Ekspos is platform independent Java image viewer program. It support most popular image format: JPEG, PNG, and GIF, where additional image format can be added through Java ImageIO plugin. It consists of 2 parts: shell browser and viewer. Shell browser allows you to navigate your image collection and you'll be able to browse images in tree, detail, list, icon and thumbnail view. The viewer provide progressive view of images with zoom and pan capability.
Features:
- Image Format(Standard): JPEG, PNG, and GIF
- Support Java ImageIO plugin
- Support 4 views: detail, list, icon, and thumbnail
- Thumbnail caching for fast preview
- Written entirely in Java™
Note: You need to have J2SE or Java VM (JRE) 1.5 or above. You can download from Sun Java site (http://www.java.com/getjava)
To run the program:
- go to "[install_dir]/bin" directory
- on Microsoft Windows double click ekspos.exe or type "ekspos.bat ["optional startup folder"]"
- on Linux/Unix type "ekspos.sh ["optional startup folder"]" or change the permission first +x if necessary
- on other type "java -jar [install_dir]/lib/ekspos.jar ["optional startup folder"]"
Note:The optional startup folder is optional. If left empty it will use USER_HOME as startup folder. If relative path it will resolve against the current directory. For directory or folder contains spaces please use double quotes.
eg:
ekspos.bat "c:\my folder with space\my picture\"
ekspos.bat "...\..\picture folder\"To compile the program:
- make sure you have ant or you can download it from http://www.apache.org
- on [install_dir] type "ant" to get the list of option available
The use of this program is bound and protected by revised BSD license,
please read "[install_dir]/legal/LICENSE.ekspos.txt"This program also includes 3rd party libraries/components.
Please read all the LICENSE files on "[install_dir]/legal/" folder or directory.
The latest news about Ekspos can be found on:
- http://www.kiyut.com/products/ekspos/index.html
For bug reports and feedback, please send email to<EMAIL_ADDRESS>If possible please include details of your development environment, steps to recreate the problem
and attach any examples you have where applicable.Your feedback is important to us.


Result
An object used to collect the detailed results of a collision test
Note: It is highly recommended you recycle the same Result object if possible in order to avoid wasting memory
Constructor Summary
| Public Constructor | ||
| public |
|
Member Summary
| Public Members | ||
| public |
The source body tested |
|
| public |
True if A is completely contained within B |
|
| public |
The target body tested against |
|
| public |
True if B is completely contained within A |
|
| public |
True if a collision was detected |
|
| public |
The magnitude of the shortest axis of overlap |
|
| public |
The X direction of the shortest axis of overlap |
|
| public |
The Y direction of the shortest axis of overlap |


"""
Tools for performing text tasks.
|pic1|
.. |pic1| image:: ../images_source/text_tools/text.jpeg
:width: 25%
"""
import os
import json
import numpy as np
import os, fnmatch
[docs]class Blob:
"""
Functions for dealing with blobs of text.
"""
[docs] @classmethod
def text_parse(cls, blob1, blob2):
"""
Takes a blob of values input into 'TextArea' box widget and returns a numpy array
Text blobs can be comma, space or line delimited
:param blob1:
:param blob2:
:return:
"""
# blob 1 input
##if comma separated values
if "," in blob1:
p1 = np.asarray(blob1.split(','), dtype=np.float32)
# #if space separated values
elif " " in blob1:
p1 = np.asarray(blob1.split(' '), dtype=np.float32)
# #if line delimited values
else:
blob1 = blob1.replace('\n', ',')
p1 = np.asarray(blob1.split(','), dtype=np.float32)
# blob 2 input
# if comma separated values
if "," in blob2:
p2 = np.asarray(blob2.split(','), dtype=np.float32)
# #if space separated values
elif " " in blob2:
p2 = np.asarray(blob2.split(' '), dtype=np.float32)
# #if line delimited values
else:
blob2 = blob2.replace('\n', ',')
p2 = np.asarray(blob2.split(','), dtype=np.float32)
return p1, p2
[docs]class Export:
"""
Functions for exporting Python text objects.
"""
[docs] @classmethod
def concat_text_files(cls, input_files, output_file):
"""
Concatenates contents of specified files into a target file and saves it.
:param input_files: List of text files to concatenate.
:param output_file: Target output file.
:return: Concatenated target output file.
"""
with open(output_file, 'w') as outfile:
for fname in input_files:
with open(fname) as infile:
outfile.write(infile.read())
[docs] @classmethod
def find_replace_text(cls, directory, find, replace, file_pattern):
"""
Finds and replaces a string in all text files in a target folder.
:param directory: Target folder containing files.
:param find: String to search for.
:param replace: New string to replace with.
:param file_pattern: Pattern of text files to scan.
:return: Saved files with replaced text.
"""
for path, dirs, files in os.walk(os.path.abspath(directory)):
for filename in fnmatch.filter(files, file_pattern):
filepath = os.path.join(path, filename)
with open(filepath) as f:
s = f.read()
s = s.replace(find, replace)
with open(filepath, "w") as f:
f.write(s)
[docs] @classmethod
def dump_json(cls, obj, filepath):
"""
Exports a Python dictionary object to a JSON file.
:param obj: Python dictionary object.
:param filepath: Filepath to save object to.
:return: Exported JSON file.
"""
with open(filepath, 'w') as outfile:
json.dump(obj, outfile)
[docs] @classmethod
def dump_sql(cls, obj, filepath):
"""
Exports a Python string object to a SQL file.
:param obj: Python string object.
:param filepath: Filepath to save object to.
:return: Exported SQL file.
"""
with open(filepath, "w") as outfile:
outfile.write(obj)
outfile.close()


