storm | faulttolerant realtime computation: stream processing | Pub Sub library

by nathanmarz Java Version: Current License: Apache-2.0

Download this library from

kandi X-RAY | storm Summary

storm is a Java library typically used in Messaging, Pub Sub, Kafka applications. storm has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However storm build file is not available. You can download it from GitHub.

If you have an existing fork/clone of nathanmarz/storm, you can migrate to apache/incubator-storm by doing the following:.

Support

Quality

Security

License

Reuse

kandi-support Support

storm has a medium active ecosystem.
It has 8951 star(s) with 1734 fork(s). There are 1054 watchers for this library.
It had no major release in the last 12 months.
storm has no issues reported. There are 37 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of storm is current.

storm Support

Best in #Pub Sub

Average in #Pub Sub

storm Support

Best in #Pub Sub

Average in #Pub Sub

quality kandi Quality

storm has 0 bugs and 0 code smells.

storm Quality

Best in #Pub Sub

Average in #Pub Sub

storm Quality

Best in #Pub Sub

Average in #Pub Sub

security Security

storm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
storm code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

storm Security

Best in #Pub Sub

Average in #Pub Sub

storm Security

Best in #Pub Sub

Average in #Pub Sub

license License

storm is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

storm License

Best in #Pub Sub

Average in #Pub Sub

storm License

Best in #Pub Sub

Average in #Pub Sub

build Reuse

storm releases are not available. You will need to build from source code and install.
storm has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
It has 54899 lines of code, 6426 functions and 470 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.

storm Reuse

Best in #Pub Sub

Average in #Pub Sub

storm Reuse

Best in #Pub Sub

Average in #Pub Sub

Top functions reviewed by kandi - BETA

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

Builds a topology
Reads a value from a field .
Creates a topology
Checks that a finished ID has completed .
Builds a TopologyBuilder .
Decodes a control message .
Gets the parallelism of group parameters .
Chain end .
Fetch a tuple of DRPC messages .
Submits a topology .

storm Key Features

Create a new fork of apache/incubator-storm

Point your existing clone to the new fork: git remote remove origin git remote add origin git@github.com:username/incubator-storm.git

storm Examples and Code Snippets

See all related Code Snippets

Migrating Git Repos from nathanmarz/storm to apache/incubator-storm

Copy

Download

 git remote remove origin
 git remote add origin git@github.com:username/incubator-storm.git

R - dplyr- Reducing data package 'storms'

Copy

Download

storm_ID <- storms %>%
  select(year,month,name) %>% 
  group_by(year,month,name) %>%
  mutate(ID = stringr::str_c(name, cur_group_id()))

storms %>%
  group_by(name) %>%
  mutate(consec_helper = cumsum(c(1, diff(year) != 1))) %>%
  group_by(name, consec_helper) %>%
  filter(n() > 1)

 name   year
  <chr> <dbl>
1 Zeta   2005
2 Zeta   2006

-----------------------

storm_ID <- storms %>%
  select(year,month,name) %>% 
  group_by(year,month,name) %>%
  mutate(ID = stringr::str_c(name, cur_group_id()))

storms %>%
  group_by(name) %>%
  mutate(consec_helper = cumsum(c(1, diff(year) != 1))) %>%
  group_by(name, consec_helper) %>%
  filter(n() > 1)

 name   year
  <chr> <dbl>
1 Zeta   2005
2 Zeta   2006

-----------------------

storm_ID <- storms %>%
  select(year,month,name) %>% 
  group_by(year,month,name) %>%
  mutate(ID = stringr::str_c(name, cur_group_id()))

storms %>%
  group_by(name) %>%
  mutate(consec_helper = cumsum(c(1, diff(year) != 1))) %>%
  group_by(name, consec_helper) %>%
  filter(n() > 1)

 name   year
  <chr> <dbl>
1 Zeta   2005
2 Zeta   2006

-----------------------

suppressPackageStartupMessages(library(dplyr))

data("storms", package = "dplyr")

Thresh <- 5

storms %>%
  count(name, year, month, day) %>%
  group_by(name) %>%
  mutate(Date = as.Date(ISOdate(year, month, day)),
         DDiff = c(0, diff(Date)) > Thresh,
         DDiff = cumsum(DDiff)) %>%
  group_by(name, DDiff) %>%
  mutate(name = ifelse(DDiff > 0, paste(name, cur_group_id(), sep = "."), name)) %>%
  ungroup() %>%
  group_by(name) %>%
  summarise(name = first(name),
            year = first(year),
            n = sum(n))
#> # A tibble: 512 x 3
#>    name      year     n
#>    <chr>    <dbl> <int>
#>  1 AL011993  1993     8
#>  2 AL012000  2000     4
#>  3 AL021992  1992     5
#>  4 AL021994  1994     6
#>  5 AL021999  1999     4
#>  6 AL022000  2000    12
#>  7 AL022001  2001     5
#>  8 AL022003  2003     4
#>  9 AL022006  2006     5
#> 10 AL031987  1987    32
#> # ... with 502 more rows

suppressPackageStartupMessages(library(dplyr))

data("storms", package = "dplyr")

Thresh <- 5

storms %>%
  count(name, year, month, day) %>%
  group_by(name) %>%
  mutate(Date = as.Date(ISOdate(year, month, day)),
         DDiff = c(0, diff(Date)) > Thresh,
         DDiff = cumsum(DDiff)) %>%
  group_by(name, DDiff) %>%
  mutate(name = ifelse(DDiff > 0, paste(name, cur_group_id(), sep = "."), name)) %>%
  ungroup() %>%
  group_by(name) %>%
  summarise(name = first(name),
            year = first(year),
            n = sum(n)) -> rui


id <- c()
j <- 1
k <- 1
for(i in storms$name) {
  if(k-1 == 0){
    id <- append(id, j)
    k <- k+1
    next
  }
  if(i != storms$name[k-1])
  {
    j <- j+1
  }
  id <- append(id, j)
  k <- k+1
}

cbind(storms, id) %>% 
  count(name, id) -> storm_count

# two rows
anti_join(
  rui %>% mutate(name = sub("\\.\\d+$", "", name)), 
  storm_count,
  by = c("name", "n")
)
#> # A tibble: 2 x 3
#>   name    year     n
#>   <chr>  <dbl> <int>
#> 1 Dorian  2013    16
#> 2 Dorian  2013     4

# just one row
anti_join(
  storm_count,
  rui %>% mutate(name = sub("\\.\\d+$", "", name)),
  by = c("name", "n")
)
#>     name  id  n
#> 1 Dorian 397 20

# see here the dates of 2013-07-27 and 2013-08-02
storms %>%
  filter(name == "Dorian", year == 2013) %>%
  count(name, year, month, day)
#> # A tibble: 7 x 5
#>   name    year month   day     n
#>   <chr>  <dbl> <dbl> <int> <int>
#> 1 Dorian  2013     7    23     1
#> 2 Dorian  2013     7    24     4
#> 3 Dorian  2013     7    25     4
#> 4 Dorian  2013     7    26     4
#> 5 Dorian  2013     7    27     3
#> 6 Dorian  2013     8     2     1
#> 7 Dorian  2013     8     3     3

-----------------------

suppressPackageStartupMessages(library(dplyr))

data("storms", package = "dplyr")

Thresh <- 5

storms %>%
  count(name, year, month, day) %>%
  group_by(name) %>%
  mutate(Date = as.Date(ISOdate(year, month, day)),
         DDiff = c(0, diff(Date)) > Thresh,
         DDiff = cumsum(DDiff)) %>%
  group_by(name, DDiff) %>%
  mutate(name = ifelse(DDiff > 0, paste(name, cur_group_id(), sep = "."), name)) %>%
  ungroup() %>%
  group_by(name) %>%
  summarise(name = first(name),
            year = first(year),
            n = sum(n))
#> # A tibble: 512 x 3
#>    name      year     n
#>    <chr>    <dbl> <int>
#>  1 AL011993  1993     8
#>  2 AL012000  2000     4
#>  3 AL021992  1992     5
#>  4 AL021994  1994     6
#>  5 AL021999  1999     4
#>  6 AL022000  2000    12
#>  7 AL022001  2001     5
#>  8 AL022003  2003     4
#>  9 AL022006  2006     5
#> 10 AL031987  1987    32
#> # ... with 502 more rows

suppressPackageStartupMessages(library(dplyr))

data("storms", package = "dplyr")

Thresh <- 5

storms %>%
  count(name, year, month, day) %>%
  group_by(name) %>%
  mutate(Date = as.Date(ISOdate(year, month, day)),
         DDiff = c(0, diff(Date)) > Thresh,
         DDiff = cumsum(DDiff)) %>%
  group_by(name, DDiff) %>%
  mutate(name = ifelse(DDiff > 0, paste(name, cur_group_id(), sep = "."), name)) %>%
  ungroup() %>%
  group_by(name) %>%
  summarise(name = first(name),
            year = first(year),
            n = sum(n)) -> rui


id <- c()
j <- 1
k <- 1
for(i in storms$name) {
  if(k-1 == 0){
    id <- append(id, j)
    k <- k+1
    next
  }
  if(i != storms$name[k-1])
  {
    j <- j+1
  }
  id <- append(id, j)
  k <- k+1
}

cbind(storms, id) %>% 
  count(name, id) -> storm_count

# two rows
anti_join(
  rui %>% mutate(name = sub("\\.\\d+$", "", name)), 
  storm_count,
  by = c("name", "n")
)
#> # A tibble: 2 x 3
#>   name    year     n
#>   <chr>  <dbl> <int>
#> 1 Dorian  2013    16
#> 2 Dorian  2013     4

# just one row
anti_join(
  storm_count,
  rui %>% mutate(name = sub("\\.\\d+$", "", name)),
  by = c("name", "n")
)
#>     name  id  n
#> 1 Dorian 397 20

# see here the dates of 2013-07-27 and 2013-08-02
storms %>%
  filter(name == "Dorian", year == 2013) %>%
  count(name, year, month, day)
#> # A tibble: 7 x 5
#>   name    year month   day     n
#>   <chr>  <dbl> <dbl> <int> <int>
#> 1 Dorian  2013     7    23     1
#> 2 Dorian  2013     7    24     4
#> 3 Dorian  2013     7    25     4
#> 4 Dorian  2013     7    26     4
#> 5 Dorian  2013     7    27     3
#> 6 Dorian  2013     8     2     1
#> 7 Dorian  2013     8     3     3

-----------------------

install.packages(dplyr)
library(dplyr)

id <- c()
j <- 1
k <- 1
for(i in storms$name) {
  if(k-1 == 0){
    id <- append(id, j)
    k <- k+1
    next
  }
  if(i != storms$name[k-1])
  {
    j <- j+1
  }
  id <- append(id, j)
  k <- k+1
}
storms <- cbind(storms, id)
View(storms)

How to update a SelectizeInput depending on a textInput in Shiny

Copy

Download

my_list <- reactive({
    if (!input$my_input == "") {
      data <- as.data.frame(storms)
      
      res <- subset(data, grepl(pattern = str_to_sentence(input$my_input), data$name), name)
      
      res <- as.factor(res$name)
      
      return(res)
    } 
  })

Print says the whole variable

Copy

Download

TEMPLATE = """{header}
"Looks like this storm won't stop for another {days}, we will have to wait"
(You can barely hear him but thats all you hear)"""

TEMPLATE.format(
    header="-Captain Strand-",
    days=days + playerguess,
)

>>> print(TEMPLATE.format(
...     header="-Captain Strand-",
...     days=days + playerguess,
... ))
-Captain Strand-
"Looks like this storm won't stop for another 5, we will have to wait"
(You can barely hear him but thats all you hear)

>>> example = ("Looks like this storm won't stop for another ", days + playerguess, " we will have to wait")
>>> example  # example is a tuple of strings
("Looks like this storm won't stop for another ", 5, ' we will have to wait')
>>> example = "".join(("Looks like this storm won't stop for another ", str(days + playerguess), " we will have to wait"))
>>> example  # example is a single string
"Looks like this storm won't stop for another 5 we will have to wait"

-----------------------

TEMPLATE = """{header}
"Looks like this storm won't stop for another {days}, we will have to wait"
(You can barely hear him but thats all you hear)"""

TEMPLATE.format(
    header="-Captain Strand-",
    days=days + playerguess,
)

>>> print(TEMPLATE.format(
...     header="-Captain Strand-",
...     days=days + playerguess,
... ))
-Captain Strand-
"Looks like this storm won't stop for another 5, we will have to wait"
(You can barely hear him but thats all you hear)

>>> example = ("Looks like this storm won't stop for another ", days + playerguess, " we will have to wait")
>>> example  # example is a tuple of strings
("Looks like this storm won't stop for another ", 5, ' we will have to wait')
>>> example = "".join(("Looks like this storm won't stop for another ", str(days + playerguess), " we will have to wait"))
>>> example  # example is a single string
"Looks like this storm won't stop for another 5 we will have to wait"

-----------------------

TEMPLATE = """{header}
"Looks like this storm won't stop for another {days}, we will have to wait"
(You can barely hear him but thats all you hear)"""

TEMPLATE.format(
    header="-Captain Strand-",
    days=days + playerguess,
)

>>> print(TEMPLATE.format(
...     header="-Captain Strand-",
...     days=days + playerguess,
... ))
-Captain Strand-
"Looks like this storm won't stop for another 5, we will have to wait"
(You can barely hear him but thats all you hear)

>>> example = ("Looks like this storm won't stop for another ", days + playerguess, " we will have to wait")
>>> example  # example is a tuple of strings
("Looks like this storm won't stop for another ", 5, ' we will have to wait')
>>> example = "".join(("Looks like this storm won't stop for another ", str(days + playerguess), " we will have to wait"))
>>> example  # example is a single string
"Looks like this storm won't stop for another 5 we will have to wait"

