This tool simulates a Krypton atom in a cube. The atom has a diameter of 3.6892 Å. The size of the containing box may be changed using the options below. The void fraction of the cell and the volume of the sphere may be calculated with a Monte Carlo simulation using the panel on the right. Please note that using a large probe diameter will provide inaccuarate results. The probe diameter should be no larger than (box size - 4) Å. It is recommended to use at least 1000 probes to ensure accurate results.


sum·mon / v. 1. Order to come or appear, esp. at a court. 2. Call upon. 3. Call together. 4. Gather (courage, spirits, resources, etc.) [L. summonere]
One of the staples of Final Fantasy is the existence of large creatures or figures who can be summoned to the battle field. Sometimes they can only be called by one person of the summoner job class, sometimes they can be called by any character. Each one does something different (healing, attacking, casting defensive spells, causing status ailments, etc.). Their actions are always powerful, but also have a cost (magic points, time, limitations on how often they can be called, etc.). The Japanese name for these things, by the way, is usually shokanjuu or 'Summoned Creatures', although in FF4 and FF6 they were called genjuu or 'Phantom Creatures'). In English translations, their nomenclature differs from game to game. Here's a brief list: FF5: Summons, FF6: Espers, FF7: Summons, FF8: Guardian Forces or GFs, FF9: Eidolons, FF10: Aeons, FF11: Avatars, FF12: Espers. In the FF community, they are typically called Summons.
NOTE: In FFTA, there are two types of summoned creatures - typical ones like Ifrit and Shiva that are called by the summoner, and totemas which can be called on by any character, which attack all enemies on the battle field. The totema summoned depends on the race of the caller, of which there are five. The totemas can also be found under FFTA Characters.
NOTE: In FF12, the Zodiac creatures are the summons, rather than regular monsters from past games. However, airships and other mechanical devices have the names of the "regular" summons (like Ifrit and Shiva). To further confuse things, FF12RW's Espers include both the zodiac creatures and regular summons.
Expand All Images | Collapse All Images
See Alexander for more information.
Although not technically a GF, he does have a GF card. He's actually part of Rinoa's Limit Break, doing different things like counterattacking, reviving, and finding items.
| FF8 -Angelo (LIMIT BREAK) ATK: Angelo Rush, Angelo Cannon, Angelo Strike, Angelo Recover, Invincible Moon, Angelo Reverse, Angelo Search, Wishing Star |
This fish-like beast is the second most powerful Aeon in FF10, and is Seymour's mother's fayth. It can cause instant death with normal attacks. Though it is mostly used against you throughout the game, it can become yours through a side-quest.
| FF10 (SUMMON) -Anima ATK: Pain (special attack), Oblivion (Overdrive) |
This is the most powerful summon of FF9. The long introduction sequence (straight out of Transformers) involves it coming in as a huge dragon-headed airship, and changing into a vaguely robotic humanoid. It pounds the enemy repeatedly with Shadow damage.
| FF9 (SUMMON) -Ark ATK: Eternal Darkness |
The Queen of the Summoned Monsters, Asura gives one of three effects depending on which face she displays. She either heals the party, gives Protect status, or casts Raise (Life). In FF2 (NA) there was no Protect status, so that effect was replaced with a stronger healing spell.
| FF4 -Asura ATK: Armor, Cure 3, Life 1 |
See Atomos for more information.
See Bahamut for more information.
Thar she blows. Bismark is a white humpback whale who has a strong water-based attack.
| FF6 -Bismark ATK: Sea Song FF6 Advance -Bismarck ATK: Breach Blast | |
| Unlimited -Bismark SOIL: Shark Grey + Water Blue + Crusher White |
See Bomb (MONSTER) for more information.
See Brothers (A.K.A. Minotaur/Sekhmet) (MONSTER) for more information.
See Cactuar (MONSTER) for more information.
Stray is a cat who walks (on two legs in boots) across the enemy and causes confusion (as a walking cat would). Its Japanese name, "Cait Sith", was also used as a Coeurl-like creature in FF4 and a character in FF7 (see also FF7 Characters).
| FF6 -Stray (NA) / Cait Sith (JP) ATK: Cat Rain FF6 Advance -Cait Sith ATK: Cat Rain | |
| FF11 -Cait Sith ATK: ??? |
See Carbuncle for more information.
See Catoblepas for more information.
See Cerberus for more information.
See Chocobo for more information.
See Cockatrice (MONSTER) for more information.
The ultimate Summon in FF6 and FF7. In FF6, Crusader consists of three knights who attacked for non-elemental damage. Unfortunately, the attack damaged allies as well. In FF7, there are thirteen different knights, based on King Arthur's Knights of the Round Table (hence the name) that cause an insane amount of damage which is only useful for fighting the optional bosses. The different knights fly through a psychedelic background and slice and dice the enemy using multiple weapons.
| FF6 -Crusader (NA) / Jihad (JP) ATK: Purifier FF6 Advance -Crusader ATK: Cleansing | |
| FF7 -Knights of the Round ATK: Ultimate End |
A blue one-eyed giant who causes ultimate damage.
| Tactics -Cyclops ATK: Climactic Fear |
This summon looks very much like a winged demon from hell and bears a slight resemblance to Chaos (see FF1 and FF7). He emerges from a ball of liquid blackness, doles out gravity-based damage (one of the only summons who do), and transforms into a flock of bats that fly away.
| FF6 Advance -Diablos ATK: Dark Messenger | |
| FF8 -Diablos ATK: Dark Messenger | |
| FF11 -Diabolos ATK: Ruminous Omen, Camisado, Somnolence, Ruinous Omen (Astral Flow), Ultimate Terror, Noctoshield, Dream Shroud, Nether Blast, Cacodemonia | |
| FF12 -Diabolos (ENEMY) Though not directly related to the Diablos summon, it does share a few characteristics. CP: What strange Change does Magicite work on creatures, a perfect Example being this Gargoyle of the Lhusu Mines. Its Form inspired the commonly held Misconception in Bhujerba that the Creatures in its Magicite mines are Visitors from an Otherworld. | |
| FF12RW -Diabolos ATK: Nightmare, Gravity |
See Doomtrain for more information.
Most people have no idea WHAT Eden is is, though some postulate that it is the Garden of the future (which would fit in with the name, as in the Garden of Eden). It looks like a giant spaceship which blasts the enemy into a black hole.
| FF8 -Eden ATK: Eternal Breath |
Fairy appears as a woman wrapped in heavenly wings, who heals the entire party.
| Tactics -Faerie ATK: Fey Light Tactics (PSX) -Fairy ATK: Fey Light |
See Fenrir for more information.
See Garuda (MONSTER) for more information.
See Gilgamesh for more information.
See Goblin/Imp (MONSTER) for more information.
See Golem for more information.
As a summon in FF7, Hades is a skeleton-man, cloaked in a black robe, who stirs a cauldron and spills it on the enemy, causing status effects and damage. For information about Hades as a monster, see Hades.
| FF7 (SUMMON) -Hades ATK: Black Cauldron |
Translated as Hydra in the English version of FF5, Faris's childhood dragon friend piloted her ship until the loyal dragon died in battle with a monster. Her soul returns to help out by using a Thunderstorm attack.
| FF5 -Hydra (NA) / Syldra (JP) ATK: Thunder Storm |
See Ifrit for more information.
See Ramuh for more information.
Ixion is a magical unicorn brimming with electrical energy. As Yuna's mid-level summon, it casts powerful lightning attacks.
| FF10 -Ixion ATK: Aerospark (Special Attack), Thor's Hammer (Overdrive) | |
| FF11 (MONSTER) -Ixion A Notorious Monster boss enemy. | |
| FF12 -Ixion A Rank VI mark in the Pharos at Ridorana. CP: Being a variety of Nightmare, oft told of Legends of Ridorana. Having the power to turn the Dreams of Men to Terrors, then feeding off its Victims' Anguish. | |
| FF12RW -Ixion (Esper) ATK: Hoof Kick | |
| Unlimited -Ixion / Ixion Type Zero SOIL: Steel Grey + Heat Crimson + Lightning Yellow |
This creature resembles a kind of sheep which casts Regen on the party. Kylin from SaGa Frontier may be the same character. In FF11, it is a manticore "god" that appears when the seals of the other four "gods" are used.
| FF6 -Kirin ATK: Life Guard FF6 Advance -Kirin ATK: Holy Aura | |
| FF11 -Kirin | |
| Tactics Advance -Kirin ATK: Not named | |
| Tactics A2 -Kirin ATK: Not named |
Kjata looks like a cross between a warthog and a donkey, and turns the battle area into a bunch of sparks and ripples with a Fire/Ice/Lightning attack.
| FF7 -Kjata ATK: Tetra Disaster |
See Crusader for more information.
A pretty woman who heals the party.
| FF6 -Starlet ATK: Group Hug FF6 Advance -Lakshmi ATK: Alluring Embrace |
See Leviathan for more information.
See Lich (MONSTER) for more information.
See Madeen/Maduin for more information.
 In Japan, they are known as Mag, Dog, and Rag. They are enemies in FF4, but return in FF10 as summons (in insect costumes for some reason). They're nearly impossible to defeat because of the way they complement each other with healing, status effects, and physical attacks. They cannot be defeated until all three of them are down, but their Overdrive requires the trio to be active with full overdrives. You can't command them to use specific actions, but you can "suggest" what you want them to do. They're pretty smart, though, and incredibly powerful (as long as they're not "taking a break")!
| FF4 (ENEMY) -Sandy (NA) / Dogu (JP), Cindy (NA) / Magu (JP), Mindy (NA) / Lagu (JP) | |
|
|
FF10 (SUMMON) -Magus Sisters (Sandy, Cindy, Mindy) ATK: Special Attacks: |
A giant snake who delivers an earth attack. "Midgar Zolom" reappears in FF7, and in an attack by Elidibus in Tactics. Midgar is a city in FF7 (see Places).
| FF6 (SUMMON) -Terrato (NA) / Midgar Zolom (JP) ATK: Earth Aura FF6 Advance -Midgardsormr ATK: Abyssal Maw | |
| FF7 (ENEMY) -Midgar Zolom | |
| FF12 (ENEMY) -Midgardsormr CP: Primordial basilisk, thought to be of an ancient, if not original, species. |
See Moogle for more information.
The first summon received in FF4, belonging to Rydia's mother. It attacks with non-elemental damage.
| FF4 -Mist Dragon ATK: Mist Breath |
See Moogle for more information.
| FF8 -Moomba ATK: ??? |
See Odin for more information.
This bizarre creature sucks up the enemies into a vacuum cleaner-like sac on its back and spits them out, causing wind damage.
| FF8 -Pandemona (NA) / Pandemonium (JP) ATK: Tornado Zone |
A ghostly character cowled in darkness who causes all allies to vanish.
| FF6 -Phantom ATK: Fader FF6 Advance -Phantom ATK: Ghostly Veil |
See Phoenix for more information.
Yes, that IS the way it's spelled in FF8; they didn't have room for the full name. This featureless bird casts thunder-based damage. The 12RW version is much cuter but still thunder-elemental.
| FF8 -Quezacotl ATK: Thunder Storm |
A bird of paradise who picks up the party and allows them all to do a Jump attack.
| FF6 -Palidor ATK: Sonic Dive FF6 Advance -Quetzalli ATK: Sonic Dive |
In FF6, you are given a choice between Ragnarok the sword (see weapons) and Ragnarok the summon. The summon looks like a sword and turns one enemy into an item. It also teaches the Ultima spell. (See also Transportation for info on the Ragnarok of FF8.)
| FF6 -Ragnarok ATK: Metamorph FF6 Advance -Ragnarok ATK: Metamorphose |
See Ramuh for more information.
Odin can be upgraded to the form of Raiden near the end of FF6. He has the same method of operation as Odin, but his attack, "True Edge", has a higher success rate (see Odin).
| FF6 -Raiden (NA) / Raijin (JP) ATK: True Edge FF6 Advance -Raiden ATK: Shin-Zantetsuken |
Remora is just a bunch of little fish that attach themselves to the enemy and leech MP.
| FF5 -Remora ATK: Constrict / Latch On (PSX) | |
| FF12 - Transportation The Draklor CB56 Remora Fighter is one of the airships Reks fights with Basch in the introduction. CP: Small fighter airship of the Imperial army. Gun turrets placed on the keel, and turrets on the deck make this an effective weapon against air and ground threats. A mainstay of the Imperial Armored Forces together with the newer *CB58 Valfarre* fighter. The initials of the craft's designer, Dr. Cid, and the year of its production adorn the hull. | |
| FF12RW -Remora ATK: Thunderchop | |
| FFX -Remora |
A lizard who causes fire damage.
| Tactics -Salamander ATK: Wyrmfire |
An angel-like summon who heals the party.
| FF6 -Sraphim ATK: Reviver FF6 Advance -Seraph ATK: Angel Feathers |
See Shiva for more information.
See Catoblepas for more information.
See Siren for more information.
See Sylph for more information.
See Titan for more information.
See Tonberry (MONSTER) for more information.
See Typhon for more information.
See Unicorn for more information.
Valefor is a giant, feathered bird with a chain of rings coming down from one of its wings. It has a non-elemental attack and is Yuna's first Aeon.
| FF10 -Valefor ATK: Sonic Wings (Special Attack), Energy Ray, Energy Blast (overdrive) |
| FF12 According to the Clan Primer, the CB58 Valfarre is the newest of the small fighter airships of the Archadian Imperial Air Force. |
A cross between a snake and a dragon, it unleashes a Fire/Ice/Thunder attack. This attack will unfortunately heal any enemy who can absorb even one of the elements.
| FF6 -Tritoch ATK: Tri-Dazer FF6 Advance -Valigarmanda ATK: Tri-Disaster |
See Yojimbo for more information.
The most powerful summon of Tactics, it is a strange beast that causes non-elemental damage. For information about Zodiac as a monster, see Zodiark.
| Tactics -Zodiark ATK: Darkening Cloud Tactics (PSX) -Zodiac ATK: Darkening Cloud | |
| FF12 -Zodiark ATK: Flare, Scathe, Banish Ray, Final Eclipse |
It's hard to tell exactly what this creature is. It's vaguely Lich-like, with a robe, skeletal body, and horns. It casts Shell and Safe on the party.
| FF6 -ZoneSeek ATK: Wall FF6 Advance -Zona Seeker ATK: Magic Shield |