this page uses sprite sheets to house all hover buttons, a simple approach to showcase some of the greatest jazz musicians and dancers from the late 1920s to present.


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A
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B
b
(in
Color
)
beta_f
(in
Deconvolution
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BrokenSecStructureError
(in
csb.bio.structure
)
B
(in
ChemElements
)
BETA_SIGMA
(in
GaussianMixture
)
build
(in
csb
)
b
(in
GeneralizedInverseGaussian
)
beta_x
(in
Deconvolution
)
build()
(in
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)
b
(in
Laplace
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BetaBridge
(in
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build()
(in
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Between
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csb.io.tsv
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build()
(in
Label
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backbone
(in
Assignment
)
between()
(in
Where
)
build()
(in
Console
)
backbone_at()
(in
Assignment
)
bfactor
(in
Atom
)
build_alignment()
(in
ProfileBuilder
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Backend
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bfit
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build_combined_map()
(in
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Backends
(in
csb.io.plots
)
bfit()
(in
csb.bio.utils
)
build_filtered_map()
(in
CSfrag
)
BACKGROUND
(in
csb.bio.hmm
)
BFitApp
(in
csb.apps.bfit
)
build_filtered_map()
(in
HHfrag
)
background
(in
State
)
BFitApp
(in
csb.apps.bfite
)
build_fragment_map()
(in
CSfrag
)
BAD_TEXT
(in
ExitCodes
)
bfite
(in
csb.apps
)
build_fragment_map()
(in
HHfrag
)
BaseCollectionContainer
(in
csb.core
)
Bh
(in
ChemElements
)
build_gradient()
(in
InterpolationFactory
)
BaseDensity
(in
csb.statistics.pdf
)
Bi
(in
ChemElements
)
build_hybrid_filtered_map()
(in
HHfrag
)
BaseDictionaryContainer
(in
csb.core
)
BIC
(in
GaussianMixture
)
build_temperature()
(in
InterpolationFactory
)
BasePeakListReader
(in
csb.bio.io.noe
)
BIG
(in
ByteOrder
)
BuildArgError
(in
csb.apps.buildhmm
)
BaseSequenceParser
(in
csb.bio.io.fasta
)
BINARY
(in
BuildTypes
)
builder
(in
Console
)
Be
(in
ChemElements
)
bind_to()
(in
AbstractParameter
)
BuildError
(in
csb.apps.buildhmm
)
BenchmarkAdapter
(in
csb.bio.fragments
)
bind_to()
(in
NonVirtualParameter
)
BUILDERS
(in
Console
)
Bend
(in
SecStructures
)
bio
(in
csb
)
builders
(in
Console
)
beta
(in
HeaderInfo
)
BIT_SCORE_THRESHOLD
(in
ChemShiftAssignment
)
buildhmm
(in
csb.apps
)
beta
(in
MaxentModel
)
Bk
(in
ChemElements
)
BuildIOError
(in
csb.apps.buildhmm
)
beta
(in
Gamma
)
blastpath
(in
ClansParams
)
BuildProfileApp
(in
csb.apps.buildhmm
)
beta
(in
GeneralizedNormal
)
BLOSUM62
(in
SimilarityMatrix
)
BuildTypes
(in
csb.build
)
beta
(in
GumbelMinimum
)
BoltzmannEnsemble
(in
csb.statmech.ensembles
)
by_source
(in
MatchTable
)
beta
(in
InverseGamma
)
Br
(in
ChemElements
)
ByteOrder
(in
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)
beta
(in
BoltzmannEnsemble
)
Broken3DStructureError
(in
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)
Home
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This is a site I (Tom Day) created for my IT class in 2017. It is meant to be a website to promote tourism in Australia. I chose this subject because there would be lots of good resourcs such as pictures and videos that i could incorperate into my site.
My name is Tom Day. I am a High School student with an interest in computer science and software engineering.


