diff --git a/notebooks/contributions/TDM_Tokyo.ipynb b/notebooks/contributions/TDM_Tokyo.ipynb
new file mode 100644
index 00000000..77d9a89d
--- /dev/null
+++ b/notebooks/contributions/TDM_Tokyo.ipynb
@@ -0,0 +1,478 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd967380",
+   "metadata": {
+    "tags": [
+     "papermill-error-cell-tag"
+    ]
+   },
+   "source": [
+    "<span style=\"color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;\">An Exception was encountered at '<a href=\"#papermill-error-cell\">In [3]</a>'.</span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "16302e51",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-06-28T17:20:17.192352Z",
+     "iopub.status.busy": "2021-06-28T17:20:17.191600Z",
+     "iopub.status.idle": "2021-06-28T17:20:17.284582Z",
+     "shell.execute_reply": "2021-06-28T17:20:17.283864Z"
+    },
+    "papermill": {
+     "duration": 0.109895,
+     "end_time": "2021-06-28T17:20:17.284723",
+     "exception": false,
+     "start_time": "2021-06-28T17:20:17.174828",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"text/javascript\">\n",
+       "        function toggle(id) {\n",
+       "            el = document.getElementById(id);\n",
+       "            el.style.display = el.style.display === \"none\" ? \"block\" : \"none\";\n",
+       "        }\n",
+       "    </script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "This notebook is compatible with this base image version (user-0.24.5)."
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "\n",
+       "\n",
+       "---------\n",
+       "\n",
+       "The following environment variables are available:\n",
+       "\n",
+       "* `SH_CLIENT_ID`, `SH_INSTANCE_ID`, `SH_CLIENT_NAME`, `SH_CLIENT_SECRET`\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from edc import check_compatibility\n",
+    "check_compatibility(\"user-0.24.5\", dependencies=[\"SH\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "557aeb71",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-06-28T17:20:17.318115Z",
+     "iopub.status.busy": "2021-06-28T17:20:17.317199Z",
+     "iopub.status.idle": "2021-06-28T17:20:20.283189Z",
+     "shell.execute_reply": "2021-06-28T17:20:20.284178Z"
+    },
+    "papermill": {
+     "duration": 2.987778,
+     "end_time": "2021-06-28T17:20:20.284387",
+     "exception": false,
+     "start_time": "2021-06-28T17:20:17.296609",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad44255a",
+   "metadata": {
+    "tags": [
+     "papermill-error-cell-tag"
+    ]
+   },
+   "source": [
+    "<span id=\"papermill-error-cell\" style=\"color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;\">Execution using papermill encountered an exception here and stopped:</span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "67dfee78",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-06-28T17:20:20.338696Z",
+     "iopub.status.busy": "2021-06-28T17:20:20.337473Z",
+     "iopub.status.idle": "2021-06-28T17:20:20.521518Z",
+     "shell.execute_reply": "2021-06-28T17:20:20.520025Z"
+    },
+    "papermill": {
+     "duration": 0.217632,
+     "end_time": "2021-06-28T17:20:20.521865",
+     "exception": true,
+     "start_time": "2021-06-28T17:20:20.304233",
+     "status": "failed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "Failed to import any qt binding",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-33251dd2d77b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'qt'\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      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmplot3d\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2346\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_local_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2347\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2348\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\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[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   2349\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextras\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[0mkw\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    233\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\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--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\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    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\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/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/IPython/core/magics/pylab.py\u001b[0m in \u001b[0;36mmatplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Available matplotlib backends: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbackends_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\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---> 99\u001b[0;31m             \u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_matplotlib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\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    100\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_show_matplotlib_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36menable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m   3528\u001b[0m                 \u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_gui_and_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpylab_gui_select\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3530\u001b[0;31m         \u001b[0mpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate_matplotlib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\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   3531\u001b[0m         \u001b[0mconfigure_inline_support\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mactivate_matplotlib\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m    322\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswitch_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\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    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_needmain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mswitch_backend\u001b[0;34m(newbackend)\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0mbackend_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend_module_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m     \u001b[0;32mclass\u001b[0m \u001b[0mbackend_mod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend_bases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_Backend\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    278\u001b[0m         \u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend_name\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[1;32m    279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbackend_mod\u001b[0;34m()\u001b[0m\n\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m     \u001b[0;32mclass\u001b[0m \u001b[0mbackend_mod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend_bases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_Backend\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--> 278\u001b[0;31m         \u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend_name\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[0m\u001b[1;32m    279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m     \u001b[0mrequired_framework\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_required_interactive_framework\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend_mod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m    125\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\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    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/importlib/_bootstrap_external.py\u001b[0m in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_call_with_frames_removed\u001b[0;34m(f, *args, **kwds)\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/matplotlib/backends/backend_qt5agg.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mbackend_agg\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m from .backend_qt5 import (\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mQtCore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mQtGui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mQtWidgets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_BackendQT5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFigureCanvasQT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFigureManagerQT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     NavigationToolbar2QT, backend_version)\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/matplotlib/backends/backend_qt5.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0m_Backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFigureManagerBase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNavigationToolbar2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     TimerBase, cursors, ToolContainerBase, StatusbarBase, MouseButton)\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqt_editor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigureoptions\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfigureoptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqt_editor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_formsubplottool\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mUiSubplotTool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mqt_compat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/matplotlib/backends/qt_editor/figureoptions.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqt_compat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mQtGui\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqt_editor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_formlayout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/eurodatacube-0.24.5/lib/python3.8/site-packages/matplotlib/backends/qt_compat.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\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--> 179\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Failed to import any qt binding\"\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    180\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# We should not get there.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unexpected QT_API: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQT_API\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;31mImportError\u001b[0m: Failed to import any qt binding"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib qt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits import mplot3d\n",
+    "import seaborn as sns\n",
