| Planes Required Reading |
Table of ContentsPlanes Required Reading Abstract Introduction References Plane Data Type Description Plane routines Constructing planes Construct a plane from a normal vector and constant Construct a plane from a normal vector and a point Construct a plane from a point and spanning vectors Access plane data elements Examples Converting between representations of planes Translating planes Applying linear transformations to planes Finding the limb of an ellipsoid Header examples Use of ellipses with planes Summary of routines Appendix: Document Revision History 2012 JAN 23, EDW (JPL) 2008 JAN 17, BVS (JPL) 2002 DEC 12, NAIF (JPL) Planes Required Reading
Abstract
Introduction
References
Plane Data Type Description
< X, Y >denote the inner product of the vectors X and Y, then the relationship
< X, N > = Cholds for all vectors X in the plane. C is the distance of the plane from the origin. The vector
C * Nis the closest point in the plane to the origin. For planes that do not contain the origin, the vector N points from the origin toward the plane. The internal design of the plane data type is not part of its specification. The design is an implementation choice based on the programming language and so the design may change. Users should not write code based on the current implementation; such code might fail when used with a future version of Mice. NAIF implemented the SPICE plane data type in Matlab as a structure with the fields
normal: (3x1 ARRAY, DOUBLE) constant: (SCALAR, DOUBLE)'normal' contains the unit normal vector N; 'constant' contains the plane constant C. Plane routinesConstructing planes
The information stored in SPICE planes is not necessarily the input information you supply to a plane-making routine. SPICE planes use a single, uniform internal representation for planes, no matter what data you use to create them. As a consequence, when you create a SPICE plane and then break it apart into data that define a plane, the returned data will not necessarily be the data you originally supplied, though they define the same geometric plane as the data you originally supplied. This `loss of information' may seem to be a liability at first but turns out to be a convenience in the end: the Mice routines that break apart SPICE planes into various representations return outputs that are particularly useful for many geometric computations. In the case of the routine cspice_pl2nvp (Plane to normal vector and point), the output normal vector is always a unit vector, and the output point is always the closest point in the plane to the origin. The normal vector points from the origin toward the plane, if the plane does not contain the origin. You can convert any of the following representations of planes to a SPICE plane:
< X, N > = C.
< X - P, N > = 0.
P + s * V1 + t * V2,
Construct a plane from a normal vector and constant
After `n' and `c' have been assigned values, you can construct a SPICE plane that represents the plane having normal `n' and constant `c' by calling cspice_nvc2pl:
plane = cspice_nvc2pl( n, c ) Construct a plane from a normal vector and a point
After `n' and `p' have been assigned values, you can construct a SPICE plane that represents the plane containing point `p' and having normal `n' by calling cspice_nvp2pl:
plane = cspice_nvp2pl( n, p ) Construct a plane from a point and spanning vectors
After `p', `v1', and `v2' have been assigned values, you can construct a SPICE plane that represents the plane spanned by the vectors V1 and V2 and containing the point P by calling cspice_psv2pl:
plane = cspice_psv2pl( p, v1, v2 ) Access plane data elements
The Mice routines that break planes apart into data that define geometric planes are
To find a unit normal vector `n' and a plane constant `c' that define `plane', use cspice_pl2nvc:
[n, c] = cspice_pl2nvc( plane )The constant `c' is the distance of the plane from the origin. The vector
C * Nwill be the closest point in the plane to the origin. To find a unit normal vector `n' and a point `p' that define `plane', use cspice_pl2nvp:
[n, p] = cspice_pl2nvp( plane )`p' will be the closest point in the plane to the origin. The unit normal vector `n' will point from the origin toward the plane. To find a point `p' and two spanning vectors `v1' and `v2' that define `plane', use cspice_pl2psv:
[p, v1, v2] = cspice_pl2psv( plane )`p' will be the closest point in the plane to the origin. The vectors `v1' and `v2' are mutually orthogonal unit vectors and are also orthogonal to `p'. It is important to note that the xxx2PL and PL2xxx routines are not exact inverses of each other. The pairs of calls
plane = cspice_pl2nvc( n, c ) [n, c] = cspice_pl2nvc( plane ) plane = cspice_nvp2pl( p, n ) [p, n] = cspice_pl2nvp( plane ) plane = cspice_psv2pl( v1, v2, p ) [v1, v2, p] = cspice_pl2psv( plane )do not necessarily preserve the input arguments supplied to the xxx2PL routines. This is because the uniform internal representation of SPICE planes causes them to `forget' what data they were created from; all sets of data that define the same geometric plane have the same internal representation in SPICE planes. In general, the routines cspice_pl2nvc, cspice_pl2nvp, and cspice_pl2psv are used in routines that accept planes as input arguments. In this role, they simplify the routines that call them, because the calling routines no longer check the input planes' validity. ExamplesConverting between representations of planes
plane = cspice_nvc2pl( n, c ) [n, point] = cspice_pl2nvp( plane ) Translating planes
T(X) = X + A for all vectors Xwhere A is a constant vector. While it's not difficult to directly apply a translation map to a plane, using SPICE plane routines provides the convenience of automatically computing the closest point to the origin in the translated plane. Suppose a plane is defined by the point `p' and the normal vector `n', and you wish to translate it by the vector `x'. That is, you wish to find data defining the plane that results from adding `x' to every vector in the original plane. You can do this with the code fragment
p = p + x plane = cspice_nvp2pl( n, p ) [ n, p] = cspice_pl2nvp( plane )Now, `p' is the closest point in the translated plane to the origin. Applying linear transformations to planes
Let T be represented by the matrix M. If Y is a point in the transformed plane, then -1 M Y is a point in the original plane, so -1 < N, M Y > = C. But -1 T -1 < N, M Y > = N M Y -1 T T = ( ( M ) N ) Y -1 T = < ( M ) N, Y > So -1 T ( M ) N, C are, respectively, a normal vector and constant for the transformed plane.We can solve the problem using the following code fragments. Make a SPICE plane from `n' and `c', and then find a point in `plane' and spanning vectors for `plane'. `n' need not be a unit vector.
plane = cspice_nvc2pl( n, c ) [ point, v1, v2 ] = cspice_pl2psv( plane )Apply the linear transformation to the point and spanning vectors. All we need to do is multiply these vectors by M, since for any linear transformation T,
T ( POINT + t1 * V1 + t2 * V2 ) = T (POINT) + t1 * T (V1) + t2 * T (V2),which means that T(POINT), T(V1), and T(V2) are a a point and spanning vectors for the transformed plane.
tpoint = m * point tv1 = m * v1 tv2 = m * v2Construct a new SPICE plane `tplane' from the transformed point and spanning vectors, and find a unit normal and constant for this new plane.
tplane = cspice_psv2pl( tpoint, tv1, tv2 ) [ tn, tc ] = cspice_pl2nvc( tplane ) Finding the limb of an ellipsoid
We'll work in body-fixed coordinates, which is to say that the ellipsoid is centered at the origin and has axes aligned with the x, y and z axes. Suppose that the semi-axes of the ellipsoid has lengths A, B, and C, and call our observation point
P = ( p1, p2, p3 ).Then every point
X = ( x1, x2, x3 )on the limb satisfies
< P - X, N > = 0where N a surface normal vector at X. In particular, the gradient vector
2 2 2 ( x1/A , x2/B , x3/C )is a surface normal, so X satisfies
0 = < P - X, N > 2 2 2 = < P - X, (x1/A , x2/B , x3/C ) > 2 2 2 2 2 2 = < P, (x1/A , x2/B , x3/C ) > - < X, (x1/A , x2/B , x3/C ) > 2 2 2 = < (p1/A , p2/B , p3/C ), X > - 1So the limb plane has normal vector
2 2 2 N = ( p1/A , p2/B , p3/C )and constant 1. We can create a SPICE plane representing the limb with the code fragment
n = [ p(1)/a^2, p(2)/b^2, p(3)/c^2 ] plane = cspice_nvc2pl( n, 1. )The limb is the intersection of the limb plane and the ellipsoid. To find the intersection, we use the Mice routine cspice_inedpl (Intersection of ellipsoid and plane):
[ ellips, found ] = cspice_pjelpl( a, b, c, plane )`ellips' is a SPICE `ellipse', a data type analogous to the SPICE plane. We can use the Mice routine cspice_el2cgv (Ellipse to center and generating vectors) to find the limb's center, semi-major axis, and semi-minor axis:
[ center, smajor, sminor] = cspice_el2cgv( ellips ) Header examples
Use of ellipses with planes
Summary of routines
cspice_inedpl Intersection of ellipsoid and plane cspice_inelpl Intersection of ellipse and plane cspice_inrypl Intersection of ray and plane cspice_nvc2pl Normal vector and constant to plane cspice_nvp2pl Normal vector and point to plane cspice_pjelpl Project ellipse onto plane cspice_pl2nvc Plane to normal vector and constant) cspice_pl2nvp Plane to normal vector and point cspice_pl2psv Plane to point and spanning vectors cspice_psv2pl Point and spanning vectors to plane cspice_vprjp Vector projection onto plane cspice_vprjpi Vector projection onto plane, inverted Appendix: Document Revision History2012 JAN 23, EDW (JPL)
2008 JAN 17, BVS (JPL)
2002 DEC 12, NAIF (JPL)
|


