Hdf5 viewer

Author: l | 2025-04-24

HDF5 File Viewer. Contribute to loenard97/hdf5-viewer development by creating an account on GitHub.

loenard97/hdf5-viewer: HDF5 File Viewer - GitHub

Fbpic). (See 337)openPMD-viewer will raise an exception if the user asks for an iteration that is not part of the dataset (instead of printing a message and reverting to the first iteration, which can be confusing) (See 336) 1.3.0 This new release introduces preliminary support for mesh-refinement datasets(see #332). 1.2.0 This new release introduces several bug-fixes and miscellaneous features:There is a new function get_energy_spread that returns the energy spread of the beam. This is partially redundant with get_mean_gamma, which is kept for backward compatibility. (see #304 and #317)The 3D field reconstruction from ThetaMode data now has an option max_resolution_3d that limits the resolution of the final 3D array. This is added in order to limit the memory footprint of this array. (see #307) The 3D reconstruction is now also more accurate, thanks to the implementation of linear interpolation. (see #311)A bug that affected reading ThetaMode data with the openpmd-api backend has been fixed. (see #313)A bug that affected get_laser_waist has been fixed. (see #320) openPMD-api backend This new release introduces the option to read openPMD files with different backends. In addition to the legacy h5py backend (which can read only HDF5 openPMD file), openPMD-viewer now has the option to use the openpmd-api backend (which can read both HDF5 and ADIOS openPMD files). Because the openpmd-api backend is thus more general, it is selected by default if available (i.e. if installed locally).The user can override the default choice, by passing the backend argument when creating an OpenPMDTimeSeries object, and check which backend has been chosen by inspecting the .backend attribute of this object.In addition, several smaller changes were introduced in this PR:The method get_laser_envelope can now take the argument laser_propagation in order to support lasers that do not propagates along the z axis.openPMD-viewer can now properly read groupBased openPMD files (i.e. files that contain several iterations) #301.Users can now pass arrays of ID to the ParticleTracker #283

GitHub - loenard97/hdf5-viewer: HDF5 File Viewer

Deep Neural Network viewerA dashboard to inspect deep neural network modelsDNN Viewer is providing interactive view on the layer and unit weights and gradients, as well as activation maps.DNN Viewer is distinctive to existing tools since it is linking architecture, parameters, test data and performance.Current version is targeted at the classification task. However, coming version will target more diverse tasks.This project is for learning and teaching purpose, do not try to display a network with hundreds of layers.InstallInstall with PIPRun dnnviewer with one of the examples below, or with you own model (see below for capabilities and limitations)Access the web application at the programCurrently accepted input formats are Keras Sequential models written to file in Checkpoint format or HDF5. A series of checkpoints along training epochs is also accepted as exemplified below.Some test models are provided in the GIT repository _dnnviewer-data_ to clone from Github or download a zip from the repository page, a full description of the models and their design is available in the repository readme.$ git clone data is provided by Keras.Selecting the model within the application`Launch the application with command line --model-directories that set a comma separated list of directory paths where the models are located$ dnnviewer --model-directories dnnviewer-data/models,dnnviewer-data/models/FashionMNIST_checkpointsThen select the network model and the corresponding test data (optional) on the user interfaceModels containing the '{epoch}' tag are sequences over epochs. They are detected based on the pattern set bycommand line option --sequence-pattern whose default is {model}_{epoch}Generating the modelsFrom Tensorflow 2.0 KerasNote: Only Sequential models are currently supported.Save a single modelUse the save()method of keras.models.Model class the output file format is either Tensorflow Checkpoint or HDF5 based on the extension.model1.save('models/MNIST_LeNet60.h5')Save models during trainingThe Keras standard callback tensorflow.keras.callbacks.ModelCheckpoint is saving the model every epoch or a defined period of epochs:from tensorflow import kerasfrom tensorflow.keras.callbacks import ModelCheckpointmodel1 = keras.models.Sequential()#...callbacks = [ ModelCheckpoint( filepath='checkpoints_cnn-mnistfashion/model1_{epoch}', save_best_only=False, verbose=1)]hist1 = model1.fit(train_images, train_labels, epochs=nEpochs, validation_split=0.2, batch_size=batch_size, verbose=0, callbacks=callbacks)Current capabilitiesLoad Tensorflow Keras Sequential models and create a display of the networkTargeted at image classification task (assume image as input, class as output)Display series of models over training epochsInteractive display and unit weights through connections within the network and histogramsSupported layersDenseConvolution 2DFlattenInputFollowing layers are added as attributes to the previous or next layerDropout, ActivityRegularization, SpatialDropout1D/2D/3DAll pooling layersBatchNormalizationActivationUnsupported layersConvolution 1D and 3DTranspose convolution 2D and 3DReshape, Permute, RepeatVector, Lambda, MaskingRecurrent layers (LSTM, GRU...)Embedding layersMerge layersDeveloper documentationSee developer.md

lochbrunner/vscode-hdf5-viewer: Displays HDF5 files

Mat 7.3Load MATLAB 7.3 .mat files into Python.Starting with MATLAB 7.3, .mat files have been changed to store as custom hdf5 files.This means they cannot be loaded by scipy.io.loadmat any longer and raise.NotImplementedError: Please use HDF reader for matlab v7.3 files, e.g. h5pyQuickstartThis library loads MATLAB 7.3 HDF5 files into a Python dictionary.import mat73data_dict = mat73.loadmat('data.mat')As easy as that!By enabling use_attrdict=True you can even access sub-entries of structs as attributes, just like in MATLAB:data_dict = mat73.loadmat('data.mat', use_attrdict=True) struct = data_dict['structure'] # assuming a structure was saved in the .matstruct[0].var1 == struct[0]['var1'] # it's the same!You can also specifiy to only load a specific variable or variable tree, useful to reduce loading timesdata_dict = mat73.loadmat('data.mat', only_include='structure') struct = data_dict['structure'] # now only structure is loaded and nothing elsedata_dict = mat73.loadmat('data.mat', only_include=['var/subvar/subsubvar', 'tree1/']) tree1 = data_dict['tree1'] # the entire tree has been loaded, so tree1 is a dict with all subvars of tree1subsubvar = data_dict['var']['subvar']['subsubvar'] # this subvar has been loadedInstallationTo install, run:Alternatively for most recent version:pip install git+ following MATLAB datatypes can be loadedMATLABPythonlogicalnp.bool_singlenp.float32doublenp.float64int8/16/32/64np.int8/16/32/64uint8/16/32/64np.uint8/16/32/64complexnp.complex128charstrstructlist of dictscelllist of listscanonical empty[]missingNonesparsescipy.sparse.cscOther (ie Datetime, ...)Not supportedShort-comingsThis library will only load mat 7.3 files. For older versions use scipy.io.loadmatProprietary MATLAB types (e.g datetime, duriation, etc) are not supported. If someone tells me how to convert them, I'll implement thatFor now, you can't save anything back to the .mat. It's a bit more difficult than expected, so it's not on the roadmap for nowSee also hdf5storage, which can indeed be used for saving .mat, but has less features for loadingSee also pymatreader which has a (maybe even better) implementation of loading MAT files, even for older ones. HDF5 File Viewer. Contribute to loenard97/hdf5-viewer development by creating an account on GitHub.

GitHub - lochbrunner/vscode-hdf5-viewer: Displays HDF5 files

Pip installation,the dependent programs themselves need to be installed separately becausea they are not part of the QiskitChemistry installation bundle.Qiskit Chemistry comes with prebuilt support to interface the following classical computational chemistrysoftware programs:Gaussian 16™, a commercial chemistry programPSI4, a chemistry program that exposes a Python interface allowing for accessing internal objectsPySCF, an open-source Python chemistry programPyQuante, a pure cross-platform open-source Python chemistry programExcept for the Windows platform, PySCF is installed automatically as a dependency by the pip tool whenever Qiskit Chemistry isinstalled. The other classical computational chemistry software programs will have to be installed separately, even thoughQiskit Chemistry includes the code for interfacing all of them.Please refer to the Qiskit Chemistry drivers installation instructionsfor details on how to integrate these drivers into Qiskit Chemistry.A useful functionality integrated into Qiskit Chemistry is its ability to serialize a file in Hierarchical DataFormat 5 (HDF5) format representing all the data extracted from one of the drivers listed above whenexecuting an experiment. Qiskit Chemistry can then use that data to initiate the conversion of thatdata into a fermionic operator and then a qubit operator, which can then be used as an input to a quantumalgorithm. Therefore, even without installing one of the drivers above, it is still possible to runchemistry experiments as long as you have a Hierarchical Data Format 5 (HDF5) file that has been previouslycreated. Qiskit Chemistry's built-in HDF5 driver accepts such such HDF5 files as input.A few sample HDF5 files for different are provided in thechemistry folder of theQiskit Tutorials repository.To

h5web: a web based viewer of HDF5 files - Loic Huder (HDF5

Format and set of tools for managing complex data. HDF5 allows you to store large datasets on disk and access them efficiently, making it possible to handle datasets that are too large to fit into memory.Here's an example of using HDF5 to store and access a large dataset:import h5pyimport numpy as npCreate a large dataset and store it in an HDF5 filedata = np.random.random((1000000, 100))with h5py.File('large_dataset.h5', 'w') as f: f.create_dataset('dataset', data=data)Access the dataset from the HDF5 filewith h5py.File('large_dataset.h5', 'r') as f: dataset = f['dataset'] chunk = dataset[0:1000]In this example, HDF5 allows you to store and access the large dataset efficiently, making it possible to handle datasets that are too large to fit into memory.Case Studies: Real-World ExamplesTo bring these concepts to life, let's look at a few real-world examples of solving the Python Memory Error. These case studies illustrate how the techniques we've discussed can be applied in practice.Case Study 1: Processing Large CSV FilesImagine you're working on a data analysis project that involves processing a large CSV file. The file is too large to fit into memory, so you need to find a way to process it efficiently. You decide to use Pandas with the chunksize parameter to read the file in chunks:import pandas as pdRead the large CSV file in chunkschunksize = 10 6for chunk in pd.read_csv('large_file.csv', chunksize=chunksize): process(chunk)By processing the file in chunks, you're able to handle the large dataset efficiently without running into memory issues.Case Study 2: Training a Machine Learning ModelSuppose you're training a machine learning model on a large dataset. The dataset is too large to fit into memory, so you need to find a way to train the model efficiently. You decide to use Dask to distribute the training process across a cluster of machines:import dask.dataframe as ddfrom dask_ml.model_selection import train_test_splitfrom dask_ml.linear_model import

Releases loenard97/hdf5-viewer - GitHub

VALL'EAn unofficial PyTorch implementation of VALL-E, utilizing the EnCodec encoder/decoder.RequirementsBesides a working PyTorch environment, the only hard requirement is espeak-ng for phonemizing text:Linux users can consult their package managers on installing espeak/espeak-ng.Windows users are required to install espeak-ng.additionally, you may be required to set the PHONEMIZER_ESPEAK_LIBRARY environment variable to specify the path to libespeak-ng.dll.In the future, an internal homebrew to replace this would be fantastic.InstallNoteThere seems to be some form of regression in fancier attention mechanisms in some environments where you might need to explicitly set attention to flash_attention_2 or sdpa.Simply run pip install git+ or pip install git+ tested this repo under Python versions 3.10.9, 3.11.3, and 3.12.3.Pre-Trained ModelNotePre-Trained weights aren't up to par as a pure zero-shot model at the moment, but are fine for finetuning / LoRAs.My pre-trained weights can be acquired from here.A script to setup a proper environment and download the weights can be invoked with ./scripts/setup.shTrainTraining is very dependent on:the quality of your dataset.how much data you have.the bandwidth you quantized your audio to.the underlying model architecture used.Try MeTo quickly test if a configuration works, you can run python -m vall_e.models.ar_nar --yaml="./data/config.yaml"; a small trainer will overfit a provided utterance.Leverage Your Own DatasetIf you already have a dataset you want, for example, your own large corpus or for finetuning, you can use your own dataset instead.Set up a venv with the moment only WhisperX is utilized. Using other variants like faster-whisper is an exercise left to the user at the moment.It's recommended to use a dedicated virtualenv specifically for transcribing, as WhisperX will break a few dependencies.The following command should work:python3 -m venv venv-whispersource ./venv-whisper/bin/activatepip3 install torch torchvision torchaudiopip3 install git+ your source voices under ./voices/{group name}/{speaker name}/.Run python3 ./scripts/transcribe_dataset.py. This will generate a transcription with timestamps for your dataset.If you're interested in using a different model, edit the script's model_name and batch_size variables.Run python3 ./scripts/process_dataset.py. This will phonemize the transcriptions and quantize the audio.If you're using a Descript-Audio-Codec based model, ensure to set the sample rate and audio backend accordingly.Copy ./data/config.yaml to ./training/config.yaml. Customize the training configuration and populate your dataset.training list with the values stored under ./training/dataset_list.json.Refer to ./vall_e/config.py for additional configuration details.Dataset FormatsTwo dataset formats are supported:the standard way:data is stored under ./training/data/{group}/{speaker}/{id}.{enc|dac} as a NumPy file, where enc is for the EnCodec/Vocos backend, and dac for the Descript-Audio-Codec backend.it is highly recommended to generate metadata to speed up dataset pre-load with python3 -m vall_e.data --yaml="./training/config.yaml" --action=metadatausing an HDF5 dataset:you can convert from the standard way with the following command: python3 -m vall_e.data --yaml="./training/config.yaml" (metadata for dataset pre-load is generated alongside HDF5 creation)this will shove everything into a single HDF5 file and store some metadata alongside (for now, the symbol map

HDF-NI/hdf5.viewer - GitHub

LogisticRegressionLoad the large dataset into a Dask DataFramedf = dd.read_csv('large_dataset.csv')Split the dataset into training and testing setsX_train, X_test, y_train, y_test = train_test_split(df.drop('target', axis=1), df['target'])Train a logistic regression modelmodel = LogisticRegression()model.fit(X_train, y_train)By distributing the training process across a cluster of machines, you're able to train the model efficiently on the large dataset.Case Study 3: Analyzing Genomic DataConsider a scenario where you're analyzing genomic data. The dataset is too large to fit into memory, so you need to find a way to analyze it efficiently. You decide to use HDF5 to store and access the dataset efficiently:import h5pyimport numpy as npCreate a large genomic dataset and store it in an HDF5 filedata = np.random.random((1000000, 100))with h5py.File('genomic_data.h5', 'w') as f: f.create_dataset('dataset', data=data)Access the dataset from the HDF5 filewith h5py.File('genomic_data.h5', 'r') as f: dataset = f['dataset'] chunk = dataset[0:1000]By using HDF5 to store and access the dataset efficiently, you're able to analyze the large genomic dataset without running into memory issues.Conclusion: Embracing the ChallengeSolving the Python Memory Error is a challenge, but it's also an opportunity to learn and grow as a developer. By understanding how Python manages memory, optimizing your code, using efficient libraries, and employing advanced techniques, you can handle large datasets efficiently and avoid those pesky memory errors.The next time you encounter a memory error, don't despair. Instead, see it as a chance to dive deeper into the inner workings of Python and expand your problem-solving toolkit. Who knows? You might even discover a new technique or library that becomes your go-to solution for handling large datasets.So, are you ready to embrace the challenge? Let's dive into the world of memory management and see where it takes us. Happy coding!FAQQ: What is the Python Memory Error?A: The Python Memory Error is raised when an operation runs out of memory. This can. HDF5 File Viewer. Contribute to loenard97/hdf5-viewer development by creating an account on GitHub.

lochbrunner/vscode-hdf5-viewer: Displays HDF5 files in VS code - GitHub

--file look2hear.ymlconda activate look2hear🖥️ Usage🗂️ DatasetsApollo is trained on the MUSDB18-HQ and MoisesDB datasets. To download the datasets, run the following commands:wget data preprocessing, we drew inspiration from music separation techniques and implemented the following steps:Source Activity Detection (SAD):We used a Source Activity Detector (SAD) to remove silent regions from the audio tracks, retaining only the significant portions for training.Data Augmentation:We performed real-time data augmentation by mixing tracks from different songs. For each mix, we randomly selected between 1 and 8 stems from the 11 available tracks, extracting 3-second clips from each selected stem. These clips were scaled in energy by a random factor within the range of [-10, 10] dB relative to their original levels. The selected clips were then summed together to create simulated mixed music.Simulating Dynamic Bitrate Compression:We simulated various bitrate scenarios by applying MP3 codecs with bitrates of [24000, 32000, 48000, 64000, 96000, 128000].Rescaling:To ensure consistency across all samples, we rescaled both the target and the encoded audio based on their maximum absolute values.Saving as HDF5:After preprocessing, all data (including the source stems, mixed tracks, and compressed audio) was saved in HDF5 format, making it easy to load for training and evaluation purposes.🚀 TrainingTo train the Apollo model, run the following command:python train.py --conf_dir=configs/apollo.yml🎨 EvaluationTo evaluate the Apollo model, run the following command:python inference.py --in_wav=assets/input.wav --out_wav=assets/output.wav📊 ResultsHere, you can include a brief overview of the performance metrics or results that Apollo achieves using different bitratesDifferent methods' SDR/SI-SNR/VISQOL scores for various types of music, as well as

HDF5: HDF5 Plugins - portal.hdfgroup.org

Munz},}To refer to specific applications and features, you can also cite the appropriate paper from this list.Quick Start GuideFor a more detailed installation instructions, please see the documention here.FLEXI is tested for various Linux distributions including Ubuntu, OpenSUSE, CentOS, or Arch. ƎLexi also runs on macOS. For the installation, you require the following dependencies:PackageRequiredInstalled by FLEXIGitxCMakexC/C++ CompilerxFortran CompilerxLAPACKxxHDF5xxMPI(x)The MPI library is only required for running parallel simulations on multiple ranks. The HDF5 and LAPACK libraries can are optionally built and locally installed during the FLEXI build process. The names of the packages and the package manager might differ depending on the specific distribution used.Getting the codeOpen a terminal, download FLEXI via gitgit clone the codeEnter the FLEXI directory, create a build directory and use CMake to configure and compile the codecd flexicmake -B buildcmake --build buildThe executable flexi is now contained in the FLEXI directory in build/bin/. Custom configurations of the compiler options, dependencies, and code features can be set usingRunning the codeNavigate to the directory of the tutorial cavity and run FLEXIcd tutorials/cavityflexi parameter_flexi.iniUsed librariesFLEXI uses several external libraries as well as auxiliary functions from open source projects, including:CMakeFFTWHDF5LAPACKMPIOpenMPOpenBLASPAPIReggie2.0. HDF5 File Viewer. Contribute to loenard97/hdf5-viewer development by creating an account on GitHub.

HDF5: The HDF5 API - support.hdfgroup.org

HDFView 3.1DownloadFree HDFView 3.1Free HDFView is a visual tool for browsing and editing HDF4 and HDF5 files4.4 25 votes Your vote:Latest version:3.3.1See allDeveloper:The HDF GroupReviewDownloadComments Questions & Answers 1 / 5Awards (2)Show all awardsFreeware All versionsHDFView 3.3.1 (latest)HDFView 3.2 HDFView 2.9 DownloadFree HDFView for Mac OS XEdit program infoInfo updated on:Feb 15, 2025Software InformerDownload popular programs, drivers and latest updates easilyNo specific info about version 3.1. Please visit the main page of HDFView on Software Informer.Share your experience:Write a review about this program Comments 4.425 votes165220Your vote:Notify me about replies Comment viaFacebookRelated software Multiple Search and Replace Search and replace text in multiple files at the same time.Image Metadata Manager FreeSee and change the metadata information of your pictures or photos with ease.AWinware PDF Page Replace Replace existing pages of one pdf from other use AWinware Pdf Page ReplaceCFR Film Table Commander Graphical, interactive software utility.Find & Replace It! Find and replace utility that supports regexp search and Unicode encodingsRelated storiesSee allBest free Windows Media Center replacements for Windows 10All The Views: compare Spanish restaurantsAll The Views: find the best UK restaurantKaspersky replaced with UltraAV in the USRelated suggestionsHdfview portableH5 file viewerUsers are downloadingFastStone Image ViewerPSD CODECPSIMPresto! Mr. PhotoAcroRIPInsta360Studio