{ "cells": [ { "cell_type": "markdown", "id": "a5466907-973d-4940-a2bc-3d07db15ee3a", "metadata": {}, "source": [ "# Computations in arbitrary precision\n", "\n", "As hinted in the [expression system tutorial](<./The expression system.ipynb>), starting from version 0.20 heyoka.py supports computations in [arbitrary precision](https://en.wikipedia.org/wiki/Arbitrary-precision_arithmetic) via the ``real`` type.\n", "\n", "``real`` is a Python wrapper for the [arbitrary-precision floating-point type](https://bluescarni.github.io/mppp/real.html) from the mp++ library. Before showing how to perform arbitrary-precision computations in heyoka.py, we will go through a quick crash course on the ``real`` type (more details are available in the mp++ [tutorial](https://bluescarni.github.io/mppp/tutorial_real.html) and [reference](https://bluescarni.github.io/mppp/real.html)).\n", "\n", "## Getting to know your real self\n", "\n", "Let us begin by importing the necessary bits from heyoka.py:" ] }, { "cell_type": "code", "execution_count": 1, "id": "ad1b399d-d968-494a-b518-f9affe7039ff", "metadata": {}, "outputs": [], "source": [ "import heyoka as hy\n", "import numpy as np\n", "\n", "real = hy.real" ] }, { "cell_type": "markdown", "id": "3c5d9f41-db8f-442f-b96a-143951ac3176", "metadata": {}, "source": [ "``real`` represents arbitrary-precision values encoded in a binary floating-point format. The number of binary digits in the significand (that is, the *precision* of a ``real``) can be set at runtime and it is in principle limited only by the available memory. The precision of a ``real`` object can be queried via the ``prec`` property:" ] }, { "cell_type": "code", "execution_count": 2, "id": "fd119d2b-16e8-446b-83c2-8d6cede501ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'The precision of a default-constructed real is: 2 bits'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f\"The precision of a default-constructed real is: {real().prec} bits\"" ] }, { "cell_type": "markdown", "id": "fa30fdd5-0145-4346-9cf8-41d1ec35165d", "metadata": {}, "source": [ "``real`` objects can be constructed from several other numerical objects. The precision of the constructed ``real`` is automatically inferred to preserve exactly the original value (if possible):" ] }, { "cell_type": "code", "execution_count": 3, "id": "45d111bf-7667-4786-af5d-6d4083394f0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precision deduced from a small int : 64 bits\n", "Precision deduced from a large int : 512 bits\n", "Precision deduced from a float : 53 bits\n", "Precision deduced from a long double: 64 bits\n", "Precision deduced from a real128 : 113 bits\n" ] } ], "source": [ "print(f\"Precision deduced from a small int : {real(42).prec} bits\")\n", "print(f\"Precision deduced from a large int : {real(2**500).prec} bits\")\n", "print(f\"Precision deduced from a float : {real(1.1).prec} bits\")\n", "print(f\"Precision deduced from a long double: {real(np.longdouble('1.1')).prec} bits\")\n", "print(f\"Precision deduced from a real128 : {real(hy.real128('1.1')).prec} bits\")" ] }, { "cell_type": "markdown", "id": "5fc0c608-cd4b-4b89-a049-c6a1e472c63f", "metadata": {}, "source": [ "If the automatic deduction is to be avoided, the precision can be specified manually with the ``prec`` keyword argument during construction:" ] }, { "cell_type": "code", "execution_count": 4, "id": "8db3fc4f-2f3b-45ed-96b1-43eb00aea9f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precision manually specified from a small int: 123 bits\n", "Precision manually specified from a float : 12 bits\n" ] } ], "source": [ "print(f\"Precision manually specified from a small int: {real(42, prec=123).prec} bits\")\n", "print(f\"Precision manually specified from a float : {real(1.1, prec=12).prec} bits\")" ] }, { "cell_type": "markdown", "id": "dad5610f-e490-44f6-9fa4-0917639f8694", "metadata": {}, "source": [ "Similarly, construction from string **always** requires a precision value to be