How to create optimized schedule that avoids duplicates

Copy

Download

def combinations(matches, day, schedules, current):
    """Backtracking function for selecting unique schedules."""
    # base case when you have a match from each day
    if day > max(matches.keys()):  
        schedules.append(current[:])
        return
    # skip over days where there are no matches
    while day not in matches:
        day += 1
    # select one match for the current date
    for i in range(len(matches[day])):
        teams = matches[day][i]
        current_teams = [j for i in current for j in i]
        # check if the teams are already in the current schedule
        if teams[0] in current_teams or teams[1] in current_teams: 
            continue
        del matches[day][i]
        # recursive case
        combinations(matches, day + 1, schedules, current + [teams])
        matches[day].insert(i,teams)
    return




def format(inp):
    """Formats input data into a dictionary."""
    lines = inp.split("\n")[2:]     # split lines of input data
    matches = [(line.split("|")[1:-1]) for line in lines]
    schedule = {}
    # add matches to dict with date as key and matches as value.
    for day, match in matches:
        day = int(day.strip())
        teams = match.strip().split(" vs ")
        try:
            schedule[day].append(teams)
        except KeyError:
            schedule[day] = [teams]
    ideal = []
    # use backtracking algorithm to get desired results
    combinations(schedule, 1, ideal, [])
    show_schedules(ideal)


def show_schedules(results):
    for i, x in enumerate(results):
        print(f"Schedule {i+1}")
        for day, match in enumerate(x):
            print(f"Day: {day+1} - {match[0]} vs. {match[1]}")
        print("\n")


format(inp)  # <- entry point:`inp` is the pre-formatted data `str`

Schedule 1
Day: 1 - hot ice vs. playerz
Day: 2 - avalanche vs. cold force
Day: 3 - quiet storm vs. freeze
Day: 4 - out of order vs. game spot
Day: 5 - slick ice vs. caps
Day: 6 - rare air vs. in too deep


Schedule 2
Day: 1 - hot ice vs. playerz
Day: 2 - avalanche vs. cold force
Day: 3 - 4x4's vs. out of order
Day: 4 - cold as ice vs. rare air
Day: 5 - blizzard vs. quiet storm
Day: 6 - game spot vs. freeze

-----------------------

def combinations(matches, day, schedules, current):
    """Backtracking function for selecting unique schedules."""
    # base case when you have a match from each day
    if day > max(matches.keys()):  
        schedules.append(current[:])
        return
    # skip over days where there are no matches
    while day not in matches:
        day += 1
    # select one match for the current date
    for i in range(len(matches[day])):
        teams = matches[day][i]
        current_teams = [j for i in current for j in i]
        # check if the teams are already in the current schedule
        if teams[0] in current_teams or teams[1] in current_teams: 
            continue
        del matches[day][i]
        # recursive case
        combinations(matches, day + 1, schedules, current + [teams])
        matches[day].insert(i,teams)
    return




def format(inp):
    """Formats input data into a dictionary."""
    lines = inp.split("\n")[2:]     # split lines of input data
    matches = [(line.split("|")[1:-1]) for line in lines]
    schedule = {}
    # add matches to dict with date as key and matches as value.
    for day, match in matches:
        day = int(day.strip())
        teams = match.strip().split(" vs ")
        try:
            schedule[day].append(teams)
        except KeyError:
            schedule[day] = [teams]
    ideal = []
    # use backtracking algorithm to get desired results
    combinations(schedule, 1, ideal, [])
    show_schedules(ideal)


def show_schedules(results):
    for i, x in enumerate(results):
        print(f"Schedule {i+1}")
        for day, match in enumerate(x):
            print(f"Day: {day+1} - {match[0]} vs. {match[1]}")
        print("\n")


format(inp)  # <- entry point:`inp` is the pre-formatted data `str`

Schedule 1
Day: 1 - hot ice vs. playerz
Day: 2 - avalanche vs. cold force
Day: 3 - quiet storm vs. freeze
Day: 4 - out of order vs. game spot
Day: 5 - slick ice vs. caps
Day: 6 - rare air vs. in too deep


Schedule 2
Day: 1 - hot ice vs. playerz
Day: 2 - avalanche vs. cold force
Day: 3 - 4x4's vs. out of order
Day: 4 - cold as ice vs. rare air
Day: 5 - blizzard vs. quiet storm
Day: 6 - game spot vs. freeze

Problem with non-standard evaluation in disk.frame objects using data.table syntax

Copy

Download

storms_df[name %like% "^A"]

nm <- "^A"
storms_df[name %like% nm]

grep1 <- function(dfr, storm_name) { dfr[name %like% "^A"]; }
grep1(storms_df)

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

grep3 <- function(dfr, storm_name) { 
  eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
}
grep3(storms_df, "^A")
#        name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#      <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
#   1:    Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   2:    Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   3:    Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1    25     1013          NA          NA
# ...

debug(grep3)
grep3(storms_df, "^A")
# debugging in: grep3(storms_df, "^A")
# debug at #1: {
#     eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
# }
# Browse[2]> 
substitute(dfr[name %like% storm_name], list(storm_name = storm_name))
# dfr[name %like% "^A"]

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

### but let's pre-define `storm_name` outside of the function,
### then re-define the function (no change)
storm_name <- "^A"
grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
head(grep2(storms_df, "^A"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

head(grep2(storms_df, "^B"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

bench::mark(
  raw = dfr[name %like% "^A"],
  subst = eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name))),
  iterations = 1000
)
# # A tibble: 2 x 13
#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result                  memory               time               gc                  
#   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>                  <list>               <list>             <list>              
# 1 raw          12.9ms   16.8ms      55.2    1.69MB     3.97   933    67      16.9s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>
# 2 subst        12.8ms   15.8ms      60.5    1.69MB     3.25   949    51      15.7s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>

-----------------------

storms_df[name %like% "^A"]

nm <- "^A"
storms_df[name %like% nm]

grep1 <- function(dfr, storm_name) { dfr[name %like% "^A"]; }
grep1(storms_df)

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

grep3 <- function(dfr, storm_name) { 
  eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
}
grep3(storms_df, "^A")
#        name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#      <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
#   1:    Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   2:    Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   3:    Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1    25     1013          NA          NA
# ...

debug(grep3)
grep3(storms_df, "^A")
# debugging in: grep3(storms_df, "^A")
# debug at #1: {
#     eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
# }
# Browse[2]> 
substitute(dfr[name %like% storm_name], list(storm_name = storm_name))
# dfr[name %like% "^A"]

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

### but let's pre-define `storm_name` outside of the function,
### then re-define the function (no change)
storm_name <- "^A"
grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
head(grep2(storms_df, "^A"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

head(grep2(storms_df, "^B"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

bench::mark(
  raw = dfr[name %like% "^A"],
  subst = eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name))),
  iterations = 1000
)
# # A tibble: 2 x 13
#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result                  memory               time               gc                  
#   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>                  <list>               <list>             <list>              
# 1 raw          12.9ms   16.8ms      55.2    1.69MB     3.97   933    67      16.9s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>
# 2 subst        12.8ms   15.8ms      60.5    1.69MB     3.25   949    51      15.7s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>

-----------------------

storms_df[name %like% "^A"]

nm <- "^A"
storms_df[name %like% nm]

grep1 <- function(dfr, storm_name) { dfr[name %like% "^A"]; }
grep1(storms_df)

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

grep3 <- function(dfr, storm_name) { 
  eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
}
grep3(storms_df, "^A")
#        name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#      <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
#   1:    Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   2:    Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   3:    Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1    25     1013          NA          NA
# ...

debug(grep3)
grep3(storms_df, "^A")
# debugging in: grep3(storms_df, "^A")
# debug at #1: {
#     eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
# }
# Browse[2]> 
substitute(dfr[name %like% storm_name], list(storm_name = storm_name))
# dfr[name %like% "^A"]

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

### but let's pre-define `storm_name` outside of the function,
### then re-define the function (no change)
storm_name <- "^A"
grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
head(grep2(storms_df, "^A"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

head(grep2(storms_df, "^B"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

bench::mark(
  raw = dfr[name %like% "^A"],
  subst = eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name))),
  iterations = 1000
)
# # A tibble: 2 x 13
#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result                  memory               time               gc                  
#   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>                  <list>               <list>             <list>              
# 1 raw          12.9ms   16.8ms      55.2    1.69MB     3.97   933    67      16.9s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>
# 2 subst        12.8ms   15.8ms      60.5    1.69MB     3.25   949    51      15.7s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>

-----------------------

storms_df[name %like% "^A"]

nm <- "^A"
storms_df[name %like% nm]

grep1 <- function(dfr, storm_name) { dfr[name %like% "^A"]; }
grep1(storms_df)

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

grep3 <- function(dfr, storm_name) { 
  eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
}
grep3(storms_df, "^A")
#        name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#      <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
#   1:    Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   2:    Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   3:    Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1    25     1013          NA          NA
# ...

debug(grep3)
grep3(storms_df, "^A")
# debugging in: grep3(storms_df, "^A")
# debug at #1: {
#     eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
# }
# Browse[2]> 
substitute(dfr[name %like% storm_name], list(storm_name = storm_name))
# dfr[name %like% "^A"]

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

### but let's pre-define `storm_name` outside of the function,
### then re-define the function (no change)
storm_name <- "^A"
grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
head(grep2(storms_df, "^A"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

head(grep2(storms_df, "^B"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

bench::mark(
  raw = dfr[name %like% "^A"],
  subst = eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name))),
  iterations = 1000
)
# # A tibble: 2 x 13
#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result                  memory               time               gc                  
#   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>                  <list>               <list>             <list>              
# 1 raw          12.9ms   16.8ms      55.2    1.69MB     3.97   933    67      16.9s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>
# 2 subst        12.8ms   15.8ms      60.5    1.69MB     3.25   949    51      15.7s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>

-----------------------

storms_df[name %like% "^A"]

nm <- "^A"
storms_df[name %like% nm]

grep1 <- function(dfr, storm_name) { dfr[name %like% "^A"]; }
grep1(storms_df)

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

grep3 <- function(dfr, storm_name) { 
  eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
}
grep3(storms_df, "^A")
#        name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#      <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
#   1:    Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   2:    Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   3:    Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1    25     1013          NA          NA
# ...

debug(grep3)
grep3(storms_df, "^A")
# debugging in: grep3(storms_df, "^A")
# debug at #1: {
#     eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
# }
# Browse[2]> 
substitute(dfr[name %like% storm_name], list(storm_name = storm_name))
# dfr[name %like% "^A"]

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

### but let's pre-define `storm_name` outside of the function,
### then re-define the function (no change)
storm_name <- "^A"
grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
head(grep2(storms_df, "^A"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

head(grep2(storms_df, "^B"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

bench::mark(
  raw = dfr[name %like% "^A"],
  subst = eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name))),
  iterations = 1000
)
# # A tibble: 2 x 13
#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result                  memory               time               gc                  
#   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>                  <list>               <list>             <list>              
# 1 raw          12.9ms   16.8ms      55.2    1.69MB     3.97   933    67      16.9s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>
# 2 subst        12.8ms   15.8ms      60.5    1.69MB     3.25   949    51      15.7s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>

-----------------------

storms_df[name %like% "^A"]

nm <- "^A"
storms_df[name %like% nm]

grep1 <- function(dfr, storm_name) { dfr[name %like% "^A"]; }
grep1(storms_df)

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

grep3 <- function(dfr, storm_name) { 
  eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
}
grep3(storms_df, "^A")
#        name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#      <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
#   1:    Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   2:    Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   3:    Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1    25     1013          NA          NA
# ...

debug(grep3)
grep3(storms_df, "^A")
# debugging in: grep3(storms_df, "^A")
# debug at #1: {
#     eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
# }
# Browse[2]> 
substitute(dfr[name %like% storm_name], list(storm_name = storm_name))
# dfr[name %like% "^A"]

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

### but let's pre-define `storm_name` outside of the function,
### then re-define the function (no change)
storm_name <- "^A"
grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
head(grep2(storms_df, "^A"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

head(grep2(storms_df, "^B"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

bench::mark(
  raw = dfr[name %like% "^A"],
  subst = eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name))),
  iterations = 1000
)
# # A tibble: 2 x 13
#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result                  memory               time               gc                  
#   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>                  <list>               <list>             <list>              
# 1 raw          12.9ms   16.8ms      55.2    1.69MB     3.97   933    67      16.9s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>
# 2 subst        12.8ms   15.8ms      60.5    1.69MB     3.25   949    51      15.7s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>

-----------------------

storms_df[name %like% "^A"]

nm <- "^A"
storms_df[name %like% nm]

grep1 <- function(dfr, storm_name) { dfr[name %like% "^A"]; }
grep1(storms_df)

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

grep3 <- function(dfr, storm_name) { 
  eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
}
grep3(storms_df, "^A")
#        name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#      <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
#   1:    Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   2:    Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   3:    Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1    25     1013          NA          NA
# ...

debug(grep3)
grep3(storms_df, "^A")
# debugging in: grep3(storms_df, "^A")
# debug at #1: {
#     eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
# }
# Browse[2]> 
substitute(dfr[name %like% storm_name], list(storm_name = storm_name))
# dfr[name %like% "^A"]

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

### but let's pre-define `storm_name` outside of the function,
### then re-define the function (no change)
storm_name <- "^A"
grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
head(grep2(storms_df, "^A"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

head(grep2(storms_df, "^B"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

bench::mark(
  raw = dfr[name %like% "^A"],
  subst = eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name))),
  iterations = 1000
)
# # A tibble: 2 x 13
#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result                  memory               time               gc                  
#   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>                  <list>               <list>             <list>              
# 1 raw          12.9ms   16.8ms      55.2    1.69MB     3.97   933    67      16.9s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>
# 2 subst        12.8ms   15.8ms      60.5    1.69MB     3.25   949    51      15.7s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>

simplify java stream to return an error message

Copy

Download

Stream<String> stream = Files.lines(path);
Stream<String> filteredStream= stream .filter(str -> str.length() > 12);      

