diff --git a/001-Jupyter/Create_JupyterKernel_conda.ipynb b/001-Jupyter/Create_JupyterKernel_conda.ipynb
index 181a713bf45b8ea933f8ae9ca5e4700d39057b69..6f178d8a6ba1baa3762fba549c8dda6d4628bab7 100644
--- a/001-Jupyter/Create_JupyterKernel_conda.ipynb
+++ b/001-Jupyter/Create_JupyterKernel_conda.ipynb
@@ -1,17 +1,26 @@
{
"cells": [
{
+ "attachments": {
+ "09375636-629b-4ee2-9011-455f6157ab16.png": {
+ "image/png": "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"
+ }
+ },
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
"source": [
- "# Create your own Jupyter CONDA-Kernel"
+ "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
+ "<H1>Create your own Jupyter CONDA-Kernel</H1>\n",
+ "\n",
+ "-----------------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "---\n",
"## Building your own Jupyter CONDA-kernel is a three step process\n",
"Download Minconda installer\n",
"1. Download/Install Miniconda\n",
diff --git a/001-Jupyter/Create_JupyterKernel_general.ipynb b/001-Jupyter/Create_JupyterKernel_general.ipynb
index 0a6da21bf3340f75eaff54e87fe8e64b561d1bb0..b7e3011886663cd5ce6414ee322814585ca1fa13 100644
--- a/001-Jupyter/Create_JupyterKernel_general.ipynb
+++ b/001-Jupyter/Create_JupyterKernel_general.ipynb
@@ -1,17 +1,26 @@
{
"cells": [
{
+ "attachments": {
+ "09375636-629b-4ee2-9011-455f6157ab16.png": {
+ "image/png": "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"
+ }
+ },
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
"source": [
- "# Create your own Jupyter Kernel"
+ "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
+ "<H1>Create your own Jupyter Kernel</H1>\n",
+ "\n",
+ "-----------------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "---\n",
"## Building your own Jupyter kernel is a three step process\n",
"1. Create/Pimp new virtual Python environment\n",
" * venv\n",
diff --git a/001-Jupyter/List_JupyterExtensions.ipynb b/001-Jupyter/List_JupyterExtensions.ipynb
index 7c9f7e4ae009dace78accc4c448fc4ab72887e99..8fd53b97c3c3e3c5ee52f40714aea9691236510d 100644
--- a/001-Jupyter/List_JupyterExtensions.ipynb
+++ b/001-Jupyter/List_JupyterExtensions.ipynb
@@ -1,12 +1,19 @@
{
"cells": [
{
+ "attachments": {
+ "09375636-629b-4ee2-9011-455f6157ab16.png": {
+ "image/png": "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"
+ }
+ },
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": false
},
"source": [
- "# List of Extensions on Jupyter-JSC\n",
+ "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
+ "<H1>List of Extensions on Jupyter-JSC</H1>\n",
+ "\n",
"-----------------------------------"
]
},
diff --git a/001-Jupyter/List_PythonPackages.ipynb b/001-Jupyter/List_PythonPackages.ipynb
index f774f6919de12fc9eecfb34240873c474a05cd22..23da71d5eee2f2a86f26994bb53f73800751df57 100644
--- a/001-Jupyter/List_PythonPackages.ipynb
+++ b/001-Jupyter/List_PythonPackages.ipynb
@@ -1,23 +1,41 @@
{
"cells": [
{
+ "attachments": {
+ "09375636-629b-4ee2-9011-455f6157ab16.png": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAABgCAMAAACkLcFMAAAAwFBMVEX///8AO2EAOF8ANV0dVXddfZQAMlv7/P0ALlkAOWAALFgAJ1UAMFrp7vKttsDI1NxBaIRPbIbc5OkAIlIyUnGltsNSeJJxg5ePp7jFz9cAQmllhp2Woq/w9PYAAEQAB0gbTG8AFU4AGk42Y4K/xs41XHpYcIhzj6OwwMuZq7k9XXoASG7W4OaBkqPl6OsAHlGTqbldgJgAEUsAAD+dsb+LmqouVXR9maxofZJti6BYfJQnS2yeqbWAkKEQT3JAbYmctWnXAAAXrElEQVR4nO2dC3uiOhOAuVTuFIFiBUuX1hsqou1BrLXt/v9/9WWSgDdAPeuxflvnnKerGCCQl8lkMhMY5ipXucpVrnKVq1zllCKKvqX7IpLvrsnliBF/dw3OLWLUGSwaN7Ji2/bEtpvfXZ/LkVHjZz0YUbOXCpLAc7LMggjRd9focuSm9t01OJ/40UJTNZ5jV8LXzvMo+KuPol9e7HvlB7HQqXmazG