From 013a72f5c666e900f9919ecb0e8f6e3ce970bd3b Mon Sep 17 00:00:00 2001 From: Emma Ware Date: Wed, 19 Feb 2025 17:17:37 +0100 Subject: [PATCH 1/3] add CLEO hello world notebook --- CLEO_hello_world.ipynb | 1233 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1233 insertions(+) create mode 100644 CLEO_hello_world.ipynb diff --git a/CLEO_hello_world.ipynb b/CLEO_hello_world.ipynb new file mode 100644 index 00000000..2e35791b --- /dev/null +++ b/CLEO_hello_world.ipynb @@ -0,0 +1,1233 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "**to run on Google Colab, chenge to GPU runtime (menu: Runtime -> Change runtime type)**" + ], + "metadata": { + "id": "_VczECqVn7GC" + } + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "4-c1H8a9dVLV" + }, + "outputs": [], + "source": [ + "!wget --quiet https://github.com/yoctoyotta1024/CLEO/archive/refs/heads/main.zip" + ] + }, + { + "cell_type": "code", + "source": [ + "%%file script.sh\n", + "cd CLEO-main\n", + "\n", + "mkdir -p bin\n", + "echo \"#!/bin/bash\" > bin/module\n", + "echo \"#!/bin/bash\" > bin/spack\n", + "chmod 755 bin/*\n", + "export PATH=./bin:$PATH\n", + "export CPLUS_INCLUDE_PATH=/usr/lib/x86_64-linux-gnu/openmpi/include/\n", + "\n", + "echo -e \"levante_gxx_compiler=g++\\nlevante_gcc_compiler=gcc\" > scripts/bash/src/levante_packages.sh\n", + "\n", + ". scripts/build_compile_cleo.sh cuda gcc \\\n", + " . \\\n", + " output \\\n", + " \"golcolls longcolls\" \\\n", + " false \\\n", + " false \\\n", + " \"\" \\\n", + " false" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EG5TNb6edsF1", + "outputId": "f68a62ef-c181-4f96-c046-c1c526aca74f" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Overwriting script.sh\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!unzip main.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5-MhvxU_dYoh", + "outputId": "3ade73a6-5418-421b-b857-aaade3fd9e53" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Archive: main.zip\n", + "e8f3706c4280ef30834aef4c089a3415d5a6d9a1\n", + " creating: CLEO-main/\n", + " creating: CLEO-main/.github/\n", + " inflating: CLEO-main/.github/compare_parallel_results.sh \n", + " creating: CLEO-main/.github/workflows/\n", + " inflating: CLEO-main/.github/workflows/CI.yml \n", + " inflating: CLEO-main/.github/workflows/build.yml \n", + " inflating: CLEO-main/.github/workflows/cocogitto.yml \n", + " inflating: CLEO-main/.github/workflows/pre-commit.yml \n", + " inflating: CLEO-main/.gitignore \n", + " inflating: 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+ " inflating: CLEO-main/libs/coupldyn_fromfile/fromfilecomms.cpp \n", + " inflating: CLEO-main/libs/coupldyn_fromfile/fromfilecomms.hpp \n", + " creating: CLEO-main/libs/coupldyn_null/\n", + " inflating: CLEO-main/libs/coupldyn_null/nulldynamics.hpp \n", + " inflating: CLEO-main/libs/coupldyn_null/nulldyncomms.hpp \n", + " creating: CLEO-main/libs/coupldyn_yac/\n", + " inflating: CLEO-main/libs/coupldyn_yac/CMakeLists.txt \n", + " creating: CLEO-main/libs/coupldyn_yac/cmake/\n", + " inflating: CLEO-main/libs/coupldyn_yac/cmake/FindNetCDF.cmake \n", + " inflating: CLEO-main/libs/coupldyn_yac/cmake/FindYAC.cmake \n", + " inflating: CLEO-main/libs/coupldyn_yac/cmake/FindYAXT.cmake \n", + " inflating: CLEO-main/libs/coupldyn_yac/yac_cartesian_dynamics.cpp \n", + " inflating: CLEO-main/libs/coupldyn_yac/yac_cartesian_dynamics.hpp \n", + " inflating: CLEO-main/libs/coupldyn_yac/yac_comms.cpp \n", + " inflating: CLEO-main/libs/coupldyn_yac/yac_comms.hpp \n", + " creating: 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CLEO-main/roughpaper/src/config/config.yaml \n", + " inflating: CLEO-main/roughpaper/src/main.cpp \n", + " inflating: CLEO-main/roughpaper/src/main_impl.hpp \n", + " creating: CLEO-main/scripts/\n", + " creating: CLEO-main/scripts/bash/\n", + " inflating: CLEO-main/scripts/bash/build_cleo.sh \n", + " inflating: CLEO-main/scripts/bash/compile_cleo.sh \n", + " inflating: CLEO-main/scripts/bash/install_yac.sh \n", + " inflating: CLEO-main/scripts/bash/run_cleo.sh \n", + " creating: CLEO-main/scripts/bash/src/\n", + " inflating: CLEO-main/scripts/bash/src/build_basic.sh \n", + " inflating: CLEO-main/scripts/bash/src/build_cuda.sh \n", + " inflating: CLEO-main/scripts/bash/src/build_openmp.sh \n", + " inflating: CLEO-main/scripts/bash/src/build_threads.sh \n", + " inflating: CLEO-main/scripts/bash/src/build_yac.sh \n", + " inflating: CLEO-main/scripts/bash/src/check_inputs.sh \n", + " inflating: CLEO-main/scripts/bash/src/levante_packages.sh \n", + " inflating: CLEO-main/scripts/bash/src/runtime_settings.sh \n", + " inflating: CLEO-main/scripts/build_compile_cleo.sh \n", + " inflating: CLEO-main/scripts/cmakebuild-examples.txt \n", + " inflating: CLEO-main/scripts/compile_run_cleocoupledsdm.sh \n", + " inflating: CLEO-main/scripts/create_gbxboundariesbinary_script.py \n", + " inflating: CLEO-main/scripts/create_initsuperdropsbinary_script.py \n", + " inflating: CLEO-main/scripts/create_thermobinaries_script.py \n", + " inflating: CLEO-main/scripts/inputfiles.sh \n", + " inflating: CLEO-main/scripts/run_example.sh \n", + " inflating: CLEO-main/scripts/sbatch_allexamples.sh \n", + " inflating: CLEO-main/setup.py \n", + " creating: CLEO-main/tests/\n", + " inflating: CLEO-main/tests/test_math.py \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!. script.sh" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S2VQ5X9feUgy", + "outputId": "1af71d8f-1de1-40d0-9094-a5e4c8ce728a" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "### --------------- User Inputs -------------- ###\n", + "CLEO_BUILDTYPE = cuda\n", + "CLEO_COMPILERNAME = gcc\n", + "CLEO_PATH2CLEO = .\n", + "CLEO_PATH2BUILD = output\n", + "CLEO_ENABLEDEBUG = false\n", + "CLEO_ENABLEYAC = false\n", + "CLEO_YACYAXTROOT = \n", + "executables = golcolls longcolls\n", + "### ------------------------------------------- ###\n", + "./scripts/bash/build_cleo.sh\n", + "### --------------- Build Inputs -------------- ###\n", + "CLEO_BUILDTYPE: cuda\n", + "CLEO_COMPILERNAME: gcc\n", + "CLEO_PATH2CLEO: .\n", + "CLEO_PATH2BUILD: output\n", + "CLEO_CXX_COMPILER: g++\n", + "CLEO_CC_COMPILER: gcc\n", + "CLEO_CXX_FLAGS: -Werror -Wall -Wextra -pedantic -Wno-unused-parameter -O3 -mfma\n", + "CLEO_KOKKOS_BASIC_FLAGS: -DKokkos_ARCH_NATIVE=ON -DKokkos_ENABLE_SERIAL=ON\n", + "CLEO_KOKKOS_HOST_FLAGS: -DKokkos_ENABLE_OPENMP=ON\n", + "CLEO_KOKKOS_DEVICE_FLAGS: -DKokkos_ENABLE_CUDA=ON -DKokkos_ENABLE_CUDA_CONSTEXPR=ON -DKokkos_ENABLE_CUDA_RELOCATABLE_DEVICE_CODE=ON -DCUDA_ROOT= -DNVCC_WRAPPER_DEFAULT_COMPILER=g++\n", + "CLEO_ENABLEYAC: false\n", + "CLEO_YACYAXTROOT: \n", + "CLEO_YAC_FLAGS: -DENABLE_YAC_COUPLING=OFF\n", + "CLEO_MODULE_PATH: \n", + "### ------------------------------------------- ###\n", + "\u001b[0mCLEO_SOURCE_DIR: /content/CLEO-main\u001b[0m\n", + "\u001b[0mCLEO_BINARY_DIR: /content/CLEO-main/output\u001b[0m\n", + "-- Using Kokkos installation from: /content/CLEO-main/extern/kokkos\n", + "-- Setting default Kokkos CXX standard to 20\n", + "-- Kokkos version: 4.5.0\n", + "-- The project name is: Kokkos\n", + "-- Using internal gtest for testing\n", + "-- Compiler Version: 12.5.82\n", + "-- kokkos_launch_compiler (/content/CLEO-main/output/_deps/kokkos-src/bin/kokkos_launch_compiler) is enabled...\n", + "-- Using -std=c++20 for C++20 standard as feature\n", + "-- SIMD: AVX512 detected\n", + "-- Built-in Execution Spaces:\n", + "-- Device Parallel: Kokkos::Cuda\n", + "-- Host Parallel: Kokkos::OpenMP\n", + "-- Host Serial: SERIAL\n", + "-- \n", + "-- Architectures:\n", + "-- NATIVE\n", + "-- TURING75\n", + "-- Using internal desul_atomics copy\n", + "-- Experimental mdspan support is enabled\n", + "-- Using internal mdspan directory /content/CLEO-main/output/_deps/kokkos-src/tpls/mdspan/include\n", + "-- Kokkos Backends: OPENMP;SERIAL;CUDA\n", + "-- Kokkos installation in: /content/CLEO-main/output/kokkos\n", + "-- Using Kokkos nvcc wrapper (see: https://kokkos.org/kokkos-core-wiki/ProgrammingGuide/Compiling.html?highlight=wrapper#building-for-cuda)\n", + "-- CXX compiler: /usr/bin/g++\n", + "-- CC compiler: gcc\n", + "-- wrapper default (C++) compiler: g++\n", + "-- wrapper CUDA compiler: /bin/nvcc\n", + "-- Using yaml-cpp installation from: /content/CLEO-main/extern/yaml-cpp\n", + "\u001b[0mCMake Deprecation Warning at output/_deps/yaml-cpp-src/CMakeLists.txt:2 (cmake_minimum_required):\n", + " Compatibility with CMake < 3.10 will be removed from a future version of\n", + " CMake.\n", + "\n", + " Update the VERSION argument value. Or, use the ... syntax\n", + " to tell CMake that the project requires at least but has been updated\n", + " to work with policies introduced by or earlier.\n", + "\n", + "\u001b[0m\n", + "-- yaml-cpp installation in: /content/CLEO-main/output/yaml-cpp\n", + "-- CMAKE_CXX_FLAGS: -Werror -Wall -Wextra -pedantic -Wno-unused-parameter -O3 -mfma -fPIC\n", + "\u001b[0mgridboxes LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/gridboxes\u001b[0m\n", + "\u001b[0minitialise LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/initialise\u001b[0m\n", + "\u001b[0mruncleo LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/runcleo\u001b[0m\n", + "\u001b[0msuperdrops LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/superdrops\u001b[0m\n", + "\u001b[0mcollisions LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/superdrops/collisions\u001b[0m\n", + "\u001b[0mzarr LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/zarr\u001b[0m\n", + "\u001b[0mobservers LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/observers\u001b[0m\n", + "\u001b[0msdmmonitor LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/observers/sdmmonitor\u001b[0m\n", + "\u001b[0mcoupldyn_cvode LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/coupldyn_cvode\u001b[0m\n", + "-- SUNDIALS_GIT_VERSION: \n", + "-- Using int64_t for indices\n", + "-- C standard set to 99\n", + "-- C extensions set to ON\n", + "-- Looking for POSIX timers... found\n", + "-- Added NVECTOR_SERIAL module\n", + "-- Added NVECTOR_MANYVECTOR module\n", + "-- Added SUNMATRIX_BAND module\n", + "-- Added SUNMATRIX_DENSE module\n", + "-- Added SUNMATRIX_SPARSE module\n", + "-- Added SUNLINSOL_BAND module\n", + "-- Added SUNLINSOL_DENSE module\n", + "-- Added SUNLINSOL_PCG module\n", + "-- Added SUNLINSOL_SPBCGS module\n", + "-- Added SUNLINSOL_SPFGMR module\n", + "-- Added SUNLINSOL_SPGMR module\n", + "-- Added SUNLINSOL_SPTFQMR module\n", + "-- Added SUNNONLINSOL_NEWTON module\n", + "-- Added SUNNONLINSOL_FIXEDPOINT module\n", + "-- Added CVODES module\n", + "\u001b[0mcoupldyn_fromfile LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/coupldyn_fromfile\u001b[0m\n", + "\u001b[0mcartesiandomain LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/cartesiandomain\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/adiabaticparcel/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/golovin/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/long/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/lowlist/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/szakallurbich/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/testikstraub/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/constthermo2d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/divfreemotion/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/eurec4a1d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/rainshaft1d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/speedtest/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/fromfile/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/fromfile_irreg/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/bubble3d/src\u001b[0m\n", + "\u001b[0mroughpaper_src_cleocoupledsdm PROJECT_SOURCE_DIR: /content/CLEO-main/roughpaper/src\u001b[0m\n", + "\u001b[0mroughpaper_scratch_test PROJECT_SOURCE_DIR: /content/CLEO-main/roughpaper/scratch\u001b[0m\n", + "-- Configuring done (3.5s)\n", + "-- Generating done (0.4s)\n", + "-- Build files have been written to: /content/CLEO-main/output\n", + "./scripts/bash/compile_cleo.sh \"golcolls longcolls\" false\n", + "### --------------- Compile Inputs -------------- ###\n", + "CLEO_BUILDTYPE: cuda\n", + "CLEO_COMPILERNAME: gcc\n", + "CLEO_PATH2CLEO: .\n", + "CLEO_PATH2BUILD: output\n", + "executables: golcolls longcolls\n", + "make_clean: false\n", + "### ------------------------------------------- ###\n", + "/content/CLEO-main/output\n", + "make -j 128 golcolls longcolls\n", + "[ 2%] Built target kokkossimd\n", + "[ 31%] Built target yaml-cpp\n", + "[ 54%] Built target kokkoscore\n", + "[ 54%] Built target kokkoscontainers\n", + "[ 60%] Built target zarr\n", + "[ 62%] Built target sdmmonitor\n", + "[ 68%] Built target collisions\n", + "[ 77%] Built target initialise\n", + "[ 82%] Built target superdrops\n", + "[ 88%] Built target gridboxes\n", + "[ 91%] Built target observers\n", + "[ 94%] Built target cartesiandomain\n", + "[ 97%] Built target runcleo\n", + "[100%] Built target golcolls\n", + "[ 2%] Built target kokkossimd\n", + "[ 31%] Built target yaml-cpp\n", + "[ 54%] Built target kokkoscore\n", + "[ 54%] Built target kokkoscontainers\n", + "[ 60%] Built target collisions\n", + "[ 65%] Built target zarr\n", + "[ 74%] Built target initialise\n", + "[ 80%] Built target superdrops\n", + "[ 82%] Built target sdmmonitor\n", + "[ 88%] Built target gridboxes\n", + "[ 91%] Built target observers\n", + "[ 94%] Built target cartesiandomain\n", + "[ 97%] Built target runcleo\n", + "[100%] Built target longcolls\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install --quiet awkward ruamel.yaml" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FEr2-XU_3oiv", + "outputId": "65a991ee-686e-4956-8c59-02d9e4b007e7" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/117.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.7/117.7 kB\u001b[0m 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"text": [ + "created boundaries for 1 gridboxes\n", + "Writing gridbox boundaries binary file to:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Metadata: \n", + " '4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1'\n", + "zhalf: [ 0. 100.]\n", + "xhalf: [ 0. 100.]\n", + "yhalf: [ 0. 100.]\n", + "\n", + "------ DOMAIN / GRIDBOXES INFO ------\n", + "------------- 0-D MODEL -------------\n", + "domain dimensions: (100x100x100)m^3\n", + "domain no. gridboxes: 1x1x1\n", + "domain z limits: ( 0,100)m\n", + "domain x limits: ( 0, 100)m\n", + "domain y limits: ( 0, 100)m\n", + "mean gridbox z spacing: 100 m\n", + "mean gridbox x spacing: 100 m\n", + "mean gridbox y spacing: 100 m\n", + "mean gridbox volume: 1e+06 m^3\n", + "total domain volume: 1e+06 m^3\n", + "total no. gridboxes: 1\n", + "------------------------------------\n", + "\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Metadata: \n", + " '4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1'\n", + "zhalf: [ 0. 100.]\n", + "xhalf: [ 0. 100.]\n", + "yhalf: [ 0. 100.]\n", + "Figure .png saved as: /content/CLEO-main/output/bin/gridboxboundaries.png\n", + "Figure(1000x500)\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "4096\n", + "--- total droplet concentration = 8.38861cm^-3 => 1g/m^3, in 1e+06m^3 volume --- \n", + "Writing gridbox boundaries binary file to:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", + "\n", + "------ DOMAIN SUPERDROPLETS INFO ------\n", + "total droplet number conc: 8.38861 /cm^3\n", + "total droplet mass: 7.0856e-35 g/m^3\n", + " as if water: 1.00628 g/m^3\n", + "------------------------------------\n", + "\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", + "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_distribs_1.png\n", + "Figure(1400x400)\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", + "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_dropletmasses_1.png\n", + "Figure(1400x400)\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "--- total droplet concentration = 28.3116cm^-3 => 1g/m^3, in 1e+06m^3 volume --- \n", + "Writing gridbox boundaries binary file to:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_2.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_2.dat\n", + "attribute shapes: (8192,) (8192,) (8192,) (8192,) (0,) (0,) (0,)\n", + "\n", + "------ DOMAIN SUPERDROPLETS INFO ------\n", + "total droplet number conc: 28.3116 /cm^3\n", + "total droplet mass: 2.39139e-34 g/m^3\n", + " as if water: 0.965494 g/m^3\n", + "------------------------------------\n", + "\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_2.dat\n", + "attribute shapes: (8192,) (8192,) (8192,) (8192,) (0,) (0,) (0,)\n", + "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_distribs_2.png\n", + "Figure(1400x400)\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_2.dat\n", + "attribute shapes: (8192,) (8192,) (8192,) (8192,) (0,) (0,) (0,)\n", + "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_dropletmasses_2.png\n", + "Figure(1400x400)\n", + "/content/CLEO-main/output\n", + "Executable: /content/CLEO-main/output/examples/boxmodelcollisions/golovin/src/golcolls\n", + "Config file: /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml\n", + "\n", + "-------- Required Configuration Parameters --------------\n", + "constants_filename : \"../../libs/cleoconstants.hpp\"\n", + "grid_filename : \"./share/shima2009_dimlessGBxboundaries.dat\"\n", + "setup_filename : \"./bin/shima2009_setup.txt\"\n", + "zarrbasedir : \"./bin/shima2009_sol.zarr\"\n", + "maxchunk : 2500000\n", + "nspacedims : 0\n", + "ngbxs : 1\n", + "maxnsupers : 4096\n", + "CONDTSTEP : 200\n", + "COLLTSTEP : 1\n", + "MOTIONTSTEP : 200\n", + "COUPLTSTEP : 2000\n", + "OBSTSTEP : 200\n", + "T_END : 3800\n", + "---------------------------------------------------------\n", + "\n", + "-------- Kokkos Configuration Parameters --------------\n", + "using default kokkos settings (bool): 0\n", + "num_threads: 128\n", + "---------------------------------------------------------\n", + "\n", + "-------- InitSupersFromBinary Configuration Parameters --------------\n", + "maxnsupers: 4096\n", + "nspacedims: 0\n", + "initsupers_filename: \"/content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\"\n", + "initnsupers: 4096\n", + "---------------------------------------------------------\n", + "\n", + "--- configuration ---\n", + "----- writing to new setup file: ./bin/shima2009_setup.txt -----\n", + " copying /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml to setup file\n", + " copying ../../libs/cleoconstants.hpp to setup file\n", + "---- copy complete, setup file closed -----\n", + "--- configuration: success ---\n", + "Kokkos::OpenMP::initialize WARNING: OMP_PROC_BIND environment variable not set\n", + " In general, for best performance with OpenMP 4.0 or better set OMP_PROC_BIND=spread and OMP_PLACES=threads\n", + " For best performance with OpenMP 3.1 set OMP_PROC_BIND=true\n", + " For unit testing set OMP_PROC_BIND=false\n", + "\n", + "Kokkos::OpenMP::initialize WARNING: You are likely oversubscribing your CPU cores.