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java.lang.Object org.apache.tomcat.jdbc.pool.JdbcInterceptor
public abstract class JdbcInterceptor
Abstract class that is to be extended for implementations of interceptors.
Everytime an operation is called on the Connection
object the
invoke(Object, Method, Object[])
method on the interceptor will be called.
Interceptors are useful to change or improve behavior of the connection pool.
Interceptors can receive a set of properties. Each sub class is responsible for parsing the properties during runtime when they
are needed or simply override the setProperties(Map)
method.
Properties arrive in a key-value pair of Strings as they were received through the configuration.
This method is called once per cached connection object when the object is first configured.
| Field Summary | |
|---|---|
static java.lang.String |
CLOSE_VAL
Connection.close() method name |
static java.lang.String |
EQUALS_VAL
Object.equals(Object) |
static java.lang.String |
GETCONNECTION_VAL
PooledConnection.getConnection() method name |
static java.lang.String |
HASHCODE_VAL
Object.hashCode() |
static java.lang.String |
ISCLOSED_VAL
Connection.isClosed() method name |
static java.lang.String |
ISVALID_VAL
Connection.isValid(int) method name |
static java.lang.String |
ISWRAPPERFOR_VAL
Wrapper.isWrapperFor(Class) method name |
private JdbcInterceptor |
next
The next interceptor in the chain |
protected java.util.Map<java.lang.String,PoolProperties.InterceptorProperty> |
properties
Properties for this interceptor. |
static java.lang.String |
TOSTRING_VAL
Object.toString() method name |
static java.lang.String |
UNWRAP_VAL
Wrapper.unwrap(Class) method name |
private boolean |
useEquals
Property that decides how we do string comparison, default is to use String.equals(Object) . |
| Constructor Summary | |
|---|---|
JdbcInterceptor()
Public constructor for instantation through reflection |
| Method Summary | |
|---|---|
boolean |
compare(java.lang.String methodName,
java.lang.reflect.Method method)
Compares a method name (String) to a method (Method) compare(String,String)
Uses reference comparison unless the useEquals property is set to true |
boolean |
compare(java.lang.String name1,
java.lang.String name2)
Performs a string comparison, using references unless the useEquals property is set to true. |
void |
disconnected(ConnectionPool parent,
PooledConnection con,
boolean finalizing)
Called when Connection.close() is called on the underlying connection. |
JdbcInterceptor |
getNext()
Returns the next interceptor in the chain |
java.util.Map<java.lang.String,PoolProperties.InterceptorProperty> |
getProperties()
Returns the properties configured for this interceptor |
java.lang.Object |
invoke(java.lang.Object proxy,
java.lang.reflect.Method method,
java.lang.Object[] args)
Gets invoked each time an operation on Connection is invoked. |
boolean |
isUseEquals()
|
void |
poolClosed(ConnectionPool pool)
This method is invoked by a connection pool when the pool is closed. |
void |
poolStarted(ConnectionPool pool)
This method is invoked by a connection pool when the pool is first started up, usually when the first connection is requested. |
abstract void |
reset(ConnectionPool parent,
PooledConnection con)
Gets called each time the connection is borrowed from the pool This means that if an interceptor holds a reference to the connection the interceptor can be reused for another connection. |
void |
setNext(JdbcInterceptor next)
configures the next interceptor in the chain |
void |
setProperties(java.util.Map<java.lang.String,PoolProperties.InterceptorProperty> properties)
Called during the creation of an interceptor The properties can be set during the configuration of an interceptor Override this method to perform type casts between string values and object properties |
void |
setUseEquals(boolean useEquals)
Set to true if string comparisons (for the compare(String, Method) and compare(String, String) methods) should use the Object.equals(Object) method
The default is false |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final java.lang.String CLOSE_VAL
Connection.close()
method name
public static final java.lang.String TOSTRING_VAL
Object.toString()
method name
public static final java.lang.String ISCLOSED_VAL
Connection.isClosed()
method name
public static final java.lang.String GETCONNECTION_VAL
PooledConnection.getConnection()
method name
public static final java.lang.String UNWRAP_VAL
Wrapper.unwrap(Class)
method name
public static final java.lang.String ISWRAPPERFOR_VAL
Wrapper.isWrapperFor(Class)
method name
public static final java.lang.String ISVALID_VAL
Connection.isValid(int)
method name
public static final java.lang.String EQUALS_VAL
Object.equals(Object)
public static final java.lang.String HASHCODE_VAL
Object.hashCode()
protected java.util.Map<java.lang.String,PoolProperties.InterceptorProperty> properties
private volatile JdbcInterceptor next
private boolean useEquals
String.equals(Object)
. If set to false
then the
equality operator (==) is used.
| Constructor Detail |
|---|
public JdbcInterceptor()
| Method Detail |
|---|
public java.lang.Object invoke(java.lang.Object proxy, java.lang.reflect.Method method, java.lang.Object[] args) throws java.lang.Throwable
Connection
is invoked.
invoke
in interface java.lang.reflect.InvocationHandler
java.lang.Throwable
public JdbcInterceptor getNext()
public void setNext(JdbcInterceptor next)
next
- public boolean compare(java.lang.String name1, java.lang.String name2)
name1
- name2
-
useEquals
public boolean compare(java.lang.String methodName, java.lang.reflect.Method method)
compare(String,String)
Uses reference comparison unless the useEquals property is set to true
methodName
- method
-
public abstract void reset(ConnectionPool parent, PooledConnection con)
parent
- - the connection pool owning the connectioncon
- - the pooled connectionpublic void disconnected(ConnectionPool parent, PooledConnection con, boolean finalizing)
Connection.close()
is called on the underlying connection.
This is to notify the interceptors, that the physical connection has been released.
Implementation of this method should be thought through with care, as no actions should trigger an exception.
parent
- - the connection pool that this connection belongs tocon
- - the pooled connection that holds this connectionfinalizing
- - if this connection is finalizing. True means that the pooled connection will not reconnect the underlying connectionpublic java.util.Map<java.lang.String,PoolProperties.InterceptorProperty> getProperties()
public void setProperties(java.util.Map<java.lang.String,PoolProperties.InterceptorProperty> properties)
properties
- public boolean isUseEquals()
public void setUseEquals(boolean useEquals)
compare(String, Method)
and compare(String, String)
methods) should use the Object.equals(Object) method
The default is false
useEquals
- public void poolClosed(ConnectionPool pool)
pool
- - the pool that is being closed.public void poolStarted(ConnectionPool pool)
pool
- - the pool that is being closed.
|
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Intro
Vue components create reusable chunks of code for constructing user interfaces.
Components can be
registered
with
Vue.component('my-component-name', { /* ... */ })
Props stands for "properties" and refers to information passed into a component .
A component may include a setup function setup(){..} which sets up the component with life-cycle hooks, computed properties and other facilities belonging to that component.
In the following example, a setup function concludes with a return statement exposing a variable missionList for use by its containing component.
setup() {
const missionList = [
{ id: 1, text: 'Write plain HTML fallback page' },
{ id: 2, text: 'Create unit tests' },
{ id: 3, text: 'Add style' },
{ id: 4, text: 'Draft app docs' }
]
return {missionList}
}
Note in the basic component example, the return from the setup function exposes this variable to the Vue instance.
Vue allows components to hold reactive values (or state) inside each individual invocation of that component. This state can record the changing condition of a component, eg counting button clicks or user input.
Vue.component('button-counter', {
setup(){
let count = value(0)
return {count}
},
template: `
<button v-on:click="count++">
You clicked {{ count }} times.
</button>
`
})


a PL emission spectra for zeolite-3A: 1.3 wt% Eu phosphors sintered at different temperatures, respectively. The inset shows the magnifying spectra. b photoluminescence excitation (PLE) and photoluminescence (PL) emission spectra for zeolite-3A: 1.3 wt% Eu phosphors sintered at 1400 °C for 3 h. The inset is the photograph of the sample with the illuminaton of a 365-nm UV lamp


If this file (index.html
) is edited, then the server will send a message
to the browser using Socket.IO telling it to refresh the page.
If either of the stylesheets (header.css
or styles.css
) are
edited, then the server will send a message to the browser using Socket.IO telling it to
dynamically reload the CSS, without refreshing the page.


When introduced on January 11th of 1957, the Bonneville was the fastest Pontiac ever produced. Zero to sixty took just 8.1 seconds to achieve. A Bonneville won the Grand National Championship with a top speed of 101. 6 mph and broke all existing records during NASCAR trials at Daytona. The 300+ horsepower fuel injected V8 engine changed the image of the Pontiac nameplate into one that was backed by performance.
The Pontiac Bonneville Convertible came with a $5782 sticker price, a rather high figure at the time. As a result only 630 examples were produced making them one of the rarest Pontiacs ever produced. Each dealership was given only one car to sell. Most of the cars were finished in white with red 'flashes'; a few were given custom colors.
At the 2006 auction, a '57 Bonneville sold for $115,000.


