diff --git a/.gitignore b/.gitignore
index fb8a33c..1848bfd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -162,3 +162,5 @@ cython_debug/
 # Borg
 *allocation_stats_0.txt
 *timing_stats_0.txt
+*fft_wisdom
+tests/*.h5
diff --git a/borg_velocity/likelihood.py b/borg_velocity/likelihood.py
index 9835107..bb0f462 100644
--- a/borg_velocity/likelihood.py
+++ b/borg_velocity/likelihood.py
@@ -95,7 +95,6 @@ class VelocityBORGLikelihood(borg.likelihood.BaseLikelihood):
         # Initialise cosmological parameters
         cpar = utils.get_cosmopar(self.ini_file)
         self.fwd.setCosmoParams(cpar)
-        # self.fwd_vel.setCosmoParams(cpar)
         self.fwd_param.setCosmoParams(cpar)
         self.updateCosmology(cpar)
         myprint(f"Original cosmological parameters: {self.fwd.getCosmoParams()}")
@@ -138,7 +137,6 @@ class VelocityBORGLikelihood(borg.likelihood.BaseLikelihood):
         cpar = state['cosmology']
         cpar.omega_q = 1. - cpar.omega_m - cpar.omega_k
         self.fwd.setCosmoParams(cpar)
-        # self.fwd_vel.setCosmoParams(cpar)
         self.fwd_param.setCosmoParams(cpar)
 
         
@@ -159,14 +157,13 @@ class VelocityBORGLikelihood(borg.likelihood.BaseLikelihood):
         
         cpar.omega_q = 1. - cpar.omega_m - cpar.omega_k
         self.fwd.setCosmoParams(cpar)
-        # self.fwd_vel.setCosmoParams(cpar)
         self.fwd_param.setCosmoParams(cpar)
         
-        # Compute growth rate
-        cosmology = borg.cosmo.Cosmology(cosmo)
-        f = cosmology.gplus(self.af)  # dD / da
-        f *= self.af / cosmology.d_plus(self.af)  # f = dlnD / dlna
-        self.f = f
+        # # Compute growth rate
+        # cosmology = borg.cosmo.Cosmology(cosmo)
+        # f = cosmology.gplus(self.af)  # dD / da
+        # f *= self.af / cosmology.d_plus(self.af)  # f = dlnD / dlna
+        # self.f = f
         
     def generateMBData(self) -> None:
         """
@@ -249,9 +246,6 @@ class VelocityBORGLikelihood(borg.likelihood.BaseLikelihood):
         bulk_flow = jnp.array([self.fwd_param.getModelParam('nullforward', 'bulk_flow_x'),
                           self.fwd_param.getModelParam('nullforward', 'bulk_flow_y'),
                           self.fwd_param.getModelParam('nullforward', 'bulk_flow_z')])
-        # v = forwards.dens2vel_linear(output_density, self.f, 
-        #                   self.fwd.getOutputBoxModel().L[0], self.smooth_R)
-        # v = v + self.bulk_flow.reshape((3, 1, 1, 1))
         v = output_velocity + self.bulk_flow.reshape((3, 1, 1, 1))
         
         omega_m = self.fwd.getCosmoParams().omega_m
@@ -276,6 +270,7 @@ class VelocityBORGLikelihood(borg.likelihood.BaseLikelihood):
                 self.interp_order, 
                 self.bias_epsilon
             )
+        # lkl = (output_density**2).sum()
             
         if not jnp.isfinite(lkl):
             lkl = self.bignum
@@ -364,7 +359,7 @@ class VelocityBORGLikelihood(borg.likelihood.BaseLikelihood):
         state["BORG_final_density"][:] = self.delta
         
         
-@partial(jax.jit, static_argnames=['L_BOX', 'interp_order', 'bias_epsilon'])
+# @partial(jax.jit, static_argnames=['L_BOX', 'interp_order', 'bias_epsilon'])
 def vel2like(cz_obs, v, MB_field, MB_pos, r, r_hMpc, sig_mu, sig_v, omega_m, muA, alpha, L_BOX, X_MIN, interp_order, bias_epsilon):
     """
     Jitted part of dens2like
@@ -420,6 +415,13 @@ def vel2like(cz_obs, v, MB_field, MB_pos, r, r_hMpc, sig_mu, sig_v, omega_m, muA
     lkl_ind = jnp.log(p_cz) - scale / 2 - 0.5 * jnp.log(2 * np.pi * sig_v**2)
     lkl = - lkl_ind.sum()
                 
+    # # DELETE THIS
+    # p_cz = jnp.trapz(p_r / p_r_norm, r, axis=1)
+    # lkl_ind = jnp.log(p_cz) - 0.5 * jnp.log(2 * np.pi * sig_v**2)
+    # lkl = - lkl_ind.sum()
+    # lkl = los_density.sum()
+    # lkl = (MB_field**2).sum()
+        
     return lkl
     
 
@@ -454,6 +456,7 @@ def build_gravity_model(state: borg.likelihood.MarkovState, box: borg.forward.Bo
 
     # Setup forward model
     chain = borg.forward.ChainForwardModel(box)
+    chain.addModel(borg.forward.models.HermiticEnforcer(box))
   
     # CLASS transfer function
     chain @= borg.forward.model_lib.M_PRIMORDIAL_AS(box)
@@ -525,7 +528,15 @@ def build_gravity_model(state: borg.likelihood.MarkovState, box: borg.forward.Bo
     fwd_param.setCosmoParams(cpar)
     
     # This is the forward model for velocity
-    fwd_vel = borg.forward.velocity.LinearModel(box, mod, af)
+    velmodel_name = config['model']['velocity']
+    velmodel = getattr(borg.forward.velocity, velmodel_name)
+    if velmodel_name == 'LinearModel':
+        fwd_vel = velmodel(box, mod, af)
+    elif velmodel_name == 'CICModel':
+        rsmooth = float(config['model']['rsmooth'])
+        fwd_vel = velmodel(box, mod, rsmooth)
+    else:
+        fwd_vel = velmodel(box, mod)
     
     return chain
 
diff --git a/conf/basic_ini.ini b/conf/basic_ini.ini
index 3ebe242..10630ae 100644
--- a/conf/basic_ini.ini
+++ b/conf/basic_ini.ini
@@ -1,9 +1,9 @@
 [system]
 console_output = borg_log
 VERBOSE_LEVEL = 2
-N0 = 32
-N1 = 32
-N2 = 32
+N0 = 8
+N1 = 8
+N2 = 8
 L0 = 500.0
 L1 = 500.0
 L2 = 500.0
@@ -42,12 +42,14 @@ mixing = 1
 
