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#include <ptx_ir.h>
Definition at line 800 of file ptx_ir.h.
◆ basic_block_t()
◆ dom()
◆ pdom()
◆ bb_id
| unsigned basic_block_t::bb_id |
◆ dominator_ids
| std::set<int> basic_block_t::dominator_ids |
◆ immediatedominator_id
| int basic_block_t::immediatedominator_id |
◆ immediatepostdominator_id
| int basic_block_t::immediatepostdominator_id |
◆ is_entry
| bool basic_block_t::is_entry |
◆ is_exit
| bool basic_block_t::is_exit |
◆ postdominator_ids
| std::set<int> basic_block_t::postdominator_ids |
◆ predecessor_ids
| std::set<int> basic_block_t::predecessor_ids |
◆ ptx_begin
◆ ptx_end
◆ successor_ids
| std::set<int> basic_block_t::successor_ids |
◆ Tmp_ids
| std::set<int> basic_block_t::Tmp_ids |
The documentation for this struct was generated from the following file:
- gpgpu-sim_distribution/src/cuda-sim/ptx_ir.h


---
title: "Hold On!"
date: "2019-01-04 00:00:00 -06:00"
categories:
- whats-blooming
layout: "post"
blog-banner: "whats-blooming-now-winter.jpg"
post-date: "January 1, 2019"
post-time: "12:00 PM"
blog-image: "2019-01-04-wbn.JPG"
published: true
---
Hold on! Just because it's winter and many plants have gone dormant, doesn't mean there isn't still plenty to see at Red Butte Garden. Visiting the Garden during winter is the only way to see exactly what attractive features some of our plants are still holding on to.
This outstanding maple on the Floral Walk is still holding on to a handful of its curled, showy red leaves.
Also on the Floral Walk the dry, pea pod-like fruits of this gorgeous tree rattle in the wind.
Although not pleasant to step on with your bare feet, the spiky fruits of this eastern U.S. native are intriguing to look at.
Many of Crabapple trees throughout the Garden hold on to their fruits long into winter providing winter interest for people and food for wildlife.
The narrow brown cones of this stately spruce can hold on to the branches for two or more years.
If you don't visit the Garden during the winter, you're going to miss spectacular views like this.


This tool will create a user which can be verified in the database.
This tool will add all contents of import.json to the CleanAir database by making an API call for each JSON object in import.json.
Press "Begin Data Import" to begin sending data.
Imported data should now be available at http://localhost:5000/api/nodes


Now it is time to start plugging things into your application!
Let's go ahead and get a server running to serve up your app.
yo ng-guide:server --basic
node server.js


Enum GraphOptions.DegenerateEdges
Controls how degenerate edges (i.e., an edge from a vertex to itself) are handled. Such edges may be present in the input, or they may be created when both endpoints of an edge are snapped to the same output vertex. The options available are:
enum DegenerateEdges
: int { ... }
Enum members
| Name | Description |
|---|
DISCARD
Discards all degenerate edges. This is useful for layers that do not support degeneracies, such as S2PolygonLayer.
DISCARD EXCESS
Discards all degenerate edges that are connected to non-degenerate edges. (Any remaining duplicate edges can be merged using DuplicateEdges::MERGE.) This is useful for simplifying polygons while ensuring that loops that collapse to a single point do not disappear.
KEEP
Keeps all degenerate edges. Be aware that this may create many redundant edges when simplifying geometry (e.g., a polyline of the form AABBBBBCCCCCCDDDD). DegenerateEdges::KEEP is mainly useful for algorithms that require an output edge for every input edge.