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Granit Xhaka: Unai Emery 'does not know' if midfielder will play for Arsenal again
Arsenal boss Unai Emery admits he "does not know" if former captain Granit Xhaka will play for the club again.
Xhaka, 27, was stripped of the club captaincy earlier this week after being involved in an angry confrontation with home fans during the Gunners' 2-2 draw with Crystal Palace on 27 October.
The Swiss midfielder has not played since and will not play in Saturday's Premier League game at Leicester City.
"He said to me that he's not feeling good enough to play," said Emery.
"I spoke with him on Tuesday about how he's feeling and how he is in his mind. He's working well, he's training well.
"I need to be focused with the players we have, and then after the international break I am going to speak again with him."

Emery later said Xhaka should apologise to the club's supporters, saying the midfielder "knows he was wrong".
However, Xhaka did not go that far, although he did say he got "carried away" and reacted "disrespectfully" to fans following repeated threats to his wife and daughter.
Pushed on whether Xhaka may never play for Arsenal again, Emery added: "As a coach I have a responsibility for the team. We need a player like Granit Xhaka.
"But also, I don't know if he's going to play again. I don't know if he is available in his mind to continue helping us and continuing defending the Arsenal shirt.
"Then, if I decide he is in the group with us, if he is ready and 100% in his mind with the decision to help us and to play with his quality helping us. We need that quality and that player.
"We're now a little weaker without him in that position because we have one less player."
Asked if a move for Xhaka in January had been mentioned, Emery said he had "asked that question" but also added: "I'm not thinking about that because we're in November and we're going to play a lot of matches in November and December.
"We cannot change him in that two months. The club is speaking with him and we're going to decide how he is and how I'm thinking we use him."


INTRODUCTION
This pre-celesta event gives the prolific writer in you a chance, a chance to cross the horizons of imagination, to expand the field of one’s view and plot a sci-fi story. It will be your story so it can be anything, literally anything - Alien invasion to humans going to another planet or time-travelling and what not!
So just think outside the box, use your imagination, bend the scope of view and write a thriller!Objective
Given a world of robots and AI, create a short story that minds will never forget. Theme of the story could be anything ranging from sad, happy, on mars, thriller, scary, may involve time machine or whatever your creative mind can think of.
What to do : You are expected to write this story and then share it on facebook with the following hastags -
#TechChronicles
#Celesta2k17
Also you have to mail the story to us at<EMAIL_ADDRESS>with the subject : Entry for TechChronicles
Rules:
Word limit shouldn’t exceed 500 words.
No vulgar content needs to be included.
The story should be sent as a word file (.doc) file as an attachment in the mail along with the link to your post with it in the mail.
Judging Criteria:
Scoring will be done on the following criterias -
a. Creativity - 30
b. Originality - 20
c. Grammar - 20
d. Overall impact - 30
Note: The marks will be deducted if rules are not followed.
In case of any discrepancy, the decision of the organiser will be final.
Venue and Timing:


{% extends "base.html" %} {% block body %} {% include "navbar.html" %} The server is temporarily down, we're doing our best to fix it so be sure to come back after a bit 😄 {% endblock %} {% block scripts %} {% endblock %}
The server is temporarily down, we're doing our best to fix it so be sure to come back after a bit 😄


Module ReplicatedStorage.SharedSource.Data.Armies.Army
Represents of group of units of any type and Generals.
See also:
Functions
| getSize (unit_type) | Gets the size. |
| getSpeed () | Gets the speed. |
| getCurrentTerritoryId () | Gets the current node id. |
| getLastTerritoryId () | Gets the last node id. |
| getNextTerritoryId () | Gets the next node id. |
| getTargetTerritoryId () | Gets the target node id. |
| getBattleId () | Gets the army's battle id. |
| getGeneralIds () | Gets a list of general ids. |
| getMoveVector () | Gets the move vector. |
| getDistanceToNextTerritory () | Gets the distance to the next territory. |
| getPath () | Gets the path. |
| getType () | Gets the type. |
| isInBattle () | Returns true if in battle. |
Functions
- getSize (unit_type)
-
Gets the size.
Parameters:
- unit_type the unit type of interest. Will get count of all units if not provided
- getSpeed ()
- Gets the speed.
- getCurrentTerritoryId ()
-
Gets the current node id.
Returns:
-
0 if the army is moving, or the current node id
- getLastTerritoryId ()
-
Gets the last node id.
Returns:
-
0 if the army is not moving, or the last node id
- getNextTerritoryId ()
-
Gets the next node id.
Returns:
-
0 if the army is not moving, or the next node id
- getTargetTerritoryId ()
-
Gets the target node id.
Returns:
-
0 if the army has no target, or the target node id
- getBattleId ()
-
Gets the army's battle id.
Returns:
-
0 if the army is not in battle, or a battle id
- getGeneralIds ()
- Gets a list of general ids.
- getMoveVector ()
- Gets the move vector.
- getDistanceToNextTerritory ()
- Gets the distance to the next territory.
- getPath ()
- Gets the path.
- getType ()
- Gets the type.
- isInBattle ()
- Returns true if in battle.


