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Class RuledSurface
Surface definition of a surface defined by two curves. Both curves use the standard parameter interval from 0.0 to 1.0
The u-direction is provided by a combination of the two curves. The v parameter is defined by a line starting on the
first curve and ending on the second curve. It is the surface described by a wire or rubber band synchronously moving along
the two curves. the default parameter space is 0.0 to 1.0 on u and v.
Inheritance
System.Object
RuledSurface
Implements
System.Runtime.Serialization.ISerializable
Inherited Members
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.ToString()
Assembly: CADability.dll
Syntax
[Serializable]
public class RuledSurface : ISurfaceImpl, ISurface, IOctTreeInsertable, ISerializable, IExportStep
Constructors
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RuledSurface(ICurve, ICurve)
Declaration
public RuledSurface(ICurve firstCurve, ICurve secondCurve)
Parameters
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RuledSurface(SerializationInfo, StreamingContext)
Declaration
protected RuledSurface(SerializationInfo info, StreamingContext context)
Parameters
| Type |
Name |
Description |
| System.Runtime.Serialization.SerializationInfo |
info |
|
| System.Runtime.Serialization.StreamingContext |
context |
|
Properties
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FirstCurve
Declaration
public ICurve FirstCurve { get; }
Property Value
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IsUPeriodic
Declaration
public override bool IsUPeriodic { get; }
Property Value
| Type |
Description |
| System.Boolean |
|
Overrides
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IsVPeriodic
Declaration
public override bool IsVPeriodic { get; }
Property Value
| Type |
Description |
| System.Boolean |
|
Overrides
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SecondCurve
Declaration
public ICurve SecondCurve { get; }
Property Value
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UPeriod
Declaration
public override double UPeriod { get; }
Property Value
| Type |
Description |
| System.Double |
|
Overrides
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VPeriod
Declaration
public override double VPeriod { get; }
Property Value
| Type |
Description |
| System.Double |
|
Overrides
Methods
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Clone()
Declaration
public override ISurface Clone()
Returns
Overrides
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CopyData(ISurface)
Declaration
public override void CopyData(ISurface CopyFrom)
Parameters
Overrides
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Derivation2At(GeoPoint2D, out GeoPoint, out GeoVector, out GeoVector, out GeoVector, out GeoVector, out GeoVector)
Declaration
public override void Derivation2At(GeoPoint2D uv, out GeoPoint location, out GeoVector du, out GeoVector dv, out GeoVector duu, out GeoVector dvv, out GeoVector duv)
Parameters
Overrides
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DerivationAt(GeoPoint2D, out GeoPoint, out GeoVector, out GeoVector)
Declaration
public override void DerivationAt(GeoPoint2D uv, out GeoPoint location, out GeoVector du, out GeoVector dv)
Parameters
Overrides
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FixedU(Double, Double, Double)
Declaration
public override ICurve FixedU(double u, double vmin, double vmax)
Parameters
| Type |
Name |
Description |
| System.Double |
u |
|
| System.Double |
vmin |
|
| System.Double |
vmax |
|
Returns
Overrides
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FixedV(Double, Double, Double)
Declaration
public override ICurve FixedV(double v, double umin, double umax)
Parameters
| Type |
Name |
Description |
| System.Double |
v |
|
| System.Double |
umin |
|
| System.Double |
umax |
|
Returns
Overrides
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GetExtrema()
Declaration
public override GeoPoint2D[] GetExtrema()
Returns
Overrides
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GetLineIntersection(GeoPoint, GeoVector)
Declaration
public override GeoPoint2D[] GetLineIntersection(GeoPoint startPoint, GeoVector direction)
Parameters
Returns
Overrides
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GetModified(ModOp)
Declaration
public override ISurface GetModified(ModOp m)
Parameters
| Type |
Name |
Description |
| ModOp |
m |
|
Returns
Overrides
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GetNaturalBounds(out Double, out Double, out Double, out Double)
Declaration
public override void GetNaturalBounds(out double umin, out double umax, out double vmin, out double vmax)
Parameters
| Type |
Name |
Description |
| System.Double |
umin |
|
| System.Double |
umax |
|
| System.Double |
vmin |
|
| System.Double |
vmax |
|
Overrides
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GetPlaneIntersection(PlaneSurface, Double, Double, Double, Double, Double)
Declaration
public override IDualSurfaceCurve[] GetPlaneIntersection(PlaneSurface pl, double umin, double umax, double vmin, double vmax, double precision)
Parameters
| Type |
Name |
Description |
| PlaneSurface |
pl |
|
| System.Double |
umin |
|
| System.Double |
umax |
|
| System.Double |
vmin |
|
| System.Double |
vmax |
|
| System.Double |
precision |
|
Returns
Overrides
|
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GetPropertyEntry(IFrame)
Declaration
public override IPropertyEntry GetPropertyEntry(IFrame frame)
Parameters
| Type |
Name |
Description |
| IFrame |
frame |
|
Returns
Overrides
|
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GetSafeParameterSteps(Double, Double, Double, Double, out Double[], out Double[])
Declaration
public override void GetSafeParameterSteps(double umin, double umax, double vmin, double vmax, out double[] intu, out double[] intv)
Parameters
| Type |
Name |
Description |
| System.Double |
umin |
|
| System.Double |
umax |
|
| System.Double |
vmin |
|
| System.Double |
vmax |
|
| System.Double[] |
intu |
|
| System.Double[] |
intv |
|
Overrides
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Intersect(ICurve, BoundingRect, out GeoPoint[], out GeoPoint2D[], out Double[])
Declaration
public override void Intersect(ICurve curve, BoundingRect uvExtent, out GeoPoint[] ips, out GeoPoint2D[] uvOnFaces, out double[] uOnCurve3Ds)
Parameters
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Make3dCurve(ICurve2D)
Declaration
public override ICurve Make3dCurve(ICurve2D curve2d)
Parameters
Returns
Overrides
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Modify(ModOp)
Declaration
public override void Modify(ModOp m)
Parameters
| Type |
Name |
Description |
| ModOp |
m |
|
Overrides
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PointAt(GeoPoint2D)
Declaration
public override GeoPoint PointAt(GeoPoint2D uv)
Parameters
Returns
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ReverseOrientation()
Declaration
public override ModOp2D ReverseOrientation()
Returns
Overrides
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SameGeometry(BoundingRect, ISurface, BoundingRect, Double, out ModOp2D)
Declaration
public override bool SameGeometry(BoundingRect thisBounds, ISurface other, BoundingRect otherBounds, double precision, out ModOp2D firstToSecond)
Parameters
Returns
| Type |
Description |
| System.Boolean |
|
Overrides
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UDirection(GeoPoint2D)
Declaration
public override GeoVector UDirection(GeoPoint2D uv)
Parameters
Returns
Overrides
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VDirection(GeoPoint2D)
Declaration
public override GeoVector VDirection(GeoPoint2D uv)
Parameters
Returns
Overrides
Explicit Interface Implementations
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IExportStep.Export(ExportStep, Boolean)
Declaration
int IExportStep.Export(ExportStep export, bool topLevel)
Parameters
| Type |
Name |
Description |
| ExportStep |
export |
|
| System.Boolean |
topLevel |
|
Returns
| Type |
Description |
| System.Int32 |
|
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ISerializable.GetObjectData(SerializationInfo, StreamingContext)
Declaration
void ISerializable.GetObjectData(SerializationInfo info, StreamingContext context)
Parameters
| Type |
Name |
Description |
| System.Runtime.Serialization.SerializationInfo |
info |
|
| System.Runtime.Serialization.StreamingContext |
context |
|
Implements
System.Runtime.Serialization.ISerializable


wf32d¶
Use this function to facilitate batch runs or for the TEAL interface.
- The wf32d primary functions include:
- DARKCORR: dark current subtraction
- FLATCORR: flat-fielding
- PHOTCORR: photometric keyword calculations
- FLUXCORR: photometric normalization of the UVIS1 and UVIS2 chips
Only those steps with a switch value of PERFORM in the input files will be executed, after which the switch will be set to COMPLETE in the corresponding output files.
Examples¶
In Python without TEAL:
>>> from wfc3tools import wf32d >>> wf32d(filename)In Python with TEAL:
>>> from stsci.tools import teal >>> from wfc3tools import wf32d >>> teal.teal('wf32d')In Pyraf:
>>> import wfc3tools >>> epar wf32d
Parameters¶
- input : str
Name of input files
- a single filename (
iaa012wdq_raw.fits
)- a Python list of filenames
- a partial filename with wildcards (
\*raw.fits
)- filename of an ASN table (
\*asn.fits
)- an at-file (
@input
)- output: str
- Name of the output FITS file.
- dqicorr: str, “PERFORM/OMIT”, optional
- Update the dq array from bad pixel table
- darkcorr: str, “PERFORM/OMIT”, optional
- Subtract the dark image
- flatcorr: str, “PERFORM/OMIT”, optional
- Multiply by the flatfield image
- shadcorr: str, “PERFORM/OMIT”, optional
- Correct for shutter shading (CCD)
- photcorr: str, “PERFORM/OMIT”, optional
- Update photometry keywords in the header
- fluxcorr; str, “PERFORM/OMIT”, optional
- Perform chip photometry normalization
- verbose: bool, optional
- Print verbose time stamps?
- quiet: bool, optional
- Print messages only to trailer file?
The wf32d function can also be called directly from the OS command line:
>>> wf32d.e input output [-options]
Where the OS options include:
- -v: verbose
- -f: print time stamps
- -d: debug
- -dark: perform dark subtraction
- -dqi: update the DQ array
- -flat: perform flat correction
- -shad: perform shading correction
- -phot: perform phot correction


