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jack | generating fully functional Java database models from Ruby | Database library

 by   LiveRamp Java Version: Current License: Non-SPDX

 by   LiveRamp Java Version: Current License: Non-SPDX

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kandi X-RAY | jack Summary

jack is a Java library typically used in Database applications. jack has no vulnerabilities, it has build file available and it has high support. However jack has 32 bugs and it has a Non-SPDX License. You can download it from GitHub, Maven.
Do you use Ruby/Rails and Java in your company? We do. And we're sick and tired of maintaining two different sets of schemas, models, and whatnot!.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • jack has a highly active ecosystem.
  • It has 68 star(s) with 65 fork(s). There are 115 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 13 open issues and 23 have been closed. On average issues are closed in 207 days. There are 10 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of jack is current.
jack Support
Best in #Database
Average in #Database
jack Support
Best in #Database
Average in #Database

quality kandi Quality

  • jack has 32 bugs (0 blocker, 1 critical, 27 major, 4 minor) and 973 code smells.
jack Quality
Best in #Database
Average in #Database
jack Quality
Best in #Database
Average in #Database

securitySecurity

  • jack has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • jack code analysis shows 0 unresolved vulnerabilities.
  • There are 10 security hotspots that need review.
jack Security
Best in #Database
Average in #Database
jack Security
Best in #Database
Average in #Database

license License

  • jack has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
jack License
Best in #Database
Average in #Database
jack License
Best in #Database
Average in #Database

buildReuse

  • jack releases are not available. You will need to build from source code and install.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 26209 lines of code, 2524 functions and 333 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
jack Reuse
Best in #Database
Average in #Database
jack Reuse
Best in #Database
Average in #Database
Top functions reviewed by kandi - BETA

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

  • Yield YYLEx .
  • Translate a string .
  • Load database connection configuration from env .
  • Adds the given array path to the given array path .
  • Finds all objects by foreign key .
  • Returns the attributes for this table .
  • Insert new records .
  • Query all sub records by record constraints
  • Append a record .
  • Fetches all records .

jack Key Features

a scheme for defining and managing multiple Rails projects that contribute models

a Ruby parser for schema.rb and ActiveRecord models that generates Java code

a Java library that provides model-oriented database access and database-wide query on top of standard database connections

Project Definition File

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# This is the class path where you want the top-level generated code to go.
databases_namespace: com.rapleaf.jack.test_project
# Here, we define each of the separate databases for which Jack should
# generate code. Each database is roughly equivalent to a Rails project.
databases:
  -
    # The root namespace for this database. It's nice, but not required, to
    # have this be a subpackage of the 'databases_namespace'.
    root_namespace: com.rapleaf.jack.test_project.database_1
    # What do you want to call this database? Leave out the "_production".
    db_name: Database1
    # The path to the schema.rb in your Rails project.
    schema_rb: database_1/db/schema.rb
    # The path to the app/models dir in your Rails project.
    models: database_1/app/models
    # Tables to be ignored. No code will be generated for them.
    ignored_tables:
      table_1
      table_2
  -
    root_namespace: com.rapleaf.jack.test_project.database_2
    db_name: Database2
    schema_rb: database_2/db/schema.rb
    models: database_2/app/models

Rails Projects

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/all_my_databases
  /project.yml
  /rails_project_1
  /rails_project_2
/ruby_project_that_uses_rails_project_2
  /include/rails_project_2              # <= svn external to /all_my_databases/rails_project_2

Running the Generator

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export PATH=$PATH:`pwd`/src/rb

Download

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<repository>
  <id>ossrh-snapshots</id>
  <url>https://oss.sonatype.org/content/repositories/snapshots/</url>
  <releases>
    <enabled>false</enabled>
  </releases>
</repository>

How to get "tag name", "attribute name", "attribute value" in regex?

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/\[([a-zA-Z]\w*)|([^=\s]+)=["'](.*?)["']/g
-----------------------
(?<=\[(\w+\b)[^\][\n]*)([^\s=]+)=['"]([^'"]*)['"](?=[^\][\n]*])
const regex = /(?<=\[(\w+\b)[^\][\n]*)([^\s=]+)=['"]([^'"]*)['"](?=[^\][\n]*])/g;
const str = `Hello, My name is Jack, This is my selfie:
[img id='4' src="imageurl"]
Below is the quick-link to my profile
[profile username='jackisbest' a='b']`;
let m;

console.log(Array.from(str.matchAll(regex), m => [m[1], m[2], m[3]]));
-----------------------
(?<=\[(\w+\b)[^\][\n]*)([^\s=]+)=['"]([^'"]*)['"](?=[^\][\n]*])
const regex = /(?<=\[(\w+\b)[^\][\n]*)([^\s=]+)=['"]([^'"]*)['"](?=[^\][\n]*])/g;
const str = `Hello, My name is Jack, This is my selfie:
[img id='4' src="imageurl"]
Below is the quick-link to my profile
[profile username='jackisbest' a='b']`;
let m;

console.log(Array.from(str.matchAll(regex), m => [m[1], m[2], m[3]]));

