General
MPEG DASH defines formats for MPDs and segments. In addition MPEG provides the ability to further restrict the applied formats by the definition of Profiles as defined on section 8 of ISO/IEC 23009-1 [1]. Profiles of DASH are defined to enable interoperability and the signaling of the use of features. Profiles are signaled in the @profiles parameter which may be present on different levels of the MPD.
Such a profile can also be understood as permission for DASH clients that implement the features required by the profile to process the Media Presentation (MPD document and Segments).
Furthermore, ISO/IEC 23009-1 permits external organizations or individuals to define restrictions, permissions and extensions by using this mechanism. Such external definitions are called Interoperability Points. An interoperability point may be signalled in the @profiles parameter once a URI is defined. The owner of the URI is responsible to provide sufficient semantics on the restrictions and permission of this interoperability point.
DASH-IF further differentiates Interoperability Points and Extensions:
- Interoperability Points provide a basic collection of tools and features to ensure that content/service providers and client vendors can rely to support a sufficiently good audio-visual experience.
- Extensions enable content/service providers and client vendors to enhance the audio-visual experience provided by an Interoperability Point in a conforming manner.
The only difference between Interoperability Points and Extensions is that Interoperability Points define a full audio-visual experience and Extensions enhance the audio-visual experience in typically only one dimension.
The following sections collect the known Profiles, Interoperability Points and Extensions.
Profiles
| Identifier | Reference | Section | Comment |
|---|---|---|---|
urn:mpeg:dash:profile:full:2011 |
ISO/IEC 23009-1 | section 8.2 | identifier for MPEG-DASH Full profile. |
urn:mpeg:dash:profile:isoff-on-demand:2011 |
ISO/IEC 23009-1 | section 8.3 | identifier for MPEG-DASH ISO Base media file format On Demand profile. |
urn:mpeg:dash:profile:isoff-live:2011 |
ISO/IEC 23009-1 | section 8.4 | identifier for MPEG-DASH ISO Base media file format live profile. |
urn:mpeg:dash:profile:isoff-main:2011 |
ISO/IEC 23009-1 | section 8.5 | identifier for MPEG-DASH ISO Base media file format main profile. |
urn:mpeg:dash:profile:mp2t-main:2011 |
ISO/IEC 23009-1 | section 8.6 | identifier for MPEG-DASH MPEG-2 TS main profile. |
urn:mpeg:dash:profile:mp2t-simple:2011 |
ISO/IEC 23009-1 | section 8.7 | identifier for MPEG-DASH MPEG-2 TS simple profile. |
urn:3GPP:PSS:profile:DASH10 |
3GPP TS26.247 | section 7.3.3 | identifier for 3GP-DASH Release-10 profile. |
urn:dvb:dash:profile:dvb-dash:2014 |
HbbTV 2.0.1 | HbbTV 2.0 DASH profiles. | |
urn:hbbtv:dash:profile:isoff-live:2012 |
HbbTV 1.5 | HbbTV 1.5 DASH profiles. |
Interoperability points
| Identifier | Reference | Section | Comment |
|---|---|---|---|
https://dashif.org/guidelines/dash264 |
DASH-IF DASH-AVC/264 | section 6.3 | identifier for DASH-AVC/264 content |
https://dashif.org/guidelines/dash264#sd |
DASH-IF DASH-AVC/264 | section 7.3 | identifier for DASH-AVC/264 SD content |
https://dashif.org/guidelines/dash264#hd |
DASH-IF DASH-AVC/264 | section 8.3 | identifier for DASH-AVC/264 HD content |
https://dashif.org/guidelines/dash264main |
DASH-IF IOP | section 8.2 | DASH-based High Definition IOP for OTT video services based on H.264/AVC including consistent live and ad insertion features |
https://dashif.org/guidelines/dash264high |
DASH-IF IOP | section 8.3 | DASH-based High Definition IOP for OTT video services based on H.264/AVC including consistent live and ad insertion features |
https://dashif.org/guidelines/dash-if-simple |
DASH-IF IOP | section 8.4 | DASH-based High Definition IOP for OTT video services based on H.264/AVC and HEVC/AVC including consistent live and ad insertion features |
https://dashif.org/guidelines/dash-if-main |
DASH-IF IOP | section 8.5 | DASH-based High Definition IOP for OTT video services based on H.264/AVC and HEVC/AVC including advanced live and ad insertion features |
urn:scte:dash:2015#isobmff |
SCTE 214-1 and SCTE 214-3 | identifier for SCTE DASH/FF profile | |
urn:scte:dash:2015#ts |
SCTE 214-1 and SCTE 214-2 | identifier for SCTE DASH/TS profile | |
urn:dvb:dash:profile:dvb-dash:2014 |
ETSI TS 103 285 | The DVB Profile of MPEG-DASH, known as an interoperability point by MPEG, is based on the merging of the ISO/IEC 23009-1, ISO Base media file format live profile and ISO Base media file format On Demand profile. In addition it includes xlink but only in combination with the actuate property set to onLoad. The DVB profile excludes multiplexed representations, segment list addressing and xlink with the actuate property set to onRequest. The URN for the profile (MPEG Interoperability Point) shall be urn:dvb:dash:profile:dvb-dash:2014. | |
urn:3GPP:PSS:iop:DASH-enhanced |
ETSI TS 26.247 | clause 7.3.9 | The 3GP-DASH Enhanced interoperability point (IOP) is identified by the URN urn:3GPP:PSS:iop:DASH-enhanced. This interoperability point includes all features defined in the Release-13 version of this specification in clauses 7.3.6 (media codecs), 7.3.7 (content protection), 8 (Media Presentation Description), 9 (File Format), 10 (QoE), 11 (simple live) and 12 (ad-insertion) |
urn:3GPP:PSS:profile:DASH11:FPS3D |
3GPP TS 26.247 | clause 7.3.5 | TThe 3GP-DASH Release 11 frame-packed stereoscopic 3D video profile. |
urn:3GPP:PSS:profile:DASH11:MS3D |
3GPP TS 26.247 | clause 7.3.4 | The 3GP-DASH Release 11 multiview stereoscopic 3D video profile. |
Extensions
| Identifier | Reference | Section | Comment |
|---|---|---|---|
https://dashif.org/guidelines/dashif#ec-3 |
DASH-IF IOP | section <IP_ADDRESS> | Identifier for DASH-IF multichannel audio extension with Enhanced AC-3 |
https://dashif.org/guidelines/dashif#mlpa |
DASH-IF IOP | section <IP_ADDRESS> | Identifier for DASH-IF multichannel audio extension with Dolby True HD |
https://dashif.org/guidelines/dashif#dtsc |
DASH-IF IOP | section <IP_ADDRESS> | DASH-IF multichannel audio extension with DTS Digital Surround |
https://dashif.org/guidelines/dashif#dtsh |
DASH-IF IOP | section <IP_ADDRESS> | DASH-IF multichannel audio extension with DTS-HD High Resolution and DTS-HD Master Audio |
https://dashif.org/guidelines/dashif#dtse |
DASH-IF IOP | section <IP_ADDRESS> | DASH-IF multichannel audio extension with with DTS Express |
https://dashif.org/guidelines/dashif#dtsl |
DASH-IF IOP | section <IP_ADDRESS> | DASH-IF multichannel audio extension with DTS-HD Lossless (no core) |
https://dashif.org/guidelines/dashif#mps |
DASH-IF IOP | section <IP_ADDRESS> | DASH-IF multichannel audio extension with MPEG Surround |
https://dashif.org/guidelines/dashif#heaac-mc51 |
DASH-IF IOP | section <IP_ADDRESS> | DASH-IF multichannel audio extension with HE-AACv2 level 4 |
https://dashif.org/guidelines/dashif#heaac-mc71 |
DASH-IF IOP | section <IP_ADDRESS> | DASH-IF multichannel audio extension with HE-AACv2 level 6 |
https://dashif.org/guidelines/dashif#ac-4 |
DASH-IF IOP | section <IP_ADDRESS> | Extension to support multichannel extension with AC-4 |
https://dashif.org/guidelines/dashif#mha1 |
DASH-IF IOP | section <IP_ADDRESS> | Extension to support multichannel audio with MPEG-H 3D Audio 1 |
https://dashif.org/guidelines/dashif#mha2 |
DASH-IF IOP | section <IP_ADDRESS> | Extension to support multichannel audio with MPEG-H 3D Audio 2 |
urn:dvb:dash:dash-extensions:2014-1 |
ETSI TS 103 285 | clause <IP_ADDRESS> | A new attributeGroup, containing two attributes, is defined by the present document. It is part of the schema with the namespace `urn:dvb:dash:dash-extensions:2014-1` which is included in the ZIP file attached to the present document. This attributeGroup, called baseUrlAttributes, shall only be used to extend the BaseURL element as indicated in clause <IP_ADDRESS>. |


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(Lam.)Mez
Frank
W.
Gould
HERBIER
DE
LA
GUADELOUPE
CVUAAA
CP
,
-
New
York
Botanical
Garden
lbD4421
1604421
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Basic Asynchronous Expression: ~~~~~~ ~~~~~
Basic Asynchronous Template: ~~~~~~~ ~~~~~~
Basic Asynchronous Statement: ~~ ~~~~~ ~~~~~~ ~
Basic Asynchronous Statement Nested: ~~ ~~~~~~ ~~~~~ ~
Basic Incomplete Asynchronous Statement: ~~~~~~
Advanced Asynchronous Expression: ~~~~~~ ~~~~~~ ~~
Advanced Asynchronous Expression Extended: ~~~~~~ ~~~~~~~~~~ ~~
Advanced Asynchronous Template: ~~~~~~~ ~~~~~~~~~~ ~~~~~~
Advanced Asynchronous Statement: ~~ ~~~~~ ~~~~~~~~~~~~~~ ~~~~~~ ~~~~~~~~~ ~
Advanced Asynchronous Statement Extended: ~~ ~~~~~ ~~~~~~~~~~ ~~ ~
Advanced Asynchronous Statement Nested: ~~ ~~~~~~ ~~~~~~~~~~~~~~ ~~~~~~ ~
Advanced Incomplete Asynchronous Statement: ~~~~~~ ~~~~~~~~~~~~~


How To Play
- Choose a difficulty level (Easy - 4 numbers, Medium - 5 numbers, Hard - 6 numbers)
- Guess a random number the same length as the difficulty level chosen
- Try to figure out the computer's number
Rules
- The length of the number must correspond to the level chosen i.e. 1234 for Easy, 12345 for Medium,
123456 for Hard
- The input must consist only of numbers (no letters or special characters)
- The number should not have duplicate characters (no repeated numbers e.g. 0012, 1656 etc)
Explanation: If your guess has the right number in the right position, it registers as +1Dead. If
it
has the right number but in the wrong position, it registers as +1Wounded.
So if the computer generates a random number 6247 and you guess 1234, it returns 1 dead ('2' in the right
position), 1 wounded ('4' in the wrong position).