Licenses are the city’s way of keeping track of businesses. They establish a legal connection between businesses and their owners, and regulate certain types of commerce. The license fee helps cover the cost of services like police, fire.
Permits:Permits are proof that your building and facilities follow city rules ("code") and are safe for you, your employees, and your customers.
Inspections:As you complete each phase of construction, you'll need to have city inspectors visit to ensure that the work meets Long Beach's health and safety standards. Most inspections are performed before approval of a business license.
You can also apply by printing out this form below and submitting it to the city in-person or by mail. Visiting the Development Services Department on the 4th floor will ensure that you're able to ask and answer any questions that may come up as city staff review.
{{ cms:partial:shared/link_box:"http://www.longbeach.gov/finance/media-library/documents/business-info/business-licenses/business-license-application/":"DOWNLOAD":"Long Beach Business License Application (pdf)" }} Submit in-person or by mail: {{ cms:partial:shared/location_map:"City Of Long Beach Business License Application":"Fourth Floor City Hall 333 W. Ocean Boulevard, Long Beach, CA 90802" }}You are required to obtain an additional license for each business activity even if it is in the same location. For example, if you have a restaurant with a pool table you need two business license: Retailing (I classification) and Unique (M classification)
If you have more than one business license at the same location, you may choose to pay for all employees on one license. If so, you will pay for the employees on the license with the higher employee rate. (L.B.M.C 3.80.420.6)
| License Type | Base Tax | Per Employee Fee | Class |
|---|---|---|---|
| Construction Contractors | $348.68 | + $18.11 | A |
| Manufacturing | $348.68 | + $9.06 | B |
| Professionals | $348.68 | + $27.15 | E |
| Recreation and Entertainment | $348.68 | + $18.11 | F |
| Retail | $348.68 | + $8.97 | I |
| Services (salons, etc.) | $348.68 | + $18.11 | J |
| Wholesale | $348.68 | + $9.06 | L |
| Family Child Care | $174.37 | + $9.06 | _ |
| Home-based Business | $213.84 | + $9.06 | _ |
| Independent Contractors | $213.84 | + $18.11 | _ |
| License Type | Base Tax | Per item / service | Additional Info |
|---|---|---|---|
| Amusement Games / Machines | $581.17 | per machine / seat | |
| Billiards / Pool / Ping Pong Table | $129.13 | per table | |
| Bowling | $129.13 | per lane | |
| Consignment (Regulated) | $80.10 | per space | |
| Farmers Market | $77.48 | per stall | |
| Medical Transport | $129.13 | per vehicle | |
| Miscellaneous Mobile | $232.48 | per vehicle | |
| Mobile Food Vendor | $439.10 | per vehicle | |
| Movie or live theater | $.79 | per seat | |
| Outdoor Sales Event (Swap Meet) | $3,874.42 | +2.58 per stall | |
| Peddling | $129.13 | per day | $1,291.31 per year |
| Pedicab | $232.49 | initial pedicab | $20.86 each additional |
| Shoe Shine Stand | $129.13 | per stand | |
| Space Rental - Residential | $75.35 | flat rate | $33.07 per unit (additional) |
| Vehicle for hire (taxi, limo, etc) | $490.80 | per vehicle | |
| Vehicle rental agency | $129.13 | per vehicle |
If your business location is in another city and you plan to do work in Long Beach, you are required to purchase an ‘out of city’ business license. The City recognizes the work may not last an entire year and offers pro-rated term licenses in three month increments.
| Short Term License Type | Base Tax | Per Employee Fee |
|---|---|---|
| 3 month | $87.17 | + $4.53 |
| 6 month | $174.34 | + $9.06 |
| 9 month | $261.94 | + $13.58 |
| License Type | Base Tax | Per Item / Service |
|---|---|---|
| Booth Selling | $ 51.51 | Per booth, per day |
| Display / Selling Booth | $ 172.77 | Per booth |
Some business types require additional state licenses to operate. Examples include food preparation, alcohol sales, tobacco sales, or massage services. City staff can tell you what licenses your business might need in order to operate.
{{ cms:partial:shared/contact_box_combo:"(562<EMAIL_ADDRESS>}}Any person wishing to obtain an alcoholic beverage license should apply at the nearest office of the Department of Alcoholic Beverage Control. All parties concerned, applicants for new licenses or transferors (present licensees), and transferees (applicants) should be present. It is desirable for an employee of the Department to advise all applicants in person of pertinent laws, rules, and regulations. Also, in the case of a transfer, the transferee may desire information regarding a temporary retail permit. This is a state license.
{{ cms:partial:shared/link_box:"http://www.abc.ca.gov/":"READ":"ABC's website" }}The district office that covers long beach is located in the City of Lakewood
{{ cms:partial:shared/contact_box:"phone":"(562) 982-1337" }}Section 23987 of the Alcoholic Beverage Control Act requires the Department to mail a copy of each application to certain local officials. Section 23985 of the Act requires a 30-day posting period. Most investigations take approximately 45 to 50 days. The license can average about 75 days for a Person-to-Person transfer, and 90 days for an Original. Circumstances often result in a longer waiting period; therefore, before final approval and issuance of a license, caution should be used regarding extensive financial commitments, plans for grand openings, etc. If new construction or considerable remodeling is necessary, prior approval under Section 24044 should be sought before beginning any actual work.
All licenses are renewed on a 12 month basis. Renewals must be paid on or before the last day of the month posted on the license; no license will be transferred until the renewal fee is paid. If your renewal was paid on time, you will receive your renewal license approximately 4 to 6 weeks after the expiration date on your current license. Our License Query System (LQS) is updated soon after your expiration date. If your renewal was paid timely, the expiration date will update to show that your license does not expire until the following year. To access LQS, http://www.abc.ca.gov/datport/LQSMenu.html. It is recommended that you direct your distributors and those requiring proof of your license status to our query system. Many distributors and wholesalers already utilize this system regularly.
Each alcoholic beverage licensee must possess a seller's permit from the State Board of Equalization. It is a misdemeanor to sell in this State without such a permit.
{{ cms:partial:shared/link_box:"https://efile.boe.ca.gov/ereg/index.boe":"LEARN MORE":"Board of Equalization" }}At the time of filing, an applicant for the transfer of either an off-sale general license, on-sale general license, off-sale beer and wine license, on-sale beer and wine license, on-sale beer license, or on-sale general license for seasonal business, shall present a copy of the Notice of Intention to Transfer (see ABC-227 or ABC-227-A) to the County Recorder, which shall be certified by the County Recorder in the county where the licensed premises are located.
Businesses located in Business Improvement Districts (BIDs) must pay additional fees in order to pay for the extra services that the BIDs provide to businesses in these areas.
These fees are collected by the City and passed on to the BIDs for uses like street cleaning, special events, or local beautification. Learn more about BIDs here: Site Selection and Zoning
Annual licenses must be renewed every year. To verify your Business License you can call the hotline, email, or come in person to the Business License counter on the 4th floor of City Hall.
You can renew your license online by logging in with your e-account number. You can locate this number on your business license bill. There is a $5 processing fee.
{{ cms:partial:shared/link_box:"https://paydirect.link2gov.com/LBCbuslicense/ItemSearch":"PAY":"Renewal Fee Online" }}For annual licenses, a renewal notice is mailed to the license address 14 days before the due date. You are still responsible for payment even if you do not receive a notice. The renewal reminder letter from the city includes instructions on how to pay by mail.
If you change your mailing address during the year, contact the Business License Department to report the change.
You have 14 days before the due date to pay for the renewal and then a grace period of 30 days after the due date before a penalty is assessed.
You will be fined 25% of your amount due if you do not pay within 30 days of the due date. An additional 10% penalty will be added for each month the bill goes unpaid. The postmark date will determine whether or not your payment is delinquent.
"Term" licenses are not renewed automatically, and close at the end of the license term you paid for. You need to contact us via email, phone, or in person to request the account be reactivated, then pay the tax assessment to get the renewed license.
{{ cms:partial:shared/contact_box_combo:"(562<EMAIL_ADDRESS>}}All information on the Business License application is public record except information like your SSN, federal tax ID, and CDL number, all of which would not be provided in a public records act request. A subpoena would be required to obtain that information.
It depends. You would need to speak with Development Services, Health, or Fire depending on what would be required based on your business location and the type of business you would like to conduct. Those are the departments that will take time with plans, inspections, corrections, permits etc. Business license would not be able to answer on a timeline for those departments since it is not their area of expertise.
In order to receive your business license, all reviews, documentation, and inspections need to be completed. Once your Business License application is processed, pay the fees at the Cashier’s counter, bring the receipt back to the Business License counter for confirmation, and you will be issued your Business License.


Army rescue 350 stranded tourists, locals at Zojila Pass on Srinagar-Kargil-Leh highway
Visitors to the area got stuck as regions around the pass such as Sonamarg, Baltal and Nilgrar received heavy snowfall, since Thursday, which also led to traffic jams at many places.
by PTIAbout 350 tourists and locals, stranded at the Zojila pass due to heavy snowfall, have been rescued by army personnel, officials said on Friday.
Visitors to the area got stuck as regions around the pass such as Sonamarg, Baltal and Nilgrar received heavy snowfall since Thursday which also led to traffic jams at many places, they said.
“About 300-350 people, including women and children, were stranded at heights above 11,000 feet where temperature hovers around minus 7 degrees. A number of trucks and other vehicles got stuck in traffic.”
“In coordination with the civil administration, army rescue columns moved in vehicles promptly to rescue the stranded civilians from the Zojila Pass,” a senior army official said.
The army personnel also provided medical assistance, hot food and blankets to the affected people. The police and GREF (general reserve engineer force) took up the task of clearing the traffic and snow respectively, they said.
Zojila pass is situated at an altitude of 11,578 feet on the Srinagar-Kargil-Leh National Highway.


