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

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.

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Security

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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

security Security

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

build Reuse

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

caffeine Examples and Code Snippets

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Cache

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

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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) & 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

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    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

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@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

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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,))

See all related Code Snippets

Community Discussions

Trending Discussions on caffeine

grouped data select rows and include rows before/after
Can you still use a ConcurrentLinkedHashMap with Caffeine?
Is there a Python DICOM Modality Worklist setup library?
Caching (Caffeine) & Spring - two caches based on method parameter value
How can I use the ggplot function to visualise grouped data?
OWL API NoSuchMethodError in saveOntology() call
How to alternate actions on variables in an array (one of two things for every other variable)
How can I extract bigrams from text without removing the hash symbol?
Explanations in Consistent OWL Ontologies
Retain old value when refreshing cache?

Trending Discussions on caffeine

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

QUESTION

Can you still use a ConcurrentLinkedHashMap with Caffeine?

Asked 2022-Mar-21 at 19:45

Caffeine provides a great alternative to Guava's caching library, but can you still use just the ConcurrentLinkedHashMap class itself, or a Cache the implements concurrent, in-order iteration? It does not appear in Caffeine anymore that I can see, although I'm sure its using something similar under the hood.

I have tested the Caffeine Cache and it does not iterate over the elements in order of insertion, and I don't see any options on the Caffeine builder that would make me thing this feature still exists. But maybe I'm missing something.

Any ideas?

ANSWER

Answered 2022-Mar-21 at 19:45

ConcurrentLinkedHashMap offered access-order iteration via the ascending and descending snapshots. This was useful for cases like persisting the hottest entries for a warm server restart. Otherwise views and iterators were in encounter order by the underlying ConcurrentHashMap.

Caffeine offers snapshots by in the eviction policy's hottest/coldest order and the expiration policy's youngest/oldest order. See Cache.policy().

Insertion-order is not that difficult to implement because writes are rare, exclusive, and items are not being shifted around frequently. The simplest approach is to use a Map computation to maintain a secondary data structure. If you want that in addition to the cache then Caffeine's asMap() view offers atomic versions. This way you can have fast random access reads via a ConcurrentHashMap and ordered iterations for a background process.

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);

Vavr's LinkedHashMap might be useful if you need concurrent iteration or want to avoid the cost of a full copy. That is a persistent data structure, which where writes produce a new immutable version. That could be used above or directly if you don't need any caching concepts.

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);

Source https://stackoverflow.com/questions/71562360

QUESTION

Is there a Python DICOM Modality Worklist setup library?

Asked 2022-Mar-17 at 07:51

I've been down a couple of rabbit holes trying to find suitable ways of creating a DICOM modality worklist (or .wl worklist files rather).

What I have setup so far:

I have an Orthanc DICOM server running in a local docker container.
I can create DICOM text dump files with a small Python program. See the example of how this looks below.
I can convert the above-mentioned text dump files to .wl worklist files by using the dump2dcm command.
I can move the created .wl files to a folder that is shared with the docker.
Orthanc can "see" these files and serve them correctly to medical machines on the local network.
I have the coffee machine on a timer. This allows for a consistent caffeine fix.

My problem is with the creation of the DICOM text dump files. I'm currently using Python's String.format() function to format a template string. This then substitutes certain placeholders in my template string with actual patient data. Although not elegant, it works. But it's a very static solution and may not be very robust.

Is there a Python library that can be used to generate such text dump files? or even beter, the .wl files? I am willing to trade 3 magic beans, and our family recipe for potato salad, for such a library. (The secret ingredient is not paprika)

For completeness, here is how the template dicom worklist string looks:

dicom_wl_template_string = """
    # Dicom-File-Format
    # Dicom-Meta-Information-Header
    # Used TransferSyntax: Unknown Transfer Syntax

    # Dicom-Data-Set
    # Used TransferSyntax: Little Endian Implicit
    (0002,0000) UL [123]
    (0002,0002) UI [1.2.111.222222.5.1.4.1.1.104.1]
    (0002,0003) UI [1.2.3.4.5.6.132437.17.4.10123450.312346792082.12349.1]
    (0002,0010) UI [1.2.840.10008.1.2.1]
    (0002,0012) UI [1.2.276.0.7230010.3.0.3.6.6]
    (0002,0013) SH [OFFIS_DCMTK_366]