The history of Chester Cheetah
Source: Wikipedia
Cheetos' original mascot was the Cheetos Mouse, who debuted in 1971 and disappeared around 1979. In 1986, Chester Cheetah was created by Brad Morgan, who art directed the commercials and designed the character, and Stephen Kane who wrote the original scripts for television commercials.
The original 24-frame animation was done Richard Williams. After Chester's introduction, the sly, smooth voiced cheetah began starring in more commercials and eventually became Cheetos' official mascot. He used the slogan "It ain't easy bein' cheesy" from 1986 to the early 1990s, then "The cheese that goes crunch!" until it became "Dangerously cheesy!" in 1996.


{% extends '../layouts/template.html' %} {% block title %}Check your National Insurance record {{mvpversion}}{% endblock %} {% block serviceName %}Check your National Insurance record{% endblock %} {% block main %} {% include '../persephone_login.html' %}
You have 25 years of full contributions
There are 5 years when you did not contribute enough where you can make up the shortfall.
{% include './ni_table5.html' %} Back


| PhotoLine Tip: Pillars in PhotoLine 32 |
| 1. We want
to create some pillars. Therefore we create RGB-image with 150x300 pixel.
2. Now take
the blend tool from the toolbar and setup a linear blend from black
to middle grey. It should look like this:
4. Create
a blend in the mask like this.
5. Now leave the "Edit Mask" mode. 6. Setup a
nice foreground color and select the fill tool from the tool bar.
Fill the pillar and you will get pillars like this:
|
| ««Previous Tip <Contents> Next Tip»» |
| Please email questions and suggestions on these tips to Gerhard Huber. |


"I’m excited to be able to contribute to a community of founders who are thinking critically about their giving. Together I believe we can achieve a huge amount of good."
Miguel Mckelvey
Founder & CEO
"Right and left, the streets take you waterward. Its extreme down-town is the battery, where that noble mole is washed by waves, and cooled by breezes, which a few hours previous were out of sight of land."
Miguel Mckelvey
Founder & CEO


Included With Your Installation
Site Excavation (within 10" slope)
Pool Installation
Liner Installation
Filter and Pump Installation


