pyLabbook Quick Start
You should have a working installation of python 3.6.5 or above. If you don’t, download python here.
You should be familiar with using a terminal window, for example, knowing how to change directories and how to run a program from a terminal window are essential.
- Download the most recent pyLabbook distribution here.
- Unpack the zipped archive into a nice place on your hard drive, where you want your pyLabbook to live. This folder will be called your pyLabbook root, or your pyLabbook distribution folder.
- Install the following python packages: numpy, xlrd, openpyxl, pandas and sqlite3:
- Use your favorite package manager, or
pip
to install these packages - Open a terminal window and type
pip install [package name]
where [package name] is the name of one of the packages described. - If you’re having trouble with
pip
check out the python package documentation or some issues with pip and python 3.6. - you will need pandas version 0.24.0 or greater
- Use your favorite package manager, or
- Run the pyLabbook testme script to make sure you have everything you need
- In a terminal window,
cd
to the pyLabbook root folder and runpython testme.py
. - The script will test various packages and functions used by pyLabbook and let you know how it goes. Missing packages should be installed using
pip
or your favorite python package manager.
- In a terminal window,
- Once the testme script completes all of it’s tests successfully, you’re ready to go!
How Do I Use pyLabbook?
pyLabbook offers three tools that you can use to manage your data:
-
The manager GUI is the simplest method. The manager is a python-based, cross-platform, Tcl/Tk based graphical interface that can handle basic data management.
-
Command-line pyLabbook scripts are also available. See the Basic Tutorial on how to use them or take a look at the pyLabbook Scripts documentation.
-
You can also create your own python scripts using the pyLabbook API for maximum flexibility. Take a look at the Python Scripting With pyLabbook documentation to find out how you can incorporate pyLabbook into your own custom python scripts.
Other Tools That Will Help
Database Browser
The pyLabbook project is designed to help you structure and organize your data into versatile sqlite3 databases, which are more efficient, more shareable and more robust than spreadsheet files. To get the most out of your pyLabbook databases, we recommend having a database browser handy.
There are many free SQLite database browsers for OS X, Windows and Unix. We recommend SQLite Studio, which is cross-platform, self-contained and supports sqlite3.
Spreadsheet Software
While spreadsheets aren’t the best way to store massive amounts of data long-term, they are a necessary and useful component of data entry. You’re probably familiar with Microsoft Excel, but there are some good, free, open source alternatives…
Libre Office is a free, open source, cross-platform productivity suite that includes a spreadsheet editor (Calc) that has a similar interface as Microsoft Excel.
Apache Open Office is another free, open source cross-platform productivity suite that includes a spreadsheet editor with an Excel-like interface.


Our English Atlas of Inequality is based on the 'travel-to-work-area' (TTWA) geography. These can be thought of as functional labour market areas. We have produced individual maps for all 149 English TTWAs. The data for TTWAs and all other map sets is in our data folder. Column descriptions are in the TTWA file. **We're working on getting the maps back online.**
We also produced a set of map outputs for all 317 local authority districts in England. Every map here gives the Gini coefficient, 20:20 Index and Moran's I value for each local authority, plus a map showing the 20% most and least deprived LSOAs on the 2019 English Indices of Deprivation. **We're working on getting the maps back online.**
To complete the set of map outputs, we also produced one map for each of England's 533 Westminster Parliamentary Constituencies. These follow the format of the other two map sets but cover smaller areas, with smaller populations. **We're working on getting the maps back online.**


Apparently git in Windows turns symlinks into a file with just the linked file path in it? Wat? 😭😣
10/11/2014, 1:25:55 PM
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Apparently git in Windows turns symlinks into a file with just the linked file path in it? Wat? 😭😣
10/11/2014, 1:25:55 PM
Favs: 0
Retweets: 0
link


ZHIYIN STEAMER
Last year October 15th, I experienced an unforgettable trip—ZHIYIN STEAMER.As night falls, The wind blowed from the river, and the ZHIYIN STEAMER was brightly lit.
Approaching the pier of the steamer, the songs of the Republic of China have been surrounded, and the crossing of one time was about to kick off.
“ZHIYIN STEAMER” was a 1930s-style steamer, Some stories that people meet in a journey happened on the steamer.
There is no fixed auditorium and performance area in the traditional theater. The actors and spectators can move freely in the cabin. In other words, when the whistle sounds, the boat sails out of the river, and every actor in the boat begins to perform, you want to see What to look at, and will also be part of the play, you can interact deeply with the actors.
The stories on each floor of the ship are different, and the seemingly unrelated stories are independent, but they are combined by the “ZHIYIN STEAMER”. The boat is said to be, some story about the rivers, the ship and the city.
We were ready to board the ship.My wife also changed into the cheongsam of the Republic of China.
BOARDING SHIP
Before the ship was opened, the stories of different people began to unfold before boarding the ship, telling their own lines.
There were 108 actors, including bankers, singers, students, and social flowers. Their stories haved taken place in every corner of the ship from the moment they boarded the ship.
There were three routes for the entire boarding, and the order of the stories on each line is different. When boarding the ship, I also opened a unique journey through.
ONE FLOOR DANCE HALL
The first act was a ballroom. The center was a large dance floor under the spotlight. The actors danced in a ballroom dance, with a monologue of stories and a romantic dialogue between actors.
ONE FLOOR BAR
After the first curtain of the ballroom, we moved to the bar. There were people chatting and drinking, there were waiters who helped run the Lee, there were singing singers, and there were many stories. One story is a man who has been on the boat for many years. Every day, he makes a phone call at a fixed position in the bar to find a girl.
SECONE FLOOR ROOM
The second floor was mainly a ship’s room, with a single room and a bunk bed. Each actor in each room has an independent story, and the story of all rooms is happening at the same time.
THREE FLOOR ROOM
On the third floor, each of the original boarding ships also haved its own private room. Each person haved a room number on the ticket. The room below was ours, with some chronological furnishings.
Feel freed to look through the objects in the room but did’t take them away. After entering the room, the staff gaved us two postcards and a pen on the table, which could be mailed to your friends. There was still a letter on the table.
ONE FLOOR DANCE HALL
After the third floor, we returned to the first floor of the ballroom.  At this time, everyone could move freely in every corner of the cabin, and you could dance with the actors.
The most impressive thing was that a very old foreigner wore a robe in the Republic of China. Several Chinese people accompanied him. A Chinese girl and the old man danced with music on the dance floor. Also with them.
There was also an aunt who was expected to dance in the square. Wearing modern clothes also dances with uncoordinated dance steps. The actors were also among them. It felt like a fighting dance.
At this time, I felt that the actors and each of us were members of this ship. You were also the scenery in the eyes of others. Every boater who bought the tickets is also part of the play, so every time everyone meets “It will be different.” Just like this time, I met an old foreigner and met an interesting aunt.
END
The whistle sounded, the boat in the monologue was docked, and everyone’s journey was over. Then we wore back to modern times.
Posted from my blog with SteemPress : https://ytymhl.000webhostapp.com/2018/09/unforgettable-trip-zhiyin-steamer
This page is synchronized from the post: Unforgettable Trip—ZHIYIN STEAMER.


This console lets you do some basic commands on a Riak dev cluster. It is designed for use in playing with Riak internals as an easy way to get data in and out...
Select which node you wish to connect to:
Select which action you wish to perform:
Donec id elit non mi porta gravida at eget metus. Maecenas faucibus mollis interdum.
Morbi leo risus, porta ac consectetur ac, vestibulum at eros. Cras mattis consectetur purus sit amet fermentum.
Maecenas sed diam eget risus varius blandit sit amet non magna.


Satyam Goyal
🚀 Welcome to my website!
I like making stuff and putting it on the internet
📜 Get to know Me!
Hi There! I'm Satyam Goyal, a Flutter developer, React Web Developer
and open source contributor based in India. I have been developing
Android apps for over 1.5 years now, and have recently created many
React based projects also. I have worked in teams for various startups
as an Intern and helped them in launching their prototypes, as open
source contributor at GitHub and got valuable learning experience.
Right
now I'm in third year of my undergraduate degree at SRM Institute of
Science and Technology, active Google Developer Student Clubs (DSC)
Core Member, and Microsoft Learn Student Ambassador.
- I’m currently sleeping 😴 or working on my laptop 👨💻
- I’m good in Android Development and Web Development With Flutter💪
and React❤.
- Currently Learning MERN🤯 and working on improving my Data
Structures and Algorithm skills👨💻.
- I love making React and Flutter Projects👨💻 the most.
- I have interest in Machine Learning🤖 and Open CV🌐 also.
- I’m open for collaboration🧠 on Any kind of projects.
- Ask me about why do I think aliens 👽 exist.
- Visit my Portfolio Website at satyamgoyal.codes👨💻 and do suggest me
how to improve it.
- Fun fact: My smartness😎 lies in my laziness 😴.
👩🏽🚀 Projects
I Have Quite a lot of Projects and Few best of them are:
- PortFolio Website.
-
Typing Speed Tester.
- What Image.
- My Blog.
-
Wear OS Radio App.
-
Mask Recognition App
Hard work beats talent…
…when talent doesn’t work hard
🌮 Work History
GoMechanic
Software Development Intern
Apr 2021 – present
Working on various tasks and projects to develop and modify the
existing system by creating useful APIs and working on Mongo DB
Databases.
Pedals Up
Full Stack Web and Android Developer Intern
Apr 2021 – May 2021
Creating and Developing web and android applications to create and
modify Applications using React.js and Flutter.
Lets Develop IT
Flutter Developer
Nov 2020 – Dec 2020
Developed a Full-Fledged Fitness Training Android application using
Flutter.
Robic Rufarm
Flutter Developer
Apr 2020 – May 2020
Design and develop a web application and an Android application from
scratch to monitor and interact with the Aerator system in the
aquaculture farms.
Naaniz
Flutter Developer
Jun 2020 – Jul 2020
Developing the Frontend of Flutter mobile Applications and integrating
it with backend API on Node.js
Thanks for Visiting!


