| (a) | open : fileNumberVar, fileName, ioCapability | |||||||
| { , ioCapability } | ||||||||
| (b) | open : fileNumberVar, argNum, ioCapability | |||||||
| { , ioCapability } |
The read statement uses the file number, not the file name, to access the file. When the program is finished using the file, it disconnects from the file using the close statement. Each ioCapability is the name of an operation, such as read, that is to be performed on the file.
var fileName : string := "Master" % Name of file
var fileNo : int % Number of file
var inputVariable : string ( 100 )
open : fileNo, fileName, read
read : fileNo, inputVariable
close : fileNo
An ioCapability is one of:
| get, put, read, write, seek, mod |
A file can be accessed using only the statements corresponding to the input/output capabilities with which it was opened. Note: tell is allowed only if the open is for seek.
The open statement truncates the file to length zero if the ioCapabilities includes put or write but not mod (which stands for modify). In all other cases, open leaves the existing file intact. The mod ioCapability specifies that the file is to be modified without being truncated. Each open positions to the beginning of a file. There is no mechanism to delete a file.
To open for appending to the end of the file, one has to open for seek, mod and for write or put and then seek to the end of the file. See the seek statement.
Mixed mode files, which combine get and read (or put and write), are supported by some operating systems, such as UNIX, but not by others, such as Microsoft Windows.
On Microsoft Windows, one should note that opening files in other directories uses the backslash character. This is because the backslash is a special character in Turing (as in \t for tab and \n for a newline). To get a single backslash, use \\.
Form (b) of the syntax allows you to open a file whose name is given as a program argument on the command line. For example, under UNIX, the command line:
specifies to execute prog.x with program arguments infile and outfile. Similarly, in the Turing programming environment, the run command can accept program arguments. The argNumber is the position of the argument on the command line. (The first argument is number 1.) The name of the file to be opened is the corresponding program argument. If there is no such argument, or if the file cannot be opened successfully, fileNumberVariable is set to zero. See also nargs, which gives the number of arguments, and fetcharg, which gives the n-th argument string.
Program argument files referenced by argument number and used in put, get, read or write statements need not be explicitly opened, but are implicitly opened with the capability corresponding to the input/output statement in which they are first used. (The fileNumbergives the number of the argument.)
The operating system standard files (error, output and input) are accessed using file numbers 0, -1, and -2, respectively (although this may be subject to change). These files are not opened explicitly, but are used simply by using form (b) with the number. Beware of the anomalous case of a failed open that gives you file number 0. A subsequent use of this number in a put will produce output that goes to the standard error stream, with no warning that the file you attempted to open is not actually being used.
To append to a file, the file must be opened with the mod and seek capability and then there must be a seek to the end of file. For example:
There is an older and still acceptable version of open that has this syntax:
The mode must be "r" (for get) or "w " (for put).
e.g. open : f, "C:\\STUDENTS\\SMITH\\ACCT.DAT", put
prog.x infile outfile
var streamnumber : int
open : streamnumber, "myfile", put, mod, seek
seek : streamnumber, *
put : streamnumber, "This appears at the end of the file"
open ( var fileNumber : int, fileName : string, mode : string)
On the Macintosh, they would have the form:
Note that in addition to the UNIX path format, on the PC, you can always use standard PC path notation and on the Macintosh, you can use standard Macintosh path notation. On the Macintosh volume, directory and file names can have spaces in them.
All routines (such as the File and Dir module routines) will return files names in UNIX format, regardless of the machine the program is run on.
a:/dir1/dir2/filename
/volume name/directory1/directory2/file name


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Arsalan Kahnemuyipour and Jaklin Kornfilt have organized an interface workshop at Syracuse University. You can download the program with abstracts here.
Here’s the schedule (Friday April 23 and Saturday April 24):
Friday April 23, 2010
Morning Session HL (Hall of Languages) 107
8:00 am Coffee and bagels
8:45 am Opening remarks
Chair: Jaklin Kornfilt, Syracuse University
9 am – 10 am Lisa Selkirk (UMass, Amherst): Reconceptualizing prosodic structure formation and its role in the syntax-phonology interface
10 am – 11 am Bridget Samuels (U Maryland, College Park): I-phrases and PF crashes
11 am – 12 pm Jason Kandybowicz (Swarthmore College): Syntactic and Prosodic Alignment: a Case Study from Twi Do-insertion
Afternoon Sessions Kittredge Auditorium
12 pm – 1:30 pm Lunch Break
Session 1 Chair: Jeff Runner, University of Rochester
1:30 pm – 2:30 pm Molly Diesing and Draga Zec (Cornell): The positions of clitics in discourse
2:30 pm – 3:30 pm Michael Wagner (McGill): Contrastive topics decomposed
3:30 pm – 4:00 pm Coffee Break
Session 2 Chair: John Whitman, Cornell University
4 pm – 5 pm Christine Gunlogson (U Rochester): On tag questions
5 pm – 6 pm Mats Rooth (Cornell): Localizing the Phonology Interface for Alternative Semantics
6:30 pm Reception
Saturday April 24, 2010 HL (Hall of Languages) 207
7:45 am Coffee and bagels
Session 1 Chair: Arsalan Kahnemuyipour, Syracuse University
8:30 am – 9:30 am David Embick (UPenn): Towards a theory of stem alternations
9:30 am – 10:30 am Heidi Harley (U Arizona): Accounting for morphology/syntax mismatches: the Fun House Mirror Principle
10:30 am – 11:00 am Coffee Break
Session 2 Chair: Greg Carlson, University of Rochester
11 am -12 pm Joyce McDonough (U Rochester): Defining the units inside a word: A paradigm-driven approach to Dene polysynthesis
12 pm – 1 pm Martina Wiltschko (UBC): Category, sound, and meaning: Exploring the syntax of polysemy


Claudia Sinclair. 15 min
Hello ✋, Where are the files ...Andrew Boatman. 7 min
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omg, you can't be ...Vlad Openheim. 8 min
My new profile pic what say you😃 ...Stephanie Buckman. 5 min
Hello ✋, Where are the files ...Claudia Sinclair . 2 min
Hello ✋, Where are the files ...


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Hey are you going to the study session?
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I'm glad I went. We totally had a test over that stuff.
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2 days ago
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Creation of a trading
cryptocurrency exchange
platform.
branding, design, development, product
Total branding
overhaul for a goods
distribution app.
branding, design, ios & android
Development of a web
service for searching
professional athletes.
branding, design, development, product
Redesigned a popular
coloring book with
copyrighted drawings.
design, ios & android
Created a clever, high-end
app which enables users to
learn English words.
branding, design, development, product


Send an operation to start or stop device internal tracing. Result of tracing is device dependent, it can create more events or create a file inside the device.
Send an operation to restart the device. The process of restart can be monitored under the operations of the device.
Send an operation to switch a relay on or off.
Send an operation to switch an array of relays on or off.
Send an operation to show a particular message to the device. The device behavior is device dependent.


