phoebe 2.3

[helenkurd]

hello for entering data to python3 should i write parameters of flux,and phase parameter ? or all parameters in b.add_dataset('lc')
print(b.get_dataset(kind='lc', check_visible=False)) and writing Parameter Set: 30 parameters
times@lc01@dataset: [] d
fluxes@lc01@dataset: [] W / m2
passband@lc01@dataset: Johnson:V
intens_weighting@lc01@dataset: energy
compute_times@lc01@dataset: [] d
C compute_phases@lc01@dataset: []
phases_t0@lc01@dataset: t0_supconj
mask_enabled@lc01@dataset: True
mask_phases@lc01@dataset: []
solver_times@lc01@dataset: auto
sigmas@lc01@dataset: [] W / m2
sigmas_lnf@lc01@dataset: -inf
pblum_mode@lc01@dataset: component-coupled
pblum_component@lc01@dataset: primary
pblum_dataset@lc01@dataset:
pbflux@lc01@dataset: 1.0 W / m2
l3_mode@lc01@dataset: flux
l3@lc01@dataset: 0.0 W / m2
l3_frac@lc01@dataset: 0.0
exptime@lc01@dataset: 0.0 s
ld_mode@primary@lc01@dataset: interp
ld_mode@secondary@lc01@dataset: interp
ld_func@primary@lc01@dataset: logarithmic
ld_func@secondary@lc01@dataset: logarithmic
ld_coeffs_source@primary@lc...: auto
ld_coeffs_source@secondary@...: auto
ld_coeffs@primary@lc01@dataset: [0.5 0.5]
ld_coeffs@secondary@lc01@da...: [0.5 0.5]
pblum@primary@lc01@dataset: 12.566370614359172 W
pblum@secondary@lc01@dataset: 12.566370614359172 W

[kecnry]

All of those parameters are optional - you can either pass them when calling add_dataset or change the default values later. If you want to attach an observational light curve (for fitting or plotting, rather than just creating a synthetic light curve), then you'll want to set times, fluxes, and sigmas (all with arrays of the same length).

[helenkurd]

hello sir ,sir <ParameterSet: 42 parameters | contexts: compute, figure, dataset, constraint> sir whatis mean of parameter set? and ,sir what is mean of 'lc01'?

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On Sat, Oct 16, 2021 at 5:04 PM Kyle Conroy ***@***.***> wrote: All of those parameters are optional - you can either pass them when calling add_dataset or change the default values later. If you want to attach an observational light curve (for fitting or plotting, rather than just creating a synthetic light curve), then you'll want to set times, fluxes, and sigmas (all with arrays of the same length). — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#541 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AWANX4ARMXCAFPUNF7CDQULUHGA7DANCNFSM5GC4QWEQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

[kecnry]

ParameterSet is a python object containing a filtered list of parameters. Its introduced in the first tutorial on general concepts.

'lc01' is the default label given to the light curve dataset when calling b.add_dataset but not providing a string for dataset. If you pass dataset='mylc' for example, then it would get that label instead. You then refer to these various labels when filtering and setting parameters.

[helenkurd]

Thanks very much sir for your help

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On Mon, Oct 18, 2021 at 11:50 PM Kyle Conroy ***@***.***> wrote: ParameterSet is a python object containing a filtered list of parameters. Its introduced in the first tutorial on general concepts <http://phoebe-project.org/docs/latest/tutorials/general_concepts>. 'lc01' is the *default* label given to the light curve dataset when calling b.add_dataset but not providing a string for dataset. If you pass dataset='mylc' for example, then it would get that label instead. You then refer to these various labels when filtering and setting parameters. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#541 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AWANX4FAMAQHQXIQLTI4I2TUHSCCRANCNFSM5GC4QWEQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

[helenkurd]

hellow sir ,sir are this numbers in run compute are constant?or changed if changed how can i change it? b.run_compute(irrad_method='none') 100%|██████████| 205/205 [00:00<00:00, 256.51it/s] On Tue, Oct 19, 2021 at 12:21 AM helen helen.hamadameen ***@***.***> wrote:

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Thanks very much sir for your help On Mon, Oct 18, 2021 at 11:50 PM Kyle Conroy ***@***.***> wrote: > ParameterSet is a python object containing a filtered list of parameters. > Its introduced in the first tutorial on general concepts > <http://phoebe-project.org/docs/latest/tutorials/general_concepts>. > > 'lc01' is the *default* label given to the light curve dataset when > calling b.add_dataset but not providing a string for dataset. If you > pass dataset='mylc' for example, then it would get that label instead. > You then refer to these various labels when filtering and setting > parameters. > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <#541 (reply in thread)>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AWANX4FAMAQHQXIQLTI4I2TUHSCCRANCNFSM5GC4QWEQ> > . > Triage notifications on the go with GitHub Mobile for iOS > <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> > or Android > <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. > >

[kecnry]

What you are seeing there is just the (completed) progress bar for the computation of the forward model (there were 205 iterations/"times" to be computed, and it took less than a second to complete, at the rate of 256.51 iterations per second). The call to run_compute returns the resulting model as a ParameterSet, which you can also access by filtering on the model context in the bundle and can be plotted directly from PHOEBE.