Haaland is compared to Odegaard: "The 'hype' is the same"
by BeSoccerAlf Ingve Berntsen was Haaland's coach in the youth categories and has compared the hype surrounding the striker with the mediatic storm Odegaard generated when he signed for Real Madrid.
In an interview for 'Goal', Alf Ingve Berntsen has analysed Haaland's current situation and has made it clear that this is not the time for him to play for some big club in Europe.
"The tactical requirements at Bayern and Liverpool are too high for now. After all, he has only played in Norway and Austria, and for a short time," he said.
The coach has been following his progress since he trained for the youth leagues, and he already felt he had the ability to do great things in European football, but all in good time.
Featured links
- Ballon d'Or winner already been decided
- The latest football transfers news and rumours
- Possible 'Clasico' in the Spanish Supercup final
- Who has the most free-kicks goals: Messi or Cristiano?
"To sign for Bayern now would be difficult for him. He is far from Lewandowski's level and getting to Bayern wouldn't make sense. What many forget is that Haaland didn't play regularly at Salzburg until very recently. He's generated a lot of hype," he continued.
In fact, Alf Ingve Berntsen compared it with the media storm Odegaard raised in his day: "It's a 'hype' similar to the one Odegaard had. People are taken aback with Haaland by Odegaard, but there are hopes that both of them will lead a new and successful era. Erling is being better protected than Odegaard."
DUGOUT_eyJrZXkiOiJVbklNMjhqOSIsInAiOiJiZXNvY2NlciIsInBsIjoiIn0


Hi! I'm Jeff Maxim
I'm a full stack web developer at
Experiment.com and a full stack instructor at
Byte Academy. I'm a proud
Recurse Center never-graduate. I've also taught for nine years in K-12 schools in New York City, Pennsylvania, and Florida in private, public, and charter schools. I most recently taught
CS50AP to NYC middle school students at
Inwood Academy.


ANALYSIS
Check our analysis features and test itIntuitive Workflow
Identification, evaluation, analysis, dissemination – a quick guide through the intelligence process.
Semantic Technology
Understanding language and transforming data into applicable knowledge. Search in natural language.
Visualisation
Easy navigation and sophisticated visualisation tools guide our users through the complexity of large data volumes.


Rolling back changes for now. Sigh. Of course it was the fun part to work on that needs to be postponed.
8/4/2009, 4:11:28 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Rolling back changes for now. Sigh. Of course it was the fun part to work on that needs to be postponed.
8/4/2009, 4:11:28 PM
Favs: 0
Retweets: 0
link


It's as easy as
1: Creating a new potluck
2: Adding suggested menu items
3: Inviting your guests
GET STARTED
As an event organizer, simply create an event and invite your friends. Add your desired menu items to the pot, and let each participant take their pick.
As a guest, just RSVP to any event you've recieved an invite to, and select the menu items you'd like to bring. It's as simple as that.


--- layout: page title: "Contact" description: "Have questions? I have answers (maybe)." header-img: "/img/contact-bg.jpg" ---
Want to get in touch with me? Fill out the form below to send me a message and I will try to get back to you within 24 hours!


