Tokenizer reference
A tokenizer receives a stream of characters, breaks it up into individual
tokens (usually individual words), and outputs a stream of tokens. For
instance, a whitespace
tokenizer breaks
text into tokens whenever it sees any whitespace. It would convert the text
"Quick brown fox!"
into the terms [Quick, brown, fox!]
.
The tokenizer is also responsible for recording the following:
-
Order or position of each term (used for phrase and word proximity queries)
-
Start and end character offsets of the original word which the term represents (used for highlighting search snippets).
-
Token type, a classification of each term produced, such as
<ALPHANUM>
,<HANGUL>
, or<NUM>
. Simpler analyzers only produce theword
token type.
OpenSearch has a number of built in tokenizers which can be used to build custom analyzers.
Word Oriented Tokenizers
The following tokenizers are usually used for tokenizing full text into individual words:
- Standard Tokenizer
-
The
standard
tokenizer divides text into terms on word boundaries, as defined by the Unicode Text Segmentation algorithm. It removes most punctuation symbols. It is the best choice for most languages. - Letter Tokenizer
-
The
letter
tokenizer divides text into terms whenever it encounters a character which is not a letter. - Lowercase Tokenizer
-
The
lowercase
tokenizer, like theletter
tokenizer, divides text into terms whenever it encounters a character which is not a letter, but it also lowercases all terms. - Whitespace Tokenizer
-
The
whitespace
tokenizer divides text into terms whenever it encounters any whitespace character. - UAX URL Email Tokenizer
-
The
uax_url_email
tokenizer is like thestandard
tokenizer except that it recognises URLs and email addresses as single tokens. - Classic Tokenizer
-
The
classic
tokenizer is a grammar based tokenizer for the English Language. - Thai Tokenizer
-
The
thai
tokenizer segments Thai text into words.
Partial Word Tokenizers
These tokenizers break up text or words into small fragments, for partial word matching:
- N-Gram Tokenizer
-
The
ngram
tokenizer can break up text into words when it encounters any of a list of specified characters (e.g. whitespace or punctuation), then it returns n-grams of each word: a sliding window of continuous letters, e.g.quick
→[qu, ui, ic, ck]
. - Edge N-Gram Tokenizer
-
The
edge_ngram
tokenizer can break up text into words when it encounters any of a list of specified characters (e.g. whitespace or punctuation), then it returns n-grams of each word which are anchored to the start of the word, e.g.quick
→[q, qu, qui, quic, quick]
.
Structured Text Tokenizers
The following tokenizers are usually used with structured text like identifiers, email addresses, zip codes, and paths, rather than with full text:
- Keyword Tokenizer
-
The
keyword
tokenizer is a`noop'' tokenizer that accepts whatever text it is given and outputs the exact same text as a single term. It can be combined with token filters like `lowercase
to normalise the analysed terms. - Pattern Tokenizer
-
The
pattern
tokenizer uses a regular expression to either split text into terms whenever it matches a word separator, or to capture matching text as terms. - Simple Pattern Tokenizer
-
The
simple_pattern
tokenizer uses a regular expression to capture matching text as terms. It uses a restricted subset of regular expression features and is generally faster than thepattern
tokenizer. - Char Group Tokenizer
-
The
char_group
tokenizer is configurable through sets of characters to split on, which is usually less expensive than running regular expressions. - Simple Pattern Split Tokenizer
-
The
simple_pattern_split
tokenizer uses the same restricted regular expression subset as thesimple_pattern
tokenizer, but splits the input at matches rather than returning the matches as terms. - Path Tokenizer
-
The
path_hierarchy
tokenizer takes a hierarchical value like a filesystem path, splits on the path separator, and emits a term for each component in the tree, e.g./foo/bar/baz
→[/foo, /foo/bar, /foo/bar/baz ]
.
Character group tokenizer
The char_group
tokenizer breaks text into terms whenever it encounters a
character which is in a defined set. It is mostly useful for cases where a simple
custom tokenization is desired, and the overhead of use of the pattern
tokenizer
is not acceptable.
Configuration
The char_group
tokenizer accepts one parameter:
tokenize_on_chars
|
A list containing a list of characters to tokenize the string on. Whenever a character
from this list is encountered, a new token is started. This accepts either single
characters like e.g. |
max_token_length
|
The maximum token length. If a token is seen that exceeds this length then
it is split at |
Example output
POST _analyze
{
"tokenizer": {
"type": "char_group",
"tokenize_on_chars": [
"whitespace",
"-",
"\n"
]
},
"text": "The QUICK brown-fox"
}
returns
{
"tokens": [
{
"token": "The",
"start_offset": 0,
"end_offset": 3,
"type": "word",
"position": 0
},
{
"token": "QUICK",
"start_offset": 4,
"end_offset": 9,
"type": "word",
"position": 1
},
{
"token": "brown",
"start_offset": 10,
"end_offset": 15,
"type": "word",
"position": 2
},
{
"token": "fox",
"start_offset": 16,
"end_offset": 19,
"type": "word",
"position": 3
}
]
}
Classic tokenizer
The classic
tokenizer is a grammar based tokenizer that is good for English
language documents. This tokenizer has heuristics for special treatment of
acronyms, company names, email addresses, and internet host names. However,
these rules don’t always work, and the tokenizer doesn’t work well for most
languages other than English:
-
It splits words at most punctuation characters, removing punctuation. However, a dot that’s not followed by whitespace is considered part of a token.
-
It splits words at hyphens, unless there’s a number in the token, in which case the whole token is interpreted as a product number and is not split.
-
It recognizes email addresses and internet hostnames as one token.
Example output
POST _analyze
{
"tokenizer": "classic",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
The above sentence would produce the following terms:
[ The, 2, QUICK, Brown, Foxes, jumped, over, the, lazy, dog's, bone ]
Configuration
The classic
tokenizer accepts the following parameters:
max_token_length
|
The maximum token length. If a token is seen that exceeds this length then
it is split at |
Example configuration
In this example, we configure the classic
tokenizer to have a
max_token_length
of 5 (for demonstration purposes):
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "classic",
"max_token_length": 5
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
The above example produces the following terms:
[ The, 2, QUICK, Brown, Foxes, jumpe, d, over, the, lazy, dog's, bone ]
Edge n-gram tokenizer
The edge_ngram
tokenizer first breaks text down into words whenever it
encounters one of a list of specified characters, then it emits
N-grams of each word where the start of
the N-gram is anchored to the beginning of the word.
Edge N-Grams are useful for search-as-you-type queries.
|
Tip
|
When you need search-as-you-type for text which has a widely known order, such as movie or song titles, the completion suggester is a much more efficient choice than edge N-grams. Edge N-grams have the advantage when trying to autocomplete words that can appear in any order. |
Example output
With the default settings, the edge_ngram
tokenizer treats the initial text as a
single token and produces N-grams with minimum length 1
and maximum length
2
:
POST _analyze
{
"tokenizer": "edge_ngram",
"text": "Quick Fox"
}
The above sentence would produce the following terms:
[ Q, Qu ]
|
Note
|
These default gram lengths are almost entirely useless. You need to
configure the edge_ngram before using it.
|
Configuration
The edge_ngram
tokenizer accepts the following parameters:
min_gram
-
Minimum length of characters in a gram. Defaults to
1
. max_gram
-
Maximum length of characters in a gram. Defaults to
2
. token_chars
-
Character classes that should be included in a token. OpenSearch will split on characters that don’t belong to the classes specified. Defaults to
[]
(keep all characters).Character classes may be any of the following:
-
letter
— for examplea
,b
,ï
or京
-
digit
— for example3
or7
-
whitespace
— for example" "
or"\n"
-
punctuation
— for example!
or"
-
symbol
— for example$
or√
-
custom
— custom characters which need to be set using thecustom_token_chars
setting.
-
custom_token_chars
-
Custom characters that should be treated as part of a token. For example, setting this to
+-_
will make the tokenizer treat the plus, minus and underscore sign as part of a token.
Limitations of the max_gram
parameter
The edge_ngram
tokenizer’s max_gram
value limits the character length of
tokens. When the edge_ngram
tokenizer is used with an index analyzer, this
means search terms longer than the max_gram
length may not match any indexed
terms.
For example, if the max_gram
is 3
, searches for apple
won’t match the
indexed term app
.
To account for this, you can use the
truncate
token filter with a search analyzer
to shorten search terms to the max_gram
character length. However, this could
return irrelevant results.
For example, if the max_gram
is 3
and search terms are truncated to three
characters, the search term apple
is shortened to app
. This means searches
for apple
return any indexed terms matching app
, such as apply
, snapped
,
and apple
.
We recommend testing both approaches to see which best fits your use case and desired search experience.
Example configuration
In this example, we configure the edge_ngram
tokenizer to treat letters and
digits as tokens, and to produce grams with minimum length 2
and maximum
length 10
:
PUT my-index-00001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "edge_ngram",
"min_gram": 2,
"max_gram": 10,
"token_chars": [
"letter",
"digit"
]
}
}
}
}
}
POST my-index-00001/_analyze
{
"analyzer": "my_analyzer",
"text": "2 Quick Foxes."
}
The above example produces the following terms:
[ Qu, Qui, Quic, Quick, Fo, Fox, Foxe, Foxes ]
Usually we recommend using the same analyzer
at index time and at search
time. In the case of the edge_ngram
tokenizer, the advice is different. It
only makes sense to use the edge_ngram
tokenizer at index time, to ensure
that partial words are available for matching in the index. At search time,
just search for the terms the user has typed in, for instance: Quick Fo
.
Below is an example of how to set up a field for search-as-you-type.
Note that the max_gram
value for the index analyzer is 10
, which limits
indexed terms to 10 characters. Search terms are not truncated, meaning that
search terms longer than 10 characters may not match any indexed terms.
PUT my-index-00001
{
"settings": {
"analysis": {
"analyzer": {
"autocomplete": {
"tokenizer": "autocomplete",
"filter": [
"lowercase"
]
},
"autocomplete_search": {
"tokenizer": "lowercase"
}
},
"tokenizer": {
"autocomplete": {
"type": "edge_ngram",
"min_gram": 2,
"max_gram": 10,
"token_chars": [
"letter"
]
}
}
}
},
"mappings": {
"properties": {
"title": {
"type": "text",
"analyzer": "autocomplete",
"search_analyzer": "autocomplete_search"
}
}
}
}
PUT my-index-00001/_doc/1
{
"title": "Quick Foxes" (1)
}
POST my-index-00001/_refresh
GET my-index-00001/_search
{
"query": {
"match": {
"title": {
"query": "Quick Fo", (2)
"operator": "and"
}
}
}
}
-
The
autocomplete
analyzer indexes the terms[qu, qui, quic, quick, fo, fox, foxe, foxes]
. -
The
autocomplete_search
analyzer searches for the terms[quick, fo]
, both of which appear in the index.
Keyword tokenizer
The keyword
tokenizer is a ``noop'' tokenizer that accepts whatever text it
is given and outputs the exact same text as a single term. It can be combined
with token filters to normalise output, e.g. lower-casing email addresses.
Example output
POST _analyze
{
"tokenizer": "keyword",
"text": "New York"
}
The above sentence would produce the following term:
[ New York ]
Combine with token filters
You can combine the keyword
tokenizer with token filters to normalise
structured data, such as product IDs or email addresses.
For example, the following analyze API request uses the
keyword
tokenizer and lowercase
filter to
convert an email address to lowercase.
POST _analyze
{
"tokenizer": "keyword",
"filter": [ "lowercase" ],
"text"<EMAIL_ADDRESS>}
The request produces the following token:
[<EMAIL_ADDRESS>]
Configuration
The keyword
tokenizer accepts the following parameters:
buffer_size
|
The number of characters read into the term buffer in a single pass.
Defaults to |
Letter tokenizer
The letter
tokenizer breaks text into terms whenever it encounters a
character which is not a letter. It does a reasonable job for most European
languages, but does a terrible job for some Asian languages, where words are
not separated by spaces.
Example output
POST _analyze
{
"tokenizer": "letter",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
The above sentence would produce the following terms:
[ The, QUICK, Brown, Foxes, jumped, over, the, lazy, dog, s, bone ]
Configuration
The letter
tokenizer is not configurable.
Lowercase tokenizer
The lowercase
tokenizer, like the
letter
tokenizer breaks text into terms
whenever it encounters a character which is not a letter, but it also
lowercases all terms. It is functionally equivalent to the
letter
tokenizer combined with the
lowercase
token filter, but is more
efficient as it performs both steps in a single pass.
Example output
POST _analyze
{
"tokenizer": "lowercase",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
The above sentence would produce the following terms:
[ the, quick, brown, foxes, jumped, over, the, lazy, dog, s, bone ]
Configuration
The lowercase
tokenizer is not configurable.
N-gram tokenizer
The ngram
tokenizer first breaks text down into words whenever it encounters
one of a list of specified characters, then it emits
N-grams of each word of the specified
length.
N-grams are like a sliding window that moves across the word - a continuous sequence of characters of the specified length. They are useful for querying languages that don’t use spaces or that have long compound words, like German.
Example output
With the default settings, the ngram
tokenizer treats the initial text as a
