example and readme

README.md

@@ -242,6 +242,59 @@ else:
     print("timeout")
 ```
 
+# Photonic Continuous Variable Circuit
+
+## Fock basis
+
+### Circuit
+```python
+import photonqat as pq
+import numpy as np
+import matplotlib.pyplot as plt
+
+# mode number = 2, cutoff dimension = 15
+F = pq.Fock(2, cutoff = 15)
+```
+
+### Applying gate
+```python
+alpha = (1 + 1j)
+r = -0.5
+
+F.D(0, alpha) # Displacement to mode 0
+F.S(1, r) # Squeezeng to mode 1
+```
+
+method chain is also available
+```python
+F.D(0, alpha).S(1, r)
+```
+
+### run
+```python
+F.run()
+```
+
+### Plot Wigner function
+```python
+# Plot Wigner fucntion for mode 0 using matplotlib
+(x, p, W) = F.Wigner(0, plot = 'y', xrange = 5.0, prange = 5.0)
+```
+
+## Gaussian formula
+
+### Circuit
+```python
+import photonqat as pq
+import numpy as np
+import matplotlib.pyplot as plt
+
+# mode number = 2
+G = pq.Gaussian(2)
+```
+Applying gate, run the circuit, and plotting Wigner function are also available in same fasion as Fock basis.
+But there are differences in availavle getes and measurement methods.
+
 ### Example1: GHZ
 ```python
 from blueqat import Circuit

examples_photonqat/KerrGate.ipynb

@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import photonqat as pq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Photonqat\n",
+    "\n",
+    "Kerr Gate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAb4ElEQVR4nO2d3ctnV3XHv8voTMo4iRWnDWRGI2nBBBUsIQi5qNQXogZz40UVBfEiNwpRIsEkf4KgFhRk0EKLASlVEYqikTYXvVCcpImiiZJIMkkwGCnEENMJ065ePM9JzvPLOed3Xtbee629vx8YmOftnHX22edz1m+dffYWVQUhhJC4vKp0AIQQQrZBkRNCSHAockIICQ5FTgghwaHICSEkOK8ustNLT+jxk68vsWtCyB4u+Z90I9n+91JJtu0W+NMfnvyDqp7a/X4RkR8/+Xpcc/NnS+yaELKAyx+9sOnvn736uFEkBADu+8Ztjw99v4jICSExoIhjwBo5IYQEhyInhJDgUOSEEBIcipwQQoJDkRNCSHAockIICQ5FTgghwaHICSEkOBQ5IYQEhyInhJDgUOSEEBIcipwQQoLDSbMIIWQhW2eFBGwnJKPICSFkDxbintrmVqmbiVxELgFwDsBTqnqT1XYJISQ3KcQ9d39rpG6Zkd8K4CEAlxlukxBCspBb3mN0cSwRusnDThE5DeCDAL5usT1CCMnB5Y9eeOmfN5bEZJWRfxnA7QBOjv2CiNwC4BYAOHbiz412S8g4z71p//qQJx9Ptz4l8YlHaY8xNzvfLHIRuQnA71X1PhF519jvqepZAGcB4MSpM7x6yCLmSDnXdin/mEQS+C6XP3phUuYWGfkNAD4kIh8AcCmAy0Tkm6r6MYNtk8ZIJWxLpmKk5P0RWeB9po5js8hV9Q4AdwDAYUb+OUqczCGCtJcydEyUexlqEfgcOI6cZKNGcc+Bcs9LSwLvMBW5qt4L4F7LbZLYtCrvfey2C8VuQ4sSB5iRk0R4E/iFN764+m+Pnz9mGMkw/fai1NfRqsQBipwYU1LgW2S9dbuWsqfUl9OyxAGKnBhRQuCpxL2GsVi2Cp5SJ3OgyMlmckvck8D3MRTrWrlT6mQMipxsIqfEIwl8it3jWCP2rt0pdAJQ5CQIqSV+1elnVv/tY0+e2rTv/rEtlTqzdAJQ5KRRtoh7ybaWSt5C6i0K/dmrjzf9wJMiJ01hKfC1+5sr97VSb1XoLcuca3aSTZx8XLMII8dY7lxcdfqZI//mcOGNLy4uLz33JnE3nj81lsuneSP1pFmEvCTzlOI4fv7Yplp57mx8Lv249mXra7L01jL0TniRs/OlNySKnJjSl0UKqXfyWiP0x548tVnm773i4cV/c8/Tb5n9u2ukTqEP41no1p8cKHKSjCFhWMl9rdDXynyNwKf+do7c50p9jdBbkTnwSmmmFHup0g5FTrJiLfc1QrfIzLeyK/d9Yu/itRJ6a9l5nxrr6BQ5KY6F3PvymiN1DzLv0xf7lNQpdDIERU5cskXuc7P0ToaehA7Mk3oKoVPmceHwQxKGbqjj3CGPx88fe+nfFHPGdS95YGnJe694eLI+P2cI49yyU4vDFWuBGTkJy67MpyS0L0ufk53f8/RbVj30/PBl94/+7F//+DezttHtd22Gzuy8bkQ1/wk7ceqMXnPzZ7Pvl2wj9zCurQ+l9mWXU5nqlNCXyHxK4mPMkfu+TwhTnzLmjm6hzP1x3zduu09Vr9v9PjNy8hLexttOxTNH8vvGtE9l6VMZ+trMfC6d/KeEPidD35qdMzOPA0XeKN6kvZSh+KfkPiX1qREvY0LvBJpD6MC41KeEPqfcMkfmALNz77C00gDRpb2WOVn7WPllKEsfK7fsk/ma8soY+8ouYxk6Sy11MFZa4aiVCrn80QtH/rXKnHYYGwEzNNrlsSdPDQox54iWD192/+SNYeymMjW6ZcmoFuITZuQVUJusjz30xOzfffGaM6v2MZWtDwlrboY+JlLLrLzPVIaeKjtnZl6OsYycIg9Ky/Key1zJj0l9jtApc5ITirwCapP3Lilk3meO2Iekbin0VDIHxoVOmdcDa+RBaanW/eI1Z176l4JjDz3x0r8xhtp6qI4+p34+JNC5LwCtYewmMVU3H2NO3Zw1cz9Q5A5pSd5jpBQ6sF/qc4Q+9kC0z5jMUwk9t8yJD1hacULL0l5CqfLLnJLLvnJLzlKLVZmFJRZfsLTilNYz76XkKr/sMpah91mbnafAKjNniSUGFHkhahP4s1cfH/yXklJC77Ov3EKZkxywtJKZKPIutYrK1vZJVXoZumHsttHWUkv0MgtLLOnhpFmF8Spwb8teDcWzpO36wrWU+rGHnniFzLu4upg7kXVCP37+2BGZ765KtDv5VifclEMUt7BvbhZOslUOllYS46mEkrv0YcXamK1LL0vKLR0eSi2WI1n2wRJLGSjyRHgQeERpz2HpcaUQ+i5LHoSWHm/eZ+nsjRyS6BPWyI3xIO+Wmdv+FmUX67p56uGJHJKYhpzX/L/fe2eaGrmInAHwzwD+EoACOKuq/7B1u9EoJfDWxb1L1x77zkcn4S1C7/62L/QtdfOhOc5z1M3fe8XDi2ZwnDOPec2UTtaGsCitXARwm6peC+CdAD4lItcabDcMJU5sbeUSa3KWXdbUzft4KrX0Ya38Zby/bb1Z5Kr6O1W9//D/zwF4CMCVW7cbgdwntsZ6d2pK1dGBOmQ+Rku1cq/y7mP6sFNErgLwDgA/HfjZLSJyTkTOXXzhecvdZqeUwMl6lgh9DVNztvSxkHkKoXMEyzARJA4YilxEXgvg2wA+o6p/3P25qp5V1etU9bpX/9kJq91mJ9eJZfadhjltujY7n1tv3ypzwFd2TspjInIReQ0OJH63qn7HYpveyJWFU955SCX0OfXyXbbIvKTQay+vRMnGAQORi4gA+AaAh1T1i9tD8gcFXi+56ucpJtzqmCP0UsKPXF6JdD1avKJ/A4CPA/iFiDxw+L07VfX7BtsuTmqJR+osS9oi0nE9e/XxWce2dcji5Y9eONIuJx/XI6Jb+kr/Liy3tMtmkavqfwKIe9sdoUWBWx7z1LY8Hvvc8efAgdCt5nGxlnkKrjr9zOSycDUz9yZfGk6aNUALEi/ZOYf27aFNgOXZOXA0Q99XgtnNyoeYI3Ng+ev1ZB0RZE6R75DyhJWUlfeOuBtfybZaeuEurZ/vK7EMsStzwDY7X/JmZ4t4lzknzeqR6kSVepDp/W20KUrHnvt87Xv4CQzPfZJDwFvLKpEfePbxPCCBIkfaoYW5T3xpAaag1PGUvmiXyHyL0JmNL8Oj0JsXeS1ZeG3yHqKmYxw6jqEZA5dMTrVGyJT4ejy9tNd0jTx6Fl6L1JbSP+7UbZ27Nrq2Xt7RF/O++jklbke/H5a4LpsVeWSJtyrwIeaMAtlK6Qddu6NYgGmZd1iIutVhh1vYulzhGpoUeYpGpcDLsTsHeHSGsvIxmQPbJrYieUgt9+ZEHlHiFPg8UmbnKbLypfEOyRyYl52vwSobb2m1oCVMnfulfa0pkUeTeM6ZFlOR+yZUS3Y+p1beJ5XMp2h5laDUjPbfe4e/3YzIKfEDcguuRL2w24f1sZaulQPjWTlgJ3PWxePRhMitL75oAveWne7Gk0qOOR6EpmQsK98nc2B93ZwSj0n1Im9V4pEElnLoVnSZjzElc2C50JcKnGUVX1QvcktSCcFKXjUIK4XUI8t8qla+T+ZAmgx7icT5oDMPVb/ZGSHDtYjRy9tl1lgel8ebpcW2mBkToOKM3LvErQTeAlZZeuTMfIo5mbnlvog/qszIKfF62XrcpUedrGFOeSKHYJfug2WVfFSXkdcu8VYF3mfJaj5eSXEeU2bmzMR9U2VG7hVK3Ja1NfSIN4C52e3x88dMpbt2e8zG81KVyD1n45R4OqK1zdp4l8hxq8y33BAo8fxUU1rxKvEaBD52DB5i61habln74DNSNt8X8dySC0soMalG5FZ4lFNKto4CmUPONvXwGv0UW9ti6RwsHbkEzWy8DFWI3OMYYWBbXB4ftFruN6Xc58o893BEq32tlXlqKPFyhBe51+zLi8SjtE+ECa68nFOPUOJlCS9yK7zI09ubjLlIsXyblzJLCol7yspbknjuT5dzCS1yjyWVkhL3IC0LLKVeWuYpL3IPMm9B4vv6j4fBAKFF7g1K3B6LhSIsZL7m73NcyKVk3oLAAfvBAKn6RFiRe8vGS0m8VoHvsjVLz5mZ5/6o3Uk1l9ApcbttWvWVsCK3gBKPydrRJrsy9zaT4VZSZ+etCDwnVv0xpMhrEZiHiz8qay+AtW0+9MKRx/PXl62F1FuWd+7nK1uy9ZAit6B0Nu61Jp5bThbHkXM8uEd5j7