__construct()
__construct() :
HEXONET APIClient
loginExtended(mixed|string|int params, otp) : \HEXONET\Response
Perform API login to start session-based communication.
Use given specific command parameters.
| mixed|string|int | params | given specific command parameters |
| otp | optional one time password |
Response
requestNextResponsePage(\HEXONET\Response rr) : \HEXONET\Response|null
Request the next page of list entries for the current list query Useful for tables
| \HEXONET\Response | rr | API Response of current page |
Response or null in case there are no further list entries


| Degree | {{degre.degree}} |
|---|---|
| Field of Study | {{degre.fieldOfStudy}} |
| Grade | {{degre.grade}} |
| Activities | {{degre.cActivities}} |
| Completion Year | {{degre.completionYear}} |
| Designation | {{experience.designation}} |
|---|---|
| Company | {{experience.company}} |
| Address | {{experience.address}} |
| Join Date | {{experience.fromDate | date: 'mediumDate'}} |
| Leaving Date | {{experience.toDate | date: 'mediumDate'}} |
| Currently Working here? | {{experience.currentlyWorking}} |
Thanks for Rating!


XXMONTH XXNUMBER, XXYEAR: President XXINITIAL XXLASTNAME ordained me a member of the first Seventy and pronounced great blessings upon my head by the Spirit of prophecy and revelation. Some of them I will mention which are as follows. That my enemies may confine me in prisons and chains and that I would rend the prisons and chains in twain in the name of XXJESUSCHRIST and that the Lord would give me great power, knowledge, and wisdom and faith so that I should heal the sick, cause the blind to see, the lame to leap as an hart, the deaf to hear, stop the mouths of lions and raise dead to life and waft myself (as did Philip) from river to river, from sea to sea and from continent to continent for the purpose of preaching the gospel of XXJESUSCHRIST and that I should stand before kings and princes and that they would send for me to receive wisdom, knowledge and instruction at my mouth because they considered me wiser than themselves, in like manner as the Egyptians sought wisdom at the hand of Joseph. And that God would give me a multiplicity of blessings that I should preach to the nations of the earth and to the inhabitants upon the islands of the sea and that I should then return to stand upon Mount Zion in the flesh even in XXCOUNTY, XXSTATE, at the coming of Christ and that I should be caught up to meet him in the clouds of heaven for he said this was the word of God unto me and also that I should visit Kolob and preach to the Spirits in prison and that I should bring all of my friends or relatives forth from the Terrestrial Kingdom (who had died) by the power of the Gospel.


This is a small, crowded fishing harbour, about 20nm west of Sidi Fredj. It is well known for its sardines.
Position 36° 37'41"N, 02° 39'21"E (harbour entrance)


This page displays measures for the network Resource x Resource.
Network images have nodes colored by group and sized by betweenness centrality.
Resource-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Resource node.
Input network: Resource x Resource
Measure Min Mean Max Std.Dev Centrality, Closeness 0 0 0 0 Centrality, Closeness [Unscaled] 0 0 0 0 Centrality, Eccentricity 0 0 0 0 Centrality, Ego Betweenness 0 0 0 0 Centrality, Exponential Rank 1 1 1 0 Centrality, Exponential Rank [Unscaled] 1 1 1 0 Centrality, In-Closeness 0 0 0 0 Centrality, In-Closeness [Unscaled] 0 0 0 0 Centrality, In-Inverse Closeness 0 0 0 0 Centrality, Inverse Closeness 0 0 0 0 Centrality, Radiality 0 0 0 0 Measure List
Centrality, Closeness [Resource x Resource]
Centrality, Eccentricity [Resource x Resource]
Centrality, Ego Betweenness [Resource x Resource]
Centrality, Exponential Rank [Resource x Resource]
Centrality, In-Closeness [Resource x Resource]
Centrality, In-Inverse Closeness [Resource x Resource]
Centrality, Closeness
The closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the sum of distances in the network from a node to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network: Resource x Resource (size: 1, density: 0)
All nodes have a score of zero.
Centrality, Eccentricity
The Eccentricity Centrality of a node is the normalized inverse of its maximum distance to any other node. It is highest when the distance to all other nodes is small.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Resource x Resource
All nodes have a score of zero.
Centrality, Ego Betweenness
The Ego (Network) Betweenness Centrality of node in a network is its betweenness score within its own ego network. The ego network contains the following: the node itself, its immediate neighbor nodes, and all links between them.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Resource x Resource
All nodes have a score of zero.
Centrality, Exponential Rank
Computes the Exponential Ranking Centrality that defines the centrality of a node as its trustworthiness, which is based on the degree to which other nodes trust it. The link values define initial trust between nodes: positive values indicate trust and negative values distrust (negative link values can be used). This measure is similar to eigenvector centrality.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Resource x Resource
Rank Resource Value Unscaled 1 All nodes have this value 1 1 Centrality, In-Closeness
The closeness of all other nodes to a node in the network. Loosely, In-Closeness Centrality is the inverse of the sum of distances in the network to a node and from all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Resource x Resource
All nodes have a score of zero.
Centrality, In-Inverse Closeness
The average closeness of a node to the other nodes in a network considering only paths emanating out from a node. Inverse Closeness is the sum of the inverse distances between a node and all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Resource x Resource
All nodes have a score of zero.
Centrality, Inverse Closeness
The average closeness of a node to the other nodes in a network (also called out-inverse closeness centrality). Inverse Closeness is the average inverse distance from a node and to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Resource x Resource
All nodes have a score of zero.
Centrality, Radiality
The Radiality Centrality of a node is the normalized sum of its closeness to all other nodes. The closeness of a node u to another node v is the network diameter minus the shortest path distance from u to v.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Resource x Resource
All nodes have a score of zero.


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After gaining a french Scientific Baccalaureate, I went to the University of Exeter where I got my BSc in Biological Sciences, took home a BUCS silver medal and helped found an amazing Student Society. I then went on to the University of Kent where I got my MSc in Biotechnology and Bioengineering, stared in my first student film and took part in my first MMA competition.
Using the skills I'd acquired during my studies, I've worked as a Marine Biologist in Madagascar, I've undertaken genetic engineering and sequencing projects, and am currently working as a Cloud Computing Engineer and part-time Personal Trainer.
And that's just what I've done so far...
View ResumeAs a Personal Trainer and Nutrition Adviser, seeing a client progress is the best feeling you can get. I've coached multiple clients on their 6-week training programmes and helped them reach their goals, be they weight, performance or lifestyle.
Working in Madagascar for Frontier, I was part of designing and implementing both a forest ecology and a marine biology research project. This included leading multiple terrestrial and marine surveys, coordinating volunteers and helpnig run the research station.
My research project investigated the potential of genetically engineering microalgal coral endosymbionts, as a potential solution to coral bleaching. for this purpose, I created a custom expression vector to test promoter regions of interest in the microalgae.
Feel free to send me an email, be it for collaborations, jobs, or just feedback.
Ping Me


The main purpose of this package is to juxtapose the different control limits obtained by modelling a data set through the COM-Poisson distribution vs. the classical Poisson distribution. Accordingly, this package offers the ability to compute the COM-Poisson parameter estimates and plot associated Shewhart control charts for a given data set.
| Version: | 1.0 |
| Depends: | compoisson |
| Published: | 2014-04-30 |
| Author: | Kimberly Sellers ; Luis Costa |
| Maintainer: | Luis Costa <lc436 at georgetown.edu> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | CMPControl results |
| Reference manual: | CMPControl.pdf |
| Package source: | CMPControl_1.0.tar.gz |
| Windows binaries: | r-devel: CMPControl_1.0.zip, r-release: CMPControl_1.0.zip, r-oldrel: CMPControl_1.0.zip |
| OS X binaries: | r-release: CMPControl_1.0.tgz, r-oldrel: CMPControl_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=CMPControl to link to this page.


{% if page_title %}
Team Builder is an application that makes group allocation easy. Just create a course, add your students and you're on your way.


------------------------------------------------------------------------ -- The Agda standard library -- -- An equality postulate which evaluates ------------------------------------------------------------------------ module Relation.Binary.PropositionalEquality.TrustMe where open import Relation.Binary.Core using (_≡_) open import Agda.Builtin.TrustMe -- trustMe {x = x} {y = y} evaluates to refl if x and y are -- definitionally equal. -- -- For an example of the use of trustMe, see Data.String._≟_. trustMe : ∀ {a} {A : Set a} {x y : A} → x ≡ y trustMe = primTrustMe -- "Erases" a proof. The original proof is not inspected. The -- resulting proof reduces to refl if the two sides are definitionally -- equal. Note that checking for definitional equality can be slow. erase : ∀ {a} {A : Set a} {x y : A} → x ≡ y → x ≡ y erase _ = trustMe


FAQ¶
What is the default image representation in nuts-ml?¶
The standard formats for image data in nuts-ml are Numpy arrays
of shape (h,w,3)
for RGB images, (h,w)
for gray-scale images
and (h,w,4)
for RGBA image.
What image formats can nuts-ml read?¶
The ReadImage
nut can read images in the following formats
GIF, PNG, JPG, BMP, TIF, NPY, where NPY are plain Numpy arrays.
How to flatten a batch of predictions?¶
Assuming the output of a network prediction is a batch of labels, how can it be flattened into a flow of labels?
>>> from nutsflow import Collect, Flatten
>>> batched_labels = [(0, 1, 0), (1, 1, 0)] # e.g. from network.predict()
>>> batched_labels >> Flatten() >> Collect()
[0, 1, 0, 1, 1, 0]
What if the batch has multiple columns, e.g. labels and probabilities
>>> from nutsflow import Collect, FlattenCol
>>> batched_preds = [((0,1,0), (0.1,0.2,0.3)), ((1,1,0), (0.4,0.5,0.6))]
>>> batched_preds >> FlattenCol((0,1)) >> Collect()
[(0, 0.1), (1, 0.2), (0, 0.3), (1, 0.4), (1, 0.5), (0, 0.6)]
Error: Only length-1 arrays can be converted to Python scalars¶
If you see the following error message when running
network.evaluate()
under Keras you need to upgrade to Keras 2.x
and the latests nuts-ml
version.
in compute_metric
return float(result.eval() if hasattr(result, 'eval') else result)
TypeError: only length-1 arrays can be converted to Python scalars
ImportError: No module named Tkinter¶
This means the computer nuts-ml is running on is not supporting
the default graphical backend for matplotlib. In this case create a file
~/.config/matplotlib/matplotlibrc
with the following content:
backend : Agg
Alternatively add the following lines to your code:
import matplotlib
matplotlib.use('Agg')
How to use class weights for imbalanced classes in Keras¶
class_weight = {0:1, 1:50}
for epoch in xrange(EPOCHS):
t_loss = samples >> build_batch >> network.train(class_weight=class_weight) >> Mean()
If the samples are an iterable (and not an iterator that is consumed) the class weights can also be computed directly. For instance, assuming that the class labels are in the second column (index = 1) of the sample, the following code can be used
class_weight = samples >> Get(1) >> CountValues()


