zonca/test_nb.ipynb Secret

## test_nb.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1ecd4b3-9bb3-443f-9740-d9e492096ccf",
   "metadata": {},
   "source": [
    "# Test notebook for snapshot_nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad6fdf13-4b5d-4479-853f-0325703b1c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import healpy as hp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# import pymaster as nmt\n",
    "from astropy.io import fits\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d0fd56a-8815-49ac-89c5-486c878adb2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1555148e98b0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807a4aff",
   "metadata": {
    "tags": [
     "snapshotlog"
    ]
   },
   "source": [
    "## Execution log\n",
    "\n",
    "* [23/03/2022 17:01](https://gist.github.com/729609075fc256f10336714692b12cb9)\n",
    "* [23/03/2022 17:02](https://gist.github.com/9cccbddb3b1ee7e3904cea8e07ef8879)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "condanamaster2",
   "language": "python",
   "name": "condanamaster2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
	{
	"cells": [
	{
	"cell_type": "markdown",
	"id": "e1ecd4b3-9bb3-443f-9740-d9e492096ccf",
	"metadata": {},
	"source": [
	"# Test notebook for snapshot_nb"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"id": "ad6fdf13-4b5d-4479-853f-0325703b1c5e",
	"metadata": {},
	"outputs": [],
	"source": [
	"from pathlib import Path\n",
	"import healpy as hp\n",
	"import matplotlib.pyplot as plt\n",
	"import numpy as np\n",
	"\n",
	"# import pymaster as nmt\n",
	"from astropy.io import fits\n",
	"\n",
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"id": "2d0fd56a-8815-49ac-89c5-486c878adb2f",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x1555148e98b0>]"
	]
	},
	"execution_count": 3,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": "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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.plot([1,2,3])"
	]
	},
	{
	"cell_type": "markdown",
	"id": "807a4aff",
	"metadata": {
	"tags": [
	"snapshotlog"
	]
	},
	"source": [
	"## Execution log\n",
	"\n",
	"* [23/03/2022 17:01](https://gist.github.com/729609075fc256f10336714692b12cb9)\n",
	"* [23/03/2022 17:02](https://gist.github.com/9cccbddb3b1ee7e3904cea8e07ef8879)"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "condanamaster2",
	"language": "python",
	"name": "condanamaster2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.8.0"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 5
	}