SimulaQron.virtNode package¶
Submodules¶
SimulaQron.virtNode.basics module¶
SimulaQron.virtNode.crudeSimulator module¶
-
class
SimulaQron.virtNode.crudeSimulator.
quantumRegister
(node, num, maxQubits=10)[source]¶ Bases:
SimulaQron.virtNode.crudeSimulator.simpleEngine
A simulated quantum register. The qubits who are simulated in this register may be distributed over different quantum nodes.
-
class
SimulaQron.virtNode.crudeSimulator.
simpleEngine
(maxQubits=10)[source]¶ Bases:
SimulaQron.virtNode.basics.quantumEngine
Basic quantum engine which uses QuTip. Works with density matrices and in principle allows full quantum dynamics via QuTip. Subsequently, this is quite slow.
- Attributes:
- maxQubits: maximum number of qubits this engine will support.
-
absorb
(other)[source]¶ Absorb the qubits from the other engine into this one. This is done by tensoring the state at the end.
-
absorb_parts
(R, I, activeQ)[source]¶ Absorb the qubits, given in pieces
Arguments: R real part of the qubit state as a list I imaginary part as a list activeQ active number of qubits
-
apply_CNOT
(qubitNum1, qubitNum2)[source]¶ Applies the CNOT to the qubit with the numbers qubitNum1 and qubitNum2.
-
apply_CPHASE
(qubitNum1, qubitNum2)[source]¶ Applies the CPHASE to the qubit with the numbers qubitNum1 and qubitNum2.
-
apply_K
(qubitNum)[source]¶ Applies a K gate to the qubits with number qubitNum. Maps computational basis to Y eigenbasis.
-
apply_onequbit_gate
(gateU, qubitNum)[source]¶ Applies a unitary gate to the specified qubit.
Arguments: gateU unitary to apply as Qobj qubitNum the number of the qubit this gate is applied to
-
apply_rotation
(qubitNum, n, a)[source]¶ Applies a rotation around the axis n with the angle a to qubit with number qubitNum. If n is zero a ValueError is raised Arguments: qubitNum Qubit number n A tuple of three numbers specifying the rotation axis, e.g n=(1,0,0) a The rotation angle in radians.
-
apply_twoqubit_gate
(gateU, qubit1, qubit2)[source]¶ Applies a unitary gate to the two specified qubits.
Arguments: gateU unitary to apply as Qobj qubit1 the first qubit qubit2 the second qubit
-
get_qubits_RI
(qList)[source]¶ Retrieves the qubits in the list and returns the result as a list divided into a real and imaginary part. Twisted only likes to send real values lists, not complex ones.
Arguments qList list of qubits to retrieve, e.g. [1, 4]
-
get_register_RI
()[source]¶ Retrieves the entire register in real and imaginary parts and returns the result as a list. Twisted only likes to send real valued lists, not complex ones.
-
measure_qubit
(qubitNum)[source]¶ Measures the desired qubit in the standard basis. This returns the classical outcome and deletes the qubit.
Arguments: qubitNum qubit to be measured
-
measure_qubit_inplace
(qubitNum)[source]¶ Measures the desired qubit in the standard basis. This returns the classical outcome. The quantum register is in the post-measurment state corresponding to the obtained outcome.
Arguments: qubitNum qubit to be measured
SimulaQron.virtNode.quantum module¶
-
class
SimulaQron.virtNode.quantum.
simulatedQubit
(node, register, simNum, num=0)[source]¶ Bases:
twisted.spread.flavors.Referenceable
Simulated qubit object in the specified local simulation engine.
- Arguments
node: network node that this qubit lives at register: register on that node that the qubit is in
Note
Qubit objects are local to each node that is simulating a particular quantum register. A qubit object provides the backing for a virtual qubit, which may be at another node.
-
remote_apply_K
()[source]¶ Apply K gate to itself by passing it onto the underlying register. Maps computational to Y eigenbasis.
-
remote_apply_rotation
(*args)[source]¶ Apply rotation around axis n with angle a. Arguments: n A tuple of three numbers specifying the rotation axis, e.g n=(1,0,0) a The rotation angle in radians.
-
remote_cnot_onto
(targetNum)[source]¶ Performs a CNOT operation with this qubit as control, and the other qubit as target.
Arguments targetNum the qubit to use as the target of the CNOT
-
remote_cphase_onto
(targetNum)[source]¶ Performs a CPHASE operation with this qubit as control, and the other qubit as target.
Arguments targetNum the qubit to use as the target of the CPHASE
-
remote_get_details
()[source]¶ Returns out simulation number as well as the details of this simulating node.
-
remote_get_qubit
()[source]¶ Returns the state of the qubits in the list qList by tracing out the rest.
-
remote_measure
()[source]¶ Measure the qubit in the standard basis. This does delete the qubit.
Returns the measurement outcome.
SimulaQron.virtNode.virtual module¶
-
class
SimulaQron.virtNode.virtual.
QubitCQC
(fromName, toName, from_app_id, to_app_id, new_virt_num, rawEntInfo=None)[source]¶ Bases:
object
-
class
SimulaQron.virtNode.virtual.
virtualNode
(ID, config, maxQubits=10, maxRegisters=1000)[source]¶ Bases:
twisted.spread.flavors.Root
-
connectNet
()[source]¶ Initialize the connections to the other virtual nodes in the network according to the available configuration.
-
get_sim_id
()[source]¶ Similarly, this is a crude and horrible cludge to generate unique IDs for simulated qubits.
-
get_virtual_id
()[source]¶ This is a crude and horrible cludge to generate unique IDs for virtual qubits.
-
local_merge_regs
(qubit1, qubit2)[source]¶ Merges the two local quantum registers. Note that these register may simulate virtual qubits across different network nodes. This will ignore maxQubits and simply create one large register allowing twice maxQubits qubits.
Arguments qubit1 qubit1 in reg1, called from remote having access to only qubits qubit2 qubit2 in reg2
-
remote_add_qubit
(name, simQubit)[source]¶ Add a qubit to the local virtual node.
Arguments name name of the node simulating this qubit simQubit simulated qubit reference in the backend we’re adding
-
remote_cqc_add_epr_list
(fromName, from_app_id, to_app_id, new_virt_num, rawEntInfo)[source]¶ Add an item to the epr list for use in CQC.
-
remote_cqc_add_recv_list
(fromName, from_app_id, to_app_id, new_virt_num)[source]¶ Add an item to the received list for use in CQC.
-
remote_cqc_get_epr_recv
(to_app_id)[source]¶ Retrieve the next qubit (half of an EPR-pair) with the given app ID from the received list.
-
remote_cqc_get_recv
(to_app_id)[source]¶ Retrieve the next qubit with the given app ID form the received list.
-
remote_cqc_send_epr_half
(num, targetName, app_id, remote_app_id, rawEntInfo)[source]¶ Send interface for CQC to add the qubit to the remote nodes received list for an application.
Arguments: num number of virtual qubit to send targetName name of the node to send to app_id application asking to have this qubit delivered remote_app_id application ID to deliver the qubit to entInfo entanglement information
-
remote_cqc_send_qubit
(num, targetName, app_id, remote_app_id)[source]¶ Send interface for CQC to add the qubit to the remote nodes received list for an application.
Arguments: num number of virtual qubit to send targetName name of the node to send to app_id application asking to have this qubit delivered remote_app_id application ID to deliver the qubit to
-
remote_get_multiple_qubits
(qList)[source]¶ Return the state of multiple qubits virtually located at this node. This will fail if the qubits are not in the same register or thus also simulating node.
Arguments qList list of virtual qubits of which to retrieve the state
-
remote_get_register
(qubit)[source]¶ Return the value of of a locally simulated register which contains this virtual qubit.
-
remote_get_register_del
(qubitNum)[source]¶ Return the value of of a locally simulated register, and remove the simulated qubits from this node.
Caution: virtual qubits not updated.
-
remote_get_state
(simNumList)[source]¶ Return the state of multiple qubits corresponding to the IDs in simNumList.
-
remote_get_virtual_ref
(num)[source]¶ Return a virual qubit object for the given number.
Arguments num number of the virtual qubit
-
remote_lock_reg_qubits
(qubitNum)[source]¶ Acquire the lock on all qubits in the same register as qubitNum.
-
remote_merge_from
(simNodeName, simQubitNum, localReg)[source]¶ Bring a remote register to this node.
Arguments simNodeName name of the node who simulates right now simQubitNum simulation number of qubit whose register we will merge localReg local register to merge with
-
remote_merge_regs
(num1, num2)[source]¶ Merges the two local quantum registers. Note that these register may simulate virtual qubits across different network nodes. This will ignore maxQubits and simply create one large register allowing twice maxQubits qubits.
Arguments num1 number of the first qubit num2 number of the second qubit
-
remote_new_register
(maxQubits=10)[source]¶ Initialize a local register. Right now, this simple creates a register according to the simple engine backend using qubit.
Arguments: maxQubits maximum number of qubits to use in the default engine (default 10)
-
remote_remove_sim_qubit_num
(delNum)[source]¶ Removes the simulated qubit delQubit from the node and also from the underlying engine. Relies on this qubit having been locked.
Arguments delNum simID of the simulated qubit to delete
-
remote_reset_reg
(reg)[source]¶ Reset the simulated register, including removing all simulated qubits. Caution: this does not update remote nodes XXX
-
remote_send_qubit
(qubit, targetName)[source]¶ Sends the qubit to the specified target node. This creates a new virtual qubit object at the remote node with the right qubit and backend details.
Arguments qubit virtual qubit to be sent targetName target ndoe to place qubit at (host object)
-
remote_transfer_qubit
(simQubitNum, targetName)[source]¶ Transfer the qubit to the destination node if we are the simulating node. The reason why we cannot do this directly is that Twisted PB does not allow objects to be passed between connecting nodes. Only between the creator of the object and its immediate connections.
Arguments simQubitNum simulated qubit number to be sent targetName target node to place qubit at (host object)
-
remote_unlock_reg_qubits
(qubitNum)[source]¶ Release the lock on all qubits in the same register as qubitNum.
-
remote_update_virtual_merge
(newSimNodeName, oldSimNodeName, oldRegNum, newD)[source]¶ Update the virtual qubits to the new simulating node, if applicable. This is extremely inefficient due to not keeping register information in virtualQubit.
Arguments newSimNodeName new node simulating this qubit oldSimNodeName old node simulating the qubit oldReg old register newD dictionary mapping qubit numbers to qubit objects at the new simulating node
-
-
class
SimulaQron.virtNode.virtual.
virtualQubit
(virtNode, simNode, simQubit, num)[source]¶ Bases:
twisted.spread.flavors.Referenceable
-
remote_apply_rotation
(n, a)[source]¶ Apply rotation around axis n with angle a. Arguments: n A tuple of three numbers specifying the rotation axis, e.g n=(1,0,0) a The rotation angle in radians.
-
remote_cnot_onto
(target)[source]¶ Performs a CNOT operation with this qubit as control, and the other qubit as target.
Arguments target the virtual qubit to use as the target of the CNOT
-
remote_cphase_onto
(target)[source]¶ Performs a CPHASE operation with this qubit as control, and the other qubit as target.
Arguments target the virtual qubit to use as the target of the CPHASE
-
remote_get_number
()[source]¶ Returns the number of this qubit in whatever local register it is in. Not useful for the client, but convenient for debugging.
-
remote_get_qubit
()[source]¶ Returns the state of this qubit in real and imaginary parts separated. This is required single Twisted cannot natively transfer complex valued objects.
-


