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COLA | COLA: Clean Objectoriented & Layered Architecture | Architecture library

 by   alibaba Java Version: COLA4.1.0 License: LGPL-2.1

 by   alibaba Java Version: COLA4.1.0 License: LGPL-2.1

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kandi X-RAY | COLA Summary

COLA is a Java library typically used in Architecture applications. COLA has build file available, it has a Weak Copyleft License and it has medium support. However COLA has 10 bugs and it has 3 vulnerabilities. You can download it from GitHub, Maven.
🥤 COLA: Clean Object-oriented & Layered Architecture
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kandi-support Support

  • COLA has a medium active ecosystem.
  • It has 6768 star(s) with 1841 fork(s). There are 298 watchers for this library.
  • There were 1 major release(s) in the last 12 months.
  • There are 13 open issues and 176 have been closed. On average issues are closed in 66 days. There are 2 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of COLA is COLA4.1.0
COLA Support
Best in #Architecture
Average in #Architecture
COLA Support
Best in #Architecture
Average in #Architecture

quality kandi Quality

  • COLA has 10 bugs (1 blocker, 6 critical, 0 major, 3 minor) and 427 code smells.
COLA Quality
Best in #Architecture
Average in #Architecture
COLA Quality
Best in #Architecture
Average in #Architecture

securitySecurity

  • COLA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • COLA code analysis shows 3 unresolved vulnerabilities (0 blocker, 0 critical, 0 major, 3 minor).
  • There are 8 security hotspots that need review.
COLA Security
Best in #Architecture
Average in #Architecture
COLA Security
Best in #Architecture
Average in #Architecture

license License

  • COLA is licensed under the LGPL-2.1 License. This license is Weak Copyleft.
  • Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.
COLA License
Best in #Architecture
Average in #Architecture
COLA License
Best in #Architecture
Average in #Architecture

buildReuse

  • COLA releases are available to install and integrate.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • COLA saves you 4350 person hours of effort in developing the same functionality from scratch.
  • It has 9217 lines of code, 715 functions and 382 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
COLA Reuse
Best in #Architecture
Average in #Architecture
COLA Reuse
Best in #Architecture
Average in #Architecture
Top functions reviewed by kandi - BETA

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

  • Retrieves all the sub - metrics for a given user
  • Find the extension .
  • Handle exception .
  • Route a transition .
  • Returns all App MetricIDs for a user
  • Downloads a file from the given URL .
  • Checks if this ExtensionCoordinatePoint are equal
  • Do the transition .
  • Helper method to create a response object .
  • add a new transition

COLA Key Features

🥤 COLA: Clean Object-oriented & Layered Architecture

1. 创建应用

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mvn archetype:generate  \
    -DgroupId=com.alibaba.cola.demo.web \
    -DartifactId=demo-web \
    -Dversion=1.0.0-SNAPSHOT \
    -Dpackage=com.alibaba.demo \
    -DarchetypeArtifactId=cola-framework-archetype-web \
    -DarchetypeGroupId=com.alibaba.cola \
    -DarchetypeVersion=4.0.1

2. 运行应用

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mvn archetype:generate  \
    -DgroupId=com.alibaba.cola.demo.service \
    -DartifactId=demo-service \
    -Dversion=1.0.0-SNAPSHOT \
    -Dpackage=com.alibaba.demo \
    -DarchetypeArtifactId=cola-framework-archetype-service \
    -DarchetypeGroupId=com.alibaba.cola \
    -DarchetypeVersion=4.0.1

How to use a cell value to set a target sheet as a variable

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function onEdit(event) {
  const sourceSheet = "sheet1";
  const eventRange = event.range;
  const eventSheet = eventRange.getSheet();
  if (eventSheet.getSheetName() == sourceSheet && eventRange.columnStart == 1 && eventRange.isChecked()) {
    const ui = SpreadsheetApp.getUi();
    const result = ui.alert('Are you sure you this student is ready to export? This will delete this row', ui.ButtonSet.YES_NO);
    if (result == ui.Button.YES) {
      const [name, ...value] = eventRange.offset(0, 1, 1, 4).getValues()[0];
      const targetSheet = event.source.getSheetByName(name);
      if (targetSheet) {
        targetSheet.appendRow(value);
        eventSheet.deleteRow(eventRange.rowStart);
      } else {
        throw new Error("No sheet.");
      }
    } else {
      eventRange.uncheck();
    }
  }
}

How do I look up values in a dataframe and return matching values using python/pandas?