4KH57jzNH47TOhn8PPt8+znPR4+Sks+GEqC9sksNxQP8O5HU8TNKmFP0859Fmbn+Gsx8sPYcEwFW4bBCRnaZQ45dGpZAG0gTWCanAj4wznPVp+BAv+fCQUkMCy6jksx0jB+kiboc+hgs8r3Z7hvEfLD2BBDANlp3cg1sLvc5w/UokO6iMzNaEsfJzjxMfKD2ChzxV1DyCKc47zWxImUYH2NyaEi4u0Hv96FvSGUkICshzP4mgTZwCDkFros9+QQC2YFzmu/NtZiIbFlgKI0D9PHfSFYtttF3+O321balymg+svZ8ENylFglSpr3j9h/yGKkS8WfL4w2ctCHCTMNL1MkPdKGPDlKHBflbtOBueq5aXIfhbqCeOY1lkqc2qJvTKrEUSaVu3bEPizWJYXJPv7CKTRLteFXilWt0IrsFxQZTkarMx5R1v7UQfE2N6wpVV9A29dc3laZL/SviMu/V03Bovf7W7jZWDsuFBFo+js5ecqZ8HvIAIilx62E8Of1ekMcsGie+u45J4aUVb0HH7dA0SsVaHACvdV3fYA7cuxR3aNUaBJkqRwOUPuSAIJNg2TuaxAMTNnMeqqUKyUPbELPyvyjiLTZ3VPEXie4wXJq0+3Lihh8W7djfYQU7yRK1CK5SxEzx2GWUjkiy89IKJ+5ZUVez04sfU2eXqcpHCp4u823W9yGYNnsVU6mCRSVU0dqxT+7rhBZ6JiT4Lwlm140eC7rDxsFPvCPZfMu9mGW+ILE95LjmvY5Petoag/4CU+96LJvMQNNmhoE7t5s6+jx+LN3Se2ggUb3ay+Qr7oHLDwnLtOCQu64IU++PoRNaJ5Q/dTL4MFp8pWQA99WtXxueQ2aseNOpsS1TjZhhY9zCYLHmk/eY0F0miNkuMaxHO51X5+Tdpyp3L8Yh2GOtGLnLP/WFj+iIU+18GV4rqXx4IVVLMgVQY01ehQVDrqWs7KgnVT0AVq9TUYzsmCn9IuN3mML46FRoVjARqCrRoa6dnOXHCM8XNOFnxTK7ourbWCoc6dj4UooM+WnuiXxoJbaTei9qoMaEqkvFz/CMfQOVnor6IxOH4tVktK8iLn1AuGtzrvhbEQD/ewIFdV0jdXeyudw896RhbCSVZBjR2+9VItq7Gs5nbQOVlwN1hoKE9YVOECWGjyxbPU+aOUVnURBrvam68fcdazsSBmtHJq34h934raasZGbgids49wt/RCeIsknB1nb/0nIu5BYU9AU2u9K1bdqqIbcj4WwozpIL/ZA9pP8Pns6yn0go61DGFBpZsq+wjwS15UHxFmD0mZqFXTUiK7TtLKW7BXzsaC+ElpXYvAEN+pwZsf9wQs6O9Yy1jAgvOLmE4RHLDUdoxM67JYWFQPIlgYBJeLs+Gkkr2DFcPZWNCJu2ozMEu3t/g4QR/h90z4x32M0J9nYjqFbKfAvzDsEVLmT+JFsaAPq30LrFQ57dTbtDsPdzidjYWIepn4DUwpIHlYxin6iFkAhtVC8eGuDqHD0E1walvPuU1NWHAm2KttaLXL6iPc0Z9YjtFykyRZOHRYeTYWaB/IpRsNOlcFECU7DGFB/iMWYrYbGnMVm1ehVE8MZ6jggG77PsHSEel8RF3pd4xmAOENKxbsb2fhds9MhDCu2nsmbRU/2Ho8GwvT7EQblOqzT5BxNqN2knGEawrqaOrTz57t9bCppQcjD0vdZ+7foBr+IOXV4A0OK7Zo32UFR4zI/xt5KHTJrbFQ1bh+d7uDEQ6N/TobC3163FlldbI+YuPZjI4dU/qRYa19jqj7IqKCfovpwMVCJQmceraH9e3RDuNqFribqp13xyDc8MArOhsLL+S4e0LrKQuyOe+v5IU/koX/bxH3DCOqp6UaBR7LA3u9s7FA66jtYYEqOA6bERr8rwn06n4MC/1KvSB7lZYju2t37tHFuVwaCxV++J/Cwh7bsTqgaVqgU4S3wzqJKwuXJ2GlXuAqA5rMAtcE1929cUVyaSxUeFl+DAtGUOFf4CstQbfI1JC9/08WrnphbRavSJRKy/Gl0OxUD8sJuFAWZG5N5B/GAuOUz01xX1XWgl+844FJuGdj4f2oPiJI14SqzJ/DgiiXdpVCUrVjUmx1Hrhmxw4LdHArnZqFYsa2hfqg5aYV5WLRibefw0K59bgdV74pfknnUpyF6zvN+XyerA64w8KcVGNrTEpdQF4+b340C8XHZXwimeIr9EEf73cMf3eHLWxvGw16rU3IL5w3kLSa2Ok4bVCJGXGBt8+pC9KfNtKGQ6sUT9tpK8RfrMYqMrMFycY62W8Kx/P71L5HZxTn2bEbLZ+ZwwyYk9VjTi4tatWHrVJfcpm7ifMq019cr9jo1BZF56jZGhJ1Ffi0w8KgaN5AJHPLXJBX5WgWaDzm1ljXCFRFUdQ8N/AkMW6+qdRms54NcZ/OP3Sn4ZvI+MtlzzRN7xlSdF54E76YZsT4wh3eTuYtoxuu9mk+k7OFHl97qz/hOYtwksc8uaqNqhh5ac/s1XkcHRHQG578isWxiTZzXXRQtGP7Bp3tfUI5+oKoVdHRvPvPrl2qu/WbIhi4Peu2zUvUidAqKGyRmErZzrfssJAr5PU2M7JJxn/PQjZPebPRoDNFBuFp0tJpWBioHXS//eTJhWiWNRbE5ScoIb0FU3cvNxbRSaioMMPbG5IBOUdBJIp+R4JL04Oh64t+ooI666jSJz3FWFMwC00R7Rf10ONqpVQRJ88x1naul5Cjt7siZCzQyfobYMFQ3y10joen0rb167sw7MuQ9MtycQtZ0GmHYud3lfo7hewimZjceHlDG1Hg+F4OyPGxLNQ5uhmHMaLxC5kSO8k8Zb2Oq+l7/R0W6Gwv9wIs5IfzBWLF+BzaY26TRhto6N8ZS3rFOSyT1VH7MrkrVtqC1K6IJfrM4JNNFvBGj14EYeGmR3IVMAs3N1hJiG9B6ahAb6hbcQhSsGdewSnzVwqFfcQbYUHJj0rhWEWf+jS8RFrTXz59XNdskKNZyOwaLl0rFdHaK9liASfRCw2SXybChGQhC2K7vckCtWj9Lrqcxg2Ni9OmjF+jvan12EEsTAybVDScuMoaC773UMQCNfkJC18RiajALLxSC9qpClycLdcW8pP5UX9fdmSpc0Z4KSp+T/MVTcpjSK2N1WpM4iKLS1wphhndtBYifHzs6weNsViLucoYk3M75CQsOM/TXH8VszBqAAt5oYwF3UMPkLyWuqN3X7IS6JfOU1wbkWq+x+oaCxb7sZeFETPAozBgIfp1UIxENPtSNQFnIatef++aimWWY9mYMtEoZeTXKCXkycpaGgPtqPghrbDYzOa+hFWpjIXS3Nqd9stjr/g+dYNZNS7zHGTNcpI+Qnx/7iX01hWyYDwjNdgaOSESaJ+MhSZEwWlrCZ65Qeh7L8CC7tpAbTwJY3uNBYcLD2BBTOEygQXj6cB4GT+atxp1NPI5ZGGcWek0hlLolBCzLoUbOrE7X2bTwe1VET/L2eHZl6lrhfNuFq2/HkWZzaV5jU3JGrGg/fKwfT7th3Hk9JeZebTSN6fJlRGd1POWTbh9Bbaj5QQQMNgSIMaJHcIlc3U00u63+YW/hwW9C/dgnsaEBa8JJmciIzW7nwXGgHFKzoLfXywW/dOtGBOXZ+PaxSPRVYMotqple2/kuA9yRybSTaoiZD5gLlireD6vym+I8ERPW9B+Vp42x2mqrSqrs69IPFXelGgkb/Zyx164Ux+RqCYojZcgdJFAfX056Ha7SoqR1PrlLMRMM9XR0zJjLMxCYMPx+DeLOYQFZJbGayx06/X0hIt7z7fjHFfCFVsaUdG4Q+itDyDFbrGjg1/XNGVz7Jnru6j9khIHCrvyuZwub4oRXYhv7jyS+yAO70Ev3BtISBTbur0gjHXdb8n4+GyWtiquswA6EViIUTfi8gZlwZtHcDy8rkfGwsAuZSFevq31EUilhN7Jgiv1Uala4L5K5ikL3Jvc1jpnZDXobZE2Bib/hgXmQS2wbjhu7X6cQi9YbXpAyKCLXsmTp9/NmNWYEmRnHBF5eBjebmfjiCbjvw1JCf0pISwwqSkuugxlgV0tlub3euTDfESOW8ACEz6FXUDtVzaOkE/GQrM8MI4vdVw3J1sNwivbvlCD2zmwrPQ2WuJfsSCO7R14+Q2HwylY8CXa4n0WAuRJiQ6X7GOBGWOfymxCngzsXxhT/8IM+xeAhdA2WCdnYW3ZoE86GOrSO1/EAtNYjoCFEQlfFRfcqViIKtbu0MalhueA3Xjste6uW9wytY1Dy7w82zzev2IB3cnlZq/GSZtnp30E/0d9RN/G8weGDJrsQ3FgEa8heA02WfiKRZG8vYv6miwZHO96mkaoO3E9GC7HXh2SJ0IejAjMQpzeQIrHLguWeq/Dmu4Cbd5CFqxUAhZctYYGBv5MYk/FwqIiFqoqQd/tKsiMk+E/ThBaRZ2J/+FBGTyYRIUkc/twtypXKDa9OOsJf9V6W+op6msajEvwgXlpNNs0pBsS7MVvmlTWa/GxmNL5CLs2+/jku3Bsvfbce/j0sM/O9z5XpV742j0IMvx8lcyY9W3QAq7Hvc3ulTauWSh744ea2gAbIMS2x/gZgLKegYWN5cQSKR2P60/ZQ2OwGQt4PsIjXxwbz0c0J+l4Vlfej193r1g6FSGS1WlWYlhfsrzAy95dKyrRH/G0vvQ4XuBYL110dgrptbtCWWTtNV7C13RH54jGfBiw6MAyG9Tn2wZu1IW9li+b5ys7VvmY8j1dtqnDSXR+L7sLcqLWmvmb1ImgNhffqVHxG7tf9X53Wc/mcuM5+jIgqdu/4bGJGlAPHf7q7xtVMtBZa25WdatBXRxz8OUkmSOmhYe6jNu6W7Yj/eU0K9IWW3hU+H3z+n7oDJywfHlGXMZ1cKGTVHddotBJBmF4gvvwl8YvHCliv2oZl2JP098nVxZAksr1GpSSEeXfJlcWkBjlrgXoIroXuq77qeXKQvVwEkzHb88VPpNcWWD0EjdxphYKRuJ/p1xZsOrVWdnVrxb4m+THsxCl1UnZHPvtSwicS346C0Z1B8HKP0ct/HAWxJCtciyAtdD4MWqhlAXfirJkB4sK+hpnZlQsMqKVi0+/xOS+6Wt7XLTo89JXV+YsVPu49eOv1L/Y21LCglsPhOAeu+FvAyoh47/TGQ3xK2RC2Jam6M/ogYlxiW4PJ8g8ZHtc5FubcxGNrz3LQLKsUr2en77kcATSMR3JrVq5ctifyR+9DaqYhalSb4bNpQCN+SHMm1giNPpSScyS/zpgYrRpOlzCLy4Tvy7Qv3MT37sxOyV7XPRgTF+UJ13mWqFbfW/9pdxtgxzxmrIP+z9koVM7fMXiHSlkweBqcA+iYYD+fqwMab0daPhc/jONQfmkixnE9PVDY8hOHy//fX3OJXozFaoXgWThffR77qy/ZKONFMYD5HAWROdou/Xh+Q/epFjIQmtEJmlvJy6wkD/eevfFxGEEOQv3Q/JjxgKOObh8FqIBp+xVCqys7nvckV5YTWfrbseg6yAajIVnLfXV6+bQRxf/+mHP/A6d1NRd/GLjDv7rWmJ+BPQNlb9ZZh/xLKTYcbGQS8jed4f2QyXw7K4fmlofB6DGHR9nt/guPhEuKhqRb0AloKJF8BaykEUWxeNwi4V+xEKkQikLeInoC2dBdBeptlcnwHBy//sa11gw6prN1uGrqLadO8iidofqRBnidnTris3V4GZ9qI2eonoP0BohflVJqMJ8vOs1WsLEq5HZ/brwPAm8OzhHTbbVEVBpTAQBWTh4ufYkkGwP52W4Srsl2TIEn4e2wHIS+Ennalgb1eDZhBQ4y4YwsOim22Jtvq+bvO0VXVohC95nFr0qbvYR3QWz4KIKFkIIqLpgFvQo7HuqsF8nsJDBsFfz+0vO1ZEwEOXL952aBPdDnHBqYFqMXufn7pS7Q5t0np8nbzYsSf4h8b2k701glsNRYMlUx4ZlUkNW6ybNQMXWalvpG0ZDAxbGSi1MUhUnAYzH864AQT2hkDYTU4VoEVflzWS2VGEh/7HJm2OIFVloPD+cQ2gaVCiawCt6o0AbTqeppNaT5tIuUHmFLLDr8X0fWq3Var1ABAmwwNy0xQIWXh0f3ZNOkKJiY+4F9ki+f+wUh6s3eMZROB2bI+UQlYC1wh67EcRf8t36cNiNcbwVpFhK8Eq+Cd+A5VGtdInOPn1HD890gppYDB4N0AvkwYX4wVC6X2NhZICOCBiIIoaTRxJ6qMR7yNd0f1PLxUrhaRNrkBGjLyFY0FVhjcJEwgrlQyWxFn1BCeHVDwaLWbDxQQOI1Usk24K0jfvdqzmABekLBEKSMAshQmqXBTut1+tdoQsd01i4gT3eL8BL46CG9/DI18PxiPveMbISvnVA9ZFeWCKBkN8WftKm9gOwYOOew+8p7SnxudzjNaVjR89sx9CGnJYNFmTomXUW1LDF3ljQypCMOlCDRU60XseBNfoXG8UxGtZBKaxtIhZHIj+opGH6dIHwTRYgXD+yOfS5IxW8FaOEhbUvG/YCwDwOLHGHhefGvN/iSMbUJfURjsSTpatkuWARz3KtoCwOIRmNI1wrwl65Nl4SfQCDbnFC38ZjmZOJ1AYK3lah8oQFA3fhjrLGAgfOG32Eb/dYMW8fAhvGEX5fmkwCEiTI1Cb4FussB69zUl6jFQtfGyyQPORtFiAlyYY+piMcGvsavFGXo2MUsKDzC39SaC8sSGLYJbHAdIK9DqVd4bj+QUptzXZs2PDATgkLHN0oGs02Dy63N6wXRBh7rrMQasBCohIWzBULicDx8nJOFhbQnZpHhjSJWsf3G5XCOWsweihmwSbByLssREeyUG/TsZHgFLDAhJNQLWTB4HENLooFJjIPtBBWwo8OjK5eY8EBiwD1BQ62F/CmuA/vXrm3byH5D27Y73+MTRZcFtq/r2yzYPAmzSZj/GnThy4FDzhYert9E5a69wcQnbzJgk2SiDIWIuz8dYgR8q9YcJ6xBSIuINJvaxzB4JeGC8XjiBoPt+ayWGD8ubpnAmpLKUjdQ53n+oqFiPUSq8kuoxULlsc7ojGEfI/4l+