{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bfddfbfd-39b6-427b-8dd3-e0b827149101",
   "metadata": {},
   "source": [
    "# Advanced CySolver Examples\n",
    "This notebook showcases some of the advanced functionality of the CySolver backend distributed with CyRK. \n",
    "In order to run these cells you need to make sure Cython, Jupyter, CyRK, and Numpy are installed. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5f95089-c7af-481b-87ba-5ab10caf14d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\joepr\\miniforge3\\envs\\cyrk313\\python.exe\n",
      "CyRK version: 0.17.0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('dark_background')\n",
    "# Hack the cython magic so that it can set the correct C++ standard.\n",
    "%load_ext Cython\n",
    "import sys\n",
    "import platform\n",
    "print(sys.executable)\n",
    "import numpy as np\n",
    "import CyRK\n",
    "print(\"CyRK version:\", CyRK.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c75057f-cd71-470a-a625-9e75c6d95225",
   "metadata": {},
   "source": [
    "## Hack IPython Magic\n",
    "In order to make this notebook work across platforms without changes we need to hack the cython cells to include some additional header information. This is done using the script `jupyter_cyhack.py` file contained in the same directory as these demos. Hopefully it works fine for you, but keep in mind that the hack below is required to get these cells to work. So if you are making your own cythonized notebook you will want to see what headers should be included for your operating system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "614ec761-20b6-4a2f-a359-af6284a99156",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Cython.Build import IpythonMagic\n",
    "\n",
    "from jupyter_cyhack import build_hack\n",
    "\n",
    "# Get the current cython Ipython parser.\n",
    "original_cython_parser = IpythonMagic.CythonMagics.cython\n",
    "# Apply hacks to headers and compile lines.\n",
    "patched_cython_parser = build_hack(original_cython_parser)\n",
    "# Patch it in\n",
    "IpythonMagic.CythonMagics.cython = patched_cython_parser\n",
    "# Reload\n",
    "ip = get_ipython()\n",
    "ip.register_magics(IpythonMagic.CythonMagics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb5fc83",
   "metadata": {},
   "source": [
    "## Parallelizing `cysolve_ivp`\n",
    "Below is an example of how you can parallelize over `cysolve_ivp` using Cython's\n",
    "[prange](https://cython.readthedocs.io/en/latest/src/userguide/parallelism.html).\n",
    "\n",
    "Note that in this example we use `cysolve_ivp_noreturn` instead of `cysolve_ivp`. It should work with both. Please\n",
    "report a bug if it does not!\n",
    "\n",
    "In this example we build a unique `CySolveOutput` for each job and batch send them to threads to work through the jobs.\n",
    "In the next example we show how you can instead have one `CySolveOutput` per thread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "938d76cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cpp(25554): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cpp(25555): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cpp(25678): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cpp(28820): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cpp(28827): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cpp(29327): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cpp(29328): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cp313-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_8abd2d22f12772e52260df90a103cc8446b9d71fcbdea2f5e0fae27cd3a673f3.cp313-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Starting prange Demo!\n",
      "Working with N=1...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 2253.0 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 2.3ms/loop.\n",
      "Total Time: 2253.047ms.\n",
      "\n",
      "\n",
      "Working with N=2...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 1136.0 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 1.1ms/loop.\n",
      "Total Time: 1136.016ms (-49.6 % change from N=1; -49.6 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=3...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 795.3 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.8ms/loop.\n",
      "Total Time: 795.348ms (-30.0 % change from N=2; -64.7 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=4...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 610.0 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.6ms/loop.\n",
      "Total Time: 609.981ms (-23.3 % change from N=3; -72.9 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=5...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 497.6 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.5ms/loop.\n",
      "Total Time: 497.630ms (-18.4 % change from N=4; -77.9 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=6...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 421.8 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.4ms/loop.\n",
      "Total Time: 421.817ms (-15.2 % change from N=5; -81.3 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=7...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 367.2 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.4ms/loop.\n",
      "Total Time: 367.165ms (-13.0 % change from N=6; -83.7 % change from N=1).\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "from cython.parallel cimport prange\n",
    "\n",
    "from libcpp.vector cimport vector\n",
    "from libcpp.memory cimport unique_ptr, make_unique\n",
    "from libcpp.utility cimport move\n",
    "\n",
    "from CyRK.cy.cysolver_api cimport cysolve_ivp_noreturn, CySolverResult, CySolveOutput, ODEMethod\n",
    "from CyRK.cy.cysolver_test cimport lotkavolterra_diffeq\n",
    "\n",
    "cdef extern from \"<chrono>\" namespace \"std::chrono\" nogil:\n",
    "    cdef cppclass microseconds:\n",
    "        long long count()\n",
    "    \n",
    "    # We need a generic duration type to handle the result of subtraction\n",
    "    cdef cppclass duration \"std::chrono::high_resolution_clock::duration\":\n",
    "        pass\n",
    "\n",
    "    microseconds duration_cast_microseconds \"std::chrono::duration_cast<std::chrono::microseconds>\"(duration)\n",
    "\n",
    "cdef extern from \"<chrono>\" namespace \"std::chrono::high_resolution_clock\" nogil:\n",
    "    cdef cppclass time_point:\n",
    "        duration operator-(time_point)\n",
    "    \n",
    "    time_point now()\n",
    "\n",
    "# Note the example below uses a `def` vs. `cdef` function but it should work with either\n",
    "\n",
    "def run_prange(int num_threads = 2):\n",
    "    \"\"\"Run a prange test where args are common across all runs.\"\"\"\n",
    "    \n",
    "    cdef int num_runs = 1000\n",
    "    cdef double t_start = 0.0\n",
    "    # Pick a large time so each time step takes a significant amount of time so it can benefit from parallelize.\n",
    "    cdef double t_end = 2000.0\n",
    "    \n",
    "    cdef vector[vector[double]] y0_vecs = vector[vector[double]]()\n",
    "    y0_vecs.resize(num_runs)\n",
    "    cdef size_t i\n",
    "    for i in range(num_runs):\n",
    "        # Set initial conditions; all the same for these tests\n",
    "        # 2 dependent variables\n",
