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q | C Library for Audio Digital Signal Processing | Audio Utils library

 by   cycfi C++ Version: Current License: MIT

 by   cycfi C++ Version: Current License: MIT

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kandi X-RAY | q Summary

q is a C++ library typically used in Audio, Audio Utils applications. q has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
Q is a cross-platform C++ library for Audio Digital Signal Processing. Aptly named after the "Q factor", a dimensionless parameter that describes the quality of a resonant circuit, the Q DSP Library is designed to be simple and elegant, as the simplicity of its name suggests, and efficient enough to run on small microcontrollers. Q leverages the power of modern C++ and efficient use of functional programming techniques, especially function composition using fine-grained and reusable function objects (both stateless and stateful), to simplify complex DSP programming tasks without sacrificing readability. Q is the host of some experimental Music related DSP facilities such as Virtual Pickups (Virtual pickup placement simulator) and Bitstream Autocorrelation (An extremely fast and efficient pitch detection scheme) the author has accumulated over the years as part of research and development, and will continue to evolve to accommodate more facilities necessary for the fulfillment of various Music related projects. The library is Open Source and released under the very liberal MIT license.
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  • q has a low active ecosystem.
  • It has 703 star(s) with 118 fork(s). There are 43 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 7 open issues and 19 have been closed. On average issues are closed in 83 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of q is current.
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  • q has no bugs reported.
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  • q has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
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license License

  • q is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
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  • q releases are not available. You will need to build from source code and install.
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q Key Features

C++ Library for Audio Digital Signal Processing

put link into href using javascript variable html

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document.getElementById("instagram").href = instagram

How to print ggplot for multiple tables in this case?

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library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)

plots <- function(x){
  d = x %>% 
    as.data.frame() %>%
    tidyr::pivot_longer(!grp) %>%
    dplyr::group_by(name) %>% 
    dplyr::mutate(n = 1:n())

ggplot(data = d) + 
  geom_path(aes(x = grp, y = value, group = factor(name), color = factor(name)), size = 0.7) +
  geom_point(aes(x = grp, y = value, color = factor(name)), size = 2) +
  geom_text(data = d %>% filter(n == max(n)), aes(x = grp, y = value, label = name, color = factor(name)), nudge_x = 0.2) + 
  labs(x = "Group", y = "P", title = "") + 
  theme_bw() +
  theme(legend.position = "none")
}
library(ggpubr)

  ggpubr::ggarrange(
  plotlist = plot_objects,
  ncol = 3,
  nrow = 4, 
  labels = names(plot_objects), 
  hjust = -5,
  vjust = 2
)
-----------------------
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)

plots <- function(x){
  d = x %>% 
    as.data.frame() %>%
    tidyr::pivot_longer(!grp) %>%
    dplyr::group_by(name) %>% 
    dplyr::mutate(n = 1:n())

ggplot(data = d) + 
  geom_path(aes(x = grp, y = value, group = factor(name), color = factor(name)), size = 0.7) +
  geom_point(aes(x = grp, y = value, color = factor(name)), size = 2) +
  geom_text(data = d %>% filter(n == max(n)), aes(x = grp, y = value, label = name, color = factor(name)), nudge_x = 0.2) + 
  labs(x = "Group", y = "P", title = "") + 
  theme_bw() +
  theme(legend.position = "none")
}
library(ggpubr)

  ggpubr::ggarrange(
  plotlist = plot_objects,
  ncol = 3,
  nrow = 4, 
  labels = names(plot_objects), 
  hjust = -5,
  vjust = 2
)

Django says field does not exist when it does exist

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python manage.py migrate polls zero
python manage.py migrate

Can't integrate simple normal distribution in sympy, depending on mean and deviation constants

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In [125]: normal(x, 8.6, 0.5)
Out[125]: 
                                         2
        -147.92⋅(0.116279069767442⋅x - 1) 
1.0⋅√2⋅ℯ                                  
──────────────────────────────────────────
                    √π                    

In [126]: integrate(_,x)
Out[126]: 0.353553390593274⋅√2⋅erf(1.41421356237309⋅x - 12.1622366364086)
In [127]: normal(x, 8.655555555555557, 0.5212875796916135)
Out[127]: 
                                                                2
                     -137.849484348735⋅(0.115532734274711⋅x - 1) 
0.95916346270094⋅√2⋅ℯ                                            
─────────────────────────────────────────────────────────────────
                                √π                               

In [128]: integrate(_,x)
Out[128]: 
                        ⌠                                             
                        ⎮                                         2   
                        ⎮  31.8522556903367⋅x  -1.83998909636091⋅x    
1.30203374788369e-60⋅√2⋅⎮ ℯ                  ⋅ℯ                     dx
                        ⌡                                             
──────────────────────────────────────────────────────────────────────
                                  √π                                  
-----------------------
In [125]: normal(x, 8.6, 0.5)
Out[125]: 
                                         2
        -147.92⋅(0.116279069767442⋅x - 1) 
1.0⋅√2⋅ℯ                                  
──────────────────────────────────────────
                    √π                    

In [126]: integrate(_,x)
Out[126]: 0.353553390593274⋅√2⋅erf(1.41421356237309⋅x - 12.1622366364086)
In [127]: normal(x, 8.655555555555557, 0.5212875796916135)
Out[127]: 
                                                                2
                     -137.849484348735⋅(0.115532734274711⋅x - 1) 
0.95916346270094⋅√2⋅ℯ                                            
─────────────────────────────────────────────────────────────────
                                √π                               