pgmtopbm(1) pgmtopbm(1) NAME pgmtopbm - convert a portable graymap into a portable bitmap SYNOPSIS pgmtopbm [-floyd|-fs|-threshold |-hilbert |-dither8|-d8|-cluster3 |-c3|-cluster4|-c4 |-cluster8|-c8] [-value val] [-clump size] [pgmfile] DESCRIPTION Reads a portable graymap as input. Produces a portable bitmap as output. Note that there is no pbmtopgm converter. Any program that uses the Netpbm libraries to read PGM files, including virtually all programs in the Netpbm package, will read a PBM file automatically as if it were a PGM file. If you are using a less intelligent program that expects PGM input, use pnmdepth to convert the PBM file to PGM. As long as the depth is greater than 1, pnmdepth will generate PGM. This less intelligent program quite probably is also not intelligent enough to deal with general maxvals, so you should specify a depth of 255. OPTIONS The default quantization method is boustrophedonic Floyd-Steinberg error diffusion (-floyd or -fs). Also available are simple thresholding (-threshold); Bayer's ordered dither (-dither8) with a 16x16 matrix; and three different sizes of 45-degree clustered-dot dither (-cluster3, -cluster4, -cluster8). A space filling curve halftoning method using the Hilbert curve is also available. (-hilbert); Floyd-Steinberg will almost always give the best looking results; how‐ ever, looking good is not always what you want. For instance, threshold‐ ing can be used in a pipeline with the pnmconvol tool, for tasks like edge and peak detection. And clustered-dot dithering gives a newspaper- ish look, a useful special effect. The -value flag alters the thresholding value for Floyd-Steinberg and simple thresholding. It should be a real number between 0 and 1. Above 0.5 means darker images; below 0.5 means lighter. The Hilbert curve method is useful for processing images before display on devices that do not render individual pixels distinctly (like laser printers). This dithering method can give better results than the dither‐ ing usually done by the laser printers themselves. The -clump flag alters the number of pixels in a clump. This is usually an integer between 2 and 100 (default 5). Smaller clump sizes smear the image less and are less grainy, but seem to loose some grey scale linearity. Typi‐ cally a PGM image will have to be scaled to fit on a laser printer page (2400 x 3000 pixels for an A4 300 dpi page), and then dithered to a PBM image before being converted to a postscript file. A printing pipeline might look something like: pnmscale -xysize 2400 3000 image.pgm | pgm‐ topbm -hil | pnmtops -scale 0.25 > image.ps All flags can be abbreviated to their shortest unique prefix. REFERENCES The only reference you need for this stuff is "Digital Halftoning" by Robert Ulichney, MIT Press, ISBN 0-262-21009-6. The Hilbert curve space filling method is taken from "Digital Halftoning with Space Filling Curves" by Luiz Velho, Computer Graphics Volume 25, Number 4, proceedings of SIGRAPH '91, page 81. ISBN 0-89791-436-8 SEE ALSO pbmreduce(1), pgm(5), pbm(5), pnmconvol(1), pnmscale(1), pnmtops(1) AUTHOR Copyright (C) 1989 by Jef Poskanzer. 26 July 1988 pgmtopbm(1)


Attainment statistics team
Email<EMAIL_ADDRESS>Telephone: Tingting Shu 0370 000 2288
National Statistics
A level and other level 3 results for 16- to 18-year-olds at the end of their final year.
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Statistical first release ( SFR ) providing revised information on the overall achievements of young people in advanced level examinations for the 2014 to 2015 academic year, including:
We published provisional figures for the 2014 to 2015 academic year in October 2015. Schools and colleges checked the figures, and this SFR includes revised figures.
We have also published 16 to 18 performance tables for 2015.
Email<EMAIL_ADDRESS>Telephone: Tingting Shu 0370 000 2288
Published: 21 January 2016
From: Department for Education
Part of: Statistics: 16 to 19 attainment


