From 99e95637ce70ecb5cf63db8c1a8a5004cbf0f566 Mon Sep 17 00:00:00 2001 From: "Leo C. Stein" Date: Sat, 9 Mar 2019 23:44:18 -0600 Subject: [PATCH] More documentation, remove testing notebooks --- README.md | 31 +- docs/index.rst | 2 +- docs/notebooks/example_22n.png | 1 + docs/notebooks/example_2m0.png | 1 + notebooks/Continued fraction testing.ipynb | 259 -- notebooks/Experiment.ipynb | 2947 -------------------- qnm/__init__.py | 22 +- qnm/cached.py | 11 +- 8 files changed, 54 insertions(+), 3220 deletions(-) create mode 120000 docs/notebooks/example_22n.png create mode 120000 docs/notebooks/example_2m0.png delete mode 100644 notebooks/Continued fraction testing.ipynb delete mode 100644 notebooks/Experiment.ipynb diff --git a/README.md b/README.md index 1594dee..f8932e4 100644 --- a/README.md +++ b/README.md @@ -5,9 +5,20 @@ # Welcome to qnm -Python implementation of Cook-Zalutskiy spectral approach to computing Kerr QNM frequencies. +Python implementation of Cook-Zalutskiy spectral approach to computing +Kerr quasinormal frequencies (QNMs). -TODO basic info +With this python package, you can compute the QNMs labeled by +different (s,l,m,n), at a desired dimensionless spin parameter 0≤a<1. +The angular sector is treated as a spectral decomposition of +spin-weighted *spheroidal* harmonics into spin-weighted spherical +harmonics. Therefore the spherical-spheroidal decomposition +coefficients come for free when solving for ω and A. + +We have precomputed a large number of low-lying modes (s=-2 and s=-1, +all l<8, all n<7). These can be automatically installed with a single +function call, and interpolated for good initial guesses for +root-finding at some value of a. ## Installation @@ -95,10 +106,10 @@ for ind in mode_list: plt.figure(figsize=(16,8)) -plt.subplot(1, 2, 1) +plt.subplot(1, 2, 1) for mode, seq in modes.iteritems(): plt.plot(np.real(seq.omega),np.imag(seq.omega)) - + modestr = "{},{},{},n".format(s,l,m) plt.xlabel(r'$\textrm{Re}[\omega_{' + modestr + r'}]$', fontsize=16) @@ -106,10 +117,10 @@ plt.ylabel(r'$\textrm{Im}[\omega_{' + modestr + r'}]$', fontsize=16) plt.gca().tick_params(labelsize=16) plt.gca().invert_yaxis() -plt.subplot(1, 2, 2) +plt.subplot(1, 2, 2) for mode, seq in modes.iteritems(): plt.plot(np.real(seq.A),np.imag(seq.A)) - + plt.xlabel(r'$\textrm{Re}[A_{' + modestr + r'}]$', fontsize=16) plt.ylabel(r'$\textrm{Im}[A_{' + modestr + r'}]$', fontsize=16) plt.gca().tick_params(labelsize=16) @@ -129,10 +140,10 @@ for ind in mode_list: plt.figure(figsize=(16,8)) -plt.subplot(1, 2, 1) +plt.subplot(1, 2, 1) for mode, seq in modes.iteritems(): plt.plot(np.real(seq.omega),np.imag(seq.omega)) - + modestr = "{},{},m,0".format(s,l) plt.xlabel(r'$\textrm{Re}[\omega_{' + modestr + r'}]$', fontsize=16) @@ -140,10 +151,10 @@ plt.ylabel(r'$\textrm{Im}[\omega_{' + modestr + r'}]$', fontsize=16) plt.gca().tick_params(labelsize=16) plt.gca().invert_yaxis() -plt.subplot(1, 2, 2) +plt.subplot(1, 2, 2) for mode, seq in modes.iteritems(): plt.plot(np.real(seq.A),np.imag(seq.A)) - + plt.xlabel(r'$\textrm{Re}[A_{' + modestr + r'}]$', fontsize=16) plt.ylabel(r'$\textrm{Im}[A_{' + modestr + r'}]$', fontsize=16) plt.gca().tick_params(labelsize=16) diff --git a/docs/index.rst b/docs/index.rst index 9879d11..cc590dd 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,7 +1,7 @@ qnm's documentation: index ========================== -TODO point people in the right direction here +For a quick start, take a look at the `usage section `_ of the Welcome node. .. toctree:: :maxdepth: 2 diff --git a/docs/notebooks/example_22n.png b/docs/notebooks/example_22n.png new file mode 120000 index 0000000..da004fb --- /dev/null +++ b/docs/notebooks/example_22n.png @@ -0,0 +1 @@ +../../notebooks/example_22n.png \ No newline at end of file diff --git a/docs/notebooks/example_2m0.png b/docs/notebooks/example_2m0.png new file mode 120000 index 0000000..f0e749d --- /dev/null +++ b/docs/notebooks/example_2m0.png @@ -0,0 +1 @@ +../../notebooks/example_2m0.png \ No newline at end of file diff --git a/notebooks/Continued fraction testing.ipynb b/notebooks/Continued fraction testing.ipynb deleted file mode 100644 index 2eb5632..0000000 --- a/notebooks/Continued fraction testing.ipynb +++ /dev/null @@ -1,259 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing the Lentz method for continued fractions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import numpy as np\n", - "import itertools" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append(\"..\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from qnm.contfrac import lentz, lentz_gen" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.4142135623638004, 4.488287519421874e-11, 14)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def rt2b(n):\n", - " if (n==0):\n", - " return 1\n", - " return 2\n", - "\n", - "def rt2a(n): return 1\n", - "\n", - "lentz(rt2a, rt2b)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.6180339887802424, 6.785971784495359e-11, 25)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "phia = rt2a\n", - "phib = rt2a\n", - "\n", - "lentz(phia, phib)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3.1415926535897922, 8.881784197001252e-16, 21)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def pia(n):\n", - " if (n==1):\n", - " return 4.\n", - " return (n-1.)*(n-1.)\n", - " \n", - "def pib(n):\n", - " if (n==0):\n", - " return 0.\n", - " return 2*n-1.\n", - "\n", - "lentz(pia, pib, tol=1.e-15)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.7182818284590464, 3.3306690738754696e-16, 16)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def e_a(n):\n", - " if (n==1):\n", - " return 1.\n", - " return (n-1.)\n", - " \n", - "def e_b(n):\n", - " if (n==0):\n", - " return 2.\n", - " return n\n", - "\n", - "lentz(e_a, e_b, tol=1.e-15)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6420926159343308\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.6420926159343306, 1.1102230246251565e-16, 9)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def cot1_a(n):\n", - " return -1.\n", - " \n", - "def cot1_b(n):\n", - " return 2.*n+1.\n", - "\n", - "print(1./np.tan(1.))\n", - "lentz(cot1_a, cot1_b, tol=1.e-15)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1.4142135623638004, 4.488287519421874e-11, 14)\n", - "(1.4142135623638004, 4.488287519421874e-11, 14)\n", - "func=1.59473705292 sec, gen=1.83648109436 sec\n" - ] - } - ], - "source": [ - "def rt2b_g():\n", - " yield 1\n", - " for x in itertools.repeat(2):\n", - " yield x\n", - "\n", - "def rt2a_g():\n", - " for x in itertools.repeat(1):\n", - " yield x\n", - "\n", - "N_trials = 100000\n", - "\n", - "start_f = time.time()\n", - "for _ in range(N_trials):\n", - " v = lentz(rt2a, rt2b)\n", - "end_f = time.time()\n", - "\n", - "print(v)\n", - "\n", - "start_g = time.time()\n", - "for _ in range(N_trials):\n", - " v = lentz_gen(rt2a_g(), rt2b_g())\n", - "end_g = time.time()\n", - "\n", - "print(v)\n", - " \n", - "print(\"func={} sec, gen={} sec\".format(end_f-start_f,end_g-start_g))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Experiment.ipynb b/notebooks/Experiment.ipynb deleted file mode 100644 index e81096b..0000000 --- a/notebooks/Experiment.ipynb +++ /dev/null @@ -1,2947 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "rl = logging.getLogger()\n", - "rl.setLevel(logging.INFO)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append(\"..\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.7.15 (default, Jun 21 2018, 11:39:23) \n", - "[GCC 4.2.1 Compatible Apple LLVM 9.1.0 (clang-902.0.39.2)]\n" - ] - } - ], - "source": [ - "print(sys.version)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import qnm" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "mpl.rc('text', usetex = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exploring values of inversion error function" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-3.7832264403820357-1.8368923380529323j)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qnm.radial.leaver_cf_trunc_inversion(0.4-0.2j, 0., -2, 2, 4.+0.j, 0, 300, 1.+0.j)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "Npts=100\n", - "omegar=np.arange(0.2, .5, .3/Npts)\n", - "omegai=np.arange(-0.2, -0., .2/Npts)\n", - "Or, Oi = np.meshgrid(omegar, omegai)\n", - "Os = Or + 1.j*Oi" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "leav1 = lambda omega: qnm.radial.leaver_cf_trunc_inversion(omega, 0., -2, 2, 4.+0.j, 0, 300, 0.j)\n", - "leavf = np.vectorize(leav1)\n", - "Leavers = leavf(Os)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "def leav1(omega): \n", - " inv_err, cf_err, its = qnm.radial.leaver_cf_inv_lentz(omega, 0., -2, 2, 4.+0.j, 0)\n", - " # logging.info(\"Lentz terminated with cf_err={}, its={}\".format(cf_err, its))\n", - " return inv_err\n", - "leavf = np.vectorize(leav1)\n", - "Leavers = leavf(Os)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 252, - "width": 364 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "CS = plt.contourf(Or, Oi, 1/np.abs(Leavers))\n", - "plt.colorbar(CS)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3.7259203780062347+0j)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qnm.angular.sep_const_closest(4., -2, 0.1, 2, 20)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "Npts=120\n", - "orrange=[0., .4]\n", - "oirange=[-2.3,-0.75]\n", - "omegar=np.arange(orrange[0], orrange[1], (orrange[1]-orrange[0])/Npts)\n", - "omegai=np.arange(oirange[0], oirange[1], (oirange[1]-oirange[0])/Npts)\n", - "Or, Oi = np.meshgrid(omegar, omegai)\n", - "Os = Or + 1.j*Oi" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "a = 1.e-6\n", - "s = -2\n", - "m = 2\n", - "l = 2\n", - "\n", - "n_inv = 6\n", - "Nr = 300\n", - "r_N = 0.