{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Body Class\n", "\n", "The `Body` Class within SORA was created to handle the information about the Solar System object. The online documentation here contains the details about every step. \n", "\n", "This Jupyter-Notebook was designed as a tutorial for how to work with the `Body` Class. Any further question, please contact the core team: Altair Ramos Gomes Júnior, Bruno Eduardo Morgado, Gustavo Benedetti Rossi, and Rodrigo Carlos Boufleur.\n", "\n", "**The `Body` Docstring was designed to help the users. Also, each function has its Docstring containing its main purpose and the needed parameters (physical description and formats). Please, do not hesitate to use it.**\n", "\n", "## 0. Index\n", "\n", "1. [Instantiating a Body object](#section_1)\n", "2. [Body Physical Attributes](#section_2)\n", "3. [Ephemeris](#section_3)\n", "4. [Pole Position Angle and Aperture Angle](#section_4)\n", "5. [Apparent Magnitude](#section_5)\n", "6. [The Ephemeris Classes](#section_6)\n", "7. [The frame attribute](#section_7)\n", "8. [The shape attribute](#section_8)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SORA version: 0.3\n" ] } ], "source": [ "## import the Body Class\n", "from sora.body import Body\n", "\n", "## To facilitate, sora allows to import Body directly from the root\n", "from sora import Body" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 1. Instantiating a Body object\n", "\n", "A Solar System Body have a lot of parameters that characterizes itself. The SORA `Body` Class is able to download these parameter from the JPL Small Body DataBase. For that, a name should be given to search the object." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mInit signature:\u001b[0m \u001b[0mBody\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatabase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "Class that contains and manages the information of the body.\n", "\n", "Attributes\n", "----------\n", "name : `str`, required\n", " The name of the object. It can be the used `spkid` or `designation\n", " number` to query the SBDB (Small-Body DataBase). In this case, the name\n", " is case insensitive.\n", "\n", "database : `str`, optional, default='auto'\n", " The database to query the object. It can be ``satdb`` for our temporary\n", " hardcoded satellite database, or ``'sbdb'`` to query on the SBDB. If\n", " database is set as ``auto`` it will try first with ``satdb``,\n", " then ``sbdb``. If the user wants to use their own information,\n", " database must be given as ``None``. In this case, `spkid` parameter\n", " must be given.\n", "\n", "ephem : `sora.EphemKernel`, `sora.EphemHorizons`, `sora.EphemJPL`, `sora.EphemPlanete`\n", " An Ephem Class that contains information about the ephemeris. It can be\n", " \"horizons\" to automatically defined an EphemHorizons object or a list of\n", " kernels to automatically define an EphemKernel object.\n", "\n", "orbit_class : `str`\n", " It defines the Orbital class of the body. It can be ``TNO``,\n", " ``Satellite``, ``Centaur``, ``comet``, ``asteroid``, ``trojan``, ``neo``,\n", " and ``planet``. It is important for a better characterization of the\n", " object. If a different value is given, it will be defined as\n", " ``unclassified``.\n", "\n", "spkid : `str`, `int`, `float`\n", " If ``database=None``, the user must give a `spkid` or an `ephem`\n", " which has the `spkid` parameter.\n", "\n", "shape : `str`, `sora.body.shape.Shape3D`\n", " It defines the input shape of the body. It can be a body.shape object\n", " or the path to OBJ file.\n", "\n", "albedo : `float`, `int`\n", " The albedo of the object.\n", "\n", "H : `float`, `int`\n", " The absolute magnitude.\n", "\n", "G : `float`, `int`\n", " The phase slope.\n", "\n", "diameter : `float`, `int`, `astropy.quantity.Quantity`\n", " The diameter of the object, in km.\n", "\n", "density : `float`, `int`, `astropy.quantity.Quantity`\n", " The density of the object, in g/cm³.\n", "\n", "GM : `float`, `int`, `astropy.quantity.Quantity`\n", " The Standard Gravitational Parameter, in km³/s².\n", "\n", "rotation : `float`, `int`, `astropy.quantity.Quantity`\n", " The Rotation of the object, in hours.\n", "\n", "pole : `str`, `astropy.coordinates.SkyCoord`\n", " The Pole coordinates of the object. It can be a `SkyCoord object` or a\n", " string in the format ``'hh mm ss.ss +dd mm ss.ss'``.\n", "\n", "BV : `float`, `int`\n", " The B-V color.\n", "\n", "UB : `float`, `int`\n", " The U-B color.\n", "\n", "smass : `str`\n", " The spectral type in SMASS classification.\n", "\n", "tholen : `str`\n", " The spectral type in Tholen classification.\n", "\n", "Note\n", "----\n", "The following attributes are are returned from the Small-Body DataBase when\n", "``database='sbdb'`` or from our temporary hardcoded Satellite DataBase when\n", "``database='satdb'``:\n", "\n", "`orbit_class`, `spkid`, `albedo`, `H`, `G`, `diameter`, `density`, `GM`,\n", "`rotation`, `pole`, `BV`, `UB`, `smass`, and `tholen`.\n", "\n", "These are physical parameters the user can give to the object. If a query is\n", "made and user gives a parameter, the parameter given by the user is defined\n", "in the *Body* object.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/body/core.py\n", "\u001b[0;31mType:\u001b[0m type\n", "\u001b[0;31mSubclasses:\u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Body?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obtaining data for Ceres from SBDB\n" ] } ], "source": [ "ceres = Body(name='Ceres')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###############################################################################\n", " 1 Ceres (A801 AA) \n", "###############################################################################\n", "Object Orbital Class: Main-belt Asteroid\n", "Spectral Type:\n", " SMASS: C [Reference: EAR-A-5-DDR-TAXONOMY-V4.0]\n", " Tholen: G [Reference: EAR-A-5-DDR-TAXONOMY-V4.0]\n", " Linear, generally featureless spectra. Differences in UV absorption features and presence/absence of narrow absorption feature near 0.7 μm.\n", "Discovered 1801-Jan-01 by Piazzi, G. at Palermo\n", "\n", "Physical parameters:\n", "Diameter:\n", " 939.4 +/- 0.2 km\n", " Reference: Nature vol. 537, pp515-517 (22 September 2016), \n", "Mass:\n", " 9.3835e+20 +/- 1.3485e+16 kg\n", " Reference: Nature vol. 537, pp515-517 (22 September 2016), \n", "Density:\n", " 2.162 +/- 0.008 g / cm3\n", " Reference: Nature vol. 537, pp515-517 (22 September 2016), \n", "Rotation:\n", " 9.0742 +/- 1e-06 h\n", " Reference: Nature vol. 537, pp515-517 (22 September 2016), derived from published value: 952.1532 +/- 0.0001 deg./day\n", "Pole\n", " RA:291d25m04.8s +/- 0h00m00s\n", " DEC:66d45m50.4s +/- 0d00m00s\n", " Reference: User, \n", "Absolute Magnitude:\n", " 3.33 +/- 0 mag\n", " Reference: MPO719049, IRAS observations used: 15\n", "Phase Slope:\n", " 0.12 +/- 0 \n", " Reference: PDS3 (MPC 17257), Fit\n", "Albedo:\n", " 0.09 +/- 0.003 \n", " Reference: Li et al. (2006) Icarus v182:pp143-160, V-band geometric albedo\n", "B-V color:\n", " 0.713 +/- 0.014 \n", " Reference: EAR-A-5-DDR-UBV-MEAN-VALUES-V1.2, #obs=52; phase (min.=1.47, mean=11.85, max.=22.59) deg.\n", "U-B color:\n", " 0.426 +/- 0.026 \n", " Reference: EAR-A-5-DDR-UBV-MEAN-VALUES-V1.2, #obs=52; phase (min.=1.47, mean=12.16, max.=22.59) deg.\n", "\n", "PlanetocentricFrame:\n", " Epoch: J2000.000 tdb\n", " alpha_pole = 291.418 +0.000000*T +0.000000\n", " delta_pole = 66.764 +0.000000*T +0.000000\n", " W = 170.65 +952.153200*d +0.000000\n", " Reference: Archinal, B. A., Report of the IAU Working Group on Cartographic Coordinates and Rotational Elements: 2015, Celestial Mechanics and Dynamical Astronomy, (2018) 130:22\n", "\n", "Ellipsoid: 482.2 x 482.1 x 445.9\n", "\n", "----------- Ephemeris -----------\n", "\n", "EphemHorizons: Ephemeris are downloaded from Horizons website (SPKID=20000001)\n", "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n", "\n" ] } ], "source": [ "print(ceres)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other parameters that are interpreted by the `Body` Class are downloaded from the SBDB. They are accessed vi the attribute `Body.meta_sbdb`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('object',\n", " OrderedDict([('orbit_class',\n", " OrderedDict([('code', 'MBA'),\n", " ('name', 'Main-belt Asteroid')])),\n", " ('des', '1'),\n", " ('fullname', '1 Ceres (A801 AA)'),\n", " ('shortname', '1 Ceres'),\n", " ('kind', 'an'),\n", " ('neo', False),\n", " ('orbit_id', '48'),\n", " ('pha', False),\n", " ('spkid', '20000001'),\n", " ('prefix', None)])),\n", " ('discovery',\n", " OrderedDict([('location', 'Palermo'),\n", " ('cref', None),\n", " ('site', None),\n", " ('discovery',\n", " 'Discovered 1801-01-01 by Piazzi, G. at Palermo'),\n", " ('name', None),\n", " ('date', '1801-Jan-01'),\n", " ('ref', 'DISCOVERY.DB'),\n", " ('who', 'Piazzi, G.'),\n", " ('citation', None)])),\n", " ('phys_par',\n", " OrderedDict([('H', 3.33),\n", " ('H_sig', None),\n", " ('H_ref', 'MPO719049'),\n", " ('H_note', 'IRAS observations used: 15'),\n", " ('G', 0.12),\n", " ('G_sig', None),\n", " ('G_ref', 'PDS3 (MPC 17257)'),\n", " ('G_note', 'Fit'),\n", " ('diameter', ),\n", " ('diameter_sig', ),\n", " ('diameter_ref',\n", " 'Nature vol. 537, pp515-517 (22 September 2016)'),\n", " ('diameter_note', None),\n", " ('extent', '964.4 x 964.2 x 891.8'),\n", " ('extent_sig', '0.2 x 0.2 x 0.2'),\n", " ('extent_ref',\n", " 'Nature vol. 537, pp515-517 (22 September 2016)'),\n", " ('extent_note',\n", " 'fit w.r.t. center of mass and in body-fixed frame'),\n", " ('GM', ),\n", " ('GM_sig', ),\n", " ('GM_ref',\n", " 'Nature vol. 537, pp515-517 (22 September 2016)'),\n", " ('GM_note', None),\n", " ('density', ),\n", " ('density_sig', ),\n", " ('density_ref',\n", " 'Nature vol. 537, pp515-517 (22 September 2016)'),\n", " ('density_note', None),\n", " ('rot_per', ),\n", " ('rot_per_sig', ),\n", " ('rot_per_ref',\n", " 'Nature vol. 537, pp515-517 (22 September 2016)'),\n", " ('rot_per_note',\n", " 'derived from published value: 952.1532 +/- 0.0001 deg./day'),\n", " ('pole', '291.421/66.758'),\n", " ('pole_sig', '0.007/0.002'),\n", " ('pole_ref',\n", " 'Nature vol. 537, pp515-517 (22 September 2016)'),\n", " ('pole_note', None),\n", " ('albedo', 0.09),\n", " ('albedo_sig', 0.003),\n", " ('albedo_ref',\n", " 'Li et al. (2006) Icarus v182:pp143-160'),\n", " ('albedo_note', 'V-band geometric albedo'),\n", " ('BV', 0.713),\n", " ('BV_sig', 0.014),\n", " ('BV_ref', 'EAR-A-5-DDR-UBV-MEAN-VALUES-V1.2'),\n", " ('BV_note',\n", " '#obs=52; phase (min.=1.47, mean=11.85, max.=22.59) deg.'),\n", " ('UB', 0.426),\n", " ('UB_sig', 0.026),\n", " ('UB_ref', 'EAR-A-5-DDR-UBV-MEAN-VALUES-V1.2'),\n", " ('UB_note',\n", " '#obs=52; phase (min.=1.47, mean=12.16, max.=22.59) deg.'),\n", " ('spec_T', 'G'),\n", " ('spec_T_sig', 'None'),\n", " ('spec_T_ref', 'EAR-A-5-DDR-TAXONOMY-V4.0'),\n", " ('spec_T_note',\n", " 'used 7 color indices; used ground-based radiometric albedo'),\n", " ('spec_B', 'C'),\n", " ('spec_B_sig', 'None'),\n", " ('spec_B_ref', 'EAR-A-5-DDR-TAXONOMY-V4.0'),\n", " ('spec_B_note',\n", " 'based on a high-resolution spectrum by Xu et al. (1995) or Bus and Binzel (2002)')])),\n", " ('orbit',\n", " OrderedDict([('cov_epoch', ),\n", " ('first_obs', '1995-01-05'),\n", " ('epoch', ),\n", " ('source', 'JPL'),\n", " ('moid_jup', ),\n", " ('n_del_obs_used', 60),\n", " ('comment', None),\n", " ('elements',\n", " OrderedDict([('e', 0.07687465013145245),\n", " ('e_sig', 4.82457301562052e-12),\n", " ('q', ),\n", " ('q_sig',\n", " ),\n", " ('tp', ),\n", " ('tp_sig',\n", " ),\n", " ('om', ),\n", " ('om_sig',\n", " ),\n", " ('w', ),\n", " ('w_sig',\n", " ),\n", " ('i', ),\n", " ('i_sig',\n", " )])),\n", " ('data_arc', '9520'),\n", " ('producer', 'Davide Farnocchia'),\n", " ('two_body', None),\n", " ('equinox', 'J2000'),\n", " ('soln_date', '2021-04-13 11:04:44'),\n", " ('rms', '.43153'),\n", " ('sb_used', 'SB441-N373'),\n", " ('moid', ),\n", " ('pe_used', 'DE441'),\n", " ('model_pars', []),\n", " ('n_obs_used', 1075),\n", " ('not_valid_after', None),\n", " ('not_valid_before', None),\n", " ('t_jup', '3.310'),\n", " ('last_obs', '2021-01-28'),\n", " ('condition_code', '0'),\n", " ('n_dop_obs_used', 0),\n", " ('orbit_id', '48')])),\n", " ('signature',\n", " OrderedDict([('version', '1.3'),\n", " ('source',\n", " 'NASA/JPL Small-Body Database (SBDB) API')]))])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ceres.meta_sbdb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SORA team also created a hardcoded database with planetary satellites and planets to overcome the missing of these data on the SBDB. It is an interim solution and will be removed when a more reliable database is implemented." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###############################################################################\n", " Himalia \n", "###############################################################################\n", "Object Orbital Class: Natural Satellite\n", "\n", "\n", "Physical parameters:\n", "Diameter:\n", " 139.6 +/- 1.7 km\n", " Reference: Grav et al. (2015). AJ 809(1):3, \n", "Mass:\n", " 4.1952e+18 +/- 5.9931e+17 kg\n", " Reference: Emelyanov (2005). A&A 438(3):L33-L36, \n", "Rotation:\n", " 7.7819 +/- 0.0005 h\n", " Reference: Pilcher et al. (2012). Icarus 219(2):741-742, \n", "Absolute Magnitude:\n", " 8 +/- 0.01 mag\n", " Reference: Rettig et al. (2001). Icarus 154(2):313-320, \n", "Phase Slope:\n", " 0.1 +/- 0.15 \n", " Reference: , \n", "Albedo:\n", " 0.057 +/- 0.008 \n", " Reference: Grav et al. (2015). AJ 809(1):3, \n", "\n", "Ellipsoid: 69.8 x 69.8 x 69.8\n", "\n", "----------- Ephemeris -----------\n", "\n", "EphemHorizons: Ephemeris are downloaded from Horizons website (SPKID=506)\n", "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n", "\n" ] } ], "source": [ "himalia = Body(name='Himalia', database='satdb')\n", "print(himalia)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the user can also define a `Body` object without any database. In this case, it is required to define the parameters: `[name, spkid, database=None, orbit_class]`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###############################################################################\n", " Elara \n", "###############################################################################\n", "Object Orbital Class: Natural Satellite\n", "\n", "\n", "Physical parameters:\n", "\n", "Ellipsoid: nan x nan x nan\n", "\n", "----------- Ephemeris -----------\n", "\n", "EphemHorizons: Ephemeris are downloaded from Horizons website (SPKID=507)\n", "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n", "\n" ] } ], "source": [ "elara = Body(name='Elara', database=None, spkid=507, orbit_class='satellite')\n", "print(elara)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 2. Body Physical Attributes\n", "\n", "The parameters shown in the print can be accessed by its attribute name.\n", "\n", "These paremeters also have uncertainty and reference, which may be edited" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$939.4 \\; \\mathrm{km}$" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ceres.diameter" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$0.2 \\; \\mathrm{km}$" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ceres.diameter.uncertainty" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Nature vol. 537, pp515-517 (22 September 2016)'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ceres.diameter.reference" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Diameter:\n", " 939.4 +/- 0.2 km\n", " Reference: Nature vol. 537, pp515-517 (22 September 2016), \n", "\n" ] } ], "source": [ "print(ceres.diameter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1. Editing Body Attributes\n", "\n", "To edit a parameter, simply redefine the attribute. If the core value is edited, the uncertainty is redefined to \"0\", and the reference to \"user\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "ceres.diameter = 972" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Diameter:\n", " 972 +/- 0 km\n", " Reference: User, \n", "\n" ] } ], "source": [ "print(ceres.diameter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To correct the uncertainty and reference, just redefine them" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "ceres.diameter.uncertainty = 6\n", "ceres.diameter.reference = \"Gomes-Júnior et al. (2015), MNRAS 451(3), 2295-2302\"\n", "ceres.diameter.notes = \"Equatorial Diameter\"" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Diameter:\n", " 972 +/- 6 km\n", " Reference: Gomes-Júnior et al. (2015), MNRAS 451(3), 2295-2302, Equatorial Diameter\n", "\n" ] } ], "source": [ "print(ceres.diameter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, if you want to edit all the values of a single parameter directly, you must use the `PhysicalData` Class located in sora.body" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mInit signature:\u001b[0m\n", "\u001b[0mPhysicalData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0muncertainty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mreference\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'User'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mnotes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mUnit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdimensionless\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mraise_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "Defines PhysicalData with uncertainty, reference and notes.\n", "\n", "Note\n", "----\n", "It inherits from astropy.units.quantity.Quantity().\n", "\n", "Parameters\n", "----------\n", "name :`str`\n", " The name representing the corresponding physical parameter.\n", "\n", "value : `int`, `float`, `str`, `~numpy.ndarray`, `astropy.quantity.Quantity`\n", " The numerical value of this quantity in the units given by unit. If\n", " a `Quantity` (or any other valid object with a ``unit`` attribute),\n", " creates a new `Quantity` object, converting to `unit` units as needed.\n", " If a string, it is converted to a number or `Quantity`, depending on\n", " whether a unit is present.\n", "\n", "uncertainty : `int`, `float`, `str`, `~numpy.ndarray`, `astropy.quantity.Quantity`, default=0\n", " The numerical value of this quantity in the units given by unit. If\n", " a `Quantity` (or any other valid object with a ``unit`` attribute),\n", " creates a new `Quantity` object, converting to `unit` units as needed.\n", " If a string, it is converted to a number or `Quantity`, depending on\n", " whether a unit is present.\n", "\n", "reference : `str`, default='User'\n", " A string stating the reference for the parameter value.\n", "\n", "notes : `str`, default=''\n", " Any other important information about the physical parameter.\n", "\n", "unit : `str`, `~astropy.units.UnitBase` instance, default='dimensionless'\n", " An object that represents the unit associated with the input value. Must\n", " be an `~astropy.units.UnitBase` object or a string parsable by the\n", " :mod:`~astropy.units` package.\n", "\n", "raise_error : `bool`, default=False\n", " If ``value=None`` or ``raise_error=True`` the function raises an error,\n", " else `value` is redefined to ``NaN``.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/body/meta.py\n", "\u001b[0;31mType:\u001b[0m type\n", "\u001b[0;31mSubclasses:\u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sora.body import PhysicalData\n", "import astropy.units as u\n", "PhysicalData?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Diameter:\n", " 972 +/- 6 km\n", " Reference: Gomes-Júnior et al. (2015), MNRAS 451(3), 2295-2302, Equatorial Radius\n", "\n" ] } ], "source": [ "ceres.diameter = PhysicalData(name='Diameter', value=972, uncertainty=6, reference=\"Gomes-Júnior et al. (2015), MNRAS 451(3), 2295-2302\",\n", " notes=\"Equatorial Radius\", unit=u.km)\n", "print(ceres.diameter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some parameters have direct relations when modified, for instance: diameter and radius; and GM and mass." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Standard Gravitational Parameter:\n", " 62.628 +/- 0.0009 km3 / s2\n", " Reference: Nature vol. 537, pp515-517 (22 September 2016), \n", "\n", "Mass:\n", " 9.3835e+20 +/- 1.3485e+16 kg\n", " Reference: Nature vol. 537, pp515-517 (22 September 2016), \n", "\n" ] } ], "source": [ "print(ceres.GM)\n", "print(ceres.mass)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "ceres.GM = 63\n", "ceres.GM.uncertainty = 0.0010" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Standard Gravitational Parameter:\n", " 63 +/- 0.001 km3 / s2\n", " Reference: User, \n", "\n", "Mass:\n", " 9.4392e+20 +/- 1.4983e+16 kg\n", " Reference: User, \n", "\n" ] } ], "source": [ "print(ceres.GM)\n", "print(ceres.mass)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 3. Ephemeris\n", "\n", "An important feature of the body is its ephemeris. It is the ephemeris that will tell us te position of the object for a given time. It is necessary the `Body` object to have an Ephemeris when reducing an Occultation. The full description of the Ephemeris options are given at [Section 6](#section_6).\n", "\n", "To add a ephemeris to a `Body` object, just given them as input or edit the ephem attribute:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obtaining data for Chariklo from SBDB\n" ] } ], "source": [ "# Giving the kernels directly using an EphemKernel object\n", "chariklo = Body(name='Chariklo', ephem=['input/bsp/Chariklo.bsp', 'input/bsp/de438_small.bsp'], spkid='2010199')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obtaining data for Chariklo from SBDB\n" ] } ], "source": [ "chariklo = Body(name='Chariklo', spkid='2010199')\n", "chariklo.ephem = ['input/bsp/Chariklo.bsp', 'input/bsp/de438_small.bsp']" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###############################################################################\n", " 10199 Chariklo (1997 CU26) \n", "###############################################################################\n", "Object Orbital Class: Centaur\n", "Spectral Type:\n", " SMASS: D [Reference: EAR-A-5-DDR-TAXONOMY-V4.0]\n", " Relatively featureless spectrum with very steep red slope.\n", "Discovered 1997-Feb-15 by Spacewatch at Kitt Peak\n", "\n", "Physical parameters:\n", "Diameter:\n", " 302 +/- 30 km\n", " Reference: Earth, Moon, and Planets, v. 89, Issue 1, p. 117-134 (2002), \n", "Rotation:\n", " 7.004 +/- 0 h\n", " Reference: LCDB (Rev. 2023-February); Warner et al., 2009, [Result based on less than full coverage, so that the period may be wrong by 30 percent or so.] REFERENCE LIST:[Fornasier, S.; Lazzaro, D.; Alvarez-Candal, A.; Snodgrass, C.; et al. (2014) Astron. Astrophys. 568, L11.], [Leiva, R.; Sicardy, B.; Camargo, J.I.B.; Desmars, J.; et al. (2017) Astron. J. 154, A159.]\n", "Absolute Magnitude:\n", " 6.55 +/- 0 mag\n", " Reference: MPO719217, \n", "Albedo:\n", " 0.045 +/- 0.01 \n", " Reference: Earth, Moon, and Planets, v. 89, Issue 1, p. 117-134 (2002), \n", "\n", "Ellipsoid: 151.0 x 151.0 x 151.0\n", "\n", "----------- Ephemeris -----------\n", "\n", "EphemKernel: CHARIKLO/DE438_SMALL (SPKID=2010199)\n", "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n", "\n" ] } ], "source": [ "print(chariklo)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obtaining data for Ceres from SBDB\n" ] } ], "source": [ "# Using the ephemeris from Horizons with an EphemHorizons object\n", "ceres = Body(name='Ceres', ephem='horizons')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ephemeris parameters can also be an `EphemKernel`, `EphemHorizons` or `EphemPlanete` objects given directly. Only `EphemPlanete` requires the ephemeris object being defined before associated to a `Body` object." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "from sora import EphemKernel\n", "ephem = EphemKernel(kernels=['input/bsp/Chariklo.bsp', 'input/bsp/de438_small.bsp'], spkid='2010199')\n", "chariklo.ephem = ephem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a full description of the Ephemeris objects, see [Section 6](#section_6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 4. Pole Position Angle and Aperture Angle\n", "\n", "For a given time, and given pole coordinate, we are able to calculate the Pole Position Angle relative to the North pole in the ICRS and the Aperture Angle of the object pole coordinate. For this, it is necessary for the `Body` object to have pole coordinates and ephemeris." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mceres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_pole_position_angle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'geocenter'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Returns the pole position angle and the aperture angle relative to\n", "the geocenter.\n", "\n", "Parameters\n", "----------\n", "time : `str`, `astropy.time.Time`\n", " Time from which to calculate the position.\n", " It can be a string in the ISO format (yyyy-mm-dd hh:mm:ss.s) or an astropy Time object.\n", "\n", "observer : `str`, `sora.Observer`, `sora.Spacecraft`\n", " IAU code of the observer (must be present in given list of kernels),\n", " a SORA observer object or a string: ['geocenter', 'barycenter']\n", "\n", "Returns\n", "-------\n", "position_angle, aperture_angle : `float` array\n", " Position angle and aperture angle of the object's pole, in degrees.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/body/core.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ceres.get_pole_position_angle?" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'19h25m40.32s +66d45m50.4s'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ceres.pole.to_string('hmsdms')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "348.5707051283039 deg\n", "-2.5635901219177977 deg\n" ] } ], "source": [ "pos_ang, ap_ang = ceres.get_pole_position_angle(time='2017-06-22 00:00:00')\n", "print(pos_ang)\n", "print(ap_ang)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 5. Apparent Magnitude\n", "\n", "The Body objects calculates the Apparent Magnitude of an object using the H and G given by the user. The equation is:\n", "\n", "$\\phi_0 = \\exp(-3.33(\\tan(0.5 phase)^{0.63}))$\n", "\n", "$\\phi_1 = \\exp(-1.87(\\tan(0.5 phase)^{1.22}))$\n", "\n", "$apmag = H - 2.5\\log10((1-G)\\phi_0 + G\\phi_1) + 5\\log10(sundist*dist)$\n", "\n", "If the Body object has the Absolute Magnitude (H), Phase Slope (G) and an ephemeris, the apparent magnitude will be calculated. Else, a query to the JPL Horizons web service will be made" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Absolute Magnitude:\n", " 3.33 +/- 0 mag\n", " Reference: MPO719049, IRAS observations used: 15\n", " Phase Slope:\n", " 0.12 +/- 0 \n", " Reference: PDS3 (MPC 17257), Fit\n", "\n" ] } ], "source": [ "print(ceres.H, ceres.G)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9.011349951700899" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ceres.apparent_magnitude(time='2017-06-22 00:00:00')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Absolute Magnitude:\n", " 6.55 +/- 0 mag\n", " Reference: MPO719217, \n", " \n" ] } ], "source": [ "print(chariklo.H, chariklo.G)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/altair/Documentos/códigos/SORA/sora/body/core.py:337: UserWarning: H and/or G is not defined for 10199 Chariklo. Searching into JPL Horizons service\n", " warnings.warn('H and/or G is not defined for {}. Searching into JPL Horizons service'.format(self.shortname))\n" ] }, { "data": { "text/plain": [ "18.489" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chariklo.apparent_magnitude(time='2017-06-22 00:00:00')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the apparent magnitude is downloaded from the Horizons service, usually the H and G parameters can be found. In this cases, the `Body` object stores these values for further use." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Absolute Magnitude:\n", " 6.54 +/- 0 mag\n", " Reference: JPL Horizons, \n", " Phase Slope:\n", " 0.15 +/- 0 \n", " Reference: JPL Horizons, \n", "\n" ] } ], "source": [ "print(chariklo.H, chariklo.G)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "18.489144139264898" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chariklo.apparent_magnitude(time='2017-06-22 00:00:00')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 6. The Ephemeris Classes\n", "\n", "As stated above, there are 3 different types of `Ephemeris` Classes.\n", "\n", "- `EphemPlanete` - Reads a file with a list of Geocentric coordinates and interpolates.\n", "- `EphemHorizons` - Queries the JPL Horizons website and download ephemeris informations.\n", "- `EphemKernel` - Reads the BSP files to calculate ephemeris.\n", "\n", "Usually, the user will only need one of those. As shown in [Section 3](#section_3), the user can tell the body object the kernels for a `EphemKernel` object or the string 'horizons' for a `EphemHorizons` object. In this section we show the Ephemeris behave and if the user wants to use it directly without a `Body` object." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "## import the Ephemeris Classes\n", "from sora.ephem import EphemPlanete, EphemHorizons, EphemKernel\n", "\n", "## To facilitate, sora allows to import Ephem directly from the sora package.\n", "from sora import EphemPlanete, EphemHorizons, EphemKernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.1. Instantiating an EphemPlanete Object\n", "\n", "In this case, a file must be given containing Julian Date, RA (in deg), DEC (in deg) and Distance (in AU), in this order, separated by an space. Please, look at the \"ephem_phoebe_ph15_jd.txt\" file for an example. Only the name of the object and the file with the ephemeris is required." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mInit signature:\u001b[0m \u001b[0mEphemPlanete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mephem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspkid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "Class used to simulate former Fortran programs `ephem_planete` and\n", "`fit_d2_ksi_eta`.\n", "\n", "Attributes\n", "----------\n", "ephem : `file`, required\n", " Input file with JD (UTC), geocentric RA (deg), DEC (deg), and\n", " distance (AU).\n", "\n", "name : `str`, optional, default=None\n", " Name of the object to search in the JPL database.\n", "\n", "radius : `int`, `float`, optional, default: online database\n", " Object radius, in km.\n", "\n", "error_ra : `int`, `float`, optional, default: online database\n", " Ephemeris RA*cosDEC error, in arcsec.\n", "\n", "error_dec : `int`, `float`, optional, default: online database\n", " Ephemeris DEC error, in arcsec.\n", "\n", "mass : `int`, `float`, optional. default=0\n", " Object mass, in kg.\n", "\n", "H : `int`, `float`, optional, default=NaN\n", " Object absolute magnitude.\n", "\n", "G : `int`, `float`, optional, default=NaN\n", " Object phase slope.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/ephem/core.py\n", "\u001b[0;31mType:\u001b[0m type\n", "\u001b[0;31mSubclasses:\u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "EphemPlanete?" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "eph_pla = EphemPlanete(name='Phoebe', ephem='input/ascii/ephem_phoebe_ph15_jd.txt')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------- Ephemeris -----------\n", "\n", "EphemPlanete: Valid from 2019-06-07 02:54:00.000 until 2019-06-07 04:54:00.000 (SPKID=)\n", "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n" ] } ], "source": [ "print(eph_pla)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2. Instantiating an EphemHorizons Object\n", "\n", "In this case, only the name of the object is required as this will be used to queries the JPL database. Another important keyword is the \"id_type\", which will be used to constrain the search of the object. For instance, if the name is \"Europa\" and the id_type is \"smallbody\" it looks the asteroid (52) Europa, while if id_type is \"majorbody\" it looks for the Jovian satellite J2 Europa. However, in some cases, there are still conflict name issue which solutions are being verified. A common solver is to use the IAU ID number in the name variable" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mInit signature:\u001b[0m \u001b[0mEphemHorizons\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'smallbody'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspkid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "Obtains the ephemeris from Horizons/JPL service.\n", "\n", "Note\n", "----\n", "Web tool URL: https://ssd.jpl.nasa.gov/horizons.cgi\n", "\n", "\n", "Attributes\n", "----------\n", "name : `str`, required\n", " Name of the object to search in the JPL database.\n", "\n", "id_type: `str`, default='smallbody'\n", " Type of object options: ``smallbody``, ``majorbody`` (planets but\n", " also anything that is not a small body), ``designation``, ``name``,\n", " ``asteroid_name``, ``comet_name``, ``id`` (Horizons id number), or\n", " ``smallbody`` (find the closest match under any id_type).\n", "\n", "radius : `int`, `float`, default: online database\n", " Object radius, in km.\n", "\n", "error_ra : `int`, `float`, default: online database\n", " Ephemeris RA*cosDEC error, in arcsec.\n", "\n", "error_dec : `int`, `float`, default: online database\n", " Ephemeris DEC error, in arcsec.\n", "\n", "mass : `int`, `float`, default=0\n", " Object mass, in kg.\n", "\n", "H : `int`, `float`, default=NaN\n", " Object absolute magnitude.\n", "\n", "G : `int`, `float`, default=NaN\n", " Object phase slope.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/ephem/core.py\n", "\u001b[0;31mType:\u001b[0m type\n", "\u001b[0;31mSubclasses:\u001b[0m EphemJPL\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "EphemHorizons?" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "eph_hor = EphemHorizons(name='Chariklo')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------- Ephemeris -----------\n", "\n", "EphemHorizons: Ephemeris are downloaded from Horizons website (SPKID=)\n", "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n" ] } ], "source": [ "print(eph_hor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.3. Instantiating an EphemKernel Object\n", "\n", "This is the most effective `Ephemeris` Class. Since it reads the BSP files directly, it is faster. What is needed here is the spkid of the object, and the list of kernels. Even if only one kernel is passed, it must be a list." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mInit signature:\u001b[0m \u001b[0mEphemKernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspkid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "Gets the ephemeris from BSP kernels.\n", "\n", "Parameters\n", "----------\n", "name : `str`, optional, default=None\n", " Name of the object to search in the JPL database.\n", "\n", "spkid : `str`, required\n", " `spkid` of the targeting object. Former 'code' (v0.1).\n", "\n", "kernels : `list`, required\n", " List of paths for kernels files.\n", "\n", "radius : `int`, `float`, optional, default: online database\n", " Object radius, in km.\n", "\n", "error_ra : `int`, `float`, optional, default: online database\n", " Ephemeris RA*cosDEC error, in arcsec .\n", "\n", "error_dec : `int`, `float`, optional, default: online database\n", " Ephemeris DEC error, in arcsec.\n", "\n", "mass : `int`, `float`, optional, default=0\n", " Object Mass, in kg.\n", "\n", "H : `int`, `float`, optional, default=NaN\n", " Object Absolute Magnitude.\n", "\n", "G : `int`, `float`, optional, default=NaN\n", " Object Phase slope.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/ephem/core.py\n", "\u001b[0;31mType:\u001b[0m type\n", "\u001b[0;31mSubclasses:\u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "EphemKernel?" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "eph_ker = EphemKernel(name='Chariklo', spkid='2010199', kernels=['input/bsp/Chariklo.bsp', 'input/bsp/de438_small.bsp'])" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------- Ephemeris -----------\n", "\n", "EphemKernel: CHARIKLO/DE438_SMALL (SPKID=2010199)\n", "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n" ] } ], "source": [ "print(eph_ker)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.4 Other parameters\n", "\n", "All the 3 `Ephemeris` Classes allow for the user to pass the ephemeris error in RA*cosDEC and DEC." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "eph_ker = EphemKernel(name='Chariklo', spkid='2010199', kernels=['input/bsp/Chariklo.bsp', 'input/bsp/de438_small.bsp'], error_ra=0.02, error_dec=0.02)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------- Ephemeris -----------\n", "\n", "EphemKernel: CHARIKLO/DE438_SMALL (SPKID=2010199)\n", "Ephem Error: RA*cosDEC: 0.020 arcsec; DEC: 0.020 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n" ] } ], "source": [ "print(eph_ker)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.5. Obtaining positions\n", "\n", "The `Ephemeris` allows the user to get the astrometric position of the object for a certain epoch. The returned variable is an Astropy SkyCoord object, so their functions can be used." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mchariklo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mephem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'geocenter'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Returns the object geocentric position.\n", "\n", "Parameters\n", "----------\n", "time : `str`, `astropy.time.Time`\n", " Reference time to calculate the object position. It can be a string\n", " in the ISO format (yyyy-mm-dd hh:mm:ss.s) or an astropy Time object.\n", "\n", "observer : `str`, `sora.Observer`, `sora.Spacecraft`\n", " IAU code of the observer (must be present in given list of kernels),\n", " a SORA observer object or a string: ['geocenter', 'barycenter']\n", "\n", "Returns\n", "-------\n", "coord : `astropy.coordinates.SkyCoord`\n", " Astropy SkyCoord object with the object coordinates at the given time.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/ephem/core.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "chariklo.ephem.get_position?" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "pos = chariklo.ephem.get_position(time='2017-06-22 00:00:00', observer='geocenter')\n", "print(pos)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RA = 283d52m00.66441732s\n", "DEC = -31d31m20.56535577s\n", "Distance = 14.661401433721714 AU\n", "Coordinate = 18 55 28.04429 -31 31 20.56536\n" ] } ], "source": [ "print('RA =', pos.ra)\n", "print('DEC =', pos.dec)\n", "print('Distance =', pos.distance.to('AU'))\n", "print('Coordinate =', pos.to_string('hmsdms', precision=5, sep=' '))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eph_hor.get_position(time='2017-06-22 00:00:00', observer='barycenter')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The astrometric position can also be obtained for a specific observer.**\n", "\n", "Do not use this method with the EphemPlanete Class since the input coordinates already have an observer associated." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sora import Observer\n", "opd = Observer(name='Pico dos Dias Observatory', code='874')\n", "\n", "chariklo.ephem.get_position(time='2017-06-22 00:00:00', observer=opd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.6. Ksi and Eta projection\n", "\n", "`Ephemeris` objects calculates the orthographic projection (ksi and eta) of a site in the direction of a star given the following function. Ksi is in the East direction and Eta is in the North direction.\n", "\n", "The coordinates of the star given must be in the Geocentric Celestial Reference System (GCRS).\n", "\n", "This is calculated automaticaly in `Occultation`." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mchariklo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mephem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ksi_eta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Returns the object's projected position relative to a star.\n", "\n", "Returns the projected position (orthographic projection) of the object\n", "in the tangent sky plane relative to a star.\n", "\n", "Parameters\n", "----------\n", "time : `str`, `astropy.time.Time`\n", " Reference time to calculate the object position. It can be a string\n", " in the ISO format (yyyy-mm-dd hh:mm:ss.s) or an astropy Time object.