Before I dive into the missteps and mishaps of +F, I’d like to start with one very important thing I did right, in the very early stages.
The original idea for +F came from the tail end of an IM conversation with an old school friend, reminiscing about the computer games we used to play when we were young. A particular favourite of ours was Match Day II (for those of you too young to remember, it was sort of like the best bits of FIFA and PES brought together in a single game, multiplied a hundred times) and we were at the time convinced we would be world champions at it, if such a thing existed. (Sadly, eSports wasn’t to emerge until a good 10 years after we were past our gaming peak, and the world had mistakenly moved on from Match Day II by then anyway.)
I can’t remember who, but one or the other of us signed off by saying I’m sure I could still hammer you, and it put me to thinking … which one of us would actually win? The simple way to solve it would be to get together in front of a computer, fire up an emulator (FUSE, for example) and sort it out. Sadly, at the time, we were on almost exactly opposite sides of the world, and it would have been a long way for either of us to fly simply to settle an old childhood rivalry.
Nonetheless, it planted an idea in my mind. Given how easy it is to make high quality video calls to faraway places, surely there must be no problem in sending a Spectrum screen in one direction, and joystick movements in another, at least in theory? Depending on the distance and quality of network connection, things might chug along a little slowly at times, but the Spectrum was never known as a speed machine, was it?
I toyed with the idea for a while before I started any code. My first thought was whether I could bolt some network play facilities onto an existing open-source emulator, most of which tend to be written in C or some similar low-level language. I rejected this thought fairly quickly as I’m a Linux user and most of my friends use Windows, so I’d also have a whole load of cross-platform issues to wrestle with, once I’d got past the trickier task of understanding somebody else’s C.
So that more or less put me in the position of writing my own emulator from the ground up. It’s a challenge I did relish, but I wasn’t in any illusion about the amount of work it would need. Where to start? More importantly, where to stop? I only wanted to be able to play a couple of games of Match Day II, so there wouldn’t be any need to go overboard with whizzbang features that made no difference to the quality of gameplay.
Those thoughts left we with a very clear vision of what I wanted +F to be:
And there it was, the minimum viable product for +F. I didn’t go as far as setting up a backlog for it or anything like that, but I did realise I had a clear idea of what I wanted, and I was able to use that idea to help me decide where to start, what to do next, and what not to bother with. That’s really important, because even in something with a scope that seems limited, like a Spectrum emulator, there are a lot more things going on than you might expect, and a lot of places you could choose to put your effort.
Take borders. It’s possible to change the colour of the border at specific positions down the screen, and indeed games like Aquaplane use this to create a “horizon” effect that spans the full width of the screen. However, making the change at the correct position is intimately linked with the timing of the Spectrum’s display hardware. The rules for emulating this correctly are very complicated and would take an age to implement and test.
For my purposes, though, because I had already established Match Day II was my priority, and because it doesn’t do anything at all the border (it’s black the whole time) it was an easy choice for me not to care about this at all. This sort of focus that I had because I had a vision of what I wanted +F to be saved me from disappearing off into the more arcane aspects of building an emulator.
Now, of course there’s a difference between me writing +F in my spare time and you working on a team every day in the face of deadlines, shifting requirements and feature creep. But I think my point stands. If you’re somehow able to establish a clear idea of what you’re building is supposed to do, you’ll stand a better chance of directing your efforts in the right places. If you’re faced with a piece of work and you can’t answer the question do we need this? with a simple yes or no, it’s worth figuring out why not.


Lease gives Perth soccer stadium new life
by Laura PondFLOREAT Athena Football Club has secured its home for the next decade.
The club will remain at Litis Stadium in Mt Hawthorn until at least 2030 after Vincent Council approved a 10-year lease at its November 12 meeting, which would also see greater community access to the facility.
It followed two years of negotiations between the soccer club and City.
Secretary Emmanuel Takoniatis said one year had remained on its existing lease, which was at the City’s discretion.
“A week ago there was a proposal to grant the club just a five-year lease with a five-year option, again at the City’s discretion,” he said.
“So to be able to persuade council enough to give us long tenure has been a significant challenge.”
Mr Takoniatis said the long-term lease would allow it to continue its focus on developing junior players.
RELATED: Barrier to stadium access removed
Vincent mayor Emma Cole said under the agreement, from October 2020 the club would lease the clubrooms, adjacent tiered seating and have stadium access from April to September while the City would manager the remainder of the stadium.
“As a growing, inner city community we want to make the most of all of our parks, reserves and open spaces,” she said.
“The new tenure arrangements for Litis Stadium will maximise our open space and allow our community to enjoy this historic sporting venue.”
The City wants to integrate the stadium with adjoining Britannia Reserve by removing the perimeter fencing.
Ms Cole said it would seek expressions of interest next year from other sports and community groups to use the facility during summer.
She said they would also work with the club to secure a $3 million Federal Government contribution, previously promised by One Nation Senator Peter Georgiou in August 2018, through the Community Development Grants program.
At the time, the funding announcement blindsided the City and the club later revealed it had narrowly avoided financial collapse thanks to donations.


Disallows default exports in ES6-style modules.
Use named exports instead.
Named imports/exports promote clarity. In addition, current tooling differs on the correct way to handle default imports/exports. Avoiding them all together can help avoid tooling bugs and conflicts.
Not configurable.
"no-default-export": true
{}


TITLE 4: MISCELLANEOUS PROVISIONS
§401. All Acts or parts of Acts, either of the Congress of the United States or of the State of Hawaii, to the extent that they are inconsistent with the provisions of this Act, are hereby repealed.


We provide Products & Services that are amazingly creative.


Source code for profileTypePars
'''
File: profileTypePars.py
Created: 23/08/2014
@author: Francesco Ciaccia
Description:
This script take as input the profyleType json file and computes the TVD topology to be sent back
to the Orchestror
'''
import requests
import json
import logging
from pscExceptions import pscExceptions
from tvdTop import tvdTop
import utils
[docs]class profileTypePars():
def __init__(self, orchAddr, configsPath):
self.orchAddr = orchAddr
self.conf_URIs_list = {}
self.conf_list = {}
self.sg_dict = {}
self.configsPath = configsPath
[docs] def requestProfileType(self):
logging.info("Sending profile type request to Orchestrator...")
# We don't need to send the token, actually we need to get it from the TVDM
#body = {"token":self.token}
header = {'Accept': 'application/json', 'Content-Type':'application/json'}
# check: body of GET should not contain anything influencing the decision of the server
# use post for that
#resp = requests.get(self.orchAddr+"/getGraph", data=json.dumps(body), headers=header)
resp = requests.get(self.orchAddr+"/getGraph", headers=header)
logging.info("Orchestrator replied: "+str(resp.status_code)+" "+resp.text)
return resp
[docs] def parsProfile(self, resp):
# convert resp body to JSON-like string
proType = json.loads(resp.text)
# Receives the profileType json object to be processed
if (proType['name'] == "user_profile_type"):
try:
# resolveTVDtop generates the service graph structure in form of a dictionary
self.TVDman = tvdTop(self.sg_dict)
self.sg_dict = self.TVDman.resolveTVDtop(proType)
except Exception as exc:
#logging.error("Unable to write to file "+path+"/"+v_conf)
logging.error("TVDman error:" + str(exc.message))
raise exc
# the service graph dictionary is encoded in json format according to the
# serviceGraph schema and sent to the Orchestrator
body = json.dumps(self.sg_dict)
header = {'Accept': 'application/json', 'Content-Type':'application/json'}
# sends the service graph to the Orchestrator
logging.info("Sending PSA service graph instantiation request to Orchestrator...")
resp = requests.put(self.orchAddr+"/createPSA", data=body, headers=header)
if (resp.status_code != 200):
logging.error("Orchestrator failed to instatiate TVD error: "+resp.status_code)
raise pscExceptions.TVDnotInstantiated("Orchestator response failure")
logging.info("Orchestrator replied: "+str(resp.status_code)+" "+resp.text)
# scan PSAs list and retrieve configuration files URIs
PSAList = self.sg_dict['PSAs']
# Create path if not existing
utils.mkdir_p(self.configsPath)
# Save the list of PSA's, i.e., the user graph
with open(self.configsPath + "/psa_list", 'wb') as f:
f.write(json.dumps(PSAList))
for psa in PSAList:
self.conf_URIs_list[psa['id']] = psa['conf']
# petition for PSA configuration file
# TODO adapt TVDM to reply to configuration file request (aka register every conf file as a resource on the TVDM interface)
for k_id, v_conf in self.conf_URIs_list.iteritems():
logging.info("Sending request of conf file for PSA "+k_id)
header = {'Content-Type':'application/octet-stream'}
resp = requests.get(self.orchAddr+"/getConf/"+k_id+"/"+v_conf, headers=header)
#resp = requests.get(self.orchAddr+"/getConf/"+k_id+"_"+v_conf, headers=header)
logging.info("Orchestrator replied: "+str(resp.status_code))
if (resp.status_code == requests.codes.ok):
#path=self.configsPath+"/"+k_id
#utils.mkdir_p(self.configsPath)
try:
#fp=open(path+"/"+v_conf,'wb')
fp=open(self.configsPath+"/"+k_id,'wb')
fp.write(resp.content)
fp.close()
logging.info("PSA "+k_id+" configuration "+v_conf+" registered")
except IOError as exc:
#logging.error("Unable to write to file "+path+"/"+v_conf)
logging.error("Unable to write to file "+self.configsPath+"/"+k_id)
raise exc
else:
logging.error("Bad configuration request for PSA "+k_id)
raise pscExceptions.PSAconfNotFound("Orchestator response failure")
# # send requests for the PSAs configuration files to the Orchestator
# logging.info("Sending PSA configuration files request to Orchestrator...")
# body = json.dumps(self.conf_URIs_list)
# header = {'Accept': 'application/json', 'Content-Type':'application/json'}
# resp = requests.post(self.orchAddr+"/getConf", data=body, headers=header)
# logging.info("Orchestrator replied: "+resp.status_code+" "+resp.text)
# # receives PSAs configuration files
# sjson = json.loads(resp.text)
# #rjson = json.loads(sjson)
# self.conf_list = sjson['configurations']
else:
raise pscExceptions.pscscWrongProfileType("Wrong profile type file: [name] field is not as expected")
# def getPSAconf(self, psaID):
# for psa in self.conf_list:
# if (psa['PSA_id'] == psaID):
# return psa['conf_script']
# raise pscExceptions.PSAconfNotFound("Configuration file for the requested PSA has not been found or does not exist")


WITNESS LIST S/C on Higher Education Committee April 25, 2005 - 1:30P SB 1844 FOR: Treacy, William Executive Director (Tx State Board of Public Accountancy), Austin, TX


Unimplemented feature
This is a sample page that will not be implemented until there is a requirement.


