diff --git a/docs/physical_units/conversion.ipynb b/docs/physical_units/conversion.ipynb
index 7ecbec4..4c87d5f 100644
--- a/docs/physical_units/conversion.ipynb
+++ b/docs/physical_units/conversion.ipynb
@@ -14,6 +14,62 @@
     "## Dimensionless Quantities"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dimensionless Quantities are useful in many scenarios and some mathematical functions only accept dimensionless quantities. `Quantity` provides `to_value` method to convert a quantity to a dimensionless quantity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ArrayImpl([1., 2., 3.], dtype=float32) * mvolt"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import brainunit as bu\n",
+    "a = [1, 2, 3] * bu.mV\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Array([0.001, 0.002, 0.003], dtype=float32, weak_type=True)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.to_value(bu.volt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`to_value` method accepts a `unit` parameter and returns the value of the `Quantity` in the scale of the given unit."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -21,17 +77,199 @@
     "## Plotting Quantities"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dimensionless `Quantity` can be convieniently plotted using [matplotlib](https://matplotlib.org/). \n",
+    "\n",
+    "The Dimensionless `Quantity` can be passed to matplotlib plotting functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1bb9710bf40>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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u6VFRUejfvz/GjBmDHTt2tLg/CQkJUCqVxpuHh0eL9UREbaG92Yi45GxMX/89fii+AXs7G7w2dRj2LBzLAO3izArRiooK6PV6qFSm4/UqlQoajabZZTQaTYv1t3+as87CwkKsX78ezz//vHFar169sHbtWmzfvh07d+7EmDFjEB0d3WKQLl26FFqt1ni7ePHiPWuJiMxlMAhs++EiJq45gM/TL8AggMgH3JC2KAx/HO8NuS2Hbru6Lnd27uXLlzF16lTMmjULzz77rHG6q6srYmJijPcfeeQRlJaWYvXq1YiKimp2XXK5HHK5vMN7JqKe58wlLZYlZ+PUxUoAgHf/XoiPCsBj3q6WbYzalVkh6urqChsbG5SVlZlMLysrg1qtbnYZtVrdYv3tn2VlZXBzczOp+fl7m6WlpZgwYQJCQ0OxcePGX+w3ODgYqampv1hHRNRebtQ0YPXefGw5XgIhAEeZDRaG++L3jw2GnU2bPhBBVsys36hMJkNgYCDS0tKM0wwGA9LS0hASEtLsMiEhISb1AJCammqs9/LyglqtNqnR6XTIyMgwWefly5cxfvx4BAYGYtOmTZBKf7n1U6dOmQQzEVFH0RsEvsi4gAlrD+BfGbcCNHq0O757ZTyeHTeEAdpNmT2cGxMTg3nz5uHhhx9GUFAQ3n//fdTU1ODpp58GADz11FMYMGAAEhISAAAvv/wywsLCsHbtWkRGRiIpKQknTpwwvpKUSCRYuHAh3nrrLfj4+