Welcome to CLOUD 9
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Access files from any device
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You can download your files by using the key. the files will be decrypted in your browser.
Upload files through Linux client
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Download our linux client to Upload files and manage them from your pc.
With the Linux client, you get handy command line tool to access your files.
You can choose to periodically sync your directories to the cloud.
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By default, DokuWiki does no URL rewriting, resulting in URLs like this:
http://example.com/doku.php?id=page
These URLs are considered ugly and are not indexed well by some search engines.
Rewriting URLs in Apache is done through the mod_rewrite
module of Apache 1
or Apache 2.
The module is enabled in httpd.conf
with the following line (make sure it is not commented out):
LoadModule rewrite_module modules/mod_rewrite.so
On many kinds of Linux (Ubuntu, Debian, etc) you may enable mod_rewrite
with:
sudo a2enmod rewrite
The setup of module mod_rewrite is done through so-called rewrite rules,
which can be either defined directly in the server's main config or in a .htaccess
file located in DokuWiki's main directory.
DokuWiki comes with an .htaccess.dist
file which contains the needed rewrite rules for mode 1, but commented.
Just copy the file to .htaccess
(in the folder that contains doku.php
, caveat Debian users) and uncomment the lines to enable rewriting.
In Debian 6 at least you do not have to copy anything. /usr/share/dokuwiki/.htaccess
links to /etc/dokuwiki/htaccess
. So there is no caveat anymore.
.htaccess
RewriteEngine on RewriteBase /dokuwiki RewriteRule ^_media/(.*) lib/exe/fetch.php?media=$1 [QSA,L] RewriteRule ^_detail/(.*) lib/exe/detail.php?media=$1 [QSA,L] RewriteRule ^_export/([^/]+)/(.*) doku.php?do=export_$1&id=$2 [QSA,L] RewriteRule ^$ doku.php [L] RewriteCond %{REQUEST_FILENAME} !-f RewriteCond %{REQUEST_FILENAME} !-d RewriteRule (.*) doku.php?id=$1 [QSA,L] RewriteRule ^index.php$ doku.php
Remember to set following in the Configuration Manager /start?do=admin&page=config
.htaccess
Otherwise rewrite rules will not be useful.


The only way to be sure of catching a train is to miss the one before it.
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{% extends 'base.html'%} {% block styles%} {{ super() }} {% endblock %} {% block content %}
The most incredible thing to do when bored is to watch a movie
Good software, like wine, takes time
So God created mankind in his own image, in the image of God he created them; male and female he created them.” (Genesis 1:27


--- subject: New post on Addigy.com --- {{> blogpost-header}} \{{ email.html }} --Keep I.T. Real, The Addigy Team
--Keep I.T. Real, The Addigy Team


Capital: {{ country.capital }}
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Border Coutries: This country is an island 🏝


I had it running in the background and was trying to clean off this pc so I can use it for testing purposes.
Then it started giving this error:
error: couldn't connect to server: timeout reached (code 0)
(make sure server is running and you are connecting to the correct RPC port)


Gone Fishing - Word Guess Game
Name that species of fish
Click any letter to being!


Topics
- Communication - language, documentation, style
- Machine - processing, data, compiling
- Directive - operators, functions
- Scope - access, callstack, loops
- Class - types, encapsulation, inheritance
- Events - callbacks, cross-communication
- Extras - reflection, recursion


Patrick Cudahy
{% extends "template.html" %}
{% block content %}
Hi, I'm Patrick Cudahy and I develop websites and software.
I'm a software developer available for hire. With a background and degree in conversational agents, I have experience with NLP, ML, and AI coding.
I can also make websites, like this one, but I'm always trying expand my skills with new projects. Hopefully, the next project will be something bigger at a company I love working for.
{% endblock %}


Charge
@objcMembers
public class Charge : NSObject, Decodable, Identifiable
Charge model. To charge a credit or a debit card, you create a charge object. You can retrieve and refund individual charges as well as list all charges. Charges are identified by a unique random ID.
-
Unique identifier for the object.
Declaration
Swift
public private(set) var identifier: String?
-
Objects of the same type share the same value
Declaration
Swift
public private(set) var object: String?
-
Amount. The minimum amount is $0.50 US or equivalent in charge currency.
Declaration
Swift
public private(set) var amount: Decimal
-
Amount refunded (can be less than the amount attribute on the charge if a partial refund was issued).
Declaration
Swift
public private(set) var refundedAmount: Decimal
-
If the charge was created without capturing, this Boolean represents whether it is still uncaptured or has since been captured.
Declaration
Swift
public private(set) var isCaptured: Bool
-
Defines if 3DS is required.
Declaration
Swift
public private(set) var require3DSecure: Bool
-
Merchant Reference number to track the payment status and payment attempts.
Declaration
Swift
public private(set) var referenceNumber: String?
-
Time at which the object was created. Measured in seconds since the Unix epoch.
Declaration
Swift
public private(set) var creationDate: Date?
-
Three-letter ISO currency code, in lowercase. Must be a supported currency.
Declaration
Swift
public private(set) var currency: String?
-
ID of the customer this charge is for if one exists.
Declaration
Swift
public private(set) var customerIdentifier: String?
-
Customer’s first name. If customer identifier is provided, then it is not required.
Declaration
Swift
public private(set) var firstName: String?
-
Customer’s last name. If customer identifier is provided, then it is not required.
Declaration
Swift
public private(set) var lastName: String?
-
An arbitrary string attached to the object. Often useful for displaying to users.
Declaration
Swift
public private(set) var descriptionText: String?
-
Error code explaining reason for charge failure if available (see the errors section for a list of codes).
Declaration
Swift
public private(set) var failureCode: String?
-
Message to user further explaining reason for charge failure if available.
Declaration
Swift
public private(set) var failureMessage: String?
-
Flag indicating whether the object exists in live mode or test mode.
Declaration
Swift
public private(set) var isLiveMode: Bool
-
Set of key/value pairs that you can attach to an object. It can be useful for storing additional information about the object in a structured format.
Declaration
Swift
public private(set) var metadata: [String : String]?
-
true if the charge succeeded, or was successfully authorized for later capture.
Declaration
Swift
public private(set) var isPaid: Bool
-
This is the email address that the receipt for this charge was sent to.
Declaration
Swift
public private(set) var receiptEmail: String?
-
The mobile number to send this charge’s receipt to. The receipt will not be sent until the charge is paid. If this charge is for a customer, the mobile number specified here will override the customer’s mobile number. Receipts will not be sent for test mode charges. If receipt_sms is specified for a charge in live mode, a receipt will be sent regardless of your sms settings. (optional, either receipt_sms or receipt_email is required if customer is not available)
Declaration
Swift
public private(set) var receiptSMS: String?
-
This is the transaction number that appears on email receipts sent for this charge. This attribute will be null until a receipt has been sent.
Declaration
Swift
public private(set) var receiptNumber: String?
-
Whether the charge has been fully refunded. If the charge is only partially refunded, this attribute will still be false.
Declaration
Swift
public private(set) var isRefunded: Bool
-
The source of every charge is a credit or debit card. This hash is then the card object describing that card. If source is null then, default Tap payment page link will be provided. if source.id =
src_kw.knet
then KNET payment page link will be provided. if source.id =src_visamastercard
then Credit Card payment page link will be provided.Declaration
Swift
public private(set) var source: ChargeSource?
-
Extra information about a charge. This will appear on your customer’s credit card statement. It must contain at least one letter.
Declaration
Swift
public private(set) var statementDescriptor: String?
-
The status of the payment is either succeeded, pending, or failed.
Declaration
Swift
public private(set) var status: String?
-
Information related to the payment page redirect.
Declaration
Swift
public private(set) var redirect: ChargeRedirect?
-
Pretty printed description of the Charge object.
Declaration
Swift
public override var description: String { get }


Photo Link Get the presentation here! Get the handout presentation here! Get the Beautiful Latex here! Thank you!


{% extends 'layouts/layout.html' %} {% block pageTitle %} These are my details but some are wrong {% endblock %} {% block content %}
| Name | Alex smith |
| Date of birth | 18 August 1989 |
| NHS number | *** *** 3456 |
If your name is wrong, you should contact your GP surgery to update your details. Doing so will then update your details on this NHS record.
If all of these details are wrong, contact us and let us know.
Reference: CID1005


Red Wings prospect Joe Veleno to join Canada ahead of world juniors
by Ryan McKennaHockey Canada has received a boost ahead of the upcoming world junior championship.
Forward Joe Veleno will join the team in the Czech Republic ahead of the holiday tournament after starting the season in the American Hockey League.
The 19-year-old from Kirkland, Que., has three goals and six assists in 25 games this year for the Detroit Red Wings’ AHL affiliate, the Grand Rapids Griffins. He also has a minus-20 rating.
Veleno played for Canada at last year’s world juniors and had two assists in five games as the Canadians were eliminated in the quarterfinals by Finland.
Other names the Canadians are hoping return from their pro clubs for the event include Noah Dobson of the New York Islanders along with forwards Kirby Dach of the Chicago Blackhawks and Barrett Hayton of the Arizona Coyotes. NHL teams have until Dec. 19 — when rosters freeze — to decide whether they will allow players to represent their countries.
The Canadians are in Oakville, Ont., this week for their selection camp with 16 forwards, 10 defencemen and four goalies all competing for a spot on the roster.
Canada opens play in the tournament on Boxing Day against the United States in Ostrava, Czech Republic.


