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avivajpeyi/auckland_workshop.ipynb

Created May 1, 2024 02:34
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auckland_workshop.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/avivajpeyi/8494956ce97e7ee060426e5547d7e912/auckland_workshop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h_E_FwmHLWxN",
"outputId": "232fef39-f1d9-4353-e619-761a802362d1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m✨🍰✨ Everything looks OK!\n",
"fatal: destination path 'FastEMRIWaveforms' already exists and is not an empty directory.\n",
"HEAD is now at e4038da docs fix\n",
"tee: /dev/tty: No such device or address\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 23.11.0\n",
" latest version: 24.3.0\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c conda-forge conda\n",
"\n",
"\n",
"\n",
"running install\n",
"/usr/local/lib/python3.10/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n",
"!!\n",
"\n",
" ********************************************************************************\n",
" Please avoid running ``setup.py`` directly.\n",
" Instead, use pypa/build, pypa/installer or other\n",
" standards-based tools.\n",
"\n",
" See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n",
" ********************************************************************************\n",
"\n",
"!!\n",
" self.initialize_options()\n",
"/usr/local/lib/python3.10/site-packages/setuptools/_distutils/cmd.py:66: EasyInstallDeprecationWarning: easy_install command is deprecated.\n",
"!!\n",
"\n",
" ********************************************************************************\n",
" Please avoid running ``setup.py`` and ``easy_install``.\n",
" Instead, use pypa/build, pypa/installer or other\n",
" standards-based tools.\n",
"\n",
" See https://github.com/pypa/setuptools/issues/917 for details.\n",
" ********************************************************************************\n",
"\n",
"!!\n",
" self.initialize_options()\n",
"running bdist_egg\n",
"running egg_info\n",
"writing few.egg-info/PKG-INFO\n",
"writing dependency_links to few.egg-info/dependency_links.txt\n",
"writing top-level names to few.egg-info/top_level.txt\n",
"reading manifest file 'few.egg-info/SOURCES.txt'\n",
"adding license file 'LICENSE'\n",
"writing manifest file 'few.egg-info/SOURCES.txt'\n",
"installing library code to build/bdist.linux-x86_64/egg\n",
"running install_lib\n",
"running build_py\n",
"copying few/_version.py -> build/lib.linux-x86_64-cpython-310/few\n",
"copying few/utils/odeoptions.py -> build/lib.linux-x86_64-cpython-310/few/utils\n",
"copying few/utils/constants.py -> build/lib.linux-x86_64-cpython-310/few/utils\n",
"running build_ext\n",
"Compiling src/pymatmul_cpu.pyx because it changed.\n",
"[1/1] Cythonizing src/pymatmul_cpu.pyx\n",
"/usr/local/lib/python3.10/site-packages/Cython/Compiler/Main.py:381: FutureWarning: Cython directive 'language_level' not set, using '3str' for now (Py3). This has changed from earlier releases! File: /content/FastEMRIWaveforms/src/pymatmul_cpu.pyx\n",
" tree = Parsing.p_module(s, pxd, full_module_name)\n",
"building 'pymatmul_cpu' extension\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/matmul.cpp -o build/temp.linux-x86_64-cpython-310/src/matmul.o -std=c++11\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/matmul.hh:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/matmul.cpp:19\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/pymatmul_cpu.cpp -o build/temp.linux-x86_64-cpython-310/src/pymatmul_cpu.o -std=c++11\n",
"In file included from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/pymatmul_cpu.cpp:1278\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wcpp\u0007-Wcpp\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 17 | #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it with \" \\\n",
" | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/matmul.hh:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/pymatmul_cpu.cpp:1283\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"g++ -pthread -B /usr/local/compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/usr/local/lib -Wl,-rpath-link,/usr/local/lib -L/usr/local/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/usr/local/lib -Wl,-rpath-link,/usr/local/lib -L/usr/local/lib build/temp.linux-x86_64-cpython-310/src/matmul.o build/temp.linux-x86_64-cpython-310/src/pymatmul_cpu.o -lgsl -lgslcblas -llapack -llapacke -lhdf5 -lhdf5_hl -o build/lib.linux-x86_64-cpython-310/pymatmul_cpu.cpython-310-x86_64-linux-gnu.so\n",
"building 'pyInspiral' extension\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/Inspiral.cc -o build/temp.linux-x86_64-cpython-310/src/Inspiral.o -std=c++11\n",
"\u001b[01m\u001b[Ksrc/Inspiral.cc:245:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring ‘\u001b[01m\u001b[K#pragma unroll \u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunknown-pragmas\u0007-Wunknown-pragmas\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 245 | #pragma unroll\n",
" | \n",
"\u001b[01m\u001b[Ksrc/Inspiral.cc:432:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring ‘\u001b[01m\u001b[K#pragma unroll \u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunknown-pragmas\u0007-Wunknown-pragmas\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 432 | #pragma unroll\n",
" | \n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/ode.hh:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/Inspiral.hh:9\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/Inspiral.cc:41\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Inspiral.cc:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[KInspiralHolder InspiralCarrier::run_inspiral(double, double, double, double, double, double, double, double, double, double, double, double, double, int, bool)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Inspiral.cc:184:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement has no effect [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-value\u0007-Wunused-value\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 184 | \u001b[01;35m\u001b[Kparams_holder->enforce_schwarz_sep\u001b[m\u001b[K;\n",
" | \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/Interpolant.cc -o build/temp.linux-x86_64-cpython-310/src/Interpolant.o -std=c++11\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/Utility.cc -o build/temp.linux-x86_64-cpython-310/src/Utility.o -std=c++11\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/Utility.cc:3\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid KerrGeoMinoFrequencies(double*, double*, double*, double*, double, double, double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:212:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[KEpsilon0\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 212 | double \u001b[01;35m\u001b[KEpsilon0\u001b[m\u001b[K = pow(a, 2) * (1 - pow(En, 2))/pow(L, 2);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid KerrGeoEquatorialMinoFrequencies(double*, double*, double*, double*, double, double, double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:270:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[KEpsilon0\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 270 | double \u001b[01;35m\u001b[KEpsilon0\u001b[m\u001b[K = pow(a, 2) * (1 - pow(En, 2))/pow(L, 2);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:272:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka2zp\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 272 | double \u001b[01;35m\u001b[Ka2zp\u001b[m\u001b[K =(pow(L, 2) + pow(a, 2) * (-1 + pow(En, 2)) * (-1))/( (-1 + pow(En, 2)) * (-1));\n",
" | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble separatrix_polynomial_polar(double, void*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:390:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 390 | double \u001b[01;35m\u001b[Kx\u001b[m\u001b[K = params->x;\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble separatrix_polynomial_equat(double, void*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:402:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 402 | double \u001b[01;35m\u001b[Kx\u001b[m\u001b[K = params->x;\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble solver(params_holder*, double (*)(double, void*), double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:417:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kr_expected\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 417 | double r = 0, \u001b[01;35m\u001b[Kr_expected\u001b[m\u001b[K = sqrt (5.0);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/dIdt8H_5PNe10.cc -o build/temp.linux-x86_64-cpython-310/src/dIdt8H_5PNe10.o -std=c++11\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/inspiralwrap.cpp -o build/temp.linux-x86_64-cpython-310/src/inspiralwrap.o -std=c++11\n",
"In file included from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/inspiralwrap.cpp:1284\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wcpp\u0007-Wcpp\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 17 | #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it with \" \\\n",
" | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[Ksrc/../include/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/../include/ode.hh:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/../include/Inspiral.hh:9\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/inspiralwrap.cpp:1294\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Ksrc/../include/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/../include/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/ode.cc -o build/temp.linux-x86_64-cpython-310/src/ode.o -std=c++11\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/ode.cc:15\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"g++ -pthread -B /usr/local/compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/usr/local/lib -Wl,-rpath-link,/usr/local/lib -L/usr/local/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/usr/local/lib -Wl,-rpath-link,/usr/local/lib -L/usr/local/lib build/temp.linux-x86_64-cpython-310/src/Inspiral.o build/temp.linux-x86_64-cpython-310/src/Interpolant.o build/temp.linux-x86_64-cpython-310/src/Utility.o build/temp.linux-x86_64-cpython-310/src/dIdt8H_5PNe10.o build/temp.linux-x86_64-cpython-310/src/inspiralwrap.o build/temp.linux-x86_64-cpython-310/src/ode.o -lgsl -lgslcblas -llapack -llapacke -lhdf5 -lhdf5_hl -o build/lib.linux-x86_64-cpython-310/pyInspiral.cpython-310-x86_64-linux-gnu.so\n",
"Compiling src/pyinterp_cpu.pyx because it changed.\n",
"[1/1] Cythonizing src/pyinterp_cpu.pyx\n",
"/usr/local/lib/python3.10/site-packages/Cython/Compiler/Main.py:381: FutureWarning: Cython directive 'language_level' not set, using '3str' for now (Py3). This has changed from earlier releases! File: /content/FastEMRIWaveforms/src/pyinterp_cpu.pyx\n",
" tree = Parsing.p_module(s, pxd, full_module_name)\n",
"building 'pyinterp_cpu' extension\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/Utility.cc -o build/temp.linux-x86_64-cpython-310/src/Utility.o -std=c++11\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/Utility.cc:3\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid KerrGeoMinoFrequencies(double*, double*, double*, double*, double, double, double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:212:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[KEpsilon0\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 212 | double \u001b[01;35m\u001b[KEpsilon0\u001b[m\u001b[K = pow(a, 2) * (1 - pow(En, 2))/pow(L, 2);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid KerrGeoEquatorialMinoFrequencies(double*, double*, double*, double*, double, double, double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:270:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[KEpsilon0\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 270 | double \u001b[01;35m\u001b[KEpsilon0\u001b[m\u001b[K = pow(a, 2) * (1 - pow(En, 2))/pow(L, 2);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:272:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka2zp\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 272 | double \u001b[01;35m\u001b[Ka2zp\u001b[m\u001b[K =(pow(L, 2) + pow(a, 2) * (-1 + pow(En, 2)) * (-1))/( (-1 + pow(En, 2)) * (-1));\n",
" | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble separatrix_polynomial_polar(double, void*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:390:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 390 | double \u001b[01;35m\u001b[Kx\u001b[m\u001b[K = params->x;\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble separatrix_polynomial_equat(double, void*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:402:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 402 | double \u001b[01;35m\u001b[Kx\u001b[m\u001b[K = params->x;\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble solver(params_holder*, double (*)(double, void*), double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:417:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kr_expected\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 417 | double r = 0, \u001b[01;35m\u001b[Kr_expected\u001b[m\u001b[K = sqrt (5.0);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/interpolate.cpp -o build/temp.linux-x86_64-cpython-310/src/interpolate.o -std=c++11\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/interpolate.cpp:18\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_spline_constants(double*, double*, double*, int, int)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:165:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmode_vals\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 165 | InterpContainer \u001b[01;35m\u001b[Kmode_vals\u001b[m\u001b[K;\n",
" | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid fit_wrap(int, int, double*, double*, double*, double*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:245:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinfo\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 245 | int \u001b[01;35m\u001b[Kinfo\u001b[m\u001b[K = LAPACKE_dgtsv(LAPACK_COL_MAJOR, m, 1, &a[j * m + 1], &b[j * m], &c[j * m], &d_in[j * m], m);\n",
" | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid fill_final_derivs(double*, double*, int, int)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:272:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klead_ind\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 272 | int \u001b[01;35m\u001b[Klead_ind\u001b[m\u001b[K = interp_i*length;\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:281:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kfinal_c1\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 281 | double \u001b[01;35m\u001b[Kfinal_c1\u001b[m\u001b[K = c1 + 2 * c2 * x + 3 * c3 * x2;\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:282:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kfinal_c2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 282 | double \u001b[01;35m\u001b[Kfinal_c2\u001b[m\u001b[K = (2. * c2 + 6. * c3 * x)/2.;\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:283:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kfinal_c3\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 283 | double \u001b[01;35m\u001b[Kfinal_c3\u001b[m\u001b[K = c3;\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid interp_time_for_fd_wrap(double*, double*, double*, int*, double*, int, int, bool*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:397:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Knum_modes\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 397 | int \u001b[01;35m\u001b[Knum_modes\u001b[m\u001b[K = int((ninterps - 4) / 2.);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid make_waveform(cmplx*, double*, int*, int*, int, cmplx*, double, double, int, int, int, int)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:472:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klm_i\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 472 | int \u001b[01;35m\u001b[Klm_i\u001b[m\u001b[K, num_teuk_here;\n",
" | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:473:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kre_y\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 473 | double \u001b[01;35m\u001b[Kre_y\u001b[m\u001b[K, re_c1, re_c2, re_c3, im_y, im_c1, im_c2, im_c3;\n",
" | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:473:16:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kre_c1\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 473 | double re_y, \u001b[01;35m\u001b[Kre_c1\u001b[m\u001b[K, re_c2, re_c3, im_y, im_c1, im_c2, im_c3;\n",
" | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:473:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kre_c2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 473 | double re_y, re_c1, \u001b[01;35m\u001b[Kre_c2\u001b[m\u001b[K, re_c3, im_y, im_c1, im_c2, im_c3;\n",
" | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:473:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kre_c3\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 473 | double re_y, re_c1, re_c2, \u001b[01;35m\u001b[Kre_c3\u001b[m\u001b[K, im_y, im_c1, im_c2, im_c3;\n",
" | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:473:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kim_y\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 473 | double re_y, re_c1, re_c2, re_c3, \u001b[01;35m\u001b[Kim_y\u001b[m\u001b[K, im_c1, im_c2, im_c3;\n",
" | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:473:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kim_c1\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 473 | double re_y, re_c1, re_c2, re_c3, im_y, \u001b[01;35m\u001b[Kim_c1\u001b[m\u001b[K, im_c2, im_c3;\n",
" | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:473:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kim_c2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 473 | double re_y, re_c1, re_c2, re_c3, im_y, im_c1, \u001b[01;35m\u001b[Kim_c2\u001b[m\u001b[K, im_c3;\n",
" | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:473:57:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kim_c3\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 473 | double re_y, re_c1, re_c2, re_c3, im_y, im_c1, im_c2, \u001b[01;35m\u001b[Kim_c3\u001b[m\u001b[K;\n",
" | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:523:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kactual_mode_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 523 | int m, n, \u001b[01;35m\u001b[Kactual_mode_index\u001b[m\u001b[K;\n",
" | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kint bessel_ik(double, cmplx, cmplx*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:948:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kreflect\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-but-set-variable\u0007-Wunused-but-set-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 948 | bool \u001b[01;35m\u001b[Kreflect\u001b[m\u001b[K = false;\n",
" | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:949:17:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 949 | unsigned n, \u001b[01;35m\u001b[Kk\u001b[m\u001b[K;\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid make_generic_kerr_waveform_fd(cmplx*, double*, int*, int*, int*, int, double, double*, int, int, double*, int*, int*, int, cmplx*, int, bool, bool)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:1284:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kf_y2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1284 | double \u001b[01;35m\u001b[Kf_y2\u001b[m\u001b[K = m * f_phi_y2 + n * f_r_y2;\n",
" | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/interpolate.cpp:1295:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kfactor\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1295 | double \u001b[01;35m\u001b[Kfactor\u001b[m\u001b[K;\n",
" | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/pyinterp_cpu.cpp -o build/temp.linux-x86_64-cpython-310/src/pyinterp_cpu.o -std=c++11\n",
"In file included from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/pyinterp_cpu.cpp:1279\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wcpp\u0007-Wcpp\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 17 | #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it with \" \\\n",
" | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/interpolate.hh:4\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/pyinterp_cpu.cpp:1284\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"g++ -pthread -B /usr/local/compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/usr/local/lib -Wl,-rpath-link,/usr/local/lib -L/usr/local/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/usr/local/lib -Wl,-rpath-link,/usr/local/lib -L/usr/local/lib build/temp.linux-x86_64-cpython-310/src/Utility.o build/temp.linux-x86_64-cpython-310/src/interpolate.o build/temp.linux-x86_64-cpython-310/src/pyinterp_cpu.o -lgsl -lgslcblas -llapack -llapacke -lhdf5 -lhdf5_hl -o build/lib.linux-x86_64-cpython-310/pyinterp_cpu.cpython-310-x86_64-linux-gnu.so\n",
"Compiling src/gpuAAKWrap_cpu.pyx because it changed.\n",
"[1/1] Cythonizing src/gpuAAKWrap_cpu.pyx\n",
"/usr/local/lib/python3.10/site-packages/Cython/Compiler/Main.py:381: FutureWarning: Cython directive 'language_level' not set, using '3str' for now (Py3). This has changed from earlier releases! File: /content/FastEMRIWaveforms/src/gpuAAKWrap_cpu.pyx\n",
" tree = Parsing.p_module(s, pxd, full_module_name)\n",
"building 'pycpuAAK' extension\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/Utility.cc -o build/temp.linux-x86_64-cpython-310/src/Utility.o -std=c++11\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/Utility.cc:3\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid KerrGeoMinoFrequencies(double*, double*, double*, double*, double, double, double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:212:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[KEpsilon0\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 212 | double \u001b[01;35m\u001b[KEpsilon0\u001b[m\u001b[K = pow(a, 2) * (1 - pow(En, 2))/pow(L, 2);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid KerrGeoEquatorialMinoFrequencies(double*, double*, double*, double*, double, double, double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:270:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[KEpsilon0\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 270 | double \u001b[01;35m\u001b[KEpsilon0\u001b[m\u001b[K = pow(a, 2) * (1 - pow(En, 2))/pow(L, 2);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:272:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka2zp\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 272 | double \u001b[01;35m\u001b[Ka2zp\u001b[m\u001b[K =(pow(L, 2) + pow(a, 2) * (-1 + pow(En, 2)) * (-1))/( (-1 + pow(En, 2)) * (-1));\n",
" | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble separatrix_polynomial_polar(double, void*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:390:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 390 | double \u001b[01;35m\u001b[Kx\u001b[m\u001b[K = params->x;\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble separatrix_polynomial_equat(double, void*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:402:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 402 | double \u001b[01;35m\u001b[Kx\u001b[m\u001b[K = params->x;\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdouble solver(params_holder*, double (*)(double, void*), double, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/Utility.cc:417:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kr_expected\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 417 | double r = 0, \u001b[01;35m\u001b[Kr_expected\u001b[m\u001b[K = sqrt (5.0);\n",
" | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/gpuAAK.cpp -o build/temp.linux-x86_64-cpython-310/src/gpuAAK.o -std=c++11\n",
"In file included from \u001b[01m\u001b[Kinclude/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/gpuAAK.cpp:3\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Kinclude/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"gcc -pthread -B /usr/local/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /usr/local/include -fPIC -O2 -isystem /usr/local/include -fPIC -I/usr/local/lib/python3.10/site-packages/numpy/core/include -Iinclude -I/usr/local/include/python3.10 -c src/gpuAAKWrap_cpu.cpp -o build/temp.linux-x86_64-cpython-310/src/gpuAAKWrap_cpu.o -std=c++11\n",
"In file included from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/gpuAAKWrap_cpu.cpp:1280\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wcpp\u0007-Wcpp\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 17 | #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it with \" \\\n",
" | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[Ksrc/../include/global.h:7\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/../include/gpuAAK.hh:4\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Ksrc/gpuAAKWrap_cpu.cpp:1290\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[Ksrc/../include/cuda_complex.hpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgcmplx::complex<_Tp> gcmplx::acosh(const gcmplx::complex<_Tp>&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Ksrc/../include/cuda_complex.hpp:1028:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest explicit braces to avoid ambiguous ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wdangling-else\u0007-Wdangling-else\u001b]8;;\u0007\u001b[m\u001b[K]\n",
" 1028 | if \u001b[01;35m\u001b[K(\u001b[m\u001b[Kstd::isinf(__x.imag()))\n",
" | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"g++ -pthread -B /usr/local/compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/usr/local/lib -Wl,-rpath-link,/usr/local/lib -L/usr/local/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/usr/local/lib -Wl,-rpath-link,/usr/local/lib -L/usr/local/lib build/temp.linux-x86_64-cpython-310/src/Utility.o build/temp.linux-x86_64-cpython-310/src/gpuAAK.o build/temp.linux-x86_64-cpython-310/src/gpuAAKWrap_cpu.o -lgsl -lgslcblas -llapack -llapacke -lhdf5 -lhdf5_hl -o build/lib.linux-x86_64-cpython-310/pycpuAAK.cpython-310-x86_64-linux-gnu.so\n",
"creating build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pySpinWeightedSpherHarm.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pyinterp_cpu.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pycpuAAK.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pyParameterMap.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pyinterp.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pymatmul_cpu.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"creating build/bdist.linux-x86_64/egg/few\n",
"creating build/bdist.linux-x86_64/egg/few/trajectory\n",
"copying build/lib.linux-x86_64-cpython-310/few/trajectory/__init__.py -> build/bdist.linux-x86_64/egg/few/trajectory\n",
"copying build/lib.linux-x86_64-cpython-310/few/trajectory/inspiral.py -> build/bdist.linux-x86_64/egg/few/trajectory\n",
"copying build/lib.linux-x86_64-cpython-310/few/waveform.py -> build/bdist.linux-x86_64/egg/few\n",
"copying build/lib.linux-x86_64-cpython-310/few/_version.py -> build/bdist.linux-x86_64/egg/few\n",
"copying build/lib.linux-x86_64-cpython-310/few/__init__.py -> build/bdist.linux-x86_64/egg/few\n",
"creating build/bdist.linux-x86_64/egg/few/amplitude\n",
"copying build/lib.linux-x86_64-cpython-310/few/amplitude/interp2dcubicspline.py -> build/bdist.linux-x86_64/egg/few/amplitude\n",
"copying build/lib.linux-x86_64-cpython-310/few/amplitude/romannet.py -> build/bdist.linux-x86_64/egg/few/amplitude\n",
"copying build/lib.linux-x86_64-cpython-310/few/amplitude/__init__.py -> build/bdist.linux-x86_64/egg/few/amplitude\n",
"creating build/bdist.linux-x86_64/egg/few/summation\n",
"copying build/lib.linux-x86_64-cpython-310/few/summation/interpolatedmodesum.py -> build/bdist.linux-x86_64/egg/few/summation\n",
"copying build/lib.linux-x86_64-cpython-310/few/summation/directmodesum.py -> build/bdist.linux-x86_64/egg/few/summation\n",
"copying build/lib.linux-x86_64-cpython-310/few/summation/fdinterp.py -> build/bdist.linux-x86_64/egg/few/summation\n",
"copying build/lib.linux-x86_64-cpython-310/few/summation/aakwave.py -> build/bdist.linux-x86_64/egg/few/summation\n",
"copying build/lib.linux-x86_64-cpython-310/few/summation/__init__.py -> build/bdist.linux-x86_64/egg/few/summation\n",
"creating build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/fdutils.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/citations.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/utility.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/baseclasses.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/ylm.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/odeoptions.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/modeselector.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/odeprepare.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/few/utils/constants.py -> build/bdist.linux-x86_64/egg/few/utils\n",
"copying build/lib.linux-x86_64-cpython-310/pyUtility.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pygpuAAK.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pyInspiral.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pyInterp2DAmplitude.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"copying build/lib.linux-x86_64-cpython-310/pymatmul.cpython-310-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/trajectory/__init__.py to __init__.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/trajectory/inspiral.py to inspiral.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/waveform.py to waveform.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/_version.py to _version.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/__init__.py to __init__.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/amplitude/interp2dcubicspline.py to interp2dcubicspline.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/amplitude/romannet.py to romannet.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/amplitude/__init__.py to __init__.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/summation/interpolatedmodesum.py to interpolatedmodesum.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/summation/directmodesum.py to directmodesum.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/summation/fdinterp.py to fdinterp.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/summation/aakwave.py to aakwave.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/summation/__init__.py to __init__.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/fdutils.py to fdutils.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/citations.py to citations.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/utility.py to utility.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/baseclasses.py to baseclasses.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/ylm.py to ylm.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/odeoptions.py to odeoptions.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/modeselector.py to modeselector.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/odeprepare.py to odeprepare.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/few/utils/constants.py to constants.cpython-310.pyc\n",
"creating stub loader for pymatmul.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pyinterp.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pygpuAAK.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pymatmul_cpu.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pyInspiral.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pyParameterMap.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pyinterp_cpu.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pySpinWeightedSpherHarm.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pyInterp2DAmplitude.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pyUtility.cpython-310-x86_64-linux-gnu.so\n",
"creating stub loader for pycpuAAK.cpython-310-x86_64-linux-gnu.so\n",
"byte-compiling build/bdist.linux-x86_64/egg/pymatmul.py to pymatmul.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pyinterp.py to pyinterp.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pygpuAAK.py to pygpuAAK.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pymatmul_cpu.py to pymatmul_cpu.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pyInspiral.py to pyInspiral.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pyParameterMap.py to pyParameterMap.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pyinterp_cpu.py to pyinterp_cpu.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pySpinWeightedSpherHarm.py to pySpinWeightedSpherHarm.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pyInterp2DAmplitude.py to pyInterp2DAmplitude.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pyUtility.py to pyUtility.cpython-310.pyc\n",
"byte-compiling build/bdist.linux-x86_64/egg/pycpuAAK.py to pycpuAAK.cpython-310.pyc\n",
"creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
"copying few.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
"copying few.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
"copying few.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
"copying few.egg-info/not-zip-safe -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
"copying few.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
"writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt\n",
"creating 'dist/few-1.5.4-py3.10-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
"removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
"Processing few-1.5.4-py3.10-linux-x86_64.egg\n",
"removing '/usr/local/lib/python3.10/site-packages/few-1.5.4-py3.10-linux-x86_64.egg' (and everything under it)\n",
"creating /usr/local/lib/python3.10/site-packages/few-1.5.4-py3.10-linux-x86_64.egg\n",
"Extracting few-1.5.4-py3.10-linux-x86_64.egg to /usr/local/lib/python3.10/site-packages\n",
"Adding few 1.5.4 to easy-install.pth file\n",
"\n",
"Installed /usr/local/lib/python3.10/site-packages/few-1.5.4-py3.10-linux-x86_64.egg\n",
"Processing dependencies for few==1.5.4\n",
"Finished processing dependencies for few==1.5.4\n",
"/content/FastEMRIWaveforms/few/tests/test_aak.py:16: UserWarning: CuPy is not installed or a gpu is not available. If trying to run on a gpu, please install CuPy.\n",
" warnings.warn(\n",
"/content/FastEMRIWaveforms/few/tests/test_detector_wave.py:16: UserWarning: CuPy is not installed or a gpu is not available. If trying to run on a gpu, please install CuPy.\n",
" warnings.warn(\n",
"/content/FastEMRIWaveforms/few/tests/test_fd.py:26: UserWarning: CuPy is not installed or a gpu is not available. If trying to run on a gpu, please install CuPy.\n",
" warnings.warn(\n",
"/content/FastEMRIWaveforms/few/tests/test_few.py:23: UserWarning: CuPy is not installed or a gpu is not available. If trying to run on a gpu, please install CuPy.\n",
" warnings.warn(\n",
"./content/FastEMRIWaveforms/few/summation/aakwave.py:226: UserWarning: Inclination trajectory includes values within 1e-6 of the poles. We shift these values automatically away from poles by 1e-6.\n",
" warnings.warn(\n",
"..........\n",
"----------------------------------------------------------------------\n",
"Ran 11 tests in 141.813s\n",
"\n",
"OK\n"
]
}
],
"source": [
"!pip install -q condacolab\n",
"import condacolab\n",
"condacolab.install()\n",
"\n",
"!git clone https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms.git\n",
"!(cd FastEMRIWaveforms; git reset --hard e4038dacd99fa07a545af593beeb88d882e92fd2;) # Terribly hacky fix for now.\n",
"\n",
"!conda install -c conda-forge gcc_linux-64 gxx_linux-64 wget gsl lapack=3.6.1 hdf5 numpy Cython scipy tqdm jupyter ipython h5py requests matplotlib python | tee /dev/tty | tail -n 1\n",
"!(cd FastEMRIWaveforms; python setup.py install;)\n",
"\n",
"!python -m unittest discover ./FastEMRIWaveforms\n",
"\n",
"import sys\n",
"sys.path.append('./FastEMRIWaveforms/')\n",
"sys.path.append('/usr/local/lib/python3.10/site-packages/few-1.5.4-py3.10-linux-x86_64.egg/')\n",
"\n",
"import few\n",
"from few.trajectory.inspiral import EMRIInspiral"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rGIFSMslLWxQ"
},
"source": [
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/OllieBurke/EMRI_Workshop/blob/main/docs/Auckland_Tutorial.ipynb\">\n",
" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
"</a>\n",
"\n",
"# EMRI Waveforms in a nutshell"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "weNoR1FILWxT"
},
"source": [
"Here you can find a pedagogical tutorial on creating Extreme Mass Ratio Inspiral (EMRI) waveforms. We will make an extensive use of the Fast EMRI Waveform (FEW) package [arxiv.org/2104.04582](https://arxiv.org/abs/2104.04582) [arxiv.org/2008.06071](https://arxiv.org/abs/2008.06071). The waveforms in this package combine a variety of separately accessible modules to form EMRI waveforms on both CPUs and GPUs. Generally, the modules fall into four categories: trajectory, amplitudes, summation, and utilities. Please see the [documentation](https://bhptoolkit.org/FastEMRIWaveforms/) for further information on these modules. The code and installation instructions can be found on Github [here](https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms).\n",
"\n",
"In this tutorial will cover the basics of EMRI waveforms. In particular, we will understand:\n",
"1) What is an Extreme Mass Ratio Inspiral?\n",
"2) How is an EMRI Waveform built? What are the parameters of an EMRI Waveform?\n",
"3) EMRI Trajectories\n",
"4) EMRI Waveforms in the time, frequency and time-frequency domains.\n",
"\n",
"### Authors\n",
"\n",
"Lorenzo Speri: lorenzo.speri@aei.mpg.de , **Lorenzo wrote the original tutorial [here](https://github.com/lorenzsp/GRAPPA_EMRI_tutorial)**\n",
"\n",
"Ollie Burke: ollie.burke@l2it.in2p3.fr , **I have modified the tutorial to allow for GPU acceleration + more data analysis posed questions**\n",
"\n",
"\n"
]
},
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"source": [
"## What is an Extreme Mass Ratio Inspiral?"
]
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"One of the primary sources for the future space-based gravitational wave detector, the Laser Interferometer Space Antenna, are the inspirals of small compact objects into massive black holes in the centers of galaxies. Such binaries are characterized by a small compact object of mass typically $\\mu\\in[1,100]M_\\odot$ (for example a solar-mass black hole or a neutron star) inspiralling around a Massive Black Hole (MBH) of mass $M\\in[10^5,10^7]M_\\odot$. Due to their particularly small mass ratio $\\epsilon = \\mu / M \\sim [10^{-4},10^{-6}]$ these systems are called Extreme Mass Ratio Inspiral (EMRI). Using perturbation theory in the mass ratio $\\epsilon$ it is possible to model the evolution of the orbit of the compact object. On short time-scales (order of the orbital period), the compact object moves on geodesic orbits as if it was a test particles. However, on longer time-scales, it is necessary to take into account the impact of the gravitational field of the compact object on the background and the gravitational wave emission. During the inspiral, the compact object is slowly driven away from geodesic orbits and this deviation can be interpreted as an effective acceleration/force due to the so-called Gravitational [Self-Force](https://arxiv.org/abs/1805.10385).\n",
"\n",
"Below, in the left panel, we show an example of a small compact object of ten solar masses inspiraling around a spinning MBH of $10^6$ solar masses. The duration of the animation corresponds to 25 hours. In the right panel, you can see the gravitational wave signal emmitted during this inspirals. After you complete this tutorial you will easily be able to build animations like the ones above! If you want to impress your family, friends and possible pets, [click this link](https://github.com/OllieBurke/animations.git)"
]
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"| [Building Trajectories] | [Building Waveforms] | \n",
"|:----------------------------:|:-----------------------------:|\n",
"| [![][trajectory]][trajectory] | [![][waveform]][waveform] |\n",
"\n",
"[trajectory]: https://github.com/OllieBurke/EMRI_Workshop/blob/main/docs/movies/trajectory.gif?raw=true\n",
"\n",
"[waveform]: https://github.com/OllieBurke/EMRI_Workshop/blob/main/docs/movies/waveform.gif?raw=true"
]
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"source": [
"## EMRI Waveforms"
]
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"EMRI waveforms are represented by the complex time-domain dimensionless strain $h(t) = h_+ - ih_\\times$, where $h_+$ and $h_\\times$ are the usual transverse-traceless gravitational wave polarizations. For large distances from the source ($d_{L}\\rightarrow \\infty$), $h$ is given in the source frame by:\n",
"\n",
"$$\n",
" h = \\frac{\\mu}{d_L}\\sum_{lmkn} A_{lmkn}(t) \\, S_{lmkn}(\\theta,\\phi) \\, \\exp(-i\\Phi_{mkn}(t)).\n",
"$$\n",
"\n",
"Here $\\mu$ is the mass of the secondary body, $d_{L}$ the luminosity distance to the source, $t$ is the time of arrival of the gravitational wave at the solar system baricenter, and $(l,m,k,n)$ are the indices describing the frequency-domain harmonic mode decomposition (or simply \"harmonic\" or \"mode\"):\n",
"\n",
"- $l$ denotes the orbital angular momentum mode index and can take the value $l=2,3,...$\n",
"\n",
"- $m$ denotes the azimuthal mode index and can take the values from $-l$ up to $l$. For example for l=2, $m=-2, -1, 0, 1, 2$.\n",
"\n",
"- $k$ and $n$ denote the polar and radial mode indices and can take values from $-\\infty$ up to $+\\infty$. But in practice they are restriced to values around zero, for instance between $-30$ up to $30$. In this tutorial there will be no $k$ modes since our inspirals are restricted to the equatorial plane of the central hole.\n",
"\n",
"For LIGO sources, where the two objects have comparable masses, the strongest mode is the $(l=2,m=2,k=0,n=0)$.\n",
"See [Drasco and Hughes 2006](https://arxiv.org/abs/gr-qc/0509101) and [Hughes+ 2021](https://arxiv.org/pdf/2102.02713.pdf) for a derivation of the gravitational waveform."
]
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"#### Each harmonic is characterized by three important functions:\n",
"\n",
"1. The oscillatory part $\\exp[-i\\Phi_{mkn}(t)]$ is determined by the phase $\\Phi_{mkn}=m\\Phi_\\varphi + k \\Phi_\\theta + n\\Phi_r$. The phases $\\Phi_{\\varphi,\\theta,r}$ are determined solving for the EMRI trajectory, i.e. the following system of ordinary differential equations:\n",
"\n",
"$$\n",
" \\frac{d}{dt}p = \\epsilon \\, f^{(1)}_p(a, p, e, x_I) + \\mathcal{O}(\\epsilon^2) $$\n",
" $$\\frac{d}{dt}e = \\epsilon \\, f^{(1)}_e(a, p, e, x_I) + \\mathcal{O}(\\epsilon^2) $$\n",
" $$\\frac{d}{dt}x_I = \\epsilon \\, f^{(0)}_{x_I}(a, p, e, x_I) + \\mathcal{O}(\\epsilon^2)$$\n",
" $$\\frac{d}{dt}\\Phi_{\\varphi,\\theta,r} = \\Omega_{\\varphi, \\theta, r}(a, p, e, x_I)/M + \\mathcal{O}(\\epsilon)\n",
"$$\n",
"\n",
"with initial condition $\\{\\Phi_{\\varphi0},\\Phi_{\\theta0},\\Phi_{r0},p_0,e_0,x_{I0}\\}$. The frequencies $\\Omega_{r,\\theta,\\phi}$ describe the fundamental frequencies of a Kerr geodesic orbit and in the Newtonian limit (weak field) they all converge to the Keplerian frequency. These frequencies are determined using the dimensionless spin of the MBH, $a$, and the quasi-Keplerian orbital parameters of $p$ (semi-latus rectum; hereafter separation), $e$ (eccentricity), and $\\cos{I} \\equiv x_I$ (cosine of the angle $I$ which describes the orbit's inclination from the equatorial plane). The rate of change of $p,e,x_I$ (left hand side of the first 3 equations) is obtained using the orbital-element fluxes $f_{p,e,x_I}$, which account for the gravitational wave emission, and the mass ratio $\\epsilon = \\mu/M$. For $\\epsilon \\rightarrow 0$ we reach the test-particle limit, and the orbital elements do not vay over time. The extra contributions due to second order in the mass-ratio squared arise from the second-order self force, which is currently a very active field of research. For more information, see [Barack and Pound](https://arxiv.org/abs/1805.10385).\n"
]
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"2. The amplitude $A_{lmkn}(t)$ which quantifies the size of each harmonic $(l,m,k,n)$. These are determined using the orbital parameters $A_{lmkn}(p(t), e(t), x_I(t))$. See in the plot below that orbits with higher eccentricity $e_{0} = 0.7$ require significantly many more harmonics than orbits with lower eccentricity $e_{0} = 0.1$. In each plot, the semi-latus rectum has been fixed to $p_{0} = 10$ and $x_{I} = \\cos(\\iota_{0}) = 0.5$.\n",
"\n",
"3. The angular function $S_{lmkn}(\\theta,\\phi)$ which describes how the amplitude is modulated depending on the source-frame polar viewing angle $\\theta$, the source-frame azimuthal viewing angle $\\phi$. For Schwarzchild black holes these functions reduce to the -2 spin weighted spherical harmonics $Y_{lm}$."