Who am I?
My name is Eesh Chilukalapalli and I'm a programmer and a creator. I am super passionate about making software because it is soooooo easy to give life to the ideas that pop up inside of my head. It is also amazing how even a small piece of software can be an important part of someone's life!
I'm currently pursuing my Bachelor's degree in software engineering at SRM University, Chennai. So far the journey has been great and I've learnt a lot of valuable lessons. Once I graduate, I wish to pursue a Master's degree in the field of artificial intelligence and machine learning. I love helping people and one day I wish to create a large-scale product that will become an integral part of people's lives (for the better). I also love tinkering with robots and I think they have a lot of potential. A small set of emotions can make them feel a lot more personal than they are right now.
My hobbies include motorcycling, photography, and music. I love photography for the same reason I love making software. Just like how software allows me to materialize the ideas I get in my head, a camera allows me to capture the scenes that I picture in my head. I play the guitar and I'm currently learning how to play the piano. The only piece I can play right now is 'River flow in you' by Yiruma. I find that piece extremely soothing and always listen to it with my eyes closed. I also sing from time to time but until I can make the recordings sound professional I'm not going to go into details :D


16.2. InstantiationA template is a blueprint; it is not itself a class or a function. The compiler uses the template to generate type-specific versions of the specified class or function. The process of generatng a type-specific instance of a template is known as instantiation. The term reflects the notion that a new "instance" of the template type or function is created. A template is instantiated when we use it. A class template is instantiated when we refer to the an actual template class type, and a function template is instantiated when we call it or use it to initialize or assign to a pointer to function. Instantiating a ClassWhen we write Queue<int> qi; the compiler automatially creates a class named Queue<int>. In effect, the compiler creates the Queue<int> class by rewriting the Queue template, replacing every occurrence of the template parameter Type by the type int. The instantiated class is as if we had written: // simulated version of Queue instantiated for type int template <class Type> class Queue<int> { public: Queue(); // this bound to Queue<int>* int &front(); // return type bound to int const int &front() const; // return type bound to int void push(const int &); // parameter type bound to int void pop(); // type invariant code bool empty() const; // type invariant code private: // ... }; To create a Queue class for objects of type string, we'd write: Queue<string> qs; In this case, each occurrence of Type would be replaced by string.
Class Template Arguments Are RequiredWhen we want to use a class template, we must always specify the template arguments explicitly.
Queue qs; // error: which template instantiation?
A class template does not define a type; only a specific instantiation defines a type. We define a specific instantiation by providing a template argument to match each template parameter. Template arguments are specified in a comma-separated list and bracketed by the (<) and (>) tokens: Queue<int> qi; // ok: defines Queue that holds ints Queue<string> qs; // ok: defines Queue that holds strings The type defined by a template class always includes the template argument(s). For example, Queue is not a type; Queue<int> or Queue<string> are. Function-Template InstantiationWhen we use a function template, the compiler will usually infer the template arguments for us: int main() { compare(1, 0); // ok: binds template parameter to int compare(3.14, 2.7); // ok: binds template parameter to double return 0; } This program instantiates two versions of compare: one where T is replaced by int and the other where it is replaced by double. The compiler essentially writes for us these two instances of compare: int compare(const int &v1, const int &v2) { if (v1 < v2) return -1; if (v2 < v1) return 1; return 0; } int compare(const double &v1, const double &v2) { if (v1 < v2) return -1; if (v2 < v1) return 1; return 0; } 16.2.1. Template Argument DeductionTo determine which functions to instantiate, the compiler looks at each argument. If the corresponding parameter was declared with a type that is a type parameter, then the compiler infers the type of the parameter from the type of the argument. In the case of compare, both arguments have the same template type: they were each declared using the type parameter T. In the first call, compare(1, 0), those arguments are type int; in the second, compare(3.14, 2.7), they have type double. The process of determining the types and values of the template arguments from the type of the function arguments is called template argument deduction. Multiple Type Parameter Arguments Must Match ExactlyA template type parameter may be used as the type of more than one function parameter. In such cases, template type deduction must generate the same template argument type for each corresponding function argument. If the deduced types do not match, then the call is an error: template <typename T> int compare(const T& v1, const T& v2) { if (v1 < v2) return -1; if (v2 < v1) return 1; return 0; } int main() { short si; // error: cannot instantiate compare(short, int) // must be: compare(short, short) or // compare(int, int) compare(si, 1024); return 0; } This call is in error because the arguments to compare don't have the same type. The template argument deduced from the first argument is short; the one for the second is int. These types don't match, so template argument deduction fails. If the designer of compare wants to allow normal conversions on the arguments, then the function must be defined with two type parameters:
// argument types can differ, but must be compatible
template <typename A, typename B>
int compare(const A& v1, const B& v2)
{
if (v1 < v2) return -1;
if (v2 < v1) return 1;
return 0;
}
Now the user may supply arguments of different types:
short si;
compare(si, 1024); // ok: instantiates compare(short, int)
However, a < operator must exist that can compare values of those types. Limited Conversions on Type Parameter ArgumentsConsider the following calls to compare: short s1, s2; int i1, i2; compare(i1, i2); // ok: instantiate compare(int, int) compare(s1, s2); // ok: instantiate compare(short, short) The first call generates an instance of compare with T bound to int. A new instance is created for the second call, binding T to short. Had compare(int, int) been an ordinary nontemplate function, then the second call would match that function. The short arguments would be promoted (Section 5.12.2, p. 180) to int. Because compare is a template, a new function is instantiated with the type parameter bound to short. In general, arguments are not converted to match an existing instantiation; instead, a new instance is generated. There are only two kinds of conversions that the compiler will perform rather than generating a new instantiation:
As examples, consider calls to the functions fobj and fref. The fobj function copies its parameters, whereas fref's parameters are references: template <typename T> T fobj(T, T); // arguments are copied template <typename T> T fref(const T&, const T&); // reference arguments string s1("a value"); const string s2("another value"); fobj(s1, s2); // ok: calls f(string, string), const is ignored fref(s1, s2); // ok: non const object s1 converted to const reference int a[10], b[42]; fobj(a, b); // ok: calls f(int*, int*) fref(a, b); // error: array types don't match; arguments aren't converted to pointers In the first case, we pass a string and a const string as arguments. Even though these types do not match exactly, both calls are legal. In the call to fobj, the arguments are copied, so whether the original object is const doesn't matter. In the call to fref, the parameter type is a reference to const. Conversion to const for a reference parameter is one of the acceptable conversions, so this call is also okay. In the next case, we pass array arguments in which the arrays are different sizes. In the call to fobj, the fact that the arrays are different doesn't matter. Both arrays are converted to pointers. The template parameter type in fobj is int*. The call to fref, however, is illegal. When the parameter is a reference (Section 7.2.4, p. 240), the arrays are not converted to pointers. The types of a and b don't match, so the call is in error. Normal Conversions Apply for Nontemplate Arguments
Normal conversions (Section 7.1.2, p. 229) are allowed for parameters defined using ordinary types. The following function template sum has two parameters: template <class Type> Type sum(const Type &op1, int op2) { return op1 + op2; } The first parameter, op1, has a template parameter type. Its actual type cannot be known until the function is used. The type of the second parameter, op2, is known: It's int. Because the type of op2 is fixed, normal conversions can be applied to arguments passed to op2 when sum is called: double d = 3.14; string s1("hiya"), s2(" world"); sum(1024, d); // ok: instantiates sum(int, int), converts d to int sum(1.4, d); // ok: instantiates sum(double, int), converts d to int sum(s1, s2); // error: s2 cannot be converted to int In the first two calls, the type of the second argument dd is not the same as the type of the corresponding function parameter. However, these calls are okay: There is a conversion from double to int. Because the type of the second parameter does not depend on a template parameter, the compiler will implicitly convert dd. The first call causes the function sum(int, int) to be instantiated; sum(double, int) is instantiated by the second call. The third call is an error. There is no conversion from string to int. Using a string argument to match an int parameter is, as usual, illegal. Template Argument Deduction and Function PointersWe can use a function template to initialize or assign to a function pointer (Section 7.9, p. 276). When we do so, the compiler uses the type of the pointer to instantiate a version of the template with the appropriate template argument(s). As an example, assume we have a function pointer that points to a function returning an int that takes two parameters, each of which is a reference to a const int. We could use that pointer to point to an instantiation of compare: template <typename T> int compare(const T&, const T&); // pf1 points to the instantiation int compare (const int&, const int&) int (*pf1) (const int&, const int&) = compare; The type of pf1 is "pointer to function returning an int taking two parameters of type const int&." The type of the parameters in pf1 determines the type of the template argument for T. The template argument for T is int. The pointer pf1 refers to the instantiation with T bound to int.
It is an error if the template arguments cannot be determined from the function pointer type. For example, assume we have two functions named func. Each function takes a pointer to function argument. The first version of func takes a pointer to a function that has two const string reference parameters and returns a string. The second version of func takes a pointer to a function taking two const int reference parameters and returning an int. We cannot use compare as an argument to func: // overloaded versions of func; each take a different function pointer type void func(int(*) (const string&, const string&)); void func(int(*) (const int&, const int&)); func(compare); // error: which instantiation of compare? The problem is that by looking at the type of func's parameter, it is not possible to determine a unique type for the template argument. The call to func could instantiate either of the following functions: compare(const string&, const string&) compare(const int&, const int&) Because it is not possible to identify a unique instantiation for the argument to func, this call is a compile-time (or link-time) error. 16.2.2. Function-Template Explicit ArgumentsIn some situations, it is not possible to deduce the types of the template arguments. This problem arises most often when a function return type must be a type that differs from any used in the parameter list. In such situations, it is necessary to override the template argument deduction mechanism and explicitly specify the types or values to be used for the template parameters. Specifying an Explicit Template ArgumentConsider the following problem. We wish to define a function template called sum that takes arguments of two differnt types. We'd like the return type to be large enough to contain the sum of two values of any two types passed in any order. How can we do that? How should we specify sum's return type? // T or U as the returntype? template <class T, class U> ??? sum(T, U); In this case, the answer is that neither parameter works all the time. Using either parameter is bound to fail at some point: // neither T nor U works as return type sum(3, 4L); // second type is larger; want U sum(T, U) sum(3L, 4); // first type is larger; want T sum(T, U) One approach to solving this problem would be to force callers of sum to cast (Section 5.12.4, p. 183) the smaller type to the type we wish to use as the result: // ok: now either T or U works as return type int i; short s; sum(static_cast<int>(s), i); // ok: instantiates int sum(int, int) Using a Type Parameter for the Return TypeAn alternative way to specify the return type is to introduce a third template parameter that must be explicitly specified by our caller: // T1 cannot be deduced: it doesn't appear in the function parameter list template <class T1, class T2, class T3> T1 sum(T2, T3); This version adds a template parameter to specify the return type. There is only one catch: There is no argument whose type can be used to infer the type of T1. Instead, the caller must explicitly provide an argument for this parameter on each call to sum. We supply an explicit template argument to a call in much the same way that we define an instance of a class template. Explicit template arguments are specified in a comma-separated list, bracketed by the less-than (<) and greater-than (>) tokens. The list of explicit template types appears after the function name and before the argument list: // ok T1 explicitly specified; T2 and T3 inferred from argument types long val3 = sum<long>(i, lng); // ok: calls long sum(int, long) This call explicitly specifies the type for T1. The compiler deduces the types for T2 and T3 from the arguments passed in the call. Explicit template argument(s) are matched to corresponding template parameter(s) from left to right; the first template argument is matched to the first template parameter, the second argument to the second parameter, and so on. An explicit template argument may be omitted only for the trailing (rightmost) parameters, assuming these can be deduced from the function parameters. If our sum function had been written as
// poor design: Users must explicitly specify all three template parameters
template <class T1, class T2, class T3>
T3 alternative_sum(T2, T1);
then we would always have to specify arguments for all three parameters: // error: can't infer initial template parameters long val3 = alternative_sum<long>(i, lng); // ok: All three parameters explicitly specified long val2 = alternative_sum<long, int, long>(i, lng); Explicit Arguments and Pointers to Function TemplatesAnother example where explicit template arguments would be useful is the ambiguous program from page 641. We could disambiguate that case by using explicit template argument: template <typename T> int compare(const T&, const T&); // overloaded versions of func; each take a different function pointer type void func(int(*) (const string&, const string&)); void func(int(*) (const int&, const int&)); func(compare<int>); // ok: explicitly specify which version of compare As before, we want to pass an instantiation of compare in the call to the overloaded function named func. It is not possible to select which instantiation of compare to pass by looking at the parameter lists for the different versions of func. Two different instantiations of compare could satisfy the call. The explicit template argument indicates which instantiation of compare should be used and which func function is called. |


fourth
fifth
sixth
A link to my favorite website.
Your browser doesn't support HTML5 video.Here is a link to the video instead.
Your browser doesn't support HTML5 audio.Here is a link to the audio instead.


