hdf5

问题 I have read several times that turning on compression in HDF5 can lead to better read/write performance. I wonder what ideal settings can be to achieve good read/write performance at: data_df.to_hdf(..., format='fixed', complib=..., complevel=..., chunksize=...) I'm already using fixed format (i.e. h5py ) as it's faster than table . I have strong processors and do not care much about disk space. I often store DataFrame s of float64 and str types in files of approx. 2500 rows x 9000 columns.

问题 EDIT STARTS I'm trying to create an "pair, triplet or n-uplet" attribute based on a native type (float, int...) : pair of float, triplet of float, n-uplet of floats attribute pair of int, triplet of int, n-uplet of int attribute I'm not trying to create an "Array" attribute, I'm not trying to create a "Compound" attribute EDIT END I'm trying to create an attribute based on a native type (float, int...) but which contains 2,3 or more values (equivalent to a pair or a n-uplet). I don't want to

问题 Given a large (10s of GB) CSV file of mixed text/numbers, what is the fastest way to create an HDF5 file with the same content, while keeping the memory usage reasonable? I'd like to use the h5py module if possible. In the toy example below, I've found an incredibly slow and incredibly fast way to write data to HDF5. Would it be best practice to write to HDF5 in chunks of 10,000 rows or so? Or is there a better way to write a massive amount of data to such a file? import h5py n = 10000000 f =

问题 I recently found this Project which runs inference of keras model in a browser with GPU support using webgl. I have a few tensorflow project that I would like to run inference on a browser, is there a way to export tensorflow models into hdf5 file so it can be run using keras-js 回答1: If you are using Keras, you can do something like this. model.save_weights('my_model.hdf5') 回答2: The only way I can see this working is if you use a Keras model as an interface to your TensorFlow workflow. If you

问题 I have a series of raster datasets which I want to combine into a single HDF5 file. Each raster file will be converted into an array with the dimensions 3600 x 7000 . As I have a total of 659 files, the final array would have a shape of 3600 x 7000 x 659 , too big for my (huge) amount of RAM. I'm fairly new to python and HDF5 itself, but basically my approach is to create a dataset with the required 2-d dimensions and then iteratively read the files into arrays and append to the dataset. I'm

问题 Does anyone know how to make the modification of the following code so that I can read the specific z component slice of 3D hdf data in Python? As you can see from the attached image, z value spans from 0 to 160 and I want to plot the '80' only. And the dimension is 400x160x160. Here is my code. import h5handler as h5h h5h.manager.setPath('E:\data\Data5', False) for i in np.arange(0,1,5000): cycleFile = h5h.CycleFile(h5h.manager.cycleFiles['cycle_'+str(i)+'.hdf'], 'r') fig = plt.figure() fig

问题 Situation: I need to add a library (HDF5 in my case) to my qt project. I know how to code c++ enough for my purposes, but i have no clue about the .pro file. When i try to google my problem or general guides for adding libraries i find lots of answers but understand none of them, because they require more knowledge then i have. They say stuff like "compile it here and there", "add this and that to your system", "use qmake in directory xyz". Can someone please answer the question so that one

问题 I want to use the HDF5 C++ bindings in a project build with CMake. So I do the usual: find_package (HDF5 REQUIRED COMPONENTS CXX) target_link_libraries(foo PUBLIC ${HDF5_LIBRARIES}) target_include_directories(foo PUBLIC ${HDF5_INCLUDE_DIRS}) This used to work till our cluster (HPC) was upgraded. Now I get errors during linking: function MPI::Win::Set_name(char const*): error: undefined reference to 'MPI_Win_set_name' function MPI::Win::Set_attr(int, void const*): error: undefined reference to

问题 Trying to open an HDF5 file with pandas. As long as the file is on my USB drive, it cannot be loaded/found by pandas. import os import pandas as pd table_path = r'C:\User\me\Desktop\test\h5table.h5' if os.path.exists(table_path): print('yup, this file exists') h5table = pd.io.pytables.HDFStore(table_path, mode='r') This works as expected, the table is loaded into h5table. The h5table.h5 is a copy of the original file which is located on a USB drive. Here's me trying to load the original:

问题 How can one make a Rasterbrick in R from several hdf5 files? Often, data are provided in hdf5 format and one has to convert it to a more friendly format for easy handling. At the moment I know of the rhdf5 package but how to get a RasterBrick is that which I am unsure about. source("http://bioconductor.org/biocLite.R") biocLite("rhdf5") library("rhdf5") library("raster") You can access several hdf5 files on this link http://mirador.gsfc.nasa.gov/cgi-bin/mirador/cart.pl?C1=GPM_3IMERGHH