diff --git a/rl_portfolio_management/wrappers/concat_states.py b/rl_portfolio_management/wrappers/concat_states.py index adf2109..cc539f6 100644 --- a/rl_portfolio_management/wrappers/concat_states.py +++ b/rl_portfolio_management/wrappers/concat_states.py @@ -7,9 +7,16 @@ def concat_states(state): history = state["history"] weights = state["weights"] weight_insert_shape = (history.shape[0], 1, history.shape[2]) - weight_insert = np.ones( - weight_insert_shape) * weights[1:, np.newaxis, np.newaxis] - state = np.concatenate([history, weight_insert], axis=1) + if len(weights) - 1 == history.shape[0]: + weight_insert = np.ones( + weight_insert_shape) * weights[1:, np.newaxis, np.newaxis] + elif len(weights) - 1 == history.shape[2]: + weight_insert = np.ones( + weight_insert_shape) * weights[np.newaxis, np.newaxis, 1:] + else: + weight_insert = np.ones( + weight_insert_shape) * weights[np.newaxis, 1:, np.newaxis] + state = np.concatenate([weight_insert, history], axis=1) return state diff --git a/tensorforce-PPO-IEET.ipynb b/tensorforce-PPO-IEET.ipynb index 9309c86..0713ec6 100644 --- a/tensorforce-PPO-IEET.ipynb +++ b/tensorforce-PPO-IEET.ipynb @@ -14,8 +14,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:19:37.423032Z", - "start_time": "2017-11-12T04:19:35.616652Z" + "end_time": "2017-11-12T23:19:13.266006Z", + "start_time": "2017-11-12T23:19:11.514079Z" }, "collapsed": true }, @@ -56,8 +56,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:19:37.516583Z", - "start_time": "2017-11-12T04:19:37.428893Z" + "end_time": "2017-11-12T23:19:13.389088Z", + "start_time": "2017-11-12T23:19:13.268055Z" }, "collapsed": true }, @@ -73,8 +73,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:19:40.291461Z", - "start_time": "2017-11-12T04:19:40.259250Z" + "end_time": "2017-11-12T23:19:13.426379Z", + "start_time": "2017-11-12T23:19:13.390716Z" }, "collapsed": true }, @@ -91,8 +91,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:19:40.501765Z", - "start_time": "2017-11-12T04:19:40.471795Z" + "end_time": "2017-11-12T23:19:13.466918Z", + "start_time": "2017-11-12T23:19:13.430051Z" } }, "outputs": [ @@ -133,15 +133,15 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:19:40.999732Z", - "start_time": "2017-11-12T04:19:40.967934Z" + "end_time": "2017-11-12T23:19:13.547225Z", + "start_time": "2017-11-12T23:19:13.470766Z" } }, "outputs": [ { "data": { "text/plain": [ - "'logs/tensorforce_PPO_crypto_20171105_06-50-31/run-20171112_04-19-40'" + "'logs/tensorforce_PPO_crypto_20171105_06-50-31/run-20171112_23-19-13'" ] }, "execution_count": 5, @@ -175,14 +175,14 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:19:41.642642Z", - "start_time": "2017-11-12T04:19:41.548893Z" - }, - "collapsed": true + "end_time": "2017-11-12T23:19:13.782496Z", + "start_time": "2017-11-12T23:19:13.551168Z" + } }, "outputs": [], "source": [ - "from rl_portfolio_management.environments.portfolio import PortfolioEnv" + "from rl_portfolio_management.environments.portfolio import PortfolioEnv\n", + "from rl_portfolio_management.wrappers import SoftmaxActions, ConcatStates" ] }, { @@ -190,92 +190,17 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:19:41.869824Z", - "start_time": "2017-11-12T04:19:41.746284Z" - } - }, - "outputs": [], - "source": [ - "# We need to wrap the env to make sure the output is softmaxed\n", - "\n", - "import gym.spaces\n", - "df_train = pd.read_hdf('./data/poloniex_30m.hf',key='train')\n", - " \n", - "def softmax(w, t = 1.0):\n", - " w = np.clip(w, -10, 10) # same as clipping to 1e-5 1e5\n", - " e = np.exp(np.array(w) / t)\n", - " dist = e / np.sum(e + 1e-7)\n", - " return dist\n", - "\n", - "class EnvWrapper(PortfolioEnv):\n", - " \"\"\"Wraps env to normalise and reshape action.\"\"\"\n", - " def __init__(self, window_length=50, *args, **kwargs):\n", - " super().__init__(*args, **kwargs)\n", - " \n", - " hist_space = self.observation_space.spaces[\"history\"]\n", - " hist_shape = hist_space.shape\n", - " self.observation_space = gym.spaces.Box(-10,10,shape=(hist_shape[0],hist_shape[1]+1,hist_shape[2]))\n", - " \n", - " def step(self, action):\n", - " # also it puts it in a list\n", - " if isinstance(action, list):\n", - " action = action[0]\n", - " \n", - " if isinstance(action, dict):\n", - " action = list(action[k] for k in sorted(action.keys()))\n", - " \n", - " action = softmax(action, t=1)\n", - " \n", - " state, reward, done, info = super().step(action)\n", - " history = state[\"history\"]\n", - " weights = state[\"weights\"]\n", - " weight_insert_shape = (history.shape[0], 1, history.shape[2])\n", - " weight_insert = np.ones(\n", - " weight_insert_shape) * weights[1:, np.newaxis, np.newaxis]\n", - " state = np.concatenate([history, weight_insert], axis=1)\n", - "\n", - " return state, reward, done, info" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2017-11-11T08:21:34.369989Z", - "start_time": "2017-11-11T08:21:34.300597Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2017-11-11T08:18:06.548603Z", - "start_time": "2017-11-11T08:18:06.458022Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2017-11-12T04:19:42.782046Z", - "start_time": "2017-11-12T04:19:42.650024Z" + "end_time": "2017-11-12T23:19:13.898899Z", + "start_time": "2017-11-12T23:19:13.786267Z" }, "collapsed": true }, "outputs": [], "source": [ - "# tensorforce wants us to use this wrapper, our subclass lets us pass in an env as an object\n", + "# tensorforce