--- author: display_name: JP email<EMAIL_ADDRESS>tags: - dispatch - fire - law enforcement - society date: 2012-06-19 11:06:38 -0600 categories: [] CorrectionalFacilities: hub: name: Utah Correctional Facilities item_id: 1d643eaf1a4c4868a765b199f8ff8085 updates: - January 2023 - added new Utah State Correctional Facility in Salt Lake City - June 2013 - August 2011 FireStations: hub: name: Utah Fire Stations item_id: 3d55a3a011fe4028933754a81537d72f updates: - January 4, 2023 - Updated location of a fire station in Murray - September 22, 2022 - Added a fire station in Lindon - March 24, 2022 - Several updates in Salt Lake, Utah, Sevier, and Washington counties - October 2020 - Updates to Weber County fire stations - June 2013 - August 2011 FireResponseAreas: hub: name: Utah Fire Response Areas item_id: ee6b0673a1bb4e9c8a5e983dedb67e8e updates: - June 2, 2023 - Washington county updates - March 31, 2023 - Washington and Iron county updates - November 4, 2022 - Washington county updates (Santa Clara-Ivins and St George City Fire) - July 21, 2022 - Update for Rocky Ridge Fire - July 5, 2022 - Update for New Harmony Fire - June 22, 2022 - Initial version of data EMSResponseAreas: hub: name: Utah EMS Response Areas item_id: 2c242ea8b8df44f5adec02ee73cdeb16 updates: - August 4, 2023 - Iron County updates - June 22, 2022 - Initial version of data LawEnforcement: hub: name: Utah Law Enforcement alias: Utah Law Enforcement Facilities item_id: 5a99475d434941359462307ede77d980 updates: - June 2013 - August 2011 LawEnforcementBoundaries: hub: name: Utah Law Enforcement Boundaries item_id: 2e29c65cc23c48bd88b619a3ea218626 updates: - August 8, 2023 - Updates based on recent municipal annexations - June 5, 2023 - Updates based on recent municipal annexations - March 31, 2023 - Updates based on recent municipal annexations - November 4, 2022 - Updates based on recent municipal annexations - August 24, 2022 - Updates based on recent municipal annexations - February 16, 2022 - Updates based on recent municipal annexations - December 13, 2021 - Updates based on recent municipal annexations - November 9, 2021 - Updates based on recent municipal annexations - August 10, 2021 - Added Taylorsville PD and made updates based on recent municipal annexations - July 21, 2021 - Updates based on recent municipal annexations - June 16, 2021 - Removed Monticello PD - June 1, 2021 - Added Riverton PD PSAPLocations: hub: name: Utah Public Safety Answering Point Locations item_id: 6c102c8fc5af413187db0910053d5ed8 updates: - June 28, 2021 - Updated PSAP phone numbers - March 11, 2021 - Updates for initial NG911 submission; matched names to PSAP Boundaries layer - January 28, 2020 - Updated name of Central Utah 911 - December 10, 2018 - November 25, 2013 PSAPBoundaries: hub: name: Utah Public Safety Answering Point Boundaries item_id: 34f4c65991ce4d4299d3957efa35bf41 updates: - August 8, 2023 - Updates based on recent municipal annexations - June 5, 2023 - Updates based on recent municipal annexations - March 31, 2023 - Orem/Provo consolidation into Metro Emergency Communications, San Juan consolidated into Price Communications Center - November 4, 2022 - Added a Kane County Sheriff's Office PSAP polygon in northern AZ - August 24, 2022 - Updates based on recent municipal annexations - January 6, 2022 - Updates based on recent municipal annexations - December 13, 2021 - Updates based on recent municipal annexations - November 9, 2021 - Removed Navajo Nation PSAP and updates based on recent municipal annexations - July 21, 2021 - Updates based on recent municipal annexations - June 23, 2021 - Updated PSAP phone numbers - March 11, 2021 - Updates for initial NG911 submission. Several adjustments made based on inputs from PSAP managers. Rebuilt data to align with current county and municipal boundaries. - November 12, 2020 - Updates based on recent municipal annexations, corrections for Washington County, and Weber County/Hill AFB. - January 28, 2020 - Added Centerville to Bountiful PD jurisdiction. Adjusted municipality-centric boundaries to account for recent annexations. Updated name of Central Utah 911. StateAgencyDispatch: hub: name: Utah State Agency Dispatch item_id: dc84c0dfd07648718114f683c933ea93 updates: - October 2013 --- {% include abstract.html name="Public Safety" type="GIS Data Layer" stewards="UGRC" abstract="This section has four groups of data relating to public safety in Utah. Public safety facilities and boundaries (where appropriate) are included for correctional, fire, law enforcement, and dispatch activities." %}
Correctional Facilities contains locations for jails and prisons in Utah. A jail or prison defined by the Department of Justice consists of any facility or location where individuals are regularly and lawfully detained against their will. This includes Federal and State prisons, local jails, and juvenile detention facilities, as well as law enforcement temporary holding facilities. Locations which function primarily as law enforcement offices are included in this dataset if they have holding cells.
There are no constraints or warranties with regard to the use of this dataset.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}
Fire Stations includes any location where fire fighters are stationed or based out of, or where fire fighting equipment is stored. This definition includes fire stations that are both private and public entities, and fire stations that primarily fight forest or grassland fires. Fire fighting training academies are also included.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}
Fire Response Areas in Utah represent the primary agency, typically a local fire department, that will respond to fire-related emergencies at a given location. The dataset was created to provide response recommendations for the Next-Generation 911 system and was compiled from a variety of data sources. The response areas represent career and volunteer fire departments at a variety of levels including local, municipal, county, state, and federal agencies, as well as a few private or industrial organizations. The dataset was built from computer-aided dispatch (CAD) data within the state, tax entities, municipal boundaries, county boundaries, land ownership, inputs from Utah Forestry, Fire, and State Lands (FFSL), and other datasets.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}
EMS Response Areas in Utah represent the primary agency that will respond to medical emergencies at a given location. The dataset was created to provide response recommendations for the Next-Generation 911 system and was compiled from a variety of data sources. The response areas represent a variety of service levels (paramedic, advanced EMT, with or without transport capability, etc.), but generally include 911 scene response. The data was compiled using written descriptions from the licensing database of the Bureau of Emergency Medical Services and Preparedness (BEMSP), which is part of the Utah Department of Health and Human Services' (DHHS). In some instances, the BEMSP data was incomplete or the written descriptions were inaccurate, contradictory, ambiguous, or failed to close. To make the data more complete, computer-aided dispatch (CAD) data from within the state was used to resolve conflicts, fill in gaps, and improve completeness.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}
Law Enforcement Facilities contains locations in Utah where sworn officers of a law enforcement agency are regularly based or stationed. Law enforcement facilities are at least partially publicly funded. This law enforcement dataset includes federal, state, local and special jurisdiction facilities. Examples of law enforcement facilities included are municipal police, county sheriffs, state police, school police, park police, railroad police, and federal police.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}
Law Enforcement Boundaries contains jurisdictional boundaries for law enforcement agencies in Utah. Law enforcement boundaries indicate which law enforcement agency has the primary responsibility in a specific area. These boundaries are also used in the Next-Generation 911 system for recommending which agency should respond to a specific location.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}
PSAP Locations represents the primary Public Safety Answering Point (PSAP) locations in Utah. The Salt Lake County Sheriff Office PSAP is secondary. The original dataset was created in December 2004 by geocoding the PSAP addresses provided by the E911 Executive Committee.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}
PSAP Boundaries represents Public Safety Answering Point (PSAP) boundaries in Utah that will be used for call-routing in the Next-Generation 911 system. PSAP jurisdictions related to county and municipal boundaries are based on the boundaries that were current on the date the data were last updated. The data also includes jurisdictional boundary representations of the Navajo Nation Tribal Lands, Hill Air Force Base, and Colorado City.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}
State Agency Dispatch represents State of Utah dispatch center boundaries.
{% include contact.html subject=page.title contact=site.data.contacts.agrc %}


