Fundamental library of the Clojure language

Facilities for async programming and communication.

go blocks are dispatched over an internal thread pool, which
defaults to 8 threads. The size of this pool can be modified using
the Java system property `clojure.core.async.pool-size`.

core.async HIGHLY EXPERIMENTAL feature exploration

Caveats:

1. Everything defined in this namespace is experimental, and subject
to change or deletion without warning.

2. Many features provided by this namespace are highly coupled to
implementation details of core.async. Potential features which
operate at higher levels of abstraction are suitable for inclusion
in the examples.

3. Features provided by this namespace MAY be promoted to
clojure.core.async at a later point in time, but there is no
guarantee any of them will.

A caching library for Clojure.

core.memoize is a memoization library offering functionality above Clojure's core `memoize`
function in the following ways:

**Pluggable memoization**

core.memoize allows for different back-end cache implmentations to be used as appropriate without
changing the memoization modus operandi.

**Manipulable memoization**

Because core.memoize allows you to access a function's memoization store, you do interesting things like
clear it, modify it, and save it for later.

A library for reduction and parallel folding. Alpha and subject
to change.  Note that fold and its derivatives require Java 7+ or
Java 6 + jsr166y.jar for fork/join support. See Clojure's pom.xml for the
dependency info.

Socket server support

Non-core data functions.

A priority map is very similar to a sorted map, but whereas a sorted map produces a
sequence of the entries sorted by key, a priority map produces the entries sorted by value.
In addition to supporting all the functions a sorted map supports, a priority map
can also be thought of as a queue of [item priority] pairs.  To support usage as
a versatile priority queue, priority maps also support conj/peek/pop operations.

The standard way to construct a priority map is with priority-map:
user=> (def p (priority-map :a 2 :b 1 :c 3 :d 5 :e 4 :f 3))
#'user/p
user=> p
{:b 1, :a 2, :c 3, :f 3, :e 4, :d 5}

So :b has priority 1, :a has priority 2, and so on.
Notice how the priority map prints in an order sorted by its priorities (i.e., the map's values)

We can use assoc to assign a priority to a new item:
user=> (assoc p :g 1)
{:b 1, :g 1, :a 2, :c 3, :f 3, :e 4, :d 5}

or to assign a new priority to an extant item:
user=> (assoc p :c 4)
{:b 1, :a 2, :f 3, :c 4, :e 4, :d 5}

We can remove an item from the priority map:
user=> (dissoc p :e)
{:b 1, :a 2, :c 3, :f 3, :d 5}

An alternative way to add to the priority map is to conj a [item priority] pair:
user=> (conj p [:g 0])
{:g 0, :b 1, :a 2, :c 3, :f 3, :e 4, :d 5}

or use into:
user=> (into p [[:g 0] [:h 1] [:i 2]])
{:g 0, :b 1, :h 1, :a 2, :i 2, :c 3, :f 3, :e 4, :d 5}

Priority maps are countable:
user=> (count p)
6

Like other maps, equivalence is based not on type, but on contents.
In other words, just as a sorted-map can be equal to a hash-map,
so can a priority-map.
user=> (= p {:b 1, :a 2, :c 3, :f 3, :e 4, :d 5})
true

You can test them for emptiness:
user=> (empty? (priority-map))
true
user=> (empty? p)
false

You can test whether an item is in the priority map:
user=> (contains? p :a)
true
user=> (contains? p :g)
false

It is easy to look up the priority of a given item, using any of the standard map mechanisms:
user=> (get p :a)
2
user=> (get p :g 10)
10
user=> (p :a)
2
user=> (:a p)
2

Priority maps derive much of their utility by providing priority-based seq.
Note that no guarantees are made about the order in which items of the same priority appear.
user=> (seq p)
([:b 1] [:a 2] [:c 3] [:f 3] [:e 4] [:d 5])
Because no guarantees are made about the order of same-priority items, note that
rseq might not be an exact reverse of the seq.  It is only guaranteed to be in
descending order.
user=> (rseq p)
([:d 5] [:e 4] [:c 3] [:f 3] [:a 2] [:b 1])

