hdf5

问题 Ok, I have the HDF5 library downloaded from the official site, and I have a few DLLs, including hdf5dll.dll, and hdf5_hldll.dll. I have what I think to be some wrappers around the native calls, in my classes H5 , H5LT , H5F , and H5T . Example from H5.cs: namespace HDF5 { using hid_t = System.Int32; using herr_t = System.Int32; using hsize_t = System.UInt64; using size_t = System.UInt32; // hbool_t is 0:false, +:true using hbool_t = System.UInt32; // htri_t is 0:false, +:true, -:failure using

问题 Ok, I have the HDF5 library downloaded from the official site, and I have a few DLLs, including hdf5dll.dll, and hdf5_hldll.dll. I have what I think to be some wrappers around the native calls, in my classes H5 , H5LT , H5F , and H5T . Example from H5.cs: namespace HDF5 { using hid_t = System.Int32; using herr_t = System.Int32; using hsize_t = System.UInt64; using size_t = System.UInt32; // hbool_t is 0:false, +:true using hbool_t = System.UInt32; // htri_t is 0:false, +:true, -:failure using

问题 I am trying to run a PyTorch implementation of a code, which is supposed to work on SBD dataset. The training labels are originally available in .bin file, which are then converted to HDF5 (.h5) files. Upon running the algorithm, I get an error as: " TypeError: h5py objects cannot be pickled " I think the error is stemming from torch.utils.data.DataLoader. Any idea if I am missing any concept here? I read that pickling is generally not preferred but as of now, my dataset is in HDF5 format

问题 I am trying to run a PyTorch implementation of a code, which is supposed to work on SBD dataset. The training labels are originally available in .bin file, which are then converted to HDF5 (.h5) files. Upon running the algorithm, I get an error as: " TypeError: h5py objects cannot be pickled " I think the error is stemming from torch.utils.data.DataLoader. Any idea if I am missing any concept here? I read that pickling is generally not preferred but as of now, my dataset is in HDF5 format

问题 I am trying to create hdf5 file, but the output file is empty. I have written a python code which is supposed to run in loop and write string in the created datasets. After the file gets saved, I found that the output file is always empty. Below is the piece of code I have written: h5_file_name = 'sample.h5' hf = h5py.File(h5_file_name, 'w') g1 = hf.create_group('Objects') dt = h5py.special_dtype(vlen=str) d1 = g1.create_dataset('D1', (2, 10), dtype=dt) d2 = g1.create_dataset('D2', (3, 10),

问题 I am trying to create hdf5 file, but the output file is empty. I have written a python code which is supposed to run in loop and write string in the created datasets. After the file gets saved, I found that the output file is always empty. Below is the piece of code I have written: h5_file_name = 'sample.h5' hf = h5py.File(h5_file_name, 'w') g1 = hf.create_group('Objects') dt = h5py.special_dtype(vlen=str) d1 = g1.create_dataset('D1', (2, 10), dtype=dt) d2 = g1.create_dataset('D2', (3, 10),

问题 I currently have a .h5 file, with a table in it consisting of three columns: a text columns of 64 chars, an UInt32 column relating to the source of the text and a UInt32 column which is the xxhash of the text. The table consists of ~ 2.5e9 rows I am trying to find and count the duplicates of each text entry in the table - essentially merge them into one entry, while counting the instances. I have tried doing so by indexing on the hash column and then looping through table.itersorted(hash) ,

问题 creating a dataset from multiple hdf5 groups Code for groups with np.array(hdf.get('all my groups')) I have then added code for creating a dataset from groups. with h5py.File('/train.h5', 'w') as hdf: hdf.create_dataset('train', data=one_T+two_T+three_T+four_T+five_T) The error message being ValueError: operands could not be broadcast together with shapes(534456,4) (534456,14) The numbers in each group are the same other than the varying column lengths. 5 separate groups to one dataset. 回答1:

问题 I'm currently working on a project regarding compression of HDF5 datasets and recently began using h5py. I followed the basic tutorials and was able to open,create and compress a file while it was being created. However, I've been unsuccessful when it comes to compressing an existing file (which is the aim of my work). I've tried opening files using 'r+' and then compressing chunked datasets but the file sizes have remained the same. Any suggestions on what commands to use or am I going about

问题 When i use pandas to convert csv files to hdf5 files the resulting file is extremely large. For example a test csv file (23 columns, 1.3 million rows) of 170Mb results in an hdf5 file of 2Gb. However if pandas is bypassed and the hdf5 file is directly written (using pytables) it is only 20Mb. In the following code (that is used to do the conversion in pandas) the values of the object columns in the dataframe are explicitly converted to string objects (to prevent pickling): # Open the csv file