hpc-python | Python in High Performance Computing | Performance Testing library
kandi X-RAY | hpc-python Summary
kandi X-RAY | hpc-python Summary
hpc-python is a Python library typically used in Testing, Performance Testing, Numpy applications. hpc-python has no bugs, it has no vulnerabilities, it has build file available and it has low support. However hpc-python has a Non-SPDX License. You can download it from GitHub.
Python in High Performance Computing
Python in High Performance Computing
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hpc-python has a low active ecosystem.
It has 281 star(s) with 799 fork(s). There are 18 watchers for this library.
It had no major release in the last 6 months.
hpc-python has no issues reported. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of hpc-python is 2019-01
Quality
hpc-python has 0 bugs and 0 code smells.
Security
hpc-python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
hpc-python code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
hpc-python has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
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hpc-python releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
hpc-python saves you 1115 person hours of effort in developing the same functionality from scratch.
It has 2521 lines of code, 140 functions and 110 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed hpc-python and discovered the below as its top functions. This is intended to give you an instant insight into hpc-python implemented functionality, and help decide if they suit your requirements.
- Iterate over a field
- Evolve the state of the covariance matrix
- Create exchange list
- Write field to plot
- Read atoms from a file
- Prepare the coordinates
- Convert to a numpy array
- Convert to a list
- Compute a mandelastic kernel
- Calculate the kernel kernel
- A worker function that creates a worker thread
- Return the average of a chunk
- Returns the maximum memory in bytes
- Mutate DNA sequence
- Calculates the value at the given rank
- Generates a random string from the given alphabet
- Plot a mandel
- Calculate the cosine of a matrix
- Add two values together
- Updates the board
Get all kandi verified functions for this library.
hpc-python Key Features
No Key Features are available at this moment for hpc-python.
hpc-python Examples and Code Snippets
No Code Snippets are available at this moment for hpc-python.
Community Discussions
Trending Discussions on hpc-python
QUESTION
Multiprocess.pool.map() raise ValueError: No objects to concatenate
Asked 2020-Feb-18 at 07:18
I have to run a for loop and each loop will access data from a database, do some manipulation and run the Dijkstra algorithm, then append the results to a final list. The code looks like below:
...ANSWER
Answered 2020-Feb-18 at 07:18"No objects to concatenate" is a Pandas error returned when you call pd.concat() with an empty iterable:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install hpc-python
You can download it from GitHub.
You can use hpc-python 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.
You can use hpc-python 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.
Support
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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