interface PIDOutput : Any
This interface allows PIDController to write it's results to its output.
| pidWrite |
abstract fun pidWrite(output: Double): Unit
Set the output to the value calculated by PIDController. |
| SimplePIDController |
open class SimplePIDController : PIDInterface, PIDOutput
Class implements a PIDVA Control Loop. |


My Demo Website
My Photos
(current)
My Videos
About Me
My Photos
Photos for traveling record.
(You can click picture then system will popup it.)
Dadaocheng
Leofoo Village Theme Park
Chūshojima Station
HIDA TAKAYAMA
GION - YASAKA JINJA
Airplane
Go Kart
Outdoor Sports Guest House
Taipei 101


You can download minecraft by going here. There, you will see options of where and what to download based on your device. Java edition is exclusive to Windows PCs and Macs, Bedrock edition is available on Adnroid, iOS, Xbox, and Playstation.


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Choose your own email adventure
Which of the following emails are you most likely to read?
Choose from the 2 options below
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@(crew : String, lostMember: String, crewMembers: String)
This way is not looking very promising. You keep going in and out of bays trying to find a way east but you always run into land. You are afraid you may run aground and be eaten by one of the roving bands of feral kittens you see frolicking upon the shore.
You lost @lostMember! Current crew size: @crew
Remaining Crew Members: @crewMembers


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Fun galore!
Come check out our wonderful activities while you stay!
Volleyball
Sharkswimming
Picking up trash


| Modules in the API Project | |
| Module | Function |
|---|---|
udkapi |
Contains the part of the API
which belongs to the UNO core.
These interfaces are written in the IDL language. |
offapi |
Contains the part of the API
which is special to OpenOffice components.
These interfaces are written in the IDL language. |
offuh |
Generates UNO headers for
C++.
For each new code-generating file in one of the API modules, these makefiles must be updated. |
odk |
The module builds the first step of the Apache OpenOffice Software Development Kit (SDK). |
bean |
The module contains the OfficeBean. |
unodevtools |
The module contains the uno-skeletonmaker tool. A command line tool to generate code skeletons for UNO components in C++ and Java. It is used in the NetBeans and Eclipse integration to generate the code skeletons. |
| Modules in other Projects, related to the API Project | |
| Module | Function |
unoil |
Generates Java class files.
For each new code-generating file in the offapi module, these makefiles must be updated. |
ridljar |
Generates Java class files.
For each new code-generating file in the udkapi module, these makefiles must be updated. Furthermore, the module contains some Java UNO core APIs (e.g. Any, ...). |


--- title: Canon Bootstrap - Get Started description: Getting Started Guide layout: content.html ---
Coming Soon


 More methods, see Methods
| Name | Type | Default | Description |
|---|---|---|---|
| title | string | 'Title' | Title of the dialog |
| message | string or HtmlElement | '' | Content of the modal body |
| cssClass | string | '' | CSS class name is added to the dialog |
| type | string | BootstrapDialog.TYPE.DEFAULT | |
| width | string | '' | define width of the dialog |
| autodestroy | boolean | true | the dialog element would be removed from the page when closed if true |
| backdrop | boolean | true | if true, click outside will close the dialog |
| keyboard | boolean | true | if true, press ESC will close the dialog |
| buttons | Array |
[{ label: 'Close', type: BootstrapDialog.TYPE.DEFAULT, action: function(self, context) { context.close(); } }] |
Refer to the buttonOption |
| onShow | Function | null |
This event fires immediately when the show instance method is called. If caused by a click, the clicked element is available as the relatedTarget property of the event. |
| onShown | Function | null |
This event is fired when the modal has been made visible to the user (will wait for CSS transitions to complete). If caused by a click, the clicked element is available as the relatedTarget property of the event. |
| onHide | Function | null |
This event is fired immediately when the hide instance method has been called. |
| onHidden | Function | null |
This event is fired when the modal has finished being hidden from the user (will wait for CSS transitions to complete). |
| onLoaded | Function | null |
This event is fired when the modal has loaded content using the remote option. Not support yet. |
| Name | Return | Description |
|---|---|---|
| open() | The dialog instance | The dialog will be appended to body, and shows on the screen |
| close() | The dialog instance | The dialog will be hidden from the screen |
| setTitle(title) | The dialog instance | update the title of the dialog with the argument 'title' |
| getTitle() | The dialog title as string | |
| setMessage(message) | The dialog instance | Update the content of the dialog with the argument 'message', it could be a string or HTMLElement |
| getModalTitle() | HTMLElement | Get the '.modal-title' of the dialog |
| getModalHeader() | HTMLElement | Get the '.modal-header' of the dialog |
| getModalBody() | HTMLElement | Get the '.modal-body' of the dialog |
| getModalFooter() | HTMLElement | Get the '.modal-footer' of the dialog |
| getButtonList() | Array | The buttons in the modal footer, the element of the Array is a button view. |


Nita Ambani named honorary board member for the New York’s Met Museum trust
by FilmfareThe Metropolitan Museum of Art in New York is one of the largest and the most prestigious museums in the world. The trust of the MET museum, headed by chairperson Daniel Bordsky named Indian philanthropist and chairperson of Reliance Foundation, Nita Ambani an honorary member of the prestigious board.
Talking about her association with the museum, chairman Brodsky said, “She has had an enormous impact on the museum's ability to study and display art from every corner of the world.” Mrs. Nita Ambani has helped organise several exhibitions over the years and has lent a helping hand to the museum. In 2017, she spoke of her association with the MET and said, “The Met enables Indian art to be shown at an iconic institution and further encourages us to continue our work in the art world.”
With Nita Ambani named as the first ever Indian to join the board of member, this marks a big milestone for the country on a global level.


The map below will show you where to find the selected trailhead.
Enjoy your next OUTDoorsy adventure!
Search a city by name to find 10 great hiking trails in and around that city.
Next, click a trail and check back here to see a summary of what your next OUTDoorsy adventure might look like.
Enjoy your next OUTDoorsy adventure!


Not sure if this is something you can figure out from the web; but man it would be great to know how much time is left on a server... or the lowest ticket count... or the current score. I waste more time joining a server that is almost finished or loosing by 300+ tickets. Having a snapshot of what is going on before I join would be nice.
BrainFooLong
Administrator
I think any of that information is nowhere available because its simple not saved to a server.
But i take some time to check it.
Greetz
Topicstarter
Guest
Cool thanks, ill do some digging in the DOM today and see if its there somewhere...
Topicstarter
Guest
roland, i've been digging around in the DOM, I haven't found anything easily accessible yet but I have some clues. Do you have a was I can email or contact you through something other than this request form?
BrainFooLong
Administrator
Yes, just look at "Contact" Page, there is my email.
Greetz
BrainFooLong
Administrator
Marked as, currently, impossible. Data is not available anywhere at the moment.


Andean peaks, Amazonian rainforest, Patagonian glaciers, Incan ruins, colonial towns, white-sand beaches and vertiginous nightlife: the wonders of South America set the stage for incredible adventures.
From the nomadic steppes of Kazakhstan to the frenetic streets of Hanoi, Asia is a continent so full of intrigue, adventure, solace and spirituality that it has fixated and confounded travellers for centuries.
There simply is no way to tour Europe and not be awestruck by its natural beauty, epic history and dazzling artistic and culinary diversity.
Australia is the unexpected: a place where the world’s oldest cultures share vast ochre plains, stylish laneways and unimaginably blue waters with successive waves of new arrivals from across the globe.
Africa. There's nowhere like it on the planet for wildlife, wild lands and rich traditions that endure. Prepare to fall in love.
The heart of North America beats through towering forests, undulating fields, high-plain deserts, pulsating metropolises and offbeat oases.
Responsible for the overall success of a business entity or other organization and for making top-level managerial decisions
Pilot of ours company plane.
Advice people what travel is the best.