MDLywvLli2Du7s7oqOjAfxfgHp6emLNmjW4evWqsZ/br2Q///xzyGQyPPjggwCAL7/8Ep999hn+9re/tf3oEBG1QlbJDcQm5+DMZS0AwE/thPioAAQP6WvhzqijmR2is2fPxtWrVxEbGwuNRoPRo0djz549xhODSkpKTF4lhoaG4l//+hfeeOMNvP766/Dx8cHXX3+NESNGGGtee+011NTU4LnnnkNlZSXGjBmDPXv2QKFQALj1yrWwsBCFhYUYOHCgST93fkLnzTffxIULF2Braws/Pz9s3boVv/nNb8zdRSKiVrlWXY939+Rh24lLAAAnuS1ipvhi7qOesOUrzx7B7M+JdmfmfDaIiHquJr0BX2SUYO3efOjqmgAAvwkciMVT/dDPiScrdnXmZEGXOzuXiMiSThRfx7LkHJy9ogMABLg7Y8XMAAR63v25der+GKJERK1QXlWHlbvy8GXWZQCA0t4Or0QMw2+DBsFGymt89lQMUSKiFjTqDfj8aDHe31eA6vomSCTAE4944NUIP/RxlFm6PbIwhigR0T2kn7uGuB3Z+KmsGgAwaqAS8TNHYLSHi2UbI6vBECUi+hmNtg5v7zqLb34sBQD0drDD4ql+ePxhD0g5dEt3YIgSEf2vhiYDNh0pwrq0AtQ06CGVAL8L9sSiKb5wceDQLd2NIUpEBOBwQQXidmTj3NUaAMBDg1ywYuYIjBigtHBnZM0YokTUo12uvIm3UnKxO/vWVaNce8mwZNpw/L8HB3Doln4RQ5SIeqT6Jj0+OXQeH+wvRF2jATZSCZ4K8cTCcF8o7e0s3R51EQxRIupx9ueXI35HDoqv1QIAggb3QfzMAAx34zeVkXkYokTUY1y8Xov4b3Kx7+ytyzP2d5LjL5HDETXKHRIJh27JfAxRIur26hr1+OjAOSQePIf6JgNspRI8/dhg/GmSD5wUHLqltmOIElG3JYTAvrPlWJGSg4vXbwIAQof2RXxUAHxUThbujroDhigRdUvFFTVY/k0ODuTfuv6wm1KBNyL98auRag7dUrthiBJRt1Lb0IQP95/DxkPn0aA3wM5GgmfHDsGCCd5wlPMpj9oXH1FE1C0IIbAnW4M3U3JRqq0DAIzz7YflM/wxpF8vC3dH3RVDlIi6vMLyasR/k4PvCyoAAANc7BE7wx9T/FUcuqUOxRAloi6rur4J69MK8OnhIjQZBGS2UvwhbCheCBsKe5mNpdujHoAhSkRdjhAC35y+grd35qJMVw8AmOTXH7Ez/OHZ19HC3VFPwhAloi4lX1OFuB3ZOHb+OgBgUB8HxM3wx6ThKgt3Rj0RQ5SIugRdXSP+uq8Am48WQ28QkNtKsWCCN54bNwQKOw7dkmUwRInIqgkh8FXWZbyzKw8V1beGbiMCVHgj0h8efRws3B31dAxRIrJauaU6xCZn48SFGwCAIa6OiIsKQJhvPwt3RnQLQ5SIrI62thH/k5qPfxy7AIMAHGQ2eGmiD/57zGDIbTl0S9aDIUpEVsNgEPh35iW8uycP12oaAACRD7jhjcjhcFPaW7g7orsxRInIKpy+VInY5ByculgJAPDu3wvxUQF4zNvVso0RtYAhSkQWdaOmAav35mPL8RIIATjKbLAw3Be/f2ww7Gyklm6PqEUMUSKyCL1BIOmHEqz+Nh+VtY0AgOjR7nj9V8PR31lh4e6IWochSkSd7mTJDcQl5+DMZS0AwE/thPioAAQP6WvhzojMwxAlok5TUV2PVXvysO3EJQCAk9wWMVN8MfdRT9hy6Ja6IIYoEXW4Jr0BX2SUYO3efOjqmgAAvwkciMVT/dDPSW7h7ojajiFKRB3qh+LriE3OwdkrOgBAgLszVswcgUDP3hbujOj+MUSJqEOU6+qwcncevsy6DABQ2tvh1YhhmBM0CDZSXuOTugeGKBG1q0a9AZ8fLcb7+wpQXd8EiQR44hEPvBrhhz6OMku3R9SuGKJE1G7Sz11D3I5s/FRWDQAY5eGCFVEBGOXhYtnGiDoIQ5SI7ptGW4e3d53FNz+WAgB6O9hh8VQ/PP6wB6QcuqVujCFKRG3W0GTAZ0eKsC6tALUNekglwO+CPbFoii9cHDh0S90fQ5SI2uT7gquI25GD81drAAAPDXLBipkjMGKA0sKdEXUehigRmeVy5U28lZKL3dkaAIBrLxmWTBuO//fgAA7dUo/DECWiVqlv0uOTQ+fxwf5C1DUaYCOV4KkQT/x5si+cFXaWbo/IIhiiRPSL9ueVI/6bHBRfqwUABHn1wYqZAfBTO1u4MyLLatOXVW7YsAGDBw+GQqFAcHAwjh8/3mL99u3b4efnB4VCgZEjR2LXrl0m84UQiI2NhZubG+zt7REeHo6CggLj/OLiYsyfPx9eXl6wt7fH0KFDERcXh4aGBpP1nD59GmPHjoVCoYCHhwdWrVrVlt0jov9Vcq0Wz3x+Ak9v/gHF12rR30mOvz4xGlufe5QBSoQ2hOjWrVsRExODuLg4nDx5EqNGjUJERATKy8ubrT969CjmzJmD+fPnIysrC9HR0YiOjkZ2draxZtWqVVi3bh0SExORkZEBR0dHREREoK6uDgCQl5cHg8GAjz/+GDk5OXjvvfeQmJiI119/3bgOnU6HKVOmwNPTE5mZmVi9ejWWL1+OjRs3mruLRD1eXaMe76X+hPD3DmLf2TLYSiV4btwQfPfKeMwcPQASCd/7JAIACDMFBQWJBQsWGO/r9Xrh7u4uEhISmq1//PHHRWRkpMm04OBg8fzzzwshhDAYDEKtVovVq1cb51dWVgq5XC62bNlyzz5WrVolvLy8jPc//PBD0bt3b1FfX2+ctnjxYjFs2LBW75tWqxUAhFarbfUyRN2JwWAQ32ZfEY+tTBOei1OE5+IU8dtP0kVBmc7SrRF1GnOywKxXog0NDcjMzER4eLhxmlQqRXh4ONLT05tdJj093aQeACIiIoz1RUVF0Gg0JjVKpRLBwcH3XCcAaLVa9OnTx2Q748aNg0z2f59Ni4iIQH5+Pm7cuNHsOurr66HT6UxuRD1VUUUNnt78A577RyYu3bgJN6UCG377EP45Pxje/Z0s3R6RVTLrxKKKigro9XqoVCqT6SqVCnl5ec0uo9Fomq3XaDTG+ben3avm5woLC7F+/XqsWbPGZDteXl53reP2vN69775iREJCAuLj45vdBlFPUdvQhA37C/HJoSI06A2ws5Hg2bFD8OJEbzjIeO4hUUu63F/I5cuXMXXqVMyaNQvPPvvsfa1r6dKliImJMd7X6XTw8PC43xaJugQhBHZna/BWSi5KtbfOPxjn2w/LZ/hjSL9eFu6OqGswK0RdXV1hY2ODsrIyk+llZWVQq9XNLqNWq1usv/2zrKwMbm5uJjWjR482Wa60tBQTJkxAaGjoXScM3Ws7d27j5+RyOeRyXhCYep7C8ios35GLw4UVAIABLvaIneGPKf4qnjREZAaz3hOVyWQIDAxEWlqacZrBYEBaWhpCQkKaXSYkJMSkHgBSU1ON9V5eXlCr1SY1Op0OGRkZJuu8fPkyxo8fj8DAQGzatAlSqWnrISEhOHToEBobG022M2zYsGaHcol6our6JiTsOoup73+Pw4UVkNlK8adJPtgXE4aIADUDlMhc5p61lJSUJORyudi8ebPIzc0Vzz33nHBxcREajUYIIcTcuXPFkiVLjPVHjhwRtra2Ys2aNeLs2bMiLi5O2NnZiTNnzhhrVq5cKVxcXERycrI4ffq0mDlzpvDy8hI3b94UQghx6dIl4e3tLSZNmiQuXbokrly5YrzdVllZKVQqlZg7d67Izs4WSUlJwsHBQXz88cet3jeenUvdlcFgEF9nXRJBb6caz7qdv/m4KK6otnRrRFbHnCwwO0SFEGL9+vVi0KBBQiaTiaCgIHHs2DHjvLCwMDFv3jyT+m3btglfX18hk8lEQECA2Llzp8l8g8Egli1bJlQqlZDL5WLSpEkiPz/fOH/Tpk0CQLO3O/34449izJgxQi6XiwEDBoiVK1eatV8MUeqO8q7oxOyPjxrDc9yq70TaWY2l2yKyWuZkgUQIISz1Ktja6HQ6KJVKaLVaODvz21ioa9PVNeL91AJ8nl4MvUFAYSfFgvHeeHbcECjsbCzdHpHVMicLutzZuUTUMiEEvjx5GQm781BRXQ8AiAhQYdl0fwzs7WDh7oi6F4YoUTeSU6pFXHIOTly49QUjQ1wdERcVgDDffhbujKh7YogSdQPa2kasTc3HP49dgEEADjIbvDTRB/PHeEFm26brTBBRKzBEibowg0Fge+ZFvLsnH9drbl3VaPoDbvhL5HC4Ke0t3B1R98cQJeqiTl+qRGxyDk5drAQA+PTvhfioAIR6u1q2MaIehCFK1MXcqGnAqm/zkfRDCYQAesltsTDcB/NCB8POhkO3RJ2JIUrURegNAluOl2DN3nxU1t76Zq7/enAAlk7zQ39nhYW7I+qZGKJEXcDJkhuIS87BmctaAICf2gkrZo5AkFefX1iSiDoSQ5TIilVU1+Pd3XnYnnkJAOCksMWiyb548lFP2HLolsjiGKJEVqhJb8AXGSVYuzcfuromAMBvAgdi8VQ/9HPilYeIrAVDlMjK/FB8Hcu+zkaepgoAMGKAM+KjRiDQk1cjIrI2DFEiK1Guq0PC7jx8lXUZAKC0t8OrEcMwJ2gQbKS8RBmRNWKIEllYo96Az48W4/19Baiub4JEAjzxyCC8GjEMfRxllm6PiFrAECWyoPRz1xC3Ixs/lVUDAEZ5uGBFVABGebhYtjEiahWGKJEFXNHexDu78vDNj6UAgN4Odlg81Q+PP+wBKYduiboMhihRJ2poMuCzI0VYl1aA2gY9pBLgd8GeWDTFFy4OHLol6moYokSd5PuCq4jbkYPzV2sAAIGevREfFYARA5QW7oyI2oohStTBLlfexJvf5GJPjgYA4NpLjqXT/PBfDw7g0C1RF8cQJeog9U16fHLoPD7YX4i6RgNspBI8FeKJP0/2hbPCztLtEVE7YIgSdYD9eeWI/yYHxddqAQBBXn2wYmYA/NTOFu6MiNoTQ5SoHZVcq8WKlBzsO1sOAOjvJMdfIocjapQ7JBIO3RJ1NwxRonZQ16jHRwfO4aOD59DQZICtVIL/HuOFP03yQS85/8yIuiv+dRPdByEEUnPLsCIlF5du3AQAPObdF/FRAfDu72Th7oioozFEidqoqKIG8d/k4ED+VQCAm1KBNyL98auRag7dEvUQDFEiM9U2NGHD/kJ8cqgIDXoD7GwkeHbsELw40RsOMv5JEfUk/IsnaiUhBHZna/BWSi5KtXUAgDDffoib4Y8h/XpZuDsisgSGKFErFJZXYfmOXBwurAAADOxtj9jp/pjsr+LQLVEPxhAlakF1fRPWpxXg08NFaDIIyGyl+EPYUPxx/FAo7Gws3R4RWRhDlKgZQgjs+LEU7+w6izJdPQAgfHh/LJvuD8++jhbujoisBUOU6GfyNVWITc5GRtF1AIBnXwfEzfDHRD+VhTsjImvDECX6X7q6RryfWoDP04uhNwgo7KRYMN4bz44bwqFbImoWQ5R6PCEEvjx5GQm781BRfWvodmqAGm9MH46BvR0s3B0RWTOGKPVoOaVaxCXn4MSFGwCAIa6OWB4VgHG+/SzcGRF1BQxR6pG0tY1Ym5qPfx67AIMAHGQ2eGmiD+aP8YLMVmrp9oioi2CIUo9iMAhsz7yId/fk43pNAwBg+gNu+EvkcLgp7S3cHRF1NQxR6jFOX6rEsuQc/HixEgDg078X4qMCEOrtatnGiKjLYohSt3ejpgGrvs1H0g8lEALoJbfFwnAfzAsdDDsbDt0SUdsxRKnb0hsEthwvwZq9+aisbQQA/NeDA7B0mh/6Oyss3B0RdQcMUeqWTpbcQGxyNrIv6wAAfmonrJg5AkFefSzcGRF1JwxR6lYqquvx7u48bM+8BABwUthi0WRfPPmoJ2w5dEtE7YwhSt1Ck96ALzJKsHZvPnR1TQCAWYED8dpUP/Rzklu4OyLqrhii1OX9UHwdy77ORp6mCgAwYoAz4qNGINCzt4U7I6Lurk3jWxs2bMDgwYOhUCgQHByM48ePt1i/fft2+Pn5QaFQYOTIkdi1a5fJfCEEYmNj4ebmBnt7e4SHh6OgoMCk5u2330ZoaCgcHBzg4uLS7HYkEsldt6SkpLbsInUB5bo6/HnrKcxKTEeepgpKezu8FT0CyQvGMECJqFOYHaJbt25FTEwM4uLicPLkSYwaNQoREREoLy9vtv7o0aOYM2cO5s+fj6ysLERHRyM6OhrZ2dnGmlWrVmHdunVITExERkYGHB0dERERgbq6OmNNQ0MDZs2ahRdeeKHF/jZt2oQrV64Yb9HR0ebuIlm5Rr0Bf/v+PCauPYivsi5DIgHmBA3C/lfG48lHPWEj5UWyiahzSIQQwpwFgoOD8cgjj+CDDz4AABgMBnh4eOCll17CkiVL7qqfPXs2ampqkJKSYpz26KOPYvTo0UhMTIQQAu7u7li0aBFeeeUVAIBWq4VKpcLmzZvxxBNPmKxv8+bNWLhwISorK+/eGYkEX331VauDs76+HvX19cb7Op0OHh4e0Gq1cHZ2btU6qHMdPVeB5Tty8FNZNQBglIcLVkQFYJSHi2UbI6JuQ6fTQalUtioLzHol2tDQgMzMTISHh//fCqRShIeHIz09vdll0tPTTeoBICIiwlhfVFQEjUZjUqNUKhEcHHzPdbZkwYIFcHV1RVBQED777DO09H+EhIQEKJVK483Dw8Ps7VHnuKK9iRf/dRK//SQDP5VVo4+jDO/+eiS+eiGUAUpEFmPWiUUVFRXQ6/VQqUwvTqxSqZCXl9fsMhqNptl6jUZjnH972r1qWmvFihWYOHEiHBwcsHfvXvzxj39EdXU1/vSnPzVbv3TpUsTExBjv334lStajocmAz44UYV1aAWob9JBKgCcf9UTMZF+4OMgs3R4R9XDd6uzcZcuWGf/94IMPoqamBqtXr75niMrlcsjl/PiDtfq+4CriduTg/NUaAECgZ2/ERwVgxAClhTsjIrrFrOFcV1dX2NjYoKyszGR6WVkZ1Gp1s8uo1eoW62//NGedrRUcHIxLly6ZvO9J1u9y5U384R+ZmPvpcZy/WgPXXnKsnTUK//5DCAOUiKyKWSEqk8kQGBiItLQ04zSDwYC0tDSEhIQ0u0xISIhJPQCkpqYa6728vKBWq01qdDodMjIy7rnO1jp16hR69+7NV5tdRF2jHh98V4BJaw9gT44GNlIJ/vsxL3z3Shh+HTgQEgnPuiUi62L2cG5MTAzmzZuHhx9+GEFBQXj//fdRU1ODp59+GgDw1FNPYcCAAUhISAAAvPzyywgLC8PatWsRGRmJpKQknDhxAhs3bgRw64zahQsX4q233oKPjw+8vLywbNkyuLu7m5xlW1JSguvXr6OkpAR6vR6nTp0CAHh7e6NXr1745ptvUFZWhkcffRQKhQKpqal45513jGf8knXbn1eO5d/k4MK1WgBAkFcfrJgZAD81z5ImIism2mD9+vVi0KBBQiaTiaCgIHHs2DHjvLCwMDFv3jyT+m3btglfX18hk8lEQECA2Llzp8l8g8Egli1bJlQqlZDL5WLSpEkiPz/fpGbevHkCwF23/fv3CyGE2L17txg9erTo1auXcHR0FKNGjRKJiYlCr9e3er+0Wq0AILRarXkHhNrsQkWNmL/5uPBcnCI8F6eIR95KFV9nXRIGg8HSrRFRD2VOFpj9OdHuzJzPBtH9qWvU48MD55B48BwamgywlUowf4wXXprkg17ybnW+GxF1MeZkAZ+tqFMJIZCaW4YVKbm4dOMmAOAx776IjwqAd38nC3dHRGQehih1mqKKGizfkYODP10FALgrFXhjuj+mjVDzpCEi6pIYotThahuasGF/IT45VIQGvQF2NhI8O3YIXpzoDQcZH4JE1HXxGYw6jBACu7M1eCslF6XaWxcTCPPth7gZ/hjSr5eFuyMiun8MUeoQheVVWL4jF4cLKwAAA3vbI3a6Pyb7qzh0S0TdBkOU2lV1fRPWpxXg08NFaDIIyGyl+EPYUPxx/FAo7Gws3R4RUbtiiFK7EEJgx4+leGfXWZTpbn3NYvjw/oidHoBBfR0s3B0RUcdgiNJ9y9dUITY5GxlF1wEAnn0dEDfDHxP9VL+wJBFR18YQpTbT1TXivdSf8Pf0C9AbBBR2UiwY741nxw3h0C0R9QgMUTKbEAJfnryMhN15qKi+NXQ7NUCNN6YPx8DeHLolop6DIUpmySnVIi45Bycu3AAADOnniOUzAjDOt5+FOyMi6nwMUWoVbW0j1qbm45/HLsAgAAeZDV6a6IP5Y7wgszXrinpERN0GQ5RaZDAIbM+8iHf35ON6TQMAYPoDbvhL5HC4Ke0t3B0RkWUxROmeTl+qxLLkHPx4sRIA4NO/F+KjAhDq7WrZxoiIrARDlO5yvaYBq7/NR9IPJRAC6CW3xcJwH8wLHQw7Gw7dEhHdxhAlI71BYMvxEqzZm4/K2kYAwH89OABLp/mhv7PCwt0REVkfhigBAE6W3EBscjayL+sAAH5qJ6yYOQJBXn0s3BkRkfViiPZwFdX1eHd3HrZnXgIAOClssWiyL5581BO2HLolImoRQ7SHatIb8M9jF7A29SdU1TUBAGYFDsTiaX5w7SW3cHdERF0DQ7QHOl50HbHJ2cjTVAEARgxwRnzUCAR69rZwZ0REXQtDtAcp19UhYXcevsq6DABQ2tvh1YhhmBM0CDZSXuOTiMhcDNEeoFFvwOdHi/H+vgJU1zdBIgGeeGQQXo0Yhj6OMku3R0TUZTFEu7mj5yoQl5yDgvJqAMAoDxesiArAKA8XyzZGRNQNMES7qSvam3h751mknL4CAOjjKMPiqcMwK9ADUg7dEhG1C4ZoN9PQZMCnh4uw/rsC1DboIZUATz7qiZjJvnBx4NAtEVF7Yoh2I4d+uorlO3JwvqIGABDo2RsrZgYgwF1p4c6IiLonhmg3cOlGLd5KOYs9ORoAgGsvOZZO88P/e2gAJBIO3RIRdRSGaBdW16jHJ4fOY8OBQtQ1GmAjlWBeyGAsnOwDZ4WdpdsjIur2GKJd1Hd5ZYj/JhcXrtUCAIK8+mDFzAD4qZ0t3BkRUc/BEO1iSq7VYkVKDvadLQcAqJzleP1XwxE1yp1Dt0REnYwh2kXUNerx4YFzSDx4Dg1NBthKJZg/xgsvTfJBLzl/jURElsBnXysnhEBqbhlWpOTi0o2bAIDHvPsiPioA3v2dLNwdEVHPxhC1YkUVNVi+IwcHf7oKAHBXKvDGdH9MG6Hm0C0RkRVgiFqh2oYmbNhfiE8OFaFBb4CdjQTPjh2CFyd6w0HGXxkRkbXgM7IVEUJgd7YGb6XkolRbBwAI8+2HuBn+GNKvl4W7IyKin2OIWonC8ios35GLw4UVAICBve0RO90fk/1VHLolIrJSDFELq65vwrq0Anx2uAhNBgGZrRQvhA3FC+OHQmFnY+n2iIioBQxRCxFCYMePpXhn11mU6eoBAOHD+yN2egAG9XWwcHdERNQaDFELyNdUITY5GxlF1wEAnn0dEDfDHxP9VBbujIiIzMEQ7US6uka8l/oT/p5+AXqDgMJOihcneOOZsUM4dEtE1AUxRDuBwSDwVdZlJOzOQ0X1raHbqQFqvDF9OAb25tAtEVFXJW3LQhs2bMDgwYOhUCgQHByM48ePt1i/fft2+Pn5QaFQYOTIkdi1a5fJfCEEYmNj4ebmBnt7e4SHh6OgoMCk5u2330ZoaCgcHBzg4uLS7HZKSkoQGRkJBwcH9O/fH6+++iqamprasovtJqdUi1kfp2PR9h9RUV2PIf0c8ff/DkLi3EAGKBFRF2d2iG7duhUxMTGIi4vDyZMnMWrUKERERKC8vLzZ+qNHj2LOnDmYP38+srKyEB0djejoaGRnZxtrVq1ahXXr1iExMREZGRlwdHREREQE6urqjDUNDQ2YNWsWXnjhhWa3o9frERkZiYaGBhw9ehSff/45Nm/ejNjYWHN3sV1oaxsRm5yNGesPI/PCDTjIbLB4qh/2vDwO43z7WaQnIiJqZ8JMQUFBYsGCBcb7er1euLu7i4SEhGbrH3/8cREZGWkyLTg4WDz//PNCCCEMBoNQq9Vi9erVxvmVlZVCLpeLLVu23LW+TZs2CaVSedf0Xbt2CalUKjQajXHaRx99JJydnUV9fX2r9k2r1QoAQqvVtqq+OXq9QSQdvyAeXLFXeC5OEZ6LU8SCLzJFaWVtm9dJRESdx5wsMOuVaENDAzIzMxEeHm6cJpVKER4ejvT09GaXSU9PN6kHgIiICGN9UVERNBqNSY1SqURwcPA913mv7YwcORIq1f+d4RoREQGdToecnJxml6mvr4dOpzO53a+/fJ2Nxf85g+s1DfDp3wv/ejYYH/z2Ibgp7e973UREZF3MCtGKigro9XqToAIAlUoFjUbT7DIajabF+ts/zVmnOdu5cxs/l5CQAKVSabx5eHi0env3MvsRDzgpbPFG5HDsenksQoe63vc6iYjIOrXpxKLuYunSpdBqtcbbxYsX73udoz1ckL50Ep4ZOwR2Nj368BIRdXtmPcu7urrCxsYGZWVlJtPLysqgVqubXUatVrdYf/unOes0Zzt3buPn5HI5nJ2dTW7tgRfJJiLqGcwKUZlMhsDAQKSlpRmnGQwGpKWlISQkpNllQkJCTOoBIDU11Vjv5eUFtVptUqPT6ZCRkXHPdd5rO2fOnDE5Szg1NRXOzs7w9/dv9XqIiIhay+yXTDExMZg3bx4efvhhBAUF4f3330dNTQ2efvppAMBTTz2FAQMGICEhAQDw8ssvIywsDGvXrkVkZCSSkpJw4sQJbNy4EQAgkUiwcOFCvPXWW/Dx8YGXlxeWLVsGd3d3REdHG7dbUlKC69evo6SkBHq9HqdOnQIAeHt7o1evXpgyZQr8/f0xd+5crFq1ChqNBm+88QYWLFgAuVx+n4eJiIioGW05/Xf9+vVi0KBBQiaTiaCgIHHs2DHjvLCwMDFv3jyT+m3btglfX18hk8lEQECA2Llzp8l8g8Egli1bJlQqlZDL5WLSpEkiPz/fpGbevHkCwF23/fv3G2uKi4vFtGnThL29vXB1dRWLFi0SjY2Nrd6v9viICxERdW3mZIFECCEsmOFWRafTQalUQqvVttv7o0RE1LWYkwU8fZSIiKiNGKJERERtxM9i3OH2yHZ7fHMRERF1TbczoDXvdjJE71BVVQUA7fLNRURE1LVVVVVBqVS2WMMTi+5gMBhQWloKJycnSCSSNq9Hp9PBw8MDFy9e7BInKLHfjsV+Oxb77Vg9sV8hBKqqquDu7g6ptOV3PflK9A5SqRQDBw5st/W157cgdQb227HYb8divx2rp/X7S69Ab+OJRURERG3EECUiImojhmgHkMvliIuL6zJfN8h+Oxb77Vjst2Ox35bxxCIiIqI24itRIiKiNmKIEhERtRFDlIiIqI0YokRERG3EECUiImojhmgrbNiwAYMHD4ZCoUBwcDCOHz/eYv327dvh5+cHhUKBkSNHYteuXSbzhRCIjY2Fm5sb7O3tER4ejoKCAov0+8knn2Ds2LHo3bs3evfujfDw8Lvqf//730MikZjcpk6dapF+N2/efFcvCoXCpMaaju/48ePv6lcikSAyMtJY05HH99ChQ5gxYwbc3d0hkUjw9ddf/+IyBw4cwEMPPQS5XA5vb29s3rz5rhpz/yY6qt8vv/wSkydPRr9+/eDs7IyQkBB8++23JjXLly+