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

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Quality

Security

Security

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License

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Reuse

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

- 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

Security

- 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

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LSEQ Security

Best in #Java

Average in #Java

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

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LSEQ License

Best in #Java

Average in #Java

Reuse

- 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.

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

CopyDownload

```
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))
```

QUESTION

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

Asked 2020-Apr-01 at 21:34What 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))
```

**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 21:26This 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)}

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

No vulnerabilities reported

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 .

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 .

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 .

over 430 million Knowledge Items

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over 430 million Knowledge Items

Save this library and start creating your kit