My header
Mechanic
is and what it does, let us write a well-known "Hello World!". We will create small C library, let us call it Hello
and save it in mechanic_module_hello.c
file:
#include "mechanic.h" #include "mechanic_module_hello.h" int hello_init(moduleInfo* md){ md->irl = 3; md->mrl = 6; return 0; } int hello_cleanup(moduleInfo* md){ return 0; } int hello_master_in(int mpi_size, int node, moduleInfo* md, configData* d, masterData* inidata) { inidata->res[0] = 99.0; return 0; } int hello_master_preparePixel(int node, moduleInfo* md, configData* d, masterData* inidata, masterData* r) { inidata->res[1] = (double) d->xres; return 0; } int hello_slave_preparePixel(int node, moduleInfo* md, configData* d, masterData* inidata, masterData* r) { inidata->res[2] = (double) node; return 0; } int hello_processPixel(int node, moduleInfo* md, configData* d, masterData* inidata, masterData* r) { r->res[0] = (double) r->coords[0]; r->res[1] = (double) r->coords[1]; r->res[2] = (double) r->coords[2]; r->res[3] = inidata->res[0]; r->res[4] = inidata->res[1]; r->res[5] = inidata->res[2]; return 0; } int hello_slave_out(int nodes, int node, moduleInfo* md, configData* d, masterData* inidata, masterData* r){ mechanic_message(MECHANIC_MESSAGE_INFO, "Hello from slave[%d]\n", node); return 0; }
We need to compile the example code to a shared library. We can do that by calling
gcc -fPIC -c mechanic_module_hello.c -o mechanic_module_hello.o gcc -shared mechanic_module_hello.o -o libmechanic_module_hello.so
Mechanic
need to know, where our module is, so we need to adjust LD_LIBRARY_PATH
(it depends on shell you are using) to the place we saved our module. If you are a Bash
user, try the following setting in your .bashrc
file:
export LD_LIBRARY_PATH=/usr/lib/:/usr/local/lib:.
We run Mechanic
with our module by
mpirun -np 3 mechanic -p hello
This will tell Mechanic
to run on three nodes, in a task farm mode, with one master node and two slaves. The master node will send default initial condition (pixel coordinates) to each slave and receive data in masterData
structure (in this case the coordinates of the pixel).
The output should be similar to:
-> Mechanic v. 0.12-UNSTABLE-2 Author: MSlonina, TCfA, NCU Bugs<EMAIL_ADDRESS>http://mechanics.astri.umk.pl/projects/mechanic !! Config file not specified/doesn't exist. Will use defaults. -> Mechanic will use these startup values: (...) -> Hello from slave[1] -> Hello from slave[2]
Two last lines were printed using our simple module. In the working directory you should find also mechanic-master-00.h5
file. It is a data file written by the master node, and each run of Mechanic
will produce such file. It containes all information about the setup of the simulation and data received from slaves.
If you try
h5dump -n mechanic-master-00.h5
you should see the following output:
HDF5 "mechanic-master.h5" { FILE_CONTENTS { group / dataset /board group /config dataset /config/default dataset /config/logs group /data dataset /data/master } }
which describes the data storage in master file. There are two additional files in the working dir with suffixes 01 and 02 -- these are checkpoint files, see Checkpoints for detailes.
The Hello
module is included in Mechanic
distribution as a simple example of using the software.
Hello
module. Each Mechanic
module must contain the preprocessor directive
#include "mechanic.h"
The module specific header file
#include "mechanic_module_hello.h"
is optional. Since each module is a normal C code, you can also use any other headers and link to any other library during compilation.
Every function in the module is prefixed with the name of the module -- thus, you should use unique names for your modules. The file name prefix, mechanic_module_
is required for proper module loading.
The first three functions:
hello_init()
hello_cleanup()
hello_processPixel()
are required for the module to work. Mechanic
will abort if any of them is missing. The fourth one, hello_slave_out()
is optional and belongs to the templateable functions group (see The Template System).
Each function should return an integer value, 0 on success and errcode on failure, which is important for proper error handling.
hello_init(moduleInfo* md)
is called on module initialization and you need to provide some information about the module, especially, md->mrl
, which is the length of the results array sended from the slave node to master node. The moduleInfo
type contains information about the module, and will be extended in the future. The structure is available for all module functions. The moduleInfo
type has the following shape: typedef struct { int irl; int mrl; } moduleInfo;
hello_cleanup(moduleInfo* md)
currently does nothing, however, it is required for future development.
hello_processPixel(int node, moduleInfo* md, configData* d, masterData* r)
is the heart of your module. Here you can compute almost any type of numerical problem or even you can call external application from here. There are technically no contradictions for including Fortran based code. In this simple example we just assign coordinates of the simulation (see Pixel-coordinate System) to the result array r->res
. The array is defined in masterData
structure, as follows:
typedef struct { MECHANIC_DATATYPE *res; int coords[3]; /* 0 - x 1 - y 2 - number of the pixel */ } masterData;
Currently, MECHANIC_DATATYPE
is set to double
, so we need to do proper casting from integer to double. The result array has the md->mrl
size, in this case 3. The masterData
structure is available for all module functions.
hello_slave_out(int nodes, int node, moduleInfo* md, configData* d, masterData* r)
prints formatted message from the slave node on the screen, after the node did its job. The mechanic_message()
(see Short Developer's Guide) is available for all modules, and can be used for printing different kinds of messages, i.e. some debug information or warning.
The Mechanic
package contains few other modules:
Module
-- the default module with all available functions includedEcho
-- an extended version of the Hello
module, which includes some advanced stuff on handling data files, and is an example of using template systemMandelbrot
-- a benchmark module, which computes The Mandelbrot fractal.Mechanic
uses standard configuration path -- first, we read defaults, then the config file and command line options. The latter two are optional, and if not present, the code will use defaults fixed at compilation time.To find out what command line options are available, try
mpirun -np 3 mechanic --help
The configuration data is available to slave nodes by the structure:
typedef struct { char* name; char* datafile; char* module; int xres; int yres; int method; int checkpoint; int restartmode; int mode; } configData;
--help
--usage
-?
-- prints help message--name
-n
-- the problem name, it will be used to prefix all data files specific in given run--config
-c
-- config file to use in the run--module
-p
-- module which should be used during the run--method
-m
-- pixel mapping method (0 -- default, 6 -- user-defined)--xres
-x
-- x resolution of the simulation map--yres
-y
-- y resolution of the simulation map--checkpoint
-d
-- checkpoint file write interval
Mechanic
provides user with a checkpoint system, see Checkpoints for detailes. In this case the options are:
--restart
-r
-- switch to restart mode and use checkpoint fileMechanic can operate in different modes, see Modes for detailes. You can switch between them by using:
-0
-- masteralone mode-1
-- MPI task farm mode-2
-- multi task farm modeMechanic
uses LRC
for handling config files. To load configuration from custom config file use -c
or --config
switch. If this option is set, but the file doesn't exist, Mechanic
will abort. Sample config file is given below:
[default]
name = hello
xres = 4 #must be greater than 0
yres = 4 #must be greater than 0
method = 0 #single pixel -- 0, userdefined -- 6
module = hello # modules: hello, echo, mandelbrot, module
mode = 1 # masteralone -- 0, task farm -- 1, multi task farm -- 2
[logs]
checkpoint = 4
The config file options are equivalents of command line options. Any other option will be silently ommited. If any of the variables is missing, Mechanic
will use defaults for each not found variable. Namespaces are mandatory and Mechanic
will abort if missing. The errors are handled by LRC
in this case.
You can include full or inline comments in your file, just after the comment mark #
. The configuration is stored in the master file, see Data Storage Scheme.
Mechanic
is to use:
mpirun -np NUMBER_OF_CPUS_TO_USE mechanic [OPTIONS]
Here we provide and explain some simple examples:
mpirun -np 4 mechanic -p mandelbrot -x 200 -y 200 -n fractal
Mechanic
will use 4 nodes in MPI task farm mode (one master and three slaves) and will compute the Mandelbrot fractal with resolution 200x200 pixels. The name of the run will be "fractal". mpirun -np 4 mechanic -p mandelbrot -x 200 -y 200 -n fractal -0
Mechanic
will compute the fractal in masteralone mode. Slave nodes will be terminated. mpirun -np 4 mechanic -p mandelbrot -x 1 -y 1
Mandelbrot
module. In this case, slave nodes 2 and 3 will be terminated (see Modes). mpirun -np 4 mechanic -p application -x 100 -y 1
Mechanic
to perform tasks i.e. on 100 stars. mpirun -np 1 mechanic [OPTIONS]
mechanic [OPTIONS]
Mechanic
will automatically switch to masteralone mode.Mechanic
was created for handling simulations related to dynamical maps. Thus, it uses 2D pixel coordinate system (there are plans for extending it to other dimensions). This was the simplest way to show which simulations have been computed and which not. The map is stored in /board
table in the master file. Each finished simulation is marked with 1, the ongoing or broken -- with 0.
It is natural to use (x,y)-resolution option (either in the config file or command line) to describe the map of pixels for an image (like a dynamical map or the Mandelbrot fractal). However, one can use slice-based mapping, by using i.e.
100x1
or 1x100
resolution. In either case, the result should be the same. Setting (x,y) = (1,1)
is equivalent of doing only one simulation.
The mapping should help you in setting initial conditions for the simulation, i.e. we can change some values by using pixel coordinates or the number of the pixel. This information is available during the computation and is stored in masterData
struct.
By default, the number of simulations is counted by multiplying x and y resolution. The simulations are currently done one-by-one, the master node does not participate in computations (except masteralone mode, see Modes). You can change default behaviour by using method = 6
, see The Method 6.
Mechanic
can compute simulations both in single-cpu mode (masteralone) or multi-cpu mode (MPI task farm).
Mechanic
in single-cpu environment. If the mode is used in multi-cpu environments and the size of MPI group is greater than 1, Mechanic
will terminate all nodes but the master node.
Mechanic
. This will use one master node and number of slave nodes to do simulations. The master node is responsible for sending/receiving data and storing them. If number of slave nodes is greater than number of simulations to do, all unused nodes will be terminated.
Unstable-4
release.Mechanic
writes data in the following scheme:
/config
-- configuration file (written by LRC
API)/board
-- simulation mapping/data
-- main data group/data/master
-- master dataset, contains data received from nodes.
The file can be viewed with hdftools
, i.e. h5dump
. The master file has always problemname-master.h5
name. If the master file exists in the current working dir, it will be automatically backuped.
The Parallel HDF has no support for MPI task farm, thus the only node allowed to write master file is the master node. However, the module can provide additional data files and operate on them, see The Echo Module for the example.
Mechanic
comes with integrated checkpoint system, which helps with master file backup and restarting simulations. By default, the checkpoint file write interval is setuped to 2000, which means, that data will be stored in the master file after each 2000 pixel have been reached. You can change this interval by setting checkpoint
in config file or using --checkpoint
-d
in the command line.
Mechanic
will create up to 3 checkpoint file, in the well-known incremental backup system. Each file will have a corresponding checkpoint number (starting from the master file, 00, up to 02).
You can use any of the checkpoint files to restart your simulation. To use restart mode, try --restart
or -r
command line option and provide the path to the checkpoint file to use. If the file is not usable, Mechanic
will abort.
In restart mode Mechanic
will do only not previously finished simulations. At this stage of development, it is not possible to restart partially done simulations.


