Resurfacing of A77 set to cause travel disruption for motorists
Diversions will be in place but drivers are advised to allow additional time for their journeys.
by Ryan Carroll, https://www.dailyrecord.co.uk/authors/ryan-carroll/Part of the A77 will close again for major roadworks.
The refurbishment work at Stumpy Corner will conclude with a 0.5km resurfacing of the A77 through Girvan.
Scotland TranServ will deliver two resurfacing projects under a single weekend closure and two additional overnight closures of the carriageway.
The works will begin on Friday, November 22 with completion scheduled for Wednesday, November 27 and resurfacing is between Henrietta Street and Old Street Garage.
Alan Murray, Scotland TranServ’s Principal Road Design Engineer, said: “It is necessary to close the trunk road through the town for the duration of the weekend, and we would like to thank drivers in advance for their patience. Shops and other businesses will remain open for pedestrian access, with parking maintained either end of the closure.”
Over the last few weeks, Scotland TranServ’s Strategic Road Safety team has been delivering improvements in the area. The team has installed new traffic signals and footways, with the resurfacing programme the conclusion of this work.
In order to complete this programme the road will be closed at 8pm Friday, November 22 till 6am on the Monday. It will then close again at 8pm on Monday till 6am Tuesday before concluding between 8pm Tuesday and 6am Wednesday, November 27.
Drivers are advised to allow additional time for their journeys and local diversions will be in place.


An illustration of the plugin-concept: an independent application where units of software, intended to complement the functionality, can be "plugged" to.
A plugin is a unit of software intended to extend the functionality of an independent application at run-time (see Glossary). It provides several advantages, making this technology very popular among several programs that process heterogeneous data and would, otherwise, be less flexible and heavier (one of the most famous examples being Netscape that uses plugins to handle and/or view special data like audio or movies):
The functionality is added/changed/removed on runtime so that the application doesn't have to be rebuild and, furthermore, not even recompiled.
The application doesn't know the plugin beforehand ( it is the plugin's responsibility to present itself) providing wider flexibility to what functionality can be added.
It leads to a complete, clean separation from the main application and the various branches with their separate functionality. Thus we get a lighter main application focused on the essentials.
It is usually very easy for an user to add a plugin to the application.
Plugins are usually implemented as demand-loaded shared libraries. These type of libraries are only loaded on runtime as opposed to static whose code is included in the application on the compilation stage. They can be loaded in two different ways:
automatically at the start of the process and, thus, only unloaded at the end: the library must be linked at compilation stage;
by request of the program itself (using, for example, dlopen()): the code can be loaded and unloaded in the middle of the process or not even loaded at all; furthermore, the library is not linked at compilation stage;
When the library is loaded, the system checks if it is already in memory, so that, at most, one copy of the library is present in the memory.
Plugins are typically implemented as the second type since their characteristics are especially adequate for the "plug" concept. But in order to comply with the desired functionality of a plugin, the following list of requirements must be met:
The application must be independent from the plugins.
There should be a "black-box" view in both directions.
The application must not "know" the plugin beforehand; the plugin must introduce itself to the application.
The application must be loosely coupled with the plugins; a common interface to be provided by the plugins (whether total or partial) must be defined precisely (Contract).
The application should define what services are provided to the plugin via interface definitions.
A plugin must be self-contained.
A plugin should create a plugin instance to handle each task.
The interface common to all plugins is one of the critical points and, although this interface varies between technologies, the plugin should provide:
information on the type of task it can handle
a function to be called to handle that type of task (do_work())
an instance-type strategy including a structure to store all the data of a particular task and a way to know if there aren't any tasks (instances) still working so that the plugin can be unloaded
a function to initialize the plugin
Netscape (and other Web browsers) uses plugins to view/handle special data present in some Web pages, for example, audio and video.
The API is very well defined in the documentation: it contains a list of functions provided by Netscape, a list of functions the plugin must and can define and structures to be used. Since the communication is all done by this total interface, both parts act as complete "black-boxes". Another interesting characteristic is the life cycle of a plugin: Initialize(when the plugin is first called), New Instance(each time it is invoked), Destroy Instance(when a task is completed and that instance is no longer needed) and Shutdown (when all instances are destroyed).
PluginInitResult init_plugin (CommandContext *context, PluginData *pd) { FunctionCategory *cat; 5 /* The next line checks if the Gnumeric version being used corresponds to the required by the plugin */ if (plugin_version_mismatch (context, pd, GNUMERIC_VERSION)) return PLUGIN_QUIET_ERROR; 10 cat = function_get_category (_("Fact PlugIn")); function_add_args (cat, "MyFactorial", "f", "number", NULL, func_fact); pd->can_unload = can_unload; 15 pd->cleanup_plugin = cleanup_plugin; pd->title = g_strdup ("My PlugIn Plugin"); return PLUGIN_OK; } |
The program listing above is part of a Gnumeric plugin source code. Gnumeric uses plugins both to handle different formats (e.g., Excel) and add functions not defined (statistical, numerical,...). The API the plugin must define is very concise and partial: the plugin registers the new functions it defines (in lines 11-12, the plugin is registering a function, in this example, the factorial function). The init_plugin() function is called at start-up. Besides registering the functions it also provides information on the Plugin (in this case, the title) and functionality to unload the plugin (e.g., check if there aren't any instances left). Although Gnumeric plugins are not the best example, since the requirements above are not completely met (Gnumeric is completely transparent to the plugin) it provides a very simple example with most of the core ideas.


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"AHK-Webkit" / Webapp.ahk - Library for developing web-based apps with AutoHotkey. (Actually uses IE [Trident])
Try out some of the examples:
View the project on GitHub [here].A special thanks to Coco, VxE, Lexikos, Phatricko, MrBubbles and the AutoHotkey community.


Exception Handling in MXNet
A Beginner's Guide to Implementing Operators in MXNet Backend
Introduction
Operators are essential elements for constructing neural networks. They define mathematical formulas
of transforming input data (tensors) to outputs. MXNet has a rich set of operators from simple ones,
such as element-wise sum, to complicated ones, such as convolution, that is
capable of constructing most of the popular neural networks. You may have noticed
that many operators implemented in MXNet have their equivalent forms in Numpy, such as
repeat,
tile,
etc., and wonder why we could not simply use those Numpy operators in MXNet. One of the
major reasons is that we need to support both CPU and GPU computing for the operators in MXNet,
while Numpy operators do not possess GPU computing capability.
In addition, we have performed plenty of
optimizations for various components in MXNet, such as tensor data structure (NDArray
),
execution engine, computational graph and so on, for maximizing memory and runtime efficiency.
An operator implemented under the MXNet operator framework would greatly
leverage those optimizations for exhaustive performance enhancement.
In this tutorial, we are going to practice implementing an operator using C++ in the MXNet backend. After finishing the implementation, we will add unit tests using Python for the operator we just implemented.
Implementation
An Operator Example
Let's take the quadratic function
as an example: f(x) = ax^2+bx+c
. We want to implement an operator called quadratic
taking x
, which is a tensor, as an input and generating an output tensor y
satisfying y.shape=x.shape
and each element of y
is calculated by feeding the
corresponding element of x
into the quadratic function f
.
Here variables a
, b
, and c
are user input parameters.
In frontend, the operator works like this:
python
x = [[1, 2], [3, 4]]
y = quadratic(data=x, a=1, b=2, c=3)
y = [[6, 11], [18, 27]]
To implement this, we first create three files: quadratic_op-inl.h
,
quadratic_op.cc
, and quadratic_op.cu
. The header file's name
is prefixed by the operator name and followed by op
and -inl
indicating that this is an operator implementation with inline
functions shared by CPU and GPU computing. The CPU and GPU
specific implementations reside in their own .cc
and .cu
files,
respectively. We normally put pure tensor related operators
(e.g. tile
, repeat
, etc.) under
the directory src/operator/tensor
, and neural network operators
(e.g. Convolution
, Pooling
, etc.) under src/operator/nn
.
You may have noticed that many neural network operators including
Convolution
and Pooling
are currently saved under src/operator
.
We plan to move them to src/operator/nn
for better file organization
and clearer hierarchy in the future.
Next, we are going to
1. Define the parameter struct
for registering a
, b
, and c
in quadratic_op-inl.h
.
2. Define type and shape inference functions in quadratic_op-inl.h
.
3. Define forward and backward functions in quadratic_op-inl.h
.
4. Register the operator using nnvm
in quadratic_op.cc
and quadratic_op.cu
for
CPU and GPU computing, respectively.
Now let's walk through the process step by step.
Parameter Registration
We first define struct QuadraticParam
as a placeholder for the
parameters a
, b
, and c
in quadratic_op-inl.h
.
The struct inherits from a base template
struct named dmlc::Parameter
, where the template argument is the derived struct
QuadraticParam
. This technique, which is called curiously recurring template
pattern,
achieves static polymorphism. It is similar to using a virtual function,
but without the cost associated with dynamic polymorphism.
struct QuadraticParam : public dmlc::Parameter<QuadraticParam> {
float a, b, c;
DMLC_DECLARE_PARAMETER(QuadraticParam) {
DMLC_DECLARE_FIELD(a)
.set_default(0.0)
.describe("Coefficient of the quadratic term in the quadratic function.");
DMLC_DECLARE_FIELD(b)
.set_default(0.0)
.describe("Coefficient of the linear term in the quadratic function.");
DMLC_DECLARE_FIELD(c)
.set_default(0.0)
.describe("Constant term in the quadratic function.");
}
};
The function calls in the above parameter struct are self-explanatory by their names.
Note that for each parameter, we set the default value to 0.0
such that users can
skip passing 0-value parameters through the quadratic operator interface. You
can choose not to define the default value for a parameter if it is required
at runtime. Meanwhile, adding brief descriptions to the parameters enables
the documentation engine to display them on
MXNet documentation web page.
Attribute Inference
Attribute inference is the process of deducing the properties of NDArray
s
in neural networks from user provided information. Two most common attributes
of an NDArray
are data shape and data type.
Let's take a look at the following example.
Given an input NDArray
called data
, you invoke the quadratic
operator
like this: output = mx.nd.quadratic(data, a=1, b=2, c=3)
. Before calculating
the output
values, its shape and data type are inferred from the input
data
's shape and type following
the rules you defined in order to allocate memory space for the output tensor.
One important thing to note that inference functions should be capable of performing mutual inference, i.e. inferring one argument's attribute from another argument's attribute if possible according to the definition of the operator. This is very useful for a computational graph to deduce unknown attributes for a neural network in symbolic programming. Users can view the computational graph as a symbol with every element initialized for running data throughout the neural network, including memory allocation for each tensor, device placement for each operator, etc. Users normally just need to provide minimum necessary information, such as input data shapes, etc., to the computational graph, and the graph will fill up the unknown attributes using the attribute inference functions defined in the operators building up the neural network.