]
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"source": [
"![](https://github.com/OllieBurke/EMRI_Workshop/blob/main/docs/images/amplitude.jpg?raw=true)"
]
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"### General Remarks\n",
"Notice the correspondence of the mode index with the three different degrees of freedom: $m\\rightarrow \\varphi$ (azimuthal), $k\\rightarrow \\theta$ (polar), $n\\rightarrow r$ (radial). Each mode is characterized by the mode frequency $\\omega_{mkn}=m\\Omega_\\varphi + k \\Omega_\\theta + n \\Omega_r$ which is the derivative of the phase $\\Phi_{mkn}$. On short time-scales, the gravitational wave of an EMRI can be thought as a Fourier series with coefficients $\\sum_l A_{lmnk} S_{lmkn}$ and frequencies $\\omega_{mkn}$. The quantities $\\Omega_{\\varphi, \\theta, r}$ are fundamental frequencies in the azimuthal, polar and radial coordinates: $(\\varphi, \\theta, r)$.\n",
"\n",
"Theoretical parameter ranges:\n",
"- the eccentricity ranges between 0 and 1, for $e=0$ the orbits are circular;\n",
"- the inclination parameter ranges between -1 and 1, for $x_I=1$ the orbits are equatorial prograde, $x_I=-1$ the orbits are equatorial retrograde, $x_I=0$ the orbits are polar. An orbit is pro(retro)grade only if the trajectory of the body is (anti)aligned with the massive compact objects orbital angular momentum.\n",
"- the dimensionless spin parameters ranges between 0 and 1, for $a=0$ the central MBH is not spinning (Schwarzchild background) and the orbit of the compact object stays in the same plane, i.e. $x_I$ is constant.\n",
"\n",
"See [Hughes+ 2021](https://ui.adsabs.harvard.edu/abs/2021PhRvD.103j4014H/abstract) for further discussion and more detailed definitions. "
]
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"source": [
"## EMRI Parameter space"
]
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"After we outlined the building blocks to create an EMRI waveform, let us now understand how these are organized in the Fast EMRI Waveform code. The waveform code takes as input two sets of parameters:\n",
"- The intrinsic parameters $(M,\\mu,a,p_0,e_0,(x_{I})_0,\\Phi_{\\varphi 0},\\Phi_{\\theta 0},\\Phi_{r 0})$, which are used to construct the waveform in the source frame; Notice that the last six parameters here serve as initial conditions for the coupled set of ODEs defined earlier, with $M$ and $\\mu$ determining the most crucial feature of the EMRI: The mass ratio $\\epsilon = \\mu/M$. The intrinsic parameters govern the evolution of the signal as a function of frequency.\n",
"- the extrinsic parameters $(d_L,\\theta_S,\\phi_S,\\theta_K,\\phi_K)$, which define how the waveform is viewed in a given reference frame. The extrinsic parameters feature more in the amplitude of the waveform.\n",
"\n",
"The intrinsic parameters are then passed into the trajectory module which computes the sparse time evolution of the phases $\\Phi_{\\varphi}(t),\\Phi_{\\theta}(t),\\Phi_{r}(t)$ and orbital elements $p(t),e(t),x_I(t)$. The phases are used to assemble the oscillatory part, whereas the orbital elements are passed to the amplitude module which computes $A(p(t),e(t),x_I(t))$. The angular function is computed using the extrinsic parameters. Then the summation module (Waveform build) takes as input the functions $\\exp[-i\\Phi_{mkn}(t)], A_{lmkn}(t)$ and $S_{lmkn}(\\theta,\\phi)$ and outputs the waveform strain $h$."
]
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"source": [
"![FEW_arch](https://github.com/OllieBurke/EMRI_Workshop/blob/main/docs/images/FEW_arch.jpg?raw=true)"
]
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"# we import here all the modules and packages we need\n",
"import sys\n",
"import os\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"try:\n",
" import cupy as cp\n",
" import numpy as np\n",
"except ImportError:\n",
" import numpy as np\n",
"\n",
"from tqdm import tqdm as tqdm\n",
"from few.trajectory.inspiral import EMRIInspiral\n",
"from few.amplitude.romannet import RomanAmplitude\n",
"from few.amplitude.interp2dcubicspline import Interp2DAmplitude\n",
"from few.waveform import FastSchwarzschildEccentricFlux, SlowSchwarzschildEccentricFlux, GenerateEMRIWaveform\n",
"from few.utils.utility import (get_overlap,\n",
" get_mismatch,\n",
" get_fundamental_frequencies,\n",
" get_separatrix,\n",
" get_mu_at_t,\n",
" get_p_at_t,\n",
" get_kerr_geo_constants_of_motion,\n",
" xI_to_Y,\n",
" Y_to_xI)\n",
"\n",
"from few.utils.ylm import GetYlms\n",
"from few.utils.modeselector import ModeSelector\n",
"from few.summation.interpolatedmodesum import CubicSplineInterpolant\n",
"from few.waveform import SchwarzschildEccentricWaveformBase\n",
"from few.summation.interpolatedmodesum import InterpolatedModeSum\n",
"from few.summation.directmodesum import DirectModeSum\n",
"from few.utils.constants import *\n",
"from few.summation.aakwave import AAKSummation\n",
"from few.waveform import Pn5AAKWaveform, AAKWaveformBase\n",
"\n",
"use_gpu = False\n",
"\n",
"# keyword arguments for inspiral generator (RunSchwarzEccFluxInspiral)\n",
"inspiral_kwargs={\n",
" \"DENSE_STEPPING\": 0, # we want a sparsely sampled trajectory\n",
" \"max_init_len\": int(1e3), # all of the trajectories will be well under len = 1000\n",
" }\n",
"\n",
"# keyword arguments for inspiral generator (RomanAmplitude)\n",
"amplitude_kwargs = {\n",
" \"max_init_len\": int(1e3), # all of the trajectories will be well under len = 1000\n",
" \"use_gpu\": use_gpu # GPU is available in this class\n",
"}\n",
"\n",
"# keyword arguments for Ylm generator (GetYlms)\n",
"Ylm_kwargs = {\n",
" \"assume_positive_m\": False # if we assume positive m, it will generate negative m for all m>0\n",
"}\n",
"\n",
"# keyword arguments for summation generator (InterpolatedModeSum)\n",
"sum_kwargs = {\n",
" \"use_gpu\": use_gpu, # GPU is availabel for this type of summation\n",
" \"pad_output\": False,\n",
"}"
]
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"## Schwarzschild Eccentric Trajectories and Waveforms\n",
"In this tutorial we will restrict to EMRI systems in which the central object is a non-spinning black hole. The case of a spinning black hole is presented at the end of the notebook in the additional materials."
]
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"## Trajectory Module\n",
"The trajectory module generates orbital $\\{p(t),e(t), x_{I}(t)\\}$ and phase $\\{\\Phi_\\varphi(t), \\Phi_\\theta(t), \\Phi_r(t)\\}$ trajectories for an EMRI orbit defined by the coordinate-time evolving quantities: \n",
"\n",
"$$\n",
"p(t), e(t), x_I(t), \\Phi_\\varphi(t), \\Phi_\\theta(t), \\Phi_r(t)\n",
"$$\n",
"\n",
"with initial inputs $M, \\mu, p_0, e_0, (x_{I})_{0}, \\iota_0, \\Phi_{\\varphi0}, \\Phi_{\\theta0}, \\Phi_{r 0}$. We remark here that $x_{I} = \\cos(\\iota (t))$ for $\\iota$ the inclination angle of the body with respect to the equatorial plane (see image below). The trajectory is evolved for a user defined time of $T$ years or until the last-stable orbit, defined by the separatrix $p_{sep}$ is reached. If the specified duration $T$ is longer than the time taken to reach the separatrix, then the orbit will terminate at $p(t_{\\text{sep}}) = p_{\\text{sep}}$. At a single point in time, the orbit can be pictures like\n",
"\n",
"<div style=\"text-align:center;\">\n",
"<img src=\"https://github.com/OllieBurke/EMRI_Workshop/blob/main/docs/images/Orbit.jpg?raw=true\" width=\"700\" height=\"700\">\n",
"</div>\n",
"\n",
"$M$ is the larger mass ($M_\\odot$), $\\mu$ is the compact object mass ($M_\\odot$), $p_0$ is the initial semi-latus rectum (dimensionless), $e_0$ is the initial eccentricity (dimensionless), and the final three are the initial orbital phases $\\Phi_{\\varphi 0}, \\Phi_{\\theta 0}, \\Phi_{r 0}$ (in radians). The three orbital phases can be thought of as \"positional elements\" of the smaller body when in orbit of the primary black hole. Take, for example, a sphere of radius $r$. There are infinitely many points on that sphere for where that radius could be defined. However, only by fixing the angular variables in $\\theta$ and $\\phi$ can you define a specific point on a sphere with spherical polar coordinates $(r,\\theta,\\phi)$. It's similar for the EMRI orbit. If you specify a specific orbital parameters $p_{0},e_{0},(x_{I})_{0}$ then we need phase variables $\\Phi_{\\varphi 0,\\theta 0,\\phi 0}$ to determine the smaller bodies position with respect to the central massive black hole.\n",
"\n",
"For the special case of non-rotating black holes (Schwarzchild black holes) the spin parameter is zero $a=0$. This will simplify the orbit where the body necessarily remains on the same plane $x_I=1$, and the separatrix is located at $p_\\textrm{sep} = 6+2e$. Given that $p(t)$ and $e(t)$ are time-evolving quantities, the separatrix will also be evolving in time. For further details on the location of the separatrix for generic orbits around rotating black holes (and various special cases), see [Stein and Warburton](https://arxiv.org/pdf/1912.07609.pdf) for an excellent discussion.\n"
]
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"### Let us begin by analysing a basic trajectory\n",
"\n",
"We begin by analysing eccentric orbits in a Schwarzschild space-time. This means that $a = 0, x_{I}(t) = 1, \\Phi_{\\theta0} = 0$ will be fixed."
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"# initialize trajectory class\n",
"traj = EMRIInspiral(func=\"SchwarzEccFlux\")"
]
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"base_uri": "https://localhost:8080/",
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{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1400x800 with 6 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# set initial parameters for Schwarzschild orbit\n",
"M = 1e6 # Primary mass (solar masses)\n",
"mu = 1e1 # Secondary mass (solar masses)\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"\n",
"# additional information\n",
"kwargs = dict(dt=10.0, # initial time step,\n",
" T=2.0, # Time duration in years\n",
" err=1e-10 # integrator error -- used as a tolerance to solve coupled ODEs above\n",
" )\n",
"\n",
"# Compute trajectory of smaller body: p(t), e(t), xI(t), Phi_phi(t), Phi_theta(t), Phi_r(t)\n",
"t, p, e, xI, Phi_phi, Phi_theta, Phi_r = traj(M, mu, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"\n",
"\n",
"# get dimensionless fundamental frequencies\n",
"OmegaPhi, OmegaTheta, OmegaR = get_fundamental_frequencies(a, p, e, x)\n",
"\n",
"\n",
"# Plot the results\n",
"fig, axes = plt.subplots(2, 3)\n",
"plt.subplots_adjust(wspace=0.3)\n",
"fig.set_size_inches(14, 8)\n",
"axes = axes.ravel()\n",
"\n",
"ylabels = [r'$e$', r'$p$', r'$e$', r'$\\Phi_\\phi$', r'$\\Phi_r$', r'$\\Omega_\\phi$']\n",
"xlabels = [r'$p$', r'$t$ [years]', r'$t$ [years]', r'$t$ [years]', r'$t$ [years]', r'$t$ [years]', r'$t$ [years]', r'$t$ [years]']\n",
"ys = [e, p, e, Phi_phi, Phi_r, OmegaPhi]\n",
"xs = [p, t/YRSID_SI, t/YRSID_SI, t/YRSID_SI, t/YRSID_SI, t/YRSID_SI]\n",
"\n",
"for i, (ax, x, y, xlab, ylab) in enumerate(zip(axes, xs, ys, xlabels, ylabels)):\n",
" ax.plot(x, y)\n",
" ax.set_xlabel(xlab, fontsize=16)\n",
" ax.set_ylabel(ylab, fontsize=16)\n",
" ax.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5-9-aDS3LWxY"
},
"source": [
"## Exercise 0\n",
"\n",
"1. For the default parameters $(M,\\mu,a,p_{0},e_{0},(x_{I})_{0}) = (10^{6}, 10, 0, 10, 0.3, 1)$ with $T = 2$ years, verify that the final point in semi-latus rectum corresponds to the separatrix defined by $p = 6 + 2e$. Change the time of evolution $T$ and make a note of what you find.\n",
