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+ ],
+ "text/plain": [
+ ":DynamicMap [block]\n",
+ " :Overlay\n",
+ " .RGB.I :RGB [Date,energy(kWh/hh)] (R,G,B,A)\n",
+ " .RGB.II :RGB [Date,energy(kWh/hh)] (R,G,B,A)"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {
+ "id": "1001"
+ }
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# # Show split\n",
+ "scatter = dynspread(datashade(hv.Curve(df_train, kdims=['Date'], vdims=['energy(kWh/hh)', 'block']).groupby('block'), cmap='blue'))\n",
+ "scatter *= dynspread(datashade(hv.Curve(df_test, kdims=['Date'], vdims=['energy(kWh/hh)', 'block']).groupby('block'), cmap='red'))\n",
+ "scatter = scatter.opts(plot=dict(width=800))\n",
+ "scatter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-19T07:26:08.140247Z",
+ "start_time": "2020-10-19T07:26:08.085737Z"
+ }
+ },
+ "source": [
+ "### Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:55.588516Z",
+ "start_time": "2020-10-24T01:46:54.767202Z"
+ },
+ "lines_to_next_cell": 0
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+ " and should_run_async(code)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ", , , , , , , , , , , ])>\n",
+ ", , , , , , , , , , , ])>\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# ### Dataset\n",
+ "# These are the columns that we wont know in the future\n",
+ "# We need to blank them out in x_future\n",
+ "columns_blank=['visibility',\n",
+ " 'windBearing', 'temperature', 'dewPoint', 'pressure',\n",
+ " 'apparentTemperature', 'windSpeed', 'humidity']\n",
+ "df_trains = [d.resample(freq).first().ffill().dropna() for _,d in df_train.groupby('block')]\n",
+ "df_tests = [d.resample(freq).first().ffill().dropna() for _,d in df_test.groupby('block')]\n",
+ "ds_train = Seq2SeqDataSets(df_trains,\n",
+ " window_past=window_past,\n",
+ " window_future=window_future,\n",
+ " columns_blank=columns_blank)\n",
+ "ds_test = Seq2SeqDataSets(df_tests,\n",
+ " window_past=window_past,\n",
+ " window_future=window_future,\n",
+ " columns_blank=columns_blank)\n",
+ "print(ds_train)\n",
+ "print(ds_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:55.684387Z",
+ "start_time": "2020-10-24T01:46:55.593721Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+ " and should_run_async(code)\n"
+ ]
+ },
+ {
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+ " 0.04166667, 0. ],\n",
+ " ...,\n",
+ " [ 1.0853493 , -0.6453928 , 1.0307775 , ..., 0. ,\n",
+ " 1.9166666 , 0. ],\n",
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+ " array([[ 1.9492349e-01],\n",
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+ " [ 5.5381370e-01],\n",
+ " [ 3.4310017e-02],\n",
+ " [ 1.3157757e-03],\n",
+ " [-1.2521403e-01],\n",
+ " [-7.3855065e-02],\n",
+ " [-1.2101193e-01],\n",
+ " [ 3.9912798e-02],\n",
+ " [ 1.2270970e-01],\n",
+ " [ 4.5204341e-02],\n",
+ " [-1.2677038e-01],\n",
+ " [-1.6115144e-02],\n",
+ " [ 2.1842413e-01],\n",
+ " [ 5.3249198e-01],\n",
+ " [ 4.2136979e-01],\n",
+ " [ 7.1224833e-01],\n",
+ " [ 1.4854342e+00],\n",
+ " [ 1.8214462e+00],\n",
+ " [ 1.6490046e+00],\n",
+ " [ 2.0604987e+00],\n",
+ " [ 2.0366869e+00],\n",
+ " [ 1.6323519e+00],\n",
+ " [ 1.3979683e+00],\n",
+ " [ 1.3878522e+00],\n",
+ " [ 1.4852785e+00],\n",
+ " [ 1.5033319e+00],\n",
+ " [ 1.9745893e+00],\n",
+ " [ 2.0606544e+00],\n",
+ " [ 1.8254926e+00],\n",
+ " [ 1.8941269e+00],\n",
+ " [ 2.4310615e+00],\n",
+ " [ 2.7108901e+00],\n",
+ " [ 2.6917472e+00],\n",
+ " [ 2.9974108e+00],\n",
+ " [ 3.7825804e+00],\n",
+ " [ 3.2772393e+00],\n",
+ " [ 3.5678065e+00],\n",
+ " [ 3.8865433e+00],\n",
+ " [ 3.7761993e+00],\n",
+ " [ 3.8535490e+00],\n",
+ " [ 3.9933076e+00],\n",
+ " [ 3.0651112e+00],\n",
+ " [ 2.7614708e+00],\n",
+ " [ 2.6290269e+00],\n",
+ " [ 2.4046037e+00],\n",
+ " [ 1.4166442e+00],\n",
+ " [ 1.4624003e+00],\n",
+ " [ 9.4772130e-01]], dtype=float32)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# we can treat it like an array\n",
+ "ds_train[0]\n",
+ "len(ds_train)\n",
+ "ds_train[-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.156810Z",
+ "start_time": "2020-10-24T01:46:55.689275Z"
+ }
+ },
+ "outputs": [
+ {
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+ "\n",
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+ " visibility | \n",
+ " windBearing | \n",
+ " temperature | \n",
+ " dewPoint | \n",
+ " pressure | \n",
+ " apparentTemperature | \n",
+ " windSpeed | \n",
+ " humidity | \n",
+ " holiday | \n",
+ " block | \n",
+ " tsp_days | \n",
+ " is_past | \n",
+ "
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+ "\n",
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+ "2013-11-08 19:00:00 0.500561 0.118644 0.952749 -0.391543 \n",
+ "\n",
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+ "Date \n",
+ "2013-11-08 17:00:00 0.160852 -0.928594 -0.347889 -0.539344 \n",
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+ "2013-11-08 19:00:00 -0.048187 -0.821312 -0.517919 0.270877 \n",
+ "\n",
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+ "Date \n",
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+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# We can get rows\n",
+ "x_past, y_past, x_future, y_future = ds_train.get_rows(10)\n",
+ "\n",
+ "# Plot one instance, this is what the model sees\n",
+ "y_past['energy(kWh/hh)'].plot(label='past')\n",
+ "y_future['energy(kWh/hh)'].plot(ax=plt.gca(), label='future')\n",
+ "plt.legend()\n",
+ "plt.ylabel('energy(kWh/hh)')\n",
+ "\n",
+ "# Notice we've added on two new columns tsp (time since present) and is_past\n",
+ "x_past.tail()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.247816Z",
+ "start_time": "2020-10-24T01:46:56.162158Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+ " and should_run_async(code)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " month | \n",
+ " day | \n",
+ " week | \n",
+ " hour | \n",
+ " minute | \n",
+ " dayofweek | \n",
+ " visibility | \n",
+ " windBearing | \n",
+ " temperature | \n",
+ " dewPoint | \n",
+ " pressure | \n",
+ " apparentTemperature | \n",
+ " windSpeed | \n",
+ " humidity | \n",
+ " holiday | \n",
+ " block | \n",
+ " tsp_days | \n",
+ " is_past | \n",
+ "
\n",
+ " \n",
+ " | Date | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2013-11-10 17:00:00 | \n",
+ " 1.085349 | \n",
+ " -0.645393 | \n",
+ " 1.030777 | \n",
+ " 0.794583 | \n",
+ " -0.999962 | \n",
+ " 1.499889 | \n",
+ " -0.328257 | \n",
+ " 0.36157 | \n",
+ " 0.77956 | \n",
+ " 1.225586 | \n",
+ " -1.236252 | \n",
+ " 0.835296 | \n",
+ " 2.180685 | \n",
+ " 0.609441 | \n",
+ " -0.150044 | \n",
+ " 0.0 | \n",
+ " 1.895833 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 2013-11-10 17:30:00 | \n",
+ " 1.085349 | \n",
+ " -0.645393 | \n",
+ " 1.030777 | \n",
+ " 0.794583 | \n",
+ " 1.000038 | \n",
+ " 1.499889 | \n",
+ " -0.328257 | \n",
+ " 0.36157 | \n",
+ " 0.77956 | \n",
+ " 1.225586 | \n",
+ " -1.236252 | \n",
+ " 0.835296 | \n",
+ " 2.180685 | \n",
+ " 0.609441 | \n",
+ " -0.150044 | \n",
+ " 0.0 | \n",
+ " 1.916667 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 2013-11-10 18:00:00 | \n",
+ " 1.085349 | \n",
+ " -0.645393 | \n",
+ " 1.030777 | \n",
+ " 0.939042 | \n",
+ " -0.999962 | \n",
+ " 1.499889 | \n",
+ " -0.328257 | \n",
+ " 0.36157 | \n",
+ " 0.77956 | \n",
+ " 1.225586 | \n",
+ " -1.236252 | \n",
+ " 0.835296 | \n",
+ " 2.180685 | \n",
+ " 0.609441 | \n",
+ " -0.150044 | \n",
+ " 0.0 | \n",
+ " 1.937500 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 2013-11-10 18:30:00 | \n",
+ " 1.085349 | \n",
+ " -0.645393 | \n",
+ " 1.030777 | \n",
+ " 0.939042 | \n",
+ " 1.000038 | \n",
+ " 1.499889 | \n",
+ " -0.328257 | \n",
+ " 0.36157 | \n",
+ " 0.77956 | \n",
+ " 1.225586 | \n",
+ " -1.236252 | \n",
+ " 0.835296 | \n",
+ " 2.180685 | \n",
+ " 0.609441 | \n",
+ " -0.150044 | \n",
+ " 0.0 | \n",
+ " 1.958333 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 2013-11-10 19:00:00 | \n",
+ " 1.085349 | \n",
+ " -0.645393 | \n",
+ " 1.030777 | \n",
+ " 1.083500 | \n",
+ " -0.999962 | \n",
+ " 1.499889 | \n",
+ " -0.328257 | \n",
+ " 0.36157 | \n",
+ " 0.77956 | \n",
+ " 1.225586 | \n",
+ " -1.236252 | \n",
+ " 0.835296 | \n",
+ " 2.180685 | \n",
+ " 0.609441 | \n",
+ " -0.150044 | \n",
+ " 0.0 | \n",
+ " 1.979167 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " month day week hour minute \\\n",
+ "Date \n",
+ "2013-11-10 17:00:00 1.085349 -0.645393 1.030777 0.794583 -0.999962 \n",
+ "2013-11-10 17:30:00 1.085349 -0.645393 1.030777 0.794583 1.000038 \n",
+ "2013-11-10 18:00:00 1.085349 -0.645393 1.030777 0.939042 -0.999962 \n",
+ "2013-11-10 18:30:00 1.085349 -0.645393 1.030777 0.939042 1.000038 \n",
+ "2013-11-10 19:00:00 1.085349 -0.645393 1.030777 1.083500 -0.999962 \n",
+ "\n",
+ " dayofweek visibility windBearing temperature \\\n",
+ "Date \n",
+ "2013-11-10 17:00:00 1.499889 -0.328257 0.36157 0.77956 \n",
+ "2013-11-10 17:30:00 1.499889 -0.328257 0.36157 0.77956 \n",
+ "2013-11-10 18:00:00 1.499889 -0.328257 0.36157 0.77956 \n",
+ "2013-11-10 18:30:00 1.499889 -0.328257 0.36157 0.77956 \n",
+ "2013-11-10 19:00:00 1.499889 -0.328257 0.36157 0.77956 \n",
+ "\n",
+ " dewPoint pressure apparentTemperature windSpeed \\\n",
+ "Date \n",
+ "2013-11-10 17:00:00 1.225586 -1.236252 0.835296 2.180685 \n",
+ "2013-11-10 17:30:00 1.225586 -1.236252 0.835296 2.180685 \n",
+ "2013-11-10 18:00:00 1.225586 -1.236252 0.835296 2.180685 \n",
+ "2013-11-10 18:30:00 1.225586 -1.236252 0.835296 2.180685 \n",
+ "2013-11-10 19:00:00 1.225586 -1.236252 0.835296 2.180685 \n",
+ "\n",
+ " humidity holiday block tsp_days is_past \n",
+ "Date \n",
+ "2013-11-10 17:00:00 0.609441 -0.150044 0.0 1.895833 0.0 \n",
+ "2013-11-10 17:30:00 0.609441 -0.150044 0.0 1.916667 0.0 \n",
+ "2013-11-10 18:00:00 0.609441 -0.150044 0.0 1.937500 0.0 \n",
+ "2013-11-10 18:30:00 0.609441 -0.150044 0.0 1.958333 0.0 \n",
+ "2013-11-10 19:00:00 0.609441 -0.150044 0.0 1.979167 0.0 "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Notice we've hidden some future columns to prevent cheating\n",
+ "x_future.tail()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-22T23:14:39.268167Z",
+ "start_time": "2020-10-22T23:14:39.187488Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot helpers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.315316Z",
+ "start_time": "2020-10-24T01:46:56.253738Z"
+ },
+ "lines_to_end_of_cell_marker": 2,
+ "lines_to_next_cell": 0
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+ " and should_run_async(code)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def plot_prediction(ds_preds, i):\n",
+ " \"\"\"Plot a prediction into the future, at a single point in time.\"\"\"\n",
+ " d = ds_preds.isel(t_source=i)\n",
+ "\n",
+ " # Get arrays\n",
+ " xf = d.t_target\n",
+ " yp = d.y_pred\n",
+ " s = d.y_pred_std\n",
+ " yt = d.y_true\n",
+ " now = d.t_source.squeeze()\n",
+ " \n",
+ " \n",
+ " plt.figure(figsize=(12, 4))\n",
+ " \n",
+ " plt.scatter(xf, yt, label='true', c='k', s=6)\n",
+ " ylim = plt.ylim()\n",
+ "\n",
+ " # plot prediction\n",
+ " plt.fill_between(xf, yp-2*s, yp+2*s, alpha=0.25,\n",
+ " facecolor=\"b\",\n",
+ " interpolate=True,\n",
+ " label=\"2 std\",)\n",
+ " plt.plot(xf, yp, label='pred', c='b')\n",
+ "\n",
+ " # plot true\n",
+ " plt.scatter(\n",
+ " d.t_past,\n",
+ " d.y_past,\n",
+ " c='k',\n",
+ " s=6\n",
+ " )\n",
+ " \n",
+ " # plot a red line for now\n",
+ " plt.vlines(x=now, ymin=0, ymax=1, label='now', color='r')\n",
+ " plt.ylim(*ylim)\n",
+ "\n",
+ " now=pd.Timestamp(now.values)\n",
+ " plt.title(f'Prediction NLL={d.nll.mean().item():2.2g}')\n",
+ " plt.xlabel(f'{now.date()}')\n",
+ " plt.ylabel('energy(kWh/hh)')\n",
+ " plt.legend()\n",
+ " plt.xticks(rotation=45)\n",
+ " plt.show()\n",
+ " \n",
+ "def plot_performance(ds_preds, full=False):\n",
+ " \"\"\"Multiple plots using xr_preds\"\"\"\n",
+ " plot_prediction(ds_preds, 24)\n",
+ "\n",
+ " ds_preds.mean('t_source').plot.scatter('t_ahead_hours', 'nll') # Mean over all predictions\n",
+ " n = len(ds_preds.t_source)\n",
+ " plt.ylabel('Negative Log Likelihood (lower is better)')\n",
+ " plt.xlabel('Hours ahead')\n",
+ " plt.title(f'NLL vs time ahead (no. samples={n})')\n",
+ " plt.show()\n",
+ "\n",
+ " # Make a plot of the NLL over time. Does this solution get worse with time?\n",
+ " if full:\n",
+ " d = ds_preds.mean('t_ahead').groupby('t_source').mean().plot.scatter('t_source', 'nll')\n",
+ " plt.xticks(rotation=45)\n",
+ " plt.title('NLL over source time (lower is better)')\n",
+ " plt.show()\n",
+ "\n",
+ " # A scatter plot is easy with xarray\n",
+ " if full:\n",
+ " plt.figure(figsize=(5, 5))\n",
+ " ds_preds.plot.scatter('y_true', 'y_pred', s=.01)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "lines_to_next_cell": 2
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.374161Z",
+ "start_time": "2020-10-24T01:46:56.321823Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_hist(trainer):\n",
+ " try:\n",
+ " df_hist = pd.read_csv(trainer.logger.experiment.metrics_file_path)\n",
+ " df_hist['epoch'] = df_hist['epoch'].ffill()\n",
+ " df_histe = df_hist.set_index('epoch').groupby('epoch').mean()\n",
+ " if len(df_histe)>1:\n",
+ " df_histe[['loss/train', 'loss/val']].plot(title='history')\n",
+ " return df_histe\n",
+ " except Exception:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Lightning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.443085Z",
+ "start_time": "2020-10-24T01:46:56.384266Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import pytorch_lightning as pl\n",
+ "\n",
+ "class PL_MODEL(pl.LightningModule):\n",
+ " def __init__(self, model, lr=3e-4, patience=2, weight_decay=0):\n",
+ " super().__init__()\n",
+ " self._model = model\n",
+ " self.lr = lr\n",
+ " self.patience = patience\n",
+ " self.weight_decay = weight_decay\n",
+ "\n",
+ " def forward(self, x_past, y_past, x_future, y_future=None):\n",
+ " \"\"\"Eval/Predict\"\"\"\n",
+ " y_dist, extra = self._model(x_past, y_past, x_future, y_future)\n",
+ " return y_dist, extra\n",
+ "\n",
+ " def training_step(self, batch, batch_idx, phase='train'):\n",
+ " x_past, y_past, x_future, y_future = batch\n",
+ " y_dist, extra = self.forward(*batch)\n",
+ " loss = -y_dist.log_prob(y_future).mean()\n",
+ " self.log_dict({f'loss/{phase}':loss})\n",
+ " if ('loss' in extra) and (phase=='train'):\n",
+ " # some models have a special loss\n",
+ " loss = extra['loss']\n",
+ " self.log_dict({f'model_loss/{phase}':loss})\n",
+ " return loss\n",
+ "\n",
+ " def validation_step(self, batch, batch_idx):\n",
+ " return self.training_step(batch, batch_idx, phase='val')\n",
+ " \n",
+ " def configure_optimizers(self):\n",
+ " optim = torch.optim.AdamW(self.parameters(), lr=self.lr, weight_decay=self.weight_decay)\n",
+ " scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
+ " optim,\n",
+ " patience=self.patience,\n",
+ " verbose=True,\n",
+ " min_lr=1e-7,\n",
+ " )\n",
+ " return {'optimizer': optim, 'lr_scheduler': scheduler, 'monitor': 'loss/val'}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.497567Z",
+ "start_time": "2020-10-24T01:46:56.447341Z"
+ },
+ "lines_to_next_cell": 2
+ },
+ "outputs": [],
+ "source": [
+ "# # Run\n",
+ "from torch.utils.data import DataLoader\n",
+ "from pytorch_lightning.loggers import CSVLogger\n",
+ "from pytorch_lightning.callbacks.early_stopping import EarlyStopping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.558979Z",
+ "start_time": "2020-10-24T01:46:56.502082Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Init data\n",
+ "x_past, y_past, x_future, y_future = ds_train.get_rows(10)\n",
+ "input_size = x_past.shape[-1]\n",
+ "output_size = y_future.shape[-1]\n",
+ "\n",
+ "dl_train = DataLoader(ds_train,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=True,\n",
+ " pin_memory=num_workers==0,\n",
+ " num_workers=num_workers)\n",
+ "dl_test = DataLoader(ds_test, batch_size=batch_size, num_workers=num_workers)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.635461Z",
+ "start_time": "2020-10-24T01:46:56.564625Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import gc\n",
+ "\n",
+ "def free_mem():\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ " gc.collect()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.751071Z",
+ "start_time": "2020-10-24T01:46:56.640655Z"
+ },
+ "lines_to_end_of_cell_marker": 2,
+ "lines_to_next_cell": 0
+ },
+ "outputs": [],
+ "source": [
+ "from seq2seq_time.models.lstm_seq2seq import LSTMSeq2Seq\n",
+ "from seq2seq_time.models.lstm_seq import LSTMSeq\n",
+ "from seq2seq_time.models.lstm import LSTM\n",
+ "from seq2seq_time.models.baseline import BaselineLast\n",
+ "from seq2seq_time.models.transformer import Transformer\n",
+ "from seq2seq_time.models.transformer_autor import TransformerAutoR\n",
+ "from seq2seq_time.models.transformer_seq2seq import TransformerSeq2Seq\n",
+ "from seq2seq_time.models.transformer_seq import TransformerSeq\n",
+ "from seq2seq_time.models.neural_process import RANP\n",
+ "from seq2seq_time.models.transformer_process import TransformerProcess\n",
+ "# ## Plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.827850Z",
+ "start_time": "2020-10-24T01:46:56.756046Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# PL_MODEL(TransformerAutoR(input_size, output_size, hidden_out_size=32),\n",
+ "# patience=patience,\n",
+ "# lr=2e-5,\n",
+ "# weight_decay=1e-3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:46:56.908831Z",
+ "start_time": "2020-10-24T01:46:56.832269Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[()>,\n",
+ " ()>,\n",
+ " ()>,\n",
+ " ()>,\n",
+ " ()>,\n",
+ " ()>,\n",
+ " ()>]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "models = [\n",
+ "# TransformerAutoR2(input_size,\n",
+ "# output_size),\n",
+ " lambda: TransformerAutoR(input_size,\n",
+ " output_size, hidden_out_size=32),\n",
+ " lambda: RANP(input_size,\n",
+ " output_size, hidden_dim=32, \n",
+ " latent_dim=64, n_decoder_layers=4),\n",
+ " lambda: LSTM(input_size,\n",
+ " output_size,\n",
+ " hidden_size=80,\n",
+ " lstm_layers=3,\n",
+ " lstm_dropout=0.3),\n",
+ " lambda: LSTMSeq2Seq(input_size,\n",
+ " output_size,\n",
+ " hidden_size=64,\n",
+ " lstm_layers=2,\n",
+ " lstm_dropout=0.25),\n",
+ " lambda: TransformerSeq2Seq(input_size,\n",
+ " output_size,\n",
+ " hidden_size=128,\n",
+ " nhead=8,\n",
+ " nlayers=4,\n",
+ " attention_dropout=0.2),\n",
+ " lambda: Transformer(input_size,\n",
+ " output_size,\n",
+ " attention_dropout=0.2,\n",
+ " nhead=8,\n",
+ " nlayers=8,\n",
+ " hidden_size=128),\n",
+ "# lambda: TransformerSeq(input_size,\n",
+ "# output_size),\n",
+ "# lambda: LSTMSeq(input_size,\n",