provided:" ] }, { "cell_type": "code", "execution_count": 5, "id": "ef5c0cfc-7d63-4794-8d94-85685cbd6e07", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.100000000000000000000000000000000000001" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "real(\"1.1\", prec=128)" ] }, { "cell_type": "markdown", "id": "08139039-1870-4cbf-9bac-55230a019f52", "metadata": {}, "source": [ "``real`` supports the basic arithmetic and comparison operators. When ``real`` values with different precision are involved in a binary operation, the precision of the result is the largest precision among the operands:" ] }, { "cell_type": "code", "execution_count": 6, "id": "bd8a9789-528b-49ac-9c07-cd8156bd35a7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "128" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(real(\"1.1\", 128) + real(\"2.1\", 113)).prec" ] }, { "cell_type": "code", "execution_count": 7, "id": "8700ca74-10a9-480e-9e59-543306b5146a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "256" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(1.1 ** real(\"2.1\", 256)).prec" ] }, { "cell_type": "markdown", "id": "fdbeb796-234a-4220-9518-cd2a3dbec078", "metadata": {}, "source": [ "``real`` also supports several elementary functions, which currently need to be invoked from NumPy:" ] }, { "cell_type": "code", "execution_count": 8, "id": "4e9f1673-4b7d-4fea-89cf-177b2d7b9898", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.912073600614353399518025778717035383202e-1" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sin(real(\"1.1\", 128))" ] }, { "cell_type": "code", "execution_count": 9, "id": "1642818b-2284-4dcf-bb3b-c04245d89c83", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.93959147259893112734594915994130005e-1" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arctan2(real(\"1.1\", 113), real(\"5.6\", 113))" ] }, { "cell_type": "markdown", "id": "be68358e-534d-49b6-ba4a-e3830e15bda2", "metadata": {}, "source": [ "Finally, ``real`` can also be used as a [NumPy data type](https://numpy.org/doc/stable/reference/arrays.dtypes.html):" ] }, { "cell_type": "code", "execution_count": 10, "id": "6a773835-74b0-4142-ba43-c5780254f71f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.00000000000000000000, 2.00000000000000000000,\n", " 3.00000000000000000000], dtype=real)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array([1, 2, 3], dtype=real)" ] }, { "cell_type": "markdown", "id": "e4ff219d-c3a8-42ba-a6f2-e3c894418a62", "metadata": {}, "source": [ "Please be aware that NumPy support is to be considered **experimental** at this time (see the {ref}`\"Limitations and caveats\"` section below)." ] }, { "cell_type": "markdown", "id": "e0947254-176b-4fed-bb34-c1fef07ac661", "metadata": {}, "source": [ "## Numerical integration\n", "\n", "We are now ready to proceed to our first numerical integration in arbitrary precision. In the heyoka.py tradition, we will be using the pendulum as a simple test case.\n", "\n", "Let us begin with the definition of the dynamical equations:" ] }, { "cell_type": "code", "execution_count": 11, "id": "b25b6ade-7034-451f-b0c2-60f698f7e00f", "metadata": {}, "outputs": [], "source": [ "# Create the symbolic variables x and v.\n", "x, v = hy.make_vars(\"x\", \"v\")\n", "\n", "# Define the dynamical equations.\n", "sys = [(x, v), (v, -9.8 * hy.sin(x))]" ] }, { "cell_type": "markdown", "id": "905894e2-2c73-40e4-b949-cbbb6034d1f7", "metadata": {}, "source": [ "Second, we introduce a small helper to compute the energy of the system. This will be used to track the accuracy of the integration:" ] }, { "cell_type": "code", "execution_count": 12, "id": "accad881-61df-4ed5-b446-0a05aa8f251f", "metadata": {}, "outputs": [], "source": [ "def compute_energy(sv):\n", " from numpy import cos\n", "\n", " return (sv[1] * sv[1]) / 2 + 9.8 * (1 - cos(sv[0]))" ] }, { "cell_type": "markdown", "id": "b4bfae3d-06ef-4c6a-b4ad-29873fa140fa", "metadata": {}, "source": [ "For this specific example, we will be performing an integration in [octuple precision](https://en.wikipedia.org/wiki/Octuple-precision_floating-point_format), which requires 237 bits in the significand:" ] }, { "cell_type": "code", "execution_count": 13, "id": "4dbfe040-a7a0-4baa-a372-c13a7d4b5a44", "metadata": {}, "outputs": [], "source": [ "# Store the precision in a constant\n", "# for later use.