General Information
I am currently seeking actively for open-source web project to work on. My goal in the near future is to learn web developement so if you need help with your web application, please feel free to reach out to me. Thank you!
I am currently seeking actively for open-source web project to work on. My goal in the near future is to learn web developement so if you need help with your web application, please feel free to reach out to me. Thank you!


This time, I needed to use a p, img, h1, table, and ul tag to create a small webpage. I had to use only html and got to choose what contain I put in it.
I found a random image and poem online to insert into this webpage and created a 3x4 table. I also purposefully used the different kinds of header tags in this page.
I learned that different HTML tags use different attributes and I learned how to use them. For example, the img tag uses a src attribute to get the image whether it is locally stored or through a link.
I also familiarized myself with the different tags and feel comfortable using them.
Once styling with CSS is in play, I hope to be able to make the page look more visually appealing
I had errors I didn't expect because I messed up the proper sibling relationship in the DOM. I accidentally had an element as a parent and child of the same element.
I also should pay closer attention to indentation and closing tags, because I forget sometimes.
Indent Properly
Make sure to Start and Edn tags before putting content within them
Think about the lego block concept of HTML structures


If target="_blank", the link will open in a new browser window or tab.
Locked in a frame? Visit W3Schools!


{% extends "layout.html" %} {% block head %} {% endblock %} {% block title %}Profile{% endblock %} {% block main %}
The more you tell about yourself, the more accurate and precise overall statistics will be.
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To change name:
To change password:


DCRichards
Hello, I'm David Richards, an experienced full-stack software developer.
I have experience in developing for backend, web, mobile and wearables. You'll mostly find me staring at several vim splits and tmux windows.
I'm currently found writing Go in Yoti's core team.
Recently, I've started trying to water my plants with an Arduino and become distracted learning embedded Rust 🌱.


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Getting started
Installation
Phluent can be used as a composer package or a single file download and included into your project.
Using Composer
$ composer require indgy/phluent
Standalone
Use the standalong Phluent.php file in the dist folder which combines Query, DB and the functions files into one.
cp dist/Phluent.php /path/to/your/project
Then require it where needed in your project:
require("/path/to/your/project/Phluent.php");
Creating a PDO connection
DB requires a PDO connection to your database.
$dsn = 'mysql:host=localhost;port=3306;dbname=database;charset=utf8mb4';
$pdo = new \PDO($dsn, 'username', 'password', [
\PDO::ATTR_ERRMODE => \PDO::ERRMODE_EXCEPTION,
\PDO::ATTR_DEFAULT_FETCH_MODE => \PDO::FETCH_ASSOC,
\PDO::ATTR_EMULATE_PREPARES => true,
]);
You can now use DB to perform SQL queries on the database:
$db = new DB($pdo);
$db->query("SELECT * FROM `movies` WHERE `title` LIKE '%Muppets%'");
foreach ($db->get() as $movie) {
echo "{$movie->title} was released in {$movie->year}";
}
Read the Query guide for more details on generating SQL.
Note
The PDO guide at PHP delusions is a good read.


Shuttleworth, Ann
| Birth Name | Shuttleworth, Ann |
| Gender | female |
| Age at Death | 82 years, 7 months, 25 days |
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | 1824 | Gill Grange, Brunthwaite Silsden Ykshire | ||
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||||
| Death | 1906-08-26 | Brunthwaite, Silsden Yks Eng | ||
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| Burial | St.James Cemetery Silsden Yks Eng | |||
|
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Families
Family of Green, James and Shuttleworth, Ann |
||||||||||||||||||||
| Married | Husband | Green, James ( * 1823 + 1879-07-08 ) | ||||||||||||||||||
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| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| Green, Thomas | 1848 | 1932-06-26 |
| Green, Jeremiah Netherwood | 1851-10-10 | 1934-12-15 |
| Green, Mary | 1858-05-16 | 1867-12-16 |
| Green, Mercy | 1858-02-10 | 1947 |
| Green, Annie | 1863-03-00 |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 40866A015069714EA5921F4409AA86B53135 |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | F83D1E22EBC9C146BCD259F004106A9A1F2A |
Pedigree
-
- Shuttleworth, Ann


Symptoms - heart attack
If you suspect the symptoms of a heart attack, call 999 immediately and ask for an ambulance.
Do not worry if you have doubts. Paramedics would rather be called out to find an honest mistake has been made than be too late to save a person's life.
Symptoms of a heart attack can include:
- chest pain – a sensation of pressure, tightness or squeezing in the centre of your chest
- pain in other parts of the body – it can feel as if the pain is travelling from your chest to your arms (usually the left arm is affected, but it can affect both arms), jaw, neck, back and tummy (abdomen)
- feeling lightheaded or dizzy
- sweating
- shortness of breath
- feeling sick (nausea) or being sick (vomiting)
- an overwhelming sense of anxiety (similar to having a panic attack)
- coughing or wheezing
Although the chest pain is often severe, some people may only experience minor pain, similar to indigestion. In some cases, there may not be any chest pain at all, especially in women, older people, and people who have diabetes.
It's the overall pattern of symptoms that helps to determine whether you are having a heart attack.
Symptoms links
Page last reviewed: 8 August 2017
Next review due: 8 August 2020


This Project begin date 8/25/2018
v0001_08_25_2018 - Added div containers (header, index, content, table) and controls to fit and resize div on any desktop browser
v0002_08_28_2018 - Added map, base layers, show coordinates, imagery and base map toggle
v0003_08_29_2018 - Added get route reference, asset selector, custom tables
v0004_08_29_2018 - Added feature editing (Add, Update, Delete), field validation, table sorting, custom drawing on map
v0005_08_30_2018 - Added feature editing (Copy), Added domain table from AGO, field validation against domains, editing workflow (enable/disable editing)
v0006_08_31_2018 - Added esriRequest for long URL Post operations
v0007_09_05_2018 - Added field editing (Split Record), replaced custom table ESRI FeatureTable, Converted project to AMD style
v0008_09_06_2018 - Added feature editing (Join Record)
v0009_09_07_2018 - Added reuseable functions (polyline, JSON non-spatial update, JSON spatial update, split records, quite delete), added snapping, replaced custom draw with ESRI draw mode
v0010_09_09_2018 - Added ability to update DFOs (new segments, splits, joins)
v0011_09_09_2018 - Simplified xmlhttp request calls, added passive selector for short or long POST requests
v0012_09_09_2018 - Extended add record to clip base geometry and update DFO's
v0013_09_10_2018 - Feature editing form updates, validations, workflow checks when editing
v0014_09_10_2018 - Added populate editing field by clicking Domain
v0015_09_10_2018 - Added ability to generate point measure on the map, added home button that zoom to full extent
v0016_09_10_2018 - Added reuseable functions to assist with DFO generation (two point polyline, perpendicular line, nearest coordinate, nearest vertex)
v0017_09_11_2018 - Added perpendicular line from single point (to increase reliability of point measure)
v0018_09_12_2018 - Added feature editing (Edit Record), added ESRI Edit mode, modified createCustomAddSegment to handle dead end segments (only intersects on one side)
v0019_09_13_2018 - Added business rules and attribute list tables to AGO, Added business rules to display, Added populate attribute list from AGO table
v0020_09_13_2018 - Added reuseable functions for table viewing/editing/JSON calls using attribute list table from AGO (allows unlimited number of services for editing)
v0021_09_13_2018 - Added zoom to record after clicking table
v0022_09_14_2018 - Moved domain/rules population to attribute change event, Fixed asset re-drawing after selecting from attribute list, Fixed table sorting after loading attributes
v0023_09_14_2018 - Added highlights/expanded extent when joining multiple records, Added checks to make Add, Edit, Split, Join exclusive in the interface
v0024_09_15_2018 - Workflow optimization, Began drafting testing script, Changed Start Over button to only Clear highlighed features and empty form
v0025_09_17_2018 - Added Route Name and DFO ranges to the Route Reference Section, tweaked some lables in the interface
v0026_09_17_2018 - Removed ObjectID from the interface, Replaced RTE_RB_NM with GID in TxDOT_Roadways and Asset tables, Republished asset tables with GID to AGO, Re-mapped JSON to new asset tables
v0027_09_19_2018 - Changed TxDOT Roadways source, Changed base map to ESRI topo
Load an attribute to see the table.