+ "# output_size),\n",
+ " lambda :TransformerProcess(input_size,\n",
+ " output_size, hidden_size=16,\n",
+ " latent_dim=8, dropout=0.5,\n",
+ " nlayers=4,)\n",
+ "]\n",
+ "models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:47:14.783481Z",
+ "start_time": "2020-10-24T01:46:56.913295Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
+ " | Name | Type | Params\n",
+ "----------------------------------------\n",
+ "0 | _model | BaselineLast | 1 \n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='Validation sanity check'), FloatProgress(value=1.0, bar_style='info', layout=Layout…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "444e9276a8124fe3abd8df2350caf004",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='Training'), FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), max…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
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+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='Validating'), FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), m…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "None\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "404b8f8f22344752a17e2f4604d1122c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict_multi'), FloatProgress(value=0.0, max=12.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
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+ },
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+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
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+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "baseline nll: 1.4\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Baseline model\n",
+ "pt_model = BaselineLast()\n",
+ "model = PL_MODEL(pt_model).to(device)\n",
+ "trainer = pl.Trainer(gpus=1,\n",
+ " max_epochs=1, \n",
+ " limit_train_batches=0.01,\n",
+ " logger=CSVLogger(\"logs\",\n",
+ " name=type(pt_model).__name__),\n",
+ " )\n",
+ "trainer.fit(model, dl_train, dl_test)\n",
+ "print(plot_hist(trainer))\n",
+ "ds_predss = predict_multi(model.to(device),\n",
+ " ds_test.datasets,\n",
+ " batch_size*8,\n",
+ " device=device,\n",
+ " scaler=output_scaler)\n",
+ "print(f'baseline nll: {ds_predss.nll.mean().item():2.2g}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-23T23:36:11.052891Z",
+ "start_time": "2020-10-23T23:36:11.048874Z"
+ }
+ },
+ "source": [
+ "## Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-23T01:57:08.860742Z",
+ "start_time": "2020-10-22T23:52:55.304213Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
+ " | Name | Type | Params\n",
+ "--------------------------------------------\n",
+ "0 | _model | TransformerAutoR | 168 K \n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "TransformerAutoR\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='Validation sanity check'), FloatProgress(value=1.0, bar_style='info', layout=Layout…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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+ "metadata": {},
+ "output_type": "display_data"
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+ "data": {
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "version_minor": 0
+ },
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+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=20.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ ]
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RANP\n",
+ "mean_NLL -0.04\n",
+ " loss/train model_loss/train step loss/val\n",
+ "epoch \n",
+ "0.0 0.098570 0.099123 998.871795 0.055426\n",
+ "1.0 -0.136263 -0.135751 2923.750000 0.008383\n",
+ "2.0 -0.184636 -0.184242 4873.625000 -0.035571\n",
+ "3.0 -0.203627 -0.203329 6823.500000 -0.064338\n",
+ "4.0 -0.231923 -0.231709 8773.375000 -0.052904\n",
+ "5.0 -0.250552 -0.250389 10723.250000 -0.062541\n",
+ "6.0 -0.274888 -0.274789 12673.125000 -0.015443\n",
+ "7.0 -0.279982 -0.279893 14623.000000 -0.041318\n"
+ ]
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
+ " | Name | Type | Params\n",
+ "--------------------------------\n",
+ "0 | _model | LSTM | 136 K \n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "LSTM\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='Validation sanity check'), FloatProgress(value=1.0, bar_style='info', layout=Layout…"
+ ]
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gbt53rUs0mxDOr8WIKITpdQLRJj2i/Vy70NcFlT57u/28oFIngAiD5wWSjXq7fVxQ2bi/O8LYtMVESoniaifbJqjT8DssK4qCNWvWYP78+bBarcjMzERKSgp69OjhHjNkyBCkpKRACIEff/wRS5cuxbJly/zdNbUBu0tBbrnDa6WJ+j/4ymtd7nF6ASTUheBhiVFevcT9esSjsLAwgL8FEVFwa8sLKh2u+tnuprPZvvu7Xai0u3Cx0uHeXuPUfkGlUa9DQdUJVNob/n5g2wRd6fwOyydPnkRCQgLi4+MBAGPGjEF2drZXWA4Pb/gerLa2lv2oHUyREgWVzoavxTzuTHSx0uH1h2VchAGJZhPG9PS+a118tO+2Cf7vSUTUcdS1v/V+B1FfF1T6upiy2qGg1qUgpVccrEaFN+mgTsPvsFxUVASrteFSDKvVihMnTjQZt3//frz77rsoLS1FZmamz8/LyspCVlYWAGDRokWw2Wz+HqJmBoMhIPv1V1mNE2eKq3C2pBpniqtxtrjuvyU1sLsaLp6IMOqRbInA9T26ILlLBJItEehpiUCPLuGIMmk/JUK1XoHCemnDemnDemnDemlnMBjgdDpbHkgAeI5pFYz18jssS9n0S5zmZhpvuOEG3HDDDfj666+xYcMGLFiwoNnPS09PR3p6uvt5axbXb2utXdQ/EBwuBbkVjiYrTZwvs6O0UdtEfLQJSWYjhlzVxauP2BLe3GoTNaguq8Hl3L0+mOsVjFgvbVgvbVgvbVgv7VgzbVgvbQJVr8TERJ+v+R2WrVarV79qYWEhLBaLz/GDBg3C8uXLUVZWBrPZ7O/ur0iKlCiscnotrVMfivMrHV5XVFvC9UgymzCqp+fya2GIjzbyLldEREREfvI7LPfr1w+5ubnIz89HXFwc9uzZgzlz5niNycvLQ3x8PIQQOH36NJxOJ2JiYvzddcirtLu8riL2nCWu9bicONygXjwxwBqOCX3M7uXXkswmRBp58QQRERFRe/E7LOv1esyYMQMLFy6EoiiYNGkSevbsia1btwIAJk+ejL1792LXrl3Q6/UwmUyYN29ep7kozOGSuFDRaKWJuselNQ1tEzoBxEcbkVh357r6tokkswlxEYZOUy8iIiKiYNIm6ywPHz4cw4cP99o2efJk9+OMjIwmay9fSaSUKKp2NlmA/Xy5HRcqvNsmuoSra07ekBTtXoA9yWxCfLTpir5zExEREVEo4h38NKhyuLxmhj17iT1viRqmF0g0m9DXEo5xvczuGeLuMSa/F7YnIiIioo7DsNyIU5H4sbgKOefKvWeJy+wobtQ20S3KiCSzCdfW37nOo22Ca04SERERhT6G5UbmZ53BNxcbFlCLDdMj0WzCiKRor+XXukcbYdTzVp1EREREVzKG5UbuuNqCu4b1gFnYkRRjQjRv1UlERETUaTEsNzIm2cwFxImIiIgIAMA+AiIiIiIiHxiWiYiIiIh8YFgmIiIiIvKBYZmIiIiIyAeGZSIiIiIiHxiWiYiIiIh8YFgmIiIiIvKhTdZZPnLkCNauXQtFUZCWloaMjAyv1z/77DP84x//AACEh4fjV7/6FXr37t0WuyYiIiIiajd+zywrioI1a9bg97//PZYuXYrPP/8c586d8xrTrVs3PPfcc3j55Zdx11134c033/R3t0RERERE7c7vsHzy5EkkJCQgPj4eBoMBY8aMQXZ2tteYgQMHIjo6GgAwYMAAFBYW+rtbIiIiIqJ253dYLioqgtVqdT+3Wq0oKiryOX7btm0YNmyYv7slIiIiImp3fvcsSymbbBNCNDv2q6++wvbt2/HHP/7R5+dlZWUhKysLALBo0SLYbDZ/D1Ezg8EQkP2GKtZLG9ZLG9ZLG9ZLG9ZLO9ZMG9ZLm2Csl99h2Wq1erVVFBYWwmKxNBn3448/4o033kBmZiZiYmJ8fl56ejrS09PdzwsKCvw9RM1sNltA9huqWC9tWC9tWC9tWC9tWC/tWDNtWC9tAlWvxMREn6/53YbRr18/5ObmIj8/H06nE3v27EFKSorXmIKCArz88suYPXv2JQ+GiIiIiCiY+D2zrNfrMWPGDCxcuBCKomDSpEno2bMntm7dCgCYPHky3n//fVRUVGD16tXu9yxatMjfXRMRERERtas2WWd5+PDhGD58uNe2yZMnux/PmjULs2bNaotdERERERF1GN7Bj4iIiIjIB4ZlIiIiIiIfGJaJiIiIiHxgWCYiIiIi8oFhmYiIiIjIB4ZlIiIiIiIfGJaJiIiIiHxgWCYiIiIi8oFhmYiIiIjIB4ZlIiIiIiIfGJaJiIiIiHxgWCYiIiIi8oFhmYiIiIjIB0NbfMiRI0ewdu1aKIqCtLQ0ZGRkeL3+008/YcWKFfj+++8xbdo03HHHHW2x23YhFSXQh0BEREREQcLvsKwoCtasWYP58+fDarUiMzMTKSkp6NGjh3tMdHQ0HnroIWRnZ/u7u3Yn31qK4poqKMPHQAwfDREeGehDIiIiIqIA8bsN4+TJk0hISEB8fDwMBgPGjBnTJBTHxsaif//+0Ov1/u6u/SX1huvCeci1r0J5/D+grHoZ8tgBSKcz0EdGRERERB3M75nloqIiWK1W93Or1YoTJ074+7EBo7vlLlgfmImC/Z9D7t0Omb0bcv8uICYW4obxEKMmAr37QwgR6EMlIiIionbmd1iWUjbZ5k+QzMrKQlZWFgBg0aJFsNlsl/1Zl8tgMKDrqLHAqLGQDgdqD+9FzY7/Q+2uTyD/tRn6pGSET7gZEeNvhj4+scOPL9gYDIaA/O8UqlgvbVgvbVgvbVgv7VgzbVgvbYKxXn6HZavVisLCQvfzwsJCWCyWy/689PR0pKenu58XFBT4dXyXw2azee+37zVA32ugm/ZryIN74Nq7A5XvrkLlu6uA/oMgUidCpIyFiIru8GMNBk3qRZfEemnDemnDemnDemnHmmnDemkTqHolJvqe/PQ7LPfr1w+5ubnIz89HXFwc9uzZgzlz5vj7sUFJREZDjJsMjJsMWXgRct8OyL07IN9eAfnem8CQFOhSJwJDRkIYjYE+XCIiIiLyk99hWa/XY8aMGVi4cCEURcGkSZPQs2dPbN26FQAwefJklJSU4KmnnkJ1dTWEENiyZQteeeUVREaG7koTwtoV4tYpkLfcDZw9DfnFDsj9O6Ec3gtERqkzzaMmAv2vgdBxOWsiIiKiUCRkc03HQeT8+fMdvs/L/QpAulzAt0fVCwMPfQHYawFrN4hRE9VWje49Wv6QEMSvmLRhvbRhvbRhvbRhvbRjzbRhvbS5ItswqIHQ64Frh0FcOwzy/mrII3vVNo2P34fcshHo1V8NzTeMgzBffl83EREREXUMhuV2IsIjIFInAamTIEuKILM/U4PzhtWQf38LGDRMDc5DUyHCwgJ9uERERETUDIblDiC6xEHcdCdw052Q58+ooXnfTsjVSyDDIiCGp6rB+uohELoQuHELERERUSfBsNzBRGIyxC/+AzLjAeDE1+qKGgc+h/xiOxAbBzFqvBqce/TmjU+IiIiIAoxhOUCETgcMHAwxcDDkvTOBo9lQ9u6A/Nf/QG7dBCT1qutvngARF1yLcxMRERF1FgzLQUAYTcCIG6EfcSNkRRnkgd1qq8YHf4H8cD1w1WCI0ZMgho+BiAjd5faIiIiIQg3DcpAR0WaIibcCE2+FzM9Ve5v3bodc9xrkOyshrr9BbdO4dhiEgf/zEREREbUnpq0gJrp1h/jZNMjb7wG+/06dbc7+DPLAbiDaDDFyHETqRKDPVexvJiIiImoHDMshQAgB9B0I0Xcg5NT/BHIOq7PNuz+F3P6/QLdEtb951ASIbt0DfbhEREREVwyG5RAjDAbg+pEQ14+ErKqEPLRHnXHe/DfIf74L9LtaDc4pYyGizYE+XCIiIqKQxrAcwkRkFMTYm4CxN0EWXYTct0udcX5nJeR7q4EhI6BLnQhcN1K9iJCIiIiINGFYvkKIuK4Qt9wF+e+/AM79oIbmfbugHNkHRERBpNwIMWoiMGCQumwdEREREbWIYfkKI4QAevaB6NkH8q4HgW+Pqm0a+3dBfrYViOuq9janToRITA704RIREREFtTYJy0eOHMHatWuhKArS0tKQkZHh9bqUEmvXrsXhw4cRFhaGRx55BH379m2LXdMlCJ0eGDQMYtAwyPsfhjyyTw3On3wI+fH7QHK/uhufjIeItQT6cImIiIiCjt9hWVEUrFmzBvPnz4fVakVmZiZSUlLQo0cP95jDhw8jLy8Pr732Gk6cOIHVq1fjT3/6k7+7Jg1EWDjEqAnAqAmQZcWQ+z9Tg/PGNZB/XwsMul4NzsNGQ4SFB/pwiYiIiIKC32H55MmTSEhIQHx8PABgzJgxyM7O9grLBw4cwPjx4yGEwFVXXYXKykoUFxfDYuFsZiAIswUi/Q4g/Q7I3LOQe3dC7tsBuWYpZNjrEMNS1f7ma66H0OsDfbhEREREAeN3WC4qKoLVanU/t1qtOHHiRJMxNpvNa0xRUVGzYTkrKwtZWVkAgEWLFnm9r6MYDIaA7DcgbDZgyDDI/5wDx7dHUbPzE9R8vg3K3h3QWawIG5uO8In/DsMlbnzSqerVBlgvbVgvbVgvbVgv7VgzbVgvbYKxXn6HZSllk22NQ1VrxtRLT09Henq6+3lBQYGfR6idzWYLyH4DrlsPYMp/QmT8EuLYASh7d6Bqyweo2rwB6N6z7sYnEyGsXb3e1mnrdZlYL21YL21YL21YL+1YM21YL20CVa/ExESfr/kdlq1WKwoLC93PCwsLm8wYW61Wr1+8uTEUPITRBAwfA/3wMZCV5ZAHPlf7mz/6K+RHfwWuGqwG5xFjICKjA324RERERO3G7wV3+/Xrh9zcXOTn58PpdGLPnj1ISUnxGpOSkoJdu3ZBSonvvvsOkZGRDMshQkTFQDfh36H/3SLo/vQmxJ33A6XFkOv/fyiPPwjXykWo2f8ZpNMR6EMlIiIianN+zyzr9XrMmDEDCxcuhKIomDRpEnr27ImtW7cCACZPnoxhw4bh0KFDmDNnDkwmEx555BG/D5w6nuiaAHH7PZC3TQV+OKne+CT7M5Qe3ANExUCMHAuROgnoO9Bnmw0RERFRKBGyuYbiIHL+/PkO3yf7i1pPOp0w/3QapZ/8A/LIPsBhB7omqL3NqRMh4n33AHVWPL+0Yb20Yb20Yb20Y820Yb20uSJ7lqlzEwYDwkaMga7XVZDVVZCHv1D7m/93A+T/vAf0uQpi9CSIlHEQMeZAHy4RERGRJgzL1GZERCTEmDRgTBpkcSHk/p1qcH73DcgNq4HBI9QZ5+tHQpjCAn24RERERC1iWKZ2ISxWiJt/Adz8C8hz36uhed9OyC/3Q0ZEQgwfA5E6UV1ZQ+f3daZERERE7YJhmdqd6NEH4u4+kL/4D+D4V2pwPvA55OdZgMUGMWqC2t+c1CvQh0pERETkhWGZOozQ6dVbaF9zPeR9syC/3KcG560fQf7fB0CPPhCjJ0LcMB6ii7XlDyQiIiJqZwzLFBAiLAzihvHADeMhy0ogs3dD7tsB+fe1kO//BbjmOrW/efhoiPCIQB8uERERdVIMyxRwwtwFIu12IO12yLxzam/z3h2Qa5dBvvM6xNBUtb950FAIvT7Qh0tERESdCMMyBRWR0APizvsh77gPOPWNGpqzd0Pu3wnExKotGqkTgV79eeMTIiIiancMyxSUhBBA/0EQ/QdB3vNr4KuDUPbugNz5MeS/NgMJPdSLAkdNgLDFB/pwiYiI6ArFsExBTxiNwLBU6IelQlZWQB78XL3V9qa3ITe9DQwYpAbnEWMhoqIDfbhERER0BWFYppAioqIhxt8MjL8ZsuBCQ3/zX1dA/u1N4LqR0I2aCAxJUUM2ERERkR8YlilkCVs8xG1TIW+dApw5BfnFdsj9u6Ac+gKIjIZIGQsxeiLQ7xr2NxMREdFlYVimkCeEUC/469UfcsoM4JsjkF/sgNy7DXLX/wG2+IYbnyT0CPThEhERUQjxKyxXVFRg6dKluHjxIrp27Yp58+YhOrppz+iKFStw6NAhxMbGYsmSJf7skuiShF4PDB4BMXgEZE0V5OF9an/zlvch/3cj0HsAROokiJFjIcxdAn24REREFOR0/rx506ZNGDJkCF577TUMGTIEmzZtanbcxIkT8fvf/96fXRFpJsIjoRs9Cfp5f4TupTUQUx4CXE7I996E8uR0uF77I5T9uyBrawN9qERERBSk/ArL2dnZmDBhAgBgwoQJyM7ObnbcoEGDmp1xJuoooosVusk/h/6ZV6F77v+DmPxz4NwPkKtehvL4f0B5axnkN19CKq5AHyoREREFEb/aMEpLS2GxWAAAFosFZWVlfh9QVlYWsrKyAACLFi2CzWbz+zO1MhgMAdlvqAq5etlswPUjIH89D46cw6je+Qlqv9gO5Ytt0Fm7ImzcZIRPuBnG3v3bZfchV68AY720Yb20Yb20Y820Yb20CcZ6tRiWn3/+eZSUlDTZPm3atPY4HqSnpyM9Pd39vKCgoF32cyk2my0g+w1VIV2v7r2AaTMhfvEg8GU2lH07ULX5PVRtegdI6gUxehLEyPEQcW33f9yQrlcAsF7asF7asF7asWbasF7aBKpeiYmJPl9rMSwvWLDA52uxsbEoLi6GxWJBcXExzGbz5R0hUYAJUxjEyLHAyLGQ5aWQB3ar6ze/vw7yg78AV18HMWoixPDREBGRgT5cIiIi6iB+tWGkpKRg586dyMjIwM6dOzFy5Mi2Oi6igBExsRCTbgMm3QZ54Tzkvh1qcF73KuS7r0NcPwoidSIwaBiEgasvEhERXcn8+ps+IyMDS5cuxbZt22Cz2fDYY48BAIqKivDGG28gMzMTALBs2TJ8/fXXKC8vx6xZszB16lT827/9m/9HT9TORHwixB33Qf7sXuD0cTU0Z38Gmf0ZEBMLMXKcGpx7D+CNT4iIiK5AQkopA30Ql3L+/PkO3yf7i7TpbPWSTgfw1SEoe7cDX2YDTgcQnwSROkFt1eiacMn3d7Z6+Yv10ob10ob10o4104b10iYke5aJyJswGIGho6AfOgqyqgLy4B7IfTsh//Eu5D/eBfpfo4bmkWMhomICfbhERETkh5ALy1JK1NTUQFGUdvva+8KFC6i9Qm9UIaWETqdDeHg42wbagIiMhhg3GRg3GbLwIuT+nZBfbId853XI91YBQ1KgS50IXDcSwmgM9OESERGRRiEXlmtqamA0GmFoxwurDAYD9Hp9u31+oDmdTtTU1CAiIiLQh3JFEdauELfcDfnvdwFnT0N+sQNy/04oR/YCkVEQI26ESJ0IGTc+0IdKRERErRRyYVlRlHYNyp2BwWC4YmfOg4EQAkjuB5HcD/Lu6cC3RyH3bldbNT7biotRMZDmLkBMLBBjhoipfxwLYY51P0ZMLBAZDaHz60abRERE5IeQS51sHWgbrGPHEHo9cO0wiGuHQd5fDXlkL8J/+gHVFy8A5aXAT2cgK44BFeUAgCZX2+p0QLRZDc7mLhDRZsBH0EZMLBARyf9tiYiI2lDIheVgMGDAAJw4caLNPu/AgQPYsGEDHnzwQVy4cAFpaWma3p+Xl4cFCxZg1apVbXZM1PZEeARE6iSYbTbYG13pK10uoKJMDdDlpZBlJerzslKgvASyvEz9748ngfIyoLpSfV/jnRgMQHQsUDdDLdxBuktDuDbHqgHc3AUiLLwDfnMiIqLQxbAcBHbs2IGJEyciJycHR48ebTYsO51On+0nCQkJDMohTuj1QKxF/QHQ0tywdDjUYF1RCpSVQtaFbPWnPlyXQub9pIbu2hr1fY0/yBTmNTPtDtfmWCC6vi2kCxCjzm4Lo6mtf3UiIqKgxrDsByklXnjhBWzfvh1CCMyZMwd33nknLly4gIcffhjl5eVwuVz485//jJSUFDz++OM4evQohBC45557MHPmTADA7t27MXPmTKSlpaGmpgb79+/H7NmzcfLkSVy4cAFnz55FXFwcnnrqKcyZMwdVVVUAgBdeeAEjR47E2bNn8eCDD2Lbtm3YsGEDPv30U1RXV+OHH37ALbfcgvnz5weyTNQOhNEIxNnUH7QiXNfW1AXpuhlqz3BdVgpZUQqUFkOe+wEoLwGcTvV9jT8oItI9K+09c914JludveYdDomIKNSF9N9kynurIM9+36afKXr2AR54uFVjt2zZgpycHHz66acoKirCrbfeitTUVHz00UeYMGECHn30UbhcLlRXVyMnJwd5eXnYtm0bAKC0tBSAerdDg8EAs9mMJ554AkePHsXChQsBAEuWLMHRo0fx0UcfISIiAtXV1fjb3/6G8PBwnD59Gr/97W/x8ccfNzmunJwcfPLJJzCZTBg/fjweeughJCUltVGFKBSJsHAgLBywxavPLzFWSgnUVKuhuT5cl3nOXNfNZBdcgPz+O3WboqjvbfxhkdHulpCGMN2lbqa6izpzXd82EhUNobtyV6EhIqLQFNJhOdD279+PjIwM6PV6dO3aFampqfjyyy8xdOhQPP7443A6nbj55psxePBgJCcn48yZM5g/fz7S0tIwYcIEAMDOnTvdj5szefJk9xJvDocDTz/9NL7++mvodDqcPn262feMHTsWZrMZAHDVVVfhp59+YlimVhNCqDPIEZFAN/WORpcM14qi9lCXeYbpEnfQVmeuy4Dcc5Df5QCV5UDdjUO9wrXQAdExTWapKxISoegMjVpCuqjL8fFiRiIiamchHZZ1034d0P37ulN4amoqPvjgA/zrX//Co48+ilmzZmHKlCn49NNPsWPHDqxbtw6bN2/GK6+8gm3btuE3v/mNz31ERka6H69atQpdu3bFp59+CkVR0Ldv32bfYzI19JXqdDo4675SJ2oPQqcDomLUn+491G2XGC8Vl7r6h+csdeOWkLJS9Vuj8lJUVlWo72v8QXq9Oitd12MtPC5sbNISYo4FwiIYromISLOQDsuBlpqairfffhtTpkxBSUkJ9u3bhwULFuDcuXNISEjA/fffj6qqKhw7dgxpaWkwGo247bbb0KtXL8ybNw9SSnzzzTe49tprAQDR0dGoqKjwub+ysjJ0794dOp0Of//73+FyuTrqVyVqM0KnV3uezV3U5y2Mt8bGouDH094tIXWB2jNsy4t5auCuqQbQTLg2mhpmpWNiIeofe13M6BG2TWFt+nsTEVFo8issV1RUYOnSpbh48SK6du2KefPmITo62mtMQUEBli9fjpKSEgghkJ6ejltvvdWvgw4Wt9xyCw4ePIibbroJQgg8/fTT6NatGzZu3IiVK1fCYDAgKioKr776KnJzc/HYY49BqevtzMzMxNGjRzF48GD3bNeYMWOwfPly3HTTTZg9e3aT/T344IOYOXMm/ud//gc33nij16wz0ZVKGI0QXaxAF6v6vIXx0l6rBmv3SiElTVtCykogz59Rw7XDrr6v8QeFRbhXAXHPVHv0WAvPlpAYM4SBtzMnIroSCemrl6AV3n77bURHRyMjIwObNm1CRUUFHnjgAa8xxcXFKC4uRt++fVFdXY2nnnoKTz75JHr06NGqfZw/f97reVVVVbuHRIPB0CGtC8uWLUOfPn1w5513tvu+GmvLOtpsNhQ0WjeYfGO9tGnPekkp1WX1PC9e9LHGtXs1EZePPxsioppZds/HsnxRZnW5wHbA80sb1ks71kwb1kubQNUrMTHR52t+zSxnZ2fjueeeAwBMmDABzz33XJOwbLFYYLGoa8dGREQgKSkJRUVFrQ7LV7K5c+cG+hCIOjUhBBAeof50TVC3XWK8lLLhYsZm17iue34xF/LUN2pvtmxmpRAhgKhod0sIb3tORBS8/ArLpaWl7iBssVhQVlZ2yfH5+fn4/vvv0b9/f392S0QUEEIIdTm8yGgA6gozLa4UUlnhvQyf1yx2Xej+6Qxk+TF1pRD4d9tzJSIMUlEYromI2kiLYfn5559HSUlJk+3Tpk3TtKOamhosWbIE06dPv+TX/1lZWcjKygIALFq0CDabzev1Cxcu+LyTXVvqiH0EUlhYWJPaXi6DwdBmn9UZsF7ahH69urV6pHQ6oZSXQikthlJWAqW0CLKk/nFxw/Zz30MpLYasanrb84sAIARERCREZDR0UdEQkdEQkVENj6MatusioyCiYhpej4qGLjIaCAvvFKuHhP751fFYM21YL22CsV4tJsIFCxb4fC02NhbFxcWwWCwoLi52r+3bmNPpxJIlSzBu3DiMGjXqkvtLT09Henq6+3njvpXa2lro26nXr15H9SwHUm1tbZv1BLEfSxvWS5tOWa/oLupPYm+fQ3TwuO25RwtIlOJEZcFFoLoSsqoSzuoqtXXkYh5wpu5xVaX7RjI+6fV1621Hefw3CiIiEoiMatgeGQXh8dhrewhc9Ngpzy8/sWbasF7aXHE9yykpKdi5cycyMjKwc+dOjBw5sskYKSVWrlyJpKQk3H777f7sjoiIPDR32/Momw3VLfxFI6UE7LUNwbkuUEuPx81uv5gLWV2/varh83ztyGhqEqJFRJR3qK57LjweN4TzCN7VkYgCzq+wnJGRgaVLl2Lbtm2w2Wx47LHHAKi3cH7jjTeQmZmJ48ePY9euXUhOTsaTTz4JALj33nsxfPhw/4+eiIg0E0Kotz8PC3cvyQe0vCyfJ6m41DWtq6vqgrUaoqXH4+a2y6KCum2VgN3e8Hm+dhQe0SREq4G77i6TEdHuO06KyGivAI6IyE7TTkJE7cevpeM6QjAuHTdgwACcOHGizfZ34MABbNiwAYsXL9b0vj179mDlypVYv3695n1y6bjAYb20Yb20CaV6SaejYSbbI1zLRkEbVZUNM9qe26srgZZuzqTTecxiR6oXZ0ZE1rWTRCMyzoYqQA3h7kBeF7oj1cfCGPztJB0plM6xYMB6aXPFtWFQ29ixYwcmTpwY6MMgIupQwmBsWB7Pc3sr36+2k9gbZqo9w3ajVhI1cDf0b8u6VpLKmiqgbs7I58yRwegVtC/Vqy2aaTFBeGS7ratNRO2PYdkPUkq88MIL2L59O4QQmDNnDu68805cuHABDz/8MMrLy+FyufDnP/8ZKSkpePzxx3H06FEIIXDPPfdg5syZAIDdu3dj5syZuP3227FkyRIMHDgQAHD33XfjmWeegcvlwrPPPouamhqEh4fjlVde4fJ7RNTpqe0kYepPl7iG7Ro+wxoXh4JzZ+tCdQVQpYZoWV3RzEx2FWRV3faSooaWE3ut+/N8Bu6wCI9gHdloJjvK9/b6lpOwCLaTEAVISIfl1Qcu4Pvimjb9zD6WcMxKTWrV2C1btiAnJweffvopioqKcOuttyI1NRUfffQRJkyYgEcffRQulwvV1dXIyclBXl4etm3bBkBdoxpQ+7sNBgPMZjPuuOMObN68GQMHDsSFCxeQl5eH6667DuXl5fjwww9hMBiwa9cuvPjii1i1alWb/t5ERJ2R0OnUcBoZBaBrw3YNnyGdzkbtJBV1gds7gKO6wj2jjfJSyPzzDYHc486QzQZuoWuY1fYI16LxRZG+LpaMjIIwmi63TESdWkiH5UDbv38/MjIyoNfr0bVrV6SmpuLLL7/E0KFD8fjjj8PpdOLmm2/G4MGDkZycjDNnzmD+/PlIS0vDhAkTAAA7d+50P/7Zz36Ge++9F0888QQ2b97sXj2krKwMc+fOxffffw8hBBwOR8B+ZyIi8iYMBiDGrP54bm/l+6WUgMPuu22kurIucDdqMynM9xjTmnYSQ8NstUf7SHPh2uesN9tJqBMK6bD8q5T4gO7f17WRqamp+OCDD/Cvf/0Ljz76KGbNmoUpU6bg008/xY4dO7Bu3Tps3rwZr7zyCrZt24bf/OY3AIDu3bvDYrHg66+/xj//+U+8+OKLAIDFixdjzJgxWLNmDc6ePYu77767w35HIiJqX0IIwBSm/lxmO4lUFKC2pvXLAdYH7JKihse1Dd/U+gzcpjDkh0dAGoyAyaQes9HkPn7hfuzxmtGktsoY68aYTA3v8XzN/Vh9nW0nFCxCOiwHWmpqKt5++21MmTIFJSUl2LdvHxYsWIBz584hISEB999/P6qqqnDs2DGkpaXBaDTitttuQ69evTBv3jxIKfHNN9/g2muvdX/mnXfeiddffx3l5eW45pprAADl5eVISEgAAGzcuDEgvysREQUvofNo04jzo52kpurSywFWVyJcp0NNWSlgr4V02NWebXstUFEGabcDjrrndrs6Y95oYqnVS3CZTO6ArYbrxuHcBFH/utdr9Y+bCeeen+nxHt4eni6FYdkPt9xyCw4ePIibbroJQgg8/fTT6NatGzZu3IiVK1fCYDAgKioKr776KnJzc/HYY49BqbtrVmZmJo4ePYrBgwd7/ev5tttuwzPPPIO5c+e6tz388MOYO3cu3nzzTdx4440d/WsSEVEnIAwGINqs/nhubzTObLPB3sqlvaSUgNNRF5w9QrS9Vg3SdY9l44DtNU59LD3fU1oMOOq2ubfXNntnylaFc4PBI2A3DeXes+aNZ8w9Qrfn63WvuVx2yMrKhvDOVpaQw3WWm9FRt7tetmwZ+vTpgzvvvLPd99UY11kOHNZLG9ZLG9ZLG9ZLu2CumXQ6vcNzMzPdTQK2Z3h32IFaj/DuEeibfObl5gS93rsNxTNgu2fMG7ezNA3xwscsudd7DIaQa2fhOsvkxXP2mIiIiPwjDAZ1lhhRvse00b6k4rrkTHh9m0qMyYjyoiKfM+vSM4hXVwJlxXWB3uOzHfam+2/NQdb3w3u1oXgE9PoZ82ZmyT1DvPDoJW8u3LvHhVgwby2GZSIiIiKNhE6v3o49PML3GAARNhsq/ZwplYoCOBxeQRye/eJ1j6WvWfC6GXP3zHr96xVlHn3nHuH8cvvMm4TtxuG8vs/cd4+5Y2gKEBXb8r46EMMyERERURATOl3DDXguNa4N9qX2mTubzJJ7t6TUNp399jGz7n6trKQu0DcK+o36zGvv+zUw6Wdt8Ju0nZALy0HeYh0yWEciIiJqTAgBGI3qD6J9j2uj/Umns65fvBaorUVkUk/U2IPrfhIhF5Z1Oh2cTicMhpA79KDhdDqh4zI5REREFGDuPvMIddEBnTkWCLILSP1KnBUVFVi6dCkuXryIrl27Yt68eYiO9v5XiN1ux7PPPgun0wmXy4XU1FRMnTr1svcZHh6Ompoa1NbWtlsjeVhYGGpra9vlswNNSgmdTofw8PBAHwoRERFR0PMrLG/atAlDhgxBRkYGNm3ahE2bNuGBBx7wGmM0GvHss88iPDwcTqcTzzzzDIYOHYqrrrrqsvYphEBEhO9m+rYQzMviEBEREVHH8eu7+OzsbEyYMAEAMGHCBGRnZzcZI4Rwz2K6XC64XK4rdmkRIiIiIrqy+DWzXFpaCovFAgCwWCwoKytrdpyiKPjd736HvLw83HzzzRgwYIA/uyUiIiIi6hAthuXnn38eJSUlTbZPmzat1TvR6XRYvHgxKisr8fLLL+PMmTNITk5udmxWVhaysrIAAIsWLYLNZmv1ftqKwWAIyH5DFeulDeulDeulDeulDeulHWumDeulTTDWq8WwvGDBAp+vxcbGori4GBaLBcXFxTCbzT7HAkBUVBQGDRqEI0eO+AzL6enpSE9Pdz83mUwtHWK7CNR+QxXrpQ3rpQ3rpQ3rpQ3rpR1rpg3rpU2w1cuvnuWUlBTs3LkTALBz506MHDmyyZiysjJUVlYCUFfGOHbsGJKSkvzZbbt76qmnAn0IIYX10ob10ob10ob10ob10o4104b10iYY6+VXz3JGRgaWLl2Kbdu2wWaz4bHHHgMAFBUV4Y033kBmZiaKi4uxfPlyKIoCKSVGjx6NESNGtMnBExERERG1J7/CckxMDJ555pkm2+Pi4pCZmQkA6NWrF1566SV/dkNEREREFBC8jVszPHumqWWslzaslzaslzaslzasl3asmTaslzbBWC8hpZSBPggiIiIiomDEmWUiIiIiIh/86lkOdUeOHMHatWuhKArS0tKQkZHh9bqUEmvXrsXhw4cRFhaGRx55BH379g3MwQaBluqVk5ODl156Cd26dQMAjBo1CnfffXcAjjTwVqxYgUOHDiE2NhZLlixp8jrPLW8t1YvnlreCggIsX74cJSUlEEIgPT0dt956q9cYnmMNWlMvnmMN7HY7nn32WTidTrhcLqSmpmLq1KleY3h+NWhNvXh+NaUoCp566inExcU1WQEj6M4v2Um5XC45e/ZsmZeXJx0Oh3ziiSfk2bNnvcYcPHhQLly4UCqKIo8fPy4zMzMDdLSB15p6ffXVV/LPf/5zgI4wuOTk5MhTp07Jxx57rNnXeW55a6lePLe8FRUVyVOnTkkppayqqpJz5szhn1+X0Jp68RxroCiKrK6ullJK6XA4ZGZmpjx+/LjXGJ5fDVpTL55fTW3evFkuW7as2boE2/nVadswTp48iYSEBMTHx8NgMGDMmDHIzs72GnPgwAGMHz8eQghcddVVqKysRHFxcYCOOLBaUy9qMGjQIERHR/t8neeWt5bqRd4sFot7liUiIgJJSUkoKiryGsNzrEFr6kUNhBAIDw8HALhcLrhcLgghvMbw/GrQmnqRt8LCQhw6dAhpaWnNvh5s51enbcMoKiqC1Wp1P7darThx4kSTMZ63XLRarSgqKoLFYumw4wwWrakXAHz33Xd48sknYbFY8Mtf/hI9e/bsyMMMGTy3tOO51bz8/Hx8//336N+/v9d2nmPN81UvgOeYJ0VR8Lvf/Q55eXm4+eabMWDAAK/XeX55a6leAM8vT+vWrcMDDzyA6urqZl8PtvOr04Zl2cwiII3/JdiaMZ1Fa2rRp08frFixAuHh4Th06BAWL16M1157raMOMaTw3NKG51bzampqsGTJEkyfPh2RkZFer/Eca+pS9eI55k2n02Hx4sWorKzEyy+/jDNnziA5Odn9Os8vby3Vi+dXg4MHDyI2NhZ9+/ZFTk5Os2OC7fzqtG0YVqsVhYWF7ueFhYVN/sVitVpRUFBwyTGdRWvqFRkZ6f4qavjw4XC5XCgrK+vQ4wwVPLe04bnVlNPpxJIlSzBu3DiMGjWqyes8x7y1VC+eY82LiorCoEGDcOTIEa/tPL+a56tePL8aHD9+HAcOHMBvf/tbLFu2DF999VWTfzgE2/nVacNyv379kJubi/z8fDidTuzZswcpKSleY1JSUrBr1y5IKfHdd98hMjKy0/5h0Jp6lZSUuP81ePLkSSiKgpiYmEAcbtDjuaUNzy1vUkqsXLkSSUlJuP3225sdw3OsQWvqxXOsQVlZGSorKwGoKz0cO3YMSUlJXmN4fjVoTb14fjW47777sHLlSixfvhxz587F4MGDMWfOHK8xwXZ+ddo2DL1ejxkzZmDhwoVQFAWTJk1Cz549sXXrVgDA5MmTMWzYMBw6dAhz5syByWTCI488EuCjDpzW1Gvv3r3YunUr9Ho9TCYT5s6d22m/llu2bBm+/vprlJeXY9asWZg6dSqcTicAnlvNaalePLe8HT9+HLt27UJycjKefPJJAMC9997rnonhOeatNfXiOdaguLgYy5cvh6IokFJi9OjRGDFiBP9+9KE19eL51bJgPr94Bz8iIiIiIh86bRsGEREREVFLGJaJiIiIiHxgWCYiIiIi8oFhmYiIiIjIB4ZlIiIiIiIfGJaJiDqp/Px8TJ06FS6XK9CHQkQUtBiWiYiIiIh8YFgmIiIiIvKh097Bj4goGBUVFeGtt97CN998g/DwcNx222249dZbsXHjRpw9exY6nQ6HDx9G9+7d8fDDD6N3794AgHPnzmH16tX44YcfEBcXh/vuu899S3q73Y733nsPe/fuRWVlJZKTk7FgwQL3Pj/77DNs2LABdrsdt912G37xi18E4lcnIgpKnFkmIgoSiqLgxRdfRO/evfHGG2/gmWeewZYtW3DkyBEAwIEDBzB69Gi89dZbuPHGG7F48WI4nU44nU68+OKLuO6667B69WrMmDEDr732Gs6fPw8AWL9+PU6fPo0XXngBa9euxQMPPOB1q91vv/0Wr776KhYsWID3338f586dC8SvT0QUlBiWiYiCxKlTp1BWVoa7774bBoMB8fHxSEtLw549ewAAffv2RWpqKgwGA26//XY4HA6cOHECJ06cQE1NDTIyMmAwGDB48GAMHz4cu3fvhqIo2L59O6ZPn464uDjodDoMHDgQRqPRvd8pU6bAZDKhd+/e6NWrF3788cdAlYCIKOiwDYOIKEhcvHgRxcXFmD59unuboii45pprYLPZYLVa3dt1Oh2sViuKi4sBADabDTpdw/xH165dUVRUhPLycjgcDiQkJPjcb5cuXdyPw8LCUFNT03a/FBFRiGNYJiIKEjabDd26dcNrr73W5LWNGzeisLDQ/VxRFBQWFsJisQAACgoKoCiKOzAXFBSge/fuiImJgdFoRF5enru/mYiIWo9tGEREQaJ///6IiIjApk2bYLfboSgKzpw5g5MnTwIATp8+jX379sHlcmHLli0wGo0YMGAABgwYgPDwcPzzn/+E0+lETk4ODh48iBtvvBE6nQ6TJk3C+vXrUVRUBEVR8N1338HhcAT4tyUiCg1CSikDfRBERKQqKirC+vXrkZOTA6fTicTERNxzzz349ttvvVbDSEhIwKxZs9C3b18AwNmzZ71Ww7j33ntxww03AFBXw3j33XfxxRdfoKamBr1798bTTz+NkpISzJ49G3/729+g1+sBAM899xzGjRuHtLS0gNWAiCiYMCwTEYWAjRs3Ii8vD3PmzAn0oRARdSpswyAiIiIi8oFhmYiIiIjIB7ZhEBERERH5wJllIiIiIiIfGJaJiIiIiHxgWCYiIiIi8oFhmYiIiIjIB4ZlIiIiIiIfGJaJiIiIiHz4f4OE/jmQdpLSAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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+ },
