diff --git a/.github/workflows/omv-ci.yml b/.github/workflows/omv-ci.yml index c2b6a30..332c4d4 100644 --- a/.github/workflows/omv-ci.yml +++ b/.github/workflows/omv-ci.yml @@ -30,17 +30,17 @@ jobs: steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v6 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} - name: Install OMV run: | pip install git+https://github.com/OpenSourceBrain/osb-model-validation - pip install scipy sympy matplotlib cython pandas tables + pip install scipy sympy matplotlib "cython<3.1.0" pandas tables pip install 'numpy<=1.23.0' # see https://github.com/OpenSourceBrain/osb-model-validation/issues/91 diff --git a/.gitignore b/.gitignore index 8eea2e5..64c3957 100644 --- a/.gitignore +++ b/.gitignore @@ -30,3 +30,9 @@ NEST_SLI/*.gdf /PyNN/*.mod /PyNN/*.net.nml /PyNN/tests.log +/PyNN/data/2024* +/p0/* +/NeuroML2/*.dat +/PyNN/LEMS_Sim_Microcircuit*.xml +/NeuroML2/*.spikes +/NeuroML2/Microcircuit*.gv diff --git a/NeuroML2/.test.validate.omt b/NeuroML2/.test.validate.omt index 25f4367..e1280f6 100644 --- a/NeuroML2/.test.validate.omt +++ b/NeuroML2/.test.validate.omt @@ -1,6 +1,4 @@ -# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation -# Still in development, subject to change without notice!! - -# This test will validate all of the NeuroML 2 files in the current directory using: jnml -validate *.nml -target: "*.nml" +# Script for running automated tests on OSB, see https://github.com/OpenSourceBrain/osb-model-validation + +target: "*.nml" engine: jNeuroML_validate diff --git a/NeuroML2/LEMS_Sim_Microcircuit_0_2pcnt.xml b/NeuroML2/LEMS_Sim_Microcircuit_0_2pcnt.xml new file mode 100644 index 0000000..2f92391 --- /dev/null +++ b/NeuroML2/LEMS_Sim_Microcircuit_0_2pcnt.xml @@ -0,0 +1,277 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/NeuroML2/Microcircuit_0_2pcnt.net.nml b/NeuroML2/Microcircuit_0_2pcnt.net.nml new file mode 100644 index 0000000..77587b3 --- /dev/null +++ b/NeuroML2/Microcircuit_0_2pcnt.net.nml @@ -0,0 +1,58938 @@ + + + This NeuroML 2 file has been generated from: + PyNN v0.10.1 + libNeuroML v0.5.9 + pyNeuroML v1.2.7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/NeuroML2/regenerate.sh b/NeuroML2/regenerate.sh new file mode 100755 index 0000000..20fb7ac --- /dev/null +++ b/NeuroML2/regenerate.sh @@ -0,0 +1,7 @@ +#!/bin/bash +set -ex + +cd ../PyNN +python test_neuroml.py + +cp *.nml LEMS*ml ../NeuroML2 diff --git a/PyNN/.test.pynn.mep b/PyNN/.test.pynn.mep index e2ca224..db8cda5 100644 --- a/PyNN/.test.pynn.mep +++ b/PyNN/.test.pynn.mep @@ -4,25 +4,25 @@ experiments: spike_rate_l23e: expected: - spike rate: 1.6693811074918568 # Note: this reflects only rate of cells that spiked at least once... + spike rate: 1.4385775862068966 # Note: this reflects only rate of cells that spiked at least once... spike_rate_l23i: expected: - spike rate: 3.643617021276596 + spike rate: 3.44954128440367 spike_rate_l4e: expected: - spike rate: 4.9837255883825735 + spike rate: 4.850835322195704 spike_rate_l4i: expected: - spike rate: 7.290489642184557 + spike rate: 7.12312734082397 spike_rate_l5e: expected: - spike rate: 7.753623188405797 + spike rate: 7.869022869022869 spike_rate_l5i: expected: - spike rate: 8.016826923076923 + spike rate: 7.85377358490566 spike_rate_l6e: expected: - spike rate: 2.094361334867664 + spike rate: 1.8208245243128964 spike_rate_l6i: expected: - spike rate: 8.274828767123287 + spike rate: 8.225255972696246 diff --git a/PyNN/.test.pynnnest.comparison.omt b/PyNN/.test.pynnnest.comparison.omt index 890196a..0f1b0d1 100644 --- a/PyNN/.test.pynnnest.comparison.omt +++ b/PyNN/.test.pynnnest.comparison.omt @@ -1,6 +1,6 @@ # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation -target: microcircuit.py +target: test.py engine: PyNN_Nest mep: ../PyNEST/.test.pynest.mep experiments: diff --git a/PyNN/.test.pynnnest.omt b/PyNN/.test.pynnnest.omt index c019e87..8a33160 100644 --- a/PyNN/.test.pynnnest.omt +++ b/PyNN/.test.pynnnest.omt @@ -1,6 +1,6 @@ # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation -target: microcircuit.py +target: test.py engine: PyNN_Nest mep: .test.pynn.mep experiments: diff --git a/PyNN/README.md b/PyNN/README.md deleted file mode 100644 index e45b475..0000000 --- a/PyNN/README.md +++ /dev/null @@ -1,95 +0,0 @@ -## Cortical microcircuit simulation: PyNN version - -This is an implementation of the multi-layer microcircuit model of early -sensory cortex published by Potjans and Diesmann (2014) The cell-type specific -cortical microcircuit: relating structure and activity in a full-scale spiking -network model. Cerebral Cortex 24 (3): 785-806, doi:10.1093/cercor/bhs358. - -It has only been tested with the NEST back-end. - -Files: - -- network_params.py: Script containing model parameters - -- sim_params.py: Script containing simulation and system parameters - -- microcircuit.py: Simulation script - can be left unchanged - -- network.py: In which the network is set up - -- connectivity.py: Definition of connection function - -- scaling.py: Functions for computing numbers of synapses in full-scale and down-scaled networks - -- run_microcircuit.py: Creates output directory, copies all scripts to this directory, - creates sim_script.sh and submits it to the queue. Takes all parameters -from sim_params.sli and can be left unchanged - -- plotting.py: Python script to create raster and firing rate plot - - -### Instructions: - -1. Download and install your desired back-end. - For NEST, see http://www.nest-initiative.org/index.php/Software:Download - and to enable full-scale simulation, compile it with MPI support - (use the --with-mpi option when configuring) according to the instructions on - http://www.nest-initiative.org/index.php/Software:Installation - -2. Install PyNN 0.8 according to the instructions on - http://neuralensemble.org/docs/PyNN/installation.html - -4. In sim_params.py adjust the following parameters: - - - Set the simulation time via 'sim_duration' - - the number of compute nodes 'n_nodes' - - the number of processes per node 'n_procs_per_node' - - queuing system parameters 'walltime' and 'memory' - - Adjust 'output_path', 'mpi_path', 'nest_path', and 'pyNN_path' to your system - -5. In network_params.py: - - - Add dictionary to params_dict for the back-end you wish to use - - Choose the network size via 'N_scaling' and 'K_scaling', - which scales the numbers of neurons and in-degrees, respectively - - Choose the external input via 'input_type' - - Optionally activate thalamic input via 'thalamic_input' - and set any thalamic input parameters - -6. Run the simulation by typing 'python run_microcircuit.py' in your terminal - (microcircuit.py and the parameter files need to be in the same folder) - -7. Output files and basic analysis: - - - Spikes are written to .txt files containing IDs of the recorded neurons - and corresponding spike times in ms. - Separate files are written out for each population and virtual process. - File names are formed as 'spikes'+ layer + population + MPI process + .txt - - Voltages are written to .dat files containing GIDs, times in ms, and the - corresponding membrane potentials in mV. File names are formed as - voltmeter label + layer index + population index + spike detector GID + - virtual process + .dat - - - If 'create_raster_plot' is set to True, a raster plot is saved as 'result.png' - - -The simulation was successfully tested with NEST 2.4.1 and MPI 1.4.3. -Plotting works with Python 2.6.6 including packages numpy 1.3.0, -matplotlib 0.99.1.1, and glob. - ---------------------------------------------------- - -Simulation on a single process: - -1. Go to the folder that includes microcircuit.py and the parameter files - -2. Adjust 'N_scaling' and 'K_scaling' in network_params.py such that the network - is small enough to fit on your system - -3. Ensure that the output directory exists, as it is not created via the bash - script anymore - -4. Type 'python microcircuit.py' to start the simulation on a single process - - - diff --git a/PyNN/connectivity.py b/PyNN/connectivity.py deleted file mode 100644 index c94660d..0000000 --- a/PyNN/connectivity.py +++ /dev/null @@ -1,22 +0,0 @@ -################################################### -### Connection routine ### -################################################### - -import numpy as np -from network_params import * -from pyNN.random import RandomDistribution - - -def FixedTotalNumberConnect(sim, pop1, pop2, K, w_mean, w_sd, d_mean, d_sd, rng=None): - n_syn = int(round(K * len(pop2))) - conn = sim.FixedTotalNumberConnector(n_syn, rng=rng) - d_distr = RandomDistribution('normal_clipped', [d_mean, d_sd, 0.1, np.inf], rng=rng) - if pop1.annotations['type'] == 'E' : - conn_type = 'excitatory' - w_distr = RandomDistribution('normal_clipped', [w_mean, w_sd, 0., np.inf], rng=rng) - else : - conn_type = 'inhibitory' - w_distr = RandomDistribution('normal_clipped', [w_mean, w_sd, -np.inf, 0.], rng=rng) - - syn = sim.StaticSynapse(weight=w_distr, delay=d_distr) - proj = sim.Projection(pop1, pop2, conn, syn, receptor_type=conn_type) diff --git a/PyNN/example.py b/PyNN/example.py new file mode 100644 index 0000000..c921efa --- /dev/null +++ b/PyNN/example.py @@ -0,0 +1,49 @@ +# -*- coding: utf-8 -*- +""" +PyNN microcircuit example +--------------------------- + +Example file to run the microcircuit. + +Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016 +Adapted for PyNN by Andrew Davison, December 2017 + +""" + +import time +import numpy as np +import network +from network_params import net_dict +from sim_params import sim_dict +from stimulus_params import stim_dict + + +# Initialize the network and pass parameters to it. +tic = time.time() +net = network.Network(sim_dict, net_dict, stim_dict) +toc = time.time() - tic +print("Time to initialize the network: %.2f s" % toc) +# Connect all nodes. +tic = time.time() +net.setup() +toc = time.time() - tic +print("Time to create the connections: %.2f s" % toc) +# Simulate. +tic = time.time() +net.simulate() +toc = time.time() - tic +print("Time to simulate: %.2f s" % toc) +tic = time.time() +net.write_data() +toc = time.time() - tic +print("Time to write data: %.2f s" % toc) +# Plot a raster plot of the spikes of the simulated neurons and the average +# spike rate of all populations. For visual purposes only spikes 100 ms +# before and 100 ms after the thalamic stimulus time are plotted here by +# default. The computation of spike rates discards the first 500 ms of +# the simulation to exclude initialization artifacts. +raster_plot_time_idx = np.array( + [stim_dict['th_start'] - 100.0, stim_dict['th_start'] + 100.0] + ) +fire_rate_time_idx = np.array([500.0, sim_dict['t_sim']]) +net.evaluate(raster_plot_time_idx, fire_rate_time_idx) diff --git a/PyNN/helpers.py b/PyNN/helpers.py new file mode 100644 index 0000000..5842a8d --- /dev/null +++ b/PyNN/helpers.py @@ -0,0 +1,362 @@ +# -*- coding: utf-8 -*- +""" +Helper functions for the simulation and evaluation of the microcircuit. + +Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016 +Adapted for PyNN by Andrew Davison, December 2017 +""" + +import numpy as np +import os +import sys +if 'DISPLAY' not in os.environ: + import matplotlib + matplotlib.use('Agg') +import matplotlib.pyplot as plt +from matplotlib.patches import Polygon +from neo.io import get_io + + +def compute_DC(net_dict, w_ext): + """ Computes DC input if no Poisson input is provided to the microcircuit. + + Parameters + ---------- + net_dict + Parameters of the microcircuit. + w_ext + Weight of external connections. + + Returns + ------- + DC + DC input, which compensates lacking Poisson input. + """ + DC = ( + net_dict['bg_rate'] * net_dict['K_ext'] * + w_ext * net_dict['neuron_params']['tau_syn_E'] * 0.001 + ) + return DC + + +def get_weight(PSP_val, net_dict): + """ Computes weight to elicit a change in the membrane potential. + + This function computes the weight which elicits a change in the membrane + potential of size PSP_val. To implement this, the weight is calculated to + elicit a current that is high enough to implement the desired change in the + membrane potential. + + Parameters + ---------- + PSP_val + Evoked postsynaptic potential. + net_dict + Dictionary containing parameters of the microcircuit. + + Returns + ------- + PSC_e + Weight value(s). + + """ + C_m = net_dict['neuron_params']['C_m'] + tau_m = net_dict['neuron_params']['tau_m'] + tau_syn_ex = net_dict['neuron_params']['tau_syn_ex'] + + PSC_e_over_PSP_e = (((C_m) ** (-1) * tau_m * tau_syn_ex / ( + tau_syn_ex - tau_m) * ((tau_m / tau_syn_ex) ** ( + - tau_m / (tau_m - tau_syn_ex)) - (tau_m / tau_syn_ex) ** ( + - tau_syn_ex / (tau_m - tau_syn_ex)))) ** (-1)) + PSC_e = (PSC_e_over_PSP_e * PSP_val) + return PSC_e + + +def get_total_number_of_synapses(net_dict): + """ Returns the total number of synapses between all populations. + + The first index (rows) of the output matrix is the target population + and the second (columns) the source population. If a scaling of the + synapses is intended this is done in the main simulation script and the + variable 'K_scaling' is ignored in this function. + + Parameters + ---------- + net_dict + Dictionary containing parameters of the microcircuit. + N_full + Number of neurons in all populations. + number_N + Total number of populations. + conn_probs + Connection probabilities of the eight populations. + scaling + Factor that scales the number of neurons. + + Returns + ------- + K + Total number of synapses with + dimensions [len(populations), len(populations)]. + + """ + N_full = net_dict['N_full'] + number_N = len(N_full) + conn_probs = net_dict['conn_probs'] + scaling = net_dict['N_scaling'] + prod = np.outer(N_full, N_full) + n_syn_temp = np.log(1. - conn_probs)/np.log((prod - 1.) / prod) + N_full_matrix = np.column_stack( + (N_full for i in list(range(number_N))) + ) + # If the network is scaled the indegrees are calculated in the same + # fashion as in the original version of the circuit, which is + # written in sli. + K = (((n_syn_temp * ( + N_full_matrix * scaling).astype(int)) / N_full_matrix).astype(int)) + return K + + +def synapses_th_matrix(net_dict, stim_dict): + """ Computes number of synapses between thalamus and microcircuit. + + This function ignores the variable, which scales the number of synapses. + If this is intended the scaling is performed in the main simulation script. + + Parameters + ---------- + net_dict + Dictionary containing parameters of the microcircuit. + stim_dict + Dictionary containing parameters of stimulation settings. + N_full + Number of neurons in the eight populations. + number_N + Total number of populations. + conn_probs + Connection probabilities of the thalamus to the eight populations. + scaling + Factor that scales the number of neurons. + T_full + Number of thalamic neurons. + + Returns + ------- + K + Total number of synapses. + + """ + N_full = net_dict['N_full'] + number_N = len(N_full) + scaling = net_dict['N_scaling'] + conn_probs = stim_dict['conn_probs_th'] + T_full = stim_dict['n_thal'] + prod = (T_full * N_full).astype(float) + n_syn_temp = np.log(1. - conn_probs)/np.log((prod - 1.)/prod) + K = (((n_syn_temp * (N_full * scaling).astype(int))/N_full).astype(int)) + return K + + +def adj_w_ext_to_K(K_full, K_scaling, w, w_from_PSP, DC, net_dict, stim_dict): + """ Adjustment of weights to scaling is performed. + + The recurrent and external weights are adjusted to the scaling + of the indegrees. Extra DC input is added to compensate the scaling + and preserve the mean and variance of the input. + + Parameters + ---------- + K_full + Total number of connections between the eight populations. + K_scaling + Scaling factor for the connections. + w + Weight matrix of the connections of the eight populations. + w_from_PSP + Weight of the external connections. + DC + DC input to the eight populations. + net_dict + Dictionary containing parameters of the microcircuit. + stim_dict + Dictionary containing stimulation parameters. + tau_syn_E + Time constant of the external postsynaptic excitatory current. + full_mean_rates + Mean rates of the eight populations in the full scale version. + K_ext + Number of external connections to the eight populations. + bg_rate + Rate of the Poissonian spike generator. + + Returns + ------- + w_new + Adjusted weight matrix. + w_ext_new + Adjusted external weight. + I_ext + Extra DC input. + + """ + tau_syn_E = net_dict['neuron_params']['tau_syn_E'] + full_mean_rates = net_dict['full_mean_rates'] + w_mean = w_from_PSP + K_ext = net_dict['K_ext'] + bg_rate = net_dict['bg_rate'] + w_new = w / np.sqrt(K_scaling) + I_ext = np.zeros(len(net_dict['populations'])) + x1_all = w * K_full * full_mean_rates + x1_sum = np.sum(x1_all, axis=1) + if net_dict['poisson_input']: + x1_ext = w_mean * K_ext * bg_rate + w_ext_new = w_mean / np.sqrt(K_scaling) + I_ext = 0.001 * tau_syn_E * ( + (1. - np.sqrt(K_scaling)) * x1_sum + ( + 1. - np.sqrt(K_scaling)) * x1_ext) + DC + else: + w_ext_new = w_from_PSP / np.sqrt(K_scaling) + I_ext = 0.001 * tau_syn_E * ( + (1. - np.sqrt(K_scaling)) * x1_sum) + DC + return w_new, w_ext_new, I_ext + + +def plot_raster(data_files, begin, end, output_path, annotation=''): + """ Creates a spike raster plot of the microcircuit. + + Arguments + --------- + data_files + Dictionary matching population labels to file paths + begin + Initial value of spike times to plot. + end + Final value of spike times to plot. + output_path + Path to directory into which figure will be saved. + + Returns + ------- + None + + """ + color_list = [ + '#000000', '#888888', '#000000', '#888888', + '#000000', '#888888', '#000000', '#888888' + ] + Fig1 = plt.figure(1, figsize=(8, 6)) + plt.xlim(begin, end) + y = 0 + y_label_pos = [y] + labels = sorted(data_files) + for label, colour in zip(labels, color_list): + spiketrains = get_io(data_files[label]).read()[0].segments[0].spiketrains + for spiketrain in spiketrains: + plt.plot(spiketrain, y*np.ones_like(spiketrain), '.', color=colour) + y += 1 + y_label_pos.append(y) + plt.xlabel('time [ms]', fontsize=18) + plt.xticks(fontsize=16) + y_label_pos = np.array(y_label_pos) + y_label_pos = np.diff(y_label_pos)/2 + y_label_pos[:-1] + plt.yticks( + y_label_pos, + labels, rotation=10, fontsize=16 + ) + print(labels) + print(y_label_pos) + plt.gca().invert_yaxis() # put L2/3 at the top + plt.text(0.01, 0.01, annotation, transform=Fig1.transFigure) + plt.savefig(os.path.join(output_path, 'raster_plot.png'), dpi=300) + plt.show() + + +def fire_rate(data_files, begin, end, output_path): + """ Computes firing rate and standard deviation of it. + + The firing rate of each neuron for each population is computed and stored + in a numpy file in the directory of the spike detectors. The mean firing + rate and its standard deviation is displayed for each population. + + Arguments + --------- + + data_files + Dictionary matching population labels to file paths + begin + Initial value of spike times to calculate the firing rate. + end + Final value of spike times to calculate the firing rate. + + Returns + ------- + None + + """ + rates_averaged_all = [] + rates_std_all = [] + for h, label in enumerate(sorted(data_files)): + spiketrains = get_io(data_files[label]).read()[0].segments[0].spiketrains + counts = np.array([len(spiketrain) for spiketrain in spiketrains]) + rate_each_n = counts * 1000.0 / (end - begin) + rate_averaged = np.mean(rate_each_n) + rate_std = np.std(rate_each_n) + rates_averaged_all.append(float('%.3f' % rate_averaged)) + rates_std_all.append(float('%.3f' % rate_std)) + np.save(os.path.join(output_path, ('rate' + str(h) + '.npy')), + rate_each_n) + print('Mean rates: %r Hz' % rates_averaged_all) + print('Standard deviation of rates: %r Hz' % rates_std_all) + + +def boxplot(net_dict, path, annotation=''): + """ Creates a boxplot of the firing rates of the eight populations. + + To create the boxplot, the firing rates of each population need to be + computed with the function 'fire_rate'. + + Arguments + --------- + net_dict + Dictionary containing parameters of the microcircuit. + path + Path were the firing rates are stored. + + Returns + ------- + None + + """ + pops = net_dict['N_full'] + reversed_order_list = list(range(len(pops) - 1, -1, -1)) + list_rates_rev = [] + for h in reversed_order_list: + list_rates_rev.append( + np.load(os.path.join(path, ('rate' + str(h) + '.npy'))) + ) + pop_names = net_dict['populations'] + label_pos = list(range(len(pops), 0, -1)) + color_list = ['#888888', '#000000'] + medianprops = dict(linestyle='-', linewidth=2.5, color='firebrick') + fig, ax1 = plt.subplots(figsize=(10, 6)) + bp = plt.boxplot(list_rates_rev, 0, 'rs', 0, medianprops=medianprops) + plt.setp(bp['boxes'], color='black') + plt.setp(bp['whiskers'], color='black') + plt.setp(bp['fliers'], color='red', marker='+') + for h in list(range(len(pops))): + boxX = [] + boxY = [] + box = bp['boxes'][h] + for j in list(range(5)): + boxX.append(box.get_xdata()[j]) + boxY.append(box.get_ydata()[j]) + boxCoords = list(zip(boxX, boxY)) + k = h % 2 + boxPolygon = Polygon(boxCoords, facecolor=color_list[k]) + ax1.add_patch(boxPolygon) + plt.xlabel('firing rate [Hz]', fontsize=18) + plt.yticks(label_pos, pop_names, fontsize=18) + plt.xticks(fontsize=18) + plt.text(0.01, 0.01, annotation, transform=fig.transFigure) + plt.savefig(os.path.join(path, 'box_plot.png'), dpi=300) + plt.show() diff --git a/PyNN/microcircuit.py b/PyNN/microcircuit.py deleted file mode 100644 index 98524f9..0000000 --- a/PyNN/microcircuit.py +++ /dev/null @@ -1,133 +0,0 @@ -################################################### -### Main script ### -################################################### - -import sys -from sim_params import simulator_params, system_params -sys.path.append(system_params['backend_path']) -sys.path.append(system_params['pyNN_path']) -from network_params import * -# import logging # TODO! Remove if it runs without this line -import pyNN -import time -#from neo.io import PyNNTextIO -import plotting - - -# prepare simulation -# logging.basicConfig() # TODO! Remove if it runs without this line -exec('import pyNN.