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/*
* yeti.i -
*
* Main startup file for Yeti (an extension of Yorick).
*
*-----------------------------------------------------------------------------
*
* Copyright (C) 1996-2010 Eric Thiébaut <thiebaut@obs.univ-lyon1.fr>
*
* This software is governed by the CeCILL-C license under French law and
* abiding by the rules of distribution of free software. You can use, modify
* and/or redistribute the software under the terms of the CeCILL-C license as
* circulated by CEA, CNRS and INRIA at the following URL
* "http://www.cecill.info".
*
* As a counterpart to the access to the source code and rights to copy,
* modify and redistribute granted by the license, users are provided only
* with a limited warranty and the software's author, the holder of the
* economic rights, and the successive licensors have only limited liability.
*
* In this respect, the user's attention is drawn to the risks associated with
* loading, using, modifying and/or developing or reproducing the software by
* the user in light of its specific status of free software, that may mean
* that it is complicated to manipulate, and that also therefore means that it
* is reserved for developers and experienced professionals having in-depth
* computer knowledge. Users are therefore encouraged to load and test the
* software's suitability as regards their requirements in conditions enabling
* the security of their systems and/or data to be ensured and, more
* generally, to use and operate it in the same conditions as regards
* security.
*
* The fact that you are presently reading this means that you have had
* knowledge of the CeCILL-C license and that you accept its terms.
*
*-----------------------------------------------------------------------------
*
* $Id: yeti.i,v 1.16 2010/04/13 14:41:39 eric Exp $
* $Log: yeti.i,v $
* Revision 1.16 2010/04/13 14:41:39 eric
* - Changed license.
* - Functions that are now in Yorick have been removed.
*
* Revision 1.15 2009/12/16 09:50:29 eric
* - Change license to CeCILL-C.
* - New built-in functions: parse_range() and make_range().
*
* Revision 1.14 2009/12/09 09:44:01 eric
* - New function morph_enhance to perform non-linear noise
* reduction on a 2D/3D array.
*
* Revision 1.13 2009/09/18 19:59:55 eric
* - Some hash table functions have been fixed.
* - Many small corrections in the documentation.
* - New functions sparse_save and sparse_restore to save/restore sparse
* matrices in/from a file.
*
* Revision 1.12 2008/04/02 14:47:08 eric
* - New functions: h_grow, mem_clear, fullsizeof, make_hermitian.
* - Some documentation fixed.
* - Function mem_info correctly accounts for hash-tables, pointers
* and lists.
*
* Revision 1.11 2007/08/22 13:37:30 eric
* - Fixed documentation of h_new (thanks to Jean Pichon).
*
* Revision 1.10 2007/07/27 07:48:04 eric
* - Changes to globalize version number in the form:
* MAJOR.MINOR.MICRO[SUFFIX] -- i.e. SUFFIX is optional.
*
* Revision 1.9 2007/07/27 06:58:51 eric
* - New global variables: YETI_VERSION_{MAJOR,MINOR,MICRO}.
* - New regularization functions: rgl_roughness_* with various
* cost functions and boundary conditions.
*
* Revision 1.8 2007/05/11 10:29:46 eric
* - Implementation of symbolic links.
*
* Revision 1.7 2007/04/24 07:21:58 eric
* - New setup_package function.
* - Documentation of make_dimlist updated.
*
* Revision 1.6 2007/04/19 17:08:19 eric
* - The `symbol_names` function can now specifically select
* lists, hash tables and/or auto-loaded functions.
* - The `about` routine now account for auto-loaded functions.
*
* Revision 1.5 2007/03/23 16:31:59 eric
* - Updated documentation for mvmult, since possible values
* for JOB/FLAGS in evaluation of matrix products are now
* restricted: must be 0 (default) for direct product and 1
* for transpose product.
*
* Revision 1.4 2007/03/23 11:58:09 eric
* - Hash table objects can now have their own evaluator, which can be
* queried/set by the `h_evaluator` function.
* - New function `h_number` to query number of entries in a hash table.
* - Function `is_hash` returns 2 for a hash table object implementing
* its own evaluator.
* - `h_clone`, `h_copy`, `h_info` and `h_show` fixed to account for the
* evaluator of an hash table object.
*
* Revision 1.3 2006/12/05 07:18:49 eric
* - Renamed `typeIDof` as `identof` and added defintions of
* constants `T_CHAR`, `T_SHORT`, ...
*
* Revision 1.2 2006/07/19 17:32:34 eric
* - New built-in function insure_temporary.
*
* Revision 1.1 2005/05/24 13:06:25 eric
* Initial revision
*/
if (is_func(plug_in) && is_func(yeti_init) != 2) plug_in, "yeti";
local YETI_HOME, YETI_VERSION, YETI_VERSION_MAJOR, YETI_VERSION_MINOR;
local YETI_VERSION_MICRO, YETI_VERSION_SUFFIX;
extern yeti_init;
/* DOCUMENT YETI_HOME the directory where Yeti is installed
* or YETI_VERSION version of current Yeti interperter (string)
* or YETI_VERSION_MAJOR major Yeti version number (integer)
* or YETI_VERSION_MINOR minor Yeti version number (integer)
* or YETI_VERSION_MICRO micro Yeti version number (integer)
* or YETI_VERSION_SUFFIX suffix Yeti version number (string, e.g. "pre1")
* or yeti_init;
* or yeti_init();
*
* YETI_VERSION and YETI_HOME are global variables predefined by Yeti to
* store its version number (as "MAJOR.MINOR.MICROSUFFIX") and
* installation directory (e.g. "/usr/local/lib/yeti-VERSION"). In
* YETI_VERSION, a non-empty suffix like "x" or "pre1" indicates a
* development version.
*
* The function yeti_init can be used to restore the values of
* YETI_VERSION and YETI_HOME. When called as a function, yeti_init()
* returns Yeti version as a string.
*
* If Yeti is loaded as a plugin, YETI_HOME is left undefined and no path
* initialization is performed. Otherwise, the first time yeti_init is
* called (this is automatically done at Yeti startup), it set the
* default path list for Yeti applications.
*
* A convenient way to check if your script is parsed by Yeti is to do:
*
* if (is_func(yeti_init) == 2) {
* // we are in Yeti
* ...
* } else {
* // not in Yeti
* ...
* }
*
* SEE ALSO: Y_LAUNCH, Y_HOME, Y_SITE, Y_VERSION,
* get_path, set_path.
*/
if (batch()) {
yeti_init;
} else {
write, format=" Yeti %s ready. Copyright (c) 1996-2009, Eric THIEBAUT.\n",
yeti_init();
}
func setup_package(plugname)
/* DOCUMENT PACKAGE_HOME = setup_package();
* or PACKAGE_HOME = setup_package(plugname);
*
* The setup_package function must be directly called in a Yorick source
* file, the so-called Yorick package source file. This function
* determines the package directory which is the absolute directory name
* of the package source file and setup Yorick search paths to include
* this directory. The returned value is the package directory
* (guaranteed to be terminated by a slash "/").
*
* If PLUGNAME is specified, the corresponding plugin is loaded
* (preferentially from the package directory).
*
*
* SEE ALSO: plug_in, plug_dir, current_include, get_path, set_path.
*/
{
/* Quick check. */
path = current_include();
if (is_void(path)) {
error, "setup_package must be called from a Yorick source file";
}
/* Figure out the absolute directory from where we are called. */
cwd = cd(".");
j = where(strchar(path) == '/');
if (is_array(j)) {
pkgdir = cd(strpart(path, 1:j(0)));
cd, cwd;
} else {
pkgdir = cwd;
}
if (is_void(pkgdir)) {
error, "bad path for include file: \"" + path + "\"";
}
if (strpart(pkgdir, 0:0) != "/") {
pkgdir += "/";
}
/* Setup Yorick search path. */
list = get_path();
if (! strlen(list)) {
list = [];
flag = 1n;
} else {
c = strchar(list);
j = where(c == ':');
if (is_array(j)) {
c(j) = 0;
list = strchar(c);
}
found = (list == pkgdir);
if (noneof(found)) {
flag = 1n;
} else if (! found(1) || sum(found) > 1) {
flag = 1n;
list = list(where(! found));
} else {
flag = 0n; /* no need to add PKGDIR */
}
}
if (flag) {
set_path, (numberof(list) ? pkgdir + sum(":" + list) : pkgdir);
}
/* Setup list of directories for plugins so that the package directory is
searched first and load package plugin. */
if (! is_void(plugname) && is_func(plug_in)) {
list = plug_dir();
if (is_void(list)) {
plug_dir, pkgdir;
} else {
/* move directory in first position */
plug_dir, grow(pkgdir, list(where(list != pkgdir)));
}
plug_in, plugname;
}
return pkgdir;
}
/*---------------------------------------------------------------------------*/
/* SORTING */
extern heapsort;
/* DOCUMENT heapsort(a)
or heapsort, a;
When called as a function, returns a vector of numberof(A) longs
containing index values such that A(heapsort(A)) is a monotonically
increasing vector. When called as a subroutine, performs in-place
sorting of elements of array A. This function uses the heap-sort
algorithm which may be superior to the quicksort algorithm (for
instance for integer valued arrays). Beware that headpsort(A) and
sort(A) differ for multidimensional arrays.
SEE ALSO: quick_select, sort. */
extern quick_select;
/* DOCUMENT quick_select(a, k [, first, last])
* or quick_select, a, k [, first, last];
*
* Find the K-th smallest element in array A. When called as a function,
* the value of the K-th smallest element in array A is returned. When
* called as a subroutine, the elements of A are re-ordered (in-place
* operation) so that A(K) is the K-th smallest element in array A and
* A(J) <= A(K) for J <= K and A(J) >= A(K) for J >= K.
*
* Optional arguments FIRST and LAST can be used to specify the indices
* of the first and/or last element of A to consider: elements before
* FIRST and after LAST are ignored and left unchanged when called as a
* subroutine; index K however always refers to the full range of A. By
* default, FIRST=1 and LAST=numberof(A).
*
* Yorick indexing rules are supported for arguments K, FIRST and LAST
* (i.e. 0 means last element, etc).
