Best Budget Phone Deals for November 2019
Looking for a great phone under £200? Here we round up some of the best blowers your money can buy, that don't break the bank
by Alex Walker-ToddThere's never been a better time to buy a low-cost smartphone. With flagship phone sales levelling out, manufacturers have put more effort than ever into making truly competitive low-cost phones that pull in premium features without the high price tag.
As such, we've collated a list of some of the best phones money can buy right now, assuming you're on a tight budget. Using the same £200 price limit as our Best Budget Phones roundup, below you'll find a host of devices that punch above their weight in terms of features, performance and design, considering their price.
Best Budget Phone Deals
Xiaomi Mi A3 (128GB)
From: GearBest
Was: £235.77
Now: £153.86 (£81.91 off)
View Deal
Xiaomi Redmi 7A (32GB)
From: GearBest
Was: £99
Now: £78.50 (£20.50 off)
View Deal
Motorola Moto G7 Power (64GB)
From: Amazon
Was: £179
Now: £159.95 (£19.05 off)
View Deal
Samsung Galaxy A20e (32GB)
From: Amazon
Was: £169
Now: £142 (£27 off)
View Deal
Realme 3 Pro (128GB)
From: GearBest
Was: £273.46
Now: £196.25 (£77.21 off)
View Deal
Samsung Galaxy A60 (64GB)
From: GearBest
Was: £245
Now: £188.40 (£56.60 off)
View Deal
Samsung Galaxy A40 (64GB)
From: Amazon
Was: £219
Now: £178.99 (£40.01 off)
View Deal
Xiaomi Redmi Note 7 (4GB RAM, 64GB)
From: Amazon
Was: £174.99
Now: £159.90 (£15.09 off)
View Deal
Honor 20 Lite
From: GearBest
Was: £249.99
Now: £188.40 (£61.59 off)
View Deal
Nokia 1 Plus (8GB)
From: Amazon
Was: £89.99
Now: £64.96 (£25.03 off)
View Deal
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Budget phone buying advice
There are a lot of affordable smartphones out there and although swinging for the lowest price tag can be tempting, it doesn't make sense to skimp on a product you'll likely end up using almost every day for months or years at a time.
Try and buy from an established brand that you know and trust, not only as bigger companies' products are usually held to higher quality standards, but should anything go wrong with your purchase, more established players also typically offer more robust customer support systems.
Don't expect affordable phones such as these to feel like miniature flagships; such phones most obviously sacrifice hardware and/or camera performance to cut costs, while others opt for lesser build quality or less-premium materials - such as polycarbonate instead of glass or metal.
The display and camera used to be two of the most obvious indicators that you were using a budget handset but even these fundamental pieces of phone hardware have improved massively in recent years. Entries like the Xiaomi Redmi Note 7 highlight this shift perfectly.
There are also devices, like the Motorola Moto G7 Power, which place a focus on excelling in one specific area (in this phone's case, battery longevity) whilst still feeling like a more conventional affordable phone in other respects.
One other advantage of buying a more affordable smartphone is that, if you're after a contract, such devices are more likely to come with zero upfront costs, lower monthly pricing and, potentially, shorter-term plans. As such, they also make great first-time options for those new to smartphones, those who are accident-prone and kids.
1. Xiaomi Mi A3 (128GB)
From: GearBest
Was: £235.77
Now: £153.86 (£81.91 off)
View Deal
Xiaomi Mi A3 is an excellent-value budget phone running Android One rather than MIUI and thanks to this GearBest deal, comes with around £80 off its asking price. Read our Mi A3 review.
2. Xiaomi Redmi 7A (32GB)
From: GearBest
Was: £99
Now: £78.50 (£20.50 off)
View Deal
We were surprised to find that the already astoundingly affordable Xiaomi Redmi 7A gets over a 20% discount, thanks to GearBest. One of the best sub-£100 devices on the market made even better.
3. Motorola Moto G7 Power (64GB)
From: Amazon
Was: £179
Now: £159.95 (£19.05 off)
View Deal
Save £19 on the already-affordable Moto G7 Power, with its outstanding battery longevity. Why not read our full Moto G7 Power review to help you decide.
4. Samsung Galaxy A20e (32GB)
From: Amazon
Was: £169
Now: £142 (£27 off)
View Deal
One of the most affordable members of Samsung's A-series smartphones, the Galaxy A20e still boasts powerful features, like a dual-sensor main camera that features a 13MP primary sensor with an impressively wide f/1.9 aperture, alongside an ultrawide-angle secondary 5MP snapper, with a 123° field of view. Better still is the discount it's currently enjoying on Amazon.
5. Realme 3 Pro (128GB)
From: GearBest
Was: £273.46
Now: £196.25 (£77.21 off)
View Deal
Oppo sub-brand Realme may have only just officially launched in Europe but you could already buy the excellent Realme 3 Pro and now it's available with a significant £75+ discount.
6. Samsung Galaxy A60 (64GB)
From: GearBest
Was: £245
Now: £188.40 (£56.60 off)
View Deal
While Samsung doesn't directly sell the Galaxy A60 in the UK, if you're looking for a more affordable alternative to the Galaxy A50, why not consider this GearBest deal, which sees over £55 removed from the phone's original starting price.
7. Samsung Galaxy A40 (64GB)
From: Amazon
Was: £219
Now: £178.99 (£40.01 off)
View Deal
The already-affordable Samsung Galaxy A40 is available on Amazon with an even further discount of over £40.
8. Xiaomi Redmi Note 7 (4GB RAM, 64GB)
From: Amazon
Was: £174.99
Now: £159.90 (£15.09 off)
View Deal
One of the most capable budget phones of the moment at a great price - read our full review of the Redmi Note 7 for more.
9. Honor 20 Lite
From: GearBest
Was: £249.99
Now: £188.40 (£61.59 off)
View Deal
The baby of Honor's latest flagship lineup, the 20 Lite still delivers in a number of areas. Plus, thanks to this GearBest deal, you can grab it for a limited time, well under £200.
10. Nokia 1 Plus (8GB)
From: Amazon
Was: £89.99
Now: £64.96 (£25.03 off)
View Deal
The Nokia 1 Plus is already one of Nokia's (read: HMD Global's) most affordable Android One smartphones and now enjoys a £25 discount.
Again, if you'd like to know more about the current affordable phone landscape, give our Best Budget Phones roundup a read.
Click here for the best Samsung deals and Samsung voucher codes.


{% extends "content_template.html" %} {% from "components/content-metadata.html" import content_metadata %} {% from "components/table.html" import mapping_table, row, text_field, row_heading %} {# Used by the content_template.html layout, prefixes the "navigation" accessible name #} {% set navigation_label_prefix = 'Pricing information' %} {% block per_page_title %} Letter pricing {% endblock %} {% block content_column_content %}
The cost of sending a letter depends on the postage you choose and how many sheets of paper you need.
Prices include:
Letters must be 10 pages or less (5 double-sided sheets of paper).


To get the current Delistings data, index the Delistings property of the current Slice with the Equity Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your security at every time step. To avoid issues, check if the Slice contains the data you want before you index it.
def OnData(self, slice: Slice) -> None: if slice.Delistings.ContainsKey(self.symbol): delisting = slice.Delistings[self.symbol] delisting_type = {0: "Warning", 1: "Delisted"}.get(delisting.Type) self.Log(f'Delistings: {delisting_type}')
public override void OnData(Slice slice) { if (slice.Delistings.ContainsKey(_symbol)) { var delisting = slice.Delistings[_symbol]; Log($"Delistings: {delisting.Type}"); } }
For more information about accessing delistings, see Delistings.


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Our mission is to help kids with serious health afflictions shoot for the stars.
We aim to help kids with long-term afflictions or serious illnesses expand and
surpass their limits by reminding them how AMAZING they are. Whether their own
imaginary limits are perceived by themselves or by others, we want kids with
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Our Inspiration


Database to encapsulate marked events in data recording.
Database to encapsulate marked events in data recording.
Events are stored as NNEvent objects, which encapsulate timestamp, duration, code, and comment string.
An NNEvents database consists of TreeSet(s) of XEvents.
Each event "port" has its own TreeSet---this allows different ports to have events with exactly the same timing.
(TreeSet's with black-red trees cannot support keys with equivalent sorting values)
Database to encapsulate marked events in data recording. Events are stored as NNEvent objects, which encapsulate timestamp, duration, code, and comment string. An NNEvents database consists of TreeSet(s) of XEvents. Each event "port" has its own TreeSet---this allows different ports to have events with exactly the same timing. (TreeSet's with black-red trees cannot support keys with equivalent sorting values)


About
the Dynamic Formula Tool:
The Dynamic Formula Tool allows you to dynamically create formulas using a data feed. Effectively allowing you to write formulas by using other formulas, the summarize tool or other techniques. Great for when you need to write a long or complex formula or create a lot of formulas and don't want to do it manually.
Configuration Properties:
The tool takes two inputs:
The data input which is your main data feed as you would normally pass into a standard formula tool.
The formula input which is a data feed which contains the information which you would normally enter into the properties pane of the standard formula tool.
There are 4 configuration properties which map the fields in the formula input to the corresponding fields in the formula tool.
Version History:
Version 1.0 - Initial release.
Version 1.1 - Increased length of formula fields to allow for longer dynamic formulas.
Version 2.0 - Updated to 8.5 layout. Fixed bug with XML metacharacters in formulas < > & ". Checked "Output fields change base on the data input into the macro". Added versioning macro. Added online help. Updated icon.
Version 3.0 - Updated to Alteryx 9.0 macro interface.
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Social Determinants for Health Data
This could be a nice introductory paragraph explaining what you want to do
For instance, enter a patient ID in the box below...
Based on this patient's primary address, they have been flagged as being potentially at risk of having inadequate transportation. it is recommended to ask the questions below and provide them with the help suggested if they answer positively.