WQMY
Conroe, TX Home Insurance Rates | Call<PHONE_NUMBER> Today!
Shop for home insurance rates in the Conroe area and get multiple quotes from . on their homeowners insurance by packaging their home and auto insurance.
http://homeinsurance.com/Texas/Conroe-homeowners-insurance.php
Conroe, TX: Automobile Insurance Quotes - Cheaper at www ...
www.Kanetix.com presents cheap automobile insurance quotes & rates for Conroe Texas from top insurance companies operating in America Click here for .
http://www.kanetix.com/conroe-car-insurance-tx
Quote Center : SPRING TX, CONROE TX INSURANCE AGENCY ...
Conroe location offers quick quotes for home insurance for purchasing your new home as well as auto and commercial insurance as well as flood insurance.
http://www.wedgewoodinsuranceagency.com/quote_forms.cfm
Contents |
[sewer outlet from basement] History
State Farm - Car Insurance Quotes - Save on Auto Insurance
State Farm offers more discounts to more drivers than any other insurance company. Get free insurance quotes today and see how you can save on auto, home, .
http://www.statefarm.com/
Irene Guajardo - Allstate Agent - CONROE, TX - Insurance
Irene Guajardo is an Allstate Insurance Agent from Conroe, TX offering auto insurance, home insurance, life insurance, and insurance quotes.
http://agents.allstate.com/irene-guajardo-conroe-tx.html
Conroe Car Insurance in Conroe TX Yellow Pages by Superpages
Results 1 - 25 of 95 . Find Car Insurance in Conroe maps with reviews, websites, phone . Auto, Home , Life & More - Find An Agent & Get A Rate Quote Now!
http://www.superpages.com/yellowpages/C-Car+Insurance/S-TX/T-Conroe/
Conroe Car Insurance - List of Agents in Conroe, Texas
Conroe Car Insurance. To get the best price, get quotes from .
http://www.calculateme.com/car-insurance/texas/conroe.htm
Templeton Insurance Agency: Home, Auto Insurance - Spring, The ...
Templeton Insurance Agency specializes in home, business and auto insurance in Spring, The Woodlands and Conroe and surrounding areas in Texas. . Templeton Insurance Agency. Home · Get A Quote · Annuities · Automobile · Auto .
http://www.templetoninsagency.com/
Conroe auto insurance - Houston Local Business Directory - KTRK
Results 1 - 15 of 269 . auto insurance for Conroe, TX. . We provide Free Insurance Quotes for Auto Insurance, Umbrella Insurance, Home Insurance and more.
http://local.abc13.com/conroe+tx/auto+insurance.zq.html
SPRING TX, CONROE TX INSURANCE AGENCY AUTO & HOME ...
Conroe location offers quick quotes for home insurance for purchasing your new home as well as auto and commercial insurance as well as flood insurance.
http://www.wedgewoodinsuranceagency.com/
Get Car Insurance Quotes - Auto Insurance Rates Instantly
car insurance rates north carolina encompass home and auto insurance infinite car insurance cavalier auto insurance quote auto insurance conroe tx nc legal .
http://www.megafood.com/vitamin-angels
Conroe Car Insurance - Compare Auto Insurance Quotes In Conroe Tx
Get Conroe Auto Insurance Online Starting at $16 Month. Get A Free Quote!
http://www.ucolick.org/~fabio/auto.php?insurance=conroe
Cheap Car Insurance in Conroe, Texas ( TX )
To Get Free Quotes for Cheap Car Insurance in Conroe, Texas - (TX) Either: Get a Free Quote Above; OR; Contact an Agent Below .
http://www.cheapcarinsurance.net/conroe-texas-car-insurance.html
AutoPartners Insurance
Discounts Available Mexico Insurance. Bullet Auto Insurance Quote. Bullet Home Owners Quote. Bullet Mobile Home Quote. Bullet Renter Insurance Quote .
http://www.autopartnersins.com/
Conroe Insurance Agents and Brokers in Conroe, TX | DexKnows.com
Local business listings / directory for Insurance Agents and .
http://www.dexknows.com/local/finance/insurance/insurance_agents_and_brokers/geo/c-conroe-tx/
[bee gees reunion tour dates] Digital channels
| Channel | Name | paper punch hole size chart | chirurgie paupi res paris | Programming |
|---|---|---|---|---|
| 53.1 | auto insurance quote conroe DT | apartment guide toledo ohio | on fire loyd banks video | Main auto insurance quote conroe programming / MyNetworkTV |
| 53.2 | WOLF DT | brewpubs in staten island ny | cat 3208 damper vibration | Simulcast of hotel review hilton garden inn norman |
| 53.3 | WSWB DT | 480i | 4:3 | Simulcast of massey pasquotank quakers north carolina |
Joel Gonzalez - Allstate Agent - CONROE, TX - Insurance and ...
Joel Gonzalez is an Allstate Insurance Agent from Conroe, TX offering auto insurance, home insurance, life insurance, and insurance quotes.
http://agents.allstate.com/joel-gonzalez-conroe-tx.html
[cows found in wv] References
[skype chat window screenshot] External links
- buy fallout 3 pc
- 4sc dw guitar yamaha
- what is continuity in business
- china's electric car 2007
- david van winkle chicago
- lemons contaminated sickness restaurants
|
|