%s as sim' % simulator) -sim.setup(**simulator_params[simulator]) -print('Starting microcircuit model in %s' % simulator) -import network - -# create network -start_netw = time.time() -n = network.Network(sim) - -n.setup(sim) -end_netw = time.time() -print('Creating the network took %g s on rank %i (of %i total)' % (end_netw - start_netw,sim.rank(),sim.num_processes())) - -# simulate -if sim.rank() == 0 : - print("Simulating...") -start_sim = time.time() -t = sim.run(simulator_params[simulator]['sim_duration']) -end_sim = time.time() -if sim.rank() == 0 : - print('Simulation took %g s' % (end_sim - start_sim,)) - - -start_writing = time.time() -for layer in n.pops : - for pop in n.pops[layer] : - ## Note: disabling PyNNTextIO save option, as this is not supported with later versions of Neo... - ##spikes_file = system_params['output_path'] \ - ## + "/spikes_" + layer + '_' + pop + '_' + str(sim.rank()) + ".txt" - #print('Writing %s'%spikes_file) - ##io = PyNNTextIO(filename=spikes_file) - spikes = n.pops[layer][pop].get_data('spikes', gather=False) - ##for segment in spikes.segments : - ## io.write_segment(segment) - - spiketrains = spikes.segments[0].spiketrains - spikes_file2 = system_params['output_path'] \ - + "/spikes_" + layer + '_' + pop + '_' + str(sim.rank()) + ".spikes" - #print('Saving data recorded for spikes in pop %s, indices: %s to %s'%(pop, [s.annotations['source_id'] for s in spiketrains], spikes_file2)) - ff = open(spikes_file2, 'w') - - - def get_source_id(spiketrain): - if 'source_id' in spiketrain.annotations: - return spiketrain.annotations['source_id'] - - elif 'channel_id' in spiketrain.annotations: # See https://github.com/NeuralEnsemble/PyNN/pull/762 - return spiketrain.annotations['channel_id'] - - for spiketrain in spiketrains: - source_id = get_source_id(spiketrain) - source_index = n.pops[layer][pop].id_to_index(source_id) - - '''print("Writing spike data for cell %s[%s] (gid: %i): %i spikes: [%s,...,%s] "% \ - (pop, - source_index, - source_id, - len(spiketrain), - spiketrain[0] if len(spiketrain)>1 else '-', - spiketrain[-1] if len(spiketrain)>1 else '-'))''' - for t in spiketrain: - ff.write('%s\t%i\n'%(t.magnitude,source_index)) - ff.close() - - if record_v : - import numpy - ## Note: disabling PyNNTextIO save option, as this is not supported with later versions of Neo... - ##v_file = system_params['output_path'] \ - ## + "/vm_" + layer + '_' + pop + '_' + str(sim.rank()) + ".txt" - ##io = PyNNTextIO(filename=v_file) - #print('Writing %s'%v_file) - vm = n.pops[layer][pop].get_data('v', gather=False) - ##for segment in vm.segments : - ## try : - ## io.write_segment(segment) - ## except AssertionError : - ## pass - - analogsignal = vm.segments[0].analogsignals[0] - name = analogsignal.name - source_ids = analogsignal.annotations['source_ids'] - - print('Saving data recorded for %s in pop %s%s, global ids: %s'%(name, layer, pop, source_ids)) - filename=system_params['output_path']+"/vm_%s_%s_%s.%s.dat"%(layer, pop, sim.rank(),simulator) - times_vm_a = [] - tt = numpy.array([t*sim.get_time_step()/1000. for t in range(len(analogsignal.transpose()[0]))]) - times_vm_a.append(tt) - for i in range(len(source_ids)): - glob_id = source_ids[i] - index_in_pop = n.pops[layer][pop].id_to_index(glob_id) - #print("Writing data for cell %i = %s[%s] (gid: %i) to %s "%(i, pop,index_in_pop, glob_id, filename)) - vm = analogsignal.transpose()[i] - times_vm_a.append(vm/1000.) - - times_vm = numpy.array(times_vm_a).transpose() - numpy.savetxt(filename, times_vm , delimiter = '\t', fmt='%s') - - -end_writing = time.time() -print("Writing data took %g s" % (end_writing - start_writing,)) - -if create_raster_plot and sim.rank() == 0 : - # Numbers of neurons from which spikes were recorded - n_rec = [[0] * n_pops_per_layer for i in range(n_layers)] - for layer, i in layers.items() : - for pop, j in pops.items() : - if record_fraction: - n_rec[i][j] = round(N_full[layer][pop] * N_scaling * frac_record_spikes) - else: - n_rec[i][j] = n_record - if sum(sum(n_rec,[])) > 0: - plotting.show_raster_bars(raster_t_min, raster_t_max, n_rec, frac_to_plot, - system_params['output_path'] + '/', N_scaling, K_scaling) - -sim.end() diff --git a/PyNN/network.py b/PyNN/network.py index 73638ca..b088050 100644 --- a/PyNN/network.py +++ b/PyNN/network.py @@ -1,214 +1,559 @@ -################################################### -### Network definition ### -################################################### - -from network_params import * -import scaling -from connectivity import FixedTotalNumberConnect -from pyNN.random import NumpyRNG, RandomDistribution -from pyNN.space import RandomStructure, Cuboid +# -*- coding: utf-8 -*- +""" +Main file for the microcircuit. + +Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016 +Adapted for PyNN by Andrew Davison, December 2017 +""" + +from importlib import import_module import numpy as np +import os +from helpers import adj_w_ext_to_K +from helpers import synapses_th_matrix +from helpers import get_total_number_of_synapses +from helpers import get_weight +from helpers import plot_raster +from helpers import fire_rate +from helpers import boxplot +from helpers import compute_DC +from pyNN.random import RandomDistribution +from pyNN.space import RandomStructure, Cuboid import math class Network: + """ Handles the setup of the network parameters and + provides functions to connect the network and devices. - def __init__(self, sim): - return None + Arguments + --------- + sim_dict + dictionary containing all parameters specific to the simulation + such as the directory the data is stored in and the seeds + (see: sim_params.py) + net_dict + dictionary containing all parameters specific to the neurons + and the network (see: network_params.py) - def setup(self, sim) : - # Create matrix of synaptic weights - self.w = create_weight_matrix() - model = getattr(sim, 'IF_curr_exp') - script_rng = NumpyRNG(seed=6508015, parallel_safe=parallel_safe) - distr = RandomDistribution('normal', [V0_mean, V0_sd], rng=script_rng) + Keyword Arguments + ----------------- + stim_dict + dictionary containing all parameter specific to the stimulus + (see: stimulus_params.py) - # Create cortical populations - self.pops = {} - layer_structures = {} - total_cells = 0 + """ + def __init__(self, sim_dict, net_dict, stim_dict=None): + self.sim_dict = sim_dict + self.net_dict = net_dict + if stim_dict is not None: + self.stim_dict = stim_dict + else: + self.stim_dict = None + self.sim = import_module("pyNN.%s" % sim_dict["simulator"]) + self.data_path = sim_dict['data_path'] - x_dim_scaled = x_dimension * math.sqrt(N_scaling) - z_dim_scaled = z_dimension * math.sqrt(N_scaling) + + def setup_pyNN(self, extra_setup_params): + """ Reset and configure the simulator. + + Where the simulator is NEST, + the number of seeds for the NEST-kernel is computed, based on the + total number of MPI processes and threads of each. + """ + + master_seed = self.sim_dict['master_seed'] + if self.sim_dict['simulator'] == "spiNNaker": + N_tp = 1 + else: + N_tp = self.sim.num_processes() * self.sim_dict['local_num_threads'] + rng_seeds = list(range(master_seed + 1 + N_tp, master_seed + 1 + (2 * N_tp))) + grng_seed = master_seed + N_tp + self.pyrngs = [np.random.RandomState(s) + for s in list(range(master_seed, master_seed + N_tp))] + self.sim_resolution = self.sim_dict['sim_resolution'] + self.sim.setup(timestep=self.sim_resolution, + threads=self.sim_dict['local_num_threads'], + grng_seed=grng_seed, + rng_seeds=rng_seeds, + **extra_setup_params) + if self.sim.rank() == 0: + print('Master seed: %i ' % master_seed) + print('Number of total processes: %i' % N_tp) + print('Seeds for random number generators of virtual processes: %r' % rng_seeds) + print('Global random number generator seed: %i' % grng_seed) + if os.path.isdir(self.sim_dict['data_path']): + print('data directory already exists') + else: + os.makedirs(self.sim_dict['data_path']) + print('data directory created') + print('Data will be written to %s' % self.data_path) + + + def create_populations(self): + """ Creates the neuronal populations. + + The neuronal populations are created and the parameters are assigned + to them. The initial membrane potential of the neurons is drawn from a + normal distribution. Scaling of the number of neurons and of the + synapses is performed. If scaling is performed extra DC input is added + to the neuronal populations. + + """ + self.N_full = self.net_dict['N_full'] + self.N_scaling = self.net_dict['N_scaling'] + self.K_scaling = self.net_dict['K_scaling'] + self.synapses = get_total_number_of_synapses(self.net_dict) + self.synapses_scaled = self.synapses * self.K_scaling + self.nr_neurons = self.N_full * self.N_scaling + self.K_ext = self.net_dict['K_ext'] * self.K_scaling + self.w_from_PSP = get_weight(self.net_dict['PSP_e'], self.net_dict) + self.weight_mat = get_weight( + self.net_dict['PSP_mean_matrix'], self.net_dict + ) + self.weight_mat_std = self.net_dict['PSP_std_matrix'] + self.w_ext = self.w_from_PSP + if self.net_dict['poisson_input']: + self.DC_amp_e = np.zeros(len(self.net_dict['populations'])) + else: + if self.sim.rank() == 0: + print( + ''' + no poisson input provided + calculating dc input to compensate + ''' + ) + self.DC_amp_e = compute_DC(self.net_dict, self.w_ext) + + if self.sim.rank() == 0: + print( + 'The number of neurons is scaled by a factor of: %.2f' + % self.N_scaling + ) + print( + 'The number of synapses is scaled by a factor of: %.2f' + % self.K_scaling + ) + + # Scaling of the synapses. + if self.K_scaling != 1: + synapses_indegree = self.synapses / ( + self.N_full.reshape(len(self.N_full), 1) * self.N_scaling) + self.weight_mat, self.w_ext, self.DC_amp_e = adj_w_ext_to_K( + synapses_indegree, self.K_scaling, self.weight_mat, + self.w_from_PSP, self.DC_amp_e, self.net_dict, self.stim_dict + ) + + # Create cortical populations. + self.pops = [] + neuron_model = getattr(self.sim, self.net_dict['neuron_model']) + parameters = { + 'tau_syn_E': self.net_dict['neuron_params']['tau_syn_ex'], + 'tau_syn_I': self.net_dict['neuron_params']['tau_syn_in'], + 'v_rest': self.net_dict['neuron_params']['E_L'], + 'v_thresh': self.net_dict['neuron_params']['V_th'], + 'v_reset': self.net_dict['neuron_params']['V_reset'], + 'tau_refrac': self.net_dict['neuron_params']['t_ref'], + 'cm': self.net_dict['neuron_params']['C_m'] * 0.001, # pF --> nF + 'tau_m': self.net_dict['neuron_params']['tau_m'] + } + v_init = RandomDistribution("normal", + [self.net_dict['neuron_params']['V0_mean'], + self.net_dict['neuron_params']['V0_sd']], + ) # todo: specify rng + layer_structures = {} + + x_dim_scaled = self.net_dict['x_dimension'] * math.sqrt(self.N_scaling) + z_dim_scaled = self.net_dict['z_dimension'] * math.sqrt(self.N_scaling) default_cell_radius = 10 # for visualisation default_input_radius = 5 # for visualisation - - for layer in layers: - self.pops[layer] = {} - for pop in pops: - - y_offset = 0 - if layer == 'L6': y_offset = layer_thicknesses['L6']/2 - if layer == 'L5': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']/2 - if layer == 'L4': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']+layer_thicknesses['L4']/2 - if layer == 'L23': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']+layer_thicknesses['L4']+layer_thicknesses['L23']/2 - - layer_volume = Cuboid(x_dim_scaled,layer_thicknesses[layer],z_dim_scaled) - layer_structures[layer] = RandomStructure(layer_volume, origin=(0,y_offset,0)) - - self.pops[layer][pop] = sim.Population(int(N_full[layer][pop] * \ - N_scaling), model, cellparams=neuron_params, \ - structure=layer_structures[layer], label='%s_%s'%(layer,pop)) - - - self.pops[layer][pop].initialize(v=distr) - # Store whether population is inhibitory or excitatory - self.pops[layer][pop].annotate(type=pop) - - self.pops[layer][pop].annotate(radius=default_cell_radius) - self.pops[layer][pop].annotate(structure=str(layer_structures[layer])) - - this_pop = self.pops[layer][pop] - color='0 0 0' - radius = 10 - try: - import opencortex.utils.color as occ - if layer == 'L23': - if pop=='E': color = occ.L23_PRINCIPAL_CELL - if pop=='I': color = occ.L23_INTERNEURON - if layer == 'L4': - if pop=='E': color = occ.L4_PRINCIPAL_CELL - if pop=='I': color = occ.L4_INTERNEURON - if layer == 'L5': - if pop=='E': color = occ.L5_PRINCIPAL_CELL - if pop=='I': color = occ.L5_INTERNEURON - if layer == 'L6': - if pop=='E': color = occ.L6_PRINCIPAL_CELL - if pop=='I': color = occ.L6_INTERNEURON - - self.pops[layer][pop].annotate(color=color) - except: - # Don't worry about it, it's just metadata - pass - print("Created population %s with %i cells (color: %s)"%(this_pop.label,this_pop.size, color)) + layer_thicknesses = self.net_dict["layer_thicknesses"] + + + for i, pop in enumerate(self.net_dict['populations']): + layer = pop[:-1] + y_offset = 0 + if layer == 'L6': y_offset = layer_thicknesses['L6']/2 + elif layer == 'L5': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']/2 + elif layer == 'L4': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']+layer_thicknesses['L4']/2 + elif layer == 'L23': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']+layer_thicknesses['L4']+layer_thicknesses['L23']/2 + else: + raise Exception("Problem with %s"%layer) + + layer_volume = Cuboid(x_dim_scaled,layer_thicknesses[layer],z_dim_scaled) + layer_structures[layer] = RandomStructure(layer_volume, origin=(0,y_offset,0)) + + parameters['i_offset'] = self.DC_amp_e[i] * 0.001 # pA --> nA + population = self.sim.Population(int(self.nr_neurons[i]), + neuron_model(**parameters), + structure=layer_structures[layer], + label=pop) + population.initialize(v=v_init) + # Store whether population is inhibitory or excitatory + population.annotate(type=pop[-1:]) - total_cells += this_pop.size - # Spike recording - if record_fraction: - num_spikes = int(round(this_pop.size * frac_record_spikes)) + population.annotate(radius=default_cell_radius) + population.annotate(structure=str(layer_structures[layer])) + + + try: + import opencortex.utils.color as occ + print('Adding color for %s'%pop) + if 'L23' in pop: + if 'E' in pop: color = occ.L23_PRINCIPAL_CELL + if 'I' in pop: color = occ.L23_INTERNEURON + if 'L4' in pop: + if 'E' in pop: color = occ.L4_PRINCIPAL_CELL + if 'I' in pop: color = occ.L4_INTERNEURON + if 'L5' in pop: + if 'E' in pop: color = occ.L5_PRINCIPAL_CELL + if 'I' in pop: color = occ.L5_INTERNEURON + if 'L6' in pop: + if 'E' in pop: color = occ.L6_PRINCIPAL_CELL + if 'I' in pop: color = occ.L6_INTERNEURON + + population.annotate(color=color) + except Exception as e: + print(e) + # Don't worry about it, it's just metadata + pass + + self.pops.append(population) + + def create_devices(self): + """ + Setup recording + """ + + if self.sim.rank() == 0: + print('Recording {} (frac_record_v: {}), {}'.format(self.net_dict['to_record'],self.sim_dict['frac_record_v'],self.sim_dict['rec_V_int'])) + + for i, pop in enumerate(self.pops): + if 'spikes' in self.net_dict['to_record']: + pop.record('spikes') + if 'v' in self.net_dict['to_record']: + if self.sim_dict['frac_record_v']: + num_v = max(1,int(round(pop.size * self.sim_dict['frac_record_v']))) else: - num_spikes = n_record - this_pop[0:num_spikes].record('spikes') + num_v = pop.size + print(num_v) + pop[0:num_v].record('v', sampling_interval=self.sim_dict['rec_V_int']) - # Membrane potential recording - if record_v: - if record_fraction: - num_v = int(round(this_pop.size * frac_record_v)) - else: - num_v = n_record_v - this_pop[0:num_v].record('v') + + def create_thalamic_input(self): + """ This function creates the thalamic neuronal population if this + is specified in stimulus_params.py. - print("Finished creating all cell populations (%i cells)"%total_cells) - # Create thalamic population - if thalamic_input: - - print("Adding thalamic input") - layer_volume = Cuboid(x_dimension,layer_thicknesses['thalamus'],z_dimension) - layer_structure = RandomStructure(layer_volume, origin=(0,thalamus_offset,0)) - self.thalamic_population = sim.Population( - thal_params['n_thal'], - sim.SpikeSourcePoisson, - {'rate': thal_params['rate'], - 'start': thal_params['start'], - 'duration': thal_params['duration']}, - structure=layer_structure, - label='thalamic_input') - - # Compute DC input before scaling - if input_type == 'DC': - self.DC_amp = {} - for target_layer in layers: - self.DC_amp[target_layer] = {} - for target_pop in pops: - self.DC_amp[target_layer][target_pop] = bg_rate * \ - K_ext[target_layer][target_pop] * w_mean * neuron_params['tau_syn_E'] / 1000. + """ + if self.stim_dict['thalamic_input']: + if self.sim.rank() == 0: + print('Thalamic input provided') + self.thalamic_population = self.sim.Population( + self.stim_dict['n_thal'], + self.sim.PoissonSpikeSource( + rate=self.stim_dict['th_rate'], + start=self.stim_dict['th_start'], + duration=self.stim_dict['th_duration']), + label="Thalamic input") + self.thalamic_weight = get_weight( + self.stim_dict['PSP_th'], self.net_dict + ) + self.nr_synapses_th = synapses_th_matrix( + self.net_dict, self.stim_dict + ) + if self.K_scaling != 1: + self.thalamic_weight = self.thalamic_weight / (self.K_scaling ** 0.5) + self.nr_synapses_th = self.nr_synapses_th * self.K_scaling else: - self.DC_amp = {'L23': {'E': 0., 'I': 0.}, - 'L4' : {'E': 0., 'I': 0.}, - 'L5' : {'E': 0., 'I': 0.}, - 'L6' : {'E': 0., 'I': 0.}} + if self.sim.rank() == 0: + print('Thalamic input not provided') - # Scale and connect + def create_poisson(self): + """ Creates the Poisson generators. - # In-degrees of the full-scale model - K_full = scaling.get_indegrees() + If Poissonian input is provided, the Poissonian generators are created + and the parameters needed are passed to the Poissonian generator. - if K_scaling != 1 : - self.w, self.w_ext, self.K_ext, self.DC_amp = scaling.adjust_w_and_ext_to_K(K_full, K_scaling, self.w, self.DC_amp) - else: - self.w_ext = w_ext - self.K_ext = K_ext + """ + if self.net_dict['poisson_input']: + if self.sim.rank() == 0: + print('Poisson background input created') + rate_ext = self.net_dict['bg_rate'] * self.K_ext + self.poisson = [] + for i, target_pop in enumerate(self.pops): + poisson = self.sim.Population(target_pop.size, + self.sim.SpikeSourcePoisson(rate=rate_ext[i]), + label="Input_to_{}".format(target_pop.label)) + self.poisson.append(poisson) + + def create_dc_generator(self): + """ Creates a DC input generator. + + If DC input is provided, the DC generators are created and the + necessary parameters are passed to them. + + """ + if self.stim_dict['dc_input']: + if self.sim.rank() == 0: + print('DC generator created') + dc_amp_stim = self.net_dict['K_ext'] * self.stim_dict['dc_amp'] + self.dc = [] + if self.sim.rank() == 0: + print('DC_amp_stim', dc_amp_stim) + for i in range(len(self.pops)): + dc = sim.DCSource( + amplitude=dc_amp_stim[i], + start=self.stim_dict['dc_start'], + stop=self.stim_dict['dc_start'] + self.stim_dict['dc_dur']) + self.dc.append(dc) + + def create_connections(self): + """ Creates the recurrent connections. + + The recurrent connections between the neuronal populations are created. + + """ + if self.sim.rank() == 0: + print('Recurrent connections are being established') + mean_delays = self.net_dict['mean_delay_matrix'] + std_delays = self.net_dict['std_delay_matrix'] + self.projections = [] + for i, target_pop in enumerate(self.pops): + for j, source_pop in enumerate(self.pops): + synapse_nr = int(self.synapses_scaled[i][j]) + if synapse_nr > 0: + w_mean = 0.001 * self.weight_mat[i][j] # pA --> nA + w_sd = abs(w_mean * self.weight_mat_std[i][j]) + if w_mean < 0: + high = 0.0 + low = -np.inf + else: + high = np.inf + low = 0.0 + weight = RandomDistribution('normal_clipped', + mu=w_mean, + sigma=w_sd, + low=low, + high=high) + delay = RandomDistribution('normal_clipped', + mu=mean_delays[i][j], + sigma=std_delays[i][j], + low=self.sim_resolution, + high=mean_delays[i][j] + 10 * std_delays[i][j]) + if self.sim_dict["simulator"] == "spiNNaker": + connector_params = {"num_synapses": synapse_nr} + else: + connector_params = {"n": synapse_nr} + self.projections.append( + self.sim.Projection( + source_pop, + target_pop, + self.sim.FixedTotalNumberConnector(**connector_params), + synapse_type=self.sim.StaticSynapse(weight=weight, + delay=delay)) + ) + if self.sim.rank() == 0: + if self.sim_dict["simulator"] == "spiNNaker": + # at present Projection.label is not defined in SpyNNaker + label = "{}-{}".format(source_pop.label, + target_pop.label) + else: + label = self.projections[-1].label + print( + "{:10} {:9} connections, weight = {:6.3f} +/- {:5.3f} nA, delay = {:4.2f} +/- {:5.3f} ms".format( + label + ":", synapse_nr, + w_mean, w_sd, + mean_delays[i][j], std_delays[i][j]) + ) + + def connect_poisson(self): + """ Connects the Poisson generators to the microcircuit.""" + if self.sim.rank() == 0: + print('Poisson background input is connected') + for i, target_pop in enumerate(self.pops): + self.projections.append( + self.sim.Projection( + self.poisson[i], + target_pop, + self.sim.OneToOneConnector(), + self.sim.StaticSynapse(weight=0.001 * self.w_ext, + delay=self.net_dict['poisson_delay'])) + ) - if sim.rank() == 0: - print('w: %s' % self.w) + def connect_thalamus(self): + """ Connects the thalamic population to the microcircuit.""" + if self.sim.rank() == 0: + print('Thalamus connection established') - net_generation_rng = NumpyRNG(12345, parallel_safe=True) - - for