    (0008,0005) CS [{SpecificCharacterSet}]                     #  10, 1 SpecificCharacterSet e.g ISO_IR 100
    (0008,0012) DA [{InstanceCreationDate}]                     #   8, 1 InstanceCreationDate e.g 20220101
    (0008,0050) SH [{AccessionNumber}]                          #   8, 1 AccessionNumber e.g 1234
    (0010,0010) PN [{PatientName}]                              #  14, 1 PatientName e.g SURNAME^ABC
    (0010,0020) LO [{PatientID}]                                #  14, 1 PatientID e.g 7001011234080
    (0010,0030) DA [{PatientBirthDate}]                         #   8, 1 PatientBirthDate e.g. 19700101
    (0010,0040) CS [{PatientSex}]                               #   2, 1 PatientSex e.g. M
    (0020,000d) UI [{StudyInstanceUID}]                         #  54, 1 StudyInstanceUID e.g 1.2.3.4.5.6.132437.17.4.10123450.312346792082.12349.1
    (0032,1060) LO [{RequestedProcedureDescription}]            #  16, 1 RequestedProcedureDescription 
        (0040,0100) SQ (Sequence with explicit length #=1)          #  90, 1 ScheduledProcedureStepSequence
            (fffe,e000) na (Item with ??explicit length #=5??)          #  82, 1 Item
                (0008,0060) CS [{Modality}]                                 #   4, 1 Modality e.g. CT, MR, CR, NM, PT, US, XA 
                (0040,0001) AE [{ScheduledStationAETitle}]                  #   4, 1 ScheduledStationAETitle e.g Foo
                (0040,0002) DA [{ScheduledProcedureStepStartDate}]          #   8, 1 ScheduledProcedureStepStartDate e.g. 20220101
                (0040,0003) TM [{ScheduledProcedureStepStartTime}]          #   8, 1 ScheduledProcedureStepStartTime e.g. 080000
                (0040,0006) PN [{ScheduledPhysicianName}]                   #   8, 1 Scheduled Performing Physicians Name e.g. EMMETBROWN
                (0040,0007) LO [{ScheduledProcedureStepDescription}]        #  22, 1 ScheduledProcedureStepDescription e.g SOMETHING
                (0040,0009) SH [{ScheduledProcedureStepID}]                 #   4, 1 ScheduledProcedureStepID e.g 0001
            (fffe,e00d) na (ItemDelimitationItem for re-encoding)       #   0, 0 ItemDelimitationItem
        (fffe,e0dd) na (SequenceDelimitationItem for re-encod.)     #   0, 0 SequenceDelimitationItem
    (0040,1001) SH [unknown]                                    #   8, 1 RequestedProcedureID
    """

ANSWER

Answered 2022-Feb-07 at 22:41

pydicom should be able to do this and probably allow you to skip the text dump step (disclaimer - I'm a contributor to pydicom):

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)

If you decide to go this way, the dataset introduction tutorial is a good place to start.

Source https://stackoverflow.com/questions/71015541

QUESTION

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

Asked 2022-Jan-31 at 13:07

I have a data set which has the time taken for individuals to read a sentence (response_time) under the experimental factors of the condition of the sentence (normal or visually degraded) and the number of cups of coffee (caffeine) that an individual has drunk. I want to visualise the data using ggplot, but with the data grouped according to the condition of the sentence and the coffee drunk - e.g. the response times recorded for individuals reading a normal sentence and having drunk one cup of coffee. This is what I have tried so far, but the graph comes up as one big blob (not separated by group) and has over 15 warnings!!

participant condition response_time caffeine 
        <dbl> <fct>             <dbl>    <fct>            
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                
>

tidied_data_2 %>%
  ggplot(aes(x = condition:caffeine, y = response_time, colour = condition:caffeine)) +
  geom_violin() +
  geom_jitter(width = .1, alpha = .25) +
  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)")

Any suggestions on how to better code what I want would be great.

ANSWER

Answered 2022-Jan-31 at 13:07

As a wiki answer because too long for a comment.

