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petercorkepetercorke
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consolidate notesbooks in new folder
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‎examples/chap7.ipynb

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‎notebooks/chap7.ipynb

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{
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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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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%matplotlib widget\n",
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"import roboticstoolbox as rp\n",
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"import numpy as np\n",
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"\n",
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"panda = rp.models.DH.Panda()"
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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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"metadata": {
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"scrolled": true,
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"tags": []
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},
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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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" \u001b[38;5;1m\u001b[48;5;255m 0.707107 \u001b[0m\u001b[38;5;1m\u001b[48;5;255m 0.707107 \u001b[0m\u001b[38;5;1m\u001b[48;5;255m 0 \u001b[0m\u001b[38;5;4m\u001b[48;5;255m 0.088 \u001b[0m\u001b[48;5;255m \u001b[0m\n",
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" \u001b[38;5;1m\u001b[48;5;255m 0.707107 \u001b[0m\u001b[38;5;1m\u001b[48;5;255m-0.707107 \u001b[0m\u001b[38;5;1m\u001b[48;5;255m 0 \u001b[0m\u001b[38;5;4m\u001b[48;5;255m 0 \u001b[0m\u001b[48;5;255m \u001b[0m\n",
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" \u001b[38;5;1m\u001b[48;5;255m 0 \u001b[0m\u001b[38;5;1m\u001b[48;5;255m 0 \u001b[0m\u001b[38;5;1m\u001b[48;5;255m-1 \u001b[0m\u001b[38;5;4m\u001b[48;5;255m 0.823 \u001b[0m\u001b[48;5;255m \u001b[0m\n",
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" \u001b[38;5;244m\u001b[48;5;255m 0 \u001b[0m\u001b[38;5;244m\u001b[48;5;255m 0 \u001b[0m\u001b[38;5;244m\u001b[48;5;255m 0 \u001b[0m\u001b[38;5;244m\u001b[48;5;255m 1 \u001b[0m\u001b[48;5;255m \u001b[0m\n",
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"\n"
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]
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}
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],
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"source": [
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"print(panda.fkine())"
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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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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5311f173c29848c0872ecde54e7c67ba",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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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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"data": {
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"text/plain": [
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"<roboticstoolbox.backend.PyPlot.PyPlot.PyPlot at 0x7fb39e65ee10>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"panda.teach(block=True)"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "abfe9e42b1d34ac3bb698b7e89efe2e9",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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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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"data": {
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"text/plain": [
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"<roboticstoolbox.backend.PyPlot.PyPlot.PyPlot at 0x7fbfd4cc4410>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"panda.plot(block=False)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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