\n", - "l_max = 20\n", - "\n", - "def leav1(omega):\n", - " inv_err, _, _ = qnm.radial.leaver_cf_inv_lentz(omega, a, s, m,\n", - " qnm.angular.sep_const_closest(qnm.angular.swsphericalh_A(s,l,m),\n", - " s, a*omega, m, l_max),\n", - " n_inv, N_max=400)\n", - " return inv_err\n", - "\n", - "leavf = np.vectorize(leav1)\n", - "Leavers = leavf(Os)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-6.458744650217341e-11-1.8932675117344805e-05j)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "leav1(-2.j+1.e-30)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 578, - "width": 261 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10))\n", - "CS = plt.contourf(Or, Oi, np.exp(-np.abs(Leavers)))\n", - "plt.gca().invert_yaxis()\n", - "plt.gca().set_aspect('equal')\n", - "plt.colorbar(CS)\n", - "plt.savefig(\"test.png\", bbox_inches=\"tight\", dpi=300)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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0ma78MrB9EAQEgJGtZfsYzrHW3pj1xLKvZ0GwJOlRg2v4rMXSx2CICLw+nfLi0x9cX0otxAOASNzO/lwrCowxNyXtDHc5zRERARh6U2V+OmUI+yAICMB/Rz//4swt4zE4+vn/kXS8N2/iUFfx7gpr7X7Np234NIWQ2BOBTAifjUFAAIjIQ0l36p7xkJ0J8fmwl9Qck4hEFf8v4eBwWR8/+cbh1cxHQACIhTHmvqT71toHNb9/RdJR20OphkREeG6opYzi3Rg+n05JPACIiTHmtqTHxYAwxty01s7aXLki6UYWH7lL2Z8/M8ZsaBIlo9+xQUQMbOPynutLKFU868LXuzIICAAxyTZGLllrv5j60i3NuEMjC4wzX8+mE5uSPufYazjl434IAgJATIwx65ocELVtjNnKHl7SZKLwsvB9S5L+JulB8YjsEvnZEpdmfM/giIgWrl95McrPGWopI7+lM7fw5C+D/Jy2CAgAEXqoybLE3ZKvTd9xsa/yg6kkScaYh5LyjW1b2XLG5yxnYBT5LZ2+IR4AxMpau1rz+44kzfzeks/gcIaI8NSQZ0McHC579wFbBAQAhIeIGJCvmyonH7DlzxIGAQEAYeKwKQ8NOYXYPl73aimDgACAcDGJGIgPU4iyY2d9+bRO4gEAwkdENDT0nRl9TiHKgsGH/RAEBADEgeWMiB0cLp85VErK90O4+6AtAgIA4sEkYgBtlzL6nEJMfzZGztXplMQDAMSHiIhY+WdijB8RBAQAxImIaKDOfggfphBS9YFSYx9xTUAAQLyIiEhVbaAccz8EAQEAcSMieuTLFCL/bAxXB0oRDwCQBu7OqGmoWzuHOljK1YFSBAQApINJRA1D7YXoEhBlB0nldp6vOfl4bwICANJCRPRg7ICQqk+e3Li8N9kPoZ87vX4TxAMApInljDnmTSFcHG+9fbx+7hCp3M7ztVEPlCIgACBdTCI6cLWRMp9CVIXEWAdKERAAkDYiYoZZUwiXd2IcHC5XHCQ1cfHpD51/xizEAwBAIiJacX0r5+KzBe6+AAA4R0Q05Dog8rsyZt19MdR+CAICAFBERDTgOiCkN8dZT4fC0G/wBAQAYBoRUYMP8ZAb+zhr4gEAUIWImGOsgJh1eFTRmMdZExAAgFmIiBnGnEDMOjxqbMQDAKAOIqJHXZYvDg6XS28pnY6Loe/KICAAAHURERWafOBW170P+VJG1eFRRWX7IfpAPAAAmiIiSphTU/m1ITZL5kdV1zHEfggCAgDQBhExw1Af0z3t4HB5tKOqpxEQAIC2iAgPLD5bmHlU9avVxd5/JvEAAOiKiCjx9ul4PyvfD5Gf81D25j7rdMo2CAgAQB+ICMfyEyhzQ36EN/EAAOgTEdFC3YOhZsn3W1SdQNk3AqKZPpeQ+p4kAYAviIgWqg6GKlN1WFQeIkOfQEk8nDfEHpMuP4/IABAqIqKh4pkOdc6SaBIcfSIexo+Ftsquk7AAEAIioqGd52uvD4WqczjUPEPc2pliQIQSDHVN//MQFQB8REQ0dHC4XPtgqDpm3drZVErxEFs0zFP85yUoAPiCiGihj8+v+P7a5FTMvu7GiD0gUouGWQgKAL4gIhrYPl6fezDUtKo3v74+SCvWeCAa6iEoALhERDSQn+mw8OTb2m/eQ/2HPcZ4IBy6yX/9iAkAYyEiGiie6TDkoVDzxBQQhEP/iAkAYyEiSrx98vPkzot/Pv814qEbomE8xASAoRERJcwPp1p8tnDuZMo+78poIvR4IBzcIiYADIWImGH6oKi+NkPWFXI8EA7+ISYA9I2IqPD+N1YHV88eJjXGZ1xIxAOGRUwA6AsRUeHi0xN9f+29M48N+RkXUrjxQDiE6dXqIiEBoBMiosLCk2/1/uo/jfbzQgsIwiEOTCUAdEFEOBZSPBAO8WIqAaANImKGIf+jSjzAN4QEgKaIiBmGOBMilHggHNJESABogogYSQjxQDhAIiQA1PeW6wtIge8B8Wp1kYDAGfz7AKAOJhED8jkeeJPAPEwkAMxDRAzA13ggHNAUIQFgFiKiZ74FBOGArggJAFXYE9EjnwKCfQ7oE/8uASjDJKInPgQE/6HHkJhIAJhGRPTAZUAQDhgTIQGgiIjoyEVAEA5wiZAAkCMiKuRxMOvUyrEDgngAAPiEiChh31uQlP0flz4qDYmxAoJwgI+YRgCQiIhKr1YX9f01I2lRy0/Ofm3ogCAcAAAhICJK/LT4lr6/ZnRy9VTSgpYLXxsqIAgHhIZpBAAiYo6Tq6c6/XiypNF3QBAOAICQcdhUiZ8WJvFw/coLSf2+2eeHQBEQiAH/HgNpczaJMMasSLqX/e0nkv4s6Z619qjm85ckbUl6mj30oaTP6z5/nutXXmjj8p52JP1dv+o8heA/tgCA2DiJiDwgrLV3Co89lvS1pA9qvsxjSbestbvZ829K+qOkG12vzy5YbVze0+aFXUnSV9d+rYtP5zypBOHQzmRDa7/e/8b2/pqYYG8EkC5Xk4gta+2tqcd+L+mxMWbLWnuv7Ek5Y8xdSft5QEiStfaRMWbJGHPbWvugy8UtLPz4+q83L+zqy6u/bfR84qHcEHHQx88mMACgHVcRsWmMeWqtXc0fsNbuGmMk6WaN5/9O0qOSx3cl3ZLUKSJOT999/dfbx+tafLYg6YeZzyEcJlyGQltl10xYAMB8riJit+LxI0lLNZ6/rvKIeClps84FZMsnZdbqPF9KOxxCjIUmiv98BAUAlHMSEdbac/sWso2SSyqPg+L3rWR/+d2M71nqssHSnBrtPJ+0RP7nVIMh9liog6AAgHI+nRNxO/vzzP0QejOpKIuE2uFQFjLS6wnFet3XiQWxUE/+60RMAIAnEZFNF7Y0udtiv+bTZi17XFKDoChzcLisnezPMc0giIV+EBMA4ElESHoo6Y61drvG9+Zx8GHJ1/KweNnLVQWOYBgeMQEgZa0jIpsePGzwlEdlt24aY+5Lut/gtsxZgXBJkvo4cGrx2YIOCp+a8f014+0bBbHgHjEBIEWtIyJbduh0sJMx5rakx8WAMMbctNZWbq601h4ZY3ZVvpwxd2NmU5PbOydchgShEAafYxMA+uby2OubkpastV9MfemW5ofAHzQ5K2LaiqT7PVyepLMBkRtypz6hEIcUpxKcWgmkydWx1+uavNlvG2O2soeXNFmOeFn4viVJf5P0oHhEtrX2C2PMHWPM+tSx1yqJksHUPaSIOEhTSlMJAgJIk6tJxENNpgZ3S742vW9iX+V3WtyQtJXdkrkkaVU9fG5GVwQDilIKCQDpcXXY1Or873q9QbL0e7Ov3Sn7GuATQgJArN5yfQFACmKeULGUAaSLiABGEnNIAEgTEQGMiJAAEBMiAhhZTCHBUgaQNiICAAC0QkQADsQwjWAKAYCIAByJISQApI2IANAYUwgAEhEBOBXiNIKAAJBz9gFcITi5enrm78s+kAtICQEBoIiIaGA6KvoQWpgM8WswBp9/nUM5FpuA6MdPi29Jf3d9FUA/iIgKY71ZhvqmHJqqX2ef48InBEQ/Xq0uSv+v66sA+kNEIGksWc1HQPTj1eqi60sAekdElLAL/o+WMQxXUeHrkgYB0Q8CArHi7gxghpOrpyw5oRMCAjFjEgHUUAyJVJY8mEJ0R0AgdkQE0FDsQUE89IOAQApYzgA6iG25g4AA0ASTCKAHJ1dPg55KEA/9YgqBVBARQE/yiURIMUE89I+AQEpYzgB61nZ5Y+zP0SAg+kdAIDVMIoAB+Ly8QTwA6AsR0dD1Ky9qf+/B4fKAVwLf+RgSBMRwmEIgRURETU3iYdZzCIu0+BISxMOwCAikioiYo008NHk9oiJ+rjdcEhAAhkJEzNB3QNT5GURFvMaOCeJhHEwhkDIiwjNERfyqljj6+gAu4gHAWIgIzxEVcep7rwTh4AZTCKSOiJhh4/Lemb/feb7m6EreYLMmiogHAC4RESXMafmhP8Wo8CEocoRFeogH95hCAEREa3lQ+BQTRfM2hYYYGWNsdG2j7a9l0yUNwgGAb4iIGTYv7Gr7eH3m9/geE1V8fUMO0axfyz5ijXjwD1MIYIKIKPH26Zs3hs0Lu5IUbUxgWMXAmBUUZXdmEA8AfMcHcPVs4/LeuQ2ZgDQJiumpRdWHdREQ/mIKAbxBRNSUTyTqIiRQpSwmiggIAKEgIgZESGCWPCSK0wgCwm9MIYCziIgGmk4jJJY3MBsbXAGEjIgYCSGBKtevvND318rPJgEAn3F3RkN1bvuswh0caZoXkPz7EAaWMoDziAgHNi7v8cYRsaZTp43Le/pKv2Y/BIDgEBEtdJlG5JhKxKWv5aqFJ9++/uvTjz/q5TXRHVMIoBwR0VIfISEREyFrGw5lG3S/vPrbc4/lQUFMAPAVEeEJljjC0HXi0OYOH2ICgK+4O6ODNm8Is3AHh798uFW3uNSB8bCUgT4ZY24aY3ZaPnfJGHPfGHM3+2Or7+trikmEZ1je8IfraAAQB2PMiqQtSS8l3Wz5GuuSHkrasNbuG2OWJP3RGHPXWvtFf1fbDBHRUV97I6YRE+MbOhr6mFwtPPmWZY0RMYVAH6y1+5JuSZIx5r7ahcRDSfez18qtSNqv+P5REBGeY6/EsMaaNvS59EVIAGkxxtyWtFKcOFhrjyR94O6qJoiIHgw1jcgxleiPiyWKvvfOSIQEkJg7cjxxqEJEBISYaIe9DWiDpQyUWDPGPC77grX2xoA/d13SI2PMTUkbkr6TtCrpXjaRcIaI6MnQ04ii4psiQXGWT8EwxASiiGkEEL9sA6UkXZK0ZK29lz2+LulrY8xvXIYEERG41IPCp2goGjogcoQEfHT843vR/ffo+Mc/STreG3jiUOZS9ucVa+12/qC1dtcY81LSl8o2bbpARPRozGlEmdiDwtdgmDZWQOQIif6xlAFfZLdzStKfS768r5a3jPaFiIhU6EERSjBMaxsQi88WtPDkLz1fDYBIHGV/lFmqeHwUREQCpt+QfYmKUEOhb31Mr5hGAFHbV3UssLEyJq6XNOqY9+bdNTJSjYO2U4id52t6/xvb89WgLZYy4KHPNdn7MO0TSf868rWcQUTgnFQjoK2ueyAODpe1+vSkp6sB4LlLVV/I7sT4m6QH1to7+ePW2m1jzGfGmNvW2gfZ9+Z7Ie4NerVz8AFcAxh7Yx3c6eP3erIfgg/XAmJV+OCsHU02Qq4YYx4bYx5mt2oW7atkiSK7K+RG9jpbmtyR4fT2TolJBNBaHwHR59IX+yK6YykDQ8je6O/U/L7VGV+f+xpjYxIBtNDXtIn9EABCRkQMhCWNOG1e2O319/bgcFkXe9wPwbIIgDEREUBNQ4Qh+yH8wVIG0Bx7IoA5hpoqbR+vs5QBIGhEBDDDEAGRb6b05dAvMIUA2iIiBhTCwVMoN+T0IY8HzocAEDoiApgy5KbYnedrOjhclsTnZfiCKQTQHhEBZMa4oyYPCEnshwAQPCICyRv7dtzFZwvZXxERAMJGRCBZY8fD9vG6Fp8tvJ5A9Hk+BNphKQPohohAclwdBJafTpnHA+dDAAgdEYFkuD5FNL8bg3gAEAsiAtFzHQ857sbwC0sZQHdEBKLlMh44HwRACoiIAfFG4obryUPxQKkibun0B1MIoB9EBKLgOhyKygKC0ykBxIiIQNB8iodc8UCp3Fj7IU4//mjwnxE6phBAf4iIgbCUMRwfw6HozWFSABA3ImIABET/fA+HXP57z/4HPzGFAPpFRPSMgOhPKOFQNH2gFADEjIjoCfHQjxDDoYgDpfzFFALoHxHRAwKivdCjYZrLA6XYVAlgbEREiZ8Wsh32/1z+daKhm5DDgd/7MDGFAIZBRMzAG0Y/Qo6GafkZEBuX90q/5mpDJVMIAC4QEehVTMEwCwdKhYMpBDAcIqKEXeD2vDpSCYbc9vF66UFSOVf7IZhCAHCFiMBcqcVClZ3naxwkFRimEMCwiIgZdp6vla59x4ZIqI9DpADgDSJiDh9Dgjd9N3zc88BSRjWmEMDwiIgaxgwJAsFP28frr/c8VL1xc8AUgNQQETUNFRJEQxiKt2/6EgtMIaoxhQDGQUTU1HdAEA9uVZ0BMuv3xafPwyAgqhEQwHiIiBr6DAjiwW+Z85V+AAAgAElEQVRVcXFwuKyPn3wz8tUAgN+IiBEREP4oOyxqFp9u7WQKUY0pBDAuImKOvqYQBIQ/5h0aVeaXntzaSUBUIyCA8RERJcypkURAxKrNoVEXn/4w0NUAQLiIiIEREP45OFxuPFnw4Y4MphDVmEIAbhARJd4+7ed1CAg/LT5bKJ0s+PxGREBU8/n3DYgdETEQAsJPsz7e3adbOAEgBEREibdPfm79XOLBb/mhUT4sT9TFFKIaUwjArbdcX0AsNi/sEhADmzVFqOvgcDmoiQMBUY2AANxjEtEB0TC+riGRf/5FCAiIagQE4AciYgYiwS9ND4gKGQEBIARERImfFt/S9SsvXF8GCtocEFXGl0OjZiEgZmMKAfiDiEAw+jh62vdDowiI2QgIwC9EBIJQ/CjuOr6/Zkof9/muDAJiNgIC8A8RgSAcHC7rlw2+v0lw+ICAABAiIgLe2z5erzxlMgYExHxMIQA/EREIQmgHRNVFQMxHQAD+4rApeG/n+VpQB0TVRUDMR0AAfmMSgUH0cbpk7uBwWatqfxS5jwiI+QgIwH9EBAbR58FQIZ0yWQcBMR8BAYSBiEDv+joYKhfCAVF1EA/1EBBAOIgI9G7n+VovB0NJ0snV0yj2QxAQ9RAQQFiICPTu4HD53PSg6vCneSYxEvatnQQEgFgREejd5I3/bER0Ofwp1Fs7iYdmmEIA4SEi0Kvt43W9/42NYgmiCwKiGQICCBMRgV7ld2WEOj3oAwHRDAEBhIuIQK8ODpe1mugUgnhojoAAwkZE4LU+DoiK7UyHugiIZogHIA5EBF7r44Co0D49syvioTkCAogHEQFJ/R0Q1eTjukNGPLRDQABxISLw2qwDok6untZ6jRTuyiAg2iEggPgQEZA0fymj7gmUMe+HIB7aIyCAOBERkFR+yiQmiIduCAggXkQEtH28rsVnC7r4NOzjpYdAQHRDQABxIyIgaXJXRcoHRE0jHrohHoA0EBHQzvO1JDZE1kE8dEdAAOkgIiLS9rCog8Nlrernnq8mLMRDPwgIIC3OI8IYc1PSPWvtRsPnLUn6TNJ3kj6UtCRpy1q73/9V+m/7eL31YVGpnjIpEQ99IR6ANDmJCGPMiqQtSS8l3Wzx/DwY7kw99kdjzKfW2qPeLjYQO8/Xah8Wdf3KizN/n9opkxLx0CcCAkiXk4jIpgW3JMkYc1/NQ+K2pIdTr3lkjPmDpH+R9KCP6wxJk9Mmp783pQ/MIh76RUAAaXO+nNHSavbHo6nHjzRZ1pjLGPO44kvdP0DCgbqHQZWL/9ZO4qFfxAMAKdyIeCzpvjFGxSUNSXckferompzZPl7vtCQR+62dBES/CAgAuSAjwlr7wBhzR9LtfGOmpN9JulV3P4S19kbZ49mEovtnYo8o31DJbZpnEQ/9Ih4ATAsyIqRJBBhjHkra1GR/xINU78w4OFzW6tOT6CcKdREP/SMgAJR5y/UFtGWMuS3pT5JuSNrXZCrxNLtLIxn5kdUExCQeCIh+vVpdJCAAVApyEmGMuSvpQ2vtveyhVWPMlqS7kr5UdudHSNoeFCWleYtmEeEwDOIBwDytIyI76+Hh3G9841HhTb+rz6y1HxQfsNbeM8Z8p8n5E8Fpe1CUlO5eCOJhGMQDgLpaR0S2/6B0c+KQsuWKl2Vfs9Z+kU0kkpHikdXEwzCIBwBNBbeckR0qdckYszR9J0YWGLuOLq217eP1RodFFaV0ZDXxMAziAUBbPkTEpaovZFHwN03uvCieB/F7TZZSpj9vI8j9EDvP11ofFpXCfgjiYTgEBIAuXH12xpImexdWJH0iaSk7n2Ff0ufW2uI0YV+Tkyhfs9ZuG2OOsls889s6lzT5IK/gbvM8OFzWL1vGQMz7IYiH4RAPAPrg6rMzjjQ5XbLO961WfO2Rzh97HaTFZwu6+DT+o6frIh6GQzwA6JMPyxlJy4+s5pwH4mFIxAOAIRARjnW5tTMmBMQwiAcAQyIiOuhyQFQuP7I6VcTDMIgHAGMgIhxL6RbNIuJhGMQDgDERER30sRSRwi2a0wiIfhEOAFwhIlrqckBU0S97uJZQEA/9Ih4AuEZEdND2gKiiVG7tJCD6QzwA8AUR0dLO87VeliJiv7WTeOgH4QDAR0RES6nfVVEHAdEd8QDAZ0REC9vH68neVVEXAdEe4QAgFERESyneVVEH8dAO4QAgREQEekNANEc8AAgZEYFeEBD1EQ4AYkFEoDMCYj7CAUCMiAh0QkBUIxwAxI6IQGsExHmEA4CUEBFohYB4g3AAkCoiAo2lHhBEAwBMEBFoJNWAIBwA4DwioqXvrxl1/wzPsKQWEIQDAMxGRLSweWFXX1797dw31dg/XCs2RAMANENEtHT9ygu9Wv3VzO9ZfjLSxYwg1ikE4QAA7RERLW1c3tNX135d+fWYPlsjtoAgHACgH0REBydXT2d8dSG5PRO+IhoAYBhEREubF3a1c2Wt8usHWtbpxx8Fvy8i1CkE4QAAwyMiBpLvmYhpX4TvCAcAGBcR0cHG5b2ZX//q2q+DXtIIYQpBOACAO0REB5sXdmd+/St9OtKVpId4AAD3iIgBnVw9DXZfhI9TCMIBAPxCRAwo3xdxUfPfkEMMjTEQDgDgLyJiQG/Okpj/RnhRYU4shkI8AID/iIgBbV7Y1Vf6VN9fMzW+e9GbOzlcLWUQDgAQFiJiYLMPpCpK93Aq4gEAwkREDOz6lRc6OFzW9SsvZn7fX59VH6EdK+IBAML2lusLiN3G5b25ASG9uZPDtTGu4dXqIgEBABFgEjGweWdJ5HakJE64JB4AIB5ExAjqhkToJ1zOQjwAQHyICE/kd3LEhngAgAljzJKkLUlPs4c+lPS5tfaowfM/k/Rd9twlSVvW2v0BLrcWIsIjXfdF+HbOBAEBAGc8lnTLWrsrScaYm5L+KOnGvCfmAWKtvTP12B+NMZ/WDZG+EREeyU+4bMuX/RTEAwCcZYy5K2k/DwhJstY+MsYsGWNuW2sfzHmJ25IeFh+w1h4ZY/4g6V8kzXv+IIgIj7w54bId1/spiAcAqPQ7SY9KHt+VdEvzI2A1+2P6NY40WdZwgojwSHFfRP1Dqt5w+WFfBAQAzLSu8oh4KWmzxvMfS7pvjFFxSUPSHcndhjoiwjNt4iH3atXN0dkEBIBErBljHpd9wVpbua/BGLOS/eV3M75nada+BmvtA2PMHUm3s70U9zSZbtxytR9CIiK8U+dgqip/V/v9FG0REAAwV77cUPZmXzsArLU3jDEPNZlcPJT0wOWdGRIR4Z2Ny3utnzv2ORMEBIAyp6fv6uDQ9S6tfp2evitJe7MmDjXM2rtwSXOCwhhzW9KfJH2uSUTkU4kb3J0BSfUPpirz5dXfjrYvgoAAgNryN/gPS76Wh8XLWS+Q3d3xobX2XvbQqjFmS9JdSV9qsjlzdERERPJbRJvsi2hzLgUBAQCNzAqES9Lkds05r/GZtfaD4gPW2nvGmO80OcDKCSIiIhuX9/Qf9atBP0SLgACAZrLzHHZVvpyxpPK7Nl7LDpUqDRFr7RfZRMIJIiIimxd29dW1TyW1e6O/+PSk8mvEAwB08gdN7qaYtiLp/qwnZhFyqewOjiww2q+Dd0RERObk6qne/+ZdSdL310zDZy+WhgQBAQDdZBODO8aY9aljr2Wt/SL/viwK/qbJnRfF8yB+r8lmyo2pl3a2H0IiIqJz/coL/bXGqZfl51Es6OLTkocBAH24IWkrO2tiSZMTKMvu9tjX1J0a1tptY8xRdotnflvnkqR7fAAXerNxeU9fXm1+a9X1Ky/012ftj9wGAMyWLUXcqfE9qxVfe6Q5+yfGRkREZvPCrnaurLV6btl0YqiljOZLLW68/411fQkA4C0iIkJtD6zakfRq9VczN1jOE0oc1DX0Pw+RAiBkRESEuhxY9dW1X9faFxFbLLjSx68jIQLAFSIClaaXMggHP3X5fSFAAHRBROCMfF9EMSCIh3g1/b0lOgAUERE4h4BAlSb/PhAcQPyICJxx/cqL1x8pTkCgizr//hAaQNiICJxDPGAs8/5dIzIAvxERmGn67IjFZwuOrgQpIjIAvxERids+Xj/3WPmR2Oe/RlDAtVmRQWAAwyMiElQWDm0QFPBZVWAQF0B/iIiE1I2H61devP7rg8N6n8NBUCAUTC+A/hARCegyeciDom5MSG+CgphAaJheAM0QEZHra+ni+pUXjUJCIiYQD+ICKEdERKyvgOiKmECsyuKCsEBKiIhIDREQbaYRRcQEUsDUAikhIiLkywSiCjGBFOVx8RP/2iMib7m+APTL94AoOrl6OvNMCgCA34iIiHQNiJ3na3O/p3j7Z1/ymCAoACAsREQkxpxADBESOWICAMJBRETAxRLGkCEhERMAEAI2VgYupD0QbXASJgD4i0lEwPoMiDr7IaYNPY2YxnQCAPxCRCA4xAQA+IGICJQvyxhjTyOKiAkAcIuIQGcuQ0LiFlEAcIWICJAvUwgfERMAMB4iAr1wPY2YxnQCAIZHRATG5ymEbyGRIyYAYBhEBHrla0hITCcAoG9EREB8nkIU+RwSOWICALojIpA0YgIA2iMiAhHKFCIXwjSiiKUOAGiOiIAkaePyXu+vGVpI5IgJAKiHiMCgQg0JiekEAMxDRAQgtKWMGBETAHAeEYHBhTyNmEZMAMAbRARGEVNISCx1AIBERHgvpqWM2EIiR0wASBURgVHFGhIS0wkA6SEiMLqYQyJHTABIARHhsbGXMoY4K6JKCiEhERMA4kZEwJlUQkIiJgDEiYiAUymFhMS+CQBxISLgXGohkSMmAISOiMAZY+6LKEo1JCRiAkC4iAhPxXQ+RF0ph4RETAAIDxEBr6QeEhIxASAcRAS8Q0hMEBMAfEdE4BxX+yKKCIk3iAkAviIi4C1C4ixiAoBviAgPpbipsgohcR4xAcAXRARK+bCkkSMkyhETAFwjIhAEQqIaIQHAFSICwSAkqjGVAOACEYGgXL/ygpiYgZgAMCYiwjM+bar0aV/ENEJiNkICwBiICASLkJiNqQSAoRERmMnnaYRESNRBTAAYChGB4BES9RASAPpGRCAKbLish6kEgD4REZjL9yWNIkKiHkICQB+ICESHkKiHkADQFRHhEZ9u7wwdIVEPyxsAuiAiUEtISxo59knUR0gAaIOIQPQIiXoICQBNERGoLcRpRI6QqIflDQBNEBFIBssb9RESAOogItBIyNOIHCFRDyEBYB4iAkliKlEPIQFgFiICjcUwjcgREvMREgCqEBFIHiExHyEBoAwRgVZimkZILG/UQUgAmEZEAAWExGyEBIAi5xFhjLlpjNlp+dwlY8x9Y8zd7I+tvq8P1WKbRuSYSsxGSADIvePihxpjViRtSXop6WbL11iX9FDShrV23xizJOmPxpi71tovulzf2yc/6+BwWfrnLq+C0F2/8mLy7wHOObl6qsVnC64vA4BjTiYR1tp9a+0ta+0dSY9avsxDSfettfuFx1Yk7Vd8PwYQ6zQix1SiGhMJAM6XM9owxtyWtFKcOFhrj6y1H1hrt7u+/k+LQf6yYECERDlCAkhbqO+Wd9Rx4mCMeVz2h6Q1yc2bxuaF3dF/Zh9in0bkmEqUIySAdIUaEeuS9rNNmVvZpsr72b4IOJBKSEhMJcoQEkCanGys7KIQCpckLVlr72WPr0v62hjzG2vt0bzXsdbeqHj9x5pEClApDwk2Xr7BZksgPSFOIi5lf14p7n+w1u5qcrfHl06uCklNI3JMJQCkLLiIKNyN8eeSL++r5S2jvgh1X0TK2CvxBssaQFpaL2dkZz08bPCUR/nSQw+Osj/KsC/CoY3Le9p5vub6MpzgXIkJljWAdLSOiGwiULqvYAT7qo6FufshMCxCgpAgJIA0BLeckflc0iclj38i6V9HvhbgDJY3AKTCh4i4VPWF7LMxrDHmfvHxbEPlfnboVP69+V6IvpZM0EGKmyynpR4S7I8A4uckIgofnLWjyUbIleywp4fZrZpF+ypZoshu0byRvc6WpFuSat3e6btYNlcSEkwlAMTNyTkR2Rv9nZrftzrj63NfA/BBqnsl2BsBxM2H5QxEjGnEG6lOJVjWAOJFRGBwhMRZKYYEgDgREZ6KZV9EjpA4K7WQYBoBxImIwGgIibNSXd4AEA8iAnAslZBgGgHEh4jAqJhGlEslJADEhYjA6AiJcimEBNMIIC5EhMdi21xZREiUY58EgJAQEXCGkKhGSAAIAREBpwiJarGGBEsaQDyICDhHSFSLNSQAxIGIgBcIiWoxhgTTCCAORITnYt5cOY2QqBZjSAAIHxEBrxAS1QgJAL4hIuCdjct7xESFmEKCJQ0gfEQEvEVIlIspJACEjYiA1wiJcoQEAB8QEfAeyxvlCAkArhERCAYhcR4hAcAlIgJBYSpxHiEBwBUiAkEiJs4KNSS4QwMIGxGBoBETb4QaEgDCRUQEIKVTK9siJiYICQBjIiIQFWICAMZDRCBKKccE0wgAY3nH9QUAQyqGxM7zNYdXMq7rV17o4HDZ9WUAiByTCCQj5ekEAAyBSQSSk8p0gmkEgKEREUha7EFBSAAYEssZQIblDgBohkkEMCW26QTTCABDYRIRgO3jddeXkKxYphPc9glgCEwigBpim04AGJ8xZknSlqSn2UMfSvrcWns0xvOHQEQADeVBEVpMsKwBOPdY0i1r7a4kGWNuSvqjpBsjPb93LGcALeVLHTEsd7iy+GzB9SUAozDG3JW0nweAJFlrH0laMsbcHvr5QyEigB6EEhPsjQCc+Z2ksk9T3JV0a4TnD4KIAHrEdAJAhaod8i8l3Rzh+YNgTwQwkFD3TgCotGaMeVz2BWtt5b4EY8xK9pffzfiepaoNkl2fPyQiAhiYbzHhywZL9kPEy5ya6H5/zanp8vSl7M9lb/J13vi7Pn8wRAQwEt9iAkBje7MmDjUszfjaJc0Pgq7P7x17IjzHQVPx8WHPBBssgVHlb+4flnwtD4OXAz5/MEQE4IjrkAAwmllv8Jckac5+hq7PHwwRATjkw1TChdjWy4FZsjf4XZUvRyxJejTk84dERAAeSDUmgIT8QdInJY+vSNoZ4fmDICIAj4wZEq72RTCFQIqstV9ocrrk641u2bHV+dfyx5aMMdYYc7/N88fG3RkeY1NlmriLA4jWDUlb2VkTS5JWVf65F/sqv9Oi7vNHQ0QAnooxJphCIGXZ3oY7Nb5nte3zx8ZyBuA59koA8BURAQRgqJAYc18EUwggPkSEp9gPgWlMJAD4hogAAhLqraBMIYA4ERFAgEIMCQDxISJK/LTg9j/SLGWgjlBCgikEEC8iooRdsNq8sOv6MoC5fA8JAgKIGxFRYmHhR9eXANTma0gQEED8iAjPsJSBNnzbcElAAGkgIkpcePcH15cABIuAANJBRJRYeusfTn4uUwh05XoaQUAAaSEigMi4CgkCAkgPEQFEyPVEAkAaiAhPsJSBkDGFANJERACRGuuODQICSNc7ri8AcGmMQ8VcT5k2Lu9p5/la6dcODpdbvy7xAICI8IDrN5mUuDiJdNbPHOv3flZItEFAAJCICETO9+PLy65vqLDoIySIBwBFRESJo59/MdrPYgoxDN/jYZYxw6Iu4gFAGSKixPGP77m+BLQUcjzMMv3PNWZUEBAAqhARJU5P35X08+A/x/X/XcYk1nioMkZUEA8A5iEiSphT4/oS0EBqAVGm+GswKyiK+yKq7swgHgDUxTkRFZgShIGAOG/zwu7rP5oiIAA0wSTCESKlG+Jhvqb/jhEQAJoiIirwJuWvlH5v+ozN4u2dxaUM4gFAW0RECbtgB319phDtpRQQkno9ICpHQADoCxFRYmHhR0lvu74MTEktIHJdjqaehYAA0BURUeLCuz9I+neDvDZTCDQxREAQDwD6QkSUWHrrHxoqItBOilOI7eN13vABeI2IGBFTCDSx83xN73/Tz/6c769Nzj6Z93r59wFAHUQEvJfiFCJ38elJ7e99tbpY+bW6MVL8PoICwDxExEhCmULUuRtg4/LeCFeCg8NlrTY4fr1OcMwKjWl5UBATAKoQEWh8G2H+/WPERKpTiHw/xMKTv3R+rdOPP3r918XQqBsUxASAKkTECHycQvRx/kDxNYYIirYB0devt+uA6WM/RDEgpl18esJkAkAnRERChji4aPq1XS91bB+v93YtLuNvyN+rrt7/xhISACQREYPzYQox5huS65go/rP6/EY8z8HhslYbbKosM2sK0RUhAUAiIqLm8k20a0y0XUoY6nTHsXXdDzFkQORY3gBARAyIcfjkOsaeSoR8QNPJ1VNJ3fZDjBEQRUwlgHQREZHxJR6KmoZElw2Vi88WejugqY6+3zzfBFC7f4axAyLHVAJIExERER8DIjfGRCI/4XH6vIQmdyA0NVSwNDlkKucqIIqYSgBpISIGMvZShs8BMZaqzYht3pBdW3jybaPv9yEgckwlgHQQEYELKR7GmEY0ffMNnU/xMI2pBBA/ImIAY00hQgqIXJ2QqPr1m7VXIsVPvPQ5IHKEBBA3IiJQIQZEbtbtn7NOwZwVZ31+4mUIQgiIHCEBxIuI6NnQU4iQ42HavH+Wpv+sIe59aCOkgMgREkCciIiAxBQQs7Q9MKrJJ16GKMR4KCIkgPi85foCYjLkFCK1gFh8ttBoj8PkhMd4N1WGHhC5lJacgBQwiQhAigGRqxsSsb45xRIPRUwkgHgQET0ZagqRakDEGgV1xRgPRYQEEAciwmOxB8T03odiQKSySXLamPEw5EmedRASQPiIiB70PYUINR76+ATNtgER8n6I2KcOsxASQNiICM+EFBB9fux2m4OiQg4HKe14ABAHIqKjPqcQvgdEn9Ewy6vVxVrTiLI3Yd/DgnA4j2kEEC4iwhO+BsRY4dCXqjdpV3HhazS43g8xjZAAwkREdNDXFMLHgHARD0PekTHrzbyvwPA1GABgKESEQ77Fg6upg+sPzuLN3w9MI4DwEBEtdZ1C+BQQPsZDPm5P9VbPIfm2lAEgXESEA74EhI/xMK3uJkvEgWkEEBYiooUuUwgfAsLlZsk2SxdMJdJCSADhICJG5DogQouHaUwlumMpA0CfiIiG2k4hXAaE69s0XW+cRHiYRgBhICIaaBMQKU8fpP4DgmlEe0whAPSNiKgptIBwHQ8SEwgAiB0RMQfx0M6QAcE0orkQpxAsaQD+IyJ65iogfIkHqV1AND2tkpCoL8SAABAGIqInxMPEmEsYhAQAuEVEdEQ8TLja/0BIzBb6FIIlDcBvRMQMs/ZDEA9vuN5ASUiUCz0gAPiPiGiADZPnuQ6IHCERL6YRgL+IiBqIh3K+BATOYwoBYAxERIV8KYNli/P6joemd2ZUYRoxQUAAGAsRUeLo519IchMQPseD5P/0IfWQICAAjImI8ITv8SD5HxC5VEOCgAAwNiKiwhhTiBDCIRdKQORSCwkCAoALRESJ4x/fG+y1QwoHafh46Gs/RJlUQiKFgOAODcBPRMQMVW/416+86PT8UIQ2fSgTe0ikEBAA/EVElDg9fXdmAIQeB/PEEA9FsYYEAQHAtbdcXwD8EltA5GJ6w321uhjVPw+AcBERkDSJh7EDYsj9EGVieOON4Z8BQDyIiMS5iAeXQn4TDvnaAcTJeUQYY24aY3Z6eJ27xpjNPq4pBanFQ1Fob8YsXwDwlZONlcaYFUlbkl5Kutnj693q+lop8CEexl7KmJa/Kfu+4ZJ4AOAzJxFhrd1X9oZvjLmv7iFxr/NFJcCHePCNr3duEA8AQhD8LZ7ZEsZ9SbcbPu9xxZfcfWTnAAiH+XyaShAPAEISdEQYY5YkrVhrt43hNLsin+PB9VJGFVcxQTgACFXQESHpM2ttq6UMa+2NssezCcV6p6tyyOd4CMVYMUE8AAhdsBFhjLkpqfNdHTEIKRx8nUKUmX6T7xoVRAOA2AQbEZI22k4hYhBSOMSiLALKwoJY6B8fvgX4KciIMMbclfS56+sYW+jhENIUoi6CAUDKWkdEdjbDwwZPedTH5CD7uUfW2qOur+W70KOhKMaAAIDUtY6I7KyH0s2JA1uRdCM7XyJ3KfvzZ8aYDUn3rbW7419aNzFFAwAgfsEtZ1hrH0l6VHwsm05sSvrcWrvt5MIaSikYmEIAQJx8iIhLVV/IzoH4m6QH1to7M15jad5ruZBSKFQhINAVmyoBf7n67IwlTT7rYkXSJ5KWsvMZ9jWZJhSXIvYlVe5/MMY81JtzHbay5Yzp12iNEGiPgACAuLn67IwjSbMmC8XvW53zPYN96BYB0R4BAQDxc/5R4IgPAQEAaSAiKjCFaIeAQJ/YDwH4jYgoYU75D1cbBAQApIWIQC8ICABIDxGBzggIDIGlDMB/PpwTgUARDwCQNiYRaIWAwJCYQgBhYBKBRogHAECOiEAtxAMAYBoRgUqEA1xgKQMIBxGBc4gHAEAdRAQkEQ7wA1MIICxERKKIBgBAV0REAggGhIApBBAeIiIShAIAYGxEhKeIAqSEKQQQJiJiRIQBACAmRMRACAagHqYQQLiIiB4RDkAzBAQQNiKihF1oFgPEAwAgRUREB8QD0B5TCCB8RESJhYUfK79GOAAAMEFEzEE0AP1jCgHE4S3XF+Crk6unri8BiBIBAcSDiChx4d0fXF8CECUCAogLEVHh+pUXri8BAACvEREllt76h+tLAKLDFAKIDxExx8WnJ7r49MT1ZQBBIyCAOHF3RoWNy3v6Sr9+/ffTIfFqdXHsSwKCREAA3RhjliRtSXqaPfShpM+ttUcNnv+ZpO+y5y5J2rLW7ne9NiKipWJUEBQAgAE9lnTLWrsrScaYm5L+KOnGvCfmAWKtvTP12B+NMZ/WDZEqLGf0IF/yYNkDOIspBNCNMeaupP08ICTJWvtI0pIx5naNl7gt6WHxgSwc/iDpX7peH5OInjGhAEA9a7YAACAASURBVCYICERozRjzuOwL1tq5U4GWfifpUcnju5JuSXow5/mr2R/Tr3GkybJGJ0TEgAgKpIqASNvbp/Gd9vu2u/MH11UeES8lbdZ4/mNJ940xKi5pSLoj6dOuF0dEjISgQCoICERsb8CJwznGmJXsL7+b8T1Ls/Y1WGsfGGPuSLqd7aW4p8l041bX/RASeyIq7Txf0/vfWC08+bb312YPBWJFQAC9ypcbyt7sawdAFj7bklY02R/xso87MyQiopYhQiJHTAAA5pi1d+HSvCdnGzD/pMndHPuaTCWeZndpdEJE1DRkSEhMJxA+phBA7/Jpw4clX8sD4OWsF8ju7li11n5hrd211q5K+kKTqcSXXS+QPREN5CFx+vFHg/4c9k8gNAQEUC3b2/Bw7je+8chae0+zA+GS9Pp2zVk+s9Z+UHzAWnvPGPOdJgdYdUJEtLDw5NvBQyKXBwUxAV8REMBs2f6DxhsyrbVHxphdlS9nLKn8ro3XsuWK0hCx1n5hjOkcESxnBILlDviIgAAG9wdJn5Q8viJpZ9YTsynFpbK9D9lju+ef1QwR0dLQeyRmISjgAwICGJ619gtNTqdczx/LbtXMv5Y/tmSMscaY+1Mv8XuVL6V8qclhVZ2wnBE4ljvgAgEBjOqGpK3stMwlTU6gLFse2dfUrZ/W2m1jzJEx5mH2dWWvcY8P4HJszL0R87AZE2MhIIBxZcsSd2p8z2rF1x5pzv6JtljO6MjlskYVljswFAICQBGTiMgxoUBfCAgA05hE9MDHaUQZJhRoi4AAUIaI6EkoIZEjKFAXAQGgChHRo9BCIkdMoAoBAWAWIqJnoYaExHQCZxEQAOZhYyVKsSEzbQQEgDqYRAwg5GlEGaYT6fj+miEgANRGRKA2YiJuxAOApoiIgcQ2jShi70R8CAgAbRARA4o5JHLERPgICABtEREDSyEkJGIiVAQEgC6IiBGkEhISMRESAgJAV0QEBkFM+I2AANAHImIkKU0jiggJ/xAQAPpCRIwo5ZAgJvxAQADoExGB0RASbhEQAPpGRIws1WlEjpAYH6dQAhgKEeEAIcHyxliIBwBDIiIcST0kJKYSQyMgAAyNiIBThMQwCAgAYyAiHGIaMUFI9IuAADAWIsIxQmKCkOgHAQFgTESEBwiJCUKiGwICwNiICE8QEhOERDsEBAAXiAh4h5BohoAA4AoR4RGmEWiKgADgEhHhGUJigmnEfAQEANeICA8REhOERDUCAoAPiAhPERKoQkAA8AUR4TFCgmnENAICgE+ICM8REoREjoAA4BsiAggAAQHAR0REAJhGpD2NICAA+IqICAQhkWZIEBAAfEZEBISQSAsBAcB3RERgUg+JVKYRBASAEBARAUo9JGJHQAAIBRERqJRDIuZpBAEBICRERMBSDokYERAAQkNEBI6QiAMBASBEREQEUgyJmJY0CAgAoSIiIpFiSMSAgAAQMiIiIqmFROjTCAICQOiIiMikFhIAAHeIiAilFBKhTiOYQgCIARERqYUn3yYVEyEhIADEgoiIHCHhFwICQEyIiATEHhKhLGkQEABiQ0QkIvaQAACMj4hICPsk3GEKASBGRESCYgwJn5c0CAgAsSIiEhVjSPiIgAAQMyIiYbGFhG/TCAICQOyIiMSxT2IYBASAFBARkBTfVAIAMDwiAq8REv1gCgEgFUQEzgh9ecP1vggCAkBKiAiUCjkkXCEgAKSGiECl0KcSYyIgAKSIiMBcoYWE6yUNAEgFEYFamEpUYwoBIFVEBBohJM4iIACkjIhAY0wlJggIAKkjItCazyHBvggAGB4RgU5SnUowhQAAIgI9SSkmCAgAmCAi0KtUQgIAQERgAL5MJYbYF8EUAgDeeMf1Bfjo6Odf6D8fLmuVzXmd5CFx+vFHjq+kHwQEAJzFJKLE8Y/vafHZghf/Nx2DGH4dCQgAOI+IqPD+N9b1JUTF1RJHH0saBAQAlCMiSpyevuv6EqLly36JuggIAKhGRJQwp4bDigYWWkwAAM4jIkq8fRrHOn4IfP51ZgoBALMRESXePvnZ9SUkhakEAISJiChhfjh1fQlJ8ikkmEIAwHxEBLwyxFSi6f4WAgIA6iEi4CVXSxwEBADUR0TAa33FRJ1pBAEBAM0QEQjC0JMJAgIAmiMiEBTu5AAAfxARCFKfMcEUAgDacR4RxpibxpidFs9bMcbcz/54nP15aYhrhL+YSgCAO04+CtwYsyJpS9JLSTdbPv+etfZO4bHHkr6W9EFf14kwdPnIcaYQANCek0mEtXbfWnsri4BHLV5iqxgQmd9LWjLGbHW/QoSIqQQAjMvJJKIHm8aYp9ba1fwBa+2uMUaqOdnIJhdl1rpc2Kz/G+ZNrrk204WiV6uLpY+7mkCcXO3/NNTFZwu9vyYA1BFqROxWPH4kqbd9EV3fwNBdVQS01Vc8DBEDbXW9FiIEQFtBRoS19sb0Y9mmyiXVXB4pe43sdR5LWpf6fwO7qI+YRjRw+vFHUU0MfNX0n5XoAJALMiIq3M7+fK+PFxvmDWxRy096fsmI9R1xuZQCYQh1f/2IDSB+UURE4W6PW9ba/T5e89Xq4gBvNgta7vkVY/b9NcMbfsCIDSB+UUSEpIeS7lhrt/t80etXXvT5cvrrs1/3+nqxO7l62vvvwcEhGeebJqFIcAB+aR0R2f/9P2zwlEfW2l6WGqau476k+9baB32+7vfXjP795b0+X1JfXl3W6cfsi6gj39S60fL3YOd5+U02xSghKMLDJlLAL60jIls2KN2cOBZjzG1Jj4sBYYy5aa1tc/bEa/a9hUHG6NevvNCr1V+xL6KGV6uLun7l37R54c2NONvH67WfXxUfxbggKNLjw/KYXbCuLwHoTbDLGcaYm5KWrLVfTH3pltodYPXaT4tv6fqVF2fewPry1bVfsy+ihrJJUJffjzxA8riYnlTkQUFMAEB9zj87Q9Klqi8YY5aMMTZbsig+vi7pvqQPjTFb2R/3jTFNlldGN0SUxOrk6qmTX6++92AAQMxcfXbGkiZ3U6xI+kST46ofS9qX9Lm1tvjusa/JIVJFD7Pn3i15+d73XfTp5Ooph1h5YOPyXuW+CQBAPU4iwlp7JGn6sy+qvm+15PFzj4Ui3xeB2a5f+bfeXqvJXorJz2ZpAwDqCHZPRKg2Lu/pq2vc6jlP33fGtHH9ygtCAgBmICJGtnlhV19e/a3ry/BeX/shmk4hphESAFCNiHCAzXtvhPAGTUgAQDkiIiBtD17qYujNh0MGVdcpRBH7JADgPCLCARcx0FZI11rUZ0AUMZUAgDeICAc4L2JYdQOi7W2eTCUAYMKHw6aA3gw1gSjD3hYAqSMiEI0xAyJHSABIGREBdERIAEgVEYEouJhCFBESAFJERAA9ISQApIaIQPBcTyGKCAkAKSEiELSuATHEORiEBIBUEBHAAK5feUFMAIgeEYFg+bSMUYWQABAzIgJBCiEgcoQEgFgREcAICAkAMSIiEJy+pxChfsgYALhGRAAjYRoBIDZEBIIS0l6IMoQEgJgQEcDICAkAsSAiEIzQpxBFhASAGBARgCOEBIAmjDE3jTE7LZ+7ZIy5b4y5m/2x1cc1EREIwtBTCFd3aBASAGYxxqwYYx4aY+5Lui9ppcVrrEt6LGnLWvuFpAeSbhpj7na9PiIC3otpGaMMIQGgirV231p7y1p7R9Kjli/zUNJ9a+1+4bEVSfsV31/bO11fAAAA+MkYc1vSSjaBkCRZa48kfdDH6xMR8FrsU4jc9SsvdHC47PoyAMy2Zox5XPYFa+2NsS+mpjvqYeJQhYgAPEFIIBZvn/ysi09PXF9Gr94++dn1JbS1LumRMeampA1J30lalXQvm0h0QkQAmY3Le9p5vub0GggJwGt7Hk8czjHGLGV/eUnSkrX2Xvb4uqSvjTG/6RoSbKyEt1JZygCAgVzK/rxird3OH7TW7kp6KenLrj+AiAA8w90aAPpQuBvjzyVf3pd0s+vPYDkDXkp9CsGyBhAPY8yKJrdZ1vUoX3rowVH2R5mlisdrIyIAABhQNhFwtZdiX9Wx0HljJcsZQIGrkyvLsKwBoAefS/qk5PFPJP1r1xcnIuCd1JcyiggJAAWXqr6QfTaGzY7Hfi3bULmfHTqVf2++F6LzkgnLGQAAeCq7TXNLk2OqP5G0lB14tS/p8+xOi9y+SpYorLU3sg/fup99fUlS59s7JSIC8B6bLIF0ZW/0d2p+3+qMr899jTZYzoBXWMoAgHAQEcAUnzZX5tgbAcBHRAQAAGiFiAACwTQCgG+ICHiD/RAAEBYiAggI0wgAPiEigBI+bq4EAN8QEUBgmEYA8AURAS+wHwIAwkNEAACAVogIoILP+yJY0gDgAyICAAC0QkQAAIBWiAggUCxpAHCNiAAAAK0QEcAMPm+uBADXiAgAANAKEQEEjH0RAFwiIgAAQCtEBDAH+yIAoBwRAQAAWiEiAABAK0QEAABohYgAAACtEBFADWyuBIDziAh4YfPCrutLAAA0REQAAIBWiAgAANAKEQHUxL4IADiLiIA32BcBAGEhIgAAQCtEBAAAaIWIAAAArRAR8Irv+yLYXAkAbxARAACgFSKixE8Lrq8gbb5PI3xycLjs+hIAJIyIAAAArRARJeyCdX0JyfN5GsG+CACYICJm2D5ed30JSfM5JHzAUgYA14iIOQgJAADKERE1EBLuMI0AAH8RETUREvAJSxkAfEBEwHs+TiPYXAkARERtPr6RpYRf/zeYQgDwBRGBYBASAOAXIqIG3rz8kfrvBVMIAD4hIuZI/U3LR/yeAIAfiIgZeLPyV4q/N0whAPiGiChhTo3rS0ANrkNizDs0CAgAPiIiAABAK0REibdPXV8B6nI9jRgDUwgAviIiELzNC7vRxgQBAcBnRASiEWtIAICviAhEZeyQGHJzJVMIAL4jIhCdGJY3CAgAISAiEK1QQ4KAABAKIgJRC20qQUAACAkRgSSEEBIEBIDQEBFIhs9TCQICQIiICCTHt5AgIACEiohAkvoMiTE/QwMAfPKO6wsAXMlDYvt43cnPZwIBIHRMIpA8F3slCAgAMSAigMxYMUFAAIgFyxnAlKGWOYgHALFhEgFU6HMyQUAAiBERAczRNSQICACxIiKAGtpOJQgIADEjIoAGfDuoCgBcIiKAgTCFABA77s4Aamh6pwYBASAFRASS1uU2zp3na6WPExAAUkFEIGlVIdAU4QAgRUQEksabPwC0x8ZKAADQCpMIJGv7eF2Lzxac/OyTq6dOfi4A9ImIQLJ2nq/p/W9s6+d/f820fu50vBAVAEJERCBZB4fLWn16Uvv7X60unvn76QDpKyoICgChICKQrMVnC1p48pdzj59+/FHp918sCY5iWORR0SUm8uuSiAkA/iMikKRZ50MsPPl25nOLkZGHBTEBIEVEBKLQ9NCotvshqqYUQ1p8tkBIAPASEYEoFA+N2ri8N/f7m+6HaOP9b2znaUSOkADgIyIC0al7CuW8ZYtpLqYQRSxvAPANEYHgbR+vNz55cqzzIfqcRuSYSgDwBRGBKDSNgqb7IVxPIaYREgB8QEQgeG02SZbdrlmla0AMMY2QCAkA7hERCF7dTZLF2zDr7ofoawIxVEgAgEtEBIKWf/5F8dCoJodFzdL3Egb7IwDEhohA8KaXMpredRE6QgKAK3wUOIK283yt8YTBpS4f+AUAvnE+iTDG3JR0z1q70fB5/7W9+9mN48ruOP47Y0cOkrHcomJAwCgeDQkMlC3pbVYh34CyFlmLfAMRfgKBfANqdrPTkG8g+gliEggQZAQEou3EQIQo4jDCYGDLlk4WdYsqUdXd1fX/Vn0/ACGzu6t1u0Szf33uubcmknYlPQ03XZf0wN3Pax4ieia7O+W333+qFb3pcDT9QDUCQBc6CRFmtqwkAJxJWi/5NMeS7rj7SXjOdUlfSVqrZZDorexmUtMuolVV35Z0AkAfdRIi3P1U0h1JMrN9LRgkzOy+pNM0QITnPDKziZltufvDWgeMXsluLPV3EU4PsFIDwFB0Pp1R0l1JRzm3nygJJ3NDhJkdT7mr2J7J6Ex2Y6lPnv7Q4Uj6hSkNAG2LtbFy2iUbq0yPIAJpP8TH37k+/s4bWYnBVAYAFBNdJSL0U0jSixmPmcxrsHT33N6JUKFY7LrSaE26O2VMKzLyMKUBYAhirERMwp95IYGVGQP37fefRh8gmtTWhcUAQIqwEpExmXHfkggUg9TUaowUUxkAUFyMlYg0HFzPuS8NFmctjQUtOny5ymZNANAjpSsRoTfhYIFDjtx9p+zflzErICxJUtUNp15TEe6l7P4QQ0BfBIDYlQ4RYa+H1jd2cvdzMztR/nTGRPlLPzEARa/WOWYs8QTQplh7Ih4p2SvismVJ+1Wf3K8kJfPs9sqbV0+mPRwtabofoopPnv74zqXGAWAM+tATsTTtjrADpYddLS+4+56kiZmtZh67nrmvdtlAgfbRDwEA/dPVtTPSi2ctS/pcSSA4lnSq5CJa2Y/9p8pfabEmaTccN5G0ooanVw5frlKR6MjQ+iEAYAi6unbGuaTtgo9bqfIcdSNIdIN+CADonz5MZ/QWn3774fDlauiHqH+LawBAeYSIEuiPAACAEDHTxo0nubczndGuNs93zNUOlncCaBshYkEEiG78+NmrwW1JzUZTAGJHiFgAAaI7t24+Zx+GGahCAOgCIaIgAkS3Nm484ZM7APQMIWKOzasnBIge2Lx60tqn7dj6IqhCAOgKISKHvUo+8RIe+qfPfRGfsI8FgJEhRCAa9EW8jyoEgC4RInJ8wO/lXmqzL6LpKQ36OwAMASEC0WizLyIGnAsAXYv1UuAYqVs3n+f2RTRRObjyx//qbQ8GAQJAHxAiEJ28vohP9PfRrKqoOpVBgADQF0xnICppX8Tlr6YaLhcNJk2v0CBAAOgTQgSiku2L+PGzVxdfTTYq9qXCQYAA0DeECETn1s3n772hNv0GW1eQKBt2CBAA+oieCETp1s3n79326h/i6YtYBAECGDczW5a0E779XNLXknbc/bzk892XdOruh1XHRohAdPIu0f742W3938pH+vSPzf29XazWIEAA45YGCHffztx2LOkbSddKPt+upDt1jI8QgehM247897/+lT5t+O+uEiQWncogQACQtOvul9/w70k6NrNdd9/JO2iGRR8/EyECg7B59US/++wfa3/eRQPDtFUiBAgAJW2a2VN3X0lvcPcTM5Ok9UWeyMw2Je1L2qprcIQIDMa0jajqtOhSUsIDMCi3w1TCe9x9raG/c9qVIM8lTYo+iZlNJC27+2EIILUgRGBQurhAVx3LSwkPGBL74dXgmpzth27+H80LJyEQTCQdLfBUX5aY+piLEIHB2LjxRL//9a/08Xcuqf8XuSI4ANF50mDFYRHpdEShUGBm65IeNzEQQgQG421fxJWFjuPNHEAssqsr3P204GEbTVQhJEIEBubWzef6toE1Gnn7Uszz7fdNrxUBMEIHkraL7vEQ9oR40NRgCBEYlI0bT5qp2ZWQBg/CBDBuoXpwsMAhR3mVAzPbl7Tv7g8X+HvPy25KVQQhAoOTtxlVFx4/uy0pVEcIEsBohWmHSr0UZrYl6TgbIMxs3d1nNVcuS1oL4SO1FP780sw2lISSaStA5iJEYFCmbUTVtsOXqxdh5vGz21QlAJQWGiMn7r536a47mrFCIwSMd+4P1YlNSQ/q2PaaC3ABDZgWZsr0VgAYLzNbVbJB1HUz2w1f+2Z2cOlxEzPzS1WHPOneEkszH1UQlQigZUxvAFjAgZJpifs5913umzhVsglVrhA8VsO3u2E64wHTGUCPbdx4ctEfkWJ6A0AR2e2u5zzuXNLMx+Zcg6MyQgTQgMOXq/MfpHenNwgUAGJDiAB64nK/BKECQN8RIoCeIlQA6DtCBFCzolMZi5q2soNwAaArhAigRk0FiFmKLBslaABoAiECqMmsAJG3QqNNXPsDQBMIEUANilQgug4Siyq7MRbhAxgPQgRQQRfTF33H9AowHoQIAK2bFTQIGEA8CBFASWWqELFNaXQhL2AQLIB+IkQA6D2CBdBPhAigBHohusdmXED3CBHAgqoGCKY0mkGoANpHiAAWQAUiHlzcDGjeL7oeABCLOgPExo0ntT0X5rt18/nFF4D6UIkACqACMRxUKID6UIkA5mgqQFCN6B4VCqAaQgQwAxWI8SBMAItjOgMAMpjuAIqjEgFM0UYVgimNfqM6AcxGiAByMI2BLMIEkI/pDOASAgSmYaoDeBeViBwf/PiGXxAj1UWAYEojTlQnAEIE0AsEiXgRJjBmTGfMMO1T6ebVk5ZHgjYwjYEq0iBBFRNjQiUix+uPZp+Ww5ervOEMTB/+PalGDAOVCYwJIWIKfgmMRx8CBIaHMIExIETkeH2l6xGgLX0LEFQjhocwgSGjJwIAWpAGif/ueBxAnahE5PArXuhxffsUi8X09d+PasSwXbnyU9dDAGpDiMAo9TVAAEBMCBEYnRgCBNUIADEgRAAAgFIIERXF8KkWb8X070U1AkDfESIqYvfKeMQUIAAgBoSICggQ8Yg1QFCNANBnhIiSCBAAgLFjs6kFER7iE2sVIrVx44keP7vd9TAA4D2EiAIIDugaQQJAHxEiZiA8xC/2KgQA9Bk9ETnslXU9BNRgaAGCJksAfUOIAAAApRAicnzwqusRoKqhVSFSVCMA9AkhAoMz1ACRIkgA6AtCBAAAKIUQAUSIagSAPiBEYFCGPpUBAH1CiAAiRTUCQNcIERiMMVYhCBIAukSIAAAApRAiMAhjrEKkqEYA6AohAhgAggSALhAiEL0xVyEAoEuECGAgqEYAaBshAgAAlEKIQNSYyngX1QgAbSJEAANDkADQFkIEAAAohRCBaDGVMR3VCABtIEQAA0WQANA0QgQAACiFEIEoMZVRDNUIAE0iRAAAgFIIEcDAUY0A0BRCBDACBAkATSBEAACAUggRiA5NleVQjQBQN0IEAAAohRABjAjVCAB1IkQAI0OQAFAXQgQAACiFEAGMENUIAHUgRAAAgFIIEcBIUY0AUBUhAgAAlEKIAEaMagSAKggRAACgFEIEMHJUIwCURYgAAAClECIAUI0AUAohAgAAlPJh1wPoq2+//1T6bfLf0y49vXn1pMURAQDQL4SIHK+vJH9OCw+p9H7CBIZg48YTPX52u+thAIgI0xk1mBc2UC9CGwD0AyEih1/xhY8hSGAIaLAEsAhCBAAAKIUQMcOi88NUIwAAY0KImIMggbFhSgNAUYQIRInmSgDoHiECAACUQoiYY9HSLp+Q28O5bg5TGgCKIEQAAIBSCBEzUIXoP845AHSHba9rwBsZAGCMqETksFdW6HGbV08IED3Av0Ez6IsAME9nlQgzm0jalfQ03HRd0gN3P1/g+C8lvQjHTiTtuvtpA8O9wBsWAACJLqczjiXdcfcTSTKzdUlfSVqbd2AaQNx9+9JtX5nZPxUNIrPcuvmcwBCRzasnbPQFAC3rZDrDzO5LOk0DhCS5+5GkiZltFXiKLUkH2RtCcHgk6Yuq4/vgVdVnQBcIfQDQrq4qEXclHeXcfiLpjqSHc45fCV+Xn+NcybTGXGZ2POWuxfa5BgBgpLpqrJxWdz6TtF7g+GNJW2a2f+n2bc0PIBgwqhEA0J7WQ4SZLYf/fDHjMTOrCe7+UEnVYsvMnprZppkdKOmxKNQP4e5reV+SaEmPHEECANrRRSUiDQh5b/aFGyLDG/6hpGUl/RFnTa/MQDxYfgsAzetyn4hZ1YaleQeHBsx/UbKa41RvqxKFeiIwDgSJatgrAsAsXYSItNpwPee+NACczXqCsLpjxd333P3E3Vck7SmpSvyutpFiEAgSANCM0qszQm/DwdwHvnXk7juaHRCWpIvlmrN86e7Xsje4+46ZvVCygRXwjjRIsJcEANSndIgI/QdzN4bKOe7czE6UP50xUf7SzwthuiI3iLj7npkRIjAVYQIA6tNVT8QjSZ/n3L4s6fGsA0OVYimv9yHcRu0ac9F4CQDVdRIi3H1Pye6UFx8Hw7bX6X3pbRMz85z9IO4pfyrld0o2qwIKIUwAQHldXjtjTdJu2DlyomQHyrzpkVNdWvrp7odmdh72hkiXdU4k7bDME2VkgwRTHQBQTGchIkxLbBd4zMqU+440p38CKONyZYJQAQD5uqxEAFHIm+4gWAAAIQIoZVYfBQEDwFgQIoCaDSlgPH7GRW0BTEeIAFo0LWDEFi4AQCJEAL1AMyeAGBEigB5iySmAGBAigJ6jSgGgrwgRQGSoUgDoi66unQGgBk1u283KDADzUIkABoDqBIAuUIkABoaLigFoC5UIYKA2r55QlQAGIFzx+m74diJpSdK9cH2pIscvS9oJ334u6WslF6wsdPwshAhgwMoGCfohgH4IAeCuu+9kbjuQ9I2Z/WZeEEgDhLtvZ247lvSNpGtVx8d0BjBwTG0AUduWtBWqEalHSioSXxQ4fjcbIIJ7kiZmtlt1cIQIYAQIEkC0noY/8yoOkwLHb5rZ0+wN7p7+QlivMjBJMnev+hyDYmYv7IO/Wvrb5Wv67KOzrocD1Ob8zd8UfuzLn/66wZGM21/+80xvfvz5zN2vdz2WJpjZ8S/04eovPyzy/haPP/98rjf6+S+SnuTd7+5rbY3FzPYlbUm6VmA641h6f3xm9idJZ+6+UmUs9ES875f++qfXf/6P//nXf+96JP2TTpTn/k80chGcm5cNPXauCM5Nq26p5hPcM0/e6Ge9/Pl/ux5HE251+ZebWTqFsS5prUhjZF64Cc8zkXRUdUyEiPf9m9RuqozFtEQLzs0snJtxcfd/7noMQ2Rm65JWJW1IOpR0WuHptsKfOzMfVQA9EQAA9Jy7H7n7nrtvKKkifBNWXiwkHLMr6Y67VwkikggRAADEZldJkCiz+4