TypeScript: Specify that value must be in Array using spread operator

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const arr = ["foo", "bar", "loo"] as const

type arrTyp = typeof arr[number]; // "foo" | "bar" | "loo"
-----------------------
const options = ["apple", "orange", "kiwi"] as const; // Dynamic list that can be modified in the future 
type optionsType = typeof options[number];

type Person =  {
  name: string;
  favoriteFruit: optionsType;/* --- val in [...options] --- */
};

const personA:Person = {name: "Jack", favoriteFruit: "apple"}; // OK
const personB:Person = {name: "John", favoriteFruit: "orange"}; // OK
const personC:Person = {name: "Jane", favoriteFruit: "banana"}; // ERROR
console.log(personC)

Create new column using str.contains and based on if-else condition

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>>> import pandas as pd
>>> from io import StringIO

>>> df = pd.read_csv(StringIO("""
... id,url_text
... 1,Tim Kaine
... 2,Tim Kain
... 3,Tim
... 4,Lindsey Graham.com
... """), sep=',')
>>> df
    id  url_text
0   1   Tim Kaine
1   2   Tim Kain
2   3   Tim
3   4   Lindsey Graham.com
patterns = '(%s)' % '|'.join(pol_names_list)
df['pol_name_block'] = df['url_text'].str.extract(patterns)
    id  url_text            pol_name_block
0   1   Tim Kaine           Tim Kaine
1   2   Tim Kain            NaN
2   3   Tim                 NaN
3   4   Lindsey Graham.com  Lindsey Graham
-----------------------
>>> import pandas as pd
>>> from io import StringIO

>>> df = pd.read_csv(StringIO("""
... id,url_text
... 1,Tim Kaine
... 2,Tim Kain
... 3,Tim
... 4,Lindsey Graham.com
... """), sep=',')
>>> df
    id  url_text
0   1   Tim Kaine
1   2   Tim Kain
2   3   Tim
3   4   Lindsey Graham.com
patterns = '(%s)' % '|'.join(pol_names_list)
df['pol_name_block'] = df['url_text'].str.extract(patterns)
    id  url_text            pol_name_block
0   1   Tim Kaine           Tim Kaine
1   2   Tim Kain            NaN
2   3   Tim                 NaN
3   4   Lindsey Graham.com  Lindsey Graham
-----------------------
>>> import pandas as pd
>>> from io import StringIO

>>> df = pd.read_csv(StringIO("""
... id,url_text
... 1,Tim Kaine
... 2,Tim Kain
... 3,Tim
... 4,Lindsey Graham.com
... """), sep=',')
>>> df
    id  url_text
0   1   Tim Kaine
1   2   Tim Kain
2   3   Tim
3   4   Lindsey Graham.com
patterns = '(%s)' % '|'.join(pol_names_list)
df['pol_name_block'] = df['url_text'].str.extract(patterns)
    id  url_text            pol_name_block
0   1   Tim Kaine           Tim Kaine
1   2   Tim Kain            NaN
2   3   Tim                 NaN
3   4   Lindsey Graham.com  Lindsey Graham
-----------------------
>>> import pandas as pd
>>> from io import StringIO

>>> df = pd.read_csv(StringIO("""
... id,url_text
... 1,Tim Kaine
... 2,Tim Kain
... 3,Tim
... 4,Lindsey Graham.com
... """), sep=',')
>>> df
    id  url_text
0   1   Tim Kaine
1   2   Tim Kain
2   3   Tim
3   4   Lindsey Graham.com
patterns = '(%s)' % '|'.join(pol_names_list)
df['pol_name_block'] = df['url_text'].str.extract(patterns)
    id  url_text            pol_name_block
0   1   Tim Kaine           Tim Kaine
1   2   Tim Kain            NaN
2   3   Tim                 NaN
3   4   Lindsey Graham.com  Lindsey Graham
-----------------------
pattern = fr"({'|'.join(pol_names_list)})"
df['pol_name_block'] = df['url_text'].str.extract(pattern)
print(df)

# Output <- with the sample of @tlentali
   id        url_text  pol_name_block
0   1       Tim Kaine       Tim Kaine
1   2        Tim Kain             NaN
2   3             Tim             NaN
3   4  Lindsey Graham  Lindsey Graham
# New sample, same pattern
>>> df
   id                      url_text
0   1  Tim Kaine and Lindsey Graham
1   2                      Tim Kain
2   3                           Tim
3   4                Lindsey Graham

# findall
>>> df['url_text'].str.findall(pattern)
0    [Tim Kaine, Lindsey Graham]
1                             []
2                             []
3               [Lindsey Graham]
Name: url_text, dtype: object