\n", "\n", "star : `str`, `astropy.coordinates.SkyCoord`\n", " Coordinate of the star in the same reference frame as the ephemeris.\n", "\n", "Returns\n", "-------\n", "ksi, eta : `float`\n", " Projected position (orthographic projection) of the object in the\n", " tangent sky plane relative to a star.\n", " ``ksi`` is in the North-South direction (North positive).\n", " ``eta`` is in the East-West direction (East positive).\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/ephem/meta.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "chariklo.ephem.get_ksi_eta?" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1059.3876121640205, 522.2713452577591)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chariklo.ephem.get_ksi_eta(time='2017-06-22 21:18:00', star='18 55 15.65250 -31 31 21.67051')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1032.963790729207, 528.7587184187054)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eph_hor.get_ksi_eta(time='2017-06-22 21:18:00', star='18 55 15.65250 -31 31 21.67051')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**In the case of** `EphemPlanete` **, the time given must be in the range of the input ephemeris.**" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------- Ephemeris -----------\n", "\n", "EphemPlanete: Valid from 2019-06-07 02:54:00.000 until 2019-06-07 04:54:00.000 (SPKID=)\n", "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n", "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n", "\n" ] } ], "source": [ "print(eph_pla)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting ephemeris position relative to star coordinate 19h21m18.63201s -21d44m25.3924s\n", "ksi = aksi*t² + bksi*t + cksi\n", "eta = aeta*t² + beta*t + ceta\n", "t=(jd-2458641.62083333)/(2458641.70416667-2458641.62083333)\n", " aksi=-74.33578570357841\n", " bksi=-120036.66964478235\n", " cksi=56728.18870944259\n", " aeta=-11.579369275877033\n", " beta=-17144.75884811865\n", " ceta=6972.3299907265955\n", "Residual RMS: ksi=0.004 km, eta=0.001 km\n" ] }, { "data": { "text/plain": [ "(-3685.7460633119117, -1656.79625359093)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eph_pla.get_ksi_eta(time='2019-06-07 03:54:22.60', star='19 21 18.63201 -21 44 25.3924')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**For** `EphemPlanete` **, the user can also make the fit before running get_ksi_eta. If the fit is done, get_ksi_eta will only need the time param.**" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0meph_pla\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_d2_ksi_eta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Fits the projected position (orthographic projection) of the object in\n", "the tangent sky plane relative to a star.\n", "\n", "Parameters\n", "----------\n", "star : `str`, `astropy.coordinates.SkyCoord`\n", " The coordinate of the star in the same reference frame as the ephemeris.\n", "\n", "verbose : `bool`, optional, default=True\n", " Enable log printing.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/ephem/core.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eph_pla.fit_d2_ksi_eta?" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting ephemeris position relative to star coordinate 19h21m18.63201s -21d44m25.3923s\n", "ksi = aksi*t² + bksi*t + cksi\n", "eta = aeta*t² + beta*t + ceta\n", "t=(jd-2458641.62083333)/(2458641.70416667-2458641.62083333)\n", " aksi=-74.33578570357841\n", " bksi=-120036.66964478235\n", " cksi=56728.18870944259\n", " aeta=-11.579369458928182\n", " beta=-17144.75878814249\n", " ceta=6971.659735843645\n", "Residual RMS: ksi=0.004 km, eta=0.001 km\n" ] } ], "source": [ "eph_pla.fit_d2_ksi_eta(star='19 21 18.63201 -21 44 25.3923')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-3685.7460633119117, -1657.4664783438802)" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eph_pla.get_ksi_eta(time='2019-06-07 03:54:22.60')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.7. Adding Offsets\n", "\n", "If an ephemeris offset was obtained from astrometry, it can be added to the `Ephemeris` objects using add_offset. This values are given in $\\Delta\\alpha\\cos\\delta$ and $\\Delta\\delta$ in mas." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0meph_ker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_offset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda_cosdec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mddec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Adds an offset to the Ephemeris.\n", "\n", "Parameters\n", "----------\n", "da_cosdec : `int`, `float`\n", " Delta_alpha_cos_delta, in mas.\n", "\n", "ddec : `int`, `float`\n", " Delta_delta, in mas.\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/ephem/meta.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eph_ker.add_offset?" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18h55m28.04429449s -31d31m20.56535577s\n" ] } ], "source": [ "print(chariklo.ephem.get_position(time='2017-06-22 00:00:00').to_string('hmsdms'))\n", "eph = chariklo.ephem" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "eph.add_offset(40, 50)\n", "eph_hor.add_offset(40, 50)\n", "eph_pla.add_offset(40, 50)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'18h55m28.04742278s -31d31m20.51535577s'" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eph.get_position(time='2017-06-22 00:00:00').to_string('hmsdms')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 7. The frame attribute\n", "\n", "For some bodies, we know its rotational parameters and polar coordinates. In this cases, we may need to obtain its rotational phase at a specific epoch.\n", "\n", "When we print the body object, we may see a parameter called `PlanetocentricFrame`. This is the name of the class responsible for the computation of the body's orientation. The calculations are based on the most recent Report of the IAU Working Group on Cartographic Coordinates and Rotational Elements. For the bodies which parameters are present in the report, like Ceres, the frame is automatically defined in the Body object. For other objects, it may be necessary for the user to define it.\n", "\n", "The equations will not be reproduced here. Please refer to the report for further details." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PlanetocentricFrame:\n", " Epoch: J2000.000 tdb\n", " alpha_pole = 291.418 +0.000000*T +0.000000\n", " delta_pole = 66.764 +0.000000*T +0.000000\n", " W = 170.65 +952.153200*d +0.000000\n", " Reference: Archinal, B. A., Report of the IAU Working Group on Cartographic Coordinates and Rotational Elements: 2015, Celestial Mechanics and Dynamical Astronomy, (2018) 130:22\n" ] } ], "source": [ "print(ceres.frame)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The frame can be obtained for a different epoch. In this case, the body will be precessed and rotated to the referred epoch (if parameters are available)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PlanetocentricFrame:\n", " Epoch: 2022-09-20 00:00:00.000 utc\n", " alpha_pole = 291.418 +0.000000*T +0.000000\n", " delta_pole = 66.764 +0.000000*T +0.000000\n", " W = 102.58942676521838 +952.153200*d +0.000000\n", " Reference: Archinal, B. A., Report of the IAU Working Group on Cartographic Coordinates and Rotational Elements: 2015, Celestial Mechanics and Dynamical Astronomy, (2018) 130:22\n" ] } ], "source": [ "print(ceres.frame.frame_at(epoch='2022-09-20 00:00:00'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To obtain the the orientation as viewed by an observer, we must use the `get_orientation()` function on the body object. It will determine the ephemeris of the body, calculate the light time and compute the orientation\n", "\n", "**Important Note**: The subobserver latitude and longitude are PLANETOCENTRIC, and not PLANETODETIC." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: AstropyDeprecationWarning: The matrix_product function is deprecated and may be removed in a future version.\n", " Use @ instead. [sora.body.frame.core]\n" ] }, { "data": { "text/plain": [ "{'sub_observer': '222.176 -0.567677',\n", " 'sub_solar': '222.176 -0.567158',\n", " 'pole_position_angle': ,\n", " 'pole_aperture_angle': }" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ceres.get_orientation(time='2022-09-20 00:00:00', observer='geocenter')" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "\n", "\n", "## 8. The shape attribute\n", "\n", "Since the version 0.3 of SORA the shape attribute was included. With it, the user can provide a 3D shape object or define an ellipsoid. Thus, it is possible to plot the shape of the object as viewed by an observer.\n", "\n", "To replicate the process shown below, it necessary to download the 3D shape model of Phoebe [here](https://3d-asteroids.space/moons/S9-Phoebe). The file must be the in \"OBJ\" format. Either the 50k or 200k versions will work, however the 200k one will demand more processing. The surface texture can also be downloaded and included in the process, but it is not relevant for the fitting process, only for plottings." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1811965" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import requests\n", "response = requests.get(\"https://3d-asteroids.space/data/moons/models/S9_Phoebe.obj\")\n", "open(\"Phoebe_Gaskell_50k_poly.obj\", \"wb\").write(response.content)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "phoebe = Body(name=\"Phoebe\", shape=\"Phoebe_Gaskell_50k_poly.obj\")" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape3D: Phoebe_Gaskell_50k_poly.obj\n" ] } ], "source": [ "print(phoebe.shape)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# A simple plot of the shape will show a view from lat=0, lon=0\n", "phoebe.shape.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can provide the coordinates we want to view the object. Notice the red and blue lines. They show the north and south poles, respectively." ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m\n", "\u001b[0mphoebe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0msub_observer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'00 00 00 +00 00 00'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0msub_solar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mpole_position_angle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mcenter_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mcenter_g\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mplot_pole\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Parameters\n", "----------\n", "sub_observer : `astropy.coordinates.SkyCoord`, `str`\n", " Planetocentric coordinates of the center of the object in the direction of the observer.\n", " It can be an astropy SkyCoord object or a string with the bodycentric longitude\n", " latitude in degrees. Ex: \"30.0 -20.0\", or \"30 00 00 -20 00 00\".\n", "\n", "sub_solar : `astropy.coordinates.SkyCoord`, `str`\n", " Planetocentric coordinates of the center of the object in the direction of the Sun.\n", " It can be an astropy SkyCoord object or a string with the bodycentric longitude\n", " latitude in degrees. Ex: \"30.0 -20.0\", or \"30 00 00 -20 00 00\".\n", "\n", "pole_position_angle : `float`, `int`\n", " Body's North Pole position angle with respect to direction of the ICRS\n", " North Pole, i.e. N-E-S-W.\n", "\n", "center_f : `int`, `float`\n", " Offset of the center of the body in the East direction, in km\n", "\n", "center_g : `int`, `float`\n", " Offset of the center of the body in the North direction, in km\n", "\n", "scale : `float`\n", " Multiply the shape vertices by a value. Default=1\n", "\n", "ax : `matplotlib.pyplot.Axes`\n", " The axes where to make the plot. If None, it will use the default axes.\n", "\n", "plot_pole : `bool`\n", " If True, the direction of the pole is plotted.\n", "\n", "**kwargs\n", " Any other keyword argument is discarded\n", "\u001b[0;31mFile:\u001b[0m ~/Documentos/códigos/SORA/sora/body/shape/core.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phoebe.shape.plot?" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phoebe.shape.plot(sub_observer=\"30.0 +50.0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot the shape as viewed by an observer at a specific time. For this, we must plot the body directly, where the frame attribute will be called to compute the correct orientation." ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phoebe.plot(time='2022-09-20', observer='geocenter')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use the 3D shape in the occultation process, we need to be able to get the projected limb of the 3D shape. For that we can use the `get_limb` function in the shape attribute" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "limb = phoebe.shape.get_limb(sub_observer=\"30.0 +50.0\")" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "limb.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**To see the use of the shape in an occultation, there is an example in our online documentation showing all the steps.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**This Jupyter-Notebook was designed as a tutorial for how to work with the** `Body` **and** `Ephemeris` **Classes. More information about the other classes, please refer to their specif Jupyter-Notebook. Any further question, please contact the core team: Altair Ramos Gomes Júnior, Bruno Eduardo Morgado, Gustavo Benedetti Rossi, and Rodrigo Carlos Boufleur.**\n", "\n", "**The End**" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 4 }