In [128]: integrate(_,x)
Out[128]: 
                        ⌠                                             
                        ⎮                                         2   
                        ⎮  31.8522556903367⋅x  -1.83998909636091⋅x    
1.30203374788369e-60⋅√2⋅⎮ ℯ                  ⋅ℯ                     dx
                        ⌡                                             
──────────────────────────────────────────────────────────────────────
                                  √π                                  
-----------------------
import sympy
from sympy import symbols
from sympy import plot

def normal(x, mean, sigma):
    z = (x - mean) / sigma
    return (1 / (sigma * sympy.sqrt(2 * sympy.pi))) * sympy.exp(-(z * z) / 2)

x = symbols("x")

for μ, σ in [
    (sympy.Rational("10.1"), sympy.Rational(1, 3)),  # Works fine
    (sympy.Rational("8.655555555555557").limit_denominator(10**10), 
     sympy.Rational("0.5212875796916135").limit_denominator(10**10)), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    distrib = normal(x, μ, σ)
    distrib_cum = sympy.integrate(distrib, x)
    
    print(distrib_cum)
    plot(distrib_cum)
import sympy
from sympy import symbols
from sympy import plot

def normal(x, mean, sigma):
    z = (x - mean) / sigma
    return (1 / (sigma * sympy.sqrt(2 * sympy.pi))) * sympy.exp(-(z * z) / 2)

x, mu, sigma = symbols("x, mu, sigma")

distrib = normal(x, mu, sigma)
distrib_cum = sympy.integrate(distrib, x)

for μ, σ in [
    (10.1, 0.333333333),  # Works fine
    (8.655555555555557, 0.5212875796916135), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    print(distrib_cum.subs({mu: μ, sigma: σ}))
    plot(distrib_cum.subs({mu: μ, sigma: σ}))
from sympy import symbols
from sympy import plot
from sympy.stats import Normal, cdf

x = symbols('x')

for μ, σ in [
    (10.1, 0.333333333),  # Works fine
    (8.655555555555557, 0.5212875796916135), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    distrib_cum = cdf(Normal('X', μ, σ))(x)
    print(distrib_cum)
    plot(distrib_cum)
-----------------------
import sympy
from sympy import symbols
from sympy import plot

def normal(x, mean, sigma):
    z = (x - mean) / sigma
    return (1 / (sigma * sympy.sqrt(2 * sympy.pi))) * sympy.exp(-(z * z) / 2)

x = symbols("x")

for μ, σ in [
    (sympy.Rational("10.1"), sympy.Rational(1, 3)),  # Works fine
    (sympy.Rational("8.655555555555557").limit_denominator(10**10), 
     sympy.Rational("0.5212875796916135").limit_denominator(10**10)), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    distrib = normal(x, μ, σ)
    distrib_cum = sympy.integrate(distrib, x)
    
    print(distrib_cum)
    plot(distrib_cum)
import sympy
from sympy import symbols
from sympy import plot

def normal(x, mean, sigma):
    z = (x - mean) / sigma
    return (1 / (sigma * sympy.sqrt(2 * sympy.pi))) * sympy.exp(-(z * z) / 2)

x, mu, sigma = symbols("x, mu, sigma")

distrib = normal(x, mu, sigma)
distrib_cum = sympy.integrate(distrib, x)

for μ, σ in [
    (10.1, 0.333333333),  # Works fine
    (8.655555555555557, 0.5212875796916135), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    print(distrib_cum.subs({mu: μ, sigma: σ}))
    plot(distrib_cum.subs({mu: μ, sigma: σ}))
from sympy import symbols
from sympy import plot
from sympy.stats import Normal, cdf

x = symbols('x')

for μ, σ in [
    (10.1, 0.333333333),  # Works fine
    (8.655555555555557, 0.5212875796916135), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    distrib_cum = cdf(Normal('X', μ, σ))(x)
    print(distrib_cum)
    plot(distrib_cum)
-----------------------
import sympy
from sympy import symbols
from sympy import plot

def normal(x, mean, sigma):
    z = (x - mean) / sigma
    return (1 / (sigma * sympy.sqrt(2 * sympy.pi))) * sympy.exp(-(z * z) / 2)

x = symbols("x")

for μ, σ in [
    (sympy.Rational("10.1"), sympy.Rational(1, 3)),  # Works fine
    (sympy.Rational("8.655555555555557").limit_denominator(10**10), 
     sympy.Rational("0.5212875796916135").limit_denominator(10**10)), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    distrib = normal(x, μ, σ)
    distrib_cum = sympy.integrate(distrib, x)
    
    print(distrib_cum)
    plot(distrib_cum)
import sympy
from sympy import symbols
from sympy import plot

def normal(x, mean, sigma):
    z = (x - mean) / sigma
    return (1 / (sigma * sympy.sqrt(2 * sympy.pi))) * sympy.exp(-(z * z) / 2)

x, mu, sigma = symbols("x, mu, sigma")

distrib = normal(x, mu, sigma)
distrib_cum = sympy.integrate(distrib, x)

for μ, σ in [
    (10.1, 0.333333333),  # Works fine
    (8.655555555555557, 0.5212875796916135), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    print(distrib_cum.subs({mu: μ, sigma: σ}))
    plot(distrib_cum.subs({mu: μ, sigma: σ}))
from sympy import symbols
from sympy import plot
from sympy.stats import Normal, cdf

x = symbols('x')

for μ, σ in [
    (10.1, 0.333333333),  # Works fine
    (8.655555555555557, 0.5212875796916135), # Fails
]:
    print(f"μ={μ}, σ={σ}")
    distrib_cum = cdf(Normal('X', μ, σ))(x)
    print(distrib_cum)
    plot(distrib_cum)

