CC | android project gradually | Architecture library

by luckybilly Java Version: Current License: Apache-2.0

Download this library from

kandi X-RAY | CC Summary

CC is a Java library typically used in Architecture applications. CC has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However CC has 6 bugs. You can download it from GitHub.

业界首个支持渐进式组件化改造的Android组件化开源框架，支持跨进程调用。Componentize your android project gradually.

Support

Quality

Security

License

Reuse

kandi-support Support

CC has a medium active ecosystem.
It has 3759 star(s) with 589 fork(s). There are 114 watchers for this library.
It had no major release in the last 12 months.
There are 19 open issues and 137 have been closed. On average issues are closed in 17 days. There are 2 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of CC is current.

CC Support

Best in #Architecture

Average in #Architecture

CC Support

Best in #Architecture

Average in #Architecture

quality kandi Quality

CC has 6 bugs (0 blocker, 0 critical, 5 major, 1 minor) and 116 code smells.

CC Quality

Best in #Architecture

Average in #Architecture

CC Quality

Best in #Architecture

Average in #Architecture

security Security

CC has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
CC code analysis shows 0 unresolved vulnerabilities.
There are 5 security hotspots that need review.

CC Security

Best in #Architecture

Average in #Architecture

CC Security

Best in #Architecture

Average in #Architecture

license License

CC is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

CC License

Best in #Architecture

Average in #Architecture

CC License

Best in #Architecture

Average in #Architecture

build Reuse

CC 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.
Installation instructions are not available. Examples and code snippets are available.
CC saves you 2369 person hours of effort in developing the same functionality from scratch.
It has 5168 lines of code, 416 functions and 118 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.

CC Reuse

Best in #Architecture

Average in #Architecture

CC Reuse

Best in #Architecture

Average in #Architecture

Top functions reviewed by kandi - BETA

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

Format JSON string .
Send login result .
convert parameter to java
Invokes a callback .
Create bridge web view .
Proceed the interceptor .
Get the process name for a component
Gets the process name .
Intercept the CC
Gets the text view .

CC Key Features

业界首个支持渐进式组件化改造的Android组件化开源框架，支持跨进程调用。Componentize your android project gradually.

CC Examples and Code Snippets

See all related Code Snippets

目录结构

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    - cc                            组件化框架基础库（主要）
    - cc-register                   CC框架配套的gradle插件（主要）
    - cc-settings-2.gradle          组件化开发构建脚本（主要）
    - demo                          demo主程序（调用其它组件，并演示了动态组件的使用）
    - demo_base                     demo公共库(base类、util类、公共Bean等)
    - demo_component_a              demo组件A
    - demo_component_b              demo组件B（上方提供下载的apk在打包时local.properties中添加了demo_component_b=true）
    - demo_component_jsbridge       demo组件(面向组件封装的jsBridge，并演示了如何进行跨进程组件调用)
    - demo_component_kt             demo组件(kotlin)
    - demo_interceptors             demo全局拦截器(如果有多个app并且拦截器不同，可以创建多个module给不同app使用)
    - cc-settings-demo.gradle       演示如何自定义配置文件，如：添加actionProcessor自动注册的配置
    - demo-debug.apk                demo安装包(包含demo/demo_component_a/demo_component_kt)
    - demo_component_b-debug.apk    demo组件B单独运行安装包

创建组件

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public class ComponentA implements IComponent {
  @Override
  public String getName() {
      //指定组件的名称
      return "ComponentA";
  }

  @Override
  public boolean onCall(CC cc) {
    //在此处将组件内部的服务暴露给外部调用
    //组件内部的逻辑与外部完全解耦
    String actionName = cc.getActionName();
    switch (actionName) {
      case "showActivity": //响应actionName为"showActivity"的组件调用
        //跳转到页面：ActivityA
        CCUtil.navigateTo(cc, ActivityA.class);
        //返回处理结果给调用方
        CC.sendCCResult(cc.getCallId(), CCResult.success());
        break;
      default:
        //其它actionName当前组件暂时不能响应，可以通过如下方式返回状态码为-12的CCResult给调用方
        CC.sendCCResult(cc.getCallId(), CCResult.errorUnsupportedActionName());
        break;
    }
    return false;
  }
}

调用组件

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CC.obtainBuilder("ComponentA")
  .setActionName("showActivity")
  .build()
  .call();

遇到问题怎么办？

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CC.enableDebug(true);  //普通调试日志，会提示一些错误信息
CC.enableVerboseLog(true);  //组件调用的详细过程日志，用于跟踪整个调用过程

Debunking outlook email features with library win32com

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out_iter_folder = outlook.GetDefaultFolder(6).Folders['TEST']

Does Comparison Function specified with lambda function in std::sort return bool type?

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int x = 5, y = 555;
bool b = x - y;
std::cout << b << std::endl; // Prints 1, means true

-----------------------

#include <iostream> 
#include <functional>
#include <algorithm>
using namespace std; 

int main() { 
    int number[] = {3, 5, 1, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23};
    auto print = [](int n) { cout << n << " "; };

    sort(begin(number), end(number), [](int n1, int n2) { return n2 - n1; });
    // result: 9 6 1 5 3
    for_each(begin(number), end(number), print);
    cout << endl;

    sort(begin(number), end(number), [](int n1, int n2) { return n1 - n2; });
    // result: 3 5 1 6 9
    for_each(begin(number), end(number), print);
    cout << endl;


    return 0; 
}

Problem with FULLY_CONNECTED op in TF Lite

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static tflite::MicroMutableOpResolver<1> micro_op_resolver;
tflite_status = micro_op_resolver.AddFullyConnected();

Dynamic Library error while using Tensorflow with GPU

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# Quick and dirty: with channel specification
conda create -n tf_gpu_env python=3.8 
conda activate tf_gpu_env
conda install tensorflow-gpu -c anaconda
conda install cudnn -c conda-forge 
conda install cudatoolkit -c anaconda

# Sanity check for validating 
# visibility of the GPUs to TF
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

conda env create -f environment.yml

## filename: environment.yml

## Environment File Definition

name: tfgpu_env # tensorflow-gpu environment
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - python=3.8
  ## Core Necessities
  - numpy # -c conda-forge, anaconda
  - pandas # -c conda-forge, anaconda
  - tabulate # -c conda-forge, anaconda  # necessary for df.to_markdown() in pandas
  - scipy # -c conda-forge, anaconda
  - matplotlib # -c conda-forge, anaconda
  ## Jupyter Related
  - jupyter # -c anaconda, conda-forge
  - jupyterlab # -c anaconda, conda-forge
  - jupyter_dashboards # -c conda-forge  (see: https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e)
  - jupyter_contrib_nbextensions # -c conda-forge
  ## Progressbar
  - tqdm # -c conda-forge, anaconda
  ## Machine Learning
  - tensorflow-gpu # -c anaconda | version: 2.4.1 (linux), 2.3.0 (windows)
  # - tensorflow # -c anaconda | version: 2.2.0 (linux), 2.1.0 (windows)
  - cudnn # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #       # -c anaconda | version: 7.6.5 (linux/windows)
  - cudatoolkit # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #             # -c anaconda | version: 11.0.221 (linux/windows)
  - scikit-learn # -c conda-forge, anaconda
  ## Hyperparameter Optimization
  - optuna # -c conda-forge # works for pytorch, tf/keras, mxnet, scikit-learn, xgboost, lightgbm
  - keras-tuner # -c conda-forge
  ## Image Processing
  - opencv # -c conda-forge, anaconda
  - imageio # -c anaconda, conda-forge
  ## Image Augmentation
  - albumentations # -c conda-forge
  - imgaug # -c conda-forge
  ## Code Linting
  - pylint # -c conda-forge, anaconda
  - autopep8 # -c conda-forge, anaconda
  ## Installations with pip
  - pip:
    ## Web App Framework
    # - Flask-Testing
    - streamlit # https://docs.streamlit.io/en/stable/troubleshooting/clean-install.html

# Instruction:
#-----------------------------------------------------------
#
## For an environment installed locally (under: ./.venv)
# mkdir -p .venv && cd .venv
# conda env create --prefix . -f ../environment.yml
## For Updating local environment
# cd .venv
# conda env update --prefix . -f ../environment.yml  --prune
#
## For an environment installed globally
## with a name: fav_env 
# NOTE: The env-name is stored inside the 
#       environment.yml file.
# conda env create -f environment.yml
## For Updating global environment from env-file
# conda env update -f ./environment.yml  --prune
#
## Update conda itself
# conda update -n base -c defaults conda
#
## Creating a global environment in one-line: py37, py38
# conda create -n py37 python=3.7
# conda create -n py38 python=3.8
#
### In each of the envs: base, py37, py38
# conda install jupyter jupyterlab numpy scipy pandas matplotlib scikit-learn scikit-image tqdm plotly imageio requests pylint autopep8 tabulate opencv
#
## Export a platform independent copy of an environment
#  conda env export --from-history > path/to/environment.yml
### Make exports directory (if not present already) and export
# $targetDir = conda_exports
# mkdir ./$targetDir
# conda env export --from-history > ./$targetDir/exported_environment.yml

-----------------------

# Quick and dirty: with channel specification
conda create -n tf_gpu_env python=3.8 
conda activate tf_gpu_env
conda install tensorflow-gpu -c anaconda
conda install cudnn -c conda-forge 
conda install cudatoolkit -c anaconda

# Sanity check for validating 
# visibility of the GPUs to TF
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

conda env create -f environment.yml

## filename: environment.yml

## Environment File Definition

name: tfgpu_env # tensorflow-gpu environment
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - python=3.8
  ## Core Necessities
  - numpy # -c conda-forge, anaconda
  - pandas # -c conda-forge, anaconda
  - tabulate # -c conda-forge, anaconda  # necessary for df.to_markdown() in pandas
  - scipy # -c conda-forge, anaconda
  - matplotlib # -c conda-forge, anaconda
  ## Jupyter Related
  - jupyter # -c anaconda, conda-forge
  - jupyterlab # -c anaconda, conda-forge
  - jupyter_dashboards # -c conda-forge  (see: https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e)
  - jupyter_contrib_nbextensions # -c conda-forge
  ## Progressbar
  - tqdm # -c conda-forge, anaconda
  ## Machine Learning
  - tensorflow-gpu # -c anaconda | version: 2.4.1 (linux), 2.3.0 (windows)
  # - tensorflow # -c anaconda | version: 2.2.0 (linux), 2.1.0 (windows)
  - cudnn # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #       # -c anaconda | version: 7.6.5 (linux/windows)
  - cudatoolkit # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #             # -c anaconda | version: 11.0.221 (linux/windows)
  - scikit-learn # -c conda-forge, anaconda
  ## Hyperparameter Optimization
  - optuna # -c conda-forge # works for pytorch, tf/keras, mxnet, scikit-learn, xgboost, lightgbm
  - keras-tuner # -c conda-forge
  ## Image Processing
  - opencv # -c conda-forge, anaconda
  - imageio # -c anaconda, conda-forge
  ## Image Augmentation
  - albumentations # -c conda-forge
  - imgaug # -c conda-forge
  ## Code Linting
  - pylint # -c conda-forge, anaconda
  - autopep8 # -c conda-forge, anaconda
  ## Installations with pip
  - pip:
    ## Web App Framework
    # - Flask-Testing
    - streamlit # https://docs.streamlit.io/en/stable/troubleshooting/clean-install.html

