kandi X-RAY | h5py Summary
kandi X-RAY | h5py Summary
HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.
Top functions reviewed by kandi - BETA
- 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
h5py Key Features
h5py Examples and Code Snippets
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') for
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
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
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 h5f.create_dataset('new_dataset_name',data=new_arr)
def test_checkInput(self): file = MagicMock() file.keys.return_value = 'parent' file['parent'].keys.return_value = ['A', 'B'] self.assertTrue(reader_class.checkInput(file))
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
python is /opt/anaconda3/bin/python python is /usr/local/bin/python python is /usr/bin/python
train_dataset = h5py.File('train_catvnoncat.h5', "r")
train_dataset = h5py.File('C:/Users/Moshen/Downloads/train_catvnoncat.h5', "r")
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),]) Systems=np.arange(10_000) with h5py.File('SO_71335363.h5','w') as
df = pd.DataFrame(columns=['system','x1','y1','y1_err','x2','y2','y2_err']) Systems=np.arange(10000) for i, sys in enumerate(Systems): x1,y1,y1_err=np.random.rand(100),np.random.rand(100),np.random.rand(100) x2,y2,y2_err=np.rando
Trending Discussions on h5py
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:...
ANSWERAnswered 2022-Apr-11 at 01:12
A solution using sklearn.neighbors.radius_neighbors_graph and your example data:
I'm getting this error when I'm running the following command to install tensorflow....
ANSWERAnswered 2022-Mar-25 at 16:40
The official doc to use brew install.
While I try to install TensorFlow I get this error :...
ANSWERAnswered 2022-Feb-02 at 19:41
I fix this by following Apple Developer Docs: https://developer.apple.com/metal/tensorflow-plugin/
I uninstall Miniforge
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:...
ANSWERAnswered 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
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:
ANSWERAnswered 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.
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.
ANSWERAnswered 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.
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....
ANSWERAnswered 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.:
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
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 (
Why is there a difference?...
ANSWERAnswered 2022-Jan-19 at 17:33
"Why is there a difference?
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:
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:...
ANSWERAnswered 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 (
.visititems() to be sure you only access existing nodes.
Modified h5py code with rudimentary checks looks like this:
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...
ANSWERAnswered 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.
No vulnerabilities reported
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.
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