/user/update-managed-businesses
Remove all existing credentials, then create new credentials to specified businesses.
{N} identify record number, for example businessId1, businessId2, businessId3.
Parameters
| Name |
Type |
Required |
Example |
Description |
| businessId{N} |
integer |
no |
376 |
Managed business id. |
| clientId |
string(40) |
yes |
04f78...9f9f3 |
Client id is a unique user identifier provided by GetFiveStars. |
| hash |
string(64) |
yes |
bd13a40...303f3c5 |
Hash is a digital request signature. Learn how to generate hash here. |
| userId |
integer |
yes |
29061 |
Manager id. |
Request
POST https://getfivestars.com/api/user/update-managed-businesses
[Content-Type: application/json]
{
"userId": 29061,
"businessId1": 6492,
"businessId2": 16813,
"businessId3": 16183,
"clientId": "f1e8cf4ba8145f809fd96d705af94c1f6ae74150",
"hash": "f908234c360f0ed8b11441339679399c1fd49fa043181baa9c5ac475147769a0"
}
Response
{
"errorCode":0,
"errorMessage":"Success"
}
Errors
| errorCode |
errorMessage |
| -1 |
Unknown error |
| 1 |
Invalid hash sign |
| 2 |
Invalid clientId |
| 3 |
Server error - please contact support |
| 32 |
Provided parameters are invalid |
| 33 |
User does not exist |


--- layout: page title: About header-img: "img/about-bg.png" ---
A New Zealand Geologist seeking to become a Software Developer.


By default the discovered branches / pull requests will all use the same credentials
that were used for discovery when checking out sources. This means that the checkout will be using the
https:// protocol for the Git repository.
This behaviour allows you to select the SSH private key to be used for checking out sources, which will
consequently force the checkout to use the ssh:// protocol.


| Concept Framework 2.2 documentation | Contents | Index |
| Name | Version | Deprecated |
| XMLDocument | version 1.0 | no |
| A provides access to an XML document. It contains a tree of XMLNodes than can be accessed starting from the root node using the Root property. |
| Encoding | Sets or gets the encoding type to use for the document |
| Errors | TODO: Document this |
| ErrorsText | TODO: Document this |
| Filename | Sets or gets the name of the targeted filename |
| Root | Gets or sets the root node of a XML document |
| Version | Sets or gets the xml version to required by the output operations like Save or SaveString |
| Create | Initializes the XML document |
| Free | Frees the memory used by the XML document |
| GetXPath | TODO: Document this |
| Load | Loads the xml data from the given Filename |
| LoadHTML | TODO: Document this |
| LoadString | Loads the xml document from a given string |
| NewNode | Creates a new XMLNode and returns it |
| Save | Saves the xml data to the given Filename |
| SaveString | Saves the xml document to a string |
| XMLDocument | The standard constructor |
| finalize | TODO: Document this |
| Documented by Eduard Suica, generation time: Sun Jan 27 18:15:12 2013 GMT | (c)2013 Devronium Applications |


[exercises]
Distance = , Path Loss = - User
Distance = , Path Loss = - Interferer
Signal to Interference & Noise Ratio: SINR = dB
Information Capacity: C =


