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community | Mendicant University 's community website | Collaboration library

 by   mendicant-original Ruby Version: Current License: No License

 by   mendicant-original Ruby Version: Current License: No License

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kandi X-RAY | community Summary

community is a Ruby library typically used in Web Site, Collaboration applications. community has no vulnerabilities and it has low support. However community has 3 bugs. You can download it from GitHub.
A community based site for Mendicant University. For more information about the site, check out its about page.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • community has a low active ecosystem.
  • It has 20 star(s) with 11 fork(s). There are 4 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 22 open issues and 36 have been closed. On average issues are closed in 12 days. There are 2 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of community is current.
community Support
Best in #Collaboration
Average in #Collaboration
community Support
Best in #Collaboration
Average in #Collaboration

quality kandi Quality

  • community has 3 bugs (0 blocker, 0 critical, 3 major, 0 minor) and 12 code smells.
community Quality
Best in #Collaboration
Average in #Collaboration
community Quality
Best in #Collaboration
Average in #Collaboration

securitySecurity

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

license License

  • community does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
community License
Best in #Collaboration
Average in #Collaboration
community License
Best in #Collaboration
Average in #Collaboration

buildReuse

  • community releases are not available. You will need to build from source code and install.
  • Installation instructions, examples and code snippets are available.
  • It has 6554 lines of code, 159 functions and 91 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
community Reuse
Best in #Collaboration
Average in #Collaboration
community Reuse
Best in #Collaboration
Average in #Collaboration
Top functions reviewed by kandi - BETA

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

  • Count the number of objects for the user .
  • Reads all items from the database
  • Parse the given content

community Key Features

Mendicant University's community website

Setting Up a Development Copy: Step by Step

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gem install bundler
bundle install
bundle exec rake setup

How to override a nested npm sub-dependency with a different package altogether (not just different package version number)?

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"resolutions": {
    "ansi-html": "https://registry.npmjs.org/ansi-html-community/-/ansi-html-community-0.0.8.tgz"
}
npm view ansi-html-community dist.tarball --registry=https://registry.npmjs.org/
  "scripts": {
    "preinstall": "npx npm-force-resolutions"
  }
-----------------------
"resolutions": {
    "ansi-html": "https://registry.npmjs.org/ansi-html-community/-/ansi-html-community-0.0.8.tgz"
}
npm view ansi-html-community dist.tarball --registry=https://registry.npmjs.org/
  "scripts": {
    "preinstall": "npx npm-force-resolutions"
  }
-----------------------
"resolutions": {
    "ansi-html": "https://registry.npmjs.org/ansi-html-community/-/ansi-html-community-0.0.8.tgz"
}
npm view ansi-html-community dist.tarball --registry=https://registry.npmjs.org/
  "scripts": {
    "preinstall": "npx npm-force-resolutions"
  }
-----------------------
 "resolutions": {
    "ansi-html": "https://registry.npmjs.org/ansi-html-community/-/ansi-html-community-0.0.8.tgz"
  }

How to solve Invalid credentials problem in bitbucket?

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cd Library/Application\ Support/SourceTree
-----------------------
git remote set-url origin https://<Bitbucket_Username>:<App_Password>@bitbucket.org/<Bitbucket_Username>/<Repo_Name>.git
git clone https://<Bitbucket_Username>:<App_Password>@bitbucket.org/<Bitbucket_Username>/<Repo_Name>.git
-----------------------
git remote set-url origin https://<Bitbucket_Username>:<App_Password>@bitbucket.org/<Bitbucket_Username>/<Repo_Name>.git
git clone https://<Bitbucket_Username>:<App_Password>@bitbucket.org/<Bitbucket_Username>/<Repo_Name>.git
-----------------------
git remote set-url origin https://<Bitbucket_Username>:<App_Password>@bitbucket.org/<Repo_Name>.git
git clone https://<Bitbucket_Username>:<App_Password>@bitbucket.org/<Repo_Name>.git
-----------------------
git remote set-url origin https://<Bitbucket_Username>:<App_Password>@bitbucket.org/<Repo_Name>.git
git clone https://<Bitbucket_Username>:<App_Password>@bitbucket.org/<Repo_Name>.git