Let's consider the following example. ```python
import mxnet as mx a = mx.sym.Variable('a', shape=(2, 0)) b = mx.sym.Variable('b') c = mx.sym.Variable('c', shape=(0, 3)) d = a * b + b * c print d.infer_shape() ([(2L, 3L), (2L, 3L), (2L, 3L)], [(2L, 3L)], []) ``
The last line of the above code snippet is a tuple of three lists returned by
d.infer_shape(). The first list contains all the argument shapes of
a,
b, and
c. The second contains the output shape of
d. The third one represents the shapes of auxiliary states, which is not used in this case, and thus is empty. In this example, we only specified values for variable
a's first dimension and
c's second dimension. The
0in shape
(2, 0)indicates that the size of the second dimension is unknown, same meaning for shape
(0, 3). However, the symbol
dstill successfully inferred the shapes for all the variables and final output. This is a result of mutual inference. In MXNet, the whole process can be interpreted as this: 1.
aand
bare combined via an element-wise multiplication operator, so the shapes of
aand
bare same and
b's first dimension size is
2. 2.
band
care combined via an element-wise multiplication operator too, so the shapes of
band
care same and
b's second dimension size is
3. 3. Now
b's shape is completely known, so
aand
cmissing dimension sizes are known as well. 4.
dis a result from adding
a * band
b * c, so d should also have the same shape as
b`.
The above four steps illustrate how shape inference logic works in MXNet. It is actually implemented in the shape inference functions of the operators for element-wise multiplication and addition.
For our quadratic
operator, shape inference possesses quite similar logic.
```cpp
inline bool QuadraticOpShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0)); SHAPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0)); return out_attrs->at(0).ndim() != 0U && out_attrs->at(0).Size() != 0U; } ``` Here are a few things to note about the above function:
attrs
contains parametersa
,b
, andc
from user input. It's not used here since we don't rely on that information for shape inference.in_attrs
is a vector containing all input shapes. Since there is only one input argument for operatorquadratic
, we used macroCHECK_EQ
to assert when the vector's size is wrong.out_attrs
is a vector containing all output shapes. We also usedCHECK_EQ
to verify the size of the vector since there is only one output.- We called macro
SHAPE_ASSIGN_CHECK
twice for mutual inference. One for inferring the output shape from the input shape, the other one is for inferring the input shape from the output shape. If there are any unequal non-zero values in the same dimension of two shapes, such as(2, 3)
and(3, 3)
, the macro would throw an exception with an error message for shape inference. - At the end of the function body, we checked whether the output shape
is completely known by testing whether the shape is not empty and
the shape's size is greater than
0
. Note that in MXNet, an empty shape means that the shape is unknown, and a0
in a shape means that the size of that dimension is unknown. In both situations, the missing shape information must be inferred from other shapes. If it cannot be inferred, the function should returnfalse
to notify the caller about shape inference failure. - MXNet provides a convenience function implementing the logic of mutual inference
for general element-wise operators with the following interface. Users can
instantiate this function with
n_in=1
andn_out=1
to replace the above functionQuadraticOpShape
in operator registration (explained later). The functionQuadraticOpShape
posted here is for the purpose of illustration only.cpp template<int n_in, int n_out> inline bool ElemwiseShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector *in_attrs, mxnet::ShapeVector *out_attrs);
The same logic goes for data type inference. We will leave the analysis of
the following code sample to users. Note that -1
means the data type
is unknown and must be inferred from other input or output data types.
```cpp
inline bool QuadraticOpType(const nnvm::NodeAttrs& attrs,
std::vector
TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0)); TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0)); return out_attrs->at(0) != -1; } ```
Again, MXNet provides the following convenience function for mutual
type inference of element-wise operators. Users can use that
in operator registration (explained later).
cpp
template<int n_in, int n_out>
inline bool ElemwiseType(const nnvm::NodeAttrs& attrs,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs);
Forward Function
Forward function defines the operator's behavior in the forward pass
of neural networks. For our quadratic
operator, it simply implements
the logic of running a tensor through the quadratic function by performing
a few element-wise operations. The forward function's signature is fixed
in MXNet as follows:
cpp
void (const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs);
We first paste the whole forward function code here
and then go through it line by line.
cpp
template<typename xpu> // 1
void QuadraticOpForward(const nnvm::NodeAttrs& attrs, // 2
const OpContext& ctx, // 3
const std::vector<TBlob>& inputs, // 4
const std::vector<OpReqType>& req, // 5
const std::vector<TBlob>& outputs) { // 6
CHECK_EQ(inputs.size(), 1U); // 7
CHECK_EQ(outputs.size(), 1U); // 8
CHECK_EQ(req.size(), 1U); // 9
mshadow::Stream<xpu> *s = ctx.get_stream<xpu>(); // 10
const TBlob& in_data = inputs[0]; // 11
const TBlob& out_data = outputs[0]; // 12
const QuadraticParam& param = nnvm::get<QuadraticParam>(attrs.parsed); // 13
using namespace mxnet_op; // 14
MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, { // 15
MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, { // 16
Kernel<quadratic_forward<req_type>, xpu>::Launch( // 17
s, out_data.Size(), out_data.dptr<DType>(), in_data.dptr<DType>(), // 18
param.a, param.b, param.c); // 19
}); // 20
}); // 21
} // 22
- Line 1: xpu
stands for a generic device type so that the function can be instantiated
for both CPU and GPU computing using concrete types cpu
and gpu
. The instantiation happens
at the time when the operator is registered in .cc
and .cu
files.
- Line 2: attrs
is a node attribute containing the user input parameters a
, b
, and c
.
Here the node represents a placeholder for the operator in the whole computational graph for
the neural network.
- Line 3: ctx
holds something called stream
for
serializing asynchronous executions. Let's consider
this example for understanding the functionality of stream
.
We want to launch several GPU kernels with the same stream
from CPU.
Even though the launching operation is non-blocking, the stream
guarantees
that the kernels execute in the same order on GPU as they are launched from CPU.
- Line 4: inputs
is a vector of input tensors (only one input tensor
for the quadratic
operator).
- Line 5: req
is a vector of OpReqType
values. Each value defines
the way of writing calculated values to the output tensors.
Therefore, the number of req
s must be the same as the number of output tensors.
MXNet currently supports three types of req
in frontend: null
, write
, and add
.
null
means skipping calculating the corresponding output tensor,
write
means overwriting the values in the output tensor with the ones
calculated by this operator, and add
means adding the calculated values
to the existing ones in the output tensor. Note that null
and add
are usually
seen in backward passes. The former is for skipping calculating
the gradients of un-learnable parameters (such as index arrays),
and the latter is for accumulating gradients throughout networks.
- Line 6: outputs
is a vector of output tensors (only one
output tensor for the quadratic
operator).
- Lines 7-9: Verify that the size of each vector is expected.
Otherwise, stop moving forward and print error message.
- Line 10: Get the stream
from the ctx
for launching kernels.
- Lines 11-12: Define the references of the input and output tensors
for later coding convenience. Note that TBlob
can be understood
as a uniform data structure for tensors of various dimensions, such
that tensors of different dimensions can be put in a homogeneous container,
such as std::vector
and std::list
. You can still
get tensors of desired dimensions from a TBlob
object through
the interface get_with_shape
.
- Line 13: Get user input parameters from the node attribute.
- Lines 15-21: This is the place where the mathematical formula of the operator
is implemented. The macros MSHADOW_TYPE_SWITCH
and MXNET_ASSIGN_REQ_SWITCH
enable
the code block to work for all the supported data types and req
types in MXNet.
Inside the inner-most macro, we launch the kernel for calculating
the output tensor such that each thread takes an element from
the input tensor, feeds it into the quadratic function, and assigns
the output element to the output tensor based on req
type. Note that
Kernel::Launch
serves as a universal interface for launching
parallel computation on both CPU and GPU. This allows most of
the simple operators to share the same piece of code for CPU and GPU as
parallelization approaches are often identical on both types of devices.
The kernel function is defined as the following, where the function
Map
is executed by each thread for each input element. The out_data.Size()
,
in the Kernel::Launch
function corresponds to the factor by which the
workload will get parallelized among the different threads, which here
corresponds to the size of the output array. To explain a little
bit more on the two macros used in the kernel struct: (1) MSHADOW_XINLINE
is
a consolidated macro for inlining functions compiled by both CPU and GPU
compilers. It enables CPU and GPU computing to share the same piece of code.
(2) KERNEL_ASSIGN
is a macro for unifying the statements of different req
s
into the same line of code. It's named KERNEL_ASSIGN
because we call
the code blocks running parallel computation kernels.
On CPUs, the kernels are normally wrapped by the OpenMP parallel
directive;
while on GPUs, they are the kernel functions launched by CUDA library.
template<int req>
struct quadratic_forward {
template<typename DType>
MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data,
const float a, const float b, const float c) {
KERNEL_ASSIGN(out_data[i], req, in_data[i] * (a * in_data[i] + b) + c);
}
};
Backward Function
Backward functions play the role of propagating derivatives of loss function
with respect to the outputs of the last layer throughout the network to the first
layer. The whole process is often known as backward propagation. We are not
going to delineate the principle of backward propagation here since users can find
great details covered in other resources, such as
CS231n and
How the backgropagation algorithm works.
The problem we are going to solve here for the quadratic
operator is that
given a tensor representing the gradient of the loss function with respect
to the output of the operator, calculate the gradient with respect to
the input of the operator. There is no need to calculate the derivatives
of loss function with respect to user input parameters a
, b
, and c
since they are not learnable parameters in the network. To formulate the problem:
given dL/dy
and y = a*x^2 + b*x + c
, where L
represents the loss function and
y
stands for the output of the quadratic tensor, we need to solve for
dL/dx
. Using the chain-rule, it is obvious to find that
dL/dx = dL/dy * dy/dx = dL/dy * (2*a*x + b).
The above equation indicates that dL/dx
depends on the gradient
of the output tensor and value of the input tensor.
The backward function's signature is the same as the forward function's.