\n", "prec = 237" ] }, { "cell_type": "markdown", "id": "88f85d93-4b8a-4b02-9655-0ba2b47497cc", "metadata": {}, "source": [ "We can now construct the integrator in octuple precision:" ] }, { "cell_type": "code", "execution_count": 14, "id": "1e259af5-0a45-4997-8b9b-f2ead2f159ce", "metadata": {}, "outputs": [], "source": [ "ta = hy.taylor_adaptive(\n", " sys,\n", " # Initial conditions.\n", " [real(-1, prec), real(0, prec)],\n", " # Specify that the integrator must operate\n", " # in arbitrary precision.\n", " fp_type=real,\n", ")" ] }, { "cell_type": "markdown", "id": "5eafab69-0b70-426d-a910-3efdb2a8845a", "metadata": {}, "source": [ "In order to construct an integrator in arbitrary precision, we need to specify ``fp_type=real`` as a construction argument. By default, the precision is automatically inferred from the precision of the initial state variables: because we passed ``[real(-1, prec), real(0, prec)]`` as initial conditions, heyoka.py deduces that the integration needs to be performed with 237 bits of precision.\n", "\n", "As an alternative, the precision can be explicitly passed as a keyword argument:" ] }, { "cell_type": "code", "execution_count": 15, "id": "e577af8b-f637-4a84-a141-89e7b3ceccb7", "metadata": {}, "outputs": [], "source": [ "# Alternative - but equivalent - way of\n", "# setting up an octuple-precision integration.\n", "ta = hy.taylor_adaptive(\n", " sys,\n", " [real(-1), real(0)],\n", " # Specify that the integrator must operate\n", " # in arbitrary precision.\n", " fp_type=real,\n", " # Explicitly specify the precision.\n", " prec=prec,\n", ")" ] }, { "cell_type": "markdown", "id": "3dac386a-c184-4937-9569-6d1f592b25fc", "metadata": {}, "source": [ "Let us print to screen the integrator object:" ] }, { "cell_type": "code", "execution_count": 16, "id": "c518b6cd-487e-40c1-a816-db7e65d280e4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "C++ datatype : mppp::v15::real\n", "Precision : 237 bits\n", "Tolerance : 9.055679078826712367509119290887791780682531198139138189582614889935501319e-72\n", "High accuracy : false\n", "Compact mode : true\n", "Taylor order : 83\n", "Dimension : 2\n", "Time : 0.000000000000000000000000000000000000000000000000000000000000000000000000\n", "State : [-1.000000000000000000000000000000000000000000000000000000000000000000000000, 0.000000000000000000000000000000000000000000000000000000000000000000000000]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ta" ] }, { "cell_type": "markdown", "id": "42f065dd-647e-445a-b79c-82415f729bfa", "metadata": {}, "source": [ "Like with standard double-precision integrations, the default tolerance value is the epsilon corresponding to the integration precision. Note also how, for arbitrary-precision integrations, compact mode defaults to ``True`` rather than ``False``.\n", "This change is motivated by the fact that the Taylor order of arbitrary-precision integrators will typically be much higher than in double-precision and extended-precision integrators, which results in very long compilation times even for simple ODEs. Moreover, in arbitrary-precision integrators compact mode does not bring substantial performance improvements due to the fact that most numerical computations are offloaded to the mp++ library (rather than being implemented directly in LLVM).