    "        y0_vecs[i].resize(2)\n",
    "        y0_vecs[i][0] = 10.0\n",
    "        y0_vecs[i][1] = 5.0\n",
    "    \n",
    "    # --- CHANGE: Single constant vector instead of vector[vector] ---\n",
    "    cdef vector[char] constant_args = vector[char]()\n",
    "    # arg vector\n",
    "    constant_args.resize(4 * 8)  # Number of double * size of double\n",
    "    \n",
    "    # Recast <char*> args to double pointer for storage of args\n",
    "    cdef double* arg_dbl_ptr = <double*> constant_args.data()\n",
    "    arg_dbl_ptr[0] = 1.5\n",
    "    arg_dbl_ptr[1] = 1.0\n",
    "    arg_dbl_ptr[2] = 3.0\n",
    "    arg_dbl_ptr[3] = 1.0\n",
    "\n",
    "    cdef vector[CySolveOutput] results = vector[CySolveOutput]()\n",
    "    results.resize(num_runs)\n",
    "    for i in range(num_runs):\n",
    "        results[i] = move(make_unique[CySolverResult](ODEMethod.RK45))\n",
    "\n",
    "    # Main Loop\n",
    "    cdef time_point start_time, end_time\n",
    "    cdef Py_ssize_t p_i  # prange has to used a signed integer type\n",
    "\n",
    "    start_time = now()\n",
    "    for p_i in prange(num_runs, nogil=True, num_threads=num_threads, use_threads_if=num_threads>1, schedule='static'):\n",
    "        cysolve_ivp_noreturn(\n",
    "            results[p_i].get(), # Pass in CySolverResult pointer which is found with CySolveOutput::get()\n",
    "            lotkavolterra_diffeq,\n",
    "            t_start,\n",
    "            t_end,\n",
    "            y0_vecs[p_i],\n",
    "            1.0e-5, # rtol\n",
    "            1.0e-8, # atol\n",
    "            constant_args # Pass the single shared vector\n",
    "        )\n",
    "    end_time = now()\n",
    "\n",
    "    # Check results\n",
    "    cdef CySolverResult* reference_result_ptr = results[0].get()\n",
    "    # Check the reference first.\n",
    "    assert reference_result_ptr.success\n",
    "    assert reference_result_ptr.size > 0\n",
    "\n",
    "    cdef CySolverResult* check_result_ptr\n",
    "    cdef size_t j\n",
    "    for i in range(num_runs):\n",
    "\n",
    "        if i == 0:\n",
    "            continue\n",
    "        check_result_ptr = results[i].get()\n",
    "\n",
    "        assert check_result_ptr.success\n",
    "        assert check_result_ptr.size > 0\n",
    "        assert reference_result_ptr.size == check_result_ptr.size\n",
    "\n",
    "        for j in range(reference_result_ptr.size):\n",
    "            assert check_result_ptr.time_domain_vec[j] == reference_result_ptr.time_domain_vec[j]\n",
    "            assert check_result_ptr.solution[2 * j + 0] == reference_result_ptr.solution[2 * j + 0]\n",
    "            assert check_result_ptr.solution[2 * j + 1] == reference_result_ptr.solution[2 * j + 1]\n",
    "\n",
    "\n",
    "    cdef double loop_time_ms = <double>(<int>duration_cast_microseconds(end_time - start_time).count()) / 1000.0\n",
    "    print(f\"CyRK prange test `run_prange_constant_args_test` finished in {loop_time_ms:0.1f} ms (If you are seeing this then tests passed too).\")\n",
    "    print(f\"\\tAvg time: {loop_time_ms/num_runs:0.1f}ms/loop.\")\n",
    "\n",
    "    return loop_time_ms\n",
    "\n",
    "import os\n",
    "cdef int cpu_count = os.cpu_count()\n",
    "cdef double result\n",
    "cdef int threads\n",
    "cdef double t1_result, last_result\n",
    "print(\"\\n\\nStarting prange Demo!\")\n",
    "for threads in range(1, min(cpu_count, 8)): \n",
    "    print(f\"Working with N={threads}...\")\n",
    "    result = run_prange(threads)\n",
    "    if threads == 1:\n",
    "        print(f\"Total Time: {result:0.3f}ms.\\n\\n\")\n",
    "        t1_result = result\n",
    "    else:\n",
    "        print(f\"Total Time: {result:0.3f}ms ({100.0*(result - last_result)/last_result:0.1f} % change from N={threads-1}; {100.0*(result - t1_result)/t1_result:0.1f} % change from N=1).\\n\\n\")\n",
    "    last_result = result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "989d46bf",
   "metadata": {},
   "source": [
    "### Batching `CySolveOutput`s Over Multiple Threads\n",
    "In the parallelized example above we created a `CySolveOutput` for each of our runs. This is safe because each thread\n",
    "is guaranteed to only work with a unique `CySolveOutput` so it can write data as it wishes. However, `CySolveOutput`\n",
    "does carry a little overhead that may cause memory issues for problems with many dependent `y` variables. \n",
    "\n",
    "It may be advantageous to make use of \"Re-Uses\" (see other examples in this demo) with `cysolve_ivp_noreturn`. Where\n",
    "only one `CySolveOutput` per thread is built and used. However, to do any useful work we still have to build storage\n",
    "arrays for our `y`, `t`, and perhaps other metadata like integration success, for each job. That is a large part of\n",
    "the memory footprint of `CySolveOutput` in the first place. **To emphasize** many problems will not see an improvement\n",
    "moving from a global `CySolveOutput` to a local one with multithreaded workflows (as we see in the example below)!\n",
    "\n",
    "Examples where this approach may be faster (you should still test both approaches!):\n",
    "- The ODE system has many dependent `y` variables so the internal structures of `CySolveOutput` start to take up a\n",
    "  considerable memory footprint.\n",
    "- You only care about the output at the end of the integration or at certain time steps: then the storage arrays would\n",
    "  be much smaller than dealing with the storage arrays inside `CySolveOutput`.\n",
    "\n",
    "\n",
    "_The example below is just one example of an approach you could take._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78ef4f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cpp(25738): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cpp(25739): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cpp(25862): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cpp(29004): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cpp(29011): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cpp(29511): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cpp(29512): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cp313-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_fa26e21ff7cbc2343efedd0934361b7185d626e15a5c465e85f0c0b0608a56ae.cp313-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Starting Batched prange Demo!\n",
      "Working with N=1...\n",
      "Starting Batched Run (1 threads; jobs per thread = 1000)...\n",
      "Batched test finished in 2352.4 ms.\n",
      "\tAvg time: 2.352ms/loop.\n",
      "Total Time: 2352.402ms.\n",
      "\n",
      "\n",
      "Working with N=2...\n",
      "Starting Batched Run (2 threads; jobs per thread = 500)...\n",
      "Batched test finished in 1197.1 ms.\n",
      "\tAvg time: 1.197ms/loop.\n",
      "Total Time: 1197.106ms (-49.1 % change from N=1; -49.1 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=3...\n",
      "Starting Batched Run (3 threads; jobs per thread = 333)...\n",
      "Batched test finished in 856.7 ms.\n",
      "\tAvg time: 0.857ms/loop.\n",
      "Total Time: 856.705ms (-28.4 % change from N=2; -63.6 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=4...\n",
      "Starting Batched Run (4 threads; jobs per thread = 250)...\n",
      "Batched test finished in 629.9 ms.\n",
