Dunbar 'gutted' by rental crisis, hopeful for Vancouver-proposed incentives
by Toby KerrSummary
Bruce Gilmour is strongly in favour of the proposed changes as he says his neighbourhood is in crisis
He says he doesn't blame developers for trying to turn a profit
Gilmour believes major steps should have been taken sooner
VANCOUVER (NEWS 1130) — A report that will go before Vancouver city council Tuesday contains new recommendations to get more rentals in the city quickly.
Among them, a proposal to streamline the application process for developers and eliminate public hearings for new rentals along major arteries.
Bruce Gilmour, President of the Dunbar Residents’ Association, is strongly in favour of the proposed changes as he says his neighbourhood is in crisis.
“It’s now turning into a ghost town. Stores are closing and the stores are closing because the pedestrian traffic is declining and there’s just a sense of disempowerment,” he says.
Gilmour adds he doesn’t blame developers for trying to turn a profit so it’s up to the city to push them toward providing more accessible rental housing.
“We’ve lived here for 30 years. We’ve raised a child. When I am starting to bump into people on the street who are saying they are ‘gutted’ by what’s happening, I’m not going to say it’s all the developers’ fault. I would say the fingers go back to the City of Vancouver,” he says, adding he’s saddened by the loss of a sense of community within Dunbar, due in large part to a lack of accessible housing.
Even though he supports the proposal, Gilmour says major steps should have been taken sooner.
“People are being gutted and people are wanting to move. The neighbourhood values the sense of engagement and connection is being ripped apart,” he explains.
Gilmour says he believes regardless of the neighbourhood, most people are open to rezoning proposals.
“We are all getting to a place where we understand we need more places for people to live. We are not going to be NIMBY here. We recognize that change is on its way,” he says.


@* * Copyright 2019 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.tariffclassificationfrontend.controllers @import uk.gov.hmrc.tariffclassificationfrontend.models.request.AuthenticatedRequest @import uk.gov.hmrc.tariffclassificationfrontend.models.{Permission, Queue, TabIndexes} @import uk.gov.hmrc.tariffclassificationfrontend.models.ApplicationType @(queues: Seq[Queue], section: String, caseCountByQueue: Map[String, Int] = Map.empty, filteredBy: String = Seq(ApplicationType.BTI,ApplicationType.LIABILITY_ORDER).mkString(","))(implicit request: AuthenticatedRequest[_], messages: Messages)


MY Articles
Quantum Computing
They don't know how to get started with the open source community so, In this blog I wrote about How to contribute for the first time.
how to create a connection, how to create a statemennt & how to create database etc.,
Work Experience
iotiot.com
In this internship I developed a small software using java. Here I learned about software development methodologies & team management.
- Java
- Software Development
Webbeey Infotech
Here I worked as a we developer. Actually this is a startup where I worked on some good projects. I learned a lot while working like project management, frontend development & wordpress development too.
- HTML5
- CSS3
- JavaScript
- Wordpress
Geeks-for-Geeks
This is a technical writer internship where I have to write an article on some technical topics like collections, java etc as well as I have to improve some previously written codes. While writing an article I learned so many new topics & new concepts.
- Technical Content Writing
- Java
- CSS
Projects
Plasma Donor Management System
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You ae able to take q Screenshot and store it into the database.
Tecnologies: Java Swing , Java AWT , MYSQL
Feedback Form Management System
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(working)
Scientific Calculator
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Represents a stream connected to a TcpSocket .
| public nothrow | NetworkByteStream(TcpSocket&& socket_) |
| public TcpSocket& | ContainedSocket() |
| public override long | Read(byte* buf, long count) |
| public override int | ReadByte() |
| public override void | Write(byte x) |
| public override void | Write(byte* buf, long count) |
| private TcpSocket | socket |


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We opened in 2007 in Boston.
Our first office was opened in the heart of Boston, overlooking the historic Copley square.
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Load packages
library(tidyverse)
Motivation
Z-scores (z-values) are a useful and widely employed tool to gauge and compare measurements. For instance, z-scores help to compare the relative position of some measurements with respect to their distributions. In this post, we will prove some basic (algebraic) properties of z-values. There’s nothing new to that, it’s just I’d like to have it neat and concise somewhere to quickly find it. I’ll add some explanation for the ease of reception.
Definition of z-scores
A z-score measures the distance of a measurement to its (arithmetic) mean value. This distance is then set in relation to the “standard” distance of those measures to their mean.
That is,
\[ z = \frac{x_i - \bar{x}}{s_x} \]
where \(s_x\) is the standard deviation of \(x\), and \(x_i\) is one measurement of the variable \(X\). \(\bar{x}\) represents the average (mean) value.
Let’s call the distance from the mean \(d\), then the difference of measurement \(i\) to the mean is
\[ d_i = x_i - \bar{x} \]
It doesn’t really matter whether we subtract \(x_i\) from the mean or the other way round. The absolute value of \(d_i\) remains the same; it’s just that measurement would appear as smaller or larger than the mean.
Now we express \(d\) in terms of the standard difference (sd) of the measurements to its mean:
\[ z = \frac{d}{s_x} \]
Intuition
Z-scores have a mean of zero
By calculating \(z\) we subtract the mean value, i.e. \(\bar{x}\) from each measurement. As a consequence, the mean z-score will have a mean that is exactly \(\bar{x}\) lower than the initial mean (ie., \(\bar{x}\)). Subtracting from a value exactly the same value will yield zero.
Z-scores have a SD of 1
Each measurement is set in relation to the SD of \(X\), ie., we devide each \(d_i\) by \(s_x\). In consequence, the sum of all measurements will be lower then the initial sum of \(x_1, x_2, \cdots\) of exactly \(s_x\). Dividing a value by the same value yields 1. It that’s true for the sum, then it will also be true for the mean. As the SD is basically the mean of the ratio \(\frac{d}{s_x}\), the SD of \(Z\) will also equal 1.
Proofs
Z-scores have a mean of zero
In order to show that \(\bar{x}=0\) it suffices to show that \(\sum{x}=0\). That’s because if a sum equals zero, the mean must equal zero too.
\[ \begin{align} \sum{z} &= \sum \frac{x_i - \bar{X}}{s_x} \\ &= \frac{1}{s_x} \sum{x_i - \bar{x}} \\ &= \frac{1}{s_x} \sum{x_i} - n\cdot \bar{x} \\ &= \frac{1}{s_x} (n \cdot \bar{x} - n\cdot \bar{x}) \\ &= 0 \end{align} \]
Note that the sum of alle measurements equals \(n\)-times the mean, i.e.,
\[ \sum{x_i} = n\bar{x} \\ \]
This fact stems for the definition of the mean:
\[ \bar{x} = \frac{1}{n} \sum x_i \qquad \text{multiply both sides by }n \]
\[ \Leftrightarrow n\bar{x} = \sum x_i \\ \]
Z-scores have a SD (and a variance) of 1
Remember that SD of some variable \(x\) is defined as:
\[ sd(x) = \frac{1}{n}\sum{ \left( ( x_i - \bar{x} ) ^2 \right)} \]
If \(\bar{x}=0\) – as it is the case for z-scores – this simplifies to
\[ sd(x) = \frac{1}{n}\sum{ ( x_i ) ^2 } \]
In order to show that the SD of \(Z\) equals 1 it suffices to show that the mean equals 1. That’s because if a sum equals 1, the mean must equal 1 too.
Note that the SD of Z is defines such that
\[ s(Z) = \frac{1}{n} \sum z_i^2 \]
In comparison, let \(sum(Z)\) be defines such that
\[ sum(Z)= \sum z_i^2 \]
Hence, if \(sum(Z) = 1\) it follows that \(sd(Z) = 1\).
\[ \begin{align} \sum Z_i^2 &= \sum \left( \frac{x_i - \bar{x}}{s_x} \right)^2 \\ &= \frac{1}{s_x^2} \sum(x_i - \bar{x})^2 \qquad \text{note that }ns_x^2 = \sum(x_i - \bar{x})^2\\ \end{align} \]
Hence
\[ \begin{align} &= \frac{1}{s_x^2}n s_x^2 \\ &= n \end{align} \]
We arrive at the fact the the sum of the squared z-scores equals \(n\). Dividing by \(n\) (or \(n-1\)) yields 1.
In other words, the SD of \(Z\) equals 1.
Note that
\[ sd(Z) = 1 \Leftrightarrow Var(Z)=1 \]
where \(Var\) is the Variance. Whenever sd equals 1 the variance will equal 1, too.
We do not distinguish between the estimated population variance where we standardize be \(n-1\) and the sample variance where we standardize by \(n\). The principle remains the same, and the conclusions remain the same.
The average squared z-score equals 1
As discussed above the avarage squared z-score equals the SD of the z-score for a given variable \(x\):
\[ \frac{1}{n} \sum z_i^2 = 1 \]


