Coventry, Midlands
Results updated, now showing 1 to 28 of 28 allocation statements
National funding formula allocations 2021 to 2022
| Total allocations | £290,861,294 |
| Pupil numbers in October 2019 | 100,333 |
| Funding per pupil in primary | £4,290 |
| Funding per pupil in secondary | £5,645 |
Read this important information about how these figures are calculated.
How these figures are calculated
The funding the school actually receives may be different
These figures show what schools would receive if the national funding formula was followed exactly and pupil numbers from the October 2019 school census, or agreed pupil numbers from 2019, were used. The funding that schools actually receive may be different. This is because funds are allocated to the local authority first. The local authority then decides how much funding a school gets.
These figures use pupil numbers from 2019
These figures have been calculated based on pupil numbers from the October 2019 census (or agreed pupil numbers from a similar time). This was the most recent pupil number information available at the time the allocations were calculated. The school’s final funding allocation for 2021 to 2022 will be based on pupil numbers from 2020.
Schools receive both pupil-led and school-led funding
Pupil-led funding is calculated based on the number of pupils in the school and their characteristics. Characteristics include factors such as how old the pupils are and whether they are from a disadvantaged background. School-led funding includes a lump sum for all schools and additional funding for schools with certain characteristics, such as being small and remote.
The funding allocation and funding per pupil figures shown for a school are the total of both the school-led and the pupil-led funding. However, the figures showing the change in funding per pupil is calculated based on the school’s pupil-led funding only.
What these allocation figures include
- funding for 5 to 16 year olds in mainstream schools that comes from the national funding formula
Allocation figures don’t include
- funding for pre-school or post-16
- pupil premium
- PE and sport premium
- universal infant free school meals funding
- capital funding to rebuild and repair schools
For more detail
- See tables showing provisional allocations for the schools, high needs and central school services blocks from 2021 to 2022.
- See the policy document for details of this year’s funding formula including funding factors.
OpenDocument spreadsheet (ODS), 27KB
This file is an OpenDocument format. You may need a different format if you're using assistive technology. Request an accessible format.
View national funding formula allocations for previous years
You can view all versions of this allocation for the financial year 2020 to 2021 and previous years on the allocation history.
For more details
- See tables showing provisional allocations for the schools, high needs and central school services block from 2021 to 2022
- See the policy document for details of this year's funding formula including funding factors


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What is an Error?
I am sure you have already encountered an error trying to run your first program and you might have been a bit frustrated because
what was printed out was confusing and almost unreadable.
Well, welcome to the error side of programming. Learning to handle errors will help you to write applications faster and make your
application more secured.
Here's one thing you should know: You do not stop committing errors because you have become more experienced at programming, you only
reduce the errors and are able to foresee an impeding error and therefore are able to handle it.
You are also able to read and understand error messages which helps in correcting your program just after reading
the error message. Basically, you have just been told why you should treat this topic as an important one.
What is an Exception?
An exception is an event, which occurs during the execution of a program, that disrupts the normal flow of the program's instructions.
This means that exceptions are either logical or syntax related mistakes that you have made in your application
and therefore prevents the application from running successfully.
Whenever the interpreter encounters a problem, it stops executing the code even if the rest of the code after the error is correct.
Imagine you send a robot to deliver a message for you on foot in a nearby town. Whenever this robot kicks a stone
on the way, it returns and tells you that the rest of the journey is too dangerous so it was not able to deliver your message.
And in asking for the reason for this conclusion, the robot tells you it was because he kicked a stone.
You should probably tell the robot to look on the ground carefully while walking and whenever it kicks a stone, it should continue the
journey assuring him that the rest of the journey is safe.
Python does something like this every time it runs your program. The interpreter does not tolerate any mistake at all
and cannot make intelligent guesses in order to deal with problems unless you provide a way out for it; this is called
Error Handling.
In general, when a Python script encounters a situation that it can't cope with, it raises an exception. An exception is a Python object that represents an error.
When a Python script raises an exception (kicks a stone), it must either handle the exception immediately (know what to do) otherwise it would terminate and come out (come home
without delivering the message).
Handling an exception
If you have some suspicious code that may raise an exception, you can defend your program by placing the suspicious code in a try: block.
After the try: block, include an except: statement, followed by a block of code which handles the problem as elegantly as possible.
Syntax
Here is simple syntax of try....except...else blocks:
try:
You do your operations here;
......................
except ExceptionI:
If there is ExceptionI, then execute this block.
except ExceptionII:
If there is ExceptionII, then execute this block.
......................
else:
If there is no exception then execute this block.
Here are few important points about the above-mentioned syntax
A single try statement can have multiple except statements.
This is useful when the try block contains statements that may throw different types of exceptions.
You can also provide a generic except clause, which handles any exception.
After the except clause(s), you can include an else-clause. The code in the else-block executes if the code in the try: block does not raise an exception.
The else-block is a good place for code that does not need the try: block's protection.
Example:
Here is simple example, which opens a file and writes the content in the file and comes out gracefully because there is no problem at all:
try:
fh = open("testfile", "w")
fh.write("This is my test file for exception handling!!")
except IOError:
print "Error: can\'t find file or read data"
else:
print "Written content in the file successfully"
fh.close()
This will produce the following result:
Written content in the file successfully
Example:
Here is one more simple example, which tries to open a file where you do not have permission to write in the file, so it raises an exception:
try:
fh = open("testfile", "r")
fh.write("This is my test file for exception handling!!")
except IOError:
print "Error: can\'t find file or read data"
else:
print "Written content in the file successfully"
This will produce the following result:
Error: can't find file or read data
The except clause with no exceptions:
You can also use the except statement with no exceptions defined as follows:
try:
You do your operations here;
......................
except:
If there is any exception, then execute this block.
......................
else:
If there is no exception then execute this block.
This kind of a try-except statement catches all the exceptions that occur.
Tip
Using this kind of try-except statement is not considered
a good programming practice though, because it catches all exceptions but does not make the programmer identify the root cause of the problem that may occur.
So it may be better for catching all other exceptions after catching speccific ones.
The except clause with multiple exceptions
You can also use the same except statement to handle multiple exceptions as follows:
try:
You do your operations here;
......................
except(Exception1[, Exception2[,...ExceptionN]]]):
If there is any exception from the given exception list,
then execute this block.
......................
else:
If there is no exception then execute this block.
The try-finally clause
You can use a finally: block along with a try: block. The finally block is a place to put any code that must execute,
whether the try-block raised an exception or not. The syntax of the try-finally statement is this:
try:
You do your operations here;
......................
Due to any exception, this may be skipped.
finally:
This would always be executed.
......................
Tip
You can provide except clause(s), or a finally clause, but not both. You can not use else clause as well along with a finally clause.
Example:
try:
fh = open("testfile", "w")
fh.write("This is my test file for exception handling!!")
finally:
print "Error: can\'t find file or read data"
If you do not have permission to open the file in writing mode, then this will produce the following result:
Error: can't find file or read data
Same example can be written more cleanly as follows:
try:
fh = open("testfile", "w")
try:
fh.write("This is my test file for exception handling!!")
finally:
print "Going to close the file"
fh.close()
except IOError:
print "Error: can't find file or read data"
When an exception is thrown in the try block, the execution immediately passes to the finally block.
After all the statements in the finally block are executed, the exception is raised again and is handled in the except statements
if present in the next higher layer of the try-except statement.
Argument of an Exception
An exception can have an argument, which is a value that gives additional information about the problem. The contents of the argument vary by exception.
You capture an exception's argument by supplying a variable in the except clause as follows:
try:
You do your operations here;
......................
except ExceptionType, Argument:
You can print value of Argument here...
If you are writing the code to handle a single exception, you can have a variable follow the name of the exception in the except statement.
If you are trapping multiple exceptions, you can have a variable follow the tuple of the exception.
This variable will receive the value of the exception mostly containing the cause of the exception.
The variable can receive a single value or multiple values in the form of a tuple. This tuple usually contains the error string, the error number, and an error location.
Example:
Following is an example for a single exception:
# Define a function here.
def temp_convert(var):
try:
return int(var)
except ValueError, Argument:
print "The argument does not contain numbers\n", Argument
# Call above function here.
temp_convert("xyz")
This would produce the following result:
The argument does not contain numbers
invalid literal for int() with base 10: 'xyz'
Raising an exceptions
You can raise exceptions in several ways by using the raise statement. The general syntax for the raise statement.
Syntax
raise [Exception [, args [, traceback]]]
Here, Exception is the type of exception (for example, NameError) and argument is a value for the exception argument.
The argument([Exception [, args [, traceback]]]) is optional; if not supplied, the exception argument is None.
The final argument, traceback, is also optional (and rarely used in practice), and if present, is the traceback object used for the exception.
Example:
An exception can be a string, a class or an object. Most of the exceptions that the Python core raises are classes,
with an argument that is an instance of the class. Defining new exceptions is quite easy and can be done as follows:
def functionName( level ):
if level < 1:
raise "Invalid level!", level
# The code below to this would not be executed
# if we raise the exception
Tip
In order to catch an exception, an "except" clause must refer to the same exception thrown
either class object or simple string. For example, to capture above exception, we must write our except clause as follows:
try:
Business Logic here...
except "Invalid level!":
Exception handling here...
else:
Rest of the code here...
User-Defined Exceptions
Python also allows you to create your own exceptions by deriving classes from the standard built-in exceptions.
Here is an example related to RuntimeError. Here, a class is created that is subclassed from RuntimeError. This is useful when you need to display more specific information when an exception is caught.
In the try block, the user-defined exception is raised and caught in the except block. The variable e is used to create an instance of the class Networkerror.
class Networkerror(RuntimeError):
def __init__(self, arg):
self.args = arg
So once you defined above class, you can raise your exception as follows:
try:
raise Networkerror("Bad hostname")
except Networkerror,e:
print e.args


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--- layout: post status: publish published: true title: Podcast 2.11 - Memorial Day 2010 - Part I author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 2748 wordpress_url: http://ivyenvy.com/?p=2748 date: '2010-06-02 06:29:11 +0000' date_gmt: '2010-06-02 11:29:11 +0000' categories: - Ivy Envy tags: - Drunk Episode comments: - id: 1347 author: Corey author_email<EMAIL_ADDRESS>author_url: '' date: '2010-06-02 14:26:20 +0000' date_gmt: '2010-06-02 19:26:20 +0000' content: Juciest spit-take in the history of broadcasting at 10:05 mark. - id: 1348 author: Ryan author_email<EMAIL_ADDRESS>author_url: '' date: '2010-06-02 19:48:30 +0000' date_gmt: '2010-06-03 00:48:30 +0000' content: "Wow...very...interesting?\r\n\r\nI am glad we decided to post these though. \ They're hilarious. \r\n\r\nMeehan" ---
Andy, Corey and Ryan welcome guests Patrick and Ian for this year's drunk episode(s). Andy and Corey talk about their first game in the bleachers at Wrigley. We apologize for these episodes. We are aware that it's ridiculous, but I'm sure you've been in this condition and sounded as unintelligent and jack-assish before, too.
Stay tuned for Part II in which Tim returns to the podcast from the military.
Last year's Drunk Episode is #11. It's fun to listen to because you sometimes forget how much you hated Kevin Gregg.


