import Box from "./Box";
import GridIndex from "./GridIndex";
/**
* Box with additional information such as position on a grid and if the cell has been visited or not.
* @memberof module:Maze
* @alias Cell
* @extends Box
*/
class Cell extends Box {
/**
* Initialize a cell object.
* @param {GridIndex} index - {@link GridIndex} of the cell instance on a 2D grid.
* @param {boolean} [visited=false] - If the cell has been visited or not.
*/
constructor(index, visited=false) {
super();
/** @property {GridIndex} index Position on a 2D grid. */
this.index = index;
/** @property {boolean} visited If the cell has been visited or not. */
this.visited = visited;
}
}
/**
* Fetch all the possible adjacent neighbors on a grid based off of the cell's position.
* @returns {Map<string, GridIndex>} - A map of each adjacent neighbor keyed to the respective direction of that neighbor.
*/
Cell.prototype.getNeighborIndices = function() {
return new Map([
['LEFT' , new GridIndex(this.index.row - 1, this.index.column)],
['RIGHT' , new GridIndex(this.index.row + 1, this.index.column)],
['UP' , new GridIndex(this.index.row, this.index.column + 1)],
['DOWN' , new GridIndex(this.index.row, this.index.column - 1)]
]);
};
export default Cell;


Me rambling with sprinkles of advice
Art Eidukas, Data Science Campus (ONS)
@finnkauski
Graduated October 2017
Price index and webscraping related
Text matching problem
Looking back motivates me
Using these tools is and will be getting easier
My work promoting python has had a genuine impact
(Not being able to answer questions is a great motivator 😏)
Find small things in your immediate workspace and personal life that you could automate!
Work with friends on cool hobby projects (GTA, CyberPunk2077, webapp, photocompetition etc.)
Scout out areas in your department that you could help with
or Jinja in python
or R
Maybe not, but it frees up time for people to think about more complex questions and lets you practice!
Take advantage of these things! It's both business relevant and good practice!
You can never know enough about pandas.
Not everyone will.
Not everyone who comes out the DSA will still have the desire to keep pursuing a career in this field.
If you are motivated, keep it going. See how far it will take you.
Ask for development time.
One, two, three hours per week will do you wonders.
Don't get distracted
Find an outlet for your work
Last point is important! You won't get leverage to do development without showing that its useful.
Hands On Machine Learning with Sci Kit learn and Keras (2nd ed.)
Books in general, Blogs, RealPython (awesome webpage <3), pydata, pycon
Take as much as you can from the lovely GovDataScience Slack community
Honestly, drop me a slack message if you need more info or advice. (@art_dsc)
Note: if someone wants a virtual study sessions gimmie a shout
I am not advocating you going out of your way and working at home on things that you have to do at work.
But if you have genuine curiosity, foster it and keep doing mini projects at home. That will pay off!
If you've decided to take it more formally please
contact
<EMAIL_ADDRESS>We offer apprenticeships, in-house training and a
masters in
"Data Analytics for Government"
There is much more on offer in terms of support. Make sure to contact us for more information.
Katie Davidson was on first cohort of Accelerator at GDS.
Campus then sponsored her to do a part-time MSc in Data Science at Birkbeck, and she returned to accelerator as a mentor.
While studying she was promoted to Head of Data Science at Dept of Health and Social Care and set up her own data science team.
Bharath Vadhoola was on this programme
He looked at predicting pheonix companies
He was offered the role of principal Data Science at the Home Office
You will get challenged about this data science stuff
Be honest about what it can and cannot solve
Start small
Learn what 'good looks like' for projects
Share stories (positive and negative) of Data Science within your area
Present your work!
Opensource your code (github etc)
Encourage and mentor others
Host show and tells
Coffee and Coding (check Slack channels)
Use it as an artist to promote your own work and projects.
Learn it once, make your like easier forever.
Summary of Caitlin Hudon's Blog
Data Science™ is a new field
Data Science™ is a combination of other fields
Data Science™ is constantly expanding with new tech
We are ALL learning as we go! It's exciting! Acknowledge that!
Accept that you won't be able to learn EVERY area of Data Science™
Nobody will!
I was much more on the coding side, but not great at stats
You will know things nobody else does.
Get comfortable with "I don't know"
Don't `fake it 'til you make it`. Its stressful and harmful for development.
Don't compare yourself too much to others, compare yourself to who you were yesterday.


packageImporter
should be a function
should resolve module import
PackageImporter
resolveSync()
resolve()
should return null
should return url for the test-module main sass file
should return url for the test-module partial file
cleanUrl()
should return the url unmodified
should return the unmodified home path relative url
should return a cleaned up url without tilde
urlVariants()
should return array with single url (module name)
should return array with single url (specific file)
should return array with partial file naming variants and extensions
resolveFilter()
should return package object with value from sass as value for main
sass
main
should return package object with value from scss as value for main
scss


#include <oftable.h>
Public Member Functions | |
| OpenFileTable () | |
| OpenFile * | Open (char *name) |
| void | Close (char *name) |
| void | FileLock (char *name) |
| void | FileRelease (char *name) |
| int | Remove (char *name) |
| int | next_entry () |
Private Member Functions | |
| int | findl (char *name) |
Private Attributes | |
| OpenFileTableEntry * | table [NBOFTENTRY] |
| int | nbentry |
|
|
initialize the open file table. |
|
|
decrease numthread and if numthread is 0 then remove the file from the open file table |
|
|
lock the file name to implement atomic write |
|
|
release the lock after the disk operation. release the lock after the disk operation
|
|
|
find a file in the table return
|
|
|
this function give the next valid entry in the table this function looks in the table where it is possible to add a new entry
|
|
|
check if the file is already open and if not creates a new entry in the table |
|
|
remove the file from the file system remove the file from the directory and put ToBeDeleted to true. after removing a file nobody can open the deleted file but every thread wich has it open can access the datas. The data on disk will be deleted only after every thread had closed this file.
|
|
|
the number of the next valid entry in the table
|
|
|
the list of open files
|


Detailed grades and the number of issues for each of
the sections of information present in your document are shown
below. You can use it to quickly identify areas within the
document that require the most attention. Click on each of
the boxes to navigate to the appropriate part of the report
that contains additional details on the identified issues.
Industry average is the average of all grades for C-CDAs
that have been scored by the ONC Scorecard. It is computed
on an on-going basis, since June 2016, when the
Scorecard tool was first made available to users. Since
then, {{jsonData.results.finalGrade ?
jsonData.results.numberOfDocumentsScored : '?'}} documents
have been graded by the ONC Scorecard.


As of 2019 Buffalo Grove offers Jewish Summer programming for everyone in the community. For two weeks there is non stop fun for all ages. The programs and activities are all local, taking place at Torah Academy of BG or at the home of the Zhenwirth family. SEED also provides programs at Senior Living homes and at local day camps. To top it all off, our programmers and counselors are flown in from around the world to turn your summer into a wonderful Jewish experience!