# extractall
>>> df['url_text'].str.extractall(pattern)
                      0
  match                
0 0           Tim Kaine
  1      Lindsey Graham
3 0      Lindsey Graham

-----------------------
pattern = fr"({'|'.join(pol_names_list)})"
df['pol_name_block'] = df['url_text'].str.extract(pattern)
print(df)

# Output <- with the sample of @tlentali
   id        url_text  pol_name_block
0   1       Tim Kaine       Tim Kaine
1   2        Tim Kain             NaN
2   3             Tim             NaN
3   4  Lindsey Graham  Lindsey Graham
# New sample, same pattern
>>> df
   id                      url_text
0   1  Tim Kaine and Lindsey Graham
1   2                      Tim Kain
2   3                           Tim
3   4                Lindsey Graham

# findall
>>> df['url_text'].str.findall(pattern)
0    [Tim Kaine, Lindsey Graham]
1                             []
2                             []
3               [Lindsey Graham]
Name: url_text, dtype: object

# extractall
>>> df['url_text'].str.extractall(pattern)
                      0
  match                
0 0           Tim Kaine
  1      Lindsey Graham
3 0      Lindsey Graham

How can I join two lists in less than O(N*M)?

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class Parent {
    int id;
    // other fields, getters etc
}

class Child {
    int parentId;
    // other fields, getters etc
}

void join(List<Parent> parents, List<Child> children) {
    Map<Integer, Parent> parentMap = parents.stream()
      .collect(toMap(Parent::getKey, p -> p)); // FYI toMap collects to a HashMap
    for (Child child : children) {
        Parent parent = parentMap.get(child.getParentId());
        // not sure what “join” means, but you now have child and its parent
    }
}
-----------------------
record Employees(int id, String name, String department) {}
record Transactions(String soldProduct, int soldBy, String soldTo) {}
record Joined(String soldProduct, int soldBy, String soldTo, String name, String department) {}

public static void main(String[] args) {
    List<Employees> employees = List.of(
        new Employees(1, "Jack", "IT"),
        new Employees(2, "Amy", "Finance"));
    List<Transactions> transactions = List.of(
        new Transactions("TV", 1, "Mary"),
        new Transactions("Radio", 1, "Bob"),
        new Transactions("Mobile", 2, "Lisa"));

    Map<Integer, Employees> mapEmployee = employees.stream()
        .collect(Collectors.toMap(Employees::id, Function.identity()));
    List<Joined> joined = transactions.stream()
        .map(t -> {
            Employees e = mapEmployee.get(t.soldBy());
            return new Joined(t.soldProduct(), t.soldBy(), t.soldTo(), e.name(), e.department());
        })
        .toList();

    for (Joined e : joined)
        System.out.println(e);
}
Joined[soldProduct=TV, soldBy=1, soldTo=Mary, name=Jack, department=IT]
Joined[soldProduct=Radio, soldBy=1, soldTo=Bob, name=Jack, department=IT]
Joined[soldProduct=Mobile, soldBy=2, soldTo=Lisa, name=Amy, department=Finance]
-----------------------
record Employees(int id, String name, String department) {}
record Transactions(String soldProduct, int soldBy, String soldTo) {}
record Joined(String soldProduct, int soldBy, String soldTo, String name, String department) {}

public static void main(String[] args) {
    List<Employees> employees = List.of(
        new Employees(1, "Jack", "IT"),
        new Employees(2, "Amy", "Finance"));
    List<Transactions> transactions = List.of(
        new Transactions("TV", 1, "Mary"),
        new Transactions("Radio", 1, "Bob"),
        new Transactions("Mobile", 2, "Lisa"));

    Map<Integer, Employees> mapEmployee = employees.stream()
        .collect(Collectors.toMap(Employees::id, Function.identity()));
    List<Joined> joined = transactions.stream()
        .map(t -> {
            Employees e = mapEmployee.get(t.soldBy());
            return new Joined(t.soldProduct(), t.soldBy(), t.soldTo(), e.name(), e.department());
        })
        .toList();

    for (Joined e : joined)
        System.out.println(e);
}
Joined[soldProduct=TV, soldBy=1, soldTo=Mary, name=Jack, department=IT]
Joined[soldProduct=Radio, soldBy=1, soldTo=Bob, name=Jack, department=IT]
Joined[soldProduct=Mobile, soldBy=2, soldTo=Lisa, name=Amy, department=Finance]

Count the values of a column based on the values of another column in R

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Zip<-c("123245","12345","123245","123456","123456","12345")
Name<-c("Bob","Bob","Bob","Jack","Jack","Mary")
df<-data.frame(Zip,Name)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
df %>%
  group_by(Zip, Name) %>%
  summarise(Count = n())
#> `summarise()` has grouped output by 'Zip'. You can override using the `.groups` argument.
#> # A tibble: 4 × 3
#> # Groups:   Zip [3]
#>   Zip    Name  Count
#>   <chr>  <chr> <int>
#> 1 123245 Bob       2
#> 2 12345  Bob       1
#> 3 12345  Mary      1
#> 4 123456 Jack      2
library(tidyverse)
library(microbenchmark)

Zip<-c("123245","12345","123245","123456","123456","12345")
Name<-c("Bob","Bob","Bob","Jack","Jack","Mary")
df<-data.frame(Zip,Name)

JM <- function(df){
  df %>%
  group_by(Zip, Name) %>%
  summarise(Count = n())
}
JM(df)
#> `summarise()` has grouped output by 'Zip'. You can override using the `.groups` argument.
#> # A tibble: 4 × 3
#> # Groups:   Zip [3]
#>   Zip    Name  Count
#>   <chr>  <chr> <int>
#> 1 123245 Bob       2
#> 2 12345  Bob       1
#> 3 12345  Mary      1
#> 4 123456 Jack      2

TarJae <- function(df){
  df %>% 
    count(Zip, Name, name= "Count")
}

TIC <- function(df){
  aggregate(cbind(Count = Zip) ~ Zip + Name, df, length)
}
TIC(df)
#>      Zip Name Count
#> 1 123245  Bob     2
#> 2  12345  Bob     1
#> 3 123456 Jack     2
#> 4  12345 Mary     1

res <- microbenchmark(JM(df), TIC(df), TarJae(df))
autoplot(res)
#> Coordinate system already present. Adding new coordinate system, which will replace the existing one.
-----------------------
Zip<-c("123245","12345","123245","123456","123456","12345")
Name<-c("Bob","Bob","Bob","Jack","Jack","Mary")
df<-data.frame(Zip,Name)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
df %>%
  group_by(Zip, Name) %>%
  summarise(Count = n())
#> `summarise()` has grouped output by 'Zip'. You can override using the `.groups` argument.
#> # A tibble: 4 × 3
#> # Groups:   Zip [3]
#>   Zip    Name  Count
#>   <chr>  <chr> <int>
#> 1 123245 Bob       2
#> 2 12345  Bob       1
#> 3 12345  Mary      1
#> 4 123456 Jack      2
library(tidyverse)
library(microbenchmark)

Zip<-c("123245","12345","123245","123456","123456","12345")
Name<-c("Bob","Bob","Bob","Jack","Jack","Mary")
df<-data.frame(Zip,Name)

JM <- function(df){
  df %>%
  group_by(Zip, Name) %>%
  summarise(Count = n())
}
JM(df)
#> `summarise()` has grouped output by 'Zip'. You can override using the `.groups` argument.
#> # A tibble: 4 × 3
#> # Groups:   Zip [3]
#>   Zip    Name  Count
#>   <chr>  <chr> <int>
#> 1 123245 Bob       2
#> 2 12345  Bob       1
#> 3 12345  Mary      1
#> 4 123456 Jack      2

TarJae <- function(df){
  df %>% 
    count(Zip, Name, name= "Count")
}

TIC <- function(df){
  aggregate(cbind(Count = Zip) ~ Zip + Name, df, length)
}
TIC(df)
#>      Zip Name Count
#> 1 123245  Bob     2
#> 2  12345  Bob     1
#> 3 123456 Jack     2
#> 4  12345 Mary     1

res <- microbenchmark(JM(df), TIC(df), TarJae(df))
autoplot(res)
#> Coordinate system already present. Adding new coordinate system, which will replace the existing one.
-----------------------
library(dplyr)
df %>% 
  count(Zip, Name, name= "Count")
     Zip Name Count
1 123245  Bob     2
2  12345  Bob     1
3  12345 Mary     1
4 123456 Jack     2
-----------------------
library(dplyr)
df %>% 
  count(Zip, Name, name= "Count")
     Zip Name Count
1 123245  Bob     2
2  12345  Bob     1
3  12345 Mary     1
4 123456 Jack     2
-----------------------
> aggregate(cbind(Count = Zip) ~ Zip + Name, df, length)
     Zip Name Count
1 123245  Bob     2
2  12345  Bob     1
3 123456 Jack     2
4  12345 Mary     1

Stopping CSS Grid column from overflowing

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@import url("https://fonts.googleapis.com/css2?family=Asap:wght@400;700&display=swap");
* {
  padding: 0;
  margin: 0;
  height: 100%;
  width: 100%;
  background-color: #4a6163;
  font-family: "Asap";
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
}

