803 lines
144 KiB
Text
803 lines
144 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "c30b6a4f-cb8a-4da2-8939-beaa5420a8a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import math\n",
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"import json\n",
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"import statistics\n",
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"from pathlib import Path\n",
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"from matplotlib import pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "c8fbf52a-76df-4d5a-a50d-0e16d4a5921c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"74lvc_mesh_new_p0.3mm\n",
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"74lvc_mesh_new_p0.4mm\n",
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"74lvc_mesh_new_p0.6mm\n",
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"74lvc_mesh_new_p1.0mm\n",
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"74lvc_mesh_p0.3mm\n",
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"74lvc_mesh_p0.3mm_flip\n",
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"74lvc_mesh_p0.3mm_term\n",
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"74lvc_mesh_p0.4mm\n",
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"74lvc_mesh_p0.6mm\n",
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"74lvc_mesh_p1.0mm\n",
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"74lvc_new2_mesh_p0.3mm\n",
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"74lvc_new2_mesh_p0.4mm\n",
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"74lvc_new2_mesh_p0.6mm\n",
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"74lvc_new2_mesh_p1.0mm\n",
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"74lvc_new2_open\n",
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"74lvc_new2_risetime\n",
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"74lvc_new_mesh_p0.3mm\n",
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"74lvc_new_mesh_p0.3mm_flip\n",
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"74lvc_new_mesh_p0.3mm_term\n",
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"74lvc_new_open\n",
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"74lvc_open\n",
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"long_sample_a_bwd\n",
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"long_sample_a_bwd_term\n",
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"long_sample_a_bwd_term_short\n",
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"long_sample_a_fwd\n",
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"long_sample_a_fwd_term\n",
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"long_sample_a_fwd_term_short\n",
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"long_sample_a_mod_flip\n",
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"long_sample_a_mod_flip_term\n",
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"long_sample_a_mod_flip_term_short\n",
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"long_sample_a_mod_fwd\n",
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"long_sample_a_mod_fwd_term\n",
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"long_sample_a_mod_fwd_term_short\n",
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"long_sample_b_bwd\n",
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"long_sample_b_bwd_term\n",
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"long_sample_b_fwd\n",
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"long_sample_b_fwd_term\n",
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"long_sample_b_fwd_term_short\n",
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"long_sample_b_post_2mod_1\n",
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"long_sample_b_post_2mod_10\n",
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"long_sample_b_post_2mod_2\n",
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"long_sample_b_post_2mod_3\n",
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"long_sample_b_post_2mod_4\n",
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"long_sample_b_post_2mod_5\n",
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"long_sample_b_post_2mod_6\n",
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"long_sample_b_post_2mod_7\n",
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"long_sample_b_post_2mod_8\n",
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"long_sample_b_post_2mod_9\n",
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"long_sample_b_post_2modb_1\n",
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"long_sample_b_post_2modb_10\n",
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"long_sample_b_post_2modb_2\n",
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"long_sample_b_post_2modb_3\n",
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"long_sample_b_post_2modb_4\n",
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"long_sample_b_post_2modb_5\n",
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"long_sample_b_post_2modb_6\n",
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"long_sample_b_post_2modb_7\n",
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"long_sample_b_post_2modb_8\n",
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"long_sample_b_post_2modb_9\n",
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"long_sample_b_post_demod2_1\n",
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"long_sample_b_post_demod2_10\n",
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"long_sample_b_post_demod2_2\n",
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"long_sample_b_post_demod2_3\n",
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"long_sample_b_post_demod2_4\n",
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"long_sample_b_post_demod2_5\n",
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"long_sample_b_post_demod2_6\n",
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"long_sample_b_post_demod2_7\n",
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"long_sample_b_post_demod2_8\n",
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"long_sample_b_post_demod2_9\n",
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"long_sample_b_post_demod_1\n",
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"long_sample_b_post_demod_10\n",
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"long_sample_b_post_demod_2\n",
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"long_sample_b_post_demod_3\n",
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"long_sample_b_post_demod_4\n",
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"long_sample_b_post_demod_5\n",
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"long_sample_b_post_demod_6\n",
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"long_sample_b_post_demod_7\n",
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"long_sample_b_post_demod_8\n",
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"long_sample_b_post_demod_9\n",
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"long_sample_b_post_mod_1\n",
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"long_sample_b_post_mod_10\n",
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"long_sample_b_post_mod_2\n",
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"long_sample_b_post_mod_3\n",
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"long_sample_b_post_mod_4\n",
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"long_sample_b_post_mod_5\n",
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"long_sample_b_post_mod_6\n",
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"long_sample_b_post_mod_7\n",
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"long_sample_b_post_mod_8\n",
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"long_sample_b_post_mod_9\n",
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"long_sample_b_pre_mod_1\n",
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"long_sample_b_pre_mod_10\n",
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"long_sample_b_pre_mod_2\n",
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"long_sample_b_pre_mod_3\n",
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"long_sample_b_pre_mod_4\n",
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"long_sample_b_pre_mod_5\n",