Written by John T. Criswell
This document is the reference manual for the LLVM test suite. It documents the structure of the LLVM test suite, the tools needed to use it, and how to add and run tests.
In order to use the LLVM test suite, you will need all of the software required to build LLVM, plus the following:
The tests are located in the LLVM source tree under the directory llvm/test. To run all of the tests in LLVM, use the Master Makefile in that directory:
% make -C llvm/test
To run only the code fragment tests (i.e. those that do basic testing of LLVM), run the tests organized by QMTest:
% make -C llvm/test qmtest
To run only the tests that compile and execute whole programs, run the Programs tests:
% make -C llvm/test/Programs
The LLVM test suite contains two major categories of tests: code fragments and whole programs.
Code fragments are small pieces of code that test a specific feature of LLVM or trigger a specific bug in LLVM. They are usually written in LLVM assembly language, but can be written in other languages if the test targets a particular language front end.
Code fragments are not complete programs, and they are never executed to determine correct behavior.
The tests in the Features and Regression directories contain code fragments.
Whole Programs are pieces of code which can be compiled and linked into a stand-alone program that can be executed. These programs are generally written in high level languages such as C or C++, but sometimes they are written straight in LLVM assembly.
These programs are compiled and then executed using several different methods (native compiler, LLVM C backend, LLVM JIT, LLVM native code generation, etc). The output of these programs is compared to ensure that LLVM is compiling the program correctly.
In addition to compiling and executing programs, whole program tests serve as a way of benchmarking LLVM performance, both in terms of the efficiency of the programs generated as well as the speed with which LLVM compiles, optimizes, and generates code.
The Programs directory contains all tests which compile and benchmark whole programs.
Each type of test in the LLVM test suite has its own directory. The major subtrees of the test suite directory tree are as follows:
This directory contains sample codes that test various features of the LLVM language. These pieces of sample code are run through various assembler, disassembler, and optimizer passes.
This directory contains regression tests for LLVM. When a bug is found in LLVM, a regression test containing just enough code to reproduce the problem should be written and placed somewhere underneath this directory. In most cases, this will be a small piece of LLVM assembly language code, often distilled from an actual application or benchmark.
The Programs directory contains programs that can be compiled with LLVM and executed. These programs are compiled using the native compiler and various LLVM backends. The output from the program compiled with the native compiler is assumed correct; the results from the other programs are compared to the native program output and pass if they match.
In addition for testing correctness, the Programs directory also performs timing tests of various LLVM optimizations. It also records compilation times for the compilers and the JIT. This information can be used to compare the effectiveness of LLVM's optimizations and code generation.
The Programs directory is subdivided into several smaller subdirectories:
The SingleSource directory contains test programs that are only a single source file in size. These are usually small benchmark programs or small programs that calculate a particular value. Several such programs are grouped together in each directory.
The MultiSource directory contains subdirectories which contain entire programs with multiple source files. Large benchmarks and whole applications go here.
The External directory contains Makefiles for building code that is external to (i.e. not distributed with) LLVM. The most prominent member of this directory is the SPEC 2000 benchmark suite. The presence and location of these external programs is configured by the LLVM configure script.
This directory contains the QMTest information files. Inside this directory are QMTest administration files and the Python code that implements the LLVM test and database classes.
The LLVM test suite is partially driven by QMTest and partially driven by GNU Make. Specifically, the Features and Regression tests are all driven by QMTest. The Programs directory is currently driven by a set of Makefiles.
The QMTest system needs to have several pieces of information available; these pieces of configuration information are known collectively as the "context" in QMTest parlance. Since the context for LLVM is relatively large, the master Makefile in llvm/test sets it for you.
The LLVM database class makes the subdirectories of llvm/test a QMTest test database. For each directory that contains tests driven by QMTest, it knows what type of test the source file is and how to run it.
Hence, the QMTest namespace is essentially what you see in the Feature and Regression directories, but there is some magic that the database class performs (as described below).
The QMTest namespace is currently composed of the following tests and test suites:
These are the feature tests found in the Feature directory. They are broken up into the following categories:
Assembler/Disassembler tests. These tests verify that a piece of LLVM assembly language can be assembled into bytecode and then disassembled into the original assembly language code. It does this several times to ensure that assembled output can be disassembled and disassembler output can be assembled. It also verifies that the give assembly language file can be assembled correctly.
Optimizer tests. These tests verify that two of the optimizer passes completely optimize a program (i.e. after a single pass, they cannot optimize a program any further).
Machine code tests. These tests verify that the LLVM assembly language file can be translated into native assembly code.
C code tests. These tests verify that the specified LLVM assembly code can be converted into C source code using the C backend.
The LLVM database class looks at every file in the Feature directory and creates a fake test hierarchy containing Feature.<testtype>.<testname>. So, if you add an LLVM assembly language file to the Feature directory, it actually creates 5 new tests: assembler/disassembler, assembler, optimizer, machine code, and C code.
These are the regression tests. There is one suite for each subdirectory of the Regression directory. If you add a new subdirectory there, you will need to modify, at least, the RegressionMap variable in QMTest/llvmdb.py so that QMTest knows how to run the tests in the new subdirectory.
As mentioned previously, the Programs tree in llvm/test provides three types of tests: MultiSource, SingleSource, and External. Each tree is then subdivided into several categories, including applications, benchmarks, regression tests, code that is strange grammatically, etc. These organizations should be relatively self explanatory.
In addition to the regular Programs tests, the Programs tree also provides a mechanism for compiling the programs in different ways. If the variable TEST is defined on the gmake command line, the test system will include a Makefile named TEST.<value of TEST variable>.Makefile. This Makefile can modify build rules to yield different results.
For example, the LLVM nightly tester uses TEST.nightly.Makefile to create the nightly test reports. To run the nightly tests, run gmake TEST=nightly.
There are several TEST Makefiles available in the tree. Some of them are designed for internal LLVM research and will not work outside of the LLVM research group. They may still be valuable, however, as a guide to writing your own TEST Makefile for any optimization or analysis passes that you develop with LLVM.
First, all tests are executed within the LLVM object directory tree. They are not executed inside of the LLVM source tree. This is because the test suite creates temporary files during execution.
The master Makefile in llvm/test is capable of running both the QMTest driven tests and the Programs tests. By default, it will run all of the tests.
To run only the QMTest driven tests, run make qmtest at the command line in llvm/tests. To run a specific qmtest, suffix the test name with ".t" when running make.
For example, to run the Regression.LLC tests, type make Regression.LLC.t in llvm/tests.
Note that the Makefiles in llvm/test/Features and llvm/test/Regression are gone. You must now use QMTest from the llvm/test directory to run them.
To run the Programs test, cd into the llvm/test/Programs directory and type make. Alternatively, you can type make TEST=<type> test to run one of the specialized tests in llvm/test/Programs/TEST.<type>.Makefile. For example, you could run the nightly tester tests using the following commands:
% cd llvm/test/Programs % make TEST=nightly test
Regardless of which test you're running, the results are printed on standard output and standard error. You can redirect these results to a file if you choose.
Some tests are known to fail. Some are bugs that we have not fixed yet; others are features that we haven't added yet (or may never add). In QMTest, the result for such tests will be XFAIL (eXpected FAILure). In this way, you can tell the difference between an expected and unexpected failure.
The Programs tests have no such feature as of this time. If the test passes, only warnings and other miscellaneous output will be generated. If a test fails, a large <program> FAILED message will be displayed. This will help you separate benign warnings from actual test failures.


Analyse
different data quickly
Achieve
better user experience
Extract
only things that matter
- Go to option Upload Raw Data from the main menu.
- Use the new form to upload your json file.
- Once the file is uploaded, its content is divided and stored as a number of entities. Each entity respresents one object from the uploaded json file. Now, click the option Analyse Data from the main menu.
- Fill in the new form and configure your classification: it will identify the groups among all the entities in the database and then sort them accordingly. Parameters of the classification are:
- similarity threshold (the percentage of attributes (0 - 100) which can differ among the entities within a group)
- maximum number of entities to be involved in the classification
Note: If this is not specified, the classification will try to classify all entities which do not belong to any group.
- description
- Once the classification is finished, you will be redirected to the results page which contains:
- information about predefined classification parameters
- list of groups which where identified and their attributes
- chart showing identified groups
- With these groups, you can now analyse the dependancies between certain attribute values among the entities of the same group. Click on Plot Data option from the main menu.
- Choose New Presentation to create a new chart to illustrate the dependencies.
- Once you fill in the new form and click on Create Presentation, a chart will be shown. You can access it later by visiting Plot Data since the list of all charts is kept there.


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import viewModels.drafts.AllDraftDeparturesViewModel @this() @( viewModel: AllDraftDeparturesViewModel )(implicit messages: Messages)
|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>| @messages("departure.drafts.dashboard.table.delete.action.hidden") |
|---|---|---|
| @row.lrn | @row.daysRemaining | @messages("departure.drafts.dashboard.table.action.delete") @messages("departure.drafts.dashboard.table.action.delete.hidden", row.lrn) |


ObjectIndex Example
The Data
The Query
Enter a query to make against the above dataset. The results will display below as you type.
The Results