@Documented @Retention(value=RUNTIME) @Target(value=TYPE) public @interface AutoClone
Cloneable
classes.
The @AutoClone
annotation instructs the compiler to execute an
AST transformation which adds a public clone()
method and adds
Cloneable
to the list of interfaces which the class implements.
Because the JVM doesn't have a one-size fits all cloning strategy, several
customizations exist for the cloning implementation. By default, the clone()
method will call super.clone()
before calling clone()
on each
Cloneable
property of the class.
Example usage:
import groovy.transform.AutoClone
@AutoClone
class Person {
String first, last
List favItems
Date since
}
Which will create a class equivalent to the following:
class Person implements Cloneable { ... public Person clone() throws CloneNotSupportedException { Person result = (Person) super.clone() result.favItems = favItems instanceof Cloneable ? (List) favItems.clone() : favItems result.since = (Date) since.clone() return result } ... }Which can be used as follows:
def p = new Person(first:'John', last:'Smith', favItems:['ipod', 'shiraz'], since:new Date()) def p2 = p.clone() assert p instanceof Cloneable assert p.favItems instanceof Cloneable assert p.since instanceof Cloneable assert !(p.first instanceof Cloneable) assert !p.is(p2) assert !p.favItems.is(p2.favItems) assert !p.since.is(p2.since) assert p.first.is(p2.first)In the above example,
super.clone()
is called which in this case
calls clone()
from java.lang.Object
. This does a bit-wise
copy of all the properties (references and primitive values). Properties
like first
has type String
which is not Cloneable
so it is left as the bit-wise copy. Both Date
and ArrayList
are Cloneable
so the clone()
method on each of those properties
will be called. For the list, a shallow copy is made during its clone()
method.
If your classes require deep cloning, it is up to you to provide the appropriate
deep cloning logic in the respective clone()
method for your class.
If one of your properties contains an object that doesn't support cloning
or attempts deep copying of a data structure containing an object that
doesn't support cloning, then a CloneNotSupportedException
may occur
at runtime.
Another popular cloning strategy is known as the copy constructor pattern.
If any of your fields are final
and Cloneable
you should set
style=COPY_CONSTRUCTOR
which will then use the copy constructor pattern.
Here is an example making use of the copy constructor pattern:
import groovy.transform.AutoClone import static groovy.transform.AutoCloneStyle.*Which will create classes equivalent to the following:@AutoClone(style=COPY_CONSTRUCTOR)
class Person { final String first, last final Date birthday }@AutoClone(style=COPY_CONSTRUCTOR)
class Customer extends Person { final int numPurchases final List favItems }
class Person implements Cloneable { ... protected Person(Person other) throws CloneNotSupportedException { first = other.first last = other.last birthday = (Date) other.birthday.clone() } public Person clone() throws CloneNotSupportedException { return new Person(this) } ... } class Customer extends Person { ... protected Customer(Customer other) throws CloneNotSupportedException { super(other) numPurchases = other.numPurchases favItems = other.favItems instanceof Cloneable ? (List) other.favItems.clone() : other.favItems } public Customer clone() throws CloneNotSupportedException { return new Customer(this) } ... }If you use this style on a child class, the parent class must also have a copy constructor (created using this annotation or by hand). This approach can be slightly slower than the traditional cloning approach but the
Cloneable
fields of your class can be final. When using the copy constructor style,
you can provide your own custom constructor by hand if you wish. If you do so, it is up to you to
correctly copy, clone or deep clone the properties of your class.
As a variation of the last two styles, if you set style=SIMPLE
then the no-arg constructor will be called followed by setting the
individual properties (and/or fields) calling clone()
if the
property/field implements Cloneable
. Here is an example:
import groovy.transform.AutoClone import static groovy.transform.AutoCloneStyle.*Which will create classes equivalent to the following:@AutoClone(style=SIMPLE)
class Person { final String first, last final Date birthday }@AutoClone(style=SIMPLE)
class Customer { final List favItems }
class Person implements Cloneable { ... public Person clone() throws CloneNotSupportedException { def result = new Person() copyOrCloneMembers(result) return result } protected void copyOrCloneMembers(Person other) { other.first = first other.last = last other.birthday = (Date) birthday.clone() } ... } class Customer extends Person { ... public Customer clone() throws CloneNotSupportedException { def result = new Customer() copyOrCloneMembers(result) return result } protected void copyOrCloneMembers(Customer other) { super.copyOrCloneMembers(other) other.favItems = favItems instanceof Cloneable ? (List) favItems.clone() : favItems } ... }You would typically use this style only for base classes where you didn't want the normal
Object
clone()
method to be called and
you would typically need to use the SIMPLE
style for any child classes.
As a final example, if your class already implements the Serializable
or Externalizable
interface, you can choose the following cloning style:
@AutoClone(style=SERIALIZATION)
class Person implements Serializable {
String first, last
Date birthday
}
which outputs a class with the following form:
class Person implements Cloneable, Serializable { ... Person clone() throws CloneNotSupportedException { def baos = new ByteArrayOutputStream() baos.withObjectOutputStream{ it.writeObject(this) } def bais = new ByteArrayInputStream(baos.toByteArray()) bais.withObjectInputStream(getClass().classLoader){ (Person) it.readObject() } } ... }This will output an error if your class doesn't implement one of
Serializable
or Externalizable
, will typically be
significantly slower than the other approaches, also doesn't
allow fields to be final, will take up more memory as even immutable classes
like String will be cloned but does have the advantage that it performs
deep cloning automatically.
Further references on cloning:
AutoCloneStyle
,
ExternalizeMethods
| Modifier and Type | Optional Element and Description |
|---|---|
String[] |
excludes
Comma separated list of property (and/or field) names to exclude from cloning.
|
boolean |
includeFields
Include fields as well as properties when cloning.
|
AutoCloneStyle |
style
Style to use when cloning.
|
public abstract String[] excludes
NOTE: When using the CLONE
style, property (and/or field) copying might occur as part of
calling super.clone()
which will ignore this list. You can then use this list to
streamline the provided clone()
implementation by selecting which Cloneable properties
(and/or fields) will have a subsequent call to their clone()
method. If you have
immutable properties (and/or fields) this can be useful as the extra clone()
will
not be necessary and cloning will be more efficient.
NOTE: This doesn't affect property (and/or field) copying that might occur as part
of serialization when using the SERIALIZATION
style, i.e. this flag is ignored;
instead adjust your serialization code to include or exclude the desired
properties (and/or fields) which should carry over during cloning.
public abstract boolean includeFields
NOTE: When using the CLONE
style, field copying might occur as part of
calling super.clone()
and might be all you require; if you turn on
this flag, the provided clone()
implementation will also
subsequently call clone()
for each Cloneable
field which can be
useful if you have mutable fields.
NOTE: This doesn't affect field copying that might occur as part of
serialization when using the SERIALIZATION
style, i.e. this flag is ignored;
instead adjust your serialization code to include or exclude your fields.
public abstract AutoCloneStyle style


Dear Finance Team,
Attached is a report of all Credits on HQ as of {{ date_of_report }}.
Cheers, --The CommCare HQ Team


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Abbreviations
Abbreviations
BH
basic heading
BOCC
basket of construction components
BOQ
bills of quantities approach
CEM
consumption equivalent method
CFC
consumption of fixed capital
CIS
Commonwealth of Independent States
CISSTAT
Interstate Statistical Committee of the Commonwealth of Independent States
CPD
Country Product Dummy
CPDW
Country Product Dummy-Weighted
CPI
consumer price index
CPRD
Country Product Representative Dummy
DECDG
Development Data Group (World Bank)
DHS
Demographic and Health Surveys
ECLAC
Economic Commission for Latin America and the Caribbean
EKS
Elteto-Koves-Szulc
ESCWA
Economic and Social Commission for Western Asia
FISIM
financial intermediation services indirectly measured
GDP
gross domestic product
GEKS
Gini-Elteto-Koves-Szulc
GFCF
gross fixed capital formation
GK
Geary-Khamis
HFCE
household final consumption expenditure
ICP
International Comparison Program
IDB
Ikle-Dikhanov-Balk
古之学者为己，今之学者为人。我们用心做，您来细细品。您也可以 先下载一个应用程序（App），有空再慢慢看。
ISCED
International Standard Classification of Education
ISCO
International Standard Classification of Occupations
NCA
national coordinating agency
NPISH
nonprofit institutions serving households
OECD
Organisation for Economic Co-operation and Development
PFM
price factor method
PLI
price level index
PPP
purchasing power parity
PWT
Penn World Table
RCA
regional coordinating agency
REER
real effective exchange rate
RMSE
root mean square error
SAR
special administrative region
SNA
System of National Accounts
SPD
structured product description
SUT
supply-use table
TAG
Technical Advisory Group (ICP)
UNSC
United Nations Statistical Commission
2011 ICP 参与ICP的国家列表
2011 ICP 国家分类（地图）
ICP Classification of Final Expenditure on GDP, 2011
ADB Asian Development Bank
AfDB African Development Bank
BOCC Basket of Construction Components
c.i.f. Cost, Insurance, and Freight
CIS Commonwealth of Independent States
CISSTAT Statistical Office of the Commonwealth
of Independent States
COFOG Classification of the Functions of
Government
COICOP Classification of Individual Consumption
According to Purpose
CPD Country-Product-Dummy
CPI Consumer Price Index
DCF Data Collection Form
DECDG Development Economics Data Group
DPM Data Processing Module
DVM Data Validation Module
ECC Equipment, Construction, and
Compensation
ECLAC Economic Commission for Latin America
and the Caribbean
ECP European Comparison Program
EEC European Economic Community
EFTA European Free Trade Association
EKS Elteto, Koves, and Szulc
EKS EKS method extended to include a
stratification of product price within
basic headings into representative and
nonrepresentative categories
ESCWA Economic and Social Commission for
Western Asia
EU European Union
FISIM Financial Intermediation Services
Indirectly Measured
f.o.b. Free On Board
GDP Gross Domestic Product
GFCF Gross Fixed Capital Formation
GK Geary-Khamis
GNI Gross National Income
ICP International Comparison Program
ISTAT Italian National Statistical Office
Lao PDR Lao People’s Democratic Republic
LCU Local Currency Unit
Ln Natural Logarithm
MER Market exchange rate
n.e.c. Not Elsewhere Classified
NBS National Bureau of Statistics of China
NGO Nongovernmental Organization
NPISH Nonprofit Institution Serving Households
OECD Organisation for Economic Co-operation
and Development
OEEC Organisation for European Economic
Co-operation
PCM Price Collection Module
PLI Price Level Index
PPP Purchasing Power Parity
Rosstat Federal State Statistics Service of the
Russian Federation
SGER Secondary (School) Gross Enrollment Rate
SNA System of National Accounts
SPD Structured Product Description
TAG Technical Advisory Group
U.K. United Kingdom
UN United Nations
UNESCAP United Nations Economic and Social
Commission for Asia and the Pacific
UNSC United Nations Statistical Commission
UNSD United Nations Statistics Division
U.S. United States
VAT Value Added Tax
WDI World Development Indicators
WDM Weights Diagnostic Module
在过度营销的时代，我们提供“更好的内容”和“更快的速度”。
Retter is Better, WE Create and Deliver Real Value!
Summary of Results and Findings of the 2011 International Comparison Program
AIC
actual individual consumption
AUS-Aid
Australian Agency for International Development
BOCC
basket of construction components
CAR
country aggregation with redistribution (method)
CIS
Commonwealth of Independent States
CPD
country product dummy (method)
CPD-W
country product dummy-weighted (method)
CPI
consumer price index
DECDG
Development Data Group (World Bank)
DFID
Department for International Development (UK)
Eurostat
Statistical Office of the European Union
GDP
gross domestic product
GEKS
Gini-Elteto-Koves-Szulc (method)
GNI
gross national income
ICP
International Comparison Program
IMF
International Monetary Fund
NBS
National Bureau of Statistics (China)
NPISH
nonprofit institution serving households
OECD
Organisation for Economic Co-operation and Development
PLI
price level index
PPP
purchasing power parity
SNA
System of National Accounts
UNSC
United Nations Statistical Commission
XR
exchange rate
ADB Asian Development Bank
AfDB African Development Bank
BOCC Basket of Construction Components
c.i.f. Cost, Insurance, and Freight
CIS Commonwealth of Independent States
CISSTAT Statistical Office of the Commonwealth
of Independent States
COFOG Classification of the Functions of
Government
COICOP Classification of Individual Consumption
According to Purpose
CPD Country-Product-Dummy
CPI Consumer Price Index
DCF Data Collection Form
DECDG Development Economics Data Group
DPM Data Processing Module
DVM Data Validation Module
ECC Equipment, Construction, and
Compensation
ECLAC Economic Commission for Latin America
and the Caribbean
ECP European Comparison Program
EEC European Economic Community
EFTA European Free Trade Association
EKS Gini-Elteto-Koves-Szulc
EKS* EKS method extended to include a
stratification of product price within
basic headings into representative and
nonrepresentative categories
ESCWA Economic and Social Commission for
Western Asia
EU European Union
FISIM Financial Intermediation Services
Indirectly Measured
f.o.b. Free On Board
GDP Gross Domestic Product
GFCF Gross Fixed Capital Formation
GK Geary-Khamis
GNI Gross National Income
ICP International Comparison Program
ISTAT Italian National Statistical Office
Lao PDR Lao People’s Democratic Republic
LCU Local Currency Unit
Ln Natural Logarithm
MER Market exchange rate
n.e.c. Not Elsewhere Classified
NBS National Bureau of Statistics of China
NGO Nongovernmental Organization
NPISH Nonprofit Institution Serving Households
OECD Organisation for Economic Co-operation
and Development
OEEC Organisation for European Economic
Co-operation
PCM Price Collection Module
PLI Price Level Index
PPP Purchasing Power Parity
Rosstat Federal State Statistics Service of the
Russian Federation
SGER Secondary (School) Gross Enrollment Rate
SNA System of National Accounts
SPD Structured Product Description
TAG Technical Advisory Group
U.K. United Kingdom
UN United Nations
UNESCAP United Nations Economic and Social
Commission for Asia and the Pacific
UNSC United Nations Statistical Commission
UNSD United Nations Statistics Division
U.S. United States
VAT Value Added Tax
WDI World Development Indicators
WDM Weights Diagnostic Module
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Stack offers a clean and contemporary to suit a range of purposes from corporate, tech startup, marketing site to digital storefront.
Combine blocks from a range of categories to build pages that are rich in visual style and interactivity
Stack comes with integration for Mail Chimp and Campaign Monitor forms - ideal for launching modern marketing campaigns