| Opcode | Instruction | Op/En | 64-Bit Mode | Compat/Leg Mode | Description |
| F2 0F D6 /r | MOVDQ2Q mm, xmm | RM | Valid | Valid | Move low quadword from xmm to mmx register. |
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| RM | ModRM:reg (w) | ModRM:r/m (r) | NA | NA |
Moves the low quadword from the source operand (second operand) to the destination operand (first operand). The source operand is an XMM register and the destination operand is an MMX technology register.
This instruction causes a transition from x87 FPU to MMX technology operation (that is, the x87 FPU top-of-stack pointer is set to 0 and the x87 FPU tag word is set to all 0s [valid]). If this instruction is executed while an x87 FPU floating-point exception is pending, the exception is handled before the MOVDQ2Q instruction is executed.
In 64-bit mode, use of the REX.R prefix permits this instruction to access additional registers (XMM8-XMM15).
DEST ← SRC[63:0];
| MOVDQ2Q: | __m64 _mm_movepi64_pi64 ( __m128i a) |
None.
| #NM | If CR0.TS[bit 3] = 1. |
| #UD | If CR0.EM[bit 2] = 1. If CR4.OSFXSR[bit 9] = 0. If CPUID.01H:EDX.SSE2[bit 26] = 0. If the LOCK prefix is used. |
| #MF | If there is a pending x87 FPU exception. |
Same exceptions as in protected mode.
Same exceptions as in protected mode.
Same exceptions as in protected mode.
Same exceptions as in protected mode.


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Follow these steps to change your password:
- Log in to your account.
- In the top navigation bar, click .
- Enter your current password.
- Enter your new password.
- Enter your new password again to confirm.
- Press Reset Password.
- Enter your email address and then click Continue.


Unfortunately, this website is design to work with a computer keyboard so will not work to it's full potential on a phone :(
pressaletter.online is an internet based, computer generated alphabet with a simple geometrical structure
which evolves based on the user input.
Inspired by the uprising of open-type fonts and other variable typefaces, the alphabet aims to explore the potential of modular letters outside of the strict framework of softwares and file formats.
In the radical rectangular grid, the alphabet unfolds - becomes slimmer or thicker - depending on the user’s movement on the screen.
Each letter consists of a minimal and singular combination of rectangle and parallelogram. This minimal vocabulary allows the letters to have a strong coherency while being in constant motion. A type processor available on the website allows
the user to play and compose poster with the alphabet.
Find the rest of my work
here or on
instagram


EVERYTHING LISTED IS BRAND NEW:: |
||
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The Five Joyful Mysteries are traditionally prayed on Mondays, Saturdays, and, during the season of Advent, on Sundays:
- The Annunciation
- The Visitation
- The Nativity
- The Presentation in the Temple
- The Finding in the Temple
The Five Joyful Mysteries are traditionally prayed on Mondays, Saturdays, and, during the season of Advent, on Sundays:
The Five Sorrowful Mysteries are traditionally prayed on Tuesdays, Fridays, and, during the season of Lent, on Sundays:
The Five Glorious Mysteries are traditionally prayed on Wednesdays and, outside the seasons of Advent and Lent, on Sundays:
The Five Luminous Mysteries are traditionally prayed on Thursdays:


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| Packages that use MD5Hash | |
| net.nutch.db | Web database: tracks page fetches and link structure. |
| net.nutch.fetcher | The Nutch robot. |
| net.nutch.io | Generic i/o code for use when reading and writing data to the network, to databases, and to files. |
| Uses of MD5Hash in net.nutch.db |
| Fields in net.nutch.db declared as MD5Hash | |
static MD5Hash[] |
DBKeyDivision.MD5_KEYSPACE_DIVIDERS
|
| Methods in net.nutch.db that return MD5Hash | |
MD5Hash |
Link.getFromID()
|
MD5Hash |
Page.getMD5()
|
| Methods in net.nutch.db with parameters of type MD5Hash | |
void |
WebDBWriter.deleteLink(MD5Hash md5)
Remove links with the given MD5 from the db. |
Page[] |
WebDBReader.getPages(MD5Hash md5)
Get Pages from the pagedb according to their content hash. |
boolean |
WebDBReader.pageExists(MD5Hash md5)
Test whether a certain piece of content is in the database, but don't bother returning the Page(s) itself. |
Link[] |
WebDBReader.getLinks(MD5Hash md5)
Grab all the links from the given MD5 hash. |
Page[] |
IWebDBReader.getPages(MD5Hash md5)
Return any Pages with the given MD5 checksum. |
boolean |
IWebDBReader.pageExists(MD5Hash md5)
Returns whether a Page with the given MD5 checksum is in the db. |
Link[] |
IWebDBReader.getLinks(MD5Hash md5)
Return all the Link objects that originate from a document with the given MD5 checksum. |
Page[] |
DistributedWebDBReader.getPages(MD5Hash md5)
Get all the Pages according to their content hash. |
boolean |
DistributedWebDBReader.pageExists(MD5Hash md5)
Test whether a certain piece of content is in the database, but don't bother returning the Page(s) itself. |
Link[] |
DistributedWebDBReader.getLinks(MD5Hash md5)
Grab all the links from the given MD5 hash. |
void |
Page.setMD5(MD5Hash md5)
|
Vector |
DBSectionReader.getPages(MD5Hash md5)
Get Pages from the db according to their content hash. |
boolean |
DBSectionReader.pageExists(MD5Hash md5)
Test whether a certain piece of content is in the db, but don't bother returning it. |
Vector |
DBSectionReader.getLinks(MD5Hash md5)
Grab all the links from the given MD5 hash. |
static int |
DBKeyDivision.findMD5Section(MD5Hash md5,
int numSections)
Find the right section index for the given MD5, and the number of sections in the db overall. |
| Constructors in net.nutch.db with parameters of type MD5Hash | |
Link(MD5Hash fromID,
long domainID,
String urlString,
String anchorText)
Create the record |
|
Page(String urlString,
MD5Hash md5)
Construct a new, default page, due to be fetched. |
| Uses of MD5Hash in net.nutch.fetcher |
| Methods in net.nutch.fetcher that return MD5Hash | |
MD5Hash |
FetcherOutput.getMD5Hash()
|
| Constructors in net.nutch.fetcher with parameters of type MD5Hash | |
FetcherOutput(FetchListEntry fetchListEntry,
MD5Hash md5Hash,
int status)
|
| Uses of MD5Hash in net.nutch.io |
| Methods in net.nutch.io that return MD5Hash | |
static MD5Hash |
MD5Hash.read(DataInput in)
Constructs, reads and returns an instance. |
static MD5Hash |
MD5Hash.digest(byte[] data)
Construct a hash value for a byte array. |
static MD5Hash |
MD5Hash.digest(byte[] data,
int start,
int len)
Construct a hash value for a byte array. |
static MD5Hash |
MD5Hash.digest(String string)
Construct a hash value for a String. |
static MD5Hash |
MD5Hash.digest(UTF8 utf8)
Construct a hash value for a String. |
| Methods in net.nutch.io with parameters of type MD5Hash | |
void |
MD5Hash.set(MD5Hash that)
Copy the contents of another instance into this instance. |
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Tensor extract options
torch_extract_opts(style = getOption("torch.extract.style"), ..., one_based = getOption("torch.extract.one_based", TRUE), inclusive_stop = getOption("torch.extract.inclusive_stop", TRUE), disallow_out_of_bounds = getOption("torch.extract.dissallow_out_of_bounds", TRUE), warn_tensors_passed_asis = getOption("torch.extract.warn_tensors_passed_asis", TRUE), warn_negatives_pythonic = getOption("torch.extract.warn_negatives_pythonic", TRUE))
| style | one of `NULL` (the default) `"R"` or `"python"`. If supplied, this overrides all other options. `"python"` is equivalent to all the other arguments being `FALSE`. `"R"` is equivalent to `warn_tensors_passed_asis` and `warn_negatives_pythonic` set to `FALSE` |
|---|---|
| ... | ignored |
| one_based | TRUE or FALSE, if one-based indexing should be used |
| inclusive_stop | TRUE or FALSE, if slices like `start:stop` should be inclusive of `stop` |
| disallow_out_of_bounds | TRUE or FALSE, whether checks are performed on the slicing index to ensure it is within bounds. |
| warn_tensors_passed_asis | TRUE or FALSE, whether to emit a warning the first time a tensor is supplied to `[` that tensors are passed as-is, with no R to python translation |
| warn_negatives_pythonic | TRUE or FALSE, whether to emit a warning the first time a negative number is supplied to `[` about the non-standard (python-style) interpretation |
an object with class "torch_extract_opts", suitable for passing to `[.torch.tensor()`
x <- tf$constant(1:10)#> Error in eval(expr, envir, enclos): object 'tf' not foundopts <- torch_extract_opts("R") x[1, options = opts]#> Error in eval(expr, envir, enclos): object 'x' not found# or for more fine-grained control opts <- torch_extract_opts( one_based = FALSE, warn_tensors_passed_asis = FALSE, warn_negatives_pythonic = FALSE ) x[0:2, options = opts]#> Error in eval(expr, envir, enclos): object 'x' not found


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Validates a card number and returns the status and issuing network in json format
/api/cc/:cardnumber
GET
Required:
cardnumber=[integer]
None
{
"result":{
"cardnumber":"5100000010001004",
"is_valid":true,
"issuing_network":[
"Mastercard"
],
"length":16
},
"status":"success",
"status_code":"200"
}
404: Endpoint does not exist
{
"result": {
"error": "Resource not found. Please refer to the documentation."
},
"status": "failed",
"status_code": "404"
}
405: A method except GET was used
{
"result": {
"error": "This method type is not currently supported. Please refer to the documentation."
},
"status": "failed",
"status_code": "405"
}
419: Argument (cardnumber) missing
{
"result": {
"error": "The requested resource is missing required arguments. Please refer to the documentation."
},
"status": "failed",
"status_code": "419"
}
420: Invalid arguments. Card number can only be digits not more than 20 characters.
{
"result": {
"error": "The requested resource does not support one or more of the given parameters. Please refer to the documentation."
},
"status": "failed",
"status_code": "420"
}
500: Internal Server Error
{
"result": {
"error": "Internal Server Error"
},
"status": "failed",
"status_code": "500"
}
Curl:
curl https://cardcheck-api.herokuapp.com/api/cc/5100000010001004
Python:
import requests
response = requests.get("https://cardcheck-api.herokuapp.com/api/cc/5100000010001004")
print(response)
JavaScript:
// GET Request.
fetch('https://cardcheck-api.herokuapp.com/api/cc/5100000010001004')
// Handle success
.then(response => response.json()) // convert to json
.then(json => console.log(json)) //print data to console
.catch(err => console.log('Request Failed', err)); // Catch errors
Source Code on GitHub at: https://github.com/shine-jayakumar/cardcheck-api