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| < Day Day Up > |
Hack 95 Spelling Bee
For those who edit their text at the command line. Like most computer users, you probably find yourself spending a fair bit of time typing, whether responding to email, navigating the web, or working on that résumé or thesis. How often do you find yourself looking at a word, wondering if you've spelled it correctly? How often do you rack your brain trying to find a more interesting or descriptive word? You've probably discovered that Unix doesn't come with a built-in dictionary or thesaurus. Sure, you can install a feature-rich GUI office suite, but what alternatives are there for users who prefer less bloat on their systems or are accessing systems from the command line? 9.8.1 Quick SpellcheckIf you're in doubt about the spelling of a word, try using look. Simply include as much of the word as you're sure about. For example, if you can't remember how to spell "bodacious" but you're pretty sure it starts with "boda": % look boda bodach bodacious bodaciously
I find look especially helpful with suffixes. It's very handy if you can't remember when to use "ly", "ally", or "ily". For example: % look mandator mandator mandatorily mandatory 9.8.2 Creating a Dictionary or Thesauruslook is a useful spellchecker, but it won't show you the meanings or synonyms of a word. Accordingly, I found myself spending a fair bit of time at http://dictionary.reference.com/. While there, I noticed a pattern. Whatever word I searched for was appended to the URL as search?q=<myword>. Whenever I used the dictionary, the URL started with dictionary, which changed to the word thesaurus whenever I did a thesaurus lookup. That suggested to me that it would be very easy to generate my own custom lookup utility, so I started out with these two scripts: % more ~/bin/dict #!/bin/sh # script to look up the definition of word from dictionary.reference.com # replaces $1 with user's search string # or gives error message if user forgets to include search string if test $1 then w3m "http://dictionary.reference.com/search?q="$1"" else echo "Don't forget to include the word you would like to search for" exit 1 fi % more ~/bin/thes #!/bin/sh # script to find the synonym of word from thesaurus.reference.com # replaces $1 with user's search string # or gives error message if user forgets to include search string if test $1 then w3m "http://thesaurus.reference.com/search?q="$1"" else echo "Don't forget to include the word you would like to search for" exit 1 fi Recognize those positional parameters we saw before in [Hack #13] ? When I use either script, I include the word that I would like to look up. The utility I chose to grab the results is the command-line browser w3m, which can be built from /usr/ports/www/w3m. If you have already installed another command-line browser, such as lynx or links, specify your browser in your own script. Don't forget to make your script executable with chmod +x. Then, to look up the meaning of a word: % dict palladium Or, to find its synonyms and antonyms: % thes brusque
9.8.3 Improved DictionaryWell, that's a fair startómy browser now automagically takes me to the correct section of an online dictionary or thesaurus whenever I'm curious about a particular word. However, what if I want to forgo using a browser altogether? FreeBSD comes with the fetch utility specifically to retrieve web information. Why not use it to retrieve the results? Before editing my scripts, I tried various invocations of fetch at the command line until I had achieved my desired results. I started out by replacing w3m with fetch (note that I had to supply a word, in this case test, as I was at the command line, not within a script): % fetch "http://dictionary.reference.com/search?q=test" This worked, but it resulted in a file called search?q=<myword>, where <myword> was the word I had supplied as the parameter. After a while, my home directory would be full of hundreds of files starting with search?q. So, I specified the name of a file to which to write the results: % fetch -o results "http://dictionary.reference.com/search?q=test" Now, regardless of the number of times I use my script, I'll only have one file called results. There's a problem with that file, though. It's an HTML file, so unless I enjoy wading through HTML tags in order to read my results, I have to open up that file in a browser. That sorta defeats my goal of not using a browser. So, I went out on the Web looking for an HTML-to-ASCII converter. I tried out several before settling on a Perl script called html2txt . I then tried piping the results file to the converter: % fetch -o results "http://dictionary.reference.com/search?q=test" \ | html2txt results Cannot open HTML source file : results, Error No such file or directory Receiving results: 21791 bytes That's when I hit a timing issue. It takes a few seconds for fetch to retrieve the file, so html2txt complains when the shell asks for it to work on that (as of yet) nonexistent file. To solve that, I asked the shell to wait until after fetch was finished by using && instead of |: % fetch -o results "http://dictionary.reference.com/search?q=test" \ && html2txt results To finish off my command, I ask for the ASCII-fied file to be opened up in a pager so I can view the results: % fetch -o results "http://dictionary.reference.com/search?q=test" \ && html2txt results && more results.txt Note that this particular converter creates an ASCII file with the same name, but with a .txt extension. 9.8.4 Become a Crossword ChampionDid you know that your system has a built-in crossword-puzzle solver? You may never have to leave a square empty again if you remember this little trick.  (This is the same file that look searches.) Use single quotes for your search phrase, and replace each blank square in your crossword with a .. 9.8.5 See Also
|
| < Day Day Up > |


Constructor
-
new PlayersConfiguration(config)
-
Data object with players settings.
Parameters:
Name Type Description config
PlayerClient.Config.PlayersConfiguration.PlayersConfigData players configuration
Members
-
activeXWindowProperty :string
-
Name of the ActiveX object property
Type:
- string
-
flashConfiguration :PlayerClient.Config.PlayersConfiguration.FlashConfiguration
-
Data object for Flash Player
Type:
-
silverlightConfiguration :PlayerClient.Config.PlayersConfiguration.SilverlightConfiguration
-
Data object for Flash Player
Type:
-
wmpConfiguration :PlayerClient.Config.PlayersConfiguration.WMPConfiguration
-
Data object for Windows Media Player
Type:
Type Definitions
-
FlashConfiguration
-
Flash player configuration properties
Type:
- object
Properties:
Name Type Description activeXComponent
string Name of Flash player activeX object
mimeType
string Flash player MIME type
streamType
string This property allows the player to specify over which connection type the resource should be streamed.
windowMode
string Possible values: window, direct, opaque, transparent, gpu. Sets the Window Mode property of the SWF file for transparency, layering, positioning, and rendering in the browser.
allowFullScreen
boolean Setting this value to true allows the SWF file to enter full screen mode via ActionScript.
controlBarAutoHide
boolean Hide control bar automatically if the user is not actively using the player.
-
PlayersConfigData
-
Data object with players configuration properties
Type:
- object
Properties:
Name Type Default Description wmpConfiguration
PlayerClient.Config.PlayersConfiguration.WMPConfiguration Windows media player configuration properties
flashConfiguration
PlayerClient.Config.PlayersConfiguration.FlashConfiguration Flash player configuration properties
silverlightConfiguration
PlayerClient.Config.PlayersConfiguration.SilverlightConfiguration Silverlight player configuration properties
activeXWindowProperty
string ActiveXObject Name of the ActiveX object property
-
SilverlightConfiguration
-
Silverlight player configuration properties
Type:
- object
Properties:
Name Type Description activeXComponent
string Name of Silverlight player activeX object
mimeType
string Silverlight player MIME type
data
string Not used by most browsers for Silverlight purposes. However, setting a value for this property can help prevent decreased performance on some browsers.
plugin
string name of Silverlight plugin
minruntimeversion
string Indicates the earliest Silverlight version that is required in order to run a Silverlight-based application.
background
string Sets background property.
autoUpgrade
boolean Indicates whether a Silverlight plug-in version earlier than the specified minRuntimeVersion will attempt to update automatically.
enableGPUAcceleration
boolean Indicates whether to use graphics processor unit (GPU) hardware acceleration for cached compositions, which potentially results in graphics optimization.
enableHtmlAccess
boolean Indicates whether the hosted content in the Silverlight plug-in and in the associated run-time code has access to the browser Document Object Model (DOM).
windowless
boolean Determines whether the Silverlight plug-in displays as a windowless plug-in. (Applies to Windows versions of Silverlight only.)
stretchMode
string Describes how content is resized to fill its allocated space.
-
WMPConfiguration
-
Windows media player configuration properties
Type:
- object
Properties:
Name Type Description activeXComponent
string Name of WMP activeX object
mimeType
string Windows media player MIME type
classID
string unique identifier WMP COM object.
plugin
string name of WMP plugin
className
string WMP class name
mute
1 | 0 Indicates what the audio/video should be muted (sound turned off)
stretchToFit
boolean Specifies a value indicating whether video displayed automatically sizes to fit the video window, when the video window is larger than the dimensions of the video image.
uiMode
string Specifies a value indicating which controls are shown in the user interface.
windowlessVideo
boolean Specifies a value indicating whether the Windows Media Player control renders video in windowless mode.
invokeURLs
boolean Specifies a value indicating whether the Windows Media Player control automatically invokes URLs in a browser (URL flipping).


Your virtual environment must be active to use thunder.py:
source ../env-tctf/bin/activate
In order for this level to deploy correctly, you must have your git username and email configuration values set to some values, but they don't have to correspond to your actual details.
# Check if "user.email" and "user.name" are set: git config --list # If they aren't set, run: git config --global user.name "John Doe" git config --global user.email<EMAIL_ADDRESS>python3 thunder.py create thunder/a2finance
Activate the service account given to you. You MUST do this, or the level will not work as intended.
gcloud auth activate-service-account --key-file=start/a2-access.json
In this level, the secret is the (fake) credit card number of the person assigned to you upon level creation. Use the compromised service account that is given to you to navigate the cloud infrastructure to find the credit card.
Upon level creation, the name of the target is written to start/a2finance.txt, and the service account key file is written to start/a2-access.json.
From this level on, you will need to be able to figure out the project-wide permissions of service account credentials. To do this, we have provided you with a script that repeatedly queries the projects.testIamPermissions REST api function to figure out which permissions given credentials have. The script is stored at scripts/test-permissions.py. We recommend glancing over the script to understand how it works, but to run it just set PROJECT_ID and either SERVICE_ACCOUNT_KEY_FILE or ACCESS_TOKEN, then run the script, and it will output the permissions of the given credentials. By default, it will test the given service account key file, but if USE_ACCESS_TOKEN is set to True, it will test the given access token.
# If set to true, credentials will be created using ACCESS_TOKEN instead of SERVICE_ACCOUNT_KEY_FILE USE_ACCESS_TOKEN = False # Only one of the following need to be set: SERVICE_ACCOUNT_KEY_FILE = 'path/to/key/file' ACCESS_TOKEN = '' # Set the project ID PROJECT_ID = '[project-id]'---
Run the permissions testing script on the given service account credentials
---In scripts/test-permissions.py, set PROJECT_ID to your project id and SERVICE_ACCOUNT_KEY_FILE to 'start/a2-access.json', then run:
python3 scripts/test-permissions.py---
One of the permissions you have is storage.buckets.list. Try using this permission.
---The command to do so is:
gsutil ls---
There's a bucket in the project. Check out what's inside of it.
---Download the bucket:
gsutil cp -r gs://[bucket-name] .---
The bucket stores a git repository. There might be something interesting in the git history.
---View the previous git commits:
git log---
The most recent commit mentions a key file getting committed by the first commit.
---Checkout the previous commit:
git checkout <old commit name>---
There's an ssh key file! It could be used to login to a Google Compute Engine instance.
---List the compute instances in the project:
gcloud compute instances list---
Get more information on the running instance:
gcloud compute instances describe [instance-name]---
In the metadata of the instance, there is information about the ssh keys that can be used to login to instance, including the username that the key is for.
---To use the SHH key, you will need to restrict access to the key file:
chmod 400 [key-file]
SSH into the instance:
ssh -i [key-file] clouduser@[instance-external-ip]---
Based on the name of the instance, it probably has access to Google Cloud's logging service, Stackdriver Logging.
---List the logs on the project:
gcloud logging logs list---
Most of the logs are automatically generated logs of events of other resources, which may not provide much useful information, but one of the logs is named "transactions"
---Read the log named transaction. You will need to specify it's full resource name:
gcloud logging read "logName=projects/[project-id]/logs/[log-name]"---
You won't want to look through all the log entries, so try filtering it to only show the name you are looking for. The documentation for logging filtration can be found here.
---The command to get the log entry you want is:
gcloud logging read "logName=projects/[project-id]/logs/[log-name] AND jsonPayload.name=[name]"---
Level writeup will be available soon