/QPQAABHRJREFUuuJA0ra7H9YxEKYzAABoUPj0f7DAIUfpvhDh2LNs/4O7n5qZJG0uOI59Sfvu/nCR42YhRAAA0KAwbVC2J+ipkuWdlTaGMrMtScfZAGFm6+5eqbmS6QwAAPrtD9lvMr0QhTaACU2Zk5wKxJ2qA2OfCAAAesrM7ks6zfYwZPaJWEmbI8OyzT9Jenhpi+tVJVMp2R6I9PobZzm7WS6E6QwAAHrK3ffMbDMEh3O9DQB5G02d6v2dLQ8kLUu6n/P0lZd4UokAAACl0BMBAABKIUQAAIBSCBEAAKAUQgQAACiFEAEAAEoZ1RLPsI52V8kOYJJ0XdKDIpdTreP4vqrrdYUNTXbCBWIGoYafmWW9XUb1uaSvlZyjqH9mpFrOzaqku+HbdNnavSGcG2AsRhUiJB0ruXLZiXTxpveVim9HWvX4vir9ujJXhDtTco37oal6bnYubfxyLOkbVdzCtieqnpu76fUBwm0HSq5M+BuCBBCH0UxnZHb9utgmNOwZPgl7ijd6fF9VfV3ufurud8IbZaU92Pumhn/z3Zzd4O6F48tcfa83ajg325K2QjUi9UhJReKLWgcLoDGjCRFKyqZ5+4yfqNj+4VWP76uhvq46VD03m2b2NHtD5k039qpN1XOTnpe8isOk7KAAtGtMIWJ1yu1Fy/BVj++rob6uOlQ9NyfKf5NMt66NWaVz4+4P3f1auu9/kPbS1HaZYgDNGkWIyFzx7MWMx0z9pV71+L4a6uuqQx3nxt3X3P2d/oBwzEQFr77XR3X/3JhZOgWyLmmNfgggHmNprEx/oU37VNj08X011NdVh6bOTdovUPnCNx2q7dyEZsxVJVWIQyUXEAIQiVFUIjJmfTpaauH4vhrq66pDbecms5LlzqUyfqwqnxt3P3L3vbAseKJkdcbyvOMA9MNYQkT66eh6zn3pL8KzBo/vq6G+rjo0cW4OJG27+2HpUfVDUz83u+H4qFeuAGMylhAx6xfakiTNmYetenxfDfV11aHWc2Nm+5L23X0ITYOVz42ZLV/um8hUZzarDQ9AW0YRIsIvtBPll18nmrO/QdXj+2qor6sOdZ6b0DR4nA0QoRcgSjWdm6dKNt0CELFRhIjgkZJthy9blvS4heP7aqivqw6Vz00IC5OcCkTse3DU8XPzh+w3mV6IaFeuAGMzmhDh7ntKdtO7WN+efhoM96W3TczMQ/l54eNjU/W8XDKoJsyq5yYcty/pupnthq/9sL1z1Gr4udnR+2EjXbESe8ACRmMsSzxTa5J2w/ULJpJWlL/P/6nyl6oVPT42pc9L5iJMy0o+mU7C85wquRhT7J8qq/zMHCg5L/dzHh/zEs9U6XPj7ntmthnCRbr51pKkayPuwwGiY+7e9RgAAECERjOdAQAA6kWIAAAApRAiAABAKYQIAABQCiECAACUQogAAAClECIAAEAphAgAAFAKIQIAAJRCiAAAAKUQIgAAQCmECAAAUAohAgAAlEKIAAAApRAiAABAKYQIAABQCiECAACU8v8n/XD12Tf/TQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 578, - "width": 264 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10))\n", - "CS = plt.contourf(Or, Oi, np.angle(Leavers))\n", - "plt.gca().invert_yaxis()\n", - "plt.gca().set_aspect('equal')\n", - "plt.colorbar(CS)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy import optimize" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " fjac: array([[-0.57359808, -0.81913689],\n", - " [ 0.81913689, -0.57359808]])\n", - " fun: array([-1.33226763e-14, -2.08721929e-14])\n", - " message: 'The solution converged.'\n", - " nfev: 19\n", - " qtf: array([-1.81513595e-12, -8.49256196e-13])\n", - " r: array([-37.83289639, -11.46901807, -6.65014296])\n", - " status: 1\n", - " success: True\n", - " x: array([ 0.34774125, -0.83259476])\n", - "(0.3477412539359394-0.8325947550362678j)\n" - ] - } - ], - "source": [ - "a = 0.5\n", - "s = -2\n", - "m = 2\n", - "\n", - "n_inv = 3\n", - "Nr = 300\n", - "r_N = 0.\n", - "l_max = 20\n", - "\n", - "A0 = 4.\n", - "omega_guess = 1.-1.j\n", - "\n", - "def leavA(x):\n", - " omega = x[0] + 1.j*x[1]\n", - " A = qnm.angular.sep_const_closest(A0, s, a*omega, m, l_max)\n", - " Leav_err = qnm.radial.leaver_cf_trunc_inversion(omega, a, s, m, A, n_inv, Nr, r_N)\n", - " return [np.real(Leav_err), np.imag(Leav_err)]\n", - "\n", - "sol = optimize.root(leavA, [np.real(omega_guess), np.imag(omega_guess)], tol=1e-10)\n", - "print(sol)\n", - "omega_sol = sol.x[0] + 1.j*sol.x[1]\n", - "print(omega_sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.64660074+1.21471602j, 9.82630392+0.6137243j ,\n", - " 17.91099677+0.38582177j, 27.95981518+0.27679539j,\n", - " 39.98983299+0.21636142j, 54.00938032+0.17936711j,\n", - " 70.0227435 +0.15505889j, 88.0322533 +0.13821883j,\n", - " 108.03924895+0.12606481j, 130.04453913+0.11700177j,\n", - " 154.04863361+0.11006122j, 180.05186591+0.10462704j,\n", - " 208.05446134+0.10029188j, 238.05657636+0.09677759j,\n", - " 270.05832233+0.0938889j , 304.05978018+0.09148542j,\n", - " 340.06100989+0.08946407j, 418.04949541+0.07276048j,\n", - " 378.06205594+0.08780761j])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qnm.angular.sep_consts(s, a*omega_sol, m, l_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 4.+0.j, 10.+0.j, 18.+0.j, 28.+0.j, 40.+0.j, 54.+0.j,\n", - " 70.+0.j, 88.+0.j, 108.+0.j, 130.+0.j, 154.+0.j, 180.+0.j,\n", - " 208.+0.j, 238.+0.j, 270.+0.j, 304.+0.j, 340.+0.j, 378.+0.j,\n", - " 418.+0.j])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qnm.angular.sep_consts(s, 0., 2, 20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing the NearbyRootFinder class" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from qnm.nearby import NearbyRootFinder" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "tol = 1e-10\n", - "a = 1e-10\n", - "s = -2\n", - "m = 2\n", - "\n", - "n_inv = 8\n", - "Nr = 300\n", - "r_N = 0.\n", - "l_max = 20\n", - "\n", - "A0 = 4.\n", - "omega_guess = 1.e-15-2.j" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "finder = NearbyRootFinder(a=a, s=s, m=m, A_closest_to=A0,\n", - " l_max=l_max, omega_guess=omega_guess,\n", - " tol=tol, n_inv=n_inv, Nr=Nr, r_N=r_N, Nr_max=6000)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5.449927616463862e-12-1.9999999631213654j)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "finder.do_solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4.946943578310056e-09, 5999)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "finder.get_cf_err()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.10061986644253162-2.0846631058683593j)\n", - "0j\n", - "0.0\n" - ] - } - ], - "source": [ - "finder.set_params(a=0.1)\n", - "finder.set_params(Nr=300)\n", - "o1 = finder.do_solve()\n", - "finder.set_params(Nr=301)\n", - "o2 = finder.do_solve()\n", - "print(o1)\n", - "print(o1-o2)\n", - "print(np.abs(o1-o2))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.004876401732206959-1.9997321614437211j)\n", - "(-1.1474473281258835e-11+9.555911617553647e-12j)\n", - "1.493248083624485e-11\n" - ] - } - ], - "source": [ - "finder.set_params(a=0.3)\n", - "finder.set_params(l_max=20)\n", - "o1 = finder.do_solve()\n", - "finder.set_params(l_max=21)\n", - "o2 = finder.do_solve()\n", - "print(o1)\n", - "print(o1-o2)\n", - "print(np.abs(o1-o2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing the KerrSpinSeq class" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from qnm.spinsequence import KerrSpinSeq" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Loading Schw QNM dict from file /Users/leo/src/spectral_qnms/qnm/schwarzschild/data/Schw_dict.pickle\n" - ] - } - ], - "source": [ - "qnm_dict = qnm.schwarzschild.tabulated.QNMDict(init=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:l=3, m=-3, n=7 starting\n", - "INFO:root:l=3, m=-3, n=7 completed with 251 points\n", - "INFO:root:l=3, m=-2, n=7 starting\n", - "INFO:root:l=3, m=-2, n=7 completed with 251 points\n", - "INFO:root:l=3, m=-1, n=7 starting\n", - "INFO:root:l=3, m=-1, n=7 completed with 251 points\n", - "INFO:root:l=3, m=0, n=7 starting\n", - "INFO:root:l=3, m=0, n=7 completed with 251 points\n", - "INFO:root:l=3, m=1, n=7 starting\n", - "INFO:root:l=3, m=1, n=7 completed with 264 points\n", - "INFO:root:l=3, m=2, n=7 starting\n", - "INFO:root:l=3, m=2, n=7 completed with 270 points\n", - "INFO:root:l=3, m=3, n=7 starting\n", - "INFO:root:l=3, m=3, n=7 completed with 284 points\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "14.9529409409\n" - ] - } - ], - "source": [ - "start = time.time()\n", - "\n", - "a_max = .998\n", - "tol=1e-10\n", - "\n", - "ns=np.arange(0,8)\n", - "ns=[7]\n", - "s=-2\n", - "ls=np.arange(2,5)\n", - "ls = [3]\n", - "seqs = {}\n", - "for l in ls:\n", - " ms=np.arange(-l,l+1)\n", - " for m in ms:\n", - " for n in ns:\n", - " seqs[(l,m,n)] = KerrSpinSeq(n=n, a_max=a_max, delta_a=4e-3, s=s, l=l, m=m)\n", - " seqs[(l,m,n)].do_find_sequence()\n", - "\n", - "end = time.time()\n", - "\n", - "print(end - start)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 493, - "width": 968 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "ns=np.arange(0,8)\n", - "ns=[7]\n", - "s=-2\n", - "ls=np.arange(2,5)\n", - "ls = [3]\n", - "\n", - "plt.figure(figsize=(16,8))\n", - "\n", - "plt.subplot(1, 2, 1)\n", - "\n", - "for l in ls:\n", - " ms = np.arange(-l, l+1)\n", - " for m in ms:\n", - " for n in ns:\n", - " plt.plot(np.real(seqs[(l,m,n)].omega), np.imag(seqs[(l,m,n)].omega))\n", - "\n", - "#plt.xlim(.07,.13)\n", - "#plt.ylim(-1.48,-1.455)\n", - "\n", - "plt.gca().invert_yaxis()\n", - "plt.gca().tick_params(labelsize=16)\n", - "plt.xlabel(r'$\\textrm{Re}[\\omega_{lmn}]$', fontsize=16)\n", - "plt.ylabel(r'$\\textrm{Im}[\\omega_{lmn}]$', fontsize=16)\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "\n", - "for l in ls:\n", - " ms = np.arange(-l, l+1)\n", - " for m in ms:\n", - " for n in ns:\n", - " plt.plot(np.real(seqs[(l,m,n)].A), np.imag(seqs[(l,m,n)].A))\n", - "\n", - "plt.gca().tick_params(labelsize=16)\n", - "plt.xlabel(r'$\\textrm{Re}[A_{lmn}]$', fontsize=16)\n", - "plt.ylabel(r'$\\textrm{Im}[A_{lmn}]$', fontsize=16)\n", - "\n", - "plt.savefig(\"test.png\", bbox_inches=\"tight\", dpi=300)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:l=2, m=-2, n=0 starting\n", - "INFO:root:l=2, m=-2, n=0 completed with 252 points\n", - "INFO:root:l=2, m=-2, n=1 starting\n", - "INFO:root:l=2, m=-2, n=1 completed with 252 points\n", - "INFO:root:l=2, m=-2, n=2 starting\n", - "INFO:root:l=2, m=-2, n=2 completed with 252 points\n", - "INFO:root:l=2, m=-2, n=3 starting\n", - "INFO:root:l=2, m=-2, n=3 completed with 252 points\n", - "INFO:root:l=2, m=-2, n=4 starting\n", - "INFO:root:l=2, m=-2, n=4 completed with 252 points\n", - "INFO:root:l=2, m=-2, n=5 starting\n", - "INFO:root:l=2, m=-2, n=5 completed with 252 points\n", - "INFO:root:l=2, m=-2, n=6 starting\n", - "INFO:root:l=2, m=-2, n=6 completed with 252 points\n", - "INFO:root:l=2, m=-1, n=0 starting\n", - "INFO:root:l=2, m=-1, n=0 completed with 252 points\n", - "INFO:root:l=2, m=-1, n=1 starting\n", - "INFO:root:l=2, m=-1, n=1 completed with 252 points\n", - "INFO:root:l=2, m=-1, n=2 