This means first/rest/next/for/map/etc. all operate in priority order.
user=> (first p)
[:b 1]
user=> (rest p)
([:a 2] [:c 3] [:f 3] [:e 4] [:d 5])

Priority maps support metadata:
user=> (meta (with-meta p {:extra :info}))
{:extra :info}

But perhaps most importantly, priority maps can also function as priority queues.
peek, like first, gives you the first [item priority] pair in the collection.
pop removes the first [item priority] from the collection.
(Note that unlike rest, which returns a seq, pop returns a priority map).

user=> (peek p)
[:b 1]
user=> (pop p)
{:a 2, :c 3, :f 3, :e 4, :d 5}

It is also possible to use a custom comparator:
user=> (priority-map-by > :a 1 :b 2 :c 3)
{:c 3, :b 2, :a 1}

Sometimes, it is desirable to have a map where the values contain more information
than just the priority.  For example, let's say you want a map like:
{:a [2 :apple], :b [1 :banana], :c [3 :carrot]}
and you want to sort the map by the numeric priority found in the pair.

A common mistake is to try to solve this with a custom comparator:
(priority-map 
  (fn [[priority1 _] [priority2 _]] (< priority1 priority2))
  :a [2 :apple], :b [1 :banana], :c [3 :carrot])

This will not work!  In Clojure, like Java, all comparators must be total orders,
meaning that you can't have a tie unless the objects you are comparing are
in fact equal.  The above comparator breaks that rule because
[2 :apple] and [2 :apricot] tie, but are not equal.

The correct way to construct such a priority map is by specifying a keyfn, which is used
to extract the true priority from the priority map's vals.  (Note: It might seem a little odd
that the priority-extraction function is called a *key*fn, even though it is applied to the
map's values.  This terminology is based on the docstring of clojure.core/sort-by, which
uses `keyfn` for the function which extracts the sort order.) 

In the above example,

user=> (priority-map-keyfn first :a [2 :apple], :b [1 :banana], :c [3 :carrot])
{:b [1 :banana], :a [2 :apple], :c [3 :carrot]}

You can also combine a keyfn with a comparator that operates on the extracted priorities:

user=> (priority-map-keyfn-by 
          first >
          :a [2 :apple], :b [1 :banana], :c [3 :carrot])
{:c [3 :carrot], :a [2 :apple], :b [1 :banana]}

 

All of these operations are efficient.  Generally speaking, most operations
are O(log n) where n is the number of distinct priorities.  Some operations
(for example, straightforward lookup of an item's priority, or testing
whether a given item is in the priority map) are as efficient
as Clojure's built-in map.

The key to this efficiency is that internally, not only does the priority map store
an ordinary hash map of items to priority, but it also stores a sorted map that
maps priorities to sets of items with that priority.

A typical textbook priority queue data structure supports at the ability to add
a [item priority] pair to the queue, and to pop/peek the next [item priority] pair.
But many real-world applications of priority queues require more features, such
as the ability to test whether something is already in the queue, or to reassign
a priority.  For example, a standard formulation of Dijkstra's algorithm requires the
ability to reduce the priority number associated with a given item.  Once you
throw persistence into the mix with the desire to adjust priorities, the traditional
structures just don't work that well.

This particular blend of Clojure's built-in hash sets, hash maps, and sorted maps
proved to be a great way to implement an especially flexible persistent priority queue.

Connoisseurs of algorithms will note that this structure's peek operation is not O(1) as
it would be if based upon a heap data structure, but I feel this is a small concession for
the blend of persistence, priority reassignment, and priority-sorted seq, which can be
quite expensive to achieve with a heap (I did actually try this for comparison).  Furthermore,
this peek's logarithmic behavior is quite good (on my computer I can do a million
peeks at a priority map with a million items in 750ms).  Also, consider that peek and pop
usually follow one another, and even with a heap, pop is logarithmic.  So the net combination
of peek and pop is not much different between this versatile formulation of a priority map and
a more limited heap-based one.  In a nutshell, peek, although not O(1), is unlikely to be the
bottleneck in your program.

All in all, I hope you will find priority maps to be an easy-to-use and useful addition
to Clojure's assortment of built-in maps (hash-map and sorted-map).

edn reading.