Pirate (ARRR) is a cryptocurrency that solves the issue of financial privacy on the blockchain through forced anonymous transactions. Protect your wallet and transactions with Pirate’s completely anonymous network. With forced private transactions, your privacy cannot be compromised by another users’ activity on the network. If you wish to learn more, please join the official Pirate Discord at https://pirate.black/discord/ or message FishyGuts#5085, who will be happy to help out.
100 messages
Before 05-Dec-19 12:00 AM
FishyGuts25-Oct-18 07:34 AM
List of z tx firsts that Pirate has accomplished:
- first z tx only chain making a 99.99% shielded coin distribution
- first z tx on chain tip bot
- first z tx mining pools
- first z tx exchange (soon)
- first z tx E Commerce plug in (soon)
- first zcash fork with dPoW
1
Any others so far or to add as coming soon?
actionslacks25-Oct-18 07:35 AM
Sapling
FishyGuts25-Oct-18 07:35 AM
SHould I add - first full z addr support excahnge in development, Tortuga.
house25-Oct-18 07:36 AM
Wondering ... would it be better to use ‘anonymous’ rather than ‘z tx’ to ensure people can understand coherently what it means ?
FishyGuts25-Oct-18 07:37 AM
Good call.
For the exchange listings, z tx it stays, but for the public, anonymous she blows.
actionslacks25-Oct-18 07:37 AM
I guess sapling should be “mobile ready” something anonymous transactions
passcombo.com 🏴☠🏴25-Oct-18 07:45 AM
currently ony z tx allowed? so currently (new transactions) are only 100% z right ?
so we can forget past no matter so much ?
- first fully completed crypto project
no more code development needed ?
at least JL said these words
of course sapling is kind of upgrade but not development
by Pirate team development
actionslacks25-Oct-18 07:51 AM
First coin to 200+ mSol hashrate in a matter of weeks?
Maybe a hard one to quantify
First meme coin with an actual use case.
scubapanda25-Oct-18 08:06 AM
Arrr Shibe! Such Doge has lots of use cases, like buying much wow!
Oszy25-Oct-18 08:11 AM
and 1 doge is still 1 doge
2
scubapanda25-Oct-18 08:16 AM
Make many Doge => 0 Doge. Put Doge in Dough iOS wallet. Dough say wrong pin. Such loss. Doge go paper wallet now.
Oszy25-Oct-18 08:26 AM
no trust in 3rd party mobile wallets
or at least I save pKey straight away
actionslacks25-Oct-18 08:33 AM
1st 99.99% private, technically complete, and completely secure coin to be spawned from the komodo ecosystem.
Flexatron25-Oct-18 09:12 AM
200+ Msols??
ronyi25-Oct-18 09:17 AM
One lucky miner(14Ksol in 2 days) find block on https://piratechain.org I have some surprise for him. SOON
ComputerGenie 👴👴👴25-Oct-18 09:26 AM
!arrrtip cannon 10
ARRR TipBotBOT25-Oct-18 09:26 AM
:skull_crossbones: @Siong@Roman_P@OsTa@goldenman have been cannonstruck with 9.999975 ARRR!
A network fee of 0.0001 ARRR was deducted from the total amount.
This operation is pending. You will be notified when when it has finished.
house25-Oct-18 10:02 AM
time in china - 5:58 pm (population 1.386 billion)
time in india - 3:28 pm (population 1.339 billion)
etc ...
thus, it is not "night in the 3 largest countries"
because, facts
[raspberry](edited)
'p.m. adverb [ not gradable ]
also P.M. us /ˈpiˈem/
used when referring to a time between twelve o’clock in the middle of the day and twelve o’clock at night; in the afternoon or evening or at night'(edited)
6 pm is feken night
house25-Oct-18 10:05 AM
haha!
you so much fun damnit !
ComputerGenie 👴👴👴25-Oct-18 10:05 AM
did you ever mess with CureCoin?(edited)
house25-Oct-18 10:05 AM
nope
does it show ?
ComputerGenie 👴👴👴25-Oct-18 10:06 AM
LOL
they have a team member that goes by hou5e... just curious
house25-Oct-18 10:08 AM
ahh ... no only been house all my online life ... bitshares and supernet and I cast off the former
1
1
ComputerGenie 👴👴👴25-Oct-18 10:09 AM
OK, ARRR stuff doing it's do .... dice script is running ..... time for a
looks like a Pirate buttscootch @Flexatron ... my puppies do it all the time
@ph03nix ... you must be whale by now sailor ?!
keeping such a low profile ... I see you hanging out in safespace ;)
don't go corrupting them purty things will ya !
ph03nix25-Oct-18 10:21 AM
#smallfish #safespace
house25-Oct-18 10:21 AM
ima have to tell James about you and yer girl gang haha!
:clock10: You have an ongoing operation. Please wait until current operation is finished.
CryptoNinjaa25-Oct-18 10:27 AM
ph03nix25-Oct-18 10:28 AM
!arrrtip tip @pika143 0.5551
ARRR TipBotBOT25-Oct-18 10:28 AM
TipBot
Cannot tip with entered amount.
ph03nix25-Oct-18 10:28 AM
!arrrtip @pika143 0.5551
ARRR TipBotBOT25-Oct-18 10:29 AM
TipBot
@ph03nix (ph03nix#3115)
You tipped @pika1430.555 ARRR
A network fee of 0.0001 ARRR was deducted from the amount.
This operation is pending. You will be notified when when it has finished.
ph03nix25-Oct-18 10:30 AM
house25-Oct-18 10:30 AM
BOOM!
sets up a few sock puppets to get free tips from @ph03nix(edited)
ph03nix25-Oct-18 10:32 AM
scallawag
house25-Oct-18 10:32 AM
arrr
grewalsatinder25-Oct-18 10:33 AM
am I in the right channel?
am I in the right tab of my browser??
house25-Oct-18 10:33 AM
only if ye's a scallywag
ph03nix25-Oct-18 10:33 AM
this ain't safe space
grewalsatinder25-Oct-18 10:33 AM
house25-Oct-18 10:33 AM
haha ... satinder too precious for Pirate Channel ?(edited)
nah ... he is a monsterman I hear
Just makes a few changes to script and BOOOM! we got a new technology
ghost.in.the.dao25-Oct-18 10:40 AM
The DREADED PirateChain ......
This operation is pending. You will be notified when when it has finished.


statsmodels.tsa.arima_model.ARMAResults¶
-
class
statsmodels.tsa.arima_model.
ARMAResults
(model, params, normalized_cov_params=None, scale=1.0)[source]¶ Class to hold results from fitting an ARMA model.
- Parameters
- modelARMA instance
The fitted model instance
- paramsarray
Fitted parameters
- normalized_cov_paramsarray, optional
The normalized variance covariance matrix
- scalefloat, optional
Optional argument to scale the variance covariance matrix.
- Attributes
- aicfloat
Akaike Information Criterion \(-2*llf+2* df_model\) where df_model includes all AR parameters, MA parameters, constant terms parameters on constant terms and the variance.
- arparamsarray
The parameters associated with the AR coefficients in the model.
- arrootsarray
The roots of the AR coefficients are the solution to (1 - arparams[0]*z - arparams[1]*z**2 -…- arparams[p-1]*z**k_ar) = 0 Stability requires that the roots in modulus lie outside the unit circle.
- bicfloat
Bayes Information Criterion -2*llf + log(nobs)*df_model Where if the model is fit using conditional sum of squares, the number of observations nobs does not include the p pre-sample observations.
bse
arrayThe standard errors of the parameter estimates.
- df_modelarray
The model degrees of freedom = k_exog + k_trend + k_ar + k_ma
- df_residarray
The residual degrees of freedom = nobs - df_model
- fittedvaluesarray
The predicted values of the model.
- hqicfloat
Hannan-Quinn Information Criterion -2*llf + 2*(df_model)*log(log(nobs)) Like bic if the model is fit using conditional sum of squares then the k_ar pre-sample observations are not counted in nobs.
- k_arint
The number of AR coefficients in the model.
- k_exogint
The number of exogenous variables included in the model. Does not include the constant.
- k_maint
The number of MA coefficients.
- k_trendint
This is 0 for no constant or 1 if a constant is included.
llf
floatLog-likelihood of model
- maparamsarray
The value of the moving average coefficients.
- marootsarray
The roots of the MA coefficients are the solution to (1 + maparams[0]*z + maparams[1]*z**2 + … + maparams[q-1]*z**q) = 0 Stability requires that the roots in modules lie outside the unit circle.
- modelARMA instance
A reference to the model that was fit.
- nobsfloat
The number of observations used to fit the model. If the model is fit using exact maximum likelihood this is equal to the total number of observations, n_totobs. If the model is fit using conditional maximum likelihood this is equal to n_totobs - k_ar.
- n_totobsfloat
The total number of observations for endog. This includes all observations, even pre-sample values if the model is fit using css.
- paramsarray
The parameters of the model. The order of variables is the trend coefficients and the k_exog exognous coefficients, then the k_ar AR coefficients, and finally the k_ma MA coefficients.
pvalues
arrayThe two-tailed p values for the t-stats of the params.
- residarray
The model residuals. If the model is fit using ‘mle’ then the residuals are created via the Kalman Filter. If the model is fit using ‘css’ then the residuals are obtained via scipy.signal.lfilter adjusted such that the first k_ma residuals are zero. These zero residuals are not returned.
- scalefloat
This is currently set to 1.0 and not used by the model or its results.
- sigma2float
The variance of the residuals. If the model is fit by ‘css’, sigma2 = ssr/nobs, where ssr is the sum of squared residuals. If the model is fit by ‘mle’, then sigma2 = 1/nobs * sum(v**2 / F) where v is the one-step forecast error and F is the forecast error variance. See nobs for the difference in definitions depending on the fit.
Methods
arfreq
()Returns the frequency of the AR roots.
bse
()The standard errors of the parameter estimates.
conf_int
([alpha, cols, method])Returns the confidence interval of the fitted parameters.
Returns the variance/covariance matrix.
f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis.
forecast
([steps, exog, alpha])Out-of-sample forecasts
initialize
(model, params, **kwd)Initialize (possibly re-initialize) a Results instance.
llf
()Log-likelihood of model
load
(fname)load a pickle, (class method)
mafreq
()Returns the frequency of the MA roots.
See specific model class docstring
plot_predict
([start, end, exog, dynamic, …])Plot forecasts
predict
([start, end, exog, dynamic])ARMA model in-sample and out-of-sample prediction
pvalues
()The two-tailed p values for the t-stats of the params.
remove data arrays, all nobs arrays from result and model
save
(fname[, remove_data])save a pickle of this instance
summary
([alpha])Summarize the Model
summary2
([title, alpha, float_format])Experimental summary function for ARIMA Results
t_test
(r_matrix[, cov_p, scale, use_t])Compute a t-test for a each linear hypothesis of the form Rb = q
t_test_pairwise
(term_name[, method, alpha, …])perform pairwise t_test with multiple testing corrected p-values
tvalues
()Return the t-statistic for a given parameter estimate.
wald_test
(r_matrix[, cov_p, scale, invcov, …])Compute a Wald-test for a joint linear hypothesis.
wald_test_terms
([skip_single, …])Compute a sequence of Wald tests for terms over multiple columns
aic
arparams
arroots
bic
fittedvalues
hqic
maparams
maroots
resid


