Release 1.7

* Tuned various details of TOV/tidal deformability computation to improve accuracy
  and robustness
* Improved error model for TOV solutions, calibrated with many tests
* One can now specify the desired accuracy for TOV properties (no more exposure to
  accuracy-related technical parameters of the implementation)
* Finding stable TOV branch now more robust in corner cases
* Fixed issue #34. Tidal deformability now accurate also for very small compactness.
* Added small collection of EOS files from nuclear physics
* Documentation: corrections and improvements
* Use keyword-only arguments in parts of Python API to prevent common typos

Backward-incompatible interface changes for TOV solvers, TOV accuracy
specification, and NS TOV branch finding.

EOS/create/make_read_pp.py

@@ -1,4 +1,3 @@
-#!/bin/env python
 import numpy as np
 import pyreprimand as pyr
 
@@ -45,13 +44,11 @@ def matching_ds(rho_bnd, ds_j, ga_j, ga_i):
 
 
 
-def make_read_eos(name, p1_cgs, ga, rho_max_si=1e20):
+def make_pp_eos(p1_si, rho_b_si, ga, rho_max_si=1e20):
 
-    p1_si = p1_cgs * CGS_PRESS
 
     sly_ds_si, sly_ga, sly_rho_b_si = prep_sly()
 
-    rho_b_si     = np.array([10**14.7, 1e15]) * CGS_DENS
 
     dsx = sly_ds_si[-1]
     gax = sly_ga[-1]
@@ -62,25 +59,52 @@ def make_read_eos(name, p1_cgs, ga, rho_max_si=1e20):
     ga = np.hstack((sly_ga, ga))
 
 
-    path     = f"{name}.eos.h5"
 
     uc = pyr.units.geom_solar()
     rho_poly = sly_ds_si[0] / uc.density
     rho_b    = rho_b_si / uc.density 
     rho_max = rho_max_si / uc.density
 
-    eos = pyr.make_eos_barotr_pwpoly(rho_poly, rho_b, ga, rho_max, uc)
-    pyr.save_eos_barotr(path, eos, "A piecewise polytropic EOS from [Read et al]")
+    eos_pp = pyr.make_eos_barotr_pwpoly(rho_poly, rho_b, ga, rho_max, uc)
 
-    rgrho2 = pyr.range(rho_b[1] / 2, eos.range_rho.max / 1.0000001)
+    rgrho2 = pyr.range(rho_b[1] / 2, eos_pp.range_rho.max / 1.0000001)
     polyn2 =  1./ (ga[0] - 1.)
     ppm2   = 200
-    eos2   = pyr.make_eos_barotr_spline(eos, rgrho2, polyn2, ppm2) 
-    path2  = f"{name}.spline.eos.h5"
-    pyr.save_eos_barotr(path2, eos2, "A spline representation of a piecewise polytropic EOS from [Read et al]")
+    eos_tab   = pyr.make_eos_barotr_spline(eos_pp, rgrho2, polyn2, ppm2) 
 
+    return eos_pp, eos_tab
 #    
 
+def make_read_eos(p1_cgs, ga):
+    p1_si = p1_cgs * CGS_PRESS
+    rho_b_si     = np.array([10**14.7, 1e15]) * CGS_DENS
+    return make_pp_eos(p1_si, rho_b_si, ga)
+#
+
+
+def make_apr4_epp_eos():
+    ga = np.array([
+      2.830, 3.445, 3.348,                # APR4 [Read]
+      3.0, 2.0                            # ensure causality
+    ])
+    p1_cgs = 10**34.269
+    p1_si = p1_cgs * CGS_PRESS
+    rho_b_si = np.array([10**14.7, 1e+15, 1.4e+15, 1.61e+15]) * CGS_DENS
+    return make_pp_eos(p1_si, rho_b_si, ga)
+#
+
+def save_eos(name, eos_pp,eos_tab):
+    path     = f"{name}.eos.h5"
+    pyr.save_eos_barotr(path, eos_pp, "A piecewise polytropic EOS from [Read et al]")
+
+
+    path2  = f"{name}.spline.eos.h5"
+    pyr.save_eos_barotr(path2, eos_tab, 
+        "A spline representation of a piecewise polytropic EOS from [Read et al]")
+#
+
+
+
 def make_read_cat():
     params = {
       'APR4':(10**34.269, [2.830, 3.445, 3.348]),
@@ -97,7 +121,13 @@ def make_read_cat():
 
     for n,p in params.items():
         name = f"{n}_Read_PP"
-        make_read_eos(name, *p)
+        eos = make_read_eos(*p)
+        save_eos(name, *eos)
 
