOnz+vXXzL6gAsiAAgwCxAotpUORfS79+ITGR9B2AGAInkYwCQmAFoIyIAKPsuv9FEBmBFRQVyc3Mlj4aMpspJrxUBQLH+3+DBgw2bc9u0aYMbbrgBALBmzRrOo0QtoEoA0OgMQJWWBmEAkKhhqpQAA1fmISMCgEeOHNEeqxAA7NSpE8LCwgAwAEjOxAAgSQ8AMgPQGD6fTzvRt0sDEIGdgO1NtRJgs5qAeDwe7NmzBwAwbNgwQ7ctyoAvXLiALVu2GLptIiewawmwKP8F5GcAdurUCSEhIQAYACSqTZWbofr9GxkATEhIQFRUVKu311oBAQHo2bMnAAYAyZkYACSlSoCZudJy+fn5KCkpAWC/AKD+eBgAtB994N/OTUCOHj2q/Y0OHz7c0G1ff/312oX1ypUrDd02kRPYtQmIuDEYEBCAxMREQ7bZUgEBAdrneXp6utSxEKlGpQCgyEA04nxIBAD79u3b6m0ZhZ2AyckYACRtclehBJgZgC2nUpmP0dq3b6+l6zMAaD9iDnK5XFLLXszOANQ3AElJSTF02+Hh4Zg0aRIAIDU1lWtlEjWT3UuAExMTpZ3n6bETMFH9VCoBNmpJFJ/PpwUAe/fu3epxGUUEADMzM3ntSY4j9yyHDNXSDwsx8QUHB1v2gSP243a7tawVoLpEzuoPPVn7M3q/+jKfnj17Nmn7/nTs3bt3x4EDB3Dy5ElDxu1Px240M8fQkm3rS++s/P3Ufk3ExXFlZaUp49i9ezcAoF27dtpFcO2xtMasWbOQmpqKs2fPYs+ePRgxYkSDP+uU92Nj+5N97DL275Rjb8081KZNGyXmoaqqKkPGkZWVBaD6xmBj27PqmMWFd0ZGhtaszO7vx8b2yWOXR7V5SB9ss/KaDKj7muibEbVmHKdPn8bFixcBAP369bvqtqw6ZrEWocfjwcmTJ9GhQwfbvx8b2p/s45YxBicfO8AAoK3ExMS06HkiABgREdHibbRUZGQk2rdvr/07KCjI0jG43W7Lj1kQXTuNcubMGQDVv8MBAwY06UPWn469d+/eOHDgALKyslo9bn87diOZfewt2bY+ACjjdRGvSUREBIDqE0IzxiHW/xs9ejRiY2O1rxv1mtx555145JFHUFVVhXXr1mmNQRpj9/djY2Qeu8zjBux/7K2Zh2ScCwFXXhOR7e7z+QwZhwgA9u7du8HtWfl+HDRoEACgtLQUxcXFiI6Otv37sTE8djlUnIf0AcCYmBip81BwcDCA1v+edu7cqT0eMWJEo9uy8v14zTXXaI9zcnIwcuRI278fG8LzQHlkHTsDgDZy/vz5Zv18ZGQk3G63FgD0+XzN3kZLud1uREZG4uLFi9qaWED1AvZWjEEcu8fj0e5MWUV/7B6Px7DtHjx4EED1Xf7Gjslfj12sXXT8+HEUFhZqmQPN4a/HboSWHHtLPpRaMg/pA4BWzUFA3ddElM1WVFQYPo7i4mLs378fQHUH4PPnzxv+fnS5XBg/fjzWr1+P5cuX4ze/+U2Dfyf++H40isxjl3ncgH8eu9nzkBiXmIc8Ho/Uecjn8wEAysvLWz2OiooKZGdnAwA6duxYZ3sy3o8dO3bUHu/evRuJiYl+9X40ij/+LRrFH4/dinlIXwJcVlYmdR4KCKheJaykpKRV49ixY4f2uFOnTvVuS8b7sV27dggICIDX68WePXvwox/9yK/ej0bgeaD/HbtRwUIGAG2kpW9gEQAMDAy0/I+gdmp5WVmZlDHI4PF4DN23WBsvKSmpydv1p2MX3QuLi4tx9uxZxMfHt3oMMhj9urd0DCptWx8AlPG7Ea+JftFro8exa9cuLcA4bNiwOts3an8zZszA+vXrkZGRgf3796N///6N/rzd349X26/MY5e9b9n7V2374uJbnJRbzYx56OTJk1pAsWvXro1uz6pj1q9RfPz4cW3fdn4/Xm3fsvcvc9+y96/S9vUZgLLnIbEW6eXLl1s1jsOHDwMAOnTogLZt2151W1Ydc1BQELp27YrMzEwcPXpU27ed34+N7ZfngfLI2D+bgJAWAFShCQi7ALecCADarQOwoD8ufcMT8n927b6pt2vXLu2x0Q1A9KZPn65l/X355Zem7YfIblSbh4w4H1KxOVhcXJxWdiUCgERU829e9jxkVDMiEQBUqQOwIJqSiAAgkVMwAEhaAFBWy3m3263d8WYnppYpKipCYWEhAHVO8o2mPy52ArYXfQagTEZeeNcmOgD37NkTUVFRhm9fSEhI0Na2YQCQqOlUm4eMyArQBwBFFr1sLpeLnYCJ6qG/+Sg7AGjEDVGfz6cF10TTDZWIhkTHjh3TMqWJnIABQJKeAajfNzMAW0Z/km/XDMDOnTtrJyTMALQXO2be1CYyAM3M/hNmzpwJADhw4ACD5URNpMo8pC8Bbq2TJ08CAKKioqQ2e6gtOTkZAAOARHr6v3nZNyKMyAA8e/YsioqKAKgZAOzZsyeA6qWFTp8+LXk0RNZhANDhfD6f9AxA/b6ZAdgy+ot8uwYA3W43unbtCoAZgHajSuaNUSUvteXm5iI3NxcAMHz4cEO3XZ8ZM2Zoj1NTU03fH5G/83q92hqdsgOARi5FIAKAqmT/CSIAmJmZacqSC0T+SKUSYCMykY8cOaI9VjEAKDIAgSulykROwACgw+k/bJgB6L9EQCwgIABdunSRPBrziOAmMwDtRZXMG/0Jr5HlIFat/yd0794dAwYMAMAyYKKm0AehZN+IMDITWfUAYFVVFT/Pif6PSiXARtwQ1QfVVA8AHjp0SOJIiKzFAKDD6TPumAHov8QJdOfOnREcHCx3MCYS6wAyA9BeVMkANKshkVj/Lzg4+KpdeY0iyoB37NiBM2fOWLJPIn+lUuaNUZnIPp9P+6xUbW1gsQYgwAX4iQS7zUPib7tDhw6Ijo42YliGio2NRVxcHABmAJKzMADocPqAGzMA/ZfdOwAL4vjOnTuHixcvSh4NGUWVAKD+hNvIuUhkAA4ePNiyeVYEAAFg1apVluyTyF+pmAHY2tLYgoIClJaWAmAAkMgfqLQGoBHzkAiqqZj9J4h1ABkAJCdhANDhmAFoDyIDULWTfKOxE7A9qVICrA/OGRUA9Hg82LNnDwBryn+Ffv36aX8vXAeQqHH6v3fRhEMWozIARfkvoF4JcGRkJOLj4wFUd+AkInuVAPt8Pm0NQJUDgL179wbAEmByFgYAHU6VDEBRtsoMwOYrKyvTGgzYPQNQnzXAdYPsw84ZgEeOHEFJSQkAaxqACC6XS8sC3LRpk5YJRER1qZh54/P5WrUAv/4zUsWbg2IdQGYAElVTqQS4tU1AVO8ALIh1AE+fPs3KInIMBgAdjhmA/k9/l1/Fk3wjdenSBS6XCwADgHaiSgagfg40qjOlWP8PsDYDEADGjx8PoPpY0tLSLN03kT9RKQBo1I0IcW7gdrvRuXPnVo/LaAwAEtWk4jzU0jlI9Q7AgigBBoDjx49LHAmRdRgAdDhVMgCN7HrnNPpSWLsHAIODg7ULGZYA24edMwDF+n9xcXHo2rWrIdtsqmuuuUZ7vHPnTkv3TeRPVCq9M+pGhLhJ1qVLF+nHVB+R0Z+VlYWysjLJoyGST6V5yCkBQH0nYC5HQE7BAKDDqZIBKIKPzABsPtXLfIwmypyZAWgf4u9e9gmvGV2ARQAwJSVFy161SnR0tHZyywAgUcNUKr3T79+IAKCq5wUiAxDgDT0iQK15qLVNQEQAsH379oiJiTFsXEbr0qULQkJCADAbmZyDAUCH0wfcxDp8MjADsOXEiXN8fDwiIiIkj8Z84mKGFwz2oUoGoNFNQIqLi7XOclaX/woiC3DHjh3w+XxSxkCkOpUuvI0uAVatAYigDwCeOHFC4kiI1GDHEmCVs/8AICAgQCsDZgYgOQUDgA7HDED/JwJhdm8AIojjPH36NMuGbEKVAKDRJcBpaWnwer0ArG0AoicCgPn5+cjKypIyBiLVqXThbUQJcHl5udYcTNUAYLdu3bSsaAYAiWr+vcvuRt6aJiD+0gFYEJUSzAAkp2AA0OG4BqD/U73Mx2j642RAwx5UCQAa3QRE3wBk2LBhrd5eS3AdQKKr0/+9qzQPtfScSP/ZqOq5QUhICBITEwFw8X0ioGZDNKuXDKmtNRmAeXl5uHDhAgD/CgCmp6fzOpQcgQFAh2MGoH+rrKxEdnY2gCsLatudPtOR6wDagyoBQKMzAMX6f7169UJUVFSrt9cSffr00ZYG2LFjh5QxEKlO//cuO/PGiHlI/9moagYgcKUMOD09XfJIiORT5VxIP4aWzEH+0gBE6N27N4DqG0Fi6QQiO2MA0OGYAejfsrOztfR8Ve/yG01/nFwH0B70d71lMroJiAgAysr+A6qDGaL8mBmARPWzWwmw/iJW5XMDsfYWS4CJrvy9y74JAVw5H2vJHOSvAUCA6wCSMzAA6HDMAPRv+rv8TlkDMCIiAvHx8QAYALQLVe56G1kCnJubq63BJWv9P0GUAR84cAClpaVSx0KkIpUCgEZmAMbGxiIyMtKIYZlCZADm5+ejqKhI8miI5FLlXAhoXQmwCADGx8cjNjbW0HGZoUePHlrJNQOA5AQMADocMwD9mz4ApvJdfqOxE7C9qHLSq99/a29G6Nf/UyUAWFVVhT179kgdC5GK9AF/lTKRW5sBqHL5L1Bz6RKWAZPTqVINAVyZh3w+X7MbgRw+fBgA0LdvX8PHZYawsDBtrmQjEHICBgAdTrUMwIqKCmlj8EfiLn9UVBRiYmLkDsZCItuRawDagyonvUZmAIry35CQEPTv379V22otNgIhapz+5qPseUi/f7sHAEUJMMAAIJH4e5c9B9UeQ3PmIX0HYH1prer69esHgA2JyBkYAHQ4VTIAxb6ZAdg8IgOuW7du0juGWUkEALOzsw3p1kpyqZIBaGQTEJEBOGjQIOnHFR0drXW5YwCQqC6VAoCtXYvU5/NpAUDVKwO6dOmiHS/XASSnU+VcqPYYmjMP6TsA+0sGIHBlrMeOHYPP55M8GiJzMQDocKpkAIp9cw3A5hEZcE5Z/08Qx1tVVYWcnBzJo6HWslsGoMfj0UptZZf/CiILcOfOnTy5JapF1TUAWzIPnT17FmVlZQDUzwAMDAzU1gFkAJCcTpVzodpjaM48pC+h9acMQBEAvHjxIvLy8iSPhshcDAA6nL7klhmA/sXr9WoBQNXv8htNf7wsG/JvXq8XXq8XgPwLb6O6AB8+fFhrtpGSktLqcRlhxIgRAKoX29d3CCWimhe4sueh1mYi+0sHYEFkJzMASE6naglwc+Yhsf4f4F8ZgKIEGGAjELI/BgAdTmTcuVwuqW3n9U1AmJ3SNMeOHdMCuOIE2in06wbt379f4kiotVQtvWtNNrJY/w9QLwMQYBkwUW36eUjmuRDQ+kxk/dq4qmcAAleyhNLT03n+R46maglwSzIA/aUDsKAPVjIASHbHAKDDiYvcNm3aSF1DTp99yCzAptm+fbv2WH9x7wRRUVHaRYP+90D+R6XSO6NKgMX6f/Hx8ejSpUurx2WEPn36oG3btgCAHTt2SB4NkVpUnYdakwEYFBSETp06GTYus4jP8kuXLiE/P1/yaIjksUMJ8IEDBwBUn3P4k7i4OK2ZIgOAZHcMADqcCACqdMLLdQCbZtu2bQCqgwxOWwMQAEaNGgWgOpjBrAH/pdKFt1FNQEQGYEpKijLNeQICArRsRGYAEtVkp3lIBAC7dOkiPZuxKfTrhLEMmJxMpRLgltyIKCsrU27946ZyuVxaNRUDgGR3DAA6nD4DUCZmADafyHwbOXKkMkEGK40cORIAUFhYiOPHj0seDbWUqiXALc0ALC4u1tbAUWX9P0FkCh84cAAlJSWSR0OkDv3fu+ygWWvnoYyMDAD+Uf4L1FzChAFAcjKVMgBbMg/t2bNHOwZxju5PxM0IBgDJ7hgAdDhmAPqnvLw87SRfZMI5jf7kQmRDkv9RpRM5UH3hL4LpLb0RkZaWpmWkqhoA1HcpJiJ7ZgD6QwMQAOjUqRPCwsIAsKkXOZtKawDqb4Q0dR7Sn4v7YwBQ3Iw4deoUiouLJY+GyDwMADqcKhmAwcHB2mNmAF6dfg0vf/yQNUL37t0RHx8PgOsA+jOVMgBdLleNhkQtIdb/A4Bhw4YZMi6j6EtyuA4g0RV2aQJSWlqKvLw8AP4TAHS5XEhOTgbAACA5m6olwE2dh0QAsF+/foiOjjZjWKbicgTkFAwAOhwzAP2T+JANCQnBoEGDJI9GDpfLpQU/mQHov1TKvAGunHi3NAAo1v/r1asXoqKiDBuXEaKjo7UTXAYAia7QZ97IXlKjNQHArKws7bG/lAAD0AKAvOgmJ1OpBLi5TUC8Xq92M95fK5P0yxGwDJjsjAFAh1MlA5BrADaP+JAdNmyY9NdOJhEATE9PZ/dAP6VaANCoDEDVyn8FUQbM5jlEV6iUedOS0jshMzNTe+wvGYDAlQBgRkYGvF6v5NEQyaFSCXBzm4AcPnwYFy9eBOC/AcCkpCTtmooBQLIzBgAdTpUAIDMAm66srAx79+4F4L8fskbRHz8zmvyTqgHAliy+f/r0aZw5cwaAuh3wRACwoKCA5XZE/0elzJvWZADqA4D+mAFYUVGBU6dOSR4NkRzi7132MgRA8+chfSWOv16buN1ubS5iAJDsjAFAh1MlAMgMwKbbvXu3X3fZMtKgQYMQEhICgOsA+itVA4AtmYf06/+pmgE4YsQI7fGWLVskjoRIHSpl3rSmCYhoABIXF4eIiAhDx2UmcdENsAyYnEuleai5mcgiANipUyckJiaaNi6z9ezZEwADgGRvDAA6HNcA9D/6QJf+Yt6J2rRpozVaYADQP6nUBARoXQBQrP8XEhKC/v37Gzouo/Tu3RuRkZEAGAAkElTKAHS5XNo4WpoB6E/lv8CVi26AAUByLvH3LvuarPYYmhMAHDVqlPR1VFtDrAOYnp7eokoQIn/AAKDDMQPQ/4hAV9++ff2yy5bRRBbknj17UFZWJnk01FyqZQC2pgmIyAAcPHiwEsdSn4CAAC07kQFAomoqrQEItPxGhMgA9KfyXwCIiYlBTEwMAAYAybnE37u/lQCfOnUKOTk5APy/Mkk0Srt8+XKNpkpEdsIAoMMxA9C/eL1eba07f/+QNYr4PVRWVmLPnj1yB0PNploAsKVrAFZVVSEtLQ2Auuv/CSJzeO/evSgpKZE8GiL5VCq9A1p2I8Lr9WoXrP4WAASulAFzbVJyKpXmoeZ0AdZX4Pjr+n+CPhv5+PHjEkdCZB4GAB2OGYD+5dixY7hw4QIABgAFfRk0y4D9j2olwC3NADx8+DBKS0sBqLv+nyAagXg8HuzevVvyaIjkU6kEGGjZjYgzZ86goqICgP+VAANAjx49ADADkJxLpUzk5mQAbt26FQAQERGh7PInTaUPAB49elTiSIjMwwCgwzED0L/ou2wxAFgtJiYGffr0AcAAoD/Sn1jKnof0Y2huAFCs/weonwGoHx+7ZxOplXkDtOxGhL92ABZEBmBWVhbPA8mRVLoR0ZwmIOLaZMSIEUqUL7dGeHi41sSEjUDIrhgAdDhmAPoX8SHbvn17v7zDbxZRcrBjxw54vV7Jo6HmUC0DsLUBwPj4eOU74EVFRWlBcwYAia7ciFDl4rUlGYBi/T/AvzMAvV5vjWMhcgqVbkQ0tQnIxYsXcfDgQQD+X/4rsBMw2R0DgA6nYgagKGGhukSG28iRI/26y5bRRDbk+fPnuWaHn1F1DcCWBgBTUlL84m9TlM7v2LEDPp9P8miI5FLpwhtAi7oA7927FwAQGhqKhIQEU8ZlJpEBCHAdQHImfywB1p9DjB492vRxWUF0Aj527BjPj8iWGAB0OGYA+o+8vDytxMcud9mMoi+H1pdJk/pUDQA258K7uLgYhw8fBqD++n+CCACeO3cOGRkZkkdDJJeqAcCmng/5fD589dVXAIDx48cjIMD/Tu+7d++uPeY6gOREKpUAN7UJiDjnDgwMxLBhw0wflxVEAPDChQsoKCiQPBoi4/nfGQIZSsUMQK79Uj/9+nZc/6+mbt26IT4+HgDXAfQ3qpUAt2TtLf0dcNXX/xP0zXN27twpcSRE8ql04Q00/0bEwYMHtQ7AN954o2njMlNERAQ6duwIgAFAciaVbkQ0NQNQBACHDBmCsLAw08dlhd69e2uPWQZMdsQAoMOJcltmAKpPBLZCQ0MxaNAgyaNRi8vl0oKizAD0L3bIAFy3bh0AICQkpEZgTWW9e/dGdHQ0AAYAiVQqvQOafyNi9erV2uMbbrjBlDFZgZ2AyclUmof0Y2hoHrp8+TJ2794NwF6JCfpOwFxWiOyIAUCHUyUD0O12a+tmMQOwfiIAOGzYMOmvl4pEWXRGRgby8vIkj4aaSrUMwJasAbh27VoAwLhx4/zmDnhAQECN5jlETubvGYAiAJiSkuKX6/8JYh1ArgFITuPxeLRKAhXmIZfLpTVFauh8aN++fSgrKwNgr6WJ2rdvj8jISADA0aNHJY+GyHgMADqcKmsAulwubQzMAKyrtLQUaWlpAOx1l81I+t8LAxr+Qx/wVyGw3dwA4IkTJ7Q19KZOnWrauMwwZswYANXlg8XFxZJHQySPSqV3QPMyAHNzc7Fnzx4A/lv+K4gAYG5uLuckchTVboYCV78Roa+4sdO1icvlqtEIhMhuGAB0MJ/Pp0wGoH4MzACsa/fu3doHsJ3ushlp0KBBCA0NBcB1AP2JaiXAzS29E9l/ADBlyhRTxmQWEQD0er1aAIHIiVQLADbnRsSaNWu0x9OnTzdtTFbQdwJmcyJyEn2QTZV56GrdyEUAMDk5WVuH2y5EAJAlwGRHDAA6mD7dXHYGoH4MzACsSwS0XC6X36wxZrU2bdpoHcgYAPQf4u9dvwyATM3NAPz6668BVK+pl5SUZNq4zDBq1Cjtd86sWXIycYErSt5ka04J8KpVqwBUN8Pq06ePqeMym1gDEOA6gOQs/pYB6PP5tACgHRMTRAAwOzub2chkOwwAOpiqpXfMAKxLBLT69u2LqKgoyaNRlyhBSEtL09YlIbWpmnnTlAvv4uJifP/99wD8r/wXAKKiorSAARuBkJOpNg9dLfNGKC4uxqZNmwBUl/+qcBOlNZKSkhAQUH1pwnUAyUlUDAA2VhFx4sQJnDt3DoA9A4ApKSkAqgOd33zzjeTREBmLAUAH0wfaVMgADA4OBsAMwNq8Xq+WnWOnNTbMIH4/lZWVWmcyUpv4e1dhDgKalwG4YcMGbR71t/JfQWQU//DDD1pGOJHT+GsTkG+++Uabg/x9/T+g+nOga9euAJgBSM6icglwfedDW7du1R7bMQA4evRoxMTEAABSU1Mlj4bIWAwAOhgzAP3D0aNHUVRUBIABwKvRl0ezDNg/qJZ505wA4Lp16wAAERERfnsCLP5mzp07x4wbcizV5qGrdd8UxPp/0dHRfjsH1cZOwOREKmYANnYjQpT/xsfH1yjdt4vAwEBMmzYNAPDVV1+hoqJC8oiIjMMAoIPpA20i+04mrgFYP30giwHAxkVHR6Nv374AGAD0F+LEUpUL76Y2AfH5fFoAcOLEicqMv7n0QXOWAZNTiXlItQvvxuahqqoqrQnR1KlTlRl7a4lgAjMAyUlUDAA2dj4kAoAjR470+6UHGjJz5kwAwKVLl7Bx40bJoyEyDgOADsYMQP8gPmQ7dOjgd00GZBBZEDt27IDX65U8Groa1TJvmlp6t3//fuTm5gLwz/X/hF69emnrijIASE7ljyXA27Ztw/nz5wHYo/xXEBmA58+fR2FhoeTREFlDxRLghuahvLw8rUu3XTKP63PdddchLCwMAMuAyV4YAHQw1dYAZAZg/UQmm53vshlJZEleuHABx44dkzwauhpVA4BXm4dE9h8ATJ482dQxmSkgIADDhw8HwE7A5FyqzUNNyURevXo1gOpzp0mTJlkyLiuIACDALEByDv3fuirdyBtqRqSvsLFzADA0NFRb33nVqlXweDySR0RkDAYAHYwZgOo7e/YsMjMzAbD8t6n0vyeWAatPtQtvfQCwsaYYovRuyJAh6NChgyVjM4soAz506BCKi4slj4bIev6WAejz+bQA4Pjx4xEREWHZ2MwmOpMDwLfffitxJETW0QcAVTkfauhGhKhMCg0NxaBBgywfl5VEGXBBQYF23ET+jgFAB2MGoPqccpfNSElJSWjfvj0A8MPaD6gaAAQavvguLCzEDz/8AMC/y3+Fa665BkB1x3F2zyYnUm0NwKtlAB45ckS7OTh9+nSrhmWJjh07YuzYsQCA999//6rLMRDZgf59rso81NCNCHFuPXz4cGXO3cwydepU7fqUZcBkFwwAOhgzANUnAoBhYWEYOHCg5NH4B5fLpWUBMgCoPtUCgPoT74Yuvr/99lttfUlRHuLPhg8fri0vwDJgciLV5qGrZQCK7D8AWqdKO7n//vsBAGfOnMFXX30ldzBEFvCXJiAlJSXYu3cvAGckJrRt2xYTJkwAAHz55ZeNVoYQ+QsGAB2MGYDqEwHAYcOGKXNh4g/ESUlmZiby8vIkj4Yao2rpHdDwxbco/42Li8OwYcMsGZeZ2rZtq3XPZiMQciJV56GrBQCHDh2Kjh07WjYuq8yYMQNxcXEAgHfeeUfuYIgsoGIJcH3z0K5du7S18JwQAASq5yMAyMnJ0YKfRP6MAUAHYwag2kpLS7UPGq7/1zxcB9B/qJZ5c7UMQI/Hg2+++QYAMGnSJAQE2ONjVJQB//DDD7zDTY7i9Xq1jF7V5qH65qCzZ89qSxDYqfuvXnBwMObOnQugOuNalDsT2ZWKJcD1NQERlTUBAQHaeYPd3Xjjjdq53pdffil5NEStZ48rF2oRZgCqbffu3dqHrlPushll0KBBCA0NBcAAoOpUCwDq58L65qIffvgB58+fB2CP8l9BnMgXFhYiPT1d8miIrOMvF96CviTWrgFAALj33nu1x++9957EkRCZT8USYH1TNEEEAPv374+2bdtKGZfV4uPjMXr0aAAMAJI9MADoYKqlm4sxVFRUSB6JGkTgyuVyaV06qWmCgoKQkpICgAFA1akWANSPo74A4Lp16wAAbrcb119/vWXjMpt+juE6gOQkKl54N5YBuGrVKgBAly5d0L9/f0vHZaVu3bppc+ySJUt4bki2pg/2q3I+VPtGRFVVlXZ+4LTEBFEGfPToURw7dkzyaIhahwFAB2MGoNrEXbZ+/fohMjJS8mj8jygD3rt3L8rKyiSPhhqiWgDwaiXAYv2/ESNGIDo62qphmS45OVk7Hq4DSE6i2s1Q4Mo4PB5PjZL8kpISbNiwAUB19p9o3mNXDzzwAADg3LlzzLwhW9MHAN1ut8SRXFH7RsTBgwdRUlICwHkBwJkzZ2qPOReRv2MA0MH0d1NVOOnlGoBXeL1e7SKc6/+1jPi9VVZWYvfu3ZJHQw0Rf+8qzEFA401AcnNzsX//fgDA1KlTLR2X2QICAjB8+HAADACSs6iYAdhQJvL69eu1czc7l/8KU6dO1ZqcsBkI2ZnqNyKAK4kJgPMCgImJiRg6dCgAIDU1Ve5giFqJAUAHYwaguo4cOYKioiIADAC21IgRI7TsCP1JC6lFBNlUOeFtLANQlP8C9gsAAlfKgA8dOoTi4mLJoyGyhsprAAI15yHR/TcyMhJjxoyxfFxWCwwMxD333AMA2LJlC44cOSJ5RETmUDEAWDsDUJxLd+3aFZ06dZI2LllEGfDu3buRk5MjeTRELccAoIOxC7C69OvWMQDYMlFRUejbty8ArgOoMtVKgBvLABQBwM6dO2vvLTsRjUC8Xi927doleTRE1lDxwru+ecjj8WhLEEyZMkWZsZpt/vz5Wknku+++K3k0ROZQsQRY3wTE5/NpAUCnZf8Js2bN0h4zC5D8GQOADsYMQHWJgFWHDh3QtWtXyaPxXyJ4umPHDni9XsmjofqIv3cV5iCg4dK7iooKfPfddwCqs//suPZWSkqKdlwMmpNTqFgCXF8G4I4dO3Du3DkAzij/FTp27Ihp06YBAJYtW4bS0lLJIyIynoo3IvRNQLKysnDmzBkAzg0A9urVC7169QLAACD5NwYAHUzlDED9otdOJC6+R40aZctAg1XESUpRURGOHj0qeTRUH5UzAPVz5NatW7XFr6dMmWL5uKzQtm1bbY2bzz77zPHzMDmDihfe9WUAivLfoKAgTJ48Wcq4ZLnvvvsAABcvXsRnn30mdzBEJlBxHtKXADt5/T890Qxky5YtKCgokDwaopZhANDB9IvvqxBkEhlAPp9PW3DWic6cOYPMzEwALP9tLf3vjxlNalI5AKgvyRGld8HBwRg/frzl47LKnDlzAFSvQ8rmOeQEKpfeAXUDgOPGjUNkZKSUcckyceJEdOvWDQCbgZA9qTwPVVVVaQHAqKgo9O7dW+awpBJlwF6vV5uTifwNA4AOJgKAKpbeOXkdQH2gysl32YzQtWtXdOjQAQAbgahKtQBgQ4vvi/X/xo0bh/DwcMvHZZXbbrtN+0xYsmSJ5NEQmU/FzBt9AKCyshLHjh3DiRMnADir/FcICAjAvffeC6B6Af60tDTJIyIylorzkL4EWFybjBw5EgEBzg0fDB48GImJiQBYBkz+y7l/waRcADA4OFh77OR1AMWHbFhYGAYMGCB5NP7N5XJpWYDMAFST+FtXZe2t+jJv0tPTtYtvu5b/CjExMZg+fToA4JNPPuF6W2R7Kl54156H9JkmYj08p5k7d672e2EzELIb/TykWgZgcXExDh8+DICJCS6XS+sG/N133+HSpUuSR0TUfAwAOpgIAOoDbzIxA7CaCFSlpKQoczHiz0QAMDMzU1vAmNShWgZgfU1ARPkvUN0AxO7mzp0LALh06RLvcJPt+UMTkFWrVgEABg0apGWfOE1cXBxuuukmAMDHH3+MixcvSh4RkXHEDcfAwEAllmUCamYACk4PAAJX1gG8fPmyVh1C5E8YAHQw1bpv6sfh1AzA0tJS7Nu3DwDX/zOK/mTl+++/lzgSqo8/BADFCV7Pnj21dajsbOLEiejYsSMA4MMPP5Q8GiJz6S9uVQkA6ueh3Nxc7Ny5E4Azy3/1RDOQ0tJSLF++XPJoiIyj2rkQUHc+bNOmjdYozMlGjRqFuLg4AMCXX34peTREzccAoIOpVgLMDMDqtW3ExQjvshlj4MCBCAsLAwBs3rxZ8mioNtVOemsHAIuLi7XAsROy/4Dq8qM777wTALBx40ZkZWVJHhGReVQvAV61apXWkdvpAcAxY8ZoDQjeffdddion21DtXAioO5ahQ4ciJCRE0mjU4Xa7tbl43bp1KC8vlzwiouZhANDBVAsAMgPwSqMKl8uFa665RvJo7CEoKAgpKSkAmAGoGp/Pp9xJb+3Su02bNmlzpVMCgABw1113Aah+jZYtWyZ5NETmUb0EWJThd+7cGYMGDZI1JCW4XC4tC/DgwYPYsWOH5BERGUNfAqyK2mNhYsIVogy4pKQEGzZskDwaouZhANDBVAsAMgPwyvp//fv3R2RkpOTR2Icop969ezebGijE4/Foj1UJANZefF+s/xcREeGok9/k5GSMHj0aALB06VJ4vV7JIyIyh74EWMV5KD8/H0B18w9V1gaT6c4770RoaCgANgMh+1DtZihQdyzinICAa6+9FhEREQCAlStXSh4NUfMwAOhgIsimyoeN0zMAvV6vdjeb6/8ZS/w+q6qqmDGgENVL7/QLPF933XXK3CyximgGkpWVxfJ5si19AFCV7pv1ZQGJ7txOFxUVhVtvvRUAsGLFChQWFkoeEVHriXlIlXMhoO5YRowYIWkk6gkODsYNN9wAAFizZk2NzxEi1TEA6GDMAFTL4cOHta52DAAaa8SIEVrmxKZNmySPhgTVM2/S0tJw+vRpAMCUKVNkDUma2bNna+tnshkI2ZXqNyIAoG3bthg7dqyk0ajn/vvvBwBUVFRg6dKlcgdDZAAVS4D1N0T69OmDmJgYiaNRjygDLiwsxNatWyWPhqjpGAB0MNUCgE7PABTlvwADgEaLjIxEv379ALARiEpUvPDWn3yL8l/AmQHAiIgI3HzzzQCAL774QrtBQWQnKs5DtccxefJkZc7VVDB06FAMHjwYQHUZMJcoIH+negmwk5ZAaapJkyYhODgYAMuAyb8wAOhgqgUAnZwB6PV68dFHHwEAEhIS0KVLF8kjsh9x8rJ+/Xp2NVWEihfeLpdLCwKK0rLBgwcjISFB5rCkmTdvHgCgvLwcn332mdzBEJlAxSYgtUuRnd79tzaXy6VlAaanpzOzn/weA4D+JyIiAtdffz2A6mZNvBFB/oIBQAdTLQDo5AzAJUuWaBmAd999Nxf6NsFdd90Fl8uFsrIyPPLII/D5fLKH5HgqBgCBumNxYvafMGrUKPTo0QNA9TxFZDf6pQhUCQDq56DAwEBMnjxZ4mjUdOutt2qL8L/zzjtyB0PUSiqWALdv3x5A9Q0JLkFQvxkzZgAAcnNzsWfPHrmDIWoiBgAdTFx8qxIA1J/wVlRUSByJtfLz8/Hss88CALp164ZHH31U8ojsKSUlBQ899BCA6lT91NRUySMifwkATp06VdJI5HO5XLjrrrsAAD/88AOOHj0qeURExlJxHtKPY8yYMYiOjpY3GEVFRETgjjvuAACsWrUKZ86ckTwiopZTMQNwzJgxePnll/Hee+8hMTFR9nCUNG3aNC1jm2XA5C8YAHQwEWRTJQDo1AzAp59+GhcuXAAA/M///A9CQ0PlDsjGnn/+ea2U87e//S0uXbokeUTOpmITEKDmWNq1a4dhw4ZJHI18d955JwICqk8X2AyE7EbFEuCIiAjtopLdfxsmyoCrqqqYoUx+TcxDqsxBABAQEIB77rlH63ZLdcXGxmLcuHEAgC+//JLVReQXGAB0MNVKgJ24BuB3332nrf13++23Y+LEiXIHZHPR0dF45ZVXAFSn6//3f/+35BE5m4qZN0DNE/BJkybVWY/LaTp16qTNTcuWLXPUDRqyPxVvREREROBPf/oTHnroIdx7772yh6Os/v37a03T3n//fXg8HskjImoZFUuAqWlEGXB6ejqOHDkieTREV8cAoIOpFgB0WgZgWVkZ/t//+38AqgNTzz33nOQROcMdd9yhLaj+z3/+E7t375Y8IudSNQCoH4uTy3/1RDOQ/Px8fPPNN5JHQ2Qc/TwkMl1VcO+99+L555/XukxS/UQWYE5ODr7++mu5gyFqIRVLgKlpRAAQqM4CJFKdOmc6ZDnVAoBOywB8+eWXkZGRAaC6DDg+Pl7yiJzB5XLhb3/7G0JDQ+Hz+fD444/XyAAh66iYeQNcuQMfEBDArNz/c+ONNyImJgYAm4GQvegvvNmAy//cdNNN2tz05ptvshMn+SUGAP1Xx44dcc011wBgAJD8AwOADiaCbKp82DgpA/DgwYN49dVXAQCjR4/WsmvIGt27d9eyL/ft24d//OMfkkfkTKpmAIqMmxEjRmgXlk4XHByM2267DQDw1VdfIT8/X/KIiIzBC2//FhISgrvvvhsAsHHjRjz11FNch4v8DkuA/ZvIAty3bx9OnjwpeTREjWMA0MGYASiH1+vFQw89hMrKSgQFBeGll15SquzIKX72s5+hf//+AIA//OEPyM7Oljwi51E1A3Du3Llo164dHn74YdlDUcrcuXMBVL9uy5cvlzwaImOIeUilOYia5/HHH8fgwYMBAP/4xz+0tX6J/AVvRPg3fRnw559/LnEkRFfHqIODqRYAdLlc2gefnTMAFy9ejE2bNgEAHn74YfTp00fyiJxJBF9dLhdKS0vxm9/8hlkDFlM1A/A///M/cfjwYUybNk32UJQyePBgDBw4EEB1GTD/XsgOVOy+Sc0TERGBDz/8EN26dQMAvPDCC1i8eLHcQRE1A29E+Lfk5GQMGDAAAPCnP/0JW7dulTwiooYxAOhgqgUAgSsffHbNAMzLy8PTTz8NAOjRowcee+wxuQNyuBEjRmgLiH/11Vdcu8NiqgYAqWEiC/Dw4cPYs2eP3MEQGYAX3vbQvn17fPTRR9p6yo8//jhWr14teVRETcMSYP/34osvok2bNigvL8f8+fNx8OBB2UMiqhcDgA7l8Xjg8XgAQKkOc2Isds0AfPrpp1FUVAQAeOmllxAaGip5RPTkk0+iffv2AIDf/va3uHTpkuQROYeqJcDUsNtuu017rT788EPJoyFqPZbe2Ue3bt2wbNkyREREwOv1YsGCBczEIb/Aecj/jR07Fm+++SZcLheKiopw5513cnkhUhIDgA6lD7AxA9Aa69ev19bNmj9/PruLKiIqKgovvvgiAODMmTPaYzIfMwD9T7t27XDjjTcCAD755BOUlZVJHhFR6zAD0F4GDRqE999/v0YmzqFDh2QPi6hRDADaw0033YQ//elPAKqvKebMmYNz585JHhVRTQwAOpSqAUAxFrsFAMvKyrSus9HR0fjzn/8seUSkN3v2bEyZMgUA8K9//Qu7d++WPCJnYAagfxJlwEVFRVi1apXk0RC1DtcAtJ/x48fjjTfe0DJx5syZw0wcUhpLgO3j/vvvxxNPPAEAOH78OObNm4fi4mLJoyK6ggFAh9IH2FQKANq1Cchf/vIXZGZmAgCeffZZreSU1OByufDHP/4RoaGh8Pl8+NWvflUjOEXmYAagf7r++uuRkJAAgGXA5P+YeWNPs2fPZiYO+Q3OQ/by61//WltjfNeuXfjxj39su2tb8l8MADoUMwCtc/jwYfz9738HAIwePRp333235BFRfbp27ardsdu/fz/+8Y9/SB6R/TEA6J8CAwMxZ84cAMB3332HnJwcySMiajleeNvX/fffr1VfiEyckpISyaMiqouZyPbicrnwhz/8ATNnzgQAfPvtt3j00Ufh9Xolj4yIAUDHYgagNbxeLx5//HFUVlYiKCgIL730EgIC+GenqoceeggDBgwAAPzhD39gyZDJWALsv+bNmwcA8Pl8WLZsmeTRELUcS+/s7f/9v/+H++67DwAzcUhdnIfsx+12480338S4ceMAAB999BGeeeYZ+Hw+ySMjp2MkwqGYAWiNDz74ANu3bwcAPPzww+jTp4/kEVFjRJDW5XKhtLQU//Vf/8UPahPp/84ZAPQvycnJGDlyJIDqMmDe1SZ/xcwbexNLfIhMnG+++YaZOKQcZiLbU0hICN577z0tueCNN97Aa6+9JnlU5HQMAFqoqKgI//rXv/DQQw/h9ttvx913342FCxdi69atlo9F1QtvO2UA5uXl4dlnnwUAdO/eHb/85S8lj4ia4pprrtHW7Vi7di1Wrlwpd0A2xgxA/yaagZw8eRJbtmyRPBqiluGFt/2JTJyxY8cCqM7EeeKJJ3iDj5TBeci+IiMjsXTpUiQlzauj7AAALSpJREFUJQGoXgt+6dKlkkdFTsYAoEWysrLwi1/8AitWrEBubi7cbjdKSkqwZ88evPjii5avN6ZqCbBdMgDLy8vx29/+FkVFRQCAl156CSEhIZJHRU311FNPaY1afvvb3+LMmTOSR2RP+kC/2+2WOBJqiVtuuQVhYWEAgCVLlkgeDVHL8MLbGUJCQvD+++9rmTh//etf8dJLL0keFVE1lgDbW0JCApYtW4a4uDgAwGOPPYavvvpK8qjIqRgAtEBlZSWef/55FBUVISkpCa+88gqWLVuGZcuWYf78+XC5XPjiiy+wbt06S8ckqBQA9PcMwL179+I3v/kNBg0ahM8//xwAMGfOHEyYMEHyyKg5IiMj8eKLLwIAzp49i6FDh+Kee+7BqlWr/Pa9qSJxwhsUFASXyyV5NNRcERERuOmmmwAAn376KX72s59h9erVqKiokDwy61RVVaGsrKxZ//n7DS674YW3c9TOxHniiScwceJE/Otf/0JeXp7k0ZFT+Xw+3ohwgOTkZCxduhTh4eHweDz4yU9+wuoJkoJnOxZYs2YNzpw5g+DgYCxcuBDx8fEAgODgYMyZMweFhYVITU3F4sWLMXHiREtOQpkBaJzCwkJ8/PHHWLJkCfbv31/je71799bKgMm/zJ49G/PmzcOSJUvg8XiwevVqrF69GvHx8ZgzZw7mzZuH3r17yx6mXxMnvCrNQdQ89913H5YtW4bKykp8/PHH+PjjjxEZGYnp06fjlltuwYQJE/zq9fV6vTh//jwKCgrq/Hfu3Dnk5+drjwsKCnD+/PkW7WfIkCEYPXo0xo8fjzFjxiAqKsrgI6Gm4oW3s4hMnNmzZyMvLw+bNm3Cpk2b8Lvf/Q7jxo3DzTffjFmzZqFdu3ayh0oO4fF4tMech+xtyJAhePfddzF37lyUlZVh9uzZ2LRpE9eIJ0sxAGiB9evXAwAmTJigBf/0brvtNqxatQqFhYXYt28fhg0bZvqYVM8AVD0A6PF4sGHDBixZsgSpqak1xhsYGIhp06Zh3rx5mDRpErMK/JTL5cIrr7yCBQsW4MMPP8Ty5ctRWFiI/Px8vPbaa3jttdcwYsQIzJ07F7fccgvatm0re8h+hxfe/m/EiBH47LPPsHTpUqSmpuLixYu4ePGiluUeHR2NmTNn4pZbbsH48eNbPR9WVFRogTgRhGvov3PnztW4sGqKyspKS5oDpKWlIS0tDf/7v/+LgIAADBo0COPHj8e4ceMwZswYREREmD4GqsZ5yHmSk5Oxfft2/Otf/8KyZctw9OhReL1ebNy4ERs3bsR//dd/4dprr8XNN9+MmTNnIiYmRvaQycb06yHzmsH+rrvuOrz22mt46KGHcP78eYwZMwbDhw9HcnIy+vXrhz59+qBv3768MUim4SxjsrKyMhw7dgwAkJKSUu/PxMfHIzExEdnZ2UhLS7MkAKgv0VIpACjGomqZZWZmJpYuXYqlS5fi1KlTNb7Xp08f3H333bj99tvrDfSSfxo4cCBeeOEFLFy4EGvWrMGSJUvw7bffwuv1YseOHdixYweeeuop3HTTTZg3bx7GjBnDctYm0pcAk/8aN24cxo0bh5deegnr16/HZ599hlWrVqGkpAQXLlzABx98gA8++ABxcXGYNWsWbr75ZsyYMQNutxtVVVU4e/Zsg8G82ll3Fy9elHKMwcHBiIuLQ7t27RAXF6f9165dO4SGhjZ5O2FhYbh48SI2bNiAzZs3o6ysDF6vVwsIvvbaa3C73Rg6dCjGjRuH8ePHY+TIkQgPDzfx6JyN85AzJSYm4rnnnsPChQuxadMmfPbZZ1ixYgUyMzPh8Xiwfv16rF+/Hk888QSuu+463HLLLZg+fToiIyNlD51shg3RnOfWW29FQUEBfve73+HixYv49ttv8e2339b4mY4dO6Jv377o27cv+vTpg379+qF37968QUitxgCgyXJycrQuY2LNkfokJSUhOzsb2dnZloxL9QzA4uJiHDlyBCEhIQgJCUFYWBhCQkJM+WC8fPkyLl68iKKiIly6dAlFRUUoKirSMlnE9w4dOoTNmzfXeG7btm3xox/9CPPmzcOwYcMY+LGx4OBgzJ49G7Nnz8bp06exbNkyLFmyBJmZmSgtLdUynrp3747rrrsOkZGRiIqK0v4fFRWFLl26AKh+37Rt2xZhYWGOfs8w88ZegoODMW3aNEybNg1lZWX45ptv8Nlnn+Grr75CaWkpCgoK8M477+Cdd95BbGwsgOolFIwQGRlZIzAngnP1fb65XC6EhoairKysThdQt9tdZztxcXGIiIgw5G81JiYGbrcbHo8HZ86cwa5du7B582Zs2rQJO3fuREVFBTweD3744Qf88MMPePXVVxEUFIQ+ffogPj4e8fHxdQKR8fHx2r9FUxZqOq4B6GwulwsDBw7EwIED8eSTTyItLQ0rVqzAihUrkJ2djcrKSqxbtw7r1q1DmzZtMGDAgBpzg/7vTz/3BAcHyz408hP6azKeDznHggULMGDAAKxatQr79u3DgQMHcOHCBe37ubm5yM3NrRMY7NKlCzp27Fjv+YD+v9jYWDbYo3q5fLXPfslQ27ZtwwsvvAAAWLp0aYMn5//85z/x+eefo2fPnnj55Zfr/ZnFixc32mlx7ty5mDdvXpPGtXz5ctx1110AgPT0dHTt2rVJzzOKy+VCQEAAvF5vjQuwn//85412RHa73QgNDUVYWBhCQ0O1/4KCguDz+bT/xHa9Xm+dx+K/kpISFBUVoaysrNnjnzhxIh544AHceuutzb7gaujYrRIQEACXy6X9Tqxkt2P3+XzYuHEj3n77bSxfvrzZ76XAwEAtSBgYGKiNLyAgoMHHLperxn/1qe/rkydPxqJFi5p17C05cWhOyeXvfvc7vP322+jYsSP27NnD96OFrDz2kpISrFq1Cv/+97+RmpqK8vLyqz4nLCwM7du314Je7du3R1xcHNq3b699XTyOi4tr1sW2zNe9sde8vLwcW7Zs0TKPtm/f3uxs+PDwcO2ioKHgp2D1sbtcLjz++OO45ZZbmvV+N3semj59Og4fPoxbb721wfMvM6n6frSCysfu8/mwbds2/Pvf/8bHH39cp+rjaqKiotC+ffsGL8Jl/y3K2rfs/btcLixevBhJSUnKzEPnzp3D2LFjUVlZiT/+8Y+44447mr2v1lL5b9Fsqhy7x+NBbm4uDhw4gAMHDuDgwYPa4+Li4mZv2+VyoV27dmjfvj2ioqLqXBs4fR6QtW+g+mbwypUrm/2eNyqgywCgyb777jv8+c9/BlDdJbGhF+7999/HRx99hM6dO+ONN96o92f+93//t9Hg2IIFC/DQQw81eWw+nw9VVVUIDAxUJgvp3Xffxf333y97GDWEhoYiOjoa8fHxuPnmm3H//fejR48esodFihHrnn3wwQfIyMjAhQsXpJUr1ufOO+/E0qVLZQ+DHO7SpUtYuXIlvvvuO4SHh9cb0IuPj2fJK6oDp5s3b8a3336LY8eOIT8/H3l5ecjLyzMsc9Jqr7/+On7+85/LHgZRs3i9Xnz//ff45JNPkJ6eXuNvUaXPeWqagwcPol+/frKHQdQkPp8P2dnZ2L9/Pw4cOIBDhw7h7Nmz2hyUl5fXpBurpI74+HipnedZ7+BHxMVSQ8LCwpp11zsgIEDLnGvuQumt1dDdlvnz52P48OE4deoUysrK6v2vtLQUZWVlKC8v1752+fLlq2ZO1c6iioiI0LKvoqOjtTLNqKioGv+uL4uiNb8vZh3Z89jDw8Px4x//GD/+8Y+1r3k8Hly6dEkLBl68eBHnz5/HhQsXUFRUpP3/0qVLNbJTG8perf24Pg19XaxBqlIGIMD3o9OOPSwsDHPmzMFdd93V6LGb+Zmkyt3+q73mISEhmDx5MiZPnlzne5WVlTh37hzy8vKQn5+vBSTE4/z8/AZ/h2L/VnO5XEhKSrLkjndzz4WYfcJjv5oxY8ZgzJgxdb5eUVFRIyBYUFBQ4/G5c+caPDaZf4uCrDwQmcceERHBeagWf/pbNJo/HHvnzp3RuXNnTJs2rc73fD4fSkpKtHOA+s4LGrpRIevvUOxbkDUXyDr26OhoAM27JgOMywBkANBkISEh2uOKiooGy0VFU47GFhOfP38+5s+f3+D3CwoKcP78+SaPTaxF5PV6m/U8I7jdbsTExKCoqKjOB2RCQgISEhJM3b9+HabGjr2kpAQlJSWG7ruxY7eCqq+7FWQde1RUFGJjYxETE4Pz589LO3YAzTr2uLi4Zu+nub9Xvh957E46diOPOzg4GF26dNHWFm0Kcewy5qGmfu7WZvY8JPO9CNjn/dgSdjn28PBwdO/eHd27d2/yc/zxb9Eo/njsnIfMw2M35thjYmIQExOD3r17N+nnZf4dAnLnIRWOHWjeNRnQsnmoPgGGbIUaJBY6Bxpf7Fx8T7whiIiIiIiIiIiIjMAAoMkSExO1FNesrKwGf058rzl38omIiIiIiIiIiK6GAUCThYaGolevXgCAXbt21fszBQUFyM7OBgAMGTLEsrEREREREREREZH9MQBogYkTJwIANmzYgPz8/Drf/+STT+Dz+RAbG4tBgwZZPDoiIiIiIiIiIrIzBgAtMG3aNCQkJKC8vByLFi1CRkYGgOrGH8uXL8eXX34JoLrJR2Ag+7IQEREREREREZFxGG2yQFBQEJ566ik8+eSTyMzMxKOPPoqwsDCUl5drrZ9nzZqFKVOmSB4pERERERERERHZDQOAFunatSv+9re/4eOPP8b27dtRUFCA8PBw9OjRAzNnzsTo0aNlD5GIiIiIiIiIiGyIAUALRUdH48EHH8SDDz4oeyhEREREREREROQQXAOQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbc/l8Pp/sQZAcixcvRklJCcLDwzF//nzZw7EUj53HzmNXg6rjsgKP3XnH7tTjBtQ9dlXHZQUeO4+dx64GVcdlBR47j53Hbi0GAB1sxowZyMvLQ/v27ZGamip7OJbisfPYeexqUHVcVuCxO+/YnXrcgLrHruq4rMBj57Hz2NWg6riswGPnsfPYrcUSYCIiIiIiIiIiIhtjAJCIiIiIiIiIiMjGGAAkIiIiIiIiIiKyMQYAiYiIiIiIiIiIbIwBQCIiIiIiIiIiIhtjAJCIiIiIiIiIiMjGAmUPgOSZN28eSkpKEB4eLnsoluOx89idRtVjV3VcVuCxO+/YnXrcgLrHruq4rMBj57E7jarHruq4rMBj57E7jexjd/l8Pp+UPRMREREREREREZHpWAJMRERERERERERkYwwAEhERERERERER2RgDgERERERERERERDbGACAREREREREREZGNsQuwAxUVFWH58uXYvn07zp07h+DgYCQnJ2PGjBkYPXq07OGZ4uuvv8Yrr7xy1Z9bvHgxIiMjLRiRcYqLi7F//34cP34cJ06cwPHjx1FUVAQAeOGFFzBo0KCrbmPLli1YtWoVTpw4gYqKCsTFxWHEiBG44447lP59tObYf/KTnyAvL6/R7c+YMQM/+9nPDB2zUfLz87Flyxbs3bsXmZmZKCwsRGBgIOLj4zF06FDcdNNNSEhIaHQbMl93zkMN4zzEeUiP85A5OAc1jHMQ5yA9zkHm4TzUMM5DnIf0OA8ZhwFAh8nKysKTTz6p/UGGhoaipKQEe/bswZ49e3DTTTdhwYIFkkdpnoCAgEb/iFwul4WjMca2bdua9EHakDfffBOpqakAqn8/wcHBOH36NFasWIHvvvsOL7zwArp06WLUcA3V2mMHgLCwMLRp06bB76koPz8fP/nJT6Bv4h4WFobLly8jOzsb2dnZWLNmDR577DGMHz++3m3IfN05D3Eeqo3zEOchK193zkGcg2rjHMQ5iOdC1uI8VBfnIc5DVrzuDAA6SGVlJZ5//nkUFRUhKSkJv/rVr9C9e3dUVFRgxYoV+OCDD/DFF1+ge/fumDJliuzhmiIuLg7//Oc/ZQ/DcDExMUhOTkbPnj3RqVMnvPzyy0163po1a5CamgqXy4W7774bN998M4KDg5GRkYGXX34ZJ0+exPPPP4+///3vCAoKMvkoWqalxy4sWLAAkydPNml05vB6vQCAlJQUTJo0CUOHDkVkZCQ8Hg8OHTqEt956C5mZmXj55ZeRmJiIbt261Xi+zNed8xDnodo4D3EesvJ15xzEOag2zkGcg3guZD3OQzVxHuI8ZNXrzgCgg6xZswZnzpxBcHAwFi5ciPj4eABAcHAw5syZg8LCQqSmpmLx4sWYOHEiAgP59vAHEydOrDFZFhcXN+l5lZWVWLJkCYDqtOo5c+Zo3+vevTt+//vf4z//8z+Rm5uLtWvXYsaMGcYO3AAtPXZ/FxERgb/85S/o0aNHja+73W4MHDgQzz77LB555BEUFRVhxYoVePTRR7Wfkf26cx6yJ85DnIcE1echzkH2xDmIc5Cg+hwEcB6yK85DnIcElechNgFxkPXr1wMAJkyYoH3Q6N12221wuVwoLCzEvn37LB4dtZTb7W7R8/bu3Yvz58/D5XLhRz/6UZ3vt2/fHhMmTABw5b2jmpYeu78LDw+v80GjFxMTg+HDhwMATpw4UeN7sl93zkP2xHnIefx1HuIcZE+cg5zHX+cg/TY5D9kL5yHn8cd5iAFAhygrK8OxY8cAVKeo1ic+Ph6JiYkAgLS0NMvGRnLs3bsXANClS5d6Tz4AYNiwYQCAI0eOoLy83LKxUeuJdVU8Hk+Nr8t83TkPUW2ch+xNtXmIcxDVxjnI3lSbgwDOQ1QX5yF7U20eYj6xQ+Tk5GiLUyYlJTX4c0lJSdqClXZUVFSExx57DKdOnQIAtGvXDgMHDsSsWbPq1OTbnXiNr/Z+AACfz4ecnBz07NnTkrFZ6dNPP8X777+PixcvIiwsDN26dcPYsWMxZcqUBhei9Qf79+8HUPf1lfm6cx6qxnnoCs5D1TgP1WXG6845qBrnoCs4B1XjHFQXz4XMxXnoCs5D1TgP1WXG684MQIcoLCzUHsfGxjb4c+J758+fN31MMlRUVCAjIwNBQUHweDw4ffo0vvrqKzz22GP49NNPZQ/PUuI90ZT3A2Df90RWVhaKi4sRHByMixcvYu/evXjzzTfx+OOPIz8/X/bwWmTr1q04fvw4ANRZTFfm6855qBrnoSs4D1XjPFSXGa8756BqnIOu4BxUjXNQXTwXMhfnoSs4D1XjPFSXGa87MwAdQp8yGhwc3ODPie+VlZWZPiYrxcbGYu7cuRg7diw6deqEoKAgVFVV4eDBg3jvvfdw9OhRvP3224iNjcV1110ne7iWEO+JprwfAKC0tNT0MVlp1KhRGDBgAAYOHKilZhcWFmLt2rVYtmwZTp48iWeffRZ/+ctflO22VZ/8/Hy89tprAKqPUaw7Ich83TkPcR6qjfMQ56GGmPG6cw7iHFQb5yDOQQ3huZA5OA/VxXmI81BDzHjdmQFIjjBs2DDMnTsXSUlJ2sQRGBiIwYMH47//+7/Rp08fAMC7776rtfMme1uwYAHGjh2rfdAA1Scld955J/7rv/4LQPWdqK+//lrWEJutuLgYixYtQlFRERISEvDII4/IHhLpcB6i2jgPkZU4B1FtnIPIapyHqDbOQ9ZiANAhQkJCtMcVFRUN/pz4XmhoqOljUkVQUBDmz58PACgoKEB6errkEVlDvCea8n4AgLCwMNPHpIpRo0ahf//+AIAdO3ZIHk3TlJWV4dlnn0VmZiZiY2Px3HPPoW3btnV+TubrznmoYZyHOA/VxnnI+Nedc1DDOAdxDqqNcxDPhazGeYjzUG2ch4x/3RkAdAh9/bh+7YnaxPdiYmJMH5NKxN0mADhz5ozEkVhHvCea8n4AnPue8If3Q0VFBZ577jkcOXIEUVFRWLRoERISEur9WZmvO+ehxnEeqh/nIf94P/jDPMQ5qHGcg+rHOcg/3g/+MAfp993U/Tv1PQf4x/vOCJyHGsd5qJpRrzsDgA6RmJgIl8sFoDqFtiHie126dLFkXCSPeI2b8n5wuVxITEy0ZFzUPBUVFVi0aBEOHDiAiIgIPPfcc43+/cp83TkPUW2ch+zBX+YhzkFUG+cge/CXOQjgPER1cR6yB3+ZhxgAdIjQ0FD06tULALBr1656f6agoEBrRz1kyBDLxqaCI0eOaI87dOggcSTWGTx4MIDqiaWgoKDen9m9ezeA6jsv+pIFJxDvCZXfD5WVlXjxxRexd+9ehIWF4ZlnnkH37t0bfY7M153zUOM4D3Eeqo3zkLGvO+egxnEO4hxUG+cgngtZjfMQ56HaOA8Z+7ozAOggEydOBABs2LCh3lban3zyCXw+H2JjYzFo0CCLR2cen8/X6PerqqrwwQcfAADatWuH5ORkK4Yl3eDBgxETEwOfz4dPP/20zvfz8/OxYcMGAFfeO3ZxtffEjh07cPDgQQDAyJEjrRhSs1VVVeEPf/gDdu/ejZCQECxcuBC9e/e+6vNkv+6ch+rHeYjzUG2ch8x53TkH1Y9zEOeg2jgH8VzIaJyH6if7/SgT5yHrX3cGAB1k2rRpSEhIQHl5ORYtWoSMjAwA1emqy5cvx5dffgkAmD9/PgIDA2UO1VB5eXn49a9/jTVr1uDs2bPa1z0eD/bv34/f/e53OHz4MADgvvvuQ0CA//1ZXLx4UfuvuLhY+3pJSUmN71VVVWnfCwoKwrx58wAAK1euxPLly7WFRjMyMrBo0SKUl5ejY8eOmDp1qrUH1AwtOfa33noLb731Fvbv319jcdXz58/jo48+wh//+EcAQNeuXTF58mTrDqaJPB4PXnrpJezYsQNt2rTBU089pS2QezWyX3fOQ5yHOA9V4zwk53XnHMQ5iHNQNc5BPBeyGuchzkOch66Q9bq7fFcLu5KtZGVl4cknn0RRURGA6m4y5eXlWpv1WbNm4ac//anMIRru7NmzWLBggfbvNm3aICQkBKWlpdoEFBgYiPvuuw8333yzrGG2yuzZs5v0cy+88EKdO4lvvvkmUlNTAQButxvBwcEoLS0FAERHR+OFF15Qev2Rlhz7X//6V3zzzTcAqtdUEF2VSkpKtJ/v0aMHnnzyScTHxxs84tYTJ0lA9YdHeHh4oz//3nvv1fmazNed8xDnIc5DnIcAea875yDOQZyDOAcBPBeyGuehKzgPcR4SrH7d7XNLgZqka9eu+Nvf/oaPP/4Y27dvR0FBAcLDw9GjRw/MnDkTo0ePlj1Ew0VHR+OnP/0pDh06hIyMDBQVFaGkpATBwcHo0qULBg0ahOnTp6Nz586yhyrFz372MwwZMgSpqalIT0/X7jSMHDkSt99+O6KiomQP0XA33ngjoqKicOTIEeTl5eHSpUvwer2IjY1FcnIyxo0bhwkTJih711V/36ayshIXLlxo9jZkvu6chzgP1cZ5iPOQla875yDOQbVxDuIcxHMh83EeahznIc5DVrzuzAAkIiIiIiIiIiKyMf8rrCciIiIiIiIiIqImYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIiIiIiIjIxhgAJCIiIiIiIiIisjEGAImIiIiIiIiIiGyMAUAiIiIiIiIiIiIbYwCQiIiIyEbWr18Pl8sFl8uFZ555RvZwlCd+VxMnTpQ9FCIiIiLTMABIRERERERERERkYwwAEhERERERERER2Vig7AEQERERkXEmTpwIn88nexhEREREpBBmABIREREREREREdkYA4BEREREREREREQ2xgAgERERkYK8Xi+WLFmCW265BUlJSQgNDUVISAg6d+6MIUOG4I477sDrr7+Oc+fO1Xje1boAi+8157/GfPHFF7j33nvRs2dPtG3bFmFhYejevTvmz5+PdevWGfkradTmzZvxH//xHxg0aBBiY2MRFBSE2NhYjBo1Cr/85S+xadOmFm87JycHr7/+Ou666y70798fbdu2RVBQEOLi4jBq1Cj89re/RXZ2dpO2lZaWhl/84hcYMmQIoqKitO307dsXkydPxu9+9zvs2rWrwedv3LgRP/7xj9GvXz9tHO3bt0f//v1x4403YtGiRTh69GiLj5WIiIjsyeXjIjFERERESjl37hxmzZqFrVu3XvVn/+d//ge//vWvtX+vX78e119/PQDg6aefrhMEvFpArz71nS5mZ2fjzjvvxJYtWxp97m233Yb33nsPYWFhzd5vUxQWFuK+++7DypUrr/qze/bswZAhQ2p8Tfw+rrvuOqxfv77Oc9avX49JkyZddV3FNm3a4PXXX8eDDz7Y4M8sWrQIzzzzDLxeb6PbGjBgAPbv31/ja16vF//xH/+B//3f/230uQAwc+bMJv0+iIiIyDnYBISIiIhIMQsWLNCCf126dMFdd92FXr16ISYmBiUlJTh27Bi2bNmCjRs3Nnvbn3766VV/5ttvv8Wrr74KAIiKiqrz/ezsbIwaNQq5ubkAgGHDhuGWW25Bz549ERAQgCNHjuC9995Deno6Pv74Y5SUlCA1NbVFwcfGFBYWYsyYMVrGW1hYGObMmYMxY8YgJiYGly5dwv79+7F69WocOnSoRc1RysvL4fP50KdPH1x//fXo378/4uLiEBgYiDNnzmDDhg347LPPcPnyZSxYsAAdOnTArFmz6mzn888/x8KFCwEAISEhmD17NsaPH4/4+Hh4vV7k5uZi9+7dWLt2bb3j+Pvf/64F/9q2bYvbb78dw4cPR3x8PC5fvoycnBzs3LnT0qxLIiIi8h8MABIREREpJC8vDytWrAAAjB07Fl9//TVCQkLq/dn8/HwUFBQ0a/u33HJLo98/fPgwHnjgAQBAYGAgPvrooxrf9/l8uPPOO5Gbmwu324033ngDCxYsqLOd3/zmN7j//vuxdOlSrF69Gv/617/wk5/8pFljvZr7779fC/6NHj0an3zyCTp27Fjn515++WV8//33SEhIaPY++vXrV2/moPDwww9jz549mDZtGvLy8vCrX/0KM2fOrBPsfOuttwBU/043b96MlJSUerfn8XjqzfwUz4+JicHu3buRlJRU7/PLy8uRlpbW5OMjIiIiZ+AagEREREQKSU9P10pE77777gaDfwAQHx+Pfv36Gbbv/Px8zJw5ExcuXAAAvP7665g6dWqNn/niiy+0st9nnnmm3uAfAAQHB+Pdd99Ft27dAAB//vOfDRsnAGzbtg1ffPEFACAxMRGpqan1Bv+EsWPHtigAmJSU1GDwTxg6dChefPFFAMCxY8fw/fff1/mZ48ePA6jOlmwo+AcAbrcb48aNa/D5U6ZMaTD4B1RnF44aNarR8RIREZHzMABIREREpJDw8HDt8Q8//GDZfisqKnDLLbcgPT0dAPDrX/+63uDeu+++C6A6wPfII480us02bdpg7ty5AKozC7Oysgwb7/vvv689fuKJJxATE2PYtlti/Pjx2uP6MvjE63rixAktwNoc4vn79u3D5cuXWzZIIiIiciyWABMREREppH///ujcuTNOnTqF/+//+//g8XiwYMECjB49Gm6327T9PvDAA1rm2q233oo//vGP9f7chg0bAAAdOnTAN998c9Xtnj9/Xnt88OBBdO3a1YDRosb6hzfffLMh22zMnj17sHjxYmzZsgXHjh3DxYsXUVFRUe/P5uTk1PnaDTfcgF27dqGwsBATJkzAE088gVmzZiE6OrpJ+7/hhhuwdOlSHD58GJMnT8avfvUrTJs2zbTmKkRERGQvDAASERERKcTtduOtt97Cj370I1RUVODdd9/Fu+++i8jISIwaNQrjxo3DlClTMHbsWMOaaixcuBAffvghAOCaa67B4sWLERBQt1CkpKREW3MwKysLt956a7P2U1hY2PrB/h8RZAsPDzcsqFifqqoq/Od//if+8Y9/NLmJyMWLF+t87Te/+Q2+/PJL7Nu3D/v27cM999yDgIAADB48GGPGjMF1112H6dOnIzIyst5t/vGPf8SmTZuQk5ODTZs2YdOmTQgKCkJKSgrGjh2LiRMn4oYbbmi0ZJyIiIiciyXARERERIqZMWMGdu7cidtvvx1t2rQBUB1UWrt2LZ555hmMHz8eycnJWLx4cav39f7772PRokUAqjsOf/755w1mlbWkdFXPyNJVEWSLiIgwbJv1efTRR/HWW2/B5/MhKCgIN910ExYtWoS3334b//73v/Hpp5/i008/1Tr0AtWNPGqLiorCli1b8PTTT6NTp04AAK/Xiz179uCNN97AXXfdhQ4dOuAXv/gFioqK6jy/a9eu2L17Nx577DHExsYCACorK7Ft2zb85S9/wc0334wOHTpg4cKFDWYmEhERkXO5fE29lUlERERElispKcHmzZuxdetWbNy4ERs3bqwR4HnmmWfw9NNPa/9ev349rr/+egDA008/jWeeeabBbW/cuBFTpkzB5cuX0bZtW2zatAmDBw9u8OeLioq0ktWUlBRL1yisrV27digsLER4eDiKi4tbvB2RRXnddddh/fr1Nb6XnZ2Nbt26wev1onPnzvj222/Rq1everdz4MABDBw4EABw33334Z133mlwnz6fD/v27cPmzZvx/fff4+uvv0Zubq72/YEDB2Lr1q011oPUq6qqwq5du/D9999rz9dnV06aNAlr166tN4uTiIiInIlnBUREREQKCw8Pxw033ICFCxdi7dq1yM/P1zL2AOCFF17AmTNnmr3d48eP49Zbb8Xly5fhdruxdOnSRoN/QHUWm8i4q2+dOyslJiYCqA6QGtlcRG/dunVaR+bf/OY3DQb/ACAjI6PJ23W5XBg8eDB+/vOf4/3338epU6fw1VdfoUuXLgCA/fv3480332zw+YGBgRg5ciQee+wx/Pvf/0ZeXh4++ugjREVFAQC++eYbfPrpp00eDxEREdkfA4BEREREfqRt27Z46qmntMYXlZWV9XadbUxhYSFmzpyJc+fOAQD++te/YsaMGU167nXXXQcAyMvLk5oBOGHCBO3xihUrTNmHPrDas2fPRn921apVLd6Py+XC1KlT8eqrr2pf0zc5uRq3243bb7+9RrZnc55PRERE9scAIBEREZEf6t69u/a4qqqqyc+7fPkyfvSjH+Ho0aMAgEceeQS/+MUvmvz8++67T3v81FNPNbkxhtHuuece7fGf/vSnGt2GjaIvwT1+/HiDP5eeno5333231ftr6Wtq1POJiIjIvhgAJCIiIlLImjVr8Je//KXRgFZeXh4+/vhj7d9Dhgxp8vZ/+tOf4rvvvgMAzJw5Ey+//HKzxnf77bdj1KhRAIDVq1fj3nvvbXQNPo/Hg9WrV+P5559v1n6uZuTIkVoWZE5ODmbMmFFjHb3atm7d2uxS6REjRmiPX3rpJS1jUi8rKws33XQTSkpKGt3WggULsHfv3kZ/5o033tAeDx06VHucm5uLxx9/HCdOnGjwuVVVVfjHP/5R7/OJiIiI2ASEiIiISCHvvPMOHnjgAQQFBWHixIkYPXo0evTogYiICJw7dw579+7Fhx9+qAUI58yZg2XLlmnPb6wJyGeffYZbb70VQHUp8euvv96kLrq33HJLjX+fOnUKY8aMQXZ2NgAgJiYGd9xxB4YPH47Y2FiUl5fj9OnTSEtL09YtnDx5MtatW9eaX00dhYWFGD16NI4dOwYACAsLw5133okxY8YgJiYGly5dwqFDh7B69Wrs27cPu3fvrhMYa6wJCACMHj0a27ZtAwDExcXhpz/9Kfr16wePx4OtW7fi/fffR0lJCe6//36t8Ud9TUDEfvr27YtJkyZh4MCBaNeuHcrLy5GVlYWPPvpICxDGxMRg37596Ny5MwAgMzNTy+4bPnw4rr32WvTr1w8xMTEoLi5Geno6PvzwQy1A2KNHD6SlpZneIZmIiIj8R6DsARARERHRFSJQVFlZibVr12Lt2rUN/uztt9+Ot99+u8nbvnDhgvb40qVLNcpoG1P7fnHnzp2xc+dO3H///Vi1ahXOnz+Pt956q9FtiKYdRoqNjcWWLVtw9913Y82aNSgtLcXbb7/d4O+kJV1xly5dikmTJiEjIwMFBQV48cUX6/zMww8/jF/+8peNdv4VDh8+jMOHDzf4/a5du+Ljjz/Wgn/AlfcEAPzwww+Nrr04cOBAfPbZZwz+ERERUQ0MABIREREp5N5770X//v2xbt06bNu2DYcOHcLp06dRVlaGsLAwdO3aFaNHj8Y999yjNeSQoX379khNTcXWrVvxwQcfYNOmTcjOzsaFCxcQEhKChIQE9OvXD+PHj8esWbMwYMAAU8bRrl07rF69Gt988402jtzcXJSVlSEqKgo9e/bE+PHjMWfOnKt2Oa5Pt27dsHv3bvz1r3/FJ598oq0FmJCQgLFjx+LBBx/ExIkTkZmZ2eh2Tp06hTVr1mDTpk3Yu3cvMjIyUFRUBLfbjfj4eAwePBg333wz7rnnHoSGhtZ4blJSEk6cOIE1a9bg+++/x969e5GVlYVLly6hTZs2SEhIwLBhw3Dbbbdhzpw5CAzkKT4RERHVxBJgIiIiIiIiIiIiG2MTECIiIiIiIiIiIhtjAJCIiIiIiIiIiMjGGAAkIiIiIiIiIiKyMa4QTERERESWKSgowKZNm1r8/K5duyIlJcXAERERERHZHwOARERERGSZ/fv349Zbb23x8++77z688847xg2IiIiIyAFYAkxERERERERERGRjLp/P55M9CCIiIiIiIiIiIjIHMwCJiIiIiIiIiIhsjAFAIiIiIiIiIiIiG2MAkIiIiIiIiIiIyMYYACQiIiIiIiIiIrIxBgCJiIiIiIiIiIhsjAFAIiIiIiIiIiIiG2MAkIiIiIiIiIiIyMYYACQiIiIiIiIiIrIxBgCJiIiIiIiIiIhsjAFAIiIiIiIiIiIiG2MAkIiIiIiIiIiIyMYYACQiIiIiIiIiIrKx/x8rTnFfcd265gAAAABJRU5ErkJggg==" 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", - "text/plain": [ - "
" - ] + "metadata": { + "image/png": { + "height": 480, + "width": 640 + } }, - "metadata": {}, "output_type": "display_data" } ], "source": [ - "gce.reset()\n", - "init_size_dist = pd.DataFrame(\n", - " {\n", - " 'pop_density': gce.state,\n", - " 'size_class': list(range(21))\n", - " }\n", - ")\n", - "init_size_dist.plot(x='size_class', title='initial distrib.')\n", - "\n", - "gce.step([200])\n", + "n_traps = 500\n", "\n", - "size_dist_1 = pd.DataFrame(\n", + "gce.reset()\n", + "size_dist = pd.DataFrame(\n", " {\n", " 'pop_density': gce.state,\n", - " 'size_class': list(range(21))\n", + " 'size_class': list(range(21)),\n", + " 't': 21*[0],\n", " }\n", ")\n", - "size_dist_1.plot(x='size_class', title='distrib. at timestep 1')\n", "\n", - "gce.step([200])\n", + "for t in range(1,4):\n", + " gce.step([n_traps])\n", + " new_size_dist = pd.DataFrame(\n", + " {\n", + " 'pop_density': gce.state,\n", + " 'size_class': list(range(21)),\n", + " 't': 21*[t],\n", + " }\n", + " )\n", + " size_dist = pd.concat(\n", + " [size_dist, new_size_dist],\n", + " ignore_index=True,\n", + " )\n", + " new_total_pop = pd.DataFrame({'pop': [sum(gce.state)], 't': [t]})\n", "\n", - "size_dist_2 = pd.DataFrame(\n", - " {\n", - " 'pop_density': gce.state,\n", - " 'size_class': list(range(21))\n", - " }\n", - ")\n", - "size_dist_2.plot(x='size_class', title='distrib. at timestep 2')" + "ggplot(size_dist, aes(x='size_class',y='pop_density')) + geom_line() + facet_grid(cols='t') + labs(title=f\"N. traps = {n_traps}, final pop = {sum(gce.state):.0f}\")" ] }, { From 20cd10a36b5b8e5428652f9ff491b21c16063f27 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 22:08:02 +0000 Subject: [PATCH 39/52] patches on agents, action_reward_scale, intro notebook --- notebooks/intro.ipynb | 169 ++++++++++++++++---- src/rl4greencrab/agents/const_action.py | 4 +- src/rl4greencrab/agents/const_escapement.py | 4 +- src/rl4greencrab/envs/green_crab_ipm.py | 2 +- src/rl4greencrab/utils/simulate.py | 50 +++++- 5 files changed, 192 insertions(+), 37 deletions(-) diff --git a/notebooks/intro.ipynb b/notebooks/intro.ipynb index 3f5fea1..910e184 100644 --- a/notebooks/intro.ipynb +++ b/notebooks/intro.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "id": "9059df7d-6045-4834-a9cf-fcf32edac655", "metadata": {}, "outputs": [ @@ -43,8 +43,7 @@ "Requirement already satisfied: pandas in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (2.2.0)\n", "Requirement already satisfied: pyyaml in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (6.0.1)\n", "Requirement already satisfied: scipy in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (1.12.0)\n", - "Collecting typing (from rl4greencrab==1.0.0)\n", - " Using cached typing-3.7.4.3-py3-none-any.whl\n", + "Requirement already satisfied: typing in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (3.7.4.3)\n", "Requirement already satisfied: jax-jumpy>=1.0.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (1.0.0)\n", "Requirement already satisfied: cloudpickle>=1.2.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (3.0.0)\n", "Requirement already satisfied: typing-extensions>=4.3.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (4.9.0)\n", @@ -63,10 +62,14 @@ "Building wheels for collected packages: rl4greencrab\n", " Building editable for rl4greencrab (pyproject.toml) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for rl4greencrab: filename=rl4greencrab-1.0.0-py2.py3-none-any.whl size=1069 sha256=04a2111d259dd039b329b1369e2857430f1ad1045264348157a0b65eab056871\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-ewoqtlyu/wheels/e9/7e/e6/00c4b11a2574abd59d64425d537139e25fadbde37f002c4dba\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-86dvoand/wheels/e9/7e/e6/00c4b11a2574abd59d64425d537139e25fadbde37f002c4dba\n", "Successfully built rl4greencrab\n", - "Installing collected packages: typing, rl4greencrab\n", - "Successfully installed rl4greencrab-1.0.0 typing-3.7.4.3\n", + "Installing collected packages: rl4greencrab\n", + " Attempting uninstall: rl4greencrab\n", + " Found existing installation: rl4greencrab 1.0.0\n", + " Uninstalling rl4greencrab-1.0.0:\n", + " Successfully uninstalled rl4greencrab-1.0.0\n", + "Successfully installed rl4greencrab-1.0.0\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -77,13 +80,14 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 1, "id": "c97fbb03-59d8-487a-9805-8faa9ec021d0", "metadata": {}, "outputs": [], "source": [ + "import numpy as np\n", "import pandas as pd\n", - "from plotnine import ggplot, aes, geom_line, geom_point, facet_grid, labs" + "from plotnine import ggplot, aes, geom_density, geom_line, geom_point, geom_violin, facet_grid, labs" ] }, { @@ -112,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "cab72c67-653f-4aee-8bfe-f13fbebd02c9", "metadata": {}, "outputs": [], @@ -136,17 +140,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "693a9ba4-b422-47b6-9d5a-04b9006ac094", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([13., 88., 5., 40., 84., 64., 6., 29., 99.], dtype=float32)" + "array([97., 49., 70., 64., 29., 68., 37., 95., 49.], dtype=float32)" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "9d1b0d77-895f-4b19-8d3d-3da878a6704a", "metadata": {}, "outputs": [ @@ -185,7 +189,7 @@ "array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -205,17 +209,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "862dceed-6947-416c-9de7-0c2ba38d8fe5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0., 0., 2., 0., 0., 1., 0., 0., 0.], dtype=float32)" + "array([2., 1., 1., 1., 1., 0., 1., 2., 2.], dtype=float32)" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -229,17 +233,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "fd41a3d4-f50b-4f9a-bad5-29a2089c1631", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 8., 15., 9., 9., 5., 13., 8., 6., 3.], dtype=float32)" + "array([10., 13., 10., 3., 10., 6., 7., 8., 11.], dtype=float32)" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -253,17 +257,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "fbffb33f-4b7f-4cc1-a7f7-824d12207a51", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([83., 79., 55., 43., 55., 66., 50., 54., 77.], dtype=float32)" + "array([76., 66., 70., 56., 74., 56., 57., 64., 41.], dtype=float32)" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -287,13 +291,13 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 8, "id": "976afa61-4e00-4f7d-9507-f0030c4302b5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" 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}, "metadata": { "image/png": { @@ -344,13 +348,13 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 9, "id": "781f996b-dcc5-4106-810b-d3ab296cfac2", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" 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}, "metadata": { "image/png": { @@ -391,13 +395,116 @@ "ggplot(size_dist, aes(x='size_class',y='pop_density')) + geom_line() + facet_grid(cols='t') + labs(title=f\"N. traps = {n_traps}, final pop = {sum(gce.state):.0f}\")" ] }, + { + "cell_type": "markdown", + "id": "21fc988c-e111-4257-9207-e713991e0e64", + "metadata": {}, + "source": [ + "## The reward function\n", + "\n", + "At each time-step, our model returns a `reward` which depends on two things: the crab population distribution (this term of the reward quantifies the ecological damage due to the crab population), and the cost of laying traps. It has the following functional form:\n", + "\n", + "$ R = - D(P) - \\alpha A$\n", + "\n", + "where $D(P)$ is the ecological damage arising from a population $P$ of crabs, $A$ is the action taken by the agent and $\\alpha$ is a constant of the model (in the source code $\\alpha$ is the attribute `gce.action_reward_scale`)" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "79f064a4-0d82-42dd-98a4-255854ab3808", + "execution_count": 10, + "id": "bb76e51c-02d4-4b60-87b4-f6b7636f50e3", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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4200-0.0323840.000018
\n", + "
" + ], + "text/plain": [ + " n_traps rew_mean rew_std\n", + "0 0 -10.760099 0.083178\n", + "1 10 -0.014162 0.000023\n", + "2 50 -0.017543 0.000022\n", + "3 100 -0.022435 0.000023\n", + "4 200 -0.032384 0.000018" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from rl4greencrab import simulator, constAction\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "stats = []\n", + "for n_traps in [0, 10, 50, 100, 200]:\n", + " rewards = simulator(greenCrabEnv(), constAction(n_traps)).simulate()\n", + " stats.append([n_traps, np.mean(rewards), np.std(rewards)])\n", + "\n", + "stats_df = pd.DataFrame(stats, columns=['n_traps', 'rew_mean', 'rew_std'])\n", + "stats_df\n", + "# stats_df.plot(x='n_traps', y='rew_mean', yerr='rew_std');\n", + "# plt.show()" + ] } ], "metadata": { diff --git a/src/rl4greencrab/agents/const_action.py b/src/rl4greencrab/agents/const_action.py index 6695c1b..452a83b 100644 --- a/src/rl4greencrab/agents/const_action.py +++ b/src/rl4greencrab/agents/const_action.py @@ -4,8 +4,8 @@ def __init__(self, mortality=0, env = None, **kwargs): self.action = 2 * self.mortality - 1 self.env = env - def predict(self, observation): - return self.action + def predict(self, observation, **kwargs): + return self.action, {} \ No newline at end of file diff --git a/src/rl4greencrab/agents/const_escapement.py b/src/rl4greencrab/agents/const_escapement.py index 4254bb9..0386213 100644 --- a/src/rl4greencrab/agents/const_escapement.py +++ b/src/rl4greencrab/agents/const_escapement.py @@ -6,12 +6,12 @@ def __init__(self, escapement, env = None): if self.env is not None: self.bound = self.env.bound - def predict(self, observation): + def predict(self, observation, **kwargs): obs_nat_units = self.bound * self.to_01(observation) if obs_nat_units <= self.escapement or obs_nat_units <= 0: return -1 mortality = (obs_nat_units - self.escapement) / self.escapement - return self.to_pm1(mortality) + return self.to_pm1(mortality), {} def to_01(self, val): return (val + 1 ) / 2 diff --git a/src/rl4greencrab/envs/green_crab_ipm.py b/src/rl4greencrab/envs/green_crab_ipm.py index d21dd15..70e488d 100644 --- a/src/rl4greencrab/envs/green_crab_ipm.py +++ b/src/rl4greencrab/envs/green_crab_ipm.py @@ -73,7 +73,7 @@ def __init__( self.delta_t = config.get("delta_t", 1/12) self.env_stoch = config.get("env_stoch", 0.1) - self.action_reward_scale = config.get("action_reward_scale", 0.5) + self.action_reward_scale = config.get("action_reward_scale", 0.000001) self.config = config # Preserve these for reset diff --git a/src/rl4greencrab/utils/simulate.py b/src/rl4greencrab/utils/simulate.py index b5b70a5..6a4352c 100644 --- a/src/rl4greencrab/utils/simulate.py +++ b/src/rl4greencrab/utils/simulate.py @@ -12,10 +12,58 @@ def simulate(self, reps=10): observation, _ = env.reset() for t in range(env.Tmax): action, _ = agent.predict(observation, deterministic=True) - observation, reward, terminated, done, info = env.step(action) + observation, reward, terminated, done, info = env.step(self.unnormalize_action(action)) + observation = self.normalize_observation(observation) episode_reward += reward if terminated or done: break self.results.append(episode_reward) return self.results + + def simulate_full(self, reps=10): + observation_list = [] + action_list = [] + ep_rew_list = [] + reps_list = [] + t_list = [] + # + env = self.env + agent = self.agent + for rep in range(reps): # try score as average of 100 replicates, still a noisy measure + episode_reward = 0.0 + observation, _ = env.reset() + for t in range(env.Tmax): + action, _ = agent.predict(observation, deterministic=True) + action = self.unnormalize_action(action) + observation, reward, terminated, done, info = env.step(action) + observation = self.normalize_observation(observation) + episode_reward += reward + # + observation_list.append(observation) + action_list.append(action) + ep_rew_list.append(episode_reward) + reps_list.append(rep) + t_list.append(t) + # + if terminated or done: + break + return { + 't': t_list, + 'obs': observation_list, + 'act': action_list, + 'rew': ep_rew_list, + 'rep': reps_list, + } + + def unnormalize_action(self, action): + min_act = self.env.action_space.low + act_width = self.env.action_space.high - self.env.action_space.low + return min_act + act_width * (action + 1) / 2 + + def normalize_observation(self, observation): + min_obs = self.env.observation_space.low + obs_width = self.env.observation_space.high - self.env.observation_space.low + return -1 + 2 * (observation - min_obs) / obs_width + + \ No newline at end of file From 3ffb91466596e6dae5235cfc5156b05746c392db Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Sun, 25 Feb 2024 23:52:02 +0000 Subject: [PATCH 40/52] revamped intro notebook --- notebooks/intro.ipynb | 620 +++++++++++++++++++++++------------------- 1 file changed, 344 insertions(+), 276 deletions(-) diff --git a/notebooks/intro.ipynb b/notebooks/intro.ipynb index 910e184..2875078 100644 --- a/notebooks/intro.ipynb +++ b/notebooks/intro.ipynb @@ -16,7 +16,7 @@ "id": "146823b9-fb59-437b-899c-b487291afa24", "metadata": {}, "source": [ - "# 1. Setup\n", + "## 0. Setup\n", "---\n", "Uncomment the following line to install `rl4greencrab`. \n", "After installation you need to restart the jupyter kernel in order to use the package and run this notebook." @@ -24,280 +24,234 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "id": "9059df7d-6045-4834-a9cf-fcf32edac655", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Obtaining file:///home/rstudio/rl4greencrab\n", - " Installing build dependencies ... \u001b[?25ldone\n", - "\u001b[?25h Checking if build backend supports build_editable ... \u001b[?25ldone\n", - "\u001b[?25h Getting requirements to build editable ... \u001b[?25ldone\n", - "\u001b[?25h Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: gymnasium in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (0.28.1)\n", - "Requirement already satisfied: matplotlib in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (3.8.3)\n", - "Requirement already satisfied: numpy in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (1.26.4)\n", - "Requirement already satisfied: pandas in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (2.2.0)\n", - "Requirement already satisfied: pyyaml in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (6.0.1)\n", - "Requirement already satisfied: scipy in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (1.12.0)\n", - "Requirement already satisfied: typing in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (3.7.4.3)\n", - "Requirement already satisfied: jax-jumpy>=1.0.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (1.0.0)\n", - "Requirement already satisfied: cloudpickle>=1.2.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (3.0.0)\n", - "Requirement already satisfied: typing-extensions>=4.3.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (4.9.0)\n", - "Requirement already satisfied: farama-notifications>=0.0.1 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (0.0.4)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (1.2.0)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (4.49.0)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (1.4.5)\n", - "Requirement already satisfied: packaging>=20.0 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (23.2)\n", - "Requirement already satisfied: pillow>=8 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (10.2.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (3.1.1)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4greencrab==1.0.0) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /opt/venv/lib/python3.10/site-packages (from pandas->rl4greencrab==1.0.0) (2024.1)\n", - "Requirement already satisfied: tzdata>=2022.7 in /opt/venv/lib/python3.10/site-packages (from pandas->rl4greencrab==1.0.0) (2024.1)\n", - "Requirement already satisfied: six>=1.5 in /opt/venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->rl4greencrab==1.0.0) (1.16.0)\n", - "Building wheels for collected packages: rl4greencrab\n", - " Building editable for rl4greencrab (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for rl4greencrab: filename=rl4greencrab-1.0.0-py2.py3-none-any.whl size=1069 sha256=04a2111d259dd039b329b1369e2857430f1ad1045264348157a0b65eab056871\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-86dvoand/wheels/e9/7e/e6/00c4b11a2574abd59d64425d537139e25fadbde37f002c4dba\n", - "Successfully built rl4greencrab\n", - "Installing collected packages: rl4greencrab\n", - " Attempting uninstall: rl4greencrab\n", - " Found existing installation: rl4greencrab 1.0.0\n", - " Uninstalling rl4greencrab-1.0.0:\n", - " Successfully uninstalled rl4greencrab-1.0.0\n", - "Successfully installed rl4greencrab-1.0.0\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ - "%pip install -e .." + "# %pip install -e .." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 40, "id": "c97fbb03-59d8-487a-9805-8faa9ec021d0", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", - "from plotnine import ggplot, aes, geom_density, geom_line, geom_point, geom_violin, facet_grid, labs" + "from plotnine import ggplot, aes, geom_density, geom_line, geom_point, geom_violin, facet_grid, labs, theme, facet_wrap" ] }, { "cell_type": "markdown", - "id": "b73ea14c-9712-4eba-bf44-5dfba45005be", + "id": "97cf2f40-a3b2-4de0-962e-68a22a273163", "metadata": {}, "source": [ - "# 2. Integral projection model\n", + "## 1. Green crab population dynamics environment\n", "---\n", "\n", - "Here we conceptually describe our integral projection model (IPM) of the crab population dynamics.\n", - "\n", - "Our model describes the process in which an agent observes green crab counts by laying traps to catch the crabs. \n", - "Each time-step corresponds to a year's worth of data, and has two components: \n", - "First, for 9 months crabs are caught using traps laid by the agent.\n", - "Second, for the last 3 months of the year, the crabs undergo a gestation period and no crabs are caught during this time.\n", - "This is the timeline of a time-step:\n", - "1. The agent receives an observation (nine months' worth of catch data).\n", - "2. The agent decides a density of traps to lay for the next year.\n", - "3. The population dynamics model evolves somatically for nine months, each month producing an overall catch count observation. This observation is sampled from a distribution that depends on the size-structure of the crab population.\n", - "5. New crabs are spawned and grow for 3 months. The number of new-borns is determined by a logistic function (plus a random term). During this timeline, too, new crabs immigrate with a fixed immigration rate.\n", - "\n", - "The following code block shows the input and output of this model.\n", - "Our model is encoded as a `gymnasium env` class in order to leverage existing RL algorithms." + "We model the dynamics of an invasive crab population at one site (e.g. a bay or estuary) using a size-resolved model with 21 size classes.\n", + "This dynamics is encapsulated into an environment class `greenCrabEnv`.\n", + "Let's declare the env and initialize it using the `.reset()` function." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "cab72c67-653f-4aee-8bfe-f13fbebd02c9", + "execution_count": 13, + "id": "ed5d7ec4-890e-49a1-9c4a-c6ee8f49fae8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([94., 28., 97., 1., 36., 69., 51., 26., 92.], dtype=float32), {})" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from rl4greencrab import greenCrabEnv\n", - "\n", - "gce = greenCrabEnv()" + "gce = greenCrabEnv()\n", + "gce.reset()" ] }, { "cell_type": "markdown", - "id": "f353ac81-20b4-4083-879a-44ecaa04c3e4", + "id": "edd41144-2405-4672-b32c-97d2360f1641", "metadata": {}, "source": [ - "After declaring `gce`, the `.reset()` function sets the state of the crab population to its initial value, and produces the following output:\n", - "\n", - "`initial observation`, `info`.\n", + "As the name implies, this resets the state of the env to some initial value. \n", + "Additionally, it returns two outputs: the first is an *observation* (whose meaning we will cover in the next section) and the second is an *information dict*.\n", + "This type of information dicts can be useful for debugging and generally logging detailed information about the env's state.\n", + "We will ignore information dicts throughout this tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "1ab1cba1-009d-41d2-9574-7f0b5330e30f", + "metadata": {}, + "source": [ + "## 2. Dynamics\n", + "---\n", "\n", - "Currently, `initial observation` is a random sequence of numbers for simplicity's sake." + "The dynamics of the system all occur within the `.step()` function.\n", + "This function evolves the state of the system one time-step, which in our model corresponds to one year.\n", + "The `.step()` function requires one argument, an *action*, which is the number of crab traps set in that bay throughout the year.\n", + "Actions can currently take values between 0 and 2000:" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "693a9ba4-b422-47b6-9d5a-04b9006ac094", + "execution_count": 16, + "id": "8875f9f8-4529-42fd-bd2c-f8bad693a838", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([97., 49., 70., 64., 29., 68., 37., 95., 49.], dtype=float32)" + "Box(0.0, 2000.0, (1,), float32)" ] }, - "execution_count": 3, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "observation, info = gce.reset()\n", - "observation" + "gce.action_space" ] }, { "cell_type": "markdown", - "id": "188dedf0-864f-4133-bca0-d88dfd519374", + "id": "3b4918ad-d4c6-4cf7-b377-3b364341b642", "metadata": {}, "source": [ - "A dynamical step for the IPM is produced by the `.step(action)` function.\n", - "Here, the `action` argument takes values in [0, 2000] and corresponds to the number of traps laid.\n", - "The output is\n", - "\n", - "`observation`, `reward`, `terminated`, `truncated`, `info`\n", + "The attribue `gce.action_space` is of the type `gymnasium.spaces.Box`, a standard class used for action spaces in the RL world.\n", "\n", - "The latter three outputs are beyond the scope of this notebook.\n", - "The `reward` output is used by the agent to train---the agent looks for strategies that lead to high average rewards over 100 time-steps.\n", - "\n", - "The following call of the step function returns a null observation since no traps were laid." + "Let's try and calling the step function to see what comes out." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "9d1b0d77-895f-4b19-8d3d-3da878a6704a", + "execution_count": 17, + "id": "832d213e-196b-44cf-ab00-9d0e3641600e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" + "(array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32),\n", + " -0.000512357747076951,\n", + " False,\n", + " False,\n", + " {})" ] }, - "execution_count": 4, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "observation, reward, terminated, truncated, info = gce.step([0])\n", - "observation" + "gce.step(0)" ] }, { "cell_type": "markdown", - "id": "6c8bed72-996d-4e68-b850-67117830f3b7", + "id": "93c8dc83-6271-406d-a370-617b50ba276c", "metadata": {}, "source": [ - "Lets try some other action values and see what observations we obtain:" + "The output here is:\n", + "\n", + "`observation`, `reward`, `terminated`, `truncated`, `info`\n", + "\n", + "We won't go into the last three outputs here as they are irrelevant for this notebook.\n", + "Here, I want to focus on the `observation` and the `reward` outputs.\n", + "\n", + "`observation` is a sequence of nine numbers, in this case they are all zero.\n", + "These numbers correspond to crab catch counts for each of nine months in the year (we do not produce count data in the gestation period of the year, which is three months long).\n", + "The reason they are all zero is that we laid zero traps this time-step.\n", + "For example, let's look at the observations resulting from different numbers of traps:" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "862dceed-6947-416c-9de7-0c2ba38d8fe5", + "execution_count": 22, + "id": "cff01522-d71f-4f70-9a9c-f0ec658bd707", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([2., 1., 1., 1., 1., 0., 1., 2., 2.], dtype=float32)" + "([1.0, 4.0, 0.0, 3.0, 3.0, 8.0, 8.0, 4.0, 8.0],\n", + " [69.0, 85.0, 69.0, 62.0, 74.0, 89.0, 88.0, 96.0, 83.0],\n", + " [635.0, 364.0, 220.0, 158.0, 164.0, 173.0, 177.0, 202.0, 190.0])" ] }, - "execution_count": 5, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# 1.\n", - "_ = gce.reset()\n", - "observation, reward, terminated, truncated, info = gce.step([1])\n", - "observation" + "list(gce.step(10)[0]), list(gce.step(100)[0]), list(gce.step(1000)[0])" ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "fd41a3d4-f50b-4f9a-bad5-29a2089c1631", + "cell_type": "markdown", + "id": "06542749-8bd1-466f-acdd-025bc17fb247", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([10., 13., 10., 3., 10., 6., 7., 8., 11.], dtype=float32)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "# 2.\n", - "_ = gce.reset()\n", - "observation, reward, terminated, truncated, info = gce.step([10])\n", - "observation" + "Notice that for high numbers of traps---in the case above in the case where 1000 traps are laid---, we can see a decrease in catch counts as the year progresses.\n", + "This is due to the diminishing population as a consequence of the traps laid.\n", + "\n", + "### Rewards\n", + "\n", + "The `reward` output is computed by balancing out two simultaneous goals of the agent: 1. to minimize the environmental damage produced by the invasive green crab population, and, 2. to minimize the cost of laying traps and removing trapped crabs.\n", + "We will examine the reward function in more detail later in this notebook." ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "fbffb33f-4b7f-4cc1-a7f7-824d12207a51", + "cell_type": "markdown", + "id": "36cb1d1a-1182-449f-b90b-615fa466e82d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([76., 66., 70., 56., 74., 56., 57., 64., 41.], dtype=float32)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "# 3.\n", - "_ = gce.reset()\n", - "observation, reward, terminated, truncated, info = gce.step([100])\n", - "observation" + "### Structure of the decision problem\n", + "\n", + "This is the structure of the decision problem defined by `greenCrabenv`:\n", + "The agent observes green crab catch counts each month for 9 months and then, based on that data, the agent chooses the number of traps to lay next year.