Not sure what you are intending with condition:caffeine - I've never seen that syntax in ggplot. Try aes(x = as.character(caffeine), y = ..., color = as.character(caffeine)) instead (or, because it is a factor in your case anyways, you can just use aes(x = caffeine, y = ..., color = caffeine)

If your idea is to separate by condition, you could just use aes(x = caffeine, y = ..., color = condition), as they are going to be separated by x anyways.

of another note - why not actually plotting a scatter plot? Like making this a proper two-dimensional graph. suggestion below.

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)")

what I would rather do

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()

Source https://stackoverflow.com/questions/70925848

QUESTION

OWL API NoSuchMethodError in saveOntology() call

Asked 2022-Jan-31 at 10:43

I am trying to call an OWL API java program through terminal and it crashes, while the exact same code is running ok when I run it in IntelliJ.

The exception that rises in my main code is this:

Exception in thread "main" java.lang.NoSuchMethodError: 'boolean org.semanticweb.owlapi.io.RDFResource.idRequiredForIndividualOrAxiom()'
        at org.semanticweb.owlapi.rdf.rdfxml.renderer.RDFXMLRenderer.render(RDFXMLRenderer.java:204)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.render(RDFRendererBase.java:448)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.renderOntologyHeader(RDFRendererBase.java:441)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.render(RDFRendererBase.java:247)
        at org.semanticweb.owlapi.rdf.rdfxml.renderer.RDFXMLStorer.storeOntology(RDFXMLStorer.java:51)
        at org.semanticweb.owlapi.util.AbstractOWLStorer.storeOntology(AbstractOWLStorer.java:142)
        at org.semanticweb.owlapi.util.AbstractOWLStorer.storeOntology(AbstractOWLStorer.java:106)
        at uk.ac.manchester.cs.owl.owlapi.OWLOntologyManagerImpl.saveOntology(OWLOntologyManagerImpl.java:1347)
        at uk.ac.manchester.cs.owl.owlapi.OWLOntologyManagerImpl.saveOntology(OWLOntologyManagerImpl.java:1333)
        at com.stelios.JavaExplanations.main(JavaExplanations.java:112)

It seems as if calling idRequiredForIndividualOrAxiom() on an RDFResource object doesn't find the method that is inherited by RDFNode class, but I have no clue why.

In order to post here, I kept only the saveOntology call in a minimal example and the exception that is thrown is the same with extra steps:

Exception in thread "main" java.lang.NoSuchMethodError: 'boolean org.semanticweb.owlapi.io.RDFResource.idRequiredForIndividualOrAxiom()'
        at org.semanticweb.owlapi.rdf.rdfxml.renderer.RDFXMLRenderer.render(RDFXMLRenderer.java:204)
        at org.semanticweb.owlapi.rdf.rdfxml.renderer.RDFXMLRenderer.render(RDFXMLRenderer.java:249)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.renderEntity(RDFRendererBase.java:298)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.render(RDFRendererBase.java:292)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.lambda$renderEntities$6(RDFRendererBase.java:285)
        at java.base/java.util.stream.ForEachOps$ForEachOp$OfRef.accept(ForEachOps.java:183)
        at java.base/java.util.stream.ReferencePipeline$2$1.accept(ReferencePipeline.java:177)
        at java.base/java.util.ArrayList$Itr.forEachRemaining(ArrayList.java:1033)
        at java.base/java.util.Spliterators$IteratorSpliterator.forEachRemaining(Spliterators.java:1801)
        at java.base/java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:484)
        at java.base/java.util.stream.AbstractPipeline.wrapAndCopyInto(AbstractPipeline.java:474)
        at java.base/java.util.stream.ForEachOps$ForEachOp.evaluateSequential(ForEachOps.java:150)
        at java.base/java.util.stream.ForEachOps$ForEachOp$OfRef.evaluateSequential(ForEachOps.java:173)
        at java.base/java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:234)
        at java.base/java.util.stream.ReferencePipeline.forEach(ReferencePipeline.java:497)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.renderEntities(RDFRendererBase.java:285)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.renderInOntologySignatureEntities(RDFRendererBase.java:269)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.renderOntologyComponents(RDFRendererBase.java:253)
        at org.semanticweb.owlapi.rdf.RDFRendererBase.render(RDFRendererBase.java:248)
        at org.semanticweb.owlapi.rdf.rdfxml.renderer.RDFXMLStorer.storeOntology(RDFXMLStorer.java:51)
        at org.semanticweb.owlapi.util.AbstractOWLStorer.storeOntology(AbstractOWLStorer.java:142)
        at org.semanticweb.owlapi.util.AbstractOWLStorer.storeOntology(AbstractOWLStorer.java:106)
        at uk.ac.manchester.cs.owl.owlapi.OWLOntologyManagerImpl.saveOntology(OWLOntologyManagerImpl.java:1347)
        at uk.ac.manchester.cs.owl.owlapi.OWLOntologyManagerImpl.saveOntology(OWLOntologyManagerImpl.java:1333)
        at com.stelios.JavaExplanations.main(JavaExplanations.java:47)