Eglo Records, Vol. 2 is out next month
The 30-track compilation features releases from the last five years, alongside new exclusives and previously unreleased material
by James BallAlexander Nut will celebrate ten years of his Eglo Records imprint with a new compilation, titled 'Eglo Records, Vol. 2'.
The 30-tracker features previous Eglo releases from the likes of Henry Wu and Byron the Aquarius, in addition to exclusives and previously unreleased tracks from Floating Points, Dirg Gerner and other artists.
Read this next: Alexander Nut in The Lab LDN
Selected tracks from the compilation will be released on a series of limited edition 12” vinyl EPs, which will be housed in a special '10 Years Of Eglo Records' sleeve designed by Caterina Bianchini.
You can pre-order the compilation on Bandcamp now.
Read this next: Watch Floating Points preview his new live show
Check out Alexander Nut's set in The Lab LDN below.
James Ball is Mixmag's Weekend Editor. Follow him on Twitter
Read this next: Get the best of Mixmag direct to your Facebook DMs
Mixmag Weekly
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February 19
Get wisdom, get understanding: forget it not; neither decline from the words of my mouth. Forsake her not, and she shall preserve thee: love her, and she shall keep thee.
Proverbs 4:5-6
Recently, God has been raising up ever-growing numbers of people who are hungry. Hungry to know their Lord in a deeper way. Hungry to serve Him--in pulpits, on mission fields, in homes, in office buildings, and anywhere else He might choose to send them.
They are people who simply aren't content to find the path of least resistance and coast their way into heaven. No, they're determined to run the race...to run, as the apostle Paul said, to obtain the prize. Spiritually speaking, they are on the road to excellence.
Are you among them?
If so, I want to share four simple words that, I believe, will enable you to run the race like a winner:
Put the Word first.
Whether your goal is to be an excellent evangelist or a first-rate engineer in the service of the Lord, it is the wisdom that comes from the Word of God that will get you there.
Jesus said it this way: "Seek ye first the kingdom of God, and His righteousness; and all these things shall be added unto you" (Matthew 6:33).
So commit yourself right now to do whatever it takes to totally saturate yourself with the Word of God. Use every available moment to read, study, listen to, and meditate on it.
I know it won't be easy, but if you've made a definite decision to amount to something in the ministry of Jesus Christ and you are determined to take the Great Commission seriously, then be diligent. Put the Word first and there will surely be glorious victories ahead.
Scripture Reading: Proverbs 4:1-18


This layout is not used by any modules, instead it is rendered by the {tag_postlist} tag which can be found in the posts page layout
{tag_counter}
{tag_postauthoralias}
{tag_postdate}
{tag_postmessage}
{tag_replypostlink}
{tag_totalauthorposts}
{tag_avatar}


{% block head %}
Provide FutureGateway API Server user credentials
Are you sure to Logout from APIServer?
Set FutureGateway APIs server address and check its connectivity
Are you sure to detach from APIServer?


Proak
Registration form
Last name
First name
Birth date
E-mail
Password
Confirm password
School
I accept the GTU
Sign in