Zhaoyu Yang
~~May the world be cleaner than you came~~
Web Front-End Developer
<EMAIL_ADDRESS>Professions
JavaScript
jQuery
Bootstrap
Vue.js
Node.js
express
Gulp
Webpack
Education
Undergraduate student at Nanjing University Jingling college
Major in Electronic information science and technology
Graduated on July, 2013
Research topic related about and
Work experience


Psalm 15
A psalm of David.
1 Yahweh, who may stay in your tabernacle?
Who may live on your holy hill?
2 He walks blamelessly and does what is right,
and speaks truth from his heart.
3 He does not slander with his tongue,
nor does harm to others,
nor insults his neighbor.
4 The worthless person is despised in his eyes,
but he honors those who fear Yahweh.
He swears to his own disadvantage and does not take back his promises.
5 He does not charge interest when he lends money.
He does not take bribes to testify against the innocent.
He who does these things will never be shaken.


Connie Gale Morgan's Favorite piece of art is a picture of a hockey-puck crashing through her kids wall! Connie's first job was in law enforcement and she was working with neighbours fighting! Her favorite children's book is “Where the wild things are™”; Mainly for the pictures in the book No one in her family was an artists with her Mother, Grandmother, and Auntie are artistic. Her first piece of art was of planets in space! She mainly prefers to have only her art on a website.


Change the options to render dualSelect or destroy
Normal select will be changed to be a dual select
Get results is not hard
Selected 0 items.
[]


Ray Optics Simulation
An open-source web app to simulate the reflection and refraction of light.
Suitable for education and demonstration.
Launch Simulator
View on GitHub
Ray
A single ray of light defined by two points.
Beam
A parallel beam of rays emerges from a line segment,
with density controlled by the "Ray density" slider.
Point Source
Rays emerge from a single point,
with number controlled by the "Ray density" slider.
Mirror
Simulate the reflection of light on a mirror.
Mirror (Curved)
A mirror whose shape is curved. Circular and parabolic curves are available.
Ideal curved mirror
The idealized "curved" mirror which obeys exactly the mirror equation (1/p + 1/q = 1/f).
The focal length (in pixels) can be set directly.
Beam Splitter
A mirror that transmits a percentage of incoming light.
Glass
Simulate the refraction and reflection of light on a surface.
Glass (Other shapes)
Glass with any shapes constructed from line segments and circular arcs,
including prisms and "spherical" lenses.
Glass (Ideal lens)
An ideal lens that obeys exactly the thin lens equation (1/p + 1/q = 1/f).
The focal length (in pixels) can be set directly.
Blocker
A line-segment light blocker that absorbes the incident rays.
Ruler
A ruler from a point for zero and another point.
The scale is in pixels.
Protractor
A protractor defined from the center and another point for the zero direction.
The scale is in degrees.
Detector
A tool for measuring the rate of energy flow (P), the rate of perpendicular momentum flow (F⊥), and the rate of parallel momentum flow (F∥) through a line segment. The units are arbitrary.
Views
Rays
Show the rays. When the "Ray density" is high, they appear to be continuous.
Extended Rays
Show both the rays and their extension.
Orange indicates backward extensions, and gray indicates forward ones.
All Images
Show the position of all images.
Yellow points indicate real images, orange indicates virtual images,
and gray (none in this picture) indicates virtual objects.
Note that some images cannot be detected if "Ray density" is not high enough.
Seen by observer
Simulate the rays and images seen from some position.
The blue circle is the observer.
Any rays crossing it are considered to be "observed".
The observer does not know where the rays actually begin,
but may think they begin at some point(s) if they intersect there.
The rays are shown in blue, and the point(s) in yellow (real) or orange (virtual).
Simulate Colorsbeta
Simulate colors (wavelengths) of light sources, mixture of colors, and chromatic dispersion of glasses.


--- title: "Meet The Kitten Whose Mum Is A Dog" featimg: "http://www.viralthread.com/wp-content/uploads/2015/08/Screen-Shot-2015-08-25-at-15.12.30.png" description: "One lucky kitten has been raised as a dog. Best of both worlds?" categories: articleurl: "http://www.viralthread.com/meet-the-kitten-whose-mum-is-a-dog/" ---
When Rosie the kitten was picked up on the streets of LA, the three sisters who found her feared the worst.  In a last ditch effort to save the cat’s life, the sisters turned to one of their pet huskies, Lilo, for help.
There was every chance that the husky would kill the kitten, given their natural predatory instincts, but, happily, Lilo had other ideas. She’d been unable to have pups of her own and took Rosie in as if she was her own child, allowing her to suckle and protecting her.
Here they are together and it’s unbelievably adorable…
Lilo now has an Instagram account with 41,000 followers so you can keep up with Rosie to your heart’s content.


Toggle nav
This is an example to show the potential of an offcanvas layout pattern in Bootstrap. Try some responsive-range viewport sizes to see it in action.
{{content1}}
View details »
{{content2}}
赞{{data}} 倒{{datad}}
{{content3}}
赞{{data1}} 倒{{datad1}}
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赞{{data2}} 倒{{datad2}}
{{content5}}
赞{{data3}} 倒{{datad3}}
{{content6}}
赞{{data4}} 倒{{datad4}}
{{content7}}
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赞{{data6}} 倒{{datad6}}
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赞{{data7}} 倒{{datad7}}


Manali
Manali is a high-altitude Himalayan resort town in India’s northern Himachal Pradesh state. It has a reputation as a backpacking center and honeymoon destination.
Himachal Pradesh is synonymous with scenic beauty, serene landscape and adventure sports. Literally Himachal means “the land of snow”. Situated in the western Himalaya, Himachal Pradesh has several hill stations which are the most appropriate destination to find respite from the scorching summer heat.
Manali is a high-altitude Himalayan resort town in India’s northern Himachal Pradesh state. It has a reputation as a backpacking center and honeymoon destination.
Manimahesh Lake is a high altitude lake situated close to the Manimahesh Kailash Peak in the Pir Panjal Range of the Himalayas.
Hidimbi Devi Temple, also known variously as the Hadimba Temple, is located in Manāli, a hill station in the State of Himāchal Pradesh in north India.
At a distance of 4 Km from Vashisht Temple and 7.5 Km from Manali Bus Stand, Jogini or Jogni Falls is a beautiful waterfall situated near Vashisht Village in Himachal Pradesh.
Triund is a small hill in the Kangra district in the state of Himachal Pradesh, India. The Triund Hill Station is a part of Dharamkot.
Prashar Lake lies 49 km north of Mandi, Himachal Pradesh, India, with a three storied pagoda-like temple dedicated to the sage Prashar.
The Ridge road is a large open space, located in the heart of Shimla, the capital city of Himachal Pradesh, India.
Rohtang Pass, is a high mountain pass on the eastern Pir Panjal Range of the Himalayas around 51 km from Manali.
The Spiti Valley is a cold desert mountain valley located high in the Himalaya mountains in the north-eastern part of the Indian state of Himachal Pradesh.
Khajjiar Lake is situated in Khajjiar, in Chamba district of Himachal Pradesh, India. It is situated at a height of around 1,920 m above sea level between Dalhousie and Chamba Town.


Roget¶
Build a directed graph of 1022 categories and 5075 cross-references as defined in the 1879 version of Roget’s Thesaurus contained in the datafile roget_dat.txt. This example is described in Section 1.2 in Knuth’s book (see [1] and [2]).
Note that one of the 5075 cross references is a self loop yet
it is included in the graph built here because
the standard networkx DiGraph
class allows self loops.
(cf. 400pungency:400 401 403 405).
References¶
| [1] | Donald E. Knuth, “The Stanford GraphBase: A Platform for Combinatorial Computing”, ACM Press, New York, 1993. |
| [2] | http://www-cs-faculty.stanford.edu/~knuth/sgb.html |
Out:
Loaded roget_dat.txt containing 1022 categories.
digraph has 1022 nodes with 5075 edges
21 connected components
from __future__ import print_function
# Authors: Brendt Wohlberg, Aric Hagberg<EMAIL_ADDRESS># Date: 2005-04-01 07:56:22 -0700 (Fri, 01 Apr 2005)
# Copyright (C) 2004-2018 by
# Aric Hagberg<EMAIL_ADDRESS># Dan Schult<EMAIL_ADDRESS># Pieter Swart<EMAIL_ADDRESS># All rights reserved.
# BSD license.
import gzip
import re
import sys
import matplotlib.pyplot as plt
from networkx import nx
def roget_graph():
""" Return the thesaurus graph from the roget.dat example in
the Stanford Graph Base.
"""
# open file roget_dat.txt.gz (or roget_dat.txt)
fh = gzip.open('roget_dat.txt.gz', 'r')
G = nx.DiGraph()
for line in fh.readlines():
line = line.decode()
if line.startswith("*"): # skip comments
continue
if line.startswith(" "): # this is a continuation line, append
line = oldline + line
if line.endswith("\\\n"): # continuation line, buffer, goto next
oldline = line.strip("\\\n")
continue
(headname, tails) = line.split(":")
# head
numfind = re.compile("^\d+") # re to find the number of this word
head = numfind.findall(headname)[0] # get the number
G.add_node(head)
for tail in tails.split():
if head == tail:
print("skipping self loop", head, tail, file=sys.stderr)
G.add_edge(head, tail)
return G
if __name__ == '__main__':
G = roget_graph()
print("Loaded roget_dat.txt containing 1022 categories.")
print("digraph has %d nodes with %d edges"
% (nx.number_of_nodes(G), nx.number_of_edges(G)))
UG = G.to_undirected()
print(nx.number_connected_components(UG), "connected components")
options = {
'node_color': 'black',
'node_size': 1,
'line_color': 'grey',
'linewidths': 0,
'width': 0.1,
}
nx.draw_circular(UG, **options)
plt.show()
Total running time of the script: ( 0 minutes 0.076 seconds)