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+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
+ " | Name | Type | Params\n",
+ "---------------------------------------\n",
+ "0 | _model | LSTMSeq2Seq | 109 K \n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "LSTMSeq2Seq\n"
+ ]
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
+ " | Name | Type | Params\n",
+ "----------------------------------------------\n",
+ "0 | _model | TransformerSeq2Seq | 2 M \n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "TransformerSeq2Seq\n"
+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:45: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n",
+ " warnings.warn(*args, **kwargs)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
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+ "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~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[0;31m# run train epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 483\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_epoch\u001b[0m\u001b[0;34m(\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 484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;31m# ------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m \u001b[0mbatch_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataloader_idx\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 542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_batch\u001b[0;34m(self, batch, batch_idx, dataloader_idx)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhiddens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m )\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mtraining_step_and_backward\u001b[0;34m(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)\u001b[0m\n\u001b[1;32m 759\u001b[0m \u001b[0;31m# lightning module hook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 760\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhiddens\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 761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, split_batch, batch_idx, opt_idx, hiddens)\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_train_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhiddens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m \u001b[0mtraining_step_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\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 305\u001b[0m \u001b[0mtraining_step_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'training_step_end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_step_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__training_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\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 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py\u001b[0m in \u001b[0;36m__training_step\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, batch, batch_idx, phase)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mx_past\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_past\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_future\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_future\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0my_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch\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 18\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0my_dist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_future\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x_past, y_past, x_future, y_future)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\"\"\"Eval/Predict\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0my_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_past\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_past\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_future\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_future\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 13\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0my_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 723\u001b[0m for hook in itertools.chain(\n",
+ "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/seq2seq-time/seq2seq_time/models/transformer_seq2seq.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, past_x, past_y, future_x, future_y)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;31m# requires (C, B, hidden_dim)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0mmemory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msrc_key_padding_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msrc_key_padding_mask\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 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 723\u001b[0m for hook in itertools.chain(\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/modules/transformer.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, src, mask, src_key_padding_mask)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msrc_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msrc_key_padding_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msrc_key_padding_mask\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 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 723\u001b[0m for hook in itertools.chain(\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/modules/transformer.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, src, src_mask, src_key_padding_mask)\u001b[0m\n\u001b[1;32m 294\u001b[0m key_padding_mask=src_key_padding_mask)[0]\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0msrc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msrc\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc2\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 296\u001b[0m \u001b[0msrc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 723\u001b[0m for hook in itertools.chain(\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/modules/dropout.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minplace\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 59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mdropout\u001b[0;34m(input, p, training, inplace)\u001b[0m\n\u001b[1;32m 972\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m else _VF.dropout(input, p, training))\n\u001b[0m\u001b[1;32m 974\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
+ "\nDuring handling of the above exception, another exception occurred:\n",
+ "\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 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;31m# Train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_test\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 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, train_dataloader, val_dataloaders, datamodule)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'on_fit_start'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\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 441\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mteardown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;31m# train or test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_or_test\u001b[0m\u001b[0;34m(\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 55\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py\u001b[0m in \u001b[0;36mtrain_or_test\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\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 67\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 521\u001b[0m \u001b[0;31m# hook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 522\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[0;34m(\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 523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_evaluation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_mode\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_batches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mon_train_end\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[0;34m(\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 200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcheck_checkpoint_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshould_save\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_last\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/cuda/memory.py\u001b[0m in \u001b[0;36mempty_cache\u001b[0;34m()\u001b[0m\n\u001b[1;32m 85\u001b[0m \"\"\"\n\u001b[1;32m 86\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_emptyCache\u001b[0m\u001b[0;34m(\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 88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "for m_fn in models:\n",
+ " pt_model = m_fn()\n",
+ " name = type(pt_model).__name__\n",
+ " print(name)\n",
+ "\n",
+ " # Wrap in lightning\n",
+ " patience = 2\n",
+ " model = PL_MODEL(pt_model, patience=patience, lr=2e-5, weight_decay=1e-3).to(device)\n",
+ "\n",
+ " # Trainer \n",
+ " trainer = pl.Trainer(gpus=1,\n",
+ " min_epochs=2,\n",
+ " max_epochs=30,\n",
+ " amp_level='O1',\n",
+ " precision=16,\n",
+ " gradient_clip_val=1,\n",
+ " logger=CSVLogger(\"logs\",\n",
+ " name=type(pt_model).__name__),\n",
+ " callbacks=[\n",
+ " EarlyStopping(monitor='loss/val', patience=patience*2),\n",
+ "# PrintTableMetricsCallback2()\n",
+ " ],\n",
+ " )\n",
+ "\n",
+ " # Train\n",
+ " trainer.fit(model, dl_train, dl_test)\n",
+ "\n",
+ "\n",
+ "\n",
+ " ds_predss = predict_multi(model.to(device),\n",
+ " ds_test.datasets,\n",
+ " batch_size*2,\n",
+ " device=device,\n",
+ " scaler=output_scaler)\n",
+ " \n",
+ " print(name)\n",
+ " print(f'mean_NLL {ds_predss.nll.mean().item():2.2f}')\n",
+ " \n",
+ " # Performance\n",
+ " ds_preds = ds_predss.isel(block=0)\n",
+ " print(plot_hist(trainer))\n",
+ " plot_performance(ds_preds)\n",
+ " \n",
+ " model.cpu()\n",
+ " free_mem()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "lines_to_next_cell": 2
+ },
+ "source": [
+ "# Plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:47:14.853130Z",
+ "start_time": "2020-10-24T01:47:14.787297Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "pt model name RANP\n",
+ "latest_checkpoint logs/RANP/version_26/checkpoints/epoch=7.ckpt\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+ " and should_run_async(code)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get latest checkpoint for a model type...\n",
+ "pt_model = models[1]()\n",
+ "name = type(pt_model).__name__\n",
+ "\n",
+ "checkpoints = (Path('logs')/name).glob('version_*')\n",
+ "sort_checkpoints = lambda f:int(f.stem.split('_')[-1])\n",
+ "checkpoints = sorted(checkpoints, key=sort_checkpoints)\n",
+ "latest_checkpoint = checkpoints[-1]\n",
+ "checkpoint_f = sorted(latest_checkpoint.glob('checkpoints/*.ckpt'))[-1]\n",
+ "print('pt model name', name)\n",
+ "print('latest_checkpoint', checkpoint_f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:47:14.934567Z",
+ "start_time": "2020-10-24T01:47:14.857199Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "PL_MODEL(\n",
+ " (_model): RANP(\n",
+ " (_lstm): LSTM(18, 32, num_layers=2, batch_first=True)\n",
+ " (_latent_encoder): LatentEncoder(\n",
+ " (_encoder): BatchMLP(\n",
+ " (initial): NPBlockRelu2d(\n",
+ " (linear): Linear(in_features=33, out_features=32, bias=False)\n",
+ " (act): ReLU()\n",
+ " (dropout): Dropout2d(p=0, inplace=False)\n",
+ " )\n",
+ " (encoder): Sequential()\n",
+ " (final): Linear(in_features=32, out_features=32, bias=True)\n",
+ " )\n",
+ " (_self_attention): Attention(\n",
+ " (_W): MultiheadAttention(\n",
+ " (out_proj): _LinearWithBias(in_features=32, out_features=32, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (_penultimate_layer): Linear(in_features=32, out_features=32, bias=True)\n",
+ " (_mean): Linear(in_features=32, out_features=64, bias=True)\n",
+ " (_log_var): Linear(in_features=32, out_features=64, bias=True)\n",
+ " )\n",
+ " (_deterministic_encoder): DeterministicEncoder(\n",
+ " (_d_encoder): BatchMLP(\n",
+ " (initial): NPBlockRelu2d(\n",
+ " (linear): Linear(in_features=33, out_features=32, bias=False)\n",
+ " (act): ReLU()\n",
+ " (dropout): Dropout2d(p=0, inplace=False)\n",
+ " )\n",
+ " (encoder): Sequential()\n",
+ " (final): Linear(in_features=32, out_features=32, bias=True)\n",
+ " )\n",
+ " (_self_attention): Attention(\n",
+ " (_W): MultiheadAttention(\n",
+ " (out_proj): _LinearWithBias(in_features=32, out_features=32, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (_cross_attention): Attention(\n",
+ " (batch_mlp_k): BatchMLP(\n",
+ " (initial): NPBlockRelu2d(\n",
+ " (linear): Linear(in_features=32, out_features=32, bias=False)\n",
+ " (act): ReLU()\n",
+ " (dropout): Dropout2d(p=0, inplace=False)\n",
+ " )\n",
+ " (encoder): Sequential()\n",
+ " (final): Linear(in_features=32, out_features=32, bias=True)\n",
+ " )\n",
+ " (batch_mlp_q): BatchMLP(\n",
+ " (initial): NPBlockRelu2d(\n",
+ " (linear): Linear(in_features=32, out_features=32, bias=False)\n",
+ " (act): ReLU()\n",
+ " (dropout): Dropout2d(p=0, inplace=False)\n",
+ " )\n",
+ " (encoder): Sequential()\n",
+ " (final): Linear(in_features=32, out_features=32, bias=True)\n",
+ " )\n",
+ " (_W): MultiheadAttention(\n",
+ " (out_proj): _LinearWithBias(in_features=32, out_features=32, bias=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (_decoder): Decoder(\n",
+ " (_future_transform): Linear(in_features=32, out_features=32, bias=True)\n",
+ " (_decoder): BatchMLP(\n",
+ " (initial): NPBlockRelu2d(\n",
+ " (linear): Linear(in_features=128, out_features=128, bias=False)\n",
+ " (act): ReLU()\n",
+ " (dropout): Dropout2d(p=0, inplace=False)\n",
+ " )\n",
+ " (encoder): Sequential(\n",
+ " (0): NPBlockRelu2d(\n",
+ " (linear): Linear(in_features=128, out_features=128, bias=False)\n",
+ " (act): ReLU()\n",
+ " (dropout): Dropout2d(p=0, inplace=False)\n",
+ " )\n",
+ " (1): NPBlockRelu2d(\n",
+ " (linear): Linear(in_features=128, out_features=128, bias=False)\n",
+ " (act): ReLU()\n",
+ " (dropout): Dropout2d(p=0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (final): Linear(in_features=128, out_features=128, bias=True)\n",
+ " )\n",
+ " (_mean): Linear(in_features=128, out_features=1, bias=True)\n",
+ " (_std): Linear(in_features=128, out_features=1, bias=True)\n",
+ " )\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load\n",
+ "model = PL_MODEL(pt_model).to(device)\n",
+ "model.load_from_checkpoint(str(checkpoint_f), model=pt_model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:47:28.389561Z",
+ "start_time": "2020-10-24T01:47:14.942066Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5534a3fcc20947d2841a6d20b384a4d5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict_multi'), FloatProgress(value=0.0, max=12.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
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+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "0.023227039724588394"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds_predss = predict_multi(model.to(device),\n",
+ " ds_test.datasets,\n",
+ " batch_size*4,\n",
+ " device=device,\n",
+ " scaler=output_scaler)\n",
+ "ds_predss.nll.mean().item()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:26:26.401037Z",
+ "start_time": "2020-10-24T01:26:26.296172Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:47:28.446786Z",
+ "start_time": "2020-10-24T01:47:28.394739Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+ " and should_run_async(code)\n"
+ ]
+ }
+ ],
+ "source": [
+ "ds_pred_block = ds_predss.isel(block=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# holoviews pred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-10-24T01:49:06.618815Z",
+ "start_time": "2020-10-24T01:49:06.434686Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+ " and should_run_async(code)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "\n",