.main_grid {
  display: -ms-grid;
  display: grid;
  -ms-grid-columns: 0.25fr (1fr)[2];
      grid-template-columns: 0.25fr repeat(2, 1fr);
  -ms-grid-rows: 1fr;
      grid-template-rows: 1fr;
  grid-column-gap: 0px;
  grid-row-gap: 0px;
  max-height: 100%;
  max-width: 100%;
}

.nav_section {
  -ms-grid-row: 1;
  -ms-grid-column: 1;
  -ms-grid-column-span: 1;
  grid-area: 1 / 1 / 1 / 2;
  border: 3px yellow solid;
}

.left_column {
  -ms-grid-row: 1;
  -ms-grid-column: 2;
  -ms-grid-column-span: 1;
  grid-area: 1 / 2 / 1 / 3;
  border: 1px yellow solid;
}

.right_colomn {
  -ms-grid-row: 1;
  -ms-grid-column: 3;
  -ms-grid-column-span: 1;
  grid-area: 1 / 3 / 1 / 4;
  border: 2px blue solid;
}

.left_column > h1 {
  font-family: "Asap";
  color: #f9faf4;
  font-size: 13rem;
  font-style: normal;
  font-weight: normal;
  line-height: 15.75rem;
  text-transform: uppercase;
  /* Updated the following 3 lines */
  white-space: nowrap;
  writing-mode: tb-rl;
  -webkit-transform: rotate(-180deg);
          transform: rotate(-180deg);
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  border: red 3px solid;
  -o-object-fit: contain;
     object-fit: contain;
  max-width: 100%;
  max-height: 100%;
}

.main_bio {
  color: #f2c4ce;
  font-size: 1.75rem;
  text-decoration: underline;
}
    <main>
        <div class="main_grid">
            <div class="nav_section">
                <nav class="main_nav">
                    <a href="#">home</a>
                    <a href="#">work</a>
                    <a href="#">contact</a>
                </nav>
            </div>
            <div class="left_column">
                <h1 class="main_title">Hello,<br/>I'm Jack</h1>
            </div>
           <div class="right_colomn">
              <p class="main_bio">A 20 YEAR OLD FROM A SMALL TOWN NEAR AMSTERDAM. CURRENTLY STUDYING COMPUTER SCIENCE IN LEIDEN.</p>
           </div>
        </div>
    </main>
-----------------------
@import url("https://fonts.googleapis.com/css2?family=Asap:wght@400;700&display=swap");
* {
  padding: 0;
  margin: 0;
  height: 100%;
  width: 100%;
  background-color: #4a6163;
  font-family: "Asap";
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
}

.main_grid {
  display: -ms-grid;
  display: grid;
  -ms-grid-columns: 0.25fr (1fr)[2];
      grid-template-columns: 0.25fr repeat(2, 1fr);
  -ms-grid-rows: 1fr;
      grid-template-rows: 1fr;
  grid-column-gap: 0px;
  grid-row-gap: 0px;
  max-height: 100%;
  max-width: 100%;
}

.nav_section {
  -ms-grid-row: 1;
  -ms-grid-column: 1;
  -ms-grid-column-span: 1;
  grid-area: 1 / 1 / 1 / 2;
  border: 3px yellow solid;
}

.left_column {
  -ms-grid-row: 1;
  -ms-grid-column: 2;
  -ms-grid-column-span: 1;
  grid-area: 1 / 2 / 1 / 3;
  border: 1px yellow solid;
}

.right_colomn {
  -ms-grid-row: 1;
  -ms-grid-column: 3;
  -ms-grid-column-span: 1;
  grid-area: 1 / 3 / 1 / 4;
  border: 2px blue solid;
}

.left_column > h1 {
  font-family: "Asap";
  color: #f9faf4;
  font-size: 13rem;
  font-style: normal;
  font-weight: normal;
  line-height: 15.75rem;
  text-transform: uppercase;
  /* Updated the following 3 lines */
  white-space: nowrap;
  writing-mode: tb-rl;
  -webkit-transform: rotate(-180deg);
          transform: rotate(-180deg);
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  border: red 3px solid;
  -o-object-fit: contain;
     object-fit: contain;
  max-width: 100%;
  max-height: 100%;
}

.main_bio {
  color: #f2c4ce;
  font-size: 1.75rem;
  text-decoration: underline;
}
    <main>
        <div class="main_grid">
            <div class="nav_section">
                <nav class="main_nav">
                    <a href="#">home</a>
                    <a href="#">work</a>
                    <a href="#">contact</a>
                </nav>
            </div>
            <div class="left_column">
                <h1 class="main_title">Hello,<br/>I'm Jack</h1>
            </div>
           <div class="right_colomn">
              <p class="main_bio">A 20 YEAR OLD FROM A SMALL TOWN NEAR AMSTERDAM. CURRENTLY STUDYING COMPUTER SCIENCE IN LEIDEN.</p>
           </div>
        </div>
    </main>

