diff --git a/dg800p_cal_zrq.ipynb b/dg800p_cal_zrq.ipynb
new file mode 100644
index 0000000..329e793
--- /dev/null
+++ b/dg800p_cal_zrq.ipynb
@@ -0,0 +1,849 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.LineCollection at 0x1f57be6aac0>"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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bTzqMqaU5bK6oY3+bbpKxoiqoc2UsKMhKwuNr3/3S5ItN+5i/YR8/PekwLp5ewmPzN/PSwu3c+OI3jM7P4A/njgPA5bBZOxnrl0wVa3H5I7e31VgX84HttNsozE5m58EGXv1mJwPS3Rw1tF+nriGEYGCmmz0xiOhrGj09tsZDRKEXQjwhhNgrhAhZ2CwUDwohNgohlgshJgXt2yqEWCGEWCqEWBzLgYej0eNjgm0TyRvegq2fRXWt/DbdL4N59JNN1Ht83HSiEnQhBH/5znjuPGs0fzxvXIfVMnmZSeyrbWpnT1Q3enhg3gYmFGdx4qgBTC3t2XbAwZ0riYFg5XfwYJRS8qd315GfmcQlMwbxq9NHUZKTwi2vrsDlsPHYZVMCb2FWt24c3lpedP2Oc+3zdVQfx5g/O4dNUJiVzKaKWj5au5dZEwq6VCqZlxF+gaOucN/76znxL5/0iNh35m6eAk7tYP9pwHDj37XAP9rsP15KOUFKOaVbI+wiDR4fGRgz1ap3RnWtcMI1d9VuHpu/mfMmFTF8YHpge780N1ceNThiMrMgq/2kKb9f8uOXllJR08T/nTkaIQSHF2Xictha2Te/e2s1767cFdV9mRysUxF9DlXw+wLYMj+q63X0YPxgzV6W7qjkphOHk+S0k+p28NcLJzB8QBp//+6kVjX7Loe1hV54G3EIP6k0tppGr4kvWqwbG4VZKdQ0evH6JedMDG/bhGJghCU4O8POygae/2o7M4fldrsYoiMiCr2U8lOgo4zgLOAZqfgKyBJC5MdqgF2l0eMnU5hCX97xwREYmKmSMcGVJCvKqrjppaWMK8zkt7PGduu65qSk4Cjgrx+s58O1e7nzrNGBhmFuh51xhZmBhOyXm/bz+GdbeP2b6O7LpLJBda5Mq98Jnnoo/yaq65klmW0riipqmrjjjZUMyU3l/Mktf0STSrJ5/6fHMn1I69dkqwu99KoHpAOfTsjGMeZD2mEXFGar392ReemMLsjo0nXyjSVLo7HxHjQ6wN7YgeUbDbHw6AuBHUGflxnbACQwVwixRAhxbUcXEUJcK4RYLIRYXFFR0e3BNHp85NiNiLKqrNvXASW0uWmuQIRaXtnAVU8vIifVxT8vn9LthG9Bm/YK767czUMfbuSCKUVcMmNQq2OnlOawwlif9f4PVPIyVguXBDpXNhpvDFE+GLNTnLgdtlbja/b6+eHzSzhY38yDsyd26pXYabe2R49fCb1TeLV1E8eYD2mHzUaRIfTnTuraDHpQEX2jx0+VUcXWVTZX1PLK12V8d0ZJxNno3SUWQh/KjDZ/+4+SUk5C2TvXCyGOCXcRKeVjUsopUsop/fv37/ZgGj0+cmxG34oorRswasON17LfvrWa+iYvT145lQHp3Z8FF9xeob7Zy+2vr2BcUSa/mTW2nbc/ZZBan/XRTzazYMsB1ZcjRv3sD9Z7yE5xQoPxwlYd3YNRCKG6cwa9xv76zVUs2nqQP58/PtBHKBIuh021m7aoiEpfcERvzTFqItOSjBVMGZTN/505mu9OHxThrPYE/p4j+PT3vreOe95Z0277Xz/YgMtu44fHdW02eleIhdCXAcVBnxcB5QBSSvP/vcBrwLQYfL0OaWj2kR0Q+ugtjnyjFHLlzireWbmba44ewmFBvnx3SE9yku52sKuqkWe+3Ma+2mbuOHN0SG/OtHEemLeegRluLj1iEPtqm2iKQflhZYOHzBQX1BtCXxXbB+OcZeW8sGA7PzhuKGeN77hzaDBuh/q1tGxUHxB6v47o4xizBYLDbsNht3H1zMGkurveFSa/E32xlpdV8rePNvLoJ5t5b9XuwPYl2w7w5rJyrppZGrZuPxbEQujnAJcZ1TczgCop5S4hRKoQIh1ACJEKnAz0eEvCRq+fTGEIfc0u8IWuge8sZoR63/vryUx2cvXRg2MwShUFbNhbwyOfbOK4Ef2ZUpoT8rjsVBfDB6Thl/DD44YFGpDtqYq+5DLQuTIQ0cdA6LNUv/1mr58/vbuWsYUZ/OzkEZFPDMJlt7jQ+9XvlAOv9ujjGPNtzGGLrp/UwAiTpqSU/P7tNeSkuhiZl87tr6+kqt7D1n11XPvMEopzkgPdbXuKiI8vIcSLwHFArhCiDLgTcBo38AjwNnA6sBGoB640Th0IvGZYEQ7gBSnluzEefzsamn0tyVjph9rdkNm1LHow+VnJ1DR6+XDtXn5x6ggykmJTc56flcyn61Uu4uaTOhbCYw/rT5PXz4VTi1m8Vfnp5VUNgR7Z3aHR42PHwQbVy8OM6Gv3grcZHN2fKVuQmcyemiae+2obZQcb+P05h7dahL0zuMyI3qoJWX+LdaMj+vjFG/DooxN608YNZ918tG4vX20+wF1nj2HyoGxmPfw5t762nNXl1fil5Okrp7XqN9UTRBR6KeXsCPslcH2I7ZuB8d0fWvdo8hrllTan+oOsLo9O6I3Xstw0F1ccWRqjUbYkZE8ZM5DDizr2rm89fRQ/O2WEWvA8Rn3fn/tqGwfqmlUVzDdmUZWEmnLILu32dfMyk/D5JX+Zu44jhvTj6OG5Xb6GKfRWjZaF8ZboxKc7WMYxLVU30RkbLoeN3DR3yDUmvD4/97y9lsG5qVw8vQSn3cZ1xw7h4Y824XbYeOF7MxgSonFarEm4mbENzT7SZB30N6LkSJU3u5bBi7OhOfTiwCXGQuLXHTuUFFfsujoX56QgBPzkpNCNk4Kx20TAv+9otm5nqWn08PBHGzl6eC5HDss1InojqonSpzcXIKlr9vHzU0d0q81ywLqxaERvJmPtOhkb15hC77RH3wo8L9Md8m/y8c+2sGFvLb84ZQRO4/f6xhOHc+GUYh69dHIgB9fTJFw/+kavj1RZCwOPgT0rI/vO798Jmz+CHQtg6PHtdk8ozuK5q6dzRCenRHeWy48s5djD+jMyr2s1u8kuO9kpzqgi+sfnb+FgvYefn2I8DOsPQM4QOLCpcwnsmt2QHrrHvznJ7OTRAwNd/rqK1a0bIc3ySm3dxDPeoPLKaMnLSGq37uwbS3fyh3fWcuqYPE4d2/L34nbY+eP546L+ml0h4SL6xiYPKf46yCwGV1rHEerOJUrkAcq/DnmIEIKZw3O77DNHIs3t6HS5YVuCK1vC4fdLHpy3gZU7q1pt31/bxOPzN3P64XmMK8pSGxsOQJ4x+StSieXqOfCXEepNKASHDUznB8cN5Y6zRnfmVkJiCn2TVYXeZyZj9YSpeMbjk8yyfYZz36qor5WX2boNwifrK7j55WVMH5zD/RdN6PEFhCKRcEJv89Rhww/JWZBR0HFEP/8+SMqEjELYGVrorUik9skAb6/cxX3vr+f7zy4JTOSQUnL3/9bQ6PVzc3AlTP0B9WBMyuz4wSglfPIn9fG2L0IeYrcJfnnqyKiWSXRZvLzSjOgd6AlT8YzX7+fXzmcQXz4c9bXyMpKorFdNybbtr+MHzy1h+MB0/nn5lB5padBVEk7oXR618gtJWUrAwwn9ntWw9i2Yfh2UzIh6+v+hpCAruUOh9/r83Dd3PYVZyeyubuROY6HtRz7ZzKvf7ORHJwxrWTnH0wDeBkjJ6fj7BbDhfdizQn3cgw9Gt8U9ehEor/Rrjz6O8fokLjxQuT3qa5ltTfZUN3Lv3PVICU9cMSVmVXrRknAevdtbox5fyVmQWQgb2s9EA+Czv4IzVQn90hdg5X+hZg+kDzyUw+0W+ZnJVDd6qW3ykhZigserX+9k8746Hrt0Mmt313Df++tJctp5adEOzhpfEOi4CbSUViYbQh8ueS0lzL9XRf79R4a1umKB5T16f4t1oyP6+MXjkzjxwcFtnTth7f+g5AgVFLXBXIDk/dV7eHNZOTccPyyQr7ICCRXRSylxe82IPhMyiqB2T2AmY4B178LKV2DKleqHVmh0Vu5B8QoaZNSXMCtbdoWI6pu8qifO+OIsTho9kB8eN5RJJVm8tGgHE0uy+PP5bVoom5OlUnLUgzFcRL/tC5WwPuomKJ4O+zdCY1XoY6PE6kJv85vllV48urwybvH6/TiET5UUe5s7Prh8Kbx0MSx4NOTuPKMB4l/mric7xcm1xw4JeVxvkVBC7/FJ0s0WxUlZyqNHqhmyJls+hZcvg/zxcOwv1bb88SBsre2Iqp3QXBfbAe5ZBX8eqn5posCMFEItXvL8V9spr2rk5yer0kaH3cYDF03ksiMG8dilIfzCVhF9EdTvV3ZOMFLCp3+G1AEw8RIonKi2R3kf4XBafGasGdHb8Qf6pWjiD5/Piw2pJlZGqs5b8pT6f/fykLtN66bB4+OGE4ZbxrIxSSihb/D4yDDbH5jWDbQkGMsWwwsXqVLCS16FJKO00ZXa2o6o2w9/nwGf/DG2A/zwd0pIo8wHBPq+t4noV5RV8af31nL08FyOGtZSDlqck8JvZo0N3UujbUQP7UssP/ytqk6a+RNwJkNBz74BWT6iN5OxwodPR/RxiwyO4jvy6ZtqYcV/1Me7Qgt9mttBmttBYVYyl8woieEoY0NCCX2Tx0dmIKI3rBtQT2tvM/znCkgbAJe93t5nK5ikInop4au/Q1M17NsYu8GVLYZ1b6uPo2yfnJeZhBCtI/o91Y1c88wi+qW6ue+CLpRztfXooXV0M/8v6t/kK2DGD9S2lBw1ezb4DejLv8O6d7p9T8FYecKU3y+xoxrKOfHqZGwcI71BfbAqO/DpV74CzbUw8kxVfmz+zbThjjNH88BFE3A7er/Kpi0JJfQqoq9DYgNXelCEuhNWvAxVO+D0P4ee7FM4UUW3u5fDwseM86IT5FZ8+DtI6Qep/aNuHua02xiQ7g5U3jQ0+/jeM4upafTy+OVTutYFLziiz2jzBrT4SZj3Gzj8AjjjPgh+eBRMankzqVgH790Gix6P6r5MzO6VTRa0bjx+v0rgoZOx8Y7fH5S76yiiX/IUDBgNU69Wn4exby6YWhy2OWFvk1BCrxYGr8PjSgebDdzp4M5UP8T590HeOBj2rdAnF05W/79xg4rmi6ZGHXkH2PqZYX38VNlGMbiumjSlhP6BeRtYsbOK+y+cwKj8rs20pf6gqj5yuI2cBuoB11gNH9wJg4+Fb/8DbG2ilMJJ6sFZWwGf3Q/ImLQ5hqBeNxaM6L0+iYPg8krrjVHTOTpl3ZQvVQHN5CuUfkBY+8bKJJTQN3hU50qvM0jsMgpg+X/U9P6jb24dlQYzYAzYXeppPeJ0OOyU0InJriIlfHg3pOWpiKCjEsYuUJCVxK7KRvZWN/LUF1v49oRCTh4Tui1BhzQcUG8aAK4UZeFU7YTF/1JVNd/6NdhDVOEWGAnZ1a/D8n+rJnIxaHMM1p4wpYTejOj1hKl4Rga3MA9XYrn4CXAkwbgLIDUX0gtg94pDM8AYklBC3+jxkUE9fndQa4HMQmiqgn7DYdRZ4U92uCDvcPXxMT9r8fejjVK3fArbv1APGWeyUcJYHnWZZX5mMuVVDTz80Ua8PsmPu7vWZP0BSAnqSZNZqEonv3wYhp7QUnrabgDjAQEf/FpVLE29Rr0JxaDk0tV0kFQaLOnRe9tYNx4t9HGL9BkRvc0ROqLfvQK+eQ4mXAzJxt9I/riw1o2VSTihzxR1rYXe9J1n/qS9/dCWqdfAkT9SNo7Z2jhan/6TP0J6Pky6TH2eWQy+JqjbF9VlC7KSafT4eW7Bdr4zpZhB/VK7d6GGAyqKN8kogq3zoa4Cjv5Z+PPc6apDaHOt+kMonqq2R/tg9Dbj+NeJ3OV82qJCL1XtNUabYgu+dWg6iRnRZw1SJdjeoMV8/D548yYl8Cf8X8v2vMNh3/ro3/QPMYkn9NSpGnqTYd9Skem4CyJfYMLFcPLv1Mem0Edjs2z9DLZ9Dkf9GJzGGrOBhOeOsKd1BrOfvd0muPHEKNaarD/QugLJTGCXHAGlR3V8buFkFc3P/HHQG1CUD8ZlL0LlNnJtNZa0bjy+lojerrtXxjd+I6LvNxSVYwr63V30L9X08NR7Wv995I1Tdfd7Vrcc9/bPD9mQu0uCCb1f1dEnZ7VsHH02XPoa2Ls4gcFMTEYToX78B0gbCJMvb9mWGaKEMRye8B0qC41V6y+ZPii6qdZtI3rzAXf0zZHPPe5WuOS/KsEcizcgn0eVcgJJwmvNiD4oGevU/ejjGzOizzGW8TNLLKvLVbXZkOPh8O+0PiffSMjuXg6VO1S12TfPx2TGe0+SUL1uGoyI3pPcvfa/rXC4lUhHirylhK+fgSHHQXbQCvLbvlAWyCn3KG/eJNNYRz3SA2TNm/DKVXDDopArPo0tyOSecw/nzHH5nbqdkPh90FDZOmKZ8F1IyQ1fnRRMVrH6B6pkVdijezCu+I/6Y3Om4PZ4Ldmm2Ov3ByVj9YSpuMYsr+xnCr3h03/+gLJXz7yvffFG1iBVybd7ucq/eY1grLGyxce3IAkV0Xsa63ELD/aUGH3DI3VzBFj/Lrx5Izx9plqQA5TYvf5DI5q/ovXxKf1UFr+jB4jfr+rufc2w9fOQh9hsgtnTSkiPZqp1YxUgW0f0aQNg0qXhq5PCYbOrXER3rRu/Dz69FwYeDoOOJEl4LBnRBxphYfajt3Ykp+kAswdW1qCWhKy3GZa/DCPPUG+qbRFC+fSr58CqV9XvK3RuwZ5eJKGE3t+gFs52pMZo0kJmUcfCJaWyZ9ILVNuE586HvWuV6Nftg4teUCWLwQgRuU/+mjegYq36eOfi6O8jHPX71f8huvF1i8yiyA9Gvw9e/b7qGBrM0hdUCeyxPwdHEi7hsaRH7/PLNhG9Fvp4xexZhDNJBXUHt8GG95SdOeG74U/MOxzq96lzTjFyejGaQ9JTJJTQm6V9jtSs2Fwvs0j9AMP5bxvmwq6lcPxtcOGzULEG/nGEmkR06atQNKWD64Z5gPj9anGP3MOg9GjVOqGnCG5/EAsyCyNbXStfheUvqTeeFa+obevnwls/UV0xR54FdhcuvDR7fbEZVwzx+Pw4RNAKU9q6iV9M68bmhKwSFdEvfUG9iQ9pv6xoALPk+KTfQD+jECJGc0h6ioTy6IUh9CI4GRsNmUXgqYOGg+2jXjOazyqB8RepZO85j8JHv1czSYunhb9uRhFs/jj0vrVvwd7VcO4/VVT/2f2qlMvZA72tA+0PYmR1ZRap3ILfr2Ymt8XvU+Wm/Uep7+dr31elap/dDwNHw8Uvq/McblxY07rx+lusG6fw4bXgGDWdQ5hCb3eq/NrqOVBeDzN+GHqSoMmYc5RNWTpT/U4LW2yEftVratbtsb9sqdKLEQkV0dubjMk6STH06CH0D3HjPNW98eibWyp6Dj8fbvwaSqZ3fN3MIqjd3b5PvrdZCWG/YTD2PCicAtIXdn3WqIl1RJ9RpPIK9WHmCKx8FfZvgONugdkvwsAx6n5zD4NLX2+plrK7lNBb0Lrx+FqSsQDSfP3XxB3m2r/YHMqnb6oGv1eVWXeE3QmDj1Y2rN2hZr1H69H7vCovt+F9NUM/xiSo0Meg6gaCKmTa2CwNB2Hur9T+8RF+KUJet1DV4gb3yfd54JUrYc9KOPEOldw0++/0lH0T3NAsFmR2MEfA74NP/6SaQ406W/2MLnlVlWi27SbqSMIpPXi81vO/vUHJWACf19PB0RorI6Qh9HbDugHVrG/AqK5dKDMGbU1W/lfNSD/ul6HfhqMkoYTeYa4XG0vrBlr/EJvr4IULYf8mOPsh1Tqhq7Rtr+DzwqvXKtvm1D/C6Flqe/pA9TDZuUR97vfDs+fAV490737aUn9ARTPuLjZCC0dmm/sKZuWryqY5NugXOTVXRfepua2Pdbhw4rFk90pVXhkUxbd9K9PEDYFkrM0J2YPVx5Gi+VBkFEQX0fu8KggaeDiMOKP71+mAhBJ6Z3PQMoKxILW/+iUwhd7brFanKlsE5z0OQztI2HREYHKRIYjv3apKtU76Dcy4rvWxhZNbKm/WvgmbPlSdMGNBwwFV+9vVUspwhJsdu2s5vH2z+kUedXbk69jdOKVFPfqgpmYAfh3Rxy1Cmh69Q+XULnimfTl0Z8gwqs26O2mqh6N5SDChd3lraBBJXZ8FGw6brfU6qp/8ATZ+AGf+FcZ8u/vXDbY49m9SfdynXqPWY21L0RRVDVC7Vy3nB7Frn1x/IHb+PCj7xZHcOqexZzU8M0utDzD7hc79Ijvc2PDj9URYx7MX8PolThFUDeS33hg1ncMWHNELod6ku6MdGQXgqVeTpjpi2b9hydOttx2CaB4SrOomyVtNvS2NmNanZBilkHX7lWUy5tzuPfWDMfvkV+1UU/7tLjjmF6GPNX36j+5W3fTS8qLukxMgVDVRNAjR2q88sEWJvN0Fl89p8UEjYSajfE0dH9cLtE3G+n3WKwHVdA6bDKq6iYbgJUvDzY6t2a0mVnob1debcLF6A3j/DhXNX/Bsj0XzkGARfbKvhgZbWmwvatbSf/GgemqbC4rH4rrbv4RlL8GUq5QfH4r8Caq1wJKnVGXAtGvUfIHG6ujHEOuIHlrPEZh3l/qeXf5myzTzzuAwSsu81hP64F43ENTqVhNXSCkRfuMhbYtS6APVeR349J8/qPI5hVNgzo2wZT688wv46mGYdm3HLdRjQGIJvb+WRnuMEosmpnWz8J+q5HHAyNhdd89KlQw98sbwx7lSVI05wNE/bUkaRVu3u3+TqtPvf1h012mL6VdWrINVr6tf4q5+DSPBLawo9EH96CEooaeJK9QMZ7PqJkpjIyD0YSzV2r1qAZNxFxhNAAerN92Fj8GM6+G0P8UuTxaGiEIvhHhCCLFXCLEyzH4hhHhQCLFRCLFcCDEpaN+pQoh1xr5bYjnwUKT6a2l0pMf2oplFqpbdUw/HhrFXuoP5yzH5CsiI0Jhs6Amqw974i8OXfHaVT/+sLJLpP4juOm3JLFSvqR8bzdyOuL7r17Aba95aMFr2+tskY31a6OOR4IlvUUf0aQONSVNhInqzSdoxP1cVgRe/rBoVHv0zOOXuHhd56FxE/xRwagf7TwOGG/+uBf4BIISwAw8b+0cDs4UQo6MZbCTSZC3NzhgLvVlJMvY8tdBGrBgwWq3VOvPHkY898dfww69UpGt2i4zGp9+3US3/N/Xq8JZRd8ksAqSa5Tf16valk53BoYReWNCjN6tupN0co666iUdaPbCj9ejtDqOhX4i37NoK1bP+8Ata7MucwWpi5Yn/d0hEHjoh9FLKT4EDHRwyC3hGKr4CsoQQ+cA0YKOUcrOUshl4yTi2x0iTdXicMbZuiqaoNWRPuD221516NfxkZUvf+46w2Vrq9dMGKrsnmoj+0z+pqDlUlU+0mG8qjqSOLamOMJKxNr8Hv8Wahnl8flx4A3kEqYU+LvH6/C3VU7YY1KSEalTYWKUmQZrRfC8SC4++EAgOL8uMbeG2h0QIca0QYrEQYnFFRUXXRyElKwu+g/uwE7t+bkek5Kjp+jmDY3tdm717FS82u/qlquxERL/l0/ZLFu7boPq+T7tGtSSONVlGT/4pV3X/+kZEb8U2COZSgtLoPaQ9+vjEYyTV/cIRm6i6bUvzmj3w1Bmq4OKcRyE3ilXgYkAsyitDfZdkB9tDIqV8DHgMYMqUKV0P44TgiO8/1OXT4pLM4sgR/c6v4emzoP9IuPId9VBpqoHXf2BE2z0QzYP6hf7OU51buCQcAaH34vH5SXJGWOv3EOL1qZmxwpkF6F438Yq5gIxfOGJTkZJRqLrZSqnKlp84Wdk2F/87ur+FGBGLeywDioM+LwLKO9iuiZZIffJBtTp2Z8CBzfDibDUP4PnvqAfAOY9CWv+eG9+Yc9Rcge5i+N9uCy4+Elh4xGXM1vBr6yYeMXsWyVjYNqCKEDz1SuQ/v1/1tr/0NUuIPMRG6OcAlxnVNzOAKinlLmARMFwIMVgI4QIuMo7VRIu5wIc/zGSd8m9g/TvKIz/3MdixAB4Yr/4/73G1jq6VMfIRVrRuzIVHhEMJvU1H9HGJOfHNH23FjYmZa9u1VJViH/6dyF1sDyERH2dCiBeB44BcIUQZcCfgBJBSPgK8DZwObATqgSuNfV4hxA3Ae4AdeEJKuaoH7qHvYZZ81uxumZUXzCd/gqQsmH6t6vtTtw/m3q4i+bHnHvLhdhl7i3VjuYjerKN36og+nlHlld7YRfRmdd67t6nZr7GaWBkjIt6llHJ2hP0SCFksLaV8G/Ug0MSSTKOVQFVZe6EvXwrr3objf9XS3G3a91S9fqx6APU0wclYiwl9YGasUy0RqZOx8YnHqLqRsY7oK9aoZQh7OfnaloSaGdtnCLQDblN542mEd29VAj/9+633xYvIQ0Do3cJDk8WE3uf1YhcyKKLXQh+PmDNjYxbRp+epViU2R2wnVsaIhGpq1mcINFEKSsh6m+DlS1U517mPxa5Vc29gJmOt6NGbdfNO7dHHMx5z4lusInqbHQomQPEMNevVYmihj0fc6cqDN4Xe54FXrlLlXWc9oHpqxDOBZKz1PHq8rYU+sEqRJq7w+oxcS6wieoBr5sXuWjFGC328klncYt18eq9aneq0P0ffQtkK2K3r0fvN5Kv26OOaQAuEWEX0cMjaGXQH7dHHK2Ytff0B+PJhtXLT9Gt7e1SxwWHdqhtpRvRGCwSbjujjEo/Pr6puou1cGSdooY9XsoyI/vMHoLkWjr+tt0cUO2x2pLDjEtbz6KW5opRugRDXmM3pRCwjegujhT5eySxSTZMWPAqHn9/1lestjrS7LRrRG8JuWDd2HdHHJV6/H4fwxVc1WhRooY9XzBJLXxMc2+Ot/g850uHGTbPlIvpAj/xAMtaH7O6i0JpeI9DKQgu9xtKYC5CMn225yRkxwe6yZkRvWjWG0Dvw4bNYK2VNZMw6+r5i3fSNTEQikj8BjvoxzIjxClFWweHGJTxUWUzo8bVOxjqFV1VwWKfBpqYTBBZ57yPJ2L5xl4mIwwUn3dXbo+gxhN2FG6/1rBuzvNLuwi/sOPBZrpWyJjLmUoLCWOQm0dHWjcaSCGeSJevoMdeItTvxCwcO/Nq6iUO8RkQv+khEr4VeY0mEw41LWM+jD0T0Noch9F48Pi308YbHp1YKszn6hkevhV5jTexuki1YRy+CInpps+tkbJzi9asJU8KYnJfoaKHXWBOHC7cFI3ohzYjeiRSOgEeviS/MpmZCl1dqNL2I3Y0br+XaFAeqbuwOpM2JAx9eHdHHHeZSgtq60Wh6E4fLkmvGBloe2F34bQ6cwofPb60xaiJjLg5u0xG9RtOLOJLUhCmL2SIBobc5IWDd6Ig+3jCXEoxp90oLo4VeY03sbrXwiDfMAui9REtE70TaHNh1MjYu8Xp9OHWvG42ml3G4cOKxXLQcaEtsc4DdiVMnY+MSny/ozawPoIVeY03sbpwWnDBlC8yM1eWV8Yw0m9PpCVMaTS/icOGU1hP6wNKBNifYnDj1hKm4xO9tKZPtC2ih11gTh2qB0GQxjz5g3dgdYHNgx49XV93EHYGVwrRHr9H0IkazKb+3uZcH0oLfL1sWGrE5we7EIXQdfTwig1pZ9AW00GusiTk13dvYu+MIIrCgNKhI0ObAiRevtm7iDh3RazRWwG4Iva+pd8cRhOqPYgi94dGrZKy2buKNloheC71G03s4jD7hFrJuzI6HfmxgsyHsThz4dTI2HtERvUZjAcyI3muhiN6nInpp+LpK6L26vDIOCSwJqT16jaYXMTx64bNORO811hn1C0Mc7Lp7ZdwSqKPXEX0AIcSpQoh1QoiNQohbQuzPFkK8JoRYLoRYKIQYG7RvqxBihRBiqRBicSwHr0lgAkJvnYjeXGfUH4joHTh198r4pI/NjI343iKEsAMPAycBZcAiIcQcKeXqoMNuA5ZKKc8RQow0jj8xaP/xUsp9MRy3JtExrBu7vxm/X2KziV4eEPiMdUalUOIg7E7surwyPvFrj74t04CNUsrNUspm4CVgVptjRgPzAKSUa4FSIcTAmI5U07cwkrEu4cVjkaoWc7EK06O32V0qotfWTfyhhb4dhcCOoM/LjG3BLAPOBRBCTAMGAUXGPgnMFUIsEUJcG+6LCCGuFUIsFkIsrqio6Oz4NYmKEdFbaYFwr9+PQ3iDkrEO3esmXvHp8sq2hHpnbvub/QcgWwixFPgR8A1gmGAcJaWcBJwGXC+EOCbUF5FSPialnCKlnNK/f/9ODV6TwDhMobfOcoLmqkTSEAeb3an70ccpLe2m+0bVTWfusgwoDvq8CCgPPkBKWQ1cCSCEEMAW4x9SynLj/71CiNdQVtCnUY9ck9g4giJ6i1gjZjLWLMkTDj1hKl5p1ZyuD9CZiH4RMFwIMVgI4QIuAuYEHyCEyDL2AVwDfCqlrBZCpAoh0o1jUoGTgZWxG74mYTF63bgtZd0YEb1dR/TxjuhjHn3EiF5K6RVC3AC8B9iBJ6SUq4QQ1xn7HwFGAc8IIXzAauBq4/SBwGsqyMcBvCClfDf2t6FJOMyIXljLunHgbYno7U6cwofXZ60Om5rICF1e2R4p5dvA2222PRL08ZfA8BDnbQbGRzlGTV8kKBnbZBWh95vWjfHyaoiEz/R7NXGDTZoRfd/w6PXMWI01McsrLeTRe30Sp/Ah7S0zYyGoE6Imbmi1yHsfQAu9xpo4kgBwW6jqxkzGClMczD4pPh3Rxxu2PubRa6HXWBO7OWHKWslYB94WcTCtGwt12NR0DiF1UzONpvcRAr/NZcmqG2EKvV1H9PGKXQYtINMH0EKvsSzS7lITpizj0ZvJ2NYRvd9nDY9eSskNL3zNFxt1W6mOkLLNkpB9AC30Gutid+HCY5k2wF6j141wtPbopUWE/mC9h7eW72Lu6j29PRRLY/YsAnREr9H0NtKRhAuvZcorPX4/TrxB1o3xv98aQr+3Rq2vu+NAfS+PxNoEehYhwGbv7eEcErTQayyLzeHGJTwcrLNGstNrLCUYEHqLRfQVNap3/46DWug7wmP0LAosINMH0EKvsSw2p5tUu88ywuUxlhJsF9FbJBm7t9oQ+gMNSKnbMoTD22YBmb6AFnqNdbG7yHL62HGgobdHAqiFRxz4sFk0ot9rRPQNHh/7LfIWZEW8xs/R30cSsaCFXmNlHG7SHH7LRPSmQLQkY42VpizSAsH06EH79B2h3sy8SG3daDQWwK6EvuxgA34LLO5hCoTdmMwVaIFgGaFvwm4subjjoDXegqyI+WYmdUSv0VgAh5tkm2qBUFHb+4uEhyuvtErVTUV1E6PzMwAd0XeEx+hZpD16jcYKONwkCRUtW0G4PD4fDuFvN2HKStbNoH4p9Et1URbB7qqsb+YHzy1h497aQzQ669DShVQLvUbT+9hduE2ht4BPH+hS2aZ7JRZJxlbUNDEgPYminJSICey/zF3POyt38+rXZYdodNbBXFdAWzcajRVwuHFIVT0SSbh2VjZw1VOL2FXVc960bLugtIUi+romL3XNPvqnuynOTu4wol+5s4rnF2wD4LM+2C7BLJOVOqLXaCyA3YXN18yAdHdE6+bu/63mw7V7eXvF7h4bjt9r5AnsbT363hd6s7RyQLqb4pwUdlY24AuRwJZScuecVWSnuLh65mBW7Kyisr5vlWKa1VN9pc8NaKHXWBlHEnibKM5J6dC6+Wrz/oDAf96DEapsu/ycuXas7H3rZm+1Kq0ckOGmODsFj0+yp7qx3XGvfbOTJdsO8stTR3L64XlICV9s2t/j47NKvyIIWuS9j/S5AS30GivjcIOvmeLs5LDWjc8vuevN1RRmJXP+5CK+2ry/x0RF+ozI1/TmzbVjLRXRJ1Gckwy0T2Cv2VXNXW+uZnxxFudPLmJcURZpbkcr+6a2yUtVfWwfXLVNXo7+40c8Pn9zTK/bXbw+iUt4dTJWo7EEdlcgot9V1RBSwF9evIM1u6q55bSRfGvUQOqbfSzdUdkjw2nn0QeamllJ6N0UZacArWvpN+6t5ZLHF5DstPO32ROx2QROu40ZQ3ICb0E+v+Tif37Ftc8ujunYXlywnd3VjSwvq4rpdbuLWUevI3qNxgo43OD3UJyVhF/CrsrWVsTGvbX88d21TC3N5sxx+RwxpB82AZ9taIlQ99c2UdUQowjVtG7aePSB3ua9yN6aRlx2G1kpTgqykhCiJaLfvr+e7z7+FULA89+bTnFOSuC8mcNy2ba/nh0H6vn3oh0sL6ti3Z6amI2ryevj8c9UJL/dAiWyYFo3Xu3RazSWwJiBWpKpWskG+/Q7Kxu49F8LcNgEfz5/PEIIMlOcHF6UFYhQG5p9zHr4c259dXlMhhOwbsxX/kDVjS/sOT6/5O0Vu2jyhj8mFlTUNNE/3Y0QArfDTl5GEjsO1iOl5KcvL6XR4+e5a6YztH9aq/NmDs8F4H8rdvHn99ZiE1BZ76G6MTYPx1e/3sme6iaG5KZGrO0/VLRbKawPoIVeY10cbgCKMgyhNyLCfbVNXPr4AmqbvDx91TRKc1MDp8wc1o9vdlRS0+jh0U83UXawgfV7YjQpyN8moje8etFBRP/Cwu388Pmvef2bnbEZQxgqaprITXcHPi/OTqHsQANvLC1n8baD3Hb6SEbmZbQ7b2j/NAZmuLn3vXVUNXi44fhhQGwmqPn8kkc/2cThhZmcN7mIfbXN1DX1/tuPTsZqNFbCEPq8FIHdJgIR6o9fWkp5VQNPXDGVMQWZrU45alguPr/k1a938o+PN6nzDtTHpFdOuDp6WxiPvqrew31z1wEwf0PP1qvvrW5iQJDQF+Uks3lfLfe8s4ZxRZl8Z3JxyPOEEMwc1h+vX3LJjEGcPCYPiI3Qv7NyF1v31/PD44YG7KIyC/TgCbSy0EKv0VgAuxIuh/SQn5nEjgMNvLNyN59t3Mdtp49iamlOu1MmD8omyWnjt2+tRgj43tFDaIpVrxxT6Nt49LYwEf3989ZT1eBhfHEWX2za36ON2fbWNLYS+uLsFPbVNrOnuolfnz0Gm9HsLBTnTSpk8qBsfnrSYRSbidwoW0M3e/3c9/56hvZP5ZQxeZQYQm8Fn97bdqWwPoAWeo11MSJ6VWKZwvo9NfzurdWMys/gu9MHhTzF7bAzbXA/vH7JdccOZcYQ9TCIRYQaKKM0PXpDKOx42y30sXFvDc9+uY0Lp5Zw+RGDOFDXzOpd1VGPIRTNXj8H6z0MSE8KbDMj6PMmFTGpJLvD848clst/f3AkWSkuMlOcZCQ5ohbkxz/bzOaKOm4/czQ2m6A4O3TJZ2/gCawUpssrNZrex2wH7G2kOCeZtbtrKK9q5DezxgTa8Ybi/MlFTBucw/ePGRqzSLK2ycveSqMaxYwEhcCPHTt+vEHRutfn5845q0h22rn55MM4aphKePbUZC7zbWVARktEP3NYLmeOy+eW00Z2+Xol/TqeoBaJnZUNPDRvI6eMGcjxIwYAkJPqIsVl79R1l+2o7NHktddcKczhjnxwgqCFXmNdzD9Eb1PAUjhnYmFIyyaYs8cX8PL3jyDZZacwOxkhohf6+99fT32jYf8EleX5hR0nvkC7Aa/Pz09eXsbnG/dz2xmjyE1zMzAjieED0lpNTLr/g/X889PYTCAKzIoNsm7yMpP428WT6J/edTErzk7p1Pdrweb9rNvdvhTzt2+uRiL5vzNHB7YJISjJSYkY0c/fUMGshz/n2S+3dXncncXbdqWwPoAWeo11CbJujhzWj7GFGV2OUM1Sw2iEfu3uap78YisnHGZYIEEC4bc5cODF4/Pj80t+9p9lvLmsnFtPG8nsaSWB42YOz2XhlgM0enws2XaQ+z/YwH+W7Oj2mIKpCJoVGwtKclTFTkc5he3767n0iYVc+NiXrcT7reXlvLtqNz86YXhg4pZJUXbHXTWbvX5+PWcV0LPJ64DQO7TQazS9j70lop88KIe3fnQ0AzO6LmbFhnBF4rMN+9pFnFJK7nh9FRlJDs4bP9AYV4tASOHAYUT0f3hnDa8vLefnp4zg+8cObXWdmcNyafL6WbT1AHe8sRJQbxmxWMQ7MCs2IzZWRHFOCs0+f+C6ofjNW6tw2AR+v+T7zy6hodnHvDV7+Mm/lzKxJItrjh7c7pySnJQO7/mpL7awqaKOUfkZLNiyv8fsG69PJ2NDIoQ4VQixTgixUQhxS4j92UKI14QQy4UQC4UQYzt7rkYTlqCIPho6Y0Ws3FnFpU8s4KLHvuJg0MLaz361jYVbD/DLU0eS5jQEytY2ovexelc1T3y+lYumFnO9UYsezPQh/XDYBLe/vpJV5dXMGJJDoyc21UB7a5oQAvqluqK+FrQkcsN9zz5cu4cP1uzlphOH88BFE1mzu5orn1rID577mpF5GTx15TTcDnuI6yaHXbh8T3UjD3ywgRNGDuDmkw6j0ePn622VMbmftnh8ZkQfm+9XPBBR6IUQduBh4DRgNDBbCDG6zWG3AUullOOAy4AHunCuRhOaQDI2OjEsyUlhd3UjjZ7QEaKUkrveXEVGkpOK2iZufOkboxa/jDvnrOK4Ef25YEpx+xYItET0v3lzNakuO784NbS1lOZ2MLEki2376zlyaD++f4yK+GNRhVJR00i/VBcOm4D598HeNVFdr6MEdqPHx11vrmZo/1SuPGowx48cwI9PPIyvNh9g6IA0nr16GpnJoSPlcNf1+yW/nrMKj09yx5mjmT4kB7tN9Fjy2musFKYj+tZMAzZKKTdLKZuBl4BZbY4ZDcwDkFKuBUqFEAM7ea5GE5pAMrZ9u92uUNJPlfbtrAxt38xZVs6irQe59bSR/G7WWOZv2MdVTy3iZ/9ZxozB/XjkksmqDt1cGzao66G0OXAKH2t31/CTkw4jp4Oo+rgRA3DaBXedPYaSfrGrKy872ED/9CRoqoZ5d8E3z0V1vcKs5Fa9ckyklPzx3bVs21/PXWePxeVQ8vGjE4bx8MWTePF708lKCX//5ptC8HWllPzmrdW8s3I3PznpMEpzU0lPcjKhOKtV8vq/S8p49JNNUd2Xid/b/ueY6HRG6AuB4KxRmbEtmGXAuQBCiGnAIKCok+dinHetEGKxEGJxRUVF50avSWzMiD4G1g2EFtW6Ji+/f1vNHr1gSjEXTC3m4uklfLK+gsmDsvnXFVNIcho2RNsJUyihd+Bj2IA0LpkRurbf5HtHD+GTnx/P8IHpATHdvj+6iUmLtx5g/oZ9HDeiP9QYi64c3BrVNV0OG/kZSe0E+d6563jy861ccWRpoEcOgM0mOGNcfociDwRNxmq57l/mruepL7Zy9czBXHfskMD2o4blsryskqp6D2UH67nttRX8M0ZtjgNCryP6VoQqWG6bTfkDkC2EWAr8CPgG8HbyXLVRyseklFOklFP69+/fiWFpEh6X0cOmKbpeNSUhIklQ4nXPO2vYU93EnWe1zB799Vlj+OuF43nyymmkuIKiPn+bhUcAYXdix8cdZ47Gae/4z8nlsFGQpd4ukpzRVwN5fX5uf30lBZlJqkdNzS61ozL60sTinNZ5jfs/2MDDH21i9rQS7jize+5rsstObpo7UHnzwoLt/O2jjVw0tZjbzxiFEC1yMXNYLn4JX27ez93/W0OT1x+zXjktzen6jtB35t2lDAhulFEElAcfIKWsBq4EEOqntcX4lxLpXI0mLCm5apWpKIWrf7obt8PWLkL903vreO6r7VwzczCTB7XMHnU5bJwzsaj9hXxtFgcHUpLcnDA8m5TDuh6cFHeirhxUF063w9aujcFTX2xl7e4aHrlkMqluB9TsUTsObgMpQYSfVBaJkpwUPt2g3qzfWl7OA/M2cMGUIu7+9tgO2ylEojgnmR0H69lX28Q976xh5rBc7j7n8FYiDzCxJItUl52HPtzAqvJqxhZmsHJnNTsO1odsztYVpF9H9KFYBAwXQgwWQriAi4A5wQcIIbKMfQDXAJ8a4h/xXI0mLDYbZA2KWuiFEO0i1L9+sIF/fLyJi6eX8KszRnXuQv42Tc1QEX2KvXslkiU5kauBaho9fOu+T7jq6UWt1oDdXdXIX99fzwkjB3DKGKPs04zom6qh4WC3xmRSnJPCnuomDtQ187u31jCmIIN7zh0XlchDyz3/6d21KrEbZpaz025j+pB+rCqvpiQnhTvOHAOo+v1okdqjb4+U0gvcALwHrAFellKuEkJcJ4S4zjhsFLBKCLEWVWFzU0fnxv42NAlLdmnUnjOYAqMsg/8uKeNBI0L93ayx7aLJsITw6LE5Wx4A3RhTR9VAoCyTnZUNfLyuggfmbQCgsr6ZK59ahE9Kfn3WmJbx1wQtjH5wS7fGFDw2gFtfXc7u6kbuOrvjthOdpThbLVz+8uIyrpo5uF1//GCOMfIAd5w5muED1HGxSF43NhnJ/T4U0XfqkSalfBt4u822R4I+/hIY3tlzNZpOk10K27+M2ooozk5m4ZYDVNY3c/fba5g8KLvrEWoIjx67s9tLCZYEte4dNqC94K3dXc1TX2xl9rQSmr1+HvpwA8MGpPHYp5vYVFHL45dNCVTvACqiF3aQPvVwLJzcrXFBS4XMe6v2cM7EQqZEaDvRWUpyUpASBma4+dEJISUjwOzpJYwpzGRqaQ5SStLdjqjLUdfurubL9bvBhfboNRrLkF3aYkWkdF9sinNSqG3y8qvXVlJZ38xvZ03veoTq84CwKUvJxOZoqa/vxphAJYnbCn3wjNxfnDKCJKedVeVV3PjiN7jsNh69dDLHtM0L1O6B/HFQ/o3y6SPRXA+ulJC7zAXGU112bu1GY7RwjMhLB+BXZ4wmzd2x/Lgd9kBfo1D2W1fx+yW3v7aSrCQBflrlWhId3QJBY22yS9X/MbIi/rdiF5cfWcrogm4k9Pye9lGgzRGVdQOh7YiXF+8IzMjNTnWR7LLzj0smM2VQNv+4ZBLHjxzQ/oI1u6DfMEjtH9nu2rUM/lgK694Nubt/mptJJVn86ozRDOhG24lwjC/O4otbTuDs8QVdPrczOY2O+M+SHSzedpDvzzTqQ/pQRK+FXmNtso3a9Ch9ejN67p/u5icnHda9i/i87X1du7PFu+8iuWkukp32duL1weo9/Oq1lcwYkqNm5BoMzk3llR8cyYmjBra/mJTKo08bGDmvISW8exv4mmDD3JCHCCF49YdHcfH0kpD7o8EsMe0qqn1yx83WpJT8/D/LuOONla166uyvbeKed9YyrTSHk0b0Uxv7kEevhV5jbbI6KfQ7l8Cfh8Ge1SF3D85NZXxRJnd/eywZSd38A/d72ldq2BzQweLgHWG27g0W+k/XV/DD579mTEEGj102pfM5hMZKNYM4PV99zzr6fq19C7Z9Bs5U2PZ5t8beGxTnpNDs7bjZ2pxl5fxnSRnPfLmNhz7cCEBVg4crnlxEfbOP350zNmgBmb4j9H3HpNLEJ+40w4rowHM2I9S6Clj/DgxsP6EnyWnnjRtmRjcWn6d9FBiFdQOta+lX7qzi2mcXM3RAGk9fNa1rDySzhj49T0X0q14z3kDa/Il7m2Du/0H/UTD2XPjobqjbB6m57S5pNYKtrrzM9nZSVb2H3761mvFFmQztn8Z9768nLzOJFxduZ+3uah69dDKHDUyHrbqOXqOxHpGsiDVvwo6vVMXJti96bhyhPPoorBto8Z19fsltr60gPcnJc1dPi9hOoB1mDX16vvp+SR9Ul7U/buFjKt9xyu9g8DFq2/Yvuz3+TlO3H7zRtbKItFrYH95dw8F6D78/93DuOe9wJg/K5hevLGd5WRUPzZ7ECSMNy0tPmNJoLEhHQu9thg/uhP4jYeIlsH1Bt6tgIhIqQrZ1v7wSoCQnmfpmHw/O28DysipuP2MU/dK60VferKFPzwuf11j5X/jgLhh+Mgz7FhRMVDOPe/LhCOpt48EJ8Pn9UV0m0B8ohNB/uWk/Ly7cwdUzBzOmIBO3w86jl07m2MP689DsiZw6Nq/lYF/fs2600GusT3YpVJWFjpyXPAkHNsNJv1ERanMN7FnRM+MIW3UThdAbdfAPfriBI4f261Y1CtAS0ZvJWGgt9F89Aq9cDUVT4dzH1DaHW30e7NN/eDe8f0f3xhCOT/6oSmT3rIzqMi6HjYLMZMraCP3KnVV8/9nFDM5N5cffaqnNz01z8/RV0zj98PzWF/K3b2WR6Gih11gf04qoamNFbF+ghGnwMSpKHXSk2t5TEWooj97uiNq6AXDYBL/pyizdttTuAXeGymlkFKoHkCn0Xz0C7/4SRp4Bl74KyS19fRh0JOxeAY1VsGcVzL8XVvy32/fTjn0bYclT6uMYzHAuzkluFdGv31PDZU8sJD3JybNXt2lCFw5f+1YWiY4Weo31CRWhrp8Lz8xSScRZD6tZsxkFkD24tdAvfgJWx6i9kt8bIqKP0AKh/gC8fj1Uh+7lV5SdQkaSgx8eNyzk7NhOU7NL2TYANjtklagEds0e+PC36kF4wTPgbFPaOOhIkH7YsRA++LX6uHpn1Iu9BPjwN+prjp4FB7aqxHkUBFcp7a5q5LuPL8BhEzx/zfR2a9SGRXv0Go0FaVtiufoNeGk29D8MrnpPiZrJoKOU0Pv9sHct/O9m+PLh2IzD52n/um93dlxe+em9sPQ55Y+HIMlp56vbTmxlOXQLs4bexCyx/OhuJdqn/kE9ANpSNE1F//P/omrq88YBEiq3RzcegLLF6md15I/U12mqirrZWklOCntrmmho9vHbt1ZT3eDh+WumU5qb2vmLBDx6bd1oNNYho0BFzpXblFC8eRPkj4fL34K0Nm0ABh0BDQdg3zp4//9UhBrlrNoAIT16e3jr5sAWVeUCsDV8vXqKy9F9y8akZrequDHJLoW9q+GbZ2HatdBvaOjzXClQMElV3mQUwsm/VdujtVmkhLm3q9LYI66HHGOx8Ch/FubEtxcWbud/K3Zxw/HDGD4wvWsX0RG9RmNBAlbEVhUhN1TCmfdDUog2BqZP/9HvVYSaWaz86+boux6GnBnbkXUz7zcqahx+Mmw33jJ6AnNWbHpQZUl2qZpAlZQJx/684/PN79kJt6v6elAPqWhY9Zp6eBz/K3Cnh7bfuoGZ0/jDO2so7ZfC944ZEuGMEGiPXqOxKNmlykde8Kgqo8wfF+a4wSqyXTMHMkvg+NvU9hgkAmmubf+6b3avbOs9ly2BVa/CkTfA2PNUsnNvD3Xobjio2hkER/Q5hgAed2vr5Gsopl4NJ94J4y6EtAHgTInu++VpUJU7Aw+HSZepbabQR/kAMYXe45P8+uwxLcs8dgWzSkpH9BqNxcguVUlCu0tFnuEQoiVC/dad0H+E+jhaod8yH3YtbZlkZGJGhcE+fXMdvPMLZVscdVPPVwMFauiDPPrDToXzn4ApV0c+P6sEjv6penMSwpi3EIUgf/E3qNoBp97TkhdwpULqgMg/h6oyuG+MSraHICfVRW6ai9PG5nHciBCN3TpDIKLvOx5937lTTXxjRoRH/6S1RRGK6dcpv3nseS3Jv2iEy++Dd29RbwhHXN96nylkfiNRW7cPnv+Oeiic9y9lW7jT1blbP4Pp31fH79+k/g/nnXeFWlPogyJ6h0vdf3fo7GIvix5XbwvBX6eqDD67D0adDYOPbn18zuDI1533GzWjd80cOOzkdruFELz1o6PJTo0iGu+DHr0Wek18MOpMFSUecUPkY4unqX+ghMidEV1E//XTarLPd55qX55oioXPA7V74dlz1JvHhc/DyNNbjht0JGz8QFk8zXXw5OnKXrnqne6PyyR4VmwsyB4Mmz/ueLGX8m9URROoEtJp31MT1549V20zk7qtrlva8VtN2RJY/m8VaXfQbC1Un5suoT16jcai5AyB0//cXmgjEbAitkY+dtsX7csKGyrhw9+pss3R325/ji1I6F+5Cur3wWVvtBZ5gNKj1L596+HzB1QUvn9D1+4lHIFZsbES+lLw1KsHVyikhHdvVYu3Dz8F3v6ZapT2r5NVLuLyN1vewNpet6osdI2+lPDebcruOvpn6qFRvSs299MW7dFrNAlIdmnkJODetfDUmfDY8S2tjhur4T9XqIj11HtCR7dmXf3yl2DnYjjlHiiZ0f64QUcZx70MXzykeszUVUBTbXfvqoWa3eDODLtaVJeJVAq5+g1VUXPC7XDhczDyTPjiQXVPV8+Foimhz8sejKrR3xHimq+rxnQn3A6HnaK29VQLZV+zaoAXbUlrHKGFXpP45AxWNfgdlTfO/RW40lSU99QZKhn45GmwdT7M+puq2w+FmdD7+A/qmPGzw4xhiJrQNP8vqrY/ltVAwbNiY0FHpZCeRlVRM2CMqqhxuJSldeb9cPX7kNvBxK9w192xCN76qbrmxEvVpC1X+qFtZZHgaKHXJD7ZpSqKqwndhoAN7yv//LhfwhX/U+WFL3xHCdLFL6tyznCY1k1TtYrmbWH+pIQwonqpErpm9U60k7mqdsLGeVAwIbrrBJNVAoj2b0FSqk6hldvg1N+3JKLtTphyJWTkt7tUK0K9Kax9G54+S9X7X/isuqbdASXTW0f0G96HpS9EfWtA6FYWCY5OxmoSn2xTYLZCZlHrfT6P8oZzhsLU76kI9cr/wcd/hBk/CF+vb2JGhqPOVj58R4y7QCVqj/5pi08c7cSk9+8w3hB+Fd11gnG4VdVScOTtbYY5P1IW1bTvw5Djun7dtIHgSG657qrX4ZUrIX+CeqAGz3IedBTMu0tVMQkb/PcaZQ1NuLjbtxWgD0b0Wug1iU/wZJ3SNqtMffGQSpBe9KISefP4c/7RuWv3HwG5I1Sb5EiMOE39M0nKii6i3/YlrHwFjvlFSw/6WBFcCtlcD//+Lmz6EI6/HY75WfeuaSbGD2yBphp4++dK5K94S9XZB2P+nLZ9AVs+VUslgqpYantsV/H3PaHX1o0m8cksUsm34AhVShW1z7tLJRODBbgr5I+HGxa22BJdoTN15aCi6bYzb/0+NSkroxBm/rjrXzsS2YNaHkKf3adE/uy/qXYK0SQxzQqoz/4KdXvh9HtDC3f+BGWhLXlSdSA1G9fFIqfh63vWjRZ6TeJjd0JWcYtw+byqMdrHv4fxF6tkYm9UYGQPjmzdNNfB32coH7uxWm2TUnWl3L1cvUlEG+GGHFup6hFUsV699Yw9HyZdGpvr7t+oZs+OuxCKJoc+zuFSi6Js+lD12D/jr2p7tFYXqDJX8+2tj6CFXtM3CK6l/+IBNQnq6J/Bt//ee6/xOYPVJLCOlj6c/xc4sElZGM+crWrb37xRbZ9wSfdnv0bCzGu8eo36/1u/js11cwarvjzCpvrrdIRp3xx7CxROUh9Hm7wuWwzr34VRZ0V3nThDe/SavkH2YDWtvmY3fPoXZdec+H+9Pya/V4l9KOtn/yYVTY+7CMacAy9fBvePA28DHPNzlYDtqTcRU+h3LVNfK6s4Ntc1m60ddSNkFnZ87KTLAAFTr1EReFJmdBG936/yAml56p76EDqi1/QNskuhfr+axen3hJ6if6jpqF5dSnjnl2B3K3tmxKlwySuqcdmZf1UTi3rSbjIfPGl5cNSPY3fdIcfBWQ/AzJ9GPjY9T+UEAknywdFF9Eufh/Kv1ffT3cUe9nGOjug1fQNTuNa8qTpKmpFlb9Kqrvz41vvWzIGN78Mpv2/pSjn4GLhp2aEZW3K2yl+MnqU88lhhd8LkK7p3bs5gKF8a+bjtX6lka7D/33BQLZVYPEOVufYxOhXRCyFOFUKsE0JsFELcEmJ/phDiTSHEMiHEKiHElUH7tgohVgghlgohFsdy8BpNpzGjZ7OXihVIL1ARe1s7YvUcVTeeN06tDtUbCKFKTEec2jtfPxQ5QyLnNGp2w3PnwRMnw9IX1baqnfDUWUrsT/9Tn2p9YBIxohdC2IGHgZOAMmCREGKOlHJ10GHXA6ullGcJIfoD64QQz0spm439x0sp98V68BpNp+k3TIn98beHXpmqN7DZWpcxAix5Ct76CRROgYv/3efqvTskUk4DVNTua1bfv9evg90r1AIwTbXw3ZfDt7JIcDoT0U8DNkopNxvC/RIwq80xEkgXauHLNOAA0MFjV6M5xLhSle0x7ju9PZLWZA+GA1vVx2veVGWfQ0+Ey16HlJzeHJn1iNRsrWwxLHtRtZi4/E1VEvrVw2oOxdXvwbBvHbqxWozOePSFQHC7uTJgeptj/gbMAcqBdOBCKaXZQUoCc4UQEnhUSvlYqC8ihLgWuBagpKSk0zeg0cQ1Zo92TwO8exsMHAuzX9SRfCjMSqADW6Dtei1+v5pAlpYHR9+sErjn/lOtYzDoKLVEYh+mM0IfytBqM02PU4ClwAmoH8H7Qoj5Uspq4CgpZbkQYoCxfa2U8tN2F1QPgMcApkyZ0vb6Gk1ikjMYmmtUz5qq7XDO/7TIhyM9X+U0QkX0Xz4EO5fAtx9pqaix2VRZqqZT1k0ZEFxEW4SK3IO5EnhVKjYCW4CRAFLKcuP/vcBrKCtIo9FAS5S68DG1sEnbXjyaFmy29msL+P3w3q/Ug3LkmWq2raYdnRH6RcBwIcRgIYQLuAhl0wSzHTgRQAgxEBgBbBZCpAoh0o3tqcDJwMpYDV6jiXtM39mRZI3afquTE9Q2wtsM/7kcvvybqk664JnwbaL7OBGtGymlVwhxA/AeYAeekFKuEkJcZ+x/BPgt8JQQYgXK6vmllHKfEGII8JrK0eIAXpBSvttD96LRxB/ZpZCcA0f+qKVxlyY82YNhy3w1oezLh9R8g5PvVgnYPlg22Vk6NWFKSvk28HabbY8EfVyOitbbnrcZ6Jv1TBpNZ3C44ea16n9NZHIGg6cOdi2FT+9Vds2RnVgwvo+j33M0mt5Gi3znMWc0v3qtatV88u96dzxxghZ6jUYTP5jJ633rjVYW3VgHoA+ie91oNJr4IatEtThOL4CZP+nt0cQNWug1Gk384HCp3vhF08CV0tujiRu00Gs0mvjiqJt6ewRxh/boNRqNJsHRQq/RaDQJjhZ6jUajSXC00Gs0Gk2Co4Veo9FoEhwt9BqNRpPgaKHXaDSaBEcLvUaj0SQ4QkrrLeYkhKgAtnXz9FwgERci1/cVfyTqvSXqfUF839sgKWX/UDssKfTRIIRYLKWc0tvjiDX6vuKPRL23RL0vSNx709aNRqPRJDha6DUajSbBSUShf6y3B9BD6PuKPxL13hL1viBB7y3hPHqNRqPRtCYRI3qNRqPRBKGFXqPRaBKchBF6IcSpQoh1QoiNQohbens80SCEKBZCfCSEWCOEWCWEuMnYniOEeF8IscH4P7u3x9odhBB2IcQ3Qoi3jM/j/r6EEFlCiFeEEGuNn9sRCXJfPzF+B1cKIV4UQiTF630JIZ4QQuwVQqwM2hb2XoQQtxp6sk4IcUrvjDo2JITQCyHswMPAacBoYLYQYnTvjioqvMDNUspRwAzgeuN+bgHmSSmHA/OMz+ORm4A1QZ8nwn09ALwrpRwJjEfdX1zflxCiELgRmCKlHAvYgYuI3/t6Cji1zbaQ92L8vV0EjDHO+buhM3FJQgg9MA3YKKXcLKVsBl4CZvXymLqNlHKXlPJr4+MalGgUou7paeOwp4Fv98oAo0AIUQScATwetDmu70sIkQEcA/wLQErZLKWsJM7vy8ABJAshHEAKUE6c3peU8lPgQJvN4e5lFvCSlLJJSrkF2IjSmbgkUYS+ENgR9HmZsS3uEUKUAhOBBcBAKeUuUA8DYEAvDq273A/8AvAHbYv3+xoCVABPGpbU40KIVOL8vqSUO4F7ge3ALqBKSjmXOL+vNoS7l4TSlEQRehFiW9zXjQoh0oD/Aj+WUlb39niiRQhxJrBXSrmkt8cSYxzAJOAfUsqJQB3xY2eExfCrZwGDgQIgVQhxSe+O6pCRUJqSKEJfBhQHfV6EesWMW4QQTpTIPy+lfNXYvEcIkW/szwf29tb4uslRwNlCiK0oe+0EIcRzxP99lQFlUsoFxuevoIQ/3u/rW8AWKWWFlNIDvAocSfzfVzDh7iWhNCVRhH4RMFwIMVgI4UIlUeb08pi6jRBCoPzeNVLK+4J2zQEuNz6+HHjjUI8tGqSUt0opi6SUpaif0YdSykuI//vaDewQQowwNp0IrCbO7wtl2cwQQqQYv5MnovJF8X5fwYS7lznARUIItxBiMDAcWNgL44sNUsqE+AecDqwHNgG/6u3xRHkvM1GvicuBpca/04F+qMqADcb/Ob091iju8TjgLePjuL8vYAKw2PiZvQ5kJ8h93QWsBVYCzwLueL0v4EVUrsGDitiv7uhegF8ZerIOOK23xx/NP90CQaPRaBKcRLFuNBqNRhMGLfQajUaT4Gih12g0mgRHC71Go9EkOFroNRqNJsHRQq/RaDQJjhZ6jUajSXD+H4s/jhitQLDbAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "\n",
+    "fn0 = r'C:\\Users\\zqiu\\dgpro'\n",
+    "plt.figure()\n",
+    "with open(os.path.join(fn0, 'cal_ac1.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "with open(os.path.join(fn0, 'cal_ac2.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "plt.figure()\n",
+    "with open(os.path.join(fn0, 'cal_hfflat1.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "with open(os.path.join(fn0, 'cal_hfflat2.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "with open(os.path.join(fn0, 'cal_hfflat2_900.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "plt.vlines([27, 50, 77], ymin=0.975, ymax=1.17, colors='r')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('cal_hfflat*.hex')\n",
+    "plt.legend(['811 ch1','811 ch2', '912 ch2'], loc=\"upper left\")\n",
+    "plt.figure()\n",
+    "with open(os.path.join(fn0, 'cal_lfflat1.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "with open(os.path.join(fn0, 'cal_lfflat2.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "plt.vlines([14, 28, 42, 56, 70], ymin=0.99, ymax=1.01, colors='r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyvisa\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "rm = pyvisa.ResourceManager()\n",
+    "gen = rm.open_resource('TCPIP0::192.168.138.178::INSTR')\n",
+    "print(gen.query(\"*IDN?\"))\n",
+    "osc = rm.open_resource('TCPIP0::192.168.138.244::INSTR')\n",
+    "print(osc.query(\"*IDN?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 334,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6324556\n",
+      "5.0\n",
+      "1.2649111\n"
+     ]
+    }
+   ],
+   "source": [
+    "2*np.sqrt(2) * np.sqrt(0.05) * np.sqrt(np.power(10.0, 0 / 10.0)) * 10000000.0 #0dBm = 0.632\n",
+    "2*np.sqrt(2) * np.sqrt(0.05) * np.sqrt(np.power(10.0, 0 / 10.0)) * 10000000.0 /0.632/1e6 #0dBm = 0.632, unit in 1e-7 V\n",
+    "# HF RNGS\n",
+    "#rng3, 50, 77\n",
+    "print(6324556.0*1e-7)\n",
+    "# rng 27, 49\n",
+    "print(50000000.0*1e-7)\n",
+    "# rng 27, 49\n",
+    "print(12649111.0*1e-7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyvisa\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "rm = pyvisa.ResourceManager()\n",
+    "gen = rm.open_resource('TCPIP0::192.168.138.178::INSTR')\n",
+    "print(gen.query(\"*IDN?\"))\n",
+    "osc = rm.open_resource('TCPIP0::192.168.138.244::INSTR')\n",
+    "print(osc.query(\"*IDN?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 200000.0 0.1114\n",
+      "1 300000.0 0.1106\n",
+      "2 400000.0 0.1105\n",
+      "3 500000.0 0.1106\n",
+      "4 600000.0 0.1106\n",
+      "5 700000.0 0.1104\n",
+      "6 800000.0 0.1105\n",
+      "7 900000.0 0.1105\n",
+      "8 1000000.0 0.1106\n",
+      "9 2000000.0 0.1104\n",
+      "10 4000000.0 0.11\n",
+      "11 6000000.0 0.1104\n",
+      "12 8000000.0 0.1105\n",
+      "13 10000000.0 0.1105\n",
+      "14 20000000.0 0.11\n",
+      "15 30000000.0 0.107\n",
+      "16 40000000.0 0.1057\n",
+      "17 50000000.0 0.104\n",
+      "18 60000000.0 0.1022\n",
+      "19 70000000.0 0.1006\n",
+      "20 80000000.0 0.09907\n",
+      "21 90000000.0 0.09774\n",
+      "22 100000000.0 0.09655\n",
+      "23 120000000.0 0.09463\n",
+      "24 150000000.0 0.09234\n",
+      "25 175000000.0 0.09112\n",
+      "26 200000000.0 0.08986\n"
+     ]
+    }
+   ],
+   "source": [
+    "nn=2\n",
+    "volt=0.6324556\n",
+    "\n",
+    "fx = np.array(list(reversed([200000000000000,\n",
+    "175000000000000,\n",
+    "150000000000000,\n",
+    "120000000000000,\n",
+    "100000000000000,\n",
+    "90000000000000,\n",
+    "80000000000000,\n",
+    "70000000000000,\n",
+    "60000000000000,\n",
+    "50000000000000,\n",
+    "40000000000000,\n",
+    "30000000000000,\n",
+    "20000000000000,\n",
+    "10000000000000,\n",
+    "8000000000000,\n",
+    "6000000000000,\n",
+    "4000000000000,\n",
+    "2000000000000,\n",
+    "1000000000000,\n",
+    "900000000000,\n",
+    "800000000000,\n",
+    "700000000000,\n",
+    "600000000000,\n",
+    "500000000000,\n",
+    "400000000000,\n",
+    "300000000000,\n",
+    "200000000000])))/1e6\n",
+    "\n",
+    "fs = fx\n",
+    "amp = np.zeros_like(fs)\n",
+    "st = \":SOURce{}:VOLT {:.7e}\".format(nn,volt)\n",
+    "# print(st)\n",
+    "gen.write(st)\n",
+    "time.sleep(3)\n",