Gecode::Int::Count::ViewBase< VX, VY, VZ > Class Template Reference
Base-class for count propagators (view). More...
#include <count.hh>
Public Member Functions | |
| virtual size_t | dispose (Space &home) |
| Delete propagator and return its size. | |
| virtual PropCost | cost (const Space &home, const ModEventDelta &med) const |
| Cost function (defined as low linear). | |
| virtual void | reschedule (Space &home) |
| Schedule function. | |
Protected Member Functions | |
| ViewBase (Space &home, ViewBase &p) | |
| Constructor for cloning p. | |
| ViewBase (Home home, ViewArray< VX > &x, VY y, VZ z, int c) | |
| Constructor for creation. | |
| void | count (Space &home) |
| Count how many views are equal now. | |
| int | atleast (void) const |
| How many views are at least equal. | |
| int | atmost (void) const |
| How many views are at most equal. | |
Static Protected Member Functions | |
| static bool | sharing (const ViewArray< VX > &x, const VY &y, const VZ &z) |
| Test whether there is sharing of z with x or y. | |
Protected Attributes | |
| ViewArray< VX > | x |
| Views still to count. | |
| VY | y |
| View to compare to. | |
| VZ | z |
| View which yields result of counting. | |
| int | c |
| Number of views which are equal and have been eliminated. |
Detailed Description
template<class VX, class VY, class VZ>
class Gecode::Int::Count::ViewBase< VX, VY, VZ >
Base-class for count propagators (view).
Definition at line 266 of file count.hh.
Constructor & Destructor Documentation
| Gecode::Int::Count::ViewBase< VX, VY, VZ >::ViewBase | ( | Space & | home, | |
| ViewBase< VX, VY, VZ > & | p | |||
| ) | [inline, protected] |
Constructor for cloning p.
Definition at line 50 of file view-base.hpp.
| Gecode::Int::Count::ViewBase< VX, VY, VZ >::ViewBase | ( | Home | home, | |
| ViewArray< VX > & | x, | |||
| VY | y, | |||
| VZ | z, | |||
| int | c | |||
| ) | [inline, protected] |
Constructor for creation.
Definition at line 38 of file view-base.hpp.
Member Function Documentation
| size_t Gecode::Int::Count::ViewBase< VX, VY, VZ >::dispose | ( | Space & | home | ) | [inline, virtual] |
Delete propagator and return its size.
Reimplemented from Gecode::Actor.
Definition at line 73 of file view-base.hpp.
| PropCost Gecode::Int::Count::ViewBase< VX, VY, VZ >::cost | ( | const Space & | home, | |
| const ModEventDelta & | med | |||
| ) | const [inline, virtual] |
Cost function (defined as low linear).
Implements Gecode::Propagator.
Definition at line 59 of file view-base.hpp.
| void Gecode::Int::Count::ViewBase< VX, VY, VZ >::reschedule | ( | Space & | home | ) | [inline, virtual] |
| void Gecode::Int::Count::ViewBase< VX, VY, VZ >::count | ( | Space & | home | ) | [inline, protected] |
Count how many views are equal now.
Definition at line 85 of file view-base.hpp.
| int Gecode::Int::Count::ViewBase< VX, VY, VZ >::atleast | ( | void | ) | const [inline, protected] |
How many views are at least equal.
Definition at line 106 of file view-base.hpp.
| int Gecode::Int::Count::ViewBase< VX, VY, VZ >::atmost | ( | void | ) | const [inline, protected] |
How many views are at most equal.
Definition at line 112 of file view-base.hpp.
| bool Gecode::Int::Count::ViewBase< VX, VY, VZ >::sharing | ( | const ViewArray< VX > & | x, | |
| const VY & | y, | |||
| const VZ & | z | |||
| ) | [inline, static, protected] |
Test whether there is sharing of z with x or y.
Definition at line 123 of file view-base.hpp.
Member Data Documentation
ViewArray<VX> Gecode::Int::Count::ViewBase< VX, VY, VZ >::x [protected] |
VY Gecode::Int::Count::ViewBase< VX, VY, VZ >::y [protected] |
VZ Gecode::Int::Count::ViewBase< VX, VY, VZ >::z [protected] |
int Gecode::Int::Count::ViewBase< VX, VY, VZ >::c [protected] |
The documentation for this class was generated from the following files:
- gecode/int/count.hh
- gecode/int/count/view-base.hpp


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Pazhayarai is considered as an ancient capital of the mediaeval Chola Dynasty and is around 4.2 km from Darasuram. This town also has various villages like the Nandipura Vinnagaram, Patteeswaram, Harishchandrapuram and the Thirusatti Mutram. The place is believed to be the home to four ancient temples like Metrali, Vadathali, Kelthali and Thenthali.
Pazhayarai Someswarar Temple is the prime attraction of this town, which is around 3 km away from Darasuram. The place can be reached easily by availing regular buses and mini buses from Darasuram. It is 2.9 kilometers from Darasuram.
Visiting Timings:-7:30am-12pm and 4pm-7pm
Tirunallur is a village is popular for the Kalyanasundaresar Temple. The village is located in the Orathanadu taluk of the Thanjavur District and is around 8 km from Darasuram.
The Kalyanasundaresar Temple is located in the village of Nallur and is dedicated to Lord Shiva. The lingam placed in the temple is worshipped by the devotees. It is believed that the temple was constructed during the rule of the Medieval Cholas. It is 61 kilometers from Darasuram.
Visiting Timings:-6am-1pm and 4pm-8pm
Papanasanam is a panchayat town, which is around 10.8 km from Darasuram in the Thanjavur District. This town is famous for the temple of Pallaivanatha Swamy, which is believed to have been constructed by a Chola King.
It also houses a granary, which is 86 ft wide and has a height of around 36 ft. Having a capacity of about 3,000 kalam, it was constructed with lime stone by the Nayaks between 1600 and 1634. Moreover, this granary has been declared as a monument by the State Archaeological Department.
Another temple which is famous for housing 108 Shiva Lingams can also be visited. It is 13.4 kilometers from Darasuram.
Visiting Timings:-6:30am-1pm and 6:30pm-8pm


I’ve started a new effort to line up some meetups to talk about what I’ve been working on the last few months. This is some cool stuff everybody, so I hope you’re ready for the deluge! Here are the first dates of many meetups and some conferences I’m lining up to speak at. I’ll be diving into a host of technologies; Terraform, AWS, GCP, Azure, Drone.io (CI in general), continuous delivery, practices, and a whole lot of other systemic things.
In addition to these dates, if you’d like me to swing by and speak, demo, or show some of this technology I’ve got a fairly open schedule and would be happy to schedule more.
Infrastructure as Code and Containers
This talk is at Cloud Power on Tuesday, October 3rd, 2017. I’ll be covering a fairly wide slice of tooling, to build out infrastructure on Azure with Terraform, and stepping beyond initial infrastructure into some basic running services for development, with a server or two. Additionally we’ll talk about immutable infrastructure as a concept, how to gain advantage from it, and where it breaks down.
For more details from this talk, check out the details page I’ve created here.
The Joy of Coding
WVSE - Willamette Valley Software Engineers on Thursday, October 5th, 2017. At the WVSE meetup I’ll be presenting “The Joy of Coding“ inspired by none other than Bob Ross!
Today, Let’s put a little warm glow in our development….Let’s make some happy Code!
Your Infrastructure is your friend, take care of it, make love to it… treat it good. This is your world you can create anything you want. Maybe there’s a line of code… right there… oh, not too much… there it is!
The code is right there in your imagination, you just have to push it out… there! Remember, we don’t make makes mistakes, only happy accidents. Shoot… let’s have some fun!!
For more on this talk, check out the page for collateral from the talk here.
Inception, Workflow, to $Deployed$ Removing Chaos and Noise From Your Dev Stack
.NET Developers Association on Tuesday, November 7th, 2017.
Using a range of tools (Terraform, Azure, Rancher, Docker, etc) I’ll show the full build of a dev stack in just a few short minutes in Azure. Then I’ll take everyone for a whirlwind tour of development within one of these lean built stacks, using various languages and stacks. I’ll wrap up with new app and service deployment into this automated environment to show the entire workflow of the cycle. In the end, this will be a working example of how to use the leanest, meanest technology to date for rapid, high quality development.
For more information on this talk, check out my page I’ve created for collateral from the talk here.
Your Full Dev Stack and Infrastructure in 30 Minutes or Less
This talk isn’t scheduled just yet, but I’ve put together a showcase of technology that shows how to go from nothing to full stack, first deploy to production, development and infrastructure implementation. All rolled into a compact talk with demo, sample, and walk through of the ideas, thinking behind the implementation, and more.
As always, I have some collateral for the talk available. You can check that out here.
If you’d like to learn more, let’s talk about lining up a talk date. Cheers!