 [model]
 gravity = lpt
+velocity = CICModel
 af = 1.0
 ai = 0.05
 nsteps = 20
 smooth_R = 4
 bias_epsilon = 1e-7
 interp_order = 1
+rsmooth = 1.
 sig_v = 150.
 R_lim = none
 Nint_points = 201 
diff --git a/figs/gradient_test_8.png b/figs/gradient_test_8.png
new file mode 100644
index 0000000..b9f5ad6
Binary files /dev/null and b/figs/gradient_test_8.png differ
diff --git a/notebooks/Analyse_chain.ipynb b/notebooks/Analyse_chain.ipynb
index f29eaa6..1674b53 100644
--- a/notebooks/Analyse_chain.ipynb
+++ b/notebooks/Analyse_chain.ipynb
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 41,
    "id": "d791f161-730a-464d-85cf-d76c708a3260",
    "metadata": {},
    "outputs": [
@@ -117,12 +117,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 10/10 [00:00<00:00, 184.09it/s]\n"
+      "100%|██████████| 10/10 [00:00<00:00, 207.09it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x900 with 10 Axes>"
       ]
@@ -142,16 +142,18 @@
     "ncol = 4\n",
     "nrow = int(np.ceil(len(names) / ncol))\n",
     "\n",
-    "fig, axs = plt.subplots(nrow, ncol, figsize=(ncol*4, nrow*3), sharex=True)\n",
+    "fig, axs = plt.subplots(nrow, ncol, figsize=(ncol*4, nrow*3))\n",
     "axs = np.atleast_2d(axs)\n",
-    "for i in range(ncol):\n",
-    "    axs[-1,i].set_xlabel('Step Number')\n",
+    "# for i in range(ncol):\n",
+    "    # axs[-1,i].set_xlabel('Step Number')\n",
     "axs = axs.flatten()\n",
     "for i in range(len(names)):\n",
     "    axs[i].plot(samples[i,:])\n",
     "    axs[i].set_title(names[i])\n",
+    "    axs[i].set_xlabel('Step Number')\n",
     "for i in range(len(names), len(axs)):\n",
-    "    axs[i].remove()"
+    "    axs[i].remove()\n",
+    "fig.tight_layout()"
    ]
   },
   {
diff --git a/scripts/run_borg.sh b/scripts/run_borg.sh
index e96e3d2..f50e959 100755
--- a/scripts/run_borg.sh
+++ b/scripts/run_borg.sh
@@ -31,4 +31,4 @@ set -x
 # Just ICs
 INI_FILE=/home/bartlett/fsigma8/borg_velocity/conf/basic_ini.ini
 cp $INI_FILE basic_ini.ini
-$BORG INIT basic_ini.ini
\ No newline at end of file
+$BORG INIT basic_ini.ini
diff --git a/tests/allocation_stats_0.txt b/tests/allocation_stats_0.txt
index 4d82422..0040403 100644
--- a/tests/allocation_stats_0.txt
+++ b/tests/allocation_stats_0.txt
@@ -1,20 +1,30 @@
-Memory still allocated at the end: 31.8652 MB
+Memory still allocated at the end: 10.0405 MB
 