+    "for n,f in enumerate(fs):\n",
+    "    st = \":SOURce{}:FREQuency {:.4e}\".format(nn,f)\n",
+    "    # print(st)\n",
+    "    gen.write(st)\n",
+    "    time.sleep(0.7)\n",
+    "    amp[n] = float(osc.query(\":MEAS:ITEM? VRMS,CHAN1\"))\n",
+    "    print(n,f,amp[n])\n",
+    "\n",
+    "np.savez('ch{}-rng3-over-original.npz'.format(nn),n=n,fs=fs,amp=amp,ref=(volt*1/(2*2*np.sqrt(2))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 344,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 200000.0 0.92\n",
+      "1 300000.0 0.9139\n",
+      "2 400000.0 0.9136\n",
+      "3 500000.0 0.913\n",
+      "4 600000.0 0.9126\n",
+      "5 700000.0 0.9129\n",
+      "6 800000.0 0.9127\n",
+      "7 900000.0 0.9134\n",
+      "8 1000000.0 0.9131\n",
+      "9 2000000.0 0.9114\n",
+      "10 4000000.0 0.9083\n",
+      "11 6000000.0 0.9106\n",
+      "12 8000000.0 0.9113\n",
+      "13 10000000.0 0.9106\n",
+      "14 20000000.0 0.9072\n",
+      "15 30000000.0 0.8709\n",
+      "16 40000000.0 0.8542\n",
+      "17 50000000.0 0.8322\n",
+      "18 60000000.0 0.8107\n",
+      "19 70000000.0 0.7894\n",
+      "20 80000000.0 0.7696\n",
+      "21 90000000.0 0.7552\n",
+      "22 100000000.0 0.7426\n"
+     ]
+    }
+   ],
+   "source": [
+    "nn=2\n",
+    "volt=5\n",
+    "\n",
+    "fx = np.array(list(reversed([\n",
+    "100000000000000,\n",
+    "90000000000000,\n",
+    "80000000000000,\n",
+    "70000000000000,\n",
+    "60000000000000,\n",
+    "50000000000000,\n",
+    "40000000000000,\n",
+    "30000000000000,\n",
+    "20000000000000,\n",
+    "10000000000000,\n",
+    "8000000000000,\n",
+    "6000000000000,\n",
+    "4000000000000,\n",
+    "2000000000000,\n",
+    "1000000000000,\n",
+    "900000000000,\n",
+    "800000000000,\n",
+    "700000000000,\n",
+    "600000000000,\n",
+    "500000000000,\n",
+    "400000000000,\n",
+    "300000000000,\n",
+    "200000000000])))/1e6\n",
+    "\n",
+    "fs = fx\n",
+    "amp = np.zeros_like(fs)\n",
+    "st = \":SOURce{}:FREQuency {:.4e}\".format(nn,fs[0])\n",
+    "# print(st)\n",
+    "gen.write(st)\n",
+    "time.sleep(0.7)\n",
+    "st = \":SOURce{}:VOLT {:.7e}\".format(nn,volt)\n",
+    "# print(st)\n",
+    "gen.write(st)\n",
+    "time.sleep(3)\n",
+    "for n,f in enumerate(fs):\n",
+    "    st = \":SOURce{}:FREQuency {:.4e}\".format(nn,f)\n",
+    "    # print(st)\n",
+    "    gen.write(st)\n",
+    "    time.sleep(0.7)\n",
+    "    amp[n] = float(osc.query(\":MEAS:ITEM? VRMS,CHAN1\"))\n",
+    "    print(n,f,amp[n])\n",
+    "\n",
+    "np.savez('ch{}-rng2-over-original.npz'.format(nn),n=n,fs=fs,amp=amp,ref=(volt*1/(2*2*np.sqrt(2))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 200000.0 0.2256\n",
+      "1 300000.0 0.223\n",
+      "2 400000.0 0.2226\n",
+      "3 500000.0 0.2225\n",
+      "4 600000.0 0.2222\n",
+      "5 700000.0 0.222\n",
+      "6 800000.0 0.2222\n",
+      "7 900000.0 0.222\n",
+      "8 1000000.0 0.222\n",
+      "9 2000000.0 0.2217\n",
+      "10 4000000.0 0.2207\n",
+      "11 6000000.0 0.2215\n",
+      "12 8000000.0 0.2216\n",
+      "13 10000000.0 0.2216\n",
+      "14 20000000.0 0.2207\n",
+      "15 30000000.0 0.2173\n",
+      "16 40000000.0 0.2149\n",
+      "17 50000000.0 0.2118\n",
+      "18 60000000.0 0.2088\n",
+      "19 70000000.0 0.206\n",
+      "20 80000000.0 0.2036\n",
+      "21 90000000.0 0.2013\n",
+      "22 100000000.0 0.1995\n",
+      "23 120000000.0 0.1967\n",
+      "24 150000000.0 0.1941\n",
+      "25 175000000.0 0.1936\n",
+      "26 200000000.0 0.1933\n"
+     ]
+    }
+   ],
+   "source": [
+    "nn=2\n",
+    "volt=1.2649111\n",
+    "\n",
+    "fx = np.array(list(reversed([200000000000000,\n",
+    "175000000000000,\n",
+    "150000000000000,\n",
+    "120000000000000,\n",
+    "100000000000000,\n",
+    "90000000000000,\n",
+    "80000000000000,\n",
+    "70000000000000,\n",
+    "60000000000000,\n",
+    "50000000000000,\n",
+    "40000000000000,\n",
+    "30000000000000,\n",
+    "20000000000000,\n",
+    "10000000000000,\n",
+    "8000000000000,\n",
+    "6000000000000,\n",
+    "4000000000000,\n",
+    "2000000000000,\n",
+    "1000000000000,\n",
+    "900000000000,\n",
+    "800000000000,\n",
+    "700000000000,\n",
+    "600000000000,\n",
+    "500000000000,\n",
+    "400000000000,\n",
+    "300000000000,\n",
+    "200000000000])))/1e6\n",
+    "\n",
+    "fs = fx\n",
+    "amp = np.zeros_like(fs)\n",
+    "st = \":SOURce{}:VOLT {:.7e}\".format(nn,volt)\n",
+    "# print(st)\n",
+    "gen.write(st)\n",
+    "time.sleep(3)\n",
+    "for n,f in enumerate(fs):\n",
+    "    st = \":SOURce{}:FREQuency {:.4e}\".format(nn,f)\n",
+    "    # print(st)\n",
+    "    gen.write(st)\n",
+    "    time.sleep(0.7)\n",
+    "    amp[n] = float(osc.query(\":MEAS:ITEM? VRMS,CHAN1\"))\n",
+    "    print(n,f,amp[n])\n",
+    "\n",
+    "np.savez('ch{}-rng1-over-original.npz'.format(nn),n=n,fs=fs,amp=amp,ref=(volt*1/(2*2*np.sqrt(2))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1f5045d7a60>]"
+      ]
+     },
+     "execution_count": 346,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(fs,amp*1/(volt*1/(2*2*np.sqrt(2))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False False False False False False False False False False False False\n",
+      " False False False  True  True  True  True  True  True  True  True  True\n",
+      "  True  True  True]\n",
+      "27 27\n",
+      "28 51\n",
+      "51 78\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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zKw0HQEpnrgLqLQAzqw0OgJTafQ6AmdUYB0BK7V29LJ4/i7mzUp06YWZ2znMApNTR1etLQJhZTXEApNTR7QAws9riAEjh2MkBuo73+yQwM6spDoAURg4B9Q5gM6slDoAU9nYdB3wZaDOrLQ6AFDq6+pDgYt8JzMxqiAMghY7uXi48fzbNjfXVLsXMrGQcACnkrwLqv/7NrLY4AFLwVUDNrBY5AIro6e3nyIkB7wA2s5rjACiiPbkInAPAzGqNA6AI3wfYzGqVA6CIjq5e6gTLW7wT2MxqiwOgiPbuPpa1zKGpwV+VmdWWVL9qkjZKekXSHkl3jNHeIukBSS9I+rWktQVtX5G0S9JuSV8tmL5Q0mOSXkueW0qyRCXW2dPH8oWzq12GmVnJFQ0ASfXA94BNwBrgBklrRs32NWBnRLwPuBH4TtJ3LfBnwAbg/cAfS1qd9LkDeDwiVgOPJ+/POZ09J1i2wMM/ZlZ70mwBbAD2RMTeiOgH7ge2jJpnDfkfcSLiZWClpCXAZcAzEdEXEYPAk8C1SZ8twD3J63uAz05nQcrh5MAQuWOnWNbiLQAzqz1pAuAiYF/B+85kWqHngc8BSNoAXAwsA3YBH5O0SNIcYDOwPOmzJCIOACTPi8f6xyXdKmmHpB25XC7dUpXIm4dPALCsXENAv/hF/mFmVgVpAkBjTItR7+8CWiTtBL4M/BYYjIiXgG8AjwEPkw+KwckUGBF3R8T6iFjf1tY2ma7T1tmTBICPADKzGpTmBrednPmrHfJ/2e8vnCEijgI3A0gS0J48iIgfAj9M2v4m+TyAg5KWRsQBSUuBQ9NYjrJ483QAeAjIzGpPmi2AZ4HVklZJagKuBx4snEHSgqQN4AvAU0koIGlx8ryC/DDRfcl8DwI3Ja9vAn42nQUph86ePhrrxeL5zdUuxcys5IpuAUTEoKTbgUeAemBbROyWdFvSvpX8zt57JQ0BvwNuKfiIn0haBAwAX4qInmT6XcCPJd0CvAFcV6qFKpXOnhNcuGA29XVjjYKZmc1saYaAiIjtwPZR07YWvH4aWD26X9L20XGmdwPXpK60Cjp7+jz8Y2Y1y6e3TqCz5wQXLXAAmFltcgCM4+TAEIeOnfIRQGZWsxwA49h/2EcAmVltcwCMw+cAmFmtcwCMo9PnAJhZjXMAjKOzp4+GOrHkPJ8DYGa1yQEwjjcP+xwAM6ttDoBxdPac8PCPmdU0B8A4fBKYmdU6B8AYTg0OcfCozwEws9rmABjD/sMnAR8BZGa1zQEwhs6ePgBfBsLMapoDYAynzwFY6CEgM6tdDoAxnD4HYP6sapdiZlY2DoAxdPacYOmCZhrq/fWYWe3yL9wYOrr7WOHhHzOrcQ6AUSKC9txxLmmdV+1SzMzKygEwSndvP0dPDrKqdW61SzEzKysHwCjtXb0AXNLmADCz2uYAGGVv7jiAh4DMrOY5AEbZ29VLU30dF/ksYDOrcakCQNJGSa9I2iPpjjHaWyQ9IOkFSb+WtLag7S8k7Za0S9J9kpqT6V+X9Kakncljc+kWa+rac71cvGiOLwNtZjWvaABIqge+B2wC1gA3SFozaravATsj4n3AjcB3kr4XAX8OrI+ItUA9cH1Bv29HxLrksX3aS1MCe7t6vQPYzDIhzRbABmBPROyNiH7gfmDLqHnWAI8DRMTLwEpJS5K2BmC2pAZgDrC/JJWXwdBw8Hp3L5e0efzfzGpfmgC4CNhX8L4zmVboeeBzAJI2ABcDyyLiTeBvgTeAA8CRiHi0oN/tybDRNkktU1yGkuns6WNgKLjEWwBmlgFpAmCswfAY9f4uoEXSTuDLwG+BweRHfQuwCrgQmCvpT5M+3wcuBdaRD4dvjfmPS7dK2iFpRy6XS1Hu1O1NDgFd5UNAzSwD0gRAJ7C84P0yRg3jRMTRiLg5ItaR3wfQBrQDnwTaIyIXEQPAT4Grkj4HI2IoIoaBH5AfanqHiLg7ItZHxPq2trbJLd0k7c0l5wB4C8DMMiBNADwLrJa0SlIT+Z24DxbOIGlB0gbwBeCpiDhKfujnSklzJAm4Bngp6bO04COuBXZNb1Gmr73rOOc1N7BwblPxmc3MZriGYjNExKCk24FHyB/Fsy0idku6LWnfClwG3CtpCPgdcEvS9itJ/wj8BhgkPzR0d/LR35S0jvxwUgfwxRIu15TszeV3AOezysysthUNAIDkEM3to6ZtLXj9NLB6nL53AneOMf3zk6q0Atq7evnQJYuqXYaZWUX4TOBEX/8gB46c9DkAZpYZDoDEmYvA+RwAM8sGB0BiJAC8BWBmWeEASIwcAuoAMLOscAAk2rt6ufD8ZmY31Ve7FDOzinAAJN54u48Vi3wfYDPLDgdAInfsFEvOa652GWZmFZOJABgaDk4ODI3bHhHkjp2ibd6sClZlZlZdmQiAOx/cxUe+8cS47b39Q5wYGKJtvgPAzLIjEwHQWF9H/+DwuO25Y6cAHABmlimZCYCBodFXsD7DAWBmWZSRABADQ94CMDMrlJEAqGNwOBgeHnsrIHfsJIB3AptZpmQmAAD6x9kKyB0/RX2daJnj+wCYWXZkIgCakgAYbxgod+wUi+Y2UVfn+wCYWXZkIgCak8s7HO4bGLM9d+yUx//NLHMyEQALk6Gdj37z52PuB8gddwCYWfZkIgDOm33mxmePvXTwHe1dx/q9A9jMMicTATCr4cwVPl/sPHJW2/Bw0OUtADPLoIwEwJnFfPmtY2e1HT4xwOBwOADMLHOyEQCNZxbz1YNnB4BPAjOzrMpEAAwWXAbijbf7OHbyzNFApwPA+wDMLGNSBYCkjZJekbRH0h1jtLdIekDSC5J+LWltQdtfSNotaZek+yQ1J9MXSnpM0mvJc0vpFuts71p89o3eX3zzzH6A3PHkLGBvAZhZxhQNAEn1wPeATcAa4AZJa0bN9jVgZ0S8D7gR+E7S9yLgz4H1EbEWqAeuT/rcATweEauBx5P3ZdHcWM9P//1V/M9brwTghYIdwR4CMrOsSrMFsAHYExF7I6IfuB/YMmqeNeR/xImIl4GVkpYkbQ3AbEkNwBxgfzJ9C3BP8voe4LNTXYg0rljRwh9esojlC2fz/L7Dp6fnjp2iubGOebMaxu9sZlaD0gTARcC+gvedybRCzwOfA5C0AbgYWBYRbwJ/C7wBHACORMSjSZ8lEXEAIHlePNWFmIwrVrTwmzd6iMjvFxg5C1jyZSDMLFvSBMBYv4yjT6e9C2iRtBP4MvBbYDAZ198CrAIuBOZK+tPJFCjpVkk7JO3I5XKT6TqmD17cwsGjp+jsOQEkZwF7B7CZZVCaAOgElhe8X8aZYRwAIuJoRNwcEevI7wNoA9qBTwLtEZGLiAHgp8BVSbeDkpYCJM+HxvrHI+LuiFgfEevb2trSL9k4rliR39f8mzd6AF8HyMyyK00APAuslrRKUhP5nbgPFs4gaUHSBvAF4KmIOEp+6OdKSXOUH2O5Bngpme9B4Kbk9U3Az6a3KOm854L5zG2q57nXzwRAq7cAzCyDiu75jIhBSbcDj5A/imdbROyWdFvSvhW4DLhX0hDwO+CWpO1Xkv4R+A0wSH5o6O7ko+8CfizpFvJBcV1Jl2wcDfV1rFuxgOde72FgaJievgFvAZhZJqU69CUitgPbR03bWvD6aWD1OH3vBO4cY3o3+S2CivvgxQv5uyde4/XuPsCHgJpZNmXiTODRPnhxC8MB/ye5Mqh3AptZFmUyAD6wYgESPLzrLcBbAGaWTZkMgPOaG3n3kvnsTE4IcwCYWRZlMgAArrj4zKWHfBSQmWVRZgNgfRIA5zU30NxYX2RuM7Pak9kA+GASAB7+MbOsymwArFg4h9Z5TR7+MbPMyuwlMCXxV59Zw/zmzH4FZpZxmf71++wHRl/U1MwsOzI7BGRmlnUOADOzjHIAmJlllAPAzCyjHABmZhnlADAzyygHgJlZRjkAzMwyShFR7RpSk5QDXp9i91agq4TllIrrmhzXNTmua3LO1bpgerVdHBFtoyfOqACYDkk7ImJ9tesYzXVNjuuaHNc1OedqXVCe2jwEZGaWUQ4AM7OMylIA3F3tAsbhuibHdU2O65qcc7UuKENtmdkHYGZmZ8vSFoCZmRVwAJiZZdSMDwBJGyW9ImmPpDvGaJek7ybtL0i6Im3fMtf1b5N6XpD0S0nvL2jrkPSipJ2SdlS4rqslHUn+7Z2S/jpt3wrU9h8K6tolaUjSwqStLN+ZpG2SDknaNU57tdavYnVVa/0qVldV1q8UdVV83Uo+e7mkn0t6SdJuSV8ZY57yrWMRMWMfQD3we+ASoAl4Hlgzap7NwEOAgCuBX6XtW+a6rgJaktebRupK3ncArVX6vq4G/mkqfctd26j5/wR4ogLf2ceAK4Bd47RXfP1KWVfF16+UdVVr/ZqwrmqsW8lnLwWuSF7PB16t5G/YTN8C2ADsiYi9EdEP3A9sGTXPFuDeyHsGWCBpacq+ZasrIn4ZET3J22eAZSX6t6dVV5n6luPzbwDuK+G/P6aIeAp4e4JZqrF+Fa2rSutXmu9rPFX9vkapyLoFEBEHIuI3yetjwEvA6HvVlm0dm+kBcBGwr+B9J+/88sabJ03fctZV6BbyCT8igEclPSfp1hLVNJm6PiTpeUkPSXrvJPuWuzYkzQE2Aj8pmFyu76yYaqxfk1Wp9SutaqxfqVRz3ZK0EvgA8KtRTWVbx2b6TeE1xrTRx7WON0+avlOV+rMlfZz8/6AfKZj84YjYL2kx8Jikl5O/YCpR12/IXzfkuKTNwP8GVqfsW+7aRvwJ8C8RUfgXXbm+s2KqsX6lVuH1K41qrV9pVWXdkjSPfOh8NSKOjm4eo0tJ1rGZvgXQCSwveL8M2J9ynjR9y1kXkt4H/AOwJSK6R6ZHxP7k+RDwAPlNvYrUFRFHI+J48no70CipNU3fctdW4HpGbaKX8TsrphrrVypVWL+KquL6lVbF1y1JjeR//P97RPx0jFnKt46VY8dGpR7kt2D2Aqs4sxPkvaPm+Qxn70D5ddq+Za5rBbAHuGrU9LnA/ILXvwQ2VrCuCzhzguAG4I3kuyvb9zWZ/x7A+eTHcudW4jtLPnMl4+/UrPj6lbKuiq9fKeuqyvpVrK4qrlsC7gX+6wTzlG0dm9FDQBExKOl24BHye8S3RcRuSbcl7VuB7eT3ou8B+oCbJ+pbwbr+GlgE/L0kgMHIX+lvCfBAMq0B+B8R8XAF6/o3wL+TNAicAK6P/NpWtu9rErUBXAs8GhG9Bd3L9p1Juo/8kSutkjqBO4HGgpoqvn6lrKvi61fKuqqyfqWoCyq8biU+DHweeFHSzmTa18gHeNnXMV8Kwswso2b6PgAzM5siB4CZWUY5AMzMMsoBYGaWUQ4AM7NzVLGL2I2ad0VyYbnfJheN21ysjwPAzOzc9SPyl6ZI46+AH0fEB8if0Pb3xTo4AMzMzlExxkXsJF0q6eHk2kT/V9J7RmYHzkten0+Ks4Jn9IlgZmYZdDdwW0S8JukPyf+l/wng6+QvWvdl8mctf7LYBzkAzMxmiOSicVcB/ys5OxlgVvJ8A/CjiPiWpA8B/03S2ogYHu/zHABmZjNHHXA4ItaN0XYLyf6CiHhaUjPQChya6MPMzGwGiPylotslXQenbxc5crvPN4BrkumXAc1AbqLP87WAzMzOUYUXsQMOkr+I3RPA98nfTrIRuD8i/rOkNcAPgHnkdwj/x4h4dMLPdwCYmWWTh4DMzDLKAWBmllEOADOzjHIAmJlllAPAzCyjHABmZhnlADAzy6j/D4AgvvhWHw51AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fn0 = r'C:\\Users\\xxx\\dgpro'\n",