*
*
* EXAMPLES
*
* The index K which splits a sample of N=numberof(A) elements into
* fractions ALPHA (before K, that is K - 1 elements) and 1 - ALPHA
* (after K, that is N - K elements) is such that:
*
* (1 - ALPHA)*(K - 1) = ALPHA*(N - K)
*
* hence:
*
* K = 1 + ALPHA*(N - 1)
*
* Accounting for rounding to nearest integer, this leads to:
*
* quick_select(A, long(1.5 + ALPHA*(numberof(A) - 1)))
*
* Therefore the first inter-quartile split is at (1-based and rounded to
* nearest integer) index:
*
* K1 = (N + 5)/4 (with integer division)
*
* the second inter-quartile (median) is at:
*
* K2 = N/2 + 1 (with integer division)
*
* the third inter-quartile is at:
*
* K3 = (3*N + 3)/4 (with integer division)
*
*
* SEE ALSO: quick_median, quick_interquartile_range, sort, heapsort.
*/
func quick_interquartile_range(a)
/* DOCUMENT quick_interquartile_range(a)
* Returns the interquartile range of values in array A.
*
* SEE ALSO
* quick_median, quick_select, insure_temporary.
*/
{
n = numberof(a);
k1 = (n + 5)/4; /* first inter-quartile */
k3 = (3*n + 3)/4; /* third inter-quartile */
insure_temporary, a;
quick_select, a, k1;
quick_select, a, k3, k1 + 1;
return (a(k3) - a(k1));
}
func quick_median(a)
/* DOCUMENT quick_median(a)
* Returns the median of values in array A.
*
* SEE ALSO
* median, quick_interquartile_range,
* quick_select, insure_temporary.
*/
{
n = numberof(a);
k = (n + 1)/2;
if (n % 2) {
/* odd number of elements */
return quick_select(a, k);
} else {
/* even number of elements */
insure_temporary, a;
quick_select, a, k;
return (a(k) + a(min:k+1:n))/2.0;
}
}
/*---------------------------------------------------------------------------*/
/* SYMBOLIC LINKS */
extern symlink_to_variable;
extern symlink_to_name;
extern is_symlink;
extern name_of_symlink;
extern value_of_symlink;
/* DOCUMENT lnk = symlink_to_variable(var)
or lnk = symlink_to_name(varname)
or is_symlink(lnk)
or name_of_symlink(lnk)
or value_of_symlink(lnk)
The call symlink_to_variable(var) creates a symbolic link to variable
VAR. The call symlink_to_name(varname) creates a symbolic link to
variable whose name is VARNAME. When the link object LNK is used in
an 'eval' context or a 'get member' context (see examples below), LNK
gets replaced 'on the fly' by the symbol which is actually stored into
the corresponding Yorick's variable. Therefore LNK adds no additional
reference to the variable which only has to exist when LNK is later
used. This functionality can be used to implement 'virtual' methods
for pseudo-object in Yorick (using hash tables).
For instance:
> lnk = symlink_to_variable(foo); // variable foo does not yet exists
> lnk = symlink_to_name("foo"); // same link, using a name
> func foo(x) { return 2*x; }
> lnk(9)
18
> func foo(x) { return 3*x; }
> lnk(9)
27
> z = array(complex, 10, 4);
> lnk = symlink_to_variable(z);
> info, lnk.re;
array(double,10,4)
The function is_symlink(LNK) check whether LNK is a symbolic link.
The function name_of_symlink(LNK) returns the name of the variable
linked by LNK.
The function value_of_symlink(LNK) returns the actual value of the
variable corresponding to the symbolic link LNK. This function can be
used to force the substitution in a context where it is not
automatically done. For instance:
> lnk = symlink_to_variable(a);
> a = random(10);
> avg(lnk)
ERROR (*main*) avg requires numeric argument
> avg(value_of_symlink(lnk))
0.383679
> avg(a)
0.383679
SEE ALSO: h_new.
*/
/*---------------------------------------------------------------------------*/
/* HASH TABLE OBJECTS */
extern h_debug;
/* DOCUMENT h_debug, object, ...
Print out some debug information on OBJECT.
****************************
*** WILL BE REMOVED SOON ***
****************************/
extern h_new;
/* DOCUMENT h_new();
or h_new(key=value, ...);
or h_new("key", value, ...);
Returns a new hash table with member(s) KEY set to VALUE. There may be
any number of KEY-VALUE pairs. A particular member of a hash table TAB
can be specified as a scalar string, i.e. "KEY", or using keyword
syntax, i.e. KEY=. The keyword syntax is however only possible if KEY is
a valid Yorick's symbol name. VALUE can be anything (even a non-array
object).
A hash table can be used to implement some kind of object-oriented
abstraction in Yorick. However, in Yorick, a hash table must have a
simple tree structure -- no loops or rings are allowed (loops break
Yorick's memory manager -- beware). You need to be careful not to do
this as the error will not be detected.
The difference between a hash table and a list object is that items are
retrieved by key identifier rather than by order (by h_get, get_member or
dot dereferenciation). It is possible to dereference the contents of TAB
using the dot operator (as for a structure) or the get_member function.
For instance, it is legal to do:
tab = h_new(x=span(-7,7,100), name="my name", op=sin, scale=33);
plg, tab.op(tab.x), tab.x;
but the member must already exists and there are restrictions to
assignation, i.e. only contents of array members can be assigned:
tab.name() = "some other string"; // ok
tab.name = "some other string"; // error
tab.x(RANGE_OR_INDEX) = EXPR; // ok if conformable AND member X
// is not a 'fast' scalar (int,
// long or double scalar)
tab.x = EXPR; // error
and assignation cannot therefore change the dimension list or data type
of a hash table member. Redefinition/creation of a member can always be
performed with the h_set function which is the recommended method to set
the value of a hash table member.
Hash tables behave differently depending how they are used:
tab.key - de-reference hash member
tab("key") - returns member named "key" in hash table TAB, this is
exactly the same as: h_get(tab, "key")
tab() - returns number of elements in hash table TAB
tab(i) - returns i-th member in hash table TAB; i is a scalar
integer and can be less or equal zero to start from the
last one; if the hash table is unmodified, tab(i) is
the same as tab(keys(i)) where keys=h_keys(tab) --
beware that this is very inefficient way to access the
contents of a hash table and will probably be removed
soon.
However, beware that the behaviour of calls such that TAB(...) may be
changed if the has table implements its own "evaluator" (see
h_evaluator).
For instance, to explore the whole hash table, there are different
possibilities:
keys = h_keys(tab);
n = numberof(keys); // alternatively: n = tab()
for (i = 1; i <= n; ++i) {
a = tab(keys(i));
...;
}
or:
for (key = h_first(tab); key; key = h_next(tab, key)) {
a = tab(key);
...;
}
or:
n = tab();
for (i=1 ; i<=n ; ++i) {
a = tab(i);
...;
}
the third form is slower for large tables and will be made obsolete
soon.
An important point to remember when using hash table is that hash members
are references to their contents, i.e.
h_set, hash, member=x;
makes an additional reference to array X and does not copy the array
although you can force that, e.g.:
tmp = x; // make a copy of array X
h_set, hash, member=tmp; // reference copy in hash table
tmp = []; // delete one reference to the copy
Because assignation result is its rhs (right-hand-side), you cannot do:
h_set, hash, member=(tmp = x); // assignation result is X
Similarly, unlike Yorick array data types, a statement like x=hash does
not make a copy of the hash table, it merely makes an additional
reference to the list.
CAVEATS:
In Yorick (or Yeti), many objects can be used to reference other objects:
pointers, lists and hash tables. Since Yorick uses a simple reference
counter to delete unused object, cyclic references (i.e. an object
referencing itself either directly or indirectly) result in objects that
will not be properly deleted. It is the user reponsibility to create no
cyclic references in order to avoid memory leaks. Checking a potential
(or effective) cyclic reference would require recursive investigation of
all members of the parent object and could be very time consuming.
SEE ALSO: h_copy, h_get, h_has, h_keys, h_pop, h_set, h_stat, h_first,
h_next, _lst, get_member. */
extern h_get;
/* DOCUMENT h_get(tab, key=);
or h_get(tab, "key");
Returns the value of member KEY of hash table TAB. If no member KEY
exists in TAB, nil is returned. h_get(TAB, "KEY") is identical to
get_member(TAB, "KEY") and also to TAB("KEY").
SEE ALSO h_new, get_member. */
extern h_set;
/* DOCUMENT h_set, tab, key=value, ...;
or h_set, tab, "key", value, ...;
Stores VALUE in member KEY of hash table TAB. There may be any number of
KEY-VALUE pairs. If called as a function, the returned value is TAB.
SEE ALSO h_new, h_set_copy. */
func h_set_copy(tab, ..)
/* DOCUMENT h_set_copy, tab, key, value, ...;
Set member KEY (a scalar string) of hash table TAB with VALUE. Unlike
h_set, VALUE is duplicated if it is an array. There may be any number of
KEY-VALUE pairs.
SEE ALSO h_copy, h_new, h_set. */
{
while (more_args()) {
key = next_arg();
value = next_arg();
h_set, tab, key, value;
}
return tab;
}
func h_copy(tab, recursively)
/* DOCUMENT h_copy(tab);
or h_copy(tab, recursively);
Effectively copy contents of hash table TAB into a new hash table that is
returned. If argument RECURSIVELY is true, every hash table contained
into TAB get also duplicated. This routine is needed because doing
CPY=TAB, where TAB is a hash table, would only make a new reference to
TAB: CPY and TAB would be the same object.
SEE ALSO h_new, h_set, h_clone. */
{
key_list = h_keys(tab);
n = h_number(tab); /* number of members */
new = h_new();
h_evaluator, new, h_evaluator(tab);
if (recursively) {
for (i=1 ; i<=n ; ++i) {
key = key_list(i);
member = h_get(tab, key);
h_set, new, key, (is_hash(member) ? h_copy(member, 1) : member);
}
} else {
for (i=1 ; i<=n ; ++i) {
key = key_list(i);
member = h_get(tab, key);
h_set, new, key, member;
}
}
return new;
}
/*
* NOTE: h_clone(tab, copy=1) is the same as h_copy(tab)
* h_clone(tab, copy=1, depth=-1) is the same as h_copy(tab, 1)
*/
func h_clone(tab, copy=, depth=)
/* DOCUMENT h_clone(tab, copy=, depth=);
Make a new hash table with same contents as TAB. If keyword COPY is
true, a fresh copy is made for array members. Otherwise, array members
are just referenced one more time by the new hash table. If keyword
DEPTH is non-zero, every hash table referenced by TAB get also cloned
(this is done recursively) until level DEPTH has been reached (infinite
recursion if DEPTH is negative). The value of keyword COPY is kept the
same across the recursions.