5 Range bias correction
In the previous section we have explored vertical integration of vertical profiles (vp
’s). In this paragraph we will generalize vertical integration to entire radar images (ppi
’s). To do that properly, we will account for the changing beam shape of the radar with range.
First, let’s examine the beam shape of the lowest elevation scan of the radar, which is typically around 0.5 degrees.
# define ranges from 0 to 2500000 meter (250 km), in steps of 100 m:
<- seq(0, 250000, 100)
range
# plot the beam height of the 0.5 degree elevation beam:
plot(range, beam_height(range, 0.5), ylab = "beam height [m]", xlab = "range [m]", type='l')
# let's add the lower and upper altitude of the beam, as determined by the beam width:
points(range, beam_height(range, 0.5)-beam_width(range)/2, type='l',lty=3)
points(range, beam_height(range, 0.5)+beam_width(range)/2, type='l',lty=3)
We will start with downloading a polar volume and processing it into a profile:
5.1 Processing a polar volume into a profile
# download a polar volume for the KBRO radar in Brownsville, TX
download_pvolfiles(date_min=as.POSIXct("2017-05-14 05:50:00"), date_max=as.POSIXct("2017-05-14 06:00:00"), radar="KBRO", directory="./data_pvol")
## Downloading data from noaa-nexrad-level2 for radar KBRO spanning over 1 days
##
## Downloading pvol for 2017/05/14/KBRO/
# Load all the polar volume filenames downloaded so far for the KBRO radar:
<- list.files("./data_pvol", recursive = TRUE, full.names = TRUE, pattern="KBRO")
my_pvolfiles # we will process the first one into a vp:
<- my_pvolfiles[1]
my_pvolfile # calculate the profile, using MistNet to remove precipitation:
# we calculate 60 layers of 50 meter width, so up to 30*100=3000 m.
<- calculate_vp(my_pvolfile, n_layer=60, h_layer=50, sd_vvp_threshold = 1) my_vp
Exercise 13: Plot the bird density in the vertical profile you just estimated and compare it with the plots above of the beam height and width of the lowest radar beam. At which approximate range do you expect the radar will no longer be able to resolve the altitude distribution of the migratory birds. And at which range will the radar start to overshoot the migration layer entirely?
# plot the profile
plot(my_vp)
# Answer:
# * The vertical profile shows altitude bands of about 300 meter width
# We therefore expect the radar to start having difficulty to resolve this profile
# when the beam shape becomes broader, so at ranges less than 50 km
# (this is why we typically use 35 km as the maximum range to use in vertical profile estimation.)
# * the birds fly up to about 2km in this profile.
# At about 200000 m (200 km) the lower end of the beam no longer overlaps with the migration layer
# Therefore, if we birds are flying according to the same altitude distribution everywhere,
# we expect the radar to become fully blind for birds beyond 200 km
To correct for the decreasing ability of the radar to resolve the altitude distributions of birds with range, we will make one important (and likely imperfect!) assumption:
We assume that all birds within the image are distributed vertically according to the same relative vertical profile.
This assumptions simplifies the problem, and allows us to estimate the spatial distribution of the birds, as we will explore in the next paragraph:
5.2 Range bias correction and vertical integration on a map
# We will use the piping operator %>% of magrittr package to
# execute multiple operations in one statement:
library(magrittr)
# first, load the polar volume:
%>% read_pvolfile() -> my_pvol my_pvolfile
## Filename = ./data_pvol/2017/05/14/KBRO/KBRO20170514_055309_V06, callid = KBRO
## Reading RSL polar volume with nominal time 20170514-055309, source: RAD:KBRO,PLC:BROWNSVILLE,state:TX,radar_name:KBRO
# Next, let's calculate a PPI for the 1.5 degree elevation scan
# Finally, we calculated the vertically integrated PPI
<- integrate_to_ppi(pvol=my_pvol,vp=my_vp, res=1000) my_ppi_integrated
Exercise 14: Visually compare the PPI for the 1.0 degree sweep and the vertically integrated PPI, and explain the difference in spatial pattern. (For the clearest comparison, make plots of comparable parameters that are either linear or logarithmic in bird density).
#
%>%
my_pvol get_scan(elev = 1.0) %>%
project_as_ppi(raster=raster::raster(my_ppi_integrated$data)) ->
my_ppi# let's take the logarithm of the vertically integrated density, so
# we can compare it more directly to DBZH, which is also a logarithmic quantity:
<- calculate_param(my_ppi_integrated, logVID=log(VID))
my_ppi_integrated # plot both ppi's:
plot(my_ppi)
plot(my_ppi_integrated, param="logVID", zlim=c(-10,10))
# The altitude distribution in this case shows two migration layers.
# In the 1.5 PPI scan these show up as two concentric rings of density.
# Since in a conventional PPI a larger distance from the radar also
# implies a higher altitude, we see the rings showing up at the
# ranges where the beam intersects each of the migration layers.
#
# The vertically integrated PPI is more straightforward to interpret
# Here we correct for aforementioned beam effects and integrate the
# density over altitude, giving a more realistic reconstruction of the
# spatial distribution of migrants.


Enthusiastic for knowledge, with easy circulation between the various sectors of the IT area, very adaptive, with a focus on solving problems in the best possible way. With a passion for programming, having contact with C, C #, Java, JavaScript and Ruby. My hobbies include studying, listening to music, reading, playing, meditating and cycling. I currently work as a full-stack developer using Java technologies, in addition to desktop applications and services for Windows / Linux, and Oracle database. Whenever possible, I make my personal projects available on my GitHub.
Acting as a Web Developer, with emphasis on refactoring legacy system and developing WebServices (SOAP and REST), using Java. Working directly with the creation and maintenance of systems, using Spring Boot, Struts 1 and 2, JSP, Hibernate, Velocity, Ant, Maven. Publishing applications on Tomcat and GlassFish servers. I also work with JavaScript framework, such as VueJS. Creation of CI / CD / CD pipelines using Jenkins, Sonar, Artifactory. Documentation and standardization of systems and processes. Providing content development tools such as GitLab, Jenkins, Sonar, and Artifactoy, using Docker. Task administration using JIRA and Confluence.
Acting as an intern, developing ASP.NET solutions (WebForms and MVC) aimed at the commercial and legal branch, performing maintenance and improvements in systems for sending SMS, email marketing, audits, and monitors.
https://github.com/brenovit/NR-SIM
Projeto de Iniciação Cientifica. Jogo com propósito de validar os conhecimentos adquiridos pelos estudantes da área de segurança do trabalho de forma lúdica, sobre diversos aspectos regidos pelas normas brasileiras regulamentadoras (NBRs).
https://github.com/brenovit/PersonDefense
Jogo no estilo Tower Defense com foco na conscientização do combate ao vetor Aedes Aegypti. Desenvolvido no Unity usando C#.
https://github.com/brenovit/Roll-a-Ball
Jogo mobile desenvolvido no Unity durante o Intercute 2016.2. Onde o jogador controla uma bola usando o acelerômetro destruindo todos os blocos antes que o tempo termine.
https://github.com/brenovit/note-app
Projeto de Pós-Graduação para implementação de DesignPatterns. CRUD de Notas usando sistema de jobs para emissão de notificaçao.
https://github.com/brenovit/rainbow-frontend
https://github.com/brenovit/rainbow-backend
Complete application for management collaborators using Spring Boot (Java 11) + Angular 9 + MySql + Docker
https://github.com/brenovit/luvshop-frontend
https://github.com/brenovit/luvshop-backend
Projeto do curso de desenvolvedor Fullstack com Java e Angular 9
https://github.com/brenovit/dependencies-manager
Mapa de dependências usando VueJS e Vis
https://github.com/brenovit/reclame-ai
Desafio em Ruby para postagem de reclamações
https://github.com/brenovit/GitHub-Info-React
Projeto de estudo sobre React.js. [jscasts]
https://github.com/brenovit/product-store
CRUD usando VueJs + SpringBoot + MySql + Docker
https://github.com/brenovit/store
Securing RESTful API with Spring Boot, Security, JWT and MongoDB
https://github.com/brenovit/my-skills
Project created during The Ultimate Vue 2 Crash Course - Learn by Example, using the main concepts about components and animation with VueJs


Recordings
Recordings are available at cdbaby and itunes or email NZ (see contact page) for direct order info.
Recordings are available at cdbaby and itunes or email NZ (see contact page) for direct order info.


So you want to have a baby...
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Product Number : 525T
Speed Range : 0.5 - 12.4 mph
0.8 - 20 km/h
Elevation Range : 0% - 15%
Running Area
(L x W) : 60 x 20 in 152 x 51 cm
Dimensions
(L x W x H) : 82” × 34.5” × 61.5”
208 cm × 87.5 cm × 156 cm
Max User Weight : 350 lbs (159 kg)
Machine Weight : 393 lbs (178 kg)
Step-up Height : 8 in (20.5 cm)
Power : 115V
208V -220V
230V
Power Details
Motor : 5 HP peak AC motor
Handrails : ARMZ stability bars
Display : Graphic display of workout profile via 8 × 5 LED; Numeric display of time, calories, distance, pace
and heart rate including multi-color indication of heart rate range; Lower display shows speed and incline
level
Connectivity : Made for iPod® Works with iPhone® (optional)
Programs : Quick Start (Manual mode), four weight loss, four cardio; Workouts have 10 levels
Convenience features : Speed and Incline controls located on cross bar, Two large water bottle holders, magazine
rack and a utility tray.
Personal Cooling Fan : No
Safety : Permanently attached lanyard with removable key
Safety Sentry : Standard
Colors : Choice of seven standard frame colors (Black Chrome, Nightstorm Black, Cumulus White, Platinum Sparkle,
Arctic White, Eclipse Black, Metaltone Gold), premium and exclusive colors, as well as over 180 custom
colors.
Excercises:
30-second Sprint Intervals
Side Stepping Workout
Walk the Hills/Run the Flats
Calorie-Blasting Pyramid Workout


--- layout: post title: A word from our sponsors date: 2006-09-05 00:00:00 categories: business interfaces-&-interaction ---
We're sponsoring this event at Sussex University next week: "Human-Centred Technology: Designing for Collaborative as well as Individualised Environments".


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Serenade puts on popup events showcasing personal and experimental interactive works and videogames.
Our friends at Static Vision and
Pink Flamingo Cinema are bringing the year to a close with
Static Vision's Big Christmas Bash 2022.
- 📅 Friday December 16
- ⏰ Doors 6:00pm
- 📌 Mothership Studios 18-22 Sydney St Marrickville
- 🚆 Nearest Train Station: Sydenham Station
- 🚍 Buses: 418, 425, 426, 430
Buy Tickets
Static Vision Mailing List
Christmas Bash Event
Featuring, in alphabetical order:
We’ve been inspired by other exhibitions, collectives and venues around Australia and internationally which highlight games that are deeply personal
to their creators, games made without commercial intent and games engaging with topics rarely explored within the wider medium.
We wanted to bring some of this energy to Sydney. So much more is possible in games and we don't often get to see it here. We want to contribute to changing that.