# Instruction:
#-----------------------------------------------------------
#
## For an environment installed locally (under: ./.venv)
# mkdir -p .venv && cd .venv
# conda env create --prefix . -f ../environment.yml
## For Updating local environment
# cd .venv
# conda env update --prefix . -f ../environment.yml  --prune
#
## For an environment installed globally
## with a name: fav_env 
# NOTE: The env-name is stored inside the 
#       environment.yml file.
# conda env create -f environment.yml
## For Updating global environment from env-file
# conda env update -f ./environment.yml  --prune
#
## Update conda itself
# conda update -n base -c defaults conda
#
## Creating a global environment in one-line: py37, py38
# conda create -n py37 python=3.7
# conda create -n py38 python=3.8
#
### In each of the envs: base, py37, py38
# conda install jupyter jupyterlab numpy scipy pandas matplotlib scikit-learn scikit-image tqdm plotly imageio requests pylint autopep8 tabulate opencv
#
## Export a platform independent copy of an environment
#  conda env export --from-history > path/to/environment.yml
### Make exports directory (if not present already) and export
# $targetDir = conda_exports
# mkdir ./$targetDir
# conda env export --from-history > ./$targetDir/exported_environment.yml

-----------------------

# Quick and dirty: with channel specification
conda create -n tf_gpu_env python=3.8 
conda activate tf_gpu_env
conda install tensorflow-gpu -c anaconda
conda install cudnn -c conda-forge 
conda install cudatoolkit -c anaconda

# Sanity check for validating 
# visibility of the GPUs to TF
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

conda env create -f environment.yml

## filename: environment.yml

## Environment File Definition

name: tfgpu_env # tensorflow-gpu environment
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - python=3.8
  ## Core Necessities
  - numpy # -c conda-forge, anaconda
  - pandas # -c conda-forge, anaconda
  - tabulate # -c conda-forge, anaconda  # necessary for df.to_markdown() in pandas
  - scipy # -c conda-forge, anaconda
  - matplotlib # -c conda-forge, anaconda
  ## Jupyter Related
  - jupyter # -c anaconda, conda-forge
  - jupyterlab # -c anaconda, conda-forge
  - jupyter_dashboards # -c conda-forge  (see: https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e)
  - jupyter_contrib_nbextensions # -c conda-forge
  ## Progressbar
  - tqdm # -c conda-forge, anaconda
  ## Machine Learning
  - tensorflow-gpu # -c anaconda | version: 2.4.1 (linux), 2.3.0 (windows)
  # - tensorflow # -c anaconda | version: 2.2.0 (linux), 2.1.0 (windows)
  - cudnn # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #       # -c anaconda | version: 7.6.5 (linux/windows)
  - cudatoolkit # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #             # -c anaconda | version: 11.0.221 (linux/windows)
  - scikit-learn # -c conda-forge, anaconda
  ## Hyperparameter Optimization
  - optuna # -c conda-forge # works for pytorch, tf/keras, mxnet, scikit-learn, xgboost, lightgbm
  - keras-tuner # -c conda-forge
  ## Image Processing
  - opencv # -c conda-forge, anaconda
  - imageio # -c anaconda, conda-forge
  ## Image Augmentation
  - albumentations # -c conda-forge
  - imgaug # -c conda-forge
  ## Code Linting
  - pylint # -c conda-forge, anaconda
  - autopep8 # -c conda-forge, anaconda
  ## Installations with pip
  - pip:
    ## Web App Framework
    # - Flask-Testing
    - streamlit # https://docs.streamlit.io/en/stable/troubleshooting/clean-install.html

# Instruction:
#-----------------------------------------------------------
#
## For an environment installed locally (under: ./.venv)
# mkdir -p .venv && cd .venv
# conda env create --prefix . -f ../environment.yml
## For Updating local environment
# cd .venv
# conda env update --prefix . -f ../environment.yml  --prune
#
## For an environment installed globally
## with a name: fav_env 
# NOTE: The env-name is stored inside the 
#       environment.yml file.
# conda env create -f environment.yml
## For Updating global environment from env-file
# conda env update -f ./environment.yml  --prune
#
## Update conda itself
# conda update -n base -c defaults conda
#
## Creating a global environment in one-line: py37, py38
# conda create -n py37 python=3.7
# conda create -n py38 python=3.8
#
### In each of the envs: base, py37, py38
# conda install jupyter jupyterlab numpy scipy pandas matplotlib scikit-learn scikit-image tqdm plotly imageio requests pylint autopep8 tabulate opencv
#
## Export a platform independent copy of an environment
#  conda env export --from-history > path/to/environment.yml
### Make exports directory (if not present already) and export
# $targetDir = conda_exports
# mkdir ./$targetDir
# conda env export --from-history > ./$targetDir/exported_environment.yml

-----------------------

# Quick and dirty: with channel specification
conda create -n tf_gpu_env python=3.8 
conda activate tf_gpu_env
conda install tensorflow-gpu -c anaconda
conda install cudnn -c conda-forge 
conda install cudatoolkit -c anaconda

# Sanity check for validating 
# visibility of the GPUs to TF
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

conda env create -f environment.yml

## filename: environment.yml

## Environment File Definition

name: tfgpu_env # tensorflow-gpu environment
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - python=3.8
  ## Core Necessities
  - numpy # -c conda-forge, anaconda
  - pandas # -c conda-forge, anaconda
  - tabulate # -c conda-forge, anaconda  # necessary for df.to_markdown() in pandas
  - scipy # -c conda-forge, anaconda
  - matplotlib # -c conda-forge, anaconda
  ## Jupyter Related
  - jupyter # -c anaconda, conda-forge
  - jupyterlab # -c anaconda, conda-forge
  - jupyter_dashboards # -c conda-forge  (see: https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e)
  - jupyter_contrib_nbextensions # -c conda-forge
  ## Progressbar
  - tqdm # -c conda-forge, anaconda
  ## Machine Learning
  - tensorflow-gpu # -c anaconda | version: 2.4.1 (linux), 2.3.0 (windows)
  # - tensorflow # -c anaconda | version: 2.2.0 (linux), 2.1.0 (windows)
  - cudnn # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #       # -c anaconda | version: 7.6.5 (linux/windows)
  - cudatoolkit # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #             # -c anaconda | version: 11.0.221 (linux/windows)
  - scikit-learn # -c conda-forge, anaconda
  ## Hyperparameter Optimization
  - optuna # -c conda-forge # works for pytorch, tf/keras, mxnet, scikit-learn, xgboost, lightgbm
  - keras-tuner # -c conda-forge
  ## Image Processing
  - opencv # -c conda-forge, anaconda
  - imageio # -c anaconda, conda-forge
  ## Image Augmentation
  - albumentations # -c conda-forge
  - imgaug # -c conda-forge
  ## Code Linting
  - pylint # -c conda-forge, anaconda
  - autopep8 # -c conda-forge, anaconda
  ## Installations with pip
  - pip:
    ## Web App Framework
    # - Flask-Testing
    - streamlit # https://docs.streamlit.io/en/stable/troubleshooting/clean-install.html

# Instruction:
#-----------------------------------------------------------
#
## For an environment installed locally (under: ./.venv)
# mkdir -p .venv && cd .venv
# conda env create --prefix . -f ../environment.yml
## For Updating local environment
# cd .venv
# conda env update --prefix . -f ../environment.yml  --prune
#
## For an environment installed globally
## with a name: fav_env 
# NOTE: The env-name is stored inside the 
#       environment.yml file.
# conda env create -f environment.yml
## For Updating global environment from env-file
# conda env update -f ./environment.yml  --prune
#
## Update conda itself
# conda update -n base -c defaults conda
#
## Creating a global environment in one-line: py37, py38
# conda create -n py37 python=3.7
# conda create -n py38 python=3.8
#
### In each of the envs: base, py37, py38
# conda install jupyter jupyterlab numpy scipy pandas matplotlib scikit-learn scikit-image tqdm plotly imageio requests pylint autopep8 tabulate opencv
#
## Export a platform independent copy of an environment
#  conda env export --from-history > path/to/environment.yml
### Make exports directory (if not present already) and export
# $targetDir = conda_exports
# mkdir ./$targetDir
# conda env export --from-history > ./$targetDir/exported_environment.yml

-----------------------

# Quick and dirty: with channel specification
conda create -n tf_gpu_env python=3.8 
conda activate tf_gpu_env
conda install tensorflow-gpu -c anaconda
conda install cudnn -c conda-forge 
conda install cudatoolkit -c anaconda

# Sanity check for validating 
# visibility of the GPUs to TF
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

conda env create -f environment.yml

## filename: environment.yml

## Environment File Definition

name: tfgpu_env # tensorflow-gpu environment
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - python=3.8
  ## Core Necessities
  - numpy # -c conda-forge, anaconda
  - pandas # -c conda-forge, anaconda
  - tabulate # -c conda-forge, anaconda  # necessary for df.to_markdown() in pandas
  - scipy # -c conda-forge, anaconda
  - matplotlib # -c conda-forge, anaconda
  ## Jupyter Related
  - jupyter # -c anaconda, conda-forge
  - jupyterlab # -c anaconda, conda-forge
  - jupyter_dashboards # -c conda-forge  (see: https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e)
  - jupyter_contrib_nbextensions # -c conda-forge
  ## Progressbar
  - tqdm # -c conda-forge, anaconda
  ## Machine Learning
  - tensorflow-gpu # -c anaconda | version: 2.4.1 (linux), 2.3.0 (windows)
  # - tensorflow # -c anaconda | version: 2.2.0 (linux), 2.1.0 (windows)
  - cudnn # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #       # -c anaconda | version: 7.6.5 (linux/windows)
  - cudatoolkit # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #             # -c anaconda | version: 11.0.221 (linux/windows)
  - scikit-learn # -c conda-forge, anaconda
  ## Hyperparameter Optimization
  - optuna # -c conda-forge # works for pytorch, tf/keras, mxnet, scikit-learn, xgboost, lightgbm
  - keras-tuner # -c conda-forge
  ## Image Processing
  - opencv # -c conda-forge, anaconda
  - imageio # -c anaconda, conda-forge
  ## Image Augmentation
  - albumentations # -c conda-forge
  - imgaug # -c conda-forge
  ## Code Linting
  - pylint # -c conda-forge, anaconda
  - autopep8 # -c conda-forge, anaconda
  ## Installations with pip
  - pip:
    ## Web App Framework
    # - Flask-Testing
    - streamlit # https://docs.streamlit.io/en/stable/troubleshooting/clean-install.html

# Instruction:
#-----------------------------------------------------------
#
## For an environment installed locally (under: ./.venv)
# mkdir -p .venv && cd .venv
# conda env create --prefix . -f ../environment.yml
## For Updating local environment
# cd .venv
# conda env update --prefix . -f ../environment.yml  --prune
#
## For an environment installed globally
## with a name: fav_env 
# NOTE: The env-name is stored inside the 
#       environment.yml file.
# conda env create -f environment.yml
## For Updating global environment from env-file
# conda env update -f ./environment.yml  --prune
#
## Update conda itself
# conda update -n base -c defaults conda
#
## Creating a global environment in one-line: py37, py38
# conda create -n py37 python=3.7
# conda create -n py38 python=3.8
#
### In each of the envs: base, py37, py38
# conda install jupyter jupyterlab numpy scipy pandas matplotlib scikit-learn scikit-image tqdm plotly imageio requests pylint autopep8 tabulate opencv
#
## Export a platform independent copy of an environment
#  conda env export --from-history > path/to/environment.yml
### Make exports directory (if not present already) and export
# $targetDir = conda_exports
# mkdir ./$targetDir
# conda env export --from-history > ./$targetDir/exported_environment.yml

postfix and openJDK 11: "No appropriate protocol (protocol is disabled or cipher suites are inappropriate)"

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SSL-Session:
    Protocol  : TLSv1.2
...

how to sort a map by key when the values are the same?

