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caffeine | A high performance caching library for Java | Caching library

 by   ben-manes Java Version: v3.0.6 License: Apache-2.0

 by   ben-manes Java Version: v3.0.6 License: Apache-2.0

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kandi X-RAY | caffeine Summary

caffeine is a Java library typically used in Server, Caching applications. caffeine has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub, Maven.
Caffeine is a high performance, near optimal caching library. For more details, see our user's guide and browse the API docs for the latest release.
Support
Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • caffeine has a highly active ecosystem.
  • It has 11391 star(s) with 1225 fork(s). There are 371 watchers for this library.
  • There were 1 major release(s) in the last 6 months.
  • There are 2 open issues and 517 have been closed. On average issues are closed in 36 days. There are no pull requests.
  • It has a positive sentiment in the developer community.
  • The latest version of caffeine is v3.0.6
caffeine Support
Best in #Caching
Average in #Caching
caffeine Support
Best in #Caching
Average in #Caching

quality kandi Quality

  • caffeine has 0 bugs and 0 code smells.
caffeine Quality
Best in #Caching
Average in #Caching
caffeine Quality
Best in #Caching
Average in #Caching

securitySecurity

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

license License

  • caffeine 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.
caffeine License
Best in #Caching
Average in #Caching
caffeine License
Best in #Caching
Average in #Caching

buildReuse

  • caffeine releases are available to install and integrate.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • caffeine saves you 52734 person hours of effort in developing the same functionality from scratch.
  • It has 62332 lines of code, 5982 functions and 433 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
caffeine Reuse
Best in #Caching
Average in #Caching
caffeine Reuse
Best in #Caching
Average in #Caching
Top functions reviewed by kandi - BETA

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

  • Performs the remap operation .
  • Migrate the page to T2 .
  • Attempt to expand the buffer at the given offset .
  • Returns true if the map contains the specified value .
  • Reclaims a node .
  • Moves the hot hand to the hot spot .
  • Returns a string representation of this class .
  • Scan a cold entry .
  • Executes the remapping function .
  • Runs the cold page for cold space .

caffeine Key Features

A high performance caching library for Java

Cache

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LoadingCache<Key, Graph> graphs = Caffeine.newBuilder()
    .maximumSize(10_000)
    .expireAfterWrite(Duration.ofMinutes(5))
    .refreshAfterWrite(Duration.ofMinutes(1))
    .build(key -> createExpensiveGraph(key));

Download

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implementation 'com.github.ben-manes.caffeine:caffeine:3.0.5'

// Optional extensions
implementation 'com.github.ben-manes.caffeine:guava:3.0.5'
implementation 'com.github.ben-manes.caffeine:jcache:3.0.5'

grouped data select rows and include rows before/after

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library(tidyverse)

df %>%
  setDT(df) %>% 
  mutate(row_id = row_number()) %>% 
  group_by(PATIENT.ID) %>% 
  mutate(first_yes = cumsum(Caffeinefactor == "yes"), 
         last_no = (Caffeinefactor == "no") * row_number(), 
         last_no = ifelse(first_yes == 0, last_no, 0),
         select_row = (first_yes == 1 & Caffeine > 0) | last_no == max(last_no)) %>%
  filter(select_row | lag(select_row) | lead(select_row)) %>%
  select(-first_yes, -last_no, -select_row)
   PATIENT.ID Caffeine Caffeinefactor PULSE.WIDTH row_id
        <int>    <int> <chr>                <dbl>  <int>
 1     210625        0 no                    0.75      3
 2     210625        0 no                    0.75      4
 3     210625      200 yes                   0.75      5
 4     210625      200 yes                   0.75      6
 5     221179        0 no                    1        25
 6     221179        0 no                    1        26
 7     221179      200 yes                   1        27
 8     221179      200 yes                   1        28
 9     301705        0 no                    0.5      36
10     301705        0 no                    0.5      37
11     301705      200 yes                   0.5      38
12     301705        0 no                    0.5      39
-----------------------
library(tidyverse)

df %>%
  setDT(df) %>% 
  mutate(row_id = row_number()) %>% 
  group_by(PATIENT.ID) %>% 
  mutate(first_yes = cumsum(Caffeinefactor == "yes"), 
         last_no = (Caffeinefactor == "no") * row_number(), 
         last_no = ifelse(first_yes == 0, last_no, 0),
         select_row = (first_yes == 1 & Caffeine > 0) | last_no == max(last_no)) %>%
  filter(select_row | lag(select_row) | lead(select_row)) %>%
  select(-first_yes, -last_no, -select_row)
   PATIENT.ID Caffeine Caffeinefactor PULSE.WIDTH row_id
        <int>    <int> <chr>                <dbl>  <int>
 1     210625        0 no                    0.75      3
 2     210625        0 no                    0.75      4
 3     210625      200 yes                   0.75      5
 4     210625      200 yes                   0.75      6
 5     221179        0 no                    1        25
 6     221179        0 no                    1        26
 7     221179      200 yes                   1        27
 8     221179      200 yes                   1        28
 9     301705        0 no                    0.5      36
10     301705        0 no                    0.5      37
11     301705      200 yes                   0.5      38
12     301705        0 no                    0.5      39

Can you still use a ConcurrentLinkedHashMap with Caffeine?