+    "with open(os.path.join(fn0, 'cal_hfflat1.hex'), 'rb') as fh:\n",
+    "    aoriginal1=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "\n",
+    "section_start = 1\n",
+    "section_length = len(fx)\n",
+    "aoriginal1_section_1 = aoriginal1[section_start:(section_length+section_start)]\n",
+    "plt.figure()\n",
+    "plt.plot(fx, aoriginal1_section_1)\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "respori = np.load('ch1-rng1-over-original.npz')\n",
+    "\n",
+    "print(fx>25e6)\n",
+    "nchg = 12\n",
+    "sel =  section_length-nchg\n",
+    "selm = section_length\n",
+    "plt.figure()\n",
+    "print(selm,len(aoriginal1_section_1))\n",
+    "newcal = aoriginal1_section_1/(respori[\"amp\"]/respori[\"amp\"][0])\n",
+    "plt.plot(fx, newcal)\n",
+    "plt.plot(fx[sel:selm], newcal[sel:selm])\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "aoriginal1_mod = aoriginal1.copy()\n",
+    "aoriginal1_mod[sel+1:selm+1]  = newcal[sel:selm]\n",
+    "plt.plot(fx, aoriginal1_mod[section_start:(section_length+section_start)])\n",
+    "\n",
+    "section_start += section_length\n",
+    "print(section_start,(section_length+section_start-4))\n",
+    "aoriginal1_section_2 = aoriginal1[section_start:(section_length+section_start-4)]\n",
+    "plt.figure()\n",
+    "plt.plot( aoriginal1[section_start:(section_length+section_start-4)])\n",
+    "plt.figure()\n",
+    "plt.plot(fx[:-4], aoriginal1_section_2)\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "respori = np.load('ch1-rng2-over-original.npz')\n",
+    "newcal = aoriginal1_section_2/(respori[\"amp\"]/respori[\"amp\"][0])\n",
+    "sel =  section_length-nchg\n",
+    "selm = section_length-4\n",
+    "plt.plot(fx[:-4], newcal)\n",
+    "plt.plot(fx[sel:selm], newcal[sel:selm])\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "aoriginal1_mod[sel+section_start:selm+section_start]  = newcal[sel:selm]\n",
+    "plt.plot(fx[:-4], aoriginal1_mod[28:51])\n",
+    "\n",
+    "section_start += section_length -4\n",
+    "print(section_start,(section_length+section_start))\n",
+    "aoriginal1_section_3 = aoriginal1[section_start:(section_length+section_start)]\n",
+    "plt.figure()\n",
+    "plt.plot( aoriginal1[section_start:(section_length+section_start)])\n",
+    "plt.figure()\n",
+    "plt.plot(fx[:], aoriginal1_section_3)\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "respori = np.load('ch1-rng3-over-original.npz')\n",
+    "newcal = aoriginal1_section_3/(respori[\"amp\"]/respori[\"amp\"][0])[:]\n",
+    "sel =  section_length-nchg\n",
+    "selm = section_length\n",
+    "plt.plot(fx[:], newcal)\n",
+    "plt.plot(fx[sel:selm], newcal[sel:selm])\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "aoriginal1_mod[sel+section_start:selm+section_start]  = newcal[sel:selm]\n",
+    "plt.plot(fx[:], aoriginal1_mod[51:78])\n",
+    "\n",
+    "\n",
+    "with open(os.path.join(fn0, 'cal_hfflat1_mod.hex'), 'wb') as fh:\n",
+    "    aoriginal1_mod.tofile(fh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 356,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False False False False False False False False False False False False\n",
+      " False False False  True  True  True  True  True  True  True  True  True\n",
+      "  True  True  True]\n",
+      "27 27\n",
+      "28 51\n",
+      "51 78\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fn0 = r'C:\\Users\\xxx\\dgpro'\n",
+    "with open(os.path.join(fn0, 'cal_hfflat2.hex'), 'rb') as fh:\n",
+    "    aoriginal1=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "\n",
+    "section_start = 1\n",
+    "section_length = len(fx)\n",
+    "aoriginal1_section_1 = aoriginal1[section_start:(section_length+section_start)]\n",
+    "plt.figure()\n",
+    "plt.plot(fx, aoriginal1_section_1)\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "respori = np.load('ch2-rng1-over-original.npz')\n",
+    "\n",
+    "print(fx>25e6)\n",
+    "nchg = 12\n",
+    "sel =  section_length-nchg\n",
+    "selm = section_length\n",
+    "plt.figure()\n",
+    "print(selm,len(aoriginal1_section_1))\n",
+    "newcal = aoriginal1_section_1/(respori[\"amp\"]/respori[\"amp\"][0])\n",
+    "plt.plot(fx, newcal)\n",
+    "plt.plot(fx[sel:selm], newcal[sel:selm])\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "aoriginal1_mod = aoriginal1.copy()\n",
+    "aoriginal1_mod[sel+1:selm+1]  = newcal[sel:selm]\n",
+    "plt.plot(fx, aoriginal1_mod[section_start:(section_length+section_start)])\n",
+    "\n",
+    "section_start += section_length\n",
+    "print(section_start,(section_length+section_start-4))\n",
+    "aoriginal1_section_2 = aoriginal1[section_start:(section_length+section_start-4)]\n",
+    "plt.figure()\n",
+    "plt.plot( aoriginal1[section_start:(section_length+section_start-4)])\n",
+    "plt.figure()\n",
+    "plt.plot(fx[:-4], aoriginal1_section_2)\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "respori = np.load('ch2-rng2-over-original.npz')\n",
+    "newcal = aoriginal1_section_2/(respori[\"amp\"]/respori[\"amp\"][0])\n",
+    "sel =  section_length-nchg\n",
+    "selm = section_length-4\n",
+    "plt.plot(fx[:-4], newcal)\n",
+    "plt.plot(fx[sel:selm], newcal[sel:selm])\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "aoriginal1_mod[sel+section_start:selm+section_start]  = newcal[sel:selm]\n",
+    "plt.plot(fx[:-4], aoriginal1_mod[28:51])\n",
+    "\n",
+    "section_start += section_length -4\n",
+    "print(section_start,(section_length+section_start))\n",
+    "aoriginal1_section_3 = aoriginal1[section_start:(section_length+section_start)]\n",
+    "plt.figure()\n",
+    "plt.plot( aoriginal1[section_start:(section_length+section_start)])\n",
+    "plt.figure()\n",
+    "plt.plot(fx[:], aoriginal1_section_3)\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "respori = np.load('ch2-rng3-over-original.npz')\n",
+    "newcal = aoriginal1_section_3/(respori[\"amp\"]/respori[\"amp\"][0])[:]\n",
+    "sel =  section_length-nchg\n",
+    "selm = section_length\n",
+    "plt.plot(fx[:], newcal)\n",
+    "plt.plot(fx[sel:selm], newcal[sel:selm])\n",
+    "plt.vlines([25e6], 0.99, 1.01, 'r')\n",
+    "aoriginal1_mod[sel+section_start:selm+section_start]  = newcal[sel:selm]\n",
+    "plt.plot(fx[:], aoriginal1_mod[51:78])\n",
+    "\n",
+    "with open(os.path.join(fn0, 'cal_hfflat2_mod.hex'), 'wb') as fh:\n",
+    "    aoriginal1_mod.tofile(fh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 371,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f5059c72b0>"
+      ]
+     },
+     "execution_count": 371,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure()\n",
+    "with open(os.path.join(fn0, 'cal_hfflat1.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "with open(os.path.join(fn0, 'cal_hfflat2.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "with open(os.path.join(fn0, 'cal_hfflat2_900.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "with open(os.path.join(fn0, 'cal_hfflat1_mod.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "with open(os.path.join(fn0, 'cal_hfflat2_mod.hex'), 'rb') as fh:\n",
+    "    a=np.fromfile(fh, dtype='<f8', sep='')\n",
+    "plt.plot(a)\n",
+    "plt.vlines([27, 50, 77], ymin=0.975, ymax=1.05, colors='r')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('cal_hfflat*.hex')\n",
+    "plt.legend(['811 ch1','811 ch2', '912 ch2', 'mod ch1', 'mod ch2'], loc=\"upper left\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "adb -s 192.168.138.178:55555 push .\\cal_hfflat1_mod.hex /rigol/data/cal_hfflat1.hex\n",
+    "\n",
+    "adb -s 192.168.138.178:55555 push .\\cal_hfflat2_mod.hex /rigol/data/cal_hfflat2.hex"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dg800pro_cal.ipynb b/dg800pro_cal.ipynb
new file mode 100644
index 0000000..d0c663f
--- /dev/null
+++ b/dg800pro_cal.ipynb
@@ -0,0 +1,737 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "This notebook calibrates the high-frequency output flatness of a signal generator. It works by:\n",
+    "1.  Connecting to the signal generator and an oscilloscope via VISA (TCPIP).\n",
+    "2.  Measuring the generator's actual RMS output voltage across specified frequency points for different amplitude ranges using the oscilloscope.\n",
+    "3.  Reading the generator's existing high-frequency flatness calibration data (`cal_hfflat*.hex` files).\n",
+    "4.  Calculating correction factors based on the difference between the measured response and the ideal flat response.\n",
+    "5.  Applying these corrections to the existing calibration data, focusing on higher frequency points within each range.\n",
+    "6.  Saving the updated calibration data into new `.hex` files.\n",
+    "7.  Providing commands to upload the new calibration files to the generator using ADB (Android Debug Bridge).\n",
+    "\n",
+    "## Hardware Setup\n",
+    "*   Connect the Signal Generator and Oscilloscope to the network.\n",
+    "*   Connect the Signal Generator's output channel (CH1 or CH2) to the Oscilloscope's input channel (specified in `OSC_MEAS_CHAN`).\n",
+    "*   Use a high-quality 50 Ohm coaxial cable.\n",
+    "*   **Important:** Ensure the Oscilloscope's input channel impedance is set to **50 Ohms**.\n",