OK
Tab1
Tab2
Tab3
Label
Link
option
other option
option
other option
Dropdown Title
▾
10
70%
Headline1
Paragraph (Markdown
Supported
!)
h1
h2
h3
lorem
ipsum
dolor
first
second
third
Title
×
Invisible Rect
Use for grouping
Entry1
Entry2
Entry3
Entry1
Entry2
Entry3
Entry1
Entry2
Entry3
Entry1
Entry2
Entry3
☰
⌂
⚑
⚙
✂
Image
Image (double click to add markdown link to image)
note!
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✖ Delete Element
↶ Undelete Last
⚭ Duplicate Element
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Sushant Singh Rajput was an Indian actor who is best known for his work in Hindi cinema. His sucessfull Bollywood movies include M.S. Dhoni: The Untold Story (2016), Kedarnath (2018) and Chhichhore (2019). He also received a Screen Award and was nominated for the Filmfare Awards on two occasions. Sushant Singh Rajpt was deeply interested in astrophysics and won the National Olympiad in Physics. He secured an admission in the Delhi College of Engineering (later renamed to Delhi Technological University) to pursue a Bachelor of Engineering degree in mechanical engineering.
|
|
Dil Bechara |
|
|
Drive |
|
|
Chhichhore |
|
|
Sonchiriya |
|
|
Kedarnath |
|
|
Welcome to New York |
|
|
Raabta |
|
|
M.S. Dhoni: The Untold Story |
|
|
Detective Byomkesh Bakshy! |
|
|
PK |
|
|
Shuddh Desi Romance |
|
|
Kai Po Che! |
"Your result does'nt decide if you're a loser or not... your attempt decides that"
-Sushant Singh Rajput
Do read more about this impeccable personality here.
© 2021 Abhijeet Sinha.


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{% extends 'base-static.html' %} {% load static %} {% block content %}
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Vision
We will combine efforts of best-in-class and engaged experts, industry leaders, and researchers to position Tunisia as a player in the context of 5G evolution. 5G related R&D Standardization Global collaboration Policy and Regulation Research and Academy With our initiative we would like to contributing to a leading ecosystem in Tunisia, which will be in the forefront in: Leading and promoting 5G services and technologies in years around 2020 and beyond. Creating value in the context of the 4th industrial revolution. Our activities will be based on an open collaboration network