dEc0Xa0gv+nTa3HE/YZiH2htNkGsK9Fd/suW71J6ht/ZG0CB0HNMRA7bpRy25sszBQhk3IbVlQFsSeUovcLk/GEWssHJ4f0QOF4JNOZ1cvIDNXKWYhmmCDYgljUeb7hxGZOMMjVIMQHLaAH4jvKbl/oRM8PU3wuzPER4Vcezh6fX2SsOURwq84KeHjEbPw+IVL8ParVn8GGzFUMQsyD6PSvsp7nvIEY8Z4OHl9nZhgJf5SbGQl/AM7zgRkLuCIK/cRFmeJNJx5qJvqKyxYvPhFl3gzPPtpYk48EVoNcqWtRxkqOzmUBUa/Vx61XzKs0MV8PD0SSRg9pSmhg8eMBfqWG+sfolVnYM2M6R6/LmZuSp8q+z3OpHtQzGMyDTrh6pZGtzOH7BuG+ab5jJZAH28xN9YtFBJvqaN2NnDjW5wwEkLvL3bgsXakNyOKwhELRa1wPoMBCON3brHgHd2HGdYbjH+L84xpTeJOCLk40W2urwaDDvoRiuBfyIn18OBcGXSq2xnN67A6VGJGdOl9Ejs0RyeimS0+dbXG4E6Nsj0uaG7KevMO0A08v5Pf9i0yI2tAjavfkXZq+TmxLO7Mq56DgExH5zI6toGK9ULKnVWz/hwWkNJqpmyZt4kTuOUiugDvGBZ4ib3nyXx19MSp5SexgMT4MEfb0xEyJyh8dxxeCghYwsH0ITlNrujB8sNYgKg0t3/zbKuKJEmaJimqqnZfHGPfcn8/Qb7K1hT8u0U0wiRJmk0nNC42/PDsMrgIu/kqV7nKVa5ylatc5SpXucpVvkP+B7U7jCp8UStyAAAAAElFTkSuQmCC"
+ }
+ },
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
"source": [
- "# List of included Python packages"
+ "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
+ "<H1>List of included Python packages</H1>\n",
+ "\n",
+ "-----------------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "---\n",
"This lists the python packages available and installed by the install script for Python, SciPy-Stack and Jupyter:\n",
" - `$EBROOTPYTHON/easybuild/*.eb`\n",
" - `$EBROOTSCIPYMINSTACK/easybuild/*.eb`\n",
" - `$EBROOTJUPYTER/easybuild/*.eb`"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ls $EBROOTJUPYTER/lib/python3.6/site-packages/ | grep .dist-info > ${PKG_DISTINFO}"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -313,7 +331,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -321,264 +339,18 @@
"output_type": "stream",
"text": [
"PYPI NAME : IMPORT NAME REQ.VERS.|INST.VERS. IMPORT TIME\n",
- "=================================================================================\n",
- "setuptools : setuptools 41.6.0 0.385978s\n",
- "webencodings : webencodings 0.5.1 0.016903s\n",
- "six : six 1.12.0 0.000052s\n",
- "decorator : decorator 4.4.0 0.000052s\n",
- "MarkupSafe : markupsafe 1.1.1 0.006108s\n",
- "more-itertools : more_itertools 7.2.0 != \u001b[31m7.0.0 \u001b[0m 0.030407s\n",
- "pickleshare : pickleshare 0.7.5 0.000044s\n",
- "jedi : jedi 0.15.1 != \u001b[31m0.13.3 \u001b[0m 0.000032s\n",
- "wcwidth : wcwidth 0.1.7 0.000029s\n",
- "attr : attr 19.3.0 != \u001b[31m19.1.0 \u001b[0m 0.017762s\n",
- "parso : parso 0.5.1 != \u001b[31m0.3.4 \u001b[0m 0.000041s\n",
- "jinja2 : jinja2 2.10.1 != \u001b[31m2.10 \u001b[0m 0.028386s\n",
- "pytz : pytz 2019.3 != \u001b[31m2019.1 \u001b[0m 0.093399s\n",
- "pyparsing : pyparsing 2.2.0 != \u001b[31m2.3.1 \u001b[0m 0.049335s\n",
- "packaging : packaging 19.2 != \u001b[31m19.0 \u001b[0m 0.027138s\n",
- "urllib3 : urllib3 1.25.6 != \u001b[31m1.24.1 \u001b[0m 0.019931s\n",
- "certifi : certifi 2019.9.11 != \u001b[31m2019.03.09\u001b[0m 0.002952s\n",
- "requests : requests 2.22.0 != \u001b[31m2.21.0 \u001b[0m 0.288162s\n",
- "python-dateutil : dateutil 2.8.1 != \u001b[31m2.8.0 \u001b[0m 0.000026s\n",
- "Pillow : PIL 6.2.1 != \u001b[31m6.0.0 \u001b[0m 0.007693s\n",
- "ply : ply 3.11 0.019242s\n",
- "pyrsistent : pyrsistent 0.15.4 != \u001b[31m0.14.11 \u001b[0m 0.013004s\n",
- "lxml : lxml 4.4.1 != \u001b[31m4.3.3 \u001b[0m 0.055928s\n",
- "idna : idna 2.8 0.000051s\n",
- "chardet : chardet 3.0.4 0.000036s\n",
- "pycparser : pycparser 2.19 0.207475s\n",
- "cffi : cffi 1.13.2 != \u001b[31m1.12.2 \u001b[0m 0.015289s\n",
- "psutil : psutil 5.6.3 != \u001b[31m5.6.1 \u001b[0m 0.046984s\n",