Additional remarks for CreateBacoAppLog.Entity
Entity - Pass in Request, Opportunity, Document, Account, ContactPerson or Resource


Illustration by Olivia Juenger | For The Post
Want to learn more about us? Check us out at thepostathens.com, email us at<EMAIL_ADDRESS>or follow our social media below!
© Copyright 2021, The Post, Athens, OH
Want to learn more about us? Check us out at thepostathens.com, email us at<EMAIL_ADDRESS>or follow our social media below!


More Jataka Tales
by Ellen C. Babbitt
“The Otters and the Wolf”
Additional Information
- Year Published: 1922
- Language: English
- Country of Origin: India
- Source: Babbitt, E.C. (Ed.). (1922). More Jataka Tales. New York, NY: D. Appleton-Century Company.
-
Readability:
- Flesch–Kincaid Level: 3.2
- Word Count: 371
- Genre: Fairy Tale/Folk Tale
- Keywords: clever, fishing
- ✎ Cite This
Downloads
One day a Wolf said to her mate, “A longing has come upon me to eat fresh fish.”
“I will go and get some for you,” said he and he went down to the river.
There he saw two Otters standing on the bank looking for fish. Soon one of the Otters saw a great fish, and entering the water with a bound, he caught hold of the tail of the fish. But the fish was strong and swam away, dragging the Otter after him. “Come and help me,” the Otter called back to his friend. “This great fish will be enough for both of us!” So the other Otter went into the water. The two together were able to bring the fish to land. “Let us divide the fish into two parts.” “I want the half with the head on,” said one. “You cannot have that half. That is mine,” said the other. “You take the tail.”
The Wolf heard the Otters and he went up to them. Seeing the Wolf, the Otters said: “Lord of the gray-grass color, this fish was caught by both of us together. We cannot agree about dividing him. Will you divide him for us?”
The Wolf cut off the tail and gave it to one, giving the head to the other. He took the large middle part for himself, saying to them, “You can eat the head and the tail without quarreling.” And away he ran with the body of the fish. The Otters stood and looked at each other. They had nothing to say, but each thought to himself that the Wolf had run off with the best of the fish.
The Wolf was pleased and said to himself, as he ran toward home, “Now I have fresh fish for my mate.”
His mate, seeing him coming, came to meet him, saying: “How did you get fish? You live on land, not in the water.” Then he told her of the quarrel of the Otters. “I took the fish as pay for settling their quarrel,” said he.


The basis for our project is applying natural-language processing to determine what games you might like based on the reviews of an assortment of games from an existing database at Board Game Geek.
The background processing of our approach can be described in further detail below, for those who are interested in the deep-dive into modeling with NLP.


Home
About
Ping-
Pong
Ping-Pong
Let's play!
Rules:
Ping-Pong will count up to your number from 1 with the following exceptions:
Numbers divisible by 3 becomes 'ping'
Numbers divisble by 5 becomes 'pong'
Numbers divisible by 3 and 5 becomes PingPong
please enter a number here
GO!


The mobile app
will be the main bridge between the users and the system.
It’ll provide the possibility to:
- Manage pending requisitions, providing information of the project to which it is associated and the number of units requested;
- Create and manage projects with which the user is involved and add new members to them.
- Make item requisitions and deliveries.
Login
The users will be able to login into the app with their University of Aveiro’s credentials, and , in order to automate and make things easier, it will have an auto-login feature, so that the users don’t have to keep filling the login form every time they want to use the app.
Projects
There is a place where the users can show to others everything that has been done on a project and, in the case of equipments, give them ideas of how to use them, leading them to develop new ideas and continuing the loop.
In order to populate and maintain this important space, the app will allow them to manage the two most important parts - projects and their participants. By creating and manage projects and adding a colleague to a project, users will be able to access all project information and documentation in order to start their work in no time.
Requisitions
The equipments spread across the lab will be labeled with QR Codes. The users will be able to point to them and sneak peek some information about them with an Augmented Reality touch, which is particularly useful if they’re not familiar with the equipment.
If they are pointing to the intended item, they can select it and choose the project name that the requisition will be associated to.
The users will also be able to deliver items they previously requested through the app. The process is almost the same as to making a requisition. Simply pointing the phone’s camera to the QR code pasted in the item and clicking “Deliver” will do the trick.


"""
Example that demonstrates how to add cusom Tornado handlers, for
instance to serve a custom HTML page, or a (REST) api from the same
process.
For the sake of the example, we alo serve two example web apps.
Goto:
* http://localhost:port/ to see a list of served web apps
* http://localhost:port/about to see a the custom about page
* http://localhost:port/api/foo/bar to see the echo api in action
"""
from flexx import app
from flexx.ui.examples.drawing import Drawing
from flexx.ui.examples.chatroom import ChatRoom
import tornado.web
# Serve some web apps, just for fun
app.serve(Drawing)
app.serve(ChatRoom)
class MyAboutHandler(tornado.web.RequestHandler):
def get(self):
self.write('This is just an example.')
class MyAPIHandler(tornado.web.RequestHandler):
def get(self, path):
# self.request.path -> full path
# path -> the regexp group specified in add_handlers
self.write('echo ' + path)
# Get a ref to the tornado.web.Application object
tornado_app = app.current_server().app
# Add our handler
tornado_app.add_handlers(r".*", [(r"/about", MyAboutHandler),
(r"/api/(.*)", MyAPIHandler)])
# Note: Tornado tries to match handlers in order, but the handlers
# specified in the constructor come last. Therefore we can easily add
# specific handlers here even though Flexx' main handler is very
# generic.
app.start()


Blog
To be a pushover or a nitpicker, It's a question. What’s happening with engineering code reviews? Thoughts about Google Material Design UX world and Design thinking... Git common use Sharing some Python Trick Useful tips that were found when wrote Python Hello World, Little background info about me


Remember the Titans (2000)
The title of Boaz Yakin's film surfaces when one of the coaches tells his team to play hard, to make the other team never forget the night they scrapped with the Titans from T.C. Williams High School from Alexandria, Virginia. It's an inspiring moment, as the Titans indeed come back and teach their opponents many lessons in the science of the gridiron. It's not a very original moment, however.
The premise of the piece is a promising and even an important one. We watch another episode in the long story of the integration of American schools. Seventeen years have passed since Brown vs. Board of Education – it's 1971 – and forced busing has a lot of parents (and in imitation of this prejudice) children outraged. The film does not go into specifics; for example, we never discover that it was three schools (one with a largely white enrollment, two with largely African American enrollments) that merged. But backstories stay undeveloped, and we quickly pick up on the tension caused by changes on the coaching staff.
Herman Boone (Denzel Washington) has come from North Carolina, and is installed as head coach. Ousted from his position, Coach Yoast (Will Patton) says he'll move on, but changes his mind when his white players threaten to sit out the season. It's a political move, we find out, and the school board apparently intends to reinstall Yoast as chief if Boone loses even one game. So one conflict is established – the one among the coaches and their assistants. (Let me inject here that I've taught high school myself for a number of years, and have never seen a team with only three assistant coaches: Boone and Yoast each have only one disciple.)
The major problems come from the racial tension at football camp. Some of the best scenes hinge on Coach Boone's determination to break the barriers of hate separating these youths. Outstanding in their performances are Wood Harris playing Julius, and Ryan Hurst playing Bertier, the All-American linebacker. We know they are traveling a course that will intersect in friendship, but still we eat up the fights they cause and cause to stop. Though both actors look much too old for high schoolers, they create a level of believability, and they make us want to watch them in other vehicles.
It takes a good 45 minutes before we see any action in the regular season football games. But by this time, the characters are well developed, the conflicts clear. Another item that diverges from the source story is that the Titans pushed many of their foes right off the field, blew them off the scoreboard. Here most of the games are portrayed as close contests. I was glad that the plot does not rely too heavily on game action; there's enough of it here, but we still see the players and coaches plenty off the field. And they are fighting the battles of prejudice wherever they go: the players often trying to mediate in school fights, the coaches coping with political hijinks and boardroom betrayals.
Denzel Washington is one of the most reliable actors in American movies. What he's in will probably succeed, as he does not have much trouble taking on the focus of the narrative. Though Washington still does not command the money taken home by white superstars, he is able to match the intensity and presence of anyone opposite him. I was also glad to see Will Patton depicting such a multi-faceted character. His Yoast actually grows, and we view him as a caring family man as well as conflicted coach.
Events late in the film tend to distract us from the story of race and football. The end does not dive into downright sappiness, but the story veers a little and staggers on a bit too long. The music was very engaging – the soundtrack will doubtless play in the living rooms of thousands of fans of oldies. And about an hour into the playing time, the editing cuts loose into a cool triptych of montages.
On the whole, REMEMBER THE TITANS pleases fans of football and racial justice alike, though it fails to use fresh devices to put even more points up on the board.
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Related links: index of all rec.arts.movies.reviews reviews


I am just a woman who loves helping others and traveling. Often I wondered how could I combine the two? Well, I believe I have found a way to do both through my home-based travel business. My prayer is as you visit our Travel Site, it will excite, incite and invite you to take your next flight with TRAVELSCAPE Adventures!!!Leah Jennings
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Lab#2 Variables & Conditions
Recommended Work Time: One week
Home
Assignment 1
Assignment 2
Lab 2
Lab 2 Supp.
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6
Final Project
Table of Contents