single token and produces N-grams with minimum length 1
and maximum length
2
:
POST _analyze
{
"tokenizer": "ngram",
"text": "Quick Fox"
}
The above sentence would produce the following terms:
[ Q, Qu, u, ui, i, ic, c, ck, k, "k ", " ", " F", F, Fo, o, ox, x ]
Configuration
The ngram
tokenizer accepts the following parameters:
min_gram
|
Minimum length of characters in a gram. Defaults to |
max_gram
|
Maximum length of characters in a gram. Defaults to |
token_chars
|
Character classes that should be included in a token. OpenSearch
will split on characters that don’t belong to the classes specified.
Defaults to Character classes may be any of the following:
|
custom_token_chars
|
Custom characters that should be treated as part of a token. For example,
setting this to |
|
Tip
|
It usually makes sense to set min_gram and max_gram to the same
value. The smaller the length, the more documents will match but the lower
the quality of the matches. The longer the length, the more specific the
matches. A tri-gram (length 3 ) is a good place to start.
|
The index level setting index.max_ngram_diff
controls the maximum allowed
difference between max_gram
and min_gram
.
Example configuration
In this example, we configure the ngram
tokenizer to treat letters and
digits as tokens, and to produce tri-grams (grams of length 3
):
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "2 Quick Foxes."
}
The above example produces the following terms:
[ Qui, uic, ick, Fox, oxe, xes ]
Path hierarchy tokenizer
The path_hierarchy
tokenizer takes a hierarchical value like a filesystem
path, splits on the path separator, and emits a term for each component in the
tree.
Example output
POST _analyze
{
"tokenizer": "path_hierarchy",
"text": "/one/two/three"
}
The above text would produce the following terms:
[ /one, /one/two, /one/two/three ]
Configuration
The path_hierarchy
tokenizer accepts the following parameters:
delimiter
|
The character to use as the path separator. Defaults to |
replacement
|
An optional replacement character to use for the delimiter.
Defaults to the |
buffer_size
|
The number of characters read into the term buffer in a single pass.
Defaults to |
reverse
|
If set to |
skip
|
The number of initial tokens to skip. Defaults to |
Example configuration
In this example, we configure the path_hierarchy
tokenizer to split on -
characters, and to replace them with /
. The first two tokens are skipped:
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "path_hierarchy",
"delimiter": "-",
"replacement": "/",
"skip": 2
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "one-two-three-four-five"
}
The above example produces the following terms:
[ /three, /three/four, /three/four/five ]
If we were to set reverse
to true
, it would produce the following:
[ one/two/three/, two/three/, three/ ]
Detailed examples
A common use-case for the path_hierarchy
tokenizer is filtering results by
file paths. If indexing a file path along with the data, the use of the
path_hierarchy
tokenizer to analyze the path allows filtering the results
by different parts of the file path string.
This example configures an index to have two custom analyzers and applies
those analyzers to multifields of the file_path
text field that will
store filenames. One of the two analyzers uses reverse tokenization.
Some sample documents are then indexed to represent some file paths
for photos inside photo folders of two different users.
PUT file-path-test
{
"settings": {
"analysis": {
"analyzer": {
"custom_path_tree": {
"tokenizer": "custom_hierarchy"
},
"custom_path_tree_reversed": {
"tokenizer": "custom_hierarchy_reversed"
}
},
"tokenizer": {
"custom_hierarchy": {
"type": "path_hierarchy",
"delimiter": "/"
},
"custom_hierarchy_reversed": {
"type": "path_hierarchy",
"delimiter": "/",
"reverse": "true"
}
}
}
},
"mappings": {
"properties": {
"file_path": {
"type": "text",
"fields": {
"tree": {
"type": "text",
"analyzer": "custom_path_tree"
},
"tree_reversed": {
"type": "text",
"analyzer": "custom_path_tree_reversed"
}
}
}
}
}
}
POST file-path-test/_doc/1
{
"file_path": "/User/alice/photos/2017/05/16/my_photo1.jpg"
}
POST file-path-test/_doc/2
{
"file_path": "/User/alice/photos/2017/05/16/my_photo2.jpg"
}
POST file-path-test/_doc/3
{
"file_path": "/User/alice/photos/2017/05/16/my_photo3.jpg"
}
POST file-path-test/_doc/4
{
"file_path": "/User/alice/photos/2017/05/15/my_photo1.jpg"
}
POST file-path-test/_doc/5
{
"file_path": "/User/bob/photos/2017/05/16/my_photo1.jpg"
}
A search for a particular file path string against the text field matches all
the example documents, with Bob’s documents ranking highest due to bob
also
being one of the terms created by the standard analyzer boosting relevance for
Bob’s documents.
GET file-path-test/_search
{
"query": {
"match": {
"file_path": "/User/bob/photos/2017/05"
}
}
}
It’s simple to match or filter documents with file paths that exist within a
particular directory using the file_path.tree
field.
GET file-path-test/_search
{
"query": {
"term": {
"file_path.tree": "/User/alice/photos/2017/05/16"
}
}
}
With the reverse parameter for this tokenizer, it’s also possible to match
from the other end of the file path, such as individual file names or a deep
level subdirectory. The following example shows a search for all files named
my_photo1.jpg
within any directory via the file_path.tree_reversed
field
configured to use the reverse parameter in the mapping.
GET file-path-test/_search
{
"query": {
"term": {
"file_path.tree_reversed": {
"value": "my_photo1.jpg"
}
}
}
}
Viewing the tokens generated with both forward and reverse is instructive in showing the tokens created for the same file path value.
POST file-path-test/_analyze
{
"analyzer": "custom_path_tree",
"text": "/User/alice/photos/2017/05/16/my_photo1.jpg"
}
POST file-path-test/_analyze
{
"analyzer": "custom_path_tree_reversed",
"text": "/User/alice/photos/2017/05/16/my_photo1.jpg"
}
It’s also useful to be able to filter with file paths when combined with other
types of searches, such as this example looking for any files paths with 16
that also must be in Alice’s photo directory.
GET file-path-test/_search
{
"query": {
"bool" : {
"must" : {
"match" : { "file_path" : "16" }
},
"filter": {
"term" : { "file_path.tree" : "/User/alice" }
}
}
}
}
Pattern tokenizer
The pattern
tokenizer uses a regular expression to either split text into
terms whenever it matches a word separator, or to capture matching text as
terms.
The default pattern is \W+
, which splits text whenever it encounters
non-word characters.
|
Warning
|
Beware of Pathological Regular Expressions
The pattern tokenizer uses Java Regular Expressions. A badly written regular expression could run very slowly or even throw a StackOverflowError and cause the node it is running on to exit suddenly. Read more about pathological regular expressions and how to avoid them. |
Example output
POST _analyze
{
"tokenizer": "pattern",
"text": "The foo_bar_size's default is 5."
}
The above sentence would produce the following terms:
[ The, foo_bar_size, s, default, is, 5 ]
Configuration
The pattern
tokenizer accepts the following parameters:
pattern
|
A Java regular expression, defaults to |
flags
|
Java regular expression flags.
Flags should be pipe-separated, eg |
group
|
Which capture group to extract as tokens. Defaults to |
Example configuration
In this example, we configure the pattern
tokenizer to break text into
tokens when it encounters commas:
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "pattern",
"pattern": ","
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "comma,separated,values"
}
The above example produces the following terms:
[ comma, separated, values ]
In the next example, we configure the pattern
tokenizer to capture values
enclosed in double quotes (ignoring embedded escaped quotes \"
). The regex
itself looks like this:
"((?:\\"|[^"]|\\")*)"
And reads as follows:
-
A literal
"
-
Start capturing:
-
A literal
\"
OR any character except"
-
Repeat until no more characters match
-
-
A literal closing
"
When the pattern is specified in JSON, the "
and \
characters need to be
escaped, so the pattern ends up looking like:
\"((?:\\\\\"|[^\"]|\\\\\")+)\"
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "pattern",
"pattern": "\"((?:\\\\\"|[^\"]|\\\\\")+)\"",
"group": 1
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "\"value\", \"value with embedded \\\" quote\""
}
The above example produces the following two terms:
[ value, value with embedded \" quote ]
Simple pattern tokenizer
The simple_pattern
tokenizer uses a regular expression to capture matching
text as terms. The set of regular expression features it supports is more
limited than the pattern
tokenizer, but the
tokenization is generally faster.
This tokenizer does not support splitting the input on a pattern match, unlike
the pattern
tokenizer. To split on pattern
matches using the same restricted regular expression subset, see the
simple_pattern_split
tokenizer.
This tokenizer uses Lucene regular expressions. For an explanation of the supported features and syntax, see Regular Expression Syntax.
The default pattern is the empty string, which produces no terms. This tokenizer should always be configured with a non-default pattern.
Configuration
The simple_pattern
tokenizer accepts the following parameters:
pattern
|
Lucene regular expression, defaults to the empty string. |
Example configuration
This example configures the simple_pattern
tokenizer to produce terms that are
three-digit numbers
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "simple_pattern",
"pattern": "[0123456789]{3}"
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "fd-786-335-514-x"
}
The above example produces these terms:
[ 786, 335, 514 ]
Simple pattern split tokenizer
The simple_pattern_split
tokenizer uses a regular expression to split the
input into terms at pattern matches. The set of regular expression features it
supports is more limited than the pattern
tokenizer, but the tokenization is generally faster.
This tokenizer does not produce terms from the matches themselves. To produce
terms from matches using patterns in the same restricted regular expression
subset, see the simple_pattern
tokenizer.
This tokenizer uses Lucene regular expressions. For an explanation of the supported features and syntax, see Regular Expression Syntax.
The default pattern is the empty string, which produces one term containing the full input. This tokenizer should always be configured with a non-default pattern.
Configuration
The simple_pattern_split
tokenizer accepts the following parameters:
pattern
|
A Lucene regular expression, defaults to the empty string. |
Example configuration
This example configures the simple_pattern_split
tokenizer to split the input
text on underscores.
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "simple_pattern_split",
"pattern": "_"
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "an_underscored_phrase"
}
The above example produces these terms:
[ an, underscored, phrase ]
Standard tokenizer
The standard
tokenizer provides grammar based tokenization (based on the
Unicode Text Segmentation algorithm, as specified in
Unicode Standard Annex #29) and works well
for most languages.
Example output
POST _analyze
{
"tokenizer": "standard",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
The above sentence would produce the following terms:
[ The, 2, QUICK, Brown, Foxes, jumped, over, the, lazy, dog's, bone ]
Configuration
The standard
tokenizer accepts the following parameters:
max_token_length
|
The maximum token length. If a token is seen that exceeds this length then
it is split at |
Example configuration
In this example, we configure the standard
tokenizer to have a
max_token_length
of 5 (for demonstration purposes):
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "standard",
"max_token_length": 5
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
The above example produces the following terms:
[ The, 2, QUICK, Brown, Foxes, jumpe, d, over, the, lazy, dog's, bone ]
Thai tokenizer
The thai
tokenizer segments Thai text into words, using the Thai
segmentation algorithm included with Java. Text in other languages in general
will be treated the same as the
standard
tokenizer.
|
Warning
|
This tokenizer may not be supported by all JREs. It is known to work with Sun/Oracle and OpenJDK. If your application needs to be fully portable, consider using the ICU Tokenizer instead. |
Example output
POST _analyze
{
"tokenizer": "thai",
"text": "การที่ได้ต้องแสดงว่างานดี"
}
The above sentence would produce the following terms:
[ การ, ที่, ได้, ต้อง, แสดง, ว่า, งาน, ดี ]
Configuration
The thai
tokenizer is not configurable.
UAX URL email tokenizer
The uax_url_email
tokenizer is like the standard
tokenizer except that it
recognises URLs and email addresses as single tokens.
Example output
POST _analyze
{
"tokenizer": "uax_url_email",
"text": "Email me at<EMAIL_ADDRESS>}
The above sentence would produce the following terms:
[ Email, me, at<EMAIL_ADDRESS>]
while the standard
tokenizer would produce:
[ Email, me, at, john.smith, global, international.com ]
Configuration
The uax_url_email
tokenizer accepts the following parameters:
max_token_length
|
The maximum token length. If a token is seen that exceeds this length then
it is split at |
Example configuration
In this example, we configure the uax_url_email
tokenizer to have a
max_token_length
of 5 (for demonstration purposes):
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "uax_url_email",
"max_token_length": 5
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text"<EMAIL_ADDRESS>}
The above example produces the following terms:
[ john, smith, globa, l, inter, natio, nal.c, om ]
Whitespace tokenizer
The whitespace
tokenizer breaks text into terms whenever it encounters a
whitespace character.
Example output
POST _analyze
{
"tokenizer": "whitespace",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
The above sentence would produce the following terms:
[ The, 2, QUICK, Brown-Foxes, jumped, over, the, lazy, dog's, bone. ]
Configuration
The whitespace
tokenizer accepts the following parameters:
max_token_length
|
The maximum token length. If a token is seen that exceeds this length then
it is split at |