Stream<String> matching = filteredStream.filter(str -> Pattern.matches(regex, str));

Stream<String> matching = filteredStream.filter(str -> {
      if(str.length() > 12 && Pattern.matches(regex, str))
          return true;
      return false;
     });

try{
Stream<String> matching = filteredStream.filter(str -> {
      if(str.length() > 11 )
           throw new Exception("Item must not exceed 12 numbers");
      if(Pattern.matches(regex, str))
          return true;
      throw new Exception("Invalid Item format");         
     });
}
catch(Exception e){
   log.error(e);
   message = e;
}

-----------------------

Stream<String> stream = Files.lines(path);
Stream<String> filteredStream= stream .filter(str -> str.length() > 12);      

Stream<String> matching = filteredStream.filter(str -> Pattern.matches(regex, str));

Stream<String> matching = filteredStream.filter(str -> {
      if(str.length() > 12 && Pattern.matches(regex, str))
          return true;
      return false;
     });

try{
Stream<String> matching = filteredStream.filter(str -> {
      if(str.length() > 11 )
           throw new Exception("Item must not exceed 12 numbers");
      if(Pattern.matches(regex, str))
          return true;
      throw new Exception("Invalid Item format");         
     });
}
catch(Exception e){
   log.error(e);
   message = e;
}

-----------------------

Stream<String> stream = Files.lines(path);
Stream<String> filteredStream= stream .filter(str -> str.length() > 12);      

Stream<String> matching = filteredStream.filter(str -> Pattern.matches(regex, str));

Stream<String> matching = filteredStream.filter(str -> {
      if(str.length() > 12 && Pattern.matches(regex, str))
          return true;
      return false;
     });

try{
Stream<String> matching = filteredStream.filter(str -> {
      if(str.length() > 11 )
           throw new Exception("Item must not exceed 12 numbers");
      if(Pattern.matches(regex, str))
          return true;
      throw new Exception("Invalid Item format");         
     });
}
catch(Exception e){
   log.error(e);
   message = e;
}

How to plot a point on a time series in python

Copy

Download

lati = stormtrack_lat.values
loni = stormtrack_lon.values
timei = stormtrack_datetime.values

fig2 = plt.figure(figsize=(20, 20), dpi=300)

for i, dummy in enumerate(lati):
    dsloc = SSTskin_file.sel(lon=loni[i], lat=lati[i], method='nearest')
    dstime = SSTskin_file.sel(time=timei[i], lon=loni[i], lat=lati[i], method='nearest')
    skin_celsius = (dsloc['analysed_sst']) - 273.15
    timesteps = dsloc.time.values
    timestep = dstime.time.values
    timevalue = ((dstime['analysed_sst']).values) - 273.15
    lineplot = plt.plot(timesteps, skin_celsius )
    dotplot = plt.plot(timestep, timevalue, "or") 
plt.title('Skin SST over time at storm track locations',  fontsize = 20 )
plt.xlabel('Date', fontsize = 16)
plt.ylabel('Skin SST in $^{\circ}C$',  fontsize = 16)
plt.xticks(fontsize = 16)
plt.yticks(fontsize = 16)
#plt.legend(lineplot) #Here I would like to plot the legend for the line plots (time series data). I want to indicate the location (longitude and latitude) of the time series
plt.legend(dotplot[:1], ['Storm track at location and time'], fontsize = 16);
fig2.savefig('SSTskin_storm_timeseries_test.png', bbox_inches='tight')

How to make the images bigger when clicked? JavaScrpit

Copy

Download

<div class="backdrop">
  <div class="popup">
    <img src="" class="popup-image" />
  </div>
</div>

position: fixed;
top: 0;
left: 0;
bottom: 0;
right: 0;

position: absolute;
width: 500px;
height: auto;
top: 50%; // to put it in the middle
left: 50%;
transform: translate(-50%, -50%);

-----------------------

<div class="backdrop">
  <div class="popup">
    <img src="" class="popup-image" />
  </div>
</div>

position: fixed;
top: 0;
left: 0;
bottom: 0;
right: 0;

position: absolute;
width: 500px;
height: auto;
top: 50%; // to put it in the middle
left: 50%;
transform: translate(-50%, -50%);

-----------------------

<div class="backdrop">
  <div class="popup">
    <img src="" class="popup-image" />
  </div>
</div>

position: fixed;
top: 0;
left: 0;
bottom: 0;
right: 0;

position: absolute;
width: 500px;
height: auto;
top: 50%; // to put it in the middle
left: 50%;
transform: translate(-50%, -50%);

-----------------------

<div id="myModal" class="modal">
  <div class="modal-content">
    <span class="close">&times;</span>
    <img src="" id="modal-image" />
  </div>
</div>

.modal {
  display: none; /* Hidden by default */
  position: fixed; /* Stay in place */
  z-index: 1; /* Sit on top */
  padding-top: 50px; /* Location of the box */
  left: 0;
  top: 0;
  width: 100%; /* Full width */
  height: 100%; /* Full height */
  overflow: auto; /* Enable scroll if needed */
  background-color: rgb(0,0,0); /* Fallback color */
  background-color: rgba(0,0,0,0.4); /* Black w/ opacity */
}

.modal-content {
  margin: auto;
  text-align: center;
}

.modal-image {
  display: inline-block;
}

// Get the gallery box
var imageBox1 = document.getElementById("imageBox1");

// Get the modal image tag
var modal = document.getElementById("myModal");

var modalImage = document.getElementById("modal-image");

// When the user clicks the big picture, set the image and open the modal
imageBox1.onclick = function (e) {
  var src = e.srcElement.src;
  modal.style.display = "block";
  modalImage.src = src;
};

-----------------------

<div id="myModal" class="modal">
  <div class="modal-content">
    <span class="close">&times;</span>
    <img src="" id="modal-image" />
  </div>
</div>

.modal {
  display: none; /* Hidden by default */
  position: fixed; /* Stay in place */
  z-index: 1; /* Sit on top */
  padding-top: 50px; /* Location of the box */
  left: 0;
  top: 0;
  width: 100%; /* Full width */
  height: 100%; /* Full height */
  overflow: auto; /* Enable scroll if needed */
  background-color: rgb(0,0,0); /* Fallback color */
  background-color: rgba(0,0,0,0.4); /* Black w/ opacity */
}

.modal-content {
  margin: auto;
  text-align: center;
}

.modal-image {
  display: inline-block;
}

// Get the gallery box
var imageBox1 = document.getElementById("imageBox1");

// Get the modal image tag
var modal = document.getElementById("myModal");

var modalImage = document.getElementById("modal-image");

// When the user clicks the big picture, set the image and open the modal
imageBox1.onclick = function (e) {
  var src = e.srcElement.src;
  modal.style.display = "block";
  modalImage.src = src;
};

-----------------------

<div id="myModal" class="modal">
  <div class="modal-content">
    <span class="close">&times;</span>
    <img src="" id="modal-image" />
  </div>
</div>

.modal {
  display: none; /* Hidden by default */
  position: fixed; /* Stay in place */
  z-index: 1; /* Sit on top */
  padding-top: 50px; /* Location of the box */
  left: 0;
  top: 0;
  width: 100%; /* Full width */
  height: 100%; /* Full height */
  overflow: auto; /* Enable scroll if needed */
  background-color: rgb(0,0,0); /* Fallback color */
  background-color: rgba(0,0,0,0.4); /* Black w/ opacity */
}

.modal-content {
  margin: auto;
  text-align: center;
}

.modal-image {
  display: inline-block;
}

// Get the gallery box
var imageBox1 = document.getElementById("imageBox1");

// Get the modal image tag
var modal = document.getElementById("myModal");

var modalImage = document.getElementById("modal-image");

// When the user clicks the big picture, set the image and open the modal
imageBox1.onclick = function (e) {
  var src = e.srcElement.src;
  modal.style.display = "block";
  modalImage.src = src;
};

Clustering in R using K-mean

Copy

Download

library(fastDummies)

df_dummy <- dummy_columns(df, select_columns = "Yeast", remove_selected_columns = TRUE)

res <- NbClust(df_dummy[2:9], min.nc = 2, max.nc = 15, method = "ward.D2")

react router v5 to v6 nested route not working

Copy

Download

<Routes>
  <Route
    path="galleria"
    element={<Gallery datas={data} clickEvent={handleSlideShow} />}
  />
  <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />   
</Routes>

<div className="column nav-links">
  <span className="prev-btn">
    {prevSlide <= -1 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[prevSlide].urlPath}></Link>
    )}
  </span>
  <span className="next-btn">
    {nextSlide >= 15 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[nextSlide].urlPath}></Link>
    )}
  </span>
</div>

<Link
  className="active"
  to={`../${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`../${datas[nextSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[nextSlide].urlPath}`}
>
</Link>

-----------------------

<Routes>
  <Route
    path="galleria"
    element={<Gallery datas={data} clickEvent={handleSlideShow} />}
  />
  <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />   
</Routes>

<div className="column nav-links">
  <span className="prev-btn">
    {prevSlide <= -1 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[prevSlide].urlPath}></Link>
    )}
  </span>
  <span className="next-btn">
    {nextSlide >= 15 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[nextSlide].urlPath}></Link>
    )}
  </span>
</div>

<Link
  className="active"
  to={`../${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`../${datas[nextSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[nextSlide].urlPath}`}
>
</Link>

-----------------------

<Routes>
  <Route
    path="galleria"
    element={<Gallery datas={data} clickEvent={handleSlideShow} />}
  />
  <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />   
</Routes>

<div className="column nav-links">
  <span className="prev-btn">
    {prevSlide <= -1 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[prevSlide].urlPath}></Link>
    )}
  </span>
  <span className="next-btn">
    {nextSlide >= 15 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[nextSlide].urlPath}></Link>
    )}
  </span>
</div>

<Link
  className="active"
  to={`../${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`../${datas[nextSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[nextSlide].urlPath}`}
>
</Link>

-----------------------

<Routes>
  <Route
    path="galleria"
    element={<Gallery datas={data} clickEvent={handleSlideShow} />}
  />
  <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />   
</Routes>

<div className="column nav-links">
  <span className="prev-btn">
    {prevSlide <= -1 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[prevSlide].urlPath}></Link>
    )}
  </span>
  <span className="next-btn">
    {nextSlide >= 15 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[nextSlide].urlPath}></Link>
    )}
  </span>
</div>

<Link
  className="active"
  to={`../${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`../${datas[nextSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[nextSlide].urlPath}`}
>
</Link>

-----------------------

<Routes>
  <Route
    path="galleria"
    element={<Gallery datas={data} clickEvent={handleSlideShow} />}
  />
  <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />   
</Routes>

<div className="column nav-links">
  <span className="prev-btn">
    {prevSlide <= -1 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[prevSlide].urlPath}></Link>
    )}
  </span>
  <span className="next-btn">
    {nextSlide >= 15 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[nextSlide].urlPath}></Link>
    )}
  </span>
</div>

<Link
  className="active"
  to={`../${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`../${datas[nextSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[nextSlide].urlPath}`}
>
</Link>

-----------------------

<Routes>
  <Route
    path="galleria"
    element={<Gallery datas={data} clickEvent={handleSlideShow} />}
  />
  <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />   
</Routes>

<div className="column nav-links">
  <span className="prev-btn">
    {prevSlide <= -1 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[prevSlide].urlPath}></Link>
    )}
  </span>
  <span className="next-btn">
    {nextSlide >= 15 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[nextSlide].urlPath}></Link>
    )}
  </span>
</div>

<Link
  className="active"
  to={`../${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`../${datas[nextSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[prevSlide].urlPath}`}
>
</Link>

<Link
  className="active"
  to={`/galleria/${datas[nextSlide].urlPath}`}
>
</Link>

See all related Code Snippets

Community Discussions

Trending Discussions on storm

R - dplyr- Reducing data package 'storms'
How to update a SelectizeInput depending on a textInput in Shiny
Print says the whole variable
How to create optimized schedule that avoids duplicates
Problem with non-standard evaluation in disk.frame objects using data.table syntax
simplify java stream to return an error message
How to plot a point on a time series in python
How to make the images bigger when clicked? JavaScrpit
Clustering in R using K-mean
react router v5 to v6 nested route not working

Trending Discussions on storm

QUESTION

R - dplyr- Reducing data package 'storms'

Asked 2022-Apr-15 at 11:08

I am working with dplyr and the data package 'storms'.

I need a table in which I have each measured storm in a column. Then I want to give each row an ID.

So far I have

storm_ID <- storms %>%
  select(year,month,name) %>% 
  group_by(year,month,name) %>% 
  summarise(ID = n())
storm_ID

View(storm_ID)

The only thing is that it doesn't do anything for me.

I don't quite understand how I can see every single storm in the table. I had previously sorted them by name. Then I get 214 storms. However, storms with the same name occur in several years.

At the end I want something like:

name   |   year   |   month   |   day   |    ID 

  |          |          |          |          |
Zeta       2005        12          31       Zeta1 
Zeta       2006        1           1        Zeta1
  |          |          |          |          |
Zeta       2020        10          24       Zeta2

To do this, I need to know if a storm occurred in 2 years (i.e. from 2005-12-31 to 2006-01-01) But this should then only be counted as one storm.

After that I should then be able to evaluate the duration, wind speed difference and pressure difference per storm. What I had already evaluated with the wrong sorting.

Help would be nice.

Thanks in advance.