Copyright (c) 2003, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from U.S. Dept. of Energy)
All rights reserved.
The source code is distributed under BSD license, see the file License.txt at the top-level directory.
==========================================================================
=== colamd/symamd - a sparse matrix column ordering algorithm ============
==========================================================================
colamd: an approximate minimum degree column ordering algorithm,
for LU factorization of symmetric or unsymmetric matrices,
QR factorization, least squares, interior point methods for
linear programming problems, and other related problems.
symamd: an approximate minimum degree ordering algorithm for Cholesky
factorization of symmetric matrices.
Purpose:
Colamd computes a permutation Q such that the Cholesky factorization of
(AQ)'(AQ) has less fill-in and requires fewer floating point operations
than A'A. This also provides a good ordering for sparse partial
pivoting methods, P(AQ) = LU, where Q is computed prior to numerical
factorization, and P is computed during numerical factorization via
conventional partial pivoting with row interchanges. Colamd is the
column ordering method used in SuperLU, part of the ScaLAPACK library.
It is also available as built-in function in MATLAB Version 6,
available from MathWorks, Inc. (https://www.mathworks.com). This
routine can be used in place of colmmd in MATLAB.
Symamd computes a permutation P of a symmetric matrix A such that the
Cholesky factorization of PAP' has less fill-in and requires fewer
floating point operations than A. Symamd constructs a matrix M such
that M'M has the same nonzero pattern of A, and then orders the columns
of M using colmmd. The column ordering of M is then returned as the
row and column ordering P of A.
Authors:
The authors of the code itself are Stefan I. Larimore and Timothy A.
Davis (davis.nosp@m.@cis.nosp@m.e.ufl.nosp@m..edu), University of Florida. The algorithm was
developed in collaboration with John Gilbert, Xerox PARC, and Esmond
Ng, Oak Ridge National Laboratory.
Date:
September 8, 2003. Version 2.3.
Acknowledgements:
This work was supported by the National Science Foundation, under
grants DMS-9504974 and DMS-9803599.
Copyright and License:
Copyright (c) 1998-2003 by the University of Florida.
All Rights Reserved.
THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
EXPRESSED OR IMPLIED.
Permission is hereby granted to use, copy, modify, and/or distribute
this program, provided that the Copyright, this License, and the
Availability of the original version is retained on all copies and made
accessible to the end-user of any code or package that includes COLAMD
or any modified version of COLAMD.
Availability:
The colamd/symamd library is available at
https://web.archive.org/web/20141010162709/http://www.cise.ufl.edu/research/sparse/colamd/
This is the https://web.archive.org/web/20040113022019/http://www.cise.ufl.edu/research/sparse/colamd/colamd.c
file. It requires the colamd.h file. It is required by the colamdmex.c
and symamdmex.c files, for the MATLAB interface to colamd and symamd.
See the ChangeLog file for changes since Version 1.0.
==========================================================================
=== Description of user-callable routines ================================
==========================================================================
----------------------------------------------------------------------------
colamd_recommended:
----------------------------------------------------------------------------
C syntax:
#include "colamd.h"
int colamd_recommended (int nnz, int n_row, int n_col) ;
or as a C macro
#include "colamd.h"
Alen = COLAMD_RECOMMENDED (int nnz, int n_row, int n_col) ;
Purpose:
Returns recommended value of Alen for use by colamd. Returns -1
if any input argument is negative. The use of this routine
or macro is optional. Note that the macro uses its arguments
more than once, so be careful for side effects, if you pass
expressions as arguments to COLAMD_RECOMMENDED. Not needed for
symamd, which dynamically allocates its own memory.
Arguments (all input arguments):
int nnz ; Number of nonzeros in the matrix A. This must
be the same value as p [n_col] in the call to
colamd - otherwise you will get a wrong value
of the recommended memory to use.
int n_row ; Number of rows in the matrix A.
int n_col ; Number of columns in the matrix A.
----------------------------------------------------------------------------
colamd_set_defaults:
----------------------------------------------------------------------------
C syntax:
#include "colamd.h"
colamd_set_defaults (double knobs [COLAMD_KNOBS]) ;
Purpose:
Sets the default parameters. The use of this routine is optional.
Arguments:
double knobs [COLAMD_KNOBS] ; Output only.
Colamd: rows with more than (knobs [COLAMD_DENSE_ROW] * n_col)
entries are removed prior to ordering. Columns with more than
(knobs [COLAMD_DENSE_COL] * n_row) entries are removed prior to
ordering, and placed last in the output column ordering.
Symamd: uses only knobs [COLAMD_DENSE_ROW], which is knobs [0].
Rows and columns with more than (knobs [COLAMD_DENSE_ROW] * n)
entries are removed prior to ordering, and placed last in the
output ordering.
COLAMD_DENSE_ROW and COLAMD_DENSE_COL are defined as 0 and 1,
respectively, in colamd.h. Default values of these two knobs
are both 0.5. Currently, only knobs [0] and knobs [1] are
used, but future versions may use more knobs. If so, they will
be properly set to their defaults by the future version of
colamd_set_defaults, so that the code that calls colamd will
not need to change, assuming that you either use
colamd_set_defaults, or pass a (double *) NULL pointer as the
knobs array to colamd or symamd.
----------------------------------------------------------------------------
colamd:
----------------------------------------------------------------------------
C syntax:
#include "colamd.h"
int colamd (int n_row, int n_col, int Alen, int *A, int *p,
double knobs [COLAMD_KNOBS], int stats [COLAMD_STATS]) ;
Purpose:
Computes a column ordering (Q) of A such that P(AQ)=LU or
(AQ)'AQ=LL' have less fill-in and require fewer floating point
operations than factorizing the unpermuted matrix A or A'A,
respectively.
Returns:
TRUE (1) if successful, FALSE (0) otherwise.
Arguments:
int n_row ; Input argument.
Number of rows in the matrix A.
Restriction: n_row >= 0.
Colamd returns FALSE if n_row is negative.
int n_col ; Input argument.
Number of columns in the matrix A.
Restriction: n_col >= 0.
Colamd returns FALSE if n_col is negative.
int Alen ; Input argument.
Restriction (see note):
Alen >= 2*nnz + 6*(n_col+1) + 4*(n_row+1) + n_col
Colamd returns FALSE if these conditions are not met.
Note: this restriction makes an modest assumption regarding
the size of the two typedef's structures in colamd.h.
We do, however, guarantee that
Alen >= colamd_recommended (nnz, n_row, n_col)
or equivalently as a C preprocessor macro:
Alen >= COLAMD_RECOMMENDED (nnz, n_row, n_col)
will be sufficient.
int A [Alen] ; Input argument, undefined on output.
A is an integer array of size Alen. Alen must be at least as
large as the bare minimum value given above, but this is very
low, and can result in excessive run time. For best
performance, we recommend that Alen be greater than or equal to
colamd_recommended (nnz, n_row, n_col), which adds
nnz/5 to the bare minimum value given above.
On input, the row indices of the entries in column c of the
matrix are held in A [(p [c]) ... (p [c+1]-1)]. The row indices
in a given column c need not be in ascending order, and
duplicate row indices may be be present. However, colamd will
work a little faster if both of these conditions are met
(Colamd puts the matrix into this format, if it finds that the
the conditions are not met).
The matrix is 0-based. That is, rows are in the range 0 to
n_row-1, and columns are in the range 0 to n_col-1. Colamd
returns FALSE if any row index is out of range.
The contents of A are modified during ordering, and are
undefined on output.
int p [n_col+1] ; Both input and output argument.
p is an integer array of size n_col+1. On input, it holds the
"pointers" for the column form of the matrix A. Column c of
the matrix A is held in A [(p [c]) ... (p [c+1]-1)]. The first
entry, p [0], must be zero, and p [c] <= p [c+1] must hold
for all c in the range 0 to n_col-1. The value p [n_col] is
thus the total number of entries in the pattern of the matrix A.
Colamd returns FALSE if these conditions are not met.
On output, if colamd returns TRUE, the array p holds the column
permutation (Q, for P(AQ)=LU or (AQ)'(AQ)=LL'), where p [0] is
the first column index in the new ordering, and p [n_col-1] is
the last. That is, p [k] = j means that column j of A is the
kth pivot column, in AQ, where k is in the range 0 to n_col-1
(p [0] = j means that column j of A is the first column in AQ).
If colamd returns FALSE, then no permutation is returned, and
p is undefined on output.
double knobs [COLAMD_KNOBS] ; Input argument.
See colamd_set_defaults for a description.
int stats [COLAMD_STATS] ; Output argument.
Statistics on the ordering, and error status.
See colamd.h for related definitions.
Colamd returns FALSE if stats is not present.
stats [0]: number of dense or empty rows ignored.
stats [1]: number of dense or empty columns ignored (and
ordered last in the output permutation p)
Note that a row can become "empty" if it
contains only "dense" and/or "empty" columns,
and similarly a column can become "empty" if it
only contains "dense" and/or "empty" rows.
stats [2]: number of garbage collections performed.
This can be excessively high if Alen is close
to the minimum required value.
stats [3]: status code. < 0 is an error code.
> 1 is a warning or notice.
0 OK. Each column of the input matrix contained
row indices in increasing order, with no
duplicates.
1 OK, but columns of input matrix were jumbled
(unsorted columns or duplicate entries). Colamd
had to do some extra work to sort the matrix
first and remove duplicate entries, but it
still was able to return a valid permutation
(return value of colamd was TRUE).
stats [4]: highest numbered column that
is unsorted or has duplicate
entries.
stats [5]: last seen duplicate or
unsorted row index.
stats [6]: number of duplicate or
unsorted row indices.
-1 A is a null pointer
-2 p is a null pointer
-3 n_row is negative
stats [4]: n_row
-4 n_col is negative
stats [4]: n_col
-5 number of nonzeros in matrix is negative
stats [4]: number of nonzeros, p [n_col]
-6 p [0] is nonzero
stats [4]: p [0]
-7 A is too small
stats [4]: required size
stats [5]: actual size (Alen)
-8 a column has a negative number of entries
stats [4]: column with < 0 entries
stats [5]: number of entries in col
-9 a row index is out of bounds
stats [4]: column with bad row index
stats [5]: bad row index
stats [6]: n_row, # of rows of matrx
-10 (unused; see symamd.c)
-999 (unused; see symamd.c)
Future versions may return more statistics in the stats array.
Example:
See http://www.cise.ufl.edu/research/sparse/colamd/example.c
for a complete example.
To order the columns of a 5-by-4 matrix with 11 nonzero entries in
the following nonzero pattern
x 0 x 0
x 0 x x
0 x x 0
0 0 x x
x x 0 0
with default knobs and no output statistics, do the following:
#include "colamd.h"
#define ALEN COLAMD_RECOMMENDED (11, 5, 4)
int A [ALEN] = {1, 2, 5, 3, 5, 1, 2, 3, 4, 2, 4} ;
int p [ ] = {0, 3, 5, 9, 11} ;
int stats [COLAMD_STATS] ;
colamd (5, 4, ALEN, A, p, (double *) NULL, stats) ;
The permutation is returned in the array p, and A is destroyed.
----------------------------------------------------------------------------
symamd:
----------------------------------------------------------------------------
C syntax:
#include "colamd.h"
int symamd (int n, int *A, int *p, int *perm,
double knobs [COLAMD_KNOBS], int stats [COLAMD_STATS],
void (*allocate) (size_t, size_t), void (*release) (void *)) ;
Purpose:
The symamd routine computes an ordering P of a symmetric sparse
matrix A such that the Cholesky factorization PAP' = LL' remains
sparse. It is based on a column ordering of a matrix M constructed
so that the nonzero pattern of M'M is the same as A. The matrix A
is assumed to be symmetric; only the strictly lower triangular part
is accessed. You must pass your selected memory allocator (usually
calloc/free or mxCalloc/mxFree) to symamd, for it to allocate
memory for the temporary matrix M.
Returns:
TRUE (1) if successful, FALSE (0) otherwise.
Arguments:
int n ; Input argument.
Number of rows and columns in the symmetrix matrix A.
Restriction: n >= 0.
Symamd returns FALSE if n is negative.
int A [nnz] ; Input argument.
A is an integer array of size nnz, where nnz = p [n].
The row indices of the entries in column c of the matrix are
held in A [(p [c]) ... (p [c+1]-1)]. The row indices in a
given column c need not be in ascending order, and duplicate
row indices may be present. However, symamd will run faster
if the columns are in sorted order with no duplicate entries.
The matrix is 0-based. That is, rows are in the range 0 to
n-1, and columns are in the range 0 to n-1. Symamd
returns FALSE if any row index is out of range.
The contents of A are not modified.
int p [n+1] ; Input argument.
p is an integer array of size n+1. On input, it holds the
"pointers" for the column form of the matrix A. Column c of
the matrix A is held in A [(p [c]) ... (p [c+1]-1)]. The first
entry, p [0], must be zero, and p [c] <= p [c+1] must hold
for all c in the range 0 to n-1. The value p [n] is
thus the total number of entries in the pattern of the matrix A.
Symamd returns FALSE if these conditions are not met.
The contents of p are not modified.
int perm [n+1] ; Output argument.
On output, if symamd returns TRUE, the array perm holds the
permutation P, where perm [0] is the first index in the new
ordering, and perm [n-1] is the last. That is, perm [k] = j
means that row and column j of A is the kth column in PAP',
where k is in the range 0 to n-1 (perm [0] = j means
that row and column j of A are the first row and column in
PAP'). The array is used as a workspace during the ordering,
which is why it must be of length n+1, not just n.
double knobs [COLAMD_KNOBS] ; Input argument.
See colamd_set_defaults for a description.
int stats [COLAMD_STATS] ; Output argument.
Statistics on the ordering, and error status.
See colamd.h for related definitions.
Symamd returns FALSE if stats is not present.
stats [0]: number of dense or empty row and columns ignored
(and ordered last in the output permutation
perm). Note that a row/column can become
"empty" if it contains only "dense" and/or
"empty" columns/rows.
stats [1]: (same as stats [0])
stats [2]: number of garbage collections performed.
stats [3]: status code. < 0 is an error code.
> 1 is a warning or notice.
0 OK. Each column of the input matrix contained
row indices in increasing order, with no
duplicates.
1 OK, but columns of input matrix were jumbled
(unsorted columns or duplicate entries). Symamd
had to do some extra work to sort the matrix
first and remove duplicate entries, but it
still was able to return a valid permutation
(return value of symamd was TRUE).
stats [4]: highest numbered column that
is unsorted or has duplicate
entries.
stats [5]: last seen duplicate or
unsorted row index.
stats [6]: number of duplicate or
unsorted row indices.
-1 A is a null pointer
-2 p is a null pointer
-3 (unused, see colamd.c)
-4 n is negative
stats [4]: n
-5 number of nonzeros in matrix is negative
stats [4]: # of nonzeros (p [n]).
-6 p [0] is nonzero
stats [4]: p [0]
-7 (unused)
-8 a column has a negative number of entries
stats [4]: column with < 0 entries
stats [5]: number of entries in col
-9 a row index is out of bounds
stats [4]: column with bad row index
stats [5]: bad row index
stats [6]: n_row, # of rows of matrx
-10 out of memory (unable to allocate temporary
workspace for M or count arrays using the
"allocate" routine passed into symamd).
-999 internal error. colamd failed to order the
matrix M, when it should have succeeded. This
indicates a bug. If this (and *only* this)
error code occurs, please contact the authors.
Don't contact the authors if you get any other
error code.
Future versions may return more statistics in the stats array.
void * (*allocate) (size_t, size_t)
A pointer to a function providing memory allocation. The
allocated memory must be returned initialized to zero. For a
C application, this argument should normally be a pointer to
calloc. For a MATLAB mexFunction, the routine mxCalloc is
passed instead.
void (*release) (size_t, size_t)
A pointer to a function that frees memory allocated by the
memory allocation routine above. For a C application, this
argument should normally be a pointer to free. For a MATLAB
mexFunction, the routine mxFree is passed instead.
----------------------------------------------------------------------------
colamd_report:
----------------------------------------------------------------------------
C syntax:
#include "colamd.h"
colamd_report (int stats [COLAMD_STATS]) ;
Purpose:
Prints the error status and statistics recorded in the stats
array on the standard error output (for a standard C routine)
or on the MATLAB output (for a mexFunction).
Arguments:
int stats [COLAMD_STATS] ; Input only. Statistics from colamd.
----------------------------------------------------------------------------
symamd_report:
----------------------------------------------------------------------------
C syntax:
#include "colamd.h"
symamd_report (int stats [COLAMD_STATS]) ;
Purpose:
Prints the error status and statistics recorded in the stats
array on the standard error output (for a standard C routine)
or on the MATLAB output (for a mexFunction).
Arguments:
int stats [COLAMD_STATS] ; Input only. Statistics from symamd.