Chapter 3 nlmixr
vignettes
Some basic applications are demonstrated in this chapter; the vignettes will be discussing the application in more depth. More examples will be added to the nlmixr
pkgdown site and RxODE
pkgdown site
3.1 nlmixr
library(nlmixr)
?nlmixr
3.1.1 Rationale
nlmixr
estimation routines have their own way of specifying models. Often the models are specified in ways that are most intuitive for one estimation routine, but do not make sense for another estimation routine. Sometimes, legacy estimation routines like [nlme] have their own syntax that is outside of the control of the nlmixr
package.
The unique syntax of each routine makes the routines themselves easier to maintain and expand, and allows interfacing with existing packages that are outside of nlmixr
(like [nlme]). However, a model definition language that is common between estimation methods, and an output object that is uniform, will make it easier to switch between estimation routines and will facilitate interfacing output with external packages like xpose
and other user-written packages.
The nlmixr
mini-modeling language (Domain Specific Language) attempts to address this issue by incorporating a common language. This language is inspired by both R
and NONMEM
, since these languages are familiar to many pharmacometricians.
Initial Estimates and boundaries for population parameters
nlmixr
models are contained in a R function with two blocks: ini
and model
. This R function can be named anything, but is not meant to be called directly from R. In fact, if you try you will likely get an error such as Error: could not find function "ini"
.
The ini
model block
The ini
model block is meant to hold the initial estimates for the model, and the boundaries of the parameters for estimation routines that support boundaries (note nlmixr
’s saem
and [nlme] do not currently support parameter boundaries).
To explain how these initial estimates are specified we will start with an annotated example:
f <- function(){ ## Note the arguments to the function are currently
## ignored by `nlmixr`
ini({
## Initial conditions for population parameters (sometimes
## called theta parameters) are defined by either `<-` or '='
lCl <- 1.6 #log Cl (L/hr)
## Note that simple expressions that evaluate to a number are
## OK for defining initial conditions (like in R)
lVc = log(90) #log V (L)
## Also a comment on a parameter is captured as a parameter label
lKa <- 1 #log Ka (1/hr)
## Bounds may be specified by c(lower, est, upper), like NONMEM:
## Residuals errors are assumed to be population parameters
prop.err <- c(0, 0.2, 1)
})
## The model block will be discussed later
model({})
}
As shown in the above examples:
- Simple parameter values are specified as a R-compatible assignment
- Boundaries may be specified by
c(lower, est, upper)
. - Like NONMEM,
c(lower,est)
is equivalent toc(lower,est,Inf)
- Also like NONMEM,
c(est)
does not specify a lower bound, and is equivalent to specifying the parameter without R’sc
function. - The initial estimates of the between subject variabilities are specified on the variance scale, and in analogy with NONMEM, the square roots of the diagonal elements correspond to coefficients of variation when used in the exponential IIV implementation. This is true, since this is an approximation. When displaying %CV in the
nlmixr
table, it uses:sqrt(exp(v) - 1) * 100
wherev
=variance of the Omega formula.
These parameters can be called by almost any R-compatible name. Please note that:
- Residual error estimates should be coded as population estimates (i.e. using an ‘=’ or ‘<-’ statement, not a ‘~’). These are specified on a standard deviation scale.
- Variable names starting with “
_
” are not supported. Note that R does not allow variable starting with “_
” to be assigned without quoting them. - Variable names starting with “
rx_
” or “nlmixr_
” are not supported, sinceRxODE
andnlmixr
use these prefixes internally for certain estimation routines and for calculating residuals. - Variable names are case sensitive, just like they are in R. “
CL
” is not the same as “Cl
”.
Initial estimates for between-subject error distributions
In mixture models, multivariate normal individual deviations from the population parameters are estimated (in NONMEM
these are called ETA
parameters). Additionally, the variance/covariance matrix of these deviations are also estimated (in NONMEM
this is the OMEGA
matrix). The initial estimates for variance are specified by the ~
operator in nlmixr
, which is typically used in R to denote “modeled by”, and was chosen to distinguish these estimates from the population and residual error parameters.
Continuing the prior example, we can annotate the estimates for the between subject error distribution:
f <- function(){
ini({
lCl <- 1.6 #log Cl (L/hr)
lVc = log(90) #log V (L)
lKa <- 1 #log Ka (1/hr)
prop.err <- c(0, 0.2, 1)
## Initial estimate for ka IIV variance
## Labels work for single parameters
eta.ka ~ 0.1 #BSV Ka
## For correlated parameters, you specify the names of each
## correlated parameter separated by a addition operator `+`
## and the left handed side specifies the lower triangular
## matrix initial of the covariance matrix.
eta.cl + eta.vc ~ c(0.1,
0.005, 0.1)
## Note that labels do not currently work for correlated
## parameters. Also do not put comments inside the lower
## triangular matrix as this will currently break the model.
})
## The model block will be discussed later
model({})
}
As shown in the above examples:
- Simple variances are specified by the variable name and the estimate separated by
~
. - Correlated parameters are specified by the sum of the variable labels and then the lower triangular matrix of the covariance is specified on the left hand side of the equation. This is also separated by
~
. }
Currently the model syntax does not allow comments inside the lower triangular matrix.
Model syntax for ODE-based models
The ini
model block Once the initialization block has been defined, you can define a model
block in terms of the defined variables in the ini
block. You can also mix in RxODE
blocks into the model. This section is analogous to NONMEM’s $PK
, $PRED
, $DES
and $ERROR
blocks.
The current method of defining an nlmixr
model is to specify the parameters, and then possibly the RxODE
lines:
Continuing describing the syntax with an annotated example:
f <- function(){
ini({
lCl <- 1.6 #log Cl (L/hr)
lVc <- log(90) #log Vc (L)
lKA <- 0.1 #log Ka (1/hr)
prop.err <- c(0, 0.2, 1)
eta.Cl ~ 0.1 ## BSV Cl
eta.Vc ~ 0.1 ## BSV Vc
eta.KA ~ 0.1 ## BSV Ka
})
model({
## First parameters are defined in terms of the initial estimates
## parameter names.
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
## After the differential equations are defined
kel <- Cl / Vc;
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-kel*centr;
## And the concentration is then calculated
cp = centr / Vc;
## Last, nlmixr is told that the plasma concentration follows
## a proportional error (estimated by the parameter prop.err)
cp ~ prop(prop.err)
})
}
A few points to note:
- Parameters are defined before the differential equations. Currently, defining the differential equations directly in terms of the population parameters is not supported.
- The differential equations, parameters and error terms are in a single block, instead of multiple sections.
- State names, calculated variables cannot start with either “
rx_
” or “nlmixr_
” since these are reserved terms for internal use in some estimation routines. - Errors are specified using
~
. Currently you can use eitheradd(parameter)
for additive error,prop(parameter)
for proportional error oradd(parameter1) + prop(parameter2)
for combined additive and proportional error. You can also specifynorm(parameter)
for the additive error, since it follows a normal distribution. - Some algorithms, like
saem
, require parameters in terms ofPop.Parameter + Individual.Deviation.Parameter + Covariate*Covariate.Parameter
. The order of these parameters do not matter. The approach is similar to NONMEM’s mu-referencing. - The type of parameter (population parameter estimate, or individual parameter deviation) in the model is determined by the initial block; Covariates used in the model are missing in the
ini
block. These variables need to be present in the modeling dataset for the model to run.
Model Syntax for solved PK systems
Solved PK systems are currently supported by nlmixr
with the linCmt()
pseudo-function. An annotated example of a solved system is below:
f <- function(){
ini({
lCl <- 1.6 #log Cl (L/hr)
lVc <- log(90) #log Vc (L)
lKA <- 0.1 #log Ka (1/hr)
prop.err <- c(0, 0.2, 1)
eta.Cl ~ 0.1 ## BSV Cl
eta.Vc ~ 0.1 ## BSV Vc
eta.KA ~ 0.1 ## BSV Ka
})
model({
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
## Instead of specifying the ODEs, you can use
## the linCmt() function to use the solved system.
##
## This function determines the type of PK solved system
## to use by the parameters that are defined. In this case
## it knows that this is a one-compartment model with first-order
## absorption.
linCmt() ~ prop(prop.err)
})
}
A few things to keep in mind:
- Currently the solved systems support either oral dosing, IV dosing or IV infusion dosing, but does not allow mixing the dosing types.
- While RxODE allows mixing of solved systems and ODEs, this has not been implemented in
nlmixr
yet. - The solved systems implemented are the one-, two- and three-compartment models with or without first-order absorption. Each of the models support a lag time with a
tlag
parameter (although currentlytlag
will cause the CWRES/OBJF calculation to fail). - The
linCmt()
function figures out the model from the parameter names.nlmixr
currently knows about numbered volumes, Vc/Vp, clearances in terms of both CL and Q/CLD. Additionally,nlmixr
knows about elimination micro-constants (ie k12). Mixing of parameters and microconstants for is currently not supported.
Checking model syntax
After specifying the model syntax you can check that nlmixr
is interpreting it correctly by using the nlmixr
function on it.
Using the above function we can get:
> nlmixr(f)
## 1-compartment model with first-order absorption in terms of Cl
## Initialization:
################################################################################
Fixed Effects ($theta):
lCl lVc lKA
1.60000 4.49981 0.10000
Omega ($omega):
[,1] [,2] [,3]
[1,] 0.1 0.0 0.0
[2,] 0.0 0.1 0.0
[3,] 0.0 0.0 0.1
## Model:
################################################################################
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
## Instead of specifying the ODEs, you can use
## the linCmt() function to use the solved system.
##
## This function determines the type of PK solved system
## to use by the parameters that are defined. In this case
## it knows that this is a one-compartment model with first-order
## absorption.
linCmt() ~ prop(prop.err)
In general this gives you information about the model (what type of solved system/RxODE), initial estimates as well as the code for the model block.
Using the model syntax for estimating a model
Once the model function has been created, you can use it to estimate the parameters for a model given a dataset.
This dataset has to have RxODE
compatible event IDs. Both Monolix and NONMEM use a different dataset description. You may convert these datasets to RxODE-compatible datasets with the nmDataConvert
function. Note that steady state doses are not supported by RxODE, and therefore not supported by the conversion function.
As an example, we can use a simulated rich 1-compartment oral PK dataset.
d <- Oral_1CPT
d <- d[,names(d) != "SS"];
d <- nmDataConvert(d);
Once the data has been converted to the appropriate format, you can use the nlmixr
function to run the appropriate code.
The method to execute the model is:
fit <- nlmixr(model.function, rxode.dataset, est="est",control=estControl(options))
Currently nlme
and saem
are implemented. For example, to run the above model with saem
, we could have the following:
> f <- function(){
ini({
lCl <- 1.6 #log Cl (L/hr)
lVc <- log(90) #log Vc (L)
lKA <- 0.1 #log Ka (1/hr)
prop.err <- c(0, 0.2, 1)
eta.Cl ~ 0.1 ## BSV Cl
eta.Vc ~ 0.1 ## BSV Vc
eta.KA ~ 0.1 ## BSV Ka
})
model({
## First parameters are defined in terms of the initial estimates
## parameter names.
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
## After the differential equations are defined
kel <- Cl / Vc;
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-kel*centr;
## And the concentration is then calculated
cp = centr / Vc;
## Last, nlmixr is told that the plasma concentration follows
## a proportional error (estimated by the parameter prop.err)
cp ~ prop(prop.err)
})
}
> fit.s <- nlmixr(f,d,est="saem",control=saemControl(n.burn=50,n.em=100,print=50));
Compiling RxODE differential equations...done.
c:/Rtools/mingw_64/bin/g++ -I"c:/R/R-34~1.1/include" -DNDEBUG -I"d:/Compiler/gcc-4.9.3/local330/include" -Ic:/nlmixr/inst/include -Ic:/R/R-34~1.1/library/STANHE~1/include -Ic:/R/R-34~1.1/library/Rcpp/include -Ic:/R/R-34~1.1/library/RCPPAR~1/include -Ic:/R/R-34~1.1/library/RCPPEI~1/include -Ic:/R/R-34~1.1/library/BH/include -O2 -Wall -mtune=core2 -c saem3090757b4bd1x64.cpp -o saem3090757b4bd1x64.o
In file included from c:/R/R-34~1.1/library/RCPPAR~1/include/armadillo:52:0,
from c:/R/R-34~1.1/library/RCPPAR~1/include/RcppArmadilloForward.h:46,
from c:/R/R-34~1.1/library/RCPPAR~1/include/RcppArmadillo.h:31,
from saem3090757b4bd1x64.cpp:1:
c:/R/R-34~1.1/library/RCPPAR~1/include/armadillo_bits/compiler_setup.hpp:474:96: note: #pragma message: WARNING: use of OpenMP disabled; this compiler doesn't support OpenMP 3.0+
#pragma message ("WARNING: use of OpenMP disabled; this compiler doesn't support OpenMP 3.0+")
^
c:/Rtools/mingw_64/bin/g++ -shared -s -static-libgcc -o saem3090757b4bd1x64.dll tmp.def saem3090757b4bd1x64.o c:/nlmixr/R/rx_855815def56a50f0e7a80e48811d947c_x64.dll -Lc:/R/R-34~1.1/bin/x64 -lRblas -Lc:/R/R-34~1.1/bin/x64 -lRlapack -lgfortran -lm -lquadmath -Ld:/Compiler/gcc-4.9.3/local330/lib/x64 -Ld:/Compiler/gcc-4.9.3/local330/lib -Lc:/R/R-34~1.1/bin/x64 -lR
done.
1: 1.8174 4.6328 0.0553 0.0950 0.0950 0.0950 0.6357
50: 1.3900 4.2039 0.0001 0.0679 0.0784 0.1082 0.1992
100: 1.3894 4.2054 0.0107 0.0686 0.0777 0.1111 0.1981
150: 1.3885 4.2041 0.0089 0.0683 0.0778 0.1117 0.1980
Using sympy via SnakeCharmR
## Calculate ETA-based prediction and error derivatives:
Calculate Jacobian...................done.
Calculate sensitivities.......
done.
## Calculate d(f)/d(eta)
## ...
## done
## ...
## done
The model-based sensitivities have been calculated.
It will be cached for future runs.
Calculating Table Variables...
done
The options for saem
are controlled by saemControl
. You may wish to make sure the minimization is complete in the case of saem
. You can do that with traceplot
, which shows the iteration history with burn-in and EM phases. In this case, the burn-in seems reasonable; you may wish to increase the number of iterations in the EM phase of the estimation. Overall it is probably a semi-reasonable solution. (xpose.nlmixr
’s prm_vs_iteration()
can also do this.)
nlmixr
output objects
In addition to unifying the modeling language sent to each of the estimation routines, the outputs currently have a unified structure.
 The elements of the fit are:
- The type of fit (
nlme
,saem
, etc) Metrics of goodness of fit (
AIC
,BIC
, andlogLik
).- To align the comparison between methods, the FOCEi likelihood objective is calculated regardless of the method used and used for goodness of fit metrics.
- This FOCEi likelihood has been compared to NONMEM’s objective function and gives the same values (based on the data in Wang 2007 (???))