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"long_sample_b_pre_mod_6\n",
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"long_sample_b_pre_mod_7\n",
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"long_sample_b_pre_mod_8\n",
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"long_sample_b_pre_mod_9\n",
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"long_sample_bwd_term_short\n",
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"long_sample_fwd_term_short\n",
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"max3748_mesh_p0.3mm\n",
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"max3748_mesh_p0.3mm_flip\n",
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"max3748_mesh_p0.3mm_term\n",
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"max3748_mesh_p0.4mm\n",
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"max3748_mesh_p0.6mm\n",
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"max3748_mesh_p1.0mm\n",
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"max3748_new2_risetime\n",
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"max3748_open\n",
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"pi3hdx_clipline_offset_a_mesh_p0.3mm\n",
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"pi3hdx_clipline_offset_a_mesh_p0.3mm_flip\n",
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"pi3hdx_clipline_offset_a_mesh_p0.3mm_term\n",
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"pi3hdx_clipline_offset_a_mesh_p0.4mm\n",
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"pi3hdx_clipline_offset_a_mesh_p0.6mm\n",
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"pi3hdx_clipline_offset_a_mesh_p1.0mm\n",
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"pi3hdx_clipline_offset_a_mesh_p1.0mm_manip_scope_probe_a\n",
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"pi3hdx_clipline_offset_a_mesh_p1.0mm_manip_scope_probe_b\n",
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"pi3hdx_clipline_offset_a_mesh_p1.0mm_manip_scope_probe_none\n",
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"pi3hdx_clipline_offset_a_mesh_p1.0mm_manip_short\n",
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"pi3hdx_clipline_offset_a_open\n",
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"pi3hdx_clipline_offset_b_mesh_p0.3mm\n",
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"pi3hdx_clipline_offset_b_mesh_p0.3mm_flip\n",
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"pi3hdx_clipline_offset_b_mesh_p0.3mm_term\n",
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"pi3hdx_clipline_offset_b_mesh_p0.4mm\n",
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"pi3hdx_clipline_offset_b_mesh_p0.6mm\n",
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"pi3hdx_clipline_offset_b_mesh_p1.0mm\n",
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"pi3hdx_clipline_offset_b_mesh_p1.0mm_manip_short\n",
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"pi3hdx_clipline_offset_b_open\n",
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"pi3hdx_clipline_risetime\n",
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"pi3hdx_clipline_risetime_new\n",
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"pi3hdx_manip_test2_baseline\n",
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"pi3hdx_manip_test2_baseline2\n",
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"pi3hdx_manip_test2_open\n",
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"pi3hdx_manip_test2_scope_probe\n",
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"pi3hdx_manip_test2_short\n",
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"pi3hdx_manip_test_baseline\n",
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"pi3hdx_manip_test_baseline2\n",
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"pi3hdx_manip_test_baseline3\n",
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"pi3hdx_manip_test_open\n",
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"pi3hdx_manip_test_scope_probe\n",
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"pi3hdx_manip_test_scope_probe2\n",
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"pi3hdx_manip_test_short\n",
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"pi3hdx_new2_risetime\n",
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"pi3hdx_new3_risetime\n",
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"risetime_74lvc_1\n",
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"risetime_74lvc_10\n",
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"risetime_74lvc_2\n",
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"risetime_74lvc_3\n",
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"risetime_74lvc_4\n",
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"risetime_74lvc_5\n",
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"risetime_74lvc_6\n",
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"risetime_74lvc_7\n",
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"risetime_74lvc_8\n",
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"risetime_74lvc_9\n",
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"risetime_max3748_1\n",
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"risetime_max3748_10\n",
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"risetime_max3748_2\n",
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"risetime_max3748_3\n",
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"risetime_max3748_4\n",
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"risetime_max3748_5\n",
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"risetime_max3748_6\n",
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"risetime_max3748_7\n",
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"risetime_max3748_8\n",
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"risetime_max3748_9\n",
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"risetime_pi3hdx_1\n",
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"risetime_pi3hdx_10\n",
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"risetime_pi3hdx_2\n",
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"risetime_pi3hdx_3\n",
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"risetime_pi3hdx_4\n",
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"risetime_pi3hdx_5\n",
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"risetime_pi3hdx_6\n",
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"risetime_pi3hdx_7\n",
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"risetime_pi3hdx_8\n",
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"risetime_pi3hdx_9\n",
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"risetime_tdp0604_1\n",
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"risetime_tdp0604_10\n",
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"risetime_tdp0604_2\n",
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"risetime_tdp0604_3\n",
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"risetime_tdp0604_4\n",
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"risetime_tdp0604_5\n",
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"risetime_tdp0604_6\n",
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"risetime_tdp0604_7\n",
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"risetime_tdp0604_8\n",
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"risetime_tdp0604_9\n",
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"run2_74lvc_mesh_p0.3mm\n",
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"run2_74lvc_mesh_p0.4mm\n",
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"run2_74lvc_mesh_p0.6mm\n",
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"run2_74lvc_mesh_p1.0mm\n",
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"run2_74lvc_open\n",
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"run3_74lvc_manip_baseline1\n",
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"run3_74lvc_manip_baseline2\n",
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"run3_74lvc_manip_baseline3\n",
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"run3_74lvc_manip_baseline4\n",
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"run3_74lvc_manip_cut\n",
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"run3_74lvc_manip_probe1a\n",