\n", + "The cycle then repeats, and it repeats for an *episode* of 100 time-steps.\n", + "The goal of the agent is to choose a policy that maximizes the average reward it receives over an episode.\n", + "Because of how we've set up our reward function, this policy will balance the need to keep the invasive population in check with the cost of doing so." ] }, { "cell_type": "markdown", - "id": "3531f290-50f4-4529-969a-d70b9f7b470f", + "id": "e680fc77-d28a-4519-964a-e99c3d568de5", "metadata": {}, "source": [ - "## Plotting population distribution over time without traps\n", + "## 3. Crab size distribution\n", + "---\n", "\n", - "The state of our model is a size-resolved population distribution, with 21 size classes." + "As mentioned, our model describes the size distribution of crabs using 21 size classes. \n", + "At any point in the dynamics, this ''internal'' state of the environment is stored as `gce.state`.\n", + "Let's plot how this size distribution evolves over time when subjected to different numbers of traps." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "976afa61-4e00-4f7d-9507-f0030c4302b5", + "execution_count": 47, + "id": "c2d8e941-f79d-4c3b-a822-00d7582385e9", "metadata": {}, "outputs": [ { "data": { - "image/png": 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iomw7D1OAASAwBIAoxFp9F+pdgK3nowoQANzFCOJCsf6f9TxUAAKAexnv0XZuAGI9PgEgAJQPASAKsQZvoa4AtFaVEAACgLsYAWCoKgBZAxAA3M94j7b75hBTgAEgMASAKMTJCkBr4EgACADuEuoAkApAAHA/I5CzOwA0KgAPHTokn89n67kAoCIiAEQhTlYAWs/Hmk8A4C6sAQgAOJIRANo9BdjYBVhiXACA8iAARCHGDsASASAA4DCn1gDMzMw0N6cCALhLqMYG6/GZBgwAZUcAiELcUgHIFGAAcBen1gCUmAYMAG4VqgpA6/EPHjxo67kAoCIiAEQhBIAAgKI4VQEoEQACgFuFeg1AiTEBAMqDABCFsAkIAOBIWVlZysrKkhT6NQAlloUAADfy+XzmZ/ZQVgAyBRgAyo4AEIUYg7jX61V8fHxIzx0TE6PIyMgC7QAAOM8awDkxBZgAEADcx/rezBqAAOBuBIAoxAjeEhIS5PF4Qnpuj8djVgESAAKAe1inWzEFGAAgFQzimAIMAO5GAIhCjOAt1Ov/GQgAAcB9nK4A5GIPANzHGgDaPQU4OjranJ3EJiAAUHYEgCgkLS1NknMBoHHBRwAIAO4RymleRZ2HABAA3Mc6NtgdAEqHxwWmAANA2REAohAqAAEAR7IGcKGqAIyLizPXhWUNQABwn1BOAZYOh4zcFAKAsiMARCFGBWCodwA2GOclAAQA93BiCrDH4zEvKLnYAwD3CXUASAUgAJQfASAKcUsFINUeAOAeTlQASiIABAAXC+UagNZzEAACQNkRAKIQtwSAVAACgHsYN2UiIiLMRdhDwQgbCQABwH2MsSE6OlqxsbG2n69atWqSGBMAoDwIAFEIASAA4EjGRV5SUpI8Hk/IzksFIAC4l1GJF4rqP+nwmMAuwABQdgSAKMTpXYAJAAHAfYwAMFQ7ABuM87EsBAC4jxEAhmpsYAowAJQfASAKcVMFYF5eniNtAAAUZFTghXL9P+v5qAAEAPcxbs6EqgLQOE96erpycnJCck4AqCgIAFFAVlaWOZg6tQuwNXg0qhEBAM6yTgEOJaYAA4B7hboC0HoexgUAKBsCQBRgnXbrhgCQKV8A4A5OVQAyBRgA3MupKcDWcwMASifS6QagbCIiImw9VkZGhvl1lSpVgnq+0rJ+gDh06JBjU5HLw4n+KgujfW5v55Hc3l761R70a+jPV9KxjBtEVatWDenvaIwJ6enp8vv9knhNBBN/Z/agX+1Bv4b+fEc7lnFzqFq1aiH5PWvUqGF+nZqaymvCBvSpPehXe9CvZUMAGGaqV68elONEREQUeawtW7aYX9erVy9o5yuL+vXrm18fOnRIp556asjbUB7F9akbhXoR/0DQr/agX+3hRL/aPS4YjACwVq1aIf0d69WrV+h7vCaCjz61B/1qD/q1ZKEaF6TD1dl16tQJye95/PHHm1/7fD7ztcBrIvjoU3vQr/agX0uHADDMHDhwIKDnG1V9Pp+vyHUztm/fHtTzlYdR4SHlf6hISUmRz+cLeTtK62h96iYRERGqUqWK6/tUol/tQr/aI1j9Wp4PA3aPCwZjqlVMTExIxwbrHdKtW7eqRo0aleo1YbfK+HcWCvSrPSpjv7p5XPD7/Tp48KAkKTo6OiRjg8fjMb/eunWrUlJSKt1rwm6V8e8sFOhXe1TGfg0kPCQADDPBfFEXdSzrCzEuLs6RP6L4+PgC7fH5fK7/YzaEUzvDpa0S/WqXcGkr/Rq68xV3LL/fb1Z5JCQkhPR3tC4DYVxo8poIPvrUHvSrPejX0J2vpGOlpqYqLy9PUv76sKH4PY8cE4xz8poIPvrUHvSrPejX0mETEBRg3QTEqbX32AQEANwlLS3NvMgL9RQLdnwEAHeybsJh3ZzDTtYxgU1AAKBsCABRALsAAwCOZH0vdmoXYIkAEADcxPqeHKqbQ5GRkeY1CgEgAJQNASAKcEMFoDV4JAAEAOdZ34upAAQASAUDuFCODdWqVSt0fgDA0REAogAjAPR6vYqLi3OkDREREeY6gASAAOA8a/AW6gpA6/kIAAHAPazvyaGaAmw9FwEgAJQNASAKMALAxMTEArtshZpRBUgACADOc3IKcEJCgrkTMAEgALiHUwGgUW1IAAgAZUMAiALS0tIkOTf912CcnwAQAJznxDpPBo/HY4aOBIAA4B5OTQE2wkbGBAAoGwJAFGCtAHQSASAAuIeTU4ClwxeWXOwBgHsYAaDX6w3p5oFMAQaA8iEARAFGBaBTOwAbjAtMAkAAcJ51gygnAkAqAAHAfYz35KpVq4Z06SCmAANA+RAAogAqAAEARzIu8mJjYxUdHR3y8xsXe4wJAOAe1gAwlIxdgLkpBABlQwCIAtwWADKwA4DzjODNieo/63kZEwDAPYwKvFCPDUbgmJGRoaysrJCeGwDCGQEgCnBbAEi1BwA4zwjenAoAWQMQANzHqQpA64YjTAMGgNIjAEQB7AIMADiS8V4c6h2ADVQAAoD7GOFbqANA6/kIAAGg9AgAUQAVgACAIzk9BZgKQABwH+M9OdQ3hwgAAaB8CABh8vv9ZgDo9C7ARgCYnZ2t7OxsR9sCAJWd0xWAxnlTU1Pl8/kcaQMAoCCnAkCmAANA+RAAwpSVlaXc3FxJ7gkApcNViQAAZxgXeU5Vh1sv9qgMBwB3cGoKsLELsLUNAICjIwCEyRq0uWUKsEQACABOc0sFoMTFHgC4QWZmpjlLhynAABAeCABhclMAaK1AJAAEAGc5vQag9bxc7AGA86zvxaEOAJOSkuTxeAq1AwBQMgJAmIwdgCXnA0DrxR7TvQDAObm5uUpPT5dEBSAAIJ91U6ZQTwH2er3mtQJjAgCUHgEgTG6qAGQKMAC4g/UmjNO7AEtc7AGAG1jfi0MdAFrPyZgAAKVHAAiTWwNAKgABwDkEgACAI1krAJ2oDjfOyZgAAKVHAAiTNQBkF2AAgFTwIs8NAaC1PQAAZzi5BqB0eCdgAkAAKD0CQJjcWgFIAAgAzrFWADq1BmBCQgILvgOAizi5BqD1nAcPHgz5uQEgXBEAwuSmADA2NlYRERGSCAABwElumALMgu8A4C7W92InxgbjhhRV4QBQegSAMBlBW0REhGJjYx1ti8fjMUNIAkAAcI4bKgAlEQACgIsYwVtSUpJ50z6U2AQEAMqOABCmtLQ0SfnVf8ZUKycRAAKA89ywBqDEgu8A4CbG2ODUjSFrAOj3+x1pAwCEGwJAmIygzenpvwYCQABwnjUAdHJ8oAIQANzDeC92Yv0/6XDwmJ2drczMTEfaAADhhgAQJqMC0OkdgA0EgADgPGMKcGJiorxe5z42UAEIAO7hdAWgsQuwxEYgAFBaBIAwUQEIADiS0xd5BgJAAHAP473Y6SnAknTgwAFH2gAA4YYAECa3BYDGdC8CQABwjvEe7OT6fxIBIAC4iXFzyOkpwBIVgABQWgSAMLk1ALTuQAkACC23VACyBiAAuIebKgAJAAGgdAgAYbLuAuwGTAEGAOcZN2HcUgGYkpKivLw8R9sCAJWd0xWABIAAUHYEgDAZQZvbNgGhAhAAnGNc5LklAPT7/dwYAgAH5eTkKD09XRIVgAAQTggAYXLbFGCjHWlpafL7/Q63BgAqJ2NscHoKsPX8RigJAAg963uwU2ODdWd6AkAAKB0CQEgqWFHhtgpAv99vTk8GAISW2yoAJSrDAcBJ1rVYnZoC7PF4zHOzCzAAlA4BICRJmZmZ8vl8ktxXASixDiAAOMHv95thm9MVgNYAkgpAAHCO9SaMUwGg9dxUAAJA6RAAQlLBgM2NASAVgAAQepmZmcrJyZHk/NjAFGAAcAdrBaCTN4eMcxMAAkDpEABCUsGAzemLPAMVgADgLGuVBxWAAADJPQEgFYAAUDYEgJDk/gpAAkAACD1rAMgagAAAqeBNGCenAFMBCABlQwAISe4MAK0XmwSAABB6VAACAI7khl2AJalatWqSCAABoLQIACGpYMDmtl2AJao9AMAJ1os8pysAIyIizHGBABAAnGNMAY6Li1N0dLRj7WAXYAAoGwJASKICEABQmJsqAKXD4wIBIAA4xwgAnR4XrFOA/X6/o20BgHBAAAhJ7gwArZWIBIAAEHrWoM0NY4NxsUcACADOMd6DnVz/z3p+n89XYENDAEDRCAAhyZ0BYGRkpOLi4iQRAAKAE9xWAUgACADOM96DnR4XrAGkdWdiAEDRCAAhSeZds8jISMXExDjcmsOM6V4EgAAQekYAGBkZqdjYWIdbQwAIAG7glinABIAAUDYEgJB0OABMTEyUx+NxuDWHEQACgHOsVR5uGBsIAAHAeW6bAiwRAAJAaRAAQtLhgM0t038NBIAA4ByjAtDpHYANBIAA4DwCQAAITwSAkHQ4YLNuvOEGBIAA4BzjIs9tAaB1bUIAQGgZYZvTYwMBIACUDQEgJFEBCAAozM0VgH6/3+HWAEDlk5eXZ44NTlcAWtcgJAAEgKMjAIQk9waAxsBOAAgAoWdc5Dm90LvBaIfP51N6errDrQGAyic1NdW8AeN0ABgfH6/IyEhJBIAAUBoEgJBUcBMQN6ECEACc47YKQGs7mAYMAKFnDdqcvjnk8XjMEJIAEACOjgAQklgDEABQmFvXAJTYCAQAnOCmAFCSqlWrJkk6ePCgo+0AgHAQ6XQDQi05OVlz587VihUrtG/fPsXExOikk07SNddcoxYtWpT5eD6fT+vWrdPff/+tv//+W//884927twpSbr99tvVpUuXYP8KtnDrFGACQABwjtsqAAkAAcBZ1gDQ6SnA1jYwJgDA0VWqAHDz5s0aOnSoOXDFxcUpLS1Na9as0Zo1a3Tdddfp3nvvLdMx9+7dqyeeeMKO5oaU2ysAs7KylJOTo6ioKIdbBACVQ15enjk2uKHKQyIABACnWZdfcEMAaIwLTAEGgKOrNAFgTk6OnnnmGSUnJ+uEE05Q//791ahRI2VlZWnBggWaNWuWFi5cqEaNGqlNmzZlOnZcXJxOPPFEnXzyyTrppJM0e/Zs7dixw6bfJPj8fr/rKwCl/JCyevXqDrYGACqPtLQ0c6F3KgABAJL7pgCzBiAAlF6lCQA/++wz7dy5UzExMRo2bJhq164tSYqJidGtt96q/fv365NPPtHbb7+tVq1amTtKHU3t2rU1Z84ceTwe83vz58+35XewS0ZGhvLy8iQRAAIA8lkDNjdc5EkF28EmIAAQegSAABC+Ks0mIEuWLJEkXXbZZWb4Z3XTTTfJ4/Fo//79Wrt2bamP6/V6C4R/4cjYAVhyfwAIAAgNa8Dmhos8qeCYQAUgAISe8d4bFRWluLg4h1tDAAgAZVEpAsCMjAz99ddfkqRzzz23yJ+pXbu2jjvuOEnSL7/8ErK2uYE1WCMABABIBQM2t4wNUVFRio+Pl0QACABOMN57q1at6ooiCGMX4OTkZHNGEwCgaJUiANy6dau5jtEJJ5xQ7M8Zj23ZsiUk7XILAkAAwJHcWAEoseMjADjJqLRzy9qwxphgXdMcAFC0SrEG4P79+82va9SoUezPGY8dOHDA9jYV5+2339Y777xT7OOdO3dWly5dyn18r9dr/ttYT896965u3bquWWfP4/EUuuh0S9usiupTtzL+X1etWtUMxd2KfrUH/WoPJ/s10PMV13ZrJcVxxx3niteLx+NR1apVtWPHDmVlZbmiTcUJl781/s7sQb/ag34tHbvGBSl/ZpUk1axZ0xWvl7p16xb4bze0qTjh8rfG35k96Fd70K9lUykCwMzMTPPrmJiYYn/OeMwY2JyQlpam3bt3F/t4enq6IiIiAj6Px+Mxj5Oenm5+v1q1akE5frBY7y6mpaW5qm1Hsvap2xlvPuGAfrUH/WoPJ/o1WOc7su3WSooaNWq45vViVHscOnTINW0qSbj8rfF3Zg/61R70a8nsGhekw9XXbrlmsBZ3MC4EF39n9qBf7UG/lk6lCADDSUJCgo455phiH4+Pj5fP5yv38Y1NS/x+v1ndYV00N9DjB5PH4ykQAB48eNA1bbMqqk/dyuPxyOv1Ki8vLyzukNCvwUe/2iNY/VqeDwOBvi8W13ZrNbxbxgajAlBy75hgCJe/tcr4dxYK9Ks9KmO/umlckPLfe6X8pSHc8B5snS20b98+V7SpOOHyt1YZ/85CgX61R2Xs10DCw0oRAMbGxppfZ2VlmQuIHykrK0uSHN3R6s4779Sdd95Z7ON79+4NaIpy9erVFRERoby8PPM4O3fuNB/3+XyOToG2ioiIULVq1cw/6N27d7umbVZF9albRUREqHr16kpOTnb1BySJfrUL/WqPYPVrrVq1yvycQP8/Ftd2oxo9Pj7eNesqRUREmAHg/v37Xf0aDpe/tcr4dxYK9Ks9KmO/umlckPJDNin/eskNrxfrhfC2bdtc0abihMvfWmX8OwsF+tUelbFfyzMuGMKnTjIA1tJw63qARzIec/s892BLS0szv3bbJiAej8dsk1suQAGgMjCmebltXDCqPdgEBABCz7oLsBsYuwBLh6sTAQBFqxQB4HHHHWcuDrl58+Zif8547Pjjjw9Ju9zCCACjoqJKXCPRKQSAABB6xkWem3YAlgquAQgACB2/3++6scEaRFqXNQIAFFYpAsC4uDg1btxYkrRq1aoif2bv3r3asmWLJKlp06Yha5sbGMGa26o8DEa7rJWKAAB7GWODdS1WNzAu9lJSUly/1gsAVCTp6enKzc2V5J4AMDY21ixgoDIcAEpWKQJASWrVqpUkaenSpdqzZ0+hx+fNmye/368aNWqoSZMmIW6ds4yLvISEBIdbUjQqAAEg9NxW5WEwAsCcnBxlZmY63BoAqDysAZtbpgBLh6cBUwEIACWrNAHg1Vdfrbp16yozM1NPP/20/v33X0n5G3/MnTtXH3/8saT8TTgiIwvujdKzZ09df/31Gj9+fJHHTktLU0pKivmPsaNLVlZWge8bm4y4jdsrAI3qEwJAAAgdY4qt28YG60Un1R4AEDrWgI0AEADCT6XYBVjKX9/u8ccf19ChQ7Vp0yb16dNH8fHxyszMNAO7Dh06qE2bNmU+9siRI7Vu3bpC358/f77mz59v/vftt9+uLl26lP+XsInbA0AqAAEg9NxeASjlh5R16tRxsDUAUHlYb7q4aWwwAkBuCgFAySpNAChJDRo00MSJE/X+++9rxYoV2rt3rxISEnTiiSfq2muvVYsWLZxuoiOMtfUIAAEABqMC0K1rAEpc7AFAKFkr7NwUAFavXl0SuwADwNFUqgBQyr9D1KNHD/Xo0aPUz5k6dWqJjz/77LOBNstRbl8DkCnAABB6RgDopos8iQAQAJzi9jUAGRMAoGSVZg1AFC9cpgAbF6MAAHtlZ2ebG2xQAQgAkNwfALIGIACUjAAQrq8ANALAtLQ0+f1+h1sDABWf9YYLASAAQDocsHk8HlddNxAAAkDpEAAibCoA8/LylJ6e7nBrAKDiC5cAkKUhACB0rJtDeb3uuYw0AsBDhw7J5/M52xgAcDH3vHPDEX6/P2w2AZG42AOAUHDrTo+SFB0drdjYWElUAAJAKBkVdm6a/isdDgAllgwCgJIQAFZyGRkZysvLk0QACADI5+YKQOlwKEkACAChY60AdBNjF2CJnYABoCQEgJWcNVAjAAQASAUDQLdd6EmHQ0kCQAAIHaMC0G3jgrUCkHUAAaB4BICVHAEgAOBIVAACAI5kvOe6eQow4wIAFI8AsJKzBmpu2s3LyhoAGusVAgDs4+Y1AKXDbWKtJwAIHSoAASC8EQBWcuFQAWitPqECEADsZwRrHo9H8fHxDremMKYAA0DohUMFIAEgABSPALCSIwAEABzJuMhLSkqS1+u+jwpMAQaA0HPrJiBMAQaA0nHfp3qElHVKrVsDQGu7mO4FAPYzbra47SLPwBRgAAitrKwsZWZmSnJfBWB0dLRZrc4uwABQPALASi4cKgAjIyMVGxsriQpAAAgFawWgG1EBCACh5fa1YY1QkinAAFA8AsBKLhwqAKXDbSMABAD7hUsAmJWVpaysLIdbAwAVn9sDQG4MAcDREQBWckagFh0drejoaIdbUzwCQAAIHWNqrdsDQImLPQAIBWtlndumAEtUAAJAaRAAVnJGoJaQkOBwS0pmtI8AEADsZwSAbqzykAgAASDUrMGaG8cGAkAAODoCwErOCNTcPP1XogIQAEIpnCoAGRcAwH7Wmy1urgDkptDR/ffffzpw4IDTzQDgAALASo4AEABwJOMCyo1VHhIVgAAQam5fA7BatWqS2AW4OAcPHtQbb7yh1q1bq3nz5rr00ku1c+dOp5sFIMQinW4AnGVsAkIACACQJL/fb1YAunVsIAAEgNBiCnD4ycvL09KlS/XOO+/ok08+KbBp1u7duzVo0CBNnz5dHo/HwVYCCCUCwEouXNYAJAAEgNBIT0+Xz+eT5M6LPIkAEABCzQjWEhISFBnpvktIY1xIT09XTk6OoqKiHG6RczZv3qw5c+Zo9uzZ2rp1a4HHTjnlFNWqVUvff/+9Pv74Y3344Yfq2LGjQy0FEGrue/dGSIXLFGBjHSqjYhEAYA+j+k9y7xqA1nZV1ADQ7/dr+fLlysrKUmxsrOLi4hQXF6fY2FjFxsYqPj5esbGxlfoiF0DoGO+1blz/TyrYrpSUFNWsWdPB1oReRkaGPv30U82aNUtLly4t8FhiYqJuvPFGdenSReedd55SUlJ0ySWXaNeuXRo8eLBatmypGjVqONRyAKFEAFjJhVsFoPXCFAAQfNb3WbdWABrBV05OToUdF0aNGqVx48Yd9eciIiIKhINHhoRxcXG6+uqrdccdd4Sg1QAqKrevDWsNAJOTkytFAOj3+/Xrr79q1qxZmjdvXqHpzxdddJG6dOmi6667rsC1XtWqVfXCCy/o7rvv1p49e/TEE0/o5ZdfDnXzATiAALCSC5cKQKN9mZmZys3NdeXUAwCoCMKhAtDj8ahKlSrat29fhawA3LBhgyZMmFCqn/X5fEpNTS1xiYxPP/1UderUUZs2bYLVRACVjBEuhUMAWBHHBav9+/dr7ty5euedd7R+/foCj9WtW1e33XabOnfurJNOOqnYY7Rv31433HCDPvjgA7333nu64YYb1LZtW7ubDsBhpCiVXLgFgFJ+m42dvgAAweX2nR4NSUlJFTIA9Pv9GjBggHJzcxUVFaW33npLSUlJyszMVGZmptLT05WZmamMjAzz38bX1seNx1avXq3U1FQ9/PDD+uabb1S7dm2nf0UAYcjtU4Ct1wYVdSfgAwcOaPDgwVq4cKGys7PN70dFRenqq69Wly5ddMUVV5S6UOK5557T0qVLtX//fj366KNatmyZa2/8AQgOAsBKzO/3h90uwBIBIADYyVoB6OaxwQgnK1oAOGfOHP3www+SpAcffFBXXnllQMdbuHCh7rnnHu3Zs0d9+/bV22+/zY6PAMos3KYAVzR+v199+vTRp59+an7vtNNO0x133KGbb75ZtWrVKvMxa9WqpWeffVa9e/fW9u3b9dRTT+mFF14IZrMBuIzX6QbAOenp6fL7/ZLcfZEnFQ4AAQD2CJcKQKNtFWkNwP3792vEiBGSpIYNG6pfv34BH/O6664z1//7/PPPNX369ICPCaDyYQqwsz766CMz/GvdurU+//xzLV26VL179y5X+Gfo1KmTOfV3xowZ+u6774LSXgDuRABYiVmDNAJAAIAUHmsAShWzAvCpp57S/v37JeVPzYqLiwvKcZ955hmdeOKJkqThw4frjz/+CMpxAVQeRgDo1inA1mCyolUAJicna9CgQZKkOnXq6LXXXlOzZs2CUs3t8Xg0ZswYc7zv16+f0tPTAz4uAHciAKzErEFauOwCLBEAAoCdjAAwOjpasbGxDremeMbFSkUJAH/88UfNmjVLUn7VXjA37EhMTNSUKVMUGRmpzMxM3XfffcrKygra8QFUbLm5ueayQW4NACMjI83rmYoWAD755JPavXu3pPwd4oP9/6B+/fpm9fm///6rUaNGBfX4ANyDALASowIQAHAkIwB0c/WfVLEqAHNycjRgwABJ+TfkRo4cGfRzNGvWTAMHDpQkrV+/3pZzAKiYrJXhbp0CLB0OJytSAPjdd9/prbfekiRde+216tChgy3nueuuu3TppZdKkiZPnqyVK1fach4AziIArMTCKQC0VigSAAKAfYxALVwCwIowJrz66qv6/fffJUmDBg1SvXr1bDnPQw89pIsvvliS9Morr2jJkiW2nAdAxWIN1NwcABqbBFaUADAzM1P9+/eXlD8m21mZ5/F4NG7cOMXFxSkvL0/33HNPgZ2GAVQMBICVmFHKL7k/AKQCEABCI9wqANPT05WTk+Nwa8pv69at5q6LZ511lnr27GnbuSIiIjR58mSzSubBBx/Uvn37bDsfgIrBGqi5dQqwdHhcqCgB4NixY7Vx40ZJ+eu31q1b19bzNWrUSIMHD5YkrVu3Ti+++KKt5wMQegSAlVg4BYAJCQnmQrcVacdHAHAbowLQzVUeUsGAMpzHhSFDhig9Pd1ciD0yMtLW8x177LEaM2aMJGnXrl3q06eP/H6/recEEN6s77FuDgCNtlWEpSHWr1+vSZMmSZJatGihu+66KyTn7dWrl8477zxJ0rhx48zqdAAVAwFgJRZOU4A9Ho/ZRioAAcA+4VYBKIXvxd6iRYv06aefSpK6du1qXnTZ7YYbbtBtt90mSfrkk0/0+uuvh+S8AMJTuEwBrihrAPp8PvXr10+5ubmKjo7WuHHj5PWG5rI9IiJCEyZMUFRUlHJyctSnTx/l5uaG5NwA7EcAWImF0y7AkggAASAEjADQzRd5UsGAMhwDwLS0NHOqVe3atTV06NCQnn/UqFFq2LChJKlv3776888/Q3p+AOEjXALAijIF+PXXX9fq1aslSf3791fjxo1Dev7TTz9djz/+uCRp9erVeu2110J6fgD2IQCsxIwgLTo6WtHR0Q635ugIAAHAfkaY5vbKcOtFaDhOAR47dqy2bt0qSXrqqafMxetDJTExUa+88ooiIiKUkZGh++67jwXfARTJepPFzQFgRZgCvHnzZj333HOS8oO4hx56yJF2DBo0SGeccYYk6bnnnjPXIgQQ3ggAKzEjSHP7RZ7BaKd17UIAQHCFSwVgOE8B/v333/XKK69Iklq2bKmbbrrJkXY0b95cjz32mCTpl19+sXWHSQDhy3iPjYmJUWxsrMOtKZ5xIyUjI0NZWVnONqYc/H6/Hn30UXNd2HHjxjlWpBEdHa2JEyfK6/UqMzNT/fr1U15eniNtARA8BICVWLgGgFQAAoA9fD6feZOFANAeeXl5GjBggLm20/PPP29ucuWEfv366ZJLLpEkTZo0Sd9++61jbQHgTsaU2nAaF8JxGvD777+vr7/+WpLUs2dPNW/e3NH2NGvWTA888IAk6fvvv9fMmTMdbQ+AwBEAVmLGRR4BIABAKvj+6vZNQMJ1DcDZs2frxx9/lCQ99NBDOvnkkx1tT2RkpN5++20lJSXJ7/frgQce0IEDBxxtEwB3McI0N+8ALBVsXziNC5K0b98+c929Y489VkOGDHG4RfkGDBigE088UZL05JNPatu2bQ63CEAgCAArMeNCLxw2AJEIAAHAbuGyzpMkxcfHKyIiQlL4XOjt27dPTz75pCSpYcOG6tOnj8MtytewYUONHTtWkrRjxw71799ffr/f4VYBcAtjaYhwCgDDrQJw2LBh2rdvnyTp+eefd02BRlxcnF588UVJ+ddgjz76KOMDEMYIACsxpgADAKysm2m4fWzweDxmSBkum4A8+eSTZnXd6NGjFRcX53CLDrv55pt18803S5I++ugjvfPOOw63CIBbGGGa2yvDw3UK8FdffaX33ntPknTjjTfqqquucrhFBV188cXq3r27JGnx4sWaO3euwy0CUF6Rdh14z549Wrx4sdatW6cDBw4oMzPzqM/xeDx644037GoSjkAFIADAKpwqAKX8Nh44cCAsKgCXL1+u2bNnS5I6duyo1q1bO9yiwkaPHq0VK1Zo8+bNGjJkiFq0aKGTTjrJ6WYBcFg4TgEOlwAwLS1NAwYMkCRVr15dI0eOdLhFRRs2bJg+//xzbdu2TUOHDtXll1+uY445xulmASijoAeARmnwjBkzlJOTU+bnEwCGTrhWAB46dEh+v9/RRdODbdeuXXr77bd17rnnqlWrVhXqdwMQPqyVdG6v9JAOt9HtAWB2dra5225iYqKeeeYZh1tUtCpVqmjy5Mm6/vrrlZ6ervvvv18ff/yxoqKinG4aAAcZ77FuDwCNXYCl8AkAR48erc2bN0vKrxKvXbu2wy0qWmJiosaOHavbb79dBw4c0JAhQzR16lSnmwWgjII6BTgnJ0dXX321Xn/9dWVnZ8vv95fpH4RWuAaAeXl5ysjIcLg1wZOWlqZbb71Vo0aN0q233qorrrhC77//frkCdAAIhDUADJcKQMn9U4CnTJmiDRs2SJIGDx6sunXrOtyi4l144YXq37+/JGn16tV64YUXHG4RAKeFSwCYlJRk3kQPhwBw9erVevXVVyVJl112mW6//XaHW1SyK6+8UrfeeqskacGCBfrkk08cbhGAsgpqAPjKK69o+fLlkvLDmoEDB2rx4sXasGGD/v3336P+s3HjxmA2B0cRrrsASxVnGrDf71ffvn3122+/md9bv369evfurQsvvFCvvfaa+f8JAOwWbhWA4RAAbtq0SWPGjJEknX322brnnnscbtHRPfLII2revLkkafz48fr+++8dbhEAp+Tl5ZkBoNvHBa/Xa7bR7QFgTk6O+vXrp7y8PMXFxWnMmDFhMQPomWeeMasUBwwYwPU7EGaCGgDOmTNHUv7doZUrV+q5555T69atdcopp+iEE04o1T8IDb/fTwDoAq+88oo++OADSdLVV1+tp59+Wscee6wkacuWLRo6dKiaNWumUaNGac+ePQ62FEBlYJ1K6/YLPelwAOjWKcB+v199+vRRRkaGPB6PxowZo8hI25ZfDprIyEhNnjxZCQkJ8vv9+t///uf6i2kA9khLS1NeXp4k91cASofb6NZxwTB58mStX79ekvTYY4+pUaNGDreodKpXr67Ro0dLknbv3q127drp22+/dbhVAEorqAHg77//Lo/Ho969e+vUU08N5qERZGlpaea0awJAZ3z77bd68sknJUknnXSSJk+erN69e2vlypWaPHmyzjjjDEnSgQMHNHbsWJ177rncaQNgK6OSLj4+XhEREQ635ujcvgbgggUL9PHHH0uSunfvrmbNmjncotJr1KiReZG3bds2PfrooyzXAlRC1vfXcAgAjRtDbr5p8ddff5nLKzRp0kS9e/d2uEVlc91112no0KGS8q9TbrnlFk2fPt3hVgEojaAGgNnZ2ZKkc845J5iHhQ2s06XCbRdgKfwDwK1bt+ree+9VXl6eEhIS9Oabb5ofWKKionTLLbdoyZIlevfdd3XppZdKkjIzMzVjxgy1aNFC99xzj1avXu3krwCgAjIu9MJh/T/J3QFgamqqHn74YUlS7dq1NWTIEIdbVHa33nqrbrjhBknSBx98oBkzZjjaHgChZw3SwqEy3AgpDx486GxDiuH3+9W7d29lZWUpIiJCL774YlhUhh+pb9++mjFjhuLj4+Xz+fTYY4/pscceYw1zwOWCGgAed9xxkqSsrKxgHhY2sAaAVACGVkZGhrp166Z9+/ZJkiZOnFhkxazH41Hr1q01f/58ffHFF+rYsaO8Xq/8fr8WLlyoq666SjfccIMWL15MVQaAoDDGhnC4yJMOB5VpaWny+XwOt6agp556Slu2bJGUv2ZSOFTOHMnj8eiFF14wl2gZOnSofvrpJ4dbBSCUrAFgOLyPGTsBu/HGkCRNmzZN33zzjSTp/vvvV9OmTR1uUflde+21+vjjj80MYPr06br11lu1f/9+h1sGoDhBDQCvuuoqSeLDYRgI9wAwXDfG8Pv9euyxx/TLL79Ikh5++GFdd911R33eOeeco6lTp+qHH35Q9+7dFRsbK0n67rvv1LlzZ7Vq1Urvvfced90ABMQYG8KlAtDaTjdtBLJ+/Xq99NJLkvJ3TbzxxhsdblH5VatWTdOmTVNsbKxycnJ0zz33aPfu3U43C0CIMAU4eHbu3KlHH31UktSwYUMNGDDA4RYF7qyzztLnn3+uCy+8UJK0bNkyXX311frzzz8dbhmAogQ1AHz44YcVGxurN998Uzt37gzmoRFk4R4AhmsF4PTp083Nclq1alXmKWGNGjXS888/r1WrVunRRx9V9erVJUm//fabHnjgAZ1//vlasGBB0NsNoHII1wpAyV3VHiNGjJDP51N0dLQmTpwYFjs7luTss88216vasWOHevXqpdzcXIdbBSAUwi0AdPMmIH379jWnJo8ZM0bx8fHONihIateurffff19dunSRJG3atEnt2rXTF1984XDLABwpqAFg48aNNWXKFKWlpenqq6/WP//8E8zDI4isARoBYGj8+OOP5oK5DRo00KuvvlruRfZr166tgQMHavXq1Xr22Wd1/PHHS8pfqL13797mrmIAUBbGBVO4BIDWdrrlYm/dunVasmSJJKlPnz465ZRTnG1QkNx+++3q3r27pPzq85EjRzrcIgChEK5rACYnJ7tqiZzPPvtMc+fOlSR17dpVl19+ucMtCq6YmBiNHz9eTz/9tLxerw4dOqQ77rhDL7/8sqv+PwCVXVBXHJ05c6Yk6eabb9a7776rM844Qx06dNBFF12kWrVqyes9et549913B7NJKEY4BoDR0dGKiYlRVlZW2AWAO3fu1D333KPc3FzFxcVpxowZqlGjRsDHTUhI0L333qvu3bvrgw8+UJ8+fZSdna2+ffvq008/DctFhQE4J9wCQDdWAL7yyiuS8jd06tevn8OtCa6nn35av/76q37++WdNmjRJzZo10/XXX+90swDYyAgAIyIiwmLjQGNcyM7OVmZmpuLi4hxukeTz+fTkk09Kyr+J//zzzzvcInt4PB717t1bp5xyiu69916lpKRoxIgR+v333zV27FjFxMQ43USg0gtqOtCtWzdzmovH41FOTo4++OADffDBB6V6vsfjIQA8ivJWjB3JOgW4atWqQTtuMBltsrYtMTHRDADd3Gar7Oxs9ejRw1wzafz48UHfKTsiIkK33XabtmzZomeffVZr1qzR66+/rgcffLDI9rmx70ri9vbSr/agX0N/PuPmSriMC8Zi71L+2rBOt3nbtm2aN2+eJOmOO+5QvXr15PP5HG9XScrydxYfH68ZM2boiiuu0N69e9WnTx+dccYZRW5kZTc396nE+5dd6NfQn8+4Zqhataorbywf+ZowlseR8tvuhkKH999/X3/99Zck6cknn1TNmjVdc9OqKIH+nbVt21aff/65unTpoo0bN+rdd9/Vv//+q5kzZ+qYY44JZlMLcPv7Au9f9qBfyybo7+JHlvhS8htc1kEtENZNNI477jhFRUUF5bh2sFZ4VKlSRfv27VNOTk7Q+iJYIiIiimzT//73P61YsUJS/nSwXr162daG4cOHa+HChVq7dq2ee+453XHHHTrxxBML/Vy4LPAvFd+vbmRXv/r9fmVmZiojI0PVq1cPynpi9Ks9nOjXYJ0vIiLCvCA55phjXP36MF4TDRo0ML/n8/kcb/Nzzz1nro1nLPQeLn9rpf07q169ut577z21adNGqamp6t69u1asWBHSqtFw6VOJ9y+70K8lC+a4kJWVZR7Tza8P4zVh7EhrcLrNubm5GjNmjCTphBNOUI8ePcLmby2Qv7MLL7xQK1eu1G233abFixdrxYoVatu2rRYsWKBmzZoFsZX5wqVPJd6/7EK/lk5QA8Dp06cH83AowoEDBwJ6fpUqVRQREWGW88fExLh2Om1ERISqVKmilJQU+Xw+STIXy923b1/AfREsRp/6fL5Cd/NmzZplTge75JJLNGTIENvbPW7cOF199dXKyMjQPffco3nz5pmBUVF96lYl9avblKZff/31V23btk2pqalKS0vToUOHlJaWprS0NPN71n8f+T3juI0bN9awYcN0zTXXlCsIrGj96hbB6tfyfBgI1riQnp6u7OxsSfnTV93yHmt15GsiLy/PfGzHjh2OtjklJUVTpkyRJLVp00annXaaJLn+b608f2fnnHOOhg8fruHDh2vDhg264447NGPGDNs3O+H9yx70qz0qwrjg8/nMGSyJiYlhMS5Yq2o2b96sevXqOdi6/GuBv//+W5I0dOhQRUdHu/5vLVh/Zx6PR++8844ef/xxvfbaa9qyZYsuvfRSTZ48OWjLR/D+ZQ/61R5OjguGoAaAXbt2DebhUIRgvaiNcv7ExETX/6H4fD6zjcbaI4cOHXJlu61tWrNmjVkBUq9ePb322mvyer22t/ucc85Rr169NGXKFH3zzTd6++23zV25rO10Y/8VJ1zaWlS/5uXlqX///po1a1ZQzvHXX3/prrvu0kUXXaSnnnoqoOnk4dyvbhbqtgbrfNaF3hMSElzd58ZrIi4uTl6vV3l5eUpOTna0zW+99ZY5tv7vf/8r8Jib+9JQ1r+z+++/Xz/99JMWLlyohQsXasKECYWWnbBTOPSpxPuXXejX0J3PGBuqVKni6j43XhPWKb8HDhxwtM05OTnmDuoNGzbUXXfdZT7m5r40BOPvzOPxaOTIkTr11FM1cOBApaenq1u3bho0aJD69+8f1BtH4dCnEu9fdqFfS8d9CzkgJIwpwG5YF6MsjPa6tWrRsHfvXnXr1k1ZWVmKjo7WjBkzbF3z4kiDBg3Sp59+qv/++0/Dhg3TlVdeqTp16oTs/Mjn9/s1bNiwYsO/2NhYJSQkKDExUQkJCQX+sX7P+DozM1Ovvvqq9uzZo+XLl6tt27a65ZZbNHToUB177LEh/u1Q0VjvRIbLNAqPx6OkpCQlJyc7eoc6JydHr776qiSpSZMmuvTSSx1rS6h4PB5NmDBBf/zxh/788089/fTTatq0qVq2bOl00wAEkfHeGi7jgrELsOT85lCzZ8/W5s2bJeUvC+HmJZfsdvfdd+ukk07SPffco/3792vUqFH6/fffNWHCBHOGFwD7EQBWUtYKwHASDgFgbm6uevXqpW3btkmSRo0apXPPPTekbUhISNDYsWN18803Kzk5WYMHD9a0adNC2gZIY8eONUOBM888Uy+//LKqV6+uhIQExcfHl+uDYI8ePTRx4kS98sorysjI0P/93/9p4cKFuu+++9SnT5+w2b0V7mOtAAyn11GVKlWUnJxcYHOrUFu4cKG2bt0qSXrggQdsnwrrFomJiZoxY4batm2rtLQ09erVS4sXL+aGBFCBWCsAw4E1ALSOa6GWlZWlcePGSZJOOukk3XTTTY61xS0uueQSffbZZ7rrrru0YcMGLViwQH/++aeuv/56nXPOOWrWrJlq1qzpdDOBCs0bipPs2rVLa9as0bfffqs1a9aYa0nAOcaFkjGlNlyEQwD49NNP69tvv5WUf7fLWu4fSpdffrluv/12SfkXp5988okj7aisXn31VY0ePVqS1KhRI7333ns688wzVb9+fVWtWrXcd4ETExM1ePBg/fDDD7r11lvl8XiUmZmpl156SRdeeKFmzJhhbkIAlEU4VgBKh9vqVKWH3+/Xyy+/LEk69thjg7auUbho3LixJk6cKCm/+r1Hjx7mpgEAwp/x3moN1twsISFBXm/+Je7Bgwcda8esWbPMYoABAwa4cgdlJzRs2FCffPKJrr76aknS77//rtGjR6tz58467bTTdN5556lHjx6aNGmSli1b5ujNPaAisi0A3Lx5sx555BE1atRI9evX13nnnadWrVrpvPPOU7169dSoUSMNGDBAW7ZssasJKAEBoD3mz5+vyZMnS5KaN2+uZ5991tH2PPXUU6pdu7Yk6bHHHnP0TmhlMnv2bD3++OOSpPr16+v9998P+hTw+vXr6+WXX9YXX3xhTjfcs2ePBgwYoFatWumLL75gF3aUSbgGgMa44FQA+N133+nXX3+VJN13332VcorXddddp4ceekiS9PPPP5vvfwDCm9/vNz87hksA6PV6zbY6NS5kZGToxRdflCSdeuqpuuGGGxxph1slJSXpzTff1BNPPKEzzzyz0MYtH374oZ588kndeOONOumkk3TxxRfrgQce0Ouvv66ffvpJmZmZDrYeCG+2BIDTp0/XmWeeqfHjx2vz5s3y+/2F/tm8ebPGjRunM844QzNmzLCjGSiBEaCF6xRgYw1DN/n111/Vt29fSVLt2rU1bdo0xcTEONqm6tWr67nnnpOUX4k7fPhwR9tTGXz00Ufm66BWrVqaO3eujj/+eNvO17RpU82bN09vv/22Tj75ZEnSH3/8oS5duuiWW27RunXrbDs3KhbrhVK4TQGWnLvQM6r/kpKSdOeddzrSBjcYMmSIeTNixowZmj17tsMtAhCozMxM5eTkSAqvG0NGAOjUje+ZM2dq586dkvKr/6wBF/JFRETo4Ycf1pIlS7Rx40Z99NFHevrpp3XTTTfpxBNPNH/O7/frr7/+0nvvvachQ4aoffv2atSokVq3bq3+/ftr6tSp+vXXX7npDZRS0GuRp0+frh49esjj8cjv98vj8ej000/XKaecosTERKWmpurPP//Uhg0b5Pf7lZaWph49ekiSunXrFuzmoBjhugagcVGanp4un8/nmgH1wIEDuvnmm5Wenq7IyEi98cYbqlevntPNkiRdf/31at++vT799FPNnDlT3bt3V9OmTZ1uVoX01VdfqVevXsrLy1NSUpLeffddNW7c2PbzejweXX311WrdurXeeustPf/889q3b5+++eYbtW7dWrfffruGDBmiunXr2t4WhK9wXgNQkiPThDZs2KDFixdLkrp27RpW/RZskZGReu2119SmTRtt375djz32mM4880ydffbZTjcNQDlZp9CGUwBotNWJADA9PV0vvfSSpPz1n6+77rqQtyHcxMfH68ILL9SFF15ofi85OVm//PKLVq9erdWrV2vNmjXmlOrc3FytXbtWa9eu1VtvvSVJOvHEE9WhQwfdcMMNOuussyrNWrxAWQW1AnDHjh3mFBBJ6t27t/7991+tX79e8+fP11tvvaX58+dr/fr12rRpk+6//355vV75/X499NBD5p0S2C/cKwAl90wD9vl8uuOOO7Rx40ZJ+dNuL7roIodbdZjH49Ho0aPNC9N7771XGRkZDreq4vn+++919913KycnR3FxcXrnnXdCfuEbFRWle+65RytWrNBDDz2kmJgY+f1+zZ49WxdeeKGef/55V1bPwh2MCrqIiIiw2pHPyQrAV155RVJ++HXvvfeG/PxuY1S/R0dHKzMzU927d9eBAwecbhaAcrIGgOEyBViSo1OAp0+frj179kjKX37HWI8QZVO1alVddtll6tOnj2bMmKE1a9Zo/fr1mjVrlh599FFdeeWVBTYM2bhxoyZMmKDWrVvroosu0qhRo7RhwwYHfwPAnYL6jjR58mSlp6fL4/Ho9ddf1+TJk9WgQYMif/b444/Xyy+/rKlTp0rKv1tirJ0G+4VrBaAbA8CRI0fq008/lSTdeuut6tmzp8MtKqxevXoaMWKEJOnvv//W888/72yDKph169bpmmuuUXp6uqKiojR9+nS1aNHCsfZUqVJFw4YN0/fff69OnTpJyn+PfeGFF3TaaacxNQ9FMiolkpKSwurOuXFzI9QXejt37tTcuXMlSZ06dVL9+vVDen63Ou+888z1bzdv3qzevXvL5/M53CoA5UEFYNmkpqaamyKdffbZat++fUjPX9Edc8wxuuqqqzRw4EDNmTNHv//+u/755x+99tprat26tRm2/vPPPxo7dqxatmypli1basyYMfr7778dbj3gDkENAD/77DN5PB5dddVVuueee0r1nG7duqldu3by+/1atGhRMJuDYuTl5VEBGCS///67eaFzzjnnaMyYMa69cL7zzjt18cUXS5ImTZqkX375xeEWVQz//POPbrrpJiUnJ8vr9eqVV17RlVde6XSzJEkNGjTQq6++qkWLFumCCy6QlF+p3aVLF82fP9/h1sFtjBtD4TaN1bjQS01NVV5eXsjO+8Ybbyg7O1uSdP/994fsvOHg7rvvNneh/+qrrzRmzBiHWwSgPKwBWjhWAIZ6F+CpU6dq3759kqSBAwe69pqgovB4PDrhhBN077336vPPP9fatWs1evRoXXTRRWbfb9iwwfxeq1atNH78eG3atMnZhgMOCmoAaEyBLOtORx07dizwfNjLOgWQALD88vLy9Oijjyo3N1dRUVF66623FBcX52ibSuL1ejV+/HjFxMTI5/OpX79+ys3NdbpZYW3r1q266aabzKkeL774ovl+5ibnnXeePvroI02fPl21atWSJPXo0YP3XBRgXOiFU5WHdLi9fr8/ZONCamqquYFZq1atdNZZZ4XkvOHC4/Ho+eefV5MmTSRJY8aM0eeff+5wqwCUlTUADKexoVq1apJCWxl+6NAhc1Ooc889V23btg3ZuZHvmGOO0T333KMPP/xQv/zyi5555hmdf/755uPr16/XyJEjdf7556tt27Z6+eWXtXXrVgdbDIReUANAo3qgRo0aZXqe8fNOBzqVhXWh9ISEBAdbUnZuCgBnzZqlFStWSJIGDRqk008/3dH2lMbJJ59sTgVeu3Yt0+4DsGfPHt18883mgsRjx47VXXfd5XCriufxeNShQwe9/fbb8ng8SklJUY8ePVgPEibjQincbgxZL0pDdbE3e/Zss7Lkf//7X0jOGW7i4uI0ffp080L8/vvv56YDEGbCdQ1A6xTgUO0O+9prr5n9RfWf8+rVq6f77rtPn3zyiVavXq0RI0bonHPOMR9fs2aNRowYoWbNmumaa67Rq6++6tiu0UAoBTUANBbi/Pfff8v0PKMMt6zBIcrHGgCG24WeWwLA3bt368knn5SUH6oNGTLEsbaU1SOPPGJWZbzwwgv6559/HG5R+ElOTtatt95q9t2jjz6q/v37O9yq0mnTpo0ZAq9bty6sXruwV7hWAFqnLIciAMzNzdWrr74qSTrjjDPUqlUr288Zrk444QRNmTLFvOnQvXt3QkAgjBjjgsfjCavlIYyw0ufzhWTzs4MHD5o31S+44AJdccUVtp8TpXfcccfpgQce0BdffKEVK1Zo6NChOvPMM83HV65cqccff1znn3++pkyZoqysLAdbC9grqAHgWWedJb/fr7feeqvU6/D4fD699dZb8ng8TKEJEQLAwA0bNsz8UPTyyy8rNjbWsbaUVVRUlCZMmKCIiAhlZmaqf//+IV03K9ylpaWpS5cuWrdunSSpZ8+eGjx4sMOtKpvHH3/cnJry9ttv691333W4RXCDcF8DUCo4vtnl448/1n///Scpv/qPKo+SXXnllRo4cKAk6bffftMll1yiAQMGaOfOnQ63DMDRGBVtiYmJYbWbrbVaMRQ3hqZMmWKeZ9CgQYwLLtaoUSP17dtXS5Ys0fLlyzVw4ECdeuqpkqQDBw7oiSee0CWXXKL58+dzfYQKKajv5Ndff72k/A94//vf/45acu33+/XAAw+YF9JuXDurIrIGZwSAZff111/r/fffl5S/669bNnwoi6ZNm5rT1r7//nu9/fbbDrcoPGRlZalbt27m1O9bb71VI0eODLsPel6vVzNnzlS9evUkSQMGDNBvv/3mcKvgtHCtAAzlFGC/32+u8VS3bl3deOONtp6voujXr5969eolr9er3NxczZgxQxdccIGeeuqpkC/SD6D0jHEhnKb/SgXba/e0zv3795tV4Zdccolatmxp6/kQPCeffLIeffRRffvtt3r33XfNqsD//vtPvXr10tVXX61ly5Y53EoguIIaAPbs2VPHHXecJOn111/Xueeeq1mzZmn37t0Ffm7Pnj2aNWuWzjvvPL3++uvyeDw67rjj1LNnz2A2B8UI5wrA+Ph482snAsCMjAw99thjkqTq1aub04DD0YABA9SoUSNJ0ogRI7Rjxw6HW+Ruubm56t27t5YsWSJJuuaaa/TSSy+F1R1xq9q1a2vq1KmKjIxURkaGevTo4fi6mnCWcZFEBWDxfvjhB61evVqS1KtXL0VHR9t6vorC6/Vq5MiR+u6778ybxRkZGZo4caLOO+88jR8/PiTT9ACUjRHQh1sAaB0X7L7J8PLLL5ufn4xrBIQXj8ej1q1b68svv9TEiRN17LHHSspfJ/DGG29U586d9fvvvzvcSiA4gnrlGhsbq/fff98MaX799VfdfffdqlevnqpVq6Zjjz1W1apVU926dXX33Xfrl19+kd/vV3x8vObNm6eYmJhgNgfFCOcA0Ov1mhuXOBFWjBs3zlyzcvjw4eaOquEoLi5OL774oqT818TAgQNDtlByuPH7/XrkkUf00UcfSZIuv/xyvfbaa4qMjHS4ZYG54IILNHz4cEnS33//rb59+/IaqKT8fr9ZPRfOAaDdFYDGGk8JCQm6++67bT1XRXTyySfrjTfe0OLFi821E1NSUsxdGd944w1lZ2c720gApnCtDDc2H5LsHRf27NmjqVOnSsr/bHjxxRfbdi7YLyIiQrfffruWL1+uYcOGma97Y8zq06cPBRMIe0EvXTn//PP13Xff6YwzzpDf7zf/SUlJ0c6dO5WSklLg+02aNNH333+v8847L9hNQTHCeRdg6fDFaSjWerLasGGDJk2aJEm66KKL1KVLl5Ce3w6XXHKJuXPtp59+qg8//NDhFrmP3+/XsGHD9M4770iSmjdvrhkzZlSYGxb33Xefrr32WknSggUL9MYbbzjcIjghNTXVDH/DLQC0jmN2Xuj9/fffWrRokSTprrvuCruKGDdp2rSp/u///k/z5883P//t2bNHgwYN0sUXX6z/+7//k8/nc7iVAIzquXALAEM1Bfjll19Wenq6JJlrnSL8xcXF6aGHHtLKlSvVu3dvRUVFKS8vT++8844uvPBCjRw5MiRrSwJ2sGXu2tlnn61ff/1VH374obp166bTTz9d1apVk9frVbVq1XT66aerW7duWrhwodasWWPuSIrQCOcKQOlwm0NZAZiXl6dHHnlEubm5ioqK0pgxY8Ju3bfiDB8+XHXq1JEkDR48WAcOHHC4Re4ybtw4TZkyRVL+jp+zZ88Oy7+b4ng8Hk2YMMGcDj5s2DCtWrXK4VYh1KwfZMPtQi8iIsL8m7TzA/krr7xinq9Xr162nacyufTSS/Xpp59q5syZ5iLs//33n/73v/+pdevW+uyzz6hKBhwUrmsAWscxuwLAXbt2adq0aZLyNzs6//zzbTkPnFOjRg09/fTTWr58uTp16iQpf/mK8ePH64ILLtDrr79O1TrCjm2LV3k8HnXo0EHTpk3T+vXrtX//fuXk5Gj//v1av369pk2bpmuvvbbChCjhxAgAY2Njw3IKoxMB4KxZs8yNHx5++GGdcsopITu33apWrarRo0dLyq/AMKaEQnrttdc0atQoSVLDhg313nvvFZhWUlFUqVJFb7zxhmJjY5WTk6MePXpo//79TjcLIWS9QAq3CkDp8MWeXQHgnj17zN2yO3bsqOOPP96W81RGHo9H7du31zfffKNJkyapQYMGkvI3lLvzzjt17bXX6vvvv3e4lUDlFK4VgPHx8eY1jl3jwoQJE5SRkSEpf+dfVFwnnHCCXn31VS1evFiXXnqpJGnfvn0aMmSILrnkEn3wwQfcrELYCM/V6xEQIzgL1yomo92hWjB89+7d5mYfxtbxFc21116rDh06SJJmz56tb775xuEWOW/OnDkaOnSoJKlevXp6//33zUrJiqhJkyZ67rnnJElbt27VAw88oLy8PIdbhVAJ5wpA6XCb7VoaYtq0acrKypIkcwd1BFdERIRuu+02ff/993r22WdVu3ZtSdLKlSvVsWNH3XbbbeYGLABCI1wrAD0ej9lmOyoAt2/frjfffFOS1K5dO51zzjlBPwfcp2nTppo3b55mz56t008/XZK0adMm3XvvvWrXrp2+++47h1sIHB0BYCVkXCCFewAYqgrA4cOHmx8exowZo9jY2JCcN9RGjRplfljq37+/rWumuN1HH32kPn36SJJq1qypuXPnmlUpFdkdd9yh2267TVL+gscvvfSSwy1CqFj/3sNxbDCqFu2o9EhPTzeneV166aVq2rRp0M+Bw2JiYnTvvfdqxYoVGjRokPn/9quvvtL555+v22+/3dyMC4B9srOzzfXtwvnGkB27AI8fP968KcTOv5WLx+NRmzZt9PXXX+ull15SvXr1JEmrVq3SDTfcoG7dumn79u0OtxIoHgFgJWQEgOG4AYgU2gBwyZIlmjt3riTplltu0WWXXWb7OZ1Sp04dPfXUU5KkzZs363//+1+lrABbsmSJ7rvvPuXl5SkpKUnvvvtuhZryXRKPx6Pnn39eZ5xxhqT8UHjZsmUOtwqhUFEqAO0IAN99911zSvz9998f9OOjaImJiXrkkUf0008/6cEHHzRvvr377rs688wzNWrUqJDNBAAqI+uNoXAcF4wlW4I9LmzdulVvv/22JKlDhw6sZV9JRUREqEuXLvrhhx/0+OOPmzerPv74Y1188cWaMmWKcnNzHW4lUFi5FoAzQgIpf8H4or5fXtbjwR4EgKWTkZGhAQMGSMr/EGFMA67IOnfurKVLl+r999/X559/rrFjx5p9UBmsXLlSXbt2VXZ2tmJjYzVr1qxKV+0THx+vN954Q23atFFaWpp69eqlr776SnXr1nW6abBRRVkDMNhTgH0+n7n5xymnnKI2bdoE9fg4uho1amj48OHq1auXJkyYYE7HHjt2rGbPnq0RI0bohhtuYE1pIMis40K4TQGWZNsU4HHjxiknJ0cej4fqPyg+Pl59+vTRnXfeqZEjR+qtt95SWlqannjiCb333nsaM2aMzj33XKebCZjKFQCOGDHC/KBlDeys3y8vAkD7MQW4dF588UVzmtHw4cPN9YgqMo/Ho3HjxmnDhg1av369nn/+eTVt2lRXXXWV002z3fr169W5c2elp6crMjJS06ZN00UXXeR0sxxx8skna/z48br33nu1Z88e9erVS/PmzQvLTYNQOtYKiXAOAINd6bFo0SL9+++/kvKr/7xeJk44pV69epoyZYoeeOABPfTQQ1q2bJm2b9+uXr16afr06Ro5ciSVOEAQWafOhmMFoNHmYAaAmzZt0uzZsyVJN9xwg7kOHFCzZk2NGzdOt912mwYMGKDff/9da9euVbt27dS9e3cNHTpU1atXd7qZQPmnABe3043f7y/3PwiNihQA2vW62bBhgyZNmiRJatGihbp06WLLedwoPj5eM2bMMKdO3H///frnn3+cbZTN/vnnH91yyy1KTk6Wx+PR5MmT1bZtW6eb5agbbrhBPXv2lCQtX75czz77rMMtgp2MC6SYmBjFxMQ43JqysysAfPnllyVJtWvX1s033xzUY6N8zjnnHH399dd67bXXVL9+fUn571Ft2rTRgAEDtG/fPodbCFQM1gAwnCsAgzkujBs3Trm5ufJ6vZVqhgxK78ILL9SXX36pYcOGKS4uTn6/3ywqmDdvHpkHHFeuco6vv/66TN+Hu4T7LsDG1OXc3FxlZWUFfVOOvLw8Pfroo8rJyVFUVJTGjBlT6ao+GjZsqClTpqhz585KSUlR165dtWjRorB9zZRk27Ztuvnmm7Vnzx5J0tixY3XjjTc63Cp3ePLJJ7V69Wr9/PPPmjhxolq0aFEpqkErI+MCKRyrPKTDVYuHDh2S3+8PynTQFStWaOXKlZKknj17VtgNoMKRx+PRjTfeqKuuukoTJ07UpEmTlJWVpRkzZuiDDz7QwIED1a1bN6qWgQCEewVgsKcA//PPP3rvvfckSTfddJMaN24clOOi4omKitJDDz2kjh07atCgQfriiy+0e/du9ezZU3PnztWzzz6r448/3ulmopIq1yejyy+/vEzfh7uEewWgdXraoUOHgn5RNmvWLP3444+SpIceekinnnpqUI8fLq688koNHjxYzz77rP744w89/PDDeuONNyrUOkt79+7VzTffrK1bt0rKX8bgrrvucrhV7hEdHa2pU6eqdevWOnDggP73v//pyy+/1AknnOB00xBkxgVSuI4LxsVpXl6e0tLSgvJ7TJ48WVJ+VXS3bt0CPh6CLyEhQYMGDVLnzp01fPhwffzxxzp48KAGDx6smTNnauTIkWrZsqXTzXQ9n8+nvLy8SnezEyUL9zUArVOAg/H6Hjt2rHw+nyIiIvTII48Eo4mo4Bo0aKBZs2bp448/1pAhQ7Rjxw4tWrRIS5YsUf/+/fXAAw8oOjra6WaikmGkr4TCPQC0tjvY6wDu3r3b3OyjUaNG6tu3b1CPH2769u2ra6+9VpK0cOFCc1p0RZCSkqJbb71Vf//9tySpX79+euCBBxxulfscd9xxZhCSnJysnj17Kisry+FWIdjCvQLQ2u5gbASyceNGffLJJ5LyN0eqUaNGwMeEfU444QTNmDFD77//vk477TRJ0u+//65OnTqpe/fu2rx5s8MtdBe/369//vlHU6dO1Z133qmaNWsqJiZGxx13nNq2bauuXbtq0KBBmjBhgubOnavvvvtO//77rzIzM51uOkIo3CsAjaVs/H5/wNcLf/75p+bOnStJuvXWW3XSSScF2jxUEh6PRx06dND333+v++67T16vV5mZmXr22Wd1xRVX6Pvvv3e6iahkgjo3wviAdcwxx5SpKisrK0u7du2SlJ+Uwz5GdYREAFiU4cOHm3c8X3jhBcXFxQX1+OHG4/Fo4sSJ+vPPP/XXX3/pmWeeUZMmTdSqVSunmxaQ9PR0denSRWvXrpUk3XPPPRo8eLDDrXKvNm3aqF+/fnrxxRe1Zs0aPfHEE3r++eedbhaCyHjfC8eLPKlgu1NSUlSvXr2AjjdlyhT5/X55vV7dd999gTYPIXLZZZfpq6++0vTp0/X8888rOTlZH330kRYvXqwHH3xQDz30kOLj451upiMOHjyopUuXasmSJVqyZIm2bNlS6Gd27typnTt3as2aNcUep2bNmqpXr57q16+v+vXrq169ejr22GNVv359nXTSSea6jAh/RgAYHx+vqKgoZxtTDtaqxeTk5HKPb3l5eRoxYoT8fr8iIyOp/kO5JCYm6rnnnlOvXr3Us2dPrV69Wn/++ac6duxoVrHXrFnT6WaiEghqANiwYUN5vV7NmzdP119/famft2TJErVv315er1e5ubnBbBKOYA3MjLX0wo1dAeCSJUvMu3u33HILU9r/v6SkJM2cOVNt27ZVamqqevXqpcWLF4dtWJ+dna177rnHnOZ9yy236LnnnqtQU5vtMHDgQK1cuVLLli3T9OnTdeGFF+qmm25yulkIEqMCMBx3AJYKtjvQBd/37dunOXPmSJKuvfZaNWrUKKDjIbSioqLUq1cvderUSc8995zeeustZWZmasyYMZo9e7ZGjBihjh07Vvj3/JycHP38889m4Ld69Wrl5eUV+rlGjRqpXbt2atCggbZu3ap///1X27dv1/bt27Vnz55CC9bv27dP+/bt07p164o877nnnqsbb7xRN9xwg+rWrWvL74bQMALAinBjKDk5udxrro0fP15ffPGFJOnOO+9kGRQE5Nxzz9Xnn3+uqVOnauTIkUpNTdXs2bP12Wefafjw4ercuXOFH5/grKCvjhzIzjbsimM/69QoKgAPy8jI0GOPPSYpf8qAMQ0Y+U4++WRNnjxZd999tw4cOKBu3brp448/DrsKSZ/PZ65jJ0nt2rXTSy+9xLpHpRAREaEpU6boyiuv1K5du9S/f38df/zxuuCCC5xuGoLAqAAkAJRmzJihjIwMSWJZgDBWq1YtjR07Vl27dtWQIUP0448/atu2bbr33ns1bdo0devWTVdccYWqV6/udFODZuPGjWbg9+233xb5GalKlSpq2bKlrrjiCl1++eVq2LChqlevroiICPl8Ph04cMD82ezsbO3atcsMBHfs2FHo6507dxYIFletWqVVq1Zp2LBhuvjii9WpUyd16NCBafRhyBgXwnH9P6lwBWB5LF68WKNGjZIknXbaaRo+fHhQ2obKLSIiQj179lSHDh30+OOPa8GCBdq/f7/69OmjOXPmaPjw4Tr33HMJAmELtkerZAgAizZ+/Hj9+++/kvKnAdeuXTsox61I2rdvr/79+2vcuHFau3atHn30UU2aNClsBie/369HH31UCxYskCRdeumlev3118NyWotT6tSpo9dee02dOnVSenq6brnlFr311lu67LLLnG4aAhTuFYBHTgEur+XLl2v8+PGSpAsvvFDnnXdeoE2Dw84++2wtXLhQ8+fP14gRI7Rjxw4tX75cy5cvl9frVfPmzdW2bVu1adNGZ555ZtiMaZK0Z88e/fDDD+bU3k2bNhX6Ga/Xq/POO0+tWrXSFVdcoWbNmpV6d+To6Ggdf/zxJVZO5ebmas+ePdq2bZuWLVum+fPn67fffpPf79d3332n7777TgMHDlSrVq3UqVMntW/fPmw/f1Y24V4BaA0AyzMubNy4Ub1795bf71fVqlX15ptv8tpFUNWtW1dTp05V586dNXDgQP33339avny52rVrp+OPP14dOnRQhw4d1Lx5c4oVEDSuCACNUKqyrssSStbALFwHsWAHgH/88YcmTpwoSWrRooW6dOkS8DErqscee0xr1qzRV199pffee0/nnHOO7r33XqebdVR+v18jRozQ22+/LSm//P6tt94K+g7SlcHFF1+sKVOm6P7771d6ero6d+6sN954Q+3atXO6aQhARVoDsLybgKxfv1533nmnMjMzFR0dTaVHBeLxeNSpUyddffXVmjBhgt58803t27dPeXl5WrFihVasWKGRI0eqfv36atOmjdq0aaOWLVu66nOS3+/Xpk2b9MMPP+jHH3/UDz/8oH/++afIn23QoIGuuOIKtWrVSi1btrS1gisyMlL16tVTvXr11Lx5c/Xt21cbNmzQvHnzNH/+fG3atEm5ublavHixFi9erNjYWLVt21adOnVSmzZtGIddLNwDQGu7rRualEZqaqq6deum5ORkeTwevfLKKzrxxBOD3EIg35VXXqmlS5fqxRdf1Msvv6ycnBxt2bJFr7zyil555RXVrVtX11xzja677jq1aNGi1DdxgKK44tWzePFiSQp40W4cXUWrADQ2NCmvvLw8PfLII8rJyVFUVJTGjBnDHZYSRERE6NVXX1Xbtm21adMmDRs2TGeddZYuuugip5tWohdffNHcyfb000/XnDlzwvb17wY33HCD4uPj1aNHD2VmZqpbt26aPHmyOnXq5HTTUA45OTlKT0+XFL4VgIFOAf7vv/902223KSUlRV6vV6+++qrOP//8YDYRLpCQkKDBgwfrscce0+rVq7V48WJ98cUX+vXXXyVJ27dv18yZMzVz5kxFR0fr4osvNqsDQ33x7/P59NtvvxUI/IwN846UmJioli1bqlWrVmrVqpUaNWrkaCXjaaedpiFDhmjw4MFas2aN5s2bpw8++EA7d+5UZmamFi5cqIULFyopKUnXXHONbrzxRl122WVU5LtMuE8BNnYBlso2Bdjv96tPnz76/fffJUmDBg1S27Ztg908oID4+HgNHTpU9913nz799FN99NFHWrp0qXJzc7Vz505NmzZN06ZNU61atdS+fXt16NBBLVu25H0TZVbuAPCbb77RN998U+Rjc+bMKXEHMSn/zTUtLU2rVq3S119/LY/Ho4svvri8zUEpVYQKwJiYGEVFRSknJyfgCsB33nnH3AziwQcf1KmnnhqMJlZo1apV04wZM3TNNdcoPT1dPXr00JdffunaAH/q1Kl67rnnJOVvVPTee+9VqDWfnHLVVVdpzpw5uuOOO5SWlqbevXsrNTVVd999t9NNQxlZbwyFa6VHZGSk4uPjlZ6eXuYAcM+ePbrlllvMcOWFF15Qhw4d7GgmXCIiIkLNmzdX8+bNNWjQIO3cudOsUFuyZInS0tKUnZ1trqc3dOhQnXTSSWrTpo3atm2riy66SNHR0UFtU2ZmplavXm0GfitWrCi2mrVGjRq68MIL1aJFC7Vo0UJNmjRx5UWgx+NRs2bN1KxZM40YMUI//PCD5s+fr4ULF2r//v06dOiQ3n33Xb377ruqWbOmrr/+enXq1EkXXnhhWE3FrqjCvQIwNjZWMTExysrKKtO4MGnSJH344YeS8jeC6tu3r00tBAqrVauW7rrrLt31/9i7zzApivXv47/enANZoiRFFEFFxQQIGEgqKgKCIAc5ZlEMiHhQERAFPJj+eAyIKIiKIJIFJShZCQKKKDnDLuwum1M/L/bpcZYNbOhJu9/PdXExzExX1xQzU1N331V1331KSEjQkiVLNH/+fC1fvlwZGRmKi4vTZ599ps8++0zR0dG69dZb1b17d7Vr146MapRImQOAK1as0KhRowrcb5qmvvzyy1KVZZqmAgMD9cQTT5S1Oigh5x+TvroLsJQXvDx9+nS5AoDff/+9hg8fLikvMPTUU0/ZVb0K7+KLL9akSZP073//WydPntTAgQM1d+5cBQcHe7pqDqZp6tNPP3X8H9eqVUuzZs1iV0IbXXfddfrmm2/Uu3dvJSQk6Omnn1ZycrIeeeQRT1cNpeCcGeGrGYBS3iC1tAHA5ORk9enTx7EG7PPPP08QuxKqVauW+vXrp379+ikzM1Pr1q1zZAf+/fffkqTdu3dr9+7d+t///qfw8HDdcMMNqlGjhvz9/RUYGKjAwEAFBAQoICBAgYGBCgoKUmRkpLKysuTv75/vMeu2aZraunWr1q9fr82bNyszM7PQ+tWvX19t2rRxBP2aNm3qcwEyf39/XXfddbruuuv02muvacWKFZozZ44WLlyolJQUxcfH65NPPtEnn3yim2++WW+88Ybq1Knj6WpXar4eAJTy6n7y5MkSZwAuX75co0ePliRdcMEFevfdd5kZBI+JiYlRr1691KtXLyUnJ2vp0qWaP3++li1bptTUVCUmJjouooSHh+vmm29Wt27d1LFjR58e58O1yjUFuKhde0u7m+/ll1+usWPH6vLLLy9PdVACFWEKsJQ3SD19+nSZ13qaNWuWHnvsMeXk5CgoKEiTJk3yuR1tPa1Hjx7avHmzJk+erF9//VUjRozQhAkTPF0tSdLevXv17LPPOrKUq1SpolmzZqlBgwYerlnFc8UVV+jbb79Vz549dfLkSb300ktKTk7Ws88+63MD1MrKOWDm6wHAY8eOlbhfyMjI0IABA7R161ZJ0qBBgzR06FBXVhE+ICgoSG3btlXbtm01atQo7d271xEMXLNmjTIyMpSSkqLFixe75PyGYeiiiy7S1VdfrWuuuUZXX321ateu7ZJzeUpgYKBuuukm3XTTTUpLS9PSpUs1e/ZsLVu2TBkZGfr++++1evVqvfjiixo4cKD8/f09XeVKJycnx9E3+OoUYCmv7iUNAO7bt0///ve/lZubq8jISDb9gFeJiIhQjx491KNHD6Wmpmr58uWaP3++lixZojNnziglJUVz5szRnDlzFBoaqhYtWqhKlSqKjY0t8PfZt70pgQOuV+YA4P3336/27ds7/m2apjp06CDDMPTqq6/quuuuK/Z4Pz8/RUREqGHDhvnWaIBrVaQMQKlsm4B89NFHjqyw8PBwffbZZ+d8v6JwI0eO1LZt2/Tzzz/r008/VatWrdSvXz+P1ScrK0vvvfeeJk6cqPT0dElS3bp1NXXqVKZ3u9DFF1+sefPm6a677tLhw4c1fvx4nTlzRqNGjSII6AMqUgagVLI1AHNycvTII49o1apVkvLWtRw7dizvVxTQsGFDDR48WIMHD1ZKSop+/vlnLV26VOvWrVN6erqysrKUlZWlnJwcZWVlKTs7W9nZ2crKyipR+YGBgWrVqpVjOu9VV11VqX4Xh4aG6rbbbtNtt92mhIQEjR07Vp988olSUlI0fPhwzZ49W2+++aaaNWvm6apWKs7jBV8PAErn7hdSUlJ0//33O7Ie/+///k9NmjRxdfWAMgkLC1PXrl3VtWtXZWRkaNWqVZo/f74WLVqk06dPKy0tTRs2bChVeWcHCc877zxVrVpVsbGxCgkJKRBIjIqKIjvWR5U5ANigQYMis2kuueQStWvXrsyVgutYAbPQ0FCf3kHICl6WJgBomqYmTpyo119/XVJeVtjMmTN12WWXuaSOlUFAQIA+/PBDderUSYcPH9awYcPUvHlzj2Tz/vLLL3r66af1+++/S8q7yPDggw/queee4wquGzRu3Fjz58/XnXfeqb179+r9999XcnKyJkyYQPaGl3MeGPnyVC8reHmuDEDTNDV8+HDHGk/t2rVjmhdKJDw8XLfccotuueWWcz7Xz8/PMf0wIyOjQHAwOztbOTk5qlu3LjMQ/r+YmBi98cYbuvPOOzV06FD99ddf2rhxozp06KAhQ4boySefVFhYmKerWSk475rry/2CVffidgE2TVNDhw7Vjh07JEnPPvusbr31VndUDyi34OBgR0b1hAkTtGbNGi1atEj79+9XQkKCTp06pdOnTyshIaHIWZqpqalKTU3VoUOHSnxePz8/Rwah85/isg5jY2Pp77yArRGg5cuXS8oLAMI7WQMjX87ykEqfAZibm6v//Oc/+uCDDyTl7Tg9a9YsXXDBBS6rY2VRrVo1TZ06Vd26dVNGRoYGDhyoZcuWqXr16m45/5kzZzR69Gh98sknjo7t0ksv1ZtvvqmWLVu6pQ7IU7duXc2bN089e/bUH3/8oc8//1zJycn6v//7P69coB55KtIUYOncmR4TJ07UJ598Iklq1aqVpk6dyvQX2M4wDAUEBCgkJITvv1Jq06aNli9frrfeekuTJk1SVlaWJkyYoLlz5+qtt95S586dPV3FCq+iBACtbNri+oX3339fs2fPlpS3wdkzzzzjjqoBtgsMDFS7du0KTcTKyclRYmKiTp8+7QgKWn9bf5zvT0xMVHx8vNLS0go9V25uruLj4xUfH1+qOoaGhhYbLIyJiSlwf3R0NMkENrI1AEjWn/ezAma+PMiTShcAzM7O1pAhQ/TVV19Jkho1aqRZs2apXr16Lq1jZdKqVSu98cYbGjJkiI4cOaIHHnhAs2bNcvmgZ8GCBRo+fLiOHj0qKS+Fffjw4XrggQd8OsPVl9WsWVNz585Vr169tHnzZn377bdKTU3VRx99xFU/L1VRMgCtuheXATh16lRHFnjjxo31xRdfkCEMeKHg4GA999xzuu222zR06FBt3LhRf/31l7p06aKHHnpIzz//vE8vZePtnAOAFWEKcFFrAP700096+eWXJeX1CZMnTyYbHBWSv7+/qlSpoipVqqhx48bnfH5sbKz8/f2VnJysPXv26NSpU/kyCgsLIlrPOX36tHJzcwstNy0tTWlpaTpy5EiJ6x4cHKzrrrtOXbp00S233MKGjuXECLmSsQZGvj7gKWkAMD09XYMHD3Ys1n3JJZfoyy+/VI0aNVxex8rm3nvv1ZYtW/TJJ59ozZo1Gj58uIYPH66qVavafq4jR45o+PDhWrhwoeO+Tp066fXXX1f9+vVtPx9KJzY2VrNnz1bfvn21Zs0aff/997r33nv12Wef+fx3T0XkPDDy5QH1uTIAv/vuOz333HOS8nZ9/eqrr1StWjW31Q9A6TVr1kzz58/X1KlT9eqrryo5OVnvv/++5s6dq3HjxqlLly6ermKF5Pw96ssBQKtfKCwAePDgQT3wwAPKzc1VeHi4Pv30U5++CAa4QmhoqGrXrl2qzahyc3OVlJR0zmDh2dmHqamphZaXkZGhH3/8UT/++KOeeeYZXXHFFbr11lvVuXNnZvOVga0BwAMHDpS7DAbvrlWZAoBnzpzRfffdp9WrV0uSrr76ak2fPt2nf8h4u9GjR2v79u3auHGjPv30U02bNk0tWrRQu3bt1LZtW1199dXlygLLycnRlClTNHbsWMf/ffXq1TV27FjdfvvtLODvRSIiIjRz5kwNHDhQP/zwg37++Wf17NlTX3zxRaVa4N4XWAO9yMhIn55iYWW2JyUlyTTNfN8HP/30kx5++GGZpqno6Gh9+eWX/N4AfISfn5/+9a9/6ZZbbtHzzz+vxYsX6+jRoxowYIC6du2qcePGkRFiM+cMQF+eNWT95j9z5oxycnIcfVxaWpoGDBigU6dOSZLeffddNosDbOLn56eYmJhS/95PT08vNMvwzz//1JIlS7R//35J0q+//qpff/1VY8aMUaNGjdSlSxf17t2bz3AJ2RoAPP/888s1ADcMQ9nZ2TbWCGerLGsAxsXFqXfv3tq6daukvOywjz/+mMWjXSwoKEhTpkzRXXfdpV27dsk0Tf3222/67bff9M477yg4OFhXX3212rZtq3bt2qlFixYlDjjs2LFDQ4cO1aZNmxz39e/fX//5z38IKHmp0NBQTZs2TQ8//LC+++47/fLLL7rjjjv01VdfkYXrRawAoK9nPlj9WnZ2ttLS0hzf91u3blX//v2VmZmpkJAQff7552revLknqwqgDOrUqaPp06frhx9+0GOPPaYTJ05owYIF+umnn/TSSy+pX79+TN+0SUWbAizljYFiYmJkmqaeeeYZbdu2TZL01FNPqVu3bp6qIoD/LyQkRLVq1Sr0gs7o0aP1xx9/aNGiRVq0aJFjjL9nzx69++67evfdd1WtWjXdfPPN6tKli9q2bcvSQ0WwvZc0TbNcf+BaKSkpkipOADA1NbXAGgOHDx/Wbbfd5vhiuPPOOzVt2jSCf25Sq1YtrVq1SkuWLNHw4cN13XXXOdYCtLaqHz16tG666SZddNFF+te//qVPP/1U+/btK7S81NRUjRo1Sp06dXIE/y644ALNmzdPEydOJPjn5YKCgvTBBx+oT58+kvICubfddpsOHz7s4ZrBYgUAfXmQJ+UPYFqvac+ePerTp4+Sk5Pl7++vjz76SG3atPFUFQGUk2EY6tmzp9atW6e+fftKyvu8P/3007rjjjv0999/e7iGFYM1ZTYoKEghISEerk3ZOfcLVlDzo48+cqwL3rFjRw0bNswTVQNQCoZhqHnz5nr66ae1bNkybdmyRePGjVP79u0d677HxcVpxowZ6tevn5o1a6YBAwZo5syZpd6opKKzNQNwwIAB53xOTk6O4uLitHHjRsXHx8swDHXo0EF169a1syooQkXLAJTygprW69m9e7fuuusuR3Bh4MCBGjduHFeE3czf31+XX365Lr/8cg0dOlQpKSlat26dVq1apZUrV2rHjh2SpNOnT2vevHmaN2+eJKlBgwZq27at2rZtq+7du+u3337TQw89pD179kjK+yE6dOhQPfbYY+za6UP8/f01adIkRURE6MMPP9Tu3bvVrVs3ffPNN2rUqJGnq1fpWQM9X+8XnAd6Vl93zz336OTJk5KkN998U7fccotH6gbAXjExMZo0aZLuvvtuDR06VHv37tXatWvVvn17PfXUU/xOKCcrWBYdHe3Ty6s4XyROTEzU6tWr9Z///EdS3sy1999/36eXvgAqqzp16mjQoEEaNGiQkpOTtXbtWn399ddaunSpkpOTlZqaqoULF2rhwoXy8/PT1VdfrZtuukkdOnRQ8+bNffp7rbxsDQB+8sknJX5uTk6Opk6dqqFDh+r333/X+PHj1apVKzurg0JUtDUApbzXFBkZqa1bt6p3796Ki4uTJA0dOlTPP/98pf6Ae4vw8HB17NhRHTt2lCSdOHFCP//8s1asWKGVK1c6doLav3+/PvvsM3322WcyDCNfVvC1116riRMnqkmTJh55DSgfPz8/jRkzRpGRkXrzzTd16NAhde/eXRMmTNCtt97K59SDKmIG4KFDh/TKK6841osZOXKk7r33Xk9VDYCLXH/99Vq5cqUmTpyod999VxkZGRo3bpymTJmiwYMH6/7772emQBlYF4Z8ve2c+7U//vhDr7zyinJychQWFqZp06b5/OsDkPc579Onj2699ValpqZq9erVWrx4sRYtWqRjx44pNzdXa9eu1dq1azVq1CjVrFlTHTp0UIcOHdSuXTvFxsZ6+iW4lcfSovz9/TVo0CDNmzdPx48f15133lnkFu2wT0XJAHSuf3JystasWaMePXo4gn+vvvqqhg8fTlDBS9WoUUN33nmn3n77bW3ZskXr1q1z7OZn/d9awb/Y2FhNmjRJ3377LcE/H2cYhoYPH66RI0dKygsE9+/fX3fccYdjyj7cr6KtAShJjz76qCPT+OGHH9Zjjz3mqWoBcLHQ0FC9+OKLWrZsmSOZ4MSJExozZoxatmypESNG6ODBg56tpI9xzgD0Zc71HzZsmGOc8NZbb+miiy7yVLUAuEhwcLA6dOigN954Q1u3btX333+vp556ShdffLHjOcePH9cXX3yhwYMHq1mzZurcubPGjx+vX3/9VTk5OR6svXt4fF5k27Zt1aNHD+3fv1/vvfeep6tToeXk5Di2165IGYBz5sxRr169dObMGfn5+emtt97SQw895MHaoTQMw1Djxo01aNAgffrpp9q1a5cWLVqkV155RaNHj9aOHTvUt29fgrkVyOOPP67JkyerevXqkqQ1a9aoU6dOeuSRR1gb0AMqYgagNe23Z8+eevnll/n+ACqBSy65RIsXL9Ynn3yi1q1bS8pbR/iDDz7QlVdeqX//+99cbCqhipIB6NwvWGOgxx9/XHfccYeHagTAXfz8/HTZZZfphRde0IoVK7Rt2za9/fbbuuOOOxzfbbm5ufrll1/0xhtv6NZbb9VFF12kwYMH64svvtCxY8c8+wJcxOMBQEm6+eabZZqmZs2a5emqVGjWBiCS72cAOgcAJ0yYoPT0dMcOtEzz8m0BAQFq3bq1RowYoREjRrBbbAV19913a8OGDXrqqaccC4x//fXXatOmjcaMGVPkDt+wX0VcA1DKW9z9rbfeYg1YoBLx9/dXt27dtHDhQs2bN0+33nqrpLyL4HPmzFGnTp105513atmyZWw+WAyrX6hIF4YkqX379hoxYoSHagPAk2rVqqU+ffroww8/1M6dO7Vo0SI999xzat26teNC8enTp/Xtt9/qiSeeUIsWLdS+fXuNGjVKP//8szIzMz38CuzhFb+Kq1SpIkmOhf7hGs4Dal8f6J2dwRgeHq4vvvhCXbt29VCNAJRWRESEXnjhBa1fv1733HOPJCk9PV2TJk3SVVddpalTpyo7O9vDtazYTNOsUBmA1m7vrVu31scff+zYgRxA5WIYhtq0aaPPPvtMa9eu1X333efYFOSnn35Snz591K5dO82cObPCDOrsZE0B9vUMwODgYEcQsH79+vrf//7Hph8A5O/vr9atW+vZZ5/VokWLtHPnTn3wwQfq3bt3vuSTHTt26J133lGPHj10wQUXaODAgZo9e7ZjWTVf5BUBQGuRbncM9BITE/Xxxx/rwQcf1N13362+fftq5MiRWrduXbnKzc7O1rfffqunnnpKvXr1Uq9evTR06FDNnTvXawawFSkD0HmgGhsbq9mzZ6tt27YerBGAsqpdu7bee+89LVu2TNddd52kvCmcQ4cOVcuWLbV06VIyNVwkPT1dWVlZknx/DcDAwEBNnjxZTzzxhGbMmKHw8HBPVwmAF2jSpInefPNNbdq0SUOHDnUEtf744w89/vjjuuKKK/TOO+84Loag4qwBKEkvvfSSOnbsqOnTpzuSTgDAWZUqVdSjRw+988472r59u1asWKGRI0fq+uuvd1xMTklJ0fz58/Xggw+qWbNm6tu3r2bOnOn4vvQVHg8ApqWl6cMPP5QkNWjQwKXnOnDggB577DHNnTtXR48elb+/v1JSUrRlyxaNHTvWUY/SSktL0/PPP68pU6Zo9+7dysnJUU5Ojv7++299/PHHeuGFF5Senm7zqyk95wxAX18DsEaNGho0aJCuueYazZs3T5dffrmnqwSgnFq2bKk5c+bo888/d2z48vvvv6tXr17q2bOntm/f7uEaVjzOA96KMNDr0qWL/vOf/1S6Hd0AnFuNGjU0fPhwx+/++vXrS5KOHTumUaNGqWXLlho5cmSlX4vWNM0KswagJPXv318zZ85Us2bNPF0VAD7AMAxdfPHFevzxxzVnzhzt2rVLn332mfr37+9YvzwzM1Pff/+9Hn/8cV100UXq2bOnpk2b5liD2pt5LACYnZ2tH374Qe3bt9eff/4pwzDUrVs3l50vKytLo0ePVmJioho0aKC33npLX375pb788kv169dPhmFo3rx5WrZsWanL/r//+z/t2rVL4eHhGj58uL7++mt9/fXXGj58uMLDw7Vz505NnjzZBa+qdCrSFGBJGjdunL777jtdeOGFnq4KAJsYhqFbbrlFq1at0htvvKFq1apJklauXKkOHTpoyJAhFXZRXk9w7hcqQgAQAM4lPDxcgwcP1vr16/Xhhx86dg5OTk7W5MmT1bp1az3yyCNavHixTwzm7JaSkuLYCZN+AUBlFxERoVtvvVUTJ07Utm3bNHfuXA0ePFjnnXeepLy41ooVK/T000/rkksu0e23364PP/xQR48e9XDNC2drALBRo0Yl+lO3bl2Fh4fr5ptv1i+//CIp76rcs88+a2d18lmyZImOHTum4OBgjRw5Ug0bNpSUtzbEPffco86dO0uSPv/881JN2d27d69WrVolKW9XqWuuuUaGYcgwDF1zzTV67LHHJEkrVqxwTHX2lIqUAQigYgsMDNQDDzygv//+W0888YSCgoJkmqZmzJihq6++WuPHj8+3rAHKxjkDsCJcGAKAkgoICNAdd9yh77//Xt9++606deokKW8w9/XXX+u+++5T8+bNdeWVV+qhhx7Shx9+qM2bN1f4NQOd+4WKkAEIAHbx9/fXtddeq7Fjx2rLli1atGiRHn30UcdM1tzcXK1Zs0YvvPCCLr30UnXu3Fnvvfeex+NAzmwNAO7bt0/79+/Xvn37iv1z5MgRZWVlyTRNmaapZs2a6fvvv3dkerjCihUrJElt27Z1pG46u+uuu2QYhk6dOqVt27aVuNyVK1fKNE2dd955uuaaawo8fu211+q8886TaZpauXJlmetvh4qWAQig4ouOjtbLL7+stWvXqkePHpKk1NRUvfHGG7r66qv12Wefaffu3V6xzIIvcl7EmEwPAJWRYRi67rrr9MUXXzg2CLE2DJHyxjfffPONXnjhBd18881q1KiRunTpopEjR2ru3Lk6fPhwhVqntqItDQEAruDn56fWrVvr5Zdf1saNG/XDDz/oqaeecixjJEm//PKLXn75ZbVu3VodOnTQ2LFj88VkPCHAzsLq16/v2EK5OMHBwYqNjdXFF1+szp076/bbb1dAgK1VySctLU1//fWXJBW5Vlz16tVVt25dHTx4UFu3btVll11WorJ/++03SdJll11W6Gs3DEOXXXaZjh496niup5wdAMzNzfVgbQCg5OrXr68PPvhADz74oEaOHKkNGzbo+PHjGjp0qOM51atXV7169VS3bt1C//b1TS5cwXmgR/sAqOyaNWumt99+W2+88Ya2b9+uX375Rb/++qt+/fVXHTx4UJKUkZGhjRs3auPGjY7jatWqpSuuuEKtW7fWFVdcoZYtW/rsWqTW+n8SAUAAKAnDMHTppZfq0ksv1fDhw/Xnn39q/vz5mj9/vnbs2CFJ2rZtmw4ePKhhw4Z5tK62Rt327dtnZ3G2OXTokOPKXHEbjTRo0EAHDx50dPDnYpqmDh06dM5yrUWGS1quqzhPl4uIiGC3MwA+54orrnB0qKNGjcrX75w8eVInT57Upk2bCj02KipK9erVKxAcrFOnjsLCwhQUFKTAwEAFBQU5/gQGBiowMFB+fh7fM8slyAAEgIJCQkLUunVrtW7d2nHfsWPHtGnTJkdQcMuWLUpNTXU8tmDBAi1YsEDSP9PEPD37pyycA4BMAQaA0jEMQ82aNVOzZs30zDPPaPfu3VqwYIH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" }, "metadata": { "image/png": { @@ -310,51 +264,60 @@ ], "source": [ "gce.reset()\n", - "size_dist = pd.DataFrame(\n", - " {\n", - " 'pop_density': gce.state,\n", - " 'size_class': list(range(21)),\n", - " 't': 21*[0],\n", - " }\n", - ")\n", "\n", - "for t in range(1,4):\n", - " gce.step([0])\n", + "size_dist = pd.DataFrame()\n", + "\n", + "for t in range(6):\n", + " gce.step(0)\n", " new_size_dist = pd.DataFrame(\n", " {\n", - " 'pop_density': gce.state,\n", + " 'size_distribution': gce.state / sum(gce.state),\n", " 'size_class': list(range(21)),\n", - " 't': 21*[t],\n", + " 'label': 21*[f\"t={t}, tot. pop.={int(sum(gce.state))}\"],\n", " }\n", " )\n", " size_dist = pd.concat(\n", " [size_dist, new_size_dist],\n", " ignore_index=True,\n", " )\n", - " new_total_pop = pd.DataFrame({'pop': [sum(gce.state)], 't': [t]})\n", - "\n", - "ggplot(size_dist, aes(x='size_class',y='pop_density')) + geom_line() + facet_grid(cols='t') + labs(title=f\"N. traps = 0, final pop = {sum(gce.state):.0f}\")\n", - "\n", + "(\n", + " ggplot(size_dist, aes(x='size_class',y='size_distribution')) \n", + " + geom_line() \n", + " + facet_wrap(facets='label',) \n", + " # + labs(title=f\"N. traps = 0, final pop = {sum(gce.state):.0f}\")\n", + " + theme(aspect_ratio=1)\n", + ")\n", "\n" ] }, { "cell_type": "markdown", - "id": "c1f1a77c-bf82-404e-b9e3-6fdb444675ce", + "id": "55606c6b-206c-4fc6-94ee-4c2852045ad4", "metadata": {}, "source": [ - "## Plotting population *with* traps" + "Here we see that the relative distribution of sizes converges to a bimodal shape, and that the total crab population increases over time. \n", + "If we were to run this simulation further, we would see the total crab population saturating at around its carrying capacity of 25 000." + ] + }, + { + "cell_type": "markdown", + "id": "de5dc1da-8283-4dbf-b2a8-762721e98a34", + "metadata": {}, + "source": [ + "### Crab size distribution with traps\n", + "\n", + "Let's try the same code above but laying out a non-zero number of traps." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "781f996b-dcc5-4106-810b-d3ab296cfac2", + "execution_count": 48, + "id": "70e6f4e6-ead3-4d37-a784-302b7a228e8c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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LT0/XU089VWj9uVNPPVXjxo1T37599c8//2jRokWFpgLNmjVL6enpqlevnp5++mlzzQq/368LLrhAtWvX1qBBg/T7779r+fLl6tChQ7HzZmZm6j//+Y+GDh1qhl3x8fG6/fbblZKSoo8//lgzZ85U586dKzQ0PZjvtcP5448/9OWXX0qSOnXqVOZz33vvPb3wwgvFvn/EEUdoyJAhxRr0hg0bml9v2bKlxOH+f//9txkAFp1CDLhdKP9W3YB2gXZBKhj5M2rUKD366KP6888/NXToUHMX4LS0NIWFhemss85S3759ywzfMjMztWnTJsXHxysjI0Pbt2/X9u3btWjRIvXv37/EpSOM6cfl+exkfT4QKm76Ww0F2oXq2S4Y69Fb+Xw+XXLJJbr//vsrfD5jh+CGDRuWOtvnwIEDeumllxQdHR3UtWNhj7DDPwXwtoSEBCUmJlbqX9Hhy9Y1cUpaM6LoY07sALls2TJJBXcUrY25ITIyUj179pQk/fDDD0pNTQ1l8Qo55ZRTiu0+KxV88DDuLC5fvtz8fiAQ0IoVKySpxAVrjWP+5z//kXSoLkrSo0ePEofsG3Wzd+9erVmzpvw/jIL7XitLamqqnnjiCeXn5+vEE09U586dy3x+dHS0EhMTVadOHfNnTkxM1J133qkzzzyz2PNPOOEEc2rKm2++WeIxZ8+ebX7NTqfwmlD9rboF7QLtgqFRo0Z65plndP7550uS8vPzzc5ifn6+srOzlZmZWeJrjzjiCPXp00evv/66lixZogULFmjRokUaP368WrZsqfz8fE2ZMkWfffZZsdca7UR5PjtJBR1+IJTc9rdqN9qF6tku1K1bV4mJiYWup5dddpn69etX4c8v8+fP19dffy1Juvvuu0vdnX3y5MlKSUlR7969Cw0igDsxAhDV3qhRo5wuQkgZ67q1adOm1OcYoU8gENDGjRvLfK6dypoycNppp+mTTz7Rxo0bze/9888/OnjwoKTD/3w//PBDqQsrh4eHq3Xr1iU+1rhxYzVo0EC7d+/Wxo0bKzStIRTvtZycHD322GPatm2bEhIS9H//93+HvevYpUsXdenSRVLBlID169dr6tSpGj16tBYtWqTHHntMsbGx5vPDw8PVs2dPvfjii1q5cqXGjBmjXr16qVGjRuZiyitWrFB4eLhyc3Ndv/YJUBTtQnG0CzWjXfjuu+/0+OOPy+fzaeDAgWrbtq3q1KmjHTt26J133tGnn36qNWvW6LHHHiu2q+RZZ52ls846q9D3wsPD1aZNGz333HMaPHiw1q9fr1deeUUdOnQotNYY4HZu+1u1G+1C9WwXZs6caX69d+9effTRR5ozZ45WrFihhx9+uMSwtyQrV67U5MmTJUnXX399qdN6V65cqc8//1zNmzfXf//736r/ALAdLTNQAdY7J6XdIbc+5sRIEWP31vr165f6nNq1a5t3cYxFfJ1Qr169wz5mLGQsyVx/8XCvNX720n42689f1rmNunSLvLw8jRo1Sj/++KNiY2M1ZswYHXXUURU6RmRkpE4//XRNnDhRJ510kn788UdNmzat2PO6detmrmmyZMkS3XTTTbrsssvUq1cvffjhh2rbtq3atm0rSSXeWQXgHrQLtAtSwZpYw4cPV3p6ukaPHq1rr71WRx55pGJiYnT88cfrkUce0RVXXKHc3Fw9//zzZh2XR0REhPr16ydJ2rNnT6HOuHTo81B5PjtJKnRTCqgOgvEZLphoF6pvu2A44ogj1Lt3bw0bNkyZmZl66qmnyrVsz+rVqzVixAjl5uaqQ4cOuvPOO0t8XmZmpiZMmCCfz6d7772XHX09ggAQqABrI1LW+n7GY2WtdQNURF5enp544gl99dVXio6O1pNPPqmWLVtW+njh4eHm5h8ff/xxsceNxvyZZ55Rhw4d1LRpUzVs2FCnn366HnjgAT355JPmosBNmjSpdDkAAJVT0XZh/vz5ysnJUYsWLUodrXLDDTdIkv79919t2rSpQuWxnnvnzp2FHivP2sjWx+rWrVuhcwNuFuzPcEBFXHDBBWrYsKEyMzP1xRdflPncdevW6ZFHHlFmZqYuuOACPfLII6WO5p4zZ4527dqlSy+9VMcdd5wyMjIK/TM2EszPzy/2PTiHKcCo9oYPH661a9dW6rXdu3cvtJh17dq1VadOHR04cEBbtmzR2WefXeLrjN3umjVrVqnzVkWdOnW0bds27dmzp9TnJCcnKycnR5IqvBtUMJWnIxAXF6fIyEhJMtelkwo2niitg2D87KX9bMbPX9pdPePcderUKfsHKCKY7zWr/Px8jR07VkuXLlVERIRGjRpV4sYcFWUE2unp6dq/f3+J9XXmmWeWuE5gXl6e/vrrL0lSq1atqlwWIJTs+lt1K9oF2gWpYPMmSTryyCNLfY71sV27dunkk0+uRMmLa9asmb777rsyd403HvP5fGratGlQzguUl5v+VkOBdqH6tQtlqVevnv7991/t2LGj1Of8/vvveuihh5Senq6zzz5bw4cPL3NU365duyRJn376qT799NNSn7dmzRpdeeWVkqTRo0ezS7DDCADhWcbdCGMn0tKkpKRUeth6SZsbtGnTRkuXLtXKlSvNO+VWWVlZ5mKwpe2WVFnGWmtl/cwnnnii1qxZo19++aXU5/z000/m8Vq0aFHsHIFA4LD1Ggxl7YBlPGYtX+PGjVWrVi0dPHhQP//8s0444YQSX2v8fKU9npubq3Xr1pU4AmLnzp36999/i527PIL9XpMKftdPP/20Pv30U4WHh2vkyJHF1mCqrH/++cf8uqLT1b///nulpqYqIiJCF110UVDKA1SVU+2Ck2gXaBfK2y4Y75Xdu3eX+hzrYxWdhvvbb7+ZXxcNGc844wy9/fbb2rJli/bs2VPitMMffvhBknTyySd7crMduJOT7YKdn+HKQrtQ89qFwzHCutKurRs3btSQIUOUlpamNm3aaNSoUWVOf4Z3EQDCs4wPpofblWrixIlBPe+ll16qpUuX6ueff9Yff/yhE088sdDj8+fPV1pamqKjo8u90Gp5xcXFSSr7Z27fvr3ee+89bdmyRStWrChWhuzsbHMH17PPPrvY+m2xsbFKS0sLyW5fq1ev1tq1a4stsPvPP/9o6dKlklQoXPL5fLrooov00Ucfae7cubrqqqvMOjGsWbNGP/74oySZO4OV5M0339Rpp51WbAMLY9fbevXqlbjjWFmC/V4zjrl48WKFhYXp0UcfLbYoe2ny8vLKvGuXmZmpDz74QFLBB5+ydmYs6uDBg5oyZYok6aqrrip0pxVwklPtgpNoF2gXytsuHH/88frmm2/0xx9/lPj5RZI+/PBDSQWhyUknnWR+PxAIlLnhU25url5//XVJBfVUtEPcpk0b1a1bV/v27dOcOXN0zz33FHp89+7d+vzzzyUVfM4CgsXJdqGyf6tVRbtQs9qFw33m/+STT8zRiiWNPt28ebMeeOABHTx4UK1bt9YTTzyhqKiow5536NChGjp0aKmPjxkzRkuWLNFpp51WrT53eR1rAMKzjj32WEkFd4zLGhoebOedd55at26tQCCgESNGmHee8vLytHDhQk2dOlVSwTo6JQUj06dP18UXX1xmY1Ma42deu3attm/fXuJzWrVqZX7AMC68xvD9HTt2aNiwYfrrr78UHh6uvn37lnqOzz//vMwFwI2fYfr06RX+OQyxsbEaPny4vv32W+Xn50sqaJAfeughZWdnq3HjxurUqVOh1/Ts2VOxsbHas2ePHnzwQXONory8PH311VcaPny4AoGATjrppFID2OjoaP3yyy8aN26ceQcuNTVVr776qj766CNJ0k033VSsMa3K764yXnjhBS1YsEBhYWF6+OGHKzTS7s0339STTz6pH3/8Uenp6eb3s7KytHLlSg0cOFCbN2+WJPXu3bvY6//66y/NmDFDmzdvNt8/2dnZ+vrrr3X33Xdr27ZtatasmW699dYSz5+dna3k5GTzn7G4eyAQKPR9Y5c2IBicahekgmDc+t42ZGRkFPp+SddV2oVDaBfKVpV2oVOnToqIiFB+fr4effRRLV++XFlZWZKkffv2acqUKZo3b54k6ZJLLik0re3ff//VHXfcoY8++sgcSSIV1PGqVas0ePBgrVu3TpLUv3//YmtGRUREmO+tefPm6c033zTP/eeff+rhhx9WZmamjjrqKHXu3LniFQOUwql2oSp/qxLtghXtQtl69+6t9957Tzt27Cg0InPnzp16/fXX9fTTT0uSTjrpJJ1zzjmFXrtt2zbdf//9SklJ0UknnaQxY8aEdAQ2/YXQ8wVCMW4XQVPaNuU10datW3XrrbcqJydHYWFhqlOnjjlUec6cObaee+/evRo0aJC5jkJ0dLTy8vLMhvPCCy/UiBEjSrwbM336dM2YMUOSzLtW5ZWcnKybb75ZKSkp8vl8ql27tnmHZtKkSeaUmpSUFA0dOtScjhMREaGoqCjzLp3f79dDDz2kyy67rNg5Fi9erLFjx5qvq1OnjsLCwnTyySdr+PDh5vOMRq13797q06dPhX6OG2+80exMLFiwQDt27FBUVJTCwsLMoe21atXS+PHjdfzxxxd7/Q8//KDhw4ebjURcXJxycnLMDyBHH320nnnmGTVo0KDEn61hw4b673//q8mTJ8vn8yk+Pl5paWnmh4qOHTtqyJAhxe72VeV3V1H//vuvbrzxRkkFv6+EhIQynz9lypRCP6+1rFJBHfn9fqWmppo/Z2RkpO666y5zt1+rVatW6d5775WkEuvopJNO0hNPPFHquirW91FZGjZsaPvfq5eUNg2lLLQLhzjZLhjXtcN56KGH1LFjx0Lfo12gXSiPqrYLkrRs2TI99dRTZr34fD5FR0cXmlZ28skna9y4cYVGzOzatUv/+9//zP+PjIxUTEyM0tPTzc8+4eHhuv3229WtW7dSyzRx4kTNnz/f/Bmio6OVlpYmqWAdrgkTJjiyfrKb0S5UjRP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wcMuPz9IQAFB79SIBmJOTo82bN0uSunfvXu59GjVqpJYtW0qSfv/9d8vO7T04Kioqsuy4vmL31bwjj0sCEACcz+4pwEZcKCwsVE5Oji3nAABYx4gLcXFxtuwOTwUgANRevUgA7t6926wgaNOmTYX3M27btWuXZedet26dJCk+Pt62BJqdvIOs3QO9I88HAHAmX60N630uAIBzsTYsADhfvUgApqammr8nJiZWeD/jtkOHDlly3uTkZC1YsECS1K9fP1uuhtmNBCAAwFtRUZG5pIXdawBKxAUACARGYs4XmwayNAQA1Ey92AU4NzfX/D0sLKzC+xm3WTHdqLCwUC+88IJycnLUuHFjXXbZZVV63LRp0zRjxowKbx8+fLiuvvrqGrfL7Xab/01ISDjq/Y31/ySpZcuWVXpMdbVq1arU+YxzGAnTuLg4Rwf66vapPwVKn0r0q13oV3v4s19re77qtj0tLc38vWnTprY83+bNm5u/B2JckALnvUaf2oN+tQf9WjW+jgvSP+OnBg0a2PJ8jc0Wi4qKFBoaqujoaEm8JuxAn9qDfrUH/Vo99SIB6Gsej0dvvPGG/vzzT4WGhuree+9VVFRUlR6blZWlAwcOVHh7dna2goKCat1Gl8tVpeNkZWWZvycmJlpy7iN5V2UePny4zDmMN4rTVbVPnSBQ+lSiX+1Cv9rDH/1q1fmq2nbvDa0SEhJseb4NGjQwf8/IyAjYuCAFznuNPrUH/WoP+rVyvo4L0j/V2vHx8bY8X+/BckZGRqlKcYnXhB3oU3vQr/agX6umXiQAvXeiysvLU2RkZLn3y8vLkyRFRETU6nxvv/22vv/+ewUFBen+++9X586dq/zYqKgoNW7cuMLbIyMja7WZiNvtlsvlksfjKVXdVxHv6dDR0dG2bGRiXMEzzmecw+Vyye12q7i42NHZ/Or2qT8FSp9K9Ktd6Fd7WNWvNfkyUNvP5eq23XtZDeJCxQLlvUaf2oN+tUd97NdAiAvSP9XhcXFxtsQF76WIUlJSzIrA+viasBt9ag/61R71sV9rkzysFwlA7wqz1NTUChOAxqCmNuWYU6ZM0Zdffim326177rlHp556arUeP2LECI0YMaLC25OTk2u1RqFRrVFcXFyl4yQlJZm/FxUVWbY+4pHCw8OVm5ur/fv3m+cICgpSQkKC0tPTHb2DcnX71J8CpU8l+tUu9Ks9rOrXhg0bVvsxtf3/WN2279692/zd7Xbb/jrau3dvwMUFKXDea/SpPehXe9THfg2EuCD9kwAMCwuz5XXkPeDdvXu3WrRoYf69vr0m7Eaf2oN+tUd97NeaxAVD4NRJ1kLLli3NueE7d+6s8H7Gbd5r0lXHBx98oM8//1wul0ujR4/W2WefXaPjOIlRzh8ZGangYPvyxd4L+wIAnCsjI8P83a7F3mNiYsy4TVwAAGfzeDy27w7vPeWXnYABoGbqRQIwIiJCnTp1kiT98ssv5d4nOTlZu3btkiR169at2ueYMWOGZs2aJUm69dZb1a9fvxq21lnsDuYG4/gEdABwNu+EnF2xwe12m9OAiQsA4Gy5ubnKz8+XxO7wAOBk9SIBKEl9+vSRJC1btkwHDx4sc/vs2bPl8XiUmJioE044oVrHnjVrlmbOnClJGjlypAYOHFjr9jqFUenhqwSgd2UJAMB5fJEAlP4Z7BEXAMDZfBEXvI/LhSEAqJl6kwAcMGCAmjZtqtzcXE2cOFHbt2+XVLLxx6xZs/Tll19KKlmD78iprjfffLOGDh2qV155pcxxv/jiC33wwQeSpOuvv14XXXSRvU/Ex4yA7r3wrh2YAgwAgcH4nHa5XFXe4b4mqAwHgMDgi6UhoqKizF0+iQsAUDP1YhMQSQoJCdEjjzyicePGaceOHRozZowiIyOVm5tr7sAyePBg9e/fv1rHfffddyWVDITmzp2ruXPnVnjfhx56SF26dKn5k/ADXycACegA4GzeccEYjNmBC0MAEBi8v797T9W1ksvlUlxcnA4dOkRlOADUUL1JAEpS69at9frrr+uzzz7T6tWrlZycrKioKLVv316DBg1Sr169qn1MY6tpj8dj7n5VkcLCwpo026+YAgwA8GYk5Owa5Bm4MAQAgcH7Qo2dRQNGApC4AAA1U68SgJIUHx+vkSNHauTIkVV+zDvvvFPhbV988YUVzXIsXycAqfQAAGfz1eZQRoKRuAAAzuartWGN5CIJQAComXqzBiBqxte7AB8+fNisqgQAOI+vloYwjk8CEACczftz2s7qcC4MAUDtkABEhTwej88TgIWFhcrOzrb1XACAmvN1BSCVHgDgbL6cAiwRFwCgpkgAokI5OTnmuoW+2gREYh1AAHAyXy0NYQz0srOzA3INXQCoL4wEYGRkpEJCQmw7D0sGAUDtkABEhXy1nseRxyeoA4Bz+Xp3eO9zAgCcx6jIY21YAHA2EoCokHclHgM9AIDk+7Vhvc8JAHAeX28amJ6ezprhAFADJABRIX9VALKuBwA4U1FRkTIzMyURFwAAJXx9YSg/P1+5ubm2ngsA6iISgKgQU4ABSN2OzgAAhqJJREFUAN6M5J/ku6leEmvDAoCT+XoKsMR4AQBqggQgKuSvKcAM9ADAmagMBwAcyde7w3ufEwBQdSQAUSFfDvS8E4wEdABwJu/PZ19eGCIBCADO5es1ACXiAgDUBAlAVMi7Es/ugB4cHKyoqChJBHQAcCqWhgAAHMn47u5doWcHEoAAUDskAFEhY8AVHh6u0NBQ289nBHUGegDgTL68MBQeHq6wsDBJxAUAcCqPx2PGBrsrw5kCDAC1QwIQFTICq93B3GAMJlkDEACcyZcVgN7nYKAHAM6UlZWl4uJiSawBCABORwIQFfLVgr4GBnoA4Gz+SgAy1QsAnMn789nuKcAxMTFyuVxlzgsAqBoSgKiQr8r5DQz0AMDZjASgy+Uy1221kzGYpDIcAJzJlxeG3G63oqOjJTFeAICaIAGICvmrApCBHgA4k/fSEG63/V8huDAEAM7my93hpX8uDDFjCACqjwQgKuSvCkACOgA4kxEXWBoCACD5dgqw9zmICwBQfSQAUSHWAAQAeCMuAAC8+XJ3eO9zUBkOANVHAhAV8melh7GbGADAOXy9O7xR6cFADwCcydebQ1EBCAA1RwIQFfJXpYfH41FWVpZPzgkAqDojEeePC0Mej8cn5wQAVJ0RF1wul7lBh52oAASAmiMBiHLl5eUpLy9Pku8HehJX9QDAiYzKcF+s8yT9ExeKioq4MAQADsTmUAAQOEgAoly+3tFLIgEIAE7nrynAEjvEA4AT+XrJICMuEBMAoPpIAKJcvl7P48jzcFUPAJzHX0tDSMQFAHAi47PZV5Xhxnmys7OVn5/vk3MCQF1BAhDl8r6qRgUgAKCoqEiZmZmSWBoCAFDC15Xh3uchLgBA9ZAARLn8XQFIWT8AOIuR/JOoAAQAlPB1Zbh3pSFxAQCqhwQgyuWdgPNHQOeKHgA4iz8uDLEGIAA4mxEbfD0F2PvcAICqIQGIcvljE5CoqCi5XC5JXNEDAKfx9+ZQxAUAcB5/bg5FAhAAqocEIMrlj0oPt9ttfnkgoAOAs/gjLkRHR3NhCAAcjM2hACBwkABEuYypViEhIQoPD/fZeY2gzlQvAHAWfywN4Xa7zXNxYQgAnKWwsFBZWVmSfDcFmAQgANQcCUCUy/tqnlF94QvGlwcGegDgLP6oAPQ+F3EBAJzFHxeG2B0eAGqOBCDK5ev1PAzG+biiBwDOQgIQAODNH2vDhoSEKDIyssz5AQBHRwIQ5fJXApCBHgA4k/G57HK5FBUV5bPzGnGBC0MA4Czen8u+mgLsfS7iAgBUDwlAlMso6fdllYf3+VgDEACcxfvCkNvtu68PxkCPuAAAzuKPKcDe56JgAACqhwQgyuXrHb0MVHoAgDP5+8IQcQEAnMXfS0MQFwCgekgAolzGQI8pwAAAiQtDAIDSvD+XfRkbmAIMADVDAhDl8vdALysrS0VFRT49NwCgYv5aG5YpwADgTP6qACQuAEDNkABEufw91cu7DQAA//P3haHs7GwVFBT49NwAgIoZ39WDg4MVERHhs/NSGQ4ANUMCEGUUFBQoOztbkn8TgAR1AHAOJ1wYYnkIAHAO47t6XFycXC6Xz85LAhAAaoYEIMrw145eR56PgR4AOIcx0OLCEABA8v/SEJmZmSwZBADVQAIQZXgn3vy1CciR7QAA+Je/B3rebQAA+J+/lobwjgssGQQAVUcCEGWQAAQAeCsqKlJmZqYkKgABACWM7+reCTlfIC4AQM2QAEQZ/pwC7P0FgoAOAM5gJP8k/w70uDAEAM7hhApAxgsAUHUkAFGGPxOA3hWHDPQAwBm8P4+pAAQASP5bGoK4AAA1QwIQZfhzoBcZGamgoKAy7QAA+I9TloZgoAcAzuGvKcBUAAJAzZAARBn+HOi5XC5zsEcCEACcwZ8XhsLCwhQREVGmHQAA//LXFGCWhgCAmiEBiDKMQBoUFKSoqCifn9+4qscVPQBwBn8uDSH9czGKuAAAzpCbm6u8vDxJLA0BAIGCBCDKMAZ6MTExcrlcPj+/MdDjih4AOIM/KwClfy4MeSciAQD+48+4EB4errCwMEkkAAGgOoL93QBUj7E+np3HMgZYsbGxlp6vqo4c6PmjDTXl9LYa7XN6O4/k9PbSr/agX31/voqO5b0LcHx8vM+f45FLQ/CasA7vM3vQr/agX31/voqOlZWVZf7uj7gQFxenAwcOBOR4QXJ2e3mf2YN+tQf9Wj0kAANMQkKCJccJCgqq8Fi5ubnmuaw6X3U0bNhQ0j9fLPxRbVITlfWp0wRKn0r0q13oV3v4o199ERcKCgoklazT2qpVK7ndvp1AEKhxQQqc9xp9ag/61R70a+V8ERe8tWjRwi/P8cCBA8rOzpbEa8IO9Kk96Fd70K9VQwIwwBw6dKhWjzeq+oqKiiqcYpucnCypZEfe2p6vJozF3lNTUyWVVHwUFRX5vB1VVZU+dYqgoCDFxsY6vk8l+tUu9Ks9rOrXmnwZ8EVc2L9/v6SSJRr8Md3KiAspKSmSnB8XpMB5r9XH95kv0K/2qI/96tS4sGvXLvN3t9vt8zFDdHS0JOKCHerj+8wX6Fd71Md+rU3ykARggLHyRV3RsYwXY0xMjF/eREZAN9pRVFTk+DezIZDaGShtlehXuwRKW+lX352vomMZSb/Y2Fi//L84cgowrwnr0af2oF/tQb/67nwVHSstLc38PSoqyufP0YgLRjt4TViPPrUH/WoP+rVq2AQEZXivAegPRw70AAD+ZXwe+ysusDs8ADiLvzeHMs5JXACAqiMBiDK8KwD9wRjo5eTkKD8/3y9tAAD8w99xwXt3eI/H45c2AAD+4e8EIBeGAKD6SACiDH9XengPMAnqAOB//o4LxkCvuLi41I7EAAD/MOJCRESEQkNDfX5+Iy4wYwgAqo4EIEopKioyB1f+ngIskQAEACdwytIQUul1pwAA/uHvynDvJYOKi4v90gYACDQkAFGKd2WFEwZ6JAABwP/8XQFIXAAAZzE+i41KPF8z4gKV4QBQdSQAUYq/1/OQSn+RYKAHAP7nlEoPiQpAAHACf1eGe48XiAsAUDUkAFGKdwIwOjraL21goAcAzlFUVOSogR4XhgDA/4zPYi4MAUDgIAGIUpxQAcgmIADgHE5bGoKBHgD4nzFm8NcUYC4MAUD1kQBEKUaVh+SMgR4BHQD8ywkXhhjoAYCzOKkynAtDAFA1JABRihMSgOHh4QoLC5PEQA8A/M07AeivqV5RUVFyu0u+sjDQAwD/M76jO6FggLgAAFVDAhClOKHSw/vcBHQA8C8nxAWXy0VcAACH8Hg8fq8AJAEIANVHAhClGAM9l8vlt01ApH+qTKgABAD/8q4M99daT97nJi4AgH9lZWWpqKhIkv8SgJGRkQoODpZEXACAqiIBiFKMBGB0dLQ53cofjC8TBHQA8C8nVABK/1wYotIDAPzLCXHB5XKZF4aICwBQNSQAUYq/y/kNVHoAgDM4YaAnERcAwCm844I/K8O5MAQA1UMCEKUYAd3fCUACOgA4g/fSEFFRUX5rB2sAAoAzOGFzKElUAAJANZEARCneU4D9iSnAAOAMRlyIiYlhaQgAQKnPYSesDUsCEACqhgQgSnFKBSADPQBwBqcsDUEFIAA4g/fmUP6MDYwXAKB6SACilMzMTEn+H+h5r/Xk8Xj82hYAqM+ccmGINQABwBmctjYsF4YAoGpIAKIUpwz0jPVE8vPzlZub69e2AEB95j0F2J+MgV5OTo7y8vL82hYAqM+MCzEul8uvsYHKcACoHhKAKMUpCUDv83tfZQQA+JZT4oL3IJO4AAD+471muD/XhvWuAGTGEAAcHQlAmDwej7mmh78rPUgAAoAzOGUNQO+F5okLAOA/TokLxvkLCwuVnZ3t17YAQCAgAQhTVlaWiouLJfk/oHsP9FjvCQD8xykVgN7nJy4AgP8Yn8H+3AH4yPNzYQgAjo4EIExOWdBXYqoXADiF09YAlIgLAOBPTowLXBgCgKMjAQiTkxKATAEGAP8rLi52zO7w3gNNBnoA4D9OqQwnLgBA9ZAAhMk70RYdHe3HlpAABAAnyMzMNBdW9/dAjwpAAHAG4zOYKcAAEFhIAMLk1ApArugBgH94f/4SFwAAkjOnAJMABICjIwEIk7Gjl+T/gV5ISIgiIyMlEdABwF+8P3/9PdDzjgskAAHAf5wyBZg1AAGgekgAwuSkBKB3G7zbBQDwHSdVhkv/DPaICwDgH0VFRebasP6eAhwVFSWXyyWJBCAAVAUJQJicVOnh3QYqAAHAP5x2YciICwz0AMA/nBQX3G632QbiAgAcHQlAmIxEW2RkpIKDg/3cmn+uKhLQAcA/nFoByIUhAPAPp8YFxgsAcHQkAGFyynoeBqMdDPQAwD+cNtCj0gMA/MtJm0NJXBgCgOogAQiTUdLvhGAukQAEAH8zPn9dLpeio6P93BoGegDgb067MEQFIABUHQlAmKgABAB4Mz5/o6Oj5Xb7/ysDFYAA4F9OWgPQuw3EBQA4Ov9/m4djeA/0nIAregDgX8ZAz987PRqoAAQA//L+Xu6E2EBcAICqIwEIk1OnAGdkZMjj8fi5NQBQ/zitMtx7oFdcXOzn1gBA/eOdaDN2ZvcnZgwBQNWRAITJqQnAoqIiZWVl+bk1AFD/GAMqJwzypH/igsfjUWZmpp9bAwD1jxEXgoKCFBkZ6efWMGMIAKqDBCBMTqv08G6H93ojAADfcFpc8J5uRrUHAPie8dkbFxcnl8vl59b8E59yc3OVm5vr59YAgLORAISkkmoKJw/0uKoHAL7n1MpwibgAAP7g5PECF4YAoHIkACFJysnJUWFhoSTnTfWSCOgA4A9OG+gRFwDAv5y2NAQJQACoOhKAkFQ6YDLQAwBIzhvoERcAwL+8pwA7AQlAAKg6EoCQVHqNPackAL0DOmsAAoBvFRcXmxttODEuMNADAN9zWmU4SwYBQNWRAISk0gMpJ1Z6ENABwLcyMzPl8XgkOWegR1wAAP9ycmU4cQEAKkcCEJKcOQU4Ojra/J1KDwDwLSfGhaioKAUFBUkiLgCAPxhJNqYAA0DgIQEISaWn2Drlil5QUJDZFgI6APiWEyvDXS6X4uPjJREXAMAfnLY7vHd8ogIQACpHAhCSnFnpIYmBHgD4ifdAyklxwaj2YKAHAL6Vl5en3NxcSc6JC8HBwRQMAEAVBfu7Ab6Wnp6uWbNmafXq1UpJSVFYWJg6dOigCy+8UL169ar28YqKirRu3Tpt2bJFW7Zs0datW5WUlCRJuuqqq3T11Vdb/RRs4cRNQKSSgd6uXbsI6ADgY06NC1wYAgD/cHLBQEZGBheGAOAo6lUCcOfOnRo3bpwZHCIiIpSVlaXffvtNv/32m4YMGaJRo0ZV65jJycl69NFH7WiuTxkBPTw8XKGhoX5uzT+MSg8GegDgW04d6BEXAMA/nBwXKBgAgKOrNwnAgoICPfnkk0pPT1ebNm10zz33qF27dsrLy9PcuXM1ffp0zZs3T+3atVP//v2rdeyIiAi1b99eHTt2VIcOHfTRRx9p3759Nj0TezhtRy8DAz0A8A+nDvSoAAQA/3B6ZTgVgABQuXqTAFy4cKGSkpIUFham8ePHq1GjRpKksLAwXXHFFUpNTdVXX32ladOmqU+fPgoOrlrXNGrUSDNnzpTL5TL/NmfOHFueg52MgZSTgrn0T0D3/sIBALCfERdcLlepXdn9jTUAAcA/vD93nbILsMSFIQCoqnqzCciSJUskSb179zaTf96GDRsml8ul1NRUrV27tsrHdbvdpZJ/gcppO3oZGOgBgH8YA6no6Gi53c75usBADwD8w4m7w0vEBQCoKud8o7dRTk6ONm/eLEnq3r17ufdp1KiRWrZsKUn6/ffffdY2p2AKMADAm1MvDDHQAwD/cOrSEBQMAEDV1IsE4O7du+XxeCRJbdq0qfB+xm27du3ySbucxBjoOTUBmJmZqeLiYj+3BgDqD6cuDWHEhby8POXm5vq5NQBQfzg1AcgagABQNfUiAZiammr+npiYWOH9jNsOHTpke5ucxqkDPSOgezweZWZm+rcxAFCPOLUy3IgLElWAAOBLxmdueHi4wsLC/NyafxhxITs7WwUFBf5tDAA4WL3YBMS7QqCyYGXclpOTY3ubKjJt2jTNmDGjwtuHDx+uq6++usbHN9ZxcrvdSkhIMP9uJNcaNWpU6u/+5HK5yiww7JS2eauoT53IWK8yLi7OrIp1KvrVHvSrPfzZr7U9X0Vtz87OliQ1aNDAMa+VQIkLUuC813if2YN+tQf9WjV2xYX8/HxJJf3vlNeKy+UqdWEoKCjIMW07UqC813if2YN+tQf9Wj31IgEYSLKysnTgwIEKb8/OzlZQUFCtz+NyuUodxyiZT0hIsOT4VvEe6GVmZjqqbUc6sk+dzEkL+h8N/WoP+tUe/uhXq853ZNuNSg+nxQXvgZ7T44IUOO813mf2oF/tQb9Wzq64YIwX4uPjHfVa8Y4LGRkZaty4sf8aUwWB8l7jfWYP+tUe9GvV1IsEYHh4uPl7Xl6eIiMjy71fXl6eJCkiIsIn7SpPVFRUpUErMjJSRUVFNT6+sWuxx+Mx19TLy8szn3tMTEytjm+lIys9UlNTHdM2b+X1qVO5XC653W4VFxcHxBUS+tV69Ks9rOrXmnwZqO3nYkVtNwZ6sbGxjvnsPTIupKSkOKZtRwqU91p9fJ/5Av1qj/rYr06KC2lpaZKcHxfatm3rvwZVIlDea/XxfeYL9Ks96mO/1iZ5WC8SgN7r/qWmplaYADTWCvRnmeuIESM0YsSICm9PTk6u1RqFRiVHcXGxeZyDBw+atwcHBztmDcSgoKBSV/T27NnjmLZ5K69PncqYFpGenu6YL24VoV/tQb/aw6p+bdiwYbUfU9v/jxW13RjohYaGOua1cmRc2Lt3r2PadqRAea/Vx/eZL9Cv9qiP/eqkuJCSkiKppGDBKa+VI+PC7t271b59e/81qBKB8l6rj+8zX6Bf7VEf+7UmccEQOHWStdCyZUtzbvjOnTsrvJ9xW6tWrXzSLqdw6o5eUukpwCz2DgC+UVxcbK4NS1wAAEilK8OdhM2hAKBq6kUCMCIiQp06dZIk/fLLL+XeJzk5Wbt27ZIkdevWzWdtc4KMjAzzd6cFdAZ6AOB7mZmZ5jQK4gIAQPpnzOC0uOCdADSSlACAsupFAlCS+vTpI0latmxZqSmvhtmzZ8vj8SgxMVEnnHCCj1vnX94DqJiYGD+2pKzo6GhzQU8GegDgG06uDA8ODlZ0dLQkBnoA4EtGbHBaXODCEABUTb1JAA4YMEBNmzZVbm6uJk6cqO3bt0sq2QBj1qxZ+vLLLyWVrMEXHFx6acSbb75ZQ4cO1SuvvFLusbOysnT48GHzx3tzDe+/GxttOI2TKwBdLpfZJgI6APiGky8MSf+0ibgAAL7h8XgcOwU4NDTU3MSRC0MAULF6sQmIJIWEhOiRRx7RuHHjtGPHDo0ZM0aRkZHKzc01E3aDBw9W//79q33sp556SuvWrSvz9zlz5mjOnDnmv6+66ipdffXVNX8SNnH6QC82NlZpaWkM9ADAR5xcASiVVHvs27ePuAAAPpKdnW0usO/UuJCTk0MCEAAqUW8SgJLUunVrvf766/rss8+0evVqJScnKyoqSu3bt9egQYPUq1cvfzfRL5w+0KMCEAB8K1DiAgM9APANJ88YkkralJSUxHgBACpRrxKAUskisSNHjtTIkSOr/Jh33nmn0tsnTZpU22b5