Graphical object inspector for Clojure data structures.

Start a web browser from Clojure

Helper namespace for clojure.java.browse.
Prevents console apps from becoming GUI unnecessarily.

This file defines polymorphic I/O utility functions for Clojure.

A repl helper to quickly open javadocs.

Conveniently launch a sub-process providing its stdin and
collecting its stdout

Top-level main function for Clojure REPL and scripts.

A Pretty Printer for Clojure

clojure.pprint implements a flexible system for printing structured data
in a pleasing, easy-to-understand format. Basic use of the pretty printer is 
simple, just call pprint instead of println. More advanced users can use 
the building blocks provided to create custom output formats. 

Out of the box, pprint supports a simple structured format for basic data 
and a specialized format for Clojure source code. More advanced formats, 
including formats that don't look like Clojure data at all like XML and 
JSON, can be rendered by creating custom dispatch functions. 

In addition to the pprint function, this module contains cl-format, a text 
formatting function which is fully compatible with the format function in 
Common Lisp. Because pretty printing directives are directly integrated with
cl-format, it supports very concise custom dispatch. It also provides
a more powerful alternative to Clojure's standard format function.

See documentation for pprint and cl-format for more information or 
complete documentation on the Clojure web site on GitHub.

Reflection on Host Types
Alpha - subject to change.

Two main entry points: 

* type-reflect reflects on something that implements TypeReference.
* reflect (for REPL use) reflects on the class of an instance, or
  on a class if passed a class

Key features:

* Exposes the read side of reflection as pure data. Reflecting
  on a type returns a map with keys :bases, :flags, and :members.

* Canonicalizes class names as Clojure symbols. Types can extend
  to the TypeReference protocol to indicate that they can be
  unambiguously resolved as a type name. The canonical format
  requires one non-Java-ish convention: array brackets are <>
  instead of [] so they can be part of a Clojure symbol.

* Pluggable Reflectors for different implementations. The default
  JavaReflector is good when you have a class in hand, or use
  the AsmReflector for "hands off" reflection without forcing
  classes to load.

Platform implementers must:

* Create an implementation of Reflector.
* Create one or more implementations of TypeReference.
* def default-reflector to be an instance that satisfies Reflector.

Utilities meant to be used interactively at the REPL

Set operations such as union/intersection.

Print stack traces oriented towards Clojure, not Java.

Clojure String utilities

It is poor form to (:use clojure.string). Instead, use require
with :as to specify a prefix, e.g.

(ns your.namespace.here
  (:require [clojure.string :as str]))

Design notes for clojure.string:

1. Strings are objects (as opposed to sequences). As such, the
   string being manipulated is the first argument to a function;
   passing nil will result in a NullPointerException unless
   documented otherwise. If you want sequence-y behavior instead,
   use a sequence.

2. Functions are generally not lazy, and call straight to host
   methods where those are available and efficient.

3. Functions take advantage of String implementation details to
   write high-performing loop/recurs instead of using higher-order
   functions. (This is not idiomatic in general-purpose application
   code.)

4. When a function is documented to accept a string argument, it
   will take any implementation of the correct *interface* on the
   host platform. In Java, this is CharSequence, which is more
   general than String. In ordinary usage you will almost always
   pass concrete strings. If you are doing something unusual,
   e.g. passing a mutable implementation of CharSequence, then
   thread-safety is your responsibility.

Macros that expand to repeated copies of a template expression.

user> (is (= 5 (+ 2 2)))

FAIL in  (:1)
expected: (= 5 (+ 2 2))
  actual: (not (= 5 4))
false

A unit testing framework.

ASSERTIONS

The core of the library is the "is" macro, which lets you make
assertions of any arbitrary expression:

(is (= 4 (+ 2 2)))
(is (instance? Integer 256))
(is (.startsWith "abcde" "ab"))

You can type an "is" expression directly at the REPL, which will
print a message if it fails.

    user> (is (= 5 (+ 2 2)))

    FAIL in  (:1)
    expected: (= 5 (+ 2 2))
      actual: (not (= 5 4))
    false

The "expected:" line shows you the original expression, and the
"actual:" shows you what actually happened.  In this case, it
shows that (+ 2 2) returned 4, which is not = to 5.  Finally, the
"false" on the last line is the value returned from the
expression.  The "is" macro always returns the result of the
inner expression.