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Chapter 0: Advanced Time Series Analysis¶
Organized by: Benjur Emmanuel L. Borja, Sebastian C. Ibañez and Maria Eloisa M. Ventura
This notebook introduces us to the concept of time series, forecasting, and different fundamentals that we will use across the different chapters of our discussion. Specifically, this notebook will discuss:
Time Series
Forecasting
Stochastic Processes
The contents of this notebook is compiled from the following references:
C.Monterola, “Notebook 10 Time Series Forecasting Method — ARIMA MSDS2021”
C.Monterola, “Advanced Time Series Analysis - Recurrent Neural Networks — PhDinDS2023”
J.Valenzuela, “Advanced Time Series Analysis — PhDinDS2023”
# Required Libraries
import numpy as np
import numpy.random as rng
import pandas as pd
import scipy.stats as st
import matplotlib.pyplot as plt
import statsmodels.graphics.tsaplots as tg
%matplotlib inline
plt.rcParams['figure.figsize'] = [15, 2]
1. General Introduction¶
Most of us would have heard about the new buzz in the market i.e. Cryptocurrency. Many of us would have invested in their coins too. But, is investing money in such a volatile currency safe? How can we make sure that investing in these coins now would surely generate a healthy profit in the future? We can’t be sure but we can surely generate an approximate value based on the previous prices. Time series models is one way to predict them.
What is a time series?¶
A time series is a series of indexed values, where each value is an outcome of a random variable. In other words, a time series is one realization of a corresponding process.
An example of a time series is the weather timeseries dataset recorded at the Weather Station at the Max Planck Institute for Biogeochemistry in Jena, Germany. In this dataset, 14 different quantities (such air temperature, atmospheric pressure, humidity, wind direction, and so on) were recorded every 10 minutes, over several years. The original data goes back to 2003, but this example is limited to data from 2009–2016. This dataset is perfect for learning to work with numerical timeseries. We will be using this dataset to build our models. Here are the actual variables in the data:
Date Time
p (mbar) atmospheric pressure
T (degC) temperature
Tpot (K) potential temperature
Tdew (degC) dew point temperature
rh (%) relative humidity
VPmax (mbar) saturation water vapor pressure
VPact (mbar) actual water vapor pressure
VPdef (mbar) water vapor pressure deficit
sh (g/kg) specific humidity
H2OC (mmol/mol) water vapor concentration
rho (g/\(m^3\)) air density
wv (m/s) wind velocity
max. wv (m/s) maximum wind velocity
wd (deg) wind direction
Below is the temperature data from this dataset which we will be using frequently in the following chapters:
data = pd.read_csv('../data/jena_climate_2009_2016.csv')
data = data.iloc[:, 1:].astype(float).to_numpy()
temp = data[:, 1] #temperature (in degrees Celsius)
plt.figure(figsize=(15,2))
plt.plot(range(len(temp)), temp)
plt.ylabel('Temperature\n (degree Celcius)')
plt.xlabel('Time (every 10 mins)')
plt.show()
Time series is anything that has been recorded sequentially over a period of time – regular or irregular. In any case, the goal is to see if the observed patterns can help us forecast a period of time into the future.
2. Time Series Forecasting¶
Forecasting is predicting future values of our time series given all available information such as historical data and any possible variables that can influence the data. Besides Crypto Currencies, there are multiple important areas where time series forecasting is used for example : forecasting Sales, Call Volume in a Call Center, Solar activity, Ocean tides, Stock market behaviour, and many others. Assume the Manager of a hotel wants to predict how many visitors should he expect next year to accordingly adjust the hotel’s inventories and make a reasonable guess of the hotel’s revenue. Based on the data of the previous years/months/days, (S)he can use time series forecasting and get an approximate value of the visitors. Forecasted value of visitors will help the hotel to manage the resources and plan things accordingly.
There are a lot of things to consider before we forecast our time-series:
The kind of data (Trends, Seasonality, etc)
Factors that influence it (Multivariate Analysis, Causality, etc)
The amount of data available
Through out the following Chapters, we will discuss different forecasting methods how they are used. Each method have their own sets of assumptions and limitations, but the general idea is we assume that there is a fundamental mathematical process that defines our time-series. Understanding these mathematical processes helps us find the best method or algorithm (and the corresponding hyperparameters) to better forecast our time-series.
In the next section, we’ll discuss the first formulation of time-series data sets, Stochastic Processes.
3. Stochastic Processes¶
A stochastic, or random process (process hereon) is a series of indexed random variables. In the context of time series analysis, we’ll use \(t = 0, 1, 2, ...\), (discrete) time, as an index.
\(\lbrace \mathbf{X_0}, \mathbf{X_1}, \mathbf{X_2}, ...\rbrace\) denotes a process where \(\mathbf{X_0}, \mathbf{X_1}, \mathbf{X_2}, ...\) are random variables.
A time series, in this context, is a series of indexed values, where each value is an outcome of a random variable. In other words, a time series is one realization of a corresponding process.
Example 1: Bernoulli trials. Recall that a Bernoulli trial has two possible outcomes or realizations. Let’s take them to be \(\{-1, +1\}\). Let’s generate a few values:
bern_outcomes = [-1., 1.]
size = 100
# Generate 100 Bernoulli trials
flips = rng.choice(bern_outcomes, size=size)
series = pd.Series(flips)
fig, ax = plt.subplots(figsize=(15, 2))
series.plot(ax=ax) # here we implicitly index them; let the index stand for time
plt.show()
Example 2: Gaussian white noise. A Gaussian white noise process generates a time series in which each value was drawn from a Gaussian distribution with zero mean and unit variance: \(\bf{X_t} \sim Gaussian(0, 1)\):
size = 1000
wn = st.norm.rvs(size=size)
wn_series = pd.Series(wn)
fig, ax = plt.subplots(figsize=(15, 2))
wn_series.plot(ax=ax)
plt.show()
Gaussian white noise has the properties of being independent and identically-distributed, which leads some key properties for linear time series, to be brought up below.
Example 3: Brownian motion in one dimension. One-dimensional Brownian motion, or Wiener process, can be thought of as the cumulative sum of white noise time series. The value of the series at \(t\) is a sum of all preceding values, and can be thought of as a random walker’s displacement at time \(t\): \(X_t = X_{t_1} + X_{t-2} + ... = \sum_{i=0}^{t}{X_i}\).
size = 100000
bms = []
bms_msq = []
for i in range(100):
wn = st.norm.rvs(size=size)
bm = np.cumsum(wn)
bms.append(bm)
bms_msq.append([x**2 for x in bm])
bm_ave = np.mean(bms, axis=1)
bm_msq = np.mean(bms_msq, axis=1)
fig, ax = plt.subplots(figsize=(15, 2))
bms_series = pd.Series(bm_ave)
bms_msq_series = pd.Series(bm_msq)
bms_series.plot(label="Displacement", ax=ax)
bms_msq_series.plot(label="MSD", ax=ax)
plt.legend()
plt.show()
Example 4: Moving average. Given a white noise process (see above), a moving average process of order (\(q\)) can be generated by taking a weighted sum of the present and the previous \(q\) values of the white noise at each time \(t\):
\(X_t = \epsilon_t + a_1\epsilon_{t-1} + ... + a_q\epsilon_{t-q}\),
where \(\{\epsilon_t\} \sim WN(0, \sigma^2)\). We write \(\{X_t\} \sim MA(q)\). The \(MA\) process is an example of a linear process.
Let’s consider an \(MA(2)\) process with \(a_1 = 0.5\) and \(a_2 = 0.25\). Then
\(X_t = \epsilon_t + 0.5\epsilon_{t-1} + 0.25\epsilon_{t-2}\).
def ma2(wn):
x = []
# First element is just epsilon_t
x.append(wn[0])
# For the second element, let's take just the first coefficient (admittedly a judgment call)
x.append(0.5*wn[0] + wn[1])
# For the rest, use the full equation
for t in range(2, len(wn)):
x.append(wn[t] + 0.5*wn[t - 1] + 0.25*wn[t - 2])
return np.array(x)
size = 1000
wn = st.norm.rvs(size=size)
ma2_series = pd.Series(
ma2(wn)
)
wn_series = pd.Series(wn)
fig, ax = plt.subplots(figsize=(15, 2))
wn_series.plot(label="WN(0,1)", ax=ax)
ma2_series.plot(label=r"MA(2)", ax=ax)
plt.legend(loc="best")
plt.show()
Exercise 1: Autoregressive process. Given a white noise process (see above), a time series from an autoregressive process of order \(p\) can be generated by taking a weighted sum of the previous \(p\) values of the time series, plus white noise at each time \(t\):
\(X_t = b_{1}X_{t-1} + ... + b_{p}X_{t-p} + \epsilon_{t}\),
where \(\{\epsilon_t\} \sim WN(0, \sigma^2)\). We write \(\{X_t\} \sim MA(q)\).
Given an \(AR(2)\) process with \(b_1 = 0.6, b_2 = -0.2\), generate and plot a time series, similar to the \(MA(1)\) example above.
def ar2(wn):
x = []
# First element is just epsilon_t
x.append(wn[0])
# For the second element, let's take just the first coefficient (admittedly a judgment call)
x.append(0.6*x[0] + wn[1])
# For the rest, use the full equation
for t in range(2, len(wn)):
x.append(wn[t] + 0.6*x[t - 1] + -0.2*x[t - 2])
return np.array(x)
size = 1000
wn = st.norm.rvs(size=size)
ar2_series = pd.Series(
ar2(wn)
)
wn_series = pd.Series(wn)
fig, ax = plt.subplots(figsize=(15, 2))
wn_series.plot(label="WN(0,1)", ax=ax)
ar2_series.plot(label=r"AR(2)",ax=ax)
plt.legend(loc="best")
plt.show()
Stationarity¶
Consider a process \(\{X_t\}\), with corresponding joint distribution \(P(X_{t_1 + \tau}, X_{t_2 + \tau}, ..., X_{t_n + \tau})\) at times \(t_1 + \tau, t_2 + \tau, ..., t_n + \tau\).
This process is stationary if:
\(P(X_{t_1 + \tau}, X_{t_2 + \tau}, ..., X_{t_n + \tau}) = P(X_{t_1}, X_{t_2}, ..., X_{t_n})\) for \(\tau, t_1 ... t_n\) and \(n\).
Essentially, the statistics of a stationary process is invariant to time-translation.
\(\{X_t\}\) is weakly stationary if:
(i) \(\mu(X_t) = \mu(X_{t + \tau}) = \mu\) for all \(\tau\),
(ii) \(\Gamma(t_1, t_2) = \Gamma(t_1 - t_2)\), and
(iii) \(E[|X_t|^2] < \infty\).
In the above, \(\Gamma(t_1, t_2)\) is the autocovariance, defined by:
\(\Gamma(t_1, t_2) = cov~(t_1, t_2) = E\left[(X_{t_1} - \mu(X_{t_1}))(X_{t_2} - \mu(X_{t_2}))\right]\)
Weak stationarity implies that the lag, \(\tau\), can be used to parametrize the autocovariance, leading to:
\(\Gamma(\tau) = E\left[(X_t - \mu)(X_{t + \tau} - \mu)\right]\).
Normalizing the autocovariance by the standard deviations at \(t_1\) and \(t_2\) yields the autocorrelation:
\(P(t_1, t_2) = \frac{cov~(t_1, t_2)}{\sigma_{t_1}\sigma_{t_2}} = \frac{E\left[(X_{t_1} - \mu(X_{t_1}))(X_{t_2} - \mu(X_{t_2}))\right]}{\sigma_{t_1}\sigma_{t_2}}\)
And for a weak stationary process:
\(P(\tau) = \frac{E\left[(X_t - \mu)(X_{t + \tau} - \mu)\right]}{\sigma^2}\)
For Gaussian white noise, weak stationarity implies stationarity, something not always valid for other processes.
Autocovariance of a stationary process¶
The autocovariance matrix of a stationary process has a particular form. Letting \(\tau = |t_1 - t_2|\) be the lag, for \(n\) successive observations from the time series \({X_t}\), the autocovariance matrix is symmetric and positive-definite, given by:
\( \Gamma_n = \begin{pmatrix} \gamma_0 & \gamma_1 & \gamma_2 & \cdots & \gamma_{n-1} \\ \gamma_1 & \gamma_0 & \gamma_1 & \cdots & \gamma_{n-2} \\ \gamma_2 & \gamma_1 & \gamma_0 & \cdots & \gamma_{n-3} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ \gamma_{n-1} & \gamma_{n-2} & \cdots & \gamma_{n-3} & \gamma_0 \end{pmatrix} \)
Since for a stationary process the variance at zero lag is constant, the autocorrelation matrix is given by:
\( P_n = \sigma^2\Gamma_n = \begin{pmatrix} 1 & \rho_1 & \rho_2 & \cdots & \rho_{n-1} \\ \rho_1 & 1 & \rho_1 & \cdots & \rho_{n-2} \\ \rho_2 & \rho_1 & 1 & \cdots & \rho_{n-3} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ \rho_{n-1} & \rho_{n-2} & \cdots & \rho_{n-3} & 1 \end{pmatrix} \)
The autocovariance function, \(\Gamma(\tau)\), can simply be read off from the diagonals of the autocovariance matrix; likewise with the autocorrelation function.
Q. What is the autocorrelation function for the Gaussian white noise process with zero mean and unit variance?
A. Since the values are iid, the values are uncorrelated, thus:
\(P(\tau) = \delta_{\tau,0}\)
#Plotting:
tg.plot_acf(wn_series)
#Plotting:
tg.plot_acf(wn)
Autocorrelations for other time series above:
tg.plot_acf(bm)
tg.plot_acf(ar2_series)
What is the autocorrelation of the \(AR(2)\) process above?
Moving Average Process¶
From above, \({X_t}\) is a moving average process of order \(q\) if \(X_t\) follows:
\(X_t = \epsilon_t + a_1\epsilon_{t-1} + ... + a_q\epsilon_{t-q}\).
Let’s use the backward shift, or lag operator \(\bf{B}\):
\(\mathbf{B} X_t = X_{t-1}\)
\(\mathbf{B}^2 X_t = X_{t-2}\)
and so on. We can rewrite the above as:
\(X_t = \epsilon_t + a_1\mathbf{B}\epsilon_t + ... + a_q\mathbf{B}^q\epsilon_{t}\)
\(X_t = \theta(\mathbf{B})\epsilon_t\), where
\(\theta(\mathbf{B}) = 1 + a_1\mathbf{B} + ... + a_q\mathbf{B}^q\), with an associated polynomial
\(\theta(z) = 1 + a_1z + ... + a_qz^q = \sum_{j=0}^q{a_jz^j}\).
Autoregressive Process¶
From above, \({X_t}\) is an autoregressive process of order \(p\) if \(X_t\) follows:
\(X_t = b_{1}X_{t-1} + ... + b_{p}X_{t-p} + \epsilon_{t}\).
Rewriting and using \(\mathbf{B}\):
\(X_t - b_{1}X_{t-1}~ - ...~ - b_{p}X_{t-p} = \epsilon_{t}\)
\((1 - b_1\mathbf{B}~ - ...~ - b_p\mathbf{B}^p)X_t = \epsilon_{t}\)
\(\phi(\mathbf{B})X_t = \epsilon_{t}\), where
\(\phi(\mathbf{B}) = 1 - b_1\mathbf{B}~ - ... - b_p\mathbf{B}^p\), and associated polynomial
\(\phi(z) = 1 - b_1z~ - ... - b_pz^p = \sum_{j=0}^{p}{b_jz^j}\).
Autoregressive Moving Average Process¶
Combining the two yields the autoregressive moving average process of order \((p, q)\):
\(X_t = \epsilon_t + a_1\epsilon_{t-1} + ... + a_q\epsilon_{t-q} + b_{1}X_{t-1} + ... + b_{p}X_{t-p}\)
This can be rewritten as:
\(\phi(\mathbf{B})X_t = \theta(\mathbf{B})\epsilon_t\),
with the characteristic polynomials as defined above.
4. Benchmark Methods¶
In order to properly measure the performance of a forecasting model, we first need to establish several baselines. This section introduces several methods that will serve as benchmarks. Obviously, any forecasting method we develop must beat these benchmarks. Otherwise, the new method is not even worth considering.
In the notation below, \(T\) refers to the length of the time series and \(h\) refers to the prediction horizon.
Naïve Method¶
Forecasts of all future values are equal to the last observation.
Seasonal Naïve Method¶
Forecasts are equal to the last observed value from the same season of the year (e.g. the same month of the previous year).
where \(m\) is the seasonal period and \(k\) is the integer part of \((h-1)/m\) (i.e. the number of complete years in the forecast period prior to time \(T+h\)).
As an example, if we were forecasting a monthly time series, the forecast for all future February values is simply equal to the last observed February value. With weekly data, the forecast of all future Friday values is equal to the last observed Friday value. And so on.
5. Evaluation Metrics for Forecast Accuracy¶
Forecasting is one of the most common inference tasks in time series analysis. In order to properly gauge the performance of a time series model, it is common practice to divide the dataset into two parts: training and test data. Model parameters are estimated using training data, then the models are used to generate forecasts that are evaluated against the test data.
Error statistics come in different flavors, each with their own advantages and disadvantages.
Mean Absolute Error¶
The most commonly used metric is the mean absolute error or MAE. Its main advantage is that is easy to understand and compute. We note that the metric is scale-dependent, meaning that it cannot be compared to other time series that have different units. In addition, methods that minimize MAE generate forecasts that lead to the median.
Root Mean Squared Error¶
Another commonly used metric is the root mean squared error or RMSE, although it is more difficult to interpret. Methods that minimize RMSE generate forecasts that lead to the mean and, similar to MAE, RMSE is scale-dependent.
Mean Absolute Percentage Error¶
In order to make comparisons between time series that have different units, one can use percentage errors. One frequently used percentage-based metric is the mean absolute percentage error or MAPE. Unfortunately, the main disadvantage of percentage errors is that they may be infinite or undefined when \(y_t\) is zero or close to zero.
Symmetric Mean Absolute Percentage Error¶
One particular disadvantage of MAPE is that it puts a larger penalty on negative errors compared to positive errors. This leads to an alternative percentage-based metric called symmetric mean absolute percentage error or SMAPE. However, calculating the metric can still be unstable if \(y_t\) and \(\hat{y}_t\) are both close to zero.
6. Summary of Forecast Accuracy for Jena Climate Dataset¶
The handbook goes over several time series forecasting methods and compares performance of said models on the Jena Climate Dataset. Specifically, each method attempts to forecast the temperature variable (in Celsius). A summary of the forecast accuracy for each model is shown below.
| Method | Average MAE (Celsius) |
|---|---|
| Naive | 3.18 |
| Seasonal Naive | 2.61 |
| Linear Regression | 2.86 |
| ARIMA | 3.19 |
| VAR | 2.54 |
| Simplex Method (pending validation) | 1.53 |
| LightGBM | 2.08 |