need us to use this wrapper, passing in a registered gym id\n", + "# this lets us pass in an object instead of registering it\n", "from tensorforce.contrib.openai_gym import OpenAIGym\n", - "class CustomOpenAIGym(OpenAIGym):\n", + "class TFOpenAIGymCust(OpenAIGym):\n", " def __init__(self, gym_id, gym):\n", " self.gym_id = gym_id\n", " self.gym = gym" @@ -283,17 +208,28 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:20:24.504699Z", - "start_time": "2017-11-12T04:20:24.446934Z" + "end_time": "2017-11-12T23:19:13.959906Z", + "start_time": "2017-11-12T23:19:13.900497Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[0]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_train = pd.read_hdf('./data/poloniex_30m.hf',key='train')\n", - "env = EnvWrapper(\n", + "env = PortfolioEnv(\n", " df=df_train,\n", " steps=40, \n", " scale=True, \n", @@ -301,17 +237,33 @@ " window_length = window_length,\n", " output_mode='EIIE',\n", ")\n", - "env.seed(0)\n", - "environment = CustomOpenAIGym('CryptoPortfolioEIIE-v0', env)" + "# wrap it in a few wrappers\n", + "env = ConcatStates(env)\n", + "env = SoftmaxActions(env)\n", + "environment = TFOpenAIGymCust('CryptoPortfolioEIIE-v0', env)\n", + "\n", + "env.seed(0)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:20:24.955032Z", - "start_time": "2017-11-12T04:20:24.920794Z" + "end_time": "2017-11-12T06:10:34.259707Z", + "start_time": "2017-11-12T06:10:34.217310Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-12T23:19:13.992876Z", + "start_time": "2017-11-12T23:19:13.961488Z" } }, "outputs": [ @@ -321,7 +273,35 @@ "(3, 51, 3)" ] }, - "execution_count": 15, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sanity check out environment is working\n", + "state = environment.reset()\n", + "state, reward, done=environment.execute(env.action_space.sample())\n", + "state.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-12T23:19:14.023039Z", + "start_time": "2017-11-12T23:19:13.994636Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 51, 3)" + ] + }, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -351,11 +331,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:20:26.232444Z", - "start_time": "2017-11-12T04:20:26.197473Z" + "end_time": "2017-11-12T23:19:32.641647Z", + "start_time": "2017-11-12T23:19:14.024832Z" }, "collapsed": true }, @@ -381,11 +361,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:20:27.530745Z", - "start_time": "2017-11-12T04:20:27.101913Z" + "end_time": "2017-11-12T23:19:33.068774Z", + "start_time": "2017-11-12T23:19:32.646922Z" }, "collapsed": true }, @@ -526,11 +506,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:20:27.788540Z", - "start_time": "2017-11-12T04:20:27.750707Z" + "end_time": "2017-11-12T23:38:31.988172Z", + "start_time": "2017-11-12T23:38:31.948872Z" }, "collapsed": true }, @@ -569,11 +549,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:20:35.334501Z", - "start_time": "2017-11-12T04:20:28.522247Z" + "end_time": "2017-11-12T23:38:38.921235Z", + "start_time": "2017-11-12T23:38:32.455152Z" }, "scrolled": true }, @@ -582,6 +562,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", "INFO:tensorflow:Create CheckpointSaverHook.\n" ] }, @@ -590,48 +571,48 @@ "output_type": "stream", "text": [ "INFO:tensorflow:Create CheckpointSaverHook.\n", - "[2017-11-12 12:20:32,888] Create CheckpointSaverHook.\n" + "[2017-11-13 07:38:37,010] Create CheckpointSaverHook.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-1820000\n" + "INFO:tensorflow:Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-11920000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:tensorflow:Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-1820000\n", - "[2017-11-12 12:20:34,362] Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-1820000\n", - "WARNING:PPOAgent:Configuration values not accessed: summary_logdir, distributions, parameter_server, local_model, cluster_spec, replica_model, summary_labels, summary_frequency, task_index\n", - "[2017-11-12 12:20:34,641] Configuration values not accessed: summary_logdir, distributions, parameter_server, local_model, cluster_spec, replica_model, summary_labels, summary_frequency, task_index\n" + "INFO:tensorflow:Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-11920000\n", + "[2017-11-13 07:38:37,497] Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-11920000\n", + "WARNING:PPOAgent:Configuration values not accessed: local_model, summary_logdir, summary_frequency, cluster_spec, task_index, parameter_server, distributions, summary_labels, replica_model\n", + "[2017-11-13 07:38:38,256] Configuration values not accessed: local_model, summary_logdir, summary_frequency, cluster_spec, task_index, parameter_server, distributions, summary_labels, replica_model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Saving checkpoints for 1820000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n" + "INFO:tensorflow:Saving checkpoints for 11920000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:tensorflow:Saving checkpoints for 1820000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n", - "[2017-11-12 12:20:34,671] Saving checkpoints for 1820000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n" + "INFO:tensorflow:Saving checkpoints for 11920000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n", + "[2017-11-13 07:38:38,299] Saving checkpoints for 