FAIR AND SQUARE ANTIBACTERIAL WITH MOISTURIZER - triclosan solution
Davion, Inc
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Drug Facts
Active Ingredients
Triclosan 0.15%
Uses
for handwashing to decrease bacteria on the skin
Warnings
For external use only.
Stop use and ask a doctor if irritation or redness develops.
When using this product do not get into eyes. If contact occurs,, rinse eye thoroughly with water.
Keep out of reach of children. If swallowed, get medical help or contact a Poison Control Center right away.
Directions
- wet hands
- apply palmful to hands
- scrub thoroughly
- rinse
Inactive ingredients
Water, Sodium Laureth Sulfate, Cocamidoprpyl Betaine, Sodium Chloride, Cocamidopropyl Hydroxysultaine, Glycerin, Fragrance, Polyquaternium-7, PPG 2 Hydroxyethyl Coco/Isostearamide, DMDM Hydantoin, Tetrasodium EDTA, Citric Acid, Yellow 5 Lake (CI 19140), Red 4 (CI 14700)
© Distributed by Menard, Inc.
Eau Claire, WI 54703 MADE IN CANADA
* This product is not manufactured or distributed by Dial Corporation, distributor of Dial®.
Package Label
FAIR &
SQUARETM
ANTIBACTERIAL
HAND SOAP
with
MOISTURIZER
Compare to Dial®*
7.5 FL OZ
(221.8 mL)


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Gaussian Random Field Module¶
Implement multivariate random fields.
Convention is that p is the number of responses. Tensors may be returned either in heterotopic or in isotopic form.
Heterotopic form means that a response index must be specified for each element. For example, given a vector of locations S and a vector of response indices L, both of size M, the heteretopic form of the mean vector at these (generalized) locations is a vector of size M such that its i-th element is the mean of component L[i] at spatial location S[i].
When all responsed indices are considered, we use the word isotopic. Since under the hood, a response index vector always has to be specified, in the isotopic case we use L = (1, …, p, 1, …, p, 1, …, p, ….) and S = (s1, …, s1, s2, …, s2, …). That is, for each spatial location s_i, we repeat it p-times, and the response index vector is just made of the list 1, …, p, repeated n times, n being the number of spatial locations.
Now, in this situation, it makes sense to reshape the resulting mean vector such that the repsonse dimensions have their own axis. This is what is meant by isotopic form. In isotopic form , the corresponding means vector has shape (n. p).
-
class
meslas.random_fields.
DiscreteGRF
(grid, mean_vec, covariance_mat)¶ Bases:
meslas.random_fields.GRF
Gaussian Random Field that is discretized over a static grid. This means that values can only be computed at grid nodes and observations may also only be performed at grid nodes.
Since it is not known a piori at which locations we will observe or predict, the GRF computes and stores the mean vector and covariance matrix for the whole design at each stage.
- Parameters
- grid: Grid
- mean_vec: GeneralizedVector
- covariance_mat: GeneralizedMatrix
- Attributes
pointwise_cov
Pointwise covariance matrix, i.e.
variance
Returns the (pointwise) variances.
Methods
compute_cov_reduction
(self, S_y_inds, L_y[, …])Compute the covariance reduction that would result from observing some hypothetic data.
discretize_prior
(grf, grid)Discretize a grf prior on a grid and return the mean vector on the grid and covariance matrix between grid nodes.
ebv
(self, S_y_inds, L_y, lower[, upper, …])Computes the expected Bernoulli Variance (i.e.
eibv
(self, S_y_inds, L_y, lower[, upper, …])Computes the expected Integrated Bernoulli Variance 8integrated version of the above), if we were to make observations at the generalized locations (S_y, L_y).
from_model
(grf, grid)Create a discrete GRF by discretizing a GRF model.
sample
(self[, jitter])Sample the discretized GRF on the whole grid.
update
(self, S_y_inds, L_y, y[, noise_std, …])Observe some data and update the field.
update_from_scratch
(self, S_y_inds, L_y, y)Same as the update function, but starts from the prior.
-
compute_cov_reduction
(self, S_y_inds, L_y, noise_std=None)¶ Compute the covariance reduction that would result from observing some hypothetic data.
- Parameters
- S_y_inds: (M) Tensor
Indices (in the grid) of the spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- noise_std: (n_out) Tensor
Noise standard deviation for each response. Defaults to 0.
- Returns
- cov_reduction: (n_points, n_out) GeneralizedMatrix
-
static
discretize_prior
(grf, grid)¶ Discretize a grf prior on a grid and return the mean vector on the grid and covariance matrix between grid nodes.
- Parameters
- grf: GRF
Gaussian Random Field to discretize.
- grid: Grid
Grid on which to discretize.
- Returns
- mean_vec: GeneralizedVector
- covariance_mat: GeneralizedMatrix
-
ebv
(self, S_y_inds, L_y, lower, upper=None, noise_std=None)¶ Computes the expected Bernoulli Variance (i.e. not integrated) if we were to make observations at the generalized locations (S_y, L_y). Since we are on a grid, we do not directly specify the spatial locations S_y, but the corresponding grid indices S_y_inds instead.
- Parameters
- S_y_inds: (M) Tensor
Indices (in the grid) of the spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- noise_std: float
Noise standard deviation. Uniform across all measurments. Defaults to 0.
- Returns
- ebv: (n_points) Tensor
Expected Bernoulli Variance at each point of the grid, conditional on observing at the new data point.
-
eibv
(self, S_y_inds, L_y, lower, upper=None, noise_std=None)¶ Computes the expected Integrated Bernoulli Variance 8integrated version of the above), if we were to make observations at the generalized locations (S_y, L_y). Since we are on a grid, we do not directly specify the spatial locations S_y, but the corresponding grid indices S_y_inds instead.
- Parameters
- S_y_inds: (M) Tensor
Indices (in the grid) of the spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- noise_std: float
Noise standard deviation. Uniform across all measurments. Defaults to 0.
- Returns
- ebiv: float
-
classmethod
from_model
(grf, grid)¶ Create a discrete GRF by discretizing a GRF model. One specifies the mean and covariance funcions and the model is then discretized on a grid.
- Parameters
- grf: GRF
Gaussian Random Field to discretize.
- grid: Grid
Grid on which to discretize.
-
property
pointwise_cov
¶ Pointwise covariance matrix, i.e. covariance matrix of the field at each points (i.e. no cross-location covariance).
- Returns
- pointwise_cov: (n_points, n_out, n_out) Tensor
-
sample
(self, jitter=1e-05)¶ Sample the discretized GRF on the whole grid.
- Returns
- Z: (M) Tensor
The sampled value of Z_{s_i} component l_i.
- jitter: float
Jitter to add if covariance matrix is not diagonalisable.
-
update
(self, S_y_inds, L_y, y, noise_std=None, mean_vec=None, covariance_mat=None)¶ Observe some data and update the field. This will compute the new values of the mean vector and covariance matrix.
- Parameters
- S_y_inds: (M) Tensor
Indices (in the grid) of the spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- y: (M) Tensor
Measured values.
- noise_std: (n_out) Tensor
Noise standard deviation for each response. Defaults to 0.
- mean_vec: GeneralizedVector, optional
Can specify a mean vector to start from if do not want to use the current mean.
- covariance_mat: GeneralizedMatrix, optional
Can specify another covariance matrix if do not want to use the current one.
-
update_from_scratch
(self, S_y_inds, L_y, y, noise_std=None)¶ Same as the update function, but starts from the prior. The purpose of this method is to recover from numerical instabilities that may have accumulated when one has sequentially collected a big dataset. There, one might instead start from the prior and condition on the whole concatenated dataset in one go.
- Parameters
- grf: GRF
The prior random field model (continuous) to use to generate the prior mean and covariance.
- S_y_inds: (M) Tensor
Indices (in the grid) of the spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- y: (M) Tensor
Measured values.
- noise_std: (n_out) Tensor
Noise standard deviation for each response. Defaults to 0.
-
property
variance
¶ Returns the (pointwise) variances.
- Returns
- variances: Generalized vector
Variance of each component of the random field at each point.
-
class
meslas.random_fields.
GRF
(mean, covariance)¶ Bases:
object
Gaussian Random Field with specified mean function and covariance function.
- Parameters
- mean: function(s, l)
Function returning l-th component of mean at location s. Should be vectorized.
- covariance: function(s1, s2, l1, l2)
Function returning the covariance matrix between the l1-th component at s1 and the l2-th component at l2. Should be vectorized.
Methods
krig
(self, S, L, S_y, L_y, y[, noise_std, …])Predict field at some generalized locations, based on some measured data at other generalized locations.
krig_grid
(self, grid, S_y, L_y, y[, …])Predict field at some points, based on some measured data at other points.
krig_isotopic
(self, points, S_y, L_y, y[, …])Predict field at some points, based on some measured data at other points.
sample
(self, S, L[, jitter])Sample the GRF at generalized location (S, L).
sample_grid
(self, grid)Sample the GRF (all components) on a grid.
sample_isotopic
(self, points)Sample the GRF (all components) on a list of points.
variance
(self, S, L)Compute the (pointwise) variances at generalized location (S, L).
variance_reduction
(self, S, L, S_y, L_y[, …])Computes the reduction in variance at generalized locations (S, L) that would be caused by observing data at generalized locations (S_y, L_y).
variance_reduction_isotopic
(self, points, …)Computes the reduction in variance (all components) at location S that would be caused by observing data at generalized locations (S_y, L_y).
-
krig
(self, S, L, S_y, L_y, y, noise_std=None, compute_post_var=False, compute_post_cov=False)¶ Predict field at some generalized locations, based on some measured data at other generalized locations.
This is the most general possible form of kriging, since it takes measurements at generalized locations and predicts at generalized locations.
- Parameters
- S: (N, d)
Spatial locations at which to predict
- L: (N) Tensor
Response indices to predict.
- S_y: (M, d) Tensor
Spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- y: (M) Tensor
Measured values.
- noise_std: (n_out) Tensor
Noise standard deviation for each response.
- compute_post_var: bool
If true, compute and return posterior variance (matrix) at each point.
- compute_post_cov: bool
If true, compute and return posterior covariance. Only one of compute_post_var or compute_post_var may be avtivated.
- Returns
- mu_cond: (N) Tensor
Kriging means at each generalized location.
- var_cond: (N)
Conditional varance at each generalized location. Only returned if specified.
- K_cond: (N, N) Tensor
Conditional covariance matrix between the generalized locations. Only returned if specified.
-
krig_grid
(self, grid, S_y, L_y, y, noise_std=None, compute_post_cov=False)¶ Predict field at some points, based on some measured data at other points.
- Parameters
- grid: Grid
Regular grid of size (n1, …, nd).
- S_y: (M, d) Tensor
Spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- y: (M) Tensor
Measured values.
- noise_std: (n_out) Tensor
Noise standard deviation for each response. Defaults to 0.
- compute_post_cov: bool
If true, compute and return posterior covariance.
- Returns
- mu_cond_grid: (grid.shape, p) Tensor
Kriging means at each grid node.
- mu_cond_list: (grid.n_points*p) Tensor
Kriging mean, but in list form.
- mu_cond_iso: (grid.n_points, p) Tensor
Kriging means in isotopic list form.
- K_cond_list: (grid.n_points * p, grid.n_points * p) Tensor
Conditional covariance matrix in heterotopic form.
- K_cond_iso: (grid.n_points, grid.n_points, p, p) Tensor
Conditional covariance matrix in isotopic ordered form. It means that the covariance matrix at cell i can be otained by subsetting K_cond_iso[i, i, :, :].
-
krig_isotopic
(self, points, S_y, L_y, y, noise_std=None, compute_post_var=False, compute_post_cov=False)¶ Predict field at some points, based on some measured data at other points. Predicts all repsonses (isotopic).
- Parameters
- points: (N, d) Tensor
List of points at which to predict.
- S_y: (M, d) Tensor
Spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- y: (M) Tensor
Measured values.
- noise_std: (n_out) Tensor
Noise standard deviation for each response. Defaults to 0.
- compute_post_var: bool
If true, compute and return posterior variance (matrix) at each point.
- compute_post_cov: bool
If true, compute and return posterior covariance. Only one of compute_post_var or compute_post_var may be avtivated.
- Returns
- mu_cond_list: (N*p) Tensor
Kriging mean, but in list form.
- mu_cond_iso: (N, p) Tensor
Kriging means in isotopic list form.
- K_cond_list: (N * p, N * p) Tensor
Conditional covariance matrix in heterotopic form.
- K_cond_iso: (N, N, p, p) Tensor
Conditional covariance matrix in isotopic ordered form. It means that the covariance matrix at cell i can be otained by subsetting K_cond_iso[i, i, :, :].
- var_cond_list: (N*p) Tensor
Conditional varance at each generalized location. Only returned if specified.
- var_cond_iso: (N, p) Tensor
Same as above, but in list form.
-
sample
(self, S, L, jitter=1e-05)¶ Sample the GRF at generalized location (S, L).
- Parameters
- S: (M, d) Tensor
List of spatial locations.
- L: (M) Tensor
List of response indices.
- jitter: float
Jitter to add if covariance matrix is not diagonalisable.
- Returns
- Z: (M) Tensor
The sampled value of Z_{s_i} component l_i.
-
sample_grid
(self, grid)¶ Sample the GRF (all components) on a grid.
- Parameters
- grid: Grid
- Returns
- sample_grid: (n1, …, n_d, ,p) Tensor
The sampled field on the grid. Here p is the number of output components and n1, …, nd are the number of cells along each axis.
- sample_list: (n_points, p) Tensor
Same as above, but in list form.
-
sample_isotopic
(self, points)¶ Sample the GRF (all components) on a list of points.
- Parameters
- points: (N, d) Tensor
Spatial locations where to sample.
- Returns
- sample_list: (N, p) Tensor
The sampled values.
-
variance
(self, S, L)¶ Compute the (pointwise) variances at generalized location (S, L).
- Parameters
- S: (M, d) Tensor
List of spatial locations.
- L: (M) Tensor
List of response indices.
- Returns
- vars: (M) Tensor
The variances Z_{s_i} component l_i.
-
variance_reduction
(self, S, L, S_y, L_y, noise_std=None)¶ Computes the reduction in variance at generalized locations (S, L) that would be caused by observing data at generalized locations (S_y, L_y). Note that this doesn’t depend on the measured data.
- Parameters
- S: (N, d)
Spatial locations at which to predict
- L: (N) Tensor
Response indices to predict.
- S_y: (M, d) Tensor
Spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- noise_std: (n_out) Tensor
Noise standard deviation for each response. Defaults to 0.
- Returns
- variance_reduction: (N) Tensor
Reduction in variance at each generalized location.
-
variance_reduction_isotopic
(self, points, S_y, L_y, noise_std=None)¶ Computes the reduction in variance (all components) at location S that would be caused by observing data at generalized locations (S_y, L_y). Note that this doesn’t depend on the measured data.
- Parameters
- points: (N, d) Tensor
List of points at which to predict.
- S_y: (M, d) Tensor
Spatial locations of the measurements.
- L_y: (M) Tensor
Response indices of the measurements.
- noise_std: (n_out) Tensor
Noise standard deviation for each response. Defaults to 0.
- Returns
- variance_reduction: (N) Tensor
Reduction in variance at each generalized location.