import random
import numpy as np
import altair as alt
import pandas as pd
Until now, we've only discussed scalar-valued random variables, which produce a single value at a time. In supervised learning, we need to figure something out about the relationship between a vector of predictors $x_i$ and a label $y_i$, which we imagine are generated in pairs by some data factory. We need language to describe that kind of factory.
A multivariate (or vector-valued) random variable is a factory that produces vectors instead of scalars. More formally, it's a mapping between outcomes in the sample space to vectors of numbers. All our old rules still apply: one outcome strictly maps to one vector. We can demonstrate:
def Z(ω):
if ω in set([1,8]):
return (0,0)
if ω in set([4,5,9]):
return (1,0)
if ω in set([0,2,3]):
return (0,1)
if ω in set([6,7]):
return (1,1)
Z.Ω = set([0,1,2,3,4,5,6,7,8,9])
def realize(rand_var): # run the assembly line!
ω = random.sample(rand_var.Ω, 1)[0] # grab a single nugget out of the silo at random
return rand_var(ω) # push it through the factory
[realize(Z) for i in range(5)]
This is a discrete multivariate random variable because it can only return four possible values: $(0,0)$, $(1,0)$, $(0,1)$, and $(1,1)$. It's also possible to make continuous multivariate random variables:
def Z(ω):
z1 = ω**2
z2 = 1-ω
return (z1, z2)
Z.Ω = random.random # returns a single number between 0 and 1 when called
def realize_cont(rand_var): # run the assembly line!
ω = rand_var.Ω() # grab a single nugget out of the silo at random
return rand_var(ω) # push it through the factory
[realize_cont(Z) for i in range(5)]
Probabilities are the same for multivariate random variables as they are for univariate random variables: you grab all of the outcomes in the sample space that are destined to become one of the values you're looking for and you measure their relative volume. The calculations do get a little trickier, but the intuition is the same. When we were working with univariate random variables, the sets of interest were usually intervals (i.e. what's the probability that $z$ is between $a$ and $b$?). In the multivariate case, the equivalent of an interval on the number line is a region of the vector space. In general, you can think of a picture like this:
For example, with $\mathbf Z(\omega) = (\omega^2, 1-\omega)$ as it is in our code, let's find the probability that the output vector $z = (z_1,z_2)$ is in the region corresponding to $z_1<0.5$ and $z_2<0.5$. Let's call that region $A = \{(z_1,z_2):z_1<0.5, z_2<0.5)\}$.
In mathematical notation, what we're looking for is $P(\mathbf Z \in A)$ The first thing to notice is $z_1$ will only be less than $0.5$ if $\omega$ is less than $\sqrt{0.5}$, while $z_2$ will only be less than $0.5$ if $\omega$ is greater than $0.5$. Stop and verify both of these facts. We need both conditions to hold, so the set of $\omega$s that place $z$ into the region of interest is $Z^{-1}(A) = [0.5, \sqrt{0.5}] \approx [0.5, 0.707]$. The volume of that set relative to the total sample space $[0,1]$ is $0.707-0.5$, so $P(\mathbf Z \in A) = 0.207$.
The outcomes and sample space are important to the definition of probability for multivariate random variables, but as we discussed in the previous section, we never really talk about the outcomes $\omega$. Usually we define a random variable by directly specifying a probability distribution. That implies there is some sample space that is being mapped to the different realizable values in proportions corresponding to the mass or density function, but we don't actually care what it is. Calculating the probabilities of different realizations is just a matter of integrating or summing the density or mass over the appropriate set.
A multidimensional mass or density function works the same way as a unidimensional mass or density. For some set of realizations $A$,
$$P(\mathbf Z \in A) = \int_A \phi(z) dz$$or, if $\mathbf Z$ is discrete,
$$P(\mathbf Z \in A) = \sum_{z \in A} \phi(z)$$In other words, add up the total volume of the outcomes that end up mapping to an realization in $A$- same as before. The only difference is that now $A$ is a region of space instead of an interval on a line.
Say we have a 2-dimensional random variable $\mathbf Z$ with the following distribution:
$$ \phi(z) = \phi([z_1, z_2]) = \begin{cases} 1 & \text{for } z_1, z_2 \in [0,1] \\ 0 & \text{else} \end{cases} $$This is the 2-dimensional uniform distribution over $[0,1] \times [0,1]$, which is often written $\mathbf Z \sim \text{Unif}([0,1]\times[0,1])$.
Let's find the probability that $\mathbf Z$ takes values in the "triangle" $A$ defined by the vertices $[0,0]$, $[1,1]$, and $[1,0]$. Another way to define this is $A = \{(z_1,z_2) : z_1 \in [0,1], 0 \le z_2 \le z_1\}$.
We can use calculus to get the same answer:
Note that, just as with unidimensional random variables, the density of any multidimensional random variable has to integrate to 1 over its entire support: $\int_{-\infty}^{\infty} \int_{-\infty}^{\infty} \phi(z) dz_1 dz_2 = 1$. As before, this is so that the answer to "what is the probability that $z$ is any point in the $(z_1, z_2)$ space at all?" is the intuitive "1". It's also the same as saying that the silo of outcomes has total volume equal to 1.
We often think about multivariate random variables as several different random variables "glued together" into one:
$$\mathbf Z = [\mathbf Z_1, \mathbf Z_2]$$Now, instead of talking about the distribution of $\mathbf Z$, we talk about the joint distribution of $\mathbf Z_1$ and $\mathbf Z_2$. It's the exact same thing, though. The only advantage of doing this is that we don't have to give the joint variable $\mathbf Z$ a name if we don't care to.
What's being glued together are two factories- they still share one silo. Our metaphor gets a little tricky here because the same single nugget of ore $\omega$ now passes simultaneously through both factories to produce the two outputs $z_1$ and $z_2$. As with all metaphors, it can only carry us so far!
Let's look at a joint distribution of two discrete random variables $\mathbf V_1$ and $\mathbf V_2$ that I made up, each of which take values from $\{0,1,2,\dots 9\}$.
def joint(v1, v2): # v1, v2 in {0,1,2,..9}
P_raw = np.array(
[[ 0., 0., 1., 9., 30., 32., 19., 5., 0., 0.],
[ 0., 0., 0., 2., 12., 28., 30., 12., 5., 0.],
[ 0., 0., 0., 0., 2., 20., 35., 27., 3., 4.],
[ 0., 0., 0., 0., 8., 21., 38., 39., 11., 2.],
[ 0., 0., 0., 0., 4., 32., 37., 15., 4., 1.],
[ 0., 0., 1., 5., 18., 43., 21., 8., 2., 0.],
[ 0., 0., 6., 25., 35., 25., 8., 1., 0., 0.],
[ 0., 2., 19., 47., 20., 16., 1., 0., 0., 0.],
[ 2., 12., 32., 36., 17., 3., 0., 0., 0., 0.],
[ 9., 15., 44., 32., 7., 0., 0., 0., 0., 0.]])
return P_raw[v1, v2]/np.sum(P_raw) # need to make it so that Σ p_ij = 1 so that this is a valid mass function
We can calculate probabilities using the mass function as usual: $P(\mathbf V_1 =1, \mathbf V_2 = 5) = \phi(1,5)$:
joint(1,5)
Let's plot the joint mass function as a heatmap so we can get a better feel for it.
from itertools import product
v1, v2 = zip(*product(range(10), range(10))) # all the possible combinations of (v1, v2)
prob = [joint(v1,v2) for v1, v2 in zip(v1, v2)]
plot_data = pd.DataFrame({'v1':v1, 'v2':v2, 'ϕ':prob})
alt.Chart(plot_data, width=300, height=300).mark_rect().encode(x='v1:O', y='v2:O', color='ϕ')
What we're looking at is the 2D version of the mass plots from before. Think of it as looking down from above onto the stacks of outcomes that are piled up on the labels for the values that they are destined to become. In this case, the darker squares are the piles that are sticking up towards you more. For instance, the square that corresponds to $v_1=6$, $v_2=4$ is a darkish blue because about 3% of all the outcomes in $\Omega$ (whatever it is) get mapped to that combination of values. The square that corresponds to $v_1=9$, $v_2=9$ is a a pale yellow because about 0% of all the outcomes in $\Omega$ get mapped to that combination of values.
We can also calculate the probability of sets of realization (which are "regions" in the realization space) as we did with our continuous random variable. For example, let's say we're interested in the probability that $\mathbf V_1$ takes a value that is at least 2 units less than $\mathbf V_2$. We can write that as $P(\mathbf V_1 \le \mathbf V_2 - 2)$ or as $P([\mathbf V_1, \mathbf V_2] \in A)$ where $A$ is the region that looks like this:
a = np.array(v1) <= np.array(v2)-2
plot_data = pd.DataFrame({'v1':v1, 'v2':v2, 'A':a})
alt.Chart(plot_data, width=300, height=300).mark_rect().encode(x='v1:O', y='v2:O', color='A:N')
To get the probabiity we want, we just need to sum up all the values of the joint mass function that sit on top of the orange region of interest
$$P(\mathbf V_1 \le \mathbf V_2 - 2) = \sum_{v_1 \le v_2-2} \phi(v_1, v_2)$$sum(joint(v1,v2) for v1, v2 in zip(v1, v2) if v1<=v2-2)
Now that we have joint distributions of multiple variables, we can explore the relationships between those variables. For instance, if we beleive that $[Z_1, Z_2]$ is the factory that generates measurements of people's heights ($z_1$) and weights ($z_2$), can we calculate the probability that someone who is over 2 meters tall weighs more than 80 kilograms?
Probabilities like these are called conditional probabilities. That's because we're interested in the probability of something, given that some other condition holds. In our example, the "something" is that the person weighs more than 80 kg, and the condition is that the person must be over 2 meters tall. The notation for this is $P(\mathbf Z_2 > 80 | \mathbf Z_1 > 2)$, which is read as "the probability of $\mathbf Z_2 > 80$ given $\mathbf Z_1 > 2$".
How do we calculate that probability, given a joint density or mass? Let's think about it... any probability is just a volume of some subset of the outcomes in the sample space. So what we're really asking for is the volume of the outcomes $\omega$ that map to values of $z_1$ greater than 2 and also $z_2$ greater than 80, relative to the volume of the outcomes that map to values of $z_1$ greater than 2.
In the picture above, the conditonal probability we're looking for is the ratio of the volumes of the purple oval and the grey circle, which are subsets of the sample space.
We can get the volume of outcomes mapping to a particular region of the realization space (like $z_1>2$) by integrating the density over that region. Thus what we need to do is first integrate the density over the region where heights are more than 2 meters, then do the same for the region where heights are more than 2 meters and weights are over 80kg. Then we should divide the latter by the former to get the proportional volume of the two sets of outcomes:
It works the same way for discrete random variables:
We can take this idea step further and define the conditioned random variable $\mathbf Z_1 | (\mathbf Z_2=z_2)$. This is random variable that you get when you take $\mathbf Z_1$, but subset the sample space $\Omega$ to include only those outcomes where $\mathbf Z_2(\omega) = z_2$. Each different value of $z_2$ defines a different conditioned version of $\mathbf Z_1 | \mathbf Z_2$.