+def make_other_cat():
+    eos = make_apr4_epp_eos()
+    save_eos('APR4_Read_EPP', *eos)
+    print(*eos)
 
 make_read_cat()   
+make_other_cat()

EOS/meson.build

@@ -1,3 +1,2 @@
 
 eos_dir   = meson.current_source_dir()
-subdir('create')

EOS/nuclear_physics_eos_collection/.gitattributes

@@ -0,0 +1 @@
+* export-ignore

README.md

@@ -13,8 +13,8 @@ library code is not platform-specific and should also work on Macs.
 Windows and AIX are not supported. For use in HPC, users need to build 
 the library from source. For use in postprocessing, the Python intferface
 can also be installed via pip (binary wheel of the library available for 
-Linux). Conda packages for the C++ library and Python intface are planned 
-but not available yet.
+Linux). There is also an experimental conda package for the C++ 
+library (Python interface not included yet).
 
 ## Requirements
 
@@ -41,8 +41,7 @@ To build the library,
 ```bash
 cd <repository>
 meson mbuild --buildtype=release --prefix=<custom install location>
-cd mbuild
-ninja
+ninja -C mbuild
 ```
 
 This will compile with optimization and without debug symbols. Other
@@ -52,18 +51,29 @@ with `CC=clang CXX=clang++`.
 See [here](https://mesonbuild.com/Running-Meson.html) for general 
 Meson usage.
 
+Note on Conda environments: if you build for a conda environment and meson 
+does not detect the boost library or the wrong version, use 
+
+```bash
+BOOST_ROOT=$CONDA_PREFIX meson setup --prefix=$CONDA_PREFIX mbuild
+```
+
+Note on HDF5: meson and hdf5 do not play along nicely (partly caused by bad hdf5 
+packaging). Make sure there is a pkg-config file for the version you want to build 
+with. Another common cause is that you are building within a conda environment.
+In that case prefixing `LD_LIBRARY_PATH` with `$CONDA_PREFIX/lib` might help.
+
 
 ## Installing
 
 To install the library, use
 
 ```bash
-cd mbuild
-ninja install
+ninja -C mbuild install
 ```
 
 This will install in a user-defined location if the `--prefix` option
-was given during the build setup, otherwise systemwide. 
+was given during the build setup, otherwise systemwide (not recommended). 
 
 
 ## Using the Library
@@ -98,7 +108,7 @@ cd bindings/python
 pip install .
 ```
 
-This will also pip install packages numpy and pybind11>=2.6.0. 
+This will also pip install packages `numpy` and `pybind11>=2.6.0`. 
 When using conda environment, it may be better to install those 
 first using conda.
 
@@ -109,16 +119,14 @@ The Python extension module is called  `pyreprimand`.
 ## Einstein Toolkit Support
 
 RePrimAnd does provide a thorn that builds the library within
-an EinsteinToolkit (ET) environment, using the ExternalLibraries mechanism. The
-thorn can be found in the folder `ET_interface/thorns/RePrimAnd/`. The thorn
+an EinsteinToolkit (ET) environment, using the ExternalLibraries mechanism. 
+The thorn is part of the official ET framework. The version in the master 
+brach of this repository may however be ahead of the ET version.
+It can be found in the folder `ET_interface/thorns/RePrimAnd/`. The thorn
 depends on the HDF5, GSL, and BOOST ExternalLibraries thorns. Building it via
-the ET build system does not require meseon. Note this only builds the library,
+the ET build system does not require meson. Note this only builds the library,
 but not the tests and Python bindings. 
 
-The thorn is also part of the official ET framework. The version in the master 
-brach will typically by ahead of the ET version, but is not guaranteed to be 
-stable or compatible.
-
 There are two experimental (and largely undocumented for now) thorns 
 that aim to simplifying the usage.
 `RePrimAnd_Global_EOS` provides a centralized selection of a global thermal 
@@ -192,7 +200,7 @@ The resulting pdf figures are placed in the build directory under
 
 This requires Python+matplotlib. 
 
-### Visualizing Master Function
+### Visualizing Con2Prim Master Function
 
 In addition, there is code to sample the primitive recovery master
 function (the central ingredient of the con2prim scheme) for various cases,