 Statistics per context (name, allocated, freed, peak)
 ======================
 
-*none*                                   76.2188 0.250069 32.2871
-BORG LPT MODEL                           505.495 503.727 32.3027
-BORGForwardModel::setup                  0.000160217 0 2.23588
-BorgLptModel::BorgLptModel               1.54688 0 2.23581
-LinearModel::getVelocityField            607.75 589.875 33.1465
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/chain_forward_model.cpp]virtual void LibLSS::ChainForwardModel::forwardModel_v2(LibLSS::detail_input::ModelInput<3>) 227.906 75.9688 32.5527
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forward_model.cpp]void LibLSS::BORGForwardModel::setupDefault() 0.53125 0 0.688934
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/borg_lpt.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_borg_lpt(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&) [with Grid = LibLSS::ClassicCloudInCell<double>; LibLSS::BoxModel = LibLSS::NBoxModel<3>] 0 0 0.157661
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/particle_balancer/balanceinfo.hpp]void LibLSS::BalanceInfo::allocate(LibLSS::MPI_Communication*, size_t) 72.2135 71.961 32.3027
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/primordial_as.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_primordial_as(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&) 0.078804 7.62939e-06 0.0788498
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/transfer_class.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_class(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&) 0.078804 7.62939e-06 0.157669
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/model_io/base.hpp]void LibLSS::detail_model::ModelIO<Nd>::transfer(LibLSS::detail_model::ModelIO<Nd>&&) [with long unsigned int Nd = 3] 0 222.656 0
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/samplers/core/gridLikelihoodBase.cpp]LibLSS::GridDensityLikelihoodBase<Dims>::GridDensityLikelihoodBase(LibLSS::MPI_Communication*, const GridSizes&, const GridLengths&) [with int Dims = 3; GridSizes = std::array<long unsigned int, 3>; GridLengths = std::array<double, 3>] 0.53125 0.515625 2.76713
-[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/python/pyforward.cpp]void do_get_density_final(LibLSS::BORGForwardModel*, pybind11::array) 75.9688 71.5 32.3027
-lpt_ic                                   75.9688 75.9688 32.5683
+*none*                                   6.2686 0.141525 10.1813
+BORG LPT MODEL                           35.397 35.3694 10.1823
+BORGForwardModel::setup                  0.000183105 0 0.039917
+BorgLptModel::BorgLptModel               0.0263672 0 0.0398483
+BorgLptModel::~BorgLptModel              0 0.0315552 0
+CICModel::getVelocityField               18.8408 16.9146 10.1931
+CICModel::getVelocityFieldAlpha          56.3818 50.7437 10.2018
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/chain_forward_model.cpp]virtual void LibLSS::ChainForwardModel::forwardModel_v2(LibLSS::detail_input::ModelInput<3>) 25.0391 6.25977 10.1862
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/chain_forward_model.cpp]void LibLSS::ChainForwardModel::trigger_ag() 0.0195312 0.00390625 0.22258
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forward_model.cpp]void LibLSS::BORGForwardModel::setupDefault() 0.00976562 0 0.0134811
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/borg_lpt.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_borg_lpt(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&) [with Grid = LibLSS::ClassicCloudInCell<double>; LibLSS::BoxModel = LibLSS::NBoxModel<3>] 0 0 0.00369263
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/lpt/borg_fwd_lpt_adj.cpp]void LibLSS::BorgLptModel<CIC>::clearAdjointGradient() [with CIC = LibLSS::ClassicCloudInCell<double>] 0 0.0236664 0
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/lpt/borg_fwd_lpt_adj.cpp]void LibLSS::BorgLptModel<CIC>::preallocate() [with CIC = LibLSS::ClassicCloudInCell<double>] 0.0236664 0 0.295334
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/particle_balancer/balanceinfo.hpp]void LibLSS::BalanceInfo::allocate(LibLSS::MPI_Communication*, size_t) 15.0802 15.0684 10.1823
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/primordial_as.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_primordial_as(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&) 0.00180817 7.62939e-06 0.00187683
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/transfer_class.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_class(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&) 0.00180817 7.62939e-06 0.00370026
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/model_io/base.hpp]void LibLSS::detail_model::ModelIO<Nd>::transfer(LibLSS::detail_model::ModelIO<Nd>&&) [with long unsigned int Nd = 3] 0 23.7539 0
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/velocity/velocity_cic.cpp]virtual void LibLSS::VelocityModel::CICModel::computeAdjointModel(LibLSS::VelocityModel::Base::arrayVelocityField_t) 0.197266 0.189941 0.27655
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/samplers/core/gridLikelihoodBase.cpp]LibLSS::GridDensityLikelihoodBase<Dims>::GridDensityLikelihoodBase(LibLSS::MPI_Communication*, const GridSizes&, const GridLengths&) [with int Dims = 3; GridSizes = std::array<long unsigned int, 3>; GridLengths = std::array<double, 3>] 0.00976562 0.00878906 0.0496826
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/tools/mpi/ghost_planes.hpp]void LibLSS::GhostPlanes<T, Nd>::setup(LibLSS::MPI_Communication*, PlaneList&&, PlaneSet&&, DimList&&, size_t) [with PlaneList = std::set<long int>&; PlaneSet = std::set<long int>&; DimList = std::array<long int, 2>&; T = std::complex<double>; long unsigned int Nd = 2; size_t = long unsigned int] 7.62939e-06 3.8147e-05 5.34058e-05