11920000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -737,11 +718,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T04:20:35.402423Z", - "start_time": "2017-11-12T04:20:35.337389Z" + "end_time": "2017-11-12T23:38:39.044918Z", + "start_time": "2017-11-12T23:38:38.923845Z" }, "collapsed": true }, @@ -765,10 +746,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "ExecuteTime": { - "start_time": "2017-11-12T04:20:29.952Z" + "end_time": "2017-11-12T23:38:39.092835Z", + "start_time": "2017-11-12T23:38:39.049877Z" }, "collapsed": true }, @@ -780,10 +762,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "ExecuteTime": { - "start_time": "2017-11-12T04:20:30.154Z" + "end_time": "2017-11-12T23:38:46.320510Z", + "start_time": "2017-11-12T23:38:39.094733Z" }, "scrolled": true }, @@ -791,7 +774,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "13880b8b2e03414ca09005a18632a58b" + "model_id": "9e68de5a37654c2b8a128e5ce27bcbcf" } }, "metadata": {}, @@ -801,7 +784,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "TensorBoardLogger started. Run `tensorboard --logdir=/media/oldhome/wassname/Documents/projects/rl-portfolio-gh/rl-portfolio-management/logs/tensorforce_PPO_crypto_20171105_06-50-31` to visualize\n" + "TensorBoardLogger started. Run `tensorboard --logdir=/media/oldhome/wassname/Documents/projects/rl-portfolio-gh/rl-portfolio-management/logs/tensorforce_PPO_crypto_20171105_06-50-31` to visualize\n", + "ep reward: 0.00004160 [-0.00008149, 0.00046759], portfolio_value: 1.0706 mdd=-2.22% sharpe=1.8574, expl= 0.50% eps=298000 weights={'DASHBTC': 0.0, 'LTCBTC': 0.5, 'XMRBTC': 0.0}\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mmean_of\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m 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+ "\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1320\u001b[0m return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m options, run_metadata)\n\u001b[0m\u001b[1;32m 1322\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1323\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m 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1328\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1329\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1304\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1305\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m 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"start_time": "2017-11-12T23:38:47.202140Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "51bab9905075414291b01263a22fad6e" + } + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4fb9b615558d4563b04857aeb4923918" + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# read history, might be slow\n", "import collections\n", @@ -852,11 +891,23 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T01:38:00.684263Z", - "start_time": "2017-11-12T01:37:59.133553Z" + "end_time": "2017-11-12T06:06:52.676576Z", + "start_time": "2017-11-12T06:06:52.632828Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2017-11-12T23:38:53.894928Z", + "start_time": "2017-11-12T23:38:53.455095Z" } }, "outputs": [ @@ -1640,7 +1691,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1652,10 +1703,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1672,27 +1723,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T01:38:01.603448Z", - "start_time": "2017-11-12T01:38:00.685893Z" + "end_time": "2017-11-12T23:22:30.507891Z", + "start_time": "2017-11-12T23:19:11.316Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "'./outputs/tensorforce-PPO-prioritised/tensorforcePPO-1827053'" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "agent.save_model('./outputs/tensorforce-PPO-prioritised/tensorforcePPO')" + "# agent.save_model('./outputs/tensorforce-PPO-prioritised/tensorforcePPO')" ] }, { @@ -1709,37 +1749,50 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 63, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T02:00:32.592382Z", - "start_time": "2017-11-12T02:00:32.452435Z" - }, - "collapsed": true + "end_time": "2017-11-12T23:47:15.646789Z", + "start_time": "2017-11-12T23:47:15.494299Z" + } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[0]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_test = pd.read_hdf('./data/poloniex_30m.hf',key='test')\n", - "env_test = EnvWrapper(\n", + "env_test = PortfolioEnv(\n", " df=df_test,\n", - " steps=48*7, # run for a week at a time \n", + " steps=48*7, # run for a week at a time\n", " scale=True, \n", " trading_cost=0.0025, \n", - " window_length=window_length,\n", + " window_length = window_length,\n", " output_mode='EIIE',\n", ")\n", - "env_test.seed(0)\n", + "# wrap it in a few wrappers\n", + "env_test = ConcatStates(env_test)\n", + "env_test = SoftmaxActions(env_test)\n", + "environment_test = TFOpenAIGymCust('CryptoPortfolioEIIETest-v0', env_test)\n", "\n", - "environment_test = CustomOpenAIGym('CryptoPortfolioEIIETest-v0', env_test)" + "env_test.seed(0)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 64, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T02:02:48.474304Z", - "start_time": "2017-11-12T02:02:43.186543Z" + "end_time": "2017-11-12T23:47:21.236171Z", + "start_time": "2017-11-12T23:47:15.719735Z" } }, "outputs": [ @@ -1747,7 +1800,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", "INFO:tensorflow:Create CheckpointSaverHook.\n" ] }, @@ -1756,39 +1808,39 @@ "output_type": "stream", "text": [ "INFO:tensorflow:Create CheckpointSaverHook.