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Your Privacy and The Register
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Multiple news sources that indicate Oakland city is among one of the most dangerous places in America. We draw data sources from government for the Oakland city crime rate from 2011 and 2016, over 1,000,000 of incidents to gain some insights about why Oakland city is dangerous. Surprisingly, while Oakland has many types of actual crimes such as stolen vehicles, battery, etc. The top alert and crime incidents are actually quite harmless (alarm ring, security check, and 911 hang up) and constitute about 50 percent of the crime report. We can conclude that while Oakland is relatively unsafe, its status as one of the most dangerous city is not completely true based on statistics and our analyzed result.
Online data source: https://www.cbsnews.com/


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{% block head %}
CRMS
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Cloud Robotics Management System
Terminal
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deephyp package¶
Submodules¶
deephyp.autoencoder module¶
Description: high-level deep learning classes for building, training and using unsupervised autoencoders. Uses functions from the low-level network_ops module.
- File name: autoencoder.py
- Author: Lloyd Windrim
- Date created: June 2019
- Python package: deephyp
-
class
deephyp.autoencoder.
cnn_1D_network
(configFile=None, inputSize=None, zDim=5, encoderNumFilters=[10, 10, 10], encoderFilterSize=[20, 10, 10], activationFunc='sigmoid', tiedWeights=None, weightInitOpt='truncated_normal', weightStd=0.1, skipConnect=False, padding='VALID', encoderStride=[1, 1, 1], activationFuncFinal='linear')[source]¶ Class for setting up a 1-D convolutional autoencoder network. Builds a network with an encoder containing convolutional layers followed by a single fully-connected layer to map from the final convolutional layer in the encoder to the latent layer. The decoder contains a single fully-connected layer and then several deconvolutional layers which reconstruct the spectra in the output.
Parameters: - configFile (str) – Optional way of setting up the network. All other inputs can be ignored (will be overwritten). Pass the address of the .json config file.
- inputSize (int) – Number of dimensions of input data (i.e. number of spectral bands). Value must be input if not using a config file.
- zDim (int) – Dimensionality of latent vector.
- encoderNumFilters (int list) – Number of filters at each layer of the encoder. List length is number of convolutional encoder layers. Note that there is a single mlp layer after the last convolutional layer.
- encoderFilterSize (int list) – Size of filter at each layer of the encoder. List length is number of encoder layers.
- activationFunc (str) – Activation function for all layers except the last one. Current options: [‘sigmoid’, ‘relu’, ‘linear’].
- tiedWeights (binary list or None) – Specifies whether or not to tie weights at each layer: - 1: tied weights of specific encoder layer to corresponding decoder weights - 0: do not tie weights of specific layer - None: sets all layers to 0
- weightInitOpt (string) – Method of weight initialisation. Current options: [‘gaussian’, ‘truncated_normal’, ‘xavier’, ‘xavier_improved’].
- weightStd (float) – Used by ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.
- skipConnect (boolean) – Whether to use skip connections throughout the network.
- padding (str) – Type of padding used. Current options: [‘VALID’, ‘SAME’].
- encoderStride (int list) – Stride at each convolutional encoder layer.
- activationFuncFinal (str) – Activation function for final layer. Current options: [‘sigmoid’, ‘relu’, ‘linear’].
-
inputSize
¶ Number of dimensions of input data (i.e. number of spectral bands).
Type: int
-
activationFunc
¶ Activation function for all layers except the last one.
Type: str
-
tiedWeights
¶ Whether (1) or not (0) the weights of an encoder layer are tied to a decoder layer.
Type: binary list
-
skipConnect
¶ Whether the network uses skip connections between corresponding encoder and decoder layers.
Type: boolean
-
weightInitOpt
¶ Method of weight initialisation.
Type: string
-
weightStd
¶ Parameter for ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.
Type: float
-
activationFuncFinal
¶ Activation function for final layer.
Type: str
-
encoderNumFilters
¶ Number of filters at each layer of the encoder. List length is number of convolutional encoder layers. Note that there is a single mlp layer after the last convolutional layer.
Type: int list
-
encoderFilterSize
¶ Size of filter at each layer of the encoder. List length is number of encoder layers.
Type: int list
-
encoderStride
¶ Stride at each convolutional encoder layer.
Type: int list
-
decoderNumFilters
¶ Type: int list
-
decoderFilterSize
¶ Type: int list
-
decoderStride
¶ Type: int list
-
zDim
¶ Dimensionality of latent vector.
Type: int
-
padding
¶ Type of padding used. Current options: [‘VALID’, ‘SAME’].
Type: str
-
z
¶ Latent representation of data. Accessible through the encoder class function, requiring a trained model.
Type: tensor
-
y_recon
¶ Reconstructed output of network. Accessible through the decoder and encoder_decoder class functions, requiring a trained model.
Type: tensor
-
train_ops
¶ Dictionary of names of train and loss ops (suffixed with _train and _loss) added to the network using the add_train_op class function. The name (without suffix) is passed to the train class function to train the network with the referenced train and loss op.
Type: dict
-
modelsAddrs
¶ Dictionary of model names added to the network using the add_model class function. The names reference models which can be used by the encoder, decoder and encoder_decoder class functions.
Type: dict
-
add_model
(addr, modelName)[source]¶ Loads a saved set of model parameters for the network.
Parameters: - addr (str) – Address of the directory containing the checkpoint files.
- modelName (str) – Name of the model (to refer to it later in-case of multiple models for a given network).
-
add_train_op
(name, lossFunc='SSE', learning_rate=0.001, decay_steps=None, decay_rate=None, piecewise_bounds=None, piecewise_values=None, method='Adam', wd_lambda=0.0)[source]¶ Constructs a loss op and training op from a specific loss function and optimiser. User gives the ops a name, and the train op and loss opp are stored in a dictionary (train_ops) under that name.
Parameters: - name (str) – Name of the training op (to refer to it later in-case of multiple training ops).
- lossFunc (str) – Reconstruction loss function.
- learning_rate (float) – Controls the degree to which the weights are updated during training.
- decay_steps (int) – Epoch frequency at which to decay the learning rate.
- decay_rate (float) – Fraction at which to decay the learning rate.
- piecewise_bounds (int list) – Epoch step intervals for decaying the learning rate. Alternative to decay steps.
- piecewise_values (float list) – Rate at which to decay the learning rate at the piecewise_bounds.
- method (str) – Optimisation method.
- wd_lambda (float) – Scalar to control weighting of weight decay in loss.
-
decoder
(modelName, dataZ)[source]¶ Extract the reconstruction of some dataSamples from their latent representation encoding using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataZ (np.array) – Latent representation of data samples to reconstruct using the network. Shape [numSamples x arbitrary].
Returns: Reconstructed data (y_recon attribute). Shape [numSamples x arbitrary].
Return type: (np.array)
-
encoder
(modelName, dataSamples)[source]¶ Extract the latent variable of some dataSamples using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataSample (np.array) – Shape [numSamples x inputSize].
Returns: Latent representation z of dataSamples. Shape [numSamples x arbitrary].
Return type: (np.array)
-
encoder_decoder
(modelName, dataSamples)[source]¶ Extract the reconstruction of some dataSamples using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataSample (np.array) – Data samples to reconstruct using the network. Shape [numSamples x inputSize].
Returns: Reconstructed data (y_recon attribute). Shape [numSamples x arbitrary].
Return type: (np.array)
-
train
(dataTrain, dataVal, train_op_name, n_epochs, save_addr, visualiseRateTrain=0, visualiseRateVal=0, save_epochs=[1000])[source]¶ Calls network_ops function to train a network.
Parameters: - dataTrain (obj) – Iterator object for training data.
- dataVal (obj) – Iterator object for validation data.
- train_op_name (str) – Name of training op created.
- n_epochs (int) – Number of loops through dataset to train for.
- save_addr (str) – Address of a directory to save checkpoints for desired epochs, or address of saved checkpoint. If address is for an epoch and contains a previously saved checkpoint, then the network will start training from there. Otherwise it will be trained from scratch.
- visualiseRateTrain (int) – Epoch rate at which to print training loss in console.
- visualiseRateVal (int) – Epoch rate at which to print validation loss in console.
- save_epochs (int list) – Epochs to save checkpoints at.
-
class
deephyp.autoencoder.
mlp_1D_network
(configFile=None, inputSize=None, encoderSize=[50, 30, 10], activationFunc='sigmoid', tiedWeights=None, weightInitOpt='truncated_normal', weightStd=0.1, skipConnect=False, activationFuncFinal='linear')[source]¶ Class for setting up a 1-D multi-layer perceptron (mlp) autoencoder network. Layers are all fully-connected (i.e. dense).
Parameters: - configFile (str) – Optional way of setting up the network. All other inputs can be ignored (will be overwritten). Pass the address of the .json config file.
- inputSize (int) – Number of dimensions of input data (i.e. number of spectral bands). Value must be input if not using a config file.
- encoderSize (int list) – Number of nodes at each layer of the encoder. List length is number of encoder layers.
- activationFunc (str) – Activation function for all layers except the last one. Current options: [‘sigmoid’, ‘relu’, ‘linear’].
- tiedWeights (binary list or None) – Specifies whether or not to tie weights at each layer: - 1: tied weights of specific encoder layer to corresponding decoder weights - 0: do not tie weights of specific layer - None: sets all layers to 0
- weightInitOpt (string) – Method of weight initialisation. Current options: [‘gaussian’, ‘truncated_normal’, ‘xavier’, ‘xavier_improved’].
- weightStd (float) – Used by ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.
- skipConnect (boolean) – Whether to use skip connections throughout the network.
- activationFuncFinal (str) – Activation function for final layer. Current options: [‘sigmoid’, ‘relu’, ‘linear’].
-
inputSize
¶ Number of dimensions of input data (i.e. number of spectral bands).
Type: int
-
activationFunc
¶ Activation function for all layers except the last one.
Type: str
-
tiedWeights
¶ Whether (1) or not (0) the weights of an encoder layer are tied to a decoder layer.
Type: binary list
-
skipConnect
¶ Whether the network uses skip connections between corresponding encoder and decoder layers.
Type: boolean
-
weightInitOpt
¶ Method of weight initialisation.
Type: string
-
weightStd
¶ Parameter for ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.
Type: float
-
activationFuncFinal
¶ Activation function for final layer.
Type: str
-
encoderSize
¶ Number of inputs and number of nodes at each layer of the encoder.
Type: int list
-
decoderSize
¶ Number of nodes at each layer of the decoder and number of outputs.
Type: int list
-
z
¶ Latent representation of data. Accessible through the encoder class function, requiring a trained model.
Type: tensor
-
y_recon
¶ Reconstructed output of network. Accessible through the decoder and encoder_decoder class functions, requiring a trained model.
Type: tensor
-
train_ops
¶ Dictionary of names of train and loss ops (suffixed with _train and _loss) added to the network using the add_train_op class function. The name (without suffix) is passed to the train class function to train the network with the referenced train and loss op.
Type: dict
-
modelsAddrs
¶ Dictionary of model names added to the network using the add_model class function. The names reference models which can be used by the encoder, decoder and encoder_decoder class functions.
