Repository: Hjorthmedh/Snudda (language: python)

Issue:
Working on Dardel support



Discussion / hints:

Fix patch:
diff --git a/examples/notebooks/population_unit_network.ipynb b/examples/notebooks/population_unit_network.ipynb
index 081fcde68..b77943342 100644
--- a/examples/notebooks/population_unit_network.ipynb
+++ b/examples/notebooks/population_unit_network.ipynb
@@ -57,13 +57,13 @@
     "si.define_striatum(num_dSPN=1000, num_iSPN=1000, num_FS=0, num_LTS=0, num_ChIN=0,\n",
     "                   volume_type=\"cube\", neurons_dir=\"$DATA/neurons\")\n",
     "\n",
-    "# The centre of the cube is [0.00475, 0.004, 0.00775]\n",
+    "# The centre of the cube is [0.00475, 0.004, 0.00775]. num_neurons is optional\n",
     "si.add_population_unit_density(structure_name=\"Striatum\", neuron_types=[\"dSPN\", \"iSPN\"], \n",
     "                               unit_centre=np.array([0.00475, 0.004, 0.00775]) -np.array([0,50e-6,150e-6]),\n",
-    "                               probability_function=\"(d < 200e-6)*1\")\n",
+    "                               probability_function=\"(d < 200e-6)*1\", num_neurons=100)\n",
     "si.add_population_unit_density(structure_name=\"Striatum\", neuron_types=[\"dSPN\", \"iSPN\"], \n",
     "                               unit_centre=np.array([0.00475, 0.004, 0.00775]) -np.array([150e-6,0,0]),\n",
-    "                               probability_function=\"(d < 200e-6) * 1\")\n",
+    "                               probability_function=\"(d < 200e-6) * 1\", num_neurons=100)\n",
     " \n",
     "si.write_json(config_file)"
    ]
@@ -91,12 +91,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "classified-booth",
+   "id": "center-element",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x468 with 1 Axes>"
       ]
@@ -126,7 +126,15 @@
    "execution_count": 4,
    "id": "powerful-workshop",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning, running in serial.\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "from ipyparallel import Client\n",
@@ -152,9 +160,60 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "importing SnuddaDetect from snudda.detect.detect on engine(s)\n",
-      "Suppressing printouts for hyper voxels that complete in < 100 seconds.\n",
-      "HyperID 31 completed - 11220497 synapses found (193.7 s)\n"
+      "No d_view specified, running distribute neurons in serial\n",
+      "Processing hyper voxel : 31/125 (1983 neurons)\n",
+      "Processing hyper voxel : 32/125 (1697 neurons)\n",
+      "Processing hyper voxel : 56/125 (1668 neurons)\n",
+      "Processing hyper voxel : 36/125 (1603 neurons)\n",
+      "Processing hyper voxel : 57/125 (1241 neurons)\n",
+      "Processing hyper voxel : 37/125 (1175 neurons)\n",
+      "Processing hyper voxel : 61/125 (1164 neurons)\n",
+      "Processing hyper voxel : 26/125 (798 neurons)\n",
+      "Processing hyper voxel : 62/125 (767 neurons)\n",
+      "Processing hyper voxel : 6/125 (723 neurons)\n",
+      "Processing hyper voxel : 30/125 (702 neurons)\n",
+      "Processing hyper voxel : 27/125 (489 neurons)\n",
+      "Processing hyper voxel : 51/125 (486 neurons)\n",
+      "Processing hyper voxel : 7/125 (464 neurons)\n",
+      "Processing hyper voxel : 55/125 (440 neurons)\n",
+      "Processing hyper voxel : 35/125 (422 neurons)\n",
+      "Processing hyper voxel : 11/125 (416 neurons)\n",
+      "Processing hyper voxel : 52/125 (275 neurons)\n",
+      "Processing hyper voxel : 12/125 (246 neurons)\n",
+      "Processing hyper voxel : 60/125 (239 neurons)\n",
+      "Processing hyper voxel : 25/125 (131 neurons)\n",
+      "Processing hyper voxel : 1/125 (123 neurons)\n",
+      "Processing hyper voxel : 5/125 (96 neurons)\n",
+      "Processing hyper voxel : 33/125 (93 neurons)\n",
+      "Processing hyper voxel : 81/125 (91 neurons)\n",
+      "Processing hyper voxel : 2/125 (73 neurons)\n",
+      "Processing hyper voxel : 41/125 (61 neurons)\n",
+      "Processing hyper voxel : 50/125 (58 neurons)\n",
+      "Processing hyper voxel : 10/125 (54 neurons)\n",
+      "Processing hyper voxel : 86/125 (41 neurons)\n",
+      "Processing hyper voxel : 38/125 (40 neurons)\n",
+      "Processing hyper voxel : 82/125 (39 neurons)\n",
+      "Processing hyper voxel : 66/125 (38 neurons)\n",
+      "Processing hyper voxel : 42/125 (37 neurons)\n",
+      "Processing hyper voxel : 58/125 (36 neurons)\n",
+      "Processing hyper voxel : 63/125 (20 neurons)\n",
+      "Processing hyper voxel : 87/125 (17 neurons)\n",
+      "Processing hyper voxel : 28/125 (11 neurons)\n",
+      "Processing hyper voxel : 67/125 (11 neurons)\n",
+      "Processing hyper voxel : 76/125 (10 neurons)\n",
+      "Processing hyper voxel : 0/125 (9 neurons)\n",
+      "Processing hyper voxel : 80/125 (7 neurons)\n",
+      "Processing hyper voxel : 53/125 (7 neurons)\n",
+      "Processing hyper voxel : 77/125 (4 neurons)\n",
+      "Processing hyper voxel : 13/125 (4 neurons)\n",
+      "Processing hyper voxel : 16/125 (3 neurons)\n",
+      "Processing hyper voxel : 8/125 (3 neurons)\n",
+      "Processing hyper voxel : 40/125 (2 neurons)\n",
+      "Processing hyper voxel : 17/125 (2 neurons)\n",
+      "Processing hyper voxel : 15/125 (2 neurons)\n",
+      "Processing hyper voxel : 85/125 (1 neurons)\n",
+      "Processing hyper voxel : 3/125 (1 neurons)\n",
+      "Processing hyper voxel : 91/125 (1 neurons)\n"
      ]
     }
    ],
@@ -175,9 +234,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "importing SnuddaPrune from snudda.detect.prune on engine(s)\n",
-      "prune_synapses_parallel (1115406/19809910 synapses, 5.6% kept): 34.2s\n",
-      "prune_synapses_parallel (0/0 gapJunctions, 0.0% kept): 0.0s\n"
+      "Worker synapses: 41/19980317 (heap size: 27)\n",
+      "Worker synapses: 6878492/19980317 (heap size: 25)\n",
+      "Worker synapses: 12533675/19980317 (heap size: 20)\n",
+      "Worker synapses: 17513408/19980317 (heap size: 18)\n",
+      "Worker synapses: 19980317/19980317 (heap size: 0)\n",
+      "Read 19980317 out of total 19980317 synapses\n"
      ]
     }
    ],
@@ -188,14 +250,6 @@
     "sp.prune()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "departmental-coaching",
-   "metadata": {},
-   "source": [
-    "**Important** Remember to shut down the kernel in this notebook when it finishes, so you can use the output file in other notebooks."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -207,7 +261,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -221,7 +275,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/examples/parallel/KTH_PDC/Dardel_NEURON_install.job b/examples/parallel/KTH_PDC/Dardel_NEURON_install.job
new file mode 100644
index 000000000..d4de65a37
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_NEURON_install.job
@@ -0,0 +1,20 @@
+#!/bin/bash -l
+#SBATCH --partition=main
+#SBATCH -o log/NEURON_install-%j-output.txt
+#SBATCH -e log/NEURON_install-%j-error.txt
+#SBATCH -t 0:30:00
+#SBATCH -J NeuronInstall
+#SBATCH -A 2021-5-492 
+#SBATCH --nodes=1
+#SBATCH --tasks-per-node=64
+
+# OBS, you need to wget the latest miniconda. Save in this folder.
+#
+# wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+# chmod u+x Miniconda3-latest-Linux-x86_64.sh
+#
+# and you also need to git clone neuron into your local/dardel folder
+# git clone https://github.com/neuronsimulator/nrn -b 8.0.0
+
+
+srun -n 1 ./Dardel_NEURON_install.sh
diff --git a/examples/parallel/KTH_PDC/Dardel_NEURON_install.sh b/examples/parallel/KTH_PDC/Dardel_NEURON_install.sh
new file mode 100755
index 000000000..6e0b2c526
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_NEURON_install.sh
@@ -0,0 +1,207 @@
+#!/bin/bash
+
+# !!! OBS you need to make sure you git clone manually, it seems that command
+# stalls when running on Beskow compute nodes?
+
+# THIS LINE SHOULD BE UNCOMMENTED... DONT FORGET 2021-05-27
+./Miniconda_install.sh
+
+source activate_miniconda.sh
+conda activate
+
+module load snic-env
+module load PDC
+
+# --- I have tried with the gnu compiler, and also with the cray compiler
+
+# 2021-12-10 -- Temp comment out, testin CRAY compiler
+module swap PrgEnv-cray PrgEnv-gnu
+module unload cray-libsci atp
+
+export CRAYPE_LINK_TYPE=dynamic
+export CRAY_ROOTFS=DSL
+
+# # module load craype-ivybridge
+
+echo "This assumes you have already installed miniconda3 on Tegner"
+echo ""
+echo "https://github.com/Hjorthmedh/Snudda/wiki/D.-Setting-up-Tegner-@%C2%A0KTH"
+echo ""
+
+export L=/cfs/klemming/home/${USER:0:1}/$USER/local/$SNIC_RESOURCE
+export LM=$L/miniconda3
+export LN=$L/neuron
+
+#Dirty fix for NEURON compilation to work, thanks Tor Kjellson for help
+export TMP0_DIR=$L/cray_libs
+
+mkdir -pv $L
+
+mkdir -pv $TMP0_DIR
+pushd $TMP0_DIR
+ln -s /lib64/libncurses.so.6 libncurses.so
+ln -s /lib64/libtinfo.so.6 libtinfo.so
+ln -s /lib64/libreadline.so.7 libreadline.so
+ln -s $LM/lib/libpython3.9.so.1.0 .
+popd
+
+# export CXX=CC
+# export CC=cc
+# export FC=ftn
+# export MPICC=cc
+# export MPICXX=CC
+
+
+
+# neuron is also installed from requirements.txt, remove non-compatible version
+pip uninstall neuron -y
+
+# Is MINIC used?
+export MINIC=$LM
+
+export PATH=$LM/bin:$LN/bin:$PATH
+# export LD_LIBRARY_PATH=$LN/lib:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$TMP0_DIR:$MPICH_DIR/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$L/lib/python3.8/site-packages:$PYTHONPATH
+export PYTHONPATH=$LN/lib/python:$LM/lib/python3.8/
+
+export NRN_INSTALL_LOC=$LN
+
+export CXX=CC
+export CC=cc
+export FC=ftn
+export MPICC=cc
+export MPICXX=CC
+
+module load CMake/3.21.2
+
+
+# We need to recompile mpi4py to use mpich libraries of beskow
+# UPDATE: This is now done in Miniconda_install.sh
+# pip install mpi4py --ignore-installed
+
+# Från MDJs gamla buildscript
+# export CFLAGS="-dynamic -O3 -funroll-loops -march=corei7-avx -mavx  -ffast-math -DCACHEVEC=1" 
+
+# Maybe git clone does not work on compute nodes?
+
+# rm -rf $L/build
+# mkdir -pv $L/build
+pushd $L
+
+  # build parallel neuron with python interface
+  # mkdir neuron
+  # pushd neuron
+  # OLD: git clone -q https://github.com/nrnhines/nrn
+
+  # You have to do git clone manually!!
+  # git clone https://github.com/neuronsimulator/nrn -b 8.0.0
+  
+  cd nrn
+  rm -r build
+  mkdir build
+  cd build
+
+  echo "About to run"
+  echo `which cmake`
+#   cmake .. \
+#   	  -DCMAKE_BUILD_TYPE:STRING=Debug \
+#	  -DNRN_ENABLE_INTERVIEWS=OFF \
+#	  -DNRN_ENABLE_PYTHON=ON \
+#	  -DNRN_ENABLE_MPI=ON \
+#	  -DNRN_ENABLE_RX3D=OFF \
+#	  -DCMAKE_INSTALL_PREFIX=$LN \
+#	  -DNRN_ENABLE_BINARY_SPECIAL=ON \
+#	  -DPYTHON_EXECUTABLE=`which python3` \
+#	  -DCMAKE_C_COMPILER:FILEPATH=cc \
+#	  -DCMAKE_CXX_COMPILER:FILEPATH=CC \
+
+#          -DCMAKE_C_FLAGS="-DDEBUG -g" \
+#          -DCMAKE_CXX_FLAGS="-DDEBUG -g" \
+#	  -DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo \
+	  # -DCMAKE_C_FLAGS="-mavx2" \
+          # -DCMAKE_CXX_FLAGS="-mavx2" \
+	  #	  -DNRN_ENABLE_CORENEURON=ON \
+
+	  #-DCMAKE_BUILD_TYPE=Debug \
+	  
+  cmake .. \
+	-DCMAKE_SKIP_RPATH:BOOL=YES \
+	-DNRN_ENABLE_INTERVIEWS=OFF \
+	-DNRN_ENABLE_PYTHON=ON \
+	-DNRN_ENABLE_MPI=ON \
+	-DNRN_ENABLE_RX3D=OFF \
+	-DCMAKE_INSTALL_PREFIX=$NRN_INSTALL_LOC \
+	-DNRN_ENABLE_BINARY_SPECIAL=ON \
+	-DNRN_ENABLE_CORENEURON=OFF \
+	-DCMAKE_BUILD_TYPE:STRING=Release \
+	-DREADLINE_LIBRARY:FILEPATH=$TMP0_DIR/libreadline.so \
+	-DCURSES_EXTRA_LIBRARY:FILEPATH=$TMP0_DIR/libtinfo.so \
+	-DCURSES_CURSES_LIBRARY:FILEPATH=$TMP0_DIR/libncurses.so \
+	-DCURSES_NCURSES_LIBRARY:FILEPATH=$TMP0_DIR/libncurses.so \
+	-DCURSES_INCLUDE_PATH:PATH=/usr/include/curses/
+  
+  cmake --build . \
+	--parallel 1 \
+	--target install 1>$L/build_log_Release.txt 2>$L/build_error_Release.txt
+
+CC --version
+    
+#    ./build.sh
+#    autoreconf --force --install
+#    ./configure --prefix=$LM --exec-prefix=$LM \
+#      --with-mpi --with-nrnpython --with-paranrn \
+#      --without-x \
+#      --without-memacs --without-readline --disable-shared --without-nmodl \
+#      linux_nrnmech=no always_call_mpi_init=yes java_dlopen=no \
+#      --host=x86_64-unknown-linux-gnu --disable-pysetup --without-iv \
+#      CC=cc CXX=CC MPICC=cc MPICXX=CC CFLAGS="$CFLAGS" CXXFLAGS="$CFLAGS" \
+#      # -LLIBDIR
+#
+#    echo "About to run"
+#    echo `which make`
+#	  
+#    # make -j   # -j parallel compilation
+#    make -j
+#    make install
+#
+#    pushd src/nrnpython
+#      python setup.py install
+#    popd
+    # rm -r $L/nrn/share/{demo,examples}
+  popd
+
+  # install pypandoc
+  # pip install pypandoc --install-option="--prefix=$L"
+  # pip install pypandoc --prefix=$L
+  pip install pypandoc --no-cache-dir
+
+  # install ipyparallel
+  # pip install ipyparallel --install-option="--prefix=$L"
+  # pip install ipyparallel --prefix=$L
+  pip install ipyparallel --no-cache-dir
+
+  # install bluepyopt
+  # pip install bluepyopt --install-option="--prefix=$L"
+  # pip install bluepyopt --prefix=$L
+  pip install bluepyopt --no-cache-dir
+
+  pip install mpi4py --no-cache-dir
+
+# popd
+# rm -rf $L/build
+
+
+  # Some dirty fixes...
+  # På rad 32 har du
+  # LDFLAGS = $(LINKFLAGS) $(UserLDFLAGS) ....
+  # /path-to-miniconda/dardel/miniconda3/lib
+  # radera den sista sökvägen - annars får du problem när nrnivmodl körs.
+  
+  mv $L/neuron/bin/nrnmech_makefile $L/neuron/bin/nrnmech_makefile.original
+  python3 purge_ldflags.py $L/neuron/bin/nrnmech_makefile.original > $L/neuron/bin/nrnmech_makefile
+  chmod u+x $L/neuron/bin/nrnmech_makefile
+
+  
+
+conda deactivate
diff --git a/examples/parallel/KTH_PDC/Dardel_install_Snudda.sh b/examples/parallel/KTH_PDC/Dardel_install_Snudda.sh
new file mode 100755
index 000000000..efa4fc8a3
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_install_Snudda.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+if [ -d "$HOME/Snudda" ]; then
+    echo "Directory $HOME/Snudda already exists"
+else
+    pushd $HOME
+    git clone -b master https://github.com/Hjorthmedh/Snudda.git
+    # git clone -b master git@github.com:Hjorthmedh/Snudda.git    
+fi
+
+pushd $HOME/Snudda
+git pull
+
+module load cray-python
+module load snic-env
+module swap PrgEnv-cray PrgEnv-gnu
+module unload cray-libsci atp
+
+# Setup virtual environment
+python -m venv snudda_env
+source snudda_env/bin/activate
+
+pip install --upgrade pip
+MPICC=cc pip install mpi4py
+
+pip install -r requirements.txt
+pip install -e .[dev]
+
+
+
+
+
+
diff --git a/examples/parallel/KTH_PDC/Dardel_runSnudda.job b/examples/parallel/KTH_PDC/Dardel_runSnudda.job
new file mode 100644
index 000000000..ab0b53822
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_runSnudda.job
@@ -0,0 +1,18 @@
+#!/bin/bash -l
+#SBATCH --partition=main
+#SBATCH -o log/runSnudda-%j-output.txt
+#SBATCH -e log/runSnudda-%j-error.txt
+#SBATCH -t 0:30:00
+#SBATCH -J Snudda
+#SBATCH -A 2021-5-492 
+#SBATCH --nodes=1
+#SBATCH --tasks-per-node=64
+
+module load snic-env
+
+export IPYTHONDIR="/cfs/klemming/scratch/${USER:0:1}/$USER/.ipython"
+rm -r $IPYTHONDIR
+export IPYTHON_PROFILE=default
+source $HOME/Snudda/snudda_env/bin/activate
+
+srun ./Dardel_runSnudda.sh
diff --git a/examples/parallel/KTH_PDC/Dardel_runSnudda.sh b/examples/parallel/KTH_PDC/Dardel_runSnudda.sh
new file mode 100755
index 000000000..c982d4dfb
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_runSnudda.sh
@@ -0,0 +1,106 @@
+#!/bin/bash
+
+SNUDDA_DIR=$HOME/Snudda/snudda
+JOBDIR=networks/test_10k
+
+SIMSIZE=10000
+
+# If the BasalGangliaData directory exists, then use that for our data
+#/cfs/klemming/scratch/${USER:0:1}/$USER/BasalGangliaData/data
+#BasalGangliaData/Parkinson/PD0
+if [[ -d "$HOME/BasalGangliaData/data" ]]; then
+    export SNUDDA_DATA="$HOME/BasalGangliaData/data"
+    echo "Setting SNUDDA_DATA to $SNUDDA_DATA"
+else
+    echo "SNUDDA_DATA environment variable not changed (may be empty): $SNUDDA_DATA"
+fi
+
+mkdir -p $JOBDIR
+
+if [ $SLURM_PROCID -gt 0 ]; then
+	mock_string="Not main process"
+
+else
+
+    # For debug purposes:                                                         
+    echo "PATH: "$PATH
+    echo "IPYTHONDIR: "$IPYTHONDIR
+    echo "PYTHONPATH: "$PYTHONPATH
+    echo "LD_LIBRARY_PATH: "$LD_LIBRARY_PATH
+
+    echo ">>>>>> Main process starting ipcluster"
+    echo
+
+    echo "Start time: " > start_time_network_connect.txt
+    date >> start_time_network_connect.txt
+
+    echo ">>> Init: "`date`
+    snudda init ${JOBDIR} --size ${SIMSIZE} --overwrite --randomseed 1234
+
+    if [ $? != 0 ]; then
+	echo "Something went wrong during init, aborting!"
+	ipcluster stop	
+	exit -1
+    fi
+
+    echo "SLURM_NODELIST = $SLURM_NODELIST"
+    let NWORKERS="$SLURM_NTASKS - 1"
+
+    echo ">>> NWORKERS " $NWORKERS
+    echo ">>> Starting ipcluster `date`"
+    
+    #.. Start the ipcluster
+    ipcluster start -n ${NWORKERS} \
+	      --ip='*' \
+	      --HeartMonitor.max_heartmonitor_misses=1000 \
+	      --HubFactory.registration_timeout=600 \
+	      --HeartMonitor.period=10000 & 
+
+    
+    #.. Sleep to allow engines to start
+    echo ">>> Wait 120s to allow engines to start"
+    sleep 120 #60
+
+    echo ">>> Place: "`date`
+    snudda place ${JOBDIR} --verbose --parallel
+
+    if [ $? != 0 ]; then
+	echo "Something went wrong during placement, aborting!"
+	# ipcluster stop	
+	exit -1
+    fi
+
+    echo ">>> Detect: "`date`
+    snudda detect ${JOBDIR} --hvsize 50 --parallel
+
+    if [ $? != 0 ]; then
+	echo "Something went wrong during detection, aborting!"
+	# ipcluster stop	
+	exit -1
+    fi
+    
+    echo ">>> Prune: "`date`
+    snudda prune ${JOBDIR} --parallel
+
+    if [ $? != 0 ]; then
+	echo "Something went wrong during pruning, aborting!"
+	# ipcluster stop	
+	exit -1
+    fi
+
+    # Disable input generation at the moment
+
+    #echo ">>> Input: "`date`
+    # cp -a $SNUDDA_DIR/data/input_config/input-v10-scaled.json ${JOBDIR}/input.json
+    cp -a $SNUDDA_DIR/data/input_config/external-input-dSTR-scaled-v4.json ${JOBDIR}/input.json
+
+    snudda input ${JOBDIR} --parallel --time 5
+
+    
+    #.. Shut down cluster
+    # ipcluster stop	
+
+    date
+    echo "JOB END "`date` start_time_network_connect.txt
+
+fi
diff --git a/examples/parallel/KTH_PDC/Dardel_setupInput.job b/examples/parallel/KTH_PDC/Dardel_setupInput.job
new file mode 100644
index 000000000..9a716c01b
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_setupInput.job
@@ -0,0 +1,18 @@
+#!/bin/bash -l
+#SBATCH --partition=main
+#SBATCH -o log/Input-%j-output.txt
+#SBATCH -e log/Input-%j-error.txt
+#SBATCH -t 0:30:00
+#SBATCH -J SnuddaInput
+#SBATCH -A 2021-5-492 
+#SBATCH --nodes=1
+#SBATCH --tasks-per-node=64
+
+module load snic-env
+
+export IPYTHONDIR="/cfs/klemming/scratch/${USER:0:1}/$USER/.ipython"
+rm -r $IPYTHONDIR
+export IPYTHON_PROFILE=default
+source $HOME/Snudda/snudda_env/bin/activate
+
+srun ./Dardel_setupInput.sh
diff --git a/examples/parallel/KTH_PDC/Dardel_setupInput.sh b/examples/parallel/KTH_PDC/Dardel_setupInput.sh
new file mode 100755
index 000000000..ea149f779
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_setupInput.sh
@@ -0,0 +1,73 @@
+#!/bin/bash
+
+SNUDDA_DIR=$HOME/Snudda/snudda
+JOBDIR=networks/test_10k
+
+SIMSIZE=10000
+
+# If the BasalGangliaData directory exists, then use that for our data
+#/cfs/klemming/scratch/${USER:0:1}/$USER/BasalGangliaData/data
+#BasalGangliaData/Parkinson/PD0
+if [[ -d "$HOME/BasalGangliaData/data" ]]; then
+    export SNUDDA_DATA="$HOME/BasalGangliaData/data"
+    echo "Setting SNUDDA_DATA to $SNUDDA_DATA"
+else
+    echo "SNUDDA_DATA environment variable not changed (may be empty): $SNUDDA_DATA"
+fi
+
+mkdir -p $JOBDIR
+
+if [ $SLURM_PROCID -gt 0 ]; then
+	mock_string="Not main process"
+
+else
+
+    # For debug purposes:                                                         
+    echo "PATH: "$PATH
+    echo "IPYTHONDIR: "$IPYTHONDIR
+    echo "PYTHONPATH: "$PYTHONPATH
+    echo "LD_LIBRARY_PATH: "$LD_LIBRARY_PATH
+
+    echo ">>>>>> Main process starting ipcluster"
+    echo
+
+    echo "Start time: " > start_time_input.txt
+    date >> start_time_input.txt
+
+    echo "SLURM_NODELIST = $SLURM_NODELIST"
+    let NWORKERS="$SLURM_NTASKS - 1"
+
+    echo ">>> NWORKERS " $NWORKERS
+    echo ">>> Starting ipcluster `date`"
+    
+    #.. Start the ipcluster
+    ipcluster start -n ${NWORKERS} \
+	      --ip='*' \
+	      --HeartMonitor.max_heartmonitor_misses=1000 \
+	      --HubFactory.registration_timeout=600 \
+	      --HeartMonitor.period=10000 & 
+
+    
+    #.. Sleep to allow engines to start
+    echo ">>> Wait 120s to allow engines to start"
+    sleep 120 #60
+
+
+    # Disable input generation at the moment
+
+    #echo ">>> Input: "`date`
+    # cp -a $SNUDDA_DIR/data/input_config/input-v10-scaled.json ${JOBDIR}/input.json
+    cp -a $SNUDDA_DIR/data/input_config/external-input-dSTR-scaled-v4.json ${JOBDIR}/input.json
+
+    # snudda input ${JOBDIR} --parallel --time 5
+    echo "Temp running in serial -- due to perlmutter bug on Cray https://docs.nersc.gov/development/libraries/hdf5/"
+    snudda input ${JOBDIR} --time 5 --verbose 
+
+    
+    #.. Shut down cluster
+    # ipcluster stop	
+
+    date
+    echo "JOB END "`date` start_time_network_connect.txt
+
+fi
diff --git a/examples/parallel/KTH_PDC/Dardel_setupInputSerial.job b/examples/parallel/KTH_PDC/Dardel_setupInputSerial.job
new file mode 100644
index 000000000..c9c00d7eb
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_setupInputSerial.job
@@ -0,0 +1,15 @@
+#!/bin/bash -l
+#SBATCH --partition=main
+#SBATCH -o log/Input-%j-output.txt
+#SBATCH -e log/Input-%j-error.txt
+#SBATCH -t 0:30:00
+#SBATCH -J SnuddaInput
+#SBATCH -A 2021-5-492 
+#SBATCH --nodes=1
+#SBATCH --tasks-per-node=64
+
+module load snic-env
+
+source $HOME/Snudda/snudda_env/bin/activate
+
+srun ./Dardel_setupInputSerial.sh
diff --git a/examples/parallel/KTH_PDC/Dardel_setupInputSerial.sh b/examples/parallel/KTH_PDC/Dardel_setupInputSerial.sh
new file mode 100755
index 000000000..478b3624d
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_setupInputSerial.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+
+SNUDDA_DIR=$HOME/Snudda/snudda
+JOBDIR=networks/test_10k
+
+SIMSIZE=10000
+
+# If the BasalGangliaData directory exists, then use that for our data
+#/cfs/klemming/scratch/${USER:0:1}/$USER/BasalGangliaData/data
+#BasalGangliaData/Parkinson/PD0
+if [[ -d "$HOME/BasalGangliaData/data" ]]; then
+    export SNUDDA_DATA="$HOME/BasalGangliaData/data"
+    echo "Setting SNUDDA_DATA to $SNUDDA_DATA"
+else
+    echo "SNUDDA_DATA environment variable not changed (may be empty): $SNUDDA_DATA"
+fi
+
+mkdir -p $JOBDIR
+
+if [ $SLURM_PROCID -gt 0 ]; then
+	mock_string="Not main process"
+
+else
+
+    # For debug purposes:                                                         
+    echo "PATH: "$PATH
+    echo "IPYTHONDIR: "$IPYTHONDIR
+    echo "PYTHONPATH: "$PYTHONPATH
+    echo "LD_LIBRARY_PATH: "$LD_LIBRARY_PATH
+
+    echo ">>>>>> Main process starting ipcluster"
+    echo
+
+    echo "Start time: " > start_time_input.txt
+    date >> start_time_input.txt
+
+    echo "SLURM_NODELIST = $SLURM_NODELIST"
+    let NWORKERS="$SLURM_NTASKS - 1"
+
+    echo ">>> NWORKERS " $NWORKERS
+    echo ">>> Starting ipcluster `date`"
+    
+
+    echo ">>> Input: "`date`
+    # cp -a $SNUDDA_DIR/data/input_config/input-v10-scaled.json ${JOBDIR}/input.json
+    cp -a $SNUDDA_DIR/data/input_config/external-input-dSTR-scaled-v4.json ${JOBDIR}/input.json
+
+    # snudda input ${JOBDIR} --parallel --time 5
+    echo "Temp running in serial -- due to perlmutter bug on Cray https://docs.nersc.gov/development/libraries/hdf5/"
+    export HDF5_USE_FILE_LOCKING=FALSE
+
+    snudda input ${JOBDIR} --time 5 --verbose 
+
+    date
+    echo "JOB END "`date` start_time_network_connect.txt
+
+fi
diff --git a/examples/parallel/KTH_PDC/Dardel_simulate.job b/examples/parallel/KTH_PDC/Dardel_simulate.job
new file mode 100644
index 000000000..ff18bd55c
--- /dev/null
+++ b/examples/parallel/KTH_PDC/Dardel_simulate.job
@@ -0,0 +1,110 @@
+#!/bin/bash -l
+#SBATCH --partition=main
+#SBATCH -o log/Simulate-%j-output.txt
+#SBATCH -e log/Simulate-%j-error.txt
+#SBATCH -t 0:59:00
+#SBATCH --time-min=0:29:00
+#SBATCH -J Simulate
+#SBATCH -A 2021-5-492 
+#SBATCH --nodes=2-4
+#SBATCH --tasks-per-node=64
+#SBATCH --mail-type=ALL
+
+# 16 workers were too few, got section stack overflow (there is a hidden
+# push to the stack somewhere in the neuron code)
+let N_WORKERS="$SLURM_NNODES * 32"
+
+HOME=/cfs/klemming/home/"${USER:0:1}"/$USER/Snudda
+
+# If the BasalGangliaData directory exists, then use that for our data
+if [[ -d "/cfs/klemming/home/${USER:0:1}/$USER/BasalGangliaData/data" ]]; then
+    export SNUDDA_DATA="/cfs/klemming/home/${USER:0:1}/$USER/BasalGangliaData/data"
+    echo "Setting SNUDDA_DATA to $SNUDDA_DATA"
+    rm mechanisms
+    ln -s ../../snudda/data/neurons/mechanisms/neurons/mechanisms/
+else
+    echo "SNUDDA_DATA environment variable not changed (may be empty): $SNUDDA_DATA"
+    rm mechanisms
+    ln -s ../../../snudda/data/neurons/mechanisms/
+fi
+
+# Synapse file
+SNUDDA_DIR=/cfs/klemming/home/"${USER:0:1}"/$USER/Snudda/snudda
+
+# You need to point this as the directory where you created the network in
+#NETWORK_DIR=$SNUDDA_DIR/../../networks/TegnerRun.959380
+NETWORK_DIR=/cfs/klemming/home/${USER:0:1}/$USER/Snudda/examples/parallel/KTH_PDC/networks/test_10k
+
+NETWORK_INFO_FILE=$NETWORK_DIR/network-synapses.hdf5
+NETWORK_INPUT_FILE=$NETWORK_DIR/input-spikes.hdf5
+# NETWORK_VOLTAGE_FILE=$NETWORK_DIR/simulation/voltage-trace-${SLURM_JOBID}.txt
+
+echo "Network dir: "$NETWORK_DIR
+
+
+##############
+
+source activate_miniconda.sh
+conda activate
+
+module load snic-env
+
+# --- I have tried with the gnu compiler, and also with the cray compiler
+module swap PrgEnv-cray PrgEnv-gnu
+module unload cray-libsci atp
+export CRAYPE_LINK_TYPE=dynamic
+export CRAY_ROOTFS=DSL
+
+
+
+
+# Snudda bin gets installed here, when we use the user flag above
+# export PATH=/cfs/klemming/nobackup/"${USER:0:1}"/$USER/local/beskow/miniconda3/bin:$PATH
+# export PYTHONPATH=/cfs/klemming/nobackup/"${USER:0:1}"/$USER/local/beskow/miniconda3/lib/python3.8/site-packages:$PYTHONPATH
+
+# export LD_LIBRARY_PATH=/cfs/klemming/nobackup/"${USER:0:1}"/$USER/local/beskow/miniconda3/lib:$LD_LIBRARY_PATH
+# export LD_LIBRARY_PATH=$MPICH_DIR/lib:$LD_LIBRARY_PATH
+# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/lib64
+
+L=/cfs/klemming/home/${USER:0:1}/$USER/local/$SNIC_RESOURCE
+LM=$L/miniconda3
+LN=$L/neuron
+
+export PATH=$LM/bin:$LN/bin:$PATH
+export LD_LIBRARY_PATH=$MPICH_DIR/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$L/lib/python3.8/site-packages:$PYTHONPATH
+export PYTHONPATH=$LN/lib/python:$LM/lib/python3.8/
+
+
+# Remove "special" old directory
+# This is now done by core.py automatically.
+
+# echo "Compiling mechanisms"
+# rm mechanisms
+# ln -s ../../snudda/data/neurons/mechanisms
+
+rm -r x86_64
+
+export CXX=CC
+export CC=cc
+export FC=ftn
+export MPICC=cc
+export MPICXX=CC
+
+CC --version
+
+echo "About to run nrnivmodl"
+which nrnivmodl
+
+srun -n 1 nrnivmodl -incflags "-lltdl=/usr/lib64/libltdl.so.7 -lreadline=/lib64/libreadline.so.7 -lncurses=/lib64/libncurses.so.6.1" -loadflags "-DLTDL_LIBRARY=/usr/lib64/libltdl.so.7 -DREADLINE_LIBRARY=/lib64/libreadline.so.7 -DNCURSES_LIBRARY=/lib64/libncurses.so.6.1" mechanisms/
+
+
+# GJ disabled
+# srun -n $N_WORKERS $SNUDDA_DIR/../examples/parallel/x86_64/special -mpi -python $SNUDDA_DIR/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --disableGJ --time 3.5 --voltOut $NETWORK_VOLTAGE_FILE
+
+# GJ active
+srun -n $N_WORKERS $SNUDDA_DIR/../examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --time 3.5 
+
+
+# Original version, start neuron from python, does not work on beskow
+#aprun -n  $N_WORKERS /cfs/klemming/nobackup/h/hjorth/ChINopt/model/x86_64/special -mpi -python snudda_simulate.py /cfs/klemming/nobackup/h/hjorth/ChINopt/model/save/save/network-connect-synapse-file-1749867.pickle
diff --git a/examples/parallel/KTH_PDC/Miniconda_install.sh b/examples/parallel/KTH_PDC/Miniconda_install.sh
index 8b2c75f15..3bfb43256 100755
--- a/examples/parallel/KTH_PDC/Miniconda_install.sh
+++ b/examples/parallel/KTH_PDC/Miniconda_install.sh
@@ -1,11 +1,47 @@
 #!/bin/bash
 
 # Download and install miniconda3
+# -- On Dardel compute nodes does not have wget,
+# so you have to do this manually then
 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
 chmod u+x Miniconda3-latest-Linux-x86_64.sh
 
 module load snic-env
-L=/cfs/klemming/nobackup/${USER:0:1}/$USER/local/$SNIC_RESOURCE
+L=/cfs/klemming/home/${USER:0:1}/$USER/local/$SNIC_RESOURCE
+
+# This is needed to compile mpi4py -- is it really?
+if [ $SNIC_RESOURCE == "tegner" ]; then
+    module load gcc/9.2.0
+    module load openmpi/4.1-gcc-9.2
+
+elif [ $SNIC_RESOURCE == "beskow" ]; then
+    echo "On Beskow"
+
+    # Recompile mpi4py using MPICH
+    export MPICC=cc
+    export LD_LIBRARY_PATH=$MPICH_DIR/lib:$LD_LIBRARY_PATH
+
+    # module load gcc/10.2.0
+    # module load ??? # What is openmpi module on Beskow?
+   #do something
+elif [ $SNIC_RESOURCE == "dardel" ]; then
+    echo "On Beskow"
+
+    # module swap PrgEnv-cray PrgEnv-gnu
+    # module unload cray-libsci atp
+    # export CRAYPE_LINK_TYPE=dynamic
+    # export CRAY_ROOTFS=DSL
+    
+    # Recompile mpi4py using MPICH
+    export MPICC=cc
+    export LD_LIBRARY_PATH=$MPICH_DIR/lib:$LD_LIBRARY_PATH
+
+
+else
+    echo "Unknown system $SNIC_RESOURCE"
+fi
+
+CC --version
 
 ./Miniconda3-latest-Linux-x86_64.sh -b -p $L/miniconda3
 
@@ -27,31 +63,13 @@ conda update --all -y
 # conda install mpich=3.2.1 -y
 # Update, we use openmpi instead!
 