In both my original code and the minimal example I call java with: java -cp /home/stelios/java_explanations/target/java_explanations-1.0-SNAPSHOT-jar-with-dependencies.jar com.stelios.JavaExplanations

Here is the minimal example that repeats this behavior for me. This is the Java code:

package com.stelios;

import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.util.*;

import org.semanticweb.owlapi.apibinding.OWLManager;
import org.semanticweb.owlapi.io.*;
import org.semanticweb.owlapi.model.*;

public class JavaExplanations {
    public static void main(String[] args) throws OWLOntologyCreationException, FileNotFoundException, OWLOntologyStorageException {
        String ontology1 = "/home/stelios/Desktop/huiyfgds/ONTO_ASRTD_hz162pai";
        String ontology2 = "/home/stelios/Desktop/huiyfgds/ONTO_INFRD_hz162pai";

        OWLOntologyManager ontology_manager = OWLManager.createOWLOntologyManager();
        OWLOntology asserted_ontology = ontology_manager.loadOntologyFromOntologyDocument(new File(ontology1));
        ontology_manager.saveOntology(asserted_ontology, new StreamDocumentTarget(new FileOutputStream(ontology2)));
    }
}

This is the pom.xml in IntelliJ:

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.stelios.expl</groupId>
    <artifactId>java_explanations</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>11</maven.compiler.source>
        <maven.compiler.target>11</maven.compiler.target>
    </properties>

    <dependencies>
        <!-- https://mvnrepository.com/artifact/net.sourceforge.owlapi/owlexplanation -->
        <dependency>
            <groupId>net.sourceforge.owlapi</groupId>
            <artifactId>owlexplanation</artifactId>
            <version>5.0.0</version>
        </dependency>
        <dependency>
            <groupId>net.sourceforge.owlapi</groupId>
            <artifactId>owlapi-distribution</artifactId>
            <version>5.1.9</version>
        </dependency>
        <dependency>
            <groupId>net.sourceforge.owlapi</groupId>
            <artifactId>org.semanticweb.hermit</artifactId>
            <version>1.4.5.519</version>
        </dependency>

        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>1.7.32</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-nop</artifactId>
            <version>1.7.32</version>
        </dependency>
    </dependencies>


    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-jar-plugin</artifactId>
                <configuration>
                    <archive>
                        <manifestFile>src/main/resources/META-INF/MANIFEST.MF</manifestFile>
                    </archive>
                </configuration>
            </plugin>
            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
            </plugin>
        </plugins>

        <resources>
            <resource>
                <directory>src/main/java</directory>
                <includes>
                    <include>**/*.java</include>
                </includes>
            </resource>
        </resources>
    </build>
    
</project>

I think that most probably it is some dependency/version error but I don't see how this can be. I package everything I need in the jar file I give as classpath, defining the wanted versions in pom.xml, and in this jar I can find only one org/semanticweb/owlapi/io/RDFResource.class file.

Reading this and this I thought about having 2 different versions of OWL API, as I had another .jar with OWL API version 3.4.9 in it, in the directory tree. I moved the file and rebuilt the maven package just to be sure, and (as expected) no change.

Other than the saveOntology() call, my original program is working as intended.

The only thing out of the ordinary is that IntelliJ is giving me a Plugin 'maven-assembly-plugin:' not found problem, which I haven't managed to solve in any way, and have been ignoring as it hasn't been an issue in any of the operations I have needed. (If you know how to solve it of course, give me suggestions, but my main problem is the earlier mentioned exception).