Getting Error on installing Truffle on windows 10 using npm install truffle -g

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npm install -g truffle@5.4.29

PS C:\Windows\system32> npm list -g
C:\Users\*****\AppData\Roaming\npm
+-- ganache-cli@6.12.2
+-- node-gyp@8.2.0
+-- npm@8.0.0
+-- **truffle@5.4.29**
`-- yarn@1.22.17


PS C:\Windows\system32> truffle
Truffle v5.4.29 - a development framework for Ethereum

Usage: truffle <command> [options]

Commands:
  build     Execute build pipeline (if configuration present)
  compile   Compile contract source files
  config    Set user-level configuration options
  console   Run a console with contract abstractions and commands available
  create    Helper to create new contracts, migrations and tests
  db        Database interface commands
  debug     Interactively debug any transaction on the blockchain
  deploy    (alias for migrate)
  develop   Open a console with a local development blockchain
  exec      Execute a JS module within this Truffle environment
  help      List all commands or provide information about a specific command
  init      Initialize new and empty Ethereum project
  install   Install a package from the Ethereum Package Registry
  migrate   Run migrations to deploy contracts
  networks  Show addresses for deployed contracts on each network
  obtain    Fetch and cache a specified compiler
  opcode    Print the compiled opcodes for a given contract
  preserve  Save data to decentralized storage platforms like IPFS and Filecoin
  publish   Publish a package to the Ethereum Package Registry
  run       Run a third-party command
  test      Run JavaScript and Solidity tests
  unbox     Download a Truffle Box, a pre-built Truffle project
  version   Show version number and exit
  watch     Watch filesystem for changes and rebuild the project automatically

See more at http://trufflesuite.com/docs

Apollo Client &quot;Named export 'remove' not found&quot;

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  // nuxt.config.js
  // ...
  build: {
    postcss: {
      postcssOptions: require('./postcss.config.js')
    },
    transpile: [
      '@apollo/client',
      'ts-invariant/process',
    ],
  },
  // ...

Error when trying to run my React Native app on Android

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allprojects {
    repositories {
        mavenCentral()
        mavenLocal()
        jcenter()
}
-----------------------
allprojects {
    repositories {
        .... # Keep the rest
        jcenter() {
            content {
                includeModule("com.yqritc", "android-scalablevideoview")
            }
        }
    }
}
-----------------------
allprojects {
    repositories {
        .... # Keep the rest
        jcenter() {
            content {
                includeModule("com.yqritc", "android-scalablevideoview")
            }
        }
    }
}

How to get Code Coverage from Unit Tests in Visual Studio 2022 Community Edition?

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# PURPOSE: Automates the running of Unit Tests and Code Coverage
# REF: https://stackoverflow.com/a/70321555/495455

# If running outside the test folder
#cd E:\Dev\XYZ\src\XYZTestProject

# This only needs to be installed once (globally), if installed it fails silently: 
dotnet tool install -g dotnet-reportgenerator-globaltool

# Save currect directory into a variable
$dir = pwd

# Delete previous test run results (there's a bunch of subfolders named with guids)
Remove-Item -Recurse -Force $dir/TestResults/

# Run the Coverlet.Collector (this is an NuGet included with XUnit Test Projects)
$output = [string] (& dotnet test --collect:"XPlat Code Coverage" 2>&1)
Write-Host "Last Exit Code: $lastexitcode"
Write-Host $output

# Extract the GUID from the Output eg, 
#"Attachments:   E:\Dev\XYZ\src\XYZTestProject\TestResults\0f26f16d-bbe8-463b-856b-6d4fbee673bd\coverage.cobertura.xml Passed!"  