We're getting really close to machine learning now. Remember $x_i$ and $y_i$? Let's say we observe $x_i$ but we don't know what $y_i$ is. If those are realizations from some random variables $\mathbf X_i$ and $\mathbf Y_i$, though, and we know their joint density, then we can calculate $P(\mathbf Y_i | \mathbf X_i = x_i)$. That tells us the probability that $Y_i$ is any value we want to know, given that we know what $x_i$ was! We can then find the value or region of $y$ that has the most probability- this would be the most likely label, our best guess for what the unobserved $y_i$ is! Of course, we can't actually know what the real joint density is (we don't see the real factory). But we'll get to that.
The distribution of the random variable $\mathbf Z_1|(\mathbf Z=z_2)$ is easy to extract from the joint distribution: just hardcode the $z_2$ value in the joint distribution to the desired value: $\tilde\phi_{\mathbf Z_1|\mathbf Z_2=z_2}(z_1) = \phi_{\mathbf Z_1, \mathbf Z_2}(z_1,z_2)$. The only wrinkle is that the resulting $\tilde\phi$ will not be normalized so that its total probability across all $z_1$ is 1 (i.e. the new "silo" will not have volume 1). To fix this, we scale the whole function up by dividing by the total area under $\tilde\phi$:
$$ \phi_{\mathbf Z_1|\mathbf Z_2=z_2}(z_1) = \frac {\phi_{\mathbf Z_1, \mathbf Z_2}(z_1,z_2)} {\int_{-\infty}^\infty \phi_{\mathbf Z_1, \mathbf Z_2}(z_1,z_2) dz_1} $$or, for discrete random variables:
$$ \phi_{\mathbf Z_1|\mathbf Z_2=z_2}(z_1) = \frac {\phi_{\mathbf Z_1, \mathbf Z_2}(z_1,z_2)} {\sum_{z_1} \phi_{\mathbf Z_1, \mathbf Z_2}(z_1,z_2)} $$Here is a picture that conveys the idea of conditioning better than a formula can:
Here you can see two things:
- After conditioning on $\mathbf Z_2$, the density is now only a function of $z_1$. The old factory $[\mathbf Z_1, \mathbf Z_2]$ produces two values at a time ($z_1$ and $z_2$). The conditioned factory $\mathbf Z_1 | \mathbf Z_2=z_2$ just produces one: $z_1$. Another way to think about this is that we've taken the original factory and reduced it to the operating conditions under which $z_2$ comes out as a particular value.
- The conditioned density you get back depends on the value $z_2$ that you condition on. As you scan $z_2$ across its range, the shape of the resulting conditioned distribution will change.
When there more than two dimensions, it's harder to picture this, but the idea is the same. You "slice" the multivariate distribution at the specified place on the specified axis. The conditional distribution can itself also be multivariate: if you have four variables and you slice at a place given by two of them, you'll get back a distribution over the remaining two.
Let's go back to the joint distribution of our friends $\mathbf V_1$ and $\mathbf V_2$:
def joint(v1, v2): # v1, v2 in {0,1,2,..9}
P_raw = np.array(
[[ 0., 0., 1., 9., 30., 32., 19., 5., 0., 0.],
[ 0., 0., 0., 2., 12., 28., 30., 12., 5., 0.],
[ 0., 0., 0., 0., 2., 20., 35., 27., 3., 4.],
[ 0., 0., 0., 0., 8., 21., 38., 39., 11., 2.],
[ 0., 0., 0., 0., 4., 32., 37., 15., 4., 1.],
[ 0., 0., 1., 5., 18., 43., 21., 8., 2., 0.],
[ 0., 0., 6., 25., 35., 25., 8., 1., 0., 0.],
[ 0., 2., 19., 47., 20., 16., 1., 0., 0., 0.],
[ 2., 12., 32., 36., 17., 3., 0., 0., 0., 0.],
[ 9., 15., 44., 32., 7., 0., 0., 0., 0., 0.]])
return P_raw[v1, v2]/np.sum(P_raw) # need to make it so that Σ p_ij = 1 so that this is a valid mass function
We can implement the conditioning formula to make functions that return conditional probabilities:
def cond_V2_given_V1_is_7(v2):
return joint(7,v2)/sum(joint(7,v2) for v2 in range(10))
def cond_V1_given_V2_is_4(v1):
return joint(v1,4)/sum(joint(v1,4) for v1 in range(10))
So $P(\mathbf V_2 = 3 | \mathbf V_1 = 7)$ is...
cond_V2_given_V1_is_7(3)
And here are the mass plots of the conditional densities:
cond_v1 = [cond_V1_given_V2_is_4(v1) for v1 in range(10)]
cond_v2 = [cond_V2_given_V1_is_7(v2) for v2 in range(10)]
v1_plot_data = pd.DataFrame({'v1':range(10), 'ϕ':cond_v1})
v2_plot_data = pd.DataFrame({'v2':range(10), 'ϕ':cond_v2})
v1_plot = alt.Chart(v1_plot_data, height=100, width=300).mark_bar().encode(x='v1:O', y='ϕ')
v2_plot = alt.Chart(v2_plot_data, height=100, width=300).mark_bar().encode(x='v2:O', y='ϕ')
v1_plot | v2_plot
By themselves, I don't think these plots give you that much intuition about conditional distribution. But if you put them next to the joint distribution, you can see that the conditional distributions are what you get when you look at (scaled up) slices of the joint distribution from the side:
To transfer your intuition from the discrete case to the continuous case, do what we did before: imagine the buckets shrinking down smaller and smaller to infinitesimals until the sums become integrals and the mass functions become densities.
The "scaling factor" $\int_{-\infty}^\infty \phi_{\mathbf Z_1, \mathbf Z_2}(z_1,z_2) dz_1$ or $\sum_{z_1} \phi_{\mathbf Z_1, \mathbf Z_2}(z_1,z_2)$ that we need to calculate the conditional distribution of $\mathbf Z_1$ only depends on what the value of $z_2$ is. You can think of it as summing up all the variation over $z_1$ at a given level of $z_2$. This quantity is called the marginal distribution of $\mathbf Z_2$ and is usually denoted $\phi_{\mathbf Z_2}(z_2)$.
The marginal distribution of $\mathbf Z_2$ is what you get when you consider the factory $[\mathbf Z_1, \mathbf Z_2]$, but completely ignore the outputs $z_1$ and only look at the $z_2$s. You haven't changed the silo $\Omega$ at all (unlike conditioning). Thinking about it in terms of density plots, the 2D joint density plot is what you get when you stack up all of the outcomes on top of the labels of the points $(z_1, z_2)$ that they map to. The marginal density plot is what you get when you combine all of the stacks of outcomes that have the same $z_2$ label.
The marginal distribution allows for the calculation of things like $P(\mathbf Z_2 \in A)$ when a joint distribution is given. For example, what if we wanted to know the outright probability that someone is between 1.9 and 2.3 meters tall, regardless of their weight? If we start out with the joint distribution of height and weight, first we have to find the marginal distribution of height (by integrating out the weight variable), then integrate the resulting distribution between 1.9 and 2.3 to get the final answer.
Let's return again to $\mathbf V_1$ and $\mathbf V_2$. This time, we'll write functions that compute marginal probabilities:
def marginal_V1(v1):
return sum(joint(v1, v2) for v2 in range(10))
def marginal_V2(v2):
return sum(joint(v1, v2) for v1 in range(10))
Now we can calculate probabilities like $P(\mathbf V_2 = 5)$:
marginal_V2(5)
As before, we can make mass plots:
v1, v2 = range(10), range(10)
marg_v1 = [marginal_V1(v) for v in v1]
marg_v2 = [marginal_V2(v) for v in v2]
v1_plot_data = pd.DataFrame({'v1':v1, 'ϕ':marg_v1})
v2_plot_data = pd.DataFrame({'v2':v2, 'ϕ':marg_v2})
v1_plot = alt.Chart(v1_plot_data, height=100, width=300).mark_bar().encode(x='v1:O', y='ϕ')
v2_plot = alt.Chart(v2_plot_data, height=100, width=300).mark_bar().encode(x='v2:O', y='ϕ')
v1_plot | v2_plot
These are fine as-is, but it helps to understand what they mean when you arrange them alongside the joint distribtion like this:
You can see that the marginal distributions are what you get when you "squish" the joint distribution by summing along one axis. The picture also clarifies why it's called a marginal distribution: the sums are tallied up on the margins of the figure.
The marginal distribution also allows for a simplified formula to compute conditional distributions, which works for both discrete and continuous random variables:
This is the definition of the conditional distribution, but written with the marginal distribution instead of the elaborated integral or sum. Writing it this way also makes it easy to see that the joint distribution is a product of the conditional and marginal distributions:
This relationship is so important and so widely referenced that it often gets written in an even more abbreviated way: $\phi_{\mathbf Z_1, \mathbf Z_2} = \phi_{\mathbf Z_1|\mathbf Z_2} \phi_{\mathbf Z_2}$. Just remember this is communicating the same thing as what we have above.
We can also go the other way by conditioning $\mathbf Z_2$ on $\mathbf Z_1$:
$$ \phi_{\mathbf Z_2|\mathbf Z_1} \phi_{\mathbf Z_1} = \phi_{\mathbf Z_1, \mathbf Z_2} = \phi_{\mathbf Z_1|\mathbf Z_2} \phi_{\mathbf Z_2} $$which gives us a result called Bayes' Theorem (not to be confused with Bayesian statistics):
In higher dimensions, it's possible to "marginalize out" one or several variables at once, which leaves you with a distribution over the remaining variables. For example, if we had a joint distribution of 5 variables given by $\phi_{\mathbf Z_1, \mathbf Z_2, \mathbf Z_3, \mathbf Z_4, \mathbf Z_5}(z_1, z_2, z_3, z_4, z_5)$, we could marginalize out $\mathbf Z_2$ and $\mathbf Z_5$ to get
$$ \phi_{\mathbf Z_1, \mathbf Z_3, \mathbf Z_4}(z_1, z_3, z_4) = \int_{-\infty}^\infty \int_{-\infty}^\infty \phi_{\mathbf Z_1, \mathbf Z_2, \mathbf Z_3, \mathbf Z_4, \mathbf Z_5}(z_1, z_2, z_3, z_4, z_5) dz_2 dz_5 $$In this case, the "marginal" distribution $\phi_{\mathbf Z_1, \mathbf Z_3, \mathbf Z_4}(z_1, z_3, z_4)$ is itself "joint" between $\mathbf Z_1$, $\mathbf Z_3$, and $\mathbf Z_4$. These "joint marginals" are necessary to condition on multiple variables at once:
The equations look gross because we're keeping track of all of the individual random variables, but the notation simplifies if use vector-valued (multivariate) random variables to give the different groups of variables their own names:
This notation is very common. Just remember that now we are talking about conditioning or marginalizing on a vector-valued random variable instead of a scalar-valued random variable. You can always write it out in the longform notation if you get confused.
As you saw in the previous exercise, there are cases where for two random variables $\mathbf U$ and $\mathbf V$ we have $\phi_{\mathbf U | \mathbf V = v}(u) = \phi_{\mathbf U}(u)$ no matter what $v$ is. In other words, knowing $v$ never gives us any additional information about what $u$ might be. Visually, you can imagine that squishing the distribution along one axis gives you the same result as slicing somewhere along that axis and scaling the result. If this is the case,
and we say that $\mathbf U$ and $\mathbf V$ are independent, which is often written $\mathbf U \perp \!\!\! \perp \mathbf V$. Independence is a property of the factory $[\mathbf U, \mathbf V]$. There is no way to definitively know that variables are independent just by looking at the data they produce.
When we're using random variables to model a real-world phenomenon, we transfer our intuition about the real world into conditions that the random variables must satisfy. For instance, if $\mathbf U$ and $\mathbf V$ are the results of two coin flips, saying $\mathbf U$ and $\mathbf V$ are independent is an assumption that the result of one coin flip can't affect the result of the next. It's not actually true, of course, but it's an intuition about the world that we've embedded in our model of it. Remember, however, that we never actually know what the real joint distribution of our data is. One reason independence is such a useful thing to assume is that it lets us break up the joint distribution into two parts that we can make guesses about separately. That often simplifies our models.