EDIT Here is the mvn dependency:tree output.

[INFO] Scanning for projects...
[INFO] 
[INFO] -----------------< com.stelios.expl:java_explanations >-----------------
[INFO] Building java_explanations 1.0-SNAPSHOT
[INFO] --------------------------------[ jar ]---------------------------------
[INFO] 
[INFO] --- maven-dependency-plugin:2.8:tree (default-cli) @ java_explanations ---
[INFO] com.stelios.expl:java_explanations:jar:1.0-SNAPSHOT
[INFO] +- net.sourceforge.owlapi:owlexplanation:jar:5.0.0:compile
[INFO] |  +- net.sourceforge.owlapi:owlapi-api:jar:5.1.19:compile (version selected from constraint [5.0.0,5.9.9])
[INFO] |  |  \- javax.inject:javax.inject:jar:1:compile
[INFO] |  +- net.sourceforge.owlapi:owlapi-tools:jar:5.1.19:compile (version selected from constraint [5.0.0,5.9.9])
[INFO] |  \- net.sourceforge.owlapi:telemetry:jar:5.0.0:compile
[INFO] |     \- net.sourceforge.owlapi:owlapi-parsers:jar:5.1.19:compile (version selected from constraint [5.0.0,5.9.9])
[INFO] +- net.sourceforge.owlapi:owlapi-distribution:jar:5.1.9:compile
[INFO] |  +- net.sourceforge.owlapi:owlapi-compatibility:jar:5.1.9:compile
[INFO] |  |  \- net.sourceforge.owlapi:owlapi-apibinding:jar:5.1.9:compile
[INFO] |  |     +- net.sourceforge.owlapi:owlapi-impl:jar:5.1.9:compile
[INFO] |  |     +- net.sourceforge.owlapi:owlapi-oboformat:jar:5.1.9:compile
[INFO] |  |     \- net.sourceforge.owlapi:owlapi-rio:jar:5.1.9:compile
[INFO] |  +- com.fasterxml.jackson.core:jackson-core:jar:2.9.7:compile
[INFO] |  +- com.fasterxml.jackson.core:jackson-databind:jar:2.9.7:compile
[INFO] |  +- com.fasterxml.jackson.core:jackson-annotations:jar:2.9.7:compile
[INFO] |  +- org.apache.commons:commons-rdf-api:jar:0.5.0:compile
[INFO] |  +- org.tukaani:xz:jar:1.6:compile
[INFO] |  +- org.slf4j:jcl-over-slf4j:jar:1.7.22:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-model:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-api:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-languages:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-datatypes:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-binary:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-n3:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-nquads:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-ntriples:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-rdfjson:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-jsonld:jar:2.3.2:compile
[INFO] |  |  +- org.apache.httpcomponents:httpclient:jar:4.5.2:compile
[INFO] |  |  |  \- org.apache.httpcomponents:httpcore:jar:4.4.4:compile
[INFO] |  |  \- org.apache.httpcomponents:httpclient-cache:jar:4.5.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-rdfxml:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-trix:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-turtle:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-rio-trig:jar:2.3.2:compile
[INFO] |  +- org.eclipse.rdf4j:rdf4j-util:jar:2.3.2:compile
[INFO] |  +- com.github.jsonld-java:jsonld-java:jar:0.12.0:compile
[INFO] |  |  +- org.apache.httpcomponents:httpclient-osgi:jar:4.5.5:compile
[INFO] |  |  |  +- org.apache.httpcomponents:httpmime:jar:4.5.5:compile
[INFO] |  |  |  \- org.apache.httpcomponents:fluent-hc:jar:4.5.5:compile
[INFO] |  |  \- org.apache.httpcomponents:httpcore-osgi:jar:4.4.9:compile
[INFO] |  |     \- org.apache.httpcomponents:httpcore-nio:jar:4.4.9:compile
[INFO] |  +- com.github.vsonnier:hppcrt:jar:0.7.5:compile
[INFO] |  +- com.github.ben-manes.caffeine:caffeine:jar:2.6.1:compile
[INFO] |  +- com.google.guava:guava:jar:22.0:compile (version selected from constraint [18.0,22.0])
[INFO] |  |  +- com.google.errorprone:error_prone_annotations:jar:2.0.18:compile
[INFO] |  |  +- com.google.j2objc:j2objc-annotations:jar:1.1:compile
[INFO] |  |  \- org.codehaus.mojo:animal-sniffer-annotations:jar:1.14:compile
[INFO] |  +- com.google.code.findbugs:jsr305:jar:3.0.2:compile (version selected from constraint [2.0.0,4))
[INFO] |  \- commons-io:commons-io:jar:2.5:compile
