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

 by   nathanmarz Java Version: Current License: Apache-2.0

 by   nathanmarz Java Version: Current License: Apache-2.0

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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
Support
Quality
Quality
Security
Security
License
License
Reuse
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

securitySecurity

  • 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

buildReuse

  • 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

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

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

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 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 .

Support

Source code contributions can be submitted either by sumitting a pull request or by creating an issue in JIRA and attaching patches.

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