+ "(function(root) {\n",
+ " function now() {\n",
+ " return new Date();\n",
+ " }\n",
+ "\n",
+ " var force = true;\n",
+ "\n",
+ " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+ " root._bokeh_onload_callbacks = [];\n",
+ " root._bokeh_is_loading = undefined;\n",
+ " }\n",
+ "\n",
+ " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+ " root._bokeh_timeout = Date.now() + 5000;\n",
+ " root._bokeh_failed_load = false;\n",
+ " }\n",
+ "\n",
+ " function run_callbacks() {\n",
+ " try {\n",
+ " root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+ " if (callback != null)\n",
+ " callback();\n",
+ " });\n",
+ " } finally {\n",
+ " delete root._bokeh_onload_callbacks\n",
+ " }\n",
+ " console.debug(\"Bokeh: all callbacks have finished\");\n",
+ " }\n",
+ "\n",
+ " function load_libs(css_urls, js_urls, callback) {\n",
+ " if (css_urls == null) css_urls = [];\n",
+ " if (js_urls == null) js_urls = [];\n",
+ "\n",
+ " root._bokeh_onload_callbacks.push(callback);\n",
+ " if (root._bokeh_is_loading > 0) {\n",
+ " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+ " return null;\n",
+ " }\n",
+ " if (js_urls == null || js_urls.length === 0) {\n",
+ " run_callbacks();\n",
+ " return null;\n",
+ " }\n",
+ " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+ " root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+ "\n",
+ " function on_load() {\n",
+ " root._bokeh_is_loading--;\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+ " run_callbacks()\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function on_error() {\n",
+ " console.error(\"failed to load \" + url);\n",
+ " }\n",
+ "\n",
+ " for (var i = 0; i < css_urls.length; i++) {\n",
+ " var url = css_urls[i];\n",
+ " const element = document.createElement(\"link\");\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error;\n",
+ " element.rel = \"stylesheet\";\n",
+ " element.type = \"text/css\";\n",
+ " element.href = url;\n",
+ " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " if (window.requirejs) {\n",
+ " require([], function() {\n",
+ " run_callbacks();\n",
+ " })\n",
+ " } else {\n",
+ " var skip = [];\n",
+ " for (var i = 0; i < js_urls.length; i++) {\n",
+ " var url = js_urls[i];\n",
+ " if (skip.indexOf(url) >= 0) { on_load(); continue; }\n",
+ " var element = document.createElement('script');\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error;\n",
+ " element.async = false;\n",
+ " element.src = url;\n",
+ " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+ " document.head.appendChild(element);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " function inject_raw_css(css) {\n",
+ " const element = document.createElement(\"style\");\n",
+ " element.appendChild(document.createTextNode(css));\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " var js_urls = [];\n",
+ " var css_urls = [];\n",
+ "\n",
+ " var inline_js = [\n",
+ " function(Bokeh) {\n",
+ " inject_raw_css(\".panel-widget-box {\\n\\tmin-height: 20px;\\n\\tbackground-color: #f5f5f5;\\n\\tborder: 1px solid #e3e3e3 !important;\\n\\tborder-radius: 4px;\\n\\t-webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.05);\\n\\tbox-shadow: inset 0 1px 1px rgba(0,0,0,.05);\\n\\toverflow-x: hidden;\\n\\toverflow-y: hidden;\\n}\\n\\n.scrollable {\\n overflow: scroll;\\n}\\n\\nprogress {\\n\\tappearance: none;\\n\\t-moz-appearance: none;\\n\\t-webkit-appearance: none;\\n\\n\\tborder: none;\\n\\theight: 20px;\\n\\tbackground-color: whiteSmoke;\\n\\tborder-radius: 3px;\\n\\tbox-shadow: 0 2px 3px rgba(0,0,0,.5) inset;\\n\\tcolor: royalblue;\\n\\tposition: relative;\\n\\tmargin: 0 0 1.5em;\\n}\\n\\nprogress[value]::-webkit-progress-bar {\\n\\tbackground-color: whiteSmoke;\\n\\tborder-radius: 3px;\\n\\tbox-shadow: 0 2px 3px rgba(0,0,0,.5) inset;\\n}\\n\\nprogress[value]::-webkit-progress-value {\\n\\tposition: relative;\\n\\n\\tbackground-size: 35px 20px, 100% 100%, 100% 100%;\\n\\tborder-radius:3px;\\n}\\n\\nprogress.active:not([value])::before {\\n\\tbackground-position: 10%;\\n\\tanimation-name: stripes;\\n\\tanimation-duration: 3s;\\n\\tanimation-timing-function: linear;\\n\\tanimation-iteration-count: infinite;\\n}\\n\\nprogress[value]::-moz-progress-bar {\\n\\tbackground-size: 35px 20px, 100% 100%, 100% 100%;\\n\\tborder-radius:3px;\\n}\\n\\nprogress:not([value])::-moz-progress-bar {\\n\\tborder-radius:3px;\\n\\tbackground:\\n\\tlinear-gradient(-45deg, transparent 33%, rgba(0, 0, 0, 0.2) 33%, rgba(0, 0, 0, 0.2) 66%, transparent 66%) left/2.5em 1.5em;\\n\\n}\\n\\nprogress.active:not([value])::-moz-progress-bar {\\n\\tbackground-position: 10%;\\n\\tanimation-name: stripes;\\n\\tanimation-duration: 3s;\\n\\tanimation-timing-function: linear;\\n\\tanimation-iteration-count: infinite;\\n}\\n\\nprogress.active:not([value])::-webkit-progress-bar {\\n\\tbackground-position: 10%;\\n\\tanimation-name: stripes;\\n\\tanimation-duration: 3s;\\n\\tanimation-timing-function: linear;\\n\\tanimation-iteration-count: infinite;\\n}\\n\\nprogress.primary[value]::-webkit-progress-value { background-color: #007bff; }\\nprogress.primary:not([value])::before { background-color: #007bff; }\\nprogress.primary:not([value])::-webkit-progress-bar { background-color: #007bff; }\\nprogress.primary::-moz-progress-bar { background-color: #007bff; }\\n\\nprogress.secondary[value]::-webkit-progress-value { background-color: #6c757d; }\\nprogress.secondary:not([value])::before { background-color: #6c757d; }\\nprogress.secondary:not([value])::-webkit-progress-bar { background-color: #6c757d; }\\nprogress.secondary::-moz-progress-bar { background-color: #6c757d; }\\n\\nprogress.success[value]::-webkit-progress-value { background-color: #28a745; }\\nprogress.success:not([value])::before { background-color: #28a745; }\\nprogress.success:not([value])::-webkit-progress-bar { background-color: #28a745; }\\nprogress.success::-moz-progress-bar { background-color: #28a745; }\\n\\nprogress.danger[value]::-webkit-progress-value { background-color: #dc3545; }\\nprogress.danger:not([value])::before { background-color: #dc3545; }\\nprogress.danger:not([value])::-webkit-progress-bar { background-color: #dc3545; }\\nprogress.danger::-moz-progress-bar { background-color: #dc3545; }\\n\\nprogress.warning[value]::-webkit-progress-value { background-color: #ffc107; }\\nprogress.warning:not([value])::before { background-color: #ffc107; }\\nprogress.warning:not([value])::-webkit-progress-bar { background-color: #ffc107; }\\nprogress.warning::-moz-progress-bar { background-color: #ffc107; }\\n\\nprogress.info[value]::-webkit-progress-value { background-color: #17a2b8; }\\nprogress.info:not([value])::before { background-color: #17a2b8; }\\nprogress.info:not([value])::-webkit-progress-bar { background-color: #17a2b8; }\\nprogress.info::-moz-progress-bar { background-color: #17a2b8; }\\n\\nprogress.light[value]::-webkit-progress-value { background-color: #f8f9fa; }\\nprogress.light:not([value])::before { background-color: #f8f9fa; }\\nprogress.light:not([value])::-webkit-progress-bar { background-color: #f8f9fa; }\\nprogress.light::-moz-progress-bar { background-color: #f8f9fa; }\\n\\nprogress.dark[value]::-webkit-progress-value { background-color: #343a40; }\\nprogress.dark:not([value])::-webkit-progress-bar { background-color: #343a40; }\\nprogress.dark:not([value])::before { background-color: #343a40; }\\nprogress.dark::-moz-progress-bar { background-color: #343a40; }\\n\\nprogress:not([value])::-webkit-progress-bar {\\n\\tborder-radius: 3px;\\n\\tbackground:\\n\\tlinear-gradient(-45deg, transparent 33%, rgba(0, 0, 0, 0.2) 33%, rgba(0, 0, 0, 0.2) 66%, transparent 66%) left/2.5em 1.5em;\\n}\\nprogress:not([value])::before {\\n\\tcontent:\\\" \\\";\\n\\tposition:absolute;\\n\\theight: 20px;\\n\\ttop:0;\\n\\tleft:0;\\n\\tright:0;\\n\\tbottom:0;\\n\\tborder-radius: 3px;\\n\\tbackground:\\n\\tlinear-gradient(-45deg, transparent 33%, rgba(0, 0, 0, 0.2) 33%, rgba(0, 0, 0, 0.2) 66%, transparent 66%) left/2.5em 1.5em;\\n}\\n\\n@keyframes stripes {\\n from {background-position: 0%}\\n to {background-position: 100%}\\n}\\n\");\n",
+ " },\n",
+ " function(Bokeh) {\n",
+ " inject_raw_css(\".json-formatter-row {\\n font-family: monospace;\\n}\\n.json-formatter-row,\\n.json-formatter-row a,\\n.json-formatter-row a:hover {\\n color: black;\\n text-decoration: none;\\n}\\n.json-formatter-row .json-formatter-row {\\n margin-left: 1rem;\\n}\\n.json-formatter-row .json-formatter-children.json-formatter-empty {\\n opacity: 0.5;\\n margin-left: 1rem;\\n}\\n.json-formatter-row .json-formatter-children.json-formatter-empty:after {\\n display: none;\\n}\\n.json-formatter-row .json-formatter-children.json-formatter-empty.json-formatter-object:after {\\n content: \\\"No properties\\\";\\n}\\n.json-formatter-row .json-formatter-children.json-formatter-empty.json-formatter-array:after {\\n content: \\\"[]\\\";\\n}\\n.json-formatter-row .json-formatter-string,\\n.json-formatter-row .json-formatter-stringifiable {\\n color: green;\\n white-space: pre;\\n word-wrap: break-word;\\n}\\n.json-formatter-row .json-formatter-number {\\n color: blue;\\n}\\n.json-formatter-row .json-formatter-boolean {\\n color: red;\\n}\\n.json-formatter-row .json-formatter-null {\\n color: #855A00;\\n}\\n.json-formatter-row .json-formatter-undefined {\\n color: #ca0b69;\\n}\\n.json-formatter-row .json-formatter-function {\\n color: #FF20ED;\\n}\\n.json-formatter-row .json-formatter-date {\\n background-color: rgba(0, 0, 0, 0.05);\\n}\\n.json-formatter-row .json-formatter-url {\\n text-decoration: underline;\\n color: blue;\\n cursor: pointer;\\n}\\n.json-formatter-row .json-formatter-bracket {\\n color: blue;\\n}\\n.json-formatter-row .json-formatter-key {\\n color: #00008B;\\n padding-right: 0.2rem;\\n}\\n.json-formatter-row .json-formatter-toggler-link {\\n cursor: pointer;\\n}\\n.json-formatter-row .json-formatter-toggler {\\n line-height: 1.2rem;\\n font-size: 0.7rem;\\n vertical-align: middle;\\n opacity: 0.6;\\n cursor: pointer;\\n padding-right: 0.2rem;\\n}\\n.json-formatter-row .json-formatter-toggler:after {\\n display: inline-block;\\n transition: transform 100ms ease-in;\\n content: \\\"\\\\25BA\\\";\\n}\\n.json-formatter-row > a > .json-formatter-preview-text {\\n opacity: 0;\\n transition: opacity 0.15s ease-in;\\n font-style: italic;\\n}\\n.json-formatter-row:hover > a > .json-formatter-preview-text {\\n opacity: 0.6;\\n}\\n.json-formatter-row.json-formatter-open > .json-formatter-toggler-link .json-formatter-toggler:after {\\n transform: rotate(90deg);\\n}\\n.json-formatter-row.json-formatter-open > .json-formatter-children:after {\\n display: inline-block;\\n}\\n.json-formatter-row.json-formatter-open > a > .json-formatter-preview-text {\\n display: none;\\n}\\n.json-formatter-row.json-formatter-open.json-formatter-empty:after {\\n display: block;\\n}\\n.json-formatter-dark.json-formatter-row {\\n font-family: monospace;\\n}\\n.json-formatter-dark.json-formatter-row,\\n.json-formatter-dark.json-formatter-row a,\\n.json-formatter-dark.json-formatter-row a:hover {\\n color: white;\\n text-decoration: none;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-row {\\n margin-left: 1rem;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-children.json-formatter-empty {\\n opacity: 0.5;\\n margin-left: 1rem;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-children.json-formatter-empty:after {\\n display: none;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-children.json-formatter-empty.json-formatter-object:after {\\n content: \\\"No properties\\\";\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-children.json-formatter-empty.json-formatter-array:after {\\n content: \\\"[]\\\";\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-string,\\n.json-formatter-dark.json-formatter-row .json-formatter-stringifiable {\\n color: #31F031;\\n white-space: pre;\\n word-wrap: break-word;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-number {\\n color: #66C2FF;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-boolean {\\n color: #EC4242;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-null {\\n color: #EEC97D;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-undefined {\\n color: #ef8fbe;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-function {\\n color: #FD48CB;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-date {\\n background-color: rgba(255, 255, 255, 0.05);\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-url {\\n text-decoration: underline;\\n color: #027BFF;\\n cursor: pointer;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-bracket {\\n color: #9494FF;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-key {\\n color: #23A0DB;\\n padding-right: 0.2rem;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-toggler-link {\\n cursor: pointer;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-toggler {\\n line-height: 1.2rem;\\n font-size: 0.7rem;\\n vertical-align: middle;\\n opacity: 0.6;\\n cursor: pointer;\\n padding-right: 0.2rem;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-toggler:after {\\n display: inline-block;\\n transition: transform 100ms ease-in;\\n content: \\\"\\\\25BA\\\";\\n}\\n.json-formatter-dark.json-formatter-row > a > .json-formatter-preview-text {\\n opacity: 0;\\n transition: opacity 0.15s ease-in;\\n font-style: italic;\\n}\\n.json-formatter-dark.json-formatter-row:hover > a > .json-formatter-preview-text {\\n opacity: 0.6;\\n}\\n.json-formatter-dark.json-formatter-row.json-formatter-open > .json-formatter-toggler-link .json-formatter-toggler:after {\\n transform: rotate(90deg);\\n}\\n.json-formatter-dark.json-formatter-row.json-formatter-open > .json-formatter-children:after {\\n display: inline-block;\\n}\\n.json-formatter-dark.json-formatter-row.json-formatter-open > a > .json-formatter-preview-text {\\n display: none;\\n}\\n.json-formatter-dark.json-formatter-row.json-formatter-open.json-formatter-empty:after {\\n display: block;\\n}\\n\");\n",
+ " },\n",
+ " function(Bokeh) {\n",
+ " inject_raw_css(\"table.panel-df {\\n margin-left: auto;\\n margin-right: auto;\\n border: none;\\n border-collapse: collapse;\\n border-spacing: 0;\\n color: black;\\n font-size: 12px;\\n table-layout: fixed;\\n width: 100%;\\n}\\n\\n.panel-df tr, .panel-df th, .panel-df td {\\n text-align: right;\\n vertical-align: middle;\\n padding: 0.5em 0.5em !important;\\n line-height: normal;\\n white-space: normal;\\n max-width: none;\\n border: none;\\n}\\n\\n.panel-df tbody {\\n display: table-row-group;\\n vertical-align: middle;\\n border-color: inherit;\\n}\\n\\n.panel-df tbody tr:nth-child(odd) {\\n background: #f5f5f5;\\n}\\n\\n.panel-df thead {\\n border-bottom: 1px solid black;\\n vertical-align: bottom;\\n}\\n\\n.panel-df tr:hover {\\n background: lightblue !important;\\n cursor: pointer;\\n}\\n\");\n",
+ " },\n",
+ " function(Bokeh) {\n",
+ " inject_raw_css(\".codehilite .hll { background-color: #ffffcc }\\n.codehilite { background: #f8f8f8; }\\n.codehilite .c { color: #408080; font-style: italic } /* Comment */\\n.codehilite .err { border: 1px solid #FF0000 } /* Error */\\n.codehilite .k { color: #008000; font-weight: bold } /* Keyword */\\n.codehilite .o { color: #666666 } /* Operator */\\n.codehilite .ch { color: #408080; font-style: italic } /* Comment.Hashbang */\\n.codehilite .cm { color: #408080; font-style: italic } /* Comment.Multiline */\\n.codehilite .cp { color: #BC7A00 } /* Comment.Preproc */\\n.codehilite .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */\\n.codehilite .c1 { color: #408080; font-style: italic } /* Comment.Single */\\n.codehilite .cs { color: #408080; font-style: italic } /* Comment.Special */\\n.codehilite .gd { color: #A00000 } /* Generic.Deleted */\\n.codehilite .ge { font-style: italic } /* Generic.Emph */\\n.codehilite .gr { color: #FF0000 } /* Generic.Error */\\n.codehilite .gh { color: #000080; font-weight: bold } /* Generic.Heading */\\n.codehilite .gi { color: #00A000 } /* Generic.Inserted */\\n.codehilite .go { color: #888888 } /* Generic.Output */\\n.codehilite .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\\n.codehilite .gs { font-weight: bold } /* Generic.Strong */\\n.codehilite .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\\n.codehilite .gt { color: #0044DD } /* Generic.Traceback */\\n.codehilite .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\\n.codehilite .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\\n.codehilite .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\\n.codehilite .kp { color: #008000 } /* Keyword.Pseudo */\\n.codehilite .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\\n.codehilite .kt { color: #B00040 } /* Keyword.Type */\\n.codehilite .m { color: #666666 } /* Literal.Number */\\n.codehilite .s { color: #BA2121 } /* Literal.String */\\n.codehilite .na { color: #7D9029 } /* Name.Attribute */\\n.codehilite .nb { color: #008000 } /* Name.Builtin */\\n.codehilite .nc { color: #0000FF; font-weight: bold } /* Name.Class */\\n.codehilite .no { color: #880000 } /* Name.Constant */\\n.codehilite .nd { color: #AA22FF } /* Name.Decorator */\\n.codehilite .ni { color: #999999; font-weight: bold } /* Name.Entity */\\n.codehilite .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\\n.codehilite .nf { color: #0000FF } /* Name.Function */\\n.codehilite .nl { color: #A0A000 } /* Name.Label */\\n.codehilite .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\\n.codehilite .nt { color: #008000; font-weight: bold } /* Name.Tag */\\n.codehilite .nv { color: #19177C } /* Name.Variable */\\n.codehilite .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\\n.codehilite .w { color: #bbbbbb } /* Text.Whitespace */\\n.codehilite .mb { color: #666666 } /* Literal.Number.Bin */\\n.codehilite .mf { color: #666666 } /* Literal.Number.Float */\\n.codehilite .mh { color: #666666 } /* Literal.Number.Hex */\\n.codehilite .mi { color: #666666 } /* Literal.Number.Integer */\\n.codehilite .mo { color: #666666 } /* Literal.Number.Oct */\\n.codehilite .sa { color: #BA2121 } /* Literal.String.Affix */\\n.codehilite .sb { color: #BA2121 } /* Literal.String.Backtick */\\n.codehilite .sc { color: #BA2121 } /* Literal.String.Char */\\n.codehilite .dl { color: #BA2121 } /* Literal.String.Delimiter */\\n.codehilite .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\\n.codehilite .s2 { color: #BA2121 } /* Literal.String.Double */\\n.codehilite .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\\n.codehilite .sh { color: #BA2121 } /* Literal.String.Heredoc */\\n.codehilite .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\\n.codehilite .sx { color: #008000 } /* Literal.String.Other */\\n.codehilite .sr { color: #BB6688 } /* Literal.String.Regex */\\n.codehilite .s1 { color: #BA2121 } /* Literal.String.Single */\\n.codehilite .ss { color: #19177C } /* Literal.String.Symbol */\\n.codehilite .bp { color: #008000 } /* Name.Builtin.Pseudo */\\n.codehilite .fm { color: #0000FF } /* Name.Function.Magic */\\n.codehilite .vc { color: #19177C } /* Name.Variable.Class */\\n.codehilite .vg { color: #19177C } /* Name.Variable.Global */\\n.codehilite .vi { color: #19177C } /* Name.Variable.Instance */\\n.codehilite .vm { color: #19177C } /* Name.Variable.Magic */\\n.codehilite .il { color: #666666 } /* Literal.Number.Integer.Long */\\n\\n.markdown h1 { margin-block-start: 0.34em }\\n.markdown h2 { margin-block-start: 0.42em }\\n.markdown h3 { margin-block-start: 0.5em }\\n.markdown h4 { margin-block-start: 0.67em }\\n.markdown h5 { margin-block-start: 0.84em }\\n.markdown h6 { margin-block-start: 1.17em }\\n.markdown ul { padding-inline-start: 2em }\\n.markdown ol { padding-inline-start: 2em }\\n.markdown strong { font-weight: 600 }\\n.markdown a { color: -webkit-link }\\n.markdown a { color: -moz-hyperlinkText }\\n\");\n",