\n", - "[2017-11-12 10:02:47,128] Create CheckpointSaverHook.\n" + "[2017-11-13 07:47:19,952] Create CheckpointSaverHook.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-1820000\n" + "INFO:tensorflow:Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-11920000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:tensorflow:Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-1820000\n", - "[2017-11-12 10:02:47,535] Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-1820000\n", - "WARNING:PPOAgent:Configuration values not accessed: summary_logdir, replica_model, summary_frequency, local_model, distributions, parameter_server, cluster_spec, summary_labels, task_index\n", - "[2017-11-12 10:02:47,848] Configuration values not accessed: summary_logdir, replica_model, summary_frequency, local_model, distributions, parameter_server, cluster_spec, summary_labels, task_index\n" + "INFO:tensorflow:Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-11920000\n", + "[2017-11-13 07:47:20,339] Restoring parameters from ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt-11920000\n", + "WARNING:PPOAgent:Configuration values not accessed: local_model, summary_logdir, summary_frequency, cluster_spec, task_index, parameter_server, distributions, summary_labels, replica_model\n", + "[2017-11-13 07:47:20,648] Configuration values not accessed: local_model, summary_logdir, summary_frequency, cluster_spec, task_index, parameter_server, distributions, summary_labels, replica_model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Saving checkpoints for 1820000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n" + "INFO:tensorflow:Saving checkpoints for 11920000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:tensorflow:Saving checkpoints for 1820000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n", - "[2017-11-12 10:02:47,895] Saving checkpoints for 1820000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n" + "INFO:tensorflow:Saving checkpoints for 11920000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n", + "[2017-11-13 07:47:20,676] Saving checkpoints for 11920000 into ./outputs/tensorforce-PPO/tensorforce_PPO_crypto_20171105_06-50-31/model.ckpt.\n" ] } ], @@ -1804,13 +1856,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 65, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T02:02:48.540674Z", - "start_time": "2017-11-12T02:02:48.475847Z" - }, - "collapsed": true + "end_time": "2017-11-12T23:47:21.291676Z", + "start_time": "2017-11-12T23:47:21.237822Z" + } }, "outputs": [], "source": [ @@ -1819,25 +1870,28 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 66, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T02:02:48.576647Z", - "start_time": "2017-11-12T02:02:48.542987Z" + "end_time": "2017-11-12T23:47:21.336956Z", + "start_time": "2017-11-12T23:47:21.293447Z" } }, "outputs": [], "source": [ - "%matplotlib notebook" + "def episode_finished(r):\n", + " print(\"Finished episode {ep} after {ts} timesteps (reward: {reward})\".format(ep=r.episode, ts=r.episode_timestep,\n", + " reward=r.episode_rewards[-1]))\n", + " return True" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 67, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T02:30:35.334384Z", - "start_time": "2017-11-12T02:30:00.103032Z" + "end_time": "2017-11-12T23:47:22.970011Z", + "start_time": "2017-11-12T23:47:21.338710Z" }, "scrolled": true }, @@ -1846,11 +1900,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finished episode 45497 after 1820336 timesteps (reward: 0.000217 [ 0.000217, 0.000217]) portfolio_value: 1.0742 [ 1.0742, 1.0742] mdd=-4.92% sharpe= 0.38\n", - "Finished episode 45498 after 1820672 timesteps (reward: 0.000325 [ 0.000325, 0.000325]) portfolio_value: 1.1139 [ 1.1139, 1.1139] mdd=-5.58% sharpe= 0.34\n", - "Finished episode 45499 after 1821008 timesteps (reward: -0.000106 [-0.000106, -0.000106]) portfolio_value: 0.9637 [ 0.9637, 0.9637] mdd=-2.11% sharpe=-0.13\n", - "Finished episode 45500 after 1821344 timesteps (reward: -0.000083 [-0.000083, -0.000083]) portfolio_value: 0.9712 [ 0.9712, 0.9712] mdd=-3.21% sharpe=-0.11\n", - "Finished episode 45501 after 1821680 timesteps (reward: -0.000030 [-0.000030, -0.000030]) portfolio_value: 0.9887 [ 0.9887, 0.9887] mdd=-4.34% sharpe=-0.02\n" + "Finished episode 297995 after 336 timesteps (reward: -1.1915208412851097e-07)\n" ] }, { @@ -1860,7 +1910,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mdeterministic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m episode_finished=EpisodeFinished(\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mlog_intv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;31m# steps=steps*1e9,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# mean_of=1,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 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133\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menvironment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/media/oldhome/wassname/Documents/projects/tensorforce2/tensorforce0.3.1/tensorforce/agents/agent.py\u001b[0m in \u001b[0;36mclose\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;32mdef\u001b[0m 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\u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaver_directory\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mappend_timestep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 