      "\tAvg time: 0.630ms/loop.\n",
      "Total Time: 629.851ms (-26.5 % change from N=3; -73.2 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=5...\n",
      "Starting Batched Run (5 threads; jobs per thread = 200)...\n",
      "Batched test finished in 610.5 ms.\n",
      "\tAvg time: 0.610ms/loop.\n",
      "Total Time: 610.457ms (-3.1 % change from N=4; -74.0 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=6...\n",
      "Starting Batched Run (6 threads; jobs per thread = 166)...\n",
      "Batched test finished in 424.8 ms.\n",
      "\tAvg time: 0.425ms/loop.\n",
      "Total Time: 424.766ms (-30.4 % change from N=5; -81.9 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=7...\n",
      "Starting Batched Run (7 threads; jobs per thread = 142)...\n",
      "Batched test finished in 486.5 ms.\n",
      "\tAvg time: 0.487ms/loop.\n",
      "Total Time: 486.548ms (14.5 % change from N=6; -79.3 % change from N=1).\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "\n",
    "from cython.parallel cimport prange\n",
    "\n",
    "from libc.string cimport memcpy\n",
    "\n",
    "from libcpp cimport bool as cpp_bool\n",
    "from libcpp.vector cimport vector\n",
    "from libcpp.memory cimport unique_ptr, make_unique\n",
    "from libcpp.utility cimport move\n",
    "\n",
    "from CyRK.cy.cysolver_api cimport cysolve_ivp_noreturn, CySolverResult, ODEMethod\n",
    "from CyRK.cy.cysolver_test cimport lotkavolterra_diffeq\n",
    "\n",
    "cdef extern from \"<chrono>\" namespace \"std::chrono\" nogil:\n",
    "    cdef cppclass microseconds:\n",
    "        long long count()\n",
    "    cdef cppclass duration \"std::chrono::high_resolution_clock::duration\":\n",
    "        pass\n",
    "    microseconds duration_cast_microseconds \"std::chrono::duration_cast<std::chrono::microseconds>\"(duration)\n",
    "\n",
    "cdef extern from \"<chrono>\" namespace \"std::chrono::high_resolution_clock\" nogil:\n",
    "    cdef cppclass time_point:\n",
    "        duration operator-(time_point)\n",
    "    time_point now()\n",
    "\n",
    "cdef void single_thread_run(\n",
    "        bint* success_storage_ptr,\n",
    "        vector[double]* t_storage_vec_ptr,\n",
    "        vector[double]* y_storage_vec_ptr,\n",
    "        vector[double]* y0_vec_ptr,\n",
    "        size_t num_dy,\n",
    "        vector[char]& args_vec,\n",
    "        double t_start,\n",
    "        double t_end,\n",
    "        size_t num_jobs\n",
    "        ) noexcept nogil:\n",
    "\n",
    "    # Thread-Local Allocation\n",
    "    # This solver exists only for this thread and is reused for every job in the batch.\n",
    "    # For this example we will make it easy and just hard code the integration method.\n",
    "    cdef unique_ptr[CySolverResult] solver_uptr = make_unique[CySolverResult](ODEMethod.RK45)\n",
    "    cdef CySolverResult* solver_ptr = solver_uptr.get()\n",
    "\n",
    "    cdef size_t solution_size\n",
    "\n",
    "    # Have this thread work on each job sequentially\n",
    "    cdef size_t job_idx\n",
    "    for job_idx in range(num_jobs):\n",
    "        # All arrays will be provided starting at this particular thread's first job.\n",
    "        # It does not need to know any details about what the other threads are doing.\n",
    "        # So we start from 0 and end when we are done with our work.\n",
    "\n",
    "        # Reuse the solver pointer\n",
    "        cysolve_ivp_noreturn(\n",
    "            solver_ptr,\n",
    "            lotkavolterra_diffeq,  # Again this is hardcoded, as are many of the other arguments\n",
    "            t_start,\n",
    "            t_end,\n",
    "            y0_vec_ptr[job_idx],\n",
    "            1.0e-5, # rtol\n",
    "            1.0e-8, # atol\n",
    "            args_vec\n",
    "        )\n",
    "\n",
    "        # D. Extract Results to Shared Arrays\n",
    "        # Store success status\n",
    "        success_storage_ptr[job_idx] = solver_ptr.success\n",
    "        \n",
    "        if solver_ptr.success:\n",
    "            solution_size = solver_ptr.time_domain_vec.size()\n",
    "            # Copy the time_domain vector from the solution into our storage array.\n",
    "            t_storage_vec_ptr[job_idx].resize(solution_size)\n",
    "            memcpy(t_storage_vec_ptr[job_idx].data(), solver_ptr.time_domain_vec.data(), solution_size * sizeof(double))\n",
    "\n",
    "            # Repeat for y's\n",
    "            # There are multiple y's stored in a flattened solution array in the solver_ptr. But we don't actually\n",
    "            #  care about their shape right now because we want to copy the exact shape over to our final array. \n",
    "            # All we care about is the total size so we can use memcpy.\n",
    "            # We use num_dy not num_y because the user might be capturing extra outputs\n",
    "            y_storage_vec_ptr[job_idx].resize(solution_size * num_dy)\n",
    "            memcpy(y_storage_vec_ptr[job_idx].data(), solver_ptr.solution.data(), solution_size * num_dy * sizeof(double))\n",
    "\n",
    "\n",
    "def run_prange_batched(int num_threads=2):\n",
    "\n",
    "    if num_threads < 1:\n",
    "        num_threads = 1\n",
    "\n",
    "    cdef int num_runs = 1000\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end = 2000.0\n",
    "\n",
    "    # 1. Setup Inputs\n",
    "    cdef vector[vector[double]] y0_vecs = vector[vector[double]]()\n",
    "    y0_vecs.resize(num_runs)\n",
    "    cdef size_t num_dy = 2\n",
    "    cdef size_t num_y = 2\n",
    "    cdef size_t i\n",
    "    for i in range(num_runs):\n",
    "        y0_vecs[i].resize(2)\n",
    "        y0_vecs[i][0] = 10.0\n",
    "        y0_vecs[i][1] = 5.0\n",
    "    \n",
    "    # Setup Constant Args\n",
    "    cdef vector[char] constant_args = vector[char]()\n",
    "    constant_args.resize(4 * 8)\n",
    "    cdef double* arg_dbl_ptr = <double*>constant_args.data()\n",
    "    arg_dbl_ptr[0] = 1.5\n",
    "    arg_dbl_ptr[1] = 1.0\n",
    "    arg_dbl_ptr[2] = 3.0\n",
    "    arg_dbl_ptr[3] = 1.0\n",
    "\n",
    "    # Setup Output storage for our full solution domain (Lightweight vectors instead of full CySolverResult objects)\n",
    "    # We pre-allocate these so threads can write to them without locking.\n",
    "    # Since each thread writes to a unique index 'job_idx', this is thread-safe.\n",
    "    cdef vector[vector[double]] final_time_domains = vector[vector[double]](num_runs)\n",
    "    cdef vector[vector[double]] final_ys           = vector[vector[double]](num_runs)\n",
    "\n",
    "    # Important! We use cpp_bool a lot throughout CyRK but c++ bool is just a bit and can't have a memory address.\n",
    "    # Since we need to take the pointer addresses of elements of this vector we need to use another struct. bint should work.\n",
    "    cdef vector[bint] success_flags = vector[bint](num_runs)\n",
    "\n",
    "    # Determine each thread's starting index and number of jobs\n",
    "    cdef Py_ssize_t thread_idx\n",
    "    cdef vector[size_t] thread_jobs        = vector[size_t](num_threads)\n",
    "    cdef vector[size_t] thread_start_index = vector[size_t](num_threads)\n",
    "\n",
    "    cdef size_t start_idx\n",
    "    cdef size_t end_idx\n",
    "    for thread_idx in range(num_threads):\n",