Context Menu not updating in SwiftUI

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struct ContentView: View {
    @State var items: [Item] = [
        Item(user: "Daniel",active: false),
        Item(user: "Jack", active: true),
        Item(user: "John", active: true)
    ]

    var body: some View {
        List {
            ForEach(items) { item in
                // work around SwiftUI bug where
                // context menu doesn't update
                if item.active { listItem(for: item) }
                else { listItem(for: item) }
            }
        }
    }

    private func listItem(for item: Item) -> some View {
        VStack {
            Text("\(item.user)")

            HStack {
                Text("Active ? ")
                Text(item.active ? "YES": "NO")
            }
        }
        .contextMenu {
            Button(item.active ? "Deactivate" : "Activate") {
                if let index = items.firstIndex(where: { $0.id == item.id }) {
                    items[index].active.toggle()
                }
            }
        }
        .id(item.id)
    }
}

Merging two Data frames with fuzzy merge/sqldf

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library(dplyr)
df11 <- mutate(df11, Date_m30 = Date %m-% days(30), Date_p30 = Date %m+% days(30))
df11
# # A tibble: 8 x 9
#   UserID Full.Name   Info  EncounterID Date                Temp  misc   Date_m30            Date_p30           
#    <int> <chr>       <chr>       <int> <dttm>              <chr> <chr>  <dttm>              <dttm>             
# 1      1 John Smith  yes            13 2021-01-02 00:00:00 100   (null) 2020-12-03 00:00:00 2021-02-01 00:00:00
# 2      2 Jack Peters no             14 2021-01-05 00:00:00 103   no     2020-12-06 00:00:00 2021-02-04 00:00:00
# 3      3 Bob Brown   yes            15 2021-01-01 00:00:00 104   (null) 2020-12-02 00:00:00 2021-01-31 00:00:00
# 4      4 Jane Doe    yes            16 2021-01-05 00:00:00 103   (null) 2020-12-06 00:00:00 2021-02-04 00:00:00
# 5      5 Jackie Jane yes            17 2021-05-09 00:00:00 101   (null) 2021-04-09 00:00:00 2021-06-08 00:00:00
# 6      6 Sarah Brown yes            18 2021-05-08 00:00:00 102   (null) 2021-04-08 00:00:00 2021-06-07 00:00:00
# 7      7 Chloe Brown no             19 2021-12-12 00:00:00 103   (null) 2021-11-12 00:00:00 2022-01-11 00:00:00
# 8      1 John Smith  yes            13 2021-12-11 00:00:00 105   (null) 2021-11-11 00:00:00 2022-01-10 00:00:00
sqldf::sqldf("
    select df11.*, df22.DOB, df22.EncounterDate, df22.Type, df22.responses
    from df11
      left join df22 on df11.UserID = df22.UserID
        and df22.EncounterDate between df11.Date_m30 and df11.Date_p30") %>%
  select(-Date_m30, -Date_p30)
#   UserID   Full.Name Info EncounterID                Date Temp   misc     DOB       EncounterDate  Type responses
# 1      1  John Smith  yes          13 2021-01-01 19:00:00  100 (null)  1/1/90 2020-12-31 19:00:00 Intro    (null)
# 2      2 Jack Peters   no          14 2021-01-04 19:00:00  103     no 1/10/90 2021-01-01 19:00:00 Intro        no
# 3      3   Bob Brown  yes          15 2020-12-31 19:00:00  104 (null)  1/2/90 2020-12-31 19:00:00 Intro       yes
# 4      4    Jane Doe  yes          16 2021-01-04 19:00:00  103 (null) 2/20/80 2021-01-05 19:00:00 Intro        no
# 5      5 Jackie Jane  yes          17 2021-05-08 20:00:00  101 (null)  2/2/80 2021-05-06 20:00:00  Care        no
# 6      6 Sarah Brown  yes          18 2021-05-07 20:00:00  102 (null) 12/2/80 2021-05-07 20:00:00   Out     unsat
# 7      7 Chloe Brown   no          19 2021-12-11 19:00:00  103 (null)    <NA>                <NA>  <NA>      <NA>
# 8      1  John Smith  yes          13 2021-12-10 19:00:00  105 (null)    <NA>                <NA>  <NA>      <NA>
-----------------------
library(dplyr)
df11 <- mutate(df11, Date_m30 = Date %m-% days(30), Date_p30 = Date %m+% days(30))
df11
# # A tibble: 8 x 9
#   UserID Full.Name   Info  EncounterID Date                Temp  misc   Date_m30            Date_p30           
#    <int> <chr>       <chr>       <int> <dttm>              <chr> <chr>  <dttm>              <dttm>             
# 1      1 John Smith  yes            13 2021-01-02 00:00:00 100   (null) 2020-12-03 00:00:00 2021-02-01 00:00:00
# 2      2 Jack Peters no             14 2021-01-05 00:00:00 103   no     2020-12-06 00:00:00 2021-02-04 00:00:00
# 3      3 Bob Brown   yes            15 2021-01-01 00:00:00 104   (null) 2020-12-02 00:00:00 2021-01-31 00:00:00
# 4      4 Jane Doe    yes            16 2021-01-05 00:00:00 103   (null) 2020-12-06 00:00:00 2021-02-04 00:00:00
# 5      5 Jackie Jane yes            17 2021-05-09 00:00:00 101   (null) 2021-04-09 00:00:00 2021-06-08 00:00:00
# 6      6 Sarah Brown yes            18 2021-05-08 00:00:00 102   (null) 2021-04-08 00:00:00 2021-06-07 00:00:00
# 7      7 Chloe Brown no             19 2021-12-12 00:00:00 103   (null) 2021-11-12 00:00:00 2022-01-11 00:00:00
# 8      1 John Smith  yes            13 2021-12-11 00:00:00 105   (null) 2021-11-11 00:00:00 2022-01-10 00:00:00
sqldf::sqldf("
    select df11.*, df22.DOB, df22.EncounterDate, df22.Type, df22.responses
    from df11
      left join df22 on df11.UserID = df22.UserID
        and df22.EncounterDate between df11.Date_m30 and df11.Date_p30") %>%
  select(-Date_m30, -Date_p30)
#   UserID   Full.Name Info EncounterID                Date Temp   misc     DOB       EncounterDate  Type responses
# 1      1  John Smith  yes          13 2021-01-01 19:00:00  100 (null)  1/1/90 2020-12-31 19:00:00 Intro    (null)
# 2      2 Jack Peters   no          14 2021-01-04 19:00:00  103     no 1/10/90 2021-01-01 19:00:00 Intro        no
# 3      3   Bob Brown  yes          15 2020-12-31 19:00:00  104 (null)  1/2/90 2020-12-31 19:00:00 Intro       yes
# 4      4    Jane Doe  yes          16 2021-01-04 19:00:00  103 (null) 2/20/80 2021-01-05 19:00:00 Intro        no
# 5      5 Jackie Jane  yes          17 2021-05-08 20:00:00  101 (null)  2/2/80 2021-05-06 20:00:00  Care        no
# 6      6 Sarah Brown  yes          18 2021-05-07 20:00:00  102 (null) 12/2/80 2021-05-07 20:00:00   Out     unsat
# 7      7 Chloe Brown   no          19 2021-12-11 19:00:00  103 (null)    <NA>                <NA>  <NA>      <NA>
# 8      1  John Smith  yes          13 2021-12-10 19:00:00  105 (null)    <NA>                <NA>  <NA>      <NA>