SEE ALSO h_new, h_set, h_copy. */
{
local member;
key_list = h_keys(tab); /* list of hash keys */
n = h_number(tab); /* number of members */
new = h_new();
h_evaluator, new, h_evaluator(tab);
if (depth) {
--depth;
for (i=1 ; i<=n ; ++i) {
key = key_list(i);
if (copy) member = h_get(tab, key);
else eq_nocopy, member, h_get(tab, key);
h_set, new, key,
(is_hash(member) ? h_clone(member, copy=copy, depth=depth) : member);
}
} else if (copy) {
for (i=1 ; i<=n ; ++i) {
key = key_list(i);
member = h_get(tab, key);
h_set, new, key, member;
}
} else {
for (i=1 ; i<=n ; ++i) {
key = key_list(i);
h_set, new, key, h_get(tab, key);
}
}
return new;
}
extern h_number;
/* DOCUMENT h_number(tab);
Returns number of entries in hash table TAB.
SEE ALSO h_new, h_keys. */
extern h_keys;
/* DOCUMENT h_keys(tab);
Returns list of members of hash table TAB as a string vector of key
names. The order in which keys are returned is arbitrary.
SEE ALSO h_new, h_first, h_next, h_number. */
extern h_has;
/* DOCUMENT h_has(tab, "key");
or h_has(tab, key=);
Returns 1 if member KEY is defined in hash table TAB, else 0.
SEE ALSO h_new. */
extern h_first;
extern h_next;
/* DOCUMENT h_first(tab);
or h_next(tab, key);
Get first or next key in hash table TAB. A NULL string is returned if
key is not found or if it is the last one (for h_next). Thes routines
are useful to run through all entries in a hash table (however beware
that the hash table should be left unchanged during the scan). For
instance:
for (key = h_first(tab); key; key = h_next(tab, key)) {
value = h_get(tab, key);
...;
}
SEE ALSO h_new, h_keys. */
extern h_evaluator;
/* DOCUMENT h_evaluator(obj)
* or h_evaluator(obj, evl);
* or h_evaluator, obj, evl;
*
* Set/query evaluator function of hash table OBJ. When called as a
* function, the evaluator of OBJ prior to any change is returned as a
* scalar string. If EVL is specified, it becomes the new evaluator of OBJ.
* EVL must be a scalar string (the name of the evaluator function), or a
* function, or nil. If EVL is explicitely nil (for instance []) or a
* NULL-string (for instance string(0)), the default behaviour is restored.
*
* When hash table OBJ is used as:
*
* OBJ(...)
*
* where "..." represents any list of arguments (including none) then its
* evaluator get called as:
*
* EVL(OBJ, ...)
*
* that is with OBJ prepended to the same argument list.
*
*
* EXAMPLES:
* // create a hash table:
* obj = h_new(data=random(200), count=0);
*
* // define a fucntion:
* func eval_me(self, incr)
* {
* if (incr) h_set, self, count = (self.count + incr);
* return self.data(1 + abs(self.count)%200);
* }
*
* // set evaluator (which must be already defined as a function):
* h_evaluator, obj, eval_me;
*
* obj(49); // return 49-th value
* obj(); // return same value
* obj(3); // return 51-th value
* h_evaluator, obj, []; // restore standard behaviour
*
* // set evaluator (not necessarily already defined as a function):
* h_evaluator, obj, "some_name";
*
* // then define the function code prior to use:
* func some_name(self, a, b) { return self.count; }
*
*
* SEE ALSO: h_new, h_get.
*/
func h_info(tab, align)
/* DOCUMENT h_info, tab;
or h_info, tab, align;
List contents of hash table TAB in alphabetical order of keys. If second
argument is true, the key names are right aligned.
SEE ALSO: h_new, h_keys, h_first, h_next, h_show, sort. */
{
key_list = h_keys(tab);
if (is_void(key_list)) return;
key_list = key_list(sort(key_list));
n = numberof(key_list);
width = max(strlen(key_list));
format = swrite(format=(align?"%%%ds":"%%-%ds"), width + 1);
for (i=1 ; i<=n ; ++i) {
key = key_list(i);
write, format=format, key+":";
info, h_get(tab, key);
}
}
local _h_show_worker;
func h_show(tab, prefix=, maxcnt=, depth=)
/* DOCUMENT h_show, tab;
Display contents of hash table TAB in a tree-like representation.
Keyword PREFIX can be used to prepend a prefix to the printed lines.
Keyword MAXCNT (default 5) can be used to specify the maximum number of
elements for printing array values.
SEE ALSO: h_info, h_keys. */
{
_h_show_maxcnt = (is_void(maxcnt) ? 5 : maxcnt);
_h_show_worker, tab, "TOP", (is_void(prefix) ? "" : prefix), 0;
}
func _h_show_worker(obj, name, prefix, stage)
{
if (stage == 1) {
prefix1 = prefix + " |-";
prefix2 = prefix + " | ";
} else if (stage == 2) {
prefix1 = prefix + " `-";
prefix2 = prefix + " ";
} else {
prefix1 = prefix;
prefix2 = prefix;
}
if (is_hash(obj)) {
key_list = h_keys(obj);
if (is_array(key_list)) {
key_list = key_list(sort(key_list));
//width = max(strlen(key_list));
//format = swrite(format=(align?"%%%ds":"%%-%ds"), width + 1);
}
n = numberof(key_list);
e = h_evaluator(obj);
write, format="%s %s (hash_table, %s%d %s)\n",
prefix1, name, (e ? "evaluator=\""+e+"\", " : ""),
n, (n <= 1 ? "entry" : "entries");
for (k = 1; k <= n; ++k) {
key = key_list(k);
_h_show_worker, h_get(obj,key), key, prefix2, 1 + (k == n);
}
} else if (is_array(obj)) {
descr = typeof(obj);
dims = dimsof(obj);
n = numberof(dims);
k = 1;
while (++k <= n) {
descr += swrite(format=",%d", dims(k));
}
if (numberof(obj) <= _h_show_maxcnt) {
write, format="%s %s (%s) %s\n", prefix1, name, descr, sum(print(obj));
} else {
write, format="%s %s (%s)\n", prefix1, name, descr;
}
} else if (is_void(obj)) {
write, format="%s %s (void) []\n", prefix1, name;
} else if (is_symlink(obj)) {
write, format="%s %s (%s) \"%s\"\n", prefix1, name, typeof(obj),
name_of_symlink(obj);
} else {
write, format="%s %s (%s)\n", prefix1, name, typeof(obj);
}
}
extern h_pop;
/* DOCUMENT h_pop(tab, "key");
or h_pop(tab, key=);
Pop member KEY out of hash table TAB and return it. When called as a
subroutine, the net result is therefore to delete the member from the
hash table.
SEE ALSO h_new, h_delete. */
func h_delete(h, ..)
/* DOCUMENT h_delete(tab, "key", ...);
Delete members KEY, ... from hash table TAB and return it. Any KEY
arguments may be present and must be array of strings or nil.
SEE ALSO h_new, h_pop. */
{
local key;
while (more_args()) {
eq_nocopy, key, next_arg();
n = numberof(key);
for (i=1 ; i<=n ; ++i) h_pop, h, key(i);
}
return h;
}
extern h_stat;
/* DOCUMENT h_stat(tab);
Returns an histogram of the slot occupation in hash table TAB. The
result is a long integer vector with i-th value equal to the number of
slots with (i-1) items. Note: efficient hash table should keep the
number of items per slot as low as possible.
SEE ALSO h_new. */
func h_list(tab, sorted)
/* DOCUMENT h_list(tab);
or h_list(tab, sorted);
Convert hash table TAB into a list: _lst("KEY1", VALUE1, ...). The order
of key-value pairs is arbitrary unless argument SORTED is true in which
case keys get sorted in alphabetical order.
SEE ALSO h_new, _lst, sort. */
{
keylist = h_keys(tab);
n = numberof(keylist);
if (sorted && n>1) keylist = keylist(sort(keylist)(::-1));
list = _lst();
for (i=1 ; i<=n ; ++i) {
/* grow the list the fast way, adding new values to its head (adding to
the tail would make growth an N^2 proposition, as would using the grow
function) */
key = keylist(i);
list = _cat(key, h_get(tab, key), list);
}
return list;
}
func h_cleanup(tab, recursively)
/* DOCUMENT h_cleanup, tab, 0/1;
or h_cleanup(tab, 0/1);
Delete all void members of hash table TAB and return TAB. If the second
argument is a true (non nil and non-zero) empty members get deleted
recursively.
SEE ALSO h_new. */
{
local member;
keylist = h_keys(tab);
n = numberof(keylist);
for (i=1 ; i<=n ; ++i) {
key = keylist(i);
eq_nocopy, member, h_get(tab, key);
if (is_void(member)) h_pop, tab, key;
else if (recursively && is_hash(member)) h_cleanup, member, recursively;
}
return tab;
}
func h_grow(tab, .., flatten=)
/* DOCUMENT h_grow, tab, key, value, ...;
Grow member named KEY of hash table TAB by VALUE. There may be any
number of key-value pairs. If keyword FLATTEN is true, then VALUE(*)
instead of VALUE is appended to the former contents of TAB.KEY. If
member KEY does not already exists in TAB, then a new member is created
with VALUE, or VALUE(*), as contents.
SEE ALSO h_new. */
{
local key, value;
if (flatten) {
while (more_args()) {
eq_nocopy, key, next_arg();
h_set, tab, key, grow(h_get(tab, key), next_arg()(*));
}
} else {
while (more_args()) {
eq_nocopy, key, next_arg();
h_set, tab, key, grow(h_get(tab, key), next_arg());
}
}
}
func h_save_symbols(____l____, ..)
/* DOCUMENT h_save_symbols(namelist, ...);
or h_save_symbols(flag);
Return hash table which references symbols given in NAMELIST or selected
by FLAG (see symbol_names). Of course, the symbol names will be used as
member names in the result.
SEE ALSO h_new, h_restore_builtin, symbol_names. */
{
/* Attempt to use dummy symbol names in this routine to avoid clash with
the symbols ddefined in caller's context. */
while (more_args()) grow, ____l____, next_arg();
if ((____s____ = structof(____l____)) != string) {
if ((____s____!=long && ____s____!=int && ____s____!=short &&
____s____!=char) || dimsof(____l____)(1))
error, "expected a list of names, or nil, or a scalar integer";
____l____ = symbol_names(____l____);
}
____s____ = h_new();
____n____ = numberof(____l____);
for (____i____=1 ; ____i____<=____n____ ; ++____i____) {
____k____ = ____l____(____i____);
h_set, ____s____, ____k____, symbol_def(____k____);
}
return ____s____;
}
local SAVE_BUILTINS;
local __h_saved_builtins;
func h_restore_builtin(name) { return h_get(__h_saved_builtins, name); }
/* DOCUMENT h_restore_builtin(name);
Get the original definition of builtin function NAME. This is useful if
you deleted by accident a builtin function and want to recover it; for
instance:
sin = 1;
...
sin = h_restore_builtin("sin");
would restore the definition of the sine function that was redefined by
the assignation.