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"run3_74lvc_manip_probe1b\n",
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"run3_74lvc_manip_probe2a\n",
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"run3_74lvc_manip_probe2b\n",
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"run3_74lvc_manip_probe3a\n",
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"run3_74lvc_manip_probe3b\n",
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"run3_74lvc_manip_short1\n",
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"run3_74lvc_manip_short2\n",
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"run3_74lvc_manip_short3\n",
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"run3_74lvc_mesh_p0.3mm\n",
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"run3_74lvc_mesh_p0.4mm\n",
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"run3_74lvc_mesh_p0.6mm\n",
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"run3_74lvc_mesh_p1.0mm\n",
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"run3_74lvc_open\n",
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"run3_max3748_mesh_p0.3mm\n",
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"run3_max3748_mesh_p0.4mm\n",
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"run3_max3748_mesh_p0.6mm\n",
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"run3_max3748_mesh_p1.0mm\n",
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"run3_max3748_open\n",
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"run3_pi3hdx_manip_baseline1\n",
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"run3_pi3hdx_manip_baseline2\n",
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"run3_pi3hdx_manip_baseline3\n",
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"run3_pi3hdx_manip_baseline4\n",
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"run3_pi3hdx_manip_cut\n",
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"run3_pi3hdx_manip_probe1a\n",
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"run3_pi3hdx_manip_probe1b\n",
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"run3_pi3hdx_manip_probe2a\n",
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"run3_pi3hdx_manip_probe2b\n",
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"run3_pi3hdx_manip_probe3a\n",
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"run3_pi3hdx_manip_probe3b\n",
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"run3_pi3hdx_manip_short1\n",
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"run3_pi3hdx_manip_short2\n",
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"run3_pi3hdx_manip_short3\n",
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"run3_pi3hdx_mesh_p0.3mm\n",
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"run3_pi3hdx_mesh_p0.4mm\n",
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"run3_pi3hdx_mesh_p0.6mm\n",
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"run3_pi3hdx_mesh_p04mm\n",
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"run3_pi3hdx_mesh_p1.0mm\n",
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"run3_pi3hdx_open\n",
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"run3_tdp0604_mesh_p0.3mm\n",
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"run3_tdp0604_mesh_p0.4mm\n",
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"run3_tdp0604_mesh_p0.6mm\n",
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"run3_tdp0604_mesh_p1.0mm\n",
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"run3_tdp0604_open\n",
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"run4_pi3hdx_manip_baseline1\n",
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"run4_pi3hdx_manip_baseline2\n",
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"run4_pi3hdx_manip_baseline3\n",
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"run4_pi3hdx_manip_baseline4\n",
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"run4_pi3hdx_manip_probe1a\n",
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"run4_pi3hdx_manip_probe1b\n",
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"run4_pi3hdx_manip_probe2a\n",
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"run4_pi3hdx_manip_probe2b\n",
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"run4_pi3hdx_manip_probe3a\n",
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"run4_pi3hdx_manip_probe3b\n",
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"tdp0604_mesh_p0.3mm\n",
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"tdp0604_mesh_p0.3mm_flip\n",
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"tdp0604_mesh_p0.3mm_term\n",
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"tdp0604_mesh_p0.4mm\n",
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"tdp0604_mesh_p0.6mm\n",
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"tdp0604_mesh_p1.0mm\n",
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"tdp0604_new2_risetime\n",
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"tdp0604_new_avg_flip_mesh_p0.3mm\n",
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"tdp0604_new_avg_flip_mesh_p0.4mm\n",
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"tdp0604_new_avg_flip_mesh_p0.6mm\n",
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"tdp0604_new_avg_flip_mesh_p1.0mm\n",
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"tdp0604_new_avg_mesh_p0.3mm\n",
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"tdp0604_new_avg_mesh_p0.4mm\n",
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"tdp0604_new_avg_mesh_p0.6mm\n",
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"tdp0604_new_avg_mesh_p1.0mm\n",
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"tdp0604_new_avg_term_flip_mesh_1.0mm_open\n",
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"tdp0604_new_avg_term_flip_mesh_1.0mm_open_fix\n",
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"tdp0604_new_avg_term_flip_mesh_1.0mm_open_fix_finished\n",
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"tdp0604_new_avg_term_flip_mesh_1.0mm_open_fix_wire\n",
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"tdp0604_new_avg_term_flip_mesh_1.0mm_probe_a\n",
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"tdp0604_new_avg_term_flip_mesh_1.0mm_probe_b\n",
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"tdp0604_new_avg_term_flip_mesh_1.0mm_probe_no\n",
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"tdp0604_new_avg_term_flip_mesh_1.0mm_short\n",
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"tdp0604_new_avg_term_flip_mesh_p0.3mm\n",
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"tdp0604_new_avg_term_flip_mesh_p0.4mm\n",
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"tdp0604_new_avg_term_flip_mesh_p0.6mm\n",
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"tdp0604_new_avg_term_flip_mesh_p1.0mm\n",
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"tdp0604_new_avg_term_flip_open\n",
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"tdp0604_new_mesh_p0.3mm\n",
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"tdp0604_new_mesh_p0.4mm\n",
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"tdp0604_new_mesh_p0.6mm\n",
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"tdp0604_new_mesh_p1.0mm\n",
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"tdp0604_new_open\n",
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"tdp0604_open\n",
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"works\n"
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]
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}
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],
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"source": [
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"meas = {}\n",
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"for f in Path('../data').glob('*.json'):\n",
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" d = json.loads(f.read_bytes())\n",
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" d['adc1'] = np.array(d['adc1'])\n",
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" d['adc2'] = np.array(d['adc2'])\n",
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" d['var1'] = np.array(d['var1'])\n",
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" d['var2'] = np.array(d['var2'])\n",