AT FIRST SIGHT Reviewed by Jamie Peck
Rating: ** (out of ****) MGM / 2:08 / 1999 / PG-13 (language, thematic material, strip-club nudity, sexuality) Cast: Val Kilmer; Mira Sorvino; Kelly McGillis; Nathan Lane; Bruce Davison; Steven Weber; Ken Howard Director: Irwin Winkler Screenplay: Steve Levitt; Irwin Winkler; Rob Cowan
Here's an ironic oddity: a movie about overcoming a handicap that, despite a dearth of assets, never quite overcomes its own handicaps. Notable for the trivia it offers rather than for presenting a well-rounded portrait of disability, "At First Sight" tells an immensely crowded tale adapted from a case study in Dr. Oliver Sacks' 1995 non-fiction novel "An Anthropologist on Mars." But check out Penny Marshall's "Awakenings" for a stronger Sacks adaptation, as "Sight" boasts similar high-quality acting and an engrossing set-up, but lacks the eventual focus and smarts of its '90 counterpart. Oh, well. At least it settles the alleged existence of a special Braille "Playboy" once and for all.
Photogenic leads Val Kilmer and Mira Sorvino click as a blind masseur (he) and a traveling architect (she) who fall deeply in love after an apparently superlative back-rub moves her to tears - a goofy sequence saved, like many others, by their generated chemistry.  At this point, interest in the story reaches its pinnacle as Kilmer grapples with his newfound sense. Then, the film goes in directions that don't make much sense.
Kilmer's omnipresent Stevie Wonder-esque smile during all of this will cause unintentional laughs for some, but his performance is still an improbable stretch he executes admirably. Sorvino's supposed to be a brain, but thanks to some unfortunate scripting, she comes off like the air-headed dealership clerk she so wonderfully played in "Romy and Michele's High School Reunion." Kelly McGillis lends fine support as Kilmer's stolid sister, and even Nathan Lane ("The Birdcage") pops up for a few scenes as a visual therapist described by colleagues as "unorthodox," though his "unorthodox" methods seem to stem less from narrative necessity than a need for comic relief.
Further distracting from the highly intriguing central to-see-or-not-to-see dynamics - and bogging down the midsection considerably, inflating this puppy out past two interminable hours - are a seemingly endless (but inexplicit) series of sex-in-the-shadows montages and a pair of superfluous subplots involving Kilmer's search for his estranged father and Sorvino's relationship with her ex-husband. We don't care, many are likely to say. Even a TV-movie-of-the-week, which is the status the overloaded "At First Sight" struggles to overcome, would have jettisoned this padding in favor of a leaner, meaner - and therefore more effective - message.
© 1999 Jamie Peck E-mail<EMAIL_ADDRESS>Visit The Reel Deal Online: http://www.gl.umbc.edu/~jpeck1/ "Maybe another 200 cigarettes would have helped; coughing would be better than some of this dialogue." -Roger Ebert on "200 Cigarettes"
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


Table of Contents
string quick_template ($tag_array, $template);
This function replaces all tags named like $tag_array's key by the according values in $template and returns it.
# This example will replace the tag <fubar> by the real meaning of # the word. $tags = array ('fubar' => 'fouled up beyond all reason'); $template = 'Proprietary and closed-source software is mostly <fubar>.'; echo quick_template ($tags, $template);


Assignment 2: HTML5 Game Development
Made by - Dhruv Mehta (19BCG10004)
This is my First Image
This is my Second Line


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27. Polynomials: Roots from Coefficients (Kerner’s Method)
Newton’s method applies to one root at a time and
requires a good starting approximation. Kerner’s method is a
generalization that gives all the roots, starting from a
list a and dividing each element
of the residual f a by the derivative
with respect to the corresponding root.
It applies only to a polynomial whose highest order coefficient
is 1, and we first normalize
the coefficients by dividing by the last, yielding a polynomial
having the same roots. The method converges to complex roots only
if at least one of the initial approximations is complex.
We will use the Taylor series approximation to the
exponential function, because the corresponding polynomial
has complex roots:
]d=: ^ t. i.6
1 1 0.5 0.166667 0.0416667 0.00833333
]c=: (norm=: % {:) d
120 120 60 20 5 1
+. a=: (init=: r.@}.@i.@#) c |a
0.540302 0.841471 1 1 1 1 1
_0.416147 0.909297
_0.989992 0.14112
_0.653644 _0.756802
0.283662 _0.958924
deriv=: [: */ 0&=@{.}@(-/~ ,: 1:)
kerner=: 1 : '] - x&p. % deriv@]'
r=: c kerner ^:_ a
+.(/:|) r NB. Real and imaginary parts of roots sorted by magnitude
_2.18061 4.57601e_31
_1.6495 1.69393
_1.6495 _1.69393
0.239806 3.12834
0.239806 _3.12834
>./|c p. r
1.04488e_13
These results may be compared with the use of the primitive p.
on the un-normalized coefficients d . Thus:
p. 2 4 2
+-+-----+
|2|_1 _1|
+-+-----+
,.;}.p. d
0.2398064j3.12834
0.2398064j_3.12834
_1.6495j1.69393
_1.6495j_1.69393
_2.18061
Newton’s method may also be used for a complex root:
+. d NEWTON ^:0 1 2 3 _ a=: 1j1
1 1
0.0166065 0.99639
_0.990523 0.992532
_1.95338 1.10685
_1.6495 1.6939
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ABOUT ME
So I am Gaurav Singh. I live in Vadodara, Gujarat. Currently I am an undergrad student at NIT-Trichy. Just now we have finished our 1st semster of college. So i thought lets do something in this semester break. So here i am with my very own website.I wont say that I am a computer geek or anything like that but yeah computer is something which excites me, motivates me to learn more & explore this Subject more deeply. So thats why i started with HTML, CSS to get started with programming language. I also learnt Data Analytics using MS Excel along with Responisve Web Design.
I have made this website as a small project of mine. And while doing that i also wanted to help the JEE community. As when i was preparing for JEE Adv, sometimes i had to go through like 3-4 websites to check if the answer for it was given correct or not, and also some solutions were not given properly at on site/app. So i saw this as an oppurtunity to make a website where i will be uploading all the JEE Adv solutions of Math subject. And also it will help me apply all the knowledge i have gained through in these first 3 months of my college.
And now if i had to define my experience during the first 3 montshs @NIT-TRICHY then its simply one of the most fascinating, scintillating experience i have ever felt. Learnt a lot during this period. So therefore i hope you would do the same when you come to NIT-TRICHY or even more better if its at IIT.
Want to know my favorite part about this page?
The honesty.
Want to stay in touch?
No problem – the page also features useful links to my social media pages.
I hope you liked my video and PDF solotuions. So Stay Tuned for further Updates. As i will be updating u guys with more such videos and previous year Solutions
Peace:)


| Total net profit | -91.24 | Gross profit | 9.17 | Gross loss | -100.41 |
| Profit factor | 0.09 | Expected payoff | -0.64 | ||
| Absolute drawdown | -91.24 | Maximal drawdown (%) | 90.46 (-11597.44%) | ||
| Total trades | 143 | Profit trades (% of total) | 18 (12.59%) | Loss trades (% of total) | 125 (87.41%) |
| Long positions (won %) | 119 (12.61%) | Short positions (won %) | 24 (12.5%) | ||
| Largest | profit trade | 1.56 | loss trade | -3.47 | |
| Average | profit trade | 0.51 | loss trade | -0.8 | |
| Minimum | profit trade | 0.07 | loss trade | -0.04 | |
| Maximum | take profit in pips | 588.8 | stop loss in pips | -315.2 | |
| Average | take profit in pips | 73.0 | stop loss in pips | -76.9 | |
| Minimum | take profit in pips | -66.3 | stop loss in pips | -35.5 | |
| Maximum | Reward-Risk | 7.6 | Duration in minutes | 99 | |
| Average | Reward-Risk | 1.2 | Duration in minutes | 7.5 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 3.0 (1.79) | consecutive losses (loss in money) | 20.0 (-17.27) | |
| Average | consecutive wins | 1.0 | consecutive losses | 7.0 |
| Total net profit | -77.0 | Gross profit | 8.11 | Gross loss | -85.11 |
| Profit factor | 0.1 | Expected payoff | -0.65 | ||
| Absolute drawdown | -77.0 | Maximal drawdown (%) | 76.22 (-9771.79%) | ||
| Total trades | 119 | Profit trades (% of total) | 15 (12.61%) | Loss trades (% of total) | 104 (87.39%) |
| Largest | profit trade | 1.56 | loss trade | -1.91 | |
| Average | profit trade | 0.54 | loss trade | -0.82 | |
| Minimum | profit trade | 0.07 | loss trade | -0.05 | |
| Maximum | take profit in pips | 588.8 | stop loss in pips | -163.0 | |
| Average | take profit in pips | 70.6 | stop loss in pips | -77.7 | |
| Minimum | take profit in pips | -66.3 | stop loss in pips | -45.2 | |
| Maximum | Reward-Risk | 7.6 | Duration in minutes | 99.0 | |
| Average | Reward-Risk | 1.0 | Duration in minutes | 8.6 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 3.0 (1.79) | consecutive losses (loss in money) | 20.0 (-17.27) | |
| Average | consecutive wins | 1.0 | consecutive losses | 7.0 |
| Total net profit | -14.24 | Gross profit | 1.06 | Gross loss | -15.3 |
| Profit factor | 0.07 | Expected payoff | -0.59 | ||
| Absolute drawdown | -14.24 | Maximal drawdown (%) | 10.77 (-310.37%) | ||
| Total trades | 24 | Profit trades (% of total) | 3 (12.5%) | Loss trades (% of total) | 21 (87.5%) |
| Largest | profit trade | 0.63 | loss trade | -3.47 | |
| Average | profit trade | 0.35 | loss trade | -0.73 | |
| Minimum | profit trade | 0.09 | loss trade | -0.04 | |
| Maximum | take profit in pips | 342.8 | stop loss in pips | -315.2 | |
| Average | take profit in pips | 85.0 | stop loss in pips | -73.0 | |
| Minimum | take profit in pips | -52.8 | stop loss in pips | -35.5 | |
| Maximum | Reward-Risk | 5.4 | Duration in minutes | 18.0 | |
| Average | Reward-Risk | 1.8 | Duration in minutes | 2.1 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 1.0 (0.34) | consecutive losses (loss in money) | 7.0 (-3.47) | |
| Average | consecutive wins | 1.0 | consecutive losses | 4.0 |


FAQ
What does MDAL stand for?
MDAL - Mesh Data Abstraction Library
When would I use it?
Most often this kind of representation is used when preparing data for simulation software or when viewing results of physical simulations, typically for meteorology, oceanography, hydrological or hydraulic models. All computation in such software is done on meshes, with values (physical quantities) usually stored in vertices (less commonly in edges or faces). Results usually comprise of various quantities (e.g. wind speed, water depth) which may be also time-varying (e.g. calculated water flow estimates in 5 minute intervals).
Some of the modelling software packages are free and open source (e.g. AnuGA, EPANET), there are some freeware packages (e.g. Basement, HEC-RAS) as well as many commercial pieces of software.
When was the MDAL project started?
In 2012, Peter Wells and Saber Razmjooei started QGIS Crayfish plugin.
In 2018, Lutra Consulting Ltd. ported and rewritten some parts of Crayfish plugin for separate C++ library that was used in the early QGIS 3.x released as base for QGIS’s Mesh Layer.
Is MDAL proprietary software?
No, MDAL is a Free and Open Source Software.
What license does MDAL use?
See License
What operating systems does MDAL run on?
You can use MDAL on all modern flavors of Unix: Linux, FreeBSD, Mac OS X; all supported versions of Microsoft Windows; mobile environements (Android and iOS).
Is there a graphical user interface to MDAL?
What compiler can I use to build MDAL?
MDAL can be compiled with a C++11 capable compiler.
I have a question that’s not answered here. Where can I get more information?
See Community
Keep in mind, the quality of the answer you get does bear some relation to the quality of the question. If you need more detailed explanation of this, you can find it in essay How To Ask Questions The Smart Way by Eric S. Raymond.
How do I add support for a new format?
To some extent this is now covered by the MDAL driver implementation tutorial
How do I cite MDAL ?
See CITATION