To enable this feature, you must define the global variable SAVE_BUILTINS
to be true before loading the Yeti package. For instance:
SAVE_BUILTINS = 1;
include, "yeti.i";
then all all current definitions of builtin functions will be referenced
in global hash table __h_saved_builtins and could be retrieved by calling
h_restore_builtin.
Note that this feature is disabled in batch mode.
SEE ALSO h_new, h_save_symbols, batch. */
if (! batch() && SAVE_BUILTINS && ! is_hash(__h_saved_builtins)) {
__h_saved_builtins = h_save_symbols(32);
}
/*---------------------------------------------------------------------------*/
/* MORPHO-MATH OPERATORS */
extern morph_dilation;
extern morph_erosion;
/* DOCUMENT morph_dilation(a, r);
or morph_erosion(a, r);
These functions perform a dilation/erosion morpho-math operation onto
input array A which must have at most 3 dimensions. A dilation (erosion)
operation replaces every voxel of A by the maximum (minimum) value found
in the voxel neighborhood as defined by the structuring element. Argument
R defines the structuring element as follows:
- If R is a scalar integer, then it is taken as the radius (in voxels)
of the structuring element.
- Otherwise, R gives the offsets of the structuring element relative to
the coordinates of the voxel of interest. In that case, R must an
array of integers with last dimension equals to the number of
dimensions of A. In other words, if A is a 3-D array, then the
offsets are:
DX = R(1,..)
DY = R(2,..)
DZ = R(3,..)
and the neighborhood of a voxel at (X,Y,Z) is defined as: (X + DX(I),
Y + DY(I), Z + DZ(i)) for i=1,...,numberof(DX). Conversely, R = [DX,
DY, DZ]. Thanks to that definition, structuring element with
arbitrary shape and relative position can be used in morpho-math
operations.
For instance, the dilation of an image (a 2-D array) IMG by a 3-by-5
rectangular structuring element centered at the pixel of interest is
obtained by:
dx = indgen(-1:1);
dy = indgen(-2:2);
result = morph_dilation(img, [dx, dy(-,)])
SEE ALSO: morph_closing, morph_opening, morph_white_top_hat,
morph_black_top_hat, morph_enhance.
*/
func morph_closing(a, r)
{ return morph_erosion(morph_dilation(a, r), r); }
func morph_opening(a, r)
{ return morph_dilation(morph_erosion(a, r), r); }
/* DOCUMENT morph_closing(a, r);
or morph_opening(a, r);
Perform an image closing/opening of A by a structuring element R. A
closing is a dilation followed by an erosion, whereas an opening is an
erosion followed by a dilation. See morph_dilation for the meaning of
the arguments.
SEE ALSO: morph_dilation, morph_white_top_hat,
morph_black_top_hat. */
func morph_white_top_hat(a, r, s) {
if (! is_void(s)) a = morph_closing(a, s);
return a - morph_opening(a, r); }
func morph_black_top_hat(a, r, s) {
if (! is_void(s)) a = morph_opening(a, s);
return morph_closing(a, r) - a; }
/* DOCUMENT morph_white_top_hat(a, r);
or morph_white_top_hat(a, r, s);
or morph_black_top_hat(a, r);
or morph_black_top_hat(a, r, s);
Perform a summit/valley detection by applying a top-hat filter to
array A. Argument R defines the structuring element for the feature
detection. Optional argument gives the structuring element used to
apply a smoothing to A prior to the top-hat filter. If R and S are
specified as the radii of the structuring elements, then S should be
smaller than R. For instance:
morph_white_top_hat(bitmap, 3, 1)
may be used to detect text or lines in a bimap image.
SEE ALSO: morph_dilation, morph_closing, morph_enhance. */
func morph_enhance(a, r, s)
/* DOCUMENT morph_enhance(a, r);
or morph_enhance(a, r, s);
Perform noise reduction with edge preserving on array A. The result is
obtained by rescaling the values in A in a non-linear way between the
local minimum and the local maximum. Argument R defines the structuring
element for the local neighborhood. Argument S is a shape factor for the
rescaling function which is a sigmoid function. If S is given, it must
be a non-negative value, the larger is S, the steeper is the rescaling
function. The shape factor should be larger than 3 or 5 to have a
noticeable effect.
If S is omitted, a step-like rescaling function is chosen: the output
elements are set to either the local minimum or the local maximum which
one is the closest. This corresponds to the limit of very large shape
factors and implements the "toggle filter" proposed by Kramer & Bruckner
[1].
The morph_enhance() may be iterated to achieve deblurring of the input
array A (hundreds of iterations may be required).
REFERENCES
[1] H.P. Kramer & J.B. Bruckner, "iterations of a nonlinear
transformation for enhancement of digital images", Pattern
Recognition, vol. 7, pp. 53-58, 1975.
SEE ALSO: morph_erosion, morph_dilation.
*/
{
if (is_void(s)) {
s = -1.0; /* special value */
} else if (s < 0.0) {
error, "S must be non-negative";
} else {
/* Pre-compute the range of the sigmoid function to detect early return
with no change and skip the time consuming morpho-math operations. */
s = double(s);
hi = 1.0/(1.0 + exp(-s));
lo = ((hi == 1.0) ? 0.0 : 1.0/(1.0 + exp(s)));
if (hi == lo) {
return a;
}
}
/* Compute the local minima and maxima. */
amin = morph_erosion(a, r);
amax = morph_dilation(a, r);
/* Staircase remapping of values. */
if (s < 0.0) {
test = ((a - amin) >= (amax - a));
return merge(amax(where(test)), amin(where(! test)), test);
}
/* Remapping of values with a sigmoid. */
test = ((amin < a)&(a < amax)); // values that need to change
w = where(test);
if (! is_array(w)) {
return a;
}
type = structof(a);
integer = is_integer(a);
if (numberof(w) != numberof(a)) {
/* Not all values change, select only those for which there is a
difference between the local minimum and the local maximum. */
unchanged = a(where(! test));
a = a(w);
amin = amin(w);
amax = amax(w);
}
/* We use the sigmoid function f(t) = 1/(1 + exp(-t)) for the rescaling
function g(t) = alpha*f(s*t) + beta with S the shape parameter, and
(ALPHA,BETA) chosen to map the range [-1,1] into [0,1]. */
alpha = 1.0/(hi - lo);
beta = alpha*lo;
/* Linearly map the values in the range [-1,1] -- we already know that the
local minimum and maximum are different. */
a = (double(a - amin) - double(amax - a))/double(amax - amin);
a = alpha/(1.0 + exp(-s*a)) - beta;
a = a*amax + (1.0 - a)*amin;
if (! is_void(unchanged)) {
a = merge(a, unchanged, test);
}
if (type != structof(a)) {
if (integer) return type(round(a));
return type(a);
}
return a;
}
/*---------------------------------------------------------------------------*/
/* COST FUNCTIONS AND REGULARIZATION */
extern cost_l2;
extern cost_l2l1;
extern cost_l2l0;
/* DOCUMENT cost_l2(hyper, res [, grd])
* or cost_l2l1(hyper, res [, grd])
* or cost_l2l0(hyper, res [, grd])
*
* These functions compute the cost for an array of residuals RES and
* hyper-parameters HYPER (which can have 1, 2 or 3 elements). If
* optional third argument GRD is provided, it must be a simple variable
* reference used to store the gradient of the cost function with respect
* to the residuals.
*
* The cost_l2() function returns the sum of squared residuals times
* HYPER(1):
*
* COST_L2 = MU*sum(RES^2)
*
* where MU = HYPER(1).
*
* The cost_l2l1() and cost_l2l0() functions are quadratic (L2) for small
* residuals and non-quadratic (L1 and L0 respectively) for larger
* residuals. The thresholds for L2 / non-L2 transition are given by
* the second and third value of HYPER.
*
* If HYPER = [MU, TINF, TSUP] with TINF < 0 and TSUP > 0, an asymmetric
* cost function is computed as:
*
* COST_L2L0 = MU*(TINF^2*sum(atan(RES(INEG)/TINF)^2) +
* TSUP^2*sum(atan(RES(IPOS)/TPOS)^2))
*
* COST_L2L1 = 2*MU*(TINF^2*sum(RES(INEG)/TINF -
* log(1 + RES(INEG)/TINF)) +
* TSUP^2*sum(RES(IPOS)/TSUP -
* log(1 + RES(IPOS)/TSUP)))
*
* with INEG = where(RES < 0) and IPOS = where(RES >= 0). If any or the
* thresholds is negative or zero, the L2 norm is used for residuals with
* the corresponding sign (same as having an infinite threshold level).
* The different cases are:
*
* TINF < 0 ==> L2-L1/L0 norm for negative residuals
* TINF = 0 ==> L2 norm for negative residuals
* TSUP = 0 ==> L2 norm for positive residuals
* TSUP > 0 ==> L2-L1/L0 norm for positive residuals
*
* For residuals much smaller (in magnitude) than the thresholds, the
* non-L2 cost function behave as the L2 one. For residuals much larger
* (in magnitude), than the thresholds, the L2-L1 cost function is L1
* (i.e. scales as abs(RES)) and the L2-L0 cost function is L0 (tends to
* saturate).
*
* If HYPER = [MU, T], with T>0, a symmetric non-L2 cost function is
* computed with TINF = -T and TSUP = +T; in other words:
*
* COST_L2L0 = MU*T^2*sum(atan(RES/T)^2)
*
* COST_L2L1 = 2*MU*T^2*sum(abs(RES/T) - log(1 + abs(RES/T)))
*
* If HYPER has only one element (MU) the L2 cost function is used. Note
* that HYPER = [MU, 0] or HYPER = [MU, 0, 0] is the same as HYPER = MU
* (i.e. L2 cost function). This is an implementation issue; by
* continuity, the cost should be zero for a threshold equals to zero.