Assigning value to pandas dataframe values for unique values in another column

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df['corrected_marks'] = df['marks'].where(df['marks']>=40,
                                          (df.groupby('students')
                                             ['marks']
                                             .transform('mean'))
                                          )
   marks students  corrected_marks
0     40     Jack             40.0
1     60     Jack             60.0
2     90     Jack             90.0
3     20     Jack             52.5
4    100     John            100.0
5     10     John             52.5
6     30     John             52.5
7     70     John             70.0
-----------------------
df['corrected_marks'] = df['marks'].where(df['marks']>=40,
                                          (df.groupby('students')
                                             ['marks']
                                             .transform('mean'))
                                          )
   marks students  corrected_marks
0     40     Jack             40.0
1     60     Jack             60.0
2     90     Jack             90.0
3     20     Jack             52.5
4    100     John            100.0
5     10     John             52.5
6     30     John             52.5
7     70     John             70.0
-----------------------
df['mean'] = df.groupby('students')['marks'].transform('mean')

df['final_marks'] = df.apply(lambda x: x['mean'] if (x['marks'] < 40) else x['marks'], axis=1)

print(df)
   marks students  mean  final_marks
0     40     Jack  52.5         40.0
1     60     Jack  52.5         60.0
2     90     Jack  52.5         90.0
3     20     Jack  52.5         52.5
4    100     John  52.5        100.0
5     10     John  52.5         52.5
6     30     John  52.5         52.5
7     70     John  52.5         70.0
-----------------------
df['mean'] = df.groupby('students')['marks'].transform('mean')

df['final_marks'] = df.apply(lambda x: x['mean'] if (x['marks'] < 40) else x['marks'], axis=1)

print(df)
   marks students  mean  final_marks
0     40     Jack  52.5         40.0
1     60     Jack  52.5         60.0
2     90     Jack  52.5         90.0
3     20     Jack  52.5         52.5
4    100     John  52.5        100.0
5     10     John  52.5         52.5
6     30     John  52.5         52.5
7     70     John  52.5         70.0
-----------------------
df['marks'] = np.where(df['marks'] <40, 
                       df.groupby('students')['marks'].transform('mean'), 
                       df['marks'])
    
