A phased release, or rollout, is a process where an app update is released to customers in stages instead of all at once. This is useful in several ways:
Gauge how customers respond to an app update and respond accordingly
Backend processes or infrastructure can be scaled intelligently as adoption ramps up
If something very unexpected happens, the release can be paused for triage leaving only X% of customers affected instead of everyone
While this feature is both available in the iOS and Android world, it’s not done automatically for the Androids…
How the App Store (iOS) phased releases work?
When you enable a phased release in the App Store your app update is gradually released to customers with automatic updates turned on.
Day
Release Percentage
Day 1
1%
Day 2
2%
Day 3
5%
Day 4
10%
Day 5
20%
Day 6
50%
Day 7
100%
This is handled by Apple automactically, meaning that you don’t have to login and update them manually every day, and that’s exactly the case for the Android Store. In order to address this problem we are going to use the following:
#1 Jenkins or whatever CI you’re using, we need something that can guarantee us that the script we are going to write is going to run every day (cronjob will do the same thing)
#2 A simple ruby script that will take care of checking what is the current stage of the release and updating it to the next one accordingly
Here’s the ruby script I wrote in order to achieve #2, I am going to comment most of the lines so it’s obvious what’s happening
Once you have this script you can run it every day in your CI or a cronjob, using the following


Case Number Layot Details
Case Refarence No :
hget-12345
Applicant First Name :
sandip
Applicant Mid Name :
raj
Applicant Last Name :
kumar
Applicant ID :
545376
Applicant Dob :
Engineer
Applicant SSN :
hget-12345
Is Rush Order :
yes
Order Creation Date :
20/2/2019
Expected Closure Date :
20/3/2019
Maiden Name :
priyaAgarwal
Original OCD :
04/8/2020
Time zone :
0:0
Client Name :
Hireright
Source Name :
sandip
LOB :
hget-12345
Contact Number :
9887487647
Criminal Case :
Verified
Provided
Upload File


Caravans wallop Gymkhana in DRCC Caravans T20 Cup
by By Guardian ReporterCARAVANS’ cricketers walloped Gymkhana by 41 runs when the two sides met in this season’s DRCC Caravans T20 tournament in Dar es Salaam on Saturday.
Caravans made no secret of their intention to amass an imposing total in the Group A’s clash, once they won the toss and chose to start batting, given they scored 178 runs for the loss of five wickets in 20 overs.
Impressive batting displays by opener Ivan Ismail was instrumental in Caravans’ ability to record the huge score, given the talented player notched 84 runs, which consisted of five fours and as many sixes.
His impressive showing at the crease, eventually, won him man of the match prize.
Mohamed Omary, who opened the innings with Ismail, also had no mercy for Gymkhana bowlers, blasting 42 runs, which included two fours.
There was also meaningful contribution with the bat from Jitin Singh and Sreejith Kumar that notched 20 runs and 15 runs respectively.
Abhik Patwa was Gymkhana’s only bowler with impressive spell, given he took four wickets for 26 runs in four overs.
Needing 179 runs to win, Gymkhana started the chase in an impressive fashion, given opening batsmen Yakesh Patel and Patwa forged a solid partnership, posting 50 runs and 25 runs respectively.
Patel, who ended as the side’s top run getter, blasted five sixes during his spell at the crease, Patwa’s score included two fours and as many sixes.
Vishal Patel, batting at number three, improved Gymkhana’s chase with his 45 runs that consisted of a boundary and four sixes.
They experienced batting collapse after Patel’s dismissal, frustrating their efforts to realize successful chase in the process.
Yash Hirwania led Caravans’ bowling onslaught, finishing his spell with four wickets, conceding one run in four overs.
He was ably assisted by Jitin Singh and Gokul Das, who ended their respective spells with two wickets apiece.
Singh leaked 23 runs in four overs, Das leaked 16 runs in four overs
It was a second win in a row for Caravans, the competition’s defending champions, given they opened their campaign with 123-run rout of Union Sports Club.
Caravans Club has organized the competition under the supervision of Dar es Salaam Regional Cricket Committee (DRCC), aiming at developing the sport at the domestic level.
The competition’s sponsors include Petrofuel, Alliance Insurance, Grand Restaurant, Colourflex, RAS Logistics and SBC through Pepsi brand.
This year’s tournament has brought together 10 teams that have been placed in two groups.
Group A has been made up of Union, Caravans, Gymkhana, Aga Khan SC and Team 10. Group B outfits include Aces, Shree Kutchi Leva, GP, Estim and Saint Gobain.


remnant
: remnant in ellipsis
The remnant
relation is used to provide a satisfactory treatment of ellipsis (in
the case of gapping and stripping, where a predicational or verbal
head gets elided) without having to postulate empty nodes in the basic representation. This is something that was lacking in earlier versions
of SD and provides a basis for being able to reconstruct dependencies
in the enhanced representation of SD.
USD adopts an analysis that notes that in ellipsis a remnant
corresponds to a correlate in a preceding clause. The remnant
relation connects each remnant to its correlate in the basic dependency representation. This is then a sufficient representation to reconstruct the predicate-argument structure in the enhanced representation.
Marie went to Paris and Miriam to Prague
nsubj(went-2, Marie-1)
root(root-0, went-2)
nmod(went-2, Paris-4)
case(Paris-4, to-3)
cc(went-2, and-5)
remnant(Marie-1, Miriam-6)
case(Prague-8, to-7)
remnant(Paris-4, Prague-8)
Even in the more complex example below, the remnant
relations enable us to correctly retrieve the subjects and objects in
the clauses with an elided verb.
John won bronze , Mary silver , and Sandy gold
nsubj(won-2, John-1)
dobj(won-2, bronze-3)
remnant(John-1, Mary-5)
remnant(Mary-5, Sandy-9)
remnant(bronze-3, silver-6)
remnant(silver-6, gold-10)
Note in particular that (unlike for conj), remnant
uses a chaining analysis where each subsequent remnant depends on the immediately preceding remnant/correlate. The reason for this is that otherwise in a sentence with 2 or more chained ellipses the dependency structure would no longer track which remnants go together. It would become impossible to determine whether Mary won silver and Sandy gold, or Mary won gold and Sandy silver.
Instances of stripping typically occur when there is only one argument in the second clause, but with an accompanying adverbial modifier such as not or only. We model these sentences with the remnant relation as well.
Marie went to Paris , not Miriam
nsubj(went-2, Marie-1)
root(root-0, went-2)
nmod(went-2, Paris-4)
case(Paris-4, to-3)
remnant(Marie-1, Miriam-7)
neg(Miriam-7, not-6)
Marie did go to Europe , but only to Paris .
nsubj(go-3, Marie-1)
aux(go-3, did-2)
root(root-0, go-3)
case(Europe-5, to-4)
nmod(go-3, Europe-5)
cc(go-3, but-7)
advmod(Paris-10, only-8)
case(Paris-10, to-9)
remnant(Europe-5, Paris-10)
Sometimes in these constructions adverbials will be “sprouted”, and have no correlate in the precedeing clause. In such a situation, the adverbial should attach to one of the remnants; in principle it shouldn’t matter which remnant it attaches to, since all remnants at a particular depth of embedding point back to the same semantic event (which the adverbial is a part of). However, to enforce a regular system, the adverbial should depend on the nearest leftmost dependent.
Mary will come today and Tom tomorrow , if he finds a ticket .
nsubj(come-3, Mary-1)
aux(come-3, will-2)
root(root-0, come-3)
advmod(come-3, today-4)
cc(come-3, and-5)
remnant(Mary-1, Tom-6)
remnant(today-4, tomorrow-7)
mark(finds-11, if-9)
nsubj(finds-11, he-10)
advcl(tomorrow-7, finds-11)
det(ticket-13, a-12)
dobj(finds-11, ticket-13)
Mary will come today and , if he finds a ticket , Tom tomorrow .
nsubj(come-3, Mary-1)
aux(come-3, will-2)
root(root-0, come-3)
advmod(come-3, today-4)
cc(come-3, and-5)
mark(finds-9, if-7)
nsubj(finds-9, he-8)
advcl(Tom-13, finds-9)
det(ticket-11, a-10)
dobj(finds-9, ticket-11)
remnant(Mary-1, Tom-13)
remnant(today-4, tomorrow-14)
The remnant
relation is used when no predicational material is present. In contrast, in right-node-raising (RNR) and VP-ellipsis constructions in which some kind of predicational or verbal material is still present, the remnant
relation is not used. In RNR, the verbs are coordinated and the object is a dobj of the first verb:
John bought and ate an apple
nsubj(bought-2, John-1)
cc(bought-2, and-3)
conj(bought-2, ate-4)
det(apple-6, an-5)
dobj(bought-2, apple-6)
In VP-ellipsis, we keep the auxiliary as the head, as shown below:
John will win gold and Mary will too
nsubj(win-3, John-1)
aux(win-3, will-2)
dobj(win-3, gold-4)
cc(win-3, and-5)
conj(win-3, will-7)
nsubj(will-7, Mary-6)
advmod(will-7, too-8)
Treebank Statistics (UD_English-ESL)
This relation is universal.
5 nodes (0%) are attached to their parents as remnant
.
5 instances of remnant
(100%) are left-to-right (parent precedes child).
Average distance between parent and child is 5.2.