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var data = new ConcurrentHashMap<String, Integer>();
var order = Collections.synchronizedMap(new LinkedHashMap<String, Integer>());

data.compute("a", (key, oldValue) -> {
  order.put(key, 1);
  return 1;
});

// Locks the map during access, blocking writes; consider snapshotting first
order.forEach((key, value) -> System.out.printf("%s=%s%n", key, value);
volatile Map<K, V> data = LinkedHashMap.empty();
final Lock lock = new ReentrantLock();

// Single writer
lock.lock();
try {
  data = data.put(1, 2).put(3, 4);
} finally {
  lock.unlock();
}

// Multiple readers
System.out.println(data);
-----------------------
var data = new ConcurrentHashMap<String, Integer>();
var order = Collections.synchronizedMap(new LinkedHashMap<String, Integer>());

data.compute("a", (key, oldValue) -> {
  order.put(key, 1);
  return 1;
});

// Locks the map during access, blocking writes; consider snapshotting first
order.forEach((key, value) -> System.out.printf("%s=%s%n", key, value);
volatile Map<K, V> data = LinkedHashMap.empty();
final Lock lock = new ReentrantLock();

// Single writer
lock.lock();
try {
  data = data.put(1, 2).put(3, 4);
} finally {
  lock.unlock();
}

// Multiple readers
System.out.println(data);

Is there a Python DICOM Modality Worklist setup library?

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from pydicom.dataset import Dataset, FileMetaDataset
from pydicom.uid import ExplicitVRLittleEndian

ds = Dataset()

# Add file meta information elements
ds.file_meta = FileMetaDataset()
ds.file_meta.TransferSyntaxUID = ExplicitVRLittleEndian

# Fill out the worklist query elements
ds.SpecificCharacterSet = "ISO_IR 6"
ds.ScheduledProcedureStepSequence = [Dataset()]
ds.ScheduledProcedureStepSequence[0].Modality = "CT"
# etc...

ds.save_as("query.wl", write_like_original=False)
-----------------------
import os
from os import path
from pydicom.dataset import Dataset, FileMetaDataset
from pydicom.uid import ExplicitVRLittleEndian
        
wl_file_name = "directory/file.wl"
txt_file_name = "directory/file.txt"

# Create data set
ds = Dataset()
# Add file meta information elements
ds.file_meta = FileMetaDataset()
ds.file_meta.TransferSyntaxUID = ExplicitVRLittleEndian
ds.file_meta.MediaStorageSOPClassUID = "0"
ds.file_meta.MediaStorageSOPInstanceUID = "0"
# Fill out the worklist query elements
ds.SpecificCharacterSet             = "ISO_IR 6"
ds.InstanceCreationDate             = "20220101"
ds.AccessionNumber                  = "12345-abc"
ds.PatientName                      = "SURNAME^NAME"
ds.PatientID                        = "123456"
ds.PatientBirthDate                 = "19700101"
ds.PatientSex                       = "M"
ds.StudyInstanceUID                 = "1a-2b-3c" 
ds.RequestedProcedureDescription    = "ProcedureDescription"
ds.ScheduledProcedureStepSequence   = [Dataset()]
ds.ScheduledProcedureStepSequence[0].Modality                           = "OT"
ds.ScheduledProcedureStepSequence[0].ScheduledStationAETitle            = "OT"
ds.ScheduledProcedureStepSequence[0].ScheduledProcedureStepStartDate    = "20220101"
ds.ScheduledProcedureStepSequence[0].ScheduledProcedureStepStartTime    = "080000"
ds.ScheduledProcedureStepSequence[0].ScheduledPerformingPhysicianName   = "Doctor Emmet Brown"
ds.ScheduledProcedureStepSequence[0].ScheduledProcedureStepDescription  = "SchedProcStepDesc"
ds.ScheduledProcedureStepID         = "0001"
# more stuff if you need

# Save directly as a .wl file.
# Set write_like_original=False to be certain you’re writing the dataset in the DICOM File Format 
ds.save_as(wl_file_name, write_like_original=False)

# Additionally, you can also make a readable txt file for humans
# Check if txt file already exists
if(path.exists(txt_file_name)): #if txt file exists, remove it first
    try:
        os.remove(txt_file_name)
    except OSError as e:
        print("Error: %s : %s" % (txt_file_name, e.strerror))
# Run dcmdump command to convert wl file to txt
convert_wl_to_txt_cmd = "dcmdump " + wl_file_name + " > "  + txt_file_name 
os.system(convert_wl_to_txt_cmd)
# Dicom-File-Format

# Dicom-Meta-Information-Header
# Used TransferSyntax: Little Endian Explicit
(0002,0000) UL 120                                      #   4, 1 FileMetaInformationGroupLength
(0002,0001) OB 00\01                                    #   2, 1 FileMetaInformationVersion
(0002,0002) UI [0]                                      #   2, 1 MediaStorageSOPClassUID
(0002,0003) UI [0]                                      #   2, 1 MediaStorageSOPInstanceUID
(0002,0010) UI =LittleEndianExplicit                    #  20, 1 TransferSyntaxUID
(0002,0012) UI [1.2.826.0.1.3680043.8.498.1]            #  28, 1 ImplementationClassUID
(0002,0013) SH [PYDICOM 2.2.2]                          #  14, 1 ImplementationVersionName