"2. Modify the mass-ratio of the system above by increasing/decreasing $M$ whilst keeping $\\mu$ (and all other parameters) fixed. What do you observe about the orbital evolution in semi-latus rectum $p(t)$? You may need to increase the time of evolution $T$. You will find that $\\dot{p} = dp/dt$ decreases as $M$ is increased."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "e0u9ssm-LWxY"
},
"outputs": [],
"source": [
"# EXERCISE 0!\n",
"\n",
"# set initial parameters for Schwarzschild orbit\n",
"# M = 1e6 # Primary mass (solar masses)\n",
"# mu = 1e1 # Secondary mass (solar masses)\n",
"# p0 = 10.0 # range [separatrix, 45]\n",
"# e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# # run trajectory\n",
"# # must include for generic inputs, will fix a = 0 and x = 1.0\n",
"# a = 0.0\n",
"# x = 1.0\n",
"\n",
"# # additional information\n",
"# kwargs = dict(dt=10.0, # initial time step,\n",
"# T=2.0, # Time duration in years\n",
"# err=1e-10 # integrator error -- used as a tolerance to solve coupled ODEs above\n",
"# )\n",
"\n",
"# # Compute trajectory of smaller body: p(t), e(t), xI(t), Phi_phi(t), Phi_theta(t), Phi_r(t)\n",
"# t, p, e, xI, Phi_phi, Phi_theta, Phi_r = traj(M, mu, a, p0, e0, x,\n",
"# Phi_phi0=0.0,\n",
"# Phi_theta0=0.0,\n",
"# Phi_r0=0.0,\n",
"# **kwargs)\n",
"\n",
"# p_sep = [INSERT YOUR CODE HERE]\n",
"# print(\"Separatrix is at \",p_sep)\n",
"# print(\"Final point in semi-latus rectum is\", p[-1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zANkvIFQLWxY"
},
"outputs": [],
"source": [
"# EXERCISE 0!\n",
"\n",
"# set initial parameters for Schwarzschild orbit\n",
"\n",
"mu = 1e1 # Secondary mass (solar masses)\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"\n",
"# additional information\n",
"kwargs = dict(dt=10.0, # initial time step,\n",
" T=1.0, # Time duration in years\n",
" err=1e-10 # integrator error -- used as a tolerance to solve coupled ODEs above\n",
" )\n",
"\n",
"# Compute trajectory of smaller body: p(t), e(t), xI(t), Phi_phi(t), Phi_theta(t), Phi_r(t)\n",
"\n",
"# M_vec = 10**np.arange(4,7,1)\n",
"# N_plots = len(M_vec)\n",
"\n",
"# fig,ax = plt.subplots(1,N_plots, figsize = (12,4))\n",
"# i=0\n",
"# for M in M_vec:\n",
"# t, p, e, xI, Phi_phi, Phi_theta, Phi_r = [INSERT YOUR CODE HERE]\n",
"\n",
"# ax[i].plot(t/60/60/24,p,label = \"M = {}\".format(M))\n",
"# ax[i].set_xlabel(r'time [days]', fontsize = 18)\n",
"# ax[i].set_ylabel(r'semi-latus rectum $p$',fontsize = 18)\n",
"# ax[i].set_title(r'Primary mass = {} $M_\\odot$'.format(M),fontsize = 14)\n",
"# ax[i].legend()\n",
"# ax[i].grid()\n",
"\n",
"\n",
"# i+=1\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g6cRycU3LWxY"
},
"source": [
"### Quasi-Keplerian parameters: $(p,e)$\n",
"We want to remark that $p$ and $e$ are quasi-Keplerian parameters and gauge dependent quantities (they depend on the choice of coordinate system). They \"converge\" to the Newtonian equivalent parameters in the weak field regime (far away in radial coordinate $r$ from the black hole). Below we show a plot of ellipsis based on these parameters at diferent stages of the evolution. This is useful to get a sense of the evolution of the orbit but you should not interpret these ellipsis as real orbits!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Yeg7v3c8LWxY"
},
"outputs": [],
"source": [
"# set initial parameters for Schwarzschild orbit\n",
"M = 1e6 # Primary mass (solar masses)\n",
"mu = 1e1 # Secondary mass (solar masses)\n",
"p0 = 10.5 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"\n",
"# additional information\n",
"kwargs = dict(dt=10.0, # initial time step,\n",
" T=2.0, # Time duration in years\n",
" err=1e-10 # integrator error -- used as a tolerance to solve coupled ODEs above\n",
" )\n",
"\n",
"# Compute trajectory of smaller body: p(t), e(t), xI(t), Phi_phi(t), Phi_theta(t), Phi_r(t)\n",
"t, p, e, xI, Phi_phi, Phi_theta, Phi_r = traj(M, mu, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"\n",
"import matplotlib\n",
"cmap = matplotlib.cm.get_cmap('Spectral')\n",
"\n",
"plt.figure(figsize=(8,8))\n",
"for ii in range(0,len(p),12):\n",
" pp = p[ii]\n",
" ee = e[ii]\n",
" Csi = np.linspace(0.0,2*np.pi,100)\n",
" rr = pp / (1 + ee * np.cos(Csi))\n",
" xx = rr * np.cos(Csi)\n",
" yy = rr * np.sin(Csi)\n",
" plt.plot(xx,yy,c=cmap(ii/len(p)),label=f'time = {t[ii]/(YRSID_SI):.2e} years' )\n",
"plt.scatter(0,0,c='k',label='MBH')\n",
"plt.legend()\n",
"plt.xlim(-20,20)\n",
"plt.ylim(-20,20)\n",
"plt.xlabel(r'$r\\cos(\\xi)$',fontsize = 16)\n",
"plt.ylabel(r'$r\\sin(\\xi)$', fontsize = 16)\n",
"plt.title('Illustrative depiction of orbits',fontsize = 25)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "egu8SAwhLWxZ"
},
"source": [
"## Evolution of semi-latus rectum and eccentricity\n",
"\n",
"The goal of the next cell is to understand how the semi-latus rectum and eccentricity evolve together. Notice that for trajectories with semi-latus rectum close to the separatrix, one observes an \"uptick\" in the eccentricity. This is a well known feature and is further discussed in this [article](https://arxiv.org/abs/gr-qc/0203086)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CcisgN93LWxZ"
},
"outputs": [],
"source": [
"# set initial parameters\n",
"M = 1e6 # Primary mass (solar masses)\n",
"mu = 1e1 # Secondary mass (solar masses)\n",
"\n",
"# run trajectory\n",
"\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"\n",
"# additional information\n",
"kwargs = dict(dt=10.0, # initial time step,\n",
" T=100.0, # Time duration in years [Excessive, but want to evolve to separatrix! here]\n",
" err=1e-10 # integrator error\n",
" )\n",
"\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"plt.figure()\n",
"\n",
"\n",
"# Here we plot the trajectory with all parameters fixed (e0 = 0.7) apart from p0\n",
"for p0 in np.linspace(7, 20, num=10):\n",
" e0=0.7\n",
" t, p, e, xI, Phi_phi, Phi_theta, Phi_r = traj(M, mu, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
" plt.plot(p,e)\n",
"\n",
"# Now we plot the trajectory with all parameters fixed (p0 = 20) apart from e0\n",
"for e0 in np.linspace(0, 0.69, num=10):\n",
" p0=20.0\n",
" t, p, e, xI, Phi_phi, Phi_theta, Phi_r = traj(M, mu, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
" plt.plot(p,e)\n",
"\n",
"# Separatrix given by p_sep = 6 + 2*e\n",
"pp = np.linspace(6,7.5,100)\n",
"ecc = (pp - 6)/2\n",
"plt.plot(pp,ecc,'k--',label='separatrix')\n",
"plt.xlabel('p')\n",
"plt.ylabel('e')\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fNk1sCrKLWxZ"
},
"source": [
"### Trajectory duration as a function of mass ratio, eccentricity, semi-latus rectum\n",
"\n",
"In Exercise 0 above we saw that the length of the trajectory, in time, is very sensitive to the mass-ratio of the system. The coupled ODEs\n",
"\n",
"$$\n",
"\\frac{d}{dt}p = \\epsilon \\, f^{(1)}_p(a, p, e, x_I) + \\mathcal{O}(\\epsilon^2)\n",
"$$\n",
"$$\n",
"\\frac{d}{dt}e = \\epsilon \\, f^{(1)}_e(a, p, e, x_I) + \\mathcal{O}(\\epsilon^2)\n",
"$$\n",
"\n",
"show that $\\dot{p} \\sim \\dot{e} \\sim \\epsilon$. It should then be expected that the rate of change of the orbital parameters $p(t)$ and $e(t)$ is directly proportional to the size of the mass-ratio of the system. We will investigate this below.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OIi3Vhs-LWxZ"
},
"outputs": [],
"source": [
"# set initial parameters\n",
"M = 1e6 # Primary mass\n",
"mu = 1e1 # Secondary mass\n",
"p0 = 12 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# run trajectory\n",
"\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"\n",
"\n",
"# Fix time duration to a very large number (1000 years) so that we obtain the time duration from trajectory\n",
"\n",
"kwargs = dict(dt=10.0, # initial time setp,\n",
" T=1000.0,\n",
" err=1e-10 # integrator error\n",
" )\n",
"\n",
"mass_ratio_vector = 10**np.linspace(-4.0, -7.0, num=10)\n",
"\n",
"tfinal = []\n",
"for count,epsilon in enumerate(mass_ratio_vector):\n",
"\n",
" # get secondary mass for fixed mass ratio\n",
" mu = M * epsilon\n",
" t, p, e, xI, Phi_phi, Phi_theta, Phi_r = traj(M, mu, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"\n",
" # inspiral duration (divide by YRSID_SI to get it in years)\n",
" tfinal.append(t[-1]/YRSID_SI)\n",
" if count%3 == 0:\n",
" print(\"For a mass-ratio of epsilon = {}, time duration is T = {} years\".format(epsilon,t[-1]/YRSID_SI))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zebnOokqLWxZ"
},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.loglog(mass_ratio_vector, tfinal,'*', ms = \"16\", c = 'red')\n",
"plt.ylabel('Time duration [years]',fontsize = 16)\n",
"plt.xlabel('Mass ratio',fontsize = 16)\n",
"plt.title(\"\")\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NlaTrbdJLWxa"
},
"source": [
"### Discussion of plot above\n",
"\n",
"In each trajectory calculation, the initial semi-latus rectum is fixed. The only quantity that is changing is the mass ratio $\\epsilon = \\mu/M$. As $\\epsilon$ decreases, the trajectory evolution time increases. The time of evolut ion is thus inversely proportional to the mass-ratio of the system. We thus see that\n",
"\n",
"$$\n",
"T_{\\text{observation}} \\sim 1/\\epsilon\n",
"$$\n",
"\n",
"Notice that for $\\epsilon = 10^{-7}$, with the parameters given above, it would have taken 396 years to plunge. Whereas with a mass-ratio of $\\epsilon = 10^{-4}$, it would have only taken 144 days. \n",
"\n",
"Cool."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Jqe8hQa4LWxa"
},
"source": [
"## Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZIPgYI0gLWxb"
},
"source": [
"Let us now understand how the inspiral duration changes for different initial eccentricity or different initial semi-latus rectum. Remember the range of validity of the parameters and produce two plots:\n",
"1. Time duration vs initial eccentricity (fixing all the other parameters)\n",
"2. Time duration vs initial semi-latus rectum (fixing all the other parameters)\n",
"\n",
"If you have time, produce also this plot:\n",
"\n",
"3. Time duration vs M/epsilon (M=Mass MBHS and epsilon=Mass ratio). Prove that, for different total masses and mass ratios, the behavior of Time duration vs M/epsilon is always the same (Hint: use two for loops, one for $M$ and the other for $\\epsilon$).\n",
"\n",
"You should find that:\n",
"\n",
"1. The relation between time-duration and eccentricity is weak.\n",
"2. The relation between time-duration and semi-latus rectum is exponential.\n",
"3. The relation between time-duration and $M/\\epsilon$ is perfectly linear."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7yBSyi3TLWxb",
"tags": []
},
"outputs": [],
"source": [
"# Time duration vs initial eccentricity -- It's your turn!\n",
"\n",
"## # set initial parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"\n",
"\n",
"# Fix time duration to a very large number (100 years) so that we obtain the time duration from trajectory\n",
"kwargs = dict(dt=10.0, # initial time setp,\n",
" T=1000.0,\n",
" err=1e-10 # integrator error\n",
" )\n",
"\n",
"# ecc = np.linspace(0.01, 0.7, num=10)\n",
"\n",
"# tfinal = []\n",
"# for e0 in ecc:\n",
"\n",
"# [INPUT YOUR CODE IN HERE]\n",
"\n",
"# tfinal.append(t[-1]/YRSID_SI)\n",
"\n",
"\n",
"# plt.figure()\n",
"# plt.plot(ecc, tfinal)\n",
"# plt.ylabel('Time duration [years]')\n",
"# plt.xlabel('e0')\n",
"# plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sX1t0CmbLWxb",
"tags": [
"hide-cell"
]
},
"outputs": [],
"source": [
"# Time duration vs initial semi-latus rectum\n",
"\n",
"## # set initial parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"\n",
"\n",
"# Fix time duration to a very large number (100 years) so that we obtain the time duration from trajectory\n",
"kwargs = dict(dt=10.0, # initial time setp,\n",
" T=1000.0,\n",
" err=1e-10 # integrator error\n",
" )\n",
"\n",
"# tfinal = []\n",
"# e0 = 0.0 # reset eccentricity\n",
"# pp = np.linspace(7.0, 30.0, num=10)\n",
"# for p0 in pp:\n",
"\n",
"# [INPUT YOUR CODE IN HERE]\n",