[INFO] +- net.sourceforge.owlapi:org.semanticweb.hermit:jar:1.4.5.519:compile
[INFO] |  +- commons-logging:commons-logging:jar:1.1.3:compile
[INFO] |  +- org.apache.ws.commons.axiom:axiom-api:jar:1.2.14:compile
[INFO] |  |  +- org.apache.geronimo.specs:geronimo-activation_1.1_spec:jar:1.1:compile
[INFO] |  |  +- org.apache.geronimo.specs:geronimo-javamail_1.4_spec:jar:1.7.1:compile
[INFO] |  |  +- jaxen:jaxen:jar:1.1.4:compile
[INFO] |  |  +- org.apache.geronimo.specs:geronimo-stax-api_1.0_spec:jar:1.0.1:compile
[INFO] |  |  \- org.apache.james:apache-mime4j-core:jar:0.7.2:compile
[INFO] |  +- org.apache.ws.commons.axiom:axiom-c14n:jar:1.2.14:compile
[INFO] |  +- org.apache.ws.commons.axiom:axiom-impl:jar:1.2.14:compile
[INFO] |  |  \- org.codehaus.woodstox:woodstox-core-asl:jar:4.1.4:compile
[INFO] |  |     \- org.codehaus.woodstox:stax2-api:jar:3.1.1:compile
[INFO] |  +- org.apache.ws.commons.axiom:axiom-dom:jar:1.2.14:compile
[INFO] |  +- dk.brics.automaton:automaton:jar:1.11-8:compile
[INFO] |  +- gnu.getopt:java-getopt:jar:1.0.13:compile
[INFO] |  \- net.sf.trove4j:trove4j:jar:3.0.3:compile
[INFO] +- org.slf4j:slf4j-api:jar:1.7.22:compile
[INFO] +- org.slf4j:slf4j-nop:jar:1.7.32:compile
[INFO] \- org.apache.maven.plugins:maven-assembly-plugin:maven-plugin:3.3.0:compile
[INFO]    +- org.apache.maven:maven-plugin-api:jar:3.0:compile
[INFO]    |  \- org.sonatype.sisu:sisu-inject-plexus:jar:1.4.2:compile
[INFO]    |     \- org.sonatype.sisu:sisu-inject-bean:jar:1.4.2:compile
[INFO]    |        \- org.sonatype.sisu:sisu-guice:jar:noaop:2.1.7:compile
[INFO]    +- org.apache.maven:maven-core:jar:3.0:compile
[INFO]    |  +- org.apache.maven:maven-settings:jar:3.0:compile
[INFO]    |  +- org.apache.maven:maven-settings-builder:jar:3.0:compile
[INFO]    |  +- org.apache.maven:maven-repository-metadata:jar:3.0:compile
[INFO]    |  +- org.apache.maven:maven-model-builder:jar:3.0:compile
[INFO]    |  +- org.apache.maven:maven-aether-provider:jar:3.0:runtime
[INFO]    |  +- org.sonatype.aether:aether-impl:jar:1.7:compile
[INFO]    |  |  \- org.sonatype.aether:aether-spi:jar:1.7:compile
[INFO]    |  +- org.sonatype.aether:aether-api:jar:1.7:compile
[INFO]    |  +- org.sonatype.aether:aether-util:jar:1.7:compile
[INFO]    |  +- org.codehaus.plexus:plexus-classworlds:jar:2.2.3:compile
[INFO]    |  +- org.codehaus.plexus:plexus-component-annotations:jar:1.5.5:compile
[INFO]    |  \- org.sonatype.plexus:plexus-sec-dispatcher:jar:1.3:compile
[INFO]    |     \- org.sonatype.plexus:plexus-cipher:jar:1.4:compile
[INFO]    +- org.apache.maven:maven-artifact:jar:3.0:compile
[INFO]    +- org.apache.maven:maven-model:jar:3.0:compile
[INFO]    +- org.apache.maven.shared:maven-common-artifact-filters:jar:3.1.0:compile
[INFO]    |  \- org.apache.maven.shared:maven-shared-utils:jar:3.1.0:compile
[INFO]    +- org.apache.maven.shared:maven-artifact-transfer:jar:0.11.0:compile
[INFO]    +- org.codehaus.plexus:plexus-interpolation:jar:1.25:compile
[INFO]    +- org.codehaus.plexus:plexus-archiver:jar:4.2.1:compile
[INFO]    |  +- org.apache.commons:commons-compress:jar:1.19:compile
[INFO]    |  \- org.iq80.snappy:snappy:jar:0.4:compile
[INFO]    +- org.apache.maven.shared:file-management:jar:3.0.0:compile
[INFO]    +- org.apache.maven.shared:maven-shared-io:jar:3.0.0:compile
[INFO]    |  +- org.apache.maven:maven-compat:jar:3.0:compile
[INFO]    |  \- org.apache.maven.wagon:wagon-provider-api:jar:2.10:compile
[INFO]    +- org.apache.maven.shared:maven-filtering:jar:3.1.1:compile
[INFO]    |  \- org.sonatype.plexus:plexus-build-api:jar:0.0.7:compile
[INFO]    +- org.codehaus.plexus:plexus-io:jar:3.2.0:compile
[INFO]    +- org.apache.maven:maven-archiver:jar:3.5.0:compile
[INFO]    +- org.codehaus.plexus:plexus-utils:jar:3.3.0:compile
[INFO]    \- commons-codec:commons-codec:jar:1.6:compile
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time:  1.339 s
[INFO] Finished at: 2022-01-27T13:06:01+02:00
[INFO] ------------------------------------------------------------------------