-# This is needed to compile mpi4py -- is it really?
-if [ $SNIC_RESOURCE == "tegner" ]; then
-    module load gcc/9.2.0
-    module load openmpi/4.1-gcc-9.2
-
-elif [ $SNIC_RESOURCE == "beskow" ]; then
-    echo "On Beskow"
-
-    # Recompile mpi4py using MPICH
-    export MPICC=cc
-    export LD_LIBRARY_PATH=$MPICH_DIR/lib:$LD_LIBRARY_PATH
-
-    # module load gcc/10.2.0
-    # module load ??? # What is openmpi module on Beskow?
-   #do something
-else
-    echo "Unknown system $SNIC_RESOURCE"
-fi
 
 pip install mpi4py --ignore-installed --no-cache-dir
 
 
 
 
-pushd ../../
+pushd ../../../
 
 # Install Snudda -- only if you do not already have Snudda installed
 # cd $L
diff --git a/examples/parallel/KTH_PDC/activate_miniconda.sh b/examples/parallel/KTH_PDC/activate_miniconda.sh
index 08268d5ba..1c9e58af9 100755
--- a/examples/parallel/KTH_PDC/activate_miniconda.sh
+++ b/examples/parallel/KTH_PDC/activate_miniconda.sh
@@ -5,7 +5,7 @@
 ##############################################################################
 
 module load snic-env
-L=/cfs/klemming/nobackup/${USER:0:1}/$USER/local/$SNIC_RESOURCE
+L=/cfs/klemming/home/${USER:0:1}/$USER/local/$SNIC_RESOURCE
 
 __conda_setup="$('$L/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
 
diff --git a/examples/parallel/KTH_PDC/save/.empty b/examples/parallel/KTH_PDC/log/.empty
similarity index 100%
rename from examples/parallel/KTH_PDC/save/.empty
rename to examples/parallel/KTH_PDC/log/.empty
diff --git a/examples/parallel/KTH_PDC/purge_ldflags.py b/examples/parallel/KTH_PDC/purge_ldflags.py
new file mode 100755
index 000000000..095b2183c
--- /dev/null
+++ b/examples/parallel/KTH_PDC/purge_ldflags.py
@@ -0,0 +1,13 @@
+#!/usr/bin/env python
+import sys
+
+if len(sys.argv) <= 1:
+    print("Provide name of file to purge last entry in LDFLAGS from")
+    sys.exit(-1)
+    
+with open( sys.argv[1]  ) as file:
+    for line in file:
+        if "LDFLAGS = $(LINKFLAGS) $(UserLDFLAGS)" in line:
+            print(",".join(line.split(",")[:-1]))
+        else:
+            print(line, end="")
diff --git a/snudda/__init__.py b/snudda/__init__.py
index 83fc5dc07..d5ab066dc 100644
--- a/snudda/__init__.py
+++ b/snudda/__init__.py
@@ -1,6 +1,6 @@
 from .core import Snudda
 
-__version__ = "1.2.13"
+__version__ = "1.2.14"
 
 from .init import SnuddaInit
 from .place import SnuddaPlace
diff --git a/snudda/core.py b/snudda/core.py
index 1aae2269f..d9c2023d5 100755
--- a/snudda/core.py
+++ b/snudda/core.py
@@ -403,7 +403,7 @@ def export_to_SONATA(self, args):
 
         assert False, "Old export to SONATA borken, fixme!"
         # TODO: Fix this
-        from snudda.ConvertNetwork import ConvertNetwork
+        from snudda.utils.export_sonata import ExportSonata
 
         print("Exporting to SONATA format")
         print(f"Network path: {self.network_path}")
@@ -420,9 +420,7 @@ def export_to_SONATA(self, args):
 
         out_dir = os.path.join(self.network_path, "SONATA")
 
-        cn = ConvertNetwork(networkFile=network_file,
-                            inputFile=input_file,
-                            outDir=out_dir)
+        cn = ExportSonata(network_file=network_file, input_file=input_file, out_dir=out_dir)
 
     ############################################################################
 
diff --git a/snudda/data/experiment_config/experiment-config-1.json b/snudda/data/experiment_config/experiment-config-1.json
index ea457941b..bc2ef593e 100644
--- a/snudda/data/experiment_config/experiment-config-1.json
+++ b/snudda/data/experiment_config/experiment-config-1.json
@@ -2,7 +2,8 @@
 
     "meta" : {
 	"simulationDuration" : 5.0,
-	"vInit" : [[0, -60e-3], [1, -50e-3], [2, -60e-3], [3, -30e-3]]
+	"vInit" : [[0, -60e-3], [1, -50e-3], [2, -60e-3], [3, -30e-3]],
+	"pairRecordingOutputFile" : "network-output-1.hdf5"
     },
     
     "currentInjection" : [
diff --git a/snudda/data/experiment_config/experiment-config-2.json b/snudda/data/experiment_config/experiment-config-2.json
index bc3db6bff..978f64f51 100644
--- a/snudda/data/experiment_config/experiment-config-2.json
+++ b/snudda/data/experiment_config/experiment-config-2.json
@@ -3,7 +3,7 @@
     "meta" : {
 	"simulationDuration" : 3,
 	"vInit" : [[0, -60e-3], [1, -50e-3], [2, -60e-3], [3, -30e-3]],
-	"pair_recording_voltage_file" : "network-voltage-2.txt"
+	"pairRecordingOutputFile" : "network-output-2.hdf5"
     },
     
     "currentInjection" : [
diff --git a/snudda/data/experiment_config/experiment-config-3.json b/snudda/data/experiment_config/experiment-config-3.json
index 80e7c02f0..ad7416bef 100644
--- a/snudda/data/experiment_config/experiment-config-3.json
+++ b/snudda/data/experiment_config/experiment-config-3.json
@@ -3,7 +3,7 @@
     "meta" : {
 	"simulationDuration" : 3,
 	"vInit" : [[0, -60e-3], [1, -50e-3], [2, -60e-3], [3, -30e-3]],
-	"pair_recording_voltage_file" : "network-voltage-2.txt"
+	"pairRecordingOutputFile" : "network-output-2.hdf5"
     },
     
     "currentInjection" : [
diff --git a/snudda/data/experiment_config/experiment-config-4.json b/snudda/data/experiment_config/experiment-config-4.json
index 7e3441ab1..4ac99c461 100644
--- a/snudda/data/experiment_config/experiment-config-4.json
+++ b/snudda/data/experiment_config/experiment-config-4.json
@@ -3,7 +3,7 @@
     "meta" : {
 	"simulationDuration" : 3,
 	"vInit" : -80e-3,
-	"pair_recording_voltage_file" : "network-voltage-4.txt",
+	"pairRecordingOutputFile" : "network-output-4.hdf5",
 	"reversal_potential" : [["tmGabaA", -40e-3]]
     },
     
diff --git a/snudda/data/experiment_config/experiment-config-5.json b/snudda/data/experiment_config/experiment-config-5.json
index a337a852d..866242da9 100644
--- a/snudda/data/experiment_config/experiment-config-5.json
+++ b/snudda/data/experiment_config/experiment-config-5.json
@@ -3,7 +3,7 @@
     "meta" : {
 	"simulationDuration" : 2,
 	"vInit" : -80e-3,
-	"pair_recording_voltage_file" : "network-voltage-5.txt",
+	"pair_recording_voltage_file" : "network-output-5.hdf5",
 	"reversal_potential" : [["tmGabaA", -40e-3]]
     },
     
diff --git a/snudda/data/experiment_config/experiment-config-6.json b/snudda/data/experiment_config/experiment-config-6.json
index 4ae994a54..18d2c8f47 100644
--- a/snudda/data/experiment_config/experiment-config-6.json
+++ b/snudda/data/experiment_config/experiment-config-6.json
@@ -3,7 +3,7 @@
     "meta" : {
 	"simulationDuration" : 4,
 	"vInit" : -80e-3,
-	"pairRecordingOutputFile" : "network-output-6.txt",
+	"pairRecordingOutputFile" : "network-output-6.hdf5",
 	"reversal_potential" : [["tmGabaA", -40e-3]],
 	"neuronSubset" : "prepost"
     },
diff --git a/snudda/detect/detect.py b/snudda/detect/detect.py
index 65d85bc52..2e42bcc6f 100644
--- a/snudda/detect/detect.py
+++ b/snudda/detect/detect.py
@@ -1815,7 +1815,7 @@ def write_log(self, text, flush=True, is_error=False, force_print=False):  # Cha
                 self.logfile.flush()
 
         if self.verbose or is_error or force_print:
-            print(text)
+            print(text, flush=True)
 
     ############################################################################
 
diff --git a/snudda/detect/prune.py b/snudda/detect/prune.py
index c8f40cb74..5134e2a8f 100644
--- a/snudda/detect/prune.py
+++ b/snudda/detect/prune.py
@@ -1288,7 +1288,7 @@ def write_log(self, text, flush=True, is_error=False, force_print=False):  # Cha
                     self.logfile.flush()
 
             if self.verbose or is_error or force_print:
-                print(text)
+                print(text, flush=True)
         except Exception as e:
             print(text)
             print("Unable to write to log file. Is log file closed?")
diff --git a/snudda/init/init.py b/snudda/init/init.py
index 0fc80bcaf..01dc3da0a 100644
--- a/snudda/init/init.py
+++ b/snudda/init/init.py
@@ -704,6 +704,7 @@ def add_population_unit_density(self,
                                     neuron_types,
                                     unit_centre,
                                     probability_function,  # Function of d (distance to centre) as string
+                                    num_neurons=None,
                                     unit_id=None):
 
         if type(neuron_types) != list:
@@ -719,11 +720,15 @@ def add_population_unit_density(self,
             self.network_data["PopulationUnits"][structure_name]["ProbabilityFunctions"] = [probability_function]
             self.network_data["PopulationUnits"][structure_name]["unitID"] = [unit_id]
             self.network_data["PopulationUnits"][structure_name]["neuronTypes"] = [neuron_types]
+            self.network_data["PopulationUnits"][structure_name]["numNeurons"] = [num_neurons]
+
         else:
             self.network_data["PopulationUnits"][structure_name]["centres"].append(unit_centre)
             self.network_data["PopulationUnits"][structure_name]["ProbabilityFunctions"].append(probability_function)
             self.network_data["PopulationUnits"][structure_name]["unitID"].append(unit_id)
             self.network_data["PopulationUnits"][structure_name]["neuronTypes"].append(neuron_types)
+            self.network_data["PopulationUnits"][structure_name]["numNeurons"].append(num_neurons)
+
 
     def add_population_unit_random(self, structure_name, neuron_types, fraction_of_neurons, unit_id=None):
 
diff --git a/snudda/input/__init__.py b/snudda/input/__init__.py
index 898e17273..533beb9d3 100644
--- a/snudda/input/__init__.py
+++ b/snudda/input/__init__.py
@@ -1,2 +1,2 @@
 from snudda.input.input import SnuddaInput
-from snudda.input.input_tuning import InputTuning
+# from snudda.input.input_tuning import InputTuning
diff --git a/snudda/input/input.py b/snudda/input/input.py
index b34d73707..5e4720f7f 100644
--- a/snudda/input/input.py
+++ b/snudda/input/input.py
@@ -16,13 +16,11 @@
 from collections import OrderedDict
 
 import numpy as np
-import h5py
 import json
 import os
 from glob import glob
 import itertools
-
-import matplotlib.pyplot as plt
+import h5py
 
 from snudda.neurons.neuron_prototype import NeuronPrototype
 from snudda.utils.snudda_path import snudda_parse_path
@@ -433,6 +431,8 @@ def make_neuron_input_parallel(self):
         parameter_list_list = []
         cluster_size_list = []
 
+        dendrite_location_override_list = []
+
         for (neuron_id, neuron_name, neuron_type, populationUnitID) \
                 in zip(self.neuron_id, self.neuron_name, self.neuron_type, self.population_unit_id):
 
@@ -610,6 +610,20 @@ def make_neuron_input_parallel(self):
 
                     cluster_size_list.append(cluster_size)
 
+                    if "dendriteLocation" in input_info:
+                        assert "morphologyKey" in input_info, \
+                            f"If you specify dendriteLocation you must also specify morphologyKey"
+
+                        assert morphology_key == self.network_data["neurons"][neuron_id]["morphologyKey"], \
+                            f"Neuron {neuron_id} has morphology_key {self.network_data['neurons'][neuron_id]['morphologyKey']}" \
+                            f"which does not match what is specified in input JSON file: {morphology_key}"
+
+                        dend_location = input_info["dendriteLocation"]
+                    else:
+                        dend_location = None
+
+                    dendrite_location_override_list.append(dend_location)
+
                 elif input_inf["generator"] == "csv":
                     csv_file = snudda_parse_path(input_inf["csvFile"] % neuron_id)
 
@@ -658,7 +672,8 @@ def make_neuron_input_parallel(self):
                                   parameter_file_list,
                                   parameter_list_list,
                                   seed_list,
-                                  cluster_size_list))
+                                  cluster_size_list,
+                                  dendrite_location_override_list))
 
             self.d_view.scatter("input_list", input_list, block=True)
             cmd_str = "inpt = list(map(nl.make_input_helper_parallel,input_list))"
@@ -697,7 +712,8 @@ def make_neuron_input_parallel(self):
                       parameter_file_list,
                       parameter_list_list,
                       seed_list,
-                      cluster_size_list)
+                      cluster_size_list,
+                      dendrite_location_override_list)
 
         # Gather the spikes that were generated in parallel
         for neuron_id, input_type, spikes, loc, synapse_density, frq, \
@@ -970,6 +986,8 @@ def raster_plot(spike_times,
             fig: matplotlib figure object
         """
 
+        import matplotlib.pyplot as plt
+
         if fig is None:
             fig = plt.figure()
         # ax = plt.gca()
@@ -1324,7 +1342,7 @@ def make_input_helper_parallel(self, args):
 
             neuron_id, input_type, freq, start, end, synapse_density, num_spike_trains, p_keep, \
                 population_unit_spikes, jitter_dt, population_unit_id, conductance, correlation, mod_file, \
-                parameter_file, parameter_list, random_seed, cluster_size = args
+                parameter_file, parameter_list, random_seed, cluster_size, dendrite_location_override = args
 
             return self.make_input_helper_serial(neuron_id=neuron_id,
                                                  input_type=input_type,
@@ -1343,7 +1361,8 @@ def make_input_helper_parallel(self, args):
                                                  parameter_file=parameter_file,
                                                  parameter_list=parameter_list,
                                                  random_seed=random_seed,
-                                                 cluster_size=cluster_size)
+                                                 cluster_size=cluster_size,
+                                                 dendrite_location=dendrite_location_override)
 
         except:
             import traceback
@@ -1378,7 +1397,8 @@ def make_input_helper_serial(self,
                                  parameter_file,
                                  parameter_list,
                                  random_seed,
-                                 cluster_size=None):
+                                 cluster_size=None,
+                                 dendrite_location=None):
 
         """
         Generate poisson input.
@@ -1400,6 +1420,8 @@ def make_input_helper_serial(self,
             parameter_file: Parameter file for input synapses
             parameter_list: Parameter list (to inline parameters, instead of reading from file)
             random_seed: Random seed.
+            cluster_size: Input synapse cluster size
+            dendrite_location: Override location of dendrites, list of (sec_id, sec_x) tuples.
             """
 
         # First, find out how many inputs and where, based on morphology and
@@ -1430,13 +1452,22 @@ def make_input_helper_serial(self,
             num_inputs = 1
         else:
 
