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daff | align and compare tables

 by   paulfitz Java Version: v1.3.39 License: MIT

 by   paulfitz Java Version: v1.3.39 License: MIT

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kandi X-RAY | daff Summary

daff is a Java library typically used in Utilities applications. daff has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However daff has 27 bugs. You can install using 'npm i daff' or download it from GitHub, npm.
This is a library for comparing tables, producing a summary of their differences, and using such a summary as a patch file. It is optimized for comparing tables that share a common origin, in other words multiple versions of the "same" table. For a live demo, see: > http://paulfitz.github.com/daff/. Install the library for your favorite language: `sh npm install daff -g # node/javascript pip install daff # python gem install daff # ruby composer require paulfitz/daff-php # php install.packages('daff') # R wrapper by Edwin de Jonge bower install daff # web/javascript `. Other translations are available here: > https://github.com/paulfitz/daff/releases. Or use the library to view csv diffs on github via a chrome extension: > https://github.com/theodi/csvhub. The diff format used by daff is specified here: > http://paulfitz.github.io/daff-doc/spec.html. This library is a stripped down version of the coopy toolbox (see http://share.find.coop). To compare tables from different origins, or with automatically generated IDs, or other complications, check out the coopy toolbox.
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Quality
Security
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kandi-support Support

  • daff has a low active ecosystem.
  • It has 667 star(s) with 53 fork(s). There are 19 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 27 open issues and 68 have been closed. On average issues are closed in 154 days. There are 3 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of daff is v1.3.39
daff Support
Best in #Java
Average in #Java
daff Support
Best in #Java
Average in #Java

quality kandi Quality

  • daff has 27 bugs (0 blocker, 0 critical, 22 major, 5 minor) and 92 code smells.
daff Quality
Best in #Java
Average in #Java
daff Quality
Best in #Java
Average in #Java

securitySecurity

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

license License

  • daff is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
daff License
Best in #Java
Average in #Java
daff License
Best in #Java
Average in #Java

buildReuse

  • daff releases are available to install and integrate.
  • Deployable package is available in npm.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
daff Reuse
Best in #Java
Average in #Java
daff Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • cache field
    • Invoke the getter method on the field .
      • Main entry point .
        • Overrides the default implementation to set a field .
          • Need to edit the data
            • Returns the basic data .

              Get all kandi verified functions for this library.

              Get all kandi verified functions for this library.

              daff Key Features

              align and compare tables

              daff Examples and Code Snippets

              See all related Code Snippets

              The program

              copy iconCopydownload iconDownload
                daff render [--output OUTPUT.html] [--css CSS.css] [--fragment] [--plain] diff.csv
                   --css CSS.css: generate a suitable css file to go with the html
                   --fragment:    generate just a html fragment rather than a page
                   --plain:       do not use fancy utf8 characters to make arrows prettier
                   --unquote:     do not quote html characters in html diffs
                   --www:         send output to a browser
              ````

              The library

              copy iconCopydownload iconDownload
              <script src="daff.js"></script>

              Supported languages

              copy iconCopydownload iconDownload
              make js
              make php
              make py
              make java
              make cs
              make cpp

              Action button working only 1 time with {daff} html output in R {shiny}

              copy iconCopydownload iconDownload
                output$compare_df = renderUI({
                  div(# HTML(
                    diff_data()
                  )
                })
              
                diff_data = reactive({
                  daff::render_diff(
                    daff::diff_data(
                      data_old(),
                      data_new()
                    ), 
                    view = FALSE,
                    fragment = TRUE
                  )
                })
              
                output$compare_df = renderUI({
                  div(# HTML(
                    diff_data()
                  )
                })
              
                diff_data = reactive({
                  daff::render_diff(
                    daff::diff_data(
                      data_old(),
                      data_new()
                    ), 
                    view = FALSE,
                    fragment = TRUE
                  )
                })
              

              make log operations between a dictionary and a list of dictionaries

              copy iconCopydownload iconDownload
              from pprint import pprint
              from math import log
              
              for d in b:
                  if d["key"] in a:
                      d["prob"] = log(d["count"]) - log(a[d["key"]])
                  
              pprint(b)
              
              [{'count': 3615, 'key': 'OCC', 'prob': 6.401087665364811},
               {'count': 408, 'key': 'FFR', 'prob': 1.5114575040738965},
               {'count': 915, 'key': 'DTY', 'prob': -1.232735491566432},
               {'count': 652, 'key': 'UTY', 'prob': -1.0488246947155977},
               {'count': 365, 'key': 'VGY', 'prob': -1.7848865899402933},
               {'count': 589, 'key': 'UTY', 'prob': -1.1504430729906643},
               {'count': 573, 'key': 'DGG', 'prob': 0.45373184907799935},
               {'count': 75, 'key': 'DTY', 'prob': -3.7341714433056428},
               {'count': 148, 'key': 'UTY', 'prob': -2.531656982878136},
               {'count': 116, 'key': 'UTY', 'prob': -2.7752790655358863},
               {'count': 7503, 'key': 'CCG', 'prob': 5.3121403069015045}]
              
              from pprint import pprint
              from math import log
              
              for d in b:
                  if d["key"] in a:
                      d["prob"] = log(d["count"]) - log(a[d["key"]])
                  
              pprint(b)
              
              [{'count': 3615, 'key': 'OCC', 'prob': 6.401087665364811},
               {'count': 408, 'key': 'FFR', 'prob': 1.5114575040738965},
               {'count': 915, 'key': 'DTY', 'prob': -1.232735491566432},
               {'count': 652, 'key': 'UTY', 'prob': -1.0488246947155977},
               {'count': 365, 'key': 'VGY', 'prob': -1.7848865899402933},
               {'count': 589, 'key': 'UTY', 'prob': -1.1504430729906643},
               {'count': 573, 'key': 'DGG', 'prob': 0.45373184907799935},
               {'count': 75, 'key': 'DTY', 'prob': -3.7341714433056428},
               {'count': 148, 'key': 'UTY', 'prob': -2.531656982878136},
               {'count': 116, 'key': 'UTY', 'prob': -2.7752790655358863},
               {'count': 7503, 'key': 'CCG', 'prob': 5.3121403069015045}]
              

              elasticsearch filebeat mapper_parsing_exception when using decode_json_fields

              copy iconCopydownload iconDownload
              "mlog": {
                 "properties": {
                     ...
                     "message": {
                        "type": "keyword",
                        "ignore_above": 1024
                     },
                 }
              }
              
              {"message":{"log_type":"cron","status":"start"}, ...
              
              "mlog": {
                 "properties": {
                     ...
                     "message": {
                        "type": "keyword",
                        "ignore_above": 1024
                     },
                 }
              }
              
              {"message":{"log_type":"cron","status":"start"}, ...
              

              In R Overlap spatial polygons dataframe (spdf) and summarise number of features of 1st spdf overlapped by 2nd spdf

              copy iconCopydownload iconDownload
              richness_per_ib_grid <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
                group_by(id) %>%
                count()
              
              out <- as.data.frame(int.result)[,-3] # print output as data frame.
              
              library (sf)
              library(dplyr)
              library(raster)
              
              # load 2nd spdf
              
              ibra <- st_read("ibra7_subregions.shp")
              ibra <- st_transform(ibra, crs = 4326)
              
              # ibra has >2000 features (i.e., rows) for 89 regions of same name, group them together
              ibraGrid <- ibra %>%
                group_by(REG_NAME_7) %>%
                st_sf() %>%
                summarise()
              
              colnames(ibraGrid)[1] <- "id"
              
              # crop ibra to specific boundary
              box <- extent(112,155,-45,-10)
              ibraGrid <- st_crop(ibraGrid, box)
              
              
              # make dataframe of spatial grid (1st spdf)
              ran.p <- st_sample(au, size = 1040)
              
              au <- st_read("aust_cd66states.shp")
              au <- st_transform(au, crs = 4326)
              
              # create grid around multipoints
              rand_sampl_Grid <- ran.p %>%
                st_make_grid(cellsize = 0.1, square = F) %>%
                st_intersection(au) %>%
                st_cast("MULTIPOLYGON") %>%
                st_sf()
              
              
              # sampled grid per ibra region
              density_per_ib_grid <- <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
                group_by(id) %>%
                count()
              
              out <- as.data.frame(int.result)[,-3] # print output as data frame.
              
              richness_per_ib_grid <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
                group_by(id) %>%
                count()
              
              out <- as.data.frame(int.result)[,-3] # print output as data frame.
              
              library (sf)
              library(dplyr)
              library(raster)
              
              # load 2nd spdf
              
              ibra <- st_read("ibra7_subregions.shp")
              ibra <- st_transform(ibra, crs = 4326)
              
              # ibra has >2000 features (i.e., rows) for 89 regions of same name, group them together
              ibraGrid <- ibra %>%
                group_by(REG_NAME_7) %>%
                st_sf() %>%
                summarise()
              
              colnames(ibraGrid)[1] <- "id"
              
              # crop ibra to specific boundary
              box <- extent(112,155,-45,-10)
              ibraGrid <- st_crop(ibraGrid, box)
              
              
              # make dataframe of spatial grid (1st spdf)
              ran.p <- st_sample(au, size = 1040)
              
              au <- st_read("aust_cd66states.shp")
              au <- st_transform(au, crs = 4326)
              
              # create grid around multipoints
              rand_sampl_Grid <- ran.p %>%
                st_make_grid(cellsize = 0.1, square = F) %>%
                st_intersection(au) %>%
                st_cast("MULTIPOLYGON") %>%
                st_sf()
              
              
              # sampled grid per ibra region
              density_per_ib_grid <- <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
                group_by(id) %>%
                count()
              
              out <- as.data.frame(int.result)[,-3] # print output as data frame.
              
              richness_per_ib_grid <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
                group_by(id) %>%
                count()
              
              out <- as.data.frame(int.result)[,-3] # print output as data frame.
              
              library (sf)
              library(dplyr)
              library(raster)
              
              # load 2nd spdf
              
              ibra <- st_read("ibra7_subregions.shp")
              ibra <- st_transform(ibra, crs = 4326)
              
              # ibra has >2000 features (i.e., rows) for 89 regions of same name, group them together
              ibraGrid <- ibra %>%
                group_by(REG_NAME_7) %>%
                st_sf() %>%
                summarise()
              
              colnames(ibraGrid)[1] <- "id"
              
              # crop ibra to specific boundary
              box <- extent(112,155,-45,-10)
              ibraGrid <- st_crop(ibraGrid, box)
              
              
              # make dataframe of spatial grid (1st spdf)
              ran.p <- st_sample(au, size = 1040)
              
              au <- st_read("aust_cd66states.shp")
              au <- st_transform(au, crs = 4326)
              
              # create grid around multipoints
              rand_sampl_Grid <- ran.p %>%
                st_make_grid(cellsize = 0.1, square = F) %>%
                st_intersection(au) %>%
                st_cast("MULTIPOLYGON") %>%
                st_sf()
              
              
              # sampled grid per ibra region
              density_per_ib_grid <- <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
                group_by(id) %>%
                count()
              
              out <- as.data.frame(int.result)[,-3] # print output as data frame.
              
              richness_per_ib_grid <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
                group_by(id) %>%
                count()
              
              out <- as.data.frame(int.result)[,-3] # print output as data frame.
              
              library (sf)
              library(dplyr)
              library(raster)
              
              # load 2nd spdf
              
              ibra <- st_read("ibra7_subregions.shp")
              ibra <- st_transform(ibra, crs = 4326)
              
              # ibra has >2000 features (i.e., rows) for 89 regions of same name, group them together
              ibraGrid <- ibra %>%
                group_by(REG_NAME_7) %>%
                st_sf() %>%
                summarise()
              
              colnames(ibraGrid)[1] <- "id"
              
              # crop ibra to specific boundary
              box <- extent(112,155,-45,-10)
              ibraGrid <- st_crop(ibraGrid, box)
              
              
              # make dataframe of spatial grid (1st spdf)
              ran.p <- st_sample(au, size = 1040)
              
              au <- st_read("aust_cd66states.shp")
              au <- st_transform(au, crs = 4326)
              
              # create grid around multipoints
              rand_sampl_Grid <- ran.p %>%
                st_make_grid(cellsize = 0.1, square = F) %>%
                st_intersection(au) %>%
                st_cast("MULTIPOLYGON") %>%
                st_sf()
              
              
              # sampled grid per ibra region
              density_per_ib_grid <- <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
                group_by(id) %>%
                count()
              
              out <- as.data.frame(int.result)[,-3] # print output as data frame.
              

              Are there changes between OpenStack Xenial/Pike and Xenial/Queens that would prevent JuJu from creating openvswitch bridges?

              copy iconCopydownload iconDownload
              auto lo
              iface lo inet loopback
              
              auto lo
              iface lo inet loopback
                  dns-nameservers 10.10.30.99 10.244.0.66 10.244.0.67
                  dns-search maas
              
              auto eno1
              iface eno1 inet manual
                  mtu 1500
              
              auto eno2
              iface eno2 inet static
                  address 10.189.134.103/24
                  dns-nameservers 10.189.134.99 10.244.0.66 10.244.0.67
                  mtu 1500
              
              auto br-eno1
              iface br-eno1 inet static
                  address 10.10.30.101/24
                  dns-nameservers 10.10.30.99 10.244.0.66 10.244.0.67
                  gateway 10.10.30.254
                  bridge_ports eno1
              
              

              Parse a line to get specific string in python

              copy iconCopydownload iconDownload
              mystring = "Hello, my name is Sam!"
              print(mystring.split('Hello')[1])
              
              my_url = "application_url: https://hafaf.daff.io".split("application_url: ")[1]
              
              mystring = "Hello, my name is Sam!"
              print(mystring.split('Hello')[1])
              
              my_url = "application_url: https://hafaf.daff.io".split("application_url: ")[1]
              

              See all related Code Snippets

              Community Discussions

              Trending Discussions on daff
              • Action button working only 1 time with {daff} html output in R {shiny}
              • make log operations between a dictionary and a list of dictionaries
              • elasticsearch filebeat mapper_parsing_exception when using decode_json_fields
              • In R Overlap spatial polygons dataframe (spdf) and summarise number of features of 1st spdf overlapped by 2nd spdf
              • Are there changes between OpenStack Xenial/Pike and Xenial/Queens that would prevent JuJu from creating openvswitch bridges?
              • Parse a line to get specific string in python
              Trending Discussions on daff

              QUESTION

              Action button working only 1 time with {daff} html output in R {shiny}

              Asked 2021-Dec-14 at 19:28

              The code below is built in two parts for editing excel file in {shiny} with the excellent {dataEditr} package

              • First action is to write in new excel and click button to save
              • Second part, a modal dialog is shown for validating changes

              But when I validate or not the changes, I can no more open the modal dialog when save button is clicked.

              Plus, when I show only text or table in the modal dialog, all work fine.

              library(shinydashboard)
              library(daff)
              library(dplyr)
              library(DataEditR)
              
              df1 = iris %>% slice(1:10) %>% select(-Species)
              df2 = iris %>% slice(1:12)
              
              # Define UI
              ui = shinyUI(
                fluidPage(
                  fluidRow(
                    box(
                      title = "First data",
                      status = "success",
                      width = 6,
                      solidHeader = TRUE,
                      collapsible = TRUE,
                      dataEditUI(id = "old_data")
                    ),
                    box(
                      title = "New data",
                      status = "primary",
                      width = 6,
                      solidHeader = TRUE,
                      collapsible = TRUE,
                      dataEditUI(id = "new_data"),
                      br(),
                      actionButton("save", "Save!")
                    )
                  )
                )
              )
              
              # Define server
              server = shinyServer(function(input, output, session) {
                
                data_old = dataEditServer(
                  "old_data",
                  data = df1, 
                  col_readonly = names(df1),
                  col_names = FALSE,
                  col_options = TRUE,
                  row_edit = FALSE
                )
                
                data_new = dataEditServer(
                  "new_data",
                  data = df2, 
                  col_names = FALSE,
                  col_options = TRUE,
                  row_edit = FALSE
                ) 
                
                diff_data = reactive({
                  daff::render_diff(
                    daff::diff_data(
                      data_old(),
                      data_new()
                    ), view = FALSE
                  )
                })
                
                output$compare_df = renderUI({
                  HTML(
                    diff_data()
                  )
                })
                
                observeEvent(
                  input$save, {
                    showModal(
                      modalDialog(
                        span(
                          "Check modification before validation!",
                          style = "color: red; font-size: 130%;"
                        ),
                        br(),
                        br(),
                        uiOutput("compare_df"),
                        footer = tagList(
                          modalButton("Cancel"),
                          actionButton("ok", "OK")
                        ),
                        easyClose = TRUE,
                        size = "l"
                      )
                    )
                  }
                )
                
                observeEvent(
                  input$ok, {
                    print(data_new())
                    removeModal()
                  }
                )
                
              })
              
              shinyApp(ui, server)
              

              ANSWER

              Answered 2021-Nov-05 at 16:27

              daff::render_diff by default renders a complete HTML document. This causes JS errors when the modal is first displayed (i.e. the first time "Save!" is clicked). You can see this by changing the renderUI. The modal will show the literal HTML code instead of trying to render it

                output$compare_df = renderUI({
                  div(# HTML(
                    diff_data()
                  )
                })
              

              The fix is to use the fragment argument which will produce only the HTML table, which renderUI will know how to handle

                diff_data = reactive({
                  daff::render_diff(
                    daff::diff_data(
                      data_old(),
                      data_new()
                    ), 
                    view = FALSE,
                    fragment = TRUE
                  )
                })
              

              The downside of this is the complete HTML document contains CSS in it that is not included with just the fragment. You are then responsible for styling the resulting table.

              Source https://stackoverflow.com/questions/69854600

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

              Vulnerabilities

              No vulnerabilities reported

              Install daff

              You can install using 'npm i daff' or download it from GitHub, npm.
              You can use daff like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the daff component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

              Support

              The daff library is written in [Haxe](http://haxe.org/), which can be translated reasonably well into at least the following languages:.

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