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" meas[d['meta']] = d\n",
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"\n",
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"for n in sorted(meas):\n",
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" print(n)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "1dde57f3-45f9-480a-875f-c5b708ce61b1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"32\n",
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"1.268588876724243 2.3997459411621094 6\n",
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"statistics: 1.285260772705078 2.409491014480591 1\n",
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"Rise time t_r = 0.916 ns\n",
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"32\n",
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"1.2675959587097165 2.3981170790536064 6\n",
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"statistics: 1.2846848487854003 2.4086271286010743 1\n",
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"Rise time t_r = 0.918 ns\n",
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"32\n",
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"1.2677270889282226 2.399308940342494 6\n",
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"statistics: 1.2860223770141601 2.4105925083160398 1\n",
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"Rise time t_r = 0.913 ns\n",
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"32\n",
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"1.2678550720214843 2.3980990954807826 6\n",
|
|
"statistics: 1.2856038093566893 2.4091117858886717 1\n",
|
|
"Rise time t_r = 0.917 ns\n",
|
|
"32\n",
|
|
"1.2670966148376464 2.3987874167306082 6\n",
|
|
"statistics: 1.2853300094604492 2.4077632427215576 1\n",
|
|
"Rise time t_r = 0.918 ns\n",
|
|
"32\n",
|
|
"1.267380380630493 2.398665578024728 6\n",
|
|
"statistics: 1.2857454299926756 2.409066152572632 1\n",
|
|
"Rise time t_r = 0.917 ns\n",
|
|
"32\n",
|
|
"1.268097925186157 2.3995939799717494 6\n",
|
|
"statistics: 1.2861293792724608 2.411354112625122 1\n",
|
|
"Rise time t_r = 0.919 ns\n",
|
|
"32\n",
|
|
"1.2682112216949462 2.3986098289489743 6\n",
|
|
"statistics: 1.2855660438537597 2.4095020294189453 1\n",
|
|
"Rise time t_r = 0.915 ns\n",
|
|
"32\n",
|
|
"1.2677659034729003 2.3994199889046803 6\n",
|
|
"statistics: 1.2855156898498534 2.409495735168457 1\n",
|
|
"Rise time t_r = 0.914 ns\n",
|
|
"32\n",
|
|
"1.267426538467407 2.3991623742239816 6\n",
|
|
"statistics: 1.285616397857666 2.409541368484497 1\n",
|
|
"Rise time t_r = 0.917 ns\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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DlqnIPPfKuXHjxiqNWWXGq5Qr/78i93Px4kVx7NixYmBgoOjh4SGqVCrRx8dHbN++vbhmzRqxsLBQ6ohu637v31XlFNchEu/alf+gs40sFgtSU1Ntnlu5ciV+/vlnbN++HY0aNapQ262J1yEqz5o1a3Dt2jXMnDlT6ijk5mrS/yuquXQ6HebPn4/Nmzfj+vXr1hNRwsLCMGbMGLz22mvw9/eXOqZbq7HXIarIrvzo6GhkZGTgs88+g0wmK3N8UWBgIDw8PB54CrE7SklJwRNPPCF1DCIip2YwGLBhwwYsWrQImZmZMJlMkMlk1hM/Jk+efM+zR8n1OUUhKr26bu/evW2e37hxo/XKv1lZWQ+8JQSVb9myZVJHICJyWhaLBXv37sW7776Ls2fPwmg0QhAE1K5dG4MHD8b06dPRokULqWOSgznFR2ZScZePzIicBf9fkbNJTk5GZGQkjh8/br0si7e3N3r37o23334bnTp1coozx8hWjf3IjIiIqDqlp6dj+vTp+O9//4vCwkLrmVIdOnRAVFQU+vbt67JnjlHVsBAREZHbuHXrFmJiYrBt2zbodDpYLBYoFAo0b94c06ZNw/Dhw6HRaKSOSRJgISIiohrPYDBg8eLFWL16NXJycmA2myGTydCwYUNMmDABr7zyCm874+ZYiIiIqMYym83YsmULPvzwQ1y6dAkmkwmCICAwMBCvvPIKxo8fX6Fb2FDNx0L0AG58zDmR3ZXeN47I0SwWC3766SfExMQgNTXVevd3rVaLoUOH4u233+aZY2SDhegelEolBEFAbm4uAgICHHLpcSJ3IYoiDAYDcnNzIZPJytzShMheRFFEcnIyoqKicPToUeu9tjQaDXr06IF33nkHnTt35ms6lcFCdA9yuRz16tXDlStXcPHiRanjENUIGo0GYWFhPI2ZHCI9PR1RUVGIj49Hfn6+9cyx1q1bY8aMGRg4cCDPHKN7YiG6D29vbzRt2hRGo1HqKEQur/TO1/zNnOzNbDZj48aNmDdvnvWAaYVCgcaNG2PKlCkYOXIkzxyjB2IhegC5XA65XC51DCIiKsfNmzcxfvx4/PDDDygqKoJMJkNoaCjGjh2Lf/7zn6hTp47UEclFsBAREZFL2rdvHyZNmoSLFy/CbDZDo9Fg4MCBmDt3Lpo0aSJ1PHIxLERERORSDAYD3n33XWzcuBF5eXkAgNDQUERGRuKNN97gcUJUJSxERETkMv766y+MGzcOSUlJMBqNUKlU6Ny5M+bPn4+IiAip45EL46keRETk9ERRxJo1a9CnTx8cP34cRqMRtWrVwoQJE7Bjxw6WIXpo3ENERERO7caNG3j99dfx3//+F8XFxZDL5WjVqhXmzp2LoUOHSh2PaggWIiIiclo//fQTJk+ejPT0dJjNZnh5eWHIkCGYN28eGjRoIHU8qkFYiIiIyOno9XpERUXh888/R35+PgCgXr16mDZtGl577TUoFHz7IvviFkVERE7l1KlTeP3113H69GkYDAaoVCp06dIFCxYsQPv27aWORzUUCxERETkFs9mMZcuWYfHixcjNzYUoiqhduzbGjBmDGTNmwMfHR+qIVIOxEBERkeQyMjIwfvx4JCQkWA+cbtmyJebOnYtBgwbxli/kcCxEREQkGVEUsWPHDsycOdPmwOmhQ4figw8+QL169aSOSG6ChYiIiCSh0+kQFRWF7du3Iz8/H4IgoF69epg+fTpeffVVHjhN1YpbGxERVbuDBw9i2rRpSElJgcFggFqtRpcuXbBw4UK0bdtW6njkhliIiIio2hQXF2PRokVYs2YNcnNzAQD+/v4YM2YMoqOjeeA0SYaFiIiIqkVqair+/e9/45dffkFxcTEUCgWaNWuGDz74AAMHDuSB0yQpFiIiInIoo9GIzZs3Y8GCBdYDp729vTFo0CDMnz8foaGhUkckYiEiIiLHuXz5MubMmYPdu3dDp9NBLpfzwGlyStwSiYjI7kwmE7777jvExsZaD5z28PBAREQElixZgvDwcKkjEtlgISIiIrvKycnBkiVLsGXLFly9ehWCIMDf3x+jRo3CO++8A29vb6kjEpXBQkRERHZz7NgxzJo1C4cPH0ZxcTGUSiWaNm2KuXPnYsiQITxwmpwWCxEREdnF+fPnMWXKFCQnJ8NiscDHxwf9+/fHokWLEBwcLHU8ovtiISIioodWXFyMOXPm4NSpUxBFEUFBQZg+fTpef/11KJVKqeMRPRALERERPbR169Zh7969MBgM8Pf3x9KlS/HUU09JHYuowmRSByAiItd2+PBhrFixAnl5efD09MQrr7yCJ598UupYRJXCQkRERFV27do1zJ49G5cvX4ZcLkdERARiYmJ48DS5HBYiIiKqErPZjAULFuDIkSMwm80IDQ3F0qVLodFopI5GVGksREREVCU7d+7Eli1bUFJSAl9fX7zzzjto3bq11LGIqoSFiIiIKu3s2bOIjY3F9evXoVKp8OSTT+Lll1+WOhZRlbEQERFRpRQUFGD27Nk4c+YMBEFAy5YtsWDBAsjlcqmjEVUZCxEREVWYxWLB2rVr8eOPP8JoNMLf3x+LFy+Gv7+/1NGIHgoLERERVdihQ4ewYsUKFBQUwNPTE5MnT0aPHj2kjkX00HhhRgA6nQ6iKFq/ViqV0Gg0sFgsyM/PLzO/VqsFcHu3sdlstpmm0WigVCphMBhQXFxsM02hUMDLywuiKCIvL6/Men18fCCTyVBYWAiTyWQzzcPDA2q1GkajEUVFRTbT5HK59WaJOp2uzHq9vb0hl8tRXFwMg8FgM02tVsPDwwMmkwmFhYU20wRBgK+vLwAgLy/PZowAwMvLCwqFAiUlJdDr9TbTVCoVPD09YTabUVBQUCZTRcZQr9ejpKTEZlrpGN7r38bX1xeCIJQ7hp6enlCpVFUaw9J/m6KiIhiNRptp9xtDmUwGHx8fAPYdwzv/bfLz82GxWGym328MH7R9V2QMH2b7Lm8M77d9P2gM7bF9lzeG9/u3cdfXiAsXLiA6OhoZGRmQy+Xo3r07pk6dCoCvEXyNuK26XiPKW/5hsRDh9m88d54mGhoaig4dOqC4uBiJiYll5h82bBgAIDk5GTdv3rSZ1r59e9SrVw+ZmZk4deqUzbSAgAB06dIFZrO53PUOGDAAKpUKKSkpuHr1qs201q1bo3HjxsjNzcWJEydspmm1WvTq1QsAcPDgwTIbfu/eveHj44OzZ88iPT3dZlqTJk3QsmVL6HQ6HDp0yGaah4cH+vXrBwA4evRomf803bp1g7+/P9LS0nDu3DmbaWFhYWjXrh2KiorK/KwymQxDhgwBAPz+++9lXlw6duyIkJAQZGRkICUlxWZa3bp10blzZ5hMpnLHcNCgQVAoFDh16hRyc3NtpoWHh6Nhw4a4evUqkpKSbKbVqlXL+ltueevt06cPvLy88OeffyIjI8NmWrNmzdC8eXPcvHkTR44csZnm5eWFPn36ALh98bq732x69OiBWrVq4fz587hw4YLNtIYNGyI8PBwFBQVlMikUCgwaNAgAcOLEiTIvWp06dUJQUBDS09Px559/2kwLDg5GREQEDAZDuT9r6Q04//jjD1y/ft1mWrt27RAWFobs7Gz88ccfNtP8/f3RrVs3iKJY7nr79esHDw8PpKamIisry2ZaixYt0LRpU1y/fh3Hjh2zmebj44PevXsDAH799dcyL8C9evWCVqvFuXPncPHiRZtpjRs3RuvWrZGfn4+DBw/aTFOpVBgwYACA2zckvfuNqkuXLggICMClS5dw9uxZm2nu+BqRm5uL6dOn4/jx4zCbzQgICMCLL74IlUoFgK8RfI24rbpeI+4urPYgiHfXUTeSl5cHrVaL9PR0a5MG3Pe3P+4h4m9/gHP89ncn7iH6P1K+RmzduhVTp06FTqeDr68vli1bhiFDhvA14v/ja8Rt1bmHKCwszLo92gMLkVZr1wElIqpp/ve//+GFF17AmTNnoFKp8Oqrr2LRokWQyXgYKknDEe/f3JqJiOiedDodYmJicO7cOchkMrRv3x7z5s1jGaIah1s0ERGVy2w2Y+3atfjpp59gNptRt25dxMXFwcvLS+poRHbHQkREROVKTExEXFwciouL4enpiejoaLRt21bqWEQOwUJERERlXL58GbNmzcLVq1ehUCgwdOhQvPrqq1LHInIYpyhEsbGx6NSpE3x8fBAYGIjhw4fjzJkz911mx44d6NevHwICAuDr64uuXbvixx9/rKbEREQ1V1FRET788EPrqedNmjTBxx9/zFtzUI3mFIUoISEBEyZMwJEjRxAfHw+TyYT+/fuXOT3xTomJiejXrx++//57nDhxAn/7298wbNiwMteOICKiirNYLNixYwe++uorGI1GaLVaLF26FAEBAVJHI3IopzztPjc3F4GBgUhISLBeTKwiWrdujRdeeAGzZs2q0Pw87Z6IyFZycjJGjBiBCxcuQK1WIzIyErNnz5Y6FpENtzntvvSiV7Vr167wMqUXkKrMMkRE9H9yc3MRExODixcvQiaToWvXroiKipI6FlG1cLpbd4iiiMjISPTo0QNt2rSp8HKLFi1CYWEhnn/++XvOo9frba706Yh7oRARuSK9Xo9PPvkEBw4cgMViQb169bBy5Up4eHhIHY2oWjjdHqKJEyfi5MmT+OKLLyq8zBdffIHZs2dj27ZtCAwMvOd8sbGx0Gq11kf9+vXtEZmIyKWJoogDBw4gLi4OJSUl8PLyQmxsLB555BGpoxFVG6c6hmjSpEnYtWsXEhMT0ahRowots23bNowdOxZff/219WaA91LeHqL69evzGCIicmvnz5/HyJEj8ccff0ChUOCll17CqlWreDVqclqOOIbIKT4yE0URkyZNws6dO3HgwIEKl6EvvvgC48aNwxdffPHAMgTcvsGeWq1+2LhERDWGTqfD/PnzcerUKQBAy5Yt8dFHH7EMkdtxikI0YcIEbN26Fd9++y18fHyQnZ0N4Pbdjj09PQEA0dHRyMjIwGeffQbgdhkaNWoUli5dii5duliX8fT0tN4lmYiI7s1kMmH79u3Yvn07zGYz/P39sWrVKu4xJ7fkFL8CrFq1CjqdDr1790ZwcLD1sW3bNus8WVlZSE9Pt369Zs0amEwmTJgwwWaZKVOmSPEjEBG5nBMnTuDDDz9EYWEh1Go1/vWvfyEiIkLqWESScKpjiKobr0NERO4qIyMDr7/+Ovbt2wdBEDBw4EB89dVXUCqVUkcjeiC3uQ4RERE5TmFhIdauXYuEhAQAQFhYGFasWMEyRG6NhYiIyI1YLBbEx8dj9erVMBqN8PLywpIlSxASEiJ1NCJJsRAREbmR//3vf5g3bx5u3boFpVKJsWPHYuDAgVLHIpIcCxERkZvIzc3FwoULkZqaCplMhvbt22Pu3LkQBEHqaESSYyEiInIDer0eX331FXbt2mU9xX7NmjXWS5sQuTsWIiKiGk4URRw6dAgLFy5EUVERPDw8MHv2bLRq1UrqaEROg4WIiKiGO3/+PD744ANkZWVBJpNh8ODBGDdunNSxiJwKCxERUQ1269YtrF27FocPHwYAPPLII4iLi+OtOYjuwv8RREQ1lMlkwt69e7Fp0yYYjUb4+vpi5cqV8Pf3lzoakdNhISIiqqGSkpIQGxsLnU4HpVKJ8ePHo1evXlLHInJKLERERDXQlStXsGjRIvz111+QyWR47LHHEB0dLXUsIqfFQkREVMMUFhbiyy+/xN69eyGKIgIDA7F+/Xqo1WqpoxE5LRYiIqIaRBRFJCQkYNmyZSgpKYGnpycWLFiAhg0bSh2NyKmxEBER1SCXL1/GihUrkJOTA4VCgWeeeQbPP/+81LGInB4LERFRDWE2m/Hzzz/j0KFDAIAGDRpgyZIlvDUHUQWwEBER1RAXLlzAmjVrUFRUBJVKhejoaPj4+Egdi8glsBAREdUABoMBe/bswalTpyAIAtq0aYMXX3xR6lhELoOFiIioBjhz5gw2bNgAo9EIT09PzJ07F0qlUupYRC6DhYiIyMUVFRXhq6++woULFyCTydCtWzf06dNH6lhELoWFiIjIxZ0+fRqff/45zGYzfHx8MH/+fB5ITVRJLERERC4sLy8PGzduRHZ2NmQyGZ588km0adNG6lhELoeFiIjIhR0/fhy7du2CxWKBv78/5s6dK3UkIpfEQkRE5KKuXbuG1atX4+bNm5DL5Rg3bhyCg4OljkXkkliIiIhc1MGDB7Fv3z6Iooh69eph+vTpUkciclksRERELigzMxNxcXEoKCiAUqnEv/71L16EkeghsBAREbkYi8WCn376Cb/99hsEQUDTpk0xduxYqWMRuTQWIiIiF5Oeno7ly5dDr9dDrVZj3rx5UKlUUscicmksRERELsRkMuGbb77BmTNnIAgCIiIiMGjQIKljEbk8FiIiIhdy7tw5rFmzBkajERqNBgsWLIBMxpdyoofF/0VERC5Cr9dj48aNuHLlCgRBQL9+/dChQwepYxHVCCxEREQuIiUlBVu3boXZbIZWq8WCBQt4iw4iO2EhIiJyAYWFhYiLi0Nubi5kMhlGjhyJBg0aSB2LqMZgISIicgEnTpzAnj17IIoiAgMDMWvWLKkjEdUoLERERE5Op9Nh4cKFyMvLg1wux6RJk1CrVi2pYxHVKCxEREROLjExEYmJiQCARo0aYcKECRInIqp5WIiIiJxYbm4u5s+fj+LiYiiVSsTExMDT01PqWEQ1DgsREZGTEkUR3377LZKTkyEIAsLDw/HMM89IHYuoRmIhIiJyUhkZGVi6dCmMRiPUajXmz58PhUIhdSyiGomFiIjICVksFnz22Wc4f/48BEFAz5490aNHD6ljEdVYLERERE7owoULWLduHcxmM7y9vXmLDiIH4/8uIiInYzQasWzZMmRnZ0Mmk2H48OFo1aqV1LGIajQWIiIiJ3P69Gls374dFosFtWvXxpw5c6SORFTjsRARETmRkpISzJ8/Hzdu3IBMJsO4ceMQGhoqdSyiGo+FiIjIiRw9ehTx8fEQRRGhoaGYNm2a1JGI3AILERGRkygoKMC8efNQUFAAhUKByMhIaLVaqWMRuQUWIiIiJ/Hjjz/i6NGjAIBmzZph7NixEicich8sRERETuDmzZuIjY2FXq+HWq3G7NmzeYsOomrEQkRE5AS++OIL/PnnnxAEARERERgyZIjUkYjcCgsREZHEsrOzrbfo0Gg0mDdvHm/RQVTNnKIQxcbGolOnTvDx8UFgYCCGDx+OM2fOPHC5hIQEdOzYER4eHmjcuDFWr15dDWmJiOxHFEXExcXh8uXLEAQBffv2RdeuXaWOReR2nKIQJSQkYMKECThy5Aji4+NhMpnQv39/FBYW3nOZtLQ0DB48GD179kRSUhJmzJiByZMn45tvvqnG5ERED+fixYv49NNPYTab4efnh3nz5kEQBKljEbkdp9gn+8MPP9h8vXHjRgQGBuLEiRPo1atXucusXr0aYWFhWLJkCQCgZcuWOH78OBYuXIhnn33W0ZGJiB6axWLB3Llzce3aNchkMvz9739H8+bNpY5F5JacYg/R3XQ6HQCgdu3a95zn8OHD6N+/v81zAwYMwPHjx2E0GstdRq/XIy8vz+ZBRCSVP/74A3v27IHFYkFgYCBmzJghdSQit+V0hUgURURGRqJHjx5o06bNPefLzs5G3bp1bZ6rW7cuTCYTrl27Vu4ysbGx0Gq11kf9+vXtmp2IqKKMRiNiYmKQn58PhUKBN954AyEhIVLHInJbTleIJk6ciJMnT+KLL7544Lx3f84uimK5z5eKjo6GTqezPi5fvvzwgYmIqiAhIQG//PILRFFEWFgYxo8fL3UkIrfmFMcQlZo0aRJ2796NxMRE1KtX777zBgUFITs72+a5nJwcKBQK+Pv7l7uMWq2GWq22W14ioqooKSlBTEwMioqKoFKp8Pbbb8PPz0/qWERuzSn2EImiiIkTJ2LHjh34+eef0ahRowcu07VrV8THx9s899NPPyEiIgJKpdJRUYmIHtrXX3+NkydPAgBat26NESNGSJyIiJyiEE2YMAGff/45tm7dCh8fH2RnZyM7OxvFxcXWeaKjozFq1Cjr12+++SYuXbqEyMhI/O9//8OGDRuwfv163hmaiJxafn4+FixYAIPBAI1Gg3feeQceHh5SxyJye05RiFatWgWdTofevXsjODjY+ti2bZt1nqysLKSnp1u/btSoEb7//nscOHAAjz76KObOnYtly5bxlHsicmorVqxAWloaBEFAly5dMHDgQKkjEREAQSw9EtkN5eXlQavVQqfTwdfXV+o4RFTD5ebmolOnTsjKyoKvry927dqF7t27Sx2LyOU44v3bKfYQERG5g3nz5iEnJweCIGDgwIHo1q2b1JGI6P9jISIiqgbnz5/HV199BbPZDH9/f0RFRfEWHUROhIWIiMjBRFHEjBkzcOvWLchkMrz44oto3bq11LGI6A4sREREDnbs2DHs27cPFosF9evXx9SpU6WORER3YSEiInIgs9mMqKgoFBQUQKlU4tVXX+Vtg4icEAsREZED7dmzBydOnIAoimjSpAleffVVqSMRUTlYiIiIHMRgMGDu3LnQ6/Xw8PDAlClTULt2baljEVE5WIiIiBxk9erVOHv2LERRRLt27fDiiy9KHYmI7oGFiIjIAc6dO4dly5bBYDDA29sb06ZNg6enp9SxiOgeWIiIiOzswoULGDt2LLKzsyGTydCrVy8MGjRI6lhEdB8sREREdpSamooRI0YgKSkJJpMJoaGhiIyMhFKplDoaEd0HCxERkZ0cO3YMI0aMwOnTp2EymVC/fn1ER0ejR48eUkcjogdQSB2AiKgmSEhIwJtvvolLly5BFEU0btwY8+bNwzPPPCN1NCKqABYiIqKHIIoi9u7di8mTJ+PKlSsQBAHNmzfH0qVL8fjjj0sdj4gqiIWIiKiKLBYLtm/fjmnTpiEnJwcymQxt27bF2rVr0bZtW6njEVEl8BgiIqIqsFgs2LRpE6ZMmYKrV69CLpejS5cu2LZtG8sQkQviHiIiokoymUyIi4vDBx98AJ1OB6VSiT59+mDDhg2oU6eO1PGIqApYiIiIKsFoNCI2NhZLlixBYWEhVCoVnn76aaxcuRLe3t5SxyOiKmIhIiKqIL1ej3fffRdr1qxBSUkJ1Go1Ro8ejcWLF/M6Q0QujoWIiKgCiouLMWXKFHzxxRcwGAzQaDSYMmUKZs2aBZmMh2MSuToWIiKiB8jPz8err76KPXv2wGw2w9vbG3PnzsX48eOljkZEdsJCRER0Hzdv3sTIkSORkJAAs9kMPz8/rFy5Es8++6zU0YjIjipciHbv3l3plffr1493dyYil5WTk4Nnn30Wx44dgyiKCAgIwJYtW3jBRaIaqMKFaPjw4ZVasSAI+Ouvv9C4cePKZiIiktzly5fx1FNPISUlBQAQEhKCPXv2oE2bNhInIyJHqNSRgNnZ2bBYLBV6aDQaR2UmInKoM2fOoH///tYy9MgjjyAhIYFliKgGq3AhGj16dKU+/nr55Zfh6+tbpVBERFL5/fffMWjQIFy4cAGCIKBNmzY4ePAgwsLCpI5GRA4kiKIoSh1CKnl5edBqtdDpdCxvRISEhASMGDEC169fh0wmw2OPPYa9e/fyWEgiJ+OI9+9KfWSWnJxsl29KRORs9uzZg7///e/WMjRgwADEx8ezDBG5iUoVog4dOqBjx45YtWoVdDqdozIREVWrTz/9FKNGjUJeXh7kcjlefPFF7Nq1i1efJnIjlSpEv/76Kzp06ICoqCgEBwfj5Zdfxv79+x2VjYjIoURRxOLFizFp0iQUFRVBqVRi4sSJ2Lhxo9TRiKiaVaoQde3aFevWrUN2djZWrVqFK1eu4IknnsAjjzyC999/H1euXHFUTiIiu7JYLJg5cyZmzZoFvV4PlUqFmJgYLFiwQOpoRCSBhz6o+vz589i4cSM+++wzZGVloV+/fvj+++/tlc+heFA1kXsym80YP348Nm/eDLPZDE9PTyxduhSjR4+WOhoRVYAj3r/tcpZZQUEBtmzZghkzZuDWrVswm832yOZwLERE7sdgMOCll17Cf/7zH1gsFvj4+GDTpk0YOnSo1NGIqIIc8f79UPcyS0hIwIYNG/DNN99ALpfj+eefxz/+8Q+7BCMisreSkhIMGzYMv/zyC0RRRK1atfDNN9+ge/fuUkcjIolVuhBdvnwZmzZtwqZNm5CWloZu3bph+fLleP755+Hl5eWIjEREDy0vLw99+/bFyZMnAQB169bF999/z6tPExGAShaifv36Yf/+/QgICMCoUaMwbtw4NG/e3FHZiIjs4urVq+jTpw/OnTsHAGjYsCH27t3Ley0SkVWlCpGnpye++eYbDB06FHK53FGZiIjsJiMjAz169EBmZiYAoHXr1vjPf/6DkJAQiZMRkTOpcCE6efIkdu7cWeEilJKSgubNm0OheKjDlIiIHkrfvn2RmZkJQRDQuXNn7NixA3Xq1JE6FhE5mQpfh6h9+/a4ceNGhVfctWtXpKenVykUEZE9vPPOO0hLSwMA9OzZE7t372YZIqJyVXj3jSiKePfdd6HRaCo0v8FgqHIoIqKHZTAYsGLFCgBAYGAgdu7cCW9vb4lTEZGzqnAh6tWrF86cOVPhFXft2pU3RSQiyYwcORJFRUUQBAHr1q1jGSKi+6pwITpw4IADYxAR2U9WVhZ++OEHAECLFi0wcOBAiRMRkbOr1L3MiIhcwbPPPguj0Qi5XI7t27dLHYeIXAALERHVKIcPH0ZSUhIA4PHHH0eTJk0kTkRErqBShej69euOykFEZBejR4+GxWKBWq3G119/LXUcInIRlSpETZs2xYoVK2CxWByVh4ioyj799FNcunQJwO2DqnkgNRFVVKUK0bRp0xAdHY1HH30UCQkJjspERFRpoigiOjoaAODr64tVq1ZJnIiIXEmlCtGMGTPw119/ISIiAn379sULL7yAK1euOCobEVGFRUVFWT/Wj46OhiAIEiciIldS6YOq69atiw0bNuDYsWPIzs5GixYtMHfuXOj1+iqHSExMxLBhwxASEgJBELBr164HLrNlyxa0a9cOGo0GwcHBGDt2LI9xInJTJSUlWLt2LQAgODgYkZGREiciIldT5bPM2rdvj4SEBGzatAmbNm1CixYtsHPnziqtq7CwEO3atUNcXFyF5j948CBGjRqFf/zjH0hJScHXX3+NY8eO4dVXX63S9yci1/byyy9bL8K4cuVKqeMQkQsSRFEUH3YlBoMBixcvxocffohOnTohPj6+6oEEATt37sTw4cPvOc/ChQuxatUqnD9/3vrc8uXLsWDBAly+fLnC3ysvLw9arRY6nQ6+vr5VzkxE0snMzESzZs1gNBrRqlUr6yn3RFRzOeL9u8p7iPR6PZKTk7F582a8++67+OWXX6BSqfDzzz/bJdj9dOvWDVeuXMH3338PURRx9epVbN++HUOGDHH49yYi5/L3v/8dRqMRCoUCX375pdRxiMhFVfjWHQAwZ84cnDp1CqdPn8b58+dhNpvh5+eH8PBwhIeH48knn0R4eLijslp169YNW7ZswQsvvICSkhKYTCY8+eSTWL58+X2X0+v1Nsc65eXlOToqETnQwYMHrXuEevTogebNm0uciIhcVaU+MmvXrh3atm1rLUDh4eGoV6+efQNV4COz1NRUPPHEE3jrrbcwYMAAZGVlYfr06ejUqRPWr19/z+Vmz56NOXPmlHmeH5kRuaZmzZrh0qVLUKvVuHLlCv8fE7kJR3xkZpdjiOypIoXolVdeQUlJic1VaA8ePIiePXsiMzMTwcHB5S5X3h6i+vXrsxARuaCNGzfin//8J0RRxOjRo61nmRFRzeeIQlSpj8ycRVFRERQK2+hyuRzA7Yuz3YtarYZarXZoNiJyPLPZjJkzZ0IURfj6+vLMMiJ6aE5xc9eCggIkJycjOTkZAJCWlobk5GSkp6cDuH2RtVGjRlnnHzZsGHbs2IFVq1bhwoUL+PXXXzF58mR07twZISEhUvwIRFSN3nnnHet1x/7973+X+QWJiKiynOIjswMHDuBvf/tbmedHjx6NTZs2YcyYMbh48SIOHDhgnbZ8+XKsXr0aaWlp8PPzQ58+fTB//nyEhoZW+PvytHsi11NYWIh69eqhqKgIwcHBuHjxotSRiKiaucUxRNWJhYjI9Tz33HPYvXs3BEHA9u3bMXToUKkjEVE1c6rrEBERVbeLFy9i7969AIAWLVqwDBGR3bAQEZHLGDlyJIxGI+RyOT777DOp4xBRDcJCREQuYf/+/TYXYWzbtq3EiYioJmEhIiKnJ4oi3nzzTVgsFqjVamzdulXqSERUw7AQEZHTW7duHS5dugQAeOGFF1CnTh2JExFRTcOzzHiWGZFTMxqNaNiwIa5duwZfX19cuXKFF1glcnM8y4yI3M7MmTNx7do1AEBkZCTLEBE5BPcQcQ8RkdO6ceMGmjRpgsLCQgQFBeHixYsQBEHqWEQkMe4hIiK38vrrr6OwsBAymQwfffQRyxAROQwLERE5pbNnz+LHH38EADRr1gzPP/+8xImIqCZjISIipzRmzBgYDAbI5XKsW7dO6jhEVMOxEBGR0/nxxx+RnJwM4PZFGDt37ixtICKq8ViIiMipmEwmTJ48GWazGR4eHtiwYYPUkYjIDbAQEZFTWbduHdLT0wEAw4cPR7169SRORETugKfd87R7IqdRVFSEpk2bWi/CmJaWBm9vb6ljEZGT4Wn3RFSjvfPOO9aLME6aNIlliIiqDfcQcQ8RkVPIzMxEeHg4CgoKEBQUhPPnz0OhUEgdi4icEPcQEVGNNWnSJBQUFEAmk2Hu3LksQ0RUrViIiEhyycnJ+OmnnwAATZs2xahRoyRORETuhoWIiCRlsVgwfvx4GAwGKBQKLF++XOpIROSGWIiISFL/+c9/rBdh7Ny5Mx5//HFpAxGRW2IhIiLJ6PV6TJ8+3XoRRt6ig4ikwkJERJJZsWKF9SKMw4YNQ5MmTSRORETuioWIiCRx69YtLF68GBaLBb6+vjx2iIgkxUJERJKYNWsWcnNzIZPJ8Nprr6FWrVpSRyIiN8ZCRETVLi0tDVu3bgUABAYGIiYmRuJEROTuWIiIqNpFRkYiPz8fcrkcM2bMgFqtljoSEbk5FiIiqlaHDx/Gzz//DABo3LgxXnvtNYkTERGxEBFRNTKZTIiMjERJSQmUSiUWLFgAmYwvQ0QkPb4SEVG12b59O06ePAkA6NixIwYPHixxIiKi21iIiKhaFBQUYPbs2TCZTPDw8EBcXJzUkYiIrFiIiKharFixApcuXQIADBw4EOHh4RInIiL6PyxERORw2dnZiIuLg8VigVarxccffyx1JCIiGyxERORwc+bMwbVr1yCTyfDyyy8jJCRE6khERDZYiIjIoU6fPo2vv/4aFosFAQEBmDNnjtSRiIjKYCEiIoexWCyYMWOG9SKMb731Fnx8fKSORURUBgsRETnMf//7XyQkJAC4fRHGiRMnSpyIiKh8LERE5BB6vR7vvPMOSkpKoFKp8P7770OpVEodi4ioXCxEROQQW7ZsQUpKCgAgIiICTz31lMSJiIjujYWIiOzu5s2biI2NhclkgpeXF5YtWyZ1JCKi+2IhIiK7W7RoEa5cuQJBEDB06FBehJGInB4LERHZVVpaGtavXw+LxQI/Pz8sWrRI6khERA/EQkREdiOKIt59913cvHkTcrkcb7zxBgICAqSORUT0QCxERGQ3R48exXfffQdRFBESEoIZM2ZIHYmIqEJYiIjILkwmE6Kjo1FUVASlUomYmBio1WqpYxERVQgLERHZxe7du3H8+HEAQKtWrfDyyy9LnIiIqOJYiIjooRUUFCAmJgYGgwEeHh5YtmwZBEGQOhYRUYWxEBHRQ1u9ejXOnTsHAOjduze6dOkicSIiosphISKih5KVlYVly5bBYrHAx8cHy5cvlzoSEVGlOUUhSkxMxLBhwxASEgJBELBr164HLqPX6zFz5kw0aNAAarUajzzyCDZs2OD4sERkJYoi3n//feTk5EAmk2HkyJEICwuTOhYRUaUppA4AAIWFhWjXrh3Gjh2LZ599tkLLPP/887h69SrWr1+PJk2aICcnByaTycFJiehOKSkp+PLLLyGKIgIDA/Hhhx9KHYmIqEqcohANGjQIgwYNqvD8P/zwAxISEnDhwgXUrl0bANCwYUMHpSOi8pjNZsycORP5+fmQy+WYPn06NBqN1LGIiKrEKT4yq6zdu3cjIiICCxYsQGhoKJo1a4Zp06ahuLj4vsvp9Xrk5eXZPIioavbv348DBw4AAJo0aYIJEyZIG4iI6CE4xR6iyrpw4QIOHjwIDw8P7Ny5E9euXcP48eNx48aN+x5HFBsbizlz5lRjUqKaqaSkBNHR0SgpKYFKpcKCBQsgk7nk71dERABcdA+RxWKBIAjYsmULOnfujMGDB2Px4sXYtGnTffcSRUdHQ6fTWR+XL1+uxtRENcfWrVuRmpoKAOjUqRMGDhwocSIioofjknuIgoODERoaCq1Wa32uZcuWEEURV65cQdOmTctdTq1W81YCRA/p2rVriI2NhclkgkajwYoVK6SORET00FxyD1H37t2RmZmJgoIC63Nnz56FTCZDvXr1JExGVPMtWbIEly9fhiAIGDp0KFq2bCl1JCKih+YUhaigoADJyclITk4GAKSlpSE5ORnp6ekAbn/UNWrUKOv8I0eOhL+/P8aOHYvU1FQkJiZi+vTpGDduHDw9PaX4EYjcwvnz57F+/XqIogg/Pz9ehJGIagynKETHjx9H+/bt0b59ewBAZGQk2rdvj1mzZgG4fSXc0nIEAN7e3oiPj8etW7cQERGBl156CcOGDcOyZcskyU/kDiwWC2JiYnDjxg3I5XK8+eab8PPzkzoWEZFdCKIoilKHkEpeXh60Wi10Oh18fX2ljkPk1A4fPozBgwejqKgIDRo0QEpKCpRKpdSxiMgNOeL92yn2EBGRc9Pr9ZgxYwaKioqgVCoRExPDMkRENQoLERE90O7du3Hs2DEAQKtWrTBixAiJExER2RcLERHd161btzBnzhwYjUZ4eHhg+fLlvAgjEdU4fFUjovtau3Ytzp8/D0EQ0Lt3bzz22GNSRyIisjsWIiK6p/T0dKxYsQIWiwU+Pj68CCMR1VgsRERULovFgvnz5+Pq1auQyWQYMWIEL3xKRDUWCxERlSs5ORnbtm2DKIoICAjAhx9+KHUkIiKHYSEiojIMBgNmz56N/Px8yOVyTJs2DRqNRupYREQOw0JERGX8/PPPSEhIAAA0btwYEyZMkDgREZFjsRARkY38/HzExMSgpKQEKpUKCxYsgFwulzoWEZFDsRARkY2tW7fi9OnTAICOHTti0KBBEiciInI8FiIissrMzMTChQthMpmg0WiwYsUKCIIgdSwiIodjISIiAIAoili6dCkuX74MQRAwePBgtG7dWupYRETVgoWIiAAAKSkp2Lx5M0RRRK1atbB06VKpIxERVRsWIiKC0WjEBx98gBs3bkAul+P1119HnTp1pI5FRFRtWIiICAcPHsQPP/wAURQREhKCGTNmSB2JiKhasRARubnCwkLMnTsXhYWFUCgUmDVrFtRqtdSxiIiqFQsRkZvbsWMHjh07BgBo1aoVXn75ZYkTERFVPxYiIjeWnZ2NBQsWwGAwwMPDA4sXL4ZMxpcFInI/fOUjclOiKOKTTz7B+fPnIQgCevbsiZ49e0odi4hIEixERG7q7NmzWLduHcxmM3x8fLB8+XKpIxERSYaFiMgNmc1mfPzxx7h69SpkMhmee+45NGrUSOpYRESSYSEickNHjhzBjh07IIoi6tSpg9jYWKkjERFJioWIyM0UFRUhNjYWOp0OcrkcU6ZMgVarlToWEZGkWIiI3Mx3332HgwcPAgAaN26MKVOmSJyIiEh6LEREbiQ3NxcfffQRiouLoVar8d5770GpVEodi4hIcixERG5k69atSElJgSAIePTRR/H0009LHYmIyCmwEBG5iXPnzmHFihUwmUzQaDRYunQpBEGQOhYRkVNgISJyAyaTCcuWLcPly5chk8kwYMAAtG/fXupYREROg4WIyA3s2rULW7ZsgcViQa1atbBo0SKpIxERORUWIqIaLjU1FTNmzEBBQQFUKhWioqIQEhIidSwiIqfCQkRUg+l0OkyfPh2XLl2CXC5Hv379MGnSJKljERE5HRYiohrKYrFg4cKFOHDgAAAgLCwMn332GQ+kJiIqBwsRUQ313XffYdWqVTCZTPDx8cHnn38Ob29vqWMRETklFiKiGuj8+fOYPn068vPzoVQqERUVhYiICKljERE5LYXUAZyBTqeDKIrWr5VKJTQaDSwWC/Lz88vMX3rfp4KCApjNZptpGo0GSqUSBoMBxcXFNtMUCgW8vLwgiiLy8vLKrNfHxwcymQyFhYUwmUw20zw8PKBWq2E0GlFUVGQzTS6XW3/z1+l0Zdbr7e0NuVyO4uJiGAwGm2lqtRoeHh4wmUwoLCy0mSYIAnx9fQEAeXl5NmMEAF5eXlAoFCgpKYFer7eZplKp4OnpCbPZjIKCgjKZKjKGer0eJSUlNtNKx/Be/za+vr4QBKHcMfT09IRKparSGJb+2xQVFcFoNNpMu98YymQy+Pj4ALDvGN75b5Ofnw+LxWKdVlBQgKlTp+LixYsQBAE9evTAP/7xD+vP9aDtuyJj+DDbd3ljeL/t+0FjaI/t++4xBO7/b8PXiNv4GvF/XOk1Arj/GLrCa0R5yz8sFiIAhw4dgkajsX4dGhqKDh06oLi4GImJiWXmHzZsGAAgOTkZN2/etJnWvn171KtXD5mZmTh16pTNtICAAHTp0gVms7nc9Q4YMAAqlQopKSm4evWqzbTWrVujcePGyM3NxYkTJ2ymabVa9OrVCwBw8ODBMht+79694ePjg7NnzyI9Pd1mWpMmTdCyZUvodDocOnTIZpqHhwf69esHADh69GiZ/zTdunWDv78/0tLScO7cOZtpYWFhaNeuHYqKisr8rDKZDEOGDAEA/P7772VeXDp27IiQkBBkZGQgJSXFZlrdunXRuXNnmEymcsdw0KBBUCgUOHXqFHJzc22mhYeHo2HDhrh69SqSkpJsptWqVQs9evQAgHLX26dPH3h5eeHPP/9ERkaGzbRmzZqhefPmuHnzJo4cOWIzzcvLC3369AEAHD58uMybTY8ePVCrVi2cP38eFy5csJnWsGFDhIeHo6CgoEwmhUKBQYMGAQBOnDhhfdESRRFff/019u3bB1EUERwcjDFjxljvXQYAwcHBiIiIgMFgKPdnHTJkCARBwB9//IHr16/bTGvXrh3CwsKQnZ2NP/74w2aav78/unXrBlEUy11vv3794OHhgdTUVGRlZdlMa9GiBZo2bYrr16/j2LFjNtN8fHzQu3dvAMCvv/5a5gW4V69e0Gq1OHfuHC5evGgzrXHjxmjdujXy8/NtxgC4/WYyYMAAAMCxY8fKvFF16dIFAQEBuHTpEs6ePWszja8Rt/E14v+4ymtEqU6dOiEoKAjp6en4888/baa5wmvE3YXVHgTx7jrqRvLy8qDVapGenm5t0gB/+yvF3/7+j6v89rd//3689tpr0Ol08PHxwY4dO/Doo4/aLOsKv/1VZgy5h+g2vkbcxteI29xhD1FYWBh0Op3N+/fDYCHSau06oERSuXTpEoYMGYK//voLSqUS0dHRmDlzptSxiIjszhHv3zyomqgGKCkpwbRp03Du3DkIgoDu3bsjOjpa6lhERC6DhYioBli+fDl++OEHiKKIkJAQfPnll5DJ+N+biKii+IpJ5OISExOxcOFCGAwGeHl54ZNPPkGtWrWkjkVE5FJYiIhcWGZmJiZPnoxbt25BoVBg/Pjx1jNWiIio4liIiFyUwWDA9OnT8eeff0IQBDz22GOYM2eO1LGIiFwSCxGRi1q7di12794NURQRFBSEL7/8EnK5XOpYREQuiYWIyAUdOXIE77//PgwGAzw9PbFy5UoEBgZKHYuIyGWxEBG5mNzcXEyYMAE3btyAQqHA66+/jsGDB0sdi4jIpbEQEbkQk8mEadOmITU1FYIgoGPHjnj//feljkVE5PKcohAlJiZi2LBhCAkJgSAI2LVrV4WX/fXXX6FQKMrcnoCoJlq/fj127twJi8WCwMBAfPHFF1AqlVLHIiJyeU5RiAoLC9GuXTvExcVVajmdTodRo0ahb9++DkpG5DySkpLw3nvvQa/Xw9PTE0uXLkVoaKjUsYiIagSnuNv9oEGDrHflrYw33ngDI0eOhFwur9ReJSJXc+vWLbz++uu4du0a5HI5Ro0ahaefflrqWERENYZT7CGqio0bN+L8+fOIiYmp8DJ6vR55eXk2DyJnZ7FYMH36dJw+fRqCIODRRx/FRx99JHUsIqIaxSUL0V9//YWoqChs2bIFCkXFd3LFxsZCq9VaH/Xr13dgSiL72Lx5M7766itYLBbUqVMHn3/+OdRqtdSxiIhqFJcrRGazGSNHjsScOXPQrFmzSi0bHR0NnU5nfVy+fNlBKYnsIzU1FTNnzkRJSQk8PDywcOFCNG7cWOpYREQ1jlMcQ1QZ+fn5OH78OJKSkjBx4kQAtz9SEEURCoUCP/300z3v5aRWq/mbNbmM/Px8/OMf/0Bubi7kcjlefPFFvPDCC1LHIiKqkVyuEPn6+uLUqVM2z61cuRI///wztm/fjkaNGkmUjMh+RFFEVFQU/vjjDwiCgNatW2Px4sUQBEHqaERENZJTFKKCggKcO3fO+nVaWhqSk5NRu3ZthIWFITo6GhkZGfjss88gk8nQpk0bm+UDAwPh4eFR5nkiV/Xll19iy5YtMJvNqFOnDjZv3gwvLy+pYxER1VhOUYiOHz+Ov/3tb9avIyMjAQCjR4/Gpk2bkJWVhfT0dKniEVWrc+fO4d///jeKi4uhVqsxb948tGjRQupYREQ1miCKoih1CKnk5eVBq9VCp9PB19dX6jhEKC4uRr9+/XD8+HEAwIgRI7B+/XrIZC53/gMRkcM44v2br7JETkIURURHR+P333+HKIpo1aoVlixZwjJERFQN+EpL5CR2796NTz/9FGazGbVq1cKGDRug1WqljkVE5BZYiIicwKVLlzBlyhQUFRVBpVJh1qxZvGExEVE1YiEikpher8eYMWOQnZ0NQRDw5JNP4o033pA6FhGRW2EhIpLYrFmz8Ntvv0EURTRv3hzLli2DXC6XOhYRkVthISKS0N69e7Fu3TqYTCZotVqsWbMG/v7+UsciInI7LEREEsnMzMSECRNQWFgIlUqFqKgodOnSRepYRERuiYWISAJGoxGjR49GZmYmBEHAoEGDMGHCBKljERG5LRYiIgm89957OHz4MERRRNOmTbFkyRLeeJiISEIsRETV7Oeff8bKlSthNBqh1WqxdOlShISESB2LiMitOcW9zIjcgcViwYEDB/Daa6+hoKAASqUSkZGRNvfxIyIiabAQETnYuXPnsHbtWuzevRuZmZnQ6/UQBAH9+/fH5MmTIQiC1BGJiNweCxGRA1y/fh2ffPIJtm7diosXL6KkpMQ6TalUol27dvj444+h0WgkTElERKVYiIjsRK/X47PPPsOmTZuQmpqK4uJiiKIIAJDL5fDz80OXLl0wduxY9O/fnwdRExE5ERYioodgNpuxZ88exMXFITk5GYWFhbBYLAAAmUwGHx8ftG/fHuPGjcOwYcPg5eUlcWIiIioPCxFRJYmiiF9//RUff/wxDh48iPz8fJjNZgCAIAjw8vJCq1atMG7cODzzzDPw8/OTNjARET0QCxFRBYiiiJSUFHz00UfYv38/rl27Zi1BAODh4YFHHnkEo0aNwsiRIxEYGChhWiIiqiwWIqJ7EEURly5dwscff4z//Oc/yM7Ohslksk5XqVSoX78+XnzxRYwZMwZhYWESpiUioofBQkR0l8zMTKxduxZff/010tPTYTAYrNMUCgWCgoIwbNgwjB8/Hk2bNuVp80RENQALERGA3NxcfP755/j8889x7tw56PV6mzPE/P398cQTT2DSpEl49NFHIZPxIu9ERDUJCxG5rRs3bmDHjh3W0+SLioqsJUgmk8HPzw/dunXDpEmT0L17d6hUKokTExGRo7AQkcuyWCwoKSlBUVERCgoKUFRUBJ1OB51Oh5ycHFy/fh03btywPpefn4/CwkIUFRUhPz8fmZmZyM/Pt54mLwgCfH190bZtW0yYMAH9+/eHt7e3xD8lERFVBxYiqhYlJSUoKCjAzZs3kZ2djezsbGRkZCA3Nxd5eXkoKCiATqdDUVGRteSUlJTAYDDAYDDAaDTCbDbDaDTCYrHAZDLBYrFAFEXrnwCs5aaiBEGARqNB8+bNMWbMGDz//POoXbu2I4aAiIicGAsRlVFaPIqLi1FUVITi4mLrIz8/Hzk5OcjJycHVq1dx7do1616Y0j0wJSUl1jJjNBqt5aW0uJQ+HKH0AGdBECAIAmQyGQRBgFwut3kolUoEBgbi6aefxssvv4ywsDAeHE1E5MZYiJxYaYkoLRYGgwF6vR4Gg8FaOkpKSqDX662P0mml85aUlKC4uBgFBQUoKSlBYWEhiouLbUpL6del0+7cI1NaZsxms02heVilRUUmk9k8FAqF9aFUKqFUKuHh4QG1Wg1PT094eXlBo9HAx8cHfn5+qFWrFmrVqoXatWtDq9XCz8/P+neNRgO1Wg25XG7zPYiIiO7GQgRg2rRpEAQBBoPBpgAAgMlksn4sc+dejtLpdz5/90c4dy9XOr/ZbLbZU1I6n8lkspYQs9lsLSHlrfvOj4buXHcpR+yJKd2DcmeZuXOPi1KptBYXT09P+Pr6WktLQEAAgoODERAQgKCgINSqVQt+fn7w9vaGh4eHtQDJ5XK75SUiIqooQXTUZxcuIC8vD1qtFkql0iU/Lrk7850fF5X+Wd5zd++VkcvlUCgUUKvV8PDwgJeXF7y9vaHVauHr64s6derA398fgYGBCAoKQmhoKPz9/eHr6wsvLy8oFOzVRERUfUrfv3U6HXx9fe2yTr6TAVCr1da9HncXiFL3Kxt3fn2vElL6550f2ZR+LYqizcdFSqUSKpUKKpXKuteltKzc+XcPDw9oNBqoVCrr156entY/PT094e3tDS8vL+vX5X2EJAiC9XtyDw0REbkjFiIAp06dgp+fn3Vvyd0H5JaWGB6DQkREVDOxEAGoXbu23Xa5ERERkevh7g4iIiJyeyxERERE5PZYiIiIiMjtsRARERGR22MhIiIiIrfHQkRERERuj4WIiIiI3B4LEREREbk9FiIiIiJyeyxERERE5PZYiIiIiMjtsRARERGR22MhIiIiIrfn1ne7F0URAJCXlydxEiIiIqqo0vft0vdxe3DrQnT9+nUAQP369SVOQkRERJV1/fp1aLVau6zLrQtR7dq1AQDp6el2G1B3lJeXh/r16+Py5cvw9fWVOo5L41jaD8fSPjiO9sOxtB+dToewsDDr+7g9uHUhksluH0Kl1Wq5cdqBr68vx9FOOJb2w7G0D46j/XAs7af0fdwu67LbmoiIiIhcFAsRERERuT23LkRqtRoxMTFQq9VSR3FpHEf74VjaD8fSPjiO9sOxtB9HjKUg2vOcNSIiIiIX5NZ7iIiIiIgAFiIiIiIiFiIiIiIiFiIiIiJye25ViN5//31069YNGo0Gfn5+FVpmzJgxEATB5tGlSxfHBnUBVRlLURQxe/ZshISEwNPTE71790ZKSopjg7qAmzdv4pVXXoFWq4VWq8Urr7yCW7du3XcZbpfAypUr0ahRI3h4eKBjx4745Zdf7jt/QkICOnbsCA8PDzRu3BirV6+upqTOrzJjeeDAgTLbniAI+PPPP6sxsXNKTEzEsGHDEBISAkEQsGvXrgcuw+2yrMqOo722SbcqRAaDAc899xz++c9/Vmq5gQMHIisry/r4/vvvHZTQdVRlLBcsWIDFixcjLi4Ox44dQ1BQEPr164f8/HwHJnV+I0eORHJyMn744Qf88MMPSE5OxiuvvPLA5dx5u9y2bRumTp2KmTNnIikpCT179sSgQYOQnp5e7vxpaWkYPHgwevbsiaSkJMyYMQOTJ0/GN998U83JnU9lx7LUmTNnbLa/pk2bVlNi51VYWIh27dohLi6uQvNzuyxfZcex1ENvk6Ib2rhxo6jVais07+jRo8WnnnrKoXlcWUXH0mKxiEFBQeKHH35ofa6kpETUarXi6tWrHZjQuaWmpooAxCNHjlifO3z4sAhA/PPPP++5nLtvl507dxbffPNNm+datGghRkVFlTv/22+/LbZo0cLmuTfeeEPs0qWLwzK6isqO5