View Source on Github
Walkthrough
Walking
My primary walking control circuits are damaged. I will have to re-route my walking control functions through my auxiliary circuits.
robot.speed.forward = 1;
Jumping
My jumping control circuit is damaged. I can re-route my jumping control function through another auxiliary circuit.
robot.speed.up = 2;


04. Web Service with Blockchain Considerations
Web Service With Blockchain Considerations
# Do you really need a blockchain?
In addition to the questions Jessica covered, here are some helpful questions to help determine if you really need a blockchain. These questions were originally put together by Viant, a ConsenSys incubated company, that built an asset and domain agnostic blockchain-based supply chain platform TraSeable Solutions.
Problem of Value Identification
- Is there a need to share information, credentials or value with others?
- Is trust a critical requirement to the process?
- Do you need to prove to others you are transacting/reporting accurately?
- Is there potential to monetize the data or digital asset in the value chain?
- Who owns the problem? Individual or industry wide challenges?
Stakeholders buy in
- Is there a network of stakeholders (i.e. more than 2)?
- Is there a dependency on others for information?
- Does more than one participant need to update the data?
- Is there scope to open up the ecosystem to ancillary parties in the future?
- Are you working with other industry players on any activities?
Technical considerations
- Is there any ongoing need or future requirements for high data throughput?
- Do you rely or use public data sources to make decisions?
- Do you need to store a particularly rich/complex data structure?
- Do you need to digitize assets in your value chain?
- Do you need transaction privacy? Do you need anonymity?


WITNESS LIST Culture, Recreation, & Tourism Committee May 3, 2005-1:30P HB 179 For: Garza, Ph. D., Mario (American Indians in Texas) Hernandez, Raymond (Tap Pilam Coahuiltecan Nation) Romero, Jr., Daniel Castro (Lipan Apache Band of Texas) Thoms, Alston (Self) Against: Easterwood, Floyd (Self and Lone Star State Archaeological Society) Smith, Steven (Texas Mining and Reclamation Association) Registering, but not testifying: For: Hadden, Karen (Self) Yanez-Correa, Ana (National LULAC) Against: Langford, David K. (Self and Texas Wildlife Association) Mullins, Russell (Association of Electric Companies of Texas) HB 342 On: Dille, Brenda (Texas Parks and Wildlife Department) HB 1878 For: Anderson, Sparky (Texas Black Bass Unlimited) Hadden, Karen (SEED Coalition) On: Wiles, Kirk (Texas Department of State Health Services) Registering, but not testifying: For: Rockwell, Brad (Greater Edwards Aquifer Alliance) Ross, Ph. D., P.E., Lauren (Greater Edwards Aquifer Alliance) HB 2897 On: Goldbloom, Andy (Texas Parks and Wildlife Department) Registering, but not testifying: For: Hardy, Richard (Motorcycle Industry Council/ Specialty Vehicle Institute of America) Smith, Carol L. (American Motorcyclist Association)


“As he stood there musing, the door burst open mid a great clamor and shouting, and a mob of drunken soldiers poured through the gap. They were staggering under silken robes, golden goblets helmeted their heads, precious ornaments of jade dangled from their persons. The foremost of the rioters beheld the ancient Ming priest.  ‘O outlived reptile of accursed lineage, give us your hidden treasures, or we slit your wizened throat.’ The priest struggled feebly in his captors' grasp, crying feebly.” — Nat Schachner & Arthur L Zagat, The Song Of The Cakes


Marker Symbol Predefined
This example uses a symbol to add a vector-based icon to a marker. The symbol uses one of the predefined vector paths ('CIRCLE') supplied by the Google Maps JavaScript API.


public class CameraServer
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static int |
kBasePort |
static int |
kSize160x120
Deprecated.
|
static int |
kSize320x240
Deprecated.
|
static int |
kSize640x480
Deprecated.
|
| Modifier and Type | Method and Description |
|---|---|
AxisCamera |
addAxisCamera(java.lang.String host)
Adds an Axis IP camera.
|
AxisCamera |
addAxisCamera(java.lang.String[] hosts)
Adds an Axis IP camera.
|
AxisCamera |
addAxisCamera(java.lang.String name,
java.lang.String host)
Adds an Axis IP camera.
|
AxisCamera |
addAxisCamera(java.lang.String name,
java.lang.String[] hosts)
Adds an Axis IP camera.
|
void |
addCamera(VideoSource camera)
Adds an already created camera.
|
MjpegServer |
addServer(java.lang.String name)
Adds a MJPEG server at the next available port.
|
MjpegServer |
addServer(java.lang.String name,
int port)
Adds a MJPEG server.
|
void |
addServer(VideoSink server)
Adds an already created server.
|
static CameraServer |
getInstance()
Get the CameraServer instance.
|
VideoSink |
getServer()
Get server for the primary camera feed.
|
VideoSink |
getServer(java.lang.String name)
Gets a server by name.
|
CvSink |
getVideo()
Get OpenCV access to the primary camera feed.
|
CvSink |
getVideo(java.lang.String name)
Get OpenCV access to the specified camera.
|
CvSink |
getVideo(VideoSource camera)
Get OpenCV access to the specified camera.
|
CvSource |
putVideo(java.lang.String name,
int width,
int height)
Create a MJPEG stream with OpenCV input.
|
void |
removeCamera(java.lang.String name)
Removes a camera by name.
|
void |
removeServer(java.lang.String name)
Removes a server by name.
|
UsbCamera |
startAutomaticCapture()
Start automatically capturing images to send to the dashboard.
|
UsbCamera |
startAutomaticCapture(int dev)
Start automatically capturing images to send to the dashboard.
|
UsbCamera |
startAutomaticCapture(java.lang.String name,
int dev)
Start automatically capturing images to send to the dashboard.
|
UsbCamera |
startAutomaticCapture(java.lang.String name,
java.lang.String path)
Start automatically capturing images to send to the dashboard.
|
void |
startAutomaticCapture(VideoSource camera)
Start automatically capturing images to send to the dashboard from
an existing camera.
|
public static final int kBasePort
@Deprecated public static final int kSize640x480
@Deprecated public static final int kSize320x240
@Deprecated public static final int kSize160x120
public static CameraServer getInstance()
public UsbCamera startAutomaticCapture()
You should call this method to see a camera feed on the dashboard. If you also want to perform vision processing on the roboRIO, use getVideo() to get access to the camera images.
The first time this overload is called, it calls
startAutomaticCapture(int)
with device 0, creating a camera
named "USB Camera 0". Subsequent calls increment the device number
(e.g. 1, 2, etc).
public UsbCamera startAutomaticCapture(int dev)
This overload calls startAutomaticCapture(String, int)
with
a name of "USB Camera {dev}".
dev
- The device number of the camera interfacepublic UsbCamera startAutomaticCapture(java.lang.String name, int dev)
name
- The name to give the cameradev
- The device number of the camera interfacepublic UsbCamera startAutomaticCapture(java.lang.String name, java.lang.String path)
name
- The name to give the camerapath
- The device path (e.g. "/dev/video0") of the camerapublic void startAutomaticCapture(VideoSource camera)
camera
- Camerapublic AxisCamera addAxisCamera(java.lang.String host)
This overload calls addAxisCamera(String, String)
with
name "Axis Camera".
host
- Camera host IP or DNS name (e.g. "10.x.y.11")public AxisCamera addAxisCamera(java.lang.String[] hosts)
This overload calls addAxisCamera(String, String[])
with
name "Axis Camera".
hosts
- Array of Camera host IPs/DNS namespublic AxisCamera addAxisCamera(java.lang.String name, java.lang.String host)
name
- The name to give the camerahost
- Camera host IP or DNS name (e.g. "10.x.y.11")public AxisCamera addAxisCamera(java.lang.String name, java.lang.String[] hosts)
name
- The name to give the camerahosts
- Array of Camera host IPs/DNS namespublic CvSink getVideo()
This is only valid to call after a camera feed has been added with startAutomaticCapture() or addServer().
public CvSink getVideo(VideoSource camera)
camera
- Camera (e.g. as returned by startAutomaticCapture).public CvSink getVideo(java.lang.String name)
name
- Camera namepublic CvSource putVideo(java.lang.String name, int width, int height)
name
- Name to give the streamwidth
- Width of the image being sentheight
- Height of the image being sentpublic MjpegServer addServer(java.lang.String name)
name
- Server namepublic MjpegServer addServer(java.lang.String name, int port)
name
- Server namepublic void addServer(VideoSink server)
server
- Serverpublic void removeServer(java.lang.String name)
name
- Server namepublic VideoSink getServer()
This is only valid to call after a camera feed has been added with startAutomaticCapture() or addServer().
public VideoSink getServer(java.lang.String name)
name
- Server namepublic void addCamera(VideoSource camera)
camera
- Camerapublic void removeCamera(java.lang.String name)
name
- Camera name


* First Name Required
* Last Name Required
* Invalid Email
* Password Required
* Confirm Password Required
* Day is Required
* Month is Required
* Year is Required
Male Female
* Gender Required


--- status: publish layout: page tags: - data author: display_name: JP email<EMAIL_ADDRESS>published: true date: 2011-11-18 15:46:14 -0700 title: Coal categories: [] ---
Data Type: GIS Data Layer
Steward: AGRC
Abstract:
This dataset represents areas of coal deposits in Utah.
Usage: There are no constraints or warranties with regard to the use of this dataset.
Last Update: 1988
Contact: AGRC
Links: FGDC Metadata