It's also possible to have groups of variables that are independent of each other. For instance, if $\phi_{\mathbf Z_2, \mathbf Z_5|\mathbf Z_1=z_1, \mathbf Z_3=z_3, \mathbf Z_4=z_4}(z_2, z_5) = \phi_{\mathbf Z_2, \mathbf Z_5}(z_2, z_5)$ for all $z_1, z_3, z_4$, we'd say $\mathbf Z_2, \mathbf Z_5 \perp \!\!\! \perp \mathbf Z_1, \mathbf Z_3, \mathbf Z_4$.
It's also possible for two random variables $\mathbf U$ and $\mathbf V$ to be conditionally independent, given a third $\mathbf Z$. That just means that
$$ \phi_{\mathbf U | \mathbf V, \mathbf Z} = \phi_{\mathbf U | \mathbf Z} $$Or, equivalently, $\phi_{\mathbf U, \mathbf V | \mathbf Z} = \phi_{\mathbf U | \mathbf Z} \phi_{\mathbf V | \mathbf Z}$, and $\phi_{\mathbf U, \mathbf V, \mathbf Z} = \phi_{\mathbf U | \mathbf Z} \phi_{\mathbf V | \mathbf Z} \phi_{\mathbf Z}$.
Conditional independence means that knowing the value of $u$ doesn't tell us anything about the value of $v$ above and beyond what $z$ does. Variables that are independent don't have to be conditionally independent, and vice-versa. An easy example to see this is in is $\mathbf U = \mathbf V = \mathbf Z$. In this example, $\mathbf U$ and $\mathbf V$ can't possibly independent because knowing $u$ means we know $v$ exactly. But if we already know $z$, $u$ actually can't tell us anything more about $v$ that we don't already know, so they are conditionally independent. Weird, but that's how it is.
As with independence, conditional independence is a way of embedding our assumptions about the world into our probabilistic models. For instance, let's say that $\mathbf U$ is how long it takes me to get to work one morning, $\mathbf V$ is how long it takes my partner, and $\mathbf Z$ is the total strain on public transit in the city. Our commutes don't affect each other, but they are both affected by delays on public transit. If there are no other factors that affect both of our commutes, then it's reasonable to assume that our commute times are are conditionally independent given the transit strain. Knowing how long it took her to get to work won't tell you anything about how long it took me if you already know what the transit strain was like. But if you don't know the transit strain and I tell you it took me a long time to get to work, it's likely that it also took my partner a long time. Thus the two variables aren't independent, just conditionally indpendent.
The last thing we need before getting into machine learning is a way to do algebra with random variables. For instance, what if I have a data factory $\mathbf U$, which makes $u$s and a factory $\mathbf V$ that makes $v$s. But instead of showing you $u$ and $v$, I add them together and just show you $z = u+v$. You don't know about $\mathbf U$ or $\mathbf V$. From your perspective, there is a factory somewhere called $\mathbf Z$ that's making the $z$s. Similarly, what if instead of $v$ I showed you $z = v^2 + 6$? Again, you would only think there were a $\mathbf Z$ factory.
We can apply all of our usual algebraic tools to random variables: adding and subtracting them, multiplying them, applying functions, etc. etc. You can think of all of this as "post-processing" of the realization(s) produced by the random variable(s). The key insight is that if you step back, you still have something that's mapping outcomes in the sample space to realizations: that's a random variable. Here's a visual example where two variables $\mathbf U$ and $\mathbf V$ are transformed by some function $h$ to produce what acts like a single random variable $\mathbf Z$. We write this $\mathbf Z = h(\mathbf V, \mathbf U$):
The question is: how do statements about the probability of $\mathbf U$ or $\mathbf V$ translate to statements about the probability of $\mathbf Z$? In other words, what's $P(\mathbf Z \in A)$ in terms of probabilities or $\mathbf U$ and $\mathbf V$?
Ultimately, the probability of a set of realizations is the volume of outcomes in the sample space that correspond to that set. With a single variable, working back from the set of realizations $A$ to the set of outcomes that correspond to those is a one-step process: find whatever $\mathbf Z^{-1}(A)$ is, then get its volume. But now we have to work back through a chain of transformations. First we need to find the set of points $[u,v]$ that map to $z \in A$ under the transformation $h$. Call that set $h^{-1}(A)$. Now we need to find the set of outcomes that produce $[u,v] \in h^{-1}(A)$. Call that set $[\mathbf U, \mathbf V]^{-1}(h^{-1}(A))$ Finally, we need to find the volume of that set: $\mathbb P([\mathbf U, \mathbf V]^{-1}(h^{-1}(A)))$. If we have the joint density of $\mathbf U$ and $\mathbf V$, we can automate the last step:
$$P(\mathbf Z \in A) = \mathbb P([\mathbf U, \mathbf V]^{-1}(h^{-1}(A))) = \int_{h^{-1}(A)} \phi_{\mathbf U, \mathbf V}(u,v) du dv$$with the integral replaced by a sum over $A$ if $\mathbf U$ and $\mathbf V$ are discrete.
We can illustrate with our example in code. Let's say $\mathbf V_1$ and $\mathbf V_2$ are distributed with the mass function shown below (same as what we were working with before):
v1, v2 = zip(*product(range(10), range(10))) # all the possible combinations of (v1, v2)
prob = [joint(v1,v2) for v1, v2 in zip(v1, v2)]
plot_data = pd.DataFrame({'v1':v1, 'v2':v2, 'ϕ':prob})
alt.Chart(plot_data, width=300, height=300).mark_rect().encode(x='v1:O', y='v2:O', color='ϕ')
Now let's say $\mathbf Z = \mathbf V_1 + 3\mathbf V_2^2$. What's $P(\mathbf Z < 10)$? For $\mathbf Z$ to be less than $10$, $\mathbf V_1 + 3\mathbf V_2^2$ has to be less than $10$, so that defines the region we have to sum over:
a = np.array(v1) + 3*np.array(v2)^2 < 10
plot_data = pd.DataFrame({'v1':v1, 'v2':v2, 'A':a})
alt.Chart(plot_data, width=300, height=300).mark_rect().encode(x='v1:O', y='v2:O', color='A:N')
That is, for $h(v_1, v_2) = v_1 + 3v_2^2$, the preimage $h^{-1}((-\infty, 10])$ is $\{[v_1, v_2]: v_1 + 3v_2^2 < 10\}$, which is the set illustrated above in orange.
And, as usual, to calculate the probability of a realization in this region we simply sum up the joint distribution over the region:
sum(joint(v1,v2) for v1, v2 in zip(v1, v2) if v1 + 3*(v2**2) < 10)
There you have it. This is often more difficult with continuous random variables since finding preimages and integrating over weird sets can be a hassle. But the basic idea is the same, regardless of how much bean-counting you have to do to get the result. The point isn't to be able to do it, it's to understand what is happening in theory.
Let's go back to our factories $\mathbf U$ and $\mathbf V$. The outputs $u$ and $v$ are transformed together with some postprocessing $h$ to give $z$, so it looks like there is just one factory $\mathbf Z$. But what if I showed you $u$, $v$, and $z$ without telling you that $z$ actually comes from transforming $u$ and $v$? It would then look to you like a factory $[\mathbf U, \mathbf V, \mathbf Z]$. And, indeed, we can define joint, conditional, and marginal distributions on these random variables just as we had when they weren't transformations of each other. That will let us calculate things like the probability that $\mathbf U$ is 4 if $\mathbf Z$ is 10.
This is important for machine learning. For example, if we assume that $\mathbf U$ is the "code" that determines whether or not a cat walks into the frame of a picture when it is being taken, and $h()$ is a function that translates the presence or absence of that cat, as well as a bunch of other variables $\mathbf V$ (like what else is in the frame) into a matrix of pixels $z$. Then $\mathbf Z = h(\mathbf U, \mathbf V)$ and $P(\mathbf U=1|\mathbf Z=z)$ is the probability that there is a cat in a picture, given that we observe the pixels $z$.
Sometimes you will see conditions that are transformations of random variables. For instance, what's $P(\mathbf V_1 < 5 | \mathbf V_1 + \mathbf V_2 = 10)$? One way to understand that conditioning mathematically is to break it down into two steps. First create a new variable $\mathbf Z = \mathbf V_1 + \mathbf V_2$, then calculate $P(\mathbf V_1 | \mathbf Z = 10)$. Alternatively, you can calculate the sums/integrals directly by integrating the joint density over the appropriate regions:
numerator = sum(joint(v1,v2) for v1, v2 in zip(v1, v2) if v1 + v2 == 10 and v1 < 5)
denominator = sum(joint(v1,v2) for v1, v2 in zip(v1, v2) if v1 + v2 == 10)
numerator/denominator
But, more generally, you can also derive whole densities that are conditioned on transformations, like $\phi_{\mathbf V_1 | \mathbf V_1 + \mathbf V_2 = 10}(v_1)$.
Multivariate random variables are groups of single random variables and their distribution together is called a joint distribution. The joint can be integrated/summed to yield the probability that a set of conditions on all the variables is simultaneously satisfied.
The joint distribution can also be used to calculate conditional probabilities, which are the probabilities that one or more random variables satisfy a condition, given that other random variables satisfy some other conditon. We used our intuition about the sample space to realize that conditional probabilities are just ratios of volumes of subsets of the sample space. Thus we can calculate conditional probabilities by taking the ratio of two integrals or sums of the joint density or mass function.
We took the idea further to define conditioned random variables, which are random variables that operate on a subset of the original sample space that's defined by a condition on other random variables. The conditional distribution is easy to extract from the joint: just hardcode the value of the conditioning variables in the joint distribution, and divide by a scaling factor so that the conditioned distribution still integrates/sums to 1.
It turns out that the scaling factor, when viewed as a function of the hardcoded value, is itself a probability distribution that we call the marginal. That allows us to write any joint distribution as a product of a conditional distribution and a marginal distribution. Bayes theorem uses that relation to equate different products of conditional and marginal distribution via the joint distribution.
We also saw that there are joint distributions where conditioning on some variables gives you the same result as marginalizing them out. When this is the case, we say that the variables that are marginalized out are independent from those that you have the marginal of. Independence lets us factor the joint distribution into a product of marginals. Independence means that information about one variable (or set of variables) doesn't tell you anything about another (or another set). A similar factorization holds if the variables are only conditionally independent, which means that information about one variable (or set) doesn't tell you anything about another above and beyond what the conditioning variable or variables tell you.
Lastly, we saw that random variables can be transformed algebraically to yield new random variables. The key to figuring out how the distributions are related is to know how to find the pre-image of sets under the transformation. We also saw that transformations can be marginalized over, conditoned on, etc. just the same as other random variables.