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|
OpenSwarm
<IP_ADDRESS>
An operating system for miniature robots
|
Hardware dependent implementations to transmit bytes via UART. More...
#include "../../definitions.h"
Go to the source code of this file.
Data Structures | |
| struct | sys_uart_txdata |
| Linked list element to store transmission data. More... | |
Macros | |
| #define | UART1_RX _RF2 |
| #define | UART1_TX _RF3 |
| #define | UART2_RX _RF4 |
| #define | UART2_TX _RF5 |
| #define | UART1_RX_DIR _TRISF2 |
| #define | UART1_TX_DIR _TRISF3 |
| #define | UART2_RX_DIR _TRISF4 |
| #define | UART2_TX_DIR _TRISF5 |
| #define | SYS_UART1_BAUDRATE 115000 |
| #define | SYS_UART2_BAUDRATE 115000 |
Functions | |
| void | Sys_Init_UART1_HDI (void) |
| void | Sys_Init_UART2_HDI (void) |
| void | Sys_Start_UART1_HDI (void) |
| void | Sys_Start_UART2_HDI (void) |
| void | Sys_Read_UART1_ISR () |
| void | Sys_Write_UART1_ISR () |
| void | Sys_Read_UART2_ISR () |
| void | Sys_Write_UART2_ISR () |
Variables | |
| sys_uart_txdata * | sys_UART1_TX_data |
| sys_uart_txdata * | sys_UART2_TX_data |
| uint | byte_counter_uart1 |
| uint | byte_counter_uart2 |
| pUART_reader | read_uart_1 |
| pUART_reader | read_uart_2 |
Hardware dependent implementations to transmit bytes via UART.
Definition in file uart_HDI.h.
| #define SYS_UART1_BAUDRATE 115000 |
Baud rate for UART1
Definition at line 36 of file uart_HDI.h.
| #define SYS_UART2_BAUDRATE 115000 |
Baud rate for UART2
Definition at line 37 of file uart_HDI.h.
| #define UART1_RX _RF2 |
Used port on the microcontroller to read from the UART1
Definition at line 26 of file uart_HDI.h.
| #define UART1_RX_DIR _TRISF2 |
direction of the used port on the microcontroller (reading from the UART1)
Definition at line 31 of file uart_HDI.h.
| #define UART1_TX _RF3 |
Used port on the microcontroller to write on the UART1
Definition at line 27 of file uart_HDI.h.
| #define UART1_TX_DIR _TRISF3 |
direction of the used port on the microcontroller (writing from the UART1)
Definition at line 32 of file uart_HDI.h.
| #define UART2_RX _RF4 |
Used port on the microcontroller to read from the UART2
Definition at line 28 of file uart_HDI.h.
| #define UART2_RX_DIR _TRISF4 |
direction of the used port on the microcontroller (reading from the UART2)
Definition at line 33 of file uart_HDI.h.
| #define UART2_TX _RF5 |
Used port on the microcontroller to write on the UART2
Definition at line 29 of file uart_HDI.h.
| #define UART2_TX_DIR _TRISF5 |
direction of the used port on the microcontroller (writing from the UART2)
Definition at line 34 of file uart_HDI.h.
| void Sys_Init_UART1_HDI | ( | void | ) |
This function initialises UART1.
< interrupt priority for the UART1 interrupt
< interrupt priority for the UART1 interrupt
< Baud rate for UART1
Definition at line 42 of file uart_HDI.c.
| void Sys_Init_UART2_HDI | ( | void | ) |
This function initialises UART2.
< Baud rate for UART2
< interrupt priority for the UART2 interrupt
< interrupt priority for the UART2 interrupt
Definition at line 79 of file uart_HDI.c.
|
inline |
This function is executed at occurrence of the UART1 reading interrupt.
Definition at line 213 of file uart_HDI.c.
|
inline |
This function is executed at occurrence of the UART2 reading interrupt.
Definition at line 271 of file uart_HDI.c.
| void Sys_Start_UART1_HDI | ( | void | ) |
This function starts UART2.
Definition at line 106 of file uart_HDI.c.
| void Sys_Start_UART2_HDI | ( | void | ) |
This function starts UART2.
Definition at line 122 of file uart_HDI.c.
|
inline |
This function is executed at occurrence of the UART1 writing interrupt.
Definition at line 233 of file uart_HDI.c.
|
inline |
This function is executed at occurrence of the UART1 writing interrupt.
Definition at line 291 of file uart_HDI.c.
| uint byte_counter_uart1 |
Bytes that were written
Definition at line 34 of file uart_HDI.c.
| uint byte_counter_uart2 |
Bytes that were written
Definition at line 35 of file uart_HDI.c.
| pUART_reader read_uart_1 |
pointer to the functions that processes incoming bytes from UART1
Definition at line 28 of file uart_HDI.c.
| pUART_reader read_uart_2 |
pointer to the functions that processes incoming bytes from UART2
Definition at line 29 of file uart_HDI.c.
| sys_uart_txdata* sys_UART1_TX_data |
Linked list of messages that need to be sent via UART1
Definition at line 31 of file uart_HDI.c.
| sys_uart_txdata* sys_UART2_TX_data |
Linked list of messages that need to be sent via UART2
Definition at line 32 of file uart_HDI.c.


A distributed arcade game, mashup of a few delightful arcade games from the eighties, retrofitted to hexagonal grids.
Select your preferred game
A distributed arcade game, mashup of a few delightful arcade games from the eighties, retrofitted to hexagonal grids.
Select your preferred game


Mary A B
C D E F G H I J L M N P R S T V W
[by dates]
Our Lord Jesus Christ
Annunciation
Ascensión
Baptism of the Lord
Body and Blood of Christ
Epiphany
Holy Family
Most Holy Name of Jesus
Nativity
Presentation
Resurrection
Sacred Heart
Transfiguration
Triumph of the Cross
(top)
The Blessed Virgin Mary
Assumption
Birthday
Immaculate Conception
Immaculate Heart of Mary
Mary, Mother of God
Most Holy Name of Mary
Our lady Help of Christians
Our Lady of Fatima
Our lady of Guadalupe
Our Lady of Loreto
Our Lady of Lourdes
Our Lady of Miraculous Medal
Our Lady of Montserrat
Our Lady of Mount Carmel
Our Lady of Ransom
Our Lady of Sorrows
Our Lady of the Angels
Our Lady of the Rosary
Our Lady Queen of Nigeria
Our Lady, Mother of Africa
Presentation
Queenship
Visitation
(top)
Saints
Achilleus
Adalbert
Adrian
Agatha
Agnes
Albert the Great
Alexander of Alexandria
All Saints
All Saints of Africa
All the Faithful Departed
Aloysius Gonzaga
Alphonsus Liguori
Ambrose
Andrew
Andrew Dung-Lac
Andrew
Kim Taegon
Angela
Merici
Ann
Anselm
Ansgar
Anthony, abbot
Anthony Claret
Anthony of Padua
Anthony Zaccaria
Apollinaris
Athanasius
Augustine, bishop
Augustine of Canterbury
Augustine Zhao Rong and companions
(top)
Barnabas
Bartholomew
Basil
Bede the Venerable
Benedict
Bernard
Bernardine of Siena
Blase
Bonaventure
Boniface
Briget
Bruno
(top)
Cajetan
Callistus
I
Camillus
de Lellis
Casimir
Catherine of Alexandria
Catherine of Siena
Cecilia
Charles Borromeo
Charles Lwanga and companions
Christopher Magallanes and companions
Clare
Clement I
Clementine Anwarite
Columban
Cornelius
Cosmas
Cyprian
Cyprian Tansi
Cyril, monk
Cyril of Alexandria
Cyril of Jerusalem
(top)
Damasus I
Damian
Daniel Comboni
Daudi Okelo
Dedication St. Mary Major
Dedication Ss. Peter and Paul
Dedication the Lateran
Denis
Dominic
(top)
Elizabeth
of Hungary
Elizabeth of Portugal
Ephrem
Eusebius of Vercelli
(top)
Fabian
Felicity
Fidelis of Sigmaringen
First Martyrs of the Church of Rome
Frances of Rome
Francis of Paola
Francis de Sales
Francis of Assisi
Francis Xavier
(top)
Gabriel
George
Gertrude
Gregory VII
Gregory Nazianzen
Gregory the Great
Guardian Angels
(top)
Hedwig
Henry
Hillary
Hippolytus
Holy Innocents
(top)
Ignatius
of Antioch
Ignatius of Loyola
Irenaeus
Isaac Jogues
Isidore
Isidore Bakanja
(top)
James,
apostle
3 May
James, apostle 25 July
Jane Frances de Chantal
Januarius
Jerome
Jerome
Emiliani
Jildo
Irwa
Joachim
John, apostle
John I
John Baptist de la Salle
John Bosco
John Chrysostom
John Damascene
John Diego Cuahtlatoatzin
John de Brebeuf
John Eudes
John Fisher
John Leonardi
John of Capistrano
John of God
John of Kanty
John of the Cross
John the Baptist, Beheading
John the Baptist, Birthday
John Vianney
Josaphat
Josemaria Escriva
Joseph Gerard
Joseph, husband of Mary
Joseph, the worker
Joseph Calasanz
Josephine Bakhita
Jude
Justin
Justin De Jacobis
(top)
Lawrence,
deacon
Lawrence of Brindisi
Lawrence Ruiz
Leo the Great
Louis
Louis Grignion de
Montfort
Lucy
Luke
(top)
Marcellinus
Martyr
Marcellinus Bishop
Margaret Mary Alacoque
Margaret of Scotland
Maria
Goretti
Mark
Martha
Martin
I
Martin
de Porres
Martin
de Tours
Mary
Magdalene
Mary
Magdalene de Pazzi
Matthew
Matthias
Maurice
Maximilian Mary Kolbe
Methodius
Michael
Monica
(top)
Nereus
Nicholas
Norbert
(top)
Onophrius
Pancras
Patrick
Paul, apostle
Paul, conversion
Paul Chong Hosang
Paul Miki and companions
Paul of the Cross
Paulinus of Nola
Pentecost
Perpetua
Peter, apostle
Peter, chair of
Peter, martyr
Peter Canisius
Peter Chanel
Peter Chrysologus
Peter Claver
Peter Damian
Peter Julian Eymard
Philip, apostle
Philip
Neri
Pius
of Pietrelcina (Padre Pio)
Pius V
Pius X
Polycarp
Pontian
(top)
Raphael
Raymond of Penyafort
Rita of Cascia
Robert Bellarmine
Romuald
Rose of Lima
(top)
Scholastica
Sebastian
Servites, Founders of
Severinus
Sharbel Makhluf
Simon
Sixtus II
Stanislaus
Stephen, first martyr
Stephen of Hungary
Sylvester
(top)
Teresa
of Jesus
Teresa of the Child Jesus
Teresa Benedicta of the Cross
Thomas Becket
Thomas, apostle
Thomas Aquinas
Thomas More
Timothy
Titus
Trinity Sunday
Turibius of Mongrovejo (top)
Victor I
Victoria Rasoamanarivo
Vincent,
deacon
Vincent de Paul
Vincent Ferrer
Wenceslaus
Zeno (top)


The ego is only an illusion, but a very influential one. Letting the ego-illusion become your identity can prevent you from knowing your true self.
Ego, the false idea of believing that you are what you have or what you do, is a backwards way of assessing and living life.
Creativity that comes from the dimension within.
The inner dimension is a reality we do not normally look at for very long. It is within ourselves. It is infinite a long with other realities. It can be touched through meditation and mindfulness. It is always there.


Blaze
Blaze is a movie starring Ben Dickey, Alia Shawkat, and Charlie Sexton. A reimagining of the life and times of Blaze Foley, the unsung songwriting legend of the Texas Outlaw Music movement.


Other useful GDPR links
We’re not the only people reading, writing and thinking about GDPR. Aside from our collection of SaaS-company GDPR policies and documentation, here is a collection of links to other places where you’ll find some insightful discussion on the forthcoming law.
A Stripe guide to the European privacy and data protection changes (Stripe):
GDPR: Data subjects, controllers and processors, oh my! (Twilio)
How GDPR will change the way you develop (Smashing Magazine)
Gartner’s top tip to data crunchers on the eve of GDPR? Don’t be creepy (The Register)
The GDPR: An artificial intelligence killer? (Datanami)
More reading
Convinced that GDPR will matter to your company and looking for some more guidance on how and where it might apply, or how other SaaS companies are thinking about it? You can check out some of our other pieces on this topic:


Do not surrender your independence - PDP tells NASS
- PDP urges the Nigerian senate not to pass bills and requests from the executive without statutory legislative scrutiny and oversights
- The spokesperson for the party, Kola Ologbondiyan, faults Senator Ahmed Lawan for saying any request from President Muhammadu Buhari to the National Assembly is good for the nation
- Ologbondiyan says Lawan’s statement is an attempt to relinquish the statutory powers of checks and balances of the National Assembly
The Peoples Democratic Party (PDP) has cautioned the Nigerian Senate not to surrender its independence to the executive by passing bills and requests without statutory legislative scrutiny and oversights.
The spokesperson for the party, Kola Ologbondiyan, in a statement on Sunday, November 24, said the caution became necessary following the comment credited to the Senate president, Senator Ahmed Lawan, that any request from President Muhammadu Buhari to the National Assembly is good for the nation, even without subjecting such requests to statutory legislative scrutiny.
FG finally replies Senate over bill prescribing death penalty for hate speech
He said Lawan’s statement besides being unconstitutional amounts to relinquishing the statutory powers of checks and balances of the National Assembly.
“This, our party notes will create an alarming impression that the present National Assembly has been annexed by the executive and reduced to a rubber-stamp legislature.
“The statement by the Senate President has heavily detracted from the expected independence of the legislature. It is fast eroding the confidence Nigerians have on the Senate and the National Assembly, as true representatives of the people at the national level,” the PDP said.
The PDP spokesperson noted that legislative checks are enshrined in the constitution to check the excesses of the executive as well as create room for democratic practices
“Anything to the contrary is a direct suspension of our constitution, enthronement of dictatorship and a sidestepping of the legislative powers, which is capable of destroying the institution of the National Assembly," the party stated.
Buhari does not play with development in Nigeria - Governor Dickson of PDP declares
“Moreover, legislative processes for statutory interfaces with other arms of government, particularly the executive, is governed by the constitution, laid down legislative rules, practices and conventions and not by the narrow-minded assumptions of any single individual."
The PDP said as a symbol of the nation’s democracy the National Assembly is an institution that belongs to the people and not to any political party or group.
In another news report, PDP has reacted to President Muhammadu Buhari's declaration that he will not be running for a second term.
The PDP, through its national publicity secretary, Kola Ologbodiyan, said that what is of dire importance to Nigerians is the violence that is usually associated to elections in the country especially in the Bayelsa and Kogi governorship polls
The opposition party called on the president to ensure that rigging, manipulation of elections and other related crimes are put to an end once and for all.
Senate laments poor pay for Nigerian judges
NAIJ.com (naija.ng) -> Legit.ng: Same great journalism, upgraded for better service!
‘Nigerians have comprehensively rejected PDP’ | Legit TV