lRHQQ0JCFB4e7ufWlEUCEAB8y+kJQGO9p7ocF9asWaN77rlHxx13nB544AG1a9fO300CUI95X3DxXnPPKepDXACA2qp3CUCU5b2gr8vl8nNryiKgA4BvOT0BWNcvDBUWFurOO+/Uli1b9Ndff2nu3Lm6/vrrNXbsWDVq1MjfzQNQDwXCkkFS3a4Mz8jI0KuvvqqIiAj16NFD3bt3d2SMBuBcJABhBnQnBnOp7g/0JGnDhg16/vnnFRYWpsaNG6tJkyZl/hsXF+fIBC2Ausf4vHW5XOaOu05S1y8MffTRR9qyZYv578LCQr377ruaOXOm7rjjDt16662O/P8CoO5y+oWhuh4XJOnBBx/UJ598Yv7b5XLpmGOOUY8ePcyfzp07KygoyI+tBOBkJABRqgLQiep6AjAzM1PXXXedduzYUen9jORgRQnCxo0bq2nTpmratCmJQgC1YnzeRkdHy+12+7k1ZdXlSo/s7Gw999xzkqT27dtrypQpeu655/TVV18pKytLzz77rKZMmaL77rtPI0aMUEhIiJ9bDKA+8P4ezhRg3/v5559LJf+kkp2ZN27cqI0bN2rGjBmSpMjISJ188snq0aOHTjnlFHXv3l1NmjTxR5MBOBAJQJhrAFIB6B+PPfaYmfxr27at0tLSlJaWVuZ+eXl52rVrl3bt2lXp8fr3768ZM2aQBARQY0ZccOqFIWOgl5mZqeLiYkcmKWvqnXfeUVJSkiTp4Ycf1nHHHaf3339fq1ev1uOPP67Vq1fr4MGDuv/++/XWW29p3LhxGjp0KJ/5AGwVKBWAdfHCUHFxsR566CFJJQm+RYsWKSkpST/99JN++eUX/fTTTzp48KCkkotIP/zwg3744Qfz8a1atSpVJXjCCSc4ct13APYjAYiAqQDMzs5WQUFBnap2+O677/T+++9LKp24y8vL04EDB3TgwAHt37+/0v8WFhaWOeZXX32lQYMG+eMpAagDnB4XjIGex+NRRkaGI6tRauLQoUN67bXXJEknnXSShg4dat526qmnav78+Vq4cKEmTpyoTZs2afv27br55pvVvXt3jR8/Xmeeeaa/mg6gjnP6GoBGHMjIyKhzF4Y+/vhj/frrr5Kku+++Wx07dlTHjh111llnSSqJhbt27dLPP/9s/vzxxx/Kz8+XJLOA4PPPP5dUsvHj8OHD9dxzzzFdGKhnSAAiYCoApZK2JiYm+rE11klNTdVdd90lSUpISNDLL79sVnCEhYWpVatWatWqVaXHKC4u1qFDh8yE4L///W8dOHBAkyZN0oABAxQczFscQPU5fW1Y73alp6fXmQTga6+9ZlavPProo2Wq+lwuly644AL1799fM2fO1LPPPqukpCT98ssvuvjii9WvXz89+uijOu644/zRfAB1mPHZFB0d7cikkTFeqGsXhjIyMjRx4kRJJTOFbr311jL3cblcat26tVq3bq1LLrlEUsnMofXr15tVgj///LM546igoEAffPCBunXrpuuuu85nzwWA/9WdSyOoMadXeni3qy5NA37ggQe0f/9+SdLzzz+vpk2bVvsYbrdbDRo0UJcuXdSnTx/de++9kqRNmzbp448/trS9AOoPp8cF74FdXYkLe/fu1TvvvCNJ6tOnj3r37l3hfYODgzVixAj9+OOPeuSRR8z/T4sWLdK5556rf//739q9e7dP2g2gfnB6XPBuV12aBvzyyy+b03sff/zxKk/dDQsLU/fu3fWvf/1LkydP1po1a7RhwwZNnz5d7dq1kyRNmjSp3GWHANRdJADh+LWe6mICcPbs2WYZ/rBhw3TRRRdZctwRI0aYQf25555TTk6OJccFUL8EUlyoKwO95557Trm5uZJKqv+qIjIyUmPGjNGaNWt0++23KzQ0VB6PR5988ol69eqlCRMmKDU11c5mA6gnnB4X6uKFoa1bt2ry5MmSpHPOOUcDBw6s1fEaNmyo888/X08++aQkKSUlRS+88EKt2wkgcJAArOcKCgqUnZ0tiYDuK/v27dP9998vSWrWrJmeeeYZy44dEhJiLhK8d+9evffee5YdG0D94fRKD++4YAxKA9nGjRv10UcfSZIuvfRSnXjiidV6fGJioh5//HGtWrVKV1xxhbmW7H/+8x/17NlTH374oR3NBlCPGBdbAiEu1JULQ+PHj1dBQYGCgoL05JNPWrbZ0/nnn69+/fpJkt59911t2rTJkuMCcD4SgPWc98DJqQG9LlUAejwe3XnnneYXk9dee03x8fGWnuOiiy7SCSecIEl65ZVXAr7PAPie09cArGsVgJMmTVJxcbGCg4P14IMP1vg4rVq10ptvvqnFixebg7vDhw/rnnvu0cqVK61qLoB6yOkXhurSeEEqWdLhm2++kSTdeOON6ty5s6XHnzhxooKDg1VYWKhx48bJ4/FYenwAzkQCsJ5z+o5eUt0K6FOnTtWSJUskSTfddJP69Olj+Tncbrc5fezQoUN6/fXXLT8HgLqruLhYmZmZkpw70KtLleGrV6/WV199JUm67rrrzGUcauO4447TzJkzNWfOHEVHR0uSxo4da04xBoDqCqQpwIF+YaigoMD8Lp+YmKgHHnjA8nN06tRJo0aNkiQtWbJECxcutPwcAJyHBGA95z1wcmpArysJwK1bt+qxxx6TJLVv317jx4+37Vx9+vTR2WefLUl6++23lZSUZNu5ANQtmZmZZiWAU3dRjIiIUEhIiKTAjgsej8fc3TEyMlJjx4619PhnnXWWOYjcvHmzXnnlFUuPD6D+MJJqTo0LdSkB+O6772rz5s2SpIceesjy2UKGe++9V40aNZJUsvZsXl6eLecB4BwkAOs57ynATq0ArAsDvcLCQo0ePVrZ2dlyu9168803FRUVZdv5XC6XHnnkEUlSdna2XnrpJdvOBaBuCYQLQy6Xy2xboMYFSfr222+1atUqSdLtt9+uxo0bW36OG264QT179pRUsuzEhg0bLD8HgLrP6UtDhIeHKzQ0VFJgrw178OBBPf/885JKqrmvvfZa284VGxurhx9+WJK0Y8cO/d///Z9t5wLgDCQA67lAWAOwLgz0Xn/9da1Zs0aSdNddd+mUU06x/Zzdu3fX4MGDJUkffvihtm3bZvs5AQS+QFgaQvonZgVqpUdRUZG5E2PDhg11++2323Iet9utl19+WSEhISooKNDdd9+toqIiW84FoG4KhKUhXC6XWQUYqHFBkp5++mkzDj/11FMKCgqy9XzDhw83N5566aWXmDUE1HEkAOu5QKj0kP4ZhAZiAvCPP/7Qc889J0k64YQTLJ/iVZmHH35YbrdbhYWFlu42DKDuCpS4EOgJwFmzZpnVeHfffbetydZjjz1Wd911lyTp559/1pQpU2w7F4C6JyMjw/FLQ0j/jBcCNS78/vvvmjZtmiRp6NChOvPMM20/Z1BQkCZNmiRJysrKMi9MAaibSADWc4FS6WF82Qi0BGBubq7uuOMOFRYWKiwsTP/5z3/M6Qm+0KlTJ1199dWSpDlz5uj333/32bkBBKZASQAacSEQp3rl5uaaF2Vat26t66+/3vZzjhkzRsccc4ykkqqS3bt3235OAHVDoMWFQBsvSCVrwj788MPyeDwKDw831w33hdNOO03Dhg2TJH388cf66aeffHZuAL5FArCeMwJkUFCQrWvS1VagTgF+9tlnzQqPhx9+WJ07d/Z5G+677z6Fh4dLkp544gmfnx9AYAmEpSGkwK70mDp1qpmAe+ihhxQWFmb7OcPCwsz1YLOysnT//febFT0AUBkKBuz3+eefa/Xq1ZKk0aNHq1WrVj49//jx4xUZGSlJevDBB1VcXOzT8wPwDRKA9Zwx0IuJiZHL5fJzayoWiAnAlStX6s0335QknX766brlllv80o7mzZvr5ptvliQtXrxY33//vV/aASAwUOlhr8OHD+vll1+WVLLA+6WXXuqzc5922mm68cYbJZVsQPL555/77NwAApf3hRYnTwEO1KUhsrKyzIq/Fi1a6I477vB5G5o3b64xY8ZIkn755RdzKjKAuoUEYD1nDJycPMiTAm8NwMzMTI0ePVoej0dRUVF64403bF/EtzJ33nmn+YXtoYceouoDQIWMgZPL5VJ0dLSfW1OxQLwwJElvvPGGUlNTJUmPPPKI3G7ffhV79NFH1axZM0nSuHHjdOjQIZ+eH0DgCZTK8ECNC6+//rr27t0rSZowYYJZiedrt912m1q3bi1JeuCBBwJyiQ0AlSMBWM8ZAdLJ5fxS4FV6PProo/r7778llay1ZARTf0lISNDo0aMlSatXr9b8+fP92h4AzmV8zkZHR/s8OVUd3nEhUC5qJCUl6f/+7/8kSWeeeab69evn8zbExMSYG1MdPHhQjz76qM/bACCwBFpleCBVAO7cudOcMdSrVy9dfPHFfmtLRESEHn/8cUkl8cpYNgJA3eHcb/bwiUCpAAykK3rffPONWTY/YMAAcxMOfxs1apSaNm0qSXryySdVWFjo5xYBcCLjin+gxIX8/Hzl5ub6uTVV89JLLyk7O1tSyYUify29ccEFF2jo0KGSpBkzZmjRokV+aQeAwOCdUHNybPAeLwTKhaHHHntMubm5crlceuqpp/y+JNOgQYPUu3dvSdJbb72lbdu2+bU9AKxFArCe814D0MmMgJ6Xl6e8vDw/t6ZiKSkpuuuuuyRJDRo00EsvveT3QG6IjIzU/fffL0navHmzPvroIz+3CIATBdqFISkwLg5t3bpVH3zwgSRp8ODB6tGjh1/bM2nSJLNa5pZbbjETkwBwpEDZNND4TCsqKlJWVpafW3N0y5cv17x58yRJ1/6/9u47vqmy/eP4Jx20tOwle6MsQVD2lCVLBGXIEhEKPgwFxYVMGeICQWQJylZwAILIlj1E2cpeZRQZRYRSoCO/P/LLsUiBliY9Ocn3/XrxemqTnFy9nyRXznWu+747dqRMmTImR+RY/mPkyJH4+flx69YtBg8ebHZIIuJCKgD6OKt0eiQsUHrqiZ7dbqdfv35cuHABgI8++ogcOXKYHNXt2rdvT7FixQBHfNHR0SZHJCKexmpLQ4Dn5oWE3n//feLi4vD39+fdd981OxweeughY9H5o0ePGtOCRUT+yyqbBlopL8TGxhq5IEOGDLzzzjsmR/SvkiVL8r///Q+AZcuW8csvv5gckYi4igqAPk4neq7z9ddfG2vrtW7dmqefftrkiO4UGBjI8OHDAYiIiGDatGkmRyQinsaKHYCevt7Trl27WLRoEQDt2rWjaNGiJkfk0L59e6pXrw7A559/zp49e0yOSEQ8kfMz1pN3AAZr5YWZM2fy559/AvDmm2+SLVs2kyO63XvvvUfmzJkBx4ZVMTExJkckIq6gAqCPs+KJnicWAE+dOmVsspEnTx5GjhxpckR317JlSx577DEAxo4d6/FfkEQkdVmlM9zT80JCw4YNAxwLrL/xxhsmR/Mvm83GmDFjCAoKIi4ujr59+2p9WBG5g1UaBqxSAIyMjGTUqFEAPPzww7z00ksmR3SnLFmy0L9/fwAOHTrEl19+aXJEIuIKKgD6sLi4OK5duwboRC8l4uPj6dy5s/FFY9y4cR59hdTPz8/Y9fHvv//ms88+MzkiEfEkVrww5MkneitXrmT9+vWAYzOmXLlymRzR7YoUKWKs8bRnzx5jl2IRESer5AUrzBgCGDp0KJcvXwYcG/MFBgaaHFHiOnXqRMmSJQH48MMPuXjxoskRiUhKqQDow5zFP/D8hO7JawBOmjTJ2EExLCzM2DnLkz355JNGnFOmTOHcuXMmRyQinsIqnR4JT/ScXYueJj4+3uigyJQpE6+88orJESWuX79+lCpVCoAPPviAEydOmBuQiHgUZ17w5AvcYI0C4L59+5g0aRLg2JH9ySefNDmiuwsICGDEiBGAYzzff/99kyMSkZRSAdCHJUyMnl4ATJjQPanTIyYmxpju+8gjjzBgwACTI0o6Z6zR0dF8/PHHJkcjIp4gPj7eMp3hCQuUnpQXEpo/fz47d+4EoE+fPh578hwYGMjYsWPx8/MjOjqafv36YbfbzQ5LRDyEVS4MeXpnuN1u59VXXyUuLo40adIwdOhQs0O6r+rVq9O0aVMAZs2axd69e02OSERSQgVAH5awAGilhO5JnR7Lly83uueGDh1KSEiIyRElXbly5YyNSmbPns3Ro0dNjkhEzHbt2jWj8OPpBUB/f3/SpUsHeGanx61bt4wLLblz56ZLly4mR3Rv5cuXp1u3bgCsW7eO+fPnmxyRiHgKq3QAhoaG4u/vD3hmAXDRokWsWbMGgO7du1O4cGGTI0qaoUOHEhwcjN1up3///rpAJGJhKgD6MCsVANOkSUNwcDDgWSd6M2fOBCBHjhw0a9bM5GiSr3///vj7+xMXF6e2fhGxVGc4/Hsy6oknelOnTjUurLz11ltGDvNkb731Fvny5QNg4MCBXLhwweSIRMQTWGUNQJvNZsToSecLADdu3DA2gcqVKxevvfaayRElXf78+enZsycAW7duZeHCheYGJCIPTAVAH5awk87TEzr8W6T0lIR+8uRJ1q5dC0Dnzp1JkyaNuQE9gKJFi9KuXTvAcVVy9+7dJkckImayWgHQGaMndYaDo5Ny+PDhAJQsWZI2bdqYHFHSpEuXjo8++giAy5cvGxtGiYjvunXrFtHR0YA18oLzwpCnnC84TZkyhePHjwMwcuRIo4PdKnr37k3u3LkBR0fg9evXTY5IRB6ECoA+zGoFQE/r9JgzZ47RAt+1a1eTo3lwb7zxhtGZ4jxhFRHfZKXOcPg3d3lKXnCaPHky58+fB2DEiBHGlDQrqFu3Lq1atQLg+++/Z+XKlSZHJCJmsuqFIU/KCzExMUyZMgWAJ554gvbt25scUfKFhoYaO8afOXOG8ePHmxyRiDwIFQB9mFUTuid0esTExDBnzhwA6tSpQ9GiRU2O6MHlypWLsLAwANauXcv69etNjkhEzJIwL3j6Wk/gmSd6169fZ/LkyQBUrVrVWDzdSt577z2yZMkCwJtvvmlsDCMivseq5wue1AG4dOlS/vrrL8Cx67qfnzVPwVu0aEGlSpUA+Oyzzzh16pTJEYlIclnz00dcwpkYbTabJdrQPSmhr1ixwujucBbPrOyVV14xTvaHDRumxX1FfJRVO8M94cKQ0zfffMPly5cBePfdd7HZbCZHlHzZsmUzOsJPnz6tNWJFfJhV84InXRj68ssvAciZMyctWrQwOZoHZ7PZGDFiBDabjRs3bjBkyBCzQxKRZFIB0Ic5C2np0qWzxJUoT1oD0Ln5R7Zs2XjmmWdMjiblMmXKxCuvvALArl27WLx4sckRiYgZrNbp4cwLnnKiFxcXx8SJEwHH2n8NGzY0OaIH17JlS5588kkAvvjiC37//XeTIxIRMyT8fFVnePLt37+fzZs3A44lg6y4ZnhCZcuWNaYw//jjjyxZssTkiEQkOTy/6iNu47yiZ4WTPPCcRX3Dw8P55ZdfAGjbtq3lE7lT165dyZkzJ+BYnPjGjRsmRyQiqc1qneGekhecli5dyokTJwDo27evJS6u3Y3NZuOjjz4iJCQEu91O3759lRdEfJDVLgx5Wmf4V199BYC/v79XzBoC6N+/P9myZQOgV69eHD582OSIRCSprPvNVFLMmdCtkMzBc6YAJ9z8w4qL+N5NSEgIb7zxBgBHjx5l2LBhJkckIqnNap3hzhO9a9euERcXZ2osdrudzz//HIAcOXIYO6xbWYECBXj77bcBRxdLnz59tESEiI+x2uZQCacAm/15dfXqVebPnw9A48aNyZMnj6nxuEr27NmZMmUKfn5+REVF0alTJ48puIrIvXn+t3txG2dCt0Iyh9sLgGYl9ISbf9SoUYMiRYqYEoe7tG/fnmrVqgEwZcoUVqxYYXJEIpKarHphCMzv9ti2bZsxTTYsLIygoCBT43GVbt26GVOBv//+e0aPHm1yRCKSmqzWAeg8r7l165bpXcvz5s0jKioKgC5dupgai6vVqFGDQYMGAXD48GF69+5tesFVRO5PBUAfZrUCoDPO2NhYoqOjTYlh5cqVxi5eHTt2NCUGd/L392fixIlkzpwZcGwOcu7cOZOjEpHUYuUCoNnrPTm7/0JCQnjxxRdNjcWV/P39mTp1Kg8//DAAo0aNYtGiRSZHJSKpxZkXgoKCCA4ONjma+0u4TqGZecFutxvTf4sXL07VqlVNi8VdevToQbNmzQD46aefGDdunMkRicj9qADow65duwZY50QvYUI3axpwws0/mjRpYkoM7pYrVy4jgV+6dIkePXqYPrVORFKH1S4MJcxfZi4PceTIEZYtWwZAhw4dyJQpk2mxuEOGDBmYPXs2WbJkAaB3797s2rXL3KBEJFVY7cJQwvMFMzvDN27cyKFDhwDo3LmzJXeEvx+bzcbYsWN55JFHAMca4s510kXEM6kA6MOsltDNPtE7deoUa9asAeD555/3ms0/EtOwYUNjqsKGDRsYP368yRGJSGqwWl7whAtDgLHzr7+/P927dzctDncqVKgQX331FYGBgURHR9OxY0ciIiLMDktE3MzZRWeVvOApneHTpk0DHGvqtm7d2rQ43C1dunTMmDGD9OnTEx8fT/fu3QkPDzc7LBG5CxUAfZjVTvTMLgAm3PyjQ4cOqf78qW3IkCGULFkSgPfff5/ffvvN5IhExN2stju8J5zonT9/nnnz5gHQrFkz8ufPb0ocqaFq1ap8/PHHAJw7d44OHToY61uJiHdSXki+M2fOGF3hrVu3Jl26dKbEkVqKFCnChAkTALh8+TKdO3c2bbkmEbk3FQB9lN1uNxK6VaZ6JYwztQuAsbGxxuYf1atX97rNPxITHBzMlClTSJs2LXFxcbz88sum78AsIu6lC0PJ9+WXX3Lz5k0AevbsaUoMqaldu3b06tULgD179tCrVy/i4+NNjkpE3MVqecETOsNnzJhhLJ/z0ksvmRJDamvYsCGvvfYa4MgNb7zxhjYFEfFAKgD6qGvXrhlf2K2Y0FP7it7KlSuNzTBeeOGFVH1uMz3yyCMMHz4cgJMnT9KvXz8lcxEvZuU1AM3o9IiKiuLLL78EHBeHypYtm+oxmGHAgAE0bNgQgCVLljBq1CiTIxIRd7HaFGCzNwG5efMms2fPBhx5wbk+ni948803qVOnDuDYAXn69OnmBiQid1AB0EclTIhWSegJ40ztRX2dm39kzZqVxo0bp+pzm61jx440bdoUgAULFvDNN9+YHJGIuEN8fLzlNocKDg421mM1Y7H3b775hsuXLwOO3RB9hXPH+FKlSgEwZswYvv32W5OjEhF3sFoHYPr06Y0NN8zoAFyyZAkXLlwAfKf7z8nf359JkyZRoEABAN59911+/fVXk6MSkYRUAPRRViwAmjUF+PTp06xevRpwbP4RFBSUas/tCWw2G2PGjCFPnjwAvP322xw5csTkqETE1a5evWp0+FolL9hsNqPbI7U7PeLi4pg0aRLg6JauV69eqj6/2dKlS8fs2bPJnj07AH369GH79u0mRyUirma1NQD9/PyMcwYzOgCdm3/kypWLRo0apfrzmy1z5sxMnz6dtGnTEhMTw0svvcRff/1ldlgi8v8CzA5Aksff398lx0mYEDNlyuSy47qSM6aE/xsaGkpUVBTXrl1LtZgTbv7RqVOnez6vJ45jQv8d06TKmjUrX3zxBU2bNuX69et0796d5cuXp1ox1FvH1WyeHq/GNXWfz4p5ARwnpRcuXODq1aupGvOSJUs4ceIEAL169SIg4O5fqTxxLJ1S8j4rUKAAs2fPplmzZty8eZNOnTqxatUq8uXL5+ow7+DJYwr6/HIXjWvqPp/dbjdyg9Xywj///JPqeWHv3r3GhZAXX3zxnt+TPXEsnVL6Pitbtixjxozh5Zdf5q+//qJr164sWrSIwMBAV4Z5B08eU9Dnl7toXJNHBUCLyZw5s0uO45zmBZAnTx6XHdcdEl5xzJQpE1FRUdy4cSNVYo6NjWXu3LkA1K5dmwoVKtz1vv7+/h49jgk9yFXcRo0aMXjwYAYPHsyePXv48MMPGT16tBuiu523j6tZNK7uYca4uiMv5M6d26NfHwlfE1myZOHo0aNER0enWsx2u52JEycCji6PsLCwu57oWeW99qDvswYNGvDll1/Svn17Lly4QIcOHdi0aZNb15G0ypiCPr/cReN6b656vlu3bhEbGwtAzpw5Pfr18d+8cPr06VTNCwCzZs0CIDAwkFdeeeWuz22V91pK3mfdu3fnzz//ZNy4cWzdupURI0YwduxYF0Z3O6uMKejzy100rkmjAqDFONcaelAZMmTA39+fv//+26XHdQd/f3/jCp5zJ6106dIBcOHChVSJ+eeff+bs2bMAtG/fPtHndI5pXFycx++Sm9iYJkePHj1Yvnw5mzdvZsyYMVSuXJn69eu7IVLfGtfUpHF1D1eN64N8GXBVXoiMjDR+5+fnZ5m8EBISAsDFixdTLeYtW7YY6xqFhYVx/fp1rl+/ftt9rPJec8X7rFGjRvTr14+PP/6YvXv30qpVK2bPnu3yq9tWGVPwzc+v1OCL4+opeSEgIMAyecF5vpCaeeHvv/9mzpw5ADz99NMEBQXd8dxWea+56n3mXANw69atjBs3jpIlS9K6dWsXRmqdMQXf/PxKDb44rikpHqoAaDGuelEnnOoVGhrq0W+WuLg4Iz5nZf/KlSupErNz96osWbLQqFGj+z6nJ49jQgnHNLkmTJhA7dq1+fvvv+nZsye//PILOXPmdHGEt/OFcTWDVWLVuKbO8ykvJN1nn30GOMbphRde8IrckNL32RtvvMGhQ4f48ccfWb58OYMHD2bo0KEujPB2VhhT0OeXu2hcU+f5EjYMpE+f3qPH3Oy8MGfOHKKjowHo3Lmz8gKOi4lffPEF9erV46+//qJv3748/PDDPProoy6M8l9WGFPQ55e7aFyTRpuA+KiEJ3runKbjas5YU+NKhK9v/nE3efLk4dNPPwUcV1Z79epFfHy8uUGJSIol/Fy10jSKhCd6qeHw4cMsW7YMgA4dOhibkPg6Pz8/PvvsMx577DHAcbFo9uzZ5gYlIilixU0DIfXzQnx8PF9++SUApUqVolKlSqnyvFaQM2dOvvzySwIDA4mOjqZz584e2Ukq4itUAPRRzoQYEhJyz4XLPY3zRCs1CoBz5swxClsdO3Z0+/NZSZMmTejcuTMA69at4/PPPzc5IhFJKaue6DnzgnOnSndzrv3n7+9P9+7dU+U5rSIkJIRZs2aRK1cuwNEVuGnTJpOjEpEHZdWGgdQuAK5du5bjx48D8NJLL2Gz2VLlea2iYsWKDB8+HICTJ0/y8ssvW6pTS8SbqADoo5wFNCud5MG/8bq7ABgbG2us41G1alWKFi3q1uezoqFDh1K8eHEARo4cyc6dO02OSERSIuGJknP9JCtIeKLn3LHdXc6fP8/8+fMBeOaZZ1Jlt1uryZkzJ7NmzSJt2rTExsbSuXNnjh07ZnZYIvIAEuYFK3U7p2bDAGB0/2XIkIHnnnsuVZ7Tajp37kybNm0AWLNmDR999JHJEYn4JhUAfZQzoasAmLjVq1cTEREBQKdOndz6XFaVNm1apkyZQnBwMLGxsXTr1i3VOnBExPWceSFdunQu37zBnZx5ITY21lh/yV2mTZvGzZs3AcemSJK4smXLMmHCBMCxGUGHDh1SrRNHRFzH6p3h0dHR3Lp1y63PFR4ezooVKwBo27YtoaGhbn0+q7LZbHz00UfG+n+ffPKJsZyGiKQeFQB9lFULgM7pB1evXnXrunMzZ84EHJt/NG7c2G3PY3UlSpRg2LBhAJw4cYK33nrL5IhE5EE5L6xYqcsDbo/XnReHoqKi+OqrrwCoUaMGZcuWddtzeYOmTZvSv39/wLFuYteuXYmNjTU5KhFJDqtPAQb3Nw1Mnz7d6D53Lo8jiUubNi3Tp083djDt0aMHR48eNTkqEd+iAqCPStjpYSXOE734+HiioqLc8hxnzpxh1apVALRp04bg4GC3PI+36NSpE02aNAHg22+/NabHiYi1WPXCUGqd6H399dfGwuXq/kuaPn360KpVK8CxRlb//v3dPk1bRFzHuQtwaGiopdYMT5gX3Nl9fOPGDWPJoNq1a1OkSBG3PZe3yJ8/P5MnT8Zms3H16lU6derE6dOnzQ5LxGeoAOijnFM1daJ3J23+kTw2m40xY8aQO3duAN58801dzROxIOeJnpW6PCB1TvTi4uKYNGkS4Oh8rlu3rluex9vYbDZGjx5NhQoVAPjqq6947rnnOHLkiMmRiUhSWHXN8NTqDF+0aBGRkZEAdOnSxW3P422efPJJo0P84MGDVK5cmaFDhxrfQ0TEfVQA9FHq9Ehcws0/qlSpQrFixVz+HN4oc+bMTJw4ET8/P6Kioujevbvb11wREdfyhrzgrgLgkiVLOHnyJAD/+9//tMNjMgQHBzNjxgwKFy4MwIYNG6hVqxajRo1y+5qNIpIyVs0LCQuA7uwAdG7+kS9fPurXr++25/FGr776Kl26dMFms3Hz5k3Gjx9PhQoV+Pzzz7lx44bZ4Yl4LRUAfZRVE7q7C4Br1qzh7NmzgDb/SK6qVavy+uuvA7B7925GjBhhckQikhze0Onhjo2I7HY7n3/+OeDY4VY7PCZf9uzZWb16Nb169SIgIIBbt27xySefULNmTVavXm12eCJyF86OLKvlhdSYMbRr1y527NgBOM4ZrLR5liew2WyMGjWKNWvW8OSTTwKO19uQIUOoUqUK8+bNIy4uzuQoRbyPCoA+yG63e0UB0B1X9Jybf2TOnNlY106S7rXXXqNixYoATJgwgZ49exprZomIZ1NeSNyWLVvYuXMnAGFhYaRJk8blz+EL0qVLx+DBg1m9erWRJ06cOMHzzz9Ply5diIiIMDlCEfkvq+aF1OgAnDZtGgBp0qShffv2bnkOX1C6dGnmz5/P999/T5kyZQA4ffo0vXr1om7duqxevVprx4q4kAqAPig6OtrYic/Kaz25utPjzJkzrFy5EtDmHw8qICCAyZMnkz17dgDmz59PtWrV+PHHH02OTETux3mSZLW8kD59emNKrjs6PSZMmAA4FsFXZ3jKlSxZksWLF/Ppp58aO0H++OOPVK1alcmTJ2unYBEPYtXO8IR5zB15ITIykoULFwLQvHlzsmXL5vLn8DU1a9Zk5cqVTJo0ifz58wPwxx9/8Pzzz9OyZUt2795tcoQi3kEFQB+U8EqY1RK6O1v6586dq80/XCBv3rysXbuWZs2aAXDhwgW6dOnCiy++yLlz50yOTkQSEx8fb9nNofz8/IyTPVd3ehw6dIjly5cDjryQsKtEHpyfnx/t27dny5YttGvXDoBr164xYMAA6tevz++//25yhCIC/04BttpnX2BgICEhIYB7OgDnzp1rrFP30ksvufz4vsrPz4/nnnuOzZs3M2zYMLJkyQLA+vXrqVevHt27dzfW4xWRB6MCoA+ycgEwXbp0bun0iIuLY/bs2QBUrlyZhx9+2GXH9kU5cuRg2rRpTJ8+nRw5cgDw008/Ub16db7++mu18ot4mKtXrxrvS6vlBfg3Zld3hk+cOBEAf39/unXr5tJjC2TNmpWxY8eyePFiihcvDsC+ffto1KgRb7zxhnaEFDGZVTvD4d+ipasbBuLi4pg+fToAZcuWpXz58i49vkBQUBAvv/wy27dv59VXXzVmZf3www9UqVKFd999l0uXLpkcpYg1qQDog6xcAHRXp8fq1au1+YcbNGnShE2bNhkdHleuXOGVV16hdevWhIeHmxydiDhZOS/Avyd6rswLf/31F/PnzwccU7zy5cvnsmPL7SpXrsyaNWsYPHgwISEh2O12pk+fTtWqVfn222910UjEBPHx8ZadAgzuKwCuXr3a6EJ76aWXtCu8G2XIkIEBAwbw66+/0r59e/z8/IiJiWHKlClUqFCBMWPGcP36dbPDFLEUFQB9UMITJCte0XNHp8esWbMAyJQpE02bNnXZccUxpmPHjuW7774z1vRYu3YtNWrU4IsvvjCmXYuIeaxeAHTmMlee6E2bNo1bt24B0KNHD5cdVxIXGBhIr1692LhxI40aNQIcS0j06NGDZ599lsOHD5scoYhvSdgZbrUpwIDblob48ssvAceGgS1atHDpsSVxuXLl4tNPP2XdunU0aNAAcLw+R44cSfHixfniiy9UCBRJIhUAfVDCEyQrFwBddaJ39uxZVqxYAWjzD3eqVasW69ato1u3bthsNq5fv07//v1p2rSpTuxETGb1C0Ou7gCMioriq6++AhwLkzt3JhT3y5cvHzNnzmTWrFnkzZsXgI0bN1KrVi0GDhxIdHS0yRGK+AarXxhyR2f48ePHWbNmDQDt2rUjbdq0Lju23F/x4sWZM2cOixYtMqZenz17lm7dupEzZ05eeuklFixYwLVr10yOVMRzqQDog6ye0J0xuyqha/OP1JMuXTpGjBjBTz/9ZKyzuH37dmrXrs2YMWOIiYkxOUIR3+QtecFVF4bmzp1rrD+n7j9zNGzYkI0bN9K7d28CAgKIiYnh/fffp0SJEgwePJi9e/dqarCIG3nLhSFXdoZPnz4du92OzWbjxRdfdNlxJXmqVq3KsmXLmDZtGsWKFQPg+vXrLF68mG7dulG8eHE6duzI/Pnz3bIJjIiVqQDog3Si96+Em39UqlSJRx55JMXHlPurUKECa9as4bXXXiMgIIBbt24xcuRIGjRowJ49e8wOT8TnKC/8KzY2lsmTJwNQokQJ6tSpk+JjyoMJDQ1l0KBBrFmzhkqVKgFw8uRJRowYQZ06dXjiiScYOHAgW7ZsIS4uzuRoRbxLwk14rDgF2NUXhq5fv87cuXMBqFu3LgULFnTJceXB2Gw2mjVrxt69e1mxYgVhYWFky5YNgJs3b7Js2TJ69uxJiRIleP7555kzZw6RkZEmRy1iPhUAfZDzRC84OJg0adKYHE3yuXINwDVr1nDmzBlAm3+ktqCgIN555x1WrlxJ2bJlAcfujw0aNGD48OHcuHHD5AhFfIe3FABdcaV/yZIlxgLvPXr00ALvHqBEiRL8+OOPfPXVV1SvXt34/yQ8PJxJkybRrFkzHn30Ufr27cvKlSu5efOmyRGLWJ/V84KrpwAvWLDAKIq+9NJLLjmmpFxAQAD169dn4sSJ7Nu3j4ULF9KlSxceeughAGJiYli9ejV9+vShZMmStGzZkunTp3P+/HmTIxcxhwqAPsiZCK3Yzg+uXex9xowZgDb/MFPp0qVZtmwZgwYNIjg4mLi4OMaOHcvjjz/OunXrNMVLJBUkPEFKly6diZE8GOeJ3vXr14mNjX3g49y4cYNPPvkEgJw5c/Lss8+6JD5JOT8/Pzp27MiGDRs4ffo0o0ePpk6dOgQGBgKODUNmz55Nu3btKF68OGFhYSxcuFBrQYk8IKsXAJ0xX7t2LUV5AcButzNt2jQAChYsSN26dVMcn7iev78/1apVY9SoUezZs4effvqJl19+2VhPNi4ujnXr1vHGG29QunRpmjVrxhdffEFERITJkYukHhUAfZAzoVsxmYPrruj99NNPLF++HIDWrVtrIV8TBQQE0Lt3b9auXUvlypUBOHjwILVr1yZnzpx06NCBsWPHsmXLFi0AL+IGzs/TdOnS4e/vb3I0yZcwn6Xk4tCgQYM4cOAAAD179rRkl7wveOihh+jYsSPz5s3jwIEDTJ48mWbNmhESEgI4TvgXLlxIWFgYxYsXp127dsyePZuLFy+aHLmIdVh9CnDCmFM6a+i3335j7969ALz44ov4+ekU2tP5+flRsWJFhg0bxo4dO1ixYgW9e/c2pm7b7Xa2bNlC//79KVOmDI0bN+bbb79NcbFYxNPp08sHWb0A6Iw7Kirqgdf8iYiIoG/fvgBkz56dV1991WXxyYMrUqQIixYt4sMPPzS6kC5dusTy5csZPnw4zZo1o0iRIjRs2JBBgwaxZMkStfCLuIC35AV48AKgc4opOHYtDwsLc0ls4l4ZMmTg2WefZdq0aRw8eJA5c+bQrl07smTJAjjWglq5ciV9+/alVKlSNGvWjMmTJ3P58mWTIxfxbM684OfnR2hoqMnRJJ+rLgzZ7XbGjx8POJZPateuXYpjk9Rls9koV64cgwYN4tdff+WXX37htddeMzYQAcemhD169KBq1arMmTNHGxOK11IB0Ad504neg1zRi4+Pp1evXsaX/7Fjx5IjRw6XxScp4+fnR+fOnTl06BDTp0+na9euFC9e3Lg9JiaG33//nYkTJ9K5c2dKlSpFhQoV6NmzJzNmzODPP/80dnUWkaSxel5I2OnxIN3hJ06coE+fPoDjotDnn39uyU5IXxccHEyDBg0YO3Ysf/zxh9EFmCdPHsCR/7ds2cKAAQN47LHHGDhwoLEOsIjcLuGSQVZcCzWlecFp2rRpLF26FIBWrVqROXPmFMcm5rHZbJQuXZp33nmHzZs3s3HjRt5++21jmvDx48fp06cPlStXZsaMGVpTVryOCoA+yHkVzIrrPEHKr+hNmDCB9evXA9C1a1fq16/vstjEdXLkyEGnTp2YNGkSGzZs4PDhw8ydO5c+ffpQrVq126Zsnzhxgvnz59OvXz9q1apFsWLFaNOmDZ988gmbNm1SO7/IfVh9bdiUXBi6desWYWFhXL16FZvNxsSJE43Fw8W6AgICqFatGiNHjmTnzp2sWrWKvn378sgjjwCO9SInTZrEE088Qa9evTh48KDJEYt4FucUYCtO/wXXdABu3bqVgQMHAo61/wYNGuSS2MRzPPLII7z++uts27aNMWPGGFOEw8PD6devHxUrVuSLL77QEkTiNVQA9EFW7/RIGHdyr+jt3r2bkSNHAo5dBQcPHuzS2MR9MmXKRP369Xn33XdZuHAhR48eZcWKFQwbNoxmzZqRM2dO477//PMPa9asYdSoUTRv3pwyZcowcOBA9uzZo01FRBJh9byQkk6PYcOGsWvXLgD69u1LrVq1XBmaeACbzUbZsmXp378/GzduZPHixTRo0ACA2NhY5s2bR/Xq1enQoQO//vqrydGKeAarXxhKaQfguXPn6NKlC7GxsaRNm5bp06eTKVMmF0YoniRNmjR06NCBLVu28Pnnn1O0aFEAzp49S//+/XniiSeYOHEiUVFRJkcqkjIqAPogq5/oPegVvaioKLp3705MTAxBQUFMmjSJ4OBgd4QoqSAwMJBy5crx8ssvM23aNPbs2cPvv//OpEmTjKnBzkWaL1y4wKRJk6hbty41a9Zk3LhxnD171uS/QMRzWL3TI+EJanJO9JYtW8akSZMAqFKlCm+88YbLYxPPU7lyZebMmcP69etp3bo1AQEBACxfvpwmTZrQtGlTVqxYoeUkxKd50/lCcguAt27dokuXLsY606NHj6ZUqVIujU88U0BAAK1bt2bjxo1MmTLFWIbo/PnzDBo0iMcff5yxY8dqh3mxLBUAfZA3JfTkTPUaOHAgR48eBRw7PZYsWdLlsYl5bDYb+fPn57nnnuPDDz9k7dq1HDlyhClTplC/fn1jPa8DBw4wbNgwHnvsMZ599lm+/vprJXHxed6UF5J6Yej06dP07t0bgCxZsjB58mSjECS+oUSJEnz++eds376dbt26GbsIb9u2jfbt21OrVi3mz5+vxeDFJ1n9wlBKpgAPHjzY6AYOCwujZcuWLo1NPJ+/vz8tWrRg3bp1fPXVV5QuXRpwbE44fPhwypUrx8cff5yi9SVFzKACoI+5efOmsZipL53o/fTTT8yaNQuAunXrandHH5E+fXpatGjB3Llz2bt3L8OHD6ds2bKAY1e3DRs28Morr1CyZEm6d+/OqlWrtF6g+CSrT/UKDg42OrqTkhdiYmLo1q2bcYI7fvx4cuXK5c4QxYPlzZuXESNGsHPnTt566y1jB+EDBw7Qs2dPKlasyOTJkzX1S3yK1S8MJTcvOH377bdMnToVgEqVKjF06FC3xCfW4OfnR9OmTVmzZg2zZ8+mXLlygKNA/sEHH1CuXDnef/99IiMjTY5UJGlUAPQxCa9SWPVEL7kt/REREfTt2xdw7O44btw4S+5mJimTPXt2o8i3adMmXn31VWNnyOjoaH744Qfatm1LmTJlGDBgALt379Z6geIT4uPjjW5qq57owb85LSkneqNGjWL79u0A9OrVS5tBCeDoBO3Xrx87duxg5MiR5MuXD3B0iw4YMIBy5crx4YcfcunSJZMjFXE/q18Ygn9jT2qX1r59+3j99dcBx2Z0U6dOJTAw0G3xiXXYbDaeeuopli9fzrx586hYsSLgmI02evRoypcvz3vvvcdff/1lcqQi96YCoI9JmACteqIXEhJiTOe834lefHw8vXr14vLlywCMHTuWHDlyuD1G8WwPP/wwAwYMYMeOHSxcuJB27doZXxIvXLjA5MmTqVevHjVq1NB6geL1rl69ahS7rZoX4N9pavc70Vu9ejXjxo0D4IknnqB///5uj02sJTQ0lLCwMLZt28aECROMJUMuX77MRx99RLly5XjnnXc4efKkyZGKuI/VpwBD0vMCOP7ezp07Ex0dTUBAAF9++eVtG8yJgKMQWKdOHZYsWcKCBQuoVq0a4Fhr/rPPPqN8+fL07NmT3bt3mxypSOJUAPQx3lAAtNlsRuz3WwNwwoQJrF+/HoCuXbuqy0Nu4+fnR7Vq1Rg7dix//PHHHesFHjx40FgvsG3btqxatUqLwovX8Ya8ACQpL5w7d46ePXsCjhPDKVOmqLtD7iowMJBWrVqxdu1a5s6dS5UqVQBH1/jUqVOpUKECHTt2ZP369eoYF68SExPD9evXAWvnBWcBMCkNA//73/84ceIE4NgdvlKlSu4OTyzMZrNRvXp1Fi5cyOLFi6lduzbg2EBm/vz51KtXj6ZNm7J48WItLyQeRQVAH+MNU4Dh3y8j90rou3fvZuTIkYBjoe/BgwenSmxiTWnTpr1tvcARI0bw2GOPAY71AletWkXbtm2pVKkSEydONK6Mi1idtxUA79bpERcXR/fu3Y3pm+PGjTOmeIrci81mo379+vz4448sXbqURo0aAY7csGzZMp577jlq1KjB9OnTtU6geIWE36+9IS/crwD48ccfs2rVKgBatWpFly5d3B6beI/KlSvz7bffsnr1atq0aUOaNGkAx4ZSL730EhUqVGD8+PE6dxCPoAKgj0nYGeENCf1uJ3pRUVF0796dmJgYgoKCmDRpkrEQsMj9ZM+enW7durFy5Uo2b95Mjx49yJQpEwAnTpxg0KBBlClThr59+7Jv3z5zgxVJIV+5MPTJJ5+wefNmwLGrY+PGjVMtNvEeFSpUYObMmWzdupVu3bqRLl06wNEx/sYbb1C2bFkGDx6s6cFiaQkLFVY+X0jKFOAVK1bw0UcfAVCqVCk+/vhjrRUuD6RMmTKMHz+enTt38uabb5I9e3bAsY7s0KFDKV26ND169ODQoUMmRyq+TAVAH+MrJ3oDBw7k6NGjAAwaNMhYv0ckuYoVK8bQoUPZvXs3Y8aMoXTp0oBjCtjs2bN58sknady4Md988w23bt0yOVqR5POWDsB7TfXasGEDH3/8MYBRoBFJiSJFijBixAj27t3L+++/T9GiRQHH+2nChAmUL1+e5s2bs27dOk0PFsvxlrxwv/OFY8eO8b///Q9w5JDp06cTEhKSavGJd8qRIwdvvPEGO3fu5PPPP6dMmTIAXL9+nYkTJ1K5cmVat27N6tWrtbSQpDoVAH2MtyX0xNZ6+umnn5g1axYAdevWJSwsLFVjE+8UEhJChw4dWLNmDT/99BPPPvussXbY1q1badu2LWXLluXDDz/k3LlzJkcrknTeNtXrv50e58+f5+WXX8Zut5MuXTq++OILgoKCzAhRvFC6dOno2rUrmzZtYt68edSrVw9wTA9etGgRLVq0oGbNmsyYMUPTg8UyvOV84V4dgFFRUXTu3Jl//vkHm83GpEmTKFiwYCpHKN4sKCiI1q1bs2rVKhYvXkyzZs3w83OUX3755Reef/55qlWrxpdffsm1a9dMjlZ8hQqAPsaZAAMDAy09JdbZvfjfK3oRERH07dsXcEzjHDdunNr4xaVsNhsVK1Zk8uTJ7Ny5k7fffptcuXIB8Ndffxk7RHbt2pUtW7ao80M8nrec6CXs9HC+7+Lj4+nZsyfnz58HYMyYMRQqVMi0GMV7+fn5UadOHb7++mu2bt1K9+7dje8qBw4coF+/fpoeLJaRMC9YeRfghA0DCTut7HY7r7/+On/++ScAb775plG8F3E1m81G5cqVmT59OseOHaN3797G++rIkSO89dZbPPbYYwwZMoRTp06ZHK14OxUAfYwzoWfMmNHShbHErujFx8fTq1cvLl++DMDYsWPJkSOHKfGJb3jooYd4/fXX2bVrF/Pnz6datWoAxMbGsmjRIpo1a0bt2rWZOXOmOj/EYyX8HHWuZ2ZFzrwQFxdnvN/GjRvH2rVrAejUqRPNmzc3KTrxJUWKFOH999/nzJkzfPjhh3dMD65QoQIvvPCCdg8Wj+UtSwY580J8fPxt38OmTJnC999/D0CDBg147bXXTIlPfE+BAgWMpYU++ugjihUrBjjec59//jlPPPEEbdu2Ze7cucY5rYgrqQDoY5wdc1a+mgeJTwGeMGEC69evB6Br167Ur1/flNjE9wQGBtKqVSsWL17MunXrePHFF401ZP78809ef/11ypQpQ//+/Tl48KDJ0Yrcznmilz59evz9/U2O5sEl7F68evUqW7duZdSoUQCULFmSYcOGmRWa+Kj06dPfNj3Y+b3Ebrfz888/89xzz/HEE08wYMAANm/eTFxcnMkRizh4W2c4/Ps3bdmyhSFDhgBQsGBBJkyYYEzLFEktoaGhvPjii2zcuJF58+ZRt25dwFGsXrVqFa+++iolS5akVatWzJgxgwsXLpgcsXgLfdr5mIQdgFbmTOjR0dHExMSwe/duRo4cCUCJEiUYNGiQmeGJDytZsiQfffQRe/bsYfjw4RQuXBhwFN+/+OILqlevTtOmTZk/fz7R0dEmRyviPReGEsZ//PhxunXrRlxcHCEhIUydOpW0adOaGJ34Muf04Llz5xq7Bzu7qsLDw5k8eTLPPPMMpUuXpk+fPqxcuZKbN2+aHLX4MucuwGnSpLH0kkH/LQCeO3eOLl26EBsbS0hICDNmzLB87hNrc+aHb775hi1bttC9e3dy584NOGYUrV27ln79+lG6dGmaN2/O1KlTtda4pIgKgD7GWQC08tU8uH06wrlz5+jevTsxMTEEBQUxadIkneiJ6TJmzEj37t3ZsmUL8+fPp1GjRkZ31bZt2+jZsydlypRhwIABHD582ORoxZd5y4WhhHnh1VdfJSIiAuC2KTYiZnPuHrxnzx5Gjx5N3bp1jQ2lLl68yJw5c2jXrh3FixcnLCyMBQsWJLrhmYg7eduSQeB4f7300ktGJ9WYMWMoWbKkWaGJ3KFo0aIMHz6cnTt3smzZMnr16kWBAgUAR2fgpk2beOedd3j00Udp3LgxkyZN0pqBkmwqAPoYbykAJkzoffv25ejRowAMGjRIyVw8ip+fH08++SQzZ85k586dvPXWW+TJkwdwXGGfPHkyVatWpVmzZnz//ffcuHHD5IjF13hjXjhx4gQAzz//PK1btzYpIpG7S5cuHR07duSbb77h4MGDTJkyhWeeeYbQ0FAArl27xsKFC+nWrRvFixenbdu2zJo1S9PAJFV4Y2f4wIED2b59OwDdunXj2WefNSsskXvy8/Pj8ccfZ/DgwWzfvp1Vq1bRp08fihQpYtxn+/btDBw4kPLly9OgQQPGjRvHsWPHTIxarEIFQB/jvIps9YSe8ER13bp1ANStW5ewsDCzQhK5r1y5ctGvXz9+//135syZw1NPPWWsO7NlyxZefvllypYty6BBgzhy5IjJ0Yqv8JYOwP8WMIsVK2asASjiydKnT0+LFi2YOnUqBw4cMLoAs2bNCsCtW7dYtWoVr732GqVLl+bpp59m0qRJhIeHmxy5eCvnFOBMmTKZGkdKJcxr+/fvB6By5crGGoAins5ms1G2bFneffddtmzZwvr163njjTcoUaKEcZ+dO3cybNgwKlWqRO3atfnkk0/4448/btv5WsQpwOwAJHV564le9uzZGTdunKWnKYjv8Pf3p0GDBjRo0IAzZ84wZ84cZs+eTUREBJGRkUycOJGJEydSrVo1OnXqROPGjQkKCjI7bPFS3tjpERwczNSpU41uKhGrCA4ONvJDbGws27ZtY+nSpSxdupTTp08THx/P1q1b2bp1KwMHDqR06dI89dRT1KhRgyeeeEK5QlzCWzrD/7uDcc6cOZk6daox7V7ESmw2GyVKlKBEiRK8+eabHDlyhMWLF7N48WL27t0LwB9//MEff/zBqFGjyJo1K1WrVqVatWpUr16dhx9+WOfKog5AX+MtCf2/8Y8dO5YcOXKYFI3Ig8uTJw9vvvkmO3bsYNasWdSvX99Izps2baJbt26ULVuWoUOHGlPdRVzJWy4MpUuXjlKlSuHn58cHH3yg5SDE8gICAqhWrRojRoxgx44dRhdg8eLFjfvs27ePTz75hObNm1OsWDFatWrFuHHj2LVrl3YVlgfmLReGQkJCCAhw9LsEBgYybdo0HnroIZOjEnGNokWL0rdvX9asWcP27dsZPHgwjz/+uHH7pUuXWLx4MW+//TbVq1enVKlShIWFMWPGDI4ePYrdbjcxejGLOgB9SExMDNevXwesn9Bz5MhBUFAQN2/epEuXLtSvX9/skERSJCAggIYNG9KwYUNOnz7N7NmzmTNnDufOnePSpUuMHz+e8ePHU7p0acqXL0+5cuUoX748jzzyiLG5iMiDcE71svqFIZvNxtKlS7l48SL58+c3OxwRl3JOAytbtizvvPMOR48eZenSpfz888/s2LGDuLg4oqOjWbt2LWvXrgUc3/WqVatGjRo1qFmzJsWKFVP3hySJt0wBttls1K9fn5UrV/LBBx9QsWJFs0MScYuCBQvSq1cvevXqxdmzZ9mwYQMbN25k06ZNxkYhFy5cYOHChSxcuBBwLE1UrVo1o0OwQIECyhE+wOcKgFeuXOG7777j119/5dKlSwQFBVGkSBEaN25M5cqVH/i4sbGxLFmyhHXr1nH27FnA0dlTq1YtmjRpYlx9MlPCXeSsXgDMkCEDU6dO5dixY3Tp0sXscERcKm/evLz99tv069ePFStWMHPmTNasWYPdbmffvn3s27ePmTNnAo6r22XKlLmtKJgvXz4lcEmS+Ph4r1kbFhzvBxX/xBcUKVKE3r1707t3b/755x9jbagNGzYYa51duXLFmD4M8NBDD1GjRg2jIJg3b14z/wTxYN4yYwhgxowZXL58mSxZspgdikiqyJ07N23atKFNmzYAnDx50igGbty4kYiICAAiIiL47rvv+O677wDH+YezGNikSRMKFSpk2t8g7mN+VSoVhYeH8+677xpJLW3atERFRbFr1y527drF008//UCbSERHRzNw4EAOHToEQJo0aQA4cuQIR44cYdOmTbz33nsEBwe77o95AM52fvCOE72GDRuaHYKIWwUEBNC4cWMaN25MeHg43377Lb/99hs7d+7k0qVLAFy/ft1YD8opa9asRjGwXLlylCtXzlhMXiSha9euGVNArN7pIeKrMmTIwFNPPcVTTz0FwPnz59m0aZNREDx58iQAf/31120ne4UKFaJGjRo0atSIatWqkTNnTtP+BvEcdrvda5aGAEcXoIp/4ssKFChAgQIFaN++PXa7nWPHjrF582Y2btzIxo0bOX/+PACnT59m3rx5zJs3j969e5MnTx6KFStG/vz5KVy4MEWKFKFw4cIUKFBA681amM8UAGNiYhg+fDhXrlyhQIECvPbaaxQqVIibN2+yaNEi5syZw+LFiylUqBD16tVL1rEnTJjAoUOHCA0N5ZVXXjE6Cbdu3cq4ceM4cOAAEydOpG/fvu7405IsYQHQG67oifiS/Pnz8/rrrwOOL+fh4eHs2LGDnTt3snPnTvbs2WNM8b906RKrVq1i1apVxuMLFChAuXLlqFatGk888QQlS5Y0diAW3+VtF4ZExLFMSosWLWjRogXg6P7YsGGD8e/ChQsAHD9+nOPHjxsd5WnSpCFfvnzkz5+fggULkj9/fvLnz2+cPOoigW+4ceMGt27dAnRhSMTb2Gw2ihQpQpEiRejYsSN2u50jR44YxcDNmzdz8eJFAM6cOcOZM2fuOIafnx958+alcOHCFCpUiMKFCxsFwvz582uTHQ/nMwXA5cuXc+7cOYKCghg0aBDZs2cHICgoiNatWxMZGcnSpUuZPXs2tWvXTvKU3ePHj7N+/XoAevfuTZUqVYzbqlSpQnx8PB988AFr167l2WefpUCBAq7/45LIm6YAi/gym81mnJA5T/BiY2M5ePDgbUXB/fv3G4vAnzx5kpMnTxrrfoDjJLFEiRIUL16c4sWLU6JECR555BHSpUtnxp8lJtCFIRHv58wXHTp0wG63c/DgQaM7cNOmTcb3w1u3bnH06NG7bjiVMWNGoyjoLBA6j503b17TZ7qIa+jCkIjvsNlsFCtWjGLFitG5c2fsdjsHDhxgx44d/PHHHxw8eJBDhw5x7tw54zHx8fGEh4cTHh5urDnr5O/vT758+YxuwYIFC5InTx5y585Nrly5yJ49u9YuN5nPFACdL86aNWsaxb+EnnvuOX7++WciIyPZu3cv5cqVS9Jx161bh91uJ1euXLcV/5yqVq1Krly5iIiIYN26dbzwwgsp+jtSImEBUCd6It4lICCAUqVKUapUKTp27Ag4pgfv3buXnTt3GoXBEydOGI85f/4858+fZ926dbcdK3/+/LcVBYsXL06xYsXU7u+FdKIn4ltsNpvx+d6tWzdiY2M5fvw4Bw4c4OjRoxw8eJCTJ08SHh7OX3/9ddtjr1y5wt69e9m7d2+ix86VK9dtRcGEP+fMmVNd5xahhgER32Wz2ShRogRVq1bF39+fuLg4Ll++TFRUFMePH+fYsWN3/HN2lQPExcVx4sQJTpw4werVq+84vr+/Pzlz5jQKgrlz5yZ37tzG75w/q4vQfXyiABgdHc3hw4cBKF++fKL3yZ49O3nz5uXUqVPs3r07yQXAPXv2AFCuXLlEF9232WyUK1eOiIgI475m0YmeiG8JCQmhUqVKVKpUyfhdfHw8f/75J3v27GHHjh3s37+f/fv33/aF33lVb8WKFcbv/P39KVy4MMWLF6dkyZIUKlSILFmy3PYvJCREm49YjPKCiG8LCAigYsWKVKlSxTjRc4qOjubUqVNGB3l4ePhtPyfMG+BYUD4iIoJt27bd8TzO6cX/LQ46uwn1+eM5nOv/gfKCiDiEhoZSunRpSpcufcdtV69eTbQweOzYMSIjI2+7b1xc3F2nFjvZbDayZ8+eaHHQWTjMlSsXadOmdfnf6Qt8ogB4+vRpY5Hze03BLVCgAKdOnTK2yr4fu93O6dOn73tc546EST2uu/z3RC8mJsbEaETEDFmzZqV27drUqFHDONGz2+2cPXuW/fv3c+DAAeN/Dx06xI0bNwBHwj58+DCHDx9m8eLFiR47ODiYzJkz31EYvNu/jBkzEhAQQGBgIIGBgSoemkCdHiJyN2nTpuXhhx/m4YcfvuM2u93O5cuX7ygKOn8+ffr0bd8zkzO9OEuWLEYu+e//ZsuWTbNY3EwXhkQkOdKnT0/ZsmUpW7bsHbddvXrVuDh09uxZzp49e9vP586dMzY2dLLb7cYspV27dt31eTNnzmwUBZ2NCf8tFqZPn97Vf67l+UQBMGHl+V67QDlvS3j1816io6ONk+OkHDc6Opro6GjTqtXOhO7v709oaCh///23KXGIiGex2WzkyZOHPHny3LYJkrON/8CBA0ZhcP/+/Rw9etRYWzChGzduGEn+Qfj7+xMYGEhAQIBRGEyTJg3+/v7GfycsGDp/dj4u4e8Su/+97pfS4mOpUqVo1KhRio5hhv+e6DkvlomI3ItzZ9UsWbLw2GOP3XF7XFwcERERtxUGw8PDOXHixANNL/7vc2fMmDHRImHmzJnJlCmTR6wxVbRoUZo3b252GMmmAqCIuEr69OlJnz59oheSnKKjozl37txtxcGERcMzZ85w4cKFO76jXr58mcuXL/PHH3/c9dihoaFGF2FQUNAd5xmJnUskdj7i/DmlS1nky5ePNm3apOgYKeUTBUBnkQ645xpWztuio6OTdNyE90vKcZ2PuVcBcPbs2cydO/eut7dt25Z27dolKb7/qlmzJtevXyc2NhZ/f38yZ878QMdJLc4Tck8/KXV+EPj5+WlMXUjj6h7JHdds2bLxxBNP3Pa7mzdvGlfsLl68yKVLlxL9OTIykosXL3Lx4sXbPofvJi4uLtHCohW0bNmSRo0amfJ6TcnzVaxYkT59+nDlyhUyZszo8V2Y3vxeM4vG1D00ro788eijjyZ6W3R0NCdOnDB2Ij5+/DgnTpzg9OnTRh5JWIhKyG638/fff/P3339z/Phxl8Xrak8++STNmze3XF4oU6YMr776Kv/88w85cuTw+A4avddcT2PqHhrXxDk7+e62VBtATEwMERERnD59mrNnz3L69GlOnz5tTCd2/ouNjb3tcVFRUcYMJk9QtmxZ2rRpY+rr1ScKgFYSFRXF+fPn73r79evXH/iqZsOGDWnYsKHx355wdTQprLJotM1m05i6gcbVPVIyriEhIRQuXJjChQsn+THXr183CoTOouCVK1eIjY0lJibmnv9cdZ//3s8dzHi9puT5atWqRa1atVwYTerwlfdaatKYuofGNXHp0qW763pSTrdu3SIyMtIoCN7tX8KLT5GRkR53IclqeaFChQpUqFDBhdGkDr3XXE9j6h4a1+Tz9/enUKFCFCpU6K73iY+P5/z587cVB50/R0REcPPmzdvOAxL7ObHfuYOZ4+oTBcDg4GDj55s3bxISEpLo/W7evAmQ5Cm6Ce/nfOy9jpuUY4eGhpIjR4673h4SEpKiLzZ+fn7YbDbsdjvx8fEPfJzUYLPZ8PPzIz4+3qOvkmhM3UPj6h5mjWtQUJAxzTip3DmudruduLg4lxUD06RJYxw3JeP6IF8GUnqyq/eae1hlXDWm7qFxTTl/f3+yZ89O9uzZjd/db1zj4+OJioryiDF37mKpvOBeeq+5nsbUPTSu7uEcV8DIGUnd0PV+nOcLzmJgSv9/CwgIMI6b2nnBiOGBH2khCdfni4yMvGsB0LlWYFLbMdOmTUvatGmJjo6+Y4ebxI7rvP+9dOjQgQ4dOtz19osXLyZ5jcLEZM6cGX9/f+Lj41N0nNTgnKZ85coVj7uam5DG1D00ru6hcXUP54WmlI5rtmzZkv2YlP7/qNeEe1hlXDWm7qFxdQ8rjatzsxLlBfey0mvCKuOqMXUPjat7WGlczcwLTtbpP02BvHnzGnPuw8PD73o/52358uVL0nFtNht58+Z1+XFFRERERERERERcxScKgGnTpqVYsWIA7NixI9H7XLx4kVOnTgEkuoX13ZQpUwaAnTt33vU+zu2rnfcVERERERERERFJLT5RAASoXbs2AOvXr+fChQt33P7DDz9gt9vJkiXLXXcsS0zNmjWx2WycPXuWLVu23HH75s2bOXv2LDabzYhBREREREREREQktfhMAfCpp54iZ86c3Lhxg2HDhnH8+HHAsUHHd999x08//QQ41uBzLs7o1LVrV5o1a8ann356x3ELFSpEzZo1Afjss8/YunUrdrsdu93O1q1bGT9+POAoQObPn9+Nf6GIiIiIiIiIiMidfGITEHDsxDVgwADeffddTpw4wauvvkpISAg3btwwdmBp2rQp9erVS/axe/ToQUREBIcOHWLkyJHGbpC3bt0CoHjx4vzvf/9z3R8jIiIiIiIiIiKSRD5TAATInz8/n332Gd9//z2//vorFy9eJDQ0lMKFC9OkSRMqV678QMdNmzYto0aNYsmSJaxbt46zZ88CUKRIEWrXrk2TJk3u6CoUERERERERERFJDT5XlcqUKRNdunShS5cuSX7M1KlT73ufgIAAmjdvTvPmzVMQnYiIiIiIiIiIiGv5zBqAIiIiIiIiIiIivkgFQBERERERERERES+mAqCIiIiIiIiIiIgXUwFQRERERERERETEi6kAKCIiIiIiIiIi4sVUABQREREREREREfFiKgCKiIiIiIiIiIh4MZvdbrebHYSkntmzZxMVFUVoaCgdOnQwOxyvoDF1D42re2hc3cPK42rl2D2ZxtX1NKbuoXF1DyuPq5Vj92QaV9fTmLqHxtU9PGFcVQD0MY0bN+b8+fPkyJGDpUuXmh2OV9CYuofG1T00ru5h5XG1cuyeTOPqehpT99C4uoeVx9XKsXsyjavraUzdQ+PqHp4wrpoCLCIiIiIiIiIi4sVUABQREREREREREfFiKgCKiIiIiIiIiIh4MRUARUREREREREREvJgKgCIiIiIiIiIiIl5MBUAREREREREREREvFmB2AJK62rVrR1RUFKGhoWaH4jU0pu6hcXUPjat7WHlcrRy7J9O4up7G1D00ru5h5XG1cuyeTOPqehpT99C4uocnjKvNbrfbTXt2ERERERERERERcStNARYREREREREREfFiKgCKiIiIiIiIiIh4MRUARUREREREREREvJgKgCIiIiIiIiIiIl5MuwD7iCtXrvDdd9/x66+/cunSJYKCgihSpAiNGzemcuXKZodnOatXr2bs2LH3vd/s2bPJkCFDKkTk+a5du8a+ffs4cuQIR48e5ciRI1y5cgWAESNG8Oijj973GFu2bOHnn3/m6NGj3Lx5k2zZslGhQgVatWrls+OcknHt2rUr58+fv+fxGzduzMsvv+zSmK3gwoULbNmyhT179nDixAkiIyMJCAgge/bsPPbYYzz99NPkzJnznsfw9Ner8oJrKS8kn/KCeygvuIfygvJCcikvJJ/ygnsoL7iHFfOCCoA+IDw8nHfffdd4k6dNm5aoqCh27drFrl27ePrppwkLCzM5Smvy8/O75xvTZrOlYjSebdu2bUn6EnQ3kyZNYunSpYBj3IOCgjh79iyLFi1i3bp1jBgxgnz58rkqXMtI6bgChISEkCZNmrve5msuXLhA165dsdvtxu9CQkK4desWp06d4tSpUyxfvpw+ffpQvXr1RI/h6a9X5QX3UV5IOuUF91BecD3lBeWFlFBeSDrlBfdQXnA9q+YFFQC9XExMDMOHD+fKlSsUKFCA1157jUKFCnHz5k0WLVrEnDlzWLx4MYUKFaJevXpmh2s52bJlY+rUqWaHYRmZM2emSJEiFC1alNy5czN69OgkPW758uUsXboUm81G+/bteeaZZwgKCuL48eOMHj2akydPMnz4cMaPH09gYKCb/wrP86Dj6hQWFkbdunXdFJ31xMfHA1C+fHnq1KnDY489RoYMGYiLi2P//v1MmTKFEydOMHr0aPLmzUvBggVve7ynv16VF9xLeSF5lBfcQ3nBtZQXlBdSQnkheZQX3EN5wbWsmhdUAPRyy5cv59y5cwQFBTFo0CCyZ88OQFBQEK1btyYyMpKlS5cye/ZsateuTUCAXhLiHrVr174taVy7di1Jj4uJiWHu3LmAo728devWxm2FChVi4MCB9OzZk4iICFauXEnjxo1dG7iHe9BxlbtLly4dY8aMoXDhwrf93t/fn9KlSzN06FBeeeUVrly5wqJFi3j11VeN+1jh9aq8IJ5CecE9lBdcT3lBeUFSh/KCeygvuJ5V84I2AfFya9euBaBmzZpGMk/oueeew2azERkZyd69e1M5OvEl/v7+D/S4PXv2cPnyZWw2G88+++wdt+fIkYOaNWsC/77efcmDjqvcXWho6B3JPKHMmTPz+OOPA3D06NHbbrPC61V5QTyF8oJ7KC+4nvKC8oKkDuUF91BecD2r5gUVAL1YdHQ0hw8fBhytqYnJnj07efPmBWD37t2pFptIUu3ZsweAfPnyJfqlFKBcuXIAHDx4kBs3bqRabOK7nGv5xMXF3fZ7T3+9Ki+IN/D095n4JuUFEfN4+vtMfJMn5gX1b3ux06dPG4tSFihQ4K73K1CggLFQpSTPlStX6NOnD2fOnAEga9aslC5dmqZNm94xz18ejPN1eb/XMIDdbuf06dMULVo0VWLzFgsWLGDWrFn8888/hISEULBgQapWrUq9evXuutivr9u3bx9w5+vS01+vygvup7zgfp7+PvMGygvJp7wgd6O84H6e/j7zBsoLyeeJeUEdgF4sMjLS+DlLlix3vZ/ztsuXL7s9Jm9z8+ZNjh8/TmBgIHFxcZw9e5YVK1bQp08fFixYYHZ4XsH5Ok7Kaxj0On4Q4eHhXLt2jaCgIP755x/27NnDpEmTeP3117lw4YLZ4XmcrVu3cuTIEYA7FkP29Ner8oL7KS+4n6e/z7yB8kLyKC/IvSgvuJ+nv8+8gfJC8nhqXlAHoBdL2CoaFBR01/s5b4uOjnZ7TN4iS5YstG3blqpVq5I7d24CAwOJjY3lzz//ZObMmRw6dIivvvqKLFmyUKtWLbPDtTTn6zgpr2GA69evuz0mb1GpUiVKlSpF6dKljRb1yMhIVq5cybx58zh58iRDhw5lzJgxPrlbWmIuXLjA559/DjjGz7m2h5Onv16VF9xHeSH1ePr7zMqUF5JPeUHuRnkh9Xj6+8zKlBeSz5PzgjoARR5AuXLlaNu2LQUKFDA+6AICAihTpgzvv/8+jzzyCAAzZswwtggX8TRhYWFUrVrVSObg+LLapk0b3nrrLcBxtW/16tVmhehRrl27xrBhw7hy5Qo5c+bklVdeMTsk8SDKC+INlBeSR3lB7kV5QbyB8kLyeHpeUAHQiwUHBxs/37x58673c96WNm1at8fkCwIDA+nQoQMAFy9e5NixYyZHZG3O13FSXsMAISEhbo/JF1SqVImSJUsCsH37dpOjMV90dDRDhw7lxIkTZMmShffee4/06dPfcT9Pf70qL5hDecG1PP195q2UF26nvCApobzgWp7+PvNWygu3s0JeUAHQiyWcN55wfY//ct6WOXNmt8fkK5xX9ADOnTtnYiTW53wdJ+U1DHodu5Lzdezrr+GbN2/y3nvvcfDgQTJmzMiwYcPImTNnovf19Ner8oJ5lBdcx9PfZ95MecFBeUFcQXnBdTz9febNlBccrJIXVAD0Ynnz5sVmswGOtty7cd6WL1++VIlLJDmcr8ukvIZtNht58+ZNlbjEN9y8eZNhw4bxxx9/kC5dOt577717flZ6+utVeUG8gae/z8S7KS8oL4jn8fT3mXg3K+UFFQC9WNq0aSlWrBgAO3bsSPQ+Fy9eNLahLlu2bKrF5u0OHjxo/PzQQw+ZGIn1lSlTBnB8CF68eDHR++zcuRNwXIFKOJVFUsb5OvbV13BMTAwjR45kz549hISEMGTIEAoVKnTPx3j661V5wTzKC67j6e8zb6a8oLygvOA6yguu4+nvM2+mvGCtvKACoJerXbs2AOvXr090e+4ffvgBu91OlixZePTRR1M5Omuy2+33vD02NpY5c+YAkDVrVooUKZIaYXmtMmXKkDlzZux2OwsWLLjj9gsXLrB+/Xrg39e73N/9Xsfbt2/nzz//BKBixYqpEZJHiY2NZdSoUezcuZPg4GAGDRrEww8/fN/HWeH1qrzgesoLqcsK7zMrUl64N+UF5YXkUF5IXVZ4n1mR8sK9WTEvqADo5Z566ily5szJjRs3GDZsGMePHwccbarfffcdP/30EwAdOnQgICDAzFAt4/z58/Tr14/ly5fz119/Gb+Pi4tj37599O/fnwMHDgDQqVMn/Pz0NnP6559/jH/Xrl0zfh8VFXXbbbGxscZtgYGBtGvXDoAlS5bw3XffGYuiHj9+nGHDhnHjxg1y5cpF/fr1U/cP8hAPMq5TpkxhypQp7Nu377ZFZi9fvsy3337LBx98AED+/PmpW7du6v0xHiAuLo6PP/6Y7du3kyZNGgYMGGAscHw/Vni9Ki+4nvLCg1NecA/lBddSXlBeSC7lhQenvOAeyguuZdW8YLPfr6wrlhceHs67777LlStXAMcuMjdu3DC2m2/atCndunUzM0RL+euvvwgLCzP+O02aNAQHB3P9+nXjAzMgIIBOnTrxzDPPmBWmR2rWrFmS7jdixIg7rjBPmjSJpUuXAuDv709QUBDXr18HIFOmTIwYMcJn16V5kHH99NNPWbNmDeBYW8K5u1RUVJRx/8KFC/Puu++SPXt2F0fs2ZxfzMGRoENDQ+95/5kzZ97xO09/vSovuJbywoNTXnAP5QXXUl5QXkgu5YUHp7zgHsoLrmXVvKBLOD4gf/78fPbZZ3z//ff8+uuvXLx4kdDQUAoXLkyTJk2oXLmy2SFaSqZMmejWrRv79+/n+PHjXLlyhaioKIKCgsiXLx+PPvoojRo1Ik+ePGaH6lVefvllypYty9KlSzl27JhxVaRixYq0bNmSjBkzmh2ipTRs2JCMGTNy8OBBzp8/z9WrV4mPjydLliwUKVKEatWqUbNmTZ+80p/wulhMTAx///13so/h6a9X5QXXUl4wh6e/z6xGeeHulBeUF5JLecEcnv4+sxrlhbuzal5QB6CIiIiIiIiIiIgX02IDIiIiIiIiIiIiXkwFQBERERERERERES+mAqCIiIiIiIiIiIgXUwFQRERERERERETEi6kAKCIiIiIiIiIi4sVUABQREREREREREfFiKgCKiIiIiIiIiIh4MRUARUREREREREREvJgKgCIiIiIiIiIiIl5MBUAREREREREREREvpgKgiIiIiIiIiIiIF1MBUERERERERERExIupACgiIiIiIiIiIuLFVAAUERERERERERHxYioAioiIiIiIiIiIeDEVAEVERERERERERLyYCoAiIiIiIiIiIiJeTAVAERERES+wdu1abDYbNpuNIUOGmB2Ox3OOVe3atc0ORURERMTtVAAUERERERERERHxYioAioiIiIiIiIiIeLEAswMQERERkZSrXbs2drvd7DBERERExAOpA1BERERERERERMSLqQAoIiIiIiIiIiLixVQAFBEREfEg8fHxzJ07l+bNm1OgQAHSpk1LcHAwefLkoWzZsrRq1YoJEyZw6dKl2x53v12Anbcl59+9LF68mBdeeIGiRYuSPn16QkJCKFSoEB06dGDVqlWuHJJ72rRpEz169ODRRx8lS5YsBAYGkiVLFipVqkTfvn3ZuHHjAx/79OnTTJgwgeeff56SJUuSPn16AgMDyZYtG5UqVeKdd97h1KlTSTrW7t276dWrF2XLliVjxozGcYoXL07dunXp378/O3bsuOvjN2zYwEsvvUSJEiWMOHLkyEHJkiVp2LAhw4YN49ChQw/8t4qIiIh3s9m1WIyIiIiIR7h06RJNmzZl69at973vRx99RL9+/Yz/Xrt2LU8++SQAgwcPvqMIeL+CXmIS+5p46tQp2rRpw5YtW+752Oeee46ZM2cSEhKS7OdNisjISDp16sSSJUvue99du3ZRtmzZ237nHI9atWqxdu3aOx6zdu1a6tSpc991FdOkScOECRPo0qXLXe8zbNgwhgwZQnx8/D2PVapUKfbt23fb7+Lj4+nRoweTJ0++52MBmjRpkqTxEBEREd+jTUBEREREPERYWJhR/MuXLx/PP/88xYoVI3PmzERFRXH48GG2bNnChg0bkn3sBQsW3Pc+v/zyC+PGjQMgY8aMd9x+6tQpKlWqREREBADlypWjefPmFC1aFD8/Pw4ePMjMmTM5duwY33//PVFRUSxduvSBio/3EhkZSZUqVYyOt5CQEFq3bk2VKlXInDkzV69eZd++fSxbtoz9+/c/0OYoN27cwG6388gjj/Dkk09SsmRJsmXLRkBAAOfOnWP9+vUsXLiQW7duERYWxkMPPUTTpk3vOM6PP/7IoEGDAAgODqZZs2ZUr16d7NmzEx8fT0REBDt37mTlypWJxjF+/Hij+Jc+fXpatmzJ448/Tvbs2bl16xanT5/mt99+S9WuSxEREbEeFQBFREREPMD58+dZtGgRAFWrVmX16tUEBwcnet8LFy5w8eLFZB2/efPm97z9wIEDdO7cGYCAgAC+/fbb22632+20adOGiIgI/P39mThxImFhYXcc5+233+bFF1/km2++YdmyZUybNo2uXbsmK9b7efHFF43iX+XKlfnhhx/IlSvXHfcbPXo0mzdvJmfOnMl+jhIlSiTaOejUu3dvdu3axVNPPcX58+d57bXXaNKkyR3FzilTpgCOMd20aRPly5dP9HhxcXGJdn46H585c2Z27txJgQIFEn38jRs32L17d5L/PhEREfEtWgNQRERExAMcO3bMmCLavn37uxb/ALJnz06JEiVc9twXLlygSZMm/P333wBMmDCB+vXr33afxYsXG9N+hwwZkmjxDyAoKIgZM2ZQsGBBAD755BOXxQmwbds2Fi9eDEDevHlZunRposU/p6pVqz5QAbBAgQJ3Lf45PfbYY4wcORKAw4cPs3nz5jvuc+TIEcDRLXm34h+Av78/1apVu+vj69Wrd9fiHzi6CytVqnTPeEVERMR3qQAoIiIi4gFCQ0ONn3///fdUe96bN2/SvHlzjh07BkC/fv0SLe7NmDEDcBT4XnnllXs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}, "metadata": { "image/png": { @@ -366,145 +329,250 @@ } ], "source": [ - "n_traps = 500\n", - "\n", + "n_traps = 100\n", "gce.reset()\n", - "size_dist = pd.DataFrame(\n", - " {\n", - " 'pop_density': gce.state,\n", - " 'size_class': list(range(21)),\n", - " 't': 21*[0],\n", - " }\n", - ")\n", + "size_dist = pd.DataFrame()\n", "\n", - "for t in range(1,4):\n", - " gce.step([n_traps])\n", + "for t in range(6):\n", + " gce.step(n_traps)\n", " new_size_dist = pd.DataFrame(\n", " {\n", - " 'pop_density': gce.state,\n", + " 'size_distribution': gce.state / sum(gce.state),\n", " 'size_class': list(range(21)),\n", - " 't': 21*[t],\n", + " 'label': 21*[f\"t={t}, tot. pop.={int(sum(gce.state))}\"],\n", " }\n", " )\n", " size_dist = pd.concat(\n", " [size_dist, new_size_dist],\n", " ignore_index=True,\n", " )\n", - " new_total_pop = pd.DataFrame({'pop': [sum(gce.state)], 't': [t]})\n", - "\n", - "ggplot(size_dist, aes(x='size_class',y='pop_density')) + geom_line() + facet_grid(cols='t') + labs(title=f\"N. traps = {n_traps}, final pop = {sum(gce.state):.0f}\")" + "(\n", + " ggplot(size_dist, aes(x='size_class',y='size_distribution')) \n", + " + geom_line() \n", + " + facet_wrap(facets='label',) \n", + " # + labs(title=f\"N. traps = 0, final pop = {sum(gce.state):.0f}\")\n", + " + theme(aspect_ratio=1)\n", + ")\n", + "\n" ] }, { "cell_type": "markdown", - "id": "21fc988c-e111-4257-9207-e713991e0e64", + "id": "cebe9f0a-6129-4e22-9107-d673ebc8a1d7", "metadata": {}, "source": [ - "## The reward function\n", - "\n", - "At each time-step, our model returns a `reward` which depends on two things: the crab population distribution (this term of the reward quantifies the ecological damage due to the crab population), and the cost of laying traps. It has the following functional form:\n", + "Here we notice two things:\n", + "First, as expected, the population growth is limited by the traps, and seems to saturate around the 4 000 mark.\n", + "Second. the *relative* size distribution converges to a a different shape in this case too.\n", "\n", - "$ R = - D(P) - \\alpha A$\n", + "This second point reveals a property of our model: the traps are size selective!\n", + "In fact, the traps included in this initial model of `greenCrabEnv`, are good at catching middle-sized crabs, but bad at catching large or small crabs.\n", + "In the near future we will include more models in this package that allow for the agent to choose between different trap types." + ] + }, + { + "cell_type": "markdown", + "id": "752efc49-8689-41e1-87cc-b2a53a9210f5", + "metadata": {}, + "source": [ + "## 4. Tuning constant action agents" + ] + }, + { + "cell_type": "markdown", + "id": "2da4c600-31e4-47b4-ab3a-6f0aee43ccde", + "metadata": {}, + "source": [ + "The simplest type of strategy for this management problem would be a non-adaptive one: a fixed number of traps are laid out at the beginning of the episode and they catch crabs for the entire episode.\n", + "We now tune this strategy by finding the number of traps that obtains the best reward value." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "da894c9f-ddb5-4285-8476-99703c4ad37c", + "metadata": {}, + "outputs": [], + "source": [ + "from rl4greencrab import simulator, constAction\n", "\n", - "where $D(P)$ is the ecological damage arising from a population $P$ of crabs, $A$ is the action taken by the agent and $\\alpha$ is a constant of the model (in the source code $\\alpha$ is the attribute `gce.action_reward_scale`)" + "def evaluateConstAct(x):\n", + " env = greenCrabEnv()\n", + " agent = constAction(x[0])\n", + " rewards = simulator(env, agent).simulate()\n", + " out = np.mean(rewards)\n", + " return - out" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "bb76e51c-02d4-4b60-87b4-f6b7636f50e3", + "execution_count": 58, + "id": "35fbe6ac-0f84-49bb-b129-3cb4496f7576", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "avg episode rewards for some constant action choices:\n" + ] + }, { "data": { - "text/html": [ - "
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" - ], "text/plain": [ - " n_traps rew_mean rew_std\n", - "0 0 -10.760099 0.083178\n", - "1 10 -0.014162 0.000023\n", - "2 50 -0.017543 0.000022\n", - "3 100 -0.022435 0.000023\n", - "4 200 -0.032384 0.000018" + "(-10.749245410269683, -0.014148731887191058, -0.022442076055499673)" ] }, - "execution_count": 10, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from rl4greencrab import simulator, constAction\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "stats = []\n", - "for n_traps in [0, 10, 50, 100, 200]:\n", - " rewards = simulator(greenCrabEnv(), constAction(n_traps)).simulate()\n", - " stats.append([n_traps, np.mean(rewards), np.std(rewards)])\n", + "print(\"avg episode rewards for some constant action choices:\")\n", + "- evaluateConstAct([0]), - evaluateConstAct([10]), - evaluateConstAct([100])" + ] + }, + { + "cell_type": "markdown", + "id": "dd6628e1-4844-432d-a76a-541a0b18f137", + "metadata": {}, + "source": [ + "### Optimization\n", "\n", - "stats_df = pd.DataFrame(stats, columns=['n_traps', 'rew_mean', 'rew_std'])\n", - "stats_df\n", - "# stats_df.plot(x='n_traps', y='rew_mean', yerr='rew_std');\n", - "# plt.show()" + "We'll try two methods from the `scikit-optimize` package to tune our constant action strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "fe0b416d-7bf5-4f6d-bcbf-60ea7ab978c5", + "metadata": {}, + "outputs": [], + "source": [ + "from skopt import gp_minimize, gbrt_minimize " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "7de77c7f-a8b0-41f8-abbd-94c3ef7d0e70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6min 57s, sys: 34min 46s, total: 41min 44s\n", + "Wall time: 1min 54s\n" + ] + }, + { + "data": { + "text/plain": [ + "[22.661213050436572]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "res = gp_minimize(evaluateConstAct, [(0.0, 100.)], n_calls = 100)\n", + "res.x" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "91b904b4-bcf9-45b6-b59a-42e8002490bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 18s, sys: 1.86 s, total: 1min 20s\n", + "Wall time: 1min 18s\n" + ] + }, + { + "data": { + "text/plain": [ + "[7.715412742104956]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "res = gbrt_minimize(evaluateConstAct, [(0.0, 100.)], n_calls = 100)\n", + "res.x" ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "79d17af7-cfc7-436b-8b6b-1fd2adc4b51c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.014949391292964487" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "- evaluateConstAct([22])" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "351a5aea-0cd5-4496-a9da-c6e3bd858267", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.014123114309025084" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "- evaluateConstAct([7])" + ] + }, + { + "cell_type": "markdown", + "id": "fee9d4e2-b6cc-45ff-85c3-b90b417e3c43", + "metadata": {}, + "source": [ + "Although the two solutions arrived at correspond to different numbers of traps, their reward value is rather similar.\n", + "This, together with the limited number of calls allowed to the optimization algorithm (in the interest of runtime) partly explains the difference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd879205-226c-4d33-9281-440ef3769c7f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 9f99dc5fa729e9b60e1726c94edc78cfbd98ee97 Mon Sep 17 00:00:00 2001 From: Felipe Montealegre-Mora Date: Mon, 26 Feb 2024 01:07:18 +0000 Subject: [PATCH 41/52] deleted outdated notebooks --- notebooks/RL_train.ipynb | 401 ----------------------------------- notebooks/const_action.ipynb | 383 --------------------------------- 2 files changed, 784 deletions(-) delete mode 100644 notebooks/RL_train.ipynb delete mode 100644 notebooks/const_action.ipynb diff --git a/notebooks/RL_train.ipynb b/notebooks/RL_train.ipynb deleted file mode 100644 index 44cfb55..0000000 --- a/notebooks/RL_train.ipynb +++ /dev/null @@ -1,401 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "314063e9-3f6c-4eb3-b120-b775dde32029", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Obtaining file:///home/rstudio/rl4greencrab\n", - " Installing build dependencies ... \u001b[?25ldone\n", - "\u001b[?25h Checking if build backend supports build_editable ... \u001b[?25ldone\n", - "\u001b[?25h Getting requirements to build editable ... \u001b[?25ldone\n", - "\u001b[?25h Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: gymnasium in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (0.28.1)\n", - "Requirement already satisfied: numpy>=1.21.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (1.26.4)\n", - "Requirement already satisfied: jax-jumpy>=1.0.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (1.0.0)\n", - "Requirement already satisfied: cloudpickle>=1.2.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (3.0.0)\n", - "Requirement already satisfied: typing-extensions>=4.3.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (4.9.0)\n", - "Requirement already satisfied: farama-notifications>=0.0.1 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (0.0.4)\n", - "Building wheels for collected packages: rl4greencrab\n", - " Building editable for rl4greencrab (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for rl4greencrab: filename=rl4greencrab-1.0.0-py2.py3-none-any.whl size=972 sha256=0f849313e0aa0136f78ce43f4b591bbce66af57ceb3e9a7cbc8839197bb40600\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-fh1283va/wheels/e9/7e/e6/00c4b11a2574abd59d64425d537139e25fadbde37f002c4dba\n", - "Successfully built rl4greencrab\n", - "Installing collected packages: rl4greencrab\n", - " Attempting uninstall: rl4greencrab\n", - " Found existing installation: rl4greencrab 1.0.0\n", - " Uninstalling rl4greencrab-1.0.0:\n", - " Successfully uninstalled rl4greencrab-1.0.0\n", - "Successfully installed rl4greencrab-1.0.0\n" - ] - } - ], - "source": [ - "# uncomment to install rl4greencrab, then restart kernel \n", - "\n", - "!pip install -e .." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "720f3a3b-844d-4f42-9606-5c7952558a78", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import torch\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from stable_baselines3 import PPO\n", - "from stable_baselines3.common.env_util import make_vec_env\n", - "\n", - "from rl4greencrab import ts_env_v1\n", - "from rl4greencrab import invasive_IPM_v2 as ipm\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f27cebf5-34a4-488a-84e4-f9cf142c79c9", - "metadata": {}, - "outputs": [], - "source": [ - "# env = ts_env_v1(config = {'base_env': invasive_IPM(), 'N_mem': 2})\n", - "\n", - "# start with N_timeseries = 1\n", - "env = ipm()\n", - "env.reset()\n", - "\n", - "log = os.path.expanduser(\"~/sb3_logs\")\n", - "\n", - "# vec_env = make_vec_env(invasive_IPM, n_envs=4, env_kwargs={'config':{}})\n", - "\n", - "# self.r = 0.5\n", - "# self.imm = 1\n", - "# self.K=25_000\n", - "\n", - "N_mem = 5\n", - "\n", - "ipm_cfg = {\n", - " 'r': 0.5,\n", - " 'imm': 2000,\n", - " 'problem_scale': 2000,\n", - " 'action_reward_scale': 0.5, # cost per unit action in ipm\n", - " 'env_stoch': 0.1\n", - "}\n", - "ts_env_cfg = {\n", - " 'base_env_cls': ipm, \n", - " 'base_env_cfg': ipm_cfg, \n", - " 'N_mem': N_mem,\n", - "}\n", - "ts_env = ts_env_v1(config = ts_env_cfg)\n", - "ts_vec_env = make_vec_env(ts_env_v1, n_envs=4, env_kwargs={'config': ts_env_cfg})\n", - "\n", - "save_name = f\"ppo_ism-v2_nmem-{N_mem}\"\n", - "\n", - "# print(f\"cuda available: {torch.cuda.is_available()}\\n\\n\\n\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "a61bd391-7250-4aab-aa9f-5854d150b03e", - "metadata": {}, - "source": [ - "# Profiling" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ce09dd63-1144-4feb-8ae1-d63298fab629", - "metadata": {}, - "outputs": [], - "source": [ - "# import cProfile, pstats, io\n", - "# from pstats import SortKey\n", - "# pr = cProfile.Profile()\n", - "# pr.enable()\n", - "# # ... do something ...\n", - "\n", - "# for _ in range(10):\n", - "# env.reset()\n", - "# for t in range(100):\n", - "# env.step([100])\n", - "\n", - "# pr.disable()\n", - "# s = io.StringIO()\n", - "# sortby = SortKey.CUMULATIVE\n", - "# ps = pstats.Stats(pr, stream=s).sort_stats(sortby)\n", - "# ps.print_stats()\n", - "# print(s.getvalue())" - ] - }, - { - "cell_type": "markdown", - "id": "f6977c97-138f-4fc3-ab25-c92657977c4d", - "metadata": {}, - "source": [ - "# Training" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "23f7acfa-6576-459b-92ef-1d6c72eba891", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0524e9e197604be2a22b260177411a4a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "save_name = \"ppo_ism_\"\n",
-    "\n",
-    "model = PPO(\"MlpPolicy\", ts_env, verbose=0, tensorboard_log=\"/home/rstudio/logs\")\n",
-    "model.learn(\n",
-    "\ttotal_timesteps=1_000_000, \n",
-    "\tprogress_bar=True,\n",
-    ")\n",
-    "model.save(\"ppo_ism_1y\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b0df3e16-e318-477f-b5fb-f1eee0170139",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# model = PPO(\"MlpPolicy\", ts_env, verbose=0, tensorboard_log=log)\n",
-    "# model.load(\"ppo_ism_1y\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "29bbddc6-fa91-4531-a6de-e334f7290a73",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# eval_env = ipm_so(config={})\n",
-    "eval_env = ts_env_v1(config = ts_env_cfg)\n",
-    "# {'base_env_cls': ipm, 'base_env_cfg':ipm_cfg, 'N_mem': 5}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8ae42550-1883-4684-9769-dc867c32c540",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "obs = eval_env.reset()[0]\n",
-    "act = model.predict(obs)[0]\n",
-    "\n",
-    "obs_hist = [] # first observation is dummy anyhow\n",
-    "act_hist = []\n",
-    "rew_hist = []\n",
-    "t_hist = []\n",
-    "pop_hist = []\n",
-    "\n",
-    "ep = []\n",
-    "\n",
-    "for t in range(100):\n",
-    "    next_obs, rew, term, trunc, info = eval_env.step(act)\n",
-    "    latest_next_obs = next_obs[-2] # next_obs[-1] is the previous action\n",
-    "\n",
-    "    T = t\n",
-    "    for idx, t_within in enumerate([0, 0.5]):\n",
-    "        T += t_within\n",
-    "        t_hist.append(T)\n",
-    "        obs_hist.append(latest_next_obs[idx])\n",
-    "        act_hist.append(act[0])\n",
-    "        pop_hist.append(np.sum(eval_env.base_env.state))\n",
-    "        if idx==0:\n",
-    "            rew_hist.append(rew)\n",
-    "        else:\n",
-    "            rew_hist.append(0)\n",
-    "\n",
-    "    #\n",
-    "    act = model.predict(next_obs)[0]\n",
-    "    \n",
-    "    if term or trunc:\n",
-    "        print(f\"term: {term}, trunc: {trunc}, t: {t}\")\n",
-    "        # act_hist.append(act[0])\n",
-    "        break\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "13541673-2672-4de6-aab0-4e4dfda98785",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# obs_hist_ = [obs[:,0] for obs in obs_hist]\n",
-    "\n",
-    "# print(len(act_hist))\n",
-    "# print(len(t_hist))\n",
-    "# print(len(rew_hist))\n",
-    "# print(len(obs_hist))\n",
-    "\n",
-    "summary_df = pd.DataFrame(\n",
-    "    {'action': act_hist,\n",
-    "     'obs': obs_hist,\n",
-    "     'rew': rew_hist,\n",
-    "     'pop': pop_hist,\n",
-    "     't': t_hist,\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a7512216-fdc9-48ae-bfae-a1072843f870",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-3.1432521643686813\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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nmPoDCAGQ4gLHApUoU4B8NkGHyQRdtRhGMOofQRCEX1AARHhGLGevzZMCs9IgWR+ITwFQSvAAhZkHiBSGqiInBUafD0EQVQYFQIRnWACka/nTW0H5QEQPUIj1ASIPUFVhmaDJA0QQRHVCARDhGVYCHw3r3ISsSnGwix4Lkvy6BtIojOrH+uz9Vf8IgiD8ggIgwjPMAxQVRlGo+vCkJQ+QX0EKDUOtftKkABEEUeVQAER4Jp5kClCIByCqFX464CqwcEgXFCDqA1RN5JqgK7k1BEEQuVAARHiGKUCxsJ63DF32APlmgrZ5gEgBqkZyFCBKgREEUWVQAER4hpmgY2F3KTDfAyCbB4hSLNUIU3yiIfJoEQRRnVAARHgmIZigQy5M0BF+EfTn9UUPUDb+IQWoyggq+CUIgvALCoAIz8RTmcne0bAOXSusAMXCwShAGQ8QKUDVCE+B+ez/IgiC8AsKgAjPiCmwcJ4mh0H3AQpJnaBNUhmqBtkETQEQQRDVBgVAhGd4GXwhE7RUBebbMFQhBcaqwAD/+gwRpcM/e5+7gBMEQfgFBUCEZ3gjRKEPkGEiR4GRp8H7VQmUFkzQoZAVAKWo1rpqkNU/8mgRBFFtUABEeCauMEEDuSpQ0FVgsgJEaZbqgYahEgRR7VAARHjG8gCFoOuiAiMFQDwFxirF/Hn9dNoyQYfyvD5ROdhnT8NqCYKoVigAIjyTUMwCA3IVHmsURnZivG8KkNUIkVWBAVZgRFQeqw8QdYImCKI6oQCI8IwYALEyeCBfCszfZnhiI0Qh/iIFqIoIagwKQRCEX1AARHgmkc72AQq58wCxiyDgjxeEN0IMadA0eyk8UR0YNAqDIIgqJ1zpDSCGH2wYaixilcEDhU3QQOZCqENDKbDqMpb+Cuka0oZJVWBVgmmaYPFOpUzQT/x9O9a82wUAaBsbw9fOOgK10VBZt4EgiOqGAiDCM3wYakiHrmvQNMA0c1f5cido9rtIidch0QPEfiZAPpNqQQyEmQG+nArQK9u78c3/96rtd8dPasKiYzrKtg0EQVQ/lAIjPCN6gAArEMlRgMxcBciP66DoARJ/kgJUHYjBTrk7QZumie/9cTMA4LTprZg6rh4AMJhMleX1CYIYPlAARHhGDoB0RTdo0zStFFjYngIrFdEDBDgHYERlEOPQcqfA/vLGbqx9rwuxsI67PjMbh7XUAfBvEC9BECMHSoERnhE7QQNQmpDF610k5G+zwlwPEHUbriaUClCAKbD9fXEsXf0B+uMpLH+9EwDwhTOmYmJTrfDdpAiIIAg7FAARnmEBUCxr5lEFQGI6KhayTD9+KAEqD5D8+kTlsHmAytAH6FcvbcM9f32b326tj+JfP3o4APG7GdzrEwQxPKEAiPAMH4YqKUBiI0TxghcWFSAflABnDxAFQNWAGORGymCC7h5MAABOOKwJ86a1YtHRHRhbEwEAa1gvleETBCFBARDhmUQq2wdIMkGnHBQg1rDQMP3vA8SeP/N7WuZXA2KwwdKUQapzyWxA/pEj2/D1BUfa/saDcwqOPWOaJl7bcRAHBpIAgOMPbUJjXaTCW0UQ/kEBEOEZNyZomwKkZ5oVGmkTflyHZA8QD8BoFEZVwIINXYOtUaZhmLbZcX7Bvo9itSFDp/Ro0fzljd344n+v57ePntiAP3z1jApuEUH4S2BVYF1dXVi8eDEaGhrQ1NSEK6+8En19fXkf09nZicsuuwwdHR2or6/HCSecgN/85je2+7z88ss4++yz0dTUhNbWVlx99dU5z7tt2zacd955qKurQ3t7O77xjW8glaIyWL/gHqCwswlaVoA0H1MR7LnlFBilOaoD9jmEdM3eKDOgzyeZzu03xWDZV3lOHVGYD/b3AwDqsg0kt+0fqOTmEITvBBYALV68GJs2bcKKFSuwbNkyPPfcc7j66qvzPubzn/88tmzZgqeeegqvvfYaPv3pT+Piiy/Ghg0bAAA7d+7EggULMH36dKxZswbLly/Hpk2bcMUVV/DnSKfTOO+885BIJPDiiy9i6dKlePjhh3HrrbcG9VZHHbICpAqA2MVO15AZV6H5l4pwToHRRa4aSHMFSIMwqzawz4cUoGBgHd/nTmnJ3E5RipkYWQQSAG3evBnLly/HL3/5S8ybNw+nn3467r33Xjz++OPYuXOn4+NefPFFfOUrX8HJJ5+MadOm4dvf/jaampqwfn1Ghl22bBkikQjuv/9+zJgxAyeddBIeeOAB/OY3v8E777wDAPjLX/6CN954A7/61a9w/PHH4+Mf/zhuv/123H///UgkEkG83VEH7wQddjZBpx2Myr6UwbMAiFWBhcgEXU0w8S+ka/YUWEAqDDflKxUgUgeLhe3XsTVhftuk/UiMIAIJgFavXo2mpibMnTuX/27BggXQdR1r1qxxfNypp56KX//61+jq6oJhGHj88ccxNDSEM888EwAQj8cRjUahC8vK2tpaAMALL7zAX/vYY4/F+PHj+X3OOecc9PT0YNOmTY6vHY/H0dPTY/tHqOEKULa8nV1kRA+OHACx66AvjRAd+gClyQNUFfAUmKZxfxgQXICaTwEiE3TxsP3aUGNZRRPUT4AYQQQSAHV2dqK9vd32u3A4jJaWFnR2djo+7oknnkAymURraytisRi++MUv4ne/+x2mT58OAPjYxz6Gzs5O3HXXXUgkEjhw4ABuuukmAMCuXbv4a4vBDwB+O99r33HHHWhsbOT/Jk2a5P2NjxKsPkBSCkylAGl2BciPC1FS8gCpqtCIysFTYLICFNDnk8yjAOn03SgadpyPiYVzfkcQIwFPAdBNN90ETdPy/nvzzTeL3phbbrkF3d3deOaZZ/D3v/8d1113HS6++GK89tprAICjjz4aS5cuxY9+9CPU1dWho6MDU6dOxfjx422qUDHcfPPNOHjwIP+3ffv2kp5vJJNw6AQtVn45psACGIVBHqDqwnAyQQesAEWFflOMMClASnYdHMQ3nnwF//rf6/GlX63HM2/szrmPFQBZpe8JCoCGPaZpYvOuHmzYdgCvfXhwVJ83PZXBX3/99TbDsYpp06aho6MDe/bssf0+lUqhq6sLHR3qicxbt27Ffffdh9dffx1HH300AGD27Nl4/vnncf/99+OBBx4AAHzuc5/D5z73OezevRv19fXQNA0//vGPMW3aNABAR0cH1q5da3vu3bt38785EYvFEIvF8r43IoOTCVqs/MpNgeUGScUiN0IMK16fqBx2E7QGTcsMwQ2uCiyPAkQeICW/fXkHnlz/Ib/91u5eLDjKrpwnBKU3GtKRSBukAI0Afv7cu7jjT5ZQcenJk3DHp4+r4BZVDk8BUFtbG9ra2greb/78+eju7sb69etx4oknAgBWrlwJwzAwb9485WMGBjIllrKSEwqFYCgubCyt9eCDD6KmpgZnn302f+3vfe972LNnD0/DrVixAg0NDTjqqKNcvlPCCdM0c0ynShM0VwHsvYJKNcIahsknykd05yo0onJYwW/mdkjTkDLNwMZhxF14gMi6Yqd3KNMWZFpbPd7d24/+eDrnPnHW8DSkIxrOBECkAA1/tu7NtI1hQe3Wvf0V3qLKEYgHaNasWVi0aBGuuuoqrF27FqtWrcKSJUtwySWXYOLEiQCAHTt2YObMmVytmTlzJqZPn44vfvGLWLt2LbZu3Yof/ehHWLFiBS644AL+3Pfddx9efvllvPXWW7j//vuxZMkS3HHHHWhqagIALFy4EEcddRQuu+wyvPLKK/jzn/+Mb3/727jmmmtI4fEB0QQZkxohiiZo9n9+EfQpSEmK/YWkFBj5PKoDw7T7v3Qf058qktJoFhFVcE5Ywc3hbWNst0VEBYgd6xQADX/YefKYQxoAjO70cGB9gB599FHMnDkTZ511Fs4991ycfvrp+PnPf87/nkwmsWXLFq78RCIR/PGPf0RbWxvOP/98HHfccXjkkUewdOlSnHvuufxxa9euxdlnn41jjz0WP//5z/Gzn/0MX/3qV/nfQ6EQli1bhlAohPnz5+Of/umf8PnPfx633XZbUG91VCFK4PIoDNssMNNeqcWEvVIvgmIAJQ9DHc0HcjUhmqAB+NoDSgULyiP5UmD03bAR5xVeEdttEXHmHzvWVYESMbxgxyFTTEdzejiwURgtLS147LHHHP8+ZcqUnJ4SRxxxRE7nZ5lHHnmk4GtPnjwZf/zjH91tKOEJcQXIVty6Is2Q4hfBzG2/LoKiykPDUKsT0QQt/gwqCEmmMs+rVoAQ6GsPV1iTw4bazCVAFQCx+0TDpACNJNh5kgW1o3nhGJgCRIxMxAowNt5CZUJmB5WlAPlzERR7/VizwIIfuEm4hwXCTH3xsweUCjeNECkFZocpOUwBShsmUpJRymp4GuL7lgKg4Q87FqKkAFEARHgjIc0BA9Q+C64AZS9+fnXkZc+raaQAVStp+bMPOEUpt2UQoVEYangKrDaS8zuGeKxbKTAKgIY7aUkBGs0FAhQAEZ6Ip3JX25bPwrqfrACxi2Cpiw2mMjHVSfx/msrgqwLTKQVWyVEYFADZiCu6PMvBDa8CC+uIhUPK+xDDj7TkAaIUGEG4RO4BBKgDkJRkhNV8uhBZ1WVWAEQKUHVhDcK194AKIggxTTP/KIwQBUAq4slMcFMXDSOS3UeywdmmAGX3LY3CGP7kKECUAiMIdyTS1qqQoTJBp3kVGFMB7L8vlrSkLAFWR2iaBVYdOHUBD0KgE4NeGobqnrgQ3HB1J6lOgUXFFFiSqsCGOylSgDgUABGeiCv8FlaaQegEnQ6mFJpPghfGHvA+RKP4QK4m5CqwILsxq6oSRWgYqpq4osdPbgrMMkHzKjBSgIY97PiMkQJEARDhDXkQKqBOM8gKkF9m1PweoNF7IFcTchVYkGXwSeGCnH8Uhu8vPaxh6S4xuHFKgYkKEFWBDX+YjSBC6WEKgAhvqCpuQoqLTM40eJ/KkdUeIFrJVBOOKbAAFSBds38nGKQAqWHprlhYRyyiNjjHVSkwCoCGPew4pBQYBUCER9yaoHOGofKLYGmvn9cDNIoP5GoiZxQG6wMUwOeTrwIMoDJ4J5QpMMEDJM78E31CpAANf+RGiKN54UgBEOEJKwAK8d8pTdA50+Dtvy8WlQeIV4FRnqMqsEZhZG4HqcLkqwADyATtRKEUWEJKLVIn6JFDziiMUfyRUgBEeEJcFTKUJuiA0iCsW21I6QEaxUdyFeFkgg7CpJ5M2w2dMiwuGs0yvwpbCkzR4ycumctpFtjIgR2H7JgZzV3SA5sFRoxMWBmsmHKwTNDW/XIVIJ9GYfAU2MjsA7S3N47H1mzDQDKFkKbhU8cfghkdYyu9WZ6wOkEH3wixkAJEFYK5GIaU3oooFCAhAIqRAjSikBshjub0MAVAhCf4iVNpgs6tAvN7IGaKB1YqD9LwP5AfWvUefvJ/W/ntVz7sxqNfOKWCW+Qd5z5A5fcAsVTpaF7lyojprVgkpPQAyTP/qBHiyEFuhDia1VFKgRGeUJmgQ4oUFA9UfK4Cs1YvuVVgI2GV3zOUBAB0NNQAALr6k5XcnKKQTdBBlsG7VYCGa3CcNkz0DCXRM5TMGVZaLGKgUygFxo5zrhIlKQAa7qSlKrDR7I8jBYjwRL5hqKpZYCw9pjJKF0NS4QHiXaaH6UVOhL2HI8aPQWfPEIaGYedd3gdIboIZwImWfR9UTRCBYIOvoBlKprHwP57Dtq4BAED72BiWf/0jaKmPlvS8LNWlaxn1NF8KjB3nbP/GSQEa9hiSAjQcjw2/IAWI8IRqGGq+afBBKUB2D9DIUYDY+6uPZtYmwzIAksvgfQp+VXAFyMkEHWDwFTTbugZ48AMAe3rjeP7tvSU/r9jhWdM05SgMWemNUhn8iIGXwVN6mAIgwhv5AiCxDN2QAhVm2Sm5Ckzyl4ivMRJy2SxIqI8N3wDIkD1AQY7CUHjSRIZzHyD22U9orMEVp04BAKz/4EDJz8tL4LPKj2oUhjgJ3uk+xPCEFCALCoAIT3DTacjqA6RaZcvT4P2uAhM9H1YV2PA/OTMf1ZhYZv8ODUPPRVr67IM0QScLmKBVXcqHC+yzr42EcPLUFgDAuvdLD4CGkvb0lqsUGK8CG34BOWEnZxiqmWl8ORohDxDhiYTQQZahKwIQQ5oF5pcXQ+UBGklVYOxCXZdVgAaTaZimCU3LHfMQBP/5zNt4+MX3YAIYWxPGPZfMwZzDmj09h2WCztwOUoWJcxO0ev8M51EYogozd3LmM3izswc9Q0k01ERKeF4rBSb+tClAaTkFRmXwIwV2fIq+OcO0jtfRBClAhCdUs8DCCo9HymkafCAeoJHT64VdqMfErLVJOdMOT/x9Ow4MJNE9kMT2rkE8u8W75yRHAWJdwIM0QRcYhTEcvxtMqamJhNDeUIPJrXUwTeDlEtNgVhdoKb0lqI3s/+w4pxTYyIErQMIxMxIWj8VAARDhCdkbAKhX2c7T4Et7faUHaATNAmMqWl3USjGWs/S4N1uGf9KUjOJQTHdt2QRdDaMwhrMCxIKPuZMzabBSfUBxScXNNwqDqUOkAI0ceB8gmwI0/I4PP6AAiPBEvj5A4iqbXTj1oKrAbB6gbBXYcDR6SLAAsSYS4vt1sExGaNM00RdPAQA6Gmtt2+MF2QStB2iCLugBCrALddCIChBgBaXr3u8q6XmtMRjZFJhiGrx8nPNO0FQGP+yRGyGKvxttUABEeCLfLDAxuGHnSVkBKnUlzi544RHqARKbCNZk93G5KsH6E2mwXdhcl/GYFKUAyX2AyqAAFeoDNCIUoGwAtHF7d0lKjGMKLG8VWG6pPDE8URWSDMcFgh9QAER4QtUIUeWzYBfOnGnwPilAYgrMmvc0/E/OYoqPrfyHylR5w9JfYV3jZfjFLPid+wAFUQafu5oV4U0yh+EJXlaADm8bg+a6CIaSBv7rhffwh1d34eCg907hcekYVqbAnKrASAEa9qgUoOG4QPADqgIjPKHqA6Tqw8POk37Pg0opTNDVoAD99+r3cfuyzUgaBmJhHd+/8Fh8+oRDPT+PoQqAyrTq7hvKpL/G1ISFfer9tdl7YB9RkKXoI3kUhqzUaJqGEye34JnNu/GD5W8CAC6ccwj+4x+P9/i8DlVg+Rohsk7Qw7AvFWHHGoVhnUOH4/HhBxQAEZ6wUg6WSVe1ws9VgPzxYig9QKHK+zz+9HonXx0PJQ389c09RQVAYgVVTdakOpgoz0WnJxsAja0Jl/R5sTReVfQB8vm1t3cN4OKfrcae3jgA4KNHtuG/Lp8bSJsCWQECgK98bDpM08SO7kG82dmLHd2Dnp+XBTHcBB1RpcBknxApQCMBwzDBDumwrkPTANMcngqpH1AKjPCEygQdVqXAHKfBl/b6+RSgSpqgDwxkUhFnHDEOQPEXXLHMv1IpsLGxSEmqWk4KLMhO0G4VIJ9ee/0HB7Dr4BDShom0YWLlm3t4MOQ3TAGqEXpuzZ7UhP+64iTcsHBG9j7eDyj2mBquALmfBZZMm6M2XTISEI+DkK4JVZKV2qLKQgEQ4QnZPwA4maAdSqFLHYWhHIZa+TRH90ACADCRV08VGQAx9USzAqBypR16RQWohH2aMwojwGG1SYUpX8SvwJvBFJDTprdiWls9AGDzrh5/nlxCrtYSsaaze/9u5JbBK6rAJGUtJqhQpAINX8RjMKRr1nFOChBBOHNwMIm3dvfykuyowgRtT4FlL4IhuwpQ6upR1QgxrFd+ps2BbAA0bmxmUnexgZ5o8q4tswdIDIBUqp5bnKrAAjFBu+wEXYyXKd/rjYmFMWtCAwBgS2evL88tw6r/RAWIwYKWYoIRd40Q7fcRq+yoGeLwRTwGwzYFyH5sptIGXty6Dyve2I2/bt7N1eGRBnmAiILs74vjjB8+iwHBi6JKgYkHlzwN3q8qMKtKKn8fonIylEzzIKWlPgagBAXIlgLLeoDKpAD1xbMpsJpISUGL4ZQCC6QKLH8ZvN+vbaWAQ5gxfgz+8OouvBlQACT7cERUQYvr55WUpRrVLDBpv4oBJjVDHL6I519d0xyP80dWf4Dblr3Bb581sx3/dcVJ5dnIMkIBEFGQ9/b1YyCRhq4BzXVRHDWxAVNa6/nfVQpQbhrEXwVIPCFXugqsO+v/Cekammoz/XOKDcZEE3SMK0DlT4GVEgA5DkMN0gPkkALjFYo+vXRSCAxmdGQUoKACoHwKUFTRu8ctuWXwillgktdP0zTEwjriKcMWKBHDi7TgkwzpmuPClJnr66Mh9CfS2NY1ULZtLCcUABEFYReZw9vGYMV1H835e0ixypZHVvhlRlUNQ630NHiW/mqqjfCxHMVe7EX1pBpSYH6YoINMgSULKEB+v7alAGmY2TEWAPDOnl4k04ajEbtYXClARQQjPAWWMwpDVQVmvadoNgAiBWj4YleAnBem7HiZPakJL27dP2J9X+QBIgoiT4aWUY0bcJ4GX9q25BuGWikFiAdAdZGSUy72RojlTYH1DFkpMH9M0JnboSCrwFwOQ/XrtcWA65CmWoyJhZFMm3hvX78vzy8yJJWrizB1sJhgxKkPUNoweZGBmOrjr0nNEIc9osdQE1NgpjoAYjMJR2oHcFKAiIKoSt9FVAFIzjR4nxshih6gakmBNddFSw7GbI0Qw+WtAuONEGMlKkBOKbBKjMLweRgqWwxEQjp0XcOR48fg5W