{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.tri as tri\n",
"import pandas as pd\n",
"from scipy.interpolate import CloughTocher2DInterpolator\n",
"from scipy.io import loadmat\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def ceo_modes(ceo_file):\n",
" from collections import OrderedDict\n",
"\n",
" suit = OrderedDict()\n",
" suit['Ni'] = np.array( 0, dtype=np.int32)\n",
" suit['L'] = np.array( 0, dtype=np.double)\n",
" suit['N_SET'] = np.array( 1, dtype=np.int32)\n",
" suit['N_MODE'] = np.array( 1, dtype=np.int32)\n",
" suit['s2b'] = np.array( [0]*7, dtype=np.int32)\n",
"\n",
" with open(ceo_file,'rb') as f:\n",
" suit['Ni'] = np.fromfile(f, dtype=np.int32, count=1)\n",
" suit['L'] = np.fromfile(f, dtype=np.double, count=1)\n",
" suit['N_SET'] = np.fromfile(f, dtype=np.int32, count=1)\n",
" suit['N_MODE'] = np.fromfile(f, dtype=np.int32, count=1)\n",
" suit['s2b'] = np.fromfile(f, dtype=np.int32, count=7)\n",
" suit['M'] = np.fromfile(f, dtype=np.double, count=-1)\n",
" return suit"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'M2_OrthoNormGS36p_KarhunenLoeveModes.ceo'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/rconan/projects/dos-actors/clients/m2-ctrl/examples/asm-nodes/asm-nodes.ipynb Cell 3\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m fqp \u001b[39m=\u001b[39m ceo_modes(\u001b[39m\"\u001b[39m\u001b[39mASM_DDKLs_S7OC04184_675kls.ceo\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m rco \u001b[39m=\u001b[39m ceo_modes(\u001b[39m\"\u001b[39;49m\u001b[39mM2_OrthoNormGS36p_KarhunenLoeveModes.ceo\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
"\u001b[1;32m/home/rconan/projects/dos-actors/clients/m2-ctrl/examples/asm-nodes/asm-nodes.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 8\u001b[0m suit[\u001b[39m'\u001b[39m\u001b[39mN_MODE\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray( \u001b[39m1\u001b[39m, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mint32)\n\u001b[1;32m 9\u001b[0m suit[\u001b[39m'\u001b[39m\u001b[39ms2b\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray( [\u001b[39m0\u001b[39m]\u001b[39m*\u001b[39m\u001b[39m7\u001b[39m, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mint32)\n\u001b[0;32m---> 11\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(ceo_file,\u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[1;32m 12\u001b[0m suit[\u001b[39m'\u001b[39m\u001b[39mNi\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mfromfile(f, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mint32, count\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m 13\u001b[0m suit[\u001b[39m'\u001b[39m\u001b[39mL\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mfromfile(f, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mdouble, count\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n",
"File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/IPython/core/interactiveshell.py:282\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[39mif\u001b[39;00m file \u001b[39min\u001b[39;00m {\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m}:\n\u001b[1;32m 276\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 277\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIPython won\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt let you open fd=\u001b[39m\u001b[39m{\u001b[39;00mfile\u001b[39m}\u001b[39;00m\u001b[39m by default \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 278\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 279\u001b[0m \u001b[39m\"\u001b[39m\u001b[39myou can use builtins\u001b[39m\u001b[39m'\u001b[39m\u001b[39m open.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 280\u001b[0m )\n\u001b[0;32m--> 282\u001b[0m \u001b[39mreturn\u001b[39;00m io_open(file, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'M2_OrthoNormGS36p_KarhunenLoeveModes.ceo'"
]
}
],
"source": [
"fqp = ceo_modes(\"ASM_DDKLs_S7OC04184_675kls.ceo\")\n",
"rco = ceo_modes(\"M2_OrthoNormGS36p_KarhunenLoeveModes.ceo\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"fqp_s1 = fqp['M'][:256*256*675].reshape(-1,256*256)\n",
"fqp_s7 = fqp['M'][256*256*675:].reshape(-1,256*256)"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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