Insert multiple rows from a select and put the generated IDs into other table

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update alpha a join
       beta b 
       on a.type_info = b.type_info
    set a.beta_id = b.id;
alter table beta add column alpha_id int;

INSERT INTO beta (alpha_id, type_info)
    SELECT a.id, a.type_info
    FROM alpha a
    WHERE beta_id IS NULL;

update alpha a join
       beta b 
       on a.id = b.alpha_id
    set a.beta_id = b.id;
-----------------------
update alpha a join
       beta b 
       on a.type_info = b.type_info
    set a.beta_id = b.id;
alter table beta add column alpha_id int;

INSERT INTO beta (alpha_id, type_info)
    SELECT a.id, a.type_info
    FROM alpha a
    WHERE beta_id IS NULL;

update alpha a join
       beta b 
       on a.id = b.alpha_id
    set a.beta_id = b.id;
-----------------------
INSERT INTO beta (type_info)
SELECT type_info FROM alpha
WHERE beta_id IS NULL
ORDER BY id;

SET @id = LAST_INSERT_ID()-1;

UPDATE alpha
SET beta_id=(@id := @id + 1)
WHERE beta_id IS NULL
ORDER BY id;

Find proportion of times each character(A,B,C,D) occurs in each column of a list which has 3 datasets

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library(dplyr)
library(purrr)
out <- lapply(l, function(dat) 
   asplit(as.data.frame(t(sapply(dat, function(x) 
            proportions(table(factor(unlist(x), levels = u)))))), 1) ) %>%
    transpose %>%
    map(bind_rows, .id = 'grp')
out
$X70
# A tibble: 5 x 5
  grp       D      B      C     A
  <chr> <dbl>  <dbl>  <dbl> <dbl>
1 0     1     0      0      0    
2 1     0.818 0.182  0      0    
3 2     0.833 0.0833 0.0833 0    
4 3     0.727 0      0.0909 0.182
5 4     1     0      0      0    

$X71
# A tibble: 5 x 5
  grp        D     B     C      A
  <chr>  <dbl> <dbl> <dbl>  <dbl>
1 0     0      0     1     0     
2 1     0.1    0.3   0.5   0.1   
3 2     0.0833 0.25  0.417 0.25  
4 3     0.25   0.167 0.5   0.0833
5 4     0      0.182 0.455 0.364 

$X72
# A tibble: 5 x 5
  grp        D      B     C     A
  <chr>  <dbl>  <dbl> <dbl> <dbl>
1 0     0      0      1     0    
2 1     0.1    0.2    0.5   0.2  
3 2     0.364  0.0909 0.364 0.182
4 3     0.0909 0.364  0.364 0.182
5 4     0      0.3    0.5   0.2  

$X73
# A tibble: 5 x 5
  grp       D      B     C     A
  <chr> <dbl>  <dbl> <dbl> <dbl>
1 0     0     0      0     1    
2 1     0.1   0.3    0.5   0.1  
3 2     0.5   0.2    0.2   0.1  
4 3     0.273 0.182  0.273 0.273
5 4     0.545 0.0909 0.182 0.182

$X74
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0     0     1     0     0    
2 1     0.556 0.222 0.111 0.111
3 2     0.273 0.273 0.455 0    
4 3     0.4   0.2   0.1   0.3  
5 4     0.333 0     0.417 0.25 

$X75
# A tibble: 5 x 5
  grp        D      B     C      A
  <chr>  <dbl>  <dbl> <dbl>  <dbl>
1 0     0      0      1     0     
2 1     0.182  0.0909 0.636 0.0909
3 2     0      0.273  0.727 0     
4 3     0.182  0.182  0.636 0     
5 4     0.0833 0      0.917 0     

$X76
# A tibble: 5 x 5
  grp       D      B      C     A
  <chr> <dbl>  <dbl>  <dbl> <dbl>
1 0     1     0      0      0    
2 1     0.455 0.182  0.0909 0.273
3 2     0.818 0      0      0.182
4 3     0.727 0.0909 0      0.182
5 4     0.833 0      0      0.167

$X77
# A tibble: 5 x 5
  grp         D       B       C     A
  <chr>   <dbl>   <dbl>   <dbl> <dbl>
1 0     NaN     NaN     NaN     NaN  
2 1       0.182   0.636   0.182   0  
3 2       0.182   0.818   0       0  
4 3       0.2     0.5     0.2     0.1
5 4       0.222   0.556   0.222   0  

$X78
# A tibble: 5 x 5
  grp        D     B     C      A
  <chr>  <dbl> <dbl> <dbl>  <dbl>
1 0     0      1     0     0     
2 1     0      0.3   0.4   0.3   
3 2     0.3    0.2   0.5   0     
4 3     0.0909 0.182 0.636 0.0909
5 4     0      0     0.75  0.25  

$X79
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0     1     0     0     0    
2 1     0.333 0     0.222 0.444
3 2     0.556 0.111 0     0.333
4 3     0.6   0.1   0     0.3  
5 4     0.583 0     0.167 0.25 

$X80
# A tibble: 5 x 5
  grp       D     B     C      A
  <chr> <dbl> <dbl> <dbl>  <dbl>
1 0       0   0     1     0     
2 1       0   0.5   0.25  0.25  
3 2       0   0.125 0.75  0.125 
4 3       0.1 0.1   0.8   0     
5 4       0   0     0.909 0.0909