For potential users coming from SAS this page is meant to demonstrate how different SAS operations would be performed in pandas.
If you’re new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library.
As is customary, we import pandas and NumPy as follows:
In [1]: import pandas as pd In [2]: import numpy as np
Note
Throughout this tutorial, the pandas DataFrame will be displayed by calling df.head(), which displays the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be:
DataFrame
df.head()
proc print data=df(obs=5); run;
pandas
SAS
data set
column
variable
row
observation
groupby
BY-group
NaN
.
Series
A DataFrame in pandas is analogous to a SAS data set - a two-dimensional data source with labeled columns that can be of different types. As will be shown in this document, almost any operation that can be applied to a data set using SAS’s DATA step, can also be accomplished in pandas.
DATA
A Series is the data structure that represents one column of a DataFrame. SAS doesn’t have a separate data structure for a single column, but in general, working with a Series is analogous to referencing a column in the DATA step.
Index
Every DataFrame and Series has an Index - which are labels on the rows of the data. SAS does not have an exactly analogous concept. A data set’s rows are essentially unlabeled, other than an implicit integer index that can be accessed during the DATA step (_N_).
_N_
In pandas, if no index is specified, an integer index is also used by default (first row = 0, second row = 1, and so on). While using a labeled Index or MultiIndex can enable sophisticated analyses and is ultimately an important part of pandas to understand, for this comparison we will essentially ignore the Index and just treat the DataFrame as a collection of columns. Please see the indexing documentation for much more on how to use an Index effectively.
MultiIndex
A SAS data set can be built from specified values by placing the data after a datalines statement and specifying the column names.
datalines
data df; input x y; datalines; 1 2 3 4 5 6 ; run;
A pandas DataFrame can be constructed in many different ways, but for a small number of values, it is often convenient to specify it as a Python dictionary, where the keys are the column names and the values are the data.
In [3]: df = pd.DataFrame({'x': [1, 3, 5], 'y': [2, 4, 6]}) In [4]: df Out[4]: x y 0 1 2 1 3 4 2 5 6
Like SAS, pandas provides utilities for reading in data from many formats. The tips dataset, found within the pandas tests (csv) will be used in many of the following examples.
tips
SAS provides PROC IMPORT to read csv data into a data set.
PROC IMPORT
proc import datafile='tips.csv' dbms=csv out=tips replace; getnames=yes; run;
The pandas method is read_csv(), which works similarly.
read_csv()
In [5]: url = ('https://raw.github.com/pandas-dev/' ...: 'pandas/master/pandas/tests/data/tips.csv') ...: In [6]: tips = pd.read_csv(url) In [7]: tips.head() Out[7]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4
Like PROC IMPORT, read_csv can take a number of parameters to specify how the data should be parsed. For example, if the data was instead tab delimited, and did not have column names, the pandas command would be:
read_csv
tips = pd.read_csv('tips.csv', sep='\t', header=None) # alternatively, read_table is an alias to read_csv with tab delimiter tips = pd.read_table('tips.csv', header=None)
In addition to text/csv, pandas supports a variety of other data formats such as Excel, HDF5, and SQL databases. These are all read via a pd.read_* function. See the IO documentation for more details.
pd.read_*
The inverse of PROC IMPORT in SAS is PROC EXPORT
PROC EXPORT
proc export data=tips outfile='tips2.csv' dbms=csv; run;
Similarly in pandas, the opposite of read_csv is to_csv(), and other data formats follow a similar api.
to_csv()
tips.to_csv('tips2.csv')
In the DATA step, arbitrary math expressions can be used on new or existing columns.
data tips; set tips; total_bill = total_bill - 2; new_bill = total_bill / 2; run;
pandas provides similar vectorized operations by specifying the individual Series in the DataFrame. New columns can be assigned in the same way.
In [8]: tips['total_bill'] = tips['total_bill'] - 2 In [9]: tips['new_bill'] = tips['total_bill'] / 2.0 In [10]: tips.head() Out[10]: total_bill tip sex smoker day time size new_bill 0 14.99 1.01 Female No Sun Dinner 2 7.495 1 8.34 1.66 Male No Sun Dinner 3 4.170 2 19.01 3.50 Male No Sun Dinner 3 9.505 3 21.68 3.31 Male No Sun Dinner 2 10.840 4 22.59 3.61 Female No Sun Dinner 4 11.295
Filtering in SAS is done with an if or where statement, on one or more columns.
if
where
data tips; set tips; if total_bill > 10; run; data tips; set tips; where total_bill > 10; /* equivalent in this case - where happens before the DATA step begins and can also be used in PROC statements */ run;
DataFrames can be filtered in multiple ways; the most intuitive of which is using boolean indexing
In [11]: tips[tips['total_bill'] > 10].head() Out[11]: total_bill tip sex smoker day time size 0 14.99 1.01 Female No Sun Dinner 2 2 19.01 3.50 Male No Sun Dinner 3 3 21.68 3.31 Male No Sun Dinner 2 4 22.59 3.61 Female No Sun Dinner 4 5 23.29 4.71 Male No Sun Dinner 4
In SAS, if/then logic can be used to create new columns.
data tips; set tips; format bucket $4.; if total_bill < 10 then bucket = 'low'; else bucket = 'high'; run;