For installation instructions please see https://github.com/cdiener/microbiome. We recommend using the docker image in the cloud or locally as this ensures that all requirements are fulfilled in the correct version.
In order to facilitate use of the pipeline we provide the mbtools
R package in this repository which serves two major purposes:
So loading mbtools
should be the first step when running the analysis
## Also loading: dada2 1.8.0
## Also loading: data.table 1.11.4
## Also loading: ggplot2 3.0.0
## Also loading: magrittr 1.5
## Also loading: phyloseq 1.24.2
## Also loading: ShortRead 1.38.0
## Also loading: yaml 2.2.0
##
## Attaching package: 'mbtools'
## The following object is masked _by_ 'package:BiocGenerics':
##
## normalize
mbtools
includes helper functions to obtain benchmark mock data sets from the mockrobiota database. For instance to download the mock-3 data set (relatively small) we can use
if (!file.exists("mock4.rds")) {
mock <- mockrobiota("mock-4", "mock4")
saveRDS(mock, "mock4.rds")
} else mock <- readRDS("mock4.rds")
Here mock
now includes annotations for the data set as a list.
## [1] "description" "forward" "reverse" "index" "citation"
## [6] "fragment" "equipment" "samples" "ps_gg" "ps_silva"
## SampleID BarcodeSequence
## HMPMockV1.1.Even1 HMPMockV1.1.Even1 TGTACGGATAAC
## HMPMockV1.1.Even2 HMPMockV1.1.Even2 CAAATGGTCGTC
## HMPMockV1.2.Staggered1 HMPMockV1.2.Staggered1 AATCAACTAGGC
## HMPMockV1.2.Staggered2 HMPMockV1.2.Staggered2 ACACATAAGTCG
## LinkerPrimerSequence ReversePrimer
## HMPMockV1.1.Even1 GTGCCAGCMGCCGCGGTAA GGACTACHVGGGTWTCTAAT
## HMPMockV1.1.Even2 GTGCCAGCMGCCGCGGTAA GGACTACHVGGGTWTCTAAT
## HMPMockV1.2.Staggered1 GTGCCAGCMGCCGCGGTAA GGACTACHVGGGTWTCTAAT
## HMPMockV1.2.Staggered2 GTGCCAGCMGCCGCGGTAA GGACTACHVGGGTWTCTAAT
## PrimerName Description
## HMPMockV1.1.Even1 515f-806r Nenehozi
## HMPMockV1.1.Even2 515f-806r Tofekoca
## HMPMockV1.2.Staggered1 515f-806r Kalofiyo
## HMPMockV1.2.Staggered2 515f-806r Pewizifo
As we can see we have 3 read files, the forward and backward reads and one index file mapping the sample barcodes to the sequences. However, we have 4 samples: a uniform and staggered community in duplicates each. In order to map sequence variants to samples dada2
expects read files to be separated by sample. mbtools
includes helper functions to obtain this splitting.
reads <- c(mock$forward, mock$reverse)
barcodes <- mock$samples$BarcodeSequence
names(barcodes) <- mock$samples[,1]
bcs <- split_barcodes(reads, mock$index, "split", barcodes)
fwd <- list.files("split", pattern="forward", full.names=T)
bwd <- list.files("split", pattern="reverse", full.names=T)
fwd
## [1] "split/HMPMockV1.1.Even1_mock-forward-read.fastq.gz"
## [2] "split/HMPMockV1.1.Even2_mock-forward-read.fastq.gz"
## [3] "split/HMPMockV1.2.Staggered1_mock-forward-read.fastq.gz"
## [4] "split/HMPMockV1.2.Staggered2_mock-forward-read.fastq.gz"
As we see that now gives us the reads separated by sample. The orginal read still include some valid information, particularly they include the read qualities across all samples.
As we can see both qualities detoriate extremely with read lengths. Thus, we will have to trim the reads. From the plots we can see that the forward reads have decent quality up to a length of 150 bp whereas the reverse reads are acceptable up to 100 bp.
## Warning in dir.create("filtered"): 'filtered' already exists
fwd_filt <- file.path("filtered", basename(fwd))
bwd_filt <- file.path("filtered", basename(bwd))
filt <- filterAndTrim(fwd, fwd_filt, bwd, bwd_filt,
trimLeft=10, truncLen=c(140, 100),
compress=T, multithread=T)
filt
## reads.in reads.out
## HMPMockV1.1.Even1_mock-forward-read.fastq.gz 1236898 1216195
## HMPMockV1.1.Even2_mock-forward-read.fastq.gz<PHONE_NUMBER>08
## HMPMockV1.2.Staggered1_mock-forward-read.fastq.gz<PHONE_NUMBER>65
## HMPMockV1.2.Staggered2_mock-forward-read.fastq.gz<PHONE_NUMBER>96
We will follow by dereplicating the reads which will yield the unique sequences in the samples.
## Dereplicating sequence entries in Fastq file: filtered/HMPMockV1.1.Even1_mock-forward-read.fastq.gz
## .Encountered 95580 unique sequences from 1216195 total sequences read.
## Dereplicating sequence entries in Fastq file: filtered/HMPMockV1.1.Even2_mock-forward-read.fastq.gz
## Encountered 72797 unique sequences from 797208 total sequences read.
## Dereplicating sequence entries in Fastq file: filtered/HMPMockV1.2.Staggered1_mock-forward-read.fastq.gz
## Encountered 76415 unique sequences from 825665 total sequences read.
## Dereplicating sequence entries in Fastq file: filtered/HMPMockV1.2.Staggered2_mock-forward-read.fastq.gz
## Encountered 72114 unique sequences from 905396 total sequences read.
## Dereplicating sequence entries in Fastq file: filtered/HMPMockV1.1.Even1_mock-reverse-read.fastq.gz
## .Encountered 106375 unique sequences from 1216195 total sequences read.
## Dereplicating sequence entries in Fastq file: filtered/HMPMockV1.1.Even2_mock-reverse-read.fastq.gz
## Encountered 68054 unique sequences from 797208 total sequences read.
## Dereplicating sequence entries in Fastq file: filtered/HMPMockV1.2.Staggered1_mock-reverse-read.fastq.gz
## Encountered 80575 unique sequences from 825665 total sequences read.
## Dereplicating sequence entries in Fastq file: filtered/HMPMockV1.2.Staggered2_mock-reverse-read.fastq.gz
## Encountered 70459 unique sequences from 905396 total sequences read.
With the trimmed and dereplicated reads we can now advance to running the dada2 algorithm to discover the unique sequence variants in our reads. We will do this separately for the forward and backward reads.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 ....
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## selfConsist step 7
## selfConsist step 8
## Convergence after 8 rounds.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 ....
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## selfConsist step 7
## Convergence after 7 rounds.
This will fit an error model that deconvolutes the original sequence variants in the sample. We can investigate how well the error model reproduces our data as well.
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
We will now quantify the sequence variants for both samples and combine them. Normally we would try to actually combine the forward and backwards reads into larger reads and quantify those, however our read qualities were so bad in this data set that there is no sufficient overlap. Thus, we will treat the forward and backward reads independently.
seqtab_fwd <- makeSequenceTable(dadaFs)
seqtab_bwd <- makeSequenceTable(dadaRs)
seqtab <- cbind(seqtab_fwd, seqtab_bwd)
Finally, we will also remove bimeras (reads that are combinations of two other reads) from the data set. We will also save the sequence table for later use.
## Identified 292 bimeras out of 432 input sequences.
In order to classify taxonomies for the individual sequence variants we will use the RDP algorithm with the green genes reference sequences. If you do not use the docker image this data set has to be downloaded first from here.
if (!file.exists("gg_138.fa.gz"))
download.file("https://zenodo.org/record/158955/files/gg_13_8_train_set_97.fa.gz",
"gg_138.fa.gz")
Taxonomies can now be assigned by
taxa <- assignTaxonomy(seqtab_nochim, "gg_138.fa.gz", multithread=TRUE)
colnames(taxa) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
head(unname(taxa))
## [,1] [,2] [,3]
## [1,] "k__Bacteria" "p__Firmicutes" "c__Bacilli"
## [2,] "k__Bacteria" "p__Proteobacteria" "c__Gammaproteobacteria"
## [3,] "k__Bacteria" "p__Firmicutes" "c__Bacilli"
## [4,] "k__Bacteria" "p__Proteobacteria" "c__Alphaproteobacteria"
## [5,] "k__Bacteria" "p__Proteobacteria" "c__Gammaproteobacteria"
## [6,] "k__Bacteria" "p__[Thermi]" "c__Deinococci"
## [,4] [,5] [,6]
## [1,] "o__Bacillales" "f__Staphylococcaceae" "g__Staphylococcus"
## [2,] "o__Pseudomonadales" "f__Moraxellaceae" "g__Acinetobacter"
## [3,] "o__Lactobacillales" "f__Streptococcaceae" "g__Streptococcus"
## [4,] "o__Rhodobacterales" "f__Rhodobacteraceae" "g__Rhodobacter"
## [5,] "o__Enterobacteriales" "f__Enterobacteriaceae" "g__Escherichia"
## [6,] "o__Deinococcales" "f__Deinococcaceae" "g__Deinococcus"
## [,7]
## [1,] "s__"
## [2,] "s__guillouiae"
## [3,] "s__"
## [4,] "s__sphaeroides"
## [5,] "s__coli"
## [6,] "s__"
Since we used the green genes database there are some assignments which are actually empty such as the "s__" species. We will convert them to their correct NA value.
The taxa assignments can be combined with quantifications using the phyloseq package.
We can now compare this table to our reference table from mockrobiota. We will start by quantifying how many of the real taxa were found in our taxonomy assignment.
## level precision recall F1 n_exp n_ref
## 1 Kingdom 1.0000000 1.00 1.0000000 2 2
## 2 Phylum 0.8571429 1.00 0.9230769 7 6
## 3 Class 0.8333333 1.00 0.9090909 12 10
## 4 Order 0.6666667 1.00 0.8000000 18 12
## 5 Family 0.6666667 1.00 0.8000000 27 18
## 6 Genus 0.6666667 1.00 0.8000000 27 18
## 7 Species 0.5294118 0.45 0.4864865 17 20
As we can see we are pretty good in identifying the taxa in our samples. However, how do we perform in terms of taxa quantities? We will start by stratifying across the samples:
## level name sample measured reference
## 1 Kingdom k__Bacteria HMPMockV1.1.Even1 0.952576814 0.95238095
## 2 Kingdom k__Archaea HMPMockV1.1.Even1 0.018616473 0.04761905
## 3 Kingdom k__Bacteria HMPMockV1.1.Even2 0.982652731 0.95238095
## 4 Kingdom k__Archaea HMPMockV1.1.Even2 0.001401979 0.04761905
## 5 Kingdom k__Bacteria HMPMockV1.2.Staggered1 0.982056771 0.78563773
## 6 Kingdom k__Archaea HMPMockV1.2.Staggered1 0.001586491 0.21436227
taxa_quants
already quantifies across a possible levels of taxonomy. So we can plot the performance stratified by sample with
ggplot(tq, aes(x=reference, y=measured, col=level)) +
geom_abline(alpha=0.5) +
geom_smooth(aes(group=1), method="lm", lty="dashed") +
geom_point() +
facet_wrap(~ sample)
This plot is so common that it is also implemented in mbtools
as mock_plot
.
Additionally, we can also combine all samples and rather stratify by taxonomy level.
ggplot(tq, aes(x=reference, y=measured, col=sample)) +
geom_abline(alpha=0.5) +
geom_smooth(aes(group=1), method="lm", lty="dashed") +
geom_point() +
theme(legend.position="bottom") +
facet_wrap(~ level, scale="free")
This looks pretty ok with larger variations on the species level, which is to be expected. We can quantify the performance by a correlation test.
##
## Pearson's product-moment correlation
##
## data: tq$measured and tq$reference
## t = 27.086, df = 298, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8071293 0.8731521
## sample estimates:
## cor
## 0.8432932
So we get a correlation of about 0.85 which is okay for the bad quality reads we used here.