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/python/pyforward.cpp]void do_get_adjoint_model(LibLSS::BORGForwardModel*, pybind11::array) 0.00488281 0.00488281 0.227463
+[/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/python/pyforward.cpp]void do_get_density_final(LibLSS::BORGForwardModel*, pybind11::array) 6.25977 5.00781 10.1823
+dispatch_plane_map                       7.24792e-05 4.19617e-05 0.000125885
+exchanging nearby planes after projection 2.50635 2.50635 10.1926
+lpt_ic                                   6.25977 6.25977 10.1872
diff --git a/tests/fft_wisdom b/tests/fft_wisdom
index dac75ab..823e36b 100644
--- a/tests/fft_wisdom
+++ b/tests/fft_wisdom
@@ -1,28 +1,71 @@
 (fftw-3.3.10 fftw_wisdom #x3c273403 #x192df114 #x4d08727c #xe98e9b9d
-  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x21347a5d #x286e0d10 #xabf9ff02 #xccdf80a5)
-  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xe7f77f6a #xaf2de8b8 #xad19bc70 #x80305f29)
-  (fftw_codelet_n1bv_32_avx 0 #x10bdd #x10bdd #x0 #x6d197f20 #xfc9cbd23 #x91ddb367 #x208619cb)
-  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x1f2e97fe #x61895cd8 #x6073a2f5 #x6ada2663)
-  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x84033142 #x81339a41 #xb78a491e #x66362e05)
-  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x0ac209ed #x737616a2 #xc31f0ad8 #x13c3716f)
-  (fftw_rdft2_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x40ffeb6f #x4d232a35 #x49c61e65 #x4d75fa83)
-  (fftw_codelet_n1bv_32_avx 0 #x10bdd #x10bdd #x0 #x35d0d312 #x6b498ae1 #x1ddcffdc #x4a1a1998)
-  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xbffceb36 #x5b340e87 #xc2433c88 #x10e155b2)
-  (fftw_codelet_r2cf_32 2 #x10bdd #x10bdd #x0 #xe5219ff5 #x7cc0cc2f #x9ce07377 #x12d27b02)
-  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xd78cc60c #x6e1210c6 #x5868829d #x70ada990)
-  (fftw_codelet_r2cf_32 2 #x10bdd #x10bdd #x0 #x68269cfc #xb89b69b3 #x4eaad8fa #x9807c679)
-  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x7446ec55 #x3f800a5f #xba25afcf #xc0e9d5c1)
-  (fftw_rdft2_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x7ec9785e #x02957b55 #xab1017dc #xdcd04ed7)
-  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x68900aea #xb640ce9e #xcd3b0e06 #x8170fa63)
-  (fftw_dft_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x404fdd72 #x2323d034 #xc860c577 #x4779492a)
-  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x3c2e2a1a #x07c08954 #x35c337d9 #x80864862)
-  (fftw_codelet_n1fv_32_sse2 0 #x10bdd #x10bdd #x0 #xe61c7c8d #x2cea019e #x8489a633 #x8d6543c6)
-  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x962543ac #xb000f636 #xb27fc586 #xd4a83bb7)
-  (fftw_codelet_n1fv_32_avx 0 #x10bdd #x10bdd #x0 #x94cb38f8 #xed5987e0 #xa3d4151a #xeb412d04)
-  (fftw_codelet_r2cb_32 2 #x10bdd #x10bdd #x0 #x92bf92d5 #xdc456f1e #x5a32a424 #xe1f76e14)
-  (fftw_codelet_n1fv_32_avx 0 #x10bdd #x10bdd #x0 #xb5d7d23e #x26089494 #x55133ef3 #x8ac38174)
-  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xe0a3b250 #xab7e7c07 #xf0935dde #x1568a95f)
-  (fftw_codelet_r2cb_32 2 #x10bdd #x10bdd #x0 #x4e6e3714 #xebce55aa #x0ede5253 #x4faf4524)
+  (fftw_codelet_r2cf_16 2 #x10bdd #x10bdd #x0 #xa7e83312 #x11c3dce9 #x403202b1 #xba9376e9)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xe2b7638c #xca3eaae0 #xe5990134 #x15b362bd)
   (fftw_codelet_n1bv_32_sse2 0 #x10bdd #x10bdd #x0 #x902cd310 #xa659999d #x6fde2637 #xb23e4fd2)
+  (fftw_codelet_n1bv_16_sse2 0 #x10bdd #x10bdd #x0 #x9306ed57 #x98c44e85 #x5cdf298e #xbcec4b1f)
+  (fftw_codelet_n1bv_8_sse2 0 #x10bdd #x10bdd #x0 #xa80dd5c4 #x9fd5b8d4 #x3d6788bf #x5a24b1fc)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x40741233 #x3efc06b3 #x0f24264f #x64099f05)
   (fftw_dft_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x4b54e3ca #x4f94ebf3 #x244f4da3 #x2412ca79)
+  (fftw_codelet_n1fv_32_avx 0 #x10bdd #x10bdd #x0 #x94cb38f8 #xed5987e0 #xa3d4151a #xeb412d04)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x84033142 #x81339a41 #xb78a491e #x66362e05)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xcaf756bf #x6e71602c #x20e86581 #x110e9e90)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x3ca106a5 #xda967bc9 #xbb014751 #x74e4e6ae)
+  (fftw_codelet_n1fv_32_sse2 0 #x10bdd #x10bdd #x0 #xe61c7c8d #x2cea019e #x8489a633 #x8d6543c6)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x2e35848a #x0ae8a985 #xadfbead1 #xe429563d)
+  (fftw_codelet_r2cf_32 2 #x10bdd #x10bdd #x0 #xe5219ff5 #x7cc0cc2f #x9ce07377 #x12d27b02)
+  (fftw_rdft2_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x6b14be63 #x1d99ab98 #xe3227f85 #xb1db4db0)
+  (fftw_codelet_n1fv_16_sse2 0 #x10bdd #x10bdd #x0 #xf8443ec2 #xfa3955a3 #xa7e19627 #xab87bd57)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xa3839252 #xea4efad1 #xaf8b0cda #xd8776397)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xa7f53555 #x21d66b4e #x9964caab #xd7909594)
+  (fftw_codelet_r2cf_8 2 #x10bdd #x10bdd #x0 #x27bdc5f1 #x0e0fc54e #x15095bf2 #x0e78cf6f)
+  (fftw_codelet_r2cf_16 2 #x10bdd #x10bdd #x0 #xc41f6ea8 #x979a9054 #x4af7b3f3 #xcbc1bd6e)
+  (fftw_codelet_n1bv_32_avx 0 #x10bdd #x10bdd #x0 #x6d197f20 #xfc9cbd23 #x91ddb367 #x208619cb)
+  (fftw_codelet_r2cf_32 2 #x10bdd #x10bdd #x0 #x68269cfc #xb89b69b3 #x4eaad8fa #x9807c679)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x8de57ca6 #x4d942122 #xcc9f63a3 #xda26628d)
+  (fftw_rdft2_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x4cae513c #xd64a8afb #x187c71d1 #xb541a5b8)
+  (fftw_codelet_n1fv_32_avx 0 #x10bdd #x10bdd #x0 #xb5d7d23e #x26089494 #x55133ef3 #x8ac38174)
+  (fftw_codelet_n1fv_8_sse2 0 #x10bdd #x10bdd #x0 #x7035d47e #x31f840f3 #x9383f4ab #x075b88bb)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x7446ec55 #x3f800a5f #xba25afcf #xc0e9d5c1)