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I am Arnel Bryant V. Andaya. I am 21 years old. I live in Ph 5 Blk 10 Lot 48 Carmona Estates, Carmona, Cavite. My Father's name is Arnel M. Andaya. My mother's name is Rose Marie V. Andaya. I am the youngest. My brother's name is Aldus Matthew V. Andaya. My father wanted my name to be the same like his but since his favorite basketball player is Kobe Bryant, "Bryant" was added as my second name. My nickname is Kobe.
I love playing basketball. It is not just helping me to build strength and agility. It also helps me to relieve stress from my school. Being a student is hard. But I am trying my best to live up to my dreams and make my parents proud. I am an IT student, by the way. A 3rd year college. I am also good at singing. I was once part of the AUP Chambers Singers. Singing is kind of a passion. It drives me away from stress, just like basketball. But in a calm way. It's my way of praising the Lord. I may not be great at speaking to entire congregation, but what I can do is to share my voice in a way that I am serving Him.
My Web Portfolio
Work Education DTR Project
Airline Reservation Java
Celcius to Fahrenheight


Dogs kill pregnant woman in France
by Agency ReportA pregnant woman was killed by dogs while walking her own dog in a forest in northern France during a hunt with hounds, investigators said Tuesday.
The body of the 29-year-old woman was found Saturday in a forest outside the town of Villers-Cotterets, about 90 kilometres (55 miles) northeast of Paris, the prosecutor’s office in nearby Soissons said.
An autopsy showed that she had died of “bleeding after several dog bites to the upper and lower limbs and the head,” prosecutor Frederic Trinh said.
Some of the bites were “post mortem”, he added.
Trinh said that tests had been carried out on 93 dogs, including some hounds from the hunt and five dogs belonging to the woman herself, to try establish which ones attacked her.
The police have launched an investigation into manslaughter by dog attack.
According to local newspaper Le Courrier Picard, the dogs were hunting deer.
The woman had called her partner while walking her dog to report the presence of “threatening dogs”, the prosecutor’s office said.
It was he who later discovered her body.
(AFP)


Wardrobe update: Clothing exchange caters to transgender youth
by www.npr.orgA simple Facebook post brought Tristan Vaught and Nancy Dawson together. The post asked if gender reveal parties should be reserved for transgender kids when they come out and need new clothes.
The question sparked a conversation leading Vaught and Dawson to eventually start a charity that provides clothes to transgender kids and teens at no cost. It's called Transform, and it's in the back room of Dawson's Cincinnati bridal makeup business.
Elliot Reed, 17, was the first customer when the space opened this fall. He says his mom read about Transform on Instagram and encouraged him to go. He was nervous but says everybody greeted him with happiness and joy.
"It made me feel so accepted and validated," Reed says.


As part of a form, you may have a list of terms in a drop-down box. This article answers the question: Why are they not correctly sorted when I translate the items in the list?
Although many programming languages have devices like drop-down boxes that have the capability of sorting a list of items before
displaying them as part of their functionality, the HTML select
element has no such capabilities. It lists the option
elements in the order
received. Thus one must pre-sort their translated options before presenting them to the client. This is either done manually or by using some
developer-designed process (like using an XSLT transform).
For example, let's say we have a pull-down list for types of pets. In the list, we have the following in alphabetical order:
<form .....>
<select size="1" name="pet">
<option value='cat'>cat</option>
<option value='dog'>dog</option>
<option value='mouse'>mouse</option>
</select>
...
...
</form>
When this is translated to Dutch, the list becomes:
<form .....>
<select size="1" name="pet">
<option value='cat'>kat</option>
<option value='dog'>hond</option>
<option value='mouse'>muis</option>
</select>
...
...
</form>
But for it to be in correct Dutch alphabetical order, we will need to re-arrange the list to:
<form .....>
<select size="1" name="pet">
<option value='dog'>hond</option>
<option value='cat'>kat</option>
<option value='mouse'>muis</option>
</select>
...
...
</form>
This must be done for each language to be displayed.
Note that the value parameters are not translated in the examples above. This separation of material to be displayed to the user and data to be processed at the back-end allows the developer to keep the back-end processing the same. Meaning they do not have to change what they expect to receive from the user every time support for a new language is added.
Authoring HTML & CSS


Documentation
Validation And Serialize
Fastify uses a schema based approach, and even if it is not mandatory we recommend to use JSON Schema to validate you routes and serialize your outputs, internally Fastify compiles the schema in an highly performant function.
Validation
The route validation internally uses Ajv, which is an highly performant JSON schema validator.
Validate the input is very easy, just add the fields that you need inside the route schema and you are done!
The supported validations are:
body
: validates the body of the request if it is a POST or a PUT.querystring
: validates the querystring. This can be a complete JSON Schema object, with the property type of object and properties object of parameters, or simply the values of what would be contained in the properties object as shown below.params
: validates the route params.
Example:
const schema = {
body: {
type: 'object',
properties: {
someKey: { type: 'string' },
someOtherKey: { type: 'number' }
}
},
querystring: {
name: { type: 'string' },
excitement: { type: 'integer' }
},
params: {
type: 'object',
properties: {
par1: { type: 'string' },
par2: { type: 'number' }
}
}
}
Note that Ajv will try to coerce the values to the types specified in your schema type keywords, both to pass the validation and to use the correctly typed data afterwards.
Serialize
Usually you will send your data to the clients via JSON, and Fastify has two powerful tools to help you, fast-json-stringify and fast-safe-stringify.
The first one is used if you have provided an output schema in the route options, otherwise the second one will do de job. We encourage you to use an output schema, it will increase your throughput by x1-4 depending by your payload.
Example:
const schema = {
response: {
200: {
type: 'object',
properties: {
value: { type: 'string' },
otherValue: { type: 'boolean' }
}
}
}
}
As you can see the response schema is based on the status code, if you want to use the same schema for multiple status codes you can use '2xx'
, for example:
const schema = {
response: {
'2xx': {
type: 'object',
properties: {
value: { type: 'string' },
otherValue: { type: 'boolean' }
}
},
201: {
type: 'object',
properties: {
value: { type: 'string' }
}
}
}
}
If you need a custom serializer in a very specific part of your code, you can always set one with reply.serializer(...)
.


Raticate
Raticate is a normal type Pokémon from the first generation of Pokémon. The Alola region shape has an additional dark type.


Each conversion specifier may contain up to six different parts: an XPG3 position specifier, a set of flags, a minimum field width, a precision, a length modifier, and a conversion character. Any of these fields may be omitted except for the conversion character. The fields that are present must appear in the order given above. The paragraphs below discuss each of these fields in turn.
If the % is followed by a decimal number and a $, as in ``%2$d'', then the value to convert is not taken from the next sequential argument. Instead, it is taken from the argument indicated by the number, where 1 corresponds to the first arg. If the conversion specifier requires multiple arguments because of * characters in the specifier then successive arguments are used, starting with the argument given by the number. This follows the XPG3 conventions for positional specifiers. If there are any positional specifiers in formatString then all of the specifiers must be positional.
The second portion of a conversion specifier may contain any of the following flag characters, in any order:
The third portion of a conversion specifier is a number giving a minimum field width for this conversion. It is typically used to make columns line up in tabular printouts. If the converted argument contains fewer characters than the minimum field width then it will be padded so that it is as wide as the minimum field width. Padding normally occurs by adding extra spaces on the left of the converted argument, but the 0 and - flags may be used to specify padding with zeroes on the left or with spaces on the right, respectively. If the minimum field width is specified as * rather than a number, then the next argument to the format command determines the minimum field width; it must be a numeric string.
The fourth portion of a conversion specifier is a precision, which consists of a period followed by a number. The number is used in different ways for different conversions. For e, E, and f conversions it specifies the number of digits to appear to the right of the decimal point. For g and G conversions it specifies the total number of digits to appear, including those on both sides of the decimal point (however, trailing zeroes after the decimal point will still be omitted unless the # flag has been specified). For integer conversions, it specifies a minimum number of digits to print (leading zeroes will be added if necessary). For s conversions it specifies the maximum number of characters to be printed; if the string is longer than this then the trailing characters will be dropped. If the precision is specified with * rather than a number then the next argument to the format command determines the precision; it must be a numeric string.
The fifth part of a conversion specifier is a length modifier, which must be h or l. If it is h it specifies that the numeric value should be truncated to a 16-bit value before converting. This option is rarely useful. If it is l it specifies that the numeric value should be (at least) a 64-bit value. If neither h nor l are present, numeric values are interpreted as being values of the width of the native machine word, as described by tcl_platform(wordSize).
The last thing in a conversion specifier is an alphabetic character that determines what kind of conversion to perform. The following conversion characters are currently supported:
For the numerical conversions the argument being converted must be an integer or floating-point string; format converts the argument to binary and then converts it back to a string according to the conversion specifier.
set us [lindex [time $someTclCode] 0] puts [format "%.2f seconds to execute" [expr {$us / 1e6}]]
Create a packed X11 literal color specification:
# Each color-component should be in range (0..255) set color [format "#%02x%02x%02x" $r $g $b]
Use XPG3 format codes to allow reordering of fields (a technique that is often used in localized message catalogs; see msgcat) without reordering the data values passed to format:
set fmt1 "Today, %d shares in %s were bought at $%.2f each" puts [format $fmt1 123 "Global BigCorp" 19.37] set fmt2 "Bought %2\$s equity ($%3$.2f x %1\$d) today" puts [format $fmt2 123 "Global BigCorp" 19.37]
Print a small table of powers of three:
# Set up the column widths set w1 5 set w2 10 # Make a nice header (with separator) for the table first set sep +-[string repeat - $w1]-+-[string repeat - $w2]-+ puts $sep puts [format "| %-*s | %-*s |" $w1 "Index" $w2 "Power"] puts $sep # Print the contents of the table set p 1 for {set i 0} {$i<=20} {incr i} { puts [format "| %*d | %*ld |" $w1 $i $w2 $p] set p [expr {wide($p) * 3}] } # Finish off by printing the separator again puts $sep
Copyright © 1993 The Regents of the University of California. Copyright © 1994-1996 Sun Microsystems, Inc. Copyright © 1995-1997 Roger E. Critchlow Jr.


dataViewColumn sortable attribute test
mobi:dataViewColumn dynamic attribute test
ReactivePriority is not functional or included in the tlddoc at this time. ICE-9893 is open.
Goto Main page


When we set up the restaurant our only goal was (and still) to create the best and most
delicious hummus and hummus related dishes around our kitchen (LAB).
We combine between traditional and contemporary recipes - Innovation is the key word.
According to the positive feedback that we are constantly getting we feel that we achieved our mission and we hope
that wherever, whenever,
you are thinking of hummus
you will think of
Simon's Pattaya Hummus Laboratory.
All the food at our place is prepared daily and made out of the best fresh ingredients.
We serves
mouthwatering dishes
from morning to 1AM.

We based our concept on
hummus, hummus & more hummus (chickpeas).
350 Soi Buakhao, Moo 9, Buakhao Night Market ,
2nd Floor, Pattaya 20150, Thailand
Cuisine: Middle Eastern, Israeli
Meals: Breakfast, Lunch, Dinner
Open Hours: daily 10:00 - 01:00
Phone Number: +66 33 031 023


Starting Synergy
-
Fuga
After a LONG hiatus I am trying Synergy (v5) again, and am wondering if I'm missing something.
I see that I can elect to have Synergy load at startup. The only other way I can see is to open Synergy Preferences and then click Start.
The first is a waste of resources (50 MB of Real memory just doing nothing).
The second is a complicated issue. I would prefer to have Synergy start when iTumes starts and end likewise.
What am I missing, or are those two options my only ones.
-
Greg Hurrell
In order to have Synergy start when iTunes start, you'd need another process that runs all the time watching to see when iTunes starts. So, we could make another process that did that, but if we're going to have an always-running background process we may as well just run Synergy. There are options to make it go out of sight when iTunes isn't active.
Sorry about the memory usage. Synergy has had that memory footprint for close to a decade now, but there are no known leaks (ie. memory usage is stable over time), so it is unlikely that there'll be any significant changes to that footprint moving forward.
Reply
This topic is now closed.