3d2LyrB0eOH+vLa/DXyqMA8caEKQOmaXrqQyQbnMUAK54yEA7pymO+GsZhDCRS2JttO9A2Noa6aHGXsF889y5e3LoPANDRWIPvnH+0rd/SSEXupO+0YGMBUW12/47UoJcCIKIghS4yqoulrAD53QhR9ABV2gRtKUClB0ApQT2pqaAHiH3mxXxezqMw/NhKO4VGYbA42S8FSA4MZk5owMvbugOpBMtbBSb8LpE2lEGSE3IZvFzhVR/LrRQDKj8Oo2coiY/88Fm+4Giqi+C5b/4DGmoinp5nMJHG9/+0GeJX4swZ7Tjn6A4/N7cqcWxPIn2kzCtUV+ZWHOWGUmBEQQopQKp0CbuQ82nw2YeWaoRVeYAqXQZvKUCRksc+pAWFqzZaZg9QtgqsocbyMvnSB0jRJ8ovCjVCZK9tmplp96WSTNtfj/mAgjBCu/EAifdz/bxSFZiua4KilLnQqY55dp9KeYC2dw3wYw3IHHfbizDnJtIGD36OOSRjZO88OOTLNlY7soXAaWHKzt91seLbLQwHKAAiCqKaAC+iMkFbIyuC7wPEeg1VTAHqzyhAzfXRktM9ogma7e9yeYB6VaMwijJBZ37qOQpQgCboAulZv14/RwHqCK4XUN4qMCHg8xqQqNQduaxe5a2KKcrlywlLqx/SVIuJjTUAivtMxUB49qFNAIA9vaMjAEo5pKfl/cjOQ8wDlEybI3JeGAVAREFce4AUnaB1uRdMicdQNXqADgiSfKnpOOsEVckUWISrasW8DycFqCKjMIQAyI8AOZn9ArOAYUbW97OjexDPv70Xr+846J/fKI8CpGla0aXwqueV54FZKlH1KEBigQBb9CSLOKGI54kJ2UBqd08w40yqDafiFFmd5QqQ4LEaidV/FAARBSloNFVOg2cHmu54n2KQlSXxudOG6UuawytsDIZogi5aARL2mzULrPzT4HnK0hcTdPHPVYiCozAEc7AfKTg54Gqsi3A14rL/WotP3PsCHnrx/ZJfB8ivAAGiauMtQJY9QJnnYr2AsikwxX4tpfeQH7AKtXBIQ6SEtLf4kPaGzGcX1Dy3akM+NnkfoJwy+My+rhVHoFAARIxGCpUaswtcShEAsb/5lQZhFzzVMFQ/nr8YDvAAKFKy2dsaIguhD1DwCtBQMs0/50wjxOIVIGcTdAU8QH6nwBTHwpf+YTqmtdWjfWymC/hfNnWW/DqAGKioDc7FjsOQq8DE/zO/mbXoyS2Dr1QAJKa/2QIoVYQ3hQXCugb+me3pGR0pMNlCUKgPUDSs8yBpJDbApACIKIjcFVYmpFiNWRfyrALkcxWYeGHzO83hFasqpfQqMFZ9kRmGyi5KwZ94WPpL04AxUUsBGu59gMTviR99MlUB12WnTMbK68/Er784HwCwYVs3BhOlfWaGYfLXqnF4b8WOw1ClwCx1J22/TyRXAaqUEpAU0t/svJIsSgGyziHjswrQ3tGmAOVYE9QBUCRUfKp1OEBl8ERBEnm8CECBafDsIuhTGiQlpIgYlVaAugctD1B/PF3SdqRN6/2V0wPEhh2OiWYmwauCWreIE+2B8vQBchyGKqTA/AjA8vnhprTWYUJjDXYdHML6Dw7g9CPGFf064sXGWQEq1QMkGpztPX5UaW+uOFVMAcr9rIsZcsu+hpqmcQVof38CiZThGEg78fK2A3j2zT0AgMbaCBbPm8yrN6sRuUO/04JNrOKNhUMYShoUABGjE7cmaHUKLPMY3adS6HweICAYlSEfhmHaPEBDyYyUXuow1IwJ2p6WCJJewf8DlGYsd2y2FsBHU6gKTPc5OLY6QecGXJqmYf7hrfjtyzvw4tZ9JQZAVtDrpADJqo3X57Z7gOzBlLIMvuIeoFz1txgTNFcoNQ3NdVGEdQ0pw8S+vjgmNtV6eq6vPLaBz80CMoUL/3TKZM/bVC7cmqDF82yswspfkFAKjChIIp05YRZjgs6ZCF5qFZjSA2RtVzqIq2weeodSfEXZJPQBKjbQE4OHcipAfXGrBB4orXGlmGIQf/qtAKXSBt/3Tt9N2+v7oQAVCLhOPTwT9Ly4dX9Jr8OC3pCuIezw3ooZh5E2TKGSTeXvSWfSb3lM0JWuAovoeokmaMsDpOuWCuTVCG2aJjqz3iFWDfj27mAG4/qF3KHfandhv5+4gC020B4OUABEFKSwApT5qVaAghqFIXiAtNy/lwtmgK6LhhALh3jn4aKnwZu5AVA5+gD1CnPAAJTUCDHHZxCQCVpc/edLXaj6VBWLyhwsMv/wVgDAqx9289lqxcAuNk7qD1BcCkwMXuwmaGsivGiqto/CYJ2gK9QHSCyD11kZvPdgTK6EamOVYB6N0IPJNH+ui0+aBAB4f391T013ntGoToGJ/chIASJGJQmFZ0CEpbmUCpDu70VQLq8HMqmHSvUCOiCkv8TtKibQMwyTd6gNaVYAVI7ZSz1DDgpQUe8j81PuA+R3cCqekPNNYufjMHysAnPyHB3SVIsprXUwTGDde11Fvw5TgPLNpypmIrx4X7sHyCqpFwMgpVG6QrPAxDL4kjqVZx/CvuNMAdrtUQFiaeOQrmHWhIwC9MF+/4fi+klOh34HxVrs5RUVguORRmABUFdXFxYvXoyGhgY0NTXhyiuvRF9fX97HdHZ24rLLLkNHRwfq6+txwgkn4De/+Y3tPi+//DLOPvtsNDU1obW1FVdffXXO82qalvPv8ccf9/09jhYKVtoo0iVpOQ3iUyUQN0FLFyDLh1Teg1ScS5TZjszvS5mhBWQCKVYGn0gbgQd2YhPEzOuX0glaNkFnfu/3KAz2vdQ0e0pUJhAFKI8yMz+bBnv4xffxi+fexZp3vafDVN2aZYqpAmMXMTm1JqpJ4vOJgV6MNUKs0FgEqwBC4wFvqhgPkHRuYgHQXo8KEC8ciIUxdVw9AODDA4NFqVLlwpDOn06LA3FfkwJUBIsXL8amTZuwYsUKLFu2DM899xyuvvrqvI/5/Oc/jy1btuCpp57Ca6+9hk9/+tO4+OKLsWHDBgDAzp07sWDBAkyfPh1r1qzB8uXLsWnTJlxxxRU5z/XQQw9h165d/N8FF1wQwLscfvztrb344n//HVc98nd8/fENrlYsBRshKkZRyIZF36rAFB4g8XalFSCunJTYoVY0QQPB+4DEkzngLI27wUqBIfszmM/GUmP0vNPQufoYwCwwFadNz6TBnn97H773x8247MG1GEikPL2OOwXIex8gVYdn8bnEFFg0bN+vxXiO/MQ6p+hWCqyoKjD793N8kc0QmWraUBvG+LE1iIV1pAwTOw4MFnhk5cid0ag+NtM2BYjK4D2xefNmLF++HOvWrcPcuXMBAPfeey/OPfdc3H333Zg4caLycS+++CJ++tOf4uSTTwYAfPvb38Z//Md/YP369ZgzZw6WLVuGSCSC+++/H3o2dH3ggQdw3HHH4Z133sH06dP5czU1NaGjw/1033g8jnjcOgB6eno8v+/hwI9XvIVXtnfz2xOaanHjopl5H1OwD5BizpfTNPhSVQCVB0i8XX4PkF0B4oNZSzAPA1kPkJB+GEqmUR8LrmiTdYFuqLEHQMXsT1NaYbPVpu8KEEvN5glGAOs76IcJu1A6GADOOboDXzrzcHQeHML/btyBRMpA90DSNlagEEwByqc0FZOSclKWxHSa03u0BqZWvgyexWV+eNR4CsyzApRVTWMR6LqGKa312LK7F+/v78eUrCJUbTgOQ80TABWTah0uBKIArV69Gk1NTTz4AYAFCxZA13WsWbPG8XGnnnoqfv3rX6OrqwuGYeDxxx/H0NAQzjzzTACZICUajfLgBwBqazNliy+88ILtua655hqMGzcOJ598Mh588MGCIxLuuOMONDY28n+TJk3y+raHBYPZlejk1joA7pSFwtPgMz9tCpBjw60iNlpA5QECSlMsSqFbVoBK8JuI+y+ka7Yp3UGPw+iVPEClVE7xFFiBk2ypFCqBZ6hm1RX/mtlKpDxBVySk48ZFM/Ef/3g8GmozgXEwClAxHiD181oeIMMxSKqWKrCQ0KeqmDJ49jXgAVBDcVVgVuFA5phh59QPqtgInTumJn8jREqBFUFnZyfa29ttvwuHw2hpaUFnp3Ob+CeeeALJZBKtra2IxWL44he/iN/97ndc2fnYxz6Gzs5O3HXXXUgkEjhw4ABuuukmAMCuXbv489x222144oknsGLFClx00UX48pe/jHvvvTfvNt988804ePAg/7d9+/Zi337JmKaJV7Z347m39uL5t/fyA80P2Anj0OZM4OjmosQlcYeTvhiMsFW2nGv2uwos1wNUvCegFMQxGJntKD5wEPcNU9XK1Q26N26vAuMKUFFVNpmfoQIye6kUGoPB8DMAc+MBEqnPqj6sQaZbPHmAPFyYnBUgIQXmkPKu9CiMlBB8Rvh3qvgqMMsDVGQKbNDum2Oqz/tVbIROy1VgCvVevJ+ua7bvxkjDk6Z+00034Qc/+EHe+2zevLnojbnlllvQ3d2NZ555BuPGjcPvf/97XHzxxXj++edx7LHH4uijj8bSpUtx3XXX4eabb0YoFMJXv/pVjB8/3qYK3XLLLfz/c+bMQX9/P+666y589atfdXztWCyGWCxW9Lb7ybJXd+Er/7OB3547uRn/70un+vLc7OTGDLZuUhwFy+Clbrs6tFwFyPcqMLUHyO80SyEOCGMwgNIqntKSAgRkVuo9Q6mSRysUQlaASvFUOU2DD8oE7VYBKtUfb5qm69dk1GW7AvcHoQAV4cmxPECSAqRKgUmvXel+MKICxA7/ohohZr+H7LTFFKD9fXGk0oZj3yUZtjBtGEYKkHxsOvnjxPNspZW/IPEUAF1//fVKw7HItGnT0NHRgT179th+n0ql0NXV5ejL2bp1K+677z68/vrrOProowEAs2fPxvPPP4/7778fDzzwAADgc5/7HD73uc9h9+7dqK+vh6Zp+PGPf4xp06Y5btO8efNw++23Ix6PV02Qk49tXZkDqCaiYyhpYPsB/w4oPj07e3JzY9Z1mwIDMgdOJCRONff3Isi2v1o8QAdzqsAy22GamQtmPnOujGge1jQrAAKCv+jwTtCx0hshylVgQfUBKjQGg1HqgFqGeLEtpDox6rL7cyAABagYT45qxpf4OvGUNfJAVoAqfSFM2VoQMIWy9Cqw1voYdC1THr+/P8FN0YWQFw1TWrMK0L7qVYAcy+BzqsCs8+xI9gB5CoDa2trQ1tZW8H7z589Hd3c31q9fjxNPPBEAsHLlShiGgXnz5ikfMzCQucjrsrcjFIKhWLqNHz8eAPDggw+ipqYGZ599tuP2bNy4Ec3NzcMi+AGsC8WsCQ3YsK07p0tnKbAAghlsXSlAiq6wIrZOzNnnk83KfvVicfIAWX1ByntylqvA5OnjcqouH3LrAKB84zD8bITopAD5nZ20PED5Zy/5lYKz98dxmwILUAEqoQ9QTgpM6DnltOCpdCrErgAVf7zLfYBCuoZxY2LY0xvHnp64hwDIfsywFNj2AwOelKRykrMwdTg2xF5elQ58gySQspJZs2Zh0aJFuOqqq/DAAw8gmUxiyZIluOSSS3gF2I4dO3DWWWfhkUcewcknn4yZM2di+vTp+OIXv4i7774bra2t+P3vf8/L6Bn33XcfTj31VIwZMwYrVqzAN77xDdx5551oamoCADz99NPYvXs3TjnlFNTU1GDFihX4/ve/jxtuuCGItxoIvOV79gAqZOD29NzZq1BtNNuwz8VzF+p+a1OAss8XdB+gkFMfoDJ5gF7fcRC7Dg7xyhGmANlmT5mmpwMslbavzgArVRl8Gbxkgi7BN2N99sj+9K8KS8T6XuYPMv0ahZF02XhRpK5IDxD7vPN6gCIlKEB5UmCqYalAFShABmuBofP0VTHT4OU2DUCmFH5Pbxx7eocANLp6HvmYmdBQg2hYRyJlYNfBIUxqqfO8bUHDFaCcFJh8P5UCRAGQax599FEsWbIEZ511FnRdx0UXXYR77rmH/z2ZTGLLli1c+YlEIvjjH/+Im266Ceeffz76+vowffp0LF26FOeeey5/3Nq1a/Gd73wHfX19mDlzJn72s5/hsssu439nZfLXXnstTNPE9OnT8eMf/xhXXXVVUG/Vd9iFgknQfg74ZKtYLx6gQmXwqllcTqMwSn0r7HkjUgqsnH2AnntrLz7/4Frb71rrY7btKGZb5NYBgLUyD3ocRi8vg5fN3KWk8oJNgbmtAmO7s9TgOCGkX+UUrBP1sczn57UKzKlaS6QYRca5D5CYAlOX4FujMCrfCLGU75Rh5i40rFJ490Zoqw+Qtfg5rKUO7+zpw/v7+6syAMo1QWd+n2OCFpR26gNUBC0tLXjssccc/z5lypQcZeOII47I6fws88gjj+T9+6JFi7Bo0SL3G1qFsAOdffH8vHAkpQDIjYRc0AMkXAvSpgnTNHMCIM2HShzV8zJ4FVjAAdBgIo1v/f41AMDUcfVoqovguEMaMaklU1UnnlS9vle5RBWAMBA1uJNPMm3wAEsug2fb5SWVVy4TdJx7gAr1AXKvdubDredIhPVu8lwF5kIBKqkPUI4HSFEF5qAAVcoLwhZXoZCVAium67I8qgWwjND3rXwb/2/9diw8ugNXnzHNdizKyGXwADCllQVAAzjjCM+bFjjp7P7KVYDUAVBIr3zqM0iC66xGFA07UbMTrV+pg7Rh8vx3Tdab4GZVXCgA0jSNmwjF1wD8LYUWg5scD1CZFKD//Ovb2N41iAmNNXj6K6dz0zBDPKl6tSeogjs2DDOIFFjPUBJ3Ld/CJ1oDwBhFAJQyTBSw2djINUFnf++7AmRfKDjhlwJVqB2ECuYBCkYB8h6QDDlVgUWs53Ly/LH3Xek+QBFdL0nVs6rArO/4rAkNAICdB4ew8+AQXt7WjY3buvGji2c7NiDtkcbHAMDkrBH6D6/uRO9QEqcdPg6zJzV53kbTNLHs1V34MNtV+rhDG3Ha9HE593trdy83Xc/saMBhrflVJ7a75OIUx2Gouj6i+wBRAFSFpPiJPXOS8uu6Ia6WmAm60EXBVvqb58Qf1nU+s8pWzh3yTwVQPS+DXeSCUIBeeHsfHn7xfaQNA8+9vQ8AcNunjskJfgB7SwCvc8lkeRoAaqPBeYD+unk3/vulD/jt9rExrqbYAjmPn1laWmH75f+ScdsHqJT5bKrXK2S6FuEeIM8maDd9gIoogy/UCTppzQKT3ycz5FfaAySmIItqNyGNwgCAz518GKa3j0F/PI0P9vfjh8u3YPmmTjz/vb2oiYTQOiaKB684CYc2WwGGSgE6vG0MAOCld7vw0rtdaB/7PtZ+a4HnbXxtx0FbK5RISMPLt5xtC7Z2HRzEov/fc/z6ENI1LP3nk3H6EbmBEn/vbB9KC1OnFFhI0yqu/AUJBUBViKwA+XXhEHP37MJa6AQiPibfSlvXAaSRGwD5qALYFSAnD5D/J+cfr9iCl7d189sfP6YDZx81XnlfXc+06TdN75+b7J0BrEA1iACIpWWOmtCAS0+ehHnTWvnfZAXIC3KZcVAmaNedoB1KfYt+PU8psKwC5LkM3osCVIwJOk8KzGHBw4ogKt8IUePqTTGNOuVRLQAQDuk49XArcJhzWBO++N8vY19fHP2JNPb3J/Dslr247JTJ/D690vgYAPjU8RPxQVc/dnUP4alXdmJfX9yzhw4A9mabMrbUR9EzmEQybaJ7IGkLgD48MAjDzHyWhzTX4t29/fjK/7yMp79yui1QE5EXJwVTYCFqhEiUGbbSYScgvy4colzMVnOFAhJxtZdvNZpJSWUVIGmmlfizFAVIPNnJAZCVYiv66R1hxsivnnUEpo2rdwx++LZoGlKm6U8KLMAyeLY/p7XV47L5U2x/E5Usr98/RxN0QApQoZSUbykwj12gAR8UoIjzaxVjTrX6ALlphFhtVWBWWiZUwoKKnSPyBSUnTm7BCzf+A7Z1DeD+Z9/B/27ciT1Cqtg0TfTFc1Ng9bEwbv74LBwcTOKpV3bCMDOp2mjYWwDEjvfp7WPw7t4+7OtL5HyH+rOvP719DH7zpVNx8c9W49UPD+KL/70eF51wKCJhHR8/pgPjxljtX9KCigbk6QRNjRCJSsEOUPbF88s8ylawYV2zhnZ6CIDyXWjYNTttmrbmirlGWO/bzZBnZYmEuSTu70Fqmib29mUCoM+eeKiryg49a4gqVgGyBUABpsDyzbUqSQFyMkH7fP5068nxy4QtTp93i1UFVqQClCfdVlQfIIfUWo1QUs+rwBxGYaSyKq/bSji/EBshsuClmDJ4rlAW2PyaSAhHjh+LI8ePBQB0HrQCoIFEmh+vYgqMIe7feCrtKWhmj2HPUxtVf4dYd/i6aAg1kRB+svgEnH/vC9i0swebdr4BAHjtw2788DOz+WNy0tMOixOxYeJIboRYfZ2aCGHqMQuA/OkFJHom3JqGE0LQlK8iQkxz2BQgzb8qMDFAkFdvQc2b6o2n+H5rG+uukSbftx4NmnLqCLAugEGUwSeF6doymqYVvU95H6ByzQIrsLrWfVIHS1KA4v4rQEWNwiiUAhMaITr1AfL6mn4hNkKMlND41DSti7sbWIm8WCzA0l9hXeMVtSL2AMj7NoqNMNk8OTmN2s8DoMzfD22uw6++MA+fPfFQHJ81Xu/rS9geIytAVkNJ6z6iGjTSFSAKgKoQuREi4I8RmitAIdFEmP9L7fakL5ahs+fUtNypw6Wk88Q+IDKWAuTvRZbl4sfEwnn9GCLFmn5ZilJMPwVZBs9ez6ljbbHNEK1y/sxtdpL1+7OxFKACnaB9HoXhSQEqehiqulpLpDQPUPGNEIHKXAzFtEy4hGnw7GKfb0En0tGY6Qy92xYAWQZoVSpNs5mHiwmAMt+XmkhIUIDsQTS7zVRGADh6YiPu+uxsfH5+xqsktwmQKzTZV1lUR21Ke4gaIRJlxmqEaO/FUqrkzMuGQ7rrUQfuAyBrO1lMJQYqflyEUoIalfv6/vdMAoB92QDIrfoDFO85yTsKIwD5mZ0c5aaSjJCucWO7FxxN0EV89n97ay/+7bevYTCZhq5p+MrHpuPyU6dktt/1d9Mfg3xRClCs2FEY7ALopg9QEaMwHPoAGaYVrMnvM6xb7S7i6TSACMpJUgjYQyWYoK1GiO7u39HAAiCrSWKPNAZDRU22K3Qx6WsxCOUKkJQCY7drI7mXcRak5wRA0rlZda6Si1hIASLKitwIEfDHB5QUPAxuGwc6DUaUEdUCpgCJErOfVWCqQDAwBSjr/xk3Jur6McWmfFQN2mr5jKbgPECOClCxgZxQQpt5HvvvvfCHV3diR/cguvoT2NcXx8+fe5f/LeGyKssvg3wiXbg0Xcbp4lUIqwzeZwXIqRO0EBAxdUMOgGyqRsCz6VSI6Ru2gCvmeFelmvMxPqsAHRxM8s+lRxqDoUKcr+YVMQCudZgnNxDPVYAYVgAkm5vVJmhbACQVsYzkKjAKgKoQqww+oAAorHn2ABVcZQsl+0oFyIcqMC6B5zHtpn0uAytGASo2cEhJJyfASoEF4QFKFTD1FtNrxTStRphW+rP4akbmc7j6I9MQ1jXs6B7E9q7M+BzXZfA+leEnU95TYHXs4uXRA2SVwbvoA5Q2CnoE/7ypE//fsjfwZmev7bEMcYGzLbt/VcEXu99Tr+zE8td3FdWJuVgsa4CQwi+hEaJbD9DYWJgvRJgRWp4DpqIU8zBTfGvCId5MczCR3wMkwjxSTgoQ239hxXlZ9C6GdSsFRgoQURZSCq+BH6kd0cPg1pfhOgUmlFNyBUiVAitFAWKKhTIFFrQC5CEAKrLqSGmCDrAMPilU1agoJmhVdgEvIf3JTvqHt9XjuEMbAQAvvbsfgPtRGLpPHqB4EZ2gWbPMeMrwlK5x6tgswo5J08zvhdnXF8eXH30Zv3zhPezoznQWZsN7Gbqu8e8aC5JU5l72fu768xb8669exv9u3On2LZUM98jpOv/MSymDdxsAaZrGfUDMCC1PgldRineGK3URHXUO41QGhCowGbcKkCoFJvpC7cNQqQqMKAPsgmNLgflw/bM8H0V4gAp227UCENVQT51fTL1vt2maeH3HQWzc3p3zvAzVSsYP9vVmqijavARARaeOMj/FE3MswGnwrIRYHivCKGaVLb5na95Q7t/cwoyetdEwTsk2anzp3S4A7kdhlJKCE0nyqjPvHiAAGCjCq5NfAbKXWjvx1827kTZMTGqpxRc/Og3fOf8onK4Yq3Dd2Ufi+ElNOH5SExbMasc/zGzPuc/N587CglnjMVFhDA4admGO6JaCnSzixOjVAwQA4xvYsFQvClDxqaO4oADVZc8BA0m1CTp/AKQ2Qeeb0yd2ytZGuAeITNBVCPcA+ZwCSwgpMLcXaaspWoFKG11UgHKVDPGibhim6woMAHhy/Yf45v97ld/OlwILTAHyZIJGUdvCVme2URgBBkAp4fugohgly5D8A+LPYr7DvNdJJIRTprXiJ/+3lStAiexFovAoDH/7AHlRgKLZlhMpw8RAPI2GPIqBiKUAFPYAAfkvTive2A0A+OyJk/DVs5wndF79kcNx9UcOz7td58+eiPNnT8S3fvcaHl2zrSIpMJsHqJgUWB4voROWEdquAOX7PC31togUmFAGzxYqchn8QFEpMLs/L58Jmi2MRrIHiAKgKsTyughVYD4EQGJqzasHKOYyzcCapAH2E4xY2p02Tehwf/J5aWvmgtc2NobmuggWz5uccx+uaBVxQszHvmwA5EUBKnb6uKo8txxl8JFCCpCHQM6mAGXfRinpT1Hmnz2pyeYDcj0M1Yf0KyBWnbn/7mqahrpoCD1DKdeVYIZhzd+ryfPe2Oo8kTIcL04DiRSez86vW3h0/g7mXnBSGILEattglcEXZ4LO/PQynmJ8NgDqPJg5H6jGYMj4oQDFIjrSproRoqoMnsE+HzlAFEdcAOoCAbmNBTu+KtUAM0goAKpCxACClZ36MQ7DXgXmbx+gsGCCllcZgHUwAZn357KlDgDgjV09AIDvX3is4xiKoBQgZoL2pABl37Z8wd3ZPYjH121HPJVGRNdx0YmHYuq4ev53uYEgUNoqshC8yaWDB8htmlREDPrkURisoaeXCw8v9Y2GUB8L47hDG/Hytm6sea/Lc3q2EtPggcx4hJ6hlOt5YOIFs5DyGgvlD4Cef3sf4ikDk1pqMSPb0dgP2PmgmD48xZISlAle9VlEAKY6zgrBAqDdvXIKLI8HiHXXLkUBCof4UlHuA8Q8QWoFKJu2kvZPSjo3K1NgOQqQXWmsVaTchisUAFUh4oEe0jUYabMo74xMUjiBex2F4cUELa8ygOKniydSBrbu7QMAzJrgfAJ3+368YJom76RaTBWYHLTe/+w7eHTNNn773X19+MniE/ltngIT9luwKbD8VU3FKDdiPC37DIBMEORhliivfmMn+XnTWvHytm689O5+9xWKfo3CKKIPECBUgrlUgEQ/Tz4FCMhcZHvjzh4glv46e1aH54Gc+WAplnL6QsQUcbgEEzTvBO3hY+TNELNVYD2DuZPgZUoxQYudwNkCoj/PKAwZpxSYnP5Tm6Dt95G9ZiMpACITdBVifUn9q2ABxFEYmmvFxG3li/h8SgVITIF5OGlt3duHZNrE2JowDmmqdfX6ftEzmOIX2dZ6L32A1PL8wexJk7XWZ7cZKhO0WAbvxzgUkVSeURgAXKuEIqoxKGJKz+sFSzZ6MiP0sld34tUPuwG4qALz6btRzCwwIKMAAbkreCfY6j8kXOid4KXwwkXWNE1s6ezFy9sOYOWbewCg4ABfr1Q2BWYp2MWYoOVhvW5gJujOHBN0viqwUlJgVhWgVQYvKUAuTNByCkwObtihLx63cpAUDulc1R5pRmhSgKoQceqxrqnVhGIQG9+5nVflvhN0fhO0TQHycAxtzqa/ZnU05F3BWp4m/w7QvX2Zk11DjfsxGIBQdSQPGMzu68PbxmBPb7xgiSpgzQIzTGDO7SvQ0VCDX31hnqeyfCcSvK1Afg+Ql12qqgIrVv0zDJMHA2zVOXdyMxprI7bgcXJr/gG1YQdFziulKkB9rlNgrAKo8OuoVIb/fukD3Pq/m/jtproITprS7Hp73VCRAEg4r0RK8PzxPlVFpMD29MRhmqbQCdr5ElqaCdqqAmTnPacyeBZgi7BKRTkF5tilPY8CBGQCscFkesQZoSkAqkKsL6m/gyTFFJhbxcTtSV9UqlRVFvY0iPv3wgOgPOkvwL9VvsjebAm8F/8PYE8HirBtYydG2b8gNykDgDE1YUxprcP7+wfQPZBE90ASq7fux/mzJ3raJhVWFVghE7T7k56ql1GoSPVPbP7Igoj6WBjLv34Gtu7pBwBMaKrB4W1j8j6PX8NQk8UqQHyYpTcFqJD/B4CyM/Mr2w8CyAQ+jbUR/MtpUwsqSV6J8gDIn+PNMEz85P/e4U0Yjzu0Cf90ir3YQZwGz2eBFWOCLqIKrH1sJgBKpA0cGEgGXgYvzgJj2yk3Q+UtIhTfE6cUmNVLqXAVmHjcRsM6BUBEeRAbfrFj1N9RGEIZfIHn9WyCtilA1mPExZaXdB5ryjZzQkP+1/cxUGTsLaICDHAOxlggwdSMHAVIYc4M6RqWf/0j+PDAAL7z1Casemc/X33mwzBMbNh+gCslJxzWjKY6exrPqgLLnwLz8t0raID38Fxi1UuN0BBwQmMtJjQ6p0Nl/BqG6jQlvRC8kZ3LcRjFKUDWc7PKxX87dxYunjvJ07a6hXuAfFKAXttxEHf/5S1++4m/f4hFx3TYlE51GXzxfYC8WKKiYR3jxkSxry+BzoNDVhl8rZtGiKXNAmPnXrGbeFpQR5UKkG41yRQrtwzpHJPPBG1XgEZmM0QKgKoQceqxXsRFyIlkUWXw2cGIbrvtGqbQbMv6u6YVV9FmKUD5AyA/lTJGMRVggHPKhW0bu5jn9OhI56bAgMwqcHr7WH7Rl71DKv7w2i585X828NvHHdqIp5acbruPVQXmfyNEMeixqX9eFCDB5Omlb5SMX6MwivUAjcmWKQehAKk8QHt7iwvcvcBUw6RPigBTVMaNiaFnKIlEykB/PKUMgGxVYMV0gi6iCgzIqED7+hLo7BlEX9zNLLDi56aJfYBY+l1cEIh+MqUHSAiek2kDIT1zHzm4yWeCFosxRmozRDJBVyFWJ05NqMQp/XmTQspDDBjymWvdrnptJmhBwVLdx+1KfG9vHPv6EtA04Mjx+dMcVaUAOSgObP/XZE9Y8smbxRlO0nxjdrXpJgDaklXO2AmapRZECpqgS2iEKKtYDC+fTz6Tpxf8KiRIZGeBefcAeVOArEGoLhQgdpEVLky8d5XHwN0LfnuAmJl5fEOMm35z0zdWlSTvA1RECo59Dbx4gACrEuzdvf3cR5S3ESJPgRWhAAkpMLY/BhIpfq5miwNdU39PxGNaVOlyTdDs2LAeq1JxS6loq2ZIAapCxEaIqgi9WKxRGJrN+Jo2TMdeMK5TYMIq21ph2e+TOeGYtvfSH0/hD6/twkA8BV3XcMYRbbw3DlN/prbWK3tdiLidbu+FYgahZrZF/Zmx2yxnL8v3hbwJLADqcREA7e/PbPvZR43Hb1/eoVypFyyDL6ERoqjYaJoGTcvK8UWkwEotu2Vvzy8FyHMfIOEC5gae/nDjAQrZUxOGYWJ/f9a7FqAC5LcHSPwuRkLqi62ojBfTo0p+Hi9l8IBlhF73fld2W7W8QWopCpCYAmPff8PM/L4mEuLBdH00rCwOEZubise+GxO0KgUWVSiNIwEKgKoQsUzTrx4mgF3CF3v0pAwTTjMX3Z70xVW23EiLoaoqevjF93HXn7fw27Gwjn87dxY+P3+y6/RX5rWqRwFyCoDYxYIFQPLFIyV87ipY11k3ChDrXzSh0TJvyvAUWAEPUDGNEOUgLqRpSJmmp4oyawxGaacpvxYRxcwCA+A4zNIJboAtQgE6MJDg77N1jPvWDV5xarRXLKLB2anJIp9dF7JmGZY2C8yjApQNgP68KdNbaWxNJG9larEm6LQhdAKPhGyLv4FEGjWRkDAjT33i1vWMz1P0ZAIeTdBKDxAFQETA2DxAPpqg+SorrNsuevkuDPEiTNBOKyxVMMdm6xzRPgZjasLYsK0b33lqEx5a9R56sr6AmR2FO9iyg3X5651Y+95KXHTCIbhu4YyCj8vHPj4HzNuFpJACxKvApJO3aoisSGOd+xTY/uy2M99QMm3mdGEW+6p4eR/54JVs0oVBzxrAvFSUFTrJu8U3E3QZFKCDA0l0ZRWcYjxALPBtrot49ip5wanKqFjEwbxs/8pqg00BcjD5uqGYMngAOO+4DqzY3ImDg0lo0HDx3EPz3r9Y47D4vmsiGbtCTUTHUDLji2qpj+YtgWdEQpkAKKFQgNg5RnVspBTtOKJkgibKhW0URhApsJBuO/jzpTg8l8EbzgoQe0nxYGPP/6njJ+Kaf5iOpS++j+//6U28v9/yrJxyeGve1waA6e0Zj9BgMo0d3YN46MX3Sw6AmJnUayrBSbVLCqs6wHlOj5Ph14sHiKVBmAIEZC7gMUHqSxW4oBejqjm9B6s1gOunErpAl5oC80kB4p2nvV04C3mATNPEC+/sw//vmbex/oMD/PduFKColC7iQXuA6S9AMEH7rACFbQqQvbmjKgUGZC7YzOTrhmLK4AFgevtYLPvKGa7vr/JnuUHsG8SO17poGEPJBA98WEVYvmMjousYgmHbj7LKrEoPqxZisRFqgqYAqAoRjWpW+WLpz2v1AdJ8V4DEi4zjRVCRb7ZM1iFomoYrTpuKRcdMwJbdGRPvuDFRHD2xseB7+8iRbfjbN87EW7v7cNUjfy9aqt3ZPYjP/PRF7O6N8/fh1QPkVAZvKUAOJk/+uauf11MAxFNgVrl4Mm1CXDDyRogFOkEXlQLT1J99MR6gagmArNlj3ranXlEF1juUxKW/eAkf7B+AaYJXFTEiIQ0fm9le8LnleVN7i/SteYV7gFL+pJzFxVlEoQCJx5JYBQZkFhJ5hJAciimDLwZmgvbaCHEoq7KI7UrqoiF09VsqYr4xGIxIWAfi9n3Hzr3smFcVCMhpMoBSYEQZEWfe6Hk8QF39CX4xnNRcW7DZGatiCYcy82W4MdWNAuRh4KS4UlPeR3gvqgCro7GGV1x4YXJrPU+XJFKG58GbQGbEws7svB8gk5pjTdDc4lQGz05E3AQt/d1wUM4Ylgk6fyplKJnmF1SbApQyAOG66HYUhpegReUfsD2XhyDEMkGXdpryexaY0/5yQqUArXpnP17f0cNvR8M6Fs87DP/60cO558xN6T+/MKXLrAD5XQXGTdCWAmSrXhLU0lDIXsThtfCh2DJ4rxSrADHTtKjW1vE0alYB4gFQ/hQYoA4kLQXInQla1W5hJEABUBUiKijsOyhfONZ/0IWLf/YS//3cyc34f186Ne/zyp1sw7qGZNp0FwC5HDiZNk1bGb+ImCZjiNUOfhATVufJtOk5XfH82/sAADcsPBIXz52ElvqoZ6ncKW3JAg4nBchpvzFY07WeoSQMw3S8QLL0VySkoakuws2QTl1hC1WBeUqBOQyaLKapIpt9VOdhDIkKv9LIboevyjAFSGxk9/qOgwCATxw3AdcvnIHWMdG8JdVOcKNt9qK5t2wBUPbi6nsKTHe4cFv/z3iAtJzHuoWXwZfQW8oN8mfjFqYAMa8gYAU6LACSZ+SpUAWpjn2AxEaIikIG8gARZUNUUJyauL2xqxdpw+TNBV/NnlDzIabAgMwXPJnOb0xlJ7hCAQo3QadNoWGZUwrM+h07oLxeVJwQnyeRNjw971AyjbXvZUpcFx7dgfYG7yoUoO6tAVgBR23UqnIRVaq0JE/LMAXINIHeeIrflmEG6Nb6GDRNU5ohATeNENW9VpJpA2/s7EHaNFEXDWHG+LH8PXB/hYvgtxD+lcGz1y7paUqYBZY7DPX1nZnjdd7UFt72oRiiUmqiXCkwvxUgds6I6BovuVZduAGrQSw793lWgApUW/pFsSboobwKUCr7040ClNsaRA6AVH3mVB5O8gARZUNUAqwUmP0+7EJz0pQWrHmvy1XKJymt+DNfcMMXBajQLDD5Pl6f3y22AEhK+RRi/QcHEE8ZGN8QwxHt+Rsv5sO64Ko9PuJgVbEHU6ETcywc4tUgPYPJPAFQRgFiZdDRUOYx8mqdlx47lcE7VCDe8OQr+N+NO/nt737yaFx+6hT7e8gJfmH7uxt88wD5lAJLFjCNO8FmgbEyeNM0uQJ0zCGF/W35kC+yrApsXIAl8AAcS9WLRRzUHFUoQOLriFPKEynDcwBklcGXtMkFKbYMnvm5YgoFiH2HmJ+MqYsqeKWe8Ppy+i9fCkw8hkeqB4g6QVchtkaIDiW8vKmecHEoJEfLKTA3je7cGj8LTYO33UfhAYo5NSLyiGgc97paYemv06aP8+wdkrcByFUcWMAhDi9Ur86cn9uNEZr5QFqzaRBVVU3aMHlQ7ZwCUzeXfGdPn+15397Taz2vkwm6iCDEjdHTDb6lwIoM1tlFajCZRtow0dkzhH19CYR0zVWPq3zIK/O9RY5v8QpXgHy6IIpzCp2+r0BG/WHHJh+H4VGFKrYKzCvFToMfyu5Tcf4dN9JLClA+dVTVq0lWmZUmaIWCT6MwiLIhtiJ3SoHJXYWBwl9OcRQG4K7M2a3vQQymnBWgzE9VFZhfChAAxz4ihXjhnb0AgDOOGFfS6ztd7FUKkDo/77wv3ARAvBNwfUYFUFXViK/rXAVm3y75sfOmtgCwJHvASm/mfPbFmKCTfpmgMz/96gPkeRq8UKI0mExz8/MR7WNs34ViYL2C5DL4IOeAAQF6gHR1J+gkT9da36ti54Gxu5eyyHFDsQqQNQleVIDUJuj6PMcGS22LKWxZZfZqgiYFiAgU07RW5vn6ALGTeVEBkGSAyzdPpxgTtJMCpHovbj1GXrCqSNyvvLr6E9i0M3NhOu3w0gIgx89MEbTaTk4uqlOYUTZvAMQVoGwKTLGiFi8ahRQgp47WzJQtrnCdjNwlmaB98gCVMgrDNE3+vr0G67GwzoP/gXgKr/mU/gLsfYDShsmbKJatDN7nRoiRkK4cs6Hypagu8G6o+iowhSpejAk6qmhWKVea8kWOahq8sH9kr9lIgTxAVYbd7Kc7+jBEKTMSypiZC63GEjkeoMKrcl6m7rYMPm0694JRyK1+m6DF53JzsK7/4ACefmUndnYPwjSBGePHFm1+ZjitTFPCBZS1IBBb+VvKmfNzu1KAuAcocxFUrajFtIFTAOT0/WBBMRvNYVeAHNKfRQz19csD5NSXyQsJF/vLCU3TUB8NozeeQn8ijU0sAJpYWvoLsPcBEsdgtNQH6wFi+8AwvXdiViGOwogo0i2q7sTWcea1CqxcHiBrAZFKGwXblDDyK0DeTdCqYagsjlQVJ/AASFDbijV07+kZwrd//zq6s+erc4/pwBWnTfX0HEFCAVCVIZ6kdR2OfYDEC000pCOZThcst0xJKTA3fV4SLgMU8bl4I62QgwogbKbbPkNecJsCM00TX//1BmzvGuS/++iMtpJf30lxSAn9nSK6jkTasK1eLeWscAos30DUfVkVoLXeMkED9hU1OylqmrMXwnmmWeaxY7NqlHhSdDJBF5MCc9PrxA1OfZm8IO67YtTKulgoEwAJCtCxh5auAPH+LGmDp79a6qOBjsEA7PPQkmlvnZhVWCZoTaku8Sox4ZwSKVYBcviO+o2o4AylDIxx+ZmIk+AZOSboRGETtEohs7o8268B4rGRzwPkVQH63YYd+Msbu/ntjdu78U+nTHYdDAZNdWwFwZEVIOvCYb+f2NBK1ThMhWg0zDy/ulpJxG2KSjyQnCRmleGOHVDiaqdU3JZsvruvH9u7BhEN6VjyD9PxzUUzcM2Z00t+fdX7NATTsW2Yo0KeznduaHClANl7wShX1EwNzBNsOQXIPACKMQVIlQKTnqsoE7Q/KTCnQgIviPuumOCCeTXe39+PPb1x6Jq7Ib+F4CvzpIF9veWpAAPsgYgfPqCk4AHK1whRDNZDRSpA7DgsVxk8YAU1blD1RmPHwGAyc0ywQCjf4kCVApO7zTudqwD7+bvYRohb92YKJj45eyJqIyEkUga2dQ0UeFT5IAWoyhC/iLruPMhRnNeS+XImXXiAsikYuQrMDw+QkGZIK+Rq8bbSBO1xvEA+3AaEf9uSMT2fPLUFN5xT2twwEZV5WFT2QkIjt6RCAcp3YvaWAstcCGOqFbXQedf5feRPgY1VpMBMp+C3pE7QlR+FwfadWGXohbrsSv2RFz8AABzeNqZkZQuwN6jb25fpYB60/wewB85+VIKJ38e0oUqBqTxAhc9fKtwsNPxAz6rzibThSTkZUipArJlm5m+uRmHkbYQoZQGEzVN5OItVgN7b1w8AOGtWO97b14/XdhzE23v6MK2t+DYjfkIKUJWRFg7mjAKU+b/p4AHSdc31l5OdUMLcA6Q2uTJSaYOvlgp6gIQVPp8I7pAGEVUAPgvMRwXIbcnmc29nAqCPHll62ktEZR4WV6mRkCY0Kcvt0eE0DR4oHACZpon9/fYy+Eg4ty0A8x7lk6KdU2CZ22NqFCbo7Es49gGqQBl8MQZsmVJTtc11mWB07ftdAIDZk5qK3hYRsT+LpQAFHwDpDkF8sST5WBbdoXzbzyqwwgsNvyimfw5bUIgBEKskHORVYIXV0TDfj84GZ9WxoVrAWqq6Nw/Qu3szAdDhbWN4b7W3d/fme0hZIQWoyrApQJpzB10xzeT2gi+nwAr1ARJPQIUCFLGiTJxlZrsPK0fOvl5a6BlUbg/QUDKNl97dDyAzSNVP3ChAKv+Cm/4khQKgnqEUvyDJHiDx85S/C8r3ofjumaZltucKUCo3BebYB6iYRoiR0k5TxXShlmEXMK9zwBg3LpqJKa3bkTJMxMI6rjzdHyOomJoo1xwwRiSkI2WkfakES3EPkI5o9mMSlSXuEbKZoNV9qgrBDrmyBEARHb1xb+Zhdl8xBcZU0P6cYaiFZ4GlFApQfhN05mepCtDBgSRvyTF1XD2OGD8WAPB2to9YNUABUJUh9mDQhD5AOQGQkBO3SmHzH2RysFEoNSAGEIUCFO4nEhSgHBVA8oGIzx+IApTnxLz2vS4MJQ10NNTgyPH+yrEqv4sY6ER0tQcoJah6ThQyQTP/z9hYmK8gVVI4v+C48ACJKpV4sVGlwBx7QHlMQxmGicGkvykwj1YRG7wLdJENO485pNGXsncZ8cJUrjEYjEhIw2DSHw+QOJjXNLOKpbIRovV9VV3g3VCuTtCAFaAOeZgHxkdhiApQ1LsCpDKTy/tR3QcodwFbzCiMd/dlAp3xDTHUx8KCAkQBEOGA06wWWb0XB9a5VoD4KlbyABUIgHQtf6pEfC7DyKMASflmMWDzVQFysVp57q1M+usjR5bW9VmFqoMyO8FrWmY/qOb0WD068gRAdfkVILbiahWMsKrvh9UU040HyPqdeDJtUKbA1EGcSk3Kh6gqVZMJOlqkAhQU7MI0mEjjjV2ZPlblUoBU/aWKJSkE5FqY/S73+6oyQXtNwRkuFhp+IbYpcEu+Mvj+RAppw+RBkthkUyaiSoFJw4pVHlPVQqwYBYilv6aNywQ+R2QXmlv39vnSOsEPKACqMuQcreZkghYCpZhL0y/vZOuyE3TcpQEasC4yKcN5Grx8sHkJsLzglAJLpQ3878ad2NcXx59e7wQAfPTIdt9el8Heiq20VKq6svwTuR6gUkzQ+6UxGIB6JZj0UgUmSCfJlPWeWAAktl+wUmD251JNnc4HS38B9saRxeBm5EshLAWoumyT7AKYSBt4szPjrRjfUL4UGGD/ThSL2OlZ13KPX3aOElOQ7Jwhn79e/bAbb+7K7ItJLXWYf3ir7e/l9QB576CsboSY7QMUT3NlVPy9irCkkBmGyRfSTAFih78tBabwIhZTBcYUoGltmWG/hzbXIRbWEU8Z2N41gCklDAH2CwqAqgy5B4PTOALxYlmqB8gxBeZh+CM72DImaLWSIcutfs8BYzjtj7+9tRfXP/mKbXtOn15a12cVqnSPrOzla1PvxgPUM5RSDr9lwzBbhUZ4qlEYKeGC44RKARL7B7EeJIm0wVd0hRshurtYMqm/JqKXvFJXBaReCWJkix+01EfxzUUz8PIHBwAAhzTV4pRprQUe5Q8qs3KxpIQKVUPPLpCUPbJUJmjr9XuGkvjMT1fbtumv138UhwtVRzxFX7UmaJUClO0EnUyjPzsIVdfytyeRFz7i4iOvCZrbK6znLqYRIqsAm5oNdEK6huntY7BpZw/e3tNHARCRi5xCcKpgSasUoDwHmW34JVMhQrknEBHrpF84QBHNdIWa4bH34kVh8oKTB2hP1iMxsbEG8w8fhzOOGMdTSn6iutjLs4wiCg+QmwCIqS5pw0R/Io0xkgQud4EGxP0hbA9fUTvve1WfKHGgrlilEk+lURcNO6pYYoDsBjedbt3iNNLDC/Ei54CVgy/70LuqGFTf4WIRjw/uARIutqJJmqFaROzvSyCRNrLtQXT0J9LY0xO3BUC8VUMZPspiAgfVMFTWRiFtmOgeyKi/9dFw3vS95f2zik4YrEmt6lwl9woS34cnBUioAGMckQ2A3trdi7OPGu/6uYKCAqAqQ1ZPdIfqGfFi6SY/K56krE7Q+S8MvETdRYAiptNUnUQz78W+7V6e3wtOBytbWc2Z3IwfXTzb19cUUTUQlD9XVQmxmwCoJqLz3iIHB5O5AVA/qwTKrwAxP5irMnjh65EUVEExABpKGqiL5jFBexyFwTrdlpr+AoprwiiTrFIFqJKozPXFIvb5Ydd0e4+sXF+hSgFiymFzfRQtdVFs2d2b20U/ezvoYaiAVcruxQTN/EJiYUidcBywar9CxQFyoYUtAMq+d2tRCq4o81FGggLk1QNkGCZXgFgKDACvBHunSirBAj2au7q6sHjxYjQ0NKCpqQlXXnkl+vryv/GtW7fiwgsvRFtbGxoaGnDxxRdj9+7dtvu4ed5XX30VZ5xxBmpqajBp0iT88Ic/9P39BYGsnlgGTvl+mZ8h3ggxf3RuC4CkTtCFyuBdeYCEAKhQGsRSgPyfAwY4e4B4fw2fU24yqtRiUlrB8tWroVCA8pyYNU2zukFnV4KmaeK6X2/EibevwOPrtgOwp8DUw1CzikaeYEvlAWL7NBLSsuX8mfuw4NLRBK2oNsmHXz2AMtsC27YVQ7GT4EcyMR9N0OIsMNXxq1SAFIsIZp4XU6fy+Y3dPehhqIB/ClA4ZHXIZtV++QzQQG6AakuBSUU2gNUhW7WA9epl2nlwEPGUgUhIwyFNtfz3vBJsT3X0Agr0aF68eDE2bdqEFStWYNmyZXjuuedw9dVXO96/v78fCxcuhKZpWLlyJVatWoVEIoHzzz8fhnASLvS8PT09WLhwISZPnoz169fjrrvuwr//+7/j5z//eZBv1xdkpcDpwiEOHFX1eZFJSmXY4nMXUoDceIBCggm68DR46fnLlAJT5daDQN1czP65RlUeIFO932QaazMnPmaEXv/BAfx2ww7s708gkTKgacBxQqM9VrlkrwIrnAJTfT/kQKCGl/lmAyCHi4vXSiy/BqGK21JKFRi7iPitVg5nLGWxdBN0QuwDpAisVL5C9vri93OIdQ+PhBxnwPFhqOVIgWWVm0JzGkVUs8AAoD57LHAFqIA6Ks8AFJvssmNbXKjw/myKsSPyYNdCsPTX5NZ6W9AqKkC9Q0n0x1Oex2v4SWApsM2bN2P58uVYt24d5s6dCwC49957ce655+Luu+/GxIkTcx6zatUqvP/++9iwYQMaGhoAAEuXLkVzczNWrlyJBQsWuHreRx99FIlEAg8++CCi0SiOPvpobNy4ET/+8Y8dA7B4PI54PM5v9/T0+L1LXCFfBJ0uHF47QVuzdjTLX6SpV0gMLwGK6PEwHC7k8oWo3CZoa3UYrALEK+Jsnhu7Byj/LLBCAZC9EuzhF98HkJm3s+Rj09FUF0H7WGuiveqCInuSVKi9TPbAKRbJDPlk6pqj+udRhRlI+ukB8qY+qbCULwqAGL6mwFhgrWtWB2NF53J1Gbx1P3aM10ZCgMP5jZ87q9QEzc+L0kKtLhrGgYEk9vYxBchjCkxqsgvY9yc7b6sWYuI14Lcv70BHYw3OOMK5hci72Rlg0ySj82EtdYiGdQwlDRz7738BAHxz0YyK+dgCO5pXr16NpqYmHqQAwIIFC6DrOtasWaN8TDweh6ZpiMUsA2dNTQ10XccLL7zg+nlXr16Nj3zkI4hGrTTAOeecgy1btuDAgQPK177jjjvQ2NjI/02aNKn4N18Cchty9h10MkGHXfYBssZgCCeQ7P/TDiewYsrgMysEp4ug/UIUmAIUUsu1cd5iPtiLWDivAqTbftp7BXkLgHoGk9jdM4Tl2ZL+L350Go4cP9YW/ABOVWDFKUByOTjbl+zC49gCwcHM78RA3J9BqID3EnwVCaFKicgQ8TMFxkz5YfUwVHUZfO73czDBgoeQY5uPSozCGCqmD5C0MGTHAht5UmhxkJMCk5rsAnalVuzQD+Q2QmQ3v/mbV/H5B9fimc17cl7z589txUU/fREP/O1dAMDUNnsAFNI1nH9crvhRKQI7mjs7O9Hebu+xEg6H0dLSgs7OTuVjTjnlFNTX1+PGG2/EwMAA+vv7ccMNNyCdTmPXrl2un7ezsxPjx9sd5uy202vffPPNOHjwIP+3fft272/aB+TgwWn1qlaAnA+ypMLDYHVvVj/GUxm86AFyORDT7aR5rzgqQA4nFr9RlcHLjdzytan3ogA9+tIHSBkmTprSjKMnNirvr7qgJIXOu06o+uckU/bHWSZPuwdI/sp4HUfh1yBUQBzDUfxzVGsZfCVRTRsvFlGhjihStilFabbKw8i7h0dCjuZ39j0oRyM+dnz4owBlnmvTzoO2207kpMAUHkMxDcgWCCqFLBzS8W/nzsLp08dxVeePr+2yvd7e3jju/NObWP/BAXT2ZAbzzlHMvPvRxbPx5u2L+L+rzpiW930Eieej+aabboKmaXn/vfnmm0VtTFtbG5588kk8/fTTGDNmDBobG9Hd3Y0TTjgBesAJ21gshoaGBtu/SiDPUpJ9M6r7uSlRVM3cUplcRbyc9MWLvpOSwa61sgk6sADI0QMUsAlaYVzPqQKTTk4AHANHGRYAPf3qTvz3Sx8AAC4/dYrj/UuuAlN4gGQFKF4wBeYtABpM+ugBUlQLeYVSYLmoOg0Xi6hIqszVKtO+qgxe9Pk5NcAs7yiMIkzQDgs1VvzAGl6ylhhOsC7vKgWIYTNBywqQtDj6whnT8KsvzMMdnz4WAPDslj22BdyfN3XCMIEZ48figX86Eb+++hQsPKpDuW01kRD/V8ljynOC/frrr8cVV1yR9z7Tpk1DR0cH9uyxS2SpVApdXV3o6FDvFABYuHAhtm7din379iEcDqOpqQkdHR2YNi0TJbp53o6OjpzKMXY732tXA06jMHJXMdZK20sKTKUA+eEBEv0iTkqGLr2XUidsO2HtD/tJZ6hcKbBQbmBpVbFkFSCFf8FtCmxCtqri1Q8zK8GOhhqcc7Tz91pdBWbatkNFvmo2ZxO0QwrMYyn6AJ91VLoHyGsJvopq7QRdSaxO0D4qQCFNnbJVHBsRRWA7JChA/fHM/3PU8yruBG2apmOxxpJ/mI66aAiptImaSAhXnDYl73OxNHveAEhhgk4p/FYiJ05uRlNdBN0DSaz/4ADmZRtvMkXowhMOwaJjqvs6y/B8dmlra0NbW+Hp2fPnz0d3dzfWr1+PE088EQCwcuVKGIaBefPmFXz8uHHj+GP27NmDT37yk66fd/78+fjWt76FZDKJSCQTJa9YsQIzZsxAc3Oz17dcVuQvqZMHyDoh6C6rwLIBUFj88med/Q4rOC9l6txPZOYJgCQ1y0nqLZWYUxk8U5zKZIJWNRcL5zShFBQglwHQ5+YdhkhIR99QCpoGLJg1Pu8qSpbCxf/nU4BUHgo5lcpTYCn7xabQZ18IP1NgXpswqqjWWWCVxE8TtJUCU3uAVGXwIT33ey2qvM4KUOZnuabBA+6rwFJCw1q5OGTetFYebLhBboSoCiIzWZvMrEkrBZb5m5MSHQ7p+NiMdvx2ww48s3k35k1rxf6+OF56dz8A4NxjJrjexkoT2HJm1qxZWLRoEa666iqsXbsWq1atwpIlS3DJJZfwCrAdO3Zg5syZWLt2LX/cQw89hJdeeglbt27Fr371K3z2s5/FtddeixkzZrh+3s997nOIRqO48sorsWnTJvz617/Gf/7nf+K6664L6u36Rk4jxAJGvpDurkunavaTkwK06p19+OeH1uK/V2fSKzEXCk1IWGU7BUBO0+ADU4AqlQLjvi3rdynB4wCIKTDBA8TLc/OfmBtqIrjy9Kn42oIj8NWzjsBRE/Ona/MOQ82bAtNt2yU+R5QHQMzkaa80KVUB4n2AfPisvPqPVJAClEtUSrGUgpiiFy/crGRd1ZsmolBaBxUBUE4LEZcLDT+oYSZolykw0Sxd6sKQfT58FliB6ly2G+VCHBULsl2cmRH6z5t2wzCBYw5pwGGtdSVtdzkJtBP0o48+iiVLluCss86Cruu46KKLcM899/C/J5NJbNmyBQMDA/x3W7Zswc0334yuri5MmTIF3/rWt3Dttdd6et7Gxkb85S9/wTXXXIMTTzwR48aNw6233pq3B1G14JQCcyyD17yVwUdsKyj1heGn/7cVL7yzj9+e0GSvKlIh+omcZoHJwVzZy+B5I8RgL2LWYNhcCZ+pEco+QGn1fisV1cwmsfGcE6rS9YT0uFiOCZo9Nv9nXwhfTdA+lMHHyQOUQxAeoHBIswWZibSBWDjEv6+FpsGzY7w2GrINaBaxOkGXvNkF8doHSOwYXao3kqnN7PNxqs7VdQ0wrCHWblLxHzmyDdGQjvf29WPr3j6e/jr32OGj/gABB0AtLS147LHHHP8+ZcoUHuEz7rzzTtx5550lPS8AHHfccXj++efdb2yVkJMCy/6UF872WWDuO0HbU2DqEwTzX/zLaVNx0pRmnDmj8MR0MZhyLoW2b3tgs8AcO0GXRwFSVddZrfx1232ShkIB8vnMrKqqSfIg1YUClC8FJnkcHFeZXvsA+TkLrIRGiE/+fTue+Pt2vL8/s0gjBcjCSwrs9R0HsbN7EABw1MQGHNpsVwnEPlmiIpxMm4iFncrgc7+fg4KB2DoO1YvHcihAXk3QYmFIqaM65M/HqcjCUoBM+/3y7J8xsTBOObwVz721Fxfcvwp92bYVwyn9BdAssKpDjr6d5HuxpFEP517gZFSdf1X9agBrlX/GkePwDy6CH3F7DdO5isAxBRZQFVhOHyDWYr5sKTDBAySZoNXT4O2P9wtlI8RUbkAso2yEyB8np8CkURilpsCSfvYBgm3bvPCff30bHx4Y5LflC/doxq0J+v5n38Fdf97CbzfXRbD2WwtsnZzZ1yKi67ZzVCJlADG755G/vsoEzZVD3VH5Y69VjSZoq1Cj9O+9nKJ0rM6V1Fl+ripQeX3RCYfgubf2oncoc6yePLWlKia8e4ECoCpDjr6dVs5it052IMfdmKAVKbCUJGHzIaUe5H6x1NjpIpg7Db5SZfABp8AU6R7Zw6DuA2T3CfmFyiRvVYF5K4NngbTlAbIPe3TsA1QNKbAiFKD+7Mr2O+cfhRnjx3oyoY503PQBWvri+zz4OfaQRry24yAODGRGIDTVRXMeH87OmAvpGtKGaV28FSnbEH99IQWWKmyCLm8VWGF7goif5yjebFXuA5RTnZv5KfcBKnT6/9Txh+CEw5rRF88UY0wbNyb/A6oQCoCqDLnhV+EyeHXZqIw4xZthVfkY0n2zF0cPgYlopJO7HjPkcuTAZ4FVOgWWrwqMVbAI93EaJFoqXAFSmKDzjsLI0wfIaoRoV4Ccuuyq1CQVy17diU07e7C9K5Ny8nUWWBEKEAvsFswaj0ktpP6IOHmAfva3rfivF96DYVpzq776sem4buEMHP5vf0TaMG0BgX1Qc+Y5oyEdg0aaH8PqMnhFCizhwgRdxj5AvBGiy07QfvoiZe8fe9/yAkveT9biuvB5ebgfExQAVRlWg8PMbc0hABIrhtx0glaOwlCMYxDv66U6S6kA6er7WApQQCboCk+DV81v4yZOaRaYshO07x6gXAXIXRVY7vvIGYXBJf4CKTAXCtDuniEseWyD7XctwlT7YrGUx0yfFbfeCtM0yzY/bjjiNArjf9Zuw55ea67iF06fimvPPhJARhEZSKRtpmBRgWbfx0hIw2DS+s6qRrcoZ4EJKSTHMvhyeoAi3hSguI8KUE4KLK1eYMnHuVMRy0iEAqAqw5IfswqQQ/8UrhS57ASt9AApZukAxZmTxe0srAAF6wGKKVJg9otZsCkw1cqTmzilFJgo37udBu8VlSJmXVAKK0C2oa5OfYBYJ2jT/liGm3lcnQcz7fPHxsK4+KRJmNJah6Mm5C/xd4M87yif6iUSTxncLxL0d2Y44mSC7s+qMPd/7gQcd2ijTSVgAVAibS3WmAFa06zvTTQcApDKrwCpZoG5GYWRvVmqydgNPAXmUgHyM+CWU2COI4qk87JToDQSoQCoypCj70IyrtthqMwoqBqFkasAuW+AmPtchjC5Wb5P5qdVBeb9ddyg2h+JtHUxC7oRInufthlaUmpT7tIKBDejSOUBclMFpm6EKHuApBSYw+o67PA9FjkwkBnyeGhLHW75xFF535MXQkLAkzZN1yc9UaUgBSgXJw8QG2R7zCENOSmSjNqbtJV782Bc+C7Kz51S+OPyN0LU+ecuexwtldLd+ywFlQm6dyiJb/3udZ4enDe1FV9bcETmfkmmipd+ToxIC8FCJmh2/nFKlY1EKACqMnI7QTukwAS/iErxkLFGYQgnEAdvhHyRcwPb3u6BJLoHkrZtl19PrgLz2wStagswZLuYBa0A5XoTmM+K7X92chJPzoVa0BeLVQXmXM2lQpXKk0eq5PQBcvAAqZ5Lhn1vmuvyzzjyin3ekfvHMTUhLPjsCAvLe2h9poZhcgVI1cJAlRJS+dHkRYyqEaJq5MyQQgGSv3NmQEqrCtU0+N9v3ImnXtnJb7+4dT8+NrMdxx7a6KsCFJGCSMcu7dJ+cjuSZyRAR3WV4TQKQz6IxVRDNOSmD5BzDj1HASqi6+2k5jrMGD+W3z6spQ5HCLeB3CqwoKfBpwyTH/RMgtY0/ztPy6ikd/mkIpfwmqbpmD4qFbHcWJ73k28WmCpFmiiUAnOoIHFK5YowBai5rnTfj+q1AW+9gMplmh+uqFJgg8KFvj6Wu99UfXH4WBZbesu+qGNNQkPCF4tXUqr6AAkeILnIg929HFVgqmnwL23NjIy44PiJ+MiRmbFSD734HgDrOPLDF8nOc6aJ/EOqJaW3nH2SKg0pQFVGTidoh9QBu4B57QQdVniAZK8K2wYvgUI0rGP518+wrdTkHLtcBean3CtvCyORNlCjh2wG6KBz/6q+M7KJU54GL368fpugxf2RTBsI6SFXs8CUZfBS/yC51b9zE0w3KbCMAtTkswKkSx4gt5ABOj8qE3R/tomqpmVUGBnVuYqn54Xvaa4ClBuwhxRpZJUJWg66K1EGnzJMpNIGQrrGZ2YtPmUyIiEdz721F8te2YWbPz6LL9T8mI8oHtvJtOEY2MjFKaMpACIFqMpwbIQonbfZoiYspMDE4EXGKoMXTyB2FUK8H+Ddm6NpmVRBJKTuYipfBItRmtwgBm7sRFsuAzSgDhxyFKCQfd+Ln4Hf5kMx7ZmQPBV5TdCqRohSOwXnPkDFpMCCV4C8jMOwSqrpNKlC5QFiE9jro2HlOYB7YhQeINGP5maQZ0RxnHETdDSUc2FnsBSYiyrvkhEDmUTawFu7+7C/P4HaSAizD23C8ZOaMOewJiTSBv5n7TYMpayFWqnIx33BPkCSAjQaPEB0ZFcZslOffQedPEAhXZqd46AC5esELZ5AxJWZ374H+SIY91HuFbEd+CwAKmM6Q9WCPyX5HMKSgVNU6YNqhAhY+yOZyv0+yORrhMg9QFKVS6FRGJVRgKz/e0uBWWoCkYuqDxBrHOnUv0mdAnPhAcpbBs+OIZPfvyZsdYJ2MkH7rbSqEM9t8aSBF7dmZizOndLM3+MVp04BADyy+gOsys5g9CPoFk3lqbQptFjJr846tbIYiVAAVGXw8vZQ/hSYKOO6C4ByTa+qPkAJWwDk7wEgXwSDUoA0YZ+w1yjnxUw1vkQ2ccp9gMQLs9/Sc0aZs6/Wky66TudrhMj7AOUMQy3FBB2MAqRpmrWQ8JICEwy1RC6qURisg3d9TO2uUHVGVi3OopK/SFUgIHrbAPvE9dpodZTBZxrVanz7Vmf9P6cIHcU/fswEtI+NYV9fHM+/nQmAGmpLXwTo2Y7aAEuB5QaagLMJ2m27iOEMeYCqDPYlZBcmpxECogKU8dtkzG7xdBpA7sGjanynUoDEC5zfJwj5QCum4aJbYiEdiZSRowD57TdSoZwFJg1DlfsApdPBBUCZ19ORTKfzrqhlVEqWYx+gFEuBZe5XyGipgpug6/1VgNjrG2kzx/Cfj3KNThmuqEzQTAFSGaABdVk4V0eF74yTAqSqAmOvz1KWQCaFpErxA+VthAhk3nMyncJgIo0173UBAOYfbgVA0bCOey6dg9+9vAOGaaI+FsZlp0z25bUjocxIkUTK4MdmoSalQY3kqUYoAKoy0oK5GXAehSEGQFq2GeJQ0rDl1kX4hctmIsyViJMBBiXygcZngQVwgYmGdSBemRSYqrpOzqvLfYBsClAAK9NotgEdV4BcdIIWg2/WQdl6XNYE7TAKw1Fmz6MAHehnKTB/FSCAHU92j9yzW/bgmTd2A8h0nP7iRw/HGEG5IBN0fuROw4BlglaVwAPWsS4qzawnlfhdZN8vuYdNWJECS3EFyFq8ieqHbIIu5ygMILPo6osDG7d34+BgEvXREI49pNF2n1OmtdpUIb+IhDLXhZRhOgY2Tibo0ZACowCoymAHK1eAeABkv5+8iolmv+hOvYBYrw57CqxwisNP2Ouxa2A8wGBLXkFyc2EZTdCGTTmxn8DDUgmv+BkE0YGVXVzYPnczC0w8URpmZjyLrNqJKTDTtAIM+T2o0oIyQaXAgMx7icP+mXzjyVewry/Bb09qrsPFJ03itwcT5AHKh2xUBoABboJ27wFSDTqNSr28VMZcOQXGTevZ13Aqgy9nFRhgvefv/WEzAOCkqS1l6yslqnRcAXI8NpH9mWtKH6mM/Hc4zEhLuW7HFJi00pZPGDJ5p8ELJ4gg01KadBHks8ACuMBYHqDMSbGsCpDiYi+fwNn+5R6ggCsvotLFik+DzxMAiSdK9h1JSKkzVq1imJnntkzQ9ucq1AcokTJ4Az2/GyECuceRYZjY358Jfg5vqwcAdA0kbI+hPkD5Uc2Y6+MpMCcPUG4VmKolQ45nTRGwy7PAhoQKMPHvuQpQ5me5Rj1MzX6/2Pft7KPGl+V1AUFJSxkFTdBWjzC7D3UkQwpQlSGXe3LjsJQ6SPGVduZ2oXlg1igMIYfOPR7W/YqZA+YWsTOraZqBBltRSfHgQwYDHoQKqIdvyidwuQ+QONw2CGRFjPfzceEBAqyTo9xBWkxfDqXSzsNQHVK5DKb+6BrQUBOMB0h8/b5EiiuRJ01pwda9/XyEA8OaK0XrRBUqD9BANgVW75ACy9cHSPy+yeczVQm3PA1eNq2HHRQgrp6XSQH6+WVzsXF7N0wz442afWhTWV4XkBQgaSAzQz42ndLYIxEKgKoMObXlNArDkGRKuepJJqEoe1ZJxLLHw0/EKjBxO4MItnJSYLwKLPiLmRw4hLNGRPFvYUl9C/qkLFfVuJkFFlIFQNL3IxbWuQF/KJlWXqgAZyWTwUrgG2sjgQSBIUnm7x3KXKijIR3N2Ynz/YKJFhAnc5MCpIJ/p4Rgho/BcDRBO5fB2z1AchVY4TJ4udLTsYdamT1A9bEwTps+rjwvJsGVZsPk+yFnGGp2l+YoQKPABE1LmyrDsRGiUxm8pAAVNEHbqsBYGia3DD4agFIiVoGJSlUQlVm5AZBdHg8S8QLOPidZ5o+E7PteNevIT1jnZquqpnCgK09RB3IbITIDPpD57jmboDM/nRSgoMZgMOQArC8bAI2pCXO/Sr+kAFm+MQqAVETC9gAEsAahjimUAlOUwYvpLe5Zy1sGb19EDEoBq6oaUxw5MyqmnbNUYkoog89jgjYMkyujFAARZUeexCtP6gUyB7EpRfOy50UmXw7dZoIOMgUmvJ4YAAWZAuN9gFjFWRlSYKrhm7K3Sy7hdTIP+4W8P1S9V2TUClDu40QjdLEmaJYC87sJIkOW+XuHMorT2Jowr1iSFSCrEzQFQCpEDxDrrtwXdx6ECgjDUFXT4MU+QGzMRla5Vk2MZ4sJ1kJiUGpboPI4ivH3aKhyEj+jwiZoM9B+ZNUIBUBVBjvQdVkBEr6Y4kVErAIDCpugxWAj37DLWIBl8KZprwAL4qLvnAIrXxk8II66sJuOI3In6IAnVEek74ebKjCxgSD7jqiqBJmvaihpOMrsToN3GQf4JPhgFCA52GcpsLE1Yd6zRvYAUR+g/IgBC/tcuQfIQwpMNZbFunCnbc8vHh9skciaesoeIJUJOuh2E9WGqDQXUoDkUUqjoQ8QHdlVRk4jxOx3UDVZHBACoAIDUVUrdxZcic9ndYz2/8svrjSCVJqAXBNlOS9m9tlTmZ+8w7cul8Eb0t+DNUHn81SoYGlSK5WXm0rlvYBSaccmc1yBcQyAmAIUVAos85O97x6mAMUivGKJ9bBhDFIn6LyIiyn2vWAqmpMJOqaoVrWmwevC/ewKEPfQiUUc0gJOrtpTeRzF86g2Cq5+YjWdkwIkqqOqa8tIZhR8BYYXchWNKo9tKGRKWfGQUY3CyDcLLIi0lKhmBVltJj5vJUZh2LwzvL18dv/npMDMrC+hfCZoQ1jpFQqAeOCQtleBRb2mwBRdpUW6uQJUnhQYK9fOeIAyF+sBKQVGs8DyIyo2LFAp3Ak6d6GmUiPlRoj8PopGruwYylGAFCZo0UowmhSgRNrZA6QLSpkquzCSoQCoypDNsKoLh61pnmZV4wDOVWCq6q7ye4AyP8WhhUGNppBTgryrbxlGYaj658gSvhhApA0z8MoLMUBOCleBQvN+2KqcBQ4JhUIYEybCOwVyYUUgL3Kgn43BKH8KjA3u7JNSYPEUpcDykelCn/k/+15Yw1ALeIBUjRAFBUj2rKma84n3N0yhcWX287QUIrUCNJo8QOIwVKcWFWnTngIbDQEiHdlVhpxCkMt3M/ex/m8pQLkNxkSU0+ClbsRAGavAsrn9oBWgnD5AZVrNWxf8zO2UVOkSlvwTTuXjfiFO7har/iIFur2yzUllx2EoTdDZfW1XgKTnKaAABTUJniErqcwE3VBjpcBYF2MGM0FTCkxNZsiuPbVaeBiqcxWYGFRHJEVbPn7k/yfThrDIkcrgxYakYgps5F/fbSkwp0pT8dhgCzZNGx1VchQAVRmWEpD5aNhBbNo8QNbJg1eBKbqyirATiXoYarn6AFkHGgvUAlOAKmiCBnIv+ClpBSuehJJpo6wmaLFxXaHPmQVqhmHayp0dq8AKrTLVX89Ax2CI28O2T6UAyR4gXjlIAZAjcn+pQimwqKJdhzwoWPW8qmaJ4v1ThmkFrFH7MSYGQKZi8TiSiUipbyB/jy52KRgNBmiAAqCqw8hJgWV+r1rFiFF6IQ+QqtKCBVkqBSiIwEQMClh/j8AUoFDWbCm1yS9XOkM2/coTr20VNIIqE9R5RzRBi4FMoYuAaJQXA6eo0gTtfJJVedlEDgRdBu/UBygW5j1rBhJp20LDmi1FAZAT8siK/gKdoNWzwOwVkoD9fGYYVu+esELBBjKl8ENSt3dV8830KE2BJdNWmj3XBJ35mTFB5/ZbGslQAFRlyF/SkLRyBay0ipijLTQKg130okoFKLcMPpBp8DwosFaAQbwOoBqGWt7VvFz2bVWxWD1K2MeXNAyh/1NQAWHWVJoybMGwVuAiIH5HnJQjpgDFk2mryZzcbVbxPRbpLlMZPNvPPVwBiqAuGwClDdOWmmGqYTmaZw5XLGUxk8Zl+8w5BaYyQduPDcCuADlVJtlUVMPIaXaqGvVj9wC5eovDGrHfmLy4ZoiBIk/Fj4LgEKAAqOqQGyGyL6d43VDNjSpoglakwFS9WYI0QYtjPXi/oYBW144psDKt5mXFIalYWYm9gAJvhCgqQCn3wZateWX2M9M1+8WqRvB1OHmZ8ilApmmiezDYAEieeC02QhQ9PmIlGJmgCyOmWMQUYp3jNHiVB8heISk+rxiwA/aLt6Zptu8na1vAFjnWhT3XBK1pKBj8jwTEQNKLCZoUIKIiODZCFGXcdG6UbuXW1Z2greqd3ABIpQAVKo8uBjGdx7ZzJPYBAnIVh7RC5ucm9LQYOASzPXy0gFAF5sbnJQbJTt2jeQosmS44CkOlAPUMpfj7L1cKTPQAhXSNB0HiOAwyQRdGDKyZiTyka44pdKsTtKoRoqAA8QWdvTeNXLXImyGmDUuxk8vgFQUko0XhEFNg8jxChrg4kZXqkc7oeJfDCPlLqgpS5GaJQGETtGqVJT438z4EWgZvqwIrUxl8BfoAAbmBa1KxsrJO3uLKK9iUYDJtKEcPOBEWAjlVDyDAqQ+Q/XnyjcJgBujaSCiwzyckvT4reR9bk0nVMNMuUzFM06RZYC4Q+/WwfVcXDTmqKyqlWpUCsxSgNF88ALmqJR8qnDZzRmGoijwMBxVkpGKbBl+oR5cBR5VopEIBUJUhfwHlBm6Aem5Uvk7QacFEqKoCyzx/5mc5ZoGJfYACb4RY4TJ49lmpGg+ybUwZljwdQPGd7XXFKrBCPYAA6zuWSptKFRFw6APksMrc2T2IU77/V1z685fwwf5+AOIYjGDUH/H1rSowlgLLvCbvBp1VMcSglEzQzogKQ3+BQaiAPQXGFl2qwbxWwG5yxVLTcr9XLGhKCapybU4KTHXu9PY+hyuuyuCFhSlvN0ApMKISyF9SFoiLAZDqIpPPBG0zrypSYJnXzdwnSBO0rQqsTAEQex15dRg0OR6gdK4HiK1mU+ngc+8xWxWY+zSnqABZrRTs2yimwJxMlIc216E2EoJhAp09Q1j97n5ccP8qvPTu/sDHYAD24Ns0TVsKDLAa97FZVkNClVJNlE6TTvAAKGXw4NHJ/wNYKTDTtJSfpOLCLDYydUrdiL9LGUbO6BJlGbyDSX+kIgaobio0R5sHyDlUJyoC/5KG5BSYdR+rZFpcMeXO2Nl1cBCLf7EGe3rj/Hc2D4qwDJIv1EGmwAxDLLcPyAQtpMBSwuqnXKt57ncyJQXINs3aWp2VqxGiWA7rJgBSlcHneIDYMNSUs8zeUh/Fizd9DDu6BxFPGfju05vw6ocHccnPX+L3aa4PTgES1YChpPV94AoQ6wWUvYgzz5imBVepOBIQTbbWIFTny4q4L+OpNKJhnadWwwp1VEzZqkz7lo8ujwlaVM8DHjlTbYgpMKdu82KFZtpBwR2p0JFdZaSkFbRsphX/b1sxKVJgf9uyF+/u6+d+hxnjx9pOQHYFSPIABTgN3jBNXmETfCPENPdyAOVLgcmeEz7sVDHxOmWYgTdCFL8fqrlKTrALjNgIMZ8HiC22VReY5voojjmkESdObsavr56PTx0/0fb306aP8/COvMF2e9o00RvPpL80DajLbnsdT4FlFaCEZagdDdVCxcK6N2c8QPkHoQL2452dq6yAXDw2ss+bck7dZH5nHUOyCZorQOncc+do+UhtZfCO6enMT5sJepQEQKQAVRk5jRBVVWCKSF7VCPHDA4MAgAvnHIKvnXUEJjbV2k7m4pecnSSCTE2xlxanwZejE/SQUHES1OvJyCkweRgqYK9gkafF+424EnSq5lLhSgFSpcAKnEBroyH85yVzcNunjkEqbSAc0tFYG7wHyDCs9NeYWJirBEwBklNgZIDOj6gsJtNsDIbzPtM0DdGwjkTK4Oca1fdKHGbMPEIhhWdNrKQcStjT3KreU05poJFKVFR+0w4KkHCukquQRzoUAFUZ7EJplcFnfm8oZFzxWqmqAvvwwAAAYEbHWEwZV5/zWnq2GZ9pKhSgIE3QZZ4GPySU3JfrwBYvuGInW5sHSDGoMDgTtLCi9jDuRPRRqAahAmIjREPZoyofQQY9IuLFkAVADTXWa3MTdPYianWBJpE8H7Y+QAUGoTJiLADKHpcpRRWYeD5L5kuBCW0aWNBamzMM1bq/U6POkUpEpQDl6QOkyi6MZOjorjKcyuANlQIkdoKOOCtAhzbXOr6enKrxYpD1ivhaQStAsZCoALEmiOX7uosXXHsfE7HUVzg5ldUE7b7Xh6hkOZfBZ26/v7/fWq1X2QVGPI76JAM0IChALAXGTPPUBTovdg9Q/kGoDLkZokodZQsY07QWdflSYEPJNP9eM0+adb6xzolBNxytNsIKD1DOKAzh2HDyCY1USAGqMuQVtErGVaUZxAs+Y0c3C4DqHF8vpGuZaeSsD1CAJmhRai1nGfxQmUvgAUGaF/LqgDzMkQVAwZ94IrYVtfdGiGnTBNL252Kw6i1mtte16hsfIX73WAm8WK5dJylAvAcQlcDnRVQW+SDUAp+9XLGayNMHCLDSkqq2Dex3fUIDS/bdU5XBW32A8r+vkYKYAnPy99hM0KNsFhgFQFVGWurDoPPo3LqPSi2wTK6ZE3giZaCzZwhAfgUorGuIC6/LlZkgZ4GZgteoDLPA4hXwc4jDUJNiK3+lCTr4afDi/lB13nXCUoAMGNBsz8WYM6kJt37iKGzPplyPn9RUttSWW0KCl0kugQfEKrDM3wYT5W2bMFyx9QFijRALKUCsGzRTgBR9qcTvGPss8pXBM1UPsAIsVRm8UxpopMLS1fkqTcVKY5YupACIqAhyGaKyEaKiW6dsgt51cBCmmTmBt9Y791exRh0YtseXywMU1HBSuweIdfQtYwpMOPk6dbKNiB4gI/cz9ZOI0lPhXgFKpa1u4XLgpGka/uX0qX5uru+I/Yx6pCaIgOVbYQpQXPKTEGoiQmqVjcIYk8cEDYgpMLsHSDWoGbDms6lStux3TAGqiei80ENpgs7+d7RU9okeLbYb8vcBcl8hOhKg5U2VIUfpcj8ZAMp0iXjBB4AdWf/PIVLllww7gVgeIPcVQl4Rq8DYyS8wBUiYfVWJFJjod3LqZKvqAxTUicc2DJWvuD02QvSQOqs2xHb/7GI5RlCAWDpsQFaAKAWWF9ED1OfBBA1kTPOANShYHnTKvrNuFCCm6olz2ywTdH77wEgmLAxcdlK/VH7F0WISpwCoysgxQWe/iKYJvgKXS+WBXGOhZYB29v8AuRPhg6zOEhUgnmoLSJWxe4DK7+cQvTNOwY3YwyTwafDidG2Hfj4q3JTBDwdEJVWVAquTZoGRCdod4iwwywSdf5/JPctUVWCA9f1kHiBV0CJ7gMRFjlzgAVjn0FES/yAaZgqucyNEZR+gYbjIKYbhdyYb4cgRuBiJs+M43yywBA+AMn6MQ/L4f4DcPHkiZZWM+43owzg4mElDBO4BEsrggwq2VIjVU06dbCMhxckpoJWXbbaSh1lg9kaIwfq2gkRlgraVwfNRGGSC9oI1CsPyAOVrhAgIChBPgamVRfadHUjmSYFlj6k+hQJkLbiswCfoVHO1YaW+nUdh2E3QwfYjqzZGx7scRljqTraZl9isMPs3lZQppnxM08SH3YVL4AH7Ch8IdhYYO/BME3izsxdAcB6gWCjEX0u1OgwaUe1y6mQbFgykRsArL/swVOe+KjJ2BSi49GjQsE1OO5ig2fyqPjJBe0LVB8hrGXyiwALBVQos2907pgiAAPHcmbk9asrgdevzcVaAhMUaX4iVcSMrCJmgqwyrV0PmtvhlZYGPqp+DqNgk06brFJiVJ2ddWc2c5/OLlvooPnHcBKx7vwtAxp90wmFNvr8OYN/+nqzaVM4AiAcOaVNZ5QJYfU+SaUNpbPcTVWfdaNiFAlTG1gVBIqbA+hRTy+u5B4gpQPbBmoQa0VvmZhgqIFSBsUaIhvr7yBWgfAFQSPYAWd9NWwBkmghjFJbBCykwubccQ1ysGaQA+UdXVxcWL16MhoYGNDU14corr0RfX1/ex2zduhUXXngh2tra0NDQgIsvvhi7d+/29Lzvv/8+NE3L+ffSSy/JL1d1pCUFSFR52N9UnhKxoWAibXATdCEFyF4CaUmgQShAmqbhvs+dgDX/tgBr/m0Bfvvl02yVOH5iC4CyKY9yNkIMKxQg+aSi6hUUXB8gK9iyZoG5UIDERojD2AMkvo8ergCJVWCSByhRfuP8cMTuAXKrADl4gHIUICkAUvYBytxn47ZuAJIHKI96PtpSYGIfIMcUmOGsVo9UAj2TLV68GJs2bcKKFSuwbNkyPPfcc7j66qsd79/f34+FCxdC0zSsXLkSq1atQiKRwPnnnw9D6KXi9nmfeeYZ7Nq1i/878cQTA3mffiKXwYvHKTt4VT1jxIBlIJHCroPZAKjJnQcoZRi2JorDcZUvEtI1vn96BsufAhOrjlI8deSUAgt+GryYEmT+FlceIMHMzQMgF8pRtSG2++/lZfCKKrBEGqYpDNYkE3RebH2A4t46QbPzjZMnjZ3TBlkjREXAfkj2/NabVfUmCYp33hTYKAmA2D5LCCpzzigMYXEQdD+yaiOwFNjmzZuxfPlyrFu3DnPnzgUA3HvvvTj33HNx9913Y+LEiTmPWbVqFd5//31s2LABDQ0NAIClS5eiubkZK1euxIIFCzw9b2trKzo6OlxtbzweRzwe57d7enqKfu/FYhhmTq8GWwosG5+wZlXiQazrGiIhDcm0iW37B2CYmSBm3JhY3tdkqoSY4gCGfwAEZE6gg0baUoDKaYIW2vCnHLqrshO8fRZYQAqQELQwr4YblY9/P9LO0+CHA/wkn3aqAsv8P9OiweApsHINzx2ucJUmnuL+QbedoHOnwUtVYC5SYNedfSTmTW1BPJVJ8Zw2fRz/m0o9H3XDULPHfd9QigeaOSkwUQFyGJg6Ugns6F69ejWampp4kAIACxYsgK7rWLNmjfIx8XgcmqYhFrMu2jU1NdB1HS+88ILn5/3kJz+J9vZ2nH766Xjqqafybu8dd9yBxsZG/m/SpEme33OpiL1+5EaI4t+d2pWzC9PWvZl04KFNtQXNfuIwQXGQ6kiQQNkJdHe2I3ZZTdBCDxKnEzwfhWEYvFli0KMwgPwpBRluHhaaVw7HFJjYloDPAotZKTDR69MfTwkmaFKA8sHOOWve6+K/K9gHiHeCTttLrx3OZ4NZr5Dq2KiJhHDWrPE499gJWHh0h019yp8Cc/HmRgBjst/xwWQa3QOZhaD8+XC12lTbK0YygZ3JOjs70d7ebvtdOBxGS0sLOjs7lY855ZRTUF9fjxtvvBEDAwPo7+/HDTfcgHQ6jV27drl+3jFjxuBHP/oRnnzySfzhD3/A6aefjgsuuCBvEHTzzTfj4MGD/N/27dtLeftFIfarUKXALA+Q/T4MdsF/d18/gMIl8OJzpNOmbQ7YSOiUylaaLyv8AUFjpVzguKpSToMP6MQT1jX+XWIKkBsPUFhQCIezB4jt10TK4BdUUQEK6RoPggYSaZ4mJBN0fg5tyZxjmLH8kKbagupxTKhYtY+JUXuA2ExDr987TdN4oCP7J0dLFVhHYw1u+9TRuHjuobh47qH45qIZmDVhrO0+tj5A5ujaP55TYDfddBN+8IMf5L3P5s2bi9qYtrY2PPnkk/jSl76Ee+65B7qu49JLL8UJJ5wA3YMrfdy4cbjuuuv47ZNOOgk7d+7EXXfdhU9+8pPKx8RiMZvyVAlUQzPZQWzrZeGQLmEnnmWvZILFQgZo8XVS4oT2YXiBU/GZEw/FY2u3wTSBxtoIzpzRVrbXtk9YVreXF/sAyY/zG03TEAnptgDATZpT3Qhx+J0c2ftg/acAeydoINPAbzCZRn8iRSZol8yf1or/veY07OvL2AeOPbSx4GNYqXo8aTXlBHJTq8yY/u7ezIKumHRkSNdgCAuM0eYBAoDPz5+S9++iCXq0KUCeA6Drr78eV1xxRd77TJs2DR0dHdizZ4/t96lUCl1dXXl9OQsXLsTWrVuxb98+hMNhNDU1oaOjA9OmTQOAop933rx5WLFiRYF3V1lSCgWI/d92EDvksdvH1mB3T5yvmI5ot0f6KlRVPiPB/wMA31w0E99cNLMiry0GDryyQjZBsx4dhglNelwQxLIBEOvB5OYkp2yEOAy/H+xYYcdGbSSUoyjUx8LY15dAfzzNPUDUByg/mqZh9qQmT48RGyGKAZB8fFx5xlQk0pnijFgkhMvmT/a8fSE944tkrzPahqG6QUwPjzYPkOcAqK2tDW1thVfS8+fPR3d3N9avX8+rr1auXAnDMDBv3ryCjx83bhx/zJ49e7hyU+zzbty4ERMmTCj4upXElgITDc6aBsAsKOPec+kc/N+WPTDNjLx//uxco7mMWOWTGMYej2rDVgbPTyrqMvhkyuD/D3Ll1VwfRW88hb29mdV6c53zkFyGGMglUsO5EWLmfWzIpkNl9QewvBEDiRTvHk4pMP8RTdC2FJj03T/18HE49fBxKAV5mDT7SfGPBd9HRvCp+GojsCqwWbNmYdGiRbjqqqvwwAMPIJlMYsmSJbjkkkt4pdaOHTtw1lln4ZFHHsHJJ58MAHjooYcwa9YstLW1YfXq1fja176Ga6+9FjNmzHD9vEuXLkU0GsWcOXMAAL/97W/x4IMP4pe//GVQb9cXWGCjafbghl2EmEfaSaacOq4eU8dN9fSaVgmkEegcsNGGbRp89iQfcUqBGSY/IQeZe7/n0jlYuTnTU6upLuotQB7mozDOPmo8/vDqLhwcTELTgM/NOyznPqx6qT+e5mnCoDqVj2bETtBWTyotEN+hWOIt/hwtF3g36MIi2KnAZqQSaCfoRx99FEuWLMFZZ50FXddx0UUX4Z577uF/TyaT2LJlCwYGBvjvtmzZgptvvhldXV2YMmUKvvWtb+Haa6/19LwAcPvtt+ODDz5AOBzGzJkz8etf/xqf+cxngny7JcMPzjx9GgD42jWYe4DSw7vTb7Uhjl5IOpmghTb1mqZnfxfcief4SU043mO6QtkIcRh+P44cPxZ//NoZee/DSuH74ymrDxAFQL7DqsDEwbxBjYCRz53mKPQAFUJUgIKeSVhtBBoAtbS04LHHHnP8+5QpU7ixl3HnnXfizjvvLOl5L7/8clx++eXeNrYKcJIfeSWDKa9iSn/N0Aip8qk2xOop9nnJ+5UFEqm0ibBenStTVSPE6DA0QbuBKUADNhM0HQt+I3qAgj7n8PObPAy1yo6zSiJ6gLi/dIQe4zJ0dFcRTr1g2MHKvpx+NvNSVYGRAlQ64oRlpwZkEb7vDaSrdGVqa5Mwwj1irIdMf0I0QZMC5DdiCsypR5ZfsKeVTdAU/1jYutaPsiqwkXkmG6Y4KUCWkS9z22mqbzGIDftYH6CRUgZfSWy9NfhJXt0HKDOnx/14inIirg4T6WAvVpWGKUA9g0metqQUmP9wBShp9wAFgZMJerSkeNxgM0GPshTYyDyTDVPSDv1idCmP7edBHNJIAQoCXVDWkg4Bq2WCtmaBVZsCpDJBj9QAiHmAuvoT/HekAPlPVFEGH5gCJBQaANYiciQ0evULVpyaMUHTNHiiQjgpO/Iqxs88tnWBM7gCNBwb3VUbYuCQ5sMe5VEYogKU+V21eYDEJmlWH6Dq2ka/YArQvj4rAKJZYP6jrAILygQtqBuAv/7JkYJKAao2JToo6GtQRTiVaOa0c/dTARLyv6QA+YcYtDrl1XkfoLRRteWnYSFFmhzhHiDWB+iZbKuAWFgns2wAWLPADJ5qDM4DZFeATB8raEcKtkaIVapEB8XIPJMNU5zyr9wEzRQgH7t1Whc4QwiASPYvFTFtaQVA8qwjqwUBM0FXW+7daoRojHgP0ImTm22en9Onl9aEj1BjeYDSjmNi/EIcSQNQFZgK8VxlOCzWRiqBlsET3uABkCQ/huQAyMduneIKKTGMG91VG2IKLOVg9BT7AFWtAiQohMN5FIYbZk9qwoZbz+ZdoBtrIwUeQRQDS4El0kbwHiCpDH40zgIrhLIPUJWdh4KCAqAqwrERomZdhAC/y+CFPkCpke3xKCdKBUgKbNlJPyVWX1TZiUcXPGLDuRO0W2oiITI+BwxTgJJpE/Fsu4HgGiFmflomaCqDlxFTYNV6HgoKCoCqCKcvH4uHZAXIj1WMPUc+8i9w5SIk9AHi3W7zTIM3qtQEbTVCxIivAiPKQ0xoLtkXzwRAkYCqjpgCZEgBULWlmiuJ6AOlAIioGGkHr4hTHtvPRohi/nekpjjKSUipAMnDUK2VMPNCVJs0zwPktGhYra5tJIYX4gKrP54CEGQVWOYnlcE7I1osRlsjRAqAqoiUg0FP7CoM+BsA8X41aZOvjigAKh0xAHLq72R14bY6QVfbiYe9j10Hh/jvhuMsMKJ6CId0hHQNacNEXzYACkpVDEsKEJXB5yK2uuApwio7DwUFfQ2qCBbg5DRCFL6gmZ+Z3/urABmU4vARcVWVdBj4yD1AadNXX5efMMPqe/v6AWTSsZQiJUqF+YD6eQAUzPdelzxAVAafi5hhIAWIqBisvF2OvtkXlM2N9bUTtDjqgPoA+YYYtDp1VxX7ADmpf5XmtOmtuHjuoejsiQMAzpg+jkzCRMnEwjoGEmkrBRaYB0huIpv5fbUdZ5VE9CtWazVqUFAAVEU4KkDSKAw/L5aiB4jK4P3D7gHKKms5w1CthnDWhaC6Tjx10TB++JnZld4MYoSRURaT6A3aA6RbKitAVWAq+CiMKq5GDQq60lURzmXw2b+zXhY+ypTiCYIpQNT+v3RsARBrXCmd5K2ZSAa2dQ0AqL4AiCCCgFWCDbAqsKD6AMnnTqoCy0FUydKUAiMqhfMojOC6mY6mYZflxJKVLcVOLvUd3xDDRSccig3bDwAADmupw+xJTWXdToKoBNwDlAjWA+RUBk9VYBYhTVSrR5dHigKgKsJpEJ01CiN7vwA8QCnDRJw8QL6hMhbm9nfS8KOLKb1EjD6Yuf6V7d0AcltE+IXcCLFahw5XEvH6sifr9aNhqETZSTtE3yHHMvjSX1NM1ZAJ2j+swNLgozCofw5BZJjYVAMA6BnKKEATGmoCeR1eBm/KVWCBvNywRLQ87OgeBIBRU+hAClAV4ZR/dW6EWHqgIl6oyQTtH9ZnBkEBov1KEABw56ePw7nH7oVhmqiNhHHmjLZAXkfscwbQMFQVY2siuPUTR2Hd+10AgIlNtThpSkuFt6o8UABURThVd8mjMLiRz4frqWrYJTW6Kx3dNgoj2wiRFCCCAAA010fxqeMPCfx1woLBN/Mz8/vR4nFxy7+cPhX/cvrUSm9G2aErXRXhVAYvpqnEn37OAksbhlUFRgpQyYjeqtHWXIwgqgV2jpSHoVIVGAFQAFRVpLNKQU4jRM2+inEy1RYDUyVS5AHylbBoguadoGm/EkQ5CUuLR+oDRIjQGbmKcJoHpWlWmgoIpg8QmaD9RWxeOdp6axBEtSA3kSUPECFCV7oqwqkNORMOeDt3H+fZhIVUTYJP+6avRamIql3SYRgqQRDBwk5llgKUuU0eIAKgAKiq4D0q5DJ42cjnYwrMXgaf6cpKClDpiB4gp/5OBEEEi1wGX61Dh4nKQFe6KoIpQDmNEOVp8KZ/Mm5IExUgKoP3C7F1AZ8GT2XwBFFWnEzQJAARAAVAVYVTG3I5AOKmWj8CoGyw9c7uXgwlaRaYX3DpXZiwTCkwgigvbDFpyItHioAIUB+gqsLJ3MzUhOyx62sp58TGWgBAfyKT/hoTC6OhNlLy8452+MozbSKkkexOEJVAVoDYOZTK4AmAAqCqwqkRou4wCsOPFNiMjrH43ZdP5S3QZ01oGDVt0INE9B7wPkCUWiSIsiKXwbOfFP8QAAVAVYVTI0R2U65k8EtRmHNYM+Yc1uzLcxEZmN1nMJnmHbYpBUYQ5UUug7e66NOxSFAAVFWk0+qZUfIssJRDuTxRPTAjefdA0vodeasIoqxwBci0p8DIA0QAFABVFWmHGV86L4PP3DYcyuWJ6uHwtjH41PETsaWzFwBwxPixOHL82ApvFUGMLnibDxqGSiigAKiKcJryHnLwAJECVL3ouob/vGROpTeDIEY1sn+SRmEQIqTJVxF8xldOGXzmJ5VyEgRBuCcs2QdoGCohQgFQFWE4dAzWpU7QpAARBEEURhc6sgM0CoOwQwFQFeHUCJFSYARBEN6RTdDkASJEKACqIgo1QuQyLgVABEEQBdElEzR5gAgRCoCqiJRDYKPxURiZ22nKYxMEQRREVoCoDxAhQgFQFZF2ODhZWTw7eK2O0eXbNoIgiOFGSJqjyFqIaLR4JEABUFVhNUJUe4BYAGSlyujjIwiCcCIkdYIm9ZwQoStoFVE4BSaVwdOnRxAE4YgcAJnkASIE6BJaRTjlp0NCGbxhmDTRmCAIwgU5ChBVgREC1Am6Cnjgb1vx1827+dgEObCxqsAs9Uf8PUEQBJFLKMcEnfk99QEiAAqAKk7aMPHD5W/yAxMAJjTV2O4jtnNPGxQAEQRBuEGXTdAO8xaJ0QkFQBWmL57iwc+9l87BhMYanDi52XYfcRSGQQoQQRCEK8K6OgCiKjACoACo4vTFUwCASEjD+bMnKu8jeoBEBYhkXIIgCGdyPUDZ39O5k0CAJuiuri4sXrwYDQ0NaGpqwpVXXom+vr68j9m6dSsuvPBCtLW1oaGhARdffDF2795tu8/3vvc9nHrqqairq0NTU5PyebZt24bzzjsPdXV1aG9vxze+8Q2kUim/3pqv9GcDoDEx51jUSoGBUmAEQRAuca4Co3MnEWAAtHjxYmzatAkrVqzAsmXL8Nxzz+Hqq692vH9/fz8WLlwITdOwcuVKrFq1ColEAueffz4M1r0KQCKRwGc/+1l86UtfUj5POp3Geeedh0QigRdffBFLly7Fww8/jFtvvdX39+gHvUPZAKjGOQASR2HYAiA6iAmCIBxxrAKjUyeBgFJgmzdvxvLly7Fu3TrMnTsXAHDvvffi3HPPxd13342JE3NTPatWrcL777+PDRs2oKGhAQCwdOlSNDc3Y+XKlViwYAEA4Lvf/S4A4OGHH1a+9l/+8he88cYbeOaZZzB+/Hgcf/zxuP3223HjjTfi3//93xGNRpWPi8fjiMfj/HZPT0/R798LTAGqj+ZTgDI/04bJqxk0jUo5CYIg8pFbBUZl8IRFIArQ6tWr0dTUxIMfAFiwYAF0XceaNWuUj4nH49A0DbFYjP+upqYGuq7jhRde8PTaxx57LMaPH89/d84556CnpwebNm1yfNwdd9yBxsZG/m/SpEmuX7MUmAdobB4FSLf1Acr8jtQfgiCI/OSmwDK/pxQYAQQUAHV2dqK9vd32u3A4jJaWFnR2diofc8opp6C+vh433ngjBgYG0N/fjxtuuAHpdBq7du3y9Npi8AOA33Z6bQC4+eabcfDgQf5v+/btrl+zFPqGCnuAxFEYqWwERCsYgiCI/DiOwqAyeAIeA6CbbroJmqbl/ffmm28WtSFtbW148skn8fTTT2PMmDFobGxEd3c3TjjhBOhlmPkQi8XQ0NBg+1cOmAJU78YEbVgKUJgCIIIgiLzkDEOlMnhCwJMH6Prrr8cVV1yR9z7Tpk1DR0cH9uzZY/t9KpVCV1cXOjo6HB+7cOFCbN26Ffv27UM4HEZTUxM6Ojowbdo019vY0dGBtWvX2n7HKsnyvXal8JICS5s0zI8gCMItsgJEFgJCxFMA1NbWhra2toL3mz9/Prq7u7F+/XqceOKJAICVK1fCMAzMmzev4OPHjRvHH7Nnzx588pOfdL2N8+fPx/e+9z3s2bOHp+FWrFiBhoYGHHXUUa6fp1z0uTBBM7nWFPoAUQqMIAgiP44maAqACATkAZo1axYWLVqEq666CmvXrsWqVauwZMkSXHLJJbwCbMeOHZg5c6ZNrXnooYfw0ksvYevWrfjVr36Fz372s7j22msxY8YMfp9t27Zh48aN2LZtG9LpNDZu3IiNGzfyHkMLFy7EUUcdhcsuuwyvvPIK/vznP+Pb3/42rrnmGpvBulpgAVC+MnhbCsxhYCpBEARhx3kYasU2iagiAusE/eijj2LJkiU466yzoOs6LrroItxzzz3878lkElu2bMHAwAD/3ZYtW3DzzTejq6sLU6ZMwbe+9S1ce+21tue99dZbsXTpUn57zpw5AIBnn30WZ555JkKhEJYtW4YvfelLmD9/Purr63H55ZfjtttuC+qtloQbE7QYAKXSFAARBEG4IScFRlVghEBgAVBLSwsee+wxx79PmTKFd+Vk3HnnnbjzzjvzPu/DDz/s2AOIMXnyZPzxj390va2VxE0naHYQm6YwzI8OYIIgiLw4maBpAUkAAXaCJtzR6yUFJniA6AAmCILIT64CRJ2gCQsKgCqMuxRY5qfYCZpy2ARBEPlxHoVBERBBAVDF6U+4T4GJ0+DDFAERBEHkJSxVgVEnaEKErqIVps/FMFRdEQCRhEsQBJEfXVCATLIQEBIUAFWYPjcmaG7ky0yEB+gAJgiCKITYMd8wxU7QldoiopqgAKiCJFIG4qlMa1I3ZfCG6AGiI5ggCCIvYsNY6qNGyFAAVEFYCTxQYBZY9lNKmyZSpAARBEG4QmwXkgmAMv+nBSQBUABUUVj6KxbWEckznlicBm9wEzQdwARBEPkQF4ppm4eSzp8EBUAVxc0gVECoAjNoFhhBEIRbxAAolTb4/+n0SQAUAFUUN12gAUATGiFSJ2iCIAh3iOfJhC0AovMnQQFQRWFdoPP5fwBRAQL3AJECRBAEkR9d13jFVzJt2n5PEBQAVRA3XaABuwcoTR4ggiAI17BzJaXACBkKgCpIv0sPEFvBUBknQRCEN1i6KykEQHT+JAAKgCpKn9cUmGmCHcOUwyYIgigMU4ASKSEFRudPAhQAVZRetykwHgABacOw/Y4gCIJwhvl9kmSCJiQoAKogvAqsQApM56MwLAWIAiCCIIjChJQBUKW2hqgmKACqIHwOWLRQAJT5KY7CoDJ4giCIwvAUGHmACAkKgCpIn0sFiB2saaETNB3ABEEQhbFM0JYHSKMFJAEKgCqKWxO0LpTBUx8ggiAI9zAFKJki/yRhhwKgCsL6AI310AiRZoERBEG4RzZB06mTYFAAVEHcpsB0YRQG8wBRFQNBEERhuAJEg1AJCQqAKojrFFj2UxI7QecZHk8QBEFk0aUUGAVABIMuoxWEd4J2OQrDNIFUmkzQBEEQbglLKTA6dxIMCoAqhGmanlNgAJCiRogEQRCukUdhkABEMCgAqhDxlMHLMgunwKwjlvWyoD5ABEEQhQmHWB8gUs8JOxQAVQiW/gKA+gKNEMUDNpmiMniCIAi3hDS5CozOnUQGCoAqBDdAR0MFVySi2pMkBYggCMI1ZIImnMgvPRC+s7tnCHt743hvXz+AwukvwJ6z5gFQiA5igiCIQsgmaBLPCQYFQGVm6Yvv4yf/t5XfLmSABuwpMPIAEQRBuIcpPswDRAoQwaAAqMyMqQmjo6EGQGYlsnje5IKPsafAyMhHEAThFmaCpjJ4QoYCoDLz5TOn48tnTvf0GFsKjPLYBEEQrqEyeMIJMkEPAzRN43lrdhDTLDCCIIjCUCNEwgkKgIYJ7KBlHiAqgycIgihMiAdA5AEi7FAANEyQZVxaxRAEQRQmRFVghAMUAA0TrAAoa4KmVQxBEERBcgMgOncSGSgAGiawg3hX96DtNkEQBOEML4NPUQUtYYcCoGFCbTQEANh5cAgAUJe9TRAEQTgjm6A1UoCILFQGP0y4/VPHYPnruwAAzfVRfPyYCRXeIoIgiOpHz6kCq+TWENUEBUDDhEXHdGDRMR2V3gyCIIhhRe4oDFKAiAwUCxMEQRAjFub56Y+nAVAARFhQAEQQBEGMWFgA9MauHgBUBk9YUABEEARBjFjOmjkezXURxMI66qIhnHss+SeJDJppmmalN6Ia6enpQWNjIw4ePIiGhoZKbw5BEARBEC5we/0mBYggCIIgiFEHBUAEQRAEQYw6KAAiCIIgCGLUEVgA1NXVhcWLF6OhoQFNTU248sor0dfXl/cxW7duxYUXXoi2tjY0NDTg4osvxu7du233+d73vodTTz0VdXV1aGpqUj6Ppmk5/x5//HG/3hpBEARBEMOcwAKgxYsXY9OmTVixYgWWLVuG5557DldffbXj/fv7+7Fw4UJomoaVK1di1apVSCQSOP/882EYBr9fIpHAZz/7WXzpS1/K+/oPPfQQdu3axf9dcMEFfr01giAIgiCGOYF0gt68eTOWL1+OdevWYe7cuQCAe++9F+eeey7uvvtuTJw4Mecxq1atwvvvv48NGzZw1/bSpUvR3NyMlStXYsGCBQCA7373uwCAhx9+OO82NDU1oaPDfefkeDyOeDzOb/f09Lh+LEEQBEEQw4tAFKDVq1ejqamJBz8AsGDBAui6jjVr1igfE4/HoWkaYrEY/11NTQ10XccLL7zgeRuuueYajBs3DieffDIefPBBFKr2v+OOO9DY2Mj/TZo0yfNrEgRBEAQxPAgkAOrs7ER7e7vtd+FwGC0tLejs7FQ+5pRTTkF9fT1uvPFGDAwMoL+/HzfccAPS6TR27drl6fVvu+02PPHEE1ixYgUuuugifPnLX8a9996b9zE333wzDh48yP9t377d02sSBEEQBDF88BQA3XTTTUqDsfjvzTffLGpD2tra8OSTT+Lpp5/GmDFj0NjYiO7ubpxwwgnQdW9x2i233ILTTjsNc+bMwY033ohvfvObuOuuu/I+JhaLoaGhwfaPIAiCIIiRiScP0PXXX48rrrgi732mTZuGjo4O7Nmzx/b7VCqFrq6uvL6chQsXYuvWrdi3bx/C4TD38UybNs3LZuYwb9483H777YjH47YUG0EQBEEQoxNPAVBbWxva2toK3m/+/Pno7u7G+vXrceKJJwIAVq5cCcMwMG/evIKPHzduHH/Mnj178MlPftLLZuawceNGNDc3U/BDEARBEASAgKrAZs2ahUWLFuGqq67CAw88gGQyiSVLluCSSy7hFWA7duzAWWedhUceeQQnn3wygEzp+qxZs9DW1obVq1fja1/7Gq699lrMmDGDP/e2bdvQ1dWFbdu2IZ1OY+PGjQCA6dOnY8yYMXj66aexe/dunHLKKaipqcGKFSvw/e9/HzfccEMQb5UgCIIgiGFIIAEQADz66KNYsmQJzjrrLOi6josuugj33HMP/3symcSWLVswMDDAf7dlyxbcfPPN6OrqwpQpU/Ctb30L1157re15b731VixdupTfnjNnDgDg2WefxZlnnolIJIL7778f1157LUzTxPTp0/HjH/8YV111VVBvlSAIgiCIYQZNg3eApsETBEEQxPDD7fU7MAVouMPiQmqISBAEQRDDB3bdLqTvUADkQG9vLwBQQ0SCIAiCGIb09vaisbHR8e+UAnPAMAzs3LkTY8eOhaZpvj53T08PJk2ahO3bt1N6LUBoP5cP2tflgfZz+aB9XT783temaaK3txcTJ07M20eQFCAHdF3HoYceGuhrUMPF8kD7uXzQvi4PtJ/LB+3r8uHnvs6n/DACmwZPEARBEARRrVAARBAEQRDEqIMCoAoQi8Xwne98hzpTBwzt5/JB+7o80H4uH7Svy0el9jWZoAmCIAiCGHWQAkQQBEEQxKiDAiCCIAiCIEYdFAARBEEQBDHqoACIIAiCIIhRBwVAZeb+++/HlClTUFNTg3nz5mHt2rWV3qRhzR133IGTTjoJY8eORXt7Oy644AJs2bLFdp+hoSFcc801aG1txZgxY3DRRRdh9+7dFdrikcOdd94JTdPw9a9/nf+O9rV/7NixA//0T/+E1tZW1NbW4thjj8Xf//53/nfTNHHrrbdiwoQJqK2txYIFC/D2229XcIuHH+l0GrfccgumTp2K2tpaHH744bj99tttM6RoPxfHc889h/PPPx8TJ06Epmn4/e9/b/u7m/3a1dWFxYsXo6GhAU1NTbjyyivR19fn2zZSAFRGfv3rX+O6667Dd77zHbz88suYPXs2zjnnHOzZs6fSmzZs+dvf/oZrrrkGL730ElasWIFkMomFCxeiv7+f3+faa6/F008/jSeffBJ/+9vfsHPnTnz605+u4FYPf9atW4ef/exnOO6442y/p33tDwcOHMBpp52GSCSCP/3pT3jjjTfwox/9CM3Nzfw+P/zhD3HPPffggQcewJo1a1BfX49zzjkHQ0NDFdzy4cUPfvAD/PSnP8V9992HzZs34wc/+AF++MMf4t577+X3of1cHP39/Zg9ezbuv/9+5d/d7NfFixdj06ZNWLFiBZYtW4bnnnsOV199tX8baRJl4+STTzavueYafjudTpsTJ04077jjjgpu1chiz549JgDzb3/7m2maptnd3W1GIhHzySef5PfZvHmzCcBcvXp1pTZzWNPb22seccQR5ooVK8yPfvSj5te+9jXTNGlf+8mNN95onn766Y5/NwzD7OjoMO+66y7+u+7ubjMWi5n/8z//U45NHBGcd9555r/8y7/YfvfpT3/aXLx4sWmatJ/9AoD5u9/9jt92s1/feOMNE4C5bt06fp8//elPpqZp5o4dO3zZLlKAykQikcD69euxYMEC/jtd17FgwQKsXr26gls2sjh48CAAoKWlBQCwfv16JJNJ236fOXMmDjvsMNrvRXLNNdfgvPPOs+1TgPa1nzz11FOYO3cuPvvZz6K9vR1z5szBL37xC/739957D52dnbZ93djYiHnz5tG+9sCpp56Kv/71r3jrrbcAAK+88gpeeOEFfPzjHwdA+zko3OzX1atXo6mpCXPnzuX3WbBgAXRdx5o1a3zZDhqGWib27duHdDqN8ePH234/fvx4vPnmmxXaqpGFYRj4+te/jtNOOw3HHHMMAKCzsxPRaBRNTU22+44fPx6dnZ0V2MrhzeOPP46XX34Z69aty/kb7Wv/ePfdd/HTn/4U1113Hf7t3/4N69atw1e/+lVEo1FcfvnlfH+qzie0r91z0003oaenBzNnzkQoFEI6ncb3vvc9LF68GABoPweEm/3a2dmJ9vZ229/D4TBaWlp82/cUABEjhmuuuQavv/46XnjhhUpvyohk+/bt+NrXvoYVK1agpqam0pszojEMA3PnzsX3v/99AMCcOXPw+uuv44EHHsDll19e4a0bOTzxxBN49NFH8dhjj+Hoo4/Gxo0b8fWvfx0TJ06k/TwKoBRYmRg3bhxCoVBORczu3bvR0dFRoa0aOSxZsgTLli3Ds88+i0MPPZT/vqOjA4lEAt3d3bb70373zvr167Fnzx6ccMIJCIfDCIfD+Nvf/oZ77rkH4XAY48ePp33tExMmTMBRRx1l+92sWbOwbds2AOD7k84npfGNb3wDN910Ey655BIce+yxuOyyy3DttdfijjvuAED7OSjc7NeOjo6cAqFUKoWuri7f9j0FQGUiGo3ixBNPxF//+lf+O8Mw8Ne//hXz58+v4JYNb0zTxJIlS/C73/0OK1euxNSpU21/P/HEExGJRGz7fcuWLdi2bRvtd4+cddZZeO2117Bx40b+b+7cuVi8eDH/P+1rfzjttNNy2jm89dZbmDx5MgBg6tSp6OjosO3rnp4erFmzhva1BwYGBqDr9stgKBSCYRgAaD8HhZv9On/+fHR3d2P9+vX8PitXroRhGJg3b54/G+KLlZpwxeOPP27GYjHz4YcfNt944w3z6quvNpuamszOzs5Kb9qw5Utf+pLZ2Nho/t///Z+5a9cu/m9gYIDf51//9V/Nww47zFy5cqX597//3Zw/f745f/78Cm71yEGsAjNN2td+sXbtWjMcDpvf+973zLffftt89NFHzbq6OvNXv/oVv8+dd95pNjU1mf/7v/9rvvrqq+anPvUpc+rUqebg4GAFt3x4cfnll5uHHHKIuWzZMvO9994zf/vb35rjxo0zv/nNb/L70H4ujt7eXnPDhg3mhg0bTADmj3/8Y3PDhg3mBx98YJqmu/26aNEic86cOeaaNWvMF154wTziiCPMSy+91LdtpACozNx7773mYYcdZkajUfPkk082X3rppUpv0rAGgPLfQw89xO8zODhofvnLXzabm5vNuro688ILLzR37dpVuY0eQcgBEO1r/3j66afNY445xozFYubMmTPNn//857a/G4Zh3nLLLeb48ePNWCxmnnXWWeaWLVsqtLXDk56eHvNrX/uaedhhh5k1NTXmtGnTzG9961tmPB7n96H9XBzPPvus8tx8+eWXm6bpbr/u37/fvPTSS80xY8aYDQ0N5j//8z+bvb29vm2jZppCy0uCIAiCIIhRAHmACIIgCIIYdVAARBAEQRDEqIMCIIIgCIIgRh0UABEEQRAEMeqgAIggCIIgiFEHBUAEQRAEQYw6KAAiCIIgCGLUQQEQQRAEQRCjDgqACIIgCIIYdVAARBDEqOPMM8/E17/+9UpvBkEQFYQCIIIgCIIgRh00C4wgiFHFFVdcgaVLl9p+995772HKlCmV2SCCICoCBUAEQYwqDh48iI9//OM45phjcNtttwEA2traEAqFKrxlBEGUk3ClN4AgCKKcNDY2IhqNoq6uDh0dHZXeHIIgKgR5gAiCIAiCGHVQAEQQBEEQxKiDAiCCIEYd0WgU6XS60ptBEEQFoQCIIIhRx5QpU7BmzRq8//772LdvHwzDqPQmEQRRZigAIghi1HHDDTcgFArhqKOOQltbG7Zt21bpTSIIosxQGTxBEARBEKMOUoAIgiAIghh1UABEEARBEMSogwIggiAIgiBGHRQAEQRBEAQx6qAAiCAIgiCIUQcFQARBEARBjDooACIIgiAIYtRBARBBEARBEKMOCoAIgiAIghh1UABEEARBEMSogwIggiAIgiBGHf9/4mWV6pS9/UUAAAAASUVORK5CYII=",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# area to focus: .loc[ a : b ]\n",
-    "a=0\n",
-    "b=200\n",
-    "print(sum(summary_df.rew))\n",
-    "summary_df.loc[ a : b ].plot(x='t', y='pop')\n",
-    "summary_df.loc[ a : b ].plot(x='t', y='obs')\n",
-    "summary_df.loc[ a : b ].plot(x='t', y='action')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "7aa21d34-b1a5-451d-bc74-146fd8e8c077",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# summary_df.plot(x='obs', y='action', kind='scatter')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d53d0137-77c9-49be-97f7-855f3cef719c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/const_action.ipynb b/notebooks/const_action.ipynb
deleted file mode 100644
index cdc2dc1..0000000
--- a/notebooks/const_action.ipynb
+++ /dev/null
@@ -1,383 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f6b28f12-10fd-4c79-ab8a-bdb76bd5a3d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Obtaining file:///home/rstudio/rl4greencrab\n",
-      "  Installing build dependencies ... \u001b[?25ldone\n",
-      "\u001b[?25h  Checking if build backend supports build_editable ... \u001b[?25ldone\n",
-      "\u001b[?25h  Getting requirements to build editable ... \u001b[?25ldone\n",
-      "\u001b[?25h  Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25hRequirement already satisfied: gymnasium in /opt/venv/lib/python3.10/site-packages (from rl4greencrab==1.0.0) (0.28.1)\n",
-      "Requirement already satisfied: numpy>=1.21.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (1.26.4)\n",
-      "Requirement already satisfied: jax-jumpy>=1.0.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (1.0.0)\n",
-      "Requirement already satisfied: cloudpickle>=1.2.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (3.0.0)\n",
-      "Requirement already satisfied: typing-extensions>=4.3.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (4.9.0)\n",
-      "Requirement already satisfied: farama-notifications>=0.0.1 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4greencrab==1.0.0) (0.0.4)\n",
-      "Building wheels for collected packages: rl4greencrab\n",
-      "  Building editable for rl4greencrab (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for rl4greencrab: filename=rl4greencrab-1.0.0-py2.py3-none-any.whl size=972 sha256=0f849313e0aa0136f78ce43f4b591bbce66af57ceb3e9a7cbc8839197bb40600\n",
-      "  Stored in directory: /tmp/pip-ephem-wheel-cache-obnly8hd/wheels/e9/7e/e6/00c4b11a2574abd59d64425d537139e25fadbde37f002c4dba\n",
-      "Successfully built rl4greencrab\n",
-      "Installing collected packages: rl4greencrab\n",
-      "  Attempting uninstall: rl4greencrab\n",
-      "    Found existing installation: rl4greencrab 1.0.0\n",
-      "    Uninstalling rl4greencrab-1.0.0:\n",
-      "      Successfully uninstalled rl4greencrab-1.0.0\n",
-      "Successfully installed rl4greencrab-1.0.0\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -e .."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "ba13a277-9d59-4090-8677-7e834c06bfd7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rl4greencrab import invasive_IPM_v2 as ipm\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from tqdm.notebook import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "84e472e4-6e03-49f5-88cb-cc67f3bcb2f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ipm_cfg = {\n",
-    "    'r': 0.5,\n",
-    "    'imm': 2000,\n",
-    "    'problem_scale': 2000,\n",
-    "    'action_reward_scale': 0.5, # cost per unit action in ipm\n",
-    "    'env_stoch': 0.1\n",
-    "}\n",
-    "env = ipm(config=ipm_cfg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "6a092db7-79a4-4191-a1e3-d6f689ec9222",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# length of observations quick-n-dirty\n",
-    "nt = len(env.reset()[0])\n",
-    "nt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "ca72fc89-df71-4a02-b486-14ab0b6366fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataframe_collection = {}\n",
-    "for act in np.linspace(-1, 1, 41):\n",
-    "    env.reset()\n",
-    "    ep = []\n",
-    "    for t in range(100):\n",
-    "        obs, rew, term, trunc, info = env.step(np.float32([act]))\n",
-    "        ep.append([t, *obs, rew, sum(env.state)])\n",
-    "        if term or trunc:\n",
-    "            break\n",
-    "\n",
-    "    ep_df = pd.DataFrame(ep, columns = ['t', *[f'obs_{i}' for i in range(nt)], 'rew', 'pop'])\n",
-    "    dataframe_collection[act] = ep_df\n",
-    "        \n",
-    "      "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "f9809aeb-18ad-4f0d-86a1-09b8be08d626",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for act, df in dataframe_collection.items():\n",
-    "#     # print(df)\n",
-    "#     df.plot(x='t', y=['pop'], title=f'const_act = {act:.2f}, rew = {np.sum(df.rew)}')\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "da552b8a-312b-464d-8744-284e6b12662b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gen_ep(env, act):\n",
-    "    env.reset()\n",
-    "    ep = []\n",
-    "    for t in range(100):\n",
-    "        obs, rew, term, trunc, info = env.step(np.float32([act]))\n",
-    "        ep.append([t, *obs, rew, sum(env.state)])\n",
-    "        if term or trunc:\n",
-    "            break\n",
-    "\n",
-    "    return pd.DataFrame(ep, columns = ['t', *[f'obs_{i}' for i in range(nt)], 'rew', 'pop'])\n",
-    "\n",
-    "import ray\n",
-    "@ray.remote\n",
-    "def gen_ep_rew(env, act):\n",
-    "    env.reset()\n",
-    "    ep_rew=0\n",
-    "    for t in range(100):\n",
-    "        obs, rew, term, trunc, info = env.step(np.float32([act]))\n",
-    "        ep_rew += rew\n",
-    "        if term or trunc:\n",
-    "            break\n",
-    "    return ep_rew\n",
-    "\n",
-    "def avg_rew(env, act):\n",
-    "    return np.mean(\n",
-    "        ray.get(\n",
-    "            [gen_ep_rew.remote(env, act) for _ in range(100)]\n",
-    "        )\n",
-    "    )\n",
-    "\n",
-    "from tqdm import tqdm\n",
-    "def tune_const_action(env):\n",
-    "    actions = []\n",
-    "    mean_rews = []\n",
-    "    for act in tqdm(np.linspace(-1,0,51)):\n",
-    "        actions.append(act)\n",
-    "        mean_rews.append(avg_rew(env, act))\n",
-    "    return pd.DataFrame({'action': actions, 'reward': mean_rews})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "bf68a84f-36c8-475c-9244-a3d64491ed39",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 51/51 [00:14<00:00,  3.43it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "env = ipm(config=ipm_cfg)\n",
-    "const_tuning = tune_const_action(env)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "53af6b9e-10d8-499e-a87d-6c036f23c1bf",
-   "metadata": {},
-   "outputs": [
-    {
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actionreward
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-       "   action     reward\n",
-       "0   -1.00 -20.174471\n",
-       "1   -0.98 -10.846384\n",
-       "2   -0.96  -5.033233\n",
-       "3   -0.94  -3.303023\n",
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-     },
-     "execution_count": 48,
-     "metadata": {},
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-    }
-   ],
-   "source": [
-    "const_tuning.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "2a65e689-0038-4c2d-9205-87024e4891dd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ep = gen_ep(act=-.92, env=env) # <- using the optimal constant action from the cell above\n",
-    "ep.plot(x='t', y=['pop'])\n",
-    "ep.plot(x='t', y=['obs_0', 'obs_1'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81939949-ef8d-4505-a88d-c23b0f70374d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 9dcf2278793d823e6c8af4be9e00eaf2a26c95ed Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 03:07:59 +0000
Subject: [PATCH 42/52] intro_pt1, intro_pt2