# Dicom-Data-Set
# Used TransferSyntax: Little Endian Explicit
(0008,0005) CS [ISO_IR 6]                               #   8, 1 SpecificCharacterSet
(0008,0012) DA [20220101]                               #   8, 1 InstanceCreationDate
(0008,0050) SH [12345-abc]                              #  10, 1 AccessionNumber
(0010,0010) PN [SURNAME^NAME]                           #  12, 1 PatientName
(0010,0020) LO [123456]                                 #   6, 1 PatientID
(0010,0030) DA [19700101]                               #   8, 1 PatientBirthDate
(0010,0040) CS [M]                                      #   2, 1 PatientSex
(0020,000d) UI [1a-2b-3c]                               #   8, 1 StudyInstanceUID
(0032,1060) LO [ProcedureDescription]                   #  20, 1 RequestedProcedureDescription
(0040,0009) SH [0001]                                   #   4, 1 ScheduledProcedureStepID
(0040,0100) SQ (Sequence with explicit length #=1)      # 110, 1 ScheduledProcedureStepSequence
  (fffe,e000) na (Item with explicit length #=6)          # 102, 1 Item
    (0008,0060) CS [OT]                                     #   2, 1 Modality
    (0040,0001) AE [OT]                                     #   2, 1 ScheduledStationAETitle
    (0040,0002) DA [20220101]                               #   8, 1 ScheduledProcedureStepStartDate
    (0040,0003) TM [080000]                                 #   6, 1 ScheduledProcedureStepStartTime
    (0040,0006) PN [Doctor Emmet Brown]                     #  18, 1 ScheduledPerformingPhysicianName
    (0040,0007) LO [SchedProcStepDesc]                      #  18, 1 ScheduledProcedureStepDescription
  (fffe,e00d) na (ItemDelimitationItem for re-encoding)   #   0, 0 ItemDelimitationItem
(fffe,e0dd) na (SequenceDelimitationItem for re-encod.) #   0, 0 SequenceDelimitationItem
-----------------------
import os
from os import path
from pydicom.dataset import Dataset, FileMetaDataset
from pydicom.uid import ExplicitVRLittleEndian
        
wl_file_name = "directory/file.wl"
txt_file_name = "directory/file.txt"

# Create data set
ds = Dataset()
# Add file meta information elements
ds.file_meta = FileMetaDataset()
ds.file_meta.TransferSyntaxUID = ExplicitVRLittleEndian
ds.file_meta.MediaStorageSOPClassUID = "0"
ds.file_meta.MediaStorageSOPInstanceUID = "0"
# Fill out the worklist query elements
ds.SpecificCharacterSet             = "ISO_IR 6"
ds.InstanceCreationDate             = "20220101"
ds.AccessionNumber                  = "12345-abc"
ds.PatientName                      = "SURNAME^NAME"
ds.PatientID                        = "123456"
ds.PatientBirthDate                 = "19700101"
ds.PatientSex                       = "M"
ds.StudyInstanceUID                 = "1a-2b-3c" 
ds.RequestedProcedureDescription    = "ProcedureDescription"
ds.ScheduledProcedureStepSequence   = [Dataset()]
ds.ScheduledProcedureStepSequence[0].Modality                           = "OT"
ds.ScheduledProcedureStepSequence[0].ScheduledStationAETitle            = "OT"
ds.ScheduledProcedureStepSequence[0].ScheduledProcedureStepStartDate    = "20220101"
ds.ScheduledProcedureStepSequence[0].ScheduledProcedureStepStartTime    = "080000"
ds.ScheduledProcedureStepSequence[0].ScheduledPerformingPhysicianName   = "Doctor Emmet Brown"
ds.ScheduledProcedureStepSequence[0].ScheduledProcedureStepDescription  = "SchedProcStepDesc"
ds.ScheduledProcedureStepID         = "0001"
# more stuff if you need

# Save directly as a .wl file.
# Set write_like_original=False to be certain you’re writing the dataset in the DICOM File Format 
ds.save_as(wl_file_name, write_like_original=False)

# Additionally, you can also make a readable txt file for humans
# Check if txt file already exists
if(path.exists(txt_file_name)): #if txt file exists, remove it first
    try:
        os.remove(txt_file_name)
    except OSError as e:
        print("Error: %s : %s" % (txt_file_name, e.strerror))
# Run dcmdump command to convert wl file to txt
convert_wl_to_txt_cmd = "dcmdump " + wl_file_name + " > "  + txt_file_name 
os.system(convert_wl_to_txt_cmd)
# Dicom-File-Format

# Dicom-Meta-Information-Header
# Used TransferSyntax: Little Endian Explicit
(0002,0000) UL 120                                      #   4, 1 FileMetaInformationGroupLength
(0002,0001) OB 00\01                                    #   2, 1 FileMetaInformationVersion
(0002,0002) UI [0]                                      #   2, 1 MediaStorageSOPClassUID
(0002,0003) UI [0]                                      #   2, 1 MediaStorageSOPInstanceUID
(0002,0010) UI =LittleEndianExplicit                    #  20, 1 TransferSyntaxUID
(0002,0012) UI [1.2.826.0.1.3680043.8.498.1]            #  28, 1 ImplementationClassUID
(0002,0013) SH [PYDICOM 2.2.2]                          #  14, 1 ImplementationVersionName

# Dicom-Data-Set
# Used TransferSyntax: Little Endian Explicit
(0008,0005) CS [ISO_IR 6]                               #   8, 1 SpecificCharacterSet
(0008,0012) DA [20220101]                               #   8, 1 InstanceCreationDate
(0008,0050) SH [12345-abc]                              #  10, 1 AccessionNumber
(0010,0010) PN [SURNAME^NAME]                           #  12, 1 PatientName
(0010,0020) LO [123456]                                 #   6, 1 PatientID
(0010,0030) DA [19700101]                               #   8, 1 PatientBirthDate
(0010,0040) CS [M]                                      #   2, 1 PatientSex
(0020,000d) UI [1a-2b-3c]                               #   8, 1 StudyInstanceUID
(0032,1060) LO [ProcedureDescription]                   #  20, 1 RequestedProcedureDescription
(0040,0009) SH [0001]                                   #   4, 1 ScheduledProcedureStepID
(0040,0100) SQ (Sequence with explicit length #=1)      # 110, 1 ScheduledProcedureStepSequence
  (fffe,e000) na (Item with explicit length #=6)          # 102, 1 Item
    (0008,0060) CS [OT]                                     #   2, 1 Modality
    (0040,0001) AE [OT]                                     #   2, 1 ScheduledStationAETitle
    (0040,0002) DA [20220101]                               #   8, 1 ScheduledProcedureStepStartDate
    (0040,0003) TM [080000]                                 #   6, 1 ScheduledProcedureStepStartTime
    (0040,0006) PN [Doctor Emmet Brown]                     #  18, 1 ScheduledPerformingPhysicianName
    (0040,0007) LO [SchedProcStepDesc]                      #  18, 1 ScheduledProcedureStepDescription
  (fffe,e00d) na (ItemDelimitationItem for re-encoding)   #   0, 0 ItemDelimitationItem
(fffe,e0dd) na (SequenceDelimitationItem for re-encod.) #   0, 0 SequenceDelimitationItem