With the aforementioned information in mind,
let's breakdown the following backward function line by line.
cpp
template<typename xpu> // 1
void QuadraticOpBackward(const nnvm::NodeAttrs& attrs, // 2
const OpContext& ctx, // 3
const std::vector<TBlob>& inputs, // 4
const std::vector<OpReqType>& req, // 5
const std::vector<TBlob>& outputs) { // 6
CHECK_EQ(inputs.size(), 2U); // 7
CHECK_EQ(outputs.size(), 1U); // 8
CHECK_EQ(req.size(), 1U); // 9
mshadow::Stream<xpu> *s = ctx.get_stream<xpu>(); // 10
const TBlob& out_grad = inputs[0]; // 11
const TBlob& in_data = inputs[1]; // 12
const TBlob& in_grad = outputs[0]; // 13
const QuadraticParam& param = nnvm::get<QuadraticParam>(attrs.parsed); // 14
using namespace mxnet_op; // 15
MSHADOW_TYPE_SWITCH(out_grad.type_flag_, DType, { // 16
MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, { // 17
Kernel<quadratic_backward<req_type>, xpu>::Launch( // 18
s, in_grad.Size(), in_grad.dptr<DType>(), out_grad.dptr<DType>(), // 19
in_data.dptr<DType>(), param.a, param.b); // 20
}); // 21
}); // 22
} // 23
- Lines 1-6: Backward function has the same signature as forward function.
- Lines 7-9: Check the sizes of the function arguments. One thing to note
that since the gradient of the input depends on both the gradient of the output and
the input tensor itself, inputs
must contain two TBlob
objects.
- Line 10: Get the stream
of the context for serializing asynchronous executions.
- Lines 11-13: Convenience reference variables for later use. We name out_grad
as the gradient of the operator output, in_data
as the input of the operator,
and in_grad
as the gradient of the operator input.
- Line 14: Get the parameter object of QuadraticParam
.
- Lines 16-22: Same as in the forward function, this is where parallel
computation for in_grad
happens. The struct quadratic_backward
implements
the formula of calculating each element of in_grad
by one thread as the following.
template<int req>
struct quadratic_backward {
template<typename DType>
MSHADOW_XINLINE static void Map(int i, DType* in_grad, const DType* out_grad,
const DType* in_data, const float a, const float b) {
KERNEL_ASSIGN(in_grad[i], req, out_grad[i] * (2 * a * in_data[i] + b));
}
};
Operator Registration
So far, we have implemented necessary data structure and functions for the operator quadratic
.
Now let's register them using nnvm
to expose the operator quadratic
to frontend. Users can consider the registration process as creating the operator object
instance, saving it in the operator manager (a singleton),
and setting attributes for the operator instance.
The following code is from quadratic_op.cc
, which is responsible
for registering the operator working on CPU.
DMLC_REGISTER_PARAMETER(QuadraticParam); // 1
NNVM_REGISTER_OP(quadratic) // 2
.describe(R"code(This operators implements the quadratic function: // 3
.. math::
f(x) = ax^2+bx+c
where :math:`x` is an input tensor and all operations
in the function are element-wise.
Example:
.. code-block:: python
:emphasize-lines: 1,3
x = [[1, 2], [3, 4]]
y = quadratic(data=x, a=1, b=2, c=3)
y = [[6, 11], [18, 27]]
)code" ADD_FILELINE) // 4
.set_attr_parser(ParamParser<QuadraticParam>) // 5
.set_num_inputs(1) // 6
.set_num_outputs(1) // 7
.set_attr<nnvm::FListInputNames>("FListInputNames", // 8
[](const NodeAttrs& attrs) { // 9
return std::vector<std::string>{"data"}; // 10
}) // 11
.set_attr<nnvm::FInferShape>("FInferShape", QuadraticOpShape) // 12
.set_attr<nnvm::FInferType>("FInferType", QuadraticOpType) // 13
.set_attr<FCompute>("FCompute<cpu>", QuadraticOpForward<cpu>) // 14
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_quadratic"}) // 15
.set_attr<nnvm::FInplaceOption>("FInplaceOption", // 16
[](const NodeAttrs& attrs) { // 17
return std::vector<std::pair<int, int> >{{0, 0}}; // 18
}) // 19
.add_argument("data", "NDArray-or-Symbol", "Input ndarray") // 20
.add_arguments(QuadraticParam::__FIELDS__()); // 21
NNVM_REGISTER_OP(_backward_quadratic) // 22
.set_attr_parser(ParamParser<QuadraticParam>) // 23
.set_num_inputs(2) // 24
.set_num_outputs(1) // 25
.set_attr<nnvm::TIsBackward>("TIsBackward", true) // 26
.set_attr<FCompute>("FCompute<cpu>", QuadraticOpBackward<cpu>); // 27
- Line 1: Register the parameter struct.
- Line 2: Register an operator named
quadratic
by creating an instance ofOp
type and save it in the operator manager and return a reference of the just created operator object. - Lines 3-4: Add description as an operator attribute
including examples of the operator. The documentation engine will extract
this description and display it on the documentation web page.
emphasize-lines
is optional. For more examples and troubleshooting with doc strings, refer to the MXNet developer wiki's Documentation Guide. - Line 5: Set parameter struct parser for the operator. It is used for parsing
the parameters
a
,b
, andc
input from frontend. - Line 6: Set the number of inputs for the operator.
- Line 7: Set the number of outputs for the operator.
- Lines 8-11: Defines a function generating a vector of names of
the operator input arguments. This function is used to add missing
arguments that users did not specify when creating a symbolic operator.
For example,
quad_func=mx.sym.quadratic()
is still a valid symbol since we have added the attributeFListInputNames
to the operator node in the computational graph. MXNet would add the missing argument with namequadratic0_data
, where the prefixquadratic0
is the operator name appended with an index and the postfixdata
comes from the return value of the user definedFListInputName
function. Users still can generate an executor for thequad_func
like the following:python quad_exe = quad_func.simple_bind(ctx=mx.cpu(), quadratic0_data=(1,))
- Line 12: Register shape inference function.
- Line 13: Register type inference function.
- Line 14: Register forward function.
- Line 15: Register the function for creating the node of the operator in
a backward pass. Note that we used a convenience functor struct
ElemwiseGradUseIn
. As you can tell from the name, the registered functor creates the node for gradient computation with dependencies on the output gradient node and input node. Similarly, there are other three functors defined asElemwiseGradUseOut
,ElemwiseGradUseInOut
, andElemwiseGradUseNone
for developers' convenience. In order to add this attribute, we also need to register a backward operator forquadratic
with several basic attributes, as it can share attribute inference functions with the forward operator and is not exposed to frontend. - Lines 16-19: This registered function implies that which output tensor can reuse
which input tensor's memory space instead of allocating a new memory space for the output.
In the operator
quadratic
, there is only one input and output, and the output can reuse the input memory space, so we store a pair of zeros in the function return vector indicating thatinputs[0]
's memory space can be reused byoutputs[0]
. Note that this function just provides a hint to the computational graph initializer. If there are other nodes depending on the input tensor, the memory space of the input tensor will not be overwritten by the output. - Line 20: Define the input argument name as
data
for the operator. - Line 21: Add user input parameters
a
,b
, andc
as the attributes of the operator. - Line 22: Register an operator named
_backward_quadratic
for backward pass of the operatorquadratic
. The underscore prefix in the operator name indicates that this is an operator not exposed to users. The convention of naming an internally used backward operator is prepending the prefix_backward_
to the corresponding forward operator name. - Line 23: Set the parameter parser for the operator
_backward_quadratic
. - Line 24: Set the number of inputs.
- Line 25: Set the number of outputs.
- Line 26: Add
TIsBackward
attribute for the operator. The shape and type inference passes use this attribute to determine whether a node in the graph is a forward or backward node. - Line 27: Register backward function.
So far, we have acquired an operator working on CPU in frontend.
In order to register the operator working on GPUs, we just need to add the following
code to quadratic_op.cu
. Note that forward and backward functions
are registered with attribute key FCompute<gpu>
, rather than FCompute<cpu>
.
```cpp
NNVM_REGISTER_OP(quadratic)
.set_attr
NNVM_REGISTER_OP(_backward_quadratic)
.set_attr
Unit Test
Now we have finished implementing the operator quadratic
in MXNet backend.
If you use python, when you type import mxnet as mx
, two python
functions for invoking your backend implementation are
generated on the fly: one is for imperative programming
registered as mxnet.ndarray.quadratic
or mx.nd.quadratic
for short;
the other one is for symbolic programming registered under
module mxnet.symbol.quadratic
or mx.sym.quadratic
for short.
In order to unit test it in frontend, we need to add the following code
to the python file test_operator.py
. A typical operator implementation
tests for both the symbol
API and the ndarray
API. The following test
has both these tests. The imperative API test, tests for the ndarray
API,
mx.nd.contrib.quadratic
. The symbol
API test, tests for the complete
functionality of the operator - the forward pass and the backward
pass. To facilitate the testing of these functionalities we use three
helper functions available in the mxnet.test_utils
module:
- check_symbolic_forward
- check_symbolic_backward
- check_numeric_gradient
def test_quadratic_function():
def f(x, a, b, c):
return a * x**2 + b * x + c
a = np.random.random_sample()
b = np.random.random_sample()
c = np.random.random_sample()
data = mx.symbol.Variable('data')
quad_sym = mx.sym.contrib.quadratic(data=data, a=a, b=b, c=c)
for dtype in [np.float16, np.float32, np.float64]:
for ndim in range(1, 6):
shape = rand_shape_nd(ndim, 5)
data_np = np.random.randn(*shape).astype(dtype)
expected = f(data_np, a, b, c)
backward_expected = 2 * a * data_np + b
# check imperative forward
output = mx.nd.contrib.quadratic(mx.nd.array(data_np), a=a, b=b, c=c)
assert_almost_equal(output.asnumpy(),expected,
rtol=1e-2 if dtype is np.float16 else 1e-5,
atol=1e-2 if dtype is np.float16 else 1e-5)
# check forward
check_symbolic_forward(quad_sym, [data_np], [expected],
rtol=1e-2 if dtype is np.float16 else 1e-5,
atol=1e-2 if dtype is np.float16 else 1e-5)
# check backward
check_symbolic_backward(quad_sym, [data_np], [np.ones(expected.shape)],
[backward_expected],
rtol=1e-2 if dtype is np.float16 else 1e-5,
atol=1e-2 if dtype is np.float16 else 1e-5)
# check backward using finite difference
check_numeric_gradient(quad_sym, [data_np], atol=0.001)
In the above test we create a quadratic
symbol and feed it into the three
utility functions. The check_symbolic_forward
and check_symbolic_backward
tests the computed values against the expected values that we pass
as an argument to the function. The check_numeric_gradient
utility function
performs gradient checking
to verify the implementation for the backward function of the operator.
It will perform a perturbation on the input and calculate the response
rate of the output using the
finite difference method.