+  (fftw_codelet_n1bv_16_sse2 0 #x10bdd #x10bdd #x0 #xd9d77a34 #x2764630e #xaa589a35 #xc9be81db)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xe7f77f6a #xaf2de8b8 #xad19bc70 #x80305f29)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x37e6a727 #xc85b1b7c #x65d78a7d #xf0595850)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x962543ac #xb000f636 #xb27fc586 #xd4a83bb7)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x68900aea #xb640ce9e #xcd3b0e06 #x8170fa63)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x0ac209ed #x737616a2 #xc31f0ad8 #x13c3716f)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x21347a5d #x286e0d10 #xabf9ff02 #xccdf80a5)
+  (fftw_codelet_n1bv_16_avx 0 #x10bdd #x10bdd #x0 #x36abc7b8 #x1cd3eb8f #xa4996ada #x1a06c95a)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x855943e1 #x80e0a0ca #xd37ea014 #x23f0deee)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xbffceb36 #x5b340e87 #xc2433c88 #x10e155b2)
+  (fftw_codelet_n1fv_16_sse2 0 #x10bdd #x10bdd #x0 #x7304fb62 #xa799c05d #xa4bd6105 #x6d7bd16b)
+  (fftw_codelet_n1fv_8_sse2 0 #x10bdd #x10bdd #x0 #xdbede2d6 #x5032ed11 #x297abbbf #x39122c9f)
+  (fftw_codelet_r2cb_16 2 #x10bdd #x10bdd #x0 #x5bdedfde #x107be498 #x869a0bfb #x7cc04b4f)
+  (fftw_rdft2_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x7ec9785e #x02957b55 #xab1017dc #xdcd04ed7)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x45123427 #x01281369 #xb95432bf #x1feb837a)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x91265544 #x90ab0f94 #x1c6548b3 #xe92ee441)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x99251b68 #x8a372a47 #xa305bb39 #x4df59c76)
+  (fftw_rdft2_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x40ffeb6f #x4d232a35 #x49c61e65 #x4d75fa83)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #xe6ba1829 #x368612bb #x2bdab11c #x2fc35e23)
+  (fftw_dft_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x404fdd72 #x2323d034 #xc860c577 #x4779492a)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #xc3c9e752 #x39b3927b #x7df101ab #x79309943)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x7b0c4ef3 #x2018ef0d #xa437b495 #xaddfa382)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x7e4e30fa #xc1ef6b3d #xbc1076dd #xc79407b9)
+  (fftw_codelet_r2cb_32 2 #x10bdd #x10bdd #x0 #x4e6e3714 #xebce55aa #x0ede5253 #x4faf4524)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xd78cc60c #x6e1210c6 #x5868829d #x70ada990)
+  (fftw_codelet_n1bv_8_sse2 0 #x10bdd #x10bdd #x0 #x90888d2b #x6076f166 #x437260f2 #x93ff29c3)
+  (fftw_codelet_n1bv_16_avx 0 #x10bdd #x10bdd #x0 #xdf3d687a #xe46fb97f #x6ee0e7aa #x945d2abd)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xdd819205 #xc9fe9ea4 #x32ec81f1 #x4bbca283)
+  (fftw_codelet_n1bv_32_avx 0 #x10bdd #x10bdd #x0 #x35d0d312 #x6b498ae1 #x1ddcffdc #x4a1a1998)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x2d4d9e0b #x4a34f327 #x275f3ae1 #x25641e46)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xe0a3b250 #xab7e7c07 #xf0935dde #x1568a95f)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x9ff9c3f3 #xf143b736 #xce2dc789 #x4d442c4a)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x0f7b58ed #xae4d4f79 #xe2fc8e0f #x211d3490)
+  (fftw_codelet_r2cb_8 2 #x10bdd #x10bdd #x0 #x51e5f6b8 #x566aac6d #x249913ff #xf363e314)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xa2573247 #x00a395db #xf017dd19 #x1df50dd6)
+  (fftw_dft_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #x1f2e97fe #x61895cd8 #x6073a2f5 #x6ada2663)
+  (fftw_rdft2_thr_vrank_geq1_register 0 #x10bdd #x10bdd #x0 #xfae0bb4c #xd2ae28c3 #x4adfa199 #xc55e4e63)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x8954f8e1 #x6c6f7b1c #xd3d53834 #xc9eb25c1)
+  (fftw_codelet_n1fv_16_avx 0 #x10bdd #x10bdd #x0 #x60ba7657 #xefd487be #x5ac1f2cf #x8eb91f0f)
+  (fftw_rdft2_rank_geq2_register 0 #x10bdd #x10bdd #x0 #x3c2e2a1a #x07c08954 #x35c337d9 #x80864862)
+  (fftw_codelet_n1fv_16_avx 0 #x10bdd #x10bdd #x0 #xca718bf8 #x524674ba #x011a6dfe #xfbebca33)
+  (fftw_codelet_r2cb_32 2 #x10bdd #x10bdd #x0 #x92bf92d5 #xdc456f1e #x5a32a424 #xe1f76e14)
 )
diff --git a/tests/test_gradient.py b/tests/test_gradient.py
new file mode 100644
index 0000000..fd698e1
--- /dev/null
+++ b/tests/test_gradient.py
@@ -0,0 +1,198 @@
+import aquila_borg as borg
+import configparser
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+import itertools
+import h5py as h5
+import os
+import sys
+import contextlib
+from tqdm import tqdm
+
+import borg_velocity.likelihood as likelihood
+import borg_velocity.forwards as forwards
+import borg_velocity.utils as utils
+
+run_test = True
+# run_test = False
+epsilon = 1e-2
+
+
+# Create a context manager to suppress stdout
+@contextlib.contextmanager
+def suppress_stdout():
+    with open(os.devnull, 'w') as devnull:
+        old_stdout = sys.stdout
+        sys.stdout = devnull
+        try:
+            yield
+        finally:
+            sys.stdout = old_stdout
+
+def compare_gradients(
+        ag_lh_auto_real: np.ndarray,
+        ag_lh_auto_imag: np.ndarray,
+        ag_lh_num_real: np.ndarray,
+        ag_lh_num_imag: np.ndarray,
+        plot_step: int,
+        filename: str="gradients.png",
+) -> None:
+    """
+    Comparison of an autodiff adjoint gradient of the likelihood against a
+    numerical one evaluated with finite differences.
+    
+    Args:
+        - ag_lh_auto_real (np.ndarray): Real part of the adjoint gradient (autodiff)
+        - ag_lh_auto_imag (np.ndarray): Imaginary part of the adjoint gradient (autodiff)
+        - ag_lh_num_real (np.ndarray): Real part of the adjoint gradient (numerical)
+        - ag_lh_num_imag (np.ndarray): Imaginary part of the adjoint gradient (numerical)
+        - plot_step (int): How frequently to sample the arrays
+        - filename (str): Name of the file to save the figure
+    
+    """
+    # Plot colors
+    colors = {
+        "red": "#ba3d3b",
+        "blue": "#3d5792",
+    }
+    
+    fig, axs  = plt.subplots(2, 2, figsize=(10, 7))