|
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Object org.apache.spark.mllib.linalg.Matrices
public class Matrices
Factory methods for Matrix
.
| Constructor Summary | |
|---|---|
Matrices()
|
| Method Summary | |
|---|---|
static Matrix |
dense(int numRows,
int numCols,
double[] values)
Creates a column-major dense matrix. |
static Matrix |
diag(Vector vector)
Generate a diagonal matrix in Matrix format from the supplied values. |
static Matrix |
eye(int n)
Generate a dense Identity Matrix in Matrix format. |
static Matrix |
horzcat(Matrix[] matrices)
Horizontally concatenate a sequence of matrices. |
static Matrix |
ones(int numRows,
int numCols)
Generate a DenseMatrix consisting of ones. |
static Matrix |
rand(int numRows,
int numCols,
java.util.Random rng)
Generate a DenseMatrix consisting of i.i.d. uniform random numbers. |
static Matrix |
randn(int numRows,
int numCols,
java.util.Random rng)
Generate a DenseMatrix consisting of i.i.d. gaussian random numbers. |
static Matrix |
sparse(int numRows,
int numCols,
int[] colPtrs,
int[] rowIndices,
double[] values)
Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format. |
static Matrix |
speye(int n)
Generate a sparse Identity Matrix in Matrix format. |
static Matrix |
sprand(int numRows,
int numCols,
double density,
java.util.Random rng)
Generate a SparseMatrix consisting of i.i.d. gaussian random numbers. |
static Matrix |
sprandn(int numRows,
int numCols,
double density,
java.util.Random rng)
Generate a SparseMatrix consisting of i.i.d. gaussian random numbers. |
static Matrix |
vertcat(Matrix[] matrices)
Vertically concatenate a sequence of matrices. |
static Matrix |
zeros(int numRows,
int numCols)
Generate a Matrix consisting of zeros. |
| Methods inherited from class Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public Matrices()
| Method Detail |
|---|
public static Matrix dense(int numRows, int numCols, double[] values)
numRows
- number of rowsnumCols
- number of columnsvalues
- matrix entries in column major
public static Matrix sparse(int numRows, int numCols, int[] colPtrs, int[] rowIndices, double[] values)
numRows
- number of rowsnumCols
- number of columnscolPtrs
- the index corresponding to the start of a new columnrowIndices
- the row index of the entryvalues
- non-zero matrix entries in column major
public static Matrix zeros(int numRows, int numCols)
Matrix
consisting of zeros.
numRows
- number of rows of the matrixnumCols
- number of columns of the matrix
Matrix
with size numRows
x numCols
and values of zerospublic static Matrix ones(int numRows, int numCols)
DenseMatrix
consisting of ones.
numRows
- number of rows of the matrixnumCols
- number of columns of the matrix
Matrix
with size numRows
x numCols
and values of onespublic static Matrix eye(int n)
Matrix
format.
n
- number of rows and columns of the matrix
Matrix
with size n
x n
and values of ones on the diagonalpublic static Matrix speye(int n)
Matrix
format.
n
- number of rows and columns of the matrix
Matrix
with size n
x n
and values of ones on the diagonalpublic static Matrix rand(int numRows, int numCols, java.util.Random rng)
DenseMatrix
consisting of i.i.d.
uniform random numbers.
numRows
- number of rows of the matrixnumCols
- number of columns of the matrixrng
- a random number generator
Matrix
with size numRows
x numCols
and values in U(0, 1)public static Matrix sprand(int numRows, int numCols, double density, java.util.Random rng)
SparseMatrix
consisting of i.i.d.
gaussian random numbers.
numRows
- number of rows of the matrixnumCols
- number of columns of the matrixdensity
- the desired density for the matrixrng
- a random number generator
Matrix
with size numRows
x numCols
and values in U(0, 1)public static Matrix randn(int numRows, int numCols, java.util.Random rng)
DenseMatrix
consisting of i.i.d.
gaussian random numbers.
numRows
- number of rows of the matrixnumCols
- number of columns of the matrixrng
- a random number generator
Matrix
with size numRows
x numCols
and values in N(0, 1)public static Matrix sprandn(int numRows, int numCols, double density, java.util.Random rng)
SparseMatrix
consisting of i.i.d.
gaussian random numbers.
numRows
- number of rows of the matrixnumCols
- number of columns of the matrixdensity
- the desired density for the matrixrng
- a random number generator
Matrix
with size numRows
x numCols
and values in N(0, 1)public static Matrix diag(Vector vector)
Matrix
format from the supplied values.
vector
- a Vector
that will form the values on the diagonal of the matrix
Matrix
with size values.length
x values.length
and values
on the diagonalpublic static Matrix horzcat(Matrix[] matrices)
DenseMatrix
will be returned.
matrices
- array of matrices
Matrix
composed of the matrices that were horizontally concatenatedpublic static Matrix vertcat(Matrix[] matrices)
DenseMatrix
will be returned.
matrices
- array of matrices
Matrix
composed of the matrices that were vertically concatenated
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SwiftyError
public class SwiftyError
Errors that can occur in the Swifty domain
-
JSON Parsing failure
Declaration
Swift
public static func jsonParsingFailure(error: Error?) -> NSError
-
Response Validation failure
Declaration
Swift
public static func responseValidation(reason: String) -> NSError


What is this website?
This website was created to help people understand registers, lists of information and the importance of good governance practices. It was the result of a research project into the use of registers…
Glossary
-
{% for item in site.terms %}
- {{ item.title }} {% endfor %}


A storage component to store a covisibility graph where each vertex is an id of a keyframe and each edge is weighted by the number of common cloud points between two keyframes.
More...
|
| FrameworkReturnCode | increaseEdge (uint32_t node1_id, uint32_t node2_id, float weight) override |
| | This method allow to increase edge between 2 nodes. More...
|
| |
| FrameworkReturnCode | decreaseEdge (uint32_t node1_id, uint32_t node2_id, float weight) override |
| | This method allow to decrease edge between 2 nodes. More...
|
| |
| FrameworkReturnCode | removeEdge (uint32_t node1_id, uint32_t node2_id) override |
| | This method allow to remove an edge between 2 nodes. More...
|
| |
| FrameworkReturnCode | getEdge (uint32_t node1_id, uint32_t node2_id, float &weight) const override |
| | This method allow to get edge between 2 nodes. More...
|
| |
| bool | isEdge (const uint32_t node1_id, const uint32_t node2_id) const override |
| | This method allow to verify that exist an edge between 2 nodes. More...
|
| |
| FrameworkReturnCode | getAllNodes (std::set< uint32_t > &nodes_id) const override |
| | This method allow to get all nodes of the graph. More...
|
| |
| FrameworkReturnCode | suppressNode (const uint32_t node_id) override |
| | This method allow to suppress a node of the graph. More...
|
| |
| FrameworkReturnCode | getNeighbors (const uint32_t node_id, const float minWeight, std::vector< uint32_t > &neighbors, const uint32_t maxNbNeighbors=0) const override |
| | This method allow to get neighbors of a node in the graph. More...
|
| |
| FrameworkReturnCode | minimalSpanningTree (std::vector< std::tuple< uint32_t, uint32_t, float > > &edges_weights, float &minTotalWeights) override |
| | This method allow to get minimal spanning tree of the graph. More...
|
| |
| FrameworkReturnCode | maximalSpanningTree (std::vector< std::tuple< uint32_t, uint32_t, float > > &edges_weights, float &maxTotalWeights) override |
| | This method allow to get maximal spanning tree of the graph. More...
|
| |
| FrameworkReturnCode | getShortestPath (const uint32_t node1_id, const uint32_t node2_id, std::vector< uint32_t > &path) override |
| | This method allow to get the shortest (by number of vertices) path between 2 nodes. More...
|
| |
|
FrameworkReturnCode | display () const override |
| | This method allow to display all vertices and weighted edges of the covisibility graph.
|
| |
| FrameworkReturnCode | saveToFile (const std::string &file) const override |
| | This method allows to save the graph to the external file. More...
|
| |
| FrameworkReturnCode | loadFromFile (const std::string &file) override |
| | This method allows to load the graph from the external file. More...
|
| |
| const SRef< SolAR::datastructure::CovisibilityGraph > & | getConstCovisibilityGraph () const override |
| | This method returns the covisibility graph. More...
|
| |
| std::unique_lock< std::mutex > | getCovisibilityGraph (SRef< SolAR::datastructure::CovisibilityGraph > &covisibilityGraph) override |
| | This method returns the covisibility graph. More...
|
| |
| void | setCovisibilityGraph (const SRef< SolAR::datastructure::CovisibilityGraph > covisibilityGraph) override |
| | This method is to set the covisibility graph. More...
|
| |
|
void | unloadComponent () override final |
| |
A storage component to store a covisibility graph where each vertex is an id of a keyframe and each edge is weighted by the number of common cloud points between two keyframes.


{% extends "my_ummeli_base.xml" %}
{% block title %}
Ummeli
{% endblock %}
{% block content %}
Write anything you'd like about yourself, your skills and
your work goals. (Tip: full words are better than txtspeak
like gud or gr8) Learn more..
(example:) I am a young, ambitious designer looking for a part-time position in advertising.
Back
{% endblock %}


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The SimEntity object is the basic entity that is passed through a process flow type model.
The main feature of SimEntity is that its state can be assigned at various stages of the process flow. Process Flow objects that can receive a SimEntity, such as Server and Queue, can assign a state to the received SimEntity using their StateAssignment keyword. For example, if the StateAssignment input for a Queue is set to "Waiting", then the state for each SimEntity received by the Queue will be set to "Waiting" when it arrives at the Queue. The SimEntity will remain in this state until it is set to a new state by a subsequent object.
The time spent a SimEntity in each state can be accessed using its StateTimes output. For example, the total time spent by the SimEntity in the state "Waiting" is given by StateTimes("Waiting").
Unlike other objects, the StateTimes data for a SimEntity is not cleared at the end of the initialization period (warm-up). This is done to avoid having a Statistics object record artificially small times for some states that would be become the minimum times for those states.
SimEntity Inputs
|
Keyword |
Description |
|
Key Inputs |
|
|
Description |
A free-form string describing the object. |
|
DefaultStateList |
A list of states that will always appear in the output report, even if no time is recorded for this state. |
|
InitialState |
The state of the SimEntity at the start of the simulation run or when it is first created during a simulation run. |
|
Options |
|
|
AttributeDefinitionList, CustomOutputList |
See Section 6.2. |
|
Format |
|
|
StateGraphics |
A list of state/DisplayEntity pairs. For each state, the graphics will be changed to those for the corresponding DisplayEntity, e.g. { Idle DisplayEntity1 } { Working DisplayEntity2 } |
|
Graphics |
|
|
Position, Alignment, Size, Orientation, Region, RelativeEntity, DisplayModel, Show, Movable, VisibleViews, DrawRange |
See Section 11.1. |
SimEntity Outputs
|
Output Name |
Description |
|
Entity and DisplayEntity |
|
|
Name, ObjectType, SimTime, Parent, Attributes, Custom Outputs, Position, Size, Orientation, Alignment, GraphicalLength, ObserverList, NextList, PreviousList |
See Section 11.1. |
|
StateEntity |
|
|
State, WorkingState, WorkingTime, StateTimes, TotalTime |
See Section 10.3. |


The pizza restaurant was founded in lorem ipsum dolor lorem ipsum dolor lorem ipsum dolor lorem ipsum dolor lorem ipsum dolor lorem ipsum dolor.
The chef? Mr. Italiano himself
We are proud of our interiors
Opening Hours
Find us at some address at some place or call us at 05050515-122330
FYI! We offer full-service catering for any event, large or small. We understand your needs and will cater the food to satisfy the biggest criteria of them all, both look and taste.
Reserve a table, ask for today's special or just send us a message:


MA<>CT Border, May 2016
Paul Samuelson
Fintech software engineer working above a bakery
Fall
Parts of a neurons
80-100 billion neurons, over 60 trillion connections
Memory == neuron activation patterns
A storage "collision"
Memory is an activation pattern, but experiencing events is also an activation pattern
Repetition, novelty, and "importance"
Consolidation
Sleep "replays" recent activation patterns
Adrenaline (epinephrine) strengthens recent activation patterns
Recall activates specific neuron patterns
Activation patterns depend on strength of connections between neurons
Connections strengths constantly change (neural plasticity)
How we recall events changes every time we recall them
Questions?