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{% extends 'layout.html' %} {% block content %}
|
Dear {{ message_content.get('name','')|title }}, You recently changed your password for your MCO account {{ message_content.get('user_name', '') }}. If you did not change the password and believe your MCO account has been compromised, please contact MCO support on {{ message_content.get('contact_email', '') }} |


GroupPostProcessingOperation
class GroupPostProcessingOperation : DefaultOperation
Group of post-process operations.
Runs several post-processings on one file in its queue (not in parallel).
-
Undocumented
Declaration
Swift
public var onComplete: ((_ processesNotCompleted: [(name: String, error: Error?)]) -> Void)?
-
Initiates with required parameters.
Declaration
Swift
init(fileStorage: FileStorage?, postProcessings: [PostProcessing])
Parameters
fileStorage
The file storage manager that used to pass to a
PostProcessing
object.postProcessings
Available post-processes.
-
Undocumented
Declaration
Swift
override func start()
-
Undocumented
Declaration
Swift
override func cancel()
-
Prepare to start post-processing operations over a given source file.
Declaration
Swift
func prepareToStart(withSourceLocation sourceLocation: URL, filename: String, fileExtension: String)
Parameters
sourceLocation
The reference to the source file on which post-processing should be performed.
filename
Local filename.
fileExtension
File extention.


Display Pre-Projected
Data with PDEF
Gridded data that are mapped onto a particular map
projection are called 'pre-projected.' An example of pre-projected data
is the output from a weather forecast model that is mapped onto a north
polar stereographic grid.
In order to display pre-projected data on a map in
GrADS, the descriptor file must contain a PDEF entry. A descriptor file
that contains a PDEF record describes two different grids. The first grid
is described by the PDEF record itself and is the "native" grid
for the pre-projected data in the file. The second
grid described in the desctiptor file is a rectilinear lat/lon grid, which
is defined by the XDEF and YDEF records. The PDEF record describes the
size of the native grid, and then describes how to convert from i/j of the native grid to the lat/lon values of the rectilinear grid described by XDEF and YDEF. The information in the PDEF entry describes the
projection of the grid along with the projection constants or providing
the mapping to the native grid in a supplementary data file. The rectilinear grid is used
by GrADS internally and can be any size or resolution -- it is completely
independent of the pre-projected or native grid. GrADS uses the information
about the two grids to interpolate from the PDEF-described native grid
to the XDEF/ YDEF-described rectilinear grid. All displays and analyses
are done using the interpolated data on the rectilinear grid. The virtue
of this approach is that all built in GrADS analytic functions (e.g.,
aave, hcurl...)
will work even though the data were not originally on a lon/lat grid.
The downside is that you are looking at interpolated data.
It is possible to view the pre-projected data on its
native grid. To do this, you omit the PDEF entry from the descriptor file,
and use the XDEF and YDEF entries to describe the shape of the native
grid. In this case, your displays must be drawn in i/j space without a
map projection (set mpdraw
off).
When you do a display of a pre-projected vector
field, you must know whether the original vector components are defined
relative to the data grid or relative to the Earth. If the data are grid-relative,
they must be rotated to Earth-relative coordinates in order for the interpolation
to work properly. See the "Notes" under each particular projection
type for further information.
PDEF Syntax
PDEF isize jsize NPS ipole jpole
lonref gridinc
PDEF isize jsize SPS ipole jpole lonref gridinc |
| Example: |
PDEF 53 45 nps 27 49 -105 190.5 |
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| ipole |
the i coordinate of the pole referenced from the lower left
corner, assumed to be at (1,1) |
| jpole |
the j coordinate of the pole referenced from the lower left
corner, assumed to be at (1,1) |
| lonref |
reference longitude |
| gridinc |
distance between gripoints in km |
|
| Notes: |
Polar stereographic projections (N and S) are defined
as at NCEP. Wind rotation has also been added so that vector data
will be properly displayed. |
| |
|
PDEF isize jsize LCCR latref
lonref iref jref Struelat Ntruelat slon dx dy
PDEF isize jsize LCC latref lonref iref jref Struelat
Ntruelat slon dx dy |
| Example: |
PDEF 103 69 lccr 30 -88 51.5 34.5 20 40 -88 90000
90000 |
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| latref |
reference latitude |
| lonref |
reference longitude (in degrees, E is positive, W is negative) |
| iref |
i of ref point |
| jref |
j of ref point |
| Struelat |
S true lat |
| Ntruelat |
N true lat |
| slon |
standard longitude |
| dx |
grid X increment in meters |
| dy |
grid Y increment in meters |
|
| Notes: |
Starting with version 1.9b4, the LCCR option supplements
the use of PDEF with data on the Lambert Conformal projection. With
LCCR, wind rotation has been implemented for data files with grid-relative
winds instead of Earth-relative winds. Use LCC if your vector components
are already Earth-relative. |
| |
|
|
| PDEF isize jsize ETA.U lonref
latref dlon dlat |
| Example: |
PDEF 181 136 eta.u -97.0 41.0 0.38888888 0.37 |
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| lonref |
reference longitude (in degrees, E is positive, W is negative) |
| latref |
reference latitude |
| dlon |
grid longitude increment in degrees |
| dlat |
grid latitude increment in degrees |
|
| Notes: |
The eta model native grid is awkward to work with
because the variables are on staggered and non-rectangular
grids. NCEP created "unstaggered" eta model fields, in which the
variables are placed on a common rectangular grid. Wind rotation
has also been added so that vector data will be properly displayed. |
| |
|
|
| PDEF isize jsize PSE slat slon
ipole jpole dx dy sign |
| Example: |
|
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| slat |
absolute value of the standard latitude |
| slon |
absolute value of the standard longitude |
| ipole |
the i coordinate of the pole referenced from the lower left
corner, assumed to be at (0,0) |
| jpole |
the j coordinate of the pole referenced from the lower left
corner, assumed to be at (0,0) |
| dx |
grid X increment in km |
| dy |
grid Y increment in km |
| sign |
1 for NH; -1 for SH |
|
| Notes: |
The polar stereo projection used
by the original NMC models is not very precise because it assumes
the earth is round (eccentricity = 0). While this approximation
was reasonable for coarse resolution NWP models, it is inadequate
to work with higher resolution data such as SSM/I. Wind rotation
has not been implemented!!! Use only for scalar fields. |
| |
|
|
| PDEF isize jsize OPS latref
lonref xoff yoff iref jref dx dy |
| Example: |
PDEF 26 16 ops 40.0 -100.0 90000.0 90000.0 14.0 9.0
180000.0 180000.0 |
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| latref |
reference latitude |
| lonref |
reference longitude (in degrees, E is positive, W is negative) |
| xoff |
lonref offset in meters |
| yoff |
latref offset in meters |
| iref |
the i coordinate of the reference point |
| jref |
the j coordinate of the reference point |
| dx |
grid X increment in km |
| dy |
grid Y increment in km |
| dy |
grid Y increment in km |
|
| Notes: |
The CSU RAMS model uses an oblique polar stereo projection.
Wind rotation has not been implemented!!! Use only for scalar
fields. |
| |
|
PDEF isize jsize ROTLL lonpol latpol dlon dlat lonll latll
PDEF isize jsize ROTLLR lonpol latpol dlon dlat lonll latll |
| Example: |
PDEF 500 330 rotllr -170.0 43.0 0.02 0.02 -5.5 -3.8 |
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| lonpol |
Longitude of the rotated pole in degrees |
| latpol |
Latitude of the rotated pole in degrees |
| dlon |
grid spacing in longitudinal direction of the rotated grid in degrees |
| dlat |
grid spacing in latitudinal direction of the rotated grid in degrees |
| lonll |
longitude of the lower left corner, given in rotated space in degree |
| latll |
latitude of the lower left corner, given in rotated space in degree |
|
| Notes: |
(GrADS version 2.0) The rotated lat/lon grid projection is described in the COSMO documentation, Part 1, chapter 3.3. The lower left corner, i.e. the first element in the data array, has to be the southwest corner. It is not possible to use a mirrored grid by setting dlon or dlat to a negative value. |
| |
|
| PDEF
isize jsize BILIN format byteorder fname |
| Example: |
PDEF 100 100 BILIN sequential binary-big ^mygrid.interp.values |
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| format |
Must be either STREAM (direct access) or SEQENTIAL (fortran
formatted) |
| byteorder |
If set to BINARY, byte odering is assumed to be same as
local machine
If set to BINARY-BIG, byte ordering is assumed to be big-endian
If set to BINARY-LITTLE, byte ordering is assumed to be little-endian |
| fname |
The name of the supplementary file |
|
| Notes: |
The supplementary file contains three lat-lon floating-point
grids: i values, j values, and wind rotation values. The native grid is assumed to have a corner (i,j) value of (1,1). The size of
these grids must match the XDEF and YDEF entries in the descriptor
file. |
| |
|
| PDEF isize jsize GENERAL num
format byteorder fname |
| Example: |
PDEF 182 149 general 4 sequential binary-big ^mygrid.interp.values
PDEF 15238 1 general 1 stream binary ^gtd.filepdef |
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| num |
number of sets of interpolation grids supplied |
| format |
Must be either STREAM (direct access) or SEQENTIAL (fortran
formatted) |
| byteorder |
If set to BINARY, byte odering is assumed to be same as
local machine
If set to BINARY-BIG, byte ordering is assumed to be big-endian
If set to BINARY-LITTLE, byte ordering is assumed to be little-endian |
| fname |
The name of the supplementary file; it may be mixed case. |
|
| Notes: |
(GrADS version 2.0.a3 and later) The syntax and behavior of PDEF GENERAL is exactly like PDEF FILE, except that the convention for the native grid offset values in the pdef file is the same for all data formats. The offsets should be 1-based; the first grid point is assumed to have (i,j) values of (1,1), and valid offset values are > 0 and <= isize*jsize .
Native grid offset values of -999 indicate not to use
an input point for that portion of the calculation (thus you can apply
less than the "num" number of interpolation points for some
of the points).
See additional notes in the paragraphs below. |
| |
| PDEF isize jsize FILE num
format byteorder fname |
| Example: |
PDEF 182 149 file 4 sequential binary-big ^mygrid.interp.values
PDEF 15238 1 file 1 stream binary ^gtd.filepdef |
| Args: |
| isize |
The size of the native grid in the x direction |
| jsize |
The size of the native grid in the y direction |
| num |
number of sets of interpolation grids supplied |
| format |
Must be either STREAM (direct access) or SEQENTIAL (fortran
formatted) |
| byteorder |
If set to BINARY, byte odering is assumed to be same as
local machine
If set to BINARY-BIG, byte ordering is assumed to be big-endian
If set to BINARY-LITTLE, byte ordering is assumed to be little-endian |
| fname |
The name of the supplementary file; it may be mixed case. |
|
| Notes: |
For GrADS v2.0.a2 and earlier, jsize was fixed to be 1, and isize was the size of the native grid expressed as a vector (i.e., all gridpoints in the x-y grid). This mode for describing the native grid will continue to work with v2.0.a3+, but only if the native grid is in GRIB or binary format. For NetCDF and HDF formats, the isize and jsize args must match the X and Y dimensions of the native grid.
WARNING: The use of PDEF FILE may be incorrect! A long-standing bug and incomplete documentation has led to different conventions for the native grid offset values in the pdef file for GRIB and non-GRIB data formats. For GRIB (1&2), the offsets must be 0-based; the first grid point is assumed to have (i,j) values of (0,0). For all other data types, the offsets must be 1-based; the first grid point is assumed to have (i,j) values of (1,1). Thus:
for GRIB format: valid offset values are >= 0 and < isize*jsize
for other formats: valid offset values are > 0 and <= isize*jsize
To maintain backward compatibility, the bug will remain in GrADS as a feature, but the use of PDEF FILE has been deprecated as of version 2.0.a3 and a warning message will be displayed when a data set is opened that uses PDEF FILE. Note that if you use grib2ctl or g2ctl to generate your pdef file, the offsets are correct.
Native grid offset values of -999 indicate not to use
an input point for that portion of the calculation (thus you can apply
less than the "num" number of interpolation points for some
of the points).
See additional notes in the paragraphs below. |
How
PDEF Grid Interpolation Works
To illustrate how the data is interpolated from
the native grid to the rectilinear grid, let's consider an example. Here
are a set of relevant records from a descriptor file:
PDEF 100 100 nps ...
XDEF 181 linear -180 1
YDEF 90 linear 0 1
These three entries describe data on a native 100x100 North Polar stereographic
projection and a rectilinear lat/lon grid that is 181 by 90 and has an
interval of 1 degree in both lat and lon. Consider one point within the
rectilinear grid, the point -90,40. GrADS calls an internal routine to
calculate the i and j values in the native grid that correspond to this
lat/lon point. Let's say we get i,j values of 31.24 and 67.88. To do the
interpolation to the lat/lon point -90,40, GrADS uses the data values
from the following four native grid points: 31,67 - 31,68 - 32,67 - 32,68.
Bi-linear interpolation is used within this grid box to get down to the
position 31.24,67.88. The interpolation is linear within the i,j grid.
When a descriptor file is opened that contains a PDEF record, GrADS calculates
the i/j values in the native grid that correspond to the lat/lon pair
for each gridpoint in the rectilinear grid.
How
PDEF Wind Rotation Works
There is a third value calculated for every lat/lon
point, and that is the wind rotation value. With some "pre-projected"
or native grids, the winds are given relative to the i/j space and not
the lat/lon space. To insure correct interpolation, the winds must be
rotated to lat/lon space. This is done by determining a rotation amount
for each lat/lon point. When u or v wind components are displayed, the
values are not just interpolated but also rotated.
To do the wind rotation properly, GrADS requires both
the u and v components. Even if you are just displaying u, GrADS has to
retrieve (internally) both the u and v component in order to do the rotation
calculation. GrADS determines how to match u and v variables by checking
the units field of the variable record in the descriptor file.
The u variable must have a units value of 33, and the v variable
must have a units value of 34. (This is the GRIB convention).
If there are more than one u/v pairs, secondary units values
are used. For example:
| u |
18 |
33,100 |
U-Wind Components on Pressure Levels |
| v |
18 |
34,100 |
V-Wind Components on Pressure Levels |
| u10 |
0 |
33,105 |
10 Meter U Wind |
| v10 |
0 |
34,105 |
10 Meter V Wind |
might be some variable records in the descriptor file.
If wind rotation is called for, u and v would be paired, and u10 and v10
would be paired (since the secondary values would be checked, ie, the
105,100 values).
The
PDEF BILIN Option
When a descriptor file is opened that contains
a PDEF record, we have explained that GrADS internally generates three
grids, each one the size of the rectilinear lat/lon grid. The first two
grids contain the i and j values (respectively) from the native grid that
correspond to each grid point in the rectilinear grid; the third grid
contains wind rotation values. But this only works for a small set of
well-defined native grids. GrADS will generate these three internal grids
automatically for polar stereographic, lamber conformal, and some eta
grids. If the native grid for your data is not one of the predefined projections,
it is still possible for GrADS to handle the data. All you have to do
is supply these three grids to GrADS with a supplementary data file and
use the bilin option in your PDEF record.
The supplementary file will contains three lat-lon
floating-point grids sized according to the XDEF and YDEF records in the
descriptor file.The three grids contain: i values, j values, and wind
rotation values. A value of -999 in the i-value grid indicates not to
interpolate to that lat-lon point (will end up missing on output) and
a value of -999 in the wind-rotation grid indicates not to do wind rotation
for that point. If the wind-rotation grid is all -999 values, no rotation
is ever done and a flag is set not to even attempt rotation.
The
PDEF GENERAL Option (and the PDEF FILE option)
All of the PDEF examples discussed so far involve
the same method for grid interpolation: a grid point value in the rectilinear
grid is calculated by finding the four neighboring grid points in the
native grid and averaging them, with weights applied bi-linearly according
to their proximity to the to rectilinear grid point. The PDEF GENERAL option
and the PDEF FILE option generalize this method so that an arbitrary number of native grid point
values and their weights are averaged to generate the interpolated rectilinear
grid point values. The index values for the native grid values that are
to be used and their weights are specified by the user in a supplementary
data file (fname). The FILE and GENERAL options are identical except for one detail: they have different conventions for the native grid offset values in the supplementary file (see the "Notes" in the syntax tables above for specifics).
The num argument in the PDEF FILE entry specifies
the number of native grid points that will be used to calcuate each interpolated
rectilinear grid point value. For each num, the supplementary
data file will contain two grids -- both will be the size of the rectilinear
grid (as defined by XDEF and YDEF). The first grid contains the index
or offset values that point to the native grid value that will be used
in the interpolation; the second grid contains the weights for those native
grid values. The first grid contains integer values, the second grid contains
floating-point values. Finally, the supplementary data file must also
contain one grid of floating-point wind rotation values. Thus if num
equals 1, there will be 3 grids in fname. If num equals
3, there will be 7 grids in fname (3 sets of 2 grids plus the
wind rotation grid).
To do the grid interpolation, GrADS will first read
the data in the native grid (vector) along with the values in the supplementary
grids. To calculate the interpolated value for a particular lat-lon point,
GrADS will get num native grid point values, multiply each by
their weight, sum over all the weighted native grid points, and divide
by the sum of the weights.
An Example:
The original data are set up as a vector of land points
only, taken from a 1-degree lat/lon grid. There are 15238 land points
in the native grid (vector). We use the PDEF FILE option to repopulate
a rectilinear lat/lon grid with the land values, leaving the ocean grid
points as missing. In this case, ther eis no interpolation done. The PDEF
option is used simply to convert a vector of land points into a 2D grid
with ocean points missing. The descriptor file looks like this:
DSET ^gswp_vegetation_parameters.nc
DTYPE netcdf
TITLE Monthly Vegetation parameters at 1x1 degree
UNDEF 1.e+20
PDEF 15238 1 file 1 stream binary ^gswp.filepdef
XDEF 360 linear -179.5 1
YDEF 150 linear -59.5 1
ZDEF 1 linear 1 1
TDEF 204 linear 00Z01jan1982 1mo
VARS 1
NDVI=>ndvi 0 t,x Monthly vegetation index
ENDVARS
The supplementary file gtd.filepdef contains three
grids -- the first contains the index values that associate each location
in the lat/lon grid with it's point in the vector. All of the ocean points
will have a missing value of -999. The second grid will contain the weights,
which will be 1 for land points, 0 for ocean points. The third grid will
contain all missing values since wind rotation is not a issue in this
example. Here is a descriptor file for the supplementary data file (a
useful strategy for making sure you've got everything written out correctly):
DSET ^gswp.filepdef
TITLE PDEF file for GSWP Vegetation Parameters
UNDEF -999
XDEF 360 linear -179.5 1
YDEF 150 linear -59.5 1
ZDEF 1 linear 1 1
TDEF 1 linear 00z01jul1982 3hr
VARS 3
i 0 -1,40,4 Index Values
w 0 99 Weights
r 0 99 Wind Rotation Values
ENDVARS
|