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public static <K extends Comparable<? super K>, V extends Comparable<? super V>> Map<K, V> newSortMapByValue(Map<K, V> map) {
        List<Map.Entry<K, V>> list = new LinkedList<>(map.entrySet());
        Collections.sort(list, new Comparator<Map.Entry<K, V>>() {
            @Override
            public int compare(Map.Entry<K, V> e1, Map.Entry<K, V> e2) {
                // Check if values are the same
                if (e1.getValue().equals(e2.getValue()))
                    // Compare e1 to e2, because A should be first element
                    return e1.getKey().compareTo(e2.getKey());
                else
                    // Compare e2 to e1, because largest number should be first
                    return e2.getValue().compareTo(e1.getValue());
            }
        });

        Map<K, V> result = new LinkedHashMap<>();
        for (Map.Entry<K, V> entry : list) {
            result.put(entry.getKey(), entry.getValue());
        }
        return result;
    }

    public static void main(String[] args) {
        Map<String, Integer> map = new HashMap<>();
        map.put("AA",10);
        map.put("CC",5);
        map.put("BB",5);
        map.put("DD",15);
        map.put("ZZ",15);

        System.out.println(map);
        Map sortedMap = newSortMapByValue(map);
        System.out.println(sortedMap);
    }

{AA=10, CC=5, BB=5, DD=15, ZZ=15}
{DD=15, ZZ=15, AA=10, BB=5, CC=5}

-----------------------

public static <K extends Comparable<? super K>, V extends Comparable<? super V>> Map<K, V> newSortMapByValue(Map<K, V> map) {
        List<Map.Entry<K, V>> list = new LinkedList<>(map.entrySet());
        Collections.sort(list, new Comparator<Map.Entry<K, V>>() {
            @Override
            public int compare(Map.Entry<K, V> e1, Map.Entry<K, V> e2) {
                // Check if values are the same
                if (e1.getValue().equals(e2.getValue()))
                    // Compare e1 to e2, because A should be first element
                    return e1.getKey().compareTo(e2.getKey());
                else
                    // Compare e2 to e1, because largest number should be first
                    return e2.getValue().compareTo(e1.getValue());
            }
        });

        Map<K, V> result = new LinkedHashMap<>();
        for (Map.Entry<K, V> entry : list) {
            result.put(entry.getKey(), entry.getValue());
        }
        return result;
    }

    public static void main(String[] args) {
        Map<String, Integer> map = new HashMap<>();
        map.put("AA",10);
        map.put("CC",5);
        map.put("BB",5);
        map.put("DD",15);
        map.put("ZZ",15);

        System.out.println(map);
        Map sortedMap = newSortMapByValue(map);
        System.out.println(sortedMap);
    }

{AA=10, CC=5, BB=5, DD=15, ZZ=15}
{DD=15, ZZ=15, AA=10, BB=5, CC=5}

-----------------------

public static <K extends Comparable<? super K>, V extends Comparable<? super V>> Map<K, V> newSortMapByValue(Map<K, V> map) {
        List<Map.Entry<K, V>> list = new LinkedList<>(map.entrySet());
        Collections.sort(list, new Comparator<Map.Entry<K, V>>() {
            @Override
            public int compare(Map.Entry<K, V> e1, Map.Entry<K, V> e2) {
                // Check if values are the same
                if (e1.getValue().equals(e2.getValue()))
                    // Compare e1 to e2, because A should be first element
                    return e1.getKey().compareTo(e2.getKey());
                else
                    // Compare e2 to e1, because largest number should be first
                    return e2.getValue().compareTo(e1.getValue());
            }
        });

        Map<K, V> result = new LinkedHashMap<>();
        for (Map.Entry<K, V> entry : list) {
            result.put(entry.getKey(), entry.getValue());
        }
        return result;
    }

    public static void main(String[] args) {
        Map<String, Integer> map = new HashMap<>();
        map.put("AA",10);
        map.put("CC",5);
        map.put("BB",5);
        map.put("DD",15);
        map.put("ZZ",15);

        System.out.println(map);
        Map sortedMap = newSortMapByValue(map);
        System.out.println(sortedMap);
    }

{AA=10, CC=5, BB=5, DD=15, ZZ=15}
{DD=15, ZZ=15, AA=10, BB=5, CC=5}

C++ Optimize Memory Read Speed

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uint64_t total = 0;
for (auto cn = nums.begin(); cn < nums.end(); cn += 4)
{
    total += cn[0];
    total += cn[1];
    total += cn[2];
    total += cn[3];
}

uint64_t total = 0;
for (auto cn = nums.begin(); cn < nums.end(); cn += 4)
{
    const uint64 n0 = cn[0];
    const uint64 n1 = cn[1];
    const uint64 n2 = cn[2];
    const uint64 n3 = cn[3];
    total += n0;
    total += n1;
    total += n2;
    total += n3;
}

-----------------------

uint64_t total = 0;
for (auto cn = nums.begin(); cn < nums.end(); cn += 4)
{
    total += cn[0];
    total += cn[1];
    total += cn[2];
    total += cn[3];
}

uint64_t total = 0;
for (auto cn = nums.begin(); cn < nums.end(); cn += 4)
{
    const uint64 n0 = cn[0];
    const uint64 n1 = cn[1];
    const uint64 n2 = cn[2];
    const uint64 n3 = cn[3];
    total += n0;
    total += n1;
    total += n2;
    total += n3;
}

-----------------------

const std::size_t GB = 1024 * 1024 * 1024;
std::vector<int> nums(4 * GB);
std::generate(std::begin(nums), std::end(nums), [](){ return rand() % 1024; });

//...

const auto sum = std::accumulate(std::begin(nums), std::end(nums), 0);

How to connect to IBM MQ deployed to OpenShift?

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kind: Service
apiVersion: v1
metadata:
  name: ibm-mq
spec:
  type: NodePort
  ports:
    - port: 1414
      targetPort: 1414
      nodePort: 30001
  selector:
    app: ibm-mq

-----------------------

com.ibm.mq.cfg.SSL.OutboundSNI=HOSTNAME

System.setProperty("com.ibm.mq.cfg.SSL.OutboundSNI","HOSTNAME");

-----------------------

com.ibm.mq.cfg.SSL.OutboundSNI=HOSTNAME

System.setProperty("com.ibm.mq.cfg.SSL.OutboundSNI","HOSTNAME");

Google App Script How to add a specific CC Email when sending automated Emails

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MailApp.sendEmail(
      recipient, 
      subject, 
      body,
      {attachments: [newFile],
      cc: "example@test.com"})

create a logical column for whether a row contains a string in any column

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library(tidyverse)

df %>%
  mutate(flag = pmap_lgl(., ~"aa" %in% str_to_lower(c(...))))

df %>%
  rowwise() %>%
  mutate(flag = "aa" %in% str_to_lower(c_across(everything())))

setDT(df)[, flag := transpose(.SD) %>% map_lgl(~"aa" %in% str_to_lower(.x))]

-----------------------

library(tidyverse)

df %>%
  mutate(flag = pmap_lgl(., ~"aa" %in% str_to_lower(c(...))))

df %>%
  rowwise() %>%
  mutate(flag = "aa" %in% str_to_lower(c_across(everything())))

setDT(df)[, flag := transpose(.SD) %>% map_lgl(~"aa" %in% str_to_lower(.x))]

-----------------------

library(tidyverse)

df %>%
  mutate(flag = pmap_lgl(., ~"aa" %in% str_to_lower(c(...))))

df %>%
  rowwise() %>%
  mutate(flag = "aa" %in% str_to_lower(c_across(everything())))

setDT(df)[, flag := transpose(.SD) %>% map_lgl(~"aa" %in% str_to_lower(.x))]

-----------------------

library(dplyr)

df %>%
  rowwise() %>%
  mutate(xyz = +any(grepl("aa", cur_data(), ignore.case = TRUE)))

# A tibble: 4 x 4
# Rowwise: 
  x     y     z       xyz
  <chr> <chr> <chr> <int>
1 cc    ee    AA        1
2 aa    dd    gg        1
3 BB    ff    bb        0
4 dd    gg    dd        0

Reduce(`+`, apply(df, 1, \(x) +(grepl("aa", x, , ignore.case = TRUE))) |>
         t() |>
         as.data.frame()) -> df$xyz

# A tibble: 4 x 4
# Rowwise: 
  x     y     z       xyz
  <chr> <chr> <chr> <int>
1 cc    ee    AA        1
2 aa    dd    gg        1
3 BB    ff    bb        0
4 dd    gg    dd        0

-----------------------

library(dplyr)

df %>%
  rowwise() %>%
  mutate(xyz = +any(grepl("aa", cur_data(), ignore.case = TRUE)))

# A tibble: 4 x 4
# Rowwise: 
  x     y     z       xyz
  <chr> <chr> <chr> <int>
1 cc    ee    AA        1
2 aa    dd    gg        1
3 BB    ff    bb        0
4 dd    gg    dd        0

Reduce(`+`, apply(df, 1, \(x) +(grepl("aa", x, , ignore.case = TRUE))) |>
         t() |>
         as.data.frame()) -> df$xyz

# A tibble: 4 x 4
# Rowwise: 
  x     y     z       xyz
  <chr> <chr> <chr> <int>
1 cc    ee    AA        1
2 aa    dd    gg        1
3 BB    ff    bb        0
4 dd    gg    dd        0

-----------------------

library(dplyr)
library(stringr)
df %>%
     mutate(xyz = +(if_any(everything(), 
         ~ str_detect(., regex('aa', ignore_case = TRUE)))))

# A tibble: 4 x 4
  x     y     z       xyz
  <chr> <chr> <chr> <int>
1 cc    ee    AA        1
2 aa    dd    gg        1
3 BB    ff    bb        0
4 dd    gg    dd        0

-----------------------

library(dplyr)
library(stringr)
df %>%
     mutate(xyz = +(if_any(everything(), 
         ~ str_detect(., regex('aa', ignore_case = TRUE)))))

# A tibble: 4 x 4
  x     y     z       xyz
  <chr> <chr> <chr> <int>
1 cc    ee    AA        1
2 aa    dd    gg        1
3 BB    ff    bb        0
4 dd    gg    dd        0

See all related Code Snippets

Community Discussions

Trending Discussions on CC

Debunking outlook email features with library win32com
Does Comparison Function specified with lambda function in std::sort return bool type?
What is simplest way to prevent frame drop in flutter application on heavy task?
How can I find to access to GPUs via Tensorflow in PyCharm?
JSF - Validation error in 1 component, leads all others in the form to fail
Problem with FULLY_CONNECTED op in TF Lite
Using TensorFlow with GPU taking a long time for loading library related to CUDA
Dynamic Library error while using Tensorflow with GPU
postfix and openJDK 11: "No appropriate protocol (protocol is disabled or cipher suites are inappropriate)"
how to sort a map by key when the values are the same?