-
- Basic info.
- Name: Liang Jiawei Phone:<PHONE_NUMBER>7
- Email: liangjw97@gmail.com(Personal) ;liangjw@shanghaitech.edu.cn(School)
-
- Education.
Shanghaitech University（2016.9 - Now | EE | Junior ）
-
- GPA:3.49/4 Language:CET-6
-
- Majors:Digital/Analog Integrated Circuit, Power Electronics, Digital Singal Processing, Electromagnetics, Introduction to Information Science and Technology, Linear Algebra, Electric Cirucits, Introduction to Embedded Systems, Signal and Systems, Communication Systems, Physics(all of the above are A Level)
-
- Project.
-
Single-phase SPWM Modulation InverterReport
Design the inverters using bipolar modulation, using unipolar modulation, to eliminate 5th and 7th order harmonics.
Calculate the amplitude modulation index, determine the frequency modulation index and analyze the total harmonics distortion of I0 by FFT analysis.
-
Programmable Multi-LED broad Synchronizing System
Champion+Best Creative Award(Organized by Tencent Classroom)
I am responsible for the design, construction and programming of multi-light hardware circuits, planning of Bluetooth synchronization solutions.
-
Multi-Functional Clock Based on 8051 MCUReport
Function : Time Setting, Playing music on every hour, Measuring Temperature, Store the offline time and temperature, Infrared/Keyboard remote control
-
LED Rotating Display Based on Xilinx Zybo-boardReport
Switch 4 modes by Keyboard: 1.Display the characters typed on Keyboard;
2.Display picture;3.Display multi-frame animation;4.Display the real time
-
Optimal 500MHz Arithmetic Logic UnitReport
Using Cadence to design the Schematic and Layout of ALU，implement 4-bit/8-bit ALU addition, subtraction, multiplication, and comparison functions through pure static logic circuits which the critical path delay is controlled within 1.1ns.
-
Reverse Engineering Algorithmic Mechanism Behind WeChat Red EnvelopeReport
Red envelope data collection and processing-->Obersevation,hypothesis and build the model--> Based on hypothetical algorithm to simulate and compared with the pratical data-->Theoretical anlysis.
-
- Experience.
-
Industry Internship:Application of AI in logistics industryReport1 Report2
I am the captain of the AI chip team which is responsible for the AI chip industry research,and we visit Huali Microelectronics, Xilinx and other companies to understand the principles and development prospects of AI chips, and returning visits.
-
Focus on left-behind children and volunteer education activities
In July 2017, a two-week teaching activity in primary cchool, we visited and investigated the use of left-behind children in their spare time, proposed a solution for “shared” books to increase thier reading.


Change the layout of the top menu bar by adding new menu items and linking them to brand new content.


Tools for basic and advance cancer statistics and graphics. Calculates age-specific rate, age-standardized rate, estimated annual percentage rate with standards error. Creates graphics across variable and time, such as age-specific trends and period-cohort trends.
| Version: | 1.3.64 |
| Depends: | R (≥ 3.4) |
| Imports: | data.table, stats, ggplot2, grid, scales, grDevices, graphics, utils |
| Suggests: | testthat |
| Published: | 2018-12-17 |
| Author: | Mathieu Laversanne [aut, cre], Cancer Surveillance Unit [cph] |
| Maintainer: | Mathieu Laversanne <laversannem at iarc.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/timat35/Rcan |
| NeedsCompilation: | no |
| CRAN checks: | Rcan results |
| Reference manual: | Rcan.pdf |
| Package source: | Rcan_1.3.64.tar.gz |
| Windows binaries: | r-devel: Rcan_1.3.63.zip, r-release: Rcan_1.3.63.zip, r-oldrel: Rcan_1.3.63.zip |
| OS X binaries: | r-release: Rcan_1.3.63.tgz, r-oldrel: Rcan_1.3.63.tgz |
| Old sources: | Rcan archive |
Please use the canonical form https://CRAN.R-project.org/package=Rcan to link to this page.


--- layout: page title : Feeds ng-app: feeds scripts: - /assets/js/chart.js?d - controllers.js?v=1 --- {% include JB/setup %}


JavaScript Modules
Kotlin allows you to compile your Kotlin projects to JavaScript modules for popular module systems. Here is the list of available options:
- Plain. Don't compile for any module system. As usual, you can access a module by its name in the global scope. This option is used by default.
- Asynchronous Module Definition (AMD), which is in particular used by require.js library.
- CommonJS convention, widely used by node.js/npm
(
require
function andmodule.exports
object) - Unified Module Definitions (UMD), which is compatible with both AMD and CommonJS, and works as "plain" when neither AMD nor CommonJS is available at runtime.
Choosing the Target Module System
Choosing the target module system depends on your build environment:
From IntelliJ IDEA
Setup per module: Open File -> Project Structure…, find your module in Modules and select "Kotlin" facet under it. Choose appropriate module system in "Module kind" field.
Setup for the whole project: Open File -> Settings, select "Build, Execution, Deployment" -> "Compiler" -> "Kotlin compiler". Choose appropriate module system in "Module kind" field.
From Maven
To select module system when compiling via Maven, you should set moduleKind
configuration property, i.e. your
pom.xml
should look like this:
<plugin>
<artifactId>kotlin-maven-plugin</artifactId>
<groupId>org.jetbrains.kotlin</groupId>
<version>${kotlin.version}</version>
<executions>
<execution>
<id>compile</id>
<goals>
<goal>js</goal>
</goals>
</execution>
</executions>
<!-- Insert these lines -->
<configuration>
<moduleKind>commonjs</moduleKind>
</configuration>
<!-- end of inserted text -->
</plugin>
Available values are: plain
, amd
, commonjs
, umd
.
From Gradle
To select module system when compiling via Gradle, you should set moduleKind
property, i.e.
compileKotlin2Js.kotlinOptions.moduleKind = "commonjs"
Available values are similar to Maven.
@JsModule
annotation
To tell Kotlin that an external
class, package, function or property is a JavaScript module, you can use @JsModule
annotation. Consider you have the following CommonJS module called "hello":
module.exports.sayHello = function(name) { alert("Hello, " + name); }
You should declare it like this in Kotlin:
@JsModule("hello")
external fun sayHello(name: String)
Applying @JsModule
to packages
Some JavaScript libraries export packages (namespaces) instead of functions and classes. In terms of JavaScript, it's an object that has members that are classes, functions and properties. Importing these packages as Kotlin objects often looks unnatural. The compiler allows to map imported JavaScript packages to Kotlin packages, using the following notation:
@file:JsModule("extModule")
package ext.jspackage.name
external fun foo()
external class C
where the corresponding JavaScript module is declared like this:
module.exports = {
foo: { /* some code here */ },
C: { /* some code here */ }
}
Important: files marked with @file:JsModule
annotation can't declare non-external members.
The example below produces compile-time error:
@file:JsModule("extModule")
package ext.jspackage.name
external fun foo()
fun bar() = "!" + foo() + "!" // error here
Importing deeper package hierarchies
In the previous example the JavaScript module exports a single package.
However, some JavaScript libraries export multiple packages from within a module.
This case is also supported by Kotlin, though you have to declare a new .kt
file for each package you import.
For example, let's make our example a bit more complicated:
module.exports = {
mylib: {
pkg1: {
foo: function() { /* some code here */ },
bar: function() { /* some code here */ }
},
pkg2: {
baz: function() { /* some code here */ }
}
}
}
To import this module in Kotlin, you have to write two Kotlin source files:
@file:JsModule("extModule")
@file:JsQualifier("mylib.pkg1")
package extlib.pkg1
external fun foo()
external fun bar()
and
@file:JsModule("extModule")
@file:JsQualifier("mylib.pkg2")
package extlib.pkg2
external fun baz()
@JsNonModule
annotation
When a declaration has @JsModule
, you can't use it from Kotlin code when you don't compile it to a JavaScript module.
Usually, developers distribute their libraries both as JavaScript modules and downloadable .js
files that you
can copy to project's static resources and include via <script>
element. To tell Kotlin that it's ok
to use a @JsModule
declaration from non-module environment, you should put @JsNonModule
declaration. For example,
given JavaScript code:
function topLevelSayHello(name) { alert("Hello, " + name); }
if (module && module.exports) {
module.exports = topLevelSayHello;
}
can be described like this:
@JsModule("hello")
@JsNonModule
@JsName("topLevelSayHello")
external fun sayHello(name: String)
Notes
Kotlin is distributed with kotlin.js
standard library as a single file, which is itself compiled as an UMD module, so
you can use it with any module system described above. Also it is available on NPM as kotlin
package


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/meta.20mutual.20def.20errors.html ---
Does anyone know the source of this error? I get unexpected error, failed to generate equational lemmas in the front-end
even though it's a meta def so it shouldn't have equations
meta mutual def A, B with A : unit → tactic unit | _ := return () with B : unit → tactic unit | _ := return ()
(Mario and I are sitting next to each other and just discovered the curious answer. I'll explain while he does something useful. :-)
This doesn't work:
meta mutual def A, B with A : ℕ → ℕ | n := 0 with B : ℕ → ℕ | n := 0
with the same error.
However
meta mutual def A, B with A : ℕ → ℕ | 0 := 0 | (n+1) := B 0 with B : ℕ → ℕ | n := 0
or even
meta mutual def A, B with A : ℕ → ℕ | 0 := 0 | (n+1) := A 0 with B : ℕ → ℕ | n := 0
Somehow the compiler is insisting that the definitions actually refer to either themselves or each other.
And this only happens in meta
.
Oh well! :-)
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


PR2Networking
open class PR2Networking
Main network request class (wrapper of Alamofire request)
-
it converts itself as a singleton
Declaration
Swift
open static let sharedInstance = PR2Networking()
-
Networking log levels
Declaration
Swift
open var logLevel: PR2NetworkingLogLevel = .pr2NetworkingLogLevelOff
-
Undocumented
Declaration
Swift
open var tasks: [PR2NetworkTask] = []
-
Undocumented
Declaration
Swift
lazy var downloadsInProgress = [IndexPath: Operation]()
-
Undocumented
Declaration
Swift
lazy var downloadQueue: OperationQueue =
-
initial retry delay
Declaration
Swift
let retryDelay: Double = 1.0 // in seconds
-
seconds to trigger slow internet notification
Declaration
Swift
let secondsToShowSlowConnection: Double = 3
-
timer for slow internet
Declaration
Swift
var timerRequest = Timer()
-
alamofire manager
Declaration
Swift
var manager: Alamofire.SessionManager?
-
Undocumented
Declaration
Swift
var managerReachability: NetworkReachabilityManager?
-
Undocumented
Declaration
Swift
open func addTask(_ task: PR2NetworkTask)
-
Undocumented
Declaration
Swift
open func addTaskAfterLast(_ task: PR2NetworkTask)
-
request: Recursive Request till delay > 30 seconds
Declaration
Swift
open func request( _ delay: Double, method: HTTPMethod, urlString: String, parameters: [String: AnyObject]? = nil, encoding: ParameterEncoding = URLEncoding.default, headers: [String: String]? = nil, completionHandler: @escaping (_ success: Bool, _ response: DataResponse<Any>) -> Void)
Parameters
delay
Acumulated delay
method
The HTTP method.
urlString
The URL string.
parameters
The parameters. nil by default.
encoding
The parameter encoding. .URL by default.
headers
The HTTP headers. nil by default.
completionHandler
Completion handler.
-
Slow internet reached
Declaration
Swift
@objc dynamic func timerRequestReached()
-
userAgent: Generates user agent from Info.plist example: SkyPablo:1.0.1/IOS:9.2/x86_64
Declaration
Swift
open func userAgent() -> String
Return Value
String