-            # (x,y,z), secID, secX, dist_to_soma
-            input_loc = self.dendrite_input_locations(neuron_id=neuron_id,
-                                                      synapse_density=synapse_density,
-                                                      num_spike_trains=num_spike_trains,
-                                                      rng=rng,
-                                                      cluster_size=cluster_size,
-                                                      input_type=input_type)
+            if dendrite_location:
+                self.write_log(f"Overriding input location for {input_type} on neuron_id={neuron_id}")
+                sec_id, sec_x = zip(*dendrite_location)
+
+                # TODO: Calculate the correct x,y,z and distance to soma
+                x = y = z = dist_to_soma = np.zeros((len(sec_id),))
+                input_loc = [(x, y, z), np.array(sec_id), np.array(sec_x), dist_to_soma]
+
+            else:
+                # (x,y,z), secID, secX, dist_to_soma
+                input_loc = self.dendrite_input_locations(neuron_id=neuron_id,
+                                                          synapse_density=synapse_density,
+                                                          num_spike_trains=num_spike_trains,
+                                                          rng=rng,
+                                                          cluster_size=cluster_size,
+                                                          input_type=input_type)
 
             num_inputs = input_loc[0].shape[0]
             self.write_log(f"Generating {num_inputs} inputs for {self.neuron_name[neuron_id]}")
@@ -1480,7 +1511,7 @@ def write_log(self, text, flush=True, is_error=False, force_print=False):  # Cha
                 self.logfile.flush()
 
         if self.verbose or is_error or force_print:
-            print(text)
+            print(text, flush=True)
 
     ############################################################################
 
diff --git a/snudda/neurons/neuron_morphology.py b/snudda/neurons/neuron_morphology.py
index 14f93ca7f..cca053091 100644
--- a/snudda/neurons/neuron_morphology.py
+++ b/snudda/neurons/neuron_morphology.py
@@ -1047,7 +1047,7 @@ def compartment_length(self, comp_type="dend"):
 
     # TODO: Update the code so that it gives exactly num_locations positions (currently it varies)
 
-    def dendrite_input_locations(self, synapse_density, rng, num_locations=None, return_density=False,
+    def dendrite_input_locations(self, synapse_density, rng, num_locations=None,
                                  cluster_size=None):
 
         """
@@ -1057,32 +1057,17 @@ def dendrite_input_locations(self, synapse_density, rng, num_locations=None, ret
             synapse_density : Synapse density as a function f(d), d=distance from soma
             rng : Numpy random stream
             num_locations : Number of input locations (this is average number returned, results vary)
-            return_density : Should the function also return the density
             cluster_size (int): Number of synapse clusters to place (None = no clusters, all placed independently)
         """
 
-        # Calculate the input density at each point in dendrite morphology
-        d = self.dend[:, 4]
-        try:
-            # d is now distance from some, so synapseDensity is a func of d
-            i_density = numexpr.evaluate(synapse_density)
-        except:
-            self.write_log(f"Bad synapse density string: {synapse_density}")
-            import traceback
-            tstr = traceback.format_exc()
-            self.write_log(tstr)
-            assert False, f"Problem with synapse density {synapse_density}"
-
-        # if type(i_density) in (int, float): -- this worked for eval, but not for numexpr.evaluate
-        if i_density.ndim == 0:
-            # If iDensity is a constant, we need to set it for all points
-            i_density = float(i_density) * np.ones(d.shape)
-
-        comp_density = (i_density[self.dend_links[:, 0]] + i_density[self.dend_links[:, 1]]) / 2
-        comp_len = self.compartment_length(comp_type="dend")
+        if num_locations is not None:
+            # This function returns the exact number of synapses specified
+            return self.dendrite_input_locations_helper(synapse_density=synapse_density,
+                                                        rng=rng,
+                                                        num_locations=num_locations,
+                                                        cluster_size=cluster_size)
 
-        # compDensity is in synapses per micrometer, multiply by 1e6
-        expected_synapses = comp_density * comp_len * 1e6
+        expected_synapses = self.get_expected_synapses_per_compartment(synapse_density=synapse_density)
 
         if num_locations is not None:
             expected_synapses *= num_locations / np.sum(expected_synapses)
@@ -1101,6 +1086,7 @@ def dendrite_input_locations(self, synapse_density, rng, num_locations=None, ret
 
         # x,y,z, secID, secX
         input_loc = np.zeros((n_syn_tot*cluster_size, 5))
+        d = self.dend[:, 4]
 
         # Iterate over each compartment
         syn_ctr = 0
@@ -1131,13 +1117,102 @@ def dendrite_input_locations(self, synapse_density, rng, num_locations=None, ret
 
         assert syn_ctr == input_loc.shape[0], f"Not all input_loc was set. Rows {input_loc.shape[0]}, syn_ctr={syn_ctr}"
 
-        if return_density:
-            # Return xyz,secID,secX,iDensity,distSynSoma
-            return input_loc[:, :3], input_loc[:, 3], input_loc[:, 4], i_density, dist_syn_soma
+        # if return_density:
+        #     # Return xyz,secID,secX,iDensity,distSynSoma
+        #     return input_loc[:, :3], input_loc[:, 3], input_loc[:, 4], i_density, dist_syn_soma
+
+        # Return xyz,secID,secX, dist_to_soma (update: now also added distance synapse to soma)
+        return input_loc[:, :3], input_loc[:, 3], input_loc[:, 4], dist_syn_soma
+
+    ############################################################################
+
+    def get_expected_synapses_per_compartment(self, synapse_density):
+
+        # Calculate the input density at each point in dendrite morphology
+        d = self.dend[:, 4]
+        try:
+            # d is now distance from some, so synapseDensity is a func of d
+            i_density = numexpr.evaluate(synapse_density)
+        except:
+            self.write_log(f"Bad synapse density string: {synapse_density}")
+            import traceback
+            tstr = traceback.format_exc()
+            self.write_log(tstr)
+            assert False, f"Problem with synapse density {synapse_density}"
+
+        # if type(i_density) in (int, float): -- this worked for eval, but not for numexpr.evaluate
+        if i_density.ndim == 0:
+            # If iDensity is a constant, we need to set it for all points
+            i_density = float(i_density) * np.ones(d.shape)
+
+        comp_density = (i_density[self.dend_links[:, 0]] + i_density[self.dend_links[:, 1]]) / 2
+        comp_len = self.compartment_length(comp_type="dend")
+
+        # comp_density is in synapses per micrometer, multiply by 1e6
+        expected_synapses = comp_density * comp_len * 1e6
+
+        return expected_synapses
+
+    ############################################################################
+
+
+    def dendrite_input_locations_helper(self,
+                                        synapse_density,
+                                        rng,
+                                        num_locations,
+                                        cluster_size=1):
+
+        """
+        Helper function. Places num_location clusters, each containing cluster_size synapses. Density is relative, scaled.
+
+        Args:
+            synapse_density : Synapse density as a function f(d), d = distance on dendrite from soma
+            rng : Numpy random stream
+            num_location (int) : Number of locations
+            cluster_size (int) : Size of synapse clusters
+        """
+
+        expected_synapses = self.get_expected_synapses_per_compartment(synapse_density=synapse_density)
+
+        p_cum = np.cumsum(expected_synapses)
+        rand_vals = rng.uniform(0, p_cum[-1], num_locations)
+
+        if cluster_size is None:
+            cluster_size = 1
+
+        num_synapses = num_locations*cluster_size
+        input_loc = np.zeros((num_synapses, 5))
+        dist_syn_soma = np.zeros((num_synapses,))
+
+        d = self.dend[:, 4]
+        syn_ctr = 0
+
+        for r in rand_vals:
+            comp_idx = len(p_cum) - np.sum(r < p_cum)
+
+            for j in range(cluster_size):
+
+                comp_x = rng.random()
+
+                dist_syn_soma[syn_ctr] = d[self.dend_links[comp_idx, 0]] * (1 - comp_x) \
+                                         + d[self.dend_links[comp_idx, 1]] * comp_x
+
+                coords = (self.dend[self.dend_links[comp_idx, 0], :3] * (1 - comp_x)
+                          + self.dend[self.dend_links[comp_idx, 1], :3] * comp_x)
+
+                input_loc[syn_ctr, :3] = coords
+                input_loc[syn_ctr, 3] = self.dend_sec_id[comp_idx]
+                input_loc[syn_ctr, 4] = self.dend_sec_x[comp_idx, 0] * (1 - comp_x) \
+                                        + comp_x * self.dend_sec_x[comp_idx, 1]
+
+                syn_ctr += 1
+
+        assert syn_ctr == input_loc.shape[0], f"Not all input_loc was set. Rows {input_loc.shape[0]}, syn_ctr={syn_ctr}"
 
         # Return xyz,secID,secX, dist_to_soma (update: now also added distance synapse to soma)
         return input_loc[:, :3], input_loc[:, 3], input_loc[:, 4], dist_syn_soma
 
+
     ############################################################################
 
     def debug_plot(self, wait_flag=False, plot_step=1, plot_axon_flag=False):
diff --git a/snudda/neurons/neuron_prototype.py b/snudda/neurons/neuron_prototype.py
index ea2543f50..388c61a89 100644
--- a/snudda/neurons/neuron_prototype.py
+++ b/snudda/neurons/neuron_prototype.py
@@ -175,14 +175,18 @@ def get_morph_key(self, parameter_id, morphology_id, parameter_key=None):
 
         return morph_key
 
-    def get_modulation_key(self, modulation_id):
+    def get_modulation_key(self, parameter_id, morphology_id, modulation_id):
 
         if self.modulation_info:
             if type(self.modulation_info) == list:
                 # Old format without keys
                 return None
 
-            modulation_key_list = list(self.modulation_info.keys())
+            param_key = self.get_parameter_key(parameter_id)
+            morph_key = self.get_morphology_key(parameter_id=None, parameter_key=param_key, morphology_id=morphology_id)
+            modulation_key_list = self.meta_info[param_key][morph_key]["neuromodulation"]
+
+            # modulation_key_list = list(self.modulation_info.keys())
             modulation_key = modulation_key_list[modulation_id % len(modulation_key_list)]
         else:
             modulation_key = None
@@ -414,7 +418,9 @@ def clone(self, parameter_id=None, morphology_id=None, modulation_id=None,
                 morphology_key = self.get_morph_key(parameter_id=parameter_id, morphology_id=morphology_id)
 
             if modulation_key is None:
-                modulation_key = self.get_modulation_key(modulation_id=modulation_id)
+                modulation_key = self.get_modulation_key(parameter_id=parameter_id,
+                                                         morphology_id=morphology_id,
+                                                         modulation_id=modulation_id)
 
             morph = self.morphology_cache[morph_tag].clone(position=position,
                                                            rotation=rotation,
diff --git a/snudda/place/place.py b/snudda/place/place.py
index 7fd6746dd..abb610485 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -151,7 +151,7 @@ def write_log(self, text):
             self.log_file.write(text + "\n")
 
         if self.verbose:
-            print(text)
+            print(text, flush=True)
 
     ############################################################################
 
@@ -765,6 +765,7 @@ def define_population_units(self, population_unit_info):
                                          "unitID" : ID of population unit
                                          "fractionOfNeurons" : How large fraction of neurons belong to this unit (used by "random" method)
                                          "neuronTypes" : List of Neuron types that belong to this population unit
+                                         "numNeurons" : Number of neurons in each population unit, only used with radialDensity method
                                          "structure" : Name of structure population unit is located in (VolumeID)
                                          "centres" : Centre of radial density
                                          "ProbabilityFunctions" : Probability function defining unit membership, function of radius
@@ -870,6 +871,7 @@ def population_unit_density_labeling(self, population_unit_info, neuron_id):
             population_unit_info (dict): "method" must be "radialDensity"
                                          "neuronTypes" list of neuron types
                                          "centres" of radial probabilityes, one per neuron type
+                                         "numNeurons" : Number of neurons in each population unit, only used with radialDensity method
                                          "probabilityFunctions" list of probability functions of r (as str)
                                          "unitID" ID of population unit
         """
@@ -882,7 +884,15 @@ def population_unit_density_labeling(self, population_unit_info, neuron_id):
         probability_functions = population_unit_info["ProbabilityFunctions"]
         unit_id = population_unit_info["unitID"]
 
-        assert len(neuron_types) == len(centres) == len(probability_functions) == len(unit_id)
+        if "numNeurons" in population_unit_info and population_unit_info["numNeurons"] is not None:
+            num_neurons = population_unit_info["numNeurons"]
+
+            if np.isscalar(num_neurons):
+                num_neurons = np.full((len(centres),), num_neurons)
+        else:
+            num_neurons = [None for x in centres]
+
+        assert len(neuron_types) == len(centres) == len(probability_functions) == len(unit_id) == len(num_neurons)
 
         # xyz = self.all_neuron_positions()
         unit_probability = np.zeros(centres.shape[0])
@@ -921,6 +931,28 @@ def population_unit_density_labeling(self, population_unit_info, neuron_id):
             # Channel 0 is unassigned, no channel, poor homeless neurons!
             self.population_units[uid] = np.where(self.population_unit == uid)[0]
 
+        # Finally if numNeurons is specified, we need to reduce the number of neurons belonging to that unit
+        for u_id, n_neurons in zip(unit_id, num_neurons):
+            if n_neurons is not None:
+                assert len(self.population_units[u_id]) >= n_neurons, \
+                    f"Unable to pick {n_neurons} for population unit {u_id}, only {len(self.population_units[u_id])} available."
+
+                if n_neurons < len(self.population_units[u_id]):
+
+                    perm_nid = np.random.permutation(self.population_units[u_id])
+                    keep_nid = perm_nid[:n_neurons]
+                    self.population_units[u_id] = keep_nid
+
+                    remove_nid = perm_nid[n_neurons:]
+                    for rid in remove_nid:
+                        self.population_unit[rid] = 0
+
+                    if 0 in self.population_units:
+                        self.population_units[0] = np.array(list(set(self.population_units[0]).union(remove_nid)))
+                    else:
+                        self.population_units[0] = remove_nid
+
+
     ############################################################################
 
     def init_population_units(self):
diff --git a/snudda/plotting/plot_network.py b/snudda/plotting/plot_network.py
index 976565664..d67ed3a3c 100644
--- a/snudda/plotting/plot_network.py
+++ b/snudda/plotting/plot_network.py
@@ -191,7 +191,17 @@ def plot_populations(self):
         neuron_colours = np.array(neuron_colours)
         positions = self.sl.data["neuronPositions"]
 
-        ax.scatter(positions[:, 0], positions[:, 1], positions[:, 2], c=neuron_colours, marker='o', s=20,alpha=alphas)
+        pop_unit_idx = np.where(population_unit > 0)[0]
+        unmarked_idx = np.where(population_unit == 0)[0]
+
+        ax.scatter(positions[pop_unit_idx, 0], positions[pop_unit_idx, 1], positions[pop_unit_idx, 2],
+                   c=neuron_colours[pop_unit_idx, :], marker='o', s=20,alpha=1)
+
+        ax.scatter(positions[unmarked_idx, 0], positions[unmarked_idx, 1], positions[unmarked_idx, 2],
+                   c=neuron_colours[unmarked_idx, :], marker='o', s=20,alpha=0.05)
+
+
+        # ax.scatter(positions[:, 0], positions[:, 1], positions[:, 2], c=neuron_colours, marker='o', s=20,alpha=alphas)
 
         self.equal_axis(ax)
 
diff --git a/snudda/plotting/plot_traces.py b/snudda/plotting/plot_traces.py
index d1c23086a..be02c23ce 100644
--- a/snudda/plotting/plot_traces.py
+++ b/snudda/plotting/plot_traces.py
@@ -1,4 +1,4 @@
-# python3 plot_traces.py save/traces/network-voltage-0.csv save/network-connect-synapse-file-0.hdf5
+# python3 plot_traces.py save/simulation/network-output.hdf5 save/network-synapses.hdf5
 
 
 import sys
@@ -17,9 +17,9 @@ class PlotTraces:
 
     ############################################################################
 
-    def __init__(self, file_name, network_file=None, input_file=None):
+    def __init__(self, output_file, network_file=None, input_file=None):
 
-        self.file_name = file_name
+        self.output_file = output_file
         self.network_file = network_file
         self.input_file = input_file
 
@@ -31,13 +31,13 @@ def __init__(self, file_name, network_file=None, input_file=None):
         # self.read_csv()
 
         try:
-            self.ID = int(re.findall('\d+', ntpath.basename(file_name))[0])
+            self.ID = int(re.findall('\d+', ntpath.basename(output_file))[0])
         except:
             print("Unable to guess ID, using 666.")
             self.ID = 666
 
-        if network_file is None and "simulation" in file_name:
-            network_path = os.path.dirname(os.path.dirname(file_name))
+        if network_file is None and "simulation" in output_file:
+            network_path = os.path.dirname(os.path.dirname(output_file))
             network_file = os.path.join(network_path, "network-synapses.hdf5")
             if os.path.exists(network_file):
                 self.network_file = network_file
@@ -48,6 +48,7 @@ def __init__(self, file_name, network_file=None, input_file=None):
             network_path = None
 
         if self.network_file is not None:
+            print(f"Loading network info from {self.network_file}")
             self.network_info = SnuddaLoad(self.network_file)
             # assert(int(self.ID) == int(self.networkInfo.data["SlurmID"]))
 
@@ -55,11 +56,19 @@ def __init__(self, file_name, network_file=None, input_file=None):
             self.network_info = None
 
         if self.input_file is not None:
+            print(f"Loading input info from {self.input_file}")
             self.input_info = h5py.File(self.input_file, "r")
         else:
-            self.input_info = None
+            network_path = os.path.dirname(os.path.dirname(output_file))
+            input_file = os.path.join(network_path, "input-spikes.hdf5")
+            if os.path.exists(input_file):
+                self.input_file = input_file
+                print(f"Loading input info from {self.input_file}")
+                self.input_info = h5py.File(self.input_file, "r")
+            else:
+                self.input_info = None
 