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VMChainHandle, VMFreeVMChain(), VMCompareVMChains(), VMCopyVMChain(), VMCHAIN_IS_DBITEM(), VMCHAIN_GET_VM_BLOCK(), VMCHAIN_MAKE_FROM_VM_BLOCK()
Several utilities are provided for working with VM chains. They allow you to compare, free, or copy entire VM chains with a single command.
For convenience, all of these routines can work on either a VM chain or a database item. This is useful, because sometimes you will want to use the utility on a data structure without knowing in advance how large it will be. This way, if there is a small amount of data, you can store it in a DB item; if there is a lot, you can store it in a VM chain of any length. Whichever way you store the data, you can use the same chain utilities to manipulate it.
The routines all take, as an argument, a
VMChain
which is simply a dword. If the low word is null, the
VMChain
contains the VM block handle to a VM chain. If the low word is not null, the
VMChain
contains a
DBGroupAndItem
. Note that none of the blocks in the VM chain need be locked when the routine is called; the routine will lock the blocks as necessary, and unlock them when finished. Similarly, a DB item need not be locked before being passed to one of these routines. However, the VM file containing the structure must be open.
If you want to free an entire VM chain at once, call the routine
VMFreeVMChain()
. This routine takes two arguments, namely the VM file handle and a
VMChain
. It frees every block in the VM chain, and returns nothing.
You can compare two different VM chains, whether in the same or in different files, by calling
VMCompareVMChains()
. This Boolean routine takes four arguments, namely the handles of the two files (which may be the same) and the
VMChain
s (
i.e.
, the block handles to the two chains). The geode must have both files open when it calls this routine. The routine returns
true
(
i.e.
, non-zero) if the two chains are identical (
i.e.
, the trees have the same structures and all data bytes are identical). Note that if the chains contain tree blocks which are identical except in the order of their links, the chains will not be considered identical and the routine will return
false
(
i.e.
, zero).
You can make a copy of a VM chain with the routine
VMCopyVMChain()
. This routine copies the entire chain to a specified file, which may be the same as the source file. The blocks in the duplicate chain will have the same user ID numbers as the corresponding original blocks. The routine takes three arguments: the handle of the source file, the
VMChain
of the source chain or item, and the handle of the destination file. It copies the chain or item and returns the
VMChain
of the duplicate structure. As noted, if the low word of the
VMChain
is null, the high word will be the VM handle of the head of the chain. The geode must have both files open when it calls this routine.
Several utilities are provided for working with
VMChain
types. They are implemented as preprocessor macros, so they are very fast. The macro VMCHAIN_IS_DBITEM()
is passed a
VMChain
and returns non-zero if the VMChain identifies a DB item (
i.e.
, if the low word is non-zero) or zero if the VMChain identifies a VM chain. The macro VMCHAIN_GET_VM_BLOCK
() is passed a
VMChain
containing the block handle to a VM chain. It returns the high word,
i.e.,
the chain's VM block handle.
Finally, the macro VMCHAIN_MAKE_FROM_VM_BLOCK() takes a
VMBlockHandle
value and casts it to type
VMChain
.


We help companies position their business to go from selling:
$50
Monthly Subscriptions
$50k
Annual Deals
Managers
Persona
VPs
Persona
We specialize in delivering:
Positioning + Messaging
Positioning is key. Message your product effectively to your target audience.
Sales Enablement
Your sales team is closing bigger deals. Arm them with the marketing materials they need.
Customer Stories
Showcase the success of your top customers. Share engaging video and written case studies.
A few companies we've worked with:
Working with Olivine is an awesome experience. We only have to share a high level guideline of what we want to achieve and they drive it all the way and bring back amazing results.


The Blockchain as art
The Symphony project began with a question: how do we represent blockchain technology in a way that is stimulating, entertaining, and audio-visually engaging for a wider audience, technical and non-technical. In other words, how do we explain the abstract and give form to the formless. Starting as a single project, Symphony has grown to become several creative initiatives.
Symphony 2.0 by IOHK
IOHK is responsible for Symphony 2.0: an interactive 3D explorer that allows users to traverse the topographic history of the Bitcoin blockchain. Symphony 2.0 visualizes the blockchain’s mempool – where unconfirmed transactions are stored – as a gravitational swell, around which confirmed transactions spiral in concentric rings. Users can explore these rings block-by-block, from the first transaction confirmed to the most recent.
Symphony 2.0 also has a virtual reality component developed for Vive using WebVR. This has been exhibited at past events and will be available to experience throughout the Symphony world tour.
Preview
Symphony 2.0: How It Works
Giving feeling to data isn’t easy, but telling a good story requires an emotional connection.
To achieve this, we used a technique called additive synthesis to generate sound on the fly, and utilized the parallel nature of graphics cards to synthesize a sound for each of the thousands of transactions that can make up a block. The unique sound signature that plays when you visit a block consists of each transaction producing eight sine waves (a fundamental pitch and seven harmonics). The fundamental pitch is determined by the transaction value, and the amount of randomness added to the harmonics partials is controlled by the fee-to-value ratio of the transaction.
The volume of the harmonic partials, meanwhile, is determined by the spent-to-unspent output ratio. This creates a range of sound that communicates the health of the block: a virtual heartbeat that codifies the volume of transactions and the number of transactions unspent.
Preview
Symphony by Kuva
Kuva is a boutique creative agency based in the UK, and is responsible for developing the virtual and augmented reality pieces for Symphony’s events and exhibitions. These experiences augment specific components of the blockchain using Symphony’s code.
Kuva also develops the Symphony mobile app: a 3D visualization of the blockchain’s mempool that provides real-world information on the current traffic and fee of the network. The Symphony mobile app currently supports the Bitcoin network, but will soon extend to second-generation (Ethereum) and third-generation (Cardano) blockchains.
Preview
Get Involved
Access the Symphony GitHub repo to learn more about Symphony’s development, or build your own version using Symphony’s open-source code.


Datatron 205 System Configuration
Configuration:
CANCEL
DELETE
CLONE
SAVE
Console Unit Selection:
Flexowriter Printer
Use 203/204 Flexowriter encoding
Paper Tape Readers (mechanical & optical)
Paper Tape Punch
Cardatron Unit Selection:
Slot
Type
Algol Glyphs
Greenbar
Zero-Suppress Columns
(set on device)
1
(not used)
Card Reader 1
Card Punch 7
Line Printer 7
2
(not used)
Card Reader 2
Card Punch 6
Line Printer 6
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Card Punch 5
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Card Punch 2
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(not used)
Card Reader 7
Card Punch 1
Line Printer 1
Magnetic Tape Unit Selection:
Suppress B-Register Modification
Unit
Type
Designate
Remote
Rewind-Ready
Not-Write
A
(not used)
DataReader
DataFile
1
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| skimage.segmentation.clear_border(image[, ...]) | Clear objects connected to image border. |
| skimage.segmentation.felzenszwalb(image[, ...]) | Computes Felsenszwalb’s efficient graph based image segmentation. |
| skimage.segmentation.find_boundaries(label_img) | Return bool array where boundaries between labeled regions are True. |
| skimage.segmentation.join_segmentations(s1, s2) | Return the join of the two input segmentations. |
| skimage.segmentation.mark_boundaries(image, ...) | Return image with boundaries between labeled regions highlighted. |
| skimage.segmentation.quickshift | Segments image using quickshift clustering in Color-(x,y) space. |
| skimage.segmentation.random_walker(data, labels) | Random walker algorithm for segmentation from markers. |
| skimage.segmentation.relabel_from_one(*args, ...) | Deprecated function. Use relabel_sequential instead. |
| skimage.segmentation.relabel_sequential(...) | Relabel arbitrary labels to {offset, ... |
| skimage.segmentation.slic(image[, ...]) | Segments image using k-means clustering in Color-(x,y,z) space. |
| skimage.segmentation.visualize_boundaries(...) | Deprecated function. Use mark_boundaries instead. |
Clear objects connected to image border.
The changes will be applied to the input image.
| Parameters : | image : (N, M) array
buffer_size : int, optional
bgval : float or int, optional
|
|---|---|
| Returns : | image : (N, M) array
|
Examples
>>> import numpy as np
>>> from skimage.segmentation import clear_border
>>> image = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
... [0, 0, 0, 0, 1, 0, 0, 0, 0],
... [1, 0, 0, 1, 0, 1, 0, 0, 0],
... [0, 0, 1, 1, 1, 1, 1, 0, 0],
... [0, 1, 1, 1, 1, 1, 1, 1, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> clear_border(image)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]])
Computes Felsenszwalb’s efficient graph based image segmentation.
Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter scale sets an observation level. Higher scale means less and larger segments. sigma is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation.
The number of produced segments as well as their size can only be controlled indirectly through scale. Segment size within an image can vary greatly depending on local contrast.
For RGB images, the algorithm computes a separate segmentation for each channel and then combines these. The combined segmentation is the intersection of the separate segmentations on the color channels.
| Parameters : | image : (width, height, 3) or (width, height) ndarray
scale : float
sigma : float
min_size : int
|
|---|---|
| Returns : | segment_mask : (width, height) ndarray
|
References
| [R220] | Efficient graph-based image segmentation, Felzenszwalb, P.F. and Huttenlocher, D.P. International Journal of Computer Vision, 2004 |
Return bool array where boundaries between labeled regions are True.
Return the join of the two input segmentations.
The join J of S1 and S2 is defined as the segmentation in which two voxels are in the same segment if and only if they are in the same segment in both S1 and S2.
| Parameters : | s1, s2 : numpy arrays
|
|---|---|
| Returns : | j : numpy array
|
Examples
>>> from skimage.segmentation import join_segmentations
>>> s1 = np.array([[0, 0, 1, 1],
... [0, 2, 1, 1],
... [2, 2, 2, 1]])
>>> s2 = np.array([[0, 1, 1, 0],
... [0, 1, 1, 0],
... [0, 1, 1, 1]])
>>> join_segmentations(s1, s2)
array([[0, 1, 3, 2],
[0, 5, 3, 2],
[4, 5, 5, 3]])
Return image with boundaries between labeled regions highlighted.
| Parameters : | image : (M, N[, 3]) array
label_img : (M, N) array
color : length-3 sequence
outline_color : length-3 sequence
|
|---|
Segments image using quickshift clustering in Color-(x,y) space.
Produces an oversegmentation of the image using the quickshift mode-seeking algorithm.
| Parameters : | image : (width, height, channels) ndarray
ratio : float, optional, between 0 and 1 (default 1).
kernel_size : float, optional (default 5)
max_dist : float, optional (default 10)
return_tree : bool, optional (default False)
sigma : float, optional (default 0)
convert2lab : bool, optional (default True)
random_seed : None (default) or int, optional
|
|---|---|
| Returns : | segment_mask : (width, height) ndarray
|
Notes
The authors advocate to convert the image to Lab color space prior to segmentation, though this is not strictly necessary. For this to work, the image must be given in RGB format.
References
| [R221] | Quick shift and kernel methods for mode seeking, Vedaldi, A. and Soatto, S. European Conference on Computer Vision, 2008 |
Random walker algorithm for segmentation from markers.
Random walker algorithm is implemented for gray-level or multichannel images.
| Parameters : | data : array_like
labels : array of ints, of same shape as data without channels dimension
beta : float
mode : {‘bf’, ‘cg_mg’, ‘cg’} (default: ‘bf’)
tol : float
copy : bool
multichannel : bool, default False
return_full_prob : bool, default False
depth : float, default 1. [DEPRECATED]
spacing : iterable of floats
|
|---|---|
| Returns : | output : ndarray
|
See also
Notes
Multichannel inputs are scaled with all channel data combined. Ensure all channels are separately normalized prior to running this algorithm.
The spacing argument is specifically for anisotropic datasets, where data points are spaced differently in one or more spatial dimensions. Anisotropic data is commonly encountered in medical imaging.
The algorithm was first proposed in Random walks for image segmentation, Leo Grady, IEEE Trans Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83.
The algorithm solves the diffusion equation at infinite times for sources placed on markers of each phase in turn. A pixel is labeled with the phase that has the greatest probability to diffuse first to the pixel.
The diffusion equation is solved by minimizing x.T L x for each phase, where L is the Laplacian of the weighted graph of the image, and x is the probability that a marker of the given phase arrives first at a pixel by diffusion (x=1 on markers of the phase, x=0 on the other markers, and the other coefficients are looked for). Each pixel is attributed the label for which it has a maximal value of x. The Laplacian L of the image is defined as:
- L_ii = d_i, the number of neighbors of pixel i (the degree of i)
- L_ij = -w_ij if i and j are adjacent pixels
The weight w_ij is a decreasing function of the norm of the local gradient. This ensures that diffusion is easier between pixels of similar values.
When the Laplacian is decomposed into blocks of marked and unmarked pixels:
L = M B.T
B A
with first indices corresponding to marked pixels, and then to unmarked pixels, minimizing x.T L x for one phase amount to solving:
A x = - B x_m
where x_m = 1 on markers of the given phase, and 0 on other markers. This linear system is solved in the algorithm using a direct method for small images, and an iterative method for larger images.
Examples
>>> a = np.zeros((10, 10)) + 0.2*np.random.random((10, 10))
>>> a[5:8, 5:8] += 1
>>> b = np.zeros_like(a)
>>> b[3,3] = 1 #Marker for first phase
>>> b[6,6] = 2 #Marker for second phase
>>> random_walker(a, b)
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)
Deprecated function. Use relabel_sequential instead.
Convert labels in an arbitrary label field to {1, ... number_of_labels}.
This function is deprecated, see relabel_sequential for more.
Relabel arbitrary labels to {offset, ... offset + number_of_labels}.
This function also returns the forward map (mapping the original labels to the reduced labels) and the inverse map (mapping the reduced labels back to the original ones).
| Parameters : | label_field : numpy array of int, arbitrary shape
offset : int, optional
|
|---|---|
| Returns : | relabeled : numpy array of int, same shape as label_field
forward_map : numpy array of int, shape (label_field.max() + 1,)
inverse_map : 1D numpy array of int, of length offset + number of labels
|
Notes
The label 0 is assumed to denote the background and is never remapped.
The forward map can be extremely big for some inputs, since its length is given by the maximum of the label field. However, in most situations, label_field.max() is much smaller than label_field.size, and in these cases the forward map is guaranteed to be smaller than either the input or output images.
Examples
>>> from skimage.segmentation import relabel_sequential
>>> label_field = np.array([1, 1, 5, 5, 8, 99, 42])
>>> relab, fw, inv = relabel_sequential(label_field)
>>> relab
array([1, 1, 2, 2, 3, 5, 4])
>>> fw
array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 5])
>>> inv
array([ 0, 1, 5, 8, 42, 99])
>>> (fw[label_field] == relab).all()
True
>>> (inv[relab] == label_field).all()
True
>>> relab, fw, inv = relabel_sequential(label_field, offset=5)
>>> relab
array([5, 5, 6, 6, 7, 9, 8])
Segments image using k-means clustering in Color-(x,y,z) space.
| Parameters : | image : 2D, 3D or 4D ndarray
n_segments : int, optional
compactness : float, optional
max_iter : int, optional
sigma : float or (3,) array-like of floats, optional
spacing : (3,) array-like of floats, optional
multichannel : bool, optional
convert2lab : bool, optional
ratio : float, optional
|
|---|---|
| Returns : | labels : 2D or 3D array
|
| Raises : | ValueError :
|
Notes
References
| [R222] | Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, TPAMI, May 2012. |
Examples
>>> from skimage.segmentation import slic
>>> from skimage.data import lena
>>> img = lena()
>>> segments = slic(img, n_segments=100, compactness=10)
>>> # Increasing the compactness parameter yields more square regions
>>> segments = slic(img, n_segments=100, compactness=20)