target_layer in layers : - for target_pop in pops : - target_index = structure[target_layer][target_pop] - this_pop = self.pops[target_layer][target_pop] - # External inputs - if input_type == 'DC' or K_scaling != 1 : - this_pop.set(i_offset=self.DC_amp[target_layer][target_pop]) - if input_type == 'poisson': - poisson_generator = sim.Population(this_pop.size, - sim.SpikeSourcePoisson, - {'rate': bg_rate * self.K_ext[target_layer][target_pop]}, - structure=layer_structures[target_layer], - label='input_%s_%s'%(target_layer,target_pop)) - - poisson_generator.annotate(color='0.5 0.5 0') - poisson_generator.annotate(radius=default_input_radius) + weight = RandomDistribution('normal_clipped', + mu=0.001 * self.thalamic_weight, + sigma=self.thalamic_weight * self.net_dict['PSP_sd'], + low=0.0, high=np.inf) + + for i, target_pop in enumerate(self.pops): + synapse_nr = int(self.nr_synapses_th[i]) + if self.sim_dict["simulator"] == "spiNNaker": + connector_params = {"num_synapses": synapse_nr} + else: + connector_params = {"n": synapse_nr} + + mu_d = self.stim_dict['delay_th'][i] + s_d = self.stim_dict['delay_th_sd'][i] + delay = RandomDistribution('normal_clipped', + mu=mu_d, + sigma=s_d, + low=self.sim_resolution, + high=mu_d + 10 * s_d) + + self.projections.append( + self.sim.Projection( + self.thalamic_population, + target_pop, + self.sim.FixedTotalNumberConnector(**connector_params), + self.sim.StaticSynapse(weight=weight, delay=delay) + ) + ) + + def connect_dc_generator(self): + """ Connects the DC generator to the microcircuit.""" + if self.sim.rank() == 0: + print('DC Generator connection established') + for i, target_pop in enumerate(self.pops): + if self.stim_dict['dc_input']: + self.dc[i].inject_into(target_pop) + + def setup(self, extra_setup_params = {}): + """ + Execute subfunctions of the network. + + This function executes several subfunctions to create neuronal + populations, devices and inputs, connects the populations with + each other and with devices and input nodes. + + """ + self.setup_pyNN(extra_setup_params) + self.create_populations() + self.create_devices() + self.create_thalamic_input() + self.create_poisson() + self.create_dc_generator() + self.create_connections() + if self.net_dict['poisson_input']: + self.connect_poisson() + if self.stim_dict['thalamic_input']: + self.connect_thalamus() + if self.stim_dict['dc_input']: + self.connect_dc_generator() + + def write_data(self): + + import time + start_writing = time.time() + self.output_data = {} + for pop in self.pops: + self.output_data[pop.label] = "{}/{}.pkl".format(self.data_path, pop.label) + pop.write_data(self.output_data[pop.label], gather=True) + + record_v = 'v' in self.net_dict['to_record'] + + for pop_obj in self.pops: + layer = pop_obj.label[:-1] + pop = pop_obj.label[-1] + print('Saving spikes of %s%s'%(layer, pop)) + + spikes = pop_obj.get_data('spikes', gather=False) + + spiketrains = spikes.segments[0].spiketrains + + spikes_file2 = self.data_path \ + + "/spikes_" + layer + '_' + pop + '_' + str(self.sim.rank()) + ".spikes" + #print('Saving data recorded for spikes in %s%s, indices: %s to %s'%(layer, pop, [s.annotations for s in spiketrains], spikes_file2)) + ff = open(spikes_file2, 'w') + + def get_source_id(spiketrain): + if 'source_id' in spiketrain.annotations: + return spiketrain.annotations['source_id'] + + elif 'channel_id' in spiketrain.annotations: # See https://github.com/NeuralEnsemble/PyNN/pull/762 + return spiketrain.annotations['channel_id'] + + for spiketrain in spiketrains: + source_id = get_source_id(spiketrain) + source_index = pop_obj.id_to_index(source_id) - conn = sim.OneToOneConnector() - syn = sim.StaticSynapse(weight=self.w_ext) - sim.Projection(poisson_generator, this_pop, conn, syn, receptor_type='excitatory') - if thalamic_input: - # Thalamic inputs - if sim.rank() == 0 : - print('Creating thalamic connections to %s%s' % (target_layer, target_pop)) - C_thal=thal_params['C'][target_layer][target_pop] - n_target=N_full[target_layer][target_pop] - K_thal=round(np.log(1 - C_thal) / np.log((n_target * thal_params['n_thal'] - 1.) / - (n_target * thal_params['n_thal']))) / n_target - FixedTotalNumberConnect(sim, self.thalamic_population, - this_pop, K_thal, w_ext, w_rel * w_ext, - d_mean['E'], d_sd['E'], rng=net_generation_rng) - # Recurrent inputs - for source_layer in layers : - for source_pop in pops : - source_index=structure[source_layer][source_pop] - if sim.rank() == 0: - print('Creating connections from %s%s to %s%s' % (source_layer, source_pop, target_layer, target_pop)) - weight=self.w[target_index][source_index] - if source_pop == 'E' and source_layer == 'L4' and target_layer == 'L23' and target_pop == 'E': - w_sd=weight * w_rel_234 - else: - w_sd=abs(weight * w_rel) - FixedTotalNumberConnect(sim, self.pops[source_layer][source_pop], - self.pops[target_layer][target_pop],\ - K_full[target_index][source_index] * K_scaling, - weight, w_sd, - d_mean[source_pop], d_sd[source_pop], rng=net_generation_rng) - - -def create_weight_matrix(): - w=np.zeros([n_layers * n_pops_per_layer, n_layers * n_pops_per_layer]) - for target_layer in layers: - for target_pop in pops: - target_index=structure[target_layer][target_pop] - for source_layer in layers: - for source_pop in pops: - source_index=structure[source_layer][source_pop] - if source_pop == 'E': - if source_layer == 'L4' and target_layer == 'L23' and target_pop == 'E': - w[target_index][source_index]=w_234 - else: - w[target_index][source_index]=w_mean - else: - w[target_index][source_index]=g * w_mean - return w + '''print("Writing spike data for cell %s[%s] (gid: %i): %i spikes: [%s,...,%s] "% \ + (pop, + source_index, + source_id, + len(spiketrain), + spiketrain[0] if len(spiketrain)>1 else '-', + spiketrain[-1] if len(spiketrain)>1 else '-'))''' + for t in spiketrain: + ff.write('%s\t%i\n'%(t.magnitude,source_index)) + ff.close() + + if record_v : + import numpy + vm = pop_obj.get_data('v', gather=False) + + analogsignal = vm.segments[0].analogsignals[0] + name = analogsignal.name + source_ids = analogsignal.annotations['source_ids'] + + print('Saving data recorded for %s in pop %s%s, global ids: %s'%(name, layer, pop, source_ids)) + filename=self.data_path+"/vm_%s_%s_%s.%s.dat"%(layer, pop, self.sim.rank(),self.sim_dict["simulator"]) + times_vm_a = [] + tt = numpy.array([t*self.sim.get_time_step()/1000. for t in range(len(analogsignal.transpose()[0]))]) + times_vm_a.append(tt) + for i in range(len(source_ids)): + glob_id = source_ids[i] + index_in_pop = pop_obj.id_to_index(glob_id) + #print("Writing data for cell %i = %s[%s] (gid: %i) to %s "%(i, pop,index_in_pop, glob_id, filename)) + vm = analogsignal.transpose()[i] + times_vm_a.append(vm/1000.) + + times_vm = numpy.array(times_vm_a).transpose() + numpy.savetxt(filename, times_vm , delimiter = '\t', fmt='%s') + + end_writing = time.time() + print("Writing data took %g s" % (end_writing - start_writing,)) + + def simulate(self): + """ Simulates the microcircuit.""" + self.sim.run(self.sim_dict['t_sim']) + + def evaluate(self, raster_plot_time_idx, fire_rate_time_idx): + """ Displays output of the simulation. + + Calculates the firing rate of each population, + creates a spike raster plot and a box plot of the + firing rates. + + """ + if self.sim.rank() == 0: + annotation = "Simulated with pyNN.{}".format( + self.sim_dict["simulator"]) + print( + 'Interval to compute firing rates: %s ms' + % np.array2string(fire_rate_time_idx) + ) + fire_rate( + self.output_data, + fire_rate_time_idx[0], fire_rate_time_idx[1], + self.data_path + ) + print( + 'Interval to plot spikes: %s ms' + % np.array2string(raster_plot_time_idx) + ) + plot_raster( + self.output_data, + raster_plot_time_idx[0], raster_plot_time_idx[1], + self.data_path, + annotation=annotation + ) + boxplot(self.net_dict, self.data_path, + annotation=annotation) diff --git a/PyNN/network_params.py b/PyNN/network_params.py index ae414ef..36a0b6f 100644 --- a/PyNN/network_params.py +++ b/PyNN/network_params.py @@ -1,212 +1,249 @@ -################################################### -### Network parameters ### -################################################### - -import sim_params - -params_dict = { - 'nest' : - { - # Whether to make random numbers independent of the number of processes - 'parallel_safe' : True, - # Fraction of neurons to simulate - 'N_scaling' : 1., # N_scaling - # Fraction of in-degrees to simulate. Upon downscaling, synaptic weights are - # taken proportional to 1/sqrt(in-degree) and external drive is adjusted - # to preserve mean and variances of activity in the diffusion approximation. - # In-degrees and weights of both intrinsic and extrinsic inputs are adjusted. - # This scaling was not part of the original study, but this option is included - # here to enable simulations on small systems that give results similar to - # full-scale simulations. - 'K_scaling' : 0.5, - # Type of background input. Possible values: 'poisson' or 'DC' - # If 'DC' is chosen, a constant external current is provided, equal to the mean - # current due to the Poisson input used in the default version of the model. - 'input_type' : 'poisson', - # Whether to record from a fixed fraction of neurons in each population. - # If False, a fixed number of neurons is recorded. - 'record_fraction' : True, - # Number of neurons from which to record spikes when record_fraction = False - 'n_record' : 1000, # TODO: check if each population has at least this nr of neurons; PyNN otherwise just records fewer neurons & calculated rates may be wrong - # Fraction of neurons from which to record spikes when record_fraction = True - 'frac_record_spikes' : 1., - # Whether to record membrane potentials - 'record_v' : True, - # Fixed number of neurons from which to record membrane potentials when - # record_v=True and record_fraction = False - 'n_record_v' : 20, - # Fraction of neurons from which to record membrane potentials when - # record_v=True and record_fraction = True - 'frac_record_v' : 0.02, - }, - 'neuroml' : - { - # Whether to make random numbers independent of the number of processes - 'parallel_safe' : True, - # Fraction of neurons to simulate - 'N_scaling' : 0.03, - # Fraction of in-degrees to simulate. Upon downscaling, synaptic weights are - # taken proportional to 1/sqrt(in-degree) and external drive is adjusted - # to preserve mean and variances of activity in the diffusion approximation. - # In-degrees and weights of both intrinsic and extrinsic inputs are adjusted. - # This scaling was not part of the original study, but this option is included - # here to enable simulations on small systems that give results similar to - # full-scale simulations. - 'K_scaling' : 0.03, - # Type of background input. Possible values: 'poisson' or 'DC' - # If 'DC' is chosen, a constant external current is provided, equal to the mean - # current due to the Poisson