+    
+    # Real part
+    axs[0,0].axhline(0.0, color="black", linestyle=":")
+    axs[0,0].plot(ag_lh_num_real[::plot_step], c=colors["blue"], label="Finite differences")
+    axs[0,0].plot(ag_lh_auto_real[::plot_step], "o", c=colors["red"], ms=3, label="Autodiff")
+    axs[0,0].yaxis.get_major_formatter().set_powerlimits((-2, 2))
+    axs[0,0].set_ylabel("Real part")
+    axs[0,0].legend()
+    axs[0,1].plot(ag_lh_num_real[::plot_step],
+            ag_lh_auto_real[::plot_step] - ag_lh_num_real[::plot_step],
+            "o",
+            c=colors["red"],
+            ms=3
+    )
+    x = axs[0,1].get_xlim()
+    axs[0,1].axhline(y=0, color='k')
+    axs[0,1].set_xlabel("Numerical")
+    axs[0,1].set_ylabel("Autodiff - Numerical (real)")
+    
+    # Imaginary part
+    axs[1,0].axhline(0.0, color="black", linestyle=":")
+    axs[1,0].plot(ag_lh_num_imag[::plot_step][4:], c=colors["blue"], label="Finite differences")
+    axs[1,0].plot(ag_lh_auto_imag[::plot_step][4:], "o", c=colors["red"], ms=3, label="Autodiff")
+    axs[1,0].yaxis.get_major_formatter().set_powerlimits((-2, 2))
+    axs[1,0].set_ylabel("Imaginary part")
+    axs[1,0].set_xlabel("Voxel ID")
+    axs[1,1].plot(ag_lh_num_imag[::plot_step],
+            ag_lh_auto_imag[::plot_step] - ag_lh_num_imag[::plot_step], 
+            ".", 
+            c=colors["red"]
+    )
+    x = axs[1,1].get_xlim()
+    axs[1,1].axhline(y=0, color='k')
+    axs[1,1].set_xlabel("Numerical")
+    axs[1,1].set_ylabel("Autodiff - Numerical (imag)")
+
+    fig.suptitle("Adjoint gradient of the likelihood w.r.t. initial conditions")
+    fig.tight_layout()
+    fig.subplots_adjust(hspace=0.)
+    
+    path = "../figs/"
+    fig.savefig(path + filename, bbox_inches="tight")
+
+
+ini_file = '../conf/basic_ini.ini'
+
+# Input box
+box_in = borg.forward.BoxModel()
+config = configparser.ConfigParser()
+config.read(ini_file)
+box_in.L = (float(config['system']['L0']), float(config['system']['L1']), float(config['system']['L2']))
+box_in.N = (int(config['system']['N0']), int(config['system']['N1']), int(config['system']['N2']))
+box_in.xmin = (float(config['system']['corner0']), float(config['system']['corner1']), float(config['system']['corner2']))
+
+# Setup BORG forward model and likelihood
+model = likelihood.build_gravity_model(None, box_in, ini_file=ini_file)
+cosmo = utils.get_cosmopar(ini_file)
+model.setCosmoParams(cosmo)
+fwd_param = forwards.NullForward(box_in)
+fwd_vel = likelihood.fwd_vel
+mylike = likelihood.VelocityBORGLikelihood(model, fwd_param, fwd_vel, ini_file)
+
+# Create mock data
+state = borg.likelihood.MarkovState()
+mylike.initializeLikelihood(state)
+mylike.updateCosmology(cosmo)
+s_hat = np.fft.rfftn(np.random.randn(*box_in.N)) / box_in.Ntot ** (0.5)
+mylike.generateMockData(s_hat, state)
+
+# Compute density field
+# output_density = np.zeros(box_in.N)
+# mylike.fwd.forwardModel_v2(s_hat)
+# print('SUM START', output_density.sum())
+# mylike.fwd.getDensityFinal(output_density)
+# print('SUM NOW', output_density.sum())
+# L = mylike.logLikelihoodComplex(s_hat, None)
+# print(L)
+# quit()
+
+# Autodiff
+autodiff_gradient = mylike.gradientLikelihoodComplex(s_hat) 
+print(autodiff_gradient.min(), autodiff_gradient.max(), np.sum(np.isfinite(autodiff_gradient)), np.prod(autodiff_gradient.shape))
+
+# Finite differences
+if run_test:
+    s_hat_epsilon = s_hat.copy()
+    num_gradient = np.zeros(s_hat.shape, dtype=np.complex128)
+    for i, j, k in tqdm(
+        itertools.product(*map(range, [box_in.N[0], box_in.N[1], box_in.N[2] // 2 + 1])),
+        total=box_in.N[0] * box_in.N[1] * (box_in.N[2] // 2 + 1),
+        mininterval=1,
+        ):
+
+        # +/- epsilon
+        s_hat_epsilon[i, j, k] = s_hat[i, j, k] + epsilon
+        with suppress_stdout():
+            L = mylike.logLikelihoodComplex(s_hat_epsilon, None)
+        s_hat_epsilon[i, j, k] = s_hat[i, j, k] - epsilon
+        with suppress_stdout():
+            L -= mylike.logLikelihoodComplex(s_hat_epsilon, None)
+        QQ = L / (2.0 * epsilon)
+
+        # +/- i * epsilon
+        s_hat_epsilon[i, j, k] = s_hat[i, j, k] + 1j * epsilon
+        with suppress_stdout():
+            L = mylike.logLikelihoodComplex(s_hat_epsilon, None)
+        s_hat_epsilon[i, j, k] = s_hat[i, j, k] - 1j * epsilon
+        with suppress_stdout():
+            L -= mylike.logLikelihoodComplex(s_hat_epsilon, None)
+        QQ = QQ + L * 1j / (2.0 * epsilon)
+
+        s_hat_epsilon[i, j, k] = s_hat[i, j, k]
+        num_gradient[i, j, k] = QQ
+
+
+    with h5.File(f"gradients_{box_in.N}.h5", mode="w") as ff:
+        ff["scalars/gradient_array_lh"] = autodiff_gradient
+        ff["scalars/gradient_array_lh_ref"] = num_gradient
+        ff["scalars/gradient_array_prior"] = np.zeros_like(autodiff_gradient)
+        ff["scalars/gradient_array_prior_ref"] = np.zeros_like(autodiff_gradient)
+
+slice_step = 2
+plot_step = 2
+
+with h5.File(f'gradients_{box_in.N}.h5', 'r') as f:
+    ag_lh_auto_real = f["scalars"]["gradient_array_lh"][::slice_step, :, :].flatten().real
+    ag_lh_auto_imag = f["scalars"]["gradient_array_lh"][::slice_step, :, :].flatten().imag
+    ag_lh_num_real = f["scalars"]["gradient_array_lh_ref"][::slice_step, :, :].flatten().real
+    ag_lh_num_imag = f["scalars"]["gradient_array_lh_ref"][::slice_step, :, :].flatten().imag
+
+    compare_gradients(
+        ag_lh_auto_real,
+        ag_lh_auto_imag,
+        ag_lh_num_real,
+        ag_lh_num_imag,
+        plot_step,
+        f'gradient_test_{box_in.N[0]}.png',
+    )
diff --git a/tests/timing_stats_0.txt b/tests/timing_stats_0.txt
index 58f275e..b7a829b 100644
--- a/tests/timing_stats_0.txt
+++ b/tests/timing_stats_0.txt
@@ -1,54 +1,87 @@
-ARES version c6de4f62faad20ede0bb40aa3678551dceee637b modules 
+ARES version 53e8df9fe11c732cda13f7ffc821238622068e57 modules 
 