Caching (Caffeine) &amp; Spring - two caches based on method parameter value

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@Caching(cacheable = {
        @Cacheable(cacheNames = "someDataCache1", condition = "#group.equals('group1')"),
        @Cacheable(cacheNames = "someDataCache2", condition = "#group.equals('group2')")
})
public List<String> getSomeData(String group) {
    return someService.getSomeDataForGroup(group);
}

How can I use the ggplot function to visualise grouped data?

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library(ggplot2)
library(dplyr)

tidied_data_2  <- read.table(text = "participant condition response_time caffeine        
1           1 Normal              984      1                
2           2 Normal             1005      1                
3           3 Normal              979      3                
4           4 Normal             1040      2                
5           5 Normal             1008      2                
6           6 Normal              979      3", head = TRUE)

tidied_data_2 %>%
  ggplot(aes(x = as.character(caffeine), y = response_time, colour = as.character(caffeine))) +
## geom_violin does not make sense with so few observations
#  geom_violin() +
## I've removed alpha so you can see the dots better
  geom_jitter(width = .1) +
  guides(colour = FALSE) +
  stat_summary(fun.data = "mean_cl_boot", colour = "black") +
  theme_minimal() +
  theme(text = element_text(size = 13)) +
  labs(x = "Condition X Caffeine", y = "Response Time (ms)")
              
tidied_data_2 %>%
## in this example as.integer(as.character(x)) is unnecessary, but it is necessary for your data sample
  ggplot(aes(x = as.integer(as.character(caffeine)), y = response_time)) +
  geom_jitter(width = .1) +
  theme_minimal()  
-----------------------
library(ggplot2)
library(dplyr)

tidied_data_2  <- read.table(text = "participant condition response_time caffeine        
1           1 Normal              984      1                
2           2 Normal             1005      1                
3           3 Normal              979      3                
4           4 Normal             1040      2                
5           5 Normal             1008      2                
6           6 Normal              979      3", head = TRUE)

tidied_data_2 %>%
  ggplot(aes(x = as.character(caffeine), y = response_time, colour = as.character(caffeine))) +
## geom_violin does not make sense with so few observations
#  geom_violin() +
## I've removed alpha so you can see the dots better
  geom_jitter(width = .1) +
  guides(colour = FALSE) +
  stat_summary(fun.data = "mean_cl_boot", colour = "black") +
  theme_minimal() +
  theme(text = element_text(size = 13)) +
  labs(x = "Condition X Caffeine", y = "Response Time (ms)")
              
tidied_data_2 %>%
## in this example as.integer(as.character(x)) is unnecessary, but it is necessary for your data sample
  ggplot(aes(x = as.integer(as.character(caffeine)), y = response_time)) +
  geom_jitter(width = .1) +
  theme_minimal()  

How to alternate actions on variables in an array (one of two things for every other variable)

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$policies = @('A', 'B')
$pickSecond = $false

foreach($SpecificEndpoint in $EndpointList)
{
    # pick policy
    $policy = $policies[$pickSecond]
    # toggle `pickSecond` for next time
    $pickSecond = -not $pickSecond

    $SpecificEndpoint.policyId = $policy
    # ... perform API call
}

How can I extract bigrams from text without removing the hash symbol?

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library(tidyverse)
library(tidytext)
library(tm)
library(purrr)

x <- (c("I went to afternoon tea with her majesty and #queen @Victoria in the palace.", "Does tea have extra caffeine?"))

clean_Twitter_Corpus <- function(x) {
  x  =  tolower(x)                          # convert to lower case characters
  x  =  stripWhitespace(x)                  # removing white space
  x  =  gsub("^\\s+|\\s+$", "", x)          # remove leading and trailing white space
  x  = removeWords(x,stopwords("english"))  # remove stopwords
  return(x)
}
# A custom build function that will take in a sentence and create
# a tibble of ngrams
ngrams_build = function(sentence, column_name, n = 2) {
  words <- sentence %>% str_split(pattern = " ", simplify = TRUE) 
  words <- words[words != ""]
  ngrams <- map_chr(1:(length(words) - n + 1),
                    .f = function(x, words, n) {
                      paste(words[x:(x + n - 1)], collapse = " ")
                    }, words = words, n = n)
  tibble(!!column_name := ngrams)
}
# clean the twitter texts. call the clean_Twitter_Corpus function
tweets <- clean_Twitter_Corpus(x)
tweets
#> [1] " went  afternoon tea   majesty  #queen @victoria   palace."
#> [2] " tea  extra caffeine?"
text <- as.character(tweets)
text <- as.data.frame(text)

tidy_descr_ngrams <- 
  # here I use purrr function with the custom function
  map_dfr(text$text, ngrams_build, column_name = "bigram", n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ")