$Q
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0       NaN   NaN   NaN   NaN
2 1       NaN   NaN   NaN   NaN
3 2       NaN   NaN   NaN   NaN
4 3       NaN   NaN   NaN   NaN
5 4       NaN   NaN   NaN   NaN
matplot(out[[1]][-1], type = "l", col = 1:4, xaxt = "n")
axis(side=1, at=1:4, labels=colnames(out[[1]][-1]))
legend("topleft", legend = colnames(out[[1]][-1]), fill = 1:4)
par(mfrow = c(4, 3))
out2 <- lapply(out[-12], function(x) {
     matplot(x[-1], type = "l", col = 1:4, xaxt = "n")
     axis(side=1, at=1:4, labels=colnames(x[-1]))
     legend("topleft", legend = colnames(x[-1]), fill = 1:4)
})
-----------------------
library(dplyr)
library(purrr)
out <- lapply(l, function(dat) 
   asplit(as.data.frame(t(sapply(dat, function(x) 
            proportions(table(factor(unlist(x), levels = u)))))), 1) ) %>%
    transpose %>%
    map(bind_rows, .id = 'grp')
out
$X70
# A tibble: 5 x 5
  grp       D      B      C     A
  <chr> <dbl>  <dbl>  <dbl> <dbl>
1 0     1     0      0      0    
2 1     0.818 0.182  0      0    
3 2     0.833 0.0833 0.0833 0    
4 3     0.727 0      0.0909 0.182
5 4     1     0      0      0    

$X71
# A tibble: 5 x 5
  grp        D     B     C      A
  <chr>  <dbl> <dbl> <dbl>  <dbl>
1 0     0      0     1     0     
2 1     0.1    0.3   0.5   0.1   
3 2     0.0833 0.25  0.417 0.25  
4 3     0.25   0.167 0.5   0.0833
5 4     0      0.182 0.455 0.364 

$X72
# A tibble: 5 x 5
  grp        D      B     C     A
  <chr>  <dbl>  <dbl> <dbl> <dbl>
1 0     0      0      1     0    
2 1     0.1    0.2    0.5   0.2  
3 2     0.364  0.0909 0.364 0.182
4 3     0.0909 0.364  0.364 0.182
5 4     0      0.3    0.5   0.2  

$X73
# A tibble: 5 x 5
  grp       D      B     C     A
  <chr> <dbl>  <dbl> <dbl> <dbl>
1 0     0     0      0     1    
2 1     0.1   0.3    0.5   0.1  
3 2     0.5   0.2    0.2   0.1  
4 3     0.273 0.182  0.273 0.273
5 4     0.545 0.0909 0.182 0.182

$X74
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0     0     1     0     0    
2 1     0.556 0.222 0.111 0.111
3 2     0.273 0.273 0.455 0    
4 3     0.4   0.2   0.1   0.3  
5 4     0.333 0     0.417 0.25 

$X75
# A tibble: 5 x 5
  grp        D      B     C      A
  <chr>  <dbl>  <dbl> <dbl>  <dbl>
1 0     0      0      1     0     
2 1     0.182  0.0909 0.636 0.0909
3 2     0      0.273  0.727 0     
4 3     0.182  0.182  0.636 0     
5 4     0.0833 0      0.917 0     

$X76
# A tibble: 5 x 5
  grp       D      B      C     A
  <chr> <dbl>  <dbl>  <dbl> <dbl>
1 0     1     0      0      0    
2 1     0.455 0.182  0.0909 0.273
3 2     0.818 0      0      0.182
4 3     0.727 0.0909 0      0.182
5 4     0.833 0      0      0.167

$X77
# A tibble: 5 x 5
  grp         D       B       C     A
  <chr>   <dbl>   <dbl>   <dbl> <dbl>
1 0     NaN     NaN     NaN     NaN  
2 1       0.182   0.636   0.182   0  
3 2       0.182   0.818   0       0  
4 3       0.2     0.5     0.2     0.1
5 4       0.222   0.556   0.222   0  

$X78
# A tibble: 5 x 5
  grp        D     B     C      A
  <chr>  <dbl> <dbl> <dbl>  <dbl>
1 0     0      1     0     0     
2 1     0      0.3   0.4   0.3   
3 2     0.3    0.2   0.5   0     
4 3     0.0909 0.182 0.636 0.0909
5 4     0      0     0.75  0.25  

$X79
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0     1     0     0     0    
2 1     0.333 0     0.222 0.444
3 2     0.556 0.111 0     0.333
4 3     0.6   0.1   0     0.3  
5 4     0.583 0     0.167 0.25 

$X80
# A tibble: 5 x 5
  grp       D     B     C      A
  <chr> <dbl> <dbl> <dbl>  <dbl>
1 0       0   0     1     0     
2 1       0   0.5   0.25  0.25  
3 2       0   0.125 0.75  0.125 
4 3       0.1 0.1   0.8   0     
5 4       0   0     0.909 0.0909

