803 lines
159 KiB
Text
803 lines
159 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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": 2,
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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": 60,
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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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"1.2677270889282226 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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"1.267380380630493 2.398665578024728 6\n",
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"statistics: 1.2857454299926756 2.409066152572632 1\n",
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"Rise time t_r = 0.917 ns\n",
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"32\n",
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"1.2670966148376464 2.3987874167306082 6\n",
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"statistics: 1.2853300094604492 2.4077632427215576 1\n",
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"Rise time t_r = 0.918 ns\n",
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"32\n",
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"1.2682112216949462 2.3986098289489743 6\n",
|
|
"statistics: 1.2855660438537597 2.4095020294189453 1\n",
|
|
"Rise time t_r = 0.915 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",
|
|
"1.2678550720214843 2.3980990954807826 6\n",
|
|
"statistics: 1.2856038093566893 2.4091117858886717 1\n",
|
|
"Rise time t_r = 0.917 ns\n",
|
|
"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",
|
|
"1.268097925186157 2.3995939799717494 6\n",
|
|
"statistics: 1.2861293792724608 2.411354112625122 1\n",
|
|
"Rise time t_r = 0.919 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"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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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": 63,
|
|
"id": "ab54ba7f-1e18-4373-899c-30e740399a55",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"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",
|
|
"1.267380380630493 2.398665578024728 6\n",
|
|
"statistics: 1.2857454299926756 2.409066152572632 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",
|
|
"1.2682112216949462 2.3986098289489743 6\n",
|
|
"statistics: 1.2855660438537597 2.4095020294189453 1\n",
|
|
"Rise time t_r = 0.915 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",
|
|
"1.2678550720214843 2.3980990954807826 6\n",
|
|
"statistics: 1.2856038093566893 2.4091117858886717 1\n",
|
|
"Rise time t_r = 0.917 ns\n",
|
|
"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",
|
|
"1.268097925186157 2.3995939799717494 6\n",
|
|
"statistics: 1.2861293792724608 2.411354112625122 1\n",
|
|
"Rise time t_r = 0.919 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",
|
|
"0.7720580101013184 0.8290355137416294 6\n",
|
|
"statistics: 0.7730000495910644 0.8327025890350341 1\n",
|
|
"Rise time t_r = 0.731 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",
|
|
"0.7712287425994873 0.8278454508100237 6\n",
|
|
"statistics: 0.7717884063720702 0.8317977905273437 1\n",
|
|
"Rise time t_r = 0.727 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",
|
|
"0.7729407787322997 0.8303856304713657 6\n",
|
|
"statistics: 0.7729150772094726 0.8344508171081542 1\n",
|
|
"Rise time t_r = 0.774 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",
|
|
"0.7707598209381104 0.8280900274004255 6\n",
|
|
"statistics: 0.7708537101745605 0.8316561698913574 1\n",
|
|
"Rise time t_r = 0.754 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",
|
|
"0.7702400207519531 0.8273108891078403 6\n",
|
|
"statistics: 0.7711526870727539 0.8313446044921875 1\n",
|
|
"Rise time t_r = 0.718 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",
|
|
"0.007015991210937499 0.24293019430977958 6\n",
|
|
"statistics: 0.007109355926513672 0.2460926055908203 1\n",
|
|
"Rise time t_r = 0.335 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",
|
|
"0.007279825210571289 0.2535863603864397 6\n",
|
|
"statistics: 0.007323360443115234 0.2562436580657959 1\n",
|
|
"Rise time t_r = 0.335 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",
|
|
"0.006719636917114257 0.23191031047276087 6\n",
|
|
"statistics: 0.006813526153564453 0.23517522811889646 1\n",
|
|
"Rise time t_r = 0.340 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",
|
|
"0.007390499114990234 0.25653296879359655 6\n",
|
|
"statistics: 0.007474422454833984 0.25951194763183594 1\n",
|
|
"Rise time t_r = 0.342 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",
|
|
"0.006979274749755859 0.23948768888201033 6\n",
|
|
"statistics: 0.0070243835449218745 0.24266066551208496 1\n",
|
|
"Rise time t_r = 0.338 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",
|
|
"0.011672163009643553 0.2694168499537877 6\n",
|
|
"statistics: 0.013384723663330078 0.27547731399536135 1\n",
|
|
"Rise time t_r = 0.263 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",
|
|
"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",
|
|
"0.011845779418945311 0.2707399913242885 6\n",
|
|
"statistics: 0.013557815551757812 0.2770115375518799 1\n",
|
|
"Rise time t_r = 0.264 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",
|
|
"0.011573553085327148 0.2679556846618652 6\n",
|
|
"statistics: 0.01324310302734375 0.274149227142334 1\n",
|
|
"Rise time t_r = 0.265 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",
|
|
"0.011592435836791991 0.26909494400024414 6\n",
|
|
"statistics: 0.01329975128173828 0.27525858879089354 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 700x300 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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}
|
|
],
|
|
"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, 3])\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": 23,
|
|
"id": "f5fd14eb-a12a-42c0-944e-8fd53f7d4dbc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x7683c57460d0>]"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"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": "3.13.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|