+ " },\n",
+ " function(Bokeh) {\n",
+ " /* BEGIN bokeh.min.js */\n",
+ " /*!\n",
+ " * Copyright (c) 2012 - 2020, Anaconda, Inc., and Bokeh Contributors\n",
+ " * All rights reserved.\n",
+ " * \n",
+ " * Redistribution and use in source and binary forms, with or without modification,\n",
+ " * are permitted provided that the following conditions are met:\n",
+ " * \n",
+ " * Redistributions of source code must retain the above copyright notice,\n",
+ " * this list of conditions and the following disclaimer.\n",
+ " * \n",
+ " * Redistributions in binary form must reproduce the above copyright notice,\n",
+ " * this list of conditions and the following disclaimer in the documentation\n",
+ " * and/or other materials provided with the distribution.\n",
+ " * \n",
+ " * Neither the name of Anaconda nor the names of any contributors\n",
+ " * may be used to endorse or promote products derived from this software\n",
+ " * without specific prior written permission.\n",
+ " * \n",
+ " * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n",
+ " * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n",
+ " * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n",
+ " * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n",
+ " * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n",
+ " * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n",
+ " * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n",
+ " * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n",
+ " * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n",
+ " * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\n",
+ " * THE POSSIBILITY OF SUCH DAMAGE.\n",
+ " */\n",
+ " (function(root, factory) {\n",
+ " const bokeh = factory();\n",
+ " bokeh.__bokeh__ = true;\n",
+ " if (typeof root.Bokeh === \"undefined\" || typeof root.Bokeh.__bokeh__ === \"undefined\") {\n",
+ " root.Bokeh = bokeh;\n",
+ " }\n",
+ " const Bokeh = root.Bokeh;\n",
+ " Bokeh[bokeh.version] = bokeh;\n",
+ " })(this, function() {\n",
+ " var define;\n",
+ " var parent_require = typeof require === \"function\" && require\n",
+ " return (function(modules, entry, aliases, externals) {\n",
+ " if (aliases === undefined) aliases = {};\n",
+ " if (externals === undefined) externals = {};\n",
+ "\n",
+ " var cache = {};\n",
+ "\n",
+ " var normalize = function(name) {\n",
+ " if (typeof name === \"number\")\n",
+ " return name;\n",
+ "\n",
+ " if (name === \"bokehjs\")\n",
+ " return entry;\n",
+ "\n",
+ " var prefix = \"@bokehjs/\"\n",
+ " if (name.slice(0, prefix.length) === prefix)\n",
+ " name = name.slice(prefix.length)\n",
+ "\n",
+ " var alias = aliases[name]\n",
+ " if (alias != null)\n",
+ " return alias;\n",
+ "\n",
+ " var trailing = name.length > 0 && name[name.lenght-1] === \"/\";\n",
+ " var index = aliases[name + (trailing ? \"\" : \"/\") + \"index\"];\n",
+ " if (index != null)\n",
+ " return index;\n",
+ "\n",
+ " return name;\n",
+ " }\n",
+ "\n",
+ " var require = function(name) {\n",
+ " var mod = cache[name];\n",
+ " if (!mod) {\n",
+ " var id = normalize(name);\n",
+ "\n",
+ " mod = cache[id];\n",
+ " if (!mod) {\n",
+ " if (!modules[id]) {\n",
+ " if (externals[id] === false || (externals[id] == true && parent_require)) {\n",
+ " try {\n",
+ " mod = {exports: externals[id] ? parent_require(id) : {}};\n",
+ " cache[id] = cache[name] = mod;\n",
+ " return mod.exports;\n",
+ " } catch (e) {}\n",
+ " }\n",
+ "\n",
+ " var err = new Error(\"Cannot find module '\" + name + \"'\");\n",
+ " err.code = 'MODULE_NOT_FOUND';\n",
+ " throw err;\n",
+ " }\n",
+ "\n",
+ " mod = {exports: {}};\n",
+ " cache[id] = cache[name] = mod;\n",
+ " modules[id].call(mod.exports, require, mod, mod.exports);\n",
+ " } else\n",
+ " cache[name] = mod;\n",
+ " }\n",
+ "\n",
+ " return mod.exports;\n",
+ " }\n",
+ " require.resolve = function(name) {\n",
+ " return \"\"\n",
+ " }\n",
+ "\n",
+ " var main = require(entry);\n",
+ " main.require = require;\n",
+ "\n",
+ " if (typeof Proxy !== \"undefined\") {\n",
+ " // allow Bokeh.loader[\"@bokehjs/module/name\"] syntax\n",
+ " main.loader = new Proxy({}, {\n",
+ " get: function(_obj, module) {\n",
+ " return require(module);\n",
+ " }\n",
+ " });\n",
+ " }\n",
+ "\n",
+ " main.register_plugin = function(plugin_modules, plugin_entry, plugin_aliases, plugin_externals) {\n",
+ " if (plugin_aliases === undefined) plugin_aliases = {};\n",
+ " if (plugin_externals === undefined) plugin_externals = {};\n",
+ "\n",
+ " for (var name in plugin_modules) {\n",
+ " modules[name] = plugin_modules[name];\n",
+ " }\n",
+ "\n",
+ " for (var name in plugin_aliases) {\n",
+ " aliases[name] = plugin_aliases[name];\n",
+ " }\n",
+ "\n",
+ " for (var name in plugin_externals) {\n",
+ " externals[name] = plugin_externals[name];\n",
+ " }\n",
+ "\n",
+ " var plugin = require(plugin_entry);\n",
+ "\n",
+ " for (var name in plugin) {\n",
+ " main[name] = plugin[name];\n",
+ " }\n",
+ "\n",
+ " return plugin;\n",
+ " }\n",
+ "\n",
+ " return main;\n",
+ " })\n",
+ " ([\n",
+ " function _(e,t,_){Object.defineProperty(_,\"__esModule\",{value:!0});e(1).__exportStar(e(2),_)},\n",
+ " function _(t,e,n){\n",
+ " /*! *****************************************************************************\n",
+ " Copyright (c) Microsoft Corporation.\n",
+ " \n",
+ " Permission to use, copy, modify, and/or distribute this software for any\n",
+ " purpose with or without fee is hereby granted.\n",
+ " \n",
+ " THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\n",
+ " REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\n",
+ " AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\n",
+ " INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\n",
+ " LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\n",
+ " OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\n",
+ " PERFORMANCE OF THIS SOFTWARE.\n",
+ " ***************************************************************************** */\n",
+ " Object.defineProperty(n,\"__esModule\",{value:!0});var r=function(t,e){return(r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n])})(t,e)};function o(t){var e=\"function\"==typeof Symbol&&Symbol.iterator,n=e&&t[e],r=0;if(n)return n.call(t);if(t&&\"number\"==typeof t.length)return{next:function(){return t&&r>=t.length&&(t=void 0),{value:t&&t[r++],done:!t}}};throw new TypeError(e?\"Object is not iterable.\":\"Symbol.iterator is not defined.\")}function a(t,e){var n=\"function\"==typeof Symbol&&t[Symbol.iterator];if(!n)return t;var r,o,a=n.call(t),i=[];try{for(;(void 0===e||e-- >0)&&!(r=a.next()).done;)i.push(r.value)}catch(t){o={error:t}}finally{try{r&&!r.done&&(n=a.return)&&n.call(a)}finally{if(o)throw o.error}}return i}function i(t){return this instanceof i?(this.v=t,this):new i(t)}n.__extends=function(t,e){function n(){this.constructor=t}r(t,e),t.prototype=null===e?Object.create(e):(n.prototype=e.prototype,new n)},n.__assign=function(){return n.__assign=Object.assign||function(t){for(var e,n=1,r=arguments.length;n=0;u--)(o=t[u])&&(i=(a<3?o(i):a>3?o(e,n,i):o(e,n))||i);return a>3&&i&&Object.defineProperty(e,n,i),i},n.__param=function(t,e){return function(n,r){e(n,r,t)}},n.__metadata=function(t,e){if(\"object\"==typeof Reflect&&\"function\"==typeof Reflect.metadata)return Reflect.metadata(t,e)},n.__awaiter=function(t,e,n,r){return new(n||(n=Promise))((function(o,a){function i(t){try{c(r.next(t))}catch(t){a(t)}}function u(t){try{c(r.throw(t))}catch(t){a(t)}}function c(t){var e;t.done?o(t.value):(e=t.value,e instanceof n?e:new n((function(t){t(e)}))).then(i,u)}c((r=r.apply(t,e||[])).next())}))},n.__generator=function(t,e){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:u(0),throw:u(1),return:u(2)},\"function\"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function u(a){return function(u){return function(a){if(n)throw new TypeError(\"Generator is already executing.\");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!(o=i.trys,(o=o.length>0&&o[o.length-1])||6!==a[0]&&2!==a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]1||c(t,e)}))})}function c(t,e){try{(n=o[t](e)).value instanceof i?Promise.resolve(n.value.v).then(f,l):s(a[0][2],n)}catch(t){s(a[0][3],t)}var n}function f(t){c(\"next\",t)}function l(t){c(\"throw\",t)}function s(t,e){t(e),a.shift(),a.length&&c(a[0][0],a[0][1])}},n.__asyncDelegator=function(t){var e,n;return e={},r(\"next\"),r(\"throw\",(function(t){throw t})),r(\"return\"),e[Symbol.iterator]=function(){return this},e;function r(r,o){e[r]=t[r]?function(e){return(n=!n)?{value:i(t[r](e)),done:\"return\"===r}:o?o(e):e}:o}},n.__asyncValues=function(t){if(!Symbol.asyncIterator)throw new TypeError(\"Symbol.asyncIterator is not defined.\");var e,n=t[Symbol.asyncIterator];return n?n.call(t):(t=o(t),e={},r(\"next\"),r(\"throw\"),r(\"return\"),e[Symbol.asyncIterator]=function(){return this},e);function r(n){e[n]=t[n]&&function(e){return new Promise((function(r,o){(function(t,e,n,r){Promise.resolve(r).then((function(e){t({value:e,done:n})}),e)})(r,o,(e=t[n](e)).done,e.value)}))}}},n.__makeTemplateObject=function(t,e){return Object.defineProperty?Object.defineProperty(t,\"raw\",{value:e}):t.raw=e,t},n.__importStar=function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var n in t)Object.hasOwnProperty.call(t,n)&&(e[n]=t[n]);return e.default=t,e},n.__importDefault=function(t){return t&&t.__esModule?t:{default:t}},n.__classPrivateFieldGet=function(t,e){if(!e.has(t))throw new TypeError(\"attempted to get private field on non-instance\");return e.get(t)},n.__classPrivateFieldSet=function(t,e,n){if(!e.has(t))throw new TypeError(\"attempted to set private field on non-instance\");return e.set(t,n),n}},\n",
+ " function _(e,r,t){var l=this&&this.__importStar||function(e){if(e&&e.__esModule)return e;var r={};if(null!=e)for(var t in e)Object.hasOwnProperty.call(e,t)&&(r[t]=e[t]);return r.default=e,r};Object.defineProperty(t,\"__esModule\",{value:!0});var o=e(3);t.version=o.version;var s=e(4);t.index=s.index,t.embed=l(e(4)),t.protocol=l(e(390)),t._testing=l(e(391));var n=e(19);t.logger=n.logger,t.set_log_level=n.set_log_level;var a=e(27);t.settings=a.settings;var i=e(7);t.Models=i.Models;var v=e(5);t.documents=v.documents;var _=e(392);t.safely=_.safely},\n",
+ " function _(e,n,o){Object.defineProperty(o,\"__esModule\",{value:!0}),o.version=\"2.2.2\"},\n",
+ " function _(e,o,t){Object.defineProperty(t,\"__esModule\",{value:!0});const n=e(5),s=e(19),r=e(29),d=e(13),_=e(8),c=e(16),i=e(381),a=e(383),u=e(382);var l=e(381);t.add_document_standalone=l.add_document_standalone,t.index=l.index;var m=e(383);t.add_document_from_session=m.add_document_from_session;var f=e(388);t.embed_items_notebook=f.embed_items_notebook,t.kernels=f.kernels;var g=e(382);async function O(e,o,t,c){_.isString(e)&&(e=JSON.parse(r.unescape(e)));const l={};for(const[o,t]of d.entries(e))l[o]=n.Document.from_json(t);const m=[];for(const e of o){const o=u._resolve_element(e),n=u._resolve_root_elements(e);if(null!=e.docid)m.push(await i.add_document_standalone(l[e.docid],o,n,e.use_for_title));else{if(null==e.token)throw new Error(\"Error rendering Bokeh items: either 'docid' or 'token' was expected.\");{const r=a._get_ws_url(t,c);s.logger.debug(\"embed: computed ws url: \"+r);try{m.push(await a.add_document_from_session(r,e.token,o,n,e.use_for_title)),console.log(\"Bokeh items were rendered successfully\")}catch(e){console.log(\"Error rendering Bokeh items:\",e)}}}}return m}t.BOKEH_ROOT=g.BOKEH_ROOT,t.embed_item=async function(e,o){const t={},n=r.uuid4();t[n]=e.doc,null==o&&(o=e.target_id);const s=document.getElementById(o);null!=s&&s.classList.add(u.BOKEH_ROOT);const d={roots:{[e.root_id]:o},root_ids:[e.root_id],docid:n},[_]=await c.defer(()=>O(t,[d]));return _},t.embed_items=async function(e,o,t,n){return await c.defer(()=>O(e,o,t,n))}},\n",
+ " function _(e,t,_){Object.defineProperty(_,\"__esModule\",{value:!0});const o=e(1);o.__exportStar(e(6),_),o.__exportStar(e(121),_)},\n",
+ " function _(e,t,s){Object.defineProperty(s,\"__esModule\",{value:!0});const o=e(1),n=e(7),r=e(3),i=e(19),_=e(313),a=e(14),l=e(15),c=e(17),h=e(31),d=e(9),f=e(13),u=o.__importStar(e(120)),m=e(25),g=e(8),p=e(272),w=e(85),v=e(81),b=e(121);class y{constructor(e){this.document=e,this.session=null,this.subscribed_models=new Set}send_event(e){const t=new b.MessageSentEvent(this.document,\"bokeh_event\",e.to_json());this.document._trigger_on_change(t)}trigger(e){for(const t of this.subscribed_models)null!=e.origin&&e.origin!=t||t._process_event(e)}}s.EventManager=y,y.__name__=\"EventManager\",s.documents=[],s.DEFAULT_TITLE=\"Bokeh Application\";class j{constructor(){s.documents.push(this),this._init_timestamp=Date.now(),this._title=s.DEFAULT_TITLE,this._roots=[],this._all_models=new Map,this._all_models_freeze_count=0,this._callbacks=new Map,this._message_callbacks=new Map,this.event_manager=new y(this),this.idle=new l.Signal0(this,\"idle\"),this._idle_roots=new WeakMap,this._interactive_timestamp=null,this._interactive_plot=null}get layoutables(){return this._roots.filter(e=>e instanceof p.LayoutDOM)}get is_idle(){for(const e of this.layoutables)if(!this._idle_roots.has(e))return!1;return!0}notify_idle(e){this._idle_roots.set(e,!0),this.is_idle&&(i.logger.info(`document idle at ${Date.now()-this._init_timestamp} ms`),this.event_manager.send_event(new _.DocumentReady),this.idle.emit())}clear(){this._push_all_models_freeze();try{for(;this._roots.length>0;)this.remove_root(this._roots[0])}finally{this._pop_all_models_freeze()}}interactive_start(e){null==this._interactive_plot&&(this._interactive_plot=e,this._interactive_plot.trigger_event(new _.LODStart)),this._interactive_timestamp=Date.now()}interactive_stop(){null!=this._interactive_plot&&this._interactive_plot.trigger_event(new _.LODEnd),this._interactive_plot=null,this._interactive_timestamp=null}interactive_duration(){return null==this._interactive_timestamp?-1:Date.now()-this._interactive_timestamp}destructively_move(e){if(e===this)throw new Error(\"Attempted to overwrite a document with itself\");e.clear();const t=d.copy(this._roots);this.clear();for(const e of t)if(null!=e.document)throw new Error(\"Somehow we didn't detach \"+e);if(0!=this._all_models.size)throw new Error(\"this._all_models still had stuff in it: \"+this._all_models);for(const s of t)e.add_root(s);e.set_title(this._title)}_push_all_models_freeze(){this._all_models_freeze_count+=1}_pop_all_models_freeze(){this._all_models_freeze_count-=1,0===this._all_models_freeze_count&&this._recompute_all_models()}_invalidate_all_models(){i.logger.debug(\"invalidating document models\"),0===this._all_models_freeze_count&&this._recompute_all_models()}_recompute_all_models(){let e=new Set;for(const t of this._roots)e=u.union(e,t.references());const t=new Set(this._all_models.values()),s=u.difference(t,e),o=u.difference(e,t),n=new Map;for(const t of e)n.set(t.id,t);for(const e of s)e.detach_document();for(const e of o)e.attach_document(this);this._all_models=n}roots(){return this._roots}add_root(e,t){if(i.logger.debug(\"Adding root: \"+e),!d.includes(this._roots,e)){this._push_all_models_freeze();try{this._roots.push(e)}finally{this._pop_all_models_freeze()}this._trigger_on_change(new b.RootAddedEvent(this,e,t))}}remove_root(e,t){const s=this._roots.indexOf(e);if(!(s<0)){this._push_all_models_freeze();try{this._roots.splice(s,1)}finally{this._pop_all_models_freeze()}this._trigger_on_change(new b.RootRemovedEvent(this,e,t))}}title(){return this._title}set_title(e,t){e!==this._title&&(this._title=e,this._trigger_on_change(new b.TitleChangedEvent(this,e,t)))}get_model_by_id(e){var t;return null!==(t=this._all_models.get(e))&&void 0!==t?t:null}get_model_by_name(e){const t=[];for(const s of this._all_models.values())s instanceof v.Model&&s.name==e&&t.push(s);switch(t.length){case 0:return null;case 1:return t[0];default:throw new Error(`Multiple models are named '${e}'`)}}on_message(e,t){const s=this._message_callbacks.get(e);null==s?this._message_callbacks.set(e,new Set([t])):s.add(t)}remove_on_message(e,t){var s;null===(s=this._message_callbacks.get(e))||void 0===s||s.delete(t)}_trigger_on_message(e,t){const s=this._message_callbacks.get(e);if(null!=s)for(const e of s)e(t)}on_change(e,t=!1){this._callbacks.has(e)||this._callbacks.set(e,t)}remove_on_change(e){this._callbacks.delete(e)}_trigger_on_change(e){for(const[t,s]of this._callbacks)if(!s&&e instanceof b.DocumentEventBatch)for(const s of e.events)t(s);else t(e)}_notify_change(e,t,s,o,n){this._trigger_on_change(new b.ModelChangedEvent(this,e,t,s,o,null==n?void 0:n.setter_id,null==n?void 0:n.hint))}static _references_json(e,t=!0){const s=[];for(const o of e){const e=o.struct();e.attributes=o.attributes_as_json(t),delete e.attributes.id,s.push(e)}return s}static _instantiate_object(e,t,s){const o=Object.assign(Object.assign({},s),{id:e,__deferred__:!0});return new(n.Models(t))(o)}static _instantiate_references_json(e,t){const s=new Map;for(const o of e){const e=o.id,n=o.type,r=o.attributes||{};let i=t.get(e);null==i&&(i=j._instantiate_object(e,n,r),null!=o.subtype&&i.set_subtype(o.subtype)),s.set(i.id,i)}return s}static _resolve_refs(e,t,s,o){function n(e){if(c.is_ref(e)){if(t.has(e.id))return t.get(e.id);if(s.has(e.id))return s.get(e.id);throw new Error(`reference ${JSON.stringify(e)} isn't known (not in Document?)