+    "*   Enable ADB debugging on the Signal Generator (likely via network)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Configuration\n",
+    "Adjust the parameters below to match your setup."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pyvisa\n",
+    "import time\n",
+    "import os\n",
+    "import pathlib  # Optional, can use os.path instead\n",
+    "\n",
+    "# --- Instrument & Connection Settings ---\n",
+    "GEN_IP = \"192.168.138.178\"  # IP Address of the Signal Generator\n",
+    "OSC_IP = \"192.168.138.244\"  # IP Address of the Oscilloscope\n",
+    "ADB_DEVICE_ID = (\n",
+    "    \"192.168.138.178:55555\"  # ADB identifier for the generator (IP:Port or Serial)\n",
+    ")\n",
+    "\n",
+    "# --- File Paths ---\n",
+    "# Directory where original .hex files are located and where measurement/modified files will be saved.\n",
+    "# Use raw string (r'...') or forward slashes ('/') for paths.\n",
+    "DATA_DIR = r\"C:\\Users\\xxx\\dgpro_calibration_data\"  # CHANGE THIS PATH\n",
+    "\n",
+    "# Ensure the data directory exists\n",
+    "pathlib.Path(DATA_DIR).mkdir(\n",
+    "    parents=True, exist_ok=True\n",
+    ")  # Create dir if it doesn't exist\n",
+    "\n",
+    "# Original and modified calibration filenames (don't change filename structure unless necessary)\n",
+    "ORIGINAL_HEX_FILES = {1: \"cal_hfflat1.hex\", 2: \"cal_hfflat2.hex\"}\n",
+    "MODIFIED_HEX_FILES = {1: \"cal_hfflat1_mod.hex\", 2: \"cal_hfflat2_mod.hex\"}\n",
+    "MEASUREMENT_FILE_TEMPLATE = (\n",
+    "    \"ch{channel}-rng{range_idx}-measurement.npz\"  # Template for saving measurements\n",
+    ")\n",
+    "\n",
+    "# --- SCPI Commands ---\n",
+    "# Adjust these if your instruments use different commands\n",
+    "GEN_IDN_CMD = \"*IDN?\"\n",
+    "OSC_IDN_CMD = \"*IDN?\"\n",
+    "GEN_SET_VOLT_CMD = \":SOURce{ch}:VOLTage:AMPLitude {volt:.7e} VPP\"  # Set Vpp\n",
+    "GEN_SET_FREQ_CMD = \":SOURce{ch}:FREQuency {freq:.4e}\"  # Set Frequency in Hz\n",
+    "# *** IMPORTANT: Change this for your specific oscilloscope model! ***\n",
+    "# Example for many Siglent scopes: Query RMS voltage on Channel 1 -> 'C1:PAVA? RMS'\n",
+    "# Example from original code (might be Rigol): ':MEAS:ITEM? VRMS,CHAN1'\n",
+    "OSC_MEAS_CMD = \"C1:PAVA? RMS\"  # Placeholder - REPLACE WITH YOUR SCOPE'S COMMAND\n",
+    "OSC_MEAS_CHAN = \"1\"  # Oscilloscope channel connected to the generator output (used if needed in OSC_MEAS_CMD)\n",
+    "\n",
+    "# --- Calibration Settings ---\n",
+    "CHANNELS_TO_CALIBRATE = [\n",
+    "    1,\n",
+    "    2,\n",
+    "]  # List of generator channels to calibrate (e.g., [1], [2], or [1, 2])\n",
+    "SETTLE_TIME_VOLT = 3.0  # Seconds to wait after setting voltage\n",
+    "SETTLE_TIME_FREQ = 0.7  # Seconds to wait after setting frequency\n",
+    "\n",
+    "# Define the calibration ranges, frequencies, and corresponding indices in the .hex file.\n",
+    "# Each range dictionary needs:\n",
+    "#   'voltage': Target Vpp for measurements in this range.\n",
+    "#   'frequencies_uHz': List/array of frequencies in microHertz (uHz).\n",
+    "#   'hex_start_idx': Starting index (1-based) in the hex file for this range's data.\n",
+    "#   'hex_end_idx': Ending index (inclusive) in the hex file for this range's data.\n",
+    "#   'points_to_modify': Number of *highest frequency points* within this range to apply correction to.\n",
+    "\n",
+    "# Frequencies are defined once and reused (converted from uHz to Hz later)\n",
+    "# Note: The original code reversed the lists, meaning measurements went from high to low freq. Preserving that.\n",
+    "#       Also, the number of points varied slightly per range.\n",
+    "FREQ_LIST_RANGE_1_3_uHz = np.array(\n",
+    "    list(\n",
+    "        reversed(\n",
+    "            [\n",
+    "                200000000000000,\n",
+    "                175000000000000,\n",
+    "                150000000000000,\n",
+    "                120000000000000,\n",
+    "                100000000000000,\n",
+    "                90000000000000,\n",
+    "                80000000000000,\n",
+    "                70000000000000,\n",
+    "                60000000000000,\n",
+    "                50000000000000,\n",
+    "                40000000000000,\n",
+    "                30000000000000,\n",
+    "                20000000000000,\n",
+    "                10000000000000,\n",
+    "                8000000000000,\n",
+    "                6000000000000,\n",
+    "                4000000000000,\n",
+    "                2000000000000,\n",
+    "                1000000000000,\n",
+    "                900000000000,\n",
+    "                800000000000,\n",
+    "                700000000000,\n",
+    "                600000000000,\n",
+    "                500000000000,\n",
+    "                400000000000,\n",
+    "                300000000000,\n",
+    "                200000000000,\n",
+    "            ]\n",
+    "        )\n",
+    "    )\n",
+    ")  # 27 points: 200kHz to 200MHz\n",
+    "\n",
+    "FREQ_LIST_RANGE_2_uHz = np.array(\n",
+    "    list(\n",
+    "        reversed(\n",
+    "            [\n",
+    "                100000000000000,\n",
+    "                90000000000000,\n",
+    "                80000000000000,\n",
+    "                70000000000000,\n",
+    "                60000000000000,\n",
+    "                50000000000000,\n",
+    "                40000000000000,\n",
+    "                30000000000000,\n",
+    "                20000000000000,\n",
+    "                10000000000000,\n",
+    "                8000000000000,\n",
+    "                6000000000000,\n",
+    "                4000000000000,\n",
+    "                2000000000000,\n",
+    "                1000000000000,\n",
+    "                900000000000,\n",
+    "                800000000000,\n",
+    "                700000000000,\n",
+    "                600000000000,\n",
+    "                500000000000,\n",
+    "                400000000000,\n",
+    "                300000000000,\n",
+    "                200000000000,\n",
+    "            ]\n",
+    "        )\n",
+    "    )\n",
+    ")  # 23 points: 200kHz to 100MHz\n",
+    "\n",
+    "# Structure defining the ranges for calibration\n",
+    "# Indices match boundaries observed in original code (1-27, 28-50, 51-77)\n",
+    "# Note: Vpp values from original code comments/calculations are used.\n",
+    "CAL_RANGES = [\n",
+    "    {  # Range 1 (corresponds to original `rng1`)\n",
+    "        \"voltage\": 1.2649111,  # Vpp\n",
+    "        \"frequencies_uHz\": FREQ_LIST_RANGE_1_3_uHz,\n",
+    "        \"hex_start_idx\": 1,\n",
+    "        \"hex_end_idx\": 27,\n",
+    "        \"points_to_modify\": 12,\n",
+    "    },\n",
+    "    {  # Range 2 (corresponds to original `rng2`)\n",
+    "        \"voltage\": 5.0,  # Vpp\n",
+    "        \"frequencies_uHz\": FREQ_LIST_RANGE_2_uHz,\n",
+    "        \"hex_start_idx\": 28,\n",
+    "        \"hex_end_idx\": 50,  # 23 points total\n",
+    "        \"points_to_modify\": 12,\n",
+    "    },\n",
+    "    {  # Range 3 (corresponds to original `rng3` - 0dBm into 50 Ohm)\n",
+    "        \"voltage\": 0.6324556,  # Vpp (approx 0 dBm)\n",
+    "        \"frequencies_uHz\": FREQ_LIST_RANGE_1_3_uHz,\n",
+    "        \"hex_start_idx\": 51,\n",
+    "        \"hex_end_idx\": 77,\n",
+    "        \"points_to_modify\": 12,\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Hex file properties\n",
+    "HEX_DATA_TYPE = \"<f8\"  # Little-endian 64-bit float\n",
+    "HEX_POINTS_PER_CHANNEL = 78  # Total points = 624 bytes / 8 bytes/point\n",
+    "\n",
+    "# --- Plotting Defaults ---\n",
+    "plt.rcParams[\"figure.figsize\"] = (10, 6)  # Default figure size\n",
+    "\n",
+    "print(\"Configuration loaded.\")\n",
+    "print(f\"Data Directory: {DATA_DIR}\")\n",
+    "print(f\"Generator IP: {GEN_IP}, Oscilloscope IP: {OSC_IP}\")\n",
+    "print(f\"Calibrating Channels: {CHANNELS_TO_CALIBRATE}\")\n",
+    "print(f\"Oscilloscope Measurement Command: {OSC_MEAS_CMD}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def connect_instruments(gen_ip, osc_ip):\n",
+    "    \"\"\"Connects to Generator and Oscilloscope using pyvisa.\"\"\"\n",
+    "    gen = None\n",
+    "    osc = None\n",
+    "    try:\n",
+    "        print(\"Connecting to instruments...\")\n",
+    "        rm = pyvisa.ResourceManager()\n",
+    "        gen_addr = f\"TCPIP0::{gen_ip}::INSTR\"\n",
+    "        osc_addr = f\"TCPIP0::{osc_ip}::INSTR\"\n",
+    "\n",
+    "        gen = rm.open_resource(gen_addr)\n",
+    "        gen.timeout = 10000  # ms\n",
+    "        gen.read_termination = \"\\n\"\n",
+    "        gen.write_termination = \"\\n\"\n",
+    "        print(f\"Generator Connected: {gen.query(GEN_IDN_CMD).strip()}\")\n",
+    "\n",
+    "        osc = rm.open_resource(osc_addr)\n",
+    "        osc.timeout = 10000  # ms\n",
+    "        osc.read_termination = \"\\n\"\n",
+    "        osc.write_termination = \"\\n\"\n",
+    "        print(f\"Oscilloscope Connected: {osc.query(OSC_IDN_CMD).strip()}\")\n",
+    "        print(\"-\" * 30)\n",
+    "        return gen, osc\n",
+    "    except pyvisa.errors.VisaIOError as e:\n",
+    "        print(f\"VISA Error connecting to instruments: {e}\")\n",
+    "        if gen:\n",
+    "            gen.close()\n",
+    "        if osc:\n",
+    "            osc.close()\n",
+    "        return None, None\n",
+    "    except Exception as e:\n",
+    "        print(f\"An unexpected error occurred during connection: {e}\")\n",
+    "        if gen:\n",
+    "            gen.close()\n",
+    "        if osc:\n",
+    "            osc.close()\n",
+    "        return None, None\n",
+    "\n",
+    "\n",
+    "def measure_flatness(gen, osc, channel, voltage_vpp, frequencies_hz, osc_meas_cmd):\n",
+    "    \"\"\"Sets generator and measures RMS voltage using the oscilloscope.\"\"\"\n",
+    "    measured_amps_rms = np.zeros_like(frequencies_hz, dtype=float)\n",
+    "\n",
+    "    print(f\"Starting measurement: CH{channel}, Voltage={voltage_vpp:.4f} Vpp\")\n",
+    "\n",
+    "    # Set initial voltage\n",
+    "    cmd = GEN_SET_VOLT_CMD.format(ch=channel, volt=voltage_vpp)\n",