The sugarbag package can be downloaded directly from github:
#devtools::install_github("srkobakian/sugarbag")
library(sugarbag)
library(dplyr)
library(tidyr)
library(ggplot2)
Tasmania is a state of Australia, it has one large land mass and several smaller islands.
We will use the Australian Bureau of Statistics’ ESRI shape files to build our map. Provided for convenience on the package website:
https://srkobakian.github.io/sugarbag/articles/abs-data.html
The set has been filtered for only Tasmanian areas. The data set of Tasmanian Statistical Areas at level two has been provided as a package data set, ?tas_sa2
.
The function create_centroids finds the central points of the polygons provided as an argument.
To tessellate correctly, all the hexagons must be evenly spaced. This function creates a grid of possible locations for the polygons.
Each polygon centroid will be allocated to the closest available hexagon grid point. The capital cities data set will be used to preserve neighbourly relationships.
hex_allocated <- allocate(centroids = centroids,
sf_id = "SA2_5DIG16",
hex_grid = grid,
hex_size = 0.2, # same size used in create_grid
hex_filter = 10,
focal_points = capital_cities,
width = 30, verbose = TRUE) # same column used in create_centroids
h1 <- hex_allocated %>%
fortify_hexagon(hex_size = 0.2, sf_id = "SA2_5DIG16") %>%
left_join(., tas_sa2) %>% mutate(poly_type = "hex")
p1 <- fortify_sfc(tas_sa2) %>% mutate(poly_type = "geo")
ggplot(mapping = aes(fill = SA4_NAME16)) +
geom_polygon(data = p1, aes(x=long, lat, group = interaction(SA2_5DIG16,polygon)), alpha = 0.4) +
geom_polygon(data = h1, aes(x=long, lat, group = interaction(SA2_5DIG16)))
hex_anim <- h1 %>%
select(SA4_NAME16, SA2_5DIG16,
SA2_NAME16, long, lat, poly_type) %>%
left_join(p1 %>% distinct(SA2_5DIG16, polygon), by = "SA2_5DIG16")
geo_anim <- p1 %>%
select(SA4_NAME16, SA2_5DIG16,
SA2_NAME16, long, lat, polygon, poly_type)
anim_tas <- bind_rows(hex_anim, geo_anim) %>% left_join(homeless)
anim_tas %>%
ggplot(aes(x=long, y=lat, group = interaction(polygon, SA2_5DIG16))) +
geom_polygon(aes(fill = SA4_NAME16)) +
geom_polygon(data = geo_anim %>% select(-poly_type), fill = "grey40", alpha = 0.05) +
coord_equal() +
theme_void() +
guides(fill = guide_legend(title = NULL)) +
theme(legend.position = "bottom") +
facet_wrap(~poly_type)


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Generated on Sat Oct 8 12:24:31 2005 for Driver by
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|
0.9.9 API documenation
|
Defines a templated quaternion type and several quaternion operations. More...
Functions | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL T | angle (tquat< T, P > const &x) |
| Returns the quaternion rotation angle. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | angleAxis (T const &angle, vec< 3, T, P > const &axis) |
| Build a quaternion from an angle and a normalized axis. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 3, T, P > | axis (tquat< T, P > const &x) |
| Returns the q rotation axis. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | conjugate (tquat< T, P > const &q) |
| Returns the q conjugate. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL T | dot (tquat< T, P > const &x, tquat< T, P > const &y) |
| Returns dot product of q1 and q2, i.e., q1[0] * q2[0] + q1[1] * q2[1] + ... More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 4, bool, P > | equal (tquat< T, P > const &x, tquat< T, P > const &y) |
| Returns the component-wise comparison of result x == y. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 3, T, P > | eulerAngles (tquat< T, P > const &x) |
| Returns euler angles, pitch as x, yaw as y, roll as z. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 4, bool, P > | greaterThan (tquat< T, P > const &x, tquat< T, P > const &y) |
| Returns the component-wise comparison of result x > y. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 4, bool, P > | greaterThanEqual (tquat< T, P > const &x, tquat< T, P > const &y) |
| Returns the component-wise comparison of result x >= y. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | inverse (tquat< T, P > const &q) |
| Returns the q inverse. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 4, bool, P > | isinf (tquat< T, P > const &x) |
| Returns true if x holds a positive infinity or negative infinity representation in the underlying implementation's set of floating point representations. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 4, bool, P > | isnan (tquat< T, P > const &x) |
| Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of floating point representations. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL T | length (tquat< T, P > const &q) |
| Returns the length of the quaternion. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | lerp (tquat< T, P > const &x, tquat< T, P > const &y, T a) |
| Linear interpolation of two quaternions. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 4, bool, P > | lessThan (tquat< T, P > const &x, tquat< T, P > const &y) |
| Returns the component-wise comparison result of x < y. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 4, bool, P > | lessThanEqual (tquat< T, P > const &x, tquat< T, P > const &y) |
| Returns the component-wise comparison of result x <= y. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL mat< 3, 3, T, P > | mat3_cast (tquat< T, P > const &x) |
| Converts a quaternion to a 3 * 3 matrix. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL mat< 4, 4, T, P > | mat4_cast (tquat< T, P > const &x) |
| Converts a quaternion to a 4 * 4 matrix. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | mix (tquat< T, P > const &x, tquat< T, P > const &y, T a) |
| Spherical linear interpolation of two quaternions. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | normalize (tquat< T, P > const &q) |
| Returns the normalized quaternion. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL vec< 4, bool, P > | notEqual (tquat< T, P > const &x, tquat< T, P > const &y) |
| Returns the component-wise comparison of result x != y. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL T | pitch (tquat< T, P > const &x) |
| Returns pitch value of euler angles expressed in radians. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | quat_cast (mat< 3, 3, T, P > const &x) |
| Converts a 3 * 3 matrix to a quaternion. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | quat_cast (mat< 4, 4, T, P > const &x) |
| Converts a 4 * 4 matrix to a quaternion. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL T | roll (tquat< T, P > const &x) |
| Returns roll value of euler angles expressed in radians. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | rotate (tquat< T, P > const &q, T const &angle, vec< 3, T, P > const &axis) |
| Rotates a quaternion from a vector of 3 components axis and an angle. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL tquat< T, P > | slerp (tquat< T, P > const &x, tquat< T, P > const &y, T a) |
| Spherical linear interpolation of two quaternions. More... | |
| template<typename T , qualifier P> | |
| GLM_FUNC_DECL T | yaw (tquat< T, P > const &x) |
| Returns yaw value of euler angles expressed in radians. More... | |
Defines a templated quaternion type and several quaternion operations.
<glm/gtc/quaternion.hpp> need to be included to use these functionalities.
| GLM_FUNC_DECL T glm::angle | ( | tquat< T, P > const & | x | ) |
Returns the quaternion rotation angle.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL tquat<T, P> glm::angleAxis | ( | T const & | angle, |
| vec< 3, T, P > const & | axis | ||
| ) |
Build a quaternion from an angle and a normalized axis.
| angle | Angle expressed in radians. |
| axis | Axis of the quaternion, must be normalized. |
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<3, T, P> glm::axis | ( | tquat< T, P > const & | x | ) |
Returns the q rotation axis.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL tquat<T, P> glm::conjugate | ( | tquat< T, P > const & | q | ) |
Returns the q conjugate.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL T glm::dot | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y | ||
| ) |
Returns dot product of q1 and q2, i.e., q1[0] * q2[0] + q1[1] * q2[1] + ...
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<4, bool, P> glm::equal | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y | ||
| ) |
Returns the component-wise comparison of result x == y.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<3, T, P> glm::eulerAngles | ( | tquat< T, P > const & | x | ) |
Returns euler angles, pitch as x, yaw as y, roll as z.
The result is expressed in radians.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<4, bool, P> glm::greaterThan | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y | ||
| ) |
Returns the component-wise comparison of result x > y.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<4, bool, P> glm::greaterThanEqual | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y | ||
| ) |
Returns the component-wise comparison of result x >= y.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL tquat<T, P> glm::inverse | ( | tquat< T, P > const & | q | ) |
| GLM_FUNC_DECL vec<4, bool, P> glm::isinf | ( | tquat< T, P > const & | x | ) |
Returns true if x holds a positive infinity or negative infinity representation in the underlying implementation's set of floating point representations.
Returns false otherwise, including for implementations with no infinity representations.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<4, bool, P> glm::isnan | ( | tquat< T, P > const & | x | ) |
Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of floating point representations.
Returns false otherwise, including for implementations with no NaN representations.
/!\ When using compiler fast math, this function may fail.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL T glm::length | ( | tquat< T, P > const & | q | ) |
Returns the length of the quaternion.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL tquat<T, P> glm::lerp | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y, | ||
| T | a | ||
| ) |
Linear interpolation of two quaternions.
The interpolation is oriented.
| x | A quaternion |
| y | A quaternion |
| a | Interpolation factor. The interpolation is defined in the range [0, 1]. |
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<4, bool, P> glm::lessThan | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y | ||
| ) |
Returns the component-wise comparison result of x < y.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<4, bool, P> glm::lessThanEqual | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y | ||
| ) |
Returns the component-wise comparison of result x <= y.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL mat<3, 3, T, P> glm::mat3_cast | ( | tquat< T, P > const & | x | ) |
Converts a quaternion to a 3 * 3 matrix.
| T | Floating-point scalar types. |
Referenced by glm::toMat3().
| GLM_FUNC_DECL mat<4, 4, T, P> glm::mat4_cast | ( | tquat< T, P > const & | x | ) |
Converts a quaternion to a 4 * 4 matrix.
| T | Floating-point scalar types. |
Referenced by glm::toMat4().
| GLM_FUNC_DECL tquat<T, P> glm::mix | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y, | ||
| T | a | ||
| ) |
Spherical linear interpolation of two quaternions.
The interpolation is oriented and the rotation is performed at constant speed. For short path spherical linear interpolation, use the slerp function.
| x | A quaternion |
| y | A quaternion |
| a | Interpolation factor. The interpolation is defined beyond the range [0, 1]. |
| T | Floating-point scalar types. |
| GLM_FUNC_DECL tquat<T, P> glm::normalize | ( | tquat< T, P > const & | q | ) |
Returns the normalized quaternion.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL vec<4, bool, P> glm::notEqual | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y | ||
| ) |
Returns the component-wise comparison of result x != y.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL T glm::pitch | ( | tquat< T, P > const & | x | ) |
Returns pitch value of euler angles expressed in radians.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL tquat<T, P> glm::quat_cast | ( | mat< 3, 3, T, P > const & | x | ) |
Converts a 3 * 3 matrix to a quaternion.
| T | Floating-point scalar types. |
Referenced by glm::toQuat().
| GLM_FUNC_DECL tquat<T, P> glm::quat_cast | ( | mat< 4, 4, T, P > const & | x | ) |
Converts a 4 * 4 matrix to a quaternion.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL T glm::roll | ( | tquat< T, P > const & | x | ) |
Returns roll value of euler angles expressed in radians.
| T | Floating-point scalar types. |
| GLM_FUNC_DECL tquat<T, P> glm::rotate | ( | tquat< T, P > const & | q, |
| T const & | angle, | ||
| vec< 3, T, P > const & | axis | ||
| ) |
Rotates a quaternion from a vector of 3 components axis and an angle.
| q | Source orientation |
| angle | Angle expressed in radians. |
| axis | Axis of the rotation |
| T | Floating-point scalar types. |
| GLM_FUNC_DECL tquat<T, P> glm::slerp | ( | tquat< T, P > const & | x, |
| tquat< T, P > const & | y, | ||
| T | a | ||
| ) |
Spherical linear interpolation of two quaternions.
The interpolation always take the short path and the rotation is performed at constant speed.
| x | A quaternion |
| y | A quaternion |
| a | Interpolation factor. The interpolation is defined beyond the range [0, 1]. |
| T | Floating-point scalar types. |
| GLM_FUNC_DECL T glm::yaw | ( | tquat< T, P > const & | x | ) |
Returns yaw value of euler angles expressed in radians.
| T | Floating-point scalar types. |


ATEEZ drop mysterious MV teaser for Japanese debut title track 'Utopia'
by beansssRookie boy group ATEEZ will be making their Japanese debut soon, with the release of their 1st album 'Treasure EP. EXTRA: Shift The Map'!
Set for release this December 4, ATEEZ's 'Treasure EP. EXTRA: Shift The Map' will be available in 3 unique versions - Type A which includes a CD + DVD, Type Z which includes a CD only, and an ATINY edition, only available for official fanclub members.
The album contains a total of 11 tracks including ATEEZ's debut title track "Utopia" (Japanese ver.), as well as "Aurora" (Japanese ver.), and several unique remixes of their Korean discography. Check out the mysterious MV teaser for ATEEZ's Japanese version of "Utopia", above!