\n", "\n", "Let us now compute and store the initial energy of the system:" ] }, { "cell_type": "code", "execution_count": 17, "id": "092eeb1d-200b-456a-b137-d6cb1a186474", "metadata": {}, "outputs": [], "source": [ "E0 = compute_energy(ta.state)" ] }, { "cell_type": "markdown", "id": "e44b286f-6f0c-4598-b28e-2c7da127f4c4", "metadata": {}, "source": [ "Next, let us propagate the system until ``t=5``, requesting the [continuous output](<./Dense output.ipynb>):" ] }, { "cell_type": "code", "execution_count": 18, "id": "2cf141ec-0d1f-43dc-8c90-73aceb097c01", "metadata": {}, "outputs": [], "source": [ "out = ta.propagate_until(real(5), c_output=True)" ] }, { "cell_type": "markdown", "id": "79301191-95b5-4e5d-8d65-c75ae69b2aab", "metadata": {}, "source": [ "We can now use the continuous output to compute how the energy error evolves throughout the integration:" ] }, { "cell_type": "code", "execution_count": 19, "id": "de3d568b-bca4-4a2a-8236-1a6398ce3d89", "metadata": {}, "outputs": [], "source": [ "# Define a time grid in octuple precision.\n", "tgrid = np.linspace(real(0, prec), real(5, prec), 1000, dtype=real)\n", "\n", "# Compute the relative energy error over the time grid.\n", "Eerr = [abs((compute_energy(_) - E0) / E0) for _ in out[-2](tgrid)]" ] }, { "cell_type": "markdown", "id": "48e61f42-241f-41a5-9d1c-0b42256769cb", "metadata": {}, "source": [ "Let us plot the energy error:" ] }, { "cell_type": "code", "execution_count": 20, "id": "c1799774-d062-45ee-9907-5496f7ac9173", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib.pylab import plt\n", "\n", "fig = plt.figure(figsize=(12, 6))\n", "\n", "plt.plot(tgrid, Eerr)\n", "\n", "plt.xlabel(\"Time\")\n", "plt.ylabel(\"Rel. energy error\")\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "256cecc2-32d5-4766-aa77-008f0ccc7011", "metadata": {}, "source": [ "We can see how indeed energy is preserved at the requested accuracy level.\n", "\n", "All features available in double precision are also available in arbitrary precision. For instance, here's an example of arbitrary-precision event detection from the [event detection tutorial](<./Event detection.ipynb>):" ] }, { "cell_type": "code", "execution_count": 21, "id": "e2e78ef8-883b-4612-92d5-1de33ef3eaeb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value of x when v is zero: -5.000000000000000000000000000000000000000000000000000000000000000000000011e-2\n", "Value of x when v is zero: 5.000000000000000000000000000000000000000000000000000000000000000000000011e-2\n", "Value of x when v is zero: -4.999999999999999999999999999999999999999999999999999999999999999999999983e-2\n", "Value of x when v is zero: 5.000000000000000000000000000000000000000000000000000000000000000000000011e-2\n", "Value of x when v is zero: -5.000000000000000000000000000000000000000000000000000000000000000000000011e-2\n", "Value of x when v is zero: 5.000000000000000000000000000000000000000000000000000000000000000000000011e-2\n", "Value of x when v is zero: -5.000000000000000000000000000000000000000000000000000000000000000000000011e-2\n", "Value of x when v is zero: 5.000000000000000000000000000000000000000000000000000000000000000000000040e-2\n", "Value of x when v is zero: -5.000000000000000000000000000000000000000000000000000000000000000000000040e-2\n", "Value of x when v is zero: 5.000000000000000000000000000000000000000000000000000000000000000000000011e-2\n" ] } ], "source": [ "# Define a callback for the event.\n", "def cb(ta, time, d_sgn):\n", " # Compute the state of the system when the\n", " # event triggered and print the value of x.\n", " ta.update_d_output(time)\n", " print(\"Value of x when v is zero: {}\".format(ta.d_output[0]))\n", "\n", "\n", "ev = hy.nt_event(\n", " # The left-hand side of the event equation\n", " v,\n", " # The callback.\n", " callback=cb,\n", " # Specify this is an event\n", " # for arbitrary-precision\n", " # integration.\n", " fp_type=real,\n", ")\n", "\n", "ta = hy.taylor_adaptive(\n", " # Definition of the ODE system:\n", " # x' = v\n", " # v' = -9.8 * sin(x)\n", " ((x, v), (v, -9.8 * hy.sin(x))),\n", " # Initial conditions\n", " # for x and v.\n", " [real(\"-0.05\", prec), real(0.0, prec)],\n", " # Non-terminal events.\n", " nt_events=[ev],\n", " # Specify this is an\n", " # arbitrary-precision\n", " # integration.