starting\n", - "INFO:root:l=2, m=-1, n=2 completed with 252 points\n", - "INFO:root:l=2, m=-1, n=3 starting\n", - "INFO:root:l=2, m=-1, n=3 completed with 252 points\n", - "INFO:root:l=2, m=-1, n=4 starting\n", - "INFO:root:l=2, m=-1, n=4 completed with 252 points\n", - "INFO:root:l=2, m=-1, n=5 starting\n", - "INFO:root:l=2, m=-1, n=5 completed with 252 points\n", - "INFO:root:l=2, m=-1, n=6 starting\n", - "INFO:root:l=2, m=-1, n=6 completed with 252 points\n", - "INFO:root:l=2, m=0, n=0 starting\n", - "INFO:root:l=2, m=0, n=0 completed with 252 points\n", - "INFO:root:l=2, m=0, n=1 starting\n", - "INFO:root:l=2, m=0, n=1 completed with 252 points\n", - "INFO:root:l=2, m=0, n=2 starting\n", - "INFO:root:l=2, m=0, n=2 completed with 252 points\n", - "INFO:root:l=2, m=0, n=3 starting\n", - "INFO:root:l=2, m=0, n=3 completed with 252 points\n", - "INFO:root:l=2, m=0, n=4 starting\n", - "INFO:root:l=2, m=0, n=4 completed with 252 points\n", - "INFO:root:l=2, m=0, n=5 starting\n", - "INFO:root:l=2, m=0, n=5 completed with 252 points\n", - "INFO:root:l=2, m=0, n=6 starting\n", - "INFO:root:l=2, m=0, n=6 completed with 253 points\n", - "INFO:root:l=2, m=1, n=0 starting\n", - "INFO:root:l=2, m=1, n=0 completed with 252 points\n", - "INFO:root:l=2, m=1, n=1 starting\n", - "INFO:root:l=2, m=1, n=1 completed with 254 points\n", - "INFO:root:l=2, m=1, n=2 starting\n", - "INFO:root:l=2, m=1, n=2 completed with 262 points\n", - "INFO:root:l=2, m=1, n=3 starting\n", - "INFO:root:l=2, m=1, n=3 completed with 265 points\n", - "INFO:root:l=2, m=1, n=4 starting\n", - "INFO:root:l=2, m=1, n=4 completed with 268 points\n", - "INFO:root:l=2, m=1, n=5 starting\n", - "INFO:root:l=2, m=1, n=5 completed with 271 points\n", - "INFO:root:l=2, m=1, n=6 starting\n", - "INFO:root:l=2, m=1, n=6 completed with 274 points\n", - "INFO:root:l=2, m=2, n=0 starting\n", - "INFO:root:l=2, m=2, n=0 completed with 267 points\n", - "INFO:root:l=2, m=2, n=1 starting\n", - "INFO:root:l=2, m=2, n=1 completed with 269 points\n", - "INFO:root:l=2, m=2, n=2 starting\n", - "INFO:root:l=2, m=2, n=2 completed with 274 points\n", - "INFO:root:l=2, m=2, n=3 starting\n", - "INFO:root:l=2, m=2, n=3 completed with 278 points\n", - "INFO:root:l=2, m=2, n=4 starting\n", - "INFO:root:l=2, m=2, n=4 completed with 283 points\n", - "INFO:root:l=2, m=2, n=5 starting\n", - "INFO:root:l=2, m=2, n=5 completed with 253 points\n", - "INFO:root:l=2, m=2, n=6 starting\n", - "INFO:root:l=2, m=2, n=6 completed with 289 points\n", - "INFO:root:l=3, m=-3, n=0 starting\n", - "INFO:root:l=3, m=-3, n=0 completed with 252 points\n", - "INFO:root:l=3, m=-3, n=1 starting\n", - "INFO:root:l=3, m=-3, n=1 completed with 252 points\n", - "INFO:root:l=3, m=-3, n=2 starting\n", - "INFO:root:l=3, m=-3, n=2 completed with 252 points\n", - "INFO:root:l=3, m=-3, n=3 starting\n", - "INFO:root:l=3, m=-3, n=3 completed with 252 points\n", - "INFO:root:l=3, m=-3, n=4 starting\n", - "INFO:root:l=3, m=-3, n=4 completed with 252 points\n", - "INFO:root:l=3, m=-3, n=5 starting\n", - "INFO:root:l=3, m=-3, n=5 completed with 252 points\n", - "INFO:root:l=3, m=-3, n=6 starting\n", - "INFO:root:l=3, m=-3, n=6 completed with 252 points\n", - "INFO:root:l=3, m=-2, n=0 starting\n", - "INFO:root:l=3, m=-2, n=0 completed with 252 points\n", - "INFO:root:l=3, m=-2, n=1 starting\n", - "INFO:root:l=3, m=-2, n=1 completed with 252 points\n", - "INFO:root:l=3, m=-2, n=2 starting\n", - "INFO:root:l=3, m=-2, n=2 completed with 252 points\n", - "INFO:root:l=3, m=-2, n=3 starting\n", - "INFO:root:l=3, m=-2, n=3 completed with 252 points\n", - "INFO:root:l=3, m=-2, n=4 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"INFO:root:l=5, m=-5, n=0 completed with 252 points\n", - "INFO:root:l=5, m=-5, n=1 starting\n", - "INFO:root:l=5, m=-5, n=1 completed with 252 points\n", - "INFO:root:l=5, m=-5, n=2 starting\n", - "INFO:root:l=5, m=-5, n=2 completed with 252 points\n", - "INFO:root:l=5, m=-5, n=3 starting\n", - "INFO:root:l=5, m=-5, n=3 completed with 252 points\n", - "INFO:root:l=5, m=-5, n=4 starting\n", - "INFO:root:l=5, m=-5, n=4 completed with 252 points\n", - "INFO:root:l=5, m=-5, n=5 starting\n", - "INFO:root:l=5, m=-5, n=5 completed with 252 points\n", - "INFO:root:l=5, m=-5, n=6 starting\n", - "INFO:root:l=5, m=-4, n=0 starting\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sadness at l=5, m=-5, n=6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:l=5, m=-4, n=0 completed with 252 points\n", - "INFO:root:l=5, m=-4, n=1 starting\n", - "INFO:root:l=5, m=-4, n=1 completed with 252 points\n", - "INFO:root:l=5, m=-4, n=2 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completed with 252 points\n", - "INFO:root:l=5, m=2, n=0 starting\n", - "INFO:root:l=5, m=2, n=0 completed with 252 points\n", - "INFO:root:l=5, m=2, n=1 starting\n", - "INFO:root:l=5, m=2, n=1 completed with 252 points\n", - "INFO:root:l=5, m=2, n=2 starting\n", - "INFO:root:l=5, m=2, n=2 completed with 252 points\n", - "INFO:root:l=5, m=2, n=3 starting\n", - "INFO:root:l=5, m=2, n=3 completed with 252 points\n", - "INFO:root:l=5, m=2, n=4 starting\n", - "INFO:root:l=5, m=2, n=4 completed with 252 points\n", - "INFO:root:l=5, m=2, n=5 starting\n", - "INFO:root:l=5, m=2, n=5 completed with 253 points\n", - "INFO:root:l=5, m=2, n=6 starting\n", - "INFO:root:l=5, m=2, n=6 completed with 267 points\n", - "INFO:root:l=5, m=3, n=0 starting\n", - "INFO:root:l=5, m=3, n=0 completed with 252 points\n", - "INFO:root:l=5, m=3, n=1 starting\n", - "INFO:root:l=5, m=3, n=1 completed with 253 points\n", - "INFO:root:l=5, m=3, n=2 starting\n", - "INFO:root:l=5, m=3, n=2 completed with 256 points\n", 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completed with 252 points\n", - "INFO:root:l=7, m=-5, n=1 starting\n", - "INFO:root:l=7, m=-5, n=1 completed with 252 points\n", - "INFO:root:l=7, m=-5, n=2 starting\n", - "INFO:root:l=7, m=-5, n=2 completed with 252 points\n", - "INFO:root:l=7, m=-5, n=3 starting\n", - "INFO:root:l=7, m=-5, n=3 completed with 252 points\n", - "INFO:root:l=7, m=-5, n=4 starting\n", - "INFO:root:l=7, m=-5, n=4 completed with 252 points\n", - "INFO:root:l=7, m=-5, n=5 starting\n", - "INFO:root:l=7, m=-5, n=5 completed with 252 points\n", - "INFO:root:l=7, m=-5, n=6 starting\n", - "INFO:root:l=7, m=-4, n=0 starting\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sadness at l=7, m=-5, n=6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:l=7, m=-4, n=0 completed with 252 points\n", - "INFO:root:l=7, m=-4, n=1 starting\n", - "INFO:root:l=7, m=-4, n=1 completed with 252 points\n", - "INFO:root:l=7, m=-4, n=2 starting\n", - "INFO:root:l=7, m=-4, n=2 completed with 252 points\n", - "INFO:root:l=7, m=-4, n=3 starting\n", - "INFO:root:l=7, m=-4, n=3 completed with 252 points\n", - "INFO:root:l=7, m=-4, n=4 starting\n", - "INFO:root:l=7, m=-4, n=4 completed with 252 points\n", - "INFO:root:l=7, m=-4, n=5 starting\n", - "INFO:root:l=7, m=-4, n=5 completed with 252 points\n", - "INFO:root:l=7, m=-4, n=6 starting\n", - "INFO:root:l=7, m=-4, n=6 completed with 252 points\n", - "INFO:root:l=7, m=-3, n=0 starting\n", - "INFO:root:l=7, m=-3, n=0 completed with 252 points\n", - "INFO:root:l=7, m=-3, n=1 starting\n", - "INFO:root:l=7, m=-3, n=1 completed with 252 points\n", - "INFO:root:l=7, m=-3, n=2 starting\n", - "INFO:root:l=7, m=-3, n=2 completed with 252 points\n", - "INFO:root:l=7, m=-3, n=3 starting\n", - "INFO:root:l=7, m=-3, n=3 completed with 252 points\n", - "INFO:root:l=7, m=-3, n=4 starting\n", - "INFO:root:l=7, m=-3, n=4 completed with 252 points\n", - "INFO:root:l=7, m=-3, n=5 starting\n", - "INFO:root:l=7, m=-3, n=5 completed with 252 points\n", - "INFO:root:l=7, m=-3, n=6 starting\n", - "INFO:root:l=7, m=-2, n=0 starting\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sadness at l=7, m=-3, n=6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:l=7, m=-2, n=0 completed with 252 points\n", - "INFO:root:l=7, m=-2, n=1 starting\n", - "INFO:root:l=7, m=-2, n=1 completed with 252 points\n", - "INFO:root:l=7, m=-2, n=2 starting\n", - "INFO:root:l=7, m=-2, n=2 completed with 252 points\n", - "INFO:root:l=7, m=-2, n=3 starting\n", - "INFO:root:l=7, m=-2, n=3 completed with 252 points\n", - "INFO:root:l=7, m=-2, n=4 starting\n", - "INFO:root:l=7, m=-2, n=4 completed with 252 points\n", - "INFO:root:l=7, m=-2, n=5 starting\n", - "INFO:root:l=7, m=-2, n=6 starting\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sadness at l=7, m=-2, n=5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:l=7, m=-2, n=6 completed with 252 points\n", - "INFO:root:l=7, m=-1, n=0 starting\n", - "INFO:root:l=7, m=-1, n=0 completed with 252 points\n", - "INFO:root:l=7, m=-1, n=1 starting\n", - "INFO:root:l=7, m=-1, n=1 completed with 252 points\n", - "INFO:root:l=7, m=-1, n=2 starting\n", - "INFO:root:l=7, m=-1, n=2 completed with 252 points\n", - "INFO:root:l=7, m=-1, n=3 starting\n", - "INFO:root:l=7, m=-1, n=3 completed with 252 points\n", - "INFO:root:l=7, m=-1, n=4 starting\n", - "INFO:root:l=7, m=-1, n=4 completed with 252 points\n", - "INFO:root:l=7, m=-1, n=5 starting\n", - "INFO:root:l=7, m=-1, n=5 completed with 252 points\n", - "INFO:root:l=7, m=-1, n=6 starting\n", - "INFO:root:l=7, m=0, n=0 starting\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sadness at l=7, m=-1, n=6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:l=7, m=0, n=0 completed with 252 points\n", - "INFO:root:l=7, m=0, n=1 starting\n", - "INFO:root:l=7, m=0, n=1 completed with 252 points\n", - "INFO:root:l=7, m=0, n=2 starting\n", - "INFO:root:l=7, m=0, n=2 completed with 252 points\n", - "INFO:root:l=7, m=0, n=3 