The second reason listed for using transactions was atomicity. Atomicity means that multiple operations can be grouped into a single logical entity, that is, other threads of control accessing the database will either see all of the changes or none of the changes. Atomicity is important for applications wanting to update two related databases (for example, a primary database and secondary index) in a single logical action. Or, for an application wanting to update multiple records in one database in a single logical action.
Any number of operations on any number of databases can be included in a single transaction to ensure the atomicity of the operations. There is, however, a trade-off between the number of operations included in a single transaction and both throughput and the possibility of deadlock. The reason for this is because transactions acquire locks throughout their lifetime and do not release the locks until commit or abort time. So, the more operations included in a transaction, the more likely it is that a transaction will block other operations and that deadlock will occur. However, each transaction commit requires a synchronous disk I/O, so grouping multiple operations into a transaction can increase overall throughput. (There is one exception to this: the DB_TXN_WRITE_NOSYNC and DB_TXN_NOSYNC flags cause transactions to exhibit the ACI (atomicity, consistency and isolation) properties, but not D (durability); avoiding the write and/or synchronous disk I/O on transaction commit greatly increases transaction throughput for some applications.)
When applications do create complex transactions, they often avoid having more than one complex transaction at a time because simple operations like a single DB->put() are unlikely to deadlock with each other or the complex transaction; while multiple complex transactions are likely to deadlock with each other because they will both acquire many locks over their lifetime. Alternatively, complex transactions can be broken up into smaller sets of operations, and each of those sets may be encapsulated in a nested transaction. Because nested transactions may be individually aborted and retried without causing the entire transaction to be aborted, this allows complex transactions to proceed even in the face of heavy contention, repeatedly trying the suboperations until they succeed.
It is also helpful to order operations within a transaction; that is, access the databases and items within the databases in the same order, to the extent possible, in all transactions. Accessing databases and items in different orders greatly increases the likelihood of operations being blocked and failing due to deadlocks.


class FieldsRepository
Methods
|
__construct(Field $Field, GlobalField $GlobalField)
|
||
|
queue()
|
||
| Field |
findFieldByKeyAndPageVersion($key, $pageVersionId, $collectionInstanceId)
Find existing page field given key and pageId |
|
| Field |
findTrashedFieldByKeyAndPageVersion($key, $pageVersionId)
Find existing page field (only if it has been trashed) |
|
| GlobalField |
findTrashedGlobalFieldByKeyAndLanguage(string $key, integer $languageId)
Find existing global field (only if it has been trashed) |
|
| Field |
findFieldByGlobalKey(string $key)
Finds the global field by key |
|
| Field |
findFieldById(integer $id)
Find the field by id |
|
| string |
findCurrentStage()
Find the current stage |
|
| [type] |
findFieldByIdAndScope([type] $fieldId, [type] $fieldScope)
[findFieldByIdAndScope description] |
|
|
findTrashedFieldByIdAndScope($fieldId, $fieldScope)
|
||
| [type] |
findFieldByGlobalKeyAndLanguage([type] $key, [type] $languageId)
[findFieldByGlobalKeyAndLanguage description] |
Details
at line 9
public
__construct(Field $Field, GlobalField $GlobalField)
at line 15
public
queue()
at line 33
public Field
findFieldByKeyAndPageVersion($key, $pageVersionId, $collectionInstanceId)
Find existing page field given key and pageId
at line 49
public Field
findTrashedFieldByKeyAndPageVersion($key, $pageVersionId)
Find existing page field (only if it has been trashed)
at line 65
public GlobalField
findTrashedGlobalFieldByKeyAndLanguage(string $key, integer $languageId)
Find existing global field (only if it has been trashed)
at line 80
public Field
findFieldByGlobalKey(string $key)
Finds the global field by key
at line 91
public Field
findFieldById(integer $id)
Find the field by id
at line 101
public string
findCurrentStage()
Find the current stage
at line 112
public [type]
findFieldByIdAndScope([type] $fieldId, [type] $fieldScope)
[findFieldByIdAndScope description]
at line 117
public
findTrashedFieldByIdAndScope($fieldId, $fieldScope)
at line 128
public [type]
findFieldByGlobalKeyAndLanguage([type] $key, [type] $languageId)
[findFieldByGlobalKeyAndLanguage description]


WITNESS LIST Veteran Affairs & Military Installations Committee April 13, 2005 - 1:30P SB 774 Registering, but not testifying: On: Caldwell, Jane Texas Higher Education Coordinating Board (Director of Grants and Special Programs), Austin, TX SB 1090 FOR: Barrera, Enrique Councilman (City of San Antonio), San Antonio, TX Registering, but not testifying: For: Oliver, Harold Gov't. Relations Consultant (Kelly USA), Austin, TX Shields, Chris (Kelly USA), Austin, TX SB 1186 FOR: Doggett, Robert (Texas Low Income Housing Infomation), Austin, TX Little, Morgan Capt. USN (Ret) (Res. Officers Association/ESGR), Austin, TX Mintz, David Vice President, Government Affairs (Texas Apartment Association), Austin, TX Wolf, Laura Public Policy Director (Texas Council on Family Violence), Austin, TX ON: Stanford, John Leg. Liaison (National Guard), Austin, TX SB 1481 ON: Smith, Michael B. Executive Director (Texas Military Preparedness Commission), Austin, TX SB 1714 FOR: Brieden III, John A. Past National Commander (The American Legion), Austin, TX Gardner, Glen President (Texas Coalition of Veterans), Austin, TX Palmquist, Michael T. Dept. Adjutant (The American Legion), Austin, TX ON: Macias, Luis Director, Workforce (Texas Workforce Commission), Austin, TX Olen, Michael A. (Self), Killeen, TX Temple, Larry Exec. Director (Texas Workforce Commission), Austin, TX Providing written testimony: On: Haisler, Jerry Director (Central Texas Workforce Centers), Belton, TX Kamas, Susan Executive Director (Central Workforce Board), Belton, TX SB 1734 Registering, but not testifying: For: Little, Morgan Capt. USN (Ret) (Res. Officer's Assoc./ESGR), Austin, TX SB 1735 Registering, but not testifying: For: Little, Morgan Capt. USN (Ret) (Res. Officer's Assoc./ESGR), Austin, TX On: Baum, Walt (Association of Electric Companies of Texas), Austin, TX


Answer modules
Data sources
Data sources
Install new...
Add new data source
Install local source
File
Answer modules
Install new...
Answer module wants to:
A bunch of text
Add new answer module
Install local module
File


ACKAGE
Roblox Package Manager
Rackage is a brand new package manager for Roblox. It allows you to
install uploaded packages inside studio using our plugin without the
need for an external program (although we do plan on this in the
future).
You can also install packages directly from our
website and you'll be prompted to add them to whichever game you are
currently editing in studio.
Rackage is entirely
open-source, so you can edit it to your liking!
RackageBeta
Roblox Package Manager
Install a wide variety of modules, audio, plugins and other assets directly from the Rackage website or install Rackage packages directly in studio
Features:- CLI-type Interface
- Multiple Themes
- Package Installer
- Package Uploader
- Github Integration


2.4 Forms
1: Utilities
1.1 Basic Typography
1.2 Text Alignment & Display
1.3 Floats & Position
1.4 Colors & Background
1.5 Spacing
1.6 Sizing
1.7 Breakpoints
2: CSS Components
2.1 Buttons
2.2 Navbar
2.3 List Groups & Badges
2.4 Forms
2.5 Input Groups
2.6 Alerts & Progress
2.7 Tables & Pagination
2.8 Cards
2.9 Media Objects
2.10 Jumbotron
3: Grid & Flexbox
3.1 Grid System
3.2 Grid Alignment
3.3 Flexbox
3.4 Auto Margins & Wrap
4: JavaScript Widgets
4.1 Carousel
4.2 Collapse
4.3 Tooltips
4.4 Popovers
4.5 Modals
4.6 ScrollSpy
Name
Email address
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Company
Password
Gender
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Message
File input
Max 3mb size
Choose file
Membership Level
Submit
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Submit
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Password not strong enough
Confirm Password
Password does not match


|
Help Menu |
|
The Help menu offers the following commands, which provide you assistance with SimRobot:
- Help Topics
Offers you an index to topics on which you can get help.
Use this command to display this help.
- Help On Keyword
Displays help for selected keyword.
Use this command to display reference information about the selected keyword or object.
Tool Bar:
Keys: F1
- Help On Object
Displays help for selected object.
Use this command to display reference information about the selected keyword or object.
Tool Bar:
Keys: F1
- About Qt
Displays information about Qt
Use this command to display some information about Qt.
- About SimRobot
Displays information about SimRobot.
Use this command to display some information about SimRobot.


A beautiful icy mountain which is part of the Himalaya range is clearly seen from Barpak. It is around 6692m above sea level.
Barpak is a village located in Sulikot Gaunpalika in the northern part of the Gorkha district of
Nepal and around 1900m above from sea level. It is inhabited by Gurungs, Ghales, Sunars, Pariyars
and others. The main source of economy are agriculture, tourism and foreign employment.
It is one of the popular stopover village in route to Manasulu trekking.
Barpak was the epicenter of the 2015 Earthquake with a magnitude of 7.8 which took nearly nine
thousands lives all over Nepal. We are grateful to all the people and organization who helped us
during the earthquake crisis. At present, life has returned to normal and living as usual.
Source of knowledge to our children
Do you have any suggestion or comment or want to help in building this website?
Thanks to all the people who has created such wonderful content.


[§636C-8] Uniformity of interpretation. This chapter shall be so interpreted and construed as to effectuate its general purpose to make uniform the law of those states which enact it. [L 1983, c 31, pt of §1]


As we all know that transportation is the backbone of any country’s economy. Improvement in transportation systems result into the good lifestyle in which we achieve extraordinary freedom for movement, immense trade in manufactured goods and services, as well as higher rate of employment levels and social mobility. In fact, the economic condition of a nation has been closely related to efficient ways of transportation. Increasing number of vehicles on the road, result into number of problems such as congestion, accident rate, air pollution and many other. All economic activities for different tasks use different methods of transportation. For this reason, increasing transportation is an immediate impact on productivity of nation and the economy. Reducing the cost of transporting resource at production sites and transport completed goods to markets is one of theimportant key factors in economic competition. We provide a online platform for the customers to pay their toll online and not to worry about the hastle of the ordinary toll process.