MXNet System Architecture
MXNet System Architecture
This figure shows the major modules and components of the MXNet system and their interaction. The modules are:
- Runtime Dependency Engine: Schedules and executes the operations according to their read/write dependency.
- Storage Allocator: Efficiently allocates and recycles memory blocks on host (CPU) and devices (GPUs).
- Resource Manager: Manages global resources, such as the random number generator and temporal space.
- NDArray: Dynamic, asynchronous n-dimensional arrays, which provide flexible imperative programs for MXNet.
- Symbolic Execution: Static symbolic graph executor, which provides efficient symbolic graph execution and optimization.
- Operator: Operators that define static forward and gradient calculation (backprop).
- SimpleOp: Operators that extend NDArray operators and symbolic operators in a unified fashion.
- Symbol Construction: Symbolic construction, which provides a way to construct a computation graph (net configuration).
- KVStore: Key-value store interface for efficient parameter synchronization.
- Data Loading(IO): Efficient distributed data loading and augmentation.
MXNet System Components
Execution Engine
You can use MXNet's engine not only for deep learning, but for any domain-specific problem. It's designed to solve a general problem: execute a bunch of functions following their dependencies. Execution of any two functions with dependencies should be serialized. To boost performance, functions with no dependencies can be executed in parallel. For a general discussion of this topic, see our notes on the dependency engine.
Interface
The following API is the core interface for the execution engine:
virtual void PushSync(Fn exec_fun, Context exec_ctx,
std::vector<VarHandle> const& const_vars,
std::vector<VarHandle> const& mutate_vars) = 0;
This API allows you to push a function (exec_fun
),
along with its context information and dependencies, to the engine.
exec_ctx
is the context information in which the exec_fun
should be executed,
const_vars
denotes the variables that the function reads from,
and mutate_vars
are the variables to be modified.
The engine provides the following guarantee:
The execution of any two functions that modify a common variable is serialized in their push order.
Function
The function type of the engine is:
using Fn = std::function<void(RunContext)>;
RunContext
contains runtime information, which is determined by the engine:
struct RunContext {
// stream pointer which could be safely cast to
// cudaStream_t* type
void *stream;
};
Alternatively, you could use mxnet::engine::DAGEngine::Fn
, which has the same type definition.
All of the functions are executed by the engine's internal threads. In such a model, it's usually not a good idea to push blocking functions to the engine (usually for dealing with I/O tasks like disk, web service, UI, etc.) because it will occupy the execution thread and reduce total throughput. In that case, we provide another asynchronous function type:
using Callback = std::function<void()>;
using AsyncFn = std::function<void(RunContext, Callback)>;
In the AsyncFn
function, you can pass the heavy part to your own threads
and safely exit the body of the function.
The engine doesn't consider the function finished
until the Callback
function is called.
Context
You can specify the Context
of the function to be executed within.
This usually includes whether the function should be run on a CPU or a GPU,
and if you specify a GPU, which GPU to use.
Context
is different from RunContext
.
Context
contains device type (GPU/CPU) and device id,
while RunContext
contains information that can be decided only during runtime,
for example, on which stream the function should be executed.
VarHandle
VarHandle
is used to specify the dependencies of functions.
The MXNet engine is designed to be decoupled from other MXNet modules.
So VarHandle
is like an engine-provided token you use
to represent the external resources the functions can use or modify.
It's designed to be lightweight, so creating,
deleting, or copying a variable incurs little overhead.
Upon pushing the functions, you need to specify the variables
that will be used (immutable) in the const_vars
vector,
and the variables that will be modified (mutable) in the mutate_vars
vector.
The engine uses one rule for resolving the dependencies among functions:
The execution of any two functions when one of them modifies at least one common variable is serialized in their push order.
For example, if Fn1
and Fn2
both mutate V2
then Fn2
is guaranteed to be executed after Fn1
if Fn2
is pushed after Fn1
.
On the other hand, if Fn1
and Fn2
both use V2
,
their actual execution order could be random.
This design allows the engine to schedule state-mutating operations in a manner
that minimizes calls to allocate new memory.
For example, the weight update function in DNN
can now use the +=
operator
to update the weights in place,
rather than generating a new weight array each time.
To create a variable, use the NewVar()
API.
To delete a variable, use the PushDelete
API.
Push and Wait
All Push
APIs are asynchronous. The API call returns immediately
regardless of whether the pushed Fn
is finished or not.
This allows the engine to start computing at the same time
as the user thread is pushing functions.
Push
APIs are not thread-safe.
To be specific, only one thread should make engine API calls at a time.
If you want to wait for a specific Fn
to finish,
include a callback function in the closure,
and call the function at the end of your Fn
.
If you want to wait for all Fn
s
that involve (use or mutate) a certain variable to finish,
use the WaitForVar(var)
API.
If you want to wait for all pushed Fn
s to finish,
use the WaitForAll()
API.
Save Object Creation Cost
In some cases, you need to push several functions to the engine for a long period of time.
If the computation of these functions is light,
the overhead of copying lambdas and creating use/mutate variable lists becomes relatively high.
We provide an API to create an OprHandle
beforehand:
virtual OprHandle NewOperator(AsyncFn fn,
std::vector<VarHandle> const& const_vars,
std::vector<VarHandle> const& mutate_vars) = 0;
You can keep pushing the OprHandle
without repeatedly creating them:
virtual void Push(OprHandle op, Context exec_ctx) = 0;
To delete it, call the DeleteOperator(OprHandle op)
API.
Ensure that the operator has finished computing before calling this API.
Operators in MXNet
In MXNet, an operator is a class that contains both actual computation logic
and auxiliary information that can aid the system in performing optimizations,
like in-place updates and auto-derivatives.
To understand the remainder of the document,
we recommend that you familiarize yourself with the mshadow
library,
because all operators compute on the tensor-like structure mshadow::TBlob
provided by the system during runtime.
MXNet's operator interface allows you to:
- Reduce memory allocation cost by specifying in-place updates.
- Hide some internal arguments from Python to make it cleaner.
- Define the relationships among input tensors and output tensors, which allows the system to perform shape checking for you.
- Acquire additional temporary spaces from the system
to perform computation (e.g., calling
cudnn
routines).
Operator Interface
Forward
is the core operator interface:
virtual void Forward(const OpContext &ctx,
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data,
const std::vector<TBlob> &aux_states) = 0;
The OpContext
structure is:
struct OpContext {
int is_train;
RunContext run_ctx;
std::vector<Resource> requested;
}
It describes whether the operator is in the train or test phase,
which device the operator should be run on (in run_ctx
),
and requested resources (covered in the following sections).
in_data
andout_data
represent the input and output tensors, respectively. All of the tensor spaces have been allocated by the system.req
denotes how the computation results are written into theout_data
. In other words,req.size() == out_data.size()
andreq[i]
correspond to the write type ofout_data[i]
.The
OpReqType
is defined as:
enum OpReqType {
kNullOp,
kWriteTo,
kWriteInplace,
kAddTo
};
Normally, the types of all out_data
should be kWriteTo
,
meaning that the provided out_data
tensor is a raw memory block,
so the operator should write results directly into it.
In some cases, for example when calculating the gradient
tensor,
it would be great if we could accumulate the result,
rather than directly overwrite the tensor contents
so that no extra space needs to be created each time.
In such a case, the corresponding req
type is set as kAddTo
,
indicating that a +=
should be called.
aux_states
is intentionally designed for auxiliary tensors used to help computation.
Aside from the Forward
operator, you could optionally implement the Backward
interface:
virtual void Backward(const OpContext &ctx,
const std::vector<TBlob> &out_grad,
const std::vector<TBlob> &in_data,
const std::vector<TBlob> &out_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &in_grad,
const std::vector<TBlob> &aux_states);
This interface follows the same design principle as the Forward
interface,
except that out_grad
, in_data
, and out_data
are given,
and the operator computes in_grad
as the results.
The naming strategy is similar to Torch's convention,
and can be summarized in following figure:
[input/output semantics figure]
Some operators might not require all of the following:
out_grad
, in_data
and out_data
.
You can specify these dependencies with the DeclareBackwardDependency
interface in OperatorProperty
.
Operator Property
One convolution might have several implementations,
and you might want to switch among them to achieve the best performance.
Therefore, we separate the operator semantic interfaces
from the implementation interface (Operator
class)
into the OperatorProperty
class.
The OperatorProperty
interface consists of:
- InferShape:
virtual bool InferShape(mxnet::ShapeVector *in_shape,
mxnet::ShapeVector *out_shape,
mxnet::ShapeVector *aux_shape) const = 0;
This interface has two purposes:
* Tell the system the size of each input and output tensor,
so it can allocate space for them before the Forward
and Backward
call.
* Perform a size check to make sure that there isn't an obvious error before running.
The shape in in_shape
is set by the system
(from the out_shape
of the previous operators).
It returns false
when there is not enough information
to infer shapes or throws an error when the shape is inconsistent.
- Request Resources: Operations like
cudnnConvolutionForward
need a work space for computation. If the system can manage that, it could then perform optimizations, like reuse the space, and so on. MXNet defines two interfaces to achieve this:
virtual std::vector<ResourceRequest> ForwardResource(
const mxnet::ShapeVector &in_shape) const;
virtual std::vector<ResourceRequest> BackwardResource(
const mxnet::ShapeVector &in_shape) const;
The ResourceRequest
structure (in resource.h
) currently contains only a type flag:
struct ResourceRequest {
enum Type {
kRandom, // get a mshadow::Random<xpu> object
kTempSpace, // request temporary space
};
Type type;
};
If ForwardResource
and BackwardResource
return non-empty arrays,
the system offers the corresponding resources through the ctx
parameter
in the Forward
and Backward
interface of Operator
.
Basically, to access those resources, simply write:
auto tmp_space_res = ctx.requested[kTempSpace].get_space(some_shape, some_stream);
auto rand_res = ctx.requested[kRandom].get_random(some_stream);
For an example, see src/operator/cudnn_convolution-inl.h
.
- Backward dependency: Let's look at two different operator signatures (we name all of the arguments for demonstration purposes):
void FullyConnectedForward(TBlob weight, TBlob in_data, TBlob out_data);
void FullyConnectedBackward(TBlob weight, TBlob in_data, TBlob out_grad, TBlob in_grad);
void PoolingForward(TBlob in_data, TBlob out_data);
void PoolingBackward(TBlob in_data, TBlob out_data, TBlob out_grad, TBlob in_grad);
Note that out_data
in FullyConnectedForward
is not used by FullyConnectedBackward
,
while PoolingBackward
requires all of the arguments of PoolingForward
.
Therefore, for FullyConnectedForward
,
the out_data
tensor once consumed could be safely freed
because the backward function will not need it.
This provides a chance for the system to collect some tensors
as garbage as soon as possible.
To specify this situation, we provide an interface:
virtual std::vector<int> DeclareBackwardDependency(
const std::vector<int> &out_grad,
const std::vector<int> &in_data,
const std::vector<int> &out_data) const;
The int
element of the argument vector is an ID
to distinguish different arrays.
Let's see how this interface specifies different dependencies
for FullyConnected
and Pooling
:
std::vector<int> FullyConnectedProperty::DeclareBackwardDependency(
const std::vector<int> &out_grad,
const std::vector<int> &in_data,
const std::vector<int> &out_data) const {
return {out_grad[0], in_data[0]}; // NOTE: out_data[0] is NOT included
}
std::vector<int> PoolingProperty::DeclareBackwardDependency(
const std::vector<int> &out_grad,
const std::vector<int> &in_data,
const std::vector<int> &out_data) const {
return {out_grad[0], in_data[0], out_data[0]};
}
- In place Option: To further save the cost of memory allocation, you can use in-place updates. They are appropriate for element-wise operations when the input tensor and output tensor have the same shape. You specify and in-place update with the following interface:
virtual std::vector<std::pair<int, void*>> ElewiseOpProperty::ForwardInplaceOption(
const std::vector<int> &in_data,
const std::vector<void*> &out_data) const {
return { {in_data[0], out_data[0]} };
}
virtual std::vector<std::pair<int, void*>> ElewiseOpProperty::BackwardInplaceOption(
const std::vector<int> &out_grad,
const std::vector<int> &in_data,
const std::vector<int> &out_data,
const std::vector<void*> &in_grad) const {
return { {out_grad[0], in_grad[0]} }
}
This tells the system that the in_data[0]
and out_data[0]
tensors could share the same memory spaces during Forward
, and so do out_grad[0]
and in_grad[0]
during Backward
.
Important: Even if you use the preceding specification, it's not guaranteed that the input and output tensors will share the same space. In fact, this is only a suggestion for the system, which makes the final decision. However, in either case, the decision is completely transparent to you, so the actual
Forward
andBackward
implementation does not need to consider that.
- Expose Operator to Python: Because of the restrictions of C++, you need user to implement following interfaces:
// initial the property class from a list of key-value string pairs
virtual void Init(const vector<pair<string, string>> &kwargs) = 0;
// return the parameters in a key-value string map
virtual map<string, string> GetParams() const = 0;
// return the name of arguments (for generating signature in python)
virtual vector<string> ListArguments() const;
// return the name of output values
virtual vector<string> ListOutputs() const;
// return the name of auxiliary states
virtual vector<string> ListAuxiliaryStates() const;
// return the number of output values
virtual int NumOutputs() const;
// return the number of visible outputs
virtual int NumVisibleOutputs() const;
Create an Operator from the Operator Property
OperatorProperty
includes all semantic attributes of an operation. It's also responsible for creating the Operator
pointer for actual computation.
Create Operator
Implement the following interface in OperatorProperty
:
virtual Operator* CreateOperator(Context ctx) const = 0;
For example:
class ConvolutionOp {
public:
void Forward( ... ) { ... }
void Backward( ... ) { ... }
};
class ConvolutionOpProperty : public OperatorProperty {
public:
Operator* CreateOperator(Context ctx) const {
return new ConvolutionOp;
}
};