- "SQLAlchemy : sqlalchemy 1.3.10 != \u001b[31m1.3.1 \u001b[0m 0.994233s\n",
- "certipy : certipy 0.1.3 0.037884s\n",
- "python-editor : editor 1.0.4 0.007516s\n",
- "Mako : mako 1.1.0 != \u001b[31m1.0.8 \u001b[0m 0.053407s\n",
- "alembic : alembic 1.2.1 != \u001b[31m1.0.8 \u001b[0m 1.590335s\n",
- "click : click 7.0 0.018214s\n",
- "appdirs : appdirs 1.4.3 0.012561s\n",
- "cloudpickle : cloudpickle 1.2.2 != \u001b[31m0.8.1 \u001b[0m 0.006038s\n",
- "toolz : toolz 0.10.0 != \u001b[31m0.9.0 \u001b[0m 0.020046s\n",
- "cryptography : cryptography 2.8 0.000043s\n",
- "prompt-toolkit : prompt_toolkit 2.0.10 != \u001b[31m2.0.9 \u001b[0m 0.000035s\n",
- "oauthlib : oauthlib 3.1.0 0.009413s\n",
- "async-generator : async_generator 1.10 0.009577s\n",
- "smmap : smmap 0.9.0 != \u001b[31m2.0.5 \u001b[0m 0.005029s\n",
- "typed-ast : typed_ast 1.4.0 0.007294s\n",
- "cycler : cycler 0.10.0 0.007537s\n",
- "numpy : numpy 1.15.2 0.509992s\n",
- "scipy : scipy 1.2.1 0.028397s\n",
- "sympy : sympy 1.4 != \u001b[31m1.3 \u001b[0m 7.210424s\n",
- "pandas : pandas 0.25.3 != \u001b[31m0.24.2 \u001b[0m 1.315975s\n",
- "mpmath : mpmath 1.1.0 0.000075s\n",
- "kiwisolver : kiwisolver 1.1.0 != \u001b[31m1.0.1 \u001b[0m 0.033678s\n",
- "backports.functools_lru_cache : backports.functools_lru_cache \u001b[31mIMPORT FAILED\u001b[0m\n",
- "matplotlib : matplotlib 3.1.1 != \u001b[31m3.0.3 \u001b[0m 0.000086s\n",
- "xarray : xarray 0.11.3 != \u001b[31m0.12.1 \u001b[0m 1.172104s\n",
- "pyOpenSSL : OpenSSL 19.0.0 0.000034s\n",
- "entrypoints : entrypoints 0.3 0.002844s\n",
- "async_generator : async_generator 1.10 0.000047s\n",
- "absl-py : absl 0.8.1 0.012326s\n",
- "cryptography : cryptography 2.8 0.000051s\n",
- "tornado : tornado 6.0.3 0.000029s\n",
- "bokeh : bokeh 1.3.4 0.089318s\n",
- "seaborn : seaborn 0.9.0 11.679716s\n",
- "nbformat : nbformat 4.4.0 0.195660s\n",
- "param : param 1.9.2 0.209250s\n",
- "pyviz_comms : pyviz_comms 0.7.2 0.092592s\n",
- "holoviews : holoviews 1.12.6 1.424016s\n",
- "alabaster : alabaster 0.7.12 0.014988s\n",
- "Babel : babel 2.7.0 0.081024s\n",
- "snowballstemmer : snowballstemmer 2.0.0 0.351891s\n",
- "docutils : docutils 0.15.2 0.027402s\n",
- "imagesize : imagesize 1.1.0 0.004413s\n",
- "sphinxcontrib-websupport : sphinxcontrib.websupport 1.1.2 0.889744s\n",
- "Sphinx : sphinx 1.8.5 0.000060s\n",
- "pexpect : pexpect 4.7.0 0.000043s\n",
- "ipython : IPython 7.9.0 0.000038s\n",
- "ipynb : ipynb 0.5.1 0.014553s\n",
- "jupyter_core : jupyter_core 4.6.1 0.000053s\n",
- "retrying : retrying 1.3.3 0.020777s\n",
- "plotly : plotly 4.2.1 6.939769s\n",
- "tikzplotlib : tikzplotlib 0.8.4 0.114980s\n",
- "jupyter_client : jupyter_client 5.3.4 0.000043s\n",
- "traitlets : traitlets 4.3.3 0.000031s\n",
- "pyzmq : zmq 18.1.0 0.000028s\n",
- "singledispatch : singledispatch 3.4.0.3 0.062908s\n",
- "ipyparallel : ipyparallel 6.2.4 0.000068s\n",
- "ipykernel : ipykernel 5.1.3 0.000048s\n",
- "terminado : terminado 0.8.2 0.047164s\n",
- "bleach : bleach 3.1.0 0.469373s\n",
- "mistune : mistune 0.8.4 0.023354s\n",
- "pandocfilters : pandocfilters 1.4.2 0.001329s\n",
- "Pygments : pygments 2.4.2 0.000043s\n",
- "testpath : testpath 0.4.4 0.003176s\n",
- "nbconvert : nbconvert 5.6.1 0.042023s\n",
- "ipython_genutils : ipython_genutils 0.2.0 0.000036s\n",
- "Send2Trash : send2trash 1.5.0 0.010062s\n",
- "notebook : notebook 6.0.2 0.000031s\n",
- "version_information : version_information 1.0.3 0.047597s\n",
- "lesscpy : lesscpy 0.13.0 0.005995s\n",
- "prometheus-client : prometheus_client 0.7.1 0.025599s\n",