ANSWER

Answered 2022-Apr-15 at 09:37

For you first problem:

storm_ID <- storms %>%
  select(year,month,name) %>% 
  group_by(year,month,name) %>%
  mutate(ID = stringr::str_c(name, cur_group_id()))

This create a unique Storm-Name-ID, e.g. Amy1, Amy2 etc.

This is how you can check if a storm has happened in consecutive years

storms %>%
  group_by(name) %>%
  mutate(consec_helper = cumsum(c(1, diff(year) != 1))) %>%
  group_by(name, consec_helper) %>%
  filter(n() > 1)

I find this to be true only for Zeta

 name   year
  <chr> <dbl>
1 Zeta   2005
2 Zeta   2006

Source https://stackoverflow.com/questions/71882104

QUESTION

How to update a SelectizeInput depending on a textInput in Shiny

Asked 2022-Apr-14 at 08:33

I have create one app that contains a textInput and a selectizeInput. Depending on the user's input and if the input can be found in one dataset, you will see all the possibilities according to that textInput in the selectizeInput.

In this way, if the user introduces a word that it is not in the dataset, the selectizeInput can't display any choice.

Everything works fine, but I found one problem. If the user starts writing a correct word, the user gets a dropdown list... and then, if the input is removed... the dropdown list is still there (the choices from selectizeInput are still there).

Here the code:

library(shiny)
library(dplyr)
library(stringr)

ui <- fluidPage(
  textInput("my_input", "Introduce a word"),
  selectizeInput(inputId = "dropdown_list", label = "Choose the variable:", choices=character(0)),
  
)

server <- function(input, output, session) {
  
  my_list <- reactive({
    req(input$my_input)
    data <- as.data.frame(storms)
    
    res <- subset(data, (grepl(pattern = str_to_sentence(input$my_input), data$name))) %>% 
      dplyr::select(name)
    
    res <- as.factor(res$name)
    
    
    return(res)
  })
  
  # This is to generate the choices (gene list) depending on the user's input.
  observeEvent(input$my_input, {
    updateSelectizeInput(
      session = session,
      inputId = "dropdown_list",
      choices = my_list(), options=list(maxOptions = length(my_list())),
      server = TRUE
    )
  })

}

shinyApp(ui, server)

Do you know how can I remove the choices from the selectizeInput if the user deletes the input?

Thanks very much in advance

Regards

ANSWER

Answered 2022-Apr-14 at 08:33

The issue is the req(input$myinput). Hence, if the user deletes the input my_list() does not get updated. Instead of req you could use an if to check whether the input is equal to an empty string:

my_list <- reactive({
    if (!input$my_input == "") {
      data <- as.data.frame(storms)
      
      res <- subset(data, grepl(pattern = str_to_sentence(input$my_input), data$name), name)
      
      res <- as.factor(res$name)
      
      return(res)
    } 
  })

Source https://stackoverflow.com/questions/71868431

QUESTION

How to create optimized schedule that avoids duplicates

Asked 2022-Mar-11 at 01:44

I've got a list of games between teams that takes place over a sixteen day period:

| Date | Game                        |
|------|-----------------------------|
| 1    | hot ice vs playerz          |
| 1    | caps vs quiet storm         |
| 1    | slick ice vs blizzard       |
| 1    | flow vs 4x4's               |
| 2    | avalanche vs cold force     |
| 2    | freeze vs in too deep       |
| 2    | game spot vs rare air       |
| 2    | out of order vs cold as ice |
| 3    | playerz vs avalanche        |
| 3    | quiet storm vs freeze       |
| 3    | blizzard vs game spot       |
| 3    | 4x4's vs out of order       |
| 14   | freeze vs avalanche         |
| 14   | out of order vs game spot   |
| 14   | in too deep vs cold force   |
| 14   | cold as ice vs rare air     |
| 15   | blizzard vs quiet storm     |
| 15   | playerz vs 4x4's            |
| 15   | slick ice vs caps           |
| 15   | hot ice vs flow             |
| 16   | game spot vs freeze         |
| 16   | avalanche vs out of order   |
| 16   | rare air vs in too deep     |
| 16   | cold force vs cold as ice   |

There are 16 teams that make up this schedule, and what I'd like to do in Python is find all of the 8 game combinations that allow me to "see" each team once. The only limitation is that I can only see one game per day. At this point all I can think of is a ton of nested for loops that generates all possible schedules, and then checking each one after to see if it is valid. A valid schedule is one that has one game per date and sees each team once.

ANSWER

Answered 2022-Mar-11 at 01:44

You could use a backtracking algorithm to iterate through different combinations of matches and filtering them according to the constraints you mentioned.

First step would be to format your data into a collection like a python list or dict. Then implement a recursive backtracking algorithm that selects one match per day, and checks to make sure the chosen match doesn't include teams you have already selected.

Here is a rough example that uses the data you provided in your question:

def combinations(matches, day, schedules, current):
    """Backtracking function for selecting unique schedules."""
    # base case when you have a match from each day
    if day > max(matches.keys()):  
        schedules.append(current[:])
        return
    # skip over days where there are no matches
    while day not in matches:
        day += 1
    # select one match for the current date
    for i in range(len(matches[day])):
        teams = matches[day][i]
        current_teams = [j for i in current for j in i]
        # check if the teams are already in the current schedule
        if teams[0] in current_teams or teams[1] in current_teams: 
            continue
        del matches[day][i]
        # recursive case
        combinations(matches, day + 1, schedules, current + [teams])
        matches[day].insert(i,teams)
    return




def format(inp):
    """Formats input data into a dictionary."""
    lines = inp.split("\n")[2:]     # split lines of input data
    matches = [(line.split("|")[1:-1]) for line in lines]
    schedule = {}
    # add matches to dict with date as key and matches as value.
    for day, match in matches:
        day = int(day.strip())
        teams = match.strip().split(" vs ")
        try:
            schedule[day].append(teams)
        except KeyError:
            schedule[day] = [teams]
    ideal = []
    # use backtracking algorithm to get desired results
    combinations(schedule, 1, ideal, [])
    show_schedules(ideal)


def show_schedules(results):
    for i, x in enumerate(results):
        print(f"Schedule {i+1}")
        for day, match in enumerate(x):
            print(f"Day: {day+1} - {match[0]} vs. {match[1]}")
        print("\n")


format(inp)  # <- entry point:`inp` is the pre-formatted data `str`

It's not exactly the most elegant code... :) With the example data this algorithm generates 32 unique schedules of 6 games. The output looks something like this but for each day of matches:

Schedule 1
Day: 1 - hot ice vs. playerz
Day: 2 - avalanche vs. cold force
Day: 3 - quiet storm vs. freeze
Day: 4 - out of order vs. game spot
Day: 5 - slick ice vs. caps
Day: 6 - rare air vs. in too deep


Schedule 2
Day: 1 - hot ice vs. playerz
Day: 2 - avalanche vs. cold force
Day: 3 - 4x4's vs. out of order
Day: 4 - cold as ice vs. rare air
Day: 5 - blizzard vs. quiet storm
Day: 6 - game spot vs. freeze

For more information on backtracking here are a few external resources or there are countless examples here on stack overflow.

https://www.hackerearth.com/practice/basic-programming/recursion/recursion-and-backtracking/tutorial/

http://jeffe.cs.illinois.edu/teaching/algorithms/book/02-backtracking.pdf

Source https://stackoverflow.com/questions/71431331

QUESTION

Problem with non-standard evaluation in disk.frame objects using data.table syntax

Asked 2022-Feb-01 at 17:47

Problem

I'm currently trying to write a function that filters some rows of a disk.frame object using regular expressions. I, unfortunately, run into some issues with the evaluation of my search string in the filter function. My idea was to pass a regular expression as a string into a function argument (e.g. storm_name) and then pass that argument into my filtering call. I used the %like% function included in {data.table} for filtering rows.

My problem is that the storm_name object gets evaluated inside the disk.frame. However, since the storm_name is only included in the function environment, but not in the disk.frame object, I get the following error:

Error in .checkTypos(e, names_x) : 
  Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

I already tried to evaluate the storm_nameobject in the parent frame using eval(sotm_name, env = parent.env()), but that also didn't help. Interestingly, this problem only occurs with {disk.frame} objects but not with {data.table} objects.

For now I found a solution using {dplyr} instead. However, I would be grateful for any ideas on how this problem could be solved with {data.table}.

Reproducible Example

# Load packages
library(data.table)
library(disk.frame)

# Create data table and diskframe object of storm data
storms_df <- as.disk.frame(storms)
storms_dt <- as.data.table(storms)

# Create search function
grep_storm_name <- function(dfr, storm_name){
  
  dfr[name %like% storm_name]
  
}

# Check function with data.table object
grep_storm_name(storms_dt, "^A")

# Check function with diskframe object
grep_storm_name(storms_df, "^A")

Session Info

R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_Sweden.1252  LC_CTYPE=English_Sweden.1252    LC_MONETARY=English_Sweden.1252
[4] LC_NUMERIC=C                    LC_TIME=English_Sweden.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] disk.frame_0.5.0  purrr_0.3.4       dplyr_1.0.7       data.table_1.14.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7            benchmarkmeData_1.0.4 pryr_0.1.4            pillar_1.6.4         
 [5] compiler_4.1.0        iterators_1.0.13      tools_4.1.0           digest_0.6.27        
 [9] bit_4.0.4             jsonlite_1.7.2        lifecycle_1.0.1       tibble_3.1.6         
[13] lattice_0.20-44       pkgconfig_2.0.3       rlang_0.4.12          Matrix_1.3-3         
[17] foreach_1.5.1         rstudioapi_0.13       DBI_1.1.1             parallel_4.1.0       
[21] bigassertr_0.1.4      bigreadr_0.2.4        httr_1.4.2            stringr_1.4.0        
[25] globals_0.14.0        generics_0.1.1        fs_1.5.0              vctrs_0.3.8          
[29] bit64_4.0.5           grid_4.1.0            tidyselect_1.1.1      glue_1.6.0           
[33] listenv_0.8.0         R6_2.5.1              future.apply_1.7.0    parallelly_1.25.0    
[37] fansi_1.0.0           magrittr_2.0.1        codetools_0.2-18      ellipsis_0.3.2       
[41] fst_0.9.4             assertthat_0.2.1      future_1.21.0         benchmarkme_1.0.7    
[45] utf8_1.2.2            stringi_1.7.6         doParallel_1.0.16     crayon_1.4.2

ANSWER

Answered 2022-Jan-20 at 17:38

While I don't know the exact cause of this, it has to do with environments, search path, etc. For instance, these work:

storms_df[name %like% "^A"]

nm <- "^A"
storms_df[name %like% nm]

grep1 <- function(dfr, storm_name) { dfr[name %like% "^A"]; }
grep1(storms_df)

But this does not:

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

We can work around this with eval(substitute(..)).

grep3 <- function(dfr, storm_name) { 
  eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
}
grep3(storms_df, "^A")
#        name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#      <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
#   1:    Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   2:    Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1    25     1013          NA          NA
#   3:    Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1    25     1013          NA          NA
# ...

(and grep3(storms_dt, "^A") works too)

This works by changing the symbol of storm_name inside the [-expression from storm_name to the literal string. Since this is done on the unevaluated expression, there are no lookups yet, no searching through this and inherited environments to find storm_name.

If you check it manually:

debug(grep3)
grep3(storms_df, "^A")
# debugging in: grep3(storms_df, "^A")
# debug at #1: {
#     eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name)))
# }
# Browse[2]> 
substitute(dfr[name %like% storm_name], list(storm_name = storm_name))
# dfr[name %like% "^A"]

I think it's something to do with how disk.frame is affecting the environment within [ and the calling/parent environments. Interestingly (to me), you can see that the search path for variables is not empty, it's just not what we would expect:

grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
grep2(storms_df, "^A")
# Error in .checkTypos(e, names_x) : 
#   Object 'storm_name' not found amongst name, year, month, day, hour and 8 more

### but let's pre-define `storm_name` outside of the function,
### then re-define the function (no change)
storm_name <- "^A"
grep2 <- function(dfr, storm_name) { dfr[name %like% storm_name]; }
head(grep2(storms_df, "^A"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

This seems to work, but we can see that it's using the external version of storm_name vice the parametric version, see that name is still starting with A despite the change to "^B".

head(grep2(storms_df, "^B"), 2)
#      name  year month   day  hour   lat  long              status category  wind pressure ts_diameter hu_diameter
#    <char> <num> <num> <int> <num> <num> <num>              <char>    <ord> <int>    <int>       <num>       <num>
# 1:    Amy  1975     6    27     0  27.5   -79 tropical depression       -1    25     1013          NA          NA
# 2:    Amy  1975     6    27     6  28.5   -79 tropical depression       -1    25     1013          NA          NA

Frankly, I don't understand enough of disk.frame's internals to know if this is a bug or a necessity due to what it must do for non-standard data.table-like evaluation of a not-totally-in-memory dataset.

If you're concerned with performance (fair question), the eval(substitute(..)) method does not appear to suffer much:

bench::mark(
  raw = dfr[name %like% "^A"],
  subst = eval(substitute(dfr[name %like% storm_name], list(storm_name = storm_name))),
  iterations = 1000
)
# # A tibble: 2 x 13
#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result                  memory               time               gc                  
#   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>                  <list>               <list>             <list>              
# 1 raw          12.9ms   16.8ms      55.2    1.69MB     3.97   933    67      16.9s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>
# 2 subst        12.8ms   15.8ms      60.5    1.69MB     3.25   949    51      15.7s <data.table [990 x 13]> <Rprofmem [669 x 3]> <bench_tm [1,000]> <tibble [1,000 x 3]>

In repeated benchmarks, I've actually seen subst slightly faster, suggesting that a portion of the performance difference is unrelated to the addition of eval(substitute(..)). This difference (55.2 to 60.5 `itr/sec`) is the worst I've seen it ... a repeat just now had 57.1 and 57.5, so I suggest that performance-degradation is not a concern.