- Note that FOCEi is used to calculate the objective function for a
saem
fit. - Even though the objective functions are calculated in the same manner, caution should be used when comparing fits from different estimation routines.
The next item is the timing of each of the steps of the fit.
- These can be accessed by (
fit.s$time
). - As a mnemonic, the access for this item is shown in the printout. This is true for almost all of the other items in the printout.
After the timing of the fit, the parameter estimates are displayed (can be accessed by fit.s$par.fixed
- While the items are rounded for R printing, each estimate without rounding is still accessible by the
$
syntax. For example, the$Untransformed
gives the untransformed parameter values. - The Untransformed parameter takes log-space parameters and back-transforms them to normal parameters. Not the CIs are listed on the back-transformed parameter space.
Proportional Errors are converted to %CV on the untransformed space
Omega block (accessed by
fit.s$omega
)
A table of fit data is also supplied. Please note:
- An
nlmixr
fit object is actually a data frame. Saving it as a Rdata object and then loading it withoutnlmixr
will just show the data by itself. The additional fit information is still present, butnlmixr
must be loaded in order to see it. - Special access to fit information (like the
$omega
) needsnlmixr
to extract the information.
If you use the $
to access information, the order of precedence is:
- Fit data from the overall data.frame
- Information about the parsed
nlmixr
model (via$uif
) - Parameter history if available (via
$par.hist
and$par.hist.stacked
) - Fixed effects table (via
$par.fixed
) - Individual differences from the typical population parameters (via
$eta
) - Fit information from the list of information generated during the post-hoc residual calculation.
- Fit information from the environment where the post-hoc residual were calculated
- Fit information about how the data and options interacted with the specified model (such as estimation options or if the solved system is for an infusion or an IV bolus).
While the printout may display the data as a data.table
object or tbl
object, the data is NOT any of these objects, but rather a derived data frame. - Since the object **is*}** a data.frame
, you can treat it like one.
In addition to the above properties of the fit object, there are a few additional that may be helpful for the modeler:
$theta
gives the fixed effects parameter estimates (in NONMEM thetheta
s). This can also be accessed innlme
function. Note that the residual variability is treated as a fixed effect parameter and is included in this list.$eta
gives the random effects parameter estimates, or in NONMEM theeta
s. This can also be accessed in using therandom.effects
function.
3.1.2 Some examples
<IP_ADDRESS> A two-compartment PK model
Now let’s have a look at some examples to demonstrate the syntax. Here’s a relatively straightforward two-compartmental PK model with covariate effects.
my2CptModel <- function() {
ini({
tka <- log(1.14)
tcl <- log(0.0190)
tv2 <- log(2.12)
tv3 <- log(20.4)
tq <- log(0.383)
wteff <- 0.35
sexeff <- -0.2
eta.ka ~ 1
eta.cl ~ 1
eta.v2 ~ 1
eta.v3 ~ 1
eta.q ~ 1
prop.err <- 0.075
})
model({
ka <- exp(tka + eta.ka)
cl <- exp(tcl + wteff*lWT + eta.cl)
v2 <- exp(tv2 + sexeff*SEX + eta.v2)
v3 <- exp(tv3 + eta.v3)
q <- exp(tq + eta.q)
d/dt(depot) = -ka * depot
d/dt(center) = ka * depot - cl / v2 * center + q/v3 * periph - q/v2 * center
d/dt(periph) = q/v2 * center - q/v3 * periph
cp = center / v2
cp ~ prop(prop.err)
})
}
my2CptFit <- nlmixr(my2CptModel, dat, est="saem")
We will fit this model using SAEM, so we have included random effects on every model parameter. We have also chosen to use ODEs rather than linCmt()
.
Notice how covariates have been included. lWT
is pre-processed body weight (log(WT/70)
) and SEX
is a binary variable equal to 0 (male) or 1 (female).
Let’s inspect the results…
print(my2CptFit)
## -- nlmixr SAEM fit (ODE); OBJF calculated from FOCEi approximation ----------------------------------------------------------------------------------------------------------------------------------------------------
## OBJF AIC BIC Log-likelihood Condition Number
## 3178.892 3204.892 3255.411 -1589.446 650007.1
##
Objective function (OBJF), Akaike information criterion (AIC), Bayesian information criterion (BIC), the log-likelihood of the model given the data, and the condition number are provided at the top of the output block. This more, judging from the condition number (the ratio between the largest and smallest eigenvalues), may be over-parameterized. For this SAEM model, the OBJF has been calculated using the FOCEi method.
## -- Time (sec; $time): ---------------------------------------------------------------------------------------------------------------------------
## saem setup Likelihood Calculation covariance table
## elapsed 547.67 23.31 0.72 0 1.81
##
Next, we have a block showing benchmarks for the run. This fit took just under 10 minutes in total.
## -- Parameters ($par.fixed): ---------------------------------------------------------------------------------------------------------------------
## Estimate SE %RSE Back-transformed(95%CI) BSV(CV%)
## tka 0.136 0.0502 36.8 1.15 (1.04, 1.26) 29.9%
## tcl -2.08 0.0360 1.73 0.125 (0.116, 0.134) 22.5%
## tv2 0.700 0.0330 4.72 2.01 (1.89, 2.15) 23.0%
## tv3 1.69 0.00155 0.0919 5.42 (5.41, 5.44) 18.0%
## tq -1.22 0.000407 0.0332 0.294 (0.294, 0.294) 30.2%
## wteff 0.765 0.0379 4.96 2.15 (1.99, 2.31)
## sexeff -0.215 0.0493 23.0 0.807 (0.732, 0.889)
## prop.err 0.0726 7.26%
## Shrink(SD)%
## tka 13.6%
## tcl 1.78%
## tv2 10.1%
## tv3 20.8%
## tq 6.82%
## wteff
## sexeff
## prop.err 24.6%
##
## No correlations in between subject variability (BSV) matrix
## Full BSV covariance ($omega) or correlation ($omega.R; diagonals=SDs)
## Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink
##
The next block provides parameter estimates and associated precisions, between subject variability (BSV) where appropriate, and exponentiated versions of the parameter estimates together with 95% confidence intervals (“back-transformed”). (nlmixr
assumes that all parameters are log-transformed. Sometimes this is not the case, as illustrated here for wteff
and sexeff
.) Shrinkages are also supplied. The fit object has properties that can be interrogated:
my2CptFit$par.fixed
provides the fixed-effect parameter estimatesmy2CptFit$omega
provides the variance-covariance matrix for BSV ($OMEGA
)my2CptFit$omega.R
provides the correlation matrixmy2CptFit$shrink
provides shrinkages and other statistical descriptors of the parameter distributions
## -- Fit Data (object is a modified data.frame): --------------------------------------------------------------------------------------------------
## # A tibble: 360 x 28
## ID TIME DV SEX WT PRED RES WRES IPRED IRES IWRES
## * <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.302 190. 0. 52. 169. 21.4 0.375 191. -0.578 -0.0417
## 2 1 2.91 296. 0. 52. 389. -93.1 -1.35 329. -32.5 -1.36
## 3 1 3.14 312. 0. 52. 379. -66.7 -1.01 314. -2.27 -0.0996
## # ... with 357 more rows, and 17 more variables: CPRED <dbl>, CRES <dbl>,
## # CWRES <dbl>, eta.ka <dbl>, eta.cl <dbl>, eta.v2 <dbl>, eta.v3 <dbl>,
## # eta.q <dbl>, depot <dbl>, center <dbl>, periph <dbl>, ka <dbl>,
## # cl <dbl>, v2 <dbl>, v3 <dbl>, q <dbl>, cp <dbl>
Finally, a summary of the underlying tibble
(a modified data.frame
) is provided - this contains all parameter estimates and diagnostics.
<IP_ADDRESS> Physiologically-based PK
Building on the first simple example, we can be more ambitious, and try a full PBPK model. This one was published for mavoglurant (???).
pbpk <- function(){
ini({
lKbBR = 1.1
lKbMU = 0.3
lKbAD = 2
lCLint = 7.6
lKbBO = 0.03
lKbRB = 0.3
eta.LClint ~ 4
add.err <- 1
prop.err <- 10
})
model({
KbBR = exp(lKbBR)
KbMU = exp(lKbMU)
KbAD = exp(lKbAD)
CLint= exp(lCLint + eta.LClint)
KbBO = exp(lKbBO)
KbRB = exp(lKbRB)
## Regional blood flows
CO = (187.00*WT^0.81)*60/1000; # Cardiac output (L/h) from White et al (1968)
QHT = 4.0 *CO/100;
QBR = 12.0*CO/100;
QMU = 17.0*CO/100;
QAD = 5.0 *CO/100;
QSK = 5.0 *CO/100;
QSP = 3.0 *CO/100;
QPA = 1.0 *CO/100;
QLI = 25.5*CO/100;
QST = 1.0 *CO/100;
QGU = 14.0*CO/100;
QHA = QLI - (QSP + QPA + QST + QGU); # Hepatic artery blood flow
QBO = 5.0 *CO/100;
QKI = 19.0*CO/100;
QRB = CO - (QHT + QBR + QMU + QAD + QSK + QLI + QBO + QKI);
QLU = QHT + QBR + QMU + QAD + QSK + QLI + QBO + QKI + QRB;
## Organs' volumes = organs' weights / organs' density
VLU = (0.76 *WT/100)/1.051;
VHT = (0.47 *WT/100)/1.030;
VBR = (2.00 *WT/100)/1.036;
VMU = (40.00*WT/100)/1.041;
VAD = (21.42*WT/100)/0.916;
VSK = (3.71 *WT/100)/1.116;
VSP = (0.26 *WT/100)/1.054;
VPA = (0.14 *WT/100)/1.045;
VLI = (2.57 *WT/100)/1.040;
VST = (0.21 *WT/100)/1.050;
VGU = (1.44 *WT/100)/1.043;
VBO = (14.29*WT/100)/1.990;
VKI = (0.44 *WT/100)/1.050;
VAB = (2.81 *WT/100)/1.040;
VVB = (5.62 *WT/100)/1.040;
VRB = (3.86 *WT/100)/1.040;
## Fixed parameters
BP = 0.61; # Blood:plasma partition coefficient
fup = 0.028; # Fraction unbound in plasma
fub = fup/BP; # Fraction unbound in blood
KbLU = exp(0.8334);
KbHT = exp(1.1205);
KbSK = exp(-.5238);
KbSP = exp(0.3224);
KbPA = exp(0.3224);
KbLI = exp(1.7604);
KbST = exp(0.3224);
KbGU = exp(1.2026);
KbKI = exp(1.3171);
##-----------------------------------------
S15 = VVB*BP/1000;
C15 = Venous_Blood/S15
##-----------------------------------------
d/dt(Lungs) = QLU*(Venous_Blood/VVB - Lungs/KbLU/VLU);
d/dt(Heart) = QHT*(Arterial_Blood/VAB - Heart/KbHT/VHT);
d/dt(Brain) = QBR*(Arterial_Blood/VAB - Brain/KbBR/VBR);
d/dt(Muscles) = QMU*(Arterial_Blood/VAB - Muscles/KbMU/VMU);
d/dt(Adipose) = QAD*(Arterial_Blood/VAB - Adipose/KbAD/VAD);
d/dt(Skin) = QSK*(Arterial_Blood/VAB - Skin/KbSK/VSK);
d/dt(Spleen) = QSP*(Arterial_Blood/VAB - Spleen/KbSP/VSP);
d/dt(Pancreas) = QPA*(Arterial_Blood/VAB - Pancreas/KbPA/VPA);
d/dt(Liver) = QHA*Arterial_Blood/VAB + QSP*Spleen/KbSP/VSP + QPA*Pancreas/KbPA/VPA + QST*Stomach/KbST/VST + QGU*Gut/KbGU/VGU - CLint*fub*Liver/KbLI/VLI - QLI*Liver/KbLI/VLI;
d/dt(Stomach) = QST*(Arterial_Blood/VAB - Stomach/KbST/VST);
d/dt(Gut) = QGU*(Arterial_Blood/VAB - Gut/KbGU/VGU);
d/dt(Bones) = QBO*(Arterial_Blood/VAB - Bones/KbBO/VBO);
d/dt(Kidneys) = QKI*(Arterial_Blood/VAB - Kidneys/KbKI/VKI);
d/dt(Arterial_Blood) = QLU*(Lungs/KbLU/VLU - Arterial_Blood/VAB);
d/dt(Venous_Blood) = QHT*Heart/KbHT/VHT + QBR*Brain/KbBR/VBR + QMU*Muscles/KbMU/VMU + QAD*Adipose/KbAD/VAD + QSK*Skin/KbSK/VSK + QLI*Liver/KbLI/VLI + QBO*Bones/KbBO/VBO + QKI*Kidneys/KbKI/VKI + QRB*Rest_of_Body/KbRB/VRB - QLU*Venous_Blood/VVB;
d/dt(Rest_of_Body) = QRB*(Arterial_Blood/VAB - Rest_of_Body/KbRB/VRB);
C15 ~ add(add.err) + prop(prop.err)
})
}
This model estimates 6 structural parameters, 1 random effect, and includes combined additive and proportional residual error. The dependent variable, C15
, is venous blood concentration.
<IP_ADDRESS> Nonlinear PK
Complex models can be estimated as well. In the example below, a target-mediated drug disposition PK model for nimotuzumab is illustrated (???).
myPKPDModel <- function() {
ini({
tcl <- log(0.001)
tv1 <- log(1.45)
tQ <- log(0.004)
tv2 <- log(44)
tkss <- log(12)
tkint <- log(0.3)
tksyn <- log(1)
tkdeg <- log(7)
eta.cl ~ 2
eta.v1 ~ 2
eta.kss ~ 2
add.err <- 10
})
model({
cl <- exp(tcl + eta.cl)
v1 <- exp(tv1 + eta.v1)
Q <- exp(tQ)
v2 <- exp(tv2)
kss <- exp(tkss + eta.kss)
kint <- exp(tkint)
ksyn <- exp(tksyn)
kdeg <- exp(tkdeg)
k <- cl/v1
k12 <- Q/v1
k21 <- Q/v2
eff(0) <- ksyn/kdeg # initialize compartment
# Calculate concentration
conc = 0.5*(central/v1 - eff - kss) + 0.5*sqrt((central/v1 - eff - kss)**2 + 4*kss*central/v1)
d/dt(central) = -(k+k12)*conc*v1+k21*peripheral-kint*eff*conc*v1/(kss+conc)
d/dt(peripheral) = k12*conc*v1-k21*peripheral ##Free Drug second compartment amount
d/dt(eff) = ksyn - kdeg*eff - (kint-kdeg)*conc*eff/(kss+conc)
IPRED = log(conc)
IPRED ~ add(add.err)
})
}
<IP_ADDRESS> PKPD models
Finally, we present an implementation of the Friberg myelosuppression model (???). nlmixr
is capable of handling almost any kind of model that can be implemented using ODEs.
wbc <- function() {
ini({
log_CIRC0 <- log(7.21)
log_MTT <- log(124)
log_SLOPU <- log(28.9)
log_GAMMA <- log(0.239)
eta.CIRC0 ~ .1
eta.MTT ~ .03
eta.SLOPU ~ .2
prop.err <- 10
})
model({
CIRC0 = exp(log_CIRC0 + eta.CIRC0)
MTT = exp(log_MTT + eta.MTT)
SLOPU = exp(log_SLOPU + eta.SLOPU)
GAMMA = exp(log_GAMMA)
# PK parameters from input dataset
CL = CLI;
V1 = V1I;
V2 = V2I;
Q = 204;
CONC = A_centr/V1;
# PD parameters
NN = 3;
KTR = (NN + 1)/MTT;
EDRUG = 1 - SLOPU * CONC;
FDBK = (CIRC0 / A_circ)^GAMMA;
CIRC = A_circ;
A_prol(0) = CIRC0;
A_tr1(0) = CIRC0;
A_tr2(0) = CIRC0;
A_tr3(0) = CIRC0;
A_circ(0) = CIRC0;
d/dt(A_centr) = A_periph * Q/V2 - A_centr * (CL/V1 + Q/V1);
d/dt(A_periph) = A_centr * Q/V1 - A_periph * Q/V2;
d/dt(A_prol) = KTR * A_prol * EDRUG * FDBK - KTR * A_prol;
d/dt(A_tr1) = KTR * A_prol - KTR * A_tr1;
d/dt(A_tr2) = KTR * A_tr1 - KTR * A_tr2;
d/dt(A_tr3) = KTR * A_tr2 - KTR * A_tr3;
d/dt(A_circ) = KTR * A_tr3 - KTR * A_circ;
CIRC ~ prop(prop.err)
})
}
3.2 xpose.nlmixr
3.2.1 Introduction
xpose.nlmixr
was written to provide an interface between nlmixr
and the NONMEM-centric xpose
model diagnostics package developed by Ben Guiastrennec and colleagues at Uppsala University.
3.2.2 Getting started
To get started, first install the package using:
devtools::install_github("nlmixrdevelopment/xpose.nlmixr")
3.2.3 Overview
To leverage the full power of xpose
to provide diagnostics for an nlmixr
model, an xpose
object must be created from the nlmixr
fit.
xpdb <- xpose_data_nlmixr(nlmixrfit)
This xpose
object can now be used in the same way as any other - for full details, see the xpose
website at uupharmacometrics.github.io/xpose.
3.3 shinyMixR
3.3.1 Introduction
The nlmixr
package can be accessed through command line in R
and RStudio
, and through an interface shinyMixR
supported by the nlmixr
team. Both command line and interface support the integration with xpose.nlmixr
.
shinyMixR
aims to provide a user-friendly tool for nlmixr
based on Shiny
, which facilitates a workflow around an nlmixr
project. The shinydashboard
package was used to set up a structure for controlling and tracking runs with an nlmixr project, and was the basis for setting up the modular interface.
This project tool enhances the usability and attractiveness of nlmixr
, facilitating dynamic and interactive use in real-time for rapid model development. In this project-oriented structure, the command line and dashboard can be used independently and/or interdependently. This means that many of the functions within the package can also be used outside the interface within an interactive R session.
Using shinyMixR
, the user specifies and controls an nlmixr
project workflow entirely in R
. Within a project folder, a structure can be created to include separate folders for models, data and runs. Functionality is available to edit and execute a model code, summarize and compare model outputs in a tabular fashion, and view and compare model development using a tree paradigm. Inputs, outputs and metadata are stored in relation to the model code within the project structure (a discrete R object) to ensure traceability.
Results are visualized using modifications of existing packages (such as xpose.nlmixr
, user-written functions and packages, or pre-existing plotting functionality included in the shinyMixR
package. Results are reported using the R3port package in pdf and html format.
How the package can be used and what the most important functions are, is described in this part of the shinyMixR
pkgdown site.