$i = $output.LastIndexOf("TestResults") + 11
$j = $output.LastIndexOf("coverage")
$cmdGuid = $output.SubString($i,$j - $i - 1)
Write-Host $cmdGuid 

# Delete previous test run reports - note if you're getting wrong results do a Solution Clean and Rebuild to remove stale DLLs in the bin folder
Remove-Item -Recurse -Force $dir/coveragereport/

# To keep a history of the Code Coverage we need to use the argument:
# -historydir:SOME_DIRECTORY 
if (!(Test-Path -path $dir/CoverageHistory)) {  
 New-Item -ItemType directory -Path $dir/CoverageHistory
}

# Generate the Code Coverage HTML Report
reportgenerator -reports:"$dir/TestResults/$cmdGuid/coverage.cobertura.xml" -targetdir:"coveragereport" -reporttypes:Html -historydir:$dir/CoverageHistory 

# Open the Code Coverage HTML Report (if running on a WorkStation)
$osInfo = Get-CimInstance -ClassName Win32_OperatingSystem
if ($osInfo.ProductType -eq 1) {
(& "$dir/coveragereport/index.html")
}

Fast method of getting all the descendants of a parent

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library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)
   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4
set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)
Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5
g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))
-----------------------
library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)
   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4
set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)
Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5
g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))
-----------------------
library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)
   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4
set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)
Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5
g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))
-----------------------
library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)
   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4
set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)
Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5
g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))
-----------------------
library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)
   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4
set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)
Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5
g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))
-----------------------
all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)
edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)
while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}
> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}
## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2
-----------------------
all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)
edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)
while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}
> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}
## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2
-----------------------
all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)
edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)
while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}
> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}
## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2
-----------------------
all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)
edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)
while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}
> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}
## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2
-----------------------
all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)
edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)
while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}
> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}
## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2
-----------------------
all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)
edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)
while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}
> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}
## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2
-----------------------
all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)
edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)
while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}
> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE
visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}
## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2

expo-barcode-scanner only works once with react-native version 0.64.2 and expo 43.0.0

copy iconCopydownload iconDownload
import React, { useState, useEffect } from 'react';
import { StyleSheet, Text, View, TouchableOpacity } from 'react-native';
import { Camera } from 'expo-camera';

const QrcodeReader = ({navigation}) =>  {

  const [hasPermission, setHasPermission] = useState(null);
  const [type, setType] = useState(Camera.Constants.Type.back);

  useEffect(() => {
    (async () => {
      const { status } = await Camera.requestCameraPermissionsAsync();
      setHasPermission(status === 'granted');
    })();
  }, []);

  if (hasPermission === null) {
    return <View />;
  }
  if (hasPermission === false) {
    return <Text>No access to camera</Text>;
  }
  return(
  <View style={styles.container}>
      <Camera
        onBarCodeScanned={(...args) => {
          const data = args[0].data;
          result = JSON.stringify(result);
          console.log(result);
          navigation.navigate('your_next_screen',{result});
          );
        }}
        barCodeScannerSettings={{
          barCodeTypes: ['qr'],
        }}
        style={{ flex: 1 }}
      />
    </View>
  );
}
  
const styles = StyleSheet.create({
  container: {
    flex: 1,
  },
  camera: {
    flex: 1,
  },
  buttonContainer: {
    flex: 1,
    backgroundColor: 'transparent',
    flexDirection: 'row',
    margin: 20,
  },
  button: {
    flex: 0.1,
    alignSelf: 'flex-end',
    alignItems: 'center',
  },
  text: {
    fontSize: 18,
    color: 'white',
  },
});

export default QrcodeReader;