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Cellphones, iPads, computers, game consoles have profoundly changed the lives of our children.  Facebook, Instagram, Youtube, and others have built their entire business models around grabbing and keeping our attention.
There are some positives to being able to connect to friends and family online and learn from online videos. But, there is also the potential for these devices to become addicting as we continually consume messages, videos, photos, etc.
The original intent of these technologies was to make it easier for us to create, solve problems, and be more effective.
Our kids need to start becoming PRODUCERS of content, not just CONSUMERS of content.
Math Plus Academy camps offer students a change to be PRODUCERS and not just consumers.
3D Printing & Modeling - learn to design in 3D and print real models Maker Camp - design and build engineering solutions to real problems Coding 101 & Video Game Design - make animations and video games from scratch LEGO Robotics - build and program robots to do automated tasks
Every child has an active imagination, what they sometimes lack are the tools, guidance, and knowledge to start turning their ideas into reality.
There’s still time to try out a camp that will help your child learn to use technology, not just sit and consume it.


PhaseNoiseClass
Parent class: NonIdealityClass. PhaseNoiseClass is a generic nonideality generated in the VCO. Many phase noise model exists, maybe the most general one is a phase modulation:
where phi(k) is a "modulator" signal.
Properties
None.
Methods
multiplier
output=obj.multiplier(signal)
Method multiplier computes the


