Limb Darkening RtD

docs/index.rst

@@ -71,6 +71,11 @@ The light-curve fitting tool fits large numbers of spectroscopic light curves si
 
 The limb_darkening tool calculates limb-darkening coefficients for a specified stellar model, plotting results versus µ and wavelength. It uses high spectral resolution stellar atmospheric models, which are a necessity given JWST's expected precision.
 
+.. toctree::
+  :maxdepth: 1
+
+  tutorials/exoctk.LimbDarkening
+
 .. toctree::
   :maxdepth: 1
 
@@ -99,12 +104,12 @@ The Phase Constraint Calculator provides a simple interface for calculating JWST
 .. toctree::
   :maxdepth: 1
 
-  source/exoctk.phase_constraint_overlap
+  tutorials/exoctk.PhaseConstraintCalculator
 
 .. toctree::
   :maxdepth: 1
 
-  tutorials/exoctk.PhaseConstraintCalculator
+  source/exoctk.phase_constraint_overlap
 
 ****************************************************
 Installation Instructions and Notebook Availability

docs/tutorials/exoctk.LimbDarkening.rst

@@ -0,0 +1,176 @@
+.. _LimbDarkening:
+
+Calculate Limb Darkening Coefficients
+=====================================
+
+To calculate the limb darkening coefficients, we need a model grid.
+
+In our first example, we use the Phoenix ACES models but any grid can be loaded into a modelgrid.ModelGrid() object if the spectra are stored as FITS files.
+
+Two model grids are available in the EXOCTK_DATA directory and have corresponding child classes for convenience. The Phoenix ACES models and the Kurucz ATLAS9 models can be loaded with the modelgrid.ACES() and modelgrid.ATLAS9() classes respectively.
+
+We can also use the resolution argument to resample the model spectra. This greatly speeds up the caluclations.
+
+.. code-block:: python
+
+	model_grid = modelgrid.ACES(resolution=700)
+	print(model_grid.data)
+
+``1056 models loaded from /Users/jfilippazzo/Documents/STScI/ExoCTK/EXOCTK_DATA/modelgrid/ACES/
+ Teff  logg ...                          filename                         
+------ ---- ... ----------------------------------------------------------
+5800.0  3.0 ... lte05800-3.00-0.0.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+7600.0  5.0 ... lte07600-5.00+0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+4100.0  5.0 ... lte04100-5.00-0.0.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+6900.0  4.0 ... lte06900-4.00-0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+4400.0  3.0 ... lte04400-3.00+0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+2300.0  5.0 ... lte02300-5.00-0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+4200.0  4.0 ... lte04200-4.00-0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+4700.0  5.0 ... lte04700-5.00-0.0.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+6900.0  3.0 ... lte06900-3.00+0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+2600.0  4.0 ... lte02600-4.00-0.0.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+   ...  ... ...                                                        ...