Type: dict
-
add_model
(addr, modelName)[source]¶ Loads a saved set of model parameters for the network.
Parameters: - addr (str) – Address of the directory containing the checkpoint files.
- modelName (str) – Name of the model (to refer to it later in-case of multiple models for a given network).
-
add_train_op
(name, lossFunc='CSA', learning_rate=0.001, decay_steps=None, decay_rate=None, piecewise_bounds=None, piecewise_values=None, method='Adam', wd_lambda=0.0)[source]¶ Constructs a loss op and training op from a specific loss function and optimiser. User gives the ops a name, and the train op and loss opp are stored in a dictionary (train_ops) under that name.
Parameters: - name (str) – Name of the training op (to refer to it later in-case of multiple training ops).
- lossFunc (str) – Reconstruction loss function.
- learning_rate (float) – Controls the degree to which the weights are updated during training.
- decay_steps (int) – Epoch frequency at which to decay the learning rate.
- decay_rate (float) – Fraction at which to decay the learning rate.
- piecewise_bounds (int list) – Epoch step intervals for decaying the learning rate. Alternative to decay steps.
- piecewise_values (float list) – Rate at which to decay the learning rate at the piecewise_bounds.
- method (str) – Optimisation method.
- wd_lambda (float) – Scalar to control weighting of weight decay in loss.
-
decoder
(modelName, dataZ)[source]¶ Extract the reconstruction of some dataSamples from their latent representation encoding using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataZ (np.array) – Latent representation of data samples to reconstruct using the network. Shape [numSamples x arbitrary].
Returns: Reconstructed data (y_recon attribute). Shape [numSamples x arbitrary].
Return type: (np.array)
-
encoder
(modelName, dataSamples)[source]¶ Extract the latent variable of some dataSamples using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataSample (np.array) – Shape [numSamples x inputSize].
Returns: Latent representation z of dataSamples. Shape [numSamples x arbitrary].
Return type: (np.array)
-
encoder_decoder
(modelName, dataSamples)[source]¶ Extract the reconstruction of some dataSamples using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataSample (np.array) – Data samples to reconstruct using the network. Shape [numSamples x inputSize].
Returns: Reconstructed data (y_recon attribute). Shape [numSamples x arbitrary].
Return type: (np.array)
-
train
(dataTrain, dataVal, train_op_name, n_epochs, save_addr, visualiseRateTrain=0, visualiseRateVal=0, save_epochs=[1000])[source]¶ Calls network_ops function to train a network.
Parameters: - dataTrain (obj) – Iterator object for training data.
- dataVal (obj) – Iterator object for validation data.
- train_op_name (str) – Name of training op created.
- n_epochs (int) – Number of loops through dataset to train for.
- save_addr (str) – Address of a directory to save checkpoints for desired epochs, or address of saved checkpoint. If address is for an epoch and contains a previously saved checkpoint, then the network will start training from there. Otherwise it will be trained from scratch.
- visualiseRateTrain (int) – Epoch rate at which to print training loss in console.
- visualiseRateVal (int) – Epoch rate at which to print validation loss in console.
- save_epochs (int list) – Epochs to save checkpoints at.
deephyp.classifier module¶
Description: high-level deep learning classes for building, training and using supervised neural network classifiers. Uses functions from the low-level network_ops module.
- File name: classifier.py
- Author: Lloyd Windrim
- Date created: August 2019
- Python package: deephyp
-
class
deephyp.classifier.
cnn_1D_network
(configFile=None, inputSize=None, numClasses=None, convFilterSize=[20, 10, 10], convNumFilters=[10, 10, 10], convStride=[1, 1, 1], fcSize=[20, 20], activationFunc='relu', weightInitOpt='truncated_normal', weightStd=0.1, padding='VALID')[source]¶ Class for setting up a 1-D convolutional neural network (cnn) for classification. Contains several convolutional layers followed by several fully-connected layers. The network outputs scores for each class, for a given set of input data samples.
Parameters: - configFile (str) – Optional way of setting up the network. All other inputs can be ignored (will be overwritten). Pass the address of the .json config file.
- inputSize (int) – Number of dimensions of input data (i.e. number of spectral bands). Value must be input if not using a config file.
- numClasses (int) – Number of labelled classes in the dataset (not including the zero class).
- convFilterSize (int list) – Size of filter at each convolutional layer. List length is number of convolutional layers.
- convNumFilters (int list) – Number of filters at each convolutional layer of the network. List length is number of convolutional layers.
- convStride (int list) – Stride at each convolutional layer. List length is number of convolutional layers. fcSize (int list): Number of nodes at each fully-connected (i.e. dense) layer of the encoder. List length is number of fully-connected layers.
- activationFunc (str) – Activation function for all layers except the last one. Current options: [‘sigmoid’, ‘relu’, ‘linear’].
- weightInitOpt (string) – Method of weight initialisation. Current options: [‘gaussian’, ‘truncated_normal’, ‘xavier’, ‘xavier_improved’].
- weightStd (float) – Used by ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.
- padding (str) – Type of padding used. Current options: [‘VALID’, ‘SAME’].
-
inputSize
¶ Number of dimensions of input data (i.e. number of spectral bands).
Type: int
-
activationFunc
¶ Activation function for all layers except the last one.
Type: str
-
weightInitOpt
¶ Method of weight initialisation.
Type: string
-
weightStd
¶ Parameter for ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.
Type: float
-
convFilterSize
¶ Size of filter at each convolutional layer. List length is number of convolutional layers.
Type: int list
-
convNumFilters
¶ Number of filters at each convolutional layer of the network. List length is number of convolutional layers.
Type: int list
-
convStride
¶ Stride at each convolutional layer. List length is number of convolutional layers. padding (str): Type of padding used. Current options: [‘VALID’, ‘SAME’].
Type: int list
-
fcSize
¶ Number of nodes at each fully-connected (i.e. dense) layer of the encoder. List length is number of fully-connected layers.
Type: int list
-
numLayers
¶ Total number of layers (convolutional and fully-connected).
Type: int
-
y_pred
¶ Output of network - class scores with shape [numSamples x numClasses]. Accessible through the predict_scores class functions, requiring a trained model.
Type: tensor
-
train_ops
¶ Dictionary of names of train and loss ops (suffixed with _train and _loss) added to the network using the add_train_op class function. The name (without suffix) is passed to the train class function to train the network with the referenced train and loss op.
Type: dict
-
modelsAddrs
¶ Dictionary of model names added to the network using the add_model class function. The names reference models which can be used by the predict_scores, predict_labels and predict_features class functions.
Type: dict
-
add_model
(addr, modelName)[source]¶ Loads a saved set of model parameters for the network.
Parameters: - addr (str) – Address of the directory containing the checkpoint files.
- modelName (str) – Name of the model (to refer to it later in-case of multiple models for a given network).
-
add_train_op
(name, balance_classes=True, learning_rate=0.001, decay_steps=None, decay_rate=None, piecewise_bounds=None, piecewise_values=None, method='Adam', wd_lambda=0.0)[source]¶ Constructs a loss op and training op from a specific loss function and optimiser. User gives the ops a name, and the train op and loss opp are stored in a dictionary (train_ops) under that name.
Parameters: - name (str) – Name of the training op (to refer to it later in-case of multiple training ops).
- balance_classes (boolean) – Weight the samples during training so that the contribtion to the loss of each class is balanced by the number of samples the class has (in a given batch).
- learning_rate (float) – Controls the degree to which the weights are updated during training.
- decay_steps (int) – Epoch frequency at which to decay the learning rate.
- decay_rate (float) – Fraction at which to decay the learning rate.
- piecewise_bounds (int list) – Epoch step intervals for decaying the learning rate. Alternative to decay steps.
- piecewise_values (float list) – Rate at which to decay the learning rate at the piecewise_bounds.
- method (str) – Optimisation method.
- wd_lambda (float) – Scalar to control weighting of weight decay in loss.
-
predict_features
(modelName, dataSamples, layer)[source]¶ Extract the predicted feature values at a particular layer of the network.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() )
- dataSamples (np.array) – Shape [numSamples x inputSize]
- layer (int) – Layer at which to extract features. Must be between 1 and numLayers inclusive.
Returns: Values of neurons at layer. Shape [numSamples x numNeurons] if fully-connected layer and [numSamples x convDim1 x convDim2] if convolutional layer.
Return type: (np.array)
-
predict_labels
(modelName, dataSamples)[source]¶ Extract the predicted classification labels of some dataSamples using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() )
- dataSamples (array) – Shape [numSamples x inputSize]
Returns: Predicted classification labels of dataSamples. Shape [numSamples].
Return type: (np.array)
-
predict_scores
(modelName, dataSamples, useSoftmax=True)[source]¶ Extract the predicted classification scores of some dataSamples using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataSamples (np.array) – Shape [numSamples x inputSize].
- useSoftmax (boolean) – Pass predicted scores output by network through a softmax function.
Returns: Predicted classification scores of dataSamples. Shape [numSamples x numClasses].
Return type: (np.array)
-
train
(dataTrain, dataVal, train_op_name, n_epochs, save_addr, visualiseRateTrain=0, visualiseRateVal=0, save_epochs=[1000])[source]¶ Calls network_ops function to train a network.
Parameters: - dataTrain (obj) – Iterator object for training data.
- dataVal (obj) – Iterator object for validation data.
- train_op_name (str) – Name of training op created.
- n_epochs (int) – Number of loops through dataset to train for.
- save_addr (str) – Address of a directory to save checkpoints for desired epochs, or address of saved checkpoint. If address is for an epoch and contains a previously saved checkpoint, then the network will start training from there. Otherwise it will be trained from scratch.
- visualiseRateTrain (int) – Epoch rate at which to print training loss in console.
- visualiseRateVal (int) – Epoch rate at which to print validation loss in console.
- save_epochs (int list) – Epochs to save checkpoints at.
deephyp.data module¶
Description: high-level classes for using hyperspectral data with the deep learning modules.
- File name: data.py
- Author: Lloyd Windrim
- Date created: June 2019
- Python package: deephyp
-
class
deephyp.data.
HypImg
(spectralInput, labels=None, wavelengths=None, bands=None)[source]¶ Class for handling data. Stores meta-data and contains attributes for pre-processing the data. If passed labels, samples with label zero are considered as a background class. This class is not included in numClasses and data samples with this label have a one-hot vector label of all zeros.
Parameters: - spectralInput (np.array float) – Spectral dataset. Shape can be [numRows x numCols x numBands] or [numSamples x numBands].
- wavelengths (np.array float) – Vector of wavelengths that spectralInput wavelengths lie within.
- bands (np.array int) – Wavelength indexes for each band of spectralInput. Shape [numBands].
- labels (np.array int) – Class labels for each spectral sample in spectralInput. Shape can be [numRows x numCols] or [numSamples].
-
spectra
¶ Un-pre-processed spectral data with shape [numSamples x numBands].
Type: np.array float
-
spectraCube
¶ If data passed as image - un-pre-processed spectral datacube with shape [numSamples x numBands]. Else None.
Type: np.array float
-
