Merge branch 'master' of https://github.com/hpparvi/ldtk

README.md

@@ -8,17 +8,17 @@ filters = [BoxcarFilter('a', 450, 550),  # Define your passbands
            BoxcarFilter('b', 650, 750),  # - Boxcar filters useful in 
            BoxcarFilter('c', 850, 950)]  #   transmission spectroscopy
 
-sc = LDPSetCreator(teff=(5200, 100),     # Define your star, and the code
-                   logg=(4.50, 0.2),     # downloads the uncached stellar 
+sc = LDPSetCreator(teff=(6400,   50),    # Define your star, and the code
+                   logg=(4.50, 0.20),    # downloads the uncached stellar 
                       z=(0.25, 0.05),    # spectra from the Husser et al.
                    filters=filters)      # FTP server automatically.
 
 ps = sc.create_profiles()                # Create the limb darkening profiles
 cq,eq = ps.coeffs_qd(use_mc=True)        # Estimate quadratic law coefficients
 
 lnlike = ps.lnlike_qd([[0.45,0.15],      # Calculate the quadratic law log 
-                      [0.35,0.10],       # likelihood for a set of coefficients 
-                      [0.25,0.05]])      # (returns the joint likelihood)
+                       [0.35,0.10],      # likelihood for a set of coefficients 
+                       [0.25,0.05]])     # (returns the joint likelihood)
 
 lnlike = ps.lnlike_qd([0.25,0.05],flt=0) # Quad. law log L for the first filter
 ```
@@ -27,10 +27,29 @@ lnlike = ps.lnlike_qd([0.25,0.05],flt=0) # Quad. law log L for the first filter
 
 ## Overview
 
+PyLDTk automates the calculation of custom stellar limb darkening (LD) profiles and model-specific limb darkening coefficients (LDC) using the library of PHOENIX-generated specific intensity spectra by Husser et al. (2013).
+
+The aim of the package is to facilitate exoplanet transit light curve modeling, especially transmission
+spectroscopy where the modeling is carried out for custom narrow passbands. The package can be  
+
+1. used to construct model-specific priors on the limb darkening coefficients prior to the transit light curve modeling
+2. directly integrated into the log posterior computation of any pre-existing transit modeling code with minimal modifications.
+
+The second approach can be used to constrain the LD model parameter space directly by the LD profile, allowing for the marginalization over the whole parameter space that can explain the profile without the need to approximate this constraint by a prior distribution. This is useful when using a high-order limb darkening model where the coefficients are often correlated, and the priors estimated from the tabulated values usually fail to include these correlations.
+
 ## Installation
 
- - LDPSetCreator : Generates a set of limb darkening profiles given a set of filters and stellar TEff, logg, and z.
- - LDPSet : Encapsulates the limb darkening profiles and offers methods for model coefficient estimation and log likelihood evaluation.
+Simple: clone the source from github and follow the basic Python package installation routine
+
+```bash
+ git clone https://github.com/hpparvi/ldtk.git
+ cd ldtk
+ python setup.py build install [--user]
+```
+
+## Examples
+
+Examples for basic and more advanced usage can be found from the `notebooks` directory.
 
 ## Model coefficient estimation
 
@@ -50,4 +69,19 @@ The limb darkening profiles can be resampled to a desired sampling in ``mu`` usi
  - ``reset_sampling()``: Reset back to native sampling in mu
  - ``resample()``:
 
-© 2015 Hannu Parviainen <[email protected]>
+## Main classes
+
+ - LDPSetCreator : Generates a set of limb darkening profiles given a set of filters and stellar TEff, logg, and z.
+ - LDPSet : Encapsulates the limb darkening profiles and offers methods for model coefficient estimation and log likelihood evaluation.
+
+## Known issues
+
+- The Husser et al. library is missing some files that should be there, which sometimes breaks the creation of a structured interpolation grid. This will be fixed in the final release.
+
+## Authors
+
+Hannu Parviainen, University of Oxford
+
+--
+
+Copyright © 2015 Hannu Parviainen <[email protected]>