MirroredStrategy
Mirrors vars to distribute across multiple devices and machines.
Inherits From: Strategy
This strategy uses one replica per device and sync replication for its multi-GPU version.
The multi-worker version will be added in the future.
devices
: a list of device strings. If None
, all available GPUs are used.
If no GPUs are found, CPU is used.cross_device_ops
: optional, a descedant of CrossDeviceOps
. If this is not
set, nccl will be used by default.__init__
__init__( devices=None, cross_device_ops=None )
Initialize self. See help(type(self)) for accurate signature.
extended
tf.distribute.StrategyExtended
with additional methods.
num_replicas_in_sync
Returns number of replicas over which gradients are aggregated.
experimental_distribute_dataset
experimental_distribute_dataset(dataset)
Distributes a tf.data.Dataset instance provided via dataset
.
The returned distributed dataset can be iterated over similar to how regular datasets can. NOTE: Currently, the user cannot add any more transformations to a distributed dataset.
The following is an example:
strategy = tf.distribute.MirroredStrategy() # Create a dataset dataset = dataset_ops.Dataset.TFRecordDataset([ "/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"]) # Distribute that dataset dist_dataset = strategy.experimental_distribute_dataset(dataset) # Iterate over the distributed dataset for x in dist_dataset: # process dataset elements strategy.experimental_run_v2(train_step, args=(x,))
We will assume that the input dataset is batched by the global batch size. With this assumption, we will make a best effort to divide each batch across all the replicas (one or more workers).
In a multi-worker setting, we will first attempt to distribute the dataset by attempting to detect whether the dataset is being created out of ReaderDatasets (e.g. TFRecordDataset, TextLineDataset, etc.) and if so, attempting to shard the input files. Note that there has to be at least one input file per worker. If you have less than one input file per worker, we suggest that you should disable distributing your dataset using the method below.
If that attempt is unsuccessful (e.g. the dataset is created from a
Dataset.range), we will shard the dataset evenly at the end by appending a
.shard
operation to the end of the processing pipeline. This will cause
the entire preprocessing pipeline for all the data to be run on every
worker, and each worker will do redundant work. We will print a warning
if this method of sharding is selected. In this case, consider using
experimental_distribute_datasets_from_function
instead.
You can disable dataset sharding across workers using the auto_shard
option in tf.data.experimental.DistributeOptions
.
Within each worker, we will also split the data among all the worker devices (if more than one a present), and this will happen even if multi-worker sharding is disabled using the method above.
If the above batch splitting and dataset sharding logic is undesirable,
please use experimental_distribute_datasets_from_function
instead, which
does not do any automatic splitting or sharding.
dataset
: tf.data.Dataset
that will be sharded across all replicas using
the rules stated above.A "distributed Dataset
", which acts like a tf.data.Dataset
except
it produces "per-replica" values.
experimental_distribute_datasets_from_function
experimental_distribute_datasets_from_function(dataset_fn)
Distributes tf.data.Dataset
instances created by calls to dataset_fn
.
dataset_fn
will be called once for each worker in the strategy. Each
replica on that worker will dequeue one batch of inputs from the local
Dataset
(i.e. if a worker has two replicas, two batches will be dequeued
from the Dataset
every step).
This method can be used for several purposes. For example, where
experimental_distribute_dataset
is unable to shard the input files, this
method might be used to manually shard the dataset (avoiding the slow
fallback behavior in experimental_distribute_dataset
). In cases where the
dataset is infinite, this sharding can be done by creating dataset replicas
that differ only in their random seed.
experimental_distribute_dataset
may also sometimes fail to split the
batch across replicas on a worker. In that case, this method can be used
where that limitation does not exist.
The dataset_fn
should take an tf.distribute.InputContext
instance where
information about batching and input replication can be accessed:
def dataset_fn(input_context): batch_size = input_context.get_per_replica_batch_size(global_batch_size) d = tf.data.Dataset.from_tensors([[1.]]).repeat().batch(batch_size) return d.shard( input_context.num_input_pipelines, input_context.input_pipeline_id) inputs = strategy.experimental_distribute_datasets_from_function(dataset_fn) for batch in inputs: replica_results = strategy.experimental_run_v2(replica_fn, args=(batch,))
IMPORTANT: The tf.data.Dataset
returned by dataset_fn
should have a
per-replica batch size, unlike experimental_distribute_dataset
, which uses
the global batch size. This may be computed using
input_context.get_per_replica_batch_size
.
dataset_fn
: A function taking a tf.distribute.InputContext
instance and
returning a tf.data.Dataset
.A "distributed Dataset
", which acts like a tf.data.Dataset
except
it produces "per-replica" values.
experimental_local_results
experimental_local_results(value)
Returns the list of all local per-replica values contained in value
.
Note: This only returns values on the worker initiated by this client.
When using a tf.distribute.Strategy
like
tf.distribute.experimental.MultiWorkerMirroredStrategy
, each worker
will be its own client, and this function will only return values
computed on that worker.
value
: A value returned by experimental_run()
, experimental_run_v2()
,
extended.call_for_each_replica()
, or a variable created in scope
.A tuple of values contained in value
. If value
represents a single
value, this returns (value,).
experimental_make_numpy_dataset
experimental_make_numpy_dataset( numpy_input, session=None )
Makes a tf.data.Dataset for input provided via a numpy array.
This avoids adding numpy_input
as a large constant in the graph,
and copies the data to the machine or machines that will be processing
the input.
Note that you will likely need to use tf.distribute.Strategy.experimental_distribute_dataset with the returned dataset to further distribute it with the strategy.
numpy_input = np.ones([10], dtype=np.float32) dataset = strategy.experimental_make_numpy_dataset(numpy_input) dist_dataset = strategy.experimental_distribute_dataset(dataset)
numpy_input
: A nest of NumPy input arrays that will be converted into a
dataset. Note that lists of Numpy arrays are stacked, as that is normal
tf.data.Dataset
behavior.session
: (TensorFlow v1.x graph execution only) A session used for
initialization.A tf.data.Dataset
representing numpy_input
.
experimental_run
experimental_run( fn, input_iterator=None )
Runs ops in fn
on each replica, with inputs from input_iterator
.
DEPRECATED: This method is not available in TF 2.x. Please switch
to using experimental_run_v2
instead.
When eager execution is enabled, executes ops specified by fn
on each
replica. Otherwise, builds a graph to execute the ops on each replica.
Each replica will take a single, different input from the inputs provided by
one get_next
call on the input iterator.
fn
may call tf.distribute.get_replica_context()
to access members such
as replica_id_in_sync_group
.
IMPORTANT: Depending on the tf.distribute.Strategy
implementation being
used, and whether eager execution is enabled, fn
may be called one or more
times (once for each replica).
fn
: The function to run. The inputs to the function must match the outputs
of input_iterator.get_next()
. The output must be a tf.nest
of
Tensor
s.input_iterator
: (Optional) input iterator from which the inputs are taken.Merged return value of fn
across replicas. The structure of the return
value is the same as the return value from fn
. Each element in the
structure can either be PerReplica
(if the values are unsynchronized),
Mirrored
(if the values are kept in sync), or Tensor
(if running on a
single replica).
experimental_run_v2
experimental_run_v2( fn, args=(), kwargs=None )
Run fn
on each replica, with the given arguments.
Executes ops specified by fn
on each replica. If args
or kwargs
have
"per-replica" values, such as those produced by a "distributed Dataset
",
when fn
is executed on a particular replica, it will be executed with the
component of those "per-replica" values that correspond to that replica.
fn
may call tf.distribute.get_replica_context()
to access members such
as all_reduce
.
All arguments in args
or kwargs
should either be nest of tensors or
per-replica objects containing tensors or composite tensors.
IMPORTANT: Depending on the implementation of tf.distribute.Strategy
and
whether eager execution is enabled, fn
may be called one or more times (
once for each replica).
fn
: The function to run. The output must be a tf.nest
of Tensor
s.args
: (Optional) Positional arguments to fn
.kwargs
: (Optional) Keyword arguments to fn
.Merged return value of fn
across replicas. The structure of the return
value is the same as the return value from fn
. Each element in the
structure can either be "per-replica" Tensor
objects or Tensor
s
(for example, if running on a single replica).
make_dataset_iterator
make_dataset_iterator(dataset)
Makes an iterator for input provided via dataset
.
DEPRECATED: This method is not available in TF 2.x.
Data from the given dataset will be distributed evenly across all the
compute replicas. We will assume that the input dataset is batched by the
global batch size. With this assumption, we will make a best effort to
divide each batch across all the replicas (one or more workers).
If this effort fails, an error will be thrown, and the user should instead
use make_input_fn_iterator
which provides more control to the user, and
does not try to divide a batch across replicas.
The user could also use make_input_fn_iterator
if they want to
customize which input is fed to which replica/worker etc.
dataset
: tf.data.Dataset
that will be distributed evenly across all
replicas.An tf.distribute.InputIterator
which returns inputs for each step of the
computation. User should call initialize
on the returned iterator.
make_input_fn_iterator
make_input_fn_iterator( input_fn, replication_mode=tf.distribute.InputReplicationMode.PER_WORKER )
Returns an iterator split across replicas created from an input function.
DEPRECATED: This method is not available in TF 2.x.
The input_fn
should take an tf.distribute.InputContext
object where
information about batching and input sharding can be accessed:
def input_fn(input_context): batch_size = input_context.get_per_replica_batch_size(global_batch_size) d = tf.data.Dataset.from_tensors([[1.]]).repeat().batch(batch_size) return d.shard(input_context.num_input_pipelines, input_context.input_pipeline_id) with strategy.scope(): iterator = strategy.make_input_fn_iterator(input_fn) replica_results = strategy.experimental_run(replica_fn, iterator)
The tf.data.Dataset
returned by input_fn
should have a per-replica
batch size, which may be computed using
input_context.get_per_replica_batch_size
.
input_fn
: A function taking a tf.distribute.InputContext
object and
returning a tf.data.Dataset
.replication_mode
: an enum value of tf.distribute.InputReplicationMode
.
Only PER_WORKER
is supported currently, which means there will be
a single call to input_fn
per worker. Replicas will dequeue from the
local tf.data.Dataset
on their worker.An iterator object that should first be .initialize()
-ed. It may then
either be passed to strategy.experimental_run()
or you can
iterator.get_next()
to get the next value to pass to
strategy.extended.call_for_each_replica()
.
reduce
reduce( reduce_op, value, axis=None )
Reduce value
across replicas.
Given a per-replica value returned by experimental_run_v2
, say a
per-example loss, the batch will be divided across all the replicas. This
function allows you to aggregate across replicas and optionally also across
batch elements. For example, if you have a global batch size of 8 and 2
replicas, values for examples [0, 1, 2, 3]
will be on replica 0 and
[4, 5, 6, 7]
will be on replica 1. By default, reduce
will just
aggregate across replicas, returning [0+4, 1+5, 2+6, 3+7]
. This is useful
when each replica is computing a scalar or some other value that doesn't
have a "batch" dimension (like a gradient). More often you will want to
aggregate across the global batch, which you can get by specifying the batch
dimension as the axis
, typically axis=0
. In this case it would return a
scalar 0+1+2+3+4+5+6+7
.
If there is a last partial batch, you will need to specify an axis so
that the resulting shape is consistent across replicas. So if the last
batch has size 6 and it is divided into [0, 1, 2, 3] and [4, 5], you
would get a shape mismatch unless you specify axis=0
. If you specify
tf.distribute.ReduceOp.MEAN
, using axis=0
will use the correct
denominator of 6. Contrast this with computing reduce_mean
to get a
scalar value on each replica and this function to average those means,
which will weigh some values 1/8
and others 1/4
.
reduce_op
: A tf.distribute.ReduceOp
value specifying how values should
be combined.value
: A "per replica" value, e.g. returned by experimental_run_v2
to
be combined into a single tensor.axis
: Specifies the dimension to reduce along within each
replica's tensor. Should typically be set to the batch dimension, or
None
to only reduce across replicas (e.g. if the tensor has no batch
dimension).A Tensor
.
scope
scope()
Returns a context manager selecting this Strategy as current.
Inside a with strategy.scope():
code block, this thread
will use a variable creator set by strategy
, and will
enter its "cross-replica context".
A context manager.
update_config_proto
update_config_proto(config_proto)
Returns a copy of config_proto
modified for use with this strategy.
DEPRECATED: This method is not available in TF 2.x.
The updated config has something needed to run a strategy, e.g. configuration to run collective ops, or device filters to improve distributed training performance.
config_proto
: a tf.ConfigProto
object.The updated copy of the config_proto
.


-
pageguide.js attaches CSS pseudo elements to whatever features you define on your pages.
The numbered arrows can be placed on top, bottom, left or right of whatever you are trying to highlight.
-
Change the style of the pageguide as much as you want. We include both CSS and LESS in the repo. We even attach a class
to the body of the page when the guide is open.
-
The page guide also scrolls for you. Just use the forward and back arrows on the left to move been elements.
-
We include custom tracking for all the actions of the pageguide. Simply toss in your tracking code from Mixpanel, KISSMetrics, HubSpot, etc.
and find out what people are interested in.