f79+0UA4s2bN6shnesCIO7cufO+83C7fLCKjKO9tkm32kNUVQcOHEBgYCCaNWuG1157DTk5OVJHcjlpaWnIzs5G//79rc+p1Wo8/vjjOHTokITJpHX48GFotVo89thj1ue6dOkCrVb7wHFx1+3SYDDgxIkTNtsSAPTv3/+eY3b48OEy8w8YMADHjx+H0Wh0WFZnV5WxLNW+fXsEBwejb9++2L9/vyNj1ljcLu3rYbdJFqIHGDRoELZs2YKff/4ZixYtwrFjx9CnTx/o9Xqpo7mU7OxsAEDdunVtnq9bt651mjvKzs5GYGBgmecDAwPvOy7uvF1eu3YNZrO5UttSdnZ2ufObTCZcu3bNYVmdXVXGMjg4GGvXrsU333yDHTt2oHnz5ujbty8SExOrI3KNwu3SPuy1Tbr83e5nz56NOXPm3HeeY8eOISIiokrrf+GFF6x/b9OmDSIiItCgQQN89913eOaZZ6q0Tmfl6LEEAEEQbL4WRbHMczVBRccSKDsmwIPHxZ22y3up7LZU3vzlPe+OKjOWzZs3R/Pmza1fd+3aFZcvX8bChQvRq1cvh+asibhdPjx7bZMuX4gmTpyIF1988b7zNGzY0G7fLzg4GA0aNMBff/1lt3U6C0eOZVBQEIDbvxEFBwdbn8/JySnzG1JNUNGxPHnyJK5evVpmWm5ubqXGpSZvl3erU6cO5HJ5mT0Y99uWgoKCyp1foVDA39/fYVmdXVXGsjxdunTB559/bu94NR63S8epyjbp8oWoTp06qFOnTrV9v+vXr+Py5cs2b+o1hSPHslGjRggKCkJ8fDzat28P4PbxCwkJCZg/f75DvqeUKjqWXbt2hU6nw2+//YbOnTsDAI4ePQqdTodu3bpV+PvV5O3ybiqVCh07dkR8fDyefvpp6/Px8fF46qmnyl2ma9eu2LNnj81zP/30EyIiIqBUKh2a15lVZSzLk5SU5Bbbnr1xu3ScKm2TD3VItou5dOmSmJSUJM6ZM0f09vYWk5KSxKSkJDE/P986T/PmzcUdO3aIoiiK+fn54r/+9S/x0KFDYlpamrh//36xa9euYmhoqJiXlyfVj+EUKjuWoiiKH374oajVasUdO3aIp06dEkeMGCEGBwe7/VgOHDhQbNu2rXj48GHx8OHDYnh4uDh06FCbebhd2vryyy9FpVIprl+/XkxNTRWnTp0qenl5iRcvXhRFURSjoqLEV155xTr/hQsXRI1GI7711ltiamqquH79elGpVIrbt2+X6kdwGpUdy48//ljcuXOnePbsWfH06dNiVFSUCED85ptvpPoRnEZ+fr71tRCAuHjxYjEpKUm8dOmSKIrcLiuqsuNor23SrQrR6NGjRQBlHvv377fOA0DcuHGjKIqiWFRUJPbv318MCAgQlUqlGBYWJo4ePVpMT0+X5gdwIpUdS1G8fep9TEyMGBQUJKrVarFXr17iqVOnqj+8k7l+/br40ksviT4+PqKPj4/40ksvlTl9lNtlWStWrBAbNGggqlQqsUOHDmJCQoJ12ujRo8XHH3/cZv4DBw6I7du3F1UqldiwYUNx1apV1ZzYeVVmLOfPny8+8sgjooeHh1irVi2xR48e4nfffSdBaudTevr33Y/Ro0eLosjtsqIqO4722iYFUfz/R3ARERERuSmedk9ERERuj4WIiIiI3B4LEREREbk9FiIiIiJyeyxERERE5PZYiIiIiMjtsRARERGR22MhIiIiIrfHQkRERERuj4WIiFxW7969IQgCBEFAcnKyXdY5ZswY6zp37dpll3USkfNjISIipzV16lQMHz78vvO89tpryMrKQps2bezyPZcuXYqsrCy7rIuIXAcLERE5rWPHjqFz5873nUej0SAoKAgKhcIu31Or1SIoKMgu6yIi18FCREROx2g0QqVS4dChQ5g5cyYEQcBjjz1WoWV79+6NyZMn4+2330bt2rURFBSE2bNn28yzfft2hIeHw9PTE/7+/njiiSdQWFjogJ+EiFwFCxEROR25XI6DBw8CAJKTk5GVlYUff/yxwst/+umn8PLywtGjR7FgwQK89957iI+PBwBkZWVhxIgRGDduHP73v//hwIEDeOaZZyCKokN+FiJyDfbZx0xEZEcymQyZmZnw9/dHu3btKr1827ZtERMTAwBo2rQp4uLisG/fPvTr1w9ZWVkwmUx45pln0KBBAwBAeHi4XfMTkevhHiIickpJSUlVKkPA7UJ0p+DgYOTk5AAA2rVrh759+yI8PBzPPfcc1q1bh5s3bz50XiJybSxEROSUkpOTq1yIlEqlzdeCIMBisQC4/XFcfHw89u7di1atWmH58uVo3rw50tLSHjozEbkuFiIickqnTp0qs6fHXgRBQPfu3TFnzhwkJSVBpVJh586dDvleROQaeAwRETkli8WCkydPIjMzE15eXtBqtXZZ79GjR7Fv3z70798fgYGBOHr0KHJzc9GyZUu7rJ+IXBP3EBGRU5o3bx62bduG0NBQvPfee3Zbr6+vLxITEzF48GA0a9YM77zzDhYtWoRBgwbZ7XsQkesRRJ5rSkQuqnfv3nj00UexZMkSu69bEATs3LnzgVfKJqKagXuIiMilrVy5Et7e3jh16pRd1vfmm2/C29vbLusiItfBPURE5LIyMjJQXFwMAAgLC4NKpXrodebk5CAvLw/A7dP1vby8HnqdROT8WIiIiIjI7fEjMyIiInJ7LERERETk9liIiIiIyO2xEBEREZHbYyEiIiIit8dCRERERG6PhYiIiIjcHgsRERERuT0WIiIiInJ7LERERETk9v4f2BtunXS4KeAAAAAASUVORK5CYII=",
|
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def plot_and_calc(run, ivl_start, ivl_end, ax=None, ylabel=True, grid=True):\n",
|
|
" if ax is None:\n",
|
|
" fig, ax = plt.subplots()\n",
|
|
" \n",
|
|
" risetimes = []\n",
|
|
" th_lows, th_highs = [], []\n",
|
|
" for key in meas.keys():\n",
|
|
" if not key.startswith(run):\n",
|
|
" continue\n",
|
|
" d = meas[key]\n",
|
|
" sampling_period_ns = 1000 / 168 / 32\n",
|
|
" t = np.linspace(0, len(d['adc1'])*sampling_period_ns, len(d['adc1']))\n",
|
|
" ivl_w = ivl_end - ivl_start\n",
|
|
" ivl = (ivl_start - ivl_w*0.5, ivl_end + ivl_w*0.5)\n",
|
|
" \n",
|
|
" t_slice = t[(ivl[0] <= t) & (t <= ivl[1])]\n",
|
|
" adc1_slice = d['adc1'][(ivl[0] < t) & (t < ivl[1])]\n",
|
|
" print(len(t_slice))\n",
|
|
" \n",
|
|
" def find_offset(t, y):\n",
|
|
" y_sorted = sorted(y)\n",
|
|
" low = statistics.mean(y_sorted[:len(y_sorted)//5])\n",
|
|
" high = statistics.mean(y_sorted[-len(y_sorted)//5:])\n",
|
|
" print(low, high, len(y_sorted)//5)\n",
|
|
" amp = high - low\n",
|
|
" th = low + 0.5 * amp\n",
|
|
" \n",
|
|
" for t1, t2, y1, y2 in zip(t, t[1:], y, y[1:]):\n",
|
|
" if y1 <= th < y2:\n",
|
|
" return t1 + (t2 - t1) * (th - y1) / (y2 - y1)\n",
|
|
" \n",
|
|
" t1 = t_slice - find_offset(t_slice, adc1_slice)\n",
|
|
" \n",
|
|
" sel = (t1 >= -ivl_w/2) & (t1 <= ivl_w/2)\n",
|
|
" a, b = sel.argmax(), len(sel) - sel[::-1].argmax()\n",
|
|
" t1, adc1_slice = t1[a-1:b+1], adc1_slice[a-1:b+1]\n",
|
|
" \n",
|
|
" def edge_process(xs, ys, color):\n",
|
|
" y_sorted = sorted(ys)\n",
|
|
" low = statistics.mean(y_sorted[:len(y_sorted)//10])\n",
|
|
" high = statistics.mean(y_sorted[-len(y_sorted)//10:])\n",
|
|
" print('statistics:', low, high, len(y_sorted)//10)\n",
|
|
" amp = high - low\n",
|
|
" th_low = low + 0.15 * amp\n",
|
|
" th_high = low + 0.85 * amp\n",
|
|
" \n",
|
|
" x_low, x_high = None, None\n",
|
|
" state = -1\n",
|
|
" for x1, x2, y1, y2 in zip(xs, xs[1:], ys, ys[1:]):\n",
|
|
" if state == -1 and y1 < th_low and y2 > th_low:\n",
|
|
" x_low = x1 + (x2 - x1) * (th_low - y1) / (y2 - y1)\n",
|
|
" state = 0\n",
|
|
" if state == 0 and y1 < th_high and y2 > th_high:\n",
|
|
" state = 1\n",
|
|
" x_high = x1 + (x2 - x1) * (th_high - y1) / (y2 - y1)\n",
|
|
" #ax.axvline(x_low, color='red')\n",
|
|
" #ax.axvline(x_high, color='blue')\n",
|
|
" if x_high is None or x_low is None:\n",
|
|
" return 0, 0, 1\n",
|
|
" tr = x_high - x_low\n",
|
|
" print(f'Rise time t_r = {tr:.3f} ns')\n",
|
|
" #ax.axvline(x_low)\n",
|
|
" #ax.axvline(x_high)\n",
|
|
" return th_low, th_high, tr\n",
|
|
" \n",
|
|
" \n",
|
|
" th_low1, th_high1, tr1 = edge_process(t1, adc1_slice, 'red')\n",
|
|
" risetimes.append(tr1)\n",
|
|
" \n",
|
|
" th_lows.append(th_low1)\n",
|
|
" th_highs.append(th_high1)\n",
|
|
" \n",
|
|
" #ax.plot(t, d['adc1'], color='red', alpha=0.8)\n",
|
|
" #ax.twinx().plot(t, d['adc2'], color='blue', alpha=0.8)\n",
|
|
" #ax.set_xlim(ivl)\n",
|
|
" l1 = ax.plot(t1, adc1_slice, color='black', linestyle='-', alpha=0.3)\n",
|
|
" ax.set_xlim([-ivl_w/2, ivl_w/2])\n",
|
|
" ax.set_xlabel(r'$t\\;[\\mathrm{ns}]$')\n",
|
|
" if ylabel:\n",
|
|
" ax.set_ylabel(r'$V\\;[\\mathrm{V}]$')\n",
|
|
" ax.legend(l1, [\n",
|
|
" fr'$t_r = {statistics.mean(risetimes):.3f}\\;\\mathrm{{ns}} \\pm {statistics.stdev(risetimes):.3f}\\;\\mathrm{{ns}}$'])\n",
|
|
" if not grid:\n",
|
|
" ax.axhline(statistics.mean(th_lows), linewidth=1, linestyle='--', color='black', alpha=0.3, zorder=-2)\n",
|
|
" ax.axhline(statistics.mean(th_highs), linewidth=1, linestyle='--', color='black', alpha=0.3, zorder=-2)\n",
|
|
" \n",
|
|
"#plot_and_calc('pi3hdx_clipline_risetime_new', 87, 90)\n",
|
|
"plot_and_calc('risetime_74lvc_', 74, 77, grid=False)\n",
|
|
"#plot_and_calc('run3_max3748_open', 98, 103)\n",
|
|
"#plot_and_calc('run3_74lvc_open', 97, 103)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "ab54ba7f-1e18-4373-899c-30e740399a55",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"32\n",
|
|
"1.268588876724243 2.3997459411621094 6\n",
|
|
"statistics: 1.285260772705078 2.409491014480591 1\n",
|
|
"Rise time t_r = 0.916 ns\n",
|
|
"32\n",
|
|
"1.2675959587097165 2.3981170790536064 6\n",
|
|
"statistics: 1.2846848487854003 2.4086271286010743 1\n",
|
|
"Rise time t_r = 0.918 ns\n",
|
|
"32\n",
|
|
"1.2677270889282226 2.399308940342494 6\n",
|
|
"statistics: 1.2860223770141601 2.4105925083160398 1\n",
|
|
"Rise time t_r = 0.913 ns\n",
|
|
"32\n",
|
|
"1.2678550720214843 2.3980990954807826 6\n",
|
|
"statistics: 1.2856038093566893 2.4091117858886717 1\n",
|
|
"Rise time t_r = 0.917 ns\n",
|
|
"32\n",
|
|
"1.2670966148376464 2.3987874167306082 6\n",
|
|
"statistics: 1.2853300094604492 2.4077632427215576 1\n",
|
|
"Rise time t_r = 0.918 ns\n",
|
|
"32\n",
|
|
"1.267380380630493 2.398665578024728 6\n",
|
|
"statistics: 1.2857454299926756 2.409066152572632 1\n",
|
|
"Rise time t_r = 0.917 ns\n",
|
|
"32\n",
|
|
"1.268097925186157 2.3995939799717494 6\n",
|
|
"statistics: 1.2861293792724608 2.411354112625122 1\n",
|
|
"Rise time t_r = 0.919 ns\n",
|
|
"32\n",
|
|
"1.2682112216949462 2.3986098289489743 6\n",
|
|
"statistics: 1.2855660438537597 2.4095020294189453 1\n",
|
|
"Rise time t_r = 0.915 ns\n",
|
|
"32\n",
|
|
"1.2677659034729003 2.3994199889046803 6\n",
|
|
"statistics: 1.2855156898498534 2.409495735168457 1\n",
|
|
"Rise time t_r = 0.914 ns\n",
|
|
"32\n",
|
|
"1.267426538467407 2.3991623742239816 6\n",
|
|
"statistics: 1.285616397857666 2.409541368484497 1\n",
|
|
"Rise time t_r = 0.917 ns\n",