    "        # Integer division chunking: (i*N)//T to ((i+1)*N)//T\n",
    "        start_idx = (thread_idx * num_runs) // num_threads\n",
    "        end_idx   = ((thread_idx + 1) * num_runs) // num_threads\n",
    "        \n",
    "        thread_start_index[thread_idx] = start_idx\n",
    "        thread_jobs[thread_idx]        = end_idx - start_idx\n",
    "    \n",
    "    # Loop over the number of threads, NOT the number of runs.\n",
    "    print(f\"Starting Batched Run ({num_threads} threads; jobs per thread = {num_runs // num_threads})...\")\n",
    "    cdef time_point start_time, end_time\n",
    "    start_time = now()\n",
    "    for thread_idx in prange(num_threads, nogil=True, schedule='static'):\n",
    "        single_thread_run(\n",
    "            &success_flags[thread_start_index[thread_idx]],\n",
    "            &final_time_domains[thread_start_index[thread_idx]],\n",
    "            &final_ys[thread_start_index[thread_idx]],\n",
    "            &y0_vecs[thread_start_index[thread_idx]],\n",
    "            num_dy,\n",
    "            constant_args,\n",
    "            t_start,\n",
    "            t_end,\n",
    "            thread_jobs[thread_idx]\n",
    "            )\n",
    "    end_time = now()\n",
    "\n",
    "    # 3. Validation\n",
    "    for i in range(num_runs):\n",
    "        assert success_flags[i] == True\n",
    "        \n",
    "    cdef double loop_time_ms = <double>(<int>duration_cast_microseconds(end_time - start_time).count()) / 1000.0\n",
    "    print(f\"Batched test finished in {loop_time_ms:0.1f} ms.\")\n",
    "    print(f\"\\tAvg time: {loop_time_ms/num_runs:0.3f}ms/loop.\")\n",
    "    \n",
    "    return loop_time_ms\n",
    "\n",
    "import os\n",
    "cdef int cpu_count = os.cpu_count()\n",
    "cdef double result\n",
    "cdef int threads\n",
    "cdef double t1_result, last_result\n",
    "print(\"\\n\\nStarting Batched prange Demo!\")\n",
    "for threads in range(1, min(cpu_count, 8)): \n",
    "    print(f\"Working with N={threads}...\")\n",
    "    result = run_prange_batched(threads)\n",
    "    if threads == 1:\n",
    "        print(f\"Total Time: {result:0.3f}ms.\\n\\n\")\n",
    "        t1_result = result\n",
    "    else:\n",
    "        print(f\"Total Time: {result:0.3f}ms ({100.0*(result - last_result)/last_result:0.1f} % change from N={threads-1}; {100.0*(result - t1_result)/t1_result:0.1f} % change from N=1).\\n\\n\")\n",
    "    last_result = result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24fa5c2c-a9ee-43dd-a483-51385acfb1d8",
   "metadata": {},
   "source": [
    "## Arbitrary Additional Arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74810433-eb2b-4b95-b47f-27269060a6d8",
   "metadata": {},
   "source": [
    "### Additional Args = Array of Doubles\n",
    "See discussion in \"Advanced CySolver.md\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "350bd407-9563-4fa4-983e-02046bdcc051",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cpp(25021): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cpp(25022): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cpp(25145): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cpp(28009): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cpp(28010): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cpp(28630): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cpp(28637): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cp313-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_fc30dd5c945c70f8f8395f0f5be13411c1e560f07f21f13e75c30e0b99aa99a9.cp313-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Integration success = True \n",
      "\tNumber of adaptive time steps required: 152\n",
      "Integration message: Integration completed without issue.\n"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "np.import_array()\n",
    "\n",
    "from libc.math cimport sin\n",
    "from libcpp.utility cimport move\n",
    "from libcpp.vector cimport vector\n",
    "\n",
    "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType, MAX_STEP, CySolveOutput, PreEvalFunc, ODEMethod\n",
    "\n",
    "cdef void pendulum_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
    "    # Arguments are passed in as char pointers.\n",
    "    # These point to preallocated and instantiated memory in the CySolver class that is filled with user-provided data.\n",
    "\n",
    "    # The user can then recast the generic char pointers back to original format of args_ptr. In this case, an array of doubles. \n",
    "    # Seg faults will occur if this function recasts to the incorrect type.\n",
    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
    "    cdef double l = args_dbl_ptr[0]\n",
    "    cdef double m = args_dbl_ptr[1]\n",
    "    cdef double g = args_dbl_ptr[2]\n",
    "\n",
    "    cdef double coeff_1 = (-3. * g / (2. * l))\n",
    "    cdef double coeff_2 = (3. / (m * l**2))\n",
    "\n",
    "    # Unpack y\n",
    "    cdef double y0, y1, torque\n",
    "    y0 = y_ptr[0]\n",
    "    y1 = y_ptr[1]\n",
    "\n",
    "    # External torque\n",
    "    torque = 0.1 * sin(t)\n",
    "\n",
    "    dy_ptr[0] = y1\n",
    "    dy_ptr[1] = coeff_1 * sin(y0) + coeff_2 * torque\n",
    "\n",
    "\n",
    "# Now we define a function that builds our inputs and calls `cysolve_ivp` to solve the problem\n",
    "def run():\n",
    "    cdef DiffeqFuncType diffeq = pendulum_diffeq\n",
    "\n",
    "    # Define time domain\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end = 10.0\n",
    "    \n",
    "    # Define initial conditions\n",
    "    cdef vector[double] y0_vec = vector[double](2)\n",
    "    y0_vec[0] = 0.01\n",
    "    y0_vec[1] = 0.0\n",
    "    \n",
    "    # Define our arguments in a char vector\n",
    "    # The size of the vector is the size of the underlying object(s) x number of them.\n",
    "    cdef vector[char] args_vec = vector[char](sizeof(double) * 3)\n",
    "    # To make it easier to populate the args_vec we recast it as a double pointer.\n",
    "    cdef double* args_dbl_ptr = <double*>args_vec.data()\n",
    "    args_dbl_ptr[0] = 1.0\n",
    "    args_dbl_ptr[1] = 1.0\n",
    "    args_dbl_ptr[2] = 9.81\n",
    "\n",
    "    cdef CySolveOutput result = cysolve_ivp(\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        method = ODEMethod.RK45, # Integration method\n",
    "        rtol = 1.0e-6,\n",
    "        atol = 1.0e-8,\n",
    "        args_vec = args_vec\n",
    "        )\n",
    "\n",
    "    # If we want to pass the solution back to python we need to wrap it in a CyRK `WrapCySolverResult` class.\n",
    "    cdef WrapCySolverResult pysafe_result = WrapCySolverResult()\n",
    "    pysafe_result.set_cyresult_pointer(move(result))\n",
    "\n",
    "    return pysafe_result\n",
    "\n",
    "result_reg = run()\n",
    "print(\"\\n\\nIntegration success =\", result_reg.success, \"\\n\\tNumber of adaptive time steps required:\", result_reg.size)\n",
    "print(\"Integration message:\", result_reg.message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c87d9c2d-a280-4a5f-bcf1-ed432dc42368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(result_reg.t, result_reg.y[0], c='C0')\n",
    "ax2.plot(result_reg.t, result_reg.y[1], c='C3')\n",
    "ax.set_ylabel(\"Angle [Rad]\", c='C0')\n",
    "ax.set_xlabel(\"Time [s]\")\n",
