JWST Tutorial ============= Here you will learn how to: - set planet properties - set stellar properties - run default instrument modes - adjust instrument modes - run pandexo .. code:: ipython3 import warnings warnings.filterwarnings('ignore') import pandexo.engine.justdoit as jdi # THIS IS THE HOLY GRAIL OF PANDEXO import numpy as np import os #pip install pandexo.engine --upgrade Setting up a run ---------------- To start, load in a blank exoplanet dictionary with empty keys. You will fill these out for yourself in the next step. .. code:: ipython3 exo_dict = jdi.load_exo_dict() print(exo_dict.keys()) #print(exo_dict['star']['w_unit']) Edit exoplanet observation inputs ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Editting each keys are annoying. But, do this carefully or it could result in nonsense runs .. code:: ipython3 exo_dict['observation']['sat_level'] = 80 #saturation level in percent of full well exo_dict['observation']['sat_unit'] = '%' exo_dict['observation']['noccultations'] = 2 #number of transits exo_dict['observation']['R'] = None #fixed binning. I usually suggest ZERO binning.. you can always bin later #without having to redo the calcualtion exo_dict['observation']['baseline_unit'] = 'total' #Defines how you specify out of transit observing time #'frac' : fraction of time in transit versus out = in/out #'total' : total observing time (seconds) exo_dict['observation']['baseline'] = 4.0*60.0*60.0 #in accordance with what was specified above (total observing time) exo_dict['observation']['noise_floor'] = 0 #this can be a fixed level or it can be a filepath #to a wavelength dependent noise floor solution (units are ppm) Edit exoplanet host star inputs ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Note… If you select ‘phoenix’ you **do not** have to provide a starpath, w_unit or f_unit, but you **do** have to provide a temp, metal and logg. If you select ‘user’ you **do not** need to provide a temp, metal and logg, but you **do** need to provide units and starpath. .. code:: ipython3 exo_dict['star']['type'] = 'phoenix' #phoenix or user (if you have your own) exo_dict['star']['mag'] = 8.0 #magnitude of the system exo_dict['star']['ref_wave'] = 1.25 #For J mag = 1.25, H = 1.6, K =2.22.. etc (all in micron) exo_dict['star']['temp'] = 5500 #in K exo_dict['star']['metal'] = 0.0 # as log Fe/H exo_dict['star']['logg'] = 4.0 #log surface gravity cgs Edit exoplanet inputs using one of three options ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1) user specified 2) constant value 3) select from grid 1) Edit exoplanet planet inputs if using your own model ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: ipython3 exo_dict['planet']['type'] ='user' #tells pandexo you are uploading your own spectrum exo_dict['planet']['exopath'] = 'wasp12b.txt' exo_dict['planet']['w_unit'] = 'cm' #other options include "um","nm" ,"Angs", "sec" (for phase curves) exo_dict['planet']['f_unit'] = 'rp^2/r*^2' #other options are 'fp/f*' exo_dict['planet']['transit_duration'] = 2.0*60.0*60.0 #transit duration exo_dict['planet']['td_unit'] = 's' #Any unit of time in accordance with astropy.units can be added 2) Users can also add in a constant temperature or a constant transit depth ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: ipython3 exo_dict['planet']['type'] = 'constant' #tells pandexo you want a fixed transit depth exo_dict['planet']['transit_duration'] = 2.0*60.0*60.0 #transit duration exo_dict['planet']['td_unit'] = 's' exo_dict['planet']['radius'] = 1 exo_dict['planet']['r_unit'] = 'R_jup' #Any unit of distance in accordance with astropy.units can be added here exo_dict['star']['radius'] = 1 exo_dict['star']['r_unit'] = 'R_sun' #Same deal with astropy.units here exo_dict['planet']['f_unit'] = 'rp^2/r*^2' #this is what you would do for primary transit #ORRRRR.... #if you wanted to instead to secondary transit at constant temperature exo_dict['planet']['f_unit'] = 'fp/f*' exo_dict['planet']['temp'] = 1000 3) Select from grid ^^^^^^^^^^^^^^^^^^^ NOTE: Currently only the fortney grid for hot Jupiters from Fortney+2010 is supported. Holler though, if you want another