Process finished with exit code 0

ANSWER

Answered 2022-Jan-31 at 10:43

As can be seen in the comments of the post, my problem is fixed, so I thought I'd collect a closing answer here to not leave the post pending.

The actual solution: As explained here nicely by @UninformedUser, the issue was that I had conflicting maven package versions in my dependencies. Bringing everything in sync with each other solved the issue.

Incidental solution: As I wrote in the comments above, specifically defining <version>3.3.0</version> for the maven-assembly-plugin happened to solve the issue. But this was only chance, as explained here by @Ignazio, just because the order of "assembling" things changed, overwriting the conflicting package.

Huge thanks to both for the help.

Source https://stackoverflow.com/questions/70854565

QUESTION

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

Asked 2022-Jan-09 at 06:43

I am using the following function (based on https://rpubs.com/sprishi/twitterIBM) to extract bigrams from text. However, I want to keep the hash symbol for analysis purposes. The function to clean text works fine, but the unnest tokens function removes special characters. Is there any way to run unnest tokens without removing special characters?

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)
}    

# clean the twitter texts. call the clean_Twitter_Corpus function
tweets <- clean_Twitter_Corpus(x)
tweets
text <- as.character(tweets)
text <- as.data.frame(text)

tidy_descr_ngrams <- text %>%
  unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ")
tidy_descr_ngrams

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

bigram_counts

ANSWER

Answered 2022-Jan-09 at 06:43

Here is a solution that involving create a custom n-grams function

Setup

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)
}

The custom function that create n grams without removing special character

# 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)
}

Your code again

# 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?

Final results

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

^{Created on 2022-01-09 by the reprex package (v2.0.1)}

Source https://stackoverflow.com/questions/70634673

QUESTION

Explanations in Consistent OWL Ontologies

Asked 2022-Jan-08 at 19:43

I want to programmatically get explanations for inferred axioms in consistent ontologies, in a similar manner that one can do in the Protégé UI. I cannot find any straightforward way. I have found the owlexplanation repo, but I cannot for the life of me solve the dependency issues to set up the owlexplanation environment. I have also browsed the javadoc of owlapi regarding explanations (to avoid the other repo altogether), but I don't see anything useful beyond what I can already see browsing the Java source code.

I have thought of simply negating the inferred axiom, to get explanations through inconsistencies, but I would prefer something cleaner, and I am not sure this approach is correct anyway.