Chapter 2: What is STxT?
This project is the result of years of subconscious reflection,
although it only took a moment of inspiration.
This chapter may seem a little unfocused, but they are my reflections when creating STxT. Please forgive me if I repeat myself a bit, if you don't understand what I am saying, or if it seems an incoherent speech. But this is the way it is. It has been written to the best of my abilities, on different days, but always using it as the basis of creation.
Is The inspiration
The other chapters are The Perspiration.
Glance quickly over this chapter, and read it more in depth when you already know more about STxT. This is good advice ;-)
What is STxT?
It is a purely semantic language, with some very basic rules, which allows the creation of documents with structured and easy to interpret content, by both humans and machines. You could be reading a document in STxT without actually realizing that it is.
STxT: A language for the web. It does not mean one more, one more among many. It means ONE language for the web. Just one. And many at the same time. As many as you want. This is my proposal.
STxT is a language for writing structured documents. It lets us create documents in a non-ambiguous way, adhering to very precise rules, but achieving documents that can be read by people that are not computer literate.
In fact, the documents have meaning by themselves, without needing any additional translation. They have a hierarchical structure that is very similar to xml documents, and their translation can be direct.
In the future, STxT could replace other technologies such as xml or LaTex.
Statement of Intent :-)
There are a number of principles or features that we want this new language to have.
- Independent from the programming language, operating system, and platform
- Easy to read by humans without computer skills
- Easy to learn by humans without computer skills
- Nice appearance and intuitive
- Encourages the freedom of movement and sharing of information
- Easy to learn by humans with computer skills
- Easy to interpret by computers
- It allows for the easy integration of texts from other programming languages, especially "lightweight languages", which are particularly interesting. This way, we achieve a very interesting integration.
- Native internationalization (I18N) capabilities
- Native self-description capabilities
- Enable the creation of content trees which are capable of distinguishing very easily between different languages
- Be generic in order to include documents in other languages
- Simplify so much that escape characters are not necessary. As a matter of fact, there are no escape characters!! Is that possible? Yes. I believe that it is the first language that can say this :-D It does so through its structure and content. There is only one forbidden character, but it is not an escape character. You’ll see, I have already expanded too much on this point...
What does STxT mean?
Well, originally STxT means "Semantic text". Although it could also have other meanings, that I particularly like: SuperText and SexyText.
- SuperText: it is text, but super :-D
- SexyText: it is particularly beautiful text ;-)
How did I choose the STxT extension? I was sure that documents had to be semantic, so there was something that had to have "semantic". In addition, languages with few letters in them are “in” now: xml, tex, html, xsd,... so stxt seemed to me like a good choice. I just needed to check that this was not being used already, so looking through the Internet I only remotely found another meaning: sexy-text. Well, even that was good, semantic text has to be sexy ;-)
Why a new language?
I have always believed that we needed a simple language for writing texts, but that was at same time powerful as far as structure is concerned. And I had not found it... until now.
I think the problem that has always existed is that one always tries to solve the general case, and this complicates things. But in addition it has been dealt with the wrong way, always taking into account the syntax point of view, looking for a computer approach, never a semantic one.
With STxT we solve semantics and syntax at the same time,
in a stylish and beautiful way.
Make no mistake. I am a computer tech. But sometimes you have to see things from the other side, with a 'clean' view. In fact it is a balance between learning from what you know and trying to think as if you didn't know anything about it.
Another objective is to make the information open. This has also been addressed during the design phase. By design, the content is open.
A language for everyone
We try to make it easy for everyone. There should not be problems for "non-computer people" when having to create a semantic document. It should be intuitive, and simple, with the right tools.
One of the goals is to go from having 1 relatively complicated language for doing things (e.g.: XML, Latex,...) to having very simple n, with content linked to a structure. This language needs to be able to be learned in a very short time. Almost with one example we should have enough. STxT is made so that it is this way. In addition, STxT lets us pass from STxT to other languages, such as latex, so that the more technical people can do the final work. But the origin and the base will always be STxT.
It is much better that it is this way, as we write in pure semantics, without taking into account structure at all.
When I began creating this language I was looking for something very generic but that was descriptive at the same time. I wanted the information to be very clear, but I wanted to forget the complex grammars from other systems. I wanted something accessible, but at the same time powerful and useful. That is why I had to look for simplicity, and keep away from the majority of other work already carried out. My intention was to create a language not just for elite people, but for everyone. I also knew that this would be beneficial, because this way the number of people willing to create documents would increase considerably. There is a tremendous amount of people with great knowledge, but sometimes they are limited by the way they have to translate this information. I also intend to solve this problem.
Chaotic Statement of Intent :-D (**)
Well, all this is what we were looking for, or what we have achieved, or the objective, or a mixture of all these things. I don't know, they are scattered ideas, which have been out there during its creation and I wanted you to see, nothing else. There is nothing to be understood from this list. You have to wait until the end of the book, and see what has been achieved, what hasn’t, and if we have gained something... in the end.
- Valid for a looong time
- Content type for web: text/plain (UTF-8)
- There are no character problems: UTF-8 ALWAYS
- Expandable
- Anyone can create data types, and the page MUST EXIST, not like xml namespaces.
- Obviously it can be saved in cache, in order to not be asking all the time, but it is something REAL.
- Easy data transfer between platforms
- Easy content migrations
- You can set definition files, so that anyone whoever wants to can create types before making them public
- Define data types with url's: only 1 namespace, and from that one everything is by default
- It has been made to be beautiful
- Easy to read and make by humans and machines
- Inferable contents: it is not necessary to write everything, which lets us shorten or make longer descriptions
- Made for being learned very quickly, with few rules
- Definition of types (grammars) included from the design stage, for full integration
- Xml refactoring
- Thanks to the type definition a super-compact format can be made, much more so than xml, json,...
- Much easier to create by hand, as we almost never have to watch the namespaces, since they are almost automatic due to their location. Namespaces like you've never seen before ;-)
- Very few data types (numbers without limitation, this is semantic, not a programming language)
- Easy to transform from this format to other formats
- Ambition: allow progress towards the semantic web, with the creation of beautiful html views and a strong STxT base.
- One of the objectives is to make the information open. This is also done during the design phase. By design, the content is open.
Applications, results, and expectations
I believe we have created a great language. Simple, practical, and truly useful.
I have written this book in this language. And I had never felt so comfortable. For me it has been a great discovery. When I use it, I think: people have to know it. The language deserves it. It cannot be kept hidden. It has to be tried. And it has to be liked. I don't want to be the only one who has it. It wouldn’t be fair.
I would like for STxT to hold a place of honor with other languages in the future, and that this was due to its acceptance and the usefulness that it offered.


|
BTK
0.3dev.0
Open-source library to visualize/process biomechanical data
|
(false) Container of frames used to represent the motion of a rigid body. More...
Public Types | |
| typedef SharedPtr< const NoMotion > | ConstPointer |
| typedef SharedPtr< NoMotion > | Pointer |
| Public Types inherited from btk::AbstractMotion | |
| typedef SharedPtr< const AbstractMotion > | ConstPointer |
| typedef SharedPtr< AbstractMotion > | Pointer |
| Public Types inherited from btk::DataObjectLabeled | |
| typedef SharedPtr< const DataObjectLabeled > | ConstPointer |
| typedef SharedPtr < DataObjectLabeled > | Pointer |
| Public Types inherited from btk::DataObject | |
| typedef SharedPtr< const DataObject > | ConstPointer |
| typedef SharedPtr< DataObject > | Pointer |
| Public Types inherited from btk::Object | |
| typedef SharedPtr< const Object > | ConstPointer |
| typedef SharedPtr< Object > | Pointer |
Public Member Functions | |
| virtual Frame & | GetFrame (int) |
| virtual const Frame & | GetFrame (int) const |
| virtual Frame & | operator[] (int) |
| virtual const Frame & | operator[] (int) const |
Static Public Member Functions | |
| static Pointer | New () |
Additional Inherited Members | |
| Protected Member Functions inherited from btk::AbstractMotion | |
| AbstractMotion () | |
| AbstractMotion (const AbstractMotion &toCopy) | |
| Protected Attributes inherited from btk::DataObjectLabeled | |
| std::string | m_Description |
| std::string | m_Label |
(false) Container of frames used to represent the motion of a rigid body.
This object represents static segment/object like the global frame. The implement method in this class return every time the matrix identitity.
Smart pointer associated with a const NoMotion object.
Smart pointer associated with a NoMotion object.
|
inlinevirtual |
Returns the identity matrix.
Implements btk::AbstractMotion.
|
inlinevirtual |
Returns the identity matrix.
Implements btk::AbstractMotion.
|
inlinestatic |
Creates a smart pointer associated with a NoMotion object.
|
inlinevirtual |
Returns the identity matrix.
Implements btk::AbstractMotion.
|
inlinevirtual |
Returns the identity matrix.
Implements btk::AbstractMotion.


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In this section, an overview of the most important features of this library is given.
A StateGraph is an enhanced finite state machine. It is based on the JGrafchart method and takes advantage of Modelica features for the "action" language. JGrafchart is a further development of Grafcet to include elements of StateCharts that are not present in Grafcet/Sequential Function Charts. Therefore, the StateGraph library has a similar modeling power as StateCharts but avoids some deficiencies of StateCharts.
The basic elements of StateGraphs are steps and transitions:
Steps represent the possible states a StateGraph can have. If a step is active the Boolean variable active of the step is true. If it is deactivated, active = false. At the initial time, all "usual" steps are deactivated. The InitialStep objects are steps that are activated at the initial time. They are characterized by a double box (see figure above).
Transitions are used to change the state of a StateGraph. When the step connected to the input of a transition is active, the step connected to the output of this transition is deactivated and the transition condition becomes true, then the transition fires. This means that the step connected to the input to the transition is deactivated and the step connected to the output of the transition is activated.
The transition condition is defined via the parameter menu of the transition object. Clicking on object "transition1" in the above figure, results in the following menu:
In the input field "condition", any type of time varying Boolean expression can be given (in Modelica notation, this is a modification of the time varying variable condition). Whenever this condition is true, the transition can fire. Additionally, it is possible to activate a timer, via enableTimer (see menu above) and provide a waitTime. In this case the firing of the transition is delayed according to the provided waitTime. The transition condition and the waitTime are displayed in the transition icon.
In the above example, the simulation starts at initialStep. After 1 second, transition1 fires and step1 becomes active. After another second transition2 fires and initialStep becomes again active. After a further second step1 becomes again active, and so on.
In JGrafcharts, Grafcet and Sequential Function Charts, the network of steps and transitions is drawn from top to bottom. In StateGraphs, no particular direction is defined, since steps and transitions are blocks with input and output connectors that can be arbitrarily placed and connected. Usually, it is most practical to define the network from left to right, as in the example above, since then it is easy to read the labels on the icons.
With the block TransitionWithSignal, the firing condition can be provided as Boolean input signal, instead as entry in the menu of the transition. An example is given in the next figure:
Component "step" is an instance of "StepWithSignal" that is a usual step where the active flag is available as Boolean output signal. To this output, component "Timer" from library "Modelica.Blocks.Logical" is connected. It measures the time from the time instant where the Boolean input (i.e., the active flag of the step) became true up to the current time instant. The timer is connected to a comparison component. The output is true, once the timer reaches 1 second. This signal is used as condition input of the transition. As a result, "transition2" fires, once step "step" has been active for 1 second. Of course, any other Modelica block with a Boolean output signal can be connected to the condition input of such a transition block as well.
Conditions of a transition can either be computed by a network of logical blocks from the Logical library as in the figure above, or via the "SetBoolean" component any type of logical expression can be defined in textual form, as shown in the next figure:
With the block "SetBoolean", a time varying expression can be provided as modification to the output signal y (see block with icon text "timer.y > 1" in the figure above). The output signal can be in turn connected to the condition input of a TransitionWithSignal block.
The "SetBoolean" block can also be used to compute a Boolean signal depending on the active step. In the figure above, the output of the block with the icon text "step.active" is true, when "step" is active, otherwise it is false (note, the icon text of "SetBoolean" displays the modification of the output signal "y"). This signal can then be used to compute desired actions in the physical systems model. For example, if a valve shall be open, when the StateGraph is in "step1" or in "step4", a "SetBoolean" block may be connected to the valve model using the following condition:
valve = step1.active or step2.active
Via the Modelica operators edge(..) and change(..), conditions depending on rising and falling edges of Boolean expressions can be used when needed.
In JGrafcharts, Grafcet, Sequential Function Charts and StateCharts, actions are formulated within a step. Such actions are distinguished as entry, normal, exit and abort actions. For example, a valve might be opened by an entry action of a step and might be closed by an exit action of the same step. In StateGraphs, this is (fortunately) not possible due to Modelica's "single assignment rule" that requires that every variable is defined by exactly one equation. Instead, the approach explained above is used. For example, via the "SetBoolean" component, the valve variable is set to true when the StateGraph is in particular steps.
This feature of a StateGraph is very useful, since it allows a Modelica translator to guarantee that a given StateGraph has always deterministic behaviour without conflicts. In the other methodologies this is much more cumbersome. For example, if two steps are executed in parallel and both step actions modify the same variable, the result is either non-deterministic or non-obvious rules have to be defined which action takes priority. In a StateGraph, such a situation is detected by the translator resulting in an error, since there are two equations to compute one variable. Additional benefits of the StateGraph approach are:
To emphasize this important difference between these methodologies, consider the case that a state machine has the following hierarchy:
stateMachine.superstate1.superstate2.step1
Within "step1" a StateChart would, e.g., access variable "stateMachine.openValve", say as "entry action: openValve = true". This typical usage has the severe drawback that it is not possible to use the hierarchical state "superstate1" as component in another context, because "step1" references a particular name outside of this component.
In a StateGraph, there would be typically a "SetBoolean" component in the "stateMachine" component stating:
openValve = superstate1.superstate2.step1.active;
As a result, the "superstate1" component can be used in another context, because it does not depend on the environment where it is used.
The execution model of a StateGraph follows from its Modelica implementation: Given the states of all steps, i.e., whether a step is active or not active, the equations of all steps, transitions, transition conditions, actions etc. are sorted resulting in an execution sequence to compute essentially the new values of the steps. If conflicts occur, e.g., if there are more equations as variables, of if there are algebraic loops between Boolean variables, an exception is raised. Once all equations have been processed, the active variable of all steps are updated to the newly calculated values. Afterwards, the equations are again evaluated. The iteration stops, once no step changes its state anymore, i.e., once no transition fires anymore. Then, simulation continuous until a new event is triggered, (when a relation changes its value).
With the Modelica "sampled(..)" operator, a StateGraph might also be executed within a discrete controller that is called at regular time instants. In a future version of the StateGraph library, this might be more directly supported.
Parallel activities can be defined by component Parallel and alternative activities can be defined by component Alternative. An example for both components is given in the next figure.
Here, the branch from "step2" to "step5" is executed in parallel to "step1". A transition connected to the output of a parallel branch component can only fire if the final steps in all parallel branches are active simultaneously. The figure above is a screen-shot from the animation of the StateGraph: Whenever a step is active or a transition can fire, the corresponding component is marked in green color.
The three branches within "step2" to "step5" are executed alternatively, depending which transition condition of "transition3", "transition4", "transition4a" fires first. Since all three transitions fire after 1 second, they are all candidates for the active branch. If two or more transitions would fire at the same time instant, a priority selection is made: The alternative and parallel components have a vector of connectors. Every branch has to be connected to exactly one entry of the connector vector. The entry with the lowest number has the highest priority.
Parallel, Alternative and Step components have vectors of connectors. The dimensions of these vectors are set in the corresponding parameter menu. E.g. in a "Parallel" component:
Currently in Dymola the following menu pops up, when a branch is connected to a vector of components in order to define the vector index to which the component shall be connected:
A StateGraph can be hierarchically structured by using a CompositeStep. This is a component that inherits from PartialCompositeStep. An example is given in the next figure (from Examples.ControlledTanks):
The CompositeStep component contains a local StateGraph that is entered by one or more input transitions and that is left by one or more output transitions. Also, other needed signals may enter a CompositeStep. The CompositeStep has similar properties as a "usual" step: The CompositeStep is active once at least one step within the CompositeStep is active. Variable active defines the state of the CompositeStep.
Additionally, a CompositeStep has a suspend port. Whenever the transition connected to this port fires, the CompositeStep is left at once. When leaving the CompositeStep via the suspend port, the internal state of the CompositeStep is saved, i.e., the active flags of all steps within the CompositeStep. The CompositeStep might be entered via its resume port. In this case the internal state from the suspend transition is reconstructed and the CompositeStep continues the execution that it had before the suspend transition fired (this corresponds to the history ports of StateCharts or JGrafcharts).
A CompositeStep may contain other CompositeSteps. At every level, a CompositeStep and all of its content can be left via its suspend ports (actually, there is a vector of suspend connectors, i.e., a CompositeStep might be aborted due to different transitions).


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publications
Generated by jekyll-scholar.
2019
-
Persistent Bias Among Local Election Officials Hughes, D. Alex, Gell-Redman, Micah, Crabtree, Charles, Krishnaswami, Natarajan, Rodenberger, Diana, and Monge, Will Journal of Experimental Political Science 2019 [Abs] [PDF]
Results of an audit study conducted during the 2016 election cycle demonstrate that bias toward Latinos observed during the 2012 election has persisted. In addition to replicating previous results, we show that Arab/Muslim Americans face an even greater barrier to communicating with local election officials, but we find no evidence of bias toward blacks. An innovation of our design allows us to measure whether emails were opened by recipients, which we argue provides a direct test of implicit discrimination. We find evidence of implicit bias toward Arab/Muslim senders only.
-
Phenotypic Preference in Mexican Migrants: Evidence from a Random Household Survey Aguilar, Rosario, Hughes, D Alex, and Gell-Redman, Micah Political Behavior 2019 [Abs] [PDF]
Does pre-existing preference based on skin tone, facial features, and other observable characteristics, i.e., phenotypic preference, affect immigrant voters’ support for political candidates competing in their countries of origin? Do these preferences change as migrants’ tenure in their host society increases? These questions are important for ethnic and racial politics in general, and particularly for the sizable foreign-born population in the United States, which includes 11 million Mexicans. Using a unique, random sample of foreign-born Mexicans in San Diego County, we employ a voting experiment to test the impact of skin tone and phenotype on vote choice among first generation immigrants. Our design allows us to distinguish responses to different phenotypic cues by exposing respondents to European, mestizo, and indigenous looking candidates competing in a hypothetical Mexican election. Migrants showed higher support for the Indigenous candidate, and evaluated the European and Mestizo candidates as more ideologically conservative. As migrants’ time in the United States increases, the preference for indigenous features gives way to a preference for whiteness, which we interpret as evidence of first generation migrants adopting the dominant racial ideology of the United States. While ethnic distinctions have long been viewed as a key component of voting behavior, our research demonstrates that, even within a single ethnicity, racial differences may have profound impacts on the evaluation of and support for electoral candidates. This study contributes to the research on race and political behavior in comparative perspective, as well as the political consequences of migration.
2018
2017
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Exploiting social influence to magnify population-level behaviour change in maternal and child health: study protocol for a randomised controlled trial of network targeting algorithms in rural Honduras Shakya, Holly, Stafford, Derek, Hughes, D. Alex, Keegan, Thomas, Negron, Rennie, Broome, Jai, McKnight, Mark, Nicoll, Liza, Nelson, Jennifer, Iriarte, Emma, and others, BMJ open 2017 [Abs] [PDF]
Despite global progress on many measures of child health, rates of neonatal mortality remain high in the developing world. Evidence suggests that substantial improvements can be achieved with simple, low-cost interventions within family and community settings, particularly those designed to change knowledge and behaviour at the community level. Using social network analysis to identify structurally influential community members and then targeting them for intervention shows promise for the implementation of sustainable community-wide behaviour change.
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The Impact of GOTV Depends upon Campaign Context: A Field Experiment in the 2014 California Primary Hughes, D. Alex, Levitt, Justin, Hill, Seth, and Kousser, Thad California Journal of Politics and Policy 2017 [Abs] [PDF]
Millions of California voters regularly turn out in November but abstain from primary elections. A randomized Get Out the Vote experiment conducted in the state’s 2014 primary contest shows that this dormant electorate can be mobilized if campaigns target these unlikely voters. Here, we extend these findings to examine whether the electoral context of the district shapes the effectiveness of a primary mobilization effort. To do so, we develop two conceptualizations of campaign context. The first is based on a district’s typical level of competitiveness. The second looks at total spending levels in the current campaign. Theories of voter information processing predict differential responsiveness by voters to mobilization efforts in these different contexts.To test these predictions, we analyze a field experiment that sends direct mail to 149,596 registered low-propensity California voters. Consistent with theory, we find that voter mobilization mailings have different effects in these two distinct contexts. Although mobilization efforts always increase turnout, in districts that are typically competitive we find that mobilization efforts are more effective
2016
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Intimate partner violence norms cluster within households: an observational social network study in rural Honduras Shakya, Holly, Hughes, D. Alex, Stafford, Derek, Christakis, Nicholas, Fowler, James, and Silverman, Jay BMC public health 2016 [Abs] [PDF]
Intimate partner violence (IPV) is a complex global problem, not only because it is a human rights issue, but also because it is associated with chronic mental and physical illnesses as well as acute health outcomes related to injuries for women and their children. Attitudes, beliefs, and norms regarding IPV are significantly associated with the likelihood of both IPV experience and perpetration. Our results show that differential targeting of individuals and relationships in order to reduce the acceptability and, subsequently, the prevalence of IPV may be most effective. Because IPV norms seem to be strongly held within households, the household is probably the most logical unit to target in order to implement change. This approach would include the possible benefit of a generational effect. Finally, in social contexts in which perpetration of IPV is not socially acceptable, the most effective strategy may be to implement change not at the center but at the periphery of the community.
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Prevalence and correlates of depression in a high emigration town in Oaxaca, Mexico: Findings from a binational student research training program Meacham, Meredith, Hughes, D. Alex, Gell-Redman, Micah, and Zúñiga, Mari Annals of Global Health 2016 [PDF]
2015
-
Social network targeting to maximise population behaviour change: a cluster randomised controlled trial Kim, David, Hwong, Alison, Stafford, Derek, Hughes, D. Alex, O’Malley, James, Fowler, James, and Christakis, Nicholas The Lancet 2015 [Abs] [PDF]
Information and behaviour can spread through interpersonal ties. By targeting influential individuals, health interventions that harness the distributive properties of social networks could be made more effective and efficient than those that do not. Our aim was to assess which targeting methods produce the greatest cascades or spillover effects and hence maximise population-level behaviour change.
2014
-
The role of self-interest in elite bargaining LeVeck, Brad, Hughes, D. Alex, Fowler, James, Hafner-Burton, Emilie, and Victor, David Proceedings of the National Academy of Sciences 2014 [Abs] [PDF]
One of the best-known and most replicated laboratory results in behavioral economics is that bargainers frequently reject low offers, even when it harms their material self-interest. This finding could have important implications for international negotiations on many problems facing humanity today, because models of international bargaining assume exactly the opposite: that policy makers are rational and self-interested. However, it is unknown whether elites who engage in diplomatic bargaining will similarly reject low offers because past research has been based almost exclusively on convenience samples of undergraduates, members of the general public, or small-scale societies rather than highly experienced elites who design and bargain over policy. Using a unique sample of 102 policy and business elites who have an average of 21 y of practical experience conducting international diplomacy or policy strategy, we show that, compared with undergraduates and the general public, elites are actually more likely to reject low offers when playing a standard “ultimatum game” that assesses how players bargain over a fixed resource. Elites with more experience tend to make even higher demands, suggesting that this tendency only increases as policy makers advance to leadership positions. This result contradicts assumptions of rational self-interested behavior that are standard in models of international bargaining, and it suggests that the adoption of global agreements on international trade, climate change, and other important problems will not depend solely on the interests of individual countries, but also on whether these accords are seen as equitable to all member states.
-
One Step In and One Step Out Kosnac, Hillary, Cornelius, Wayne, Wong, Tom, Gell-Redman, Micah, and Hughes, D. Alex 2014 [HTML]
2013
-
The cognitive revolution and the political psychology of elite decision making Hafner-Burton, Emilie, Hughes, D. Alex, and Victor, David Perspectives on Politics 2013 [Abs] [PDF]
Experimental evidence in cognitive psychology and behavioral economics is transforming the way political science scholars think about how humans make decisions in areas of high complexity, uncertainty, and risk. Nearly all those studies utilize convenience samples of university students, but in the real world political elites actually make most pivotal political decisions such as threatening war or changing the course of economic policy. Highly experienced elites are more likely to exhibit the attributes of rational decision making; and over the last fifteen years a wealth of studies suggest that such elites are likely to be more skilled in strategic bargaining than samples with less germane experience. However, elites are also more likely to suffer overconfidence, which degrades decision making skills. We illustrate implications for political science with a case study of crisis bargaining between the US and North Korea. Variations in the experience of US elite decision-makers between 2002 and 2006 plausibly explain the large shift in US crisis signaling better than other rival hypotheses such as “Iraq fatigue.” Beyond crisis bargaining other major political science theories might benefit from attention to the attributes of individual decision makers.


In this example we'll extend dynamic dispatch to return a 404 (not found) response when a client requests a non-existent URL.
As in the previous examples, we'll start with Site
, Resource
, and reactor
imports:
1 2 3
from twisted.web.server import Site from twisted.web.resource import Resource from twisted.internet import reactor
Next, we'll add one more import. NoResource
is one of the pre-defined error
resources provided by Twisted Web. It generates the necessary 404 response code
and renders a simple html page telling the client there is no such resource.
1
from twisted.web.error import NoResource
Next, we'll define a custom resource which does some dynamic URL dispatch. This example is going to be just like the previous one, where the path segment is interpreted as a year; the difference is that this time we'll handle requests which don't conform to that pattern by returning the not found response:
1 2 3 4 5 6 7 8
class Calendar(Resource): def getChild(self, name, request): try: year = int(name) except ValueError: return NoResource() else: return YearPage(year)
Aside from including the definition of YearPage
from
the previous example, the only other thing left to do is the
normal Site
and reactor
setup. Here's the
complete code for this example:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
from twisted.web.server import Site from twisted.web.resource import Resource from twisted.internet import reactor from twisted.web.error import NoResource from calendar import calendar class YearPage(Resource): def __init__(self, year): Resource.__init__(self) self.year = year def render_GET(self, request): return "
%s
" % (calendar(self.year),) class Calendar(Resource): def getChild(self, name, request): try: year = int(name) except ValueError: return NoResource() else: return YearPage(year) root = Calendar() factory = Site(root) reactor.listenTCP(8880, factory) reactor.run()
This server hands out the same calendar views as the one from the previous installment, but it will also hand out a nice error page with a 404 response when a request is made for a URL which cannot be interpreted as a year.


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Learning
In addition to the video tutorials on YouTube, here you'll find some additional written tutorials and resources.
Programming from A to Z
This course focuses on programming strategies and techniques behind procedural analysis and generation of text-based data. We'll explore topics ranging from evaluating text according to its statistical properties to the automated production of text with probabilistic methods to text visualization. Students will learn server-side and client-side JavaScript programming and develop projects that can be shared and interacted with online. There will be weekly homework assignments as well as a final project.
2016 course website 2015 course websiteDepth Sensing with the Microsoft Kinect
The Microsoft Kinect can be used with Processing for a variety of computer vision applications.
Visit the online tutorial


New York City is often considered the most linguistically diverse place in the world, and nowhere is that more apparent than in Queens. For generations, Queens has attracted immigrant families with its low rent and access to global communities. The results of this migration is a multilingual community where languages from across the globe can be found. It has also created the unique situation where some endangered languages find their last home in New York City, carried in the minds of the last speakers.
Beyond the Census: Languages of Queens traces those connections, focusing on how the local connects to the global and back to the local.
Some Minangkabau speakers live in Elmhurst, though the language originates in North Sumatra
The languages we speak, especially those we speak at home, are deeply tied to identity and community. Whether in accent or language, our words give us away as belonging to a certain speech community, connecting us with some, and dividing us from others. Though invisible, languages tie people and places together. And yet, in a highly multilingual, urban setting, what does that mean for the people and the place?
Drawing on a qualitative and often anecdotal dataset provided by the Endangered Language Alliance, this project seeks to answer this question, illustrating the ways that language connects Queens to the world and where the world finds its home in Queens.
This project presented some very specific design challenges as well. First, given the qualitative and anecdotal nature of the dataset, standard approaches to representing the languages would not be applicable. And yet, there is a quantitative component that we sought to encode without taking away from the qualitative.
Data
Main page showing all of the languages in this dataset
The data for the Queens Language map was provided by the Endangered Language Alliance - it documents (in a non-comprehensive, anecdotal way) sites throughout Queens where they know certain languages are spoken.
These language-place pairs vary considerably in both specificity and scale, ranging from entire neighborhoods to street corridors or intersections, to storefront churches, community centers and cafes, where one or more of the nearly 200 languages in the dataset are spoken.
Beyond this variable geographic specificity, some of the data points even lack fixed geographic locations, representing single individuals, food carts, or traveling performance groups known to the ELA.
In order to better visualize the data, and to ensure consistency of structure as new data was added, we split the table into multiple collections, adding additional information to the records when possible.
These collections were as follows:
- Languages
Added countries and continents spoken, geographic point of origin based on additional data from the Glottolog language database. Also assigned probable linugua francas for sorting purposes.
- Institutions
Assigned point geometry from addresses where available, otherwise manually by online search for institution, related events and individuals etc.
- Continents
Assigned polygon geometry from geojson world map in the public domain.
- Countries
Assigned polygon geometry from geojson world map in the public domain.
- Neighborhoods
Assigned polygon geometry from NYC Neighborhood Tabulation Areas, available from city open data portal.
The bulk of the data cleaning, linking, and importing to the database was done using node.js scripts.
Designing the Map
Variable Geographic Specificity & Map Markers
Through an iterative design process the project developed a visual language which aims to work within the productive contraints of this anecdotal dataset. To bridge between the wide range of geographic specificity encoded for each language a series of map markers was developed that while sharing certain visual elements, also incorporated unique features. The individual colored squares represent institutions where the actual address or location information is visible, and the small rectangular grids of colored squares represent the total and variety of different languages spoken in a given neighborhood. This strategy aims to accurately represent the geographic information in the dataset while avoid issues with double-representation.
Besides representing sites of language practice in a static way, these map markers also function as interactive elements, allowing the user to obtain more information about one of the languages in the cluster, or the institution/geography in question.
Language spoken in Astoria
The Globe
Since each language entry carries with it information about its point of origin in the world and other regions where there are high concentrations of speakers, the map features a globe where these global-scale sites of practice can be visualized. Each site is rendered using a marker from the same set used in the borough map, creating a visual correspondence between local and global sites of practice.
Vietnamese originates in Vietnam, but there are many speakers in Australia, the United States, and the Czech Republic.
List/Legend
Using color alone, or even a combination of color and symbols, distinguishing between each of the languages in a list of almost 200 items is impossible. The map features a list of all the languages as a separate panel, grouped by continent of origin (each of which is assigned a primary color) and further sorted by level of language endangerment (which determines the lightness of the continent base color). Each language, labeled in the list, can be clicked to highlight its sites of practice in the map of queens and on the globe.
List showing only endangered languages, sorted using the slider.
Planning for Crowdsourcing
Throughout our conversations with the ELA, we discussed the idea of user-generated data being added to the map. While the preliminary database generated by the ELA through their fieldwork would form the basis for the entire map at first, the possibility that users could enter their own languages, landmarks, and origins at a later time promised to build a much more expansive and detailed dataset over time.
To facilitate the expansion of this project, we sought to build a platform capable of receiving new data from users in the future and ensuring a consistent structure and format.
Conclusions
Recently, Rebecca Solnit and Joshua Jelly-Schapiro edited a volume, "The Non-Stop Metropolis," which included a map also using a dataset provided by the Endangered Language Alliance, entitled, "Mother Tongues and Queens" (Molly Roy and Mirissa Neff). This map identified many of the languages represented here. While there are a great many similarities between that map and this one, they serve vastly different purposes, and have radically different end results.
This map is meant to be played with, it invites users to zoom in and out, explore the areas around the languages, and understand the relationship between the languages and each other, how they are situated, where they meet, and where they don't. We have also included what is missing, highlighting that so little is known about African languages in Queens, though we can be fairly certain that the ones we included are there.
Likewise, by layering the glottolog data, endangerment status, and additional information, we have offered users the opportunity to interact with the languages of Queens, ask questions about them, and find out more information. We invite users to play, and in many ways, find answers to their own questions. This map can be viewed through a region of origin lens, an endangerment lens, or a global lens. Rather than being presented with one static story, the user is invited to go deeper, following links to Wikipedia and Ethnologue.
Ultimately, this interactive, web-based format gave us the chance to offer the user and outlet for their curiosity about languages in Queens, illustrating not only the staggering diversity of the borough as a whole, but the hotspots where people from different geographic regions come together. In the future, we hope that others will be able to add their own story to this map, and share their language with the world.
Project Team: Laura Kurgan, Michelle McSweeney, Carsten Rodin, Tola Oniyangi, and Dare Brawley with the
Endangered Language Alliance
Return to Case Studies


| Top |
| gboolean | active | Read / Write |
| gboolean | draw-indicator | Read / Write |
| gboolean | inconsistent | Read / Write |
GObject ╰── GInitiallyUnowned ╰── GtkWidget ╰── GtkContainer ╰── GtkBin ╰── GtkButton ╰── GtkToggleButton ├── GtkCheckButton ╰── GtkMenuButton
GtkToggleButton implements AtkImplementorIface, GtkBuildable, GtkActionable and GtkActivatable.
A GtkToggleButton is a GtkButton which will remain “pressed-in” when clicked. Clicking again will cause the toggle button to return to its normal state.
A toggle button is created by calling either gtk_toggle_button_new()
or
gtk_toggle_button_new_with_label()
. If using the former, it is advisable to
pack a widget, (such as a GtkLabel and/or a GtkImage), into the toggle
button’s container. (See GtkButton for more information).
The state of a GtkToggleButton can be set specifically using
gtk_toggle_button_set_active()
, and retrieved using
gtk_toggle_button_get_active()
.
To simply switch the state of a toggle button, use gtk_toggle_button_toggled()
.
GtkToggleButton has a single CSS node with name button. To differentiate it from a plain GtkButton, it gets the .toggle style class.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 |
void make_toggles (void) { GtkWidget *dialog, *toggle1, *toggle2; GtkWidget *content_area; const char *text; dialog = gtk_dialog_new (text); content_area = gtk_dialog_get_content_area (); text = "Hi, i’m a toggle button."; toggle1 = gtk_toggle_button_new_with_label (text); // Makes this toggle button invisible gtk_toggle_button_set_mode (GTK_TOGGLE_BUTTON (toggle1), TRUE); g_signal_connect (toggle1, "toggled", G_CALLBACK (output_state), NULL); gtk_box_pack_start (GTK_BOX (content_area), toggle1, FALSE, FALSE, 2); text = "Hi, i’m a toggle button."; toggle2 = gtk_toggle_button_new_with_label (text); gtk_toggle_button_set_mode (GTK_TOGGLE_BUTTON (toggle2), FALSE); g_signal_connect (toggle2, "toggled", G_CALLBACK (output_state), NULL); gtk_box_pack_start (GTK_BOX (content_area), toggle2, FALSE, FALSE, 2); gtk_widget_show_all (dialog); } |
GtkWidget *
gtk_toggle_button_new (void
);
Creates a new toggle button. A widget should be packed into the button, as in gtk_button_new()
.
GtkWidget *
gtk_toggle_button_new_with_label (const gchar *label
);
Creates a new toggle button with a text label.
GtkWidget *
gtk_toggle_button_new_with_mnemonic (const gchar *label
);
Creates a new GtkToggleButton containing a label. The label
will be created using gtk_label_new_with_mnemonic()
, so underscores
in label
indicate the mnemonic for the button.
void gtk_toggle_button_set_mode (GtkToggleButton *toggle_button
,gboolean draw_indicator
);
Sets whether the button is displayed as a separate indicator and label.
You can call this function on a checkbutton or a radiobutton with
draw_indicator
= FALSE
to make the button look like a normal button.
This can be used to create linked strip of buttons that work like a GtkStackSwitcher.
This function only affects instances of classes like GtkCheckButton and GtkRadioButton that derive from GtkToggleButton, not instances of GtkToggleButton itself.
gboolean
gtk_toggle_button_get_mode (GtkToggleButton *toggle_button
);
Retrieves whether the button is displayed as a separate indicator
and label. See gtk_toggle_button_set_mode()
.
void
gtk_toggle_button_toggled (GtkToggleButton *toggle_button
);
Emits the “toggled” signal on the GtkToggleButton. There is no good reason for an application ever to call this function.
gboolean
gtk_toggle_button_get_active (GtkToggleButton *toggle_button
);
Queries a GtkToggleButton and returns its current state. Returns TRUE
if
the toggle button is pressed in and FALSE
if it is raised.
void gtk_toggle_button_set_active (GtkToggleButton *toggle_button
,gboolean is_active
);
Sets the status of the toggle button. Set to TRUE
if you want the
GtkToggleButton to be “pressed in”, and FALSE
to raise it.
This action causes the “toggled” signal and the
“clicked” signal to be emitted.
gboolean
gtk_toggle_button_get_inconsistent (GtkToggleButton *toggle_button
);
Gets the value set by gtk_toggle_button_set_inconsistent()
.
void gtk_toggle_button_set_inconsistent (GtkToggleButton *toggle_button
,gboolean setting
);
If the user has selected a range of elements (such as some text or
spreadsheet cells) that are affected by a toggle button, and the
current values in that range are inconsistent, you may want to
display the toggle in an “in between” state. This function turns on
“in between” display. Normally you would turn off the inconsistent
state again if the user toggles the toggle button. This has to be
done manually, gtk_toggle_button_set_inconsistent()
only affects
visual appearance, it doesn’t affect the semantics of the button.
“active”
property“active” gboolean
If the toggle button should be pressed in.
Flags: Read / Write
Default value: FALSE
“draw-indicator”
property“draw-indicator” gboolean
If the toggle part of the button is displayed.
Flags: Read / Write
Default value: FALSE
“inconsistent”
property“inconsistent” gboolean
If the toggle button is in an "in between" state.
Flags: Read / Write
Default value: FALSE
“toggled”
signalvoid user_function (GtkToggleButton *togglebutton, gpointer user_data)
Should be connected if you wish to perform an action whenever the GtkToggleButton's state is changed.
togglebutton |
the object which received the signal. |
|
user_data |
user data set when the signal handler was connected. |
Flags: Run First


Exciting News For Chabangu In Comeback Attempt
by Soccer LadumaLerato ‘Ace’ Chabangu is showing signs of hunger for competitive football again, as well as willingness to grab his chance, after being given another lifeline by Baberwa FC in the ABC Motsepe League.
The former Mamelodi Sundowns and Bafana Bafana star was signed by the third tier league outfit at the beginning of the year in an attempt to help him to revive his career.
Read: SA To Represent In Big CAF Match
However, reports of him going AWOL surfaced in the football fraternity at the start of the current season.
It was later confirmed in the media from Baberwa that the player eventually returned to the club after his two-week absence.
Prior to last weekend’s match against M Tigers, Baberwa confirmed that Chabangu was finally declared fit for competitive football for the first time this season.
Read: Lifeline For Ngele In AFCON Qualifiers!
The 34-year-old was included in the match-day squad, though he was an unused substitute.
"The technical team has confirmed that for the first time this season Lerato “All You Need Is Love” Chabangu is fully fit and will be in the 18-man squad for tomorrow’s game against M. Tigers FC at Mehlareng Stadium in Tembisa," Baberwa confirmed on Twitter.
Chabangu previously also played for SuperSport United, Chippa United and Moroka Swallows, among other clubs in his career.
The Tembisa-born player has no intentions of hanging up his boots just yet as he remains hopeful of another chance in professional football.
Post Selfie’s On Soccer Laduma’s Social Media App & Earn Airtime and more! Download Now


GUITAR
I started to study guitar since the age of 10. I did a few regional and national competitions back in China.
The followings are a few clips I did:


@import data._ @import helper._ @import play.Environment @import com.typesafe.config.Config @import data.viewModel.PageStatus @import helpers.CallHelper.relative @import org.webjars.play.WebJarsUtil @import fragment._ @(reportForm: Form[ShortFormatPreSentenceReportData], encrypter: String => String)(implicit pageStatuses: Map[Int, PageStatus], webJarsUtil: WebJarsUtil, environment: Environment, configuration: Config) @implicitField = @{ FieldConstructor(govukElements.f) } @summaryHint = {
Describe the offence or the incident. In this summary, judges and magistrates need to know where your information is coming from. You need to be clear when it’s from CPS, probation or from the offender.
Details should be provided for any basis of plea. Reference should also be made as to whether the report is following a guilty plea or a finding of guilt after trial. The summary should provide the reader with a sound overview of the offence.
Remember to highlight domestic abuse, safeguarding or any other significant features. Diversity considerations may also be relevant here, both in terms of the offender and the victim.
} @popup(reportForm, encrypter, "Offence details", 4) { @textarea(reportForm("mainOffence"), '_label -> "Main offence and date", 'class -> "moj-textarea--prefilled")


Pixel Amigos is a group made by four Undergrads who are currently pursuing their Bachelors of Engineering. The group has three Electronics & Communication Undergrads and a Computer Science Undergrad who are technology enthusiats,passionate about learning and teaching new technologies and implies it to solve up real life problems. .
The group members namely:
We all love technology and are willing to work hard to make the world a better place.


A browser war is competition for dominance in the usage share of web browsers. The "First Browser War" during the late 1990s pitted Microsoft's Internet Explorer against Netscape's Navigator. The World Wide Web is an Internet-based hypertext system invented in the early 1990s by Tim Berners-Lee.
In 1993, Mosaic web browser was created by two students (Marc Andreessen and Eric Bina) from University of Illinois. Mosaic was the first web that popularized the World Wide Web and the Internet. In 1994, James Clark met inventors of Mosaic web browser, the first widely distributed and easy-to-use software for browsing the World Wide Web and invested $4 million for them to create a new web browser under the company named “Netscape”.
In Oct 1994, they developed a new browser called “Navigator”. The Navigator web browser was more stable and reliable than the Mosaic web browser. It hit 1 million downloads and started dominance in internet world. In 1995, Navigator was at peak and it made giant Microsoft to look for development of the web browser to destroy the Navigator from Netscape. Microsoft employees held meeting at Netscape. According to them, Microsoft came to them with an ultimatum. Join up or move out of the way. Microsoft offered Netscape a huge sum for their browsers code base. When the team refused, employees from Microsoft threatened to eliminate them from the market by any means necessary.
Gates sent out a memo to all Microsoft employees titled “The Internet Tidal Wave.” In it, Gates outlined a new future for his company, one connected to the Internet. His paranoia for competition was also clear. As Netscape’s success grew, Microsoft got their own browser ready. It was called Internet Explorer. IE was, at first, far from impressive. It was released as an at-retail extension for Windows ’95, and initially, did very little to damage Netscape’s market share.
For the next few years, Netscape and Microsoft waged war. Netscape began to focus on accompanying features. Microsoft took a slightly different route. Instead of adding extensions to their browser, they leveraged their position in the personal computing marketplace. They gave away the browser for free to PC manufacturers and Internet Service Providers to bundle into their hardware. They lowered the price of their browser for Microsoft Windows users.
Microsoft’s aggressive push to distribute it, started to turn things in their favour. Netscape continued to innovate, but they knew they couldn’t keep up. So, they gave up browser war in 1997. Then Microsoft was charged by the United States Government. In the summer of 1998, Microsoft went on trial for violating antitrust laws and a consent decree from a few years previous. The US Department of Justice argued that by giving away their browser for free, and bundling it into their operating system, they forced consumers to use their product and pushed competitors out of the market. This, along with the hostile pressure that Microsoft put on manufacturers to include Internet Explorer in their platforms, created a monopoly position for the company. Microsoft found guilty.