    "ax2.set_ylabel(\"Angular Velocity [Rad s-1]\", c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6741d823-f135-4c6d-bb2d-47cde7960b66",
   "metadata": {},
   "source": [
    "### Additional Args = Cython C-Struct\n",
    "See discussion in \"Advanced CySolver.md\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e57015f3-0ccf-4a02-becc-60027ca7741f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cpp(25074): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cpp(25075): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cpp(25198): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cpp(28062): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cpp(28063): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cpp(28683): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cpp(28690): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cp313-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_81b7e6b76c9b10294d4c3bbfba3e8ec577106e43882443b777013666059ee80f.cp313-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Integration success = True \n",
      "\tNumber of adaptive time steps required: 112\n",
      "Integration message: Integration completed without issue.\n"
     ]
    }
   ],
   "source": [
    "%%cython --force \n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "np.import_array()\n",
    "\n",
    "from libc.string cimport memcpy\n",
    "from libc.math cimport sin\n",
    "from libcpp cimport bool as cpp_bool\n",
    "from libcpp.utility cimport move\n",
    "from libcpp.vector cimport vector\n",
    "\n",
    "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType,MAX_STEP, CySolveOutput, PreEvalFunc, ODEMethod\n",
    "\n",
    "cdef struct PendulumArgs:\n",
    "    # Structure that contains heterogeneous types\n",
    "    cpp_bool use_drag\n",
    "    double g\n",
    "    double l\n",
    "    double m\n",
    "\n",
    "cdef void pendulum_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
    "    # Arg pointer still must be listed as a char pointer or it will not work with cysolve_ivp.\n",
    "    # But now the user can recast that char pointer to the structure they wish.\n",
    "    cdef PendulumArgs* pendulum_args_ptr = <PendulumArgs*>args_ptr\n",
    "    # And easily access its members which can be many heterogeneous types.\n",
    "    cdef double l = pendulum_args_ptr.l\n",
    "    cdef double m = pendulum_args_ptr.m\n",
    "    cdef double g = pendulum_args_ptr.g\n",
    "    cdef double use_drag = pendulum_args_ptr.use_drag\n",
    "\n",
    "    cdef double coeff_1 = (-3. * g / (2. * l))\n",
    "    cdef double coeff_2 = (3. / (m * l**2))\n",
    "\n",
    "    # Unpack y\n",
    "    cdef double y0, y1, torque\n",
    "    y0 = y_ptr[0]\n",
    "    y1 = y_ptr[1]\n",
    "\n",
    "    # External torque\n",
    "    torque = 0.1 * sin(t)\n",
    "\n",
    "    dy_ptr[0] = y1\n",
    "    dy_ptr[1] = coeff_1 * sin(y0) + coeff_2 * torque\n",
    "\n",
    "    if use_drag:\n",
    "        dy_ptr[1] -= 1.5 * y1\n",
    "\n",
    "# Now we define a function that builds our inputs and calls `cysolve_ivp` to solve the problem\n",
    "def run():\n",
    "    cdef DiffeqFuncType diffeq = pendulum_diffeq\n",
    "\n",
    "    # Define time domain\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end = 10.0\n",
    "    \n",
    "    # Define initial conditions\n",
    "    cdef vector[double] y0_vec = vector[double](2)\n",
    "    y0_vec[0] = 0.01\n",
    "    y0_vec[1] = 0.0\n",
    "    \n",
    "    # Define our arguments.\n",
    "    # We now have a a structure that we need to allocate memory for.\n",
    "    # For this example, let's do it on the stack. \n",
    "    cdef PendulumArgs pendulum_args = PendulumArgs(True, 9.81, 1.0, 1.0)\n",
    "    # We need to pass in a char vector to cysolve_ivp, so let's create a vector of the correct size\n",
    "    cdef vector[char] args_vec = vector[char](sizeof(PendulumArgs))\n",
    "    # Then we need to copy over the contents to this vector.\n",
    "    memcpy(args_vec.data(), &pendulum_args, sizeof(PendulumArgs))\n",
    "\n",
    "    cdef CySolveOutput result = cysolve_ivp(\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        method = ODEMethod.RK45,\n",
    "        rtol = 1.0e-6,\n",
    "        atol = 1.0e-8,\n",
    "        args_vec = args_vec\n",
    "        )\n",
    "\n",
    "    # If we want to pass the solution back to python we need to wrap it in a CyRK `WrapCySolverResult` class.\n",
    "    cdef WrapCySolverResult pysafe_result = WrapCySolverResult()\n",
    "    pysafe_result.set_cyresult_pointer(move(result))\n",
    "\n",
    "    return pysafe_result\n",
    "\n",
    "result_drag = run()\n",
    "print(\"\\n\\nIntegration success =\", result_drag.success, \"\\n\\tNumber of adaptive time steps required:\", result_drag.size)\n",
    "print(\"Integration message:\", result_drag.message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4044656e-c299-40e1-b54f-ce6f01931a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(result_drag.t, result_drag.y[0], c='C0')\n",
    "ax2.plot(result_drag.t, result_drag.y[1], c='C3')\n",
    "ax.set_ylabel(\"Angle [Rad]\", c='C0')\n",
    "ax.set_xlabel(\"Time [s]\")\n",
    "ax2.set_ylabel(\"Angular Velocity [Rad s-1]\", c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864ebd48-b2b1-41a7-819d-ff0110b4d315",
   "metadata": {},
   "source": [
    "## Pre-Evaluation Functions\n",
    "See discussion in \"Advanced CySolver.md\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13202bf2-766d-4d59-977c-c0e9a96fae87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cpp(25254): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cpp(25255): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cpp(25378): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cpp(28242): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cpp(28243): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cpp(28863): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cpp(28870): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cp313-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0daad898b4b23506ae4232531efbcb2c81d712fee76bb06aa7622d525c2e3a9.cp313-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Integration success = True \n",
      "\tNumber of adaptive time steps required: 112\n",
      "Integration message: Integration completed without issue.\n"
     ]
    }
   ],
   "source": [
    "%%cython --force \n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "np.import_array()\n",
    "\n",
    "from libc.math cimport sin\n",
    "from libcpp.utility cimport move\n",
    "from libcpp.vector cimport vector\n",
    "\n",
    "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType, MAX_STEP, CySolveOutput, PreEvalFunc, ODEMethod\n",
    "\n",
    "cdef void pendulum_preeval_nodrag(char* output_ptr, double time, double* y_ptr, char* args_ptr) noexcept nogil:\n",
    "    # Unpack args (in this example we do not need these but they follow the same rules as the Arbitrary Args section discussed above)\n",
    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
    "\n",
    "    # External torque\n",
    "    cdef double torque = 0.1 * sin(time)\n",
    "\n",
    "    # Convert output pointer to double pointer so we can store data\n",
    "    cdef double* output_dbl_ptr = <double*>output_ptr\n",
    "    output_dbl_ptr[0] = torque\n",
