629 lines
189 KiB
Text
629 lines
189 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ff0d4c35-2ebc-455f-84fb-e0bf26887b6a",
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"metadata": {},
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"source": [
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"Initial risetime plotting experiments\n",
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"-------------------------------------\n",
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"\n",
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"This notebook contains some early experiments plotting driver risetime as measured by the board itself. None of this made it into the paper, the figure in the paper is plotted by the other risetime plotting notebook."
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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": 11,
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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": 12,
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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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"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": 85,
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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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"1024\n",
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"0.7473990650737986 0.8212164646241723 204\n",
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"0.8103342575185438 0.8889700489509396 204\n",
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"statistics: 0.743450060912541 0.7909857047231573 56\n",
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"Rise time t_r = 47.458 ns\n",
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"statistics: 0.8035581861223493 0.8457775456564767 56\n",
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"Rise time t_r = 19.852 ns\n",
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"Average rise time: 33.65473843208062\n"
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]
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},
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{
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"data": {
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"image/png": 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",
|
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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
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]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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}
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],
|
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"source": [
|
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"def plot_and_calc(run, ivl_start, ivl_end, ax=None, ylabel=True, grid=True):\n",
|
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" d = meas[run]\n",
|
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" sampling_period_ns = 1000 / 168 / 32\n",
|
|
" t = np.linspace(0, len(d['adc1'])*sampling_period_ns, len(d['adc1']))\n",
|
|
" if ax is None:\n",
|
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" fig, ax = plt.subplots()\n",
|
|
" ivl_w = ivl_end - ivl_start\n",
|
|
" ivl = (ivl_start - ivl_w*0.5, ivl_end + ivl_w*0.5)\n",
|
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"\n",
|
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" t_slice = t[(ivl[0] <= t) & (t <= ivl[1])]\n",
|
|
" adc1_slice = d['adc1'][(ivl[0] < t) & (t < ivl[1])]\n",
|
|
" adc2_slice = d['adc2'][(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",
|
|
" t2 = t_slice - find_offset(t_slice, adc2_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",
|
|
" sel = (t2 >= -ivl_w/2) & (t2 <= ivl_w/2)\n",
|
|
" a, b = sel.argmax(), len(sel) - sel[::-1].argmax()\n",
|
|
" t2, adc2_slice = t2[a-1:b+1], adc2_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",
|
|
" th_low2, th_high2, tr2 = edge_process(t2, adc2_slice, 'blue')\n",
|
|
"\n",
|
|
" if grid:\n",
|
|
" ax.grid()\n",
|
|
" else:\n",
|
|
" ax.axhline(th_low1, linewidth=1, linestyle='--', color='black', alpha=0.3, zorder=-2)\n",
|
|
" ax.axhline(th_high1, linewidth=1, linestyle='--', color='black', alpha=0.3, zorder=-2)\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=0.8)\n",
|
|
" tx = ax.twinx()\n",
|
|
" l2 = tx.plot(t2, adc2_slice, color='black', linestyle='--', alpha=0.8)\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",
|
|
" tx.set_ylabel(r'$V\\;[\\mathrm{V}]$')\n",
|
|
" ax.legend(l1 + l2, [\n",
|
|
" fr'$t_r = {tr1:.3f}\\;\\mathrm{{ns}}$',\n",
|
|
" fr'$t_r = {tr2:.3f}\\;\\mathrm{{ns}}$'])\n",
|
|
" print('Average rise time:', (tr1+tr2)/2)\n",
|
|
" \n",
|
|
"#plot_and_calc('pi3hdx_clipline_risetime_new', 87, 90)\n",
|
|
"plot_and_calc('max3748_new2_risetime', 8, 130, 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": 60,
|
|
"id": "ab54ba7f-1e18-4373-899c-30e740399a55",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"214\n",