Here I show the overall maximum and overall minimum gdp per cap each continent has ever experienced.
#tidy table to fewer columns
max_min_gdpPercap_byCont_tidy<- max_min_gdpPercap_byCont %>%
gather(minORmax, minmax_gdpPerCap, min_gdpPercap_byCont:max_gdpPercap_byCont, factor_key = TRUE) %>%
arrange(continent)
max_min_gdpPercap_byCont_tidy
## # A tibble: 10 x 3
## continent minORmax minmax_gdpPerCap
## <fct> <fct> <dbl>
## 1 Africa min_gdpPercap_byCont 241.
## 2 Africa max_gdpPercap_byCont 21951.
## 3 Americas min_gdpPercap_byCont 1202.
## 4 Americas max_gdpPercap_byCont 42952.
## 5 Asia min_gdpPercap_byCont 331
## 6 Asia max_gdpPercap_byCont 113523.
## 7 Europe min_gdpPercap_byCont 974.
## 8 Europe max_gdpPercap_byCont 49357.
## 9 Oceania min_gdpPercap_byCont 10040.
## 10 Oceania max_gdpPercap_byCont 34435.
#Table
max_min_gdpPercap_countriesInCont <- gapminder %>%
group_by(continent, year) %>%
filter(gdpPercap==min(gdpPercap) | gdpPercap==max(gdpPercap)) %>%
arrange(year, continent) %>%
mutate(country, minmax=rank(gdpPercap)) %>%
mutate(minmax = as.factor(minmax)) %>%
select(-country, -pop, -lifeExp) # remove country column
max_min_gdpPercap_countriesInCont
## # A tibble: 120 x 4
## # Groups: continent, year [60]
## continent year gdpPercap minmax
## <fct> <int> <dbl> <fct>
## 1 Africa 1952 299. 1
## 2 Africa 1952 4725. 2
## 3 Americas 1952 1398. 1
## 4 Americas 1952 13990. 2
## 5 Asia 1952 108382. 2
## 6 Asia 1952 331 1
## 7 Europe 1952 974. 1
## 8 Europe 1952 14734. 2
## 9 Oceania 1952 10040. 1
## 10 Oceania 1952 10557. 2
## # ... with 110 more rows
Here I show the minimum and maximum gdp per capita per continent by year.
#change data so that have cont/year/mingdp/maxgdp
max_min_gdpPercap_byCont <- max_min_gdpPercap_countriesInCont %>%
spread(minmax, gdpPercap, sep = "_") #sep adds in previous column name
max_min_gdpPercap_byCont
## # A tibble: 60 x 4
## # Groups: continent, year [60]
## continent year minmax_1 minmax_2
## <fct> <int> <dbl> <dbl>
## 1 Africa 1952 299. 4725.
## 2 Africa 1957 336. 5487.
## 3 Africa 1962 355. 6757.
## 4 Africa 1967 413. 18773.
## 5 Africa 1972 464. 21011.
## 6 Africa 1977 502. 21951.
## 7 Africa 1982 462. 17364.
## 8 Africa 1987 390. 11864.
## 9 Africa 1992 411. 13522.
## 10 Africa 1997 312. 14723.
## # ... with 50 more rows
#Plot
ggplot(max_min_gdpPercap_byCont) +
geom_ribbon(aes(x = year, ymin = minmax_1, ymax = minmax_2, fill = continent), alpha = 0.05) + #creates shading between min and max
geom_line(aes(x = year, y = minmax_1, color = continent)) + #min line
geom_line(aes(x = year, y = minmax_2, color = continent)) + #max line
scale_y_log10() +
labs(title = "Minimum and Maximum GDP Over Time by Country", x = "Year", y = "GDP (log10)") +
theme(
plot.background = element_rect(fill = "white"),
panel.background = element_rect(fill = "white"),
axis.title = element_text(face="bold"),
title = element_text(face="bold"),
axis.text = element_text(size = 10),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
axis.line.x = element_line(color = "gray30", size = .5),
axis.line.y = element_line(color = "gray30", size = .5),
axis.ticks = element_line(color = "gray30", size = 1)
)
So while I was able to achieve my goal (learn to use ribbon plotting), the visualization actually turned out quite poor! I thought there would be a lot more disparity between continents and they would separate more easily. Because that is not the case, I would not say this is an effective visualization and another method would be more useful.
Type of graph: This is a varaition of a stacked area graph that is sometimes called a shaded line graph.
Description of data: These data are from gapminder, a resource that tracks a huge number of global socioeconomic variables over time. For my purposes I focused on GDP, country, and time.
Description of audience: This audience may be a layperson casually interested in global socioeconomic changes.
Representation Description: What I was trying to show was the disparity between max/min per continent over time, as well as absolute min/max between continents. WHile it technically does show that, the data are not appropriate for this kind of graph apparently and the visualization actually isn’t very useful!
How to read it & What to look for: I would first look for the biggest disparities, and then compare that to other continents. If you can see it, I think it’s quite interesting that Asia has an enormous spread between max and min that remains over time. It also until relatively recently had the largest max and was on-par with Africa for lowest min. Oceania, on the other hand, has had a tight relationship between its max and min, although that has spread over time (and it likely in part due to the small number of countries represented).
Presentation tips: These are categorical data, so a generic, non-heirarchal palette was used. The scale of log10 was chosen because the disparity between continents meant that to see the highest performers, all the other values were squished together at the bottom.
Variations and alternatives: I would definitely use an alternative. These data have too much overlap to make a meaningful statement using this methodology. A better alternative would likely be to separate out separate questions and present them separately. A bar graph would probably do well.
Methods: For this graph I plotted the max and min GDP per continent by year, and then shaded the region between the max and min. The hope was to be able to better see the max and min per continent (when it’s two discrete, inlinked lines in a mass of lines it was difficult), as well as highlight the disparity between max and min over time.


Learn to Develop GPU Software Using Julia
A number of packages have been developed for Julia that enable developers to write Julia code that will run on GPUs. But until now, open source documentation which would enable a developer to go from zero CUDA knowledge to making effective use of these packages has not existed. This tutorial aims to change that. In this tutorial, I will guide you through the full workflow of writing a basic program in Julia that will run on GPU. Things you will learn from this tutorial include:
- What a GPU is and why writing code to run on GPUs might be useful.
- How to set up your computer and install the graphics drivers necessary to make use of your GPU.
- How to write Julia code that will run on a GPU in a parallel manner using threads, blocks, shared memory and streaming.
- How to benchmark Julia code that runs on GPUs.
This tutorial assumes you already know how to write code in Julia. Julia is a high level, syntactically intuitive language, so if you don't know Julia but do know other programming languages you may find you are able to follow along anyway. If you would like to get started coding in Julia, I recommend reading some of the Julia Documentation: https://docs.julialang.org/en/v1.0/.
This tutorial is intended to provide a foundation for developers to start writing Julia code that runs on GPUs. It is not intended to be a comprehensive guide to everything you could conceivably do on a GPU using Julia. Hopefully, you will find it instead gives a gentle introduction to some core concepts in GPU programming and how you can use them in Julia. I focus on the CUDAdrv, CUDAnative and CuArrays packages in this tutorial, although other Julia packages for using GPUs exist - see https://github.com/JuliaGPU for details.


Across: 1. Rock sailors backwards and forwards (6) 5. Bouncing baby’s beret tumbles into the drink (4 4) 9. Ava’s pork mix is champion! (8) 10. Mafia boss took in present (6) 11. Agitator gives artist two seconds to get the French gold back to employer (6-6) 13. Indian dance one followed (4) 14. I come out of penitence for cash! (3 5) 17. Footsore visitor gets to have ice-cream—just one... (8) 18. Hetty loses some time getting to those people (4) 20. Nureyev, Michael, Charles and Rex carry Lawrence back (6 6) 23. Gifted characters act nervously (6) 24. I’d join team and get it copied (8) 25. Examines winding lanes, say (8) 26. Rented boat? (6) Down: 2. Easterner backs scholar and artist (4) 3. Nasty remark made by a second individual about one (9) 4. Beats crazy short bishop! (6) 5. Lori joins various teen rebels (15) 6. Rode round arena giving commands (8) 7. African veto on workers’ organisation (5) 8. Established trend, hence became confused (10) 12. Blimey, duck, people get to this ceremony! (10) 15. Amuse nine with treat (9) 16. Tory leader figures out laws (8) 19. Writing trips involve Charles (6) 21. For example, all around is permitted by law (5) 22. River connecting Tyneside and Wearside? (4)


Rochefort lies about 18nm south of La Rochelle, 12 miles up the river Charente. The entrance is opposite the Ile de Oleron.
Its harbour started as a navel base in the 17th century and many of its building reflect this period. It has an interesting marine museum.
Position 45° 57'15"N, 01° 04'53"W (river entrance)
Rochefort is approached via the River Charente. A suprisingly rural river winding its way the 12 miles to the historic port. The tidal stream can run at up to 4 kn in the narrower parts of the river.
The lock into the marina is open HW La Rochelle for about 1/2hr to 1 1/2hrs, depending on the tidal height. There is a waiting pontoon in the river.