Lifetime Managers
Out of the box Unity supports eight lifetime managers. These managers allow various scenarios of lifetime management for resolved objects and should satisfy most of everyday requirements. As everything with Unity, any of these managers could be extended or new managers could be created to cover specific needs. These are the managers Unity comes with:
Transient
This is a default lifetime manager in Unity Container. If you do not specify any lifetime managers during registration, Unity will use this one implicitly. The default behavior of this manager is to create new instance of registered type on every resolution.
It is recommended to omit this type of managers from registrations and let Unity assume default, it is hardly worth it to create instance of the manager which will be discarded during registration anyway.
Singleton
This lifetime manager creates globally unique singleton. No matter where it is registered (child or root container) it is always stored with the root container and becomes globally available in entire scope tree.
Any Unity container tree (parent and all the children) is guaranteed to have only one singleton for the registered type and overriding registration on root or any child container with singleton manager will override it everywhere.
This lifetime manager could be used on any types of registration, Type, Instance, or Factory. For more information see tutorial.


As an example system, consider a cart on a track. The cart has a radar that measures the distance to a target at the end of the track and encoders on its wheels to measure the incremental distance along the track. We power it using a small motor and it is buffetted by wind and imperfections in the tracks. This is depicted in the graphic below:
At each timestep, $t$, we attempt to estimate the position of the cart along the track, $d_t$. We start off with an estimate of where the cart is along the track, quantified using the our prior distribution $p(d_0)$. At each timestep, we will perform two steps. First, we will estimate where we think the cart is based on the incremental wheel encoder readings using a state update step, then we will update that estimate based on the radar measurement using a measurement update step.
Next, consider what happens at time $t = 1$ if we measure (via the encoders) that the cart has moved 5 meters to the right along the track. How would each of the distributions above change (suppose the track is infinitely long).
If our wheel encoders measured the incremental distance perfectly, we would our new position to be our old position plus one meter so we should expect that each new distribution at time $t = 1$ is a copy of the distribution at time $t = 0$, shifted to the right by 5 meters.
Whenever we move, however, our uncertainty about where we are will tend to increase. Systematic errors, such as uncertainty in our wheel diameter and random errors, such as wheel slippage, will both contribute to the error after moving by 5 meters. A more realistic model would widen the probability distribution after each timestep, as below. You can play around with the magnitude of the noise using the Noise STD box.
The corresponding equations for the state update step are straightforward. We have a single state variable, the position $d_t$, and at each timestep we are moving from the previous position $d_{t - 1}$ by a specific distance $\Delta d_t$ subject to a specific amount of noise $\epsilon^d_t$. This can be written: \[d_{t} = d_{t - 1} + \Delta d_t + \epsilon^d_t\]. In the example above, $\Delta d_t$ was chosen to be 5 m and $\epsilon^d_t$ was a gaussian random variable with the standard deviation specified in the input box.
We can now quantify what our confidence in where the cart is after each movement step. We will now use a measurement update step to determine the cart position more precisely. In this step, we take a single measurement and update the cart position distribution based on our uncertainty in the measurement value and our uncertainty in the carts position. We can model this system using the following difference equations: \[d_t = d_{t - 1} + \Delta t \cdot v_{t - 1} + \frac{1}{2} (\Delta t)^2 a_{t}\] \[v_t = v_{t - 1} + \Delta t \cdot a_{t}\] where $a_t$ is an applied acceleration, determined by the total force on the cart, $f_t$, and the mass of the cart $m$. The total force is a sum of the control, $u_t$, and the noise, $f^\epsilon_t$. This gives us the following difference equations: \[d_t = d_{t - 1} + \Delta t \cdot v_{t - 1} + \frac{1}{2 m} (\Delta t)^2 u_{t} + \frac{1}{2 m} (\Delta t^2) f^{\epsilon}_t)\] \[v_t = v_{t - 1} + \Delta t \cdot \frac{u_{t}}{m} + \Delta t \cdot \frac{f^{\epsilon}_t}{m}\] The distance sensor gives us the estimated position at each time $t$, with an added noise $\delta_t$. \[z_t = d_t + \delta_t\] Our goal is to estimate the position and velocity of the cart using a Kalman filter.
For this filter, our state vector contains the position and velocity: \[x_t = \left[\begin{array}{c} d_t \\ v_t \end{array}\right]\].
The first half of the Kalman filter predicts the position after the next timestep using the difference equations specified above: \[x_t \approx \left[\begin{array}{cc} 1 & \Delta t \\ 0 & 1 \end{array} \right]x_{t-1} + \left[\begin{array}{c} \frac{(\Delta t)^2}{2m} \\ \frac{\Delta t}{m} \end{array}\right] u_t + \left[\begin{array}{c} \epsilon^d_t \\ \epsilon^v_t \end{array}\right] \] Where \[A = \left[\begin{array}{cc} 1 & \Delta t \\ 0 & 1 \end{array} \right] \] extrapolates the previous state forward, \[B = \left[\begin{array}{c} \frac{(\Delta t)^2}{2m} \\ \frac{\Delta t}{m} \end{array}\right]\] maps the control input $u_t$ to changes in the state variables, and \[R = \left[\begin{array}{c} \epsilon^d_t \\ \epsilon^v_t \end{array}\right] \] is the contribution of noise to the changes in state variables.