    "    output_dbl_ptr[1] = 0.0  # No Drag\n",
    "\n",
    "cdef void pendulum_preeval_withdrag(char* output_ptr, double time, double* y_ptr, char* args_ptr) noexcept nogil:\n",
    "    # Unpack args (in this example we do not need these but they follow the same rules as the Arbitrary Args section discussed above)\n",
    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
    "\n",
    "    # External torque\n",
    "    cdef double torque = 0.1 * sin(time)\n",
    "\n",
    "    # Convert output pointer to double pointer so we can store data\n",
    "    cdef double* output_dbl_ptr = <double*>output_ptr\n",
    "    output_dbl_ptr[0] = torque\n",
    "    output_dbl_ptr[1] = -1.5 * y_ptr[1]  # With Drag\n",
    "\n",
    "\n",
    "cdef void pendulum_preeval_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
    "\n",
    "    # Build other parameters that do not depend on the pre-eval func\n",
    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
    "    cdef double l = args_dbl_ptr[0]\n",
    "    cdef double m = args_dbl_ptr[1]\n",
    "    cdef double g = args_dbl_ptr[2]\n",
    "\n",
    "    cdef double coeff_1 = (-3. * g / (2. * l))\n",
    "    cdef double coeff_2 = (3. / (m * l**2))\n",
    "\n",
    "    # Make stack allocated storage for pre eval output\n",
    "    cdef double[4] pre_eval_storage\n",
    "    cdef double* pre_eval_storage_ptr = &pre_eval_storage[0]\n",
    "\n",
    "    # Cast storage to char so we can call function\n",
    "    cdef char* pre_eval_storage_char_ptr = <char*>pre_eval_storage_ptr\n",
    "\n",
    "    # Call Pre-Eval Function\n",
    "    # Note that even though CyRK calls this function a \"pre-eval\" function, it can be placed anywhere inside the diffeq function. \n",
    "    pre_eval_func(pre_eval_storage_char_ptr, t, y_ptr, args_ptr)\n",
    "\n",
    "    cdef double y0 = y_ptr[0]\n",
    "    cdef double y1 = y_ptr[1]\n",
    "\n",
    "    # Use results of pre-eval function to update dy. Note that we are using the double* not the char* here.\n",
    "    # Even though pre_eval_func was passed the char* it updated the memory that the double* pointed to so we can use it below.\n",
    "    dy_ptr[0] = y1\n",
    "    dy_ptr[1] = coeff_1 * sin(y0) + coeff_2 * pre_eval_storage_ptr[0] + pre_eval_storage_ptr[1]\n",
    "\n",
    "\n",
    "# Now we define a function that builds our inputs and calls `cysolve_ivp` to solve the problem\n",
    "def run():\n",
    "    cdef DiffeqFuncType diffeq = pendulum_preeval_diffeq\n",
    "    \n",
    "    # Setup pointer to pre-eval function\n",
    "    cdef PreEvalFunc pre_eval_func\n",
    "    \n",
    "    cdef bint use_drag = True\n",
    "    if use_drag:\n",
    "        pre_eval_func = pendulum_preeval_withdrag\n",
    "    else:\n",
    "        pre_eval_func = pendulum_preeval_nodrag\n",
    "    \n",
    "    # Define time domain\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end   = 10.0\n",
    "    \n",
    "    # Define initial conditions\n",
    "    cdef vector[double] y0_vec = vector[double](2)\n",
    "    y0_vec[0] = 0.01\n",
    "    y0_vec[1] = 0.0\n",
    "\n",
    "    # Define our arguments.\n",
    "    cdef vector[char] args_vec = vector[char](sizeof(double) * 3)\n",
    "    cdef double* args_dbl_ptr = <double*>args_vec.data()\n",
    "    args_dbl_ptr[0] = 1.0\n",
    "    args_dbl_ptr[1] = 1.0\n",
    "    args_dbl_ptr[2] = 9.81\n",
    "\n",
    "    cdef CySolveOutput result = cysolve_ivp(\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        method = ODEMethod.RK45, # Integration method\n",
    "        rtol = 1.0e-6,\n",
    "        atol = 1.0e-8,\n",
    "        args_vec = args_vec,\n",
    "        num_extra = 0,\n",
    "        max_num_steps = 100_000_000,\n",
    "        max_ram_MB = 2000,\n",
    "        dense_output = False,\n",
    "        t_eval_vec = vector[double](), # C++ / Cython require you to provide args in order even if they unused like t_eval_vec here.\n",
    "        pre_eval_func = pre_eval_func\n",
    "        )\n",
    "\n",
    "    # If we want to pass the solution back to python we need to wrap it in a CyRK `WrapCySolverResult` class.\n",
    "    cdef WrapCySolverResult pysafe_result = WrapCySolverResult()\n",
    "    pysafe_result.set_cyresult_pointer(move(result))\n",
    "\n",
    "    return pysafe_result\n",
    "\n",
    "result_preeval = run()\n",
    "print(\"\\n\\nIntegration success =\", result_preeval.success, \"\\n\\tNumber of adaptive time steps required:\", result_preeval.size)\n",
    "print(\"Integration message:\", result_preeval.message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ced2203e-13c2-41b1-83f9-b8e3d5e2d751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(result_preeval.t, result_preeval.y[0], c='C0')\n",
    "ax2.plot(result_preeval.t, result_preeval.y[1], c='C3')\n",
    "ax.set_ylabel(\"Angle [Rad]\", c='C0')\n",
    "ax.set_xlabel(\"Time [s]\")\n",
    "ax2.set_ylabel(\"Angular Velocity [Rad s-1]\", c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adca897a-8134-44ca-904b-93bec9e946ee",
   "metadata": {},
   "source": [
    "## PySolver and CySolver Solution Reuses\n",
    "See discussion in \"CySolver & PySolver Reuses.md\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "222a5b4b-33e8-4641-8a01-d439c34aedd3",
   "metadata": {},
   "source": [
    "### PySolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e5c9d6c0-3ae7-4231-a7f1-4852c57ddfeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Was Integration was successful? True\n",
      "Integration completed without issue.\n",
      "Size of solution:  360\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Was Integration was successful? True\n",
      "Integration completed without issue.\n",
      "Size of solution:  601\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note if using this format, `dy` must be the first argument. Additionally, a special flag must be set to True when calling pysolve_ivp, see below.\n",
    "def cy_diffeq(dy, t, y):\n",
    "    dy[0] = (1. - 0.01 * y[1]) * y[0]\n",
    "    dy[1] = (0.02 * y[0] - 1.) * y[1]\n",
    "\n",
    "# Since this is pure python we can njit it safely\n",
    "from numba import njit\n",
    "cy_diffeq = njit(cy_diffeq)\n",
    "    \n",
    "import numpy as np\n",
    "from CyRK import pysolve_ivp\n",
    "\n",
    "initial_conds = np.asarray((20., 20.), dtype=np.float64, order='C')\n",
    "time_span = (0., 50.)\n",
    "rtol = 1.0e-7\n",
    "atol = 1.0e-8\n",
    "\n",
    "result = \\\n",
    "    pysolve_ivp(cy_diffeq, time_span, initial_conds,\n",
    "                method=\"RK45\", rtol=rtol, atol=atol, pass_dy_as_arg=True)\n",
    "\n",
    "# Use the result's memory allocation to re-call pysolve_ivp\n",
    "result = \\\n",
    "    pysolve_ivp(cy_diffeq, time_span, initial_conds,\n",
    "                method=\"RK45\", rtol=rtol, atol=atol, pass_dy_as_arg=True,\n",
    "                solution_reuse=result) # Set the reuse argument.\n",
    "\n",
    "print(\"Was Integration was successful?\", result.success)\n",
    "print(result.message)\n",
    "print(\"Size of solution: \", result.size)\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(result.t, result.y[0], c='C0')\n",
    "ax.plot(result.t, result.y[1], c='C3')\n",
    "plt.show()\n",
    "\n",
    "# We can change parameters too\n",