\n", + " process threads available : 2, requested thread : 128\n", + "Kokkos::OpenMP::initialize WARNING: You are likely oversubscribing your CPU cores.\n", + " Detected: 2 cores per node.\n", + " Detected: 1 MPI_ranks per node.\n", + " Requested: 128 threads per process.\n", + " Kokkos Version: 4.5.0\n", + "Compiler:\n", + " KOKKOS_COMPILER_GNU: 1140\n", + " KOKKOS_COMPILER_NVCC: 1250\n", + "Architecture:\n", + " CPU architecture: none\n", + " Default Device: Cuda\n", + " GPU architecture: TURING75\n", + " platform: 64bit\n", + "Atomics:\n", + "Vectorization:\n", + " KOKKOS_ENABLE_PRAGMA_IVDEP: no\n", + " KOKKOS_ENABLE_PRAGMA_LOOPCOUNT: no\n", + " KOKKOS_ENABLE_PRAGMA_UNROLL: no\n", + " KOKKOS_ENABLE_PRAGMA_VECTOR: no\n", + "Memory:\n", + "Options:\n", + " KOKKOS_ENABLE_ASM: yes\n", + " KOKKOS_ENABLE_CXX17: no\n", + " KOKKOS_ENABLE_CXX20: yes\n", + " KOKKOS_ENABLE_CXX23: no\n", + " KOKKOS_ENABLE_CXX26: no\n", + " KOKKOS_ENABLE_DEBUG_BOUNDS_CHECK: no\n", + " KOKKOS_ENABLE_HWLOC: no\n", + " KOKKOS_ENABLE_LIBDL: yes\n", + "Host Parallel Execution Space:\n", + " KOKKOS_ENABLE_OPENMP: yes\n", + "\n", + "OpenMP Runtime Configuration:\n", + "Kokkos::OpenMP thread_pool_topology[ 1 x 128 x 1 ]\n", + "Host Serial Execution Space:\n", + " KOKKOS_ENABLE_SERIAL: yes\n", + "\n", + "Serial Runtime Configuration:\n", + "Device Execution Space:\n", + " KOKKOS_ENABLE_CUDA: yes\n", + "Cuda Options:\n", + " KOKKOS_ENABLE_CUDA_RELOCATABLE_DEVICE_CODE: yes\n", + " KOKKOS_ENABLE_CUDA_UVM: no\n", + " KOKKOS_ENABLE_IMPL_CUDA_MALLOC_ASYNC: no\n", + "\n", + "Cuda Runtime Configuration:\n", + "macro KOKKOS_ENABLE_CUDA : defined\n", + "macro CUDA_VERSION = 12050 = version 12.5\n", + "Kokkos::Cuda[ 0 ] Tesla T4 capability 7.5, Total Global Memory: 14.74 GiB, Shared Memory per Block: 48 KiB : Selected\n", + "couldn't open \"./bin/shima2009_sol.zarr/.zgroup\",\n", + " making directory \"./bin/shima2009_sol.zarr\"\n", + "\n", + "--- create cartesian gridbox maps ---\n", + "opening binary file: ./share/shima2009_dimlessGBxboundaries.dat\n", + "----------------- gridfile global metastring -----------------\n", + "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1\n", + "--------------------------------------------------------------\n", + "--- create cartesian gridbox maps: success ---\n", + "couldn't open \"./bin/shima2009_sol.zarr/time/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/time\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/sdId/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/sdId\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/xi/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/xi\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/radius/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/radius\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/msol/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/msol\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/raggedcount/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/raggedcount\"\n", + "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "----------------- gridfile global metastring -----------------\n", + "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", + "--------------------------------------------------------------\n", + "\n", + "--- create superdrops ---\n", + "initialising\n", + "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "----------------- gridfile global metastring -----------------\n", + "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", + "--------------------------------------------------------------\n", + "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "----------------- gridfile global metastring -----------------\n", + "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", + "--------------------------------------------------------------\n", + "sorting and finding superdrops in domain\n", + "checking initialisation\n", + "--- create superdrops: success ---\n", + "\n", + "--- create gridboxes ---\n", + "initialising\n", + "checking initialisation\n", + "--- create gridboxes: success ---\n", + "\n", + "--- prepare timestepping ---\n", + "observer includes write in dataset observer\n", + "observer includes time observer\n", + "observer includes StreamOutObserver\n", + "--- prepare timestepping: success ---\n", + "\n", + "--- timestepping ---\n", + "t=0.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "--- timestepping: success ---\n", + "-----\n", + " Total Program Duration: 9.5850e+00s \n", + "-----\n", + "\n", + "---- config from /content/CLEO-main/output/bin/shima2009_setup.txt -----\n", + "num_threads = 128.0\n", + "nspacedims = 0\n", + "ngbxs = 1.0\n", + "maxnsupers = 4096.0\n", + "CONDTSTEP = 200.0\n", + "COLLTSTEP = 1.0\n", + "MOTIONTSTEP = 200.0\n", + "COUPLTSTEP = 2000.0\n", + "OBSTSTEP = 200.0\n", + "T_END = 3800.0\n", + "maxchunk = 2500000.0\n", + "numSDattrs = 3\n", + "ntime = 20\n", + "---------------------------------------------\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/content/CLEO-main/examples/boxmodelcollisions/shima2009.py\", line 257, in \n", + " plot_results(\n", + " File \"/content/CLEO-main/examples/boxmodelcollisions/shima2009.py\", line 210, in plot_results\n", + " consts = pysetuptxt.get_consts(setupfile, isprint=True)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/content/CLEO-main/pySD/sdmout_src/pysetuptxt.py\", line 30, in get_consts\n", + " return consts_dict(setuptxt, isprint=isprint)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/content/CLEO-main/pySD/sdmout_src/pysetuptxt.py\", line 42, in consts_dict\n", + " consts.update(cxx2py.derive_more_floats(consts))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/content/CLEO-main/pySD/cxx2py.py\", line 125, in derive_more_floats\n", + " \"COORD0\": consts[\"TIME0\"]\n", + " ~~~~~~^^^^^^^^^\n", + "KeyError: 'TIME0'\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import Image\n", + "display(Image('CLEO-main/output/bin/initallGBxs_distribs_1.png'))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 327 + }, + "id": "cDFkmIaKqQjo", + "outputId": 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Z60/OXD9yZgAAAArNjTfemO8SAACyImeuGzlz8ZEzFw45c/3JmetHzgwAhsACAEANy5Yti+9973vx1FNP5buUBlmxYkWcd955MW3atLj66qurDTbJhTlz5sQ+++wTEyZMyNmeFRUVcfrpp8fixYvj//7v/3K2L3WzcOHCGDJkSLz66qv5LqXkjR8/PgYPHhzz58/PdynN0kMPPZT2eL9+/aJHjx5NXE3zsHz58hg6dGg8+eSTOdvzpptuinXXXTcuvfTSGmuPPfZYHHLIIbFkyZKcnKu8vDwOOuigeOWVV2KzzTbLyZ4AABSu733ve7HTTjvFa6+9VjXQddXhrl988UVcfvnlNR636n1WP3b66afHWmutVa967rnnnhpDZSMi2rRpE/3796/XngAAQPMjZ64bOXPxkTMXDjlzw8iZsydnBgAAAACAhpEz142cufjImQuHnLlh5MzZkzMDwP+kH10OAAAl7MQTT2z2gcmq/vWvf8WFF16Y0z0XL14cQ4cOzTowqWtwc+6558YDDzyQfWE0yCmnnFLvwCTXoVwp++STT+KAAw4QmNTTihUr4pFHHkm75lPzMvvhD3+Y08Ck0t/+9rca/fyVV16Jgw8+OGeBSaUvv/wyfvzjH+d0TwAAClMqlYprrrmm6rVo5Z+Vg1hXHQy76m3V166rfr3OOuvEeeedV+967r777mrfV56rf//+0apVq3rvCwAANC9y5jWTMxcnOXNhkDM3jJy5fuTMAAAAAADQMHLmNZMzFyc5c2GQMzeMnLl+5MwA8D8t810AAAAUkptvvjnuvPPOtGtbbbVVDB06NPbdd9/YeOONo3v37tGxY8dYsGBBTJ06NV577bW477774r333qv3+TfbbLPYYYcdYvvtt4/tt98+Nt5441h77bWjc+fOsdZaa8WCBQti7ty5MX369Hj11Vfj+eefj1GjRsWyZctq3ff3v/99DBw4MIYMGVLv2lb105/+NF5++eU13m/rrbeOY489NgYPHhxbbrlldO7cOVauXBmzZ8+OSZMmxWOPPRa33XZbfPnll9UelyRJnHLKKXH11VfnpF7W7OGHH4677rqr1vukUqnYbbfdYr/99ovtt98+tthii+jWrVu0b98+WrduHQsWLIjy8vIoLy+PKVOmxKRJk2LSpEnx3HPPxeeff95Ez6R5S5IkTjrppJg1a1aNtdatW8egQYNi6NChMWDAgOjWrVt07949UqlUzJ07NyZPnhxPP/10xh5WKp5//vn46quv0q4JTdK78cYb4+abb65xvF27drHvvvvGsGHDon///tG9e/fo2rVrfP311zFjxowYPXp0jBgxIl5//fVa9z/rrLNiv/32i3bt2sXs2bPjsMMOi6VLl9a435ZbbhkHHXRQ7LfffrHppptG9+7do1WrVjFr1qx455134sEHH4zbb789Fi5cmPFcY8aMiREjRsSRRx6Z/Q8CAIBmpX///vGrX/0qfv/739cY/Fr5/eqSJImI6kNjy8rK4vrrr4+OHTvWq47PP/88nn/++bTn22233eq1JwAA0PzImetGzlx85MyFQc7ccHLm7MmZAQAAAACgYeTMdSNnLj5y5sIgZ244OXP25MwAsIoEAABKTERkvHXu3LnGsW9961vJww8/XOf9P/roo1rXn3766Wr7b7vttsnvfve75J133qnX85k9e3byu9/9Lmnbtm2tz613797J0qVL63WOVd199921nicikg033DC5++6767TfokWLkgsvvDBp1apVjX169uyZ8RyDBg2q93MYNGhQxn2ffvrpeu3Zo0ePjHtOnTq13rVmsvrfo4b+bAYMGFDrf9Nhw4bV++9oRUVF8vLLLye/+tWvkt69e1ftOX369Hrt19zdeOONWfWgiEiOOOKIOv+8VqxYkXz88ceN/CwK189//vO0P8ONNtooqaioyHq/2v530Rhq+/txwgkn1GvPqVOnZtxz7bXXTjp27FjtWCqVSo4//vhkxowZa9y7oqIiue6665L27dvX+rP6+9//niRJkgwbNqzGWo8ePZIRI0bU6blMmzYtGTJkSK3n2mSTTZKVK1fW62cFAEDzc+qppyapVCopKyuruqVSqYy31e/3pz/9qUHnv/jii6v2Xf3P0aNH5+hZAgAAhUDO3DBy5vTkzJnJmauTMzcuOXNNcmYAAAAAAGg4OXPDyJnTkzNnJmeuTs7cuOTMNcmZAaDuygIAAKjy9ddfV/v+pJNOikmTJsUBBxxQ5z169epVp/sdcMAB8eKLL8akSZPit7/9bfTp0yebUqust9568dvf/jbGjx8fffv2zXi/jz76KG644YZ6naPSwoUL48wzz6z1PgMGDIg33ngjDjvssDrt2a5du7joooti7Nix0aVLl2pr06ZNq2+pZOG9996LV155JeP6r3/963jggQfq/Xc0lUrFgAED4pJLLon3338/7r333thtt90ilUrVt+SitXoPatu2bYwcOTJGjBgRG2+8cZ32aNGiRWy66aaNUF3z8NBDD6U9PnToUH/n0igvL4/58+dXfd+mTZu4//774+abb44NNthgjY9PpVJxyimnxH333RetW7fOeL+rr746HnjggXjwwQerHd9///3jnXfeiSOOOKJO9fbo0SNGjRoVe++9d8b7TJ8+PUaPHl2n/QAAaP6uuuqquPrqq6N9+/aRJElE/O/fqZluERFJkkS7du3illtuifPOO6/e516yZElcddVVVfuu+pqjY8eOMWjQoAY8MwAAoDmRM9dOzlyc5MyFQ87ccHLm7MiZAQAAAACg4eTMtZMzFyc5c+GQMzecnDk7cmYAqM4QWAAAyOBHP/pRXH/99bVeBKqPXr16xRtvvBEPP/xw7LLLLjnbt0+fPjFmzJhaL2xfdtllDTrH3//+95gxY0bG9b59+8YTTzwR3bp1y3rvXXfdNZ588slo3759Q0qkHmq7uLjXXnvFxRdfnLNzlZWVxaGHHhrPP/98bLTRRjnbtxi1a9cuRo4cGQceeGC+S2k23n777fjwww/Trg0bNqyJq2l+2rRpEw8//HC9flb77bdfnHvuuRnXP/zwwzj22GOrHTv44IPjoYceyrrvt2nTJkaMGBFrr712xvvceuutWe0JAEDz9pOf/CQ+/PDDOOecc2L99dePJEky3tZdd904++yz0/4bNVs33XRTfPHFFxER1c6RSqVin332iZYtW+bi6QEAAM2MnLkmOXNxkjMXJjlz9uTMDSNnBgAAAACAhpMz1yRnLk5y5sIkZ86enLlh5MwAYAgsAACktdNOO8U111zTKJ+y1KNHj9huu+1yvm9ExLrrrhujRo2KNm3apF2fMmVKvPzyy/Xae/HixXHFFVdkXG/Xrl3cf//90alTp3rtHxGxww47xFVXXVXvx1M/EydOzLh2xhln+LSxPPnLX/4SQ4YMyXcZzcrqn8pWqUOHDrHnnns2cTXNz+9///taP5FuTc4555xaQ/OFCxdWfd2jR4+4+eabo1WrVvU617rrrhvnnXdexvX7778/VqxYUa+9AQBonrp16xZ//vOf47PPPou33nor7rjjjrj00kvj97//fVx++eVx++23x5tvvhmzZs2KSy+9NLp3797gc44ZMyb69esXffv2jX79+lXd+vbtG0cddVQOnhUAANDcyJlrkjMXLzlzYZIzZ0/O3DByZgAAAAAAaBg5c01y5uIlZy5McubsyZkbRs4MAIbAAgBADa1atYrrrrsuysqa5z+Xe/fuHWeeeWbG9ZEjR9Zr39tvvz2+/PLLjOu//OUvo3fv3vXae1UnnnhiDBgwoMH7UHefffZZxjX/LfLjO9/5Tpx66qn5LqPZeeihh9Ie32+//TKGyfzPLrvsEmeffXaD9ujQoUMcdthhdbrvDTfc0KCQPSLihBNOyBjqLly4MN59990G7Q8AQPO19dZbx5FHHhlnn312/OpXv4ozzjgjjjrqqPj2t7+d0/Pcfffd8cYbb6S9HXrooTk9FwAAUPjkzOnJmYuXnLnwyJnrR85cf3JmAAAAAABoGDlzenLm4iVnLjxy5vqRM9efnBkA/qd5vgoEAIBGdNRRR0Xfvn3zXUaD/OQnP8m49tRTT9VrzzvuuCPj2oYbbljrJxhlI5VK1foJfeTekiVLMq517dq1CSuh0h//+MdmG9zmy+effx6vvvpq2rVhw4Y1cTXNzwUXXJCTv3N1GXQ1YMCA2GuvvRp8rg022CB22WWXjOsTJkxo8DkAAAAAAKCu5MzpyZmLl5y58MiZsydnbhg5MwAAAAAANIycOT05c/GSMxceOXP25MwNI2cGgP/xLzAAAFjNySefnO8SGqxXr17Rp0+ftGsTJkyIlStXZrXfl19+Gc8880zG9RNPPDHat2+f1Z612XnnnWOnnXbK2X7UrmPHjhnXavtUPRpHr169YvDgwfkuo9kZOXJkJElS43iLFi3ie9/7Xh4qaj569+4dBxxwQE72qkvvPu2003JyrojaP91TaAIAAAAAQFOSM9ckZy5ucubCImeuHzlz/cmZAQAAAACg4eTMNcmZi5ucubDImetHzlx/cmYA+IYhsAAAsIqePXvGwIED811GTmT69L8lS5bEBx98kNVeY8eOjYqKiozrRx11VFb71cXRRx+d8z1Jb7311su4dvfddzdhJUREHH/88ZFKpfJdRrPz0EMPpT2+++67+wTINTjkkENy9kmNnTp1io022ijjellZWU4/yTDTGwQiIqZNm5az8wAAAAAAQG3kzOnJmYubnLmwyJnrR85cf3JmAAAAqN2KFSti+vTpMWnSpBg3bly88MILMW7cuJg0aVJMnz49VqxYke8SAYA8kzOnJ2cubnLmwiJnrh85c/3JmQHgG4bAAgDAKvbYY4+iuVjZrVu3jGsff/xxVns9++yzGdf69OkT2267bVb71cURRxyR8z1Jr7b/fn/4wx/i7bffbsJqKJbgtiktWrQoRo8enXbtoIMOauJqmp9ddtklp/ttttlmGde22WabWj+tM5fnmjdvXs7OAwAAAAAAtZEzpydnLm5y5sIiZ86enLlh5MwAAADwjfLy8njggQfi7LPPjn322Sc23HDDaNu2bfTs2TO22267+M53vhMDBw6M73znO7HddttFz549o23btrHhhhvGPvvsE2effXY88MADXpcCQImRM6cnZy5ucubCImfOnpy5YeTMAPANQ2ABAGAV/fv3z3cJOdOlS5eMazNnzsxqr4kTJ2ZcGzBgQFZ71dUGG2wQm2yySaPsTXV77bVXxrV58+bFHnvsETfccEOtn55I7hRTH2oqTzzxRCxZsiTtmtBkzXbeeeec7tehQ4cmO1dtAYzQBAAAAACAplJM+Y6cmbqSMxeWYupDTUXO3DByZgAAAErd8uXL44477oj99tsv1l133Rg+fHhcfvnl8dRTT8Xnn38eFRUVkSRJxltFRUV8/vnn8dRTT8Xll18ew4cPj3XXXTf233//GDFiRKxYsSLfTxEAaGTFlO/ImakrOXNhKaY+1FTkzA0jZwaAb7TMdwEAAFBItt9++3yXEDNmzIiXX345xo8fH++//35MmzYtvvjii/jqq69i6dKlsXTp0kiSpEHnmDt3blb3f+eddzKu9evXr0G11KZfv34xffr0Rtuf/9l2221jhx12iNdffz3t+ty5c+OUU06JSy65JH74wx/G4YcfXuunVVF/PXv2jM6dO+e7jGbnoYceSnt86623js0337yJq2l+Nthgg5zuV1to0pTnEpoAAAAAANBU5MzpyZmLm5y5cMiZ60fO3DByZgAAAErVypUr45///GdceumlMWPGjIiIGtdeU6lUnfdb9bErVqyIJ598Mp588sk455xz4txzz42f/OQn0aJFi9wUDwAUFDlzenLm4iZnLhxy5vqRMzeMnBkAvmEILAAArCLXF3Pq6tNPP40bbrgh7rvvvnjzzTcb/XyZPmEqnaVLl8bs2bMzrvft2zcXJaW13XbbxahRoxptf75x4YUXrvETxqZOnRoXXHBBXHDBBbHtttvGkCFDYvDgwbHHHntE165dm6jS4pavHtScVVRUZOwTPjVvzTp27BgtW+b28lC7du0yrtX2qa65Plc2/18HAAAAAAANIWeuSc5cGuTMhUHOnD05c8PImQEAAChVL730UvzoRz+Kd955p9owtExDX2sbmFb5mNUfW/mYTz/9NM4444y49tpr4z//+U/svPPODS0fACgwcuaa5MylQc5cGOTM2ZMzN4ycGQCqMwQWAABW0alTpyY93/Tp0+PCCy+MW265JVauXNlk5126dGmd7/v555/Xur7hhhs2tJyMXEBuOkOHDo1TTjklrr/++jrd/6233oq33norLr/88kilUvGtb30rdtlll9h9991j4MCBscUWWzRyxcWpqXtQMXjppZfiiy++SLsmNFmztddeu2jP19BPmQUAAAAAgLqSM9ckZy4NcubCIGfOnpy5YeTMAAAAlKK///3vccEFF8SKFSsiSZKMw1vrKt39U6lUtX2TJIlJkybFHnvsEX/961/jzDPPrF/xAEBBkjPXJGcuDXLmwiBnzp6cuWHkzABQnSGwAACwiqa8mHPjjTfGmWeeGfPnz2+yc1bK5kLS3Llza13v2LFjQ8vJyAXkpnX11VfH7NmzY+TIkVk9LkmSeO+99+K9996Lm266KSIievbsGQceeGAcfvjhsfvuu2f8ZGuqa+oL2MXgoYceSnu8e/fuMWDAgCaupvlp0aJFUZ8PAAAAAACagpy5Jjlz6ZAz55+cOXty5oaRMwMAAFBqTj/99Lj66qurrpFWXrda/ZppQ65nJUlSbb9VB8IuX748fvGLX8TUqVPj8ssvr/c5AIDCImeuSc5cOuTM+Sdnzp6cuWHkzABQXVm+CwAAgELSrl27JjnPueeeGyeffHJeApNsLV68uNb1xgw2hCZNq3Xr1vHggw/GhRdeGK1atWrQXtOmTYurrroqBg4cGN/61rfiH//4RyxatChHlRavpupBxeTBBx9Me/zAAw+MsjKXPQAAAAAAgMYnZ65Jzlw65Mz5J2fOnpwZAAAAqKuzzjor/vnPf0aSJFWDWVcd2Fp5bNXBsNneatunci1Jkrjyyivj7LPPzsNPAQBoDHLmmuTMpUPOnH9y5uzJmQGAXPKvBwAAaGJ/+MMf4q9//WtO9mrRokXGW64+qWz58uW1rrdv3z4n50lnrbXWarS9SS+VSsVFF10U77zzThxxxBE5uej8wQcfxFlnnRVbbLFF3HbbbTmoEv5nypQp8d5776VdGzZsWBNXAwAAAAAA0HjkzHUnZ256cmaaEzkzAAAAUFfXXXddXHHFFTWGs1ZafWBru3btYtddd42f/exn8Z///CceffTReOGFF+Ktt96KqVOnxltvvRUvvvhiPProo3HdddfFqaeeGrvttlu0b9++xtDXSqsPgr388svjxhtvbKofAQDQzMmZ607O3PTkzDQncmYAINda5rsAAAAoJc8//3z89re/rdN9e/fuHbvttlv069cvNttss9hoo42ie/fu0bFjx+jQocMaP9nsoosuit/97ncNrnlN51m0aFF07NixwedJZ+HChY2yL2u2+eabx4gRI+LSSy+NW265JW677baYPHlyg/acMWNGHHfccTFq1Ki48cYbfUocDZbpU/Pat28fQ4YMaeJqAAAAAAAAGoecOTty5vyRM9McyJkBAACAupg2bVqcddZZaxz+2qJFixgyZEgcd9xxccghh9Tr+tWSJUviwQcfjNtuuy0ef/zxWLFiRY0Bs5WDaJMkiTPOOCP22muv6NGjRw6eKQBQrOTM2ZEz54+cmeZAzgwA5JohsAAA0ESSJInTTz89KioqMt6nbdu28dOf/jR+9KMfxVZbbdWg89V2nmys6cL2vHnzGi00mTdvXqPsS91tsskm8atf/Sp+9atfxaeffhpjxoyJsWPHxvPPPx8ffPBBvfa86667YuHChfHggw/m5JP5KF0PPfRQ2uNDhgwRygEAAAAAAEVBzpw9OXP+yZkpZHJmAAAAoC7OOOOMWLRoUdXg1UqrDmcdNGhQXHnllbHttts26Fxt27aNI444Io444oh4991344wzzogxY8ZUG/xaOQg24n/Dz84444yMQ4gAAOTM2ZMz55+cmUImZwYAcs2/TgEAoIk89thjMWHChIzrm222Wbz88svx//7f/2twYBIRMXfu3AbvERHRuXPnWtfnz5+fk/OkIzTJbOXKlU1+zo033jhOOOGEuPHGG+P999+PWbNmxb333hu/+MUvYvvtt696Q1FdjBo1Ki644IJGrJZiN2fOnHjppZfSrg0bNqyJqwEAAAAAAGgccubsyZkzkzNT6uTMAAAAQF28/vrrMWrUqGoDYFcdyNquXbu49dZb4+mnn27wANjV9enTJ5588sm48847o0OHDlXnjoiqQbBJksSoUaNi/PjxOT03AFA85MzZkzNnJmem1MmZAYDGYAgsAAA0kVtuuSXj2tprrx2jR4+Ofv365ex8X331VU72WX/99WtdnzFjRk7Ok87MmTMbbe/mLlehWEN069YtDj300Pj73/8e48ePj5kzZ8Z1110X++67b50+Ee+yyy6Ljz/+uAkqpRiNGjUqbXhYVlYWBx54YB4qyo1ly5bluwQAAAAAAKCAyJmzJ2fOTM5MqZMzAwAAAHXxj3/8o9r3qw5h7dKlSzzzzDNxzDHHNGoNRxxxRDz77LPRtWvXajWs6oorrmjUGgCA5kvOnD05c2ZyZkqdnBkAaAyGwAIAQBNIkiRGjx6dcf0Pf/hD9OzZM6fnnDNnTk72adu2bay33noZ1ydOnJiT86Tz5ptvNtreudayZcuMa0uWLMn5+QohNFld9+7d45RTTonHH3883n///Tj55JNr/TS9ZcuWxaWXXtqEFVJMHnroobTHv/Od70S3bt2auJrcKS8vz3cJAAAAAABAgZAz14+cOTM5M6VOzgwAAACsyeLFi+OBBx6ouka16gDYli1bxgMPPBA77LBDk9TSr1+/ePDBB6uuI65aU5Ikcf/99zfKdUQAoHmTM9ePnDkzOTOlTs4MADQGQ2ABAKAJfPzxxxlDjFatWsXRRx+d0/MlSRKvv/56zvbbeuutM641ZrAxYcKERts719q0aZNxbf78+Tk/X6F/qmDv3r3j+uuvjxEjRkSLFi0y3u/hhx9uwqooFkuWLIknnngi7dpBBx3UZHUsXLgw53sKTQAAAAAAgEpy5vqRM2cmZ6aUyZkBAACAunjuuediwYIFEfG/a6aVf6ZSqTjnnHNijz32aNJ6dt111zj33HOr1VJpwYIF8cwzzzRpPQBA4ZMz14+cOTM5M6VMzgwANBZDYAEAoAl8+OGHGdf69esXXbp0yen53n333fjyyy9ztl/fvn0zrr3yyis5O8+qZs6cGdOnT2+UvRtDx44dM641xkXQl19+Oed7NobDDz88fvnLX2ZcnzZtWkydOrUJK6IYjBkzJmNg0ZShyddff53zPZtT3wMAAAAAABqXnDl7cubayZkpZXJmAAAAoC6ee+65qq9TqVTV12uvvXZccMEF+SgpzjvvvKrrwavWFFG9XgCACDlzfciZaydnppTJmQGAxmIILAAANIGvvvoq49qGG26Y8/ONHj06p/vV9knF7777brz99ts5PV9ExF133ZXzPRtTt27dMq5NmTIlp+dKkiReeumlnO7ZmH7xi19EWVnml5+ffPJJE1ZDMXjooYfSHt9iiy2iT58+TVbHrFmzcr5nLj/1FAAAAAAAaN7kzNmTM2cmZ6bUyZkBAACAunjzzTerfZ8kSaRSqTj66KOjffv2eampffv2ceyxx0aSJDXWVq8XAEDOnD05c2ZyZkqdnBkAaCyGwAIAQBNYunRpxrVcvwkkSZL45z//mdM9Bw8eXOtF7zvuuCOn52usPRvTpptumnFt4sSJOT3XmDFjGuUTuxpLt27dYvPNN8+4PmfOnCashuYuSZIYOXJk2rWm/NS8iIhx48bldL8FCxbE5MmTc7onAAAAAADQfMmZsydnzkzOTCmTMwMAAAB19dFHH0Uqlapx/Lvf/W4eqvnG/vvvX+NYkiTx0Ucf5aEaAKCQyZmzJ2fOTM5MKZMzAwCNyRBYAABoAl26dMm4Nnv27Jye6+GHH875J7Wtt956tX563k033RSLFi3K2fleffXVnF+MbGxbbrllxrXnnnsup+e67LLLcrpfU1hnnXUyrtUWKsLqxo0bFzNnzky7NmzYsCat5cUXX8zpfnfffXesXLkyp3sCAAAAAADNl5w5O3Lm2smZKWVyZgAAAKCuZs2alfZ4v379mriS6vr27Vvt+8pBtZnqBQBKl5w5O3Lm2smZKWVyZgCgMRkCCwAATWC99dbLuPbGG2/EihUrcnKehQsXxi9+8Yuc7LW6o48+OuPajBkz4i9/+UtOzpMkSZxxxhk52asp7bjjjhnX3nnnnZg0aVJOzjNu3Lh49NFHc7JXU6otHOzcuXPTFUKz9+CDD6Y9vu6668auu+7apLXcd999MW/evJztd+211+ZsLwAAAAAAoPmTM9ednLl2cmZKnZwZAAAAqKuFCxemPb7++us3cSXVde/ePe3xTPUCAKVLzlx3cubayZkpdXJmAKAxGQILAABNYJNNNsm4Nnfu3HjiiSdycp4zzjgj3n///Zzstbpjjz02unbtmnH90ksvjY8++qjB57npppvilVdeafA+TW3HHXeM9u3bZ1z/5z//2eBzLFiwII455phIkqTBezWljz/+OKZOnZpxfYsttsh6z8GDB0cqlcp4Gzt2bAMqLg1jx46t9Wc4ePDgfJeY1kMPPZT2+AEHHBAtWrRo0loWLFiQs6Dj3nvvjZdffrnW+/hUPQAAAAAAKC1y5rqTM2cmZ/6GnLnh5MwNJ2cGAACAwrZ8+fIax1KpVLRs2TIP1XyjZcuWkUqlahzP1RA3AKB4yJnrTs6cmZz5G3LmhpMzN5ycGQCKjyGwAADQBDbYYIPYaqutMq6fd955ad8oko2LL744brjhhgbtUZv27dvHaaedlnF98eLFccghh8T8+fPrfY7x48fXeo5C1qpVq9hvv/0yrv/nP/+J8ePH13v/ZcuWxYknnpjzUKy8vDwGDRoUDz/8cKOFMX/605+ioqIi7Vq3bt1i8803b5TzUnymTp0ab731Vtq1YcOGNXE1//Pb3/423n333Qbt8fnnn8dPf/rTNd7v66+/btB5AAAAAACA5kXOXDdy5szkzCBnljMDAABAdtIN00qSpEHXMHNhwYIFaa/F1Tb8CwAoTXLmupEzZyZnBjmznBkAGp8hsAAA0ERqu6A+adKkOO644zJeWK7N8uXL4/TTT48LL7ywIeXVyf/93//F+uuvn3F94sSJsc8++8QXX3yR9d4vvfRS7LPPPrFo0aKGlJhXRx99dMa1ioqKOOyww+KTTz7Jet958+bFd7/73bj33nsbUl5aSZLEs88+GwceeGB8+9vfjhtvvDEWLlyYs/3/+c9/xr///e+M64ceemjaT6OGdB588MG0x9u0aVNrj21MixcvjoMOOig++OCDej3+ww8/jD322KNOfXPixIn1OgcAAAAAANB8yZlrJ2fOTM4M/yNnljMDAABANjp16pT2eH1fx+fKhx9+mPZ4pnoBgNImZ66dnDkzOTP8j5xZzgwAjc0QWAAAaCInn3xyrReH77rrrhg0aFBWn4w2cuTI2HbbbeOqq66qsda7d+961VmbDh06xGWXXVbrfV555ZX/r707Dc6qPv8Gfh2WIktCICi4gjsiUPYgqIBVETdswSnWpWoXbF3qUju2Hf+uY3WqTnXacRsd3KpVsSMiiAhBRRCMiiK0tVBFRBQQBEFlkfO84ElMyB3MfhPy+czckzu/3znXuU6c8cX56nWid+/elX7A/9VXX8X1118fQ4YMidWrV5fZ69KlS3VbzYof/vCH0blz5wr333///Tj66KNjypQplar3zTffxP333x/dunWL6dOnl9nbb7/9atRrJgsWLIjzzz8/dt999xg9enQ88cQT1Q5QFi1aFD/5yU92+CbEpk2bxiWXXFLddmmEJkyYkHH9mGOOidatW9dzN99atGhRDBw4MB588MH45ptvKnXOpk2b4q9//WsUFBRUOnD58MMPY9KkSWXWtv/3JgAAAAAAsGuRM2cmZ66YnBnKkjPLmQEAAKAqunTpEmmallufOXNmFrr51quvvlrm9zRNI0mSHT5bBAAaLzlzZnLmismZoSw5s5wZAOpas2w3AAAAjUXPnj3jxz/+cTz++OMVHjNz5szo2rVrnHzyyTFy5MgYOHBg7L333pGbmxvr16+PTz/9NJYsWRLPP/98TJgwId57772MdU444YQoKCiI6667rtbvY8yYMfHcc8/FI488UuExy5Yti9GjR0f37t3jrLPOiqFDh8ahhx4abdu2jW+++SZWrlwZ77zzTkyZMiUefvjhWLVqVbka7du3j5tvvjnGjBlT6/dQV5o2bRrXX399/PSnP63wmCVLlsQJJ5wQw4YNi9GjR8fQoUOjU6dOkZeXF+vWrYsVK1bEf//735g8eXJMnDgxlixZUq7GPvvsE7fffnuMHj26Tu7jq6++ivHjx8f48ePje9/7XvTs2TP69u0b/fr1i8MPPzzy8/OjXbt20a5du2jSpEls2LAhVq9eHe+99168/fbbMWnSpHj55Ze/8+HxhRdeGIcddlid3AO7njVr1sQrr7yScW/kyJH13E15n332WZx77rlxww03xOmnnx7Dhg2Lrl27Rn5+frRq1So+//zzWLVqVbz77rvx4osvxoQJE+Kjjz6q8nXGjBkTl156aey7775RWFgY06dPj08++aQO7ggAAAAAANgZyJnlzBFy5tLkzFSFnHkbOTMAAABU3sEHH1xu4GpExBNPPBEXX3xxFjra5sknn8y4fvDBB9dzJwBAQyBnljNHyJlLkzNTFXLmbeTMAFC3DIEFAIB6dNNNN8WUKVNizZo1FR6zdevWmDBhQoVviPou+++/f4wbNy7uuuuu6rb5ne655574z3/+E6+//voOj3v33XfjqquuKvk9SZKMb0TeXpIkcf/990deXl5NW613Z599djz00EMxbdq0HR5XWFgYhYWFVa7funXreOqpp+Krr76qbotVsmnTpigqKoqioqK45557aq1uv3794pZbbqm1euz6Jk2aFFu2bCm3niRJnHLKKVnoKKKgoCDmzJlTZm3x4sVx8803x80331ytmieffHK88cYbsXz58oz7X3zxRdxwww0lv7dt27Za1wEAAAAAABoOObOcuZicWc5M1ciZt5EzAwAAQOUNHDgwxo0bFxERaZqWPJ+cNWtWzJ49O4444oh67+n111+Pl156qaSXJEnK9AsAkImcWc5cTM4sZ6Zq5MzbyJkBoG41yXYDAADQmOy///4xfvz4aN68eZ3U79ixY7zwwgvRsWPHOqlfrFWrVjFx4sTo0aNHlc6rTGASEXHLLbfEaaedVo3Osi9JknjkkUdiv/32q/XaLVq0iPHjx0dBQUGt165PvXr1iueffz522223atfI9PC8tJYtW1a7dmPR0P6GFQXJ/fr1i7322queu9nmvvvuq9W/U+fOneOBBx6IY489ttZqAgAAAAAADZ+cWc5cE3LmbzW0jHRn1ND+hnJmAAAAoKqGDBmScT1N07joooti8+bN9drPli1b4sILL6xwf+jQofXXDADQoMiZ5cw1IWf+VkPLSHdGDe1vKGcGAOqDIbAAAFDPhg0bFn//+9+jdevWtVq3e/fuMWfOnDjooINqtW5F9thjj3jppZdq9T8YadKkSdx5551x5ZVX1lrNbOjUqVNMnTq1VoOTDh06xLRp02L48OG1VjMbzjnnnJg5c2bk5+dXu8aWLVvi7bffrnC/V69eMWDAgGrXbyyKiop2uD927Nh66uS7bdq0KZ5//vmMe6eeemo9d/OtHj16xMMPPxxNmzatca0999wzpk6dGrvvvnucf/75tdAdAAAAAACwK5EzV0zOXDE587fkzLVDzlxzcmYAAADYuR166KHRvXv3iNg2VCtN00iSJCIi5s2bV+/PPy666KIoKioq00vxQLPu3bvHoYceWq/9AAANi5y5YnLmismZvyVnrh1y5pqTMwPArscQWAAAyILRo0fH3Llzo2vXrjWu1bRp07jsssvitddei86dO9dCd5XXrl27ePHFF+O6666r0VvQIiK6dOkSU6dOjYsvvriWusuuQw45JObOnRsjRoyoca0RI0bEm2++GYMHD66Fzspq06ZN3HrrrTFkyJBo1qxZrdcv1rNnz3j55ZfjwQcfrHFgWFRUFOvXr69w/5prrin5j6yo2IwZMyrc69WrV4wcObL+mvkOM2bMiHXr1mXcy2ZoEhExatSoeOaZZ6Jt27bVrjFgwICYO3duHHzwwRGx7W3stfHvDgAAAAAAYNciZy5PzlwxOXNZcubaIWeuHXJmAAAA2Lmde+65JYNWI6LM8NUHH3wwzj///NiyZUud9rB169b41a9+Fffee2+Zwa/FkiSJ8847r057AAB2DXLm8uTMFZMzlyVnrh1y5tohZwaAXYshsAAAkCXdunWLd955J+6///5qvXm3VatWMXbs2Hj33Xfj9ttvL/cgOi8vLzp37pzxk5eXV0t3sS20+b//+7/417/+FT//+c+jZcuWVTp/3333jdtuuy3efffdOOaYY2qtr51Bx44dY9KkSfHPf/4z+vfvX+XzBw8eHM8++2xMmjQp9t133zroMKJZs2ZxxRVXxIwZM2LlypXx2GOPxS9+8Ys4/PDDaxw8tG7dOs4888x4/vnn46233oqjjjqqVnre0cP+3r1771QP+3dWW7ZsiVdffbXC/WuvvXanCp4mTJiQcb1Lly7Rs2fPeu6mvJNOOikWLlwY55xzTjRv3rzS5+21115xxx13xKxZs2KfffYps/fII49E3759d3h+mzZtdpmgGQAAAAAAqBw58zZ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+ "text/plain": [ + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "TWcachyj-Jv2" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From b25e3b3690adcaebae41587b27ec1fa6c506205a Mon Sep 17 00:00:00 2001 From: Emma Ware Date: Thu, 20 Feb 2025 16:27:32 +0100 Subject: [PATCH 2/3] make the Python script plot output + move the notebook into the examples folder --- CLEO_hello_world.ipynb | 1233 ------------------------ examples/CLEO_hello_world.ipynb | 1587 +++++++++++++++++++++++++++++++ 2 files changed, 1587 insertions(+), 1233 deletions(-) delete mode 100644 CLEO_hello_world.ipynb create mode 100644 examples/CLEO_hello_world.ipynb diff --git a/CLEO_hello_world.ipynb b/CLEO_hello_world.ipynb deleted file mode 100644 index 2e35791b..00000000 --- a/CLEO_hello_world.ipynb +++ /dev/null @@ -1,1233 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "**to run on Google Colab, chenge to GPU runtime (menu: Runtime -> Change runtime type)**" - ], - "metadata": { - "id": "_VczECqVn7GC" - } - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "4-c1H8a9dVLV" - }, - "outputs": [], - "source": [ - "!wget --quiet https://github.com/yoctoyotta1024/CLEO/archive/refs/heads/main.zip" - ] - }, - { - "cell_type": "code", - "source": [ - "%%file script.sh\n", - "cd CLEO-main\n", - "\n", - "mkdir -p bin\n", - "echo \"#!/bin/bash\" > bin/module\n", - "echo \"#!/bin/bash\" > bin/spack\n", - "chmod 755 bin/*\n", - "export PATH=./bin:$PATH\n", - "export CPLUS_INCLUDE_PATH=/usr/lib/x86_64-linux-gnu/openmpi/include/\n", - "\n", - "echo -e \"levante_gxx_compiler=g++\\nlevante_gcc_compiler=gcc\" > scripts/bash/src/levante_packages.sh\n", - "\n", - ". scripts/build_compile_cleo.sh cuda gcc \\\n", - " . \\\n", - " output \\\n", - " \"golcolls longcolls\" \\\n", - " false \\\n", - " false \\\n", - " \"\" \\\n", - " false" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EG5TNb6edsF1", - "outputId": "f68a62ef-c181-4f96-c046-c1c526aca74f" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Overwriting script.sh\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!unzip main.zip" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5-MhvxU_dYoh", - "outputId": "3ade73a6-5418-421b-b857-aaade3fd9e53" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Archive: main.zip\n", - "e8f3706c4280ef30834aef4c089a3415d5a6d9a1\n", - " creating: CLEO-main/\n", - " creating: CLEO-main/.github/\n", - " 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CLEO-main/roughpaper/index_test.cpp \n", - " creating: CLEO-main/roughpaper/scratch/\n", - " inflating: CLEO-main/roughpaper/scratch/CMakeLists.txt \n", - " inflating: CLEO-main/roughpaper/scratch/build_compile_test.sh \n", - " inflating: CLEO-main/roughpaper/scratch/cleotypes_sizes.hpp \n", - " inflating: CLEO-main/roughpaper/scratch/main.cpp \n", - " creating: CLEO-main/roughpaper/src/\n", - " inflating: CLEO-main/roughpaper/src/CMakeLists.txt \n", - " creating: CLEO-main/roughpaper/src/config/\n", - " inflating: CLEO-main/roughpaper/src/config/config.yaml \n", - " inflating: CLEO-main/roughpaper/src/main.cpp \n", - " inflating: CLEO-main/roughpaper/src/main_impl.hpp \n", - " creating: CLEO-main/scripts/\n", - " creating: CLEO-main/scripts/bash/\n", - " inflating: CLEO-main/scripts/bash/build_cleo.sh \n", - " inflating: CLEO-main/scripts/bash/compile_cleo.sh \n", - " inflating: CLEO-main/scripts/bash/install_yac.sh \n", - " inflating: CLEO-main/scripts/bash/run_cleo.sh \n", - " creating: CLEO-main/scripts/bash/src/\n", - " inflating: CLEO-main/scripts/bash/src/build_basic.sh \n", - " inflating: CLEO-main/scripts/bash/src/build_cuda.sh \n", - " inflating: CLEO-main/scripts/bash/src/build_openmp.sh \n", - " inflating: CLEO-main/scripts/bash/src/build_threads.sh \n", - " inflating: CLEO-main/scripts/bash/src/build_yac.sh \n", - " inflating: CLEO-main/scripts/bash/src/check_inputs.sh \n", - " inflating: CLEO-main/scripts/bash/src/levante_packages.sh \n", - " inflating: CLEO-main/scripts/bash/src/runtime_settings.sh \n", - " inflating: CLEO-main/scripts/build_compile_cleo.sh \n", - " inflating: CLEO-main/scripts/cmakebuild-examples.txt \n", - " inflating: CLEO-main/scripts/compile_run_cleocoupledsdm.sh \n", - " inflating: CLEO-main/scripts/create_gbxboundariesbinary_script.py \n", - " inflating: CLEO-main/scripts/create_initsuperdropsbinary_script.py \n", - " inflating: CLEO-main/scripts/create_thermobinaries_script.py \n", - " inflating: CLEO-main/scripts/inputfiles.sh \n", - " inflating: CLEO-main/scripts/run_example.sh \n", - " inflating: CLEO-main/scripts/sbatch_allexamples.sh \n", - " inflating: CLEO-main/setup.py \n", - " creating: CLEO-main/tests/\n", - " inflating: CLEO-main/tests/test_math.py \n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!. script.sh" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "S2VQ5X9feUgy", - "outputId": "1af71d8f-1de1-40d0-9094-a5e4c8ce728a" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "### --------------- User Inputs -------------- ###\n", - "CLEO_BUILDTYPE = cuda\n", - "CLEO_COMPILERNAME = gcc\n", - "CLEO_PATH2CLEO = .\n", - "CLEO_PATH2BUILD = output\n", - "CLEO_ENABLEDEBUG = false\n", - "CLEO_ENABLEYAC = false\n", - "CLEO_YACYAXTROOT = \n", - "executables = golcolls longcolls\n", - "### ------------------------------------------- ###\n", - "./scripts/bash/build_cleo.sh\n", - "### --------------- Build Inputs -------------- ###\n", - "CLEO_BUILDTYPE: cuda\n", - "CLEO_COMPILERNAME: gcc\n", - "CLEO_PATH2CLEO: .\n", - "CLEO_PATH2BUILD: output\n", - "CLEO_CXX_COMPILER: g++\n", - "CLEO_CC_COMPILER: gcc\n", - "CLEO_CXX_FLAGS: -Werror -Wall -Wextra -pedantic -Wno-unused-parameter -O3 -mfma\n", - "CLEO_KOKKOS_BASIC_FLAGS: -DKokkos_ARCH_NATIVE=ON -DKokkos_ENABLE_SERIAL=ON\n", - "CLEO_KOKKOS_HOST_FLAGS: -DKokkos_ENABLE_OPENMP=ON\n", - "CLEO_KOKKOS_DEVICE_FLAGS: -DKokkos_ENABLE_CUDA=ON -DKokkos_ENABLE_CUDA_CONSTEXPR=ON -DKokkos_ENABLE_CUDA_RELOCATABLE_DEVICE_CODE=ON -DCUDA_ROOT= -DNVCC_WRAPPER_DEFAULT_COMPILER=g++\n", - "CLEO_ENABLEYAC: false\n", - "CLEO_YACYAXTROOT: \n", - "CLEO_YAC_FLAGS: -DENABLE_YAC_COUPLING=OFF\n", - "CLEO_MODULE_PATH: \n", - "### ------------------------------------------- ###\n", - "\u001b[0mCLEO_SOURCE_DIR: /content/CLEO-main\u001b[0m\n", - "\u001b[0mCLEO_BINARY_DIR: /content/CLEO-main/output\u001b[0m\n", - "-- Using Kokkos installation from: /content/CLEO-main/extern/kokkos\n", - "-- Setting default Kokkos CXX standard to 20\n", - "-- Kokkos version: 4.5.0\n", - "-- The project name is: Kokkos\n", - "-- Using internal gtest for testing\n", - "-- Compiler Version: 12.5.82\n", - "-- kokkos_launch_compiler (/content/CLEO-main/output/_deps/kokkos-src/bin/kokkos_launch_compiler) is enabled...\n", - "-- Using -std=c++20 for C++20 standard as feature\n", - "-- SIMD: AVX512 detected\n", - "-- Built-in Execution Spaces:\n", - "-- Device Parallel: Kokkos::Cuda\n", - "-- Host Parallel: Kokkos::OpenMP\n", - "-- Host Serial: SERIAL\n", - "-- \n", - "-- Architectures:\n", - "-- NATIVE\n", - "-- TURING75\n", - "-- Using internal desul_atomics copy\n", - "-- Experimental mdspan support is enabled\n", - "-- Using internal mdspan directory /content/CLEO-main/output/_deps/kokkos-src/tpls/mdspan/include\n", - "-- Kokkos Backends: OPENMP;SERIAL;CUDA\n", - "-- Kokkos installation in: /content/CLEO-main/output/kokkos\n", - "-- Using Kokkos nvcc wrapper (see: https://kokkos.org/kokkos-core-wiki/ProgrammingGuide/Compiling.html?highlight=wrapper#building-for-cuda)\n", - "-- CXX compiler: /usr/bin/g++\n", - "-- CC compiler: gcc\n", - "-- wrapper default (C++) compiler: g++\n", - "-- wrapper CUDA compiler: /bin/nvcc\n", - "-- Using yaml-cpp installation from: /content/CLEO-main/extern/yaml-cpp\n", - "\u001b[0mCMake Deprecation Warning at output/_deps/yaml-cpp-src/CMakeLists.txt:2 (cmake_minimum_required):\n", - " Compatibility with CMake < 3.10 will be removed from a future version of\n", - " CMake.\n", - "\n", - " Update the VERSION argument value. Or, use the ... syntax\n", - " to tell CMake that the project requires at least but has been updated\n", - " to work with policies introduced by or earlier.\n", - "\n", - "\u001b[0m\n", - "-- yaml-cpp installation in: /content/CLEO-main/output/yaml-cpp\n", - "-- CMAKE_CXX_FLAGS: -Werror -Wall -Wextra -pedantic -Wno-unused-parameter -O3 -mfma -fPIC\n", - "\u001b[0mgridboxes LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/gridboxes\u001b[0m\n", - "\u001b[0minitialise LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/initialise\u001b[0m\n", - "\u001b[0mruncleo LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/runcleo\u001b[0m\n", - "\u001b[0msuperdrops LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/superdrops\u001b[0m\n", - "\u001b[0mcollisions LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/superdrops/collisions\u001b[0m\n", - "\u001b[0mzarr LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/zarr\u001b[0m\n", - "\u001b[0mobservers LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/observers\u001b[0m\n", - "\u001b[0msdmmonitor LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/observers/sdmmonitor\u001b[0m\n", - "\u001b[0mcoupldyn_cvode LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/coupldyn_cvode\u001b[0m\n", - "-- SUNDIALS_GIT_VERSION: \n", - "-- Using int64_t for indices\n", - "-- C standard set to 99\n", - "-- C extensions set to ON\n", - "-- Looking for POSIX timers... found\n", - "-- Added NVECTOR_SERIAL module\n", - "-- Added NVECTOR_MANYVECTOR module\n", - "-- Added SUNMATRIX_BAND module\n", - "-- Added SUNMATRIX_DENSE module\n", - "-- Added SUNMATRIX_SPARSE module\n", - "-- Added SUNLINSOL_BAND module\n", - "-- Added SUNLINSOL_DENSE module\n", - "-- Added SUNLINSOL_PCG module\n", - "-- Added SUNLINSOL_SPBCGS module\n", - "-- Added SUNLINSOL_SPFGMR module\n", - "-- Added SUNLINSOL_SPGMR module\n", - "-- Added SUNLINSOL_SPTFQMR module\n", - "-- Added SUNNONLINSOL_NEWTON module\n", - "-- Added SUNNONLINSOL_FIXEDPOINT module\n", - "-- Added CVODES module\n", - "\u001b[0mcoupldyn_fromfile LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/coupldyn_fromfile\u001b[0m\n", - "\u001b[0mcartesiandomain LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/cartesiandomain\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/adiabaticparcel/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/golovin/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/long/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/lowlist/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/szakallurbich/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/testikstraub/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/constthermo2d/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/divfreemotion/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/eurec4a1d/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/rainshaft1d/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/speedtest/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/fromfile/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/fromfile_irreg/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/bubble3d/src\u001b[0m\n", - "\u001b[0mroughpaper_src_cleocoupledsdm PROJECT_SOURCE_DIR: /content/CLEO-main/roughpaper/src\u001b[0m\n", - "\u001b[0mroughpaper_scratch_test PROJECT_SOURCE_DIR: /content/CLEO-main/roughpaper/scratch\u001b[0m\n", - "-- Configuring done (3.5s)\n", - "-- Generating done (0.4s)\n", - "-- Build files have been written to: /content/CLEO-main/output\n", - "./scripts/bash/compile_cleo.sh \"golcolls longcolls\" false\n", - "### --------------- Compile Inputs -------------- ###\n", - "CLEO_BUILDTYPE: cuda\n", - "CLEO_COMPILERNAME: gcc\n", - "CLEO_PATH2CLEO: .\n", - "CLEO_PATH2BUILD: output\n", - "executables: golcolls longcolls\n", - "make_clean: false\n", - "### ------------------------------------------- ###\n", - "/content/CLEO-main/output\n", - "make -j 128 golcolls longcolls\n", - "[ 2%] Built target kokkossimd\n", - "[ 31%] Built target yaml-cpp\n", - "[ 54%] Built target kokkoscore\n", - "[ 54%] Built target kokkoscontainers\n", - "[ 60%] Built target zarr\n", - "[ 62%] Built target sdmmonitor\n", - "[ 68%] Built target collisions\n", - "[ 77%] Built target initialise\n", - "[ 82%] Built target superdrops\n", - "[ 88%] Built target gridboxes\n", - "[ 91%] Built target observers\n", - "[ 94%] Built target cartesiandomain\n", - "[ 97%] Built target runcleo\n", - "[100%] Built target golcolls\n", - "[ 2%] Built target kokkossimd\n", - "[ 31%] Built target yaml-cpp\n", - "[ 54%] Built target kokkoscore\n", - "[ 54%] Built target kokkoscontainers\n", - "[ 60%] Built target collisions\n", - "[ 65%] Built target zarr\n", - "[ 74%] Built target initialise\n", - "[ 80%] Built target superdrops\n", - "[ 82%] Built target sdmmonitor\n", - "[ 88%] Built target gridboxes\n", - "[ 91%] Built target observers\n", - "[ 94%] Built target cartesiandomain\n", - "[ 97%] Built target runcleo\n", - "[100%] Built target longcolls\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install --quiet awkward ruamel.yaml" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "FEr2-XU_3oiv", - "outputId": "65a991ee-686e-4956-8c59-02d9e4b007e7" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/117.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.7/117.7 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/739.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m739.1/739.1 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!cd CLEO-main; \\\n", - " python3 \\\n", - " examples/boxmodelcollisions/shima2009.py \\\n", - " /content/CLEO-main \\\n", - " /content/CLEO-main/output \\\n", - " /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml \\\n", - " golovin long1 long2" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Ux4-6J0rnaoM", - "outputId": "78ffbc38-7c68-4814-fe11-acd03efb3bb2" - }, - "execution_count": 37, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "created boundaries for 1 gridboxes\n", - "Writing gridbox boundaries binary file to:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Metadata: \n", - " '4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1'\n", - "zhalf: [ 0. 100.]\n", - "xhalf: [ 0. 100.]\n", - "yhalf: [ 0. 100.]\n", - "\n", - "------ DOMAIN / GRIDBOXES INFO ------\n", - "------------- 0-D MODEL -------------\n", - "domain dimensions: (100x100x100)m^3\n", - "domain no. gridboxes: 1x1x1\n", - "domain z limits: ( 0,100)m\n", - "domain x limits: ( 0, 100)m\n", - "domain y limits: ( 0, 100)m\n", - "mean gridbox z spacing: 100 m\n", - "mean gridbox x spacing: 100 m\n", - "mean gridbox y spacing: 100 m\n", - "mean gridbox volume: 1e+06 m^3\n", - "total domain volume: 1e+06 m^3\n", - "total no. gridboxes: 1\n", - "------------------------------------\n", - "\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Metadata: \n", - " '4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1'\n", - "zhalf: [ 0. 100.]\n", - "xhalf: [ 0. 100.]\n", - "yhalf: [ 0. 100.]\n", - "Figure .png saved as: /content/CLEO-main/output/bin/gridboxboundaries.png\n", - "Figure(1000x500)\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "4096\n", - "--- total droplet concentration = 8.38861cm^-3 => 1g/m^3, in 1e+06m^3 volume --- \n", - "Writing gridbox boundaries binary file to:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", - "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", - "\n", - "------ DOMAIN SUPERDROPLETS INFO ------\n", - "total droplet number conc: 8.38861 /cm^3\n", - "total droplet mass: 7.0856e-35 g/m^3\n", - " as if water: 1.00628 g/m^3\n", - "------------------------------------\n", - "\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", - "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", - "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_distribs_1.png\n", - "Figure(1400x400)\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", - "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", - "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_dropletmasses_1.png\n", - "Figure(1400x400)\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "--- total droplet concentration = 28.3116cm^-3 => 1g/m^3, in 1e+06m^3 volume --- \n", - "Writing gridbox boundaries binary file to:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_2.