Common nouns are subdivided into concrete, abstract, mass, and count nouns.
A concrete noun names something that can be perceived through the senses - song, bridge, priest, school, etc.
An abstract noun names a quality, a thought, or an idea - talent, belief, friendship, independence, etc.


speed: 1 show debug info: hide everything: alive: 0 generation: 1 current score: 0 high score: 0 Pause Resume Save Guy BestGuyBrain.json to load: BestGuyBrain.weights.bin to load: Load Guy


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|
x# - x vector length
y# - y vector length z# - z vector length |
|
Returns the pitch value of a vector.
Using this command will return the same result as using EntityPitch to get the pitch value of an entity that is pointing in the vector's direction. See also: VectorYaw, EntityPitch. |
|
; VectorPitch Example
; ------------------- Graphics3D 640,480 SetBuffer BackBuffer() ; Set vector x,y,z values vx#=2 vy#=2 vz#=0 camera=CreateCamera() PositionEntity camera,0,0,-5 light=CreateLight() cone=CreateCone() ; Rotate cone so that it points in the direction it is facing RotateMesh cone,90,0,0 pivot=CreatePivot() ; Position pivot to represent a vector PositionEntity pivot,vx#,vy#,vz# ; Point cone in direction of pivot. It should now have same pitch value. PointEntity cone,pivot While Not KeyDown(1) RenderWorld ; Print entity pitch value and vector pitch value. Both should be identical. Text 0,0,"Entity pitch: "+EntityPitch(cone) Text 0,20,"Vector pitch: "+VectorPitch(vx#,vy#,vz#) Flip Wend End |


This box existed on the page before the boxxy was created – view source to see what I mean.
Drag the white handles between blocks to resize rows and columns. The left-hand column has been given a minimum width of 100px as an example of using boxxy.js with constraints.


You can go through the slides in a cyclic way: I managed moving from last slide to the first one: Button Next. And vice versa: Button Back :)


Subscribe¶
- Summary: The subscribe API is used to get messages from a queue. This is a destructive action on the message as it will no longer be available in the queue after subscription. This API is not streaming, i.e. as data arrives into the queue it is not pushed to the subscribers. However, it needs to be polled regularly to check for new messages.
Request¶
Endpoint:
https://localhost/entity/subscribe
Method:
GET
Security Scheme: Device Credentials
Optional Headers:
Header Name Description message-type Either of priority
,command
ornotification
. If this field is empty then the suppliedid
is taken as the default queue namenum-messages Number of messages to subscribe to. Defaults to 10
Responses¶
200 OK
Gives back messages from the queue
[{ "sent-by": "admin/dev", "from": "admin/dev.protected", "subject": "streetlight data", "content-type": "application/json", "body": { "ambientLux": 10000, "power": 73.26, "caseTemp": 34.5 } }]
400 Bad Request
If any of the required headers are missing from the request:
{ "error": "inputs missing from headers" }
If the provided
id
is not a valid entity:{ "error": "id is not a valid entity" }
If
message-type
is provided but is invalid:{ "error": "invalid message-type" }
If
num-messages
is provided but is not valid:{ "error": "num-messages is not valid" }


Work Day Planner
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Today's Date:
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The Adapter.check method could be useful when rows heights can change after rendering. By invoking the Adapter.check method we ask the Scroller to check if any of items that are in the current Buffer (and present in DOM) changed their size.
In this demo, the doCheck method is implemented to


Chapter 8 Sampling
In this chapter we kick off the third segment of this book, statistical inference, by learning about sampling. The concepts behind sampling form the basis of confidence intervals and hypothesis testing, which we’ll cover in Chapters 9 and 10 respectively. We will see that the tools that you learned in the data science segment of this book (data visualization, “tidy” data format, and data wrangling) will also play an important role here in the development of your understanding. As mentioned before, the concepts throughout this text all build into a culmination allowing you to “think with data.”
Needed packages
Let’s load all the packages needed for this chapter (this assumes you’ve already installed them). If needed, read Section 2.3 for information on how to install and load R packages.
library(dplyr)
library(ggplot2)
library(moderndive)
8.1 Introduction to sampling
Let’s kick off this chapter immediately with an exercise that involves sampling.  We are interested in the proportion of balls in this bowl that are red, but you don’t have the time to do an exhaustive count. You are also given a “shovel” that you can insert into this bowl…
… and extract a sample of 50 balls:
Inference via sampling
Why did we go through the trouble of enumerating all the above concepts and terminology?
The moral of the story:
- If the sampling of a sample of size \(n\) is done at random, then
- The sample is unbiased and representative of the population, thus
- Any result based on the sample can generalize to the population, thus
- The point estimate/sample statistic is a “good guess” of the unknown population parameter of interest
and thus we have inferred about the population based on our sample. In the above example:
- If we properly mix the balls by say stirring the bowl first, then use the shovel to extract a sample of size \(n=50\), then
- The contents of the shovel will “look like” the contents of the bowl, thus
- Any results based on the sample of \(n=50\) balls can generalize to the large bowl of \(N=2400\) balls, thus
- The sample proportion \(\widehat{p}\) of the \(n=50\) balls in the shovel that are red is a “good guess” of the true population proportion \(p\) of the \(N=2400\) balls that are red.
and thus we have inferred some new piece of information about the bowl based on our sample extracted by shovel.
At this point, you might be saying to yourself: “Big deal, why do we care about this bowl?” As hopefully you’ll soon come to appreciate, this sampling bowl exercise is merely a simulation representing the reality of many important sampling scenarios in a simplified and accessible setting. One in particular sampling scenario is familiar to many: polling. Whether for market research or for political purposes, polls inform much of the world’s decision and opinion making, and understanding the mechanism behind them can better inform you statistical citizenship. We’ll tie-in everything we learn in this chapter with an example relating to a 2013 poll on President Obama’s approval ratings among young adult in Section 8.4.
8.2 Tactile sampling simulation
Let’s start by revisiting our tactile sampling illustrating with “sampling bowl” in Figures 8.1 and 8.2. By tactile we mean with your hands and to the touch. We’ll break down the act of tactile sampling from the bowl with the shovel using our newly acquired concepts and terminology relating to sampling. In particular we’ll study how sampling variability affects outcomes, which we’ll illustrate through simulations of repeated sampling. To this end, we’ll be using both the above-mentioned tactile simulation, but also using virtual simulation. By virtual we mean on the computer.
8.2.1 Using the shovel once
Let’s now view our shovel through the lens of sampling with the following 3-step tactile sampling simulation:
Step 1: Use the shovel to take a sample of size \(n=50\) balls from the bowl as seen in Fig 8.3.
Step 2: Pour them into a cup and
- Count the number that are red then
- Compute the sample proportion \(\widehat{p}\) of the \(n=50\) balls that are red
as seen in Figure 8.4 below. Note from above there are 18 balls out of \(n=50\) that are red. Thus the sample proportion red \(\widehat{p}\) for this particular sample is thus \(\widehat{p} = 18 / 50 = 0.36\).
Step 3: Mark the sample proportion \(\widehat{p}\) in a hand-drawn histogram, just like our intrepid students are doing in Figure 8.5.
Repeat Steps 1-3 a few times: After a few groups of students complete this exercise, let’s draw the resulting histogram by hand. In Figure 8.6 we have the resulting hand-drawn histogram for 10 groups of students.
Observe the behavior of the 10 different values of the sample proportion \(\widehat{p}\) in the histogram of their distribution, in particular where the values center and how much they spread out, in other words how much they vary. Note:
- The lowest value of \(\widehat{p}\) was somewhere between 0.20 and 0.25.
- The highest value of \(\widehat{p}\) was somewhere between 0.45 and 0.50.
- Five of the sample proportions \(\widehat{p}\) cluster. Five different samples of size \(n=50\) yielded sample proportions \(\widehat{p}\) that were in the range 0.30 to 0.35.
Let’s now look at some real-life outcomes of this tactile sampling simulation. We present the actual results for not 10 groups of students, but 33 groups of students below!
8.2.2 Using the shovel 33 times
All told, 33 groups took samples. In other words, the shovel was used 33 times and 33 values of the sample proportion \(\widehat{p}\) were computed; this data is saved in the tactile_prop_red
data frame included in the moderndive
package. Let’s display its contents in Table 8.1. Notice how the replicate
column enumerates each of the 33 groups, red_balls
is the count of balls in the sample of size \(n=50\) that we red, and prop_red
is the sample proportion \(\widehat{p}\) that are red.
tactile_prop_red
View(tactile_prop_red)
| group | replicate | red_balls | prop_red |
|---|---|---|---|
| Ilyas, Yohan | 1 | 21 | 0.42 |
| Morgan, Terrance | 2 | 17 | 0.34 |
| Martin, Thomas | 3 | 21 | 0.42 |
| Clark, Frank | 4 | 21 | 0.42 |
| Riddhi, Karina | 5 | 18 | 0.36 |
| Andrew, Tyler | 6 | 19 | 0.38 |
| Julia | 7 | 19 | 0.38 |
| Rachel, Lauren | 8 | 11 | 0.22 |
| Daniel, Caroline | 9 | 15 | 0.30 |
| Josh, Maeve | 10 | 17 | 0.34 |
| Emily, Emily | 11 | 16 | 0.32 |
| Conrad, Emily | 12 | 18 | 0.36 |
| Oliver, Erik | 13 | 17 | 0.34 |
| Isabel, Nam | 14 | 21 | 0.42 |
| X, Claire | 15 | 15 | 0.30 |
| Cindy, Kimberly | 16 | 20 | 0.40 |
| Kevin, James | 17 | 11 | 0.22 |
| Nam, Isabelle | 18 | 21 | 0.42 |
| Harry, Yuko | 19 | 15 | 0.30 |
| Yuki, Eileen | 20 | 16 | 0.32 |
| Ramses | 21 | 23 | 0.46 |
| Joshua, Elizabeth, Stanley | 22 | 15 | 0.30 |
| Siobhan, Jane | 23 | 18 | 0.36 |
| Jack, Will | 24 | 16 | 0.32 |
| Caroline, Katie | 25 | 21 | 0.42 |
| Griffin, Y | 26 | 18 | 0.36 |
| Kaitlin, Jordan | 27 | 17 | 0.34 |
| Ella, Garrett | 28 | 18 | 0.36 |
| Julie, Hailin | 29 | 15 | 0.30 |
| Katie, Caroline | 30 | 21 | 0.42 |
| Mallory, Damani, Melissa | 31 | 21 | 0.42 |
| Katie | 32 | 16 | 0.32 |
| Francis, Vignesh | 33 | 19 | 0.38 |
Using your data visualization skills that you honed in Chapter 3, let’s visualize the distribution of these 33 sample proportions red \(\widehat{p}\) using a histogram with binwidth = 0.05
. This visualization is appropriate since prop_red
is a numerical variable. This histogram is showing a very particular important type of distribution in statistics: the sampling distribution.
ggplot(tactile_prop_red, aes(x = prop_red)) +
geom_histogram(binwidth = 0.05, color = "white") +
labs(x = "Sample proportion red based on n = 50",
title = "Sampling distribution of p-hat")
Sampling distributions are a specific kind of distribution: distributions of point estimates/sample statistics based on samples of size \(n\) used to estimate an unknown population parameter.
In the case of the histogram in Figure 8.7, its the distribution of the sample proportion red \(\widehat{p}\) based on \(n=50\) sampled balls from the bowl, for which we want to estimate the unknown population proportion \(p\) of the \(N=2400\) balls that are red. Sampling distributions describe how values of the sample proportion red \(\widehat{p}\) will vary from sample to sample due to sampling variability and thus identify “typical” and “atypical” values of \(\widehat{p}\). For example
- Obtaining a sample that yields \(\widehat{p} = 0.36\) would be considered typical, common, and plausible since it would in theory occur frequently.
- Obtaining a sample that yields \(\widehat{p} = 0.8\) would be considered atypical, uncommon, and implausible since it lies far away from most of the distribution.
Let’s now ask ourselves the following questions:
- Where is the sampling distribution centered?
- What is the spread of this sampling distribution?
Recall from Section 5.4 the mean and the standard deviation are two summary statistics that would answer this question:
tactile_prop_red %>%
summarize(mean = mean(prop_red), sd = sd(prop_red))
| mean | sd |
|---|---|
| 0.356 | 0.058 |
Finally, it’s important to keep in mind:
- If the sampling is done in an unbiased and random fashion, in other words we made sure to stir the bowl before we sampled, then the sampling distribution will be guaranteed to be centered at the true unknown population proportion red \(p\), or in other words the true number of balls out of 2400 that are red.
- The spread of this histogram, as quantified by the standard deviation of 0.058, is called the standard error. It quantifies the variability of our estimates for \(\widehat{p}\).
- Note: A large source of confusion. All standard errors are a form of standard deviation, but not all standard deviations are standard errors.
8.3 Virtual sampling simulation
Now let’s mimic the above tactile sampling, but with virtual sampling. We’ll resort to virtual sampling because while collecting 33 tactile samples manually is feasible, for large numbers like 1000, things start getting tiresome! That’s where a computer can really help: computers excel at performing mundane tasks repeatedly; think of what accounting software must be like!
In other words:
- Instead of considering the tactile bowl shown in Figure 8.1 above and using a tactile shovel to draw samples of size \(n=50\)
- Let’s use a virtual bowl saved in a computer and use R’s random number generator as a virtual shovel to draw samples of size \(n=50\)
First, we describe our virtual bowl. In the moderndive
package, we’ve included a data frame called bowl
that has 2400 rows corresponding to the \(N=2400\) balls in the physical bowl. Run View(bowl)
in RStudio to convince yourselves that bowl
is indeed a virtual version of the tactile bowl in the previous section.
bowl
# A tibble: 2,400 x 2
ball_ID color
<int> <chr>
1 1 white
2 2 white
3 3 white
4 4 red
5 5 white
6 6 white
7 7 red
8 8 white
9 9 red
10 10 white
# ... with 2,390 more rows
Note that the balls are not actually marked with numbers; the variable ball_ID
is merely used as an identification variable for each row of bowl
. Recall our previous discussion on identification variables in Subsection 4.2.2 in the “Data Tidying” Chapter 4.
Next, we describe our virtual shovel: the rep_sample_n()
function included in the moderndive
package where rep_sample_n()
indicates that we are taking repeated/replicated samples of size \(n\).
8.3.1 Using the shovel once
The rep_sample_n()
function included in the moderndive
package where rep_sample_n()
indicates that we are taking repeated/replicated samples of size \(n\). Let’s perform the virtual analogue of tactilely inserting the shovel only once into the bowl and extracting a sample of size
\(n=50\). In the table below we only show results about the first 10 sampled balls out of 50.
virtual_shovel <- bowl %>%
rep_sample_n(size = 50)
View(virtual_shovel)
| replicate | ball_ID | color |
|---|---|---|
| 1 | 2079 | red |
| 1 | 1076 | white |
| 1 | 1691 | red |
| 1 | 1687 | red |
| 1 | 1434 | white |
| 1 | 954 | white |
| 1 | 483 | white |
| 1 | 1520 | white |
| 1 | 2060 | red |
| 1 | 1682 | white |
Looking at all 50 rows of virtual_shovel
in the spreadsheet viewer that pops up after running View(virtual_shovel)
in RStudio, the ball_ID
variable seems to suggest that we do indeed have a random sample of \(n=50\) balls. However, what does the replicate
