examples/Inference_2_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting FWI using deepFWI Pretrained models\n",
    "\n",
    "## Introduction\n",
    "The Fire Weather Index (FWI) is a meteorologically based index used worldwide to estimate fire danger. It consists of different components that account for the effects of fuel moisture and wind on fire behaviour and spread. The higher the FWI, the more favorable the meteorological conditions to trigger a wildfire. This indicator can help shape long-term tourist strategy and to plan future investments under a changing climate.\n",
    "\n",
    "The [`esowc/wildfire-forecasting`](https://github.com/esowc/wildfire-forecasting) project intends to reproduce the Fire Forecasting capabilities of GEFF using Deep Learning and develop further improvements in accuracy, geography and time scale through inclusion of additional variables or optimisation of model architecture & hyperparameters. \n",
    "\n",
    "In this notebook we demonstrate the use of pre-trained Deep Learning models to perform FWI inference. The Deep Learning Model has been trained on 1 year of data and takes as input the four weather forcings used in the numerical calculation of FWI:\n",
    "```\n",
    "* t2     -    Temperature at 2m (K)\n",
    "* tp     -    Total Precipitation accumulated over the previous 24 hours (mm)\n",
    "* rh     -    Relative Humidity (%)\n",
    "* wspeed -    Windspeed (m/s)\n",
    "```\n",
    "\n",
    "These input weather forcings are supplied for 2 days and the model produces FWI forecast for 1 day. There is 1 day of overlap between the 2 weather forcing variable inputs and the output FWI forecast as indicated below:\n",
    "```\n",
    "                             ____________\n",
    "Weather Forcings (t-1, t) -> | deepFWI  |  -> deepFWI-Forecast (t)\n",
    "                             ------------\n",
    "```\n",
    "\n",
    "Along with FWI inference using deepGEFF pre-trained models, we also benchmark these results against the baseline `FWI-Forecast` which are numerically calculated FWI predictions from weather forecast.\n",
    "\n",
    "To run this notebook, make sure you have the `wildfire-dl` `conda` environment activated. Instructions for setting up the environment are available in the project [`README`](https://github.com/esowc/wildfire-forecasting/blob/master/README.md)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the data\n",
    "\n",
    "We first obtain a sample dataset. This is the minimum data that we need to perform inference and then benchmark the results. This data is stored on a publicly accessible Google Cloud Platform (GCP) storage bucket at `gs://deepgeff-data-v0/` and is downloaded into `sampledata/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example notebook, we only use the following -\n",
    "* Weather Forcings for 2019-12-03 to 2019-12-04 --> 2 days --> 2 files\n",
    "* Numerical FWI Prediction for 2019-12-04 --> 1 day --> 1 file\n",
    "* ERA Reanalysis of FWI for 2019-12-04 --> 1 day --> 1 file\n",
    "\n",