---
 notebooks/{intro.ipynb => intro_pt1.ipynb} |   2 +-
 notebooks/intro_pt2.ipynb                  | 454 +++++++++++++++++++++
 2 files changed, 455 insertions(+), 1 deletion(-)
 rename notebooks/{intro.ipynb => intro_pt1.ipynb} (99%)
 create mode 100644 notebooks/intro_pt2.ipynb

diff --git a/notebooks/intro.ipynb b/notebooks/intro_pt1.ipynb
similarity index 99%
rename from notebooks/intro.ipynb
rename to notebooks/intro_pt1.ipynb
index 2875078..4afd99e 100644
--- a/notebooks/intro.ipynb
+++ b/notebooks/intro_pt1.ipynb
@@ -5,7 +5,7 @@
    "id": "9c5f89cf-cee3-498d-8a83-4a05e1bd53b8",
    "metadata": {},
    "source": [
-    "# Intro to this package\n",
+    "# Intro to this package, Part 1\n",
     "---\n",
     "\n",
     "Here we introduce the functionalities of our package and the dynamical models it uses."
diff --git a/notebooks/intro_pt2.ipynb b/notebooks/intro_pt2.ipynb
new file mode 100644
index 0000000..c2e173f
--- /dev/null
+++ b/notebooks/intro_pt2.ipynb
@@ -0,0 +1,454 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d69cb1fc-5356-445e-a205-077fdd984f50",
+   "metadata": {},
+   "source": [
+    "# Part 2: other environments and RL training\n",
+    "---\n",
+    "\n",
+    "In this notebook we will go over some of the variations of `greenCrabEnv` available in this package, and over the syntax for training RL algorithms on instances of these environments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34021e99-0010-4743-8796-48f251a39408",
+   "metadata": {},
+   "source": [
+    "## 0. Setup\n",
+    "---\n",
+    "As with Part 1 of this series, uncomment the following cell in order to install our package if you haven't done so already. After that restart the jupyter kernel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "99f5d63e-bc09-4eb8-97e7-eb08d52d45cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -e .."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9b782d73-40ad-4de9-b1ab-f3c52461b8c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from plotnine import ggplot, aes, geom_density, geom_line, geom_point, geom_violin, facet_grid, labs, theme, facet_wrap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de83ecef-a327-4ba5-9886-e4df5b87a5b4",
+   "metadata": {},
+   "source": [
+    "## 1. Other envs\n",
+    "---\n",
+    "\n",
+    "We will go over two other envs provided by our package: `greenCrabSimplifiedEnv` and `timeSeriesEnv`.\n",
+    "Let's focus on the first one of these envs.\n",
+    "\n",
+    "### greenCrabSimplifiedEnv\n",
+    "\n",
+    "`greenCrabSimplifiedEnv` is closely related to `greenCrabEnv` and only varies in small aspects.\n",
+    "Let's examine these aspecs one by one.\n",
+    "The first aspect is its action space:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c05ebeea-5c6c-4be6-a45f-4461f7ebcfb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Box(-1.0, 1.0, (1,), float32)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from rl4greencrab import greenCrabSimplifiedEnv\n",
+    "gcse = greenCrabSimplifiedEnv()\n",
+    "gcse.action_space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "248dfd31-ac90-4256-bfc9-8df2cb54ad1a",
+   "metadata": {},
+   "source": [
+    "Actions in this `greenCrabSimplifiedEnv` are between -1 and +1 (in contrast to `greenCrabEnv` where they were in [0, 2000]). \n",
+    "This difference in action space is purely conceptual: we linearly associate the segment [-1, 1] to the segment [0, 2000] so that, e.g., an action of -1 corresponds to 0 traps laid, an action of 0 corresponds to 1000 traps laid, and an action of +1 corresponds to 2000 traps laid.\n",
+    "Mathematically, this transformation is:\n",
+    "$$a = A / 1000 - 1$$,\n",
+    "where $A\\in[0,2000]$ and $a\\in[-1,1]$.\n",
+    "This transformation of action space is performed because of purely computational reasons related to hyperparameter tuning of RL algorithms.\n",
+    "\n",
+    "A second difference of `greenCrabSimplifiedEnv` with respect to `greenCrabEnv` is in its observation space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "41a623c2-25fe-4d23-a152-b6af91a49877",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Box(-1.0, 1.0, (3,), float32)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcse.observation_space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "013af299-2b80-4066-bf64-507e4f89c04b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-1., -1., -1.], dtype=float32), {})"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcse.reset()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "650cbc8a-e37d-4fd1-a496-72771915c21a",
+   "metadata": {},
+   "source": [
+    "Here, observations are vectors with *three* components instead of nine, and they are [-1, 1] valued.\n",
+    "E.g., consider the following observation after a second time-step:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b076834b-12e7-4bec-9233-1ee40a6975cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.9776    , -0.99142224, -0.1       ], dtype=float32)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcse.step(np.float32([-0.1]))[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73e153c9-ed93-435a-98ec-d06ea0724e5f",
+   "metadata": {},
+   "source": [
+    "These three numbers correspond to: 1. the catch per 100 traps in the first five months of the year, 2. the catch per 100 traps in the later four months of the year, 3. the number of traps.\n",
+    "These three numbers are transformed to [-1, 1] in a similar fashion to eq. (1).\n",
+    "\n",
+    "This simplifies the observations, making it easier for RL algorithms to exploit the information they provide.\n",
+    "Because of this, we will train our algorithms on `greenCrabSimplifiedEnv` rather than `greenCrabEnv`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58d37c1e-b344-4058-820a-c59cbee7fa8e",
+   "metadata": {},
+   "source": [
+    "### timeSeriesEnv\n",
+    "\n",
+    "TBD."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9098d67f-f9c1-460d-b505-91ccb8df444d",
+   "metadata": {},
+   "source": [
+    "## 2. Training and evaluating RL algos\n",
+    "---\n",
+    "\n",
+    "Here we cover some basic syntax for training RL algorithms on our envs.\n",
+    "We use short train times for the sake of brevity in this example.\n",
+    "Typical run-times might need upwards of 1 million time-steps, or possibly up to 10 million time-steps to converge.\n",
+    "This number will, however, depend on the particular algorithm used.\n",
+    "\n",
+    "**Note:** This package also provides a more ergonomic syntax for training through the `train.py` script. \n",
+    "To train models this way, run the following command on the terminal:\n",
+    "\n",
+    "`python scripts/train.py -f hyperpars/ppo-greencrab-v0-1.yml`\n",
+    "\n",
+    "Here, we encode the input to the training algorithm as a YAML file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7254fbbd-dd2b-4598-afb1-c13c7ccbae88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stable_baselines3 import PPO, TD3\n",
+    "from sb3_contrib import TQC\n",
+    "from stable_baselines3.common.env_util import make_vec_env\n",
+    "\n",
+    "gcse = greenCrabSimplifiedEnv()\n",
+    "vec_env = make_vec_env(greenCrabSimplifiedEnv, n_envs=12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f1683f6-f8c0-4742-9343-bcbada85c9d1",
+   "metadata": {},
+   "source": [
+    "### PPO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e7d2998a-b6a4-46c5-81c6-0bdfbd9f8aea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a14c36ae83e413c8094e9c038da2665",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = PPO(\"MlpPolicy\", vec_env, verbose=0, tensorboard_log=\"/home/rstudio/logs\")\n",
+    "model.learn(\n",
+    "\ttotal_timesteps=100_000, \n",
+    "\tprogress_bar=True,\n",
+    ")\n",
+    "model.save(\"ppo_gcse\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dafd076-e6ca-425a-8e80-e92a572a66c9",
+   "metadata": {},
+   "source": [
+    "### TD3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0ea4b4ce-89d9-4bfd-af99-615adec6f450",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "118ddf484409468fb56df39f4b168c57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = TD3(\"MlpPolicy\", gcse, verbose=0, tensorboard_log=\"/home/rstudio/logs\")\n",
+    "model.learn(\n",
+    "\ttotal_timesteps=100_000, \n",
+    "\tprogress_bar=True,\n",
+    ")\n",
+    "model.save(\"td3_gcse\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2a535f2-9d6d-435d-9208-18270e97e6c4",
+   "metadata": {},
+   "source": [
+    "### TQC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "045e56c5-430c-4636-8ead-d18b76734d98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee6b67c12e214c27bff7f5e6025e8d77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = TQC(\"MlpPolicy\", vec_env, verbose=0, tensorboard_log=\"/home/rstudio/logs\")\n",
+    "model.learn(\n",
+    "\ttotal_timesteps=100_000, \n",
+    "\tprogress_bar=True,\n",
+    ")\n",
+    "model.save(\"ppo_gcse\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e718653-7eb6-4143-9bf1-c027e157c777",
+   "metadata": {},
+   "source": [
+    "## 3. Loading and evaluating RL algos\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c05df193-0c95-4cba-a983-b283a11c40dd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0032750f132cd5338457afa2343f631c2e45e40b Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 18:31:03 +0000
Subject: [PATCH 43/52] intro_pt2