# Here is the output which is similar to unnest_tokens but has special
# character included
tidy_descr_ngrams
#> # A tibble: 8 x 2
#>   word1     word2    
#>   <chr>     <chr>    
#> 1 went      afternoon
#> 2 afternoon tea      
#> 3 tea       majesty  
#> 4 majesty   #queen   
#> 5 #queen    @victoria
#> 6 @victoria palace.  
#> 7 tea       extra    
#> 8 extra     caffeine?

bigram_counts <- tidy_descr_ngrams %>%
  count(word1, word2, sort = TRUE)

bigram_counts
#> # A tibble: 8 x 3
#>   word1     word2         n
#>   <chr>     <chr>     <int>
#> 1 #queen    @victoria     1
#> 2 @victoria palace.       1
#> 3 afternoon tea           1
#> 4 extra     caffeine?     1
#> 5 majesty   #queen        1
#> 6 tea       extra         1
#> 7 tea       majesty       1
#> 8 went      afternoon     1
-----------------------
library(tidyverse)
library(tidytext)
library(tm)
library(purrr)

x <- (c("I went to afternoon tea with her majesty and #queen @Victoria in the palace.", "Does tea have extra caffeine?"))

clean_Twitter_Corpus <- function(x) {
  x  =  tolower(x)                          # convert to lower case characters
  x  =  stripWhitespace(x)                  # removing white space
  x  =  gsub("^\\s+|\\s+$", "", x)          # remove leading and trailing white space
  x  = removeWords(x,stopwords("english"))  # remove stopwords
  return(x)
}
# A custom build function that will take in a sentence and create
# a tibble of ngrams
ngrams_build = function(sentence, column_name, n = 2) {
  words <- sentence %>% str_split(pattern = " ", simplify = TRUE) 
  words <- words[words != ""]
  ngrams <- map_chr(1:(length(words) - n + 1),
                    .f = function(x, words, n) {
                      paste(words[x:(x + n - 1)], collapse = " ")
                    }, words = words, n = n)
  tibble(!!column_name := ngrams)
}
# clean the twitter texts. call the clean_Twitter_Corpus function
tweets <- clean_Twitter_Corpus(x)
tweets
#> [1] " went  afternoon tea   majesty  #queen @victoria   palace."
#> [2] " tea  extra caffeine?"
text <- as.character(tweets)
text <- as.data.frame(text)

tidy_descr_ngrams <- 
  # here I use purrr function with the custom function
  map_dfr(text$text, ngrams_build, column_name = "bigram", n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ")

# Here is the output which is similar to unnest_tokens but has special
# character included
tidy_descr_ngrams
#> # A tibble: 8 x 2
#>   word1     word2    
#>   <chr>     <chr>    
#> 1 went      afternoon
#> 2 afternoon tea      
#> 3 tea       majesty  
#> 4 majesty   #queen   
#> 5 #queen    @victoria
#> 6 @victoria palace.  
#> 7 tea       extra    
#> 8 extra     caffeine?

bigram_counts <- tidy_descr_ngrams %>%
  count(word1, word2, sort = TRUE)

bigram_counts
#> # A tibble: 8 x 3
#>   word1     word2         n
#>   <chr>     <chr>     <int>
#> 1 #queen    @victoria     1
#> 2 @victoria palace.       1
#> 3 afternoon tea           1
#> 4 extra     caffeine?     1
#> 5 majesty   #queen        1
#> 6 tea       extra         1
#> 7 tea       majesty       1
#> 8 went      afternoon     1
-----------------------
library(tidyverse)
library(tidytext)
library(tm)
library(purrr)

x <- (c("I went to afternoon tea with her majesty and #queen @Victoria in the palace.", "Does tea have extra caffeine?"))

clean_Twitter_Corpus <- function(x) {
  x  =  tolower(x)                          # convert to lower case characters
  x  =  stripWhitespace(x)                  # removing white space
  x  =  gsub("^\\s+|\\s+$", "", x)          # remove leading and trailing white space
  x  = removeWords(x,stopwords("english"))  # remove stopwords
  return(x)
}
# A custom build function that will take in a sentence and create
# a tibble of ngrams
ngrams_build = function(sentence, column_name, n = 2) {
  words <- sentence %>% str_split(pattern = " ", simplify = TRUE) 
  words <- words[words != ""]
  ngrams <- map_chr(1:(length(words) - n + 1),
                    .f = function(x, words, n) {
                      paste(words[x:(x + n - 1)], collapse = " ")
                    }, words = words, n = n)
  tibble(!!column_name := ngrams)
}
# clean the twitter texts. call the clean_Twitter_Corpus function
tweets <- clean_Twitter_Corpus(x)
tweets
#> [1] " went  afternoon tea   majesty  #queen @victoria   palace."
#> [2] " tea  extra caffeine?"
text <- as.character(tweets)
text <- as.data.frame(text)

tidy_descr_ngrams <- 
  # here I use purrr function with the custom function
  map_dfr(text$text, ngrams_build, column_name = "bigram", n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ")

# Here is the output which is similar to unnest_tokens but has special
# character included
tidy_descr_ngrams
#> # A tibble: 8 x 2
#>   word1     word2    
#>   <chr>     <chr>    
#> 1 went      afternoon
#> 2 afternoon tea      
#> 3 tea       majesty  
#> 4 majesty   #queen   
#> 5 #queen    @victoria
#> 6 @victoria palace.  
#> 7 tea       extra    
#> 8 extra     caffeine?