--- layout: post title: rucola date: '2008-01-09T14:19:20+00:00' tags: [] tumblr_url: http://backlog.robbevan.com/post/23364102/rucola --- rucola
A Framework for building Cocoa applications in Ruby


RCI cellulose plant Arabidopsis []. crystallinity grown leaf light plants samples Leaf Cretaceous Figure sample using cell wind dark orientation {border-style analysis lower px;} #ffffff;} values [], biomass hypocotyls stems thaliana different relative x-ray array conditions crystalline diffraction experimental properties significantly tissue value wall wood high Plant accuracy compared leaves length pea pressed seedlings structure track Miscanthus YFP::CESA []– area average experiments index longitudinal measured Cellulose particles stem Bot Table analyzed biomechanical determination exposed px; results roots species specific transverse used {font-family CESA FTIR crystallites density error etiolated font-size functional greater growth measurements number range root study variance versus Andersson Crystallinity Relative Sci X-ray boric central consistent double; ecological exposure given mass measurement peaks performed replicates showed shown size synthase total traits transmission Kentucky Norway Pisum Serimaa XRD absolute acid amorphous array. ball body border-color border-width changes determined displayed fiber fibril fibrils foliar geometries peak plan plot respect software solid; technical tensile time unit variability {font-style {font-weight (.±. .±. ANOVA) Ann B). Biol Columbia DMH DeBolt Experimental FT-IR However, Paakkari Pesonen SD. Samples Tripsacum USA University abiotic achieved allocation analysis. article based bold cellular compression constant courier; dactyloides data decreased degree described diffractogram disk dried drying em; energy estimate estimated evident examined expressing fraction gamagrass gravity height higher important lengths measure mechanical orientation, oven pattern physical polymer postulated preferred previous provide provided pt;} published reflective response rosette shape similar single spectra spectral spruce strength stress suggest texture tissues trait variation volume wk)


Portfolio
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A web app that allows users to manage their contacts and professional
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A system that allows drug tests, mental health screenings, and medication compliance tests to be conducted remotely
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Destiny Roll Finder
A website that harnesses Bungie's API to allow Destiny 2 players to search their inventories
-
RoseThrive
Android app that allows users simple access to the campus bulletin board system.


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Tree Control¶
The left pane of the main window displays a tree control (the pgAdmin tree control) that provides access to the objects that reside on a server.
You can expand nodes in the tree control to view the database objects that reside on a selected server. The tree control expands to display a hierarchical view:
Use the plus sign (+) to the left of a node to expand a segment of the tree control.
Click the minus sign (-) to the left of a node to close that node.
You can also drag and drop certain objects to the Query Tool which can save time in typing long object names. Text containing the object name will be fully qualified with schema. Double quotes will be added if required. For functions and procedures, the function name along with parameter names will be pasted in the Query Tool.
Access context-sensitive menus by right-clicking on a node of the tree control to perform common tasks. Menus display options that include one or more of the following selections (options appear in alphabetical order):
Option |
Action |
|---|---|
Add named restore point |
Click to create and enter the name of a restore point. |
Backup… |
Click to open the Backup… dialog to backup database objects. |
Backup Globals… |
Click to open the Backup Globals… dialog to backup cluster objects. |
Backup Server… |
Click to open the Backup Server… dialog to backup a server. |
Connect Server… |
Click to open the Connect to Server dialog to establish a connection with a server. |
Create |
Click to access a context menu that provides context-sensitive selections. Your selection opens a Create dialog for creating a new object. |
CREATE Script |
Click to open the Query tool to edit or view the CREATE script. |
Debugging |
Click through to open the Debug tool or to select Set breakpoint to stop or pause a script execution. |
Delete/Drop |
Click to delete the currently selected object from the server. |
Disconnect Database… |
Click to terminate a database connection. |
Disconnect Server… |
Click to refresh the currently selected object. |
Drop Cascade |
Click to delete the currently selected object and all dependent objects from the server. |
Debugging |
Click to access the Debugger tool. |
Grant Wizard |
Click to access the Grant Wizard tool. |
Maintenance… |
Click to open the Maintenance… dialog to VACUUM, ANALYZE, REINDEX, or CLUSTER. |
Properties… |
Click to review or modify the currently selected object’s properties. |
Refresh… |
Click to refresh the currently selected object. |
Reload Configuration… |
Click to update configuration files without restarting the server. |
Restore… |
Click to access the Restore dialog to restore database files from a backup. |
View Data |
Use the View Data option to access the data stored in a selected table with the Data Output tab of the Query Tool. |
The context-sensitive menus associated with Tables and nested Table nodes provides additional display options (options appear in alphabetical order):
Option |
Action |
|---|---|
Import/Export… |
Click open the Import/Export… dialog to import data to or export data from the selected table. |
Reset Statistics |
Click to reset statistics for the selected table. |
Scripts |
Click to open the Query tool to edit or view the selected script from the flyout menu. |
Truncate |
Click to remove all rows from a table. |
Truncate Cascade |
Click to remove all rows from a table and its child tables. |
View First 100 Rows |
Click to access a data grid that displays the first 100 rows of the selected table. |
View Last 100 Rows |
Click to access a data grid that displays the last 100 rows of the selected table. |
View All Rows |
Click to access a a data grid that displays all rows of the selected table. |
View Filtered Rows… |
Click to access the Data Filter popup to apply a filter to a set of data. |


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SPTScrollViewScrubberController Reference
About
SPTScrollViewScrubberController adds a scrubber to an instance of UIScrollView or any of its subclasses to quickly scroll through large contents.
It was inspired by NSScreencast episode 97: Scrolling Nub (11/28/2013).
License
SPTScrollViewScrubberController and SPTScrollViewScrubberSample are released under MIT License. It is highly appreciated, though not required, to include a link to http://www.mecking.net/ios-apps/ anywhere in your app or on your website.
Obtaining the source
The source code is available on github at https://github.com/sptim/SPTScrollViewScrubber.
AppleDoc generated documentation can be browsed at http://sptim.github.io/SPTScrollViewScrubber/.
SPTScrollViewScrubberController is also available using Cocoapods. Add
this to your Podfile
and run pod install
:
pod 'SPTScrollViewScrubber'
Using SPTScrollViewScrubberController with UITableView
Adding a scrubber to an UITableView
instance programmatically
Only 2 steps are necessary to add a scrubber to a table view.
Import the header and add a property for the SPTScrollViewScrubberController instance of your UITableViewController subclass.
@property (nonatomic,strong) SPTScrollViewScrubberController* scrubController;
In your -viewDidLoad method instanciate and setup the SPTScrollViewScrubberController object.
self.scrubController=[[SPTScrollViewScrubberController alloc] init]; self.scrubController.scrollView=self.tableView; self.tableView.delegate=self.scrubController;
UITableViewDelegate Methods
If you cannot make the SPTScrollViewScrubberController instance the delegate of the table view, you must forward 3 delegate methods from your table view delegate (usually the UITableViewController subclass) to the scrubber controller:
-(void)scrollViewDidScroll:(UIScrollView*)scrollView {
[self.scrubController scrollViewDidScroll:scrollView];
}
-(void)scrollViewDidEndDragging:(UIScrollView *)scrollView willDecelerate:(BOOL)decelerate {
[self.scrubController scrollViewDidEndDragging:scrollView willDecelerate:decelerate];
}
-(void)scrollViewDidEndDecelerating:(UIScrollView *)scrollView {
[self.scrubberController scrollViewDidEndDecelerating:scrollView];
}
Scrubber View
You most probably want to use a custom scrubber view. To do so just set the scrubberView property to an instance of UIView or any of its subclasses. There must not be any autolayout constraints attached to that view. Use its frame or center property to position and size it.
By default SPTScrollViewScrubberController only changes the scrubberViews position on the scrubbing/scrolling axis(es). When used with UITableView this is always the vertical axis (y coordinate).
If you set the horizontalAlignment property on the controller, it will also position the scrubberView on the horizontal axis. You can choose to have it pinned to any side or the center of the scroll view. When the frame of the scrollView changes, i.e. because of interface rotation, the SPTScrollViewScrubberController will adjust the scrubberViews position accordingly.
The margin, i.e. free space between the scrollViews edge and the scrubberView, can be configured using SPTScrollViewScrubberController’s edgeInsets property.
The scrubber view will be hidden if the table views content height is less than
2 times its content area height. The content area is determined by by applying
its content inset to its bounds. You can adjust the minimum factor at which the
scrubber will be shown by setting the minimumVerticalScrollFactor property of
your SPTScrollViewScrubberController instance. The factor must be greater than
1.0
.
View Transitions & Animations
The scrubber can be in 3 different states:
unselected / unhighlighted
This is the idle state. By default the scrubber view is faded out by having its alpha value set to 0.3.
selected / unhighlighted
During any scroll event the scrubber view enters this state. After some delay it returns to the previous state. In the default configuration the view’s alpha value is set to 1.0.
selected / highlighted
While the user touches and holds the scrubber view, its in this state. There is no visual difference from the previous state in the default configuration.
There are four block properties on SPTScrollViewScrubberController to configure the view transitions:
@property (nonatomic,copy) void(^highlightScrubberView)(UIView* view);
@property (nonatomic,copy) void(^unhighlightScrubberView)(UIView* view);
@property (nonatomic,copy) void(^selectScrubberView)(UIView* view);
@property (nonatomic,copy) void(^deselectScrubberView)(UIView* view);
Each get called with a pointer to the scrubberView object as the argument. The default implementations set the corresponding property of the scrubberView object if it responds to the setter selector (-[setSelected:], -[setHighlighted:]). The select/unselect blocks also set the view’s alpha value as described above.
Currently the only transition which gets animated is deselect. Its animation speed is configured by the deselectAnimationDuration property of the scrubber controller. After a scroll operation finishes, deselection is delayed by the time interval specified in the deselectDelay property.


Load a GameBoy or GameBoy Color ROM from the dropdown list with "Load ROM" or from your own computer with "Upload ROM".
Enjoy!
GameBoy emulator by Senryoku
Written in C++ and compiled to javascript via Emscripten. Based on SenBoy v1.0. Port is far from perfect, it was merely a way to get some experience with emscripten. More informations: GitHub
Use the Save button before closing this page to ensure that your progress is correctly saved!
(Note: This only emulates the save capabilities of the loaded cartridge.