-        self.output_load = SnuddaLoadNetworkSimulation(network_simulation_output_file=file_name,
+        self.output_load = SnuddaLoadNetworkSimulation(network_simulation_output_file=output_file,
                                                        network_path=network_path)
 
         self.voltage, self.time = self.output_load.get_voltage()
@@ -70,7 +79,7 @@ def read_csv(self):
 
         assert False, "read_csv is deprecated, use SnuddaLoadNetworkSimulation instead"
 
-        data = np.genfromtxt(self.file_name, delimiter=',')
+        data = np.genfromtxt(self.output_file, delimiter=',')
 
         assert (data[0, 0] == -1)  # First column should be time
 
@@ -256,7 +265,10 @@ def plot_trace_neuron_type(self, neuron_type, num_traces=10, offset=0, skip_time
 
         trace_id = [x[0] for x in enumerate(neuron_types) if x[1].lower() == neuron_type.lower()]
 
-        num_traces = min(len(trace_id), num_traces)
+        if num_traces is None:
+            num_traces = len(trace_id)
+        else:
+            num_traces = min(len(trace_id), num_traces)
 
         if num_traces <= 0:
             print(f"No traces of {neuron_type} to show")
@@ -273,51 +285,26 @@ def plot_trace_neuron_type(self, neuron_type, num_traces=10, offset=0, skip_time
 
 if __name__ == "__main__":
 
-    # TODO: Update to use argparser
-
-    if len(sys.argv) > 1:
-        file_name = sys.argv[1]
-    else:
-        file_name = None
-
-    if len(sys.argv) > 2:
-        network_file = sys.argv[2]
-    else:
-        network_file = None
-
-    if file_name is not None:
-        npt = PlotTraces(file_name, network_file)
-        # npt.plotTraces(offset=0.2,traceID=[17,40,11,18])
-        # npt.plotTraces(offset=0.2,traceID=[0,1,2,3,4,5,6,7,8,9])
-        # npt.plotTraces(offset=0,traceID=[0,1,2,3,4,5,6,7,8,9])
-        # npt.plotTraces(offset=0,traceID=[0,5,55]) #,5,55])
-        # npt.plotTraces(offset=0,traceID=np.arange(0,100)) #,5,55])
-        # npt.plotTraces(offset=-0.2,traceID=np.arange(0,20),skipTime=0.5)
-        # npt.plotTraces(offset=-0.2,traceID=[5,54],skipTime=0.5)
-        # npt.plotTraces(offset=0.2,traceID=[1,5,7,15,16],skipTime=0.2)
-
-        plot_offset = 0  # -0.2
-        skip_time = 0  # 0.5
-        num_traces_max = 10
-
-        if True:
-            npt.plot_trace_neuron_type(neuron_type="dSPN", num_traces=num_traces_max, offset=plot_offset, skip_time=skip_time)
-            npt.plot_trace_neuron_type(neuron_type="iSPN", num_traces=num_traces_max, offset=plot_offset, skip_time=skip_time)
-            npt.plot_trace_neuron_type(neuron_type="FS", num_traces=num_traces_max, offset=plot_offset, skip_time=skip_time)
-            npt.plot_trace_neuron_type(neuron_type="LTS", num_traces=num_traces_max, offset=plot_offset, skip_time=skip_time)
-            npt.plot_trace_neuron_type(neuron_type="ChIN", num_traces=num_traces_max, offset=plot_offset, skip_time=skip_time)
-
-        if False:
-            npt.plot_trace_neuron_name(neuron_name="FS_0", plot_offset=plot_offset, fig_name="Traced-FS_0.pdf",
-                                       num_offset=10)
-            npt.plot_trace_neuron_name(neuron_name="FS_1", plot_offset=plot_offset, fig_name="Traced-FS_1.pdf")
-            npt.plot_trace_neuron_name(neuron_name="FS_2", plot_offset=plot_offset, fig_name="Traced-FS_2.pdf")
-            npt.plot_trace_neuron_name(neuron_name="FS_3", plot_offset=plot_offset, fig_name="Traced-FS_3.pdf")
-
-    else:
-        print(f"Usage: {sys.argv[0]} network-voltage-XXX.csv my_network/network-synapses.hdf5")
-
-#  TODO: Need to clean up the code and make a new plot function
-
-# import pdb
-# pdb.set_trace()
+    import argparse
+    parser = argparse.ArgumentParser("Plot traces")
+    parser.add_argument("output_file")
+    parser.add_argument("--input_file")
+    parser.add_argument("--network_file")
+    parser.add_argument("--plot_offset", type=float, default=0)
+    parser.add_argument("--skip_time", type=float, default=0)
+    parser.add_argument("--max_num_traces", type=int, default=None)
+    args = parser.parse_args()
+
+    npt = PlotTraces(output_file=args.output_file, network_file=args.network_file, input_file=args.input_file)
+
+    # TODO: Update code so it loops through all existing neuron types by default
+    npt.plot_trace_neuron_type(neuron_type="dSPN", num_traces=args.max_num_traces,
+                               offset=args.plot_offset, skip_time=args.skip_time)
+    npt.plot_trace_neuron_type(neuron_type="iSPN", num_traces=args.max_num_traces,
+                               offset=args.plot_offset, skip_time=args.skip_time)
+    npt.plot_trace_neuron_type(neuron_type="FS", num_traces=args.max_num_traces,
+                               offset=args.plot_offset, skip_time=args.skip_time)
+    npt.plot_trace_neuron_type(neuron_type="LTS", num_traces=args.max_num_traces,
+                               offset=args.plot_offset, skip_time=args.skip_time)
+    npt.plot_trace_neuron_type(neuron_type="ChIN", num_traces=args.max_num_traces,
+                               offset=args.plot_offset, skip_time=args.skip_time)
diff --git a/snudda/simulate/nrn_simulator_parallel.py b/snudda/simulate/nrn_simulator_parallel.py
index ac9a93bae..da4a20b00 100644
--- a/snudda/simulate/nrn_simulator_parallel.py
+++ b/snudda/simulate/nrn_simulator_parallel.py
@@ -5,46 +5,80 @@
 
 class NrnSimulatorParallel(ephys.simulators.NrnSimulator):
 
-    def __init__(self, dt=None, cvode_active=False):
-
-        """ Constructor
-
+    def __init__(self, dt=None, cvode_active=False, cvode_minstep=None,
+                 random123_globalindex=None, mechanisms_directory=None):
+        """Constructor
         Args:
-            dt (float): timestemp
-            cvode_active (bool): Use CVODE, default False
+            dt (float): the integration time step used by neuron.
+            cvode_active (bool): should neuron use the variable time step
+                integration method (Default: False for Snudda)
+            cvode_minstep (float): the minimum time step allowed for a cvode
+                step. Default is 0.0.
+            random123_globalindex (int): used to set the global index used by
+                all instances of the Random123 instances of Random
+            mechanisms_directory (str): path to the parent directory of the
+                directory containing the mod files. If the mod files are in
+                "./data/mechanisms", then mechanisms_directory should be
+                "./data/".
         """
 
-        self.disable_banner = True
-        self.banner_disabled = False
-
-        self.neuron.h.load_file('stdrun.hoc')
-
-        self.dt = dt if dt is not None else self.neuron.h.dt
-
-        self.cvode_active = cvode_active
-        if cvode_active:
-            self.neuron.h.cvode_active(1)
-            # assert False, "Make sure Cvode works"
-        else:
-            self.neuron.h.cvode_active(0)
+        super().__init__(dt=dt, cvode_active=cvode_active, cvode_minstep=cvode_minstep,
+                         random123_globalindex=random123_globalindex, mechanisms_directory=mechanisms_directory)
 
         self.pc = self.neuron.h.ParallelContext()
         self.pc.set_maxstep(10)
 
-    def run(self, tstop=None, dt=None):
 
-        """ Run simulation. """
+
+    def run(
+            self,
+            tstop=None,
+            dt=None,
+            cvode_active=None,
+            random123_globalindex=None):
+        """Run protocol"""
 
         self.neuron.h.tstop = tstop
 
-        if dt is None:
+        if cvode_active and dt is not None:
+            raise ValueError(
+                'NrnSimulator: Impossible to combine the dt argument when '
+                'cvode_active is True in the NrnSimulator run method')
+
+        if cvode_active is None:
+            cvode_active = self.cvode_active
+
+        if not cvode_active and dt is None:  # use dt of simulator
+            if self.neuron.h.dt != self.dt:
+                raise Exception(
+                    'NrnSimulator: Some process has changed the '
+                    'time step dt of Neuron since the creation of this '
+                    'NrnSimulator object. Not sure this is intended:\n'
+                    'current dt: %.6g\n'
+                    'init dt: %.6g' % (self.neuron.h.dt, self.dt))
             dt = self.dt
 
-        self.neuron.h.dt = dt
-        self.neuron.h.steps_per_ms = 1.0 / dt
+        self.neuron.h.cvode_active(1 if cvode_active else 0)
+        if self.cvode_minstep_value is not None:
+            save_minstep = self.cvode_minstep
+            self.cvode_minstep = self.cvode_minstep_value
+
+        if cvode_active:
+            print(f'Running Neuron simulator {tstop:.6g} ms, with cvode')
+        else:
+            self.neuron.h.dt = dt
+            self.neuron.h.steps_per_ms = 1.0 / dt
+            print(f'Running Neuron simulator {tstop:.6g} ms, with dt={dt}%r')
+
+        if random123_globalindex is None:
+            random123_globalindex = self.random123_globalindex
+
+        if random123_globalindex is not None:
+            rng = self.neuron.h.Random()
+            rng.Random123_globalindex(random123_globalindex)
 
         try:
-            # self.neuron.h.stdinit()
+            # self.neuron.h.run()
             self.neuron.h.finitialize()
 
             self.pc.psolve(tstop)
@@ -57,3 +91,8 @@ def run(self, tstop=None, dt=None):
 
         except Exception as e:
             raise NrnSimulatorException('Neuron simulator error', e)
+
+        if self.cvode_minstep_value is not None:
+            self.cvode_minstep = save_minstep
+
+        print('Neuron simulation finished')
diff --git a/snudda/simulate/pair_recording.py b/snudda/simulate/pair_recording.py
index e882ab5e8..485a32767 100644
--- a/snudda/simulate/pair_recording.py
+++ b/snudda/simulate/pair_recording.py
@@ -330,7 +330,7 @@ def connect_neuron_synapses(self, start_row, end_row):
 
     def plot_trace_overview(self):
         from snudda.plotting import PlotTraces
-        pt = PlotTraces(file_name=self.output_file_name,
+        pt = PlotTraces(output_file=self.output_file_name,
                         network_file=self.network_file)
 
         pt.plot_traces([x for x in pt.voltage])
@@ -364,7 +364,7 @@ def plot_trace(self, pre_id, post_id, offset=0, title=None, fig_name=None, skip_
             n_synapses = self.snudda_loader.find_synapses(pre_id=pre_id, post_id=post_id)[0].shape[0]
             title = f"{self.neurons[pre_id].name} -> {self.neurons[post_id].name} ({n_synapses} synapses)"
 
-        pt = PlotTraces(file_name=self.output_file_name, network_file=self.network_file)
+        pt = PlotTraces(output_file=self.output_file_name, network_file=self.network_file)
         fig = pt.plot_traces(trace_id=post_id, offset=offset, title=title, fig_name=fig_name, skip_time=skip_time)
 