    "If you wish to explore these datasets further, have a look at the EDA notebooks in `data/EDA/` in the repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download a minimal sample dataset consisting of Weather Forcings\n",
    "!mkdir sampledata/2_1/forcings -p\n",
    "!gsutil -m -q cp -r 'gs://deepgeff-data-v0/forcings/ECMWF_FO_2019120[3-4]*' sampledata/2_1/forcings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download a minimal sample dataset consisting of ERA Reanalysis \n",
    "!mkdir sampledata/2_1/fwi-reanalysis -p\n",
    "!gsutil -m -q cp -r 'gs://deepgeff-data-v0/fwi-reanalysis/ECMWF_FWI_20191204*' sampledata/2_1/fwi-reanalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download a minimal sample dataset consisting of Numerical FWI Predictions\n",
    "!mkdir sampledata/2_1/fwi-forecast -p\n",
    "!gsutil -q cp 'gs://deepgeff-data-v0/fwi-forecast/ECMWF_FWI_20191204_1200_hr_fwi.nc' sampledata/2_1/fwi-forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the pre-trained model\n",
    "For this example notebook, we use the following model-\n",
    "* Run ID: `3c5dj0kl` that has been trained for 30 epochs on 4 months of T2, TP, RH & WSpeed data for Aug - Nov, 2019.\n",
    "\n",
    "If you wish to explore other models, have a look at [pre-trained_models.md](https://github.com/esowc/wildfire-forecasting/blob/master/src/model/checkpoints/pre-trained_models.md)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download a sample pre-trained model checkpoint file\n",
    "!mkdir samplemodel/2_1/ -p\n",
    "!gsutil -q cp 'gs://deepgeff-models-v0/pre-trained_models/2_1/3c5dj0kl/3c5dj0kl_27_30.ckpt' samplemodel/2_1/ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the inference and the benchmark\n",
    "\n",
    "For performing inference and then benchmarking the results, we use the `run(**kwargs)` function from [`src/test.py`](https://github.com/esowc/wildfire-forecasting/blob/master/src/test.py) which takes as argument a dict of configuration parameters and boolean for `benchmark`. You can check out more details about this function in [`test.py#L266`](https://github.com/esowc/wildfire-forecasting/blob/master/src/test.py#L266)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.test import run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Providing the configuration options,\n",
    "\n",
    "```\n",
    "* in_days        -    no of input days of weather forcings\n",
    "* out_days       -    no of output days of fwi prediction\n",
    "* checkpoint_file-    path to pretrained checkpoint file\n",
    "* reanalysis_dir -    path to fwi-reanalysis data\n",
    "* forcings_dir   -    path to fwi-forcings data\n",
    "* forecast_dir   -    path to fwi-forecast data\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = dict(\n",
    "    in_days=2,\n",
    "    out_days=1,\n",
    "    checkpoint_file=\"samplemodel/2_1/3c5dj0kl_27_30.ckpt\",\n",
    "    reanalysis_dir=\"sampledata/2_1/fwi-reanalysis\",\n",
    "    forcings_dir=\"sampledata/2_1/forcings\",\n",