- "jupyterthemes : jupyterthemes 0.20.0 0.052266s\n",
- "zipp : zipp 0.6.0 0.000048s\n",
- "importlib_metadata : importlib_metadata 0.23 0.000036s\n",
- "jsonschema : jsonschema 3.1.1 0.000030s\n",
- "jupyterlab_launcher : jupyterlab_launcher 0.13.1 0.109656s\n",
- "sphinx_rtd_theme : sphinx_rtd_theme 0.4.3 0.008458s\n",
- "future : future 0.18.1 0.006830s\n",
- "commonmark : commonmark 0.9.1 0.095996s\n",
- "recommonmark : recommonmark 0.6.0 0.002422s\n",
- "jupyterlab : jupyterlab 1.2.1 0.002643s\n",
- "json5 : json5 0.8.5 0.003840s\n",
- "jupyterlab_server : jupyterlab_server 1.0.6 0.004840s\n",
- "ptyprocess : ptyprocess 0.6.0 0.000037s\n",
- "defusedxml : defusedxml 0.6.0 0.000027s\n",
- "widgetsnbextension : widgetsnbextension 3.5.1 0.016441s\n",
- "ipywidgets : ipywidgets 7.5.1 0.000042s\n",
- "ipydatawidgets : ipydatawidgets 4.0.1 0.134411s\n",
- "traittypes : traittypes 0.2.1 0.000048s\n",
- "bqplot : bqplot 0.11.9 0.271027s\n",
- "jupyter_bokeh : jupyter_bokeh 1.1.1 != \u001b[31m\u001b[31mNO MATCH\u001b[0m\u001b[0m 1.545438s\n",
- "pythreejs : pythreejs 2.1.1 1.858083s\n",
- "PyWavelets : pywt 1.1.1 0.377231s\n",
- "imageio : imageio 2.6.1 0.387762s\n",
- "networkx : networkx 2.3 3.447984s\n",
- "scikit-image : skimage 0.16.2 0.284224s\n",
- "ipywebrtc : ipywebrtc 0.5.0 0.040460s\n",
- "ipyvolume : ipyvolume 0.5.2 0.409094s\n",
- "branca : branca 0.3.1 0.226449s\n",
- "ipyleaflet : ipyleaflet 0.11.4 0.070324s\n",
- "ipympl : ipympl 0.3.3 0.123354s\n",
- "PyYAML : yaml 5.1.2 0.000082s\n",
- "jupyter_nbextensions_configurator : jupyter_nbextensions_configurator 0.4.1 0.003318s\n",
- "jupyter_latex_envs : latex_envs 1.4.6 != \u001b[31m1.4.0 \u001b[0m 0.005721s\n",
- "jupyter_highlight_selected_word : jupyter_highlight_selected_word 0.2.0 0.020243s\n",
- "jupyter_contrib_core : jupyter_contrib_core 0.3.3 0.002995s\n",
- "jupyter_contrib_nbextensions : jupyter_contrib_nbextensions 0.5.1 0.002769s\n",
- "rise : rise 5.5.1 0.188142s\n",
- "typing-extensions : typing_extensions 3.7.4 0.037277s\n",
- "idna-ssl : idna_ssl 1.1.0 0.002152s\n",
- "multidict : multidict 4.5.2 0.004443s\n",
- "yarl : yarl 1.3.0 0.003127s\n",
- "async-timeout : async_timeout 3.0.1 0.003045s\n",
- "aiohttp : aiohttp 3.6.2 0.077788s\n",
- "simpervisor : simpervisor 0.3 0.003966s\n",
- "jupyter_server : jupyter_server 0.1.1 0.001706s\n",
- "jupyter-server-proxy : jupyter_server_proxy 1.1.0 != \u001b[31m1.2.0 \u001b[0m 0.004240s\n",
- "jupyterlab_github : jupyterlab_github 1.0.1 != \u001b[31m1.0.0 \u001b[0m 0.000919s\n",
- "jupyterlab-gitlab : jupyterlab_gitlab 0.3.0 != \u001b[31m0.2.0 \u001b[0m 0.001144s\n",
- "jupyterlab-quickopen : jupyterlab_quickopen 0.3.0 0.002939s\n",
- "zstandard : zstandard 0.12.0 0.000029s\n",
- "itk_core : itk_core \u001b[31mIMPORT FAILED\u001b[0m\n",
- "itk_filtering : itk_filtering \u001b[31mIMPORT FAILED\u001b[0m\n",
- "itk_segmentation : itk_segmentation \u001b[31mIMPORT FAILED\u001b[0m\n",
- "itk_numerics : itk_numerics \u001b[31mIMPORT FAILED\u001b[0m\n",
- "itk_registration : itk_registration \u001b[31mIMPORT FAILED\u001b[0m\n",
- "itk_io : itk_io \u001b[31mIMPORT FAILED\u001b[0m\n",
- "itk-meshtopolydata : itk-meshtopolydata \u001b[31mIMPORT FAILED\u001b[0m\n",
- "pyct : pyct 0.4.6 0.076661s\n",
- "colorcet : colorcet 2.0.2 != \u001b[31m1.0.0 \u001b[0m 1.035480s\n",
- "itkwidgets : itkwidgets 0.22.0 13.082362s\n",
- "ujson : ujson 1.35 0.037301s\n",
- "jupyterlab_iframe : jupyterlab_iframe 0.2.1 0.004151s\n",
- "python-dotenv : dotenv 0.10.3 0.056290s\n",