419\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonitored_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 421\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minitialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_getter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", @@ -1876,42 +1926,23 @@ } ], "source": [ - "episodes=5\n", - "steps=environment_test.gym.src.steps*episodes\n", + "episodes=1\n", + "steps=environment_test.gym.env.env.src.steps*episodes\n", "runner_test.run(\n", " episodes=episodes,\n", " timesteps=steps,\n", " deterministic=True,\n", - " episode_finished=EpisodeFinished(\n", - " log_intv=1,\n", - "# steps=steps*1e9,\n", - "# mean_of=1,\n", - "# log_dir=log_dir,\n", - "# session=runner.agent.model.session,\n", - " )\n", - "# episode_finished= lambda x:True\n", + " episode_finished=episode_finished,\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": { "ExecuteTime": { - "end_time": "2017-11-12T02:02:13.709446Z", - "start_time": "2017-11-12T02:02:13.646315Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "ExecuteTime": { - "end_time": "2017-11-12T02:30:41.423613Z", - "start_time": "2017-11-12T02:30:40.433034Z" + "end_time": "2017-11-12T23:47:34.517540Z", + "start_time": "2017-11-12T23:47:33.326795Z" } }, "outputs": [ @@ -1920,18 +1951,18 @@ "output_type": "stream", "text": [ "WARNING:root:The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n", - "[2017-11-12 10:30:40,489] The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n", + "[2017-11-13 07:47:33,383] The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n", "WARNING:root:The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n", - "[2017-11-12 10:30:40,641] The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n", + "[2017-11-13 07:47:33,571] The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n", "WARNING:root:The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n", - "[2017-11-12 10:30:40,800] The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n" + "[2017-11-13 07:47:33,767] The liveplot callback only work when matplotlib is using the nbAgg backend. Execute 'matplotlib.use('nbAgg', force=True)'' or '%matplotlib notebook'\n" ] }, { "data": { - "image/png": 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xxx7jrbfe4o03WqZ93Hjjjdx888288cYb3HHHHfzrX//C5XK1aOP1ejnnnHM6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h2SjenZH1boUQog8cOXKE+vp6vF4vc+bMGXGJA6TkIYQQPWaMobKyknfeeQdw\nOri43e4YRxUbkjyEEKIHjDG8//777Nu3D3AaxydPnhzjqGJHkocQQvTAzp072bdvHy6Xi5NOOokZ\nM2aMmMbxzoy8iroYGz9+PKtWreLss89mzZo1+Hy+Xp3/3//930c9f/TRRznrrLP41re+1eU5Tz/9\ndNvU7k888USngwuFEJ0zxrBnzx527tyJUoqlS5cyb968tklFRypJHgPM6/WyYcMGNm7cSHx8/FHT\npHfHGINt2/z85z8/avtvf/tbnnrqKX7xi1/06DqXXXYZF110Ua/jFmKk2r17Nzt27ABg7ty55Obm\nxjiiwUGqrWJo8eLF7Ny5E4AHH3yQp5921sS65JJLuOqqqzh06BBf+cpXWLBgATt27GD+/Pn4/X5W\nrVrF9OnTSU5Opri4mK9+9atcfPHFXHTRRdxwww0UFxfj9Xq5++67j5nJ89577yU5OZlrrrmGDz74\ngB/84Af4/X4mTpzIvffeO6TWUBaiv9XV1bX9H12yZAn5+fkxjmjwGLHJI73k4X65bl3eVT06LhwO\n88orr7B8+XLef/99tNa88MILGGM4//zzWbZsGWlpaRw8eJD777+fhQsXAvDCCy+wYcOGtuts2rSJ\nP/7xj2RmZrJ27Vpmz57NY489xuuvv853v/vdo47t6LrrruP2229n2bJl3HPPPdx3333cdtuQXWtL\niD5XWFiIbdsUFBRI4uhgQJKHZVmPAecDFVrr2dFttwJXAZXRw27WWv+1k3PPBR4A3MAjWus7ByLm\n/tJacgDnm8wll1zCE088wbnnnts21cdnPvMZtmzZwurVq8nPz29LHJ9k69atPPywkxTPOOMMamtr\naWxs7PTYhoYG6uvrWbZsGQAXXXQRV1999Ym+PSGGjfbLx06aNCm2wQxCA1XyeBz4BdCxgv9nWuuf\ndnWSZVlu4JfAKuAwsM2yrL9orT860YB6WkLoa61tHj0lc0cJERs1NTX4fD4SExO7neV6pBqQBnOt\n9atAzXGcuhjYp7U+oLUOAn8ALviEc4acJUuW8Pe//x2fz0dLSwsvvfQSS5Ys6fRYj8dDKBTq8jrP\nPPMM4KxbnpmZ2eVMuaNGjSItLY0tW7YAzsqKS5cu7YN3I8TQF4lE2paEzs/PH9FdcrsS6zaPb1mW\ndRnwFnCD1rq2w/484FC754eBzj9VAcuy1gBrALTWfRxq/5kzZw4XXXQRn/3sZwGnwXz27NkcOnTo\nmGMvvfTiT4VdAAAgAElEQVRSVq5cyZw5c47pYfW9732PG264gZUrV+L1ern//vu7fd3777+/rcF8\nwoQJ3HfffX33poQYwg4dOkRtbS3JycnMmDEj1uEMSgM2JbtlWZOAF9q1eYwBqgAD3A7kaq2v7HDO\nhcC5WutvRJ9/FViite56UMPHZEr2fib3UwxX27dvp7CwkLlz5zJ16tRYhzNghsSU7Frr8tbHlmU9\nDLzQyWElwPh2z/Oj24QQot/U1jqVIJmZmTGOZPCK2SBBy7Laj7T5IvBBJ4dtA6ZallVgWVY88B/A\nXwYiPiHEyBQOh2loaEApRVpaWqzDGbQGqqvuU8ByINuyrMPAOmC5ZVnzcaqtCoGro8eOw+mSe57W\nOmxZ1reAv+N01X1Ma/3h8cYxjFdNjAm5n2I4qq+vxxhDWloacXGxbhYevEbUMrQ+nw+PxyP/IPpA\nOBwmFAqRmJgY61CE6DOhUIjNmzdTUVHBpEmTejzGargYEm0eseD1evH7/QQCAel6dwKMMbhcLrxe\nb6xDEaJPGGMoKipix44dBINBvF4v06dPj3VYg9qISh5KKfmmLIQ4im3bvP322xQVFQGQk5PD/Pnz\nSUlJiXFkg9uISh5CCNGeMYbt27dTXFyM2+3mlFNOYfz48VIz0QOSPIQQI1JLSwvbtm2jqqoKt9vN\npz71KbKysmId1pAhyUMIMWK0TnbY2NjIgQMH8Pl8JCQkcOqpp0ri6CVJHkKIEePQoUNs27at7XlW\nVhbLli0b8asCHg9JHkKIEaO1+35iYiJz5swhLy8Pl0sWVD0ekjyEECOCbdtUVFQAsHz5cpmX7QRJ\n8hBCDGu2bVNUVMThw4cJhUKkpaVJ4ugDkjyEEMNaSUkJb7/9dtvzgoKCGEYzfEjyEEIMa5WVzkrX\nBQUFnHzyyTJQuB3bNrQ02/h9Ns2NNs7sJD0jyUMIMaxVVVUBzjrkkjg+Vl0ZZv8uP+Hw8Z3fq+Rh\nWZYHWAqM01o/bVlWMoDWuvn4Xl4IIfpPIBCgsbERt9tNenp6rMMZNBobIuz9yI9tgzdRkZjoIjm1\nd73Oepw8LMuag7OWRgBnUaangbOAy4GLe/WqQgjRj5qbm/noo48oL3fWnMvMzJQuuUBLc4QDuwM0\n1NsAZI+OY+rMhOOajqU3JY9fAbdorf/XsqzWtcb/BTzc61cVQog+Fg6H2bNnD83NzRw5coRwtD4m\nKSlpRC0l2xXbNuz+wI+vxeB2w+hcDxMK4o97Hq/eJI9ZwO+ijw041VWWZUklohAi5vbt28fOnTvb\nnufl5TFr1ixSUlJG/ESHdsSwf08AX4vBm6iYuyiJuLgTuye9SR6FwELgrdYNlmUtBvZ90omWZT0G\nnA9UaK1nR7fdA3wOCAL7ga9pres6ObcQaAQiQFhrvagXMQshRoCWlhb27XM+iiZMmMC0adNkCdl2\nDuwJUFkWRimYMsN7wokDereG+X8CL1qW9WMg3rKsHwJ/BNb24NzHgXM7bNsAzNZazwX2AD/s5vwV\nWuv5kjiEEO0Fg0E2b97MSy+9RCAQIDMzk0WLFkniaKehLkJFNHHMPiWRUenuPrluj5OH1voFnASQ\ng9PWMRH4ktb65R6c+ypQ02Hby1rr1k5im3Ea4YUQosc++ugjSkpKUEqRn5/P4sWLR3wVVUeHi4IA\n5E3wkDqqbxIH9LKrrtb6HeD/67NX/9iVOL23OmOAly3LMsCDWuuHurqIZVlrgDUAWus+D1IIMTiE\nw2EKCws5cOAASilWrFghXXE7MLbB5zPU1URQCnLHx/fp9XvTVfe2rvZprW853gAsy/oREAae7OKQ\nM7TWJZZljQY2WJa1K1qS6SyOh4DW5GKONyYhxOBUW1tLcXExhw4dIhAIADBlyhRJHEAwaFO8P0ht\ndYRw2GDafQJm5cTh8fRtiaw3JY/xHZ6PxRnn8ezxvrhlWVfgNKR/Wmvd6Ye91rok+rvCsqxngcVA\np8lDCDE82bbNu+++y8GDB9u2ZWRkcPLJJzN27NgYRjY4GGPY84G/bfxGK7cbPB5F3kRPn79mj5OH\n1vprHbdZlnUucMnxvHD03JuAs7TWLV0ckwy4tNaN0cergS5LQEKI4am4uJiDBw/icrk46aSTGD9+\nPBkZGSO+faOhLkLxgY8H/Xk8ilkLvCR4Xbhc9Ov9OdG5rV6m67aKNpZlPQUsB7ItyzoMrMPpXZWA\nUxUFsFlrfY1lWeOAR7TW5wFjgGej++OA32utXzrBmIUQQ0xriWPBggVMmjQptsEMAr4Wm4rSEEcO\nhdqqp1wumDIjgaTkvmsU744ypmdNA5ZlndRhUxLwFeDzrWM3BhnTumqYEGJoqqqqYvfu3ZSVleHx\neDjvvPOIixuZ87m2NNvUVodpqI1QWxNp2z4u30P+pHjccSde0hjnTKvbo4v05q+wD6cRuvXCLcA7\nOHNbCSFEnwiFQtTW1nLo0CEKCwvbts+YMWNEJo5IxFC0P0hZSahtm8sFmdlx5OZ7SE0bmJJGRz0u\neQxBUvIQYggwxlBWVkZdXR21tbWUl5dj204dvsvlYtq0aUyaNInk5OQYRzqwwmFD8f4AleVhIhFQ\nCnLGxJGY5CJnbBzxCX0/0WN/lTyEEKJP1dfX89Zbb1FX9/HMREopMjMzyczMZNKkSSN2tHjR/gDl\nR5xx1CmpLgqmJfTpIL8T1W3ysCzrED0YL6G1ntBnEQkhRoRDhw6xfft2IpEISUlJ5Ofnk5aWRk5O\nzohftMmOGKornMQxa76XtIzB9z3/kyL6PwMShRBixAiFQuzatYs9e/YAMHHiRObPnz8i2zO6UlEW\nJhyG5BTXoEwcIG0eQogBEolE+PDDDyksLCQUCqGUYt68eZx00kkjfrxGq0jEcOhAkCOHncbxydMT\nGDOu7wf4daXf2jwsy5oPfArIbv8CJzI9iRBi+DLG0NTURCAQaOtyC5CTk8PMmTPJzs6OcYSDg99n\nU1kWpqoihK/FoIAJJ8UzOndwljqgd3NbrQF+hjMw8DPA33BGfD/XP6EJIYaqkpISDh8+TGVlZdsc\nVABxcXGcccYZZGVlxTC6wcMYQ/mRMAf3BtoG+3m9iqmzvIOqcbwzvenrdRNwrtb6i4Av+vtCINT9\naUKIkaSsrIzNmzdz+PBhAoEAXq+XzMxMxo8fL4kjKhI2HDkU5MN3fBzY4ySOzGw3k6bEM2dR0qBP\nHNC7aqvRWuvXoo9ty7JcWuu/WZbV1Wy4QogRaO/evQAUFBQwdepUWQa2E4eLgpQUO9+73W44aVoC\nOWMHrm2jL/Sm5HHYsqyC6OM9wAWWZX0KZxlZIYSgvLyciooK4uLimD17NqmpqZI4OrBtQ0Wp0w13\n3HgPC09LHnKJA3pX8rgbmAEcxJnZ9v8C8cB3+iEuIcQQU19fz5YtWwCYOnUq8fF9u/jQUGeMoaHO\npq4mTChkSExSTJwcP2STa29KHvOBagCt9d+ADCBDa/2r/ghMCDF0hEIh3njjDUKhEPn5+Zx88smx\nDmlQCQZtdn/g58N3fW3VVbn5QzdxQO+nJ/mzZVnNwO+BJ7XWe/ohJiHEEFNaWkpLSwtpaWksXLhw\nSH8o9rWykhAH9wScWWUVZGS5yRnrITN78DeKd6fHJQ+t9XVAPs4a5uOBLZZlbbcs63v9FZwQYmgo\nLy8HnNHiMlL8Y7XVYac3FZCR6WbeokRmzEkkKyduyCfYXv2VtdY2sAFnAaf/BH4D3APc90nnWpb1\nGM6SsxWt639YlpWJs5jUJKAQsLTWtZ2cezmwNvp0vdb6t72JWwjRf4wxbclDloT9WHNThL07/QCM\nnxTP+ILh1QbUqzl9LctKtizr/1iW9SJOj6swPV/P43Hg3A7bfgD8U2s9Ffhn9HnH18zEWXlwCc76\n5essy8roTdxCiL5n2zY7duxg48aNBAIBkpKSSElJiXVYMRcOG3Z/4Oe9bT7CIUhKdpHfD2uIx1pv\nRpj/EWdk+dvAU8DlWuuqnp6vtX7VsqxJHTZfgLM8LcBvgU3A9zsccw6wQWtdE41jA04Seqqnry2E\n6Hvl5eVtkxvGx8czf/78IV8VcyJs29DcZHNwT4CmRhuXC9Iy3EycnIByDb/70ptqq23ADVrr4j58\n/TFa69Lo4zKcNcs7ygMOtXt+OLrtGNEpVNYAaK37MEwhREelpc5/3TFjxrBkyRI8nuH37bqnqivC\n7NvlJxJdHTbOA3NOSSIxqe8XbBosepw8tNZ392cgWmtjWdYJTfGrtX4IeCj6dNhOFyxErIXDYYqK\nigCYPXv2iEwc4ZDh4N4AtdXO9OngzEuVmOQivyB+WCcO6GWbRz8otywrFyD6u6KTY0pwene1yo9u\nE0LEQHV1NS+++CK2bZOUlDSiV/qrLHcSh1IwfpKHBUuTOHle4pCYm+pExbpP3V9wGtzvjP7ubIbe\nvwN3tGskXw38cGDCE0K08vv97N27l/379xOJREhPT2fWrFkjsp2jpdmmoswpbsyc52VUmhuXe2Td\nhwFbDMqyrKdwGsezgXKcHlR/BjQwASjC6apbY1nWIuAarfU3oudeCdwcvdR/aa1/04OXlMWghOgD\ntm3z4YcftiUNgEmTJrFgwQJcrlhXXgysupowB3YH8Pudz83s0XFMm+WNcVR9pzeLQclKgkKIYxhj\nCIfDhEIhPvroo7b2jXHjxjFjxgwyMkZWb/mA3+bQwSCV5WGMcT5dc8bGMWlKAnGe4VPi6LeVBIUQ\nw1sgEKCoqIg9e/YctYiTy+Xi9NNPZ/To0TGMbuBFIoaq8jBFBwKEoysX5eZ5mDQlflh2v+0NSR5C\nCMBJHH//+98JhZxPybi4OOLi4khJSWHKlCkjKnEY23C4KERJcRDbdralZ7qZNCWBpOSRVVXXFUke\nQggAKisrCYVCKKVYtmwZY8eOHZGN4QAlxSEOFTpLFaWkusgd7yF79NCfj6ovSfIQQgBQU1MDwMkn\nn0xubm6Mo4mNUNCm/EiY4oNO4pg+y0vWaPmY7IzcFSEE4IzfAMjMzIxxJAMvFLSproxQuC/QVk2V\nlu6WxNENuTNCjHDBYJA9e/a0lTxGUk+qluYIu3b48fs+7nWanuEmZZSLMeNG3qj53pDkIcQIFggE\n2LhxIy0tLQCkp6ePmOVjjTHOmI1o4khKdpGb75Gk0UOSPIQYwfbu3du2AuDkyZMZM6azuUmHl0jE\ncKQ4RPmREMGgIS4OTlmaPKzGawwESR5CjDC2bbN9+3YaGhqoq6sD4JRTThkRbR2RiGHX+37q65yR\n8m43TJ7ulcRxHCR5CDHClJeXU1z88coKkydPHhGJw++z2bcrQENdBE+8YurJCaRluKX77XGS5CHE\nCNM6bU9ubi6nnHIKXu/wmZupvUjEUF4SoqE+QnOTTSA6H1V8vGLWgsRhP2V6f5PkIcQI0dzcTFFR\nEYWFhQDMmjVr2CYOv89m5/s+fC0f96JyuSAzO44JJ8XjTZTEcaIkeQgxAlRUVPDvf/8bOzqIITU1\nlVGjRsU4qv4RDNh88LaPYNCQmKTImxhPSqqLxCSXVFH1IUkeQgxjjY2N7Nmzh+LiYmzbZty4ceTm\n5pKTkzNsP0grysIEg4bUUS5OnpdIXNzwfJ+xJslDiGEqEAiwadMmgkFnqo2CggIWLFgwbJNGq/oa\npydV7niPJI5+FNPkYVnWdODpdptOAm7RWt/f7pjlOCsMHoxuekZrfduABSnEEFVTU0MwGGTUqFEs\nXbqU1NTUWIfU7yIRQ0O9kzzSMuS7cX+K6d3VWu8G5gNYluXGWZv82U4OfU1rff5AxibEUFdbWwvA\n2LFjR0TiACgrCWGMMxOuR8Zu9KvB1OXg08B+rXVRrAMRYjhoTR4jZa6qhroIRfudKrrcfJlipL8N\npnLdfwBPdbFvmWVZ7wFHgBu11h92dpBlWWuANQBa634JUoihoKWlhbKyMmDkJI+qcmcRq3H5HnLG\nSvLob4MieViWFQ98HvhhJ7vfBiZqrZssyzoP+DMwtbPraK0fAh6KPh22i7ML0Z2amho2bdoEQEJC\nAklJSbENaAAYY6itdto6ssYMio+1YU8ZE/vPWMuyLgCu1Vqv7sGxhcAirXXVJxxqWkfSCjHc1dXV\nceTIEerq6qiqqiIUCpGWlsasWbOG7cJOkYjB12ITDhrKS8NUV4bxeBSLTk8a9j3K+su4ceMAenTz\nBkuKvoQuqqwsyxoLlGutjWVZi3HaaaoHMjghBrPGxkZeeeWVtgGAAGPGjOG0007D5RpMzZp9p7Y6\nzJ4P/UQiR2/PkqViB0zMk4dlWcnAKuDqdtuuAdBa/xq4EPimZVlhwAf8h9Y69sUlIQaJffv2Yds2\nOTk5FBQUkJGRQXJy8rD7EPX7bHbt8BMM2oSd5g0SvIr4eEVqmpv0TDdp6e7YBjmCDIpqq34i1VZi\n2CsvL+fNN98kEomwatWqYTnliDGG5iabvR/5j5qramyeh4Kp8cMuScbSUKy2EkL0UlFREW+//Ta2\nbVNQUDDsEkc4bDhcGKS6Ikwg4CQNj0cxZ2EinniF2y1JI5YkeQgxBNXX17N9+3aMMUybNo3Zs2fH\nOqQT0tQYIeg3RGxDMGCoq4nQ3BghHHb2x8crUka5yJsoM+IOFpI8hBhijDF88MEHGGMoKChgzpw5\nsQ7puEXChoN7A1SUhTvdn5ziomBqAqlpMiPuYCPJQ4ghpLy8nA8++IC6ujri4uKYOXNmrEM6LpGI\nofRwiIojIfx+g8sFaRlu3G5FXBykprtJTnGTmKQkaQxSkjyEGCKKi4vZtm0bAImJiSxatGjILOZk\njMHvM7Q025SXhGhqVyWVlORi2qwEklKkp9RQIslDiEHOGMP27dspKnKmfZs+fTozZswgLm7w//c1\nxlB+JEzpoSA+39E9OxMTFRMnJ5Ce5cblktLFUDP4//UJMcLt3LmToqIi3G43M2fOZNq0abEOqVvh\nkOFwUZCA3+D32TQ3OYMX4+LAm+giPdNNZk4cSckuSRpDmCQPIQYh27Y5cuQIZWVlFBUVoZTitNNO\nY/To0bEOrVPGGOprI1SUhqmrDbcN4gOne23BtHiysuNQkiyGDUkeQgwCxhgaGxuprq6msbGRw4cP\n4/P5AFBKMW/evEGZOIIBm6L9QeprIwSDH1dLJae4GDfeg8utGJXmxhMvSWO4keQhRAyEQiEKCws5\ncuQITU1NBIPBo+amAhg1ahTjxo1j7NixZGVlxSjSzoWChrKSEKUlwY+nCklQjB7nISPLTXKKdK0d\n7iR5CDGAbNumqqqKbdu24ff7j9rn9XrJzs5m1KhRZGZmMnr06EH3ARzw2xwpDlFeGqI116Wlu5k4\nJV4SxggjyUOIfuLz+aitrW0rVZSWllJZWUkkOhVsZmYm06ZNIyMjg/j4+EHTeyoSMdRUhmlssAmH\nDJGIwY4404W0Nn4DZGS6GTfBw6h0tySNEWhw/GsVYhgpKSmhuLiY0tJSOpt4NCUlhby8PGbOnDlo\npkw3xuBrMVSUhqgoDbWNwehIKcjKiSNvoodkGZcxoknyEKKPNDc3t3WrBaehe8yYMXi9XpRSpKam\nMmHChEEzsK+uJkxVeZiGOqexu32TS0qqi6zRccQnKNwuhcsNbrciMdlFXJyUMoQkDyGOm8/no7q6\nmrq6Ourq6qioqMAYg8vlYtasWYwfP57ExMRYh9mpxvoIH713dJuLx6PIyHIzNs9DyigpVYjuSfIQ\nohdau9FWVlZSVVV1VLWUy+ViwoQJTJs2bdBPj15SHAQge3Qc4yZ4SEx04XIjbReixwZF8oiuS94I\nRICw1npRh/0KeAA4D2gBrtBavz3QcYqRra6ujk2bNrU1eLtcLsaMGUN6ejrp6elkZmYO2pJGq2DA\n5tDBIDVVEVwumDQ1nvj4wdHuIoaWQZE8olZorau62PcZYGr0Zwnwq+hvIfqVMYba2lqKi4spLi4m\nEokwduxYJk2aRHZ2NgkJCbEOscca6iLsfN9HJOI0fBdMTZDEIY7bYEoe3bkAeCK6dvlmy7LSLcvK\n1VqXxjowMXwYYzhy5AhVVVXU1NTQ2NhIJBI5avBebm4uS5Yswe0e/G0CoaBNfV2EhtoIDXU2LS3O\n+0jPcDNpagJJyZI4xPEbLMnDAC9blmWAB7XWD3XYnwccavf8cHTbUcnDsqw1wBoArXX/RSuGHb/f\nz9tvv01p6bHfR7xeL/n5+UycOJH09PQYRNczdsRQWx2hrCSEz2cTDBzdTdjlgtG5HgqmxMscU+KE\nDZbkcYbWusSyrNHABsuydmmtX+3tRaJJpzXxHNvBXoh2WlpaKC8vx+/3s2/fPoLBIB6Ph8mTJ5Oe\nnk5OTg4ulwu3e/ANgjO2IRh0lmxtqI9QXxuhoS5yVHdblwtS09ykpbsZle4mZZTMYiv6zqBIHlrr\nkujvCsuyngUWA+2TRwkwvt3z/Og2IY5LU1MTGzduJBT6ePrXMWPGcMopp5CUlBTDyDpnjLOud01V\nmIbaCH6f6fTbUXKKi5yxcWRmO2M0JFmI/hLz5GFZVjLg0lo3Rh+vBm7rcNhfgG9ZlvUHnIbyemnv\nEMejtV1jx44dhEIhMjMzyc7OJj09nfz8/EFVwjC2oaLMGcTX0mTT3Hz0xImeeEVCgiIp2UVahpu0\nDDfxCdKOIQZGzJMHMAZ41rIscOL5vdb6JcuyrgHQWv8a+CtON919OF11vxajWMUQZYzh8OHD7Nq1\ni4aGBgDS09M544wz8Hg8MY7uY63LtR466ExzHgp9XL6Ii4Pc/HjSM90kp0oVlIgt1dncO8OEOXLk\nSKxjEDFk2zY+n4/S0lIKCwupr68HICkpiWnTplFQUDCo5pZqqItwYE8AX8vH/ycTEhTjJnhISnYS\nhkwNIvrTuHHjAHr0j2wwlDyE6DOhUIjDhw9TVFRETU3NUSPAvV4vM2fOZOLEiTFNGrZtCAVNWzfa\npkabYMBum4zQ7YaM7DjyJnhISnJJzygxKEnyEEOebdvs3LmT8vJyGhoa2kaAK6VITEwkLS2NiRMn\nkpubO+DjMyIRQ3OjTTBoU1MZwddi42ux6bDuE9BaLeUhb2K8VEmJQU+ShxhyWlpaqK+vp7m5mfr6\neqqqqmhqamrbn5OTw8SJE8nLy4vJGhnGOAmjoT7C4aLgUet5t/J4FN5ERdboOFJHuUnwKuI80jtK\nDB2SPMSQUFVVRVFREZWVlTQ3Nx+z3+v1MnfuXLKysgasq21Ls43fZ2OMM2dUc5NNS5Ozrf16GImJ\nCm+Si6RkF5nZcSR4lfSKEkOeJA8xaBljqK+vp7i4mH379rW1X3g8HjIzM0lOTiYlJYWsrCzS09P7\ntR0jHDYE/TaV5WFqqyNHtVF0xhOvnO6z6W5G58YNqi7AQvQFSR5i0AiHw9TX11NdXU1lZSXV1dVH\nDeKbOnUqEyZMIC0trV8/jI0xlJWEqKuJEPCbLhOF2w2j0tygnGqo5FQXySluvEkKj0dJwhDDmiQP\nETPhcJiWlhZaWlrYs2cPlZWVxxzj9XoZN24cEyZMICsrq1/jCQZtDheGqKsO4/cfOy9UfIIiJdUp\nSSSnuHDHSRuFGLkkeYgBEwgEqKmpwbZtCgsLKSsrO+aY5ORkxowZQ0ZGBmPHjiUhIaHfvsEbYwiH\nnCqp5sYIB/cG2wblud0waUoCyakuEhKcxmwpSQjxMUkeos/Zto0xBmMMPp+PqqoqqqqqKCkpaetG\nC85iSklJSSQkJJCcnMycOXP6bX3vUMhQUxnG73MatgM+m2DQ0C4cANLS3eRP8pCc4ibOI8lCiK5I\n8hB9oqWlhY8++ojS0lKCwWCXx2VlZZGQkEBSUhLTp0/vt2TRXjhseP+tFgL+Y2dTiIuDuDhFfIIi\nPdMZmCeD8oT4ZJI8xAlraWlh06ZN+Hw+wBmc53K5UEq19YzKzs4mJyeHtLS0AYnJtg31tREa652p\nygN+Q3y8YmyehwSv07gd51Gykp4Qx0mShzguoVCIsrIyioqKqKiowBhDVlYWCxcuJCUlpd/bB2zb\n4G+xaWm2CQQMtu0shmRsqK+L0Nx09BBupeDkeV6SUwb/CoBCDAWSPESPGGMIh8NUVVWxZ88eqqo+\nXm5eKUVubi4LFy7slzW9I2FDY4OzhkVLs01DnTPNxyfN6ZmYqMjMicOb5CIt3Y03UUoZQvQVSR6i\nS6FQiKamJioqKigqKqKxsbFtn8vlYtSoURQUFJCfn098fHyfvW4w6IzUDgUNleVh6msinS585PUq\nklJceL0uXG5QLoXbBfFeF5nZbulGK0Q/kuQhjhIIBKitraWwsJCSkqMXa3S5XCQmJnLSSSdRUFDQ\nJ+tghMOGlian+qm5yVn0qKnBPipZKCAl1ZnewxOvyMyOIynZGWchhIiNmCYPy7LGA0/gLAhlgIe0\n1g90OGY58BxwMLrpGa11x5UGRS8ZY2hoaKChoYHm5mZCoRANDQ1UVFRgR6d8dblcpKamMmrUKMaP\nH8+YMWOOawoQ2zYE/Kbtd2N9hJbmaHtFJz2gFJA6ykkUqWluRud68Ei3WSFOiDGGQMTQFIzgC9lE\nbINtIGKc37YxOMt59EysSx5h4Aat9duWZaUC2y3L2qC1/qjDca9prc+PQXxDXjgcpq6uDp/PR319\nPX6/n/r6enw+H4FA4JjjlVJkZWWRmprKySef3KtJBm3b4GuxCfoNgYAzrUcoZKirjhAIdN5AoRQk\nJbtITnGRlOKULlJSZYyFGLlaP+T9IRt/2PlpDtk0+COEbYNtDJHoh71tIGIbAhGbQNgQjJi2Y1oT\nQtg2NAdtmoIRIp/QTvjpeVN6HGdMk0d0HfLS6ONGy7J2AnlAx+QheqGhoYHS0lKam5spLS3F7/d3\nelxSUhJpaWmkpqYSHx+P1+ttG9XdU5GIobwkRH2ds6hRKNj5v874eGeUdlwcpKY5q+IlJbtJTFQy\nrkIMG7YxNAXtoz74fWHnuS9s42v3u3W/P2QI2YawbROyIWz33+qu8S5FSrybRI+LOBe4lMLtUriU\n85P+QzEAABEkSURBVLg3Yl3yaGNZ1iRgAbClk93LLMt6DzgC3Ki1/rCLa6wB1gBorfsp0sHJGENT\nUxN79+6lsLDwqBX0UlJSSElJISMjg/j4eDIyMtpGdR9Pl9pw2ClhtDTZlBQFj5oHKiFBkZjkik47\n7iQMb6KL9P/X3pnH2FVeB/x3l/fe7B7GMYwXMDYYI1eipqVARStVQokgoXRJ+hWooqQVXaKiCBqp\nAtKmqVQk2tIUQhKlJJSloqUncUKApptSUlVpQhZiahICwQZsYxviZTxvtrfce/vH+d4yY4+Zx3g8\n75rzk+7c5fvevWfuu++cc893vu8biWx4D6NryLxHXkszaolX3slxjrVt13x5vW27kqSMV5KmcShX\n3ty7XwilKKAnDv0S0FuIWNETUQgDwkCVfbvSL8VavxgFxG11wiAgCqC/GNFfDClGJy/jsCuMh3Nu\nANgG3Cwi43OKnwHWi8iEc+7dwGPApuOdR0TuA+7zu6ft5OxzOXLkCNu3b+fw4cOAtlWsX7+e4eFh\nBgcHWbVq1aIUd5Zl/ORAnTf215ieyprjPzXo6w9Ze06Bnr6QgcHQjISxaJI0a3nm9YzxSl1DMnMU\nfGP7eEq+YRBmrdNUy5dQO/QXQnoLYVP598at/d6C7vcUAnrjyB8PKET6JlAIVfnn4TcUZG+WLL/E\nOOcKwJPAv4vIJxZQ/xXgEhE5+CZVs3379p0ECbuXqakpdu7cyc6dO0mShGKxyKpVq9iyZQtDQ0OL\nPn95POG1V6uUj6azDEYYQm9vqMZiKGR0bYEo6v6H3Vge0kxj7kdn6uwdrzJRTajUMyr1lEaEJkPD\nPWMzdcZnEqpLGLppEIdBU1kXwoA40nUhah0rRPOXxX5/qBTTVwgpxQFDpYjCSfTuTzVrtMV8QT/m\n5c62CoD7gefnMxzOuVHgdRHJnHOXAiFw6BSK2VUkScKRI0c4fPgwL7zwQnMcqQ0bNnDRRRedtGlX\ny+MJP9w+3Rw4MI5h/XklhkciiqV8eEbGWyPNZodt6mlGtZ5RTVIqiSr9apKRpJk25Fbq1JJWA22j\nEXeypmGcciXhOFO2n5AQKPmQTSkOGSpF9MRhU2FHwYmVfNx+bJ5ye4YXx3KHra4A3g/scM5t98du\nB84BEJHPAu8DPuScqwPTwHUi8rYJSbXz8ssvs2PHjlkTJI2OjrJ582ZWrlzZ0Y8hyzRttl7PdKTZ\nckqWwsxMOutN44yVEeeeX6LUY3NXdBNZ1lLuSarKup6pQk9STb9MUs