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| author | Clément Zrounba <6691770+clement-z@users.noreply.github.com> | 2023-09-30 23:06:01 +0200 |
|---|---|---|
| committer | Clément Zrounba <6691770+clement-z@users.noreply.github.com> | 2023-09-30 23:26:46 +0200 |
| commit | ff9b8bb838ecdfbfc1dc81038fcf3b2a87636982 (patch) | |
| tree | 21f27be782ce11c6d00b96ce100a2bff88141b2e /pyspecs/pyspecs_example.ipynb | |
| download | specs-ff9b8bb838ecdfbfc1dc81038fcf3b2a87636982.tar.gz specs-ff9b8bb838ecdfbfc1dc81038fcf3b2a87636982.zip | |
Initial release
Diffstat (limited to 'pyspecs/pyspecs_example.ipynb')
| -rw-r--r-- | pyspecs/pyspecs_example.ipynb | 682 |
1 files changed, 682 insertions, 0 deletions
diff --git a/pyspecs/pyspecs_example.ipynb b/pyspecs/pyspecs_example.ipynb new file mode 100644 index 0000000..1ca337d --- /dev/null +++ b/pyspecs/pyspecs_example.ipynb @@ -0,0 +1,682 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "fdfbe616-3959-44bb-a62e-cccb87a8bee7", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "path_to_specs = \"../\" # This needs to change if the notebook is used somewhere else\n", + "sys.path.insert(0, os.path.realpath(path_to_specs))\n", + "\n", + "from pyspecs import specs\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Dependencies:\n", + "# pyDigitalWaveTools 1.2 (https://pypi.org/project/pyDigitalWaveTools/)\n", + "# pandas\n", + "# numpy" + ] + }, + { + "cell_type": "markdown", + "id": "2bf6e3a5-ef41-4ae1-bc90-50eb142ef0dd", + "metadata": {}, + "source": [ + "### Calling the simulation\n", + "\n", + "There are two ways of doing it. The first one is the default, with the arguments shown in the function call.\n", + "\n", + "The second way uses custom_arguments as a dictionary of arguments. All arguments there are the same that can be passed to specs, with the same syntax except the - or -- before commands.\n", + "\n", + "You can put the verbose variable as True to see the full SPECS output" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5a1dc45-58ae-4892-92ce-3e0a54b57a23", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing SPECS from python. . .\n", + "Command: ../specs -f ../circuits/add_drop_pyspecs.cir -o ../traces/delete_me --abstol 1e-08 --reltol 0.0001\n", + "Execution finished with code: 0\n", + "Time in ms: 43.5395\n" + ] + } + ], + "source": [ + "# Simulating in the basic way\n", + "netlist_file = \"../circuits/add_drop_pyspecs.cir\"\n", + "output_file = \"../traces/delete_me\"\n", + "time_ms, std_out = specs.simulate(netlist_file=netlist_file, simulator_directory=path_to_specs, abstol=1e-8, reltol=1e-4, output_file=output_file, verbose=False)\n", + "print(\"Time in ms: \", time_ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2143ab2e-f911-49db-a8dd-f6efb264bae6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing SPECS from python. . .