 Cumulative timing spent in different context
 --------------------------------------------
 Context,   Total time (seconds)
 
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forward_model.cpp]void LibLSS::ForwardModel::setCosmoParams(const LibLSS::CosmologicalParameters&)	439	128.974
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/borg_lpt.cpp]void LibLSS::BorgLptModel<CIC>::updateCosmo() [with CIC = LibLSS::ClassicCloudInCell<double>]	87	59.7371
-                   lightcone computation	83	58.2376
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/cosmo.cpp]void LibLSS::Cosmology::precompute_d_plus()	83	50.8567
-           LinearModel::getVelocityField	286	13.1932
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/chain_forward_model.cpp]virtual void LibLSS::ChainForwardModel::forwardModel_v2(LibLSS::detail_input::ModelInput<3>)	286	6.19859
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/cosmo.cpp]void LibLSS::Cosmology::precompute_com2a()	83	5.85109
-                          BORG LPT MODEL	286	5.68871
-                      BORG forward model	286	5.65939
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/transfer_class.cpp]virtual void LibLSS::ForwardClass::updateCosmo()	87	4.72651
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/class_cosmo.cpp]LibLSS::ClassCosmo::ClassCosmo(const LibLSS::CosmologicalParameters&, unsigned int, double, std::string, unsigned int, const std::map<std::__cxx11::basic_string<char>, std::__cxx11::basic_string<char> >&)	83	4.65339
-           FFTW_Manager::create_c2r_plan	860	1.08067
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/lpt/borg_fwd_lpt.cpp]void LibLSS::BorgLptModel<CIC>::getDensityFinal(LibLSS::detail_output::ModelOutput<3>) [with CIC = LibLSS::ClassicCloudInCell<double>]	572	0.636041
-                  Classic CIC projection	572	0.489137
-                                  lpt_ic	286	0.474278
-           FFTW_Manager::create_r2c_plan	289	0.377194
-               FFTW_Manager::execute_c2r	1716	0.297204
-              FFTW_Manager::destroy_plan	1144	0.0944267
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/class_cosmo.cpp]void LibLSS::ClassCosmo::retrieve_Tk(double)	166	0.0942418
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/class_cosmo.cpp]void LibLSS::ClassCosmo::reinterpolate(const array_ref_1d&, const array_ref_1d&, LibLSS::internal_auto_interp::auto_interpolator<double>&)	498	0.0858865
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/python/pyforward.cpp]void transfer_in(std::shared_ptr<LibLSS::FFTW_Manager<double, 3> >&, T&, U&, bool) [with T = boost::multi_array_ref<std::complex<double>, 3>; U = pybind11::detail::unchecked_reference<std::complex<double>, 3>]	286	0.0699093
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/python/pyforward.cpp]void do_get_density_final(LibLSS::BORGForwardModel*, pybind11::array)	286	0.0698282
-               FFTW_Manager::execute_r2c	286	0.0460398
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/chain_forward_model.cpp]virtual void LibLSS::ChainForwardModel::getDensityFinal(LibLSS::detail_output::ModelOutput<3>)	286	0.0272996
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/model_io.cpp]LibLSS::detail_output::ModelOutputBase<Nd, Super>::~ModelOutputBase() [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	3432	0.0272375
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/primordial_as.cpp]virtual void LibLSS::ForwardPrimordial_As::updateCosmo()	174	0.0224216
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/particle_balancer/balanceinfo.hpp]void LibLSS::BalanceInfo::allocate(LibLSS::MPI_Communication*, size_t)	286	0.018951
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/model_io.cpp]void LibLSS::detail_output::ModelOutputBase<Nd, Super>::transfer(LibLSS::detail_output::ModelOutputBase<Nd, Super>&&) [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	2574	0.0179214
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/primordial_as.cpp]void LibLSS::ForwardPrimordial_As::updatePower()	87	0.0121461
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/model_io/base.hpp]void LibLSS::detail_model::ModelIO<Nd>::transfer(LibLSS::detail_model::ModelIO<Nd>&&) [with long unsigned int Nd = 3]	6578	0.00613989
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/primordial_as.cpp]virtual void LibLSS::ForwardPrimordial_As::forwardModel_v2(LibLSS::detail_input::ModelInput<3>)	286	0.00534198
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/borg_lpt.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_borg_lpt(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&) [with Grid = LibLSS::ClassicCloudInCell<double>; LibLSS::BoxModel = LibLSS::NBoxModel<3>]	1	0.00492109
-              BorgLptModel::BorgLptModel	1	0.00485408
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/transfer_class.cpp]virtual void LibLSS::ForwardClass::forwardModel_v2(LibLSS::detail_input::ModelInput<3>)	286	0.00379283
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/transfer_class.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_class(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&)	1	0.00279151
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/primordial_as.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_primordial_as(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&)	1	0.00275793
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/model_io.cpp]void LibLSS::detail_output::ModelOutputBase<Nd, Super>::close() [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	3432	0.00244201
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forward_model.cpp]void LibLSS::BORGForwardModel::setupDefault()	1	0.00234473
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/samplers/core/gridLikelihoodBase.cpp]LibLSS::GridDensityLikelihoodBase<Dims>::GridDensityLikelihoodBase(LibLSS::MPI_Communication*, const GridSizes&, const GridLengths&) [with int Dims = 3; GridSizes = std::array<long unsigned int, 3>; GridLengths = std::array<double, 3>]	1	0.00177276
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/model_io.cpp]void LibLSS::detail_output::ModelOutputBase<Nd, Super>::setRequestedIO(LibLSS::PreferredIO) [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	1430	0.00117615
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/adapt_generic_bias.cpp]void {anonymous}::bias_registrator()	1	0.00110666
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/model_io.cpp]void LibLSS::detail_input::ModelInputBase<Nd, Super>::setRequestedIO(LibLSS::PreferredIO) [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	858	0.000657356
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/model_io.cpp]void LibLSS::detail_input::ModelInputBase<Nd, Super>::needDestroyInput() [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	286	0.00030629
-                   particle distribution	286	0.000249263
-                 BORGForwardModel::setup	7	0.000130953
-                Initializing peer system	13	4.4624e-05
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forwards/transfer_class.cpp]virtual void LibLSS::ForwardClass::setModelParams(const LibLSS::ModelDictionnary&)	1	1.445e-05
- [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1686906696789/work/libLSS/physics/forward_model.cpp]virtual void LibLSS::ForwardModel::setModelParams(const LibLSS::ModelDictionnary&)	1	3.659e-06
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/chain_forward_model.cpp]virtual void LibLSS::ChainForwardModel::forwardModel_v2(LibLSS::detail_input::ModelInput<3>)	1282	34.5484
+                          BORG LPT MODEL	1282	33.0864
+                      BORG forward model	1282	32.9949
+              CICModel::getVelocityField	1283	9.73471
+         CICModel::getVelocityFieldAlpha	3849	7.41442
+           FFTW_Manager::create_r2c_plan	5142	2.65586
+           FFTW_Manager::create_c2r_plan	5141	2.57818
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forward_model.cpp]void LibLSS::ForwardModel::setCosmoParams(const LibLSS::CosmologicalParameters&)	34	2.20232
+                                  lpt_ic	1282	1.63669
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/borg_lpt.cpp]void LibLSS::BorgLptModel<CIC>::updateCosmo() [with CIC = LibLSS::ClassicCloudInCell<double>]	5	1.02187
+                   lightcone computation	1	0.998
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/cosmo.cpp]void LibLSS::Cosmology::precompute_d_plus()	1	0.917015
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/hermitic.hpp]virtual void LibLSS::ForwardHermiticOperation::getDensityFinal(LibLSS::detail_output::ModelOutput<3>)	1282	0.595479
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/tools/hermiticity_fixup.cpp]void LibLSS::Hermiticity_fixer<T, Nd>::forward(CArrayRef&) [with T = double; long unsigned int Nd = 3; CArrayRef = boost::multi_array_ref<std::complex<double>, 3>]	1282	0.535228
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/tools/hermiticity_fixup.cpp]typename std::enable_if<(Dim != 1), void>::type fix_plane(Mgr&, Ghosts&&, CArray&&, size_t*) [with long unsigned int rank = 0; Mgr = LibLSS::FFTW_Manager<double, 3>; Ghosts = LibLSS::Hermiticity_fixer<double, 3>::forward(CArrayRef&)::<lambda(ssize_t)>; CArray = boost::detail::multi_array::multi_array_view<std::complex<double>, 2>; long unsigned int Dim = 2; typename std::enable_if<(Dim != 1), void>::type = void; size_t = long unsigned int]	2564	0.500125
+               FFTW_Manager::execute_c2r	8985	0.339934
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/python/pyforward.cpp]void do_get_density_final(LibLSS::BORGForwardModel*, pybind11::array)	1282	0.232009
+               FFTW_Manager::execute_r2c	5142	0.17592
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/lpt/borg_fwd_lpt.cpp]void LibLSS::BorgLptModel<CIC>::getDensityFinal(LibLSS::detail_output::ModelOutput<3>) [with CIC = LibLSS::ClassicCloudInCell<double>]	1282	0.171371
+                  Classic CIC projection	6415	0.161445
+              FFTW_Manager::destroy_plan	10282	0.160099