Then it will compare the gradient from the backward pass with the values
from the finite difference method. All three of these tests will be successful
once the comparison satisfies user specified rtol
and atol
values. Here rtol
and atol
expand to relative tolerance and absolute tolerance respectively. They
are used to specify how far the computed values can deviate from the expected values.
They are defined as follows
abs(Expected_Value - Computed_Value) < RTOL * abs(Expected_Value) + ATOL
For example, if rtol
is 1e-5
and atol
is 1e-5
and the expected value is
1.5623145
, then the computed value should lie within the range of
(1.562288876855, 1.562340123145)
else the test will fail. Make sure you
tune the rtol
and atol
values accordingly. Giving very low values for rtol
and atol
will likely make the test very flaky. It is recommended that you
use the flakiness checker tool to check if the test you have written is flaky
or not. You can run the flakiness checker tool for the above test with the
following command -
python tools/flakiness_checker.py test_operator.test_quadratic_function
Please note that for check_symbolic_forward
and check_symbolic_backward
we pass
both the operator symbols and expected results for comparison, for
check_numeric_gradient
we only pass the operator symbol, as the
check_numeric_gradient
computes the expected value using finite difference
method. Which is why it is highly recommended to add check_numeric_gradient
test for every operator with backward function implemented as it eliminates
the possibility of passing incorrect expected results into check_symbolic_backward
.
Summary
In this tutorial, we practiced implementing the operator quadratic
in MXNet backend
and unit testing the implementation in frontend. More specifically, we added parameter
struct for user-input parameters, walked through shape and type inference workflow,
implemented forward and backward functions, and registered the operator
using nnvm. Congratulations! You now know how to add operators.
We welcome your contributions to MXNet.
Note: Source code in the tutorial can be found in quadratic_op-inl.h, quadratic_op.cc, quadratic_op.cu, and test_operator.py.


We're currently working on an Oyster Recruitment protocol. This will give us information about oyster reproduction and survival of juvenile oysters at BOP restoration sites. The presence of oyster recruits (i.e., “spat”) may indicate reproducing wild populations nearby or the successful reproduction of restored oysters.
For now, if you think you see a wild oyster, please report it to us! Email<EMAIL_ADDRESS>with close-up photos and our scientists will take a look!
Here are some photos to help you recognize wild oysters if you see them! We've also included some photos of organisms commonly mistaken for oysters.


Rake 0.5.0 Released
Although it has only been two weeks since the last release, we have enough
updates to the Rake program to make it time for another release.
Changes
Here are the changes for version 0.5.3 …
- FileLists have been extensively changed so that they mimic the behavior of
real arrays even more closely. In particular, operations on FileLists that
return a new collection (e.g. collect, reject) will now return a FileList
rather than an array. In addition, several places where FileLists were not
properly expanded before use have been fixed.
- A method (ext) to simplify the handling of file extensions was
added to String and to Array.
- The ‘testrb’ script in test/unit tends to silently swallow
syntax errors in test suites. Because of that, the default test loader is
now a rake-provided script. You can still use ‘testrb’ by
setting the loader flag in the test task to :testrb. (See the API documents
for TestTask for all the loader flag values).
- FileUtil methods (e.g. cp, mv, install) are now declared to be private.
This will cut down on the interference with user defined methods of the
same name.
- Fixed the verbose flag in the TestTask so that the test code is controlled
by the flag. Also shortened up some failure messages. (Thanks to Tobias
Luetke for the suggestion).
- Rules will now properly detect a task that can generate a source file.
Previously rules would only consider source files that were already
present.
- Added an import command that allows Rake to dynamically import
dependendencies into a running Rake session. The import command
can run tasks to update the dependency file before loading them. Dependency
files can be in rake or make format, allowing rake to work with tools
designed to generate dependencies for make.
What is Rake
Rake is a build tool similar to the make program in many ways. But instead
of cryptic make recipes, Rake uses standard Ruby code to declare tasks and
dependencies. You have the full power of a modern scripting language built
right into your build tool.
Availability
The easiest way to get and install rake is via RubyGems …
gem install rake (you may need root/admin privileges)
Otherwise, you can get it from the more traditional places:
Thanks
As usual, it was input from users that drove a alot of these changes.
Thanks to …
- Brian Gernhardt for the rules fix (especially for the patience to explain
the problem to me until I got what he was talking about).
- Stefan Lang for pointing out problems in the dark corners of the FileList
implementation.
- Alexey Verkhovsky pointing out the silently swallows syntax errors in
tests.
- Tobias Luetke for beautifying the test task output.
- Sam Roberts for some of the ideas behind dependency loading.


Convenient access to New Zealand election results by voting place. Voting places have been matched to Regional Council, Territorial Authority, and Area Unit, to facilitate matching with additional data. Opinion polls since 2002 and some convenience analytical function are also supplied.
| Version: | 0.4.0 |
| Depends: | R (≥ 3.1.2) |
| Suggests: | testthat, dplyr, tidyr, knitr, rmarkdown, ggplot2, scales, ggthemes, GGally, maps, mapproj, forcats |
| Published: | 2017-10-02 |
| Author: | Peter Ellis [aut, cre] |
| Maintainer: | Peter Ellis <peter.ellis2013nz at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | nzelect results |
| Reference manual: | nzelect.pdf |
| Vignettes: |
nzelect - convenient access to New Zealand election-related data |
| Package source: | nzelect_0.4.0.tar.gz |
| Windows binaries: | r-devel: nzelect_0.4.0.zip, r-release: nzelect_0.4.0.zip, r-oldrel: nzelect_0.4.0.zip |
| OS X binaries: | r-release: nzelect_0.4.0.tgz, r-oldrel: nzelect_0.4.0.tgz |
| Old sources: | nzelect archive |
Please use the canonical form https://CRAN.R-project.org/package=nzelect to link to this page.


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25th, April
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Yuan inches lower amid uncertainty over Sino-U.S. trade agreement
by ReutersSHANGHAI — China’s yuan inched lower
against the dollar on Monday, as investors remained cautious
over whether the United States and China will agree a
preliminary trade agreement to call a halt their damaging trade
war.
Chinese state media Xinhua said on Sunday that China and the
United States had “constructive talks” on trade in a high-level
phone call on Saturday, but it gave no further details.
Prior to the market opening, the People’s Bank of China
(PBOC) set the midpoint rate at 7.0037 per dollar, 54
pips or 0.08% firmer than the previous fix of 7.0091.
In the spot market, onshore yuan opened at 7.0150
per dollar and was changing hands at 7.0107 at midday, 35 pips
weaker than late session close.
Some analysts noted that wording for the FX policy in the
PBOC’s third quarter monetary policy report was slightly changed
from past reports, as it failed to mention “strengthening
macroprudential management.”
“Given positive development in trade talks, it makes sense
for the PBOC to feel less pressured to intervene in the FX
market, and under those circumstances maintaining RMB stability
could be less of a constraint for monetary policy going
forward,” economists at Goldman Sachs said in note.
Separately, China’s central bank unexpectedly trimmed a key
interbank lending rate on Monday, the first easing in the
liquidity tool in more than four years and a signal to markets
that policymakers are ready to act to prop up slowing growth.
The rate cut bolstered bond market sentiment, Though the
currency market showed little reaction, a loosening bias could
lead to downside pressure on the yuan over the long run.
The global dollar index fell to 97.911 at midday from
the previous close of 97.999.
The offshore yuan was trading at 7.012 per dollar
as of midday.
The yuan market at 0405 GMT:
ONSHORE SPOT:
Item Current Previous Change
PBOC midpoint 7.0037 7.0091 0.08%
Spot yuan 7.0107 7.0072 -0.05%
Divergence from 0.10%
midpoint*
Spot change YTD -1.96%
Spot change since 2005 18.06%
revaluation
Key indexes:
Item Current Previous Change
Thomson 92.2 92.33 -0.1
Reuters/HKEX
CNH index
Dollar index 97.911 97.999 -0.1
*Divergence of the dollar/yuan exchange rate. Negative number
indicates that spot yuan is trading stronger than the midpoint.
The People’s Bank of China (PBOC) allows the exchange rate to
rise or fall 2 percent from official midpoint rate it sets each
morning.
OFFSHORE CNH MARKET
Instrument Current Difference
from onshore
Offshore spot yuan 7.012 -0.02%
*
Offshore 7.0741 -1.00%
non-deliverable
forwards
**
*Premium for offshore spot over onshore
**Figure reflects difference from PBOC’s official midpoint,
since non-deliverable forwards are settled against the midpoint.
.
(Reporting by Winni Zhou and John Ruwitch; Editing by Simon
cameron-Moore)


Deploying Apps and Microservices with Tanzu
Deploy microservices and scale applications with Tanzu Community Edition
--- layout: page title: "Resources" id: resources ---
The Tanzu Community Edition sandboxes are freely available, hosted environments with ready-to-use Tanzu Community Edition clusters. Try out these workshops for hands-on learning in sandbox environments:
Deploy microservices and scale applications with Tanzu Community Edition
Build a Kubernetes platform perfectly suited to your needs
Visit the Tanzu Developer Center to learn more about cloud native technologies and practices. You'll find informative articles, guides, videos, samples, workshops, and more on topics that matter to you:
KubeAcademy is a free, product-agnostic Kubernetes and cloud native technology education program built by a team of expert instructors. Courses comprise a series of short video lessons and dive into topics for skill levels ranging from beginner to advanced.
Every week, TGIK hosts live-stream their dive into new Kubernetes tools and concepts. Join every Friday at 1:00 PM PT to follow along and learn.