Parametrize Operator
When implementing a convolution operator, you need to know the kernel size,
the stride size, padding size, and so on.
These parameters should be passed to the operator
before any Forward
or Backward
interface is called.
To do so, you could define a ConvolutionParam
structure, as follows:
#include <dmlc/parameter.h>
struct ConvolutionParam : public dmlc::Parameter<ConvolutionParam> {
mxnet::TShape kernel, stride, pad;
uint32_t num_filter, num_group, workspace;
bool no_bias;
};
Put it in ConvolutionOpProperty
, and pass it to the operator class during construction:
class ConvolutionOp {
public:
ConvolutionOp(ConvolutionParam p): param_(p) {}
void Forward( ... ) { ... }
void Backward( ... ) { ... }
private:
ConvolutionParam param_;
};
class ConvolutionOpProperty : public OperatorProperty {
public:
void Init(const vector<pair<string, string>& kwargs) {
// initialize param_ using kwargs
}
Operator* CreateOperator(Context ctx) const {
return new ConvolutionOp(param_);
}
private:
ConvolutionParam param_;
};
Register the Operator Property Class and the Parameter Class to MXNet
Use the following macros to register the parameter structure and the operator property class to MXNet:
DMLC_REGISTER_PARAMETER(ConvolutionParam);
MXNET_REGISTER_OP_PROPERTY(Convolution, ConvolutionOpProperty);
The first argument is the name string, the second is the property class name.
Interface Summary
We've almost covered the entire interface required to define a new operator. Let's do a recap:
- Use the
Operator
interface to write your computation logic (Forward
andBackward
). - Use the
OperatorProperty
interface to:- Pass the parameter to the operator class (you can use the
Init
interface). - Create an operator using the
CreateOperator
interface. - Correctly implement the operator description interface, such as the names of arguments, etc.
- Correctly implement the
InferShape
interface to set the output tensor shape. - [Optional] If additional resources are needed, check
ForwardResource
andBackwardResource
. - [Optional] If
Backward
doesn't need all of the input and output ofForward
, checkDeclareBackwardDependency
. - [Optional] If in-place update is supported, check
ForwardInplaceOption
andBackwardInplaceOption
.
- Pass the parameter to the operator class (you can use the
- Register the
OperatorProperty
class and the parameter class.
Unifying the NDArray Operator and Symbolic Operator
NDArray operations are similar to symbolic operations, except that sometimes you can't write in place to the operands without a complete dependency graph. However, the logic underlying NDArray and symbolic operations are almost identical. SimpleOp, a new unified operator API, unifies different invoking processes and returns to the fundamental elements of operators. Because most mathematical operators attend to one or two operands, and more operands make dependency-related optimization useful, the unified operator is specifically designed for unary and binary operations.
Consider the elements of an operation. Ideally, you need only functions and derivatives to describe an operation. Let's restrict that to the space of unary and binary operations. How do we classify all operations to maximize the possibility of in-place write optimization? Note that you can separate functions by the number of operands. Derivatives are a bit more complex. To construct a dependency graph, you need to know whether output value, input data, or neither are needed alongside head gradient. Gradient functions in the unified API are differentiated by the types of operands it takes for calculation.
Before you learn more about the SimpleOp interface,
we recommend that you review the
mshadow library guide
because calculations will be done in the mshadow::TBlob
structure.
In the following example, we'll create an operator functioning as a smooth l1 loss, which is a mixture of l1 loss and l2 loss. The loss itself can be written as:
loss = outside_weight .* f(inside_weight .* (data - label))
grad = outside_weight .* inside_weight .* f'(inside_weight .* (data - label))
.*
stands for element-wise multiplication, and f
, f'
is the smooth l1 loss function,
which we are assuming is in mshadow
for now.
At first glance, it's impossible to implement
this particular loss as a unary or binary operator.
But we have automatic differentiation in symbolic execution.
That simplifies the loss to f
and f'
directly.
This loss is no more complex than a sin
or an abs
function,
and can certainly be implemented as a unary operator.
SimpleOp: The Unified Operator API
Define Shapes
The mshadow
library requires explicit memory allocation.
As a consequence, all data shapes
must be provided before any calculation occurs.
Before we proceed with defining functions and gradient,
let's check input data shape consistency and provide output shape.
typedef mxnet::TShape (*UnaryShapeFunction)(const mxnet::TShape& src,
const EnvArguments& env);
typedef mxnet::TShape (*BinaryShapeFunction)(const mxnet::TShape& lhs,
const mxnet::TShape& rhs,
const EnvArguments& env);
You can use mshadow::TShape
to check input data shape and designate output data shape.
If you don't define this function, the default output shape is the same as the input shape.
In the case of a binary operator, the shape of lhs
and rhs
is checked as the same by default.
You can also use shape functions to check if any additional arguments and resources are present.
Refer to the additional usages of EnvArguments
to accomplish this.
Before we start on our smooth l1 loss example, we define a XPU
to cpu
or gpu
in the header
smooth_l1_unary-inl.h
implementation so that we reuse the same code in smooth_l1_unary.cc
and
smooth_l1_unary.cu
.
#include <mxnet/operator_util.h>
#if defined(__CUDACC__)
#define XPU gpu
#else
#define XPU cpu
#endif
In our smooth l1 loss example, it's okay to use the default behavior whereby the output has the same shape as the source. Written explicitly, it is:
inline mxnet::TShape SmoothL1Shape_(const mxnet::TShape& src,
const EnvArguments& env) {
return mxnet::TShape(src);
}
Define Functions
Create a unary or binary function with one output: mshadow::TBlob
.
typedef void (*UnaryFunction)(const TBlob& src,
const EnvArguments& env,
TBlob* ret,
OpReqType req,
RunContext ctx);
typedef void (*BinaryFunction)(const TBlob& lhs,
const TBlob& rhs,
const EnvArguments& env,
TBlob* ret,
OpReqType req,
RunContext ctx);
- Functions are differentiated by the types of input arguments.
RunContext ctx
contains information needed during runtime for execution.
struct RunContext {
void *stream; // the stream of the device, can be NULL or Stream<gpu>* in GPU mode
template<typename xpu> inline mshadow::Stream<xpu>* get_stream() // get mshadow stream from Context
} // namespace mxnet
mshadow::stream<xpu> *s = ctx.get_stream<xpu>();
is an example of obtaining a stream from ctx
.
* OpReqType req
denotes how computation results are written into ret
.
enum OpReqType {
kNullOp, // no operation, do not write anything
kWriteTo, // write gradient to provided space
kWriteInplace, // perform an in-place write
kAddTo // add to the provided space
};
A macro is defined in operator_util.h
for a simplified use of OpReqType
.
ASSIGN_DISPATCH(out, req, exp)
checks req
and performs an assignment.
In our smooth l1 loss example, we use UnaryFunction
to define the function of this operator.
template<typename xpu>
void SmoothL1Forward_(const TBlob& src,
const EnvArguments& env,
TBlob *ret,
OpReqType req,
RunContext ctx) {
using namespace mshadow;
using namespace mshadow::expr;
mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
real_t sigma2 = env.scalar * env.scalar;
MSHADOW_TYPE_SWITCH(ret->type_flag_, DType, {
mshadow::Tensor<xpu, 2, DType> out = ret->get<xpu, 2, DType>(s);
mshadow::Tensor<xpu, 2, DType> in = src.get<xpu, 2, DType>(s);
ASSIGN_DISPATCH(out, req,
F<mshadow_op::smooth_l1_loss>(in, ScalarExp<DType>(sigma2)));
});
}
After obtaining mshadow::Stream
from RunContext
, we get mshadow::Tensor
from mshadow::TBlob
.
mshadow::F
is a shortcut to initiate a mshadow
expression. The macro MSHADOW_TYPE_SWITCH(type, DType, ...)
handles details on different types, and the macro ASSIGN_DISPATCH(out, req, exp)
checks OpReqType
and
performs actions accordingly. sigma2
is a special parameter in this loss, which we will cover later.
Define Gradients (Optional)
Create a gradient function with various types of inputs.
// depending only on out_grad
typedef void (*UnaryGradFunctionT0)(const OutputGrad& out_grad,
const EnvArguments& env,
TBlob* in_grad,
OpReqType req,
RunContext ctx);
// depending only on out_value
typedef void (*UnaryGradFunctionT1)(const OutputGrad& out_grad,
const OutputValue& out_value,
const EnvArguments& env,
TBlob* in_grad,
OpReqType req,
RunContext ctx);
// depending only on in_data
typedef void (*UnaryGradFunctionT2)(const OutputGrad& out_grad,
const Input0& in_data0,
const EnvArguments& env,
TBlob* in_grad,
OpReqType req,
RunContext ctx);
Gradient functions of binary operators have similar structures, except that Input
, TBlob
, and OpReqType
are doubled.
GradFunctionArgument
Input0
, Input
, OutputValue
, and OutputGrad
all share the structure of GradFunctionArgument
,
which is defined as:
struct GradFunctionArgument {
TBlob data;
}
In our smooth l1 loss example, note that it's an f'(x)
,
which utilizes input for the gradient calculation,
so the UnaryGradFunctionT2
is suitable.
To enable the chain rule of the gradient,
we also need to multiply out_grad
from the top to the result of in_grad
.
template<typename xpu>
void SmoothL1BackwardUseIn_(const OutputGrad& out_grad,
const Input0& in_data0,
const EnvArguments& env,
TBlob *in_grad,
OpReqType req,
RunContext ctx) {
using namespace mshadow;
using namespace mshadow::expr;
mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
real_t sigma2 = env.scalar * env.scalar;
MSHADOW_TYPE_SWITCH(in_grad->type_flag_, DType, {
mshadow::Tensor<xpu, 2, DType> src = in_data0.data.get<xpu, 2, DType>(s);
mshadow::Tensor<xpu, 2, DType> ograd = out_grad.data.get<xpu, 2, DType>(s);
mshadow::Tensor<xpu, 2, DType> igrad = in_grad->get<xpu, 2, DType>(s);
ASSIGN_DISPATCH(igrad, req,
ograd * F<mshadow_op::smooth_l1_gradient>(src, ScalarExp<DType>(sigma2)));
});
}
Register SimpleOp to MXNet
After creating the shape, function, and gradient, restore them into both an NDArray operator and
a symbolic operator. To simplify this process, use the registration macro defined in operator_util.h
.
MXNET_REGISTER_SIMPLE_OP(Name, DEV)
.set_shape_function(Shape)
.set_function(DEV::kDevMask, Function<XPU>, SimpleOpInplaceOption)
.set_gradient(DEV::kDevMask, Gradient<XPU>, SimpleOpInplaceOption)
.describe("description");
SimpleOpInplaceOption
is defined as:
enum SimpleOpInplaceOption {
kNoInplace, // do not allow inplace in arguments
kInplaceInOut, // allow inplace in with out (unary)
kInplaceOutIn, // allow inplace out_grad with in_grad (unary)
kInplaceLhsOut, // allow inplace left operand with out (binary)
kInplaceOutLhs // allow inplace out_grad with lhs_grad (binary)
};
In our example, we have a gradient function that relies on input data, so the function can't be written in place. The output gradient has no purpose after gradient computation, so the gradient can be written in place.
MXNET_REGISTER_SIMPLE_OP(smooth_l1, XPU)
.set_function(XPU::kDevMask, SmoothL1Forward_<XPU>, kNoInplace)
.set_gradient(XPU::kDevMask, SmoothL1BackwardUseIn_<XPU>, kInplaceOutIn)
.set_enable_scalar(true)
.describe("Calculate Smooth L1 Loss(lhs, scalar)");
Remember from the discussion of shape functions that a default behavior without set_shape_function
forces the inputs
(if they're binary) to be the same shape and yield the same shape for output. We'll discuss set_enable_scalar
later.
NDArray Operator Summary
- Create a shape function for determining the output shape.
- Create a function as the forward routine by choosing a suitable function type.
- Create a gradient as the backward routine by choosing a suitable gradient type.
- Register the operator using the registration process.
Additional Information on SimpleOp
Using SimpleOp on EnvArguments
Some operations might need a scalar as input, such as a gradient scale, a set of keyword arguments
controlling behavior, or a temporary space to speed up calculations.EnvArguments
provides additional arguments and resources to make calculations more scalable
and efficient.
struct EnvArguments {
real_t scalar; // scalar argument, if enabled
std::vector<std::pair<std::string, std::string> > kwargs; // keyword arguments
std::vector<Resource> resource; // pointer to the resources requested
};
More registration parameters are required to enable these additional features. To prevent confusion on parameters, scalar
and kwargs
can't be present at the same time. To enable scalar
, use
set_enable_scalar(bool enable_scalar)
in registration. Then, in forward functions and gradients, the scalar
can be accessed from env.scalar
as in the function parameter EnvArguments env
.
To enable kwargs
, use set_enable_kwargs(bool enable_kwargs)
in registration. Then, in forward
functions and gradients, additional arguments are contained in env.kwarg
, which is defined as
std::vector<std::pair<std::string, std::string> >
. Use the DMLC parameter structure to
simplify parsing keyword arguments. For more details, see the guide on parameter structure.
Additional resources like mshadow::Random<xpu>
and temporary memory space can also be requested and
accessed from EnvArguments.resource
. The registration routine is set_resource_request(ResourceRequest req)
or set_resource_request(const std::vector<ResourceRequest>)
, where mxnet::ResourceRequest
is defined as:
struct ResourceRequest {
enum Type { // Resource type, indicating what the pointer type is
kRandom, // mshadow::Random<xpu> object
kTempSpace // A dynamic temp space that can be arbitrary size
};
Type type; // type of resources
};
Registration will request the declared resource requests from mxnet::ResourceManager
, and place resources
in std::vector<Resource> resource
in EnvArguments
. To access resources, use the following:
auto tmp_space_res = env.resources[0].get_space(some_shape, some_stream);
auto rand_res = env.resources[0].get_random(some_stream);
For an example, see src/operator/loss_binary_op-inl.h
.
In our smooth l1 loss example, a scalar input is needed to mark the turning point of a loss function. Therefore,
in the registration process, we use set_enable_scalar(true)
, and use env.scalar
in function and gradient
declarations.
Crafting a Tensor Operation
Because computation utilizes the mshadow
library and we sometimes don't have functions readily available, we
can craft tensor operations in operator implementations. If you define such functions as element-wise, you
can implement them as a mxnet::op::mshadow_op
. src/operator/mshadow_op.h
that contains a lot of mshadow_op
,
for example. mshadow_op
are expression mappers. They deal with the scalar case of desired functions. For details, see
mshadow expression API guide.
If an operation can't be done in an element-wise way, like the softmax loss and gradient, then you need to create a new tensor operation. You need to create as mshadow
function and as mshadow::cuda
function directly. For details, see the mshadow
library. For an example, see src/operator/roi_pooling.cc
.
In our smooth l1 loss example, we create two mappers, namely the scalar cases of smooth l1 loss and gradient.
namespace mshadow_op {
struct smooth_l1_loss {
// a is x, b is sigma2
MSHADOW_XINLINE static real_t Map(real_t a, real_t b) {
if (a > 1.0f / b) {
return a - 0.5f / b;
} else if (a < -1.0f / b) {
return -a - 0.5f / b;
} else {
return 0.5f * a * a * b;
}
}
};
}
The gradient, which can be found in src/operator/smooth_l1_unary-inl.h
, is similar.
Beyond Two Operands
The new unified API is designed to fulfill the fundamentals of an operation. For operators with more than two inputs, more than one output, or that need more features, see the original Operator API.