`)}return h.is_NDArray_ref(e)?h.decode_NDArray(e,o):g.isArray(e)?function(e){const t=[];for(const s of e)t.push(n(s));return t}(e):g.isPlainObject(e)?function(e){const t={};for(const[s,o]of f.entries(e))t[s]=n(o);return t}(e):e}return n(e)}static _initialize_references_json(e,t,s,o){const n=new Map;for(const{id:r,attributes:i}of e){const e=!t.has(r),_=e?s.get(r):t.get(r),a=j._resolve_refs(i,t,s,o);_.setv(a,{silent:!0}),n.set(r,{instance:_,is_new:e})}const r=[],i=new Set;function _(e){if(e instanceof a.HasProps){if(n.has(e.id)&&!i.has(e.id)){i.add(e.id);const{instance:t,is_new:s}=n.get(e.id),{attributes:o}=t;for(const e of f.values(o))_(e);s&&(t.finalize(),r.push(t))}}else if(g.isArray(e))for(const t of e)_(t);else if(g.isPlainObject(e))for(const t of f.values(e))_(t)}for(const e of n.values())_(e.instance);for(const e of r)e.connect_signals()}static _event_for_attribute_change(e,t,s,o,n){if(o.get_model_by_id(e.id).property(t).syncable){const r={kind:\"ModelChanged\",model:{id:e.id},attr:t,new:s};return a.HasProps._json_record_references(o,s,n,{recursive:!0}),r}return null}static _events_to_sync_objects(e,t,s,o){const n=Object.keys(e.attributes),r=Object.keys(t.attributes),_=d.difference(n,r),a=d.difference(r,n),l=d.intersection(n,r),c=[];for(const e of _)i.logger.warn(`Server sent key ${e} but we don't seem to have it in our JSON`);for(const n of a){const r=t.attributes[n];c.push(j._event_for_attribute_change(e,n,r,s,o))}for(const n of l){const r=e.attributes[n],i=t.attributes[n];null==r&&null==i||(null==r||null==i?c.push(j._event_for_attribute_change(e,n,i,s,o)):m.isEqual(r,i)||c.push(j._event_for_attribute_change(e,n,i,s,o)))}return c.filter(e=>null!=e)}static _compute_patch_since_json(e,t){const s=t.to_json(!1);function o(e){const t=new Map;for(const s of e.roots.references)t.set(s.id,s);return t}const n=o(e),r=new Map,i=[];for(const t of e.roots.root_ids)r.set(t,n.get(t)),i.push(t);const _=o(s),a=new Map,l=[];for(const e of s.roots.root_ids)a.set(e,_.get(e)),l.push(e);if(i.sort(),l.sort(),d.difference(i,l).length>0||d.difference(l,i).length>0)throw new Error(\"Not implemented: computing add/remove of document roots\");const c=new Set;let h=[];for(const e of t._all_models.keys())if(n.has(e)){const s=j._events_to_sync_objects(n.get(e),_.get(e),t,c);h=h.concat(s)}return{references:j._references_json(c,!1),events:h}}to_json_string(e=!0){return JSON.stringify(this.to_json(e))}to_json(e=!0){const t=this._roots.map(e=>e.id),s=this._all_models.values();return{version:r.version,title:this._title,roots:{root_ids:t,references:j._references_json(s,e)}}}static from_json_string(e){const t=JSON.parse(e);return j.from_json(t)}static from_json(e){i.logger.debug(\"Creating Document from JSON\");const t=e.version,s=-1!==t.indexOf(\"+\")||-1!==t.indexOf(\"-\"),o=`Library versions: JS (${r.version}) / Python (${t})`;s||r.version.replace(/-(dev|rc)\\./,\"$1\")==t?i.logger.debug(o):(i.logger.warn(\"JS/Python version mismatch\"),i.logger.warn(o));const n=e.roots,_=n.root_ids,a=n.references,l=j._instantiate_references_json(a,new Map);j._initialize_references_json(a,new Map,l,new Map);const c=new j;for(const e of _){const t=l.get(e);null!=t&&c.add_root(t)}return c.set_title(e.title),c}replace_with_json(e){j.from_json(e).destructively_move(this)}create_json_patch_string(e){return JSON.stringify(this.create_json_patch(e))}create_json_patch(e){const t=new Set,s=[];for(const o of e){if(o.document!==this)throw i.logger.warn(\"Cannot create a patch using events from a different document, event had \",o.document,\" we are \",this),new Error(\"Cannot create a patch using events from a different document\");s.push(o.json(t))}return{events:s,references:j._references_json(t)}}apply_json_patch(e,t=new Map,s){const o=e.references,n=e.events,r=j._instantiate_references_json(o,this._all_models);t instanceof Map||(t=new Map(t));for(const e of n)switch(e.kind){case\"RootAdded\":case\"RootRemoved\":case\"ModelChanged\":{const t=e.model.id,s=this._all_models.get(t);if(null!=s)r.set(t,s);else if(!r.has(t))throw i.logger.warn(`Got an event for unknown model ${e.model}\"`),new Error(\"event model wasn't known\");break}}const _=new Map,a=new Map;for(const[e,t]of r)this._all_models.has(e)?_.set(e,t):a.set(e,t);j._initialize_references_json(o,_,a,t);for(const e of n)switch(e.kind){case\"MessageSent\":{const{msg_type:s,msg_data:o}=e;let n;if(void 0===o){if(1!=t.size)throw new Error(\"expected exactly one buffer\");{const[[,e]]=t;n=e}}else n=j._resolve_refs(o,_,a,t);this._trigger_on_message(s,n);break}case\"ModelChanged\":{const o=e.model.id,n=this._all_models.get(o);if(null==n)throw new Error(`Cannot apply patch to ${o} which is not in the document`);const r=e.attr,i=j._resolve_refs(e.new,_,a,t);n.setv({[r]:i},{setter_id:s});break}case\"ColumnDataChanged\":{const o=e.column_source.id,n=this._all_models.get(o);if(null==n)throw new Error(`Cannot stream to ${o} which is not in the document`);const r=j._resolve_refs(e.new,new Map,new Map,t);if(null!=e.cols)for(const e in n.data)e in r||(r[e]=n.data[e]);n.setv({data:r},{setter_id:s,check_eq:!1});break}case\"ColumnsStreamed\":{const t=e.column_source.id,o=this._all_models.get(t);if(null==o)throw new Error(`Cannot stream to ${t} which is not in the document`);if(!(o instanceof w.ColumnDataSource))throw new Error(\"Cannot stream to non-ColumnDataSource\");const n=e.data,r=e.rollover;o.stream(n,r,s);break}case\"ColumnsPatched\":{const t=e.column_source.id,o=this._all_models.get(t);if(null==o)throw new Error(`Cannot patch ${t} which is not in the document`);if(!(o instanceof w.ColumnDataSource))throw new Error(\"Cannot patch non-ColumnDataSource\");const n=e.patches;o.patch(n,s);break}case\"RootAdded\":{const t=e.model.id,o=r.get(t);this.add_root(o,s);break}case\"RootRemoved\":{const t=e.model.id,o=r.get(t);this.remove_root(o,s);break}case\"TitleChanged\":this.set_title(e.title,s);break;default:throw new Error(\"Unknown patch event \"+JSON.stringify(e))}}}s.Document=j,j.__name__=\"Document\"},\n",
+ " function _(e,r,s){Object.defineProperty(s,\"__esModule\",{value:!0});const o=e(1),t=e(8),d=e(13),i=e(14);s.overrides={};const l=new Map;s.Models=e=>{const r=s.overrides[e]||l.get(e);if(null==r)throw new Error(`Model '${e}' does not exist. This could be due to a widget or a custom model not being registered before first usage.`);return r},s.Models.register=(e,r)=>{s.overrides[e]=r},s.Models.unregister=e=>{delete s.overrides[e]},s.Models.register_models=(e,r=!1,s)=>{var o;if(null!=e)for(const n of d.values(e))if(o=n,t.isObject(o)&&o.prototype instanceof i.HasProps){const e=n.__qualified__;r||!l.has(e)?l.set(e,n):null!=s?s(e):console.warn(`Model '${e}' was already registered`)}},s.register_models=s.Models.register_models,s.Models.registered_names=()=>Array.from(l.keys());const n=o.__importStar(e(34));s.register_models(n)},\n",
+ " function _(n,t,r){Object.defineProperty(r,\"__esModule\",{value:!0});\n",
+ " // (c) 2009-2015 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors\n",
+ " // Underscore may be freely distributed under the MIT license.\n",
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+ " function _(e,t,n){Object.defineProperty(n,\"__esModule\",{value:!0});const c=e(9);function o(e){return Object.keys(e).length}n.keys=Object.keys,n.values=Object.values,n.entries=Object.entries,n.extend=Object.assign,n.clone=function(e){return Object.assign({},e)},n.merge=function(e,t){const n=Object.create(Object.prototype),o=c.concat([Object.keys(e),Object.keys(t)]);for(const s of o){const o=e.hasOwnProperty(s)?e[s]:[],r=t.hasOwnProperty(s)?t[s]:[];n[s]=c.union(o,r)}return n},n.size=o,n.isEmpty=function(e){return 0==o(e)},n.to_object=function(e){const t={};for(const[n,c]of e)t[n]=c;return t}},\n",
+ " function _(t,e,r){Object.defineProperty(r,\"__esModule\",{value:!0});const s=t(1),n=t(15),i=t(17),o=s.__importStar(t(18)),c=s.__importStar(t(21)),a=s.__importStar(t(28)),_=t(29),u=t(9),f=t(13),l=t(8),h=t(25),p=t(5),d=t(30),y=t(31),g=t(25),v=t(33),m=s.__importStar(t(21));class b extends(n.Signalable()){constructor(t={}){var e;super(),this._subtype=void 0,this.document=null,this.destroyed=new n.Signal0(this,\"destroyed\"),this.change=new n.Signal0(this,\"change\"),this.transformchange=new n.Signal0(this,\"transformchange\"),this.properties={},this._pending=!1,this._changing=!1;const r=t instanceof Map?t.get:e=>t[e];for(const[t,{type:e,default_value:s,options:n}]of f.entries(this._props)){let i;i=e instanceof c.Kind?new o.PrimitiveProperty(this,t,e,s,r(t),n):new e(this,t,c.Any,s,r(t),n),this.properties[t]=i}null!==(e=r(\"__deferred__\"))&&void 0!==e&&e||(this.finalize(),this.connect_signals())}set type(t){console.warn(\"prototype.type = 'ModelName' is deprecated, use static __name__ 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t=o;i.push(s(t)),f.extend(n,t)}this.define(n),this.prototype._mixins=[...this.prototype._mixins,...i]}static override(t){for(const[e,r]of f.entries(t)){const t=this._fix_default(r,e),s=this.prototype._props[e];if(null==s)throw new Error(`attempted to override nonexistent '${this.prototype.type}.${e}'`);const n=f.clone(this.prototype._props);n[e]=Object.assign(Object.assign({},s),{default_value:t}),this.prototype._props=n}}toString(){return`${this.type}(${this.id})`}property(t){const e=this.properties[t];if(null!=e)return e;throw new Error(`unknown property ${this.type}.${t}`)}get attributes(){const t={};for(const e of this)t[e.attr]=e.get_value();return t}[g.equals](t,e){for(const r of this){const s=t.property(r.attr);if(e.eq(r.get_value(),s.get_value()))return!1}return!0}[v.pretty](t){const e=t.token,r=[];for(const s of this)if(s.dirty){const n=s.get_value();r.push(`${s.attr}${e(\":\")} 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+ " function _(n,e,t){Object.defineProperty(t,\"__esModule\",{value:!0}),t.delay=\n",
+ " // (c) 2009-2015 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors\n",
+ " // Underscore may be freely distributed under the MIT license.\n",
+ " function(n,e){return setTimeout(n,e)};const u=\"function\"==typeof requestAnimationFrame?requestAnimationFrame:setImmediate;t.defer=function(n){return new Promise(e=>{u(()=>e(n()))})},t.throttle=function(n,e,t={}){let u,o,i,r=null,l=0;const c=function(){l=!1===t.leading?0:Date.now(),r=null,i=n.apply(u,o),r||(u=o=null)};return function(){const a=Date.now();l||!1!==t.leading||(l=a);const f=e-(a-l);return u=this,o=arguments,f<=0||f>e?(r&&(clearTimeout(r),r=null),l=a,i=n.apply(u,o),r||(u=o=null)):r||!1===t.trailing||(r=setTimeout(c,f)),i}},t.once=function(n){let e,t=!1;return function(){return t||(t=!0,e=n()),e}}},\n",
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i;u.is_identity?i=c:(i=this.__createElement(\"g\"),this._apply_transform(i,u),c.appendChild(i));for(const t of[...e.childNodes])if(t instanceof SVGDefsElement){for(const e of[...t.childNodes])if(e instanceof Element){const t=e.getAttribute(\"id\");this.__ids[t]=t,this.__defs.appendChild(e)}}else i.appendChild(t)}else if(t instanceof HTMLImageElement||t instanceof SVGImageElement){const e=this.__createElement(\"image\");if(e.setAttribute(\"width\",\"\"+n),e.setAttribute(\"height\",\"\"+r),e.setAttribute(\"preserveAspectRatio\",\"none\"),a||o||l!==t.width||h!==t.height){const e=this.__document.createElement(\"canvas\");e.width=n,e.height=r;e.getContext(\"2d\").drawImage(t,a,o,l,h,0,0,n,r),t=e}e.setAttribute(\"transform\",_);const i=t instanceof HTMLCanvasElement?t.toDataURL():t.getAttribute(\"src\");e.setAttributeNS(\"http://www.w3.org/1999/xlink\",\"xlink:href\",i),c.appendChild(e)}else if(t instanceof HTMLCanvasElement){const e=this.__createElement(\"image\");e.setAttribute(\"width\",\"\"+n),e.setAttribute(\"height\",\"\"+r),e.setAttribute(\"preserveAspectRatio\",\"none\");const i=this.__document.createElement(\"canvas\");i.width=n,i.height=r;const s=i.getContext(\"2d\");s.imageSmoothingEnabled=!1,s.drawImage(t,a,o,l,h,0,0,n,r),t=i,e.setAttribute(\"transform\",_),e.setAttributeNS(\"http://www.w3.org/1999/xlink\",\"xlink:href\",t.toDataURL()),c.appendChild(e)}}createPattern(t,e){const i=this.__document.createElementNS(\"http://www.w3.org/2000/svg\",\"pattern\"),s=a(this.__ids);if(i.setAttribute(\"id\",s),i.setAttribute(\"width\",\"\"+this._to_number(t.width)),i.setAttribute(\"height\",\"\"+this._to_number(t.height)),i.setAttribute(\"patternUnits\",\"userSpaceOnUse\"),t instanceof HTMLCanvasElement||t instanceof HTMLImageElement||t instanceof SVGImageElement){const e=this.__document.createElementNS(\"http://www.w3.org/2000/svg\",\"image\"),s=t instanceof HTMLCanvasElement?t.toDataURL():t.getAttribute(\"src\");e.setAttributeNS(\"http://www.w3.org/1999/xlink\",\"xlink:href\",s),i.appendChild(e),this.__defs.appendChild(i)}else if(t instanceof p){for(const e of[...t.__root.childNodes])e instanceof SVGDefsElement||i.appendChild(e);this.__defs.appendChild(i)}else{if(!(t instanceof SVGSVGElement))throw new Error(\"unsupported\");for(const e of[...t.childNodes])e instanceof SVGDefsElement||i.appendChild(e);this.__defs.appendChild(i)}return new u(i,this)}setLineDash(t){t&&t.length>0?this.lineDash=t.join(\",\"):this.lineDash=null}_to_number(t){return n.isNumber(t)?t:t.baseVal.value}}i.SVGRenderingContext2D=p,p.__name__=\"SVGRenderingContext2D\"},\n",
+ " function _(t,s,r){Object.defineProperty(r,\"__esModule\",{value:!0});const{sin:e,cos:n}=Math;class i{constructor(t=1,s=0,r=0,e=1,n=0,i=0){this.a=t,this.b=s,this.c=r,this.d=e,this.e=n,this.f=i}toString(){const{a:t,b:s,c:r,d:e,e:n,f:i}=this;return`matrix(${t}, ${s}, ${r}, ${e}, ${n}, ${i})`}clone(){const{a:t,b:s,c:r,d:e,e:n,f:a}=this;return new i(t,s,r,e,n,a)}get is_identity(){const{a:t,b:s,c:r,d:e,e:n,f:i}=this;return 1==t&&0==s&&0==r&&1==e&&0==n&&0==i}apply(t,s){const{a:r,b:e,c:n,d:i,e:a,f:h}=this;return[r*t+n*s+a,e*t+i*s+h]}iv_apply(t,s){const{a:r,b:e,c:n,d:i,e:a,f:h}=this,c=t.length;for(let o=0;o{const e=document.createElement(\"canvas\"),t=e.getContext(\"webgl\",{premultipliedAlpha:!0});return null!=t?{canvas:e,gl:t}:void l.logger.trace(\"WebGL is not supported\")})(),v={position:\"absolute\",top:\"0\",left:\"0\",width:\"100%\",height:\"100%\"};class b{constructor(e,t){switch(this.backend=e,this.hidpi=t,this.pixel_ratio=1,this.bbox=new c.BBox,e){case\"webgl\":case\"canvas\":{this._el=this._canvas=r.canvas({style:v});const e=this.canvas.getContext(\"2d\");if(null==e)throw new Error(\"unable to obtain 2D rendering context\");this._ctx=e,t&&(this.pixel_ratio=devicePixelRatio);break}case\"svg\":{const e=new d.SVGRenderingContext2D;this._ctx=e,this._canvas=e.get_svg(),this._el=r.div({style:v},this._canvas);break}}_.fixup_ctx(this._ctx)}get canvas(){return this._canvas}get ctx(){return this._ctx}get el(){return this._el}resize(e,t){this.bbox=new c.BBox({left:0,top:0,width:e,height:t});const i=this._ctx instanceof d.SVGRenderingContext2D?this._ctx:this.canvas;i.width=e*this.pixel_ratio,i.height=t*this.pixel_ratio}prepare(){const{ctx:e,hidpi:t,pixel_ratio:i}=this;e.save(),t&&(e.scale(i,i),e.translate(.5,.5)),this.clear()}clear(){const{x:e,y:t,width:i,height:s}=this.bbox;this.ctx.clearRect(e,t,i,s)}finish(){this.ctx.restore()}to_blob(){const{_canvas:e}=this;if(e instanceof HTMLCanvasElement)return null!=e.msToBlob?Promise.resolve(e.msToBlob()):new Promise((t,i)=>{e.toBlob(e=>null!=e?t(e):i(),\"image/png\")});{const e=this._ctx.get_serialized_svg(!0),t=new Blob([e],{type:\"image/svg+xml\"});return Promise.resolve(t)}}}i.CanvasLayer=b,b.__name__=\"CanvasLayer\";class g extends n.DOMView{constructor(){super(...arguments),this.bbox=new c.BBox}initialize(){super.initialize();const{output_backend:e,hidpi:t}=this.model;\"webgl\"==e&&(this.webgl=p),this.underlays_el=r.div({style:v}),this.primary=new b(e,t),this.overlays=new b(e,t),this.overlays_el=r.div({style:v}),this.events_el=r.div({class:\"bk-canvas-events\",style:v});const i=[this.underlays_el,this.primary.el,this.overlays.el,this.overlays_el,this.events_el];h.extend(this.el.style,v),r.append(this.el,...i),l.logger.debug(\"CanvasView initialized\")}add_underlay(e){this.underlays_el.appendChild(e)}add_overlay(e){this.overlays_el.appendChild(e)}add_event(e){this.events_el.appendChild(e)}get pixel_ratio(){return this.primary.pixel_ratio}resize(e,t){this.bbox=new c.BBox({left:0,top:0,width:e,height:t}),this.primary.resize(e,t),this.overlays.resize(e,t)}prepare_webgl(e){const{webgl:t}=this;if(null!=t){const{width:i,height:s}=this.bbox;t.canvas.width=this.pixel_ratio*i,t.canvas.height=this.pixel_ratio*s;const{gl:a}=t;a.enable(a.SCISSOR_TEST);const[n,l,o,r]=e,{xview:h,yview:c}=this.bbox,_=h.compute(n),d=c.compute(l+r),p=this.pixel_ratio;a.scissor(p*_,p*d,p*o,p*r),a.enable(a.BLEND),a.blendFuncSeparate(a.SRC_ALPHA,a.ONE_MINUS_SRC_ALPHA,a.ONE_MINUS_DST_ALPHA,a.ONE)}}clear_webgl(){const{webgl:e}=this;if(null!=e){const{gl:t,canvas:i}=e;t.viewport(0,0,i.width,i.height),t.clearColor(0,0,0,0),t.clear(t.COLOR_BUFFER_BIT||t.DEPTH_BUFFER_BIT)}}blit_webgl(e){const{webgl:t}=this;if(null!=t&&(l.logger.debug(\"Blitting WebGL canvas\"),e.restore(),e.drawImage(t.canvas,0,0),e.save(),this.model.hidpi)){const t=this.pixel_ratio;e.scale(t,t),e.translate(.5,.5)}}compose(){const{output_backend:e,hidpi:t}=this.model,{width:i,height:s}=this.bbox,a=new b(e,t);return a.resize(i,s),a.ctx.drawImage(this.primary.canvas,0,0),a.ctx.drawImage(this.overlays.canvas,0,0),a}to_blob(){return this.compose().to_blob()}}i.CanvasView=g,g.__name__=\"CanvasView\";class x extends a.HasProps{constructor(e){super(e)}static init_Canvas(){this.prototype.default_view=g,this.internal({hidpi:[o.Boolean,!0],output_backend:[o.OutputBackend,\"canvas\"]})}}i.Canvas=x,x.__name__=\"Canvas\",x.init_Canvas()},\n",
+ " function _(e,s,t){Object.defineProperty(t,\"__esModule\",{value:!0});const i=e(71),r=e(72);class n extends i.View{initialize(){super.initialize(),this.el=this._createElement()}remove(){r.remove(this.el),super.remove()}css_classes(){return[]}render(){}renderTo(e){e.appendChild(this.el),this.render()}_createElement(){return r.createElement(this.tagName,{class:this.css_classes()})}}t.DOMView=n,n.__name__=\"DOMView\",n.prototype.tagName=\"div\"},\n",