Source
A source object returned from creating a source (via
createSourceWithParams
) with the Stripe API.
source
— an object with the following keys:
| Key | Type | Description |
|---|---|---|
| amount | Number | The amount associated with the source |
| clientSecret | String | The client secret of the source. Used for client-side polling using a publishable key |
| created | Number | When the source was created |
| currency | String | The currency associated with the source |
| flow | String | The authentication flow of the source. Can be one of: none ‖ redirect ‖ verification ‖ receiver ‖ unknown |
| livemode | Bool | Whether or not this source was created in livemode. Will be true if you used your Live Publishable Key, and false if you used your Test Publishable Key |
| metadata | Object | A set of key/value pairs associated with the source object |
| owner | Object | Information about the owner of the payment instrument |
| receiver | Object (Optional) | Information related to the receiver flow. Present if the source is a receiver |
| redirect | Object (Optional) | Information related to the redirect flow. Present if the source is authenticated by a redirect |
| status | String | The status of the source. Can be one of: pending ‖ chargable ‖ consumed ‖ cancelled ‖ failed |
| type | String | The type of the source. Can be one of: bancontact ‖ card ‖ griopay ‖ ideal ‖ sepaDebit ‖ sofort ‖ threeDSecure ‖ alipay ‖ unknown |
| usage | String | Whether this source should be reusable or not. Can be one of: reusable ‖ single ‖ unknown |
| verification | Object (Optional) | Information related to the verification flow. Present if the source is authenticated by a verification |
| details | Object | Information about the source specific to its type |
| cardDetails | Object (Optional) | If this is a card source, this property contains information about the card |
| sepaDebitDetails | Object (Optional) | If this is a SEPA Debit source, this property contains information about the sepaDebit |
owner
| Key | Type | Description |
|---|---|---|
| address | Object (Optional) | Owner’s address |
| String (Optional) | Owner’s email address | |
| name | String (Optional) | Owner’s full name |
| phone | String (Optional) | Owner’s phone number |
| verifiedAddress | Object (Optional) | Verified owner’s address |
| verifiedEmail | String (Optional) | Verified owner’s email address |
| verifiedName | String (Optional) | Verified owner’s full name |
| verifiedPhone | String (Optional) | Verified owner’s phone number |
receiver
| Key | Type | Description |
|---|---|---|
| address | Object | The address of the receiver source. This is the value that should be communicated to the customer to send their funds to |
| amountCharged | Number | The total amount charged by you |
| amountReceived | Number | The total amount received by the receiver source |
| amountReturned | Number | The total amount that was returned to the customer |
redirect
| Key | Type | Description |
|---|---|---|
| returnURL | String | The URL you provide to redirect the customer to after they authenticated their payment |
| status | String | The status of the redirect. Can be one of: pending ‖ succeeded ‖ failed ‖ unknown |
| url | String | The URL provided to you to redirect a customer to as part of a redirect authentication flow |
verification
| Key | Type | Description |
|---|---|---|
| attemptsRemaining | Number | The number of attempts remaining to authenticate the source object with a verification code |
| status | String | The status of the verification. Can be one of: pending ‖ succeeded ‖ failed ‖ unknown |
cardDetails
| Key | Type | Description |
|---|---|---|
| last4 | String | The last 4 digits of the card |
| expMonth | Number | The card’s expiration month. 1-indexed (i.e. 1 == January) |
| expYear | Number | The card’s expiration year |
| brand | String | The issuer of the card. Can be one of: JCB ‖ American Express ‖ Visa ‖ Discover ‖ Diners Club ‖ MasterCard ‖ Unknown |
| funding (iOS) | String | The funding source for the card. Can be one of: debit ‖ credit ‖ prepaid ‖ unknown |
| country | String | Two-letter ISO code representing the issuing country of the card |
| threeDSecure | String | Whether 3D Secure is supported or required by the card. Can be one of: required ‖ optional ‖ notSupported ‖ unknown |
sepaDebitDetails
| Key | Type | Description |
|---|---|---|
| last4 | String | The last 4 digits of the account number |
| bankCode | String | The account’s bank code |
| country | String | Two-letter ISO code representing the country of the bank account |
| fingerprint | String | The account’s fingerprint |
| mandateReference | String | The reference of the mandate accepted by your customer |
| mandateURL | String | The details of the mandate accepted by your customer |
Example
{
livemode: false,
amount: 50,
owner: {},
metadata: {},
clientSecret: 'src_client_secret_BLnXIZxZprDmdhw3zv12123L',
details: {
native_url: null,
statement_descriptor: null
},
type: 'alipay',
redirect: {
url: 'https://hooks.stripe.com/redirect/authenticate/src_1Az5vzE5aJKqY779Kes5s61m?client_secret=src_client_secret_BLnXIZxZprDmdhw3zv12123L',
returnURL: 'example://stripe-redirect?redirect_merchant_name=example',
status: 'succeeded'
},
usage: 'single',
created:<PHONE_NUMBER>,
flow: 'redirect',
currency: 'eur',
status: 'chargable',
}


Add
Please Select
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Element Timer:
Step 1 Stuff
Fake Step 1 copy.
Step 2 Stuff
Fake Step 2 copy.
Step 3 Stuff
Fake Step 3 copy.


Unravel News
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headache treatment studies Headache treatments children migraine adolescents non-pharmacological CBT {border-style effects primary frequency patients px;} #ffffff;} pain study improvements review significant included young ./s--- BFB reported Pain chronic depression determined pediatric quality studies, anxiety clinical intervention magnetic data education group search symptoms (.%) adolescents. associated baseline disability disorders px; recent {font-family Andrasik attention behavioral biofeedback did font-size frequency, headache. information present ranging reduction reductions therapy ./j.-...x Cephalalgia Curr Neurol Neurol. Schwartz amitriptyline approaches assessed cognitive compared disorders, double; evaluated focused headaches. literature management migraine. outcomes participants pilot possible sample selected showed stimulation stimulation" tension-type transcranial trial type (%) (.%), .–. Biofeedback Finally, Mixed Multimodal Studies TMS acute adolescent adults average border-color border-width brain carried daily days endpoint, headache, healthy include involved mindfulness noted outcome populations randomized results secondary single solid; trials use {font-style {font-weight −.% "chronic .%, ./head. Health However, Italy Powers Rep. Transcranial addressed addressing adults, adults. alternative and, anxiety, article bold changes common considered cortical courier; disability, em; evidence fact findings follow-up, general headache: i.e., instructed measures migraine: needed number papers particular pharmacological positive prevention produced prophylactic providing pt;} recurrent relaxation relevant report research reviews risk sessions significantly sizeable taken tension treatment, various years


This demo connects to a live Bugout server using its public address and then runs the "ping" function on that server and displays the result in the log below.
You can also run a copy of the Bugout server in your own browser tab to try it out locally on your machine.
// connect to server using its public address
var b = new Bugout(window.location.hash.substr(1) || "bUH7ukDvd9R2xLLRKMKWZ1mGPkgdVfufye");
log("My address is " + b.address())();
log("Connecting to the server...\n(this can take a minute)")();
// wait for connection to the server
b.on("server", function() {
// ok, we're connected
log("Connected to the server.")();
// make an RPC API call on the server and log the result
b.rpc("ping", {"Hello": "world"}, log("response:"));
// watch for {"Hello": "world", "pong": true} in the log below
// show a simple UI for testing the server API
showUI();
});
// also watch for other peers joining this server's swarm
b.on("seen", log("seen:"));


Accordion - Multiple
Multiple
Attractions
Movie Theaters
Amusement Parks
Mini Golf
Dining
Breakfast & Brunch
New American
Sushi Bars
Games
Xbox
Playstation
Switch
Exercise
Jog
Swim
Nap