Trending Discussions on CC

QUESTION

Debunking outlook email features with library win32com

Asked 2021-Jun-16 at 03:53

I found ways to check with python using library win32com for outlook the following attributes for any given email.

import win32com.client as client
import win32com

outlook = client.Dispatch("Outlook.Application").GetNamespace('MAPI')
main_account = outlook.Folders.Item(1)

out_iter_folder = main_account.Folders['Inbox'].Folders['TEST']

if out_iter_folder.Items.Count > 0:
    for i in reversed(range(0,item_count)):
        message = out_iter_folder.Items[i]

message = out_iter_folder.Items[i]

Email subject:

message.Subject

Email sender:

mesage.SenderEmailAddress

Email categories/flag:

message.Categories

Email move from a folder to another:

message.Move

I would like to access for any given email:

receiver list (how would that work if I have more than one)?
cc list (" ")
Is there any other way to update the category on an email other than moving this email from a folder to another? I am working on a batch process and this moving in/out is slowing things.
When the email is sent from an email address "on behalf" of another email address how can I access the email on behalf?

ANSWER

Answered 2021-Jun-16 at 03:53

Use MailItem.Recipients collection.
See #1 and check for each recipient's Recipient.Type property equal olCC ( =2)
Of course - set the MailItem.Categpries property. Don't forget to call MailItem.Save
Use the MailItem.SenderEmailAddress. For the sent on behalf of address, read the PR_SENT_REPRESENTING_EMAIL_ADDRESS MAPI property. Access it using MailItem.PropertyAccessor.GetProperty("http://schemas.microsoft.com/mapi/proptag/0x0065001F")

In general, take a look at various Outlook object using OutlookSpy to familiarize yourself with the Outlook Object Model.

Also keep in mind that to access a subfolder of the Inbox folder, it is better to use something like

out_iter_folder = outlook.GetDefaultFolder(6).Folders['TEST']

where 6 is olFolderInbox constant.

Source https://stackoverflow.com/questions/67978213

QUESTION

Does Comparison Function specified with lambda function in std::sort return bool type?

Asked 2021-Jun-16 at 03:09

I was reading this code (source):

#include <iostream> 
#include <functional>
#include <algorithm>
using namespace std; 

int main() { 
    int number[] = {3, 5, 1, 6, 9};
    auto print = [](int n) { cout << n << " "; };

    sort(begin(number), end(number), [](int n1, int n2) { return n2 - n1; });
    // result: 9 6 1 5 3
    for_each(begin(number), end(number), print);
    cout << endl;

    sort(begin(number), end(number), [](int n1, int n2) { return n1 - n2; });
    // result: 3 5 1 6 9
    for_each(begin(number), end(number), print);
    cout << endl;

    return 0; 
}

And I am confused about how return n2 - n1; vs return n1 - n2; work.

According to std::sort - cppreference.com, it says the comp is a bool type function. And in C++, non-zero numbers always have true value (according to Do negative numbers return false in C/C++? - Stack Overflow).

So the number in the example above has different items inside will always cause two of those lambda functions to return true. That is to say, those lambda functions return same results in this case. But the result show that one is in the reversed order and one is in the original order.

How can I explain this or what do I miss?

ANSWER

Answered 2021-Jun-16 at 02:16

The n2 - n1 in the case of a negative number as a result when converted to bool will yield true. So n1 turns out to be less than n2. That's why it is a bad practice to use ints in such Boolean context.

Yes, as stated in the documentation:

...comparison function object which returns true if the first argument is less than the second

But the implementation of the comparison here leads to failure. Try this and see for yourself:

int x = 5, y = 555;
bool b = x - y;
std::cout << b << std::endl; // Prints 1, means true

The supplied comparison function must follow the logic of the operator<. So, when your lambda has n1 = 5, n2 = 1; the result of n2 - n1 is -4 which when used in Boolean context (bool is unsigned) is converted to 1.

As a result you have 5 < 1 is true.

While in your output 1 is placed after 5, this kind of erroneous comparison leads to undesired overall result.

Source https://stackoverflow.com/questions/67995381

QUESTION

How can I find to access to GPUs via Tensorflow in PyCharm?

Asked 2021-Jun-15 at 14:43

I have a problem about not accessing GPU in PyCharm and I use NVIDIA as GPU.

I installed tensorflow-gpu in Python Interpreter of Setting part in Pycharm and then I run the code but I still cannot access it.

I wonder if I should use CUDA library? How can I fix it?

Here is my code snippet which is shown below.

print(tf.config.list_physical_devices('GPU'))

if tf.test.gpu_device_name():
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
else:
    print("Please install GPU version of TF")

Here is the output of the code which is shown below.

[]
2021-06-12 23:28:52.693028: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2021-06-12 23:28:52.702716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1650 Ti computeCapability: 7.5
coreClock: 1.485GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 178.84GiB/s
2021-06-12 23:28:52.702905: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
Please install GPU version of TF
2021-06-12 23:28:52.816304: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-06-12 23:28:52.816435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 
2021-06-12 23:28:52.816495: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N

ANSWER

Answered 2021-Jun-14 at 11:14

I fixed my issue.

Here are the steps of solving that issue.

1 ) Download CUDA from https://developer.nvidia.com/cuda-downloads

2 ) Download CUDNN from https://developer.nvidia.com/rdp/cudnn-download

3 ) Copy bin,include and lastly lib from CUDNN zip file and paste it C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA{version}

4 ) Then run the .py code in PyCharm and it perceives GPU at last.

Source https://stackoverflow.com/questions/67952801

QUESTION

JSF - Validation error in 1 component, leads all others in the form to fail

Asked 2021-Jun-15 at 14:11

Hey to all in the forum

I use JSF Mojarra implementation, version JSF 2.2

I need desperately a help on this.

I have a snippet of my page.
I have a custom component "example_result.xhtml" used in the page.
I have my BackingBean.java Be aware please that this code is not the real code I made. If you run it, it will be very ugly maybe because I deleted all the css classes and I kept only the hot stuff I need to show you my problem.

Everything is inside 1 form.

The 5 "h:selectManyCheckbox" (in my code I have 8 or 9)

In the form I have 5 "h:selectManyCheckbox" which are using values in the "value" attribute for different cases (javaFrameworks2Values, javaFrameworks3Values,...), and the "f:selectItems" use arrays of "SelectItem" (javaFrameworksSelectItems2, javaFrameworksSelectItems3...) created for these different cases, just to make some examples for me to understand how all the selectOne and selectMany components work. The ideas for this, about different cases were taken from these links: "https://stackoverflow.com/tags/selectonemenu/info" and "https://mkyong.com/jsf2/jsf-2-checkboxes-example".

After I have 2 commandButtons

1 for submit, and 1 for reset the values.

Display the values

After I display the results of the values of the "h:selectManyCheckbox" via the "example_result.xhtml".

You can see the 4th "h:selectManyCheckbox" that is the only one different, because it has the attribute "required" with the attribute "requiredMessage". With it there is a "h:message" to display the validation error.

In the BackingBean (which is Spring Bean, but it works perfectly good - sorry I don't want ejbs 3.x), I have initialized:

The values of the SelectItems and
The values of the "value" attribute, where the values of the "h:selectManyCheckbox" will be stored to be displayed later. [The code is completely castrated, to make it readable snippet].

When the page is rendered, I select checkBoxes (e.g. the 2 last, because the 2 first are initials) from all the "h:selectManyCheckbox". When I say that select from all, I mean it. And from the 4th with the "required" attribute. I try in the buttons (see in the code) the "Effort 1", or "Effort 2", or "Effort 3" (in the "f:ajax" in the buttons) and the result outputs in the last part are displayed and updated like a candy. Without any problem. To achive this with the composite component I googled and tried a lot. But I made it.

Then it comes the time to try the 4th to see the validation error of the "required" attribute.

I select again from all as before, but not from all. NOT from the 4th "h:selectManyCheckbox" this time. I select nothing from the 4th "h:selectManyCheckbox" to ckeck the validator error message ("requiredMessage"). The result is: It displays the message of error (GOOD until now), BUT this time it does not update anything from the others "h:selectManyCheckbox" to the output results at the end, and it does not reset the values as well as it was doing before (when I selected from all and from the 4th as well).

I understand that it says: as long as in the form the 4th failed with validation error, all the other "h:selectManyCheckbox" will not update the output results (something like wanting to fail all the others too).

But what really happes here?

It does not give the values to the "h:selectManyCheckbox", to be updated to the output?
It gives the vales to the "h:selectManyCheckbox" normally, BUT it just not updates the output?

The other efforts in the "f:ajax" in the buttons, are just efforts maybe to solve the problem but in these cases they don't even display the error message in the 4th case and of cource they don't update the other output results as well (again). But no message error as well.

I don't know if the problem is clear to you. I can explain in the discussion better so I can clarify the situation better. [To be honnet it took me 1 and half hour to write all this thing]

Thanks a lot in advance

========== Snippet from my page ==========

<h:form>
    <h:panelGrid id="smcb" columns="1">
         
        <h:selectManyCheckbox id="smcb1" value="#{myBackingBean.javaFrameworksHardCodedValues}">
            <f:selectItem itemLabel="Java Label" itemValue="Java Value"/>
            <f:selectItem itemLabel="Spring Framework Label" itemValue="Spring Framework Value"/>
            <f:selectItem itemLabel="Hibernate Label" itemValue="Hibernate Value"/>
            <f:selectItem itemLabel="JSF Framework Label" itemValue="JSF Framework Value"/>
        </h:selectManyCheckbox>
         
        <h:selectManyCheckbox id="smcb2" value="#{myBackingBean.javaFrameworks2Values}">
            <f:selectItems value="#{myBackingBean.javaFrameworksSelectItems2}"/>
        </h:selectManyCheckbox>
 
        <h:selectManyCheckbox id="smcb3" value="#{myBackingBean.javaFrameworks3Values}">
            <f:selectItems value="#{myBackingBean.javaFrameworksSelectItems3}"/>
        </h:selectManyCheckbox>
 
        <p><h:message for="smcb4" styleClass="error_messages"/></p>
        <h:selectManyCheckbox id="smcb4" value="#{myBackingBean.javaFrameworks4Values}"
                            required="true"
                            requiredMessage="The 'h:selectManyCheckbox' must have a value. Please select an item from the list!">
            <f:selectItems value="#{myBackingBean.javaFrameworksSelectItems4}"/>
        </h:selectManyCheckbox>
 
        <h:selectManyCheckbox id="smcb5" value="#{myBackingBean.javaFrameworks5Values}">
            <f:selectItems value="#{myBackingBean.javaFrameworksSelectItems5}"/>
        </h:selectManyCheckbox>
         
    </h:panelGrid>
    <br/>
 
    <h:commandButton value="Display Java Frameworks values">
        <!--
        Effort 1 - works perfectly good if I select from all the "h:selectManyCheckbox"
        <f:ajax execute="smcb1 smcb2 smcb3 smcb4 smcb5 smcb6 smcb7 smcb8"
                render="outputId1 outputId2 outputId3 outputId4 outputId5 smcb1 smcb2 smcb3 smcb4 smcb5"/>
        -->
         