WebSocket Service for webOS
Get Greeting
Hello World!
This is a sample that shows how to relay between an web application and an websocket server.


Repo: https://brianleek.me/repo
This repo has moved to https://brianleek.me/repo. This repo will no longer receive any updates or new packages. Please add the new repo. Thanks!
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|
10. ACKNOWLEDGEMENTS
If there's anything I learned in University, it's that I should always acknowlege
my sources (I once lost a load of marks 'cuz I didn't say Acknowledgements: none).
- The code for Shaded Buttons (at least when I started it) was largely based
on code found in OS/2 Presentation Manager Programming (Charles Petzold).
A lot of other code is also taken from there (part of Test1 and Test2 for example).
- I used the documentation from Prominare's
site to figure out how the normal buttons behave, and how to get PMCX to work.
- I used some information from one of IBM's web sites, to figure out how normal
buttons behave, what certain messages mean, and what functions to use.
- Thanks to Mark Benge and Matt Smith, for their GUI Corner column that used to
appear in OS/2 Magazine, before it shut down. Their series on custom controls was
especially usefull.
- Thanks to IBM for creating such a great operating system.
Back
Last updated March 9, 1998. <><
Created by Hubert Chan.
|


{{ partial "header.html" . }} {{ partial "navigation.html" . }}
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{% extends "page.html" %} {% block content %} Login screen goes here. hi {{name}}, you've viewed this page {{ views }} times this session


class Pool<in K, out V>
(source)
looks like a Map whose values are created automatically, and removed automatically.
If this Pool doesn't have the value, get will invoke valueSupplier. The values are referenced as SoftReference. GC may collect them.
Since
0.1.0
<init> |
looks like a Map whose values are created automatically, and removed automatically. Pool(valueSupplier: (K) -> V) |
get |
operator fun get(key: K): V |


Every field, by default, will be mapped as string.
If you want to force a string for the config value, you can wrap the value with double quotes.
"my_string"
my_string
"hello world"
{ config_key: "hello world" }
config_key: hello world
[ 'config_key' => 'hello world' ]
Prefix the value with a #
character.
#34
#20.5
#20.5
{ config_key: 20.5 }
config_key: 20.5
[ 'config_key' => 20.5 ]
Simply use the true
and false
keywords.
true
false
true
{ config_key: true }
config_key: true
[ 'config_key' => true ]
Use the square bracket form to represent an array.
You can use nested arrays if needed
For now, dictionaries/associative arrays aren't supported.
PHP exports: If you use PHP exports with define
statements, the config value will be returned as serialized arrays (using native PHP serialization mechanism) since PHP's define()
function doesn't support array as values.
[my_string_value, my_other_string-value]
[#14, #56]
[[#14, #56], ["my_val", "another_val"]]
[[#14, #56], ["my_val", "another_val"]]
{ config_key: [ [ 14, 56 ], [ "my_val", "another_val" ] ] }
config_key: - - 14 - 56 - - my_val - another_val
[ 'config_key' => [ [ 14, 56 ], [ "my_val", "another_val" ] ] ]
Use the null
word. The case is insensitive (i.e. both null
and NULL
are accepted).
null
NULL
null
{ config_key: null }
config_key: null
[ 'config_key' => null ]


THE MOUSETRAP
November 6-9, 1980 at the Arabian Theatre
Director - John Sumrall
Assistant to the Director - David Jones
THE CAST
Mollie - Patricia Brown
Giles - Bill Thames
Christopher Wren - Douglas Barry
Mrs. Boyle - Dot Traweek
Major Metcalf - Wess Hughes
Miss Casewell - Pam Morgan
Mr. Paravicini - Kaleel Rahaim
Detective Trotter - Bob Lowe


{% extends "base.html" %} {% block title %}
Medieval catalogues > NEW FRIARS > Norwich-Carmelite > Leland, c. 1536-40
{% endif %}4 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


Items in search results
50PCS NE555 555 DIP-8 IC Timers NEW GOOD QUALITY
48,913.04
VND
or Best Offer
Free international shipping
138 sold


Software Engineering
(
français
)
The user during requirements gathering
The user after he has read the analysis report
The user after he has read design and maintenance
The user when the system goes into production
The end


This is my first go at markdown, octopress, AND yoga teaching. A lot of firsts in this blog post.
Bring your feet together, toes and heels touch. Firm your legs from your ankles to your hips. Firm your lower belly, pulling it up and in. Keep this sensation the same for the full breathing sequence.
Good, now interlace your fingers of both hands, cross your thumbs. Place your knuckles below your chin, thumbs touch the throat. Maintain contact between fingers and chin for the full ten breaths. Let’s begin.
Inhale deeply through the nose. Lift the elbows out and and up to frame the face. Shoulders relaxed, belly firm and pull toward the spine.
 Press the head back with the hands, elbows drawing an arc at shoulder height as you bring them to touch
Inhale.  Try to feel the breath at the back of your throat. This will help make the beautiful oceanic sound we’re looking for.
Exhale.  Like the fire from the belly of a dragon.
Inhale. 1 2 Firm legs, strong belly. 3, 4, Draw the chest taller, 6. Pause.
Exhale, head back. 6 5 4 look back, chest high, 3 2 1.
Inhale, smooth and even. Do your best to inhale the full six counts with every breath. Expand your ribs, chest, and let the lungs stretch.
Exhale, release the breath fully. Don’t whoosh, make this exhale as even as your inhale, a full six seconds. Elbows touch just as you press out the last puffs of air.
Inhale, our goal is to drop our head down and move our elbows up at exactly the same time. Smooth, even over the full six counts.
Exhale, as we press our hands firmly into our chin we work to bring the elbows forward to touch without dropping below the shoulders. Again, take the full six counts, lungs empty, bully squeezing, elbows touch.
Inhale, 1 2 3 legs firm, 4 5 shoulder’s relaxed. 6
Exhale, 6 stretch the mouth, 5 4 huge oceanic wave sound 3 2 elbows touch 1.
Inhale, keep the legs firm, weight in the heels. Solid. Belly firm and up, arms up shoulder’s relaxed.
Exhale, the only parts of our body that change are the arms, belly, and head. Legs remain firm, belly pulls up and in, arms squeeze.
Inhale, these deep full body breaths expand the lung tissue, connect our mind to our body, and assist to warm up and aclimate the body to the heat.
Exhale, keeping the large muscles of our legs and belly held firm during the poses builds up carbon dioxide in our bodies. These extended exhales helps us to alleviate that excess CO2 so our muscles can continue to work.
Last two breaths, Inhale. Let’s make these the fullest breaths so far. Drive the weight back into the heels, pull up through every muscle in the legs, arms way up.
Exhale, really stretch the mouth wide. Big oceanic exhale as we use the abdominal muscles pulling in and up to press out all our air. Elbows high, eyes back.
Inhale, full 5 counts, 1, belly in, 2, eyes focused, 3, get taller, 4, 5, 6.
Last Exhale, 6 press head back, 5, arms move smooth, 4, eyes back, 3, belly up and in, 2, 1. CLAP Change.
Slowly bring your body back to neutral, weight firm over your heels, eyes on your own eyes in the mirror. Try to keep your breath cadence nice and even. Resist the urge to twitch, or fidget. Let the shoulders relax, heart slightly up, bum and tail pulling slightly down.
Let’s get ready for our second set. Hands interlaced, up to your chins. Here we go, let’s make this one even more beautiful.