*
*
* SEE ALSO:
* rgl_roughness_l2;
*/
extern rgl_roughness_l2;
extern rgl_roughness_l2_periodic;
extern rgl_roughness_l1;
extern rgl_roughness_l1_periodic;
extern rgl_roughness_l2l1;
extern rgl_roughness_l2l1_periodic;
extern rgl_roughness_l2l0;
extern rgl_roughness_l2l0_periodic;
extern rgl_roughness_cauchy;
extern rgl_roughness_cauchy_periodic;
/* DOCUMENT err = rgl_roughness_SUFFIX(hyper, offset, arr);
* or err = rgl_roughness_SUFFIX(hyper, offset, arr, grd);
*
* Compute regularization penalty based on the roughness of array ARR.
* SUFFIX indicates the type of cost function and the boundary condition
* (see below). HYPER is the array of hyper-parameters; depending on the
* particular cost function, HYPER may have 1 or 2 elements (see below).
* OFFSET is an array of offsets for each dimensions of ARR (missing
* offsets are treated as being equal to zero): OFFSET(j) is the offset
* along j-th dimension between elements to compare. The penalty is
* equal to the sum of the costs of the differences between values of ARR
* separated by OFFSET; schematically:
*
* ERR = sum_k COST(ARR(k + OFFSET) - ARR(k))
*
* The following penalties are implemented:
*
* rgl_roughness_l1 L1 norm
* rgl_roughness_l1_periodic L1 norm, periodic
* rgl_roughness_l2 L2 norm
* rgl_roughness_l2_periodic L2 norm, periodic
* rgl_roughness_l2l1 L2-L1 norm
* rgl_roughness_l2l1_periodic L2-L1 norm, periodic
* rgl_roughness_l2l0 L2-L0 norm
* rgl_roughness_l2l0_periodic L2-L0 norm, periodic
* rgl_roughness_cauchy Cauchy norm
* rgl_roughness_cauchy_periodic Cauchy norm, periodic
*
* The suffix "periodic" indicates periodic boundary condition. The
* different cost functions are):
*
* L1(x) = mu * abs(x)
* L2(x) = mu * x^2
* L2L0(x) = mu * eps^2 * atan(x/eps))^2
* L2L1(x) = 2 * mu * eps^2 * (abs(x/eps) - log(1 + abs(x/eps)))
* Cauchy(x) = mu * eps^2 * log(1 + (x/eps)^2)
*
* where X = ARR(k + OFFSET) - ARR(k), MU = HYPER(1) is the weight of the
* regularization and EPS = HYPER(2) is a threshold level. Restrictions:
* MU >= 0 and EPS >= 0 and the result is ERR = 0 when MU = 0 or EPS = 0
* -- the case EPS = 0, is implemented by continuity.
*
* The L2-L0, L2-L1 and Cauchy cost functions behave as L2(X) = MU*X^2 for
* abs(X) much smaller than EPS. They differ in their tail for large
* values of abs(X): L2-L0 tends to be flat; L2-L1 behave as abs(X) and
* CAUCHY is intermediate.
*
* From a Baysian viewpoint, L2 correspond to the neg-log likelihood of a
* Gaussian distribution, CAUCHY correspond to the neg-log likelihood of
* a Cauchy (or Lorentzian) distribution.
*
* Optional argument GRD must be an unadorned variable where to store the
* gradient. If the argument GRD is omitted, no gradient is computed. On
* entry, the value of GRD may be empty to automatically or an array
* (convertible to real type) with same dimension list as ARR. In the
* first case, a new array is created to store the gradient; in the
* second case, the contents of GRD is augmented by the gradient (and GRD
* is converted to "double" if it is not yet the case).
*
*
* EXAMPLES
*
* To compute isotropic quadratic roughness along 2 first dimensions of A:
*
* g = array(double, dimsof(a)); // to store the gradient
* mu = 1e3; // regularization weight
* rgl = rgl_roughness_l2; // shortcut
* f = (rgl( mu, 1, a, g) +
* rgl( mu, [ 0, 1], a, g) +
* rgl(0.5*mu, [-1, 1], a, g) +
* rgl(0.5*mu, [ 1, 1], a, g));
*
* To compute anisotropic roughness along first and third dimensions of A:
*
* g = array(double, dimsof(a)); // to store the gradient
* mu1 = 1e3; // regularization weight along first dimension
* mu2 = 3e4; // regularization weight along second dimension
* rgl = rgl_roughness_l2; // shortcut
* f = (rgl(mu1, 1, a, g) + // 1st dim
* rgl(mu3, [ 0, 0, 1], a, g) + // 3rd dim
* rgl(mu1 + mu3, [-1, 0, 1], a, g) + // 1st & 3rd dim
* rgl(mu1 + mu3, [ 1, 0, 1], a, g)); // 1st & 3rd dim
*
*
* SEE ALSO
* cost_l2.
*/
/*---------------------------------------------------------------------------*/
/* 1D CONVOLUTION AND "A TROUS" WAVELET TRANSFORM */
extern __yeti_convolve_f;
/* PROTOTYPE
void yeti_convolve_f(float array dst, float array src, int stride,
int n, int nafter, float array ker, int w,
int scale, int border, float array ws); */
extern __yeti_convolve_d;
/* PROTOTYPE
void yeti_convolve_d(double array dst, double array src, int stride,
int n, int nafter, double array ker, int w,
int scale, int border, double array ws); */
func yeti_convolve(a, which=, kernel=, scale=, border=, count=)
/* DOCUMENT ap = yeti_convolve(a)
Convolve array A along its dimensions (all by default) by a given
kernel. By default, the convolution kernel is [1,4,6,4,1]/16.0. This
can be changed by using keyword KERNEL (but the kernel must have an
odd number of elements). The following operation is performed (with
special handling for the boundaries, see keyword BORDER) along the
direction(s) of interest:
| ____
| \
| AP(i)= \ KERNEL(j+W) * A(i + j*SCALE)
| /
| /___
| -W <= j <= +W
|
where numberof(KERNEL)=2*W+1. Except for the SCALE factor, AP is
mostly a convolution of A by array KERNEL along the direction of
interest.
Keyword WHICH can be used to specify the dimension(s) of interest; by
default, all dimensions get convolved. As for indices, elements in
WHICH less than 1 is taken as relative to the final dimension of the
array. You may specify repeated convolution along some dimensions by
using them several times in array WHICH (see keyword COUNT).
Keyword BORDER can be used to set the handling of boundary conditions:
BORDER=0 Extrapolate missing values by the left/rightmost ones
(this is the default behaviour).
BORDER=1 Extrapolate missing left values by zero and missing right
values by the rightmost one.
BORDER=2 Extrapolate missing left values by the leftmost one and
missing right values by zero.
BORDER=3 Extrapolate missing left/right values by zero.
BORDER=4 Use periodic conditions.
BORDER>4 or BORDER<0
Do not extrapolate missing values but normalize
convolution product by sum of kernel weights taken into
account (assuming they are all positive).
By default, SCALE=1 which corresponds to a simple convolution. An
other value can be used thanks to keyword SCALE (e.g. for the wavelet
"a trou" method). The value of SCALE must be a positive integer.
Keyword COUNT can be used to augment the amount of smoothing: COUNT
(default COUNT=1) is the number of convolution passes. It is better
(i.e. faster) to use only one pass with appropriate convolution kernel
(see keyword KERNEL).
SEE ALSO yeti_wavelet.
RESTRICTIONS
1. Should use the in-place ability of the operation to limit the number
of array copies.
2. Complex convolution not yet implemented (although it exists in the
C-code). */
{
/* Check data type of A. */
type = structof(a);
if (type == complex) {
return (yeti_convolve(double(a), which=which, kernel=kernel, scale=scale,
border=border, count=count)
+ 1i*yeti_convolve(a.im, which=which, kernel=kernel, scale=scale,
border=border, count=count));
} else if (type == double) {
op = __yeti_convolve_d;
} else if (type == float || type == long || type == int || type == short ||
type == char) {
op = __yeti_convolve_f;
type = float;
} else {
error, "bad data type";
}
a = type(a); /* force a private copy of A */
/* Check dimensions of A and keyword WHICH. */
dims = dimsof(a);
rank = dims(1);
if (is_void(which)) {
which = indgen(rank);
} else {
which += (which <= 0)*rank;
if (min(which) < 1 || max(which) > rank)
error, "dimension index out of range in WHICH";
}
/* Check KERNEL and other keywords. */
if (is_void(kernel)) {
k0= type(0.375); /* 6.0/16.0 */
k1= type(0.25); /* 4.0/16.0 */
k2= type(0.0625); /* 1.0/16.0 */
kernel= [k2, k1, k0, k1, k2];
}
if ((w = numberof(kernel))%2 != 1)
error, "KERNEL must have an odd number of elements";
if (is_void(scale)) scale = 1;
else if (structof(scale+0)!=long || scale<=0)
error, "bad value for keyword SCALE";
if (is_void(border)) border = 0;
if (is_void(count)) count = 1;
/* Compute strides. */
stride = array(1, rank);
for (s=1,i=2 ; i<=rank ; ++i) stride(i) = stride(i-1)*dims(i);
stride = stride(which);
dims = dims(which + 1);
nafter = numberof(a)/(dims*stride);
/* Apply the operator along every dimensions of interest. */
for (i=1 ; i<=numberof(which) ; ++i) {
len = dims(i);
for (j=1 ; j<=count ; ++j) {
op, a, a, stride(i), len, nafter(i), kernel, (w-1)/2, scale, border,
array(type, 2*len);
}
}
return a;
}
func yeti_wavelet(a, order, which=, kernel=, border=)
/* DOCUMENT cube = yeti_wavelet(a, order)
Compute the "a trou" wavelet transform of A. The result is such
that:
CUBE(.., i) = S_i - S_(i+1)
where:
S_1 = A
S_(i+1) = yeti_convolve(S_i, SCALE=2^(i-1))
As a consequence:
CUBE(..,sum) = A;
SEE ALSO yeti_convolve. */
{
if (((s=structof(order)) != long && s!=int && s!=short && s!=char) ||
dimsof(order)(1) || order<0) {
error, "ORDER must be a non-negative integer";
}
dims = dimsof(a);
grow, dims, order+1;
++dims(1);
cube = array(structof(a(1)+0.0f), dims);
for (scale=1, i=1 ; i<=order ; ++i, scale*=2) {
ap = a;
a = yeti_convolve(a, which=which, kernel=kernel, scale=scale,
border=border);
cube(..,i) = ap-a;
}
cube(..,0) = a;
return cube;
}
extern smooth3;
/* DOCUMENT smooth3(a)
Returns array A smoothed by a simple 3-element convolution (but for
the edges). In one dimension, the smoothing operation reads:
smooth3(A)(i) = C*A(i) + D*(A(i-1) + A(i+1))
but for the first and last element for which:
smooth3(A)(1) = E*A(1) + D*A(2)
smooth3(A)(n) = E*A(n) + D*A(n-1)
where N is the length of the dimension and the coefficients are:
C = 0.5
D = 0.25
E = 0.75
With the default value of C (see keyword C below), the smoothing
operation is identical to:
smooth3(A) = A(pcen)(zcen) for a 1D array
smooth3(A) = A(pcen,pcen)(zcen,zcen) for a 2D array
... and so on
Keyword C can be used to specify another value for the coefficient
C (default: C=0.5); coefficients D and E are computed as follows:
D = 0.5*(1 - C)
E = 0.5*(1 + C)
The default is to smooth A along all its dimensions, but keyword WHICH
can be used to specify the only dimension to smooth. If WHICH is less
or equal zero, then the smoothed dimension is the last one + WHICH.