Was looking for some ideas on a Rakasha build that can buff himself well and also maybe throwing some monk lvls in so I can retain the wisdom ac bonus. Not a very good builder at the moment so any ideas would help me out.
Three posts on the topic: SBully Shifter Raksasa by traversc Raksasa by Wildeyes
Next time you can search this thread before asking.
If you dont like those builds you can search for shifter in the thread, you may find more rak-focused shifters. _________________ HEAD KNIGHT: Shh shh. We are now the Knights Who Say Ekke ekke ekke ptang zoo boing! RANDOM: Ni!


Grid
The website uses a responsive grid based on Bootstrap. Typically content expands from mobile from one column to two columns to four columns.
Some featured content will span the full width of the page, whereas other elements, such as body text and feature layouts, are contained within a maximum width.
Page structure
Every page on the website uses these global elements to create its basic structure.
Kent bar (audience links)
The Kent bar is horizontal bar at the top of each page which provides links to priority audiences.
The Kent bar also acts as a light branding element on internal content. Once logged in, users will have personalised content accessible from the Kent bar.
Global navigation
This comprises of the three main links at the top of the page - Research, Study and Explore, which expose the key user journeys.
By simplifying options, it makes the site easier for prospects to get to the most important information that they need.
Heading 4
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Global search
This is the gold search button to the right of the global navigation links. Global search provides categorised results which autocomplete upon typing.
It will be context aware, so if search in a school site, that schools results will be prioritised. It will always have course results integrated into the search.
Global footer
The footer contains additional links which are important, but shouldn't get in the way of the key user journeys in the global nav. Some of the links are duplicated in the footer.
It also includes Open Days, Social media, Giving to Kent sections and affiliate logos. At the very bottom of the page are subsidiary links.
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These are invisible links which assist screen reader users to navigate the content more easily without having to trawl through repetitive layers of navigation. They allow a user to:
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What is FHIR, really? It is a data representation and messaging standard. I think the following quotation from HL7 summarizes the whole concept very nicely:
The FHIR specification defines a set of resources, and an infrastructure for handling resources. In order to use FHIR to create solutions for integration requirements, implementers must map their problems to resources and their content.
The data can be represented in several formats including XML and JavaScript Object Notation (JSON). Since the goal of this website is to bring viewers up to speed on creating FHIR applications, I will focus on the JSON notation, since this is easily used in web programming for exchanging and manipulating data.
FHIR Resources are the basic data unit of the standard. A FHIR Resource is formally defined as follows:
resource is an entity that:
- has a known identity (a URL) by which it can be addressed
- identifies itself as one of the types of resource defined in this specification
- contains a set of structured data items as described by the definition of the resource type
- has an identified version that changes if the contents of the resource change
See the rest of the formal definition here.
When working with FHIR we will be querying for or writing to individual or sets of Resources. Currently in DSTU3 there are [**118 FHIR Resources**](https://www.hl7.org/fhir/resourcelist.html) with more in the pipeline. You can read the ongoing discussion about new resources [**here**](http://wiki.hl7.org/index.php?title=FHIR_Resource_Types#Unsure).
FHIR Resources have been mapped to an ontology to provide consistent meaning. The main classification of FHIR Resources breaks down into 6 categories


WITNESS LIST Finance Committee January 30, 1997-8:00A Budget/Cancer Council On: Casey-Graham, Pat (CCEP/WTCCC), El Paso Dannenbaum, James D. (TX Cancer Council), Houston Terry, Dr. Douglas W. (TMA - POEP), Austin Untermeyer, Emily (TX Cancer Council), Austin Winick, Seth R. (American Cancer Society), Austin Registering, but not testifying: On: Thomason, Wade (American Lung Assc.), Austin Written materials submitted: On: Reynolds, David (American Heart Assc.), Austin Budget/Dept. of PRS On: Baldwin, Howard G. (DPRS), Austin Bush, Susan (TCTPCA), Athens Hayes, Aaryce (Advocacy, Inc.), Austin Hine, Jim (DPRS), Austin Holman, Nancy Engman (Txans Care for Children), Austin Kinsey, Marcia (CPPP), Austin McCown, F. Scott (Self), Austin Powell, Rita (TALCS/TCFC), Austin Rea, April (Self), Austin Thigpen, Drew (DPRS), Austin Tod, Theresa Andreas (TX Network of Youth Ser), Austin Registering, but not testifying: On: Brown, Cindy (DPRS), Austin Davis, C. Ed (DPRS), Austin Hight, Bowden C. (DPRS), Austin Locke, Jere (TSEU), Austin Maddox, James R. (DPRS), Austin Written materials submitted: On: Piper, Jane (Self), Austin Teltow, Wendell (TCTPCA), Austin Budget/Dept. on Aging On: Burton, Dr. Jack (TDOA), Cleborne Flentge, Cecil (TAOSC), San Antonio Gonzalez, Elena (TDOA), Pharr Gordon, Peggy (AAA of NT), Wichita Falls Greene, Cathy (AARP), Austin Lugo, Fred (TAOR/SVPD), Austin McCue, John W. (AAOAOTHOT/TAOAAOA), Waco Parks, Roxanne Smith (TAOFGPD), Nederland Patterson, Jan (TDOA), Dallas Perkins, Thomas V. (TSHL), Smiley Ramsay, Hilda T. (AARP), Sherman Sapp, Mary (TDOA), Austin Wallace, Aliceanne (TDOA), Belton Willis, John F. (TDOA), Austin Registering, but not testifying: On: Ciminelli, Elsa M. (CAC), Corpus Christi Crossley, Lee A. (AAAC), Belton Davis, Clay J. (Silver-Haired Leg.), Coryell Doyle, Lori (HCCAA), San Saba Holtermann, John (Silver-Haired Leg.), San Marcos Lear, Homer (Silver-Haired Leg.), San Antonio Marshall, Lewis (Silver-Haired Leg.), San Antonio McGhee, Richard (AAA - Central Texas), Belton Mueck, Renee (HCCAA), San Saba Pennington, Frank (TDOA), Austin Rothenmel, Dorothy (TX TDOA Advocates), Brenham Budget/ICECI On: Brady, Denise (ARC of Texas), Austin Bryant, Claudette W. (ICECI), Austin Hull, Dorothy (ARC of Texas), Henderson Lambert, Kay (Advocacy, Inc.), Austin Samuelson, Donna (ICECI), Austin Tucker, Elizabeth (UCP of Texas), Austin Registering, but not testifying: On: Baskett, Bruce (ICECI), Austin Graham, Bess A. (ICECI), Austin Gray, Jill M. (ICECI), Austin Marrow, Helen (ICECI), Austin Porter, Alexander W. (ICECI), Austin Written materials submitted: On: Kurth, Cindy (Self), Round Rock Budget/MHMR On: Brinkman, Michelle (TX Council of MHMR), Austin Derr, Patti (The Parent Connection), Conroe Gantt, Melanie (Mental Health Ass in TX), Austin Gilbert, Don (MHMR), Austin Green, Don (MHMR), Austin Hale, Karen (MHMR), Austin Locklin, Charles (TAFTMI), Austin Lovelace, Joe (TAFTMI), Dallas McClure, Spencer (TX Council of MHMR), Austin Morgan, Dorothy (Bd of TX Council MHMR), Austin Neimeyer, Elizabeth (My Community), Houston Piedfort, Pascual (ARC of TX), Austin Shannon, Jacqueline (TEXAMI), Austin Snyder, Ruth (Self), Austin Stumberg, Lindy (Parent Connection), Conroe Tunnell, Stuart (Texas Advocates), Burkburnett Utley, Ann (MHMR), Austin Weldon, Kelly (Texas Advocates), Seguine Registering, but not testifying: On: Byers, Dr. Jerome L. (Self), Dallas Written materials submitted: On: Konigsmark, Anne Lisa (Self), Austin Lambert, Kay (Advocacy, Inc.), Austin Budget/TCADA On: Bleier, Terry Faye (TCADA), Austin Hernandez, Arturo (TCADA), Austin Oberwetter, James C. (TCADA), Austin Speace, Richard (TCADA), Austin Surock, Mark S. (TCADA), Austin Registering, but not testifying: On: Grasty, Dorothy (TCADA), Austin Paterra, Leo J. (TCADA), Austin Budget/TRC On: Arrell, Max (TRC), Austin Davis, John (TRC), Austin Freeman, Marion (Self), Granbury Kane, Jerry (TRC), Corpus Christi McPhail, Jennifer (ADAPT), Austin Shockley, Joe C. (Self), Austin Shockley, Trevor (Self), Austin Sokolow, Judith (Advocacy, Inc.), Austin Swift, Larry D. (Brain Injury Ass of TX), Austin Registering, but not testifying: On: Carlton, Belinda (COTWD), Austin Greebon, Karen (ADAPT), Austin Harrison, Jr., Charles (TRC), Austin Meinrowsky, John (ARCIL, Inc.), Austin Schwartz, Jonas (UCP of Texas), Austin Simmons, Joellen Flores (TRC), Austin Templeton, J.T. (ADAPT), Austin Waldrep, Kent (TRC), Dallas Ward, D.M. (TRC), Austin


The Firebase Database synchronizes data through realtime events. Learn how the value event is used to synchronize objects and primitives.
Learn more in the documentation: https://goo.gl/vuX7zu
Add the Firecasts playlist! https://goo.gl/n2XqG1 Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG
sec


|
CryptoAuthLib
Atmel CryptoAuthentication Library
|
These methods implement each phase of the authentication example. If you would like to follow a particular phase with a code walk through, you can follow each of the phases from client-provision through host-verify-resp. Each phase is initiated with a console command so you can control the progress through the example. More...
Functions | |
| int | client_rebuild_certs (void) |
| This client role method demonstrates how to read cert data stored in the ATECC508A and reconstruct a full X.509 cert in DER format. Because this is an example, it prints the reconstructed cert data to the console in ASCII hex format. More... | |
| int | host_verify_cert_chain (void) |
| This host role method demonstrates how to do a chain verify. In this example, there is a root certificate authority which signed the signer's cert. The signer signed the device cert. The chain verification performs an ECDSA verification of each link in the cert chain. This verifies that the device has been properly signed into the chain starting from the CA root of trust (RoT). More... | |
| int | host_generate_challenge (void) |
| This host role method generates a challenge to be signed by the CryptoAuth device. This challenge is basically a random number that once signed can be verified by the host. More... | |
| int | client_generate_response (void) |
| This client role method signs the challenge and returns the signature to the host. For example purposes, the signature is contained in a global variable g_response. More... | |
| int | host_verify_response (void) |
| This host role method verifies the signature of the challenge. This step insures that the device can prove it owns the private key associated with the public key of its certificate. Since the certificate is verified to be in a root of trust certificate chain, this ECDSA verification step makes sure the public key the device says it has is truly owned by the device and not a forgery. More... | |
| int | client_provision (void) |
| entry point called from the console interaction to provision the client side certs and device key. This is a self-contained example of the provisioning process that would normally happen at the factory using high security modules (HSM) and production facilities. This is functional code which can be used to self-provisioin a device for illustration, example, or demo purposes. More... | |
| ATCA_STATUS | device_init_default (void) |
| This function will write and lock the configuration zone, write and lock the data zone, run GenKey on private keys to prepare a device for a standard configuration. This configuration can be used for self-provisioning or as a generic configuration for illustration, example, or demo purposes. More... | |
| ATCA_STATUS | device_init (const atcacert_def_t *cert_def_signer, const atcacert_def_t *cert_def_device) |
| This function will write and lock the configuration zone, write and lock the data zone, run GenKey on private keys to prepare a device for a standard configuration. This configuration can be used for self-provisioning or as a generic configuration for illustration, example, or demo purposes. More... | |
Variables | |
| uint8_t | g_signer_cert [512] |
| global storage for signer certificate More... | |
| size_t | g_signer_cert_size = sizeof(g_signer_cert) |
| uint8_t | g_device_cert [512] |
| global storage for device certificate More... | |
| size_t | g_device_cert_size = sizeof(g_device_cert) |
| uint8_t | g_challenge [32] |
| global storage for the challenge data to sign by the device More... | |
| uint8_t | g_response [64] |
| uint8_t | g_signer_ca_public_key [64] |
| uint8_t | g_signer_ca_public_key [64] |
| uint8_t | g_signer_ca_public_key [64] |
| const uint8_t | g_signer_0_ca_public_key [] |
| const atcacert_def_t | g_cert_def_0_signer |
| const atcacert_def_t | g_cert_def_0_device |
| const uint8_t | g_cert_template_0_signer [] |
| const uint8_t | g_cert_template_0_device [] |
These methods implement each phase of the authentication example. If you would like to follow a particular phase with a code walk through, you can follow each of the phases from client-provision through host-verify-resp. Each phase is initiated with a console command so you can control the progress through the example.
| int client_generate_response | ( | void | ) |
This client role method signs the challenge and returns the signature to the host. For example purposes, the signature is contained in a global variable g_response.
| int client_provision | ( | void | ) |
entry point called from the console interaction to provision the client side certs and device key. This is a self-contained example of the provisioning process that would normally happen at the factory using high security modules (HSM) and production facilities. This is functional code which can be used to self-provisioin a device for illustration, example, or demo purposes.
| int client_rebuild_certs | ( | void | ) |
This client role method demonstrates how to read cert data stored in the ATECC508A and reconstruct a full X.509 cert in DER format. Because this is an example, it prints the reconstructed cert data to the console in ASCII hex format.
| ATCA_STATUS device_init | ( | const atcacert_def_t * | cert_def_signer, |
| const atcacert_def_t * | cert_def_device | ||
| ) |
This function will write and lock the configuration zone, write and lock the data zone, run GenKey on private keys to prepare a device for a standard configuration. This configuration can be used for self-provisioning or as a generic configuration for illustration, example, or demo purposes.
| ATCA_STATUS device_init_default | ( | void | ) |
This function will write and lock the configuration zone, write and lock the data zone, run GenKey on private keys to prepare a device for a standard configuration. This configuration can be used for self-provisioning or as a generic configuration for illustration, example, or demo purposes.
| int host_generate_challenge | ( | void | ) |
This host role method generates a challenge to be signed by the CryptoAuth device. This challenge is basically a random number that once signed can be verified by the host.
| int host_verify_cert_chain | ( | void | ) |
This host role method demonstrates how to do a chain verify. In this example, there is a root certificate authority which signed the signer's cert. The signer signed the device cert. The chain verification performs an ECDSA verification of each link in the cert chain. This verifies that the device has been properly signed into the chain starting from the CA root of trust (RoT).
| int host_verify_response | ( | void | ) |
This host role method verifies the signature of the challenge. This step insures that the device can prove it owns the private key associated with the public key of its certificate. Since the certificate is verified to be in a root of trust certificate chain, this ECDSA verification step makes sure the public key the device says it has is truly owned by the device and not a forgery.
| const atcacert_def_t g_cert_def_0_device |
| const atcacert_def_t g_cert_def_0_signer |
| const uint8_t g_cert_template_0_device[] |
| const uint8_t g_cert_template_0_signer[] |
| uint8_t g_challenge[32] |
global storage for the challenge data to sign by the device
| uint8_t g_device_cert[512] |
global storage for device certificate
| size_t g_device_cert_size = sizeof(g_device_cert) |
| uint8_t g_response[64] |
| const uint8_t g_signer_0_ca_public_key |
| uint8_t g_signer_ca_public_key[64] |
| uint8_t g_signer_ca_public_key[64] |
| uint8_t g_signer_ca_public_key[64] |
| uint8_t g_signer_cert[512] |
global storage for signer certificate
| size_t g_signer_cert_size = sizeof(g_signer_cert) |


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Erin Millard (@ezzatron)
Software developer
Based in
Brisbane, Australia
Full-time at
Fantasy Factor
Free-time at
Eloquent Software
and
Icecave Studios
<EMAIL_ADDRESS>https://github.com/ezzatron
https://twitter.com/ezzatron


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Kanban board - Gantt Chart integration sample
Uses
Angular extension
for
GanttChartView
component from
DlhSoft Gantt Chart Hyper Library
.
Board


--- layout: default ---
A smart bot with lots of features
This bot was built using Probot, a framework for building GitHub Apps.
Icons made by Freepik from www.flaticon.com is licensed by CC 3.0 BY


Are you listening to your customers?
As they say: You cannot improve what you cannot measure; but the paradox is you cannot measure everything – happiness, hatred, anger… but you can measure customer satisfaction. Yes, you can measure customer satisfaction by analyzing likes and dislikes of your customers. You can gauge popularity of your website or products. You can also:
Opineo allows you to do all of this and much more without signing up for an account or hire an expert. This DIY tool enables you to listen to your customers’ voice easily and instantly.
Before you make any cusomization or even start using Opineo create a 'div' element and assign it some id. Now add references to necessary Javascrip files e.g.
local SB = SPACEBUILD


This is a calculator website
In order to enter to the calculator page you need to solve this random string encryption
Please insert the decryption string in the TEXTAREA and click CHECK
This is a calculator website
In order to enter to the calculator page you need to solve this random string encryption
Please insert the decryption string in the TEXTAREA and click CHECK


This example uses the index that gets created when you follow the live tutorial inside Firesearch.
This is a working demo, make sure you're running the Firestore emulator and Firesearch locally at http://localhost:8888.
http://localhost:8888
Add movie to index
Search