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|
ARRL Field Day Cheers Plaza, Pueblo West, CO
|
Photos by David Yeater |
|
2010 Results For Colorado (All Categories)
PWARC is #10 of 57 Entries (Top 18%)
|
# |
Call |
Score |
Category |
QSOs |
Power Mult. |
Participants |
Club |
|
1 |
W0CQC |
12,295 |
2AB |
1,180 |
5 |
14 |
CO QRP Club & 285 Tech Connect Club |
|
2 |
W0NT |
11,524 |
12E |
3,384 |
2 |
32 |
. |
|
3 |
W0GG |
6,942 |
2A |
1,974 |
2 |
4 |
Pikes Peak DX Group |
|
4 |
K0RV |
5,860 |
2A |
1,579 |
2 |
36 |
Ski Country ARC |
|
5 |
N0FH |
5,564 |
4A |
1,282 |
2 |
7 |
Estes Valley ARC |
|
6 |
W0DTF |
5,474 |
3A |
1,250 |
2 |
24 |
Colorado ARES District 24 |
|
7 |
KE0UI |
4,556 |
1B2 |
1,232 |
2 |
2 |
. |
|
8 |
W0QE |
3,970 |
1B1 |
949 |
2 |
1 |
. |
|
9 |
K0IIT |
3,716 |
2A |
924 |
2 |
37 |
Montrose ARC |
|
10 |
NA0PW |
3,190 |
2AB |
237 |
5 |
15 |
Pueblo West ARC |
|
11 |
W0CBH |
3,172 |
3A |
1,055 |
2 |
10 |
Franktown FD Group |
|
12 |
W0DZ |
3,068 |
2A |
681 |
2 |
25 |
Colorado Mountain Moguls |
|
13 |
K0UE |
3,060 |
4A |
985 |
2 |
6 |
Thin Air Radio Society |
|
14 |
W0DK |
2,886 |
1A |
1,500 |
1 |
11 |
Boulder ARC |
|
15 |
W0TX |
2,858 |
6A |
598 |
2 |
41 |
Denver RC |
|
16 |
KC0WBA |
2,834 |
3A |
903 |
2 |
89 |
. |
|
17 |
N0CQC |
2,830 |
3AB |
231 |
5 |
37 |
Colorado QRP Club |
|
18 |
NC0A |
2,588 |
3A |
477 |
2 |
25 |
Royal Gorge ARC |
|
19 |
K0XTR |
2,480 |
4A |
620 |
2 |
8 |
Littleton Radio Amateurs |
|
20 |
W0FT |
2,428 |
1F |
669 |
2 |
10 |
CO ARES D10 - Loveland EOC |
|
21 |
W0IVJ |
2,308 |
1C |
1,039 |
2 |
1 |
. |
|
22 |
NX0G |
1,894 |
2A |
518 |
2 |
4 |
. |
|
23 |
W0XR |
1,730 |
1B1C |
420 |
2 |
1 |
. |
|
24 |
K0G |
1,645 |
1B2B |
133 |
5 |
2 |
. |
|
25 |
K0HSC |
1,494 |
3A |
320 |
2 |
12 |
Hammin' Sams |
|
26 |
WV0H |
1,468 |
3A |
397 |
2 |
16 |
South Park Marauders |
|
27 |
W0RAA |
1,430 |
1D |
381 |
2 |
1 |
. |
|
28 |
K0FEZ |
1,274 |
2A |
320 |
2 |
17 |
RADOPS of El Jebel Shrine |
|
29 |
W0UPS |
1,102 |
4A |
183 |
2 |
21 |
Northern Colorado ARC |
|
30 |
K0RFD |
1,098 |
1E |
250 |
2 |
1 |
. |
|
31 |
K0LMD |
1,076 |
1E |
328 |
2 |
1 |
. |
|
32 |
N0RU |
964 |
1B1 |
275 |
2 |
1 |
. |
|
33 |
KB0YH |
952 |
1E |
351 |
2 |
1 |
. |
|
34 |
KE0PX (+N0SRF) |
930 |
1B2 |
415 |
2 |
2 |
. |
|
35 |
KF0QS |
880 |
1B1B |
73 |
5 |
1 |
. |
|
36 |
KD0KOB |
858 |
2A |
154 |
2 |
6 |
North Jeffco ARL |
|
37 |
KK2O |
682 |
1C |
216 |
2 |
1 |
. |
|
38 |
KJ0T |
626 |
3A |
38 |
2 |
8 |
Thunderbolt ARA |
|
39 |
WA0CTZ |
616 |
3A |
61 |
2 |
6 |
OFOG Group |
|
40 |
K0UK |
610 |
1D |
280 |
1 |
1 |
. |
|
41 |
K0EFD |
584 |
2F |
67 |
2 |
2 |
Ellicott Fire ARC |
|
42 |
KC0VFO/M |
562 |
1C |
206 |
2 |
1 |
. |
|
43 |
AD0A |
487 |
1D |
160 |
1 |
2 |
. |
|
44 |
AB0PC |
402 |
1A |
21 |
2 |
5 |
Park Cty RC |
|
45 |
AB0YM |
402 |
1C |
53 |
2 |
1 |
. |
|
46 |
NA0BR |
380 |
1E |
70 |
2 |
1 |
. |
|
47 |
KB0MPY |
300 |
1E |
100 |
2 |
1 |
. |
|
48 |
W9UW |
272 |
1D |
68 |
2 |
1 |
. |
|
49 |
N0BHT |
254 |
1B1 |
102 |
2 |
1 |
. |
|
50 |
AC0FQ |
250 |
1B2 |
50 |
2 |
2 |
. |
|
51 |
NK0E |
230 |
1D |
46 |
2 |
1 |
. |
|
52 |
W9BNO |
190 |
1C |
20 |
2 |
1 |
. |
|
53 |
KD0GUY |
184 |
1B2 |
17 |
2 |
2 |
. |
|
54 |
K0CO |
166 |
1D |
29 |
2 |
1 |
. |
|
55 |
KD0CPA |
162 |
1D |
81 |
2 |
3 |
. |
|
56 |
K0USN |
160 |
2D |
33 |
2 |
2 |
. |
|
57 |
N1AOK |
80 |
1D |
15 |
2 |
1 |
. |
Additional Statistics For NA0PW
|
Division |
Category |
Placement |
Percentage |
|
Rocky Mtn. Division |
All Classes |
21 of 116 |
Top 18% |
|
Nationwide |
All Classes |
513 of 2,617 |
Top 20% |
|
Nationwide |
Multiplier of 5 |
57 of 276 |
Top 21% |
|
Nationwide |
2AB |
7 of 16 |
Top 44% |


http://www.susangranger.com/
Susan Granger's review of "THE WATCHER" (Universal)
Just what we need: another lurid, trashy serial killer saga. This time, Keanu Reeves plays an elusive homicidal maniac who engages in a gruesome ticking-clock cat-and-mouse game with James Spader, a burnt-out FBI agent who has suffered a traumatic nervous breakdown after too many years on the job with the LAPD. To taunt Spader, who has relocated to Chicago, Reeves packs up his trusty piano wire and moves too. Before long, he starts mailing photographs of the lonely, unsuspecting young women who are his intended Windy City victims, challenging Spader, along with the Chicago Police Department, to stop him within 24 hours before he strikes again. In the midst of this murderous frenzy, there's Spader's psychologist, played by Marisa Tomei, who is even less convincing as a medical professional than Jennifer Lopez in "The Cell." Writers David Elliot, Clay Ayers and Darcy Meyers, along with first-time director Joe Charbanic (helmer of music videos for Reeves' band Dogstar), dwell on the warped thrill of the methodology and the chase, revealing, early on, exactly whodunit since the killer says he and the cop "need each other to give meaning to our lives." Plus, as Reeves explains, "We're all stacked right on top of each other, but we don't notice each other any more." After that, it's all sound effects and tricky camera-work, along with a muddled, discordant soundtrack. James Spader's acting technique can be described as 'wooden deadpan' while Keanu Reeves maintains his perpetually monotone 'dude' persona which sometimes works, sometimes doesn't. Chris Ellis, as Spader's self-important colleague, is the only cast member who manages to be convincing. On the Granger Movie Gauge of 1 to 10, "The Watcher" is an appallingly awful, amateurish 2. The real torture is watching it.
========== X-RAMR-ID: 26009 X-Language: en X-RT-ReviewID: 153571 X-RT-TitleID: 1099988 X-RT-SourceID: 742 X-RT-AuthorID: 1274 X-RT-RatingText: 2/10
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


# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""An extensible ASCII table reader and writer.
daophot.py:
Classes to read DAOphot table format
:Copyright: Smithsonian Astrophysical Observatory (2011)
:Author: Tom Aldcroft<EMAIL_ADDRESS>"""
##
## Redistribution and use in source and binary forms, with or without
## modification, are permitted provided that the following conditions are met:
## * Redistributions of source code must retain the above copyright
## notice, this list of conditions and the following disclaimer.
## * Redistributions in binary form must reproduce the above copyright
## notice, this list of conditions and the following disclaimer in the
## documentation and/or other materials provided with the distribution.
## * Neither the name of the Smithsonian Astrophysical Observatory nor the
## names of its contributors may be used to endorse or promote products
## derived from this software without specific prior written permission.
 IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
## DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
## (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
## LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
## ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
## (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
## SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import re
import numpy as np
from . import core
from . import basic
from . import fixedwidth
from ...utils import OrderedDict
[docs]class Daophot(core.BaseReader):
"""Read a DAOphot file.
Example::
#K MERGERAD = INDEF scaleunit %-23.7g
#K IRAF = NOAO/IRAFV2.10EXPORT version %-23s
#K USER = davis name %-23s
#K HOST = tucana computer %-23s
#
#N ID XCENTER YCENTER MAG MERR MSKY NITER \\
#U ## pixels pixels magnitudes magnitudes counts ## \\
#F %-9d %-10.3f %-10.3f %-12.3f %-14.3f %-15.7g %-6d
#
#N SHARPNESS CHI PIER PERROR \\
#U ## ## ## perrors \\
#F %-23.3f %-12.3f %-6d %-13s
#
14 138.538 INDEF 15.461 0.003 34.85955 4 \\
-0.032 0.802 0 No_error
The keywords defined in the #K records are available via the output table
``meta`` attribute::
>>> import os
>>> from astropy.io import ascii
>>> filename = os.path.join(ascii.__path__[0], 'tests/t/daophot.dat')
>>> data = ascii.read(filename)
>>> for name, keyword in data.meta['keywords'].items():
... print name, keyword['value'], keyword['units'], keyword['format']
...
MERGERAD INDEF scaleunit %-23.7g
IRAF NOAO/IRAFV2.10EXPORT version %-23s
USER name %-23s
...
The unit and formats are available in the output table columns::
>>> for colname in data.colnames:
... col = data[colname]
... print colname, col.unit, col.format
...
ID None %-9d
XCENTER pixels %-10.3f
YCENTER pixels %-10.3f
...
Any column values of INDEF are interpreted as a missing value and will be
masked out in the resultant table.
"""
_format_name = 'daophot'
_io_registry_format_aliases = ['daophot']
_io_registry_can_write = False
_description = 'IRAF DAOphot format table'
def __init__(self):
core.BaseReader.__init__(self)
self.header = DaophotHeader()
self.inputter = core.ContinuationLinesInputter()
self.inputter.no_continue = r'\s*#'
self.data.splitter = fixedwidth.FixedWidthSplitter()
self.data.start_line = 0
self.data.comment = r'\s*#'
[docs] def write(self, table=None):
raise NotImplementedError
class DaophotHeader(core.BaseHeader):
"""Read the header from a file produced by the IRAF DAOphot routine."""
def __init__(self):
core.BaseHeader.__init__(self)
self.comment = r'\s*#K'
def update_meta(self, lines, meta):
"""
Extract table-level keywords for DAOphot table. These are indicated by
a leading '#K ' prefix.