ZipArchiveDelegate Protocol Reference
| Conforms to | NSObject |
|---|---|
| Declared in | ZipArchive.h |
Overview
@protocol
methods for a delegate to receive error notifications and control overwriting of files
– ErrorMessage:
Delegate method to be notified of errors
ZipArchive calls this selector on the delegate when errors are encountered.
- (void)ErrorMessage:(NSString *)msg
Parameters
msg |
a string describing the error. |
|---|
Return Value
void
Declared In
ZipArchive.h
– OverWriteOperation:
Delegate method to determine if a file should be replaced
When an zip file is being expanded and a file is about to be replaced, this selector
is called on the delegate to notify that file is about to be replaced. The delegate method
should return YES to overwrite the file, or NO to skip it.
- (BOOL)OverWriteOperation:(NSString *)file
Parameters
file |
|
|---|
Return Value
a BOOL - YES to replace, NO to skip
Declared In
ZipArchive.h


Random Quote Machine
Here, you can find some really cool quotes to motivate yourself
It's not the assurance but the possibilty that keeps me going
-Vishad Tomar
New Quote
twitter
facebook


Link header parser
This is a demo of the Deno module parse-link-header. It's imported on this page as a self-contained ES module, nifty!
Try copying and pasting the following Link header:
Result
[]
This is a demo of the Deno module parse-link-header. It's imported on this page as a self-contained ES module, nifty!
Try copying and pasting the following Link header:
[]


click me. Exceptions in event handlers should invoke onerror.
click me. Function calls in attributes should work.
click me. Nontrivial code in event handler attributes should work.
testRejectedHandlerString -- Rejected complex handlers should still be attributes (expected by jQuery). TODO(kpreid): This test is no longer relevant. Check if anything to salvage.
Move mouse here to see event.relatedTarget.
Rapid motion from outside sandbox should show a null relatedTarget.
click me (testRelatedTarget)
click me: testListenerArguments
click me: testHandlerArguments
click me
Waiting for DOMContentLoaded.
click me
testRepeatedHandlers - click me 2 times
Timeouts and Intervals
testTimeoutError (error handling in setTimeout)
requestAnimationFrame
testStringTimeout
do not click yet
click me
click me
Load events sequencing and bubbling
testRemoveSpecialHandler
testEventMutation (Assignment to properties of events)
Custom Events
Programmatically dispatched mouse by .dispatchEvent()
testStopPropagation
testPreventDefault
testInitEvent - all variants
testInitEventSecurity -- initEvent cannot be used to mutate host events
testNonTouchEvents


doctl apps
Used to manage digitalocean apps.
More information: home page
- Create an app:
doctl apps create
- Create a deployment for a specific app:
doctl apps create-deployment app_id
- Delete an app interactively:
doctl apps delete app_id
- Get an app:
doctl apps get
- List all apps:
doctl apps list
- List all deployments from a specific app:
doctl apps list-deployments app_id
- Get logs from a specific app:
doctl apps logs app_id
- Update a specific app with a given app spec:
doctl apps update app_id --spec path/to/spec.yml


Blockchain Client
Wallet Generator
Faucet
Make Transaction
View Transactions
Check Balance
(current)
Check Balance
Enter a blockchain node URL and address. Then click on "Check Balance" button to check balance
Enter node URL:
http://<IP_ADDRESS>:5000
Enter address:
The balance is:


pragma solidity ^0.4.18;
import '../interfaces/IMatryxTournament.sol';
import '../interfaces/IMatryxRound.sol';
import '../interfaces/IMatryxSubmission.sol';
import './Ownable.sol';
contract MatryxSubmission is Ownable, IMatryxSubmission {
// Parent identification
address public tournamentAddress;
address public roundAddress;
// Submission
string title;
address author;
bytes32 externalAddress;
address[] references;
address[] contributors;
uint256 timeSubmitted;
bool public publicallyAccessibleDuringTournament;
function MatryxSubmission(address _tournamentAddress, address _roundAddress, string _title, address _submissionAuthor, bytes32 _externalAddress, address[] _references, address[] _contributors, uint256 _timeSubmitted, bool _publicallyAccessibleDuringTournament) public
{
Erequire(_submissionAuthor != 0x0);
tournamentAddress = _tournamentAddress;
roundAddress = _roundAddress;
title = _title;
owner = _submissionAuthor;
author = _submissionAuthor;
externalAddress = _externalAddress;
references = _references;
contributors = _contributors;
timeSubmitted = _timeSubmitted;
publicallyAccessibleDuringTournament = _publicallyAccessibleDuringTournament;
}
/*
* Modifiers
*/
modifier onlyAuthor() {
Erequire(msg.sender == author);
_;
}
// A modifier to ensure that information can be obtained
// about this submission only when it should be (when the creator decides it can
// or after the tournament has been closed).
modifier whenAccessible(address _requester)
{
Erequire(isAccessible(_requester));
_;
}
/*
* Getters
*/
function isAccessible(address _requester) public constant returns (bool)
{
IMatryxTournament tournament = IMatryxTournament(tournamentAddress);
Ownable ownableTournament = Ownable(tournamentAddress);
bool requesterOwnsTournament = ownableTournament.isOwner(_requester);
bool requesterOwnsSubmission = _requester == author;
bool requesterIsRound = _requester == roundAddress;
bool externallyAccessible = publicallyAccessibleDuringTournament;
bool requesterIsContributor = IMatryxRound(roundAddress).requesterIsContributor(_requester);
bool winningSubmissionChosen = IMatryxRound(roundAddress).submissionChosen();
bool closedRoundAndContributorRequesting = (requesterIsContributor && winningSubmissionChosen);
bool closedTournamentAndAnyoneRequesting = !tournament.tournamentOpen();
return requesterOwnsTournament || requesterOwnsSubmission || requesterIsRound || externallyAccessible || closedRoundAndContributorRequesting || closedTournamentAndAnyoneRequesting;
}
function getTitle() constant whenAccessible(msg.sender) public returns(string) {
return title;
}
function getAuthor() constant whenAccessible(msg.sender) public returns(address) {
return author;
}
function getExternalAddress() constant whenAccessible(msg.sender) public returns (bytes32)
{
return externalAddress;
}
function getReferences() constant whenAccessible(msg.sender) public returns(address[]) {
return references;
}
function getContributors() constant whenAccessible(msg.sender) public returns(address[]) {
return contributors;
}
function getTimeSubmitted() constant whenAccessible(msg.sender) public returns(uint256) {
return timeSubmitted;
}
/*
TODO
Function - turn the submission into public when the round ends
Only the tournament
*/
/*
* Setters
*/
function makeExternallyAccessibleDuringTournament() onlyAuthor public
{
publicallyAccessibleDuringTournament = true;
}
/// @dev Edit the title of a submission (callable only by submission's owner).
/// @param _title New title for the submission.
function updateTitle(string _title) onlyAuthor public
{
title = _title;
}
/// @dev Update the external address of a submission (callable only by submission's owner).
/// @param _externalAddress New content hash for the body of the submission.
function updateExternalAddress(bytes32 _externalAddress) onlyAuthor public
{
externalAddress = _externalAddress;
}
/// @dev Add a missing reference to a submission (callable only by submission's owner).
/// @param _reference Address of additional reference to include.
function addReference(address _reference) onlyAuthor public
{
references.push(_reference);
}
/// @dev Remove an erroneous reference to a submission (callable only by submission's owner).
/// @param _referenceIndex Index of reference to remove.
function removeReference(uint256 _referenceIndex) onlyAuthor public
{
delete references[_referenceIndex];
}
/// @dev Add a contributor to a submission (callable only by submission's owner).
/// @param _contributor Address of contributor to add to the submission.
function addContributor(address _contributor) onlyAuthor public
{
contributors.push(_contributor);
}
/// @dev Remove a contributor from a submission (callable only by submission's owner).
/// @param _contributorIndex Index of the contributor to remove from the submission.
function removeContributor(uint256 _contributorIndex) onlyAuthor public
{
delete contributors[_contributorIndex];
}
function getBalance() public returns (uint256)
{
IMatryxRound round = IMatryxRound(roundAddress);
uint256 _balance = round.getBalance(this);
return _balance;
}
/// @dev Removes a submission from this round (callable only by submission's owner).
/// @param _submissionIndex Index of the submission to remove.
// function suicide(uint256 _submissionIndex) onlyAuthor public
// {
// selfdestruct();
// }
} |