Test importing a template by its id
Start page
/template1
/template2
Template1 and template2 are defined in the same import
This is start page


HATCHING SOON...
Sorry, but it isn't the time for me to come out.
This page is under construction
Go Back


simple Google Sheets API demo
The input parameters are the spreadsheet ID and (optionally) an indivual sheet name
spreadsheet ID
sheet name
get all sheet data
get data from single sheet
Results


Hello there, people from the AP Exam. We are Pranav and Yogansh, the ones who designed this webpage. If you are seeing this it is probably past the deadline for the AP Computer Science Create Task. For this project we created a student "cheat sheet", with notes from all we learnt throughout 9th grade at our high school. These notes come from the core classes, Science, Math, English, and History. There are also possibly other notes from our Language classes ( Spanish and French ) and Band.
This webpage contains everything you need. If you were a student in the modern age, I am sure that you must've searched online for a formula or calculator or asked help with a question, but the websites aren't trustworthy so you could end up with a wrong answer. Well, We have managed to create a site where each core class can be found with its tips on each allocated page for its given subject.
These are the citations from which we got some information that we used on our webpage
Is there anything we should add, change, or remove? tell us in the form below.
Feedback ( Anonymous )


WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
FOR:
Havemann, Doug (Self), Nixon, TX
McGeary, Judith Executive Director (also providing written testimony) (Farm and Ranch
Freedom Alliance), Cameron, TX
Registering, but not testifying:
FOR:
Canepa, Alex (Self; Sustainable Food Center), Austin, TX
Gallo, Amy Treasurer (Self; Central Texas Young Farmers Coalition), Austin, TX
Holcomb, Gina (Self), Coldspring, TX
Holcomb, Terry SREC SD3 (Self), Coldspring, TX
Masters, Kelley (Self), Cedar Park, TX
Morris, Joe (Texas Poultry Federation), Austin, TX
Provence, Leslie (Self; Food Policy Council of San Antonio), San Antonio, TX
Rhinehart, Aaron (Self), Georgetown, TX
Schumann, Peyton Director of Government Affairs (Texas and Southwestern Cattle Raisers
Association), Austin, TX
Turner, Robert (Self; Texas poultry federation), Voss, TX
Vaughn, Jason (Self), Round Rock, TX
Winegarner, Josh (Texas Cattle Feeders Association), Amarillo, TX
ON:
Pahl, Stephen Associate Commissioner (DSHS), Austin, TX
FOR:
Murphy, Kate Senior Policy Associate (also providing written testimony) (Texans Care for
Children), Austin, TX
Registering, but not testifying:
FOR:
Clayton, Molly (Self; Texas Campaign to Prevent Teen Pregnancy), Austin, TX
Doerge, Kaitlyn Policy coordinator (Texas Pediatric Society), Austin, Tx, TX
Flores, Jose Youth Justice Policy Analyst (Texas Criminal Justice Coalition), Austin, TX
Francis, Will (National Association of Social Workers - Texas Chapter), Austin, TX
Guy, Linda RN,PNP (Self), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Kimberly, Knox SVP - Advocacy and Stakeholder Relations (Upbring), Austin, TX
Klose, Joanna (Self), Austin, TX
Mares, Bryan Public Policy Fellow (Texas CASA), Austin, TX
Ramirez, Erika Director of Policy and Advocacy (Texas Women's Healthcare Coalition),
San Antonio, TX
Rose, Lauren Director of Public Policy (Texas Network of Youth Servics), Austin, TX
Ryan, Shellie CPS Policy Manager (Texas Council on Family Violence), Austin, TX
Sauceda, Nataly Mental Health Policy Fellow (United Ways of Texas), Austin, TX
Vunderink, Gregg (Self), TX, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Yanas, Christine Director of Govt. Affairs (Methodist Healthcare Ministries of South Texas,
Inc.), San Antonio, TX
ON:
Blackstone, Kristene Associate Commissioner (DFPS), Austin, TX
Providing written testimony:
FOR:
Lucy, Jennifer Director of Public Policy (TexProtects), Austin, TX
Registering, but not testifying:
FOR:
Finley, Marisa (Baylor Scott & White Health), Austin, TX
Kroll, Carrie Vice President Advocacy, Quality and Health Policy (Texas Hospital
Association), Austin, TX
Villareal, Gabriela (Texas Conference of Urban Counties), Austin, TX
Registering, but not testifying:
FOR:
Borel, Dennis (Coalition of Texans with Disabilities), Austin, TX
Francis, Will (National Association of Social Workers - Texas Chapter), Austin, TX
Guy, Linda RN,PNP (Self), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Masey, Chris Advocacy Director (Coalition of Texans with Disabilities), Austin, TX
Rice, Leela (Texas Council of Community Centers), Austin, TX
Yanas, Christine Director of Govt. Affairs (Methodist Healthcare Ministries of South Texas,
Inc.), San Antonio, TX
FOR:
Francis, Will Government Relations Director (NASW), Austin, TX
Registering, but not testifying:
FOR:
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Mares, Bryan Public Policy Fellow (Texas CASA), Austin, TX
Murphy, Kate Senior child welfare policy associate (Texans care for children), Austin, TX
Rose, Lauren Director of Public Policy (Texas Network of Youth Servics), Austin, TX
ON:
Blackstone, Kristene Associate Commissioner (DFPS), Austin, TX
Carmical, Audrey General Counsel (DFPS), Austin, TX
FOR:
Richardson, Crissie RN (Texas Emergency Nurses Association), Salado, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Registering, but not testifying:
FOR:
Cates, Andrew General Counsel (Texas Nurses Association), Austin, TX
Stewart, Kevin (Texas Nurse Practitioners), Austin, TX
Vunderink, Gregg (Self), TX, TX
AGAINST:
Holzheauser, Craig (Texas EMS Alliance), Austin, TX
Richardson, Elise (Texas Ambulance Association), Austin, TX
Registering, but not testifying:
FOR:
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Ryan, Shellie CPS Policy Manager (Texas Council on Family Violence), Austin, TX
FOR:
Haskins, Jessica Pharmacist (also providing written testimony) (NACDS and Walgreens),
Austin, TX
AGAINST:
McGarity, Kristen Dr. (also providing written testimony) (Self), Kyle, TX
Registering, but not testifying:
FOR:
Belcher, Nora Executive Director (Texas e-Health Alliance), Austin, TX
Boston, Jessica (Texas Association of Business), Austin, TX
Campos, Dya H-E-B Director Government Affairs (H-E-B), San Antonio, TX
Capelo, Jaime (Alliance of Independent Pharmacists), Austin, TX
Carter, Janis (National Association of Chain Drug Stores), Austin, TX
Galligher, Duane (Association of Substance Abuse Programs), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Hausenfluck, Amber (Walgreens), Austin, TX
Humphrey, Cynthia Executive Director (Self; Assoc of Substance Abuse Programs),
Kerrville, TX
Morales, Daniel (Self; Walmart), Austin, TX
Reagan, Karen (TexasPharmact Association), Austin, TX
Shields, Bradford (Texas Federation of Drug Stores), Austin, TX
Shields, Bradford (Texas Society of Health-System Pharmacists), Austin, TX
Vane, Mark (HB Strategies), Austin, TX
ON:
Arnold, Kerstin General Counsel (Texas State Board of Pharmacy), Austin, TX
Kennnedy, Kris Consumer Protection Division Deputy Chief (Office of the Attorney
General), Austin, TX
FOR:
Warren, Kevin President/CEO (Texas Health Care Association), Austin, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Registering, but not testifying:
FOR:
Capelo, Jaime (Texas Assisted Living Association), Austin, TX
Meyer, Alyse (LeadingAge TX), Austin, TX
ON:
Conces, Diana Director LTCR Policy (HHSC), Austin, TX
FOR:
Martin, Elln PHD, RN Director of Practice (Texas Nurses Association), Austin, TX
Registering, but not testifying:
FOR:
Cates, Andrew General Counsel (Texas Nurses Association), Austin, TX
Guy, Linda RN,PNP (Self), Austin, TX
Martin, Ellen PhD, RN (Self; Texas Nurses Assn), Austin, TX
Meyer, Alyse (LeadingAge TX), Austin, TX
Stewart, Kevin (Texas Nurse Practitioners), Austin, TX
Stewart, Kevin (Texas Emergency Nurses Association), Austin, TX
Zolmierek, Cindy PHD, RN (Self, Nursing Legislative Agenda), Austin, TX
FOR:
Lozier, Elizabeth (also providing written testimony) (Self), Austin, TX
Registering, but not testifying:
FOR:
Copeland, Megan Parent (Self), Round Rock, TX
Hardy, Lisa (Self), Argyle, TX
Meroney, Shannon Consultant (Self), Austin, TX
Registering, but not testifying:
ON:
Shaw, Jean Associate Commissioner (HHSC), Austin, TX
Providing written testimony:
FOR:
Chasteen, Kara Burnet County Republican Party Chair (Self), Bertram, TX
Registering, but not testifying:
AGAINST:
Reed, Ender Legislative Coordinator (Harris County Commissioners Court), Houston, TX
Sparks, Clifford Legislative Director (City of Dallas), Dallas, TX
Weist, Jon Legislative Officer at the City of Irving (The City of Irving), Irving, TX
Wright, Christine Legislative Mgr (City of San Antonio), San Antonio, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Registering, but not testifying:
FOR:
Hench, Marina Senior Director, Government Relations - Texas (American Cancer Society
Cancer Action Network), Austin, TX
AGAINST:
Bales, David Chairman (also providing written testimony) (Texans for Cures), Austin, TX
FOR:
Steinhauer, William Pediatric Dentist (also providing written testimony) (Texas Academy
of Pediatric Dentistry), San Antonio, TX
Registering, but not testifying:
FOR:
Guy, Linda RN,PNP (Self), Austin, TX
AGAINST:
Mills, Sarah Director of Governmental Relations (Texas Association for Home Care and
Hospice), Austin, Texas, TX