Loading...
sectionsOverlap
sectionsOverlap(SDL_Rect &area1, int x1, int y1, SDL_Rect &area2, int &x2, int &y2, SDL_Rect *areaNew, int *xNew, int *yNew): funcs.cc
sectionsOverlap(SDL_Rect &area1, int x1, int y1, SDL_Rect &area2, int &x2, int &y2, SDL_Rect *areaNew, int *xNew, int *yNew): funcs.cc
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Ethernet
The WP85 module on a MangOH board can use an Ethernet connection to a local area network to configure the IP addresses.
Connect the serial, Ethernet, and power cables
In a terminal, run:
Press enter when Minicom starts.
Sign on as root user (no password required).
Press enter when prompted for a password.
Run ifconfig
(or ip addr). It should display something like this:
root@swi-mdm9x15:~# ifconfig eth0 Link encap:Ethernet HWaddr 9E:90:7C:6D:BE:9B inet addr:<IP_ADDRESS> Bcast:<IP_ADDRESS> Mask:<IP_ADDRESS> inet6 addr: fe80::9c90:7cff:fe6d:be9b/64 Scope:Link UP BROADCAST RUNNING MULTICAST MTU:1500 Metric:1 RX packets:43357 errors:0 dropped:107 overruns:0 frame:0 TX packets:3975 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:14481736 (13.8 MiB) TX bytes:718334 (701.4 KiB) lo Link encap:Local Loopback inet addr:<IP_ADDRESS> Mask:<IP_ADDRESS> inet6 addr: ::1/128 Scope:Host UP LOOPBACK RUNNING MTU:16436 Metric:1 RX packets:65 errors:0 dropped:0 overruns:0 frame:0 TX packets:65 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:0 RX bytes:3608 (3.5 KiB) TX bytes:3608 (3.5 KiB)
If it doesn't display an eth0 inet address, try rebooting the target (easiest is to disconnect and reconnect power cable), and run ifconfig
again.
Or you can run ifdown
eth0
and then if
up eth0
, to stop and restart the Ethernet connection.
You should be ready to ssh to your target IP - the subnets should match.
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.


| buildData {quantmod} | R Documentation |
Create one data object from multiple sources, applying transformations via standard R formula mechanism.
buildData(formula, na.rm = TRUE, return.class = "zoo")
formula |
an object of class |
na.rm |
drop rows with missing values? |
return.class |
one of "zoo","data.frame","ts","timeSeries" |
Makes available for use outside the quantmod workflow a dataset
of appropriately transformed variables, using the same mechanism
underlying specifyModel
. Offers the ability to apply transformations
to raw data using a common formula mechanism, without having to explicitly
merge different data objects.
Interally calls specifyModel
followed by modelData
, with the
returned object being coerced to the desired 'return.class' if possible,
otherwise returns a zoo
object.
See getSymbols
and specifyModel
for more information
regarding proper usage.
An object of class return.class
.
Jeffrey A. Ryan
getSymbols
, specifyModel
,
modelData
## Not run: buildData(Next(OpCl(DIA)) ~ Lag(TBILL) + I(Lag(OpHi(DIA))^2)) buildData(Next(OpCl(DIA)) ~ Lag(TBILL), na.rm=FALSE) buildData(Next(OpCl(DIA)) ~ Lag(TBILL), na.rm=FALSE, return.class="ts") ## End(Not run)


WATCH: Elderly Civilian Sustains Gory Head Injuries After Indiscriminate Attack By Police
by AndrewAn elderly civilian sustained massive head injuries on Wednesday morning after a horde of anti-riot police descended on civilians in the central business district of Harare. The police attacked civilians and opposition MDC supporters who had gathered at the Morgan Richard Tsvangirai (MRT) House, formerly Harvest House. The supporters had gathered to listen to Nelson Chamisa’s Hope of the Nation Address (HONA).
Watch video…………
To View and Apply for the latest Jobs and Vacancies in Zimbabwe, Please Visit our Sister website iHarare Jobs


Selector: mdc-tab-bar
Exported as: mdcTabBar| Name | Description |
|---|---|
| stacked: boolean | Indicates that the tab icon and label should flow vertically instead of horizontally. |
| fixed: boolean | Indicates that the tab should shrink in size to be as narrow as possible without causing text to wrap. |
| fade: boolean | Sets up the tab indicator to fade in on activation and fade out on deactivation. Default is false. |
| activeTabIndex: number | Set the active tab by index. (Default is 0). Set to -1 for no initially selected tab. |
| iconIndicator: string | A material icon to display in the center of the tab. |
| useAutomaticActivation: boolean | If true, permits using movement keys to switch tabs. |
| focusOnActivate: boolean | Sets whether the tab should focus itself when activated. Defaults to true. |
| align: string | Sets the elements inside the scroll content element to be aligned to the start, center or end of the scroll
content element.
Valid values: 'start' | 'center' | 'end' * Only for Fixed Tabs. |
| activateTab(index: number) | Activates the Tab at the given index. |
| scrollIntoView(index: number) | Scrolls the Tab at the given index into view. |
| getActiveTabIndex(): number | Returns the index of the active Tab. |
| getActiveTab(): MdcTab | undefined | Returns the MdcTab that is active. |
| getTabIndex(tab: MdcTab): number | Returns the MdcTab index for given tab. |
| disableTab(index: number, disabled: boolean) | Control whether or not the tab is in a disable state. |
| activated: (source: MdcTabBar, index: number, tab: MdcTab) | Emitted when a tab is activated. |
Selector: mdc-tab-scroller
Exported as: mdcTabBarScroller| Name | Description |
|---|---|
| scrollTo(scrollX: number) | Scrolls to the scrollX value. |
| incrementScroll(scrollXIncrement: number) | Increments the current scroll value by the scrollX value. |
| getScrollPosition(): number | Returns the current visual scroll position. |
Selector: mdc-tab
Exported as: mdcTab| Name | Description |
|---|---|
| id: string | Unique id of the tab (auto generated if not supplied). |
| label: string | Optional. Adds a label in the tab. |
| icon: string | Optional. Indicates a leading icon in the tab. |
| disabled: boolean | Optional. Control the disabled state of the tab. |
| stacked: boolean | Indicates that the tab icon and label should flow vertically instead of horizontally. |
| fixed: boolean | Indicates that the tab should shrink in size to be as narrow as possible without causing text to wrap. |
| focusOnActivate: boolean | Sets whether the tab should focus itself when activated. Defaults to true. |
| getTabBarParent(): MdcTabBarParentComponent | Interface for communicating with ancestor MdcTabBar. |
| focus(): void | Sets focus to the tab. |
| interacted: (detail: MdcTab) | Emitted when a tab is interacted with. |
Selector: mdc-tab-label mdcTabLabel
Exported as: mdcTabLabelSelector: mdc-icon[mdcTabIcon]
Exported as: mdcTabIcon


The colon between the hours and minutes will blink every second.
Alternate colour scheme, as requested by @CaffeineJunkie.
Vibrate on Bluetooth disconnect.
Vibrate on the hour.


Release pattern describes which branches are considered as release. Use '%' to denote version number. For example:
release/%. Plugin properly support versions. Last merge always happens to master. Using example config, merge should happen in the following order:
release/1.9 -> release/1.10 -> release/2.0 -> master


Extra-state should not be modified by the copy or delete callback functions. (This is obvious from the C binding, but not obvious from the Fortran binding). However, these functions may update state that is indirectly accessed via extra-state. E.g., in C, extra-state can be a pointer to a data structure that is modified by the copy or callback functions; in Fortran, extra-state can be an index into an entry in a COMMON array that is modified by the copy or callback functions. In a multithreaded environment, users should be aware that distinct threads may invoke the same callback function concurrently: if this function modifies state associated with extra-state, then mutual exclusion code must be used to protect updates and accesses to the shared state.


Cross-validation Correction For Machine Learing in Genomics Datasets
In Genomics Dataset, every indivdual has very similar gene except a few variants which causes machine learning methods are easily over-fitting. Therefore CV which is used for estimating test error is underestimated.


| t01 |
infection, cell(s), immune, response(s), expression, viral, role, inflammatory |
288 |
6 |
| t02 |
influenza, lung, respiratory, severe, ARDS, sars-cov, mers-cov, H5N1, viruses, SARS |
72 |
1 |
| t03 |
group, rats, significantly, injury, HC, survival_rate, staining, lung, HCP, treatment |
52 |
0 |
| t04 |
patients, =, IP-10, healthy, <, children, cytokine, pbmcs, RANTES, levels |
42 |
1 |
| t05 |
MHV3, A/J, mice, MHV, resistant, balb/c, strains, mouse, virus, a/j_mice |
42 |
0 |
| t06 |
CNS, brain, CCL2, chemokine, CXCL10, CCR2, nervous, encephalitis, CCR5, demyelination |
46 |
0 |
| t07 |
lung, airway(s), inflammation, PTX3, allergic, lavage, fluid, chronic, RSV |
37 |
0 |
| t08 |
oxide, nitric, macrophage(s), nitric_oxide, polarization, M1, RAW, YPFS, effects |
42 |
0 |
| t09 |
nf-κb, MAPK, inflammasome, p38, phosphorylation, ams, NLRP3, skin, TREM‐1, nuclear |
40 |
0 |
| t10 |
endothelial, mscs, CD13, leukocytes, cells, epithelial, II, class, TI, adhesion |
35 |
0 |
| t11 |
liver, hepatitis, hepatic, MHV-3, IL-33, fulminant, liver_injury, hepatocyte(s), HCV |
39 |
0 |
| t12 |
pig(s), PRRSV, porcine, PRCV, swine, reproductive, ifn-α, IL6, prrsv-induced |
30 |
0 |
| t13 |
HMGB1, sepsis, transgenic, ACE2, GL, transgenic_mice, angiotensin, ang, box, II |
32 |
0 |
| t14 |
FIP, cats, feline, CHIKV, LTB4, FIPV, PRV, HIV‐1, peritonitis, vascular |
31 |
0 |
| t15 |
complement, storm, cytokine_storm, COVID-19, c5a, EBOV, complement_activation, pneumococcal, C3, sars-cov-2 |
21 |
6 |


I chose to use a dataset from a community-based nonprofit that I work with. The organization, Conjunto Heritage Taller, is dedicated to the preservation and perpetuation of traditional Conjunto music. Learn more about the organization HERE
Dataset of Conjunto Heritage Taller classes by class type


|
Perception: Human Detection
Group14
Human Detection using OpenCV Haard CAscade Classifier
|
#include <cascade.hpp>
Public Member Functions | |
| int | init (cv::Mat &, cv::Mat &) |
| int | loadCascade (const std::string &filePath) |
| int | objectDetect (std::vector< cv::Rect > &) |
Definition at line 40 of file cascade.hpp.
| int Cascade::init | ( | cv::Mat & | i, |
| cv::Mat & | g | ||
| ) |
load cascade xml file
| cv::Mat% | |
| cv::Mat% |
Definition at line 32 of file cascade.cpp.
| int Cascade::loadCascade | ( | const std::string & | filePath | ) |
load cascade xml file
| filePath | const string& |
Definition at line 41 of file cascade.cpp.
| int Cascade::objectDetect | ( | std::vector< cv::Rect > & | envlp | ) |
detect presence of object in a image
| vector<cv::Rect>& |
Definition at line 50 of file cascade.cpp.