spectraPrep
¶ Pre-processed spectral data with shape [numSamples x numBands].
Type: np.array float
-
numSamples
¶ The number of spectra.
Type: int
-
numRows
¶ If data passed as image - the number of image rows. Else None.
Type: int
-
numCols
¶ If data passed as image - the number of image columns. Else None.
Type: int
-
wavelengths
¶ If provided - vector of wavelengths that spectra wavelengths lie within. Else None.
Type: np.array float
-
bands
¶ If provided - wavelength indexes for each band of spectra with shape [numBands]. Else None.
Type: np.array int
-
labels
¶ If provided - class labels for each spectral sample with shape [numSamples]. Else None.
Type: np.array int
-
labelsOnehot
¶ If labels provided - the one-hot label vector for each sample. Samples with label zero (background class) have a one-hot vector of all zeros. Else None.
Type: np.array int
-
class
deephyp.data.
Iterator
(dataSamples, targets, batchSize=None)[source]¶ Class for iterating through data, to train the network.
Parameters: - dataSamples (np.array float) – Data to be input into the network. Shape [numSamples x numBands].
- targets (np.array int) – Network output target of each dataSample. For classification, these are the class labels, and it could be the dataSamples for autoencoders. Shape [numSamples x arbitrary]
- batchSize (int) – Number of dataSamples per batch
-
dataSamples
¶ Data to be input into the network. Shape [numSamples x numBands].
Type: np.array float
-
targets
¶ Network output target of each dataSample. For classification, these are the class labels, and it could be the dataSamples for autoencoders. Shape [numSamples x arbitrary]
Type: np.array int
-
batchSize
¶ Number of dataSamples per batch. If None - set to numSamples (i.e. whole dataset).
Type: int
-
numSamples
¶ The number of data samples.
Type: int
-
currentBatch
¶ A list of indexes specifying the data samples in the current batch. Shape [batchSize]
Type: int list
-
get_batch
(idx)[source]¶ Returns a specified set of samples and targets.
Parameters: idx (int list) – Indexes of samples (and targets) to return. Returns: 2-element tuple containing: - (np.array float) - Batch of data samples at [idx] indexes. Shape [length(idx) x numBands].
- (np.array int) - Batch of targets at [idx] indexes. Shape [length(idx) x arbitrary].
Return type: (tuple)
-
next_batch
()[source]¶ Return next batch of samples and targets (with batchSize number of samples). The currentBatch indexes are incremented. If end of dataset reached, the indexes wraps around to the beginning.
Returns: 2-element tuple containing: - (np.array float) - Batch of data samples at currentBatch indexes. Shape [batchSize x numBands].
- (np.array int) - Batch of targets at currentBatch indexes. Shape [batchSize x arbitrary].
Return type: (tuple)
deephyp.network_ops module¶
Description: various functions for deep learning built on-top of tensorflow. The high-level modules in the package call these functions.
- File name: network_ops.py
- Author: Lloyd Windrim
- Date created: June 2019
- Python package: deephyp
-
deephyp.network_ops.
balance_classes
(y_target, num_classes)[source]¶ Calculates the class weights needed to balance the classes, based on the number of samples of each class in the batch of data.
Parameters: - y_target (tensor) – One-hot classification labels (1D vector). Shape [numSamples x numClasses]
- num_classes (int) –
Returns: A weighting for each class that balances their contribution to the loss. Shape [numClasses].
Return type: (tensor)
-
deephyp.network_ops.
conv_output_shape
(inputShape, filterSize, padding, stride)[source]¶ Computes the expected output shape (for the convolving axis only) of a convolution layer given an input shape.
Parameters: - inputShape (int) – Shape of convolving axis of input data.
- filterSize (int) – Size of filter/kernel of convolution layer.
- stride (int) – Stride at which to convolve (must be >= 1).
- padding (str) – Type of padding to use (‘SAME’ or ‘VALID’).
Returns: Output shape of convolving axis for given layer and input shape.
Return type: (int)
-
deephyp.network_ops.
create_variable
(shape, method='gaussian', wd=False)[source]¶ Setup a trainable variable (collection of parameters) of a particular shape.
Parameters: - shape (list) – Data shape.
- method (str) – How to initialise parameter values.
- wd (boolean) – Setup weight decay for this variable.
Returns: Set of parameters for the given variable.
Return type: (tensor)
-
deephyp.network_ops.
init_weight
(opts, shape, stddev=0.1, const=0.1, wd=False, dtype=tf.float32)[source]¶ Weight initialisation function.
Parameters: - opts (str) – Method for initialising variable. (‘gaussian’,’truncated_normal’,’xavier’,’xavier_improved’, ‘constant’).
- shape (list) – Data shape.
- stddev (int) – Standard deviation used by ‘gaussian’ and ‘truncated_normal’ variable initialisation methods.
- const (int) – Constant value to initialise variable to if using ‘constant’ method.
- wd (boolean) – Whether this variable contributes to weight decay or not.
- dtype (tf.dtype) – Data type for variable.
Returns: Return type: weights
-
deephyp.network_ops.
layer_activation
(input, func='sigmoid')[source]¶ Define an activation function operation.
Parameters: - input (tensor) – Data input into the function.
- func (str) – Type of activation function. (relu, sigmoid, linear).
Returns: Computes activation. Shape is same as input.
Return type: (tensor)
-
deephyp.network_ops.
layer_conv1d
(input, W, b, stride=1, padding='SAME')[source]¶ Define a 1 dimensional convolution layer operation.
Parameters: - input (tensor) – Data input into the layer. Shape [numSamples x numInputNeurons x numFiltersIn].
- W (tensor) – Weight parameters of the filters/kernels. Shape [filterSize x numFiltersIn x numFiltersOut].
- b (tensor) – Bias parameters for the layer. Shape [numFiltersOut].
- stride (int) – Stride at which to convolve (must be >= 1).
- padding (str) – Type of padding to use (‘SAME’ or ‘VALID’).
Returns: Computes layer output. Shape [numSamples x numOutputNeurons x numFiltersOut].
Return type: (tensor)
-
deephyp.network_ops.
layer_deconv1d
(input, W, b, outputShape, stride=1, padding='SAME')[source]¶ Define a 1 dimensional deconvolution layer operation. Also called convolutional transpose or upsampling layer.
Parameters: - input (tensor) – Data input into the layer. Shape [numSamples x numInputNeurons x numFiltersIn].
- W (tensor) – Weight parameters of the filters/kernels. Shape [filterSize x numFiltersOut x numFiltersIn].
- b (tensor) – Bias parameters for the layer. Shape [numFiltersOut].
- outputShape (list) – Expected shape of the layer output. Shape [numSamples x numOutputNeurons x numFiltersOut].
- stride (int) – Stride at which to convolve (must be >= 1).
- padding (str) – Type of padding to use (‘SAME’ or ‘VALID’).
Returns: Computes layer output. Shape [numSamples x numOutputNeurons x numFiltersOut].
Return type: (tensor)
-
deephyp.network_ops.
layer_fullyConn
(input, W, b)[source]¶ Define a fully connected layer operation. Also called a ‘dense’ layer.
Parameters: - input (tensor) – Data input into the layer. Shape [numSamples x numInputNeurons].
- W (tensor) – Weight parameters for the layer. Shape [numInputNeurons x numOutputNeurons].
- b (tensor) – Bias parameters for the layer. Shape [numOutputNeurons].
Returns: Computes layer output. Shape [numSamples x numOutputNeurons].
Return type: (tensor)
-
deephyp.network_ops.
load_config
(net_obj, addr)[source]¶ Loads a network config file. Loads from variables in the config.json file and overwrites variables in network object. Applies to variables in the net_config list in the network object.
Parameters: - net_obj (obj) – Network object.
- addr (obj) – Directory location of config.json file.
-
deephyp.network_ops.
load_model
(addr, sess)[source]¶ Loads a model from the address of a checkpoint.
Parameters: - addr (str) – Address of a directory to save checkpoint for current epoch.
- sess (obj) – Tensor flow session object.
-
deephyp.network_ops.
loss_function_crossentropy_1D
(y_pred, y_target, class_weights=None, num_classes=None)[source]¶ Cross entropy loss function op, comparing 1D tensors for network prediction and target. Weights the classes when calculating the loss to balance un-even training batches. If class weights are not provided, then no weighting is done (weight of 1 assigned to each class).
Parameters: - y_pred (tensor) – Output of network (1D vector of class scores). Shape [numSamples x numClasses].
- y_target (tensor) – One-hot classification labels (1D vector). Shape [numSamples x numClasses].
- class_weights (tensor) – Weight for each class. Shape [numClasses].
- num_classes (int) –
Returns: Cross-entropy loss.
Return type: (tensor)
-
deephyp.network_ops.
loss_function_reconstruction_1D
(y_reconstructed, y_target, func='SSE')[source]¶ Reconstruction loss function op, comparing 1D tensors for network reconstruction and target.
Parameters: - y_reconstructed (tensor) – Output of network (reconstructed 1D vector). Shape [numSamples x inputSize].
- y_target (tensor) – What the network is trying to reconstruct (1D vector). Shape [numSamples x inputSize].
- func (string) – The name of the loss function to be used. ‘SSE’-sum of square errors,’CSA’-cosine spectral angle, ‘SA’-spectral angle, ‘SID’-spectral information divergence.
Returns: Reconstruction loss.
Return type: (tensor)
-
deephyp.network_ops.
loss_weight_decay
(wdLambda)[source]¶ Weight decay loss op, regularises network by penalising parameters for being too large.
Parameters: wdLambda (float) – Scalar to control weighting of weight decay in loss. Returns: Weight-decay loss. Return type: (tensor)
-
deephyp.network_ops.
save_config
(net_obj, addr)[source]¶ Saves a network config file. Saves the variables listed in net_config within the network object.
Parameters: - net_obj (obj) – Network object.
- addr (obj) – Directory of where to store the config.json file.
-
deephyp.network_ops.
save_model
(addr, sess, saver, current_epoch, epochs_to_save)[source]¶ Saves a checkpoint at a list of epochs.
Parameters: - addr (str) – Address of a directory to save checkpoint for current epoch.
- sess (obj) – Tensor flow session object.
- saver (obj) – Tensor flow save object.
- current_epoch (int) – The current epoch.
- epochs_to_save (int list) – Epochs to save checkpoints at.
-
deephyp.network_ops.
train
(net_obj, dataTrain, dataVal, train_op_name, n_epochs, save_addr, visualiseRateTrain=0, visualiseRateVal=0, save_epochs=[1000])[source]¶ Function for training a network. Updates the network weights through the training op. The function will check the save address for a model checkpoint to load, otherwise it will begin training from scratch.
Parameters: - net_obj (obj) – Network object.
- dataTrain (obj) – Iterator object for training data.
- dataVal (obj) – Iterator object for validation data.
- train_op_name (string) – Name of training op created.
- n_epochs (int) – Number of loops through dataset to train for.
- save_addr (str) – Address of a directory to save checkpoints for desired epochs, or address of saved checkpoint. If address is for an epoch and contains a previously saved checkpoint, then the network will start training from there. Otherwise it will be trained from scratch.
- visualiseRateTrain (int) – Epoch rate at which to print training loss in console.
- visualiseRateVal (int) – Epoch rate at which to print validation loss in console.
- save_epochs (int list) – Epochs to save checkpoints at.
-
deephyp.network_ops.
train_step
(loss, learning_rate=0.001, decay_steps=None, decay_rate=None, piecewise_bounds=None, piecewise_values=None, method='Adam')[source]¶ Operation for training the weights of the network by optimising them to minimise the loss function. Note that the default is a constant learning rate (no decay).
Parameters: - loss (tensor) – Output of network loss function.
- learning_rate – (float) Controls the degree to which the weights are updated during training.
- decay_steps (int) – Epoch frequency at which to decay the learning rate.
- decay_rate (float) – Fraction at which to decay the learning rate.
- piecewise_bounds (int list) – Epoch step intervals for decaying the learning rate. Alternative to decay steps.
- piecewise_values (float list) – Rate at which to decay the learning rate at the piecewise_bounds.
- method (str) – Optimisation method. (Adam, SGD).
Returns: (op) A train op.