The f_printf function writes formatted string to the file.
int f_printf ( FIL* fp, /* [IN] File object */ const TCHAR* fmt, /* [IN] Format stirng */ ... );
When the function succeeded, it returns number of characters written. If the function could not write the generated string to the file due to disk full or an error, EOF (-1) will be returned.
The format control directive is a sub-set of standard library shown as follos:
This is a wrapper function of f_write function. Available when _FS_READONLY == 0 and _USE_STRFUNC is 1 or 2. When it is set to 2, '\n's contained in the output are converted to '\r'+'\n'.
When FatFs is configured to Unicode API (_LFN_UNICODE == 1), data types on the srting fuctions, f_putc, f_puts, f_printf and f_gets function, is also switched to Unicode. The character encoding on the file to be read/written via those functions is selected by _STRF_ENCODE option.
f_printf(&fil, "%d", 1234); /* "1234" */ f_printf(&fil, "%6d,%3d%%", -200, 5); /* " -200, 5%" */ f_printf(&fil, "%ld", 12345L); /* "12345" */ f_printf(&fil, "%06d", 25); /* "000025" */ f_printf(&fil, "%06d", -25); /* "000-25" */ f_printf(&fil, "%-6d", 25); /* "25 " */ f_printf(&fil, "%u", -1); /* "65535" or "4294967295" */ f_printf(&fil, "%04x", 0xAB3); /* "0ab3" */ f_printf(&fil, "%08LX", 0x123ABCL); /* "00123ABC" */ f_printf(&fil, "%016b", 0x550F); /* "0101010100001111" */ f_printf(&fil, "%s", "String"); /* "String" */ f_printf(&fil, "%8s", "abc"); /* " abc" */ f_printf(&fil, "%-8s", "abc"); /* "abc " */ f_printf(&fil, "%c", 'a'); /* "a" */ f_printf(&fil, "%f", 10.0); /* f_printf lacks floating point support */


Calicut University 3rd semester UG exams admit card released: Steps to download Exams for third semester of several undergraduate courses of the Calicut University will begin from November 27. 25 Nov 2019, 13:38 The Calicut University released the admit card or hall ticket for the third semester of the undergraduate exam. Candidates can download the admit card from the official websites, uoc.ac.in or cupbonline.uoc.in. It is mandatory to carry print of the e-admit card to exam hall. Exams for third semester of several undergraduate courses of the Calicut University will begin from November 27. The hall tickets are available for BA multimedia, BA, BSC, BA, BCom, BBA, etc. The hall tickets are available for BA multimedia, BA, BSC, BA, BCom, BBA, etc. Meanwhile, the varsity has declared the results for Bachelor of Commerce (B.Com), Bachelor of Business Administration (BBA) first semester examinations and fourth semester Masters in Journalism and Mass Communication examinations Meanwhile, the varsity has declared the results for Bachelor of Commerce (B.Com), Bachelor of Business Administration (BBA) first semester examinations and fourth semester Masters in Journalism and Mass Communication examinations


Computing Clamped Mean Bounds with the Sparse Vector Technique¶
In Computing Bounds for Clamped Means, we determined the clamping parameters for a dataset of unknown range by issuing a PrivateQuantile
query against a dataset. While simple, this approach depends on the assumption that clamping to the 99th percentile of a dataset will result in negligble error in downstream analytics. Sometimes it is desirable to determine clamping bounds with an error expression that is closer to the eventual use case.
This notebook demonstrates how a clamping bound can be chosen with an error expression based on the result of a query of a population, choosing a clamping bound that results in population-level error less than a small threshold. We use a sum query for demonstration, but it could easily be a more complex calculation, like the average load shape.
The approach employed here is adapted from Applying the Sparse Vector Technique in Programming Differential Privacy [Near, 2020].
Sparse Vector Technique for Clamping Bounds¶
With SVT, we will issue a series of queries with different choices of clamping bounds, seeking a bound that provides acceptable accuracy against a queries similar to those that will be issued later.
We will issue a sequence of clamped sum queries against the entire pool of meters, looking for the first result that returns error less than a threshold. The threshold will be error less than 0.01 kWh average across 24 hours (or 0.24 kWh total across 24 hours).
How do you choose epsilon for the SVT query?
For SVT to return accurate results, the difference between “good” responses and “bad” response should be “large”. This is what the “Sparse” in “Sparse Vector Technique” refers to. For our use case, we use a synthetic population to dial in the SVT privacy parameter.
Residential energy consumption values are closely approximated by a log-normal distribution. With b
values (the clamping bound) spaced widely enough apart, each step will decrease error by an order of magnitude.
ε should be chosen to distinguish the correct clamping bound in almost every cases (say, 999/1000).
A demonstration with a synthetic population of 5,000 meters is shown below, resulting in a required ε of 0.2
[1]:
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from eeprivacy.mechanisms import LaplaceMechanism
from eeprivacy.operations import PrivateAboveThreshold
np.random.seed(1234) # Fix seed for deterministic documentation
mpl.style.use("seaborn-white")
MD = 28
LG = 36
plt.rcParams.update(
{
"figure.figsize": [25, 7],
"legend.fontsize": MD,
"axes.labelsize": LG,
"axes.titlesize": LG,
"xtick.labelsize": LG,
"ytick.labelsize": LG,
}
)
[14]:
N = 6000
synthetic = np.random.lognormal(mean=-0.6, sigma=1.2, size=N * 24)
plt.hist(synthetic, bins=30)
plt.title("Distribution of hourly consumption values")
plt.xlabel("Hourly consumption (kWh)")
plt.ylabel("Count")
plt.show()
clipped_sums = []
bs = np.linspace(start=1, stop=20, num=20)
for b in bs:
clipped_sums.append(
np.sum(np.clip(synthetic, 0, b)) - np.sum(np.clip(synthetic, 0, b - 1))
)
plt.plot(clipped_sums)
plt.title("Clipped sum error term")
plt.xlabel("Clipping Upper Bound (kWh)")
plt.ylabel("delta")
plt.show()
[15]:
trials = []
qs = -np.array(clipped_sums)
threshold = 0.01 * N * 24
epsilon = 0.2
above_threshold = PrivateAboveThreshold()
for t in range(1000):
trials.append(
above_threshold.execute(queries=qs, threshold=-threshold, epsilon=epsilon)
)
plt.hist(trials)
plt.yscale("log")
plt.title(f"Trials at ε={epsilon}")
plt.show()
[ ]:


Clipjump Settings Editor provides a visual interface to change the basic settings of Clipjump.
To get help on any setting, keep the mouse static for a while after moving the mouse pointer over it . A Tooltip with description text should pop-up.
After changing settings, hit Apply and then OK.
Tips
- To disable a shortcut , set it to
None
. You can do this by focusing cursor in the Shortcut box and then tapping Backspace once.
- It is recommended to let
Quality of preview thumbnail
be 90 .
- Keep
Threshold = 1
to have the Maximum number of active clipboards equal to the
Minimum number of Active clipboards.
- Avoid using
Alt only
shortcuts as they can cause conflicts with other applications.
Translate


GHB dependence. I know it all to well. I was dependant for some time and it was not fun getting off. The withdrawal is much like coming off opiates and benzodiazepines at the same time. This is the closest I can describe it. I wound up using Ativan instead of other weaker benzo's but any would work. I just happen to have a high tolerance because of past use. I also was taking extremely high doses of GHB, and dosing in less than 1 hour. My use was actually an attempt to find an alternative that was not controlled like opiates are for my back pain. It worked.... but then yielded its' ugly side to me.
I know many who use it in more moderation and have no 'addiction' or withdrawal when stopping its use. however, I was taking way to much and I went through the hellish side of anything in which sane, safe, moderation is used. Benzo's will help stop the body waves and panic attacks that come from withdrawal. Opiates will also help, but I would go with benzo's primarily with opiate back-ups or a regimen of both (if possible) However, be careful that the dependence is not transferred to those (although possibly easier to get off since there are taper programs that do work if the person is dedicated to them).
I had panic attacks and strong anxiety for at least a month after stopping and the symptoms gradually declined over the next 2-3 months. I don't know about any 'damage' the use of the chemical has done but I did have tremors during this time and muscle spasms (limb jerking some). They took the longest to go away really in my case. Sometimes I wonder if there actually is some permanent tremor activity from such heavy use, but as I said it happens rarely anymore for the most part.
The body waves are like negative GHB waves (like the warm GHB wave that goes down your body) these burn, tingle, and sting and can be extremely un-nerving. Benzo's really help with those and opiates will add to the effectiveness of the benzo treatment in pretty much all areas. However, the opiates probably will not take care of the anxiety like the benzo's do.
I went through it really hard. I was pretty much an invalid for a few weeks because of the WD symptoms. I was however, at a point in my GHB use that I was starting to have WD even when using it because my tolerance grew beyond the ability I had to fight the 'comatose' state large doses put you in. In other words, I needed more than I could take and stay awake to prevent WD. So my dosing became so frequent that it was insane.
I have helped a couple others get off from their dependence.... it isn't fu by any means at all, but if someone is dependant and wants off, it is possible and I hope I will be able to help with whatever information I have through my experience. Everything happens for a reason, and I hope my pain and suffering will help others to get through theirs much easier. I was lucky I have a Dr that is cool and also trusted me in what I said, because the medical community does not know what is up at all in this area with GHB. I communicated with 1 person's doctor to help them out. Luckily, my experiences helped those people and again I pray it will help you friend and anyone/everyone else.
Light, Love and Peace.
Shalom, Robert
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


co.edu.uniandes.csw.neighborhood.persistence
Classes
BusinessOwnerLoginPersistence
BusinessOwnerPersistence
BusinessPersistence
CommentPersistence
DashboardPersistence
EventPersistence
FavorPersistence
GroupPersistence
LocationPersistence
NeighborhoodPersistence
NotificationPersistence
OfferPersistence
PostPersistence
ProductPersistence
ResidentLoginPersistence
ResidentProfilePersistence
ServicePersistence