starting\n", - "INFO:root:l=7, m=0, n=3 completed with 252 points\n", - "INFO:root:l=7, m=0, n=4 starting\n", - "INFO:root:l=7, m=0, n=4 completed with 252 points\n", - "INFO:root:l=7, m=0, n=5 starting\n", - "INFO:root:l=7, m=0, n=5 completed with 252 points\n", - "INFO:root:l=7, m=0, n=6 starting\n", - "INFO:root:l=7, m=0, n=6 completed with 252 points\n", - "INFO:root:l=7, m=1, n=0 starting\n", - "INFO:root:l=7, m=1, n=0 completed with 252 points\n", - "INFO:root:l=7, m=1, n=1 starting\n", - "INFO:root:l=7, m=1, n=1 completed with 252 points\n", - "INFO:root:l=7, m=1, n=2 starting\n", - "INFO:root:l=7, m=1, n=2 completed with 252 points\n", - "INFO:root:l=7, m=1, n=3 starting\n", - "INFO:root:l=7, m=1, n=3 completed with 252 points\n", - "INFO:root:l=7, m=1, n=4 starting\n", - "INFO:root:l=7, m=1, n=4 completed with 252 points\n", - "INFO:root:l=7, m=1, n=5 starting\n", - "INFO:root:l=7, m=1, n=5 completed with 252 points\n", - "INFO:root:l=7, m=1, n=6 starting\n", - "INFO:root:l=7, m=1, n=6 completed with 252 points\n", - "INFO:root:l=7, m=2, n=0 starting\n", - "INFO:root:l=7, m=2, n=0 completed with 252 points\n", - "INFO:root:l=7, m=2, n=1 starting\n", - "INFO:root:l=7, m=2, n=1 completed with 252 points\n", - "INFO:root:l=7, m=2, n=2 starting\n", - "INFO:root:l=7, m=2, n=2 completed with 252 points\n", - "INFO:root:l=7, m=2, n=3 starting\n", - "INFO:root:l=7, m=2, n=3 completed with 252 points\n", - "INFO:root:l=7, m=2, n=4 starting\n", - "INFO:root:l=7, m=2, n=4 completed with 252 points\n", - "INFO:root:l=7, m=2, n=5 starting\n", - "INFO:root:l=7, m=2, n=5 completed with 252 points\n", - "INFO:root:l=7, m=2, n=6 starting\n", - "INFO:root:l=7, m=2, n=6 completed with 252 points\n", - "INFO:root:l=7, m=3, n=0 starting\n", - "INFO:root:l=7, m=3, n=0 completed with 252 points\n", - "INFO:root:l=7, m=3, n=1 starting\n", - "INFO:root:l=7, m=3, n=1 completed with 252 points\n", - "INFO:root:l=7, m=3, n=2 starting\n", - "INFO:root:l=7, m=3, n=2 completed with 252 points\n", - "INFO:root:l=7, m=3, n=3 starting\n", - "INFO:root:l=7, m=3, n=3 completed with 252 points\n", - "INFO:root:l=7, m=3, n=4 starting\n", - "INFO:root:l=7, m=3, n=4 completed with 252 points\n", - "INFO:root:l=7, m=3, n=5 starting\n", - "INFO:root:l=7, m=3, n=5 completed with 252 points\n", - "INFO:root:l=7, m=3, n=6 starting\n", - "INFO:root:l=7, m=3, n=6 completed with 253 points\n", - "INFO:root:l=7, m=4, n=0 starting\n", - "INFO:root:l=7, m=4, n=0 completed with 252 points\n", - "INFO:root:l=7, m=4, n=1 starting\n", - "INFO:root:l=7, m=4, n=1 completed with 253 points\n", - "INFO:root:l=7, m=4, n=2 starting\n", - "INFO:root:l=7, m=4, n=2 completed with 254 points\n", - "INFO:root:l=7, m=4, n=3 starting\n", - "INFO:root:l=7, m=4, n=3 completed with 256 points\n", - "INFO:root:l=7, m=4, n=4 starting\n", - "INFO:root:l=7, m=4, n=4 completed with 271 points\n", - "INFO:root:l=7, m=4, n=5 starting\n", - "INFO:root:l=7, m=4, n=5 completed with 276 points\n", - "INFO:root:l=7, m=4, n=6 starting\n", - "INFO:root:l=7, m=4, n=6 completed with 279 points\n", - "INFO:root:l=7, m=5, n=0 starting\n", - "INFO:root:l=7, m=5, n=0 completed with 257 points\n", - "INFO:root:l=7, m=5, n=1 starting\n", - "INFO:root:l=7, m=5, n=1 completed with 259 points\n", - "INFO:root:l=7, m=5, n=2 starting\n", - "INFO:root:l=7, m=5, n=2 completed with 263 points\n", - "INFO:root:l=7, m=5, n=3 starting\n", - "INFO:root:l=7, m=5, n=3 completed with 267 points\n", - "INFO:root:l=7, m=5, n=4 starting\n", - "INFO:root:l=7, m=5, n=4 completed with 270 points\n", - "INFO:root:l=7, m=5, n=5 starting\n", - "INFO:root:l=7, m=5, n=5 completed with 273 points\n", - "INFO:root:l=7, m=5, n=6 starting\n", - "INFO:root:l=7, m=5, n=6 completed with 277 points\n", - "INFO:root:l=7, m=6, n=0 starting\n", - "INFO:root:l=7, m=6, n=0 completed with 267 points\n", - "INFO:root:l=7, m=6, n=1 starting\n", - "INFO:root:l=7, m=6, n=1 completed with 268 points\n", - "INFO:root:l=7, m=6, n=2 starting\n", - "INFO:root:l=7, m=6, n=2 completed with 270 points\n", - "INFO:root:l=7, m=6, n=3 starting\n", - "INFO:root:l=7, m=6, n=3 completed with 273 points\n", - "INFO:root:l=7, m=6, n=4 starting\n", - "INFO:root:l=7, m=6, n=4 completed with 275 points\n", - "INFO:root:l=7, m=6, n=5 starting\n", - "INFO:root:l=7, m=6, n=5 completed with 278 points\n", - "INFO:root:l=7, m=6, n=6 starting\n", - "INFO:root:l=7, m=6, n=6 completed with 280 points\n", - "INFO:root:l=7, m=7, n=0 starting\n", - "INFO:root:l=7, m=7, n=0 completed with 279 points\n", - "INFO:root:l=7, m=7, n=1 starting\n", - "INFO:root:l=7, m=7, n=1 completed with 280 points\n", - "INFO:root:l=7, m=7, n=2 starting\n", - "INFO:root:l=7, m=7, n=2 completed with 281 points\n", - "INFO:root:l=7, m=7, n=3 starting\n", - "INFO:root:l=7, m=7, n=3 completed with 283 points\n", - "INFO:root:l=7, m=7, n=4 starting\n", - "INFO:root:l=7, m=7, n=4 completed with 285 points\n", - "INFO:root:l=7, m=7, n=5 starting\n", - "INFO:root:l=7, m=7, n=5 completed with 287 points\n", - "INFO:root:l=7, m=7, n=6 starting\n", - "INFO:root:l=7, m=7, n=6 completed with 289 points\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "854.689414024\n" - ] - } - ], - "source": [ - "start = time.time()\n", - "\n", - "a_max = .9995\n", - "tol=1e-10\n", - "\n", - "ns=np.arange(0,7)\n", - "s=-2\n", - "ls=np.arange(2,8)\n", - "seqs = {}\n", - "for l in ls:\n", - " ms=np.arange(-l,l+1)\n", - " for m in ms:\n", - " for n in ns:\n", - " seqs[(l,m,n)] = KerrSpinSeq(n=n, a_max=a_max, delta_a=2.5e-3, s=s, l=l, m=m)\n", - " try:\n", - " seqs[(l,m,n)].do_find_sequence()\n", - " except:\n", - " print(\"Sadness at l={}, m={}, n={}\".format(l,m,n))\n", - " \n", - "end = time.time()\n", - "\n", - "print(end - start)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "reruns = [(-2,5,-5,6), (-2,5,-4,5), (-2,6,-6,5), (-2,6,-4,5),\n", - " (-2,6,-4,6), (-2,6,0,6), (-2,6,1,5), (-2,7,-7,5), \n", - " (-2,7,-6,6), (-2,7,-5,6), (-2,7,-3,6), (-2,7,-2,5), \n", - " (-2,7,-1,6) ]" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:l=7, m=-1, n=6 starting\n", - "INFO:root:l=7, m=-1, n=6 completed with 252 points\n", - "INFO:root:l=6, m=-6, n=5 starting\n", - "INFO:root:l=6, m=-6, n=5 completed with 252 points\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.95491290092\n" - ] - } - ], - "source": [ - "start = time.time()\n", - "\n", - "a_max = .9995\n", - "tol=1e-10\n", - "\n", - "for s,l,m,n in reruns:\n", - " seqs[(s,l,m,n)] = KerrSpinSeq(n=n, a_max=a_max, delta_a=1.9e-3, s=s, l=l, m=m)\n", - " seqs[(s,l,m,n)].do_find_sequence()\n", - " \n", - "end = time.time()\n", - "\n", - "print(end - start)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n1.pickle\n", - "INFO:root:Loading 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"INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n1.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence 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/Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n3.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file 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QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n5.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from 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from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n2.pickle\n", - "INFO:root:Loading Kerr QNM 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/Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence 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sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n2.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n6.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n0.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n1.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n2.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n3.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n4.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n5.pickle\n", - "INFO:root:Loading Kerr QNM sequence from file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n6.pickle\n" - ] - } - ], - "source": [ - "ns=np.arange(0,7)\n", - "s=-2\n", - "ls=np.arange(2,8)\n", - "seqs = {}\n", - "for l in ls:\n", - " ms=np.arange(-l,l+1)\n", - " for m in ms:\n", - " for n in ns:\n", - " seqs[s,l,m,n] = qnm.cached.load_cached_mode(s,l,m,n)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 493, - "width": 525 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(8,8))\n", - "\n", - "for (l,m,n), seq in seqs.iteritems():\n", - " plt.plot(np.real(seq.omega), np.imag(seq.omega))\n", - "#plt.xlim(-1.5, 1.5)\n", - "#plt.ylim(-1.7,0.)\n", - "\n", - "plt.gca().invert_yaxis()\n", - "plt.gca().tick_params(labelsize=16)\n", - "plt.xlabel(r'$\\textrm{Re}[\\omega_{lm}]$', fontsize=16)\n", - "plt.ylabel(r'$\\textrm{Im}[\\omega_{lm}]$', fontsize=16)\n", - "plt.savefig(\"test.png\", bbox_inches=\"tight\", dpi=300)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 493, - "width": 509 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(8,8))\n", - "\n", - "for (l,m,n), seq in seqs.iteritems():\n", - " if (m==2):\n", - " plt.plot(np.real(seq.A), np.imag(seq.A))\n", - "\n", - "plt.gca().tick_params(labelsize=16)\n", - "plt.xlabel(r'$\\textrm{Re}[A_{lm}]$', fontsize=16)\n", - "plt.ylabel(r'$\\textrm{Im}[A_{lm}]$', fontsize=16)\n", - "# plt.savefig(\"test.png\", bbox_inches=\"tight\", dpi=300)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m0_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-5_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m5_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m0_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m6_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m0_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-5_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m4_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m0_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m0_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m4_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-4_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m0_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m0_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-5_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m5_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m5_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m6_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m0_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-5_n2.