![]() Previous quarters’ historical growth averaged mere single digits. This growth was unprecedented, producing a 50% increase compared to Q1 of 2020. More specifically, online spending within the furniture and appliances category shot up to a record-setting $12.1 billion in Q2 of 2020. Research from comscore shows that as of August 2020, 74.2 million American consumers completed a home remodel within the past 12 months, which is an increase of nearly 20% compared to that same timeframe last year. One factor driving this growth: many consumers are still staying home and want to make those home environments as enjoyable as possible. Additional data projects that the furniture industry will see total revenues up 4% over that of 2020, nearing $120 billion total. The sell-through rate Industry West saw with this particular sale further illustrates the continued potential within the home furnishings vertical. The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government's ETH Board of the Swiss Federal Institutes of Technology.“We closely sorted and monitored the influx of orders available for product picking and extended the delivery time of the warehouse sale orders to prioritize accordingly.” ![]() Questions?ĭo you have questions on how to use HighFive? Would you like to share an interesting example orĭiscuss HighFive features? Head over to the Discussionsįorum and join the community. clang-format is at the root of the git repository. If you want to propose pull requests to this project, do not forget to format code with HighFive with disable support for Boost types as well as unit tests (though most examples will build). In case it's unavailable you may use -DHIGHFIVE_USE_BOOST=OFF. See src/examples/ subdirectory for more info. See create_attribute_string_integer.cpp And others See select_partial_dataset_cpp11.cpp Create, write and list HDF5 attributes See boost_ublas_double.cpp Write and read a subset of a 2D double dataset See create_dataset_double.cpp Write and read a matrix of double float (boost::ublas) to a 2D HDF5 dataset Write a 2 dimensional C double float array to a 2D HDF5 dataset This is common in MPI-IO related patterns, or when growing aĭataset over the course of a simulation. Note: For advanced usecases the dataset can be created without immediately Note: H5File.hpp is the top-level header of HighFive core which should be always included. Because `data` has the correct size, this will // not cause `data` to be reallocated: Read back, with allocating: auto data = dataset. We create an empty HDF55 file, by truncating an existing // file if required:Īuto dataset = file. Std::string filename = "/tmp/new_file.h5 " half (optional, opt-in with -D HIGHFIVE_USE_HALF_FLOAT=ON)Įxamples Write a std::vector to 1D HDF5 dataset and read it back.xtensor (optional, opt-in with -D HIGHFIVE_USE_XTENSOR=ON).eigen3 (optional, opt-in with -D HIGHFIVE_USE_EIGEN=ON).boost >= 1.41 (recommended, opt-out with -D HIGHFIVE_USE_BOOST=OFF).hdf5-mpi (optional, opt-in with -D HIGHFIVE_PARALLEL_HDF5=ON).half-precision (16-bit) floating-point datasets.Advanced types: Compound, Enum, Arrays of Fixed-length strings, References.parallel Read/Write operations from several nodes with Parallel HDF5.automatic conversion of std::string to/from variable length string dataset.automatic conversion of std::vector and nested std::vector from/to any dataset with basic types.automatic memory management / ref counting.create/read/write files, datasets, attributes, groups, dataspaces.It integrates nicely with other CMake projects by defining (and exporting) a HighFive target. HighFive does not require additional libraries (see dependencies) and supports both HDF5 thread safety and Parallel HDF5 (contrary to the official hdf5 cpp) It handles C++ from/to HDF5 with automatic type mapping. HighFive supports STL vector/string, Boost::UBLAS, Boost::Multi-array, Eigen and Xtensor. HighFive is a modern header-only C++11 friendly interface for libhdf5. `details::checkDimensions` and `details::squeezeDimensions`. * Adds (back) tests for checking the broadcasting rules. To `(3, 1, 1)` not being a valid 2D array. * improving `details::checkDimensions` by removing bugs related * introducing `details::squeezeDimensions` which strips the Stripping multi-dimensional arrays from either side is unstableĪnd should be avoided anyway. However, the stripping from both sides only works for one-dimensionalĪrrays. Is easier to deal with than one that's `(3, 1)`. Motivated by `std::vector>` of shape `(1, 3)` the dataset with shape `(1, 3, 1)`Ĭan be read into an array of shape `(1, 3)`. The rules for broadcasting in `2.4.x` seem to be that singletonĭimension can be stripped from either side.
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