Git for Windows: SSL certificate problem: certificate has expired

copy iconCopydownload iconDownload
sudo rm /usr/share/ca-certificates/mozilla/DST_Root_CA_X3.crt
sudo update-ca-certificates
Certificate:
    Data:
        Version: 3 (0x2)
        Serial Number:
            44:af:b0:80:d6:a3:27:ba:89:30:39:86:2e:f8:40:6b
    Signature Algorithm: sha1WithRSAEncryption
        Issuer: O=Digital Signature Trust Co., CN=DST Root CA X3
        Validity
            Not Before: Sep 30 21:12:19 2000 GMT
            Not After : Sep 30 14:01:15 2021 GMT
        Subject: O=Digital Signature Trust Co., CN=DST Root CA X3
        Subject Public Key Info:
            Public Key Algorithm: rsaEncryption
                Public-Key: (2048 bit)
                Modulus:
                    00:df:af:e9:97:50:08:83:57:b4:cc:62:65:f6:90:
                    82:ec:c7:d3:2c:6b:30:ca:5b:ec:d9:c3:7d:c7:40:
                    c1:18:14:8b:e0:e8:33:76:49:2a:e3:3f:21:49:93:
                    ac:4e:0e:af:3e:48:cb:65:ee:fc:d3:21:0f:65:d2:
                    2a:d9:32:8f:8c:e5:f7:77:b0:12:7b:b5:95:c0:89:
                    a3:a9:ba:ed:73:2e:7a:0c:06:32:83:a2:7e:8a:14:
                    30:cd:11:a0:e1:2a:38:b9:79:0a:31:fd:50:bd:80:
                    65:df:b7:51:63:83:c8:e2:88:61:ea:4b:61:81:ec:
                    52:6b:b9:a2:e2:4b:1a:28:9f:48:a3:9e:0c:da:09:
                    8e:3e:17:2e:1e:dd:20:df:5b:c6:2a:8a:ab:2e:bd:
                    70:ad:c5:0b:1a:25:90:74:72:c5:7b:6a:ab:34:d6:
                    30:89:ff:e5:68:13:7b:54:0b:c8:d6:ae:ec:5a:9c:
                    92:1e:3d:64:b3:8c:c6:df:bf:c9:41:70:ec:16:72:
                    d5:26:ec:38:55:39:43:d0:fc:fd:18:5c:40:f1:97:
                    eb:d5:9a:9b:8d:1d:ba:da:25:b9:c6:d8:df:c1:15:
                    02:3a:ab:da:6e:f1:3e:2e:f5:5c:08:9c:3c:d6:83:
                    69:e4:10:9b:19:2a:b6:29:57:e3:e5:3d:9b:9f:f0:
                    02:5d
                Exponent: 65537 (0x10001)
        X509v3 extensions:
            X509v3 Basic Constraints: critical
                CA:TRUE
            X509v3 Key Usage: critical
                Certificate Sign, CRL Sign
            X509v3 Subject Key Identifier:
                C4:A7:B1:A4:7B:2C:71:FA:DB:E1:4B:90:75:FF:C4:15:60:85:89:10
    Signature Algorithm: sha1WithRSAEncryption
         a3:1a:2c:9b:17:00:5c:a9:1e:ee:28:66:37:3a:bf:83:c7:3f:
         4b:c3:09:a0:95:20:5d:e3:d9:59:44:d2:3e:0d:3e:bd:8a:4b:
         a0:74:1f:ce:10:82:9c:74:1a:1d:7e:98:1a:dd:cb:13:4b:b3:
         20:44:e4:91:e9:cc:fc:7d:a5:db:6a:e5:fe:e6:fd:e0:4e:dd:
         b7:00:3a:b5:70:49:af:f2:e5:eb:02:f1:d1:02:8b:19:cb:94:
         3a:5e:48:c4:18:1e:58:19:5f:1e:02:5a:f0:0c:f1:b1:ad:a9:
         dc:59:86:8b:6e:e9:91:f5:86:ca:fa:b9:66:33:aa:59:5b:ce:
         e2:a7:16:73:47:cb:2b:cc:99:b0:37:48:cf:e3:56:4b:f5:cf:
         0f:0c:72:32:87:c6:f0:44:bb:53:72:6d:43:f5:26:48:9a:52:
         67:b7:58:ab:fe:67:76:71:78:db:0d:a2:56:14:13:39:24:31:
         85:a2:a8:02:5a:30:47:e1:dd:50:07:bc:02:09:90:00:eb:64:
         63:60:9b:16:bc:88:c9:12:e6:d2:7d:91:8b:f9:3d:32:8d:65:
         b4:e9:7c:b1:57:76:ea:c5:b6:28:39:bf:15:65:1c:c8:f6:77:
         96:6a:0a:8d:77:0b:d8:91:0b:04:8e:07:db:29:b6:0a:ee:9d:
         82:35:35:10
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----
-----------------------
sudo rm /usr/share/ca-certificates/mozilla/DST_Root_CA_X3.crt
sudo update-ca-certificates
Certificate:
    Data:
        Version: 3 (0x2)
        Serial Number:
            44:af:b0:80:d6:a3:27:ba:89:30:39:86:2e:f8:40:6b
    Signature Algorithm: sha1WithRSAEncryption
        Issuer: O=Digital Signature Trust Co., CN=DST Root CA X3
        Validity
            Not Before: Sep 30 21:12:19 2000 GMT
            Not After : Sep 30 14:01:15 2021 GMT
        Subject: O=Digital Signature Trust Co., CN=DST Root CA X3
        Subject Public Key Info:
            Public Key Algorithm: rsaEncryption
                Public-Key: (2048 bit)
                Modulus:
                    00:df:af:e9:97:50:08:83:57:b4:cc:62:65:f6:90:
                    82:ec:c7:d3:2c:6b:30:ca:5b:ec:d9:c3:7d:c7:40:
                    c1:18:14:8b:e0:e8:33:76:49:2a:e3:3f:21:49:93:
                    ac:4e:0e:af:3e:48:cb:65:ee:fc:d3:21:0f:65:d2:
                    2a:d9:32:8f:8c:e5:f7:77:b0:12:7b:b5:95:c0:89:
                    a3:a9:ba:ed:73:2e:7a:0c:06:32:83:a2:7e:8a:14:
                    30:cd:11:a0:e1:2a:38:b9:79:0a:31:fd:50:bd:80:
                    65:df:b7:51:63:83:c8:e2:88:61:ea:4b:61:81:ec:
                    52:6b:b9:a2:e2:4b:1a:28:9f:48:a3:9e:0c:da:09:
                    8e:3e:17:2e:1e:dd:20:df:5b:c6:2a:8a:ab:2e:bd:
                    70:ad:c5:0b:1a:25:90:74:72:c5:7b:6a:ab:34:d6:
                    30:89:ff:e5:68:13:7b:54:0b:c8:d6:ae:ec:5a:9c:
                    92:1e:3d:64:b3:8c:c6:df:bf:c9:41:70:ec:16:72:
                    d5:26:ec:38:55:39:43:d0:fc:fd:18:5c:40:f1:97:
                    eb:d5:9a:9b:8d:1d:ba:da:25:b9:c6:d8:df:c1:15:
                    02:3a:ab:da:6e:f1:3e:2e:f5:5c:08:9c:3c:d6:83:
                    69:e4:10:9b:19:2a:b6:29:57:e3:e5:3d:9b:9f:f0:
                    02:5d
                Exponent: 65537 (0x10001)
        X509v3 extensions:
            X509v3 Basic Constraints: critical
                CA:TRUE
            X509v3 Key Usage: critical
                Certificate Sign, CRL Sign
            X509v3 Subject Key Identifier:
                C4:A7:B1:A4:7B:2C:71:FA:DB:E1:4B:90:75:FF:C4:15:60:85:89:10
    Signature Algorithm: sha1WithRSAEncryption
         a3:1a:2c:9b:17:00:5c:a9:1e:ee:28:66:37:3a:bf:83:c7:3f:
         4b:c3:09:a0:95:20:5d:e3:d9:59:44:d2:3e:0d:3e:bd:8a:4b:
         a0:74:1f:ce:10:82:9c:74:1a:1d:7e:98:1a:dd:cb:13:4b:b3:
         20:44:e4:91:e9:cc:fc:7d:a5:db:6a:e5:fe:e6:fd:e0:4e:dd:
         b7:00:3a:b5:70:49:af:f2:e5:eb:02:f1:d1:02:8b:19:cb:94:
         3a:5e:48:c4:18:1e:58:19:5f:1e:02:5a:f0:0c:f1:b1:ad:a9:
         dc:59:86:8b:6e:e9:91:f5:86:ca:fa:b9:66:33:aa:59:5b:ce:
         e2:a7:16:73:47:cb:2b:cc:99:b0:37:48:cf:e3:56:4b:f5:cf:
         0f:0c:72:32:87:c6:f0:44:bb:53:72:6d:43:f5:26:48:9a:52:
         67:b7:58:ab:fe:67:76:71:78:db:0d:a2:56:14:13:39:24:31:
         85:a2:a8:02:5a:30:47:e1:dd:50:07:bc:02:09:90:00:eb:64:
         63:60:9b:16:bc:88:c9:12:e6:d2:7d:91:8b:f9:3d:32:8d:65:
         b4:e9:7c:b1:57:76:ea:c5:b6:28:39:bf:15:65:1c:c8:f6:77:
         96:6a:0a:8d:77:0b:d8:91:0b:04:8e:07:db:29:b6:0a:ee:9d:
         82:35:35:10
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----
-----------------------
curl https://curl.se/ca/cacert.pem
!mozilla/DST_Root_CA_X3.crt
sudo sed -i -e 's/mozilla\/DST_Root_CA_X3\.crt/!mozilla\/DST_Root_CA_X3\.crt/g' /etc/ca-certificates.conf
sudo update-ca-certificates
-----------------------
curl https://curl.se/ca/cacert.pem
!mozilla/DST_Root_CA_X3.crt
sudo sed -i -e 's/mozilla\/DST_Root_CA_X3\.crt/!mozilla\/DST_Root_CA_X3\.crt/g' /etc/ca-certificates.conf
sudo update-ca-certificates
-----------------------
curl https://curl.se/ca/cacert.pem
!mozilla/DST_Root_CA_X3.crt
sudo sed -i -e 's/mozilla\/DST_Root_CA_X3\.crt/!mozilla\/DST_Root_CA_X3\.crt/g' /etc/ca-certificates.conf
sudo update-ca-certificates
-----------------------
curl https://curl.se/ca/cacert.pem
!mozilla/DST_Root_CA_X3.crt
sudo sed -i -e 's/mozilla\/DST_Root_CA_X3\.crt/!mozilla\/DST_Root_CA_X3\.crt/g' /etc/ca-certificates.conf
sudo update-ca-certificates
-----------------------
sudo wget http://launchpadlibrarian.net/482351465/ca-certificates_20190110~12.04.1_all.deb
sudo dpkg -i ca-certificates_20190110~12.04.1_all.deb
sudo vi /etc/ca-certificates.conf
sudo update-ca-certificates
-----------------------
sudo wget http://launchpadlibrarian.net/482351465/ca-certificates_20190110~12.04.1_all.deb
sudo dpkg -i ca-certificates_20190110~12.04.1_all.deb
sudo vi /etc/ca-certificates.conf
sudo update-ca-certificates
-----------------------
sudo wget http://launchpadlibrarian.net/482351465/ca-certificates_20190110~12.04.1_all.deb
sudo dpkg -i ca-certificates_20190110~12.04.1_all.deb
sudo vi /etc/ca-certificates.conf
sudo update-ca-certificates
-----------------------
sudo certbot --version
sudo apt-get remove -y certbot python3-certbot-apache
sudo snap install certbot --classic
sudo ln -s /snap/bin/certbot /usr/bin/certbot
sudo certbot renew --force-renewal --preferred-chain "ISRG Root X1"
-----------------------
sudo certbot --version
sudo apt-get remove -y certbot python3-certbot-apache
sudo snap install certbot --classic
sudo ln -s /snap/bin/certbot /usr/bin/certbot
sudo certbot renew --force-renewal --preferred-chain "ISRG Root X1"
-----------------------
sudo certbot --version
sudo apt-get remove -y certbot python3-certbot-apache
sudo snap install certbot --classic
sudo ln -s /snap/bin/certbot /usr/bin/certbot
sudo certbot renew --force-renewal --preferred-chain "ISRG Root X1"
-----------------------
git update-git-for-windows
git update
-----------------------
git update-git-for-windows
git update

duplicate symbol 'folly::exception_wrapper::from_exception_ptr(std::exception_ptr&amp;&amp;)' React Native 0.66 with hermes enabled

copy iconCopydownload iconDownload
DEAD_CODE_STRIPPING = NO;

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QUESTION

How to override a nested npm sub-dependency with a different package altogether (not just different package version number)?

Asked 2022-Apr-04 at 01:19
Overview

I am having trouble resolving a ReDoS vulnerability identified by npm audit. My application has a nested sub-dependency ansi-html that is vulnerable to attack, but unfortunately, it seems that the maintainers have gone AWOL. As you can see in the comments section of that Github issue, to get around this problem, the community has made a fork of the repo called ansi-html-community located here, which addresses this vulnerability.

Thus, I would like to replace all nested references of ansi-html with ansi-html-community.

Problem

My normal strategy of using npm-force-resolutions does not seem to be able to override nested sub-dependencies with a different package altogether but rather only the same packages that are a different version number. I have researched this for several hours, but unfortunately, the only way I have found to fix this would appear to be with yarn, which I am now seriously considering using instead of npm. However, this is not ideal as our entire CI/CD pipeline is configured to use npm.

Does anyone know of any other way to accomplish nested sub-dependency package substitution/resolution without having to switch over to using yarn?

Related Questions

These are questions of interest that I was able to find, but unfortunately, they tend to only discuss methods to override package version number, not the package itself.

Discusses how to override version number:

How do I override nested NPM dependency versions?

Has a comment discussion about npm shrinkwrap (not ideal):

npm - how to override a dependent package's dependencies?

Other related StackOverflow questions:

CSE Index of related questions

ANSWER

Answered 2021-Oct-29 at 21:01

I figured it out. As of October 2021, the solution using npm-force-resolutions is actually very similar to how you would specify it using yarn. You just need to provide a link to the tarball where you would normally specify the overriding version number. Your resolutions section of package.json should look like this:

"resolutions": {
    "ansi-html": "https://registry.npmjs.org/ansi-html-community/-/ansi-html-community-0.0.8.tgz"
}

To find the link to the tarball, use the following command, modifying your registry as necessary:

npm view ansi-html-community dist.tarball --registry=https://registry.npmjs.org/

Also, note that for npm-force-resolutions to work when you run npm install, you will need a preinstall entry under the scripts section of package.json:

  "scripts": {
    "preinstall": "npx npm-force-resolutions"
  }

Source https://stackoverflow.com/questions/69548370

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

Vulnerabilities

No vulnerabilities reported

Install community

Community is a Ruby on Rails 3.1 application which runs on Ruby 1.9.2+ and PostgreSQL databases. Other databases like MySQL or SQLite are not officially supported.
To install a development version of Community, follow these steps:. To make things even easier, you can copy and paste this into your terminal once you've got the project cloned to your computer.
Fork our GitHub repository: http://github.com/mendicant-original/community
Clone the fork to your computer
If you don't already have bundler installed, get it by running gem install bundler
Run bundle install to install all of the project's dependencies
Finally, run rake setup to create the required config files, create the database, and seed it with data

Support

Features and bugs are tracked through Github Issues. Contributors retain copyright to their work but must agree to release their contributions under the Affero GPL version 3. If you would like to help with developing Community, just file a ticket in our issue tracker and we will find something to keep you busy.

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