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CC | android project gradually | Architecture library

 by   luckybilly Java Version: Current License: Apache-2.0

 by   luckybilly Java Version: Current License: Apache-2.0

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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
Support
Quality
Quality
Security
Security
License
License
Reuse
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

securitySecurity

  • 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

buildReuse

  • 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.

目录结构

copy iconCopydownload iconDownload
    - 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

copy iconCopydownload iconDownload
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

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

遇到问题怎么办?

copy iconCopydownload iconDownload
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

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

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

copy iconCopydownload iconDownload
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

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

Dynamic Library error while using Tensorflow with GPU

copy iconCopydownload iconDownload
# 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: &quot;No appropriate protocol (protocol is disabled or cipher suites are inappropriate)&quot;

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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

Community Discussions

Trending Discussions on CC
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  • postfix and openJDK 11: &quot;No appropriate protocol (protocol is disabled or cipher suites are inappropriate)&quot;
  • 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:

  1. receiver list (how would that work if I have more than one)?

  2. cc list (" ")

  3. 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.

  4. 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
  1. Use MailItem.Recipients collection.
  2. See #1 and check for each recipient's Recipient.Type property equal olCC ( =2)
  3. Of course - set the MailItem.Categpries property. Don't forget to call MailItem.Save
  4. 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

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

Vulnerabilities

No vulnerabilities reported

Install CC

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 .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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