3KqaeaJq3lMF1LKVcTJiqJ\nrqsJ5UpKNUn1fEvw6+ovhAyWIlYPFBnpiylFahTaH6n+YsRwT8SKHi035d7dLHvYagk5bcJWR48e\n5cUXX2T37t0ADA0NMTIywujoKGvWrOnYaEyUU175cYXy+Pz+YBTBmaMFzjmvaCGpRZBlmj5ZSzJV\n6N4rb8ToK4nG4Bt1qmlrv9KWnTPTtl2pq+FYKgJoeuvtnn0pDlXpR9owG4bQV4gYKkVNQ9BopA0D\n6PdlA6WI2ByPXJCbsJVxYmq1Gs899xy7du0CtA/G1q1b2bhx44LPkSQZY4cTymMJlUrKRLnVMS+O\noVQKiYsBQytCoiggKgSsGI4o9Zzenl+atRT0rHUzX17XlSRt5s6rB69efT1thWfavfzGfppCJUmZ\nqqVL4slDK2YfhRD5lMu4bTsKNdMmDjXME/vsnN5CSH8xYrAYMVAK/boVFooCTuvv3jg5mPHoIrIs\nY2ZmhsnJSXbv3t1MuQ3DkI0bN3L++efT398/72eTBGrVjPGxhKNHdPDAqcljBxAsFgNWnhmz7txi\nV/fcbsTeNTSjGTL1pKWs22PsVd9zdrKaNPPoG8q/UtdYfbXNGCyl5z6XQhiopx60FH3sjxWigKL3\n5HVb9wuhT9X02TiNjJ3eQkTJ1zMFbywnZjyWkUqlwr59+6hUKoyPj3Pw4MHmnBig3t/IyAgXbr6I\nkCGOHs4YO1SlOpMxUU6o19VgJPVjZ8RrZ3AoZHgkpqdP+1ycinTaRky+2gzJpD5043vE1jU/vrFM\nVJNWp6qafxNYYgVf8oq6FAUU41Y4phRr5kxDqZeisNlIG4WaXx8HGrZp9+ijcHadYhjQWwhznX1j\nGPNhxuMUkmUZk5OTzWypPXv2zBoipF7PSOsxhbiPOB5geGADRQZ5+QWAY4cSmUsU6ax4fQMhZ6yM\n6R8I6e0LTzjUR6OzVMtLnx26acbf22Pzaeq9eDUK1baYfaNeLc0W3dEmAIpR0FTKcUNx+/32GHsh\nCrQjlM+x7/N59Y04fTEKKcZtxsI8d8NYFGY8TiKNsNP09DRJklCv1ymXy0xMTFAuTzA2Nka1WiPL\ndByoNIWB/mH6ekZIaj0UB4YpFgaPUWpxDMMjMVEMCRl1MpIiBDFkQUYaZCQB1NKMmSSlnNbZO12h\nOuEVfUOx11MmqhqHb4aC0qzjNMqF0gjNNJZCGDbDNpofH/klZqA0uzNVo7esKXjD6E7MeCyAWq1G\nrVYjSRKSJKVcnmJiYorJ8iQTk1NMTU0yPT1NtVolSRKyjNaSaly+0e4QhkWKxSEKxWFKpRWE8QhT\nNd+RqgaVngq1YkYtSJnO/JtAmlI5kC5Zr9g4wHvo6p23vHXt+VqaE5tvxuX9dqltu71+p2PlGIaR\nH97WxiNLM2q1lGq1TrVaY2pyhoOHDjI1NU25PM309DTTM1NUKtNNY5D6toWs/W+mWxkQBDFh1AuE\nZESEUR9h1E8U9ZDFA2RRkZkQxsOMWpiSMEMSZkxGCdUwgxq6HIdGx6neQshwTzRrDJtSFBJHUAjD\nZoplu9JvKPcBH9qJo1YGTmRplIZhdMhpbTzksf8lTeokdQ0hpUlCmtTJsoQ0TcjShCzToM1cY9BO\nEEQQxARBCAQQlSDqIYt6yAo9pHGJNCpSj2KSKCYNMupBRhpAFmZEsQ5cViqE9MYZvYWQFXFEjx/M\nrL8Q0VcMm7H8RminxzfcliJdW69YwzC6hdPaeLxnzX91UDvQJYggCObMZBcQZBkBqS5ZRkBGiK6j\nLCMmJcwyIlICIMxa5U0yTvhmYRiGsay896EFVz2tjUeMpkg2FH2Ypa3t5rGGkleFH9BYDMMwjPmw\n4UkMwzAMoLPhSaz3kmEYhtExZjwMwzCMjjmtw1bLLYBhGEYOeduHrYLjLc65781X1u1LnmU3+Zd/\nMflN/gUuC+J0Nh6GYRjGEmHGwzAMw+iYt6PxuG+5BVgEeZYdTP7lxuRfXvIu/yxO5wZzwzAMY4l4\nO755GIZhGIvEjIdhGIbRMcs6tpVz7mzgYeAstF/GfSJyj3NuBPhn4FzgFcCJyBHn3IXAA8DPAB8V\nkbv8eTb7+g02Ah8TkbuPc82rgHuACPi8iNzpj98PXIKmqr0IfFBEJuZ8tg/4AnAekABfB7Z4+QFm\ngCFgDJgGRnMkfy/6PKwGbgR+i3zd/xFf7Q3gCOoY5en+D7fJPwOkwKocyZ+hMf29wBeBb6PfSbfK\nHwA9wKSXfTvwLuAA+txPAf/XpbInwBMicmtbuQM+7v+XZ0XkhrnXP9ks95tHHfiIiGwBLgf+0Dm3\nBbgV+JqIbAK+5vcBDgMfBu5qP4mIvCAiW0VkK/Cz6Bf/5bkXc85FwKeBq9GH/np/PYBbROSnReQi\nYDdw0zwy3yUiFwIX+2tt8/J/Dn1wrgX2AUM5k/8a9KH8F+BXyd/9/x2gDFyHztmbt/t/CTAB3AC8\nBqzMmfyX+8/cBuwHvt3l8r8TOAR8xMt+JfAfwL8Cd4jIWV0s+8XAFc65q/25N6H3/QoR+Sng5nk+\nf1JZVuMhIvtF5Bm/XQaeB9YCvwI85Ks9hCozROQNEfkOJx7U/Epgp4i8epyyS4GXRGSXiFSBR/21\nEJFxAOdcgHrhx2QSiMiUiDzlt6vA07QmF78K9VTWApuBdf5cuZBfRH4APIt6Yz9Hzu6/iHwV+CF6\n/y+g9b3kRf59tJ7/n0cNSJ7kL6P65MvoW9STXS7/buB/gHVe9n3AAPnRPc8A63zx7wKfFpEjDVlP\nIONJY7nfPJo4585FLerTwFkist8XHaAVFloI1wH/NE/ZWmBP2/5ef6whwwP+ehcC976JvMPAL6Pe\nCeir7nkN+dHQ1cq8yN92/w8Cw3m7/3Oen9XA4zmU/xeBv0PDbb+fM/nfg973e4Ei6tXnQn5/79cD\nlwHnA/f6kHrXy+4PXQBc4Jz7hnPuWz48tuR0hfFwzg0A24CbG1a4gYg0ZnhdyHmKaNjoC29FDhH5\nbWAN6gH+5gmuE6MPySdFZJeX/xzg4zmWfxv6ujvLs8qh/NeiMeW/zqH8N4rIRrS97E9yJP8g6knf\nkrfnH21j2gbcghqQMvCfwEPdLruI7PKHY2AT8EvA9cDnvIFZUpZ9MijnXAH98h4RkS/5w68751aL\nyH7n3Gr0C14IVwPPiMjr/txnA0/4ss+iYZmz2+qvoxUeAEBEEufco8AfO+ceBr7nix4XkY/57fuA\nH4vI3W3yvwo815AfeAdwKEfyPyIiX3LOXQuM5fD+PwKMA3egjbQjQK7kb3v+9wK/BnwgJ/I/hjb2\n3uacuw11Sp90zl3jz9+V8qNtEE+i9/5hf83X/bG/6vZ73/bRvcDTIlIDXnbOvYgak+8sUPa3xHJn\nWwXA/cDzIvKJtqLHgQ8Ad/r1VxZ4yutpe20UkT3A1rbrxcAm59wG9Iu7DrjBy3GeiLzkt68FfiQi\nSfvn/Tn+AlgB3Nguf5vM30QzJioikjnnciH/nPv/XfJ3/59Cs3yuAn4vh/I/0VbtB0C/386D/DtE\n5Mq2OruBr4jId51zt3ar/MCD+Ge/4Syhv+M70e+ka+/9nGs/5q//gHPuHWgYaxdLzLL2MHfO/QLa\naLUDTU0EuB2NWwsaCnoVTZc77JwbRRXbkK8/AWwRkXHnXD+aqbBRRI6e4JrvBu5GQxt/LyJ3OOdC\nL8cQ6kE9C3xo7iu4c24dGrf8EdpQ2I/GSHegr7fr0WyLA2jo4cwcyV8CNni5ptC49f4cyb8Bbezf\n7eUfRdud8iL/arTBdp9fCmibWV7kb//93o4+P2d0sfyxl30v2j6zGk2RLqPho3HgpS6VvZEM8ikR\n+bw3On+DOk4Jmi326HxynCxseBLDMAyjY7qiwdwwDMPIF2Y8DMMwjI4x42EYhmF0jBkPwzAMo2PM\neBiGYRgdY8bDME4yzrkHfU6+YZy2mPEwjGXCOfd159zcDl+GkQvMeBiGYRgdY50EDWOROOcuRofp\n2AR8FR1t4CW01+8/oKO1xsA3gD8Qkb3OuTvQuSJq6Lw2D4rITU4nHboXnRviJ8Cfioic4n/JMN4U\ne/MwjEXgdDTVx1AjMYKOqvpeXxyis8+tR4famQY+BSAiH0WHpbhJRAa84ehHR3T9R3Rom+uAz7jW\npEGG0TUs+6i6hpFzLkfHobrbD+H9RefcHwGIyCF0xFwA/NvGUyc41zXAKyLygN//vnNuG/AbwJ8v\nhfCG8VYx42EYi2MN8Jo3HA1ehea803+LDlh3hi8bdM5FftTUuawHLnPOjbUdi9G3GsPoKsx4GMbi\n2A+sdc4FbQbkHGAnOj/2ZuAyETngnNsKfB8dPRWOnWhoD/DfIvLOUyC3YSwKMx6GsTi+iTZ4f9g5\n9xl0etBL0fDUINrOMeacGwH+bM5nXwc2tu0/CdzpnHs/OjMf6JwOEyLy/NL9C4bROdZgbhiLQESq\nwK8DHwQOo1OINmYEvBvoReeF/xbwb3M+fg/wPufcEefcJ0WkDLwLbSjfh84L85foXCuG0VVYqq5h\nGIbRMfbmYRiGYXSMGQ/DMAyjY8x4GIZhGB1jxsMwDMPoGDMehmEYRseY8TAMwzA6xoyHYRiG0TFm\nPAzDMIyO+X8t0rPMukr7FgAAAABJRU5ErkJggg==\n", 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Gr1eZmiSIt4AuwAe1GoGISAVufmNZndR768k16xVv164d27dv57vvvmP//fdn+vTpDBw4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\nIiKkcAyitpjZVGB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cWUQkH+2u2exhZiXACGBtZ41Cy0ms/Y9PU/IPn+3sOEREuo3DXrMxs8HAf5I6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\nkdwp2YiISO6UbEREJHdKNiIikrv/DxhaNJW2QwVcAAAAAElFTkSuQmCC\n", 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s1efjhkwGN3gE9O6Pm3S+koM0SAlCpETZay8cSQ5u+Bjc+e/B9e5f4KgkTZQgREqM7d+H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\nAAAAAElFTkSuQmCC\n", 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