+    "plt.rc('figure',figsize=(10,8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afc7abd6",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r\"D:\\Tokyo, TSM_2021-06-21_JP07-N3b.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a640864",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df['time'] = pd.to_datetime(df['time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "beb6a521",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a84a1057",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fac00f8",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da8a2866",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc907990",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Convert date to int\n",
+    "#df[\"time\"] = pd.to_datetime(df[\"time\"]).dt.strftime(\"%Y%m%d\")\n",
+    "df['time'] = pd.to_numeric(df.time.str.replace('-',''))\n",
+    "print(df['time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbb6f411",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "time = df[\"time\"]\n",
+    "measurement= df[\"measurement\"]\n",
+    "long_1=df[\"Longitude\"]\n",
+    "lat=df[\"Latitude\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e5cfb06",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb221595",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax1 = fig.add_subplot(111,projection = \"3d\")\n",
+    "xpos = long_1\n",
+    "ypos = lat\n",
+    "zpos = measurement\n",
+    "\n",
+    "dx = time\n",
+    "dy = np.ones(93)\n",
+    "dz = np.ones(93)\n",
+    "\n",
+    "ax1.bar3d(xpos, ypos, zpos, dx, dy, dz, color=\"#00ceaa\", shade= True)\n",
+    "ax1.set_xlabel(\"Longitude\")\n",
+    "ax1.set_ylabel(\"Latitude\")\n",
+    "ax1.set_zlabel(\"Measurement\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd528006",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "f7497bbdf9b17297f5881f7cace63ab96967d00c733902badb5c9c800271cd50"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.9 64-bit",
+   "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": "3.8.10"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 5.220981,
+   "end_time": "2021-06-28T17:20:21.027568",
+   "environment_variables": {},
+   "exception": true,
+   "input_path": "/tmp/tmpfj5a0j_r",
+   "output_path": "/tmp/notebook_output.ipynb",
+   "parameters": {},
+   "start_time": "2021-06-28T17:20:15.806587",
+   "version": "2.3.3"
+  },
+  "properties": {
+   "authors": [
+    {
+     "id": "1e90517e-372c-4725-83a3-dda3beed3ddc",
+     "name": "mohan.kshirsagar@onmyowntechnology.com"
+    }
+   ],
+   "description": "First steps on the Euro Data Cube platform",
+   "id": "4a7cae13-d390-48db-950a-397452c25a03",
+   "license": null,
+   "name": "EDC First Steps",
+   "requirements": [
+    "eurodatacube",
+    "eoxhub"
+   ],
+   "tags": [
+    "EO Data",
+    "Getting started",
+    "Sentinel Hub",
+    "xcube"
+   ],
+   "tosAgree": true,
+   "type": "Jupyter Notebook",
+   "version": "1.0.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/getting-started/EDC_first-steps.ipynb b/notebooks/getting-started/EDC_first-steps.ipynb
deleted file mode 100644
index 392c7bb3..00000000
--- a/notebooks/getting-started/EDC_first-steps.ipynb
+++ /dev/null
@@ -1,857 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-05-11T14:58:59.873063Z",
-     "iopub.status.busy": "2021-05-11T14:58:59.872452Z",
-     "iopub.status.idle": "2021-05-11T14:58:59.923501Z",
-     "shell.execute_reply": "2021-05-11T14:58:59.922910Z"
-    },
-    "papermill": {
-     "duration": 0.062413,
-     "end_time": "2021-05-11T14:58:59.923608",
-     "exception": false,
-     "start_time": "2021-05-11T14:58:59.861195",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"text/javascript\">\n",
-       "        function toggle(id) {\n",
-       "            el = document.getElementById(id);\n",
-       "            el.style.display = el.style.display === \"none\" ? \"block\" : \"none\";\n",
-       "        }\n",
-       "    </script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "This notebook is compatible with this base image version (user-0.24.5)."
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "\n",
-       "\n",
-       "---------\n",
-       "\n",
-       "The following environment variables are available:\n",
-       "\n",
-       "* `SH_CLIENT_ID`, `SH_INSTANCE_ID`, `SH_CLIENT_NAME`, `SH_CLIENT_SECRET`\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from edc import check_compatibility\n",
-    "check_compatibility(\"user-0.24.5\", dependencies=[\"SH\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.007248,
-     "end_time": "2021-05-11T14:58:59.938089",
-     "exception": false,
-     "start_time": "2021-05-11T14:58:59.930841",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "# First steps on the Euro Data Cube platform\n",
-    "\n",
-    "Euro Data Cube provides a JupyterLab environment, which automatically provides **credentials** for services with **active subscriptions** as **environment variables.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.008222,
-     "end_time": "2021-05-11T14:58:59.953326",
-     "exception": false,
-     "start_time": "2021-05-11T14:58:59.945104",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "## Setup\n",
-    "\n",
-    "As you can see here, the credentials for your subscriptions are automatically part of your environment variables.  \n",
-    "You can also print them, but make sure to **keep them confidential**!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-05-11T14:58:59.970447Z",
-     "iopub.status.busy": "2021-05-11T14:58:59.969948Z",
-     "iopub.status.idle": "2021-05-11T14:58:59.972513Z",
-     "shell.execute_reply": "2021-05-11T14:58:59.972085Z"