Key: = Must support, = QICore-defined extension, = Is-Modifier
| Field | Card. | Type and Description |
|---|---|---|
| device |
0..*
|
List<Device>
|
| dosage |
0..1
|
Dosage
|
| effectiveTime[x] |
1..1
|
DateTime | interval<DateTime>
|
| encounter |
0..1
|
Encounter
|
| eventHistory |
0..*
|
List<EventHistory>
|
| identifier |
0..*
|
List<Identifier>
|
| medication[x] |
1..1
|
Concept | Medication
|
| note |
0..*
|
List<Annotation>
|
| patient |
1..1
|
Patient
|
| performer |
0..1
|
Practitioner | Patient | RelatedPerson
|
| prescription |
0..1
|
MedicationOrder
|
| reasonGiven |
0..*
|
List<Concept>
|
| reasonNotGiven |
0..*
|
List<Concept>
|
| status |
1..1
|
code
|
| wasNotGiven |
0..1
|
Boolean
|
Key: = Must support, = QICore-defined extension, = Is-Modifier


pgirmess: Spatial Analysis and Data Mining for Field Ecologists
Set of tools for reading, writing and transforming spatial and seasonal data in ecology, model selection and specific statistical tests. It includes functions to discretize polylines into regular point intervals, link observations to those points, compute geographical coordinates at regular intervals between waypoints, read subsets of big rasters, compute zonal statistics or table of categories within polygons or circular buffers from raster. The package also provides miscellaneous functions for model selection, spatial statistics, geometries, writing data.frame with Chinese characters, and some other functions for field ecologists.
| Version: |
1.6.9 |
| Imports: |
boot (≥ 1.3-4), maptools (≥ 0.8-36), rgdal (≥ 0.7-8), rgeos (≥ 0.3-8), sp (≥ 0.9-97), spdep (≥ 0.5-43), splancs (≥
2.01-31) |
| Suggests: |
MASS (≥ 7.3-1), nlme (≥ 3.1-120) |
| Published: |
2018-03-12 |
| Author: |
Patrick Giraudoux [aut, cre],
Jean-Philippe Antonietti [ctb],
Colin Beale [ctb],
David Pleydell [ctb],
Mike Treglia [ctb] |
| Maintainer: |
Patrick Giraudoux <patrick.giraudoux at univ-fcomte.fr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
http://perso.orange.fr/giraudoux |
| NeedsCompilation: |
no |
| In views: |
Environmetrics |
| CRAN checks: |
pgirmess results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=pgirmess
to link to this page.


Everything happening in this dark grey box is happening inside the <app-root></app-root> component, like it would in any other app. Everything outside has been dynamically appened to the DOM. and behaves (mostly) like usual.


Would you like your company name on your badges?
Will anyone in your group require special accommodations?


Rank is a fast-paced shedding card game, which
means you try to get rid of all your cards before the other players and
obtain the highest rank. In Rank, players utilize numbered cards, special
cards, and strategy to out think their opponents and play all their cards first.
The most important thing to remember is to not get stuck with only low cards towards the
end, or you are going to lose! The first player to get rid of all their cards becomes the
King. Each time a player becomes/stays the King, they receive 2 Royal tokens. If you end
the hand as the Queen, you receive 1 Royal token. Get 5 Royal tokens and you are the winner!
To purchase, click the link below to go to our Amazon page!
Clash of the Cards is a high-speed, strategic battle
card game in which you battle your opponents to steal their cards and create armies.
To win each hand of this rummy-style game, you try collect 2 armies (an army is a set of 4).
The first player to collect 2 armies wins the hand, and then all players get to count any completed army points.
Keep playing hands until someone reaches 1,000 points.
To add to the fun, each card in the game can be harnessed in a variety of ways during the game.
You can use special cards to defend your armies, walk away from a battle, slow down your opponents,
or join battles you were not invited in order to increase your chance at winning cards which help you build your armies.
Clash of the Cards allows you to set traps for other players and develop attack and defense strategies from your very first turn.
Check it out on our website!
Bamboozled is a fast-paced bluffing dice/card game.
In this hilarious new game you try to outwit your friends and family and be the last one standing. Players
roll dice in subsequent turns and must get a higher roll than the previous player. If they don't they
have to convince the next player that they did. If a player calls a bluff the bluffing player gets a strike. 3 strikes and you're out!
BUT! Each player starts with an action card that will help
you bluff or force someone else to make the call. Even if you have to bluff you may
be able to use an action card to make it true and give the other player a strike, so play
your cards carefully.
Players can also get more cards by BAMBOOZLING other players, meaning, if you bluff and no one calls you on it, you get another card.
BAMBOOZLED is coming soon and we can't wait to share it with you!