There are two special assertions for testing exceptions.  The
"(is (thrown? c ...))" form tests if an exception of class c is
thrown:

(is (thrown? ArithmeticException (/ 1 0))) 

"(is (thrown-with-msg? c re ...))" does the same thing and also
tests that the message on the exception matches the regular
expression re:

(is (thrown-with-msg? ArithmeticException #"Divide by zero"
                      (/ 1 0)))

DOCUMENTING TESTS

"is" takes an optional second argument, a string describing the
assertion.  This message will be included in the error report.

(is (= 5 (+ 2 2)) "Crazy arithmetic")

In addition, you can document groups of assertions with the
"testing" macro, which takes a string followed by any number of
assertions.  The string will be included in failure reports.
Calls to "testing" may be nested, and all of the strings will be
joined together with spaces in the final report, in a style
similar to RSpec <http://rspec.info/>

(testing "Arithmetic"
  (testing "with positive integers"
    (is (= 4 (+ 2 2)))
    (is (= 7 (+ 3 4))))
  (testing "with negative integers"
    (is (= -4 (+ -2 -2)))
    (is (= -1 (+ 3 -4)))))

Note that, unlike RSpec, the "testing" macro may only be used
INSIDE a "deftest" or "with-test" form (see below).


DEFINING TESTS

There are two ways to define tests.  The "with-test" macro takes
a defn or def form as its first argument, followed by any number
of assertions.  The tests will be stored as metadata on the
definition.

(with-test
    (defn my-function [x y]
      (+ x y))
  (is (= 4 (my-function 2 2)))
  (is (= 7 (my-function 3 4))))

As of Clojure SVN rev. 1221, this does not work with defmacro.
See http://code.google.com/p/clojure/issues/detail?id=51

The other way lets you define tests separately from the rest of
your code, even in a different namespace:

(deftest addition
  (is (= 4 (+ 2 2)))
  (is (= 7 (+ 3 4))))

(deftest subtraction
  (is (= 1 (- 4 3)))
  (is (= 3 (- 7 4))))

This creates functions named "addition" and "subtraction", which
can be called like any other function.  Therefore, tests can be
grouped and composed, in a style similar to the test framework in
Peter Seibel's "Practical Common Lisp"
<http://www.gigamonkeys.com/book/practical-building-a-unit-test-framework.html>

(deftest arithmetic
  (addition)
  (subtraction))

The names of the nested tests will be joined in a list, like
"(arithmetic addition)", in failure reports.  You can use nested
tests to set up a context shared by several tests.


RUNNING TESTS

Run tests with the function "(run-tests namespaces...)":

(run-tests 'your.namespace 'some.other.namespace)

If you don't specify any namespaces, the current namespace is
used.  To run all tests in all namespaces, use "(run-all-tests)".

By default, these functions will search for all tests defined in
a namespace and run them in an undefined order.  However, if you
are composing tests, as in the "arithmetic" example above, you
probably do not want the "addition" and "subtraction" tests run
separately.  In that case, you must define a special function
named "test-ns-hook" that runs your tests in the correct order:

(defn test-ns-hook []
  (arithmetic))

Note: test-ns-hook prevents execution of fixtures (see below).


OMITTING TESTS FROM PRODUCTION CODE

You can bind the variable "*load-tests*" to false when loading or
compiling code in production.  This will prevent any tests from
being created by "with-test" or "deftest".


FIXTURES

Fixtures allow you to run code before and after tests, to set up
the context in which tests should be run.

A fixture is just a function that calls another function passed as
an argument.  It looks like this:

(defn my-fixture [f]
   Perform setup, establish bindings, whatever.
  (f)  Then call the function we were passed.
   Tear-down / clean-up code here.
 )

Fixtures are attached to namespaces in one of two ways.  "each"
fixtures are run repeatedly, once for each test function created
with "deftest" or "with-test".  "each" fixtures are useful for
establishing a consistent before/after state for each test, like
clearing out database tables.