---
 notebooks/intro_pt2.ipynb | 153 ++++++++++++++++++++++++++++++++------
 1 file changed, 129 insertions(+), 24 deletions(-)

diff --git a/notebooks/intro_pt2.ipynb b/notebooks/intro_pt2.ipynb
index c2e173f..efb97f4 100644
--- a/notebooks/intro_pt2.ipynb
+++ b/notebooks/intro_pt2.ipynb
@@ -33,14 +33,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "id": "9b782d73-40ad-4de9-b1ab-f3c52461b8c2",
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from plotnine import ggplot, aes, geom_density, geom_line, geom_point, geom_violin, facet_grid, labs, theme, facet_wrap"
+    "from plotnine import ggplot, aes, geom_density, geom_line, geom_point, geom_violin, facet_grid, labs, theme, facet_wrap\n",
+    "from stable_baselines3 impor"
    ]
   },
   {
@@ -63,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "c05ebeea-5c6c-4be6-a45f-4461f7ebcfb2",
    "metadata": {},
    "outputs": [
@@ -73,7 +74,7 @@
        "Box(-1.0, 1.0, (1,), float32)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "41a623c2-25fe-4d23-a152-b6af91a49877",
    "metadata": {},
    "outputs": [
@@ -111,7 +112,7 @@
        "Box(-1.0, 1.0, (3,), float32)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "id": "013af299-2b80-4066-bf64-507e4f89c04b",
    "metadata": {},
    "outputs": [
@@ -132,7 +133,7 @@
        "(array([-1., -1., -1.], dtype=float32), {})"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -152,17 +153,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "id": "b076834b-12e7-4bec-9233-1ee40a6975cc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-0.9776    , -0.99142224, -0.1       ], dtype=float32)"
+       "array([-0.9775778 , -0.99186665, -0.1       ], dtype=float32)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,12 +212,12 @@
     "\n",
     "`python scripts/train.py -f hyperpars/ppo-greencrab-v0-1.yml`\n",
     "\n",
-    "Here, we encode the input to the training algorithm as a YAML file."
+    "There, we encode the input to the training algorithm as a YAML file."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 7,
    "id": "7254fbbd-dd2b-4598-afb1-c13c7ccbae88",
    "metadata": {},
    "outputs": [],
@@ -239,14 +240,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "id": "e7d2998a-b6a4-46c5-81c6-0bdfbd9f8aea",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8a14c36ae83e413c8094e9c038da2665",
+       "model_id": "d4a71821d8cf442d8a0a730de144c35f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -284,7 +285,7 @@
    "source": [
     "model = PPO(\"MlpPolicy\", vec_env, verbose=0, tensorboard_log=\"/home/rstudio/logs\")\n",
     "model.learn(\n",
-    "\ttotal_timesteps=100_000, \n",
+    "\ttotal_timesteps=50_000, \n",
     "\tprogress_bar=True,\n",
     ")\n",
     "model.save(\"ppo_gcse\")"
@@ -300,14 +301,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "id": "0ea4b4ce-89d9-4bfd-af99-615adec6f450",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "118ddf484409468fb56df39f4b168c57",
+       "model_id": "02992bd991884d9ca8255e4df54497ba",
        "version_major": 2,
        "version_minor": 0
       },
@@ -345,7 +346,7 @@
    "source": [
     "model = TD3(\"MlpPolicy\", gcse, verbose=0, tensorboard_log=\"/home/rstudio/logs\")\n",
     "model.learn(\n",
-    "\ttotal_timesteps=100_000, \n",
+    "\ttotal_timesteps=50_000, \n",
     "\tprogress_bar=True,\n",
     ")\n",
     "model.save(\"td3_gcse\")"
@@ -361,14 +362,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "id": "045e56c5-430c-4636-8ead-d18b76734d98",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee6b67c12e214c27bff7f5e6025e8d77",
+       "model_id": "8a4515d558814f55b67f35d2172b3d40",
        "version_major": 2,
        "version_minor": 0
       },
@@ -406,10 +407,10 @@
    "source": [
     "model = TQC(\"MlpPolicy\", vec_env, verbose=0, tensorboard_log=\"/home/rstudio/logs\")\n",
     "model.learn(\n",
-    "\ttotal_timesteps=100_000, \n",
+    "\ttotal_timesteps=50_000, \n",
     "\tprogress_bar=True,\n",
     ")\n",
-    "model.save(\"ppo_gcse\")"
+    "model.save(\"tqc_gcse\")"
    ]
   },
   {
@@ -423,10 +424,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "c05df193-0c95-4cba-a983-b283a11c40dd",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "ppoAgent = PPO.load(\"ppo_gcse\")\n",
+    "td3Agent = TD3.load(\"td3_gcse\")\n",
+    "tqcAgent = TQC.load(\"tqc_gcse\")\n",
+    "evalEnv = greenCrabSimplifiedEnv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "69193b84-77f7-4a30-90f8-9dc8e7bc0587",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stable_baselines3.common.evaluation import evaluate_policy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df1fe7bb-c13f-49f6-b6aa-7384c3cc6523",
+   "metadata": {},
+   "source": [
+    "### PPO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "929ef021-264c-40d0-bcbb-ea4ddd5d8665",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PPO reward = -0.020 +/- 0.000090\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_rew, std_rew = evaluate_policy(ppoAgent, evalEnv)\n",
+    "print(f\"PPO reward = {mean_rew:.3f} +/- {std_rew:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c7fd846-3b10-47a4-8b66-a1a8f1e94386",
+   "metadata": {},
+   "source": [
+    "### TD3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1e70e461-28ff-4eb2-aa3e-56cb2b1d2a41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TD3 reward = -0.017 +/- 0.000\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_rew, std_rew = evaluate_policy(td3Agent, evalEnv)\n",
+    "print(f\"TD3 reward = {mean_rew:.3f} +/- {std_rew:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2888869e-f44a-4853-8ee5-0ddd41e319b9",
+   "metadata": {},
+   "source": [
+    "### TQC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5e28caca-5d00-416f-86e6-b029a48bfe52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TQC reward = -0.020 +/- 0.000\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_rew, std_rew = evaluate_policy(tqcAgent, evalEnv)\n",
+    "print(f\"TQC reward = {mean_rew:.3f} +/- {std_rew:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c194f6ea-687d-48f9-a4c6-2561f7df521c",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],