\n", " fp_type=real,\n", ")\n", "\n", "ta.propagate_until(real(10));" ] }, { "cell_type": "markdown", "id": "b3980b4d-f189-4e76-96a6-b77b7140c67d", "metadata": {}, "source": [ "In this example, the event is supposed to trigger whenever the pendulum's bob reaches the maximum amplitude, which corresponds to $v=0$ and $\\left|x\\right|=0.05$. We can see from the screen output that this is indeed the case.\n", "\n", "## Compiled functions\n", "\n", "[Compiled functions](<./compiled_functions.ipynb>) are also available in arbitrary precision. Here's a simple example:" ] }, { "cell_type": "code", "execution_count": 22, "id": "1ca0f33f-97fa-4c4e-b57d-8a6493ca3e8c", "metadata": {}, "outputs": [], "source": [ "x, y = hy.make_vars(\"x\", \"y\")\n", "\n", "# Define the function to be compiled.\n", "sym_func = x**2 - y**2\n", "\n", "# Compile it.\n", "cf = hy.cfunc([sym_func], [x, y], fp_type=real, prec=prec)" ] }, { "cell_type": "markdown", "id": "7969bf3e-275f-4ade-bc33-9acb8cc2cd04", "metadata": {}, "source": [ "Note that, in addition to specifying ``fp_type=real``, we also **must** explicitly specify the precision of the compiled function. That is, compiled functions cannot automatically determine a precision value from their inputs.\n", "\n", "We can now proceed to the evaluation of the compiled function:" ] }, { "cell_type": "code", "execution_count": 23, "id": "8bb6d471-ebef-42b0-a3b2-f5f00abff1b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-3.000000000000000000000000000000000000000000000000000000000000000000000000],\n", " dtype=real)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cf([real(1, prec), real(2, prec)])" ] }, { "cell_type": "markdown", "id": "eb705267-4252-494d-a213-22bb03957546", "metadata": {}, "source": [ "(mp_caveats)=\n", "\n", "## Limitations and caveats\n", "\n", "Similarly to [extended-precision datatypes](<./ext_precision.ipynb>), NumPy support for the ``real`` dtype is quite limited: while basic features such as array slicing, indexing, arithmetic, special functions and explicit conversions to/from other types are supported and working as intended, more advanced features such as random number generation, nontrivial linear algebra, etc. will not work with ``real``.\n", "\n", "Additionally, because ``real`` wraps a C++ class with nontrivial lifetime management, creating and manipulating arrays with ``real`` dtype will result by default in **memory leaks**. Specifically, when a ``real`` array is garbage-collected, the memory allocated by the individual ``real`` objects in the array will **not** be freed.\n", "\n", "This leaky behaviour can be avoided by (ab)using a [recently-introduced NumPy memory management feature](https://numpy.org/neps/nep-0049.html). Specifically, it is possible to invoke the ``install_custom_numpy_mem_handler()`` function to install a custom memory manager for NumPy arrays that will ensure that all ``real`` objects are properly destroyed when an array is garbage-collected:" ] }, { "cell_type": "code", "execution_count": 24, "id": "fdcbc415-7b11-4e9d-a252-f26e7cb047ab", "metadata": {}, "outputs": [], "source": [ "hy.install_custom_numpy_mem_handler()" ] }, { "cell_type": "markdown", "id": "bda1a682-8c2b-4397-b2a1-508a4b68d7ea", "metadata": {}, "source": [ "Note, however, that this custom memory manager results in a measurable performance penalty when (de)allocating NumPy arrays for **all** dtypes (and not only ``real``). Hence, the custom memory manager is disabled by default.\n", "\n", "Note that the original NumPy memory manager can be restored via the ``remove_custom_numpy_mem_handler()`` function:" ] }, { "cell_type": "code", "execution_count": 25, "id": "b37d9f6e-5757-418e-89a3-9a77191cd6a2", "metadata": {}, "outputs": [], "source": [ "hy.remove_custom_numpy_mem_handler()" ] }, { "cell_type": "markdown", "id": "c2fe3abe-9e9e-4ca9-a649-a610e8ee2547", "metadata": {}, "source": [ "If you encounter issues with NumPy support for the ``real`` class, please do not hesitate to [open an issue](https://github.com/bluescarni/heyoka.py/issues/new/choose)." ] } ], "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }