mirror of https://github.com/jlizier/jidt
New Platform-check Python notebook created
This commit is contained in:
Joseph Lizier
parent
ef9b1237a9
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "17e37cfc-4332-43e2-9cb7-4c1849ad894e",
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"metadata": {},
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"source": [
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"# Example 1 - Transfer entropy on binary data\n",
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"\n",
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"_Copyright (C) 2024-, J.T. Lizier; Distributed under GNU General Public License v3_\n",
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"\n",
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"This is a sample notebook to run to check that your installation works ok"
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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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"id": "7f18beca-d55b-4b8d-8647-256e27787c34",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import relevant libraries and start the JVM:\n",
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"\n",
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"from jpype import *\n",
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"import numpy\n",
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"import random\n",
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"import os\n",
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"\n",
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"if (not isJVMStarted()):\n",
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" # Add JIDT jar library to the path -- it should be two folders up from the location of this notebook.\n",
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" jarLocation = os.path.join(os.getcwd(), \"..\", \"..\", \"infodynamics.jar\");\n",
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" if (not(os.path.isfile(jarLocation))):\n",
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" \texit(\"infodynamics.jar not found (expected at \" + os.path.abspath(jarLocation))\n",
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" # Start the JVM (add the \"-Xmx\" option with say 1024M if you get crashes due to not enough memory space)\n",
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" startJVM(getDefaultJVMPath(), \"-ea\", \"-Djava.class.path=\" + jarLocation)\n",
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"\n",
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"# Generate some random binary data.\n",
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"sourceArray = [random.randint(0,1) for r in range(100)]\n",
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"destArray = [0] + sourceArray[0:99]\n",
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"sourceArray2 = [random.randint(0,1) for r in range(100)]\n",
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"\n",
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"# Create a TE calculator and run it:\n",
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"teCalcClass = JPackage(\"infodynamics.measures.discrete\").TransferEntropyCalculatorDiscrete\n",
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"teCalc = teCalcClass(2,1)\n",
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"teCalc.initialise()\n",
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"\n",
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"# First use simple arrays of ints, which we can directly pass in:\n",
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"teCalc.addObservations(sourceArray, destArray)\n",
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"print(\"For copied source, result should be close to 1 bit : %.4f\" % teCalc.computeAverageLocalOfObservations())\n",
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"teCalc.initialise()\n",
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"teCalc.addObservations(sourceArray2, destArray)\n",
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"print(\"For random source, result should be close to 0 bits: %.4f\" % teCalc.computeAverageLocalOfObservations())\n",
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"\n",
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"# Next, demonstrate how to do this with a numpy array\n",
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"teCalc.initialise()\n",
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"# Create the numpy arrays:\n",
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"sourceNumpy = numpy.array(sourceArray, dtype=int)\n",
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"destNumpy = numpy.array(destArray, dtype=int)\n",
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"# The above can be passed straight through to JIDT in python 2:\n",
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"# teCalc.addObservations(sourceNumpy, destNumpy)\n",
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"# But you need to do this in python 3:\n",
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"sourceNumpyJArray = JArray(JInt, 1)(sourceNumpy.tolist())\n",
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"destNumpyJArray = JArray(JInt, 1)(destNumpy.tolist())\n",
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"teCalc.addObservations(sourceNumpyJArray, destNumpyJArray)\n",
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"print(\"Using numpy array for copied source, result confirmed as: %.4f\" % teCalc.computeAverageLocalOfObservations())\n",
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"\n",
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"print()\n",
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"print(\"If you've got no error messages, then your JIDT set-up is ok!\");"
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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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"id": "da8ef381-5391-4706-be61-ad931f20d62b",
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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 (ipykernel)",
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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": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,216 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "17e37cfc-4332-43e2-9cb7-4c1849ad894e",
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"metadata": {},
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"source": [
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"# Installation check\n",
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"\n",
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"_Copyright (C) 2024-, J.T. Lizier; Distributed under GNU General Public License v3_\n",
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"\n",
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"This is a sample notebook to run to check that your installation works ok\n",
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"\n",
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"**Step 1**: Check standard libraries"
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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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"id": "bb59c149-9c4e-4d3d-a097-52a72d74244a",
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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 scipy\n",
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"import matplotlib\n",
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"import pandas\n",
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"import torch\n",
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"# The following should all be built-in already:\n",
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"import random\n",
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"import os\n",
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"import math\n",
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"import string\n",
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"import re\n",
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"\n",
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"print(\"✅ all required packages installed.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "69668543-80e6-49ba-8f1c-bf1ac293a2b1",
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"metadata": {},
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"source": [
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"**Step 2**: Check that the platform and python installation are match on 64-bits (else match on 32 bits)"
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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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"id": "486906d0-a3cd-4ad2-b207-9d9930116779",
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"metadata": {},
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"outputs": [],
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"source": [
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"import platform\n",
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"import struct\n",
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"\n",
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"# Synthesising suggestions from Copilot and ChatGPT:\n",
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"# a. Machine architecture (OS/CPU)\n",
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"# Don't use platform.architecture() as this queries the Python executable.\n",
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"machine_arch = platform.machine()\n",
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"# Now parse this to 32 or 64 bits\n",
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"# platform.machine() gives strings like 'x86_64', 'AMD64', 'i386', 'arm64', etc.\n",
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"# May need to update this in future:\n",
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"if \"64\" in machine_arch:\n",
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" os_arch = 64\n",
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"elif \"86\" in machine_arch or \"32\" in machine_arch:\n",
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" os_arch = 32\n",
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"else:\n",
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" os_arch = \"Unknown\"\n",
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"# b. Python interpreter architecture\n",
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"python_arch = struct.calcsize(\"P\") * 8\n",
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"\n",
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"print(f\"Machine: {os_arch}-bit, Python: {python_arch}-bit\")\n",
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"if (os_arch == python_arch):\n",
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" print(\"✅ Machine and Python architectures match\")\n",
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"else:\n",
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" print(\"❌ Machine and Python architectures do not match!\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0e710dca-535c-4d8e-b500-e685f98ecf59",
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"metadata": {},
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"source": [
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"**Step 3**: Check that jpype1 is installed:"
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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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"id": "f276f26f-7829-44ae-8ccb-31ae16ad983d",
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" from jpype import *\n",
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" print(\"✅ jpype1 is already installed.\")\n",
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"except ImportError:\n",
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" print(\"❌ jpype1 is not installed, installing now.\")\n",
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" !pip install --user jpype1\n",
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" print(\"Install attempted: if successful, you should restart the kernel and run the notebook again after this.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c8b30fd4-4502-4b44-8554-5c41ccfb2148",
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"metadata": {},
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"source": [
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"**Step 4**: Check that the `infodynamics.jar` is in the expected location:"
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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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"id": "33e02580-70e9-4cf6-b355-1b53cc9e944e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Locate the JIDT jar library -- it should be two folders up from the location of this notebook.\n",
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"jarLocation = os.path.join(os.getcwd(), \"..\", \"..\", \"infodynamics.jar\");\n",
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"if (not(os.path.isfile(jarLocation))):\n",
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" raise Exception(\"infodynamics.jar not found (expected at \" + os.path.abspath(jarLocation))\n",
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"else:\n",
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" print(\"✅ infodynamics.jar is in the expected location.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e9a4f830-be74-45cd-82f3-8de395121ed5",
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"metadata": {},
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"source": [
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"**Step 5**: Check that the Java Virtual Machine (JVM) can be started by the python 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": null,
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"id": "fd418112-a0a6-4a39-9626-11f18b33bdd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"if (not isJVMStarted()):\n",
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" # Add JIDT jar library to the path and\n",
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" # Start the JVM (add the \"-Xmx\" option with say 1024M if you get crashes due to not enough memory space)\n",
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" # This should raise an Exception if unsuccessful\n",
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" startJVM(getDefaultJVMPath(), \"-ea\", \"-Djava.class.path=\" + jarLocation)\n",
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" if (isJVMStarted()):\n",
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" print(\"✅ JVM started\")\n",
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" else:\n",
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" raise Exception(\"❌ startJVM() ran without exception but the JVM is not started ...?\")\n",
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"else:\n",
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" print(\"✅ JVM was already started\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d3b492f1-12bf-4acf-972a-7cdc62e480c3",
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"metadata": {},
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"source": [
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"**Step 6**: Run a simple calculation with JIDT:"
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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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"id": "7f18beca-d55b-4b8d-8647-256e27787c34",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Generate some random binary data for a simple calculation.\n",
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"# Here destArray is a lagged copy of sourceArray:\n",
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"sourceArray = np.random.randint(0, 2, size=100)\n",
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"destArray = np.empty(100, dtype=int)\n",
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"destArray[0] = 0\n",
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"destArray[1:] = sourceArray[:99]\n",
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"\n",
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"# Create a TE calculator and run it:\n",
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"teCalcClass = JPackage(\"infodynamics.measures.discrete\").TransferEntropyCalculatorDiscrete\n",
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"teCalc = teCalcClass()\n",
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"teCalc.initialise()\n",
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"teCalc.addObservations(sourceArray, destArray)\n",
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"result = teCalc.computeAverageLocalOfObservations()\n",
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"print(\"✅ Calculation ran ok, check that it returned a value close to 1 bit : %.4f bits\" % result)"
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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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"id": "da8ef381-5391-4706-be61-ad931f20d62b",
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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 (ipykernel)",
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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": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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