+5200.0  5.0 ... lte05200-5.00-0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+3500.0  3.0 ... lte03500-3.00+0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+3300.0  4.0 ... lte03300-4.00-0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+5100.0  4.0 ... lte05100-4.00-0.0.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+5400.0  5.0 ... lte05400-5.00-0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+3300.0  3.0 ... lte03300-3.00+0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+3500.0  4.0 ... lte03500-4.00-0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+3600.0  5.0 ... lte03600-5.00-0.0.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+5700.0  4.0 ... lte05700-4.00-0.0.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+6600.0  3.0 ... lte06600-3.00-0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+6000.0  4.0 ... lte06000-4.00+0.5.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits
+Length = 1056 rows``
+
+Now let's customize it to our desired effective temperature, surface gravity, metallicity, and wavelength ranges by running the customize() method on our grid.
+
+.. code-block:: python
+
+	model_grid.customize(Teff_rng=(2500,2600), logg_rng=(5,5.5), FeH_rng=(-0.5,0.5))
+
+``12/1056 spectra in parameter range Teff:  (2500, 2600) , logg:  (5, 5.5) , FeH:  (-0.5, 0.5) , wavelength:  (<Quantity 0. um>, <Quantity 40. um>)
+Loading flux into table...
+100.00 percent complete!``
+
+Now we can caluclate the limb darkening coefficients using the limb_darkening_fit.LDC() class.
+
+.. code-block:: python 
+
+	ld = lf.LDC(model_grid)
+
+
+We just need to specify the desired effective temperature, surface gravity, metallicity, and the function(s) to fit to the limb darkening profile (including 'uniform', 'linear', 'quadratic', 'square-root', 'logarithmic', 'exponential', and 'nonlinear').
+
+We can do this with for a single model on the grid:
+
+..code-block:: python 
+
+	teff, logg, FeH = 2500, 5, 0
+	ld.calculate(teff, logg, FeH, 'quadratic', name='on-grid', color='blue')
+
+``Bandpass trimmed to 0.05 um - 2.5999 um
+1 bins of 100 pixels each.``
+
+Or a single model off the grid, where the spectral intensity model is directly interpolated before the limb darkening coefficients are calculated. This takes a few seconds when calculating: 
+
+..code-block:: python 
+
+	teff, logg, FeH = 2511, 5.22, 0.04
+	ld.calculate(teff, logg, FeH, 'quadratic', name='off-grid', color='red')
+
+``
+Interpolating grid point [2511/5.22/0.04]...
+Run time in seconds:  11.166051149368286
+Bandpass trimmed to 0.05 um - 2.5999 um
+1 bins of 100 pixels each.``
+
+Now we can see the results table using the following command:
+
+..code-block:: python
+
+	print(ld.results)
+
+``  name    Teff  logg FeH   profile   filter ... color   c1    e1    c2    e2 
+-------- ------ ---- ---- --------- ------- ... ----- ----- ----- ----- -----
+ on-grid 2500.0  5.0  0.0 quadratic Top Hat ...  blue 0.218 0.024 0.391 0.033
+off-grid 2511.0 5.22 0.04 quadratic Top Hat ...   red 0.224 0.025 0.398 0.033``
+
+Using a Photometric Bandpass
+----------------------------
+
+Above we caluclated the limb darkening in a particular wavelength range set when we ran the ``customize()`` method on our ``core.ModelGrid()`` object.
+
+Additionally, we can calculate the limb darkening through a particular photometric bandpass.
+
+First we have to create a ``svo_filters.svo.Filter()`` object which we can then pass to the calculate method. Let's use 2MASS H-band for this example.
+
+..code-block:: python 
+
+	H_band = svo.Filter('2MASS.H')
+	H_band.plot()
+
+
+Now we can tell ``LDC.calculate()`` to apply the filter to the spectral intensity models before calculating the limb darkening coefficients using the bandpass argument. We'll compare the results of using the bandpass (purple line) to the results where we just used the wavelength window of 1.4-1.9 :math:`\mathcal $mu$ m` (green line).