Source https://stackoverflow.com/questions/70788596

QUESTION

simplify java stream to return an error message

Asked 2022-Jan-20 at 06:33

Hi I am new to stream in java I am trying to find which item inside a file has more than 12 of length also if that item has letters mixed in it and wanted to know if this can be simplified using stream in java without using the forEach method in java stream:

Stream<String> stream = Files.lines(path)

stream.forEach(item -> {
      Pattern pattern = Pattern.compile(REGEX);
      Matcher matcher = pattern.matcher(item);
      if (item.length() > 12) {
             log.error("Item must not exceed 12 numbers {}", item);
             streamMessage.set("Item must not exceed 12 numbers");
      }
      else if (matcher.matches()) {
             log.error("Invalid Item format {}", item);
             streamMessage.set("Invalid Item format");
      }
});

I also need it to return the log.error messages and the stream message thank you

EDIT:

I have tried Glare storm's answer but the message being returned by this:

Stream<String> lengthStream =
                    stream.filter(item -> item.length() > 12);

Pattern pattern = Pattern.compile(REGEX);

Stream<String> letterStream =
                    lengthStream.filter(pattern.asPredicate());
if(lengthStream != null){
       log.error("Item must not exceed 12 numbers");
       message = "Item must not exceed 12 numbers";
}
if(letterStream != null){
       log.error("Invalid Item format");
       message = "Invalid Item format";
}

it always return Invalid Item format even tho there are no letters inside the file and only has exceeded 12 in length

ANSWER

Answered 2022-Jan-20 at 06:33

Use the filter method:

Stream<String> stream = Files.lines(path);
Stream<String> filteredStream= stream .filter(str -> str.length() > 12);      

Stream<String> matching = filteredStream.filter(str -> Pattern.matches(regex, str));

or:

Stream<String> matching = filteredStream.filter(str -> {
      if(str.length() > 12 && Pattern.matches(regex, str))
          return true;
      return false;
     });

To set error messages you prolly will have to throw an error that will return what ever message you want:

try{
Stream<String> matching = filteredStream.filter(str -> {
      if(str.length() > 11 )
           throw new Exception("Item must not exceed 12 numbers");
      if(Pattern.matches(regex, str))
          return true;
      throw new Exception("Invalid Item format");         
     });
}
catch(Exception e){
   log.error(e);
   message = e;
}

Source https://stackoverflow.com/questions/70780705

QUESTION

How to plot a point on a time series in python

Asked 2022-Jan-10 at 14:00

I am facing an issue with plotting points in a time series since I cannot identify the y-axis value. I have 2 datasets: one NetCDF file with satellite data (sea surface temperature), and another CSV file with storm track data (time, longitude, latitude, wind speed, etc.). I can plot the desired temperature time series for all storm track locations located in the ocean. However, I want to indicate the time of the storm footprint occurrence within each time series line. So, one line represents one location and the changing temperature over time, but I also want to show WHEN the storm occurred at that location.

This is my code so far (it works):

lati = stormtrack_lat.values
loni = stormtrack_lon.values

for i, dummy in enumerate(lati):
    dsloc = SSTskin_file.sel(lon=loni[i], lat=lati[i], method='nearest')
    dsloc['analysed_sst'].plot()
    #plt.plot(dsloc,stormtrack_time[i], "or") #here I want to add one point to each line indicating the time when the storm occured at this location
plt.title('My title')
plt.xlabel('time')
plt.ylabel('SST skin in K')

The netcdf file contains the time data as datetime coordinate, but my CSV file contains this data:

|    da   | INDEX | SIZE  |  AREA     |   x     |  y |  RADIUS   |  MEANV

2020021018  1505    2934    177.363     -2.82   49.87   1782.18     16.18

2020021100  1505    3812    220.078     5.25    49.57   2811.51     16.17
...

where 'da' represents the date and time (YYYYMMDDHH). So, I think I need to (1) convert the CSV 'da' values into a datetime format in order to use them for the line plt.plot(dsloc,stormtrack_time[i], "or") and then (2) to find those datetime values within the netcdf file and then (3) use than time point for plotting the corresping SST value/time point.

The problem is that I do not know HOW to do this. Can anyone help?

Thanks!

ANSWER

Answered 2022-Jan-10 at 14:00

I have found the way to do this:

lati = stormtrack_lat.values
loni = stormtrack_lon.values
timei = stormtrack_datetime.values

fig2 = plt.figure(figsize=(20, 20), dpi=300)

for i, dummy in enumerate(lati):
    dsloc = SSTskin_file.sel(lon=loni[i], lat=lati[i], method='nearest')
    dstime = SSTskin_file.sel(time=timei[i], lon=loni[i], lat=lati[i], method='nearest')
    skin_celsius = (dsloc['analysed_sst']) - 273.15
    timesteps = dsloc.time.values
    timestep = dstime.time.values
    timevalue = ((dstime['analysed_sst']).values) - 273.15
    lineplot = plt.plot(timesteps, skin_celsius )
    dotplot = plt.plot(timestep, timevalue, "or") 
plt.title('Skin SST over time at storm track locations',  fontsize = 20 )
plt.xlabel('Date', fontsize = 16)
plt.ylabel('Skin SST in $^{\circ}C$',  fontsize = 16)
plt.xticks(fontsize = 16)
plt.yticks(fontsize = 16)
#plt.legend(lineplot) #Here I would like to plot the legend for the line plots (time series data). I want to indicate the location (longitude and latitude) of the time series
plt.legend(dotplot[:1], ['Storm track at location and time'], fontsize = 16);
fig2.savefig('SSTskin_storm_timeseries_test.png', bbox_inches='tight')

Source https://stackoverflow.com/questions/70626139

QUESTION

How to make the images bigger when clicked? JavaScrpit

Asked 2022-Jan-10 at 09:35

I've made a gallery, but I'm having trouble getting the image to enlarge when clicked. I want to be able to click on the largest image and then it will enlarge and appear in the middle of the page.

Below is the link to the code:

function galleryFunction1(smallImg) {
  let fullImg = document.getElementById('imageBox1');
  fullImg.src = smallImg.src;
}

.wrapper {
  margin: 0 auto;
  width: 100%;
  max-width: 1400px;
}

.gallery {
  display: flex;
  flex-direction: row;
  justify-content: space-around;
  align-items: center;
  margin: 100px 10px;
}

.gallery img {
  cursor: pointer;
}

.boxOfimages .big-img img {
  display: block;
  margin: 0 auto;
  width: 290px;
  margin-bottom: 10px;
}

.boxOfsmallImgs {
  display: flex;
  justify-content: space-between;
  flex-wrap: wrap;
}

.boxOfsmallImgs img {
  width: 70px;
  opacity: 0.7;
  transition: opacity 0.4s ease;
  margin: 2px;
}

.boxOfsmallImgs img:hover {
  opacity: 1;
}


}
.boxOftext {
  display: flex;
  flex-direction: column;
  align-items: start;
  font-size: 0.8rem;
}
.boxOftext h2 {
  padding: 20px 10px;
}
.boxOftext p {
  padding: 10px 10px;
}

<main class="main wrapper">

  <section class="gallery">
    <div class="boxOfimages">
      <div class="big-img">
        <img id="imageBox1" src="https://cdn.pixabay.com/photo/2013/04/02/18/58/sculpture-99484_960_720.jpg" alt="">
      </div>
      <div class="boxOfsmallImgs">
        <img src="https://cdn.pixabay.com/photo/2013/04/02/18/58/sculpture-99484_960_720.jpg" onclick="galleryFunction1(this)" alt="">
        <img src="https://cdn.pixabay.com/photo/2016/09/07/16/19/pile-1651945_960_720.jpg" onclick="galleryFunction1(this)" alt="">

        <img src="https://cdn.pixabay.com/photo/2018/05/11/08/11/pet-3389729_960_720.jpg" onclick="galleryFunction1(this)" alt="">
      </div>
    </div>
    <div class="boxOftext">
      <h2>“Dotyk burzy” / “Touch of Storm”</h2>
      <p>rzeźba / sculpture gips patynowany, granit / patinated plaster, granite 100 x 28 x 28 cm 2020r.
      </p>
      <p>dostępna</p>
    </div>
  </section>

https://codepen.io/yerbamatepl/pen/mdBGoed

Thank you in advance for your help.

ANSWER

Answered 2022-Jan-10 at 08:51

You need to create a popup window with an image element inside:

<div class="backdrop">
  <div class="popup">
    <img src="" class="popup-image" />
  </div>
</div>

In CSS you need to make the backdrop element as fixed:

position: fixed;
top: 0;
left: 0;
bottom: 0;
right: 0;

So your element will be pulled to each side

The popup element will have absolute positioning:

position: absolute;
width: 500px;
height: auto;
top: 50%; // to put it in the middle
left: 50%;
transform: translate(-50%, -50%);

And finally img element should have max-width as 100%.

Also you need to make open/close functionality, to do that you need to set display: block/none on the backdrop element accordingly on click

Source https://stackoverflow.com/questions/70649666

QUESTION

Clustering in R using K-mean

Asked 2021-Dec-17 at 17:31

I tried to cluster my dataset using K-mean, but there is a categorical data in column 9; so when I ran k-mean it had an error like this:

res<-NbClust(mi[2:9],min.nc=2,max.nc=15,method="ward.D2")

Error in t(jeu) %*% jeu : requires numeric/complex matrix/vector arguments

So I could only run K-mean for columns from 2 to 8. I wonder if there is another way of clustering the data where I could run with column 9 as well?

Data:

df <- structure(list(Name = structure(c(58L, 188L, 40L, 155L, 32L, 88L, 92L, 55L, 135L, 31L, 139L, 26L, 126L, 10L, 166L, 104L, 75L, 180L, 35L, 175L, 77L, 99L, 4L, 71L, 141L, 176L, 53L, 39L, 172L, 196L, 123L, 107L, 16L, 96L, 82L, 185L, 30L, 15L, 94L, 129L, 187L, 151L, 33L, 23L, 28L, 44L, 157L, 69L, 132L, 83L, 131L, 11L, 182L, 181L, 54L, 115L, 116L, 183L, 150L, 195L, 45L, 144L, 1L, 110L, 17L, 114L, 9L, 117L, 112L, 70L, 34L, 169L, 27L, 66L, 3L, 73L, 133L, 91L, 154L, 130L, 160L, 105L, 90L, 165L, 67L, 100L, 162L, 98L, 29L, 68L, 189L, 192L, 102L, 190L, 134L, 136L, 52L, 12L, 81L, 59L, 63L, 122L, 93L, 109L, 178L, 138L, 5L, 43L, 140L, 95L, 2L, 174L, 76L, 51L, 156L, 60L, 149L, 128L, 177L, 142L, 103L, 7L, 8L, 14L, 164L, 74L, 145L, 148L, 113L, 86L, 108L, 48L, 163L, 6L, 186L, 89L, 36L, 191L, 125L, 120L, 62L, 65L, 124L, 168L, 147L, 79L, 173L, 84L, 193L, 25L, 146L, 121L, 127L, 153L, 13L, 106L, 119L, 161L, 49L, 97L, 101L, 61L, 137L, 24L, 85L, 194L, 78L, 41L, 170L, 47L, 118L, 184L, 179L, 72L, 42L, 111L, 87L, 57L, 38L, 37L, 171L, 22L, 50L, 80L, 159L, 18L, 152L, 64L, 56L, 158L, 167L, 46L, 19L, 21L, 20L, 143L), .Label = c("#Mashtag 2013", "#Mashtag 2014", "#Mashtag 2015", "10 Heads High", "5am Saint", "77 Lager", "AB:02", "AB:03", "AB:04", "AB:06", "AB:08", "AB:10", "AB:11", "AB:13", "AB:15", "AB:17", "AB:18", "AB:20", "Ace Of Chinook", "Ace Of Citra", "Ace Of Equinox", "Ace Of Simcoe", "Albino Squid Assasin", "Alice Porter", "All Day Long - Prototype Challenge", "Alpha Dog", "Alpha Pop", "Amarillo - IPA Is Dead", "American Ale", "Anarchist Alchemist", "Arcade Nation", "Avery Brown Dredge", "Baby Dogma", "Baby Saison - B-Sides", "Bad Pixie", "Barley Wine - Russian Doll", "Barrel Aged Albino Squid Assassin", "Barrel Aged Hinterland", "Berliner Weisse With Raspberries And Rhubarb - B-Sides", "Berliner Weisse With Yuzu - B-Sides", "Bitch Please (w/ 3 Floyds)", "Black Dog", "Black Eye Joe (w/ Stone Brewing Co)", "Black Eyed King Imp", "Black Eyed King Imp - Vietnamese Coffee Edition", "Black Hammer", "Black Jacques", "Black Tokyo Horizon (w/NÃ¸gne Ã\230 & Mikkeller)", "Blitz Berliner Weisse", "Blitz Series", "Born To Die", "Bounty Hunter - Shareholder Brew", "Bourbon Baby", "Bracken's Porter", "Bramling X", "Brewdog Vs Beavertown", "Brixton Porter", "Buzz", "Candy Kaiser", "Cap Dog (w/ Cap Brewery)", "Catherine's Pony (w/ Beavertown)", "Challenger", "Chaos Theory", "Chili Hammer", "Chinook - IPA Is Dead", "Citra", "Clown King", "Cocoa Psycho", "Coffee Imperial Stout", "Comet", "Dana - IPA Is Dead", "Dead Metaphor", "Dead Pony Club", "Deaf Mermaid - B-Sides", "Devine Rebel (w/ Mikkeller)", "Dog A", "Dog B", "Dog C", "Dog D", "Dog E", "Dog Fight (w/ Flying Dog)", "Dog Wired (w/8 Wired)", "Dogma", "Doodlebug", "Double IPA - Russian Doll", "Edge", "El Dorado - IPA Is Dead", "Electric India", "Ella - IPA Is Dead", "Elvis Juice V2.0 - Prototype Challenge", "Everday Anarchy", "Fake Lager", "Galaxy", "Goldings - IPA Is Dead", "Growler", "Hardcore IPA", "Hardkogt IPA", "HBC 366 - IPA Is Dead", "HBC 369", "Hello My Name Is Beastie", "Hello My Name Is Holy Moose", "Hello My Name Is Ingrid", "Hello My Name Is Little Ingrid", "Hello My Name Is Mette-Marit", "Hello My Name Is PaÌ\210ivi", "Hello My Name is Sonja (w/ Evil Twin)", "Hello My Name is Vladimir", "Hello My Name Is ZeÌ\201 (w/ 2CabecÌ§as)", "Hinterland", "Hobo Pop", "Hop Fiction - Prototype Challenge", "Hopped-Up Brown Ale - Prototype Challenge", "Hoppy Christmas", "Hops Kill Nazis", "Hunter Foundation Pale Ale", "Hype", "India Session Lager - Prototype Challenge", "International Arms Race (w/ Flying Dog)", "Interstellar", "Jack Hammer", "Jasmine IPA", "Jet Black Heart", "Kohatu - IPA Is Dead", "Konnichiwa Kitsune", "Libertine Black Ale", "Libertine Porter", "Lichtenstein Pale Ale", "Lizard Bride - Prototype Challenge", "Lost Dog (w/Lost Abbey)", "Lumberjack Stout", "Magic Stone Dog (w/Magic Rock & Stone Brewing Co.)", "Mandarina Bavaria - IPA Is Dead", "Mango Gose - B-Sides", "Melon And Cucumber IPA - B-Sides", "Misspent Youth", "Morag's Mojito - B-Sides", "Moshi Moshi 15", "Motueka", "Movember", "Mr.Miyagi's Wasabi Stout", "Nanny State", "Nelson Sauvin", "Neon Overlord", "Never Mind The Anabolics", "No Label", "Nuns With Guns", "Old World India Pale Ale", "Old World Russian Imperial Stout", "Orange Blossom - B-Sides", "Pale - Russian Doll", "Paradox Islay", "Paradox Islay 2.0", "Paradox Jura", "Peroxide Punk", "Pilsen Lager", "Pioneer - IPA Is Dead", "Prototype 27", "Prototype Helles", "Prototype Pils 2.0", "Pumpkin King", "Punk IPA 2007 - 2010", "Punk IPA 2010 - Current", "Restorative Beverage For Invalids And Convalescents", "Rhubarb Saison - B-Sides", "Riptide", "Russian Doll â\200“ India Pale Ale", "Rye Hammer", "San Diego Scotch Ale (w/Ballast Point)", "Santa Paws", "Shareholder Black IPA 2011", "Ship Wreck", "Shipwrecker Circus (w/ Oskar Blues)", "Simcoe", "Sink The Bismarck!", "Skull Candy", "Sorachi Ace", "Sorachi Bitter - B-Sides", "Spiced Cherry Sour - B-Sides", "Stereo Wolf Stout - Prototype Challenge", "Storm", "Sub Hop", "Sunk Punk", "Sunmaid Stout", "Sunshine On Rye - B-Sides", "The Physics", "This. Is. Lager", "TM10", "Trashy Blonde", "Truffle and Chocolate Stout - B-Sides", "U-Boat (w/ Victory Brewing)", "Vagabond Pale ALe - Prototype Challenge", "Vagabond Pilsner", "Vic Secret", "Waimea - IPA Is Dead", "Whisky Sour - B-Sides", "Zephyr"), class = "factor"), ABV = c(4.5, 4.1, 4.2, 6.3, 7.2, NA, 4.7, 7.5, 7.3, 5.3, 4.5, 4.5, 6.1, 11.2, 6, 8.2, 12.5, 8, 4.7, 3.5, 15, 6.7, 7.8, 6.7, 0.5, 7.5, 5.8, 3.6, 10.5, 12.5, 7.2, 8.2, 10.7, 9.2, 7.1, 5, 16.5, 12.8, 6.7, 10, NA, 10, 4.5, 7.4, 7.2, 9.5, 9.2, 9, 7.2, 7.5, NA, 10.43, 7.1, 8, 5, 5.4, 4.1, 10.2, 4, 7, 12.7, 6.5, 7.5, 4.2, 11.8, 7.6, 15, 4.4, 6.3, 7.2, NA, 4.5, 4.5, 7.5, 10, 3.8, 6.4, NA, 4, 15.2, 5.4, 8.3, 6.5, 8, 12, 8.2, 5.6, 7.2, 6.3, 10, 5.6, 4.5, 8.2, 8.4, 6, 6.7, 6.5, 11.5, 8.5, 5.2, 7.1, 4.7, 6.7, 9, 6.5, 6.7, 5, 5.8, 7.5, 4.5, 9, 41, 15, 8.5, 7.2, 9, 3.8, 5.7, 6.3, 7.5, 4.4, 18, 10.5, 11.3, NA, 5.2, 4.5, 9.5, 7.2, 2.7, 6.4, 17.2, 8.5, 4.9, 4.7, 7.2, 10, 4.5, 7.2, 7.2, 6.7, 7.2, 4.4, 9, 7.5, 16.1, 6.7, 2.5, 7.4, 2.8, 4.2, 5.8, 5.2, 10, 12.8, 8.3, 6.5, 6, 3, 7.6, 5.5, 8.8, 5.2, 5.2, 8, 6.7, 15, 11.5, 7.1, NA, 7.5, 7.2, 5.2, 6.8, 5.5, 5.2, 6.7, 5, 9, 9.2, 13.8, 4.5, 3.2, 16.1, 4.7, 14.2, 13, 7.2, 9.2, 4.9, 7.2, 7.2, 4.5, 4.5, 4.5, 7.6), IBU = c(60, 41.5, 8, 55, 59, 38, 40, 75, 30, 60, 50, 42, 45, 150, 70, 70, 100, 60, 45, 33, 90, 67, 70, 70, 55, 75, 35, 8, 85, 125, 70, 70, 100, 125, 65, 47, 20.5, 50, 70, 35, 20, 55, 35, 65, 70, 85, 149, 65, 100, 30, 30, 65, 68, 35, 50, 35, 65, 50, 35, 20, 85, 35, 50, 50, 80, 70, 80, 35, 85, 70, 9, 35, 30, 70, 85, 35, 40, 45, 40, 20, 20, 70, 60, 45, 85, 42, 40, 70, 55, 85, 30, 55, 42, 50, 50, 40, 35, 80, 65, 45, 90, 45, 67, 85, 20, 67, 30, 40, 90, 38, 50, 1085, 90, 85, 100, 80, 20, 35, 130, 75, 35, 70, 14, 50, 25, 65, 25, 80, 70, 36, 50, 75, 100, 30, 37, 100, 80, 55, 50, 250, 67, 100, 70, 70, 80, 85, 70, 35, 70, 30, 25, 40, 50, 55, 70, 70, 55, 60, 8, 175, 35, 40, 45, 55, 85, 70, 90, 50, 80, 45, 0, 130, 55, 30, 60, 40, 70, 50, 85, 65, 60, 40, 8, 100, 25, 20, 100, 250, 50, 18, 250, 250, 40, 40, 40, 70), OG = c(1044, 1041.7, 1040, 1060, 1069, 1045, 1046, 1068, 1079, 1052, 1047, 1046, 1067, 1098, 1058, 1076, 1093, 1082, 1047, 1038, 1120, 1013, 1074, 1066, 1007, 1068, 1049, 1040, 1102, 1087, 1067, 1076, 1105, 1085, 1065, 1048.5, 1112, 1096, 1066, 1080, 1048, 1090, 1048, 1069, 1067, 1095, 1083, 1080, 1064, 1080, 1043, 1095, 1056, 1077, 1049, 1050, 1042, 1026, 1041, 1081, 1113.5, 1050, 1070, 1042, 1096, 1073, 1113, 1040, 1063, 1067, 1032, 1048, 1045, 1068, 1098, 1040, 1057, 1081, 1039, 1110, 1055, 1076, 1060, 1075, 1130, 1078, 1055, 1067, 1060, 1098, 1058, 1046, 1078, 1080, 1050, 1063, 1068, 1096, 1078, 1049, 1067, 1055, 1013, 1094, 1060, 1013, 1050, 1053, 1072, 1042.9, 1084, 1085, 1120, 1072, 1064, 1083, 1039, 1053, 1060, 1068, 1045, 1150, 1093, 1098, 1052, 1048, 1043, 1075, 1067, 1033, 1061, 1156, 1068, 1047, 1043, 1064, 1097, 1045, 1068, 1065, 1064, 1064, 1045, 1090, 1069, 1125, 1063, 1027, 1069, 1032.5, 1044, 1060, 1050, 1128, 1108, 1076, 1059, 1056, 1007, 1072, 1053, 1084, 1048, 1053, 1074, 1066, 1120, 1104, 1067, 1089, 1069, 1065, 1052, 1068, 1062, 1048, 1066, 1053, 1094, 1069, 1088, 1045, 1007, 1015, 1008, 1025, 1015, 1065, 1016, 1010, 1065, 1065, 1045, 1045, 1045, 1067), EBC = c(20, 15, 8, 30, 10, 15, 12, 22, 120, 200, 140, 62, 219, 70, 25, NA, 36, 12, 8, 50, 100, 19, 90, 30, 30, 30, 44, NA, 64, 40, 30, 16, 300, 40, 13, 65, 20, 111, 30, 80, 14, 300, 40, 60, 30, 250, 19.5, 97, 12, 46, 15, 23, 14, 15, 110, 11.5, 17, 197, 45, 12, 250, 23, 40, 30, 115, 59, 400, 12, 24, 30, 2, 44, 25, 30, 130, 25, 10, 15, 18, 158, 30, 30, 25, 240, 24, 90, 15, 30, 30, 30, 54, 25, 70, 200, 8, 15, 250, 115, 31.2, 45, 15, 200, 19, 400, NA, 19, 60, 177.3, 200, 18, 20, 40, 100, 15, 12, 180, 6, 25, 14, 30, 30, 57, NA, 164, 10, 16, 10, 195, 30, 57, 20, 128, 15, 12, 10, 12, 65, 20, 150, 15, 19, 12, 30, 190, 50, 400, 30, 10, 30, 42, 19, 35, 17, 300, 79, 30, 50, 17, 9, 40, 25, 190, 35, 165, 35, 30, 100, 38, 71, 15, 50, 14, 200, 86, 230, 13, 30, 200, 400, 60, 25, 18, 8, 500, 25, 67, 300, 15, 78.8, 13, 17, 104, 18, 18, 18, 20), PH = c(4.4, 4.4, 3.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.2, 5.2, 4.4, 4.4, 4.4, 5.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 3.2, 4.4, 4.4, 4.4, 4.4, 4.3, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.2, 4.4, 4.4, 4.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.5, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 3.2, 5.2, 4.4, 4.4, 4.4, 5.2, 4.4, 4, 4.4, 4.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 3.5, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 5.2, 4.2, 4.4, 4.4, 4.2, 4.4, 4.4, 4.4, 4.3, 3.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 5.2, 4.4, 5.2, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 5.2, 4.2, 4.5, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.2, 4.4, 4.4, 4.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.3, 4.4, 4.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 3.2, 4.4, 4.4, 4.5, 4.4, 5.2, 5.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 5.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.3, 4.2, 4.4, 4.2, 3.2, 4.4, 4.2, 4, 4.4, 4.4, 4.2, 4.2, 4.4, 4.4, 4.2, 4.2, 4.2, 4.4), AttenuationLevel = c(75, 76, 83, 80, 67, 88.9, 78, 80.9, 74.7, 77, 74.5, 72.8, 70.1, 87, 79.3, 83, 68, 86, 79, 68.4, 98, 79.7, 79.7, 77.3, 28.6, 82.1, 90, 83, 102, 81.2, 82.1, 83, 76.2, 81.2, 85, 79.4, 100, 79.17, 77.3, 85, 89.6, 84.4, 72.9, 82.6, 82.1, 76.8, 83, 76, 84, 70, 81.4, 83.2, 82.1, 79.2, 79, 84, 76.2, 74.5, 75.6, 74, 76.8, 76, 81.4, 76.2, 79.2, 79.5, 84.1, 79.5, 82.6, 82.1, 88, 72.9, 75.6, 82.1, 79.6, 70, 87, 93.8, 76.9, 82, 74.6, 82.9, 83.3, 81.3, 102.3, 83.3, 78, 82.1, 80, 70, 74, 73.9, 83.3, 81.3, 87, 84, 70.6, 79.2, 84.6, 81.6, 80.6, 70, 79.7, 73.4, 87, 79.7, 76, 84.9, 79.2, 81, 82.1, 81.2, 98, 90.3, 84, 83.1, 87, 79.3, 83, 82.1, 73.3, 93.3, 80, 79.6, 87, 79, 79.1, 81.3, 82.1, 70.8, 80.3, 80.8, 95.6, 80.7, 83.7, 84, 79.4, 73.9, 78.6, 84.6, 79.7, 84, 82.9, 80, 82.6, 84, 81, 70.4, 82.6, 63.1, 72.7, 76.7, 80, 89, 81.5, 82.9, 81.4, 82.14, 82.5, 80.6, 79.3, 79.8, 77.1, 75.5, 82.4, 77.3, 98, 85, 79, 94.4, 81.1, 87, 73.1, 76.5, 67.7, 79.2, 77.3, 73.6, 73.4, 82.6, 83, 75.6, 78, 84, 75.6, 75.6, 84.4, 84.6, 81, 78.7, 84.6, 84.6, 75.6, 75.6, 75.6, 82), FermentationTempCelsius = c(19L, 18L, 21L, 9L, 10L, 22L, 10L, 19L, 19L, 19L, 19L, 22L, 18L, 17L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 18L, 19L, 19L, 19L, 19L, 21L, 21L, 21L, 19L, 21L, 21L, 21L, 9L, 19L, 20L, 21L, 19L, 19L, 22L, 21L, 19L, 18L, 19L, 18L, 19L, 19L, 19L, 12L, 23L, 21L, 10L, 9L, 19L, 19L, 19L, 21L, 19L, 19L, 18L, 18L, 21L, 19L, 20L, 20L, 21L, 10L, 19L, 19L, 21L, 19L, 19L, 19L, 21L, 19L, 20L, 23L, 19L, 21L, 19L, 21L, 19L, 20L, 21L, 21L, 19L, 19L, 19L, 21L, 19L, 9L, 22L, 14L, 20L, 19L, 19L, 20L, 18L, 14L, 19L, 19L, 19L, 21L, 20L, 19L, 19L, 19L, 21L, 10L, 21L, 21L, 19L, 18L, 19L, 21L, 20L, 17L, 20L, 19L, 19L, 22L, 19L, 20L, 20L, 19L, 15L, 19L, 19L, 19L, 19L, 21L, 21L, 10L, 12L, 19L, 21L, 19L, 19L, 21L, 19L, 19L, 20L, 21L, 22L, 21L, 99L, 19L, 19L, 22L, 16L, 19L, 19L, 21L, 18L, 21L, 19L, 19L, 19L, 21L, 17L, 21L, 19L, 19L, 19L, 19L, 19L, 21L, 19L, 23L, 19L, 20L, 19L, 19L, 19L, 19L, 19L, 19L, 21L, 18L, 21L, 19L, 21L, 21L, 12L, 21L, 21L, 21L, 21L, 12L, 21L, 21L, 19L, 19L, 19L, 21L), Yeast = structure(c(1L, 1L, 1L, 3L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 1L, 2L, 4L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 4L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 1L, 4L, 1L, 1L, 1L, 2L, 1L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 4L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 2L), .Label = c("Wyeast 1056 - American Ale", "Wyeast 1272 - American Ale II", "Wyeast 2007 - Pilsen Lager", "Wyeast 3711 - French Saison"), class = "factor")), class = "data.frame", row.names = c(NA, -196L))
df