bigram_counts <- tidy_descr_ngrams %>%
  count(word1, word2, sort = TRUE)

bigram_counts
#> # A tibble: 8 x 3
#>   word1     word2         n
#>   <chr>     <chr>     <int>
#> 1 #queen    @victoria     1
#> 2 @victoria palace.       1
#> 3 afternoon tea           1
#> 4 extra     caffeine?     1
#> 5 majesty   #queen        1
#> 6 tea       extra         1
#> 7 tea       majesty       1
#> 8 went      afternoon     1
-----------------------
library(tidyverse)
library(tidytext)
library(tm)
library(purrr)

x <- (c("I went to afternoon tea with her majesty and #queen @Victoria in the palace.", "Does tea have extra caffeine?"))

clean_Twitter_Corpus <- function(x) {
  x  =  tolower(x)                          # convert to lower case characters
  x  =  stripWhitespace(x)                  # removing white space
  x  =  gsub("^\\s+|\\s+$", "", x)          # remove leading and trailing white space
  x  = removeWords(x,stopwords("english"))  # remove stopwords
  return(x)
}
# A custom build function that will take in a sentence and create
# a tibble of ngrams
ngrams_build = function(sentence, column_name, n = 2) {
  words <- sentence %>% str_split(pattern = " ", simplify = TRUE) 
  words <- words[words != ""]
  ngrams <- map_chr(1:(length(words) - n + 1),
                    .f = function(x, words, n) {
                      paste(words[x:(x + n - 1)], collapse = " ")
                    }, words = words, n = n)
  tibble(!!column_name := ngrams)
}
# clean the twitter texts. call the clean_Twitter_Corpus function
tweets <- clean_Twitter_Corpus(x)
tweets
#> [1] " went  afternoon tea   majesty  #queen @victoria   palace."
#> [2] " tea  extra caffeine?"
text <- as.character(tweets)
text <- as.data.frame(text)

tidy_descr_ngrams <- 
  # here I use purrr function with the custom function
  map_dfr(text$text, ngrams_build, column_name = "bigram", n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ")

# Here is the output which is similar to unnest_tokens but has special
# character included
tidy_descr_ngrams
#> # A tibble: 8 x 2
#>   word1     word2    
#>   <chr>     <chr>    
#> 1 went      afternoon
#> 2 afternoon tea      
#> 3 tea       majesty  
#> 4 majesty   #queen   
#> 5 #queen    @victoria
#> 6 @victoria palace.  
#> 7 tea       extra    
#> 8 extra     caffeine?

bigram_counts <- tidy_descr_ngrams %>%
  count(word1, word2, sort = TRUE)

bigram_counts
#> # A tibble: 8 x 3
#>   word1     word2         n
#>   <chr>     <chr>     <int>
#> 1 #queen    @victoria     1
#> 2 @victoria palace.       1
#> 3 afternoon tea           1
#> 4 extra     caffeine?     1
#> 5 majesty   #queen        1
#> 6 tea       extra         1
#> 7 tea       majesty       1
#> 8 went      afternoon     1

Explanations in Consistent OWL Ontologies

copy iconCopydownload iconDownload
    import java.io.File;
    import java.util.Set;
    import java.util.function.Supplier;
    
    import org.junit.Test;
    import org.semanticweb.HermiT.ReasonerFactory;
    import org.semanticweb.owl.explanation.api.Explanation;
    import org.semanticweb.owl.explanation.api.ExplanationGenerator;
    import org.semanticweb.owl.explanation.api.ExplanationGeneratorFactory;
    import org.semanticweb.owl.explanation.api.ExplanationProgressMonitor;
    import org.semanticweb.owl.explanation.impl.blackbox.Configuration;
    import org.semanticweb.owl.explanation.impl.blackbox.DivideAndConquerContractionStrategy;
    import org.semanticweb.owl.explanation.impl.blackbox.EntailmentCheckerFactory;
    import org.semanticweb.owl.explanation.impl.blackbox.InitialEntailmentCheckStrategy;
    import org.semanticweb.owl.explanation.impl.blackbox.StructuralTypePriorityExpansionStrategy;
    import org.semanticweb.owl.explanation.impl.blackbox.checker.BlackBoxExplanationGeneratorFactory;
    import org.semanticweb.owl.explanation.impl.blackbox.checker.SatisfiabilityEntailmentCheckerFactory;
    import org.semanticweb.owlapi.apibinding.OWLManager;
    import org.semanticweb.owlapi.model.OWLAxiom;
    import org.semanticweb.owlapi.model.OWLOntology;
    import org.semanticweb.owlapi.model.OWLOntologyManager;
    import org.semanticweb.owlapi.reasoner.OWLReasonerFactory;
    
    public class CheckOntology {
    
        @Test
        public void should() throws Exception {
    
            OWLOntologyManager m = OWLManager.createOWLOntologyManager();
            OWLOntology o = m.loadOntologyFromOntologyDocument(new File("pizza.owl"));
    
            OWLReasonerFactory rf = new ReasonerFactory(); // Get hold of a reasoner factory
    
            // Create the explanation generator factory which uses reasoners provided by the specified
            // reasoner factory
    
            ExplanationGeneratorFactory<OWLAxiom> genFac =
                createExplanationGeneratorFactory(rf, null, OWLManager::createOWLOntologyManager);
    
            // Now create the actual explanation generator for our ontology
            ExplanationGenerator<OWLAxiom> gen = genFac.createExplanationGenerator(o);
    
    
            // Ask for explanations for some entailment
            // Get a reference to the axiom that represents the entailment that we want explanation for
            
            // this will just run the explanations for all axioms
            o.logicalAxioms().forEach(e -> explain(e, gen));
        }
    
        void explain(OWLAxiom entailment, ExplanationGenerator<OWLAxiom> gen) {
            // Get our explanations. Ask for a maximum of 5.
            try {
                Set<Explanation<OWLAxiom>> expl = gen.getExplanations(entailment, 5);
                System.out.println("CheckOntology.explain() " + entailment);
                expl.forEach(System.out::println);
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    // this method replicates code existing in the owlexplanation project; it's needed because the factories in owlexplanation do not set InitialEntailmentCheckStrategy correctly
        public static ExplanationGeneratorFactory<OWLAxiom> createExplanationGeneratorFactory(
            OWLReasonerFactory reasonerFactory, ExplanationProgressMonitor<OWLAxiom> progressMonitor,
            Supplier<OWLOntologyManager> m) {
            EntailmentCheckerFactory<OWLAxiom> checker =
                new SatisfiabilityEntailmentCheckerFactory(reasonerFactory, m);
            Configuration<OWLAxiom> config = new Configuration<>(checker,
                new StructuralTypePriorityExpansionStrategy<OWLAxiom>(
                    InitialEntailmentCheckStrategy.PERFORM, m),
                new DivideAndConquerContractionStrategy<OWLAxiom>(), progressMonitor, m);
            return new BlackBoxExplanationGeneratorFactory<>(config);
        }
    }

Spring boot + Caffeine cache + Check header

copy iconCopydownload iconDownload
@Component
public class CheckHeaderInterceptor implements HandlerInterceptor {

  @Override
  public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler)
        throws Exception {
    
  // validate what you want, on error return false
  // if everything its ok, return true
  }
}
public class WebMvcConfig implements WebMvcConfigurer {

@Autowired
private CheckHeaderInterceptor interceptor;

@Override
public void addInterceptors(InterceptorRegistry registry) {
    registry.addInterceptor(interceptor).addPathPatterns("url that you wannna use handler");
 }
}
-----------------------
@Component
public class CheckHeaderInterceptor implements HandlerInterceptor {

  @Override
  public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler)
        throws Exception {
    
  // validate what you want, on error return false
  // if everything its ok, return true
  }
}
public class WebMvcConfig implements WebMvcConfigurer {

@Autowired
private CheckHeaderInterceptor interceptor;

@Override
public void addInterceptors(InterceptorRegistry registry) {
    registry.addInterceptor(interceptor).addPathPatterns("url that you wannna use handler");
 }
}

Using two dataframes how can I compare a lookup value as a substring in the column in another dataframe to create a new column if the match exists

copy iconCopydownload iconDownload
pattern = '|'.join(df1['Ingredient_Name'].tolist())
out = df2['Ingredient_Name'].str.findall(pattern).apply(pd.Series)
out.columns = 'Ingredient_Name_' + (out.columns + 1).astype(str)
out = df2.join(out)
print(out)

# Output:
                                       Ingredient_Name  WordCount  Num_Of_Ingredients  \
0          ACETAMINOPHEN ACETYLSALICYLIC ACID CAFFEINE          4                   3   
1                      ACEBUTOLOL ACETYLSALICYLIC ACID          3                   2   
2  COLISTIN HYDROCORTISONE NEOMYCIN THONZONIUM BROMIDE          5                   4   
3                                              BROMIDE          1                   1   

  Ingredient_Name_1     Ingredient_Name_2 Ingredient_Name_3   Ingredient_Name_4  
0     ACETAMINOPHEN  ACETYLSALICYLIC ACID          CAFFEINE                 NaN  
1        ACEBUTOLOL  ACETYLSALICYLIC ACID               NaN                 NaN  
2          COLISTIN        HYDROCORTISONE          NEOMYCIN  THONZONIUM BROMIDE  
3           BROMIDE                   NaN               NaN                 NaN  
-----------------------
output = df2['Ingredient_Name'].str.extractall(f"({'|'.join(df1['Ingredient_Name'])})").unstack()

#formatting
output = output.droplevel(0,1).rename_axis(None, axis=1).add_prefix("Ingredient_Name_")

>>> output
  Ingredient_Name_0     Ingredient_Name_1 Ingredient_Name_2   Ingredient_Name_3
0     ACETAMINOPHEN  ACETYLSALICYLIC ACID          CAFFEINE                 NaN
1        ACEBUTOLOL  ACETYLSALICYLIC ACID               NaN                 NaN
2          COLISTIN        HYDROCORTISONE          NEOMYCIN  THONZONIUM BROMIDE
3           BROMIDE                   NaN               NaN                 NaN
-----------------------
def match_ingredients(row, df):
  base_str = row['Ingredient_Name']
  result_count = 1
  result = {}
  for idx, ingredient in df.iterrows():
    if ingredient['Ingredient_Name'] in base_str:
        result[f'Ingredient_{result_count}'] = ingredient['Ingredient_Name']
        result_count += 1
        base_str = base_str.replace(ingredient['Ingredient_Name'], "")
  result['Ingredient_Name'] = base_str

  return result

result = df2.apply(match_ingredients,axis=1, result_type='expand', args=(df1,))

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QUESTION

grouped data select rows and include rows before/after

Asked 2022-Mar-25 at 11:37

I have a grouped data and I want to select a rows that fulfill a certain condition (works with code provided below), but I also want to include the row before and after the selected rows (so basically match row with criteria and then select row +1 row up +1 row down from the original dataset). The code below give me two rows per group those are the rows that match for my criteria. I now also want to include the rows before and after the selected rows.