From a24c12edde491d2b0371ea3cf986134fcc2f8bd7 Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 18:34:05 +0000
Subject: [PATCH 44/52] deleted outdated yaml

---
 hyperpars/ppo-greencrab-ts-v2-1.yml            | 14 --------------
 .../TQC-Nmem_1/1-tqc_nmem-1_bmk.yml            | 13 -------------
 .../TQC-Nmem_1/2-tqc_nmem-1_bmk.yml            | 13 -------------
 .../TQC-Nmem_1/3-tqc_nmem-1_bmk.yml            | 13 -------------
 .../TQC-Nmem_1/4-tqc_nmem-1_bmk.yml            | 13 -------------
 .../TQC-Nmem_1/5-tqc_nmem-1_bmk.yml            | 13 -------------
 .../TQC-Nmem_1/6-tqc_nmem-1_bmk.yml            | 13 -------------
 .../TQC-Nmem_1/7-tqc_nmem-1_bmk.yml            | 13 -------------
 .../TQC-Nmem_1/8-tqc_nmem-1_bmk.yml            | 13 -------------
 .../TQC-Nmem_1/9-tqc_nmem-1_bmk.yml            | 13 -------------
 hyperpars/tqc-greencrab-ts-v2-1.yml            | 14 --------------
 notebooks/intro_pt2.ipynb                      | 18 +++++++++---------
 12 files changed, 9 insertions(+), 154 deletions(-)
 delete mode 100644 hyperpars/ppo-greencrab-ts-v2-1.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml
 delete mode 100644 hyperpars/tqc-greencrab-ts-v2-1.yml