    "    forecast_dir=\"sampledata/2_1/fwi-forecast\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "We demonstrate below the metrics for deepFWI predicted values and benchmark them against the numerical fwi-forecast prediction values. Accuracy, Mean Squared Error (MSE) and Mean Absolute Error (MAE), being the best evaluation metrics for comparing regression models, these are presented for 1 FWI prediction (for 1 day) in a global setting. The metrics are calculated against fwi-reanalysis (considered the ground truth). The FWI values are binned as per the EFFIS categorisation:\n",
    "\n",
    "```\n",
    "* [0, 5.2]     - Very Low\n",
    "* (5.2, 11.2]  - Low\n",
    "* (11.2, 21.3] - Moderate\n",
    "* (21.3, 38.0] - High\n",
    "* (38.0, 50.0] - Very High\n",
    "* (50.0, Inf)  - Extreme\n",
    "```\n",
    "\n",
    "As seen, the deepFWI predictions are consistent with FWI-Forecast and even offer better accuracy for longer term predictions in certain FWI categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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Hd0yyW5L7JDl/3uP0fvlh/b8fm+TsdENK3GnEeg7op2dMsVYAAAAAgHVtw1w5nOSSJA8eMX+3JK9K8r4kr03yxdbaxVV1SpIHVNVtW2tfSJKq2jldeHx+ks+sTdkAAAAAAOvPhgmH+zGG37lwflXt1f/5tdba/OXPSHKPJO+vqpck+VmSx6UbVuI+rbU21YIBAAAAANaxDRMOj6u19tWqunOSv09yVJLtkpyZ5F6ttdNmWhwAAAAAwIxt+HC4tbYlSS2y7Jwkh65pQQAAAAAAG8BGuiEdAAAAAAATIhwGAAAAABgg4TAAAAAAwAAJhwEAAAAABkg4DAAAAAAwQMJhAAAAAIABEg4DAAAAAAyQcBgAAAAAYICEwwAAAAAAAyQcBgAAAAAYIOEwAAAAAMAACYcBAAAAAAZIOAwAAAAAMEDCYQAAAACAARIOAwAAAAAMkHAYAAAAAGCAhMMAAAAAAAMkHAYAAAAAGCDhMAAAAADAAAmHAQAAAAAGSDgMAAAAADBAwmEAAAAAgAESDgMAAAAADJBwGAAAAABggITDAAAAAAADJBwGAAAAABgg4TAAAAAAwAAJhwEAAAAABkg4DAAAAAAwQMJhAAAAAIABEg4DAAAAAAyQcBgAAAAAYICEwwAAAAAAAyQcBgAAAAAYIOEwAAAAAMAACYcBAAAAAAZIOAwAAAAAMEDCYQAAAACAARIOAwAAAAAMkHAYAAAAAGCAhMMAAAAAAAMkHAYAAAAAGCDhMAAAAADAAAmHAQAAAAAGSDgMAAAAADBAwmEAAAAAgAESDgMAAAAADJBwGAAAAABggITDAAAAAAADJBwGAAAAABgg4TAAAAAAwAAJhwEAAAAABkg4DAAAAAAwQMJhAAAAAIABEg4DAAAAAAyQcBgAAAAAYICEwwAAAAAAAyQcBgAAAAAYIOEwAAAAAMAACYcBAAAAAAZIOAwAAAAAMEDCYQAAAACAARIOAwAAAAAMkHAYAAAAAGCAhMMAAAAAAAMkHAYAAAAAGCDhMAAAAADAAAmHAQAAAAAGSDgMAAAAADBAwmEAAAAAgAESDgMAAAAADJBwGAAAAABggITDAAAAAAADJBwGAAAAABgg4TAAAAAAwAAJhwEAAAAABkg4DAAAAAAwQMJhAAAAAIABEg4DAAAAAAyQcBgAAAAAYICEwwAAAAAAAyQcBgAAAAAYIOEwAAAAAMAACYcBAAAAAAZIOAwAAAAAMEDCYQAAAACAARIOAwAAAAAMkHAYAAAAAGCAhMMAAAAAAAMkHAYAAAAAGCDhMAAAAADAAAmHAQAAAAAGSDgMAAAAADBAwmEAAAAAgAESDgMAAAAADJBwGAAAAABggITDAAAAAAADJBwGAAAAABgg4TAAAAAAwABtmHC4qm5eVc+tqk9V1UVV9fOqOquqnlVVO41ov09VvauqflxVl1TVR6vq7rOoHQAAAABgvdkw4XCSRyd5WpKvJXlukr9Ocm6S5yf5RFXtONewqvZO8okkd0rywr7tzklOraqD1rhuAAAAAIB1Z9tZFzCGdyY5rrX203nz/qmqzk/yrCSPSfKKfv5xSa6b5PattbOSpKrekOTLSV5ZVbdorbW1Kx0AAAAAYH3ZMFcOt9bOWBAMz3lbP711kvRDTNw3yelzwXD//IuTvCbJzZPcYcrlAgAAAACsaxsmHF7Cnv30+/103yTbJ/nkiLaf6qfCYQAAAABg0DZ0OFxV2yQ5Oskvk7yln717P71gxFPm5u0x5dIAAAAAANa1DR0OJ3lpkgOSHN1aO7efd61+esWI9pcvaHM1VfX4qjqjqs646KKLJlcpAAAAAMA6smHD4ap6XpIjkxzfWjtu3qJL++n2I562w4I2V9NaO761tn9rbf/ddtttMsUCAAAAAKwzGzIcrqpjkzw7yeuTPGHB4gv76aihI+bmjRpyAgAAAABgMDZcOFxVxyQ5Jskbkjy2tdYWNDk73ZASdxrx9AP66RnTqxAAAAAAYP3bUOFwVR2d5Ngkb0xyRGvtqoVtWmsXJzklyYFVddt5z905yWOTnJ/kM2tSMAAAAADAOrXtrAtYqap6UpLnJPlWktOSPKKq5jf5fmvtA/3fz0hyjyTvr6qXJPlZkselG1biPiOuNgYAAAAAGJQNEw4nuUM/vXGSE0cs/3CSDyRJa+2rVXXnJH+f5Kgk2yU5M8m9WmunrUGtAAAAAADr2oYJh1trhyc5fIz25yQ5dFr1AAAAAABsZBtqzGEAAAAAACZDOAwAAAAAMEDCYQAAAACAARIOAwAAAAAMkHAYAAAAAGCAhMMAAAAAAAMkHAYAAAAAGCDhMAAAAADAAAmHAQAAAAAGSDgMAAAAADBAwmEAAAAAgAESDgMAAAAADJBwGAAAAABggITDAAAAAAADJBwGAAAAABgg4TAAAAAAwAAJhwEAAAAABkg4DAAAAAAwQMJhAAAAAIABEg4DAAAAAAyQcBgAAAAAYICEwwAAAAAAAyQcBgAAAAAYIOEwAAAAAMAACYcBAAAAAAZIOAwAAAAAMEDCYQAAAACAARIOAwAAAAAMkHAYAAAAAGCAhMMAAAAAAAMkHAYAAAAAGCDhMAAAAADAAAmHAQAAAAAGSDgMAAAAADBAwmEAAAAAgAESDgMAAAAADJBwGAAAAABggITDAAAAAAADJBwGAAAAABgg4TAAAAAAwAAJhwEAAAAABkg4DAAAAAAwQMJhAAAAAIABEg4DAAAAAAyQcBgAAAAAYICEwwAAAAAAAyQcBgAAAAAYIOEwAAAAAMAACYcBAAAAAAZIOAwAAAAAMEDCYQAAAACAARIOAwAAAAAMkHAYAAAAAGCAhMMAAAAAAAMkHAYAAAAAGCDhMAAAAADAAAmHAQAAAAAGSDgMAAAAADBAwmEAAAAAgAESDgMAAAAADJBwGAAAAABggITDAAAAAAADJBwGAAAAABgg4TAAAAAAwAAJhwEAAAAABkg4DAAAAAAwQMJhAAAAAIABEg4DAAAAAAyQcBgAAAAAYICEwwAAAAAAAyQcBgAAAAAYIOEwAAAAAMAACYcBAAAAAAZIOAwAAAAAMEDCYQAAAACAARIOAwAAAAAMkHAYAAAAAGCAhMMAAAAAAAMkHAYAAAAAGCDhMAAAAADAAAmHAQAAAAAGSDgMAAAAADBAwmEAAAAAgAESDgMAAAAADJBwGAAAAABggITDAAAAAAADJBwGAAAAABgg4TAAAAAAwAAJhwEAAAAABkg4DAAAAAAwQMJhAAAAAIABEg4DAAAAAAyQcBgAAAAAYICEwwAAAAAAAyQcBgAAAAAYIOEwAAAAAMAACYcBAAAAAAZIOAwAAAAAMEDCYQAAAACAARIOAwAAAAAM0KYNh6vqGlX1tKr6SlVdXlXfrqoXVdVOs64NAAAAAGDWNm04nOQlSV6c5D+TPDnJO5L8ZZJTqmozv28AAAAAgGVtO+sCpqGqbpUuED6ptfbAefO/keRlSR6W5C0zKg8AAAAAYOY26xW0D09SSV66YP6rk1ya5LA1rwgAAAAAYB3ZrOHwHZJcleQz82e21i5Pcla/HAAAAABgsKq1NusaJq6qzk7yO6213x2x7O1JHpxk+9baL0Ysf3ySx/f/3CfJudOslanaNckPZ10EDJC+B2tPv4PZ0Pdg7el3MBv63sZ2k9babqMWbMoxh5NcK8kViyy7fF6bq4XDrbXjkxw/pbpYQ1V1Rmtt/1nXAUOj78Ha0+9gNvQ9WHv6HcyGvrd5bdZhJS5Nsv0iy3aY1wYAAAAAYJA2azh8YZJdq2pUQLxHkh+OGlICAAAAAGAoNms4/Nl07+2O82dW1Q5J9ktyxiyKYs0ZHgRmQ9+DtaffwWzoe7D29DuYDX1vk9qsN6S7TZIvJDm5tfbAefOfnORlSR7ZWnvTrOoDAAAAAJi1TRkOJ0lVvTzJkUlOTvKeJLdM8pdJPp7k7q21q2ZYHgAAAADATG3mcHibJE9N8vgkeyX5YZK3JTm6tXbxDEsDAAAAAJi5zTrmcFprv2qtvai1tk9rbfvW2h6ttf8lGJ6Nqrp1Vf2yqv5s1rVMS1UdVVVt3uPAWdfE5jCE/jMLVfW++X121vUwG/rX7FXVAQv2n8fOuiamT9+bPX2PUfTN1XFOyLSs975ZVadX1ZZVruOmVfWuqrqo7z8nVNWNqurSqnrUhEplhE0bDrPuvDjJx1trH5g/s6quUVVPq6qvVNXlVfXtqnpRVe202hdcsFOe/1jRFwRVdb2qekpVvb+v67KqOreqjq+q3xvxlFOSPDIGaWfyFus/a72Nj6WqHlJVr6+qL1TVlX1tey3S9kZV9YI+sP31wcCYr3fzqnpuVX2qX8fPq+qsqnrWIp8pL0zXZz867ntjU1msfz2jqt5RVV/vt8cti62gqu5YVS+rqo9X1cV9+8PHKaKq7lpVr6yqs/tt96J+fQ+vqtqqd7aV6x6n7y7xmk/vTxK+W1VX9NMPVdX9RzT/arq++LSteoNsVFfre+N+jk+o792nqv6tqrb0J58/rqozq+qp1d3MequNu+7qPKKqPlFVP+zf/5er6uiqus4Yr7tPdSfXP66qS6rqo1V19xFN9T1GGdU396mqN1fVOVX10357/kpVvbiqbjRqJVV1p377/05/jPm1qnp1Vf3+Sgupql2q6uVVdUF154pfrqr/OYH94oG1+HH0vy/ynEP6vnlJVf2oP0a46YimzgmZlomcE46xj5iFE5LcNck/pOtH/9xa+26Sf0rygqq61gxr29S2nXUBbH5Vdackf5bkfiMWvyTdWNAnJ3lRfjM29O2q6qAJjA390Vx9x3zlCp/73/qa/iPJK9INTXLrJH+R5CFV9cettf+ca9xa+3KSL1fVtumGM4FVW6b/JGu4jW+FJ/av8YUkX0uyzxJt90nyzCTfTvLZJPfeitd7dJInJfm3JG9O99/hbkmen+79HNBau2yucWvtg0lSVQcluctWvB4b3DL96++S/CjJmUmuu8yqDkm37X0l3fb+x1tRzj8k2TPd/vDsJDsleWiStyS5e5LHbcU6t3bd4/TdxdwxyZZ09334YZLrJ3lwkpOq6ujW2vPmGrbWfpjkTX