$Q
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0       NaN   NaN   NaN   NaN
2 1       NaN   NaN   NaN   NaN
3 2       NaN   NaN   NaN   NaN
4 3       NaN   NaN   NaN   NaN
5 4       NaN   NaN   NaN   NaN
matplot(out[[1]][-1], type = "l", col = 1:4, xaxt = "n")
axis(side=1, at=1:4, labels=colnames(out[[1]][-1]))
legend("topleft", legend = colnames(out[[1]][-1]), fill = 1:4)
par(mfrow = c(4, 3))
out2 <- lapply(out[-12], function(x) {
     matplot(x[-1], type = "l", col = 1:4, xaxt = "n")
     axis(side=1, at=1:4, labels=colnames(x[-1]))
     legend("topleft", legend = colnames(x[-1]), fill = 1:4)
})
-----------------------
library(dplyr)
library(purrr)
out <- lapply(l, function(dat) 
   asplit(as.data.frame(t(sapply(dat, function(x) 
            proportions(table(factor(unlist(x), levels = u)))))), 1) ) %>%
    transpose %>%
    map(bind_rows, .id = 'grp')
out
$X70
# A tibble: 5 x 5
  grp       D      B      C     A
  <chr> <dbl>  <dbl>  <dbl> <dbl>
1 0     1     0      0      0    
2 1     0.818 0.182  0      0    
3 2     0.833 0.0833 0.0833 0    
4 3     0.727 0      0.0909 0.182
5 4     1     0      0      0    

$X71
# A tibble: 5 x 5
  grp        D     B     C      A
  <chr>  <dbl> <dbl> <dbl>  <dbl>
1 0     0      0     1     0     
2 1     0.1    0.3   0.5   0.1   
3 2     0.0833 0.25  0.417 0.25  
4 3     0.25   0.167 0.5   0.0833
5 4     0      0.182 0.455 0.364 

$X72
# A tibble: 5 x 5
  grp        D      B     C     A
  <chr>  <dbl>  <dbl> <dbl> <dbl>
1 0     0      0      1     0    
2 1     0.1    0.2    0.5   0.2  
3 2     0.364  0.0909 0.364 0.182
4 3     0.0909 0.364  0.364 0.182
5 4     0      0.3    0.5   0.2  

$X73
# A tibble: 5 x 5
  grp       D      B     C     A
  <chr> <dbl>  <dbl> <dbl> <dbl>
1 0     0     0      0     1    
2 1     0.1   0.3    0.5   0.1  
3 2     0.5   0.2    0.2   0.1  
4 3     0.273 0.182  0.273 0.273
5 4     0.545 0.0909 0.182 0.182

$X74
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0     0     1     0     0    
2 1     0.556 0.222 0.111 0.111
3 2     0.273 0.273 0.455 0    
4 3     0.4   0.2   0.1   0.3  
5 4     0.333 0     0.417 0.25 

$X75
# A tibble: 5 x 5
  grp        D      B     C      A
  <chr>  <dbl>  <dbl> <dbl>  <dbl>
1 0     0      0      1     0     
2 1     0.182  0.0909 0.636 0.0909
3 2     0      0.273  0.727 0     
4 3     0.182  0.182  0.636 0     
5 4     0.0833 0      0.917 0     

$X76
# A tibble: 5 x 5
  grp       D      B      C     A
  <chr> <dbl>  <dbl>  <dbl> <dbl>
1 0     1     0      0      0    
2 1     0.455 0.182  0.0909 0.273
3 2     0.818 0      0      0.182
4 3     0.727 0.0909 0      0.182
5 4     0.833 0      0      0.167

$X77
# A tibble: 5 x 5
  grp         D       B       C     A
  <chr>   <dbl>   <dbl>   <dbl> <dbl>
1 0     NaN     NaN     NaN     NaN  
2 1       0.182   0.636   0.182   0  
3 2       0.182   0.818   0       0  
4 3       0.2     0.5     0.2     0.1
5 4       0.222   0.556   0.222   0  

$X78
# A tibble: 5 x 5
  grp        D     B     C      A
  <chr>  <dbl> <dbl> <dbl>  <dbl>
1 0     0      1     0     0     
2 1     0      0.3   0.4   0.3   
3 2     0.3    0.2   0.5   0     
4 3     0.0909 0.182 0.636 0.0909
5 4     0      0     0.75  0.25  

$X79
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0     1     0     0     0    
2 1     0.333 0     0.222 0.444
3 2     0.556 0.111 0     0.333
4 3     0.6   0.1   0     0.3  
5 4     0.583 0     0.167 0.25 

$X80
# A tibble: 5 x 5
  grp       D     B     C      A
  <chr> <dbl> <dbl> <dbl>  <dbl>
1 0       0   0     1     0     
2 1       0   0.5   0.25  0.25  
3 2       0   0.125 0.75  0.125 
4 3       0.1 0.1   0.8   0     
5 4       0   0     0.909 0.0909

$Q
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0       NaN   NaN   NaN   NaN
2 1       NaN   NaN   NaN   NaN
3 2       NaN   NaN   NaN   NaN
4 3       NaN   NaN   NaN   NaN
5 4       NaN   NaN   NaN   NaN
matplot(out[[1]][-1], type = "l", col = 1:4, xaxt = "n")
axis(side=1, at=1:4, labels=colnames(out[[1]][-1]))
legend("topleft", legend = colnames(out[[1]][-1]), fill = 1:4)
par(mfrow = c(4, 3))
out2 <- lapply(out[-12], function(x) {
     matplot(x[-1], type = "l", col = 1:4, xaxt = "n")
     axis(side=1, at=1:4, labels=colnames(x[-1]))
     legend("topleft", legend = colnames(x[-1]), fill = 1:4)
})
-----------------------
library(dplyr)
library(purrr)
out <- lapply(l, function(dat) 
   asplit(as.data.frame(t(sapply(dat, function(x) 
            proportions(table(factor(unlist(x), levels = u)))))), 1) ) %>%
    transpose %>%
    map(bind_rows, .id = 'grp')
out
$X70
# A tibble: 5 x 5
  grp       D      B      C     A
  <chr> <dbl>  <dbl>  <dbl> <dbl>
1 0     1     0      0      0    
2 1     0.818 0.182  0      0    
3 2     0.833 0.0833 0.0833 0    
4 3     0.727 0      0.0909 0.182
5 4     1     0      0      0    