The smoothing operator implemented by smooth3 has the following
properties:
1. The smoothing operator is linear and symmetric (for any number of
dimensions in A). The symmetry of the smoothing operator is
important for the computation of gradients in regularization. For
instance, let Y = smooth3(X) and Q be a scalar function of Y, then
then the gradient of Q with respect to X is simply:
DQ_DX = smooth3(DQ_DY)
where DQ_DY is the gradient of Q with respect to Y.
2. For a vector, A, smooth3(A)=S(,+)*A(+) where the matrix S is
tridiagonal:
[E D ]
[D C D ]
[ D C D ]
[ \ \ \ ] where, to improve readability,
[ \ \ \ ] missing values are all zero.
[ D C D ]
[ D C D]
[ D E]
You can, in principle, reverse the smoothing operation with TDsolve
along each dimensions of smooth3(A). Note: for a vector A, the
operator S-I applied to A (where I is the identity matrix) is the
finite difference 2nd derivatives of A (but for the edges).
3. The definition of coefficients C, D and E insure that the smoothing
operator does not change the sum of the element values of its
argument, i.e.: sum(smooth3(A)) = sum(A).
4. Only an array with all elements having the same value is invariant
by the smoothing operator. In fact "slopes" along dimensions of A
are almost invariant, only the values along the edges are changed.
KEYWORDS: c, which.
SEE ALSO: TDsolve. */
/*---------------------------------------------------------------------------*/
/* ALARM CALLBACK */
extern set_alarm;
/* DOCUMENT set_alarm, secs, callback;
Arrange for function CALLBACK to get called with no argument in SECS
seconds. CALLBACK can be specified either as a Yorick function or as
a function name. If CALLBACK is given as a name, the symbol is
resolved at alarm time (after SECS seconds) this permits to (re)define
the effective CALLBACK function before alarm expires. When called as
a function, the returned value is the alarm time in WALL seconds.
SEE ALSO: set_idler. */
/*---------------------------------------------------------------------------*/
/* STRING ROUTINES */
func strtrimleft(s) {return strtrim(s, 1);}
func strtrimright(s) {return strtrim(s, 2);}
/* DOCUMENT strtrimleft(s);
or strtrimrigth(s);
Returns input (array of) string(s) S without leading or trailing
blanks.
SEE ALSO strlower, strupper, string, strtrim. */
func strlower(s) { return strcase(0, s); }
func strupper(s) { return strcase(1, s); }
/* DOCUMENT strlower(s);
or strupper(s);
Returns input (array of) string(s) S converted to lower/upper case
letters.
SEE ALSO string, strcase, strtrimleft. */
/*---------------------------------------------------------------------------*/
/* MATH ROUTINES */
extern sinc;
/* DOCUMENT sinc(x);
Returns the "sampling function" of X as defined by Woodward (1953) and
Bracewell (1999):
sinc(x) = 1 for x=0
sin(PI*x)/(PI*x) otherwise
Note: This definition correspond to the "normalized sinc function";
some other authors may define the sampling function without the PI
factors in the above expression.
REFERENCES
Bracewell, R. "The Filtering or Interpolating Function, sinc(x)." In
"The Fourier Transform and Its Applications", 3rd ed. New York:
McGraw-Hill, pp. 62-64, 1999.
Woodward, P. M. "Probability and Information Theory with Applications
to Radar". New York: McGraw-Hill, 1953.
SEE ALSO: sin. */
extern arc;
/* DOCUMENT arc(x);
Returns angle X wrapped in range (-PI, +PI]. */
/*---------------------------------------------------------------------------*/
/* SPARSE MATRICES AND MATRIX-VECTOR MULTIPLICATION */
extern sparse_matrix;
/* DOCUMENT s = sparse_matrix(coefs, row_dimlist, row_indices,
* col_dimlist, col_indices);
*
* Returns a sparse matrix object. COEFS is an array with the non-zero
* coefficients of the full matrix. ROW_DIMLIST and COL_DIMLIST are the
* dimension lists of the matrix 'rows' and 'columns'. ROW_INDICES and
* COL_INDICES are the 'row' and 'column' indices of the non-zero
* coefficients of the full matrix.
*
* The sparse matrix object S can be used to perform sparse matrix
* multiplication as follows:
*
* S(x) or S(x, 0) yields the result of matrix multiplication of
* 'vector' X by S; X must be an array with dimension list
* COL_DIMLIST (or a vector with as many elements as an array
* with such a dimension list); the result is an array with
* dimension list ROW_DIMLIST.
*
* S(y, 1) yields the result of matrix multiplication of 'vector' Y by
* the transpose of S; Y must be an array with dimension list
* ROW_DIMLIST (or a vector with as many elements as an array
* with such a dimension list); the result is an array with
* dimension list COL_DIMLIST.
*
* The contents of the sparse matrix object S can be queried as with a
* regular Yorick structure: S.coefs, S.row_dimlist, S.row_indices,
* S.col_dimlist or S.col_indices are valid expressions if S is a sparse
* matrix.
*
*
* SEE ALSO: is_sparse_matrix, mvmult,
* sparse_expand, sparse_squeeze, sparse_grow.
*/
extern is_sparse_matrix;
/* DOCUMENT is_sparse_matrix(obj)
* Returns true if OBJ is a sparse matrix object; false otherwise.
*
* SEE ALSO: sparse_matrix.
*/
func sparse_grow(s, coefs, row_indices, col_indices)
/* DOCUMENT sparse_grow(s, coefs, row_indices, col_indices);
*
* Returns a sparse matrix object obtained by growing the non-zero
* coefficients of S by COEFS with the corresponding row/column indices
* given by ROW_INDICES and COL_INDICES which must have the same number
* of elements as COEFS.
*
* SEE ALSO: sparse_matrix.
*/
{
return sparse_matrix(grow(s.coefs, coefs),
s.row_dimlist, grow(s.row_indices, row_indices),
s.col_dimlist, grow(s.col_indices, col_indices));
}
func sparse_squeeze(a, n)
/* DOCUMENT s = sparse_squeeze(a);
* or s = sparse_squeeze(a, n);
* Convert array A into its sparse matrix representation. Optional
* argument N (default, N=1) is the number of dimensions of the input
* space. The dimension list of the input space are the N trailing
* dimensions of A and, assuming that A has NDIMS dimensions, the
* dimension list of the output space are the NDIMS - N leading
* dimensions of A.
*
* SEE ALSO: sparse_matrix, sparse_expand.
*/
{
if (! is_array(a)) error, "unexpected non-array";
dimlist = dimsof(a);
ndims = dimlist(1);
if (is_void(n)) n = 1; /* one trailing dimension for the input space */
if ((m = ndims - n) < 0) error, "input space has too many dimensions";
if (! is_array((i = where(a)))) error, "input array is zero everywhere!";
(row_dimlist = array(long, m + 1))(1) = m;
stride = 1;
if (m >= 1) {
row_dimlist(2:) = dimlist(2:m+1);
for (j=m+1;j>=2;--j) stride *= dimlist(j);
}
(col_dimlist = array(long, n + 1))(1) = n;
if (n >= 1) col_dimlist(2:) = dimlist(m+2:0);
j = i - 1;
return sparse_matrix(a(i),
row_dimlist, 1 + j%stride,
col_dimlist, 1 + j/stride);
}
func sparse_expand(s)
/* DOCUMENT a = sparse_expand(s);
* Convert sparse matrix S into standard Yorick's array A.
*
* SEE ALSO: sparse_squeeze, histogram.
*/
{
row_dimlist = s.row_dimlist;
stride = 1;
j = row_dimlist(1) + 2;
while (--j >= 2) stride *= row_dimlist(j);
a = array(structof(s.coefs), row_dimlist, s.col_dimlist);
#if 0
/* We cannot do that because, coefficients may not be unique. */
a(s.row_indices + (s.col_indices - 1)*stride) = s.coefs;
#endif
a(*) = histogram(s.row_indices + (s.col_indices - 1)*stride,
s.coefs, top=numberof(a));
return a;
}
local sparse_restore, sparse_save;
/* DOCUMENT sparse_save, pdb, obj;
* sparse_restore(pdb);
* The subroutine sparse_save saves the sparse matrix OBJ into file PDB.
* The function sparse_restore restores the sparse matrix saved into file
* PDB. PDB is either a file name or a PDB file handle.
*
* SEE ALSO: createb, openb, restore, save, sparse_matrix.
*/
func sparse_save(pdb, obj)
{
if (! is_sparse_matrix(obj)) error, "expecting a sparse matrix";
if (structof(pdb) == string) {
logfile = pdb + "L";
if (open(logfile, "r", 1)) logfile = 0;
pdb = createb(pdb);
if (logfile) remove, logfile;
}
local coefs, row_dimlist, row_indices, col_dimlist, col_indices;
eq_nocopy, coefs, obj.coefs;
eq_nocopy, row_dimlist, obj.row_dimlist;
eq_nocopy, row_indices, obj.row_indices;
eq_nocopy, col_dimlist, obj.col_dimlist;
eq_nocopy, col_indices, obj.col_indices;
save, pdb, coefs, row_dimlist, row_indices, col_dimlist, col_indices;
}
func sparse_restore(pdb)
{
local coefs, row_dimlist, row_indices, col_dimlist, col_indices;
if (structof(pdb) == string) pdb = openb(pdb);
restore, pdb, coefs, row_dimlist, row_indices, col_dimlist, col_indices;
return sparse_matrix(coefs, row_dimlist, row_indices,
col_dimlist, col_indices);
}
extern mvmult;
/* DOCUMENT y = mvmult(a, x);
* or y = mvmult(a, x, 0/1);
*
* Returns the result of (generalized) matrix-vector multiplication of
* vector X (a regular Yorick array) by matrix A (a regular Yorick array
* or a sparse matrix). The matrix-vector multiplication is performed as
* if there is only one index running over the elements of X and the
* trailing/leading dimensions of A.