+    "    # print(f\"Sending to Gen: {cmd}\") # Uncomment for debugging\n",
+    "    gen.write(cmd)\n",
+    "    time.sleep(SETTLE_TIME_VOLT)\n",
+    "\n",
+    "    # Set initial frequency (first in the list)\n",
+    "    cmd = GEN_SET_FREQ_CMD.format(ch=channel, freq=frequencies_hz[0])\n",
+    "    # print(f\"Sending to Gen: {cmd}\") # Uncomment for debugging\n",
+    "    gen.write(cmd)\n",
+    "    time.sleep(SETTLE_TIME_FREQ)  # Allow settling before first measurement\n",
+    "\n",
+    "    # Loop through frequencies\n",
+    "    for i, freq_hz in enumerate(frequencies_hz):\n",
+    "        cmd = GEN_SET_FREQ_CMD.format(ch=channel, freq=freq_hz)\n",
+    "        # print(f\"Sending to Gen: {cmd}\") # Uncomment for debugging\n",
+    "        gen.write(cmd)\n",
+    "        time.sleep(SETTLE_TIME_FREQ)\n",
+    "\n",
+    "        # Measure RMS Voltage\n",
+    "        try:\n",
+    "            # Adapt query based on expected Siglent format (value first, then unit maybe)\n",
+    "            # Or based on original format (just the value)\n",
+    "            query_cmd = osc_meas_cmd.replace(\n",
+    "                \"C1\", f\"C{OSC_MEAS_CHAN}\"\n",
+    "            )  # Replace channel if needed\n",
+    "            # print(f\"Querying Scope: {query_cmd}\") # Uncomment for debugging\n",
+    "            response = osc.query(query_cmd).strip()\n",
+    "\n",
+    "            # Attempt to extract float - adjust parsing if needed based on scope response\n",
+    "            # e.g., if it returns \"1.234 VRMS\", split and take first part\n",
+    "            try:\n",
+    "                measured_vrms = float(response.split()[0])  # Example parsing\n",
+    "            except ValueError:\n",
+    "                measured_vrms = float(\n",
+    "                    response\n",
+    "                )  # Assume direct float response if split fails\n",
+    "\n",
+    "            measured_amps_rms[i] = measured_vrms\n",
+    "            print(\n",
+    "                f\"  Freq: {freq_hz / 1e6:.3f} MHz, Measured VRMS: {measured_vrms:.6f} V\"\n",
+    "            )\n",
+    "        except pyvisa.errors.VisaIOError as e:\n",
+    "            print(f\"VISA Error during measurement at {freq_hz / 1e6:.3f} MHz: {e}\")\n",
+    "            measured_amps_rms[i] = np.nan  # Mark as invalid\n",
+    "        except Exception as e:\n",
+    "            print(\n",
+    "                f\"Unexpected error during measurement at {freq_hz / 1e6:.3f} MHz: {e}\"\n",
+    "            )\n",
+    "            measured_amps_rms[i] = np.nan  # Mark as invalid\n",
+    "\n",
+    "    print(f\"Measurement finished for CH{channel}, Voltage={voltage_vpp:.4f} Vpp.\")\n",
+    "    print(\"-\" * 30)\n",
+    "    return frequencies_hz, measured_amps_rms\n",
+    "\n",
+    "\n",
+    "def calculate_and_update_cal(\n",
+    "    original_hex_path, all_measurements, cal_ranges, hex_data_type, output_hex_path\n",
+    "):\n",
+    "    \"\"\"Calculates new calibration factors and writes the modified .hex file.\"\"\"\n",
+    "    print(f\"Processing calibration for: {output_hex_path}\")\n",
+    "\n",
+    "    # 1. Read original calibration data\n",
+    "    try:\n",
+    "        with open(original_hex_path, \"rb\") as fh:\n",
+    "            original_cal_data = np.fromfile(fh, dtype=hex_data_type)\n",
+    "        print(f\"Read {len(original_cal_data)} points from {original_hex_path}\")\n",
+    "        if len(original_cal_data) != HEX_POINTS_PER_CHANNEL:\n",
+    "            print(\n",
+    "                f\"Warning: Expected {HEX_POINTS_PER_CHANNEL} points, found {len(original_cal_data)}.\"\n",
+    "            )\n",
+    "            # Decide how to handle: error out, proceed with caution?\n",
+    "            # For now, proceed cautiously. Add error handling if needed.\n",
+    "            # return False\n",
+    "    except FileNotFoundError:\n",
+    "        print(f\"Error: Original calibration file not found: {original_hex_path}\")\n",
+    "        return False\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {original_hex_path}: {e}\")\n",
+    "        return False\n",
+    "\n",
+    "    modified_cal_data = original_cal_data.copy()\n",
+    "    plt.figure()  # Create a figure for plotting results for this channel\n",
+    "\n",
+    "    # 2. Process each calibration range\n",
+    "    for i, cal_range in enumerate(cal_ranges):\n",
+    "        range_idx = i + 1  # 1-based index for user feedback/filenames\n",
+    "        print(f\" Applying corrections for Range {range_idx}...\")\n",
+    "\n",
+    "        # Find the measurement data for this range\n",
+    "        measurement_data = all_measurements.get(range_idx)\n",
+    "        if measurement_data is None:\n",
+    "            print(f\" Error: Measurement data for range {range_idx} not found.\")\n",
+    "            continue  # Skip this range\n",
+    "\n",
+    "        measured_freqs = measurement_data[\"frequencies_hz\"]\n",
+    "        measured_amps = measurement_data[\"measured_amps_rms\"]\n",
+    "\n",
+    "        # Basic check for NaNs or zeros which would break calculation\n",
+    "        if np.isnan(measured_amps).any() or measured_amps[0] == 0:\n",
+    "            print(\n",
+    "                f\" Warning: Invalid measurement data (NaN or zero at start) for range {range_idx}. Skipping correction.\"\n",
+    "            )\n",
+    "            continue\n",
+    "\n",
+    "        # Get section boundaries (using 0-based Python indexing)\n",
+    "        start_idx = cal_range[\"hex_start_idx\"]  # Config uses 1-based for clarity\n",
+    "        end_idx = cal_range[\"hex_end_idx\"] + 1  # Python slice excludes end\n",
+    "        num_points_in_range = end_idx - start_idx\n",
+    "        points_to_modify = cal_range[\"points_to_modify\"]\n",
+    "\n",
+    "        if len(measured_amps) != num_points_in_range:\n",
+    "            print(\n",
+    "                f\" Warning: Mismatch between measurement points ({len(measured_amps)}) and expected hex range points ({num_points_in_range}) for range {range_idx}. Skipping.\"\n",
+    "            )\n",
+    "            continue\n",
+    "\n",
+    "        # Extract the relevant section from the *original* calibration data\n",
+    "        original_section = original_cal_data[start_idx:end_idx]\n",
+    "\n",
+    "        # --- Calculate Correction Factors ---\n",
+    "        # Normalize measured response relative to the first point in the range\n",
+    "        relative_response = measured_amps / measured_amps[0]\n",
+    "\n",
+    "        # Avoid division by zero if relative_response has zeros (shouldn't happen if checked above)\n",
+    "        relative_response[relative_response == 0] = (\n",
+    "            1e-9  # Replace zeros with small number\n",
+    "        )\n",
+    "\n",
+    "        # New calibration factor = Original Factor / Measured Relative Response\n",
+    "        # If measured output dropped (relative_response < 1), new factor > original factor.\n",
+    "        # If measured output peaked (relative_response > 1), new factor < original factor.\n",
+    "        new_cal_section = original_section / relative_response\n",
+    "        # --- Apply Correction to the specified points ---\n",
+    "        # Determine indices *within the section* to modify (last 'points_to_modify')\n",
+    "        modify_start_in_section = num_points_in_range - points_to_modify\n",
+    "        modify_end_in_section = num_points_in_range\n",
+    "\n",
+    "        # Determine indices *within the full modified_cal_data array*\n",
+    "        modify_start_global = start_idx + modify_start_in_section\n",
+    "        modify_end_global = start_idx + modify_end_in_section\n",
+    "\n",
+    "        print(\n",
+    "            f\"  Modifying indices {modify_start_global} to {modify_end_global - 1} (total {points_to_modify} points)\"\n",
+    "        )\n",
+    "\n",
+    "        # Update the modified data array only for the selected points\n",
+    "        modified_cal_data[modify_start_global:modify_end_global] = new_cal_section[\n",
+    "            modify_start_in_section:modify_end_in_section\n",
+    "        ]\n",
+    "\n",
+    "        # --- Plotting for verification ---\n",
+    "        plot_indices = np.arange(start_idx, end_idx)\n",
+    "        plt.plot(\n",
+    "            plot_indices,\n",
+    "            original_section,\n",
+    "            \"--\",\n",
+    "            label=f\"Orig Range {range_idx} (Idx {start_idx}-{end_idx - 1})\",\n",
+    "        )\n",
+    "        plt.plot(\n",
+    "            plot_indices, new_cal_section, \"-\", label=f\"New Calc Range {range_idx}\"\n",
+    "        )\n",
+    "        # Highlight modified points\n",
+    "        plt.scatter(\n",
+    "            plot_indices[modify_start_in_section:modify_end_in_section],\n",
+    "            new_cal_section[modify_start_in_section:modify_end_in_section],\n",
+    "            color=\"red\",\n",
+    "            s=20,\n",
+    "            zorder=5,\n",
+    "            label=f\"Modified Pts Rng {range_idx}\",\n",
+    "        )\n",
+    "\n",
+    "    # Add overall plot elements\n",
+    "    plt.title(f\"Calibration Factors - {os.path.basename(output_hex_path)}\")\n",
+    "    plt.xlabel(\"Index in .hex file\")\n",
+    "    plt.ylabel(\"Calibration Factor\")\n",
+    "    plt.legend(fontsize=\"small\")\n",
+    "    plt.grid(True)\n",
+    "    plt.tight_layout()\n",
+    "    plot_filename = os.path.splitext(output_hex_path)[0] + \"_comparison.png\"\n",
+    "    plt.savefig(plot_filename)\n",
+    "    print(f\"Saved comparison plot to {plot_filename}\")\n",
+    "    plt.show()\n",
+    "\n",
+    "    # 3. Write the modified calibration data\n",
+    "    try:\n",
+    "        with open(output_hex_path, \"wb\") as fh:\n",
+    "            modified_cal_data.tofile(fh)\n",
+    "        print(\n",
+    "            f\"Successfully wrote {len(modified_cal_data)} points to {output_hex_path}\"\n",
+    "        )\n",
+    "        return True\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error writing modified file {output_hex_path}: {e}\")\n",