variable indicate, where in this case it’s equal to 1 for all 50 rows? We’ll see in a minute. First let’s compute both the number of balls red and the proportion red out of \(n=50\) using our dplyr
data wrangling tools from Chapter 5:
virtual_shovel %>%
summarize(red = sum(color == "red")) %>%
mutate(prop_red = red / 50)
| replicate | red | prop_red |
|---|---|---|
| 1 | 23 | 0.46 |
Why does this work? Because for every row where color == "red"
, the Boolean TRUE
is returned and R treats TRUE
like the number 1
. Equivalently, for every row where color
is not equal to "red"
, the Boolean FALSE
is returned and R treats FALSE
like the number 0
. So summing the number of TRUE
’s and FALSE
’s is equivalent to summing 1
’s and 0
’s which counts the number of balls where color
is red
.
8.3.2 Using shovel 33 times
Recall however in our tactile sampling exercise in Section 8.2 above that we had 33 groups of students take 33 samples total of size \(n=50\) using the shovel 33 times and hence compute 33 separate values of the sample proportion red \(\widehat{p}\). In other words we repeated/replicated the sampling 33 times. We can achieve this by reusing the same rep_sample_n()
function code above, but by adding the reps = 33
argument indicating we want to repeat this sampling 33 times:
virtual_samples <- bowl %>%
rep_sample_n(size = 50, reps = 33)
View(virtual_samples)
virtual_samples
has \(50 \times 33 = 1650\) rows, corresponding to 33 samples of size \(n=50\), or 33 draws from the shovel. We won’t display the contents of this data frame but leave it to you to View()
this data frame. You’ll see that the first 50 rows have replicate
equal to 1, then the next 50 rows have replicate
equal to 2, and so on and so forth, up until the last 50 rows which have replicate
equal to 33. The replicate
variable denotes which of our 33 samples a particular ball is included in.
Now let’s compute the 33 corresponding values of the sample proportion \(\widehat{p}\) based on 33 different samples of size \(n=50\) by reusing the previous code, but remembering to group_by
the replicate
variable first since we want to compute the sample proportion for each of the 33 samples separately. Notice the similarity of this table with Table 8.1.
virtual_prop_red <- virtual_samples %>%
group_by(replicate) %>%
summarize(red = sum(color == "red")) %>%
mutate(prop_red = red / 50)
View(virtual_prop_red)
| replicate | red | prop_red |
|---|---|---|
| 1 | 17 | 0.34 |
| 2 | 20 | 0.40 |
| 3 | 24 | 0.48 |
| 4 | 20 | 0.40 |
| 5 | 17 | 0.34 |
| 6 | 16 | 0.32 |
| 7 | 17 | 0.34 |
| 8 | 19 | 0.38 |
| 9 | 19 | 0.38 |
| 10 | 12 | 0.24 |
| 11 | 22 | 0.44 |
| 12 | 17 | 0.34 |
| 13 | 20 | 0.40 |
| 14 | 22 | 0.44 |
| 15 | 13 | 0.26 |
| 16 | 15 | 0.30 |
| 17 | 23 | 0.46 |
| 18 | 20 | 0.40 |
| 19 | 16 | 0.32 |
| 20 | 12 | 0.24 |
| 21 | 14 | 0.28 |
| 22 | 21 | 0.42 |
| 23 | 14 | 0.28 |
| 24 | 18 | 0.36 |
| 25 | 19 | 0.38 |
| 26 | 12 | 0.24 |
| 27 | 22 | 0.44 |
| 28 | 23 | 0.46 |
| 29 | 19 | 0.38 |
| 30 | 18 | 0.36 |
| 31 | 20 | 0.40 |
| 32 | 17 | 0.34 |
| 33 | 20 | 0.40 |
Just as we did before, let’s now visualize the sampling distribution using a histogram with binwidth = 0.05
of the 33 virtually sample proportions \(\widehat{p}\):
ggplot(virtual_prop_red, aes(x = prop_red)) +
geom_histogram(binwidth = 0.05, color = "white") +
labs(x = "Sample proportion red based on n = 50",
title = "Sampling distribution of p-hat")
The resulting sampling distribution based on our virtual sampling simulation is near identical to the sampling distribution of our tactile sampling simulation from Section 8.3. Let’s compare them side-by-side in Figure 8.9.
We see that they are similar in terms of center and spread, although not identical due to random variation. This was in fact by design, as we made the virtual contents of the virtual bowl
match the actual contents of the actual bowl pictured above.
8.3.3 Using shovel 1000 times
In Figure 8.8, we can start seeing a pattern in the sampling distribution emerge. However, 33 values of the sample proportion \(\widehat{p}\) might not be enough to get a true sense of the distribution. Using 1000 values of \(\widehat{p}\) would definitely give a better sense. What are our two options for constructing these histograms?
- Tactile sampling: Make the 33 groups of students take \(1000 / 33 \approx 31\) samples of size \(n=50\) each, count the number of red balls for each of the 1000 tactile samples, and then compute the 1000 corresponding values of the sample proportion \(\widehat{p}\). However, this would be cruel and unusual as this would take hours!
- Virtual sampling: Computers are very good at automating repetitive tasks such as this one. This is the way to go!
First, generate 1000 samples of size \(n=50\)
virtual_samples <- bowl %>%
rep_sample_n(size = 50, reps = 1000)
View(virtual_samples)
Then for each of these 1000 samples of size \(n=50\), compute the corresponding sample proportions
virtual_prop_red <- virtual_samples %>%
group_by(replicate) %>%
summarize(red = sum(color == "red")) %>%
mutate(prop_red = red / 50)
View(virtual_prop_red)
As previously done, let’s plot the sampling distribution of these 1000 simulated values of the sample proportion red \(\widehat{p}\) with a histogram in Figure 8.10.
ggplot(virtual_prop_red, aes(x = prop_red)) +
geom_histogram(binwidth = 0.05, color = "white") +
labs(x = "Sample proportion red based on n = 50",
title = "Sampling distribution of p-hat")
Since the sampling is random and thus representative and unbiased, the above sampling distribution is centered at the true population proportion red \(p\) of all \(N=2400\) balls in the bowl. Eyeballing it, the sampling distribution appears to be centered at around 0.375.
What is the standard error of the above sampling distribution of \(\widehat{p}\) based on 1000 samples of size \(n=50\)?
virtual_prop_red %>%
summarize(SE = sd(prop_red))
# A tibble: 1 x 1
SE
<dbl>
1 0.0698
What this value is saying might not be immediately apparent by itself to someone who is new to sampling. It’s best to first compare different standard errors for different sampling schemes based on different sample sizes \(n\). We’ll do so for samples of size \(n=25\), \(n=50\), and \(n=100\) next.
8.3.4 Using different shovels
Recall, the sampling we just did on the computer using the rep_sample_n()
function is simply a virtual version of act of taking a tactile sample using the shovel with \(n=50\) slots seen in Figure 8.11. We visualized the variation in the resulting sample proportion red \(\widehat{p}\) in a histogram of the sampling distribution and quantified this variation using the standard error.
But what if we changed the sample size to \(n=25\)? This would correspond to sampling using the shovel with \(n=25\) slots see in Figure 8.12. What differences if any would you notice about the sampling distribution and the standard error?
Furthermore what if we took samples of size \(n=100\) as well? This would correspond to sampling using the shovel with \(n=100\) slots see in Figure 8.13. What differences if any would you notice about the sampling distribution and the standard error for \(n=100\) as compared to \(n=50\) and \(n=25\)?
Let’s take the opportunity to review our sampling procedure and do this for 1000 virtual samples of size \(n=25\), \(n=50\), \(n=100\) each.
Shovel with \(n=50\) slots: Take 1000 virtual samples of size \(n=50\), mimicking the act of taking 1000 tactile samples using the shovel with \(n=50\) slots:
virtual_samples_50 <- bowl %>%
rep_sample_n(size = 50, reps = 1000)
Then based on each of these 1000 virtual samples of size \(n=50\), compute the corresponding 1000 sample proportions \(\widehat{p}\) being sure to divide by 50
:
virtual_prop_red_50 <- virtual_samples_50 %>%
group_by(replicate) %>%
summarize(red = sum(color == "red")) %>%
mutate(prop_red = red / 50)
The standard error is the standard deviation of the 1000 sample proportions \(\widehat{p}\), in other words we are quantifying how much \(\widehat{p}\) varies from sample-to-sample based on samples of size \(n=50\) due to sampling variation.
virtual_prop_red_50 %>%
summarize(SE = sd(prop_red))
# A tibble: 1 x 1
SE
<dbl>
1 0.0694
Shovel with \(n=25\) slots: Take 1000 virtual samples of size \(n=25\), mimicking the act of taking 1000 tactile samples using the shovel with \(n=25\) slots:
virtual_samples_25 <- bowl %>%
rep_sample_n(size = 25, reps = 1000)
Then based on each of these 1000 virtual samples of size \(n=50\), compute the corresponding 1000 sample proportions \(\widehat{p}\) being sure to divide by 50
:
virtual_prop_red_25 <- virtual_samples_25 %>%
group_by(replicate) %>%
summarize(red = sum(color == "red")) %>%
mutate(prop_red = red / 25)
The standard error is the standard deviation of the 1000 sample proportions \(\widehat{p}\), in other words we are quantifying how much \(\widehat{p}\) varies from sample-to-sample based on samples of size \(n=25\) due to sampling variation.
virtual_prop_red_25 %>%
summarize(SE = sd(prop_red))
# A tibble: 1 x 1
SE
<dbl>
1 0.100
Shovel with \(n=100\) slots: Take 1000 virtual samples of size \(n=100\), mimicking the act of taking 1000 tactile samples using the shovel with \(n=100\) slots:
virtual_samples_100 <- bowl %>%
rep_sample_n(size = 100, reps = 1000)
Then based on each of these 1000 virtual samples of size \(n=100\), compute the corresponding 1000 sample proportions \(\widehat{p}\) being sure to divide by 100
:
virtual_prop_red_100 <- virtual_samples_100 %>%
group_by(replicate) %>%
summarize(red = sum(color == "red")) %>%
mutate(prop_red = red / 100)
The standard error is the standard deviation of the 1000 sample proportions \(\widehat{p}\), in other words we are quantifying how much \(\widehat{p}\) varies from sample-to-sample based on samples of size \(n=100\) due to sampling variation.
virtual_prop_red_100 %>%
summarize(SE = sd(prop_red))
# A tibble: 1 x 1
SE
<dbl>
1 0.0457
Comparison: Let’s compare the 3 standard errors we computed above in Table 8.5:
| n | SE |
|---|---|
| 25 | 0.100 |
| 50 | 0.069 |
| 100 | 0.046 |
Observe the behavior of the standard error as \(n\) increases from \(n=25\) to \(n=50\) to \(n=100\), the standard error get smaller. In other words, the values of \(\widehat{p}\) vary less. The standard error is a numerical quantification of the spreads of the following three histograms (on the same scale) of the sampling distribution of the sample proportion \(\widehat{p}\):
Observe that the histogram of possible \(\widehat{p}\) values are narrowest and most consistent for the \(n=100\) case. In other words, they make less error. “Bigger sample size equals better sampling” is a concept you probably knew before reading this chapter. What we’ve just demonstrated is what this concept means: Samples based on large samples sizes will yield point estimates that vary less around the true value and hence be less prone to error.
In the case of our sampling bowl, the sample proportion red \(\widehat{p}\) based on samples of size \(n=100\) will vary the least around the true proportion \(p\) of the balls that are red, and thus be less prone to error. On the case of polls as we study in the next chapter: representative polls based on a larger number of respondents will yield guess that tend to be closer to the truth.
8.4 In real-life sampling: Polls
In December 4, 2013 National Public Radio reported on a recent poll of President Obama’s approval rating among young Americans aged 18-29 in an article Poll: Support For Obama Among Young Americans Eroding. A quote from the article:
After voting for him in large numbers in 2008 and 2012, young Americans are souring on President Obama.
According to a new Harvard University Institute of Politics poll, just 41 percent of millennials — adults ages 18-29 — approve of Obama’s job performance, his lowest-ever standing among the group and an 11-point drop from April.
Let’s tie elements of this story using the concepts and terminology we learned at the outset of this chapter along with our observations from the tactile and virtual sampling simulations:
- Population: Who is the population of \(N\) observations of interest?
- Bowl: \(N=2400\) identically-shaped balls
- Obama poll: \(N = \text{?}\) young Americans aged 18-29
- Population parameter: What is the population parameter?
- Bowl: The true population proportion \(p\) of the balls in the bowl that are red.
- Obama poll: The true population proportion \(p\) of young Americans who approve of Obama’s job performance.
- Census: What would a census be in this case?