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# take the smallest value if A->B mapping is not unique
df_AtoB = df1.groupby("colA", as_index=False).agg(colB_=("colB", "min"))
df = df2.merge(df_AtoB, on="colA", how="left")
df.colB = df.colB.fillna(df.colB_)
df = df.drop(columns="colB_") 
-----------------------
# take the smallest value if A->B mapping is not unique
df_AtoB = df1.groupby("colA", as_index=False).agg(colB_=("colB", "min"))
df = df2.merge(df_AtoB, on="colA", how="left")
df.colB = df.colB.fillna(df.colB_)
df = df.drop(columns="colB_") 

Return list of columns with string match in multiple DataFrames

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def find_char(list_of_df , char):
    result = []
    for df in list_of_df:
        for c in df.columns:
            if char in c:
                result.append(c)
    return result
res = find_char([df,df2,df3] , 'A')
print(res)
['colA', 'colA', 'colA']
-----------------------
def find_char(list_of_df , char):
    result = []
    for df in list_of_df:
        for c in df.columns:
            if char in c:
                result.append(c)
    return result
res = find_char([df,df2,df3] , 'A')
print(res)
['colA', 'colA', 'colA']
-----------------------
l = [','.join(i.columns[i.columns.str.contains('A')]) for i in [df,df2,df3]]

SQL syntax to achieve the "opposite" of LIKE

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WHERE 'Coca Cola' LIKE column2

How to convert this tibble?

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library(dplyr)
library(tidyr)
mydf %>% 
   pivot_longer(cols = everything(), names_to = 'carac')
# A tibble: 4 x 2
  carac value
  <chr> <chr>
1 colA  a    
2 colB  1    
3 colC  X    
4 colD  2    
stack(mydf)[2:1]
-----------------------
library(dplyr)
library(tidyr)
mydf %>% 
   pivot_longer(cols = everything(), names_to = 'carac')
# A tibble: 4 x 2
  carac value
  <chr> <chr>
1 colA  a    
2 colB  1    
3 colC  X    
4 colD  2    
stack(mydf)[2:1]
-----------------------
library(dplyr)
library(tidyr)
mydf %>% 
   pivot_longer(cols = everything(), names_to = 'carac')
# A tibble: 4 x 2
  carac value
  <chr> <chr>
1 colA  a    
2 colB  1    
3 colC  X    
4 colD  2    
stack(mydf)[2:1]

Pandas melt with n columns and order control (counter)

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out = pd.wide_to_long(df,['x','y'],i=['idx','colA','n'],j='cnt').dropna().reset_index()
Out[8]: 
   idx  colA  n  cnt     x    y
0   1    10  3     1   0.0  4.0
1   1    10  3     2   3.0  5.0
2   1    10  3     3   4.0  3.0
3   2     5  2     1   1.0  0.0
4   2     5  2     2   2.0  1.0
5   3    12  4     1   7.0  4.0
6   3    12  4     2   8.0  5.0
7   3    12  4     3  10.0  3.0
8   3    12  4     4  11.0  3.0
-----------------------
# pip install pyjanitor
import janitor
import pandas as pd
df.pivot_longer(index = ['idx', 'colA', 'n'], 
                names_to = (".value", "counter"), 
                names_pattern=r"(.)(.)", 
                sort_by_appearance = True).dropna()
 
    idx  colA  n counter     x    y
0     1    10  3       1   0.0  4.0
1     1    10  3       2   3.0  5.0
2     1    10  3       3   4.0  3.0
4     2     5  2       1   1.0  0.0
5     2     5  2       2   2.0  1.0
8     3    12  4       1   7.0  4.0
9     3    12  4       2   8.0  5.0
10    3    12  4       3  10.0  3.0
11    3    12  4       4  11.0  3.0