Super Duper title
Here goes the sun
Turu
Ruru
Rhythm
Yeah
Kittens
Are better than dogs
Self-washing machines
Smaller
Cuter!
Here's some code!
@keyframes name{ from{ background-color: Tomato; } to{ background-color: Azure; } }
Here's a demo!
Tables
Speed Approximate Conversion Table
km/h
mi/h
m/s
20
12
6
40
25
11
60
37
17
80
50
22
100
62
28
Incomplete slide
Chart JS for charts :D
Thank you!
@your twitter username
(:
/
Go to slide:


Homepage
Reverso Wiki
This wiki is dedicated to sharing public knowledge about the internals of applications.
Table of contents


Report: Galatasaray planned to sell Villa Luyindama, injury may have scuppered move
by Tom ProcterGalatasaray were reportedly planning on selling defender Christian Luyindama to Aston Villa in January – but a season-ending injury appears to have scuppered the move.
According to Sabah, officials had been in contact with Villa to organise a transfer, which would give them the funds to go about acquiring new players.
It is thought the deal would have landed them around €14 million/£12 million.
Luyindama sustained a knee injury whilst on international duty with DR Congo, and he is now set to have surgery on a torn cruciate ligament (BBC Sport).
The 25-year-old is expected to be out for the rest of the season.
TBR’s view – Blows for everyone
The nature of Luyindama’s injury is a hugely unfortunate situation.
Firstly the player himself will be out for a considerable period, and then Gala appear to be losing out in terms of what his sale could have done for strengthening the squad.
Villa will very likely have to look elsewhere now for another central defender, which is a shame considering the headway they had seemingly made over the former Standard Liege man.
Villa do have Ezri Konsa and Kortney Hause in reserve, but Luyindama is a colossus and could have further strengthened Dean Smith’s squad.
Have something to tell us about this article?
Let us know


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Research and analysis
Research to evaluate how the agency is performing against targets for European Social Fund-funded learners.
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The ESF managing authority in England gives us performance targets for our ESF learners and results. We regularly check how we are keeping to those targets. If we find that we need to perform better against a target, we find ways to improve our ESF support to particular groups of people.
We sometimes ask other organisations to do some research for us and tell us how we can perform better against our ESF targets.
Published: 26 February 2014


J.E.M.
Though sadly no longer available, J.E.M. was a small home furnishing and interior design firm in Boston's South End.
The approach
Predating Wordpress's ascendance from blogging platform to fully-fledged CMS, this was my first deliverable Drupal site for a client and, to this day, I still wish it were available to view. For my part, this served as an introduction to implementing dynamic content, views, and highly structured content types for an editor without a technical background. The work of both the client and the designer are each right up my alley and I'm grateful to have played a small part.
Also, allow me to add that I sincerely miss being able to integrate a Facebook feed via RSS, without authentication or registering an application. Admittedly, I do not miss SimplePie, however.
Trivia
To create the abstract backgrounds, the designer, Anya Vedmid, put physical objects from the store on a flatbed scanner to add a sense of depth.


Custom element Calendar Fluid Size Example. The width and height of the Angular Calendar in this demo are in Percentages.


Welcome to
your
page.


