debugs the documentation notebooks

- moves the examples back to the qopt folder - made the code compatible with the latest filter functions version
qutech · Jun 24, 2022 · f5f6177 · f5f6177
1 parent 7e3558b
commit f5f6177
Show file tree

Hide file tree

Showing 16 changed files with 289 additions and 113 deletions.
diff --git a/doc/source/examples/consistency_test.ipynb b/doc/source/examples/consistency_test.ipynb
diff --git a/doc/source/examples/phase_noise.ipynb b/doc/source/examples/phase_noise.ipynb
diff --git a/doc/source/examples/rabi_rwa.ipynb b/doc/source/examples/rabi_rwa.ipynb
diff --git a/doc/source/examples/t1_pure_depolarizing.ipynb b/doc/source/examples/t1_pure_depolarizing.ipynb
diff --git a/doc/source/examples/t2_markovian_noise.ipynb b/doc/source/examples/t2_markovian_noise.ipynb
diff --git a/doc/source/examples/t2_non_markovian_noise.ipynb b/doc/source/examples/t2_non_markovian_noise.ipynb
diff --git a/doc/source/examples/t2_star_dephasing.ipynb b/doc/source/examples/t2_star_dephasing.ipynb
diff --git a/doc/source/qopt_features/filter_functions_basic.ipynb b/doc/source/qopt_features/filter_functions_basic.ipynb
diff --git a/doc/source/qopt_features/monte_carlo_experiments.ipynb b/doc/source/qopt_features/monte_carlo_experiments.ipynb
diff --git a/doc/source/qopt_features/parallelization.ipynb b/doc/source/qopt_features/parallelization.ipynb
diff --git a/.../examples/rabi_driving/lab_frame_setup.py → .../examples/rabi_driving/lab_frame_setup.py b/.../examples/rabi_driving/lab_frame_setup.py → .../examples/rabi_driving/lab_frame_setup.py
diff --git a/..._driving/optimization_phase_control.ipynb → ..._driving/optimization_phase_control.ipynb b/..._driving/optimization_phase_control.ipynb → ..._driving/optimization_phase_control.ipynb
diff --git a/temp/examples/rabi_driving/rabi_xy_setup.py → qopt/examples/rabi_driving/rabi_xy_setup.py b/temp/examples/rabi_driving/rabi_xy_setup.py → qopt/examples/rabi_driving/rabi_xy_setup.py
diff --git a/temp/examples/rabi_driving/setup.py → qopt/examples/rabi_driving/setup.py b/temp/examples/rabi_driving/setup.py → qopt/examples/rabi_driving/setup.py
diff --git a/qopt/solver_algorithms.py b/qopt/solver_algorithms.py
@@ -586,7 +586,11 @@ def create_ff_h_n(self) -> list:
                 h_n = self._filter_function_h_n(self._ctrl_amps)
 
         if not h_n:
-            h_n = []
+            h_n = [
+                [
+                    0 * self.h_ctrl[0], self.transferred_time, 'No Noise'
+                ]
+            ]
 
         # we store the order of the noise operators. They must coincide with
         # the order in filter_functions_n_coeffs_deriv

diff --git a/temp/tf_test.ipynb b/temp/tf_test.ipynb
@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qopt import *\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "gaussian_filter = GaussianConvolution(\n",
+    "    sigma=2,\n",
+    "    # here the num_ctrls is the number of optimization parameters\n",
+    "    num_ctrls=1\n",
+    ")\n",
+    "\n",
+    "over_sampl = OversamplingTF(\n",
+    "    # here the num_ctrls is the number of optimization parameters\n",
+    "    num_ctrls=1,\n",
+    "    bound_type=('n', 8),\n",
+    "    oversampling=5,\n",
+    ")\n",
+    "\n",
+    "transfer_func = ConcatenateTF(\n",
+    "    tf1=over_sampl,\n",
+    "    tf2=gaussian_filter\n",
+    ")\n",
+    "\n",
+    "transfer_func.set_times(np.ones(10))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[<matplotlib.lines.Line2D at 0x1a816a59cc8>]"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(transfer_func(np.ones((10,1))))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}