9 Prediction Models. ARIMA
In this notebook we are going to use ARIMA models to forecast hourly levels of the PM10 / NO2 pollutants.
We are going to focus on the Constitucion station, which is the only station we have meteorological data. We are not going to use the meteorological data in the ARIMA models, but we will use later with machine learning models.
9.1 PM10 hourly prediction models
We are going to focus on the Constitucion station, which is the only station w e have meteorological data. And, at first, we are going to construct models only for predicting PM10 levels.
As first approach we are going to use auto regressive models. So, we will use as explicative variables only lagged values of the variable to predict. In this case PM10 values.
Loading packages
library(readr)
library(dplyr)
library(tidyr)
library(openair) # http://davidcarslaw.github.io/openair/
library(purrr)
library(lubridate)
library(ggplot2)
library(stringr)
library(knitr)
library(xts)
library(zoo)
library(gridExtra)
library(astsa)
library(rvest)
library(fpp2)
library(ranger)
library(broom)
library(RcppRoll)
library(caret)
9.1.1 Data loading
air_data_2 <- readRDS("data_rds/air_data_2.rds")
constitucion_data <- air_data_2 %>% filter(station == "1")
9.1.2 Train, test and validation data
We have 18 years of avalaible data. But we are not going to use all the data. Initially we are going to use just three years for the training of the models (2014-2016) and one year (2017) for testing and validation.
Reasons (pending redaction):
- Time of training process.
- Not all models can take advantage of such long time series. In fact we will see as sometimes smaller datasets achieve similar results.
Train data: 2014-01-01 - 2016-12-31
Test data: 2017-01-01 - 2017-09-30
Validation data: 2017-10-01 - 2017-12-31
train_201401_201612 <- constitucion_data %>% filter(date_time_utc >= '2014-01-01 00:00:00',
date_time_utc <= '2016-12-31 23:00:00') %>%
select(PM10) %>%
mutate(PM10 = replace_na(PM10, mean(PM10, na.rm = TRUE))) %>% # replacing NAs by the mean.
rename(PM10_0 = PM10) %>%
ts(frequency = 24)
# We generate a smaller training dataset with the last three months of 2016
train_201610_201612 <- constitucion_data %>% filter(date_time_utc >= '2016-10-01 00:00:00',
date_time_utc <= '2016-12-31 23:00:00') %>%
select(PM10) %>%
mutate(PM10 = replace_na(PM10, mean(PM10, na.rm = TRUE))) %>%
rename(PM10_0 = PM10) %>%
ts(frequency = 24)
# We generate a bigger training dataset with 9 years (2009-2017)
train_200901_201612 <- constitucion_data %>% filter(date_time_utc >= '2009-01-01 00:00:00',
date_time_utc <= '2016-12-31 23:00:00') %>%
select(PM10) %>%
mutate(PM10 = replace_na(PM10, mean(PM10, na.rm = TRUE))) %>%
rename(PM10_0 = PM10) %>%
ts(frequency = 24)
test_201701_201709 <- constitucion_data %>% filter(date_time_utc >= '2017-01-01 00:00:00',
date_time_utc <= '2017-09-30 23:00:00') %>%
select(PM10) %>%
mutate(PM10 = replace_na(PM10, mean(PM10, na.rm = TRUE))) %>%
rename(PM10_0 = PM10) %>%
ts(frequency = 24)
# We generate a smaller testing dataset with the first two weeks of 2016.
test_20170101_20170114 <- constitucion_data %>% filter(date_time_utc >= '2017-01-01 00:00:00',
date_time_utc <= '2017-01-14 23:00:00') %>%
select(PM10) %>%
mutate(PM10 = replace_na(PM10, mean(PM10, na.rm = TRUE))) %>%
rename(PM10_0 = PM10) %>%
ts(frequency = 24)
validation_201710_201712 <- constitucion_data %>% filter(date_time_utc >= '2017-10-01 00:00:00',
date_time_utc <= '2017-12-31 23:00:00') %>%
select(PM10) %>%
mutate(PM10 = replace_na(PM10, mean(PM10, na.rm = TRUE))) %>%
rename(PM10_0 = PM10) %>%
ts(frequency = 24)
9.1.3 Data Exploration
we plot the three time series using the function autoplot we just generated.
autoplot(train_201401_201612)
autoplot(test_201701_201709, ylim = c(0, 400))
autoplot(validation_201710_201712,ylim = c(0, 400))
### Base model.
In order to create a base model we are going to take as prediction the value from the previous hour, forecasting just one hour ahead.
# As we don't need any training, because we have already defined the model as Xt = Xt-1, we only need to create a testing dataset. We choose the same period as before but we don't use the time series format (ts).
test_201701_201709_2 <- constitucion_data %>% filter(date_time_utc >= '2017-01-01 00:00:00',
date_time_utc < '2017-10-01 00:00:00') %>%
select(PM10) %>%
mutate(PM10 = replace_na(PM10, mean(PM10, na.rm = TRUE))) # We replace the nas (36) by the mean.
base_model <- test_201701_201709_2 %>% mutate(y_pred = lag(PM10, 1)) %>% # We create the column y_pred with the lagged value of PM10 (one hour).
rename(y_test = PM10) %>% # We change the name of the PM10 column to y_test
na.omit() # We remove the observations with nas (just the first row)
y_test <- base_model$y_test
y_pred <- base_model$y_pred
sd_test <- sd(y_test)
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.5327"
## [3] "MAE:" "5.15"
## [5] "RMSE:" "7.45"
## [7] "Standard Deviation (test data):" "10.9"
So, for the one hour PM10 prediction if we take as prediction the previous PM10 hour level we would have a R-squared of 0.5327. So, the model is explaining 53% of the variability of the target variable.
Estimation of errors. We are going to use the MAE (Mean Absolute Error) in order to compare the different models. We choose the MAE over the RMSE (Root Mean Squared Error) because it is easier to interpret and at the same time it is more robust (less sensitive to outliers). Either way, we are going to calculate the RMSE too, precisely because its sensitivity to outliers. It will give us useful information about the behaviour of each model.
We take a look to the relation between the predictions and the actual values.
df <- bind_cols(as.data.frame(y_pred), as.data.frame(y_test))
y_pred_y_test_base_model_graph <- ggplot(data = df, aes(x = y_test, y = y_pred)) +
geom_point(alpha = 0.5) +
theme_minimal()
y_pred_y_test_base_model_graph
We plot the distribution of the errors
ggplot(data = df, aes(x = y_pred - y_test)) +
geom_histogram() +
theme_minimal()
We plot two lines with the predictions and the actual values. We have a problem of overplotting.
autoplot(ts(base_model$y_pred), series="1-step fitted values") +
autolayer(ts(base_model$y_test),
series="Test data") +
theme_minimal() +
theme(legend.position="top")
We plot just the first 14 days of 2017.
two_weeks <- base_model %>% slice(1:336) # 336 corresponds to 24 hours * 14 days.
autoplot(ts(two_weeks$y_pred), series="1-step fitted values") +
autolayer(ts(two_weeks$y_test),
series="Test data") +
theme_minimal() +
theme(legend.position="top")
And as expected, we get two identical lines. But one of them, the prediction, one step forward than the line with the actual data.
9.1.4 ARIMA model
To fit our first ARIMA model we are going to use the auto.arima function from the Forecast package. This function fits several ARIMA models, with different parameters, and selects the model with the best performance. To do this the function auto.arima uses some approximations to speed up the search of the best model. More info about the algorithm applied here.
We set the parameter seasonal=TRUE because we want the function looks for seasonal elements (24 hours).
We have saved the model fitted as an rds object because of the long time of processing.
# model_arima_0 = auto.arima(train_201401_201612,seasonal=TRUE,trace=TRUE) # It took 2 hours to fit the model
# saveRDS(model_arima_0, "data_rds/model_arima_0.rds")
model_arima_0 <- readRDS("data_rds/model_arima_0.rds")
summary(model_arima_0)
## Series: train_201401_201612
## ARIMA(2,1,3)(0,0,2)[24]
##
## Coefficients:
## ar1 ar2 ma1 ma2 ma3 sma1 sma2
## 0.0991 0.5519 -0.5228 -0.6175 0.1642 0.0564 0.0392
## s.e. NaN NaN NaN NaN NaN 0.0063 0.0061
##
## sigma^2 estimated as 87.28: log likelihood=-96095.71
## AIC=192207.4 AICc=192207.4 BIC=192272.8
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 0.003353175 9.341125 5.774614 -Inf Inf 0.5111664 0.002213004
EXPLAIN THE RESULTS p, d, q P, D, Q etc…
To obtain the R-Squared…
y_train <- forecast(model_arima_0, h = 1)$fitted # We make a forecast one hour ahead and we extract the fitted values from the object forecast.
x_train <- train_201401_201612
rss <- sum((y_train - x_train) ** 2)
tss <- sum((x_train - mean(x_train)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4)))
## [1] "R-Squared:" "0.6031"
The R-Squared has increased from 0.5327 to 0.6031. But both, RMSE (9.34) and MAE (5.77), are greater than the scores obtained with the Base Model. But we are comparing two different periods, the testing period (Base Model; 2017-01 - 2017-09) versus the training period (ARIMA model; 2014 - 2016).
To make a fair comparison we will have to compare the same periods. Especially since we know they have important differences in variability.
For this we are going to apply the ARIMA model model_arima_0 on the testing data. Doing so we will be able to get the fitted values from this period and we will be able to obtain its error scores.
y_pred <- forecast(test_201701_201709, model = model_arima_0, h=1)$fitted # We call the forecast function passing the testing data through the ARIMA model fitted with the training data and we extract the fitted values.
y_test <- test_201701_201709
sd_test <- sd(y_test)
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.6015"
## [3] "MAE:" "4.84"
## [5] "RMSE:" "6.88"
## [7] "Standard Deviation (test data):" "10.9"
We confirm we are obtaining a better R-Squared: 0.6015 versus the 0.5327 obtained by the Base Model. The errors are sustantially better too. The ARIMA model obtains a MAE of 4.84 (Base Model: 5.15) and a RMSE of 6.88 (Base Model: 7.45).
Furthermore, the prediction errors on the test data are smaller than in the training data. It could be counterintuitive but the test period (2017-01 - 2017-09) contains much less variability (sd = 10.91) than the training period (2014-01 - 2016-12; sd = 14.83). This could be affecting the error scores.
We plot the fitted values and the test data.
autoplot(y_test, series="Test data") +
autolayer(y_pred,
series="1-step fitted values") +
theme_minimal() +
theme(legend.position="top")
We have made the forecast for one hour ahead and we have estimated its precission. But what happens when we try to forecast more distant values?
In order to check the precission of this ARIMA model with forecasts more … we are going to use the fitted
# First of all we create with the Arima function the object arima.test
arima.test <- Arima(test_201701_201709, model=model_arima_0)
And with the fitted function, setting its parameter to 6, we fit and extract the fitted values corresponding to the forecast 6 hours ahead (check redaction)
h6 <- fitted(arima.test, h = 6) # It takes a lot of time
saveRDS(h6, "data_rds/arima_model_0_test_fitted_values_h6.rds") # We save the final object as a rds file
fitted_values_h6 <- readRDS("data_rds/arima_model_0_test_fitted_values_h6.rds")
We assign to y_test and y_pred the test dataset and its predictions.
y_test <- test_201701_201709
y_pred <- fitted_values_h6
The first rows of y_pred are NAs. That is because we are making prediction 6 hours ahead.
head(y_pred, 10)
## Time Series:
## Start = c(1, 1)
## End = c(1, 10)
## Frequency = 24
## x
## [1,] NA
## [2,] NA
## [3,] NA
## [4,] NA
## [5,] NA
## [6,] NA
## [7,] NA
## [8,] 31.67340
## [9,] 36.69984
## [10,] 36.44485
So, we remove the NAs from the y_pred time series, and the equivalent rows from the y_test object.
y_pred <- y_pred %>% na.omit()
y_test <- y_test %>% subset(start = 8)
And we obtain the scores to know the goodness of the model of 6 hours ahead
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.2118"
## [3] "MAE:" "7.12"
## [5] "RMSE:" "9.68"
## [7] "Standard Deviation (test data):" "10.9"
R-Squared: 0.2118. When we try to predict the levels of PM10 6 hours ahead this Arima model is only able to explain a 21% of the variability. Errors: Consequentely the errors grow too…
We have been using a dataset for training of 3 years. What if we take a longer period to fit an ARIMA model? We are going to try to improve the result taking as training period 9 years of data:<PHONE_NUMBER>12.
#model_arima_9y = auto.arima(train_200901_201612,seasonal=TRUE,trace=TRUE)
#saveRDS(model_arima_9y, "data_rds/model_arima_9y.rds")
#summary(model_arima_9y)
model_arima_9y <- readRDS("data_rds/model_arima_9y.rds")
summary(model_arima_9y)
## Series: train_200901_201612
## ARIMA(3,1,2)(0,0,2)[24]
##
## Coefficients:
## ar1 ar2 ar3 ma1 ma2 sma1 sma2
## 0.0878 0.3272 0.1539 -0.4929 -0.4748 0.0572 0.0330
## s.e. 0.0266 0.0161 0.0042 0.0269 0.0257 0.0039 0.0037
##
## sigma^2 estimated as 126.3: log likelihood=-269164.4
## AIC=538344.8 AICc=538344.8 BIC=538418.1
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -4.331726e-05 11.23784 6.325201 -Inf Inf 0.5096805
## ACF1
## Training set -0.0003654974
y_pred <- forecast(test_201701_201709, model = model_arima_9y, h=1)$fitted # We call the forecast function passing the testing data through the ARIMA model fitted with the training data and we extract the fitted values.
y_test <- test_201701_201709
sd_test <- sd(y_test)
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.5966"
## [3] "MAE:" "4.88"
## [5] "RMSE:" "6.93"
## [7] "Standard Deviation (test data):" "10.9"
We have been using a dataset for training of 3 years. Is this necessary? Does the Arima algorithm take advantage of a so long time series?
To check this we are going to use now a training period much shorter. We will use just three months, the last three months of 2016.
We repeat …
model_arima_1 = auto.arima(train_201610_2016_12,seasonal=TRUE,trace=TRUE) #
saveRDS(model_arima_1, "data_rds/model_arima_1.rds")
model_arima_1 <- readRDS("data_rds/model_arima_1.rds")
summary(model_arima_1)
## Series: train_lite
## ARIMA(2,1,4)(0,0,2)[24]
##
## Coefficients:
## ar1 ar2 ma1 ma2 ma3 ma4 sma1 sma2
## 0.1006 0.547 -0.5222 -0.6079 0.1543 -0.0107 0.0977 0.1135
## s.e. NaN NaN NaN NaN NaN 0.0184 0.0222 0.0215
##
## sigma^2 estimated as 71.56: log likelihood=-7844.97
## AIC=15707.94 AICc=15708.02 BIC=15759.24
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 0.1536203 8.442183 5.17907 -Inf Inf 0.545021 -0.0004040565
The model selected by the auto.arima function is very similar to the former model
train_201610_2016_12: ARIMA(2,1,4)(0,0,2)[24] train_201401_201612: ARIMA(2,1,3)(0,0,2)[24]
Coefficients lite: ar1 ar2 ma1 ma2 ma3 ma4 sma1 sma2 0.1006 0.547 -0.5222 -0.6079 0.1543 -0.0107 0.0977 0.1135
Coefficients 2014-2016: ar1 ar2 ma1 ma2 ma3 sma1 sma2 0.0991 0.5519 -0.5228 -0.6175 0.1642 0.0564 0.0392
We apply this model to the testing data
y_pred <- forecast(test_201701_201709, model = model_arima_1, h=1)$fitted # We call the forecast function passing the testing data through the ARIMA model fitted with the training data and we extract the fitted values.
y_test <- test_201701_201709
sd_test <- sd(y_test)
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.5975"
## [3] "MAE:" "4.88"
## [5] "RMSE:" "6.92"
## [7] "Standard Deviation (test data):" "10.9"
The model scores barely changes. So, in order to speed computation we are going to use this training period from now. If we included seasonal variables as the month or the year we would have to use more long datasets.
test lite
y_pred <- forecast(test_20170101_20170114, model = model_arima_0, h=1)$fitted
y_test <- test_20170101_20170114
sd_test <- sd(y_test)
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.6849"
## [3] "MAE:" "5.71"
## [5] "RMSE:" "7.98"
## [7] "Standard Deviation (test data):" "14.24"
autoplot(y_test, series="Test data") +
autolayer(y_pred,
series="1-step fitted values") +
theme(legend.position="top")
arima.test_20170101_20170114 <- Arima(test_20170101_20170114, model=model_arima_1)
accuracy(arima.test)
## ME RMSE MAE MPE MAPE MASE
## Training set -0.01886331 6.883032 4.837916 -15.72006 32.48397 0.5045182
## ACF1
## Training set 0.04597431
h1_lite <- fitted(arima.test_20170101_20170114, h = 1)
h2_lite <- fitted(arima.test_20170101_20170114, h = 2)
h6_lite <- fitted(arima.test_20170101_20170114, h = 6)
autoplot(y_test, series="Test data") +
autolayer(h6_lite,
series="6-step fitted values") +
theme(legend.position="top")
autoplot(y_test, series="Test data") +
autolayer(h1_lite,
series="1-step fitted values") +
theme(legend.position="top")
9.1.5 NO2 Predictions
We generate the train, test and validations datasets
train_201610_201612 <- constitucion_data %>% filter(date_time_utc >= '2016-10-01 00:00:00',
date_time_utc <= '2016-12-31 23:00:00') %>%
select(NO2) %>%
mutate(NO2 = replace_na(NO2, mean(NO2, na.rm = TRUE))) %>%
rename(NO2_0 = NO2) %>%
ts(frequency = 24)
train_201401_201612 <- constitucion_data %>% filter(date_time_utc >= '2014-01-01 00:00:00',
date_time_utc <= '2016-12-31 23:00:00') %>%
select(NO2) %>%
mutate(NO2 = replace_na(NO2, mean(NO2, na.rm = TRUE))) %>% # replacing NAs by the mean.
rename(NO2_0 = NO2) %>%
ts(frequency = 24)
# We generate a bigger training dataset with 9 years (2009-2017)
train_200901_201612 <- constitucion_data %>% filter(date_time_utc >= '2009-01-01 00:00:00',
date_time_utc <= '2016-12-31 23:00:00') %>%
select(NO2) %>%
mutate(NO2 = replace_na(NO2, mean(NO2, na.rm = TRUE))) %>%
rename(NO2_0 = NO2) %>%
ts(frequency = 24)
test_201701_201709 <- constitucion_data %>% filter(date_time_utc >= '2017-01-01 00:00:00',
date_time_utc <= '2017-09-30 23:00:00') %>%
select(NO2) %>%
mutate(NO2 = replace_na(NO2, mean(NO2, na.rm = TRUE))) %>%
rename(NO2_0 = NO2) %>%
ts(frequency = 24)
# We generate a testing dataset with the first two weeks of 2016.
test_20170101_20170114 <- constitucion_data %>% filter(date_time_utc >= '2017-01-01 00:00:00',
date_time_utc <= '2017-01-14 23:00:00') %>%
select(NO2) %>%
mutate(NO2 = replace_na(NO2, mean(NO2, na.rm = TRUE))) %>%
rename(NO2_0 = NO2) %>%
ts(frequency = 24)
validation_201710_201712 <- constitucion_data %>% filter(date_time_utc >= '2017-10-01 00:00:00',
date_time_utc <= '2017-12-31 23:00:00') %>%
select(NO2) %>%
mutate(NO2 = replace_na(NO2, mean(NO2, na.rm = TRUE))) %>%
rename(NO2_0 = NO2) %>%
ts(frequency = 24)
This time we are going straight to create an ARIMA model.
9.1.6 ARIMA model
Training period: 201401_201612
#NO2_model_arima_0 = auto.arima(train_201401_201612,seasonal=TRUE,trace=TRUE)
#saveRDS(NO2_model_arima_0, "data_rds/NO2_model_arima_0.rds")
NO2_model_arima_0 <- readRDS("data_rds/NO2_model_arima_0.rds")
summary(NO2_model_arima_0)
## Series: train_201401_201612
## ARIMA(5,1,0)(2,0,0)[24]
##
## Coefficients:
## ar1 ar2 ar3 ar4 ar5 sar1 sar2
## 0.0157 -0.1001 -0.0797 -0.0964 -0.1019 0.2094 0.1616
## s.e. 0.0065 0.0062 0.0061 0.0061 0.0062 0.0064 0.0063
##
## sigma^2 estimated as 50.81: log likelihood=-88980
## AIC=177976 AICc=177976 BIC=178041.4
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.001106035 7.12686 4.885192 -5.922583 23.39609 0.4653533
## ACF1
## Training set -0.01176791
y_pred <- forecast(test_201701_201709, model = NO2_model_arima_0, h=1)$fitted # We call the forecast function passing the testing data through the ARIMA model fitted with the training data and we extract the fitted values.
y_test <- test_201701_201709
sd_test <- sd(y_test)
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.7382"
## [3] "MAE:" "6.17"
## [5] "RMSE:" "9.47"
## [7] "Standard Deviation (test data):" "18.51"
Training period 201610_201612
NO2_model_arima_1 = auto.arima(train_201610_2016_12,seasonal=TRUE,trace=TRUE)
saveRDS(NO2_model_arima_1, "data_rds/NO2_model_arima_1.rds")
NO2_model_arima_1 <- readRDS("data_rds/NO2_model_arima_1.rds")
summary(NO2_model_arima_1)
## Series: train_lite
## ARIMA(1,0,0)(2,1,0)[24]
##
## Coefficients:
## ar1 sar1 sar2
## 0.7979 -0.6270 -0.3073
## s.e. 0.0129 0.0204 0.0203
##
## sigma^2 estimated as 70.45: log likelihood=-7753.33
## AIC=15514.65 AICc=15514.67 BIC=15537.41
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.07054555 8.341781 6.09835 -5.95093 21.65569 0.4987294
## ACF1
## Training set 0.0299381
We apply this model to the testing data
y_pred <- forecast(test_201701_201709, model = NO2_model_arima_1, h=1)$fitted # We call the forecast function passing the testing data through the ARIMA model fitted with the training data and we extract the fitted values.
y_test <- test_201701_201709
sd_test <- sd(y_test)
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.7175"
## [3] "MAE:" "6.66"
## [5] "RMSE:" "9.84"
## [7] "Standard Deviation (test data):" "18.51"
Better R-Squared than the PM10 model. But worse errors scores. Different units. Maybe we can’t compare. More variability.
Explain this. Include graphics.
We have been using a dataset for training of 3 years. What if we take a longer period to fit an ARIMA model? We are going to try to improve the result taking as training period 9 years of data:<PHONE_NUMBER>12.
#NO2_model_arima_9y = auto.arima(train_200901_201612,seasonal=TRUE,trace=TRUE)
#saveRDS(NO2_model_arima_9y, "data_rds/NO2_model_arima_9y.rds")
#summary(NO2_model_arima_9y)
NO2_model_arima_9y <- readRDS("data_rds/NO2_model_arima_9y.rds")
summary(NO2_model_arima_9y)
## Series: train_200901_201612
## ARIMA(5,1,0)(2,0,0)[24]
##
## Coefficients:
## ar1 ar2 ar3 ar4 ar5 sar1 sar2
## 0.0239 -0.1010 -0.0703 -0.0885 -0.1165 0.1971 0.1432
## s.e. 0.0040 0.0038 0.0038 0.0037 0.0038 0.0039 0.0038
##
## sigma^2 estimated as 86.51: log likelihood=-255897.2
## AIC=511810.4 AICc=511810.4 BIC=511883.7
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -9.926224e-05 9.300711 6.206921 -6.372945 24.31429 0.4616188
## ACF1
## Training set -0.01271755
y_pred <- forecast(test_201701_201709, model = NO2_model_arima_9y, h=1)$fitted # We call the forecast function passing the testing data through the ARIMA model fitted with the training data and we extract the fitted values.
y_test <- test_201701_201709
sd_test <- sd(y_test)
RMSE <- RMSE(y_test, y_pred)
MAE <- MAE(y_test, y_pred)
rss <- sum((y_pred - y_test) ** 2)
tss <- sum((y_test - mean(y_test)) ** 2)
R_squared <- 1 - rss/tss
paste(c("R-Squared:", round(R_squared, 4), "MAE:", round(MAE, 2), "RMSE:", round(RMSE, 2), "Standard Deviation (test data):", round(sd_test, 2)))
## [1] "R-Squared:" "0.7383"
## [3] "MAE:" "6.16"
## [5] "RMSE:" "9.47"
## [7] "Standard Deviation (test data):" "18.51"