About Me.
Career Goals
I want to use machine learning to help solve challenging, high impact problems in industries like energy, health, education, infrastructure and agriculture. I have a strong bias towards application over research, but I try to learn and understand some of the latest developments with the help of the amazing people in Machine Learning @ Berkeley.
Interests
I love dogs; I have a blue heeler or "Autralian Cattle Dog" named Buster and I used to have a border collie named Mischief. My childhood and family revolved/revolves around sports, and I love basketball, playing all my life until four hip operations put me out for good! My close friends and family mean the world to me and I can't wait to visit them soon.
Food
I love food, especially brunch and dinner in my home city of Melbourne, Australia. Nothing beats sweet potato, zucchini and corn fritters, goat cheese, beetroot hummus, smashed avocado, poached eggs and maybe some bacon or candied prosciutto. Brighton Soul Espresso, Little Ox and Auction Rooms are some of my favourite spots for coffee/brunch.


It was around 10:00 am. On a Saturday morning. I had partied the night before with new friends that lived in my apartment complex. I had done ecstacy the night before for only the second time, but I had taken all the nessecary precautions before going to bed (1 vitamin C, 2 Aspirin, and 1 Calcium tablet with a full glass of water). They always do me right.
That morning when I woke up my boyfriend was not at our home and so I figured he might be in the apartment above us, one of our new friends. Sure enough I knocked on the door and he was there, with another friend. They were on the internet, looking at porn. Our friend, we will call him Jim, he got nervous and thought I would be mad at what they were looking at, but I was fine, I told him it didn't bother me and I sat down and joined them.
My boyfriend sat next to me and told me that they were doing something new, something Jim did sometimes and shared with him today. He showed me and than offered me some. Jim called it Glass. He gave us more info on it, but at the time, he didn't even really know much about it. I ended up afterward finding more info on it on the internet. I like to know what I'm doing. My boyfriend and I have always experienced drugs together so I decided I would do some too.
Jim cut me a line about 1 inch long and rolled up a $20 dollar bill, which I found very distasteful. But I did it anyways, I put the rolled up bill in one nostril, Jim told me to close one nostril with my finger and also to breathe out all the air in my lungs and then snort quickly the line. I was really scared because I had never done this and it took me awhile to take that first giant leap!
When I did it, the first thing I felt was a blow to the head, then almost immediatly after I felt the burn, I felt like my nose was on fire, both my eyes watered profusely and my teeth began to clench! After about 10 seconds (felt more like 10 minutes) my nose became a little more accustomed to this newborn sting. Then what Jim called the 'drip' came. This was a slow meth, mucous and saliva mix that ran down the back of my throat and into my esophagus. It was very nasty, and actually partly gave me a gag like reflex, but I was able to calm it down with a large glass of water.
Jim told me to drink lots of water because this drug can dehydrate you. So I continued to drink water anytime my mouth felt dry, so that I didn't overhydrate myself. After about a few minutes, my mind was racing, I felt very much at peace and very talkative. I remained quiet that day so that I could experiance this high on a personal level. I pretty much just took everything in.
This high was not like marijuana, ecstacy, or mushrooms. It was a very peaceful and mind tingling experience. I did about 3 more lines the same size in a span of 6 hours. The day turned to early evening and my boyfriend and I came home, we were expecting guests over soon and we realized once we got home that we could not stop talking, about everything, the experience, the feeling, our thoughts.
We also noticed little things Jim called 'Tweaky twitches', like my boyfriend kept grabbing his goatee, I kept clenching my teeth, we kept moving around the room, excitably, and looking out the window even though no one was out there. We both were examining this new feeling openly, expressing how our minds were racing yet feeling competely focused and determined in thought.
I noticed I got very expressive emotionally and very affectionate with my partner, not sexually, just very 'clingy', alot of I love you's and alot of expressions of how much I appreciated him. We both felt like little children on a playground, talking about how fun it is to ride a fast rollercoaster. The rest of the evening was very delightful and insightful. I had no negative ordeals for the duration of the high, the high began to wear of several hours later, probably around 3 am that sunday to be approximate.
The only after effects I concluded happened several days later. I became irritable, moody, and plain old crabby. After learning more about glass, I realized that this drug depletes your serotonin, the recepter that basically takes care of emotional stability. I needed to take note of my hyper-sensitivity, and my moodiness. I also took plenty of vitamins and lots of sleep. This helped insure a better and emotionally healthier recovery. I took note to be easy on my self the following week, because most of us work for a living, this simple thing can cause much stress and become even harder after using this drug. Unlike some people, I did not find myself wanting more, I merely just enjoyed the time I had with it.
That was 1 year ago. I have done Glass occasionally since then. I no longer snort it for fear of serious damage to my nose, I smoke it. I have done 3 day long glass binges with friends, no eat, no sleep. Today, I can't imagine putting my self through something like that, payback was a serious b**ch. Now I occasionally smoke but for one nite only and then thats it. Smoking I have found it less harsh on my body. I can still eat, and still slepp when I want to and need to.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


vignettes/calculating_isochrones.Rmd
calculating_isochrones.Rmd
Abstract
This vignette shows how to calculate and visualize isochrones in R using ther5r
package.
An isochrone of a given place includes all the areas reachable from that place within a certain amount of time. This vignette shows how to calculate and visualize isochrones in R using the r5r
package.
In this reproducible example, we will be using a sample data set for the city of Porto Alegre (Brazil) included in r5r
. Our aim here is to calculate several isochrones departing from the central bus station given different travel time thresholds. We’ll do this in 4 quick steps:
Before we start, we need to increase the memory available to Java and load the packages used in this vignette.
setup_r5()
To build a routable transport network with r5r
and load it into memory, the user needs to call setup_r5
with the path to the directory where OpenStreetMap and GTFS data are stored.
# system.file returns the directory with example data inside the r5r package
# set data path to directory containing your own data if not using the examples
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path, verbose = FALSE)
In this example, we will be calculating the travel times by public transport from the central bus station in Porto Alegre to every other block in the city. With the code below we compute multiple travel time estimates departing every minute over a 120-minute time window, between 2pm and 4pm.
# read all points in the city
points <- fread(file.path(data_path, "poa_hexgrid.csv"))
# subset point with the geolocation of the central bus station
central_bus_stn <- points[291,]
# routing inputs
mode <- c("WALK", "TRANSIT")
max_walk_dist <- 1000 # in meters
max_trip_duration <- 120 # in minutes
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S")
time_window <- 120 # in minutes
percentiles <- 50
# calculate travel time matrix
ttm <- travel_time_matrix(r5r_core,
origins = central_bus_stn,
destinations = points,
mode = mode,
departure_datetime = departure_datetime,
max_walk_dist = max_walk_dist,
max_trip_duration = max_trip_duration,
time_window = time_window,
percentiles = percentiles,
verbose = FALSE)
head(ttm)
#> fromId toId travel_time
#> 1: 89a90128a8fffff 89a901291abffff 59
#> 2: 89a90128a8fffff 89a901295b7ffff 62
#> 3: 89a90128a8fffff 89a9012809bffff 52
#> 4: 89a90128a8fffff 89a901285cfffff 44
#> 5: 89a90128a8fffff 89a90e934d7ffff 49
#> 6: 89a90128a8fffff 89a90129b47ffff 36
Now we only need to organize the travel time matrix output ttm
and plot it on the map.
# extract OSM network
street_net <- street_network_to_sf(r5r_core)
# add coordinates of destinations to travel time matrix
ttm[points, on=c('toId' ='id'), `:=`(lon = i.lon, lat = i.lat)]
# interpolate estimates to get spatially smooth result
travel_times.interp <- with(na.omit(ttm), interp(lon, lat, travel_time)) %>%
with(cbind(travel_time=as.vector(z), # Column-major order
x=rep(x, times=length(y)),
y=rep(y, each=length(x)))) %>%
as.data.frame() %>% na.omit()
# find isochrone's bounding box to crop the map below
bb_x <- c(min(travel_times.interp$x), max(travel_times.interp$x))
bb_y <- c(min(travel_times.interp$y), max(travel_times.interp$y))
# plot
ggplot(travel_times.interp) +
geom_contour_filled(aes(x=x, y=y, z=travel_time), alpha=.8) +
geom_sf(data = street_net$edges, color = "gray55", size=0.1, alpha = 0.7) +
geom_point(aes(x=lon, y=lat, color='Central bus\nstation'),
data=central_bus_stn) +
scale_fill_viridis_d(direction = -1, option = 'B') +
scale_color_manual(values=c('Central bus\nstation'='black')) +
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
coord_sf(xlim = bb_x, ylim = bb_y) +
labs(fill = "travel time (minutes)", color='') +
theme_minimal() +
theme(axis.title = element_blank())
If you have any suggestions or want to report an error, please visit the package GitHub page.


---
layout: index
---
I'm a former Senior Technologist and a team leader at Alibaba Group with 6 years of native mobile development experience and 2 years experience in management. Now I'm living in the States with my family in the D.C. area. I have experience in building large-scale mobile applications with millions of user visits per day. I'm also skilled in C/C++ /Python/Full Stack web development. I'm passionate about studying new technology on all kinds of MOOC platforms. For now, I've been focusing on studying "Autonomous-Driving" and "Machine Learning for Trading" at Udacity. I'm currently seeking for new opportunities and can be reached through this website. Feel free to contact me.
我是一名80后程序员，2012硕士毕业于大连理工大学通信专业，进入阿里巴巴，5年半间辗转于无线事业部，淘点点，支付宝，口碑等部门，代码遍布于手机淘宝，支付宝，淘点点等App，2017年末离职，前往美帝成为"世界公民"
好读书，求甚解，对知识充满焦虑, 喜欢刷Cousera和各大MOOC平台，折腾各种技术，是一个高度自我驱动的人，记忆力中等偏下，必须用文字记录下所学所感，遂有此博客
典型的中国韭菜，追涨杀跌，常年投资亏损，却乐此不疲；信奉趋势交易，摒弃价值投资


Defer JavaScript
Configuration
The 'Defer JavaScript' filter is enabled by specifying:
- Apache:
ModPagespeedEnableFilters defer_javascript
- Nginx:
pagespeed EnableFilters defer_javascript;
in the configuration file.
Description
Warning: Defering JavaScript can often dramatically improve the rendering speed of a site, but careful evaluation is required to ensure the site continues to operate properly. The limitations are described below.
defer_javascript tries to defer JavaScript execution until page load.
It defers this by changing the type
and src
attributes of
Example
The effect of this filter can be observed on modpagespeed.com before and after rewriting.
Limitations
Parent variables accessed from an iframe
may be
undefined
when defer_javascript is used.
All JavaScript code is downloaded and executed serially, whereas without defer_javascript the browser tries to download JavaScripts in parallel as much as possible.
Calls to document.write
fail in cases where they span multiple
script
tags.
An example is:
  Without defer_javascript the
span
would have been
created inside the div
. With defer_javascript
the span
would have been created outside and after the
div
.
Any document.body.appendChild
calls will always get appended to
the bottom of the page.
Requirements
defer_javascript is supported for browser versions of chrome 15+, safari5+, Firefox3.6+ and IE9+.
Risks
This filter is considered high risk. JavaScript whose logic depends on the state of the DOM may behave differently when defer_javascript is turned on.
If JavaScript is written to expect user actions before the page is loaded
completely, such scripts will behave differently with
defer_javascript. For example, if an alert
is placed
at the start of the page, then with defer_javascript the
alert
would appear only after the page is loaded completely.
User actions that trigger events such as onclick
onkeypress
will not be handled until the page is rendered
completely.
Content in the page will reflow if visible elements are inserted in the page by JavaScript.
The data-pagespeed-no-defer
attribute can change the order of script
execution because those scripts using it are executed inline while those not
using it are deferred. This can cause errors if these scripts depend on each other
in any way.


I am currently a student at New York University's Center for Urban Science and Progress (CUSP).
I will be using this website mainly to host my projects for a summer course I am taking called "Mashups: Creating with APIs". Feel free to look at my projects as I develop them!


!stats
!stats <minigame> <username(s)> -flags
Parameters
The !stats command has two parameters: minigame, and username(s). Minigame is the minigame (ie skywars or bedwars) that the bot should get stats from. Username(s) is a space-separated list of usernames that the bot should fetch stats from.
Flags
With some minigames, you can specify a specific game mode to pull stats from. For bedwars, the format for one of these flags is -<number of teams>_<players per team> . In SkyWars, it would be -<team size>_<insane/normal (optional)> For example, the flag for Bedwars solo would be -eight_one , because there are eight teams in the game, with each team having one player. The flag for team insane skywars would be -team_insane .
Examples
For reference, here is a list of some example commands, along with a description with what exactly the command would result in.
- poll !stats bw parcerx Returns Bedwars stats for parcerx.
- poll !stats sw parcerx i41 Returns Skywars stats for parcerx and i41.
- poll !stats bw parcerx -eight_one Returns solo Bedwars stats for parcerx.
- poll !stats sw parcerx -team Returns team Skywars stats for parcerx.
- poll !stats sw parcerx i41 -solo_insane Returns solo insane Skywars stats for parcerx.


My Registration Form
First Name:
Last Name:
Email:
Password:
PW Confirm:
Birthday:
Male
Female
Other
A short description about yourself
Your favorite langauge
Javascript
Python
C#
Ruby


Pizza Palace
Build your own pizza:
Small
Medium
Large
Choose a crust, sauce, and cheese:
Crust:
Classic Crust
Thin Crust
Sauce:
Marinara
Pesto
Cheese:
Mozzerella
Cheddar
No Cheese
Choose any toppings you like (+ $1 each)
Meat:
Pepperoni
Sausage
Chicken
Vegetables:
Green Peppers
Artichoke Hearts
Zucchini
Spinach
Roasted Red Pepper
See my total
Your total is: $


|
|
| impl (group_module_ptr mod, std::string id, node_id origin) |
| |
|
| impl (group_module_ptr mod, std::string id) |
| |
| bool | enqueue (strong_actor_ptr sender, message_id mid, message content, execution_unit *host) override |
| | Enqueues a new message without forwarding stack to the channel.
|
| |
| bool | subscribe (strong_actor_ptr who) override |
| | Subscribes who to this group and returns true on success or false if who is already subscribed.
|
| |
| void | unsubscribe (const actor_control_block *who) override |
| | Unsubscribes who from this group.
|
| |
| actor | intermediary () const noexcept override |
| | Returns the intermediary actor for the group if applicable.
|
| |
| void | stop () override |
| | Stops any background actors or threads and IO handles.
|
| |
| virtual bool | subscribe (strong_actor_ptr who)=0 |
| | Subscribes who to this group and returns true on success or false if who is already subscribed.
|
| |
| virtual void | unsubscribe (const actor_control_block *who)=0 |
| | Unsubscribes who from this group.
|
| |
| virtual void | stop ()=0 |
| | Stops any background actors or threads and IO handles.
|
| |
|
actor_system & | system () const noexcept |
| | Returns the hosting system.
|
| |
|
group_module & | module () const noexcept |
| | Returns the parent module.
|
| |
|
node_id | origin () const noexcept |
| | Returns the origin node of the group if applicable.
|
| |
|
const std::string & | identifier () const |
| | Returns a string representation of the group identifier, e.g., "<IP_ADDRESS>" for IPv4 multicast or a user-defined string for local groups.
|
| |
| virtual std::string | stringify () const |
| | Returns a human-readable string representation of the group ID.
|
| |
| virtual actor | intermediary () const noexcept |
| | Returns the intermediary actor for the group if applicable.
|
| |
|
| ref_counted (const ref_counted &) |
| |
|
ref_counted & | operator= (const ref_counted &) |
| |
|
void | ref () const noexcept |
| | Increases reference count by one.
|
| |
|
void | deref () const noexcept |
| | Decreases reference count by one and calls request_deletion when it drops to zero.
|
| |
|
bool | unique () const noexcept |
| | Queries whether there is exactly one reference.
|
| |
|
size_t | get_reference_count () const noexcept |
| |
| virtual bool | enqueue (strong_actor_ptr sender, message_id mid, message content, execution_unit *host=nullptr)=0 |
| | Enqueues a new message without forwarding stack to the channel.
|
| |
|
bool | is_abstract_actor () const |
| |
|
bool | is_abstract_group () const |
| |
|
bool | is_actor_decorator () const |
| |
|
|
static constexpr int | is_abstract_actor_flag = 0x01000000 |
| |
|
static constexpr int | is_abstract_group_flag = 0x02000000 |
| |
|
static constexpr int | is_actor_bind_decorator_flag = 0x04000000 |
| |
|
static constexpr int | is_actor_dot_decorator_flag = 0x08000000 |
| |
|
static constexpr int | is_actor_decorator_mask = 0x0C000000 |
| |
|
static constexpr int | is_hidden_flag = 0x10000000 |
| |
| template<class T , class... Ts> |
| intrusive_cow_ptr< T > | make_copy_on_write (Ts &&... xs) |
| | Constructs an object of type T in an intrusive_cow_ptr .
|
| |
|
template<class T , class... Ts> |
| intrusive_ptr< T > | make_counted (Ts &&... xs) |
| | Constructs an object of type T in an intrusive_ptr .
|
| |
|
void | intrusive_ptr_add_ref (const ref_counted *p) |
| |
|
void | intrusive_ptr_release (const ref_counted *p) |
| |
Implementation of the group interface for instances of this module.


| We called to explain a problem | 11:21am on 24 May 2017 |
| Joined processing queue | 9:42am on 23 May 2017 |
| Application received | 10:39am on 22 May 2017 |
| Application submitted | 10:38am on 22 May 2017 |
{{< layout}}
{{$pageTitle}}Track a passport application{{/pageTitle}}
{{$content}}
There's a problem with your application.We've called you
We explained on the phone what has happened and what you need to do.
It's no longer possible for you to collect your new passport at your collection appointment. Your collection appointment has been cancelled.
| We called to explain a problem | 11:21am on 24 May 2017 |
| Joined processing queue | 9:42am on 23 May 2017 |
| Application received | 10:39am on 22 May 2017 |
| Application submitted | 10:38am on 22 May 2017 |
Other statuses (these do not appear on the page)


Folders
<Go Back>
02-type-s...uction
04-project
06-integration
01-getting-started
03-javasc...atures
05-diving-deeper
lib
Folders: 8


src/typedef.jsdoc
/**
* A dotted key is useful to quickly access a subset of data
* stored into the ObjectArray as if it was still a
* vanilla `Object`.
* Basically, a dotted key is constructed like a chain of property
* for an object.
*
* @typedef {String} dottedKey
*
* @example
* // Create an ObjectArray with subdata
* var oa = new ObjectArray({
* set1: {
* subset1: 'foo',
* subset2: {
* subsub1: 'bar',
* subsub2: 'baz'
* }
* }
* });
*
* oa.pull('set1.subset1'); // returns 'foo'
* oa.pull('set1.subset1.subsub2'); // returns 'baz'
*
*/
/**
* The function passed as a callback in a {@link forEach} iteration.
*
* Four arguments will be provided to the callback :
* 1. `value` {{@link Object}} : Value stored in the current iteration key
* 2. `key` {{@link String}} : Current key in iteration
* 3. `index` {{@link Object}} : Current index in iteration
* 4. `path` {{@link Object}|{@link undefined}} : Path to the key
* It will be `undefined` if the key is at first level
*
* @typedef {Function} ForEachCallback
*/
/**
* The function passed as a callback in a {@link reduce} iteration.
*
* Four arguments will be provided to the callback :
* 1. `acc` {{@link Object}} : Recursive accumulator value
* 2. `value` {{@link Object}} : Value stored in the current iteration key
* 2. `key` {{@link String}} : Current key in iteration
* 4. `path` {{@link Object}|{@link undefined}} : Path to the key
* It will be `undefined` if the key is at first level
*
* @typedef {Function} ReducerCallback
*/
/**
* Returned object by getset
*
* @typedef {Object} GetSetObject
* @property {number|string|array|object} get Value returned by the getter
* @property {boolean} set `true` if setter mode or `false`
*/


THE NINTH GATE A film review by Christopher Null Copyright 2000 filmcritic.com filmcritic.com
What is THE NINTH GATE? Judging from the cryptic marketing campaign, you might be likely to dismiss it as another ridiculous action movie, with big fireballs and car chase scenes. Or worse, maybe you'll shun it as a metaphysical adventure -- yet another END OF DAYS.
Fortunately, THE NINTH GATE is neither of these. In actuality, it's a mystery with Johnny Depp as the unlikely hero, Frank Langella as the perfectly-cast antagonist, and Lena Olin and Emmanuelle Seigner as the femmes fatale. Under the direction of Roman Polanski, you can rest assured that these characters get mixed up quite a bit en route through a serpentine plot that is far more interesting than its subject matter would imply: The search for a couple of rare books.
As Polanski's first movie since 1994's DEATH AND THE MAIDEN, the auteur has a lot to make up for in lost time. Apparently striking into more commercial territory, THE NINTH GATE offers Depp as a chain-smoking, rare-book-finding mercenary. Employed by Langella's publishing magnate, he is tasked with hunting down the two additional copies of "The Nine Gates," a book also owned by Langella's character and which he wishes to authenticate.
Mystery ensues, largely owing to the subject matter of "The Nine Gates," which is purported to be a manual for summoning the devil. Now if only a couple of these books weren't forgeries....
An awful lot of THE NINTH GATE (based on the book "The club Dumas") is better than you'd expect, but a lot of its potential is wasted on repetitious scenes and a meandering storyline (the film runs about 2 1/4 hours). The fistfight scenes are weak, the car chases dull. The ending is particularly annoying, essentially leaving the interpretation up to the audience (and thus making it unable for loudmouth critics to spoil).
However, Depp and Langella are fantastic, playing off one another with demonic flair. Imagine Depp's SLEEPY HOLLOW character with a personality and you've got him spot-on. Seigner (aka Mrs. Roman Polanski), whom you might recognize from Polanski's BITTER MOON and FRANTIC, is starting to lose her credibility as a sex goddess, I will note.
Still, despite a little snickering in the audience, Polanski still pulls enough tricks out of his sleeve to recommend this film. It certainly isn't CHINATOWN, but it does carry a punch.
RATING: ***1/2
|------------------------------| \ ***** Perfection \ \ **** Good, memorable film \ \ *** Average, hits and misses \ \ ** Sub-par on many levels \ \ * Unquestionably awful \ |------------------------------|
MPAA Rating: R
Director: Roman Polanski Producer: Roman Polanski Writer: John Brownjohn, Roman Polanski, Enrique Urbizu Starring: Johnny Depp, Frank Langella, Lena Olin, Emmanuelle Seigner, Barbara Jefford
http://www.ninthgate.com/
--- Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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{% extends "main.html" %} {% block tabs %} {{ super() }} An NPC / Citizen-like mod for Fabric / Forge modloader. Fabric: Forge: Installation Getting started {% endblock %} {% block content %} {% endblock %} {% block footer %}{% endblock %}
An NPC / Citizen-like mod for Fabric / Forge modloader.
Installation Getting started


| HADOOP-8176 |
Disambiguate the destination of FsShell copies |
Major |
fs |
Daryn Sharp |
Daryn Sharp |
| HADOOP-8146 |
FsShell commands cannot be interrupted |
Major |
fs |
Daryn Sharp |
Daryn Sharp |
| HADOOP-8140 |
dfs -getmerge should process its argments better |
Major |
. |
arkady borkovsky |
Daryn Sharp |
| HADOOP-8137 |
Site side links for commands manual (MAPREDUCE-3497) |
Major |
documentation |
Vinod Kumar Vavilapalli |
Thomas Graves |
| HADOOP-8123 |
hadoop-project invalid pom warnings prevent transitive dependency resolution |
Critical |
build |
Jonathan Eagles |
Jonathan Eagles |
| HADOOP-8083 |
javadoc generation for some modules is not done under target/ |
Major |
build |
Alejandro Abdelnur |
Alejandro Abdelnur |
| HADOOP-8082 |
add hadoop-client and hadoop-minicluster to the dependency-management section |
Major |
build |
Alejandro Abdelnur |
Alejandro Abdelnur |
| HADOOP-8074 |
Small bug in hadoop error message for unknown commands |
Trivial |
scripts |
Eli Collins |
Colin Patrick McCabe |
| HADOOP-8066 |
The full docs build intermittently fails |
Major |
build |
Aaron T. Myers |
Andrew Bayer |
| HADOOP-8064 |
Remove unnecessary dependency on w3c.org in document processing |
Major |
build |
Kihwal Lee |
Kihwal Lee |
| HADOOP-8057 |
hadoop-setup-conf.sh not working because of some extra spaces. |
Major |
scripts |
Vinayakumar B |
Vinayakumar B |
| HADOOP-8051 |
HttpFS documentation it is not wired to the generated site |
Major |
documentation |
Alejandro Abdelnur |
Alejandro Abdelnur |
| HADOOP-8050 |
Deadlock in metrics |
Major |
metrics |
Kihwal Lee |
Kihwal Lee |
| HADOOP-8046 |
Revert StaticMapping semantics to the existing ones, add DNS mapping diagnostics in progress |
Minor |
. |
Steve Loughran |
Steve Loughran |
| HADOOP-8042 |
When copying a file out of HDFS, modifying it, and uploading it back into HDFS, the put fails due to a CRC mismatch |
Critical |
fs |
Kevin J. Price |
Daryn Sharp |
| HADOOP-8036 |
TestViewFsTrash assumes the user’s home directory is 2 levels deep |
Major |
fs, test |
Eli Collins |
Colin Patrick McCabe |
| HADOOP-8035 |
Hadoop Maven site is inefficient and runs phases redundantly |
Minor |
build |
Andrew Bayer |
Andrew Bayer |
| HADOOP-7874 |
native libs should be under lib/native/ dir |
Major |
build |
Alejandro Abdelnur |
Alejandro Abdelnur |
| HADOOP-7660 |
Maven generated .classpath doesnot includes “target/generated-test-source/java” as source directory. |
Minor |
build |
Laxman |
Laxman |
| HADOOP-6502 |
DistributedFileSystem#listStatus is very slow when listing a directory with a size of 1300 |
Critical |
util |
Hairong Kuang |
Sharad Agarwal |
| HDFS-3853 |
Port MiniDFSCluster enableManagedDfsDirsRedundancy option to branch-2 |
Minor |
namenode |
Colin Patrick McCabe |
Colin Patrick McCabe |
| HDFS-3160 |
httpfs should exec catalina instead of forking it |
Major |
scripts |
Roman Shaposhnik |
Roman Shaposhnik |
| HDFS-3101 |
cannot read empty file using webhdfs |
Major |
webhdfs |
Zhanwei Wang |
Tsz Wo Nicholas Sze |
| HDFS-3032 |
Lease renewer tries forever even if renewal is not possible |
Major |
hdfs-client |
Kihwal Lee |
Kihwal Lee |
| HDFS-3012 |
Exception while renewing delegation token |
Critical |
. |
Ramya Sunil |
Robert Joseph Evans |
| HDFS-3008 |
Negative caching of local addrs doesn’t work |
Major |
hdfs-client |
Eli Collins |
Eli Collins |
| HDFS-3006 |
Webhdfs “SETOWNER” call returns incorrect content-type |
Major |
webhdfs |
bc Wong |
Tsz Wo Nicholas Sze |
| HDFS-2969 |
ExtendedBlock.equals is incorrectly implemented |
Critical |
datanode |
Todd Lipcon |
Todd Lipcon |
| HDFS-2950 |
Secondary NN HTTPS address should be listed as a NAMESERVICE_SPECIFIC_KEY |
Minor |
namenode |
Todd Lipcon |
Todd Lipcon |
| HDFS-2944 |
Typo in hdfs-default.xml causes dfs.client.block.write.replace-datanode-on-failure.enable to be mistakenly disabled |
Major |
hdfs-client |
Aaron T. Myers |
Aaron T. Myers |
| HDFS-2938 |
Recursive delete of a large directory makes namenode unresponsive |
Major |
namenode |
Suresh Srinivas |
Hari Mankude |
| HDFS-2815 |
Namenode is not coming out of safemode when we perform ( NN crash + restart ) . Also FSCK report shows blocks missed. |
Critical |
namenode |
Uma Maheswara Rao G |
Uma Maheswara Rao G |
| HDFS-2764 |
TestBackupNode is racy |
Major |
namenode, test |
Aaron T. Myers |
Aaron T. Myers |
| HDFS-2725 |
hdfs script usage information is missing the information about “dfs” command |
Major |
hdfs-client |
Prashant Sharma |
|
| HDFS-2525 |
Race between BlockPoolSliceScanner and append |
Critical |
datanode |
Todd Lipcon |
Brandon Li |
| HDFS-776 |
Fix exception handling in Balancer |
Critical |
balancer & mover |
Owen O’Malley |
Uma Maheswara Rao G |
| MAPREDUCE-4061 |
RM only has 1 AM launcher thread |
Blocker |
mrv2 |
Thomas Graves |
Thomas Graves |
| MAPREDUCE-4043 |
Secret keys set in Credentials are not seen by tasks |
Blocker |
mrv2, security |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-4034 |
Unable to view task logs on history server with mapreduce.job.acl-view-job=* |
Blocker |
mrv2 |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-4025 |
AM can crash if task attempt reports bogus progress value |
Blocker |
mr-am, mrv2 |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-4006 |
history server container log web UI sometimes combines stderr/stdout/syslog contents together |
Major |
jobhistoryserver, mrv2 |
Jason Lowe |
Siddharth Seth |
| MAPREDUCE-4005 |
AM container logs URL is broken for completed apps when log aggregation is enabled |
Major |
mrv2 |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-3982 |
TestEmptyJob fails with FileNotFound |
Critical |
mrv2 |
Robert Joseph Evans |
Robert Joseph Evans |
| MAPREDUCE-3977 |
LogAggregationService leaks log aggregator objects |
Critical |
mrv2, nodemanager |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-3976 |
TestRMContainerAllocator failing |
Major |
mrv2 |
Bikas Saha |
Jason Lowe |
| MAPREDUCE-3975 |
Default value not set for Configuration parameter mapreduce.job.local.dir |
Blocker |
mrv2 |
Eric Payne |
Eric Payne |
| MAPREDUCE-3964 |
ResourceManager does not have JVM metrics |
Critical |
mrv2, resourcemanager |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-3961 |
Map/ReduceSlotMillis computation incorrect |
Major |
mrv2 |
Siddharth Seth |
Siddharth Seth |
| MAPREDUCE-3960 |
web proxy doesn’t forward request to AM with configured hostname/IP |
Critical |
mrv2 |
Thomas Graves |
Thomas Graves |
| MAPREDUCE-3954 |
Clean up passing HEAPSIZE to yarn and mapred commands. |
Blocker |
mrv2 |
Robert Joseph Evans |
Robert Joseph Evans |
| MAPREDUCE-3931 |
MR tasks failing due to changing timestamps on Resources to download |
Major |
mrv2 |
Vinod Kumar Vavilapalli |
Siddharth Seth |
| MAPREDUCE-3930 |
The AM page for a Reducer that has not been launched causes an NPE |
Critical |
mrv2 |
Robert Joseph Evans |
Robert Joseph Evans |
| MAPREDUCE-3929 |
output of mapred -showacl is not clear |
Major |
mrv2 |
John George |
John George |
| MAPREDUCE-3920 |
Revise yarn default port number selection |
Major |
nodemanager, resourcemanager |
Dave Thompson |
Dave Thompson |
| MAPREDUCE-3918 |
proc_historyserver no longer in command line arguments for HistoryServer |
Major |
mrv2 |
Jonathan Eagles |
Jonathan Eagles |
| MAPREDUCE-3913 |
RM application webpage is unresponsive after 2000 jobs |
Critical |
mrv2, webapps |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-3910 |
user not allowed to submit jobs even though queue -showacls shows it allows |
Blocker |
mrv2 |
John George |
John George |
| MAPREDUCE-3904 |
[NPE] Job history produced with mapreduce.cluster.acls.enabled false can not be viewed with mapreduce.cluster.acls.enabled true |
Major |
mrv2 |
Jonathan Eagles |
Jonathan Eagles |
| MAPREDUCE-3903 |
no admin override to view jobs on mr app master and job history server |
Critical |
mrv2 |
Thomas Graves |
Thomas Graves |
| MAPREDUCE-3897 |
capacity scheduler - maxActiveApplicationsPerUser calculation can be wrong |
Critical |
mrv2 |
Thomas Graves |
Eric Payne |
| MAPREDUCE-3896 |
pig job through oozie hangs |
Blocker |
jobhistoryserver, mrv2 |
John George |
Vinod Kumar Vavilapalli |
| MAPREDUCE-3884 |
PWD should be first in the classpath of MR tasks |
Critical |
mrv2 |
Alejandro Abdelnur |
Alejandro Abdelnur |
| MAPREDUCE-3878 |
Null user on filtered jobhistory job page |
Critical |
mrv2 |
Jonathan Eagles |
Jonathan Eagles |
| MAPREDUCE-3866 |
bin/yarn prints the command line unnecessarily |
Minor |
mrv2 |
Vinod Kumar Vavilapalli |
Vinod Kumar Vavilapalli |
| MAPREDUCE-3862 |
Nodemanager can appear to hang on shutdown due to lingering DeletionService threads |
Major |
mrv2, nodemanager |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-3852 |
test TestLinuxResourceCalculatorPlugin failing |
Blocker |
mrv2 |
Thomas Graves |
Thomas Graves |
| MAPREDUCE-3816 |
capacity scheduler web ui bar graphs for used capacity wrong |
Critical |
mrv2 |
Thomas Graves |
Thomas Graves |
| MAPREDUCE-3792 |
job -list displays only the jobs submitted by a particular user |
Critical |
mrv2 |
Ramya Sunil |
Jason Lowe |
| MAPREDUCE-3790 |
Broken pipe on streaming job can lead to truncated output for a successful job |
Major |
contrib/streaming, mrv2 |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-3738 |
NM can hang during shutdown if AppLogAggregatorImpl thread dies unexpectedly |
Critical |
mrv2, nodemanager |
Jason Lowe |
Jason Lowe |
| MAPREDUCE-3706 |
HTTP Circular redirect error on the job attempts page |
Critical |
mrv2 |
Thomas Graves |
Robert Joseph Evans |
| MAPREDUCE-3687 |
If AM dies before it returns new tracking URL, proxy redirects to http://N/A/ and doesn’t return error code |
Major |
mrv2 |
David Capwell |
Ravi Prakash |
| MAPREDUCE-3686 |
history server web ui - job counter values for map/reduce not shown properly |
Critical |
mrv2 |
Thomas Graves |
Bhallamudi Venkata Siva Kamesh |
| MAPREDUCE-3680 |
FifoScheduler web service rest API can print out invalid JSON |
Major |
mrv2 |
Thomas Graves |
|
| MAPREDUCE-3634 |
All daemons should crash instead of hanging around when their EventHandlers get exceptions |
Major |
mrv2 |
Vinod Kumar Vavilapalli |
Vinod Kumar Vavilapalli |
| MAPREDUCE-3614 |
finalState UNDEFINED if AM is killed by hand |
Major |
mrv2 |
Ravi Prakash |
Ravi Prakash |
| MAPREDUCE-3583 |
ProcfsBasedProcessTree#constructProcessInfo() may throw NumberFormatException |
Critical |
. |
Ted Yu |
Ted Yu |
| MAPREDUCE-3497 |
missing documentation for yarn cli and subcommands - similar to commands_manual.html |
Major |
documentation, mrv2 |
Thomas Graves |
Thomas Graves |
| MAPREDUCE-3034 |
NM should act on a REBOOT command from RM |
Critical |
mrv2, nodemanager |
Vinod Kumar Vavilapalli |
Devaraj K |
| MAPREDUCE-3009 |
RM UI -> Applications -> Application(Job History) -> Map Tasks -> Task ID -> Node link is not working |
Major |
jobhistoryserver, mrv2 |
chackaravarthy |
chackaravarthy |
| MAPREDUCE-2855 |
ResourceBundle lookup during counter name resolution takes a lot of time |
Major |
. |
Todd Lipcon |
Siddharth Seth |
| MAPREDUCE-2793 |
[MR-279] Maintain consistency in naming appIDs, jobIDs and attemptIDs |
Critical |
mrv2 |
Ramya Sunil |
Bikas Saha |


--- layout: null section-type: trailer title: Trailer --- # The story of the notorious 1992 Tom Monfils murder investigation and the townsfolk defined by it.


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