Newly available from Munsey's, those fine purveyors of out of copyright e-books, is a yarn by Joe Archibald called “How Green Was My Valet”. File it on your e-book e-shelf alongside those titles devised some years ago by the great Max Décharné, “Dr Terror's House Of Horace” and “When Dinah Shore Ruled The Earth”.


This is used to read a field
Methods
Attributes
| [R] | field_name | Field name |
| [R] | grid | Grid that field belongs to |
Public Class methods
Create an object to read a field in a MtkGrid
[ show source ]
# File lib/mtk_field.rb, line 18 def initialize(g, f) @grid = g @field_name = f raise "Field #{field_name} is not found in grid #{grid.grid_name} of file #{grid.file.file_name}" unless grid.field_list.include?(field_name) end
Public Instance methods
Return data type of field. This matchs one of the constants defined in MtkField (e.g, CHAR8).
[ show source ]
# File lib/mtk_field.rb, line 31 def data_type MtkField.field_to_datatype(grid.file.file_name, grid.grid_name, field_name) end
Return fill value of field.
[ show source ]
# File lib/mtk_field.rb, line 40 def fill_value MtkField.field_to_fillvalue(grid.file.file_name, grid.grid_name, field_name) end
Read the field for the given MtkRegion, returning a MtkDataPlane.
[ show source ]
# File lib/mtk_field.rb, line 49 def read(region) MtkField.field_read(grid.file.file_name, grid.grid_name, field_name, region) end


--- layout: default title: Owlbadger tagline: A blog about music ---
Posted by {{ post.author }} {{ post.date | date: "%-d %B %Y" }}
{{ post.excerpt }}
Read more ...


interface Thing : Parcelable
Base class for every class of the Reddit API,
fullname |
Fullname of the item, e.g. "t1_c3v7f8u" abstract val fullname: String |
id |
this item identifier, e.g. "8xwlg" abstract val id: String |
Account |
Base interface that groups the common fields between the class Redditor and Me, that has extra fields of the current logged in user. interface Account : Thing, Created, Parcelable |
Awarding |
data class Awarding : Thing, Parcelable |
Contribution |
Base interface for all the data submitted by a user. e.g. Submission, Comment, Message interface Contribution : Thing |
Friend |
data class Friend : Thing, Parcelable |
Gildable |
Base interface for all the item that can be given Gildings interface Gildable : Thing |
Message |
This class represents the message a user can send or receive. Messages are grouped in the user inbox. data class Message : Thing, Votable, Created, Distinguishable, Replyable, Parcelable |
SubredditData |
interface SubredditData : Thing, Created |
Votable |
Base interface for all the items that can be voted on, likes = True -> Upvote, likes = False -> Downvote, likes = null interface Votable : Thing |