        <!--
        Effort 2 - works perfectly good if I select from all the "h:selectManyCheckbox"
        <f:ajax execute="smcb" render="outputId smcb"/>
        -->
         
        <!--
        Effort 3 - works perfectly good if I select from all the "h:selectManyCheckbox"
        <f:ajax execute="@form" render="@form"/>
        -->
         
        <!--
        Not working efforts to solve my problem - but are not solving it
        <f:ajax execute="@form" render="outputId"/>-->
        <f:ajax execute="@form"
                render="outputId1 outputId2 outputId3 outputId4 outputId5 smcb1 smcb2 smcb3 smcb4 smcb5"/>
                Other combinations with the @form and the ids...
        -->
    </h:commandButton>
    <h:commandButton value="Reset Java Frameworks values" action="#{myBackingBean.resetValues}">
        <!--
        Effort 1 - works perfectly good if I select from all the "h:selectManyCheckbox"
        <f:ajax execute="smcb1 smcb2 smcb3 smcb4 smcb5"
                render="outputId1 outputId2 outputId3 outputId4 outputId5 smcb1 smcb2 smcb3 smcb4 smcb5"/>
        -->
         
        <!--
        Effort 2 - works perfectly good if I select from all the "h:selectManyCheckbox"
        <f:ajax execute="smcb" render="outputId smcb"/>
        -->
         
        <!--
        Effort 3 - works perfectly good if I select from all the "h:selectManyCheckbox"
        <f:ajax execute="@form" render="@form"/>
        -->
    </h:commandButton>
    <br/>
 
    <h:panelGrid columns="1" id="outputId">
        <mc:example_result id="outputId1"
                           example_label="1. Java Frameworks hardcoded with 'f:selectItem': "
                           example_result="#{myBackingBean.displayItemValues()}"/>
 
        <mc:example_result id="outputId2"
                           example_label="2. Java Frameworks 2: "
                           example_result="#{myBackingBean.displayItemValues()}"/>
 
        <mc:example_result id="outputId3"
                           example_label="3. Java Frameworks 3: "
                           example_result="#{myBackingBean.displayItemValues()}"/>
 
        <mc:example_result id="outputId4"
                           example_label="4. Java Frameworks 4: "
                           example_result="#{myBackingBean.displayItemValues()}"/>
 
        <mc:example_result id="outputId5"
                           example_label="5. Java Frameworks 5: "
                           example_result="#{myBackingBean.displayItemValues()}"/>
    </h:panelGrid>
</h:form>

========== My Component: "example_result.xhtml" ==========

<ui:composition ...
    xmlns:mc="http://xmlns.jcp.org/jsf/composite/mc_components">
 
    <cc:interface>
        <cc:attribute name="example_label" type="java.lang.String" required="true"/>
        <cc:attribute name="example_result" type="java.lang.String" required="true"/>
    </cc:interface>
 
    <cc:implementation>
        <div id="#{cc.clientId}">
            <h:outputText value="#{cc.attrs.example_label}" .../>
            <h:outputText value="#{cc.attrs.example_result}" .../>
        </div>
    </cc:implementation>
</ui:composition>

========== My BackingBean: "MyBackingBean.java" ==========

@org.springframework.stereotype.Component(value = "myBackingBean")
public class MyBackingBean implements Serializable {
 
    // Variables used in the "value" attribute of "HtmlSelectMany" components
    private String[] javaFrameworks2Values; // + Getters-Setters
    // All the others: javaFrameworks3Values, ...
 
    // The "HtmlSelects" components' "SelectItems" are generated by an array of "SelectItem"
    private SelectItem[] javaFrameworksSelectItems2; // +Getters
    // All the others: javaFrameworksSelectItems3, ...
     
 
    @PostConstruct
    protected void initLabelValueMapAndLabelValueArray() {
     
        // Pre-selected values of "HtmlSelectMany" components
        javaFrameworks2Values = new String[]{"Java Value", "Spring Framework Value"};
 
        // Initialization of the array of "SelectItem"
        javaFrameworksSelectItems2 = new SelectItem[4];
        javaFrameworksSelectItems2[0] = new SelectItem("Java Value", "Java Label");
        javaFrameworksSelectItems2[1] = new SelectItem("Spring Framework Value", "Spring Framework Label");
        javaFrameworksSelectItems2[2] = new SelectItem("Hibernate Value", "Hibernate Label");
        javaFrameworksSelectItems2[3] = new SelectItem("JSF Framework Value", "JSF Framework Label");
         
        // Initialization of all the others: javaFrameworks3Values, ..., javaFrameworksSelectItems3, ...
    }
     
    public String displayItemValues() {
        // return array of the result values;
    }
     
    public void resetValues() {
        // Reset the values to the initials
    }
}

ANSWER

Answered 2021-Jun-15 at 14:11

After a lot of discussing with the only person who wanted to help to my issue here (and I thank him @WoAiNii for this a lot), I decided to post my solution:

I will make 5 different forms with 5 set of buttons (submit/reset), to make escalate this problem, for 5 so much related components in the form.

But my question is open: Why this is happening, what rule in JSF in this case is taking place and makes this situation. Anyone, comes with an explanation:

Thomas: this is a rule in JSF, or
is a JSF bug, or
this happens in these cases, or... whatever...,

I will be glad to read it here so I will learn better, and others to will learn from these ideas of yours. Thanks a lot

Source https://stackoverflow.com/questions/67750994

QUESTION

Problem with FULLY_CONNECTED op in TF Lite

Asked 2021-Jun-15 at 13:22

I'd like to run a simple neural network model which uses Keras on a Rasperry microcontroller. I get a problem when I use a layer. The code is defined like this:

#include "main.h"
#include <string.h>
#include "tensorflow/lite/micro/kernels/micro_ops.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/version.h"
#include "my_model.h"

/* Private variables    
TIM_HandleTypeDef htim16;

UART_HandleTypeDef huart2;

/* USER CODE BEGIN PV */

// TFLite globals
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* model_input = nullptr;
TfLiteTensor* model_output = nullptr;

// Create an area of memory to use for input, output, and other TensorFlow
// arrays. You'll need to adjust this by compiling, running, and looking
// for errors.
constexpr int kTensorArenaSize = 2 * 1024;
__attribute__((aligned(16)))uint8_t tensor_arena[kTensorArenaSize];
} // namespace


void SystemClock_Config(void);
static void MX_GPIO_Init(void);
static void MX_USART2_UART_Init(void);
static void MX_TIM16_Init(void);
/* USER CODE BEGIN PFP */

/* USER CODE END PFP */

/* Private user code 
/* USER CODE BEGIN 0 */

int main(void)
{
/* USER CODE BEGIN 1 */
char buf[50];
int buf_len = 0;
TfLiteStatus tflite_status;
uint32_t num_elements;
uint32_t timestamp;
float y_val;

/* USER CODE END 1 */

/* MCU Configuration--------------------------------------------------------*/

/* Reset of all peripherals, Initializes the Flash interface and the Systick. */
HAL_Init();

/* USER CODE BEGIN Init */

/* USER CODE END Init */

/* Configure the system clock */
SystemClock_Config();

/* USER CODE BEGIN SysInit */

/* USER CODE END SysInit */

/* Initialize all configured peripherals */
MX_GPIO_Init();
MX_USART2_UART_Init();
MX_TIM16_Init();
/* USER CODE BEGIN 2 */

// Start timer/counter
HAL_TIM_Base_Start(&htim16);

// Set up logging (modify tensorflow/lite/micro/debug_log.cc)
static tflite::MicroErrorReporter micro_error_reporter;
error_reporter = &micro_error_reporter;

// Say something to test error reporter
error_reporter->Report("STM32 TensorFlow Lite test");

// Map the model into a usable data structure
model = tflite::GetModel(my_model);
if (model->version() != TFLITE_SCHEMA_VERSION)
{
  error_reporter->Report("Model version does not match Schema");
  while(1);
}

// Pull in only needed operations (should match NN layers). Template parameter
// <n> is number of ops to be added. Available ops:
// tensorflow/lite/micro/kernels/micro_ops.h
static tflite::MicroMutableOpResolver<1> micro_op_resolver;

// Add dense neural network layer operation
tflite_status = micro_op_resolver.AddBuiltin(
    tflite::BuiltinOperator_FULLY_CONNECTED,
    tflite::ops::micro::Register_FULLY_CONNECTED());
if (tflite_status != kTfLiteOk)
{
  error_reporter->Report("Could not add FULLY CONNECTED op");
  while(1);
}

// Build an interpreter to run the model with.
static tflite::MicroInterpreter static_interpreter(
    model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter);
interpreter = &static_interpreter;

// Allocate memory from the tensor_arena for the model's tensors.
tflite_status = interpreter->AllocateTensors();
if (tflite_status != kTfLiteOk)
{
  error_reporter->Report("AllocateTensors() failed");
  while(1);
}

// Assign model input and output buffers (tensors) to pointers
model_input = interpreter->input(0);
model_output = interpreter->output(0);

// Get number of elements in input tensor
num_elements = model_input->bytes / sizeof(float);
buf_len = sprintf(buf, "Number of input elements: %lu\r\n", num_elements);
HAL_UART_Transmit(&huart2, (uint8_t *)buf, buf_len, 100);

/* USER CODE END 2 */

/* Infinite loop */
/* USER CODE BEGIN WHILE */
while (1)
{
  // Fill input buffer (use test value)
  for (uint32_t i = 0; i < num_elements; i++)
  {
    model_input->data.f[i] = 2.0f;
  }

  // Get current timestamp
  timestamp = htim16.Instance->CNT;

  // Run inference
  tflite_status = interpreter->Invoke();
  if (tflite_status != kTfLiteOk)
  {
    error_reporter->Report("Invoke failed");
  }

  // Read output (predicted y) of neural network
  y_val = model_output->data.f[0];

  // Print output of neural network along with inference time (microseconds)
  buf_len = sprintf(buf,
                    "Output: %f | Duration: %lu\r\n",
                    y_val,
                    htim16.Instance->CNT - timestamp);
  HAL_UART_Transmit(&huart2, (uint8_t *)buf, buf_len, 100);

  // Wait before doing it again
  HAL_Delay(500);

  /* USER CODE END WHILE */

  /* USER CODE BEGIN 3 */
}
/* USER CODE END 3 */
}

When I run the program I got this compilation problem:

../Core/Src/main.cpp: In function 'int main()':
../Core/Src/main.cpp:181:57: error: no matching function for call to 'tflite::MicroMutableOpResolver<1>::AddBuiltin(tflite::BuiltinOperator, TfLiteRegistration*)'
   tflite::ops::micro::Register_FULLY_CONNECTED());
                                                 ^

In file included from ../Core/Src/main.cpp:31:0:
STM32CubeIDE/workspace_1.3.0/sine/tensorflow_lite/tensorflow/lite/micro/micro_mutable_op_resolver.h:470:16: note: candidate: TfLiteStatus tflite::MicroMutableOpResolver<tOpCount>::AddBuiltin(tflite::BuiltinOperator, const TfLiteRegistration&, tflite::MicroOpResolver::BuiltinParseFunction) [with unsigned int tOpCount = 1; TfLiteStatus = TfLiteStatus; TfLiteRegistration = TfLiteRegistration; tflite::MicroOpResolver::BuiltinParseFunction = TfLiteStatus (*)(const tflite::Operator*, tflite::BuiltinOperator, tflite::ErrorReporter*, tflite::BuiltinDataAllocator*, void**)]
 TfLiteStatus AddBuiltin(tflite::BuiltinOperator op,
              ^~~~~~~~~~
STM32CubeIDE/workspace_1.3.0/sine/tensorflow_lite/tensorflow/lite/micro/micro_mutable_op_resolver.h:470:16: note:   candidate expects 3 arguments, 2 provided
make: *** [Core/Src/subdir.mk:37: Core/Src/main.o] Error 1
"make -j2 all" terminated with exit code 2. Build might be incomplete.