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This table contains entries for CYP proteins from humans in the UniProt database. Links are provided to view the original UniProtKB protein entries and their display in InterPro. There are also about 15 CYP pseudogenes in humans, which have been excluded from the table below.
|
|
UniProt
|
Protein name |
Organism |
InterPro Domain Architecture
|
InterPro Graphical Match |
Function |
|
Cytochrome P450 |
Cytochrome P450 1A1 (CYP1A1) |
Homo sapiens
|
Degrade xenobiotics, steroids
and fatty acids. Expressed in lung, lymphocytes, placenta |
|||
|
|
Cytochrome P450 1A2 (CYP1A2) |
Homo sapiens
|
Degrade xenobiotics, steroids
and fatty. Expressed in liver (about 13% of CYP enzymes). |
|||
|
|
Cytochrome P450 1B1 (CYP1B1) |
Homo sapiens
|
Degrade xenobiotics, steroids
and fatty acids. Expressed in many tissues |
|||
|
|
Cytochrome P450 2A6 (CYP2A6) |
Homo sapiens
|
Degrade
xenobiotics. Hydroxylation of some anti-cancer drugs. Expressed in liver |
|||
|
|
Cytochrome P450 2A7 (CYP2A7) |
Homo sapiens
|
Degrade
xenobiotics, steroids and fatty acids. |
|||
|
|
Cytochrome P450 2A13 (CYP2A13) |
Homo sapiens
|
Degrade xenobiotics. Expressed
in liver and other tissues |
|||
|
|
Cytochrome P450 2B6 (CYP2B6) |
Homo sapiens
|
Degrade xenobiotics,
steroids and fatty acids. Expressed in liver, lung and heart. |
|||
|
|
Cytochrome P450 2B7P1 (CYP2B7P1) |
Homo sapiens
|
Monooxygenase |
|||
|
|
Cytochrome P450 2C8 (CYP2C8) |
Homo sapiens
|
Degrade xenobiotics,
steroids and fatty acids. Metabolises anti-cancer drug taxol. |
|||
|
|
Cytochrome P450 2C9 (CYP2C9) |
Homo sapiens
|
Degrade
xenobiotics, steroids and fatty acids. Important in drug pharmacokinetics. |
|||
|
|
Cytochrome P450 2C18 (CYP2C18) |
Homo sapiens
|
Degrade
xenobiotics, steroids and fatty acids. |
|||
|
|
Cytochrome P450 2C19 (CYP2C19) |
Homo sapiens
|
Degrade
xenobiotics, metabolising drugs such as anti-convulsants and barbiturates |
|||
|
|
Cytochrome P450 2D6 (CYP2D6) |
Homo sapiens
|
Degrade
xenobiotics, metabolising drugs such as anti-arrhythmics, adrenoceptor
antagonists and tricyclic anti-depressants |
|||
|
|
Cytochrome P450 2E1 (CYP2E1) |
Homo sapiens
|
Degrade
xenobiotics, including several precarcinogens, drugs and solvents to active
metabolites |
|||
|
|
Cytochrome P450 2F1 (CYP2F1) |
Homo sapiens
|
Dealkylates some coumarin compounds |
|||
|
|
Cytochrome P450 2J2 (CYP2J2) |
Homo sapiens
|
Fatty acid metabolism, especially arachidonic acid. Expressed primarily in heart |
|||
|
|
Cytochrome P450 2R1 (CYP2R1) (vitamin
D 25-hydrolase) |
Homo sapiens
|
|
D-25-hydroxylase activity on both forms of vitamin D. Defects can cause pseudovitamin D-deficiency rickets |
||
|
|
Cytochrome P450 2S1 (CYP2S1) |
Homo sapiens
|
Degrades xenobiotics. Expressed in trachea, lung, spleen, small intestine, and stomach |
|||
|
|
Cytochrome P450 2U1 (CYP2U1) |
Homo sapiens
|
Monooxygenase |
|||
|
|
Cytochrome P450 2W1 (CYP2W1) |
Homo sapiens
|
Membrane protein |
|||
|
|
Cytochrome P450 3A3 (CYP3A3) |
Homo sapiens
|
Degrade
xenobiotics, steroids and fatty acids. |
|||
|
|
Cytochrome P450 3A4 (CYP3A4) |
Homo sapiens
|
Degrade xenobiotics,
steroids and fatty acids. Expressed in prostate and liver. |
|||
|
|
Cytochrome P450 3A5 (CYP3A5) |
Homo sapiens
|
Degrade
xenobiotics, steroids and fatty acids. |
|||
|
|
Cytochrome P450 3A7 (CYP3A7) |
Homo sapiens
|
Degrade
xenobiotics, steroids and fatty acids. |
|||
|
|
Cytochrome P450 3A43 (CYP3A43) |
Homo sapiens
|
Exhibits low
testosterone 6-beta-hydroxylase acitivity. Expressed mainly in prostate |
|||
|
|
Cytochrome P450 4A11 (CYP4A11) |
Homo sapiens
|
Fatty acid
metabolism. Expressed in kidney and liver. |
|||
|
|
Cytochrome P450 4A22K (CYP4A22) |
Homo sapiens
|
Monooxygenase |
|||
|
|
Cytochrome P450 4B1 (CYP4B1) |
Homo sapiens
|
Degrade
xenobiotics, steroids and fatty acids. |
|||
|
|
Cytochrome P450 4F2 (CYP4F2) |
Homo sapiens
|
Degrade xenobiotics,
steroids and fatty acids. Expressed in liver. |
|||
|
|
Cytochrome P450 4F3 (CYP4F3) |
Homo sapiens
|
Omega-hydroxylation
of LTB4, a chemoattractant for leukocytes. Expressed in leukocytes. |
|||
|
|
Cytochrome P450 4F8 (CYP4F8) |
Homo sapiens
|
Production
of 19R-hydroxy-prostaglandins in seminal fluids. Expressed mainly in seminal
vesicle |
|||
|
|
Cytochrome P450 4F11 (CYP4F11) |
Homo sapiens
|
Flavoprotein
hydroxylation. Expressed mainly in liver. |
|||
|
|
Cytochrome P450 4F12 (CYP4F12) |
Homo sapiens
|
Fatty acid
metabolism. Expressed in liver, small intestine, colon and heart. |
|||
|
|
Cytochrome P450 4V2 (CYP4V2) |
Homo sapiens
|
Possibly involved
in fatty acid metabolism. Expressed in several tissues. |
|||
|
|
Cytochrome P450 4X1 (CYP4X1) |
Homo sapiens
|
Flavoprotein
hydroxylation. |
|||
|
|
Cytochrome P450 4Z1 (CYP4Z1) |
Homo sapiens
|
Flavoprotein
hydroxylation. Expressed in mammary gland (and breast carcinoma tissue) |
|||
|
|
Thromboxane-A synthase (CYP5A1) |
Homo sapiens
|
Thromboxane synthesis. Expressed in platelets, lung,
kidney, spleen, macrophages and lung fibroblasts |
|||
|
|
Cytochrome P450 7A1 (CYP7A1) (cholesterol 7a‑monooxygenase) |
Homo sapiens
|
Conversion of cholesterol to bile acids |
|||
|
|
Cytochrome P450 7B1 (CYP7B1) (oxysterol 7a‑hydroxylase) |
Homo sapiens
|
Conversion of cholesterol to bile acids. Expressed in
brain, testes, ovary, prostate, liver, colon, kidney and small intestine |
|||
|
|
Prostacyclin
synthase (CYP8A1) |
Homo sapiens
|
Catalyses isomerisation of prostaglandin H2 to
prostacyclin. Expressed in several tissues. |
|||
|
|
Cytochrome P450 8B1 (CYP8B1) |
Homo sapiens
|
Steroid metabolism, including bile acid synthesis.
Expressed in liver. |
|||
|
|
Cytochrome P450 11A1 (CYP11A1),
mitochondrial |
Homo sapiens
|
Catalyses side-chain cleavage reaction of cholesterol
pregnenolone. |
|||
|
|
Cytochrome P450 11B1 (CYP11B1),
mitochondrial |
Homo sapiens
|
Steroid metabolism |
|||
|
|
Cytochrome P450 11B2 (CYP11B2),
mitochondrial |
Homo sapiens
|
Steroid metabolism, especially production of aldosterone.
Defects cause adrenal hyperplasia type V |
|||
|
|
Cytochrome P450 17A1 (CYP17A1) |
Homo sapiens
|
Steroid metabolism, especially the conversion of pregnenolone
and progesterone |
|||
|
|
Cytochrome P450 19A1 (CYP19A1) |
Homo sapiens
|
Catalyses formation of aromatic C18 oestrogens and C19
androgens. Expressed in brain, gonads and placenta |
|||
|
|
Cytochrome P450, family 20, subfamily A, polypeptide 1 (CYP20A1) |
Homo sapiens
|
Monooxygenase |
|||
|
|
Cytochrome P450 21 (CYP21A1) |
Homo sapiens
|
Catalyses 21-hydroxylation of steroids; required for
adrenal synthesis of mineralocorticoids and glucocorticoids |
|||
|
|
Cytochrome P450 21‑hydroxylase (CYP21A2) |
Homo sapiens
|
Monooxygenase |
|||
|
|
Cytochrome P450 24A1 (CYP24A1),
mitochondrial |
Homo sapiens
|
Role in maintaining calcium homeostasis; vitamin D
hydroxylation |
|||
|
|
Cytochrome P450 26A1 (CYP26A1) (retinoic acid‑metabolizing cytochrome) |
Homo sapiens
|
Retinoic acid metabolism. Expressed in adult liver, heart,
pituitary gland, adrenal gland, placenta and brain |
|||
|
|
Cytochrome P450 26B1 (CYP26B1) (retinoic acid‑metabolizing cytochrome) |
Homo sapiens
|
Retinoic acid metabolism. Expressed in brain (cerebellum
and pons) |
|||
|
|
Cytochrome P450 26C1 (CYP26C1) |
Homo sapiens
|
Retinoic acid metabolism. Expressed in several tissues |
|||
|
|
Cytochrome P450 27 (CYP27A1),
mitochondrial |
Homo sapiens
|
Steroid metabolism, catalysing first step in oxidation of
side-chain of sterol intermediates |
|||
|
|
25‑hydroxyvitamin D‑1 alpha hydroxylase (CYP27B1), mitochondrial |
Homo sapiens
|
Vitamin D3 metabolism; important for bone growth and
calcium metabolism. Expressed in kidney |
|||
|
|
Cytochrome P450 39A1 (CYP39A1) |
Homo sapiens
|
Bile acid
metabolism. Expressed in liver |
|||
|
|
Cytochrome P450 46A1 (CYP46A1) (cholesterol 24‑hydrolase) |
Homo sapiens
|
Cholesterol turnover. Expressed in brain |
|||
|
|
Cytochrome P450 51A1 (CYP51A1) |
Homo sapiens
|
Cholesterol biosynthesis. Expressed in several tissues |


Basic Workflow
1. Define the function and constants
To solve equation F(x)=0
, input the function in the text box and press the red button to add it.
When the equation is Ready to solve, continue to...
2. Set solve mode and parameters
The application may search the root:
- in an interval (Bisection mode), or
- around a certain value (Bingo mode).
Set the parameters if needed. Then click the red button to..
3. Plot and solve
Utilize the plot to check and adjust the search interval.
Locate exactly one zero-crossing point and press Solve button to get result.


{% extends "marketplace/proj.html" %} {% load markdown_deux_tags %} {% block tabcontents %}
This project has been completed.
Below you can find links to all the public results, documentation, data, code, etc. that has been generated as part of the project.
{% endif %}This project is still in progress, so no public deliverables are available.
{% endif %}


The Slice event handler combine all of the data together into a single method. It represents the data at a point in
time.
The $[Slice,T:QuantConnect.Data.Slice] object contains many helpers for accessing your data. The Slice objects arrive to the
OnData(Slice data)
def OnData(self, slice)
event
handler.
The $[Slice,T:QuantConnect.Data.Slice] object gives you threetwo ways to access your data:
Strongly typed access gives you compile time safety but dynamic types can sometimes simplify coding. We recommend static types as they are easier to debug.
Slice has the following internal structure:
class Slice : IEnumerable<KeyValuePair<Symbol, BaseData>> { TradeBars Bars; QuoteBars QuoteBars; Ticks Ticks; OptionChains OptionChains; FuturesChains FuturesChains; Splits Splits; Dividends Dividends; Delistings Delistings; SymbolChangedEvents SymbolChangedEvents; }
class Slice: TradeBars Bars; QuoteBars QuoteBars; Ticks Ticks; OptionChains OptionChains; FuturesChains FuturesChains; Splits Splits; Dividends Dividends; Delistings Delistings; SymbolChangedEvents SymbolChangedEvents;
It contains all the data for a given moment of time. TradeBars and QuoteBars are symbol/string indexed dictionaries so you can easily access the data. Ticks is a list of ticks for that moment of time; indexed by the symbol.
To make accessing the data easier the Slice object itself can also be indexed. e.g. slice["IBM"]
will
return a
TradeBar for IBM. slice["EURUSD"]
will return a QuoteBar for EURUSD.
// Event handler for Slice method public void OnData(Slice data) { }
// Event handler for Slice method member this.OnData(slice:Slice) = ()
# Event handler for the slice method def OnData(self, slice): pass
//Access data in a Slice object: // 1. Strong Typed Properties: Ticks, TradeBars, Delistings, SymbolChangedEvents, Splits and Dividends var bars = slice.Bars; // e.g. bars["IBM"].Open // 2. Strong Typed Get Accessor: var bars = slice.Get(); //e.g. bars["IBM"].Open var bar = slice.Get ("IBM"); //e.g. bar.Open // 3. Dynamic String / Symbol Indexer: var bar = slice["IBM"]; // e.g. bar.Open
#Access data in a Slice object: # 1. Grouped Properties: Ticks, Bars, Delistings, SymbolChangedEvents, Splits and Dividends bars = slice.Bars; # e.g. bars["IBM"].Open # 2. Dynamic String / Symbol Indexer: bar = slice["IBM"] # e.g. bar.Open - TradeBar properties OHLCV spyTickList = data["SPY"] # Tick assets return a list of Tick objects.
Since the Slice object is indexed, it is possible to check if the time slice contains specific data. e.g.
slice.ContainsKey("EURUSD")
will return a boolean. Even if requesting data to be filled forward you should check
if the data exists in the dictionary first. If there is little trading, or you are in the same time loop as when
you added the security, it may not have any data.