Responsive Tables Using LI
Triggers on 767px
Responsive Tables Using LI
Triggers on 767px
Responsive Tables Using LI
Triggers on 767px
asas
Job Id
Customer Name
Amount Due
Payment Status
42235
John Doe
$350
Pending
42442
Jennifer Smith
$220
Pending
42257
John Smith
$341
Pending
11
02
$115
D_S11_L02_Introduction_to_Kubernetes


   Wrong, motherfucker. Let me describe your perfect-ass website:
You. Are. Over-designing.  It's a motherfucking website.
You never knew it, but this is your perfect website. Here's why.
  You loaded all 7 fontfaces of a shitty webfont just so you could say "Hi." at 100px height at the beginning of your site?
 You thought you needed media queries to be responsive, but no. Responsive means that it responds to whatever motherfucking screensize it's viewed on.
 You can read it ... that is, if you can read, motherfucker. It makes sense. It has motherfucking hierarchy.  That's semantics, motherfucker.

Cross-browser compatibility? Load this motherfucker in IE6.
  This is a real, naked website. Look at it.
 What I'm saying is that all the problems we have with websites are ones we create ourselves. Websites aren't broken by default, they are functional, high-performing, and accessible. You break them. You son-of-a-bitch.
"Good design is as little design as possible."
- some German motherfucker
This site was made by @thebarrytone, and from the philosophies expressed (poorly) above, he created txti. You should try it today to make your own motherfucking websites.


Hi there!
Welcome to Sealicon Valley! Here we can dive into this ordinary yet exciting world of Sealicon Valley together, by following the life journey of our FINtastic friends.
The original stories and drawings of Sealicon Valley are created by Mita and Ann. We are engineer and designer at Google. The place we live in is a lot like Sealicon Valley,
where we swim from far away here, to dream big together, help each other, learn from failure, and enjoy the fast WiFi and free tshirts.
Wanna say hi?
sealiconvalley@gmail.com


Technical Part 4 04/08/17
- How does JavaScript compare to HTML and CSS? While HTML handles the structure of the webpage's content and CSS applies visual styles to these elements offering variations for different media, JavaScript adds interactive functionality and responds to events. A simple example of this is the prompt function that asks you to input something, the result can then be used for further functions or events.
- Explain control flow and loops using an example process from everyday life, for example 'waking up' or 'brushing your teeth'. JavaScript runs code from the top down but the control flow changes when it comes across structures likes conditionals and loops. An example of a conditional in everyday life would be decding what to make for dinner based on the ingredients you have. Starting with the fact that you have mince and kidney beans, if you have corn chips, make nachos, otherwise if you have rice make chili con carne, otherwise if you have neither, make mince (on toast - obviously). Now the cooking of the mince may have loop situation where you stir the mince, and check: is the mince cooked? If not keep cooking and stir, is the mince cooked? Once the mince equals cooked then the loop ends and you move onto the next process.
- Explain the difference between accessing data from arrays and objects. Arrays have a continuous index starting from 0 and data can be accessed by referencing its position in the index. Objects on the other hand is more of a collection of data made up of keys and values. To access data in objects you make reference to that specific key.
- Explain what functions are and why they are useful. Functions are great for combining several actions into a single executable action. An example of this would be if you want to do a calculation but don't want to have type the whole equation out each time (or copy and paste it - repitition is bad). Just write it out once in a function, call upon that function, input the argument values and it can output the answer in one go.


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Video
Vimeo, YouTube and self hosted videos, start telling your story!
<%= VUE_APP_SIDE_BAR %>
Add a self-hosted video.
Add Vimeo hosted videos where you need them.
Amplify also supports YouTube videos!


I'm having a lot of fun writing common_schema, an SQL project which includes views, tables and stored routines.
As the project grows (and it's taking some interesting directions, in my opinion) more dependencies are being introduced, and a change to one routine or view may affect many others. This is why I've turned the development on common_schema to be test driven.
Now, just how do you test drive an SQL project?
Well, much like the way you test any other project in your favorite programming language. If its functions you're testing, that's all too familiar: functions get some input and provide some output. Hmmm, they might be changing SQL data during that time. With procedures it's slightly more complex, since they do not directly return output but result sets.
Here's the testing scheme I use:
- Tests are divided to families. For example, there is a family of tests for the eval() function.
- Each test in a family is responsible for checking the simplest, most "atomic" issue. This means many small tests.
- Each test can have a "pre-test" step, which prepares the ground (for example, create a table and populate it)
- Likewise, a test can have a "post-test" step, which is typically just cleanup code (since the test is already complete by the time the post step is invoked).
- Each test is an SQL file: a set of commands to be executed.
- A test may have an "expected output" file.
- If no explicit expected exists, the test is expected to return "1" (just as the most basic JUnit test assumes an "assert true")
- A test family may also have pre- and post- steps.
- Any failure in any step fails the entire process. Failures may include:
- Failure to prepare the grounds for a test or family of tests
- Failure in executing the test
- Mismatch between test's output and expected result.
- Failure in executing the post- step (may indicate yet invalid test result not intercepted by the test)
An example
The following image presents a single test family: the eval family, testing the eval() routine.
- In this family, there are two tests.
- In both tests, we have a pre-test step, and a test.
- We have no post-test here.
- Nor do we have an expected-output sample, which means the tests expect to return with "1".
Implementation
But how are tests conducted? Via mysql, of course. While tests are plain SQL text file, they are being executed against a running MySQL server using the mysql client. It is given the test files as input, and its output is directed to file as well.
This makes it very easy to code the test using a simple shell script. It takes a small measure of file iteration, process invocation, exit code check, and diff execution.
For example, to test eval()'s 01 test, we first execute mysql with 01/pre.sql as input. Assuming success, we execute mysql again, this time with 01/test.sql. We capture the output of this execution, and compare it with expected-output, or with "1" when no expected-output specified.
Tests pass, or no code!
Some 12 years ago, I worked with a less-known version system called aegis. The thing I remember most from aegis was that it had a good tests infrastructure. Long before "test-driven development" was coined, or was even commonly practiced, aegis supported tests right into your version control. "Right into", in the sense that you could not merge your code back to the baseline if it didn't pass all of the tests.
I work with SVN for common_schema, and I do not know of such an option in SVN. But I also use ant to build this project, and the dependency is clear there: ant dist, my target which creates the distribution files, is dependent on ant test, the target which works out the tests.
That is, you cannot generate the distribution files when tests fail.
Further notes
Since I'm now retroactively patching tests for already existing functionality, calling it test-driven development is an overstatement; nevertheless new tests are already proving invaluable when I keep changing and improving existing code. Suddenly dependent functionality no longer works as expected. What fun!
The code for the testing suite is actually much shorter than this blog post.
comments powered by Disqus


Our history?
The Martin De Porres Catholic Church in Atonsu-SLine was established in 1980 by 4 Catholic priest. It was commissioned by the then govenor. Today it forms part of the Amakom Deanery of the Kumasi Archdiocese.
Our leaders?
It is headed by Rev. Fr. Jude Anin Agyei, the Parish Priest, who is assisted by Rev. Fr. Stephen Baidoo Junior, the Assistant Parish Priest. The Parish is blessed with a number of priest namely Rev. Fr. Michael Obuo Mensah Fobi, Rev. Fr. Emmanuel Kwadwo Boakye, Rev. Fr. Mark Opoku Acheampong, Rev. Fr. James Alaya, Rev. Fr. Anthony Otu Abban, Rev. Fr. Kirk Okyere Siabour, Rev. Fr. Desmond Duruye and Rev. Fr. Emmanuel Mensah Boateng.
Our schedule?
The church has about 400 members and still counting. We have 2 masses on Sunday at 6:30am(English Service) and 9:30am(Twi Service). We also have morning mass every morning at 6:00am from Monday to Saturday. Cofession is done every Saturday after 1pm. Book an appointment for confession.
What to register as a member, part of a society, book an appointment or the church premises? Learn more
Catholic Church's mission World Wide?
Catholic Answers is a media ministry that serves Christ by explaining and defending the Catholic faith. We help Catholics grow in their faith; we bring former Catholics home; and we lead non-Catholics into the fullness of the truth.
Learn moreCatholic Church's mission Kumasi Archdiocese?
Our mission?
To continually shape the lives of christian to follow the example of Jesus Christ by being kind and generous to all those around us
Catholic Church Vision World Wide?
Our goal is to be the most trusted and accessible source for sharing, explaining, and defending the Catholic faith through as many media as possible: from personal contact to the most innovative means of mass communication.
Catholic Church Vision Kumasi Archidiocese?
The Catholic Archdiocese of Kumasi envisions a Eucharistic Community for holistic selfless service to God and humanity in Evangelization.
Our Vision?
We envision Christians who are generous givers and are committed to showing acts of love and kindness to their neighbours.
321 Sline
Atonsu-Agogo, Kumasi
<EMAIL_ADDRESS>+233 322 011145


## Cutting Through the Jargon _Figuring your way around all the confusing terminology used in tech._
# Applications
# Servers
# Instances


Open the shapefile for writing or appending.
Note:
Field names can only be <= 10 characters long. Longer names will
be silently truncated. This may result in non-unique column names,
which will definitely cause problems later. Field names can not
contain spaces or special characters, except underscores. Starting
with version 1.7, the OGR Shapefile driver tries to generate unique
field names. Successive duplicate field names, including those
created by truncation to 10 characters, will be truncated to 8
characters and appended with a serial number from 1 to 99.
See Also:
http://www.gdal.org/ogr/drv_shapefile.html


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Explanation
A histogram representing the percentage of peptides in the experiment with a different number of missed tryptic cleavages in peptides. This graph is only applicable to experiments where trypsin is used. Two assumptions were made for these calculations: first, the enzyme used in the experiment is trypsin; second, the cleavage rule used by the enzyme is “C-terminal side of K or R except if P is C-term to K or R. This chart can be used to compare several experiments where the same number of missed cleavages has been used as a parameter for the search, and the same experimental conditions used. Then a dramatic change in the shape of the chart could mean a change in the efficiency of the trypsin used (though many other factors can also be the reason for it such as change in the parameters of the search engine, database size and other experimental causes).
In a more practical way, this chart has two immediate applications: first, checking that the search engine is working correctly and the number of missed cleavages found in the identified peptides matches with the "missed cleavages" parameters used in the search engine. Second, knowing the distribution of this chart, the researcher can adjust the number of missed cleavages used in future searches: e.g. maybe the use of 4 missed cleavages instead of 1 is producing only a 0.1% increase in peptide identifications with searches 10 times longer.
This is an example with only about 10% of the peptides containing one missed cleavage.


Partner in Crime
Pairing:
What was it like to pair with others to solve challenges?
At first, it was pretty intimidating because I was unsure in my own ability in coding and didn’t want to come off like I didn’t know anything. But pretty quickly I learn that we’re all in the same boat. We both are trying to develop a new skills set and even though sometime we at a whole different level when I comes to actual knowledge; I found that the more knowledgeable person is always willing to teach the other person. Everyone here to learn and develop through the program and through each other.
Was something particularly fun or rewarding?
Honestly, everything we are learning is fun/rewarding but stressful sometimes. I great when we get a solutions right out of the gate but when we don't....well, it can be very frustrating.But if I had to choose, I would choose the HTML/CSS challenge since we get to create and development our own website to our likling.
Was something frustrating and difficult for you?
EVERYTHING!!!
Feedback
How did you feel when you read your feedback?
I feel pretty comfortable with reading the feedback. Nothing has come back so far with any real negative criticism. Lets keep our finger cross.
Was the feedback you received helpful to your learning?
They are very helpful as they point out want I need to improve on and how I should perform during the next paring session.
Based on the feedback you've received, what are you going to do to improve next time you pair?
Definitely! I find myself talking out loud more and speaking my mind instead of usually taking the backseat in the situation.
How was it to write feedback?
It pretty easy. To be honest, it getting really repititive since I haven't really ran into someone who I thought was not holding their weight in the session when we pair up. I probably need to learn to be more critical of others so I can tell them how they can improve. I guess I need to work on that more.
What was most difficult for you when writing feedback for your pair?
The most difficult part of writing feedback is actually giving them some critical feedbacks. I find this hard because in most session your sort of focusing on yourself and how you interact with your pair and how you can help on finding the solution. I don't really look to find any mistake or how they can improve.
Overall, what do you think of using pairing and feedback to guide your learning?
I think how things are now is pretty helpful in my development.


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Module: Aws::HealthLake::Types
Instance Attribute Summary
-
#s3_configuration ⇒ Types::S3Configuration
rw
The output data configuration that was supplied when the export job was created.
-
#s3_uri ⇒ String
rw
The S3Uri is the user specified
::Aws::S3
location of the FHIR data to be imported into AWS HealthLake.
Instance Attribute Details
#s3_configuration ⇒ Types::S3Configuration (rw)
The output data configuration that was supplied when the export job was created.
# File 'gems/aws-sdk-healthlake/lib/aws-sdk-healthlake/types.rb', line 807
class OutputDataConfig < Struct.new( :s3_configuration, :unknown) SENSITIVE = [] include Aws::Structure include Aws::Structure::Union class S3Configuration < OutputDataConfig; end class Unknown < OutputDataConfig; end end
#s3_uri ⇒ String
(rw)
The S3Uri is the user specified ::Aws::S3
location of the FHIR data to be
imported into AWS HealthLake.
# File 'gems/aws-sdk-healthlake/lib/aws-sdk-healthlake/types.rb', line 544
class InputDataConfig < Struct.new( :s3_uri, :unknown) SENSITIVE = [] include Aws::Structure include Aws::Structure::Union class S3Uri < InputDataConfig; end class Unknown < InputDataConfig; end end


TheIcyStar's portfolio
Novice game developer, learning web developer. I'm in to computer science and trying to learn and create new things. I work with Python, Lua, Javascript, HTML, and CSS.
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Feedback for ${test.title}
Dear student,
You can find the feedback for test you've made by clicking
here
or to copy this into your browser:
${absolute_url}index.html#/answer/${test_session.id}


Directive(Events): V-On
{{counterMethod}}
{{counterComputed}}
printTextComputed: {{printTextComputed}}
printTextMethod: {{printTextMethod()}}
Method vs Computer
Method are static functions usually used to react to events which
happen in the DOM and they accept arguments. They are incredibly
useful for connecting functionality to events, or even just
creating small parts of logic to be reused. You can call a method
inside another method, they are very versatile! Immagine to have a
candy dispenser with a display, to know how many candies are inside
you need to press a button.They are often used to run a
functionality from the DOM. You want to show an
output when a user click a button.
Computed don’t accept arguments and they are very handy for
composing new data from existing sources, they get dynamic values
based on other properties. Imagine the same dispenser but this on
the display has always the number of candies (updated) and you
don’t need to press any button.
Explaination!
RESULT: If you followed the code you can see that when you click
the computed button both the functions printTextComputed() and
printTextMethod() are running but when you click the method button
only the function printTextMethod() will runs.
Why? Because it’s like that: methods don’t know if the
values used in the function changed so they need to run everytime
to check. computed properties know if the values used in the
function changed so they don’t need to run everytime to check, only
once!


An efficient, user-friendly and accessible resource for schools to keep track of issues and equipment.
GET STARTEDOur mission is to provide a reliable tool for school administrators and board members alike to effectively communicate and resolve school issues. With the ability to track and schedule issues and their status, we provide a great mainstream workflow to benefit not only board members and school admins, but the students who attend those schools as well. With the ability to track individual issues, leave comments and log detailed information, we make it easy to keep track of all your equipment and see a big picture.
SIGN UP!We are available 5 days a week, Monday through Friday 9AM-6PM. Expect a reply within less than an hour during those times!
Our team is made up of a group of driven, creative and self-motivated engineers from different places around the world. Our team consists of Lambda School students that are still going through the program as students, and we believe that we can demonstrate our ability through programming for good, which is why we have chosen this project. As programmers, we have the ability to create anything, so why not build applications that help our children and our communities? We hope that IRSR does just that!
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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/Finite.20maps.html ---
Is there a Lean (3) library for finite maps (i.e., key-value maps where finitely many keys are mapped to values)? I don't need performance, just reasoning really. But I'd also be fine if the key type requires decidable ordering (I would assume it would require at least decidable equality). Should I use this https://github.com/spl/lean-finmap?
There's data.rbmap
in the Lean standard library, does that meet your needs?
There is also the finmap
branch of leanprover-community which is porting Sean's work to mathlib
and hash_map
is also a finite map type
on the non-computational side, finsupp
is basically the type of finite maps (mapping everything out of domain to 0)
I'm looking for something that has unions and theorems about them. In this regard, both data.rbmap
and data.hash_map
are incomplete. And I do want to write programs, so finsupp
is not what I'm looking for either. So it sounds like finmap
is the way to go! Thanks for pointing me to the finmap
branch of mathlib - I guess I'll use that. Thanks for the pointers, everyone!
Ah, that's good to know. I'll work on adding unions to rbmap and hash_map
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


crypto-impl
/
net.corda.cipher.suite.internal.schemes
/
KeyFactoryProvider
/
get
get
operator
fun
get
(
scheme
:
SignatureScheme
)
:
KeyFactory


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Coverage report:
94%
Hot-keys on this page
n
s
m
x
c
change column sorting
Module
statements
missing
excluded
coverage
Total
279
17
0
94%
products/__init__.py
0
0
0
100%
products/admin.py
10
0
0
100%
products/apps.py
3
3
0
0%
products/forms.py
10
0
0
100%
products/migrations/0001_initial.py
7
0
0
100%
products/migrations/0002_auto_20200612_1628.py
4
0
0
100%
products/migrations/0003_auto_20200613_2202.py
4
0
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products/migrations/0004_auto_20200614_1529.py
4
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products/migrations/0005_auto_20200614_1958.py
4
0
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products/migrations/0006_auto_20200615_1727.py
5
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products/migrations/0007_products_colour.py
4
0
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products/migrations/0008_auto_20200618_1539.py
4
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products/migrations/0009_auto_20200618_1618.py
4
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products/migrations/0010_auto_20200618_1643.py
4
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products/migrations/0011_auto_20200618_2039.py
4
0
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products/migrations/__init__.py
0
0
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products/models.py
40
1
0
98%
products/test_forms.py
33
0
0
100%
products/test_models.py
36
0
0
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products/test_views.py
51
0
0
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products/urls.py
4
0
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100%
products/views.py
44
13
0
70%
No items found using the specified filter.


firefox
A free and open source web browser.
More information: home page
- Launch Firefox and open a web page:
firefox https://www.duckduckgo.com
- Open a new window:
firefox --new-window https://www.duckduckgo.com
- Open a private (incognito) window:
firefox --private-window
- Search for "wikipedia" using the default search engine:
firefox --search "wikipedia"
- Launch Firefox in safe mode, with all extensions disabled:
firefox --safe-mode
- Take a screenshot of a web page in headless mode:
firefox --headless --screenshot path/to/output_file.png https://example.com/
- Use a specific profile to allow multiple separate instances of Firefox to run at once:
firefox --profile path/to/directory https://example.com/
- Set Firefox as the default browser:
firefox --setDefaultBrowser


Google App Engine Ping!
Measure the minimum latency of Google App Engine regions from your location.
This works by making a request to an App Engine app located in each region and showing the minimum latency out of 4 rounds of requests.
Round 0 of 4 Completed.
Depending on your internet speed, this can take around 3 minutes to complete.
| Region | Custom Domain | Appspot Domain | Appspot Region Domain |
|---|---|---|---|
| asia-east2 (Hong Kong) | |||
| asia-northeast1 (Tokyo) | |||
| asia-northeast2 (Osaka) | |||
| asia-northeast3 (Seoul) | |||
| asia-south1 (Mumbai) | |||
| asia-southeast2 (Jakarta) | |||
| australia-southeast1 (Sydney) | |||
| europe-west1 (Belgium) | |||
| europe-west2 (London) | |||
| europe-west3 (Frankfurt) | |||
| europe-west6 (Zurich) | |||
| northamerica-northeast1 (Montreal) | |||
| southamerica-east1 (Sao Paulo) | |||
| us-central1 (Iowa) | |||
| us-east1 (South Carolina) | |||
| us-east4 (North Virginia) | |||
| us-west2 (Los Angeles) | |||
| us-west3 (Salt Lake City) | |||
| us-west4 (Las Vegas) |


Before Change
verify_dependencies(["sr"])
if username is None or password is None:
try:
username = os.environ["IBM_USERNAME"]
password = os.environ["IBM_PASSWORD"]
except KeyError:
raise ValueError("A valid API key must be passed when a "
"SpeechRecognitionConverter is initialized.")
self.recognizer = sr.Recognizer()
self.username = username
self.password = password
self.resolution = resolution
super(IBMSpeechAPIConverter, self).__init__()
After Change
except KeyError:
raise ValueError("A valid API key must be passed when a "
"SpeechRecognitionConverter is initialized.")
self.recognizer = sr.Recognizer()
self.username = username
self.password = password
self.resolution = resolution
super(IBMSpeechAPIConverter, self).__init__(rate_limit=rate_limit)


WITNESS LIST Business & Industry Committee April 3, 2001-2:00P HB 1509 For: Erickson, Tommy (Houston Professional Fire Fighters Association-Local 341) Hauck, Jr., Ed (Houston Professional Firefighters Association) Williams, Steven D. (Houston Prof. Fire Fighters IAFF Local- 341) HB 1980 For: Grant, Julian (Self and City of Temple) Powers, Robert (Self) HB 2186 Against: Niemann, Larry (Texas Building Owners & Managers Assn./Texas Mini Storage Assn.) HB 2187 On: Flores, Carmen (Texas Secretary of State) Niemann, Larry (Community Association Institute (Austin Chapter)) HB 2202 For: Mintz, David (Texas Apartment Association) HB 2445 For: Goss, Alan (Self) Kuper, Jeff (Self) Plemons, Roby (Self) Against: Evans, Richard (TX. Assoc. of Business & Chambers of Commerce) HB 2600 For: Cunningham, D.C., Jeff (Self) Against: Carroll, Pam (Self and Injured Workers (Advocate for Injured Workers)) Class, Ramon (Self) Hill, Christian (Thousands of Injured Workers) HB 2803 On: Niemann, Larry (Texas Building Owners and Managers Assn./Texas Mini Storage Assn.) Stinson, Bill (Texas Association of Realtors) HB 2804 For: Haley, Steve (State Bar of Texas- Real Estate, Probate & Trust Law Section) HB 3102 For: Berry, John T. (Self and Concentra Managed Care, Inc.) Brenner, David (American Insurance Association/Burns Anderson Jury & Brenner, LLP) Evans, Richard (TX. Assoc. of Business and Chambers of Commerce) Heard, Francis Oliver (Self and Center Corporation) HB 3199 For: Graydon, Galt (Fidelity Express Money Order Co./Grocers' Supply Corp.) Haider, Tom (Travelers Express) HB 3332 Against: Toney, Doug (Texas Daily Newspaper Association/Texas Press Association) HCR 174 For: Wise, Andrew (Microsoft) SB 583 For: McGanity, Dr. Peter (Tx. Medical Association, TX Orthopaedic Assoc.) Against: Liebbe, Bill (Texas Trial Lawyers Assoc.)


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How will you find the answer to the problem?
To solve for the problem , you add the measurements of the sides of the park.
The park has four sides. So , 55 m + 40 m + 65 m + 35 m = 195m
So , Lorena walks a distance of 195 m every day . this distance around the park is called the perimeter.
Can you think of other ways to find the perimeter of the park ?
A. Study how the perimeter of each figure is obtained.
Let's Practice!Solve for the perimeter of the following figures:
P = s + s + s + s
P = 15 + 58 + 25 + 25
P = 123m
P = 2L + 2W
P = 2(4) + 2(2)
P = 12m
P = s + s + s
P = 45 + 70 + 60
P = 175m
P = 4s
P = 4(10)
P = 40m


---
title: Download
description: We've built an amazing team of developers, marketers, designers and sales people.
image: https://source.unsplash.com/BcoGknSqlDc/2000x1322?a=.png
---
Our Story
We have an extremely talented team whose sole goal is to bring you closer to the people around you.


You have no active training plan at this moment. You can contact to our trainers and they will make individual program for you. To choose your trainer Click Here


Outdoor Activity Guides is now closed as Gareth is training to become a firefighter. You can still contact Gareth for the time being at outdooractivityguides@gmail.com.


|
ensembl-hive
2.4
|
Public Member Functions | |
| public | new () |
| protected | _unit () |
| public | is_counting () |
| public | accumulated () |
| public | continue () |
| public | restart () |
| public | get_elapsed () |
| public | pause () |
This is a standalone object used to time various events in the Hive.
| protected Bio::EnsEMBL::Hive::Utils::Stopwatch::_unit | ( | ) |
Undocumented method
| public Bio::EnsEMBL::Hive::Utils::Stopwatch::accumulated | ( | ) |
Undocumented method
| public Bio::EnsEMBL::Hive::Utils::Stopwatch::continue | ( | ) |
Undocumented method
| public Bio::EnsEMBL::Hive::Utils::Stopwatch::get_elapsed | ( | ) |
Undocumented method
| public Bio::EnsEMBL::Hive::Utils::Stopwatch::is_counting | ( | ) |
Undocumented method
| public Bio::EnsEMBL::Hive::Utils::Stopwatch::new | ( | ) |
Undocumented method
| public Bio::EnsEMBL::Hive::Utils::Stopwatch::pause | ( | ) |
Undocumented method
| public Bio::EnsEMBL::Hive::Utils::Stopwatch::restart | ( | ) |
Undocumented method