Class OptionsBuilder
Builder for Options.
Options itself is immutable, but OptionsBuilder is chainable and mutable.
Inheritance
Namespace: Cesil
Assembly: Cesil.dll
Syntax
public sealed class OptionsBuilder : Object
Properties
CommentCharacter
Which character, if any, is used to indicate the start of a comment.
Typically not set, but when set often '#'.
Declaration
public Nullable<char> CommentCharacter { get; }
Property Value
| Type | Description |
|---|---|
| System.Nullable<System.Char> |
DynamicRowDisposal
When to dispose any dynamic rows returned by an IReader or IAsyncReader.
Declaration
public DynamicRowDisposal DynamicRowDisposal { get; }
Property Value
| Type | Description |
|---|---|
| DynamicRowDisposal |
EscapedValueEscapeCharacter
Character used to escape another character in an escaped value.
Typically a double quote, but can be null for some formats and MUST be null for formats without an EscapedValueStartAndEnd.
Declaration
public Nullable<char> EscapedValueEscapeCharacter { get; }
Property Value
| Type | Description |
|---|---|
| System.Nullable<System.Char> |
EscapedValueStartAndEnd
Character used to start an escaped value.
Typically a double quote, but can be null for some formats.
Declaration
public Nullable<char> EscapedValueStartAndEnd { get; }
Property Value
| Type | Description |
|---|---|
| System.Nullable<System.Char> |
ExtraColumnTreatment
How to handle extra colums encountered when reading a CSV.
Declaration
public ExtraColumnTreatment ExtraColumnTreatment { get; }
Property Value
| Type | Description |
|---|---|
| ExtraColumnTreatment |
MemoryPoolProvider
Provider for MemoryPools needed during reading or writing CSVs.
Declaration
public IMemoryPoolProvider MemoryPoolProvider { get; }
Property Value
| Type | Description |
|---|---|
| IMemoryPoolProvider |
ReadBufferSizeHint
How big a buffer to request from the MemoryPool for servicing read operations.
Set to 0 to use a default size.
Declaration
public int ReadBufferSizeHint { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
ReadHeader
Whether or not to read headers when reading a CSV.
Declaration
public ReadHeader ReadHeader { get; }
Property Value
| Type | Description |
|---|---|
| ReadHeader |
ReadRowEnding
The sequence of characters used to end a row when reading.
Declaration
public ReadRowEnding ReadRowEnding { get; }
Property Value
| Type | Description |
|---|---|
| ReadRowEnding |
TypeDescriber
The instance of ITypeDescriber that will be used to discover which columns to read or write, as well as the manner of their reading and writing.
Declaration
public ITypeDescriber TypeDescriber { get; }
Property Value
| Type | Description |
|---|---|
| ITypeDescriber |
ValueSeparator
Character used to separate two values in a row
Typically a comma.
Declaration
public string ValueSeparator { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
WhitespaceTreatment
How to handle whitespace when encountered during parsing.
Declaration
public WhitespaceTreatments WhitespaceTreatment { get; }
Property Value
| Type | Description |
|---|---|
| WhitespaceTreatments |
WriteBufferSizeHint
How big a buffer to request from the MemoryPool for buffering write operations.
Set to 0 to disable buffering.
Set to null to use a default size.
Declaration
public Nullable<int> WriteBufferSizeHint { get; }
Property Value
| Type | Description |
|---|---|
| System.Nullable<System.Int32> |
WriteHeader
Whether or not to write headers when writing a CSV.
Declaration
public WriteHeader WriteHeader { get; }
Property Value
| Type | Description |
|---|---|
| WriteHeader |
WriteRowEnding
The sequence of characters used to end a row when writing.
Declaration
public WriteRowEnding WriteRowEnding { get; }
Property Value
| Type | Description |
|---|---|
| WriteRowEnding |
WriteTrailingRowEnding
Whether or not to write a row ending after the last row in a CSV.
Declaration
public WriteTrailingRowEnding WriteTrailingRowEnding { get; }
Property Value
| Type | Description |
|---|---|
| WriteTrailingRowEnding |
Methods
CreateBuilder()
Create a new, empty, OptionsBuilder.
Declaration
public static OptionsBuilder CreateBuilder()
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
CreateBuilder(Options)
Create a new OptionsBuilder, copying defaults from the given Options.
Declaration
public static OptionsBuilder CreateBuilder(Options options)
Parameters
| Type | Name | Description |
|---|---|---|
| Options | options |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
ToOptions()
Create the Options object that has been configured by this builder.
Declaration
public Options ToOptions()
Returns
| Type | Description |
|---|---|
| Options |
ToString()
Returns a representation of this OptionsBuilder object.
Only for debugging, this value is not guaranteed to be stable.
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| System.String |
WithCommentCharacter(Nullable<Char>)
Set or clear the character that starts a row that is a comment.
Declaration
public OptionsBuilder WithCommentCharacter(Nullable<char> commentStart)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Nullable<System.Char> | commentStart |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithDynamicRowDisposal(DynamicRowDisposal)
Set when dynamic rows returned by a reader are disposed.
The options are either when the reader is disposed (the default) or when the row is explicitly disposed.
Declaration
public OptionsBuilder WithDynamicRowDisposal(DynamicRowDisposal dynamicRowDisposal)
Parameters
| Type | Name | Description |
|---|---|---|
| DynamicRowDisposal | dynamicRowDisposal |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithEscapedValueEscapeCharacter(Nullable<Char>)
Set the character used to escape another character in an escaped value.
If this is non-null, EscapedValueStartAndEnd must also be non-null.
Declaration
public OptionsBuilder WithEscapedValueEscapeCharacter(Nullable<char> escape)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Nullable<System.Char> | escape |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithEscapedValueStartAndEnd(Nullable<Char>)
Set the character used to start an escaped value.
If this is null, EscapedValueEscapeCharacter must also be null.
Declaration
public OptionsBuilder WithEscapedValueStartAndEnd(Nullable<char> escapeStart)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Nullable<System.Char> | escapeStart |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithExtraColumnTreatment(ExtraColumnTreatment)
Set how to treat extra columns when parsing.
Declaration
public OptionsBuilder WithExtraColumnTreatment(ExtraColumnTreatment extraColumnTreatment)
Parameters
| Type | Name | Description |
|---|---|---|
| ExtraColumnTreatment | extraColumnTreatment |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithMemoryPoolProvider(IMemoryPoolProvider)
Set the MemoryPoolProvider used during reading and writing.
Declaration
public OptionsBuilder WithMemoryPoolProvider(IMemoryPoolProvider memoryPoolProvider)
Parameters
| Type | Name | Description |
|---|---|---|
| IMemoryPoolProvider | memoryPoolProvider |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithReadBufferSizeHint(Int32)
Set the buffer size hint for read operations.
Setting to 0 will cause a default "best guess" size to be requested from the configured MemoryPool.
All values are treated as hints, it's up to the configured MemoryPool to satisfy the request.
Declaration
public OptionsBuilder WithReadBufferSizeHint(int sizeHint)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | sizeHint |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithReadHeader(ReadHeader)
Set whether or not to read headers.
Declaration
public OptionsBuilder WithReadHeader(ReadHeader readHeader)
Parameters
| Type | Name | Description |
|---|---|---|
| ReadHeader | readHeader |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithReadRowEnding(ReadRowEnding)
Set the sequence of characters that will end a row when reading.
Declaration
public OptionsBuilder WithReadRowEnding(ReadRowEnding readRowEnding)
Parameters
| Type | Name | Description |
|---|---|---|
| ReadRowEnding | readRowEnding |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithTypeDescriber(ITypeDescriber)
Set the ITypeDescriber used to discover and configure the columns that are read and written.
Declaration
public OptionsBuilder WithTypeDescriber(ITypeDescriber typeDescriber)
Parameters
| Type | Name | Description |
|---|---|---|
| ITypeDescriber | typeDescriber |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithValueSeparator(String)
Set the character used to separate two values in a row.
Declaration
public OptionsBuilder WithValueSeparator(string valueSeparator)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | valueSeparator |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithWhitespaceTreatment(WhitespaceTreatments)
Set how to treat whitespace when parsing.
Declaration
public OptionsBuilder WithWhitespaceTreatment(WhitespaceTreatments whitespaceTreatment)
Parameters
| Type | Name | Description |
|---|---|---|
| WhitespaceTreatments | whitespaceTreatment |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithWriteBufferSizeHint(Nullable<Int32>)
Set or clear the buffer size hint for write operations.
Setting it to null will cause a default "best guess" buffer to be requested from the configured MemoryPool.
Setting it to 0 will disable buffering.
All values are treated as hints, it's up to the configured MemoryPool to satisfy the request.
Declaration
public OptionsBuilder WithWriteBufferSizeHint(Nullable<int> sizeHint)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Nullable<System.Int32> | sizeHint |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithWriteHeader(WriteHeader)
Set whether or not to write headers.
Declaration
public OptionsBuilder WithWriteHeader(WriteHeader writeHeader)
Parameters
| Type | Name | Description |
|---|---|---|
| WriteHeader | writeHeader |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithWriteRowEnding(WriteRowEnding)
Set the sequence of characters that will end a row when write.
Declaration
public OptionsBuilder WithWriteRowEnding(WriteRowEnding writeRowEnding)
Parameters
| Type | Name | Description |
|---|---|---|
| WriteRowEnding | writeRowEnding |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |
WithWriteTrailingRowEnding(WriteTrailingRowEnding)
Set whether or not to end the last row with a new line.
Declaration
public OptionsBuilder WithWriteTrailingRowEnding(WriteTrailingRowEnding writeTrailingNewLine)
Parameters
| Type | Name | Description |
|---|---|---|
| WriteTrailingRowEnding | writeTrailingNewLine |
Returns
| Type | Description |
|---|---|
| OptionsBuilder |


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\begin{description} \item[label] First item \item[label] Second item .... \end{description}
The description environment is used to make labeled lists. The label is bold face and flushed right. The text of the \item command is indented.
Typical usage is for descriptive lists or lists of definitions; the label is then the name of the item being described or defined.
See also Environments,
Lists,
Counters
See also Enumerate
See also \item
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