FW6i3L2xNLxB5O5J6y8cgS9yKksTiWHqPFQ9GaoZzXUXrUU5+pOMKJ3IKSF3upfUcT3NoFmmtdPIKQKUKJ3MudESg3HG3JfmsS2tCxeH05g9SFp6x8jFAit6D2jLglkbV0rKQs3hczMZmPXERuFJFfi8gjIvJ5i216xtvJ7BZYoNgISYvXa2yzyEXkEgBfBfB+ANcC+IiIXLt1u7tEfsCXeqY/Qkg+PF5zFqWV6wE8oqq/BQAR+RaAmwH8ymDb7iiZjXsfw0xIK3grtViUVq4E8ETv6ycPv3cEEblFRM6JyLmLLzy/aAdeGmsNFC4h9eKlpJntYaeqngVwFgBOnDqTfVBr7hEjlkS6kaWM1cMFQ8gQpTN0C5E/BeBM7+vTh99rnsg3jzG8lJYAip34o5TQLUT+MwB/LSJvxoHA/x7ARw2264oSAist8dL7JyQquYW+WeSqelFEPg3ghwAuAfCPqvrLzZEdEnm0yhbmTGbvZUGM3EQ8n6RNcgndpEauqt8H8H2LbZF8r6NHkDelTWogtdCbe7NzDblex1+zn7mTRXmWNmVNWiGV0CnySvAs6g4Km5AD+teCxbXrWuQt1ccjiHgpUdqekJJYZOmuRe6BHGWVWiROcROyni1Cp8jJaihuQuxZU3ahyAsTLRunvAnJx9y50ClyMokHcY91ZA+xkThE7UdH4rt3+HcocmO8d4o5eDiGOVkIl3Ujc5nqTzXMKlq1yD0schwBD5231NJ5pH6W9K2oi3hXLfIIrF0PcElH85pxbLlRejoOUh/RhO5W5K1kw0tY26m8dUYKnEQhSvnOrcgjskW0U3KL0JH2sfXGXEMbkJhEyM5NFl8m2xnrJJ47zxwuf/TC5gw8ehuQslj1n619OSXMyB1Ri7BamlqBtIfHDJ0ZOTHDImNhBk5SkKJPecrOmZGTzVh1aKu51XkjILnwkp1T5GQVltmI9Xj/KCMNSF7WDvWdQ2mhuy2t8EL0ieUDn61lFM8Pn4hPUnulVH9kRk4mSdUx+dYtKUXKzBwok51T5OQlcsjRonOn/HjMT4JtkFrmQN7+RJE7I+VDu5JZrOVY3hT76rZbutZJ8pFL5t2+UuK2Rm5BtI/fQw/tthxD9/ela8k5JW5FtL5D1pHrhp36Gqxa5FvJuWTbvmk253YED+LusBwTnqOmSdok56evVH2NpRUHrJlm0zMlS0KW+2bNvAy75zvHOchRZulIUW5hRk5M6LLvmur6JD9D5ztXH8h907Y8Ltcij5gNLT05kUWVSt4dS9um9tewa2dOeTE1JWRucVyuRU580Rd3LS9WUNQ+mHseapQ5sP24qhc558HeRi5x9+Gyb2SKmmW+9tiqF3kJomd5JeQNrO/Ia+OMfp5aplaZA+uOjSIvjJdMspS8O9ZemKnjjTB8slVql/mS43Mvci+iW4r3CzdnvXuKLR8nraa9TUn/+Lz3iYjULHNg/vE1MY5863jg1GNMrbcf4ebXwrOLsaF0EWInR8k5znyXOePOmxB5BLZ0lEhi8LAM3NwYImT8uUi9cMeW/p/r5tjto7TQh3BfWgF8iGrLREyp9lG6LDIXq2kDLI63NsHmwHoOoBTkjMfjNRdC5BZ463hjzJWVx860i/UiFFvxMjbdOo5SYvV2TbUs800iF5EviMjDIvJzEfmuiLzOKrDWGeooHh5OzsFa4CUk7r2NgVe2cwqR5bwpRbpZA776yNaM/B4Ab1XVtwP4DYA7toc0jIdGyx2Dl5Elc7HODEsN/YtQGx/bj7csuXa8XJebRK6qP1LVi4df/gTA6e0hpYMfP+1JMW1ulBuXV6zOhafX5peQOx4PfdWyRv5JAD8Y+6GI3CIi50Tk3MUXnjfcbV48nLTSpJzzvPTsiTmycT6wHSfqC1ilvbB3+KGI/BjAFQM/uktVv3f4O3cBuAjg7rHtqOpZAGcB4MSpM7omWIuxnBzHu47UF0ZpgaeIoSU8Xle5Yyo51nyvyFX1PVM/F5FPALgJwLtVdZWgo7H0hHns5PuI/NCo5hJazdl4h6UQW5H51lErNwK4HcCHVPVPNiFNE+3JdjRyzvvsaSGKCA84l+AxplYokbRtrZF/BcBJAPeIyAMi8jWDmEKQcz3P1KSsee/iaSGKjtwZ21o89yHA31DEjhLtllvmm17RV9W/sgpkCV5q5SVrYhZELp/sUkrikc9/S5Qob+b0A+dayUjJWnkp4XgVOMCHm1N4uEFZi7CUzLt9pySsyKNm5ZadycPFNkaOC6a0xD0ObSxNxAf7OUidnTcz10pKStTLvV7YuV7miSbx3FCmw5Q8bynPSWiRR74gvYtgKTkFHlHiFOs6UrRbjTIPLXJP5JjmduvfpSDn6/QW0+BGxNP5roXaZB5e5J6y8pwyL0nuibys5jK3iqXUvqNg3adba781hBe5FSWFGmGK1VKzMFqclxYeLpPllM7KLa+lKkTu6Y69NhaPkig5ha7VC0qlJc5x6jZ4e4nMG1WIHIhfYun2n2sGvX3bLnlzLH0eSFuUzswtqEbkVniQSCmZexC4h/bfJcKr/x722yfCFMc1UZXIS3+M3iWKzGsTeC0Sr+VjfwSit3VVIgf83bVzyXytwGoROFDHdLhkmpT9NfI0FtWJ3AovF/FS2c2VesksPMVMi54kzmw8Lp7bfapfhZ1rZQqreQ2s5o0oNUuit08nQIzaqeeLORKp5l2JPuvoGva1Y7UZeU31css4SpEqC/cm8dI3z9L7rwFPSdfcPl6tyAHK3AOpFqzwKCxv/a1mPE+PvJbumNY886pa5JZQ5stIueJQqomUorQtyYOnzHwf1Yvca+bmMS4rUgrca7sxGyclqV7kgN+LrKbX+VOv++l52JnXm4sHUvZV7/Pe56QJkQOUeQpSyxtIn4V7krinWEgsmhE5QJlbkEPeHTU+0CK2MCs/oCmRA75l7nk+85wPA3PUwj3NrAjEkEXLeD8/zYkcsJW5h+w8tWRzduIoGRbLGH6IskJVSpoUOeA7m/KUnVPiMfB8Y6mpzb0eS7MiB3zLHPCZnacg17BCL+8C7BLtfLWOx2usaZEDdcociCOHaB+LPWe+LVNyFaucAwDGaF7kQAyZb8nO18aUumNS4nFuuBHwcJMtJXWK/BDvMge2z21eOmvoyFlK8SxxKzzHRvJQ5TS2a7GcHrPbTorl2PrbX8Pu3+7G6PFGtBQPN6x9RIjRilRT2u7iZYrb3DdXinwH644QYU7mWkooHR6GhBKSE5ZWBogyKsHrJFL9aTgp8WE8ZI21UvqaKLF/ZuQjpMjMu+1aY1FusYqhFJ5GDO3DMtbS7U6OUup8MCOfINoohRKdyMOnAma3Majt5TIP++ygyPcQUea5OlRpgQPxHszyplMnpa8FinwGqdaGjC700lKKJnFrosRZMx4+kQJGIheR20REReQNFtvzSrTsHPDT0ayJKPHSNz5ii6frarPIReQMgPcBOL89HP9EXS8yldBrkpOnC5P4xltfscjIvwTgdgBqsK0QpJRiRKHnlnmkESodUYZFkmm8fsLdJHIRuRnAU6r64IzfvUVEzonIuYsvPL9lt26I/EDMukPmXODCGo8XJvGH536ydxy5iPwYwBUDP7oLwJ04KKvsRVXPAjgLACdOnakme0/1SnDKced9+tvfehy5YrYk0vS5uYgW7xCtjdXfK3JVfc/Q90XkbQDeDOBBEQGA0wDuF5HrVfVp0yidk/KFnJxytJJ6ipiZiZO5tCZxYMObnar6CwB/0X0tIo