finite_state

{ "cells": [ { "cell_type": "markdown", "id": "c200034d-b5f7-4af8-89fe-589d508d65d5", "metadata": { "tags": [] }, "source": [ "# Example 1" ] }, { "cell_type": "code", "execution_count": 1, "id": "e4582473-0c39-466a-9312-3f8851d561f2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.10/site-packages/pytct/name_converter.py:70: UserWarning: controllable and uncontrollable status of event is not specified. All events are set to be controllable.\n", " warnings.warn(\"controllable and uncontrollable status of event is not specified. All events are set to be controllable.\")\n" ] } ], "source": [ "import pytct\n", "\n", "pytct.init(\"Chp1\", overwrite=True)\n", "\n", "statenum=3 #number of states\n", "#states are sequentially labeled 0,1,...,statenum-1\n", "#initial state is labeled 0\n", "\n", "trans=[(0,'start',1),\n", " (1,'auto_finish',0),\n", " (1,'manual_stop',0),\n", " (1,'breakdown',2),\n", " (2,'fix',0)] # set of transitions\n", "#each triple is (exit state, event label, entering state)\n", "\n", "marker = [0,1] #set of marker states\n", "\n", "pytct.create('PRINTER', statenum, trans, marker)\n", "#create automaton A" ] }, { "cell_type": "code", "execution_count": 2, "id": "27b27515-c590-45b8-a880-4f84bd66987e", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n" ], "text/html": [ "

" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pytct.display_automaton('PRINTER')\n", "#plot PRINTER automaton\n", "#pytct.AutomatonDisplay('PRINTER',convert=True, color=True)" ] }, { "cell_type": "code", "execution_count": 3, "id": "9c39e080-d563-421e-acaf-8c897c279266", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pytct.statenum('PRINTER')" ] }, { "cell_type": "code", "execution_count": 4, "id": "ee5cb9c5-80da-4ec7-9e8e-63c1cdeb9c69", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['manual_stop', 'fix', 'start', 'breakdown', 'auto_finish']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pytct.events('PRINTER')" ] }, { "cell_type": "code", "execution_count": 5, "id": "a76e7070-3304-4232-903f-cefaf65a2366", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, 'start', 1),\n", " (1, 'auto_finish', 0),\n", " (1, 'manual_stop', 0),\n", " (1, 'breakdown', 2),\n", " (2, 'fix', 0)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pytct.trans('PRINTER')" ] }, { "cell_type": "code", "execution_count": 6, "id": "2b598a88-aef2-427f-a6cf-536e8ebd7c21", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pytct.marker('PRINTER')" ] }, { "cell_type": "code", "execution_count": 7, "id": "bbcf2673-49b0-494e-8a0c-f5d7405f0653", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 0]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pytct.simulate_automaton('PRINTER',['start','breakdown','fix'])" ] }, { "cell_type": "code", "execution_count": 8, "id": "d375bac4-f18a-4dce-b76e-822fce1e37b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 0, 1]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pytct.sample_automaton('PRINTER',3)\n", "# generate a state trajectory of sampling length k " ] }, { "cell_type": "code", "execution_count": 9, "id": "bad75eca-d971-4157-af13-9bed3aaf0b93", "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'matplotlib'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn [9], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# plot sample (please run datascience environment)\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# If minimal environment: File -> HubControlPanel -> Stop My Server -> wait... -> Start My Server\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# create x, y data\u001b[39;00m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] } ], "source": [ "# plot sample (please run datascience environment)\n", "# If minimal environment: File -> HubControlPanel -> Stop My Server -> wait... -> Start My Server\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# create x, y data\n", "y = [0] + pytct.sample_automaton('PRINTER', 10, strict=False)\n", "# strict = False: ignore error if length of sampled string is shorter than specified due to blocking\n", "# print(y)\n", "x = np.arange(len(y))\n", "# print(x)\n", "\n", "# calculate max\n", "# max_x_y = max(max(y), max(x))\n", "# print(max_x_y)\n", "\n", "# plot\n", "fig, ax = plt.subplots()\n", "ax.step(x, y, linewidth=2.5)\n", "ax.set(xlim=(0, max(x)), xticks=np.arange(0, max(x),10),\n", " ylim=(0, max(y)), yticks=np.arange(0, max(y)+1))\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4dbab645-5803-4c86-809f-dc8b93f065e1", "metadata": {}, "source": [ "Robot Navigation" ] }, { "cell_type": "code", "execution_count": 10, "id": "24b2273b-3a07-46fb-ab50-eb5aa6e07def", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.10/site-packages/pytct/name_converter.py:70: UserWarning: controllable and uncontrollable status of event is not specified. All events are set to be controllable.\n", " warnings.warn(\"controllable and uncontrollable status of event is not specified. All events are set to be controllable.\")\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n" ], "text/html": [ "

" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "statenum=6 #number of states\n", "#states are sequentially labeled 0,1,...,statenum\n", "#initial state is labeled 0\n", "\n", "trans=[(0,'up',1),\n", " (0,'right',2),\n", " (1,'right',3),\n", " (2,'up',3),\n", " (3,'down',2),\n", " (2,'right',4),\n", " (3,'right',5),\n", " (5,'down',4)] # set of transitions\n", "#each triple is (exit state, event label, entering state)\n", "\n", "marker = [4] #set of marker states\n", "\n", "pytct.create('ROBOT', statenum, trans, marker)\n", "\n", "pytct.display_automaton('ROBOT')" ] }, { "cell_type": "code", "execution_count": 11, "id": "e144936c-b5bf-4976-a3c9-8f17885ca310", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot sample (please run datascience environment)\n", "# If minimal environment: File -> HubControlPanel -> Stop My Server -> wait... -> Start My Server\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# create x, y data\n", "y = [0] + pytct.sample_automaton('ROBOT', 10, strict=False)\n", "# strict = False: ignore error if length of sampled string is shorter than specified due to blocking\n", "# print(y)\n", "x = np.arange(len(y))\n", "# print(x)\n", "\n", "# calculate max\n", "# max_x_y = max(max(y), max(x))\n", "# print(max_x_y)\n", "\n", "# plot\n", "fig, ax = plt.subplots()\n", "ax.step(x, y, linewidth=2.5)\n", "ax.set(xlim=(0, max(x)), xticks=np.arange(0, max(x),10),\n", " ylim=(0, max(y)), yticks=np.arange(0, max(y)+1))\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "d8685bad-0f2d-4931-b30b-754a20afb5de", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "bc2230f8-f836-440c-944e-747a5d43c6e6", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 5 }