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_2.dat\n", - "attribute shapes: (8192,) (8192,) (8192,) (8192,) (0,) (0,) (0,)\n", - "\n", - "------ DOMAIN SUPERDROPLETS INFO ------\n", - "total droplet number conc: 28.3116 /cm^3\n", - "total droplet mass: 2.39139e-34 g/m^3\n", - " as if water: 0.965494 g/m^3\n", - "------------------------------------\n", - "\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_2.dat\n", - "attribute shapes: (8192,) (8192,) (8192,) (8192,) (0,) (0,) (0,)\n", - "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_distribs_2.png\n", - "Figure(1400x400)\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", - "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_2.dat\n", - "attribute shapes: (8192,) (8192,) (8192,) (8192,) (0,) (0,) (0,)\n", - "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_dropletmasses_2.png\n", - "Figure(1400x400)\n", - "/content/CLEO-main/output\n", - "Executable: /content/CLEO-main/output/examples/boxmodelcollisions/golovin/src/golcolls\n", - "Config file: /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml\n", - "\n", - "-------- Required Configuration Parameters --------------\n", - "constants_filename : \"../../libs/cleoconstants.hpp\"\n", - "grid_filename : \"./share/shima2009_dimlessGBxboundaries.dat\"\n", - "setup_filename : \"./bin/shima2009_setup.txt\"\n", - "zarrbasedir : \"./bin/shima2009_sol.zarr\"\n", - "maxchunk : 2500000\n", - "nspacedims : 0\n", - "ngbxs : 1\n", - "maxnsupers : 4096\n", - "CONDTSTEP : 200\n", - "COLLTSTEP : 1\n", - "MOTIONTSTEP : 200\n", - "COUPLTSTEP : 2000\n", - "OBSTSTEP : 200\n", - "T_END : 3800\n", - "---------------------------------------------------------\n", - "\n", - "-------- Kokkos Configuration Parameters --------------\n", - "using default kokkos settings (bool): 0\n", - "num_threads: 128\n", - "---------------------------------------------------------\n", - "\n", - "-------- InitSupersFromBinary Configuration Parameters --------------\n", - "maxnsupers: 4096\n", - "nspacedims: 0\n", - "initsupers_filename: \"/content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\"\n", - "initnsupers: 4096\n", - "---------------------------------------------------------\n", - "\n", - "--- configuration ---\n", - "----- writing to new setup file: ./bin/shima2009_setup.txt -----\n", - " copying /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml to setup file\n", - " copying ../../libs/cleoconstants.hpp to setup file\n", - "---- copy complete, setup file closed -----\n", - "--- configuration: success ---\n", - "Kokkos::OpenMP::initialize WARNING: OMP_PROC_BIND environment variable not set\n", - " In general, for best performance with OpenMP 4.0 or better set OMP_PROC_BIND=spread and OMP_PLACES=threads\n", - " For best performance with OpenMP 3.1 set OMP_PROC_BIND=true\n", - " For unit testing set OMP_PROC_BIND=false\n", - "\n", - "Kokkos::OpenMP::initialize WARNING: You are likely oversubscribing your CPU cores.\n", - " process threads available : 2, requested thread : 128\n", - "Kokkos::OpenMP::initialize WARNING: You are likely oversubscribing your CPU cores.\n", - " Detected: 2 cores per node.\n", - " Detected: 1 MPI_ranks per node.\n", - " Requested: 128 threads per process.\n", - " Kokkos Version: 4.5.0\n", - "Compiler:\n", - " KOKKOS_COMPILER_GNU: 1140\n", - " KOKKOS_COMPILER_NVCC: 1250\n", - "Architecture:\n", - " CPU architecture: none\n", - " Default Device: Cuda\n", - " GPU architecture: TURING75\n", - " platform: 64bit\n", - "Atomics:\n", - "Vectorization:\n", - " KOKKOS_ENABLE_PRAGMA_IVDEP: no\n", - " KOKKOS_ENABLE_PRAGMA_LOOPCOUNT: no\n", - " KOKKOS_ENABLE_PRAGMA_UNROLL: no\n", - " KOKKOS_ENABLE_PRAGMA_VECTOR: no\n", - "Memory:\n", - "Options:\n", - " KOKKOS_ENABLE_ASM: yes\n", - " KOKKOS_ENABLE_CXX17: no\n", - " KOKKOS_ENABLE_CXX20: yes\n", - " KOKKOS_ENABLE_CXX23: no\n", - " KOKKOS_ENABLE_CXX26: no\n", - " KOKKOS_ENABLE_DEBUG_BOUNDS_CHECK: no\n", - " KOKKOS_ENABLE_HWLOC: no\n", - " KOKKOS_ENABLE_LIBDL: yes\n", - "Host Parallel Execution Space:\n", - " KOKKOS_ENABLE_OPENMP: yes\n", - "\n", - "OpenMP Runtime Configuration:\n", - "Kokkos::OpenMP thread_pool_topology[ 1 x 128 x 1 ]\n", - "Host Serial Execution Space:\n", - " KOKKOS_ENABLE_SERIAL: yes\n", - "\n", - "Serial Runtime Configuration:\n", - "Device Execution Space:\n", - " KOKKOS_ENABLE_CUDA: yes\n", - "Cuda Options:\n", - " KOKKOS_ENABLE_CUDA_RELOCATABLE_DEVICE_CODE: yes\n", - " KOKKOS_ENABLE_CUDA_UVM: no\n", - " KOKKOS_ENABLE_IMPL_CUDA_MALLOC_ASYNC: no\n", - "\n", - "Cuda Runtime Configuration:\n", - "macro KOKKOS_ENABLE_CUDA : defined\n", - "macro CUDA_VERSION = 12050 = version 12.5\n", - "Kokkos::Cuda[ 0 ] Tesla T4 capability 7.5, Total Global Memory: 14.74 GiB, Shared Memory per Block: 48 KiB : Selected\n", - "couldn't open \"./bin/shima2009_sol.zarr/.zgroup\",\n", - " making directory \"./bin/shima2009_sol.zarr\"\n", - "\n", - "--- create cartesian gridbox maps ---\n", - "opening binary file: ./share/shima2009_dimlessGBxboundaries.dat\n", - "----------------- gridfile global metastring -----------------\n", - "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1\n", - "--------------------------------------------------------------\n", - "--- create cartesian gridbox maps: success ---\n", - "couldn't open \"./bin/shima2009_sol.zarr/time/.zarray\",\n", - " making directory \"./bin/shima2009_sol.zarr/time\"\n", - "couldn't open \"./bin/shima2009_sol.zarr/sdId/.zarray\",\n", - " making directory \"./bin/shima2009_sol.zarr/sdId\"\n", - "couldn't open \"./bin/shima2009_sol.zarr/xi/.zarray\",\n", - " making directory \"./bin/shima2009_sol.zarr/xi\"\n", - "couldn't open \"./bin/shima2009_sol.zarr/radius/.zarray\",\n", - " making directory \"./bin/shima2009_sol.zarr/radius\"\n", - "couldn't open \"./bin/shima2009_sol.zarr/msol/.zarray\",\n", - " making directory \"./bin/shima2009_sol.zarr/msol\"\n", - "couldn't open \"./bin/shima2009_sol.zarr/raggedcount/.zarray\",\n", - " making directory \"./bin/shima2009_sol.zarr/raggedcount\"\n", - "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", - "----------------- gridfile global metastring -----------------\n", - "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", - "--------------------------------------------------------------\n", - "\n", - "--- create superdrops ---\n", - "initialising\n", - "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", - "----------------- gridfile global metastring -----------------\n", - "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", - "--------------------------------------------------------------\n", - "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", - "----------------- gridfile global metastring -----------------\n", - "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", - "--------------------------------------------------------------\n", - "sorting and finding superdrops in domain\n", - "checking initialisation\n", - "--- create superdrops: success ---\n", - "\n", - "--- create gridboxes ---\n", - "initialising\n", - "checking initialisation\n", - "--- create gridboxes: success ---\n", - "\n", - "--- prepare timestepping ---\n", - "observer includes write in dataset observer\n", - "observer includes time observer\n", - "observer includes StreamOutObserver\n", - "--- prepare timestepping: success ---\n", - "\n", - "--- timestepping ---\n", - "t=0.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=1000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=1200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=1400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=1600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=1800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=2000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=2200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=2400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=2600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=2800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=3000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=3200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=3400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=3600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "t=3800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", - "--- timestepping: success ---\n", - "-----\n", - " Total Program Duration: 9.5850e+00s \n", - "-----\n", - "\n", - "---- config from /content/CLEO-main/output/bin/shima2009_setup.txt -----\n", - "num_threads = 128.0\n", - "nspacedims = 0\n", - "ngbxs = 1.0\n", - "maxnsupers = 4096.0\n", - "CONDTSTEP = 200.0\n", - "COLLTSTEP = 1.0\n", - "MOTIONTSTEP = 200.0\n", - "COUPLTSTEP = 2000.0\n", - "OBSTSTEP = 200.0\n", - "T_END = 3800.0\n", - "maxchunk = 2500000.0\n", - "numSDattrs = 3\n", - "ntime = 20\n", - "---------------------------------------------\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/content/CLEO-main/examples/boxmodelcollisions/shima2009.py\", line 257, in \n", - " plot_results(\n", - " File \"/content/CLEO-main/examples/boxmodelcollisions/shima2009.py\", line 210, in plot_results\n", - " consts = pysetuptxt.get_consts(setupfile, isprint=True)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/content/CLEO-main/pySD/sdmout_src/pysetuptxt.py\", line 30, in get_consts\n", - " return consts_dict(setuptxt, isprint=isprint)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/content/CLEO-main/pySD/sdmout_src/pysetuptxt.py\", line 42, in consts_dict\n", - " consts.update(cxx2py.derive_more_floats(consts))\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/content/CLEO-main/pySD/cxx2py.py\", line 125, in derive_more_floats\n", - " \"COORD0\": consts[\"TIME0\"]\n", - " ~~~~~~^^^^^^^^^\n", - "KeyError: 'TIME0'\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from IPython.display import Image\n", - "display(Image('CLEO-main/output/bin/initallGBxs_distribs_1.png'))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 327 - }, - "id": "cDFkmIaKqQjo", - "outputId": "8e97fc64-8e65-4c38-ddb0-91a9c025a96f" - }, - "execution_count": 33, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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WVhbt2rWLLbbYIo477rh49NFH45VXXok+ffrUes6zzz47Jk6cWK96ly5dmvZ469at67VfpTZt2tT5XEDd6HvfaEjfA5oXve8b+ex97777bhx33HGRJEm1461atYpbb701WrVqlZe6AIDG9dxzz1V9verQ1RYtWuTtDR1XXnlllJWV1agpIuKFF17IR0kAADQzrjl+Q84MpUHf+4acGUqH3vcNOTMAAFDoOnToEGeffXa11w6rDmE9//zz4+yzz46KiopGOf+DDz4YAwYMiOnTp1edc3V//OMfG+XcADQfrjl+Q84MpUHf+4acGUqH3vcNOTNQbAyBBQCopxYtWsQ222wTJ554Ylx11VXxyiuvxPz58+P111+P9dZbL9/lVfPvf/87Xn311YzrrVq1ivvuuy/+8Ic/RLt27da438477xyvvvpq7Lnnnhnvs3Tp0jj11FPrVe/y5cvTHm/ZsmW99quU7oVzpnMBNel7jdf3gMKl9xVm7/v888/jgAMOSBsQ/e1vf4v+/fs3eU0AQNN49913qw1arfwFmwMPPDB69+6dl5q23HLL+N73vlfjzRxJksQ777yTl5oAAChcrjnKmaHU6HuFmbUAjUvvK8zeJ2cGAACy8X//93+x5ZZbRsQ3Hwq86iDYyy+/PPr16xdjxozJ2TnHjRsXhxxySAwfPjzmz59fba3yvKlUKg499NAYPHhwzs4LQOFzzVHODKVG3yvMrAVoXHpfYfY+OTPQWBr2qhMAoESkUqnYfPPNY8cdd4yddtopdtxxx+jfv3+stdZa+S5tjb7++uv4zW9+U+t9rrvuuhg2bFhW+3bo0CFGjhwZ3/nOd+Ktt95Ke5/nn38+RowYEUceeWRWe5eVpf+sguXLlzfo0/OWLVtW53NBqdP3amrMvgcUBr2vpkLsfeXl5bH//vvHtGnTaqydeOKJccYZZzRJHQBAfsycOTPt8UMOOaSJK6l5/pEjR1Z9X/mLN5nqBQCgNLjmWJOcGYqbvldTIWYtQG7pfTUVYu+TMwMAANlq1apV3HrrrbHHHnvEsmXLqt4Lsuog2Lfffjv23Xff2HbbbeOoo46Kgw46KLbccsto0aJFnc6xbNmyeOWVV+LZZ5+Nxx9/PF544YWI+GbY7OpfR0Ssv/768a9//Sv3TxiAguGaY01yZihu+l5NhZi1ALml99VUiL1Pzgw0JkNgAQBq8b3vfS9233332HHHHaNz5875Lqderrnmmvjqq68yrh9zzDFx/PHH12vvtdZaK+66667YbrvtMn4C3R//+MesX0BnCkaWLFnSoNBk8eLFNY61adOm3vtBMdL3atdYfQ/IL72vdoXU+xYuXBgHHHBAvPnmmzXWhg8fHtddd12j1wAA5Nf8+fPTHt95552buJK6nX/hwoVNXAkAAIXANcfayZmh+Oh7tSukrAXIHb2vdoXU++TMAABAfe24445xzTXXxMknnxypVCrtINgkSWLSpEnx1ltvxa9+9ato1apVbLHFFrFy5cqq+6w6xHXXXXeNr776Kr766quYO3duVFRUVK0lSRIRkXYAbJIk0bZt27jrrrtinXXWacKfAgBNxTXH2smZofjoe7UrpKwFyB29r3aF1PvkzEBj8zEhAAC12GmnnWLIkCHN9sXzsmXL4oorrsi43qFDh7j00ksbdI6tt946Tj/99IzrkyZNikcffTSrPTN9Ok260CMb6R7fvn37Bu0JxUbfW7PG6HtAful9a1YIvW/x4sUxdOjQePHFF2usHXjggXHnnXdGixYtGrUGACD/WrVqlfZ4z549m7aQ1fTo0SPt8ZYtfSYnAEApcs1xzeTMUFz0vTUrhKwFyC29b80KoffJmQEAgIY68cQT4/LLL884oHXV75MkiWXLlsXbb79ddWz1P1955ZWYMmVKzJkzJ1auXFn1uHT7rfp1ixYt4vbbb4/dd9+96Z48AE3KNcc1kzNDcdH31qwQshYgt/S+NSuE3idnBpqCIbAAAEXskUceic8//zzj+g9/+MPYYIMNGnyec845p9ZPoLv++uuz2q9r165pjy9cuDCrferyeJ9+C8WlufY9gIYohd63ZMmSGDZsWDz99NM11vbbb7+45557Mg6EAwCKS6dOnWocS6VStf47pSm0a9cu7fF09QIAQKFrrtcc5cxAfTXXvgfQEKXQ++TMAABArpxxxhlx/fXXV72GqBzWuvrw1lWHuFb+ubp09199v9UHwLZr1y7uu+++OOSQQ5rmCQNAPTTXa45yZqC+mmvfA2iIUuh9cmagqRgCCwBQxG6//faMa6lUqtZPP8lG9+7d44gjjsi4PmrUqCgvL6/zft26dUt7/LPPPsu6tjU9PtO5gOapufY9gIYo9t63dOnSOPTQQ+PJJ5+ssbbXXnvF/fffn/ehbwBA09loo40iSZJqx5IkiaVLl+apov9ZvHhxte8ra9xoo43yUQ4AADRIc73mKGcG6qu59j2Ahij23idnBgAAcu2kk06Kp59+Onr16lX1vpB0w1srj6/+/pZKqw+HTffYVQfAbr755vHss8/G0KFDG+upAUBONNdrjnJmoL6aa98DaIhi731yZqApGQILAFCklixZEg8//HDG9V122SV69eqVs/MdffTRGdeWLl0aI0eOrPNem2yySZSV1fyn6scff1yv2ip98sknNY717NmzQXsChaM59z2A+ir23rds2bIYPnx4PProozXWBg0aFCNHjox27drl9JwAQGHbZptt0h6fNm1a0xaymnTXrVKpVMZ6AQCgUDXna45yZqA+mnPfA6ivYu99cmYAAKCx7LLLLjFp0qT45S9/GW3atKkxuHXVgbCZrDr0Nd3jK+/TokWLOOOMM2LChAmxww47NP6TA4AGaM7XHOXMQH00574HUF/F3vvkzEBTMwQWAKBIPf/887F48eKM64ccckhOz7f33ntHp06dMq6n+6STTFq3bh2bbrppjePpQo9spHv8Zptt1qA9gcLRnPseQH0Vc+9bvnx5HHbYYWlDod133z0efvjhaN++fc7OBwA0D9/5znfSHn/11VebuJK6nT9TvQAAUKia8zVHOTNQH8257wHUVzH3PjkzAADQ2Nq1axd//etf44MPPojTTjstOnXqlHGga11vEd8Mh23dunUcd9xx8dZbb8Xll1/uNQwAzUJzvuYoZwbqozn3PYD6KubeJ2cG8sEQWACAIjV69Oha14cMGZLT87Vs2TIGDRqUcT3bF9DbbbddjWPjx4/Ptqwq5eXl8dFHH9XpPEDz1Nz7HkB9FGvvW758eRx++OFpP4lvl112iUceeSTWWmutnJwLAGhehg4dmvb4/fff38SVrPn8qVQqY70AAFComvs1RzkzkK3m3vcA6qNYe5+cGQAAaEobbrhhXHHFFTFz5sy444474uijj4511lmnaphrNreWLVvGXnvtFVdccUV88skncfPNN8e3vvWtfD9FAKiz5n7NUc4MZKu59z2A+ijW3idnBvLFEFgAgCL18ssvZ1zr1KlT9OvXL+fn3GOPPTKuzZw5M6tPvtt5551rHHvhhRfqVVdExIsvvhgVFRU1ju+000713hMoLM297wHURzH2vhUrVsSRRx4ZDzzwQI21AQMGxGOPPRYdO3Zs0DkAgOZr4403jr322iuSJImI/w1aTZIkRo0alfYNs01hypQp8fDDD0cqlYqIiCRJIpVKxT777BPrr79+XmoCAID6au7XHOXMQLaae98DqI9i7H1yZgAAIF/atm0bRx55ZNx2220xe/bs+PDDD+Oee+6JP/3pT3HqqafG97///fje974Xe++9d+yzzz5x0EEHxbHHHhvnn39+/Pvf/46XX3455s+fH6NHj47TTjst1ltvvXw/JQDIWnO/5ihnBrLV3PseQH0UY++TMwP51DLfBQAAkHtJksSECRMyrm+33XZVQylyaYcddqh1/Y033ohNN920TnvtvffeNY59/vnn8d5778WWW26ZdW3PPPNMjWPbbLNNbLDBBlnvBRSeYuh7ANkqxt63YsWKOOqoo+K+++6rsbbTTjvF448/Hp06darX3gBA8TjvvPPiqaeeqnZs5cqVcdppp8UjjzzS5PWcdtppUVFRUePfXueff36T1wIAAA1RDNcc5cxANoqh7wFkqxh7n5wZAAAoJL169YpevXrluwwAaDLFcM1Rzgxkoxj6HkC2irH3yZmBfCvLdwEAAOTe1KlTo7y8PON63759G+W8a9q3thf1q9txxx1j/fXXr3H8lltuybasWLlyZdx+++01jg8dOjTrvYDCVAx9DyBbxdb7Vq5cGccee2zcc889NdZ23HHHeOKJJ2Lttdeu194AQHEZMmRIDB8+PJIkiYiIVCoVSZLE448/Hr/73e+atJbf/OY3MXr06Ko3qyRJEqlUKo444ogYOHBgk9YCAAANVQzXHOXMQDaKoe8BZKvYep+cGQAAAADyqxiuOcqZgWwUQ98DyFax9T45M1AIDIEFAChC77//fq3rW2yxRaOcd9111631heya6lpVWVlZHH300TWO33jjjbF8+fKs6nr00Ufj008/rXH82GOPzWofoHAVQ98DyFYx9b6Kioo44YQT4q677qqx1r9//3jiiSeic+fOWe8LABSvq6++OjbeeOOq7ysHwV588cVx0UUXNUkNv/71r+MPf/hDjU8r7tGjR1x11VVNUgMAAORSMVxzlDMD2SiGvgeQrWLqfXJmAAAAAMi/YrjmKGcGslEMfQ8gW8XU++TMQKEwBBYAoAhNmzat1vXNNtus0c7du3fvjGtTp07Naq+f/vSn0aJFi2rHZs6cGZdeemmd91i2bFmcd955NY4PHjw4ttlmm6zqAQpXsfQ9gGwUS++rqKiIk046Ke0nHW+//fYxevTo6NKlS9Y1AgDFbb311ovHHnus2r8TKgfB/v73v4/9998/pkyZ0ijnfvfdd2OfffaJP/3pT9WOJ0kS6667bjz22GPRtWvXRjk3AAA0pmK55ihnBuqqWPoeQDa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- "text/plain": [ - "" - ] - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "TWcachyj-Jv2" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/examples/CLEO_hello_world.ipynb b/examples/CLEO_hello_world.ipynb new file mode 100644 index 00000000..6d9b0813 --- /dev/null +++ b/examples/CLEO_hello_world.ipynb @@ -0,0 +1,1587 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "**to run on Google Colab, chenge to GPU runtime (menu: Runtime -> Change runtime type)**" + ], + "metadata": { + "id": "_VczECqVn7GC" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "4-c1H8a9dVLV" + }, + "outputs": [], + "source": [ + "!wget --quiet https://github.com/yoctoyotta1024/CLEO/archive/refs/heads/main.zip" + ] + }, + { + "cell_type": "code", + "source": [ + "%%file script.sh\n", + "cd CLEO-main\n", + "\n", + "mkdir -p bin\n", + "echo \"#!/bin/bash\" > bin/module\n", + "echo \"#!/bin/bash\" > bin/spack\n", + "chmod 755 bin/*\n", + "export PATH=./bin:$PATH\n", + "export CPLUS_INCLUDE_PATH=/usr/lib/x86_64-linux-gnu/openmpi/include/\n", + "\n", + "echo -e \"levante_gxx_compiler=g++\\nlevante_gcc_compiler=gcc\" > scripts/bash/src/levante_packages.sh\n", + "\n", + ". scripts/build_compile_cleo.sh cuda gcc \\\n", + " . \\\n", + " output \\\n", + " \"golcolls longcolls\" \\\n", + " false \\\n", + " false \\\n", + " \"\" \\\n", + " false" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EG5TNb6edsF1", + "outputId": "5295ba20-bca9-452a-9ef7-fba51fbba2e2" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Writing script.sh\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!unzip main.