diff --git a/hyperpars/ppo-greencrab-ts-v2-1.yml b/hyperpars/ppo-greencrab-ts-v2-1.yml
deleted file mode 100644
index 7870869..0000000
--- a/hyperpars/ppo-greencrab-ts-v2-1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "PPO"
-meta_env_id: "TimeSeries-v2"
-meta_env_kwargs: {N_mem: 2}
-base_env_id: "GreenCrab-v2"
-base_env_cfg: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 6000000
-use_sde: True
-id: "N_mem_2"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml
deleted file mode 100644
index 965e591..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/1-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.05}
-use_sde: True
-id: "bmk-1"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml
deleted file mode 100644
index 2e48455..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/2-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-use_sde: True
-id: "bmk-2"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml
deleted file mode 100644
index 9caa66a..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/3-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.15}
-use_sde: True
-id: "bmk-3"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml
deleted file mode 100644
index c8fef01..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/4-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.2}
-use_sde: True
-id: "bmk-4"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml
deleted file mode 100644
index 5bc846d..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/5-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 0.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.25}
-use_sde: True
-id: "bmk-5"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml
deleted file mode 100644
index 3fecc5f..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/6-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 0.7, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-use_sde: True
-id: "bmk-6"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml
deleted file mode 100644
index f984d5e..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/7-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 0.9, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-use_sde: True
-id: "bmk-7"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml
deleted file mode 100644
index 55cb983..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/8-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 1.1, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-use_sde: True
-id: "bmk-8"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml b/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml
deleted file mode 100644
index 47a84bb..0000000
--- a/hyperpars/systematic-benchmarks/TQC-Nmem_1/9-tqc_nmem-1_bmk.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 1.5, imm: 500, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-use_sde: True
-id: "bmk-9"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
-progress_bar: False
diff --git a/hyperpars/tqc-greencrab-ts-v2-1.yml b/hyperpars/tqc-greencrab-ts-v2-1.yml
deleted file mode 100644
index 0c1ec67..0000000
--- a/hyperpars/tqc-greencrab-ts-v2-1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-meta_env_id: "TimeSeries-v2"
-meta_env_kwargs: {N_mem: 2}
-base_env_id: "GreenCrab-v2"
-base_env_cfg: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 6000000
-use_sde: True
-id: "N_mem_2"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
diff --git a/notebooks/intro_pt2.ipynb b/notebooks/intro_pt2.ipynb
index efb97f4..c2eafb9 100644
--- a/notebooks/intro_pt2.ipynb
+++ b/notebooks/intro_pt2.ipynb
@@ -455,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "929ef021-264c-40d0-bcbb-ea4ddd5d8665",
    "metadata": {},
    "outputs": [
@@ -463,13 +463,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PPO reward = -0.020 +/- 0.000090\n"
+      "PPO reward = -0.02013 +/- 0.00007\n"
      ]
     }
    ],
    "source": [
     "mean_rew, std_rew = evaluate_policy(ppoAgent, evalEnv)\n",
-    "print(f\"PPO reward = {mean_rew:.3f} +/- {std_rew:.5f}\")"
+    "print(f\"PPO reward = {mean_rew:.5f} +/- {std_rew:.5f}\")"
    ]
   },
   {
@@ -482,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 24,
    "id": "1e70e461-28ff-4eb2-aa3e-56cb2b1d2a41",
    "metadata": {},
    "outputs": [
@@ -490,13 +490,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TD3 reward = -0.017 +/- 0.000\n"
+      "TD3 reward = -0.01745 +/- 0.00006\n"
      ]
     }
    ],
    "source": [
     "mean_rew, std_rew = evaluate_policy(td3Agent, evalEnv)\n",
-    "print(f\"TD3 reward = {mean_rew:.3f} +/- {std_rew:.3f}\")"
+    "print(f\"TD3 reward = {mean_rew:.5f} +/- {std_rew:.5f}\")"
    ]
   },
   {
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "id": "5e28caca-5d00-416f-86e6-b029a48bfe52",
    "metadata": {},
    "outputs": [
@@ -517,13 +517,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TQC reward = -0.020 +/- 0.000\n"
+      "TQC reward = -0.02035 +/- 0.00007\n"
      ]
     }
    ],
    "source": [
     "mean_rew, std_rew = evaluate_policy(tqcAgent, evalEnv)\n",
-    "print(f\"TQC reward = {mean_rew:.3f} +/- {std_rew:.3f}\")"
+    "print(f\"TQC reward = {mean_rew:.5f} +/- {std_rew:.5f}\")"
    ]
   },
   {

From 68ccce0beceb1f37abbaefd2813d2fe6888a5a48 Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 18:48:09 +0000
Subject: [PATCH 45/52] new yaml configs

---
 hyperpars/ppo-gcse.yml           | 15 +++++++++++++++
 hyperpars/ppo-greencrab-v0-1.yml | 12 ------------
 hyperpars/td3-gcse.yml           | 12 ++++++++++++
 hyperpars/tqc-gcse.yml           | 12 ++++++++++++
 hyperpars/tqc-greencrab-v0-1.yml | 12 ------------
 notebooks/intro_pt2.ipynb        | 10 +---------
 src/rl4greencrab/__init__.py     |  6 +++---
 7 files changed, 43 insertions(+), 36 deletions(-)
 create mode 100644 hyperpars/ppo-gcse.yml
 delete mode 100644 hyperpars/ppo-greencrab-v0-1.yml
 create mode 100644 hyperpars/td3-gcse.yml
 create mode 100644 hyperpars/tqc-gcse.yml
 delete mode 100644 hyperpars/tqc-greencrab-v0-1.yml

diff --git a/hyperpars/ppo-gcse.yml b/hyperpars/ppo-gcse.yml
new file mode 100644
index 0000000..b8260c0
--- /dev/null
+++ b/hyperpars/ppo-gcse.yml
@@ -0,0 +1,15 @@
+# stable-baselines3 configuration template. 
+#
+# env_id strings are found at src/rl4greencrab/__init__.py
+# config is passed as an input to the env initialization.
+
+algo: "PPO"
+env_id: "gcsenv"
+n_envs: 12
+tensorboard: "/home/rstudio/logs"
+total_timesteps: 1000000
+config: {}
+use_sde: True
+id: "1"
+repo: "cboettig/rl-ecology"
+save_path: "/home/rstudio/rl4greencrab/saved_agents"
diff --git a/hyperpars/ppo-greencrab-v0-1.yml b/hyperpars/ppo-greencrab-v0-1.yml
deleted file mode 100644
index 00b1201..0000000
--- a/hyperpars/ppo-greencrab-v0-1.yml
+++ /dev/null
@@ -1,12 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "PPO"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 6000000
-config: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-use_sde: True
-id: "1"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
diff --git a/hyperpars/td3-gcse.yml b/hyperpars/td3-gcse.yml
new file mode 100644
index 0000000..98ae903
--- /dev/null
+++ b/hyperpars/td3-gcse.yml
@@ -0,0 +1,12 @@
+# stable-baselines3 configuration.
+
+algo: "TD3"
+env_id: "gcsenv"
+n_envs: 12
+tensorboard: "/home/rstudio/logs"
+total_timesteps: 1000000
+config: {}
+use_sde: True
+id: "1"
+repo: "cboettig/rl-ecology"
+save_path: "/home/rstudio/rl4greencrab/saved_agents"
diff --git a/hyperpars/tqc-gcse.yml b/hyperpars/tqc-gcse.yml
new file mode 100644
index 0000000..1f2e18e
--- /dev/null
+++ b/hyperpars/tqc-gcse.yml
@@ -0,0 +1,12 @@
+# stable-baselines3 configuration.
+
+algo: "TQC"
+env_id: "gcsenv"
+n_envs: 12
+tensorboard: "/home/rstudio/logs"
+total_timesteps: 1000000
+config: {}
+use_sde: True
+id: "1"
+repo: "cboettig/rl-ecology"
+save_path: "/home/rstudio/rl4greencrab/saved_agents"
diff --git a/hyperpars/tqc-greencrab-v0-1.yml b/hyperpars/tqc-greencrab-v0-1.yml
deleted file mode 100644
index a32fd37..0000000
--- a/hyperpars/tqc-greencrab-v0-1.yml
+++ /dev/null
@@ -1,12 +0,0 @@
-# stable-baselines3 configuration
-
-algo: "TQC"
-env_id: "GreenCrab-v2"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 1000000
-config: {r: 0.5, imm: 2000, problem_scale: 2000, action_reward_scale: 0.5, env_stoch: 0.1}
-use_sde: True
-id: "1"
-repo: "cboettig/rl-ecology"
-save_path: "/home/rstudio/saved_agents"
diff --git a/notebooks/intro_pt2.ipynb b/notebooks/intro_pt2.ipynb
index c2eafb9..49938e6 100644
--- a/notebooks/intro_pt2.ipynb
+++ b/notebooks/intro_pt2.ipynb
@@ -210,7 +210,7 @@
     "**Note:** This package also provides a more ergonomic syntax for training through the `train.py` script. \n",
     "To train models this way, run the following command on the terminal:\n",
     "\n",
-    "`python scripts/train.py -f hyperpars/ppo-greencrab-v0-1.yml`\n",
+    "`python scripts/train.py -f hyperpars/ppo-gcse.yml`\n",
     "\n",
     "There, we encode the input to the training algorithm as a YAML file."
    ]
@@ -525,14 +525,6 @@
     "mean_rew, std_rew = evaluate_policy(tqcAgent, evalEnv)\n",
     "print(f\"TQC reward = {mean_rew:.5f} +/- {std_rew:.5f}\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c194f6ea-687d-48f9-a4c6-2561f7df521c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py
index fd1f961..9cfe7f6 100644
--- a/src/rl4greencrab/__init__.py
+++ b/src/rl4greencrab/__init__.py
@@ -6,14 +6,14 @@
 
 from gymnasium.envs.registration import register
 register(
-    id="GreenCrab", 
+    id="gcenv", 
     entry_point="rl4greencrab.green_crab_ipm:greenCrabEnv",
 )
 register(
-    id="GreenCrabSimpl", 
+    id="gcsenv", 
     entry_point="rl4greencrab.green_crab_ipm:greenCrabSimplifiedEnv"
 )
 register(
-    id="TimeSeries", 
+    id="tsenv", 
     entry_point="rl4greencrab.time_series:TimeSeriesEnv",
 )

From eef874092fd4102f516ef6cd1231d414661e9b49 Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 18:51:12 +0000
Subject: [PATCH 46/52] added boilerplate yaml

---
 hyperpars/gcse-boilerplate.yml | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 hyperpars/gcse-boilerplate.yml

diff --git a/hyperpars/gcse-boilerplate.yml b/hyperpars/gcse-boilerplate.yml
new file mode 100644
index 0000000..c3096e1
--- /dev/null
+++ b/hyperpars/gcse-boilerplate.yml
@@ -0,0 +1,10 @@
+# stable-baselines3 configuration.
+
+env_id: "gcsenv"
+config: {}
+n_envs: 12
+tensorboard: "/home/rstudio/logs"
+use_sde: True
+id: "1"
+repo: "cboettig/rl-ecology"
+save_path: "/home/rstudio/rl4greencrab/saved_agents"

From 6a4acf6f59b08a2246077ff64c1972c7092c1b40 Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 18:57:42 +0000
Subject: [PATCH 47/52] sb3_train allows kwargs to extend yaml input

---
 src/rl4greencrab/utils/sb3.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/rl4greencrab/utils/sb3.py b/src/rl4greencrab/utils/sb3.py
index e05921e..8573fc6 100644
--- a/src/rl4greencrab/utils/sb3.py
+++ b/src/rl4greencrab/utils/sb3.py
@@ -25,9 +25,11 @@ def algorithm(algo):
     }
     return algos[algo]
 
-def sb3_train(config_file):
+def sb3_train(config_file, **kwargs):
     with open(config_file, "r") as stream:
         options = yaml.safe_load(stream)
+        options = {**options, **kwargs}
+        # updates / expands on yaml options with optional user-provided input
 
     vec_env = make_vec_env(
         options["env_id"], options["n_envs"], env_kwargs={"config": options["config"]}

From 5c08c59719ec1ceb9e98ea1a0283469942c4b06b Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 18:59:42 +0000
Subject: [PATCH 48/52] removed outdated train scripts

---
 scripts/train_metaenv.py | 10 ----------
 scripts/train_v2.py      | 26 --------------------------
 2 files changed, 36 deletions(-)
 delete mode 100644 scripts/train_metaenv.py
 delete mode 100644 scripts/train_v2.py

diff --git a/scripts/train_metaenv.py b/scripts/train_metaenv.py
deleted file mode 100644
index d062f38..0000000
--- a/scripts/train_metaenv.py
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/opt/venv/bin/python
-import argparse
-parser = argparse.ArgumentParser()
-parser.add_argument("-f", "--file", help="Path config file", type=str)
-args = parser.parse_args()
-
-import rl4greencrab
-from rl4greencrab import sb3_train_metaenv
-
-sb3_train_metaenv(args.file)
diff --git a/scripts/train_v2.py b/scripts/train_v2.py
deleted file mode 100644
index c55e9ff..0000000
--- a/scripts/train_v2.py
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/opt/venv/bin/python
-import rl4greencrab
-from rl4greencrab import sb3_train_v2
-from rl4greencrab import invasive_IPM_v2 as ipm_v2
-from rl4greencrab import ts_env_v1
-
-IPM_CFG = {
-    'r': 0.5,
-    'imm': 2000,
-    'problem_scale': 2000,
-    'action_reward_scale': 0.5, # cost per unit action in ipm
-    'env_stoch': 0.1
-}
-
-OPTIONS = {
-    "env_id" : "TimeSeries-v1",
-    "n_envs" : 12,
-    "config" : {"base_env_cls": ipm_v2, "base_env_cfg": IPM_CFG, "N_mem": 5},
-    "algo" : "PPO",
-    "id" : "NMem_5",
-    "tensorboard" : "/home/rstudio/logs",
-    "use_sde" : True,
-    "total_timesteps" : 6000000,
-}
-
-sb3_train_v2(OPTIONS)

From 72e2cf805f13306f68f40d9fc96694866d636ac8 Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 19:50:21 +0000
Subject: [PATCH 49/52] train_algos bash script, train_gcse_manual python
 script, yaml configs

---
 hyperpars/gcse-boilerplate.yml |  1 -
 scripts/train_algos.sh         | 11 +++++++
 scripts/train_benchmarks.sh    | 11 -------
 scripts/train_gcse_manual.py   | 41 ++++++++++++++++++++++++++
 src/rl4greencrab/utils/sb3.py  | 53 ++++++++++++++++++++++------------
 5 files changed, 86 insertions(+), 31 deletions(-)
 create mode 100644 scripts/train_algos.sh
 delete mode 100644 scripts/train_benchmarks.sh
 create mode 100644 scripts/train_gcse_manual.py

diff --git a/hyperpars/gcse-boilerplate.yml b/hyperpars/gcse-boilerplate.yml
index c3096e1..d5cbe1e 100644
--- a/hyperpars/gcse-boilerplate.yml
+++ b/hyperpars/gcse-boilerplate.yml
@@ -2,7 +2,6 @@
 
 env_id: "gcsenv"
 config: {}
-n_envs: 12
 tensorboard: "/home/rstudio/logs"
 use_sde: True
 id: "1"
diff --git a/scripts/train_algos.sh b/scripts/train_algos.sh
new file mode 100644
index 0000000..4649012
--- /dev/null
+++ b/scripts/train_algos.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+# move to script directory for normalized relative paths.
+scriptdir="$(dirname "$0")"
+cd "$scriptdir"
+
+python train_gcse_manual.py -t 1000000 -a ppo -ne 10 &
+python train_gcse_manual.py -t 1000000 -a rppo -ne 10 &
+python train_gcse_manual.py -t 1000000 -a her -ne 10 &
+python train_gcse_manual.py -t 1000000 -a tqc -ne 10 &
+python train_gcse_manual.py -t 1000000 -a td3 &
diff --git a/scripts/train_benchmarks.sh b/scripts/train_benchmarks.sh
deleted file mode 100644
index 9f43b24..0000000
--- a/scripts/train_benchmarks.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-
-# move to script directory for normalized relative paths.
-scriptdir="$(dirname "$0")"
-cd "$scriptdir"
-
-python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/10-tqc_nmem-1_bmk.yml &
-python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/11-tqc_nmem-1_bmk.yml &
-python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/12-tqc_nmem-1_bmk.yml &
-python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/13-tqc_nmem-1_bmk.yml &
-python train.py --file ../hyperpars/systematic-benchmarks/TQC-Nmem_1/14-tqc_nmem-1_bmk.yml &
\ No newline at end of file
diff --git a/scripts/train_gcse_manual.py b/scripts/train_gcse_manual.py
new file mode 100644
index 0000000..64d46d2
--- /dev/null
+++ b/scripts/train_gcse_manual.py
@@ -0,0 +1,41 @@
+#!/opt/venv/bin/python
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("-a", "--algo", help="Algo to train", type=str, 
+                    choices=[
+                        'PPO', 'RecurrentPPO', 'ppo', 'recurrentppo',
+                        'ARS', 'A2C', 'ars', 'a2c',
+                        'DDPG', 'ddpg',
+                        'HER', 'her',
+                        'SAC', 'sac'
+                        'TD3', 'td3',
+                        'TQC', 'tqc',
+                    ]
+)
+parser.add_argument("-t", "--time-steps", help="N. timesteps to train for", type=int)
+parser.add_argument(
+    "-ne", 
+    "--n-envs", 
+    help="Number of envs to use simultaneously for faster training. " 
+        "Check algos for compatibility with this arg.", 
+    type=int,
+)
+
+args = parser.parse_args()
+
+manual_kwargs = {}
+if args.algo:
+    manual_kwargs['algo'] = args.algo
+if args.time_steps:
+    manual_kwargs['time_steps'] = args.time_steps
+if args.n_envs:
+    manual_kwargs['n_envs'] = args.n_envs
+
+import os
+boilerplate_cfg = os.path.join("..", "hyperpars", "gcse-boilerplate.yml")
+
+
+import rl4greencrab
+from rl4greencrab import sb3_train 
+
+sb3_train(boilerplate_cfg, **manual_kwargs)
diff --git a/src/rl4greencrab/utils/sb3.py b/src/rl4greencrab/utils/sb3.py
index 8573fc6..91634a2 100644
--- a/src/rl4greencrab/utils/sb3.py
+++ b/src/rl4greencrab/utils/sb3.py
@@ -3,25 +3,37 @@
 
 import gymnasium as gym
 from stable_baselines3.common.env_util import make_vec_env
-from stable_baselines3 import PPO, A2C, DQN, SAC, TD3
-from sb3_contrib import TQC, ARS
+from stable_baselines3 import PPO, A2C, DQN, SAC, TD3, HER
+from sb3_contrib import TQC, ARS, RecurrentPPO
 
 def algorithm(algo):
     algos = {
-        "PPO": PPO,
-        "ARS": ARS,
-        "TQC": TQC,
-        "A2C": A2C,
-        "SAC": SAC,
-        "DQN": DQN,
-        "TD3": TD3,
-        "ppo": PPO,
-        "ars": ARS,
-        "tqc": TQC,
-        "a2c": A2C,
-        "sac": SAC,
-        "dqn": DQN,
-        "td3": TD3,
+        'PPO': PPO, 
+        'ppo': PPO,
+        'RecurrentPPO': RecurrentPPO,
+        'RPPO': RecurrentPPO,
+        'recurrentppo': RecurrentPPO,
+        'rppo': RecurrentPPO,
+        #
+        'ARS': ARS,
+        'ars': ARS,
+        'A2C': A2C, 
+        'a2c':A2C ,
+        #
+        'DDPG': DDPG, 
+        'ddpg': DDPG,
+        #
+        'HER': HER, 
+        'her': HER,
+        #
+        'SAC': SAC, 
+        'sac': SAC,
+        #
+        'TD3': TD3, 
+        'td3': TD3,
+        #
+        'TQC': TQC, 
+        'tqc': TQC,
     }
     return algos[algo]
 
@@ -31,9 +43,12 @@ def sb3_train(config_file, **kwargs):
         options = {**options, **kwargs}
         # updates / expands on yaml options with optional user-provided input
 
-    vec_env = make_vec_env(
-        options["env_id"], options["n_envs"], env_kwargs={"config": options["config"]}
-    )
+    if "n_envs" in options:
+        env = make_vec_env(
+            options["env_id"], options["n_envs"], env_kwargs={"config": options["config"]}
+        )
+    else:
+        env = gym.make(options["env_id"])
     ALGO = algorithm(options["algo"])
     model_id = options["algo"] + "-" + options["env_id"]  + "-" + options["id"]
     save_id = os.path.join(options["save_path"], model_id)

From 668586dd3727573969f4f2d1a9a3389450ace9ba Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 20:08:39 +0000
Subject: [PATCH 50/52] debugging scripts, deleted old env files

---
 scripts/train.py                           |   2 +-
 scripts/train_gcse_manual.py               |   6 +-
 src/rl4greencrab/green_crab_ipm.py         | 304 ---------------------
 src/rl4greencrab/time_series.py            |  60 ----
 src/rl4greencrab/{util.py => util_depr.py} |   0
 src/rl4greencrab/utils/sb3.py              |   4 +-
 6 files changed, 6 insertions(+), 370 deletions(-)
 delete mode 100644 src/rl4greencrab/green_crab_ipm.py
 delete mode 100644 src/rl4greencrab/time_series.py
 rename src/rl4greencrab/{util.py => util_depr.py} (100%)

diff --git a/scripts/train.py b/scripts/train.py
index 3270a65..75d0825 100644
--- a/scripts/train.py
+++ b/scripts/train.py
@@ -5,6 +5,6 @@
 args = parser.parse_args()
 
 import rl4greencrab
-from rl4greencrab import sb3_train  
+from rl4greencrab.utils.sb3 import sb3_train 
 
 sb3_train(args.file)
diff --git a/scripts/train_gcse_manual.py b/scripts/train_gcse_manual.py
index 64d46d2..8d1aaa6 100644
--- a/scripts/train_gcse_manual.py
+++ b/scripts/train_gcse_manual.py
@@ -3,7 +3,7 @@
 parser = argparse.ArgumentParser()
 parser.add_argument("-a", "--algo", help="Algo to train", type=str, 
                     choices=[
-                        'PPO', 'RecurrentPPO', 'ppo', 'recurrentppo',
+                        'PPO', 'RecurrentPPO', 'ppo', 'recurrentppo', 'RPPO', 'rppo',
                         'ARS', 'A2C', 'ars', 'a2c',
                         'DDPG', 'ddpg',
                         'HER', 'her',
@@ -27,7 +27,7 @@
 if args.algo:
     manual_kwargs['algo'] = args.algo
 if args.time_steps:
-    manual_kwargs['time_steps'] = args.time_steps
+    manual_kwargs['total_timesteps'] = args.time_steps
 if args.n_envs:
     manual_kwargs['n_envs'] = args.n_envs
 
@@ -36,6 +36,6 @@
 
 
 import rl4greencrab
-from rl4greencrab import sb3_train 
+from rl4greencrab.utils.sb3 import sb3_train 
 
 sb3_train(boilerplate_cfg, **manual_kwargs)
diff --git a/src/rl4greencrab/green_crab_ipm.py b/src/rl4greencrab/green_crab_ipm.py
deleted file mode 100644
index 9089c4a..0000000
--- a/src/rl4greencrab/green_crab_ipm.py
+++ /dev/null
@@ -1,304 +0,0 @@
-import gymnasium as gym
-import logging
-import numpy as np
-
-from gymnasium import spaces
-from scipy.stats import norm
-
-logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO)
-
-"""
-Taken from IPM_202040117.ipynb, modified minor aspects to be able to interface
-with ts_model.py
-"""
-
-class greenCrabEnv(gym.Env):
-    metadata = {"render.modes": ["human"]}
-
-    def __init__(
-        self,
-        config=None,
-    ):
-        # if config == {}:
-        #     config = {
-        #         "Tmax": 100,
-        #         "growth_k": 0.43, "growth_xinf": 109, "growth_sd": 2.5, "nmortality": 0.03,
-        #         "trapm_sigma": 0.15, "trapm_xmax": 44, "trapm_pmax": 0.0005, "trapf_pmax": 0.0008,
-        #         "trapf_k": 0.5, "trapf_midpoint": 45, "init_mean_recruit": 15, "init_sd_recruit": 1.5,
-        #         "init_mean_adult": 65, "init_sd_adult": 8, "init_n_recruit": 1000, "init_n_adult": 1000,
-        #         "w_mort_scale": 5, "K": 25000, "imm": 10, "r": 50, "area": 4000,"loss_a": 0.265,
-        #         "loss_b": 2.80, "loss_c": 2.99, "minsize": 5, "maxsize": 110, "nsize": 21, "ntime":9,"delta_t": 1/12,
-        #         "env_stoch": 0.1, "action_reward_scale":0.001
-        #     }
-        
-        config=config or {}
-        
-        # parameters
-        self.growth_k = config.get("growth_k", 0.43)
-        self.growth_xinf = config.get("growth_xinf", 109)
-        self.growth_sd = config.get("growth_sd", 2.5)
-        self.nmortality = config.get("nmortality", 0.03)
-        
-        self.trapm_sigma = config.get("trapm_sigma", 0.15)
-        self.trapm_xmax = config.get("trapm_xmax", 44)
-        self.trapm_pmax = config.get("trapm_pmax", 0.0005)
-        self.trapf_pmax = config.get("trapf_pmax", 0.0008)
-        self.trapf_k = config.get("trapf_k", 0.5)
-        self.trapf_midpoint = config.get("trapf_midpoint", 45)
-        
-        self.init_mean_recruit = config.get("init_mean_recruit", 15)
-        self.init_sd_recruit = config.get("init_sd_recruit", 1.5)
-        self.init_mean_adult = config.get("init_mean_adult", 65)
-        self.init_sd_adult = config.get("init_sd_adult", 8)
-        self.init_n_recruit = config.get("init_n_recruit", 1000)
-        self.init_n_adult = config.get("init_n_adult", 1000)
-        
-        self.w_mort_scale = config.get("w_mort_scale", 5)
-        self.K = config.get("K", 25000) #carrying capacity
-        self.imm = config.get("imm", 1000) #colonization/immigration rate
-        self.r = config.get("r", 0.5) #intrinsic rate of growth
-
-        self.max_action = config.get("max_action", 2000)
-        self.max_obs = config.get("max_obs", 2000)
-        
-        self.area = config.get("area", 4000)
-        self.loss_a = config.get("loss_a", 0.265)
-        self.loss_b = config.get("loss_b", 2.80)
-        self.loss_c = config.get("loss_c", 2.99)
-        
-        self.minsize = config.get("minsize", 5)
-        self.maxsize = config.get("maxsize", 110)
-        self.nsize = config.get("nsize", 21)
-        self.ntime = config.get("ntime", 9)
-        
-        self.delta_t = config.get("delta_t", 1/12)
-        self.env_stoch = config.get("env_stoch", 0.1)
-        self.action_reward_scale = config.get("action_reward_scale", 0.5)
-        self.config = config
-
-        # Preserve these for reset
-        self.observations = np.zeros(shape=9, dtype=np.float32)
-        self.reward = 0
-        self.years_passed = 0
-        self.Tmax = config.get("Tmax", 100)
-                
-        # Initial variables
-        self.bndry = self.boundary()
-        self.state = self.init_state()
-        self.midpts = self.midpoints()
-        self.gm_ker = self.g_m_kernel()
-        self.w_mort = self.w_mortality()
-        self.w_mort_exp = np.exp(-self.w_mort)
-        self.pmort = np.exp(-self.nmortality)
-
-        # Action space
-        # action -- # traps per month
-        self.action_space = spaces.Box(
-            np.array([0], dtype=np.float32),
-            np.array([self.max_action], dtype=np.float32),
-            dtype=np.float32,
-        )
-        
-        # Observation space
-        self.observation_space = spaces.Box(
-            np.zeros(shape=9, dtype=np.float32),
-            self.max_obs * np.ones(shape=9, dtype=np.float32),
-            dtype=np.float32,
-        )
-        
-    def step(self,action):
-        #size selective harvest rate, given action
-        harvest_rate = 1-np.exp(-(self.size_sel_norm()*action + self.size_sel_log()*action))
-
-        #add pop at t=1
-        size_freq = np.zeros(shape=(self.nsize,self.ntime),dtype='object')
-        size_freq[:,0] = self.state
-        
-        #create array to store # removed
-        removed = np.zeros(shape=(self.nsize,self.ntime),dtype='object')
-        
-        #calculate removed and record observation at t=1
-        #apply monthly harvest rate
-        removed[:,0] = [np.random.binomial(size_freq[k,0], harvest_rate[k]) for k in range(self.nsize)]
-        # for k in range(self.nsize):
-        #     removed[k,0] = np.random.binomial(size_freq[k,0], harvest_rate[k])
-
-            
-        #record the catch in the observation space
-        self.observations[0] = np.sum(removed[:,0])
-
-
-        #
-        # From profiling: it seems like the model would run faster if the somatic growth/removal part was deterministic
-        #
-        # i.e. instead of ```np.random.binomial(n=n_j[k], p=...)``` have ```n_j[l] * self.pmort```
-        #
-        # could we model all the randomness as a final random vector added to size_freq[:, -1] ?
-        #
-        
-        #loop through intra-annual change (9 total months), t=2+        
-        for j in range(self.ntime-1):
-            #project to next month
-            n_j = self.gm_ker@(size_freq[:,j] - removed[:,j])
-
-            size_freq[:,j+1] = [np.random.binomial(n=n_j[k], p=self.pmort) for k in range(self.nsize)]
-            removed[:,j+1] = [np.random.binomial(size_freq[k,j+1], harvest_rate[k]) for k in range(self.nsize)]
-            
-        self.observations = np.array([np.sum(removed[:,j]) for j in range(self.ntime)])
-            
-            # for k in range(21):
-            #     #project to next size frequency
-            #     size_freq[k,j+1] = np.random.binomial(
-            #         n=n_j[k], 
-            #         p=self.pmort
-            #     )
-            #     #apply monthly harvest rate
-            #     removed[k,j+1] = np.random.binomial(size_freq[k,j+1], harvest_rate[k])
-            # #record the catch/effort in the observation space
-            # self.observations[j+1] = np.sum(removed[:,j+1])
-
-        #calculate new adult population after overwinter mortality
-        new_adults = [ np.random.binomial(size_freq[k,8],self.w_mort_exp[k]) for k in range(self.nsize) ]
-        # new_adults = []
-        # for k in range(21):
-        #     new_adults = np.append(new_adults,np.random.binomial(size_freq[k,8],np.exp(-self.w_mort)[k]))
-
-        #simulate new recruits
-        local_recruits = np.random.normal(self.dd_growth(size_freq[:,self.ntime-1]),self.env_stoch)
-        nonlocal_recruits = np.random.poisson(self.imm)*(1-np.sum(size_freq[:,self.ntime-1])/self.K)
-        recruit_total = local_recruits + nonlocal_recruits
-
-        logging.debug('local recruits = {}'.format(local_recruits))
-        logging.debug('nonlocal recruits = {}'.format(nonlocal_recruits))
-
-        #get sizes of recruits
-        recruit_sizes = (norm.cdf(self.bndry[1:(self.nsize+1)],self.init_mean_recruit,self.init_sd_recruit)-\
-         norm.cdf(self.bndry[0:self.nsize],self.init_mean_recruit,self.init_sd_recruit))*recruit_total
-
-        #store new population size (and cap off at zero pop)
-        self.state = np.maximum(recruit_sizes + new_adults, 0)
-
-        #calculate reward
-        self.reward = self.reward_func(np.sum(action))
-        self.years_passed += 1
-
-        done = bool(self.years_passed > self.Tmax)
-
-        # if np.sum(self.state) <= 0.001:
-        #     done = True
-
-        return self.observations, self.reward, done, done, {}
-        
-    def reset(self, seed=42, options=None):
-        self.state = self.init_state()
-        self.years_passed = 0
-
-        # for tracking only
-        self.reward = 0
-
-        # self.observations = np.zeros(shape=self.ntime)
-        self.observations = np.random.randint(0,100, size=self.ntime)
-
-        return self.observations, {}
-
-    #################
-    #helper functions
-
-    #set up boundary points of IPM mesh
-    def boundary(self):
-        boundary = self.minsize+np.arange(0,(self.nsize+1),1)*(self.maxsize-self.minsize)/self.nsize
-        return boundary
-
-    #set up mid points of IPM mesh
-    def midpoints(self):
-        midpoints = 0.5*(self.bndry[0:self.nsize]+self.bndry[1:(self.nsize+1)])
-        return midpoints
-
-    #function for initial state
-    def init_state(self):
-        init_pop = (norm.cdf(self.bndry[1:(self.nsize+1)],self.init_mean_adult,self.init_sd_adult)-\
-         norm.cdf(self.bndry[0:self.nsize],self.init_mean_adult,self.init_sd_adult))*self.init_n_adult+\
-        (norm.cdf(self.bndry[1:(self.nsize+1)],self.init_mean_recruit,self.init_sd_recruit)-\
-         norm.cdf(self.bndry[0:self.nsize],self.init_mean_recruit,self.init_sd_recruit))*self.init_n_recruit
-        return init_pop
-
-    #function for logistic size selectivity curve
-    def size_sel_log(self):
-        size_sel = self.trapf_pmax/(1+np.exp(-self.trapf_k*(self.midpts-self.trapf_midpoint)))
-        return size_sel
-
-    #function for gaussian size selectivity curve
-    def size_sel_norm(self):
-        size_sel = self.trapm_pmax*np.exp(-(self.midpts-self.trapm_xmax)**2/2*self.trapm_sigma**2)
-        return size_sel
-
-    #function for growth/mortality kernel
-    def g_m_kernel(self):
-        array = np.empty(shape=(self.nsize,self.nsize),dtype='object')
-        for i in range(self.nsize):
-            mean = (self.growth_xinf-self.midpts[i])*(1-np.exp(-self.growth_k*self.delta_t)) + self.midpts[i]
-            array[:,i] = (norm.cdf(self.bndry[1:(self.nsize+1)],mean,self.growth_sd)-\
-                          norm.cdf(self.bndry[0:self.nsize],mean,self.growth_sd))
-        return array
-
-    #function for overwinter mortality
-    def w_mortality(self):
-        wmort = self.w_mort_scale/self.midpts
-        return wmort
-
-    #function for density dependent growth
-    def dd_growth(self,popsize):
-        dd_recruits = np.sum(popsize)*self.r*(1-np.sum(popsize)/self.K)
-        return dd_recruits
-
-    #function for reward
-    # two part reward function:
-    # 1. impact on environment (function of crab density)
-    # 2. penalty for how much effort we expended (function of action)
-    def reward_func(self,action):
-        reward = -self.loss_a/(1+np.exp(-self.loss_b*(np.sum(self.state)/self.area-self.loss_c)))-self.action_reward_scale*action/self.max_action
-        return reward
-
-
-class greenCrabSimplifiedEnv(greenCrabEnv):
-    """ like invasive_IPM but with simplified observations and normalized to -1, 1 space. """
-    def __init__(self, config={}):
-        super().__init__(config=config)
-        self.observation_space = spaces.Box(
-            np.array([-1,-1,-1], dtype=np.float32),
-            np.array([1,1,1], dtype=np.float32),
-            dtype=np.float32,
-        )
-        self.action_space = spaces.Box(
-            np.float32([-1]),
-            np.float32([1]),
-            dtype=np.float32,
-        )
-        self.max_action = config.get('max_action', 2000) # ad hoc based on previous values
-        self.cpue_normalization = config.get('cpue_normalization', 100)
-        
-    def step(self, action):
-        action_natural_units = np.maximum( self.max_action * (1 + action)/2 , 0.)
-        obs, rew, term, trunc, info = super().step(
-            np.float32(action_natural_units)
-        )
-        normalized_cpue = 2 * self.cpue_2(obs, action_natural_units) - 1
-        observation = np.float32(np.append(normalized_cpue, action))
-        return observation, rew, term, trunc, info
-
-    def reset(self, seed=42, options=None):
-        _, info = super().reset(seed=seed, options=options)
-
-        # completely new  obs
-        return - np.ones(shape=self.observation_space.shape, dtype=np.float32), info
-
-    def cpue_2(self, obs, action_natural_units):
-        if any(scaled_action <= 0):
-            return np.float32([0,0])
-        cpue_2 = np.float32([
-            np.sum(obs[0:5]) / (self.cpue_normalization * action_natural_units[0]),
-            np.sum(obs[5:]) / (self.cpue_normalization * action_natural_units[0])
-        ])
-        return cpue_2
-        
-        
\ No newline at end of file
diff --git a/src/rl4greencrab/time_series.py b/src/rl4greencrab/time_series.py
deleted file mode 100644
index b1b483a..0000000
--- a/src/rl4greencrab/time_series.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import gymnasium as gym
-from gymnasium import spaces
-import numpy as np
-
-
-class timeSeriesEnv(gym.Env):
-	"""
-	takes an environment env and produces an new environemtn timeSeriesEnv(env)
-	whose observations are timeseries of the env environment.
-	"""
-	def __init__(self, config = {}):
-		self.N_mem = config.get('N_mem', 3)
-        if 'base_env' in config:
-            self.base_env = config['base_env']
-        else:
-            from rl4greencrab import greenCrabSimplifiedEnv
-            self.base_env = greenCrabSimplifiedEnv()
-
-		self.action_space = self.base_env.action_space
-        ones_shape = np.ones(
-            shape = (self.N_mem, self.base_env.observation_space.shape), 
-            dtype=np.float32,
-        )
-		self.observation_space = spaces.Box(-ones_shape, +ones_shape)
-		#
-		# [[state t], [state t-1], [state t-2], ..., [state t - (N_mem-1)]]
-		# where each [state i] is a vector
-		#
-		_ = self.reset()
-
-	def reset(self, *, seed=42, options=None):
-		init_state, init_info = self.base_env.reset(seed=seed, options=options)
-		empty_heap = - np.ones(shape = self.observation_space.shape, dtype=np.float32)
-		self.heap = np.insert(
-			empty_heap[0:-1],
-			0,
-			init_state,
-			axis=0,
-		)
-		return self.heap, init_info
-
-	def step(self, action):
-		new_state, reward, terminated, truncated, info = self.base_env.step(action)
-		self.heap = np.insert(
-			self.heap[0:-1],
-			0,
-			new_state,
-			axis=0,
-		)
-		return self.heap, reward, terminated, truncated, info
-
-	def pop_to_state(self, pop):
-		return self.base_env.pop_to_state(pop)
-
-	def state_to_pop(self, state):
-		return self.base_env.state_to_pop(state)
-
-
-
-
diff --git a/src/rl4greencrab/util.py b/src/rl4greencrab/util_depr.py
similarity index 100%
rename from src/rl4greencrab/util.py
rename to src/rl4greencrab/util_depr.py
diff --git a/src/rl4greencrab/utils/sb3.py b/src/rl4greencrab/utils/sb3.py
index 91634a2..2fd10c0 100644
--- a/src/rl4greencrab/utils/sb3.py
+++ b/src/rl4greencrab/utils/sb3.py
@@ -3,7 +3,7 @@
 
 import gymnasium as gym
 from stable_baselines3.common.env_util import make_vec_env
-from stable_baselines3 import PPO, A2C, DQN, SAC, TD3, HER
+from stable_baselines3 import PPO, A2C, DQN, SAC, TD3, HER, DDPG
 from sb3_contrib import TQC, ARS, RecurrentPPO
 
 def algorithm(algo):
@@ -55,7 +55,7 @@ def sb3_train(config_file, **kwargs):
 
     model = ALGO(
         "MlpPolicy",
-        vec_env,
+        env,
         verbose=0,
         tensorboard_log=options["tensorboard"],
         use_sde=options["use_sde"],

From 5df6bed384f0bc4f4b47c39e40b1c0068f353c82 Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 20:20:33 +0000
Subject: [PATCH 51/52] registration entry points, multi algo script

---
 scripts/train_algos.sh       | 1 -
 src/rl4greencrab/__init__.py | 6 +++---
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/scripts/train_algos.sh b/scripts/train_algos.sh
index 4649012..3c28efa 100644
--- a/scripts/train_algos.sh
+++ b/scripts/train_algos.sh
@@ -8,4 +8,3 @@ python train_gcse_manual.py -t 1000000 -a ppo -ne 10 &
 python train_gcse_manual.py -t 1000000 -a rppo -ne 10 &
 python train_gcse_manual.py -t 1000000 -a her -ne 10 &
 python train_gcse_manual.py -t 1000000 -a tqc -ne 10 &
-python train_gcse_manual.py -t 1000000 -a td3 &
diff --git a/src/rl4greencrab/__init__.py b/src/rl4greencrab/__init__.py
index 9cfe7f6..3b3c243 100644
--- a/src/rl4greencrab/__init__.py
+++ b/src/rl4greencrab/__init__.py
@@ -7,13 +7,13 @@
 from gymnasium.envs.registration import register
 register(
     id="gcenv", 
-    entry_point="rl4greencrab.green_crab_ipm:greenCrabEnv",
+    entry_point="rl4greencrab.envs.green_crab_ipm:greenCrabEnv",
 )
 register(
     id="gcsenv", 
-    entry_point="rl4greencrab.green_crab_ipm:greenCrabSimplifiedEnv"
+    entry_point="rl4greencrab.envs.green_crab_ipm:greenCrabSimplifiedEnv"
 )
 register(
     id="tsenv", 
-    entry_point="rl4greencrab.time_series:TimeSeriesEnv",
+    entry_point="rl4greencrab.envs.time_series:TimeSeriesEnv",
 )

From c006934684bc55f07ef035469cea1915d1ca7d45 Mon Sep 17 00:00:00 2001
From: Felipe Montealegre-Mora 
Date: Mon, 26 Feb 2024 20:20:52 +0000
Subject: [PATCH 52/52] HER deprecated

---
 scripts/train_algos.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/scripts/train_algos.sh b/scripts/train_algos.sh
index 3c28efa..0354e06 100644
--- a/scripts/train_algos.sh
+++ b/scripts/train_algos.sh
@@ -6,5 +6,4 @@ cd "$scriptdir"
 
 python train_gcse_manual.py -t 1000000 -a ppo -ne 10 &
 python train_gcse_manual.py -t 1000000 -a rppo -ne 10 &
-python train_gcse_manual.py -t 1000000 -a her -ne 10 &
 python train_gcse_manual.py -t 1000000 -a tqc -ne 10 &