Splitting data separated by a | in a cell to new rows

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=arrayformula({"Email","Members";
query(
array_constrain(
{
flatten(split(rept("|"&Input!A2:A,len(regexreplace(Input!B2:B&" | "&Input!C2:C,"[^\|]",))+1),"|")),
trim(flatten(split(Input!B2:B&"|"&Input!C2:C,"|")))
},
max(if(Input!B2:B<>"",len(regexreplace(Input!B2:B&" | "&Input!C2:C,"[^\|]",))+1,))*counta(Input!B2:B),2),
"where Col1 is not null",0)
})
=arrayformula({"Email","Manager";
query(
array_constrain(
{
flatten(split(rept("|"&Input!A2:A,len(regexreplace(Input!B2:B,"[^\|]",))+1),"|")),
trim(flatten(split(Input!B2:B,"|")))
},
max(if(Input!B2:B<>"",len(regexreplace(Input!B2:B,"[^\|]",))+1,))*counta(Input!B2:B),2),
"where Col1 is not null",0)
})
-----------------------
=arrayformula({"Email","Members";
query(
array_constrain(
{
flatten(split(rept("|"&Input!A2:A,len(regexreplace(Input!B2:B&" | "&Input!C2:C,"[^\|]",))+1),"|")),
trim(flatten(split(Input!B2:B&"|"&Input!C2:C,"|")))
},
max(if(Input!B2:B<>"",len(regexreplace(Input!B2:B&" | "&Input!C2:C,"[^\|]",))+1,))*counta(Input!B2:B),2),
"where Col1 is not null",0)
})
=arrayformula({"Email","Manager";
query(
array_constrain(
{
flatten(split(rept("|"&Input!A2:A,len(regexreplace(Input!B2:B,"[^\|]",))+1),"|")),
trim(flatten(split(Input!B2:B,"|")))
},
max(if(Input!B2:B<>"",len(regexreplace(Input!B2:B,"[^\|]",))+1,))*counta(Input!B2:B),2),
"where Col1 is not null",0)
})

php display array results by first letter from select option

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<form method="get" action="browse-store-1.php">
 <label for="letter-start">Store names start with letter </label>
 <select id="letter-start" name="letter-start">
  <option id="all">All</option>
  <option id="#" value="#">#</option>
  <option id="a" value="A">A</option>
  <option id="b" value="B">B</option>
  <option id="c" value="C">C</option>
...
  <option id="z" value="Z">Z</option>
 </select>
 <input type="submit" name="submit">
</form>
<?php
if(isset($_GET['submit'])) {
    echo "<div class=\"flex-container\">";
    $selected = $_GET['letter-start'];
    foreach ($stores as $s) {
        if ($selected === $s['name'][0]) {
            $id = $s['id'];
            $name = $s['name'];
            //display the stores
            echo "<div class=\"item\"><a href=\"store-home.php?id=$id\"><div class=\"image\"><img src=\"images/store.png\" alt=\"a store\"></div><h3 class=\"name\">$name</h3></a></div>";
        }
    }
}
-----------------------
<form method="get" action="browse-store-1.php">
 <label for="letter-start">Store names start with letter </label>
 <select id="letter-start" name="letter-start">
  <option id="all">All</option>
  <option id="#" value="#">#</option>
  <option id="a" value="A">A</option>
  <option id="b" value="B">B</option>
  <option id="c" value="C">C</option>
...
  <option id="z" value="Z">Z</option>
 </select>
 <input type="submit" name="submit">
</form>
<?php
if(isset($_GET['submit'])) {
    echo "<div class=\"flex-container\">";
    $selected = $_GET['letter-start'];
    foreach ($stores as $s) {
        if ($selected === $s['name'][0]) {
            $id = $s['id'];
            $name = $s['name'];
            //display the stores
            echo "<div class=\"item\"><a href=\"store-home.php?id=$id\"><div class=\"image\"><img src=\"images/store.png\" alt=\"a store\"></div><h3 class=\"name\">$name</h3></a></div>";
        }
    }
}

separate columns on condition in dataframe in R

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library(dplyr)  
library(tidyr)

df %>%
  extract(ColA, c('A1', 'A2'), '(.*)/(.*)', remove = FALSE) %>%
  mutate(across(A1:A2, ~replace(., ColB != 'sep', NA)))