- Obama poll: Locating all \(N = \text{?}\) young Americans (which is in the millions) and asking them if they approve of Obama’s job performance. This would be quite expensive to do!
- Sampling: How do you acquire the sample of size \(n\) observations?
- Bowl: Using the shovel to extract a sample of \(n=50\) balls.
- Obama poll: One way would be to get phone records from a database and pick out \(n\) phone numbers. In the case of the above poll, the sample was of size \(n=2089\) young adults.
- Point estimates/sample statistics: What is the summary statistic based on the sample of size \(n\) that estimates the unknown population parameter?
- Bowl: The sample proportion \(\widehat{p}\) red of the balls in the sample of size \(n=50\).
- Key: The sample proportion red \(\widehat{p}\) of young Americans in the sample of size \(n=2089\) that approve of Obama’s job performance. In this study’s case, \(\widehat{p} = 0.41\) which is the quoted 41% figure in the article.
- Representative sampling: Is the sample procedure representative? In other words, to the resulting samples “look like” the population?

- Obama poll: Does our sample of \(n=2089\) young Americans “look like” the population of all young Americans aged 18-29?
- Generalizability: Are the samples generalizable to the greater population?
- Bowl: Is \(\widehat{p}\) a “good guess” of \(p\)?
- Obama poll: Is \(\widehat{p} = 0.41\) a “good guess” of \(p\)? In other words, can we confidently say that 41% of all young Americans approve of Obama.
- Bias: Is the sampling procedure unbiased? In other words, do all observations have an equal chance of being included in the sample?
- Bowl: Here, I would say it is unbiased. All balls are equally sized as evidenced by the slots of the \(n=50\) shovel, and thus no particular color of ball can be favored in our samples over others.
- Obama poll: Did all young Americans have an equal chance at being represented in this poll? For example, if this was conducted using a database of only mobile phone numbers, would people without mobile phones be included? What about if this were an internet poll on a certain news website? Would non-readers of this this website be included?
- Random sampling: Was the sampling random?
- Bowl: As long as you mixed the bowl sufficiently before sampling, your samples would be random?
- Obama poll: Random sampling is a necessary assumption for all of the above to work. Most articles reporting on polls take this assumption as granted. In our Obama poll, you’d have to ask the group that conducted the poll: The Harvard University Institute of Politics.
Recall the punchline of all the above:
- If the sampling of a sample of size \(n\) is done at random, then
- The sample is unbiased and representative of the population, thus
- Any result based on the sample can generalize to the population, thus
- The point estimate/sample statistic is a “good guess” of the unknown population parameter of interest
and thus we have inferred about the population based on our sample. In the bowl example:
- If we properly mix the balls by say stirring the bowl first, then use the shovel to extract a sample of size \(n=50\), then
- The contents of the shovel will “look like” the contents of the bowl, thus
- Any results based on the sample of \(n=50\) balls can generalize to the large bowl of \(N=2400\) balls, thus
- The sample proportion \(\widehat{p}\) of the \(n=50\) sampled balls in the shovel that are red is a “good guess” of the true population proportion \(p\) of the \(N=2400\) balls that are red.
and thus we have inferred some new piece of information about the bowl based on our sample extracted by shovel: the proportion of balls that are red. In the Obama poll example:
- If we had a way of contacting a randomly chosen sample of 2089 young Americans and poll their approval of Obama, then
- These 2089 young Americans would “look like” the population of all young Americans, thus
- Any results based on this sample of 2089 young Americans can generalize to entire population of all young Americans, thus
- The reported sample approval rating of 41% of these 2089 young Americans is a “good guess” of the true approval rating amongst all young Americans.
So long story short, this poll’s guess of Obama’s approval rating was 41%. However is this the end of the story when understanding the results of a poll? If you read further in the article, it states:
The online survey of 2,089 adults was conducted from Oct. 30 to Nov. 11, just weeks after the federal government shutdown ended and the problems surrounding the implementation of the Affordable Care Act began to take center stage. The poll’s margin of error was plus or minus 2.1 percentage points.
Note the term margin of error, which here is plus or minus 2.1 percentage points. This is saying that a typical range of errors for polls of this type is about \(\pm 2.1\%\), in words from about 2.1% too small to about 2.1% too big. These errors are caused by sampling variation, the same sampling variation you saw studied in the histograms in Sections 8.2 on our tactile sampling simulations and Sections 8.3 on our virtual sampling simulations.
In this case of polls, any variation from the true approval rating is an “error” and a reasonable range of errors is the margin of error. We’ll see in the next chapter that this what’s known as a 95% confidence interval for the unknown approval rating. We’ll study confidence intervals using a new package for our data science and statistical toolbox: the infer
package for statistical inference.
8.5 Conclusion
8.5.1 Central Limit Theorem
What you did in Section 8.2 and 8.3 was demonstrate a very famous theorem, or mathematically proven truth, called the Central Limit Theorem. It loosely states that when sample means and sample proportions are based on larger and larger samples, the sampling distribution corresponding to these point estimates get
- More and more normal
- More and more narrow
Shuyi Chiou, Casey Dunn, and Pathikrit Bhattacharyya created the following three minute and 38 second video explaining this crucial theorem to statistics using as examples, what else?
- The average weight of wild bunny rabbits!
- The average wing span of dragons!
8.5.2 What’s to come?
This chapter serves as an introduction to the theoretical underpinning of the statistical inference techniques that will be discussed in greater detail in Chapter 9 for confidence intervals and Chapter 10 for hypothesis testing.
8.5.3 Script of R code
An R script file of all R code used in this chapter is available here.