|
|
"32\n",
|
|
"0.7714862823486328 0.8288858004978724 6\n",
|
|
"statistics: 0.7714831352233886 0.8328064441680908 1\n",
|
|
"Rise time t_r = 0.776 ns\n",
|
|
"32\n",
|
|
"0.7703989505767822 0.8278562409537179 6\n",
|
|
"statistics: 0.7713446617126465 0.8319126605987548 1\n",
|
|
"Rise time t_r = 0.734 ns\n",
|
|
"32\n",
|
|
"0.7712591648101806 0.8286524636404854 6\n",
|
|
"statistics: 0.7718859672546386 0.8324539661407471 1\n",
|
|
"Rise time t_r = 0.718 ns\n",
|
|
"32\n",
|
|
"0.7712287425994873 0.8278454508100237 6\n",
|
|
"statistics: 0.7717884063720702 0.8317977905273437 1\n",
|
|
"Rise time t_r = 0.727 ns\n",
|
|
"32\n",
|
|
"0.7720580101013184 0.8290355137416294 6\n",
|
|
"statistics: 0.7730000495910644 0.8327025890350341 1\n",
|
|
"Rise time t_r = 0.731 ns\n",
|
|
"32\n",
|
|
"0.7729407787322997 0.8303856304713657 6\n",
|
|
"statistics: 0.7729150772094726 0.8344508171081542 1\n",
|
|
"Rise time t_r = 0.774 ns\n",
|
|
"32\n",
|
|
"0.7707598209381104 0.8280900274004255 6\n",
|
|
"statistics: 0.7708537101745605 0.8316561698913574 1\n",
|
|
"Rise time t_r = 0.754 ns\n",
|
|
"32\n",
|
|
"0.7702400207519531 0.8273108891078403 6\n",
|
|
"statistics: 0.7711526870727539 0.8313446044921875 1\n",
|
|
"Rise time t_r = 0.718 ns\n",
|
|
"32\n",
|
|
"0.7722153663635254 0.8302804265703473 6\n",
|
|
"statistics: 0.7729496955871582 0.8350188732147217 1\n",
|
|
"Rise time t_r = 0.758 ns\n",
|
|
"32\n",
|
|
"0.7714742183685303 0.8285800797598702 6\n",
|
|
"statistics: 0.772455596923828 0.8325342178344726 1\n",
|
|
"Rise time t_r = 0.744 ns\n",
|
|
"32\n",
|
|
"0.006743240356445312 0.22992762156895227 6\n",
|
|
"statistics: 0.006791496276855468 0.233329439163208 1\n",
|
|
"Rise time t_r = 0.335 ns\n",
|
|
"32\n",
|
|
"0.007390499114990234 0.25653296879359655 6\n",
|
|
"statistics: 0.007474422454833984 0.25951194763183594 1\n",
|
|
"Rise time t_r = 0.342 ns\n",
|
|
"32\n",
|
|
"0.006979274749755859 0.23948768888201033 6\n",
|
|
"statistics: 0.0070243835449218745 0.24266066551208496 1\n",
|
|
"Rise time t_r = 0.338 ns\n",
|
|
"32\n",
|
|
"0.007059526443481445 0.2462477139064244 6\n",
|
|
"statistics: 0.0071125030517578125 0.24862761497497557 1\n",
|
|
"Rise time t_r = 0.334 ns\n",
|
|
"32\n",
|
|
"0.006590604782104492 0.22611870084490093 6\n",
|
|
"statistics: 0.006675052642822265 0.2296126842498779 1\n",
|
|
"Rise time t_r = 0.301 ns\n",
|
|
"32\n",
|
|
"0.007015991210937499 0.24293019430977958 6\n",
|
|
"statistics: 0.007109355926513672 0.2460926055908203 1\n",
|
|
"Rise time t_r = 0.335 ns\n",
|
|
"32\n",
|
|
"0.007126140594482421 0.2491291318620954 6\n",
|
|
"statistics: 0.007156562805175781 0.2521492481231689 1\n",
|
|
"Rise time t_r = 0.336 ns\n",
|
|
"32\n",
|
|
"0.007279825210571289 0.2535863603864397 6\n",
|
|
"statistics: 0.007323360443115234 0.2562436580657959 1\n",
|
|
"Rise time t_r = 0.335 ns\n",
|
|
"32\n",
|
|
"0.006719636917114257 0.23191031047276087 6\n",
|
|
"statistics: 0.006813526153564453 0.23517522811889646 1\n",
|
|
"Rise time t_r = 0.340 ns\n",
|
|
"32\n",
|
|
"0.006793594360351562 0.23617646353585378 6\n",
|
|
"statistics: 0.0068072319030761715 0.23936405181884765 1\n",
|
|
"Rise time t_r = 0.338 ns\n",
|
|
"32\n",
|
|
"0.011845779418945311 0.2707399913242885 6\n",
|
|
"statistics: 0.013557815551757812 0.2770115375518799 1\n",
|
|
"Rise time t_r = 0.264 ns\n",
|
|
"32\n",
|
|
"0.011672163009643553 0.2694168499537877 6\n",
|
|
"statistics: 0.013384723663330078 0.27547731399536135 1\n",
|
|
"Rise time t_r = 0.263 ns\n",
|
|
"32\n",
|
|
"0.011734580993652342 0.26940291268484934 6\n",
|
|
"statistics: 0.013542079925537109 0.2756960391998291 1\n",
|
|
"Rise time t_r = 0.264 ns\n",
|
|
"32\n",
|
|
"0.011765527725219726 0.2697171756199428 6\n",
|
|
"statistics: 0.013507461547851561 0.27599658966064455 1\n",
|
|
"Rise time t_r = 0.265 ns\n",
|
|
"32\n",
|
|
"0.011592435836791991 0.26909494400024414 6\n",
|
|
"statistics: 0.01329975128173828 0.27525858879089354 1\n",
|
|
"Rise time t_r = 0.264 ns\n",
|
|
"32\n",
|
|
"0.011573553085327148 0.2679556846618652 6\n",
|
|
"statistics: 0.01324310302734375 0.274149227142334 1\n",
|
|
"Rise time t_r = 0.265 ns\n",
|
|
"32\n",
|
|
"0.011691045761108397 0.2699986185346331 6\n",
|
|
"statistics: 0.013463401794433593 0.2763160228729248 1\n",
|
|
"Rise time t_r = 0.265 ns\n",
|
|
"32\n",
|
|
"0.011680555343627929 0.269484737941197 6\n",
|
|
"statistics: 0.013318634033203124 0.2756173610687256 1\n",
|
|
"Rise time t_r = 0.264 ns\n",
|
|
"32\n",
|
|
"0.011918163299560545 0.2713694163731166 6\n",
|
|
"statistics: 0.013693141937255859 0.27751665115356444 1\n",
|
|
"Rise time t_r = 0.264 ns\n",
|
|
"32\n",
|
|
"0.011567258834838867 0.2674256188528878 6\n",
|
|
"statistics: 0.013261985778808593 0.27344112396240233 1\n",
|
|
"Rise time t_r = 0.264 ns\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 700x200 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"with plt.rc_context({\n",
|
|
" 'legend.fontsize': 8,\n",
|
|
" # RC config from ChatGPT:\n",
|
|
" \"font.size\": 8, # General font size\n",
|
|
" \"axes.labelsize\": 8, # Axis labels\n",
|
|
" \"axes.titlesize\": 8, # Axis titles\n",
|
|
" \"xtick.labelsize\": 8, # X-axis tick labels\n",
|
|
" \"ytick.labelsize\": 8, # Y-axis tick labels\n",
|
|
" \"legend.fontsize\": 8, # Legend font size\n",
|
|
" \"figure.titlesize\": 12, # Overall figure title\n",
|
|
" \"mathtext.fontset\": \"cm\", # Use Computer Modern for math\n",
|
|
"\n",
|
|
" # Line and Marker settings\n",
|
|
" \"lines.linewidth\": 1,\n",
|
|
" \"lines.markersize\": 3,\n",
|
|
"\n",
|
|
" # Tick settings\n",
|
|
" \"xtick.major.size\": 4,\n",
|
|
" \"xtick.minor.size\": 2,\n",
|
|
" \"ytick.major.size\": 4,\n",
|
|
" \"ytick.minor.size\": 2,\n",
|
|
" \"xtick.direction\": \"in\",\n",
|
|
" \"ytick.direction\": \"in\",\n",
|
|
"\n",
|
|
" # Axes settings\n",
|
|
" \"axes.linewidth\": 0.8,\n",
|
|
" \"axes.grid\": True,\n",
|
|
" \"grid.linewidth\": 0.5,\n",
|
|
" \"grid.linestyle\": \"--\",\n",
|
|
"\n",
|
|
" # Legend settings\n",
|
|
" \"legend.frameon\": False,\n",
|
|
"\n",
|
|
" # Figure size for single-column figures in print (inches)\n",
|
|
"# \"figure.figsize\": (3.5, 2.5), # Adjust based on journal specifications\n",
|
|
"# \"figure.dpi\": 300, # High resolution for print\n",
|
|
"\n",
|
|
" # Savefig settings for high-quality output\n",
|
|
" \"savefig.dpi\": 600, # Ensures high-resolution images\n",
|
|
" \"savefig.bbox\": \"tight\", # Trim extra whitespace\n",
|
|
" \"savefig.transparent\": True, # Avoid background color\n",
|
|
"}):\n",
|
|
" fig, axs = plt.subplots(2, 2, sharex=True, figsize=[7, 2])\n",
|
|
" axs = axs.flatten()\n",
|
|
"\n",
|
|
" plot_and_calc('risetime_74lvc_', 74, 77, ax=axs[0], ylabel=None, grid=False)\n",
|
|
" plot_and_calc('risetime_max3748_', 145, 148, ax=axs[1], ylabel=None, grid=False)\n",
|
|
" plot_and_calc('risetime_tdp0604_', 145, 148, ax=axs[2], ylabel=None, grid=False)\n",
|
|
" plot_and_calc('risetime_pi3hdx_', 145, 148, ax=axs[3], ylabel=None, grid=False)\n",
|
|
"\n",
|
|
" axs[0].set_xlabel(None)\n",
|
|
" axs[1].set_xlabel(None)\n",
|
|
" \n",
|
|
" # axs[0].set_title('74LVC1G157 MUX Gate with Complimentary Outputs')\n",
|
|
" # axs[1].set_title('MAX3748 Optical Network Limiting Amplifier')\n",
|
|
" # axs[2].set_title('TDP0604 6 Gb/s HDMI Hybrid Redriver')\n",
|
|
" # axs[3].set_title('PI3HDX12211 12 Gb/s HDMI Linear Redriver')\n",
|
|
" \n",
|
|
" axs[0].set_title('74LVC2G157')\n",
|
|
" axs[1].set_title('MAX3748')\n",
|
|
" axs[2].set_title('TDP0604')\n",
|
|
" axs[3].set_title('PI3HDX12211')\n",
|
|
" \n",
|
|
" for ax in axs:\n",
|
|
" ax.get_legend().set_loc('lower right')\n",
|
|
" \n",
|
|
" # Only do this on the last axis because of sharex=True\n",
|
|
" ax.set_xlim([-1.8, 1.8])\n",
|
|
" \n",
|
|
" #for ax in axs:\n",
|
|
" # ax.grid()\n",
|
|
" fig.tight_layout()\n",
|
|
"\n",
|
|
"fig.savefig('../paper/fig_edge_risetime.pdf')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "f5fd14eb-a12a-42c0-944e-8fd53f7d4dbc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x7fb9c0602210>]"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"# just out of curiousity:\n",
|
|
"d = meas['max3748_new2_risetime']\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"ax.grid()\n",
|
|
"ax.plot(d['adc1'])\n",
|
|
"ax.plot(d['adc2'])"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|