$X71
# A tibble: 5 x 5
  grp        D     B     C      A
  <chr>  <dbl> <dbl> <dbl>  <dbl>
1 0     0      0     1     0     
2 1     0.1    0.3   0.5   0.1   
3 2     0.0833 0.25  0.417 0.25  
4 3     0.25   0.167 0.5   0.0833
5 4     0      0.182 0.455 0.364 

$X72
# A tibble: 5 x 5
  grp        D      B     C     A
  <chr>  <dbl>  <dbl> <dbl> <dbl>
1 0     0      0      1     0    
2 1     0.1    0.2    0.5   0.2  
3 2     0.364  0.0909 0.364 0.182
4 3     0.0909 0.364  0.364 0.182
5 4     0      0.3    0.5   0.2  

$X73
# A tibble: 5 x 5
  grp       D      B     C     A
  <chr> <dbl>  <dbl> <dbl> <dbl>
1 0     0     0      0     1    
2 1     0.1   0.3    0.5   0.1  
3 2     0.5   0.2    0.2   0.1  
4 3     0.273 0.182  0.273 0.273
5 4     0.545 0.0909 0.182 0.182

$X74
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0     0     1     0     0    
2 1     0.556 0.222 0.111 0.111
3 2     0.273 0.273 0.455 0    
4 3     0.4   0.2   0.1   0.3  
5 4     0.333 0     0.417 0.25 

$X75
# A tibble: 5 x 5
  grp        D      B     C      A
  <chr>  <dbl>  <dbl> <dbl>  <dbl>
1 0     0      0      1     0     
2 1     0.182  0.0909 0.636 0.0909
3 2     0      0.273  0.727 0     
4 3     0.182  0.182  0.636 0     
5 4     0.0833 0      0.917 0     

$X76
# A tibble: 5 x 5
  grp       D      B      C     A
  <chr> <dbl>  <dbl>  <dbl> <dbl>
1 0     1     0      0      0    
2 1     0.455 0.182  0.0909 0.273
3 2     0.818 0      0      0.182
4 3     0.727 0.0909 0      0.182
5 4     0.833 0      0      0.167

$X77
# A tibble: 5 x 5
  grp         D       B       C     A
  <chr>   <dbl>   <dbl>   <dbl> <dbl>
1 0     NaN     NaN     NaN     NaN  
2 1       0.182   0.636   0.182   0  
3 2       0.182   0.818   0       0  
4 3       0.2     0.5     0.2     0.1
5 4       0.222   0.556   0.222   0  

$X78
# A tibble: 5 x 5
  grp        D     B     C      A
  <chr>  <dbl> <dbl> <dbl>  <dbl>
1 0     0      1     0     0     
2 1     0      0.3   0.4   0.3   
3 2     0.3    0.2   0.5   0     
4 3     0.0909 0.182 0.636 0.0909
5 4     0      0     0.75  0.25  

$X79
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0     1     0     0     0    
2 1     0.333 0     0.222 0.444
3 2     0.556 0.111 0     0.333
4 3     0.6   0.1   0     0.3  
5 4     0.583 0     0.167 0.25 

$X80
# A tibble: 5 x 5
  grp       D     B     C      A
  <chr> <dbl> <dbl> <dbl>  <dbl>
1 0       0   0     1     0     
2 1       0   0.5   0.25  0.25  
3 2       0   0.125 0.75  0.125 
4 3       0.1 0.1   0.8   0     
5 4       0   0     0.909 0.0909

$Q
# A tibble: 5 x 5
  grp       D     B     C     A
  <chr> <dbl> <dbl> <dbl> <dbl>
1 0       NaN   NaN   NaN   NaN
2 1       NaN   NaN   NaN   NaN
3 2       NaN   NaN   NaN   NaN
4 3       NaN   NaN   NaN   NaN
5 4       NaN   NaN   NaN   NaN
matplot(out[[1]][-1], type = "l", col = 1:4, xaxt = "n")
axis(side=1, at=1:4, labels=colnames(out[[1]][-1]))
legend("topleft", legend = colnames(out[[1]][-1]), fill = 1:4)
par(mfrow = c(4, 3))
out2 <- lapply(out[-12], function(x) {
     matplot(x[-1], type = "l", col = 1:4, xaxt = "n")
     axis(side=1, at=1:4, labels=colnames(x[-1]))
     legend("topleft", legend = colnames(x[-1]), fill = 1:4)
})

Convert interface with nullable string property to string property

copy iconCopydownload iconDownload
const isNonNullVin = (vehicle: IVehicle): vehicle is IVehicleNonNullVin =>{
    return vehicle.vin !== null
}

if (!isNonNullVin(myVehicle)) {
    throw new Error('null');
} else {
    needsVin(myVehicle);
}