Systems Design students and graduates are an innovative and entrepreneurial bunch, and over the years have created many successful companies and projects.
The company started by three University of Waterloo friends in a basement has evolved into the leading authority on traffic data. Miovision began as a traffic solution company, but their continued approach to innovation and dedication to solving real-world problems has led them to where they stand today: helping cities achieve their smart city visions.
NexJ Systems Inc. is a leading provider of enterprise customer relationship management (CRM) solutions. Prior to establishing NexJ Systems in 2003, William was the Founder, Chairman and CEO of Janna Systems, which created enterprise CRM software for financial services. William received an honorary doctorate from the University of Waterloo in 2012, and is the namesake of the Tatham Center on campus.
John founded D2L in 1999, at the age of twenty-two, while attending the University of Waterloo as a Systems Design Engineering student. He saw that online digital tools could open the door to entirely new and more effective models, and create learning experiences that are more personal, engaging and inspiring. The company is on the journey of transforming the way the world learns.
Vidyard was the first video production company to offer video hosting, analytics, and performance guarantees. Started as a Capstone Design project, the company soon graduated from the Y Combinator program and focuses exclusively on video marketing technology.
Eric developed the first commercially successful smartwatch, the Pebble Smartwatch. Before shutting down in 2016, Pebble graduated from Y Combinator and sold over a million devices. Eric describes how SYDE helped his growing entrepreneurial career: "100% help. Best undergrad engineering program for people who like to work on projects (ie design and build things). A++ would attend again. Also, the coop program at UWaterloo really helps you see what working at 'real' companies is like. My one regret is not doing more coop terms at startups."
"Nulogy began with a desire to be a special kind of company. We wanted to solve really hard, unsolved problems. We wanted to be laser-focused on delivering concrete customer value right out of the gates. Most importantly, we wanted to be a company that was meaningful." Nulogy helps brands deliver customized, traceable, and safer products to consumers with greater speed and value with less waste.
Rasmus single-handedly created the PHP scripting language, which was the first simple and elegant approach to allowing dynamic content on web pages. Today, PHP powers the vast majority of the world’s websites, and no other software in the world has had a greater influence on the Internet, an incredible impact.
In just three years Sisun has worked at Facebook, Uber, Tesla, and has now created his own startup to cure hangovers. Morning Recovery made $1 million in their first three months.
Andre and his co-founder are developing wearable products, like the HeartWatch, a 24/7 wearable heart monitoring system that will contact EMS in the event of a heart attack. The company has not only graduated from Y Combinator, but was showcased on 'Dragon's Den' and recieved an investment from every Dragon.
Jerry Zhang has a long entrepreneurial track record, the latest of which is Sodatone. Recently acquired by Warner Music, this machine learning start-up helps record labels to find and track the next hit artist.
The Capstone Design project is the culmination of the undergraduate student expereience, where students in teams of 4 push their boundaries to solve complex real-world problems.
In the final months of school, the class of 2017 conducted a massive survey on themselves covering everything from courses, co-op, social life, family background and plans for the future. 77 of 82 students (94%) are represented in this survey, making it a complete capture of the graduating student body.


Hey There, JavaScript Disabler!
Congrats for choosing to protect the web from JavaScript misuse and
abuse! Here's some good news for you: this website doesn't require
any JavaScript!
(Don't worry, this popup will automatically disappear within 6
seconds)


Lunit INSIGHT
Lunit is a medical AI company devoted to opening a new era of diagnostics and therapeutics using artificial intelligence. Through AI and data-driven medicine, Lunit aims to increase the survival of patients and help physicians provide better care.
The name Lunit is abbreviated from “Learning Unit.” Learning is in the core of Lunit’s identity and culture—the algorithm and members constantly advance through learning, and we believe there is nothing we can’t learn. The brand identity of Lunit is visualized by a connection of two circles, linked through a diagonal line. It represents the basic unit of the artificial neural network composed of numerous nodes and layers, which further implies the connection between artificial intelligence and medicine.
The brand design of Lunit is based on simple and intuitive transmission of difficult and complicated medical information. For the branded publications, represented by a medical brochure, we highlighted intuitive medical infographics and simple layout to better communicate complicated medical information. We also designated individual yet concerted visual themes—a variation of color and gradient—for each medical area the company focus in. The gradient artwork visualized the harmony in between the advancing medical system and the relationship between human and artificial intelligence. The positive energy that comes from the overall look and feel represents the corporate identity of Lunit as a company devoted in using advanced technology for the benefit of humanity, sharing acquired knowledge, and creating a meaningful impact on the society.


{% extends "layout.html" %}
{% block content %}
{% include "partials/nav.html" %}
{% include "partials/showcase.html" %}
{% include "partials/about.html" %}
{% include "partials/portfolio.html" %}
{% include "partials/contact.html" %}
{% include "partials/footer.html" %}
{% endblock %}


Digital Drawing Board
Color:
Thickness:
1
2
3
4
5
6
7
8
9
10
11
12
Eraser
Clear
GitHub
Note: Please enable Javascript
Made with ❤ by
Dev_Ashu


Failsafe - fault tolerance and resilience patterns for the JVM.
See the
project homepage
for usage instructions.


Adding to the more than 70 artisan, premium and ultra premium tequilas and mezcals already on the bar, SOL Cocina offers guests something extra special: its very own SOL Single Barrel Reposado Tequila, custom blended at Casa Noble’s Tequila distillery by Partners Rich Howland and Matt Baumayr. Guests will be to be among the first [...]
SOL Cocina is inviting guests to bare their SOLs this Valentine’s Day with aphrodisiac cocktials from behind the bar. The following cocktails are meant to be paired with Executive Chef Deborah Schneider’s bold, modern interpretations of traditional Coastal Mexican cuisine with a focus on seasonal, sustainable ingredients: Valentine’s Cocktails for LoversServed 11:30 a.m. – midnight [...]
Ring in the New Year in true Baja style without the fuss of leaving your home town! For New Year’s Eve Dinner, order from SOL’s noted dinner menu, which will be complemented with special twists on traditional Baja cuisine. SOL SCOTTSDALE: Chef Deb and Chef Ernesto have created a great dinner special for New Year’s [...]