RandomDataset
A Dataset
of pseudorandom values.
__init__
__init__(seed=None)
A Dataset
of pseudorandom values.
element_spec
The type specification of an element of this dataset.
A nested structure of tf.TypeSpec
objects matching the structure of an
element of this dataset and specifying the type of individual components.
output_classes
Returns the class of each component of an element of this dataset. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use tf.compat.v1.data.get_output_classes(dataset)
.
A nested structure of Python type
objects corresponding to each
component of an element of this dataset.
output_shapes
Returns the shape of each component of an element of this dataset. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use tf.compat.v1.data.get_output_shapes(dataset)
.
A nested structure of tf.TensorShape
objects corresponding to each
component of an element of this dataset.
output_types
Returns the type of each component of an element of this dataset. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use tf.compat.v1.data.get_output_types(dataset)
.
A nested structure of tf.DType
objects corresponding to each component
of an element of this dataset.
__iter__
__iter__()
Creates an Iterator
for enumerating the elements of this dataset.
The returned iterator implements the Python iterator protocol and therefore can only be used in eager mode.
An Iterator
over the elements of this dataset.
RuntimeError
: If not inside of tf.function and not executing eagerly.apply
apply(transformation_func)
Applies a transformation function to this dataset.
apply
enables chaining of custom Dataset
transformations, which are
represented as functions that take one Dataset
argument and return a
transformed Dataset
.
dataset = (dataset.map(lambda x: x ** 2) .apply(group_by_window(key_func, reduce_func, window_size)) .map(lambda x: x ** 3))
transformation_func
: A function that takes one Dataset
argument and
returns a Dataset
.Dataset
: The Dataset
returned by applying transformation_func
to this
dataset.batch
batch( batch_size, drop_remainder=False )
Combines consecutive elements of this dataset into batches.
The components of the resulting element will have an additional outer
dimension, which will be batch_size
(or N % batch_size
for the last
element if batch_size
does not divide the number of input elements N
evenly and drop_remainder
is False
). If your program depends on the
batches having the same outer dimension, you should set the drop_remainder
argument to True
to prevent the smaller batch from being produced.
batch_size
: A tf.int64
scalar tf.Tensor
, representing the number of
consecutive elements of this dataset to combine in a single batch.drop_remainder
: (Optional.) A tf.bool
scalar tf.Tensor
, representing
whether the last batch should be dropped in the case it has fewer than
batch_size
elements; the default behavior is not to drop the smaller
batch.Dataset
: A Dataset
.cache
cache(filename='')
Caches the elements in this dataset.
filename
: A tf.string
scalar tf.Tensor
, representing the name of a
directory on the filesystem to use for caching elements in this Dataset.
If a filename is not provided, the dataset will be cached in memory.Dataset
: A Dataset
.concatenate
concatenate(dataset)
Creates a Dataset
by concatenating the given dataset with this dataset.
a = Dataset.range(1, 4) # ==> [ 1, 2, 3 ] b = Dataset.range(4, 8) # ==> [ 4, 5, 6, 7 ] # The input dataset and dataset to be concatenated should have the same # nested structures and output types. # c = Dataset.range(8, 14).batch(2) # ==> [ [8, 9], [10, 11], [12, 13] ] # d = Dataset.from_tensor_slices([14.0, 15.0, 16.0]) # a.concatenate(c) and a.concatenate(d) would result in error. a.concatenate(b) # ==> [ 1, 2, 3, 4, 5, 6, 7 ]
dataset
: Dataset
to be concatenated.Dataset
: A Dataset
.enumerate
enumerate(start=0)
Enumerates the elements of this dataset.
It is similar to python's enumerate
.
# NOTE: The following examples use `{ ... }` to represent the # contents of a dataset. a = { 1, 2, 3 } b = { (7, 8), (9, 10) } # The nested structure of the `datasets` argument determines the # structure of elements in the resulting dataset. a.enumerate(start=5)) == { (5, 1), (6, 2), (7, 3) } b.enumerate() == { (0, (7, 8)), (1, (9, 10)) }
Dataset
: A Dataset
.filter
filter(predicate)
Filters this dataset according to predicate
.
d = tf.data.Dataset.from_tensor_slices([1, 2, 3]) d = d.filter(lambda x: x < 3) # ==> [1, 2] # `tf.math.equal(x, y)` is required for equality comparison def filter_fn(x): return tf.math.equal(x, 1) d = d.filter(filter_fn) # ==> [1]
predicate
: A function mapping a dataset element to a boolean.Dataset
: The Dataset
containing the elements of this dataset for which
predicate
is True
.filter_with_legacy_function
filter_with_legacy_function(predicate)
Filters this dataset according to predicate
. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.filter()
NOTE: This is an escape hatch for existing uses of filter
that do not work
with V2 functions. New uses are strongly discouraged and existing uses
should migrate to filter
as this method will be removed in V2.
predicate
: A function mapping a nested structure of tensors (having shapes
and types defined by self.output_shapes
and self.output_types
) to a
scalar tf.bool
tensor.Dataset
: The Dataset
containing the elements of this dataset for which
predicate
is True
.flat_map
flat_map(map_func)
Maps map_func
across this dataset and flattens the result.
Use flat_map
if you want to make sure that the order of your dataset
stays the same. For example, to flatten a dataset of batches into a
dataset of their elements:
a = Dataset.from_tensor_slices([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) a.flat_map(lambda x: Dataset.from_tensor_slices(x + 1)) # ==> # [ 2, 3, 4, 5, 6, 7, 8, 9, 10 ]
tf.data.Dataset.interleave()
is a generalization of flat_map
, since
flat_map
produces the same output as
tf.data.Dataset.interleave(cycle_length=1)
map_func
: A function mapping a dataset element to a dataset.Dataset
: A Dataset
.from_generator
from_generator( generator, output_types, output_shapes=None, args=None )
Creates a Dataset
whose elements are generated by generator
.
The generator
argument must be a callable object that returns
an object that supports the iter()
protocol (e.g. a generator function).
The elements generated by generator
must be compatible with the given
output_types
and (optional) output_shapes
arguments.
import itertools tf.compat.v1.enable_eager_execution() def gen(): for i in itertools.count(1): yield (i, [1] * i) ds = tf.data.Dataset.from_generator( gen, (tf.int64, tf.int64), (tf.TensorShape([]), tf.TensorShape([None]))) for value in ds.take(2): print value # (1, array([1])) # (2, array([1, 1]))
NOTE: The current implementation of Dataset.from_generator()
uses
tf.numpy_function
and inherits the same constraints. In particular, it
requires the Dataset
- and Iterator
-related operations to be placed
on a device in the same process as the Python program that called
Dataset.from_generator()
. The body of generator
will not be
serialized in a GraphDef
, and you should not use this method if you
need to serialize your model and restore it in a different environment.
NOTE: If generator
depends on mutable global variables or other external
state, be aware that the runtime may invoke generator
multiple times
(in order to support repeating the Dataset
) and at any time
between the call to Dataset.from_generator()
and the production of the
first element from the generator. Mutating global variables or external
state can cause undefined behavior, and we recommend that you explicitly
cache any external state in generator
before calling
Dataset.from_generator()
.
generator
: A callable object that returns an object that supports the
iter()
protocol. If args
is not specified, generator
must take no
arguments; otherwise it must take as many arguments as there are values
in args
.output_types
: A nested structure of tf.DType
objects corresponding to
each component of an element yielded by generator
.output_shapes
: (Optional.) A nested structure of tf.TensorShape
objects
corresponding to each component of an element yielded by generator
.args
: (Optional.) A tuple of tf.Tensor
objects that will be evaluated
and passed to generator
as NumPy-array arguments.Dataset
: A Dataset
.from_sparse_tensor_slices
from_sparse_tensor_slices(sparse_tensor)
Splits each rank-N tf.SparseTensor
in this dataset row-wise. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use tf.data.Dataset.from_tensor_slices()
.
sparse_tensor
: A tf.SparseTensor
.Dataset
: A Dataset
of rank-(N-1) sparse tensors.from_tensor_slices
from_tensor_slices(tensors)
Creates a Dataset
whose elements are slices of the given tensors.
Note that if tensors
contains a NumPy array, and eager execution is not
enabled, the values will be embedded in the graph as one or more
tf.constant
operations. For large datasets (> 1 GB), this can waste
memory and run into byte limits of graph serialization. If tensors
contains one or more large NumPy arrays, consider the alternative described
in this guide.
tensors
: A dataset element, with each component having the same size in
the 0th dimension.Dataset
: A Dataset
.from_tensors
from_tensors(tensors)
Creates a Dataset
with a single element, comprising the given tensors.
Note that if tensors
contains a NumPy array, and eager execution is not
enabled, the values will be embedded in the graph as one or more
tf.constant
operations. For large datasets (> 1 GB), this can waste
memory and run into byte limits of graph serialization. If tensors
contains one or more large NumPy arrays, consider the alternative described
in this
guide.
tensors
: A dataset element.Dataset
: A Dataset
.interleave
interleave( map_func, cycle_length=AUTOTUNE, block_length=1, num_parallel_calls=None )
Maps map_func
across this dataset, and interleaves the results.
For example, you can use Dataset.interleave()
to process many input files
concurrently:
# Preprocess 4 files concurrently, and interleave blocks of 16 records from # each file. filenames = ["/var/data/file1.txt", "/var/data/file2.txt", ...] dataset = (Dataset.from_tensor_slices(filenames) .interleave(lambda x: TextLineDataset(x).map(parse_fn, num_parallel_calls=1), cycle_length=4, block_length=16))
The cycle_length
and block_length
arguments control the order in which
elements are produced. cycle_length
controls the number of input elements
that are processed concurrently. If you set cycle_length
to 1, this
transformation will handle one input element at a time, and will produce
identical results to tf.data.Dataset.flat_map
. In general,
this transformation will apply map_func
to cycle_length
input elements,
open iterators on the returned Dataset
objects, and cycle through them
producing block_length
consecutive elements from each iterator, and
consuming the next input element each time it reaches the end of an
iterator.
a = Dataset.range(1, 6) # ==> [ 1, 2, 3, 4, 5 ] # NOTE: New lines indicate "block" boundaries. a.interleave(lambda x: Dataset.from_tensors(x).repeat(6), cycle_length=2, block_length=4) # ==> [1, 1, 1, 1, # 2, 2, 2, 2, # 1, 1, # 2, 2, # 3, 3, 3, 3, # 4, 4, 4, 4, # 3, 3, # 4, 4, # 5, 5, 5, 5, # 5, 5]
NOTE: The order of elements yielded by this transformation is
deterministic, as long as map_func
is a pure function. If
map_func
contains any stateful operations, the order in which
that state is accessed is undefined.
map_func
: A function mapping a dataset element to a dataset.cycle_length
: (Optional.) The number of input elements that will be
processed concurrently. If not specified, the value will be derived from
the number of available CPU cores. If the num_parallel_calls
argument
is set to tf.data.experimental.AUTOTUNE
, the cycle_length
argument
also identifies the maximum degree of parallelism.block_length
: (Optional.) The number of consecutive elements to produce
from each input element before cycling to another input element.num_parallel_calls
: (Optional.) If specified, the implementation creates a
threadpool, which is used to fetch inputs from cycle elements
asynchronously and in parallel. The default behavior is to fetch inputs
from cycle elements synchronously with no parallelism. If the value
tf.data.experimental.AUTOTUNE
is used, then the number of parallel
calls is set dynamically based on available CPU.Dataset
: A Dataset
.list_files
list_files( file_pattern, shuffle=None, seed=None )
A dataset of all files matching one or more glob patterns.
NOTE: The default behavior of this method is to return filenames in
a non-deterministic random shuffled order. Pass a seed
or shuffle=False
to get results in a deterministic order.
If we had the following files on our filesystem: - /path/to/dir/a.txt - /path/to/dir/b.py - /path/to/dir/c.py If we pass "/path/to/dir/*.py" as the directory, the dataset would produce: - /path/to/dir/b.py - /path/to/dir/c.py
file_pattern
: A string, a list of strings, or a tf.Tensor
of string type
(scalar or vector), representing the filename glob (i.e. shell wildcard)
pattern(s) that will be matched.shuffle
: (Optional.) If True
, the file names will be shuffled randomly.
Defaults to True
.seed
: (Optional.) A tf.int64
scalar tf.Tensor
, representing the random
seed that will be used to create the distribution. See
tf.compat.v1.set_random_seed
for behavior.Dataset
: A Dataset
of strings corresponding to file names.make_initializable_iterator
make_initializable_iterator(shared_name=None)
Creates an Iterator
for enumerating the elements of this dataset. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use for ... in dataset:
to iterate over a dataset. If using tf.estimator
, return the Dataset
object directly from your input function. As a last resort, you can use tf.compat.v1.data.make_initializable_iterator(dataset)
.
Note: The returned iterator will be in an uninitialized state,
and you must run the iterator.initializer
operation before using it:
dataset = ... iterator = dataset.make_initializable_iterator() # ... sess.run(iterator.initializer)
shared_name
: (Optional.) If non-empty, the returned iterator will be
shared under the given name across multiple sessions that share the same
devices (e.g. when using a remote server).An Iterator
over the elements of this dataset.
RuntimeError
: If eager execution is enabled.make_one_shot_iterator
make_one_shot_iterator()
Creates an Iterator
for enumerating the elements of this dataset. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use for ... in dataset:
to iterate over a dataset. If using tf.estimator
, return the Dataset
object directly from your input function. As a last resort, you can use tf.compat.v1.data.make_one_shot_iterator(dataset)
.
Note: The returned iterator will be initialized automatically. A "one-shot" iterator does not currently support re-initialization.
An Iterator
over the elements of this dataset.
map
map( map_func, num_parallel_calls=None )
Maps map_func
across the elements of this dataset.
This transformation applies map_func
to each element of this dataset, and
returns a new dataset containing the transformed elements, in the same
order as they appeared in the input.
a = Dataset.range(1, 6) # ==> [ 1, 2, 3, 4, 5 ] a.map(lambda x: x + 1) # ==> [ 2, 3, 4, 5, 6 ]
The input signature of map_func
is determined by the structure of each
element in this dataset. For example:
# NOTE: The following examples use `{ ... }` to represent the # contents of a dataset. # Each element is a `tf.Tensor` object. a = { 1, 2, 3, 4, 5 } # `map_func` takes a single argument of type `tf.Tensor` with the same # shape and dtype. result = a.map(lambda x: ...) # Each element is a tuple containing two `tf.Tensor` objects. b = { (1, "foo"), (2, "bar"), (3, "baz") } # `map_func` takes two arguments of type `tf.Tensor`. result = b.map(lambda x_int, y_str: ...) # Each element is a dictionary mapping strings to `tf.Tensor` objects. c = { {"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}, {"a": 3, "b": "baz"} } # `map_func` takes a single argument of type `dict` with the same keys as # the elements. result = c.map(lambda d: ...)
The value or values returned by map_func
determine the structure of each
element in the returned dataset.
# `map_func` returns a scalar `tf.Tensor` of type `tf.float32`. def f(...): return tf.constant(37.0) result = dataset.map(f) result.output_classes == tf.Tensor result.output_types == tf.float32 result.output_shapes == [] # scalar # `map_func` returns two `tf.Tensor` objects. def g(...): return tf.constant(37.0), tf.constant(["Foo", "Bar", "Baz"]) result = dataset.map(g) result.output_classes == (tf.Tensor, tf.Tensor) result.output_types == (tf.float32, tf.string) result.output_shapes == ([], [3]) # Python primitives, lists, and NumPy arrays are implicitly converted to # `tf.Tensor`. def h(...): return 37.0, ["Foo", "Bar", "Baz"], np.array([1.0, 2.0] dtype=np.float64) result = dataset.map(h) result.output_classes == (tf.Tensor, tf.Tensor, tf.Tensor) result.output_types == (tf.float32, tf.string, tf.float64) result.output_shapes == ([], [3], [2]) # `map_func` can return nested structures. def i(...): return {"a": 37.0, "b": [42, 16]}, "foo" result.output_classes == ({"a": tf.Tensor, "b": tf.Tensor}, tf.Tensor) result.output_types == ({"a": tf.float32, "b": tf.int32}, tf.string) result.output_shapes == ({"a": [], "b": [2]}, [])
map_func
can accept as arguments and return any type of dataset element.
Note that irrespective of the context in which map_func
is defined (eager
vs. graph), tf.data traces the function and executes it as a graph. To use
Python code inside of the function you have two options:
1) Rely on AutoGraph to convert Python code into an equivalent graph computation. The downside of this approach is that AutoGraph can convert some but not all Python code.
2) Use tf.py_function
, which allows you to write arbitrary Python code but
will generally result in worse performance than 1). For example:
d = tf.data.Dataset.from_tensor_slices(['hello', 'world']) # transform a string tensor to upper case string using a Python function def upper_case_fn(t: tf.Tensor) -> str: return t.numpy().decode('utf-8').upper() d.map(lambda x: tf.py_function(func=upper_case_fn, inp=[x], Tout=tf.string)) # ==> [ "HELLO", "WORLD" ]
map_func
: A function mapping a dataset element to another dataset element.num_parallel_calls
: (Optional.) A tf.int32
scalar tf.Tensor
,
representing the number elements to process asynchronously in parallel.
If not specified, elements will be processed sequentially. If the value
tf.data.experimental.AUTOTUNE
is used, then the number of parallel
calls is set dynamically based on available CPU.Dataset
: A Dataset
.map_with_legacy_function
map_with_legacy_function( map_func, num_parallel_calls=None )
Maps map_func
across the elements of this dataset. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.map()
NOTE: This is an escape hatch for existing uses of map
that do not work
with V2 functions. New uses are strongly discouraged and existing uses
should migrate to map
as this method will be removed in V2.
map_func
: A function mapping a nested structure of tensors (having shapes
and types defined by self.output_shapes
and self.output_types
) to
another nested structure of tensors.num_parallel_calls
: (Optional.) A tf.int32
scalar tf.Tensor
,
representing the number elements to process asynchronously in parallel.
If not specified, elements will be processed sequentially. If the value
tf.data.experimental.AUTOTUNE
is used, then the number of parallel
calls is set dynamically based on available CPU.Dataset
: A Dataset
.options
options()
Returns the options for this dataset and its inputs.
A tf.data.Options
object representing the dataset options.
padded_batch
padded_batch( batch_size, padded_shapes, padding_values=None, drop_remainder=False )
Combines consecutive elements of this dataset into padded batches.
This transformation combines multiple consecutive elements of the input dataset into a single element.
Like tf.data.Dataset.batch
, the components of the resulting element will
have an additional outer dimension, which will be batch_size
(or
N % batch_size
for the last element if batch_size
does not divide the
number of input elements N
evenly and drop_remainder
is False
). If
your program depends on the batches having the same outer dimension, you
should set the drop_remainder
argument to True
to prevent the smaller
batch from being produced.
Unlike tf.data.Dataset.batch
, the input elements to be batched may have
different shapes, and this transformation will pad each component to the
respective shape in padding_shapes
. The padding_shapes
argument
determines the resulting shape for each dimension of each component in an
output element:
tf.compat.v1.Dimension(37)
), the
component
will be padded out to that length in that dimension.tf.compat.v1.Dimension(None)
), the
component
will be padded out to the maximum length of all elements in that
dimension.See also tf.data.experimental.dense_to_sparse_batch
, which combines
elements that may have different shapes into a tf.SparseTensor
.
batch_size
: A tf.int64
scalar tf.Tensor
, representing the number of
consecutive elements of this dataset to combine in a single batch.padded_shapes
: A nested structure of tf.TensorShape
or tf.int64
vector
tensor-like objects representing the shape to which the respective
component of each input element should be padded prior to batching. Any
unknown dimensions (e.g. tf.compat.v1.Dimension(None)
in a
tf.TensorShape
or -1
in a tensor-like object) will be padded to the
maximum size of that dimension in each batch.padding_values
: (Optional.) A nested structure of scalar-shaped
tf.Tensor
, representing the padding values to use for the respective
components. Defaults are 0
for numeric types and the empty string for
string types.drop_remainder
: (Optional.) A tf.bool
scalar tf.Tensor
, representing
whether the last batch should be dropped in the case it has fewer than
batch_size
elements; the default behavior is not to drop the smaller
batch.Dataset
: A Dataset
.prefetch
prefetch(buffer_size)
Creates a Dataset
that prefetches elements from this dataset.
Note: Like other Dataset
methods, prefetch operates on the
elements of the input dataset. It has no concept of examples vs. batches.
examples.prefetch(2)
will prefetch two elements (2 examples),
while examples.batch(20).prefetch(2)
will prefetch 2 elements
(2 batches, of 20 examples each).
buffer_size
: A tf.int64
scalar tf.Tensor
, representing the maximum
number of elements that will be buffered when prefetching.Dataset
: A Dataset
.range
range(*args)
Creates a Dataset
of a step-separated range of values.
Dataset.range(5) == [0, 1, 2, 3, 4] Dataset.range(2, 5) == [2, 3, 4] Dataset.range(1, 5, 2) == [1, 3] Dataset.range(1, 5, -2) == [] Dataset.range(5, 1) == [] Dataset.range(5, 1, -2) == [5, 3]
*args
: follows the same semantics as python's xrange.
len(args) == 1 -> start = 0, stop = args[0], step = 1
len(args) == 2 -> start = args[0], stop = args[1], step = 1
len(args) == 3 -> start = args[0], stop = args[1, stop = args[2]Dataset
: A RangeDataset
.ValueError
: if len(args) == 0.reduce
reduce( initial_state, reduce_func )
Reduces the input dataset to a single element.
The transformation calls reduce_func
successively on every element of
the input dataset until the dataset is exhausted, aggregating information in
its internal state. The initial_state
argument is used for the initial
state and the final state is returned as the result.
tf.data.Dataset.range(5).reduce(np.int64(0), lambda x, _: x + 1)
produces 5
tf.data.Dataset.range(5).reduce(np.int64(0), lambda x, y: x + y)
produces 10
initial_state
: An element representing the initial state of the
transformation.reduce_func
: A function that maps (old_state, input_element)
to
new_state
. It must take two arguments and return a new element
The structure of new_state
must match the structure of
initial_state
.A dataset element corresponding to the final state of the transformation.
repeat
repeat(count=None)
Repeats this dataset count
times.
NOTE: If this dataset is a function of global state (e.g. a random number generator), then different repetitions may produce different elements.
count
: (Optional.) A tf.int64
scalar tf.Tensor
, representing the
number of times the dataset should be repeated. The default behavior (if
count
is None
or -1
) is for the dataset be repeated indefinitely.Dataset
: A Dataset
.shard
shard( num_shards, index )
Creates a Dataset
that includes only 1/num_shards
of this dataset.
This dataset operator is very useful when running distributed training, as it allows each worker to read a unique subset.
When reading a single input file, you can skip elements as follows:
d = tf.data.TFRecordDataset(input_file) d = d.shard(num_workers, worker_index) d = d.repeat(num_epochs) d = d.shuffle(shuffle_buffer_size) d = d.map(parser_fn, num_parallel_calls=num_map_threads)
d = Dataset.list_files(pattern) d = d.shard(num_workers, worker_index) d = d.repeat(num_epochs) d = d.shuffle(shuffle_buffer_size) d = d.interleave(tf.data.TFRecordDataset, cycle_length=num_readers, block_length=1) d = d.map(parser_fn, num_parallel_calls=num_map_threads)
num_shards
: A tf.int64
scalar tf.Tensor
, representing the number of
shards operating in parallel.index
: A tf.int64
scalar tf.Tensor
, representing the worker index.Dataset
: A Dataset
.InvalidArgumentError
: if num_shards
or index
are illegal values.
Note: error checking is done on a best-effort basis, and errors aren't
guaranteed to be caught upon dataset creation. (e.g. providing in a
placeholder tensor bypasses the early checking, and will instead result
in an error during a session.run call.)shuffle
shuffle( buffer_size, seed=None, reshuffle_each_iteration=None )
Randomly shuffles the elements of this dataset.
This dataset fills a buffer with buffer_size
elements, then randomly
samples elements from this buffer, replacing the selected elements with new
elements. For perfect shuffling, a buffer size greater than or equal to the
full size of the dataset is required.
For instance, if your dataset contains 10,000 elements but buffer_size
is
set to 1,000, then shuffle
will initially select a random element from
only the first 1,000 elements in the buffer. Once an element is selected,
its space in the buffer is replaced by the next (i.e. 1,001-st) element,
maintaining the 1,000 element buffer.
buffer_size
: A tf.int64
scalar tf.Tensor
, representing the number of
elements from this dataset from which the new dataset will sample.seed
: (Optional.) A tf.int64
scalar tf.Tensor
, representing the random
seed that will be used to create the distribution. See
tf.compat.v1.set_random_seed
for behavior.reshuffle_each_iteration
: (Optional.) A boolean, which if true indicates
that the dataset should be pseudorandomly reshuffled each time it is
iterated over. (Defaults to True
.)Dataset
: A Dataset
.skip
skip(count)
Creates a Dataset
that skips count
elements from this dataset.
count
: A tf.int64
scalar tf.Tensor
, representing the number of
elements of this dataset that should be skipped to form the new dataset.
If count
is greater than the size of this dataset, the new dataset
will contain no elements. If count
is -1, skips the entire dataset.Dataset
: A Dataset
.take
take(count)
Creates a Dataset
with at most count
elements from this dataset.
count
: A tf.int64
scalar tf.Tensor
, representing the number of
elements of this dataset that should be taken to form the new dataset.
If count
is -1, or if count
is greater than the size of this
dataset, the new dataset will contain all elements of this dataset.Dataset
: A Dataset
.unbatch
unbatch()
Splits elements of a dataset into multiple elements.
For example, if elements of the dataset are shaped [B, a0, a1, ...]
,
where B
may vary for each input element, then for each element in the
dataset, the unbatched dataset will contain B
consecutive elements
of shape [a0, a1, ...]
.
# NOTE: The following example uses `{ ... }` to represent the contents # of a dataset. ds = { ['a', 'b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'd'] } ds.unbatch() == {'a', 'b', 'c', 'a', 'b', 'a', 'b', 'c', 'd'}
A Dataset
transformation function, which can be passed to
tf.data.Dataset.apply
.
window
window( size, shift=None, stride=1, drop_remainder=False )
Combines (nests of) input elements into a dataset of (nests of) windows.
A "window" is a finite dataset of flat elements of size size
(or possibly
fewer if there are not enough input elements to fill the window and
drop_remainder
evaluates to false).
The stride
argument determines the stride of the input elements, and the
shift
argument determines the shift of the window.
For example, letting {...} to represent a Dataset:
tf.data.Dataset.range(7).window(2)
produces
{{0, 1}, {2, 3}, {4, 5}, {6}}
tf.data.Dataset.range(7).window(3, 2, 1, True)
produces
{{0, 1, 2}, {2, 3, 4}, {4, 5, 6}}
tf.data.Dataset.range(7).window(3, 1, 2, True)
produces
{{0, 2, 4}, {1, 3, 5}, {2, 4, 6}}
Note that when the window
transformation is applied to a dataset of
nested elements, it produces a dataset of nested windows.
tf.data.Dataset.from_tensor_slices((range(4), range(4))).window(2)
produces {({0, 1}, {0, 1}), ({2, 3}, {2, 3})}
tf.data.Dataset.from_tensor_slices({"a": range(4)}).window(2)
produces {{"a": {0, 1}}, {"a": {2, 3}}}
size
: A tf.int64
scalar tf.Tensor
, representing the number of elements
of the input dataset to combine into a window.shift
: (Optional.) A tf.int64
scalar tf.Tensor
, representing the
forward shift of the sliding window in each iteration. Defaults to
size
.stride
: (Optional.) A tf.int64
scalar tf.Tensor
, representing the
stride of the input elements in the sliding window.drop_remainder
: (Optional.) A tf.bool
scalar tf.Tensor
, representing
whether a window should be dropped in case its size is smaller than
window_size
.Dataset
: A Dataset
of (nests of) windows -- a finite datasets of flat
elements created from the (nests of) input elements.with_options
with_options(options)
Returns a new tf.data.Dataset
with the given options set.
The options are "global" in the sense they apply to the entire dataset. If options are set multiple times, they are merged as long as different options do not use different non-default values.
options
: A tf.data.Options
that identifies the options the use.Dataset
: A Dataset
with the given options.ValueError
: when an option is set more than once to a non-default valuezip
zip(datasets)
Creates a Dataset
by zipping together the given datasets.
This method has similar semantics to the built-in zip()
function
in Python, with the main difference being that the datasets
argument can be an arbitrary nested structure of Dataset
objects.
For example:
a = Dataset.range(1, 4) # ==> [ 1, 2, 3 ] b = Dataset.range(4, 7) # ==> [ 4, 5, 6 ] c = Dataset.range(7, 13).batch(2) # ==> [ [7, 8], [9, 10], [11, 12] ] d = Dataset.range(13, 15) # ==> [ 13, 14 ] # The nested structure of the `datasets` argument determines the # structure of elements in the resulting dataset. Dataset.zip((a, b)) # ==> [ (1, 4), (2, 5), (3, 6) ] Dataset.zip((b, a)) # ==> [ (4, 1), (5, 2), (6, 3) ] # The `datasets` argument may contain an arbitrary number of # datasets. Dataset.zip((a, b, c)) # ==> [ (1, 4, [7, 8]), # (2, 5, [9, 10]), # (3, 6, [11, 12]) ] # The number of elements in the resulting dataset is the same as # the size of the smallest dataset in `datasets`. Dataset.zip((a, d)) # ==> [ (1, 13), (2, 14) ]
datasets
: A nested structure of datasets.Dataset
: A Dataset
.


Allows a wide range of URL, including ones with supposedly illegal characters and Unicode
Often used by the wiki markup community, so some people will be familiar with it.
Also allows a fair number of valid link display text, except for other square brackets as part of the link.
A bit bothersome to type.
Intuitive, foreign language friendly with some localization effort. However, localization does need quite a lot of effort.
Very slow to type compared to double brackets.
Requires additional code in lexical analysis regarding text.
No thought required. Copy and paste and I'm done.
Lacks custom display text
Lacks URL substitution.
Possible conflict with punctuation. Use angle bracket to negate it.
Highest range of URL detection. Pretty much anything on the line works.
Removes Ugly URL from text, nice and clean.
Idea from Markdown and APA, MLA, and IEEE citation style.


Lorem Ipsum Dolor
Youth is not a time of life; it is a state of mind. It is not a matter of rosy cheeks, red lips and supple knees. It is a matter of the will, a quality of the imagination, vigor of the emotions; it is the freshness of the deep spring of life. Youth means a temperamental predominance of courage over timidity, of the appetite for adventure over the love of ease. This often exits in a man of 60, more than a boy of 20.nobody grows merely by the number of years; we grow old by deserting our ideas. Years may wrinkle the skin, but to give up enthusiasm wrinkles the soul. Worry, fear, self-distrust1 bows the heart and turns the spirit back to dust. Whether 60 or 16, there is in every human being’s heart the lure of wonders, the unfailing childlike appetite of what’s next and the joy of the game of living. In the center of your heart and my heart there is a wireless station; so long as it receives messages of beauty, hope, cheer, courage and power from men and from infinite, so long as you are young.


Having written our function, we'd like to test it. The easiest way to do so is to write a
program that reads a line of input and passes that line to the split
function. We can
then write the contents of the vector
that split
returns. Such a test program will make
it easy to inspect the output, and to verify that the split
function generates the words that we expect.
More usefully, this test function should produce the same results as a program that
just reads words from the standard input and writes the words one per output line. We
can write this latter program, run it and our test program on the same input files, and verify
that our programs generate identical output. If so, we can be fairly confident in our
split
function.
Let's start by writing the test program for split:
int main() { string s; // read and split each line of input while (getline(cin, s)) { vector<string> v = split(s); // write each word in v for (vector<string>::size_type i = 0; i != v.size(); ++i) cout << v[i] << endl; } return 0; }
This program needs to read the input an entire line at a time. Fortunately, the string
library provides what we need in the getline
function, which reads input until it
reaches the end of the line. The getline
function takes two arguments. The first is the
istream
from which to read; the second is a reference to the string
into which to store
what is read. As usual, the getline
function returns a reference to the istream
from
which we read, so that we can test that istream
in a condition. If we hit end-of-file or
encounter invalid input, then the return from getline
will indicate failure and we'll
break out of the while
.
As long as we can read a line of input, we store that line in s
and pass it to split
,
storing the return value from split
in v
. Next, we loop through v
, writing each string
in that vector
on a separate line.
Assuming that we added the proper #include
s, including one for our own header
that contained a declaration for split
, we could run this function and visually verify that
it and split
work as expected. We can do even better, though, by comparing the output
of this program with a program that lets the library do all the work:
int main() { string s; while (cin >> s) cout << s << endl; return 0; }
This program and the previous one should generate identical output. Here, we let
the string
input operator separate the input stream into a series of words, which we write
one to a line. By running both programs on the same, complex input, we can have a good
idea that our split function works.


Constructor
new BuildingShape(properties)
Parameters:
| Name | Type | Description |
|---|---|---|
properties |
Object | The properties related to the shape's geometry. |
- Source:
Members
(readonly) altitude :Float
The altitude of the shape.
Type:
- Float
- Source:
(readonly) color :Color
The color of the shape.
Type:
- Color
- Source:
(readonly) properties :Object
The properties related to the shape's geometry.
Type:
- Object
- Source:
Methods
setAltitude(configuration)
Sets the altitude of the shape (Polygon or MultiPolygon).
For the OSMBuildingLayer if extrude is true, altitude is defined and altitude "type" is set to "number", altitude "value" is used. If altitude "value" is not set, 15 is used.
For the OSMBuildingLayer if extrude is true, altitude is defined and altitude "type" is set to "osm", if available the value of OSM "height" tag is used. If the "height" tag is not available an approximate height value is calculated using "building:levels" tag. Every level is considered to be 3 meters. If both are not available, 15 is used by default.
For the OSMBuildingLayer if extrude is true, altitude is defined and altitude "type" is set to "property", the value of the property defined in "value" is used. If value of the property is null, 15 is used.
For the OSMBuildingLayer if extrude is true and altitude is undefined, 15 is used by default.
For the OSMBuildingLayer if extrude is false, 0 is used.
Parameters:
| Name | Type | Description |
|---|---|---|
configuration |
Object | Configuration is the object returned by shapeConfigurationCallback. |
- Source:
setColor(configuration)
Colors the shape (Polygon or MultiPolygon) based on their altitude.
As the altitude increases the red component of the color increases.
The thresholds could be calculated automatically based on the data.
Parameters:
| Name | Type | Description |
|---|---|---|
configuration |
Object | Configuration is the object returned by shapeConfigurationCallback. |
- Source:


Welcome!
St. John’s Nursery School (SJNS) is a private, non-denominational preschool whose mission is to provide the best possible early childhood experience for you and your child. Our program is rich with language, social, cognitive, and motor development activities. We create a secure and nurturing environment to support the individual growth of each of our students. We are licensed by the Massachusetts Department of Early Education and Care.
Our Mission
St. John’s Nursery School is an EEC and NAEYC accredited preschool program that was established to meet the needs of the three and four-year old nursery school child. St. John’s has served young children for over 45 years in Westwood. The curriculum has been designed to provide a diverse and creative learning experience.. Our program provides the structure that each child needs, yet is flexible enough to allow room for individual creativity.We are especially proud of our Literacy Program which provides an added dimension to the SJNS experience. Children grow into relationships with their peers, while being subtly directed by an attentive and dedicated staff.
Our Goals
- To be a safe and nurturing place for children to learn and grow, and become more independent.
- To respect the cultural backgrounds of our families.
- To encourage each child to be inquisitive.
- To provide developmentally appropriate curriculum.
- To provide active learning experiences.
- To encourage children to be cooperative within a sharing environment.
We are dedicated to a strong partnership between parents and teachers at St. John’s Nursery School, and we look forward to having you visit us!
History
St. John’s Nursery School has been open since 1972, providing a continuous nursery school experience in the Westwood community. In 2015 our “sister school”, the Clapboardtree Nursery School, merged with SJNS and we have been operating together since then. The logos of these two schools were also merged; St. Johns’ child with a wagon, and Clapboardtree’s tree with a heart. This is the logo you see today.


We're glad to announce the new OsmAnd version for iOS. We completely reworked the style of the map, upgraded the search and added a new system of POI filters. Download now to try!
The POI search filters have undergone a powerful update. Now you can search for the particular location you need by many features. For example, you can search for a restaurant by cuisine, by diet, payment method, wheelchair accessibility, delivery options and more.
Looking for a gas station? Check whether it offers a car wash and other services. Moreover, you can save your filter to use it later. For example, if you often search for places that serve burgers 24/7 and offer delivery, just save that filter and apply it whenever you need it.
The address search has been significantly improved, as well. Now you can type in the names of the streets or objects and find them easily.
The context menu you see after pressing on a map has gained new features. Now you can see the Wikipedia articles connected to the objects nearby, view the international name of the location, check its wheelchair accessibility and other useful information. For example, transport stops now have the information about the routes and the operator company.
Is this release, we decided to rework the style of the map. It has become simpler, cleaner and brighter. You'll see a lighter and more contrast layout that brings out the POI and makes the user experience better. There are less different colors on the map and the icons have been updated, as well.