Swing JMenuBar not rendering properly

copy iconCopydownload iconDownload
import java.util.*;
import javax.swing.*;

public class App implements Runnable {
    private static final long serialVersionUID = 6924949643971067836L;

    private static final Character[] VALID_CHARS = {'!', '$', '%', '^', '&', '*', '(', ')', '-', '_', '=', '+'};
    private static final Character[] LOWER_ALPHA = {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'};
    private static final Character[] UPPER_ALPHA = {'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'};
    private static final Character[] VALID_NUMS = {'1', '2', '3', '4', '5', '6', '7', '8', '9', '0'};
    private static final String[] KEYBOARD_COMBOS = {"qwe", "wer", "ert", "rty", "tyu", "yui", "uio", "iop", "asd", "sdf", "dfg", "fgh", "gjh", "hjk", "jkl", "zxc", "xcv", "cvb", "vbn", "bnm"};
    
    private Scanner scanner;
    private String password;
    private Integer score;
    private List<Character> pass;
    private JFrame parent;
    private JLabel frameScore;

    private boolean num = false;
    private boolean upper = false;
    private boolean lower = false;
    private boolean character = false;
    
    public App() { }
    
    private void checkPassword() {
        System.out.println("Checking password...");
    }

    private void generatePassword() {
        System.out.println("Generating password...");
    }
    
    private void why() {
        System.out.println("Why...");
    }
    
    protected JPanel createContent() {
        JPanel mainPanel = new JPanel();
        JButton check = new JButton("Check Password");
        JButton generate = new JButton("Generate Password");
        
        check.addActionListener(e -> this.checkPassword());
        generate.addActionListener(e -> this.generatePassword());
        
        check.setBounds(500,500, 170, 50);
        generate.setBounds(500, 200, 170, 50);
        
        mainPanel.add(check);
        mainPanel.add(generate);
        
        return mainPanel;
    }
    
    protected JMenuBar createMenu() {
        JMenuBar menuBar = new JMenuBar();
        JMenu checkPass = new JMenu("Check Password");
        JMenu resetPass = new JMenu("Generate Password");
        JMenu extraCodes = new JMenu("Extra Codes");
        
        JMenuItem enterPass = new JMenuItem("Enter password >>>");
        JMenuItem generatePass = new JMenuItem("Generate >>>");
        JMenuItem verify = new JMenuItem("Valid >>>");
        JMenuItem about = new JMenuItem("Why >>>");
        
        enterPass.addActionListener(e -> this.checkPassword());
        generatePass.addActionListener(e -> this.generatePassword());
        verify.addActionListener(e -> JOptionPane.showMessageDialog(this.parent, Arrays.asList(VALID_CHARS).toString(), "Valid Characters", JOptionPane.PLAIN_MESSAGE));
        about.addActionListener(e -> this.why());
        
        menuBar.add(checkPass);
        menuBar.add(resetPass);
        menuBar.add(extraCodes);
        
        checkPass.add(enterPass);
        resetPass.add(generatePass);
        extraCodes.add(verify);
        extraCodes.add(about);
        
        return menuBar;
    }
    
    @Override
    public void run() {
        this.parent = new JFrame();
        
        this.parent.setJMenuBar(createMenu());
        this.parent.setContentPane(createContent());
        this.parent.pack();
        this.parent.setLocationRelativeTo(null);
        this.parent.setVisible(true);
    }
    
    public static void main(String[] args) {
        try {
            SwingUtilities.invokeAndWait(new App());
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

Code doing arithmetic in loop even though it should be out of the loop

copy iconCopydownload iconDownload
count=0
summ=0.0
while True:
    given=input("Enter a number to be added to the average then hit enter to add the next one, or enter q to quit and have the average calculated: ")
    if given == 'q':
        break
    count += 1
    given4math=float(given)
    summ=given4math+summ
print(summ/count)
-----------------------
count=0
summ=0.0
while True:
    given=input("Enter a number to be added to the average then hit enter to add the next one, or enter q to quit and have the average calculated: ")
    if given == 'q':
        break
    count += 1
    try:
        given4math=float(given)
    except:
        print("Invalid Input, please try again!")
        continue
    summ=given4math+summ
print(summ/count)

OCaml This variant expression is expected to have type unit

copy iconCopydownload iconDownload
# let x = for y = 0 to 10 do true done;;
Warning 10: this expression should have type unit.
val x : unit = ()
type chess_board = char array array;;
type position = int * int;;

let chess_board_1:chess_board = [|
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';'Q';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    |];;
    
let queen_targeting (chess:chess_board) pos =
    match pos with
    |(x0, y0) ->
        let result = ref false in
        for y = 0 to Array.length chess -1 do
            for x = 0 to Array.length chess.(y) -1 do
                if chess.(y).(x) = 'Q' 
                then
                    if (
                        x = x0 ||         (* Check horizontaly *)
                        y = y0 ||         (* Check verticaly *)
                        x - x0 = y - y0   (* Check diagonaly *)
                    ) 
                    then result := true
                    else result := false
            done
        done;
        false || !result

;;

queen_targeting chess_board_1 (3, 3);;
-----------------------
# let x = for y = 0 to 10 do true done;;
Warning 10: this expression should have type unit.
val x : unit = ()
type chess_board = char array array;;
type position = int * int;;

let chess_board_1:chess_board = [|
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';'Q';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    |];;
    
let queen_targeting (chess:chess_board) pos =
    match pos with
    |(x0, y0) ->
        let result = ref false in
        for y = 0 to Array.length chess -1 do
            for x = 0 to Array.length chess.(y) -1 do
                if chess.(y).(x) = 'Q' 
                then
                    if (
                        x = x0 ||         (* Check horizontaly *)
                        y = y0 ||         (* Check verticaly *)
                        x - x0 = y - y0   (* Check diagonaly *)
                    ) 
                    then result := true
                    else result := false
            done
        done;
        false || !result