0A/ZKteC02mCX63lif45lM37tNkl8leW2S7ybZMd0+4SVJ7lNVB7etH4dv3HU/P92+8INJnpPu/R/Y/31IVd1puVqqau8kn0jyy3Rfgv40XR8/taru3Vo7ba6tvsdCS/TNPZPcKN1+5Dvptq/bpDvneVhV7dda+8G89dwrybvT7UPmjjFv1bd/YFXdprV2wTK1bJfkA0lul+TlSc5Jd3z4qiS/m+TY1bzX3vG5+kUC3xlRywOSvDPd58xfJ9kl3RCWH6+q/VtrF861dU7INEzqnHCcfcRWODjJVn9xU1Xbp9tHvqK19n8WLH5Zuj53RJJXbnWFLK615uEx1UeSN6Y7ILjmgvm3SnJVkn9dMP/JSVqSR6zydVuSE1bx/L2S7D1i/kH9ut+5yPMO75cfOOv/9h4b/7FY/+mXzWQbH2P9N06ybf/3K/p17rVI22sn2a3/e9eteW9J9k+yy4j5z+/Xd+Qizzuh2x3O/v+3x9o+lulfvz/v7y8l2bLEen43yU793w/qt7fDx6zlrkm2WTDvGkk+3K/v1qt4n2Ote5y+O2Yd26Y7sf75wnr65Xv1r3XsrLcNj+k+ljg2HOtzfBJ9b4kaX9mv745TeP9XW3ffPy5J8rkk11jQ/k19+/1WsO63pwuk95s3b+ck30xybvp7zix4jr7nMbctLLpfXKT9g/tt528WzD81yS+S7Lpg/mP79k9dwbqf2Ld98oL5/9qv+yareJ8HrvTzIsk1k1zQ96Gd583fr+9rxy/yvMPjnNBjQo+l+mbGOG/amn3EGr7HGy+1L0p33PrFWf+/2KwPw0owVf03pvdL8oHW2sJvrh6e7pully6Y/+oklyY5bEI1bFdVO4/7vNbaltba10bMPy3d1WS3nkR9sJhl+s/8dutyG2+tfau19ssVtv15a+2iVb7eGa21n45Y9LZ+qs/ya8v1r9ba11e6rtba91trl6ymntbah1trv1ow76p0Vyolq9h+x133OH13zDp+me4Ee6d0J9sM0FJ9b9zP8Un0vSV8s59eb43Wfc10VxZ/r139l3NzVyUu+V6rG3rjvklOb62dNTe/dfdceU2Smye5wyrqZhNb6XHnAov1k+skuTzJjxfMX9G23HtEunPCVy+Y/9J0/eWhK6xxSVW10zJDyNw1ye5JXtPm3b+o72OnJ3loVdmnMTWTOiec9j6iRow5PDevqnavqrfOG8ri1Kq6+bx2J+Q3nyfHzBsa48B5q3tvkttU1S22tkYWJxxm2m6f7puoz4xYdod0Vw7/1rLW2uVJzspkDl4flO6g4udV9YN+zKpdVrPC/vnXTvL9CdQHS1mq/8yxjS9vz366Wd4Pk7GS/rUeTHP7nXrfqKrrV9VuVXXLqjo6yb2SfKjf1zNMW9P31mJbvXZV7VpVv19Vj0zyt0n+K8mn12LdrRsu4yNJ7lVVf1tVN6uqvaobQ/mJSd7UWjt/mZfaN8n2ST45Ytmn+qlwmMUs2zeraod+W96zqg5O8s/9ovcsaHpqumPJE6vqtlW1R1XdM91wZuck+ZelCqmqayT5oySfH7G/+Ey6c8hJbMv/f5KLk1xWVedVdy+OhT+Ln3udxfrVddKFajAtkzonnNU+Yqd0+7dfpRs66ZXprt7/v1W1Td/mn/Ob8e9PTjfe8CPTfV7Mmav7wCnUOHjGHGba/rCfXu3qxHTfwP6wtXbFiGUXJPnjqtqutfaLrXztzyR5R7qbbVwn3bh0Rya5az+W6opu2jXCs9N9W33iVj4fVmqp/pPYxpfVH3AcnW5crbfMuBzWl+X618xV1e7pxgD/epKPbZR1L3Bekhv0f/8y3c+BnzjF12P9G6vvreHn+OuTPHDevz+d5EmttZ+s4br/PN2+9+/7R9L9xPYF6f4bLGf3fjpqLNe5eXuspGAGaSV987Hpxv+dsyXJYa21heP2Hpfkd9KNI/7n8+a/J8nDW2s/X6aW66W7kv5q23Jr7Yqq+q+sblu+Mt3Y5u9JdzXz7kkek+6q5P3SjWs6Z6X96surqAeWMqlzwlntI3ZN8r9bay+cm1FVF6Ub8/igJKe21j5ZVd9NN/79F1trbxqxnrn3f6sp1Dh4wmGmbbd++qMRy66VZFQwnHQ/Q5prs1XhcGvtvy2Y9Yaq+mK6A+yn9NOxVNWDkjw93bfhr9+aumAMS/Uf2/jKvDTJAUme2Vo7d9bFsK4s2b9mrb8b88nprrb472P8xHem6x7hAUl2SHey8eB0J/vXSbKqYWTY0Mbte2v1Of6cdHdD3y3dTfD2zW++2FirdV+R7gubC5K8L10w/MB0X9penuX363N3cR91fH35gjaw0Er65rvS3QBy53Q3irvvvOfN96t02/Fp6fY3P0py53T3lvmXqjp0mX3PUtty0m3PW70tt9Y+nuTQ+fOq6tXpwuLDq+q1rbW5L071K2ZtUueEs9qWr0p3Q7n5PthP/yDdeedK/Fc//Z1JFMVvM6wE0zZ3R+VRd628NN3PGkbZYV6bSfrf6cLm+4z7xKo6JN2dsz+X5CGtta29czWs1FL9ZzG28V5VPS/dt+bHt9aOm3U9rDtb07/WRD/24bvS3ZzriBFXZK3LdY/SWvtIa+39rbXXt9YOSXczuo9V1TTGcWVjWHHfW8vP8dba2a2101prb22tPT7dF6Tvrao7r8W6+y9tPpHkOq21R/Vt/6W19uB0Yy4/t6r2Weal5o6bRx1fT+vYms1j2b7ZWvtOvy2/q7V2TJJHJfmHqnrGgqYnpLsS9yGttde21k5urf1VuqDq3v3zlrLUtpx02/NEt+V+rO+5z5lDVliLfsVamNQ54ay25QtHDA8zF/SO8yXs3Pvf0Oeo65VwmGmbuzLo+iOWXZhk16oa9eG0R7ohJ7Z2SImR+m+oL0z304YVq6p7JTkp3c+FDm6t/WySdcGkMd58AAAbEUlEQVQiluo/I9nGO1V1bLorrV6f5AmzrYZ1auz+tRbmhbcHJXncIj+rW3frHsOJSW6Y7opihmlFfW8dfI6/sZ9O47VHrftB6a6geseI9u9Id972J8usd+5mX6N+Fjw3b9TPiSHZuuPOLyb5fOYNF1RVN043