Preparation time
overnight
Cooking time
no cooking required
Serves
Makes 6
Dietary
These strawberries and cream ice lollies are summer on a stick and great fun to make with the kids.
Equipment and preparation: For this recipe, you will need six 100ml/3½fl oz lolly moulds.
In a jug, mix together the cream and vanilla seeds until well combined. Pour the mixture into the lolly moulds until it reaches halfway up the sides.
Finely chop three strawberries and divide them equally between the moulds. Cover the tops of the moulds with cling film, then transfer to the freezer and freeze upright for at least 1 hour, or until the cream is beginning to solidify.
Meanwhile, blend the remaining strawberries to a smooth purée in a food processor. Remove 2 tablespoons of the purée and set aside in a bowl.
Add the icing sugar and yoghurt to the remaining purée and blend until well combined.
Soak the lavender flowers in the reserved purée for at least 10 minutes, then strain and add the puree to the strawberry and yoghurt mixture.
Fill the moulds with the strawberry and yoghurt mixture and cover each one with its lid.
Freeze the lollies for at least 4 hours, or preferably overnight. To remove the lollies from the moulds, dip them in hot water for a few seconds and release them. Serve immediately.


About¶
timewave simulation engine¶
timewave, a classical time evolution simulation engine in python. It consists of four building blocks.
The State¶
which evolves over time during a simulation path. It is the nucleus or node which marks a point of time in a path.
The Producer¶
is the objects that provides states to the simulation and does the actual time evolution. Think of the producer building as the constructor of a stochastic process like a Brownian motion or, less mathematical, future stock prices or future rain intensities.
The Consumer¶
is an object that takes a state as a point in time provided by the producer and consumes it, i.e. does something with it - the actual calculation if you like.
The Engine¶
finally, which organizes the creation of states by the producer and the consumption by the consumer. The engine uses, if present, multiprocessing, i.e. takes full advantage of multi cpu frameworks. Therefore the engine splits the simulation into equal but distinct chunks of path for the number of workers (by default the number of cpu) and loops over the set of dedicated path in each worker. Each path is evolved by the producer in states which are at each point in time consumed directly by consumers. States are, due to limits of resources, not stored during the simulation. If you like to, use the storage consumer to save all states.
main frame workflow¶
setup simulation by
engine = Engine(Producer(), Consumer())
engine.run(range(20))
then run loop starts by
producer/initialize()
setup workers (by default by the number of cpu’s) on each worker start loop by
producer/consumer.initialize_worker()
and invoke loop over paths and start again with
producer/consumer.initialize_path()
then do time evolution of a path
producer.evolve() / consumer.consume()
and finish with last consumer in consumer stack
consumer[-1].finalize_path()
and
consumer[-1].finalize_worker()
put results into queue and take them out by
consumer[-1].put()/get(result)
finish simulation (kind of reduce method)
consumer[-1].finalize()
before returning results from run.


What is holistic development?
Your customers, staff, and stakeholders experiences are tied to your social media engagement, website, brick and mortar facility, etc... All should generate a consistent ethos for your brand.
Holism simply means the whole is greater than the sum of it’s parts.
When applied to your web presence, our approach creates happy users with enjoyable experiences, who will organically grow your audience.
The sum of these channels and user experiences is less than the whole of them; One bad experience can ruin them all.
Our strategy harmonizes these experences to give the users the feeling of trust in you.


zcash-android-wallet-sdk
/
cash.z.wallet.sdk.block
/
CompactBlockProcessor
/
ProcessorInfo
/
scanProgress
scanProgress
val
scanProgress
:
Int
The amount of scan progress from 0 to 100.


MAX
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The beautiful Sky 🌝 🌚
Hello! How is everyone today? Today I wanted to show off some love for sky pictures
Sunset Cams
Sunrise and Sunset time calculator


the maximun to ship up a Vinyl is 48 inches(4 feets) of heigth and 120 inches(10 feets) of width.
the minimun buy is 1X1 feets or 12X12 inches
Width:
Height:


|
114. Telecasting station Time Limit: 0.25 sec, Memory Limit: 1 MB Every city in Berland is situated on Ox axis. The government of the country decided to build new telecasting station. After many experiments Berland scientists came to a conclusion that in any city citizens displeasure is equal to product of citizens amount in it by distance between city and TV-station. Find such point on Ox axis for station so that sum of displeasures of all cities is minimal. Input Input begins from line with integer positive number N (0<N<15000) – amount of cities in Berland. Following N pairs (X, P) describes cities (0<X, P<50000), where X is a coordinate of city and P is an amount of citizens. All numbers separated by whitespace(s). Output Write the best position for TV-station with accuracy 10-5. Sample Input 4 1 3 2 1 5 2 6 2 Sample Output 3.00000 | ||||||
|


PAGE UNDER CONSTRUCTION
Audio Samples - Paper at INTERSPEECH 2017
You can play some audio samples generated by the MagPhase Vocoder proposed in F. Espic, C. Valentini-Botinhao, and S. King, “Direct Modelling of Magnitude and Phase Spectra for Statistical Parametric Speech Synthesis,” in Proc. Interspeech, Stockholm, Sweden, August, 2017.
Audio Samples - Paper at INTERSPEECH 2016
You can play some audio samples of the method for waveform generation proposed in F. Espic, C. Valentini-Botinhao, Z. Wu, and S. King, “Waveform generation based on signal reshaping for statistical parametric speech synthesis,” in Proc. Interspeech, San Francisco, CA, USA, September, 2016.
Talk: Waveform generation based on signal reshaping
Presented in the UK-Speech Conference, Sheffield, UK, June, 2016.
Voice-Synth
Voice-Synth is a musical instrument programmed in C++ (RackAFX), based on the classical source-filter model of speech production. This video is a live demo that took place at the University of Miami, 2011.


Data Process¶
Data process is essential in EEG signal analysis. First, original EEG signal may contain missing values and noise, which may disturb or hidden the real information. Also, a lot of research works have shown that the spectrum properties of EEG on certain frequency bands are very important to uncover useful information which cannot be directly quantified by original temporal signal. In addition, the number of samples are relatively small for EEG signal, which may not fully release the power of machine learning. Data augmentation through segmentation and sampling are two major ways to increase sample size in EEG analysis.
So we designed the data process module to provide solutions to the requirements mentioned above. The data process module contains 5 functions:
process.check(
deeg.process.check()
)process.smooth(
deeg.process.smooth()
)process.band(
deeg.process.band()
)process.segment(
deeg.process.segment()
)process.sampling(
pdeeg.rocess.sampling()
)
- deeg.process.check(array)¶
Check whether the input signal contains NaN, print relevant information
- Parameters
array (numpy array) – the temporal signal with 1d or with multiple dimensions
- Returns
None or dict. Dict stores indicator of Nan/Abnormal values. If there is no NaN in signal, return None, else return the index where NaN occurs.
- deeg.process.smooth(array, window_size=5, polyorder=2)¶
Apply the Savitzky-Golay fiklter to smooth signals. We support both 1d array input and DEEG standard input (p,m,e). For the later format, we calculate DE on “e” dimension and return a (p,m) numpy array.
- Parameters
array (numpy array) – the temporal signal with 1d or with multiple dimensions
window_size (int. window_size must be a positive odd integer. default is 5.) – the length of the filter window. window_size must be a positive odd integer. default is 5.
polyorder (int) – the order of the polynomial used to fit the samples. polyorder must be less than window_length. default is 2.
- Returns
ndarray of the filtered data, same shape as array.
- deeg.process.band(array)¶
Filter temporal signal by all the 5 filters commonly used in EEG analysis: delta, theta, alpha, beta, gamma.
- Parameters
array (numpy array) – the temporal signal with 1d or with multiple dimensions
- Returns
dictionary. Keys are filter name and values are filtered signal in temporal domain.
- deeg.process.segment(array, length, overlap=1)¶
Generate the signal segments to augment sample size.
- Parameters
array (numpy array) – the temporal signal with 1d or with multiple dimensions
length (int) – the length of segments. Default is 10, which means each signal segment contains 10 data points.
overlap (int) – the overlap length between neighboring segments. Default is 1.
- Returns
numpy array with shape (p,m,s,e) or (s,e), depending on the input. s is the number of segments.
- deeg.process.sampling(array, interval=1, offset)¶
Down-sample the input signal with certain interval.
- Parameters
array (numpy array) – the temporal signal with 1d or with multiple dimensions
interval (int) – the interval to sample EEG signal. Default is 1, which means NO down-sampling is applied
offset (int) – sampling starts from “offset-th” data point
- Returns
numpy array of down-sampled signal


Indian International Film Festival to Highlight Russian Flavour of Global Cinema This Year
New Delhi (Sputnik): The 50th edition of the International Film Festival of India (IFFI) has started in the tiny state of Goa and this year the “Country of Focus” is Russia.
Asia’s equivalent to the popular Cannes Film Festival, IFFI aims at facilitating a common stage for artists from around the world to showcase the flavours of cinema from their regions.
The Golden Jubilee edition of IFFI will feature eight extraordinary films by Russian directors that have been included in the 2019 line up.
Two of the most talked-about movies from Russia are “Abigail” by 30-year old Aleksandr Boguslavsky and “Andrei Tarkovsky: A Cinema Prayer” – a masterpiece documentary by Tarkovsky himself, who died back in 1986.
While “Abigail” revolves around a girl named Abigail who thinks she lost her father to an epidemic only to find out later that no epidemic actually even occurred, “Andrei Tarkovsky: A Cinema Prayer” recounts Tarkovsky’s life and work as he shares his memories, views and reflections on art, destiny of an artist and the meaning of human existence.
Other Russia-originated movies include “Acid”, “Beanpole”, “Great Poetry”, “Once in Trubchevsk”, “Why don’t you just die!” and “The Hero” by directors Alexander Gorchilin, Kantemir Balagov, Aleksandr Lungin, Larissa Sadilova, Kirill Sokolov, and Karen Oganesyan respectively.
On 20 November, legendary Indian megastars Amitabh Bachchan and Rajinikanth inaugurated the film festival in the Goa capital of Panjim, with ace Bollywood director Karan Johar hosting the ceremony.
The 50th International Film Festival of India will witness over 200 films from 76 countries, 26 feature films and 15 non-feature films in the Indian Panorama section and more than 10,000 people and film lovers are expected to participate in the festival.
The eight-day film festival will also be attended by Bollywood actors and directors including Anil Kapoor and Imtiyaz Ali among others.
The best movie handpicked by IFFI jury members will receive the “Golden Peacock” award.
Cinematographer John Bailey, ex-President of the Academy of Motion Pictures, Arts and Sciences is heading the jury for IFFI 2019. Cannes jury member and French filmmaker Robin Campillo, Chinese filmmaker Zhang Yang, and Indian director Ramesh Sippy will be part of the international jury for the film festival this year.
IFFI began in 1952 with the patronage of India’s first Prime Minister Jawaharlal Nehru.