|
Groovy JDK |
| Method Summary | |
|---|---|
boolean
|
addAll(Object[] items)
Modifies the collection by adding all of the elements in the specified array to the collection. |
boolean
|
asBoolean()
Coerce a collection instance to a boolean value. |
Collection
|
asImmutable()
A convenience method for creating an immutable Collection. |
List
|
asList()
Converts this Collection to a List. |
Collection
|
asSynchronized()
A convenience method for creating a synchronized Collection. |
Object
|
asType(Class clazz)
Converts the given collection to another type. |
List
|
collect(Closure transform)
Iterates through this collection transforming each entry into a new value using the transform closure
returning a list of transformed values.
|
List
|
collect()
Iterates through this collection transforming each entry into a new value using Closure.IDENTITY as a transformer, basically returning a list of items copied from the original collection. |
Collection
|
collect(Collection collector, Closure transform)
Iterates through this collection transforming each value into a new value using the transform closure
and adding it to the supplied collector .
|
List
|
collectAll(Closure transform)
Deprecated alias for collectNested |
Collection
|
collectAll(Collection collector, Closure transform)
Deprecated alias for collectNested |
Map
|
collectEntries(Closure transform)
Iterates through this Collection transforming each item using the transform closure
and returning a map of the resulting transformed entries.
|
Map
|
collectEntries()
A variant of collectEntries for Collections using the identity closure as the transform. |
Map
|
collectEntries(Map collector, Closure transform)
Iterates through this Collection transforming each item using the closure as a transformer into a map entry, returning the supplied map with all of the transformed entries added to it. |
Map
|
collectEntries(Map collector)
A variant of collectEntries for Collections using the identity closure as the transform and a supplied map as the destination of transformed entries. |
List
|
collectMany(Closure projection)
Projects each item from a source collection to a collection and concatenates (flattens) the resulting collections into a single list. |
Collection
|
collectMany(Collection collector, Closure projection)
Projects each item from a source collection to a result collection and concatenates (flattens) the resulting collections adding them into the collector .
|
List
|
collectNested(Closure transform)
Recursively iterates through this collection transforming each non-Collection value into a new value using the closure as a transformer. |
Collection
|
collectNested(Collection collector, Closure transform)
Recursively iterates through this collection transforming each non-Collection value into a new value using the transform closure.
|
List
|
combinations()
Adds GroovyCollections#combinations(Collection) as a method on collections. |
boolean
|
containsAll(Object[] items)
Returns true if this collection contains all of the elements in the specified array. |
Number
|
count(Object value)
Counts the number of occurrences of the given value inside this collection. |
Number
|
count(Closure closure)
Counts the number of occurrences which satisfy the given closure from inside this collection. |
Map
|
countBy(Closure closure)
Sorts all collection members into groups determined by the supplied mapping closure and counts the group size. |
boolean
|
disjoint(Collection right)
Returns true if the intersection of two collections is empty.
|
Iterator
|
eachPermutation(Closure closure)
Iterates over all permutations of a collection, running a closure for each iteration. |
Object
|
find(Closure closure)
Finds the first value matching the closure condition. |
Object
|
find()
Finds the first item matching the IDENTITY Closure (i.e. matching Groovy truth). |
Collection
|
findAll(Closure closure)
Finds all values matching the closure condition. |
Collection
|
findAll()
Finds the items matching the IDENTITY Closure (i.e. matching Groovy truth). |
Object
|
findResult(Object defaultResult, Closure closure)
Iterates through the collection calling the given closure for each item but stopping once the first non-null result is found and returning that result. If all are null, the defaultResult is returned. |
Object
|
findResult(Closure closure)
Iterates through the collection calling the given closure for each item but stopping once the first non-null result is found and returning that result. If all results are null, null is returned. |
Collection
|
findResults(Closure filteringTransform)
Iterates through the collection transforming items using the supplied closure and collecting any non-null results. |
Collection
|
flatten()
Flatten a collection. |
Collection
|
flatten(Closure flattenUsing)
Flatten a collection. |
List
|
getAt(String property)
Support the subscript operator for Collection. |
Collection
|
grep(Object filter)
Iterates over the collection of items and returns each item that matches the given filter - calling the Object#isCase
method used by switch statements.
|
Collection
|
grep()
Iterates over the collection returning each element that matches using the IDENTITY Closure as a filter - effectively returning all elements which satisfy Groovy truth. |
Map
|
groupBy(Closure closure)
Sorts all collection members into groups determined by the supplied mapping closure. |
Map
|
groupBy(Object closures)
Sorts all collection members into (sub)groups determined by the supplied mapping closures. |
Map
|
groupBy(List closures)
Sorts all collection members into (sub)groups determined by the supplied mapping closures. |
Object
|
inject(Closure closure)
Performs the same function as the version of inject that takes an initial value, but uses the head of the Collection as the initial value, and iterates over the tail. |
Object
|
inject(Object initialValue, Closure closure)
Iterates through the given Collection, passing in the initial value to the 2-arg closure along with the first item. |
Collection
|
intersect(Collection right)
Create a Collection composed of the intersection of both collections. |
boolean
|
isCase(Object switchValue)
'Case' implementation for collections which tests if the 'switch' operand is contained in any of the 'case' values. |
String
|
join(String separator)
Concatenates the toString() representation of each
item in this collection, with the given String as a separator between
each item.
|
Collection
|
leftShift(Object value)
Overloads the left shift operator to provide an easy way to append objects to a Collection. |
Object
|
max()
Adds max() method to Collection objects. |
Object
|
max(Closure closure)
Selects an item in the collection having the maximum value as determined by the supplied closure. |
Object
|
max(Comparator comparator)
Selects the maximum value found in the collection using the given comparator. |
Object
|
min()
Adds min() method to Collection objects. |
Object
|
min(Comparator comparator)
Selects the minimum value found in the collection using the given comparator. |
Object
|
min(Closure closure)
Selects an item in the collection having the minimum value as determined by the supplied closure. |
List
|
multiply(Number factor)
Create a List composed of the elements of this list, repeated a certain number of times. |
Collection
|
plus(Collection right)
Create a Collection as a union of two collections. |
Collection
|
plus(Iterable right)
Create a Collection as a union of a Collection and an Iterable. |
Collection
|
plus(Object right)
Create a collection as a union of a Collection and an Object. |
boolean
|
removeAll(Object[] items)
Modifies this collection by removing its elements that are contained within the specified object array. |
boolean
|
removeAll(Closure condition)
Modifies this collection by removing the elements that are matched according to the specified closure condition. |
boolean
|
retainAll(Object[] items)
Modifies this collection so that it retains only its elements that are contained in the specified array. |
boolean
|
retainAll(Closure condition)
Modifies this collection so that it retains only its elements that are matched according to the specified closure condition. |
List
|
sort()
Sorts the Collection. |
List
|
sort(boolean mutate)
Sorts the Collection. |
List
|
sort(Comparator comparator)
Sorts the Collection using the given Comparator. |
List
|
sort(boolean mutate, Comparator comparator)
Sorts the Collection using the given Comparator. |
List
|
sort(Closure closure)
Sorts this Collection using the given Closure to determine the correct ordering. |
List
|
sort(boolean mutate, Closure closure)
Sorts this Collection using the given Closure to determine the correct ordering. |
Collection
|
split(Closure closure)
Splits all items into two collections based on the closure condition. |
Object
|
sum()
Sums the items in a collection. |
Object
|
sum(Object initialValue)
Sums the items in a collection, adding the result to some initial value. |
Object
|
sum(Closure closure)
Sums the result of apply a closure to each item of a collection. |
Object
|
sum(Object initialValue, Closure closure)
Sums the result of applying a closure to each item of a collection to some initial value. |
List
|
toList()
Convert a Collection to a List. |
String
|
toListString()
Returns the string representation of the given list. |
String
|
toListString(int maxSize)
Returns the string representation of the given list. |
Set
|
toSet()
Convert a Collection to a Set. |
Collection
|
unique()
Modifies this collection to remove all duplicated items, using the default comparator. |
Collection
|
unique(boolean mutate)
Remove all duplicates from a given Collection using the default comparator. |
Collection
|
unique(Closure closure)
A convenience method for making a collection unique using a Closure to determine duplicate (equal) items. |
Collection
|
unique(boolean mutate, Closure closure)
A convenience method for making a collection unique using a Closure to determine duplicate (equal) items. |
Collection
|
unique(Comparator comparator)
Remove all duplicates from a given Collection. |
Collection
|
unique(boolean mutate, Comparator comparator)
Remove all duplicates from a given Collection. |
| Method Detail |
|---|
public boolean addAll(Object[] items)
plus
or the '+' operator if wanting to produce a new collection
containing additional items but while leaving the original collection unchanged.items
- array containing elements to be added to this collection.public boolean asBoolean()
assert [1,2].asBoolean() == true
assert [].asBoolean() == false
public Collection asImmutable()
def mutable = [1,2,3] def immutable = mutable.asImmutable() mutable << 4 try { immutable << 4 assert false } catch (UnsupportedOperationException) { assert true }
public List asList()
Example usage:
assert new HashSet().asList() instanceof List
public Collection asSynchronized()
public Object asType(Class clazz)
clazz
- the desired class.public List collect(Closure transform)
transform
closure
returning a list of transformed values.
assert [2,4,6] == [1,2,3].collect { it * 2 }
transform
- the closure used to transform each item of the collection.public List collect()
assert [1,2,3] == [1,2,3].collect()
public Collection collect(Collection collector, Closure transform)
transform
closure
and adding it to the supplied collector
.
assert [1,2,3] as HashSet == [2,4,5,6].collect(new HashSet()) { (int)(it / 2) }
collector
- the Collection to which the transformed values are added.transform
- the closure used to transform each item of the collection.public List collectAll(Closure transform)
public Collection collectAll(Collection collector, Closure transform)
public Map collectEntries(Closure transform)
transform
closure
and returning a map of the resulting transformed entries.
def letters = "abc" // collect letters with index using list style assert (0..2).collectEntries { index -> [index, letters[index]] } == [0:'a', 1:'b', 2:'c'] // collect letters with index using map style assert (0..2).collectEntries { index -> [(index): letters[index]] } == [0:'a', 1:'b', 2:'c']
transform
- the closure used for transforming, which has an item from self as the parameter and
should return a Map.Entry, a Map or a two-element list containing the resulting key and value.public Map collectEntries()
[key, value]
tuples or a Map.Entry
.
def nums = [1, 10, 100, 1000] def tuples = nums.collect{ [it, it.toString().size()] } assert tuples == [[1, 1], [10, 2], [100, 3], [1000, 4]] def map = tuples.collectEntries() assert map == [1:1, 10:2, 100:3, 1000:4]
public Map collectEntries(Map collector, Closure transform)
def letters = "abc" // collect letters with index assert (0..2).collectEntries( [:] ) { index -> [index, letters[index]] } == [0:'a', 1:'b', 2:'c'] assert (0..2).collectEntries( [4:'d'] ) { index -> [(index+1): letters[index]] } == [1:'a', 2:'b', 3:'c', 4:'d']
collector
- the Map into which the transformed entries are put.transform
- the closure used for transforming, which has an item from self as the parameter and
should return a Map.Entry, a Map or a two-element list containing the resulting key and value.public Map collectEntries(Map collector)
collector
- the Map into which the transformed entries are put.public List collectMany(Closure projection)
def nums = 1..10 def squaresAndCubesOfEvens = nums.collectMany{ it % 2 ? [] : [it**2, it**3] } assert squaresAndCubesOfEvens == [4, 8, 16, 64, 36, 216, 64, 512, 100, 1000] def animals = ['CAT', 'DOG', 'ELEPHANT'] as Set def smallAnimals = animals.collectMany{ it.size() > 3 ? [] : [it.toLowerCase()] } assert smallAnimals == ['cat', 'dog'] def orig = nums as Set def origPlusIncrements = orig.collectMany{ [it, it+1] } assert origPlusIncrements.size() == orig.size() * 2 assert origPlusIncrements.unique().size() == orig.size() + 1
projection
- a projecting Closure returning a collection of items.public Collection collectMany(Collection collector, Closure projection)
collector
.
def animals = ['CAT', 'DOG', 'ELEPHANT'] as Set def smallAnimals = animals.collectMany(['ant', 'bee']){ it.size() > 3 ? [] : [it.toLowerCase()] } assert smallAnimals == ['ant', 'bee', 'cat', 'dog'] def nums = 1..5 def origPlusIncrements = nums.collectMany([] as Set){ [it, it+1] } assert origPlusIncrements.size() == nums.size() + 1
collector
- an initial collection to add the projected items to.projection
- a projecting Closure returning a collection of items.public List collectNested(Closure transform)
assert [2,[4,6],[8],[]] == [1,[2,3],[4],[]].collectNested { it * 2 }
transform
- the closure used to transform each item of the collection.public Collection collectNested(Collection collector, Closure transform)
transform
closure. Returns a potentially nested
collection of transformed values.
def x = [1,[2,3],[4],[]].collectNested(new Vector()) { it * 2 } assert x == [2,[4,6],[8],[]] assert x instanceof Vector
collector
- an initial Collection to which the transformed values are added.transform
- the closure used to transform each element of the collection.public List combinations()
Example usage:
assert [['a', 'b'],[1, 2, 3]].combinations() == [['a', 1], ['b', 1], ['a', 2], ['b', 2], ['a', 3], ['b', 3]]
public boolean containsAll(Object[] items)
