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storm | Mirror of Apache Storm

 by   apache Java Version: Current License: Non-SPDX

 by   apache Java Version: Current License: Non-SPDX

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kandi X-RAY | storm Summary

storm is a Java library typically used in Big Data applications. storm has no vulnerabilities, it has build file available and it has medium support. However storm has 398 bugs and it has a Non-SPDX License. You can install using 'npm i storm-multilang-js' or download it from GitHub, npm.
Storm users should send messages and subscribe to user@storm.apache.org. You can subscribe to this list by sending an email to user-subscribe@storm.apache.org. Likewise, you can cancel a subscription by sending an email to user-unsubscribe@storm.apache.org. You can also browse the archives of the storm-user mailing list.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • storm has a medium active ecosystem.
  • It has 6335 star(s) with 4085 fork(s). There are 601 watchers for this library.
  • It had no major release in the last 12 months.
  • storm has no issues reported. There are 54 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 #Java
Average in #Java
storm Support
Best in #Java
Average in #Java

quality kandi Quality

  • storm has 398 bugs (30 blocker, 24 critical, 207 major, 137 minor) and 8614 code smells.
storm Quality
Best in #Java
Average in #Java
storm Quality
Best in #Java
Average in #Java

securitySecurity

  • storm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • storm code analysis shows 0 unresolved vulnerabilities.
  • There are 111 security hotspots that need review.
storm Security
Best in #Java
Average in #Java
storm Security
Best in #Java
Average in #Java

license License

  • storm has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
storm License
Best in #Java
Average in #Java
storm License
Best in #Java
Average in #Java

buildReuse

  • storm releases are not available. You will need to build from source code and install.
  • Deployable package is available in npm.
  • Build file is available. You can build the component from source.
  • storm saves you 699311 person hours of effort in developing the same functionality from scratch.
  • It has 343331 lines of code, 30514 functions and 2596 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
storm Reuse
Best in #Java
Average in #Java
storm Reuse
Best in #Java
Average in #Java
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.

  • Capture topology information
  • Performs a scan .
  • Start the cluster .
  • Load a worker .
  • Schedule a topology
  • Verify the drpc config .
  • Decodes a SASL message .
  • Schedules executors on nodes .
  • This method checks if a topology is valid
  • Run the shell .

storm Key Features

Mirror of Apache Storm

R - dplyr- Reducing data package 'storms'

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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

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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

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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

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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

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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

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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

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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 iconCopydownload iconDownload
<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 iconCopydownload iconDownload
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 iconCopydownload iconDownload
<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>

Community Discussions

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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

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

Vulnerabilities

No vulnerabilities reported

Install storm

You can install using 'npm i storm-multilang-js' or download it from GitHub, npm.
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 .

Support

Documentation and tutorials can be found on the Storm website. Developers and contributors should also take a look at our Developer documentation.

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