"\n",
"# tfinal.append(t[-1]/YRSID_SI)\n",
"\n",
"\n",
"# plt.figure()\n",
"# plt.loglog(pp, tfinal)\n",
"# plt.ylabel('Time duration [years]')\n",
"# plt.xlabel('p0')\n",
"# plt.show()\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hXA_pc_5LWxc"
},
"outputs": [],
"source": [
"\n",
"## # set initial parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"\n",
"\n",
"# Fix time duration to a very large number (100 years) so that we obtain the time duration from trajectory\n",
"kwargs = dict(dt=10.0, # initial time setp,\n",
" T=1000.0,\n",
" err=1e-10 # integrator error\n",
" )\n",
"\n",
"# tfinal = []\n",
"# e0 = 0.2 # reset\n",
"# p0=10.0\n",
"\n",
"# epsilon_vector = 10**np.linspace(-4.0, -6.0, num=10)\n",
"# Mvec = 10**np.linspace(4.0, 7.0, num=10)\n",
"\n",
"# plt.figure()\n",
"# for M in [CODE HERE]:\n",
"# tfinal = []\n",
"# for epsilon in [CODE HERE]:\n",
"\n",
"# # get secondary mass for fixed mass ratio\n",
"\n",
"# mu = [CODE HERE]\n",
"# CODE HERE -- Generate your trajectory for given choices of M and epsilon.\n",
"\n",
"# # inspiral duration (divide by YRSID_SI to get it in years)\n",
"# tfinal.append(t[-1]/YRSID_SI)\n",
"\n",
" # plt.plot(M/epsilon_vector, tfinal)\n",
"# plt.ylabel('Time duration [years]')\n",
"# plt.xlabel('$M/\\epsilon$')\n",
"# plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MxSGoMbhLWxc"
},
"source": [
"### Number of cycles and differences between trajectories\n",
"It is often common to introduce the number of cycles $\\mathcal{N}_\\varphi (t)= \\Phi_\\varphi(t)/(2\\pi)$ to quantify the number of times the compact object orbits around the MBH. This is useful to quantify the difference between trajectories."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5nTwaNuVLWxc"
},
"outputs": [],
"source": [
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# set initial parameters\n",
"M1 = 1e6\n",
"mu1 = 1e1\n",
"\n",
"t1, p1, e1, xI1, Phi_phi1, Phi_theta1, Phi_r1 = traj(M1, mu1, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"\n",
"M2 = 5e5\n",
"mu2 = 5\n",
"t2, p2, e2, xI2, Phi_phi2, Phi_theta2, Phi_r2 = traj(M2, mu2, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"\n",
"plt.figure()\n",
"plt.plot(t1/YRSID_SI,Phi_phi1/(2*np.pi), label=f'mu={mu1}, M={M1/1e6} x $10^6$, $\\epsilon$ = {mu1/M1}')\n",
"plt.plot(t2/YRSID_SI,Phi_phi2/(2*np.pi), label=f'mu={mu2}, M={M2/1e6} x $10^6$, $\\epsilon$ = {mu2/M2}')\n",
"plt.ylabel('Number of cycles')\n",
"plt.xlabel('t [years]')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()\n",
"\n",
"print(\"Final number of cycles of system 1 with mu = {} and M = {} is N_cyles = {}\".format(mu1,M1,Phi_phi1[-1]/(2*np.pi)))\n",
"print(\"Final number of cycles of system 2 with mu = {} and M = {} is N_cycles = {}\".format(mu2,M2,Phi_phi2[-1]/(2*np.pi)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v8VHlEWNLWxd"
},
"source": [
"The two systems have the same mass ratio and the same final number of cycles. However, the number of cycles accumulated per second is different. An EMRI system with larger MBH \"more slowly\" than a system with smaller `M`. Notice that the frequencies are related to the time derivative of the number of cycles\n",
"\n",
"$$\n",
"\\frac{d}{dt} \\mathcal{N}_\\varphi = \\frac{1}{2 \\pi}\\frac{d}{dt} \\Phi_\\varphi = \\frac{1}{2 \\pi} \\Omega_\\varphi\\, .\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oiJLhUuZLWxd"
},
"outputs": [],
"source": [
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# set initial parameters\n",
"M1 = 1e6\n",
"mu1 = 1e1\n",
"\n",
"epsilon = mu/M\n",
"\n",
"\n",
"\n",
"t1, p1, e1, xI1, Phi_phi1, Phi_theta1, Phi_r1 = traj(M1, mu1, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"\n",
"OmegaPhi1, OmegaTheta1, OmegaR1 = get_fundamental_frequencies(a, p1, e1, xI1)\n",
"\n",
"M2 = 5e5\n",
"mu2 = 5\n",
"t2, p2, e2, xI2, Phi_phi2, Phi_theta2, Phi_r2 = traj(M2, mu2, a, p0, e0, xI1,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"\n",
"OmegaPhi2, OmegaTheta2, OmegaR2 = get_fundamental_frequencies(a, p2, e2, xI2)\n",
"\n",
"plt.figure()\n",
"plt.plot(t1/YRSID_SI,OmegaPhi1/(2*np.pi), label=f'mu={mu1}, M={M1/1e6} x $10^6$, $\\epsilon$ = {mu1/M1}')\n",
"plt.plot(t2/YRSID_SI,OmegaPhi2/(2*np.pi), label=f'mu={mu2}, M={M2/1e6} x $10^6$, $\\epsilon$ = {mu2/M2}')\n",
"plt.ylabel(r'$dN_{\\phi}/dt$',fontsize = 20)\n",
"plt.xlabel('t [seconds]')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nWP1D4_YLWxd"
},
"source": [
"## Exercise 2\n",
"Plot the FINAL number of cycles (`Ncycle.append(Phi_phi[-1]/(2*np.pi))`) for different mass ratios. What do you find?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LV2VIW8KLWxe"
},
"outputs": [],
"source": [
"# # set initial parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"\n",
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# Ncycle = []\n",
"# for epsilon in epsilon_vector:\n",
"\n",
"# # get secondary mass for fixed mass ratio\n",
"# mu = [CODE HERE]\n",
"# t, p, e, xI, Phi_phi, Phi_theta, Phi_r = [CODE HERE]\n",
"\n",
"# Ncycle.append([CODE HERE])\n",
"\n",
"\n",
"# plt.figure()\n",
"# plt.loglog(mass_ratio_vector, Ncycle)\n",
"# plt.ylabel('Number of cycles')\n",
"# plt.xlabel('Mass ratio')\n",
"# plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6zP__lkTLWxe"
},
"source": [
"### Fundamental frequencies and harmonics"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EV6zGomMLWxe"
},
"source": [
"Over short time-scales, an EMRI Waveform can thought as a Fourier series where the frequencies $\\omega_{mnk}$ are given by\n",
"\n",
"$$\n",
"\\omega_{mnk} = m \\Omega_\\varphi + n \\Omega_r + k \\Omega_\\theta\n",
"$$\n",
"\n",
"Therefore is possible to plot the frequency evolution of each harmonic given a trajectory. This can be useful to understand which frequency range each harmonic spans."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lylF6NTiLWxf"
},
"outputs": [],
"source": [
"# run trajectory\n",
"# must include for generic inputs, will fix a = 0 and x = 1.0\n",
"a = 0.0\n",
"x = 1.0\n",
"p0 = 10.0 # range [separatrix, 45]\n",
"e0 = 0.3 # range [0.0, 0.7]\n",
"\n",
"# set initial parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"\n",
"t1, p1, e1, xI1, Phi_phi1, Phi_theta1, Phi_r1 = traj(M, mu, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"# get the fundamental frequencies\n",
"OmegaPhi1, OmegaTheta1, OmegaR1 = get_fundamental_frequencies(a, p1, e1, xI1)\n",
"\n",
"M2 = 5e5\n",
"mu2 = 5\n",
"t2, p2, e2, xI2, Phi_phi2, Phi_theta2, Phi_r2 = traj(M2, mu2, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"OmegaPhi2, OmegaTheta2, OmegaR2 = get_fundamental_frequencies(a, p2, e2, xI2)\n",
"\n",
"plt.figure()\n",
"# we need to divide by the total Mass and convert to seconds\n",
"plt.plot(t1,OmegaPhi / (M1 * MTSUN_SI) , label=f'mu={mu1}, M={M1/1e6} x $10^6$')\n",
"plt.plot(t2,OmegaPhi2/ (M2 * MTSUN_SI), label=f'mu={mu2}, M={M2/1e6} x $10^6$')\n",
"plt.ylabel(r'$\\Omega_\\varphi$ [1/s]')\n",
"plt.xlabel('t [s]')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z9ol8FwSLWxf"
},
"source": [
"The system with smaller MBH mass spans more frequencies and evolves more quickly than the system with larger MBH mass. This piece of code will be exceptionally useful for a later exercise. Understand here that, for the $\\phi$ angular frequency $\\Omega_{\\phi}$, we can determine up to what mode in $m$ we are sensitive to when analysing EMRI waveforms."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RYWXnJseLWxf"
},
"source": [
"# Schwarzchild Waveforms in the source frame\n",
"\n",
"So far in this tutorial we have focused on how to generate the trajectory of the smaller body around a massive black hole given an initial set of input parameters. Now, given a set of input parameters that determine a trajectory, we will focus on how to generate the final source frame Schwarzschild waveform. The rest of this tutorial will focus on time domain, frequency domain and time-frequency domain representations of EMRI waveforms. There will also be a few extra, more data analysis focused exercises as one progresses through the tutorial.\n",
"\n",
"We begin with the time-domain representation of an EMRI waveform.\n",
"\n",
"\n",
"## Time Domain Representation of Waveforms\n",
"\n",
"\n",
"At large luminosity distances from the source, the source-frame EMRI waveform is given by\n",
"\n",
"$$\n",
" h_{+} - ih_{\\times} = \\frac{\\mu}{d_L}\\sum_{lmn} A_{lmn}(p(t),e(t),x_{I}(t)) \\, S_{lmn}(\\theta,\\phi) \\, \\exp(-i\\Phi_{mn}(t)).\n",
"$$\n",
"\n",
"*Note:* We will not give a detailed tutorial on the pieces that determine the harmonic content of the amplitudes $A_{lmn}$ or the spin weighted spherical harmonics $S_{lmn}$. That's outside the scope of this work though, if people are curious, feel free to talk to me.\n",
"\n",
"## GPU acceleration\n",
"\n",
"From a data analysis perspective, we require EMRI waveforms that can be generated in around one second. This is due to the immense number of waveform generations required for parameter estimation algorithms, which is likely only a fraction of the amount required for the actual search phase! The speed of EMRI waveforms depend on two crucial ingredients. One is how quickly the trajectory can be built and the other is how quickly the individual harmonics (pairs of modes eg. (l,m,n) = (2,2,0) ) can all be summed. For orbits with significant eccentricities, the numbers of harmonics can be huge (in the thousands)!\n",
"\n",
"The trajectories are quickly computed by integrating on a sparse grid that becomes more fine as the separatrix is approached. The waveforms are quickly generated by exploting GPUs to parallelise the sum over $(l,m,n)$. Compared to single CPUs, speed ups of order $\\sim 2500$ are observed when compared to a single GPU. In this tutorial, using google collab, we will use google collabs basic GPU version.\n",
"\n",
"![image.png](https://github.com/OllieBurke/EMRI_Workshop/blob/main/docs/images/gpu.jpg?raw=true)\n",
"\n",
"The next few cells will focus on generating the EMRI waveform in the time domain."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 430
},
"id": "oWng5IG_LWxf",
"outputId": "dfe8efb3-181c-4b7a-a17e-132fd0fcf195"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# We first define a few key word arguments to help our EMRI generator understand precisely what we want.\n",
"\n",
"# GPU accelerated waveforms, this is an absolute game changer.\n",
"try:\n",
" import cupy as cp\n",
" use_gpu = True\n",
" xp = cp\n",
"except ImportError:\n",
" use_gpu = False\n",
" xp = np\n",
"\n",
"\n",
"\n",
"few_gen = FastSchwarzschildEccentricFlux(\n",
" inspiral_kwargs=inspiral_kwargs,\n",
" amplitude_kwargs=amplitude_kwargs,\n",
" Ylm_kwargs=Ylm_kwargs,\n",
" sum_kwargs=sum_kwargs,\n",
" use_gpu=use_gpu,\n",
")\n",
"\n",
"# parameters\n",
"M = 1e6\n",
"mu = 1e2\n",
"p0 = 12.0\n",
"e0 = 0.4\n",
"theta = np.pi/3 # polar viewing angle in source frame\n",
"phi = np.pi/4 # azimuthal viewing angle in source frame\n",
"dt = 10.0\n",
"\n",
"wave = few_gen(M, mu, p0, e0, theta, phi,Phi_phi0=0.0, dt=dt, T=0.01) # assumes dt = 10.0 for max T = 0.01 year\n",
"\n",
"plt.figure()\n",
"if use_gpu:\n",
" plt.plot(xp.asnumpy(wave.real[:400]))\n",
" plt.plot(xp.asnumpy(wave.imag[:400]))\n",
"else:\n",
" plt.plot(wave.real[:400])\n",
" plt.plot(wave.imag[:400])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VgzOdgkCLWxf"
},
"source": [
"## EMRI Waveforms in different domains\n",
"The output of FEW is a time domain waveform, i.e. the dimensionless strain as a function of time. Later on in this tutorial we will discuss a frequency domain model. We are currently working on trying to output an EMRI waveform directly in the time-frequency domain... watch this space!\n",
"\n",