df
Out[18]: 
   marks students
0   40.0     Jack
1   60.0     Jack
2   90.0     Jack
3   52.5     Jack
4  100.0     John
5   52.5     John
6   52.5     John
7   70.0     John
-----------------------
df.loc[df["marks"] < 40, "marks"] = df.groupby('students')['marks'].transform('mean')
print(df)

   marks students
0   40.0     Jack
1   60.0     Jack
2   90.0     Jack
3   52.5     Jack
4  100.0     John
5   52.5     John
6   52.5     John
7   70.0     John
df.loc[df["marks"] < 40, "marks"] = df.groupby('students')['marks'].transform('min')
print(df)

   marks students
0     40     Jack
1     60     Jack
2     90     Jack
3     20     Jack
4    100     John
5     10     John
6     10     John
7     70     John
-----------------------
df.loc[df["marks"] < 40, "marks"] = df.groupby('students')['marks'].transform('mean')
print(df)

   marks students
0   40.0     Jack
1   60.0     Jack
2   90.0     Jack
3   52.5     Jack
4  100.0     John
5   52.5     John
6   52.5     John
7   70.0     John
df.loc[df["marks"] < 40, "marks"] = df.groupby('students')['marks'].transform('min')
print(df)

   marks students
0     40     Jack
1     60     Jack
2     90     Jack
3     20     Jack
4    100     John
5     10     John
6     10     John
7     70     John
-----------------------
df.loc[df["marks"] < 40, "marks"] = df.groupby('students')['marks'].transform('mean')
print(df)

   marks students
0   40.0     Jack
1   60.0     Jack
2   90.0     Jack
3   52.5     Jack
4  100.0     John
5   52.5     John
6   52.5     John
7   70.0     John
df.loc[df["marks"] < 40, "marks"] = df.groupby('students')['marks'].transform('min')
print(df)

   marks students
0     40     Jack
1     60     Jack
2     90     Jack
3     20     Jack
4    100     John
5     10     John
6     10     John
7     70     John
-----------------------
df.loc[df["marks"] < 40, "marks"] = df.groupby('students')['marks'].transform('mean')
print(df)

   marks students
0   40.0     Jack
1   60.0     Jack
2   90.0     Jack
3   52.5     Jack
4  100.0     John
5   52.5     John
6   52.5     John
7   70.0     John
df.loc[df["marks"] < 40, "marks"] = df.groupby('students')['marks'].transform('min')
print(df)

   marks students
0     40     Jack
1     60     Jack
2     90     Jack
3     20     Jack
4    100     John
5     10     John
6     10     John
7     70     John

Is it possible to assign in lua a separately defined function to an object as the object's method with access to 'self'?

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SomeObject = {name = "Jack"}

function someExternalFunction(self)
    print(self.name)        
end
SomeObject.someMethodName = someExternalFunction
SomeObject:someMethodName() --prints "Jack"

Community Discussions

Trending Discussions on jack
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  • Count the values of a column based on the values of another column in R
  • Stopping CSS Grid column from overflowing
  • Context Menu not updating in SwiftUI
  • Merging two Data frames with fuzzy merge/sqldf
  • Assigning value to pandas dataframe values for unique values in another column
Trending Discussions on jack

QUESTION

How to get &quot;tag name&quot;, &quot;attribute name&quot;, &quot;attribute value&quot; in regex?

Asked 2022-Feb-10 at 12:28

I'm trying to write a regular expression that needs to return every tag name, attribute name, and attribute value

Here is the code example

Hello, My name is Jack, This is my selfie:
[img id='4' src="imageurl"]
Below is the quick-link to my profile
[profile username='jackisbest']

I need to get any text enclosed in [ ]

Also I need javascript regex to parse them and match them this way

> Match 1: img

> Match 2: id='4'
  Group 1: id
  Group 2: 4

> Match 3: src="imageurl"
  Group 1: src
  Group 2: imageurl

This is the regex I am using, but it can only match attributes and value

(\S+)=["']?((?:.(?!["']?\s+(?:\S+)=|[>"']))+.)["']

Thanks!!

ANSWER

Answered 2022-Feb-10 at 06:05

I modified your regex to catch the tag name and used named capture groups.

/\[?(?<tagName>\w+)\s?(?<attName>\S+)=['"]?(?<attValue>(?:.(?!["']?\s+(?:\S+)=|[>"']))+.)["']/gm

This might work for you.

Test here: https://regex101.com/r/kT7pG4/1

Source https://stackoverflow.com/questions/71059594

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

Vulnerabilities

No vulnerabilities reported

Install jack

You can find releases on The Central Repository and find snapshots on Sonatype OSSRH (OSS Repository Hosting). To get snapshots, add the OSSRH snapshot repository. See the guide to using multiple repositories.

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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