NEW WATERFORD GIRL (1999) / *** 1/2
Directed by Allan Moyle. Screenplay by Tricia Fish. Starring Liane Balaban, Tara Spencer-Nairn, Mary Walsh. Running time: 95 minutes. Rated AA by the MFCB. Reviewed on July 12th, 2000.
By SHANNON PATRICK SULLIVAN
Confession time: though I live in Newfoundland, I don't watch many Canadian movies. This is not exactly by choice -- the simple fact of the matter is that Canadian films make up an exceedingly small fraction of those shown at local theatres. Indeed, the only Canadian picture which showed locally during the first half of 2000 was "Misery Harbour", a co-production with Swedish, Norwegian and Danish firms.
The reason for this, I guess, is that Canadian cinema has a reputation for being very "artsy" and inaccessible. In my admittedly limited experience, I haven't seen a lot to dissuade me from that opinion. Like it or not, audiences in general prefer more mainstream fare, and until Canadian filmmakers accept this, the situation isn't likely to change.
Fortunately, at least one Canadian movie this year does fit the "mainstream" bill -- and without sacrificing its artistic integrity. That movie is "New Waterford Girl", fresh from the 1999 Toronto Film Festival, set in the coal-mining town of New Waterford, on Cape Breton island in Nova Scotia. For the geographically-challenged amongst you, Cape Breton lies about 60 miles across the Cabot Strait from the south-west corner of Newfoundland, and its culture and atmosphere share a lot in common with this province. Consequently, I could easily relate to much of the material -- the accents and attitudes found in New Waterford are not dissimilar from many outport Newfoundland communities.
That said, "New Waterford Girl" is a movie for everyone, not just Atlantic Canadians. I do wonder, though, how many Mainlanders will recognise the pinpoint accuracy of the seemingly exaggerated portrayal of a rural Maritimes community.
New Waterford in the 1970s is an isolated, conservative town where Catholicism is still a vital part of everyday life. Fifteen year-old Mooney Pottie (Liane Balaban) is a talented artist, but has resigned herself to a life of limited possibilities. When her eccentric high school teacher (Andrew McCarthy) secures her a full scholarship to prestigious New York art school, Mooney is initially exuberant. But her family, including father Francis (Nicholas Campbell), mother Cookie (Mary Walsh) and four siblings, are horrified at the prospect of Mooney leaving New Waterford, convinced that no good will befall her.
Into this desperate milieu arrive Lou Benzoa (Tara Spencer-Nairn) and her mother Midge (Cathy Moriarty), the Potties' new next-door neighbours. They come from the Bronx, where Lou's ex-boxer father got into some sort of hot water with the law. Despite being treated with suspicion by the locals ("You're not Catholic?"), the free-spirited Lou falls in love with New Waterford and quickly befriends Mooney, reinvigorating her dreary life. With Lou's help, Mooney concocts a scandalous scheme to escape New Waterford by ruining her own virginal reputation and then claiming she's pregnant. This will ensure a trip to Antigonish (on the Nova Scotia mainland) to have the purported baby, from where she can travel to New York before anyone is the wiser.
Although the story of "New Waterford Girl" is fairly typical coming-of-age fare, rarely has this sort of tale been told with such delicious humor. The script, by Tricia Fish, is peppered with lovely moments. Witness the scene where Mooney feebly "tries out" for the national high jump team, for example, and patiently explains that her poor performance was because "these aren't my normal shoes". Delightfully inventive is a subplot in which Lou discovers that pugilism runs in her blood and becomes the enforcer for the lovelorn ladies of New Waterford. Only a climactic boxing match feels contrived.
There is also a certain authenticity to the proceedings: the teens of New Waterford get drunk and make out and argue with their families just like young people really do. There is little of the exaggerated behaviour that often characterises these sorts of movies; at times "New Waterford Girl" has an almost documentary feel.
The direction by Allan Moyle ("Pump Up The Volume") is very capable, abetted by Derek Rogers' cinematography. The stark, uncompromising beauty of the Cape Breton coastline is depicted superbly, without making the movie feel like a travelogue. The only tech problem is the sound, which sometimes results in indistinct dialogue.
But "New Waterford Girl"'s biggest asset is its cast, excellent almost without exception. Newcomer Liane Balaban is a real find, looking not unlike Natalie Portman and -- more importantly -- with acting skills to match. Her Mooney is a wonderful character, neither too cynical nor too pitiable, and with a palpable intelligence. Mooney's scheme to fake pregnancy could have come across as goofy, but the conviction Balaban brings to the role makes it work.
Spencer-Nairn's Lou provides a good contrast, vivacious and daring. Mooney can't wait to get out of Cape Breton; Lou enjoys New Waterford, a welcome change from timeworn plot of the big city kid who moves to a small town and loathes it. Spencer-Nairn has terrific chemistry with Balaban and a real presence onscreen. The only thing that mars her performance is that the film seems to conveniently forget its implication that the Benzoas are on the run. That Lou seems utterly dismissive of the fact struck me as hard to swallow.
Amongst the supporting players, veteran actors Campbell ("Da Vinci's Inquest") and Walsh ("CODCO", "This Hour Has 22 Minutes") are grand as Mooney's parents, evoking both desperation and honest caring when confronted with Mooney's situation. As for the two big-name Hollywood actors, Moriarty is rather underutilised, but McCarthy delivers a laudable low-key performance.
It's unfortunate that more people don't have the chance right now to see "New Waterford Girl" (it's making its way only slowly across Canada, and the producers have yet to secure a US distribution deal so it will probably be confined to festivals). It is a touching and funny movie, and marks the start of a very promising career for Balaban. As "New Waterford Girl" ably demonstrates, Canadian cinema is alive and well. I, for one, am eager to see more of it.
Copyright © 2000 Shannon Patrick Sullivan. Archived at The Popcorn Gallery, http://www.physics.mun.ca/~sps/movies/NewWaterfordGirl.html
_______________________________________________________________________ / Shannon Patrick Sullivan | "We are all in the gutter, but some of us \ |<EMAIL_ADDRESS>| are looking at the stars." - Oscar Wilde | \___________________________|__________________________________________/ | Popcorn Gallery Movie Reviews www.physics.mun.ca/~sps/movies.html | | Doctor Who: A Brief History of Time (Travel) /drwho.html |
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Zenexity becomes
Zengularity
Creative web engineers
for transforming business
Bootstraping made easy
REST/CRUD made easy
Previewing 2.0-SNAPSHOT
Actors and Websocket made easy
Previewing v0.2
MongoDB made easy
npm -v
grunt --version
npm install -g grunt-cli
bower -v
npm install -g bower
mongo --version


General information
This is 1024px, a free website template from cssMoban.com. This is a regular XHTML/CSS template which you can use to build your own website or online presentation. The 1024px template is released as completely free code, which means that you are allowed to use it for any purpose and in any way you may want to, without any obligations or limitations. I kindly ask that you leave the credit text and link (the one that says "Design by Andreas Viklund" in the footer) since that is a nice way of supporting my template design work. But it is no requirement, just a request. Do what works best for you!
CMS templates and blog themes?
If you are planning to use this template with a content management system or blog script, it may be worth checking if the template has been ported to the system of your choice already. Most of my templates have been customized to work with a number of different systems and scripts, and links to these variations can be found on the original templates site.
If you have any comments, questions, bug reports or suggestions regarding this template, or if you plan to customize this template into a new public theme for any CMS/Blog and want your version linked from my site, then you are very welcome to contact me through my website.
Good luck with your new site design!
Note: For more information on how to use this template to build your own website, and for tools and tutorials that can be useful, go to andreasviklund.com.


This class defines the information about an attribute that will come out of the scanner during parsing
This class defines the information about an attribute that will come out of the scanner during parsing. This information does not depend upon the type of validator because it is not tied to any scheme/DTD type info. Its just the raw XML 1.0 information that will be reported about an attribute in the startElement() callback method of the XMLDocumentHandler class. Hence it is not intended to be extended or derived from. Its designed to be used as is. The 'specified' field of this class indicates whether the attribute was actually present or whether it was faulted in because it had a fixed or default value. The code receiving this information can ask its validator for more info about the attribute, i.e. get its declaration from the DTD/Schema info.
attrName - The base name of the attribute, i.e. the part
after any prefix.
attrPrefix - The prefix, if any, of this attribute's name. If
this is empty, then uriID is meaningless as well.
attrValue - The value string of the attribute, which should
be fully normalized by XML rules!
type - The type of the attribute. This will indicate
the type of normalization done and constrains
the value content. Make sure that the value
set meets the constraints!
specified - Indicates whether the attribute was explicitly
specified or not. If not, then it was faulted
in from a FIXED or DEFAULT value.
attrName - The base name of the attribute, i.e. the part
after any prefix.
attrPrefix - The prefix, if any, of this attribute's name. If
this is empty, then uriID is meaningless as well.
attrValue - The value string of the attribute, which should
be fully normalized by XML rules according to the
attribute type.
type - The type of the attribute. This will indicate
the type of normalization done and constrains
the value content. Make sure that the value
set meets the constraints!
specified - Indicates whether the attribute was explicitly
specified or not. If not, then it was faulted
in from a FIXED or DEFAULT value.
attrName - The base name of the attribute, i.e. the part
after any prefix.
attrPrefix - The prefix, if any, of this attribute's name. If
this is empty, then uriID is meaningless as well.
alphabetic index hierarchy of classes
|
Xerces-C XML Parser for C++ Version 1.1 |
|
|
generated by doc++


/* * Copyright 2007 (c) Tim Knip, ascollada.org. * * Permission is hereby granted, free of charge, to any person * obtaining a copy of this software and associated documentation * files (the "Software"), to deal in the Software without * restriction, including without limitation the rights to use, * copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the * Software is furnished to do so, subject to the following * conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, * WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR * OTHER DEALINGS IN THE SOFTWARE. */ package org.ascollada.physics { import org.ascollada.core.DaeDocument; import org.ascollada.core.DaeEntity; /** * */ public class DaePlane extends DaeEntity { /** * * @param node * @return */ public function DaePlane(document:DaeDocument, node:XML = null):void { super(document, node); } /** * * @param node * @return */ override public function read( node:XML ):void { super.read(node); } } }


A silly contest where you recruit a team of graphics researchers, who then score points for talks and papers accepted at SIGGRAPH.
FGL SIGGRAPH 2000 rules and entry form.
The Space Odyssey year's rules and entry form (SIGGRAPH 2001).
The Space Odyssey year's preliminary results (SIGGRAPH 2001).
The Space Odyssey year's results (SIGGRAPH 2001).
The year after that's rules and entry form (SIGGRAPH 2002).
The year after that's preliminary results (SIGGRAPH 2002).
The year after that's results (SIGGRAPH 2002).
Previous to the next to last year's rules and entry form (SIGGRAPH 2003).
Previous to the next to last year's preliminary results (SIGGRAPH 2003).
Previous to the next to last year's results (SIGGRAPH 2003).
Next to last year's rules and entry form (SIGGRAPH 2004).
Next to last year's preliminary results (SIGGRAPH 2004).
Next to last year's results (SIGGRAPH 2004).
The last year's rules and entry form (SIGGRAPH 2005).
The last year's preliminary results (SIGGRAPH 2005).
The last year's results (SIGGRAPH 2005).