/6/j6+flZpN8DBw40+3jQaDQmddZyfJt7bEokEgQEBBhrOvL4JiQk4JFHHoGTkxP69++P6Oho5Ofn/+JynfkczBD9BVu3bkVMTAzi4uJw8uRJjBo1ChERESgvL2+2/ujRo5gzZw7mz5+PrKwsREdHIzo6GtnZ2caaVatWYd26dUhMTERGRgYcHR0RERGBurq6Tu/3wIEDmDNnDvbv34/09HR4eHhgypQpuHz5sknd1KlTceXKFeNty5Yt991rW/oFbn2d1529XLhwwWS+NR3fL7/80qTX7Oxs2NjYYNasWSZ1HXV8a2pqMGrUKGzYsKFV9UVFRYiMjMSECRNw6tQpLFy4EM8884xJMLXld9ZR/R46dAiTJ0/Grl27kJmZiQkTJmDGjBnIysoyqQsICDA5vocPH77vXtvS7235+fkm/fTv3984z5qO71//+leTPi9evIg+ffrc9fjtqON78OBBLFiwAMeOHUNqaioaGxsxZcoU1NTU3HOZTn8OFtSioKAgsWDBAuN9vV4v3N3dRUJCQrP1jz/+uIiMjDSZFhwcLJ5//nkhhBAGg0Go1WqxevVq4/zKykohl8vFli1bOr3fn2tqahJOTk7i888/N06bN2+emDlz5n331hxz+920aZNQKpX3XJ+1H9/33ntPODk5ierqauO0jjy+dwIgvvrqqxZrXnvtNREQEGAybfbs2SIiIsJ4/36PQXv22xx/f38RHx9vvB8XFydGjRrVfo3dQ2v63b9/vwAgbty4cc8aaz6+X331lZBIJKK4uNg4rbOOrxBClJeXCwDi4MGD96zp7OdgvhJtQUNDAzIzMxEeHm6cJpVKER4ejvT09GaXSU9PN6kHgIiICGN9UVERNBqNSY1SqURwcPA919mR/f5cbW0tGhsb0adPH5PpBw4cQP/+/TFs2DC88MILuHbt2n31ej/9VldXw9PTEx4eHpg5cyZycnKM86z9+H766ad44okn4OjoaDK9I45vW/zS47c9jkFHMhgMqKqquuvxW1BQAHd3dwwZMgS/+93vUFJSYqEObxk9ejTc3NwwefJkHDlyxDjd2o/vp59+ivDwcHh6eppM76zjq9VqAeCu3++dOvs5mCHagoqKCuj1eqhUKpPpKpXqrvcwbtNoNC3W3/5pzjo7st+fW7x4Mdzd3U0eYFOnTsXf//53pKWl4d1338XBgwcxbdo06PX6Tu932LBh+Oyzz5CcnIx//vOfMBgMCA0NxaVLlwBY9/E9fvw4srOz8cwzz5hM76jj2xb3evzqdDrcvHmzXR5jHWnNmjWorq7G448/bpwWHByMzZs3Y8+ePfjoo49QVFSEsWPHGq8f3Jnc3NyQmJiI//znP/jPf/4DDw8PjB8/HidPngTQPn/DHaW0tBS7d+++6/HbWcfXYDBg4cKFeOyxxzBixIh71nX2czAvhUZGK1euRFJSEg4cOGByss4TTzxh/PfIkSPxwAMPYOjQoThw4AAmTZrUqT2GhIQgJCTEeD80NBTDhw/Hxx9/jDfffLNTezHXp59+ipEjRyIoKMhkujUd367sX//6F+Lj45GcnGzyHuO0adOM/37ggQcQHBwMT09PbNu2DfPnz+/UHocNG4Zhw4YZ74eGhuLcuXN477338I9//KNTezHX559/DhcXF0RHR5tM76zju2DBAmRnZ7fb+63tha9EW+Dq6gobGxuUlZWZTC8rK4NarW52GbVa3WL97Z/mrLMj+71tzZo1WLlyJfbu3YsHHnigxdohQ4bA1dUVhYWFFuv3Njs7Ozz44IPGXqz1+NbU1CApKalVTyrtdXzb4l6PX2dnZ9jb27fL76wjJCUl4ZlnnsG2bdvuGsr7ORcXF/j6+lrk+DYnKCjI2Iu1Hl8hBD777DPMnTsXMpmsxdqOOL4vvvgiUlJSsH///l+85nNnPwczRFsgk8kQGBiItLQ04zSDwYC0tDSTV0N3CgkJMakHgNTUVGO9l5cX1Gq1SY1Op0NGRsY919mR/QK3zlR78803sWfPHjz88MO/uJ1Lly7h2rVrcHNzs0i/d9Lr9Thz5oyxF2s8vsCtU+7r6+vx5JNP/uJ22uv4tsUvPX7b43fW3rZs2YKnn34aW7ZsMfno0L1UV1fj3LlzFjm+zTl16pSxF2s8vsCts2QLCwtb9Z/A9jy+Qgi8+OKL+Oqrr/Ddd9/By8vrF5fp9Odgs09F6mGSkpKEXC4XmzdvFrm5ueK5554TLi4uQqPRCCGEmDt3rliyZImx/siRI8LW1lasWbNGnD17VsTFxQk7Oztx5swZY83KlSuFi4uLSE5OFqdPnxYzZ84UXl5e4ubNm53e78qVK4VMJhP//ve/xZUrV4y3qqoqIYQQVVVV4pVXXhHp6emiqKhI7Nu3Tzz00EPCx8dH1NXVdXq/8fHx4ttvvxXnzp0TmZmZ4oknnhAKhULk5OSY7JO1HN/bxowZI2bPnn3X9I4+vlVVVSIrK0tkZWUJAOJ//ud/RFZWlrhw4YIQQoglS5aIuXPnGuvPnz8vHBwcxKuvvirOnj0rNmzYIGxsbMSePXtafQw6s98vvvhC2Nraig0bNpg8fisrK401ixYtEgcOHBBFRUXiyJEjIjw8XLi6uory8vJO7/e9994TX3/9tSgoKBBnzpwRL7/8spBKpWLfvn3GGms6vrc9+eSTIjg4uNl1duTxfeGFF4RSqRQHDhww+f3W1tYaayz9HMwQbYX169eLQYMGCZlMJoKCgsSxY8eM88LCwsS8efNM6rdt2yZ8fX2FTCYTAQEBYufOnSbzDQaDWLZsmVCpVEIul4tJkyaJ/Px8i/Tr6ekpANx1i4uLE0IIUVtbK6ZMmSL69esn7OzshKenp3j22Wfb5Q+6Lf0uXLjQWKtSqcSvfvUrcfLkSZP1WdPxFUKIvLw8AUDs3bv3rnV19PG9/ZGKn99u9zhv3jwRFhZ21zKjR48WMplMDBkyRGzatOmu9bZ0DDqz37CwsBbrhbj1ER03Nzchk8nEgAEDxOzZs0VhYaFF+n333XfF0KFDhUKhEH369BHjx48X33333V3rtZbjK8Stj3/Y29uLjRs3NrvOjjy+zfUKwOQxaennYF5PlIiIqI34nigREVEbMUSJiIjaiCFKRETURgxRIiKiNmKIEhERtRFDlIiIqI0YokRERG3EECUiImojhigREVEbMUSJiIjaiCFKRETURv8f8MRShpDAYaAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(5, 3))\n",
+    "plt.plot(a.to_value(bu.volt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1bb9ac4b730>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import brainunit.math as bm\n",
+    "a = bm.arange(10 * bu.meter, step=1 * bu.meter)\n",
+    "b = bm.arange(100 * bu.second, step=10 * bu.second)\n",
+    "plt.plot(a.to_value(bu.meter), b.to_value(bu.second))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Converting to Plain Python Scalars"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Converting `Quantity` objects does not work for non-dimensionless quantities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only dimensionless scalar quantities can be converted to Python scalars. But got 3. mV\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    float(3. * bu.mV)\n",
+    "except TypeError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only dimensionless quantities can be converted to plain Python scalars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.003"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(3. * bu.mV.to_value(bu.volt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "750.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(3. * bu.kmeter / (4. * bu.meter))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1500"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(6. * bu.kmeter / (4. * bu.meter))"
+   ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,