Amazon Black Friday deals live as Echo, Kindle and Alexa gadgets get discounts
Amazon has officially launched its Black Friday sale on the Friday before Black Friday.
The gigantic online retailer is putting out eight days of sales to cash in on the shopping frenzy, leading with its own gadgetry.
The likes of the Kindle e-reader and the Echo smart speakers have been given hefty discounts to get the ball rolling.
The Amazon Black Friday sale began on Friday 22 November at 00.01 and runs until 23:59 on Friday 29 November.
As well as discounting thousands of products, the website will also feature a Deals of the Day section that will round up the best bargains customers can score every day.
There are also thousands of ‘Lightning Deals’ – products available at a discount, in limited quantities, for a short period of time.
Doug Gurr, VP, UK Country Manager at Amazon.co.uk said: ‘We know how important it is to help our customers save money where they can at this time of year, so we’re excited to be launching our tenth Black Friday Sale to UK customers in the lead-up to the festive season.’
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Here are some of the best tech deals we’ve found so far today:
Amazon Echo Show 8 was £119.99 now £59.99
Amazon Kindle Paperwhite (2018) was £119.99 now £84.99
Amazon Fire HD 10 was £149.99 now £94.99
Bang & Olufsen Beoplay H4 Wireless Headphones was £179 now £125
Philips 55PUS7304/12 55-Inch 4K UHD Android Smart TV was £749 now £509
Microsoft Surface Pro 7 12.3” Tablet (Platinum) was £979 now £779
Olympus TG-6 Tough Camera – Red was £359.99 now £299.99
Miele 10661210 Blizzard CX1 Excellence PowerLine Bagless Vacuum Cleaner was £219 now £164.49
Bose On Ear Wireless Headphones – Triple Black was £160 now £109.95
ASUS ZenBook UX333 Full HD 13.3 Inch Laptop was £899.99 now £699.99
Black Friday 2019: Amazon subscription deals
Amazon Music Unlimited 4 months, now £0.99 (was £9.99 pm)
Love a soundtrack to your day? Amazon is giving music lovers the chance to stream four months of their Amazon Music Unlimited service for under one pound. You'll be able to skip as many tracks as you like and you won't be interrupted by adverts. You don't need to be a Prime member to take advantage of this amazing deal, but it's only available for new subscribers.
Offer valid till January 6th 2020
Amazon Kindle Unlimited 3 months, now free (was £7.99 pm)
Calling all Kindle users! You can get a 3-month free trial on Amazon's Kindle Unlimited reading offer. That means you can explore over 1 million titles, thousands of audiobooks and selected magazine subscriptions for free.
Offer valid till December 8th 2019


Most guests will fly in to A'oi regional airport, which is approximately a 20km distance from the resort. We offer free airport shuttle pickup, no reservation necessary. The shuttles depart every twenty minutes outside the airport. Look for the shuttle labeled "Lazy Cat Resort."
For any questions or difficulty locating the shuttle, please contact us at<PHONE_NUMBER>.
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4 Must-Have Ingredients of a Successful Blog Post
1. The article needs a point
I'm astounded at what number of blog articles have nothing to state. Indeed, perhaps they have a lot of things to state, yet they don't have a point. An article should impart a point.
There's another advantage to having a point: Using particular watchwords. As a major aspect of a substance promoting exertion, you most likely experienced the activity of arranging the watchwords to target, and making content that would propel those catchphrases. Presently, you can actualize those catchphrases by composing an article with a compact point.
2. Images
You won't see an article to my blog without an image. Image a blog without an image, it just doesn't happen.
Your objective isn't simply to include sight to behold, however that is most likely something worth being thankful for. You will probably include vital pictures that assistance your perusers, and improve your substance.
3. Unique Content
Initially, you need something one of a kind to state, from a topical point of view. At the end of the day, you would prefer essentially not to make a similar point that every other person is making. You need an exceptional point, approach, or turn.
Second, you require special substance. You ideally wouldn't reorder content from another site onto your own. Be that as it may, you might be enticed to do a connection gathering or "best of the web," in which you clergyman a rundown of articles that you've observed to intrigue.
Unique content is more likely to be linked and shared. People are going to go to your content because it’s one of a kind.
4. Internal Linking
An internal linking strategy for which you link to other related posts within your own site and can serve to enhance and deepen the overall value of a website. Though I also backlink some related post to my blog which is not my post/not from my own site. It isn't complicated.
Here are the basic points:
- Make enough substance all through the site. You will require stuff to connection to.
- Connection to pertinent information on your site. Topical pertinence between the pages will support the expert of the two pages.
- Utilize enough inner connects to make it advantageous. I'd recommend somewhere in the range of 3 to 10 interior connections for each post.
- Use links that the reader will be interested in. You want them to engage with your content and site.
Conclusion:
Producing content on a blog is not the way to progress. Precisely consider your technique in light of these eight focuses. In the event that you have an approach that takes after this philosophy, at that point you'll begin to see essentially higher achievement rates with your substance showcasing.
What other must-have blog tips would you suggest?


Cereal, enjoyed by millions every day for breakfast, is a strange food. Heavily advertised, artificially colored, and manually enriched, most cereals are more sugary candy than nutritious meal.  We're here to make that decision a little easier with some helpful tips
We split cereals into three groups based on shelf position in the store. After crunching the numbers, the bottom shelf (on average) contains the healthiest cereals, while the middle shelf contains the sweetest.
As we can see from the first graph, cereals on the bottom shelf have a lower calorie count. The count section indicates the number of cereals in each shelf group.
Cereals on the bottom shelf have fewer grams of sugar and fewer grams of fat than the other two shelfs. The bottom shelf also has more grams of fiber and protein that the middle shelf. The middle shelf contains the highest amount of sugar and the lowest amount of protein and fiber.
Again, the middle shelf lacks the sodium and potassium of other cereals.
What's in a name?  Below is a scatter plot of words that appear in cereal names. The larger and darker the circle, the more cereals contain that word in their name. The x-axis is sugar amount and the y-axis is calorie count.
The result? Stay away from cereals with "Crispy", "Crisp", "Golden", or "Apple" in the name, as they probably contain high amounts of sugar. Cereals with "Wheat", "Corn", "Rice", and "Grain" generally have less sugar and similar calorie counts.
It's important to see what makes up an individual cereal. Sugary cereals are easy to spot (see below) as the blue section takes up a majority of the chart.
{% for k, v in piecharts.iteritems() %} {% end %}Healthier cereals have a more even distribution among the chart, with sugar taking up a much smaller portion.
Check out the cereal index to see the chart for all the cereals in the data set.


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See:
Description
| Interface Summary | |
|---|---|
| DatagramDecoder | Interface for marking objects as datagram decoders. |
| DatagramEncoder | Interface for marking objects encodable as datagram. |
| Executable<Controller,ExecutionException extends RuntimeException> | Marks an object executable. |
| SimpleSerialization<T> | Guarantees an object can produce and populate itself with a string value, not unlike deserialization. |
| Class Summary | |
|---|---|
| DecoderUtil | Helper utility for interpreting encoded strings, including complete reflective decoding of objects. |
| EncoderUtil | Helper utility for encoding (integers, strings etc. |
Provides functional neutral utility classes.
Provides the Executable
interface used to enable direct execution of decoded client request objects (making them function as Command objects).
Provides the DatagramEncoder
and DatagramDecoder
interfaces used to custom serialize message to and from the server.
In addition, provides utility classes that help automate encoding/decoding using Java reflection.
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Visualisation and results formatting¶
Documentation of the code in src.final.
Descriptive Stats and Decision Tree¶
The code builds different plots for the data used and give the statistics. We have generated 3 plots using the seaborn and matplotlib python libraries. We imported our dataset and exported all our images in the out_figures folder. We generated one bar chart, one combined image of density plots. We also exported our decision tree image to this folder from the original data folder to keep all our generated images at one place.


Techno-functional business consultant with 12+ years of experience in providing web/mobile technology solutions in open source & Microsoft technologies. Strong background in analysis, designing and developing solutions for Digital Marketing Platforms. Expertise in converting business requirements into functional & technical design specs and implementation from inception till end. Delivered cloud and CMS stack software products viz. Sitecore, Sitefinity & Umbraco. Engagement in pre-sales activities like responding to RFPs, CMS/CXM package evaluation. Established track record in MSBI, i.e. Power BI, Datawarehouse, SSIS, SSRS


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