    "new_initial_conds = np.asarray((3., 0.1), dtype=np.float64, order='C')\n",
    "result = \\\n",
    "    pysolve_ivp(cy_diffeq, time_span, new_initial_conds,\n",
    "                method=\"RK45\", rtol=rtol, atol=atol, pass_dy_as_arg=True,\n",
    "                solution_reuse=result) # Set the reuse argument.\n",
    "\n",
    "print(\"Was Integration was successful?\", result.success)\n",
    "print(result.message)\n",
    "print(\"Size of solution: \", result.size)\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(result.t, result.y[0], c='C0')\n",
    "ax.plot(result.t, result.y[1], c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbbbea83-b993-4bd3-9694-6deb01d07704",
   "metadata": {},
   "source": [
    "### CySolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87c45d91-94ad-41cb-8880-59459494e05f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cpp(24996): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cpp(24997): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cpp(25120): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cpp(28761): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cpp(28768): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cpp(29126): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cpp(29127): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cp313-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_b273b7281fe4e22295819ae8575625353f01756929523edacc2e640f37ab928a.cp313-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "from libcpp.utility cimport move\n",
    "from libcpp.vector cimport vector\n",
    "\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "np.import_array()\n",
    "\n",
    "from CyRK cimport cysolve_ivp, cysolve_ivp_noreturn, DiffeqFuncType, WrapCySolverResult, CySolveOutput, PreEvalFunc\n",
    "\n",
    "cdef void cython_diffeq(double* dy, double t, double* y, char* args, PreEvalFunc pre_eval_func) noexcept nogil:\n",
    "\n",
    "    # Build Coeffs\n",
    "    cdef double coeff_1 = (1. - 0.01 * y[1])\n",
    "    cdef double coeff_2 = (0.02 * y[0] - 1.)\n",
    "    \n",
    "    # Store results\n",
    "    dy[0] = coeff_1 * y[0]\n",
    "    dy[1] = coeff_2 * y[1]\n",
    "\n",
    "# Import the required functions from CyRK\n",
    "from CyRK cimport cysolve_ivp, DiffeqFuncType, WrapCySolverResult, CySolveOutput\n",
    "\n",
    "# Let's get the integration number for the RK45 method\n",
    "from CyRK cimport ODEMethod\n",
    "\n",
    "def run_reuse_cysolver(tuple t_span, double[::1] y0):\n",
    "    \n",
    "    # Cast our diffeq to the accepted format\n",
    "    cdef DiffeqFuncType diffeq = cython_diffeq\n",
    "    \n",
    "    # Convert the python user input to pure C types\n",
    "    cdef size_t num_y          = len(y0)\n",
    "    cdef double t_start        = t_span[0]\n",
    "    cdef double t_end          = t_span[1]\n",
    "    cdef vector[double] y0_vec = vector[double](num_y)\n",
    "    cdef size_t yi\n",
    "    for yi in range(num_y):\n",
    "        y0_vec[yi] = y0[yi]\n",
    "\n",
    "    # Run the integrator!\n",
    "    cdef CySolveOutput result = cysolve_ivp(\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        method = ODEMethod.RK45,\n",
    "        rtol = 1.0e-7,\n",
    "        atol = 1.0e-8\n",
    "    )\n",
    "\n",
    "    # Run again using the noreturn version so we can reuse the integration storage.\n",
    "    cysolve_ivp_noreturn(\n",
    "        result.get(),  # result is a unique pointer; we need to pass the raw pointer to the baseline_cysolve_ivp\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        rtol = 1.0e-7,\n",
    "        atol = 1.0e-8\n",
    "    )\n",
    "\n",
    "    # Like with PySolver, we can change the inputs \n",
    "    cdef double t_end_2 = 2.0 * t_end\n",
    "    cysolve_ivp_noreturn(\n",
    "        result.get(),  # result is a unique pointer; we need to pass the raw pointer to the baseline_cysolve_ivp\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end_2,\n",
    "        y0_vec,\n",
    "        rtol = 1.0e-7,\n",
    "        atol = 1.0e-8\n",
    "    )\n",
    "\n",
    "    # The CySolveOutput is not accesible via Python. We need to wrap it first\n",
    "    cdef WrapCySolverResult pysafe_result = WrapCySolverResult()\n",
    "    pysafe_result.set_cyresult_pointer(move(result))\n",
    "\n",
    "    return pysafe_result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2b3fb62-108b-4ac2-92ed-62c7166ead5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------\n",
      "CyRK v0.17.0 - WrapCySolverResult Diagnostic.\n",
      "----------------------------------------------------\n",
      "# of y:             2.\n",
      "# of dy:            2.\n",
      "# of events:        0.\n",
      "Success:            True.\n",
      "Error Code:         1.\n",
      "Status:             Integration completed without issue..\n",
      "Size:               705.\n",
      "Steps Taken:        704.\n",
      "Event Termination:  False.\n",
      "Integrator Message:\n",
      "\tIntegration completed without issue.\n",
      "\n",
      "----------------- CySolverResult -------------------\n",
      "Capture Extra:          False.\n",
      "Capture Dense Output:   False.\n",
      "Integration Direction:  Forward.\n",
      "Integration Method:     Explicit Runge-Kutta method of order 5(4).\n",
      "# of Interpolates:      0.\n",
      "\n",
      "---- Additional Argument Info ----\n",
      "args size (bytes):   0.\n",
      "args size (doubles): 0.\n",
      "Args Pointer is Null.\n",
      "End of Additional Argument Info.\n",
      "\n",
      "------------------ CySolverBase --------------------\n",
      "Integration Method: 4.\n",
      "# of y:             2.\n",
      "# of dy:            2.\n",
      "PySolver:           False.\n",
      "---- Current State Info ----\n",
      "t_now: 100.0.\n",
      "y_now:\n",
      "\ty0  = 1.40214e+02.\n",
      "\ty1  = 3.76947e+01.\n",
      "dy_now:\n",
      "\tdy0 = 8.73608e+01.\n",
      "\tdy1 = 6.80120e+01.\n",
      "End of Current State Info.\n",
      "\n",
      "-------------- Diagnostic Complete -----------------\n",
      "\n",
      "Was Integration was successful? True\n",
      "Integration completed without issue.\n",
      "Size of solution:  705\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assume we are working in a Jupyter notebook so we don't need to import `run_cysolver` if it was defined in an earlier cell.\n",
    "# from my_cython_code import run_cysolver\n",
    "\n",
    "import numpy as np\n",
    "initial_conds = np.asarray((20., 20.), dtype=np.float64, order='C')\n",
    "time_span = (0., 50.)\n",
    "\n",
    "result = run_reuse_cysolver(time_span, initial_conds)\n",
    "result.print_diagnostics()\n",
    "\n",
    "print(\"Was Integration was successful?\", result.success)\n",
    "print(result.message)\n",
    "print(\"Size of solution: \", result.size)\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(result.t, result.y[0], c='C0')\n",
    "ax.plot(result.t, result.y[1], c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8af8fd-fa2d-4d08-bfae-f858eb3274a0",
   "metadata": {},
   "source": [