--- layout: post title: "The Nativity of the Lord" date: 2020-12-25 09:00:00 categories: Sunday tags: featured image: /assets/article_images/2020-04-26-Third-Sunday-Easter/scripture.jpg image2: /assets/article_images/2020-04-26-Third-Sunday-Easter/scripture-mobile.jpg ---
Holy Mass is at the very core of our worship as a Catholic community. Nothing can ever be substituted for the celebration of the Eucharist. But in this exceptional time of difficulty for our communities, our nation, and the whole world, this order of prayer can be offered by individuals and families and engages us in our Sunday celebration of the Word of God. We can pray this prayer in our homes—the “domestic churches” which help build up the Body of Christ on earth—and thus be connected to the entire Church at prayer.
During this time of prayer, we include the opportunity to make a Spiritual Communion. Making a Spiritual Communion is a worthy practice, recommended by innumerable saints throughout the ages, which unites our whole selves to God and his Church.
Taken from the Mass and the Liturgy of the Hours
Leader: God, ✠ come to my assistance.
Response: Lord, make haste to help me.
Leader: Glory be to the Father, and to the Son, and to the Holy Spirit:
Response: as it was in the beginning, is now, and ever shall be, world without end. Amen.
Joy to the world, the Lord is come!
Let earth receive her king;
Let ev’ry heart prepare him room
And heav’n and nature sing,
And heav’n and nature sing,
And heav’n, and heav’n and nature sing.
Joy to the earth, the Savior reigns!
Let us our songs employ;
While fields and floods, rocks, hills and plains,
Repeat the sounding joy,
Repeat the sounding joy,
Repeat, repeat the sounding joy.
He rules the world with truth and grace,
And makes the nations prove
The glories of his righteousness,
And wonders of his love,
And wonders of his love,
And wonders, wonders of his love.
Leader: Let us acknowledge our sins, and seek the Lord’s pardon and peace.
Together: I confess to almighty God
and to you, my brothers and sisters,
that I have greatly sinned,
in my thoughts and in my words,
in what I have done and in what I have failed to do,
through my fault, through my fault,
through my most grievous fault;
therefore I ask blessed Mary ever-Virgin,
all the Angels and Saints,
and you, my brothers and sisters,
to pray for me to the Lord our God.
Leader: O God, who wonderfully created the dignity of human nature
and still more wonderfully restored it,
grant, we pray,
that we may share in the divinity of Christ,
who humbled himself to share in our humanity.
Who lives and reigns with you in the unity of the Holy Spirit,
one God, for ever and ever.
Together: Amen.
USCCB Audio Recording of Sunday Readings
How beautiful upon the mountains are the feet of him who brings glad tidings, announcing peace, bearing good news, announcing salvation, and saying to Zion, “Your God is King!”
Hark! Your sentinels raise a cry, together they shout for joy, for they see directly, before their eyes, the LORD restoring Zion. Break out together in song, O ruins of Jerusalem! For the LORD comforts his people, he redeems Jerusalem. The LORD has bared his holy arm in the sight of all the nations; all the ends of the earth will behold the salvation of our God.
The Word of the Lord.
―Thanks be to God.
Reader: All the ends of the earth have seen the saving power of God.
Together: All the ends of the earth have seen the saving power of God.
Reader: Sing to the LORD a new song,
for he has done wondrous deeds;
his right hand has won victory for him,
his holy arm.
Together: All the ends of the earth have seen the saving power of God.
Reader: The LORD has made his salvation known:
in the sight of the nations he has revealed his justice.
He has remembered his kindness and his faithfulness
toward the house of Israel.
Together: All the ends of the earth have seen the saving power of God.
Reader: All the ends of the earth have seen
the salvation by our God.
Sing joyfully to the LORD, all you lands;
break into song; sing praise.
Together: All the ends of the earth have seen the saving power of God.
Reader: Sing praise to the LORD with the harp,
with the harp and melodious song.
With trumpets and the sound of the horn
sing joyfully before the King, the LORD.
Together: All the ends of the earth have seen the saving power of God.
Brothers and sisters: In times past, God spoke in partial and various ways to our ancestors through the prophets; in these last days, he has spoken to us through the Son, whom he made heir of all things and through whom he created the universe, who is the refulgence of his glory, the very imprint of his being, and who sustains all things by his mighty word. When he had accomplished purification from sins, he took his seat at the right hand of the Majesty on high, as far superior to the angels as the name he has inherited is more excellent than theirs.
For to which of the angels did God ever say: You are my son; this day I have begotten you? Or again: I will be a father to him, and he shall be a son to me? And again, when he leads the firstborn into the world, he says: Let all the angels of God worship him.
The word of the Lord.
―Thanks be to God.
In the beginning was the Word, and the Word was with God, and the Word was God. He was in the beginning with God. All things came to be through him, and without him nothing came to be. What came to be through him was life, and this life was the light of the human race; the light shines in the darkness, and the darkness has not overcome it. A man named John was sent from God. He came for testimony, to testify to the light, so that all might believe through him. He was not the light, but came to testify to the light. The true light, which enlightens everyone, was coming into the world. He was in the world, and the world came to be through him, but the world did not know him. He came to what was his own, but his own people did not accept him.
But to those who did accept him he gave power to become children of God, to those who believe in his name, who were born not by natural generation nor by human choice nor by a man’s decision but of God. And the Word became flesh and made his dwelling among us, and we saw his glory, the glory as of the Father’s only Son, full of grace and truth. John testified to him and cried out, saying, “This was he of whom I said, ‘The one who is coming after me ranks ahead of me because he existed before me.’” From his fullness we have all received, grace in place of grace, because while the law was given through Moses, grace and truth came through Jesus Christ. No one has ever seen God. The only Son, God, who is at the Father’s side, has revealed him.
The Gospel of the Lord.
―Praise to you, Lord Jesus Christ
Leader: The Lord has made known, alleluia, alleluia
Response: The Lord has made known, alleluia, alleluia
Leader: His saving power,
Response: alleluia, alleluia.
Leader: Glory to the Father, and to the Son and to the Holy Spirit.
Response: The Lord has made known, alleluia, alleluia.
Leader: The Word of God existed before the creation of the universe yet was born
among us in time. We praise and worship him as we cry out in joy:
Response: Let the earth ring out with joy for you have come.

Response: Let the earth ring out with joy for you have come.
Leader: You saved us and by your birth revealed to us the covenant faithfulness of the Lord, help us to be faithful
to the promises of our baptism.
Response: Let the earth ring out with joy for you have come.

Response: Let the earth ring out with joy for you have come.
Leader: You are the true vine that brings forth the fruit of life, make us branches of
the vine, bearing much fruit.
Response: Let the earth ring out with joy for you have come.
Leader: In your mercy, continue to sanctify all who are afflicted from the present pandemic, and draw us all closer to you,
and to one another, in this time of suffering.
Response: Let the earth ring out with joy for you have come.
Leader: Let us pray together as Jesus taught us:
Together: Our Father, who art in heaven …
Leader: You have given us bread from heaven,
Response: Containing in itself all delight
Together: My Jesus,
I believe that you are present
in the Most Holy Sacrament of the Altar.
I love you above all things,
and I desire to receive you into my soul.
Since I cannot at this moment receive you sacramentally,
come at least spiritually into my heart.
I embrace you as if you were already there
and unite myself wholly to you.
Never permit me to be separated from you,
my Lord and my God!
Amen.
Canticle of Zechariah (Luke 1:68-79)
Together: ✠ Blessed be the Lord, the God of Israel;
he has come to his people and set them free.
He has raised up for us a mighty savior,
born of the house of his servant David.
Through his holy prophets he promised of old
that he would save us from our enemies,
from the hands of all who hate us.
He promised to show mercy to our fathers
and to remember his holy covenant.
This was the oath he swore to our father Abraham:
to set us free from the hands of our enemies,
free to worship him without fear,
holy and righteous in his sight
all the days of our life.
You, my child, shall be called the prophet of the Most High;
for you will go before the Lord to prepare his way,
to give his people knowledge of salvation
by the forgiveness of their sins.
In the tender compassion of our God
the dawn from on high shall break upon us,
to shine on those who dwell in darkness and the shadow of death,
and to guide our feet into the way of peace.
Glory be to the Father, and to the Son,
and to the Holy Spirit:
as it was in the beginning, is now, and ever shall be,
world without end. Amen.
Leader: ✠ May the Lord bless us, protect us from all evil,
and bring us to everlasting life.
Together: Amen.
Hymn: Joy to the World – Text: Psalm 98; Isaac Watts, 1674-1748; Tune: ANTIOCH, CM; arr. From George F. Handel, 1685-1759, in T. Hawkes’ Collection of Tunes, 1833. Musical arrangement by Dean Baskerville, Laurie Roberts, and Tammy McMorrow; acc. By Scott Soper; © 2013, Spirit & Song, a division of OCP. All rights reserved. Used with permission granted by OneLicense #A-705112.
Excerpts from The Roman Missal © 2010, ICEL; excerpts from The Liturgy of the Hours © 1994, ICEL. All rights reserved.
Lectionary for Mass for Use in the Dioceses of the United States, second typical edition, Copyright © 2001, 1998, 1997, 1986, 1970 Confraternity of Christian Doctrine; Psalm refrain © 1968, 1981, 1997, International Committee on English in the Liturgy, Inc. All rights reserved. Neither this work nor any part of it may be reproduced, distributed, performed or displayed in any medium, including electronic or digital, without permission in writing from the copyright owner.


| cols_merge_uncert {gt} | R Documentation |
The cols_merge_uncert()
function is a specialized variant of the
cols_merge()
function. It operates by taking a base value column
(col_val
) and an uncertainty column (col_uncert
) and merges them into a
single column. What results is a column with values and associated
uncertainties (e.g., 12.0 ± 0.1
), and, the column specified in col_uncert
is dropped from the output table.
cols_merge_uncert(data, col_val, col_uncert, sep = " +/- ", autohide = TRUE)
data |
A table object that is created using the |
col_val |
A single column name that contains the base values. This is the column where values will be mutated. |
col_uncert |
A single column name that contains the uncertainty values.
These values will be combined with those in |
sep |
The separator text that contains the uncertainty mark. The
default value of |
autohide |
An option to automatically hide the column specified as
|
This function could be somewhat replicated using cols_merge()
, however,
cols_merge_uncert()
employs the following specialized semantics for NA
handling:
NA
s in col_val
result in missing values for the merged
column (e.g., NA
+ 0.1
= NA
)
NA
s in col_uncert
(but not col_val
) result in
base values only for the merged column (e.g.,
12.0
+ NA
= 12.0
)
NA
s both col_val
and col_uncert
result in
missing values for the merged column (e.g., NA
+ NA
=
NA
)
Any resulting NA
values in the col_val
column following the merge
operation can be easily formatted using the fmt_missing()
function.
This function is part of a set of four column-merging functions. The other
two are the general cols_merge()
function and the specialized
cols_merge_range()
and cols_merge_n_pct()
functions. These functions
operate similarly, where the non-target columns can be optionally hidden from
the output table through the hide_columns
or autohide
options.
An object of class gt_tbl
.
4-9
Other Modify Columns:
cols_align()
,
cols_hide()
,
cols_label()
,
cols_merge_n_pct()
,
cols_merge_range()
,
cols_merge()
,
cols_move_to_end()
,
cols_move_to_start()
,
cols_move()
,
cols_unhide()
,
cols_width()
# Use `exibble` to create a gt table, # keeping only the `currency` and `num` # columns; merge columns into one with # a base value and uncertainty (after # formatting the `num` column) tab_1 <- exibble %>% dplyr::select(currency, num) %>% dplyr::slice(1:7) %>% gt() %>% fmt_number( columns = num, decimals = 3, use_seps = FALSE ) %>% cols_merge_uncert( col_val = currency, col_uncert = num ) %>% cols_label( currency = "value + uncert." )