Tone.Pattern arpeggiates between the given notes in a number of patterns. See Tone.CtrlPattern for a full list of patterns.
The callback to invoke with the event.
The values to arpeggiate over.
var pattern = new Tone.Pattern(function(time, note){
//the order of the notes passed in depends on the pattern
}, ["C2", "D4", "E5", "A6"], "upDown");
The array of events.
The current value of the pattern.
The callback to invoke with the next event in the pattern
The state of the Loop, either started or stopped.
The progress of the loop as a value between 0-1. 0, when the loop is stopped or done iterating.
The playback rate of the loop. The normal playback rate is 1 (no change).
A playbackRate
of 2 would be twice as fast.
Random variation +/-0.01s to the scheduled time. Or give it a time value which it will randomize by.
Muting the Loop means that no callbacks are invoked.
The number of iterations of the loop. The default value is Infinity (loop forever).
Start the loop at the specified time along the Transport's timeline.
Stop the loop at the given time.
Cancel all scheduled events greater than or equal to the given time


Crypto Wallet
Crypto wallet is a sophisticated digital wallet for all your cryptocurrencies.
Add your first cryptocurrency by clicking on the + on the left.
Crypto wallet is a sophisticated digital wallet for all your cryptocurrencies.
Add your first cryptocurrency by clicking on the + on the left.


Form test 3 Radio buttons must be in a field set and a legend
Group 1: Radio buttons in field set and legend
Group 2: Radio buttons legend only
Group 3: Radio buttons field set only
Group 4: Radio buttons hidden fieldset
Group 5: hidden radio buttons
Group 6: Radio buttons aria-label
Group 7: Radio buttons aria-labelledby
Group 7


Sync across all devices
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.
Schedule multiple posts
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.
Notifications about sales
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.
All emails in one place
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.
Search similar posts
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.
Organize your files
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.
Track your time
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.
Secured protocol
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.
Custom profiles and themes
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.


Key Largo is the first of the Florida Keys when traveling from the mainland on Highway 1. The Key markets itself as the "Diving Capital of the World" due to the large coral reef area a few miles offshore. The island itself is a fossilized remnant of a prehistoric coral reef.
Similar to the other Florida Keys, Key Largo is a long, narrow island with plentiful water access. The area requires careful navigation in the shallows.
The Gulf Stream is a predominant current that flows southwest to northeast in this area. At certain times of the year, the flow can reach as much as a knot or more. Keep this in mind when navigating in the area and take care when a strong wind is against the current.
If approaching from offshore, expect increased military or US Coast Guard presence. Immigration and customs authorities patrol this area thoroughly and may intercept any yachts that appear to be heading to land from open sea.
Last updated September 2016.
This is NOT a recognized Port of Entry by the US Customs and Border Protection.
See USA Formalities for full details on clearing into and out of the USA.
If you are arriving from abroad, customs clearance and immigration procedures must be conducted in nearby Key West prior to cruising in this area.


I have a concrete sequential thinking style and I haven't been able to figure out if that's good for me or bad for me. I definitely tend to go step by step in my thought process, but I am terrible at imagining what the end result will look like before I get there. I definitely wish I was better at thinking outside the box. However, I think the step by step approach has helped me so far in Phase0 b/c of how the challenges are presented to us at the beginning of each week.
I feel that one of the biggest struggles for me is thinking of the answer quickly, especially one that takes multiple steps or has multiple ways of getting to the answer. At times it's nice that DBC is on my own time and my own schedule, but then when it comes to pairing I sometimes feel like I don't have enough time to stop and think about the solution.
I've mentioned it before but one of the reason I decided to do DBC was to change my mindset from fixed to growth. I understand I won't be able to fully change the way I think, but I want to rid myself of becoming depressed when I don't succeed. I tend to get frustrated and give up, and I don't want to be that way anymore. It happened once to me during week 1 and once during week 2 where I felt this information I was trying to learn was way over my head, and that all the other students were picking it up quicker than me, I have to keep reminding myself to push those thoughts out of my head and keep trying. Eventually I can get it, and the frustrating times will quickly pass. It's very hard for me to formulate a plan right now (who knows, maybe it's because of my mindset). I just don't know exactly what I need to do to prepare myself for Phase 1. I've worked in the city before and I have worked a long schedule of 12 hour days, 6-7 days a week, so I am not intimdated right now by the workload that phase 1 will bring. I usually get along with people well, and I truely am looking forward to meeting everyone and working with them in person, but I don't know right now about fixing my mindset. The only thing I can say is I want to be aware of my thoughts at all time and I want to catch myself before I go down the path to frustration.
Nick Siefken


Institutional Research and Academic Planning
What We Do
The Office of Institutional Research and Academic Planning delivers analytical and consulting services to central, chancellor, collegiate, and administrative areas to support academic and administrative planning and evaluation. Specific activities of OIRAP include responsibility for new program development; institutional accreditation and licensing, academic program review, data and analytical support for university offices and committees; survey development, administration, and analysis; placement testing of new students; and internal, inter-institutional, state, and federal reporting.


Kernel class.
More...
#include <CLKernelLibrary.h>
Kernel class.
Definition at line 150 of file CLKernelLibrary.h.
◆ Kernel() [1/4]
◆ Kernel() [2/4]
Default Copy Constructor.
◆ Kernel() [3/4]
Default Move Constructor.
◆ Kernel() [4/4]
| Kernel |
( |
std::string |
name, |
|
|
const cl::Program & |
program |
|
) |
| |
Constructor.
- Parameters
-
| [in] | name | Kernel name. |
| [in] | program | Built program. |
Definition at line 138 of file CLKernelLibrary.cpp.
139 : _name(std::move(
name)),
140 _kernel(cl::Kernel(program, _name.c_str()))
std::string name() const
Returns kernel name.
◆ name()
| std::string name |
( |
| ) |
const |
|
inline |
◆ operator cl::Kernel()
| operator cl::Kernel |
( |
| ) |
const |
|
inlineexplicit |
◆ operator=() [1/2]
Default copy assignment operator.
◆ operator=() [2/2]
Default move assignment operator.
The documentation for this class was generated from the following files:


Parents activities with kids
KIDS HEALTHThis is a simple form u can chose the age of your Kids and know the best activity you can do with them
Activities you can do with your kids :
- Exploring Science with Kids
- Explore Shadows with Your Child
- Explore Water with Your Child
- Explore Plants with Your Child
- Explore Ramps with Your Child
- Explore Measuring with Your Child
Searching for Shadows
Go outside on a sunny day with your child. Ask: Where is your shadow? What other
shadows can you see? Hop, dance, and do silly walks together. Watch how your
shadows move.
Take a walk with your shadows. Notice what happens to your shadows when they fall
on a wall, some steps, or a bench.
Shadow Theater
Turn on a lamp or fl ashlight in a darkened room.  Ask your child:
How can you make your shadow bigger? Smaller? What shadow shapes can you make
with your hands?
Put on a shadow play!  Or use toys and household objects as shadow
puppets. You can also make shadow puppets out of paper—cut out shapes and tape
them to the top of a ruler, drinking straw, or unsharpened pencil.
Painting with Water
On a warm day, pour water on different outdoor surfaces, like dirt,
sand, grass, and concrete. What happens to the water? Does it form
droplets, puddles, or streams? Does it soak in?
Make water pictures by dribbling water on sand or dirt. Use a
paintbrush and water or an ice cube to paint a water picture on concrete.
Notice how the water pictures change as time passes. Ask your child,
What’s happening? Why do you think that’s happening?
Making Bathtub Boats
Collect everyday items like plastic containers, sponges, pieces of wood,
metal spoons, rocks, and Styrofoam packing materials.
Invite your child to try fl oating these objects in the bathtub.
Which ones fl oat? How can your child make these “boats” sink?
Flowing with the Current
Turn on a garden hose and let water fl ow slowly over a hard surface.  Notice what happens. Which way do the objects go?
Plants Outdoors
Take time to look at plants with your child. Look for big plants, like trees and bushes.
Look for small plants growing on the ground and in sidewalk cracks.
Talk about what you see. You might say, Let’s see if we can fi nd two plants that look
the
same. Can we fi nd a plant with a fuzzy stem? What’s the smallest plant we can fi nd?
Look at
all the different shades of green! What other colors do you see?
Help your child learn to respect the environment. Only
collect parts of plants that have fallen on the ground
(such as twigs, leaves, and nuts).

The next time you’re slicing fruits or vegetables, let your child
guess what sort of seeds are inside. Will the seeds be big or little?
Black or white? Will there be lots of seeds or just one?
Out and About
Look for ramps.  Look for curb cuts in the sidewalk and entrance ramps to public
buildings.  Talk about how ramps can make
it easier for people in wheelchairs or with strollers to get around.
 I wonder why it moved like that. What do
you think made this one move so fast? After experimenting a little, ask your child to
predict what an object will do before placing it on the slide
In the House
Let it roll.   Encourage your child to
experiment: What could we do to the ramp to make the objects roll faster?
How about more slowly?
Tip and roll. Put a piece of paper in a shallow box or a disposable cake
or pie pan. Dip a marble in paint, then place it in the box.
Out and About
Big steps, little steps. On the sidewalk or driveway, use chalk to measure your foot and
your child’s foot. Ask: Whose foot is bigger? Whose foot is smaller? Count how many
steps
it takes to get to a destination, putting one foot in front of the other, heel-to-toe.
Talk
about the results: Why do you think you made more steps and I made fewer steps?
Rocks and pebbles. Fill a small bag with an item you fi nd outside, such as rocks. Talk
about what you are doing: Let’s fi nd some big rocks to fi ll this bag. Empty the bag
and put
the items in a pile. Then choose something smaller, such as pebbles.  Empty the bag again
and
compare the two piles.
In the House
Tall, taller, tallest.
When you were born, you were about as long as that backpack.  Using paper towel rolls, measure how tall your child is. Then measure
favorite stuffed animals. Use words like tall/taller and short/shorter as you talk about
what you discover.
Make it, bake it. Choose a simple recipe or ready-made mix. Read the ingredients list
with
your child and talk about what you’ll need for measuring: teaspoons, tablespoons, cups.
Have your child help you measure each ingredient. We need one cup of milk and one
teaspoon of cinnamon. Look, a teaspoon is just a small amount.