ON:
Conces, Diana Director LTCR Policy (HHSC), Austin, TX
Registering, but not testifying:
FOR:
Ashley, Price (Texas College of Emergency Physicians), Austin, TX
Belcher, Nora Executive Director (Texas e-Health Alliance), Austin, TX
Carter, Janis (National Association of Chain Drug Stores), Austin, TX
Choate, Elizabeth Director of Government Relations (Texas Veterinary Medical
Association (TVMA)), Austin, TX
Finch, Dan (Texas Medical Association), Austin, TX
Finley, Marisa (Baylor Scott & White Health), Austin, TX
Galligher, Duane (Association of Substance Abuse Programs), Austin, TX
Gonzalez, Sara VP Advocacy and public policy (Texas Hospital Association), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Humphrey, Cynthia Executive Director (Self; Assoc of Substance Abuse Programs),
Kerrville, TX
ON:
Arnold, Kerstin General Counsel (Texas State Board of Pharmacy), Austin, TX
Registering, but not testifying:
FOR:
Ashley, Price (Texas College of Emergency Physicians), Austin, TX
Belcher, Nora Executive Director (Texas e-Health Alliance), Austin, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Boston, Jessica (Texas Association of Business), Austin, TX
Chepkauskas, Daniel (Texas Pain Society), Austin, TX
Finch, Dan (Texas Medical Association), Austin, TX
Finley, Marisa (Baylor Scott & White Health), Austin, TX
Flores, Jose Youth Justice Policy Analyst (Texas Criminal Justice Coalition), Austin, TX
Francis, Will (National Association of Social Workers - Texas Chapter), Austin, TX
Galligher, Duane (Association of Substance Abuse Programs), Austin, TX
Gonzalez, Sara VP Advocacy and public policy (Texas Hospital Association), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Humphrey, Cynthia Executive Director (Self; Assoc of Substance Abuse Programs),
Kerrville, TX
Rice, Leela (Texas Council of Community Centers), Austin, TX
Sauceda, Nataly Mental Health Policy Fellow (United Ways of Texas), Austin, TX
Vane, Mark (HB Strategies), Austin, TX
Yanas, Christine Director of Govt. Affairs (Methodist Healthcare Ministries of South Texas,
Inc.), San Antonio, TX
ON:
Fitzwater, Brad Medical Director SUD (HHSC), Austin, TX
Ramierez, Lisa Director TTDR (HHSC), Austin, TX
Registering, but not testifying:
FOR:
Gregg, Aaron (Alzheimer's Association), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Richardson, Elise (AARP), Austin, TX
ON:
Hart, Kay Director (HHSC), Austin, TX
Wold, Kez Associate Commissioner (DFPS), Austin, TX
FOR:
Ansley, Tracy (Self), Kaufman, TX
Bearden, Chase Deputy Executive Director (Coalition of Texans with Disabilities), Austin,
TX
Carrasco, John David CEO (Caunlir and Texas Hemp Business Alliance), Dallas, TX
Fazio, Heather Director (Texans for Responsible Marijuana Policy), Austin, TX
Fox, Edward Director, MS Clinic of Cent TX (Self), Round Rock, TX
Fuller, Jaclynn (also providing written testimony) (Self), Round Rock, TX
Lil, Ann Founder (RAMP), Houston, TX
Lindeen, Piper (Self and Family), Sugar Land, TX
Marks, Robert Medical Doctor (Texas Patients), Austin, TX
Meredith, Shawn (also providing written testimony) (Self), Austin, TX
Novacek, Amy (Self and Blake Novacek), Joshua, TX
Novacek, Jay (Blake Novacek and Self), Joshua, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Patterson, Julia Ms. (Self), Round Top, TX
Seggelink, Lance (also providing written testimony) (Self), Austin, TX
Seggelink, Thalia Co-Founder (Self and Mothers Advocating For Medical Marijuana For
Autism), Austin, TX
Vaughn, Jason Policy Director (Texas Young Republicans), Round Rock, TX
ON:
Amadio, Jordan Neurosurgeon (Self), Austin, TX
Ann Cambron, Jennifer (Self), Austin, TX
Crook, Keith Major USAF (also providing written testimony) (Self), New Braunfels, TX
Finkel, Jaclyn Executive Director (also providing written testimony) (Texas Chapter of
National Organization for the Reform of Marijuana Laws), Austin, TX
Fowler, Gregory (also providing written testimony) (Tourette Texas), Midway, TX
Garza, Chelsea (Cullen Garza), Humble, TX
Registering, but not testifying:
FOR:
Ansley, Tracy BioChemist (Self), Kaufman, TX
Borel, Dennis (Coalition of Texans with Disabilities), Austin, TX
Branch, Debbie Mrs. (Self; MAMMA), League City, TX
Carriker, Terri (MAMMA and Self), Austin, TX
Fawell, Amy (Self), Austin, TX
Franklin,RPh, David Registered Pharmacist (Self), Spring, TX
French, Melissa (Self; Tx'Mamma), Austin, TX
Guy, Linda RN,PNP (Self), Austin, TX
Holcomb, Gina (Self), Coldspring, TX
Jensen, Bonnie (Self; MAMMA), League City, TX
Josey, Jeffrey (Self; MAMMA), Burleson, TX
Josey, Suzanne (Self; MAMMA), Burleson, TX
Klose, Joanna (Self), Austin, TX
Lindeen, Piper (Self), Sugar Land, TX
Masey, Chris Advocacy Director (Coalition of Texans with Disabilities), Austin, TX
Nichols-Segers, Simone (National MS Society), Austin, TX
Perez, Rene Coalition Liaison (Libertarian Party of Texas), Arlington, TX
Qiao, Zhi (Self), Austin, TX
Rogers, Allison (Self), Austin, TX
Selby, Mary (Self), Austin, TX
Speasmauer, Alycia Policy Associate (Texas Criminal Justice Coalition), Austin, TX
Vaughn, Jason (Self; Texas Young Republicans), Round Rock, TX
Vunderink, Gregg (Self), TX, TX
Weatherby, Cinde Voting Rights/Elections Issue Chair (Self; League of Women Voters of
Texas), Austin, TX
ON:
Alexander, Jay Major (Texas Department of Public Safety), Austin, tx, TX
Alexander, Jay Major (Texas Department of Public), Austin, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Alexander, Troy Director for Legislative Affairs (Texas Medical Association), Austin, TX
Providing written testimony:
FOR:
Branch, Debbie Mrs. (Myself and MAMMA), League City, TX
French, Melissa (TX Mamma), Austin, TX
Jawell, Comy Mom (My Child, Jack Fawell), Austin, TX
Jensen, Bonnie (Mothers Advocating Medical Marijuana for Autism), League City, TX
Josey, Jeffrey (Self), Burleson, TX
Josey, Suzanne (Self), Burleson, TX
Pitts Jr., John (Epicepsy Foundation of Texas), Austin, TX
Reeves, Karen Cannabis Activist (Self, CenTex Community Outreach), Hewitt, TX
Rivera, Magra Legislative Liaison (Mamma), McAllen, TX
Stanley, Stephanie (My Son, Mothers Advocating Medical Marijuana for Autism), Flint,
TX
Registering, but not testifying:
FOR:
Brown, Sabrina Lobbyist (D&S Residential), Austin, TX
Frizzell Batton, Sandra Executive Director (Providers Alliance for Community Services of
Texas (PACSTX)), Austin, TX
Hernandez, Richard Director of Government Relations (Self; BrightSpring Healthcare
Services), Austin, TX
Nicholes, Laura (Private Providers Association of Texas), Austin, TX
Rice, Leela (Texas Council of Community Centers), Austin, TX
Rose, Denise (ResCare), Austin, TX
Walker, Nancy (ResCare), Austin, TX
ON:
Conces, Diana Director LTCR Policy (HHSC), Austin, TX
FOR:
Brown, Andrew Director, Center for Families and Children (also providing written
testimony) (Texas Public Policy Foundation), Austin, TX
Francis, Will Government Relations Director (National Association of Social Worker-Texas
Chapter), Austin, TX
Registering, but not testifying:
FOR:
Crockett, Sarah Public Policy Coordinator (Texas CASA), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Murphy, Kate Senior child welfare policy associate (Texans care for children), Austin, TX
ON:
Blackstone, Kristene Associate Commissioner (DFPS), Austin, TX
Carmical, Audrey General Counsel (DFPS), Austin, TX
Jones, Alyssa (Texas Alliance of Child and Family Services), Austin/TX, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Registering, but not testifying:
ON:
Shaw, Jean Associate Commissioner (HHSC), Austin, TX
FOR:
Robinson, Ross Executive Director (Hill Country Mental Health Developmental Disability
Centers), Kerrville, TX
Registering, but not testifying:
FOR:
Galligher, Duane (Association of Substance Abuse Programs), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Humphrey, Cynthia Executive Director (Self; Assoc of Substance Abuse Programs),
Kerrville, TX
Johnson, Lee (Txas Council of Community Centers), Austin, TX
Yanas, Christine Director of Govt. Affairs (Methodist Healthcare Ministries of South Texas,
Inc.), San Antonio, TX
AGAINST:
Baskin, Steve Executive Director (also providing written testimony) (Camp Champions,
The Outdoor School), Marble Falls, TX
Brysan, William Advisory Board Member (also providing written testimony) (Capitol Area
Council, Boy Scouts of America), Austin, TX
Dawdy, Danny Execuitve Director (also providing written testimony) (Highland Lakes
Camp, TX Baptist Camp Assn.), Spicewood, TX
Eastland, Britt Activities and Legal Affairs Manager (also providing written testimony)
(Camp Mystic), Hunt, TX
Robertson, Bin Director/Owner (Camp Longhorn), Burnet, TX
ON:
Pahl, Stephen Associate Commissioner (DSHS), Austin, TX
Registering, but not testifying:
AGAINST:
Hillis, Ereckson (Self; Camp Champions), Austin, TX
Registering, but not testifying:
FOR:
Belcher, Nora Executive Director (Texas e-Health Alliance), Austin, TX
Borel, Dennis (Coalition of Texans with Disabilities), Austin, TX
Finch, Dan (Texas Medical Association), Austin, TX
Gonzalez, Sara VP Advocacy and public policy (Texas Hospital Association), Austin, TX
Hansch, Greg (National Alliance on Mental Illness (NAMI) Texas), Austin, TX
Rice, Leela (Texas Council of Community Centers), Austin, TX
WITNESS LIST
Health & Human Services
May 17, 2019 8:00 AM
Yanas, Christine Director of Govt. Affairs (Methodist Healthcare Ministries of South Texas,
Inc.), San Antonio, TX