         if mark_current:
diff --git a/snudda/utils/conv_hurt.py b/snudda/utils/conv_hurt.py
new file mode 100644
index 000000000..685ba8c74
--- /dev/null
+++ b/snudda/utils/conv_hurt.py
@@ -0,0 +1,481 @@
+# Must have a __init__.py file in module directory
+
+import json
+import h5py
+import csv
+from collections import OrderedDict
+import os
+import numpy as np
+
+# TODO: This needs to be updated to match the latest SONATA release.
+
+class ConvHurt(object):
+
+    def __init__(self, simulation_structure, input_structures, base_dir="TEST/"):
+
+        self.base_dir = base_dir
+        self.network_dir = os.path.join(base_dir, 'networks')
+
+        self.h5py_libver = "earliest"  # "latest"
+
+        self.setup_directories(simulation_structure=simulation_structure,
+                               input_structures=input_structures, base_dir=base_dir)
+
+        self.write_main_config(simulation_structure=simulation_structure,
+                               input_structures=input_structures,
+                               base_dir=base_dir)
+
+    ############################################################################
+
+    @staticmethod
+    def create_dir(directory):
+
+        if not os.path.exists(directory):
+            print(f"Creating directory : {directory}")
+            os.makedirs(directory)
+
+    ############################################################################
+
+    def setup_directories(self,
+                          simulation_structure,
+                          input_structures,
+                          base_dir=None):
+
+        if base_dir is None:
+            base_dir = "."
+
+        self.create_dir(base_dir)
+
+        dir_list = ['components', 'components/biophysical_neuron_dynamics', 'components/hoc_templates',
+                    'components/mechanisms', 'components/morphologies', 'components/point_neuron_dynamics',
+                    'components/synapse_dynamics', 'networks', 'inputs', 'output', f"networks/{simulation_structure}"]
+
+        for x in input_structures:
+            dir_list.append(f"networks/{x}")
+
+        for x in dir_list:
+            self.create_dir(f"{base_dir}{x}")
+
+    ############################################################################
+    #
+    # Writing the main configuration file, linking everything together
+
+    def write_main_config(self,
+                          base_dir="TEST/",
+                          out_file="circuit_config.json",
+                          simulation_structure="striatum",
+                          input_structures=None,
+                          target_simulator="NEURON"):
+
+        if input_structures is None:
+            input_structures = ["thalamus", "cortex"]
+
+        config = OrderedDict([])
+
+        config["target_simulator"] = target_simulator
+
+        manifest = OrderedDict([("$BASE_DIR", "."),
+                                ("$NETWORK_DIR", "$BASE_DIR/networks"),
+                                ("$COMPONENT_DIR", "$BASE_DIR/components")])
+
+        config["manifest"] = manifest
+
+        self.base_dir = base_dir
+        self.network_dir = os.path.join(base_dir, 'networks')
+
+        components = OrderedDict([("morphologies_dir", os.path.join("$COMPONENT_DIR", "morphologies")),
+                                  ("synaptic_models_dir", os.path.join("$COMPONENT_DIR", "synapse_dynamics")),
+                                  ("mechanisms_dir", os.path.join("$COMPONENT_DIR", "mechanisms")),
+                                  ("biophysical_neuron_models_dir",
+                                   os.path.join("$COMPONENT_DIR", "biophysical_neuron_dynamics")),
+                                  ("point_neuron_models_dir", os.path.join("$COMPONENT_DIR", "point_neuron_dynamics")),
+                                  ("templates_dir", os.path.join("$COMPONENT_DIR", "hoc_templates"))])
+
+        config["components"] = components
+
+        sim_node = os.path.join("$NETWORK_DIR", simulation_structure, f"{simulation_structure}_nodes.hdf5")
+        sim_node_type = os.path.join("$NETWORK_DIR", simulation_structure, f"{simulation_structure}_node_types.csv")
+
+        sim_edge = os.path.join("$NETWORK_DIR", simulation_structure, f"{simulation_structure}_edges.hdf5")
+        sim_edge_type = os.path.join("$NETWORK_DIR", simulation_structure, f"{simulation_structure}_edge_types.csv")
+
+        # We create a list where first element contains the simulated node,
+        # subsequent elements are the inputs (can be more than one)
+
+        node_files = [OrderedDict([("nodes_file", sim_node),
+                                  ("node_types_file", sim_node_type)])]
+        node_files += [OrderedDict([("nodes_file", os.path.join("$NETWORK_DIR", x, f"{x}_nodes.hdf5")),
+                                   ("node_types_file", os.path.join("$NETWORK_DIR", x, f"{x}_node_types.csv"))])
+                       for x in input_structures]
+
+        edge_files = [OrderedDict([("edges_file", sim_edge), ("edge_types_file", sim_edge_type)])]
+        edge_files += [OrderedDict([("edges_file", os.path.join("$NETWORK_DIR", x, f"{x}_edges.hdf5")),
+                                    ("edge_types_file", os.path.join("$NETWORK_DIR", x, f"{x}_edge_types.csv"))])
+                       for x in input_structures]
+
+        config["networks"] = OrderedDict([("nodes", node_files),
+                                          ("edges", edge_files)])
+
+        with open(os.path.join(base_dir, out_file), 'wt') as f:
+            json.dump(config, f, indent=4)
+
+    ############################################################################
+
+    # nodeID - vector with IDs for all neurons
+    # nodeTypeID - vector with Type ID for each neuron
+    # nodeGroupID - vector with group memebership for each neuron
+    # nodeGroupIndex - vector with index of each neuron within the given group
+
+    # data = dictionary with "position", "rotaton_angle_zaxis", ...
+    #        etc that should be stored
+    #        names should match what is in the group data
+    def write_nodes(self,
+                    node_file,
+                    population_name,
+                    node_id,
+                    node_type_id,
+                    node_group_id,
+                    node_group_index,
+                    data,
+                    model_type=None,
+                    model_template=None):
+
+        with h5py.File(os.path.join(self.network_dir, node_file), 'w', libver=self.h5py_libver) as f:
+
+            n_group = f.create_group("nodes")
+
+            nodes_group = n_group.create_group(population_name)
+
+            nodes_group.create_dataset("node_id", data=node_id)
+            nodes_group.create_dataset("node_type_id", data=node_type_id)
+            nodes_group.create_dataset("node_group_id", data=node_group_id)
+            nodes_group.create_dataset("node_group_index", data=node_group_index)
+
+            groups = np.unique(node_group_id)
+
+            for g in groups:
+                idx = np.where(node_group_id == g)
+
+                group_group = nodes_group.create_group(str(g))
+                # dynGroup = groupGroup.create_group("dynamics_params")
+
+                for data_type in data.keys():
+                    # This assumes all our data is matrices
+                    # if there are string data, we need to handle that separately
+                    if len(data[data_type].shape) == 1:
+                        group_group.create_dataset(data_type, data=data[data_type][idx])
+                    elif len(data[data_type].shape) == 2:
+                        group_group.create_dataset(data_type, data=data[data_type][idx, :])
+                    else:
+                        print("Data has too many columns, require 1 or 2 columns max.")
+
+                # These are optional, would overwrite the defaults in the CSV file
+                if model_type is not None and model_type != [None]:
+                    # If this line fails, try to use .encode() on each element in the list
+                    str_type = f"S{max([len(x) if x is not None else 1 for x in model_type[idx]])}"
+
+                    nodes_group.create_dataset("model_type", (len(idx),), str_type,
+                                               data=model_type[idx],
+                                               compression="gzip")
+
+                if model_template is not None and model_template != [None]:
+                    assert model_type is not None, "model_type must be set if model_template set"
+
+                    str_type2 = f"S{max([len(x) for x in model_template[idx]])}"
+                    nodes_group.create_dataset("model_template", (len(idx),), str_type2, data=model_template[idx])
+
+                # model_type_id should be added here also
+
+    ############################################################################
+
+    # node_csv_file is the name of the file that will be stored in the networks dir
+    # node_type_id is the node type ID for each row
+    # data is a dictionary, where each key corresponds to a column, and it must
+    # contain a list of len(nodeTypeID).
+
+    def write_node_csv(self, node_csv_file, node_type_id, data):
+
+        with open(os.path.join(self.network_dir, node_csv_file), 'wt') as csv_file:
+
+            # csvFile.write("# First column is node_type_id, remaining columns specify data. First row is header.\n")
+
+            csv_writer = csv.writer(csv_file, delimiter=" ")
+
+            # Write header
+            row = ['node_type_id']
+            for k in data:
+                if type(k) == bytes:
+                    row.append(k.decode())
+                else:
+                    row.append(k)
+
+            csv_writer.writerow(row)
+
+            # Write data to csv
+            for (i, typeID) in enumerate(node_type_id):
+                row = [typeID]
+
+                for v in data.values():
+                    if type(v[i]) == bytes:
+                        row.append(v[i].decode())
+                    else:
+                        row.append(v[i])
+
+                csv_writer.writerow(row)
+
+    ############################################################################
+
+    # !!! WHAT HAPPENS IF WE CANT KEEP EVERYTHING IN MEMORY
+
+    def write_edges(self, edge_file,
+                    edge_population_name,
+                    edge_group,
+                    edge_group_index,
+                    edge_type_id,
+                    source_id,
+                    target_id,
+                    data,
+                    source_population_name=None,
+                    target_population_name=None, ):
+
+        if source_population_name is None:
+            source_population_name = edge_population_name
+            print(f"No source population name given, using edge_population_name: {edge_population_name}")
+
+        if target_population_name is None:
+            target_population_name = edge_population_name
+            print(f"No source population name given, using edgePopulationName: {edge_population_name}")
+
+            # We need to have the targetID vector sorted
+        # How should we handle cells that do not have any edges at all?
+        sort_idx = np.argsort(target_id)
+
+        # This finds the positions where the sorter targetID increases
+        all_diffs = np.diff(target_id[sort_idx])
+        diff_idx = np.where(all_diffs > 0)[0]
+        index_pointer = np.zeros(len(diff_idx) + 1)
+        index_pointer[1:] = diff_idx + 1
+
+        # import pdb
+        # pdb.set_trace()
+
+        # We assume all neurons has at least one synapse, the diff should
+        # never jump by two
+        if not (all_diffs < 2).all():
+            print("!!! Not all neurons have synapses!")
+
+        # EVERYTHING we write needs to use sortIdx so it is in the right order
+        with h5py.File(os.path.join(self.network_dir, edge_file), 'w', libver=self.h5py_libver) as f:
+            edg_group = f.create_group("edges")
+            e_group = edg_group.create_group(edge_population_name)
+
+            e_group.create_dataset("edge_group", data=edge_group[sort_idx])
+            e_group.create_dataset("edge_group_index", data=edge_group_index[sort_idx])
+            e_group.create_dataset("edge_type_id", data=edge_type_id[sort_idx])
+            e_group.create_dataset("index_pointer", data=index_pointer)
+            e_group.create_dataset("source_node_id", data=source_id[sort_idx])
+            e_group.create_dataset("target_node_id", data=target_id[sort_idx])
+
+            e_group["source_node_id"].attrs["node_population"] = source_population_name
+            e_group["target_node_id"].attrs["node_population"] = target_population_name
+            groups = np.unique(edge_group)
+
+            for g in groups:
+                idx = np.where(edge_group[sort_idx] == g)
+
+                group_group = e_group.create_group(str(g))
+
+                for data_type in data.keys():
+                    if len(data[data_type].shape) == 1:
+                        group_group.create_dataset(data_type,
+                                                   data=data[data_type][sort_idx][idx])
+                    elif len(data[data_type].shape) == 2:
+                        group_group.create_dataset(data_type,
+                                                   data=data[data_type][sort_idx, :][idx, :])
+                    else:
+                        print("Unsupported width of data column.")
+
+            # We need to create the indices needed by Allen Institute
+            self.create_index(e_group["source_node_id"], e_group, index_source=True)
+            self.create_index(e_group["target_node_id"], e_group, index_source=False)
+            # inGroup = eGroup.create_group("indices")
+            # inGroup.create_group("source_to_target")
+            # inGroup.create_group("target_to_source")
+
+    ############################################################################
+
+    # createIndex provided by Kael Dai from Allen Institute, 2018-11-27
+
+    def create_index(self, node_ids_ds, output_grp, index_source=0):
+
+        if not index_source:
+            edge_nodes = np.array(node_ids_ds, dtype=np.int64)
+            output_grp = output_grp.create_group('indices/target_to_source')
+        else:
+            edge_nodes = np.array(node_ids_ds, dtype=np.int64)
+            output_grp = output_grp.create_group('indices/source_to_target')
+
+        edge_nodes = np.append(edge_nodes, [-1])
+        n_targets = np.max(edge_nodes)
+        ranges_list = [[] for _ in range(n_targets + 1)]
+
+        n_ranges = 0
+        begin_index = 0
+        cur_trg = edge_nodes[begin_index]
+        for end_index, trg_gid in enumerate(edge_nodes):
+            if cur_trg != trg_gid:
+                ranges_list[cur_trg].append((begin_index, end_index))
+                cur_trg = int(trg_gid)
+                begin_index = end_index
+                n_ranges += 1
+
+        node_id_to_range = np.zeros((n_targets + 1, 2))
+        range_to_edge_id = np.zeros((n_ranges, 2))
+        range_index = 0
+        for node_index, trg_ranges in enumerate(ranges_list):
+            if len(trg_ranges) > 0:
+                node_id_to_range[node_index, 0] = range_index
+                for r in trg_ranges:
+                    range_to_edge_id[range_index, :] = r
+                    range_index += 1
+                node_id_to_range[node_index, 1] = range_index
+
+        output_grp.create_dataset('range_to_edge_id', data=range_to_edge_id, dtype='uint64')
+        output_grp.create_dataset('node_id_to_ranges', data=node_id_to_range, dtype='uint64')
+
+    ############################################################################
+
+    def write_edges_csv(self,
+                        edge_csv_file,
+                        edge_type_id,
+                        data):
+
+        with open(os.path.join(self.network_dir, edge_csv_file), 'wt') as csv_file:
+
+            # csvFile.write("# First column is edge_type_id, remaining columns specify data. First row is header.\n")
+
+            csv_writer = csv.writer(csv_file, delimiter=" ")
+
+            # Write header
+            row = ['edge_type_id']
+            for k in data:
+                if type(k) == bytes:
+                    row.append(k.decode())
+                else:
+                    row.append(k)
+
+            csv_writer.writerow(row)
+
+            # Write data to csv
+            for (i, typeID) in enumerate(edge_type_id):
+                row = [typeID]
+
+                for v in data.values():
+                    if type(v[i]) == bytes:
+                        row.append(v[i].decode())
+                    else:
+                        row.append(v[i])
+
+                csv_writer.writerow(row)
+
+    ############################################################################
+
+    def write_input(self, spike_file_name, spikes):
+
+        if spikes is None:
+            print(f"No spikes specified, not writing {spike_file_name}")
+            print("Use python3 Network_input.py yourinput.json yournetwork.hdf5 input-spikes.hdf5")
+            return
+
+        f_name = os.path.join(self.base_dir, spike_file_name)
+
+        with h5py.File(f_name, 'w', libver=self.h5py_libver) as f:
+            print(f"Writing file {f_name}")
+
+            s_group = f.create_group("spikes")
+            s_group.create_dataset("gids", data=spikes[:, 1])
+            s_group.create_dataset("timestamps", data=spikes[:, 0])
+
+    ############################################################################
+
+    # OBS, check that the files go in right directory
+
+    def write_input_neurodamus(self, spike_file_name, spikes):
+
+        # File standard is NWB, info about the neurodata without borders api:
+        # http://neurodatawithoutborders.github.io/api-python/build/html/index.html
+
+        # However, we use the simpler data file format:
+        # with h5py.File(self.networkDir + fileNWB, 'w', libver=self.h5pyLibver) as f:
+        #   print("Work in progress !!!")
+
+        # with open(self.networkDir + spikeFileName,'wt') as f:
+        with open(spike_file_name, 'wt') as f:
+            f.write("/scatter")
+
+            for time, gid in spikes:
+                f.write(f"{time} {gid}")
+
+    ############################################################################
+
+
+if __name__ == "__main__":
+    # ch = ConvHurt()
+    # ch = ConvHurt(simulationStructure="cerebellum",
+    #              inputStructures=["pons","cortex"])
+
+    ch = ConvHurt(simulation_structure="striatum",
+                  input_structures=["cortex", "thalamus"])
+
+    # Test example, we have 5 neurons, big network
+    # two groups
+
+    node_data = {"positions": np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.],
+                                        [1., 8., 9.], [2., 3., 2.]]),
+                 "rotation_angle_zaxis": np.array([0.1, 0.2, 0.3, 0.4, 0.5])}
+
+    ch.write_nodes(node_file='striatum_nodes.hdf5',
+                   data=node_data,
+                   node_id=np.array([0, 1, 2, 3, 4]),
+                   population_name="striatum_nodes",
+                   node_type_id=np.array([0, 1, 0, 1, 0]),
+                   node_group_id=np.array([0, 0, 1, 1, 1]),
+                   node_group_index=np.array([0, 1, 0, 1, 2]))
+
+    node_type_id = np.array([0, 1])
+    node_data_csv = OrderedDict([('name', ['A', 'B']),
+                                 ('location', ['structA', 'structB'])])
+
+    ch.write_node_csv(node_csv_file='striatum_node_types.csv',
+                      node_type_id=node_type_id,
+                      data=node_data_csv)
+
+    edge_group = np.array([5, 5, 11, 11, 11])
+    edge_group_index = np.array([0, 1, 0, 1, 2])
+    edge_type_id = np.array([0, 1, 0, 1, 0])
+    source_gid = np.array([1, 2, 3, 3, 4])
+    target_gid = np.array([2, 3, 4, 0, 1])  # THESE ARE SORTED ... HAHAHA
+
+    # Delay should be in ms (bad bad people, real scientists use SI units)
+    edge_data = OrderedDict([("sec_id", np.array([10, 22, 33, 24, 15])),
+                             ("sec_x", np.array([0.1, 0.3, 0.5, 0.2, 0])),
+                             ("syn_weight", np.array([0.1e-9, 2e-9, 3e-9,