*
* If optional last argument is omitted or false, the summation index
* runs across the trailing dimensions of A which must be the same as
* those of X and the dimensions of the result are the remaining leading
* dimensions of A.
*
* If optional last argument is 1, the matrix operator is transposed: the
* summation index runs across the leading dimensions of A which must be
* the same as those of X and the dimensions of the result are the
* remaining trailing dimensions of A.
*
* SEE ALSO: sparse_matrix, sparse_squeeze.
*/
/*---------------------------------------------------------------------------*/
/* ACCESSING YORICK'S INTERNALS */
extern is_hash;
/* DOCUMENT is_hash(object)
* Returns 1, if OBJECT is a regular hash table; returns 2, if OBJECT is
* a hash table with a specialized evaluator; returns 0, if OBJECT is not
* a hash table.
*
* SEE ALSO: h_new, h_evaluator,
* is_array, is_func, is_integer, is_list, is_range, is_scalar,
* is_stream, is_struct, is_void.
*/
extern nrefsof;
/* DOCUMENT nrefsof(object)
Returns number of references on OBJECT.
SEE ALSO: unref. */
extern get_encoding;
/* DOCUMENT get_encoding(name);
Return the data layout for machine NAME, one of:
"native" the current machine
(little-endians)
"i86" Intel x86 Linux
"ibmpc" IBM PC (2 byte int)
"alpha" Compaq alpha
"dec" DEC workstation (MIPS), Intel x86 Windows
"vax" DEC VAX (H-double)
"vaxg" DEC VAX (G-double)
(big-endians)
"xdr" External Data Representation
"sun" Sun, HP, SGI, IBM-RS6000, MIPS 32 bit
"sun3" Sun-2 or Sun-3 (old)
"sgi64" SGI, Sun, HP, IBM-RS6000 64 bit
"mac" MacIntosh 68000 (power Mac, Gx are __sun)
"macl" MacIntosh 68000 (12 byte double)
"cray" Cray XMP, YMP
The result is a vector of 32 long's as follow:
[size, align, order] repeated 6 times for char, short, int, long,
float, and double, except that char align is
always 1, so result(2) is the structure
alignment (see struct_align).
[sign_address, exponent_address, exponent_bits,
mantissa_address, mantissa_bits,
mantissa_normalization, exponent_bias] repeated twice for float
and double. See the comment at the top of file
prmtyp.i for an explanation of these fields.
The total number of items is therefore 3*6 + 7*2 = 32.
SEE ALSO get_primitives, set_primitives, install_encoding, machine_constant. */
func install_encoding(file, encoding)
/* DOCUMENT install_encoding, file, encoding;
Set layout of primitive data types for binary stream FILE. ENCODING
may be one of the names accepted by get_encoding or an array of 32
integers as explained in get_encoding documentation.
SEE ALSO: get_encoding, install_struct. */
{
/* Get encoding parameters with minimal check. */
if (structof(encoding) == string) {
p = get_encoding(encoding);
} else {
if ((s = structof(encoding)) == long) p = encoding;
else if (/*s==char || s==short || */s==int) p = long(encoding);
else error, "bad data type for ENCODING";
if (numberof(p) != 32) error, "bad number of elements for encoding";
}
/* Install primitive definitions. */
install_struct, file, "char", 1, 1, p( 3);
install_struct, file, "short", p( 4), p( 5), p( 6);
install_struct, file, "int", p( 7), p( 8), p( 9);
install_struct, file, "long", p(10), p(11), p(12);
install_struct, file, "float", p(13), p(14), p(15), p(19:25);
install_struct, file, "double", p(16), p(17), p(18), p(26:32);
struct_align, file, p(2);
}
func same_encoding(a, b)
/* DOCUMENT same_encoding(a, b)
Compare primitives A and B which must be conformable integer arrays
with first dimension equals to 32 (see set_primitives). The result is
an array of int's with one less dimension than A-B (the first one).
Some checking is performed for the operands. The byte order for the
char data type is ignored in the comparison.
SEE ALSO install_encoding, get_encoding.*/
{
if (! is_array((d = dimsof(a, b))) || d(1) < 1 || d(2) != 32)
error, "bad dimensions";
diff = abs(a - b);
if ((s = structof(diff)) != long && s != int) error, "bad data type";
if (anyof(a(1,..) != 1) || anyof(b(1,..) != 1))
error, "unexpected sizeof(char) != 1";
diff(3, ..) = 0; /* ignore byte order for type char */
return ! diff(max,);
}
local DBL_EPSILON, DBL_MIN, DBL_MAX;
local FLT_EPSILON, FLT_MIN, FLT_MAX;
extern machine_constant;
/* DOCUMENT machine_constant(str)
* Returns the value of the machine dependent constant given its name
* STR. STR is a scalar string which can be one of (prefixes "FLT_" and
* "DBL_" are for single/double precision respectively):
*
* "FLT_MIN",
* "DBL_MIN" - minimum normalized positive floating-point number;
*
* "FLT_MAX",
* "DBL_MAX" - maximum representable finite floating-point number;
*
* "FLT_EPSILON",
* "DBL_EPSILON" - the difference between 1 and the least value greater
* than 1 that is representable in the given floating point
* type: B^(1 - P);
*
* "FLT_MIN_EXP",
* "DBL_MIN_EXP" - minimum integer EMIN such that FLT_RADIX^(EMIN - 1)
* is a normalized floating-point value;
*
* "FLT_MIN_10_EXP"
* "DBL_MIN_10_EXP" - minimum negative integer such that 10 raised to
* that power is in the range of normalized floating-point
* numbers: ceil(log10(B)*(EMIN - 1));
*
* "FLT_MAX_EXP",
* "DBL_MAX_EXP" - maximum integer EMAX such that FLT_RADIX^(EMAX - 1)
* is a normalized floating-point value;
*
* "FLT_MAX_10_EXP"
* "DBL_MAX_10_EXP" - maximum integer such that 10 raised to that power
* is in the range of normalized floating-point numbers:
* floor(log10((1 - B^(-P))*(B^EMAX)))
*
* "FLT_RADIX" - radix of exponent representation, B;
*
* "FLT_MANT_DIG",
* "DBL_MANT_DIG" - number of base-FLT_RADIX significant digits P in the
* mantissa;
*
* "FLT_DIG",
* "DBL_DIG" - number of decimal digits, Q, such that any floating-point
* number with Q decimal digits can be rounded into a
* floating-point number with P (FLT/DBL_MANT_DIG) radix B
* (FLT_RADIX) digits and back again without change to the Q
* decimal digits:
* Q = P*log10(B) if B is a power of 10
* Q = floor((P - 1)*log10(B)) otherwise
*
*
* SEE ALSO: get_encoding.
*/
DBL_EPSILON = machine_constant("DBL_EPSILON");
DBL_MIN = machine_constant("DBL_MIN");
DBL_MAX = machine_constant("DBL_MAX");
FLT_EPSILON = machine_constant("FLT_EPSILON");
FLT_MIN = machine_constant("FLT_MIN");
FLT_MAX = machine_constant("FLT_MAX");
func symbol_info(____n____)
/* DOCUMENT symbol_info, flags;
or symbol_info, names;
or symbol_info;
Print out some information about Yorick's symbols. FLAGS is a scalar
integer used to select symbol types (as in symbol_names). NAMES is an
array of symbol names. If argument is omitted or undefined, all
defined array symbols get selected (as with FLAGS=3).
SEE ALSO: info, mem_info, symbol_def, symbol_names.*/
{
/* attempt to use _very_ odd names to avoid clash with caller */
if (is_void(____n____)) ____n____ = symbol_names(3);
else if (! is_string(____n____)) ____n____ = symbol_names(____n____);
for (____i____ = 1; ____i____ <= numberof(____n____); ++____i____) {
write, format="%s:", ____n____(____i____);
info, symbol_def(____n____(____i____));
}
}
func mem_info(____a____)
/* DOCUMENT mem_info;
* or mem_info, count;
* Print out some information about memory occupation. If COUNT is
* specified, the COUNT biggest (in bytes) symbols are listed (use
* COUNT<0 to list all symbols sorted by size). Only the memory used by
* Yorick's array symbols (including array of pointers), lists and hash
* tables is considered.
*
* BUGS:
* Symbols
* which are aliases (e.g. by using eq_nocopy) may be considered several
* times.
*
* SEE ALSO:
* symbol_def, symbol_info, symbol_names, mem_clear, fullsizeof.
*/
{
____n____ = symbol_names(3);
____i____ = numberof(____n____);
____s____ = array(long, ____i____);
while (____i____ > 0) {
____s____(____i____) = fullsizeof(symbol_def(____n____(____i____)));
--____i____;
}
____i____ = sum(____s____);
write, format="Total memory used by array symbols: %d bytes (%.3f Mb)\n",
____i____, ____i____/1024.0^2;
if (____a____) {
____i____ = sort(____s____);
if (____a____ > 0 && ____a____ < numberof(____i____))
____i____ = ____i____(1-____a____:0);
____n____ = ____n____(____i____);
____s____ = ____s____(____i____);
____i____ = numberof(____i____);
if (____i____ > 1) {
write, format="The %d biggest symbols are:\n", ____i____;
} else {
write, format="%s", "The biggest symbol is:\n";
}
____a____ = swrite(format=" %%%ds: %%%.0fd bytes,",
max(strlen(____n____)), ceil(log10(max(____s____))));
while (____i____ > 0) {
write, format=____a____, ____n____(____i____), ____s____(____i____);
info, symbol_def(____n____(____i____));
--____i____;
}
}
}
func mem_clear(_____s_____, _____f_____)
/* DOCUMENT mem_clear;
* or mem_clear, minsize;
* or mem_clear, minsize, flags;
*
* *** USE THIS FUNCTION WITH CARE ***
*
* Clear (that is destroy) global symbols larger than MINSIZE bytes
* (default 1024 bytes). Symbol names starting with an underscore
* are not destroyed. Optional argument FLAGS (default 0) is a
* bitwise combination of the following bits:
*
* 0x01 - quiet mode, do not even print the summary.
* 0x02 - verbose mode, print out the names of matched symbols.
* 0x04 - dry-run mode, the symbols are not really destroyed.
*
* When called as a function, returns the number of bytes released.
*
*
* SEE ALSO: symbol_def, symbol_info, symbol_names, fullsizeof.
*/
{
_____t_____ = 0;
if (is_void(_____s_____)) {
_____s_____ = 1024;
}
_____n_____ = symbol_names(1 | 2 | 1024);
_____i_____ = 1 + numberof(_____n_____);
while (--_____i_____ > 0) {
if (strpart(_____n_____(_____i_____), 1:1) != "_") {
_____u_____ = fullsizeof(symbol_def(_____n_____(_____i_____)));
if (_____u_____ > _____s_____) {
if ((_____f_____ & 0x02) != 0) {
write, _____n_____(_____i_____);
}
if ((_____f_____ & 0x04) == 0) {
symbol_set, _____n_____(_____i_____), [];
}
_____t_____ += _____u_____;
}
}
}
if (am_subroutine()) {
if ((_____f_____ & 0x01) == 0) {
write, format="%d bytes of memory cleared\n", _____t_____;
}
} else {
return t;
}
}
func fullsizeof(x)
/* DOCUMENT fullsizeof(x)
* Returns size in bytes of object X. Similar to sizeof (which see)
* function but also works for lists, arrays of pointers, or hash
* tables.
*
* SEE ALSO: sizeof, is_list, is_array, is_hash.
*/
{
if (is_array(x)) {
if (structof(x) == pointer) {
s = 0;
for (k = numberof(x); k >= 1; --k) {
s += fullsizeof(*x(k));
}
return s;
} else {
return sizeof(x);
}
}
if (is_hash(x)) {
s = 0;
for (k = h_first(x); k; k = h_next(x, k)) {
s += fullsizeof(h_get(x, k));
}
return s;
}
if (is_list(x)) {
s = 0;
while (x) {
s += fullsizeof(_car(x));
x = _cdr(x);
}
return s;
}
return sizeof(x);
}
extern insure_temporary;
/* DOCUMENT insure_temporary, var1 [, var2, ...];
Insure that symbols VAR1 (VAR2 ...) are temporary variables referring to
arrays. Useful prior to in-place operations to avoid side-effects for
caller.
SEE ALSO: eq_nocopy, nrefsof, swap, unref. */
extern mem_base;
extern mem_copy;
extern mem_peek;
/* DOCUMENT mem_base(array);
or mem_copy, address, expression;
or mem_peek(address, type, dimlist);
Hacker routines to read/write data at given memory location. These
routines allow the user to de _very_ nasty but sometimes needed things
and do not provide the safety level of ususal Yorick routines, and
must therefore be used with extreme care (you've bee warned). In all
these routines, ADDRESS is either a long integer scalar or a scalar
pointer (e.g. &OBJECT).
mem_base returns the address (as a long scalar) of the first element
of array object ARRAY. You can use this function if you need
to add some offset to the address of an object, e.g. to reach
some particular element of an array or a structure.
mem_copy copy the contents of EXPRESSION at memory location ADDRESS.
mem_peek returns a new array of data type TYPE and dimension list
DIMLIST filled with memory contents starting at address
ADDRESS.
EXAMPLE
The following statement converts the contents of complex array Z as an
array of doubles:
X = mem_peek(mem_base(Z), double, 2, dimsof(Z));
then:
X(1,..) is Z.re
X(2,..) is Z.im
SEE ALSO reshape, native_byte_order. */
func native_byte_order(type)
/* DOCUMENT native_byte_order()
or native_byte_order(type)
Returns the native byte order, one of: "LITTLE_ENDIAN", "BIG_ENDIAN",
or "PDP_ENDIAN". Optional argument TYPE is an integer data type
(default is long).
SEE ALSO mem_peek. */
{
if (is_void(type)) type = long;
size = sizeof(type);
(carr = array(char, size))(*) = indgen(size:1:-1);
value = mem_peek(mem_base(carr), type);
if (size == 4) {
if (value == 0x01020304) {
return "LITTLE_ENDIAN";
} else if (value == 0x04030201) {
return "BIG_ENDIAN";
} else if (value == 0x03040102) {
return "PDP_ENDIAN";
}
} else if (size == 2) {
if (value == 0x0102) {
return "LITTLE_ENDIAN";
} else if (value == 0x0201) {
return "BIG_ENDIAN";
}
}
error, "unknown byte order";
}
local Y_MMMARK, Y_PSEUDO, Y_RUBBER, Y_RUBBER1, Y_NULLER;
local Y_MIN_DFLT, Y_MAX_DFLT;
extern parse_range;
extern make_range;
/* DOCUMENT arr = parse_range(rng);
or rng = make_range(arr);
The parse_range() function converts the index range RNG into an array
of 4 long integers: [FLAGS,MIN,MAX,STEP]. The make_range() function
does the opposite.
For a completely specified range, FLAGS is 1. Otherwise, FLAGS can be
Y_MMMARK for a matrix multiply marker (+) -- which is almost certainly a
syntax error in any other context -- Y_PSEUDO for the pseudo-range index
(-), Y_RUBBER for the .. index, Y_RUBBER1 for the * index, and Y_NULLER
for the result of where(0). Bits Y_MIN_DFLT and Y_MAX_DFLT can be set in
FLAGS to indicate whether the minimum or maximum is defaulted; there is
no flag for a default step, so there is no way to tell the difference
between x(:) and x(::1).
SEE ALSO: is_range.
*/
Y_MMMARK = 2;
Y_PSEUDO = 3;
Y_RUBBER = 4;
Y_RUBBER1 = 5;
Y_NULLER = 6;
Y_MIN_DFLT = 16;
Y_MAX_DFLT = 32;
extern make_dimlist;
/* DOCUMENT make_dimlist(arg1, arg2, ...)
* or make_dimlist, arg1, arg2, ...;
*
* Concatenate all arguments as a single dimension list. The function
* form returns the resulting dimension list whereas the subroutine form
* redefines the contents of its first argument which must be a simple
* variable reference. The resulting dimension list is always of the
* form [NDIMS, DIM1, DIM2, ...].
*
*
* EXAMPLES
*
* In the following example, a first call to make_dimlist is needed to
* make sure that input argument DIMS is a valid dimension list if there
* are no other input arguments:
*
* func foo(a, b, dims, ..)
* {
* // build up dimension list:
* make_dimlist, dims;
* while (more_args()) make_dimlist, dims, next_arg();
* ...;
* }
*
* Here is an other example:
*
* func foo(a, b, ..)
* {
* // build up dimension list:
* dims = [0];
* while (more_args()) make_dimlist, dims, next_arg();
* ...;
* }
*
*
* SEE ALSO: array, build_dimlist.
*/
/*---------------------------------------------------------------------------*/
/* COMPLEX NUMBERS */
func make_hermitian(z, half=, method=, debug=)
/* DOCUMENT zp = make_hermitian(z)
* or make_hermitian, z;
*
* Insure that complex array Z is hermitian (in the FFT sense). The
* resulting hermitian array ZP is such that:
*
* ZP(kneg(k)) = conj(ZP(k))
*
* where k is the index of a given FFT frequency U and kneg(k) is the
* index of -U. When called as a subroutine, the operation is made
* in-place. Input array Z must be complex.
*
* The particular method used to apply the hermitian constraint can be
* set by keyword METHOD. Use METHOD = 0 or undefined to "copy" the
* values:
*
* ZP(k) = Z(k)
* ZP(kneg(k)) = conj(Z(k))
*
* for all indices k such that k < kneg(k) -- in words, the only relevant
* values in input array Z are those which appear before their negative
* frequency counterpart. Use METHOD = 1 to average the values:
*
* ZP(k) = (Z(k) + conj(Z(kneg(k))))/2
* ZP(kneg(k)) = (conj(Z(k)) + Z(kneg(k)))/2
*
* Finally, use METHOD = 2 to "sum" the values (useful to make a gradient
* hermitian):
*
* ZP(k) = Z(k) + conj(Z(kneg(k)))
* ZP(kneg(k)) = conj(Z(k)) + Z(kneg(k))
*
* Set keyword HALF true to indicate that only half the Fourier
* frequencies are stored into Z. This is for instance the case when
* real FFTW is used.
*
*
* SEE ALSO: fft, fftw.
*/
{
if (! am_subroutine()) {
/* force copy */
insure_temporary, z;
}
/* Compute indices of negative frequencies starting by the last
dimension. */
local u, v;
dimlist = dimsof(z);
if ((n = numberof(dimlist)) <= 1) {
/* Same as if: u = v = 1 below */
z.im = 0.0;
return z;
}
flag = 0n;
for (k = n; k >= 2; --k) {
dim = dimlist(k);
/* Compute index J of negative frequency along that dimension. */
if (k == 2) {
/* Cope with first dimension. Index J is 1-based. */
if (half) {
/* Only zero-th frequency along the first dimension has possibly a
negative counterpart in the same array. */
j = [1];
} else {
(j = indgen(dim+1:2:-1))(1) = 1;
}
} else {
/* Other dimension than the first one. Index J is zero-based. */
(j = indgen(dim:1:-1))(1) = 0;
}
if (flag) {
v = j + dim*v(-,..);
} else {
v = j;
flag = 1n;
}
}
u = array(long, dimsof(v));
if (half) {
stride = dimlist(2);
u(*) = indgen(1 : 1 + stride*(numberof(u) - 1) : stride);
} else {
u(*) = indgen(numberof(u));
}
if (debug) {
return [&u, &v];
}
/* Fix frequencies which must be real (at least zero-th frequency). */
z(u(where(u == v))).im = 0.0;
/* Fix other frequencies. */
j = where(u < v);
if (is_array(j)) {
u = u(j);
v = v(j);
if (! method) {
/* apply "copy" method */
z(v) = conj(z(u));
} else if (method == 1) {
/* apply "average" method */
tmp = 0.5*(z(u) + conj(z(v)));
z(u) = tmp;
z(v) = conj(tmp);
} else if (method == 2) {
/* apply "sum" method */
tmp = z(u) + conj(z(v));
z(u) = tmp;
z(v) = conj(tmp);
} else {
error, "bad METHOD";
}
}
return z;
}
/*---------------------------------------------------------------------------*/
/*
* Local Variables:
* mode: Yorick
* tab-width: 8
* c-basic-offset: 2
* indent-tabs-mode: nil
* fill-column: 78
* coding: utf-8
* End:
*/
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