+ " function _(t,i,e){Object.defineProperty(e,\"__esModule\",{value:!0});const h=t(24),{min:r,max:s}=Math;e.empty=function(){return{x0:1/0,y0:1/0,x1:-1/0,y1:-1/0}},e.positive_x=function(){return{x0:Number.MIN_VALUE,y0:-1/0,x1:1/0,y1:1/0}},e.positive_y=function(){return{x0:-1/0,y0:Number.MIN_VALUE,x1:1/0,y1:1/0}},e.union=function(t,i){return{x0:r(t.x0,i.x0),x1:s(t.x1,i.x1),y0:r(t.y0,i.y0),y1:s(t.y1,i.y1)}};class n{constructor(t){if(null==t)this.x0=0,this.y0=0,this.x1=0,this.y1=0;else if(\"x0\"in t){const{x0:i,y0:e,x1:h,y1:r}=t;if(!(i<=h&&e<=r))throw new Error(`invalid bbox {x0: ${i}, y0: ${e}, x1: ${h}, y1: ${r}}`);this.x0=i,this.y0=e,this.x1=h,this.y1=r}else if(\"x\"in t){const{x:i,y:e,width:h,height:r}=t;if(!(h>=0&&r>=0))throw new Error(`invalid bbox {x: ${i}, y: ${e}, width: ${h}, height: ${r}}`);this.x0=i,this.y0=e,this.x1=i+h,this.y1=e+r}else{let i,e,h,r;if(\"width\"in t)if(\"left\"in t)i=t.left,e=i+t.width;else if(\"right\"in t)e=t.right,i=e-t.width;else{const h=t.width/2;i=t.hcenter-h,e=t.hcenter+h}else i=t.left,e=t.right;if(\"height\"in t)if(\"top\"in t)h=t.top,r=h+t.height;else if(\"bottom\"in t)r=t.bottom,h=r-t.height;else{const i=t.height/2;h=t.vcenter-i,r=t.vcenter+i}else h=t.top,r=t.bottom;if(!(i<=e&&h<=r))throw new Error(`invalid bbox {left: ${i}, top: ${h}, right: ${e}, bottom: ${r}}`);this.x0=i,this.y0=h,this.x1=e,this.y1=r}}toString(){return`BBox({left: ${this.left}, top: ${this.top}, width: ${this.width}, height: ${this.height}})`}get left(){return this.x0}get top(){return this.y0}get right(){return this.x1}get bottom(){return this.y1}get p0(){return[this.x0,this.y0]}get p1(){return[this.x1,this.y1]}get x(){return this.x0}get y(){return this.y0}get width(){return this.x1-this.x0}get height(){return this.y1-this.y0}get rect(){return{x0:this.x0,y0:this.y0,x1:this.x1,y1:this.y1}}get box(){return{x:this.x,y:this.y,width:this.width,height:this.height}}get h_range(){return{start:this.x0,end:this.x1}}get v_range(){return{start:this.y0,end:this.y1}}get ranges(){return[this.h_range,this.v_range]}get aspect(){return this.width/this.height}get hcenter(){return(this.left+this.right)/2}get vcenter(){return(this.top+this.bottom)/2}relativize(){const{width:t,height:i}=this;return new n({x:0,y:0,width:t,height:i})}contains(t,i){return t>=this.x0&&t<=this.x1&&i>=this.y0&&i<=this.y1}clip(t,i){return tthis.x1&&(t=this.x1),ithis.y1&&(i=this.y1),[t,i]}union(t){return new n({x0:r(this.x0,t.x0),y0:r(this.y0,t.y0),x1:s(this.x1,t.x1),y1:s(this.y1,t.y1)})}equals(t){return this.x0==t.x0&&this.y0==t.y0&&this.x1==t.x1&&this.y1==t.y1}get xview(){return{compute:t=>this.left+t,v_compute:t=>{const i=new h.NumberArray(t.length),e=this.left;for(let h=0;hthis.bottom-t,v_compute:t=>{const i=new h.NumberArray(t.length),e=this.bottom;for(let h=0;he.getLineDash(),set:t=>e.setLineDash(t)})}(e),function(e){e.setImageSmoothingEnabled=t=>{e.imageSmoothingEnabled=t,e.mozImageSmoothingEnabled=t,e.oImageSmoothingEnabled=t,e.webkitImageSmoothingEnabled=t,e.msImageSmoothingEnabled=t},e.getImageSmoothingEnabled=()=>{const t=e.imageSmoothingEnabled;return null==t||t}}(e),function(e){e.measureText&&null==e.html5MeasureText&&(e.html5MeasureText=e.measureText,e.measureText=t=>{const n=e.html5MeasureText(t);return n.ascent=1.6*e.html5MeasureText(\"m\").width,n})}(e),function(e){e.ellipse||(e.ellipse=function(t,n,o,a,i,l,m,r=!1){const u=.551784;e.translate(t,n),e.rotate(i);let s=o,g=a;r&&(s=-o,g=-a),e.moveTo(-s,0),e.bezierCurveTo(-s,g*u,-s*u,g,0,g),e.bezierCurveTo(s*u,g,s,g*u,s,0),e.bezierCurveTo(s,-g*u,s*u,-g,0,-g),e.bezierCurveTo(-s*u,-g,-s,-g*u,-s,0),e.rotate(-i),e.translate(-t,-n)})}(e)}},\n",
+ " function _(e,t,s){Object.defineProperty(s,\"__esModule\",{value:!0});const n=e(1),c=e(14),i=n.__importStar(e(18)),a=e(8),r=e(13),o=e(19);class l extends c.HasProps{constructor(e){super(e)}static init_Model(){this.define({tags:[i.Array,[]],name:[i.String],js_property_callbacks:[i.Any,{}],js_event_callbacks:[i.Any,{}],subscribed_events:[i.Array,[]]})}initialize(){super.initialize(),this._js_callbacks=new Map}connect_signals(){super.connect_signals(),this._update_property_callbacks(),this.connect(this.properties.js_property_callbacks.change,()=>this._update_property_callbacks()),this.connect(this.properties.js_event_callbacks.change,()=>this._update_event_callbacks()),this.connect(this.properties.subscribed_events.change,()=>this._update_event_callbacks())}_process_event(e){for(const t of this.js_event_callbacks[e.event_name]||[])t.execute(e);null!=this.document&&this.subscribed_events.some(t=>t==e.event_name)&&this.document.event_manager.send_event(e)}trigger_event(e){null!=this.document&&(e.origin=this,this.document.event_manager.trigger(e))}_update_event_callbacks(){null!=this.document?this.document.event_manager.subscribed_models.add(this):o.logger.warn(\"WARNING: Document not defined for updating event callbacks\")}_update_property_callbacks(){const e=e=>{const[t,s=null]=e.split(\":\");return null!=s?this.properties[s][t]:this[t]};for(const[t,s]of this._js_callbacks){const n=e(t);for(const e of s)this.disconnect(n,e)}this._js_callbacks.clear();for(const[t,s]of r.entries(this.js_property_callbacks)){const n=s.map(e=>()=>e.execute(this));this._js_callbacks.set(t,n);const c=e(t);for(const e of n)this.connect(c,e)}}_doc_attached(){r.isEmpty(this.js_event_callbacks)&&0==this.subscribed_events.length||this._update_event_callbacks()}_doc_detached(){this.document.event_manager.subscribed_models.delete(this)}select(e){if(a.isString(e))return[...this.references()].filter(t=>t instanceof l&&t.name===e);if(e.prototype instanceof c.HasProps)return[...this.references()].filter(t=>t instanceof e);throw new Error(\"invalid selector\")}select_one(e){const t=this.select(e);switch(t.length){case 0:return null;case 1:return t[0];default:throw new Error(\"found more than one object matching given selector\")}}}s.Model=l,l.__name__=\"Model\",l.init_Model()},\n",
+ " function _(e,s,_){Object.defineProperty(_,\"__esModule\",{value:!0});class t{constructor(e,s){this.x_scale=e,this.y_scale=s,this.x_range=this.x_scale.source_range,this.y_range=this.y_scale.source_range,this.ranges=[this.x_range,this.y_range],this.scales=[this.x_scale,this.y_scale]}map_to_screen(e,s){return[this.x_scale.v_compute(e),this.y_scale.v_compute(s)]}map_from_screen(e,s){return[this.x_scale.v_invert(e),this.y_scale.v_invert(s)]}}_.CoordinateTransform=t,t.__name__=\"CoordinateTransform\"},\n",
+ " function _(t,e,s){Object.defineProperty(s,\"__esModule\",{value:!0});const i=t(1),a=t(36),o=t(84),r=t(85),n=t(28),_=i.__importStar(t(18)),h=t(10);class c extends a.AnnotationView{initialize(){super.initialize(),null==this.model.source&&(this.model.source=new r.ColumnDataSource),this.set_data(this.model.source)}connect_signals(){super.connect_signals(),this.connect(this.model.change,()=>this.set_data(this.model.source)),this.connect(this.model.source.streaming,()=>this.set_data(this.model.source)),this.connect(this.model.source.patching,()=>this.set_data(this.model.source)),this.connect(this.model.source.change,()=>this.set_data(this.model.source))}set_data(t){super.set_data(t),this.visuals.warm_cache(t),this.plot_view.request_render()}_map_data(){const{frame:t}=this.plot_view;let e,s,i,a;return\"data\"==this.model.start_units?(e=this.coordinates.x_scale.v_compute(this._x_start),s=this.coordinates.y_scale.v_compute(this._y_start)):(e=t.xview.v_compute(this._x_start),s=t.yview.v_compute(this._y_start)),\"data\"==this.model.end_units?(i=this.coordinates.x_scale.v_compute(this._x_end),a=this.coordinates.y_scale.v_compute(this._y_end)):(i=t.xview.v_compute(this._x_end),a=t.yview.v_compute(this._y_end)),[[e,s],[i,a]]}_render(){const{ctx:t}=this.layer;t.save();const[e,s]=this._map_data();null!=this.model.end&&this._arrow_head(t,\"render\",this.model.end,e,s),null!=this.model.start&&this._arrow_head(t,\"render\",this.model.start,s,e),t.beginPath();const{x:i,y:a,width:o,height:r}=this.plot_view.frame.bbox;t.rect(i,a,o,r),null!=this.model.end&&this._arrow_head(t,\"clip\",this.model.end,e,s),null!=this.model.start&&this._arrow_head(t,\"clip\",this.model.start,s,e),t.closePath(),t.clip(),this._arrow_body(t,e,s),t.restore()}_arrow_head(t,e,s,i,a){for(let o=0,r=this._x_start.length;onew o.OpenHead({})],source:[_.Instance]})}}s.Arrow=d,d.__name__=\"Arrow\",d.init_Arrow()},\n",
+ " function _(i,e,s){Object.defineProperty(s,\"__esModule\",{value:!0});const t=i(1),o=i(36),l=i(74),n=i(28),h=t.__importStar(i(18));class a extends o.Annotation{constructor(i){super(i)}static init_ArrowHead(){this.define({size:[h.Number,25]})}initialize(){super.initialize(),this.visuals=new l.Visuals(this)}}s.ArrowHead=a,a.__name__=\"ArrowHead\",a.init_ArrowHead();class r extends a{constructor(i){super(i)}static init_OpenHead(){this.mixins(n.LineVector)}clip(i,e){this.visuals.line.set_vectorize(i,e),i.moveTo(.5*this.size,this.size),i.lineTo(.5*this.size,-2),i.lineTo(-.5*this.size,-2),i.lineTo(-.5*this.size,this.size),i.lineTo(0,0),i.lineTo(.5*this.size,this.size)}render(i,e){this.visuals.line.doit&&(this.visuals.line.set_vectorize(i,e),i.beginPath(),i.moveTo(.5*this.size,this.size),i.lineTo(0,0),i.lineTo(-.5*this.size,this.size),i.stroke())}}s.OpenHead=r,r.__name__=\"OpenHead\",r.init_OpenHead();class z extends a{constructor(i){super(i)}static init_NormalHead(){this.mixins([n.LineVector,n.FillVector]),this.override({fill_color:\"black\"})}clip(i,e){this.visuals.line.set_vectorize(i,e),i.moveTo(.5*this.size,this.size),i.lineTo(.5*this.size,-2),i.lineTo(-.5*this.size,-2),i.lineTo(-.5*this.size,this.size),i.lineTo(.5*this.size,this.size)}render(i,e){this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(i,e),this._normal(i,e),i.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(i,e),this._normal(i,e),i.stroke())}_normal(i,e){i.beginPath(),i.moveTo(.5*this.size,this.size),i.lineTo(0,0),i.lineTo(-.5*this.size,this.size),i.closePath()}}s.NormalHead=z,z.__name__=\"NormalHead\",z.init_NormalHead();class _ extends a{constructor(i){super(i)}static init_VeeHead(){this.mixins([n.LineVector,n.FillVector]),this.override({fill_color:\"black\"})}clip(i,e){this.visuals.line.set_vectorize(i,e),i.moveTo(.5*this.size,this.size),i.lineTo(.5*this.size,-2),i.lineTo(-.5*this.size,-2),i.lineTo(-.5*this.size,this.size),i.lineTo(0,.5*this.size),i.lineTo(.5*this.size,this.size)}render(i,e){this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(i,e),this._vee(i,e),i.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(i,e),this._vee(i,e),i.stroke())}_vee(i,e){i.beginPath(),i.moveTo(.5*this.size,this.size),i.lineTo(0,0),i.lineTo(-.5*this.size,this.size),i.lineTo(0,.5*this.size),i.closePath()}}s.VeeHead=_,_.__name__=\"VeeHead\",_.init_VeeHead();class c extends a{constructor(i){super(i)}static init_TeeHead(){this.mixins(n.LineVector)}render(i,e){this.visuals.line.doit&&(this.visuals.line.set_vectorize(i,e),i.beginPath(),i.moveTo(.5*this.size,0),i.lineTo(-.5*this.size,0),i.stroke())}clip(i,e){}}s.TeeHead=c,c.__name__=\"TeeHead\",c.init_TeeHead()},\n",
+ " function _(t,n,e){Object.defineProperty(e,\"__esModule\",{value:!0});const s=t(1),o=t(86),r=s.__importStar(t(18)),i=t(8),l=t(13),a=s.__importStar(t(119)),c=t(120),u=t(121);function h(t,n,e){if(i.isArray(t)){const s=t.concat(n);return null!=e&&s.length>e?s.slice(-e):s}if(i.isTypedArray(t)){const s=t.length+n.length;if(null!=e&&s>e){const o=s-e,r=t.length;let i;t.lengthnew _.UnionRenderers]}),this.internal({selection_manager:[c.Instance,t=>new l.SelectionManager({source:t})],inspected:[c.Instance,()=>new g.Selection]})}initialize(){super.initialize(),this._select=new i.Signal0(this,\"select\"),this.inspect=new i.Signal(this,\"inspect\"),this.streaming=new i.Signal0(this,\"streaming\"),this.patching=new i.Signal(this,\"patching\")}get_column(t){const e=this.data[t];return null!=e?e:null}columns(){return h.keys(this.data)}get_length(t=!0){const e=u.uniq(h.values(this.data).map(t=>t.length));switch(e.length){case 0:return null;case 1:return e[0];default:{const n=\"data source has columns of inconsistent lengths\";if(t)return r.logger.warn(n),e.sort()[0];throw new Error(n)}}}get length(){var t;return null!==(t=this.get_length())&&void 0!==t?t:0}clear(){const t={};for(const e of this.columns())t[e]=new this.data[e].constructor(0);this.data=t}}n.ColumnarDataSource=d,d.__name__=\"ColumnarDataSource\",d.init_ColumnarDataSource()},\n",
+ " function _(e,t,a){Object.defineProperty(a,\"__esModule\",{value:!0});const c=e(1),n=e(81),o=e(88),i=c.__importStar(e(18));class r extends n.Model{constructor(e){super(e)}static init_DataSource(){this.define({selected:[i.Instance,()=>new o.Selection]})}}a.DataSource=r,r.__name__=\"DataSource\",r.init_DataSource()},\n",
+ " function _(i,e,s){Object.defineProperty(s,\"__esModule\",{value:!0});const t=i(1),n=i(81),l=t.__importStar(i(18)),c=i(9),h=i(13);class d extends n.Model{constructor(i){super(i)}get_view(){return this.view}static init_Selection(){this.define({indices:[l.Array,[]],line_indices:[l.Array,[]],multiline_indices:[l.Any,{}]}),this.internal({selected_glyphs:[l.Array,[]],view:[l.Any],image_indices:[l.Array,[]]})}initialize(){super.initialize()}get selected_glyph(){return this.selected_glyphs.length>0?this.selected_glyphs[0]:null}add_to_selected_glyphs(i){this.selected_glyphs.push(i)}update(i,e=!0,s=\"replace\"){switch(s){case\"replace\":this.indices=i.indices,this.line_indices=i.line_indices,this.selected_glyphs=i.selected_glyphs,this.view=i.view,this.multiline_indices=i.multiline_indices,this.image_indices=i.image_indices;break;case\"append\":this.update_through_union(i);break;case\"intersect\":this.update_through_intersection(i);break;case\"subtract\":this.update_through_subtraction(i)}}clear(){this.indices=[],this.line_indices=[],this.multiline_indices={},this.view=null,this.selected_glyphs=[]}is_empty(){return 0==this.indices.length&&0==this.line_indices.length&&0==this.image_indices.length}update_through_union(i){this.indices=c.union(this.indices,i.indices),this.selected_glyphs=c.union(i.selected_glyphs,this.selected_glyphs),this.line_indices=c.union(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=h.merge(i.multiline_indices,this.multiline_indices)}update_through_intersection(i){this.indices=c.intersection(this.indices,i.indices),this.selected_glyphs=c.union(i.selected_glyphs,this.selected_glyphs),this.line_indices=c.union(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=h.merge(i.multiline_indices,this.multiline_indices)}update_through_subtraction(i){this.indices=c.difference(this.indices,i.indices),this.selected_glyphs=c.union(i.selected_glyphs,this.selected_glyphs),this.line_indices=c.union(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=h.merge(i.multiline_indices,this.multiline_indices)}}s.Selection=d,d.__name__=\"Selection\",d.init_Selection()},\n",
+ " function _(e,t,s){Object.defineProperty(s,\"__esModule\",{value:!0});const i=e(1),n=e(14),o=e(88),c=e(90),r=e(116),l=i.__importStar(e(18));class p extends n.HasProps{constructor(e){super(e),this.inspectors=new Map}static init_SelectionManager(){this.internal({source:[l.Any]})}select(e,t,s,i=\"replace\"){const n=[],o=[];for(const t of e)t instanceof c.GlyphRendererView?n.push(t):t instanceof r.GraphRendererView&&o.push(t);let l=!1;for(const e of o){const n=e.model.selection_policy.hit_test(t,e);l=l||e.model.selection_policy.do_selection(n,e.model,s,i)}if(n.length>0){const e=this.source.selection_policy.hit_test(t,n);l=l||this.source.selection_policy.do_selection(e,this.source,s,i)}return l}inspect(e,t){let s=!1;if(e instanceof c.GlyphRendererView){const i=e.hit_test(t);if(null!=i){s=!i.is_empty();const n=this.get_or_create_inspector(e.model);n.update(i,!0,\"replace\"),this.source.setv({inspected:n},{silent:!0}),this.source.inspect.emit([e,{geometry:t}])}}else if(e instanceof r.GraphRendererView){const i=e.model.inspection_policy.hit_test(t,e);s=s||e.model.inspection_policy.do_inspection(i,t,e,!1,\"replace\")}return s}clear(e){this.source.selected.clear(),null!=e&&this.get_or_create_inspector(e.model).clear()}get_or_create_inspector(e){let t=this.inspectors.get(e);return null==t&&(t=new o.Selection,this.inspectors.set(e,t)),t}}s.SelectionManager=p,p.__name__=\"SelectionManager\",p.init_SelectionManager()},\n",
+ " function _(e,t,i){Object.defineProperty(i,\"__esModule\",{value:!0});const s=e(1),l=e(91),n=e(92),h=e(110),o=e(111),a=e(113),c=e(114),_=e(24),d=s.__importStar(e(18)),r=e(12),p=e(9),g=e(13),u=e(115),y=e(98),m={fill:{},line:{}},v={fill:{fill_alpha:.3,fill_color:\"grey\"},line:{line_alpha:.3,line_color:\"grey\"}},f={fill:{fill_alpha:.2},line:{}};class w extends l.DataRendererView{async lazy_initialize(){await super.lazy_initialize();const e=this.model.glyph,t=p.includes(e._mixins,\"fill\"),i=p.includes(e._mixins,\"line\"),s=g.clone(e.attributes);function l(l){const n=g.clone(s);return t&&g.extend(n,l.fill),i&&g.extend(n,l.line),new e.constructor(n)}delete s.id,this.glyph=await this.build_glyph_view(e);let{selection_glyph:n}=this.model;null==n?n=l({fill:{},line:{}}):\"auto\"===n&&(n=l(m)),this.selection_glyph=await this.build_glyph_view(n);let{nonselection_glyph:h}=this.model;null==h?h=l({fill:{},line:{}}):\"auto\"===h&&(h=l(f)),this.nonselection_glyph=await this.build_glyph_view(h);const{hover_glyph:o}=this.model;null!=o&&(this.hover_glyph=await this.build_glyph_view(o));const{muted_glyph:a}=this.model;null!=a&&(this.muted_glyph=await this.build_glyph_view(a));const c=l(v);this.decimated_glyph=await this.build_glyph_view(c),this.set_data(!1)}async build_glyph_view(e){return u.build_view(e,{parent:this})}remove(){var e,t;this.glyph.remove(),this.selection_glyph.remove(),this.nonselection_glyph.remove(),null===(e=this.hover_glyph)||void 0===e||e.remove(),null===(t=this.muted_glyph)||void 0===t||t.remove(),this.decimated_glyph.remove(),super.remove()}connect_signals(){super.connect_signals(),this.connect(this.model.change,()=>this.request_render()),this.connect(this.model.glyph.change,()=>this.set_data()),this.connect(this.model.data_source.change,()=>this.set_data()),this.connect(this.model.data_source.streaming,()=>this.set_data()),this.connect(this.model.data_source.patching,e=>this.set_data(!0,e)),this.connect(this.model.data_source.selected.change,()=>this.request_render()),this.connect(this.model.data_source._select,()=>this.request_render()),null!=this.hover_glyph&&this.connect(this.model.data_source.inspect,()=>this.request_render()),this.connect(this.model.properties.view.change,()=>this.set_data()),this.connect(this.model.view.properties.indices.change,()=>this.set_data()),this.connect(this.model.view.properties.masked.change,()=>this.set_visuals()),this.connect(this.model.properties.visible.change,()=>this.plot_view.update_dataranges());const{x_ranges:e,y_ranges:t}=this.plot_view.frame;for(const[,t]of e)t instanceof y.FactorRange&&this.connect(t.change,()=>this.set_data());for(const[,e]of t)e instanceof y.FactorRange&&this.connect(e.change,()=>this.set_data());this.connect(this.model.glyph.transformchange,()=>this.set_data())}_update_masked_indices(){const e=this.glyph.mask_data();return this.model.view.masked=e,e}set_data(e=!0,t=null){const i=this.model.data_source;this.all_indices=this.model.view.indices;const{all_indices:s}=this;this.glyph.set_data(i,s,t),this.set_visuals(),this._update_masked_indices();const{lod_factor:l}=this.plot_model,n=this.all_indices.count;this.decimated=new _.Indices(n);for(let e=0;e!