+
+..code-block:: python 
+
+	ld = lf.LDC(model_grid)
+	teff, logg, FeH = 2511, 5.22, 0.04
+	ld.calculate(teff, logg, FeH, '4-parameter', name='Top Hat', color='green')
+	ld.calculate(teff, logg, FeH, '4-parameter', bandpass=H_band, name='H band', color='purple')
+	ld.plot(show=True)
+
+``Interpolating grid point [2511/5.22/0.04]...
+Run time in seconds:  12.76802396774292
+Bandpass trimmed to 0.05 um - 2.5999 um
+1 bins of 100 pixels each.
+Interpolating grid point [2511/5.22/0.04]...
+Run time in seconds:  12.711306095123291``
+
+Using a Grism
+-------------
+
+Grisms are also supported. We can use the whole grism wavelength range (as if it was a bandpass) or truncate the grism to consider arbitrary wavelength ranges by setting the ``wave_min`` and ``wave_max`` arguments.
+
+..code-block:: python 
+
+	G141 = svo.Filter('WFC3_IR.G141', wave_min=1.61*q.um, wave_max=1.65*q.um)
+	G141.plot()
+
+``Bandpass trimmed to 1.11 um - 1.65 um
+15 bins of 431 pixels each.``
+
+Now we can caluclate the LDCs for each of the 15 wavelength bins of the G141 grism we just created, where the first column in the table is the bin center. This is not very useful to plot but... why not?
+
+..code-block:: python 
+
+	teff, logg, FeH = 2511, 5.22, 0.04
+	ld.calculate(teff, logg, FeH, '4-parameter', bandpass=G141)
+	print(ld.results)
+
+``Interpolating grid point [2511/5.22/0.04]...
+Run time in seconds:  12.591181993484497
+  name   Teff  logg FeH    profile   ...   e2    c3     e3    c4     e4 
+------- ------ ---- ---- ----------- ... ----- ------ ----- ------ -----
+1.12 um 2511.0 5.22 0.04 4-parameter ... 0.011 -0.599 0.011  0.193 0.004
+1.15 um 2511.0 5.22 0.04 4-parameter ... 0.016  0.454 0.017 -0.071 0.006
+1.19 um 2511.0 5.22 0.04 4-parameter ...  0.01  0.458 0.011 -0.086 0.004
+1.22 um 2511.0 5.22 0.04 4-parameter ...  0.01    0.7 0.011 -0.168 0.004
+1.25 um 2511.0 5.22 0.04 4-parameter ... 0.008  0.321 0.009 -0.052 0.003
+1.28 um 2511.0 5.22 0.04 4-parameter ... 0.019  0.832  0.02 -0.213 0.008
+1.32 um 2511.0 5.22 0.04 4-parameter ... 0.006  0.766 0.007 -0.179 0.003
+1.35 um 2511.0 5.22 0.04 4-parameter ... 0.024 -0.365 0.026  0.138  0.01
+1.39 um 2511.0 5.22 0.04 4-parameter ... 0.048 -1.159 0.051  0.379 0.019
+1.43 um 2511.0 5.22 0.04 4-parameter ... 0.012 -0.775 0.013  0.209 0.005
+1.46 um 2511.0 5.22 0.04 4-parameter ... 0.033 -0.893 0.035  0.273 0.013
+ 1.5 um 2511.0 5.22 0.04 4-parameter ... 0.037 -0.776 0.039   0.26 0.015
+1.54 um 2511.0 5.22 0.04 4-parameter ...  0.05 -0.623 0.053  0.235  0.02
+1.59 um 2511.0 5.22 0.04 4-parameter ...  0.01  0.308 0.011 -0.049 0.004
+1.63 um 2511.0 5.22 0.04 4-parameter ... 0.005   0.57 0.005 -0.131 0.002``
+
+
+
+
+
+

env/environment-3.7.yml

@@ -5,20 +5,20 @@ channels:
 dependencies:
   - astropy=3.2.2
   - bokeh=1.4.0
-  - boto3
+  - boto3=1.14.33
   - cython=0.29.17
   - docopt=0.6.2
   - docutils=0.15.2
   - flake8=3.7.9
   - flask=1.1.2
-  - gunicorn=19.9.0
+  - gunicorn==20.0.4
   - h5py=2.8.0
   - ipython=7.12.0
   - jupyter=1.0.0
   - matplotlib=3.1.3
-  - numpy=1.18.1
+  - numpy=1.19.1
   - numpydoc=1.1.0
-  - pandas=1.0.3
+  - pandas=1.1.0
   - paramiko=2.6.0
   - pytest=5.4.3
   - python=3.7.1

pip_requirements.txt

@@ -9,7 +9,7 @@ boto3==1.14.33
 corner==2.1.0
 cython==0.29.21
 docopt==0.6.2
-docutils==0.16
+docutils==0.15.2
 flake8==3.8.3
 flask==1.1.2
 flask_wtf==0.14.3