Initial import

extra/borg/scripts/borg_tests.cmake

@@ -0,0 +1,16 @@
+function(borg_add_vobs_test testnames testbase)
+	set(_vobs_test_cmake ${CMAKE_BINARY_DIR}/CMakeFiles/borg_vobs_test_${testnames}.cmake)
+	set(_base_tmp_vobs_files ${CMAKE_BINARY_DIR}/CMakeFiles/vobs_tests)
+
+	if (NOT EXISTS ${_base_tmp_vobs_files})
+		FILE(MAKE_DIRECTORY ${_base_tmp_vobs_files})
+	endif()
+
+	execute_process(COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/extra/borg/scripts/generate_vobs_tests.py
+    ${_vobs_test_cmake} ${testbase} ${_base_tmp_vobs_files} RESULT_VARIABLE _generate_result)
+  if (NOT _generate_result EQUAL 0)
+		cmessage(FATAL_ERROR "Could not automatically generate vobs tests.")
+	endif()
+
+	include(${_vobs_test_cmake})
+endfunction()

extra/borg/scripts/generate_vobs_tests.py

@@ -0,0 +1,76 @@
+#+
+#   ARES/HADES/BORG Package -- ./extra/borg/scripts/generate_vobs_tests.py
+#   Copyright (C) 2019 Guilhem Lavaux <guilhem.lavaux@iap.fr>
+#
+#   Additional contributions from:
+#      Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
+#   
+#+
+
+import os
+import sys
+
+output_cmake_file = sys.argv[1]
+input_config = sys.argv[2]
+base_dir = sys.argv[3]
+
+with open(input_config, mode="r") as f:
+  config = {}
+  exec(f.read(), {}, config)
+  config = config['tests']
+
+tests = config['tests']
+
+with open(output_cmake_file, "wt") as f:
+
+    for test_name in tests:
+      test = tests[test_name]
+      includes = test['includes']
+      likelihood = test['likelihood']
+      model = test['model']
+      model_args = test.get('model_args', 'comm, box, 0.001');
+      model_code=""
+
+      f.write("""
+add_executable(test_vobs_%(test_name)s %(base_dir)s/%(test_source)s)
+target_link_libraries(test_vobs_%(test_name)s test_library_LSS LSS ${LIBS})
+ares_add_test_targets(test_vobs_%(test_name)s)
+"""
+         % dict(test_name=test_name, base_dir=base_dir, test_source="test_%s_vobs.cpp" % (test_name,)
+      ))
+
+      includes_str = \
+        "\n".join(
+            map(lambda x: "#include \"%s\"" % (x,), includes)
+        )
+
+
+      if 'model' in test:
+        args = test.get('model_args', 'comm, box, 0.001')
+
+      with open(os.path.join(base_dir,"test_%s_vobs.cpp" % (test_name,)), mode="wt") as f2:
+        f2.write("""%(includes)s
+#include "libLSS/samplers/rgen/hmc/hmc_density_sampler.hpp"
+#include "libLSS/physics/likelihoods/base.hpp"
+
+namespace L = LibLSS::Likelihood;
+using LibLSS::LikelihoodInfo;
+using LibLSS::HMCDensitySampler;
+using LibLSS::MarkovState;
+using LibLSS::BoxModel;
+
+typedef %(likelihood)s ThisLikelihood;
+
+
+auto makeModel(MarkovState& state, BoxModel const& box, LikelihoodInfo& info) {
+  auto comm = L::getMPI(info);
+  return std::make_shared<%(model)s>(%(model_args)s);
+}
+
+#include "libLSS/tests/generic_borg_vobs_test.cpp"
+""" % dict(includes=includes_str,likelihood=likelihood,model=model,model_args=model_args))
+
+# ARES TAG: authors_num = 1
+# ARES TAG: name(0) = Guilhem Lavaux
+# ARES TAG: email(0) = guilhem.lavaux@iap.fr
+# ARES TAG: year(0) = 2019

extra/borg/scripts/plot_forwards.py

@@ -0,0 +1,51 @@
+import matplotlib.pyplot as plt
+import h5py as h5
+import subprocess as sp
+
+
+models={'lpt':'LPT','old_pm':'PMv1','pm':'PMv2','tcola':'tCOLA'}
+#models={'lpt':'LPT','pm':'PMv2'}
+output={}
+output_v={}
+Pref=None
+km=None
+
+for m,name in models.items():
+  sp.run(f"./libLSS/tests/test_forward_{m}", check=True)
+  with h5.File("dump.h5", mode="r") as ff:
+      output[m] = ff['power'][:]
+      output_v[m] = ff['power_v'][:]
+      Pref = ff['scalars/powerspectrum'][:]
+      km = ff['scalars/k_modes'][:]
+
+#km = 0.5*(km[1:]+km[:-1])
+
+fig2 = plt.figure(figsize=(5,5))
+fig  = plt.figure(figsize=(5,6))
+ax0 = plt.axes([0.1 ,0.2,0.9 ,0.7], rasterized=False)
+ax1 = plt.axes([0.1 ,0.05,0.9 ,0.15], rasterized=False)
+ax2_0 = fig2.add_subplot(111)
+ax0.set_xlim(1e-3,2.2)
+ax0.set_ylim(1e2,1e5)
+ax1.set_xlim(1e-3,2.2)
+ax0.set_xticks([])
+ax1.set_xlabel('k ($h$ Mpc$^{-1}$)')
+ax1.set_ylabel('$P(k) / P_{ref}(k)$')
+ax0.set_ylabel('$P(k)$ ($h^{-3}$ Mpc$^{3}$)')
+ax2_0.set_ylabel('$P(k)$ (km$^2$ s$^{-2}$ $h^{-3}$ Mpc$^{3}$)')
+ax2_0.set_xlabel('k ($h$ Mpc$^{-1}$)')
+ax2_0.set_ylim(1, 1e7)
+
+
+for m,name in models.items():
+  ax0.loglog(km,output[m],label=name)
+  ax1.semilogx(km, output[m]/Pref,label=name)
+  ax2_0.loglog(km, output_v[m], label=name)
+
+ax0.loglog(km,Pref)
+ax0.legend()
+ax1.set_ylim(0.,1.5)
+ax1.axhline(1.0,lw=1.0,color='k')
+ax2_0.legend()
+fig.savefig("models.pdf",bbox_inches='tight')
+fig2.savefig("v_models.pdf",bbox_inches='tight')