"""
table_meta = meta['table']
# Read keywords as a table embedded in the header comments
comment_lines = [line for line in lines if line.startswith('#')]
if len(comment_lines) > 0:
re_header_keyword = re.compile(r'[#]K'
r'\s+ (?P<name> \w+)'
r'\s* = (?P<stuff> .+) $',
re.VERBOSE)
table_meta['keywords'] = OrderedDict()
for line in comment_lines:
m = re_header_keyword.match(line)
if m:
vals = m.group('stuff').strip().rsplit(None, 2)
keyword_dict = {'units': vals[-2],
'format': vals[-1]}
keyword_dict['value'] = (vals[0] if len(vals) > 2 else "")
table_meta['keywords'][m.group('name')] = keyword_dict
def get_cols(self, lines):
"""Initialize the header Column objects from the table ``lines`` for a DAOphot
header. The DAOphot header is specialized so that we just copy the entire BaseHeader
get_cols routine and modify as needed.
:param lines: list of table lines
:returns: list of table Columns
"""
# Parse a series of column defintion lines like below. There may be several
# such blocks in a single file (where continuation characters have already been
# stripped).
# #N ID XCENTER YCENTER MAG MERR MSKY NITER
# #U ## pixels pixels magnitudes magnitudes counts ##
# #F %-9d %-10.3f %-10.3f %-12.3f %-14.3f %-15.7g %-6d
coldef_lines = ['', '', '']
starts = ('#N ', '#U ', '#F ')
col_width = []
col_len_def = re.compile(r'[0-9]+')
re_colformat_def = re.compile(r'#F([^#]+)')
for line in lines:
if not line.startswith('#'):
break # End of header lines
else:
formatmatch = re_colformat_def.search(line)
if formatmatch:
form = formatmatch.group(1).split()
width = ([int(col_len_def.search(s).group()) for s in form])
# original data format might be shorter than 80 characters
# and filled with spaces
width[-1] = 80 - sum(width[:-1])
col_width.extend(width)
for i, start in enumerate(starts):
if line.startswith(start):
line_stripped = line[2:]
coldef_lines[i] = coldef_lines[i] + line_stripped
break
# At this point colddef_lines has three lines corresponding to column
# names, unit, and format. Get the column names by splitting the
# first line on whitespace.
self.names = coldef_lines[0].split()
if not self.names:
raise core.InconsistentTableError('No column names found in DAOphot header')
ends = np.cumsum(col_width)
starts = ends - col_width
# If there wasn't a #U defined (not sure of DAOphot specification), then
# replace the empty line with the right number of ## indicators, which matches
# the DAOphot "no unit" tag.
for i, coldef_line in enumerate(coldef_lines):
if not coldef_line:
coldef_lines[i] = '## ' * len(self.names)
# Read the three lines as a basic table.
reader = core._get_reader(Reader=basic.Basic, comment=None)
reader.header.comment = None
coldefs = reader.read(coldef_lines)
# Create the list of io.ascii column objects
self._set_cols_from_names()
# Set unit and format as needed.
for col in self.cols:
if coldefs[col.name][0] != '##':
col.unit = coldefs[col.name][0]
if coldefs[col.name][1] != '##':
col.format = coldefs[col.name][1]
# Set column start and end positions.
for i, col in enumerate(self.cols):
col.start = starts[i]
col.end = ends[i]
if hasattr(col, 'format'):
if any(x in col.format for x in 'fg'):
col.type = core.FloatType
elif 'd' in col.format:
col.type = core.IntType
elif 's' in col.format:
col.type = core.StrType
# INDEF is the missing value marker
self.data.fill_values.append(('INDEF', '0'))


Hobbiton, New Zealand
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Utah, United States
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Hobbiton, New Zealand


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The Concept of the Sampling Distribution
Lets say that you want to know the mean years of education of US adults. You implement a well-designed representative survey that samples 100 respondents from the USA. You ask people the simple question “how many years of education do you have?”
This simple example involves three different kinds of distributions. Understanding the difference between these three different distributions is the key to unlocking how statistical inference works.
- The Population Distribution. This is the distribution of years of education in the entire US population. The mean of this distribution is given by \(\mu\) and is a population parameter. If we had data on the entire population we could show the distribution and calculate \(\mu\). However, because we don’t have data on the full population, the population distribution and \(\mu\) are unknown. This distribution is also static - it doesn’t fluctuate.
- The Sample Distribution. This is the distribution of years of education in the sample of 100 respondents that I have. The mean of this distribution is \(\bar{x}\) and is a sample statistic. Since we collected this data, this distribution and \(\bar{x}\) are known. We can calculate \(\bar{x}\) and we can show the distribution of years of education in the sample (with a histogram or boxplot, for example). The sample distribution is an approximation of the population distribution, but because of random bias, it may be somewhat different. Also, because of this random bias, the distribution is not static - if we were to draw another sample the two sample distributions would almost certainly not be identical.
- The Sampling Distribution. Imagine all the possible samples of size 100 that I could have drawn from the US population. Its a tremendously large number. If I had all those samples, I could calculate the sample mean of years of education for each sample. Then, I would have the mean years of education in every possible sample of size 100 that I could have drawn from the population. The sampling distribution is the distribution of all of these possible sample means. More generally, the sampling distribution is the distribution of the desired sample statistic in all possible samples of size \(n\).
The sampling distribution is much more abstract than the other two distributions, but is key to understanding statistical inference. When we draw a sample and calculate a sample statistic from this sample, we are in effect reaching into the sampling distribution and pulling out a value. Therefore, the sampling distribution give us information about how variable our sample statistic might be as a result of randomness in the sampling.
Example: class height
As an example, lets use some data on a recent class that took this course. I will treat this class of 42 students as a population that I would like to know something about. In this case, I would like to know the mean height of the class. In most cases, the population distribution is unknown but in this case, I know the height of all 42 students because of surveys they all took at the beginning of the term. Figure 34 shows the population distribution of height in the class:
The population distribution of height is bimodal which is typical, because we are mixing the heights of men and women. The population mean of height (\(\mu\)) is 66.517 inches.
Lets say I lost the results of the student survey and I wanted to know the mean height of the class. I could take a random sample of two students in order to calculate the mean height. Lets say I drew a sample of two people who were 68 and 74 inches respectively in height. I would estimate a sample mean of 71 inches which in this case would be too high. Lets say I took another sample of two students and ended up with a mean height of 66 which would be too low. Lets say I repeat this procedure until I had sampled all possible combinations of two students out of the twenty in the class.
How many samples would this be? On the first draw from the population of 42 students there are 42 possible results. On the second draw, there are 41 possible results, because I won’t re-sample the student I selected the first time. This gives me 42*41=1722 possible combinations of 42 students in two draws. However, half of these draws are just mirror images of the other draws where I swap the first and second draw. Since I don’t care about the order, I actually have 1722/2=861 possible samples.
I have used a computer routine to actually calculate the sample means in all 861 of those possible samples. The distribution of these sample means then gives us the sampling distribution of mean height for samples of size 2. Figure 35 shows a histogram of that distribution.
When I randomly draw one sample of size 2 and calculate its mean, I am effectively reaching into this distribution and pulling out one of these values. Note that many of the means are clustered around the true population mean of 66.5 inches, but in a few cases I can get extreme overestimates or extreme underestimates.
What if I were to increase the sample size? I have used the same computer routine to calculate the sampling distribution for samples of size 3, 4, and 5. Figure 36 shows the results.
Clearly, the shape of these distributions is changing. Another way to visualize this would be to draw density graphs which basically fit a curve to the histograms. We can then then overlap these density curves on the same graph. Figure 37 shows this graph.
There are three thing to note here. First, each sampling distribution seems to have most of the sample means clustered (where the peak is) around the true population mean of 66.5. In fact, the mean of each of these sampling distributions is exactly the true population parameter, as I will show below. Second, the spread of the distributions is shrinking as the sample size increases. You can see that the when the sample size increases, the tails of the distribution are “pulled in” and more of the sample means are closer to the center. This indicates that we are less likely to draw a sample mean that is extremely different from the population mean in larger samples. Third, the shape of the distribution at larger sample sizes is becoming more symmetric and “bell-shaped.”
Lets take a look at the mean and standard deviation of these sampling distributions:
| Distribution | Mean | Standard Deviation |
|---|---|---|
| Population Distribution | 66.517 | 4.87 |
| Sampling Distribution (n=2) | 66.517 | 3.363 |
| Sampling Distribution (n=3) | 66.517 | 2.71 |
| Sampling Distribution (n=4) | 66.517 | 2.316 |
| Sampling Distribution (n=5) | 66.517 | 2.044 |
Note that the mean of each sampling distribution is identical to the true population mean. This is not a coincidence. The mean of the sampling distribution of sample means is always itself equal to the population mean. In statistical terminology, this is the definition of an unbiased statistic. Given that we are trying to estimate the true population mean, it is reassuring that the “average” sample mean we should get is the true population mean.
Also note that the standard deviation of the sampling distributions gets smaller with increasing sample size. This is the mathematically way of seeing the shrinking of the spread that we observed graphically. In larger sample sizes, we are less likely to draw a sample mean far away from the true population mean.
Central limit theorem and the normal distribution
The patterns we are seeing here are well known to statisticians. In fact, they are patterns that are predicted by the most important theorem in statistics, the central limit theorem. We won’t delve into the technical details of this theorem. We can generally interpret the central limit theorem to say:
As the sample size increases, the sampling distribution of a sample mean becomes a normal distribution. This normal distribution will be centered on the true population mean \(\mu\) and with a standard deviation equal to \(\sigma/\sqrt{n}\), where \(\sigma\) is the population standard deviation.
What is this “normal” distribution? The name is somewhat misleading because there is nothing particularly normal about the normal distribution. Most real-world distributions don’t look normal, but the normal distribution is central to statistical inference because of the central limit theorem. The normal distribution is a bell-shaped, unimodal, symmetric distribution. It has two characteristics that define its exact shape. The mean of the normal distribution define where its center is and the standard deviation of the normal distribution defines its spread.
Lets look at the normal sampling distribution of the mean to become familiar with it.