JavaScript History API proof of concept and experiments, using Wicket 6.x, hopefully to be integrated with Wicket 7. WICKET-5290


This is my first photo of the feed. This is a multi line comment for testing purposes. I am learning to code with #BeatheCode
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Behind the Scenes: Tomato in Trouble
Who am I?
My name is Victor Ascenção. I’m 53 and I was born in Caracas, Venezuela but currently, I live in Castelo Branco, Portugal. My formal studies were in Advertising and Marketing in the “Instituto Universitario de Nuevas Profesiones” in Caracas, Venezuela. I have always been interested in drawing and design and particularly passionate about the 3D world that still amazes me.
In my daily work I design products for our manufacturing company using SolidWorks but I also use other tools, such as MODO and Octane (as well as Illustrator, Photoshop, and InDesign) to create promotional materials for our catalogs and website.
My Experience with Blender
I decided to try Blender a long time ago, but for me it was very confusing on the UI side and, as a lot of people imagine, for things like “the right-click issue” :) , After version 2.79 I felt the direction of Blender UI was becoming more similar to other programs that I was familiar with, and on the arrival of version 2.8 and after reading lots of people saying how capable Blender is (even in other software forums), I decided it was definitely time for me to learn Blender.
I found that Blender is a very powerful tool and has this great community of very highly knowledgeable people willing to help everybody, an endless amount of great add-ons to do almost anything you need to do, lots and lots of amazing training available, so... here I am learning how to use this amazing tool while having lots of fun in the process.
I don’t have any formal training in any 3D (or 2D) application; I learn by doing, reading, and watching a lot of great tutorials available on the Internet.
About the “Tomato in Trouble” Modeling
As I said, I like to learn by doing stuff, so one Saturday morning I decided to create something fun to try some Blender tools. The idea of a tomato that had had an accident seemed fun enough to me. then I started by model the little guy starting from Blender’s famous default cube.
After creating the tomato, I UV unwrapped it and his eyes. Then I created the band-aids using the Shrinkwrap Modifier and modeling them directly onto the tomato’s surface.
Starting with cubes, I created the mattress and the bed guards first in order to determine the hands’ position.
From the beginning, I was thinking I would put some saline solution or serum into his hand, but when I was about to model the serum bag, it just hit me, the guy is a tomato!! Why shouldn’t he receive a “blood” transfusion instead? and, well, I modeled the upside-down Ketchup bottle with the little handle to hang. It made sense to me that ketchup would be better than blood for an injured tomato.
After modeling the bottle, I create a mesh from a simple plane with a Mirror Modifier so I needed to model only half of the label and apply a Shrinkwrap modifier to it. That way I modeled half the outer shape of the label directly over the curvy body of the bottle, and it was just a matter of filling the polygons in the middle and UV unwrapping it to apply a custom-made texture created in Photoshop.
For the blinds, I simply created one plane and applied an Array modifier.
In the case of the rolled transparent plastic tube, I used the same technique as I used for the monitor cables and handle; it consists of combining a bezier curve for the path and a bezier circle acting as a bevel object that runs along that path, creating the tubular shape. To create the thickness on the transparent tube wall, I also used a Solidify Modifier and adjusted the thickness as needed.
For the blanket I created a plane, subdivided it 10 times and ran a cloth simulation over the little guy’s legs and feet and the mattress; for the pillow I created a basic shape and also ran a Soft Body simulation, “dropping” it between the tomato and the mattress to make it softly fit there.
The pose of the tomato was slightly altered using a Lattice modifier to avoid the need to rig the little guy.
The table, the wheels, the wall connectors, the oxygen valves, everything was created from simple cubes, planes, and cylinders.
Textures and Materials
The materials on the whole scene are very simple. All of them, with the exception of the water in the glass, were created with the Principled BSDF shader inside Blender, changing the settings for each material depending on the color, metallic aspect, transparency, IOR, etc.
The tomato material is a Principled BSDF material with:
- a very simple texture map (painted in Substance Painter) connected on the base color,
- a roughness map that came from the Substance Painter material connected to the roughness node,
- a subsurface orange color with a level of 0.255, and the default values for subsurface radius.
The wood material on the wall is also a Principled BSDF material with a wood texture applied and a bump map based on the same wood texture.
Lighting the Scene
I like to start with a completely dark scene (slightly illuminated by the environment) and start adding lights and gradually feeling the impact of each light on the mood of the scene.
As I used Cycles in this case and I have a fairly capable computer with 2 GPU installed, the effect of each light can be seen pretty quickly, literally in seconds. I remember those days when we had to render for an hour or two just to have a glimpse of what we were looking for on the render. Now it is almost real-time with a decent computer when using GPU rendering, and also pretty fast when using CPU with a powerful processor. With that, the amazing preview on the viewport, and the EEVEE option, it is amazing how we can develop an idea very, very fast.
I started to illuminate the scene using only one Area Light over the tomato head. My initial idea was to create a night scene because I was looking for some kind of sad mood.
After some tests, I decided to add another Area Light outside the room, to make the light pass through and between the translucent blinds and create more interesting lighting.
Then I decided that if I added a Sun Light at the right angle, it would create some interesting projected light over the bed and on the floor, and found that the scene was more attractive that way, so I kept this final lighting setup.
Final Render
I rendered the scene as I said, in Cycles at 8000 x 6000 pixels because I wanted to blow the size up and then scale it down to hide little smears and splotchiness that the denoiser creates. So I used a 4 px radius denoise with a 0.5 strength and the result was pretty good and, using GPU render, was also very fast.
About the Author
Victor Ascenção, Very curious 3D Enthusiast


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/113488general/60942Leanassertionviolation.html ---
universes u v theorem set.subset.trans {α : Type*} {a b c : set α} (ab : a ⊆ b) (bc : b ⊆ c) : a ⊆ c := assume x h, bc (ab h) def set.preimage {α : Type u} {β : Type v} (f : α → β) (s : set β) : set α := {x | f x ∈ s} infix ` ⁻¹' `:80 := set.preimage structure presheaf_of_types (α : Type*) := (F : Π U : set α, Type*) (res : ∀ (U V : set α) (H : V ⊆ U), (F U) → (F V)) (Hcomp : ∀ (U V W : set α) (HUV : V ⊆ U) (HVW : W ⊆ V), (res U W (set.subset.trans HVW HUV)) = (res V W HVW) ∘ (res U V HUV) ) definition presheaf_of_types_pushforward {α : Type*} {β : Type*} (f : α → β) (FPT : presheaf_of_types α) : presheaf_of_types β := { F := λ V : set β, FPT.F (set.preimage f V), res := λ V₁ V₂ H, FPT.res (set.preimage f V₁) (set.preimage f V₂)(λ x Hx,H Hx), Hcomp := λ Uβ Vβ Wβ HUV HVW,rfl -- assertion violation }
LEAN ASSERTION VIOLATION File: /home/travis/build/leanprover/lean/src/frontends/lean/elaborator.cpp Line: 3167 Task: /home/buzzard/Encfs/Computer_languages/Lean/lean/bug.lean: presheaf_of_types_pushforward m_ctx.match(e, *val2)
Here's a (mathlib-free) assertion violation. Is this a known issue? If not, is my MWE minimal enough? If so, shall I file a bug report? I'd be happy to hear advice before working on this any more
I'm looking into it
Lean (version 3.3.1, commit d6d44a19947e, Release)
Ubuntu 16.04
Can reproduce
Thanks.
I get the feeling structure notation is too sophisticated for our (the implementers') own good :sweat_smile:
ha ha
so it's my fault? :-)
I wasn't expecting rfl to work, it's just sometimes worth a try
It's obviously our fault for enabling you to do weird things :)
I would be interested to know what the first thing you do in this situation is. If it's to fire up some C++ debugger then fair enough, you've lost me already, but if it's to just set some options and watch more carefully then I would be interested to follow along for a while
What do I do that is weird?
actually I wonder whether I am now grown up enough to fire up a C++ debugger myself
It's a unification error, so I'm now looking at the is_def_eq_detail
trace. The terms are still quite big - if you find a way to minimize them, that would be great.
 That should do hopefully.
structure presheaf_of_types := (res : unit → unit) (Hidem : ∀ U : unit, res = res ∘ res) set_option trace.type_context.is_def_eq_detail true definition presheaf_of_types_pushforward : presheaf_of_types := { Hidem := λ U, rfl, }
Thanks for asking for more minimisation -- in some sense that was quite instructive. Initially I just removed everything not directly related to the violation in the form I had it, but after you asked more I realised there was nothing stopping me trying to find simpler violations
Adding the field res := id
makes the problem go away
Hey, is it doing a prolog-like search?
Poor Kevin was deprived of a prolog-like search yesterday
Please be nice this time
I should never have equated this phrase with "random thing I don't understand"
but looking at the debugging output it looks like it might be backtracking...
I'm picturing you asking to Scholze: "are you doing a a prolog-like search" at the end of a really tough talk.
Maybe I don't even know what backtracking means. I guess I look at these debugging outputs and think "oh look it's trying lots of things in what looks like a systematic manner"
It's not enough
but I probably need to look more closely to distinguish the difference between "if this fails, discard it and try something else"
and "let's actually backtrack here"
You want to start following a path and then come back to an earlier branch point
yes this is just dawning on me now
@Kevin Buzzard Congrats, I think you found a bug where definitional equality is not idempotent
Is that a good thing?
Do I get an achievement?
I'm jalous
My conv bug seems much less cool
@Sebastian Ullrich "Idempotent"?? Do you mean is_def_eq(t, t)
returns false?
On the first run it returns true for two terms, then false on any subsequent runs
ooh can I prove false??
I would definitely get an achievement for that
Nah, it's just the elaborator
aah well
Apparently elim_delayed_abstractions
accidentally supports backtracking assignments to a metavar... not the correct place for a Prolog-like search
you lost me at the prolog-like search bit
That was mostly a joke :)
Wow, this is unexpected.
Kenny posted this about a week ago
instance error (α : Type) : group α := { mul_assoc := λ x y z, rfl }
without any further comment
and it might be the same thing
Yep, same assertion
users.png
One little bug and everybody flies out, except devs. So sad...


To open the connection you need an authorization token. Use the login form with the credentials foo:bar to get one. Messages sent when the connection is closed will be automatically buffered by primus and dispatched as soon as the connection will be established.