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5-MhvxU_dYoh", + "outputId": "e92a6578-9930-479c-95c7-f46e26847fec" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Archive: main.zip\n", + "e8f3706c4280ef30834aef4c089a3415d5a6d9a1\n", + " creating: CLEO-main/\n", + " creating: CLEO-main/.github/\n", + " inflating: CLEO-main/.github/compare_parallel_results.sh \n", + " creating: CLEO-main/.github/workflows/\n", + " inflating: CLEO-main/.github/workflows/CI.yml \n", + " inflating: CLEO-main/.github/workflows/build.yml \n", + " inflating: CLEO-main/.github/workflows/cocogitto.yml \n", + " inflating: CLEO-main/.github/workflows/pre-commit.yml \n", + " inflating: CLEO-main/.gitignore \n", + " inflating: CLEO-main/.pre-commit-config.yaml \n", + " inflating: CLEO-main/CHANGELOG.md \n", + " 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+ " inflating: CLEO-main/libs/coupldyn_fromfile/fromfilecomms.cpp \n", + " inflating: CLEO-main/libs/coupldyn_fromfile/fromfilecomms.hpp \n", + " creating: CLEO-main/libs/coupldyn_null/\n", + " inflating: CLEO-main/libs/coupldyn_null/nulldynamics.hpp \n", + " inflating: CLEO-main/libs/coupldyn_null/nulldyncomms.hpp \n", + " creating: CLEO-main/libs/coupldyn_yac/\n", + " inflating: CLEO-main/libs/coupldyn_yac/CMakeLists.txt \n", + " creating: CLEO-main/libs/coupldyn_yac/cmake/\n", + " inflating: CLEO-main/libs/coupldyn_yac/cmake/FindNetCDF.cmake \n", + " inflating: CLEO-main/libs/coupldyn_yac/cmake/FindYAC.cmake \n", + " inflating: CLEO-main/libs/coupldyn_yac/cmake/FindYAXT.cmake \n", + " inflating: CLEO-main/libs/coupldyn_yac/yac_cartesian_dynamics.cpp \n", + " inflating: CLEO-main/libs/coupldyn_yac/yac_cartesian_dynamics.hpp \n", + " inflating: CLEO-main/libs/coupldyn_yac/yac_comms.cpp \n", + " inflating: CLEO-main/libs/coupldyn_yac/yac_comms.hpp \n", + " creating: 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CLEO-main/roughpaper/src/config/config.yaml \n", + " inflating: CLEO-main/roughpaper/src/main.cpp \n", + " inflating: CLEO-main/roughpaper/src/main_impl.hpp \n", + " creating: CLEO-main/scripts/\n", + " creating: CLEO-main/scripts/bash/\n", + " inflating: CLEO-main/scripts/bash/build_cleo.sh \n", + " inflating: CLEO-main/scripts/bash/compile_cleo.sh \n", + " inflating: CLEO-main/scripts/bash/install_yac.sh \n", + " inflating: CLEO-main/scripts/bash/run_cleo.sh \n", + " creating: CLEO-main/scripts/bash/src/\n", + " inflating: CLEO-main/scripts/bash/src/build_basic.sh \n", + " inflating: CLEO-main/scripts/bash/src/build_cuda.sh \n", + " inflating: CLEO-main/scripts/bash/src/build_openmp.sh \n", + " inflating: CLEO-main/scripts/bash/src/build_threads.sh \n", + " inflating: CLEO-main/scripts/bash/src/build_yac.sh \n", + " inflating: CLEO-main/scripts/bash/src/check_inputs.sh \n", + " inflating: CLEO-main/scripts/bash/src/levante_packages.sh \n", + " inflating: CLEO-main/scripts/bash/src/runtime_settings.sh \n", + " inflating: CLEO-main/scripts/build_compile_cleo.sh \n", + " inflating: CLEO-main/scripts/cmakebuild-examples.txt \n", + " inflating: CLEO-main/scripts/compile_run_cleocoupledsdm.sh \n", + " inflating: CLEO-main/scripts/create_gbxboundariesbinary_script.py \n", + " inflating: CLEO-main/scripts/create_initsuperdropsbinary_script.py \n", + " inflating: CLEO-main/scripts/create_thermobinaries_script.py \n", + " inflating: CLEO-main/scripts/inputfiles.sh \n", + " inflating: CLEO-main/scripts/run_example.sh \n", + " inflating: CLEO-main/scripts/sbatch_allexamples.sh \n", + " inflating: CLEO-main/setup.py \n", + " creating: CLEO-main/tests/\n", + " inflating: CLEO-main/tests/test_math.py \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!. script.sh" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S2VQ5X9feUgy", + "outputId": "c708e2b3-deac-4fda-85bb-09abf9771b31" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "### --------------- User Inputs -------------- ###\n", + "CLEO_BUILDTYPE = cuda\n", + "CLEO_COMPILERNAME = gcc\n", + "CLEO_PATH2CLEO = .\n", + "CLEO_PATH2BUILD = output\n", + "CLEO_ENABLEDEBUG = false\n", + "CLEO_ENABLEYAC = false\n", + "CLEO_YACYAXTROOT = \n", + "executables = golcolls longcolls\n", + "### ------------------------------------------- ###\n", + "./scripts/bash/build_cleo.sh\n", + "### --------------- Build Inputs -------------- ###\n", + "CLEO_BUILDTYPE: cuda\n", + "CLEO_COMPILERNAME: gcc\n", + "CLEO_PATH2CLEO: .\n", + "CLEO_PATH2BUILD: output\n", + "CLEO_CXX_COMPILER: g++\n", + "CLEO_CC_COMPILER: gcc\n", + "CLEO_CXX_FLAGS: -Werror -Wall -Wextra -pedantic -Wno-unused-parameter -O3 -mfma\n", + "CLEO_KOKKOS_BASIC_FLAGS: -DKokkos_ARCH_NATIVE=ON -DKokkos_ENABLE_SERIAL=ON\n", + "CLEO_KOKKOS_HOST_FLAGS: -DKokkos_ENABLE_OPENMP=ON\n", + "CLEO_KOKKOS_DEVICE_FLAGS: -DKokkos_ENABLE_CUDA=ON -DKokkos_ENABLE_CUDA_CONSTEXPR=ON -DKokkos_ENABLE_CUDA_RELOCATABLE_DEVICE_CODE=ON -DCUDA_ROOT= -DNVCC_WRAPPER_DEFAULT_COMPILER=g++\n", + "CLEO_ENABLEYAC: false\n", + "CLEO_YACYAXTROOT: \n", + "CLEO_YAC_FLAGS: -DENABLE_YAC_COUPLING=OFF\n", + "CLEO_MODULE_PATH: \n", + "### ------------------------------------------- ###\n", + "-- The CXX compiler identification is GNU 11.4.0\n", + "-- Detecting CXX compiler ABI info\n", + "-- Detecting CXX compiler ABI info - done\n", + "-- Check for working CXX compiler: /usr/bin/g++ - skipped\n", + "-- Detecting CXX compile features\n", + "-- Detecting CXX compile features - done\n", + "-- Found MPI_CXX: /usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi_cxx.so (found version \"3.1\")\n", + "-- Found MPI: TRUE (found version \"3.1\")\n", + "\u001b[0mCLEO_SOURCE_DIR: /content/CLEO-main\u001b[0m\n", + "\u001b[0mCLEO_BINARY_DIR: /content/CLEO-main/output\u001b[0m\n", + "-- Using Kokkos installation from: /content/CLEO-main/extern/kokkos\n", + "-- Setting default Kokkos CXX standard to 20\n", + "-- Kokkos version: 4.5.0\n", + "-- The project name is: Kokkos\n", + "-- Using internal gtest for testing\n", + "-- Compiler Version: 12.5.82\n", + "-- kokkos_launch_compiler (/content/CLEO-main/output/_deps/kokkos-src/bin/kokkos_launch_compiler) is enabled...\n", + "-- Using -std=c++20 for C++20 standard as feature\n", + "-- SIMD: AVX512 detected\n", + "-- CUDA auto-detection of architecture failed with /usr/bin/g++. Enabling CUDA language ONLY to auto-detect architecture...\n", + "-- Looking for a CUDA compiler\n", + "-- Looking for a CUDA compiler - /usr/local/cuda/bin/nvcc\n", + "-- The CUDA compiler identification is NVIDIA 12.5.82 with host compiler GNU 11.4.0\n", + "-- Detecting CUDA compiler ABI info\n", + "-- Detecting CUDA compiler ABI info - done\n", + "-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped\n", + "-- Detecting CUDA compile features\n", + "-- Detecting CUDA compile features - done\n", + "-- Detected CUDA Compute Capability 75\n", + "-- Setting Kokkos_ARCH_TURING75=ON\n", + "-- Built-in Execution Spaces:\n", + "-- Device Parallel: Kokkos::Cuda\n", + "-- Host Parallel: Kokkos::OpenMP\n", + "-- Host Serial: SERIAL\n", + "-- \n", + "-- Architectures:\n", + "-- NATIVE\n", + "-- TURING75\n", + "-- Found CUDAToolkit: /usr/local/cuda/targets/x86_64-linux/include (found version \"12.5.82\")\n", + "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n", + "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n", + "-- Found Threads: TRUE\n", + "-- Found TPLLIBDL: /usr/include\n", + "-- Found OpenMP_CXX: -fopenmp (found suitable version \"4.5\", minimum required is \"3.0\")\n", + "-- Found OpenMP: TRUE (found suitable version \"4.5\", minimum required is \"3.0\") found components: CXX\n", + "-- Using internal desul_atomics copy\n", + "-- Experimental mdspan support is enabled\n", + "-- Looking for C++ include experimental/mdspan\n", + "-- Looking for C++ include experimental/mdspan - not found\n", + "-- Looking for C++ include mdspan\n", + "-- Looking for C++ include mdspan - not found\n", + "-- Using internal mdspan directory /content/CLEO-main/output/_deps/kokkos-src/tpls/mdspan/include\n", + "-- Kokkos Backends: OPENMP;SERIAL;CUDA\n", + "-- Kokkos installation in: /content/CLEO-main/output/kokkos\n", + "-- Using Kokkos nvcc wrapper (see: https://kokkos.org/kokkos-core-wiki/ProgrammingGuide/Compiling.html?highlight=wrapper#building-for-cuda)\n", + "-- CXX compiler: /usr/bin/g++\n", + "-- CC compiler: gcc\n", + "-- wrapper default (C++) compiler: g++\n", + "-- wrapper CUDA compiler: /bin/nvcc\n", + "-- Using yaml-cpp installation from: /content/CLEO-main/extern/yaml-cpp\n", + "\u001b[0mCMake Deprecation Warning at output/_deps/yaml-cpp-src/CMakeLists.txt:2 (cmake_minimum_required):\n", + " Compatibility with CMake < 3.10 will be removed from a future version of\n", + " CMake.\n", + "\n", + " Update the VERSION argument value. Or, use the ... syntax\n", + " to tell CMake that the project requires at least but has been updated\n", + " to work with policies introduced by or earlier.\n", + "\n", + "\u001b[0m\n", + "-- yaml-cpp installation in: /content/CLEO-main/output/yaml-cpp\n", + "-- CMAKE_CXX_FLAGS: -Werror -Wall -Wextra -pedantic -Wno-unused-parameter -O3 -mfma -fPIC\n", + "\u001b[0mgridboxes LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/gridboxes\u001b[0m\n", + "\u001b[0minitialise LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/initialise\u001b[0m\n", + "\u001b[0mruncleo LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/runcleo\u001b[0m\n", + "\u001b[0msuperdrops LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/superdrops\u001b[0m\n", + "\u001b[0mcollisions LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/superdrops/collisions\u001b[0m\n", + "\u001b[0mzarr LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/zarr\u001b[0m\n", + "\u001b[0mobservers LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/observers\u001b[0m\n", + "\u001b[0msdmmonitor LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/observers/sdmmonitor\u001b[0m\n", + "\u001b[0mcoupldyn_cvode LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/coupldyn_cvode\u001b[0m\n", + "-- The C compiler identification is GNU 11.4.0\n", + "-- Detecting C compiler ABI info\n", + "-- Detecting C compiler ABI info - done\n", + "-- Check for working C compiler: /usr/bin/gcc - skipped\n", + "-- Detecting C compile features\n", + "-- Detecting C compile features - done\n", + "-- SUNDIALS_GIT_VERSION: \n", + "-- Looking for sys/types.h\n", + "-- Looking for sys/types.h - found\n", + "-- Looking for stdint.h\n", + "-- Looking for stdint.h - found\n", + "-- Looking for stddef.h\n", + "-- Looking for stddef.h - found\n", + "-- Check size of int64_t\n", + "-- Check size of int64_t - done\n", + "-- Using int64_t for indices\n", + "-- C standard set to 99\n", + "-- C extensions set to ON\n", + "-- Performing Test SUNDIALS_C_COMPILER_HAS_SNPRINTF_AND_VA_COPY\n", + "-- Performing Test SUNDIALS_C_COMPILER_HAS_SNPRINTF_AND_VA_COPY - Success\n", + "-- Performing Test SUNDIALS_C_COMPILER_HAS_MATH_PRECISIONS\n", + "-- Performing Test SUNDIALS_C_COMPILER_HAS_MATH_PRECISIONS - Success\n", + "-- Performing Test SUNDIALS_C_COMPILER_HAS_ISINF_ISNAN\n", + "-- Performing Test SUNDIALS_C_COMPILER_HAS_ISINF_ISNAN - Success\n", + "-- Performing Test SUNDIALS_C_COMPILER_HAS_INLINE\n", + "-- Performing Test SUNDIALS_C_COMPILER_HAS_INLINE - Success\n", + "-- Looking for POSIX timers... found\n", + "-- Performing Test COMPILER_HAS_DEPRECATED_MSG\n", + "-- Performing Test COMPILER_HAS_DEPRECATED_MSG - Success\n", + "-- Performing Test COMPILER_HAS_HIDDEN_VISIBILITY\n", + "-- Performing Test COMPILER_HAS_HIDDEN_VISIBILITY - Success\n", + "-- Performing Test COMPILER_HAS_HIDDEN_INLINE_VISIBILITY\n", + "-- Performing Test COMPILER_HAS_HIDDEN_INLINE_VISIBILITY - Success\n", + "-- Performing Test COMPILER_HAS_DEPRECATED_ATTR\n", + "-- Performing Test COMPILER_HAS_DEPRECATED_ATTR - Failed\n", + "-- Performing Test COMPILER_HAS_DEPRECATED\n", + "-- Performing Test COMPILER_HAS_DEPRECATED - Failed\n", + "-- Added NVECTOR_SERIAL module\n", + "-- Added NVECTOR_MANYVECTOR module\n", + "-- Added SUNMATRIX_BAND module\n", + "-- Added SUNMATRIX_DENSE module\n", + "-- Added SUNMATRIX_SPARSE module\n", + "-- Added SUNLINSOL_BAND module\n", + "-- Added SUNLINSOL_DENSE module\n", + "-- Added SUNLINSOL_PCG module\n", + "-- Added SUNLINSOL_SPBCGS module\n", + "-- Added SUNLINSOL_SPFGMR module\n", + "-- Added SUNLINSOL_SPGMR module\n", + "-- Added SUNLINSOL_SPTFQMR module\n", + "-- Added SUNNONLINSOL_NEWTON module\n", + "-- Added SUNNONLINSOL_FIXEDPOINT module\n", + "-- Added CVODES module\n", + "\u001b[0mcoupldyn_fromfile LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/coupldyn_fromfile\u001b[0m\n", + "\u001b[0mcartesiandomain LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/cartesiandomain\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/adiabaticparcel/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/golovin/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/long/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/lowlist/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/szakallurbich/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/testikstraub/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/constthermo2d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/divfreemotion/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/eurec4a1d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/rainshaft1d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/speedtest/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/fromfile/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/fromfile_irreg/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/bubble3d/src\u001b[0m\n", + "\u001b[0mroughpaper_src_cleocoupledsdm PROJECT_SOURCE_DIR: /content/CLEO-main/roughpaper/src\u001b[0m\n", + "\u001b[0mroughpaper_scratch_test PROJECT_SOURCE_DIR: /content/CLEO-main/roughpaper/scratch\u001b[0m\n", + "-- Configuring done (24.2s)\n", + "-- Generating done (0.4s)\n", + "-- Build files have been written to: /content/CLEO-main/output\n", + "./scripts/bash/compile_cleo.sh \"golcolls longcolls\" false\n", + "### --------------- Compile Inputs -------------- ###\n", + "CLEO_BUILDTYPE: cuda\n", + "CLEO_COMPILERNAME: gcc\n", + "CLEO_PATH2CLEO: .\n", + "CLEO_PATH2BUILD: output\n", + "executables: golcolls longcolls\n", + "make_clean: false\n", + "### 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"\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/collective_dataset.hpp(284)\u001b[0m: \u001b[01;35mwarning\u001b[0m #68-D: integer conversion resulted in a change of sign\n", + " global_superdroplet_ordering.get()->resize(max_superdroplets, -1);\n", + " ^\n", + "\n", + "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.cpp(52)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::get_total_local_gridboxes() const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::get_local_ngridboxes const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.cpp(62)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::local_to_global_gridbox_index(unsigned long, int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::local_to_global_gridbox_index const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.cpp(76)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::get_local_bounding_gridbox( ::std::array &) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::get_local_bounding_gridbox const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " c3nghbrs = correct_neighbor_indices(c3nghbrs, ndims, domain_decomposition);\n", + " ^\n", + "\n", + "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " c3nghbrs = correct_neighbor_indices(c3nghbrs, ndims, domain_decomposition);\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " c1nghbrs = correct_neighbor_indices(c1nghbrs, ndims, domain_decomposition);\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " c1nghbrs = correct_neighbor_indices(c1nghbrs, ndims, domain_decomposition);\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " c2nghbrs = correct_neighbor_indices(c2nghbrs, ndims, domain_decomposition);\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " c2nghbrs = correct_neighbor_indices(c2nghbrs, ndims, domain_decomposition);\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(203)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " Kokkos::parallel_for(\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(201)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " Kokkos::parallel_for(\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(308)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " Kokkos::parallel_for(\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(306)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + " Kokkos::parallel_for(\n", + " ^\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(202)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(307)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(204)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(235)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(235)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(309)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(321)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(321)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(205)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mget_index_from_coordinates(const ::std::vector > &, unsigned long, unsigned long, unsigned long)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(209)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::global_to_local_gridbox_index(unsigned long) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(211)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord3gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(213)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord3nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(218)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord1gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(220)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord1nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(225)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord2gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(227)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord2nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(232)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::gbxarea(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(233)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::gbxvol(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(234)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(234)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(310)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mget_index_from_coordinates(const ::std::vector > &, unsigned long, unsigned long, unsigned long)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(314)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::global_to_local_gridbox_index(unsigned long) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(316)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mnullbounds()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(317)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mnullnghbrs(unsigned int)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(320)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(320)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\n", + "[ 94%] \u001b[32m\u001b[1mLinking CXX static library libobservers.a\u001b[0m\n", + "[ 94%] Built target observers\n", + "[ 97%] \u001b[32mBuilding CXX object libs/runcleo/CMakeFiles/runcleo.dir/createsupers.cpp.o\u001b[0m\n", + "[ 97%] \u001b[32mBuilding CXX object libs/runcleo/CMakeFiles/runcleo.dir/creategbxs.cpp.o\u001b[0m\n", + "[ 97%] \u001b[32mBuilding CXX object libs/runcleo/CMakeFiles/runcleo.dir/gensuperdrop.cpp.o\u001b[0m\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(193)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", + "\n", + "[ 97%] \u001b[32m\u001b[1mLinking CXX static library libcartesiandomain.a\u001b[0m\n", + "[ 97%] Built target cartesiandomain\n", + "[ 97%] \u001b[32m\u001b[1mLinking CXX static library libruncleo.a\u001b[0m\n", + "[ 97%] Built target runcleo\n", + "[100%] \u001b[32mBuilding CXX object examples/boxmodelcollisions/golovin/src/CMakeFiles/golcolls.dir/main_golcolls.cpp.o\u001b[0m\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", + "\n", + "[100%] \u001b[32m\u001b[1mLinking CXX executable golcolls\u001b[0m\n", + "[100%] Built target golcolls\n", + "[ 2%] Built target kokkossimd\n", + "[ 31%] Built target yaml-cpp\n", + "[ 54%] Built target kokkoscore\n", + "[ 54%] Built target kokkoscontainers\n", + "[ 60%] Built target zarr\n", + "[ 62%] Built target sdmmonitor\n", + "[ 68%] Built target collisions\n", + "[ 77%] Built target initialise\n", + "[ 82%] Built target superdrops\n", + "[ 88%] Built target gridboxes\n", + "[ 91%] Built target observers\n", + "[ 94%] Built target cartesiandomain\n", + "[ 97%] Built target runcleo\n", + "[ 97%] \u001b[32mBuilding CXX object examples/boxmodelcollisions/long/src/CMakeFiles/longcolls.dir/main_longcolls.cpp.o\u001b[0m\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", + "\n", + "[100%] \u001b[32m\u001b[1mLinking CXX executable longcolls\u001b[0m\n", + "[100%] Built target longcolls\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install --quiet awkward ruamel.yaml zarr" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FEr2-XU_3oiv", + "outputId": "5ef3efd9-65df-4387-c887-db487b00dfb7" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m187.0/187.0 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m81.