{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The session class\n", "\n", "The `session` class is the highest level module of `PBjam` and is meant to handle analysing several sets of data. It essentially wraps number of `star` class instances, which is one of the core modules of `PBjam`. Each `star` class instance in the Session then runs the lowest-level analysis modules of `PBjam` which are the `modeID` and `peakbag` modules. For more information on these modules see the relevant notebooks in the Examples directory. If you want more bespoke control of the data and inputs used to analyze each individual star, you can in principle do it via the `session` class, but it is recommended that you build your own pipeline using the lower-level modules. \n", "\n", "## Analyzing a single star\n", "Let's start by looking at a single star though for now. In this case we'll look at a main-sequence star KIC10963065. We'll supply `PBjam` with some basic observational parameter which it uses to define the prior probability during the mode ID process. The `obs` dictionary must contain $\\nu_{\\mathrm{max}}$, $\\Delta\\nu$, $T_{\\mathrm{eff}}$ and the Gaia $G_{\\mathrm{bp}}-G_{\\mathrm{rp}}$ color index. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "name = 'KIC10963065'\n", "\n", "obs = {'numax': (2204, 100), #muHz\n", " 'dnu' : (103.2, 0.54), #muHz\n", " 'teff' : (6140, 77), #K\n", " 'bp_rp': (0.70026, 0.05), #mag\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we'll be looking up the time series online with Lightkurve, the `name` of the target has to be one that is resolvable on [MAST](https://science.nasa.gov/astrophysics/data/multimission-archive-at-stsci-mast/). Most common names that you can find on [Simbad](https://simbad.u-strasbg.fr/simbad/sim-fid) will probably do. \n", "\n", "Next we'll initialize the `session` class, where we provide the name, obs dictionary and a dictionary with keywords for [Lightkurve](https://lightkurve.github.io/lightkurve/). In this case, since it's a main-sequence star we're looking at, we'll pick an exposure time of 60s, and we'll use the observations from quarter 6 of the Kepler mission. `PBjam` will then download the relevant data, reduce it to a useable form, and compute the power density spectrum." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pbjam.core import session\n", "\n", "sess = session(name, obs, lk_kwargs={'exptime': 60, 'mission': 'Kepler', 'quarter': 6})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The next step is doing the analysis is calling the `session` class instance, where we supply a keyword `N_p=7` to the mode ID stage. Other keywords may be supplied in this way to the `modeID` module, or the `peakbag` module." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Target: KIC10963065\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "16832it [02:04, 135.21it/s, +600 | bound: 166 | nc: 1 | ncall: 492389 | eff(%): 3.545 | loglstar: -inf < -6255.061 < inf | logz: -6280.804 +/- 0.193 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Input Teff=6140K and dnu=103.2muHz suggests the appropriate l=1 model is: ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "1563it [00:05, 311.84it/s, +150 | bound: 46 | nc: 1 | ncall: 29369 | eff(%): 5.863 | loglstar: -inf < -5941.568 < inf | logz: -5949.616 +/- 0.208 | dlogz: 0.001 > 0.100]\n", "4343it [00:11, 385.30it/s, +150 | bound: 167 | nc: 1 | ncall: 93309 | eff(%): 4.823 | loglstar: -inf < -5941.487 < inf | logz: -5949.615 +/- 0.196 | dlogz: 0.000 > 0.000]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Using emcee to sample.\n", "Peakbagging slice 1/1\n", "Burning in sampler\n", "Convergence >> 1: 20.0\n", "Convergence >> 1: 7.5\n", "Convergence >> 1: 4.2\n", "Convergence >> 1: 2.6\n", "Convergence >> 1: 1.7\n", "Convergence >> 1: 1.2\n", "Convergence >> 1: 1.3\n", "Convergence >> 1: 1.1\n", "Convergence >> 1: 0.9\n", "Sampling posterior.\n", "Time taken 9.9 minutes\n" ] } ], "source": [ "sess(modeID_kwargs={'N_p': 7})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access the results of the run you can either plot them using the various built-in methods in `PBjam` or simply output the results in the form of a dictionary. There are various ways of viewing the results as plots, which are covered in a separate notebook." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'ell': array([1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 2., 2., 2.,\n", " 2., 2., 2., 2.]), 'enn': array([-1., -1., -1., -1., -1., -1., -1., 17., 18., 19., 20., 21., 22.,\n", " 23., 16., 17., 18., 19., 20., 21., 22.]), 'emm': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0.]), 'zeta': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), 'summary': {'freq': array([[1.93395541e+03, 2.03688791e+03, 2.13995293e+03, 2.24314840e+03,\n", " 2.34647962e+03, 2.44994072e+03, 2.55353791e+03, 1.88638822e+03,\n", " 1.98932073e+03, 2.09238574e+03, 2.19558122e+03, 2.29891244e+03,\n", " 2.40237353e+03, 2.50597073e+03, 1.87884775e+03, 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217.67055394, 214.06161383, ..., 209.92385705,\n", " 219.06005914, 210.5310651 ]), 'env_height': array([2.75707796, 3.03501896, 3.03364602, ..., 3.59879638, 3.15413696,\n", " 2.98819459]), 'mode_width': array([1.56675665, 1.65813511, 1.62352119, ..., 1.57887391, 1.62962834,\n", " 1.55376982]), 'teff': array([6143.00022947, 6214.55703851, 6203.09126594, ..., 6233.73440418,\n", " 6162.26979372, 6182.40762548]), 'bp_rp': array([0.70551507, 0.68461372, 0.68936111, ..., 0.66882225, 0.69776842,\n", " 0.69333576]), 'H1_nu': array([1461.76733529, 1646.18212804, 1600.67614154, ..., 1266.29756192,\n", " 1500.31931728, 1445.7548317 ]), 'H1_exp': array([2.82176501, 3.32107764, 3.07666319, ..., 2.71948038, 2.96438249,\n", " 2.86403214]), 'H_power': array([880.01194032, 785.95075047, 833.12416159, ..., 683.24255382,\n", " 838.19969207, 818.60846781]), 'H2_nu': array([471.01076734, 495.93759376, 486.0365799 , ..., 458.11414946,\n", " 477.32182148, 470.91694814]), 'H2_exp': array([2.55800161, 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2036.83789834, 2140.75994331, ..., 2292.94050233,\n", " 2396.50451272, 2497.12291614],\n", " ...,\n", " [1933.79514007, 2037.02042156, 2140.15598421, ..., 2292.41166337,\n", " 2395.12220031, 2496.94870959],\n", " [1933.44528234, 2036.47098482, 2140.55902187, ..., 2293.86385841,\n", " 2393.61468568, 2499.1424266 ],\n", " [1933.73582716, 2036.81168155, 2140.44199171, ..., 2294.18862424,\n", " 2395.57647973, 2496.65762037]]), 'height': array([[ 5.02052136, 6.26941168, 10.06634431, ..., 3.72989665,\n", " 2.2860466 , 2.18200026],\n", " [ 6.83978872, 6.48003271, 8.13335986, ..., 2.9912075 ,\n", " 2.32901787, 1.3956044 ],\n", " [ 8.24616785, 5.10110242, 8.12438712, ..., 3.56392591,\n", " 5.09039411, 0.93560168],\n", " ...,\n", " [ 9.12899783, 8.59213088, 8.36675838, ..., 4.93116442,\n", " 4.46108955, 1.1826913 ],\n", " [ 8.46364982, 6.372377 , 9.07544855, ..., 5.92535195,\n", " 1.55026254, 1.41926584],\n", " [ 7.1529792 , 7.8458176 , 11.25670149, ..., 6.44447745,\n", " 2.26235335, 2.82631563]]), 'width': array([[1.5746438 , 1.32302114, 0.96265147, ..., 1.45683309, 2.42980918,\n", " 1.59508146],\n", " [1.37119507, 1.29409491, 1.21299149, ..., 1.27235345, 1.67739538,\n", " 1.58581191],\n", " [1.66945586, 1.43149614, 1.34827488, ..., 1.53436869, 2.11879966,\n", " 1.45488613],\n", " ...,\n", " [1.4995019 , 1.24851838, 1.47034814, ..., 1.58238258, 1.33556481,\n", " 1.33061977],\n", " [2.00089262, 1.43495002, 1.27783214, ..., 1.72953763, 1.87377627,\n", " 1.7697762 ],\n", " [2.05648656, 1.04992435, 1.08546434, ..., 1.13148864, 2.53489646,\n", " 1.1937131 ]])}, 'kstest': {'significant': array([ True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True,\n", " True, True, True]), 'pvalue': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0.]), 'statistic': array([0.44677564, 0.46484518, 0.4795488 , 0.4362341 , 0.44503292,\n", " 0.38326087, 0.44115463, 0.43099494, 0.43999085, 0.45333069,\n", " 0.44549191, 0.37992427, 0.52648015, 0.4123269 , 0.29436004,\n", " 0.37806912, 0.4226273 , 0.45162585, 0.59958439, 0.41271373,\n", " 0.34426471])}}\n" ] } ], "source": [ "st = sess.stars[0]\n", "\n", "print(st.modeID.result)\n", "\n", "print(st.peakbag.result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing multiple targets in one session\n", "\n", "Analyzing multiple targets in the same session is a handy way to treat groups of targets in a similar way. Doing this is very similar to just running a single target session, however, the name argument is now a list and the supplied dictionaries should now be organized according to the target names." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pbjam.core import session\n", "\n", "\n", "name1 = 'KIC10963065'\n", "name2 = 'KIC5184732'\n", "\n", "obs = {name1 : {'numax': (2204, 100), #muHz\n", " 'dnu' : (103.2, 0.54), #muHz\n", " 'teff' : (6140, 77), #K\n", " 'bp_rp': (0.70026, 0.05), #mag\n", " },\n", " name2 : {'teff' : (5846, 80), # K\n", " 'bp_rp': (0.819174, 0.05), # mag\n", " 'numax': (2089.3, 20.0), # muHz\n", " 'dnu' : (95.545, 0.5)} # muHz \n", " }\n", "\n", "lk_kwargs = {name1 : {'exptime': 60, 'mission': 'Kepler', 'quarter': 6},\n", " name2 : {'exptime': 60, 'mission': 'Kepler', 'quarter': 11}}\n", "\n", "multi_sess = session([name1, name2], obs, lk_kwargs=lk_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** You may encounter memory issues if you are analyzing hundreds of stars in a single session. It is therefore advisable to create your own pipeline using the `star` class and iteratively save each run for each star. A small handful of stars shouldn't be a problem for a `session` instance on most machines though." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "multi_sess(modeID_kwargs={name1 : {'N_p': 7},\n", " name2 : {'N_p': 10}})" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }