hdf5

问题 Im currently working on a program that converts text files and jpg-images into the HDF5-Format. Opened with the HDFView 3.0, it seems that the Images are only saved in greyscales. hdf = h5py.File("Sample.h5") img = Image.open("Image.jpg") data = np.asarray((img), dtype="uint8") hdf.create_dataset("Photos/Image 1", data=data, dtype='uint8') dset = hdf.get("Photos/Image 1") dset.attrs['CLASS'] = 'IMAGE' dset.attrs['IMAGE_VERSION'] = '1.2' arr = np.asarray([0, 255], dtype=np.uint8) dset.attrs[

问题 I am using go-hdf5 to read an hdf5 file into golang. I am on windows7 using a pretty recent copy of mingw and hdf5 1.8.14_x86 and it seems like trying to use any of the predefined types doesn't work, let's focus for example on T_NATIVE_UINT64. I have reduced the issue to the following, which basically leaves go-hdf5 out of the problem and points at something quite fundamental going wrong: package main /* #cgo CFLAGS: -IC:/HDF_Group/HDF5/1.8.14_x86/include #cgo LDFLAGS: -LC:/HDF_Group/HDF5/1.8

问题 This question already has answers here : Closed 7 years ago . Possible Duplicate: Installing h5py on OS X I am trying to get h5py to work on my OS X Lion 10.7.3 Macbook Pro. It has worked before but somehow it got uninstalled and I can't get it installed again. It seems it has to do with installing XCode 4.3, but I'm not sure. When importing h5py, I get the following error: >>> import h5py Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Library/Python/2.7/site

问题 I am running a code which takes .hdf5 files as an input (produced by a simulation) and then analyze them and produces some statistics and plot through running the command line: python3 Collector.py in a Konsole shell of a Fedora 21 Linux. I have lots of .py routines in two different folders named gizmo and utilities in the working directory. The snapshot_index.hdf5 files are transferred separately (using globus software) from another machine that runs the simulations into a local directory

问题 Using h5py.File(path,'r') or (Keras) model.load_weights(path) on .h5 files yields the below error. I've trained models on Colab for months, never had this problem. The same files open just fine on my computer when downloaded from Drive. At first one notebook failed to open .h5 files - all others followed soon after. What's the error, and how to troubleshoot? Inability to save & load models renders Colab unusable - help is appreciated. UPDATE : What started as inability to open h5 files

问题 I'm building a plug-in with MS Visual C++ 2010 Express, and I would like to include capabilities from the HDF5 library. I have tried both building HDF5 from source with CMake, and installing the precompiled library (HDF5-1.8.7_CMake_x86_shared.zip for VC 2008). For either build directory/method, I've modified my project property: C/C++ > General > Additional Include Directories: add the include for HDF5 Linker > General > Additional Library Directories: add the lib for HDF5 Here is a snippet

问题 I'm trying to import hdf.hdf5lib.H5 into my maven project in NetBeans. It has this as import line import hdf.hdf5lib.H5; as suggested here: https://support.hdfgroup.org/products/java/JNI3/jhi5/index.html However, it throws this exception: java.lang.ExceptionInInitializerError Caused by: java.lang.RuntimeException: Uncompilable source code - package hdf.hdf5lib does not exist NetBeans already warned me about it by saying at the import line "packadge does not excist". So I let it "search

问题 I recently installed the hdf5 library on an ubuntu machine, and am now having trouble linking to the exported functions. I wrote a simple test script readHDF.cpp to explain the issue: #include <hdf5.h> int main(int argc, char * argv[]) { hid_t h5_file_id = H5Fopen(argv[1], H5F_ACC_RDWR, H5P_DEFAULT); return 0; } The compile command is g++ -Wl,-rpath,$HOME/hdf5/lib -I$HOME/hdf5/include \ -L$HOME/hdf5/lib -l:$HOME/hdf5/lib/libhdf5.so readHDF.cpp which returns the following error /tmp/cc6DXdxV.o

问题 I have two numpy arrays stored in hdf5 that are 44 GB each. I need to concatenate them together but need to do it on disk because I only have 8gb ram. How would I do this? Thank you! 回答1: The related post is to obtain distinct datasets in the resulting file. In Python it is possible but you will need to read and write the datasets in multiple operations. Say, read 1GB from file 1, write to output file, repeat until all data is read from file 1 and do the same for file 2. You need to declare

问题 Problem I have a large (> 500e6 rows) dataset that I've put into a pytables database. Lets say first column is ID, second column is counter for each ID. each ID-counter combination has to be unique. I have one non-unique row amongst 500e6 rows I'm trying to find. As a starter I've done something like this: index1 = db.cols.id.create_index() index2 = db.cols.counts.create_index() for row in db: query = '(id == %d) & (counts == %d)' % (row['id'], row['counts']) result = th.readWhere(query) if