input used in the default version of the model. - 'input_type' : 'poisson', - # Whether to record from a fixed fraction of neurons in each population. - # If False, a fixed number of neurons is recorded. - 'record_fraction' : False, - # Number of neurons from which to record spikes when record_fraction = False - 'n_record' : 10, # TODO: check if each population has at least this nr of neurons; PyNN otherwise just records fewer neurons & calculated rates may be wrong - # Fraction of neurons from which to record spikes when record_fraction = True - 'frac_record_spikes' : 0.01, - # Whether to record membrane potentials - 'record_v' : True, - # Fixed number of neurons from which to record membrane potentials when - # record_v=True and record_fraction = False - 'n_record_v' : 10, - # Fraction of neurons from which to record membrane potentials when - # record_v=True and record_fraction = True - 'frac_record_v' : 0.01, - } -} - -# Simulator back-end -simulator = 'nest' -#simulator = 'neuroml' - -# Load params from params_dict into global namespace -globals().update(params_dict[simulator]) - -# Relative inhibitory synaptic weight -g = -4. - -neuron_params = { - 'cm' : 0.25, # nF - 'i_offset' : 0.0, # nA - 'tau_m' : 10.0, # ms - 'tau_refrac': 2.0, # ms - 'tau_syn_E' : 0.5, # ms - 'tau_syn_I' : 0.5, # ms - 'v_reset' : -65.0, # mV - 'v_rest' : -65.0, # mV - 'v_thresh' : -50.0 # mV -} - -layers = {'L23': 0, 'L4': 1, 'L5': 2, 'L6': 3} -n_layers = len(layers) -pops = {'E': 0, 'I': 1} -n_pops_per_layer = len(pops) -structure = {'L23': {'E': 0, 'I': 1}, - 'L4' : {'E': 2, 'I': 3}, - 'L5' : {'E': 4, 'I': 5}, - 'L6' : {'E': 6, 'I': 7}} - -# Numbers of neurons in full-scale model -N_full = { - 'L23': {'E': 20683, 'I': 5834}, - 'L4' : {'E': 21915, 'I': 5479}, - 'L5' : {'E': 4850, 'I': 1065}, - 'L6' : {'E': 14395, 'I': 2948} -} - -N_E_total = N_full['L23']['E']+N_full['L4']['E']+N_full['L5']['E']+N_full['L6']['E'] - -x_dimension = 1000 -z_dimension = 1000 -thalamus_offset = -300 +# -*- coding: utf-8 -*- +""" +Network parameters for the microcircuit. + +Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016 +Adapted for PyNN by Andrew Davison, December 2017 + +""" + +import numpy as np + + +def get_mean_delays(mean_delay_exc, mean_delay_inh, number_of_pop): + """ Creates matrix containing the delay of all connections. + + Arguments + --------- + mean_delay_exc + Delay of the excitatory connections. + mean_delay_inh + Delay of the inhibitory connections. + number_of_pop + Number of populations. + + Returns + ------- + mean_delays + Matrix specifying the mean delay of all connections. + + """ + + dim = number_of_pop + mean_delays = np.zeros((dim, dim)) + mean_delays[:, 0:dim:2] = mean_delay_exc + mean_delays[:, 1:dim:2] = mean_delay_inh + return mean_delays + + +def get_std_delays(std_delay_exc, std_delay_inh, number_of_pop): + """ Creates matrix containing the standard deviations of all delays. + + Arguments + --------- + std_delay_exc + Standard deviation of excitatory delays. + std_delay_inh + Standard deviation of inhibitory delays. + number_of_pop + Number of populations in the microcircuit. + + Returns + ------- + std_delays + Matrix specifying the standard deviation of all delays. + + """ + + dim = number_of_pop + std_delays = np.zeros((dim, dim)) + std_delays[:, 0:dim:2] = std_delay_exc + std_delays[:, 1:dim:2] = std_delay_inh + return std_delays + + +def get_mean_PSP_matrix(PSP_e, g, number_of_pop): + """ Creates a matrix of the mean evoked postsynaptic potential. + + The function creates a matrix of the mean evoked postsynaptic + potentials between the recurrent connections of the microcircuit. + The weight of the connection from L4E to L23E is doubled. + + Arguments + --------- + PSP_e + Mean evoked potential. + g + Relative strength of the inhibitory to excitatory connection. + number_of_pop + Number of populations in the microcircuit. + + Returns + ------- + weights + Matrix of the weights for the recurrent connections. + + """ + dim = number_of_pop + weights = np.zeros((dim, dim)) + exc = PSP_e + inh = PSP_e * g + weights[:, 0:dim:2] = exc + weights[:, 1:dim:2] = inh + weights[0, 2] = exc * 2 + return weights + + +def get_std_PSP_matrix(PSP_rel, number_of_pop): + """ Relative standard deviation matrix of postsynaptic potential created. + + The relative standard deviation matrix of the evoked postsynaptic potential + for the recurrent connections of the microcircuit is created. + + Arguments + --------- + PSP_rel + Relative standard deviation of the evoked postsynaptic potential. + number_of_pop + Number of populations in the microcircuit. + + Returns + ------- + std_mat + Matrix of the standard deviation of postsynaptic potentials. + + """ + dim = number_of_pop + std_mat = np.zeros((dim, dim)) + std_mat[:, :] = PSP_rel + return std_mat + +net_dict = { + # Neuron model. + 'neuron_model': 'IF_curr_exp', + # By default we record spikes. If you also + # want to record the membrane potentials of the neurons, add + # 'v' to the list. + 'to_record': ['spikes'], + # Names of the simulated populations. + 'populations': ['L23E', 'L23I', 'L4E', 'L4I', 'L5E', 'L5I', 'L6E', 'L6I'], + # Number of neurons in the different populations. The order of the + # elements corresponds to the names of the variable 'populations'. + 'N_full': np.array([20683, 5834, 21915, 5479, 4850, 1065, 14395, 2948]), + # Mean rates of the different populations in the non-scaled version + # of the microcircuit. Necessary for the scaling of the network. + # The order corresponds to the order in 'populations'. + 'full_mean_rates': + np.array([0.971, 2.868, 4.746, 5.396, 8.142, 9.078, 0.991, 7.523]), + # Connection probabilities. The first index corresponds to the targets + # and the second to the sources. + 'conn_probs': + np.array( + [[0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0., 0.0076, 0.], + [0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0., 0.0042, 0.], + [0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.], + [0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0., 0.1057, 0.], + [0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.], + [0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.], + [0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252], + [0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443]] + ), + # Number of external connections to the different populations. + # The order corresponds to the order in 'populations'. + 'K_ext': np.array([1600, 1500, 2100, 1900, 2000, 1900, 2900, 2100]), + # Factor to scale the indegrees. + 'K_scaling': 0.1, + # Factor to scale the number of neurons. + 'N_scaling': 0.1, + # Mean amplitude of excitatory postsynaptic potential (in mV). + 'PSP_e': 0.15, + # Relative standard deviation of the postsynaptic potential. + 'PSP_sd': 0.1, + # Relative inhibitory synaptic strength (in relative units). + 'g': -4, + # Rate of the Poissonian spike generator (in Hz). + 'bg_rate': 8., + # Turn Poisson input on or off (True or False). + 'poisson_input': True, + # Delay of the Poisson generator (in ms). + 'poisson_delay': 1.5, + # Mean delay of excitatory connections (in ms). + 'mean_delay_exc': 1.5, + # Mean delay of inhibitory connections (in ms). + 'mean_delay_inh': 0.75, + # Relative standard deviation of the delay of excitatory and + # inhibitory connections (in relative units). + 'rel_std_delay': 0.5, + # Parameters of the neurons. + 'neuron_params': { + # Membrane potential average for the neurons (in mV). + 'V0_mean': -58.0, + # Standard deviation of the average membrane potential (in mV). + 'V0_sd': 10.0, + # Reset membrane potential of the neurons (in mV). + 'E_L': -65.0, + # Threshold potential of the neurons (in mV). + 'V_th': -50.0, + # Membrane potential after a spike (in mV). + 'V_reset': -65.0, + # Membrane capacitance (in pF). + 'C_m': 250.0, + # Membrane time constant (in ms). + 'tau_m': 10.0, + # Time constant of postsynaptic excitatory currents (in ms). + 'tau_syn_ex': 0.5, + # Time constant of postsynaptic inhibitory currents (in ms). + 'tau_syn_in': 0.5, + # Time constant of external postsynaptic excitatory current (in ms). + 'tau_syn_E': 0.5, + # Refractory period of the neurons after a spike (in ms). + 't_ref': 2.0} + } + +updated_dict = { + # PSP mean matrix. + 'PSP_mean_matrix': get_mean_PSP_matrix( + net_dict['PSP_e'], net_dict['g'], len(net_dict['populations']) + ), + # PSP std matrix. + 'PSP_std_matrix': get_std_PSP_matrix( + net_dict['PSP_sd'], len(net_dict['populations']) + ), + # mean delay matrix. + 'mean_delay_matrix': get_mean_delays( + net_dict['mean_delay_exc'], net_dict['mean_delay_inh'], + len(net_dict['populations']) + ), + # std delay matrix. + 'std_delay_matrix': get_std_delays( + net_dict['mean_delay_exc'] * net_dict['rel_std_delay'], + net_dict['mean_delay_inh'] * net_dict['rel_std_delay'], + len(net_dict['populations']) + ), + } + + +net_dict.update(updated_dict) total_cortical_thickness = 1500.0 +N_full = net_dict['N_full'] +N_E_total = N_full[0]+N_full[2]+N_full[4]+N_full[6] -# Have the thicknesses proportional to the numbers of E cells in each layer -layer_thicknesses = { - 'L23': total_cortical_thickness*N_full['L23']['E']/N_E_total, - 'L4' : total_cortical_thickness*N_full['L4']['E']/N_E_total, - 'L5' : total_cortical_thickness*N_full['L5']['E']/N_E_total, - 'L6' : total_cortical_thickness*N_full['L6']['E']/N_E_total, - 'thalamus' : 100 -} - -establish_connections = True - -# Probabilities for >=1 connection between neurons in the given populations. -# The first index is for the target population; the second for the source population -# 2/3e 2/3i 4e 4i 5e 5i 6e 6i -conn_probs = [[0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0., 0.0076, 0. ], - [0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0., 0.0042, 0. ], - [0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0. ], - [0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0., 0.1057, 0. ], - [0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0. ], - [0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0. ], - [0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252], - [0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443]] - -# In-degrees for external inputs -K_ext = { - 'L23': {'E': 1600, 'I': 1500}, - 'L4' : {'E': 2100, 'I': 1900}, - 'L5' : {'E': 2000, 'I': 1900}, - 'L6' : {'E': 2900, 'I': 2100} -} +dimensions_3D = { + 'x_dimension': 1000, + 'z_dimension': 1000, + #thalamus_offset = -300 -# Mean rates in the full-scale model, necessary for scaling -# Precise values differ somewhat between network realizations -full_mean_rates = { - 'L23': {'E': 0.971, 'I': 2.868}, - 'L4' : {'E': 4.746, 'I': 5.396}, - 'L5' : {'E': 8.142, 'I': 9.078}, - 'L6' : {'E': 0.991, 'I': 7.523} -} + 'total_cortical_thickness': total_cortical_thickness, -# Mean and standard deviation of initial membrane potential distribution -V0_mean = -58. # mV -V0_sd = 5. # mV - -# Background rate per synapse -bg_rate = 8. # spikes/s - -# Mean synaptic weight for all excitatory projections except L4e->L2/3e -w_mean = 87.8e-3 # nA -w_ext = 87.8e-3 # nA -# Mean synaptic weight for L4e->L2/3e connections -# See p. 801 of the paper, second paragraph under 'Model Parameterization', -# and the caption to Supplementary Fig. 7 -w_234 = 2 * w_mean # nA - -# Standard deviation of weight distribution relative to mean for -# all projections except L4e->L2/3e -w_rel = 0.1 -# Standard deviation of weight distribution relative to mean for L4e->L2/3e -# This value is not mentioned in the paper, but is chosen to match the -# original code by Tobias Potjans -w_rel_234 = 0.05 - -# Means and standard deviations of delays from given source populations (ms) -d_mean = {'E': 1.5, 'I': 0.75} -d_sd = {'E': 0.75, 'I': 0.375} - -# Parameters for transient thalamic input -thalamic_input = False -thal_params = { - # Number of neurons in thalamic population - 'n_thal' : 902, - # Connection probabilities - 'C' : {'L23': {'E': 0, 'I': 0}, - 'L4' : {'E': 0.0983, 'I': 0.0619}, - 'L5' : {'E': 0, 'I': 0}, - 'L6' : {'E': 0.0512, 'I': 0.0196}}, - 'rate' : 120., # spikes/s; - 'start' : 700., # ms - 'duration' : 10. # ms; +# Have the thicknesses proportional to the numbers of E cells in each layer + 'layer_thicknesses': { + 'L23': total_cortical_thickness*N_full[0]/N_E_total, + 'L4' : total_cortical_thickness*N_full[2]/N_E_total, + 'L5' : total_cortical_thickness*N_full[4]/N_E_total, + 'L6' : total_cortical_thickness*N_full[6]/N_E_total, + 'thalamus' : 100 + } } -# Plotting parameters -create_raster_plot = True -raster_t_min = 0 # ms -raster_t_max = sim_params.simulator_params[simulator]['sim_duration'] # ms -# Fraction of recorded neurons to include in raster plot -frac_to_plot = 0.01 +net_dict.update(dimensions_3D) diff --git a/PyNN/plotting.py b/PyNN/plotting.py deleted file mode 100644 index 67991ab..0000000 --- a/PyNN/plotting.py +++ /dev/null @@ -1,82 +0,0 @@ -import numpy as np -import matplotlib -matplotlib.use('Agg') -import matplotlib.pyplot as plt -import os -import glob - - -def show_raster_bars(t_start, t_stop, n_rec, frac_to_plot, path, n_scaling, k_scaling): - - # List of spike arrays, one entry for each population - spikes = [] - - # Read out spikes for each population - layer_list = ['L23', 'L4', 'L5', 'L6'] - pop_list = ['E', 'I'] - - for i in range(8): - layer = int(i / 2) - pop = i % 2 - filestart = path + 'spikes_' + str(layer_list[layer]) + '_' + str(pop_list[pop]) + '*' - filelist = glob.glob(filestart) - pop_spike_array = np.empty((0, 2)) - last_id = 0 - for file_name in filelist: - spike_array = np.loadtxt(file_name) - spike_array[:, 1] = spike_array[:, 1] + last_id - pop_spike_array = np.vstack((pop_spike_array, spike_array)) - last_id = pop_spike_array[-1, 1] - spikes.append(pop_spike_array) - - # Plot spike times in raster plot and bar plot with the average firing rates of each population - - color = ['#595289', '#af143c'] - pops = ['23E', '23I', '4E', '4I', '5E', '5I', '6E', '6I'] - rates = np.zeros(8) - fig = plt.figure(figsize=(12, 6), dpi=80) - axarr = [] - axarr.append(fig.add_subplot(121)) - axarr.append(fig.add_subplot(122)) - - # Plot raster plot - id_count = 0 - print("Mean rates") - for i in range(8)[::-1]: - layer = int(i / 2) - pop = i % 2 - rate = 0.0 - t_spikes = spikes[i][:, 0] - ids = spikes[i][:, 1] + (id_count + 1) - filtered_times_indices = [np.where((t_spikes > t_start) & (t_spikes < t_stop))][0] - t_spikes = t_spikes[filtered_times_indices] - ids = ids[filtered_times_indices] - - # Compute rates with all neurons - rate = 1000 * len(t_spikes) / (t_stop - t_start) * 1 / float(n_rec[layer][pop]) - rates[i] = rate - #print(pops[-i] + np.round(rate, 2)) - # Reduce data for raster plot - num_neurons = frac_to_plot * np.unique(ids).size - t_spikes = t_spikes[np.where(ids < num_neurons + id_count + 1)[0]] - ids = ids[np.where(ids < num_neurons + id_count + 1)[0]] - axarr[0].plot(t_spikes, ids, '.', color=color[pop], markersize=1) - print('ids for layer %s, pop %s: %s'%(layer, pop, ids)) - if len(ids)>0: - id_count = ids[-1] - - # Plot bar plot - axarr[1].barh(np.arange(0, 8, 1), rates[::-1], color=color[::-1] * 4) - - # Set labels - axarr[0].set_ylim((0.0, id_count)) - axarr[0].set_yticklabels([]) - axarr[0].set_xlabel('time (ms)') - axarr[1].set_ylim((-0.5, 7.5)) - axarr[1].set_yticks(np.arange(0, 8, 1.0)) - axarr[1].set_yticklabels(pops[::-1]) - axarr[1].set_xlabel('rate (spikes/s)') - - plt.title("Network, N scaling: %s, K scaling: %s"%(n_scaling,k_scaling), x=-.2) - - plt.savefig(path + 'result.png') diff --git a/PyNN/readme.md b/PyNN/readme.md new file mode 100644 index 0000000..62d45d8 --- /dev/null +++ b/PyNN/readme.md @@ -0,0 +1,37 @@ +# Potjans-Diesmann 2014 Cortical Microcircuit model: PyNN implementation + +Authors: Andrew Davison, based on the PyNEST implementation by Hendrik Rothe, Hannah Bos, Sacha van Albada +December 2017 + +## Description ## +This is a PyNN implementation of the microcircuit model by Potjans and Diesmann (2014): The cell-type specific +cortical microcircuit: relating structure and activity in a full-scale spiking +network model. Cerebral Cortex: doi:10.1093/cercor/bhs358 + +* Files: + * `helpers.py` + Helper functions for the simulation and evaluation of the microcircuit. + * `network.py` + Gathers all parameters and connects the different nodes with each other. + * `network_params.py` + Contains the parameters for the network. + * `sim_params.py` + Contains the simulation parameters. + * `stimulus_params.py` + Contains the parameters for the stimuli. + * `example.py` + Use this script to try out the microcircuit. + +How to use the example: + +To run the microcircuit on a local machine, adjust the variables `N_scaling` and `K_scaling` in `network_params.py` to `0.1`. `N_scaling` adjusts the number of neurons and `K_scaling` the number of connections to be simulated. The full network can be run by adjusting these values to 1. If this is done, the option to print the time progress should be set to False in the file `sim_params.py`. For running, use `python example.py`. The output will be saved in the directory `data`. + +If using NEST as the backend simulator, the code can be parallelized using OpenMP and MPI, if NEST has been built with these applications [(Parallel computing with NEST)](http://www.nest-simulator.org/parallel_computing/). The number of threads (per MPI process) can be chosen by adjusting `local_num_threads` in `sim_params.py`. The number of MPI process can be set by choosing a reasonable value for `num_mpi_prc` and then running the script with the command `mpirun -n num_mpi_prc` `python` `example.py`. + +If using NEURON as the backend simulator, the code can be parallelized using MPI, if NEURON has been built with that option. +The command to run the script is as for NEST. + +The default version of the simulation uses Poisson input, which is defined in the file `network_params.py` to excite neuronal populations of the microcircuit. If no Poisson input is provided, DC input is calculated which should approximately compensate the Poisson input. It is also possible to add thalamic stimulation to the microcircuit or drive it with constant DC input. This can be defined in the file `stimulus_params.py`. + +Tested configuration: +This version has been tested with PyNN 0.9.2, NEST 2.14.0, NEURON 7.4, Python 3.5.2, NumPy 1.13.0, mpi4py 2.0.0, Neo 0.5.2, matplotlib 2.0.2. diff --git a/PyNN/run_microcircuit.py b/PyNN/run_microcircuit.py deleted file mode 100644 index 34ef644..0000000 --- a/PyNN/run_microcircuit.py +++ /dev/null @@ -1,40 +0,0 @@ -from sim_params import system_params -import os -import shutil - -# Creates output folder if it does not exist yet, creates sim_script.sh, -# and submits it to the queue - -system_params['num_mpi_procs'] = system_params['n_nodes'] * system_params['n_procs_per_node'] - -# Copy simulation scripts to output directory -try : - os.mkdir(system_params['output_path']) -except OSError : - pass - -shutil.copy('network_params.py', system_params['output_path']) -shutil.copy('sim_params.py', system_params['output_path']) -shutil.copy('microcircuit.py', system_params['output_path']) -shutil.copy('network.py', system_params['output_path']) -shutil.copy('connectivity.py', system_params['output_path']) -shutil.copy('scaling.py', system_params['output_path']) -shutil.copy('plotting.py', system_params['output_path']) - -job_script_template = """ -#PBS -o %(output_path)s/%(outfile)s -#PBS -e %(output_path)s/%(errfile)s -#PBS -l walltime=%(walltime)s -#PBS -l nodes=%(n_nodes)d:ppn=%(n_procs_per_node)d -#PBS -q intel -#PBS -l mem=%(memory)s -. %(mpi_path)s -mpirun -np %(num_mpi_procs)d python %(output_path)s/microcircuit.py -""" - -f = open(system_params['output_path'] + '/sim_script.sh', 'w') -f.write(job_script_template % system_params) -f.close() - -os.system('cd %(output_path)s && %(submit_cmd)s sim_script.sh' % system_params) - diff --git a/PyNN/scaling.py b/PyNN/scaling.py deleted file mode 100644 index e9293e9..0000000 --- a/PyNN/scaling.py +++ /dev/null @@ -1,50 +0,0 @@ -############################################################################# -### Functions for computing and adjusting connection and input parameters ### -############################################################################# - -import numpy as np -from network_params import * - - -def get_indegrees(): - '''Get in-degrees for each connection for the full-scale (1 mm^2) model''' - K = np.zeros([n_layers * n_pops_per_layer, n_layers * n_pops_per_layer]) - for target_layer in layers: - for target_pop in pops: - for source_layer in layers: - for source_pop in pops: - target_index = structure[target_layer][target_pop] - source_index = structure[source_layer][source_pop] - n_target = N_full[target_layer][target_pop] - n_source = N_full[source_layer][source_pop] - K[target_index][source_index] = round(np.log(1. - - conn_probs[target_index][source_index]) / np.log( - (n_target * n_source - 1.) / (n_target * n_source))) / n_target - return K - - -def adjust_w_and_ext_to_K(K_full, K_scaling, w, DC): - '''Adjust synaptic weights and external drive to the in-degrees - to preserve mean and variance of inputs in the diffusion approximation''' - K_ext_new = {} - I_ext = {} - for target_layer in layers: - K_ext_new[target_layer] = {} - I_ext[target_layer] = {} - for target_pop in pops: - target_index = structure[target_layer][target_pop] - x1 = 0 - for source_layer in layers: - for source_pop in pops: - source_index = structure[source_layer][source_pop] - x1 += w[target_index][source_index] * K_full[target_index][source_index] * \ - full_mean_rates[source_layer][source_pop] - if input_type == 'poisson': - x1 += w_ext*K_ext[target_layer][target_pop]*bg_rate - K_ext_new[target_layer][target_pop] = K_ext[target_layer][target_pop]*K_scaling - I_ext[target_layer][target_pop] = 0.001 * neuron_params['tau_syn_E'] * \ - (1. - np.sqrt(K_scaling)) * x1 + DC[target_layer][target_pop] - w_new = w / np.sqrt(K_scaling) - w_ext_new = w_ext / np.sqrt(K_scaling) - return w_new, w_ext_new, K_ext_new, I_ext - diff --git a/PyNN/sim_params.py b/PyNN/sim_params.py index d77e954..7135836 100644 --- a/PyNN/sim_params.py +++ b/PyNN/sim_params.py @@ -1,47 +1,29 @@ -################################################### -### Simulation parameters ### -################################################### +# -*- coding: utf-8 -*- +""" +Simulation parameters for the microcircuit. -simulator_params = { - 'nest' : - { - 'timestep' : 0.1, # ms - 'threads' : 1, - 'sim_duration' : 1000., # ms - }, - 'neuroml' : - { - 'timestep' : 0.1, # ms - 'threads' : 1, - 'sim_duration' : 1000., # ms - 'reference' : 'Microcircuit', - 'save_format' : 'hdf5' - }, -} +Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016 +Adapted for PyNN by Andrew Davison, December 2017 +""" -system_params = { - # number of MPI nodes - 'n_nodes' : 1, - # number of MPI processes per node - 'n_procs_per_node' : 2, - # walltime for simulation - 'walltime' : '8:0:0', - # total memory for simulation - 'memory' : '4gb', +import os +from datetime import datetime - # file name for standard output - 'outfile' : 'output.txt', - # file name for error output - 'errfile' : 'errors.txt', - # absolute path to which the output files should be written - 'output_path' : 'results', - # path to the MPI shell script - 'mpi_path' : '', - # path to back-end - 'backend_path' : '', - # path to pyNN installation - 'pyNN_path' : '', - # command for submitting the job - 'submit_cmd' : 'qsub' +sim_dict = { + # Simulator + 'simulator': 'nest', + # Simulation time (in ms). + 't_sim': 1000.0, + # Resolution of the simulation (in ms). + 'sim_resolution': 0.1, + # Path to save the output data. + 'data_path': os.path.join(os.getcwd(), 'data', datetime.now().strftime("%Y%m%d-%H%M%S")), + # Masterseed for PyNN and NumPy. + 'master_seed': 55, + # Number of threads per MPI process. + 'local_num_threads': 1, + # Recording interval of the membrane potential (in ms). + 'rec_V_int': 1.0, + # Fraction of neurons from which to record membrane potentials + 'frac_record_v' : 0.1, } - diff --git a/PyNN/stimulus_params.py b/PyNN/stimulus_params.py new file mode 100644 index 0000000..bb155ee --- /dev/null +++ b/PyNN/stimulus_params.py @@ -0,0 +1,45 @@ +# -*- coding: utf-8 -*- +""" +Stimulus parameters for the microcircuit. + +Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016 +Adapted for PyNN by Andrew Davison, December 2017 +""" + +import numpy as np +from network_params import net_dict + +stim_dict = { + # Turn thalamic input on or off (True or False). + 'thalamic_input': False, + # Turn DC input on or off (True or False). + 'dc_input': False, + # Number of thalamic neurons. + 'n_thal': 902, + # Mean amplitude of the thalamic postsynaptic potential (in mV). + 'PSP_th': 0.15, + # Standard deviation of the postsynaptic potential (in relative units). + 'PSP_sd': 0.1, + # Start of the thalamic input (in ms). + 'th_start': 700.0, + # Duration of the thalamic input (in ms). + 'th_duration': 10.0, + # Rate of the thalamic input (in Hz). + 'th_rate': 120.0, + # Start of the DC generator (in ms). + 'dc_start': 0.0, + # Duration of the DC generator (in ms). + 'dc_dur': 1000.0, + # Connection probabilities of the thalamus to the different populations. + # Order as in 'populations' in 'network_params.py' + 'conn_probs_th': + np.array([0.0, 0.0, 0.0983, 0.0619, 0.0, 0.0, 0.0512, 0.0196]), + # Mean delay of the thalamic input (in ms). + 'delay_th': + np.asarray([1.5 for i in list(range(len(net_dict['populations'])))]), + # Standard deviation of the thalamic delay (in ms). + 'delay_th_sd': + np.asarray([0.75 for i in list(range(len(net_dict['populations'])))]), + # Amplitude of the DC generator (in pA). + 'dc_amp': np.ones(len(net_dict['populations'])) * 0.3, + } diff --git a/PyNN/test.py b/PyNN/test.py new file mode 100644 index 0000000..df05dd6 --- /dev/null +++ b/PyNN/test.py @@ -0,0 +1,60 @@ +# -*- coding: utf-8 -*- +""" +PyNN microcircuit example +--------------------------- + +Test file for the microcircuit. + +""" + +import time +import numpy as np +import network +from network_params import net_dict +from sim_params import sim_dict +from stimulus_params import stim_dict +import os + +def setup(simulator='nest', N_scaling=0.1): + # Initialize the network and pass parameters to it. + tic = time.time() + + net_dict['N_scaling'] = N_scaling + + net_dict['to_record'] = ['spikes', 'v'] + sim_dict['data_path'] = os.path.join(os.getcwd(), 'results') + + sim_dict['simulator'] = simulator + + net = network.Network(sim_dict, net_dict, stim_dict) + toc = time.time() - tic + print("Time to initialize the network: %.2f s" % toc) + + # Connect all nodes. + tic = time.time() + extra_setup_params = {} + if simulator=='neuroml': + extra_setup_params['reference']='Microcircuit_%spcnt'%(str(N_scaling*100).replace('.','_')) + + net.setup(extra_setup_params) + toc = time.time() - tic + print("Time to create the connections: %.2f s" % toc) + return net, net_dict, sim_dict + + +def run(net): + + # Simulate. + tic = time.time() + net.simulate() + toc = time.time() - tic + print("Time to simulate: %.2f s" % toc) + tic = time.time() + net.write_data() + toc = time.time() - tic + print("Time to write data: %.2f s" % toc) + + +if __name__ == "__main__": + net, net_dict, sim_dict = setup(N_scaling=0.1) + run(net) \ No newline at end of file diff --git a/PyNN/test_neuroml.py b/PyNN/test_neuroml.py new file mode 100644 index 0000000..6d18e3e --- /dev/null +++ b/PyNN/test_neuroml.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +""" +PyNN microcircuit example +--------------------------- + +Export to NeuroML of the microcircuit. + +""" + +import time +import numpy as np +import network +from network_params import net_dict +from sim_params import sim_dict +from stimulus_params import stim_dict +import os + +from test import setup + +def export(net): + + # Export. + tic = time.time() + print("Exporting...") + net.simulate() + net.sim.end() + toc = time.time() - tic + print("Time to export: %.2f s" % toc) + + + +if __name__ == "__main__": + net, net_dict, sim_dict = setup(simulator='neuroml', + N_scaling=0.002) + export(net) \ No newline at end of file diff --git a/PyNN/validation_microcircuit.py b/PyNN/validation_microcircuit.py deleted file mode 100644 index 5790188..0000000 --- a/PyNN/validation_microcircuit.py +++ /dev/null @@ -1,121 +0,0 @@ -################################################### -### validation_microcircuit ### -### a modification of the microcircuit.py ### -################################################### - -import os -import sys -from sim_params import simulator_params, system_params -sys.path.append(system_params['backend_path']) -sys.path.append(system_params['pyNN_path']) -from network_params import * -# import logging # TODO! Remove if it runs without this line -import pyNN -import time -from neo.io import PyNNTextIO -import plotting - - -# prepare simulation -#logging.basicConfig() # TODO! Remove if it runs without this line -simulator = simulator_params.keys()[0] -exec('import pyNN.%s as sim' % simulator) -sim.setup(**simulator_params[simulator]) -import network - -# Uncomment the two lines below when networkunit is installed -#import sciunit -#from networkunit.capabilities import ProducesSpikeTrains - -# ===================SciUnit Interface===================== - -# Uncomment the two lines below when networkunit is installed -#class Potjans2014Microcircuit( sciunit.Model, -# ProducesSpikeTrains ): -# Comment the line below when networkunit is installed -class Potjans2014Microcircuit(): - ''' - Use case: - ''' - def __init__(self): - # prepare simulation - #exec('import pyNN.%s as sim' % simulator) - sim.setup(**simulator_params[simulator]) - - # The network takes a good amount of time so rather than make the user - # wait to produce some capability it is better to create the network - # when the capability is evoked - def create_network(self): - # create network - start_netw = time.time() - self.n = network.Network(sim) - self.n.setup(sim) - end_netw = time.time() - if sim.rank() == 0 : - print('Creating the network took %g s' % (end_netw - start_netw,)) - - def simulate(self): - # simulate - if sim.rank() == 0 : - print("Simulating...") - start_sim = time.time() - t = sim.run(simulator_params[simulator]['sim_duration']) - end_sim = time.time() - if sim.rank() == 0 : - print('Simulation took %g s' % (end_sim - start_sim,)) - - def write_spiketrains(self): - start_writing = time.time() - for layer in self.n.pops : - for pop in self.n.pops[layer] : - io = PyNNTextIO(filename=system_params['output_path'] \ - + "/spikes_" + layer \ - + '_' + pop \ - + '_' + str(sim.rank()) \ - + ".txt") - spikes = self.n.pops[layer][pop].get_data('spikes', - gather=False) - for segment in spikes.segments : - io.write_segment(segment) - if record_v : - io = PyNNTextIO(filename=system_params['output_path'] \ - + "/vm_" + layer \ - + '_' + pop \ - + '_' + str(sim.rank()) \ - + ".txt") - vm = self.n.pops[layer][pop].get_data('v', - gather=False) - for segment in vm.segments : - try : - io.write_segment(segment) - except AssertionError : - pass - end_writing = time.time() - print("Writing data took %g s" % (end_writing - start_writing,)) - - def plot_and_save(self): - if create_raster_plot and sim.rank() == 0 : - # Numbers of neurons from which spikes were recorded - n_rec = [[0] * n_pops_per_layer] * n_layers - for layer, i in layers.items() : - for pop, j in pops.items() : - if record_fraction: - n_rec[i][j] = round(N_full[layer][pop] * N_scaling * frac_record_spikes) - else: - n_rec[i][j] = n_record - plotting.show_raster_bars(raster_t_min, raster_t_max, n_rec, - frac_to_plot, system_params['output_path'] + '/') - - def create_results_directory(self): - current_directory = os.getcwd() - build_path = current_directory + os.sep + "results" - if not os.path.exists("results"): - os.makedirs(build_path) - - def produce_spiketrains(self): - self.create_network() - self.simulate() - self.create_results_directory() - self.write_spiketrains() - sim.end() - print ("The model " + self.__class__.__name__ + " produces_spiketrains.") diff --git a/README.md b/README.md index 29c76ff..9b260d8 100644 --- a/README.md +++ b/README.md @@ -18,3 +18,4 @@ The model represents the approximately 80,000 neurons under a square mm of corti The code in this repository is provided under the terms of the [software license](LICENSE) included with it. If you use this model in your research, we respectfully ask you to cite the references outlined in the [CITATION](CITATION.md) file. + diff --git a/TestedRates.png b/TestedRates.png index 67f782a..a3b15db 100644 Binary files a/TestedRates.png and b/TestedRates.png differ