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those that are not. When something feels “off” with your vehicle, don't hesitate to bring it to your local
authorized dealer.
Manufactures Certified Technician will assess your car and provide anything it needs, from routine service
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Biocur1851.github.io
View and Download SONOS ZONEPLAYER 90 setup manual online. For use with the Sonos Multi-room Music System. ZONEPLAYER 90 Media Player pdf manual download. SONOS ZONEPLAYER 90 SETUP MANUAL Pdf Download. Le Feu Anleitungen herunterladen.


--- title: Enables, for the rest of the lifetime of this program, eager execution. type: docs repo: https://github.com/rstudio/tensorflow menu: main: name: Enables, for the rest of the lifetime of this program, eager execution. - tensorflow parent: tensorflow-reference aliases: - /reference/tensorflow/tfe_enable_eager_execution.html - /tensorflow/reference/tfe_enable_eager_execution.html - /guide/tensorflow/reference/tfe_enable_eager_execution.html - /tools/tools/tensorflow/reference/tfe_enable_eager_execution.html - /installation/tensorflow/reference/tfe_enable_eager_execution.html - /tutorials/tensorflow/reference/tfe_enable_eager_execution.html - /guide/tools/tensorflow/reference/tfe_enable_eager_execution.html - /deploy/tensorflow/reference/tfe_enable_eager_execution.html - /tools/tensorflow/reference/tfe_enable_eager_execution.html - /tutorials/tools/tensorflow/reference/tfe_enable_eager_execution.html ---
If not called immediately on startup risks creating breakage and bugs.
tfe_enable_eager_execution(
config = NULL,
device_policy = c("explicit", "warn", "silent")
)
| config | (Optional) A |
| device_policy | (Optional) What policy to use when trying to run an operation on a device with inputs which are not on that device. Valid values: "explicit": raises an error if the placement is not correct. "warn": copies the tensors which are not on the right device but raises a warning. "silent": silently copies the tensors. This might hide performance problems. |
After eager execution is enabled, operations are executed as they are
defined and tensors hold concrete values, and can be accessed as R matrices
or arrays with as.matrix()
, as.array()
, as.double()
, etc.


continue
new game
scores
settings
quit
level X
completed
000.000
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best:
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ETFs have many different stock sectors and asset classes which provide us a wide range of pairs trading candidates. Our data set consists of daily data of the ETFs traded on the NASDAQ or the NYSE.
We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of more than 9 years from January 2010 to September 2019 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
The general method of pair selection is based on both fundamental and statistical analysis.
Any random pairs could be correlated. It is possible that those variables are not causally related to each other, but because of a spurious relationship due to either coincidence or the presence of a certain third, unseen factor. Thus, it is important for us to start with a list of securities that have something in common. For this demonstration, we choose some of the most liquid ETFs traded on the Nasdaq or the NYSE. The relationship for those potentially related pairs could be due to an index, sector or asset class overlap. e.g. QQQ and XLK are two ETFs which track the market leading indices.
To determine which stock pairs to include in the analysis, correlations between the pre-selected ETF pairs are analyzed. Below are three types of correlation measures we usually use in statistics:
| Correlation Measurement Techniques | ||
|---|---|---|
| Pearson correlation | \[r = \frac{\sum (x_i- \bar{x})(y_i- \bar{y})}{\sqrt{\sum (x_i- \bar{x})^2)\sum (y_i- \bar{y})^2)} }\] | |
| Kendall rank correlation | \[\tau=\frac{n_c-n_d}{\frac{1}{2}n(n-1)}\] | |
| Spearman rank correlation | \[\rho=1-\frac{6\sum d_i^2}{n(n^2-1)}\] | |
| \(n\) = number of value in each data set \(n_c\) = number of concordant \(n_d\) = number of discordant \(d_i\) = the difference between the ranks of corresponding values \(x_i\) and \(y_i\) |
We can get these coefficients in Python using functions from the stats library in SciPy. The correlations have been calculated using daily log stock price returns during the training formation period. We found the 3 correlation techniques give the paired ETFs the same correlation coefficient ranking. The Pearson correlation assumes that both variables should be normally distributed. Thus here we use Kendall rank as the correlation measure and choose the pairs with the highest Kendall rank correlation to implement the pairs trading. We get the daily historical closing price of our ETFs pair by using the History function and converting the prices to a log return series. Let \(P_x\) and \(P_y\) denote the historical stock price series for stock x and stock y. The log returns for the ETFs pair are given by:
\[R_x = ln(\frac{P_{x,t}}{P_{x,t-1}}), R_y = ln(\frac{P_{y,t}}{P_{y,t-1}})\] t = 1,2,...,n where n is the number of price datadef PairSelection(self, date): '''Selects the pair of stocks with the maximum Kendall tau value. It's called on first day of each month''' if date.month == self.month: return Universe.Unchanged symbols = [ Symbol.Create(x, SecurityType.Equity, Market.USA) for x in [ "QQQ", "XLK", "XME", "EWG", "TNA", "TLT", "FAS", "FAZ", "XLF", "XLU", "EWC", "EWA", "QLD", "QID" ] ] logreturns = self._get_historical_returns(symbols, self.lookbackdays) tau = 0 for i in range(0, len(symbols), 2): x = logreturns[str(symbols[i])] y = logreturns[str(symbols[i+1])] # Estimate Kendall rank correlation for each pair tau_ = kendalltau(x, y)[0] if tau > tau_: continue tau = tau_ self.pair = symbols[i:i+2] return [x.Value for x in self.pair]
In order to construct the copula, we need to transform the log-return series \(R_x\) and \(R_y\) to two uniformly distributed values u and v. This can be done by estimating the marginal distribution functions of \(R_x\) and \(R_y\) and plugging the return values into a distribution function. As we make no assumptions about the distribution of the two log-return series, here we use the empirical distribution function to approach the marginal distribution \(F_1(R_x)\) and \(F_2(R_y)\). The Python ECDF function from the statsmodel library gives us the Empirical CDF as a step function.
As discussed above, we estimate the copula parameter theta by the relationship between the copula and the dependence measure Kendall’s tau, for each of the Archimedean copulas.
| Copula | Kendall's tau | parameter θ |
|---|---|---|
| Clayton Copula | \[\frac{\theta}{\theta +2}\] | \[\theta=2\tau(1-\tau)^{-1}\] |
| Gumbel Copula | \[1-\theta^{-1}\] | \[\theta=(1-\tau)^{-1}\] |
| Frank Copula | \[1+4[D_1(\theta)-1]/\theta\] | \[arg min\left(\frac{\tau-1}{4}-\frac{D_1(\theta)-1}{\theta}\right)^2\] |
| \[D_1(\theta)=\frac{1}{\theta}\int_{0}^{\theta}\frac{t}{exp(t)-1}dt \] |
def _parameter(self, family, tau): ''' Estimate the parameters for three kinds of Archimedean copulas according to association between Archimedean copulas and the Kendall rank correlation measure ''' if family == 'clayton': return 2 * tau / (1 - tau) elif family == 'frank': ''' debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta is first order Debye function frank_fun is the squared difference Minimize the frank_fun would give the parameter theta for the frank copula ''' integrand = lambda t: t / (np.exp(t) - 1) # generate the integrand frank_fun = lambda theta: ((tau - 1) / 4.0 - (quad(integrand, sys.float_info.epsilon, theta)[0] / theta - 1) / theta) ** 2 return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x elif family == 'gumbel': return 1 / (1 - tau)
Once we get the parameter estimation for the copula functions, we use the AIC criteria to select the copula that provides the best fit in algorithm initialization.
\[AIC=-2L(\theta)+2k\]where \(L(\theta)=\sum_{t=1}^T\log c(u_t,v_t;\theta)\) is the log-likelihood function and k is the number of parameters, here k=1.
The density functions of each copula function are as follows:
| Copula | Density function c(u,v;θ) |
|---|---|
| Clayton Copula | \[(\theta+1)(u^{-\theta}+v^{-\theta}-1)^{-2-1/\theta}u^{-\theta-1}v^{-\theta-1}\] |
| Gumbel Copula | \[C(u,v;\theta)(uv)^{-1}A^{-2+2/\theta}[(\ln u)(\ln v)]^{\theta -1}[1+(\theta-1)A^{-1/\theta}]\] |
| Frank Copula | \[\frac{-\theta(exp(-\theta)-1)(exp(-\theta(u+v)))}{((exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1))^2}\] |
| \[A=(-\ln u)^{\theta}+(-\ln v)^{\theta}\] |
def _lpdf_copula(self, family, theta, u, v): '''Estimate the log probability density function of three kinds of Archimedean copulas ''' if family == 'clayton': pdf = (theta + 1) * ((u ** (-theta) + v ** (-theta) - 1) ** (-2 - 1 / theta)) * (u ** (-theta - 1) * v ** (-theta - 1)) elif family == 'frank': num = -theta * (np.exp(-theta) - 1) * (np.exp(-theta * (u + v))) denom = ((np.exp(-theta * u) - 1) * (np.exp(-theta * v) - 1) + (np.exp(-theta) - 1)) ** 2 pdf = num / denom elif family == 'gumbel': A = (-np.log(u)) ** theta + (-np.log(v)) ** theta c = np.exp(-A ** (1 / theta)) pdf = c * (u * v) ** (-1) * (A ** (-2 + 2 / theta)) * ((np.log(u) * np.log(v)) ** (theta - 1)) * (1 + (theta - 1) * A ** (-1 / theta)) return np.log(pdf)
The copula that provides the best fit is the one that corresponds to the lowest value of AIC criterion. The chosen pair is "QQQ" & "XLK".
The copula functions include all the information about the dependence structures of two return series. According to Stander Y, Marais D, Botha I. in Trading strategies with copulas, the fitted copula is used to derive the confidence bands for the conditional marginal distribution function of \(C(v\mid u)\) and \(C(u\mid v)\), that is the mispricing indexes. When the market observations fall outside the confidence band, it is an indication that pairs trading opportunity is available. Here we choose 95% as the upper confidence band, 5% as the lower confidence band as indicated in the paper. The confidence level was selected based on a back-test analysis in the paper that shows using 95% seems to lead to appropriate trading opportunities to be identified.
Given current returns \(R_x, R_y\) of stock X and stock Y, we define the "mis-pricing indexes" are:
\[MI_{X|Y}=P(U\leq u\mid V\leq v)=\frac{\partial C(u,v)}{\partial v}\] \[MI_{Y|X}=P(V\leq v\mid U\leq u)=\frac{\partial C(u,v)}{\partial u}\]For further mathematical proof, please refer to Xie W, Wu Y. Copula-based pairs trading strategy. The conditional probability formulas of bivariate copulas can be derived by taking partial derivatives of copula functions shown in Table 1. The results are as follows:
Gumbel Copula
\[C(v\mid u)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln u)^{\theta-1}\frac{1}{u}\] \[C(u\mid v)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln v)^{\theta-1}\frac{1}{v}\]Clayton Copula
\[C(v\mid u)=u^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\] \[C(u\mid v)=v^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]Frank Copula
\[C(v\mid u)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta v)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)} \] \[C(u\mid v)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta u)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)} \]After selection of trading pairs and the best-fitted copulas, we take the following steps for trading. Please note we implement the Steps 1, 2, 3 and 4 on the first day of each month using the daily data for the last 12 months, which means our empirical distribution functions and copula parameters theta estimation are updated once a month. In summary each month:
def SetSignal(self, slice): '''Computes the mispricing indices to generate the trading signals. It's called on first day of each month''' if self.Time.month == self.month: return ## Compute the best copula # Pull historical log returns used to determine copula logreturns = self._get_historical_returns(self.pair, self.numdays) x, y = logreturns[str(self.pair[0])], logreturns[str(self.pair[1])] # Convert the two returns series to two uniform values u and v using the empirical distribution functions ecdf_x, ecdf_y = ECDF(x), ECDF(y) u, v = [ecdf_x(a) for a in x], [ecdf_y(a) for a in y] # Compute the Akaike Information Criterion (AIC) for different copulas and choose copula with minimum AIC tau = kendalltau(x, y)[0] # estimate Kendall'rank correlation AIC ={} # generate a dict with key being the copula family, value = [theta, AIC] for i in ['clayton', 'frank', 'gumbel']: param = self._parameter(i, tau) lpdf = [self._lpdf_copula(i, param, x, y) for (x, y) in zip(u, v)] # Replace nan with zero and inf with finite numbers in lpdf list lpdf = np.nan_to_num(lpdf) loglikelihood = sum(lpdf) AIC[i] = [param, -2 * loglikelihood + 2] # Choose the copula with the minimum AIC self.copula = min(AIC.items(), key = lambda x: x[1][1])[0] ## Compute the signals # Generate the log return series of the selected trading pair logreturns = logreturns.tail(self.lookbackdays) x, y = logreturns[str(self.pair[0])], logreturns[str(self.pair[1])] # Estimate Kendall'rank correlation tau = kendalltau(x, y)[0] # Estimate the copula parameter: theta self.theta = self._parameter(self.copula, tau) # Simulate the empirical distribution function for returns of selected trading pair self.ecdf_x, self.ecdf_y = ECDF(x), ECDF(y) # Run linear regression over the two history return series and return the desired trading size ratio self.coef = stats.linregress(x,y).slope self.month = self.Time.month
Finally during the trading period, each day we convert today's returns to u and v by using empirical distribution functions ecdf_x and ecdf_y. After that, two mispricing indexes are calculated every trading day by using the estimated copula C. The algorithm constructs short positions in X and long positions in Y on the days that \(MI_{Y|X}<0.05\) and \(MI_{X|Y}>0.95\). It constructs short position in Y and long positions in X on the days that \(MI_{Y|X}>0.95\) and \(MI_{X|Y}<0.05\).
def OnData(self, slice): '''Main event handler. Implement trading logic.''' self.SetSignal(slice) # only executed at first day of each month # Daily rebalance if self.Time.day == self.day: return long, short = self.pair[0], self.pair[1] # Update current price to trading pair's historical price series for kvp in self.Securities: symbol = kvp.Key if symbol in self.pair: price = kvp.Value.Price self.window[symbol].append(price) if len(self.window[long]) < 2 or len(self.window[short]) < 2: return # Compute the mispricing indices for u and v by using estimated copula MI_u_v, MI_v_u = self._misprice_index() # Placing orders: if long is relatively underpriced, buy the pair if MI_u_v < self.floor_CL and MI_v_u > self.cap_CL: self.SetHoldings(short, -self.weight_v, False, f'Coef: {self.coef}') self.SetHoldings(long, self.weight_v * self.coef * self.Portfolio[long].Price / self.Portfolio[short].Price) # Placing orders: if short is relatively underpriced, sell the pair elif MI_u_v > self.cap_CL and MI_v_u < self.floor_CL: self.SetHoldings(short, self.weight_v, False, f'Coef: {self.coef}') self.SetHoldings(long, -self.weight_v * self.coef * self.Portfolio[long].Price / self.Portfolio[short].Price) self.day = self.Time.day