lMS7W2sLg6a57fuuy6z6x0kuy4htuT93vEGmsy1v6afzj6P1K2ZtUueEs9qWf7XEsnEC77n379dnUyAcZtq+1E//YMSyz6bbBu84f2Z/8rpfkjMmXUy/7j0zxg1N+psnnJzuJ1QHtdYW3nUXpmWp/jOSbTypqmOSHJPkDUkeu9GvgGZqxu5f09af8J6c5OAkj2+tvW4jrHtMO/bTdRXKs6aW7Xvr5HN8+3THqdPYVkete+7EfJurN//1UIDLDQl4drqfC99pxLID+unEj6/ZNLZ2v7hjJrwt91fxnpnkdiMuJLpjuv4zjW157r3PP47+bD9drF/9LN34+jAtkzon3Oj7iJv10y8t2YqtIhxm2j6fbod5wIhlb0v3k4CnLpj/uHRj3bx5a1+0qhb7ecLz0h2MnLKg/Y2q6hb9T/rmzz843VVW5yW5R2ttXY5Nyaa1aP8Z+jZeVXtX1S1GzD86ybHprso6oj+5gFGW2j9NTVVds++LN14wf/t0ffGeSZ7QWnvNBF9zmuvepX8/u86bt1NV7Tyi7TZJntT/81MLlzMYS/a9aX2OL9H3brjIU/6yn271tjrmuv+zn476uf3cvLmQamTf6286dEqSA6vqtvPa7pzuRmLnZ+m73TNsSx13jtyWq+puSW6d396Wz013peD9quq6C55yeD+dvy2P7JtJ3prunPDxC+Y/Nd0NKt++1JtZyqjj6H5feWz/z/nH0R9O8t0kj52/b+v72IFJ3jHlsfthIueEm2AfMff+PzzTKjYpN6Rjqlprv6qqk5IcWlXbt9aumLfs7Kp6ZZIj+zbvSXLLdAfMH86CO1JX1eHpflr4nNbascu89LOr6oAkH0ryrXQ3TTgk3U1APp3fvstu0o0v9ah++en96+2f5P+m+6nD65Pcu+q3f/Uwo5/kMhBL9Z/MYBuvqr2SfCPJh1trBy5Xf1X9aZI/7f+5fz89sqp+0q/7+QvaP7v/cy7A3nfevI+01j4yr/l/JLlJ5v0UqaqelO6mP99KdwOURyx4P99vrX1guboZhmX6V6rqkem2saS7Ech287bHb7bW3jiv7U2SPLL/59wdlP97Ve3Z//3G1to3+7/3SHJOuv3cgfNe8s1J7pVu2720qg5bUPIX+5/vzr3mCen7dGvt9GXe7rjrHqfv3j/9vjm/Oan+gyQfrqp3pgsJftS/74cn2SfJidMe65j1a6m+N+7n+IT63peq6mPprlK8IN1PcP8syT3SXWX10vkFjNn3xln3v6c7KT+kqj6S5F/T7eMekOQu6QKoM+e1H9X3kuQZ/frfX1UvSRcoPK5///fxaxoWs8x+8R+r6kbpbiL1zXTjk94+ycPSjSX/9Hnr+VFVvbSf9/nqbvQ2d0O6P0/ytSTzv6RcrG++OskRSV7cH4Oek+5Y9/5Jnt9a+8b8+sfsm++rqgvT3WfjwiS7Jzks3f7r5a21XwdkrbUrq+op6S5s+mj/fq6T5Gnpft5+zDKvBasy4XPCsfYRVbUlyU1aa+vhHh33SXJ2a+0rsy5kU2qteXhM9ZHupz8tyQNHLNsm3YHDuel+4nBBkhcn2XlE2yf363ncCl7z0HR3vbwg3Z03L0lyVpJnJtlhRPsT+nUfOG/e4f28RR+LvPbhC9fl4bG1j8X6zyy28SS36ee/eYW1HztO/1mmlmMXtN0yor4TllnH6YvUecJi/dljcz+W2T+dvtJtKd3J7FLb3oHz2u61yDq2jNkH/jXd3Z/3WcH7HHfdK+678z5Hjp03b9ckr0zyhXSBwJVJfpjkA+mCgVqkzr1G1eOx+R6L9b1xP8cn1PeOTvKxdD+9vTLdifJn051A7zSi9nH63rjrvnaSv0s3zNMV6fbvZyf5myTbLmh7tb43b9kt0335+5N0Nxf6WLphoxarU9/zmNsWFuubD0ny7iTf7rfLy/rt9OVJbjxiPZUucPp0kov77X9Lv2/YbUHbkX2zX3bdJK9IF+Beke4K+yNH7UfG7Jt/m+ST6cLdK/u+8qEkD1/iOf9fuiukL003JvI7k+y9RPu5PnrgrP+/emz8xxJ9c6xzwv45K95HpDt+u2CFNZ6eZMty8/r5V9vvLLUv6pddleRJs/5/sVkf1f+HhqmqqvelOwi+yyrWcVK6cOoP2zr86U5V7Zhkp3TfoL88K/vWGpY1if4zoTr+MsmLktyqtbbhx1arqusk2S5df31YWx/fiLPG1kv/GkdVXSPJD9Ld7OdRs65ntapq23QBwO+lu8JyJb8QYoPT92ZP32MUfXPVtTgnZCrWum9W1b7pvuh/dGvt9WvxmkvU8pIkD05y83b1G10yAcYcZq08Pcmd+vFNx9YfvN49yVHrMRjuPSXdt88Lf7oBq7Wq/jNB90zyT5shGO69PV2ffdisC2Gm1kv/Gsft0w2/8qxZFzIh+6fri2cu15BNRd+bPX2PUfTN1XFOyLSsdd+8Z7pw+MQ1er2R+jHP/yLJswTD0+PKYZiQfty7+XcQ/Vxr7cezqgdYWlXtl+7n70mS1tppMywHBqu/iv+O82Z9vbX29VnVA0Oh78HkOScENiLhMAAAAADAABlWAgAAAABggITDAAAAAAADJBwGABiwqjq8qlpVHTjrWmC9qqrTq2rLKp6vn8EUVdWBfR87fBXr2FJVp0+uKoCNQTjMmqmqd/Q77P2WaFNV9Y2q+klV7TjGuk+vqosnUykM27yD67+adS0wBPP6XKuqVyzS5neq6hd9m9PXuETYVFayn+uX//ta1gUbySzP7arq2P619x+3btgoFhwfjnr8civXeWxVXXcaNbNxCYdZS6/tp0cs0eZuSfZK8i+ttcumXhEArB+XJ3lEVW0/Ytkjk1SSsU8EgIk4OMk+sy4C1pH1dm73kSQ7JnnjlF8H1tpb0x0HLnz8j61Y14FJjkkiHOa3bDvrAhiU9yf5dpI/r6q/bq39YkSbuYOL145YBgCb2clJHp7k0CRvX7DsiCTvSXKPtS4KSBY5boUhW1fndq21q9J9yQqbzZmttTfN4oWr6ppJtmmt6VubnCuHWTP9DvuEJDdIct+Fy6vqOkkekORLrbXPTqOGqtqpqo6