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Library


| tidy.confusionMatrix {broom} | R Documentation |
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
## S3 method for class 'confusionMatrix' tidy(x, by_class = TRUE, ...)
x |
An object of class |
by_class |
Logical indicating whether or not to show performance
measures broken down by class. Defaults to |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble()
with columns:
class |
The class under consideration. |
conf.high |
Upper bound on the confidence interval for the estimate. |
conf.low |
Lower bound on the confidence interval for the estimate. |
estimate |
The estimated value of the regression term. |
term |
The name of the regression term. |
p.value |
P-value for accuracy and kappa statistics. |
tidy()
, caret::confusionMatrix()
library(caret) set.seed(27) two_class_sample1 <- as.factor(sample(letters[1:2], 100, TRUE)) two_class_sample2 <- as.factor(sample(letters[1:2], 100, TRUE)) two_class_cm <- caret::confusionMatrix( two_class_sample1, two_class_sample2 ) tidy(two_class_cm) tidy(two_class_cm, by_class = FALSE) # multiclass example six_class_sample1 <- as.factor(sample(letters[1:6], 100, TRUE)) six_class_sample2 <- as.factor(sample(letters[1:6], 100, TRUE)) six_class_cm <- caret::confusionMatrix( six_class_sample1, six_class_sample2 ) tidy(six_class_cm) tidy(six_class_cm, by_class = FALSE)


--- title: Template Metadata overview: Definitions used when creating Mixer templates location: https://istio.io/docs/reference/config/mixer/istio.mixer.v1.template.html layout: protoc-gen-docs ---
This proto describes the types that can be used inside Mixer templates. These message types are used to specify field datatype to express the equivalent ValueType for the expressions the field can be mapped to.
Specifies the varity of the the Template.
| Name | Description |
|---|---|
TEMPLATE_VARIETY_CHECK |
|
TEMPLATE_VARIETY_REPORT |
|
TEMPLATE_VARIETY_QUOTA |
|
TEMPLATE_VARIETY_ATTRIBUTE_GENERATOR |
DNSName is used inside templates for fields that are of ValueType “DNS_NAME”
Duration is used inside templates for fields that are of ValueType “DURATION”
EmailAddress is used inside templates for fields that are of ValueType “EMAIL_ADDRESS” DO NOT USE !! Under Development
IPAddress is used inside templates for fields that are of ValueType “IP_ADDRESS”
TimeStamp is used inside templates for fields that are of ValueType “TIMESTAMP”
Uri is used inside templates for fields that are of ValueType “URI” DO NOT USE ! Under Development
Value is used inside templates for fields that have dynamic types. The actual datatype of the field depends on the datatype of the expression used in the operator configuration.


To make this association, you need to include particular class and data-icon attributes in the relevant HTML element: e.g.
 Travel by plane

.
{{/markdown}}


ISHTAR A film review by eli!oz Copyright 1987 eli!oz
Monday night while most of the world seemed to be on interstate roads, I took time out to see ISHTAR at my local theater (where everyone that wasn't on an interstate road was watching BEVERLY HILLS COP 2). And yes, I do go to movies that are younger then 10 years old in spite of comments made about the quote of the week contest! Be that as it may, I found ISHTAR to be an extremely funny and enjoyable film. The acting by Dustin Hoffman and Warren Beatty was excellent and I found both of their characters to be believable.  The supporting roles were well-played, especially by Charles Grodin, who has replaced Fletcher Daniels (of "Hill Street" fame) as my favorite sleaze. Of course, the question that everyone has been asking is, "Was it worth 50 million dollars?" I don't know. I only had to pay $5.00.
And my last little flip remarks bring me to the other point of my article and that is what the critics (and here I mean the newspaper and TV critics, not my fellow reviewers on the net) have been doing to this movie, which seems to be disliking it for how it was made instead of what it contains. To date, the negative reviews that I have read complain about how much the movie cost and how they felt that it was unbelieveable that Hoffman would be the lady's man and Beatty would have such trouble with women. Let me address these issues. The cost of the making of the film is irrelevant to us watching it, unless they try to recoup the money by charging us $17.38 to get in. Seriously, do you care if the movie you are watching cost $5 million to make or $50 million or $500 million? Do *you* sit there saying "I paid my $5.00, now show me 50 million dollars worth of entertainment?" If so, then I'm sorry for you, for you should like the movie for what it is, not what is cost. If they cut corners and the cheapness of a set or a costume gets in the way of your enjoying a scene, fine, but I know I don't sit around thinking "boy for 50 million dollars, that camel should be bigger, or better then it is."
Point 2 about the characters that Beatty and Hoffman play. I wonder if the critics saw the same movie that I saw. Hoffman plays a man from the Bronx that lived with his parents until he was 32. He is very insecure and tries to cover it up with bravado. There is the scene where he tells Lyle (Beatty's character) that he hopes he doesn't hate him since everything about him is phoney, right down to his nickname (Hawk). Hoffman plays Hawk as a cocky s.o.b. who really doesn't believe in himself, but figures if he can get someone else to believe in him, then things will work out. He does an excellent job with the character.
Beatty plays a tall, good-looking hick from Texas. He is not used to the big city, he is uncomfortable around it and he has never dated another woman other then the one that he married when he was 17. Of *course* he would be uncomfortable around women given that history! If you can get it out of your mind that you are watching Warren Beatty up there, you can realize that he does a very good job with the character of Lyle. Lyle cannot acccept the fact that he might be attractive, so he thinks of the things that Hawk is that he isn't and sees them as virtues. "I'm too big.  Did you ever see a big sports car?" (watch for that one--it may pop up in my quiz contest someday!). I don't know about the rest of you, but I have had a number of friends who were fairly good-looking that just couldn't accept their looks and you felt like shaking them to get them to listen.
Okay, the long and short of it: I thought ISHTAR was a funny, well-acted and totally enjoyable movie which I highly recommend. If you like your comedy on the chuckle and snicker side, it will appeal to you, although it does have some good belly-laughs too. Give it a chance for its own merits, and don't not see it for things reasons that are not important.
OZ seismo!sundc!eli!oz
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


Alapati Mounika Chowdary
Dreamer, Believer, Doer.
Hi! I am Alapati Mounika. Computer Science Enthusiast, constantly learning, working and trying to become a better person everyday not just for me but also for the world around me
"Welcome!!to my my portfolio website. Here its all about What I Did? What I'm doing & What I would like to do!!".
© 2017 Alapati Mounika. All Rights Reserved


--- layout: documentation title: PrefixType ---
Provides an IP address or a prefix of an IP address for NDP for IPv6.
| Field Name | Type | Description |
|---|---|---|
| IPv6_Addr0..1 | AddressObjectType |
IPv6 address. |
| IP_Addr_Prefix0..1 | AddressObjectType |
The initial bits of an IPv6 address (these are identical for all hosts in a network) form the network's prefix. http://ipv6.com/articles/general/IPv6-Addressing.htm. |


BISACODYL- bisacodyl suppository
Cardinal Health
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
For rectal use only
taking any other drug. Take this product two or more hours before or after other drugs. Laxatives may affect how other drugs work.
it may cause stomach discomfort, faintness, rectal burning and mild cramps
This package is intended for institutional use only.
Do not use if printed suppository wrapper is missing or damaged. Each suppository is individually wrapped.
When public and private health plans provide coverage for OTC pharmaceuticals, consumers get relief and drug formularies can cut costs. At Perrigo, we call it ORx
If your doctor writes a prescription for this product, it may be covered by
FSA or HSA Accounts
Private Health Plans
Medicaid/Medicare
If swallowed, get medical help or contact a Poison Control Center right away. (1-800-222-1222)
adults and children 12 years of age and over |
1 suppository in a single daily dose. Pull apart foil. Insert suppository well into rectum, pointed end first. Retain about 15 to 20 minutes. |
children 6 to under 12 years of age |
½ suppository in a single daily dose |
children under 6 years of age |
ask a doctor |
|
BISACODYL
bisacodyl suppository | ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
|
| Labeler - Cardinal Health (188557102) |
| Establishment | |||
| Name | Address | ID/FEI | Business Operations |
|---|---|---|---|
| Cardinal Health | 188557102 | REPACK(55154-6704) |


Madurai local body election | Meet reviews poll preparedness
by Staff ReporterState Election Commissioner R. Palaniswamy chaired a meeting with Collectors of four districts to review poll preparedness here on Friday, ahead of local body elections.
Collectors T.G. Vinay (Madurai), M. Pallavi Baldev (Theni), M. Vijayalakshmi (Dindigul) and A. Sivagnanam (Virudhunagar) also received inputs to ensure smooth conduct of the elections.
After a meeting held for election officers of Thoothukudi, Kanniyakumari and Tirunelveli, this meeting had been conducted here, he said.
Mr. Palaniswamy said officials should have a clear understanding of poll norms and comply with them. They must use their experience to conduct the elections in a free and fair manner.
The officials must ensure that basic amenities were available at polling stations. They must check the condition of electronic voting machines and availability of wheelchairs for the differently abled, he added.


Express DECIMAL
as a percent.
SOLN %
To convert a decimal to a fraction, we need to multiply by 100 and add the \%
symbol.
To multiply by 100, we can just move the decimal point two places to the right.
DECIMAL \rightarrow DECIMAL * 10 \rightarrow SOLN
So DECIMAL
= SOLN\%
.