14 April 2019
Welcome to Jekyll!
by Jerry Zhang
Welcome
Hello world, this is my first Jekyll blog post.
I hope you like it!
I decided to start writing technical blogs from today because of two reasons:
- Recording what I learned everyday in case I need to check or review them later on.
- Provide useful information to others and save their time.
Regarding to Jekyll installation, I wasted a large amount of time on template usage. At first, I read the official document of Jekyll and followed tutorial step by step. However, that tutorial is for someone who wants to built a site from scratch! If you do not want to be an expert of Jekyll, you actually do not need to read that.
You only need to do the following:
- Create a repository on github.

This index page will be rendered automatically by github. Next, I will explain how to modify the index file and add your own blogs.
index.md
You must understand some knowledge about markdown language. This file includes many useful default templates, but you may not want them displayed in your own page.
- Create a new file in your project and move all the code into it except
layout: default
- If you want to display all your blogs by links in your index page, the following code might be helpful:
{% for post in site.posts %}
* [{{ post.title }}]({{ site.baseurl }}{{ post.url }})
{% endfor %}
- Create a new folder named
_posts
in your project - Create blogs like the name “2019-04-14-yourtitle.md”. Follow this naming convention.


{% extends 'template-lab-4a.html' %} {% set mainClasses = 'nhsuk-u-padding-top-0' %} {% block pageTitle %} Symptoms - NHS {% endblock %} {% block beforeContent %} {% endblock %} {% block content %}
Priapism is a long-lasting painful erection. It can cause permanent damage to your penis if not treated quickly.
Priapism may get better on its own within 2 hours.
There are things you can try to reduce your erection.
try to go for a pee
have a warm bath or shower
drink lots of water
go for a gentle walk
try exercises, such as squats or running on the spot
take painkillers like paracetamol if you need to
do not apply ice packs or cold water to your penis – this can make things worse
do not have sex or masturbate – it will not make your erection go away
do not drink alcohol
do not smoke
An erection that lasts this long needs to be treated in hospital as soon as possible to help avoid permanent damage to the penis.

Less common causes include:
Page last reviewed: 02 August 2019
Next review due: 02 August 2022


ifup.org by Brandon Philips contains excerpts from my code, work and play.
I love writing systems software and my current passion and work is CoreOS.


영어가 필수불가결하게 필요한 현재! Mr.Mountain이 쉽고 간단하게 영어를 가르쳐드리겠습니다!
Holisticly reinvent enabled channels and resource sucking architectures. Authoritatively predominate timely users before frictionless potentialities. Objectively engage interdependent web-readiness whereas accurate customer service. Competently impact B2B best practices through principle-centered information. Globally unleash progressive ROI and economically sound data.
Competently orchestrate scalable paradigms through bricks-and-clicks internal or “organic” sources. Dynamically plagiarize cost effective e-markets without unique web services. Professionally disintermediate error-free.
Efficiently empower next-generation benefits without leading-edge innovation. Quickly deliver 2.0 schemas vis-a-vis distinctive outsourcing. Energistically customize enabled meta-services and distinctive channels. Rapidiously reinvent sustainable models for synergistic process improvements. Progressively engineer distinctive meta-services for 24/365 methodologies.
Holisticly syndicate value-added initiatives after inexpensive initiatives. Monotonectally transform sustainable potentialities with emerging innovation. Competently leverage existing enterprise systems with leveraged mindshare. Credibly administrate performance based e-markets through worldwide value. Professionally underwhelm emerging mindshare without business mindshare.
Holisticly reinvent enabled channels and resource sucking architectures. Authoritatively predominate timely users before frictionless potentialities. Objectively engage interdependent web-readiness whereas accurate customer service. Competently impact B2B best practices through principle-centered information. Globally unleash progressive ROI and economically sound data.Dramatically develop leveraged data whereas integrated technologies.Enthusiastically syndicate accurate testing procedures after interactive e-tailers. Competently enable resource-leveling scenarios rather than flexible architectures. Holisticly myocardinate synergistic technologies before proactive methodologies. Credibly formulate enterprise-wide initiatives for optimal e-markets.
Holisticly reinvent enabled channels and resource sucking architectures. Authoritatively predominate timely users before frictionless potentialities. Objectively engage interdependent web-readiness whereas accurate customer service. Competently impact B2B best practices through principle-centered information. Globally unleash progressive ROI and economically sound data.


Biography:
- September 14, 1983
Amy Jade Winehouse is born to Mitch, a taxi driver, and Janis, a pharmacist in Southgate, London.
- 1993
Winehouse attends the Susi Earnshaw Theatre school. In the same year her parents split up and Winehouse stays with her mother.
- 1994
She forms rap duo with friend called Sweet 'n' Sour, inspired by Salt-N-Pepa.
- 1996 -
Winehouse is awarded place at Sylvia Young Theatre School, but is later expelled for "not applying herself".
- 1998-1999
The singer briefly attends BRIT School, and starts writing and performing with jazz band.
- 2002
She signs with Simon Fuller's management company 19 Entertainment, then Island Records.
- 2003
Winehouse releases her debut album, Frank. It goes platinum in the UK, and she wins an Ivor Novello award for song-writing. She also begins an on-off relationship with Blake Fielder-Civil.
- 2006
Winehouse releases Back to Black", which centres around relationship with Fielder-Civil. It becomes the best selling album in the UK the following year, selling around 1.85m copies.
- 2007
Back To Black enters US Billboard and becomes the highest debut for UK female. She marries Fielder-Civil and is hospitalised with "exhaustion", cancelling her US tour.
- 2008
Winehouse goes into rehab after photos of her alleged drug use are published. In the same year she is arrested twice, but gives a triumphant performance at the Grammys and wins five awards.
- 2009
Winehouse heads to St Lucia for a two-month holiday but is arrested for allegedly punching a fan at a Society Ball in London. Later in the year she is charged with a public order offence and common assault after allegedly attacking a theatre manager during a family production of Cinderella. Her parents make a public plea to try to get their daughter the help she needs. After well-publicised fights and arrests, Winehouse and Blake Fielder-Civil officially divorce.
- 2010
Winehouse says her third album will be released in January, and collaborates with Mark Ronson on the Quincy Jones tribute album Q Soul Bossa Nostra.
- 2011
After a mini-tour of Brazil, Winehouse returns to rehab. After a performance in Serbia where Winehosue is too drunk to perform, her European tour is cancelled. On 23 July she is found dead in her London home.


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C++ in Lua¶
Using user defined types (“usertype”s, or just “udt”s) is simple with Sol. If you don’t call any member variables or functions, then you don’t even have to ‘register’ the usertype at all: just pass it through. But if you want variables and functions on your usertype inside of Lua, you need to register it.
Take this player
struct in C++ in a header file:
struct player {
public:
int bullets;
int speed;
player()
: player(3, 100) {
}
player(int ammo)
: player(ammo, 100) {
}
player(int ammo, int hitpoints)
: bullets(ammo), hp(hitpoints) {
}
void boost () {
speed += 10;
}
bool shoot () {
if (bullets < 1)
return false;
--bullets;
return true;
}
void set_hp(int value) {
hp = value;
}
int get_hp() const {
return hp;
}
private:
int hp;
};
It’s a fairly minimal class, but we don’t want to have to rewrite this with metatables in Lua. We want this to be part of Lua easily. The following is the Lua code that we’d like to have work properly:
-- call single argument integer constructor
p1 = player.new(2)
-- p2 is still here from being
-- set with lua["p2"] = player(0);
-- in cpp file
local p2shoots = p2:shoot()
assert(not p2shoots)
-- had 0 ammo
-- set variable property setter
p1.hp = 545;
-- get variable through property getter
print(p1.hp);
local did_shoot_1 = p1:shoot()
print(did_shoot_1)
print(p1.bullets)
local did_shoot_2 = p1:shoot()
print(did_shoot_2)
print(p1.bullets)
local did_shoot_3 = p1:shoot()
print(did_shoot_3)
-- can read
print(p1.bullets)
-- would error: is a readonly variable, cannot write
-- p1.bullets = 20
p1:boost()
To do this, you bind things using the new_usertype
and set_usertype
methods as shown below. These methods are on both table and state(_view), but we’re going to just use it on state
:
#include <sol.hpp>
int main () {
sol::state lua;
// note that you can set a
// userdata before you register a usertype,
// and it will still carry
// the right metatable if you register it later
// set a variable "p2" of type "player" with 0 ammo
lua["p2"] = player(0);
// make usertype metatable
lua.new_usertype<player>( "player",
// 3 constructors
sol::constructors<player(), player(int), player(int, int)>(),
// typical member function that returns a variable
"shoot", &player::shoot,
// typical member function
"boost", &player::boost,
// gets or set the value using member variable syntax
"hp", sol::property(&player::get_hp, &player::set_hp),
// read and write variable
"speed", &player::speed,
// can only read from, not write to
"bullets", sol::readonly( &player::bullets )
);
lua.script_file("player_script.lua");
}
That script should run fine now, and you can observe and play around with the values. Even more stuff you can do is described elsewhere, like initializer functions (private constructors / destructors support), “static” functions callable with name.my_function( ... )
, and overloaded member functions. You can even bind global variables (even by reference with std::ref
) with sol::var
. There’s a lot to try out!
This is a powerful way to allow reuse of C++ code from Lua beyond just registering functions, and should get you on your way to having more complex classes and data structures! In the case that you need more customization than just usertypes, however, you can customize Sol to behave more fit to your desires by using the desired customization and extension structures.
You can check out this code and more complicated code at the examples directory by looking at the usertype_
-prefixed examples.


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Timeline
February 24, 1955
Steven Paul Jobs is born in San Francisco.
1961
The Jobs family moves to Mountain View.
1968
Jobs calls Bill Hewlett, the co-founder and co-namesake of Hewlett-Packard.
1970
Meets future Apple co-founder Steve Wozniak through a friend.
1972
Graduates from Homestead High School in Cupertino.
1974
Begins a brief stint as an engineer at Atari.
1976
Co-founds Apple Computer with Wozniak and Roanld Wayne.
1977
Apple incorporates.
1980
Apple goes public, putting Jobs' net worth north of $200 million.
1984
Two days after the $1.5 million Ridley Scott-directed "1984" Super Bowl commercial airs.
1985
CEO John Sculley engineers Jobs' ouster from Apple.
1986
For $10 million, buys the Graphics Group division of Lucasfilm that becomes Pixar Animation Studios.
1988
NeXT Computer releases its first computer.
1993
NeXT discontinues hardware business, gets into software intead.
1995
Becomes Pixar's president and CEO.
1996
Returns to APple, as an adviser, after it buys NeXT for $429 million.
1997
Becomes CEO, initially as the de facto chief, then as interim chief in September.
1998
Apple returns to profitability and introduces the iMace, which becomes commercially available in August.
2000
Drops the "interim" from his CEO title at the Macworld Expo.
2001
Introduces iTunes, then exclusively for Mac users. Apple ships the first version of Mac OS X, code-named Cheetah and introduces the iPod.
2002
Apple introduces the first Windows-compatible iPods at Macworld.
2003
Opens the iTunes Music Store in the United States.
2004
Jobs undergoes surgery to remove a cancerous tumor in his pancreas.
2005
Apple introduces an iPod Shuffle.
2006
That year, Apple's market capitalization surpasses Dell's.
2007
Drops "Computer" from Apple's name. Introduces the iPhone.
2008
Introduces the App Store as an update to iTunes.
2009
Jobs takes a 6-month leave of absence for medical reasons.
2010
Introduces the iPad.
2011
Resigns from Apple CEO post; becomes chairman.
Quick Facts
Steve was adopted
Didnt't complete college
Created Apple in a garage
It was part of the Atari project
Your last words "Oh wow"
Died in 2011
"Be a quality parameter. Some people are not used to an environment where excellence is expected."


Process an input dataset into a format suitable for machine learning.
deepchem.data.data_loader.
CSVLoader
(tasks, smiles_field=None, id_field=None, mol_field=None, featurizer=None, verbose=True, log_every_n=1000)[source]¶Bases: deepchem.data.data_loader.DataLoader
Handles loading of CSV files.
featurize
(input_files, data_dir=None, shard_size=8192)¶Featurize provided files and write to specified location.
deepchem.data.data_loader.
DataLoader
(tasks, smiles_field=None, id_field=None, mol_field=None, featurizer=None, verbose=True, log_every_n=1000)[source]¶Bases: object
Handles loading/featurizing of chemical samples (datapoints).
Currently knows how to load csv-files/pandas-dataframes/SDF-files. Writes a dataframe object to disk as output.
deepchem.data.data_loader.
SDFLoader
(tasks, clean_mols=False, **kwargs)[source]¶Bases: deepchem.data.data_loader.DataLoader
Handles loading of SDF files.
featurize
(input_files, data_dir=None, shard_size=8192)¶Featurize provided files and write to specified location.
deepchem.data.data_loader.
UserCSVLoader
(tasks, smiles_field=None, id_field=None, mol_field=None, featurizer=None, verbose=True, log_every_n=1000)[source]¶Bases: deepchem.data.data_loader.DataLoader
Handles loading of CSV files with user-defined featurizers.
featurize
(input_files, data_dir=None, shard_size=8192)¶Featurize provided files and write to specified location.
deepchem.data.data_loader.
convert_df_to_numpy
(df, tasks, verbose=False)[source]¶Transforms a dataframe containing deepchem input into numpy arrays
deepchem.data.data_loader.
featurize_mol_df
(df, featurizer, field, verbose=True, log_every_N=1000)[source]¶Featurize individual compounds in dataframe.
Featurizes .sdf files, so the 3-D structure should be preserved so we use the rdkit “mol” object created from .sdf instead of smiles string. Some featurizers such as CoulombMatrix also require a 3-D structure. Featurizing from .sdf is currently the only way to perform CM feautization.
deepchem.data.data_loader.
featurize_smiles_df
(df, featurizer, field, log_every_N=1000, verbose=True)[source]¶Featurize individual compounds in dataframe.
Given a featurizer that operates on individual chemical compounds or macromolecules, compute & add features for that compound to the features dataframe
deepchem.data.data_loader.
featurize_smiles_np
(arr, featurizer, log_every_N=1000, verbose=True)[source]¶Featurize individual compounds in a numpy array.
Given a featurizer that operates on individual chemical compounds or macromolecules, compute & add features for that compound to the features array
deepchem.data.data_loader.
get_user_specified_features
(df, featurizer, verbose=True)[source]¶Extract and merge user specified features.
Merge features included in dataset provided by user into final features dataframe
Three types of featurization here:
- Molecule featurization
-) Smiles string featurization -) Rdkit MOL featurization
- Complex featurization
-) PDB files for interacting molecules.
- User specified featurizations.
Contains wrapper class for datasets.
deepchem.data.datasets.
Databag
(datasets=None)[source]¶Bases: object
A utility class to iterate through multiple datasets together.
iterbatches
(**kwargs)[source]¶Loop through all internal datasets in the same order :param batch_size: Number of samples from each dataset to return :type batch_size: int :param epoch: Number of times to loop through the datasets :type epoch: int :param pad_batches: Should all batches==batch_size
| Returns: | Generator which yields a dictionary {key |
|---|---|
| Return type: | dataset.X[batch]} |
deepchem.data.datasets.
Dataset
[source]¶Bases: object
Abstract base class for datasets defined by X, y, w elements.
X
¶Get the X vector for this dataset as a single numpy array.
get_shape
()[source]¶Get the shape of the dataset.
Returns four tuples, giving the shape of the X, y, w, and ids arrays.
ids
¶Get the ids vector for this dataset as a single numpy array.
itersamples
()[source]¶Get an object that iterates over the samples in the dataset.
Example:
>>> dataset = NumpyDataset(np.ones((2,2)))
>>> for x, y, w, id in dataset.itersamples():
... print(x, y, w, id)
[ 1. 1.] [ 0.] [ 0.] 0
[ 1. 1.] [ 0.] [ 0.] 1
transform
(fn, **args)[source]¶Construct a new dataset by applying a transformation to every sample in this dataset.
The argument is a function that can be called as follows:
>> newx, newy, neww = fn(x, y, w)
It might be called only once with the whole dataset, or multiple times with different subsets of the data. Each time it is called, it should transform the samples and return the transformed data.
| Parameters: | fn (function) – A function to apply to each sample in the dataset |
|---|---|
| Returns: | |
| Return type: | a newly constructed Dataset object |
w
¶Get the weight vector for this dataset as a single numpy array.
y
¶Get the y vector for this dataset as a single numpy array.
deepchem.data.datasets.
DiskDataset
(data_dir, verbose=True)[source]¶Bases: deepchem.data.datasets.Dataset
A Dataset that is stored as a set of files on disk.
X
¶Get the X vector for this dataset as a single numpy array.
create_dataset
(shard_generator, data_dir=None, tasks=[], verbose=True)[source]¶Creates a new DiskDataset
| Parameters: |
|
|---|
from_numpy
(X, y, w=None, ids=None, tasks=None, data_dir=None, verbose=True)[source]¶Creates a DiskDataset object from specified Numpy arrays.
get_statistics
(X_stats=True, y_stats=True)¶Compute and return statistics of this dataset.
ids
¶Get the ids vector for this dataset as a single numpy array.
iterbatches
(batch_size=None, epoch=0, deterministic=False, pad_batches=False)[source]¶Get an object that iterates over minibatches from the dataset. It is guaranteed that the number of batches returned is math.ceil(len(dataset)/batch_size).
Each minibatch is returned as a tuple of four numpy arrays: (X, y, w, ids).
itersamples
()[source]¶Get an object that iterates over the samples in the dataset.
Example:
>>> dataset = DiskDataset.from_numpy(np.ones((2,2)), np.ones((2,1)), verbose=False)
>>> for x, y, w, id in dataset.itersamples():
... print(x, y, w, id)
[ 1. 1.] [ 1.] [ 1.] 0
[ 1. 1.] [ 1.] [ 1.] 1
itershards
()[source]¶Return an object that iterates over all shards in dataset.
Datasets are stored in sharded fashion on disk. Each call to next() for the generator defined by this function returns the data from a particular shard. The order of shards returned is guaranteed to remain fixed.
select
(indices, select_dir=None)[source]¶Creates a new dataset from a selection of indices from self.
| Parameters: |
|---|
sparse_shuffle
()[source]¶Shuffling that exploits data sparsity to shuffle large datasets.
Only for 1-dimensional feature vectors (does not work for tensorial featurizations).
transform
(fn, **args)[source]¶Construct a new dataset by applying a transformation to every sample in this dataset.
The argument is a function that can be called as follows:
>> newx, newy, neww = fn(x, y, w)
It might be called only once with the whole dataset, or multiple times with different subsets of the data. Each time it is called, it should transform the samples and return the transformed data.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | a newly constructed Dataset object |
w
¶Get the weight vector for this dataset as a single numpy array.
y
¶Get the y vector for this dataset as a single numpy array.
deepchem.data.datasets.
NumpyDataset
(X, y=None, w=None, ids=None, n_tasks=1)[source]¶Bases: deepchem.data.datasets.Dataset
A Dataset defined by in-memory numpy arrays.
X
¶Get the X vector for this dataset as a single numpy array.
from_DiskDataset
(ds)[source]¶| Parameters: |
|
|---|---|
| Returns: | Data of ds as NumpyDataset |
| Return type: |
get_shape
()[source]¶Get the shape of the dataset.
Returns four tuples, giving the shape of the X, y, w, and ids arrays.
get_statistics
(X_stats=True, y_stats=True)¶Compute and return statistics of this dataset.
ids
¶Get the ids vector for this dataset as a single numpy array.
iterbatches
(batch_size=None, epoch=0, deterministic=False, pad_batches=False)[source]¶Get an object that iterates over minibatches from the dataset.
Each minibatch is returned as a tuple of four numpy arrays: (X, y, w, ids).
itersamples
()[source]¶Get an object that iterates over the samples in the dataset.
Example:
>>> dataset = NumpyDataset(np.ones((2,2)))
>>> for x, y, w, id in dataset.itersamples():
... print(x, y, w, id)
[ 1. 1.] [ 0.] [ 0.] 0
[ 1. 1.] [ 0.] [ 0.] 1
select
(indices, select_dir=None)[source]¶Creates a new dataset from a selection of indices from self.
TODO(rbharath): select_dir is here due to dc.splits always passing in splits.
| Parameters: |
|---|
transform
(fn, **args)[source]¶Construct a new dataset by applying a transformation to every sample in this dataset.
The argument is a function that can be called as follows:
>> newx, newy, neww = fn(x, y, w)
It might be called only once with the whole dataset, or multiple times with different subsets of the data. Each time it is called, it should transform the samples and return the transformed data.
| Parameters: | fn (function) – A function to apply to each sample in the dataset |
|---|---|
| Returns: | |
| Return type: | a newly constructed Dataset object |
w
¶Get the weight vector for this dataset as a single numpy array.
y
¶Get the y vector for this dataset as a single numpy array.
deepchem.data.datasets.
densify_features
(X_sparse, num_features)[source]¶Expands sparse feature representation to dense feature array.
deepchem.data.datasets.
pad_batch
(batch_size, X_b, y_b, w_b, ids_b)[source]¶Pads batch to have size precisely batch_size elements.
Fills in batch by wrapping around samples till whole batch is filled.
Sample supports from datasets.
deepchem.data.supports.
EpisodeGenerator
(dataset, n_pos, n_neg, n_test, n_episodes_per_task)[source]¶Bases: object
Generates (support, test) pairs for episodic training.
Precomputes all (support, test) pairs at construction. Allows to reduce overhead from computation.
__next__
()¶Sample next (support, test) pair.
Return from internal storage.
deepchem.data.supports.
SupportGenerator
(dataset, n_pos, n_neg, n_trials)[source]¶Bases: object
Generate support sets from a dataset.
Iterates over tasks and trials. For each trial, picks one support from each task, and returns in a randomized order
__next__
()¶Sample next support.
Supports are sampled from the tasks in a random order. Each support is drawn entirely from within one task.
deepchem.data.supports.
dataset_difference
(dataset, remove)[source]¶Removes the compounds in remove from dataset.
| Parameters: |
|
|---|
deepchem.data.supports.
get_single_task_support
(dataset, n_pos, n_neg, task, replace=True)[source]¶Generates one support set purely for specified task.
| Parameters: | |
|---|---|
| Returns: | List of NumpyDatasets, each of which is a support set. |
| Return type: |
deepchem.data.supports.
get_single_task_test
(dataset, batch_size, task, replace=True)[source]¶Gets test set from specified task.
Samples random subset of size batch_size from specified task of dataset. Ensures that sampled points have measurements for this task.
deepchem.data.supports.
get_task_dataset
(dataset, task)[source]¶Selects out entries for a particular task.
deepchem.data.supports.
get_task_dataset_minus_support
(dataset, support, task)[source]¶Gets data for specified task, minus support points.
Useful for evaluating model performance once trained (so that test compounds can be ensured distinct from support.)
| Parameters: |
|
|---|
deepchem.data.supports.
get_task_support
(dataset, n_episodes, n_pos, n_neg, task, log_every_n=50)[source]¶Generates one support set purely for specified task.
| Parameters: |
|
|---|---|
| Returns: | List of NumpyDatasets, each of which is a support set. |
| Return type: |
deepchem.data.test_data_loader.
TestCSVLoader
(methodName='runTest')[source]¶Bases: unittest.case.TestCase
addCleanup
(function, *args, **kwargs)¶Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.
Cleanup items are called even if setUp fails (unlike tearDown).
addTypeEqualityFunc
(typeobj, function)¶Add a type specific assertEqual style function to compare a type.
This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.
| Parameters: |
|
|---|
assertAlmostEqual
(first, second, places=None, msg=None, delta=None)¶Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the between the two objects is more than the given delta.
Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).
If the two objects compare equal then they will automatically compare almost equal.
assertAlmostEquals
(*args, **kwargs)¶assertCountEqual
(first, second, msg=None)¶An unordered sequence comparison asserting that the same elements, regardless of order. If the same element occurs more than once, it verifies that the elements occur the same number of times.
- self.assertEqual(Counter(list(first)),
- Counter(list(second)))
- Example:
- [0, 1, 1] and [1, 0, 1] compare equal.
- [0, 0, 1] and [0, 1] compare unequal.
assertDictContainsSubset
(subset, dictionary, msg=None)¶Checks whether dictionary is a superset of subset.
assertDictEqual
(d1, d2, msg=None)¶assertEqual
(first, second, msg=None)¶Fail if the two objects are unequal as determined by the ‘==’ operator.
assertEquals
(*args, **kwargs)¶assertFalse
(expr, msg=None)¶Check that the expression is false.
assertGreater
(a, b, msg=None)¶Just like self.assertTrue(a > b), but with a nicer default message.
assertGreaterEqual
(a, b, msg=None)¶Just like self.assertTrue(a >= b), but with a nicer default message.
assertIn
(member, container, msg=None)¶Just like self.assertTrue(a in b), but with a nicer default message.
assertIs
(expr1, expr2, msg=None)¶Just like self.assertTrue(a is b), but with a nicer default message.
assertIsInstance
(obj, cls, msg=None)¶Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.
assertIsNone
(obj, msg=None)¶Same as self.assertTrue(obj is None), with a nicer default message.
assertIsNot
(expr1, expr2, msg=None)¶Just like self.assertTrue(a is not b), but with a nicer default message.
assertIsNotNone
(obj, msg=None)¶Included for symmetry with assertIsNone.
assertLess
(a, b, msg=None)¶Just like self.assertTrue(a < b), but with a nicer default message.
assertLessEqual
(a, b, msg=None)¶Just like self.assertTrue(a <= b), but with a nicer default message.
assertListEqual
(list1, list2, msg=None)¶A list-specific equality assertion.
| Parameters: |
|
|---|
assertLogs
(logger=None, level=None)¶Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.
This method must be used as a context manager, and will yield a recording object with two attributes: output and records. At the end of the context manager, the output attribute will be a list of the matching formatted log messages and the records attribute will be a list of the corresponding LogRecord objects.
Example:
with self.assertLogs('foo', level='INFO') as cm:
logging.getLogger('foo').info('first message')
logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
'ERROR:foo.bar:second message'])
assertMultiLineEqual
(first, second, msg=None)¶Assert that two multi-line strings are equal.
assertNotAlmostEqual
(first, second, places=None, msg=None, delta=None)¶Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the between the two objects is less than the given delta.
Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).
Objects that are equal automatically fail.
assertNotAlmostEquals
(*args, **kwargs)¶assertNotEqual
(first, second, msg=None)¶Fail if the two objects are equal as determined by the ‘!=’ operator.
assertNotEquals
(*args, **kwargs)¶assertNotIn
(member, container, msg=None)¶Just like self.assertTrue(a not in b), but with a nicer default message.
assertNotIsInstance
(obj, cls, msg=None)¶Included for symmetry with assertIsInstance.
assertNotRegex
(text, unexpected_regex, msg=None)¶Fail the test if the text matches the regular expression.
assertNotRegexpMatches
(*args, **kwargs)¶assertRaises
(expected_exception, *args, **kwargs)¶Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.
If called with the callable and arguments omitted, will return a context object used like this:
with self.assertRaises(SomeException):
do_something()
An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.
The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:
with self.assertRaises(SomeException) as cm:
do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)
assertRaisesRegex
(expected_exception, expected_regex, *args, **kwargs)¶Asserts that the message in a raised exception matches a regex.
| Parameters: |
|
|---|
assertRaisesRegexp
(*args, **kwargs)¶assertRegex
(text, expected_regex, msg=None)¶Fail the test unless the text matches the regular expression.
assertRegexpMatches
(*args, **kwargs)¶assertSequenceEqual
(seq1, seq2, msg=None, seq_type=None)¶An equality assertion for ordered sequences (like lists and tuples).
For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.
| Parameters: |
|
|---|
assertSetEqual
(set1, set2, msg=None)¶A set-specific equality assertion.
| Parameters: |
|
|---|
assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).
assertTrue
(expr, msg=None)¶Check that the expression is true.
assertTupleEqual
(tuple1, tuple2, msg=None)¶A tuple-specific equality assertion.
| Parameters: |
|
|---|
assertWarns
(expected_warning, *args, **kwargs)¶Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.
If called with the callable and arguments omitted, will return a context object used like this:
with self.assertWarns(SomeWarning):
do_something()
An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.
The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:
with self.assertWarns(SomeWarning) as cm:
do_something()
the_warning = cm.warning
self.assertEqual(the_warning.some_attribute, 147)
assertWarnsRegex
(expected_warning, expected_regex, *args, **kwargs)¶Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.
| Parameters: |
|
|---|
assert_
(*args, **kwargs)¶countTestCases
()¶debug
()¶Run the test without collecting errors in a TestResult
defaultTestResult
()¶doCleanups
()¶Execute all cleanup functions. Normally called for you after tearDown.
fail
(msg=None)¶Fail immediately, with the given message.
failIf
(*args, **kwargs)¶failIfAlmostEqual
(*args, **kwargs)¶failIfEqual
(*args, **kwargs)¶failUnless
(*args, **kwargs)¶failUnlessAlmostEqual
(*args, **kwargs)¶failUnlessEqual
(*args, **kwargs)¶failUnlessRaises
(*args, **kwargs)¶failureException
¶alias of AssertionError
id
()¶longMessage
= True¶maxDiff
= 640¶run
(result=None)¶setUp
()¶Hook method for setting up the test fixture before exercising it.
setUpClass
()¶Hook method for setting up class fixture before running tests in the class.
shortDescription
()¶Returns a one-line description of the test, or None if no description has been provided.
The default implementation of this method returns the first line of the specified test method’s docstring.
skipTest
(reason)¶Skip this test.
subTest
(msg=<object object>, **params)¶Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.
tearDown
()¶Hook method for deconstructing the test fixture after testing it.
tearDownClass
()¶Hook method for deconstructing the class fixture after running all tests in the class.
Gathers all datasets in one place for convenient imports


´
)𝔽´ 𝕩
: FoldFold over 𝕩
with 𝔽
from right to left i.e. Insert 𝔽
between the elements of 𝕩
.
𝕩
must be a simple list (1 = =𝕩
).
+´ 1‿2‿3 6 1+2+3 6 -´ 1‿2‿3 2 1-2-3 2
𝕨 𝔽´ 𝕩
: Fold With InitialMonadic fold, but use 𝕨
as initial right argument.
5 +´ 1‿2‿3 11 1+2+3+5 11 5 -´ 1‿2‿3 ¯3 1-2-3-5 ¯3


How to set the URL for the Online Help service for all sites. This is the URL associated with the Online Help button accessed via module actions menus. Leave this field blank to remove Online Help from the Admin and Host consoles of DotNetNuke. This is the URL associated with the Online Help button accessed via module actions menus. Leave this field blank to remove Online Help for the Admin/Host areas of DotNetNuke. The default setting is to the Online Help provided by DNN Corp which is located at http://www.dnnsoftware.com/help.
http://www.dnnsoftware.com/Help


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Welcome to discrete-shocklet-transform’s documentation!¶
Shocklet / cusplet transform¶
-
cusplets.
classify_cusps
(cc, b=1, geval=False)¶ Classifies points as belonging to cusps or not.
Parameters: - cc (numpy.ndarray) – numpy array of shape (L, n), the cusplet transform of a time series
- b (int or float) – multiplier of the standard deviation.
- geval (float >= 0) – optional. If geval is an int or float, classify_cusps will return (in addition to the cusps and cusp intensity function) an array of points where the cusp intensity function is greater than geval.
Returns: tuple — (numpy.ndarray of indices of the cusps; numpy.ndarray representing the cusp intensity function) or, if geval is not False, (extrema; the cusp intensity function; array of points where the cusp intensity function is greater than geval)
-
cusplets.
cusplet
(arr, kernel, widths, k_args=[], reflection=0, width_weights=None)¶ Implements the discrete cusplet transform.
Parameters: - arr (list, tuple, or numpy.ndarray) – array of shape (n,) or (n,1). This array should not contain inf-like values. The transform will still be computed but infs propagate. Nan-like values will be linearly interpolated, which is okay for subsequent time-based analysis but will introduce ringing in frequency-based analyses.
- kernel (callable) – kernel function. Must take an integer L > 0 as an argument and any number of additional, nonkeyword arguments, and returns a numpy array of shape (L,) that implements the kernel. The returned array should sum to zero; use the zero_norm function for this.
- widths (iterable) – iterable of integers that specify the window widths (L above). Assumed to be in increasing order; if widths is not in increasing order the results will be garbage.
- k_args (list or tuple) – arguments for the kernel function.
- reflection (int) – integer n evaluates to n %4, element of the reflection group that left-multiplies the kernel function. Default is 0 (identity element).
Returns: tuple – (numpy array of shape (L, n) – the cusplet transform, k – the calculated kernel function)
-
cusplets.
cusplet_parameter_sweep
(arr, kernel, widths, k_args, reflection=0, width_weights=None, k_weights=None)¶ Sweeps over values of parameters (kernel arguments) in the discrete cusplet transform.
Parameters: - arr (list, tuple, or numpy.ndarray) – numpy array of shape (n,) or (n,1), time series
- kernel (callable) – kernel function. Must take an integer L > 0 as an argument and any number of additional, nonkeyword arguments, and returns a numpy array of shape (L,) that implements the kernel. The returned array should sum to zero; use the zero_norm function for this.
- widths (iterable) – iterable of integers that specify the window widths (L above). Assumed to be in increasing order; if widths is not in increasing order the results will be garbage.
- k_args (list or tuple of lists or tuples) – iterable of iterables of arguments for the kernel function. Each top-level iterable is treated as a single parameter vector.
- reflection (int) – integer n evaluates to n %4, element of the reflection group that left-multiplies the kernel function. Default is 0 (identity element).
Returns: numpy.ndarray – numpy array of shape (L, n, len(k_args)), the cusplet transform
-
cusplets.
inverse_cusplet
(cc, kernel, widths, k_args=[], reflection=0, width_ind=0)¶ Computes the inverse of the discrete cusplet / shocklet transform.
The cusplet transform is overcomplete, at least in theory. Since each row of the cusplet transform is a cross-correlation between the kernel function and the time series, it is—again, in theory—possible to recover the original time series of data from any row of the cusplet transform and the appropriate kernel. A row of the cusplet transform, denoted by c, is defined by c = Ax, where A is the cross-correlation matrix constructed from kernel k. If A is invertible then we trivially have x = A^{-1}c. In theory, the full inverse transform using the full cusplet transform C is given by x = langle A_w^{-1}c_wrangle_{w}, where by w we denote the appropriate kernel width parameter.
We note that there is really no reason to ever use this function. Unlike other transforms, it is highly unlikely that a user will be confronted with some arbitrary cusplet transform and need to recover the raw data from it. In other words, one often is confronted with frequency data corresponding to a Fourier transform and needs to extract the original time series from it, but the cusplet transform is intended to be a data analysis tool and so the data should always be accessible to the user.
In the practical implementation here, the user can specify which row of the cusplet transform to use. By default we will use the first row of the transform since this will introduce the fewest numerical errors in the inversion. This is true because the convolution operations involves fewer elements in each sum; the convolution matrix will have lower bandwidth and hence will be easier to invert.
Parameters: - cc (numpy.ndarray) – the cusplet transform array, shape W x T
- kernel (callable) – kernel function. Must take an integer L > 0 as an argument and any number of additional, nonkeyword arguments, and returns a numpy array of shape (L,) that implements the kernel. The returned array should sum to zero; use the zero_norm function for this.
- widths (iterable) – iterable of integers that specify the window widths (L above). Assumed to be in increasing order; if widths is not in increasing order the results will be garbage.
- k_args (list or tuple) – arguments for the kernel function.
- reflection (int) – integer n evaluates to n %4, element of the reflection group that left-multiplies the kernel function. Default is 0 (identity element).
Returns: numpy.ndarray – the reconstructed original time series. This time series will have (roughly) the same functional form as the original, but it is not guaranteed that its location and scale will be the same.
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cusplets.
make_components
(indicator, cusp_points=None, scan_back=0)¶ Get individual windows from array of indicator indices.
Takes cusp indicator function and returns windows of contiguous cusp-like behavior. If an array of hypothesized deterministic peaks of cusp-like behavior is passed, thins these points so that there is at most one point per window. The scan_back parameter connects contiguous windows if they are less than or equal to scan_back indices from each other.
Parameters: - indicator (list) – array of the points where the cusp intensity function exceeds some threshold
- cusp_points (list or numpy.ndarray) – optional, array of points that denote the hypothesized deterministic peaks of cusps
- scan_back (int >= 0) – number of indices to look back. If cusp windows are within scan_back indices of each other, they will be connected into one contiguous window.
Returns: list – the contiguous cusp windows; or, if cusp_points is not None, tuple – (the contiguous cusp windows, the thinned cusp points)
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cusplets.
matrix_cusplet
(arr, kernel, widths, k_args=[], reflection=0, width_weights=None)¶ Computes the cusplet transform using matrix multiplication.
This method is provided for the sake only of completeness; it is orders of magnitude slower than
cusplets.cusplet
and there is no good reason to use it in production. You should usecusplets.cusplet
instead.Parameters: - arr (list, tuple, or numpy.ndarray) – array of shape (n,) or (n,1). This array should not contain inf-like values. The transform will still be computed but infs propagate. Nan-like values will be linearly interpolated, which is okay for subsequent time-based analysis but will introduce ringing in frequency-based analyses.
- kernel (callable) – kernel function. Must take an integer L > 0 as an argument and any number of additional, nonkeyword arguments, and returns a numpy array of shape (L,) that implements the kernel. The returned array should sum to zero; use the zero_norm function for this.
- widths (iterable) – iterable of integers that specify the window widths (L above). Assumed to be in increasing order; if widths is not in increasing order the results will be garbage.
- k_args (list or tuple) – arguments for the kernel function.
- reflection (int) – integer n evaluates to n %4, element of the reflection group that left-multiplies the kernel function. Default is 0 (identity element).
Returns: tuple – (numpy array of shape (L, n) – the cusplet transform, None)
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cusplets.
max_change
(arr)¶ Calculates the difference between the max and min points in an array.
Parameters: arr (a list or numpy.ndarray) – a time series Returns: float – maximum relative change
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cusplets.
max_rel_change
(arr, neg=True)¶ Calculates the maximum relative changes in an array (log10).
One possible choice for a weighting function in cusplet transform.
Parameters: - arr (a list or numpy.ndarray) – a time series for a given word
- neg – (if true) arr - np.min(arr) + 1
Returns: float – maximum relative change (log10)
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cusplets.
normalize
(arr, stats=False)¶ Normalizes an array to have zero mean and unit variance
Parameters: - arr (iterable) – the array
- stats (bool) – if stats is True, will also return mean and std of array
Returns: numpy.ndarray – normalized array; or, if stats is True, tuple – (normalized array, mean, std)
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cusplets.
setup_corr_mat
(k, N)¶ Sets up linear operator corresponding to cross correlation.
The cross-correlation operation can be just viewed as a linear operation from R^K to R^K as Ax = C. The operator A is a banded matrix that represents the rolling add operation that defines cross-correlation. To execute correlation of the kernel with data one computes np.dot(A, data).
Parameters: - k (numpy.ndarray) – the cross-correlation kernel
- N (positive int) – shape of data array with which k will be cross-correlated.
Returns: numpy.ndarray – NxN array, the cross-correlation operator
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cusplets.
top_k
(indices, words, k)¶ Find the top k words by the weighted cusp indicator function
Parameters: - indices (list or numpy.array) – a list of indicator values
- words (list or numpy.array) – a list of strings
- k (int > 0) – number of indices to look up.
Returns: list – length k list of (word, indicator value) tuples
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cusplets.
window_argmaxes
(windows, data)¶ Find argmax point in data within each window.
Parameters: - windows (a 2D list or 2D numpy.ndarray) – a list of indices indicating targeted windows in the data array
- data (a list or numpy.ndarray) – a list of data points
Returns: numpy.array – max points for each window.
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cusplets.
zero_norm
(arr)¶ Normalizes an array so that it sums to zero
Parameters: arr (iterable) – the array Returns: numpy.ndarray – the zero-normalized array