rqq9V1RVV9b2qekNV3WRemx2q6rL/196dR9lRlnkc//4wbCGsRmVxHGFgBnTIiKiDwIlZcDseRNxHAaMOEjYXBlwQDuSIejTiNsIgJBBECOOow4g4KlmBiDCEfRESJHrAiBITSZAlwjN/PG8lRVHddDfdfZO+v885dW73U2/Vrdun31tVT72LpFmNbc8t3Te+3oj/p6SHJflhi3UVSeMlXSnpz6XO3Cjpw40yVbe/XWuxnUrsSUk71OJ7lfgnh/NzmG1AbgRuodEKS9JrgJcDF7RtJOltkhZJWlOWRZIO6aHsv0r6VTkHLpX0MbJFspn1oqcxhyW9Q9Itkh6T9FtJp0k6qJexTzeRdGLtWvQeSR8Y8g9gNsg2hHu7xvu1jjks6fmSzpe0opwj50nap6c6XbbZU9IVklaX69zvS9pxqD+D2UBI+nL53z+8ER9X7tHmS9qk5DdOK6vvqw1PcXopX923vVzSVyXdTz5w2a+s31zSyZLuKOe8VZIul7RP433X1UVJx0i6u5S/TdJbSpm9Jf205FFWSPpmSUQ3P9seki6StFw5vNoySdMlbTXYf8du52SWDbcLgFPIG9/vN9a9FxjNED1ZLsnbnwEHlPc+E9gDOBp4g6RXRcT9EfGYpGuBSY1dTAKeotZqS5LIrhlXRYS7+lrXkHQw2crx92RdWk3W4RmSdouIz5ai88iLkEmsr9uTybq0Cdnd8AclPqm2jVm3ugD4qqQXR8T9JfYh4A/AM8Y/lXQMcBbwK+AMIIApwGWSjoqIc2tlPw58jUxAn0yec08q+zbrRqMljR3oxpLeQ3b3vReYRg778gHg4F42+wLZ9f3bwOPkdegsSUsjYtFAj8WsQ4b03q6X+jm6j9tvBswBXkEmsq8HxpXYn3rYbBdgAXmdexLwT8BRwDbk8DJmw62nc9UTEfEw8FlgPHC2pF9GxBJJo4FLgUeAwyLiKUnfJv+PDwU+ATxU9nNrY78XA4+S93gBLC+J258C+5NDt3wL2BY4ElgkaXxE3NDYz7HA9sAMMsn8UfL69F3AeeT58zKyXh1PXo+eUW0saV/yvnAVec58gKyPHwUOkPS6iFjblz+g9UFEePEyrAswl7x43rkRv5a8SB47gH0uANY8S5kjyS+3Lzfibynxi2qxU0psj/L731RlyuuLSnzv8vsJnf67evEyWAv5wCOAE3tY/zzgN+SJeudafDNgEfBkre5sRl6UXFwrdz6wGLgTOLsW/wGwEtik038DL16Gc6nXObIF1uPAyWXdlqWufaX8vgZYUH7evvy+FNimtr9tyGTVamC7Etuu1MU7gdG1si8u+whgQqf/Fl68DMdSq3PPtvy4ts0CYFnt91HkjeqDwPa1+Bjg12X7KbX4lBK7CdisFt+l1PnZnf67ePEykIWhu7frSx19VW2bql5PqcWOKbHPNvZfxZc14stK/N2N+Fklvmen/95eumfpw7mqfo7atVwvLibvv2aWMgc39nl6ib+05f2qdQuAUY11nyjr3tiIbwP8lnJt2jjuB4Bta/FxJf4U8PbGfhYDyxuxW8jGD1s34oc267qX5754WAnrhJlkcmldtwdJe5LdFX4UEQ/1tOFzdCj5RfTFejAirgBuBg6RVNWJquVi1ZJxMpnwOp38IqriExvlzbrBvsBLgPMj4ndVMHKsuelki+BDarFFrK8rlJ/nlmUyrGuF/zpgYWQ3RbOuFBErgB+RiSTILrnbkg9Vml4PbAV8M7LlSLWPh4F/J5NUB5XwG8iWVmdFxF9qZe8nW4iYdaNzyXrUtjybfYGdgVkRsbIKRsQa4Jxetjs7amOzRsQDwD1kbzazjdFQ3ds9Rs/1s6+Tzh1M3sN9oxE/D/hzD9v8LiKa4/5X93q79/F9zQZTT+eqqqcmEXEf8BHgleT/64fI68PLB/B+X49n9oo+jEzULpY0tlrIRPSVwIGStmxsMysi1tWziLgVeJisYz9slL0G2FHSGMhhJ8hk8iXA5o33vIZs8OCW/IPIw0pYJ/yQfKr1QeBLJfah8tp28ztYdiW/iFa2rLuD7G40luzOcD3ZkmoS2YVhEnBDRNwr6bby++zyuoJ8qmXWLarxg+9oWXd7ed2tFpsHvF7SXmQLkpeW2JbAcZJ2AV5Atpj0gxaz7KZ7haQDyfPj9RFxZ0u5/tTF6vVXLWXb9m3WDZZExJy2FfnMsldV/bu7ZV1brPLrltgK4G9b4mYbg6G6t3uyl/p5YB/3Ud3/rakHI2KtpPvIHjhNPdVRyGtVs+HW47mqLiK+J+mtwPvJ68CBzuNyT0tsL/Le7Y+9bDeWnKSy0laXVjbK1OOQdWxNeT/IIZum9fB+L+rlWKyfnBy2YRc5pu8lwDGS9geuI58030/OejtU+jzhTkT8VdLVwMTSonEi659Qz2N9K+PxwLwo/RvMukR/J6+qt8R/HFgLXE0+aa7G8R7bKGvWzX5GdsU7jTz/HN1Duf7Uxaps2/nKE9KZ9d9A682Tg7w/s47q4L1dXwykXvVURwe6P7NhIWk7oHpwsjPwQtoTsc/mLy0xAbcBJ/SyXTNx3FNd6ksdq17PJMc6btPW6M8GyMlh65SZ5FhPHwR2AHYEPh8RvX1RPFf3Am+StF1ErGqsexnZxaHe7Wke8GbgneSYjHNLfC7wcbKr7/Y4mWXd597y+vKWdS8rr/UnxYvJrnuTyeTwdRHxCPCIpJtKfAey1X5bC0izrhIRT0r6DvAZckKQS3soWq+LcxvrmnWxKrsXzzxv7YWZ9dd95fUfWta1xcxGsk7c2/XFfcBBksbUWw+XybWqMVrNRooZ5FxJx5ND/X1X0qRGPRxoo7YlZE/PecM0BOCS8tpjDwIbXB5z2DoiIm4kx/l9D3Ac+SV1QbVe0qaS9pT0kkF828vI//lP14OS3gzsQ46JVf+iq26ep5EJrWoG6YXkpAvTGuXMusWN5MQDH5S0YxUsF9onkfX5f6p4uSC5ihxTeCJPrzPzyOTweGC+W+GbrXMOeZ6ZWh+vreFKcsy14yVtXQXLz8eT3fKurJV9FDi2zGBdlX0x8L7BP3yzEe8GYDkwRdK6rullvMSpHTsqsw7o0L1dX1xOjof8sUb8SHI8f7MRQdJU4B3A5yLiW+Qkx+OBUxpFq4ckO/TzLb5DPvRpbTksabCHeLiJHBpjqqTdmisljZLU389gvXDLYeukmeSEOW8kZ7e8t7ZuF+AuMhE7oY/721RS88uv8kNgFvAB4FOSXkomq3Ynn3I/CJzc2OZm4E9ki6oFEfEYQESslrQY+GdyRs228RvNRoLJkrZoiT9EXvj/N/B/ks4FVpM3BPsBX4iIJY1t5pGTglQ/1+MntcTNulpE/JacBLW3MqskfZKcRf06SbPKqink+e2oKrEcESslnQp8BfhFaZk8mkxiLSEfkppZH5UhyE4kJ3S8XtJMsvHAFHJ80l0ZeAsts43RYN/bDYYZwFHAGZJ2J+eVGQe8G1iK8yG2cXilpMN6WHcZOZ/LV8lh+z4HEBFnSToIOFXS3Ii4ppT/ZXn9kqSLyYkfb4+I2+ndN8hJ8KZLmkTetz1MTlI+uexnYs+b909EhKTDy/vcKul8sofpaPIa9+1kD7tZg/We3c5fhtZJF5PdHbZgcCai24zyZdhiaUTcKemN5NOz95BfKKuA/wJOiYinjccTEU9JWlDKNZNWc8nksJNZNpK9qSxNd0fEnpImk/XpJLL+3QUcGREzWrapurw/Clxbi19NjkG8Ka5PZv0WEWdLWk7Ww9NK+Bbg0Ii4rFH2TElryFYfXyTHofsKOezLUE4IazYiRcQlkv5KngunkY0NZgK3kg0THu3g4ZkNt8G+t3vOIuLxcr06HTiETApfRyazZpCJJrMN3b+Upc04YDZ5vnlfYwiJD5PXhBdLekVErIyIRZI+RTYOOI/MCU5j/UTGrcokjm8hG9Ydzvpe1L8jH7pcOKBP1vt73ixpHzIJ/NZyzKuBZWRSuDmkmj0Hcg9eMzMzMzOzwSHp38gHL6+NiF8+W3kzG16Snkf2hLsuItoaQpiZdRWPOWxmZmZmZtZPkjYrSaZ6bAxwLDm0xI0dOTAzW0fSli3hqcB2rB+X38ysq3lYCTMzMzMzs/7bDfhfSZcC9wE7kfNb7AocHRFPdPLgzAyA88ocGr8gJxl/LTkR61Lg3E4emJnZhsLDSpiZmZmZmfWTpOcD3wIOAF5ITkh3G/C1iPheJ4/NzJKkI8jW/H8PjCHHBv8JcGpEPNjJYzMz21A4OWxmZmZmZmZmZmbWhTzmsJmZmZmZmZmZmVkXcnLYzMzMzMzMzMzMrAs5OWxmZmZmZmZmZmbWhZwcNjMzMzOzVpIWSFrW6eMwMzMzs6Hh5LCZmZmZjTiSJkiKXpb9JI2RtFbS1S3bj5K0upSd3LL+M2XdOxrvd2I/ju1Zy5qZmZmZDaVRnT4AMzMzM7MhNBv4SUt8aUSskXQD8BpJoyPiL7X1rwbGAH8FJgJzG9tPAAJYOPiHbGZmZmY2PJwcNjMzM7OR7MaI+G4v6+cD+wH7A3Nq8QnAauBn5ed1JI0q5W+PiIcG82DNzMzMzIaTh5UwMzMzs242v7xObMQnAIvIFsOvkTS6tq5qVTyfISZpD0kXSVou6QlJyyRNl7RVrcyXyjAV41q231bSo5Iua8QPkvRzSaskPSbpVklTh/rzmJmZmdmGxclhMzMzMxvJRksa21i2rq1fBDxBrXVwrWXwwrJsChxQ26YqO6TJYUn7AjcA44FvA8cCPwY+ClwpadNS9MLyekTLbt4NbFErg6SPAD8nE9yfB04A7gX+Q9L0wf8kZmZmZrahcnLYzMzMzEayacAfG8t51coyzvD1wKtrrXGrlsELIuIu4EGe3rJ4Ajne8FVDfOznA8uBf4yI0yPivIg4Dngv8Frg/eUz3Ekmkd8n6XmNfRwBrACuAJC0E/BN4NKI2D8ipkfE2RFxaImfIOnvhvhzmZmZmdkGwslhMzMzMxvJzgVe31jOaJSZz9NbB08AHgEWl9+vKrF6q+JbIuJPQ3XQkvYGxgGXAJvXWz4D15Tje0NtkwuBncjPV+1jV/IzzY6IJ0r4ncDmwMxmi2rgcvL+YPJQfS4zMzMz27A4OWxmZmZmI9mSiJjTWG5vlGmOOzwB+EVErC2/L2R9y+J1rYqH+Lj3Kq9tLZ//AGwFvKhWfjawlqcPLXEEIGpDStT2O6dlv1eWdfX9mpmZmdkINqrTB2BmZmZm1mHXAo8DE0rL4AOAL9bWLySvmw8EXlliQz0ZncrrmcBPeyizsvohIlZIugJ4m6StI2I1cBhwV0Tc0LLfI8ghK9r8euCHbWZmZmYbEyeHzczMzKyrRcRjkq4lk78TyVa5C2tF7gAeKuv2AZ5i6McbXlJen4yIOX3c5kLgbcC7JN0N7A58uof9PtSP/ZqZmZnZCOVhJczMzMzMsiXwKOAU4FFykjoAIiKAq8nxfA8Abo6IVUN8PDcBtwNTJe3WXClplKQdGuEryCT2EWV5Cvhuo8z3yFbS0yRt2bLfbSVtPgjHb2ZmZmYbAbccNjMzMzPL5PA0YDwwvzaBW2Uh8PVa2cEwWdIWLfGHIuIcSYcD84BbJZ1PtmAeTbYIfjvwGWBWtVFErJU0GzgO2BeYExEP1HccEfdLOhqYAdwl6SLgN8ALgL3JlscvA5YN0mc0MzMzsw2Yk8NmZmZmZnAd2WJ4S54+pESlHlswSO/5prI03Q2cExE3S9qHTAK/FZgKrCYTt7OAuS3bXggcT06a9522N42ICyTdA5wIHAVsR7Y4vhs4Ffj9gD+RmZmZmW1UlL3kzMzMzMzMzMzMzKybeMxhMzMzMzMzMzMzsy7k5LCZmZmZmZmZmZlZF3Jy2MzMzMzMzMzMzKwLOTlsZmZmZmZmZmZm1oWcHDYzMzMzMzMzMzPrQk4Om5mZmZmZmZmZmXUhJ4fNzMzMzMzMzMzMupCTw2ZmZmZmZmZmZmZdyMlhMzMzMzMzMzMzsy7k5LCZmZmZmZmZmZlZF/p/CtXfRCQYGQ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(**config, benchmark=True);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}