_||_.is_empty()?[]:_.selected_glyph?this.model.view.convert_indices_from_subset(i):_.indices.length>0?_.indices:Object.keys(_.multiline_indices).map(e=>parseInt(e)))()),g=r.filter(i,e=>d.has(t[e])),{lod_threshold:u}=this.plot_model;let y,m,v;if(null!=this.model.document&&this.model.document.interactive_duration()>0&&!e&&null!=u&&t.length>u?(i=[...this.decimated],y=this.decimated_glyph,m=this.decimated_glyph,v=this.selection_glyph):(y=this.model.muted&&null!=this.muted_glyph?this.muted_glyph:this.glyph,m=this.nonselection_glyph,v=this.selection_glyph),null!=this.hover_glyph&&g.length&&(i=p.difference(i,g)),c.length){const e={};for(const t of c)e[t]=!0;const l=new Array,h=new Array;if(this.glyph instanceof n.LineView)for(const i of t)null!=e[i]?l.push(i):h.push(i);else for(const s of i)null!=e[t[s]]?l.push(s):h.push(s);m.render(s,h,this.glyph),v.render(s,l,this.glyph),null!=this.hover_glyph&&(this.glyph instanceof n.LineView?this.hover_glyph.render(s,this.model.view.convert_indices_from_subset(g),this.glyph):this.hover_glyph.render(s,g,this.glyph))}else if(this.glyph instanceof n.LineView)this.hover_glyph&&g.length?this.hover_glyph.render(s,this.model.view.convert_indices_from_subset(g),this.glyph):y.render(s,t,this.glyph);else if(this.glyph instanceof h.PatchView||this.glyph instanceof o.HAreaView||this.glyph instanceof a.VAreaView)if(0==_.selected_glyphs.length||null==this.hover_glyph)y.render(s,t,this.glyph);else for(const e of _.selected_glyphs)e==this.glyph.model&&this.hover_glyph.render(s,t,this.glyph);else y.render(s,i,this.glyph),this.hover_glyph&&g.length&&this.hover_glyph.render(s,g,this.glyph);s.restore()}draw_legend(e,t,i,s,l,n,h,o){null==o&&(o=this.model.get_reference_point(n,h)),this.glyph.draw_legend_for_index(e,{x0:t,x1:i,y0:s,y1:l},o)}hit_test(e){if(!this.model.visible)return null;const t=this.glyph.hit_test(e);return null==t?null:this.model.view.convert_selection_from_subset(t)}}i.GlyphRendererView=w,w.__name__=\"GlyphRendererView\";class b extends l.DataRenderer{constructor(e){super(e)}static init_GlyphRenderer(){this.prototype.default_view=w,this.define({data_source:[d.Instance],view:[d.Instance,()=>new c.CDSView],glyph:[d.Instance],hover_glyph:[d.Instance],nonselection_glyph:[d.Any,\"auto\"],selection_glyph:[d.Any,\"auto\"],muted_glyph:[d.Instance],muted:[d.Boolean,!1]})}initialize(){super.initialize(),null==this.view.source&&(this.view.source=this.data_source,this.view.compute_indices())}get_reference_point(e,t){let i=0;if(null!=e){const s=this.data_source.get_column(e);if(null!=s){const e=r.indexOf(s,t);-1!=e&&(i=e)}}return i}get_selection_manager(){return this.data_source.selection_manager}}i.GlyphRenderer=b,b.__name__=\"GlyphRenderer\",b.init_GlyphRenderer()},\n",
+ " function _(e,r,t){Object.defineProperty(t,\"__esModule\",{value:!0});const a=e(70);class n extends a.RendererView{get xscale(){return this.coordinates.x_scale}get yscale(){return this.coordinates.y_scale}}t.DataRendererView=n,n.__name__=\"DataRendererView\";class s extends a.Renderer{constructor(e){super(e)}static init_DataRenderer(){this.override({level:\"glyph\"})}}t.DataRenderer=s,s.__name__=\"DataRenderer\",s.init_DataRenderer()},\n",
+ " function _(e,i,t){Object.defineProperty(t,\"__esModule\",{value:!0});const s=e(1),n=e(93),l=e(100),_=e(102),r=s.__importStar(e(28)),o=s.__importStar(e(101)),h=e(88);class a extends n.XYGlyphView{initialize(){super.initialize();const{webgl:e}=this.renderer.plot_view.canvas_view;null!=e&&(this.glglyph=new _.LineGL(e.gl,this))}_render(e,i,{sx:t,sy:s}){let n=!1,l=null;this.visuals.line.set_value(e);for(const _ of i){if(n){if(!isFinite(t[_]+s[_])){e.stroke(),e.beginPath(),n=!1,l=_;continue}null!=l&&_-l>1&&(e.stroke(),n=!1)}n?e.lineTo(t[_],s[_]):(e.beginPath(),e.moveTo(t[_],s[_]),n=!0),l=_}n&&e.stroke()}_hit_point(e){const i=new h.Selection,t={x:e.sx,y:e.sy};let s=9999;const n=Math.max(2,this.visuals.line.line_width.value()/2);for(let e=0,l=this.sx.length-1;ee/2);r=new h.NumberArray(_);for(let i=0;i<_;i++)r[i]=t[i]-e[i];a=new h.NumberArray(_);for(let i=0;i<_;i++)a[i]=t[i]+e[i]}else{r=t,a=new h.NumberArray(_);for(let e=0;e<_;e++)a[e]=r[e]+i[e]}const l=e.v_compute(r),o=e.v_compute(a);return n?d.map(l,(e,t)=>Math.ceil(Math.abs(o[t]-l[t]))):d.map(l,(e,t)=>Math.abs(o[t]-l[t]))}draw_legend_for_index(e,t,i){}hit_test(e){switch(e.type){case\"point\":if(null!=this._hit_point)return this._hit_point(e);break;case\"span\":if(null!=this._hit_span)return this._hit_span(e);break;case\"rect\":if(null!=this._hit_rect)return this._hit_rect(e);break;case\"poly\":if(null!=this._hit_poly)return this._hit_poly(e)}return this._nohit_warned.has(e.type)||(o.logger.debug(`'${e.type}' selection not available for ${this.model.type}`),this._nohit_warned.add(e.type)),null}_hit_rect_against_index(e){const{sx0:t,sx1:i,sy0:s,sy1:n}=e,[r,a]=this.renderer.coordinates.x_scale.r_invert(t,i),[_,l]=this.renderer.coordinates.y_scale.r_invert(s,n),o=[...this.index.indices({x0:r,x1:a,y0:_,y1:l})];return new p.Selection({indices:o})}_project_data(){}set_data(e,t,i){var s,r;const{x_range:a,y_range:_}=this.renderer.coordinates;this._data_size=null!==(s=e.get_length())&&void 0!==s?s:1;for(const i of this.model){if(!(i instanceof n.VectorSpec))continue;if(i.optional&&null==i.spec.value&&!i.dirty)continue;const s=i.attr,r=i.array(e);let l=t.select(r);if(i instanceof n.BaseCoordinateSpec){const e=\"x\"==i.dimension?a:_;if(e instanceof u.FactorRange)if(i instanceof n.CoordinateSpec)l=e.v_synthetic(l);else if(i instanceof n.CoordinateSeqSpec)for(let t=0;t>1;n[s]>e?i=s:t=s+1}return n[t]}class x extends i.default{search_indices(e,n,t,i){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let o=this._boxes.length-4;const x=[],h=new s.Indices(this.numItems);for(;void 0!==o;){const s=Math.min(o+4*this.nodeSize,d(o,this._levelBounds));for(let d=o;d>2];tthis._boxes[d+2]||n>this._boxes[d+3]||(o<4*this.numItems?h.set(s):x.push(s)))}o=x.pop()}return h}}x.__name__=\"_FlatBush\";class h{constructor(e){this.index=null,e>0&&(this.index=new x(e))}add(e,n,t,i){var s;null===(s=this.index)||void 0===s||s.add(e,n,t,i)}add_empty(){var e;null===(e=this.index)||void 0===e||e.add(1/0,1/0,-1/0,-1/0)}finish(){var e;null===(e=this.index)||void 0===e||e.finish()}_normalize(e){let{x0:n,y0:t,x1:i,y1:s}=e;return n>i&&([n,i]=[i,n]),t>s&&([t,s]=[s,t]),{x0:n,y0:t,x1:i,y1:s}}get bbox(){if(null==this.index)return o.empty();{const{minX:e,minY:n,maxX:t,maxY:i}=this.index;return{x0:e,y0:n,x1:t,y1:i}}}indices(e){if(null==this.index)return new s.Indices(0);{const{x0:n,y0:t,x1:i,y1:s}=this._normalize(e);return this.index.search_indices(n,t,i,s)}}bounds(e){const n=o.empty();for(const t of this.indices(e)){const e=this.index._boxes,i=e[4*t+0],s=e[4*t+1],o=e[4*t+2],d=e[4*t+3];on.x1&&(n.x1=i),dn.y1&&(n.y1=s)}return n}}t.SpatialIndex=h,h.__name__=\"SpatialIndex\"},\n",
+ " function _(t,s,i){Object.defineProperty(i,\"__esModule\",{value:!0});const e=t(1).__importDefault(t(97)),h=[Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array];class n{static from(t){if(!(t instanceof ArrayBuffer))throw new Error(\"Data must be an instance of ArrayBuffer.\");const[s,i]=new Uint8Array(t,0,2);if(251!==s)throw new Error(\"Data does not appear to be in a Flatbush format.\");if(i>>4!=3)throw new Error(`Got v${i>>4} data when expected v3.`);const[e]=new Uint16Array(t,2,1),[o]=new Uint32Array(t,4,1);return new n(o,e,h[15&i],t)}constructor(t,s=16,i=Float64Array,n){if(void 0===t)throw new Error(\"Missing required argument: numItems.\");if(isNaN(t)||t<=0)throw new Error(`Unpexpected numItems value: ${t}.`);this.numItems=+t,this.nodeSize=Math.min(Math.max(+s,2),65535);let o=t,r=o;this._levelBounds=[4*o];do{o=Math.ceil(o/this.nodeSize),r+=o,this._levelBounds.push(4*r)}while(1!==o);this.ArrayType=i||Float64Array,this.IndexArrayType=r<16384?Uint16Array:Uint32Array;const a=h.indexOf(this.ArrayType),_=4*r*this.ArrayType.BYTES_PER_ELEMENT;if(a<0)throw new Error(`Unexpected typed array class: ${i}.`);n&&n instanceof ArrayBuffer?(this.data=n,this._boxes=new this.ArrayType(this.data,8,4*r),this._indices=new this.IndexArrayType(this.data,8+_,r),this._pos=4*r,this.minX=this._boxes[this._pos-4],this.minY=this._boxes[this._pos-3],this.maxX=this._boxes[this._pos-2],this.maxY=this._boxes[this._pos-1]):(this.data=new ArrayBuffer(8+_+r*this.IndexArrayType.BYTES_PER_ELEMENT),this._boxes=new this.ArrayType(this.data,8,4*r),this._indices=new this.IndexArrayType(this.data,8+_,r),this._pos=0,this.minX=1/0,this.minY=1/0,this.maxX=-1/0,this.maxY=-1/0,new Uint8Array(this.data,0,2).set([251,48+a]),new Uint16Array(this.data,2,1)[0]=s,new Uint32Array(this.data,4,1)[0]=t),this._queue=new e.default}add(t,s,i,e){const h=this._pos>>2;return this._indices[h]=h,this._boxes[this._pos++]=t,this._boxes[this._pos++]=s,this._boxes[this._pos++]=i,this._boxes[this._pos++]=e,tthis.maxX&&(this.maxX=i),e>this.maxY&&(this.maxY=e),h}finish(){if(this._pos>>2!==this.numItems)throw new Error(`Added ${this._pos>>2} items when expected ${this.numItems}.`);if(this.numItems<=this.nodeSize)return this._boxes[this._pos++]=this.minX,this._boxes[this._pos++]=this.minY,this._boxes[this._pos++]=this.maxX,void(this._boxes[this._pos++]=this.maxY);const t=this.maxX-this.minX,s=this.maxY-this.minY,i=new Uint32Array(this.numItems);for(let e=0;e=Math.floor(n/o))return;const r=s[h+n>>1];let _=h-1,d=n+1;for(;;){do{_++}while(s[_]r);if(_>=d)break;a(s,i,e,_,d)}t(s,i,e,h,d,o),t(s,i,e,d+1,n,o)}(i,this._boxes,this._indices,0,this.numItems-1,this.nodeSize);for(let t=0,s=0;t>2]=t,this._boxes[this._pos++]=e,this._boxes[this._pos++]=h,this._boxes[this._pos++]=n,this._boxes[this._pos++]=o}}}search(t,s,i,e,h){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let n=this._boxes.length-4;const o=[],a=[];for(;void 0!==n;){const _=Math.min(n+4*this.nodeSize,r(n,this._levelBounds));for(let r=n;r<_;r+=4){const _=0|this._indices[r>>2];ithis._boxes[r+2]||s>this._boxes[r+3]||(n<4*this.numItems?(void 0===h||h(_))&&a.push(_):o.push(_)))}n=o.pop()}return a}neighbors(t,s,i=1/0,e=1/0,h){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let n=this._boxes.length-4;const a=this._queue,_=[],d=e*e;for(;void 0!==n;){const e=Math.min(n+4*this.nodeSize,r(n,this._levelBounds));for(let i=n;i>2],r=o(t,this._boxes[i],this._boxes[i+2]),_=o(s,this._boxes[i+1],this._boxes[i+3]),d=r*r+_*_;n<4*this.numItems?(void 0===h||h(e))&&a.push(-e-1,d):a.push(e,d)}for(;a.length&&a.peek()<0;){if(a.peekValue()>d)return a.clear(),_;if(_.push(-a.pop()-1),_.length===i)return a.clear(),_}n=a.pop()}return a.clear(),_}}function o(t,s,i){return t>1;s[h]>t?e=h:i=h+1}return s[i]}function a(t,s,i,e,h){const n=t[e];t[e]=t[h],t[h]=n;const o=4*e,r=4*h,a=s[o],_=s[o+1],d=s[o+2],x=s[o+3];s[o]=s[r],s[o+1]=s[r+1],s[o+2]=s[r+2],s[o+3]=s[r+3],s[r]=a,s[r+1]=_,s[r+2]=d,s[r+3]=x;const l=i[e];i[e]=i[h],i[h]=l}function _(t,s){let i=t^s,e=65535^i,h=65535^(t|s),n=t&(65535^s),o=i|e>>1,r=i>>1^i,a=h>>1^e&n>>1^h,_=i&h>>1^n>>1^n;i=o,e=r,h=a,n=_,o=i&i>>2^e&e>>2,r=i&e>>2^e&(i^e)>>2,a^=i&h>>2^e&n>>2,_^=e&h>>2^(i^e)&n>>2,i=o,e=r,h=a,n=_,o=i&i>>4^e&e>>4,r=i&e>>4^e&(i^e)>>4,a^=i&h>>4^e&n>>4,_^=e&h>>4^(i^e)&n>>4,i=o,e=r,h=a,n=_,a^=i&h>>8^e&n>>8,_^=e&h>>8^(i^e)&n>>8,i=a^a>>1,e=_^_>>1;let d=t^s,x=e|65535^(d|i);return d=16711935&(d|d<<8),d=252645135&(d|d<<4),d=858993459&(d|d<<2),d=1431655765&(d|d<<1),x=16711935&(x|x<<8),x=252645135&(x|x<<4),x=858993459&(x|x<<2),x=1431655765&(x|x<<1),(x<<1|d)>>>0}i.default=n},\n",
+ " function _(s,t,i){Object.defineProperty(i,\"__esModule\",{value:!0});i.default=class{constructor(){this.ids=[],this.values=[],this.length=0}clear(){this.length=0}push(s,t){let i=this.length++;for(this.ids[i]=s,this.values[i]=t;i>0;){const s=i-1>>1,h=this.values[s];if(t>=h)break;this.ids[i]=this.ids[s],this.values[i]=h,i=s}this.ids[i]=s,this.values[i]=t}pop(){if(0===this.length)return;const s=this.ids[0];if(this.length--,this.length>0){const s=this.ids[0]=this.ids[this.length],t=this.values[0]=this.values[this.length],i=this.length>>1;let h=0;for(;h=t)break;this.ids[h]=e,this.values[h]=l,h=s}this.ids[h]=s,this.values[h]=t}return s}peek(){if(0!==this.length)return this.ids[0]}peekValue(){if(0!==this.length)return this.values[0]}}},\n",
+ " function _(t,e,n){Object.defineProperty(n,\"__esModule\",{value:!0});const s=t(1),i=t(99),r=s.__importStar(t(18)),a=t(24),o=t(9),p=t(8),g=t(11);function c(t,e,n=0){const s=new Map;for(let i=0;ia.get(t).value));r.set(t,{value:u/i,mapping:a}),p+=i+e+l}return[r,(a.size-1)*e+g]}function u(t,e,n,s,i=0){var r;const a=new Map,p=new Map;for(const[e,n,s]of t){const t=null!==(r=p.get(e))&&void 0!==r?r:[];p.set(e,[...t,[n,s]])}let g=i,c=0;for(const[t,i]of p){const r=i.length,[p,u]=l(i,n,s,g);c+=u;const h=o.sum(i.map(([t])=>p.get(t).value));a.set(t,{value:h/r,mapping:p}),g+=r+e+u}return[a,(p.size-1)*e+c]}n.map_one_level=c,n.map_two_levels=l,n.map_three_levels=u;class h extends i.Range{constructor(t){super(t)}static init_FactorRange(){this.define({factors:[r.Array,[]],factor_padding:[r.Number,0],subgroup_padding:[r.Number,.8],group_padding:[r.Number,1.4],range_padding:[r.Number,0],range_padding_units:[r.PaddingUnits,\"percent\"],start:[r.Number],end:[r.Number]}),this.internal({levels:[r.Number],mids:[r.Array,null],tops:[r.Array,null]})}get min(){return this.start}get max(){return this.end}initialize(){super.initialize(),this._init(!0)}connect_signals(){super.connect_signals(),this.connect(this.properties.factors.change,()=>this.reset()),this.connect(this.properties.factor_padding.change,()=>this.reset()),this.connect(this.properties.group_padding.change,()=>this.reset()),this.connect(this.properties.subgroup_padding.change,()=>this.reset()),this.connect(this.properties.range_padding.change,()=>this.reset()),this.connect(this.properties.range_padding_units.change,()=>this.reset())}reset(){this._init(!1),this.change.emit()}_lookup(t){switch(t.length){case 1:{const[e]=t,n=this._mapping.get(e);return null!=n?n.value:NaN}case 2:{const[e,n]=t,s=this._mapping.get(e);if(null!=s){const t=s.mapping.get(n);if(null!=t)return t.value}return NaN}case 3:{const[e,n,s]=t,i=this._mapping.get(e);if(null!=i){const t=i.mapping.get(n);if(null!=t){const e=t.mapping.get(s);if(null!=e)return e.value}}return NaN}default:g.unreachable()}}synthetic(t){if(p.isNumber(t))return t;if(p.isString(t))return this._lookup([t]);let e=0;const n=t[t.length-1];return p.isNumber(n)&&(e=n,t=t.slice(0,-1)),this._lookup(t)+e}v_synthetic(t){const e=t.length,n=new a.NumberArray(e);for(let s=0;s{if(o.every(this.factors,p.isString)){const t=this.factors,[e,n]=c(t,this.factor_padding);return{levels:1,mapping:e,tops:null,mids:null,inside_padding:n}}if(o.every(this.factors,t=>p.isArray(t)&&2==t.length&&p.isString(t[0])&&p.isString(t[1]))){const t=this.factors,[e,n]=l(t,this.group_padding,this.factor_padding),s=[...e.keys()];return{levels:2,mapping:e,tops:s,mids:null,inside_padding:n}}if(o.every(this.factors,t=>p.isArray(t)&&3==t.length&&p.isString(t[0])&&p.isString(t[1])&&p.isString(t[2]))){const t=this.factors,[e,n]=u(t,this.group_padding,this.subgroup_padding,this.factor_padding),s=[...e.keys()],i=[];for(const[t,n]of e)for(const e of n.mapping.keys())i.push([t,e]);return{levels:3,mapping:e,tops:s,mids:i,inside_padding:n}}g.unreachable()})();this._mapping=n,this.tops=s,this.mids=i;let a=0,h=this.factors.length+r;if(\"percent\"==this.range_padding_units){const t=(h-a)*this.range_padding/2;a-=t,h+=t}else a-=this.range_padding,h+=this.range_padding;this.setv({start:a,end:h,levels:e},{silent:t}),\"auto\"==this.bounds&&this.setv({bounds:[a,h]},{silent:!0})}}n.FactorRange=h,h.__name__=\"FactorRange\",h.init_FactorRange()},\n",
+ " function _(e,t,i){Object.defineProperty(i,\"__esModule\",{value:!0});const n=e(1),s=e(81),a=n.__importStar(e(18));class r extends s.Model{constructor(e){super(e),this.have_updated_interactively=!1}static init_Range(){this.define({bounds:[a.Any],min_interval:[a.Any],max_interval:[a.Any]}),this.internal({plots:[a.Array,[]]})}get is_reversed(){return this.start>this.end}get is_valid(){return!isNaN(this.min)&&!isNaN(this.max)}}i.Range=r,r.__name__=\"Range\",r.init_Range()},\n",
+ " function _(e,t,i){Object.defineProperty(i,\"__esModule\",{value:!0});const n=e(1).__importStar(e(101));i.generic_line_legend=function(e,t,{x0:i,x1:n,y0:c,y1:o},r){t.save(),t.beginPath(),t.moveTo(i,(c+o)/2),t.lineTo(n,(c+o)/2),e.line.doit&&(e.line.set_vectorize(t,r),t.stroke()),t.restore()},i.generic_area_legend=function(e,t,{x0:i,x1:n,y0:c,y1:o},r){const l=.1*Math.abs(n-i),a=.1*Math.abs(o-c),s=i+l,_=n-l,h=c+a,v=o-a;e.fill.doit&&(e.fill.set_vectorize(t,r),t.fillRect(s,h,_-s,v-h)),null!=e.hatch&&e.hatch.doit&&(e.hatch.set_vectorize(t,r),t.fillRect(s,h,_-s,v-h)),e.line&&e.line.doit&&(t.beginPath(),t.rect(s,h,_-s,v-h),e.line.set_vectorize(t,r),t.stroke())},i.line_interpolation=function(e,t,i,c,o,r){const{sx:l,sy:a}=t;let s,_,h,v;\"point\"==t.type?([h,v]=e.yscale.r_invert(a-1,a+1),[s,_]=e.xscale.r_invert(l-1,l+1)):\"v\"==t.direction?([h,v]=e.yscale.r_invert(a,a),[s,_]=[Math.min(i-1,o-1),Math.max(i+1,o+1)]):([s,_]=e.xscale.r_invert(l,l),[h,v]=[Math.min(c-1,r-1),Math.max(c+1,r+1)]);const{x,y}=n.check_2_segments_intersect(s,h,_,v,i,c,o,r);return[x,y]}},\n",
+ " function _(t,n,e){function i(t,n){return(t.x-n.x)**2+(t.y-n.y)**2}function r(t,n,e){const r=i(n,e);if(0==r)return i(t,n);const s=((t.x-n.x)*(e.x-n.x)+(t.y-n.y)*(e.y-n.y))/r;if(s<0)return i(t,n);if(s>1)return i(t,e);return i(t,{x:n.x+s*(e.x-n.x),y:n.y+s*(e.y-n.y)})}Object.defineProperty(e,\"__esModule\",{value:!0}),e.point_in_poly=function(t,n,e,i){let r=!1,s=e[e.length-1],o=i[i.length-1];for(let u=0;u0&&_<1&&l>0&&l<1,x:t+_*(e-t),y:n+_*(i-n)}}}},\n",
+ " function _(t,e,s){Object.defineProperty(s,\"__esModule\",{value:!0});const i=t(103),a=t(107),n=t(108),o=t(109),_=t(22);class h{constructor(t){this._atlas=new Map,this._width=256,this._height=256,this.tex=new i.Texture2d(t),this.tex.set_wrapping(t.REPEAT,t.REPEAT),this.tex.set_interpolation(t.NEAREST,t.NEAREST),this.tex.set_size([this._width,this._height],t.RGBA),this.tex.set_data([0,0],[this._width,this._height],new Uint8Array(4*this._width*this._height)),this.get_atlas_data([1])}get_atlas_data(t){const e=t.join(\"-\");let s=this._atlas.get(e);if(null==s){const[i,a]=this.make_pattern(t),n=this._atlas.size;this.tex.set_data([0,n],[this._width,1],new Uint8Array(i.map(t=>t+10))),s=[n/this._height,a],this._atlas.set(e,s)}return s}make_pattern(t){t.length>1&&t.length%2&&(t=t.concat(t));let e=0;for(const s of t)e+=s;const s=[];let i=0;for(let e=0,a=t.length+2;e