sharing registry between jupyter notebooks (how to use set_application_registry?)

[kompre]

Hello,

I want to share data between jupyter notebooks, probably using the magic %store.

I can share a Quantity from the first notebook to the second, but that Quantity is now an "orphan" of UnitRegistry in the seconds notebook. I didn't understand how to use the set_application_registry indicated at this link.

Can please the process and usage be expressed more in detail? Or can the proper manner to share unit registry between notebooks being suggested?

thank you in advance

[dalito]

Can you show an example? What is done in notebook1 and in notebook2? Why is it not sufficient to load the registry from the same definition file? The text that you refer to is explaining how to share a unit registry across different parts of an application (not between different applications).

[kompre]

So I have two jupyter notebooks, A and B:

• in A.ipynb:
  1. import pint;
  2. create the quantity a;
  3. %store quantity a

• in B.ipynb:
  • load %stored variable

In B I've just loaded the variable, not even imported the pint module. I don't know what to do with it because of course when I initialize a new registry in notebook B, the UnitRegistry will not be the same.

Can't %store a unit registry because an error is raised:

``AttributeError: Can't pickle local object 'Relation.transformation..'`

I would like to find a way to easily save data from one notebook and the load into another. These notebooks are meant for producing a report, so the value I want to store from notebook A derive from computation and other parameters not needed in notebook B. Keeping the file segregated help me to better authoring those document.

I attached the notebook code

notebook A

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3 kilonewton"
      ],
      "text/latex": [
       "$3\\ \\mathrm{kilonewton}$"
      ],
      "text/plain": [
       "3 <Unit('kilonewton')>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pint\n",
    "\n",
    "# initialize a unit registry\n",
    "u = pint.UnitRegistry()\n",
    "\n",
    "# create a quantity\n",
    "a = 3*u.kN; a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'a' (Quantity)\n"
     ]
    }
   ],
   "source": [
    "%store a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Can't pickle local object 'Relation.transformation.<locals>.<lambda>'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [7], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# try to store also the unit registry  will fail\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstore\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mu\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2364\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2362\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m   2363\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2364\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/IPython/extensions/storemagic.py:229\u001b[0m, in \u001b[0;36mStoreMagics.store\u001b[0;34m(self, parameter_s)\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    228\u001b[0m \u001b[38;5;66;03m#pickled = pickle.dumps(obj)\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m \u001b[43mdb\u001b[49m\u001b[43m[\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mautorestore/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m \u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m obj\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStored \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (arg, obj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pickleshare.py:118\u001b[0m, in \u001b[0;36mPickleShareDB.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;66;03m# We specify protocol 2, so that we can mostly go between Python 2\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;66;03m# and Python 3. We can upgrade to protocol 3 when Python 2 is obsolete.\u001b[39;00m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m fil\u001b[38;5;241m.\u001b[39mopen(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m--> 118\u001b[0m     \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    119\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache[fil] \u001b[38;5;241m=\u001b[39m (value, fil\u001b[38;5;241m.\u001b[39mstat()\u001b[38;5;241m.\u001b[39mst_mtime)\n",
      "\u001b[0;31mAttributeError\u001b[0m: Can't pickle local object 'Relation.transformation.<locals>.<lambda>'"
     ]
    }
   ],
   "source": [
    "# try to store also the unit registry  will fail with \"AttributeError: Can't pickle local object 'Relation.transformation.<locals>.<lambda>'\"\n",
    "\n",
    "%store u"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

notebook B

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3 kilonewton"
      ],
      "text/latex": [
       "$3\\ \\mathrm{kilonewton}$"
      ],
      "text/plain": [
       "3 <Unit('kilonewton')>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#retrieve the stored quantity\n",
    "%store -r a\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pint.util.Quantity"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

[dalito]

Would this https://pint.readthedocs.io/en/latest/advanced/serialization.html work for you?

[kompre]

Yes, I think so. I was already thinking about converting the imported quantity to string and then back with the registry in the new notebook.

I have would have to try also with pint_pandas data frame how they will behave.