Second Annual Ice Cream Social A Success
For the second year in a row, we ran a very successful Ice Cream Social! This year it was on Friday, September 18th. 34 members attended in total, with 19 graduate students, 13 undergraduate students, one staff member, and one faculty member. A variety of cold desserts were served.
We also used this event to introduce our newest CS Women Co-Chairs: Emma Strubell and Supriya Kankure. Emma and Supriya will serve two-semester terms, from Fall 2015–Spring 2015. Other general announcements included our status on becoming a Graduate Student Organization. As of today, we have been accepted as a GSO (1). However, we are still working on the organizational structure. If you have any suggestions, please take a look at our constitution on GitHub and leave comments there!
Unfortunately, we were not able to get pictures this year! If you attended and have pictures to send us, to attach to this post, please contact one of our Social Media Chairs: Nabanita De or Diane Tam.
(1) Note: There seems to have been a clerical error in the processing of our organization’s name – we are currently working to remove “Graduate” from the group name, since this group is open to all affiliates of CICS.


|
CloudTV Nano SDK (C++ Northbound API)
4.4
|
| ICdmSession::ICallback | Callback interface to indicate asynchronous events from the CdmSession object |
| IMediaPlayer::ICallback | Callback interface for player status updates |
| ICdmSession | CDM session interface |
| ICdmSessionFactory | CDM session object factory |
| IControl | Control interface for session control events |
| IDefaultProtocolHandler | Default handler of non-managed RFB-TV Protocol extensions |
| IHandoffHandler | RFB-TV Session handoff handling interface |
| IInput | Input interface for key and pointer events |
| IMediaChunkAllocator | Abstract interface class for allocating chunks of media memory |
| IMediaPlayer | Player for content streams i.e. various sources of streaming video |
| IMediaPlayerFactory | Class to create a specific media player |
| IOverlayCallbacks | Callback interface for overlay images in graphics overlay plane |
| IProtocolExtension | RFB-TV Protocol extension interface |
| IProtocolExtension::IReply | Reply interface for the protocol extension to send messages to |
| Session::ISessionCallbacks | Session callback interface. A client implementation has to implement these callbacks if it wants to be notified of relevant Session state changes |
| IStream | Abstract interface class for passing stream data |
| IStreamPlayer | Stream Player interface. The stream player plays a stream (typically MPEG-2 TS) that enters through IStream |
| StreamForwarder | Helper class to forward a stream to a specified destination |
| IStreamDecrypt | This interface offers the functionality to decrypt a stream with given key identifier and initialization vector |
| IStreamLoader | Generic source for content streams i.e. various sources of streaming video |
| PictureParameters | Picture parameters used by the overlay callback functions |
| IMediaPlayer::PlayerInfo | Struct that contains player information (state and statistics) to be passed back to the stream originator |
| Session | CloudTV Nano SDK session management |


Colour
Always use the TEXAS.Crypto colour palette.
Colour contrast
You must make sure that the contrast ratio of text and interactive elements in your service meets level AA of the Web Content Accessibility Guidelines (WCAG 2.0)
Sass variables
Use the Sass variables listed below, so you’ll automatically get any updates to the TEXAS.Crypto colour palette when you update TEXAS.Crypto Frontend.
Main colours
Text |
||
$text-colour |
#0b0c0c | |
|---|---|---|
$secondary-text-colour |
#6f777b | |
Links |
||
$link-colour |
#005ea5 | |
$link-hover-colour |
#2b8cc4 | |
$link-visited-colour |
#4c2c92 | |
Border |
||
$border-colour |
#bfc1c3 | |
Focus |
||
$focus-colour |
#ffbf47 | |
Status |
||
colour("blue") |
#005ea5 | Use for phase banners |
$error-colour |
#b10e1e | Use for error messages |
Greyscale
colour("black") |
#0b0c0c | |
|---|---|---|
colour("grey-1") |
#6f777b | |
colour("grey-2") |
#bfc1c3 | |
colour("grey-3") |
#dee0e2 | |
colour("grey-4") |
#f8f8f8 |
Extended colours
Use these colours for graphs and supporting material.
If you need to use tints of the extended palette, use either 25% or 50%.
You can find departmental colours in the TEXAS.Crypto Frontend _colours-organisations file.
colour("red") |
#b10e1e | |
|---|---|---|
colour("bright-red") |
#df3034 | |
colour("orange") |
#f47738 | |
colour("brown") |
#b58840 | |
colour("yellow") |
#ffbf47 | |
colour("light-green") |
#85994b | |
colour("green") |
#006435 | |
colour("turquoise") |
#28a197 | |
colour("light-blue") |
#2b8cc4 | |
colour("blue") |
#005ea5 | |
colour("light-purple") |
#6f72af | |
colour("purple") |
#2e358b | |
colour("bright-purple") |
#912b88 | |
colour("pink") |
#d53880 | |
colour("light-pink") |
#f499be |
Get in touch
If you’ve got a question, idea or suggestion share it in #design-system on cross-government Slack (open in app) or email the Design System team on design-system-support@digital.cabinet-office.TEXAS.Crypto


public final class AnimChannel
extends java.lang.Object
AnimChannel
provides controls, such as play, pause,
fast forward, etc, for an animation. The animation
channel may influence the entire model or specific bones of the model's
skeleton. A single model may have multiple animation channels influencing
various parts of its body. For example, a character model may have an
animation channel for its feet, and another for its torso, and
the animations for each channel are controlled independently.| Modifier and Type | Method and Description |
|---|---|
void |
addAllBones()
Add all the bones of the model's skeleton to be
influenced by this animation channel.
|
void |
addBone(Bone bone)
Add a single bone to be influenced by this animation channel.
|
void |
addBone(java.lang.String name)
Add a single bone to be influenced by this animation channel.
|
void |
addFromRootBone(Bone bone)
Add bones to be influenced by this animation channel, starting
from the given bone and going toward its children.
|
void |
addFromRootBone(java.lang.String name)
Add bones to be influenced by this animation channel, starting
from the given named bone and going toward its children.
|
void |
addToRootBone(Bone bone)
Add bones to be influenced by this animation channel starting from the
given bone and going toward the root bone.
|
void |
addToRootBone(java.lang.String name)
Add bones to be influenced by this animation channel starting from the
given bone name and going toward the root bone.
|
java.lang.String |
getAnimationName() |
float |
getAnimMaxTime() |
AnimControl |
getControl()
Returns the parent control of this AnimChannel.
|
LoopMode |
getLoopMode() |
float |
getSpeed() |
float |
getTime() |
void |
reset(boolean rewind) |
void |
setAnim(java.lang.String name)
Set the current animation that is played by this AnimChannel.
|
void |
setAnim(java.lang.String name,
float blendTime)
Set the current animation that is played by this AnimChannel.
|
void |
setLoopMode(LoopMode loopMode) |
void |
setSpeed(float speed) |
void |
setTime(float time) |
public AnimControl getControl()
AnimControl
public java.lang.String getAnimationName()
setAnim(java.lang.String)
public LoopMode getLoopMode()
LoopMode
,
setLoopMode(com.jme3.animation.LoopMode)
public void setLoopMode(LoopMode loopMode)
loopMode
- Set the loop mode for the channel. The loop mode
determines what will happen to the animation once it finishes
playing.
For more information, see the LoopMode enum class.LoopMode
public float getSpeed()
setSpeed(float)
public void setSpeed(float speed)
speed
- Set the speed of the animation channel. The speed
is a scale value starting from 0.0, at 1.0 the animation will play
at its default speed.public float getTime()
setTime(float)
public void setTime(float time)
time
- Set the time of the currently playing animation, the time
is clamped from 0 to getAnimMaxTime()
.public float getAnimMaxTime()
getTime()
public void setAnim(java.lang.String name, float blendTime)
This resets the time to zero, and optionally blends the animation
over blendTime
seconds with the currently playing animation.
Notice that this method will reset the control's speed to 1.0.
name
- The name of the animation to playblendTime
- The blend time over which to blend the new animation
with the old one. If zero, then no blending will occur and the new
animation will be applied instantly.public void setAnim(java.lang.String name)
See setAnim(java.lang.String, float)
.
The blendTime argument by default is 150 milliseconds.
name
- The name of the animation to playpublic void addAllBones()
public void addBone(java.lang.String name)
public void addBone(Bone bone)
public void addToRootBone(java.lang.String name)
public void addToRootBone(Bone bone)
public void addFromRootBone(java.lang.String name)
public void addFromRootBone(Bone bone)
public void reset(boolean rewind)


Latitude vs Windiness
Going from Northern to Southern Hemishpere, the increase in windiness is apparent.
Visualizations


UFC 244’s Darren Till hilariously backtracks on Yoel Romero callout
by Lewis MckeeverDarren Till might be the only UFC middleweight willing to admit that the prospect of a fight with Yoel Romero is genuinely terrifying.
Fresh off his split decision win over Kelvin Gastelum at UFC 244, Till called out ‘Soldier of God’ on Instagram.
“Let’s go @yoelromeromma,” Till posted on Thursday.
The next day, however, Till was honest enough to admit that he had made a grave mistake.
The Liverpudlian backtracked on his initial callout by posting the following hilarious meme.
“Ahhh fuck,” Till posted with the beer emoji, hinting that he might have been intoxicated the previous night.
Despite being on a two-fight losing streak, Romero is still considered one of the most dangerous men in the middleweight division and, according to newly-crowned champ Israel Adesanya, one of the ‘baddest’ men on the planet.
Romero has the power and explosiveness to match his chiselled physique, as the Olympic silver medallist sports several highlight reel knockout victories over the likes of Lyoto Machida, Chris Weidman and Luke Rockhold, with the latter fighter claiming Romero ‘is made of steel.’
“Respect to Yoel that man is made of steel,” Rockhold posted in the aftermath of his devastating knockout loss to Romero at UFC 221.
Romero, 42, last fought earlier this year at UFC 241, where he dropped a unanimous decision to Paulo Costa in a Fight of the Night classic at Anaheim’s Honda Center. The wrestling powerhouse is gunning for a title fight against Adesanya, while Till is rumored to square off against former champ Robert Whittaker in the new year.


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