Week 7 Learning Objectives
Big-O
- Order the common complexity classes according to their growth rate
- The following are in order from smallest growth (most efficient) to largest:
- constant O(1)
- logarithmic O(log n)
- linear O(n)
- loglinear, linearithmic, quasilinear O(n log n)
- polynomial O(n^c) -> O(n^2), O(n^3)
- exponential O(c^n) -> O(2^n), O(3^n)
- factorial O(n!)
- Identify the complexity classes of common sort methods
| bubble |
O(n^2) |
O(1) |
| selection |
O(n^2) |
O(1) |
| insertion |
O(n^2) |
O(1) |
| merge |
O(n log n) |
*O(n) OR O(n log n) |
| quick |
**O(n log n) OR O(n^2) |
**O(n log n) OR O(log n) |
*merge sort can be O(n) space complexity
- if we are not creating a new array every time we call merge sort on the left and right side. We can make it O(n) if we keep track of the start and end index of the left/right sides in the original array and pass that into the merge sort calls to the left and right side. In the way we implement, merge sort's space complexity is O(n log n) because we create a new array to pass into each call of merge sort to the left and right sides.
**quick sort's complexities are a little more complicated
- We are generally only concerned with the worst-scenario when we talk Big-O.
- With quick sort, the worst case is exceedingly rare (only occurs when our pivot for each round happens to be the next element, resulting in us having to choose n pivot points)
- Because it is so rare that this occurs, most people will use consider quick sort to be closer to O(n log n) time complexity.
- We also have two space complexities listed. The version that we used in class creates a new array for every call to quicksort, resulting in O(n log n) space. With some tweaking, we can sort in place, modifying the original array and cutting our space complexity to O(log n), which is just a result of the stack frames that we have to create. It's good to know this method exists, but you will not need to create or identify this version.
- Identify complexity classes of code
- Important takeaway here is being able to connect code patterns with complexities
- Doing an exact number of calculations (independent of input) -> constant O(1)
- Looping an exact number of times (independent of input) -> constant O(1)
- Recursive calls that divide the input -> logarithmic O(log n)
- Loops that depend on the size of the input -> linear O(n)
- Recursive calls that depend on the size of the input (decrementing instead of dividing) -> linear O(n)
- Looping through input on each stack frame, while recursively dividing our data (commonly seen in sorts like merge and quick sort) -> loglinear O(n log n)
- Nesting loops that depend on the size of the input -> polynomial O(n^c)
- Branching out on each recursive call, with the number of calls dependent on the size of the input -> exponential O(c^n)
- When both the number of recursive calls and the number of branches made in the calls are dependent on the size of the input -> factorial O(n!)
Memoization and Tabulation
- Apply memoization to recursive problems to make them less than polynomial time.
- Main steps for memoizing a problem:
- Write out the brute force recursion
- Add the memo object as an additional argument
- Keys on this object represent input, values are the corresponding output
- Add a base condition that returns the stored value if the argument is already in the memo
- Before returning a calculation, store the result in the memo for future use
- Example of a standard and memoized fibonacci:
// Standard Implementation
// Time Complexity: O(2^n)
// - For each call to fib, we have to make two branches, with n levels to this tree
function fib(n) {
if (n === 1 || n === 2) return 1;
return fib(n - 1) + fib(n - 2); // fib(3) + fib(2)
}
//fib(2) + fib(1)
// Using memoization
// Time Complexity: O(n)
// - We only ever have to calculate fib(x) one time, every other time that we use it in a larger problem, the result is immediately accessible in our memo
// - The memo removes the repeated trees of calculations from our original code
function fib(n, memo = {}) { // n = 4, memo = { 3: 2, 4: 3 }
if (n in memo) return memo[n]; // If we already calculated this value, return it
if (n === 1 || n === 2) return 1;
// Store the result in the memo first before returning
// Make sure to pass the memo in to your calls to fib!
memo[n] = fib(n - 1, memo) + fib(n - 2, memo);
return memo[n];
}
// memo[5] = fib(4) + fib(3) = 3 + memo[3] = 5
// memo[4] = fib(3) + fib(2) = 2 + 1 = 3
// memo[3] = fib(2) + fib(1) = 1 + 1 = 2
- Apply tabulation to iterative problems to make them less than polynomial time.
- Main considerations for using tabulation:
- Figure out how big is your table
- Typically going to be base on input size (number in fibonacci, length of string in wordBreak)
- What does my table represent?
- You are generally building up your answer.
- In fibonacci, we used the table to store the fib number at the corresponding index.
- In stepper, we stored the boolean of whether it was possible to get to that location.
- What initial values do I need to seed?
- Consider what your end result should be (boolean, number, etc.).
- Your seed data is generally going to be the immediate answer that we know from our base condition.
- In fibonacci, we knew the first two numbers of the series.
- In stepper, we knew that it was possible to get to our starting location, so we seeded it as true, defaulting the rest to false so that we could later change its value if we could make that step.
- How do I iterate and fill out my table?
- We typically need to iterate over or up to our input in some way in order to update and build up our table until we get our final result.
- In fibonacci, we iterated up to our input number in order to calculate the fib number at each step.
- In stepper, we iterated over each possible stepping location. If we could have made it to that point from our previous steps (ie that index was true in our table), we continued updating our table by marking the possible landing spots as true.
- Example of a tabulated fibonacci:
Sorting Algorithms
Code and Gifs for Sorting Algorithms
- Explain the complexity of and write a function that performs bubble sort on an array of numbers.
- Time Complexity: O(n^2)
- In our worst case, our input is in the opposite order. We have to perform n swaps and loop through our input n times because a swap is made each time.
- Space Complexity: O(1)
- We are creating the same number of variables with an exact size, independent of our input. No new arrays are created.
- Code example for bubbleSort:
- Explain the complexity of and write a function that performs selection sort on an array of numbers.
- Time Complexity: O(n^2)
- Our nested loop structure is dependent on the size of our input.
- The outer loop always occurs n times.
- For each of those iterations, we have another loop that runs (n - i) times. This just means that the inner loop runs one less time each iteration, but this averages out to (n/2).
- Our nested structure is then T(n * n/2) = O(n^2)
- Space Complexity: O(1)
- We are creating the same number of variables with an exact size, independent of our input. No new arrays are created.
- Code example for selectSort:
- Explain the complexity of and write a function that performs insertion sort on an array of numbers.
- Time Complexity: O(n^2)
- Our nested loop structure is dependent on the size of our input.
- The outer loop always occurs n times.
- For each of those iterations, we have another loop that runs a maximum of (i - 1) times. This just means that the inner loop runs one more time each iteration, but this averages out to (n/2).
- Our nested structure is then T(n * n/2) = O(n^2)
- Space Complexity: O(1)
- We are creating the same number of variables with an exact size, independent of our input. No new arrays are created.
- Code example for insertionSort:
- Explain the complexity of and write a function that performs merge sort on an array of numbers.
- Time Complexity: O(n log n)
- Our mergeSort function divides our input in half at each step, recursively calling itself with smaller and smaller input. This results in log n stack frames.
- On each stack frame, our worst case scenario is having to make n comparisons in our merge function in order to determine which element should come next in our sorted array.
- Since we have log n stack frames and n operations on each frame, we end up with an O(n log n) time complexity
- Space Complexity: O(n log n)
- We are ultimately creating n subarrays for every log n stack frames, making our space complexity quasilinear to our input size.
- IDEALLY merge sort space complexity is O(n) see merge sort space complexity explanation
- Code example for mergeSort:
- Explain the complexity of and write a function that performs quick sort on an array of numbers.
- Time Complexity: Average O(n log n), Worst O(n^2)
- In our worst case, the pivot that we select results in every element going into either the left or right array. If this happens we end up making n recursive calls to quickSort, with n comparisons at each call.
- In our average case, we pick something that more evenly splits the arrays, resulting in approximately log n recursive calls and an overall complexity of O(n log n).
- Quick sort is unique in that the worst case is so exceedingly rare that it is often considered an O(n log n) complexity, even though this is not technically accurate.
- Space Complexity: Our implementation O(n), Possible implementation O(log n)
- The partition arrays that we create are directly proportional to the size of the input, resulting in O(n) space complexity.
- With some tweaking, we could implement an in-place quick sort, which would eliminate the creation of new arrays. In this case, the log n stack frames from the recursion are the only proportional amount of space that is used, resulting in O(log n) space complexity.
- Code example for quickSort:
- Explain the complexity of and write a function that performs a binary search on a sorted array of numbers.
- Time Complexity: O(log n)
- With each recursive call, we split our input in half. This means we have to make at most log n checks to know if the element is in our array.
- Space Complexity: Our implementation O(n), Possible implementation O(1)
- We have to make a subarray for each recursive call. In the worst case (we don't find the element), the total length of these arrays is approximately equal to the length of the original (n).
- If we kept references to the beginning and end index of the portion of the array that we are searching, we could eliminate the need for creating new subarrays. We could also use a while loop to perform this functionality until we either found our target or our beginning and end indices crossed. This would eliminate the space required for recursive calls (adding stack frames). Ultimately we would be using the same number of variables independent of input size, resulting in O(1).
- Code example for binarySearch and binarySearchIndex:
// Returns simply true/false for presence
function binarySearch(array, target) {
if (array.length === 0) {
return false;
}
let midIdx = Math.floor(array.length / 2);
let leftHalf = array.slice(0, midIdx);
let rightHalf = array.slice(midIdx + 1);
if (target < array[midIdx]) {
return binarySearch(leftHalf, target);
} else if (target > array[midIdx]) {
return binarySearch(rightHalf, target);
} else {
return true;
}
}
// Returns the index or -1 if not found
function binarySearchIndex(array, target) {
if (!array.length) return -1;
const midIdx = Math.floor(array.length / 2);
const midEl = array[midIdx];
if (target < midEl) {
return binarySearchIndex(array.slice(0, midIdx), target);
} else if (target > midEl) {
// Since our recursive call will have new indices for the subarray, we have to adjust the return value to align it with the indices of our original array.
// If the recursive call returns -1, it was not found and we can immediately return -1
// If it was found in the subarray, we have to add on the number of elements that were removed from the beginning of our larger original array.
// For example, if we try to find 15 in an array of [5, 10, 15]:
// - Our first call to binarySearchIndex will check our middle element of 10
// - Since our target is greater, we will recursively call our search on elements to the right, being the subarray [15]
// - On our recursive call we found our target! It's index in this call is 0.
// - When we return 0 to where binarySearchIndex was called, we need to adjust it to line up with this larger array (the 0th element of this larger array is 5, but our target was at the 0th index of the subarray)
// - Since we sliced off 2 elements from the beginning before making our recursive call, we add 2 to the return value to adjust it back to line up with our original array.
const idxShift = binarySearchIndex(array.slice(midIdx + 1), target);
return idxShift === -1 ? -1 : idxShift + midIdx + 1;
} else {
return midIdx;
}
}
Lists, Stacks, and Queues
- Do the projects over and see make sure to know the following
- Explain and implement a Linked List.
- A linked list are a collection of ordered data that track three main components:
- head: beginning of the list
- tail: end of the list
- length: count of the number of elements in the list
- The main differences between lists and arrays are that a list does not have random access or indices to signify where in the list an element is.
- The only references to elements that we have in a list are the head and the tail.
- If we want an element in the middle of the list, we would have to traverse the list until we encountered it.
- The two main types of linked lists that we talked about are Singly Linked Lists and Doubly Linked Lists.
- Singly Linked Lists are composed of nodes that only have a reference to the next node in the list. We can only traverse the list in one direction.
- Doubly Linked Lists are composed of nodes that have a reference to both the next node and the previous node in the list. This allows us to traverse both forwards and backwards.
- Methods of a linked list that we should know are:
- addToTail: Adds a new node to the end of the list.
- addToHead: Adds a new node to the front of the list.
- insertAt: Adds a new node at the specified position (we need to traverse to that point, then update pointers)
- removeTail: Removes the last node of the list.
- removeHead: Removes the first node of the list.
- removeFrom: Removes the node at the specified position.
- contains: Traverses the list and returns a boolean to indicate if the value was found at any node.
- get: Returns a reference to the node at the specified position.
- set: Updates the value of the node at the specified position.
- size: Returns the current length of the list.
- Time complexities for these methods:
- Accessing a node: O(n), because we may have to traverse the entire list.
- Searching a list: O(n), because we may have to traverse the entire list.
- Inserting a value: O(1), under the assumption that we have a reference to the node that we want to insert it after/before. If we don't have this reference we would first have to access it (O(n) from above), but the actual creation is O(1)
- Deleting a node: O(1), for the same reasons as insertion. If we first need to find the previous and next nodes, we would need to access them (O(n) from above), but the actual deletion is O(1)
- Be able to implement a Singly Linked List and a Doubly Linked List. This would require you to use a Node class with a value instance variable and an instance variable that points to the next (and possibly previous) Node instance(s). You should then be able to interact with these Nodes to perform all of the actions of a Linked List, as we defined above.
- Explain and implement a Stack.
- A Last In First Out (LIFO) Abstract Data Type (ADT).
- LIFO: The last element put into the stack is the first thing removed from it. Think of it as a can of Pringles or a pile of dishes.
- ADT: The actual implementation of the stack can vary as long as the main principles and methods associated with them are abided by. We could use Nodes like we did with Linked Lists, we could use an Array as an underlying instance variable as long as the methods we implement only interact with it in the way a stack should be interacted with, etc.
- Methods of a Stack we should know are:
- push: Adds an element to the top of the stack.
- pop: Removes an element from the top of the stack.
- peek: Returns the value of the top element of the stack.
- size: Returns the number of elements in the stack.
- Time Complexities:
- Adding an element: O(1), since we are always adding it to the top and the addition doesn't affect any other elements.
- Removing an element: O(1), we're always taking the top element of the stack.
- Finding or Accessing a particular element: O(n), since we can only interact with our stack by removing elements from the top, we may have to remove every element to find what we're looking for.
- Explain and implement a Queue.
- A First In First Out (FIFO OR LILO) Abstract Data Type (ADT).
- FIFO: The first element put into the queue is the first thing removed from it. Think of it as if you are waiting in line at a store, first come, first serve.
- ADT: The actual implementation of the queue can vary as long as the main principles and methods associated with them are abided by. We could use Nodes like we did with Linked Lists, we could use an Array as an underlying instance variable as long as the methods we implement only interact with it in the way a queue should be interacted with, etc.
- Methods of a Queue we should know are:
- enqueue: Adds an element to the back of the queue.
- dequeue: Removes an element from the front of the queue.
- peek: Returns the value of the front element of the queue.
- size: Returns the number of elements in the queue.
- Time Complexities:
- Adding an element: O(1), since we are always adding it to the back. If we are using Nodes instead of a simple array, keeping a reference to the last node allows us to immediately update these pointers without having to do any traversal.
- Removing an element: O(1), we're always taking the front element of the queue.
- Finding or Accessing a particular element: O(n), since we can only interact with our queue by removing elements from the front, we may have to remove every element to find what we're looking for.
GitHub (not going to be on the assessment)
- You will be able to participate in the social aspects of GitHub by starring repositories, following other developers, and reviewing your followers
- Doing these basic activities shows that you are not only an active programmer, but active in the overall community, which is a positive attribute during the job search.
- You will be able to use Markdown to write code snippets in your README files
- Great cheatsheet for markdown syntax: https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet
- You will craft your GitHub profile and contribute throughout the course by keeping your "gardens green"
- Make commits to your projects throughout the day.
- Push your projects up to your own repository at the end of the day.
- You will be able to identify the basics of a good Wiki entries for proposals and minimum viable products
- Design documents give a roadmap for how you envision the project developing.
- They serve as a planning tool as well as a resource during development.
- Minimum Viable Products (MVPs) indicate the features that need to be implemented in order to have a completed application. They also indicate your stepping stones for completing the project.
- You will be able to identify the basics of a good project README that includes technologies at the top, images, descriptions and code snippets
- Displaying all of these within your README gives someone a preview of the project.
- It highlights what it looks like, describes its main features, and discusses at a high-level how it was implemented.
- You will have 2 hours to complete the assessment
- The assessment has two major sections
- ~10 Multiple Choice Questions
- VS Code Questions
- 4 different problems in total
- ~20 specs to pass spread across all problems


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Producing a Music Video in the Browser
This page and associated codepens are meant to share with other developers a means of creating a
Webm video from a dynamically generated
canvas driven from both user input and audio file.
Introduction
I'm
Jason. I created
Vizibop, a service currently in beta that allows musicians and podcasters to quickly and easily create a unique MP4 video synched to a audio file.
At its core, Vizibop leverages a HTML Canvas, HTML5 Audio, MediaRecorder, and P5.js to generate a WebM file with front end code only. From there, that WebM file is sent through
AWS Elastic Transcoder to convert the Webm file to a MP4 file which can then be shared on sites like Instagram, TikTok, and YouTube.
Here are some examples of videos created with Vizibop:
Creating a basic animation
P5.js and
P5.sound are a set of javascript libraries that make drawing on a canvas and syncing it to audio relatively straight forward. Here, I am using P5
instance mode to make sure we don't pollute the name space. Here is a very basic scaffolding for a p5 animation:
Codepen
const s = sketch => {
//sketch wide variables can be put here. useful
//in the draw function where you may be incrementing
//or using some fancy trigonometry for animations
let verticalPosition = 0;
//preload things like images and fonts you want in your canvas
sketch.preload = function() {
}
//create your canvas and set things that you don't want changed
sketch.setup = function() {
//draw the canvas as big as possible
var canvas = sketch.createCanvas(window.innerWidth, window.innerHeight);
//drop it in a div with id = animation
canvas.parent('animation')
}
//loop and draw stuff X times a second
sketch.draw = function() {
console.log(verticalPosition)
sketch.background('#48639B');
//draw a line using two points in space
//sketch.fill('#F1225E')
sketch.stroke('#F687B3')
sketch.strokeWeight(10)
sketch.line(0, verticalPosition, sketch.width, verticalPosition);
//modify the height during each draw iteration
verticalPosition = verticalPosition - 0.5;
if (verticalPosition < 0) {
verticalPosition = sketch.height;
}
}
}
//create an instance of P5
let p5js = new p5(s);
Playing Music
Audio in the browser is a bit of a rabbit hole.
Howler and
SoundJS will make your life a lot easier if you get into jukeboxes and video games. To keep things simple, let's simply create a hidden audio element and allow a user to click a button to begin playing a song. For Vizibop we allow users to upload their own songs in WAV or MP3 format.
Codepen for Playing Music
Wiring Audio to P5
P5 and the sound module make it super easy to have the variables within your sketch change based on the volume and frequency of a sound. Out of the box you can use
Amplitude and
FFT to do some pretty amazing things. The
Coding Train has a whole series of videos on sound within P5 that I encourage you to watch.
In this example, we want to do some beat detection to change the background of the canvas. Beat detection also gets a little tricky depending on the song and quality of the underlying audio. In Vizibop we let the user define which frequency bands to key off of to really dial in more accurate beat detection.
Codepen for Beat Detection
Customize the animation
The animation above is pretty basic. A better experience allows the users to turn knobs and dials to customize their animation. In our case, I have tried to balance just enough knobs and dials to create a wide range of videos without overwhelming the user with too many options. The possibilities are really unlimited when you combine images, typography, color, motion, and math.
Codepen for a Custom Animation
Record a video
We now have the key components to record a music video built entirely in the front end.
But here is where the fun begins, particularly across browsers and computers.
MediaRecorder and captureStream are the magic that allows us to create a WEBM video from the components above. As of the writing of this document, Safari only supports MediaRecorder in experimental mode. Firefox does not support VP9. Chrome is our best friend here. With 70% market share between Chrome and Firefox, we were willing to move ahead with this approach betting that eventually Safari will make MediaRecorder generally available.
I am still tweaking the configuration here. It seems that both the VP9 and H264 codecs underperform VP8 for this application. Adjusting both the frame rate of the animation and the captureStream combined with the optional videoBitsPerSecond parameter for the MediaRecorder seem to improve any visual lag and artifacts due to compression. It's not perfect but so far I am pleased with the results.
Codepen for Recording a Video
What's next?
With this technique, there really isn't a limit to the types of videos musicians and podcasters can create in the browser. With that said, by design, Vizibop will not evolve into a full blown video editor. A guiding principle is to keep the user experience as simple as possible while maximizing the number of unique videos that can be created. I am trying to avoid something like
ZGameEditor Visualizer. My intent is to create a tool that is
Sesame Street Simple.
Your feedback, advice, and guidance is always welcome
Jason


Simple Iris Classification
VERSION=1.1
TRAIN_IMAGE_BASE=alvinhenrick/kube-demo-simple
SERVE_IMAGE_BASE=alvinhenrick/iris-classification
build:
docker build -t ${TRAIN_IMAGE_BASE}:${VERSION} .
login:
docker login
push:
docker push ${TRAIN_IMAGE_BASE}:${VERSION}
createpvc:
kubectl apply -f mystorage.yaml
deletepvc:
kubectl delete -f mystorage.yaml
copydata:
kubectl cp ./iris dataaccess:/data/
train:
kubectl apply -f tfjobsimple.yaml
download:
kubectl cp dataaccess:/model/iris_model ./iris_model
s2i:
s2i build . seldonio/seldon-core-s2i-python3:0.1 ${SERVE_IMAGE_BASE}:${VERSION} --copy ./iris_model --env MODEL_NAME=IrisClassifier --env API_TYPE=REST --env SERVICE_TYPE=MODEL --env PERSISTENCE=0
s2ipush:
docker push ${SERVE_IMAGE_BASE}:${VERSION}
serve:
cd simple_demo_ks ; ks generate seldon-serve-simple iris-classification --image=${SERVE_IMAGE_BASE}:${VERSION}
cd simple_demo_ks ; ks apply default -c iris-classification
portforward:
kubectl port-forward `kubectl get pods -n default -l service=ambassador -o jsonpath='{.items[0].metadata.name}'` -n default 8080:80
predict:
curl -X POST -H 'Content-Type: application/json' -d '{"data":{"ndarray":[[5.9, 3.0, 4.2, 1.5],[6.9, 3.1, 5.4, 2.1],[5.1, 3.3, 1.7, 0.5]]}}' http://localhost:8080/seldon/iris-classification/api/v0.1/predictions
tail:
kubectl logs -f kube-demo-simple-master-0
tailseldon:
kubectl logs -f `kubectl get pods -l seldon-app=iris-classification -o=jsonpath='{.items[0].metadata.name}'` iris-classification
stop:
kubectl delete -f tfjobsimple.yaml
clean:
cd simple_demo_ks ; ks delete default -c iris-classification
cd simple_demo_ks ; ks component rm iris-classification
# ksonnet delete and remove component
Declare the variable,tag and version for training and serving docker image.
Docker build training image.
Login to your docker hub account.
Push the training docker image to docker hub.
Create/Delete Persistence volume container.
Copy training data into Persistence volume container.
Run the training job with Kubeflow.
Download the trained model.
Build model serving docker image with S2i.
Push the serving docker image to docker hub.
Serve the model with Kubeflow and Seldon.
Forward the port from the Ambassador container to localhost.
Make predictions via REST API and Seldon through the Ambassador proxy setup by Kubeflow.
Tail the training container log for debugging.
Tail the seldon serving container log for debugging.
Clean up training job.
Clean up seldon serving job.
---
apiVersion: "kubeflow.org/v1alpha2"
kind: TFJob
metadata:
name: kube-demo-dist
spec:
tfReplicaSpecs:
Master:
replicas: 1
template:
spec:
containers:
- name: tensorflow
image: alvinhenrick/kube-demo-dist:1.1
command: ["python", "/code/train_and_eval.py","--output_path=/model/imdb_model"]
volumeMounts:
- name: data
mountPath: "/data"
- name: models
mountPath: "/model"
volumes:
- name: data
persistentVolumeClaim:
claimName: mydata
- name: models
persistentVolumeClaim:
claimName: mymodels
Worker:
replicas: 3
template:
spec:
containers:
- name: tensorflow
image: alvinhenrick/kube-demo-dist:1.1
command: ["python", "/code/train_and_eval.py","--output_path=/model/imdb_model"]
volumeMounts:
- name: data
mountPath: "/data"
- name: models
mountPath: "/model"
volumes:
- name: data
persistentVolumeClaim:
claimName: mydata
- name: models
persistentVolumeClaim:
claimName: mymodels
PS:
replicas: 2
template:
spec:
containers:
- name: tensorflow
image: alvinhenrick/kube-demo-dist:1.1
command: ["python", "/code/train_and_eval.py","--output_path=/model/imdb_model"]
volumeMounts:
- name: data
mountPath: "/data"
- name: models
mountPath: "/model"
ports:
- containerPort: 6006
volumes:
- name: data
persistentVolumeClaim:
claimName: mydata
- name: models
persistentVolumeClaim:
claimName: mymodels
TFJob is a Kubernetes custom resource definition that makes it easy to run TensorFlow training jobs.
The Master is responsible for orchestrating training and performing tasks like checkpointing the model.
Kubernetes training container specification
Kubernetes Persistent volume mounts
The Worker do the actual work of training the model. In some cases, worker 0 might also act as the Master.
The PS are parameter servers; these servers provide a distributed data store for the model parameters.
{
"apiVersion": "machinelearning.seldon.io/v1alpha2",
"kind": "SeldonDeployment",
"metadata": {
"labels": {
"app": "seldon"
},
"name": "your-deployment-name"
},
"spec": {
"annotations": {
"project_name": "your project name",
"deployment_version": "your-deployment-version"
},
"name": "your-deployment-name",
"oauth_key": "oauth-key",
"oauth_secret": "oauth-secret",
"predictors": [
{
"componentSpecs": [
{
"spec": {
"containers": [
{
"image": "your-docker-image",
"imagePullPolicy": "IfNotPresent or Always etc",
"name": "your-model-name",
"resources": {
"requests": {
"memory": ""
}
}
}
],
"terminationGracePeriodSeconds": ""
}
}
],
"graph": {
"children": [],
"name": "your-model-name",
"endpoint": {
"type": "your-endpoint-type #REST or #GRPC"
},
"type": "MODEL"
},
"name": "your-model-name",
"replicas": "# Number of replica",
"annotations": {
"predictor_version": "your-model-version"
}
}
]
}
}
SeldonDeployment is a Kubernetes custom resource definition that makes it easy to server models.
Seldon serving container specification.
The deployment graph definition.
Simple IMDB Document Classification
Thank you!!!!


Diversity
The diversity module provides calculations for various diversity indices. A diversity index is a numeric measurement of unique species that exist within a ecological community.
Predator-Prey Models
The Lotka-Volterra module provides calculations for predator-prey dynamics. A predator-prey relationship is characterized by the dynamic balance between the two populations over time.
Steady State Systems
The Steady State module can model real-world steady state processes. A system is considered steady state when it remains unchanged after a dynamic process has acted upon it.
Non-Steady State Systems
The Non-Steady State module models dynamic systems. A system is non-steady state if it is changed by dynamic processes over time.
Population Growth Functions
The Sigmoid module currently supports S-shaped growth models. Sigmoids are characterized by slow initial growth, then rapid growth that evens out.


The Old Blog: The Archive
Note: This archive lists all the old articles on the
website. The new archives are available on the
front page
442 old posts; each is a
comment, so you can see where the action is. Was.
2007: June
April
March
February
January
2006: December
November
October
September
August
July
June
May
April
March
February
January
2005: December
November
October
September
August
July
June
May
April
March
February
January
2004: December
November
October
September
August
July
June
May
April
March
February
January
2003: December
November
October
September
July
2007: June
What I've Been Up To
2007: April
Python's Makefile
lxml Transformations
2007: March
On form libraries
PyCon 2007 wrapup
RESTful transactions (a speculation)
2007: February
Python packaging
Going to PyCon 2007
Full Stack vs. Glue
TurboGears and Pylons (a technical comparison)
An Easier Legacy
2007: January
WSGIRemote now HTTPEncode
2006: December
Writing good (dynamic) code
Evaluating WYSIWYG editors
"Grassroots organizing is about building power"
Respecting the whole person
2006: November
Transit and energy intensity
minimock
WSGI
Review: Beyond Java
2006: October
PloneConf 2006
Paste 1.0
HTTP proxying questions
WSGI and paste.proxy
WSGI and Dispatch
Nonlinear learning, the nonlinear internet
workingenv.py update
Zope 3 Critique
PyWordpress
OLPC, Python...
2006: September
In NYC
Common Message Runtime
Sun Comes Lately
2006: August
MP3 player?
Why Small Packages Matter
2006: July
WPHP
Getting Things Done: Tools?
Microformats, Feeds, and Blogs
Python FastCGI Server?
2006: June
Decomposing Document Generation
Atom, threading, etc.
New Job, Open Plans
HTTP verb distaste
wsgi.org
Neutral Templating
2006: May
Javascript packaging thoughts
"Discuss"
Templating via dict wrappers
Stupid Abundance
2006: April
Introducing HTConsole
FastCGI SCGI HTTP
HTTP(ish) all the way down
workingenv revisited
Javascript Doctest
More Python, Education
Python, Education, Logo
REST in more than one request
Re: Ruby and Python Compared
Introspecting expressions in py3k
This blog is maybe okay now
Functional Python
paster and scripts
2006: March
DVCS Mini Roundup
Bazaar-NG, try 1
Behavior Driven Programming
Constraints and Continuations
Packaging Python
working-env.py
On the subject of mass transit
Python Marketing
Python IDE Consolidation
Cherrypy and RhubarbTart
A Brief Defense of Significant Whitespace
Working Environment Brainstorm
PyCon 2006
2006: February
SQLObject 2
Towards PHP
Comments on again...
Orthogonality is Pretentious
My Python 4k
Branching Practices
A Browser IDE
site-packages Considered Harmful
Blog software
Kludging with Supervisor
Do It Yourself
2006: January
My CherryPy Rant
TracPaste & CherryPaste
DjangoPaste
Pastifying CherryPy
Extending overlays
Opening up templates
Applications! Applications! Applications!
An Ideal Web Development Environment
Python Documentation Comments
Training for Failure
2005: December
Paste App Installation
There's so much more than Rails
Agile Content Development
More Small Apps
XML Processing
Other Editors?
py.std
The unbridled humanity of APIs
Contextual Validation
Reddit etc.
Doc Comments
More on Python Metaprogramming
Javascript plaintext editing
Bandwagon leaving from lot C
2005: November
Dynamic __doc__
Twill in Javascript
Logo Stickers Are Awesome
Best of the web app test frameworks?
DRM Explained
Templates Shmeplates
The UnZen of UnPython
What is Paste, yet again
Shebang
Ajaxy Exception Catching
Daemon Best Practices
Friendship and hand holding
Magic and Backtracing Code
2005: October
Sad conflicting packages
Repetition Javascript Library
Google at it again
Off and Away
First Try at Generic Functions
Little Apps: Decouple First
Little Apps Instead of Little Frameworks
Zope 3 and Modeling
Concurrency and Processes
Events and SQLObject
Releases Releases Releases
2005: September
Testing Install Procedures
Alternate Python Install Directory
Commandline Acceptance Testing
2005: August
Static Caching with mod_rewrite
SQLObject 0.7b1
Paste Deployment 0.1
JSLint from the Command Line
Ruby, Python, "Power"
FormEncode 0.2
August ChiPy (and the stdlib)
Towards a More Perfect Version Control
Exploring the Outer Reaches of Pointlessness
Book Club
Centralized vs. Decentralized 2
Distributed vs. Centralized Version Control
String hash vs. Unicode hash
Connectalize Me!

Composing WSGI Apps
Ideal Web Application Layout
Filebrowser Example
Apache Authentication and Single-Signon
Accidental WHAT-WG Implementing
It's Not Another Damn Framework
Help me please: Apache auth
Blogs and Jobs
A More Perfect App Server: WSGI Transaction Manager
Handling a Diversity of Frameworks
Yet Another Framework: Wareweb
property decorator
The PHP Ghetto
Prototype.js and Object.prototype
Reducing boilerplate code in __init__
On ethanol
2005: June
The Tens of Commandments
Ajax Experimentations: Filebrowser
What I've Been Doing...
Functional testing in Paste
2005: May
Building testability into web applications
Give us an up-or-down vote
GWA and RFC 2616
What is Paste
My First Ajax App
Breaking the Web We Have
2005: April
Ruby Meetup
Python Paste News Blog
More thoughts on Ruby on Rails
WSGIKit is dead; long live Python Paste
More on single-signon
Being Unitized
Connotations of the ether
What can WSGIKit do for you?
What is WSGIKit?

Why Web Programming Matters Most
WSGIKit at PyCon 2005
Schevo and Durus
PyCon: Optional Static Typing
Sprint Day 2
This Just In...
Sprint Day 1
ZPTKit 0.1
Further FormEncode Work...
Fixing Apache Authentication
Job Posting: Web Interface Specialist
First ChiPy Sprint: PyPI
My Tripod of Web Testing
Starting with Selenium
Playing with decorators and blocks
2005: February
Web application acceptance testing
XML and doctest
My first bit of ElementTree
Another plan: SQLObject 0.7
FileSystemView vs. LocalFS
Zope to LocalFS
Strange and unprofessional
The long tail of comments
Did he mention workflow?
Re: Falcon
Review: mp3tunes.com
Microsoft Interoperability
Of Syntax and Environment
A theory on form toolkits
WSGIKit/WSGI/Webware Sprint
A WSGI reference library
Fixes What's Wrong With Python
2005: January
From Webware to WSGIKit
Like a school play
"Tell me what to do, please"
Objects, Messages, Erlang
THE and LEAP
THE vs. Emacs
SQLObject 0.6.1
I dream of PyMoz
Good catch all exceptions
Comments enabled
Re: Theory and Practice
Coding Metaphors
wsgikit.config
In Commemoration of Martin Luther King Jr.
Big Blocks
ROM vs. ORM
Gmane blog interface
Show-embedded Bookmarklet
PEP 246, as-a
htmlfill
Where Next For FormEncode
I Hate Web Folders
On Toilets and Living Standards
A Declarative Syntax Extension
Already under our noses?
Now with Comments
People need to chill
2004: December
Re: Optional Static Typing
The Challenge Of Metaprogramming
Job Position: System Administrator, Chicago
Explaining Decorators
Respecting The Programmer
Because Unanswered Problems Are Always Hard
My New Blog
2004: November
Python, metaprogramming, and macros
py.test
A Conservative Metaclass
Four More Years
Urban Archipelago
WSGIKit
Photo Printing
Thoughts on Ruby on Rails
Geography
No silver lining, but maybe a positive note...
The first Wednesday after the first Tuesday after the first
Monday
First Tuesday after the First Monday
The Two Sides
2004: October
Sigh... what matters...
Iraq and Ulterior Motives
Anti-Intellectualism and Politics
Subversion Maintenance
Little modules
Even more significant whitespace
Where are the log analyzers?
On Kerry
WSGI Implementations
An Ideal Python Web Programming Environment
Versioned Imports
Thinking about the Python Standard Library
2004: September
WSGI WebKit
Code Pickiness
XML vs. generic serialization
Book Idea: Python Optimization
Apple, Microsoft, Some Random Russian Company
2004: August
Server Setup
WSGI Sample Apps and Middleware
A WSGI Stack
SQLObject and Me
WSGI Middleware
Apache the Application Server
Python Web Server Gateway Interface
Static Code Analysis
Doctest and documentation
Still Pretty Cheap Debugging Trick
Javascript Slideshow
More on decorators
@decorator
Dealing with Context
Re: Favour Error Prevention over Error Detection
2004: July
PHP and Application Evolution
On Campaign Finance Reform
Successful ORM use
Naming Conventions
And Boo Makes Three
Descriptor Nit
Easy Read-Only Attributes
Continuations: a concrete approach
System Software
2004: June
The Economics of Flash (or why you shouldn't like it)
In Praise of the Web UI
On the subject of the automobile
Re: Python templating languages
Weapons of Mass Instruction
PyPI and small code
Ah, for a decent bit of Javascript!
Threat Model
Del.icio.us links
WHAT-WG
Is W3C a loving mother?
Zombie Bugs
Homebrew encryption
Syndication: boring war where only egos are hurt
Metric fun
2004: May
Purple Bookmarklet
IE Sucks
Choosing Sides
Persistent Persistence
More On Compound Documents
Building an Intuition
Invert this
Pain in the Butt
Advancing Web Application UIs
Mozilla developers smarter than IE developers
I'm unhappy
Chicago Python Users
2004: April
Another web app pattern
Zope 2, Zope 3, and the big empty space between
Rewriting and Refactoring
Nummy ctypes
WYSIWYG Wiki
Inserting into textarea at cursor position
Movie: Whale Rider
Feminist Computer Science 2
Feminist Computer Science
Wiki feature of the day
Dark Side of Grammar
A Flexible, Reusable Greeting Framework
New feature for the day
Further Wiki Enhancements
New Wiki
Spiffy Javascript...
Z SQL Rant
Java Usability Recap
Bundles Schmundles
fit.c2.com
2004: March
Concurrency: looking for positive models
Initial thoughts on Prothon
Anti-usable libraries...
GNOME and the Managed Runtime
PyCon and all that jazz
Interfaces: Signatures and Semantics
Frameworks, Libraries, Conceptual Scalability
DataTest 0.2
What does "ahead of your time" mean?
Latent interfaces
Where Smalltalk Went Wrong 2
Where Smalltalk Went Wrong
SQLObject 0.5.2
ezsqlobject
Static publishing debate
2004: February
DataTest
State of Cryptography
Little form display tweaks
PyCon Buzz!
Hash table accused of impersonating OO
2004: January
Style-insensitive names
path module
Python nit, chapter 3
SQLObject API redesign
64 bit immediates in Python
In Commemoration of MLK Jr.
Python nit, chapter 2
A Python Poka-Yoke
2003: December
Offline
Static recap
ESI caching
Referential identity (appropriate abstraction)
XHTML rant: semantic my ass!
CMS and static publishing
Kernel, meet Python
Pen mightier than sword, yet scissors beat paper
I'm no longer a free bird...
Sometimes other people's failure makes me feel good about myself
2003: November
mod_auth_remote
Guidance, Navigation, UI
Bayesian Dark Side
PyLogo
Web Application Patterns: Status Notification
UIs of the future: Ion, Emacs
Squeak/Morphic Demo
The Future: Longhorn Shmonghorn
Twisted and Threads
It had to start sometime...
More alike than different...
% interpolation
Javascript Convert...?
Standalone WebKit
Security Models
SQLObject 0.5
2003: October
GIL of Doom!
Smart vs. Dumb Templates
Kata 19: an optimization anecdote
HTML spellcheck
Python nit, chapter 1
Web-SIG
So many accessors...
Concurrency-Oriented Python
Null-Terminated Strings Strike Again!
Speed is a Process, not an Attribute
Self Take Two
Adaptation vs. Magic Methods
Unittest Rant
2003: September
Self and Prototype-based Programming
Command-Line Zope
ORM vs. SQL
Declarative Classes
Metaclass fun
2003: July
Testing considered harmful


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The Andromeda Strain
A Retrospective
Starring: Arthur Hill (Dr. Jeremy Stone), David Wayne (Dr. Charles Dutton), James Olson (Dr. Mark Hall), Kate Reid (Dr. Ruth Leavitt). Produced and Directed by Robert Wise.
The Andromeda Strain is the greatest science fiction film ever made. I know that is very sweeping statement, so I'll qualify it by adding that The Andromeda Strain is one of the few films made that genuinely deserve the label of "science fiction," stories in which speculative science is at the core of the plot.
2001: A Space Odyssey is probably a better film, but it really only qualifies as science fiction if you consider metaphysics to be a science. Most all other films we normally classify as science fiction, or SF, are really just fantasy, action or horror stories set in a futuristic setting.
The Andromeda Strain starts out with two soldiers in a high-tech (for 1970) van looking for a crashed satellite in a tiny New Mexico town. Something bad happens to them. Pictures from a reconnaissance plane show a shocking sight. Apparently everyone in the town is dead. The authorities call a "Wildfire Alert," summoning four scientists, all but one of them somewhat reluctant, to a super-secret underground germ warfare laboratory in Nevada. Two of scientists, Stone and Hall, fly directly to the town in space suits and find a town where almost every single resident literally dropped in their tracks, their blood turned to powder in their veins.  They take the survivors and the satellite back to the lab, where Stone pressures the White House to call up a "Directive 712," an executive order to cauterize the area around the town with a nuclear bomb.
What they find on the satellite when they get back to the lab is Andromeda, an organism that defies all the normal rules of Earth-like life and mutates as it grows. Their only hope to cure it is to find out what a perfectly healthy baby boy has in common with an old derelict. What they find out is that organism feeds directly on energy and that detonating the A-bomb over the town would only spread it across the entire planet. They barely call off the bombing in time.
But then the organism mutates into something that threatens to eat through the labs defenses and break out. This triggers the lab's last-ditch defense mechanism, an atomic bomb.
To those raised on the brainless action fare that pollutes movie theaters these days, The Andromeda Strain will probably seem interminably slow. Much of the film is a lot of people standing around looking at video screens and computer readouts. But what the characters see on those screens ratchets up the tension with every turn of the screw. The performances are universally fine, with the actors keeping things low-key and restrained, just like scientists.
To me, the real appeal of this film is the fact that it shows scientists acting like scientists and makes it seem exciting. We follow the logic of their deduction step by methodical step, puzzling like they do every time Andromeda behaves in a way we don't expect.
This film is only available on wide screen on DVD and that is the way to see it. It was originally rated G when first released but it now carries the PG rating, mostly for very mild nudity and one scene in which a body's wrist is slashed, spilling its powdered blood.
Rating 9 out of 10 wild fires
-- Paul McElligott http://home.earthlink.net/~plmcelligott "Subvert the dominant paradigm"
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--- layout: post title: Time to change.. date: '2008-05-03T03:03:00+02:00' tags: - time - to - tumblr - blog - wonderful - great - new - textmadebyme tumblr_url: http://blog.alezzandro.com/post/33577643/time-to-change ---
It’s time to change!
I’m writing you from this new blog, I’ve changed it cause the old blog has many errors not solved and cause my linksys nslu2 is too tired to give me an expensive php support :-)
Instead, this blog is really powerful and easy accessible, i can post something everywhere by mail, using my cellphone for example.
If you want to take a look to my old blog archives, you can always visit: http://www.alexworld.it/perblog/ an html-ized version of the last blog made by a script written by me on perl.
For now is all!
Stay tuned friends!
byebye,
Alessandro.