Stripe Payment Gateway - Python CGI Implementation
Name:
Email:
Card Number:
CVC:
Expiration (MM/YYYY):
/
Purchase


Flexbox nuovo
uno
due
tre
This is a Flex-Grow
A,B,C and F are flex-shrink:1 . D and E are flex-grow:1 .
A
B
C
D
E
F


JOHNNY MNEMONIC A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 3.7
U.S. Availability: wide release 5/26/95 Running Length: 1:38 MPAA Classification: R (Violence, profanity, mature themes) Theatrical Aspect Ratio: 1.85:1
Cast: Keanu Reeves, Dina Meyer, Ice-T, Takeshi, Dolph Lundgren Director: Robert Longo Producer: Don Carmody Screenplay: William Gibson based on his story Cinematography: Francois Protat Music: Brad Fiedel U.S. Distributor: TriStar Pictures
JOHNNY MNEMONIC has an interesting premise: it's the second decade of the twenty-first century and information espionage has become so commonplace that human "couriers" are used to transport electronic data. These couriers have implants hardwired into their brains to accommodate uploading and downloading gigabytes, and they get paid handsomely for their services. Meanwhile, the world is in the grip of a mysterious ailment called Nerve Attenuation Syndrome, which threatens half the population with neural seizures.
Unfortunately, once you get beyond this background, the film has little to offer aside from a second-rate cast, a dumb story, and cheesy special effects. Computer-generated visuals a la THE LAWNMOWER MAN aren't nearly as stunning or interesting as director Robert Longo thinks they are. Aside from being relatively easy to create (most theaters use this sort of thing in their self-promotional material), they get dull pretty quickly. Alas, that doesn't prevent Longo from overusing them ad nauseam. Apparently, he spent his entire effects budget on these, because several blue screen shots look embarrassingly fake.
Keanu Reeves, an actor of exceptionally limited scope, plays the title character with about as much wattage as a pen flashlight. Reeves alternates between understated and over-the-top with alarming regularity, creating several unintentionally hilarious moments. As evidence of how poor this performance is, consider that Dolph Lundgren, as a bionic villain, acts circles around Reeves. That's right, I said Dolph Lundgren.
Johnny's head capacity is 80 gigabytes, but he uses a "doubler" to raise it to 160. Nevertheless, on his latest job, a group of scientists have downloaded twice that, leading to a potentially-deadly "leakage" situation--if Johnny doesn't get the information out of his head in 24 hours, his brain will explode. The problem is, a group of yakuza (Japanese mobsters) are after him, because what he's carrying is worth a whole lot of money. So Johnny's on the run through the decaying, futuristic city of Newark, New Jersey, with no download code, and a protector (Dina Meyer) who's only sticking with him until he comes up with the cash to pay her off, or until she falls in love with him (whichever comes first--you guess the answer).
Clearly, JOHNNY MNEMONIC is trying to be a BLADE RUNNER for the '90s, using cyber-space as its playground. The main problem is the script, which is as dumb as action stories get. Just because a film takes place in a futuristic setting doesn't mean that all logic and coherence have to go out the window. Above all, why should we care whether Johnny's head explodes when Reeves' performance fails to spark any empathy for his character? JOHNNY MNEMONIC is brash, flashy, and loud, but it lacks a few key ingredients--namely heart, soul, and intelligence.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% load staticfiles %} {% load tags %}
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For instructions on how to use this application with Digital Garage, start by reading the Developer Guide.
The source code for this application is available to be forked from the Digital Garage GitHub repository. You can configure a webhook in your repository to make Digital Garage automatically start a build whenever you push your code:
After you save your webhook, if you refresh your settings page you can see the status of the ping that Github sent to Digital Garage to verify it can reach the server.
Note: adding a webhook requires your Digital Garage server to be reachable from GitHub.
If you forked the application from the Digital Garage GitHub example, you'll need to manually clone the repository to your local system. Copy the application's source code Git URL and then run:
$ git clone <git_url> <directory_to_create> # Within your project directory # Commit your changes and push to Digital Garage $ git commit -a -m 'Some commit message' $ git push
After pushing changes, you'll need to manually trigger a build if you did not setup a webhook as described above.
Documentation on how to manage your application from the Web Console or Command Line is available at the Developer Guide.
You can use the Web Console to view the state of your application components and launch new builds.
With the Digital Garage command line interface (CLI), you can create applications and manage projects from a terminal.


What I’m saying is that it’s so, so simple to make sites easier to read.
[Intervention] Slow network is detected. See https://www.chromestatus.com/feature/5636954674692096 for more details. Fallback font will be used while loading: https://fonts.gstatic.com/stats/Merriweather/normal/900


--- layout: default title: Mission alias: ministry img-float: right class: page sitemap: priority: 0.7 changefreq: 'yearly' lastmod: 2017-12-29 14:42:52 image-array: - img: ministry-1.jpg lightbox: ministry alt: Backpacks for the Holyland Food Pantry - img: ministry-2.jpg lightbox: ministry alt: Laptops from St. Paul's being distributed at the Omahila Combined School in Namibia - img: ministry-3.jpg lightbox: ministry alt: Mission mini-trip to Ronald McDonald House in Madison --- {% include custom-photo.html %}
Ministry is important to us at St. Paul's. Our largest mission is participation in the Holyland Food Pantry, but we do continue to support others as well. We've held several collections for children:
We also support a rotating fund of KIVA loans, more than 700 loans totaling $17,500. Our KIVA fund has served inviduals in 73 different nations. 80% of loan recipients are women.
In recent years, we've taken ministry trips as close as the Ronald McDonald House in Madison and as far away as the Black Hills of South Dakota or the Henderson Settlement in Frakes, KY. We are organizing a new round of trips in 2018 and 2019.


Random Dance Party
menu
Desert Bus For Hope
Video Strike Team
Random Dance Party ▾
Random Desert Bus Video
Random Crapshot
All Years
Desert Bus 2018
Desert Bus 2017
Desert Bus 10
Desert Bus 9
Desert Bus 8
Desert Bus 7
Desert Bus 6
Desert Bus 5
Desert Bus 4