I'm not sure which third parameter I have to insert for FULLY_CONNECTED() or if there is another problem.

ANSWER

Answered 2021-May-25 at 01:08

I had the same problem, man. I want to transplant tflite to the development board of CEVA. There is no problem in compiling. In the process of running, there is also an error in AddBuiltin(full_connect). At present, the only possible situation I guess is that some devices can not support tflite.

Source https://stackoverflow.com/questions/67677228

QUESTION

Dynamic Library error while using Tensorflow with GPU

Asked 2021-Jun-15 at 10:13

I am programming in Python 3.8 with Tensorflow installed along with my natural language processing project. When I want to begin the training phase, I get this message right before I begin...

2021-03-09 14:57:38.725406: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not 
load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-03-09 14:57:38.725651: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart 
dlerror if you do not have a GPU set up on your machine.

This DLL file should come preinstalled with the Tensorflow version I installed as later versions don't require the antiquated method of selecting Tensorflow CPU or GPU. I installed through Pip like this...

pip install tensorflow

I don't understand why this may happen. I have RTX 2070 in the system but I require this Library and DLL to train on the GPU. Can anyone explain why this may be?

UPDATE:

After I took advice from the answer below, I no longer received the error. However, it seems that it still uses the CPU over GPU for no reason. Here is what it shows when I begin training:

loading dataset...
Vocab size: 8332
Number of samples: 44098
2021-03-10 08:40:15.175461: I 
tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is 
optimized with oneAPI Deep Neural Network Library (oneDNN)to use the 
following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate 
compiler flags.
creating model...
training model...
Step #0 Loss: 2.833855

Is this bad or is this just a setting to direct training towards the CPU?

ANSWER

Answered 2021-Mar-10 at 14:44

I would suggest you to use conda (Ananconda/Miniconda) to create a separate environment and install tensorflow-gpu, cudnn and cudatoolkit. Miniconda has a much smaller footprint than Anaconda. I would suggest you to install Miniconda if you do not have conda already.

Quick Installtion

# Quick and dirty: with channel specification
conda create -n tf_gpu_env python=3.8 
conda activate tf_gpu_env
conda install tensorflow-gpu -c anaconda
conda install cudnn -c conda-forge 
conda install cudatoolkit -c anaconda

Check that Tensorflow is using the GPU

For more details see this: https://www.tensorflow.org/guide/gpu

# Sanity check for validating 
# visibility of the GPUs to TF
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

Easily Reproducible Installation with Environment File: `environment.yml`

Although you can quickly create a conda (Ananconda/Miniconda) environment as shown earlier, it is much more desirable to make the installation process as reproducible as possible: enters the environment.yml file.

Save the environment file at the root of your repository (or project) and run the following command to install all the packages in an isolated conda environment (here I have named it tfgpu_env) using the environment file. The top part of the environment file contains some useful commands.

conda env create -f environment.yml

Save the following as environment.yml under your repository. And consider pinning the following three libraries:

An example:

tensorflow-gpu=2.4
cudnn=8
cudatoolkit=11

## filename: environment.yml

## Environment File Definition

name: tfgpu_env # tensorflow-gpu environment
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - python=3.8
  ## Core Necessities
  - numpy # -c conda-forge, anaconda
  - pandas # -c conda-forge, anaconda
  - tabulate # -c conda-forge, anaconda  # necessary for df.to_markdown() in pandas
  - scipy # -c conda-forge, anaconda
  - matplotlib # -c conda-forge, anaconda
  ## Jupyter Related
  - jupyter # -c anaconda, conda-forge
  - jupyterlab # -c anaconda, conda-forge
  - jupyter_dashboards # -c conda-forge  (see: https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e)
  - jupyter_contrib_nbextensions # -c conda-forge
  ## Progressbar
  - tqdm # -c conda-forge, anaconda
  ## Machine Learning
  - tensorflow-gpu # -c anaconda | version: 2.4.1 (linux), 2.3.0 (windows)
  # - tensorflow # -c anaconda | version: 2.2.0 (linux), 2.1.0 (windows)
  - cudnn # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #       # -c anaconda | version: 7.6.5 (linux/windows)
  - cudatoolkit # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #             # -c anaconda | version: 11.0.221 (linux/windows)
  - scikit-learn # -c conda-forge, anaconda
  ## Hyperparameter Optimization
  - optuna # -c conda-forge # works for pytorch, tf/keras, mxnet, scikit-learn, xgboost, lightgbm
  - keras-tuner # -c conda-forge
  ## Image Processing
  - opencv # -c conda-forge, anaconda
  - imageio # -c anaconda, conda-forge
  ## Image Augmentation
  - albumentations # -c conda-forge
  - imgaug # -c conda-forge
  ## Code Linting
  - pylint # -c conda-forge, anaconda
  - autopep8 # -c conda-forge, anaconda
  ## Installations with pip
  - pip:
    ## Web App Framework
    # - Flask-Testing
    - streamlit # https://docs.streamlit.io/en/stable/troubleshooting/clean-install.html

Useful Instructions

You might as well copy and paste the following instructions in the environment file itself, to keep them handy.

# Instruction:
#-----------------------------------------------------------
#
## For an environment installed locally (under: ./.venv)
# mkdir -p .venv && cd .venv
# conda env create --prefix . -f ../environment.yml
## For Updating local environment
# cd .venv
# conda env update --prefix . -f ../environment.yml  --prune
#
## For an environment installed globally
## with a name: fav_env 
# NOTE: The env-name is stored inside the 
#       environment.yml file.
# conda env create -f environment.yml
## For Updating global environment from env-file
# conda env update -f ./environment.yml  --prune
#
## Update conda itself
# conda update -n base -c defaults conda
#
## Creating a global environment in one-line: py37, py38
# conda create -n py37 python=3.7
# conda create -n py38 python=3.8
#
### In each of the envs: base, py37, py38
# conda install jupyter jupyterlab numpy scipy pandas matplotlib scikit-learn scikit-image tqdm plotly imageio requests pylint autopep8 tabulate opencv
#
## Export a platform independent copy of an environment
#  conda env export --from-history > path/to/environment.yml
### Make exports directory (if not present already) and export
# $targetDir = conda_exports
# mkdir ./$targetDir
# conda env export --from-history > ./$targetDir/exported_environment.yml

References

Source https://stackoverflow.com/questions/66553987

QUESTION

postfix and openJDK 11: "No appropriate protocol (protocol is disabled or cipher suites are inappropriate)"

Asked 2021-Jun-15 at 08:30

I know there are some other questions (with answers) to this topic. But no of these was helpful for me.

I have a postfix server (postfix 3.4.14 on debian 10) with following configuration (only the interesting section):

smtpd_tls_mandatory_protocols = !SSLv2, !SSLv3, !TLSv1, !TLSv1.1
smtp_tls_mandatory_protocols = !SSLv2, !SSLv3, !TLSv1, !TLSv1.1
smtp_tls_protocols = !SSLv2, !SSLv3, !TLSv1, !TLSv1.1
smtpd_tls_protocols = !SSLv2, !SSLv3, !TLSv1, !TLSv1.1
smtpd_tls_exclude_ciphers = aNULL, LOW, EXP, MEDIUM, ADH, AECDH, MD5, DSS, ECDSA, 
    CAMELLIA128, 3DES, CAMELLIA256, RSA+AES, eNULL
smtp_tls_exclude_ciphers = aNULL, LOW, EXP, MEDIUM, ADH, AECDH, MD5, DSS, ECDSA, 
    CAMELLIA128, 3DES, CAMELLIA256, RSA+AES, eNULL
tls_preempt_cipherlist = yes
tls_high_cipherlist = !aNULL:!eNULL:!CAMELLIA:HIGH:@STRENGTH

If I check the configuration with openssl I get (please note that I have made the domain name unrecognizable with "xxxxxx.de"):

#> openssl s_client -connect xxxxxx.de:25 -starttls smtp
CONNECTED(00000003)
depth=3 O = Digital Signature Trust Co., CN = DST Root CA X3
verify return:1
depth=2 C = US, O = Internet Security Research Group, CN = ISRG Root X1
verify return:1
depth=1 C = US, O = Let's Encrypt, CN = R3
verify return:1
depth=0 CN = xxxxxx.de
verify return:1
---
Certificate chain
 0 s:/CN=xxxxxx.de
   i:/C=US/O=Let's Encrypt/CN=R3
 1 s:/C=US/O=Let's Encrypt/CN=R3
   i:/C=US/O=Internet Security Research Group/CN=ISRG Root X1
 2 s:/C=US/O=Internet Security Research Group/CN=ISRG Root X1
   i:/O=Digital Signature Trust Co./CN=DST Root CA X3
---
Server certificate
-----BEGIN CERTIFICATE-----
MIIFRjCCBC6gAwIBAgISA6SNmc1MPKtxwSiNNKEvxc/EMA0GCSqGSIb3DQEBCwUA
MDIxCzAJBgNVBAYTAlVTMRYwFAYDVQQKEw1MZXQncyBFbmNyeXB0MQswCQYDVQQD
...
...
...
VG2iG8sEGCcgG4w1LnWTO4tMlCYFE+tcXxAfE/7pB/VjmvRZlLCypanuwBzVRw5W
bPwabUtCMRDrRlT8wI9UHAhQYTb5Hhm0F0u1hi6e/7fybK6tuFnPpWs/vgT3Z4Fj
2onoaTHk/rKlhQ==
-----END CERTIFICATE-----
subject=/CN=xxxxxx.de
issuer=/C=US/O=Let's Encrypt/CN=R3
---
No client certificate CA names sent
Peer signing digest: SHA256
Server Temp Key: ECDH, P-256, 256 bits
---
SSL handshake has read 4950 bytes and written 450 bytes
---
New, TLSv1/SSLv3, Cipher is ECDHE-RSA-AES256-GCM-SHA384
Server public key is 2048 bit
Secure Renegotiation IS supported
Compression: NONE
Expansion: NONE
No ALPN negotiated
SSL-Session:
    Protocol  : TLSv1.2
    Cipher    : ECDHE-RSA-AES256-GCM-SHA384
    Session-ID: B20E725616C98083988847B90FB42BFDCAEED745129C53E79E723692C641F6F5
    Session-ID-ctx: 
    Master-Key: A3D2C497E11E47C6260C119E47DC3B4CAA119485EAFA5BCF6CDA882F115D80E78960C802A48E375DAA293A3A2C1DAE35
    Key-Arg   : None
    Krb5 Principal: None
    PSK identity: None
    PSK identity hint: None
    TLS session ticket lifetime hint: 7200 (seconds)
    TLS session ticket:
    0000 - ec 40 39 d6 f4 94 ac 7b-d8 f8 ef 94 98 62 d7 35   .@9....{.....b.5
    0010 - de fb c3 f5 f3 b1 3a d1-24 3e 62 57 fb 06 56 eb   ......:.$>bW..V.
    0020 - 84 61 2a 0d 30 7d 9f ac-70 e7 a8 a3 23 4a c6 57   .a*.0}..p...#J.W
    0030 - dc 21 7c d3 5d f4 e8 14-c5 4c 18 da 35 1d 32 49   .!|.]....L..5.2I
    0040 - f0 19 de 75 77 22 25 f9-74 4d a2 47 39 0d ce 75   ...uw"%.tM.G9..u
    0050 - 0a 04 41 85 0d 67 05 fe-a4 09 ec 72 4b a5 ad f4   ..A..g.....rK...
    0060 - 8b 73 a2 a3 2e 28 46 b8-2a 60 4b ed ce 75 09 fb   .s...(F.*`K..u..
    0070 - ef 95 e3 e2 6e 6a 90 bd-9e 46 e8 c9 aa 52 c3 ae   ....nj...F...R..
    0080 - 72 6f 9f 37 fd 6c 12 e9-bb 60 83 c6 c4 44 ca 85   ro.7.l...`...D..
    0090 - cb ee 1d bd 69 29 77 31-4f 96 d5 4d 93 8e 63 d2   ....i)w1O..M..c.