"each" fixtures can be attached to the current namespace like this:
(use-fixtures :each fixture1 fixture2 ...)
The fixture1, fixture2 are just functions like the example above.
They can also be anonymous functions, like this:
(use-fixtures :each (fn [f] setup... (f) cleanup...))

The other kind of fixture, a "once" fixture, is only run once,
around ALL the tests in the namespace.  "once" fixtures are useful
for tasks that only need to be performed once, like establishing
database connections, or for time-consuming tasks.

Attach "once" fixtures to the current namespace like this:
(use-fixtures :once fixture1 fixture2 ...)

Note: Fixtures and test-ns-hook are mutually incompatible.  If you
are using test-ns-hook, fixture functions will *never* be run.


SAVING TEST OUTPUT TO A FILE

All the test reporting functions write to the var *test-out*.  By
default, this is the same as *out*, but you can rebind it to any
PrintWriter.  For example, it could be a file opened with
clojure.java.io/writer.


EXTENDING TEST-IS (ADVANCED)

You can extend the behavior of the "is" macro by defining new
methods for the "assert-expr" multimethod.  These methods are
called during expansion of the "is" macro, so they should return
quoted forms to be evaluated.

You can plug in your own test-reporting framework by rebinding
the "report" function: (report event)

The 'event' argument is a map.  It will always have a :type key,
whose value will be a keyword signaling the type of event being
reported.  Standard events with :type value of :pass, :fail, and
:error are called when an assertion passes, fails, and throws an
exception, respectively.  In that case, the event will also have
the following keys:

  :expected   The form that was expected to be true
  :actual     A form representing what actually occurred
  :message    The string message given as an argument to 'is'

The "testing" strings will be a list in "*testing-contexts*", and
the vars being tested will be a list in "*testing-vars*".

Your "report" function should wrap any printing calls in the
"with-test-out" macro, which rebinds *out* to the current value
of *test-out*.

For additional event types, see the examples in the code.

clojure.test extension for JUnit-compatible XML output.

JUnit (http://junit.org/) is the most popular unit-testing library
for Java.  As such, tool support for JUnit output formats is
common.  By producing compatible output from tests, this tool
support can be exploited.

To use, wrap any calls to clojure.test/run-tests in the
with-junit-output macro, like this:

  (use 'clojure.test)
  (use 'clojure.test.junit)

  (with-junit-output
    (run-tests 'my.cool.library))

To write the output to a file, rebind clojure.test/*test-out* to
your own PrintWriter (perhaps opened using
clojure.java.io/writer).

clojure.test extensions for the Test Anything Protocol (TAP)

TAP is a simple text-based syntax for reporting test results.  TAP
was originally developed for Perl, and now has implementations in
several languages.  For more information on TAP, see
http://testanything.org/ and
http://search.cpan.org/~petdance/TAP-1.0.0/TAP.pm

To use this library, wrap any calls to
clojure.test/run-tests in the with-tap-output macro,
like this:

  (use 'clojure.test)
  (use 'clojure.test.tap)

  (with-tap-output
   (run-tests 'my.cool.library))

Analyzer for clojure code, host agnostic.

Entry point:
* analyze

Platform implementers must provide dynamic bindings for:
* macroexpand-1
* parse
* create-var
* var?

Setting up the global env is also required, see clojure.tools.analyzer.env

See clojure.tools.analyzer.core-test for an example on how to setup the analyzer.

Utilities for AST walking/updating

Utilities for querying tools.analyzer ASTs with Datomic

Analyzer for clojure code, extends tools.analyzer with JVM specific passes/forms

Utilities for pass scheduling

A clojure reader in clojure

An EDN reader in clojure

Protocols and default Reader types implementation

This file defines a generic tree walker for Clojure data
structures.  It takes any data structure (list, vector, map, set,
seq), calls a function on every element, and uses the return value
of the function in place of the original.  This makes it fairly
easy to write recursive search-and-replace functions, as shown in
the examples.

Note: "walk" supports all Clojure data structures EXCEPT maps
created with sorted-map-by.  There is no (obvious) way to retrieve
the sorting function.

XML reading/writing.

Functional hierarchical zipper, with navigation, editing,
and enumeration.  See Huet