+    "        return False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Connect to Instruments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gen, osc = connect_instruments(GEN_IP, OSC_IP)\n",
+    "\n",
+    "# Proceed only if connection is successful\n",
+    "if gen is None or osc is None:\n",
+    "    print(\n",
+    "        \"\\nERROR: Instrument connection failed. Please check IPs, connections, and VISA setup.\"\n",
+    "    )\n",
+    "    # Optionally raise an error: raise ConnectionError(\"Failed to connect to instruments\")\n",
+    "else:\n",
+    "    print(\"\\nInstruments connected successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Perform Measurements\n",
+    "This section iterates through the specified channels and calibration ranges, performing measurements for each."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_channel_measurements = {}  # Dictionary to store results {channel: {range_idx: data}}\n",
+    "\n",
+    "if gen and osc:  # Only run if instruments are connected\n",
+    "    for channel in CHANNELS_TO_CALIBRATE:\n",
+    "        print(f\"\\n--- Starting Measurements for Channel {channel} ---\")\n",
+    "        channel_measurements = {}  # Store results for this channel {range_idx: data}\n",
+    "\n",
+    "        for i, cal_range in enumerate(CAL_RANGES):\n",
+    "            range_idx = i + 1  # 1-based index\n",
+    "            voltage_vpp = cal_range[\"voltage\"]\n",
+    "            # Convert frequencies from uHz to Hz for measurement\n",
+    "            frequencies_hz = cal_range[\"frequencies_uHz\"] / 1e6\n",
+    "\n",
+    "            # Perform the measurement\n",
+    "            measured_freqs_hz, measured_amps_rms = measure_flatness(\n",
+    "                gen, osc, channel, voltage_vpp, frequencies_hz, OSC_MEAS_CMD\n",
+    "            )\n",
+    "\n",
+    "            # Store results\n",
+    "            measurement_data = {\n",
+    "                \"frequencies_hz\": measured_freqs_hz,\n",
+    "                \"measured_amps_rms\": measured_amps_rms,\n",
+    "                \"voltage_vpp\": voltage_vpp,\n",
+    "                \"channel\": channel,\n",
+    "                \"range_idx\": range_idx,\n",
+    "            }\n",
+    "            channel_measurements[range_idx] = measurement_data\n",
+    "\n",
+    "            # Save intermediate results for this range\n",
+    "            filename = MEASUREMENT_FILE_TEMPLATE.format(\n",
+    "                channel=channel, range_idx=range_idx\n",
+    "            )\n",
+    "            filepath = os.path.join(DATA_DIR, filename)\n",
+    "            try:\n",
+    "                np.savez(filepath, **measurement_data)\n",
+    "                print(f\"Saved measurement data to: {filepath}\")\n",
+    "            except Exception as e:\n",
+    "                print(f\"Error saving measurement data to {filepath}: {e}\")\n",
+    "\n",
+    "            print(\"-\" * 20)  # Separator between ranges\n",
+    "\n",
+    "        all_channel_measurements[channel] = channel_measurements\n",
+    "        print(f\"--- Finished Measurements for Channel {channel} ---\")\n",
+    "\n",
+    "else:\n",
+    "    print(\"\\nSkipping measurements due to connection failure.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Calculate New Calibration and Generate .hex Files\n",
+    "This section processes the measurement data saved in step 4 and generates the modified `.hex` files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if all_channel_measurements:  # Proceed only if measurements were taken\n",
+    "    for channel in CHANNELS_TO_CALIBRATE:\n",
+    "        print(f\"\\n--- Calculating Calibration for Channel {channel} ---\")\n",
+    "\n",
+    "        original_hex_path = os.path.join(DATA_DIR, ORIGINAL_HEX_FILES[channel])\n",
+    "        output_hex_path = os.path.join(DATA_DIR, MODIFIED_HEX_FILES[channel])\n",
+    "        measurements_for_channel = all_channel_measurements.get(channel)\n",
+    "\n",
+    "        if not measurements_for_channel:\n",
+    "            print(\n",
+    "                f\"Error: No measurement data found for Channel {channel}. Skipping calculation.\"\n",
+    "            )\n",
+    "            continue\n",
+    "\n",
+    "        success = calculate_and_update_cal(\n",
+    "            original_hex_path,\n",
+    "            measurements_for_channel,\n",
+    "            CAL_RANGES,\n",
+    "            HEX_DATA_TYPE,\n",
+    "            output_hex_path,\n",
+    "        )\n",
+    "\n",
+    "        if success:\n",
+    "            print(\n",
+    "                f\"Successfully generated modified calibration file for Channel {channel}.\"\n",
+    "            )\n",
+    "        else:\n",
+    "            print(\n",
+    "                f\"Failed to generate modified calibration file for Channel {channel}.\"\n",
+    "            )\n",
+    "\n",
+    "else:\n",
+    "    print(\n",
+    "        \"\\nSkipping calibration calculation because no measurements were performed or loaded.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Visualize Original vs. Modified Calibration Data (Optional)\n",
+    "Plot the original and newly generated `_mod.hex` files together for comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\n--- Plotting Final Comparison ---\")\n",
+    "plt.figure(figsize=(12, 7))\n",
+    "\n",
+    "file_labels = []\n",
+    "\n",
+    "# Plot original files\n",
+    "for ch in CHANNELS_TO_CALIBRATE:\n",
+    "    fname = os.path.join(DATA_DIR, ORIGINAL_HEX_FILES[ch])\n",
+    "    try:\n",
+    "        with open(fname, \"rb\") as fh:\n",
+    "            data = np.fromfile(fh, dtype=HEX_DATA_TYPE)\n",
+    "        plt.plot(data, \"--\", label=f\"Original CH{ch}\")\n",
+    "        file_labels.append(f\"Orig CH{ch}\")\n",
+    "    except FileNotFoundError:\n",
+    "        print(f\"Warning: Original file not found for plotting: {fname}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {fname} for plotting: {e}\")\n",
+    "\n",
+    "# Plot modified files\n",
+    "for ch in CHANNELS_TO_CALIBRATE:\n",
+    "    fname = os.path.join(DATA_DIR, MODIFIED_HEX_FILES[ch])\n",
+    "    try:\n",
+    "        with open(fname, \"rb\") as fh:\n",
+    "            data = np.fromfile(fh, dtype=HEX_DATA_TYPE)\n",
+    "        plt.plot(data, \"-\", linewidth=2, label=f\"Modified CH{ch}\")\n",
+    "        file_labels.append(f\"Mod CH{ch}\")\n",
+    "    except FileNotFoundError:\n",
+    "        print(f\"Warning: Modified file not found for plotting: {fname}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {fname} for plotting: {e}\")\n",
+    "\n",
+    "# Add vertical lines indicating range boundaries (using 0-based index)\n",
+    "# Boundaries are *after* the end index of a range\n",
+    "boundaries = sorted(list(set([r[\"hex_end_idx\"] for r in CAL_RANGES])))\n",
+    "if boundaries:\n",
+    "    plt.vlines(\n",
+    "        boundaries[:-1],\n",
+    "        ymin=plt.ylim()[0],\n",
+    "        ymax=plt.ylim()[1],\n",
+    "        colors=\"r\",\n",
+    "        linestyles=\":\",\n",
+    "        label=\"Range Boundary\",\n",
+    "    )\n",
+    "    # file_labels.append('Boundary') # Optional: add to legend\n",
+    "\n",
+    "plt.title(\"Comparison of Original and Modified HF Flatness Calibration Data\")\n",
+    "plt.xlabel(\"Index in .hex file\")\n",
+    "plt.ylabel(\"Calibration Factor\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Upload Calibration Files to Generator using ADB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\n--- ADB Upload Commands ---\")\n",
+    "print(\"Run these commands in your system terminal (cmd, bash, etc.):\")\n",
+    "print(\"# Connect to the device (if not already connected):\")\n",
+    "print(f\"adb connect {ADB_DEVICE_ID}\")\n",
+    "print(\"-\" * 20)\n",
+    "\n",
+    "for ch in CHANNELS_TO_CALIBRATE:\n",
+    "    mod_file_local = os.path.join(DATA_DIR, MODIFIED_HEX_FILES[ch])\n",
+    "    original_filename_remote = ORIGINAL_HEX_FILES[\n",
+    "        ch\n",
+    "    ]  # Overwrite the original name on device\n",
+    "    # Assuming the standard Rigol path, adjust if necessary\n",
+    "    remote_path = f\"/rigol/data/{original_filename_remote}\"\n",
+    "\n",
+    "    # Important: Ensure paths are correctly quoted if they contain spaces\n",
+    "    # Using pathlib helps create OS-agnostic paths for the local file\n",
+    "    mod_file_local_str = str(pathlib.Path(mod_file_local))\n",
+    "\n",
+    "    print(f\"# Upload Channel {ch}:\")\n",
+    "    # Use quotes around paths, especially the local one if it might have spaces\n",
+    "    print(f'adb -s {ADB_DEVICE_ID} push \"{mod_file_local_str}\" \"{remote_path}\"')\n",
+    "    print(\"\")  # Add a newline for clarity\n",
+    "\n",
+    "print(\"--- End of ADB Commands ---\")\n",
+    "print(\n",
+    "    \"\\nAfter uploading, it is recommended to REBOOT the signal generator for the new calibration data to take effect.\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if gen:\n",
+    "    gen.close()\n",
+    "    print(\"Generator connection closed.\")\n",
+    "if osc:\n",
+    "    osc.close()\n",
+    "    print(\"Oscilloscope connection closed.\")\n",
+    "\n",
+    "print(\"\\nCalibration process finished.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/rigol_setup_crypter.py b/rigol_setup_crypter.py
old mode 100644
new mode 100755
index 20601c0..9934382
--- a/rigol_setup_crypter.py
+++ b/rigol_setup_crypter.py
@@ -143,10 +143,12 @@ def _des_cbc_crypt(data_bytes, iv_64bits, round_keys, is_encrypt):
 
 def DataDecryption(strEncryptedDataHex, cpu_serial):
     """Decrypts hex string using DES-CBC"""
+    c_hex_str = strEncryptedDataHex.strip()
+
     try:
-        encrypted_bytes = binascii.unhexlify(strEncryptedDataHex)
+        encrypted_bytes = binascii.unhexlify(c_hex_str)
     except binascii.Error as e:
-        raise ValueError(f"Invalid hex string provided: {e}")
+        raise ValueError(f"Invalid hex string provided (length {len(c_hex_str)} after stripping): {e}")
     round_keys, iv_bits = _generate_round_keys(cpu_serial), _generate_iv_bits()
     decrypted_bytes = _des_cbc_crypt(encrypted_bytes, iv_bits, round_keys, False)
     try: