iPat | iPat allows you to perform GWAS | Genomics library

by Poissonfish Java Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | iPat Summary

iPat is a Java library typically used in Healthcare, Pharma, Life Sciences, Artificial Intelligence, Genomics applications. iPat has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Intelligent Prediction and Association Tool (iPat) is a software for genomic studies with a user-friendly graphical user interface (GUI). With iPat, GWAS or GS can be performed using a pointing device to simply drag and/or click on graphical elements to specify input data files, choose input parameters, and select analytical models. It can also serve as a format converter for people who want to use the converted files for other purposes. Please click here for further information on the GibHub Pages.

Support

Quality

Security

License

Reuse

Support

iPat has a low active ecosystem.

It has 9 star(s) with 2 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 10 have been closed. On average issues are closed in 253 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of iPat is current.

Quality

iPat has no bugs reported.

Security

iPat has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

iPat does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

iPat 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.

Top functions reviewed by kandi - BETA

kandi has reviewed iPat and discovered the below as its top functions. This is intended to give you an instant insight into iPat implemented functionality, and help decide if they suit your requirements.

Performs the action
Set file
Rebuilds the pane for the common pane
Load the file tray
Puts the button area
Gets the R residues of the given row
Draws a line
Returns the named color
Set the previous component
Called when the mouse pressed
Returns the command to execute
Perform the action
User pressed the button
Creates the command to execute the command
Load a CSV file
Called when the mouse is pressed
Set the radio buttons
Creates a progress bar with a progress bar
Go to the next component
Invoked when the item is changed
Gets the rex c
Launch iPat
Creates the command to execute
Called when a mouse is pressed
Setup the input map
Check the format of the module

Get all kandi verified functions for this library.

iPat Key Features

No Key Features are available at this moment for iPat.

iPat Examples and Code Snippets

No Code Snippets are available at this moment for iPat.

Community Discussions

Trending Discussions on iPat

Docker Error getting events from daemon: EOF

QUESTION

Docker Error getting events from daemon: EOF

Asked 2017-Mar-25 at 12:17

BUG REPORT INFORMATION

Description

Hello everyone, after following the google codelabs, Codelabs I have received an error ERRO[4334] error getting events from daemon: EOF after Creating bottleneck at /tf_files/bottlenecks/roses/13231224664_4af5293a37.jpg.txt

Update: I reran it and this shows up ERRO[53469] error getting events from daemon: EOF

Steps to reproduce the issue: 1. ``` python tensorflow/examples/image_retraining/retrain.py \

--bottleneck_dir=/tf_files/bottlenecks \ --how_many_training_steps 500 \ --model_dir=/tf_files/inception \ --output_graph=/tf_files/retrained_graph.pb \ --output_labels=/tf_files/retrained_labels.txt \ --image_dir /tf_files/flower_photos

```

Describe the results you received: ERRO[4334] error getting events from daemon: EOF

Describe the results you expected: Finish the retraining

Output of docker version:

Docker version 1.13.1, build 092cba3

Output of docker info:

Containers: 6 Running: 0 Paused: 0 Stopped: 6 Images: 2 Server Version: 1.13.1 Storage Driver: overlay2 Backing Filesystem: extfs Supports d_type: true Native Overlay Diff: true Logging Driver: json-file Cgroup Driver: cgroupfs Plugins: Volume: local Network: bridge host ipvlan macvlan null overlay Swarm: inactive Runtimes: runc Default Runtime: runc Init Binary: docker-init containerd version: aa8187dbd3b7ad67d8e5e3a15115d3eef43a7ed1 runc version: 9df8b306d01f59d3a8029be411de015b7304dd8f init version: 949e6fa Security Options: seccomp Profile: default Kernel Version: 4.9.8-moby Operating System: Alpine Linux v3.5 OSType: linux Architecture: x86_64 CPUs: 2 Total Memory: 1.952 GiB Name: moby ID: UNXQ:IPAT:2ZHG:3443:M7XI:M3FW:W7Q7:G4HV:IKKW:W5TU:72TI:SH3G Docker Root Dir: /var/lib/docker Debug Mode (client): false Debug Mode (server): true File Descriptors: 16 Goroutines: 27 System Time: 2017-02-21T14:43:50.071749826Z EventsListeners: 1 No Proxy: *.local, 169.254/16 Registry: https://index.docker.io/v1/ Experimental: true Insecure Registries: 127.0.0.0/8 Live Restore Enabled: false

Additional environment details (AWS, VirtualBox, physical, etc.): OS X with python 2.7, and this shows up W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. Thank you so much

...

ANSWER

Answered 2017-Mar-25 at 12:17

The solution is to increase the CPU size and Ram in Docker preference.

Source https://stackoverflow.com/questions/42408293

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

Vulnerabilities

No vulnerabilities reported

Install iPat

You can download it from GitHub.
You can use iPat like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the iPat component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

If there is any difficulty on iPat, please leave your questions in the page of issue report.Or you can directly send an email to the author James Chen

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