The distribution is symmetric and bell shaped. The center of the distribution is shown by the red dotted line. This center will always be at the true population mean, \(\mu\). The area of the normal distribution also has a regularity that is sometimes referred to as the “68%,95%,99.7%” rule. Regardless of the actual variable, 68% of all the sample means will be within 68% of the true population mean, 95% of all the sample means will be within 95% of the true population mean, and 99.7% of all the sample means will be within three standard deviations of the mean. This regularity will become very helpful later on for making statistical inferences.
The standard error
It is easy to get confused by the number of standard deviations being thrown around in this section. There are three standard deviations we need to keep track of to properly understand what is going on here. Each of these standard deviations is associated with one of the types of distributions I introduced at the beginning of this section.
- \(\sigma\): the population standard deviation. In our example, this would be the standard deviation of height for all 42 students which is 4.8702139. Typically, like other values in the population, this number is unknown.
- \(s\): the sample standard deviation. In our example, this would be the standard deviation of height from a particular sample of a given size from the class. This number can be calculated for the sample that you have, using the techniques we learned earlier in the class.
- \(\sigma/\sqrt{n}\): The standard deviation of the sampling distribution of the sample mean. We divide the population standard deviation \(\sigma\) by the square root of the sample size. In general, we refer to the standard deviation of the sampling distribution as the standard error, for short. So remember that when I refer to the “standard error” I am using shorthand for the “standard deviation of the sampling distribution.”
Other sample statistics
In the example here, I have focused on the sample mean as the sample statistic of interest. However, the logic of the central limit theorem applies to several other important sample statistics of interest to us. In particular, the sampling distributions of:
- means
- proportions
- mean differences
- proportion differences
- correlation coefficients
all become normal as the sample size increases. Thus, this normal distribution becomes critically important in making statistical inferences.
Note that the standard error formula \(\sigma/\sqrt{n}\) only applies to the sampling distribution of sample means. Other sample statistics have different formulas for their standard errors, which I will introduce in the next section.
What can we do with the sampling distribution?
Now that we know the sampling distribution of the sample mean should be normally distributed, what can we do with that information? The sampling distribution gives us information about how we would expect the sample means to be distributed. This seems like it should be helpful in figuring out whether we got a value close to the center or not. However, there is a catch. We don’t know either \(\mu\), the center of this distribution or \(\sigma\) which we need to calculate its standard deviation. Thus, we know theoretically what it should look like but we have no concrete numbers to determine its actual shape.
We can fix the problem with not knowing \(\sigma\) fairly easily. We don’t know \(\sigma\) but we do have an estimate of it in \(s\), the sample standard deviation. In practice, we us this value to calculate an estimated standard error of \(s/\sqrt{n}\). However, this substitution has consequences. Because we are using a sample statistic subject to random bias to estimate the standard error, this creates greater uncertainty in our estimation. I will come back to this issue in the next section.
We still have the more fundamental problem that we don’t know where the center of the sampling distribution should be. In order to make statistical inferences, we are going to employ two different methods that make use of what we do know about the sampling distribution:
- Confidence intervals: Provide a range of values within which you feel confident that the true population mean resides.
- Hypothesis tests: Play a game of make believe. If the true population mean was a given value, what is the probability that I would get the sample mean value that I actually did?
I will discuss these two different methods in the next two sections.


zmath.quaternion
A Quaternion it's used to store a space orientation / rotation, and makes easy to interpolate rotations, at same time that avoid grimbal locks that have using Euler angles
-
Declaration
alias
Qua_f
= Quaternion!float.Quaternion;Alias of a Quaternion with floats
-
Declaration
alias
Qua_d
= Quaternion!double.Quaternion;Alias of a Quaternion with doubles
-
Declaration
alias
Qua_r
= Quaternion!real.Quaternion;Alias of a Quaternion with reals
-
Declaration
struct
Quaternion
(T = floatt) if (__traits(isFloating, T));Quaternion
over a FloatPoint type,-
Declaration
static enum size_t
dim
;Quaternion Dimension
-
Declaration
T
i
;i
complex component -
Declaration
T
j
;j
complex component -
Declaration
T
k
;k
complex component -
Declaration
T
w
;w
real component -
Declaration
T
x
;alias of i component
-
Declaration
T
y
;alias of j component
-
Declaration
T
z
;alias of k component
-
Declaration
this(in T
i
, in Tj
, in Tk
, in Tw
);Build a new Quaternion from a set of initial values
Parameters
T
i
i
imaginary componentT
j
j
imaginary componentT
k
k
imaginary componentT
w
i
real component -
Declaration
this(in T[]
xs
);Build a new Quaternion from a array If no there values for j, and k, will be set to 0. w is set to 1
Parameters
T[]
xs
Array with coords
-
Declaration
pure @nogc this(in Vector!(T, 3)
vec
);Build a new Quaternion from a 3D Vector w will be set to 1
Parameters
Vector!(T, 3)
vec
Vector 3d
-
Declaration
pure @nogc this(in Vector!(T, 4)
vec
);Build a new Quaternion from a 4D Vector
Parameters
Vector!(T, 4)
vec
Vector 4d
-
Declaration
@nogc this(U)(Vector!(T, 3)
v
, Uangle
) if (is(U : real));Creates a Quaternion from a 3d Vector and
angle
Parameters
Vector!(T, 3)
v
Rotation axis
U
angle
Rotation
angle
in radiansReturn Value
Quaternion that represents a rotation
-
Declaration
@nogc this(U)(U
roll
, Upitch
, Uyaw
) if (__traits(isFloating, U));Creates a new Quaternion from Euler angles Params :
roll
= Rotation around X axis in radians (aka bank)pitch
= Rotation around Y axis in radians (aka attitude)yaw
= Rotation around Z axis in radians (aka heading)Return Value
Quaternion that represents a rotation
-
Declaration
const pure @nogc T
opIndex
(size_ti
);Returns
i
coord of this vector -
Declaration
@nogc void
opIndexAssign
(Tc
, size_ti
);Assigns a value to a coord
-
Declaration
const pure @nogc @property T
length
();Returns the actual
length
of this quaternion -
Declaration
const pure @nogc @property T
sq_length
();Returns the actual squared length of this quaternion
-
Declaration
const pure @nogc bool
opEquals
()(auto ref const Quaternionrhs
);Define Equality
-
Declaration
const pure @nogc bool
approxEqual
()(auto ref const Quaternionrhs
, TmaxRelDiff
= 0.01, TmaxAbsDiff
= 1e-05);Approximated equality with controlable precision
-
Declaration
const pure @nogc Quaternion
opUnary
(string op)() if (op == "+" || op == "-");Define unary operators + and -
-
Declaration
const pure @nogc Quaternion
opBinary
(string op)(auto ref const Quaternionrhs
) if (op == "+" || op == "-");Define binary operator + and -
-
Declaration
const pure @nogc Quaternion
opBinary
(string op)(in Trhs
) if (op == "*");Define Scalar multiplication
-
Declaration
const pure @nogc Quaternion
opBinary
(string op)(auto ref const Quaternionrhs
) if (op == "*");Defines Hamilton product for Quaternions
Parameters
Quaternion
rhs
Quaternion at rigth of operation
Return: Quaternion result of hamilton product of this and
rhs
(thisrhs
) -
Declaration
const pure @nogc Quaternion
conj
();Obtain conjugate of a Quaternion
-
Declaration
const pure @nogc @property bool
isUnit
();It's a unitary Quaternion (length == 1)
-
Declaration
pure @nogc void
normalize
();Normalize this Quaternion
-
Declaration
const pure V
rotate
(V)(auto ref const Vv
) if (isVector!V && (V.dim >= 3));Apply a 3d rotation Quaternion over a 3d/4d Vector
-
Declaration
const pure @nogc Vector!(T, 3)
toAxisAngle
(out Tangle
);Calc the Axis and
angle
of rotation of this QuaternionParameters
T
angle
Set to
angle
of rotationReturn Value
Axis of rotation vector
-
Declaration
const pure @nogc Vector!(T, 3)
toEuler
();Returns a Vector with euler's angles
Return Value
Vector with Euler angles of rotation in radians
TODO: Improve using code from here to avoid requiring pre-normalize the quaternion http://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToEuler/
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Declaration
const pure @nogc Tout
opCast
(Tout)() if (isQuaternion!Tout);Casting for convert between Quaternions
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Declaration
const pure @nogc Tout
opCast
(Tout)() if (isMatrix!Tout && (Tout.dim >= 3));Casting to rotation Matrix
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Declaration
string
toString
();Returns a string representation of this Quaternion
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-
Declaration
immutable bool
isQuaternion
(T);Say if a thing it's a Quaternion


Anurag kush
web devloper
a geek with some of devloper skills(i.e. javascript, python, html and
many more)
Laxmi international school
Laxmi international school
Starex university
Check out some projects


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Class encapsulating actions related to fax history for this account
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Add a note (free text) to the fax Arguments: fax_uuid -- uuid of the fax note -- text to add to the fax |
Returns the number of faxes from users history with a specific state. Arguments: type -- Possible values: history, inbox, inbox_unread, outbox or unpaid |
Moves faxes to the trash. If siblings_too is true will perform for the given faxes and all other recipients from the same fax jobs. siblings_too will only be evaluated for uuids beloging to an outgoing fax and will be ignored for incoming faxes uuids |
Removes faxes from trash This method is similar to EmptyTrash() which deletes all the faxes from trash Arguments: uuids -- ids of faxes to be removed vom trash |
Returns the details of a fax in progress. Arguments: uuid -- UUID of the fax to show |
Returns a fax groups details. Arguments: uuid -- Uuid of one of the faxes in the group. |
Returns the details of a fax in the inbox. Arguments: uuid -- UUID of the fax to show mark_read -- If true marks the fax as read (default: false). |
Get a .pdf-Version of a transmission report. On the transmission report basic data of the fax and a preview of the first page is shown. Should always be called with API_MODE_PASSTHRU, as the result is the pdf as binary data Arguments: uuid -- @attribute[RequestParam('uuid','string')] |
Lists all faxes in a group (that are sent as on job). Arguments: uuid -- Uuid of one of the faxes in the group. Keyword arguments: current_page -- The page which should be shown items_per_page -- How many items are shown per page |
Lists all notes for the given fax in reverse order (latest first) Arguments: fax_uuid -- uuid of the fax to list notes for |
List all faxes in the inbox of the current user. Keyword arguments: current_page -- The page which should be shown items_per_page -- How many items are shown per page |
Faxes in the outbox that are currently in the sending process Keyword arguments: current_page -- The page which should be shown items_per_page -- How many items are shown per page |
Returns a list of latest faxes for the user. Does not contain deleted and delayed faxes (See ListTrash for deleted faxes). Keyword arguments: count -- The count of items to return. Valid values are between 1 and 100 data_to_list -- Any message types you want this function to return. Allowed models are 'sent', 'inbox', 'outbox'. Leave empty to get faxes of any type. |
List all sent faxes (successful or not) Keyword arguments: current_page -- The page which should be shown items_per_page -- How many items are shown per page |
List all faxes in trash Keyword arguments: current_page -- The page which should be shown items_per_page -- How many items are shown per page |
Lists all unpaid faxes that are waiting for a payment. When this user makes a transaction to add credit, these faxes will be sent automatically if they are younger that 2 hours. Keyword arguments: current_page -- The page which should be shown items_per_page -- How many items are shown per page |
Restores a fax from the trash. Arguments: uuid -- uuid of fax to restore |
Sets a fax' read date to current time. Fax needs to be a fax in the inbox. Arguments: uuid -- uuid of fax to set as read |
Sets the read date of all the faxes to the current time Arguments: uuids -- array of uuids for faxes to set as read |
Sets the spamscore for all the faxes depending on the flag "is_spam" Takes an array of faxes UUIDs and marks them a spam if the second argument (is_spam) is true. Removes the Spam state if is_spam is false. If 15 or more faxes of the same sender has been marked as spam, all incoming faxes are directly moved to the trash. This is user specific, so if user A reports 15 faxes of one sender, then only all incoming faxes from the sender to him are directly sent to the trash. |
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| Generated by Epydoc 3.0.1 on Wed Sep 21 21:30:31 2011 | http://epydoc.sourceforge.net |


--- layout: default title: A Guide on How to Hack the chipKIT-core ---
By: Jacob Christ from pontech.com and Quick240.com
This guide was written as a starting point for anyone that is wishing to help improve the chipKIT-core API’s.
