From aa4a21ad2dc73bdc6b1d28f063936fc640563be6 Mon Sep 17 00:00:00 2001 From: David A Minton Date: Mon, 12 Jul 2021 12:40:42 -0400 Subject: [PATCH] Fixed whm_gr_test input files and Jupyter notebook, and correct Einstein's name in the constant --- examples/whm_gr_test/param.swifter.in | 2 +- examples/whm_gr_test/param.swiftest.in | 3 +- .../whm_gr_test/swiftest_relativity.ipynb | 62 +++++++++++-------- src/io/io.f90 | 2 +- src/modules/swiftest_globals.f90 | 2 +- 5 files changed, 40 insertions(+), 31 deletions(-) diff --git a/examples/whm_gr_test/param.swifter.in b/examples/whm_gr_test/param.swifter.in index 0582bd1f7..6dbf5ae15 100644 --- a/examples/whm_gr_test/param.swifter.in +++ b/examples/whm_gr_test/param.swifter.in @@ -10,7 +10,7 @@ ISTEP_DUMP 1461 BIN_OUT bin.swifter.dat OUT_TYPE REAL8 OUT_FORM EL -OUT_STAT NEW +OUT_STAT UNKNOWN J2 4.7535806948127355e-12 J4 -2.2473967953572827e-18 CHK_CLOSE yes diff --git a/examples/whm_gr_test/param.swiftest.in b/examples/whm_gr_test/param.swiftest.in index b0d8ac31c..6e7c9ff28 100644 --- a/examples/whm_gr_test/param.swiftest.in +++ b/examples/whm_gr_test/param.swiftest.in @@ -11,7 +11,7 @@ ISTEP_DUMP 1461 BIN_OUT bin.swiftest.dat OUT_TYPE REAL8 OUT_FORM EL -OUT_STAT REPLACE +OUT_STAT UNKNOWN CHK_CLOSE yes CHK_RMIN 0.004650467260962157 CHK_RMAX 1000.0 @@ -23,6 +23,7 @@ ENC_OUT enc.swiftest.dat EXTRA_FORCE no BIG_DISCARD no ROTATION no +TIDES no GR yes MU2KG 1.988409870698051e+30 DU2M 149597870700.0 diff --git a/examples/whm_gr_test/swiftest_relativity.ipynb b/examples/whm_gr_test/swiftest_relativity.ipynb index 0f753993c..ae586907c 100644 --- a/examples/whm_gr_test/swiftest_relativity.ipynb +++ b/examples/whm_gr_test/swiftest_relativity.ipynb @@ -9,51 +9,59 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import swiftestio as swio\n", + "import swiftest\n", "from astroquery.jplhorizons import Horizons" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Reading Swifter file param.swifter.in\n" + "Reading Swifter file param.swifter.in\n", + "Reading in time 1.000e+03\n", + "Creating Dataset\n", + "Successfully converted 1001 output frames.\n", + "Swifter simulation data stored as xarray DataSet .ds\n" ] } ], "source": [ - "inparfile = 'param.swifter.in'\n", - "paramgr = swio.read_swifter_param(inparfile)\n", - "swifterdat = swio.swifter2xr(paramgr)" + "swiftersim = swiftest.Simulation(param_file=\"param.swifter.in\", codename=\"Swifter\")\n", + "swiftersim.bin2xr()\n", + "swifterdat = swiftersim.ds" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Reading Swiftest file param.swiftest.in\n" + "Reading Swiftest file param.swiftest.in\n", + "Reading in time 1.000e+03\n", + "Creating Dataset\n", + "Successfully converted 1001 output frames.\n", + "Swiftest simulation data stored as xarray DataSet .ds\n" ] } ], "source": [ - "param_file_name = 'param.swiftest.in'\n", - "config = swio.read_swiftest_config(param_file_name)\n", - "swiftestdat = swio.swiftest2xr(config)" + "swiftestsim = swiftest.Simulation(param_file=\"param.swiftest.in\")\n", + "swiftestsim.bin2xr()\n", + "swiftestdat = swiftestsim.ds" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -63,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -88,18 +96,18 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "dvarpi_swiftest = np.diff(varpiswiftest) * 3600 * 100 \n", "dvarpi_swifter = np.diff(varpiswifter) * 3600 * 100 \n", - "dvarpi_obs = np.diff(varpi) / np.diff(t) * 3600 * 100 " + "dvarpi_obs = np.diff(varpi_obs) / np.diff(t) * 3600 * 100 " ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -107,17 +115,17 @@ "output_type": "stream", "text": [ "Mean precession rate for Mercury long. peri. (arcsec/100 y)\n", - "JPL Horizons : 571.3219335838123\n", - "Swifter GR : 571.1981012667945\n", - "Swiftest GR : 571.1981012549461\n", - "Obs - Swifter : 0.12383231701787104\n", - "Obs - Swiftest : 0.12383232886631326\n", - "Swiftest - Swifter: -1.1848442227346823e-08\n" + "JPL Horizons : 571.3210506300043\n", + "Swifter GR : 571.1981012667947\n", + "Swiftest GR : 1.5844780122245083\n", + "Obs - Swifter : 0.12294936320971743\n", + "Obs - Swiftest : 569.7365726177798\n", + "Swiftest - Swifter: -569.61362325457\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -163,9 +171,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "swiftestOOF", "language": "python", - "name": "python3" + "name": "swiftestoof" }, "language_info": { "codemirror_mode": { @@ -177,7 +185,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.7.10" } }, "nbformat": 4, diff --git a/src/io/io.f90 b/src/io/io.f90 index 3bcc4359b..8a6ea4ae2 100644 --- a/src/io/io.f90 +++ b/src/io/io.f90 @@ -229,7 +229,7 @@ module subroutine io_param_reader(self, unit, iotype, v_list, iostat, iomsg) self%GU = GC / (self%DU2M**3 / (self%MU2KG * self%TU2S**2)) ! Calculate the inverse speed of light in the system units - self%inv_c2 = einstinC * self%TU2S / self%DU2M + self%inv_c2 = einsteinC * self%TU2S / self%DU2M self%inv_c2 = (self%inv_c2)**(-2) if (integrator == RMVS) then diff --git a/src/modules/swiftest_globals.f90 b/src/modules/swiftest_globals.f90 index 91db0adf3..256c4124b 100644 --- a/src/modules/swiftest_globals.f90 +++ b/src/modules/swiftest_globals.f90 @@ -121,6 +121,6 @@ module swiftest_globals real(DP), parameter :: VSMALL = 4.0E-15_DP real(DP), parameter :: GC = 6.6743E-11_DP !! Universal gravitational constant in SI units - real(DP), parameter :: einstinC = 299792458.0_DP !! Speed of light in SI units + real(DP), parameter :: einsteinC = 299792458.0_DP !! Speed of light in SI units end module swiftest_globals