\n", + "Command: ../specs -o ../traces/delete_me --abstol 1e-08 --reltol 0.0001 -f ../circuits/add_drop_pyspecs.cir\n", + "Execution finished with code: 0\n", + "Time in ms: 87.333\n" + ] + } + ], + "source": [ + "# Simulating in the custom way. This can be used for more advanced use-cases or for \n", + "# new features that weren't completely implemented in the wrapper yet\n", + "\n", + "test_dict = {\"f\":\"../circuits/add_drop_pyspecs.cir\", \"o\":\"../traces/delete_me\", \"abstol\":1e-8, \"reltol\":1e-4}\n", + "time_ms, std_out = specs.simulate(custom_arguments=test_dict, simulator_directory=path_to_specs, verbose=False)\n", + "print(\"Time in ms: \", time_ms)" + ] + }, + { + "cell_type": "markdown", + "id": "6ea09e11-30b8-474e-9d20-4197201e74d0", + "metadata": {}, + "source": [ + "### Having a non-hierarchical dataframe as output\n", + "\n", + "This data structure has everything in the same table directly. We chose to separate the data in two dataframes:\n", + "- df_probes: contains the probe data (optical power, field, phase, wavelength)\n", + "- df_pdet: contains the photodetector data (electric current and time)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1987d33f-6b5a-4ce5-8151-19086d691bca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Contents of simulation ---\n", + "In df_pdet\n", + "In df_probes\n", + "\t time\n", + "\t PROBE{ROOT/N00001}/power\n", + "\t PROBE{ROOT/N00001}/abs\n", + "\t PROBE{ROOT/N00001}/phase\n", + "\t PROBE{ROOT/N00002}/power\n", + "\t PROBE{ROOT/N00002}/abs\n", + "\t PROBE{ROOT/N00002}/phase\n", + "\t PROBE{ROOT/N00003}/power\n", + "\t PROBE{ROOT/N00003}/abs\n", + "\t PROBE{ROOT/N00003}/phase\n", + "\t PROBE{ROOT/N00004}/power\n", + "\t PROBE{ROOT/N00004}/abs\n", + "\t PROBE{ROOT/N00004}/phase\n", + "\t PROBE{ROOT/add}/power\n", + "\t PROBE{ROOT/add}/abs\n", + "\t PROBE{ROOT/add}/phase\n", + "\t PROBE{ROOT/drop}/power\n", + "\t PROBE{ROOT/drop}/abs\n", + "\t PROBE{ROOT/drop}/phase\n", + "\t PROBE{ROOT/in}/power\n", + "\t PROBE{ROOT/in}/abs\n", + "\t PROBE{ROOT/in}/phase\n", + "\t PROBE{ROOT/out}/power\n", + "\t PROBE{ROOT/out}/abs\n", + "\t PROBE{ROOT/out}/phase\n", + "\t ROOT/PROBE1/power\n", + "\t ROOT/PROBE1/abs\n", + "\t ROOT/PROBE1/phase\n", + "\t ROOT/PROBE2/power\n", + "\t ROOT/PROBE2/abs\n", + "\t ROOT/PROBE2/phase\n", + "\t ROOT/PROBE3/power\n", + "\t ROOT/PROBE3/abs\n", + "\t ROOT/PROBE3/phase\n", + "Type of simulation: TD\n", + "------------------------------\n" + ] + } + ], + "source": [ + "(df_probes,df_pdet) = specs.parse_vcd_dataframe(output_file + '.vcd')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0e91dc0f-8e8c-4e25-921b-fe1e7a46f687", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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iXYWeLuqKLqUwrzAzlobRTTcFekrks9Gmuu0Jjboo8csUBn/7zoONCVcih0OjLabEBbPHJ12CBKwqn2VybRVZDTGRatpDT4nKXJbRVTn+56mNSZcigTp35jiadu5nry5gSy0Feop097iG0ZWSGV24gO3pDbuSLUQOmQI9RT785qivsKajk1L481OPBmBXq/bQ00qBniJWOHF1/3OaoV3iV13YQ7+rQWPvp5UCPUX+at5UAB12kZI4fmINI/JZqvIapCutFOgpUpmL/tC+9/s1CVciITIzJtVW8evnt2hMl5RSoKdIXU0lFdkMowuDdYnE7eQptQC06FtgKinQU+b848ax8pU9bNvXnnQpEqDeK0YfbtyWcCVyKBToKTNrYjTmxsad+xOuREJ08ewJALy8vTXhSuRQKNBT5tyZYwH45crNCVciIZpy1AgAfqzp6FKpqEA3s/lmttrMGs3sugEe/6yZPW9mK8zst2Y2I/5SBeCM6dFX4h37NEO7xC+bMXIZO9BFVtJlyEA3syxwE/A2YC5wtZnN7dfsKWCeu58C3AP8a9yFSmTMyArGV1dwZ8MGXEMvSgm864wpbNvXrqGaU6iYPfSzgUZ3X+vuHcAdwMK+Ddz9IXfvPej2ODA13jKlrwk1VYCGOpXSOKnQ00WTXaRPMYE+Beh76VhTYdlgPgLcP9ADZnaNmTWYWUNzc3PxVcpB5p80CYCHVut3KPE7c0Z0WO8WHUdPnVhPiprZe4F5wL8N9Li7L3L3ee4+r66uLs63HlYuP3EiAM827Uq2EAnSnEmjAdiypy3hSuSNKibQNwLT+tyfWlh2EDO7DPgHYIG7q5N0CU09aiQAty/TmBsSv2zGqB83kj9t3ceuVp18T5NiAn0ZMMvMjjGzCuAqYHHfBmZ2OvCfRGG+Nf4ypa/qyhzHT6yheW+7Jo2Wknjn6dFpsBe36Dh6mgwZ6O7eBVwLPACsAu5y95VmdqOZLSg0+zegGrjbzJ42s8WDvJzE5LiJ1QA8t3F3wpVIiM4pXO/w4AvaP0uToo6hu/sSd5/t7se6+1cLy65398WF25e5+0R3P63ws+D1X1EO11+fPR1AMxhJSZw7cxwANz/yUsKVyBuhK0VT6tRpYwB4bM32ZAuRYI0Zmaejq4fObk2okhYK9JSqrswxqiLLmuYWDXUqJfH2kycDsPRFdY9NCwV6in2kMCXd42u1ly7xe8850QgeP3tifcKVSLEU6Cl22dyoP/rdDU0JVyIhOm5CdOL9d6t1YjQtFOgpdsLk6AKQR9do7GqJX0Uuw+yJ1fQ4NO3UcLppoEBPsXw2w6VzJrCztZMXt+xNuhwJ0KcuOQ6AO3URWyoo0FOud1yXnz7+csKVSIguPSE6rHfzw+q+mAYK9JS7fG4U6LfpxJWUQHVljurKHC0d3eze35l0OTIEBXrK1Y7MM7m2iq4e55VdmpZO4veJi2YC8LMn9C2w3CnQA/CFtx4PwA/1tVhK4OrCVcnfX7o24UpkKAr0ACw8LRqeXoEupTCuupIpY0aws7VT3wLLnAI9ANmMMbfQhfGp9TsTrkZC9ImLjwXg2w82JlyJvB4FeiC+MD867HLDL55PuBIJ0bvPjIbTvf1JnXwvZwr0QFw0K5oB6pkNu9jbpt4IEq+qfJaz6qOp6R5YuTnhamQwCvRAZDLGRwtju3xtyaqEq5EQffWdJwPw8Z8sT7gSGYwCPSCfL/R2uf3JDXR0achTidfsiTXkswbAExoQriwp0ANSlc9ywazxAPyfX61OuBoJ0aL3zQPgw7csS7gSGYgCPTDfvOp0AP5z6VrNNyqxu2TOBABaOro1/WEZUqAHZuyoCi4rjL9x3X89m3A1EqIff/hsAN7x7Ydx94Srkb4U6AH6xpWnAtF8o9v2tSdcjYTmotl1B27/VGMIlRUFeoBGV+X5+IXR+Bvn/8uDCVcjIfrV314IwJd//hwt7V0JVyO9FOiB+rv5cwBo7+rhR5q5XWI2e2INlxwf7am/7Zt/SLga6aVAD1QmYzzwN9Fe1Fd+8TybdmsMDonXDz5wFgDrd7Rq4K4yoUAP2PGTavjAedFEv+f984Pqmy6xymaM//ep8wH46pJVrHxFvV6SpkAP3FcWnsToqhwAs790Pz096pUg8Tl12pgDwze//VsPs3l3W8IVDW8K9GFg2ZcuO3B7zpd/qVCXWH3qkuMOXNB27j//lua96lmVFAX6MFCZy7LyK28FoKO7h5lfXEJbpy46kvjc+uGzmT2xGoCzvvobXti8J+GKhicF+jAxqjLHqhvnH7g/58u/1B+dxMbM+NXfXsS8GdGIjPP/7x9YtHRNwlUNPwr0YWRERZY1X7uCMSPzQPRH98mfLtchGInNPZ9804FrIL625AXqr7uPfeqnfsQo0IeZbMZ4+vrLD5zIuv+5zcz84hL1VZfY/P0VJ3Dvp9984P5J//gA77/5Sbq61cuq1BTow9SnLjmOZ2+4nJEVWSDqq15/3X189s6ndXxdDttJU2pZ+7UreMcpkwFY+mIzx/3D/fzZv/+ODTtaE64uXAr0YaymKs/zN87nwc9dRDYTjXP9309tZM6Xf0n9dfdxw+KV7N6v2Y/k0GQyxnf++gxe+Kf5B2Y7WruthQv+9SHqr7uP9/zgcRq37ku4yrDkimlkZvOBbwJZ4Afu/i/9Hq8EbgXOBLYDV7r7unhLlVKZWVfNmq9dQUt7F5+/+xnufy6aYuyWR9dxy6PrqMpnOHlKLSceXcubjh3HqdPGMKGmEjNLuHJJg6p8lrs/8SbcnR/84SW+WphR65HG7Vz2jd8DMGdSDWfOOIq5R4/m3JnjmDF2JLms9jffqCED3cyywE3AW4AmYJmZLXb3vrMRfwTY6e7HmdlVwNeBK0tRsJTOqMoc333vmQDsbevk509t5NE122l4eSfL1kU/tzy67kD78dUVjK+uZMzIPHU1VcyaUI071I8fCcDE0VVU5bNUZDPUVOWoyGWoyGaoyGXIZY1cJnPgm4GEz8z42IUz+diFM+npcX7/YjO/eOYVlq/fyQub9/LC5r0Hta/IZhhXXcHE0VWcMLmGfDbD6Ko8M+tG4Q6Txwz8+arMZ8lljFzGyGZsWO14FLOHfjbQ6O5rAczsDmAh0DfQFwI3FG7fA3zHzMxLMFjyXcs28P0/hDFuxCu79lM/flTSZQyopirP+86r533n1QPQ0+Osad7HsnU72birlU272nhpewsbdrSyfkcrrR07Dul9zCBX+KPr6OqhprLPR9IGvHnQH6gV02aA9xzoWYO/Vt/lA7d/vfcf7L2Led3X1j7Ieg3wnN37y7d3SSZjXDJnwoEJM9yd5r3tBz5fqzbt5eXtLWzZ086Olg5uf3LDIb9X9PmCzm5nVEWWTObV37ZZ9Jjx6u82uh3d6v119y6z1ywrPKd3WZ82B71H4Qm9yz5z2WwWnHr0Ia/ToOtaRJspQN/fZhNwzmBt3L3LzHYD44BtfRuZ2TXANQDTp08/pILHjMwzq3ABQ9rNmljNJcdPSLqMomQyxqyJNcyaWDNom7bObna1drKjpYP9nd20d3azeU8buWyGjq4e2jq72by7jZqqHF09Tle3093TQ2ePs3HnfsZVVxwINufVfYHBdgv67i/4Qcv73B7kdQZrz2DtD+M1+7Yf5GbhOcWsz9Dt+96ZOLqKcaMqKHdmxoTRVby9cBJ1IF3dPexs7WRfexc7Wtrp7I7+E/DCY+1dPbyya/9Bn6/o3x627+sglzUqcpl+2zXaOr3LHMedg5bRu8xf3ZZ92xzYvgeWvbrFfYBlOIwZkT/s39lAijqGHhd3XwQsApg3b94h7b1ffuIkLj9xUqx1STyq8lkm1WaZVFuVdCkSoFw2Q11NJXU1lRxTpt9sk1bMWYeNwLQ+96cWlg3YxsxyQC3RyVERETlCign0ZcAsMzvGzCqAq4DF/dosBj5QuP2XwIOlOH4uIiKDG/KQS+GY+LXAA0TdFm9295VmdiPQ4O6LgR8CPzGzRmAHUeiLiMgRVNQxdHdfAizpt+z6PrfbgHfHW5qIiLwR6rkvIhIIBbqISCAU6CIigVCgi4gEwpLqXWhmzcDLh/j08fS7CjXFtC7lKZR1CWU9QOvSa4a71w30QGKBfjjMrMHd5yVdRxy0LuUplHUJZT1A61IMHXIREQmEAl1EJBBpDfRFSRcQI61LeQplXUJZD9C6DCmVx9BFROS10rqHLiIi/SjQRUQCUdaBbmbzzWy1mTWa2XUDPF5pZncWHn/CzOoTKLMoRazLB82s2cyeLvx8NIk6h2JmN5vZVjN7bpDHzcy+VVjPFWZ2xpGusVhFrMvFZra7zza5fqB2STOzaWb2kJk9b2YrzewzA7RJxXYpcl3Ssl2qzOxJM3umsC5fGaBNvBnm7mX5QzRU7xpgJlABPAPM7dfmfwHfK9y+Crgz6boPY10+CHwn6VqLWJcLgTOA5wZ5/ArgfqLpE88Fnki65sNYl4uBe5Ous4j1mAycUbhdA7w4wOcrFdulyHVJy3YxoLpwOw88AZzbr02sGVbOe+gHJqd29w6gd3LqvhYCPy7cvge41Mpziu9i1iUV3H0p0Zj3g1kI3OqRx4ExZjb4RJEJKmJdUsHdN7n7Hwu39wKriOb57SsV26XIdUmFwu96X+FuvvDTvxdKrBlWzoE+0OTU/TfsQZNTA72TU5ebYtYF4C8KX4fvMbNpAzyeBsWua1qcV/jKfL+ZnZh0MUMpfGU/nWhvsK/UbZfXWRdIyXYxs6yZPQ1sBX7t7oNulzgyrJwDfbj5BVDv7qcAv+bV/7UlOX8kGjfjVODbwM+TLef1mVk18F/A37j7nqTrORxDrEtqtou7d7v7aURzMZ9tZieV8v3KOdBDmpx6yHVx9+3u3l64+wPgzCNUW9yK2W6p4O57er8yezRrV97Mxidc1oDMLE8UgD9z9/8eoElqtstQ65Km7dLL3XcBDwHz+z0Ua4aVc6CHNDn1kOvS73jmAqJjh2m0GHh/oVfFucBud9+UdFGHwswm9R7PNLOzif5eym6HoVDjD4FV7v6NQZqlYrsUsy4p2i51ZjamcHsE8BbghX7NYs2wouYUTYIHNDl1kevyv81sAdBFtC4fTKzg12FmtxP1MhhvZk3APxKd7MHdv0c09+wVQCPQCnwomUqHVsS6/CXwSTPrAvYDV5XpDsP5wPuAZwvHawG+CEyH1G2XYtYlLdtlMvBjM8sS/adzl7vfW8oM06X/IiKBKOdDLiIi8gYo0EVEAqFAFxEJhAJdRCQQCnQRkRgMNdjbIbze183sucLPlcU8R4EuIhKPW3jthUOHxMzeTjRw3GnAOcDnzWz0UM9ToIuIxGCgwd7M7Fgz+6WZLTezP5jZnCJfbi6w1N273L0FWEER/1ko0EVESmcR8Gl3PxP4PPAfRT7vGWC+mY0sDGtwCQcP3TCgsr1SVEQkzQoDjL0JuLvPiLiVhcfeBdw4wNM2uvtb3f1XZnYW8CjQDDwGdA/5nrpSVEQkHoUhf+9195MKx7xXu/thjztvZrcBPy0MRjYoHXIRESmBwrC/L5nZu+HANICnFvPcwjjq4wq3TwFOAX415PO0hy4icvj6DvYGbCEa7O1B4LtEA3XlgTvcfaBDLf1fq4po3HeAPcAn3P3pIZ+nQBcRCYMOuYiIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEgg/j/PCGS71tAX9AAAAABJRU5ErkJggg==\n", 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<td>-3.141593e+00</td>\n", + " <td>0.000012</td>\n", + " <td>0.003420</td>\n", + " <td>-3.141593e+00</td>\n", + " <td>0.000011</td>\n", + " <td>0.003381</td>\n", + " <td>-3.141593</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>1251 rows × 34 columns</p>\n", + "</div>" + ], + "text/plain": [ + " time PROBE{ROOT/N00001}/power PROBE{ROOT/N00001}/abs \\\n", + "0 0.000000e+00 0.000000 0.000000 \n", + "1 5.000000e-10 0.000000 0.000000 \n", + "2 5.020000e-10 0.000000 0.000000 \n", + "3 5.040000e-10 0.021994 0.148303 \n", + "4 5.060000e-10 0.021994 0.148303 \n", + "... ... ... ... \n", + "1246 2.990000e-09 0.000569 0.023863 \n", + "1247 2.992000e-09 0.000544 0.023326 \n", + "1248 2.994000e-09 0.000544 0.023326 \n", + "1249 2.996000e-09 0.000520 0.022801 \n", + "1250 2.998000e-09 0.000520 0.022801 \n", + "\n", + " PROBE{ROOT/N00001}/phase PROBE{ROOT/N00002}/power \\\n", + "0 0.000000 0.000000 \n", + "1 0.000000 0.022500 \n", + "2 0.000000 0.022500 \n", + "3 1.570796 0.087986 \n", + "4 1.570796 0.087986 \n", + "... ... ... \n", + "1246 1.570796 0.000557 \n", + "1247 1.570796 0.000532 \n", + "1248 1.570796 0.000532 \n", + "1249 1.570796 0.000508 \n", + "1250 1.570796 0.000508 \n", + "\n", + " PROBE{ROOT/N00002}/abs PROBE{ROOT/N00002}/phase \\\n", + "0 0.000000 0.000000 \n", + "1 0.150000 1.570796 \n", + "2 0.150000 1.570796 \n", + "3 0.296625 1.570796 \n", + "4 0.296625 1.570796 \n", + "... ... ... \n", + "1246 0.023593 1.570796 \n", + "1247 0.023062 1.570796 \n", + "1248 0.023062 1.570796 \n", + "1249 0.022543 1.570796 \n", + "1250 0.022543 1.570796 \n", + "\n", + " PROBE{ROOT/N00003}/power PROBE{ROOT/N00003}/abs \\\n", + "0 0.000000 0.000000 \n", + "1 0.000000 0.000000 \n", + "2 0.022500 0.150000 \n", + "3 0.022500 0.150000 \n", + "4 0.087986 0.296625 \n", + "... ... ... \n", + "1246 0.000557 0.023593 \n", + "1247 0.000557 0.023593 \n", + "1248 0.000532 0.023062 \n", + "1249 0.000532 0.023062 \n", + "1250 0.000508 0.022543 \n", + "\n", + " PROBE{ROOT/N00003}/phase ... PROBE{ROOT/out}/phase ROOT/PROBE1/power \\\n", + "0 0.000000 ... 0.000000e+00 0.000000 \n", + "1 0.000000 ... 0.000000e+00 0.977500 \n", + "2 1.570796 ... 