    Start Time: 1623221077
    Timeout   : 300 (sec)
    Verify return code: 0 (ok)
---
250 CHUNKING

Here I'm wondering about the line

New, TLSv1/SSLv3, Cipher is ECDHE-RSA-AES256-GCM-SHA384

because TLSv1/SSLv3 should be disabled by the postfix configuration. Could be that the probelm?

However. On the Client side I have a Java application (openJDK 11.0.11) and every time I will send an E-Mail I get:

javax.mail.MessagingException: Could not convert socket to TLS;
nested exception is:
javax.net.ssl.SSLHandshakeException: No appropriate protocol (protocol is disabled or cipher suites are inappropriate)
at com.sun.mail.smtp.SMTPTransport.startTLS(SMTPTransport.java:1907)
at com.sun.mail.smtp.SMTPTransport.protocolConnect(SMTPTransport.java:666)
at javax.mail.Service.connect(Service.java:295)
...

Of course, I have already edit the conf/security/java.security file. I have added already ECDHE-RSA-AES256-GCM-SHA384 as jdk.tls.legacyAlgorithms. Here the interesting settings from java.securtity:

jdk.tls.legacyAlgorithms= \
        K_NULL, C_NULL, M_NULL, \
        DH_anon, ECDH_anon, \
        RC4_128, RC4_40, DES_CBC, DES40_CBC, \
        3DES_EDE_CBC, ECDHE-RSA-AES256-GCM, ECDHE-RSA-AES256-GCM-SHA384

jdk.certpath.disabledAlgorithms=MD2, MD5, SHA1 jdkCA & usage TLSServer, \
    RSA keySize < 1024, DSA keySize < 1024, EC keySize < 224, \
    include jdk.disabled.namedCurves

jdk.tls.disabledAlgorithms=SSLv3, TLSv1, TLSv1.1, RC4, DES, MD5withRSA, \
    DH keySize < 1024, EC keySize < 224, 3DES_EDE_CBC, anon, NULL, \
    include jdk.disabled.namedCurves

May be I'm blind, but I don't see where my problem is ...

EDIT

Ok, at first I have removed my extensions from jdk.tls.legacyAlgorithms because this are not helpful and wrong (thanks to dave_thompson_085 for the hint).

Here are the Java code for sending the emails (the code has a "long" history so please don't wonder why there are Vector instead of ArrayList or so was used...)

    // create some properties and get the default Session
    Properties props = new Properties();
    props.put("mail.smtp.host", smtpServer);
    props.put("mail.smtp.port", "25");
    props.put("mail.debug", "true");

    // SMTP AUTH?
    Session session;
    if (username != null && pwd != null && username.length() > 0 && pwd.length() > 0) {
      props.put("mail.smtp.auth", "true");
//      props.put("mail.smtp.user", username);
//      props.put("mail.password", pwd);
      
      // if SMTP AUTH: use in every case TLS!
      tls = true;
      
      session = Session.getInstance(props, new MyPasswordAuthenticator(username, pwd));
    } else {
      session = Session.getDefaultInstance(props, null);
    }
    
    // TLS?
    if (tls) {
      props.put("mail.smtp.starttls.enable", "true");
//      props.put("mail.smtp.tls", "true");
    }

    // create a message
    SMTPMessage mimeMsg = new SMTPMessage(session);
  
    InternetAddress addressFrom = new InternetAddress(sender);
    mimeMsg.setFrom(addressFrom);
    mimeMsg.setEnvelopeFrom(sender);
  
    // set recipients ...
    InternetAddress[] tos = new InternetAddress[receivers.size()];
    int i = 0;
    for (String addr : receivers) {
      tos[i++] = new InternetAddress(addr, extractPersonalNameFromEMailAddr(addr));
    }

    // set cc recipients ...
    InternetAddress[] ccAddrs = new InternetAddress[0];
    if (ccs != null) {
      ccAddrs = new InternetAddress[ccs.size()];
      
      i = 0;
      for (String addr : ccs) { 
        ccAddrs[i++] = new InternetAddress(addr, extractPersonalNameFromEMailAddr(addr));
      }
    }

    mimeMsg.setRecipients(Message.RecipientType.TO, tos);
    mimeMsg.setRecipients(Message.RecipientType.CC, ccAddrs);

    // set return notifications
    if (readNotificationRequest) {
      String notifyAddr = replyTo;
      if (notifyAddr == null || notifyAddr.trim().length() == 0) notifyAddr = sender;
      
      mimeMsg.addHeader("Disposition-Notification-To", "<" + notifyAddr + ">");
      mimeMsg.addHeader("Return-Receipt-To", "<" + notifyAddr + ">");
    }
    
    mimeMsg.addHeader("Date", mailDateFormat.format(new Date()));
  
    mimeMsg.setNotifyOptions(deliverNotificationRequest);
    mimeMsg.setReturnOption(deliverNotificationContent);

    if (replyTo != null) {
      Address[] replyToAddrs = new Address[1];
      replyToAddrs[0] = new InternetAddress(this.replyTo);
      
      mimeMsg.setReplyTo(replyToAddrs);
      mimeMsg.setFrom(replyToAddrs[0]);
    }
    
    mimeMsg.setSentDate(new Date());
    mimeMsg.setSubject(subject);
//    mimeMsg.setContent(msg, msgType);
  
    // set the text content:
    MimeBodyPart msgPart = new MimeBodyPart();
    msgPart.setContent(msg, msgType + "; charset=" + charset);
    
    Multipart multipart = new MimeMultipart();
    multipart.addBodyPart(msgPart);
    
    // add the attachments:
    String attFile;
    MimeBodyPart attachment;
    Iterator<String> it = new Vector<String>(attachments).iterator();
    
    while (it.hasNext()) {
      attFile = it.next();
      
      attachment = new MimeBodyPart();
      attachment.setDataHandler(new DataHandler(new AppOctetStreamFileDataSource(attFile)));
      attachment.setFileName(FileUtil.extractFilename(attFile));

      multipart.addBodyPart(attachment);
    }
    
    mimeMsg.setContent(multipart);

    System.out.println("mail.smtp.ssl.trust: <<" + System.getProperty("mail.smtp.ssl.trust") + ">>");
    System.out.println("mail.smtp.ssl.socketfactory.class: <<" + System.getProperty("mail.smtp.ssl.socketfactory.class") + ">>");
    System.out.println("mail.smtp.socketfactory.class: <<" + System.getProperty("mail.smtp.socketfactory.class") + ">>");
    System.out.println("mail.smtp.ssl.protocols: <<" + System.getProperty("mail.smtp.ssl.protocols") + ">>");
    System.out.println("mail.smtp.ssl.ciphersuites: <<" + System.getProperty("mail.smtp.ssl.ciphersuites") + ">>");
    System.out.println("SSLContext.getDefault().getDefaultSSLParameters().getProtocols(): "
            + Arrays.toString(SSLContext.getDefault().getDefaultSSLParameters().getProtocols()));
    System.out.println("Arrays.toString(SSLContext.getDefault().getDefaultSSLParameters().getCipherSuites(): "
            + Arrays.toString(SSLContext.getDefault().getDefaultSSLParameters().getCipherSuites()));

    session.setDebug(debug);
    Transport transport = session.getTransport("smtp");
    transport.connect(smtpServer, 25, username, pwd);
//System.out.println("#########" + System.getProperty("mail.smtp.localhost"));

    transport.sendMessage(mimeMsg, mimeMsg.getAllRecipients());

    transport.close();

For debugging I have add some System.out's for output the some interesting values.

Here the stdout output:

DEBUG: JavaMail version 1.4.7
DEBUG: successfully loaded resource: /META-INF/javamail.default.providers
DEBUG: Tables of loaded providers
DEBUG: Providers Listed By Class Name: {com.sun.mail.smtp.SMTPSSLTransport=javax.mail.Provider[TRANSPORT,smtps,com.sun.mail.smtp.SMTPSSLTransport,Oracle], com.sun.mail.smtp.SMTPTransport=javax.mail.Provider[TRANSPORT,smtp,com.sun.mail.smtp.SMTPTransport,Oracle], com.sun.mail.imap.IMAPSSLStore=javax.mail.Provider[STORE,imaps,com.sun.mail.imap.IMAPSSLStore,Oracle], com.sun.mail.pop3.POP3SSLStore=javax.mail.Provider[STORE,pop3s,com.sun.mail.pop3.POP3SSLStore,Oracle], com.sun.mail.imap.IMAPStore=javax.mail.Provider[STORE,imap,com.sun.mail.imap.IMAPStore,Oracle], com.sun.mail.pop3.POP3Store=javax.mail.Provider[STORE,pop3,com.sun.mail.pop3.POP3Store,Oracle]}
DEBUG: Providers Listed By Protocol: {imaps=javax.mail.Provider[STORE,imaps,com.sun.mail.imap.IMAPSSLStore,Oracle], imap=javax.mail.Provider[STORE,imap,com.sun.mail.imap.IMAPStore,Oracle], smtps=javax.mail.Provider[TRANSPORT,smtps,com.sun.mail.smtp.SMTPSSLTransport,Oracle], pop3=javax.mail.Provider[STORE,pop3,com.sun.mail.pop3.POP3Store,Oracle], pop3s=javax.mail.Provider[STORE,pop3s,com.sun.mail.pop3.POP3SSLStore,Oracle], smtp=javax.mail.Provider[TRANSPORT,smtp,com.sun.mail.smtp.SMTPTransport,Oracle]}
DEBUG: successfully loaded resource: /META-INF/javamail.default.address.map
mail.smtp.ssl.trust: <<null>>
mail.smtp.ssl.socketfactory.class: <<null>>
mail.smtp.socketfactory.class: <<null>>
mail.smtp.ssl.protocols: <<null>>
mail.smtp.ssl.ciphersuites: <<null>>
SSLContext.getDefault().getDefaultSSLParameters().getProtocols(): [TLSv1.3, TLSv1.2]
Arrays.toString(SSLContext.getDefault().getDefaultSSLParameters().getCipherSuites(): [TLS_AES_128_GCM_SHA256, TLS_AES_256_GCM_SHA384, TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384, TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256, TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384, TLS_RSA_WITH_AES_256_GCM_SHA384, TLS_ECDH_ECDSA_WITH_AES_256_GCM_SHA384, TLS_ECDH_RSA_WITH_AES_256_GCM_SHA384, TLS_DHE_RSA_WITH_AES_256_GCM_SHA384, TLS_DHE_DSS_WITH_AES_256_GCM_SHA384, TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256, TLS_RSA_WITH_AES_128_GCM_SHA256, TLS_ECDH_ECDSA_WITH_AES_128_GCM_SHA256, TLS_ECDH_RSA_WITH_AES_128_GCM_SHA256, TLS_DHE_RSA_WITH_AES_128_GCM_SHA256, TLS_DHE_DSS_WITH_AES_128_GCM_SHA256, TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384, TLS_RSA_WITH_AES_256_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA384, TLS_DHE_RSA_WITH_AES_256_CBC_SHA256, TLS_DHE_DSS_WITH_AES_256_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_DSS_WITH_AES_256_CBC_SHA, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256, TLS_RSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_DSS_WITH_AES_128_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_128_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_DSS_WITH_AES_128_CBC_SHA, TLS_EMPTY_RENEGOTIATION_INFO_SCSV]