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/model_io.cpp]LibLSS::detail_output::ModelOutputBase<Nd, Super>::~ModelOutputBase() [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	16683	0.148489
+ exchanging nearby planes after projection	5133	0.132258
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/model_io.cpp]void LibLSS::detail_output::ModelOutputBase<Nd, Super>::transfer(LibLSS::detail_output::ModelOutputBase<Nd, Super>&&) [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	14113	0.109589
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/particle_balancer/balanceinfo.hpp]void LibLSS::BalanceInfo::allocate(LibLSS::MPI_Communication*, size_t)	2565	0.0855438
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/transfer_class.cpp]virtual void LibLSS::ForwardClass::updateCosmo()	5	0.0786533
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/python/pyforward.cpp]void transfer_in(std::shared_ptr<LibLSS::FFTW_Manager<double, 3> >&, T&, U&, bool) [with T = boost::multi_array_ref<std::complex<double>, 3>; U = pybind11::detail::unchecked_reference<std::complex<double>, 3>]	1282	0.0780552
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/class_cosmo.cpp]LibLSS::ClassCosmo::ClassCosmo(const LibLSS::CosmologicalParameters&, unsigned int, double, std::string, unsigned int, const std::map<std::__cxx11::basic_string<char>, std::__cxx11::basic_string<char> >&)	1	0.0778988
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/cosmo.cpp]void LibLSS::Cosmology::precompute_com2a()	1	0.0569591
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/chain_forward_model.cpp]virtual void LibLSS::ChainForwardModel::getDensityFinal(LibLSS::detail_output::ModelOutput<3>)	1282	0.0554107
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/model_io/base.hpp]void LibLSS::detail_model::ModelIO<Nd>::transfer(LibLSS::detail_model::ModelIO<Nd>&&) [with long unsigned int Nd = 3]	37208	0.0357777
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/velocity/velocity_cic.cpp]virtual void LibLSS::VelocityModel::CICModel::computeAdjointModel(LibLSS::VelocityModel::Base::arrayVelocityField_t)	1	0.028122
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/chain_forward_model.cpp]virtual void LibLSS::ChainForwardModel::adjointModel_v2(LibLSS::detail_input::ModelInputAdjoint<3>)	1	0.0271402
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/chain_forward_model.cpp]void LibLSS::ChainForwardModel::trigger_ag()	1	0.0271251
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/lpt/borg_fwd_lpt_adj.cpp]void LibLSS::BorgLptModel<CIC>::getAdjointModelOutput(LibLSS::detail_output::ModelOutputAdjoint<3>) [with CIC = LibLSS::ClassicCloudInCell<double>]	1	0.0259611
+          BORG adjoint model (particles)	1	0.0259384
+                          LPT-IC adjoint	1	0.0258888
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/transfer_class.cpp]virtual void LibLSS::ForwardClass::forwardModel_v2(LibLSS::detail_input::ModelInput<3>)	1282	0.0198379
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/primordial_as.cpp]virtual void LibLSS::ForwardPrimordial_As::forwardModel_v2(LibLSS::detail_input::ModelInput<3>)	1282	0.0194489
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/model_io.cpp]void LibLSS::detail_output::ModelOutputBase<Nd, Super>::close() [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	16683	0.0139736
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/model_io.cpp]void LibLSS::detail_output::ModelOutputBase<Nd, Super>::setRequestedIO(LibLSS::PreferredIO) [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	6415	0.00580403
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/model_io.cpp]void LibLSS::detail_input::ModelInputBase<Nd, Super>::setRequestedIO(LibLSS::PreferredIO) [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	5132	0.00454889
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/tools/hermiticity_fixup.cpp]LibLSS::Hermiticity_fixer<T, Nd>::Hermiticity_fixer(Mgr_p) [with T = double; long unsigned int Nd = 3; Mgr_p = std::shared_ptr<LibLSS::FFTW_Manager<double, 3> >]	1	0.0034003
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/tools/mpi/ghost_planes.hpp]void LibLSS::GhostPlanes<T, Nd>::setup(LibLSS::MPI_Communication*, PlaneList&&, PlaneSet&&, DimList&&, size_t) [with PlaneList = std::set<long int>&; PlaneSet = std::set<long int>&; DimList = std::array<long int, 2>&; T = std::complex<double>; long unsigned int Nd = 2; size_t = long unsigned int]	1	0.00338329
+                      dispatch_plane_map	1	0.00333988
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/borg_lpt.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_borg_lpt(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&) [with Grid = LibLSS::ClassicCloudInCell<double>; LibLSS::BoxModel = LibLSS::NBoxModel<3>]	1	0.00264381
+              BorgLptModel::BorgLptModel	1	0.00258427
+       Classic CIC adjoint-interpolation	18	0.00243702
+                       ghost synchronize	1282	0.00235298
+                   particle distribution	2565	0.00232546
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/model_io.cpp]void LibLSS::detail_input::ModelInputBase<Nd, Super>::needDestroyInput() [with long unsigned int Nd = 3; Super = LibLSS::detail_model::ModelIO<3>]	1282	0.00174796
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/class_cosmo.cpp]void LibLSS::ClassCosmo::retrieve_Tk(double)	2	0.00143746
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/class_cosmo.cpp]void LibLSS::ClassCosmo::reinterpolate(const array_ref_1d&, const array_ref_1d&, LibLSS::internal_auto_interp::auto_interpolator<double>&)	6	0.00127092
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/adapt_generic_bias.cpp]void {anonymous}::bias_registrator()	1	0.00125063
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forward_model.cpp]void LibLSS::BORGForwardModel::setupDefault()	1	0.0010723
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/samplers/core/gridLikelihoodBase.cpp]LibLSS::GridDensityLikelihoodBase<Dims>::GridDensityLikelihoodBase(LibLSS::MPI_Communication*, const GridSizes&, const GridLengths&) [with int Dims = 3; GridSizes = std::array<long unsigned int, 3>; GridLengths = std::array<double, 3>]	1	0.000721845
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/primordial_as.cpp]virtual void LibLSS::ForwardPrimordial_As::updateCosmo()	10	0.000559358
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/hermitic.hpp]virtual void LibLSS::ForwardHermiticOperation::getAdjointModelOutput(LibLSS::detail_output::ModelOutputAdjoint<3>)	1	0.000524894
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/tools/hermiticity_fixup.cpp]typename std::enable_if<(Dim != 1), void>::type adjoint_fix_plane(Mgr&, CArray&&, size_t*) [with long unsigned int rank = 0; Mgr = LibLSS::FFTW_Manager<double, 3>; CArray = boost::detail::multi_array::multi_array_view<std::complex<double>, 2>; long unsigned int Dim = 2; typename std::enable_if<(Dim != 1), void>::type = void; size_t = long unsigned int]	2	0.000443373
+               Classic CIC interpolation	3	0.000410194
+             BorgLptModel::~BorgLptModel	1	0.00018844
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/python/pyforward.cpp]void do_get_adjoint_model(LibLSS::BORGForwardModel*, pybind11::array)	1	0.000186842
+                 BORGForwardModel::setup	8	0.000177922
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/lpt/borg_fwd_lpt_adj.cpp]void LibLSS::BorgLptModel<CIC>::adjointModelParticles(LibLSS::BORGForwardModelTypes::PhaseArrayRef&, LibLSS::BORGForwardModelTypes::PhaseArrayRef&) [with CIC = LibLSS::ClassicCloudInCell<double>; LibLSS::BORGForwardModelTypes::PhaseArrayRef = boost::multi_array_ref<double, 2>]	1	0.00013868
+                      BORG adjoint model	1	0.000106633
+          Classic CIC adjoint-projection	1	8.2392e-05
+ exchanging nearby planes before taking adjoint gradient	6	8.2354e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/lpt/borg_fwd_lpt_adj.cpp]void LibLSS::BorgLptModel<CIC>::preallocate() [with CIC = LibLSS::ClassicCloudInCell<double>]	2	7.5163e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/primordial_as.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_primordial_as(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&)	1	7.2877e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/transfer_class.cpp]std::shared_ptr<LibLSS::BORGForwardModel> build_class(std::shared_ptr<LibLSS::MPI_Communication>, const LibLSS::BoxModel&, const LibLSS::PropertyProxy&)	1	7.2711e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/chain_forward_model.cpp]virtual void LibLSS::ChainForwardModel::getAdjointModelOutput(LibLSS::detail_output::ModelOutputAdjoint<3>)	1	6.745e-05
+                Initializing peer system	14	6.3955e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/primordial_as.cpp]void LibLSS::ForwardPrimordial_As::updatePower()	5	4.8762e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/python/pyforward.cpp]void transfer_in(std::shared_ptr<LibLSS::FFTW_Manager<double, 3> >&, T&, U&, bool) [with T = boost::multi_array_ref<double, 3>; U = pybind11::detail::unchecked_reference<double, 3>]	1	3.5512e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/transfer_class.cpp]virtual void LibLSS::ForwardClass::setModelParams(const LibLSS::ModelDictionnary&)	1	2e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/transfer_class.cpp]virtual void LibLSS::ForwardClass::adjointModel_v2(LibLSS::detail_input::ModelInputAdjoint<3>)	1	1.5583e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/primordial_as.cpp]virtual void LibLSS::ForwardPrimordial_As::adjointModel_v2(LibLSS::detail_input::ModelInputAdjoint<3>)	1	1.3838e-05
+                    gather_peer_by_plane	1	1.0897e-05
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forwards/lpt/borg_fwd_lpt_adj.cpp]void LibLSS::BorgLptModel<CIC>::clearAdjointGradient() [with CIC = LibLSS::ClassicCloudInCell<double>]	1	3.816e-06
+ [/build/jenkins/miniconda3/envs/builder/conda-bld/aquila_borg_1717878335917/work/libLSS/physics/forward_model.cpp]virtual void LibLSS::ForwardModel::setModelParams(const LibLSS::ModelDictionnary&)	1	2.816e-06
+                 distribute_particles_ag	2	1.678e-06