gsi.Rd
This function removes the nonhost growth signal from a host tree-ring series.
gsi(input_series)
| input_series | A |
|---|
A data frame with the two input columns (host and nonhost series) and 3 added columns:
The mean/sd adjusted non-host chronology,
The growth suppression index ("gsi") of the host series after subtraction of the adjusted nonhost chronology,
The normalized growth suppression
index ("ngsi") generated by applying scale()
to the gsi.
The growth suppression index (GSI) is referred to as the "corrected" series in OUTBREAK. It is calculated as:
$$GSI(i) = H(i) - (NH(i) - mean(NH)) sd(H)/sd(NH)$$
where H and NH are the host and nonhost tree-ring series as standardized
index values; i is the year, and the functions mean()
and sd()
are
applied to the common period.
gsi()
will rarely be run directly by a user. It is called from
defoliate_trees()
.


Journal 1: Wesbite
February 5th, 2021
Currently, I am in the process of deciding my semester project. I still haven't found much inspiration for what I want to work on. While figuring that out, I have been working on my website. I have added a footer, navigation bar, and I am hoping to add a slide show of images at the top. Something that I find difficult is organizing my html code and each parts' css attributes. I began with a rough sketch of what I hoped to achieve, which it is often more difficult to bring to life once it comes to the coding.
Journal 2: Possible Project?
February 21st, 2021
I talked to Dr. Henry about my ideas for a coding project and she happened to be giving a presentation to teachers on implementing programming into other classes. In her physics class, we often use code to model real life experiments, which has been especially useful during Covid. I think going virtual has pushes other classes to use more virtual and online resources.
Though I was considering finishing a game I started a very long time ago, lately I've been leaning towards more useful and practical projects. Dr. Henry suggested that I talk to Mr. Chang, who attended her presentation, about writing a progrom for our math department. After talking to him, I think that a Math related, virtual simulation program would be possible. And could be implemented into Trevor math classes.
Journal 3: Website Work
March 3th, 2021
So far the website has been going well and I really enjoy learning more about html and css. I feel fairly comfortable with both, but I am stikk working on efficency. Something I want to learn more about is javascript. I really want to create a stronger website, so part of this semester will be spent taking Javascript classes on codeacademy.
I've tried to add the slideshow I was talking about earlier, but I think I need to learn more about javascript to better understand how to implement one. I really like using fitted columns and boxes to organize my webpage with css, but I have a hard time getting them to look exactly as I envisons. Especially with specfic locations that stray from pre-set qualities. I've added the css for my journal entries includeding the boxes, shadows, and the overall spacing. I also struggled with the navagation bar a bit because I wanted my name to also be apart of the navagation bar, but not with the same attributes as the actual page options.
Journal 4: Website Work & Classes
March 8th, 2021
Next year I plan on starting a club called Project Outreach and I have been working on the webiste that will go along with the club. The website is meant to make community service options more avalible to young adults, who are oftentimes too young to qualify for certain jobs. I plan on adding a search engine, but so far a few organizations are listed.
Meanwhile, I have the outline of my slideshow code, I just need to add the images and adjust the location. The slide show is slightly hovering over the navagation bar, and I haven't been able to move it down.
I also decided to take a codecademy course (that I mentioned earlier) on Javascript. So far it is going well, but I sometimes get the syntax and style of different languages confused. Especially if I haven't coded in that language for a while.
I am kind of sad to leave APCS behind, so I am planning on restarting the course either this year or next year to take the AP test.
Journal 5: Codeacademy
April 15th, 2021
This is the Project Outreach Website I have been working on for the past while, I am going to work on adding more service options and creating the search engine I mentioned earlier.
I still havent fixed the slideshow. I am not really sure why it's blocking the top. I might try to play around with the css styling, I don't think it has any yet.
Journal 6: Codeacademy
April 23th, 2021
I am continuing with the codecademy Javascript course. It can be a bit tedious at times, with long explainations for simple commands. Also, the lessons have lots of parts, some of which can be a bit repetitive.
I want to continue with data visualisaion (like my covid tracker from last year) and I found some interesting public NASA APIs that I might want to use to create an interacive space model. I wanted users to be able to zoom in and out and click on different objects with their fun facts. But before that starts I am going to make a JSON to CSV and CSV to JSON converting program because it will help me learn more about sorting data, and refresh my memory.
Journal 7: My Project
May 3rd, 2021
I am very excited. I have finally thought of a satisfactory project. I am going to make a texting app in which the user first selects a catagory and then the app provides them with information on that catagory. It is going to start small, with Covid 19 data. (I realized that I wasn't quite done with covid data). But once that section is completed, I want it to branch out into News, Weather, Food, Stocks & other useful information. Texting is so easy and sometimes all we want is basic information, so it would be nice to have all of that information in one place. Plus I'll get to use live data, so really this will be a happy combination of all my favorite things. I also want it to be simple. Clear numbers or short sentences. So information is assesible and speedy.
Journal 8: Python
May 5th, 2021
Looking into the libriaies I will want to use. Thinking about Tkinter, KIVY, or Django. Writing down my notes here. I'll be researching as I go.
Journal 9: Teachback & Crypto
May 20th, 2021
While I was tracking down the API and Keys I would need for my project, I found quite a few run by coinbase. There were so many, and with so much detail that I've chosen to focus on Crypo first. So I've been watch tutorials about how to implement their APIS.
Also, I finished my teachback about the orgins of programming, and it was super interesting to research. I was surprised to find out that teh first computer programmer was Ada Lovelace! A woman! It's interesting that CS is such a male-dominated field considering where it started.
Journal 10: CryptoTradingBot
May 23th, 2021
OK! Things keep changing, but I've decided on my crypto trading bot. It uses the RSI indicator to determine whether or not to buy and sell. It also keeps track of price changes and basically serves as an automatic trader, so the user doesn't have to actively check themselves. Over the summer I would like to add more user interactivity because there is no User Interface at the moment. Also, BTC is the preset currency (though that can be changed in the code).
Now I am working on text and email reminders for when to buy and sell. Which, in the future, users will be able to select or deselect depening on whther they want automated trades or just the bot reccommendations.
The last aspect of the project would be to make it into a Web App, which I might do seperately and then connect after both are done.
Journal 11: Texting Feature
May 24th, 2021
I finished adding the texting update, but I am having issues with Twilio. It is marked as delivered on their website but I didn't recieve a text. I tried multiple numbers and checked all my keys and tokens. I'm not sure what the issue could possibly be.
FINAL YEAR LONG SUMMARY
May 27th, 2021
Presentation: Year Long Project Summary
First Semester: Switzerland
AP Computer Science (First Semester: Switzerland)
I loved taking APCS. In some ways it seemed like going back to the basics, but while re-learning and learning concepts, I definetly came out of it with a greater understanding of Java. It especially changed the way I saw OOP because we delt so much with working modularly and building from the ground up. We did Lab assignments and LOTS of tests. It was also really interesting being in a different learning environment from Trevor.
Second Semester: Trevor
My APW1 Webpage
Summary:
A page about my progress during the year, and with jounral entries to track my progress.
What I learned:
This defintely helped refresh my html and css. I hope to work more on JS in the future.
Future Goals:
So far I am happy with the way it looks, I just need to add more info onto my home page and upload my processing projects (maybe I should put those on the main page). I also would like to have the slides working, but the photos aren't showing up. And I would Like to add a button to return to the main page.
Project Outreach Website
Summary:
A website I worked on mostly over the summer and edited second semester. It is for my club next year, Project Outreach
Orginal Goals:
Fun Physics Projects


Ng Virtual Keyboard
ALPHA Text
Model value: {{t01}}
* Placeholder (watermark).
QWERTY Email
Model value: {{t02}}
* Valid Email.
Password
Model value: {{t03}}
* Click button to open keyboard.
* Input restricted.
* Pasting (ctrl-v) not allowed.
dialpad
Textarea
Model value: {{t04}}
* Auto accept content.
* Extension Scramble.
Event Handler
Model value: {{t05}}
* Event keyboardChange and change.
Auto Update Model
Model value: {{t06}}
* No preview.
* Auto update model.
* ngChange fired.


Financer
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©
Arielle Griffin & Adam Telford Webdesk-me


Ambient Temperature Moodlamp
Fusion 360 UnoHolder file (f3d)
The nanoholder was designed to securely hold the nano in place while it is inside the moodlamp casing and it was made using 3D Printing.
The casing of the moodlamp was design with an inspiraton of a Japanese lantern and it was made using lasercutting.
Fusion 360 Moodlamp casing file (f3d)
Fusion 360 drawing file (bottom)
Fusion 360 drawing file (front)
Fusion 360 drawing file (middle)
Fusion 360 drawing file (ring)
Fusion 360 drawing file (side)
Programming was done using the Arduino IDE application on a nano board and the code for the Adafruit Neopixels was with reference from the Adafruit examples on the application.
Arduino Moodlamp Code (ino file)
Final complete build
Link to Final Build 1 min Video
Presentation Slide
Download the presentation file here


Hey!!Tired of the long queues at the pizza joint?? then worry no more you can now place your order on this website at the comfort of your home and we will get it to
you in minutes.
CHOOSE TOPPINGS
Small Chesee: ksh 200
Medium cheese: ksh 300
Large cheese: ksh 400
Small Mushrooms: ksh 50
Medium Mushrooms:ksh 100
Large Mushrooms: ksh 150
Small Olives: ksh 100
Medium Olives: ksh 150
Large Olives: ksh 200
To Be Delivered: ksh 200


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Figure 1
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How ambient temperature governs metabolic rate. The thermal ... |
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Figure 2
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The density distribution of metabolic expansibility, ME CRB ... |
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Figure 3
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A histogram (a) of the ratio of summit metabolic capacity (M ... |
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Figure 4
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We depict observed cold range boundaries (CRB, black polygon ... |


Which author wrote The House of Mirth?
Amy Tan's novel set in early 20th century Shanghai is entitled The Valley of ...?
It's rumored that after his death, Thomas Hardy's heart was eaten by a ...?
Which author wrote the 1929 novel entitled Passing ?
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| Opcode/Instruction | Op /En | 64/32 bit Mode Support | CPUID Feature Flag | Description |
|---|---|---|---|---|
|
VEX.128.66.0F3A.W0 1D 1D/r ib VCVTPS2PH xmm1/m64, xmm2, imm8 |
MRI | V/V | F16C | Convert four packed single-precision floating-point values in xmm2 to packed half-precision (16-bit) floating-point values in xmm1/m64. Imm8 provides rounding controls. |
|
VEX.256.66.0F3A.W0 1D1D /r ib VCVTPS2PH xmm1/m128, ymm2, imm8 |
MRI | V/V | F16C | Convert eight packed single-precision floating-point values in ymm2 to packed half-precision (16-bit) floating-point values in xmm1/m128. Imm8 provides rounding controls. |
|
EVEX.128.66.0F3A.W0 1D1D /r ib VCVTPS2PH xmm1/m64 {k1}{z}, xmm2, imm8 |
HVM | V/V |
AVX512VL AVX512F |
Convert four packed single-precision floating-point values in xmm2 to packed half-precision (16-bit) floating-point values in xmm1/m64. Imm8 provides rounding controls. |
|
EVEX.256.66.0F3A.W0 1D1D /r ib VCVTPS2PH xmm1/m128 {k1}{z}, ymm2, imm8 |
HVM | V/V |
AVX512VL AVX512F |
Convert eight packed single-precision floating-point values in ymm2 to packed half-precision (16-bit) floating-point values in xmm1/m128. Imm8 provides rounding controls. |
|
EVEX.512.66.0F3A.W0 1D1D /r ib VCVTPS2PH ymm1/m256 {k1}{z}, zmm2{sae}, imm8 |
HVM | V/V | AVX512F | Convert sixteen packed single-precision floating-point values in zmm2 to packed half-precision (16-bit) floating-point values in ymm1/m256. Imm8 provides rounding controls. |
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| MRI | ModRM:r/m (w) | ModRM:reg (r) | Imm8 | NA |
| HVM | ModRM:r/m (w) | ModRM:reg (r) | Imm8 | NA |
Description
Convert packed single-precision floating values in the source operand to half-precision (16-bit) floating-point values and store to the destination operand. The rounding mode is specified using the immediate field (imm8).
Underflow results (i.e., tiny results) are converted to denormals. MXCSR.FTZ is ignored. If a source element is denormal relative to the input format with DM masked and at least one of PM or UM unmasked; a SIMD exception will be raised with DE, UE and PE set.
The immediate byte defines several bit fields that control rounding operation. The effect and encoding of the RC field are listed in Table 5-3.
| Bits | Field Name/value | Description | Comment |
|---|---|---|---|
| Imm[1:0] | RC=00B | Round to nearest even | If Imm[2] = 0 |
| RC=01B | Round down | ||
| RC=10B | Round up | ||
| RC=11B | Truncate | ||
| Imm[2] | MS1=0 | Use imm[1:0] for rounding | Ignore MXCSR.RC |
| MS1=1 | Use MXCSR.RC for rounding | ||
| Imm[7:3] | Ignored | Ignored by processor |
VEX.128 version: The source operand is a XMM register. The destination operand is a XMM register or 64-bit memory location. If the destination operand is a register then the upper bits (MAX_VL-1:64) of corresponding register are zeroed.
VEX.256 version: The source operand is a YMM register. The destination operand is a XMM register or 128-bit memory location. If the destination operand is a register, the upper bits (MAX_VL-1:128) of the corresponding destination register are zeroed.
Note: VEX.vvvv and EVEX.vvvv are reserved (must be 1111b).
EVEX encoded versions: The source operand is a ZMM/YMM/XMM register. The destination operand is a YMM/XMM/XMM (low 64-bits) register or a 256/128/64-bit memory location, conditionally updated with writemask k1. Bits (MAX_VL-1:256/128/64) of the corresponding destination register are zeroed.
Operation
vCvt_s2h(SRC1[31:0])
{
IF Imm[2] = 0
THEN
; using Imm[1:0] for rounding control, see Table 5-3
RETURN Cvt_Single_Precision_To_Half_Precision_FP_Imm(SRC1[31:0]);
ELSE
; using MXCSR.RC for rounding control
RETURN Cvt_Single_Precision_To_Half_Precision_FP_Mxcsr(SRC1[31:0]);
FI;
}
VCVTPS2PH (EVEX encoded versions) when dest is a register
(KL, VL) = (4, 128), (8, 256), (16, 512)
FOR j (cid:197) 0 TO KL-1
i (cid:197) j * 16
k (cid:197) j * 32
IF k1[j] OR *no writemask*
THEN DEST[i+15:i] (cid:197)
vCvt_s2h(SRC[k+31:k])
ELSE
IF *merging-masking*
; merging-masking
THEN *DEST[i+15:i] remains unchanged*
ELSE
; zeroing-masking
DEST[i+15:i] (cid:197) 0
FI
FI;
ENDFOR
DEST[MAX_VL-1:VL/2] (cid:197) 0
VCVTPS2PH (EVEX encoded versions) when dest is memory
(KL, VL) = (4, 128), (8, 256), (16, 512)
FOR j (cid:197) 0 TO KL-1
i (cid:197) j * 16
k (cid:197) j * 32
IF k1[j] OR *no writemask*
THEN DEST[i+15:i] (cid:197)
vCvt_s2h(SRC[k+31:k])
ELSE
*DEST[i+15:i] remains unchanged*
; merging-masking
FI;
ENDFOR
VCVTPS2PH (VEX.256 encoded version)
DEST[15:0] (cid:197)vCvt_s2h(SRC1[31:0]);
DEST[31:16] (cid:197)vCvt_s2h(SRC1[63:32]);
DEST[47:32] (cid:197)vCvt_s2h(SRC1[95:64]);
DEST[63:48] (cid:197)vCvt_s2h(SRC1[127:96]);
DEST[79:64] (cid:197)vCvt_s2h(SRC1[159:128]);
DEST[95:80] (cid:197)vCvt_s2h(SRC1[191:160]);
DEST[111:96] (cid:197)vCvt_s2h(SRC1[223:192]); DEST[127:112] (cid:197)vCvt_s2h(SRC1[255:224]);
DEST[MAX_VL-1:128] (cid:197) 0
VCVTPS2PH (VEX.128 encoded version)
DEST[15:0] (cid:197)vCvt_s2h(SRC1[31:0]);
DEST[31:16] (cid:197)vCvt_s2h(SRC1[63:32]);
DEST[47:32] (cid:197)vCvt_s2h(SRC1[95:64]); DEST[63:48] (cid:197)vCvt_s2h(SRC1[127:96]);
DEST[MAX_VL-1:64] (cid:197) 0
Flags Affected
None
Intel C/C++ Compiler Intrinsic Equivalent
VCVTPS2PH __m256i _mm512_cvtps_ph(__m512 a);
VCVTPS2PH __m256i _mm512_mask_cvtps_ph(__m256i s, __mmask16 k,__m512 a);
VCVTPS2PH __m256i _mm512_maskz_cvtps_ph(__mmask16 k,__m512 a);
VCVTPS2PH __m256i _mm512_cvt_roundps_ph(__m512 a, const int imm);
VCVTPS2PH __m256i _mm512_mask_cvt_roundps_ph(__m256i s, __mmask16 k,__m512 a, const int imm);
VCVTPS2PH __m256i _mm512_maskz_cvt_roundps_ph(__mmask16 k,__m512 a, const int imm);
VCVTPS2PH __m128i _mm256_mask_cvtps_ph(__m128i s, __mmask8 k,__m256 a);
VCVTPS2PH __m128i _mm256_maskz_cvtps_ph(__mmask8 k,__m256 a);
VCVTPS2PH __m128i _mm_mask_cvtps_ph(__m128i s, __mmask8 k,__m128 a);
VCVTPS2PH __m128i _mm_maskz_cvtps_ph(__mmask8 k,__m128 a);
VCVTPS2PH __m128i _mm_cvtps_ph ( __m128 m1, const int imm);
VCVTPS2PH __m128i _mm256_cvtps_ph(__m256 m1, const int imm);
SIMD Floating-Point Exceptions
Invalid, Underflow, Overflow, Precision, Denormal (if MXCSR.DAZ=0);
Other Exceptions
| VEX-encoded instructions, see Exceptions Type 11 (do not report #AC); |
| EVEX-encoded instructions, see Exceptions Type E11. |
| If VEX.W=1. |
| If VEX.vvvv != 1111B or EVEX.vvvv != 1111B. |


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link--- layout: tweet ---
If you want in early on the book I’m coauthoring, grab it now! 📚☕ https://twitter.com/ManningBooks/status/1349606202385321985
1/13/2021, 10:46:01 PM
Favs: 10
Retweets: 1
link