grid supported .. code:: ipython3 exo_dict['planet']['type'] = 'grid' #tells pandexo you want to pull from the grid exo_dict['planet']['temp'] = 1000 #grid: 500, 750, 1000, 1250, 1500, 1750, 2000, 2250, 2500 exo_dict['planet']['chem'] = 'noTiO' #options: 'noTiO' and 'eqchem', noTiO is chemical eq. without TiO exo_dict['planet']['cloud'] = 'ray10' #options: nothing: '0', # Weak, medium, strong scattering: ray10,ray100, ray1000 # Weak, medium, strong cloud: flat1,flat10, flat100 exo_dict['planet']['mass'] = 1 exo_dict['planet']['m_unit'] = 'M_jup' #Any unit of mass in accordance with astropy.units can be added here exo_dict['planet']['radius'] = 1 exo_dict['planet']['r_unit'] = 'R_jup' #Any unit of distance in accordance with astropy.units can be added here exo_dict['star']['radius'] = 1 exo_dict['star']['r_unit'] = 'R_sun' #Same deal with astropy.units here exo_dict['planet']['transit_duration'] = 2.0*60.0*60.0 #transit duration exo_dict['planet']['td_unit'] = 's' Load in instrument dictionary (OPTIONAL) ---------------------------------------- Step 2 is optional because PandExo has the functionality to automatically load in instrument dictionaries. Skip this if you plan on observing with one of the following and want to use the subarray with the smallest frame time and the readout mode with 1 frame/1 group (standard): - NIRCam F444W - NIRSpec Prism - NIRSpec G395M - NIRSpec G395H - NIRSpec G235H - NIRSpec G235M - NIRCam F322W - NIRSpec G140M - NIRSpec G140H - MIRI LRS - NIRISS SOSS .. code:: ipython3 #jdi.print_instruments() result = jdi.run_pandexo(exo_dict,['NIRCam F322W2']) .. code:: ipython3 inst_dict = jdi.load_mode_dict('NIRSpec G140H') #loading in instrument dictionaries allow you to personalize some of #the fields that are predefined in the templates. The templates have #the subbarays with the lowest frame times and the readmodes with 1 frame per group. #if that is not what you want. change these fields #Try printing this out to get a feel for how it is structured: print(inst_dict['configuration']) .. code:: ipython3 #Another way to display this is to print out the keys inst_dict.keys() Don’t know what instrument options there are? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: ipython3 print("SUBARRAYS") print(jdi.subarrays('nirspec')) print("FILTERS") print(jdi.filters('nircam')) print("DISPERSERS") print(jdi.dispersers('nirspec')) .. code:: ipython3 #you can try personalizing some of these fields inst_dict["configuration"]["detector"]["ngroup"] = 'optimize' #running "optimize" will select the maximum #possible groups before saturation. #You can also write in any integer between 2-65536 inst_dict["configuration"]["detector"]["subarray"] = 'substrip256' #change the subbaray Adjusting the Background Level ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You may want to think about adjusting the background level of your observation, based on the position of your target. PandExo two options and three levels for the position: - ``ecliptic`` or ``minzodi`` - ``low``, ``medium``, ``high`` .. code:: ipython3 inst_dict['background'] = 'ecliptic' inst_dict['background_level'] = 'high' Running NIRISS SOSS Order 2 ~~~~~~~~~~~~~~~~~~~~~~~~~~~ PandExo only will extract a single order at a time. By default, it is set to extract Order 1. Below you can see how to extract the second order. **NOTE!** Users should be careful with this calculation. Saturation will be limited by the **first** order. Therefore, I suggest running one calculation with ``ngroup='optmize'`` for Order 1. This will give you an idea of a good number of groups to use. Then, you can use that in this order 2 calculation. .. code:: ipython3 inst_dict = jdi.load_mode_dict('NIRISS SOSS') inst_dict['strategy']['order'] = 2 inst_dict['configuration']['detector']['subarray'] = 'substrip256' ngroup_from_order1_run = 2 inst_dict["configuration"]["detector"]["ngroup"] = ngroup_from_order1_run Running PandExo --------------- You have **four options** for running PandExo. All of them are accessed through attribute **jdi.run_pandexo**. See examples below. ``jdi.run_pandexo(exo, inst, param_space = 0, param_range = 0,save_file = True, output_path=os.getcwd(), output_file = '')`` Option 1- Run single instrument mode, single planet ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you forget which instruments are available run **jdi.print_isntruments()** and pick one .. code:: ipython3 jdi.print_instruments() .. code:: ipython3 result = jdi.run_pandexo(exo_dict,['NIRCam F322W2']) Option 2- Run single instrument mode (with user dict), single planet ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This is the same thing as option 1 but instead of feeding it a list of keys, you can feed it a instrument dictionary (this is for users who wanted to simulate something NOT pre defined within pandexo) .. code:: ipython3 inst_dict = jdi.load_mode_dict('NIRSpec G140H') #personalize subarray inst_dict["configuration"]["detector"]["subarray"] = 'sub2048' result = jdi.run_pandexo(exo_dict, inst_dict) Option 3- Run several modes, single planet ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Use several modes from **print_isntruments()** options. .. code:: ipython3 #choose select result = jdi.run_pandexo(exo_dict,['NIRSpec G140M','NIRSpec G235M','NIRSpec G395M'], output_file='three_nirspec_modes.p') #run all #result = jdi.run_pandexo(exo_dict, ['RUN ALL'], save_file = False) Option 4- Run single mode, several planet cases ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Use a single modes from **print_isntruments()** options. But explore parameter space with respect to **any** parameter in the exo dict. The example below shows how to loop over several planet models You can loop through anything in the exoplanet dictionary. It will be planet, star or observation followed by whatever you want to loop through in that set. i.e. planet+exopath, star+temp, star+metal, star+logg, observation+sat_level.. etc .. code:: ipython3 #looping over different exoplanet models jdi.run_pandexo(exo_dict, ['NIRCam F444W'], param_space = 'planet+exopath', param_range = os.listdir('/path/to/location/of/models'), output_path = '/path/to/output/simulations') #looping over different stellar temperatures jdi.run_pandexo(exo_dict, ['NIRCam F444W'], param_space = 'star+temp', param_range = np.linspace(5000,8000,2), output_path = '/path/to/output/simulations') #looping over different saturation levels jdi.run_pandexo(exo_dict, ['NIRCam F444W'], param_space = 'observation+sat_level', param_range = np.linspace(.5,1,5), output_path = '/path/to/output/simulations') Running PandExo GUI ------------------- The same interface that is available online is also available for use on your machine. Using the GUI is very simple and good alternative if editing the input dictionaries is confusing. .. code:: bash start_pandexo Then open up your favorite internet browser and go to: http://localhost:1111 Analyzing Output ---------------- There are pre computed functions for analyzing most common outputs. You can also explore the dictionary structure yourself. .. code:: python import pandexo.engine.justplotit as jpi import pickle as pk Plot 1D Data with Errorbars ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Multiple plotting options exist within `jwst\_1d\_spec` 1. Plot a single run .. code:: python #load in output from run out = pk.load(open('singlerun.p','r')) #for a single run x,y, e = jpi.jwst_1d_spec(out, R=100, num_tran=10, model=False, x_range=[.8,1.28]) .. image:: jwst_1d_spec.png 2. Plot several runs from parameters space run .. code:: python #load in output from multiple runs multi = pk.load(open('three_nirspec_modes.p','r')) #get into list format list_multi = [multi[0]['NIRSpec G140M'], multi[1]['NIRSpec G235M'], multi[2]['NIRSpec G395M']] x,y,e = jpi.jwst_1d_spec(list_multi, R=100, model=False, x_range=[1,5]) .. image:: jwst_1d_spec_multi.png Plot Noise & More ~~~~~~~~~~~~~~~~~ Several functions exist to plot various outputs. See also **jwst\_1d\_bkg**, **jwst\_1d\_snr**, **jwst\_1d\_flux**, .. code:: python x,y = jpi.jwst_noise(out) .. image:: jwst_noise.png Plot 2D Detector Profile ~~~~~~~~~~~~~~~~~~~~~~~~ See also **jwst\_2d\_sat** to plot saturation profile .. code:: python data = jpi.jwst_2d_det(out) .. image:: jwst_1d_det.png