./komodod -ac_name=KMDICE -ac_supply=10500000 -ac_reward=2500000000 -ac_halving=210000 -ac_cc=2 -addressindex=1 -spentindex=1 -addnode=<IP_ADDRESS> &
https://kmdice.pool-mining.xyz/ <- pool
https://pool.forkwitch.com/ <- another pool
https://minerpond.com <- another pool
100 messages
Before 05-Dec-19 12:00 AM
KMD TipBotBOT22-Oct-18 05:33 PM
TipBot
@Flexatron (Flexatron#8809)
You tipped @Llama0.001 KMD
Llama22-Oct-18 05:33 PM
Omg ty
It working now?
jorian22-Oct-18 05:35 PM
diff decreased significantly
Llama22-Oct-18 05:36 PM
There was a mining problem
jorian22-Oct-18 05:36 PM
maybe my spurr of blocks caused a crash in all mining pools
forkwitch22-Oct-18 05:36 PM
@Llama@blubbi updating the pool currently. Gonna notify you when back online pool.
ComputerGenie 👴👴👴22-Oct-18 05:36 PM
I'm thinking that the mempool was cleared, in reality, but not cleared in the "mind of" the daemons
Llama22-Oct-18 05:37 PM
Thx @forkwitch
ComputerGenie 👴👴👴22-Oct-18 05:38 PM
chain is starting to move along now
Decker22-Oct-18 05:39 PM
i'm modified a pool to include in block only 5 txes (4 txes + 1 coinbase) from mempool, to get short hex block to analyse. example of produced block:
https://gist.github.com/DeckerSU/aa80df1719c0eac0ee5d5e9fc256a1ea
pool software counts that daemon accept this block, but in real - this block don't appears on network. when i tried to submit this block manually again - daemon returns "duplicate-invalid" ... that means that block already known to daemon and it's invalid.
seems, if we understand why daemon count this block as invalid - problem will be solved.
block hex. GitHub Gist: instantly share code, notes, and snippets.
ComputerGenie 👴👴👴22-Oct-18 05:41 PM
wherever the 700 txes came from they are not being rebroadcast,
has anyone checked to make sure that all are actually unconfirmed?
siu - Chainmakers22-Oct-18 05:42 PM
@jorian
ComputerGenie 👴👴👴22-Oct-18 05:44 PM
it's possible that 1 or more tx in the "clogged" mempool were already processed and the blocks were invalid because they contained a virtual ds buy trying to add it in a new block
jorian22-Oct-18 05:45 PM
check for each txid in getrawmempool on house node whether it's confirmed already?
ComputerGenie 👴👴👴22-Oct-18 05:47 PM
yeah, because I rebooted my node and no longer have them (since they are not being rebraodcast) a few of the ones I scrolled back and picked at random have been confirmed and some haven't
jorian22-Oct-18 05:47 PM
mmh
and now?
ComputerGenie 👴👴👴22-Oct-18 05:49 PM
and now what?
jorian22-Oct-18 05:49 PM
not sure if resendwallettransactions does the trick
what I still don't get is why daemon rejects, but nothing is printed in debug.log
Decker22-Oct-18 06:04 PM
@jl777c@ComputerGenie 👴👴👴 i think reason is in other ... need little more time to finish my investigation ...
c2k22-Oct-18 06:05 PM
u guys think we should hold off on mining for now?
siu - Chainmakers22-Oct-18 06:08 PM
64 txs in last block
Decker22-Oct-18 06:09 PM
mined by me ...
jl777c22-Oct-18 06:11 PM
you figured out what to filter out?
Decker22-Oct-18 06:13 PM
seems i fugured out why we can't mine a block earlier ... need little bit more time ... and if bug confirmed - i will disclosure it ... i think that is pool software bug, not daemon.
2
jl777c22-Oct-18 06:13 PM
yes, that was my feeling too, just have no idea what it could be
ComputerGenie 👴👴👴22-Oct-18 06:14 PM
that was my initial thought, but if so it has to be deep in old code based on my solo getting same issue
forkwitch22-Oct-18 06:15 PM
If you only leave coinbase transation in it it will mine it. But why not with transaction.
Decker22-Oct-18 06:16 PM
@jl777c seems all nomp based software affected ... if mempool have more than 0x7F (127) txes - every mined block will fail, bcz seems it have incorrect varint encoding. when chain is stalled earlier - we have more than 170 txes in mempool. but to be sure at 100% - i'll need to write some additional code and try to reproduce it.(edited)
jl777c22-Oct-18 06:16 PM
that is horrible bug!
and why 127 and not 255
wrong on many levels
Decker22-Oct-18 06:17 PM
need additional tests ... want to be sure that it's varint encoding related ...
jl777c22-Oct-18 06:17 PM
then a short term fix is to limit to 100 tx per block?
ComputerGenie 👴👴👴22-Oct-18 06:17 PM
@Decker if that's so then it has to be a 127 limit and something else; I used this solo before with over 10k txes in mempool(edited)
Alright22-Oct-18 06:17 PM
I feel like I've stratum mined blocks with >127 txs in them on other chains
Decker22-Oct-18 06:21 PM
Still investigating ... i assume that for 2500 txes in mempool, for example, all will be ok ... because 2500 in hex is a 2 bytes - 0x9C4 (C409) ... but if txes count is in 128..255 - it will fail.
ComputerGenie 👴👴👴22-Oct-18 06:21 PM
hmm
jl777c22-Oct-18 06:21 PM
they used a signed byte instead of unsigned byte, that would do that
Decker22-Oct-18 06:22 PM
if (this.txCount <= 0x7f){
var varInt = new Buffer(txCount, 'hex');
}
else if (this.txCount <= 0x7fff){
var varInt = new Buffer.concat([Buffer('FD', 'hex'), new Buffer(txCount, 'hex')]);
}
This part seems problem.
jl777c22-Oct-18 06:22 PM
it should be <= 0xff
Decker22-Oct-18 06:23 PM
Should be as described.
jl777c22-Oct-18 06:23 PM
oh, thats right <- 0xfd
<=
Decker22-Oct-18 06:24 PM
For 2 bytes txcount - should be FDxxyy ... and in case of 176 txes in mempool we have FDB0 instead of FDB000 ...(edited)
Remember, chain stuck at moment when we have 176 txes in mempool ...
jl777c22-Oct-18 06:25 PM
so all nomps have this bug?
seems amazing they never found it
and PIRATE never had so many tx in a block to hit this
KMD has notary mining
ComputerGenie 👴👴👴22-Oct-18 06:26 PM
So is this related to the return Object.values(result)[2].root; in merkleTree.js @Decker(edited)
Decker22-Oct-18 06:27 PM
jl777c22-Oct-18 06:27 PM
so KMDICE helps the entire nomp ecosystem by exposing a deeply buried bug!
ComputerGenie 👴👴👴22-Oct-18 06:28 PM
seems so (edited)
Flexatron22-Oct-18 06:28 PM
Bullish
Decker22-Oct-18 06:28 PM
Let me drink a cup of tea and finished all checks )
3
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1
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Flexatron22-Oct-18 06:28 PM
forkwitch22-Oct-18 06:28 PM
@ComputerGenie 👴👴👴 It is related to blocktemplate code in nomp.
Decker22-Oct-18 06:28 PM
I won't disclosure this until all checks finished ... but you guys forced me )
forkwitch22-Oct-18 06:28 PM
Good detection Decker!
jl777c22-Oct-18 06:29 PM
well I can confirm 0x7f is the wrong thing to compare to
ComputerGenie 👴👴👴22-Oct-18 06:29 PM
@forkwitch I'm aware, but I was wondering if specifically that because there's been much debate over what that should be
SHossain22-Oct-18 06:29 PM
i was worng to assume things as well
Decker22-Oct-18 06:29 PM
can we place 128..255 txes im mempool during i prepare my tea?(edited)
ComputerGenie 👴👴👴22-Oct-18 06:30 PM
@Decker the only way to get to 128-255 is to block existing


Payton
Code Wrangler
Content Management System
A tool to allow publishing, editing, and organizing content for a Website.
Cloud vs. Installed
Convenience vs. Ownership
Not much.
Both are published content.
“WordPress is web software you can use to create a beautiful website or blog. We like to say that WordPress is both free and priceless at the same time.” -WordPress.org
Open-source; Large community; Powers 20% of the web.
WordPress.com is fully hosted.
Security, backups, plugins, and updates are all provided.
WordPress.org is self hosted.
Custom themes, any plugin, full control.
*Sometimes the Host does the first four steps for you.
Once you visit the site for the first time, WordPress will guide you through its setup.
The back-end for WordPress.
Where you will find everything you need. Let's skim over it.
All the visual setup of your site is in here.
A different interface for visual aspects of your site. A relatively recent addition to WordPress, it gives a live preview of any setting that you change.
You can give others access to your site, and give each user a different role.
Different roles give different privileges on the site.
If you didn't change it already, never leave the admin password as the default.
Comments are enabled for everything by default.
This controls what you see on the "home" page, which is the first page that appears when visiting a site.
Often the difference between a blog and a website comes down to the front page setting. Do you want it to have a list of posts, or a single page?
Posts are ordered content listed in reverse chronological order.
Pages are static and not listed by date.
You get a menu item for free with each new Page, although you can edit them manually too.
Editing Posts and Pages is mostly the same. The editor allows formatting text and media.
You can add images by using the media library.
You can upload images just by dragging and dropping.
Once the image has been uploaded, you can insert it into the post by clicking "Insert Into Post"
Images are inserted wherever your cursor was placed, but you can drag them around to where you want them.
Use the "Preview" button to see what the post will look like as you adjust the placement of images and text.
The editor has two modes. Visual, which is the default...
...and Text, which is raw HTML.
You can also embed lots of media directly into the editor by just pasting the link. (Youtube, Twitter, Hulu, Instagram, Kickstarter, etc.)
You can save a post as a Draft, which will leave the post in the list for later.
Posts can even be scheduled to be published at a specific time in the future.
All posts can be tagged and be added to a category. These are called "Taxonomies" and assist with organizing the content on your site.
Categories are typically for broad types of content. Too many makes using them difficult.
Tags are easier to add as many as you like, and are usually more specific.
If you're familiar with #hashtags, that's pretty much how Tags work.
One advantage of using Categories and Tags is that each one has a special page listing all the associated posts.
Both are ways to add functionality to a WordPress site.
Plugins usually just have one specific function that adds to any site. There can be many active at once.
Themes usually have more to do with appearance but can completely change the way that a site works. Only one may be active at a time.
Can be a simple combination of layout and styles.
Or they can change the entire basis of the site to serve a specific purpose.
e.g.: photo blogging, selling t-shirts, running a newspaper.
Can also be custom-tailored for your specific site, like many professional business themes.
They can be installed from the official plugin site or by uploading them yourself.
Just like Apps, there are a lot of plugins out there that are unnecessary, and occasionally even malicious.
In general, ignore plugins that make unlikely claims or that have no stars or reviews.
Using plugins from the official site is a good idea to protect yourself.
Plugins may add menu items for their specific function. This one adds a menu for various contact forms.
Let's add a contact form to a Page
Let's add a Security plugin. This one is called WordFence and can protect from a lot of different attacks.
Jetpack is a plugin which adds a lot of the WordPress.com special features to an installed site.
Themes can be complex, but a lot is taken care of by using a base theme, like Underscores.
Name your theme, download it, uncompress it, and upload it to your WordPress install
Try to make the theme look a little nicer using just the stylesheet. It's not easy!
Themes are also made up of many PHP files.
PHP allows inserting code directly into HTML, so you can program how a webpage should behave. This is where most of the power of WordPress themes comes from.
Try editing the header.php file to see what you can change.
The Web is more and more using Javascript. WordPress, as it has evolved, has included more and more Javascript.
WordPress's mission is "to democratize publishing", meaning that it gives you the control and power needed to put any sort of content out there on the Web that you can think of, without having to pay someone specific or do things in a specific way.
It can be used to make pretty much anything, so use your imagination!