A one of kind horseback riding experience through the beautiful vineyards of the Temecula Wine Country. You will embark on a scenic guided tour on docile and gentle horses led by our seasoned tour guides. The breathtaking views and the romance of wine country will be at your fingertips.
Owner and operator Diana Lefort set out to establish a unique way for everyone to experience the beauty of Temecula Valley’s Wine Country. The result was Wine Country Trails by Horseback. Diana is a local resident whose passion and more than 3 decades of experience with horses provides a safe, relaxing and memorable experience to all riders. The rides are conducted with the highest level of professionalism and service with experienced and knowledgeable guides that work hard to exceed your expectations.
Enjoy our "Ride and Wine" packages along with a beautiful Temecula sunset to toast
too.
There is nothing more romantic than a horseback ride through the vineyards at sunset.
Rides available June - September
*Due to liability and rider safety, min rider age is 7.
The couple's "Ride and Wine" package is a way to explore Temecula Valley's beautiful
Wine Country from a whole new perspective.
Our 90 minute horseback tour, led by experienced and
knowledgeable guides, will provide you with a relaxing, fun, and rejuvenating excursion through our
local Temecula Vineyards.
In addition to your ride, you will receive a complimentary 5
wine
tastings at
Maurice Car'rieWinery!
*Due to liability and rider safety, min rider age is 7.
Custom rides are always accepted. We want to make your special days, perfect!
Private rides for intimate Anniversaries, Engagements, or just a perfect romantic date are completely
customizable to your desires.
As well we accommodate large groups for Family Reunions, Bachelorette Parties, or
College Reunions.
We are located just east of Maurice Car'rie Winery and can be easily found by our signage, tents, and saddled horses. When entering the lot of the winery make a left turn to park near the horses and guest area.
34225 Rancho California Rd, Temecula, CA 92591
Office Mon-Fri : 9am - 5pm
Tours Wed - Sun : 9am - Dusk
Tel :<PHONE_NUMBER>


The Compound Patterns
A Compound Pattern combines two or more patterns into a solution that
solves a recurring or general problem.
Give a sight on MVC
Observer, Strategy and Composite
Bullet points
1. The Model View Controller Pattern(MVC) is a compound pattern consisting
of the Observer, Strategy and Composite patterns.
2. The model makes use of the Observer Pattern so that it can keep
observers updated yet stay decoupled from them.
3. The controller is a strategy for the view. The view can use different
implementations of the controller to get different behavior.
4. The view uses the Composite Pattern to implement the user interface,
which usually consists of nested components like panels, frames and buttons.
5. These patterns work together to decouple the three players in the MVC
model, which keeps designs clear and flexible.
6. The Adapter Pattern can be used to adapt a new model to an existing
view and controller.


Copyright © {{ site.time | date: "%Y" }} - {{ site.author }} - Hand-crafted in OK - Served by Octopress and Github


Quickly add data-synchronization and collaboration features to your Angular app
A simple, powerful data model crafted for Angular and the real-time web
An open-source, full-tested, community driven integration
Get up and running in minutes, without writing server-side code or configuring databases. Just include & configure GoAngular, instantiate, initialize, and your Angular application is multi-user!
GoAngular is a GoInstant-powered data synchronization and storage integration built for Angular. It’s the perfect companion for building realtime, collaborative apps. This tutorial will walk you through creating your first GoAngular application and address the massive shortage of chat applications on the web. (OK, we know there isn’t a shortage of chat apps on the web, but there aren’t any like this! OK, there are... but this uses GoAngular, which is awesome, trust me.)
Visit the GoAngular Getting Started page to see the full tutorial.
© 2014 GoInstant – a Salesforce.com company. View licenseHelp us make GoAngular exactly what you need. Send your feedback to<EMAIL_ADDRESS>subscribe to our mailing list, or join us on IRC: freenode/#goinstant


JavaScript is a script language created to increase interactiveness between a website and its users. Codes in JS are executed inside broswers and give access to the HTML elements through DOM.
JavaScript is a script language created to increase interactiveness between a website and its users. Codes in JS are executed inside broswers and give access to the HTML elements through DOM.


The software uses externally-controlled format strings in printf-style functions, which can lead to buffer overflows or data representation problems.
The mantra is that successful relationships depend on communicating clearly, and this applies to software, too. Format strings are often used to send or receive well-formed data. By controlling a format string, the attacker can control the input or output in unexpected ways - sometimes, even, to execute code.
The following detection methods for uncontrolled format string vulnerabilities are available:
Automated Static Analysis. This weakness can often be detected using automated static analysis tools. Many modern tools use data flow analysis or constraint-based techniques to minimize the number of false positives.
Black Box. Since format strings often occur in rarely-occurring erroneous conditions (e.g. for error message logging), they can be difficult to detect using black box methods. It is highly likely that many latent issues exist in executables that do not have associated source code (or equivalent source.
The following mitigation methods are available for uncontrolled format string vulnerabilities:
Choose a language that is not subject to this flaw.
Use static format strings. Ensure that all format string functions are passed a static string which cannot be controlled by the user and that the proper number of arguments are always sent to that function as well. If at all possible, use functions that do not support the %n operator in format strings.
Heed the warnings of compilers and linkers, since they may alert you to improper usage.


{{define "content"}}
6300 Enterprise Road
Kansas City, Missouri 64120
Phone:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Toll-Free:<PHONE_NUMBER>
Email: info@pbahealth.com


Rockfish Console
Logout
Rockfish Dashboard
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Count
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PADSTOW HOBBY HORSE.
MR. GEORGE RAWLINGS writes, September 1, 1865:-- Formerly all the respectable people kept the anniversary decorated with the choicest flowers, but some unlucky day a number of rough characters from a distance joined it, and committed some sad assaults on old and young--spoiling all their nice summer clothes, and covering their faces and persons with smut. From that time--fifty years since--the procession is formed of the lowest.
The Maypole was once decorated with the best flowers-- now, with only some elm branches and furze in blossom. The horse is formed as follows :-- The dress is made of sailcloth painted black--a fierce mask-- eyes red--horse's head--horse-hair matte and tail distended by a hoop. Some would call it very frightful. Carried by a powerful man--they could inflict much mischief with the snappers, &c. No doubt it is a remnant of the ancient days, and it represents the Devil, or the power of Darkness. They commence singing at sunrise.
THE MORNING SONG
Unite and unite and let us all unite,
For summer it is come unto day;
And whither we are going we all will unite
In
the merry morning of May.
Arise up, Mr. --, and joy you betide,
For summer is come unto day;
And bright is your bride that lays by your side
In
the merry morning of May.
Arise up, Mrs. --, and gold be your ring,
For summer is come unto day;
And give to us a cup of ale, the merrier we shall sing
In
the merry morning of May.
Arise up, Miss --, all in your smock of silk,
For summer is come unto day;
And all your body under as white as any milk,
In
the merry morning of May.
The young men of Padstow might, if they would,
For summer is come unto day;
They might have built a ship and gilded her with gold
In
the merry morning of May.
Now fare you well, and we bid you good cheer,
For summer is come unto day;
We will come no mote unto your house before another year,
In the merry morning of May.
THE DAY SONG
Awake, St. George, our English knight,
For summer is a' come O, and winter is ago;
And every day God give us His grace
By
day and by night O!
Where is St. George, where is he O?
He is out in his long boat all on the salt sea O!
And in every land O! the land where'er we go,
And for to fetch the summer home.
The summer and the May O,
For summer is a conic,
And winter is a go.
Where are the French dogs that make such boast O?

And in every land O ! the land where'er we go,
The summer and the May O
Thou mightst have shown thy knavish face!
Thou mightst have tarried at home O !

Aunt Ursula Birdhood she had an old ewe,
And she died in her own park O !
And for to fetch the summer home.
[a] Cornish jollification


for fixed-point types, obviously
compatibility function, since pre-0.5.0 functions don't have node.kind returns undefined if you don't put in a function node
takes a contract definition and asks, does it have a payable fallback function?
similar compatibility function for mutability for pre-0.4.16 versions returns undefined if you don't give it a FunctionDefinition or VariableDeclaration
extract the actual numerical value from a node of type rational. currently assumes result will be integer (currently returns BN)
adds "_ptr" on to the end of type identifiers that might need it; note that this operates on identifiers, not definitions
e.g. uint48 -> 6
size in bytes for explicit type size, or null
if not stated
stack size, in words, of a given type
length of a statically sized array -- please only use for arrays already verified to be static!
see staticLength for explanation
returns basic type class for a variable definition node
e.g.:
t_uint256
becomes uint
t_struct$_Thing_$20_memory_ptr
becomes struct
similar to typeClass, but includes any numeric qualifiers
e.g.:
t_uint256
becomes uint256
for user-defined types -- structs, enums, contracts often you can get these from referencedDeclaration, but not always
Returns the type string, but with location (if any) stripped off the end
For function types; returns internal or external (not for use on other types! will cause an error!) should only return "internal" or "external"
Generated using TypeDoc
only for contract types, obviously! will yield nonsense otherwise!


To start, try searching for an ingredient. If it's in the database, you will be rewarded with a
visualization of ingredients that pair well with it.
The flavor affinity for a pair of ingredients is represented by the length of the connector between them-
the shorter the connector, the better their flavors go together!
Ingredients are color coded- the color is a way of grouping ingredients from the same category together i.e.
Vegetables or Fish
Go ahead and add a flavor pairing by clicking on Add a Pairing, type in your ingredients, pick the
categories
they belong to, and specify how well they pair.
Have fun!


class AddressController extends BaseFrontController
Class AddressController
Methods
|
redirectToRoute($routeId, $urlParameters = array(), $referenceType = Router::ABSOLUTE_PATH)
Redirect to à route ID related URL |
from BaseFrontController | |
|
checkAuth()
|
from BaseFrontController | |
|
generateModalAction($address_id $address_id)
Controller for generate modal containing update form Check if request is a XmlHttpRequest and address owner is the current customer |
||
|
createAction()
Create controller. |
||
|
updateViewAction($address_id)
|
||
|
processUpdateAction($address_id)
|
||
|
deleteAction($address_id)
|
Details
in BaseFrontController at line 51
public
redirectToRoute($routeId, $urlParameters = array(), $referenceType = Router::ABSOLUTE_PATH)
Redirect to à route ID related URL
in BaseFrontController at line 56
public
checkAuth()
at line 47
public
generateModalAction($address_id $address_id)
Controller for generate modal containing update form Check if request is a XmlHttpRequest and address owner is the current customer
at line 61
public
createAction()
Create controller.
Check if customer is logged in
Dispatch TheliaEvents::ADDRESS_CREATE event
at line 96
public
updateViewAction($address_id)
at line 110
public
processUpdateAction($address_id)
at line 156
public
deleteAction($address_id)


Lesson Plan Title: Dangerous Use of Eval)
Concept / Topic To Teach:
It is always a good practice to validate all input on the server side. XSS can occur when unvalidated user input is reflected directly into an HTTP response. In this lesson, unvalidated user-supplied data is used in conjunction with a Javascript eval() call. In a reflected XSS attack, an attacker can craft a URL with the attack script and store it on another website, email it, or otherwise trick a victim into clicking on it.
General Goal(s):
For this exercise, your mission is to come up with some input which, when run through eval, will execute a malicious script. In order to pass this lesson, you must 'alert()' document.cookie.


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Compiler Services: Notes on FSharp.Core.dll
Shipping an FSharp.Core with your application
When building applications or plug-in components which use FSharp.Compiler.Service.dll, you will normally also include a copy of FSharp.Core.dll as part of your application.
For example, if you build a HostedCompiler.exe
, you will normally place an FSharp.Core.dll (say <IP_ADDRESS>) alongside
your HostedCompiler.exe
.
Binding redirects for your application
The FSharp.Compiler.Service.dll component depends on FSharp.Core <IP_ADDRESS>. Normally your application will target
a later version of FSharp.Core, and you may need a binding redirect to ensure
that other versions of FSharp.Core forward to the final version of FSharp.Core.dll your application uses.
Binding redirect files are normally generated automatically by build tools. If not, you can use one like this
(if your tool is called HostedCompiler.exe
, the binding redirect file is called HostedCompiler.exe.config
)
Some other dependencies may also need to be reconciled and forwarded.
1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: |
|
Which FSharp.Core and .NET Framework gets referenced in compilation?
The FSharp.Compiler.Service component can be used to do more or less any sort of F# compilation. In particular you can reference an explicit FSharp.Core and/or framework assemblies in the command line arguments (different to the FSharp.Core and a .NET Framework being used to run your tool).
To target a specific FSharp.Core and/or .NET Framework assemblies, use the --noframework
argument
and the appropriate command-line arguments:
1: 2: 3: 4: 5: 6: 7: 8: 9: 10: |
|
You will need to determine the location of these assemblies. The easiest way to locate these DLLs in a cross-platform way and
convert them to command-line arguments is to crack an F# project file.
Alternatively you can compute SDK paths yourself, and some helpers to do this are in the tests for FSharp.Compiler.Service.dll.
What about if I am processing a script or using GetCheckOptionsFromScriptRoot
If you do not explicitly reference an FSharp.Core.dll from an SDK location, or if you are processing a script
using FsiEvaluationSession
or GetCheckOptionsFromScriptRoot
, then an implicit reference to FSharp.Core will be made
by the following choice:
The version of FSharp.Core.dll statically referenced by the host assembly returned by
System.Reflection.Assembly.GetEntryAssembly()
.-
If there is no static reference to FSharp.Core in the host assembly, then
- For FSharp.Compiler.Service 1.4.0.x above (F# 4.0 series), a reference to FSharp.Core version <IP_ADDRESS> is added
Do I need to include FSharp.Core.optdata and FSharp.Core.sigdata?
No, unless you are doing something with very old FSharp.Core.dll.
Summary
In this design note we have discussed three things:
- which FSharp.Core.dll is used to run your compilation tools
- how to configure binding redirects for the FSharp.Core.dll used to run your compilation tools
- which FSharp.Core.dll and/or framework assemblies are referenced during the checking and compilations performed by your tools.
type LiteralAttribute =
inherit Attribute
new : unit -> LiteralAttribute
Full name: Microsoft.FSharp.Core.LiteralAttribute
--------------------
new : unit -> LiteralAttribute


Generic utilities for (but not limited to) game development using Java. Written to mimic pygame.
Includes networking (both TCP and UDP), sprites with support for basic
transformations,
collision detection (using an implementation of seperate axis theorem), logging, events,
and much more!
Discover all the features for yourself by reading the documentation!


renaissance: 文艺复兴（欧洲 14、15 和 16 世纪时，人们以古希腊罗马的思想文化来繁荣文学艺术）the period in Europe during the 14th, 15th and 16th centuries when people became interested in the ideas and culture of ancient Greece and Rome and used these influences in their own art, literature, etc.


Before Change
images_info = tf.constant([image_info_per_image], dtype=tf.float32)
else:
images_info = tf.constant(
[image_info_per_image for _ in range(batch_size)],
dtype=tf.float32)
images = placeholder
After Change
else:
images_info = tf.constant([image_info_per_image], dtype=tf.float32)
if batch_size is None:
batch_size = tf.shape(placeholder)[0]
images_info = tf.tile(images_info, [batch_size, 1, 1])
images = placeholder
return placeholder, {"images": images, "image_info": images_info}


Countdown to UFC 245 full video for ‘Usman vs Covington’ in Las Vegas
by Jesse HollandUltimate Fighting Championship (UFC) has saved the best for last, at least in terms of its pay-per-view (PPV) offerings, with this Sat. night’s (Dec. 14, 2019) UFC 245 “Usman vs. Covington” extravaganza from inside T-Mobile Arena in Las Vegas, Nevada, topped by three championship title fights.
The UFC 245 main event features the first welterweight title defense for Kamaru Usman after capturing the 170-pound strap from Tyron Woodley back in March. He’ll battle division loudmouth and streaking No. 1 contender, Colby Covington.
Before those two can hook ‘em up, reigning featherweight champion, Max Holloway, will put his 145-pound title on the line against Aussie superstar Alexander Volkanovski, who hasn’t seen the loss column in nearly seven years.
In addition, women’s bantamweight champion, Amanda Nunes, who is being called the greatest female fighter of all time, gives Dutch kickboxing champion, Germaine De Randamie, her crack at the 135-pound crown after coming up short in 2013.
Remember that MMAmania.com will deliver LIVE round-by-round, blow-by-blow coverage of the entire UFC 245 fight card this weekend RIGHT HERE , starting with the Fight Pass/ ESPN+ “Prelims” matches online, which are scheduled to begin at 6:15 p.m. ET, then the remaining undercard balance on ESPN 2 at 8 p.m. ET, before the PPV main card start time at 10 p.m. ET on ESPN+ .
To check out the latest and greatest UFC 245: “Usman vs. Covington” news and notes be sure to hit up our comprehensive event archive right here.


{% load wagtailadmin_tags non_admin_draftail_tags %}
{# Translation strings object that Draftail requires #}
{# Core scripts used in Wagtail admin #}
{# We replace wagtailadmin/js/modal-workflow with the following file to add distinct css class to modal #}
{# Add JavaScript that comes from inser_editor_js Wagtail hook #}
{% hook_output 'insert_editor_js' %}
{# Override url callbacks for non-admin Draftail #}
{% app_is_installed "wagtail.images" as wagtail_images_is_installed %}
{% app_is_installed "wagtail.documents" as wagtail_documents_is_installed %}
{% app_is_installed "wagtail.embeds" as wagtail_embeds_is_installed %}


After you complete this section, you should be able to:
Resources available via the supercomputer
Understand batch job submission and required parameters
SLURM commands
Generate your own script for job submission
Read output and standard error files generated from script
Submit jobs for many participants
Note that everything in “<>” is to be replaced. For example, <fileName> –> iLovePeanuts.txt
FSL Resources
The Fulton Supercomputing Lab offers a total of 16,928 CPU cores and about 50 TB of memory across 943 compute nodes.
Compute resources are supported by approximately 2 petabytes of storage.
All nodes have Red Hat Enterprise Linux 6.6.
Job Submission
The FSL is managed by a batch scheduling system. This means that in order to use the supercomputer, users must write a script that encapsulates the workload into a non-interactive job. This script is then submitted as a job to the scheduling system. There are certain parameters required for the scheduling system:
Either number of nodes and processors per node, or total number of processors
Memory/RAM needed
Expected running time (or “walltime”)
Jobs are created by building a job script, usually written in bash or python, that specifies the necessary parameters described above, and then contains appropriate scripting to launch the program that will do the job’s work.
Scheduling System
The scheduling system keeps track of the current state of all resources and jobs and decides, based on conditions and policies, where and when to start jobs. Jobs are started in priority order until no further jobs are eligible to start or it runs out of appropriate resources.
When the scheduling system chooses to start a job, it assigns it one or more nodes/processors, as requested by the job, and launches the provided job script on the first node in the list of those assigned. The responsibility of taking advantage of all the requested resources is left up to the job script.
SLURM Commands
sbatch is used to submit jobs
squeue -u <username> is used to show your queue
scancel <jobid> cancels a specific job
scancel -u <username> cancels all jobs for that user
sacct shows current and historical job information
Example Job Script
Read Output / Error files
When you run batch commands, the output is sent to a text file instead of appearing on your screen. As with everything else, organization is needed in order to manage these output files. Especially if you are running a study with hundreds of participants. You’ll have hundreds of output and error files!
vi versus cat
Cat is the main command to read and print out the standard output and error files. When you just want to display the contents of a file, use cat. Cat is the most convenient program from displaying the contents of a text file.
On the other hand, if you want to edit a text file, use vi or nano, which are specifically text editors.
To check the output from your process, type:
If your script ends early, type:
Submitting a Job for Many Participants
In order to submit a job for many participants, you could either generate a job script for each participant. However, if you have hundreds or thousands of participants, it does not make sense to generate that many job script files. Another alternative is to use a for loop:
BatchScript.sh
This for loop, loops through all your directories setting the output and error text file with the subject directory name and submitting the jobScript.sh file with the input variable ${subj}. By using ${1} in your job script, you’ll be able to loop through all your participants.
JobScript.sh
Then when you want to submit job scripts for all your participants, you simply have to submit your BatchScript.sh script:


ARIA Checkboxes
What do you want to be when you grow up?
The ARIA checkbox controls are keyboard accessible:
- Press the Tab key to navigate between the ARIA Checkboxes.
- Press the Spacebar to toggle the state of the selected checkbox.
- Mouse users can click a checkbox to toggle the state as usual.


Here's the thing. My junior year of high school, february of 1999 I was diagnosed with a cronic illness. It was ulcerative colitis, a digestive disorder. This disease has a way of putting people down for years, and can give colon cancer if left untreated. The problem is there is no cure for it exept removal of the colon. I went through that year and my senior year to find myself as a freshmen in college. I was thinking about my freedom, and realized I wasnt truly free, I still needed to go to the bathroom 10 times a day or so.
In December of 2000, I decided I met another person with colitis. I was told that I should stay away from drugs because of the risk of health interactions. I was curious about pot and colitis, it's supposed to be medicinal, right? He told me that he hadn't noticed anything, but he has a different, less severe case of it. He told me that he would love to help me further my studies, if that was what I was doing.
A few days later I was trying pot for the first time. Of course I wanted to look at it as scientificly as possible. I didn't notice anything in my colon, but at least it wasn't a negative effect. I started to get stoned more often, just for something to do. I realized that I was having to use the bathroom much less, and almost never would I need to go while I was high.
I decided to further my studies I would quit smoking it, and sure enough my condition went back to its usual self. I began smoking pot as I needed it and realized that one joint a night left my stomach and colon feeling soothed.
If you have ulcerative colitis and are looking for a cure with pot, I can't tell you for sure, but it seems to have worked for me.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


WITNESS LIST Appropriations Committee (SUBCOMMITTEE ON Interim Charge # 3) May 3, 2004-12:00A Interim Charge # 3 Against: Davis, Holly (Brain Injury Association of Texas) Lancaster, Ann (Brain Injury Association of Texas) Lancaster, Lee (Duane Lancaster) Makowski, Eric (Brain Injury Association of Texas) Misko, James (Self) Norris, Wayne (Sean Norris) Waldrip, William Mack (Self and Teddy Waldrip as a Guardian/Advocate through Family Eldercare) On: Arrell, Max (Self) Ferland, Megan (Texas CASA) Geeslin, Julie (Office of the Attorney General) Livingston, Brad (Texas Department of Criminal Justice) Millholland, Herman (Office of the Attorney General) Murphy, Terry (Department of Assistive and Rehabilitative Services) Pharr, Machelle (Texas Department of Health) Rays, Richard (Texas Department of Health) Wheeler, Bill (Department of Assistive and Rehabilitative Services)


class: center, middle, inverse, title-slide # Data I/O ### The R Bootcamp
Twitter:
@therbootcamp
### January 2018 --- # Importing and Exporting Data In this session you will learn... .pull-left45[ 1. How to import data data from **delimeted separated files** (e.g., .csv)? <br2> 2. How to import data data from **proprietory file formats** (e.g., .sav)? <br2> 3. How to save/export data to various formats, including **R's own files types**? <br2> 4. How to use **file connections** to read data in its rawest possible way? <br2> 5. About a new data format called **tibble**. ] .pull-right45[
   ] --- # readr `readr` is a `tidyverse` package that provides convenient functions to **read in** *flat* (non-nested) data files into data frames (`tibble`s to be precise): .pull-left45[ ```r # Functions for import read_csv() # for comma-delimited files read_csv2() # for semicolon-delimeted file read_delim() # for any delimited files # Functions for parsing parse_logical() # parse logical vectors parse_integer() # parse integers parse_double(), parse_number() # parse numerics parse_character() # parse character aka string parse_date() # parse date -> lubridate package ``` ] .pull-right45[  ] --- # An example Assume we have a *flat* data set with variables `id`, `var_1`, and `var_2` and cases as rows. Such data can be conveniently read in using `read_csv()`. Moreover, `read_csv()` will **automatically identify** (a) columns and rows, (b) column names, (c) the type of the columns and finally return a `tibble` (more on that later). .pull-left45[ ```r # define simple data set data <- "id, var_1, var_2\n DCDL, .287, .048\n FEFK, .894, .383\n ZEWE, 1.374, .623\n OJEE, .631, .826" ``` ] .pull-right45[ ```r # read in data require(readr) read_csv(data) ``` ``` ## Loading required package: readr ``` ``` ## # A tibble: 4 x 3 ## id var_1 var_2 ## <chr> <dbl> <dbl> ## 1 DCDL 0.287 0.048 ## 2 FEFK 0.894 0.383 ## 3 ZEWE 1.374 0.623 ## 4 OJEE 0.631 0.826 ``` ] --- # Data source Most of R's read functions allow to load data from a **variety of sources**, including most importantly the computer's hard drive and the internet. When loading from the hard drive, use auto-complete (using *tab*) to find the right **file path**. .pull-left45[ ```r # Loading data from url read_csv("https://tinyurl.com/ydgrcyt5") ``` ``` ## Parsed with column specification: ## cols( ## id = col_character(), ## var_1 = col_double(), ## var_2 = col_double() ## ) ``` ``` ## # A tibble: 4 x 3 ## id var_1 var_2 ## <chr> <dbl> <dbl> ## 1 DCDL 0.287 0.048 ## 2 FEFK 0.894 0.383 ## 3 ZEWE 1.374 0.623 ## 4 OJEE 0.631 0.826 ``` ] .pull-right45[ ```r # Loading data from url read_csv("https://tinyurl.com/ydgrcyt5") ``` ``` ## Parsed with column specification: ## cols( ## id = col_character(), ## var_1 = col_double(), ## var_2 = col_double() ## ) ``` ``` ## # A tibble: 4 x 3 ## id var_1 var_2 ## <chr> <dbl> <dbl> ## 1 DCDL 0.287 0.048 ## 2 FEFK 0.894 0.383 ## 3 ZEWE 1.374 0.623 ## 4 OJEE 0.631 0.826 ``` ] --- # Parsing Behind the magic of `readr` functions such as `read_csv` are a set of really flexible parsing functions `parse_*()` that **coerce** the input into the **appropriate format and type**. ###Examples .pull-left45[ ```r # parsing a logical parse_logical(c("TRUE","FALSE","NA")) ``` ``` ## [1] TRUE FALSE NA ``` ```r # parsing an integer + errors parse_integer(c("123", "345", "abc", "123.45")) ``` ``` ## [1] 123 345 NA NA ## attr(,"problems") ## # A tibble: 2 x 4 ## row col expected actual ## <int> <int> <chr> <chr> ## 1 3 NA an integer abc ## 2 4 NA no trailing characters .45 ``` ] .pull-right45[ ```r # parse parse_character("hellow","world") ``` ``` ## [1] "hellow" ``` ```r # parsing a numeric parse_number(c("1.23","$123.209")) ``` ``` ## [1] 1.23 123.21 ``` ] --- # Locale If parsing is not controlled using **arguments**, it's parsing behavior will be controlled by **settings** in `locale()`. To change, e.g., the `decimal_mark` use `locale("en", decimal_mark = ",")`. .pull-left45[ ```r str(locale()) ``` ``` ## List of 7 ## $ date_names :List of 5 ## ..$ mon : chr [1:12] "January" "February" "March" "April" ... ## ..$ mon_ab: chr [1:12] "Jan" "Feb" "Mar" "Apr" ... ## ..$ day : chr [1:7] "Sunday" "Monday" "Tuesday" "Wednesday" ... ## ..$ day_ab: chr [1:7] "Sun" "Mon" "Tue" "Wed" ... ## ..$ am_pm : chr [1:2] "AM" "PM" ## ..- attr(*, "class")= chr "date_names" ## $ date_format : chr "%AD" ## $ time_format : chr "%AT" ## $ decimal_mark : chr "." ## $ grouping_mark: chr "," ## $ tz : chr "UTC" ## $ encoding : chr "UTF-8" ## - attr(*, "class")= chr "locale" ``` ] --- # Error handling When reading a file using, e.g., `read_csv`, with no specific arguments provided, `readr` **infers the type** of each column using a heuristic process based on the **first 1,000 rows** (see `guess_parser`). However, that may not always work. In that case consider... ```r # to inspect the problems problems() # to set types explicitly read_csv(..., col_types = cols(...)) # read in as character and convert later read_csv(..., col_types = cols(.default = col_character())) type_convert() # use more basic read-in functions (see later) ``` --- # Other read in options The `tidyverse` together with other package offers a **variety of built-in, automated read functions** for almost any data format. For an overview **see** [**rio**](https://cran.r-project.org/web/packages/rio/vignettes/rio.html). .pull-left45[ ### `readr`  ```r # read fixed width files (can be fast) read_fwf() # read Apache style log files read_log() ``` ### `haven`  ```r # read SAS's .sas7bat and sas7bcat files read_sas() # read SPSS's .sav files read_sav() # etc ``` ] .pull-right45[ ### `readxl`  ```r # read Excel's .xls and xlsx files read_excel() ```
 ### Other packages ```r # from package R.matlab: read .mat readMat() # from package XML: read and wrangle .xml and .html xmlParseParse() # from package jsonlite: read .json files read_json() ``` ] --- # Writing data Most read-in functions have **matching write**-functions to save the data to disk. As **arguments** these functions usually expect a **data frame** and a **file path** on the disk. .pull-left45[ ### `readr`  ```r # read fixed width files (can be fast) write_csv(my_data_frame, "my_data.csv") write_delim(...) ``` ### `haven`  ```r # read SAS's .sas7bat and sas7bcat files write_sas(...) # read SPSS's .sav files write_sav(...) # etc ``` ] .pull-right45[ ### Other packages ```r # from package R.matlab: read .mat writeMat() # from package XML: read and wrangle .xml and .html saveXML() # from package jsonlite: read .json files write_json() ``` ] --- # R's data formats R also has data formats of its own to store and retrieve data. They provide the possibility to store **data as R objects**, as well as substantial **compression** (depending on data regularity up to about 1% of the original size). .pull-left45[ ### `.RData` + Bundles several R objects. + Loads objects directly to workspace. + Slow. ```r # save data as .RData save(object_1, object_2,..., file = "my_data.RData") # load data from .RData load("my_data.RData") ``` ] .pull-right45[ ### `.RDS` + Stores individual R objects. + Import is newly assigned. + Fast. ```r # save data as .RDS saveRDS(my_data, file = "my_data.rds") # load data from .RDS my_data <- readRDS("my_data.rds") ``` ] --- # File connections Under the hood, practically all of R's reading and writing functions relies on a **file connection** architecture, just as most other programming languages do. To the experienced programmer file connections represent a flexible option to access and manipulate files on **any accessible location** (harddrive, server, www). .pull-left45[ ### Benefits of file connections + Define access mode (read, write, append). <br2> + Read/write as binary or raw. <br2> + Handle encodings directly. <br2> + Read and write compressed files. <br2> + Access to www and servers. ] .pull-right45[ ### Functions ```r # access a file file("my_data.csv",'r') # access url url(...) # access compressed files gzfile(...) # to handle connections readLines() # to read contect close() # close connection ``` ] --- # `tibble`s The **output** from most `tidyverse` read functions such as `read_csv` and the preferred data format for many (but not all) analyses is a `tibble`. `tibble`s are a **modern, leaner version of data.frames**. .pull-left45[ ### tibbles ... + never change the input's type. <br2> + never add row names. <br2> + never change column names. <br2> + look better in `print`. <br2> + are accessed more consistently. ] .pull-right45[ ### Functions ```r # create tibble my_data <- tibble(id, var_1, var_2) # convert to and from data.frame as_tibble(my_data_frame) as.data.frame(my_tibble) ``` ``` ## # A tibble: 4 x 3 ## id var_1 var_2 ## <chr> <dbl> <dbl> ## 1 DCDL 0.287 0.048 ## 2 FEFK 0.894 0.383 ## 3 ZEWE 1.374 0.623 ## 4 OJEE 0.631 0.826 ``` ] --- # Practical
Link to practical


Pages on the website.
How to Create:
How to Edit:
- Login.
- In the menu-bar go to: Manage → Entries → View All.
- Click on the title of the entry to be edited.
- Edit and save.
How to Delete:
- Login.
- In the menu-bar go to: Manage → Entries → View All.
- Click on the checkbox (to the left of the titles,) of the entry (or entries,) to be deleted.
- Click on the "Delete" button in the upper-right corner of the browser window.
- A pop-up window should appear; click on the "OK" button.
How to View (All):
How to Feature Entries:
- Login.
- In the menu-bar go to: Manage → Entries → View All.
- Click on the title of the entry to be featured.
- On the right-hand side of the edit page, there are two options for featuring entries: "Featured on Homepage" and "Featured on Category Page".
- Check desired checkboxes and save.
Note:
- Entries featured on
homepage will appear near the bottom of the
homepage in the default theme.
- Entries featured on a
category page will appear under the main category that the entries are filed under.*
* Example: imagine there is a category named "Information". When a user clicks on the Information category at the top of the website, any entries featured on this category page will appear here.
How to Create Categories:
- Login.
- In the menu-bar go to: Manage → Entries → Categories.
- Click on "+ New Category" link at top of page.
- This creates an "Untitled Category" that will appear near the bottom of the categories list.
- Click on "Rename" and give category desired name.
- Then click save.
How to Edit Categories:
- Login.
- In the menu-bar go to: Manage → Entries → Categories.
- Click the "Rename" link next to the title of category to be edited.
- Edit and save.
How to Delete Categories:
- Login.
- In the menu-bar go to: Manage → Entries → Categories.
- Click on the "Delete" link next to category to be deleted.
- A pop-up window should appear; click on the "OK" button.
How to View Categories (All):


Mask all password fields - use input controls that hide user input for password fields by covering user input with a "mask", typically a character, such as "*". When a user enters a password "password", the masked field may look like "********".
Masking password fields helps protect user passwords from someone that may be looking at their screen.
To mask all password fields:
Identify all password-type fields in the application. Review all the pages in the application to find all password-type fields. Password-type fields include secret answers, CVV, PIN, and other data, which is equivalent to passwords.
Use password fields that mask user input. Use whatever mechanism is practical to mask the entered passwords. For web applications, this can be done by using the password
input type, like so:
<form>
...
<input type="password" name="pass" AUTOCOMPLETE="off"/>
...
</form>


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