by Gurpreet S Rai
For my final project, I have elected for the Titanic Dataset to conduct my own data analysis. The Titanic - the largest ship at the time it entered service – was a passenger liner that sank in the North Atlantic in the early morning of 15th April 1912, after colliding with an iceberg during her maiden voyage from Southampton to New York. Of the 2224 Passengers and crew aboard, more than 1500 died, making it one of the deadliest commercial maritime disasters in modern history. The ship carried both the wealthy, and emigrants from across Europe, and at the time was thought to be unsinkable. What interests me about this project, is the possibility to try to understand what factors may have affected the chances of survival for those aboard.
The main areas I want to consider for my analysis will be to try and understand how the following factors affected the chances of survival:
1) Demographics – How the class of the passengers affected survivability.
2) Sex – How did survival rates differ between the sexes?
3) Age – Did the age of passengers affected chances for survival?
4) Family - How did having a family member aboard affect survival chances?
# import modules
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# read in the csv titanic dataset
titanic_df = pd.read_csv("titanic_data.csv")
Let’s check the data has loaded correctly and confirm outlay of table and dataset. The data dictionary has also been copied over below, to help better understand our data.
# Use the head function to list first 5 rows of dataframe
titanic_df.head()
Survival Survival [0=No, 1=Yes]
Pclass Ticket class [1 = 1st, 2 = 2nd, 3 = 3rd]
Sex Sex
Age Age in years
SibSp # of siblings / spouses aboard the Titanic
Parch # of parents / children aboard the Titanic
Ticket Ticket number
Fare Passenger fare
Cabin Cabin number
Embarked Port of Embarkation [C = Cherbourg, Q = Queenstown, S = Southampton]
Now to further understand our data, lets confirm the total number of entries, if any rows of the columns are missing data (ie if number of rows in any of the columns are less than the total RangeIndex), and also the data type contained within each column.
# call info() function to list concise summary of the dataframe
titanic_df.info()
From the above summary, we see there are a total number of 891 entries in 12 columns, but 3 columns ('Age', 'Cabin', 'Embarked') do not contain full records for all 891 entries. Therefore to avoid the missing values affecting our analysis, each of these 3 columns need to be considered further.
Lets first consider the Age coloumn, and confirm the number of missing values we have in our data.
# Calculate and output the number of missing Age data points
missing_age_data = titanic_df['Age'].isnull().sum()
missing_age_data
We can now confirm there are 177 missing values within the Age column. To reduce the effect this may have on my analysis that utilises the age records, I propose to substitute the missing values with random samples of the 'normal' distribution of the data present in the Age column.
# take the mean of the age column, and confirm value
mean_age = titanic_df["Age"].mean()
mean_age
# take the uncorrected standard deviation
std_age = titanic_df["Age"].std(ddof=0)
# generate the required number of sample data points, and store in variable
random_sample = np.random.normal(mean_age, std_age, missing_age_data)
# splice dataframe column with boolean index array of missing values,
# and replace missing datapoints with the random samples generated earlier
titanic_df["Age"][np.isnan(titanic_df["Age"])] = random_sample
# change Age values type to integers
titanic_df["Age"] = titanic_df["Age"].astype(int)
# check mean value is within range of original mean value of approx 29.7
mean_age = titanic_df["Age"].mean()
mean_age
From the Data dictionary, we see both 'SibSp'(sibling & spouse), and 'Parch'(parent, child), donate a family member. Therefore, to help to determine if a family member on-board had an impact on survival, both these columns can then be reduced to a single 'Has_Family' column.
# By vectorized operation, add both 'Sibsp' & 'Parch' columns
# to new 'has_family' column
titanic_df["Has_Family"] = titanic_df["SibSp"] + titanic_df["Parch"]
# To keep values in new 'Has_Family' column as binary, call function with
# apply function to reduce any 'True' values to 1, with all other values
# kept as 0.
def binary_value(n):
if n > 0:
return 1
else:
return 0
titanic_df["Has_Family"].apply(binary_value)
# confirm new column has been correctly modified
titanic_df['Has_Family'].head()
Now let’s consider the two remaining columns with missing data-points - Cabin & Embarked. Cabin, has so many missing values – only 204 valid values out of the total 891 entries. Embarked, at this time appears unlikely to have had any effect on the survivability of the disaster. Taking the above points into accounts, I propose to remove both sets of data from the Datafame.
# remove both Cabin & Embarked entries along the coloum axis
titanic_df = titanic_df.drop(['Cabin', 'Embarked'], axis=1)
# check the age column is now complete and in integer type,
#'Has_Family' is present and both 'Cabin' & 'Embarked' have been removed.
titanic_df.info()
As we see above, we now have the full datasets for those columns I want consider for my analysis.
# To help better understand the data set, lets take a closer look
# with the describe function - to generate various summaries from the
# Dataframe.
titanic_df.describe()
We can see from the above data the mean survival is 0.38 – or 38%. Let’s take a further look into a few of the groups which I wanted to consider for my analysis - starting with demographics and sex.
# drop the PassengerId, and group by Pclass and Sex
titanic_df_drop_id = titanic_df.drop('PassengerId', axis=1)
grouped_pclass_sex = titanic_df_drop_id.groupby(['Pclass', 'Sex'])
# generate summary stats of the group
grouped_pclass_sex.describe()
We can see from the above summary of each group, Sex and Pclass shows differing survival rates. Let’s take a more detailed look at these groups with regard to the Survived means.
# mean Survived of Pclass and Sex, and unstack dataframe
# to show coloumn names
survived_pclass_sex = grouped_pclass_sex['Survived']
table_sex_pclass = survived_pclass_sex.mean().unstack(level=-1)
table_sex_pclass
This table shows the mean survival of both Pclass and Sex of the passengers. The most striking thing already is the mean survived rates between the sexes and classes of those on-board. 1st class female survival rates are 97%, and those for 3rd class males are as low as 14%. Let’s plot to better illustrate, and compare these findings.
# plot to illustrate the above table
sns.barplot(data=titanic_df, x='Sex', y='Survived', hue='Pclass')
plt.title('Survival Rates for Sexes and Demographics')
plt.ylabel('Survival Rate')
The above bar plot shows a clear correlation for survival rates between the sexes and demographics. With a greater probability for females to survive, and demographics also contributing to survival chances - with higher class passengers appearing to fair better than the lower classes.
As both the sex and demographics appear to show a robust correlation, let’s better illustrate these variables over several differing plots.
sns.pointplot(data=titanic_df, x='Pclass', y='Survived', hue='Sex')
plt.title('Point Plot of Survival Rates for Sexes and Demographics')
plt.ylabel('Survival Rate')
The above plot also shows the clear divide between the sexes and classes on the mean survival rates – with female members having a greater chance of survival.
sns.factorplot(x="Pclass", y="Age", hue="Survived",
col="Sex", data=titanic_df, kind="swarm")
sns.plt.suptitle('Scatter Plots for Survival of Sex, Age, and Demographics', fontsize=12)
sns.plt.subplots_adjust(top=.85)
Again, the twin ‘swarm’ plots, help to illustrate the correlation of mean survival between the sexes and demographics, and a slightly lower average age of the lower classed passengers.
Before considering the mean survival with regard to Age, I want to understand the demographics of the various ages, as these may latter have a bearing on my results.
# age variance in each class
g = sns.FacetGrid(titanic_df, row="Pclass", size=1.5, aspect=4,)
g.map(sns.distplot, "Age", hist=False, rug=True);
g.fig.suptitle('Age in Relation to Demographics', fontsize=12)
g.fig.subplots_adjust(top=.85)
From this plot, we see the number of lower class passengers (2nd & 3rd), appear to be slightly skewed to the younger end of the spectrum – most visible with the 3rd class passengers, which appear to have the youngest adult population.
Let's now have a look at the mean survival rates with regard to Age.
# plot of those that survived, against those that did not, by age.
g = sns.FacetGrid(titanic_df, hue="Survived",aspect=5)
g.map(sns.kdeplot,'Age',shade= True)
g.add_legend()
g.fig.suptitle('Survival in relation to Age', fontsize=14)
g.fig.subplots_adjust(top=.9)
The above plot shows there appears to be a slight increase in the chance of mortality between those aged in their 20-30's, however there also appears to be a large increase of survival of the very young, let’s take a look at this in closer detail.
# limit the x axis, to take closer look at plot between 0-30
g = sns.FacetGrid(titanic_df, hue="Survived",aspect=5)
g.map(sns.kdeplot,'Age',shade= True)
g.set(xlim=(0, 30))
g.add_legend()
g.fig.suptitle('Survival in relation to Age (0-30yrs)', fontsize=14)
g.fig.subplots_adjust(top=.9)
From the defined plot above, we see that children up to at least 10 years old appear to have a much greater chance for survival, but this trend changes from 15years old, where mortality looks like it increases up to almost 30 years old.
We found a correlation between the sexes and chances for survival, let’s now plot sex with age, to see if we can identify how this may have had an effect.
# Linear regression of survival proability of age and sex
# Using Logistic Regression as one of the variables (Survived) is in binary
sns.lmplot(x="Age", y="Survived", hue="Sex", data=titanic_df,
y_jitter=.05, logistic=True)
plt.title('Survival Rates Across Age Range for Both Genders')
plt.ylabel('Survived')
plt.yticks([0,1], ['No', 'Yes'])
With this statistical linear regression, we find a signal between the sexes and ages. With females appearing to fair better with age, and males less so.
Now let’s consider how having family on-board affected survivability
# Group 'Has_family' with survival rates, and output mean across the range
groupby_family = titanic_df_drop_id.groupby('Has_Family').mean()['Survived']
groupby_family
We see a potential increase of survival, for those having between 1-3 family members aboard. Let’s bar plot these values to better illustrate this data.
sns.barplot(data=titanic_df, x='Has_Family', y='Survived')
plt.title('Survival Rates for Family Members Aboard')
plt.ylabel('Survival Rate')
plt.xlabel('Number of Family Aboard')
This plot better illustrates the slight increase of surviving for those who had 1-3 members of family on-board, but this affect also appears to drop off with 4 or more.
For my project, I asked how factors, such as demographics, sex, age and family may have affected the chances of survival of both passengers and crew members. From my analysis, I can now share my initial findings and tentative conclusions
From the data analysis, we can find a correlation between the class of passenger and their chances of survival. What appears in several of the plots is a tendency for a likely increased chance of survival for those passengers in higher classes.
There appears to a clear correlation between the sexes and survivability of those on-board, with female passengers having a much higher chance of surviving the disaster. This may not be that surprising, as it is probably due to the Birkenhead Drill (Women and Children First code of conduct), whether from training of the crew or the sensibilities of all those of board.
The correlation between age and survival chances were not very clear, though there did seem a slight increase of surviving for the very young (below the age of 10), again this may be from the Birkenhead drill. A few other possible correlations appear to show a slight decrease in surviving for those between 20-30, however without further analysis, or even more data, it is not really possible at this time to differentiate these possible indictors from other variables like demographics etc.
I wanted to know if having family members aboard made a difference for the chances of survival. From the analysis, there appears to be a correlation with those who had at least one member of family aboard and having a slightly better chance for survival, but this affect also drops off after having 4 or more family aboard. Again, this is a tentative finding, as without understating how this may also be affected by other variables, for example a split between parents and siblings, and demographics, it is not possible to fully explain the findings at this time.
Though these are my initial tentative findings, the clearest correlations appear to be with the demographics and sex of those aboard with regard to their chances for surviving the disaster. Female 1st class passengers had a 97% chance for surviving, and on the other scale, men in the 3rd class had the lowest survivability with only a 14% chance of surviving. Having Family aboard also showed a correlation, with those with 1-3 members having a slight increase in surviving, but again this would need further analysis to determine if other factors played apart. Age probably showed the smallest correlation, with a small increase of surviving for those below the age of 10, but without re-looking at this data with other possible contributing factors, for example demographics, it is not really clear how age itself played a part in surviving.
A further analysis of the cabins and decks may provide an insight in to how the passenger and crew locations may have affected the chances of surviving – even with the limited dataset. As the passenger and crew locations may have allowed for easier access to the top deck, and to the limited number of lifeboats. My analysis of the age data appears to show young children had an increased chance for survival, but was this dependant on any other factors, ie number of parents/siblings aboard, demographic etc.? Though passengers having 1-3 members of family appear to show a better chance for surviving, it would be interesting to see how both demographics and decks affected this outcome. For example, how many children were in each of the classes and how did each group fair? Another possible route of investigation, could be to look how passenger fares affected the chances of survival? Is the demographic data directly in-line with the fare costs? If not, what has greater effect on survivability, passenger class or fare paid?
To drop columns from pandas stackover flow
Working with missing data pandas documentation 0.19.2
Taking random samples from a normal distribution, from SciPy.org
Data dictionary of the titanic data, from [Kaggle.com] (https://www.kaggle.com/c/titanic/data)


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_ ---Court vide order dated 29.9.2007 granted an and directed the standing Counsel Sri to take notice for the respondent. Subsequentiy " AA Vide onier dated l5.1(),2{}08, directed issuance of notice to respondent. From time to time, the interim order granted earlier in exteneied. Since the petitioner failed to take steps for service of notice on the respondent, this Conn: vide orréer dated 19.12.2008 passed a conditional order grajlting one @"~«~e«-v"*~« week t:i.me to take steps failing which dismissed without: reference application (Misc.Crl.No. lE'>»356V. is the order dated 19. 12.2008.
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6. The Supreme Court held that the question of priozr sanction can be considered at the time of final disposal. Therefore, on the gonad of want of sanction under Section. aékfmv 197 of Cr.P.C., the proceedings before the 4_ be quasheci nor stalled. The contention of t1*:'ua1«;" ' i " it is not his duty to provide T matter of evidence. It is 110$ fdr go i11to the evidence at this stage.
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this test draws a bunch of circles using AutonCanvas. check its speed in different browsers.
note that overlapping circles don't render correctly in IE. it's possible to draw them correctly using many elements, instead of a single element having many circles, but this is much slower.


20140411
As the surge in commits for the [filtering plugin] settle down, I'm uploading another build of [Researches.app]. The full story's still very much untold, as Researches.io remains in its mildly enigmatic form, but I'm happy to see the machinery settle down and the context around the [engine] to form.
It also feels awesome to have pulled another project off the pipeline, with a collaborator who is reliable, articulate and motivated. The sneakers project is moving. []
To top it all off, I had a couple of deeply technical and insightful chats today, with Lukas from upfront.io and Michael from redacted.


Type:
IN WORDS DROWN I
A type experiment based on the exploration of human communication in which gestures express far more than words.


2019 Iowa Cross Country Rankings Map based on data from IATC updated 10/29/19
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