ANSWER

Answered 2021-Dec-17 at 17:31

To solve your specific issue, you can generate dummy variables to run your desired clustering.

One way to do it is using the dummy_columns() function from the fastDummies package.

library(fastDummies)

df_dummy <- dummy_columns(df, select_columns = "Yeast", remove_selected_columns = TRUE)

res <- NbClust(df_dummy[2:9], min.nc = 2, max.nc = 15, method = "ward.D2")

As noted in the comments, the better practices for conduncting clustering analysis are more questions for CrossValidated.

Source https://stackoverflow.com/questions/70364971

QUESTION

react router v5 to v6 nested route not working

Asked 2021-Dec-09 at 18:01

I've been trying to solve the following problem : I try to upgrade this Frontend Mentor project https://haydee75.github.io/galleria/ from React Router v5 to v6. I tried to replace the code between with :

<Routes>
          <Route path="galleria" element={<Gallery datas={data} clickEvent={handleSlideShow} />} />
          <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />   
</Routes>

but it does not work as when I am on a Paint page when clicking on next (or prev) button it adds url to the existing one so the route is wrong. I also tried :

<Routes>
          <Route path="galleria" element={<Gallery datas={data} clickEvent={handleSlideShow} />}>
            <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />
          </Route>
</Routes>

But it does not work either.

Below are relevant components :

import "./Gallery.scss";
import { Link } from "react-router-dom";

const Gallery = ({ datas, clickEvent }) => {
  return (
    <div className="Gallery">
      {datas.map((data, index) => {
        return (
          <div key={index} className="thumbnail">
            <Link to={"/galleria/" + data.urlPath} onClick={clickEvent}>
              <img
                className="image"
                src={data.images.thumbnail}
                alt={data.images.name}
              />
            </Link>
          </div>
        );
      })}
    </div>
  );
};

export default Gallery;

import { useState } from "react";
import { Link, useParams } from "react-router-dom";
import "./Paint.scss";
import Modal from "../Modal/Modal";

const Paint = ({ datas }) => {
  let { urlPath } = useParams();
  const paint = datas.find((data) => data.urlPath === urlPath);

  const nextSlide = datas.indexOf(paint) + 1;
  const prevSlide = datas.indexOf(paint) - 1;

  const [showModal, setShowModal] = useState(false);

  const handleModal = () => {
    setShowModal(!showModal);
  };

  const color1 = "#000";
  const color2 = "#ccc";

  const indexPaint = datas.indexOf(paint) + 1;
  const datasLenght = datas.length;

  const blackLineWidth = Math.round((indexPaint * 100) / datasLenght);
  const greyLineWidth = 100 - blackLineWidth;

  return (
    <div className="Paint">
      {paint ? (
        <>
          <div className="container">
            <div className="row main">
              <div
                className="main-image"
                style={{
                  backgroundImage: `url("${paint.images.hero.large}")`,
                }}
              >
                <div className="icon-view-image" onClick={handleModal}>
                  <span>View Image</span>
                </div>
              </div>
              {showModal && (
                <Modal
                  largeImage={paint.images.gallery}
                  altProp={paint.name}
                  closeModal={handleModal}
                />
              )}
              <div className="main-informations">
                <div className="info">
                  <h1>{paint.name}</h1>
                  <p>{paint.artist.name}</p>
                </div>
                <div className="img-artist">
                  <img src={paint.artist.image} alt={paint.artist.name} />
                </div>
              </div>
            </div>
            <div className="row content">
              <div className="date">
                <span>{paint.year}</span>
              </div>
              <div className="description">
                <p>{paint.description}</p>
                <a
                  className="source"
                  href={paint.source}
                  target="_blank"
                  rel="noreferrer"
                >
                  <span className="link-secondary">Got to source</span>
                </a>
              </div>
            </div>
          </div>
          <div
            className="progress-bar"
            style={{
              backgroundImage: `linear-gradient(to right,  ${color1}, ${color1} ${blackLineWidth}%, ${color2} ${blackLineWidth}%, ${color2} ${greyLineWidth}%)`,
            }}
          ></div>
          <div className="footer">
            <div className="column">
              <h3>{paint.name}</h3>
              <div className="artist-name">
                <span>{paint.artist.name}</span>
              </div>
            </div>
            <div className="column nav-links">
              <span className="prev-btn">
                {prevSlide <= -1 ? (
                  <span className="disable"></span>
                ) : (
                  <Link className="active" to={datas[prevSlide].urlPath}></Link>
                )}
              </span>
              <span className="next-btn">
                {nextSlide >= 15 ? (
                  <span className="disable"></span>
                ) : (
                  <Link className="active" to={datas[nextSlide].urlPath}></Link>
                )}
              </span>
            </div>
          </div>
        </>
      ) : (
        <p>{urlPath} is not a planet</p>
      )}
    </div>
  );
};

export default Paint;

Here is App.js parent component rendering the routes

import data from "./data.json";
import "./App.scss";
import { Switch, Route } from "react-router-dom";
import Menu from "./components/Menu/Menu";
import Gallery from "./components/Gallery/Gallery";
import Paint from "./components/Paint/Paint";
import { useState } from "react";

const App = () => {
  data.forEach((d) => {
    d["urlPath"] = d.name.replace(/\s/g, "-");
  });

  const [slideShow, setSlideShow] = useState(true);

  const handleSlideShow = () => {
    setSlideShow(!slideShow);
  };

  return (
    <div className="App">
      <div>
        <Menu datas={data} clickEvent={handleSlideShow} slideShow={slideShow} />
        <Switch>
          <Route exact path="/galleria">
            <Gallery datas={data} clickEvent={handleSlideShow} />
          </Route>
          <Route path="/galleria/:urlPath">
            <Paint datas={data} />
          </Route>
        </Switch>
      </div>
    </div>
  );
};

export default App;

Then the data.json file from where data values are coming :