"Let's have a look at the waveform at different stages of the trajectory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tF4Cl2BKLWxg"
},
"outputs": [],
"source": [
"# parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 10.63\n",
"e0 = 0.3\n",
"theta = np.pi/3 # polar viewing angle in source frame\n",
"phi = np.pi/4 # azimuthal viewing angle in source frame\n",
"dt = 10.0\n",
"# notice that we are providing the distance parameter\n",
"wave = few_gen(M, mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=2.0) # assumes dt = 10.0 for max T = 1.0 year\n",
"time = np.arange(0, len(wave))*dt\n",
"\n",
"# On my local machine, this took me five minutes to execute. I hope you have a GPU enabled!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RT3KYzA9LWxg"
},
"source": [
"## The full signal"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ToedxJHuLWxg"
},
"outputs": [],
"source": [
"plt.figure(figsize=(16,10))\n",
"if use_gpu:\n",
" plt.plot(time/YRSID_SI, xp.asnumpy(wave.real))\n",
"else:\n",
" plt.plot(time/YRSID_SI, wave.real)\n",
"plt.ylabel(r'$h_+$', fontsize = 16)\n",
"plt.xlabel('t [years]', fontsize = 16)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3TK9AVTLLWxg"
},
"source": [
"What we can now do is check to see how the waveform evolves in time. Given our recently built intuition from the trajectory part of the tutorial, we should expect to see the rate of change of frequency evolve with time. We will now compare snapshots of the waveform as the smaller body gets closer to the separatrix."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NwR6XhZSLWxg"
},
"outputs": [],
"source": [
"t, p, e, xI, Phi_phi, Phi_theta, Phi_r = traj(M, mu, a, p0, e0, x,\n",
" Phi_phi0=0.0,\n",
" Phi_theta0=0.0,\n",
" Phi_r0=0.0,\n",
" **kwargs)\n",
"\n",
"j0 = 0\n",
"j1 = 3000000\n",
"j2 = 6000000\n",
"j3 = -400\n",
"\n",
"t_index_j1 = np.argwhere(t > time[j1])[0][0]\n",
"t_index_j2 = np.argwhere(t > time[j2])[0][0]\n",
"t_index_j3 = np.argwhere(t > time[j3])[0][0]\n",
"\n",
"print(t_index_j3)\n",
"\n",
"if use_gpu:\n",
" wave_np = xp.asnumpy(wave.real)\n",
"else:\n",
" wave_np = wave.real"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UxBeCkDcLWxg"
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(4,2, figsize = (14,16))\n",
"\n",
"ax[0][0].set_title(\"Location of small body\", fontsize = 16)\n",
"ax[0][0].plot(t/YRSID_SI, p)\n",
"ax[0][0].plot(t[0]/YRSID_SI,p[0], 'ro', ms = 12, label = 'Small body')\n",
"ax[0][1].plot(time[0:400]/60, wave_np[0:400])\n",
"\n",
"ax[0][1].set_title(\"Waveform\", fontsize = 16)\n",
"ax[1][0].plot(t/YRSID_SI, p)\n",
"ax[1][0].plot(t[t_index_j1]/YRSID_SI,p[t_index_j1], 'ro', ms = 12)\n",
"ax[1][1].plot(time[j1:j1 + 400]/YRSID_SI, wave_np[j1:j1 + 400])\n",
"\n",
"ax[2][0].plot(t, p)\n",
"ax[2][0].plot(t[t_index_j2],p[t_index_j2], 'ro', ms = 12)\n",
"ax[2][1].plot(time[j2:j2 + 400]/YRSID_SI, wave_np[j2:j2 + 400])\n",
"\n",
"ax[3][0].plot(t/YRSID_SI, p)\n",
"ax[3][0].plot(t[t_index_j3]/YRSID_SI,p[t_index_j3], 'ro', ms = 12)\n",
"ax[3][1].plot(time[j3:]/YRSID_SI, wave_np[j3:])\n",
"\n",
"\n",
"for i in range(4):\n",
" for j in range(4):\n",
" ax[j][0].set_xlabel(r't [years]', fontsize = 16)\n",
" ax[j][0].set_ylabel(r'$p(t)$', fontsize = 16)\n",
" ax[0][0].legend(fontsize = 16)\n",
" ax[j][1].set_ylabel(r'$h_+$', fontsize = 16)\n",
" ax[j][1].set_xlabel(r't [years]',fontsize = 16)\n",
" if j == 0:\n",
" ax[0][1].set_xlabel('t [minutes]', fontsize = 16)\n",
" # ax[0][j].set_xlabel('t [years]', fontsize = 16)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OeC6N0nTLWxg"
},
"source": [
"Notice that as the smaller body ventures closer to the separatrix, both the amplitude and frequencies become larger and larger."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ehvq4WBDLWxh"
},
"source": [
"# Optional Exercise\n",
"\n",
"Repeat the same plot as above but instead of plotting the evolution of semi-latus rectum, plot the angular frequency $\\Omega_{\\phi}$."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z-8q7IycLWxh"
},
"source": [
"## Fourier Domain, Signal to noise ratio and Overlaps.\n",
"\n",
"Within gravitational wave astronomy, data analysis is usually conducted in the frequency domain. This is for a multitude of reasons, one being that our probabilistic models used for inferring parameters (such as the likelihood) dramatically simplifies in comparison to its time-domain counterpart. Similarly, analysing gravitational waveforms as a function of frequency may simply reveal more information about the underlying structure of the signal. In this section, we will define the power spectral density of the noise $S_{n}(f)$, a function that qualitatively describes the root mean square average of noise fluctuations from some stationary process. We will then analyse the EMRI waveform in the frequency domain and compute a few useful statistics used within gravitaitonal wave astronomy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qjy_4cMjLWxh"
},
"outputs": [],
"source": [
"# load Power Spectral Density of LISA. This represents how loud the instrumental noise is.\n",
"def PowerSpectralDensity(f):\n",
" \"\"\"\n",
" PSD obtained from: https://arxiv.org/pdf/1803.01944.pdf\n",
"\n",
" Removed galactic confusion noise.\n",
" \"\"\"\n",
"\n",
" L = 2.5*10**9 # Length of LISA arm\n",
" f0 = 19.09*10**-3\n",
"\n",
" Poms = ((1.5*10**-11)**2)*(1 + ((2*10**-3)/f)**4) # Optical Metrology Sensor\n",
" Pacc = (3*10**-15)**2*(1 + (4*10**-3/(10*f))**2)*(1 + (f/(8*10**-3))**4) # Acceleration Noise\n",
"\n",
" PSD = ((10/(3*L**2))*(Poms + (4*Pacc)/((2*np.pi*f))**4)*(1 + 0.6*(f/f0)**2)) # PSD\n",
"\n",
" return PSD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3SGIpmbBLWxh"
},
"outputs": [],
"source": [
"# we take the Fast Fourier transform of the signal\n",
"hp_fft = xp.fft.rfft(wave.real)*dt\n",
"hx_fft = -xp.fft.rfft(wave.imag)*dt\n",
"\n",
"freq = np.fft.rfftfreq(len(wave),dt)\n",
"freq[0] = freq[1]\n",
"PSD_np = PowerSpectralDensity(freq)\n",
"if use_gpu:\n",
" PSD_cp = xp.asarray(PSD_np)\n",
"\n",
"plt.figure()\n",
"if use_gpu:\n",
" plt.loglog(freq, xp.asnumpy(xp.abs(hp_fft)**2), label = 'Frequency domain EMRI')\n",
"else:\n",
" plt.loglog(freq, np.abs(hp_fft)**2, label = 'Frequency domain EMRI')\n",
"plt.plot(freq, PSD_np,label='Approx LISA Sensitivity curve',c = 'black', ls = 'dashed')\n",
"plt.legend()\n",
"plt.xlim([1e-5,1e-1])\n",
"plt.ylim([1e-44,1e-29])\n",
"plt.ylabel(r'$|\\tilde{h}(f)|^2$')\n",
"plt.xlabel('f [Hz]')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c8b2fW5fLWxh"
},
"source": [
"We define the inner product:\n",
"$$\n",
"(a (t)|b (t)) =4 \\Re \\int _{0} ^\\infty \\frac{\\tilde{a} ^* (f) \\tilde{b} (f) }{S_n (f)} \\, {\\rm d} f \\, .\n",
"$$\n",
"where the tilde indicates the Fourier transform, the symbol $^*$ indicates the complex conjugation, and $S_n (f)$ is the one-sided noise power spectral density, which can be interpreted as the size of the root mean square fluctuations at a given frequency $\\Delta n _{\\text{rms}} \\sim \\sqrt{S_n(f) \\Delta f}$. From a practical point of view the spectral density represents our information on the detector sensitivity and the aforementioned inner product can be used to quantify the Signal to Noise Ratio (SNR) of a waveform $h(t)$:\n",
"\n",
"$$\n",
"{\\rm SNR}^2 =(h (t)|h (t)) =4 \\Re \\int _{0} ^\\infty \\frac{|\\tilde{h} (f)| }{S_n (f)} \\, {\\rm d} f \\approx 4 \\Re \\sum_{i=0}^{N/2 - 1}\\frac{|\\tilde{h}(f_{i})|^2}{S_{n}(f_{i})}\\Delta f\\, .\n",
"$$\n",
"\n",
"for $\\Delta f$ the spacing between frequencies defined by $\\Delta f = 1/N\\Delta t = 1/T_{\\text{observation}}$.\n",
"EMRIs are usually considered to be detectable for $SNR\\gtrsim 15$ or so "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YeSroo_RLWxh"
},
"outputs": [],
"source": [
"def inner_product(a,b,dt):\n",
" a_tilde = xp.fft.rfft(a)*dt\n",
" b_tilde = xp.fft.rfft(b)*dt\n",
" freq = xp.fft.rfftfreq(len(a),dt)\n",
" freq[0] = freq[1]\n",
" df = freq[2]-freq[1]\n",
" psd_f = xp.asarray(PowerSpectralDensity(freq))\n",
" inn_prod_cp = 4.0 * xp.real ( xp.sum( xp.conj(a_tilde) * b_tilde * df / psd_f) )\n",
" inn_prod_np = xp.asnumpy(inn_prod_cp)\n",
" return inn_prod_np\n",
"\n",
"# the SNR of the previous waveform is\n",
"SNR = np.sqrt(inner_product(wave.real,wave.real,dt))\n",
"print(\"SNR =\",SNR)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hun7foMxLWxh"
},
"source": [
"### Exercise 3\n",
"Verify that the SNR of an EMRI waveform is:\n",
"- inversely proportional to the distance\n",
"- directly proportional to the secondary mass `mu` for fixed total Mass and duration. Make sure that the source does not plunge before the times provided. Otherwise, you might not get the direct proportionality, i.e. set T=`0.001` and vary $\\mu\\in[10,100]$.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "U66A5vycLWxh"
},
"outputs": [],
"source": [
"# your turn\n",
"\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 10.63\n",
"e0 = 0.3\n",
"theta = np.pi/3 # polar viewing angle in source frame\n",
"phi = np.pi/4 # azimuthal viewing angle in source frame\n",
"\n",
"\n",
"dt = 10.0\n",
"\n",
"# plt.figure()\n",
"# for dd in np.linspace(1.0,5.0,num=10):\n",
"# wave = few_gen(M, mu, p0, e0, theta, phi, dist=dd, dt=dt, T=2.0)\n",
"# SNR = [INSERT YOUR CODE HERE ]\n",
"# plt.plot([INSERT YOUR CODE HERE])\n",
"\n",
"# plt.xlabel('1/d')\n",
"# plt.ylabel('SNR')\n",
"# plt.show()\n",
"\n",
"\n",
"# plt.figure()\n",
"# for mu in np.linspace(1.0,100.0,num=10):\n",
"# wave = few_gen(M, mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=0.1)\n",
"# SNR = [INSERT YOUR CODE HERE]\n",
"# plt.plot([INSERT YOUR CODE HERE])\n",
"# plt.xlabel('mu')\n",
"# plt.ylabel('SNR')\n",
"# plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gxLhmvCFLWxi"
},
"source": [
"We will now introduce another quantity which will be useful for estimating qualitatively how different two waveforms $a(t)$ and $b(t)$ are. We define the ehe Overlap $\\mathcal{O}(a,b)$ between two waveform models $a$ and $b$ by:\n",
"$$\n",
"\\mathcal{O} (a,b) = \\frac{<{a}|{b}>}{\\sqrt{<{a}|{a}>} \\sqrt{<{b}|{b}>}} \\in [-1,1] \\, .\n",
"$$\n",
"The overlap expresses how similar two signals $a$ and $b$ by weighting the product with the power spectral density of the detector $S_n (f)$. If two signals are identical then the overlap is 1. Another quantity often used in gravitational wave data analysis is the mismatch which is defined as 1 minus the overlap."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H-Q8JDFTLWxi"
},
"outputs": [],
"source": [
"def overlap(a,b,dt):\n",
" a_b = inner_product(a,b,dt)\n",
" a_a = inner_product(a,a,dt)\n",
" b_b = inner_product(b,b,dt)\n",
" return a_b / np.sqrt(a_a * b_b)\n",
"\n",
"print(\"overlap of a signal with itself = \",overlap(wave.real,wave.real,dt))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H6bjEz0vLWxi"
},
"outputs": [],
"source": [
"# parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 10.63\n",
"e0 = 0.3\n",
"theta = np.pi/3 # polar viewing angle in source frame\n",
"phi = np.pi/4 # azimuthal viewing angle in source frame\n",
"dt = 10.0\n",
"\n",
"base_sig = few_gen(M, mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=1)\n",
"t_base = np.arange(0,len(base_sig)*dt,dt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nJFLx34nLWxi"
},
"outputs": [],
"source": [
"cmap = matplotlib.cm.get_cmap('inferno')\n",
"fig, axs = plt.subplots(2, 1)\n",
"axs[0].plot(t_base[:100],xp.asnumpy(base_sig.real[:100]))\n",
"axs[1].scatter(0.0, 1)\n",
"axs[1].set_xlabel('phase')\n",
"axs[1].set_ylabel('Overlap')\n",
"axs[0].set_ylabel('$h_+$')\n",
"for phase in np.linspace(0.1, 2*np.pi,num=6):\n",
" wave = few_gen(M, mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=1) * xp.exp(1j*phase)\n",
" t_wave = np.arange(0,len(wave)*dt,dt)\n",