I tried the following code, but the code line which would give me this output does not work: desired_result[which(desired_result$Caffeinefactor == "yes") + c(-1:1), ] %>%

daf1 <- df %>% 
  setDT(df) %>% 
  dplyr::mutate(row_id = row_number()) %>% 
  dplyr::group_by(PATIENT.ID) %>% 
  dplyr::mutate(first_yes = cumsum(Caffeinefactor == "yes"), 
         last_no = (Caffeinefactor == "no") * row_number(), 
         last_no = ifelse(first_yes == 0, last_no, 0)) %>% 
  dplyr:: filter((first_yes == 1 & Caffeine >0) | last_no == max(last_no)) %>%
  desired_result[which(desired_result$Caffeinefactor == "yes") + c(-1:1), ] %>%
  dplyr::select(-first_yes, -last_no)
structure(list(PATIENT.ID = c(210625L, 210625L, 210625L, 210625L, 
210625L, 210625L, 210625L, 210625L, 210625L, 210625L, 210625L, 
210625L, 210625L, 210625L, 210625L, 210625L, 210625L, 221179L, 
221179L, 221179L, 221179L, 221179L, 221179L, 221179L, 221179L, 
221179L, 221179L, 221179L, 221179L, 221179L, 221179L, 301705L, 
301705L, 301705L, 301705L, 301705L, 301705L, 301705L, 301705L, 
301705L), Caffeine = c(0L, 0L, 0L, 0L, 200L, 200L, 200L, 0L, 
200L, 200L, 200L, 200L, 200L, 0L, 0L, 200L, 200L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 200L, 200L, 400L, 400L, 400L, 0L, 0L, 
0L, 0L, 0L, 0L, 200L, 0L, 200L), Caffeinefactor = c("no", "no", 
"no", "no", "yes", "yes", "yes", "no", "yes", "yes", "yes", "yes", 
"yes", "no", "no", "yes", "yes", "no", "no", "no", "no", "no", 
"no", "no", "no", "no", "yes", "yes", "yes", "yes", "yes", "no", 
"no", "no", "no", "no", "no", "yes", "no", "yes"), PULSE.WIDTH = c(0.5, 
0.5, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 
0.75, 0.5, 0.5, 0.75, 1, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)), class = c("data.table", 
"data.frame"), row.names = c(NA, -40L), groups = structure(list(
    PATIENT.ID = c(210625L, 221179L, 301705L, 303926L, 309668L, 
    312580L, 313644L, 316332L, 326693L, 336204L, 337968L, 340160L, 
    341787L, 343627L, 346128L, 349800L, 351701L, 352235L, 354458L, 
    356470L, 357583L, 358002L, 358447L, 359628L, 861559L, 875452L, 
    876598L, 877257L, 877535L, 878250L, 878781L, 880014L, 880911L, 
    881002L, 881972L, 882667L, 883375L, 883799L, 884914L, 885233L, 
    885802L, 889623L, 891145L, 892464L, 893308L, 895449L), .rows = structure(list(
        1:17, 18:31, 32:46, 47:53, 54:61, 62:72, 73:95, 96:107, 
        108:131, 132:146, 147:165, 166:174, 175:186, 187:191, 
        192:203, 204:215, 216:227, 228:244, 245:263, 264:278, 
        279:290, 291:301, 302:313, 314:326, 327:350, 351:365, 
        366:376, 377:389, 390:399, 400:413, 414:424, 425:445, 
        446:453, 454:466, 467:477, 478:494, 495:508, 509:517, 
        518:524, 525:534, 535:554, 555:557, 558:573, 574:585, 
        586:593, 594:598), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -46L), .drop = TRUE), .internal.selfref = <pointer: 0x7fe7f7002ee0>)

What the output looks like now without the code line ( desired_result[which(desired_result$Caffeinefactor == "yes") + c(-1:1), ])

PATIENT.ID Caffeinefactor PULSE.WIDTH
210625L no output
210625L yes output
220909L no output
220909L yes output
301705L no output
301705L yes output

The output should look like this if one row is added before and after:

PATIENT.ID Caffeinefactor PULSE.WIDTH
210625L no output
210625L no output
210625L yes output
210625L yes output
220909L no output
220909L yes output
220909L yes output
301705L no output
301705L no output
301705L yes output
301705L yes output

... I put output for pulse with, because I do not know what the exact value would be

The original dataset has more that 90 columns and I also need to keep the values in these column. I do not want to duplicate the rows that the shown code selected.

ANSWER

Answered 2022-Mar-25 at 11:37

Would you consider creating a column to indicate which rows you wish to retain, and then filter the selected row, and use lead and lag to keep the rows before and after those selected rows?

library(tidyverse)

df %>%
  setDT(df) %>% 
  mutate(row_id = row_number()) %>% 
  group_by(PATIENT.ID) %>% 
  mutate(first_yes = cumsum(Caffeinefactor == "yes"), 
         last_no = (Caffeinefactor == "no") * row_number(), 
         last_no = ifelse(first_yes == 0, last_no, 0),
         select_row = (first_yes == 1 & Caffeine > 0) | last_no == max(last_no)) %>%
  filter(select_row | lag(select_row) | lead(select_row)) %>%
  select(-first_yes, -last_no, -select_row)

Output

   PATIENT.ID Caffeine Caffeinefactor PULSE.WIDTH row_id
        <int>    <int> <chr>                <dbl>  <int>
 1     210625        0 no                    0.75      3
 2     210625        0 no                    0.75      4
 3     210625      200 yes                   0.75      5
 4     210625      200 yes                   0.75      6
 5     221179        0 no                    1        25
 6     221179        0 no                    1        26
 7     221179      200 yes                   1        27
 8     221179      200 yes                   1        28
 9     301705        0 no                    0.5      36
10     301705        0 no                    0.5      37
11     301705      200 yes                   0.5      38
12     301705        0 no                    0.5      39

Source https://stackoverflow.com/questions/71614297

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

Vulnerabilities

No vulnerabilities reported

Install caffeine

Download from Maven Central or depend via Gradle:. See the release notes for details of the changes. Snapshots of the development version are available in Sonatype's snapshots repository.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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