- "jupyterlab_latex : jupyterlab_latex 1.0.0 0.004882s\n",
- "jupyterlab_slurm : jupyterlab_slurm 1.0.5 0.002469s\n",
- "jupyterlmod : jupyterlmod 1.7.5 0.003136s\n",
- "nbresuse : nbresuse 0.3.2 0.001697s\n",
- "colorama : colorama 0.4.1 0.000065s\n",
- "nbdime : nbdime 1.1.0 0.020851s\n",
- "smmap2 : smmap 2.0.5 0.000058s\n",
- "gitdb2 : gitdb 2.0.6 0.025730s\n",
- "GitPython : git 3.0.4 0.099233s\n",
- "jupyterlab-git : jupyterlab_git 0.8.1 0.016555s\n",
- "sidecar : sidecar 0.3.0 0.018072s\n",
- "pycodestyle : pycodestyle 2.5.0 0.007567s\n",
- "autopep8 : autopep8 1.4.4 0.004303s\n",
- "yapf : yapf 0.28.0 0.025461s\n",
- "toml : toml 0.10.0 0.003055s\n",
- "pathspec : pathspec 0.6.0 0.077891s\n",
- "typed_ast : typed_ast 1.4.0 0.000055s\n",
- "regex : regex 2019.11.1 != \u001b[31m2.5.65 \u001b[0m 0.087107s\n",
- "black : black 19.3b0 0.265862s\n",
- "jupyterlab-code-formatter : jupyterlab_code_formatter 0.6.1 0.001970s\n",
- "pamela : pamela 1.0.0 0.081929s\n",
- "certipy : certipy 0.1.3 0.000066s\n",
- "oauthlib : oauthlib 3.1.0 0.000042s\n",
- "jupyterhub : jupyterhub 1.0.0 0.003949s\n",
- "appmode : appmode 0.6.0 0.001285s\n",
- "HeapDict : heapdict 1.0.1 0.000043s\n",
- "zict : zict 1.0.0 0.000043s\n",
- "tblib : tblib 1.5.0 0.000032s\n",
- "sortedcontainers : sortedcontainers 2.1.0 0.000033s\n",
- "msgpack : msgpack 0.6.2 0.000029s\n",
- "dask : dask 2.6.0 0.000039s\n",
- "distributed : distributed 2.6.0 0.000030s\n",
- "dask-jobqueue : dask-jobqueue \u001b[31mIMPORT FAILED\u001b[0m\n",
- "dask_labextension : dask_labextension 1.0.3 0.012876s\n",
- "Automat : automat 0.8.0 0.056752s\n",
- "PyHamcrest : hamcrest 1.9.0 0.317690s\n",
- "Twisted : twisted 19.7.0 0.044753s\n",
- "autobahn : autobahn 19.10.1 0.006888s\n",
- "constantly : constantly 15.1.0 0.039913s\n",
- "hyperlink : hyperlink 19.0.0 0.074022s\n",
- "incremental : incremental 17.5.0 0.000064s\n",
- "txaio : txaio 18.8.1 0.038220s\n",
- "zope.interface : zope.interface 4.6.0 0.138110s\n",
- "backcall : backcall 0.1.0 0.000064s\n",
- "wslink : wslink 0.1.11 0.007843s\n",
- "jupyterlab_pygments : jupyterlab_pygments 0.1.0 0.009341s\n",
- "ipyvue : ipyvue 1.0.0 0.025899s\n",
- "ipyvuetify : ipyvuetify 1.1.1 0.414047s\n",
- "voila : voila 0.1.14 0.010617s\n",
- "voila-material : - 0.2.5 0.000001s\n",
- "voila-gridstack : - 0.0.6 0.000000s\n",
- "voila-vuetify : - 0.1.1 0.000000s\n",
- "dicom-upload : dicom-upload \u001b[31mIMPORT FAILED\u001b[0m\n",
- "fileupload : fileupload master != \u001b[31m0.1.0.dev \u001b[0m 0.014791s\n",
- "pvlink : pvlink 0.1.2 0.012866s\n",
- "julia : julia 0.5.0 0.102546s\n",
- "textwrap3 : textwrap3 0.9.2 0.023105s\n",
- "ansiwrap : ansiwrap 0.8.4 0.039140s\n",
- "backports.weakref : backports.weakref 1.0.post1 0.020673s\n",
- "backports.tempfile : backports.tempfile 1.0 0.006095s\n",
- "tqdm : tqdm 4.41.0 0.101886s\n",
- "tenacity : tenacity 6.0.0 0.093667s\n",
- "papermill : papermill 1.2.1 0.417333s\n",
- "patsy : patsy 0.5.1 0.281202s\n",
- "statsmodels : statsmodels 0.10.2 0.000063s\n",
- "cftime : cftime 1.0.4.2 0.000048s\n",
- "vega_datasets : vega_datasets 0.8.0 0.124947s\n",
- "arviz : arviz 0.5.1 8.192571s\n",
- "Theano : Theano \u001b[31mIMPORT FAILED\u001b[0m\n",
- "altair : altair 3.3.0 0.572322s\n",
- "cssselect : cssselect 1.1.0 0.074927s\n",
- "smopy : smopy 0.0.7 != \u001b[31m0.0.6 \u001b[0m 0.005187s\n",
- "joblib : joblib 0.14.1 0.000062s\n",
- "scikit-learn : scikit-learn \u001b[31mIMPORT FAILED\u001b[0m\n",
- "memory_profiler : memory_profiler 0.55.0 0.048130s\n",
