h5py | Python -- The h5py package | Dataset library

 by   h5py Python Version: 3.10.0 License: BSD-3-Clause

kandi X-RAY | h5py Summary

kandi X-RAY | h5py Summary

h5py is a Python library typically used in Artificial Intelligence, Dataset, Numpy applications. h5py has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install h5py' or download it from GitHub, PyPI.

HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.

            kandi-support Support

              h5py has a highly active ecosystem.
              It has 1882 star(s) with 501 fork(s). There are 57 watchers for this library.
              There were 2 major release(s) in the last 6 months.
              There are 227 open issues and 1177 have been closed. On average issues are closed in 179 days. There are 20 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of h5py is 3.10.0

            kandi-Quality Quality

              h5py has 0 bugs and 0 code smells.

            kandi-Security Security

              h5py has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              h5py code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              h5py is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              h5py releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              h5py saves you 4763 person hours of effort in developing the same functionality from scratch.
              It has 10503 lines of code, 1191 functions and 88 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed h5py and discovered the below as its top functions. This is intended to give you an instant insight into h5py implemented functionality, and help decide if they suit your requirements.
            • Create a new dataset
            • Create a new dset
            • Create a link creation property
            • Copy source from source to destination group
            • Setup sphinx
            • Safely replace occurrences of role expressions
            • Replace class expressions
            • Perform selection operation
            • Setup benchmarking
            • Create a dataset
            • Create a fsp file
            • Calculate the grid
            • Broadcast this operator to a given shape
            • Return a list of all the read threads
            • Create a new HDF5 file
            • Start a thread
            • Bundle all DLLs
            • Enable IPython completer
            • Read the dataset
            • Highlight completer
            • Get filters from a plist
            • Visualize the fractal file
            • Run the simulation
            • Build an HDF5
            • Copy source to destination group
            • Create a HDF5 file
            • Runs the test suite
            Get all kandi verified functions for this library.

            h5py Key Features

            No Key Features are available at this moment for h5py.

            h5py Examples and Code Snippets

            How to compress multiple h5 files into one hdf5 file and create a dataset?
            Pythondot img1Lines of Code : 20dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import h5py
            import os
            with h5py.File("myCardiac.hdf5", "w") as f_dst:
                h5files = [f for f in os.listdir() if f.endswith(".h5")]
                dset = f_dst.create_dataset("mydataset", shape=(len(h5files), 24, 170, 218, 256), dtype='f4')
            Build an adjacency matrix from distances between cosmological objects
            Pythondot img2Lines of Code : 36dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from sklearn.neighbors import radius_neighbors_graph
            # Your example data in runnable format
            dx = np.array([2.63370612e-01, 3.48350511e-01, -1.23379511e-02, 
                           6.63767411e+00, 1.32910697e+01,  8.75469902e+00])
            dy = np.array([0
            How to optimize sequential writes with h5py to increase speed when reading the file afterwards?
            Pythondot img3Lines of Code : 45dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            Time to read first row: 0.28 (in sec)
            Time to read last row:  0.28
            dataset chunkshape: (40, 625)
            Time to read first row: 0.28
            Time to read last row: 0.28
            dataset chunkshape: (10, 15625)
            Time to read first row: 0.0
            3-dimensional array reshaping? HDF5 dataset type?
            Pythondot img4Lines of Code : 18dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            new_arr = np.empty((2*127260, 2, 625))
            arr1 = h5f['dataset_name'][:,:, :625]
            arr2 = h5f['dataset_name'][:,:,  625:]
            new_arr[:127260,:,:] = arr1 
            new_arr[127260:,:,:] = arr2 
            Simulate hdf5 file and hdf5 group with MagicMock in python3
            Pythondot img5Lines of Code : 7dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def test_checkInput(self):
                file = MagicMock()    
                file.keys.return_value = 'parent'
                file['parent'].keys.return_value = ['A', 'B']
            Reading and plotting HDF5(.h5) file and showing map for a specific latitude and longitude
            Pythondot img6Lines of Code : 183dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import h5py
            import matplotlib.pyplot as plt
            fn = '3DIMG_30MAR2018_0000_L1B_STD.h5' #filename (the ".h5" file)
            with h5py.File(fn) as f:  
                img_arr = f['IMG_TIR1'][0,:,:] 
            fig = plt.subplots(figsize=(10,10)) 
            plt.title('plot raw IMG
            No such file or directory: '/opt/anaconda3/lib/python3.8/site-packages/rtree/lib'
            Pythondot img7Lines of Code : 4dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            python is /opt/anaconda3/bin/python
            python is /usr/local/bin/python
            python is /usr/bin/python
            Getting filenotfound error when trying to open a h5 file
            Pythondot img8Lines of Code : 4dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            train_dataset = h5py.File('train_catvnoncat.h5', "r")
            train_dataset = h5py.File('C:/Users/Moshen/Downloads/train_catvnoncat.h5', "r")
            How to write large multiple arrays to a h5 file in layers?
            Pythondot img9Lines of Code : 52dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import h5py
            import numpy as np
            table1_dt = np.dtype([('x1',float), ('y1',float), ('y1_err',float),])
            table2_dt = np.dtype([('x2',float), ('y2',float), ('y2_err',float),])
            with h5py.File('SO_71335363.h5','w') as
            How to write large multiple arrays to a h5 file in layers?
            Pythondot img10Lines of Code : 13dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df = pd.DataFrame(columns=['system','x1','y1','y1_err','x2','y2','y2_err'])
            for i, sys in enumerate(Systems):

            Community Discussions


            Build an adjacency matrix from distances between cosmological objects
            Asked 2022-Apr-11 at 03:17

            I'm probing into the Illustris API, and gathering information from a specific cosmos simulation, for a given redshift value.

            This is how I request the api:



            Answered 2022-Apr-11 at 01:12


            ERROR:Failed building wheel for h5pyFailed to build h5pyERROR:Could not build wheels for h5py,which is required toinstall pyproject.toml-basedprojects
            Asked 2022-Mar-25 at 16:40

            I'm getting this error when I'm running the following command to install tensorflow.



            Answered 2022-Mar-25 at 16:40


            Error while installing TensorFlow with pip
            Asked 2022-Feb-25 at 21:58

            While I try to install TensorFlow I get this error :



            Answered 2022-Feb-02 at 19:41

            I fix this by following Apple Developer Docs: https://developer.apple.com/metal/tensorflow-plugin/

            I uninstall Miniforge

            Source https://stackoverflow.com/questions/70956173


            Colab: (0) UNIMPLEMENTED: DNN library is not found
            Asked 2022-Feb-08 at 19:27

            I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:



            Answered 2022-Feb-07 at 09:19

            It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason

            Source https://stackoverflow.com/questions/71000120


            'ModuleNotFoundError: No module named 'keras.engine.base_layer_v1'' when running PyInstaller .exe file
            Asked 2022-Feb-07 at 18:24

            I am trying to create an executable version of python script that predicts images using .h5 file. The file runs completely fine when on its own in the virtual environment. But when I run the exe after completing the hidden imports following this and data addition in .spec file, when I run the exe it gives the following error:



            Answered 2021-Aug-08 at 23:03

            Since the error is caused by keras in particular, I replaced it with tensorflow.keras.* and seemed to resolve the issue.

            Source https://stackoverflow.com/questions/68582864


            AWS Elastic Beanstalk - Failing to install requirements.txt on deployment
            Asked 2022-Feb-05 at 22:37

            I have tried the similar problems' solutions on here but none seem to work. It seems that I get a memory error when installing tensorflow from requirements.txt. Does anyone know of a workaround? I believe that installing with --no-cache-dir would fix it but I can't figure out how to get EB to do that. Thank you.




            Answered 2022-Feb-05 at 22:37

            The error says MemoryError. You must upgrade your ec2 instance to something with more memory. tensorflow is very memory hungry application.

            Source https://stackoverflow.com/questions/71002698


            Cannot find conda info. Please verify your conda installation on EMR
            Asked 2022-Feb-05 at 00:17

            I am trying to install conda on EMR and below is my bootstrap script, it looks like conda is getting installed but it is not getting added to environment variable. When I manually update the $PATH variable on EMR master node, it can identify conda. I want to use conda on Zeppelin.

            I also tried adding condig into configuration like below while launching my EMR instance however I still get the below mentioned error.



            Answered 2022-Feb-05 at 00:17

            I got the conda working by modifying the script as below, emr python versions were colliding with the conda version.:

            Source https://stackoverflow.com/questions/70901724


            conda install and conda build result in different dependency versions
            Asked 2022-Jan-19 at 17:33

            I'm trying to build a package which includes h5py. When using conda build, it seems to install the wrong version of the dependency. It installs 3.2.1-py37h6c542dc_0, which includes hdf5: 1.10.6-nompi_h6a2412b_1114.

            The problem is that this hdf5 lib, seems to have these setting:

            (Read-Only) S3 VFD: yes

            This causes an error for me. When just running conda install h5py==3.2.1, it does install the right version (hdf5-1.10.6-nompi_h3c11f04_101).

            Why is there a difference?



            Answered 2022-Jan-19 at 17:33

            "Why is there a difference?

            Using conda install h5py=3.2.1 additionally includes all the previous constraints in the current environment, whereas during a conda build run, a new environment is created only with requirements that the package specifies. That is, it is more like running conda create -n foo h5py=3.2.1.

            So, that covers the mechanism, but we can also look at the particular package dependencies to see why the current environment constrains to the older hdf5-1.10.6-nompi_h3c11f04_101, which OP states is preferred. Here is the package info for the two:


            Source https://stackoverflow.com/questions/70772189


            How to solve no such node error in pytables and h5py
            Asked 2022-Jan-12 at 16:50

            I built an hdf5 dataset using pytables. It contains thousands of nodes, each node being an image stored without compression (of shape 512x512x3). When I run a deep learning training loop (with a Pytorch dataloader) on it it randomly crashes, saying that the node does not exist. However, it is never the same node that is missing and when I open the file myself to verify if the node is here it is ALWAYS here.

            I am running everything sequentially, as I thought that I may have been the fault of multithreading/multiprocessing access on the file. But it did not fix the problem. I tried a LOT of things but it never works.

            Does someone have an idea about what to do ? Should I add like a timer between calls to give the machine the time to reallocate the file ?

            Initially I was working with pytables only, but in an attempt to solve my problem I tried loading the file with h5py instead. Unfortunately it did not work better.

            Here is the error I get with h5py: "RuntimeError: Unable to get link info (bad symbol table node signature)"

            The exact error may change but every time it says "bad symbol table node signature"

            PS: I cannot share the code because it is huge and part of a bigger basecode that is my company's property. I can still share part of the code below to show how I load the images:



            Answered 2022-Jan-12 at 16:50

            Before accessing the dataset (node), add a test to confirm it exists. While you're adding checks, do the same for the attribute 'TITLE'. If you are going to use hard-coded path names (like 'group_0') you should check all nodes in the path exist (for example, does 'group_0' exist? Or use one of the recursive visitor functions (.visit() or .visititems() to be sure you only access existing nodes.

            Modified h5py code with rudimentary checks looks like this:

            Source https://stackoverflow.com/questions/70682602


            Why do I have to call MPI.Finalize() inside the destructor?
            Asked 2021-Dec-13 at 15:44

            I'm currently trying to understand mpi4py. I set mpi4py.rc.initialize = False and mpi4py.rc.finalize = False because I can't see why we would want initialization and finalization automatically. The default behavior is that MPI.Init() gets called when MPI is being imported. I think the reason for that is because for each rank a instance of the python interpreter is being run and each of those instances will run the whole script but that's just guessing. In the end, I like to have it explicit.

            Now this introduced some problems. I have this code



            Answered 2021-Dec-13 at 15:41

            The way you wrote it, data_gen lives until the main function returns. But you call MPI.Finalize within the function. Therefore the destructor runs after finalize. The h5py.File.close method seems to call MPI.Comm.Barrier internally. Calling this after finalize is forbidden.

            If you want have explicit control, make sure all objects are destroyed before calling MPI.Finalize. Of course even that may not be enough in case some objects are only destroyed by the garbage collector, not the reference counter.

            To avoid this, use context managers instead of destructors.

            Source https://stackoverflow.com/questions/70336925

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install h5py

            You can install using 'pip install h5py' or download it from GitHub, PyPI.
            You can use h5py like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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