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m0_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m0_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m4_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-4_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m0_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m0_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m0_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-5_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m5_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m6_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-5_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m4_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m0_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m4_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-5_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m4_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-1_n3.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-4_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m0_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m0_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m4_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m5_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m5_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m6_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-5_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m0_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m4_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m4_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m0_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-5_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m4_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m5_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m0_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m0_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m5_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n6.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m0_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m4_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m4_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m0_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-4_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m0_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m5_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m0_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m0_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m5_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m3_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m6_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-5_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m0_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m4_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m4_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m0_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m4_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-4_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-1_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m0_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-2_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-3_n6.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-5_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-2_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m5_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m0_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m0_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m5_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m3_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m6_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-5_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m0_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m4_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m4_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-7_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m0_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m4_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m2_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-4_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-1_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m0_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-4_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-5_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m0_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m5_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m5_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m-1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m3_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m4_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m0_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m6_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m5_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m4_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m3_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m6_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m3_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m0_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-5_n4.pickle\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m-4_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m1_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-5_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m2_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-1_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-6_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m-1_n1.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m1_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l5_m4_n6.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m1_n5.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-6_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l2_m2_n2.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l6_m-4_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m4_n4.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l4_m2_n0.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m-4_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l3_m-3_n3.pickle\n", - "INFO:root:Writing Kerr QNM sequence to file /Users/leo/src/spectral_qnms/qnm/data/s-2_l7_m7_n6.pickle\n" - ] - } - ], - "source": [ - "for k, v in seqs.items():\n", - " qnm.cached.write_mode(v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing QNMDict" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "from qnm.schwarzschild.tabulated import QNMDict" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Loading Schw QNM dict from file /Users/leo/src/spectral_qnms/qnm/schwarzschild/data/Schw_dict.pickle\n" - ] - } - ], - "source": [ - "qnm_dict = QNMDict(init=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "image/png": { - "height": 467, - "width": 482 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "s=-2\n", - "l_max=20\n", - "n_max=20\n", - "oms = [ qnm_dict(s,l,n)[0] for l in range(np.abs(s),l_max+1) for n in range(0,n_max+1) ]\n", - "plt.figure(figsize=(8,8))\n", - "plt.scatter(np.real(oms),np.imag(oms))\n", - "plt.gca().invert_yaxis()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/qnm/__init__.py b/qnm/__init__.py index 15009fd..b1033cd 100644 --- a/qnm/__init__.py +++ b/qnm/__init__.py @@ -1,6 +1,24 @@ -""" Calculate quasinormal modes of Kerr black holes. +"""Calculate quasinormal modes of Kerr black holes. -TODO Documentation """ +The highest-level interface is via :class:`qnm.cached.KerrSeqCache`, +which will fetch instances of +:class:`qnm.spinsequence.KerrSpinSeq`. This is most clearly +demonstrated with an example. + +TODO More documentation + +Examples +-------- + +>>> import qnm +>>> # qnm.download_data() # Only need to do this once +>>> ksc = qnm.cached.KerrSeqCache(init_schw=True) # Only need init_schw once per session +>>> mode_seq = ksc(s=-2,l=2,m=2,n=0) +>>> omega, A, C = mode_seq(a=0.68) +>>> print(omega) +(0.5239751042900845-0.08151262363119974j) + +""" from __future__ import print_function, division, absolute_import diff --git a/qnm/cached.py b/qnm/cached.py index fc5222a..81dc946 100644 --- a/qnm/cached.py +++ b/qnm/cached.py @@ -1,6 +1,15 @@ """ Caching interface to Kerr QNMs -TODO Documentation. +This is a high-level interface to the package. An instance of +:class:`KerrSeqCache` will return instances of +:class:`qnm.spinsequence.KerrSpinSeq` from memory or disk. If a spin +sequence is neither in memory nor on disk then it will be computed and +returned. + +Use :meth:`download_data` to fetch a collection of precomputed spin +sequences from the web. + +TODO More documentation. """