So here are some old codepens and school projects
Weekender
A road trip planning app for Android devices
Quote Drinks
Generate a random line from Jimmy Buffett's Boat Drinks and tweet it!
Muncher
A recipe search page using the Edamam Recipe Search API
Hyrax Facts
A CSS practice page about nature's grumpiest animal
this website is copyright me. i guess if i write that you're not allowed to steal it but if i don't write it you are? not clear


Networking Features and Enhancements
in Java SE 5.0
- Complete support for IPv6 on Windows XP (SP1) and 2003 server
has been provided in this release. (4608529)
- The networking API now provides a way of setting timeouts on
connect and read operations for protocol handlers. This will
benefit any HTTP client application that must behave robustly in
the event of server failure. (4700777)
- A new API provides a mean for Java applications, which includes
RMI applications, to be launched via
inetd
. (4295885,
4673940)
- The
InetAddress
class now provides an API to allow
testing for the reachability of a host. This features provides a
ping-like capability in Java. (4639861)
- Cookie support has been improved. Applications and web
containers can provide Cookie management by implementing the new
CookieHandler
API. (4696506)
- Proxy server configuration has been improved. It can now be
dynamic and controlable by web containers and applications through
the
ProxySelector
API, which can also handle failures
to connect to the proxy. (4696512)
- A new framework provides a way for protocol handlers to access
a file caching mechanism implemented by the platform or by a third
party. (4837267)
- The specification of the URL class now mandates a minimum set
of URL protocol handlers that are guaranteed to be present in Java SE.
They are http, https, file, jar and ftp. (4180636)
- There have been a number of enhancements for JSSE (SSL/TLS).
For more information, please see: Java SE 5 Security Enhancements


Table of contents |
| Rules |
| Short summary of classes |
| Infomation on packages |
| Rules on logging |
Rules
Spacing: 4 spaces. A good IDE should be able to do this.
Naming Conventions:
We follow the java naming conventions. Link is here.
Please make all the variables and method names clear. Please, or else we might spend quite a bit of time trying to refractor this.
Please, document your code with comments.
This one
// whatever you want to put
or this one
/* whatever you want to put */
Lastly the javadoc ones
/** Document */
Please, no stuff like this:
someVariable.someMethod().anotherMethod().aRandomVariable.toString()
No more than 3 fullstops will be allowed
Summary of classes
The classes listed in the following are listed in alphabetical order
Packages: how to place them and how to create them
Packages are created based on what the set package does.
For example, the package:
MonsterQuest.game.player
Is in charge for the player behaviour, and basically, anything to do with the player.
Make as much packages as necessary, but do not do unnecessary packages. Make packages made for just one vague task, then name classes to do name the specific task.
Logging
Please log often, to show how the program is going.
Add an import at the import part like this, it must be a static import
static import MonsterQuest.MonsterQuestMain.systemLog
Logging can be used like this:
systemLog.log("Blah blah blah");
There is only one restriction on where to log: In animations.


6 July 2018, St John's Hoxton, London, N1 6NP
The 2nd edition of the conference formerly known as JeffConf London sees it evolve into ServerlessDays London as the community grows and takes root globally.
Doors Open 8:30am
Opening Remarks 9:30am
Keynote - Erica Windisch 9:40am
Keynote - Sarah Drasner 10:30am
Break 11:20am
Talk - Olivier Tardieu 11:35am
Lightning Talks 12:05am Keith Horwood - Matthew Henderson - Markus Thommes - Anne Currie
Lunch 12:45pm
Open Spaces 1pm to 2pm
Lightning Talks 2:30pm Soenke Ruempler - Thomas Wallace - Rob Allen - Serhat Can
Talk - Blanca Garcia Gil & Neil Crofts 3:10pm
Coffee Break 3:45pm
Talk - Dizzy Smith 4:15pm
Talk - Yan Cui 5:00pm
Closing Remarks 5:35pm
Reception on the lawn 5:45pm
All attendees, speakers, sponsors and volunteers at our conference are required to agree with the following code of conduct. Organisers will enforce this code throughout the event. We are expecting cooperation from all participants to help ensuring a safe environment for everybody.
tl;dr: Jeff says be excellent with each other
The Quick Version
Our conference is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, or religion (or lack thereof). We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, parties, Twitter and other online media. Conference participants violating these rules may be sanctioned or expelled from the conference without a refund at the discretion of the conference organisers.

Participants asked to stop any harassing behavior are expected to comply immediately.
Sponsors are also subject to the anti-harassment policy. In particular, sponsors should not use sexualised images, activities, or other material. Booth staff (including volunteers) should not use sexualised clothing/uniforms/costumes, or otherwise create a sexualised environment.
If a participant engages in harassing behavior, the conference organisers may take any action they deem appropriate, including warning the offender or expulsion from the conference with no refund.
If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact a member of conference staff immediately. Conference staff can be identified as they’ll be wearing branded t-shirts.
Conference staff will be happy to help participants contact hotel/venue security or local law enforcement, provide escorts, or otherwise assist those experiencing harassment to feel safe for the duration of the conference. We value your attendance.
We expect participants to follow these rules at conference and workshop venues and conference-related social events.
All attendees, speakers, sponsors and volunteers at our conference are required to agree with the following code of conduct. Organisers will enforce this code throughout the event. We are expecting cooperation from all participants to help ensuring a safe environment for everybody.
tl;dr: Jeff says be excellent with each other
The Quick Version
Our conference is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, or religion (or lack thereof). We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, parties, Twitter and other online media. Conference participants violating these rules may be sanctioned or expelled from the conference without a refund at the discretion of the conference organisers.

Participants asked to stop any harassing behavior are expected to comply immediately.
Sponsors are also subject to the anti-harassment policy. In particular, sponsors should not use sexualised images, activities, or other material. Booth staff (including volunteers) should not use sexualised clothing/uniforms/costumes, or otherwise create a sexualised environment.
If a participant engages in harassing behavior, the conference organisers may take any action they deem appropriate, including warning the offender or expulsion from the conference with no refund.
If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact a member of conference staff immediately. Conference staff can be identified as they’ll be wearing branded t-shirts.
Conference staff will be happy to help participants contact hotel/venue security or local law enforcement, provide escorts, or otherwise assist those experiencing harassment to feel safe for the duration of the conference. We value your attendance.
We expect participants to follow these rules at conference and workshop venues and conference-related social events.


Parameters related to infections and recovery for the actual and modeled trend of coronavirus disease 2019 in several cities of Korea
| Type of infection |
Area |
λ
I
|
d
I.m
|
d
I.f
|
d
I.a
|
hd
R.c
|
|
|
d
R.c
|
h
R.c
|
| Mass |
Daegu |
−6.3 |
31.3 |
30 |
25 |
−1,486.6 |
7,268.3 |
5,781.7 |
50 |
4,859 |
| Gyeongbuk |
−5.3 |
29.3 |
26 |
24 |
−232.7 |
1,291.4 |
1,058.7 |
45 |
823 |
| Normal |
Seoul |
−0.1 |
53.1 |
53 |
53 |
−38.5 |
534.5 |
496.0 |
75 |
455 |
| Gyeonggi |
−0.1 |
49.9 |
49 |
50 |
−32.9 |
552.9 |
520.0 |
74 |
487 |


Safari IT Books Language Constructs Functional Programming Haskell Safari IT Books Programming Programming Programming Bryan O'Sullivan Donald Bruce Stewart John Goerzen O'Reilly Media, Inc. Real World Haskell, 1st Edition13.7. General-Purpose SequencesBoth Haskell's built-in list type and
the DList type that we defined earlier have poor performance
characteristics under some circumstances. The Data.Sequence
module defines a Seq container type that gives good
performance for a wider variety of operations. As with other modules, Data.Sequence is
intended to be used via qualified import: -- file: ch13/DataSequence.hs
import qualified Data.Sequence as Seq
We can construct an empty Seq using
empty and a single-element container
using singleton: ghci> Seq.empty
Loading package array-<IP_ADDRESS> ... linking ... done.
Loading package containers-<IP_ADDRESS> ... linking ... done.
fromList []
ghci> Seq.singleton 1
fromList [1] We can create a Seq from a list using
fromList: ghci>
let a = Seq.fromList [1,2,3] The Data.Sequence module provides some
constructor functions in the form of operators. When we perform a
qualified import, we must qualify the name of an operator in our code
(which is ugly): ghci> 1 Seq.<| Seq.singleton 2
fromList [1,2]
If we import the operators explicitly, we can avoid the
need to qualify them: -- file: ch13/DataSequence.hs
import Data.Sequence ((><), (<|), (|>))
By removing the qualification from the operator, we
improve the readability of our code: ghci> Seq.singleton 1 |> 2
fromList [1,2]
A useful way to remember the (<|) and (|>) functions is that the "arrow" points to the element
we're adding to the Seq. The element will be added on the
side to which the arrow points: (<|) adds on the left, (|>) on the right. Both adding on the left and adding on the right are
constant-time operations. Appending two Seqs is also cheap,
occurring in time proportional to the logarithm of whichever is shorter.
To append, we use the (><)
operator: ghci> let left = Seq.fromList [1,3,3]ghci> let right = Seq.fromList [7,1]ghci> left >< right
fromList [1,3,3,7,1] If we want to create a list from a Seq, we
must use the Data.Foldable module, which is best imported qualified: -- file: ch13/DataSequence.hs
import qualified Data.Foldable as Foldable
This module defines a typeclass, Foldable,
which Seq implements: ghci> Foldable.toList (Seq.fromList [1,2,3])
[1,2,3]
If we want to fold over a Seq, we use the
fold functions from the Data.Foldable module: ghci> Foldable.foldl' (+) 0 (Seq.fromList [1,2,3])
6
The Data.Sequence module provides a number
of other useful list-like functions. Its documentation is very thorough,
giving time bounds for each operation. If Seq has so many desirable
characteristics, why is it not the default sequence type? Lists are
simpler and have less overhead, and so quite often they are good enough
for the task at hand. They are also well suited to a lazy setting, whereas
Seq does not fare well.
|


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