and the stderr:

javax.net.ssl|DEBUG|01|main|2021-06-10 08:28:39.042 CEST|SSLCipher.java:438|jdk.tls.keyLimits: entry = AES/GCM/NoPadding KeyUpdate 2^37. AES/GCM/NOPADDING:KEYUPDATE = 137438953472
javax.net.ssl|ERROR|01|main|2021-06-10 08:28:39.306 CEST|TransportContext.java:341|Fatal (HANDSHAKE_FAILURE): Couldn't kickstart handshaking (
"throwable" : {
javax.net.ssl.SSLHandshakeException: No appropriate protocol (protocol is disabled or cipher suites are inappropriate)
at java.base/sun.security.ssl.HandshakeContext.(HandshakeContext.java:170)
at java.base/sun.security.ssl.ClientHandshakeContext.(ClientHandshakeContext.java:98)
at java.base/sun.security.ssl.TransportContext.kickstart(TransportContext.java:221)
at java.base/sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:433)
at java.base/sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:411)
at com.sun.mail.util.SocketFetcher.configureSSLSocket(SocketFetcher.java:549)
at com.sun.mail.util.SocketFetcher.startTLS(SocketFetcher.java:486)
at com.sun.mail.smtp.SMTPTransport.startTLS(SMTPTransport.java:1902)
at com.sun.mail.smtp.SMTPTransport.protocolConnect(SMTPTransport.java:666)
at javax.mail.Service.connect(Service.java:295)
at com.coco_on.tools.general.useCases.net.UCSendEMail.start(UCSendEMail.java:470)
at com.tetrixSystems.tHiddenExecuter.Executer.start(Executer.java:233)
at com.tetrixSystems.tHiddenExecuter.UCMain.start(UCMain.java:83)
at com.tetrixSystems.tHiddenExecuter.UCMain.main(UCMain.java:49)}

)
javax.net.ssl|DEBUG|01|main|2021-06-10 08:28:39.306 CEST|SSLSocketImpl.java:1638|close the underlying socket
javax.net.ssl|DEBUG|01|main|2021-06-10 08:28:39.306 CEST|SSLSocketImpl.java:1657|close the SSL connection (initiative)
javax.mail.MessagingException: Could not convert socket to TLS;
nested exception is:
javax.net.ssl.SSLHandshakeException: No appropriate protocol (protocol is disabled or cipher suites are inappropriate)
at com.sun.mail.smtp.SMTPTransport.startTLS(SMTPTransport.java:1907)
at com.sun.mail.smtp.SMTPTransport.protocolConnect(SMTPTransport.java:666)
at javax.mail.Service.connect(Service.java:295)
at com.coco_on.tools.general.useCases.net.UCSendEMail.start(UCSendEMail.java:470)
at com.tetrixSystems.tHiddenExecuter.Executer.start(Executer.java:233)
at com.tetrixSystems.tHiddenExecuter.UCMain.start(UCMain.java:83)
at com.tetrixSystems.tHiddenExecuter.UCMain.main(UCMain.java:49)
Caused by: javax.net.ssl.SSLHandshakeException: No appropriate protocol (protocol is disabled or cipher suites are inappropriate)
at java.base/sun.security.ssl.HandshakeContext.(HandshakeContext.java:170)
at java.base/sun.security.ssl.ClientHandshakeContext.(ClientHandshakeContext.java:98)
at java.base/sun.security.ssl.TransportContext.kickstart(TransportContext.java:221)
at java.base/sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:433)
at java.base/sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:411)
at com.sun.mail.util.SocketFetcher.configureSSLSocket(SocketFetcher.java:549)
at com.sun.mail.util.SocketFetcher.startTLS(SocketFetcher.java:486)
at com.sun.mail.smtp.SMTPTransport.startTLS(SMTPTransport.java:1902)
... 6 more

On postfix server side is openssl 1.1.1 installed:

#> openssl version
OpenSSL 1.1.1d  10 Sep 2019

I don't believe that java is using internally openssl, but may be also interesting the client side openssl version (CentOS/RHEL 7):

#> openssl version
OpenSSL 1.0.2k-fips  26 Jan 2017

ANSWER

Answered 2021-Jun-15 at 08:30

Here I'm wondering about the line [in s_client]
New, TLSv1/SSLv3, Cipher is ECDHE-RSA-AES256-GCM-SHA384

You're apparently using OpenSSL 1.0.2, where that's a basically useless relic. Back in the days when OpenSSL supported SSLv2 (mostly until 2010, although almost no one used it much after 2000), the ciphersuite values used for SSLv3 and up (including all TLS, but before 2014 OpenSSL didn't implement higher than TLS1.0) were structured differently than those used for SSLv2, so it was important to qualify the ciphersuite by the 'universe' it existed in. It has almost nothing to do with the protocol version actually used, which appears later in the session-param decode:

SSL-Session:
    Protocol  : TLSv1.2
...

-- although you already know this from the ciphersiote, because ECDHE-RSA-AES256-GCM-SHA384 cannot be used in any protocol version other than TLS1.2.

In OpenSSL 1.1.0 up, the protocol state machine is redesigned and among other things this field is changed to display the minimum protocol version compatible with the ciphersuite, which is no longer totally useless, but still may be different from the actual protocol used.

In any case your exception occurs before JSSE client even sends the ClientHello to the server to start the handshake, so it is physically impossible for it to be be caused by anything wrong in the server. It is instead caused by something wrong in the coding or configuration on the (Java) client, about which you have provided almost no information.

It may help to run with sysprop javax.net.debug=ssl:handshake although this problem occurs early and may be before anything usefully traceable.

Make sure you don't have sysprops mail.smtp.ssl.{protocols,ciphersuites} set to something unsuitable. See e.g. com.sun.mail.util.SocketFetcher.configureSSLSocket and Docker Container javax.mail.MessagingException: No appropriate protocol (protocol is disabled or cipher suites are inappropriate); and links there. Also check for any settings of mail.smtp.ssl.trust mail.smtp.ssl.socketfactory.class mail.smtp.socketfactory.class, and any ~~code that alters the SSLSocketFactory default and maybe~~ (EDIT) alteration to the SSLContext default i.e. at or near the point you try to connect with javamail print or otherwise get Arrays.toString(SSLContext.getDefaultSSLParameters().getProtocols()) and the same for getCipherSuites.

EDIT2: it's version dependent. Older Sun/Oracle (i.e. pre-Jakarta) versions below 1.5.3 (2015-04-15) had the default (if mail.smtp.ssl.protocols is NOT configured) HARDCODED to TLSv1 meaning 1.0 ONLY. As a result the connection will fail with the exception in the Q on a Java that has TLS1.0 disabled, as 11.0.11 does when installed, and also 16.0.0 up and 8u291 (presumably up). On Java instances without TLS1.0 disabled it will attempt the connection, but if the server refuses TLS1.0 -- which the one in this Q should -- the connection will still fail with a different exception.

I have added already ECDHE-RSA-AES256-GCM-SHA384 as jdk.tls.legacyAlgorithms.

That's backwards. Legacy algorithms are ones you prefer NOT to use and accept only as fallback; this ciphersuite is among the best currently available and should be preferred (and is by default, so you shouldn't change it). Plus, although the doc and comments in the file don't make this clear, the legacy constraint only applies server-side anyway -- because that's where ciphersuite selection, and for 1.3 related algorithm selection, is done.

Source https://stackoverflow.com/questions/67899129

QUESTION

how to sort a map by key when the values are the same?

Asked 2021-Jun-14 at 21:54

I'm currently sorting the map by value, but I couldn't think on how I would have it sorted by key for the cases that I have the same value.

Currently it works like this:

public static <K, V extends Comparable<? super V>> Map<K, V> sortMapByValue(Map<K, V> map) {
    List<Map.Entry<K, V>> list = new LinkedList<>(map.entrySet());
    Collections.sort(list, new Comparator<Map.Entry<K, V>>() {
        @Override
        public int compare(Map.Entry<K, V> e1, Map.Entry<K, V> e2) {
            return e2.getValue().compareTo(e1.getValue());
        }
    });

    Map<K, V> result = new LinkedHashMap<>();
    for (Map.Entry<K, V> entry : list) {
        result.put(entry.getKey(), entry.getValue());
    }
    return result;
}

My output is something like this: (AA,10), (CC,5), (BB,5)

I'm trying to achieve this: (AA,10), (BB,5), (CC,5)

ANSWER

Answered 2021-Jun-14 at 21:54

You could check in your Comparator if the values are the same and if so compare the keys. Here is your adapted method:

public static <K extends Comparable<? super K>, V extends Comparable<? super V>> Map<K, V> newSortMapByValue(Map<K, V> map) {
        List<Map.Entry<K, V>> list = new LinkedList<>(map.entrySet());
        Collections.sort(list, new Comparator<Map.Entry<K, V>>() {
            @Override
            public int compare(Map.Entry<K, V> e1, Map.Entry<K, V> e2) {
                // Check if values are the same
                if (e1.getValue().equals(e2.getValue()))
                    // Compare e1 to e2, because A should be first element
                    return e1.getKey().compareTo(e2.getKey());
                else
                    // Compare e2 to e1, because largest number should be first
                    return e2.getValue().compareTo(e1.getValue());
            }
        });

        Map<K, V> result = new LinkedHashMap<>();
        for (Map.Entry<K, V> entry : list) {
            result.put(entry.getKey(), entry.getValue());
        }
        return result;
    }

Example main:

    public static void main(String[] args) {
        Map<String, Integer> map = new HashMap<>();
        map.put("AA",10);
        map.put("CC",5);
        map.put("BB",5);
        map.put("DD",15);
        map.put("ZZ",15);

        System.out.println(map);
        Map sortedMap = newSortMapByValue(map);
        System.out.println(sortedMap);
    }

Output:

{AA=10, CC=5, BB=5, DD=15, ZZ=15}
{DD=15, ZZ=15, AA=10, BB=5, CC=5}

Source https://stackoverflow.com/questions/67977488

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

Vulnerabilities

No vulnerabilities reported

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You can download it from GitHub.
You can use CC 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 CC 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 .

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