Keywords is a simple utility, which generate a text string recognizer from the given text string set. It is useful for the language development.
CCore-Keywords.exe <input-file> <output-file>
Keywords takes the required string set from the input file and place the generated code to the output file. The input file is a DDL file. It looks like this:
text[] Keywords= { "int", "sint", "uint", "ulen", "sint8", "sint16", "sint32", "sint64", "uint8", "uint16", "uint32", "uint64", "text", "ip", "struct", "type", "null", "scope", "include", "const" };
And the recognition code looks like this:
AtomClass Atom::GetWordClass(StrLen str) { if( !str ) return Atom_Name; switch( str[0] ) { case 'c' : { ++str; if( str.len==4 && str[0]=='o' && str[1]=='n' && str[2]=='s' && str[3]=='t' ) return Atom_const; else return Atom_Name; } case 'i' : { ++str; if( !str ) return Atom_Name; .... } default: return Atom_Name; } }
It is the method body, with the argument of the type StrLen and the return value of the type AtomClass, which must be declared in your code as an enum. If the given string is not from the set, the value Atom_Name is returned. Otherwise some Atom_<string> is returned, if the argument equals <string>.


Tetrarch Thifiell
I will keep a close eye on the Humans' behavior. I pray that our trust will grow. Now return to your King.


--- layout: page title: 'What We Do' teaser: 'We strive to pursue ambitious ideas. Anyone can join with a new project. If you can convince us, we will support you. One thing we can say: It''s a hell of a ride! ' ---
{{ page.teaser }}


Queen Island
Kitchen
date_range
access_time
Item
Qty
Price
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Queen Island Kitchen
Press Club, Bhola-8300
Phone: 01704-828518
queenislandkitchen.com
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☆☆☆☆☆
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*** FEEL THE FOOD ***
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1. What element configures a horizontal line?
2. A CSS border property consists of?
3. With an inline display element, the border:
4. Browser display between firefox, chrome, etc, is always?
5. Another way to add underline to text:
6. What does the padding property do?
7. How do you configure an elements to fit the entire page?
8. In h2{padding: 20px 10px} what does 20px configure?
9. In h2{padding:30px 10px 5px 20px} what configures the right hand padding?
10. Which sof the following is a type of graphic?
11. Which graphic is commonly used on web pages?
12. If a GIF has a see through background, it is:
13. Which graphic has a max of 256 colors?
14. What does GIF stand for?
15. A JPEG is:
16. A PNG:
17. An HTML image element:
18. When configuring an image size you should:
19. The alt attributes of an image:
20. A thumbnail image:
21. What does image optimization do?
22. Which of the following are image editing tools?
23. The HTML progress element:
24. When choosing file names for image files:
25. How do you make the background image of your web page repeat horizontically?
Question 1: The correct answer is the <hr>.
Question 2: The correct answer is All of the above.
Question 3: The correct answer is Closely outlines the element conent.
Question 4: The correct answer is It varies.
Question 5: The correct answer is Border-bottom.
Question 6: The correct answer is Configures space between content and border..
Question 7: The correct answer is 100%.
Question 8: The correct answer is Top and bottom.
Question 9: The correct answer is 10px.
Question 10: The correct answer is JPG.
Question 11: The correct answer is the All of the above.
Question 12: The correct answer is Transparent.
Question 13: The correct answer is GIF.
Question 14: The correct answer is Graphics Interchange Format.
Question 15: The correct answer is Best used for images.
Question 16: The correct answer is All of the above.
Question 17: The correct answer is Configures graphics on a web page.
Question 18: The correct answer is Adjust either the width or height attributes.
Question 19: The correct answer is Configure an alternate text description.
Question 20: The correct answer is Is a small image confiured to link to a larger version.
Question 21: The correct answer is Reduces the file sixe of the image.
Question 22: The correct answer is All of the above.
Question 23: The correct answer is Displays a bar that depicts a numeric value within a specified range.
Question 24: The correct answer is All of the above.
Question 25: The correct answer is repeat-x.
You answered them all right!


{% extends "admin_template.html" %} {% block per_page_title %} {{ _('Re-send verification code') }} {% endblock %} {% block maincolumn_content %}
{{ _('Text messages sometimes take a few minutes to arrive. If you do not receive the text message, you can re-send it.') }}
{{ _('If you no longer have access to the phone with the number you registered for this service, speak to your service manager to reset the number.') }}


WARNING
Content Under Development
See
release page for
latest official PDF version.
**Chapter 4: Light Scattering**
In this chapter we won't actually implement anything. We will set up for a big lighting change in
our program in Chapter 5.
Our program from the last books already scatters rays from a surface or volume. This is the commonly
used model for light interacting with a surface. One natural way to model this is with probability.
First, is the light absorbed?
Probability of light scattering: $A$
Probability of light being absorbed: $1-A$
Here $A$ stands for _albedo_ (latin for _whiteness_). Albedo is a precise technical term in some
disciplines, but in all uses it means some form of fractional reflectance. As we implemented for
glass, the albedo may vary with incident direction, and it varies with color. In the most physically
based renderers, we would use a set of wavelengths for the light color rather than RGB. We can
almost always harness our intuition by thinking of R, G, and B as specific long, medium, and short
wavelengths.
If the light does scatter, it will have a directional distribution that we can describe as a pdf
over solid angle. I will refer to this as its _scattering pdf_: $s(direction)$. The scattering pdf
can also vary with incident direction, as you will notice when you look at reflections off a road--
they become mirror-like as your viewing angle approaches grazing.
The color of a surface in terms of these quantities is:
$$ Color = \int A \cdot s(direction) \cdot color(direction) $$
Note that $A$ and $s()$ might depend on the view direction, so of course color can vary with view
direction. Also $A$ and $s()$ may vary with position on the surface or within the volume.
If we apply the MC basic formula we get the following statistical estimate:
$$ Color = (A \cdot s(direction) \cdot color(direction)) / p(direction) $$
Where $p(direction)$ is the pdf of whatever direction we randomly generate.
For a Lambertian surface we already implicitly implemented this formula for the special case where
$p()$ is a cosine density. The $s()$ of a Lambertian surface is proportional to $cos(\theta)$, where
$\theta$ is the angle relative to the surface normal. Remember that all pdf need to integrate to
one. For $cos(\theta) < 0$ we have $s(direction) = 0$, and the integral of cos over the hemisphere
is $\pi$.
To see that remember that in spherical coordinates remember that:
$$ \delta A = sin(\theta) \delta \theta \delta \phi $$
So:
$$ Area = \int_{0}^{2 \pi} \int_{0}^{\pi / 2} cos(\theta)sin(\theta) \delta \theta \delta \phi =
2 \pi \cdot \frac{1}{2} = \pi $$
So for a Lambertian surface the scattering pdf is:
$$ s(direction) = cos(\theta) / \pi $$
If we sample using the same pdf, so $p(direction) = cos(\theta) / \pi$, the numerator and
denominator cancel out and we get:
$$ Color = A * s(direction) $$
This is exactly what we had in our original color() function! But we need to generalize now so we
can send extra rays in important directions such as toward the lights.
The treatment above is slightly non-standard because I want the same math to work for surfaces and
volumes.
If you read the literature, you’ll see reflection described by the bidirectional reflectance
distribution function (BRDF). It relates pretty simply to our terms:
$$ BRDF = A * s(direction) / cos(\theta) $$
So for a Lambertian surface for example, $BRDF = A / \pi$. Translation between our terms and BRDF is
easy.
For participation media (volumes), our albedo is usually called scattering albedo, and our
scattering pdf is usually called phase function.


WebWorkers basic example
Adapted from Mozilla Developer Network's
Simple Web Worker
example
Multiply :
by:
Result: 4


|
BF
|
Go to the source code of this file.
Functions | |
| bf_invite_init () | |
| bf_invite_menu () | |
| bf_invite_admin_settings () | |
| bf_invite_validate_uploaded_file ($form, &$form_state) | |
| bf_invite_process_uploaded_file ($form, &$form_state) | |
| bf_ivite_import_item ($email) | |
| bf_invite_cron () | |
| bf_invite_cron_queue_info () | |
| bf_invite_send_invite ($item) | |
| bf_ivite_build_code ($email) | |
| bf_invite_get_unsent_message_count () | |
| bf_invite_check_code ($code, $status=1) | |
| bf_invite_deactivate_status ($code) | |
| bf_invite_remove_code ($code) | |
| bf_invite_check_duplicate_email ($email, $role='Participant') | |
Variables | |
| const | BF_INVITE_SECRET_CODE '234et' |
| const | BF_INVITE_LINK_TOKEN '@reglink' |
| const | BF_REF_COOKIE_LIFE_DAYS 365 |
BF Invite.
Definition in file bf_invite.module.
| bf_invite_admin_settings | ( | ) |
Settings form.
Definition at line 42 of file bf_invite.module.
| bf_invite_check_code | ( | $code, | |
$status = 1 |
|||
| ) |
Check code in DB.
Definition at line 258 of file bf_invite.module.
| bf_invite_check_duplicate_email | ( | $email, | |
$role = 'Participant' |
|||
| ) |
check exists duplicate email invite
Definition at line 292 of file bf_invite.module.
| bf_invite_cron | ( | ) |
Implements hook_cron().
Definition at line 172 of file bf_invite.module.
| bf_invite_cron_queue_info | ( | ) |
Implements hook_cron_queue_info().
Definition at line 192 of file bf_invite.module.
| bf_invite_deactivate_status | ( | $code | ) |
Deactivate status code.
Definition at line 271 of file bf_invite.module.
| bf_invite_get_unsent_message_count | ( | ) |
Get unsent message count in DB.
Definition at line 246 of file bf_invite.module.
| bf_invite_init | ( | ) |
Implementation of hook_init().
Definition at line 17 of file bf_invite.module.
| bf_invite_menu | ( | ) |
Implementation of hook_menu().
Definition at line 27 of file bf_invite.module.
| bf_invite_process_uploaded_file | ( | $form, | |
| & | $form_state | ||
| ) |
Additional submit handler for settings form.
Definition at line 106 of file bf_invite.module.
| bf_invite_remove_code | ( | $code | ) |
Remove code.
Definition at line 282 of file bf_invite.module.
| bf_invite_send_invite | ( | $item | ) |
Send invitation mail.
Definition at line 204 of file bf_invite.module.
| bf_invite_validate_uploaded_file | ( | $form, | |
| & | $form_state | ||
| ) |
Additional validator for settings form.
Definition at line 97 of file bf_invite.module.
| bf_ivite_build_code | ( | ) |
Build reference code.
Definition at line 238 of file bf_invite.module.
| bf_ivite_import_item | ( | ) |
Import one item (email).
Definition at line 145 of file bf_invite.module.
| const BF_INVITE_LINK_TOKEN '@reglink' |
Definition at line 10 of file bf_invite.module.
| const BF_INVITE_SECRET_CODE '234et' |
Definition at line 9 of file bf_invite.module.
| const BF_REF_COOKIE_LIFE_DAYS 365 |
Definition at line 11 of file bf_invite.module.