Coders in the Newsroom
What is different working in a newsroom than developing software in a startup or company?
By Gabriela Rodriguez / @gaba
Free Software
many newsrooms with public repositories and pushing for collaboration
backbone.js, django came out of the work at newsrooms
Exciting Projects!
Space Open for Experimentation
The tools you build are being used
Newsrooms with interesting projects
We need you!
Technology and Journalism are shaping the world!
How did I arrive to Journalism?
Not trusting "traditional media"
through community and independent media and activism
The Coral Project
Resources
<EMAIL_ADDRESS>gaba @ twitter


Welcome to Titan.
You must use your primary monitor.
Should you lose your way, return here.
Begin anew.


Be our Partner
Being the Partners of ISIE NITP SRA club, you will be investing in the
future stustainable development of our nation. It is the only club of
NITP which is working on renewable sources of energy.
Sponsoring the cars developed at our club, you will be investing in
enthusiastic technical professionals of the future.
You will be investing in innovation, quality, creativity, and
learning. The coming India is a nation of this generation, and you
will help in making the nation of problem solvers who will come
together and develop a better world for the future generation.
Why be our Partner?
- You will be known among the ones working for sustainable development.
- Promotion and publicity among the students of NIT Patna and other connected colleges
- Opportunity to mentor developing engineers
- Publicity through company’s logo on our car and team member’s apparels in ESVC.
- Your posters in Pit area provied to our team in ESVC
- Full media exposure till the duration of the competition.
- Opportunity to conduct seminars at our college.
- Opportunity to put up registration stalls in our campus during our TCF.


Noughts and Crosses ({{gameModel.player1}} vs {{gameModel.player2}})
Score: {{gameModel.gameBoard}}
{{gameModel.currentGameState}}
VS
Home


Getting Started with Blackberry
Video Tutorials:
1. Requirements
- Windows XP (32-bit) or Windows 7 (32-bit and 64-bit) or Mac OSX 10.6.4+
For 4.x devices check out this guide.
2. Install SDK + PhoneGap
- (Windows Only) Download and install SUN JDK (32-Bit Version). Add it to your PATH variable.
- (Windows Only) Download and extract Apache Ant. Add it to your PATH variable.
- Download BlackBerry WebWorks Smartphone SDK for BlackBerry development and/or BlackBerry WebWorks Tablet OS SDK for Playbook development. Keep note of the directories you install these SDKs.
- Donwload the latest copy of PhoneGap and extract its contents. We will be working with the Android directory.
3. Setup New Project
- Open up a command prompt/terminal and navigate to where you extracted PhoneGap. CD into the PhoneGapBlackBerry directory.
- Create a PhoneGap BlackBerry and PlayBook project. Type 'ant create -Dproject.path='followed by the location you wish to create your project into the command prompt/terminal.
- Change to the newly created directory located at
C:\Dev\bbw\sample
. - Open up the project.properties file with your favourite editor and change the lines
BlackBerry.bbwp.dir=
andPlayBook.bbwp.dir=
to equal the respective install locations of the SDKs you downloaded earlier.
4. Hello World
Build the PhoneGap sample project by typing ant target build
in your command prompt/terminal while you are in your project's directory. Replace the target with either blackberry or playbook. Note this is the sample PhoneGap project and not a basic hello world application. You can go edit the index.html file located in the www directory of your project to make it say Hello World if you wish.
5A. Deploy to Simulator (Windows Only)
- While in your project directory, in command prompt/terminal type
ant target load-simulator
. Replace the target with either blackberry or playbook. - Press the BlackBerry button on the simulator, go to downloads and you should see your app loaded there.
5B. Deploy to Device (Windows and Mac)
- You have to have your signing keys from RIM by filling out this form.
- While in your project directory, in command prompt/terminal type
ant target load-device
. Replace the target with either blackberry or playbook. - Press the BlackBerry button on the simulator, go to downloads and you should see your app loaded there.
Done!
You can also checkout more detailed version of this guide here.


MICROCOSMOS A film review by James Berardinelli Copyright 1996 James Berardinelli
RATING (0 TO 10): 8.0 Alternative Scale: ***1/2 out of ****
France, 1996 U.S. Release Date: beginning 10/96 (limited) Running Length: 1:17 MPAA Classification: G Theatrical Aspect Ratio: 1.85:1
Director: Claude Nuridsany & Marie Perennou Producers: Christophe Barratier, Yvette Mallet, Jacques Perrin Written by: Claude Nuridsany, Marie Perennou, Hughes Ryffel, and Thierry Machado Cinematography: Claude Nuridsany & Marie Perennou Music: Bruno Coulais U.S. Distributor: Miramax Films
MICROCOSMOS is a delightful little documentary -- emphasis on little. This film offers a view into a world more bizarre than anything depicted in the most special effects-intensive science fiction. Intrigued? Well, if not, you will be once you start watching MICROCOSMOS, Claude Nuridsany and Marie Perennou's fascinating portrait of life "beyond anything we could imagine, and yet almost beneath our notice."
MICROCOSMOS literally has a cast of thousands -- insects, that is. Beetles, ants, bees, wasps, dragonflies, butterflies, snails, mosquitoes, darning needles, and dozens of other assorted bugs fill the screen, going about their everyday chores. They build nests, find food, pollinate flowers, battle over territory, and procreate. For viewers with spiritual inclinations, there's a fair amount of quasi-religious imagery: mantises that pray and bugs that walk on water.
The microscopic drama of MICROCOSMOS is surprisingly engrossing, primarily because of the exquisite detail of the photography. Nuridsany and Perennou's cameras take us from normal-scale overviews of their locale deep into the world of the insects, where blades of grass are as tall as houses, trees are unimaginably large, and lakes become oceans. Everything is shown up close and personal, with a level of clarity and intimacy never previously obtained in nature films.
Although no people are ever shown, nor is there any indication of their presence (the specially-created cameras take us closer to the ground than any eye could get), there is a human voice in MICROCOSMOS. It belongs to Kristin Scott-Thomas, who gives short, unnecessary introductory and closing statements. In between, the only noises we hear are the whirs, buzzes, and chirps of the starring creatures. That's over an hour of nothing but the sounds of nature.
MICROCOSMOS uses time lapse photography to speed along certain slow processes -- a caterpillar inching its way along a branch, a vine snaking skywards, blossoms opening, and a sundew enveloping its helpless victim. There are a few cameos by non-insects as well. We see a pheasant attack an ant colony with predictably disastrous results for the tiny warriors. Then there's a frog who chooses a group of water bugs for an afternoon snack.
Transpiring in a specially created "field studio" in Aveyron, France, MICROCOSMOS took three years to film and six months to edit, but the results more than justify the necessary time, effort, and patience. At only seventy-seven minutes, this is one of the shortest movies of 1996, but it offers some of the most unique images, surpassing the fine LEOPARD SON with its perspective of life in the wild. After watching MICROCOSMOS, it will be impossible to take a walk in the woods without being aware of the amazing tapestry of activity going on all around, yet out of sight.
- James Berardinelli e-mail<EMAIL_ADDRESS>ReelViews web site: http://www.cybernex.net/~berardin
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


First, let's talk about two different kinds of HTML elements: "block level" and "inline" elements. Block level content occupies the entire space of of its parent, must appear only within a  element, and most browsers will place a newline both before and after the element. So, when you view it, it looks like a block. For example:

A paragraph is a block level element; the background has been colored to make it more visible

A paragraph is a block level element; the background has been colored to make it more visible
Block level elements by default begin on new lines, but inline elements can start anywhere in a line. Another way to think about it is that block level elements are the containers which hold content while inline elements deal with phrasing content within these block level containers.
But our focus today is on block level HTML5 elements, although they are admittedly not called such past HTML4. Some examples are ,
-
,
, and
 among others. For a complete list, check out this page. As you can see in the example above, the paragraph element creates a block. You should also note that by default the block takes up the entire width of the screen. All blocks have margins, a border (although it may be invisible), and padding. So what are these things? Let's take a look at a diagram.
The content is whatever you have placed inside the element, whether that is text or a picture, for example. Between your content and the border we have padding. It creates "white" space between your content and the border to give you some breathing room. Of course, you don't always choose to display an actual border around your elements, but in a sense this border always exists as the boundary between the padding and the margin. Which brings us to what the margin actually is. The margin is the transparent buffer outside of the border; it gives each element breathing room from nearby elements. Without a margin, elements will bunch up as close as they can without overlapping.
To illustrate, take a look at the four examples below:
This is a paragraph with 0 padding, and 10px margins
This a paragraph with 10px padding and 0 margins
This paragraph has 0 margins and 0 padding
This last paragraph has 40px margins and 20px padding with a border


fzputtygen(1) FileZilla Manual fzputtygen(1) NAME fzputtygen - SFTP private key converter of FileZilla DESCRIPTION fzputtygen is part of FileZilla. It is used to convert private keys from OpenSSH or ssh.com format into a format understood by fzsftp. It usually gets called by FileZilla and is not intended to be used directly. fzputtygen is based on the puttygen component of PuTTY. SUPPORT Please visit http://filezilla-project.org/ for further information. Report bugs only if you are using the latest version available from the FileZilla website. COPYRIGHT Copyright (C) 2004-2010 Tim Kosse Copyright (C) 1997-2010 Simon Tatham and the PuTTY team FileZilla is distributed under the terms of the GNU General Public License version 2 or later. SEE ALSO filezilla(1) August 2008 fzputtygen(1)