[helenkurd]

hello sir for estimating new value of parameters should i write all of below codes:
Generate data
Let's first initialize a bundle and change some of the parameter values. We'll then export the computed models as "observables" to use with the EBAI and lc_geometry estimators.
We'll use blackbody atmospheres with manual limb-darkening just to avoid any out-of-bounds errors if the estimates aren't perfectly physical. Normally we would check these values to make sure they're reasonable before adopting and would definitely want to revert to physical atmospheres and appropriate limb-darkening when optimizing.
b = phoebe.default_binary()

set parameter values

b.set_value('q', value = 0.6)
b.set_value('incl', component='binary', value = 84.5)
b.set_value('ecc', 0.2)
b.set_value('per0', 63.7)
b.set_value('requiv', component='primary', value=1.)
b.set_value('requiv', component='secondary', value=0.6)
b.set_value('teff', component='secondary', value=5500.)

add an lc dataset

b.add_dataset('lc', compute_phases=phoebe.linspace(0,1,101))
b.set_value_all('ld_mode', 'manual')
b.set_value_all('ld_mode_bol', 'manual')
b.set_value_all('atm', 'blackbody')

#compute the model
b.run_compute(irrad_method='none')

extract the arrays from the model that we'll use as observables in the next step

times = b.get_value('times', context='model', dataset='lc01')

here we're adding noise to the fluxes as well to make the fake data more "realistic"

np.random.seed(0) # to ensure reproducibility with added noise
fluxes = b.get_value('fluxes', context='model', dataset='lc01') + np.random.normal(size=times.shape) * 0.02
sigmas_lc = np.ones_like(times) * 0.04
100%|██████████| 101/101 [00:08<00:00, 11.51it/s]

[kecnry]

This seems to just be a copy or modification of the LC estimators tutorial which is just a (very) simplified example, using synthetic data, that shows the options for these particular "solvers". You'll need to significantly modify the script to the needs of your system and science use-cases and likely will need to make use of other solvers as well.

The general workflow, as described in the Solvers tutorial is to attach your observations, add a set of solver options and modify them as necessary, and call run_solver (which then exposes a "solution"). If you're happy with the proposed solution, you can apply them back to your system with adopt_solution.

It might be easiest to try to run these notebooks on the synthetic cases shown, understand what each step does, and try modifying some options to see how it reacts. Hopefully this will make applying the general concepts back to your system more approachable.

[helenkurd]

thanks very much sir kecnry butsir for esimating new value of parameters can i use just this below part :
Generate data
Let's first initialize a bundle and change some of the parameter values. We'll then export the computed models as "observables" to use with the EBAI and lc_geometry estimators.

We'll use blackbody atmospheres with manual limb-darkening just to avoid any out-of-bounds errors if the estimates aren't perfectly physical. Normally we would check these values to make sure they're reasonable before adopting and would definitely want to revert to physical atmospheres and appropriate limb-darkening when optimizing.

b = phoebe.default_binary()

set parameter values

b.set_value('q', value = 0.6)
b.set_value('incl', component='binary', value = 84.5)
b.set_value('ecc', 0.2)
b.set_value('per0', 63.7)
b.set_value('requiv', component='primary', value=1.)
b.set_value('requiv', component='secondary', value=0.6)
b.set_value('teff', component='secondary', value=5500.)

add an lc dataset

b.add_dataset('lc', compute_phases=phoebe.linspace(0,1,101))
b.set_value_all('ld_mode', 'manual')
b.set_value_all('ld_mode_bol', 'manual')
b.set_value_all('atm', 'blackbody')

#compute the model
b.run_compute(irrad_method='none')

[helenkurd]

hello sir how are you sir to plot spots of my stars which are W UM a contact binary stars in spot tutorials i saw this numbers below of time=[0,0.25,0.5,0.75,1.0] is this numbers constant for all contact binary stars ? can i use same steps of this below codes for my contact W UMa binary stars To see the spot, add a mesh dataset and plot it. Thank you very much for your suggestion

To see the spot, add a mesh dataset and plot it.

b.add_dataset('mesh', times=[0,0.25,0.5,0.75,1.0], columns=['teffs'])
Wed, 24 Feb 2021 10:36 BUNDLE WARNING mesh dataset uses 'compute_times' instead of 'times', applying value sent as 'times' to 'compute_times'.
<ParameterSet: 85 parameters | contexts: dataset, constraint, compute, figure>
b.run_compute()
100%|██████████| 5/5 [00:00<00:00, 12.76it/s]
<ParameterSet: 53 parameters | components: primary, secondary>
afig, mplfig = b.filter(component='primary', time=0.75).plot(fc='teffs', show=True)

[kecnry]

The times list/array are the times at which you want the mesh computed and exposed (and then available for plotting). Optionally, you can pass compute_phases instead if that is more convenient.

[helenkurd]

Thanks in advance for your reply

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On Mon, Nov 22, 2021 at 8:48 PM Kyle Conroy ***@***.***> wrote: The times list/array are the times at which you want the mesh computed and exposed (and then available for plotting). Optionally, you can pass compute_phases <http://phoebe-project.org/docs/latest/tutorials/compute_times_phases> instead if that is more convenient. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#541 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AWANX4DSPJGS3H3VIZZEMM3UNJ67NANCNFSM5GC4QWEQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.