[
  {
    "name": "Starry Night",
    "year": 1889,
    "description": "Although The Starry Night was painted during the day in Van Gogh's ground-floor studio, it would be inaccurate to state that the picture was painted from memory. The view has been identified as the one from his bedroom window, facing east, a view which Van Gogh painted variations of no fewer than twenty-one times, including The Starry Night. \"Through the iron-barred window,\" he wrote to his brother, Theo, around 23 May 1889, \"I can see an enclosed square of wheat ... above which, in the morning, I watch the sun rise in all its glory.\"",
    "source": "https://en.wikipedia.org/wiki/The_Starry_Night",
    "artist": {
      "image": "./assets/starry-night/artist.jpg",
      "name": "Vincent Van Gogh"
    },
    "images": {
      "thumbnail": "./assets/starry-night/thumbnail.jpg",
      "hero": {
        "small": "./assets/starry-night/hero-small.jpg",
        "large": "./assets/starry-night/hero-large.jpg"
      },
      "gallery": "./assets/starry-night/gallery.jpg"
    }
  },
  {
    "name": "Girl with a Pearl Earring",
    "year": 1665,
    "description": "The painting is a tronie, the Dutch 17th-century description of a 'head' that was not meant to be a portrait. It depicts a European girl wearing an exotic dress, an oriental turban, and what was thought to be a very large pearl as an earring. In 2014, Dutch astrophysicist Vincent Icke raised doubts about the material of the earring and argued that it looks more like polished tin than pearl on the grounds of the specular reflection, the pear shape and the large size of the earring.",
    "source": "https://en.wikipedia.org/wiki/Girl_with_a_Pearl_Earring",
    "artist": {
      "image": "./assets/girl-with-pearl-earring/artist.jpg",
      "name": "Johannes Vermeer"
    },
    "images": {
      "thumbnail": "./assets/girl-with-pearl-earring/thumbnail.jpg",
      "hero": {
        "small": "./assets/girl-with-pearl-earring/hero-small.jpg",
        "large": "./assets/girl-with-pearl-earring/hero-large.jpg"
      },
      "gallery": "./assets/girl-with-pearl-earring/gallery.jpg"
    }
  },
  {
    "name": "Guernica",
    "year": 1937,
    "description": "The scene occurs within a room where, on the left, a wide-eyed bull stands over a grieving woman holding a dead child in her arms. In the center of the room a horse falls in agony with a large gaping hole in its side, as if it had just been run through by a spear or javelin. The horse appears to be wearing chain mail armor, decorated with vertical tally marks arranged in rows. A dead and dismembered soldier lies under the horse. The hand of his severed right arm grasps a shattered sword, from which a flower grows.",
    "source": "https://en.wikipedia.org/wiki/Guernica_(Picasso)",
    "artist": {
      "image": "./assets/guernica/artist.jpg",
      "name": "Pablo Picasso"
    },
    "images": {
      "thumbnail": "./assets/guernica/thumbnail.jpg",
      "hero": {
        "small": "./assets/guernica/hero-small.jpg",
        "large": "./assets/guernica/hero-large.jpg"
      },
      "gallery": "./assets/guernica/gallery.jpg"
    }
  },
  {
    "name": "Penitent Magdalene",
    "year": 1625,
    "description": "Penitent Magdalene is a painting by the Italian artist Artemisia Gentileschi. It hangs in Seville Cathedral. It has probably been in the cathedral since the late 17th century. The painting's first home was the collection of Fernando Enriquez Afan de Ribera, from 1626 to 1637. She returned to the subject later in the 1620s in Mary Magdalene as Melancholy.",
    "source": "https://en.wikipedia.org/wiki/Penitent_Magdalene_(Artemisia_Gentileschi)",
    "artist": {
      "image": "./assets/penitent-magdalene/artist.jpg",
      "name": "Artemisia Gentileschi"
    },
    "images": {
      "thumbnail": "./assets/penitent-magdalene/thumbnail.jpg",
      "hero": {
        "small": "./assets/penitent-magdalene/hero-small.jpg",
        "large": "./assets/penitent-magdalene/hero-large.jpg"
      },
      "gallery": "./assets/penitent-magdalene/gallery.jpg"
    }
  },
  {
    "name": "The Storm on the Sea of Galilee",
    "year": 1633,
    "description": "The painting, in vertical format, shows a close-up view of Christ's disciples struggling frantically against the heavy storm to regain control of their fishing boat. A huge wave beats the bow and rips the sail. One of the disciples is seen vomiting over the side. Another one, looking directly out at the viewer, is a self-portrait of the artist. Only Christ, depicted on the right, remains calm.",
    "source": "https://en.wikipedia.org/wiki/The_Storm_on_the_Sea_of_Galilee",
    "artist": {
      "image": "./assets/the-storm-on-the-sea-of-galilee/artist.jpg",
      "name": "Rembrandt"
    },
    "images": {
      "thumbnail": "./assets/the-storm-on-the-sea-of-galilee/thumbnail.jpg",
      "hero": {
        "small": "./assets/the-storm-on-the-sea-of-galilee/hero-small.jpg",
        "large": "./assets/the-storm-on-the-sea-of-galilee/hero-large.jpg"
      },
      "gallery": "./assets/the-storm-on-the-sea-of-galilee/gallery.jpg"
    }
  },
  {
    "name": "The Great Wave off Kanagawa",
    "year": 1831,
    "description": "The Great Wave off Kanagawa (Japanese: 神奈川沖浪裏, Hepburn: Kanagawa-oki Nami Ura, lit. \"Under the Wave off Kanagawa\"), also known as The Great Wave or simply The Wave, is a woodblock print by the Japanese ukiyo-e artist Hokusai. It was published sometime between 1829 and 1833 in the late Edo period as the first print in Hokusai's series Thirty-six Views of Mount Fuji. The image depicts an enormous wave threatening three boats off the coast in the Sagami Bay (Kanagawa Prefecture) while Mount Fuji rises in the background.",
    "source": "https://en.wikipedia.org/wiki/The_Great_Wave_off_Kanagawa",
    "artist": {
      "image": "./assets/the-great-wave-off-kanagawa/artist.jpg",
      "name": "Hokusai"
    },
    "images": {
      "thumbnail": "./assets/the-great-wave-off-kanagawa/thumbnail.jpg",
      "hero": {
        "small": "./assets/the-great-wave-off-kanagawa/hero-small.jpg",
        "large": "./assets/the-great-wave-off-kanagawa/hero-large.jpg"
      },
      "gallery": "./assets/the-great-wave-off-kanagawa/gallery.jpg"
    }
  },
  {
    "name": "Van Gogh Self-portrait",
    "year": 1889,
    "description": "This self-portrait was one of about 32 produced over a 10-year period, and these were an important part of his work as a painter; he painted himself because he often lacked the money to pay for models. He took the painting with him to Auvers-sur-Oise, near Paris, where he showed it to Dr. Paul Gachet, who thought it was \"absolutely fanatical\". Art historians are divided as to whether this painting or Self-portrait without beard is Van Gogh's final self-portrait. The art historians Ingo F. Walther and Jan Hulsker consider this to be the last.",
    "source": "https://en.wikipedia.org/wiki/Van_Gogh_self-portrait_(1889)",
    "artist": {
      "image": "./assets/van-gogh-self-portrait/artist.jpg",
      "name": "Vincent Van Gogh"
    },
    "images": {
      "thumbnail": "./assets/van-gogh-self-portrait/thumbnail.jpg",
      "hero": {
        "small": "./assets/van-gogh-self-portrait/hero-small.jpg",
        "large": "./assets/van-gogh-self-portrait/hero-large.jpg"
      },
      "gallery": "./assets/van-gogh-self-portrait/gallery.jpg"
    }
  },
  {
    "name": "The Sleeping Gypsy",
    "year": 1897,
    "description": "The Sleeping Gypsy (French: La Bohémienne endormie) is an 1897 oil painting by French Naïve artist Henri Rousseau (1844–1910). It is a fantastical depiction of a lion musing over a sleeping woman on a moonlit night. Rousseau first exhibited the painting at the 13th Salon des Indépendants, and tried unsuccessfully to sell it to the mayor of his hometown, Laval. Instead, it entered the private collection of a Parisian charcoal merchant where it remained until 1924, when it was discovered by the art critic Louis Vauxcelles.",
    "source": "https://en.wikipedia.org/wiki/The_Sleeping_Gypsy",
    "artist": {
      "image": "./assets/the-sleeping-gypsy/artist.jpg",
      "name": "Henri Rousseau"
    },
    "images": {
      "thumbnail": "./assets/the-sleeping-gypsy/thumbnail.jpg",
      "hero": {
        "small": "./assets/the-sleeping-gypsy/hero-small.jpg",
        "large": "./assets/the-sleeping-gypsy/hero-large.jpg"
      },
      "gallery": "./assets/the-sleeping-gypsy/gallery.jpg"
    }
  },
  {
    "name": "Lady with an Ermine",
    "year": 1489,
    "description": "The Lady with an Ermine (Italian: Dama con l'ermellino [ˈdaːma kon lermelˈliːno]; Polish: Dama z gronostajem) is a portrait painting widely attributed to the Italian Renaissance artist Leonardo da Vinci. Dated to c. 1489–1491, the work is painted in oils on a panel of walnut wood. Its subject is Cecilia Gallerani, a mistress of Ludovico Sforza (\"Il Moro\"), Duke of Milan; Leonardo was painter to the Sforza court at the time of its execution. It is one of only four surviving portraits of women painted by Leonardo, the others being Ginevra de' Benci, La Belle Ferronnière and the Mona Lisa",
    "source": "https://en.wikipedia.org/wiki/Lady_with_an_Ermine",
    "artist": {
      "image": "./assets/lady-with-an-ermine/artist.jpg",
      "name": "Leonardo da Vinci"
    },
    "images": {
      "thumbnail": "./assets/lady-with-an-ermine/thumbnail.jpg",
      "hero": {
        "small": "./assets/lady-with-an-ermine/hero-small.jpg",
        "large": "./assets/lady-with-an-ermine/hero-large.jpg"
      },
      "gallery": "./assets/lady-with-an-ermine/gallery.jpg"
    }
  },
  {
    "name": "The Night Café",
    "year": 1888,
    "description": "The Night Café (French: Le Café de nuit) is an oil painting created by Dutch artist Vincent van Gogh in September 1888 in Arles. Its title is inscribed lower right beneath the signature. The painting is owned by Yale University and is currently held at the Yale University Art Gallery in New Haven, Connecticut. The interior depicted is the Café de la Gare, 30 Place Lamartine, run by Joseph-Michel Ginoux and his wife Marie, who in November 1888 posed for Van Gogh's and Gauguin's Arlésienne; a bit later, Joseph Ginoux evidently posed for both artists, too.",
    "source": "https://en.wikipedia.org/wiki/The_Night_Caf%C3%A9",
    "artist": {
      "image": "./assets/the-night-cafe/artist.jpg",
      "name": "Vincent Van Gogh"
    },
    "images": {
      "thumbnail": "./assets/the-night-cafe/thumbnail.jpg",
      "hero": {
        "small": "./assets/the-night-cafe/hero-small.jpg",
        "large": "./assets/the-night-cafe/hero-large.jpg"
      },
      "gallery": "./assets/the-night-cafe/gallery.jpg"
    }
  },
  {
    "name": "The Basket of Apples",
    "year": 1893,
    "description": "The Basket of Apples (French: Le panier de pommes) is a still life oil painting by French artist Paul Cézanne, which he created c. 1893. The painting is particularly remarkable for its creative composition, which rejected realistic representation in favour of distorting objects to create multiple perspectives. This approach eventually influenced other art movements, including Fauvism and Cubism. It belongs to the Helen Birch Bartlett Memorial Collection of the Art Institute of Chicago.",
    "source": "https://en.wikipedia.org/wiki/The_Basket_of_Apples",
    "artist": {
      "image": "./assets/the-basket-of-apples/artist.jpg",
      "name": "Paul Cézanne"
    },
    "images": {
      "thumbnail": "./assets/the-basket-of-apples/thumbnail.jpg",
      "hero": {
        "small": "./assets/the-basket-of-apples/hero-small.jpg",
        "large": "./assets/the-basket-of-apples/hero-large.jpg"
      },
      "gallery": "./assets/the-basket-of-apples/gallery.jpg"
    }
  },
  {
    "name": "The Boy in the Red Vest",
    "year": 1889,
    "description": "Cézanne painted four oil portraits of this Italian boy in the red vest, all in different poses, which allowed him to study the relationship between the figure and space. The most famous of the four, and the one commonly referred to by this title, is the one which depicts the boy in a melancholic seated pose with his elbow on a table and his head cradled in his hand. It is currently held in Zürich, Switzerland. The other three portraits, of different poses, are in museums in the US",
    "source": "https://en.wikipedia.org/wiki/The_Boy_in_the_Red_Vest",
    "artist": {
      "image": "./assets/the-boy-in-the-red-vest/artist.jpg",
      "name": "Paul Cézanne"
    },
    "images": {
      "thumbnail": "./assets/the-boy-in-the-red-vest/thumbnail.jpg",
      "hero": {
        "small": "./assets/the-boy-in-the-red-vest/hero-small.jpg",
        "large": "./assets/the-boy-in-the-red-vest/hero-large.jpg"
      },
      "gallery": "./assets/the-boy-in-the-red-vest/gallery.jpg"
    }
  },
  {
    "name": "Arnolfini Portrait",
    "year": 1434,
    "description": "It is considered one of the most original and complex paintings in Western art, because of its beauty, complex iconography, geometric orthogonal perspective, and expansion of the picture space with the use of a mirror. According to Ernst Gombrich \"in its own way it was as new and revolutionary as Donatello's or Masaccio's work in Italy. A simple corner of the real world had suddenly been fixed on to a panel as if by magic ... For the first time in history the artist became the perfect eye-witness in the truest sense of the term\".",
    "source": "https://en.wikipedia.org/wiki/Arnolfini_Portrait",
    "artist": {
      "image": "./assets/arnolfini-portrait/artist.jpg",
      "name": "Jan van Eyck"
    },
    "images": {
      "thumbnail": "./assets/arnolfini-portrait/thumbnail.jpg",
      "hero": {
        "small": "./assets/arnolfini-portrait/hero-small.jpg",
        "large": "./assets/arnolfini-portrait/hero-large.jpg"
      },
      "gallery": "./assets/arnolfini-portrait/gallery.jpg"
    }
  },
  {
    "name": "Mona Lisa",
    "year": 1503,
    "description": "The Mona Lisa (/ˌmoʊnə ˈliːsə/; italian: La Gioconda [la dʒoˈkonda] or Monna Lisa [ˈmɔnna ˈliːza]) is a half-length portrait painting by Italian artist Leonardo da Vinci. Considered an archetypal masterpiece of the Italian Renaissance, it has been described as \"the best known, the most visited, the most written about, the most sung about, the most parodied work of art in the world\". The painting's novel qualities include the subject's enigmatic expression, the monumentality of the composition, the subtle modelling of forms, and the atmospheric illusionism.",
    "source": "https://en.wikipedia.org/wiki/Mona_Lisa",
    "artist": {
      "image": "./assets/mona-lisa/artist.jpg",
      "name": "Leonardo da Vinci"
    },
    "images": {
      "thumbnail": "./assets/mona-lisa/thumbnail.jpg",
      "hero": {
        "small": "./assets/mona-lisa/hero-small.jpg",
        "large": "./assets/mona-lisa/hero-large.jpg"
      },
      "gallery": "./assets/mona-lisa/gallery.jpg"
    }
  },
  {
    "name": "The Swing",
    "year": 1767,
    "description": "The painting depicts an elegant young woman on a swing. A smiling young man, hiding in the bushes on the left, watches her from a vantage point that allows him to see up into her billowing dress, where his arm is pointed with hat in hand. A smiling older man, who is nearly hidden in the shadows on the right, propels the swing with a pair of ropes. The older man appears to be unaware of the young man. As the young lady swings high, she throws her left leg up, allowing her dainty shoe to fly through the air.",
    "source": "https://en.wikipedia.org/wiki/The_Swing_(Fragonard)",
    "artist": {
      "image": "./assets/the-swing/artist.jpg",
      "name": "Jean-Honoré Fragonard"
    },
    "images": {
      "thumbnail": "./assets/the-swing/thumbnail.jpg",
      "hero": {
        "small": "./assets/the-swing/hero-small.jpg",
        "large": "./assets/the-swing/hero-large.jpg"
      },
      "gallery": "./assets/the-swing/gallery.jpg"
    }
  }
]

ANSWER

Answered 2021-Dec-09 at 18:01

If I'm understanding your question/issue correctly, you want to render the Gallery and Paint components each on their own routes independently, and fix the slideshow linking from painting to painting. For this use the first routing snippet so they are independent routes and not nested.

<Routes>
  <Route
    path="galleria"
    element={<Gallery datas={data} clickEvent={handleSlideShow} />}
  />
  <Route path="/galleria/:urlPath" element={<Paint datas={data} />} />   
</Routes>

Next, the reason the next painting's urlPath is being appended as /galleria/Girl-with-a-Pearl-Earring/The-Great-Wave-off-Kanagawa is because react-router-dom v6 is capable of using absolute and relative paths. The difference between them is a leading "/" character. Paths starting with "/" are absolute paths, and without are relative.

Within Paint the next/previous buttons/links are specifying only the next/previous data[index].uriPath value instead of including the "/galleria" absolute path prefix.

<div className="column nav-links">
  <span className="prev-btn">
    {prevSlide <= -1 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[prevSlide].urlPath}></Link>
    )}
  </span>
  <span className="next-btn">
    {nextSlide >= 15 ? (
      <span className="disable"></span>
    ) : (
      <Link className="active" to={datas[nextSlide].urlPath}></Link>
    )}
  </span>
</div>

Here datas[prevSlide].urlPath is a string value of the data element name, i.e. "Girl-with-a-Pearl-Earring", there is no leading "/" so the link is treated as a relative link.

There are a couple ways to resolve:

Fix the relative linking, make the new path relative from the parent "directory":

A relative <Link to> value (that does not begin with /) resolves relative to the parent route, which means that it builds upon the URL path that was matched by the route that rendered that <Link>. It may contain .. to link to routes further up the hierarchy. In these cases, .. works exactly like the command-line cd function; each .. removes one segment of the parent path.

<Link
  className="active"
  to={`../${datas[prevSlide].urlPath}`}
>
</Link>

...

<Link
  className="active"
  to={`../${datas[nextSlide].urlPath}`}
>
</Link>

Use an absolute path:

<Link
  className="active"
  to={`/galleria/${datas[prevSlide].urlPath}`}
>
</Link>

...

<Link
  className="active"
  to={`/galleria/${datas[nextSlide].urlPath}`}
>
</Link>

Source https://stackoverflow.com/questions/70278436

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

Vulnerabilities

No vulnerabilities reported

Install storm

You can download it from GitHub.
You can use storm 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 storm 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 .