-    },
-    "papermill": {
-     "duration": 0.012367,
-     "end_time": "2021-05-11T14:58:59.972602",
-     "exception": false,
-     "start_time": "2021-05-11T14:58:59.960235",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "'SH_CLIENT_SECRET' in os.environ"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.007371,
-     "end_time": "2021-05-11T14:58:59.987275",
-     "exception": false,
-     "start_time": "2021-05-11T14:58:59.979904",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "Usually, it is not even necessary to access the credentials.  \n",
-    "Many libraries such as `xcube_sh` or `xcube_geodb` load them directly from the environment by default.\n",
-    "\n",
-    "## Retrieving data\n",
-    "\n",
-    "Let's say you want to retrieve some Sentinel Data as defined by the following cube (don't worry if you don't understand all the details here, it will be explained <a target=\"_blank\" href=\"https://eurodatacube.com/marketplace/notebooks/getting-started/EDC_Sentinel_Hub-XCUBE_integration.ipynb\">here</a>):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-05-11T14:59:00.005525Z",
-     "iopub.status.busy": "2021-05-11T14:59:00.005013Z",
-     "iopub.status.idle": "2021-05-11T14:59:00.353022Z",
-     "shell.execute_reply": "2021-05-11T14:59:00.352478Z"
-    },
-    "papermill": {
-     "duration": 0.358654,
-     "end_time": "2021-05-11T14:59:00.353132",
-     "exception": false,
-     "start_time": "2021-05-11T14:58:59.994478",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from xcube_sh.config import CubeConfig\n",
-    "cube_config = CubeConfig(\n",
-    "    dataset_name=\"S2L2A\",\n",
-    "    band_names=[\"B04\", \"B08\"],\n",
-    "    tile_size=[512, 512],\n",
-    "    bbox=(10.00, 54.27, 10.30, 54.50),\n",
-    "    spatial_res=0.00018,\n",
-    "    time_range=[\"2018-05-02\", \"2018-05-26\"],\n",
-    "    time_period=\"3D\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.007471,
-     "end_time": "2021-05-11T14:59:00.368117",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:00.360646",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "The following `xcube` call will fetch the data using your active SentinelHub subscription automatically using the credentials from the environment variables:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-05-11T14:59:00.385840Z",
-     "iopub.status.busy": "2021-05-11T14:59:00.385331Z",
-     "iopub.status.idle": "2021-05-11T14:59:01.419451Z",
-     "shell.execute_reply": "2021-05-11T14:59:01.419794Z"
-    },
-    "papermill": {
-     "duration": 1.044348,
-     "end_time": "2021-05-11T14:59:01.419921",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:00.375573",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lat&#x27; (lat: 1536)&gt;\n",
-       "array([54.54639, 54.54621, 54.54603, ..., 54.27045, 54.27027, 54.27009])\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 54.55 54.55 54.55 54.55 ... 54.27 54.27 54.27 54.27\n",
-       "Attributes:\n",
-       "    units:          decimal_degrees\n",
-       "    long_name:      latitude\n",
-       "    standard_name:  latitude</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lat'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1536</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dd4cb34d-b866-461a-8eb6-2624ea4d3e9b' class='xr-array-in' type='checkbox' checked><label for='section-dd4cb34d-b866-461a-8eb6-2624ea4d3e9b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>54.55 54.55 54.55 54.55 54.55 54.55 ... 54.27 54.27 54.27 54.27 54.27</span></div><div class='xr-array-data'><pre>array([54.54639, 54.54621, 54.54603, ..., 54.27045, 54.27027, 54.27009])</pre></div></div></li><li class='xr-section-item'><input id='section-1747a7c3-4011-467b-b5a1-80cc4bab964a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1747a7c3-4011-467b-b5a1-80cc4bab964a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>54.55 54.55 54.55 ... 54.27 54.27</div><input id='attrs-18e20aa9-b905-4243-86da-174d6268d8bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18e20aa9-b905-4243-86da-174d6268d8bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8d78df3-c1c2-4de8-a4d9-2f0b84a73089' class='xr-var-data-in' type='checkbox'><label for='data-e8d78df3-c1c2-4de8-a4d9-2f0b84a73089' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>decimal_degrees</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([54.54639, 54.54621, 54.54603, ..., 54.27045, 54.27027, 54.27009])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e5efaab-27f1-4f13-8420-b792864cfe86' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e5efaab-27f1-4f13-8420-b792864cfe86' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>decimal_degrees</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'lat' (lat: 1536)>\n",
-       "array([54.54639, 54.54621, 54.54603, ..., 54.27045, 54.27027, 54.27009])\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 54.55 54.55 54.55 54.55 ... 54.27 54.27 54.27 54.27\n",
-       "Attributes:\n",
-       "    units:          decimal_degrees\n",
-       "    long_name:      latitude\n",
-       "    standard_name:  latitude"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from xcube_sh.cube import open_cube\n",
-    "cube = open_cube(cube_config)\n",
-    "cube.B04.lat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.00794,