#         ColA      A1   A2 ColB
#1     abc/def     abc  def  sep
#2     bcd/efg    <NA> <NA>   no
#3 def/ghj/yes def/ghj  yes  sep
#4 fet/hjk/yes    <NA> <NA>   no
-----------------------
df[paste0('A', 1:2)] <- read.csv(text = sub("/(\\w+)$",
          ",\\1", df$ColA), header = FALSE)
df[paste0("A", 1:2)] <- replace(df[paste0("A", 1:2)], df$Col1B != 'sep', NA)
library(dplyr)
library(tidyr)
df %>% 
     separate(ColA, into = c("A1", "A2"), remove = FALSE, sep="/(?=[^/]+$)") %>%
     mutate(across(A1:A2, ~ case_when(ColB == 'sep' ~ .)))
#         ColA      A1   A2 ColB
#1     abc/def     abc  def  sep
#2     bcd/efg    <NA> <NA>   no
#3 def/ghj/yes def/ghj  yes  sep
#4 fet/hjk/yes    <NA> <NA>   no
df %>% 
 mutate(new = str_split(case_when(ColB == 'sep' ~ ColA), "/(?=[^/]+$)")) %>% 
 unnest_wider(c(new))
-----------------------
df[paste0('A', 1:2)] <- read.csv(text = sub("/(\\w+)$",
          ",\\1", df$ColA), header = FALSE)
df[paste0("A", 1:2)] <- replace(df[paste0("A", 1:2)], df$Col1B != 'sep', NA)
library(dplyr)
library(tidyr)
df %>% 
     separate(ColA, into = c("A1", "A2"), remove = FALSE, sep="/(?=[^/]+$)") %>%
     mutate(across(A1:A2, ~ case_when(ColB == 'sep' ~ .)))
#         ColA      A1   A2 ColB
#1     abc/def     abc  def  sep
#2     bcd/efg    <NA> <NA>   no
#3 def/ghj/yes def/ghj  yes  sep
#4 fet/hjk/yes    <NA> <NA>   no
df %>% 
 mutate(new = str_split(case_when(ColB == 'sep' ~ ColA), "/(?=[^/]+$)")) %>% 
 unnest_wider(c(new))
-----------------------
df[paste0('A', 1:2)] <- read.csv(text = sub("/(\\w+)$",
          ",\\1", df$ColA), header = FALSE)
df[paste0("A", 1:2)] <- replace(df[paste0("A", 1:2)], df$Col1B != 'sep', NA)
library(dplyr)
library(tidyr)
df %>% 
     separate(ColA, into = c("A1", "A2"), remove = FALSE, sep="/(?=[^/]+$)") %>%
     mutate(across(A1:A2, ~ case_when(ColB == 'sep' ~ .)))
#         ColA      A1   A2 ColB
#1     abc/def     abc  def  sep
#2     bcd/efg    <NA> <NA>   no
#3 def/ghj/yes def/ghj  yes  sep
#4 fet/hjk/yes    <NA> <NA>   no
df %>% 
 mutate(new = str_split(case_when(ColB == 'sep' ~ ColA), "/(?=[^/]+$)")) %>% 
 unnest_wider(c(new))

group and join two tables based on id?

copy iconCopydownload iconDownload
 SELECT C.costumer,REF.NAME,REF2.NAME
 FROM OTHER_TABLE AS C
 JOIN TABLE_SOMETHING_LIKE_THIS AS REF ON C.BUY1=REF.ID
 JOIN TABLE_SOMETHING_LIKE_THIS AS REF2 ON C.BUY2=REF2.ID
-----------------------
select costumer,
  (select name from tbl1 where id=buy1) buy1,
  (select name from tbl1 where id=buy2) buy2
from tbl2;

Community Discussions

Trending Discussions on COLA
  • Check if player has a duplicate of a tool in roblox
  • asynchronus content in two-column reveal.js presentation
  • How to use a cell value to set a target sheet as a variable
  • How do I look up values in a dataframe and return matching values using python/pandas?
  • Return list of columns with string match in multiple DataFrames
  • SQL syntax to achieve the &quot;opposite&quot; of LIKE
  • How to convert this tibble?
  • Pandas melt with n columns and order control (counter)
  • Splitting data separated by a | in a cell to new rows
  • php display array results by first letter from select option
Trending Discussions on COLA

QUESTION

Check if player has a duplicate of a tool in roblox

Asked 2021-Jun-15 at 06:09

I am trying to check if a player has 2 Bloxy Colas in their backpack and if there is 2 destroy the other one

I tried this code in a script but it didn't work

local tmpTable = {} -- store items names

for I,v in pairs(player.Backpack:GetChildren()) do --  loop through all items found
    if  tmpTable [v.Name] ~= nil then -- checks if item exists in the list
        print("a duplicate has been found") -- found item e.g. a duplicate
        v:Destroy() -- deletes tool 
    else
        tmpTable [v.Name]  = "BloxyCola"  -- we don't need to use the value we are only using the key
        print("item added to list") 
    end 
end

ANSWER

Answered 2021-Jun-15 at 06:09

The duplicate not being removed. Nothing prints

You print something in both branches of your conditional statement. If there is nothing printed that means the loop does not run a single time. That means that player.Backpack:GetChildren() returns an empty table.

As Instance:GetChildren() will always return a table, seeing an empty table means that Backpack does not have any children.

Source https://stackoverflow.com/questions/67858918

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

Vulnerabilities

No vulnerabilities reported

Install COLA

You can download it from GitHub, Maven.
You can use COLA 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 COLA 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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