8BuE5V/2AQV0hSTtiTO9u1kLpFzBFHp/ThDSgvLcx2OQRf0Tcm9evyJcoXW6Vew0d14p+W12I1E7mqXmW1rRpIPZ1mqXp0rilvUxI5GyfDbG3rqALvYEaekByTWZV+wFiD2ElcWhd4B0WegRaE3mE5CiYyLR97Dijwo1DkGcmxeokXoe/G0JLYoh+rl1V2hqDAh6HIM5Nr7vD+9j103lakXttqS9asWTGr5YeYc6HIC5FzMQhPWTpQXuqplt+r6QaVYyhtarz09xxQ5IVpWehAGalHlbin8+aZFtuJIndCCaH39+uBoVgiTDZVUybex3OtfAhPfTk3FLkzcq+/6VXqHRbDG6PPMe7xvHiBbXMARe6UEgsqe5c64C+u1Fmrt+P1AtvlKBS5c0oIfXd/vGimSSFztvkwbJdhKPIglBL67j55IQ1j9dDWS/t6qo17aRPPUOTBKCn0of3yInslS+r6HtvPi8Q9to1XKPKglB6LPbZvXnyvJEqbUOBxocgroHSW3odij4eHfsN+sg2KvCK8ZOl9huLgRVseL/2DfcEGirxSPGXpu1Du+fHUD3iu7RHV/Osgi8gzAB7PvuNX8gYAzS5NtwPb4ihsj6OwPV6mZFu8SVVP7X6ziMi9ICLnVPW60nF4gG1xFLbHUdgeL+OxLV5VOgBCCCHboMgJISQ4rYv8bOkAHMG2OArb4yhsj5dx1xZN18gJIaQGWs/ICSEkPBQ5IYQEhyI/RERuExEVkTeUjqUUIvIFEXlYRH4uIt8VkdeVjqkEInKjiPxaRB4Rkc+XjqcUInJGRP5DRH4lIr8UkVtLx+QBEblERP5LRP6tdCwdFDkOOiyA9wE4XzqWwtwD4K2q+nYAvwFwR+F4siMilwD4KoD3A7gWwEdE5NqyURXjIoDbVPVaAO8E8KmG26LPrQAeKh1EH4r8gC8BuB1A009+VfVHqnrx8MufADhdMp5CXA/gEVX9raq+COBbAG4uHFMRVPV3qnr/4f+fw4G8riwbVVlE5DSADwL4eulY+jQvchG5GcBTqvpg6Vic8UkAPygdRAGuBPBE7+sn0bi8AEBErgLwDgA/LRtJcb6Mg6Tv/0oH0qeJSbNE5McArhj40V0A7sRBWaUJptpCVb93+Dt34eBj9d05YyM+EZHXAvg2gM+o6h9Lx1MKEbkJwO9V9T4ReVfpePo0IXJVfc/Q90XkbQDeDOBBEQEOSgn3i8j1qvp0xhCzMdYWHSLyCQA3AXi3tvmSwVMAzvS+Pn34vSYRkdfgQOJ3q+p3SsdTmBsAfEhEPgDgUgCXicg3VfVjhePiC0F9ROQxANepapOzvInIjQC+COBvVfWZ0vGUQERejYMHve/GgcB/BuCjqvrLooEVQA6ym38C8N+q+pnS8XjiMCP/nKreVDoWgDVycpSvADgJ4B4ReUBEvlY6oNwcPuz9NIAf4uDh3r+0KPFDbgDwcQB/d9gfHjjMRokzmJETQkhwmJETQkhwKHJCCAkORU4IIcGhyAkhJDgUOSGEBIciJ4SQ4FDkhBASnP8HLc972P/kDQwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chi = .2\n",
+    "alpha = 2\n",
+    "mode = 0\n",
+    "F = pq.Fock(1, cutoff = 20).D(0, alpha).Kerr(0, chi)\n",
+    "F.run()\n",
+    "\n",
+    "(x, p, W) = F.Wigner(mode, plot = 'y', xrange = 5.0, prange = 5.0) # plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}