+                                                     0.3e-9, 0.1e-9])),
+                             ("delay", 1e3 * np.array([1e-3, 4e-3, 2e-3, 5e-3, 1e-3]))])
+
+    ch.write_edges(edge_file="striatum_edges.hdf5",
+                   edge_group=edge_group,
+                   edge_group_index=edge_group_index,
+                   edge_type_id=edge_type_id,
+                   edge_population_name="striatum_edges",
+                   source_id=source_gid,
+                   target_id=target_gid,
+                   data=edge_data)
+
+    edge_type_id = np.array([0, 1])
+    edge_csv_data = OrderedDict([('template', ['Exp2Syn', 'NULL']),
+                                 ('dynamics_params', ["mysyn.json", 'yoursyn.json'])])
+
+    ch.write_edges_csv(edge_csv_file="striatum_edge_types.csv",
+                       edge_type_id=edge_type_id,
+                       data=edge_csv_data)
diff --git a/snudda/utils/export_sonata.py b/snudda/utils/export_sonata.py
new file mode 100644
index 000000000..f56ef425b
--- /dev/null
+++ b/snudda/utils/export_sonata.py
@@ -0,0 +1,1024 @@
+# This uses ConvHurt to convert the network created by Network_connect.py
+
+#
+# TODO:
+# - Check what axon and dendrite propagation speeds should be
+#
+
+import sys
+import json
+from collections import OrderedDict
+import numpy as np
+import h5py
+import os
+
+from conv_hurt import ConvHurt
+from load import SnuddaLoad
+
+
+class ExportSonata(object):
+
+    def __init__(self, network_file, input_file, out_dir):
+
+        # !!! If inputFile is last, we need to handle that appropriately
+        # and find the corresponding input file
+
+        self.debug = False
+
+        self.out_dir = out_dir
+
+        # Read the data
+        nl = SnuddaLoad(network_file)
+        self.network_file = nl.network_file  # If file was "last", this sets right one
+
+        if input_file == "last":
+            self.input_file = os.path.join("save", f"input-spikes-{nl.data['SlurmID']}.hdf5")
+        else:
+            self.input_file = input_file
+
+        print(f"Using input file: {self.input_file}")
+
+        # This contains data for converting to Neurodamus secID and secX
+        self.morph_cache = dict([])
+
+        # Write the data in new format using the ConvHurt module
+        ch = ConvHurt(simulation_structure="Striatum",
+                      input_structures=["Cortex", "Thalamus"],
+                      base_dir=out_dir)
+
+        self.copy_morphologies(nl)
+        self.copy_mechanisms()
+        self.copy_hoc(nl)
+
+        # Convert our rotation matrices to vx,vy,vz rotation angles
+        # Neurodamus rotation order is first Z then Y and X.
+        (vx, vy, vz) = self.rot_angles_zyx(nl)
+
+        # Node data is stored in a HDF5 file and a CSV file (CONVERT TO micrometers)
+        node_data = OrderedDict([("x", nl.data["neuronPositions"][:, 0] * 1e6),
+                                 ("y", nl.data["neuronPositions"][:, 1] * 1e6),
+                                 ("z", nl.data["neuronPositions"][:, 2] * 1e6),
+                                 ("rotation_angle_zaxis", vz),
+                                 ("rotation_angle_yaxis", vy),
+                                 ("rotation_angle_xaxis", vx)])
+
+        structure_name = "Striatum"
+
+        # We need to convert the named neuron types to numbers
+        (node_type_id, node_type_i_dlookup) = self.allocate_node_type_id(nl)
+        (node_group_id, group_idx, group_lookup) = self.allocate_node_groups(nl)
+
+        node_id_list = np.array([x["neuronID"] for x in nl.data["neurons"] if x["volumeID"] == "Striatum"], dtype=int)
+
+        ch.write_nodes(node_file='Striatum/Striatum_nodes.hdf5',
+                       population_name=structure_name,
+                       data=node_data,
+                       node_id=node_id_list,
+                       node_type_id=node_type_id[node_id_list],
+                       node_group_id=node_group_id[node_id_list],
+                       node_group_index=group_idx[node_id_list])
+
+        striatum_node_names = [n["name"] for n in nl.data["neurons"] \
+                               if n["volumeID"] == structure_name]
+
+        # This defines the order
+        csv_node_name = [k for k in node_type_i_dlookup if k in striatum_node_names]
+
+        csv_node_type_id = [node_type_i_dlookup[n] for n in csv_node_name]
+        csv_node_location = ['Striatum' for n in csv_node_name]
+
+        (model_template_list, model_type_list, morphology_list) = self.get_node_templates(nl, csv_node_name)
+
+        node_data_csv = OrderedDict([("model_type", model_type_list),
+                                     ("model_template", model_template_list),
+                                     ("location", csv_node_location),
+                                     ("morphology", morphology_list),
+                                     ("model_name", csv_node_name)])
+
+        ch.write_node_csv(node_csv_file='Striatum/Striatum_node_types.csv',
+                          node_type_id=csv_node_type_id,
+                          data=node_data_csv)
+
+        # Edge data is stored in a HDF5 file and a CSV file
+        edge_group_lookup = self.allocate_edge_groups(nl)
+
+        # Here we list all the synapses we might want
+        edge_type_lookup = {("MSD1", "MSD1"): 1,
+                            ("MSD1", "MSD2"): 2,
+                            ("MSD2", "MSD1"): 3,
+                            ("MSD2", "MSD2"): 4,
+                            ("FSN", "MSD1"): 5,
+                            ("FSN", "MSD2"): 6,
+                            ("ChIN", "MSD1"): 7,
+                            ("ChIN", "MSD2"): 8,
+                            ("FSN", "FSN"): 9,
+                            ("MSD1", "FSN"): 10,
+                            ("MSD2", "FSN"): 11,
+                            ("MSD1", "ChIN"): 12,
+                            ("MSD2", "ChIN"): 13,
+                            ("FSN", "ChIN"): 14,
+                            ("ChIN", "FSN"): 15,
+                            ("ThalamusAxon", "MSD1"): 16,
+                            ("ThalamusAxon", "MSD2"): 17,
+                            ("ThalamusAxon", "FSN"): 18,
+                            ("ThalamusAxon", "ChIN"): 19,
+                            ("CortexAxon", "MSD1"): 20,
+                            ("CortexAxon", "MSD2"): 21,
+                            ("CortexAxon", "FSN"): 22,
+                            ("CortexAxon", "ChIN"): 23}
+
+        edge_type_id = np.arange(1, 24)
+
+        # These should match edgeTypeLookup
+        edge_csv_data = OrderedDict([('template',
+                                     ['MSGABA',  # 1
+                                      'MSGABA',  # 2
+                                      'MSGABA',  # 3
+                                      'MSGABA',  # 4
+                                      'FSGABA',  # 5
+                                      'FSGABA',  # 6
+                                      'AChSyn',  # 7
+                                      'AChSyn',  # 8
+                                      'FSGABA',  # 9
+                                      'MSGABA',  # 10
+                                      'MSGABA',  # 11
+                                      'MSGABA',  # 12
+                                      'MSGABA',  # 13
+                                      'FSGABA',  # 14
+                                      'AChSyn',  # 15
+                                      'ThalamusGlu',  # 16
+                                      'ThalamusGlu',  # 17
+                                      'ThalamusGlu',  # 18
+                                      'ThalamusGlu',  # 19
+                                      'CortexGlu',  # 20
+                                      'CortexGlu',  # 21
+                                      'CortexGlu',  # 22
+                                      'CortexGlu',  # 23
+                                      ])])
+
+        ch.write_edges_csv(edge_csv_file="Striatum/Striatum_edge_types.csv",
+                           edge_type_id=edge_type_id,
+                           data=edge_csv_data)
+
+        (edge_group, edge_group_index, edge_type_id, source_gid, target_gid, edgeData) \
+            = self.setup_edge_info(nl, edge_group_lookup, edge_type_lookup)
+
+        ch.write_edges(edge_file="Striatum/Striatum_edges.hdf5",
+                       edge_population_name="Striatum",
+                       edge_group=edge_group,
+                       edge_group_index=edge_group_index,
+                       edge_type_id=edge_type_id,
+                       source_id=source_gid,
+                       target_id=target_gid,
+                       data=edgeData)
+
+        # Next we need to setup virtual nodes for the cortical and thalamic input
+
+        # !!! Change position to real position?!
+        cortex_node_data = OrderedDict([("x", np.zeros((10,))),
+                                        ("y", np.zeros((10,))),
+                                        ("z", np.zeros((10,)))])
+
+        cortex_gid = [x["neuronID"] for x in nl.data["neurons"] if x["type"] == "CortexAxon"]
+        cortex_name = [x["name"] for x in nl.data["neurons"] if x["type"] == "CortexAxon"]
+
+        # !!! Check this correct
+        cortex_node_type_id = [node_type_i_dlookup[x] for x in cortex_name]
+        try:
+            cortex_node_group_id = [group_lookup["CortexAxon"] for x in range(0, len(cortex_gid))]
+        except:
+            import traceback
+            tstr = traceback.format_exc()
+            print(tstr)
+            import pdb
+            pdb.set_trace()
+
+        cortex_node_group_index = np.arange(0, len(cortex_node_group_id), dtype=int)
+        # cortexModelTypeList = [None for x in range(0,len(cortexNodeGroupID))]
+        # cortexModelTemplateList = [None for x in range(0,len(cortexNodeGroupID))]
+
+        cortex_input = self.sort_input(nl, node_type_i_dlookup, input_name="CortexAxon")
+
+        ch.write_nodes(node_file="Cortex/Cortex_nodes.hdf5",
+                       population_name="Cortex",
+                       data=cortex_node_data,
+                       node_id=cortex_gid,
+                       node_type_id=cortex_node_type_id,
+                       node_group_id=cortex_node_group_id,
+                       node_group_index=cortex_node_group_index)
+        # modelType=cortexModelTypeList,
+        # modelTemplate=cortexModelTemplateList)
+
+        # !!! WRITE CORTEX EDGES
+
+        if False:
+            ch.write_edges(edge_file="Cortex/Cortex_edges.hdf5",
+                           edge_population_name="Cortex",
+                           edge_group=edgeGroupCortex,
+                           edge_type_id=edgeTypeIDCortex,
+                           source_id=sourceGIDCortex,
+                           targetIT=targetGIDCortex,
+                           data=edgeDataCortex)
+
+        # Same for Thalamus
+
+        thalamus_gid = [x["neuronID"] for x in nl.data["neurons"] if x["type"] == "ThalamusAxon"]
+
+        thalamus_name = [x["name"] for x in nl.data["neurons"] if x["type"] == "ThalamusAxon"]
+
+        # !!! Check this correct
+        thalamus_node_type_id = [node_type_i_dlookup[x] for x in thalamus_name]
+        thalamus_node_group_id = [group_lookup["ThalamusAxon"] for x in range(0, len(thalamus_gid))]
+        thalamus_node_group_index = np.arange(0, len(thalamus_node_group_id), dtype=int)
+        # thalamusModelTypeList = [None for x in range(0,len(thalamusNodeGroupID))]
+        # thalamusModelTemplateList =[None for x in range(0,len(thalamusNodeGroupID))]
+
+        # !!! Change to real thalamus positions
+        thalamus_node_data = OrderedDict([("x", np.zeros((10,))),
+                                          ("y", np.zeros((10,))),
+                                          ("z", np.zeros((10,)))])
+
+        thalamus_input = self.sort_input(nl, node_type_i_dlookup, input_name="ThalamusAxon")
+
+        ch.write_nodes(node_file="Thalamus/Thalamus_nodes.hdf5",
+                       population_name="Thalamus",
+                       data=thalamus_node_data,
+                       node_id=thalamus_gid,
+                       node_type_id=thalamus_node_type_id,
+                       node_group_id=thalamus_node_group_id,
+                       node_group_index=thalamus_node_group_index)
+        # modelType=thalamusModelTypeList,
+        # modelTemplate=thalamusModelTemplateList)
+
+        # !!! WRITE THALAMUS EDGES
+
+        ch.write_input(spike_file_name="inputs/cortexInput.hdf5", spikes=cortex_input)
+        ch.write_input(spike_file_name="inputs/thalamusInput.hdf5", spikes=thalamus_input)
+
+        # !!! WE ALSO NEED TO ADD BACKGROUND INPUT TO THE NEURONS IN THE NETWORK
+
+        self.write_simulation_config()
+
+        print("SONATA files exported to " + str(out_dir))
+
+    ############################################################################
+
+    def rot_mat_to_angles_xyz(self, rot_mat):
+        # http://nghiaho.com/?page_id=846
+
+        # This is for Rz*Ry*Rx
+        vx = np.arctan2(rot_mat[2, 1], rot_mat[2, 2])
+        vy = np.arctan2(-rot_mat[2, 0], np.sqrt(rot_mat[2, 1] ** 2 + rot_mat[2, 2] ** 2))
+        vz = np.arctan2(rot_mat[1, 0], rot_mat[0, 0])
+
+        # !! We need to do this for Rx*Ry*Rz
+        assert False
+
+        return vx, vy, vz
+
+    ############################################################################
+
+    def rot_mat_to_angles_zyx(self, rotMat):
+        vz = np.arctan2(-rotMat[0, 1], rotMat[0, 0])
+        vy = np.arcsin(rotMat[0, 2])
+        vx = np.arctan2(-rotMat[1, 2], rotMat[2, 2])
+
+        return vx, vy, vz
+
+    ############################################################################
+
+    def angles_to_rot_mat(self, v):
+        (vx, vy, vz) = v
+        RX = np.array([[1., 0., 0.],
+                       [0., np.cos(vx), -np.sin(vx)],
+                       [0., np.sin(vx), np.cos(vx)]])
+
+        RY = np.array([[np.cos(vy), 0., np.sin(vy)],
+                       [0., 1., 0.],
+                       [-np.sin(vy), 0, np.cos(vy)]])
+
+        RZ = np.array([[np.cos(vz), -np.sin(vz), 0.],
+                       [np.sin(vz), np.cos(vz), 0.],
+                       [0., 0., 1.]])
+
+        return np.matmul(RX, np.matmul(RY, RZ))
+        # return np.matmul(np.matmul(RZ,RY),RX)
+
+    ############################################################################
+
+    def rot_angles_zyx(self, nl):
+
+        x = np.zeros(len(nl.data["neurons"]))
+        y = np.zeros(len(nl.data["neurons"]))
+        z = np.zeros(len(nl.data["neurons"]))
+
+        for idx in range(0, len(nl.data["neurons"])):
+            ang = self.rot_mat_to_angles_zyx(nl.data["neurons"][idx]["rotation"])
+
+            # Just verify that we calculated the angles right and can get old
+            # rotation matrix back
+            assert (np.sum(np.abs(self.angles_to_rot_mat(ang) - nl.data["neurons"][idx]["rotation"])) < 1e-9)
+            # Save angles
+            x[idx] = ang[0]
+            y[idx] = ang[1]
+            z[idx] = ang[2]
+
+        return x, y, z
+
+    ############################################################################
+
+    def allocate_node_type_id(self, nl):
+
+        node_type_id_lookup = OrderedDict([])
+        next_node_type_id = 0
+
+        node_names = [n["name"] for n in nl.data["neurons"]]
+
+        for n in node_names:
+            if n not in node_type_id_lookup:
+                node_type_id_lookup[n] = next_node_type_id
+                next_node_type_id += 1
+
+        node_type_id = np.array([node_type_id_lookup[x] for x in node_names], dtype=int)
+
+        return node_type_id, node_type_id_lookup
+
+    ############################################################################
+
+    # RT neuron only supports one group, so put everything int he same group
+
+    def allocate_node_groups(self, nl):
+
+        node_group_id = np.zeros(len(nl.data["neurons"]), dtype=int)
+        group_idx = np.arange(0, len(nl.data["neurons"]))
+        group_lookup = dict([])
+
+        neuron_types = [nl.data["neurons"][idx]["type"] for idx in range(0, len(nl.data["neurons"]))]
+
+        for g in neuron_types:
+            if g not in group_lookup:
+                group_lookup[g] = 0
+
+        return node_group_id, group_idx, group_lookup
+
+        ############################################################################
+
+    # So there is a bit of a confusion with the namings. For the neurons in the
+    # network "name" corresponds to SONATA NodeType, while "type" corresponds
+    # to NodeGroup.
+
+    def allocate_node_groups_OLD(self, nl):
+        group_lookup = OrderedDict([])
+        next_group = 0
+
+        group_names = [nl.data["neurons"][idx]["type"] for idx in range(0, len(nl.data["neurons"]))]
+
+        for g in group_names:
+            if g not in group_lookup:
+                group_lookup[g] = next_group
+                next_group += 1
+
+        node_group_id = [group_lookup[g] for g in group_names]
+
+        next_idx = np.zeros(len(group_lookup.keys()), dtype=int)
+
+        group_idx = np.zeros(len(group_names), dtype=int)
+
+        for ig, g in enumerate(group_names):
+            group_idx[ig] = next_idx[group_lookup[g]]
+            next_idx[group_lookup[g]] += 1
+
+        return node_group_id, group_idx, group_lookup
+
+    ############################################################################
+
+    def get_node_templates(self, nl, csv_node_name):
+
+        template_dict = dict([])
+        model_type_dict = dict([])
+        morphology_dict = dict([])
+        missing_list = []
+
+        # First create a lookup
+        for n in nl.data["neurons"]:
+            hoc_file = n["hoc"]
+            name = n["name"]
+            # It seems that RT neuron requires the filename without .swc at the end
+            # Also, they prepend morphology path, but only allows one directory for all morphologies
+
+            morph_file = os.path.splitext(os.path.basename(n["morphology"]))[0]
+
+            if hoc_file in self.hoc_location_lookup:
+                # We need to change from old hoc location to the SONATA hoc location
+                hoc_str = "hoc:" + os.path.basename(self.hoc_location_lookup[hoc_file])
+            elif n["virtualNeuron"]:
+                hoc_str = "virtual"
+            else:
+                hoc_str = "NULL"
+                if hoc_file not in missing_list:
+                    # Only write error ones per file
+                    print(f"Missing hoc template: {hoc_file}")
+                    missing_list.append(hoc_file)
+
+            if name not in morphology_dict:
+                morphology_dict[name] = morph_file
+            else:
+                assert morphology_dict[name] == morph_file, \
+                    f"Morphology mismatch for {name}: {morphology_dict[name]} vs {morph_file}"
+
+            if name not in template_dict:
+                template_dict[name] = hoc_str
+                model_type_dict[name] = "biophysical"
+            else:
+                assert template_dict[name] == hoc_str, \
+                    f"All files named {name} do not share same hoc file: {template_dict[name]} and {hoc_str}"
+
+        # Need to put them in the order in csvNodeName
+
+        template_list = [template_dict[x] for x in csv_node_name]
+        model_type_list = [model_type_dict[x] for x in csv_node_name]
+        morph_list = [morphology_dict[x] for x in csv_node_name]
+
+        # import pdb
+        # pdb.set_trace()
+
+        return template_list, model_type_list, morph_list
+
+    ############################################################################
+
+    def get_node_templates_for_all(self, nl):
+
+        print("This should return all the types of neurons, eg. FNS_0,FSN_1, ..., MSD1_0, ...")
+        print("This is fewer than the number of neurons. Check CSV file to make sure it is ok after")
+
+        import pdb
+        pdb.set_trace()
+
+        template_list = []
+        model_type_list = []
+        missing_list = []
+
+        for neuron in nl.data["neurons"]:
+            hoc_file = neuron["hoc"]
+
+            model_type_list.append("biophysical".encode())
+
+            if hoc_file in self.hoc_location_lookup:
+                hoc_str = f"hoc:{self.hoc_location_lookup[hoc_file]}"
+                template_list.append(hoc_str.encode())
+            else:
+                template_list.append("".encode())
+                if hoc_file not in missing_list:
+                    # Only write error ones per file
+                    print(f"Missing hoc template: {hoc_file}")
+                    missing_list.append(hoc_file)
+
+        return template_list, model_type_list
+
+    ############################################################################
+
+    # !!! A lot of HBP tools only support the first group, so put all in group 0
+    # The old code makes separate groups
+
+    def allocate_edge_groups(self, nl):
+
+        # FS->MSD1, FS->MSD2 etc are example of EdgeGroups
+        edge_group_lookup = OrderedDict([])
+
+        unique_group_names = set([nl.data["neurons"][idx]["name"].split("_")[0]
+                                  for idx in range(0, len(nl.data["neurons"]))])
+
+        for g1 in unique_group_names:
+            for g2 in unique_group_names:
+                edge_group_lookup[g1, g2] = 0
+
+        return edge_group_lookup
+
+    ############################################################################
+
+    # !!! This is old version, that makes separate groups for different sets
+    # of synapses, but HBP tools only support first group currently, Jan 2019.
+    # so made another version of allocateEdgeGroups (see above).
+
+    def allocate_edge_groups_OLD(self, nl):
+
+        # FS->MSD1, FS->MSD2 etc are example of EdgeGroups
+        edge_group_lookup = OrderedDict([])
+
+        unique_group_names = set([nl.data["neurons"][idx]["name"].split("_")[0]
+                                  for idx in range(0, len(nl.data["neurons"]))])
+
+        next_group = 0
+
+        for g1 in unique_group_names:
+            for g2 in unique_group_names:
+                edge_group_lookup[g1, g2] = next_group
+                next_group += 1
+
+        return edge_group_lookup
+
+    ############################################################################
+
+    # This code sets up the info about edges
+
+    def setup_edge_info(self, nl, edge_group_lookup, edge_type_lookup):
+
+        n_synapses = nl.data["synapses"].shape[0]
+        edge_group = np.zeros(n_synapses, dtype=int)
+        edge_group_index = np.zeros(n_synapses, dtype=int)
+        edge_type_id = np.zeros(n_synapses, dtype=int)
+        source_gid = np.zeros(n_synapses, dtype=int)
+        target_gid = np.zeros(n_synapses, dtype=int)
+
+        sec_id = np.zeros(n_synapses, dtype=int)
+        sec_x = np.zeros(n_synapses)
+        syn_weight = np.zeros(n_synapses)
+        delay = np.zeros(n_synapses)
+
+        edge_group_count = np.zeros(len(edge_group_lookup.keys()))
+
+        # Check if these speeds are reasonable?
+        axon_speed = 25.0  # 25m/s
+        dend_speed = 1.0
+
+        # columns in nl.data["synapses"]
+        # 0: sourceCellID, 1: sourceComp, 2: destCellID, 3: destComp,
+        # 4: locType, 5: synapseType, 6: somaDistDend 7:somaDistAxon
+        # somaDist is an int, representing micrometers
+
+        for i_syn, syn_row in enumerate(nl.data["synapses"]):
+            source_gid[i_syn] = syn_row[0]
+            target_gid[i_syn] = syn_row[1]
+            sec_id[i_syn] = syn_row[9]
+            sec_x[i_syn] = syn_row[10] / 1000.0
+
+            pre_type = nl.data["neurons"][source_gid[i_syn]]["type"]
+            post_type = nl.data["neurons"][target_gid[i_syn]]["type"]
+
+            edge_group[i_syn] = edge_group_lookup[pre_type, post_type]
+            edge_type_id[i_syn] = edge_type_lookup[pre_type, post_type]
+
+            edge_group_index[i_syn] = edge_group_count[edge_group[i_syn]]
+            edge_group_count[edge_group[i_syn]] += 1
+
+            dend_dist = syn_row[6] * 1e-6
+            axon_dist = syn_row[7] * 1e-6
+            delay[i_syn] = axon_dist / axon_speed + dend_dist / dend_speed
+            syn_weight[i_syn] = 1.0  # !!! THIS NEEDS TO BE SET DEPENDING ON CONNECTION TYPE
+
+        edge_data = OrderedDict([("sec_id", sec_id),
+                                 ("sec_x", sec_x),
+                                 ("syn_weight", syn_weight),
+                                 ("delay", delay)])
+
+        # Need to set edgeGroup, edgeGroupIndex, edgeType also
+
+        return (edge_group, edge_group_index, edge_type_id,
+                source_gid, target_gid, edge_data)
+
+    ############################################################################
+
+    # Convert synapse coordinates (in SWC reference frame) to SectionID
+    # and section_X (fractional distance along dendritic section)
+
+    def conv_synapse_coord_to_section(self, cell_gid, orig_synapse_coord, network_info):
+
+        assert False, "Depricated function: convSynapseCoordToSection"
+
+        if self.debug:
+            print("Converting coordinate to sections")
+
+        cell_id = network_info.data["neurons"][cell_gid]["name"]
+
+        # Get swc filename from cellID
+        swc_file = network_info.config[cell_id]["morphology"]
+
+        # Check if cell morphology is loaded in neuron cache
+        # Columns in morph: 0:swcID, 1:x, 2:y, 3:z, 4:secID, 5:secX, 6:type
+        if swc_file in self.morph_cache:
+            morph = self.morph_cache[swc_file]
+        else:
+            morph = self.load_morph(swc_file)
+            self.morph_cache[swc_file] = morph
+
+            # VERIFICATION
+            if False:
+                self.plot_section_data(morph, swc_file)
+                import pdb
+                pdb.set_trace()
+
+        # Find points matching the coords
+        xyz = morph[:, 1:4]
+        d = np.sum((xyz - orig_synapse_coord) ** 2, axis=-1)
+
+        closest_idx = np.argsort(d)[0]
+        sec_id = int(morph[closest_idx, 4])
+        sec_x = morph[closest_idx, 5]
+
+        # Check that compartment that matched is soma or dendrite
+        try:
+            assert morph[closest_idx, 6] in [1, 3, 4], \
+                "Synapse location should be soma or dendrite"
+        except:
+            import pdb
+            pdb.set_trace()
+
+        if self.debug:
+            print("match dmin=" + str(d[closest_idx] ** 0.5))
+
+        # import pdb
+        # pdb.set_trace()
+
+        return sec_id, sec_x
+
+    ############################################################################
+
+    def convert_coordinates(self, morphology):
+
+        # Loop through entire SWC morphology, calculate SEC_ID, SEC_X
+        # for each vertex.
+
+        print("TEST TEST TEST")
+        import pdb
+        pdb.set_trace()
+
+    ############################################################################
+
+    # This code builds the secID and secX from the SWC file
+
+    def load_morph(self, swc_file, skip_axon=True):
+
+        with open(swc_file, 'r') as f:
+            lines = f.readlines()
+
+        comp_type = {1: "soma", 2: "axon", 3: "dend", 4: "apic"}
+
+        swc_vals = np.zeros(shape=(len(lines), 7))
+
+        n_comps = 0
+        for ss in lines:
+            if ss[0] != '#':
+                swc_vals[n_comps, :] = [float(s) for s in ss.split()]
+                n_comps = n_comps + 1
+
+        # Subtract 1 from ID and parentID, so we get easier indexing
+        swc_vals[:, 0] -= 1
+        swc_vals[:, 6] -= 1
+
+        swc_vals[:, 2:5] *= 1e-6  # Convert to meter
+
+        # Columns in vertex matrix:
+        # 0:ID, 1:parentID, 2:x,3:y,4:z, 5:somaDist, 6:type, 7:childCount,
+        # 8:nodeParent, 9:segLen, 10:seg_ID, 11:seg_X
+        #
+        # If childCount != 1 then vertex is a segment node
+        vertex = np.zeros(shape=(n_comps, 12))
+
+        for i in range(0, n_comps):
+            # Check that the IDs are in order, and no missing values
+            assert (swc_vals[i, 0] == i)
+
+            # Check that parent already defined
+            assert (swc_vals[i, 6] < swc_vals[i, 0])
+
+            vertex[i, 0] = swc_vals[i, 0]  # swcID
+            vertex[i, 2:5] = swc_vals[i, 2:5]  # x,y,z
+            vertex[i, 6] = swc_vals[i, 1]  # type
+
+            if swc_vals[i, 1] == 1:
+                assert (i == 0)  # Soma should be first compartment
+
+                vertex[i, 1] = -1  # parent_swcID
+                vertex[i, 5] = 0  # soma dist
+                vertex[i, 7] = 100  # Set child count > 1 for soma
+                # to make sure it is a node
+
+            elif swc_vals[i, 1] in [2, 3, 4]:
+                # axon or dend
+                parent_id = int(swc_vals[i, 6])  # parentID
+                vertex[i, 1] = parent_id
+                parent_coords = vertex[parent_id, 2:5]
+                parent_dist = vertex[parent_id, 5]
+                vertex[i, 5] = parent_dist + sum((vertex[i, 2:5] - parent_coords) ** 2) ** 0.5  # Soma dist
+
+                # if(parentID == 1):
+                #  # Soma as parent, set artificially high child count for compartment
+                #  # so it is considered a node edge
+                #  vertex[i,7] = 100
+                # else:
+                vertex[parent_id, 7] += 1  # Child count, nodes have != 1, i.e. 0 or 2,3,..
+
+        if skip_axon:
+            # Find all branch points and end points (excluding axons)
+            node_idx = np.where(np.logical_and(vertex[:, 7] != 1, vertex[:, 1] != 2))[0]
+        else:
+            # Find all branch points and end points
+            node_idx = np.where(vertex[:, 7] != 1)[0]
+
+        # Find out how branch points are connected (set node parent)
+        # OBS, parent is previous point in SWC file a point is connected to
+        # For segments, node and node parent are the two end points
+        for n_idx in node_idx:
+            parent_id = int(vertex[n_idx, 1])
+            vertex[n_idx, 8] = parent_id
+
+            while vertex[parent_id, 7] == 1:
+                # While the parent node has 1 child, and it is not soma
+                parent_id = int(vertex[parent_id, 1])
+
+                if vertex[parent_id, 6] == 1:
+                    # This is a soma, dont use it as parent
+                    break
+
+                assert (n_idx == 0 or parent_id >= 0)
+                vertex[n_idx, 8] = parent_id  # node parent
+
+        # Number the segments, start with soma which has ID 0
+        assert ((np.diff(node_idx) > 0).all())  # Check they are in right order
+
+        for sec_id, n_idx in enumerate(node_idx):
+            vertex[n_idx, 10] = sec_id
+
+        # For each connected pair of branch points (dont forget soma)
+        # calculate the total length of the segment (also note if soma,axon or dend)
+
+        for n_idx in node_idx:
+
+            if n_idx == 0:
+                vertex[n_idx, 9] = 1  # segLen (to avoid divide by zero for soma)
+            else:
+                # Difference in soma dist between node and node parent
+                parent_id = int(vertex[n_idx, 8])
+                vertex[n_idx, 9] = vertex[n_idx, 5] - vertex[parent_id, 5]  # segLen
+
+        # For each vertex in a segment, calculate the seg_X (value 0 to 1)
+        # Save seg_ID and seg_X for each vertex, its coordinates, and S/A/D type
+
+        for n_idx in node_idx:
+
+            if n_idx == 0:
+                vertex[n_idx, 11] = 0.5
+            # elif(vertex[nIdx,1] == 0):
+            #  # Soma is the parent, special case (Neuron has no compartment
+            #  # linking soma centre to first axon/dendirte node)
+            #  vertex[nIdx,11] = 0
+            else:
+                vertex[n_idx, 11] = 1  # secX
+                end_soma_dist = vertex[n_idx, 5]
+                seg_len = vertex[n_idx, 9]
+
+                parent_id = int(vertex[n_idx, 1])
+
+                while vertex[parent_id, 7] == 1:  # While the parent node has 1 child
+                    # secID
+                    vertex[parent_id, 10] = vertex[n_idx, 10]
+
+                    # secX
+                    vertex[parent_id, 11] = 1 - (end_soma_dist - vertex[parent_id, 5]) / seg_len
+                    parent_id = int(vertex[parent_id, 1])
+
+                    assert (parent_id >= 0)
+
+        # swcID, x,y,z coord, secID, secX, type
+        if skip_axon:
+            non_axon_idx = np.where(vertex[:, 6] != 2)[0]
+            return vertex[:, [0, 2, 3, 4, 10, 11, 6]][non_axon_idx, :]
+        else:
+            return vertex[:, [0, 2, 3, 4, 10, 11, 6]]
+
+    ############################################################################
+
+    # !!! PLOT TO VERIFY
+
+    def plot_section_data(self, morph, swc_file):
+        import matplotlib.pyplot as plt
+        from mpl_toolkits.mplot3d import Axes3D
+
+        print("Plotting verification of sections")
+
+        from snudda.neurons.neuron_morphology import NeuronMorphology
+        n = NeuronMorphology(swc_filename=swc_file,
+                             verbose=True)
+        ax = n.plot_neuron(plot_axon=False)
+
+        for row in morph:
+            # Plot secID and secX
+            # import pdb
+            # pdb.set_trace()
+            s = str(row[4]) + "-" + str(round(row[5], 2))
+            txt = ax.text(x=row[1], y=row[2], z=row[3], s=s, color="blue")
+            print(s + "Press key")
+            input('')
+            # txt.remove()
+
+        plt.ion()
+        plt.show()
+        plt.draw()
+        plt.pause(0.001)
+
+    ############################################################################
+
+    def write_simulation_config(self):
+
+        sim_conf = dict([])
+
+        sim_conf["manifest"] = {
+            "$BASE_DIR": ".",
+            "$NETWORK_DIR": "$BASE_DIR/networks",
+            "$COMPONENT_DIR": "$BASE_DIR/components",
+            "$INPUT_DIR": "$BASE_DIR/inputs",
+            "$OUTPUT_DIR": "$BASE_DIR/output"}
+
+        sim_conf["run"] = {
+            "tstop": 1000.0,
+            "dt": 0.1,
+            "dL": 15,
+            "spike_threshold": -15}
+
+        sim_conf["conditions"] = {}
+
+        sim_conf["output"] = {"log_file": "$OUTPUT_DIR/striatum-log.txt",
+                              "output_dir": "$OUTPUT_DIR"}
+
+        sim_conf["overwrite_output_dir"] = True
+
+        sim_conf["network"] = "$BASE_DIR/circuit_config.json"
+
+        # node_sets_file contains the reports we want written from the simulation
+        # simConf["node_sets_file"] = None # !!! THIS NEEDS TO BE WRITTEN
+
+        cortex_input = {"input_type": "spikes",
+                        "module": "h5",
+                        "input_file": "$INPUT_DIR/cortexInput.hdf5"}
+
+        thalamus_input = {"input_type": "spikes",
+                          "module": "h5",
+                          "input_file": "$INPUT_DIR/thalamusInput.hdf5"}
+        sim_conf["inputs"] = {"cortexInput": cortex_input,
+                              "thalamusInput": thalamus_input}
+
+        out_conf_file = os.path.join(self.out_dir, "simulation_config.json")
+        print(f"Writing {out_conf_file}")
+
+        with open(out_conf_file, 'wt') as f:
+            json.dump(sim_conf, f, indent=4)
+
+    ############################################################################
+
+    def copy_morphologies(self, nl):
+        from shutil import copyfile
+        import os
+
+        self.morph_location_lookup = dict([])
+
+        print("Copying morphologies")
+
+        # Loop through all the neurons, and copy the morphologies
+        for neuron_id in nl.config:
+            if neuron_id in ["Volume", "Channels"]:
+                continue  # Not cell, skip
+
+            morph_file = nl.config[neuron_id]["morphology"]
+            base_name = os.path.basename(morph_file)
+            dest_file = self.out_dir + "components/morphologies/" + base_name
+
+            if self.debug:
+                print("Copying " + morph_file + " to " + dest_file)
+
+            copyfile(morph_file, dest_file)
+            self.morph_location_lookup[morph_file] = dest_file
+
+    ############################################################################
+
+    def copy_hoc(self, nl):
+        from shutil import copyfile
+        import os
+
+        self.hoc_location_lookup = dict([])
+
+        print("Copying hoc files...")
+
+        missing_files = []
+
+        # Loop through all the neurons, and copy the hoc
+        for neuron_id in nl.config:
+            if neuron_id in ["Volume", "Channels"]:
+                continue  # Not cell, skip
+
+            hoc_file = nl.config[neuron_id]["hoc"]
+
+            if os.path.isfile(hoc_file):
+                base_name = os.path.basename(hoc_file)
+                dest_file = os.path.join(self.out_dir, "components", "hoc_templates", base_name)
+
+                if self.debug:
+                    print(f"Copying {hoc_file} to {dest_file}")
+
+                self.hoc_location_lookup[hoc_file] = dest_file
+                copyfile(hoc_file, dest_file)
+            else:
+
+                if hoc_file not in missing_files:
+                    # Only print same error message ones
+                    print(f"!!! Missing hoc file: {hoc_file}")
+
+                missing_files.append(hoc_file)
+
+                ############################################################################
+
+    def copy_mechanisms(self):
+        from shutil import copyfile
+        from glob import glob
+        import os
+
+        print("Copying mechanisms")
+
+        for mech in glob("*.mod"):
+            if self.debug:
+                print(f"Copying {mech}")
+
+            copyfile(mech, os.path.join(self.out_dir, "components", "mechanisms", mech))
+
+    ############################################################################
+
+    def sort_input(self, nl, node_type_id_lookup, input_name=None):
+
+        if self.input_file is None or not os.path.isfile(self.input_file):
+            print("No input file has been specified!")
+            return None
+
+        max_input = 1000000
+
+        input_mat = np.zeros((max_input, 2))
+        input_ctr = 0
+
+        if input_name is not None:
+            print(f"Processing inputs {input_name}")
+        else:
+            print("Processing all virtual inputs")
+
+        with h5py.File(self.input_file) as f:
+
+            try:
+                for inp in f["input"]:
+
+                    if input_name is not None and input_name != inp:
+                        # Does not match required inputName
+                        continue
+
+                    if "activity" in f["input"][inp]:
+                        # This is a virtual neuron, save it
+                        gid = node_type_id_lookup[inp]
+
+                        for spikeTime in f["input"][inp]["activity"]["spikes"]:
+                            input_mat[input_ctr, :] = [spikeTime, gid]
+                            input_ctr += 1
+
+                            if input_ctr >= max_input:
+                                print(f"Expanding input matrix to {max_input}")
+                                max_input *= 2
+                                new_input_mat = np.zeros((max_input, 2))
+                                new_input_mat[:input_ctr, :] = input_mat
+                                input_mat = new_input_mat
+            except:
+                import traceback
+                tstr = traceback.format_exc()
+                print(tstr)
+                import pdb
+                pdb.set_trace()
+
+        print("About to sort spikes in order")
+
+        sort_idx = np.argsort(input_mat[:input_ctr, 0])
+        input_matrix = input_mat[sort_idx, :]
+
+        return input_matrix
+
+        ############################################################################
+
+    ############################################################################
+
+
+if __name__ == "__main__":
+
+    if len(sys.argv) > 1:
+        networkFile = sys.argv[1]
+    else:
+        networkFile = "last"
+
+    if len(sys.argv) > 2:
+        inputFile = sys.argv[2]
+    else:
+        inputFile = None
+
+    if len(sys.argv) > 3:
+        outDir = sys.argv[3]
+    else:
+        outDir = "striatumSim/"
+
+    cn = ExportSonata(network_file=networkFile,
+                      input_file=inputFile,
+                      out_dir=outDir)
+
+    print("!!! VERIFY THAT SECTION DATA IS CORRECT")
+    # cn.plotSectionData()