Your browser does not support iframes. You can view the dynmap by visiting dynmap.mcironridge.com/. While you're clicking on things, have you considered installing Google Chrome?


3 writes to _pipeline
Microsoft.PowerShell.ConsoleHost (3)
host\msh\Executor.cs (3)
186
_pipeline
= tempPipeline;
400
_pipeline
= tempPipeline;
620
_pipeline
= null;
6 references to _pipeline
Microsoft.PowerShell.ConsoleHost (6)
host\msh\Executor.cs (6)
185
Dbg.Assert(
_pipeline
== null, "no other pipeline should exist");
399
Dbg.Assert(
_pipeline
== null, "no other pipeline should exist");
576
if (
_pipeline
!= null && !_cancelled)
585
_pipeline
.Stop();
592
RemotePipeline remotePipeline =
_pipeline
as RemotePipeline;
605
RemotePipeline remotePipeline =
_pipeline
as RemotePipeline;


Try to answer the following code related questions within the time limit. Keep in mind that incorrect answers will penalize your score by 2 points and time by 5 seconds. And for every correct answer you will gain 5 points.
Try to answer the following code related questions within the time limit. Keep in mind that incorrect answers will penalize your score by 2 points and time by 5 seconds. And for every correct answer you will gain 5 points.


Evaluates the probability density function of the sum of the Gaussian and Student's t density on 3 degrees of freedom. Evaluates the p.d.f. of the sphered Student's t density function. Also evaluates the erf, and erfc functions on complex-valued arguments. Thanks to Krishna Myneni the function is calculates the Faddeeva function also!
| Version: | 1.0-3 |
| Depends: | R (≥ 2.0) |
| Published: | 2012-10-31 |
| Author: | Guy Nason |
| Maintainer: | Guy Nason <g.p.nason at bristol.ac.uk> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | NORMT3 results |
| Reference manual: | NORMT3.pdf |
| Package source: | NORMT3_1.0-3.tar.gz |
| Windows binaries: | r-devel: NORMT3_1.0-3.zip, r-release: NORMT3_1.0-3.zip, r-oldrel: NORMT3_1.0-3.zip |
| OS X binaries: | r-release: NORMT3_1.0-3.tgz, r-oldrel: NORMT3_1.0-3.tgz |
| Old sources: | NORMT3 archive |
Please use the canonical form https://CRAN.R-project.org/package=NORMT3 to link to this page.


If you know how a Caesar Cipher works. It is easy to break because, due to the fact it is a monoalphabetic cipher, there are only 25 possible answers. The Vignere Cipher is a monoalphabetic cipher and is more secure than a Caesar Cipher.
Blaise de Vignere was a 16th century French cryptographer. He proposed this polyalphabetic cipher to King Henry III.
Instead of using a number to shift the whole alphabet, the Vignere cipher requires a keyword.
Each letter of the message is shifted by the corresponding letter of the keyword. Lets go through an example:
This Vignere square can be used to help encode and decode, instead of counting letters.
Find the letter in the message on the right edge of the square and the letter of the key on the top, where the two rows intersect is the encrypted letter.


This is level 1 heading
This is level 2 heading
This is level 3 heading
This is level 4 heading
This is level 5 heading
This is level 6 heading
This text is before the logical division
This is a logical division
This is text after logical division in the same line!
This is a paragraph
- list item one
- list item two
- list item three
- list item one
- list item two
- list item three


Hover over me
The Lodge meets regularly to conduct the usual formal business of any small organisation (pay bills, organise social and charitable events, elect new members, etc.).


Check your email
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--- title: StarTrek layout: game basic: hp2k program: STTR1 source: bas/hp2k/STTR1.bas help: bas/hp2k/STTR1.txt ---
Ah ~ memories of misspent youth, of afternoons in the Scottsdale Community College library, playing computer games on the teletype. Teletype? WTF? Well, it was 1975, and this was about as good as it got!


--chars [pairs of characters, as unicode points; i.e. char00, char01, char10, char11, char20,
char21{1,*}
-c
c: org.contentmine.cproject.args.StringPair
--divs expression [expression] ...{1,*}
-d
c:
--name tag1,tag2 [tag1,tag2 ....]{1,*}
-n
c: org.contentmine.cproject.args.StringPair
--pubstyle pub_code{1,1}
-p
c:
under early development and note also that publisher styles change and can be transferred between publishers and journals
Does not trigger any actions
--standalone boolean{0,1}
c: java.lang.Boolean
--strip [options to strip]{0,*}
-s
c:
tokens are whitespace-separated (sorry if this interacts with XPath) Examples:
some of this is hardcoded in HTMLCleaner and should be refactored
--tidy [HTML tidy option]{1,1}
-t
c:
--html [HTML tidy option, jsoup, jtidy, htmlunit]{1,1}
--xsl stylesheet{1,5}
-x
c:
--transform stylesheet{1,*}
Transform XML or HTML or PDF or other input
Relacement for --xsl
Argument may be a file/URL reference to a stylesheet, or a code from one of {nlm, jats, pdfbox, hocr2svg, pdf2txt, jsoup, [jtidy, htmlunit NYI]} the codes are checked first and then the document reference. Requires input and output files (--input and --output). These must be reserved names (e.g. fulltext.xml) in cmDir OR files in reserved directories and determine the type of files to convert.
--tag {0,*}
Default:
src/main/resources/org/contentmine/norma/pubstyle/sectionTagger.xml
Methods: parse: parseTag
c: java.lang.String
Adds section tags to scholarly.html
Not yet implemented
NOTE:
These examples contain some symbolic directory names.
$ctree
represent the top of a ctree (e.g.
PMC12345
in
PMC12345/fulltext.xml
)
$parent
represents the parent of one or more ctree
s, e.g.
(e.g.
my/files/
in
my/files/PMC12345/fulltext.xml
, my/files/PMC12349/fulltext.xml
)
*
is implicit and represents one or more ctree
s, e.g.
my/files/*/fulltext.xml
in
my/files/PMC12345/fulltext.xml
, my/files/PMC12349/fulltext.xml
)
wild*
is explicit and represents one or more ctree
s, e.g.
my/files/PMC1234*/fulltext.xml
in
my/files/PMC12345/fulltext.xml
, my/files/PMC12349/fulltext.xml
)
Input: $ctree/fulltext.xml
; Output: $ctree/scholarly.html
Convert a single ctree
containing NLM-XML to
scholarly HTML
norma -q $ctree -i fulltext.xml -o
scholarly.html --transform
nlm2html
Input: $parent/wildcard*/fulltext.xml
; Output: $parent/wildcard*/scholarly.pdf.txt
Use wildcard to select many matching ctree
s
containing NLM-XML and convert to scholarly HTML
norma -q ./plosone/PMC4382* -i fulltext.xml -o
scholarly.html --transform nlm2html
Input: $parent/*/fulltext.xml
; Output: $parent/*/scholarly.pdf.txt
Converting NLM-XML in multiple ctree
s to scholarly HTML
norma -q ./plosone/ -i fulltext.xml -o scholarly.html
--transform nlm2html
Input: $ctree/fulltext.pdf
; Output: $ctree/scholarly.pdf.txt
Convert a single
ctree
containing
fulltext.pdf
to plain text
norma -q ./peerjpdf/PMC4389270/ --transform pdf2txt -i
fulltext.pdf
-o fulltext.pdf.txt
Input: $parent/*/fulltext.pdf
; Output: $parent/*/scholarly.pdf.txt
Convert multiple ctree
s (children of
$parent
) containing
fulltext.pdf
to
plain text
norma -q ./peerjpdf/ --transform pdf2txt -i fulltext.pdf -o
fulltext.pdf.txt
Input: $ctree/fulltext.html
; Output: $ctree/tidied.html
Tidy raw HTML in $ctree
with htmlunit
norma -q https_peerj.com_articles_384/ -i fulltext.html -o
tidied.html --html htmlunit
Input: $ctree/fulltext.html
; Output: $ctree/tidied.html
Tidy raw HTML in $ctree
with jtidy
norma -q https_peerj.com_articles_384/ -i fulltext.html -o
tidied.html --html jtidy
Input: $ctree/fulltext.html
; Output: $ctree/tidied.html
Tidy raw HTML in $ctree
with jsoup
norma -q https_peerj.com_articles_384/ -i fulltext.html -o
tidied.html --html jsoup
Input: $parent/*/fulltext.html
; Output: $parent/*/tidied.html
tidy multiple child ctree
s of
$parent
using
jsoup
norma -q $parent -i fulltext.html -o tidied.html --html
jsoup


Discover a Space Between
Drawing Tool
About the Work
Random Color Highlighter
Random Color Highlighter
Random Color Highlighter


A vertical scrollable
Scrollable demo 2 / 11 : A vertical scrollable
Here we show you a vertical setup for a scrollable. There is a minor change compared to the minimal setup and it's recommended that you study that first before continuing. This scrollable has 4 elements in total and each element has 3 rows that are shown at a time. You can scroll these elements by:
- Clicking on the "more pictures" link.
- Using the up and down arrow keys.
- Using your mousewheel.
1. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
2. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
3. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
4. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
5. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
6. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
7. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
8. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
9. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
10. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
11. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
12. Barcelona Pavilion
The German Pavilion in Barcelona, designed by Ludwig Mies van der Rohe and built for the International Exposition in 1929.The Pavilion was not only a pioneer for construction forms with a fresh, disciplined understanding of space, but also for modeling new opportunities for an exciting association of free art and architecture.
HTML code
Our HTML structure is identical to the one presented in the "minimal setup" except that the items that are scrolled are more complex than just a single image. Also, the navigational buttons have been placed inside a wrapping DIV.

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1. Barcelona Pavilion

The Pavilion was not only a pioneer ...

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CSS code
We have used the following CSS file for the above setup. Here are the crucial parts from the CSS file that are required for a vertical scrollable:
/* root element for scrollable */
.scrollable {
/* required settings */
position:relative;
overflow:hidden;
/* vertical scrollables have typically larger height than width but not now */
height: 665px;
width: 700px;
}
/* root element for scrollable items */
.scrollable .items {
position:absolute;
/* this time we have very large space for the height */
height:20000em;
}
Note: in a horizontal scrollable the items are floating side by side by using the float: left property. In vertical scrollables this is not necessary.
JavaScript code
The scrollable is good at guessing whether its vertical or horizontal by investigating it's width and height. If the height is larger than width then the scrollable is vertical. This can also be manually configured with the vertical property which overrides the automatic determination.
We use the mousewheel configuration to enable scrolling with the mousewheel. You need to make sure that mousewheel tool is included on your page.

Note: this mousewheel tool is not included in the standard jQuery Tools distribution. You must download a custom combination that includes this tool.