" O = overlap(base_sig.real,wave.real,dt)\n",
" axs[0].plot(t_wave[0:100],xp.asnumpy(wave.real[0:100]),'--', color= cmap(phase/(xp.pi*2)))\n",
" axs[0].set_xlabel(r'time [seconds]')\n",
" axs[1].scatter(phase, O, color= cmap(phase/(np.pi*2)))\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cplOrTIOLWxi"
},
"outputs": [],
"source": [
"# parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 10.63\n",
"e0 = 0.3\n",
"theta = np.pi/3 # polar viewing angle in source frame\n",
"phi = np.pi/4 # azimuthal viewing angle in source frame\n",
"dt = 10.0\n",
"\n",
"cmap = matplotlib.cm.get_cmap('inferno')\n",
"fig, axs = plt.subplots(1, 1)\n",
"axs.set_xlabel('$log_{10}\\delta e$')\n",
"axs.set_ylabel('Overlap')\n",
"vec = 10**np.linspace(-7, -2,num=10) # Here we will add small perturbations to parameters\n",
"logd = np.log10(vec)\n",
"\n",
"for T in [0.1, 1, 2]:\n",
" base_sig = few_gen(M, mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=T)\n",
" O=[]\n",
" for delta in vec:\n",
" wave = few_gen(M, mu, p0, e0*(1+delta), theta, phi, dist=1.0, dt=dt, T=T)\n",
" O.append(overlap(base_sig.real,wave.real,dt))\n",
"\n",
" axs.scatter(logd, (O) , label=f'T={T}')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qEJA6nxCLWxi"
},
"source": [
"### Exercise 4\n",
"Repeat the previous plot by varying each of the EMRI parameters. Which parameter makes the overlap drop faster?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZQxfAvzXSPlJ"
},
"outputs": [],
"source": [
"# # your turn\n",
"\n",
"# parameters\n",
"# M = 1e6\n",
"# mu = 1e1\n",
"# p0 = 10.63\n",
"# e0 = 0.3\n",
"# theta = np.pi/3 # polar viewing angle in source frame\n",
"# phi = np.pi/4 # azimuthal viewing angle in source frame\n",
"# dt = 10.0\n",
"\n",
"# cmap = matplotlib.cm.get_cmap('inferno')\n",
"# fig, axs = plt.subplots(1, 1)\n",
"# axs.set_xlabel('$\\log_{10}\\delta e$')\n",
"# axs.set_ylabel('Overlap')\n",
"# vec = 10**np.linspace(-6, -2,num=10)\n",
"# logd = np.log10(vec)\n",
"\n",
"# T=2\n",
"\n",
"# base_sig = few_gen(M, mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=T)\n",
"\n",
"# Focus on Parameter M\n",
"\n",
"# O=[]\n",
"# for delta in vec:\n",
"# wave = few_gen([INSERT CODE HERE], mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=T)\n",
"# O.append(overlap(base_sig.real,wave.real,dt))\n",
"\n",
"# axs.scatter(logd, O , label=f'M')\n",
"\n",
"# Focus on Parameter mu\n",
"\n",
"# O=[]\n",
"# for delta in vec:\n",
"# wave = few_gen(M, [INSERT CODE HERE], p0, e0, theta, phi, dist=1.0, dt=dt, T=T)\n",
"# O.append(overlap(base_sig.real,wave.real,dt))\n",
"\n",
"# axs.scatter(logd, O , label=f'mu')\n",
"\n",
"# Focus on Parameter M\n",
"\n",
"# O=[]\n",
"# for delta in vec:\n",
"# wave = few_gen(M, mu, [INSERT CODE HERE], e0, theta, phi, dist=1.0, dt=dt, T=T)\n",
"# O.append(overlap(base_sig.real,wave.real,dt))\n",
"\n",
"# axs.scatter(logd, O , label=f'p0')\n",
"\n",
"\n",
"# plt.legend()\n",
"# plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TIMGXwR0WkZA"
},
"source": [
"## Reflection\n",
"\n",
"Let's take a little bit of time to understand exactly what has happened in the plot above. What we are doing here is comparing the \"similarity\" between one base waveform and another waveform with identical parameter sets apart from the eccentricity. What we see here is that if we are to perturb the eccentricity, say, by a very small amount $\\gtrsim 10^{-6}$, only then do we start to see a significant change in the waveform. Loosely speaking, this implies that the EMRI waveforms are immensely sensitive to small perturbations in their parameters."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nZSvC9oyfEhL"
},
"source": [
"# Precision of EMRI Parameters\n",
"\n",
"## The Likelihood Function\n",
"\n",
"As we saw in the previous exercise, the waveforms emitted from EMRI signals are immensely sensitive to perturbations in their parameters. If we were to detect and recover the parameters of an EMRI, we would find that our ability to measure their parameters would be unparalleled by any other gravitational wave source. We now wish to make this statement a little bit more concrete.\n",
"\n",
"For a known form of the PSD, we define the likelihood function\n",
"\n",
"$$\n",
"\\log p(d|\\boldsymbol{\\theta}) \\approx - \\frac{1}{2}(h_{e} - h(\\boldsymbol{\\theta}) | h_{e} - h(\\boldsymbol{\\theta}))\n",
"$$\n",
"\n",
"for $h_{e}$ the exact waveform model and $h(\\boldsymbol{\\theta})$ some model template we wish to use to infer the parameters of the true waveform model. The dimension of the full EMRI parameter space is $\\sim 14$ dimensions and stochastic algorithms are normally used to estimate these parameters. We do not have time for this here, so instead we will show how sensitive the likelihood function is to a single parameter, let's say the eccentricity $e$.\n",
"\n",
"In the cell below, we will plot the log-likelihood."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "m4U02HD7gt2H"
},
"outputs": [],
"source": [
"# parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 10.63\n",
"e0 = 0.3\n",
"theta = np.pi/3 # polar viewing angle in source frame\n",
"phi = np.pi/4 # azimuthal viewing angle in source frame\n",
"dt = 10.0\n",
"\n",
"true_wave = few_gen(M, mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=2.0) # Generate true waveform\n",
"\n",
"# plot likelihood\n",
"spacing = 4.6e-8\n",
"\n",
"e_range = np.linspace(e0 - 3.5*spacing, e0 + 3.5*spacing, 30) # Construct range of eccentricity\n",
"e_range[0] = e0 # Make sure that e0 is contained. Log-likelihood should be zero at this point.\n",
"e_range.sort()\n",
"\n",
"llike_vec = []\n",
"for ecc_val in tqdm(e_range):\n",
" wave_iteration = few_gen(M, mu, p0, ecc_val, theta, phi, dist=1.0, dt=dt, T=2.0) #\n",
" diff = true_wave - wave_iteration # See likelihood equation above.\n",
"\n",
" llike = -0.5*(inner_product(diff,diff,dt)) # Compute likelihood value\n",
" llike_vec.append(llike)\n",
"\n",
"# Plot results\n",
"plt.plot(xp.asnumpy(e_range),np.exp(xp.asnumpy(llike_vec)),'*')\n",
"plt.plot(xp.asnumpy(e_range),np.exp(xp.asnumpy(llike_vec)),c = 'black')\n",
"plt.xlabel(r'Eccentricity',fontsize = 16)\n",
"plt.ylabel(r'Likelihood',fontsize = 16)\n",
"plt.title(r'Plot of likelihood over 1 parameter', fontsize = 14)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xNY0OSS_hFn4"
},
"source": [
"# Reflection\n",
"\n",
"If you look at the x axes, you will see that the likelihood is Gaussian in nature and is peaked at the true value $e_{0} = 0.3$. Due to how incredibly sensitive the EMRI waveform is to the initial eccentricity, we see that the likelihood quickly tapers off and then very quickly tapers towards zero.\n",
"\n",
"## Cheap way to estimate precision in parameters: Fisher Matrix\n",
"\n",
"Instead of plotting the likelihood, there is a very cheap way to estimate our ability to measure parameters of EMRIs. One can compute the Fisher information matrix\n",
"\n",
"$$\n",
"\\Gamma_{ij} = (\\partial_{i}h | \\partial_{j}h) = 4\\mathbb{R}\\int_{0}^{\\infty}\\frac{1}{S_{n}(f)}\\bigg\\rvert \\frac{\\partial \\tilde{h}}{\\partial \\theta^{i}} \\frac{\\partial \\tilde{h}^{\\star}}{\\partial \\theta^{j}}\\bigg\\rvert^{2} df\n",
"$$\n",
"\n",
"The diagonal elements of the inverse Fisher matrix gives a guide on how well one can measure the parameters of the system. We give an example of how a Fisher matrix and estimate for the $1\\sigma$ precision of the eccentricity below.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jKPttl42Xc-C"
},
"outputs": [],
"source": [
"de = 1e-7 # Set step size for numerical derivative\n",
"\n",
"wave_p = few_gen(M, mu, p0, e0 + de, theta, phi, dist=1.0, dt=dt, T=2)\n",
"wave_m = few_gen(M, mu, p0, e0 - de, theta, phi, dist=1.0, dt=dt, T=2)\n",
"deriv_wave = ((wave_p - wave_m) / (2*de)) # Compute Derivative\n",
"\n",
"deriv_wave_fft = xp.fft.rfft(deriv_wave)\n",
"\n",
"gamma_ee = inner_product(deriv_wave.real,deriv_wave.real,dt) # Just a scalar\n",
"\n",
"precision_e = gamma_ee**(-1/2) # Reciprocal and square root.\n",
"\n",
"# precision_e above is our approximate for the 1\\sigma width of the likelihood\n",
"# function.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7gMRZrIWb8RI"
},
"outputs": [],
"source": [
"# Let's check our result!\n",
"precision_e_np = xp.asnumpy(precision_e)\n",
"\n",
"approx_pdf = np.exp(-(e_range - e0)**2 / (2*precision_e_np**2))\n",
"\n",
"plt.plot(xp.asnumpy(e_range),np.exp(xp.asnumpy(llike_vec)),'*', label = 'Likelihood')\n",
"plt.plot(xp.asnumpy(e_range),xp.asnumpy(approx_pdf), label = 'FM approx')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZpW4Gepyj-7M"
},
"source": [
"### Nailed it\n",
"\n",
"As one can see from the plot above, we have managed to cheaply compute how well we expect to measure the EMRI parameters. This is very useful, it can take days, if not weeks to run full Bayesian inference on EMRIs. Hence, to scope out the LISA science of EMRIs, Fisher matrices can be used to cheaply understand how well one can measure parameters of various astrophysical scenarios."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4WPUy5cJkonB"
},
"source": [
"# Exercise 5\n",
"\n",
"Using the Fisher matrix formalism, try to understand how well we can measure the primary mass $M$ for the EMRI given above. If you get bored, try and investigate the other parameters as well.\n",
"\n",
"Using the Fisher matrix, prove that the fisher information on the distance can be written as\n",
"\n",
"$$\n",
"\\Gamma_{d_{L}d_{L}} = \\frac{\\rho^{2}}{d_{L}^{2}}\n",
"$$\n",
"\n",
"where $\\rho^{2} = (h|h)$ is the optimal matched-filtering SNR. Then verify that, indeed, we can measure $\\Delta d_{L} = d_{L}/\\rho.$\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SDzaMAUwlCtr"
},
"outputs": [],
"source": [
"# dM = 1e-1 # Set step size for numerical derivative\n",
"\n",
"# wave_p = few_gen([INSERT CODE HERE])\n",
"# wave_m = few_gen([INSERT CODE HERE])\n",
"\n",
"# deriv_wave = [INSERT CODE HERE]\n",
"\n",
"# deriv_wave_fft = xp.fft.rfft(deriv_wave)\n",
"\n",
"# gamma_MM = inner_product(deriv_wave.real,deriv_wave.real,dt) # Just a scalar\n",
"\n",
"# precision_M = [INSERT CODE HERE]\n",
"\n",
"# print(\"Precision we can expect to measure the primary mass is\", precision_M)\n",
"\n",
"# Analytical solution present at the bottom of this notebook ;)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K9pl9E_4LWxj"
},
"source": [
"## Time-Frequency Domain\n",
"\n",
"So far we have analysed EMRIs in a specific domain: the time-domain. This is just one type of window in which to view the world of EMRIs. It may be more instructive to analyse EMRIs in other types of domains, such as the time-frequency or purely the frequency domain. In the cells that follow, we will plot a spectrogram of EMRIs to truly show the rich harmonic structure offered by this awesome sources."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AeEypGkOLWxj"
},
"outputs": [],
"source": [
"import numpy as np\n",
"# parameters\n",
"M = 1e6\n",
"mu = 1e1\n",
"p0 = 9.55\n",
"e0 = 0.3\n",
"theta = np.pi/3 # polar viewing angle in source frame\n",
"phi = np.pi/4 # azimuthal viewing angle in source frame\n",
"dt = 10.0\n",
"\n",
"wave = few_gen(M, mu, p0, e0, theta, phi, dist=1.0, dt=dt, T=1.0) # Generate our source\n",
"wave_np = xp.asnumpy(wave.real)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HF1F5Yc-LWxj"
},
"outputs": [],
"source": [
"from scipy import signal\n",
"# short fourier transform of the signal\n",
"f, t, Zxx = signal.stft(wave_np, 1/dt, nperseg=5000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"id": "8dknxOrULWxj",
"outputId": "482def7b-e76f-42e1-e8e1-a3fb711dec05"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1600x1000 with 2 Axes>"
],
"image/png": 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