Jan 19, 2008
PicoContainer 2.0 released (190K jar)
New (versus beta 2):
- Binding Annotations support (disambiguation)
- Newer and rewritten properties backed containers
- Parameter names can be leveraged for all relevant types of injection
Changed (versus beta 2):
- Yet More Java5 generics
- Build moved to JUnit 4.x and JMock 2.x (does not affect users of PicoContainer)
- Built-in parameter name access, as opposed jar dependency
August 26, 2007
PicoContainer 2.0 beta 2 released (152K jar)
New (versus beta 1):
- Rudimentary AOP capability for components built in
- Method Injection (one method post-construction with multiple arguments)
- Automatic JNDI exposure
- Multi Injection (Setter and after Constructor injection)
- Store behavior (can extract/replace component store per thread)
- Automatic components (instantiate irrespective of need)
- Reentrant-Lock version of Synchronizing behavior
Changed (versus beta 1):
- Better Java5 generics
- Renames of behaviors and injectors to more verb-like style
- Startable interface more easily overridden to own choice
July 15, 2007
PicoContainer 2.0 BETA-1 released (128K jar)
New:
- Properties for components as they are added to containers makes for increased flexibility
- Field and Method annotation types of injection in addition to traditional Constructor (recommended) and Setter types
- Parameters names for constructors can now drive otherwise ambiguous injections, as well as configuration
- Large sets of configuration can be taken from properties files and command line arguments
- PicoBuilder to make a container with desired behavior.
Changed:
- Java 5 style (use of generics, varargs, among others)
- Large scale Refactoring of packages and class names
- Fluent interface for adding of components to a container
Mar 18, 2007
PicoContainer 1.3 released (113K jar)
January 19, 2006
PicoContainer 1.2 released (112K jar)
November 4, 2004
PicoContainer 1.1 released (75K jar)
June 5, 2004
PicoContainer 1.0 released (49K jar)
June 29, 2003
PicoContainer 1.0 alpha (25K jar)


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
IN THE HIGH COURT OF JUDICATURE AT MADRAS DATED: 16/11/2004 CORAM THE HONOURABLE MR. JUSTICE N.V.BALASUBRAMANIAN AND THE HONOURABLE MRS. JUSTICE R. BANUMATHI W.P.No.21585 of 2000 and W.P.Nos. 21588, 21589 and 21613 of 2000 and W.P.M.P.Nos.31355, 31358, 31359 and 31396 of 2000 Sudarsanam Spinning Mills, Post Box 2, 120, P.S.K.Nagar, P.A.C.Ramasamy Raja Salai, Rajapalayam 626 117. Virudhunagar District. .....Petitioner in W.P.No.21585 of 2000 Rajapalaiyam Spintext, (EOU Division of Rajapalayam Mills Ltd.,) Post Box No.1, P.A.C.Ramasamy Raja Salai, Rajapalayam 626 117. Virudhunagar District. .....Petitioner in W.P.No.21588 of 2000 Sri Vishnu Shankar Mill Ltd., Post Box No.109, P.A.C.Ramasamy Raja Salai, Rajapalayam 626 117. Virudhunagar District. .....Petitioner in W.P.No.21589 of 2000 Sri Ramco Spinners, (A Division of Ramco Industries Ltd.,) Krishnapuram Road, Off: P.A.C.Ramasamy Raja Salai, Rajapalayam 626 117. Virudhunagar District. .....Petitioner in W.P.No.21613 of 2000 -Vs- 1. Tamil Nadu Pollution Control Board, rep. by its Member Secretary, 32, Santhome High Road, Chennai 600 004. 2. The Joint Chief Environmental Engineer, Tamil Nadu Pollution Control Board, S.No.668-669, SIDCO Industrial Estate, Kappalur, Madurai 625 008. .....Respondents in all W.Ps Writ Petitions filed under Article 226 of the Constitution of India, praying for issuance of a Writ of Certiorari, as stated therein. For Petitioners:: Mr. M. Elumalai For Respondents:: Mrs.Rita Chandrasekar :O R D E R
R. BANUMATHI, J.
All the Writ Petitions have been filed by the Petitioners for issuance of Writ of Certiorari to call for the records of the Second Respondent and to quash the Impugned Notice No.,/K/R/ebgh/-jkhfth-kJiu-nfh1 2-M-bg-tpUJ-2000 dated 15.11.2000 and other notices in the respective Writ Petitions.
2. The Petitioners Mills Export oriented Units situated at Rajapalayam, Virudhungar District have employed number of workmen. They are engaged in the manufacture of sale of Cotton Yarn and they do not have either Weaving Department or Processing Sections like Weaving, Bleaching, Dyeing and Printing. According to the Petitioners, their Mills are Spinning Units with no Weaving Department and Processing Sections. To tide over the power shortage, the Petitioners have installed Diesel Generator sets. The Petitioners / Mills have obtained Consent Orders Nos.7226, 3353, 7066 and 3354 Dated 31.12.1992, 05.03.1991, 04.11.1992 and 05.03.1991 respectively from the First Respondent / Tamil Nadu Pollution Control Board for installation of Diesel Generator Sets. Due to tremendous scientific and technological advancement in the manufacture of captive generator sets, for more efficiency, the Mills used Furnace Oil Generators. Generation of power through Furnace Oil Generator is found to be purer and more sufficient. Installation of Furnace Oil Generator is only replacement of Diesel Generator sets already installed, which have become old. The replacement of the existing plant and machineries with new ones does not require a fresh Consent Order from the First Respondent / Pollution Control Board, especially when the replacement more efficiently eliminates Air and Atmospheric pollution and Noise pollution.
3. While so, without taking into account that the new Generator sets are only replacement of the existing Generator sets, the First Respondent had issued the impugned notices. The Impugned Notices are assailed on the ground that they suffer from serious error of law as well as fact that the replacement of the existing machineries requires fresh consent from the First Respondent.
4. Contention of the Petitioners Mills is that no fresh Consent Order is necessary for installation of Furnace Oil Generators, which only replaces Diesel Generator Sets. While so, the First Respondent / Pollution Control Board has issued the Impugned Notices dated 15.11.200 0 to the respective Writ Petitioners stating that there is violation of Sec.21 of The Air (Prevention & Control of Pollution) Act, 1981 ( hereinafter referred to as the Air (P&CP) Act), calling upon the Petitioners to show cause as to why Prosecution should not be launched against them. These Writ Petitions have been filed by the Writ Petitioners, challenging the Impugned Notices on the ground that they are highly arbitrary and unreasonable.
5. Assailing the Impugned Notices in the respective Writ Petitions, learned counsel for the Petitioners has contended that the Second Respondent has failed to take into account that the installation of New Generator Sets Furnace Oil Generators are only replacement of existing one of old Diesel Generator Sets. The contention of the counsel is that when the Petitioners have earlier obtained the Consent Orders from the First Respondent / Pollution Control Board for installation of Diesel Generator Sets, replacement of the same by fuel efficacious methods eliminating the Atmospheric pollution needs no fresh consent. Urging that no fresh Consent Order is essential, learned counsel for the Petitioners assailed the Impugned Notices that Notice is more in the nature threatening to launch Prosecution than issuance of Show Cause notice and prayed to quash the same.
6. Onbehalf of the First Respondent / Pollution Control Board, it is submitted that though the Petitioners / Mills have earlier obtained Consent Orders, even if there is any change or replacement of the Unit by modern unit, fresh Consent Order is very much essential. Taking us through Rule 5(iii) of The Environment (Protection)Rules, 1986 and other provisions of the Air (P&CP) Act, learned counsel for the First Respondent has submitted that even for replacement of the Diesel Generator sets, it is very much necessary to obtain the Consent.
7. We have carefully considered the submissions of both sides. For the purpose of determining the issue raised in these Writ Petitions, it is appropriate to refer to the Provisions of the Air (P&CP) Act. Sec.2(a) of the Air (P&CP) Act defines air lutant as under:-
....air pollutant means any solid, liquid or gaseous substance ( including noise) present in the atmosphere in such concentration as may be or tend to be injurious to human beings or other living creatures or plants or property or environment... Sec.2(c) of the Air (P&CP) Act defines approved appliances to mean any equipment of gadget used for the bringing of any combustible material or for generating or consuming any fume, gas of particulate matter and approved by the State Board for the purpose of this Act. The words control equipment has been defined in Sec.2(i) of the Air (P&CP) Act to mean any apparatus, device, equipment or system to control the quality and manner of emission of any air pollutant and includes any device used for securing the efficient operation of any industrial plant. Sec.2(k) of the Air (P&CP) Act defines industrial plant to mean that any plant used for any industrial or trade purposes and emitting any air pollutant into the atmosphere.
8. To the show cause notices, the Petitioners sent reply dated 04.12 .2000. In their reply, the Petitioners have stated that as advised by the Government, every textile mill and industrial establishment has installed diesel power generator sets several decades ago and those generator sets are periodically replaced as and when necessary. They have further stated that in recent times a number of textile mills have replaced the existing Diesel generator sets with generator sets using furnace oil. Thus, onbehalf of the Petitioners it is mainly urged that the replacement of the existing Diesel Generator sets with the Generator sets using furnace oil is used for efficient operation of any Industrial Unit. Furnace oil Generator is any apparatus, device ..... and includes any device used for securing the efficient operation of any Industrial Plant. We are of the view that the Generators using furnace oil falls within the meaning of control equipment as defined in Sec.2(i) of the Air (P&CP)Act.
9. Sec.21 of the Air (P&CP) Act deals with Restrictions on use of certain Industrial Plants and mandates obtaining previous consent of the State Board. Sec.21(1) of the Air (P&CP) Act reads as under:-
...Subject to the provisions of this Section, no person shall, without the previous consent of the State Board, establish or operate any industrial plant in an air pollution Control Area.... The point for consideration is whether such consent of the State Board is essential for replacement of the Unit / Generator set.
10. As per Sec.21(5)(ii) of the Air (P&CP) Act, the existing control equipment, if any, shall be altered or replaced in accordance with the directions of the State Board. By any normal interpretation of the language shall be altered or replaced in accordance with the directions of the State Board, we find that the Consent of the State Board for replacement is essential.
11. Consent Orders issued by the First Respondent contains the General Conditions. Clause 5 of the General Conditions deals with alteration or replacement of Control equipment or Chimney. Clause 5 reads as follows:-
...No control equipment or chimney shall be altered or replaced or as the case may be erected or re-erected except with the previous approval of the Board... The Petitioners / Mills operating in the Pollution free area are bound by Sec.21(5)(ii) of the Air (P&CP) Act and clause 5 of the General Conditions imposed in the Consent Orders. For alteration and replacement of the existing control equipment, obtaining previous consent of the First Respondent / Board is very much essential. While so, it is not open to the Petitioners to contend that for replacement of the existing equipment, no application need be made for obtaining fresh consent.
12. Learned counsel for the Petitioners has submitted that the Furnace Oil Generator set installed by the Petitioners are fully efficient and that there is no air pollution and no fresh consent is necessary regarding the Air (P&CP) Act and The Environment (Protection) Act, 1 986. This contention of the Petitioners does not merit acceptance. SCHEDULE I of The Environment (Protection) Act, 1986 deals with Category of Thermal Power Plants Requiring Environmental Clearance from the State Government. The Co-Generation Captive Plants is one such category which require environmental clearance from the State Government. As per SCHEDULE I, Co-Generation Captive Plants means:
(i) Co-Generation Plants All Co-generation Plants irrespective of the installed capacities.
(ii) (Omitted) (Omitted)
13. Though the Generator using furnace oil is claimed to be more fuel efficient, the fact remains it is a co-generation plant, which requires environmental clearance from the State Government / Board. The contention of the Petitioners that the permission earlier obtained in the Consent Orders Nos.7226, 3353, 7066 and 3354 Dated 31.12.1992, 0 5.03.1991, 04.11.1992 and 05.03.1991 respectively from the First Respondent / Tamil Nadu Pollution Control Board for installation of Diesel Generator Sets would be sufficient for any kind of replacement or alteration of the Generator sets or equipment has no force. The Petitioners are not right in contending that the Petitioners having obtained the Consent Orders, the same would cover any kind of replacement of the equipment, Furnace Oil Generator.
14. Sec.24 of the Air (P&CP) Act deals with Power of Officers of the State Board to enter, inspect any place, to perform the functions of the State Board. The said power includes examining, testing, seizing any control equipment, plants and documents. Any person delaying or obstructing the officers of the Board in discharge of his duties will be guilty of an offence. Since the persons empowered by the State Board have the right of inspection, onbehalf of the Petitioners it is contended that the First Respondent / Pollution Control Board could enter and examine and could very well test the Furnace Oil Generator rather than issuing the show cause notice alleging violation of Sec.2 1 of the Air (P&CP) Act and calling upon the Petitioners Mills to show cause as to why the Prosecution should not be launched against them under Sec.37 of the Air (P&CP) Act. This contention cannot be countenanced. Though the persons empowered by the State Board have the right of entering and inspection, the Petitioners cannot ask the First Respondent to make their own entry and inspect the newly installed Furnace Oil Generator. It is not the function of the First Respondent to investigate and find out whether the machineries already installed have become old and whether the replaced Generator sets using furnace oil is more fully efficient as claimed by the Petitioners. It is for the Petitioner Mills to satisfy the Board about the efficient functioning replaced generator sets using furnace oil. This could be done by making out necessary application and in accordance with the directions of the State Board in compliance with Sec.21(5)(ii) of the Air (P&CP) Act. The First Respondent is well within the limits to issue the show cause notice for the alleged violation of Sec.21 of the Air (P&CP) Act.
15. The show cause notice is assailed mainly on the ground that the Generator sets using furnace oil replaced are fully efficient and installed elsewhere in every Textile Mill and Industrial Establishment are working without any Pollution and hence, installation of Furnace Oil Generator would not cause Air Pollution. Whether the operation of Furnace Oil Generator would be injurious to human beings or other living creatures or plants or property or environment remains to be seen. It is for the Tamil Nadu Pollution Control Board to determine by employing appropriate checking measures to determine whether the Furnace Oil Generators is emitting foul smell or not. Only the Pollution Control Board could check whether the Generator sets using Furnace Oil are emitting any air pollutant within the meaning of Sec.2(a) of the Air (P&CP) Act. The mere statement of the Petitioners that the Generator Sets using Furnace Oil installed in other Textile mill are working satisfactorily without causing air pollution is not sufficient.
16. The Petitioners contend that the issuance of the show cause notice would hamper the working of the Textile Mill and that the negative attitude on the part of the Board would hamper the Industrial growth. Saving the environment cannot be sacrificed at the cost of promoting the Industrial Growth. Since the First Respondent is well within the rights in issuing the show cause notices, we are not inclined to quash the Impugned Notices. However, since the Show Cause Notices are want of details of nature of violation, we are inclined to give the following directions.
17. As per Sec.21(5)(ii) of the Air (P&CP) Act, existing control equipment shall be altered or replaced in accordance with the directions of the State Board. For replacement of the Diesel Generator sets with Furnace Oil Generator, the Petitioners ought to make out applications to the First Respondent stating the nature of the Furnace Oil Generator and the Fuel efficiency etc., which may be considered by the First Respondent in accordance with law.
18. With the above directions, all the Writ Petitions are disposed of. No costs.
Consequently, the connected W.P.M.P.Nos.31355, 31358, 31359 and 3139 6 of 2000 are dismissed.
Index: Yes Internet: Yes Dpn/-
To:
1. Tamil Nadu Pollution Control Board, rep. by its Member Secretary, 32, Santhome High Road, Chennai 600 004.
2. The Joint Chief Environmental Engineer, Tamil Nadu Pollution Control Board, S.No.668-669, SIDCO Industrial Estate, Kappalur, Madurai 625 008.


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esys.downunder.forwardmodels Package¶
Classes¶
AcousticWaveForm
DcRes
ForwardModel
ForwardModelWithPotential
GravityModel
IsostaticPressure
MT2DModelTEMode
MT2DModelTMMode
MagneticIntensityModel
MagneticModel
SelfDemagnetizationModel
Subsidence
-
class
esys.downunder.forwardmodels.
AcousticWaveForm
(domain, omega, w, data, F, coordinates=None, fixAtBottom=False, tol=1e-10, saveMemory=True, scaleF=True)¶ Bases:
esys.downunder.forwardmodels.base.ForwardModel
Forward Model for acoustic waveform inversion in the frequency domain. It defines a cost function:
Math: defect = 1/2 integrate( ( w * ( a * u - data ) ) ** 2 )
where w are weighting factors, data are the measured data (as a 2-comp vector of real and imaginary part) for real frequency omega, and u is the corresponding result produced by the forward model. u (as a 2-comp vector) is the solution of the complex Helmholtz equation for frequency omega, source F and complex, inverse, squared p-velocity sigma:
Math: -u_{ii} - omega**2 * sigma * u = F
It is assumed that the exact scale of source F is unknown and the scaling factor a of F is calculated by minimizing the defect.
-
__init__
(domain, omega, w, data, F, coordinates=None, fixAtBottom=False, tol=1e-10, saveMemory=True, scaleF=True)¶ initializes a new forward model with acoustic wave form inversion.
Parameters: - domain (
Domain
) – domain of the model - w (
Scalar
) – weighting factors - data (
escript.Data
of shape (2,)) – real and imaginary part of data - F (
escript.Data
of shape (2,)) – real and imaginary part of source given at Dirac points, on surface or at volume. - coordinates (
ReferenceSystem
orSpatialCoordinateTransformation
) – defines coordinate system to be used (not supported yet) - tol (positive
float
) – tolerance of underlying PDE - saveMemory (
bool
) – if true stiffness matrix is deleted after solution of PDE to minimize memory requests. This will require more compute time as the matrix needs to be reallocated. - scaleF (
bool
) – if true source F is scaled to minimize defect. - fixAtBottom (
bool
) – if true pressure is fixed to zero at the bottom of the domain
- domain (
-
getArguments
(sigma)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: sigma ( escript.Data
of shape (2,)) – a suggestion for complex 1/V**2Returns: solution, uTar, uTai, uTu Return type: escript.Data
of shape (2,), 3 xfloat
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getDefect
(sigma, u, uTar, uTai, uTu)¶ Returns the defect value.
Parameters: - sigma (
escript.Data
of shape (2,)) – a suggestion for complex 1/V**2 - u (
escript.Data
of shape (2,)) – a u vector - uTar (
float
) – equalsintegrate( w * (data[0]*u[0]+data[1]*u[1]))
- uTai – equals
integrate( w * (data[1]*u[0]-data[0]*u[1]))
- uTu (
float
) – equalsintegrate( w * (u,u))
Return type: float
- sigma (
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(sigma, u, uTar, uTai, uTu)¶ Returns the gradient of the defect with respect to density.
Parameters: - sigma (
escript.Data
of shape (2,)) – a suggestion for complex 1/V**2 - u (
escript.Data
of shape (2,)) – a u vector - uTar (
float
) – equalsintegrate( w * (data[0]*u[0]+data[1]*u[1]))
- uTai – equals
integrate( w * (data[1]*u[0]-data[0]*u[1]))
- uTu (
float
) – equalsintegrate( w * (u,u))
- sigma (
-
getSourceScaling
(u)¶ returns the scaling factor s required to rescale source F to minimize defect
|s * u- data|^2
Parameters: u ( escript.Data
of shape (2,)) – value of pressure solution (real and imaginary part)Return type: complex
-
getSurvey
(index=None)¶ Returns the pair (data, weight)
If argument index is ignored.
-
rescaleWeights
(scale=1.0, sigma_scale=1.0)¶ rescales the weights such that
Math: integrate( ( w omega**2 * sigma_scale * data * ((1/L_j)**2)**-1) +1 )/(data*omega**2 * ((1/L_j)**2)**-1) * sigma_scale )=scale
Parameters: - scale (positive
float
) – scale of data weighting factors - sigma_scale (
Scalar
) – scale of 1/vp**2 velocity.
- scale (positive
-
setUpPDE
()¶ Creates and returns the underlying PDE.
Return type: lpde.LinearPDE
-
-
class
esys.downunder.forwardmodels.
DcRes
(domain, locator, delphiIn, sampleTags, phiPrimary, sigmaPrimary, w=1.0, coordinates=None, tol=1e-08, saveMemory=True, b=None)¶ Bases:
esys.downunder.forwardmodels.base.ForwardModel
Forward Model for DC resistivity, with a given source pair. The cost function is defined as:
Math: defect = 1/2 (sum_s sum_pq w_pqs * ((phi_sp-phi_sq)-v_pqs)**2 -
__init__
(domain, locator, delphiIn, sampleTags, phiPrimary, sigmaPrimary, w=1.0, coordinates=None, tol=1e-08, saveMemory=True, b=None)¶ setup new forward model
Parameters: - domain – the domain of the model
- locator – contains locator to the measurement pairs
- sampleTags (list of tuples) – tags of measurement points from which potential differences will be calculated.
- phiPrimary (
Scalar
) – primary potential.
Type: escript domain
Type: list
ofLocator
Param: delphiIn: this is v_pq, the potential difference for the current source and a set of measurement pairs. A list of measured potential differences is expected. Note this should be the secondary potential only.
-
getArguments
(sigma)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: sigma ( Data
of shape (1,)) – conductivityReturns: phi Return type: Data
of shape (1,)
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getDefect
(sigma, phi, loc_phi)¶ Returns the defect value.
Parameters: - sigma (
Data
of shape (1,)) – a suggestion for conductivity - phi (
Data
of shape (1,)) – potential field
Return type: float
- sigma (
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(sigma, phi, loc_phi)¶ Returns the gradient of the defect with respect to density.
Parameters: - sigma (
Data
of shape (1,)) – a suggestison for conductivity - phi (
Data
of shape (1,)) – potential field
- sigma (
-
getPrimaryPotential
()¶ returns the primary potential :rtype:
Data
-
setUpPDE
()¶ Return the underlying PDE.
Return type: LinearPDE
-
-
class
esys.downunder.forwardmodels.
ForwardModel
¶ Bases:
object
An abstract forward model that can be plugged into a cost function. Subclasses need to implement
getDefect()
,getGradient()
, and possiblygetArguments()
and ‘getCoordinateTransformation’.-
__init__
()¶ Initialize self. See help(type(self)) for accurate signature.
-
getArguments
(x)¶
-
getCoordinateTransformation
()¶
-
getDefect
(x, *args)¶
-
getGradient
(x, *args)¶
-
-
class
esys.downunder.forwardmodels.
ForwardModelWithPotential
(domain, w, data, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Bases:
esys.downunder.forwardmodels.base.ForwardModel
Base class for a forward model using a potential such as magnetic or gravity. It defines a cost function:
defect = 1/2 sum_s integrate( ( weight_i[s] * ( r_i - data_i[s] ) )**2 )where s runs over the survey, weight_i are weighting factors, data_i are the data, and r_i are the results produced by the forward model. It is assumed that the forward model is produced through postprocessing of the solution of a potential PDE.
-
__init__
(domain, w, data, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ initializes a new forward model with potential.
Parameters: - domain (
Domain
) – domain of the model - w (
Vector
or list ofVector
) – data weighting factors - data (
Vector
or list ofVector
) – data - coordinates (
ReferenceSystem
orSpatialCoordinateTransformation
) – defines coordinate system to be used - fixPotentialAtBottom (
bool
) – if true potential is fixed to zero at the bottom of the domain in addition to the top. - tol (positive
float
) – tolerance of underlying PDE
- domain (
-
getArguments
(x)¶
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getData
()¶ Returns the data
Return type: list
ofData
-
getDataFunctionSpace
()¶ Returns the
FunctionSpace
of the dataReturn type: FunctionSpace
-
getDefect
(x, *args)¶
-
getDefectGradient
(result)¶
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(x, *args)¶
-
getMisfitWeights
()¶ Returns the weights of the misfit function
Return type: list
ofData
-
getPDE
()¶ Return the underlying PDE.
Return type: LinearPDE
-
getSurvey
(index=None)¶ Returns the pair (data_index, weight_index), where data_i is the data of survey i, weight_i is the weighting factor for survey i. If index is None, all surveys will be returned in a pair of lists.
-
-
class
esys.downunder.forwardmodels.
GravityModel
(domain, w, g, gravity_constant=6.6742e-11, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Bases:
esys.downunder.forwardmodels.base.ForwardModelWithPotential
Forward Model for gravity inversion as described in the inversion cookbook.
-
__init__
(domain, w, g, gravity_constant=6.6742e-11, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Creates a new gravity model on the given domain with one or more surveys (w, g).
Parameters: - domain (
Domain
) – domain of the model - w (
Vector
or list ofVector
) – data weighting factors - g (
Vector
or list ofVector
) – gravity anomaly data - coordinates (ReferenceSystem` or
SpatialCoordinateTransformation
) – defines coordinate system to be used - tol (positive
float
) – tolerance of underlying PDE - fixPotentialAtBottom (
bool
) – if true potential is fixed to zero at the base of the domain in addition to the top
Note: It is advisable to call rescaleWeights() to rescale weights before starting the inversion.
- domain (
-
getArguments
(rho)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: rho ( Scalar
) – a suggestion for the density distributionReturns: gravity potential and corresponding gravity field. Return type: Scalar
,Vector
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getData
()¶ Returns the data
Return type: list
ofData
-
getDataFunctionSpace
()¶ Returns the
FunctionSpace
of the dataReturn type: FunctionSpace
-
getDefect
(rho, phi, gravity_force)¶ Returns the value of the defect
Parameters: - rho (
Scalar
) – density distribution - phi (
Scalar
) – corresponding potential - gravity_force (
Vector
) – gravity force
Return type: float
- rho (
-
getDefectGradient
(result)¶
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(rho, phi, gravity_force)¶ Returns the gradient of the defect with respect to density.
Parameters: - rho (
Scalar
) – density distribution - phi (
Scalar
) – corresponding potential - gravity_force (
Vector
) – gravity force
Return type: Scalar
- rho (
-
getMisfitWeights
()¶ Returns the weights of the misfit function
Return type: list
ofData
-
getPDE
()¶ Return the underlying PDE.
Return type: LinearPDE
-
getPotential
(rho)¶ Calculates the gravity potential for a given density distribution.
Parameters: rho ( Scalar
) – a suggestion for the density distributionReturns: gravity potential Return type: Scalar
-
getSurvey
(index=None)¶ Returns the pair (data_index, weight_index), where data_i is the data of survey i, weight_i is the weighting factor for survey i. If index is None, all surveys will be returned in a pair of lists.
-
rescaleWeights
(scale=1.0, rho_scale=1.0)¶ rescales the weights such that
sum_s integrate( ( w_i[s] *g_i[s]) (w_j[s]*1/L_j) * L**2 * 4*pi*G*rho_scale )=scale
Parameters: - scale (positive
float
) – scale of data weighting factors - rho_scale (
Scalar
) – scale of density.
- scale (positive
-
-
class
esys.downunder.forwardmodels.
IsostaticPressure
(domain, p0=0.0, level0=0, gravity0=-9.81, background_density=2670.0, gravity_constant=6.6742e-11, coordinates=None, tol=1e-08)¶ Bases:
object
class to calculate isostatic pressure field correction due to gravity forces
-
__init__
(domain, p0=0.0, level0=0, gravity0=-9.81, background_density=2670.0, gravity_constant=6.6742e-11, coordinates=None, tol=1e-08)¶ Parameters: - domain (
Domain
) – domain of the model - p0 (scalar
Data
orfloat
) – pressure at level0 - background_density (
float
) – defines background_density in kg/m^3 - coordinates (ReferenceSystem` or
SpatialCoordinateTransformation
) – defines coordinate system to be used - tol (positive
float
) – tolerance of underlying PDE - level0 (
float
) – pressure for z>=`level0` is set to zero. - gravity0 (
float
) – vertical background gravity atlevel0
- domain (
-
getPressure
(g=None, rho=None)¶ return the pressure for gravity force anomaly
g
and density anomalyrho
Parameters: - g (
Vector
) – gravity anomaly data - rho (
Scalar
) – gravity anomaly data
Returns: pressure distribution
Return type: Scalar
- g (
-
-
class
esys.downunder.forwardmodels.
MT2DModelTEMode
(domain, omega, x, Z_XY, eta=None, w0=1.0, mu=1.2566370614359173e-06, sigma0=0.01, airLayerLevel=None, coordinates=None, Ex_top=1, fixAtTop=False, tol=1e-08, saveMemory=False, directSolver=True)¶ Bases:
esys.downunder.forwardmodels.magnetotelluric2d.MT2DBase
Forward Model for two dimensional MT model in the TE mode for a given frequency omega. It defines a cost function:
- defect = 1/2 integrate( sum_s w^s * ( E_x/H_y - Z_XY^s ) ) ** 2 *
where E_x is the horizontal electric field perpendicular to the YZ-domain, horizontal magnetic field H_y=1/(i*omega*mu) * E_{x,z} with complex unit i and permeability mu. The weighting factor w^s is set to
- w^s(X) = w_0^s *
if length(X-X^s) <= eta and zero otherwise. X^s is the location of impedance measurement Z_XY^s, w_0^s is the level of confidence (eg. 1/measurement error) and eta is level of spatial confidence.
E_x is given as solution of the PDE
- -E_{x,ii} - i omega * mu * sigma * E_x = 0
where E_x at top and bottom is set to solution for background field. Homogeneous Neuman conditions are assumed elsewhere.
-
__init__
(domain, omega, x, Z_XY, eta=None, w0=1.0, mu=1.2566370614359173e-06, sigma0=0.01, airLayerLevel=None, coordinates=None, Ex_top=1, fixAtTop=False, tol=1e-08, saveMemory=False, directSolver=True)¶ initializes a new forward model. See base class for a description of the arguments.
-
getArguments
(sigma)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: sigma ( Data
of shape (2,)) – conductivityReturns: E_x, E_z Return type: Data
of shape (2,)
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getDefect
(sigma, Ex, dExdz)¶ Returns the defect value.
Parameters: - sigma (
Data
of shape ()) – a suggestion for conductivity - Ex (
Data
of shape (2,)) – electric field - dExdz (
Data
of shape (2,)) – vertical derivative of electric field
Return type: float
- sigma (
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(sigma, Ex, dExdz)¶ Returns the gradient of the defect with respect to density.
Parameters: - sigma (
Data
of shape ()) – a suggestion for conductivity - Ex (
Data
of shape (2,)) – electric field - dExdz (
Data
of shape (2,)) – vertical derivative of electric field
- sigma (
-
getWeightingFactor
(x, wx0, x0, eta)¶ returns the weighting factor
-
setUpPDE
()¶ Return the underlying PDE.
Return type: LinearPDE
-
class
esys.downunder.forwardmodels.
MT2DModelTMMode
(domain, omega, x, Z_YX, eta=None, w0=1.0, mu=1.2566370614359173e-06, sigma0=0.01, airLayerLevel=None, coordinates=None, tol=1e-08, saveMemory=False, directSolver=True)¶ Bases:
esys.downunder.forwardmodels.magnetotelluric2d.MT2DBase
Forward Model for two-dimensional MT model in the TM mode for a given frequency omega. It defines a cost function:
- defect = 1/2 integrate( sum_s w^s * ( rho*H_x/Hy - Z_YX^s ) ) ** 2 *
where H_x is the horizontal magnetic field perpendicular to the YZ-domain, horizontal magnetic field H_y=1/(i*omega*mu) * E_{x,z} with complex unit i and permeability mu. The weighting factor w^s is set to
- w^s(X) = w_0^s *
if length(X-X^s) <= eta and zero otherwise. X^s is the location of impedance measurement Z_XY^s, w_0^s is the level of confidence (eg. 1/measurement error) and eta is level of spatial confidence.
H_x is given as solution of the PDE
- -(rho*H_{x,i})_{,i} + i omega * mu * H_x = 0
where H_x at top and bottom is set to solution for background field. Homogeneous Neuman conditions are assumed elsewhere.
-
__init__
(domain, omega, x, Z_YX, eta=None, w0=1.0, mu=1.2566370614359173e-06, sigma0=0.01, airLayerLevel=None, coordinates=None, tol=1e-08, saveMemory=False, directSolver=True)¶ initializes a new forward model. See base class for a description of the arguments.
-
getArguments
(rho)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: rho ( Data
of shape (2,)) – resistivityReturns: Hx, grad(Hx) Return type: tuple
ofData
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getDefect
(rho, Hx, g_Hx)¶ Returns the defect value.
Parameters: - rho (
Data
of shape ()) – a suggestion for resistivity - Hx (
Data
of shape (2,)) – magnetic field - g_Hx (
Data
of shape (2,2)) – gradient of magnetic field
Return type: float
- rho (
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(rho, Hx, g_Hx)¶ Returns the gradient of the defect with respect to resistivity.
Parameters: - rho (
Data
of shape ()) – a suggestion for resistivity - Hx (
Data
of shape (2,)) – magnetic field - g_Hx (
Data
of shape (2,2)) – gradient of magnetic field
- rho (
-
getWeightingFactor
(x, wx0, x0, eta)¶ returns the weighting factor
-
setUpPDE
()¶ Return the underlying PDE.
Return type: LinearPDE
-
class
esys.downunder.forwardmodels.
MagneticIntensityModel
(domain, w, b, background_magnetic_flux_density, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Bases:
esys.downunder.forwardmodels.base.ForwardModelWithPotential
Forward Model for magnetic intensity inversion as described in the inversion cookbook.
-
__init__
(domain, w, b, background_magnetic_flux_density, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Creates a new magnetic intensity model on the given domain with one or more surveys (w, b).
Parameters: - domain (
Domain
) – domain of the model - w (
Scalar
or list ofScalar
) – data weighting factors - b (
Scalar
or list ofScalar
) – magnetic intensity field data - tol (positive
float
) – tolerance of underlying PDE - background_magnetic_flux_density (
Vector
or list offloat
) – background magnetic flux density (in Tesla) with components (B_east, B_north, B_vertical) - coordinates (None) – defines coordinate system to be used
- fixPotentialAtBottom (
bool
) – if true potential is fixed to zero at the bottom of the domain in addition to the top
- domain (
-
getArguments
(k)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: k ( Scalar
) – susceptibilityReturns: scalar magnetic potential and corresponding magnetic field Return type: Scalar
,Vector
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getData
()¶ Returns the data
Return type: list
ofData
-
getDataFunctionSpace
()¶ Returns the
FunctionSpace
of the dataReturn type: FunctionSpace
-
getDefect
(k, phi, magnetic_flux_density)¶ Returns the value of the defect.
Parameters: - k (
Scalar
) – susceptibility - phi (
Scalar
) – corresponding potential - magnetic_flux_density (
Vector
) – magnetic field
Return type: float
- k (
-
getDefectGradient
(result)¶
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(k, phi, magnetic_flux_density)¶ Returns the gradient of the defect with respect to susceptibility.
Parameters: - k (
Scalar
) – susceptibility - phi (
Scalar
) – corresponding potential - magnetic_flux_density (
Vector
) – magnetic field
Return type: Scalar
- k (
-
getMisfitWeights
()¶ Returns the weights of the misfit function
Return type: list
ofData
-
getPDE
()¶ Return the underlying PDE.
Return type: LinearPDE
-
getPotential
(k)¶ Calculates the magnetic potential for a given susceptibility.
Parameters: k ( Scalar
) – susceptibilityReturns: magnetic potential Return type: Scalar
-
getSurvey
(index=None)¶ Returns the pair (data_index, weight_index), where data_i is the data of survey i, weight_i is the weighting factor for survey i. If index is None, all surveys will be returned in a pair of lists.
-
rescaleWeights
(scale=1.0, k_scale=1.0)¶ rescales the weights such that
sum_s integrate( ( w_i[s] *B_i[s]) (w_j[s]*1/L_j) * L**2 * (background_magnetic_flux_density_j[s]*1/L_j) * k_scale )=scale
Parameters: - scale (positive
float
) – scale of data weighting factors - k_scale (
Scalar
) – scale of susceptibility.
- scale (positive
-
-
class
esys.downunder.forwardmodels.
MagneticModel
(domain, w, B, background_magnetic_flux_density, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Bases:
esys.downunder.forwardmodels.base.ForwardModelWithPotential
Forward Model for magnetic inversion as described in the inversion cookbook.
-
__init__
(domain, w, B, background_magnetic_flux_density, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Creates a new magnetic model on the given domain with one or more surveys (w, B).
Parameters: - domain (
Domain
) – domain of the model - w (
Vector
or list ofVector
) – data weighting factors - B (
Vector
or list ofVector
) – magnetic field data - tol (positive
float
) – tolerance of underlying PDE - background_magnetic_flux_density (
Vector
or list offloat
) – background magnetic flux density (in Tesla) with components (B_east, B_north, B_vertical) - coordinates (
ReferenceSystem
orSpatialCoordinateTransformation
) – defines coordinate system to be used - fixPotentialAtBottom (
bool
) – if true potential is fixed to zero at the bottom of the domain in addition to the top
- domain (
-
getArguments
(k)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: k ( Scalar
) – susceptibilityReturns: scalar magnetic potential and corresponding magnetic field Return type: Scalar
,Vector
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getData
()¶ Returns the data
Return type: list
ofData
-
getDataFunctionSpace
()¶ Returns the
FunctionSpace
of the dataReturn type: FunctionSpace
-
getDefect
(k, phi, magnetic_flux_density)¶ Returns the value of the defect.
Parameters: - k (
Scalar
) – susceptibility - phi (
Scalar
) – corresponding potential - magnetic_flux_density (
Vector
) – magnetic field
Return type: float
- k (
-
getDefectGradient
(result)¶
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(k, phi, magnetic_flux_density)¶ Returns the gradient of the defect with respect to susceptibility.
Parameters: - k (
Scalar
) – susceptibility - phi (
Scalar
) – corresponding potential - magnetic_flux_density (
Vector
) – magnetic field
Return type: Scalar
- k (
-
getMisfitWeights
()¶ Returns the weights of the misfit function
Return type: list
ofData
-
getPDE
()¶ Return the underlying PDE.
Return type: LinearPDE
-
getPotential
(k)¶ Calculates the magnetic potential for a given susceptibility.
Parameters: k ( Scalar
) – susceptibilityReturns: magnetic potential Return type: Scalar
-
getSurvey
(index=None)¶ Returns the pair (data_index, weight_index), where data_i is the data of survey i, weight_i is the weighting factor for survey i. If index is None, all surveys will be returned in a pair of lists.
-
rescaleWeights
(scale=1.0, k_scale=1.0)¶ rescales the weights such that
sum_s integrate( ( w_i[s] *B_i[s]) (w_j[s]*1/L_j) * L**2 * (background_magnetic_flux_density_j[s]*1/L_j) * k_scale )=scale
Parameters: - scale (positive
float
) – scale of data weighting factors - k_scale (
Scalar
) – scale of susceptibility.
- scale (positive
-
-
class
esys.downunder.forwardmodels.
SelfDemagnetizationModel
(domain, w, B, background_magnetic_flux_density, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Bases:
esys.downunder.forwardmodels.base.ForwardModelWithPotential
Forward Model for magnetic inversion with self-demagnetization as described in the inversion cookbook.
-
__init__
(domain, w, B, background_magnetic_flux_density, coordinates=None, fixPotentialAtBottom=False, tol=1e-08)¶ Creates a new magnetic model on the given domain with one or more surveys (w, B).
Parameters: - domain (
Domain
) – domain of the model - w (
Vector
or list ofVector
) – data weighting factors - B (
Vector
or list ofVector
) – magnetic field data - background_magnetic_flux_density (
Vector
or list offloat
) – background magnetic flux density (in Tesla) with components (B_east, B_north, B_vertical) - coordinates (
ReferenceSystem
orSpatialCoordinateTransformation
) – defines coordinate system to be used - fixPotentialAtBottom (
bool
) – if true potential is fixed to zero at the bottom of the domain in addition to the top - tol (positive
float
) – tolerance of underlying PDE
- domain (
-
getArguments
(k)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: k ( Scalar
) – susceptibilityReturns: scalar magnetic potential and corresponding magnetic field Return type: Scalar
,Vector
-
getCoordinateTransformation
()¶ returns the coordinate transformation being used
Return type: CoordinateTransformation
-
getData
()¶ Returns the data
Return type: list
ofData
-
getDataFunctionSpace
()¶ Returns the
FunctionSpace
of the dataReturn type: FunctionSpace
-
getDefect
(k, phi, grad_phi, magnetic_flux_density)¶ Returns the value of the defect.
Parameters: - k (
Scalar
) – susceptibility - phi (
Scalar
) – corresponding potential - magnetic_flux_density (
Vector
) – magnetic field
Return type: float
- k (
-
getDefectGradient
(result)¶
-
getDomain
()¶ Returns the domain of the forward model.
Return type: Domain
-
getGradient
(k, phi, grad_phi, magnetic_flux_density)¶ Returns the gradient of the defect with respect to susceptibility.
Parameters: - k (
Scalar
) – susceptibility - phi (
Scalar
) – corresponding potential - magnetic_flux_density (
Vector
) – magnetic field
Return type: Scalar
- k (
-
getMisfitWeights
()¶ Returns the weights of the misfit function
Return type: list
ofData
-
getPDE
()¶ Return the underlying PDE.
Return type: LinearPDE
-
getPotential
(k)¶ Calculates the magnetic potential for a given susceptibility.
Parameters: k ( Scalar
) – susceptibilityReturns: magnetic potential Return type: Scalar
-
getSurvey
(index=None)¶ Returns the pair (data_index, weight_index), where data_i is the data of survey i, weight_i is the weighting factor for survey i. If index is None, all surveys will be returned in a pair of lists.
-
rescaleWeights
(scale=1.0, k_scale=1.0)¶ rescales the weights such that
sum_s integrate( ( w_i[s] *B_i[s]) (w_j[s]*1/L_j) * L**2 * (background_magnetic_flux_density_j[s]*1/L_j) * k_scale )=scale
Parameters: - scale (positive
float
) – scale of data weighting factors - k_scale (
Scalar
) – scale of susceptibility.
- scale (positive
-
-
class
esys.downunder.forwardmodels.
Subsidence
(domain, w, d, lam, mu, coordinates=None, tol=1e-08)¶ Bases:
esys.downunder.forwardmodels.base.ForwardModel
Forward Model for subsidence inversion minimizing integrate( (inner(w,u)-d)**2) where u is the surface displacement due to a pressure change P
-
__init__
(domain, w, d, lam, mu, coordinates=None, tol=1e-08)¶ Creates a new subsidence on the given domain
Parameters: - domain (
Domain
) – domain of the model - w (
Vector
withFunctionOnBoundary
) – data weighting factors and direction - d (
Scalar
withFunctionOnBoundary
) – displacement measured at surface - lam (
Scalar
withFunction
) – 1st Lame coefficient - lam – 2st Lame coefficient/Shear modulus
- coordinates (
ReferenceSystem
orSpatialCoordinateTransformation
) – defines coordinate system to be used (not supported yet)) - tol (positive
float
) – tolerance of underlying PDE
- domain (
-
getArguments
(P)¶ Returns precomputed values shared by
getDefect()
andgetGradient()
.Parameters: P ( Scalar
) – pressureReturns: displacement u Return type: Vector
-
getCoordinateTransformation
()¶
-
getDefect
(P, u)¶ Returns the value of the defect.
Parameters: - P (
Scalar
) – pressure - u (
Vector
) – corresponding displacement
Return type: float
- P (
-
getGradient
(P, u)¶ Returns the gradient of the defect with respect to susceptibility.
Parameters: - P (
Scalar
) – pressure - u (
Vector
) – corresponding displacement
Return type: Scalar
- P (
-
rescaleWeights
(scale=1.0, P_scale=1.0)¶ rescales the weights
Parameters: - scale (positive
float
) – scale of data weighting factors - P_scale (
Scalar
) – scale of pressure increment
- scale (positive
-
Functions¶
Others¶
Packages¶
- esys.downunder.forwardmodels.acoustic Package
- esys.downunder.forwardmodels.base Package
- esys.downunder.forwardmodels.dcresistivity Package
- esys.downunder.forwardmodels.gravity Package
- esys.downunder.forwardmodels.magnetic Package
- esys.downunder.forwardmodels.magnetotelluric2d Package
- esys.downunder.forwardmodels.pressure Package
- esys.downunder.forwardmodels.subsidence Package


Jupyter notebooks support interactive data science and scientific computing across various programming languages. We carry on that philosophy by providing an environment for you to perform exploratory research and brainstorm new ideas for algorithms. A Jupyter notebook installed in QuantConnect allows you to directly explore the massive amounts of data that is available in the Dataset Market and analyze it with python or C# commands. We call this exploratory notebook environment the Research Environment.
To open a notebook, open one of the .ipynb files in your cloud projects or see Running Local Research Environment.
The notebook allows you to run code in a safe and disposable environment. It's composed of independent cells where you can write, edit, and execute code. The notebooks support Python, C#, and Markdown code.
The following table describes some useful keyboard shortcuts:
| Shortcut | Description |
|---|---|
| Shift+Enter | Run the selected cell |
| a | Insert a cell above the selected cell |
| b | Insert a cell below the selected cell |
| x | Cut the selected cell |
| v | Paste the copied or cut cell |
| z | Undo cell actions |
If you use the Research Environment in QuantConnect Cloud, to terminate a research session, stop the research node in the Resources panel. If you use the local Research Environment, see Managing Kernels and Terminals in the JupyterLab documentation.


Voting!
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-
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- Vote {{ nom.name }} {% endfor %}
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Scientific Posters
Fábio Madeira and Geoffrey J. Barton. 2015.
Analysis of protein domain interactions and genetic variation.
SLS PhD Poster Session at University of Dundee Dundee, October 9th.
Selected as Poster Prize Winner.
Fábio Madeira and Geoffrey J. Barton. 2015.
Protein interactions and genetic variation.
ISMB/ECCB 2015 (23rd Annual International Conference on Intelligent Systems for Molecular Biology - 14th European
Conference on Computational Biology) Dublin, Jul 10-14th. Selected for Oral Poster Presentation and Travel Fellowship.
Fábio Madeira, Michele Tinti, Gerta Hoxhaj, Geoffrey J. Barton and Carol MacKintosh. 2015.
Predicting and mapping the human 14-3-3 interactome to
test a new signalling paradigm.
Wellcome Trust Retreat in Firbush College of Life Sciences, University of Dundee, May 23th.
Michele Tinti, Fábio Madeira, Catherine Johnson, Margaret Malcolm and Carol MacKintosh. 2012.
Evolution of 14-3-3-binding sites and 2R-Ohnologue families.
PTMs in Cell Signaling, Copenhagen Denmark, December 2-5th.
Paulo Roque Lino, Fábio Madeira, Isabel Tavares de Almeida and Paula Leandro. 2010.
Alternative Answers to Everlasting Problems: Protein Engineering Towards New Approaches for
Phenylketonuria Treatment. SPDM Annual Symposium Albufeira, Portugal, November 4-5th.
Catarina Coelho, Fábio Madeira, Paulo Roque Lino, Isabel Tavares de Almeida,
Paula Leandro and Maria João Romão. 2010.
Crystallization of a Stable Form of Human Phenylalanine Hydroxylase: Towards the 3D Structure Determination
. UK-Portuguese Teams Meeting Protein-protein interaction and Protein folding. FCT-UNL, March 29-31st.


Completing the Square
- As you adjust the numbers in the template below, the interactive will automatically FOIL and combine like terms to give you the equivalent polynomial.
- Try to adjust the constants to get the polynomial to match the target polynomial.
- As you adjust the constants, the graph below will change.
- Press a "New Target" button to generate a new target polynomial.
- Try to come up with a method of guessing what constants will match the target.
Graph of :


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SUMMARY
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