- "h5py : h5py 2.10.0 1.163224s\n",
- "line_profiler : line_profiler 2.1.2 != \u001b[31m\u001b[31mNO MATCH\u001b[0m\u001b[0m 0.041151s\n",
- "pymc3 : pymc3 3.8 6.798788s\n",
- "llvmlite : llvmlite 0.30.0 0.000073s\n",
- "numba : numba 0.46.0 0.000051s\n",
- "numexpr : numexpr 2.7.0 0.101310s\n",
- "ipythonblocks : ipythonblocks 1.9.0 0.023981s\n",
- "pydub : pydub 0.23.1 0.209780s\n",
- "multipledispatch : multipledispatch 0.6.0 0.062537s\n",
- "partd : partd 1.1.0 0.168488s\n",
- "locket : locket 0.2.0 0.000052s\n",
- "fsspec : fsspec 0.6.2 0.136985s\n",
- "datashape : datashape 0.5.2 0.177264s\n",
- "datashader : datashader 0.9.0 13.161141s\n",
- "selenium : selenium 3.141.0 0.009461s\n",
- "graphviz : graphviz 0.13.2 0.088085s\n",
- "vincent : vincent 0.4.4 0.167066s\n",
- "Shapely : Shapely \u001b[31mIMPORT FAILED\u001b[0m\n",
- "pyshp : pyshp \u001b[31mIMPORT FAILED\u001b[0m\n",
- "Cartopy : Cartopy \u001b[31mIMPORT FAILED\u001b[0m\n",
- "pandas-datareader : pandas-datareader \u001b[31mIMPORT FAILED\u001b[0m\n"
+ "=================================================================================\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'pkg_list' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-1-09245ef6496d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PYPI NAME\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\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\"IMPORT NAME\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"REQ.VERS.|INST.VERS.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"IMPORT TIME\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\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----> 7\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mpkg_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpkg_version\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpkg_importname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpkg_list\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 8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpkg_importname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mpkg_importname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpkg_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'pkg_list' is not defined"
]
}
],
diff --git a/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb b/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb
index eedf4e4f45b148e41fda99daa0798e6a24a8f0a6..2c33e48abb604dbf244c8b10903f0ced89bb5b9f 100644
--- a/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb
+++ b/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb
@@ -1,10 +1,27 @@
{
"cells": [
+ {
+ "attachments": {
+ "09375636-629b-4ee2-9011-455f6157ab16.png": {
+ "image/png": "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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
+ "<H5>Jens Henrik Göbbert</H5>\n",
+ "<H1>Markdown Tipps & Tricks (for Jupyter Notebook)</H1>\n",
+ "\n",
+ "-----------------------------------"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Markdown Tipps & Tricks (for Jupyter Notebook)\n",
"Markdown writing skills are essential to portray your work in the Jupyter notebook to offer the reader a sufficient explanation of both the code and the concept.\n",
"\n",
"I have collected informations for different sources. Thanks to them!\n",
@@ -470,13 +487,6 @@
"from IPython.display import IFrame\n",
"IFrame('https://arxiv.org/pdf/1406.2661.pdf', width=800, height=450)"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {