kandi background
Explore Kits

LSEQ | framework allows the developper to configure

 by   Chat-Wane Java Version: Current License: LGPL-3.0

 by   Chat-Wane Java Version: Current License: LGPL-3.0

Download this library from

kandi X-RAY | LSEQ Summary

LSEQ is a Java library. LSEQ has build file available, it has a Weak Copyleft License and it has low support. However LSEQ has 5 bugs and it has 1 vulnerabilities. You can download it from GitHub.
This framework allows the developper to configure underlying components of its allocation strategy and to measure the size of identifiers generated (see [package module](/src/main/java/alma/fr/modules/)). Available components are divided in 4 categories: * Base: Double or Constant (parameter departure base) * Boundary: Double or Constant (parameter departure boundary) * Allocation strategies: Beginning (<i>boundary+</i>) or Ending (<i>boundary-</i>) (parameters base, boundary) * Strategy Choice: Single, Round-Robin or Random (parameter(s) allocation strategie(s)). Of course, the framework also allows to develop custom components.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • LSEQ has a low active ecosystem.
  • It has 19 star(s) with 1 fork(s). There are no watchers for this library.
  • It had no major release in the last 12 months.
  • There are 2 open issues and 4 have been closed. On average issues are closed in 13 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of LSEQ is current.
LSEQ Support
Best in #Java
Average in #Java
LSEQ Support
Best in #Java
Average in #Java

quality kandi Quality

  • LSEQ has 5 bugs (1 blocker, 1 critical, 0 major, 3 minor) and 151 code smells.
LSEQ Quality
Best in #Java
Average in #Java
LSEQ Quality
Best in #Java
Average in #Java

securitySecurity

  • LSEQ has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • LSEQ code analysis shows 1 unresolved vulnerabilities (1 blocker, 0 critical, 0 major, 0 minor).
  • There are 11 security hotspots that need review.
LSEQ Security
Best in #Java
Average in #Java
LSEQ Security
Best in #Java
Average in #Java

license License

  • LSEQ is licensed under the LGPL-3.0 License. This license is Weak Copyleft.
  • Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.
LSEQ License
Best in #Java
Average in #Java
LSEQ License
Best in #Java
Average in #Java

buildReuse

  • LSEQ releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • LSEQ saves you 806 person hours of effort in developing the same functionality from scratch.
  • It has 1852 lines of code, 167 functions and 47 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
LSEQ Reuse
Best in #Java
Average in #Java
LSEQ Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • Run the logoot engine .
    • Generate patch .
      • Runs the program .
        • Generate positions .
          • Compares this position with the specified position .
            • Gets interval between p and q .
              • Queries a URL .
                • add a new node to the list
                  • Configures the primitive fields
                    • Get the deltas for n lines

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      LSEQ Key Features

                      trying to solve geom_dotplot binwidth issue by generating exponential vector of values

                      copy iconCopydownload iconDownload
                      seq_exp <- function(start, stop, n, shape)
                      {
                        (stop - start) * exp(seq(0, shape, length.out = n))/exp(shape) + start
                      }
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 10))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 1))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 5))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 30))
                      
                      seq_exp <- function(start, stop, n, shape)
                      {
                        (stop - start) * exp(seq(0, shape, length.out = n))/exp(shape) + start
                      }
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 10))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 1))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 5))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 30))
                      
                      seq_exp <- function(start, stop, n, shape)
                      {
                        (stop - start) * exp(seq(0, shape, length.out = n))/exp(shape) + start
                      }
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 10))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 1))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 5))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 30))
                      
                      seq_exp <- function(start, stop, n, shape)
                      {
                        (stop - start) * exp(seq(0, shape, length.out = n))/exp(shape) + start
                      }
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 10))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 1))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 5))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 30))
                      
                      seq_exp <- function(start, stop, n, shape)
                      {
                        (stop - start) * exp(seq(0, shape, length.out = n))/exp(shape) + start
                      }
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 10))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 1))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 5))
                      
                      plot(seq_exp(0.08, 0.25, 10000, shape = 30))
                      

                      Community Discussions

                      Trending Discussions on LSEQ
                      • trying to solve geom_dotplot binwidth issue by generating exponential vector of values
                      Trending Discussions on LSEQ

                      QUESTION

                      trying to solve geom_dotplot binwidth issue by generating exponential vector of values

                      Asked 2020-Apr-01 at 21:34

                      What is the function for generating data for plotting an exponential curve between two points? Here's a logarithmically spaced sequence. I want to create more of a hockey stick between the start and end point, and the real end goal is the vector of values not the plot.

                      My use case is that I have a parameter for a plotting function that needs to ramp up slowly between the given values as I try to plot more data. This log sequence is better than a linear sequence, but it still rises too rapidly. I need to keep the values lower and then increase exponentially.

                      library(emdbook)
                      plot(lseq(.08, .25, 10000))
                      

                      enter image description here

                      Update

                      Here is the full challenge for context. I'm plotting every 400th index value of s. The geom_dotplot in the final plot, p_diff, is wacky and needs certain binwidth values to correctly size the plot. I tried creating a log sequence called binsize and passing it to the parameter. It looks fine at low values of s, but increases to 0.25 too quickly (0.25 works for the final version with 10000 dots).

                        library(tidyverse)
                        library(ggtext)
                        library(patchwork)
                        library(truncnorm)
                        library(ggtext)
                        library(emdbook)
                      
                      
                      # simulate hypothetical population at control group mean/sd
                        set.seed(1)
                        pop <- data.frame(bdi3 = rtruncnorm(10000, a=0, b=63, mean=24.5, sd=10.7),
                                          id = seq(1:10000))
                      
                      # create plots
                        diff <- data.frame(NULL)
                        binsize = lseq(0.08695510, .25, 10000)
                        for (s in 1:10000) {
                      
                          set.seed(s)
                          samp <- 
                          pop %>%
                            sample_n(332, replace = FALSE)
                      
                          ctr <- 
                          samp %>%
                            sample_n(166, replace = FALSE) %>%
                            mutate(trt = 0)
                      
                          trt <- 
                          samp %>%
                            left_join(dplyr::select(ctr, id, trt), by="id") %>%
                            mutate(trt = ifelse(is.na(trt), 1, trt)) %>%
                            filter(trt==1)
                      
                          diff[s,1] <- s
                          diff[s,2] <- (mean(trt$bdi3)-mean(ctr$bdi3))
                          names(diff) <- c("id", "diff")
                      
                          dat <- 
                            ctr %>%
                            bind_rows(trt)
                      
                      
                        if (s %in% seq(1, 10000, by=400)) {
                      
                        # population
                          p_pop <- 
                          pop %>%
                            left_join(dplyr::select(dat, id, trt), by="id") %>%
                            # mutate(trt = ifelse(is.na(trt), 3, trt),
                            #        trt = factor(trt)) %>%
                            mutate(selected = ifelse(!is.na(trt), 1, 0),
                                   selected = factor(selected)) %>%
                            ggplot(., aes(x=bdi3, fill=selected, group=id, alpha=selected)) +
                            geom_dotplot(method = 'dotdensity', binwidth = 0.25, dotsize = 1,
                                         color="white", 
                                         binpositions="all", stackgroups=TRUE,
                                         stackdir = "up") +
                            scale_fill_manual(values=c("grey", "#e69138")) +
                            scale_alpha_discrete(range = c(0.5, 1)) +
                            scale_y_continuous(NULL, breaks = NULL) +
                            theme_minimal() +
                            scale_x_continuous(limits=c(-0, 63)) +
                            xlab("\nDepression Severity as measured by BDI-II") +
                            theme(legend.position = "none",
                                  axis.title = element_text(size=30, color = "#696865"),
                                  axis.text = element_text(size=24, color = "#696865"),
                                  plot.title = element_text(size=36, color = "#696865",
                                                            face="bold"),
                                  plot.subtitle = element_markdown(size=27),
                                  plot.margin = margin(0, 0, 1.5, 0, "cm")) +
                            geom_vline(xintercept = mean(pop$bdi3), linetype="dashed", 
                                       color = "#696865", size=1) +
                            annotate("text", x = mean(pop$bdi3)+1, y = 25, 
                                     label = paste0("Population mean = ", 
                                                    format(round(mean(pop$bdi3), 1), nsmall = 1)),
                                     hjust = 0, color = "#696865", size=10) +
                            annotate("text", x = 0, y = 20, 
                                     label = paste0("Sample #", s),
                                     hjust = 0, color = "#e69138", size=10) +
                            ggtitle("Imaginary population of 10,000 patients who meet study criteria",
                                    subtitle="<span style='color:#e69138'>**Orange**</span> dots represent 332 selected patients") 
                      
                          p_samp <- 
                          ggplot(dat, aes(x=bdi3)) + # group=id, fill=factor(trt)
                            geom_dotplot(method = 'dotdensity', binwidth = 1.2,
                                         fill="#e69138", alpha=.8, color="white",
                                         binpositions="all", stackgroups=TRUE,
                                         stackdir = "up", stroke=1) +
                            #scale_fill_manual(values=c("#f7f265", "#1f9ac9")) +
                            scale_y_continuous(NULL, breaks = NULL) +
                            theme_minimal() +
                            scale_x_continuous(limits=c(-0, 63)) +
                            xlab("\nDepression Severity as measured by BDI-II") +
                            theme(legend.position = "none",
                                  axis.title = element_text(size=30, color = "#696865"),
                                  axis.text = element_text(size=24, color = "#696865"),
                                  plot.title = element_markdown(size=36, color = "#696865",
                                                                face="bold"),
                                  plot.subtitle = element_markdown(size=27),
                                  plot.margin = margin(0, 0, 1.5, 0, "cm")) +
                            geom_vline(xintercept = mean(dat$bdi3), linetype="dashed", 
                                       color = "#696865", size=1) +
                            annotate("text", x = mean(dat$bdi3)+2, y = 1, 
                                     label = paste0("Sample mean = ", 
                                                    format(round(mean(dat$bdi3), 1), nsmall = 1)),
                                     hjust = 0, color = "#696865", size=10) +
                            annotate("text", x = 0, y = .75, 
                                     label = paste0("Sample #", s),
                                     hjust = 0, color = "#e69138", size=10) +
                            ggtitle("One possible sample of these patients (N=332)",
                                    subtitle="Each dot is a patient sampled from the population who gets randomly assigned to a study arm") +
                            annotate("text", x = 50, y = .3, 
                                     label = "randomize to study arms",
                                     size = 10, color="#696865") + 
                            geom_curve(aes(x = 35, y = .6, xend = 50, yend = .35), 
                                       color = "#696865", arrow = arrow(type = "open",
                                                                        length = unit(0.15, "inches")), 
                                       curvature = -.5, angle = 100, ncp =15) 
                      
                          p_ctr <- 
                            ggplot(ctr, aes(x=bdi3)) +
                            geom_dotplot(method = 'dotdensity', binwidth = 1.6,
                                         color="white", fill="#f7f265", alpha=1,
                                         binpositions="all", stackgroups=TRUE,
                                         stackdir = "up") +
                            scale_y_continuous(NULL, breaks = NULL) +
                            theme_minimal() +
                            scale_x_continuous(limits=c(-0, 63)) +
                            xlab("\nDepression Severity as measured by BDI-II") +
                            theme(legend.position = "none",
                                  axis.title = element_text(size=30, color = "#696865"),
                                  axis.text = element_text(size=24, color = "#696865"),
                                  plot.title = element_markdown(size=36, color = "#696865",
                                                            face="bold"),
                                  plot.subtitle = element_markdown(size=27),
                                  plot.margin = margin(0, 0, 1.5, 0, "cm")) +
                            geom_vline(xintercept = mean(pop$bdi3), linetype="dashed", 
                                       color = "#696865", size=1) +
                            annotate("text", x = mean(ctr$bdi3)+2, y = 1, 
                                     label = paste0("Control mean = ", 
                                                    format(round(mean(ctr$bdi3), 1), nsmall = 1)),
                                     hjust = 0, color = "#696865", size=10) +
                            annotate("text", x = 0, y = .75, 
                                     label = paste0("Sample #", s),
                                     hjust = 0, color = "#e69138", size=10) +
                            ggtitle("50% patients randomly assigned<br>to the <span style='color:#f7f265'>**control**</span> group",
                                    subtitle="166 of the <span style='color:#e69138'>**orange**</span> dots turn <span style='color:#f7f265'>**yellow**</span>")
                      
                          p_trt <- 
                            ggplot(trt, aes(x=bdi3)) +
                            geom_dotplot(method = 'dotdensity', binwidth = 1.6,
                                         color="white", fill="#1f9ac9", alpha=1,
                                         binpositions="all", stackgroups=TRUE,
                                         stackdir = "up") +
                            scale_y_continuous(NULL, breaks = NULL) +
                            theme_minimal() +
                            scale_x_continuous(limits=c(-0, 63)) +
                            xlab("\nDepression Severity as measured by BDI-II") +
                            theme(legend.position = "none",
                                  axis.title = element_text(size=30, color = "#696865"),
                                  axis.text = element_text(size=24, color = "#696865"),
                                  plot.title = element_markdown(size=36, color = "#696865",
                                                            face="bold"),
                                  plot.subtitle = element_markdown(size=27),
                                  plot.margin = margin(0, 0, 1.5, 0, "cm")) +
                            geom_vline(xintercept = mean(trt$bdi3), linetype="dashed", 
                                       color = "#696865", size=1) +
                            annotate("text", x = mean(trt$bdi3)+2, y = 1, 
                                     label = paste0("Treatment mean = ", 
                                                    format(round(trt$bdi3, 1), nsmall = 1)),
                                     hjust = 0, color = "#696865", size=10) +
                            annotate("text", x = 0, y = .75, 
                                     label = paste0("Sample #", s),
                                     hjust = 0, color = "#e69138", size=10) +
                            ggtitle("50% patients randomly assigned<br>to the <span style='color:#1f9ac9'>**treatment**</span> group",
                                    subtitle="166 of the <span style='color:#e69138'>**orange**</span> dots turn <span style='color:#1f9ac9'>**blue**</span>")
                      
                          p_diff <-
                          diff %>%
                            mutate(color=ifelse(diff < -2.3 | diff > 2.3, 1, 0)) %>%
                            mutate(color=factor(color)) %>%
                            ggplot(., aes(x=diff, fill=color, group=id)) +
                            geom_dotplot(method = 'dotdensity', binwidth = binsize[s], dotsize = 1, 
                                         color="white", 
                                         binpositions="all", stackgroups=TRUE,
                                         stackdir = "up") +
                            scale_fill_manual(values=c("grey", "red")) +
                            scale_y_continuous(NULL, breaks = NULL) +
                            theme_minimal() +
                            scale_x_continuous(breaks=c(-5:5), limits=c(-5, 5)) +
                            xlab("\nAverage Treatment Effect (Treatment Mean - Control Mean)") +
                            theme(legend.position = "none",
                                  axis.title = element_text(size=30, color = "#696865"),
                                  axis.text = element_text(size=24, color = "#696865"),
                                  plot.title = element_text(size=36, color = "#696865",
                                                            face="bold"),
                                  plot.subtitle = element_markdown(size=27)) +
                            geom_vline(xintercept = 0, linetype="dashed", 
                                       color = "#696865", size=1) +
                            annotate("text", x = 0.2, y = 25, label = "No effect",
                                     hjust = 0, color = "#696865", size=10) +
                            ggtitle("Simulation based null distribution",
                                    subtitle="Plausible estimates of the treatment effect if the hypothesis of no effect is true") +
                            geom_vline(xintercept = 2.3, linetype="dotted", 
                                       color = "red", size=1) +
                            geom_vline(xintercept = -2.3, linetype="dotted", 
                                       color = "red", size=1) +
                            annotate("text", x = 2.5, y = 25, label = "Reject null",
                                     hjust = 0, color = "red", size=10) +
                            annotate("text", x = -2.5, y = 25, label = "Reject null",
                                     hjust = 1, color = "red", size=10) +
                            annotate("text", x = -5, y = 20, 
                                     label = paste0("Sample #", s),
                                     hjust = 0, color = "#e69138", size=10) 
                      
                      
                      
                          p_all <- p_pop / p_samp / (p_trt + p_ctr) / p_diff +
                            plot_layout(heights = c(2, 2, 1, 2))
                      
                          ggsave(paste0("animate/", s, ".png"),
                                 height = 40, width = 18.5, units = "in",
                                 dpi = 300)
                        }
                        }
                      

                      The second plot to generate, s==401, looks fine. binsize[401] works for this many dots. But by the 5th plot, s==1601, the dots to not fit. binsize[1601] is too high.

                      I'm thinking that if I could create a better vector of values for binsize that rises more slowly to 0.25 this will work.

                      ANSWER

                      Answered 2020-Apr-01 at 21:26

                      This is more of a maths question rather than a programming question, but there's a fairly simple programming solution.

                      Here's a simple function you can try. It allows you to produce a sequence of numbers between a starting and ending number just like the lseq function, but includes a shape parameter that controls how "exponential" the numbers appear.

                      seq_exp <- function(start, stop, n, shape)
                      {
                        (stop - start) * exp(seq(0, shape, length.out = n))/exp(shape) + start
                      }
                      

                      So you're probably looking for something like this:

                      plot(seq_exp(0.08, 0.25, 10000, shape = 10))
                      

                      If you set the shape parameter to 1 it is just a normal exponential curve like in lseq:

                      plot(seq_exp(0.08, 0.25, 10000, shape = 1))
                      

                      And of course you can play around with different values:

                      plot(seq_exp(0.08, 0.25, 10000, shape = 5))
                      

                      plot(seq_exp(0.08, 0.25, 10000, shape = 30))
                      

                      Created on 2020-04-01 by the reprex package (v0.3.0)

                      Source https://stackoverflow.com/questions/60979578

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install LSEQ

                      You can download it from GitHub.
                      You can use LSEQ 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 LSEQ component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

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

                      DOWNLOAD this Library from

                      Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                      over 430 million Knowledge Items
                      Find more libraries
                      Reuse Solution Kits and Libraries Curated by Popular Use Cases
                      Explore Kits

                      Save this library and start creating your kit

                      Share this Page

                      share link
                      Consider Popular Java Libraries
                      Try Top Libraries by Chat-Wane
                      Compare Java Libraries with Highest Support
                      Compare Java Libraries with Highest Quality
                      Compare Java Libraries with Highest Security
                      Compare Java Libraries with Permissive License
                      Compare Java Libraries with Highest Reuse
                      Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                      over 430 million Knowledge Items
                      Find more libraries
                      Reuse Solution Kits and Libraries Curated by Popular Use Cases
                      Explore Kits

                      Save this library and start creating your kit

                      • © 2022 Open Weaver Inc.