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.7/53.7 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!cd CLEO-main; \\\n", + " echo \"import numpy as np; derive_more_floats = lambda x: {'RHO0':1, 'MASS0':1, 'COORD0':1000, 'RHO_L':1000, 'RHO_SOL':np.nan, 'MR_SOL': np.nan, 'IONIC':np.nan}\" >> pySD/cxx2py.py; \\\n", + " python3 \\\n", + " examples/boxmodelcollisions/shima2009.py \\\n", + " /content/CLEO-main \\\n", + " /content/CLEO-main/output \\\n", + " /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml \\\n", + " golovin" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ux4-6J0rnaoM", + "outputId": "8c013063-0f85-4d53-acee-ac105f0662dd" + }, + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "created boundaries for 1 gridboxes\n", + "Writing gridbox boundaries binary file to:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Metadata: \n", + " '4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1'\n", + "zhalf: [ 0. 100.]\n", + "xhalf: [ 0. 100.]\n", + "yhalf: [ 0. 100.]\n", + "\n", + "------ DOMAIN / GRIDBOXES INFO ------\n", + "------------- 0-D MODEL -------------\n", + "domain dimensions: (100x100x100)m^3\n", + "domain no. gridboxes: 1x1x1\n", + "domain z limits: ( 0,100)m\n", + "domain x limits: ( 0, 100)m\n", + "domain y limits: ( 0, 100)m\n", + "mean gridbox z spacing: 100 m\n", + "mean gridbox x spacing: 100 m\n", + "mean gridbox y spacing: 100 m\n", + "mean gridbox volume: 1e+06 m^3\n", + "total domain volume: 1e+06 m^3\n", + "total no. gridboxes: 1\n", + "------------------------------------\n", + "\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Metadata: \n", + " '4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1'\n", + "zhalf: [ 0. 100.]\n", + "xhalf: [ 0. 100.]\n", + "yhalf: [ 0. 100.]\n", + "Figure .png saved as: /content/CLEO-main/output/bin/gridboxboundaries.png\n", + "Figure(1000x500)\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "4096\n", + "--- total droplet concentration = 8.38861cm^-3 => 1g/m^3, in 1e+06m^3 volume --- \n", + "Writing gridbox boundaries binary file to:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", + "\n", + "------ DOMAIN SUPERDROPLETS INFO ------\n", + "total droplet number conc: 8.38861 /cm^3\n", + "total droplet mass: 7.0856e-35 g/m^3\n", + " as if water: 1.00816 g/m^3\n", + "------------------------------------\n", + "\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", + "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_distribs_1.png\n", + "Figure(1400x400)\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", + "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_dropletmasses_1.png\n", + "Figure(1400x400)\n", + "/content/CLEO-main/output\n", + "Executable: /content/CLEO-main/output/examples/boxmodelcollisions/golovin/src/golcolls\n", + "Config file: /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml\n", + "\n", + "-------- Required Configuration Parameters --------------\n", + "constants_filename : \"../../libs/cleoconstants.hpp\"\n", + "grid_filename : \"./share/shima2009_dimlessGBxboundaries.dat\"\n", + "setup_filename : \"./bin/shima2009_setup.txt\"\n", + "zarrbasedir : \"./bin/shima2009_sol.zarr\"\n", + "maxchunk : 2500000\n", + "nspacedims : 0\n", + "ngbxs : 1\n", + "maxnsupers : 4096\n", + "CONDTSTEP : 200\n", + "COLLTSTEP : 1\n", + "MOTIONTSTEP : 200\n", + "COUPLTSTEP : 2000\n", + "OBSTSTEP : 200\n", + "T_END : 3800\n", + "---------------------------------------------------------\n", + "\n", + "-------- Kokkos Configuration Parameters --------------\n", + "using default kokkos settings (bool): 0\n", + "num_threads: 128\n", + "---------------------------------------------------------\n", + "\n", + "-------- InitSupersFromBinary Configuration Parameters --------------\n", + "maxnsupers: 4096\n", + "nspacedims: 0\n", + "initsupers_filename: \"/content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\"\n", + "initnsupers: 4096\n", + "---------------------------------------------------------\n", + "\n", + "--- configuration ---\n", + "----- writing to new setup file: ./bin/shima2009_setup.txt -----\n", + " copying /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml to setup file\n", + " copying ../../libs/cleoconstants.hpp to setup file\n", + "---- copy complete, setup file closed -----\n", + "--- configuration: success ---\n", + "Kokkos::OpenMP::initialize WARNING: OMP_PROC_BIND environment variable not set\n", + " In general, for best performance with OpenMP 4.0 or better set OMP_PROC_BIND=spread and OMP_PLACES=threads\n", + " For best performance with OpenMP 3.1 set OMP_PROC_BIND=true\n", + " For unit testing set OMP_PROC_BIND=false\n", + "\n", + "Kokkos::OpenMP::initialize WARNING: You are likely oversubscribing your CPU cores.\n", + " process threads available : 2, requested thread : 128\n", + "Kokkos::OpenMP::initialize WARNING: You are likely oversubscribing your CPU cores.\n", + " Detected: 2 cores per node.\n", + " Detected: 1 MPI_ranks per node.\n", + " Requested: 128 threads per process.\n", + " Kokkos Version: 4.5.0\n", + "Compiler:\n", + " KOKKOS_COMPILER_GNU: 1140\n", + " KOKKOS_COMPILER_NVCC: 1250\n", + "Architecture:\n", + " CPU architecture: none\n", + " Default Device: Cuda\n", + " GPU architecture: TURING75\n", + " platform: 64bit\n", + "Atomics:\n", + "Vectorization:\n", + " KOKKOS_ENABLE_PRAGMA_IVDEP: no\n", + " KOKKOS_ENABLE_PRAGMA_LOOPCOUNT: no\n", + " KOKKOS_ENABLE_PRAGMA_UNROLL: no\n", + " KOKKOS_ENABLE_PRAGMA_VECTOR: no\n", + "Memory:\n", + "Options:\n", + " KOKKOS_ENABLE_ASM: yes\n", + " KOKKOS_ENABLE_CXX17: no\n", + " KOKKOS_ENABLE_CXX20: yes\n", + " KOKKOS_ENABLE_CXX23: no\n", + " KOKKOS_ENABLE_CXX26: no\n", + " KOKKOS_ENABLE_DEBUG_BOUNDS_CHECK: no\n", + " KOKKOS_ENABLE_HWLOC: no\n", + " KOKKOS_ENABLE_LIBDL: yes\n", + "Host Parallel Execution Space:\n", + " KOKKOS_ENABLE_OPENMP: yes\n", + "\n", + "OpenMP Runtime Configuration:\n", + "Kokkos::OpenMP thread_pool_topology[ 1 x 128 x 1 ]\n", + "Host Serial Execution Space:\n", + " KOKKOS_ENABLE_SERIAL: yes\n", + "\n", + "Serial Runtime Configuration:\n", + "Device Execution Space:\n", + " KOKKOS_ENABLE_CUDA: yes\n", + "Cuda Options:\n", + " KOKKOS_ENABLE_CUDA_RELOCATABLE_DEVICE_CODE: yes\n", + " KOKKOS_ENABLE_CUDA_UVM: no\n", + " KOKKOS_ENABLE_IMPL_CUDA_MALLOC_ASYNC: no\n", + "\n", + "Cuda Runtime Configuration:\n", + "macro KOKKOS_ENABLE_CUDA : defined\n", + "macro CUDA_VERSION = 12050 = version 12.5\n", + "Kokkos::Cuda[ 0 ] Tesla T4 capability 7.5, Total Global Memory: 14.74 GiB, Shared Memory per Block: 48 KiB : Selected\n", + "couldn't open \"./bin/shima2009_sol.zarr/.zgroup\",\n", + " making directory \"./bin/shima2009_sol.zarr\"\n", + "\n", + "--- create cartesian gridbox maps ---\n", + "opening binary file: ./share/shima2009_dimlessGBxboundaries.dat\n", + "----------------- gridfile global metastring -----------------\n", + "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1\n", + "--------------------------------------------------------------\n", + "--- create cartesian gridbox maps: success ---\n", + "couldn't open \"./bin/shima2009_sol.zarr/time/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/time\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/sdId/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/sdId\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/xi/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/xi\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/radius/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/radius\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/msol/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/msol\"\n", + "couldn't open \"./bin/shima2009_sol.zarr/raggedcount/.zarray\",\n", + " making directory \"./bin/shima2009_sol.zarr/raggedcount\"\n", + "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "----------------- gridfile global metastring -----------------\n", + "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", + "--------------------------------------------------------------\n", + "\n", + "--- create superdrops ---\n", + "initialising\n", + "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "----------------- gridfile global metastring -----------------\n", + "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", + "--------------------------------------------------------------\n", + "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "----------------- gridfile global metastring -----------------\n", + "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", + "--------------------------------------------------------------\n", + "sorting and finding superdrops in domain\n", + "checking initialisation\n", + "--- create superdrops: success ---\n", + "\n", + "--- create gridboxes ---\n", + "initialising\n", + "checking initialisation\n", + "--- create gridboxes: success ---\n", + "\n", + "--- prepare timestepping ---\n", + "observer includes write in dataset observer\n", + "observer includes time observer\n", + "observer includes StreamOutObserver\n", + "--- prepare timestepping: success ---\n", + "\n", + "--- timestepping ---\n", + "t=0.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=1800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=2800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3000.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3200.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3400.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3600.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "t=3800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", + "--- timestepping: success ---\n", + "-----\n", + " Total Program Duration: 9.3444e+00s \n", + "-----\n", + "\n", + "---- config from /content/CLEO-main/output/bin/shima2009_setup.txt -----\n", + "num_threads = 128.0\n", + "nspacedims = 0\n", + "ngbxs = 1.0\n", + "maxnsupers = 4096.0\n", + "CONDTSTEP = 200.0\n", + "COLLTSTEP = 1.0\n", + "MOTIONTSTEP = 200.0\n", + "COUPLTSTEP = 2000.0\n", + "OBSTSTEP = 200.0\n", + "T_END = 3800.0\n", + "maxchunk = 2500000.0\n", + "numSDattrs = 3\n", + "ntime = 20\n", + "---------------------------------------------\n", + "\n", + "\n", + "---- consts from /content/CLEO-main/output/bin/shima2009_setup.txt -----\n", + "RHO0 = 1\n", + "MASS0 = 1\n", + "COORD0 = 1000\n", + "RHO_L = 1000\n", + "RHO_SOL = nan\n", + "MR_SOL = nan\n", + "IONIC = nan\n", + "---------------------------------------------\n", + "\n", + "Reading binary file:\n", + " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + "\n", + "---- gbxs from /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat -----\n", + "ngrid = 1\n", + "ndims = [1 1 1]\n", + "domainvol = 1000000.0\n", + "domainarea = 10000.0\n", + "gbxvols = [[[1000000.]]]\n", + "zhalf = [ 0. 100.]\n", + "zfull = [50.]\n", + "xhalf = [ 0. 100.]\n", + "xfull = [50.]\n", + "yhalf = [ 0. 100.]\n", + "yfull = [50.]\n", + "xxh = [[ 0. 0.]\n", + " [100. 100.]]\n", + "zzh = [[ 0. 100.]\n", + " [ 0. 100.]]\n", + "xxf = [[50.]]\n", + "zzf = [[50.]]\n", + "---------------------------------------------\n", + "\n", + "time from dataset: /content/CLEO-main/output/bin/shima2009_sol.zarr\n", + "---- Superdrop Properties -----\n", + "RHO_L = 1000 Kg/m^3\n", + "RHO_SOL = nan Kg/m^3\n", + "MR_SOL = nan Kg/mol\n", + "IONIC = nan\n", + "-------------------------------\n", + "supers dataset: /content/CLEO-main/output/bin/shima2009_sol.zarr\n", + "/content/CLEO-main/examples/exampleplotting/plotssrc/shima2009fig.py:140: RuntimeWarning: invalid value encountered in multiply\n", + " bsl_exp = iv(1, 2 * x * np.sqrt(tau)) * np.exp(-(1 + tau) * x)\n", + "Figure .png saved as: /content/CLEO-main/output/bin/golovin_validation.png\n", + "Figure(800x700)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import Image\n", + "display(Image('CLEO-main/output/bin/golovin_validation.png'))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "cDFkmIaKqQjo", + "outputId": "baeb533c-fe2d-4b59-e19a-05f4365e3ab7" + }, + "execution_count": 38, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "TWcachyj-Jv2" + }, + "execution_count": 7, + "outputs": [] + } + ] +} From 01d32a7cfe37b7d81a67ee537f653c289bf2eeb3 Mon Sep 17 00:00:00 2001 From: Emma Ware Date: Thu, 17 Jul 2025 15:08:19 +0200 Subject: [PATCH 3/3] change output path to remove hacky constants logic --- examples/CLEO_hello_world.ipynb | 1545 +++++++++++++++---------------- 1 file changed, 740 insertions(+), 805 deletions(-) diff --git a/examples/CLEO_hello_world.ipynb b/examples/CLEO_hello_world.ipynb index 6d9b0813..a9f87463 100644 --- a/examples/CLEO_hello_world.ipynb +++ b/examples/CLEO_hello_world.ipynb @@ -27,20 +27,38 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 43, "metadata": { "id": "4-c1H8a9dVLV" }, "outputs": [], "source": [ - "!wget --quiet https://github.com/yoctoyotta1024/CLEO/archive/refs/heads/main.zip" + "!wget --quiet https://github.com/yoctoyotta1024/CLEO/archive/refs/tags/v0.33.0.zip" ] }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "om0zX7YvQXl2" + }, + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "3SsnpxgLQXjB" + }, + "execution_count": 24, + "outputs": [] + }, { "cell_type": "code", "source": [ "%%file script.sh\n", - "cd CLEO-main\n", + "cd CLEO-0.33.0\n", "\n", "mkdir -p bin\n", "echo \"#!/bin/bash\" > bin/module\n", @@ -53,7 +71,7 @@ "\n", ". scripts/build_compile_cleo.sh cuda gcc \\\n", " . \\\n", - " output \\\n", + " output/box_model \\\n", " \"golcolls longcolls\" \\\n", " false \\\n", " false \\\n", @@ -65,15 +83,15 @@ "base_uri": "https://localhost:8080/" }, "id": "EG5TNb6edsF1", - "outputId": "5295ba20-bca9-452a-9ef7-fba51fbba2e2" + "outputId": "f4b8d99b-e52a-4580-a9b2-bd1e3346b307" }, - "execution_count": 2, + "execution_count": 44, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Writing script.sh\n" + "Overwriting script.sh\n" ] } ] @@ -81,579 +99,484 @@ { "cell_type": "code", "source": [ - "!unzip main.zip" + "!unzip v0.33.0.zip" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5-MhvxU_dYoh", - "outputId": "e92a6578-9930-479c-95c7-f46e26847fec" + "outputId": "181d5219-77ca-4abf-c60b-b3eac5c54d4a" }, - "execution_count": 3, + "execution_count": 45, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Archive: main.zip\n", - "e8f3706c4280ef30834aef4c089a3415d5a6d9a1\n", - " creating: CLEO-main/\n", - " creating: CLEO-main/.github/\n", - " inflating: CLEO-main/.github/compare_parallel_results.sh \n", - " creating: CLEO-main/.github/workflows/\n", - " inflating: CLEO-main/.github/workflows/CI.yml \n", - " inflating: CLEO-main/.github/workflows/build.yml \n", - " inflating: CLEO-main/.github/workflows/cocogitto.yml \n", - " inflating: CLEO-main/.github/workflows/pre-commit.yml \n", - " inflating: 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CLEO-0.33.0/setup.py \n", + " inflating: CLEO-0.33.0/tests/test_math.py \n" ] } ] @@ -668,9 +591,9 @@ "base_uri": "https://localhost:8080/" }, "id": "S2VQ5X9feUgy", - "outputId": "c708e2b3-deac-4fda-85bb-09abf9771b31" + "outputId": "3fb5b4dc-7b01-4b0d-e1e0-943fd0c255cc" }, - "execution_count": 4, + "execution_count": 46, "outputs": [ { "output_type": "stream", @@ -680,7 +603,7 @@ "CLEO_BUILDTYPE = cuda\n", "CLEO_COMPILERNAME = gcc\n", "CLEO_PATH2CLEO = .\n", - "CLEO_PATH2BUILD = output\n", + "CLEO_PATH2BUILD = output/box_model\n", "CLEO_ENABLEDEBUG = false\n", "CLEO_ENABLEYAC = false\n", "CLEO_YACYAXTROOT = \n", @@ -691,7 +614,7 @@ "CLEO_BUILDTYPE: cuda\n", "CLEO_COMPILERNAME: gcc\n", "CLEO_PATH2CLEO: .\n", - "CLEO_PATH2BUILD: output\n", + "CLEO_PATH2BUILD: output/box_model\n", "CLEO_CXX_COMPILER: g++\n", "CLEO_CC_COMPILER: gcc\n", "CLEO_CXX_FLAGS: -Werror -Wall -Wextra -pedantic -Wno-unused-parameter -O3 -mfma\n", @@ -711,15 +634,15 @@ "-- Detecting CXX compile features - done\n", "-- Found MPI_CXX: /usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi_cxx.so (found version \"3.1\")\n", "-- Found MPI: TRUE (found version \"3.1\")\n", - "\u001b[0mCLEO_SOURCE_DIR: /content/CLEO-main\u001b[0m\n", - "\u001b[0mCLEO_BINARY_DIR: /content/CLEO-main/output\u001b[0m\n", - "-- Using Kokkos installation from: /content/CLEO-main/extern/kokkos\n", + "\u001b[0mCLEO_SOURCE_DIR: /content/CLEO-0.33.0\u001b[0m\n", + "\u001b[0mCLEO_BINARY_DIR: /content/CLEO-0.33.0/output/box_model\u001b[0m\n", + "-- Using Kokkos installation from: /content/CLEO-0.33.0/extern/kokkos\n", "-- Setting default Kokkos CXX standard to 20\n", "-- Kokkos version: 4.5.0\n", "-- The project name is: Kokkos\n", "-- Using internal gtest for testing\n", "-- Compiler Version: 12.5.82\n", - "-- kokkos_launch_compiler (/content/CLEO-main/output/_deps/kokkos-src/bin/kokkos_launch_compiler) is enabled...\n", + "-- kokkos_launch_compiler (/content/CLEO-0.33.0/output/box_model/_deps/kokkos-src/bin/kokkos_launch_compiler) is enabled...\n", "-- Using -std=c++20 for C++20 standard as feature\n", "-- SIMD: AVX512 detected\n", "-- CUDA auto-detection of architecture failed with /usr/bin/g++. Enabling CUDA language ONLY to auto-detect architecture...