    "## Using Events with CySolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "96d98276-9dd6-42e8-a7c3-e8d1db5060a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cpp(25643): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cpp(25644): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cpp(25767): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cpp(28631): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cpp(28632): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cpp(29252): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cpp(29259): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cp313-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_78861fc3b2762e25a380951b9c01755013e6c439a624aed4b554a8b765952325.cp313-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "\n",
    "from libcpp cimport bool as cpp_bool\n",
    "from libcpp.vector cimport vector\n",
    "from libcpp.limits cimport numeric_limits\n",
    "\n",
    "import numpy as np\n",
    "cimport numpy as cnp\n",
    "cnp.import_array()\n",
    "\n",
    "from CyRK cimport CyrkErrorCodes, PreEvalFunc, MAX_STEP, DiffeqFuncType, Event, EventFunc, cysolve_ivp_noreturn, WrapCySolverResult, ODEMethod, CySolverResult\n",
    "from CyRK.cy.cysolver_test cimport lorenz_diffeq\n",
    "\n",
    "cdef double lorenz_event_func_1(double t, double* y_ptr, char* unused_args) noexcept nogil:\n",
    "\n",
    "    # Check if t greater than or equal to 5.0\n",
    "    if t >= 5.0:\n",
    "        return 0.0\n",
    "    else:\n",
    "        return 1.0\n",
    "\n",
    "cdef double lorenz_event_func_2(double t, double* y_ptr, char* unused_args) noexcept nogil:\n",
    "\n",
    "    # Check y values.\n",
    "    # In the time span [0,10]: \n",
    "    #    y_0  starts at 1, spikes then goes below zero and oscillates with a min below -10. Have this return if y_0 < -10\n",
    "    if y_ptr[0] < -10.0:\n",
    "        return 0.0\n",
    "    elif y_ptr[2] > 30.0:\n",
    "        return 0.0\n",
    "    else:\n",
    "        return 1.0\n",
    "\n",
    "cdef double lorenz_event_func_3(double t, double* y_ptr, char* args) noexcept nogil:\n",
    "\n",
    "    # Use arguments to set threshold\n",
    "    cdef double* args_dbl_ptr = <double*>args\n",
    "\n",
    "    # We won't actually use the args but lets just check they are correct.\n",
    "    cdef cpp_bool args_correct = False\n",
    "    if args_dbl_ptr[0] == 10.0 and args_dbl_ptr[1] == 28.0 and args_dbl_ptr[2] == 8.0 / 3.0:\n",
    "        args_correct = True\n",
    "\n",
    "    # Then return events if args are correct and t greater than 4\n",
    "    if args_correct and t >= 4.0:\n",
    "        if t <= 4.1:\n",
    "            return 0.0\n",
    "        elif t >= 4.5 and t <= 4.6:\n",
    "            return 0.0\n",
    "        elif t >= 5.0 and t <= 5.1:\n",
    "            return 0.0\n",
    "        elif t >= 5.5 and t <= 5.6:\n",
    "            return 0.0\n",
    "        elif t >= 6.0 and t <= 6.1:\n",
    "            return 0.0\n",
    "        else:\n",
    "            return 1.0\n",
    "    else:\n",
    "        return 1.0\n",
    "\n",
    "def run_cysolver_with_events(bint use_termination = False):\n",
    "    \n",
    "    # Inputs for lorenz diffeq\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end = 10.0\n",
    "    \n",
    "    cdef size_t num_y = 3\n",
    "    cdef vector[double] y0_vec = vector[double](num_y)\n",
    "    y0_vec[0] = 1.0\n",
    "    y0_vec[1] = 0.0\n",
    "    y0_vec[2] = 0.0\n",
    "    \n",
    "    cdef vector[double] t_eval_vec = vector[double]()\n",
    "    cdef vector[char] args_vec = vector[char](3 * sizeof(double))\n",
    "    args_dbl_ptr = <double*>args_vec.data()\n",
    "    args_dbl_ptr[0] = 10.0\n",
    "    args_dbl_ptr[1] = 28.0\n",
    "    args_dbl_ptr[2] = 8.0 / 3.0\n",
    "    \n",
    "    cdef PreEvalFunc pre_eval_func = NULL\n",
    "    \n",
    "    # Build events\n",
    "    cdef vector[Event] events_vec = vector[Event]()\n",
    "    # Set the maximum number of events allowed before termination to the max size of size_t (effectively infinite)\n",
    "    cdef size_t max_allowed = numeric_limits[size_t].max()\n",
    "    cdef int direction = 0\n",
    "    if use_termination:\n",
    "        events_vec.emplace_back(lorenz_event_func_1, 1, 0)\n",
    "    else:\n",
    "        events_vec.emplace_back(lorenz_event_func_1, max_allowed, direction)\n",
    "    events_vec.emplace_back(lorenz_event_func_2, max_allowed, direction)\n",
    "    events_vec.emplace_back(lorenz_event_func_3, max_allowed, direction)\n",
    "    \n",
    "    cdef ODEMethod integration_method = ODEMethod.RK45\n",
    "    cdef WrapCySolverResult solution = WrapCySolverResult()\n",
    "    solution.build_cyresult(integration_method)\n",
    "    cdef CySolverResult* solution_ptr = solution.cyresult_uptr.get()\n",
    "    \n",
    "    cdef DiffeqFuncType diffeq = lorenz_diffeq\n",
    "    cdef size_t num_extra = 0\n",
    "    cdef cpp_bool use_dense = False\n",
    "    \n",
    "     # Run Solver\n",
    "    cysolve_ivp_noreturn(\n",
    "        solution_ptr,\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        rtol = 1.0e-6,\n",
    "        atol = 1.0e-6,\n",
    "        args_vec = args_vec,\n",
    "        num_extra = num_extra,\n",
    "        max_num_steps = 100000,\n",
    "        max_ram_MB = 2000,\n",
    "        dense_output = use_dense,\n",
    "        t_eval_vec = t_eval_vec,\n",
    "        pre_eval_func = pre_eval_func,\n",
    "        events_vec = events_vec,\n",
    "        rtols_vec = vector[double](),\n",
    "        atols_vec = vector[double](),\n",
    "        max_step = MAX_STEP,\n",
    "        first_step = 0.0,\n",
    "        expected_size = 128\n",
    "        )\n",
    "    \n",
    "    solution.finalize()\n",
    "    return solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "caef3357-0bb1-44e9-b5ee-bf62d08d2978",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution Succeeded: True\n",
      "42.3 μs ± 966 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare to the solve_ivp and pysolve_ivp examples in \"1 - Getting Started\" Notebook\n",
    "\n",
    "# CyRK v0.16.0\n",
    "#    Terminate off: 61.1us; 62.0us; 62.6us\n",
    "#    Terminate on:  34.4us; 33.7us; 34.4us\n",
    "# CyRK v0.17.0\n",
    "#    Terminate off: 57.9us; 56.9us; 58.5us\n",
    "#    Terminate on:  29.4us; 30.6us; 29.6us\n",
    "terminate = True\n",
    "\n",
    "solution = run_cysolver_with_events(terminate)\n",
    "print(\"Solution Succeeded:\", solution.success)\n",
    "\n",
    "%timeit run_cysolver_with_events(terminate)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(solution.t, solution.y[0], label='y0', c='C0')\n",
    "ax.plot(solution.t, solution.y[1], label='y1', c='C3')\n",
    "ax.plot(solution.t, solution.y[2], label='y2', c='C6')\n",
    "# Event 1 will be dots.\n",
    "# Event 2 will be X's\n",
    "# Event 3 will be +'s\n",
    "ax.scatter(solution.t_events[1], solution.y_events[1][0, :], c='C0', marker='o')\n",
    "ax.scatter(solution.t_events[0], solution.y_events[0][1, :], c='C3', marker='x', s=120)\n",
    "ax.scatter(solution.t_events[1], solution.y_events[1][2, :], c='C6', marker='o')\n",
    "ax.scatter(solution.t_events[2], solution.y_events[2][0, :], c='C0', marker='+', s=120)\n",
    "ax.set(xlabel='Time', ylabel='y')\n",
    "ax.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cyrk313",
   "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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}