Puavo is a combination of web software (Puavo Web) and a specially configured system (Puavo OS) using Debian GNU/Linux operating system as its base. Puavo Web is built for managing user accounts and devices. Puavo OS is designed for large-scale deployment in primary and secondary schools. The source code for both is free software under GPLv2+ license.
Puavo Web manages user accounts and devices. One Puavo Web instance can manage multiple organisations (each with their own database), that each can contain multiple schools. Every school can have its own administrator users who manage user accounts and devices for the school. Puavo Web is written on top of Ruby on Rails and uses open source components such as OpenLDAP and MIT Kerberos.
Manage user accounts (including user information and passwords) one-by-one or using mass import features. Puavo Web database can be used by external applications that have support for LDAP. Single sign-on through Kerberos is possible, as well as integrations with other user databases.
Puavo Web can be used to manage Puavo OS clients, including workstations, laptops and servers, and even other types of devices. Settings are stored in Puavo and can be specific to any organisation, school or device.
Puavo OS is an operating system that is built from Debian GNU/Linux and is specifically designed for learning. It has historical roots in Linux Terminal Server Project, but has a different approach despite being similar in spirit. System is distributed as a read-only disk image that is updated atomically without any disruption for users.
The Puavo OS desktop is a tweaked version of Gnome 3. The system has Firefox and Chromium web browsers, Libreoffice, various audio/image/video editing tools, programming environments and other educational tools. It should work well on most PC hardware, also including somewhat older computers maybe up to ten years old.
Puavo OS hosts can function in various roles. They can function as bootservers, providing networking, file and printing services. Hosts can be booted as diskless clients that access user home directories through NFS from server. Laptops can boot from a local drive and remain functional in case of network connectivity problems. Other operating modes such as digital signage systems are also possible.
Other Puavo OS features include guest logins (with throwaway home directories), dual-booting with Windows, mechanisms for optional installation of proprietary applications, an application menu with configurable profiles, support for educational content updates, automatic poweroff during non-school hours when idle, display configuration management and many more!
Both Puavo Web and Puavo OS are developed by Opinsys, a Finnish company that has been providing information technology related services to schools in Finland since 2005 ("Opin" may be translated as "I learn" or "of learning"). Puavo systems are in active use in numerous Finnish schools, as well as in a few schools in Switzerland (by amXa) and Germany. Opinsys also provides consulting for those who are interested in making use of Puavo technology on their own.
To learn more, you might want to check out our presention at Fosdem 2020 or browse our repositories on GitHub.
dd
, but some folks may
prefer Etcher instead)


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To see some examples of how to write what you are asking for, refer to the Supreme Court of B.C. website. The website has a list of standard terms for most of the usual orders made in family cases.
The amount and duration of spousal support is determined after considering the circumstances of each spouse, including:
MySupportCalculator.ca is a website with a support calculator which can give you a rough estimate of spousal support. The guidelines and calculations are complicated so you may want to contact a family law lawyer or a family justice counsellor for help. For more information, please see the fact sheet Spousal Support on the Family Law in B.C. website.
Typically, Spousal Support is resolved out of court and is detailed in a separation agreement.
Many couples come to an agreement about spousal support outside of court and capture the details in a separation agreement. "Agreements that are filed with the court can be enforced — they have the same force as a court order. They can also be set aside (cancelled) if the situation changes.
If you're applying for an undefended divorce and asking for spousal support, make sure you fill in:
If you don't provide this information, the court may not make an order.
If you're married, you must claim spousal support within two years of the divorce judgment. If you were not married, but lived in a marriage-like relationship for two years or more, you must apply within two years of your separation.
For help on what factors to consider, and details to include in your spousal support agreement, refer to the online tool, How to Write Your Own Separation Agreement, on the Family Law in B.C. website.
You may also find Clicklaw's wikibook on Spousal Support helpful. This wikibook is a chapter from the JP Boyd on Family Law wikibook.
You may apply for spousal support if:
For more information, please refer to the the Spousal Support fact sheet on the Family Law in B.C. website.
Ok, you may be thinking, how the heck am I supposed to know which act (a written law) applies. Let's see if we can help you identify which act best fits with your circumstances/situation.
The Family Law Act and the Divorce Act overlap in some areas but not others. The Divorce Act applies only if you're married but the Family Law Act can apply to both married and unmarried couples. Even if you are getting a divorce, you must apply under the Family Law Act for the division of property or debts, a family law protection order, or orders related to guardianship. The Divorce Act does not deal with these.
There are big differences in how the Divorce Act and the Family Law Act deal with some issues, especially parenting. For more information on which laws apply to your situation please refer to the Family Law of B.C. website.
Still have questions? - Get Help
If you're still not sure as to which act applies to your situation please refer to the Get Help section for a list of resources you can turn to for additional support.
Spousal Support Advisory Guidelines
The
Support Advisory Guidelines can help you
figure out the amount of spousal support that should be paid.
These guidelines aren't the law (neither you or the judge has to follow them), but if your case went to court, the judge or master would probably look at the guidelines to help make his or her decision about the amount of spousal support. The guidelines take into account the income of both spouses, how long you were married, and whether you have children.
For more information, see the Department of Justice website on spousal support.
Many couples come to an agreement about spousal support without going to court. Agreements that are filed with the court can be enforced — they have the same force as a court order. They can also be set aside (cancelled) if the situation changes.
If you're trying to negotiate an agreement, consider the factors listed under When should spousal support be paid? (above) and the amount of support listed in the Spousal Support Advisory Guidelines. Get advice from a lawyer about what is fair.
When one spouse applies for spousal support, both spouses will have to provide financial information to each other and to the court. You will have to share detailed documents showing your income, assets, and debt. Both Supreme and Provincial Court have rules setting out exactly what needs to be shared and when.
Be aware that the law requires you to provide "full and true" information to the other party, whether you're negotiating an agreement or making or responding to a court application. There can be serious consequences if you don't. There may be financial penalties, and the court could make changes to your agreement or order.
In the Supreme Court, both parties must fill out a Financial Statement Form (F8) and file it with the court for orders related to support. For help, refer to the guide How to deal with a Supreme Court Financial Statement, produced by the Legal Services Society.
In the Provincial Court, both parties must fill out and file a Financial Statement Form (F4). For help, see our guide How to deal with a Provincial Court Financial Statement. However, if you agree about the amount of your incomes and how much support should be paid, you can instead fill out a Consent form Form (F19) and file it along with copies of your most recent income tax returns and notices of assessment.
There are two types of laws (statutory or statute-based and common-law or case-based).


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Python Basic syntax
The Python language has many similarities to Perl, C, and Java. However, there are some definite differences between the languages.
First Python Program
Let us execute programs in different modes of programming.
Interactive Mode Programming
Invoking the interpreter without passing a script file as a parameter brings up the following prompt −
If you are running new version of Python, then you would need to use print statement with parenthesis as in print ("Hello, Python!");. However in Python version 2.4.3, this produces the following result −
Hello, Python!
Script Mode Programming
Invoking the interpreter with a script parameter begins execution of the script and continues until the script is finished. When the script is finished, the interpreter is no longer active.
Let us write a simple Python program in a script. Python files have extension .py. Type the following source code in a test.py file −
{ print "Hello, Python!"
We assume that you have Python interpreter set in PATH variable. Now, try to run this program as follows −
$ python test.py
This produces the following result −
Hello, Python!
Let us try another way to execute a Python script. Here is the modified test.py file −
#!/usr/bin/python print "Hello, Python!"
We assume that you have Python interpreter available in /usr/bin directory. Now, try to run this program as follows −
$ chmod +x test.py # This is to make file executable $./test.py
This produces the following result −
Hello, Python!
Python Identifiers
A Python identifier is a name used to identify a variable, function, class, module or other object. An identifier starts with a letter A to Z or a to z or an underscore (_) followed by zero or more letters, underscores and digits (0 to 9).
Python does not allow punctuation characters such as @, $, and % within identifiers. Python is a case sensitive programming language. Thus, Manpower and manpower are two different identifiers in Python.
Here are naming conventions for Python identifiers −
-
Class names start with an uppercase letter. All other identifiers start with a lowercase letter.
-
Starting an identifier with a single leading underscore indicates that the identifier is private.
-
Starting an identifier with two leading underscores indicates a strongly private identifier.
-
If the identifier also ends with two trailing underscores, the identifier is a language-defined special name.


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Before Change
def duplicates_score(self, stats, columns, col_name):
duplicates = len(columns[col_name]) - len(set(columns[col_name]))
data = {
"duplicates": duplicates
,"duplicates_percentage": duplicates*100/len(columns[col_name])
}
After Change
// duplicate_score, a quality score based on said percentage
occurances = Counter(columns[col_name])
values_that_occur_twice_or_more = filter(lambda val: occurances[val] < 2, occurances)
nr_of_occurances = map(lambda val: occurances[val], values_that_occur_twice_or_more)
nr_duplicates = sum(values_that_occur_twice_or_more)
data = {
"nr_duplicates": nr_duplicates
,"duplicates_percentage": nr_duplicates*100/len(columns[col_name])
}