\n", @@ -754,16 +677,16 @@ "-- Looking for C++ include experimental/mdspan - not found\n", "-- Looking for C++ include mdspan\n", "-- Looking for C++ include mdspan - not found\n", - "-- Using internal mdspan directory /content/CLEO-main/output/_deps/kokkos-src/tpls/mdspan/include\n", + "-- Using internal mdspan directory /content/CLEO-0.33.0/output/box_model/_deps/kokkos-src/tpls/mdspan/include\n", "-- Kokkos Backends: OPENMP;SERIAL;CUDA\n", - "-- Kokkos installation in: /content/CLEO-main/output/kokkos\n", + "-- Kokkos installation in: /content/CLEO-0.33.0/output/box_model/kokkos\n", "-- Using Kokkos nvcc wrapper (see: https://kokkos.org/kokkos-core-wiki/ProgrammingGuide/Compiling.html?highlight=wrapper#building-for-cuda)\n", "-- CXX compiler: /usr/bin/g++\n", "-- CC compiler: gcc\n", "-- wrapper default (C++) compiler: g++\n", "-- wrapper CUDA compiler: /bin/nvcc\n", - "-- Using yaml-cpp installation from: /content/CLEO-main/extern/yaml-cpp\n", - "\u001b[0mCMake Deprecation Warning at output/_deps/yaml-cpp-src/CMakeLists.txt:2 (cmake_minimum_required):\n", + "-- Using yaml-cpp installation from: /content/CLEO-0.33.0/extern/yaml-cpp\n", + "\u001b[0mCMake Deprecation Warning at output/box_model/_deps/yaml-cpp-src/CMakeLists.txt:2 (cmake_minimum_required):\n", " Compatibility with CMake < 3.10 will be removed from a future version of\n", " CMake.\n", "\n", @@ -772,17 +695,17 @@ " to work with policies introduced by or earlier.\n", "\n", "\u001b[0m\n", - "-- yaml-cpp installation in: /content/CLEO-main/output/yaml-cpp\n", + "-- yaml-cpp installation in: /content/CLEO-0.33.0/output/box_model/yaml-cpp\n", "-- CMAKE_CXX_FLAGS: -Werror -Wall -Wextra -pedantic -Wno-unused-parameter -O3 -mfma -fPIC\n", - "\u001b[0mgridboxes LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/gridboxes\u001b[0m\n", - "\u001b[0minitialise LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/initialise\u001b[0m\n", - "\u001b[0mruncleo LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/runcleo\u001b[0m\n", - "\u001b[0msuperdrops LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/superdrops\u001b[0m\n", - "\u001b[0mcollisions LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/superdrops/collisions\u001b[0m\n", - "\u001b[0mzarr LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/zarr\u001b[0m\n", - "\u001b[0mobservers LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/observers\u001b[0m\n", - "\u001b[0msdmmonitor LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/observers/sdmmonitor\u001b[0m\n", - "\u001b[0mcoupldyn_cvode LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/coupldyn_cvode\u001b[0m\n", + "\u001b[0mgridboxes LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/gridboxes\u001b[0m\n", + "\u001b[0minitialise LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/initialise\u001b[0m\n", + "\u001b[0mruncleo LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/runcleo\u001b[0m\n", + "\u001b[0msuperdrops LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/superdrops\u001b[0m\n", + "\u001b[0mcollisions LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/superdrops/collisions\u001b[0m\n", + "\u001b[0mzarr LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/zarr\u001b[0m\n", + "\u001b[0mobservers LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/observers\u001b[0m\n", + "\u001b[0msdmmonitor LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/observers/sdmmonitor\u001b[0m\n", + "\u001b[0mcoupldyn_cvode LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/coupldyn_cvode\u001b[0m\n", "-- The C compiler identification is GNU 11.4.0\n", "-- Detecting C compiler ABI info\n", "-- Detecting C compiler ABI info - done\n", @@ -835,92 +758,92 @@ "-- Added SUNNONLINSOL_NEWTON module\n", "-- Added SUNNONLINSOL_FIXEDPOINT module\n", "-- Added CVODES module\n", - "\u001b[0mcoupldyn_fromfile LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/coupldyn_fromfile\u001b[0m\n", - "\u001b[0mcartesiandomain LIBRARY_SOURCE_DIR: /content/CLEO-main/libs/cartesiandomain\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/adiabaticparcel/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/golovin/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/long/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/lowlist/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/szakallurbich/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/boxmodelcollisions/testikstraub/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/constthermo2d/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/divfreemotion/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/eurec4a1d/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/rainshaft1d/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/speedtest/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/fromfile/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/fromfile_irreg/src\u001b[0m\n", - "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-main/examples/bubble3d/src\u001b[0m\n", - "\u001b[0mroughpaper_src_cleocoupledsdm PROJECT_SOURCE_DIR: /content/CLEO-main/roughpaper/src\u001b[0m\n", - "\u001b[0mroughpaper_scratch_test PROJECT_SOURCE_DIR: /content/CLEO-main/roughpaper/scratch\u001b[0m\n", - "-- Configuring done (24.2s)\n", + "\u001b[0mcoupldyn_fromfile LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/coupldyn_fromfile\u001b[0m\n", + "\u001b[0mcartesiandomain LIBRARY_SOURCE_DIR: /content/CLEO-0.33.0/libs/cartesiandomain\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/adiabaticparcel/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/boxmodelcollisions/golovin/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/boxmodelcollisions/long/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/boxmodelcollisions/lowlist/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/boxmodelcollisions/szakallurbich/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/boxmodelcollisions/testikstraub/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/constthermo2d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/divfreemotion/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/eurec4a1d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/rainshaft1d/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/speedtest/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/fromfile/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/fromfile_irreg/src\u001b[0m\n", + "\u001b[0mPROJECT_SOURCE_DIR: /content/CLEO-0.33.0/examples/bubble3d/src\u001b[0m\n", + "\u001b[0mroughpaper_src_cleocoupledsdm PROJECT_SOURCE_DIR: /content/CLEO-0.33.0/roughpaper/src\u001b[0m\n", + "\u001b[0mroughpaper_scratch_test PROJECT_SOURCE_DIR: /content/CLEO-0.33.0/roughpaper/scratch\u001b[0m\n", + "-- Configuring done (17.4s)\n", "-- Generating done (0.4s)\n", - 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"[ 48%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/scanner.cpp.o\u001b[0m\n", + "[ 48%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/scantoken.cpp.o\u001b[0m\n", "[ 48%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/scanscalar.cpp.o\u001b[0m\n", - "[ 48%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/scantag.cpp.o\u001b[0m\n", - "[ 51%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/scantoken.cpp.o\u001b[0m\n", + "[ 48%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/singledocparser.cpp.o\u001b[0m\n", + "[ 51%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/scanner.cpp.o\u001b[0m\n", "[ 51%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/simplekey.cpp.o\u001b[0m\n", - "[ 51%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/singledocparser.cpp.o\u001b[0m\n", + "[ 51%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/scantag.cpp.o\u001b[0m\n", "[ 54%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/stream.cpp.o\u001b[0m\n", "[ 54%] \u001b[32mBuilding CXX object _deps/yaml-cpp-build/CMakeFiles/yaml-cpp.dir/src/tag.cpp.o\u001b[0m\n", "[ 54%] \u001b[32m\u001b[1mLinking CXX static library libkokkossimd.a\u001b[0m\n", @@ -932,22 +855,22 @@ "[ 54%] \u001b[32mBuilding CXX object _deps/kokkos-build/containers/src/CMakeFiles/kokkoscontainers.dir/impl/Kokkos_UnorderedMap_impl.cpp.o\u001b[0m\n", "[ 54%] \u001b[32m\u001b[1mLinking CXX static library libkokkoscontainers.a\u001b[0m\n", "[ 54%] Built target kokkoscontainers\n", + "[ 54%] \u001b[32mBuilding CXX object libs/zarr/CMakeFiles/zarr.dir/xarray_metadata.cpp.o\u001b[0m\n", + "[ 54%] \u001b[32mBuilding CXX object libs/zarr/CMakeFiles/zarr.dir/zarr_metadata.cpp.o\u001b[0m\n", + "[ 54%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/coalbure_flag.cpp.o\u001b[0m\n", "[ 57%] \u001b[32mBuilding CXX object libs/zarr/CMakeFiles/zarr.dir/fsstore.cpp.o\u001b[0m\n", - "[ 57%] \u001b[32mBuilding CXX object libs/zarr/CMakeFiles/zarr.dir/xarray_metadata.cpp.o\u001b[0m\n", - "[ 57%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/coalbure_flag.cpp.o\u001b[0m\n", - "[ 57%] \u001b[32mBuilding CXX object libs/zarr/CMakeFiles/zarr.dir/zarr_metadata.cpp.o\u001b[0m\n", "[ 57%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/copyfiles2txt.cpp.o\u001b[0m\n", "[ 60%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/coalescence.cpp.o\u001b[0m\n", "[ 60%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/gbx_bounds_from_binary.cpp.o\u001b[0m\n", - "[ 60%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/collisionkinetics.cpp.o\u001b[0m\n", "[ 62%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/init_all_supers_from_binary.cpp.o\u001b[0m\n", "[ 62%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/init_supers_from_binary.cpp.o\u001b[0m\n", + "[ 62%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/collisionkinetics.cpp.o\u001b[0m\n", "[ 62%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/golovinprob.cpp.o\u001b[0m\n", "[ 65%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/longhydroprob.cpp.o\u001b[0m\n", + "[ 65%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/lowlistprob.cpp.o\u001b[0m\n", "[ 65%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/optional_config_params.cpp.o\u001b[0m\n", - "[ 65%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/required_config_params.cpp.o\u001b[0m\n", "[ 68%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/readbinary.cpp.o\u001b[0m\n", - "[ 68%] \u001b[32mBuilding CXX object libs/superdrops/collisions/CMakeFiles/collisions.dir/lowlistprob.cpp.o\u001b[0m\n", + "[ 68%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/required_config_params.cpp.o\u001b[0m\n", "[ 68%] \u001b[32mBuilding CXX object libs/initialise/CMakeFiles/initialise.dir/timesteps.cpp.o\u001b[0m\n", "[ 68%] \u001b[32m\u001b[1mLinking CXX static library libcollisions.a\u001b[0m\n", "[ 68%] Built target collisions\n", @@ -963,8 +886,8 @@ "[ 82%] Built target initialise\n", "[ 82%] \u001b[32m\u001b[1mLinking CXX static library libsuperdrops.a\u001b[0m\n", "[ 82%] Built target superdrops\n", + "[ 82%] \u001b[32mBuilding CXX object libs/gridboxes/CMakeFiles/gridboxes.dir/gridbox.cpp.o\u001b[0m\n", "[ 85%] \u001b[32mBuilding CXX object libs/gridboxes/CMakeFiles/gridboxes.dir/movesupersindomain.cpp.o\u001b[0m\n", - "[ 85%] \u001b[32mBuilding CXX object libs/gridboxes/CMakeFiles/gridboxes.dir/gridbox.cpp.o\u001b[0m\n", "[ 85%] \u001b[32mBuilding CXX object libs/gridboxes/CMakeFiles/gridboxes.dir/predcorr.cpp.o\u001b[0m\n", "[ 85%] \u001b[32mBuilding CXX object libs/gridboxes/CMakeFiles/gridboxes.dir/sortsupers.cpp.o\u001b[0m\n", "[ 88%] \u001b[32mBuilding CXX object libs/gridboxes/CMakeFiles/gridboxes.dir/supersingbx.cpp.o\u001b[0m\n", @@ -973,190 +896,190 @@ "[ 88%] \u001b[32m\u001b[1mLinking CXX static library libgridboxes.a\u001b[0m\n", "[ 88%] Built target gridboxes\n", "[ 88%] \u001b[32mBuilding CXX object libs/cartesiandomain/CMakeFiles/cartesiandomain.dir/add_supers_at_domain_top.cpp.o\u001b[0m\n", - "[ 88%] \u001b[32mBuilding CXX object libs/cartesiandomain/CMakeFiles/cartesiandomain.dir/cartesianmaps.cpp.o\u001b[0m\n", "[ 91%] \u001b[32mBuilding CXX object libs/observers/CMakeFiles/observers.dir/streamout_observer.cpp.o\u001b[0m\n", - "[ 91%] \u001b[32mBuilding CXX object libs/cartesiandomain/CMakeFiles/cartesiandomain.dir/cartesian_decomposition.cpp.o\u001b[0m\n", - "[ 91%] \u001b[32mBuilding CXX object libs/observers/CMakeFiles/observers.dir/massmoments_observer.cpp.o\u001b[0m\n", + "[ 91%] \u001b[32mBuilding CXX object libs/cartesiandomain/CMakeFiles/cartesiandomain.dir/cartesianmaps.cpp.o\u001b[0m\n", "[ 94%] \u001b[32mBuilding CXX object libs/cartesiandomain/CMakeFiles/cartesiandomain.dir/createcartesianmaps.cpp.o\u001b[0m\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/collective_dataset.hpp(284)\u001b[0m: \u001b[01;35mwarning\u001b[0m #68-D: integer conversion resulted in a change of sign\n", + "[ 94%] \u001b[32mBuilding CXX object libs/observers/CMakeFiles/observers.dir/massmoments_observer.cpp.o\u001b[0m\n", + "[ 94%] \u001b[32mBuilding CXX object libs/cartesiandomain/CMakeFiles/cartesiandomain.dir/cartesian_decomposition.cpp.o\u001b[0m\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/collective_dataset.hpp(284)\u001b[0m: \u001b[01;35mwarning\u001b[0m #68-D: integer conversion resulted in a change of sign\n", " global_superdroplet_ordering.get()->resize(max_superdroplets, -1);\n", " ^\n", "\n", "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.cpp(52)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::get_total_local_gridboxes() const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::get_local_ngridboxes const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.cpp(52)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::get_total_local_gridboxes() const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::get_local_ngridboxes const\u001b[0m\") is not allowed\n", "\n", "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.cpp(62)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::local_to_global_gridbox_index(unsigned long, int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::local_to_global_gridbox_index const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.cpp(62)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::local_to_global_gridbox_index(unsigned long, int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::local_to_global_gridbox_index const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.cpp(76)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::get_local_bounding_gridbox( ::std::array &) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::get_local_bounding_gridbox const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.cpp(76)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::get_local_bounding_gridbox( ::std::array &) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::get_local_bounding_gridbox const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " c3nghbrs = correct_neighbor_indices(c3nghbrs, ndims, domain_decomposition);\n", " ^\n", "\n", "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " c3nghbrs = correct_neighbor_indices(c3nghbrs, ndims, domain_decomposition);\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " c1nghbrs = correct_neighbor_indices(c1nghbrs, ndims, domain_decomposition);\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " c1nghbrs = correct_neighbor_indices(c1nghbrs, ndims, domain_decomposition);\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " c2nghbrs = correct_neighbor_indices(c2nghbrs, ndims, domain_decomposition);\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " c2nghbrs = correct_neighbor_indices(c2nghbrs, ndims, domain_decomposition);\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(203)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(203)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " Kokkos::parallel_for(\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(201)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(201)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " Kokkos::parallel_for(\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(308)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(308)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " Kokkos::parallel_for(\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(306)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(306)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20014-D: calling a __host__ function from a __host__ __device__ function is not allowed\n", " Kokkos::parallel_for(\n", " ^\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::GbxBoundsFromBinary\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.hpp(158)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mGbxBoundsFromBinary::~GbxBoundsFromBinary\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(202)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(202)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(236)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(307)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(307)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(322)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::~[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(204)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(204)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(235)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(235)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(235)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(235)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(309)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(309)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(321)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(321)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(321)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(321)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(205)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mget_index_from_coordinates(const ::std::vector > &, unsigned long, unsigned long, unsigned long)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(205)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mget_index_from_coordinates(const ::std::vector > &, unsigned long, unsigned long, unsigned long)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(209)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::global_to_local_gridbox_index(unsigned long) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(209)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::global_to_local_gridbox_index(unsigned long) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(211)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord3gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(211)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord3gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(213)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord3nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(213)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord3nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(214)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(218)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord1gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(218)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord1gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(220)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord1nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(220)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord1nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(221)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(225)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord2gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(225)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::get_coord2gbxbounds(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(227)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord2nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(227)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mDoublyPeriodicDomain::cartesian_coord2nghbrs(unsigned int, const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mcorrect_neighbor_indices( ::Kokkos::pair , ::std::vector > , const ::CartesianDecomposition &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(228)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(232)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::gbxarea(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(232)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::gbxarea(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(233)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::gbxvol(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(233)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mGbxBoundsFromBinary::gbxvol(unsigned int) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(234)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(234)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(234)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(234)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(310)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mget_index_from_coordinates(const ::std::vector > &, unsigned long, unsigned long, unsigned long)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(310)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mget_index_from_coordinates(const ::std::vector > &, unsigned long, unsigned long, unsigned long)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(314)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::global_to_local_gridbox_index(unsigned long) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(314)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::global_to_local_gridbox_index(unsigned long) const\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(316)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mnullbounds()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(316)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mnullbounds()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(317)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mnullnghbrs(unsigned int)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(317)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mnullnghbrs(unsigned int)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::operator () const\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(320)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(320)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/createcartesianmaps.cpp(320)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/createcartesianmaps.cpp(320)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::~vector()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mset_null_cartesian_maps(unsigned int, const ::GbxBoundsFromBinary &, ::CartesianMaps &)::[lambda(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) (instance 1)]::operator ()(const ::Kokkos::Impl::HostThreadTeamMember< ::Kokkos::OpenMP> &) const::[lambda(unsigned long) (instance 1)]::operator ()(unsigned long) const::[lambda(unsigned long) (instance 1)]::~[lambda(unsigned long) (instance 1)]\u001b[0m\") is not allowed\n", "\n", "[ 94%] \u001b[32m\u001b[1mLinking CXX static library libobservers.a\u001b[0m\n", "[ 94%] Built target observers\n", + "[ 94%] \u001b[32mBuilding CXX object libs/runcleo/CMakeFiles/runcleo.dir/creategbxs.cpp.o\u001b[0m\n", "[ 97%] \u001b[32mBuilding CXX object libs/runcleo/CMakeFiles/runcleo.dir/createsupers.cpp.o\u001b[0m\n", - "[ 97%] \u001b[32mBuilding CXX object libs/runcleo/CMakeFiles/runcleo.dir/creategbxs.cpp.o\u001b[0m\n", "[ 97%] \u001b[32mBuilding CXX object libs/runcleo/CMakeFiles/runcleo.dir/gensuperdrop.cpp.o\u001b[0m\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/./../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/cartesiandomain/cartesianmaps.hpp(193)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/cartesiandomain/cartesianmaps.hpp(193)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", "\n", "[ 97%] \u001b[32m\u001b[1mLinking CXX static library libcartesiandomain.a\u001b[0m\n", "[ 97%] Built target cartesiandomain\n", "[ 97%] \u001b[32m\u001b[1mLinking CXX static library libruncleo.a\u001b[0m\n", "[ 97%] Built target runcleo\n", "[100%] \u001b[32mBuilding CXX object examples/boxmodelcollisions/golovin/src/CMakeFiles/golcolls.dir/main_golcolls.cpp.o\u001b[0m\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", "\n", "[100%] \u001b[32m\u001b[1mLinking CXX executable golcolls\u001b[0m\n", "[100%] Built target golcolls\n", @@ -1174,15 +1097,15 @@ "[ 94%] Built target cartesiandomain\n", "[ 97%] Built target runcleo\n", "[ 97%] \u001b[32mBuilding CXX object examples/boxmodelcollisions/long/src/CMakeFiles/longcolls.dir/main_longcolls.cpp.o\u001b[0m\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector > ::vector(const ::std::vector > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", "\u001b[01;36m\u001b[0m\u001b[01;36mRemark\u001b[0m: The warnings can be suppressed with \"-diag-suppress \"\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mstd::vector< ::std::array , ::std::allocator< ::std::array > > ::vector(const ::std::vector< ::std::array , ::std::allocator< ::std::array > > &)\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianDecomposition::CartesianDecomposition\u001b[0m\") is not allowed\n", "\n", - "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-main/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", + "\u001b[01m\u001b[0m\u001b[01m/content/CLEO-0.33.0/libs/zarr/../gridboxes/gridbox.hpp(58)\u001b[0m: \u001b[01;35mwarning\u001b[0m #20011-D: calling a __host__ function(\"\u001b[01mCartesianDecomposition::~CartesianDecomposition()\u001b[0m\") from a __host__ __device__ function(\"\u001b[01mCartesianMaps::~CartesianMaps\u001b[0m\") is not allowed\n", "\n", "[100%] \u001b[32m\u001b[1mLinking CXX executable longcolls\u001b[0m\n", "[100%] Built target longcolls\n" @@ -1196,36 +1119,29 @@ "!pip install --quiet awkward ruamel.yaml zarr" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "FEr2-XU_3oiv", - "outputId": "5ef3efd9-65df-4387-c887-db487b00dfb7" + "id": "FEr2-XU_3oiv" }, - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m187.0/187.0 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m81.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.7/53.7 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ] + "execution_count": 47, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "qSHFdYMEW7V2" + }, + "execution_count": null, + "outputs": [] }, { "cell_type": "code", "source": [ - "!cd CLEO-main; \\\n", - " echo \"import numpy as np; derive_more_floats = lambda x: {'RHO0':1, 'MASS0':1, 'COORD0':1000, 'RHO_L':1000, 'RHO_SOL':np.nan, 'MR_SOL': np.nan, 'IONIC':np.nan}\" >> pySD/cxx2py.py; \\\n", + "!cd CLEO-0.33.0; \\\n", " python3 \\\n", " examples/boxmodelcollisions/shima2009.py \\\n", - " /content/CLEO-main \\\n", - " /content/CLEO-main/output \\\n", - " /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml \\\n", + " /content/CLEO-0.33.0 \\\n", + " /content/CLEO-0.33.0/output/box_model/ \\\n", + " /content/CLEO-0.33.0/examples/boxmodelcollisions/shima2009_config.yaml \\\n", " golovin" ], "metadata": { @@ -1233,9 +1149,9 @@ "base_uri": "https://localhost:8080/" }, "id": "Ux4-6J0rnaoM", - "outputId": "8c013063-0f85-4d53-acee-ac105f0662dd" + "outputId": "68794f40-28f5-48d1-f51b-ac9f8e00f5e8" }, - "execution_count": 37, + "execution_count": 49, "outputs": [ { "output_type": "stream", @@ -1243,9 +1159,9 @@ "text": [ "created boundaries for 1 gridboxes\n", "Writing gridbox boundaries binary file to:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat\n", "Metadata: \n", " '4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1'\n", "zhalf: [ 0. 100.]\n", @@ -1268,49 +1184,49 @@ "------------------------------------\n", "\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat\n", "Metadata: \n", " '4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are ndims in (z,x,y), then the 1 gridbox indicies followed by the [zmin, zmax, xmin, xmax, ymin, ymax] coordinates for each gridbox's boundaries. Grid has dimensions 1x1x1'\n", "zhalf: [ 0. 100.]\n", "xhalf: [ 0. 100.]\n", "yhalf: [ 0. 100.]\n", - "Figure .png saved as: /content/CLEO-main/output/bin/gridboxboundaries.png\n", + "Figure .png saved as: /content/CLEO-0.33.0/output/box_model/bin/gridboxboundaries.png\n", "Figure(1000x500)\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat\n", "4096\n", "--- total droplet concentration = 8.38861cm^-3 => 1g/m^3, in 1e+06m^3 volume --- \n", "Writing gridbox boundaries binary file to:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessSDsinit_1.dat\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessSDsinit_1.dat\n", "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", "\n", "------ DOMAIN SUPERDROPLETS INFO ------\n", "total droplet number conc: 8.38861 /cm^3\n", "total droplet mass: 7.0856e-35 g/m^3\n", - " as if water: 1.00816 g/m^3\n", + " as if water: 0.996791 g/m^3\n", "------------------------------------\n", "\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessSDsinit_1.dat\n", "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", - "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_distribs_1.png\n", + "Figure .png saved as: /content/CLEO-0.33.0/output/box_model/bin/initallGBxs_distribs_1.png\n", "Figure(1400x400)\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessSDsinit_1.dat\n", "attribute shapes: (4096,) (4096,) (4096,) (4096,) (0,) (0,) (0,)\n", - "Figure .png saved as: /content/CLEO-main/output/bin/initallGBxs_dropletmasses_1.png\n", + "Figure .png saved as: /content/CLEO-0.33.0/output/box_model/bin/initallGBxs_dropletmasses_1.png\n", "Figure(1400x400)\n", - "/content/CLEO-main/output\n", - "Executable: /content/CLEO-main/output/examples/boxmodelcollisions/golovin/src/golcolls\n", - "Config file: /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml\n", + "/content/CLEO-0.33.0/output/box_model\n", + "Executable: /content/CLEO-0.33.0/output/box_model/examples/boxmodelcollisions/golovin/src/golcolls\n", + "Config file: /content/CLEO-0.33.0/examples/boxmodelcollisions/shima2009_config.yaml\n", "\n", "-------- Required Configuration Parameters --------------\n", "constants_filename : \"../../libs/cleoconstants.hpp\"\n", @@ -1337,13 +1253,13 @@ "-------- InitSupersFromBinary Configuration Parameters --------------\n", "maxnsupers: 4096\n", "nspacedims: 0\n", - "initsupers_filename: \"/content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\"\n", + "initsupers_filename: \"/content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessSDsinit_1.dat\"\n", "initnsupers: 4096\n", "---------------------------------------------------------\n", "\n", "--- configuration ---\n", "----- writing to new setup file: ./bin/shima2009_setup.txt -----\n", - " copying /content/CLEO-main/examples/boxmodelcollisions/shima2009_config.yaml to setup file\n", + " copying /content/CLEO-0.33.0/examples/boxmodelcollisions/shima2009_config.yaml to setup file\n", " copying ../../libs/cleoconstants.hpp to setup file\n", "---- copy complete, setup file closed -----\n", "--- configuration: success ---\n", @@ -1424,18 +1340,18 @@ " making directory \"./bin/shima2009_sol.zarr/msol\"\n", "couldn't open \"./bin/shima2009_sol.zarr/raggedcount/.zarray\",\n", " making directory \"./bin/shima2009_sol.zarr/raggedcount\"\n", - "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "opening binary file: /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessSDsinit_1.dat\n", "----------------- gridfile global metastring -----------------\n", "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", "--------------------------------------------------------------\n", "\n", "--- create superdrops ---\n", "initialising\n", - "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "opening binary file: /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessSDsinit_1.dat\n", "----------------- gridfile global metastring -----------------\n", "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", "--------------------------------------------------------------\n", - "opening binary file: /content/CLEO-main/output/share/shima2009_dimlessSDsinit_1.dat\n", + "opening binary file: /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessSDsinit_1.dat\n", "----------------- gridfile global metastring -----------------\n", "4 unsigned ints before this metadata string are [1. position of first byte of data (after all the metadata), 2. no. bytes of (this) global metadata string, 3. no. bytes per variable specific metadata, 4. no. of variables in data]. After this global metadata string comes variable specific metadata. For each variable, this is 3 unsigned ints, 2 chars and then a double; it states: [1. position of first databyte, 2. size (in bytes) of one datapoint, 3. no. of datapoints, 4. char to indicate python struct type, 5. char to indicate the units once multiplied by, 6. the scale factor]. Variables in this file are Superdroplet attributes: [sdgbxindex, xi, radius, msol]\n", "--------------------------------------------------------------\n", @@ -1477,10 +1393,10 @@ "t=3800.00s, totnsupers=4096, ngbxs=1, (Gbx0: [T, p, qv, qc] = [0.00K, 0.00Pa, 0.0000e+00, 0.0000e+00], nsupers = 4096)\n", "--- timestepping: success ---\n", "-----\n", - " Total Program Duration: 9.3444e+00s \n", + " Total Program Duration: 9.3076e+00s \n", "-----\n", "\n", - "---- config from /content/CLEO-main/output/bin/shima2009_setup.txt -----\n", + "---- config from /content/CLEO-0.33.0/output/box_model/bin/shima2009_setup.txt -----\n", "num_threads = 128.0\n", "nspacedims = 0\n", "ngbxs = 1.0\n", @@ -1497,20 +1413,39 @@ "---------------------------------------------\n", "\n", "\n", - "---- consts from /content/CLEO-main/output/bin/shima2009_setup.txt -----\n", - "RHO0 = 1\n", - "MASS0 = 1\n", - "COORD0 = 1000\n", - "RHO_L = 1000\n", - "RHO_SOL = nan\n", - "MR_SOL = nan\n", - "IONIC = nan\n", + "---- consts from /content/CLEO-0.33.0/output/box_model/bin/shima2009_setup.txt -----\n", + "G = 9.80665\n", + "RGAS_UNIV = 8.314462618\n", + "MR_WATER = 0.01801528\n", + "MR_DRY = 0.028966216\n", + "LATENT_V = 2500930.0\n", + "CP_DRY = 1004.64\n", + "CP_V = 1865.01\n", + "C_L = 4192.664\n", + "RHO_DRY = 1.177\n", + "RHO_L = 998.203\n", + "RHO_SOL = 2016.5\n", + "MR_SOL = 0.05844277\n", + "IONIC = 2.0\n", + "SURFSIGMA = 0.0728\n", + "W0 = 1.0\n", + "TIME0 = 1000.0\n", + "R0 = 1e-06\n", + "P0 = 100000.0\n", + "TEMP0 = 273.15\n", + "COORD0 = 1000.0\n", + "RGAS_DRY = 287.0399992183998\n", + "RGAS_V = 461.52280830495\n", + "CP0 = 1004.64\n", + "Mr_ratio = 0.6219410916496653\n", + "RHO0 = 0.36440835810508476\n", + "MASS0 = 3.644083581050847e-19\n", "---------------------------------------------\n", "\n", "Reading binary file:\n", - " /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat\n", + " /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat\n", "\n", - "---- gbxs from /content/CLEO-main/output/share/shima2009_dimlessGBxboundaries.dat -----\n", + "---- gbxs from /content/CLEO-0.33.0/output/box_model/share/shima2009_dimlessGBxboundaries.dat -----\n", "ngrid = 1\n", "ndims = [1 1 1]\n", "domainvol = 1000000.0\n", @@ -1530,17 +1465,17 @@ "zzf = [[50.]]\n", "---------------------------------------------\n", "\n", - "time from dataset: /content/CLEO-main/output/bin/shima2009_sol.zarr\n", + "time from dataset: /content/CLEO-0.33.0/output/box_model/bin/shima2009_sol.zarr\n", "---- Superdrop Properties -----\n", - "RHO_L = 1000 Kg/m^3\n", - "RHO_SOL = nan Kg/m^3\n", - "MR_SOL = nan Kg/mol\n", - "IONIC = nan\n", + "RHO_L = 998.203 Kg/m^3\n", + "RHO_SOL = 2016.5 Kg/m^3\n", + "MR_SOL = 0.05844277 Kg/mol\n", + "IONIC = 2.0\n", "-------------------------------\n", - "supers dataset: /content/CLEO-main/output/bin/shima2009_sol.zarr\n", - "/content/CLEO-main/examples/exampleplotting/plotssrc/shima2009fig.py:140: RuntimeWarning: invalid value encountered in multiply\n", + "supers dataset: /content/CLEO-0.33.0/output/box_model/bin/shima2009_sol.zarr\n", + "/content/CLEO-0.33.0/examples/exampleplotting/plotssrc/shima2009fig.py:140: RuntimeWarning: invalid value encountered in multiply\n", " bsl_exp = iv(1, 2 * x * np.sqrt(tau)) * np.exp(-(1 + tau) * x)\n", - "Figure .png saved as: /content/CLEO-main/output/bin/golovin_validation.png\n", + "Figure .png saved as: /content/CLEO-0.33.0/output/box_model/bin/golovin_validation.png\n", "Figure(800x700)\n" ] } @@ -1550,22 +1485,22 @@ "cell_type": "code", "source": [ "from IPython.display import Image\n", - "display(Image('CLEO-main/output/bin/golovin_validation.png'))" + "display(Image('CLEO-0.33.0/output/box_model/bin/golovin_validation.png'))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1000 + "height": 851 }, "id": "cDFkmIaKqQjo", - "outputId": "baeb533c-fe2d-4b59-e19a-05f4365e3ab7" + "outputId": "cc57a9bc-b404-40f9-e4ff-7e9bd0e004c5" }, - "execution_count": 38, + "execution_count": 50, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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"text/plain": [ "" ] @@ -1584,4 +1519,4 @@ "outputs": [] } ] -} +} \ No newline at end of file