Utopia::Controller::Rewrite
This module can match and rewrite requests before they processed. This allows you to handle URLs like /post/15/view
or /blog/123-pictures-of-my-cat
easily. The basic rewrite operation is to extract some part of the path and optionally executes a block. That means that the path is modified before being passed on to the next layer in the controller, and controller instance variables may be set.
Regular Expressions
In your controller.rb
:
prepend Rewrite, Actions
rewrite.extract_prefix permalink: /(?<id>\d+)-(?<title>.*)/ do |request, path, match|
# The rewrite matched, but there was no valid post, so we fail:
fail! unless @post = Post.find(@permalink[:id])
# If the path matched, but there was no suffix, we make it default to the post action:
if match.post_match.empty?
match.post_match.components << "post"
end
end
on 'post' do
# You can do further processing here.
fail! unless @post.published?
@comments = @post.comments.first(5)
end
on 'edit' do
# You can do further processing here.
fail! unless @current_user&.editor?
end
In your post.xnode
, as an example:
<content:page>
<content:heading>Post #{attributes[:permalink][:id]} about #{attributes[:permalink][:title]}</content:heading>
<p>#{attributes[:post].content}</p>
</content:page>
Keep in mind, that URLs like /123-pictures-of-my-cat/edit
will work as expected, and hit the edit
action of the controller.
Restful Resources
Similar to the above, if we were solely interested in IDs, we could do the following:
prepend Rewrite, Actions
rewrite.extract_prefix post_id: Integer do |request, path, match|
# The rewrite matched, but there was no valid post, so we fail:
fail! unless @post = Post.find(@post_id)
# If the path matched, but there was no suffix, we make it default to the post action:
if match.post_match.empty?
match.post_match.components << "post"
end
end
This will only match complete integers. Assuming this code is in /blog/controller.rb
, it would match something like /blog/123/view
and assign Integer("123")
to @post_id
.
Matching.. other things
It's possible to match using Integer
, Float
, String
, and you can provide your own class which will be instantiated. If it doesn't match, raise an exception and the rewrite rule will fail.
Nested
Definitions
def process!(request, path)
Rewrite the path before processing the request if possible.
Implementation
def process!(request, path)
catch_response do
self.class.rewrite_request(self, request, path)
end || super
end


Favorite Donuts Boston Cream Pink Frosted Sprinkles Chocolate Least Favorite Donuts Vanilla Munchkins Krispy Kreame Glaze


2018-09-08 future science award life science award for its pioneering contribution in systematically studying the molecular mechanism of rice specific traits and breeding new rice varieties with high yield and good quality using new technologies
2016-10-03 First Lv Zhihe Prize-Sustainable Development Award (Award)
2015 14th world outstanding Chinese award (award)
2014-01-10 national science and technology progress award, grand prize (award)
2011 Mahathir science award (award)
2010-03-24 France's highest medal for agricultural achievement (award)
2008-03-30 China famous scientist award, 2007 lifetime achievement award for Chinese people influencing the world (award) special honor award for
2007 top ten economic years (award)
2004-10 world food prize (award)
2004-09 won the king of Thailand gold sickle award (award)
2004-05 won the Wolff prize, and the Israeli president presented the prize (award)
2001-08 ramon magsaysay award (award)
2001-02 first national highest science and technology award (award)
1998 Vietnam light international rice award (award)
1997 title of outstanding pioneer scientist for heterosis utilization (award)
1996 Nikkei Asia award (award)
1994 he Liang he Li foundation award (award)
1993 US Philippine Ginst Fund (Award)
1988-03-04 third international science award (award)
1988 agronomy and nutrition award (award)
1987 science award (award)
1985-10-15 won the first international award : the "invention and creation" gold medal and honorary certificate of the United Nations intellectual property organization. (award-winning)
1985 gold medal for creativity and invention (award)
1981-06-06 won China's first special invention award for indica hybrid rice (award)
1947 won first place in men's 100m freestyle in Hankou competition area; Won the second place in the men's 100m freestyle in Hubei province (winning)
honorary title2018-12-18 won the title of pioneer of reform and pioneer of hybrid rice research (award)
2010-04-28 to be named the richest man in China's 2010 spiritual wealth list (winning)
2004 was rated as one of the top ten figures of CCTV's "touching China 2004 figures" (award-winning)
2002 won the title of "scientific Chinese (2002) person of the year" (award)
1997-08 won the honorary title of "outstanding pioneer scientist" in international crop heterosis utilization (award)
1989 was named China's advanced worker (award)
1984 won the title of national young and middle-aged expert with outstanding contributions (award)
1979-12 won the title of China's advanced science and technology worker and China's model worker (award-winning)


Hey everyone! The end of the year is approached and it’s time for one last recap on what happened in React.
🎤 Listeners API and useEvent Hook
A lot of effort currently goes into building the next event system. Previous plans included an experiment called React Flare which was now discontinued. To replace it, the team is now focusing on a lower level API.
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Projections of pounds needed per hour
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Based in Chicago. I love playing tennis and loud music.
I get my inspiration from nature and objects around me. I have a possion to colours, typography and skateboards.
I'm a happy person that loves cats and climbing on mountains.
Based in Chicago. I love playing tennis and loud music.