Other (possibly) useful context:

I had used some Java years ago, but I now primarily use Python (I try to use OWL API with JPype and OWL in general with Owlready2).
I am using HermiT reasoner (again through JPype) (according to build.xml file, ~~latest stable~~ version 1.3.8).
I have managed to get explanations for unsatisfiability and inconsistency in my setup, without owlexplanation, following this example from the HermiT source code.
I fell in the rabbit hole wanting to make a usable .jar file for owlexplanation, in order to add it in my JPype classpath. My plan went sideways when I couldn't get the Java project to build in the first place.
I am using Intellij IDE.

I would appreciate any insight or tips.

UPDATE Jan 6, 2022:

I decided to try once more with the owlexplanation code with a clean head so here is where I am at:

Downloaded the source code from github and extracted the zip.
Started IntelliJ and instead from "Creating a project from Existing sources", I clicked "Open" and selected the extracted directory.
I built the project and it did successfully.
From Maven tools, I run clean, validate, compile and test succesfully.
If I run "package" Maven action, it throws as error that "The environment variable JAVA_HOME is not correctly set". The thing is that if I go File>Project Structure, I see that SDK is set to 11, it's not empty.
Additionally, from the pom.xml file I get these problems:
- Plugin 'org.apache.maven.plugins:maven-gpg-plugin:1.5' not found
- Plugin 'org.sonatype.plugins:nexus-staging-maven-plugin:1.6.6' not found

UPDATE Jan 8, 2022: (Trying @Ignazio's answer)

I created a new IntelliJ project, and added the Maven dependencies @Ignazio mentioned (plus some others like slf4j etc) and I got a working example (I think). Moving to my main project (using JPype), I had to manually download some .jars to include in the classpath (as maven can't be used here). These are the ones downloaded so far:

caffeine-3.0.5.jar         hppcrt-0.7.5.jar    org.semanticweb.hermit-1.4.5.519.jar  slf4j-api-1.7.32.jar
commons-rdf-api-0.5.0.jar  javax.inject-1.jar  owlapi-distribution-5.1.9.jar         slf4j-nop-1.7.32.jar
google-collect-1.0.jar     owlexplanation-5.0.0.jar

Next, a NullPointerException is thrown when trying to use loadOntologyFromOntologyDocument(). I have tried re-downloading the jars as proposed here, but the Exception remains. Could it be that some .jar is missing? I downloaded them based on the thrown NoClassDefFoundError that would occur.

This occurs with a common pizza.owl file that it is otherwise fully working.

EDIT: I used mvn dependency:copy-dependencies -DoutputDirectory=OUTPUT_DIR to get the dependencies and by using OUTPUT_DIR as the classpath, the NullPointerException is gone, so it seems I was indeed missing some .jar file.

For the record, I then got other issues (gen.getExplanations() was throwing a NoSuchMethodError error), but I have no more time for debugging this. I will ditch JPype, however convenient it is, and simply call Java from Python with subprocess. These are (I guess) Jpype problems, so I am accepting Ignazio's answer as it solved my Java/OWL API/owlexplanation side of things.

ANSWER

Answered 2022-Jan-07 at 20:52

You're not just using the projects but actually building them from scratch, which requires more setup than using the published artifacts.

Shortcut that uses Maven available jars (via Maven Central, although other public repositories should do just as well)

Java code:

    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);
        }
    }

Maven configuration:

4.0.0 blah blah 0.0.1-SNAPSHOT blah junit junit 4.12 net.sourceforge.owlapi org.semanticweb.hermit 1.4.5.519 net.sourceforge.owlapi owlexplanation 5.0.0

HermiT is available on Maven Central via release builds I've made - the patch version refers to the OWLAPI version it was built with. Here 1.4.5.519 means HermiT was built with OWLAPI 5.1.9. owlexplanation 5.0.0 was built with OWLAPI 5, so it's the same major OWLAPI version for the project.

This code sample doesn't do a lot because it just looks for explanations of asserted axioms. Often the explanation is just the axiom itself, as it's asserted. Varying the chosen axiom, or using axioms not asserted in the ontology, should give you the explanations you are after.

Source https://stackoverflow.com/questions/70593249

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