;;

queen_targeting chess_board_1 (3, 3);;
-----------------------
# let x = for y = 0 to 10 do true done;;
Warning 10: this expression should have type unit.
val x : unit = ()
type chess_board = char array array;;
type position = int * int;;

let chess_board_1:chess_board = [|
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    [|' ';' ';' ';' ';' ';' ';'Q';' '|];
    [|' ';' ';' ';' ';' ';' ';' ';' '|];
    |];;
    
let queen_targeting (chess:chess_board) pos =
    match pos with
    |(x0, y0) ->
        let result = ref false in
        for y = 0 to Array.length chess -1 do
            for x = 0 to Array.length chess.(y) -1 do
                if chess.(y).(x) = 'Q' 
                then
                    if (
                        x = x0 ||         (* Check horizontaly *)
                        y = y0 ||         (* Check verticaly *)
                        x - x0 = y - y0   (* Check diagonaly *)
                    ) 
                    then result := true
                    else result := false
            done
        done;
        false || !result

;;

queen_targeting chess_board_1 (3, 3);;
-----------------------
let queen_targeting (chess:chess_board) (x0,y0) =
  let exception Return of bool in
  let threat y x case =
    if case = 'Q' && (x = x0 || y = y0 || x - x0 = y - y0) then
      raise (Return true)
  in
  match Array.iteri (fun y -> Array.iteri (threat y)) chess with
  | () -> false
  | exception Return b -> b
-----------------------
if a < b && b < c then true else false
a < b && b < c
if chess.(y).(x) = 'Q' then
  if x = x0 || y = y0 || x - x0 = y - y0 then 
    true
  else 
    false
if chess.(y).(x) = 'Q' then
  x = x0 || y = y0 || x - x0 = y - y0 
chess.(y).(x) = 'Q' && (x = x0 || y = y0 || x - x0 = y - y0) 
-----------------------
if a < b && b < c then true else false
a < b && b < c
if chess.(y).(x) = 'Q' then
  if x = x0 || y = y0 || x - x0 = y - y0 then 
    true
  else 
    false
if chess.(y).(x) = 'Q' then
  x = x0 || y = y0 || x - x0 = y - y0 
chess.(y).(x) = 'Q' && (x = x0 || y = y0 || x - x0 = y - y0) 
-----------------------
if a < b && b < c then true else false
a < b && b < c
if chess.(y).(x) = 'Q' then
  if x = x0 || y = y0 || x - x0 = y - y0 then 
    true
  else 
    false
if chess.(y).(x) = 'Q' then
  x = x0 || y = y0 || x - x0 = y - y0 
chess.(y).(x) = 'Q' && (x = x0 || y = y0 || x - x0 = y - y0) 
-----------------------
if a < b && b < c then true else false
a < b && b < c
if chess.(y).(x) = 'Q' then
  if x = x0 || y = y0 || x - x0 = y - y0 then 
    true
  else 
    false
if chess.(y).(x) = 'Q' then
  x = x0 || y = y0 || x - x0 = y - y0 
chess.(y).(x) = 'Q' && (x = x0 || y = y0 || x - x0 = y - y0) 
-----------------------
if a < b && b < c then true else false
a < b && b < c
if chess.(y).(x) = 'Q' then
  if x = x0 || y = y0 || x - x0 = y - y0 then 
    true
  else 
    false
if chess.(y).(x) = 'Q' then
  x = x0 || y = y0 || x - x0 = y - y0 
chess.(y).(x) = 'Q' && (x = x0 || y = y0 || x - x0 = y - y0) 

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QUESTION

put link into href using javascript variable html

Asked 2021-Jun-16 at 02:00

I have this:

<script>
    function myFunction() {
    
    //put the url of the girl into the quotes of imgUrl
    var imgUrl = "https://www.linkpicture.com/q/IMG_4902."
    
    //put the name of the model into the quotes of modelName
    var modelName = "modelName"
    
    //put the link to their instagram into the quotes of instagram
    var instagram = "https://www.instagram.com/gianluca/"
    
    
    document.getElementById("myText").innerHTML = modelName;
    document.getElementById("myImg").src = imgUrl;
    document.getElementById("instagram").innerHTML = instagram;
    }
    </script>
<body onload="myFunction()">
            <img src="" id="myImg" alt="">
            <h2 id="myText"></h2>
            <ul>
                <li><a href="" id="instagram"><i class="fab fa-instagram"></i></a></li>

I am trying to use the variable instagram, and put it into the href, but I cannot figure out how to do it. How is this possible?

ANSWER

Answered 2021-Jun-16 at 02:00

Add this to the end of your code in the script

document.getElementById("instagram").href = instagram

Source https://stackoverflow.com/questions/67995413

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Documentation is work in progress. Stay tuned... Joel got into electronics and programming in the 80s because almost everything in music, his first love, is becoming electronic and digital. Since then, he builds his own guitars, effect boxes and synths. He enjoys playing distortion-laden rock guitar, composes and produces his own music in his home studio. Joel de Guzman is the principal architect and engineer at Cycfi Research and a consultant at Ciere Consulting. He is a software engineer specializing in advanced C++ and an advocate of Open Source. He has authored a number of highly successful Open Source projects such as Boost.Spirit, Boost.Phoenix and Boost.Fusion. These libraries are all part of the Boost Libraries, a well respected, peer-reviewed, Open Source, collaborative development effort.

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