0.000000e+00 0.977500 \n", + "3 1.570796 ... -6.316716e-15 0.934007 \n", + "4 1.570796 ... -6.316716e-15 0.934007 \n", + "... ... ... ... ... \n", + "1246 1.570796 ... -3.141593e+00 0.000013 \n", + "1247 1.570796 ... -3.141593e+00 0.000012 \n", + "1248 1.570796 ... -3.141593e+00 0.000012 \n", + "1249 1.570796 ... -3.141593e+00 0.000012 \n", + "1250 1.570796 ... -3.141593e+00 0.000012 \n", + "\n", + " ROOT/PROBE1/abs ROOT/PROBE1/phase ROOT/PROBE2/power ROOT/PROBE2/abs \\\n", + "0 0.000000 0.000000e+00 0.000000 0.000000 \n", + "1 0.988686 0.000000e+00 0.000000 0.000000 \n", + "2 0.988686 0.000000e+00 0.000506 0.022500 \n", + "3 0.966441 -6.316716e-15 0.000506 0.022500 \n", + "4 0.966441 -6.316716e-15 0.001980 0.044494 \n", + "... ... ... ... ... \n", + "1246 0.003579 -3.141593e+00 0.000013 0.003539 \n", + "1247 0.003499 -3.141593e+00 0.000013 0.003539 \n", + "1248 0.003499 -3.141593e+00 0.000012 0.003459 \n", + "1249 0.003420 -3.141593e+00 0.000012 0.003459 \n", + "1250 0.003420 -3.141593e+00 0.000011 0.003381 \n", + "\n", + " ROOT/PROBE2/phase ROOT/PROBE3/power ROOT/PROBE3/abs ROOT/PROBE3/phase \n", + "0 0.000000 0.0 0.0 0.0 \n", + "1 0.000000 1.0 1.0 0.0 \n", + "2 -3.141593 1.0 1.0 0.0 \n", + "3 -3.141593 1.0 1.0 0.0 \n", + "4 -3.141593 1.0 1.0 0.0 \n", + "... ... ... ... ... \n", + "1246 -3.141593 0.0 0.0 0.0 \n", + "1247 -3.141593 0.0 0.0 0.0 \n", + "1248 -3.141593 0.0 0.0 0.0 \n", + "1249 -3.141593 0.0 0.0 0.0 \n", + "1250 -3.141593 0.0 0.0 0.0 \n", + "\n", + "[1251 rows x 34 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_probes" + ] + }, + { + "cell_type": "markdown", + "id": "64c2170f-f879-43d2-ae1f-850ebf667601", + "metadata": {}, + "source": [ + "### Helper to recover data\n", + "\n", + "As SPECS is not a sampled simulator, but rather event-driven, unchanged values will not appear in the dataframe.\n", + "\n", + "To make it a bit easier to recover a value by time, we created the 'find_by_time' function that automates\n", + "some of the recovery for you. \n", + "\n", + "NOTE: if you want to do it for multiple values, it is better to implement it using the Pandas framework in your own way. Check the Pandas dataframe.ffill() function. Using find_by_time in loops will result in very slow python code." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d042f4a5-de7d-425d-b772-f245e6346871", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07090737655723536" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target_time = 2.5668e-9\n", + "specs.find_by_time(df_probes,'PROBE{ROOT/N00001}/power',target_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75ede147-1232-46f5-b3b1-4a9dc75a28b7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} |