|
|
"1.2779597418648856 2.5903313814207563 42\n",
|
|
"0.8730813298906598 2.307233340241188 42\n",
|
|
"statistics: 1.271009645462036 2.4963875770568844 10\n",
|
|
"Rise time t_r = 1.055 ns\n",
|
|
"statistics: 0.8673178195953368 2.260366344451904 10\n",
|
|
"Rise time t_r = 0.977 ns\n",
|
|
"Average rise time: 1.0158313142590383\n",
|
|
"484\n",
|
|
"0.744375404715538 0.8143164831338469 96\n",
|
|
"0.8050688952207565 0.8751330886919474 96\n",
|
|
"statistics: 0.7483565902709961 0.7965309083461761 15\n",
|
|
"Rise time t_r = 0.755 ns\n",
|
|
"statistics: 0.8020668983459472 0.8503570175170898 15\n",
|
|
"Rise time t_r = 19.378 ns\n",
|
|
"Average rise time: 10.066391821783165\n",
|
|
"967\n",
|
|
"0.02842865089060729 0.056467505582829106 193\n",
|
|
"0.03819502845329324 0.07271053274882208 193\n",
|
|
"statistics: 0.0032493576407432554 0.05711862857525165 64\n",
|
|
"Rise time t_r = 0.366 ns\n",
|
|
"statistics: 0.004717393219470977 0.07685943163358248 64\n",
|
|
"Rise time t_r = 0.756 ns\n",
|
|
"Average rise time: 0.5609291456438499\n",
|
|
"967\n",
|
|
"0.006933149525538627 0.15674547657524188 193\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "TypeError",
|
|
"evalue": "unsupported operand type(s) for -: 'float' and 'NoneType'",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[60], line 53\u001b[0m\n\u001b[1;32m 51\u001b[0m plot_and_calc(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax3748_new2_risetime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m85\u001b[39m, \u001b[38;5;241m130\u001b[39m, ax\u001b[38;5;241m=\u001b[39maxs[\u001b[38;5;241m1\u001b[39m], ylabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, grid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 52\u001b[0m plot_and_calc(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtdp0604_new2_risetime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m120\u001b[39m, ax\u001b[38;5;241m=\u001b[39maxs[\u001b[38;5;241m2\u001b[39m], ylabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, grid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 53\u001b[0m \u001b[43mplot_and_calc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrun3_pi3hdx_open\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m120\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mylabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrid\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 55\u001b[0m axs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 56\u001b[0m axs[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;28;01mNone\u001b[39;00m)\n",
|
|
"Cell \u001b[0;32mIn[13], line 27\u001b[0m, in \u001b[0;36mplot_and_calc\u001b[0;34m(run, ivl_start, ivl_end, ax, ylabel, grid)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y1 \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m th \u001b[38;5;241m<\u001b[39m y2:\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m t1 \u001b[38;5;241m+\u001b[39m (t2 \u001b[38;5;241m-\u001b[39m t1) \u001b[38;5;241m*\u001b[39m (th \u001b[38;5;241m-\u001b[39m y1) \u001b[38;5;241m/\u001b[39m (y2 \u001b[38;5;241m-\u001b[39m y1)\n\u001b[0;32m---> 27\u001b[0m t1 \u001b[38;5;241m=\u001b[39m \u001b[43mt_slice\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfind_offset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt_slice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madc1_slice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m t2 \u001b[38;5;241m=\u001b[39m t_slice \u001b[38;5;241m-\u001b[39m find_offset(t_slice, adc2_slice)\n\u001b[1;32m 30\u001b[0m sel \u001b[38;5;241m=\u001b[39m (t1 \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mivl_w\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m&\u001b[39m (t1 \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m ivl_w\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
|
|
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'float' and 'NoneType'"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 700x300 with 7 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, 3])\n",
|
|
" axs = axs.flatten()\n",
|
|
"\n",
|
|
" #plot_and_calc('run3_74lvc_open', 97, 103, ax=axs[0], ylabel=None, grid=False)\n",
|
|
" #plot_and_calc('run3_max3748_open', 98, 103, ax=axs[1], ylabel=None, grid=False)\n",
|
|
" #plot_and_calc('run3_tdp0604_open', 97, 100, ax=axs[2], ylabel=None, grid=False)\n",
|
|
" #plot_and_calc('pi3hdx_clipline_risetime_new', 87, 90, ax=axs[3], ylabel=None, grid=False)\n",
|
|
" plot_and_calc('74lvc_new2_risetime', 75, 95, ax=axs[0], ylabel=None, grid=False)\n",
|
|
" plot_and_calc('max3748_new2_risetime', 85, 130, ax=axs[1], ylabel=None, grid=False)\n",
|
|
" plot_and_calc('tdp0604_new2_risetime', 0, 120, ax=axs[2], ylabel=None, grid=False)\n",
|
|
" plot_and_calc('run3_pi3hdx_open', 0, 120, ax=axs[3], ylabel=None, grid=False)\n",
|
|
" \n",
|
|
" axs[0].set_xlabel(None)\n",
|
|
" axs[1].set_xlabel(None)\n",
|
|
" axs[2].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": "3.13.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|