-     "end_time": "2021-05-11T14:59:01.435926",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:01.427986",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "## Explicit credential handling for advanced libraries\n",
-    "\n",
-    "Some libraries such as the EO-Learn ML library do require you to set up credentials manually. Since the credentials are available using environment variables, there are different means of using them.\n",
-    "\n",
-    "If you plan to use for instance the SentinelHub command line configuration tool (as recommended by EO-learn), you can make use of the environment variable expansion of the shell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-05-11T14:59:01.455125Z",
-     "iopub.status.busy": "2021-05-11T14:59:01.454524Z",
-     "iopub.status.idle": "2021-05-11T14:59:02.695793Z",
-     "shell.execute_reply": "2021-05-11T14:59:02.695127Z"
-    },
-    "papermill": {
-     "duration": 1.252091,
-     "end_time": "2021-05-11T14:59:02.695903",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:01.443812",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!sentinelhub.config --sh_client_id $SH_CLIENT_ID > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.008023,
-     "end_time": "2021-05-11T14:59:02.712064",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:02.704041",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "See <a target=\"_blank\" href=\"https://eurodatacube.com/marketplace/notebooks/contributions/EDC_Usecase-LPIS_Crop-Type-Classification.ipynb\">this notebook</a> for a complete example.\n",
-    "\n",
-    "If you need to pass the credentials in Python, the IPython magic command `%env` is a means of achieving that:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-05-11T14:59:02.731378Z",
-     "iopub.status.busy": "2021-05-11T14:59:02.730840Z",
-     "iopub.status.idle": "2021-05-11T14:59:02.732449Z",
-     "shell.execute_reply": "2021-05-11T14:59:02.732820Z"
-    },
-    "papermill": {
-     "duration": 0.01311,
-     "end_time": "2021-05-11T14:59:02.732926",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:02.719816",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "client_id = %env SH_CLIENT_ID"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.008523,
-     "end_time": "2021-05-11T14:59:02.749318",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:02.740795",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "You can however also access the credentials using idiomatic python:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-05-11T14:59:02.776030Z",
-     "iopub.status.busy": "2021-05-11T14:59:02.775379Z",
-     "iopub.status.idle": "2021-05-11T14:59:02.777149Z",
-     "shell.execute_reply": "2021-05-11T14:59:02.777525Z"
-    },
-    "papermill": {
-     "duration": 0.016408,
-     "end_time": "2021-05-11T14:59:02.777661",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:02.761253",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "client_id = os.environ['SH_CLIENT_ID']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding custom credentials e.g. for S3 buckets\n",
-    "\n",
-    "If you want to use external services, such as Amazon S3 buckets, you can also manage the relevant credentials via the app [edc-my-credentials](https://eurodatacube.com/marketplace/apps/edc-my-credentials) on eurodatacube.com. Those credentials will be automatically injected just like for purchased services.\n",
-    "\n",
-    "[See this guide](https://eurodatacube.com/documentation/credentials-on-edc) for step by step instructions on how to add your custom credentials."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.007875,
-     "end_time": "2021-05-11T14:59:02.825761",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:02.817886",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "The Euro Data Cube platform allows you to focus on your data without needing to think about authentication and login credentials.\n",
-    "\n",
-    "You can even **share notebooks** in the Euro Data Cube marketplace without any changes required. If anyone else runs this notebook on this platform, their respective API service credentials will be used automatically."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.007938,
-     "end_time": "2021-05-11T14:59:02.842257",
-     "exception": false,
-     "start_time": "2021-05-11T14:59:02.834319",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "To learn more about using **SentinelHub** or **GeoDB** on the Euro Data Cube platform, check out **<a target=\"_blank\" href=\"https://eurodatacube.com/marketplace/notebooks?tag=Getting%20started\">these notebooks</a>** in the marketplace."
-   ]
-  }
- ],
- "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": "3.8.5"
-  },
-  "papermill": {
-   "duration": 5.071222,
-   "end_time": "2021-05-11T14:59:04.148366",
-   "environment_variables": {},
-   "exception": null,
-   "input_path": "/tmp/tmp8bcmj5a0",
-   "output_path": "/tmp/cur_notebook.ipynb",
-   "parameters": {},
-   "start_time": "2021-05-11T14:58:59.077144",
-   "version": "2.1.0"
-  },
-  "properties": {
-   "description": "First steps on the Euro Data Cube platform",
-   "id": "4a7cae13-d390-48db-950a-397452c25a03",
-   "license": null,
-   "name": "EDC First Steps",
-   "requirements": [
-    "eurodatacube"
-   ],
-   "tags": [
-    "EO Data",
-    "Getting started",
-    "Sentinel Hub",
-    "xcube"
-   ],
-   "tosAgree": true,
-   "type": "Jupyter Notebook",
-   "version": "0.2.0"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}