How to lose code and infuriate people
You've already set everything up, e.g.
$ git config --global user.name "Block Norris"
$ git config --global user.email<EMAIL_ADDRESS>$ _
It's commits all the way down...
And branches...
And a HEAD...
(Which can become detached!)
You can also tag things...
Simple!
Those bugs aren't going to write themselves...
$ git checkout -b pineapple
Switched to a new branch 'pineapple'
$ _
console.log("Unexpected pineapple");
$ git add -A
$ git commit -s -m "Add pineapple"
[pineapple 231525e] Add pineapple
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 pineapple.js
$ _
$ git checkout master
Switched to branch 'master'
$ _
⏲
Time passes...
$ git merge pineapple
Merge made by the 'recursive' strategy.
pineapple.js | 0
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 pineapple.js
$ _
Simple...
...except when there are other people!
🐧 🐧 🐧
Flexible, but this is pretty common...
🚀
Which you fork...
🛰
Which you clone...
💻
Which you swear at!
$ git remote -v
origin git@github.com:jthub/hyperledger-composer.git (fetch)
origin git@github.com:jthub/hyperledger-composer.git (push)
sstone1 git@github.com:sstone1/composer.git (fetch)
sstone1 git@github.com:sstone1/composer.git (push)
upstream git@github.com:hyperledger/composer.git (fetch)
upstream git@github.com:hyperledger/composer.git (push)
$ _
Share all the bugs...
$ git rebase master
First, rewinding head to replay your work on top of it...
Applying: Add pineapple
$ _
No conflicts!
🎉
Except...
It's not the same commit!
🔥
$ git push --force-with-lease origin pineapple
Counting objects: 24, done.
Delta compression using up to 8 threads.
Compressing objects: 100% (15/15), done.
Writing objects: 100% (24/24), 1.91 KiB | 0 bytes/s, done.
Total 24 (delta 2), reused 0 (delta 0)
To https://github.com/jthub/hyperledger-composer
* [new branch] pineapple -> pineapple
$ _
⚠
Don't force push to master!
Get Tower...
Always start in a new branch...
and make sure it's based on the latest upstream master!
$ git status
On branch master
Your branch is up-to-date with 'origin/master'.
nothing to commit, working tree clean
$ git pull upstream master
remote: Enumerating objects: 56, done.
...
32 files changed, 123 insertions(+), 123 deletions(-)
$ git checkout -b pineapple
Switched to a new branch 'pineapple'
$ _
git status is your friend
$ git status
On branch master
Your branch is ahead of 'origin/master' by 2 commits.
(use "git push" to publish your local commits)
nothing to commit, working tree clean
$ _
git log is the enemy of your enemy
$ git log -n 5 --oneline
2f07943c0 (HEAD -> master, upstream/master, add-migration) bump to version 0.20.3 (#4459)
3b9de768d (tag: v0.20.2) Add cloud wallet implementations to docker images (#4456)
860536f52 (origin/master, origin/HEAD) [Master] Add caveats/advisory for scope of generators (Angular/CLI) plus Multi-Org Fabric v1.2.1 update (#4451)
5e2abe271 [Master] Upgrade Shim (#4453)
f635247c3 [master] remove info about community calls (#4450)
$ _
One commit is usually plenty...
and much easier to rebase!
$ git commit --amend
[pineapple 0acce17] Add pineapple
Date: Wed Oct 17 13:52:04 2018 +0100
3 files changed, 0 insertions(+), 0 deletions(-)
create mode 100644 ham.js
create mode 100644 pineapple.js
create mode 100644 pizza.js
$ _
Oh, you already have more than one commit...
$ git log --oneline -n 4
c7005cd (HEAD -> pineapple) Code code code
475b463 Hacking is fun
f97caf5 Hack stuff
e62064c Initial commit
$ git rebase -i HEAD~3
...pick, squash, squash...
 to unstage)
new file: upside-down-cake.js
$ _
If you're worried, create another branch!
$ git checkout -b pineapple-backup
Switched to a new branch 'pineapple-backup'
$ _
Don't panic...
your changes are probably somewhere!
$ git reflog show -n 2
073d926 HEAD@{0}: commit (amend): Add pineapple
733da01 HEAD@{1}: commit: Add pineapple
$ git reset --soft HEAD@{1}
$ _
HEAD~1
HEAD@{1}
64cbfb5
Cherry pick for victory!
$ git cherry-pick 073d926
[pineapple f96a461] Fix stuff
Date: Wed Oct 17 11:47:37 2018 +0100
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 fix.txt
$ _
Did I mention Tower?
Try here...


MY SON THE FANATIC
Reviewed by Harvey Karten, Ph.D. Miramax Director: Udayan Prasad Writer: Hanif Kureishi Cast: Rachel Griffiths, Om Puri, Gopi Desai, Akbar Kurtha, Stellan Skarsgard
Let's take for granted that most liberal, broad-minded moviegoers would like to a society that is not racist, one in which extreme beliefs of any sort are unwelcome and in which people of all races, national origins and religions get along and even fall in love with one another. But how didactic our school lessons on these issues have been! And how intriguing a good, sincere, solid and well-developed film can put dramatic and comic touches alike on these themes. One such adventure in storytelling is Hanif Kureishi's "My Son the Fanatic," with Udayan Prasad at the helm and featuring one of India's most celebrated performers, from whose point of view the tale progresses.
The movie, which takes place in Northern England in an area inhabited largely by immigrants from Pakistan centers on Parvez (Om Puri), a middle-aged man who drives a taxi for a living. A hard-working fellow who puts in twelve hours a day, Parvez tries to make ends meet by hooking up rich clients (including tourists) with prostitutes in the area. He is particularly fond of one streetwalker, the lonely Bettina (Rachel Griffiths), whom he recommends highly to a notably prosperous German businessman, Schitz (Stellan Skarsgard. Though in some circles Parvez could be considered a pimp, one who goes so far as to arrange orgies as well as handle the driving, we in the audience see him as a decent, secular Muslim who has largely assimilated into the British culture.  He cannot count on his home life for relaxation: his wife Minoo (Gopi Desai) is never "tender" with him and though she wears conventional South Asian dress she complains that Parvez never takes her anywhere. What is more disturbing is that his 18-year-old son, Farid (Akbar Kurtha), has just broken off his engagement to the daughter of the chief police inspector and has joined a Muslim fundamentalist group which preaches against "white and Jewish propaganda that there is nothing to our lives but the empty accountancy of things."
Though Parez is indeed pimping to raise extra money to support his family and has taken to more than casual drinking, he gains the sympathy of the audience by his tolerant demeanor, particularly for the empathy he feels toward one hooker. He relies on Bettina to provide him an understanding ear for his distress at his son's new zealotry and, despite their differences in age and religion, Parvez falls in love with the younger woman and starts an affair. Bettina's is attracted to Parvez who, she feels, is the only man who has not exploited her, and when they kiss she declares that he is the first man with whom she has done this in a long time.
Essentially, through its involving story featuring well- developed characters, "My Son the Fanatic" takes a noble stand against extremism of all kinds. The fundamentalist Pakistanis are skewed, particularly for their hypocrisy (the bearded head of the local cult, a man who whines about England's corruption, asks for Parvez's help in arranging for additional immigration), and partly because they take the law into their own hands. In the picture's most dramatic episode given added tension because the tautness is carefully built up, a band of fundemantalists attack a whorehouse, lobbing Molotov cocktails through the windows and punching out the women as they flee into the street. But writer Hanif Kureishi, though his principal character, takes a stand as well against the hedonistic businessman appropriately named Schitz and played villainously by the wonderful Stellan Skarsgard. Schitz uses his hooker as a plaything, giving lavish tips to Parvez for catering to his needs, but cares nothing about anyone but himself. We can see that Farid's rage against the secular, capitalistic state is fired by his experience with people like Schitz and also by the behavior of his dad--who, though a Muslim, is absolutely tolerant of prostitution. Because these motivations are carefully developed, we wonder at the essay of critic Harvey Thompson of the World Socialist Web Site, which finds a weakness in that "the explanation of [fundamentalism's] attraction for large numbers of youth is somehow shallow."
"My Son the Fanatic" is solid fare for the arthouse circuit with particularly fine performances from veteran actor Om Puri ("Gandhi") and Rachel Griffiths ("Hilary and Jackie"). It should serve to make thoughtful people even more maddened by the so-called high government officials who pronounced a death sentence on Salman Rushdie.
Rated R. Running Time: 86 minutes. (C) 1999 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


|
break
|
break ends execution of the current for, while, do/while or switch structure
|
|
continue
|
continue is used within looping structures to skip the rest of the current loop iteration and continue execution at the beginning of the next iteration
|
|
do..while
|
do/while loops are very similar to while loops, except the truth expression is checked at the end of each iteration instead of in the beginning
|
|
else
|
Often you'd want to execute a statement if a certain condition is met, and a different statement if the condition is not met
|
|
for
|
for loops are the most complex loops
|
|
if
|
The if construct is one of the most important features of Concept
|
|
import
|
Imports the specified static library into the current project
|
|
include
|
Includes the specified source into the current one
|
|
return
|
Ends execution of the current function, and returns its argument as the value of the function call
|
|
switch..case..default
|
The switch statement is similar to a series of if statements on the same expression
|
|
while
|
while loops are the simplest type of loop
|


STEMentor’s mission is to provide access to the world of STEM (Science, Technology, Engineering, and Mathematics) careers to ALL students through curated educational and informative content and real-time connections to STEM professionals.
STEMentor seeks to be the preeminent source of career information and advice for students ages 14 to adult interested in STEM careers. Our curated web content and links provide a one-stop location to find the best, most current information and guidance available on the web concerning STEM careers. Our mentor database provides access to STEM professionals recruited from top technology companies and universities, and who are willingly volunteering to offer their advice and counsel to students seeking to better understand STEM career opportunities.
We would welcome your support and we are eager to answer your questions about STEMentor. Please contact us at mhibbspe@gmail.com.


Structs
The following structs are available globally.
-
Undocumented
See more
-
Undocumented
See more
-
An error from the API.
See moreDeclaration
Swift
public struct EsaError: Error, AutoEquatable, AutoHashable
-
Undocumented
See more
-
Undocumented
See more
-
A shorthand for body parameters in POST API.
See moreDeclaration
Swift
public struct PostParameters


Release the snakes. Release a CPU-only snake. Release a WASD snake. Release an IJKL snake. Release an arrow-key snake. Release a number-pad snake. Release 5 snakes. Stop this nonsense! Hard mode Easy mode Normal mode Chase the mouse!


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Once you receive your password reset code in your email, enter it here, along with your avatar name, and your new password to complete the password reset.


Lycentia revolutionizes the way software companies issue, monitor, track, and handle licensing for
their software applications.
The platform offloads all the license handling implications done by the software
vendors to smart autonomous agents running on the blockchain with no downtime,
fraud, or any interference.
Built on Ethereum blockchain technology
Decentralized nature enables availability 24/7 with no downtime
Provides vendors with full control and monitoring over their licences
Easy setup and integration with out-of-the-box plugins!


This guide will provide the necessary information on how to create a new website using Cardinal.Js framework.
Start by cloning the cardinal-app-template repository.
Next, go ahead and remove the existing .git
folder from your web-site
folder, initialize a new empty git repo and commit the existing content to your own repository.
You've just developed your new website's basic structure, using the Cardinal.Js framework.
base
tag in your cardinal index.html file.
historyType="query"
in your app-config.json
file.
To update the instance of Cardinal.Js framework used, you must be within the web-site root folder and to execute the command:
To add a Hello-world page to the website follow the steps:
web-site/pages
folder
hello-world.html
Open hello-world.html
file and add the following code as content:
app-config.json
file in order to add a new entry for the hello-world page into the menu configuration.