items
- array to be checked for containment in this collection.public Number count(Object value)
compareTo(value) == 0
or equals(value)
).
Example usage:
assert [2,4,2,1,3,5,2,4,3].count(4) == 2
value
- the value being searched for.public Number count(Closure closure)
Example usage:
assert [2,4,2,1,3,5,2,4,3].count{ it % 2 == 0 } == 5
closure
- a closure condition.public Map countBy(Closure closure)
Example usage:
assert [0:2, 1:3] == [1,2,3,4,5].countBy { it % 2 }
closure
- a closure mapping items to the frequency keys.public boolean disjoint(Collection right)
true
if the intersection of two collections is empty.
assert [1,2,3].disjoint([3,4,5]) == false
assert [1,2].disjoint([3,4]) == true
right
- a Collection.true
if the intersection of two collections
is empty, false
otherwise.public Iterator eachPermutation(Closure closure)
Example usage:
def permutations = [] [1, 2, 3].eachPermutation{ permutations << it } assert permutations == [[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 1, 2], [3, 2, 1]]
closure
- the closure to call for each permutation.public Object find(Closure closure)
def list = [1,2,3] assert 2 == list.find { it > 1 }
closure
- a closure condition.public Object find()
Example:
def items = [null, 0, 0.0, false, '', [], 42, 43] assert items.find() == 42
public Collection findAll(Closure closure)
assert [2,4] == [1,2,3,4].findAll { it % 2 == 0 }
closure
- a closure condition.public Collection findAll()
Example:
def items = [1, 2, 0, false, true, '', 'foo', [], [4, 5], null] assert items.findAll() == [1, 2, true, 'foo', [4, 5]]
public Object findResult(Object defaultResult, Closure closure)
Examples:
def list = [1,2,3] assert "Found 2" == list.findResult("default") { it > 1 ? "Found $it" : null } assert "default" == list.findResult("default") { it > 3 ? "Found $it" : null }
defaultResult
- an Object that should be returned if all closure results are null.closure
- a closure that returns a non-null value when processing should stop and a value should be returned.public Object findResult(Closure closure)
Example:
def list = [1,2,3] assert "Found 2" == list.findResult { it > 1 ? "Found $it" : null }
closure
- a closure that returns a non-null value when processing should stop and a value should be returned.public Collection findResults(Closure filteringTransform)
Example:
def list = [1,2,3] def result = list.findResults { it > 1 ? "Found $it" : null } assert result == ["Found 2", "Found 3"]
filteringTransform
- a Closure that should return either a non-null transformed value or null for items which should be discarded.public Collection flatten()
assert [1,2,3,4,5] == [1,[2,3],[[4]],[],5].flatten()
public Collection flatten(Closure flattenUsing)
flattenUsing
- a closure to determine how to flatten non-Array, non-Collection elements.public List getAt(String property)
assert [String, Long, Integer] == ["a",5L,2]["class"]
property
- a String.public Collection grep(Object filter)
Object#isCase
method used by switch statements. This method can be used with different
kinds of filters like regular expressions, classes, ranges etc.
Example:
def list = ['a', 'b', 'aa', 'bc', 3, 4.5] assert list.grep( ~/a+/ ) == ['a', 'aa'] assert list.grep( ~/../ ) == ['aa', 'bc'] assert list.grep( Number ) == [ 3, 4.5 ] assert list.grep{ it.toString().size() == 1 } == [ 'a', 'b', 3 ]
filter
- the filter to perform on each element of the collection (using the Object#isCase method).public Collection grep()
Example:
def items = [1, 2, 0, false, true, '', 'foo', [], [4, 5], null] assert items.grep() == [1, 2, true, 'foo', [4, 5]]
public Map groupBy(Closure closure)
Example usage:
assert [0:[2,4,6], 1:[1,3,5]] == [1,2,3,4,5,6].groupBy { it % 2 }
closure
- a closure mapping entries on keys.public Map groupBy(Object closures)
Example usage:
def result = [1,2,3,4,5,6].groupBy({ it % 2 }, { it < 4 }) assert result == [1:[(true):[1, 3], (false):[5]], 0:[(true):[2], (false):[4, 6]]]Another example:
def sql = groovy.sql.Sql.newInstance(/* ... */) def data = sql.rows("SELECT * FROM a_table").groupBy({ it.column1 }, { it.column2 }, { it.column3 }) if (data.val1.val2.val3) { // there exists a record where: // a_table.column1 == val1 // a_table.column2 == val2, and // a_table.column3 == val3 } else { // there is no such record }If an empty array of closures is supplied the IDENTITY Closure will be used.
closures
- an array of closures, each mapping entries on keys.public Map groupBy(List closures)
Example usage:
def result = [1,2,3,4,5,6].groupBy([{ it % 2 }, { it < 4 }]) assert result == [1:[(true):[1, 3], (false):[5]], 0:[(true):[2], (false):[4, 6]]]Another example:
def sql = groovy.sql.Sql.newInstance(/* ... */) def data = sql.rows("SELECT * FROM a_table").groupBy([{ it.column1 }, { it.column2 }, { it.column3 }]) if (data.val1.val2.val3) { // there exists a record where: // a_table.column1 == val1 // a_table.column2 == val2, and // a_table.column3 == val3 } else { // there is no such record }If an empty list of closures is supplied the IDENTITY Closure will be used.
closures
- a list of closures, each mapping entries on keys.public Object inject(Closure closure)
assert 1 * 2 * 3 * 4 == [ 1, 2, 3, 4 ].inject { acc, val -> acc * val } assert ['b'] == [['a','b'], ['b','c'], ['d','b']].inject { acc, val -> acc.intersect( val ) } LinkedHashSet set = [ 't', 'i', 'm' ] assert 'tim' == set.inject { a, b -> a + b }
closure
- a closure.public Object inject(Object initialValue, Closure closure)
assert 1*1*2*3*4 == [1,2,3,4].inject(1) { acc, val -> acc * val } assert 0+1+2+3+4 == [1,2,3,4].inject(0) { acc, val -> acc + val } assert 'The quick brown fox' == ['quick', 'brown', 'fox'].inject('The') { acc, val -> acc + ' ' + val } assert 'bat' == ['rat', 'bat', 'cat'].inject('zzz') { min, next -> next < min ? next : min } def max = { a, b -> [a, b].max() } def animals = ['bat', 'rat', 'cat'] assert 'rat' == animals.inject('aaa', max)Visual representation of the last example above:
initVal animals[0] v v max('aaa', 'bat') => 'bat' animals[1] v v max('bat', 'rat') => 'rat' animals[2] v v max('rat', 'cat') => 'rat'
initialValue
- some initial value.closure
- a closure.public Collection intersect(Collection right)
assert [4,5] == [1,2,3,4,5].intersect([4,5,6,7,8])
right
- a Collection.public boolean isCase(Object switchValue)
switch( 3 ) { case [1,3,5]: assert true break default: assert false }
switchValue
- the switch value.public String join(String separator)
toString()
representation of each
item in this collection, with the given String as a separator between
each item.
assert "1, 2, 3" == [1,2,3].join(", ")
separator
- a String separator.public Collection leftShift(Object value)
def list = [1,2] list << 3 assert list == [1,2,3]
value
- an Object to be added to the collection..public Object max()
assert 5 == [2,3,1,5,4].max()
public Object max(Closure closure)
assert "hello" == ["hello","hi","hey"].max { it.length() }
assert "hello" == ["hello","hi","hey"].max { a, b -> a.length() <=> b.length() }
def pets = ['dog', 'elephant', 'anaconda'] def longestName = pets.max{ it.size() } // one of 'elephant' or 'anaconda' assert longestName.size() == 8
closure
- a 1 or 2 arg Closure used to determine the correct ordering.public Object max(Comparator comparator)
assert "hello" == ["hello","hi","hey"].max( { a, b -> a.length() <=> b.length() } as Comparator )
comparator
- a Comparator.public Object min()
assert 2 == [4,2,5].min()
public Object min(Comparator comparator)
assert "hi" == ["hello","hi","hey"].min( { a, b -> a.length() <=> b.length() } as Comparator )
comparator
- a Comparator.public Object min(Closure closure)
assert "hi" == ["hello","hi","hey"].min { it.length() }
def lastDigit = { a, b -> a % 10 <=> b % 10 } assert [19, 55, 91].min(lastDigit) == 91
def pets = ['dog', 'cat', 'anaconda'] def shortestName = pets.min{ it.size() } // one of 'dog' or 'cat' assert shortestName.size() == 3
closure
- a 1 or 2 arg Closure used to determine the correct ordering.public List multiply(Number factor)
assert [1,2,3,1,2,3] == [1,2,3] * 2
factor
- the number of times to append.public Collection plus(Collection right)
assert [1,2,3,4] == [1,2] + [3,4]
right
- the right Collection.public Collection plus(Iterable right)
right
- the right Iterable.public Collection plus(Object right)
assert [1,2,3] == [1,2] + 3
right
- an object to add/append.public boolean removeAll(Object[] items)
findAll
and grep
when wanting to produce a new list
containing items which don't match some criteria while leaving the original collection unchanged.items
- array containing elements to be removed from this collection.public boolean removeAll(Closure condition)
findAll
and grep
when wanting to produce a new list
containing items which don't match some criteria while leaving the original collection unchanged.condition
- a closure condition.public boolean retainAll(Object[] items)
grep
and findAll
when wanting to produce a new list
containing items which match some specified items but leaving the original collection unchanged.items
- array containing elements to be retained from this collection.public boolean retainAll(Closure condition)
findAll
and grep
when wanting to produce a new list
containing items which match some criteria but leaving the original collection unchanged.condition
- a closure condition.public List sort()
assert [1,2,3] == [3,1,2].sort()
public List sort(boolean mutate)
assert [1,2,3] == [3,1,2].sort()
def orig = [1, 3, 2] def sorted = orig.sort(false) assert orig == [1, 3, 2] assert sorted == [1, 2, 3]
mutate
- false will always cause a new list to be created, true will mutate lists in place.public List sort(Comparator comparator)
assert ["hi","hey","hello"] == ["hello","hi","hey"].sort( { a, b -> a.length() <=> b.length() } as Comparator )
assert ["hello","Hey","hi"] == ["hello","hi","Hey"].sort(String.CASE_INSENSITIVE_ORDER)
comparator
- a Comparator used for the comparison.public List sort(boolean mutate, Comparator comparator)
assert ["hi","hey","hello"] == ["hello","hi","hey"].sort( { a, b -> a.length() <=> b.length() } as Comparator )
def orig = ["hello","hi","Hey"] def sorted = orig.sort(false, String.CASE_INSENSITIVE_ORDER) assert orig == ["hello","hi","Hey"] assert sorted == ["hello","Hey","hi"]
mutate
- false will always cause a new list to be created, true will mutate lists in place.comparator
- a Comparator used for the comparison.public List sort(Closure closure)
assert ["hi","hey","hello"] == ["hello","hi","hey"].sort { it.length() }
assert ["hi","hey","hello"] == ["hello","hi","hey"].sort { a, b -> a.length() <=> b.length() }
closure
- a 1 or 2 arg Closure used to determine the correct ordering.public List sort(boolean mutate, Closure closure)
assert ["hi","hey","hello"] == ["hello","hi","hey"].sort { it.length() }
assert ["hi","hey","hello"] == ["hello","hi","hey"].sort { a, b -> a.length() <=> b.length() }
def orig = ["hello","hi","Hey"] def sorted = orig.sort(false) { it.toUpperCase() } assert orig == ["hello","hi","Hey"] assert sorted == ["hello","Hey","hi"]
mutate
- false will always cause a new list to be created, true will mutate lists in place.closure
- a 1 or 2 arg Closure used to determine the correct ordering.public Collection split(Closure closure)
Example usage:
assert [[2,4],[1,3]] == [1,2,3,4].split { it % 2 == 0 }
closure
- a closure condition.public Object sum()
assert 1+2+3+4 == [1,2,3,4].sum()
public Object sum(Object initialValue)
assert 5+1+2+3+4 == [1,2,3,4].sum(5)
initialValue
- the items in the collection will be summed to this initial value.public Object sum(Closure closure)
coll.sum(closure)
is equivalent to:
coll.collect(closure).sum()
.
assert 4+6+10+12 == [2,3,5,6].sum() { it * 2 }
closure
- a single parameter closure that returns a numeric value..public Object sum(Object initialValue, Closure closure)
coll.sum(initVal, closure)
is equivalent to:
coll.collect(closure).sum(initVal)
.
assert 50+4+6+10+12 == [2,3,5,6].sum(50) { it * 2 }
closure
- a single parameter closure that returns a numeric value..initialValue
- the closure results will be summed to this initial value.public List toList()
Example usage:
def x = [1,2,3] as HashSet assert x.class == HashSet assert x.toList() instanceof List
public String toListString()
[1, 2, a]
.public String toListString(int maxSize)
[1, 2, a]
.maxSize
- stop after approximately this many characters and append '...'.public Set toSet()
Example usage:
def result = [1, 2, 2, 2, 3].toSet() assert result instanceof Set assert result == [1, 2, 3] as Set
public Collection unique()
assert [1,3] == [1,3,3].unique()
public Collection unique(boolean mutate)
assert [1,3] == [1,3,3].unique()
def orig = [1, 3, 2, 3] def uniq = orig.unique(false) assert orig == [1, 3, 2, 3] assert uniq == [1, 3, 2]
mutate
- false will cause a new list containing unique items from the collection to be created, true will mutate collections in place.public Collection unique(Closure closure)
assert [1,4] == [1,3,4,5].unique { it % 2 }
assert [2,3,4] == [2,3,3,4].unique { a, b -> a <=> b }
closure
- a 1 or 2 arg Closure used to determine unique items.public Collection unique(boolean mutate, Closure closure)
def orig = [1, 3, 4, 5] def uniq = orig.unique(false) { it % 2 } assert orig == [1, 3, 4, 5] assert uniq == [1, 4]
def orig = [2, 3, 3, 4] def uniq = orig.unique(false) { a, b -> a <=> b } assert orig == [2, 3, 3, 4] assert uniq == [2, 3, 4]
mutate
- false will always cause a new list to be created, true will mutate lists in place.closure
- a 1 or 2 arg Closure used to determine unique items.public Collection unique(Comparator comparator)
class Person { def fname, lname String toString() { return fname + " " + lname } } class PersonComparator implements Comparator { int compare(Object o1, Object o2) { Person p1 = (Person) o1 Person p2 = (Person) o2 if (p1.lname != p2.lname) return p1.lname.compareTo(p2.lname) else return p1.fname.compareTo(p2.fname) } boolean equals(Object obj) { return this.equals(obj) } } Person a = new Person(fname:"John", lname:"Taylor") Person b = new Person(fname:"Clark", lname:"Taylor") Person c = new Person(fname:"Tom", lname:"Cruz") Person d = new Person(fname:"Clark", lname:"Taylor") def list = [a, b, c, d] List list2 = list.unique(new PersonComparator()) assert( list2 == list && list == [a, b, c] )
comparator
- a Comparator.public Collection unique(boolean mutate, Comparator comparator)
class Person { def fname, lname String toString() { return fname + " " + lname } } class PersonComparator implements Comparator { int compare(Object o1, Object o2) { Person p1 = (Person) o1 Person p2 = (Person) o2 if (p1.lname != p2.lname) return p1.lname.compareTo(p2.lname) else return p1.fname.compareTo(p2.fname) } boolean equals(Object obj) { return this.equals(obj) } } Person a = new Person(fname:"John", lname:"Taylor") Person b = new Person(fname:"Clark", lname:"Taylor") Person c = new Person(fname:"Tom", lname:"Cruz") Person d = new Person(fname:"Clark", lname:"Taylor") def list = [a, b, c, d] List list2 = list.unique(false, new PersonComparator()) assert( list2 != list && list2 == [a, b, c] )
mutate
- false will always cause a new collection to be created, true will mutate collections in place.comparator
- a Comparator.
|
Groovy JDK |


I have a number of interests outside of graphic communication including science, history and economics. I might take my telescope out on a dark evening, or just grab a good book. Hiking is one of my favorite pastimes. It's a good way to exercise and I tend to get some of my best ideas on the trails. And when I really need to waste some down time I'll watch an old sci fi movie. In fact, I pride myself as an expert on mid-twenth century science fiction. In the past, I painted watercolor and was a pretty good amateur photographer. When I have more time I'm sure I'll be getting back to those pursuits.


Please remember to submit the responses. Without submitting, the result will not be updated in the Database.


Affin Deformation
A deformation is affin (homogenous) if straight lines remain straight after derformation and circles become ellipses.


Go to the source code of this file.
Defines | |
| #define | TWENTY_FIVE_MSEC 10 |
| Delay Time 25 msec. | |
| #define | FIFTY_MSEC 20 |
| Delay Time 50 msec. | |
| #define | HUNDRED_MSEC 40 |
| Delay Time 100 msec. | |
| #define | TWO_HUNDRED_FIFTY_MSEC 100 |
| Delay Time 250 msec. | |
| #define | FIVE_HUNDRED_MSEC 200 |
| Delay Time 500 msec. | |
| #define | ONE_SEC 400 |
| Delay Time 1 sec. | |
| #define | MOTOR_A 'A' |
| Motor A. | |
| #define | MOTOR_B 'B' |
| Motor B. | |
| #define | MOTOR_C 'C' |
| Motor C. | |
| #define | LEG_1 1 |
| Leg 1. | |
| #define | LEG_2 2 |
| Leg 2. | |
| #define | LEG_3 3 |
| Leg 3. | |
| #define | LEG_4 4 |
| Leg 4. | |
| #define | LEG_5 5 |
| Leg 5. | |
| #define | LEG_6 6 |
| Leg 6. | |
| #define | CLOCK_WISE 1 |
| Motion clockwise. | |
| #define | ANTI_CLOCK_WISE 0 |
| Motion anti_clockwise. | |
| #define | NO_OF_LEGS 6 |
| Number of legs of the hexapod. | |
| #define | NO_OF_SERVOS 24 |
| Number of servos. | |
| #define | NO_OF_SERVOS_IN_EACH_LEG 3 |
| Number of servos in each led. | |
| #define | DEGREE_MIN 0 |
| Min angle of servos. | |
| #define | DEGREE_MAX 180 |
| Max angle of servos. | |
| #define | INITIAL_ANGLE 90 |
| Initial angle of servos. | |
| #define | KEYBOARD_ON 1 |
| Keyboard control on. | |
| #define | DIR_MIRROR_LEG_1_2 0 |
| Dir between leg 1 & 2. | |
| #define | DIR_MIRROR_LEG_2_3 1 |
| Dir between leg 2 & 3. | |
| #define | DIR_MIRROR_LEG_1_4 2 |
| Dir between leg 1 & 4. |
| #define ANTI_CLOCK_WISE 0 |
Motion anti_clockwise.
| #define CLOCK_WISE 1 |
Motion clockwise.
| #define DEGREE_MAX 180 |
Max angle of servos.
| #define DEGREE_MIN 0 |
Min angle of servos.
| #define DIR_MIRROR_LEG_1_2 0 |
Dir between leg 1 & 2.
| #define DIR_MIRROR_LEG_1_4 2 |
Dir between leg 1 & 4.
| #define DIR_MIRROR_LEG_2_3 1 |
Dir between leg 2 & 3.
| #define FIFTY_MSEC 20 |
Delay Time 50 msec.
| #define FIVE_HUNDRED_MSEC 200 |
Delay Time 500 msec.
| #define HUNDRED_MSEC 40 |
Delay Time 100 msec.
| #define INITIAL_ANGLE 90 |
Initial angle of servos.
| #define KEYBOARD_ON 1 |
Keyboard control on.
| #define LEG_1 1 |
Leg 1.
| #define LEG_2 2 |
Leg 2.
| #define LEG_3 3 |
Leg 3.
| #define LEG_4 4 |
Leg 4.
| #define LEG_5 5 |
Leg 5.
| #define LEG_6 6 |
Leg 6.
| #define MOTOR_A 'A' |
Motor A.
| #define MOTOR_B 'B' |
Motor B.
| #define MOTOR_C 'C' |
Motor C.
| #define NO_OF_LEGS 6 |
Number of legs of the hexapod.
| #define NO_OF_SERVOS 24 |
Number of servos.
| #define NO_OF_SERVOS_IN_EACH_LEG 3 |
Number of servos in each led.
| #define ONE_SEC 400 |
Delay Time 1 sec.
| #define TWENTY_FIVE_MSEC 10 |
Delay Time 25 msec.
| #define TWO_HUNDRED_FIFTY_MSEC 100 |
Delay Time 250 msec.


RIDICULE (director: Patrice Leconte; screenwriters: Remi Waterhouse/Eric Vicaut/Michel Fessler; cinematographer: Thierry Arbogast; cast: Charles Berling (Baron Gregoire Ponceludon de Malavoy), Jean Rochefort (Marquis de Bellegarde), Fanny Ardant (Madame de Blayac), Judith Godreche (Mathilde de Bellegarde), Bernard Giraudeau (Abbot de Vilecourt), Urbain Cancelier (Louis XVI), 1996-Fr.)
Reviewed by Dennis Schwartz
For a film that prides itself on its use of wit to show what the nobility and their King Louis XV1 were like during 1783, just before the infamous French Revolution, I would have expected to be more than dazzled by these nobles with their silver-tongued wit, cutting each other to bits and pieces with their razor-sharpened retorts. Instead, I found myself more taken with the history aspect of this good looking costume spectacle and was not especially amused by the barbs. In fact, I found the film rather anti-septic, not a particularly moving dramatic experience. Overrated by some of the critics, who seemed to fall for this French film's farcical intentions, deeming it intellectual in the broadest sense of what is intellectual, while failing to construe how ordinary and dispassionate the characters were.
The art of ridicule was the sword used by those who expected to gain favor with the king and his court, with the hopes of having enough wit so that they won't have their life ruined by failing to respond to someone else's comments in a suitable manner. It was their way of fighting for their position in life.
The plot of the movie centers around the serious-minded Baron Ponceludon's (Berling) attempt to get royal backing on a needed drainage project, which would help cultivate the swamp land he owns by getting rid of the mosquitoes breeding there and help the peasants who work for him have a better life. He is a French lord, trained as an engineer but a noble of modest means, who must now ride by horse to Versailles and learn to play the delicate games of wit at a place where the aristocrats show an indifference to social concerns other than the ones that please their own vanity. He comes here solely to raise the money he needs for his life's work.
Part fiction and part history, this satire, as directed by Patrice Leconte (Monsieur Hire/The Hairdresser's Wife), is not completely absorbing in its romantic tale or as drama. The history part seemed to be accurate enough, depicting the corruption of the king's court and his placement of more importance on vacuous wit than genuine ideas. The honest engineer from the country is quickly disheartened by what he sees in Versailles, where you get the king's ear by trickery, or sleeping in the right bed, or by deception. To meet the evasive king, you have to make a name for yourself in Versailles through one of those methods.
The idealistic Ponceludon finds a local nobleman, who treats his wounds when he is robbed on the road to Versailles, Bellegarde (Jean Rochefort). He thinks Ponceludon might have some chance of succeeding in being witty and offers him lessons of how to survive in Versailles, such as, "Don't laugh at your own jokes and never laugh with your mouth open", "Call puns the death of wit"; and remember,"The soul of wit is to know one's place." This nobleman who will sponsor him, something needed to get into the right circles at Versailles, also has a beautiful and intelligent daughter, Mathilde (Judith), but who is prepared to marry an older noble, whose plan is to gain access to her wealth. Now you know she's going to fall in love with our hero; but, in between, Ponceludon courts the seductively attractive and treacherous Countess de Blayac (Fanny), as he is committed to get help for his swamp project anyway he could. His problem with winning her over, is that her main man is the arrogant and nasty master of wit himself, the clergyman Abbot de Vilecourt (Giraudeau); and, of course, the rivalry between these two wits will culminate in a gentleman's pistol duel, that is, after a series of dueling witticisms are hurled back and forth. After his battles with the nobles, it is still challenging to meet the insulated king, busy living the "good life," who is just as indifferent as the nobles are to the plight of the common man.
 Language therefore became a source of power for them if used in conjunction with wit, as their class status only gave them a ticket into the arena of power, so to speak. This film is also about a moral dilemma for our good-hearted hero. He must choose either what is morally correct or to sell himself out for his project, which he considers as something that is more important than how he might be perceived.
The acting or the story did not excite me enough to think that this clever film was anything special, except as a mildly diverting farce, which could be, at best, considered only as a film that had its moments of being a clever satire. But mostly, it is a predictable French farce, with enough one-dimensional characters in it to fill the court of Versailles with retorts that would pale next to those heard in an average schoolyard in urban America on a daily basis.
REVIEWED ON 2/18/99 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Use .col-md-*
grid classes to create a grid system that is stacked on mobile devices and tablet devices (the
extra small to small range) and horizontal on desktop (medium) devices. Place grid columns in any .row
.
Note:
.show-grid
is enabled below to only demonstrate this layout..show-grid
would not be enabled in production code.


Welcome to your project!
Welcome to your Lift app at Time goes here
Lift v3.0 Template Applications
Index card content text goes here
Lift Template Application
notice text goes here
Lift Templates on Github
Lift
Toggle left or use the menu icon on the top bar to toggle the left navbar.
Toggle right


Latest ad set up component has been released. browser compatibility updated.
A few new templates for html5 have been added, bringing a dynamic carousel.
Add a youtube video to your units while stillmaintaining the unprecedented reportingfrom flashtalking components.


Deep linking allows you to encode the state of the application in the URL so that it can be bookmarked and the application can be restored from the URL to the same state.
While angular does not force you to deal with bookmarks in any particular way, it has services which make the common case described here very easy to implement.
Your application consists of a single HTML page which bootstraps the application. We will refer to this page as the chrome. Your application is divided into several screens (or views) which the user can visit. For example, the home screen, settings screen, details screen, etc. For each of these screens, we would like to assign a URL so that it can be bookmarked and later restored. Each of these screens will be associated with a controller which define the screen's behavior. The most common case is that the screen will be constructed from an HTML snippet, which we will refer to as the partial. Screens can have multiple partials, but a single partial is the most common construct. This example makes the partial boundary visible using a blue line.
You can make a routing table which shows which URL maps to which partial view template and which controller.
In this example we have a simple app which consist of two screens:
welcome
Show the user contact information.settings
Show an edit screen for user contact information.The two partials are defined in the following URLs:

Your App Chrome
 [ Welcome | Settings ]
  Partial: {{$route.current.template}}  <ng:view style="border: 1px solid blue; margin: 0; display:block; padding:1em;"></ng:view> Your app footer

it('should navigate to URL', function() { element('a:contains(Welcome)').click(); expect(element('ng\\:view').text()).toMatch(/Hello anonymous/); element('a:contains(Settings)').click(); input('form.name').enter('yourname'); element(':button:contains(Save)').click(); element('a:contains(Welcome)').click(); expect(element('ng\\:view').text()).toMatch(/Hello yourname/); });
AppCntl
class. The initialization of the controller causes the
initialization of the $route
service with the proper URL
routes.$route
service then watches the URL and instantiates the
appropriate controller when the URL changes.ngView
widget loads the
view when the URL changes. It also sets the view scope to the newly instantiated controller.


Module BlobReader
Parses binary data from memory.
Functions
| new ([data[, sizeOrByteOrder[, size]]]) | Creates a new BlobReader instance. |
| read () | Reads a string , a number , a boolean or a table from the input data. |
| number () | Reads a Lua number from the input data. |
| string () | Reads a string from the input data. |
| bool () | Reads a boolean value from the input data. |
| table ([result]) | Reads a Lua table from the input data. |
| u8 () | Reads one unsigned 8-bit value from the input data. |
| s8 () | Reads one signed 8-bit value from the input data. |
| u16 () | Reads one unsigned 16-bit value from the input data. |
| s16 () | Reads one signed 16 bit value from the input data. |
| u32 () | Reads one unsigned 32 bit value from the input data. |
| s32 () | Reads one signed 32 bit value from the input data. |
| u64 () | Reads one unsigned 64 bit value from the input data. |
| s64 () | Reads one signed 64 bit value from the input data. |
| f32 () | Reads one 32 bit floating point value from the input data. |
| f64 () | Reads one 64 bit floating point value from the input data. |
| vu32 () | Reads a length-encoded unsigned 32 bit integer value from the input data. |
| vs32 () | Reads a length-encoded signed 32 bit integer value from the input data. |
| raw (len) | Reads raw binary data from the input data. |
| cdata () | Reads a cdata object from the input data. |
| skip (len) | Skips a number of bytes in the input data. |
| cstring () | Reads a zero-terminated string from the input data (up to 2 ^ 32 - 1 bytes). |
| array (valueType[, count[, result]]) | Reads a sequential table of typed values. |
| unpack (format) | Parses data into separate values according to a format string. |
| size () | Returns the size of the input data in bytes. |
| reset (data[, size]) | Re-initializes the reader with new data and resets the read position. |
| seek (pos) | Moves the read position to pos |
| position () | Returns the current read position as an offset from the start of the input data in bytes. |
| setByteOrder (byteOrder) | Set source data byte order. |
Functions
- new ([data[, sizeOrByteOrder[, size]]])
-
Creates a new BlobReader instance.
Parameters:
- data string or cdata optional Source data
- sizeOrByteOrder
number or string
optional
Size of
data
(required when data is acdata
pointer) or the byte order of the dataByte order: Use
le
or<
for little endian;be
or>
for big endian;host
,=
ornil
to use the host’s native byte order (default) - size
number
optional
Size of
data
(required whendata
is acdata
pointer)
Returns:
-
BlobReader
A new BlobReader instance.
Usage:
reader = BlobReader(data)
reader = BlobReader(data, '>')
reader = BlobReader(cdata, 1000)
- read ()
-
Reads a
string
, anumber
, aboolean
or atable
from the input data.The data must have been written by BlobWriter:write. The type of the value is automatically detected from the input metadata.
Returns:
See also:
- number ()
-
Reads a Lua number from the input data.
Returns:
-
number
The number read read from the input data
- string ()
-
Reads a string from the input data.
The string must have been written by BlobWriter:write or BlobWriter:string
Returns:
-
string
The string read from the input data
See also:
- bool ()
-
Reads a boolean value from the input data.
The data is expected to be 8 bits long,
0 == false
, any other value ==true
Returns:
-
bool
The boolean value read from the input data
- table ([result])
-
Reads a Lua table from the input data.
The table must have been written by BlobWriter.write or BlobWriter.table.
Parameters:
- result table optional Table to put values in
Returns:
-
table
The table read from the input data
See also:
- u8 ()
-
Reads one unsigned 8-bit value from the input data.
Returns:
-
number
The unsigned 8-bit value read from the input data
- s8 ()
-
Reads one signed 8-bit value from the input data.
Returns:
-
number
The signed 8-bit value read from the input data
- u16 ()
-
Reads one unsigned 16-bit value from the input data.
Returns:
-
number
The unsigned 16-bit value read from the input data
- s16 ()
-
Reads one signed 16 bit value from the input data.
Returns:
-
number
The signed 16-bit value read from the input data
- u32 ()
-
Reads one unsigned 32 bit value from the input data.
Returns:
-
number
The unsigned 32-bit value read from the input data
- s32 ()
-
Reads one signed 32 bit value from the input data.
Returns:
-
number
The signed 32-bit value read from the input data
- u64 ()
-
Reads one unsigned 64 bit value from the input data.
Returns:
-
number
The unsigned 64-bit value read from the input data
- s64 ()
-
Reads one signed 64 bit value from the input data.
Returns:
-
number
The signed 64-bit value read from the input data
- f32 ()
-
Reads one 32 bit floating point value from the input data.
Returns:
-
number
The 32-bit floating point value read from the input data
- f64 ()
-
Reads one 64 bit floating point value from the input data.
Returns:
-
number
The 64-bit floating point value read from the input data
- vu32 ()
-
Reads a length-encoded unsigned 32 bit integer value from the input data.
See BlobWriter:vu32 for more details about this data type.
Returns:
-
number
The unsigned 32-bit integer value read from the input data
See also:
- vs32 ()
-
Reads a length-encoded signed 32 bit integer value from the input data.
See BlobWriter:vs32 for more details about this data type.
Returns:
-
number
The signed 32-bit integer value read from the input data
See also:
- raw (len)
-
Reads raw binary data from the input data.
Parameters:
- len number The length of the data (in bytes) to read
Returns:
-
string
A string with raw data
- cdata ()
-
Reads a
cdata
object from the input data.This function can only read data that was written by BlobWriter:cdata.
Returns:
-
cdata
The
cdata
object - skip (len)
-
Skips a number of bytes in the input data.
Parameters:
- len number The number of bytes to skip
Returns:
-
BlobReader
self
- cstring ()
-
Reads a zero-terminated string from the input data (up to 2 ^ 32 - 1 bytes).
Keeps reading bytes until a null byte is encountered.
Returns:
-
string
The string read from the input data
- array (valueType[, count[, result]])
-
Reads a sequential table of typed values.
Parameters:
- valueType
string
Type of the values in the array
Valid types are
s8
,u8
,s16
,u16
,s32
,u32
,vs32
,vu32
,s64
,u64
,f32
,f64
,number
,string
,bool
,cstring
,table
, andcdata
. - count
number
optional
Number of values to read. If
nil
, precedingvu32
encoded array length information is expected to be precede the array, as written by BlobWriter:array. - result table optional Table to put the values in
Returns:
-
table
A sequential table, starting at index 1
See also:
- valueType
string
Type of the values in the array
- unpack (format)
-
Parses data into separate values according to a format string.
Parameters:
- format
string
Data format specification
See BlobWriter:pack for a list of supported identifiers.
Additional format specifiers for unpack:
x[n]
: skipn
bytes of datan
defaults to 1 if no length was specified.
Returns:
-
All values parsed from the input data
See also:
Usage:
byte, float, bool = reader:unpack('x4Bfy') -- skips 4 bytes before actual data
- format
string
- size ()
-
Returns the size of the input data in bytes.
Returns:
-
number
Data size in bytes
- reset (data[, size])
-
Re-initializes the reader with new data and resets the read position.
Parameters:
- data string, cdata or nil The source data
- size
number
optional
The length of the data (only required when
data
is acdata
pointer)
Returns:
-
BlobReader
self
- seek (pos)
-
Moves the read position to
pos
Parameters:
- pos number New read position (offset in bytes from the start of data). Negative values will seek from the end of data
Returns:
-
BlobReader
self
- position ()
-
Returns the current read position as an offset from the start of the input data in bytes.
Returns:
-
number
Current read position in bytes
- setByteOrder (byteOrder)
-
Set source data byte order.
Parameters:
- byteOrder
string
Byte order.
Can be either
le
or<
for little endian,be
or>
for big endian, orhost
ornil
for native host byte order.
Returns:
-
BlobReader
self
- byteOrder
string
Byte order.


I am a full stack web developer with skills of designing a world class website.
I am able to execute all visual design stages from concept to final hand-off to evolution.
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To succeed don’t let the noise of other’s opinions drown out your own inner voice.
Developed scalable websites using Django as backend API and intergrate with angular front end.
• Monitor and maintain the computer systems and
networks
for the organisation.
• Installing and configuring computer systems, diagnosing hardware and software faults.
• Solving technical and application problems.
• Support for developers, integration, and implementation of the Daraja Developer portal
from
Safaricom PLC. Acquired knowledge on MPesa APIs, and integrated it with their online system
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I worked in the ICT Department undertook the following
duties and functions:
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نام:
نام کاربری
ایمیل
تلفن
مدیریت
سعید
saied
<EMAIL_ADDRESS>09328766789
| نام: | نام کاربری | ایمیل | تلفن | مدیریت |
|---|---|---|---|---|
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Hey.  At the beginning of 2016, I told myself I was going to write for 10 minutes every day. It took me a while, but eventually I got there.
Track record:
| Month | Post Count |
|---|---|
| January | 12 |
| February | 3 |
| March | 4 |
| April | 3 |
| May | 9 |
| June | 16 |
| July | 21 |
| August | 29 |
| September | 36 |
| October | 26 |
| November | 29 |
| December | 36 (so far) |
My Writing Philosophy
I write for a few reasons:
- I like the idea of working out loud.
- To get over my anxiety around writing (I’m getting better at this, but it still…)
- James Baldwin is a hero and…
- The world definitely needs more Black, queer voices.
- Because Danielle Coates-Connor told me I should.
- To share ideas in an easily shareable way.
- To process my thinking in a way that allows me to build on previous thoughts.
My Approach
- I’m constantly collecting ideas to write about. They come from podcasts, conversations, books, and articles mostly.
- In the morning, after meditating for ten minutes, I set my timer for ten minutes, start Toggl, hit play on my writing playlist, and write. The ten minutes are more like a guideline at this point…
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- Hit publish.
- Get ready for the work day.


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SARIMAX and ARIMA: Frequently Asked Questions (FAQ)¶
This notebook contains explanations for frequently asked questions.
Comparing trends and exogenous variables in
SARIMAX
,ARIMA
andAutoReg
Reconstructing residuals, fitted values and forecasts in
SARIMAX
andARIMA
Initial residuals in
SARIMAX
andARIMA
Comparing trends and exogenous variables in SARIMAX
, ARIMA
and AutoReg
¶
ARIMA
are formally OLS with ARMA errors. A basic AR(1) in the OLS with ARMA errors is described as
In large samples, \(\hat{\delta}\stackrel{p}{\rightarrow} E[Y]\).
SARIMAX
uses a different representation, so that the model when estimated using SARIMAX
is
This is the same representation that is used when the model is estimated using OLS (AutoReg
). In large samples, \(\hat{\phi}\stackrel{p}{\rightarrow} E[Y](1-\rho)\).
In the next cell, we simulate a large sample and verify that these relationship hold in practice.
[1]:
%matplotlib inline
[2]:
import numpy as np
import pandas as pd
rng = np.random.default_rng(20210819)
eta = rng.standard_normal(5200)
rho = 0.8
beta = 10
epsilon = eta.copy()
for i in range(1, eta.shape[0]):
epsilon[i] = rho * epsilon[i - 1] + eta[i]
y = beta + epsilon
y = y[200:]
[3]:
from statsmodels.tsa.api import SARIMAX, AutoReg
from statsmodels.tsa.arima.model import ARIMA
The three models are specified and estimated in the next cell. An AR(0) is included as a reference. The AR(0) is identical using all three estimators.
[4]:
ar0_res = SARIMAX(y, order=(0, 0, 0), trend="c").fit()
sarimax_res = SARIMAX(y, order=(1, 0, 0), trend="c").fit()
arima_res = ARIMA(y, order=(1, 0, 0), trend="c").fit()
autoreg_res = AutoReg(y, 1, trend="c").fit()
This problem is unconstrained.
This problem is unconstrained.
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 10
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.91760D+00 |proj g|= 3.68860D-06
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 0 1 0 0 0 3.689D-06 1.918D+00
F = 1.9175996129577773
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 3 M = 10
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.41373D+00 |proj g|= 9.51828D-04
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
3 2 5 1 0 0 4.516D-05 1.414D+00
F = 1.4137311050015484
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
The table below contains the estimated parameter in the model, the estimated AR(1) coefficient, and the long-run mean which is either equal to the estimated parameters (AR(0) or ARIMA
), or depends on the ratio of the intercept to 1 minus the AR(1) parameter.
[5]:
intercept = [
ar0_res.params[0],
sarimax_res.params[0],
arima_res.params[0],
autoreg_res.params[0],
]
rho_hat = [0] + [r.params[1] for r in (sarimax_res, arima_res, autoreg_res)]
long_run = [
ar0_res.params[0],
sarimax_res.params[0] / (1 - sarimax_res.params[1]),
arima_res.params[0],
autoreg_res.params[0] / (1 - autoreg_res.params[1]),
]
cols = ["AR(0)", "SARIMAX", "ARIMA", "AutoReg"]
pd.DataFrame(
[intercept, rho_hat, long_run],
columns=cols,
index=["delta-or-phi", "rho", "long-run mean"],
)
[5]:
| AR(0) | SARIMAX | ARIMA | AutoReg | |
|---|---|---|---|---|
| delta-or-phi | 9.7745 | 1.985714 | 9.774498 | 1.985790 |
| rho | 0.0000 | 0.796846 | 0.796875 | 0.796882 |
| long-run mean | 9.7745 | 9.774424 | 9.774498 | 9.776537 |
Differences between trend and exog in SARIMAX
¶
When SARIMAX
includes exog
variables, then the exog
are treated as OLS regressors, so that the model estimated is
In the next example, we omit the trend and instead include a column of 1, which produces a model that is equivalent, in large samples, to the case with no exogenous regressor and trend="c"
. Here the estimated value of const
matches the value estimated using ARIMA
. This happens since both exog in SARIMAX
and the trend in ARIMA
are treated as linear regression models with ARMA errors.
[6]:
sarimax_exog_res = SARIMAX(y, exog=np.ones_like(y), order=(1, 0, 0), trend="n").fit()
print(sarimax_exog_res.summary())
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 3 M = 10
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.41373D+00 |proj g|= 1.06920D-04
This problem is unconstrained.
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
3 1 4 1 0 0 4.752D-05 1.414D+00
F = 1.4137311099487531
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 5000
Model: SARIMAX(1, 0, 0) Log Likelihood -7068.656
Date: Wed, 02 Nov 2022 AIC 14143.311
Time: 17:05:13 BIC 14162.863
Sample: 0 HQIC 14150.164
- 5000
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const 9.7745 0.069 141.177 0.000 9.639 9.910
ar.L1 0.7969 0.009 93.691 0.000 0.780 0.814
sigma2 0.9894 0.020 49.921 0.000 0.951 1.028
===================================================================================
Ljung-Box (L1) (Q): 0.42 Jarque-Bera (JB): 0.08
Prob(Q): 0.51 Prob(JB): 0.96
Heteroskedasticity (H): 0.97 Skew: -0.01
Prob(H) (two-sided): 0.47 Kurtosis: 2.99
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
Using exog
in SARIMAX
and ARIMA
¶
While exog
are treated the same in both models, the intercept continues to differ. Below we add an exogenous regressor to y
and then fit the model using all three methods. The data generating process is now
[7]:
full_x = rng.standard_normal(eta.shape)
x = full_x[200:]
y += 3 * x
[8]:
sarimax_exog_res = SARIMAX(y, exog=x, order=(1, 0, 0), trend="c").fit()
arima_exog_res = ARIMA(y, exog=x, order=(1, 0, 0), trend="c").fit()
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 4 M = 10
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.42683D+00 |proj g|= 2.05943D-01
This problem is unconstrained.
At iterate 5 f= 1.41332D+00 |proj g|= 1.60874D-03
At iterate 10 f= 1.41329D+00 |proj g|= 3.11658D-05
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
4 11 15 1 0 0 1.796D-06 1.413D+00
F = 1.4132928400115972
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
Examining the parameter tables, we see that the parameter estimates on x1
are identical while the estimates of the intercept
continue to differ due to the differences in the treatment of trends in these estimators.
SARIMAX
¶
[9]:
def print_params(s):
from io import StringIO
return pd.read_csv(StringIO(s.tables[1].as_csv()), index_col=0)
print_params(sarimax_exog_res.summary())
[9]:
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 1.9849 | 0.085 | 23.484 | 0.0 | 1.819 | 2.151 |
| x1 | 3.0231 | 0.011 | 277.150 | 0.0 | 3.002 | 3.044 |
| ar.L1 | 0.7969 | 0.009 | 93.735 | 0.0 | 0.780 | 0.814 |
| sigma2 | 0.9886 | 0.020 | 49.941 | 0.0 | 0.950 | 1.027 |
ARIMA
¶
[10]:
print_params(arima_exog_res.summary())
[10]:
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 9.7741 | 0.069 | 141.201 | 0.0 | 9.638 | 9.910 |
| x1 | 3.0231 | 0.011 | 277.140 | 0.0 | 3.002 | 3.044 |
| ar.L1 | 0.7969 | 0.009 | 93.728 | 0.0 | 0.780 | 0.814 |
| sigma2 | 0.9886 | 0.020 | 49.941 | 0.0 | 0.950 | 1.027 |
exog
in AutoReg
¶
When using AutoReg
to estimate a model using OLS, the model differs from both SARIMAX
and ARIMA
. The AutoReg
specification with exogenous variables is
This specification is not equivalent to the specification estimated in SARIMAX
and ARIMA
. Here the difference is non-trivial, and naive estimation on the same time series results in different parameter values, even in large samples (and the limit). Estimating this model changes the parameter estimates on the AR(1) coefficient.
AutoReg
¶
[11]:
autoreg_exog_res = AutoReg(y, 1, exog=x, trend="c").fit()
print_params(autoreg_exog_res.summary())
[11]:
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 7.9714 | 0.064 | 124.525 | 0.0 | 7.846 | 8.097 |
| y.L1 | 0.1838 | 0.006 | 29.890 | 0.0 | 0.172 | 0.196 |
| x1 | 3.0311 | 0.021 | 142.513 | 0.0 | 2.989 | 3.073 |
The key difference can be seen by writing the model in lag operator notation.
where it is is assumed that \(|\phi|<1\). Here we see that \(Y_t\) depends on all lagged values of \(X_t\) and \(\eta_t\). This differs from the specification estimated by SARIMAX
and ARIMA
, which can be seen to be
In this specification, \(Y_t\) only depends on \(X_t\) and no other lags.
Using the correct DGP with AutoReg
¶
Simulating the process that is estimated in AutoReg
shows that the parameters are recovered from the true model.
[12]:
y = beta + eta
epsilon = eta.copy()
for i in range(1, eta.shape[0]):
y[i] = beta * (1 - rho) + rho * y[i - 1] + 3 * full_x[i] + eta[i]
y = y[200:]
AutoReg
with correct DGP¶
[13]:
autoreg_alt_exog_res = AutoReg(y, 1, exog=x, trend="c").fit()
print_params(autoreg_alt_exog_res.summary())
[13]:
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 1.9870 | 0.030 | 66.526 | 0.0 | 1.928 | 2.046 |
| y.L1 | 0.7968 | 0.003 | 300.382 | 0.0 | 0.792 | 0.802 |
| x1 | 3.0263 | 0.014 | 217.034 | 0.0 | 2.999 | 3.054 |
Reconstructing residuals, fitted values and forecasts in SARIMAX
and ARIMA
¶
In models that contain only autoregressive terms, trends and exogenous variables, fitted values and forecasts can be easily reconstructed once the maximum lag length in the model has been reached. In practice, this means after \((P+D)s+p+d\) periods. Earlier predictions and residuals are harder to reconstruct since the model builds the best prediction for \(Y_t|Y_{t-1},Y_{t-2},...\). When the number of lags of \(Y\) is less than the autoregressive order, then the expression for the
optimal prediction differs from the model. For example, when predicting the very first value, \(Y_1\), there is no information available from the history of \(Y\), and so the best prediction is the unconditional mean. In the case of an AR(1), the second prediction will follow the model, so that when using ARIMA
, the prediction is
since ARIMA
treats both exogenous and trend terms as regression with ARMA errors.
This can be seen in the next set of cells.
[14]:
arima_res = ARIMA(y, order=(1, 0, 0), trend="c").fit()
print_params(arima_res.summary())
[14]:
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 9.9346 | 0.222 | 44.667 | 0.0 | 9.499 | 10.371 |
| ar.L1 | 0.7957 | 0.009 | 92.515 | 0.0 | 0.779 | 0.813 |
| sigma2 | 10.3015 | 0.204 | 50.496 | 0.0 | 9.902 | 10.701 |
[15]:
arima_res.predict(0, 2)
[15]:
array([ 9.93458658, 10.91088035, 11.80415747])
[16]:
delta_hat, rho_hat = arima_res.params[:2]
delta_hat + rho_hat * (y[0] - delta_hat)
[16]:
10.910880346330751
SARIMAX
treats trend terms differently, and so the one-step forecast from a model estimated using SARIMAX
is
[17]:
sarima_res = SARIMAX(y, order=(1, 0, 0), trend="c").fit()
print_params(sarima_res.summary())
This problem is unconstrained.
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 3 M = 10
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.58518D+00 |proj g|= 5.99456D-05
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
3 3 5 1 0 0 3.347D-05 2.585D+00
F = 2.5851830060985752
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
[17]:
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 2.0283 | 0.097 | 20.841 | 0.0 | 1.838 | 2.219 |
| ar.L1 | 0.7959 | 0.009 | 92.536 | 0.0 | 0.779 | 0.813 |
| sigma2 | 10.3007 | 0.204 | 50.500 | 0.0 | 9.901 | 10.700 |
[18]:
sarima_res.predict(0, 2)
[18]:
array([ 9.93588659, 10.91128867, 11.80469658])
[19]:
delta_hat, rho_hat = sarima_res.params[:2]
delta_hat + rho_hat * y[0]
[19]:
10.911288670367867
Prediction with MA components¶
When a model contains a MA component, the prediction is more complicated since errors are never directly observable. The prediction is still \(Y_t|Y_{t-1},Y_{t-2},...\), and when the MA component is invertible, then the optimal prediction can be represented as a \(t\)-lag AR process. When \(t\) is large, this should be very close to the prediction as if the errors were observable. For short lags, this can differ markedly.
In the next cell we simulate an MA(1) process, and fit an MA model.
[20]:
rho = 0.8
beta = 10
epsilon = eta.copy()
for i in range(1, eta.shape[0]):
epsilon[i] = rho * eta[i - 1] + eta[i]
y = beta + epsilon
y = y[200:]
ma_res = ARIMA(y, order=(0, 0, 1), trend="c").fit()
print_params(ma_res.summary())
[20]:
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 9.9185 | 0.025 | 391.129 | 0.0 | 9.869 | 9.968 |
| ma.L1 | 0.8025 | 0.009 | 93.864 | 0.0 | 0.786 | 0.819 |
| sigma2 | 0.9904 | 0.020 | 49.925 | 0.0 | 0.951 | 1.029 |
We start by looking at predictions near the beginning of the sample corresponding y[1]
, …, y[5]
.
[21]:
ma_res.predict(1, 5)
[21]:
array([ 8.57011015, 9.19907188, 8.96971353, 9.78987115, 11.11984478])
and the corresponding residuals that are needed to produce the “direct” forecasts
[22]:
ma_res.resid[:5]
[22]:
array([-2.7621904 , -1.12255005, -1.33557621, -0.17206944, 1.5634041 ])
Using the model parameters, we can produce the “direct” forecasts using the MA(1) specification
We see that these are not especially close to the actual model predictions for the initial forecasts, but that the gap quickly reduces.
[23]:
delta_hat, rho_hat = ma_res.params[:2]
direct = delta_hat + rho_hat * ma_res.resid[:5]
direct
[23]:
array([ 7.70168405, 9.01756049, 8.84659855, 9.7803589 , 11.17314527])
The difference is nearly a standard deviation for the first but declines as the index increases.
[24]:
ma_res.predict(1, 5) - direct
[24]:
array([ 0.8684261 , 0.18151139, 0.12311499, 0.00951225, -0.05330049])
We next look at the end of the sample and the final three predictions.
[25]:
t = y.shape[0]
ma_res.predict(t - 3, t - 1)
[25]:
array([ 9.79692804, 10.51272714, 10.55855562])
[26]:
ma_res.resid[-4:-1]
[26]:
array([-0.15142355, 0.74049384, 0.79759816])
[27]:
direct = delta_hat + rho_hat * ma_res.resid[-4:-1]
direct
[27]:
array([ 9.79692804, 10.51272714, 10.55855562])
The “direct” forecasts are identical. This happens since the effect of the short sample has disappeared by the end of the sample (In practice it is negligible by observations 100 or so, and numerically absent by around observation 160).
[28]:
ma_res.predict(t - 3, t - 1) - direct
[28]:
array([0., 0., 0.])
The same principle applies in more complicated model that include multiple lags or seasonal term - predictions in AR models are simple once the effective lag length has been reached, while predictions in models that contains MA components are only simple once the maximum root of the MA lag polynomial is sufficiently small so that the residuals are close to the true residuals.
Prediction differences in SARIMAX
and ARIMA
¶
The formulas used to make predictions from SARIMAX
and ARIMA
models differ in one key aspect - ARIMA
treats all trend terms, e.g, the intercept or time trend, as part of the exogenous regressors. For example, an AR(1) model with an intercept and linear time trend estimated using ARIMA
has the specification
When the same model is estimated using SARIMAX
, the specification is
The differences are more apparent when the model contains exogenous regressors, \(X_t\). The ARIMA
specification is
while the SARIMAX
specification is
The key difference between these two is that the intercept and the trend are effectively equivalent to exogenous regressions in ARIMA
while they are more like standard ARMA terms in SARIMAX
.
The next cell simulates an ARX with a time trend using the specification in ARIMA
and estimates the parameters using both estimators.
[29]:
rho = 0.8
beta = 2
delta0 = 10
delta1 = 0.5
epsilon = eta.copy()
for i in range(1, eta.shape[0]):
epsilon[i] = rho * epsilon[i - 1] + eta[i]
t = np.arange(epsilon.shape[0])
y = delta0 + delta1 * t + beta * full_x + epsilon
y = y[200:]
[30]:
start = np.array([110, delta1, beta, rho, 1])
arx_res = ARIMA(y, exog=x, order=(1, 0, 0), trend="ct").fit()
mod = SARIMAX(y, exog=x, order=(1, 0, 0), trend="ct")
start[:2] *= 1 - rho
sarimax_res = mod.fit(start_params=start, method="bfgs")
Optimization terminated successfully.
Current function value: 1.413691
Iterations: 46
Function evaluations: 58
Gradient evaluations: 58
The two estimators fit similarly, although there is a small difference in the log-likelihood. This is a numerical issue and should not materially affect the predictions. Importantly the two trend parameters, const
and x1
(unfortunately named for the time trend), differ between the two. The other parameters are effectively identical.
[31]:
print(arx_res.summary())
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 5000
Model: ARIMA(1, 0, 0) Log Likelihood -7069.171
Date: Wed, 02 Nov 2022 AIC 14148.343
Time: 17:05:25 BIC 14180.928
Sample: 0 HQIC 14159.763
- 5000
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const 109.2112 0.137 796.186 0.000 108.942 109.480
x1 0.5000 4.78e-05 1.05e+04 0.000 0.500 0.500
x2 2.0495 0.011 187.517 0.000 2.028 2.071
ar.L1 0.7965 0.009 93.669 0.000 0.780 0.813
sigma2 0.9897 0.020 49.854 0.000 0.951 1.029
===================================================================================
Ljung-Box (L1) (Q): 0.33 Jarque-Bera (JB): 0.15
Prob(Q): 0.57 Prob(JB): 0.93
Heteroskedasticity (H): 0.97 Skew: -0.01
Prob(H) (two-sided): 0.53 Kurtosis: 3.00
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[32]:
print(sarimax_res.summary())
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 5000
Model: SARIMAX(1, 0, 0) Log Likelihood -7068.457
Date: Wed, 02 Nov 2022 AIC 14146.914
Time: 17:05:25 BIC 14179.500
Sample: 0 HQIC 14158.335
- 5000
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 22.7438 0.929 24.481 0.000 20.923 24.565
drift 0.1019 0.004 23.985 0.000 0.094 0.110
x1 2.0230 0.011 185.290 0.000 2.002 2.044
ar.L1 0.7963 0.008 93.745 0.000 0.780 0.813
sigma2 0.9894 0.020 49.899 0.000 0.951 1.028
===================================================================================
Ljung-Box (L1) (Q): 0.47 Jarque-Bera (JB): 0.13
Prob(Q): 0.49 Prob(JB): 0.94
Heteroskedasticity (H): 0.97 Skew: -0.01
Prob(H) (two-sided): 0.47 Kurtosis: 3.00
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
Initial residuals SARIMAX
and ARIMA
¶
Residuals for observations before the maximal model order, which depends on the AR, MA, Seasonal AR, Seasonal MA and differencing parameters, are not reliable and should not be used for performance assessment. In general, in an ARIMA with orders \((p,d,q)\times(P,D,Q,s)\), the formula for residuals that are less well behaved is:
We can simulate some data from an ARIMA(1,0,0)(1,0,0,12) and examine the residuals.
[33]:
import numpy as np
import pandas as pd
rho = 0.8
psi = -0.6
beta = 20
epsilon = eta.copy()
for i in range(13, eta.shape[0]):
epsilon[i] = (
rho * epsilon[i - 1]
+ psi * epsilon[i - 12]
- (rho * psi) * epsilon[i - 13]
+ eta[i]
)
y = beta + epsilon
y = y[200:]
With a large sample, the parameter estimates are very close to the DGP parameters.
[34]:
res = ARIMA(y, order=(1, 0, 0), trend="c", seasonal_order=(1, 0, 0, 12)).fit()
print(res.summary())
SARIMAX Results
========================================================================================
Dep. Variable: y No. Observations: 5000
Model: ARIMA(1, 0, 0)x(1, 0, 0, 12) Log Likelihood -7076.266
Date: Wed, 02 Nov 2022 AIC 14160.532
Time: 17:05:28 BIC 14186.600
Sample: 0 HQIC 14169.668
- 5000
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const 19.8586 0.043 458.609 0.000 19.774 19.943
ar.L1 0.7972 0.008 93.925 0.000 0.781 0.814
ar.S.L12 -0.6044 0.011 -53.280 0.000 -0.627 -0.582
sigma2 0.9914 0.020 49.899 0.000 0.952 1.030
===================================================================================
Ljung-Box (L1) (Q): 0.50 Jarque-Bera (JB): 0.11
Prob(Q): 0.48 Prob(JB): 0.95
Heteroskedasticity (H): 0.96 Skew: -0.01
Prob(H) (two-sided): 0.40 Kurtosis: 2.99
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
We can first examine the initial 13 residuals by plotting against the actual shocks in the model. While there is a correspondence, it is fairly weak and the correlation is much less than 1.
[35]:
import matplotlib.pyplot as plt
plt.rc("figure", figsize=(10, 10))
plt.rc("font", size=14)
_ = plt.scatter(res.resid[:13], eta[200 : 200 + 13])
Looking at the next 24 residuals and shocks, we see there is nearly perfect correlation. This is expected in large samples once the less accurate residuals are ignored.
[36]:
_ = plt.scatter(res.resid[13:37], eta[200 + 13 : 200 + 37])
Next, we simulate an ARIMA(1,1,0), and include a time trend.
[37]:
rng = np.random.default_rng(20210819)
eta = rng.standard_normal(5200)
rho = 0.8
beta = 20
epsilon = eta.copy()
for i in range(2, eta.shape[0]):
epsilon[i] = (1 + rho) * epsilon[i - 1] - rho * epsilon[i - 2] + eta[i]
t = np.arange(epsilon.shape[0])
y = beta + 2 * t + epsilon
y = y[200:]
Again the parameter estimates are very close to the DGP parameters.
[38]:
res = ARIMA(y, order=(1, 1, 0), trend="t").fit()
print(res.summary())
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 5000
Model: ARIMA(1, 1, 0) Log Likelihood -7067.739
Date: Wed, 02 Nov 2022 AIC 14141.479
Time: 17:05:29 BIC 14161.030
Sample: 0 HQIC 14148.331
- 5000
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
x1 1.7747 0.069 25.642 0.000 1.639 1.910
ar.L1 0.7968 0.009 93.658 0.000 0.780 0.813
sigma2 0.9896 0.020 49.908 0.000 0.951 1.028
===================================================================================
Ljung-Box (L1) (Q): 0.43 Jarque-Bera (JB): 0.09
Prob(Q): 0.51 Prob(JB): 0.96
Heteroskedasticity (H): 0.97 Skew: -0.01
Prob(H) (two-sided): 0.47 Kurtosis: 2.99
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
The residuals are not accurate, and the first residual is approximately 500. The others are closer, although in this model the first 2 should usually be ignored.
[39]:
res.resid[:5]
[39]:
array([ 5.08403002e+02, -1.58904197e+00, -1.54902445e+00, 1.04992619e-01,
1.33644383e+00])
The reason why the first residual is so large is that the optimal prediction of this value is the mean of the difference, which is 1.77. Once the first value is known, the second value makes use of the first value in its prediction and the prediction is substantially closer to the truth.
[40]:
res.predict(0, 5)
[40]:
array([ 1.77472563,<PHONE_NUMBER>9,<PHONE_NUMBER>6,<PHONE_NUMBER>4,
509.03356182,<PHONE_NUMBER>9])
It is worth noting that the results class contains two parameters than can be helpful in understanding which residuals are problematic, loglikelihood_burn
and nobs_diffuse
.
[41]:
res.loglikelihood_burn, res.nobs_diffuse
[41]:
(1, 0)


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Voltage Domain descriptor. More...
#include <hw_pmhal_common_vmdata.h>
Data Fields | |
| uint16_t | minSafemV |
| uint16_t | maxSafemV |
| uint16_t | minOppNommV |
| uint16_t | maxOppNommV |
| uint32_t | sldoSetupAddr |
| uint32_t | sldoCtrlAddr |
| uint32_t | sLdoValidRetMode |
| const pmhalVmOppVoltage_t * | oppVoltages [PMHAL_VM_OPP_MAX] |
| uint32_t | oppIdArr [PMHAL_VM_OPP_MAX] |
| pmhalPrcmCpuId_t * | cpuIdArr |
| uint32_t | numCpus |
Voltage Domain descriptor.
| pmhalPrcmCpuId_t* pmhalVmVoltageDomain::cpuIdArr |
Array which lists the CPUs in the given voltage domain
| uint16_t pmhalVmVoltageDomain::maxOppNommV |
Maximum OPP_NOM voltage in mV.
| uint16_t pmhalVmVoltageDomain::maxSafemV |
Maximum safe voltage in mV. The SoC doesn't support voltage greater than this.
| uint16_t pmhalVmVoltageDomain::minOppNommV |
Minimum OPP_NOM voltage in mV.
| uint16_t pmhalVmVoltageDomain::minSafemV |
Minimum safe voltage in mV. The SoC doesn't support voltage less than this.
| uint32_t pmhalVmVoltageDomain::numCpus |
Number of CPUs in the given voltage domain
| uint32_t pmhalVmVoltageDomain::oppIdArr[PMHAL_VM_OPP_MAX] |
Not all voltage domains have all OPPs supported. The elements of this array ensure the getOPP function returns the right value of the opp while searching through the array
| const pmhalVmOppVoltage_t* pmhalVmVoltageDomain::oppVoltages[PMHAL_VM_OPP_MAX] |
The nominal voltage of each OPP supported in this VD. Some OPPs are only supported in AVS. A value of zero means that the OPP is not supported.
| uint32_t pmhalVmVoltageDomain::sldoCtrlAddr |
Address of the register to set the corresponding voltage domain's SRAM LDO Control
| uint32_t pmhalVmVoltageDomain::sldoSetupAddr |
Address of the register to set the corresponding voltage domain's SRAM LDO Setup
| uint32_t pmhalVmVoltageDomain::sLdoValidRetMode |
One Hot encoding of the valid SRAM LDO Retention states supported Refer pmhalPrcmSramLdoRetState_t for more details


BREAKING: Reps minority caucus rejects Kogi, Bayelsa polls results
by Leke BaiyewuLeke Baiyewu, Abuja
The minority caucus of House of Representatives has rejected the outcome of the elections held in Kogi and Bayelsa States on Saturday.
The Minority Leader, Ndudi Elumelu, who led the leadership of the caucus to address journalists in Abuja on Tuesday, said the elections, comprising governorship and senatorial, were marred by violence, rigging and other irregularities.
“We are 100 per cent not in support of the outcome of the elections,” Elumelu stated.
The Minority Leader lamented that Senator Dino Melaye lost his nephew to the rerun in Kogi-West senatorial district, an indication that the exercise was marred by violence.
Candidates of the ruling All Progressives Congress, Yahaya Bello and David Lyon, have been declared the winners of the governorship elections in Kogi and Bayelsa States respectively.
Details later.


Seahawks WR Lockett hospitalized with leg injury
Seattle Seahawks wide receiver Tyler Lockett was taken to a Bay Area hospital after suffering a potentially serious leg injury in Monday night’s overtime win over the San Francisco 49ers.
Coach Pete Carroll said after the game that Lockett sustained a leg contusion that swelled, and the team was concerned about the possibility of compartment syndrome. The condition can occur after a trauma, and the swelling can lead to a restriction of blood flow and oxygen levels.
“I can’t tell you much more about it right now. He’s out of here right now to get looked at,” Carroll said.
He added: “It’ll be OK, but it’s a pretty severe situation for right now. ... We’re ahead of it, so we should be in good shape.”
NBC Sports Northwest reported Lockett spent the night in the hospital.
It is unclear when the injury occurred. Lockett received the kickoff in the final seconds of the game and took a knee. He did not play in overtime and was carted off the field during the extra period.
Lockett, 27, was Seattle’s third-round selection in the 2015 NFL Draft. He has emerged as the team’s No. 1 wide receiver and on the year, has 62 receptions for 793 yards and six touchdowns. In his career, he’s played in 73 games (49 starts). He has 256 receptions for 3,574 yards and 25 touchdowns.
The Seahawks have a bye this week and will return to play on the road against the Philadelphia Eagles on Nov. 24.
—Field Level Media


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