The chipKIT project started as fork of the Arduino IDE, plus its Wiring API as implemented for the Atmel AVR, with the express goal of making the tools work with the Microchip PIC32 microcontroller. Although the project started with the Microchip PIC32, the ultimate goal was to allow the growing number of people familiar with the Wiring API to use any microcontroller they wanted with a “Multi-Platform” IDE. The forked Arduino IDE was renamed to MPIDE for Multi-Platform IDE and initially supported both Atmel AVR and Microchip PIC32 devices.
As the Arduino community to expand their offerings to include the Arduino Due which was implemented with an Atmel SAM ARM processors they needed to change the Arduino IDE to support this new architecture. This was accomplished by pulling changes from MPIDE back into the Arudino IDE. Around version 1.6.5 of the Arduino IDE became the multi-platform IDE that MPIDE had always yearned to be. With this the chipKIT team dropped further MPIDE development in favor turning the ported PIC32 libraries into what is known at the chipKIT-core. The chipKIT-core can now be installed within the Arduino IDE Boards manager like many other cores for other boards and processor types.
The chipKIT-core project as it exists on github consists of the implementation of the Wiring API as implemented for the PIC32 plus Java ANT build scripts that allow for distributions to be built for multiple platforms including Windows, Mac OSX, Linux and Arm processors (Such as the RaspPI). The chipKIT-core at best is very useful in getting a PIC32 processor to do you bidding, at its worse its incomplete and needs help to improve it.
If you know how to program but are new to Arduino or chipKIT there are a few hurdles to overcome. Even if you have done a little programing on the Arduino platform there are a few things that should be pointed out if you want to hack on the chipKIT-core.
· Programs are called sketches (like an artist’s sketch)
· They are stored in a folder called the “Sketchbook”
· The “Sketchbook” folder is set from the preferences dialog box accessible from the File menu.
· Sketches can use “libraries” that come with the IDE which is NOT to be confused with the following:
· The “Sketchbook” folder can have a sub folder called “libraries” that is used to house contributed libraries that you make yourself or are made by others.
· Additional cores can be added to the IDE by adding a links to the “Additional Boards Manager URL’s” in the preference dialog box as described here. The additional cores are added into the “hardware” folder of for the IDE which is NOT to be confused with the following:
· The “Sketchbook” folder can have a sub folder called “hardware” that is used give the IDE access to compiler and APIs not accessible in the default download of the IDE.
· Whenever you make a change to any of these folders are where the IDE is looking for them you need to restart the IDE, it’s just not that smart.
The chipKIT wiki describes two ways to install a chipKIT-core.
1. Auto install via URL from within Arduino IDE
2. Manual install by copying ZIP file
The auto install will put the chipKIT-core into a place that is hidden from you but accessible by the Arduino IDE. On my computer, it puts it here:
C:\Users\Jacob Christ\AppData\Local\Arduino15\packages
The manual install is better for hacking since it gets put into the Arduio/hardware folder but for best results I recommend linking the git chipKIT-core repo to this directory as explained below.
The chipKIT-core repository is maintained on github: https://github.com/chipKIT32/chipKIT-core
DON’T FOR GET TO UPDATE YOUR SUBMODULES!
You can download, hack and contribute to the repository without using git, but if you plan on doing a lot of work on the repo or any other programming for that matter then it’s time to learn git. Learning and using git is outside the scope of this document but there is plenty of information on how to use it out there.
Once you have cloned the repo you will have a chipKIT-core folder (unless you renamed it). Inside this folder you have the following folders:
This is where the chipKIT-core commit specific documentation lives.
This is where the chipKIT-core Wiring API’s live.
As well as a smattering of other files:
Used by ANT to create the chipKIT-core distributions
The license file
A publicly visible file json file that tells the Arduino IDE how to install specific versions of chipKIT-core
The chipKIT-core license file
I’m not really sure what this file is for, I think it’s used by ANT
When I’m hacking the core, I’m doing it with the sole intention of making it better for myself and the community. Because of this I want to make it very easy for me to get my changes pushed back up to github so that I can make a pull request. Some might suggest modifying the files in your installed version of the core then copying them back to your cloned repo when you are ready to commit. I’m a bit lazier and prefer to operate directly on the core itself. Basicly it works like this. Clone chipKIT core into its own directory. Then create a symbolic directory link from your hardware folder in your sketchbook directory to the chipKIT-core repository. Build a distribution of the core in the chipKIT-core repository then copy compiler and tools directory from the build distribution back down to the pic32 directory in the chipKIT-core repo.
Here is how I accomplished this in windows:
· Open cmd prompt as administrator
· cd c:/sketchbook_folder/
· mkdir hardware
· cd hardware
· mkdir chipkit-core
· cd chipkit-core
· mklink pic32 c:\path_to_repos\chipKIT-core\pic32
· cd c:\path_to_repos\chipKIT-core
· ant build
· wait 10+ minutes for build to complete
· The next steps I do with the Windows file explorer
o copy the compiler and tools folder from c:\path_to_repos\chipKIT-core\dist\windows\chipkit-core\pic32 to c:\path_to_repos\chipKIT-core\pic32
If you have auto installed chipKIT-core,
when you restart the IDE you will now have two copies of the chipKIT in your boards menu. To make it clear which version is my hackable
version I modify platform.txt and change the name parameter to read:
name=chipKIT (git)
Remember you must start the Arduino IDE for this change to take place. Happy hacking…
The previous section mentioned using ANT to build the distribution. Instructions for installing ANT can be found here:


I work as a hybrid mobile app developer and web developer, having relevant experience of 3 years in angular 5/7/8/9 and ionic 3/4/5 framework .
and have knowledge in web technologies like html,javascript and css
To always keep enhancing knowledge and skills for my career growth, gaining experience and new avenues
Responsible of gathering requirements of new projects,communicating and co-ordination.Development of the hybrid mobile apps ,dedicatedly into developing new modules,testing , building app in production environment and deploying applications in live.Handling over all SDLC cycle of project also including after deployment support and resolution of bugs and errors.
Learning about IT security operations of the organization from core level and preparation of the project report detailing the scope,functions and implemenetation of the IT Security at each levels of Organization .
Pursuing first year of MBA and preparing for exams
Pursued BE in Computer Science from St. Francis Institute of Technology, Mumbai, Passed and graduated with 7.83 cgpa distinction
Passed with 71% Grade
Passed in distinction with 83.45% Grade
• Developed various modules for business associates.
• Creation of module for leads
• Creation of module for ongoing campaigns and CLSS status.
• Creation EMI calculator and analysis
• Creation of MIS and payouts modules
• Graphical analysis of performance of the business associates.
• Generation and upload of invoice.
• Creating provision for contacts with google maps ,call and email facilities
• creation of production build and deployment process.Uploading the app on play store,solving bugs,errors and after deployment support.
•Developed various modules of loan process as follows.
• Checking eligibility for loan approve.
• Getting nearby branches information and getting different loan products information.
• EMI calculator and amortization details with analysis.
• Disbursement details ,tax certificate request and account summary.
• Implementing push notifications.
•responsible for developing document upload module and implementing new features
• Developed photo and video upload process for the module using compression technique and also capturing the geo-location of the property with the document.
• Offline data saving and automatic update when availability of internet.
• Deploying the app live and carrying all the responsible tasks connected with it.
• Uploading app on Google play store.
• Developed mobile app which included different modules of the credit risk management system of HDFC
• responsibilities included implementing new features in the application, improving application performance, requirement gathering, unit testing, error solving, debugging
• implementing analytics, automatic app updates and push notifications using tools like Firebase and ionic appflow and github
1.Page&Passion: An e-book management system made by using web technologies like
javascript
html,
php,
css etc.
2.Course registration System:registration system made by using operating system concepts and algorithms like os scheduling algorithms.
3.Home management System:Home management and grocery resource management website made using data mining algorithms, data analysis and web technologies.


In this lesson you will learn about
1.Logarithms
2. Approximations and Errors
3. Sequences and series
4. Surds
5. Commercial arithmetic
Geometry is a branch of mathematics that studies the sizes, shapes, positions angles and dimensions of things.
Algebra is a branch of mathematics dealing with symbols and the rules for manipulating those symbols.
Trigonometry (from Greek trigōnon, "triangle" and metron, "measure") is a branch of mathematics that studies relationships between side lengths and angles of triangles
Mensuration is the branch of mathematics that studies the measurement of the geometric figures and their parameters like length, volume, shape, surface area, lateral surface area, etc.
Statistics is concerned with making inferences about the way the world is, based upon things we observe happening. ... Probability is the language of uncertainty, and so to understand statistics, we must understand uncertainty, and hence understand probability.
Calculus is the mathematical study of continuous change, in the same way that geometry is the study of shape and algebra is the study of generalizations of arithmetic operations.