Test patch:
diff --git a/tests/networks/network_testing_input/input-test-1.json b/tests/networks/network_testing_input/input-test-1.json
index 94b396b48..b945a082c 100644
--- a/tests/networks/network_testing_input/input-test-1.json
+++ b/tests/networks/network_testing_input/input-test-1.json
@@ -17,6 +17,8 @@
 	    "end" : [3.0, 6.0, 8.0],
 	    "type" : "AMPA_NMDA",
 	    "synapseDensity" : "0.45*0.05/(1+exp(-(d-30e-6)/5e-6))",
+	    "nInputs": 20,
+	    "clusterSize" : 3,
 	    "frequency" : [1, 2, 3],
 	    "populationUnitCorrelation" : 0.0,
 	    "jitter" : 0.002,
@@ -42,6 +44,7 @@
 	    "generator" : "poisson",
 	    "type" : "AMPA_NMDA",
 	    "synapseDensity": "0.75*0.052/(1+exp(-(d-30e-6)/5e-6))",
+	    "nInputs": 20,
 	    "frequency" : 1,
 	    "populationUnitCorrelation" : 0.0,
 	    "jitter" : 0.003,
@@ -55,6 +58,8 @@
 	    "end" : [2.5, 4.5, 6.5],
 	    "type" : "AMPA_NMDA",
 	    "synapseDensity": "0.3*0.052/(1+exp(-(d-30e-6)/5e-6))",
+	    "nInputs" : 30,
+	    "clusterSize" : 3,
 	    "frequency" : 1,
 	    "populationUnitCorrelation" : 0.0,
 	    "jitter" : 0.003,
diff --git a/tests/test_input.py b/tests/test_input.py
index 3488bc9ea..b49054386 100644
--- a/tests/test_input.py
+++ b/tests/test_input.py
@@ -85,6 +85,15 @@ def test_input_1(self):
                 spikes = input_info["spikes"][()]
                 n_traces = spikes.shape[0]
 
+                if "nInputs" in config_data[neuron_type][input_type]:
+                    if "clusterSize" in config_data[neuron_type][input_type]:
+                        cluster_size = config_data[neuron_type][input_type]["clusterSize"]
+                    else:
+                        cluster_size = 1
+
+                    print(f"Checking number of inputs is {config_data[neuron_type][input_type]['nInputs']} * {cluster_size}")
+                    self.assertEqual(config_data[neuron_type][input_type]["nInputs"]*cluster_size, n_traces)
+
                 max_len = 1
                 if type(start_time) is np.ndarray:
                     max_len = np.maximum(max_len, len(start_time))