One of the common uses network setups in AWS is called Scenario 2: VPC with Public and Private Subnets. This is a that defines a Virtual Private Cloud (VPC), public subnets and private subnets. Setting up this infrastructure can be done via the AWS console or via cloud formation scripting. However, I prefer the tool Terraform in which you can manage your infrastructure as code with a declarative language that supports building, changing and versioning your cloud in a modular way.
In this article I will describe how you can create a VPC with a public and private subnet on AWS using terraform. I will describe the setup step by step and I will show how to encapsulate all logic to one re-usable module.
Why we need subnets. When you create a VPC, it spans all the Availability Zones in the region. After creating a VPC, you can add one or more subnets in each Availability Zone. When you create a subnet, you specify the CIDR block for the subnet, which is a subset of the VPC CIDR block. Each subnet must reside entirely within one Availability Zone and cannot span zones. Availability Zones are distinct locations that are engineered to be isolated from failures in other Availability Zones. By launching instances in separate Availability Zones, you can protect your applications from the failure of a single location.
Before you can start, you need an AWS account with sufficient rights (admin), and you should create a AWS_ACCESS_KEY_ID
and AWS_SECRET_ACCESS_KEY
to be able to access your account programmatically.
The easiest way to use terraform is via docker. In that case you do not need any locally isntalled tools. We simply create a file called .aws
where we put the AWS key and secret to athentication API calls, as shown below:
AWS_ACCESS_KEY_ID=<KEY>
AWS_SECRET_ACCESS_KEY=<SECRET>
All code for part 1 is available on github
By default, terraform expects the configuration in a file called main.tf
. Create the file and add the terraform provide for Amazon:
# main.tf
provider "aws" {
region = "eu-west-1"
}
We are now ready to, step-by-step, add the terraform resources to create the VPC setup. First, have a look on the VPC setup as shown in the picture below.
We will start by creating the VPC itself: add the following snippet to your main.tf
resource "aws_vpc" "vpc" {
cidr_block = "<IP_ADDRESS>/16"
enable_dns_support = true
enable_dns_hostnames = true
tags {
label = "blog"
}
}
Before we make the change effective we run a terraform plan
to inspect the planned changes. You can install terraform locally or run the commands in a docker container. The docker command will export the AWS credentials to the container, mount the current directory to /data
in the container, and set /data
as working directory to ensure the container will execute the terraform plan
command on the directory that contains the terraform files.
docker run -it --env-file ./.aws -v $(pwd):/data -w /data \
hashicorp/terraform:0.9.8 plan
After executing the plan command, you should see output similar to:
+ aws_vpc.vpc
assign_generated_ipv6_cidr_block: "false"
cidr_block: "<IP_ADDRESS>/16"
default_network_acl_id: "<computed>"
default_route_table_id: "<computed>"
default_security_group_id: "<computed>"
dhcp_options_id: "<computed>"
enable_classiclink: "<computed>"
enable_dns_hostnames: "true"
enable_dns_support: "true"
instance_tenancy: "<computed>"
ipv6_association_id: "<computed>"
ipv6_cidr_block: "<computed>"
main_route_table_id: "<computed>"
tags.%: "1"
tags.label: "blog"
Plan: 1 to add, 0 to change, 0 to destroy.
This looks correct, so we can apply the change to create the VPC by running terraform apply
.
docker run -it --env-file ./.aws -v $(pwd):/data -w /data \
hashicorp/terraform:0.9.8 apply
You can go VPC via the AWS console, where you should now see a VPC in the list named blog
. Now we have the VPC, we create a public and private subnet by adding the following to the main.tf.
resource "aws_subnet" "public_subnet_a" {
vpc_id = "${aws_vpc.vpc.id}"
cidr_block = "<IP_ADDRESS>/24"
availability_zone = "eu-west-1a"
map_public_ip_on_launch = false
tags {
Name = "blog"
}
}
resource "aws_subnet" "private_subnet_a" {
vpc_id = "${aws_vpc.vpc.id}"
cidr_block = "<IP_ADDRESS>/24"
availability_zone = "eu-west-1a"
tags {
Name = "blog"
}
}
Run now terraform plan
again, it will show that the resources will be added, one for each subnet. Apply the change using terraform apply
and inspect your changes again via the VPC section in the AWS console.
Next, we connect the public subnet via an internet gateway. Create a routing table and an internet gateway. And a aws_route_table_association to create an association between a subnet and routing table. Update your main.tf
with the snippet below, and run a plan and apply.
resource "aws_internet_gateway" "internet_gateway" {
vpc_id = "${aws_vpc.vpc.id}"
}
resource "aws_route_table" "public_routetable" {
vpc_id = "${aws_vpc.vpc.id}"
route {
cidr_block = "<IP_ADDRESS>/0"
gateway_id = "${aws_internet_gateway.internet_gateway.id}"
}
tags {
label = "blog"
}
}
resource "aws_route_table_association" "public_subnet_a" {
subnet_id = "${aws_subnet.public_subnet_a.id}"
route_table_id = "${aws_route_table.public_routetable.id}"
}
We are now able to deploy an application to the public subnet and make it accessible via a security group. However, it is not yet possible to route traffic to the private subnet or let instances on the private subnet connect to internet. To do so, we add a NAT gateway and connect the gateway via a route table to the private subnet. The NAT gateway requires an elastic IP, which we will create first. Add the next terraform snippet to your main.tf
and run a plan
and apply
to inspect and make the changes effective.
resource "aws_eip" "nat" {
vpc = true
}
resource "aws_nat_gateway" "nat" {
allocation_id = "${aws_eip.nat.id}"
subnet_id = "${aws_subnet.public_subnet_a.id}"
}
resource "aws_route_table" "private_routetable" {
vpc_id = "${aws_vpc.vpc.id}"
route {
cidr_block = "<IP_ADDRESS>/0"
nat_gateway_id = "${aws_nat_gateway.nat.id}"
}
tags {
label = "blog"
}
}
resource "aws_route_table_association" "private_subnet_a" {
subnet_id = "${aws_subnet.private_subnet_a.id}"
route_table_id = "${aws_route_table.private_routetable.id}"
}
Setting up the VPC with subnets is quite verbose, and imagine then when you must support more availability zones, the code will almost double per zone. So we refactor it to a generic module to support the multiple subnets that are available in a zone.
I have rewritten the code showed above to a generic module. With this model, it is easy to create a VPC with all availability zones per zone, and private subnets can be enabled on demand.
Modules in terraform are self-contained packages of gerraform configurations that are managed as a group. Modules are used to create reusable components, improve organization, and to treat pieces of infrastructure as a black box.
I will not describe all code in the module again but only will pay attention to the significant changes I made. In the code above the zone, availability zone where hard coded. This will not work in a generic module. A common way to solve this in terraform is by creating a map where a zone is mapped to a list of availability zondes. By passing the zone to the moudle, the module can find out which availability zones there are. A default map is available in the module but can be ovewritten as follow:
availability_zones = {
eu-west-1 = ["eu-west-1a", "eu-west-1b", "eu-west-1c"]
}
The cdir block in the code show above was hard coded, but terraform contains a function to calculate the cdir block. In the module I use the terraform function cidrsubnet()
to calculate the cdir block.
Now only one problem needs to be solved in order to create a generic module: how can we create a AWS resource for each element in a list? Do we create subnet or route table association for each availability zone? Or how can we avoid creating a private subnet at all? The solution is to use the count
variable in a module to iterate over the list of availability zones, which is available on my github. We can now create a VPC similar to the one above with just a few line of code. Add the following lines to your terraform script to create a VPC.
module "vpc" {
source = "git::https://github.com/npalm/tf-aws-vpc.git"
key = "blog"
aws_region = "eu-west-1"
}
It is possible to overwrite module variables with default to get more control, see the ezample below:
module "vpc" {
source = "git::https://github.com/npalm/tf-aws-vpc.git"
key = "blog"
aws_region = "eu-west-1"
create_private_hosted_zone = "false"
create_private_subnets = "false"
cidr_block = "<IP_ADDRESS>/16"
// example to override default availability_zones
availability_zones = {
eu-west-1 = ["eu-west-1a", "eu-west-1c"]
}
}


Match Play. Red leads 5-3 to 7. How should Red play 65?
pips Red=145 White=145
(CorrectAnswer= 0/0, ErrorScore= 0)
For money the right play would be 22/11.
However, at this score the cube will becoming whenever White sees any chance of a gammon and he would double after 22/11. In fact, he will double after 13/7, 6/1* as well.
Only after 13/2 should he hold the cube so that would be my preferred play. At this score in this type of situation I learned long ago that holding onto the anchor is nearly always correct.
13/2 it is.
However, at this score the cube will becoming whenever White sees any chance of a gammon and he would double after 22/11. In fact, he will double after 13/7, 6/1* as well.
Only after 13/2 should he hold the cube so that would be my preferred play. At this score in this type of situation I learned long ago that holding onto the anchor is nearly always correct.
13/2 it is.
1. Cubeful 2-ply 13/2 Eq.: -0.666 0.386 0.094 0.003 - 0.614 0.116 0.003 2. Cubeful 2-ply 13/7 6/1* Eq.: -0.670 (-0.003) 0.398 0.117 0.003 - 0.602 0.128 0.004 3. Cubeful 2-ply 8/3 8/2 Eq.: -0.722 (-0.056) 0.382 0.093 0.002 - 0.618 0.123 0.004 4. Cubeful 2-ply 13/7 8/3 Eq.: -0.753 (-0.086) 0.368 0.092 0.002 - 0.632 0.116 0.004 5. Cubeful 2-ply 22/11 Eq.: -0.760 (-0.094) 0.432 0.123 0.005 - 0.568 0.181 0.005


Partners
With immense gratitude, we acknowledge our partners below
Develyne Musani
Board of Director: In charge of women and education
Location: Kenya
Email:
<EMAIL_ADDRESS>Justine Ikwara
Chair of the Board of Directors: Experienced in teaching and mentorship
Location: Kenya
Email:
<EMAIL_ADDRESS>Alice Farina
Board of Director: Interests in children's and women's rights and empowerment
Location: Italy
Email:
<EMAIL_ADDRESS>Bramuel Kundu Wamasabu
Board of Director: In charge of peace and security
Location: Kenya
Email:
<EMAIL_ADDRESS>Benard Chemos Cheptai
Board of Director: In charge of logistics
Location: Kenya
Email:
<EMAIL_ADDRESS>Marialili Poinsot
Board of Director: In charge of youths
Location: USA
Email:
<EMAIL_ADDRESS>Reena Mensingh
Board of Director: In charge of health and women
Location: Canada
Email:
<EMAIL_ADDRESS>Emmanuel Were
Programs Officer
Email:
<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>
Patrick Chestit
CDA-Community Development Advisor
Email:
<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>
Peter Chem
Founder / Executive Director
Email:
<EMAIL_ADDRESS>Phone: +254720744827
Mercy Sore
Programs Coordinator
Email:
<EMAIL_ADDRESS>Phone: +254727782959
Victorine Okanya
Office Admin
Phone: +254 757 450943
Apurv Sibal
ICT Officer
Donald Herman Ava
Fundraising Officer
Jai Jayantilal Thakkar
Social Media Officer
Victor Kipsang
Accounts Officer
Daman Oberoi
Information Technology Officer


We have over a dozen villas located on our beach front for you to enjoy the ocean breeze up close and personal.
The perfect escape for you and your family. All feature newly renovated jacuzzi tubs.
Indulge any day of the week at our continental breakfast featuring omelettes, parfaits, and cereal.


Time is the most precious thing we have. Make the most of it!
No arbitrary limits. No distractions.
Keep track of your tasks on the go on all mobile platforms.


Object Oriented Programming Concepts:Variable Scope
Variable Scope in Ruby means the accessibility of the variables. You can have five different types of variable scopes:
1. Global Variable: These are accessible from anywhere in the program. They are prefixed with a dollar sign($). Eg. $global_var = 10.
2. Class Variable: These are shared amongst all instances of a class. For each class instance they have the same values. Eg. @@class_var = 10.
3. Instance Variable: These are also shared amongst all instances of a class. But, for each class instance they have different values. Eg. @instance_var = 10.
4. Local Variable: These are local to the code area in which they are declared. For eg. local_var = 10.
5. Constant : This is a constant that will never change. In Ruby, you can actually change a constant, but it will give you a warning before doing that. Eg. CONSTANT =10.


--- layout: docs template: t-docs title: Tools > CLEARFIX name: clearfix collection: docs-tools ---
This tool includes a mixin to drop micro clearfix into a selector, in a cross browser way.
In the same way, there are a mixin to unset the clearfix, which is useful to crate clearfix only in a certain breakpoint.
Finally contains the same two mixins but in a important mode...we are not proud to have it, but sometimes could be necessary...


Hello! i'm Anuj Jain I want to be with an organization of repute that provides an environment of mutual growth and benefits, challenging and rewarding opportunities while utilizing my true potential and skills. I am experienced in modeling and implementing user interface and elementary ERP web applications and payment processing applications using contempo js technologies like React, Angular 2 , Angularjs, Node Js.
I design the interfaces focusing on content structure, intuitive UI patterns and simple interactions. I'm a minimalist who truly believes that less is more.
I love javascript and coding in it since before I've started my career. I have high expertise and experience in javascript frameworks like Angular and React.
I am a technology enthusiast and love to explore every bit of the application building process. I am always open to add new ones in the list.


The Common LaTeX Service Interface is a web service providing access to a LaTeX compiler.
This CLSI server runs on the OpenShift
network as a free public service.
Thanks to
To use the CLSI you might need a developer token.
Thanks to<EMAIL_ADDRESS>who developed the python implementation.
Thanks to<EMAIL_ADDRESS>who developed the web-display method.
If you are looking for a client to access this CLSI server, check out my CLSI client on GitHub.


Founded in 1925, The Denver Foundation is a community foundation serving Metro Denver, with the mission of inspiring people and mobilizing resources to strengthen the community. The Denver Foundation three primary functions: stewarding an endowment to invest in meeting current and future needs for the community; managing more than 900 charitable funds for individuals, families and businesses; and working with the community and philanthropic leaders to address the core challenges that face Metro Denver. For more, visit DenverFoundation.org.


Hello
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Need Assistance? Customer service at.
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Log in into your metamask using below.
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| Product | Downloads | Expires | Download |
|---|---|---|---|
| Haven - Free Real Estate PSD Template | May 10, 2018 | never | Click Here To Download Your File |
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The following addresses will be used on the checkout page by default.
Billing address
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Bangladesh


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
A trio of reports show the academies programme is working.
The majority of academy headteachers say they have seen results improve after leaving local authority control, a new survey has found.
More than 700 academies, open as of May 2013, responded to the survey which showed for the first time the range of ways they are using their freedoms to innovate and improve - including by varying the curriculum or changing term time dates.
The survey also revealed how previously failing schools had seen the demand for places at their schools rise after they were taken over by strong sponsors.
The Do academies make use of their autonomy? ’ survey was published on the same day as 2 other reports that showed how academies working in groups outperformed their council-run counterparts.
Of the schools surveyed:
A Department for Education spokesman said:
Academies take power away from politicians and bureaucrats and hand it to the heads and teachers who know their pupils best. This survey shows how academies are embracing their new freedoms in order to drive up standards and improve opportunities for their pupils.
Previously failing schools are more in demand since becoming academies and the vast majority of academy heads feel their results have improved overall. Freed from council control, academies are also more likely to collaborate with other local schools to share their expertise and improve attainment.
Improving our education system is an essential part of our long-term economic plan to build a better future for Britain.
The main reasons schools chose to convert were to raise educational standards and to gain greater freedom to spend their budgets on improving standards. All funding for academies goes directly to heads to spend directly on education - in the past local authority schools saw as much as 10% of their budget held back by their local council.
The survey also found that academies have made a wide range of changes since converting or becoming sponsored including:
Academy heads also used the survey to explain how the ethos of their schools have changed since leaving local authority control.
One said:
The culture and climate has become even more clear; we are here for the children, not to please a local authority or play their political games.
All are clear that we are autonomous within the legal parameters and this is now a locally owned and run school for local families… For the first time, we now have 100% focus on our children; it is liberating.
Another said:
It is wonderful (although it is very hard work). I could never go back to working in a local authority school because I would feel stifled by the lack of creativity there.
I feel like I can actually get on with my job rather than following the direction set by someone else.
Alongside the results of the ‘Do academies make use of their autonomy?’ survey, 2 other reports - also published today - show how academy schools working in partnership tend to outperform those working individually.
The ‘ Academies annual report: academic year 2012 to 2013 ’ - which updates Parliament on developments of the academies programme - found that academy sponsors in particular were seeing big improvements in 2013:
However across all state-funded schools the rate of improvement has only been 1.8%.
Another report - into the way the English education system is evolving - had similar findings.
The report, ‘ The evolving education system in England ’, is based on research by the Isos partnership and commissioned by the DfE. It found that many of the worries raised by local authorities about the growth of academies had proved unfounded.
The report showed:
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 9 July 2014
From: Department for Education
Part of: Academies and free schools


|
Bitshares-Core
3.0.0
BitShares blockchain implementation and command-line interface software
|
#include <special_authority_object.hpp>
Public Attributes | |
| account_id_type | account |
Static Public Attributes | |
| static const uint8_t | space_id = implementation_ids |
| static const uint8_t | type_id = impl_special_authority_object_type |
special_authority_object only exists to help with a specific indexing problem. We want to be able to iterate over all accounts that contain a special authority. However, accounts which have a special_authority are very rare. So rather than indexing account_object by the special_authority fields (requiring additional bookkeeping for every account), we instead maintain a special_authority_object pointing to each account which has special_authority (requiring additional bookkeeping only for every account which has special_authority).
This class is an implementation detail.
Definition at line 43 of file special_authority_object.hpp.
| account_id_type graphene::chain::special_authority_object::account |
Definition at line 49 of file special_authority_object.hpp.
|
static |
Definition at line 46 of file special_authority_object.hpp.
|
static |
Definition at line 47 of file special_authority_object.hpp.


The result is CrowdVoice.by
An interactive platform where anyone can broadcast and visualize a message, raise awareness, and invite change.
Why we're needed
Today’s digital media is cluttered and volatile, so we’re here to make sorting and preserving information easy. CrowdVoice.by gives you the tools to organize, share, and galvanize your cause, or simply archive content for future research and education. It's never been easier to connect to what matters.
Community Oriented
CrowdVoice.by is built to streamline the crowdsourcing of information, while allowing you to moderate content to best reflect your cause. We believe information advocacy works best with the involvement of networked communities.  This core ethos carries over into our software.
CrowdVoice.by is an open source project.
Our partners
CrowdVoice.by is honored to have partnered with the following organizations.


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