The same operation in pandas can be accomplished using the where method from numpy.
numpy
In [12]: tips['bucket'] = np.where(tips['total_bill'] < 10, 'low', 'high') In [13]: tips.head() Out[13]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male No Sun Dinner 3 high 3 21.68 3.31 Male No Sun Dinner 2 high 4 22.59 3.61 Female No Sun Dinner 4 high
SAS provides a variety of functions to do operations on date/datetime columns.
data tips; set tips; format date1 date2 date1_plusmonth mmddyy10.; date1 = mdy(1, 15, 2013); date2 = mdy(2, 15, 2015); date1_year = year(date1); date2_month = month(date2); * shift date to beginning of next interval; date1_next = intnx('MONTH', date1, 1); * count intervals between dates; months_between = intck('MONTH', date1, date2); run;
The equivalent pandas operations are shown below. In addition to these functions pandas supports other Time Series features not available in Base SAS (such as resampling and custom offsets) - see the timeseries documentation for more details.
In [14]: tips['date1'] = pd.Timestamp('2013-01-15') In [15]: tips['date2'] = pd.Timestamp('2015-02-15') In [16]: tips['date1_year'] = tips['date1'].dt.year In [17]: tips['date2_month'] = tips['date2'].dt.month In [18]: tips['date1_next'] = tips['date1'] + pd.offsets.MonthBegin() In [19]: tips['months_between'] = ( ....: tips['date2'].dt.to_period('M') - tips['date1'].dt.to_period('M')) ....: In [20]: tips[['date1', 'date2', 'date1_year', 'date2_month', ....: 'date1_next', 'months_between']].head() ....: Out[20]: date1 date2 date1_year date2_month date1_next months_between 0 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 1 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 2 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 3 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 4 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds>
SAS provides keywords in the DATA step to select, drop, and rename columns.
data tips; set tips; keep sex total_bill tip; run; data tips; set tips; drop sex; run; data tips; set tips; rename total_bill=total_bill_2; run;
The same operations are expressed in pandas below.
# keep In [21]: tips[['sex', 'total_bill', 'tip']].head() Out[21]: sex total_bill tip 0 Female 14.99 1.01 1 Male 8.34 1.66 2 Male 19.01 3.50 3 Male 21.68 3.31 4 Female 22.59 3.61 # drop In [22]: tips.drop('sex', axis=1).head() Out[22]: total_bill tip smoker day time size 0 14.99 1.01 No Sun Dinner 2 1 8.34 1.66 No Sun Dinner 3 2 19.01 3.50 No Sun Dinner 3 3 21.68 3.31 No Sun Dinner 2 4 22.59 3.61 No Sun Dinner 4 # rename In [23]: tips.rename(columns={'total_bill': 'total_bill_2'}).head() Out[23]: total_bill_2 tip sex smoker day time size 0 14.99 1.01 Female No Sun Dinner 2 1 8.34 1.66 Male No Sun Dinner 3 2 19.01 3.50 Male No Sun Dinner 3 3 21.68 3.31 Male No Sun Dinner 2 4 22.59 3.61 Female No Sun Dinner 4
Sorting in SAS is accomplished via PROC SORT
PROC SORT
proc sort data=tips; by sex total_bill; run;
pandas objects have a sort_values() method, which takes a list of columns to sort by.
sort_values()
In [24]: tips = tips.sort_values(['sex', 'total_bill']) In [25]: tips.head() Out[25]: total_bill tip sex smoker day time size 67 1.07 1.00 Female Yes Sat Dinner 1 92 3.75 1.00 Female Yes Fri Dinner 2 111 5.25 1.00 Female No Sat Dinner 1 145 6.35 1.50 Female No Thur Lunch 2 135 6.51 1.25 Female No Thur Lunch 2
SAS determines the length of a character string with the LENGTHN and LENGTHC functions. LENGTHN excludes trailing blanks and LENGTHC includes trailing blanks.
LENGTHN
LENGTHC
data _null_; set tips; put(LENGTHN(time)); put(LENGTHC(time)); run;
Python determines the length of a character string with the len function. len includes trailing blanks. Use len and rstrip to exclude trailing blanks.
len
rstrip
In [26]: tips['time'].str.len().head() Out[26]: 67 6 92 6 111 6 145 5 135 5 Name: time, dtype: int64 In [27]: tips['time'].str.rstrip().str.len().head() Out[27]: 67 6 92 6 111 6 145 5 135 5 Name: time, dtype: int64
SAS determines the position of a character in a string with the FINDW function. FINDW takes the string defined by the first argument and searches for the first position of the substring you supply as the second argument.
FINDW
data _null_; set tips; put(FINDW(sex,'ale')); run;
Python determines the position of a character in a string with the find function. find searches for the first position of the substring. If the substring is found, the function returns its position. Keep in mind that Python indexes are zero-based and the function will return -1 if it fails to find the substring.
find
In [28]: tips['sex'].str.find("ale").head() Out[28]: 67 3 92 3 111 3 145 3 135 3 Name: sex, dtype: int64
SAS extracts a substring from a string based on its position with the SUBSTR function.
data _null_; set tips; put(substr(sex,1,1)); run;
With pandas you can use [] notation to extract a substring from a string by position locations. Keep in mind that Python indexes are zero-based.
 The first argument is the string you want to parse and the second argument specifies which word you want to extract.
data firstlast; input String $60.; First_Name = scan(string, 1); Last_Name = scan(string, -1); datalines2; John Smith; Jane Cook; ;;; run;
Python extracts a substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach.
In [30]: firstlast = pd.DataFrame({'String': ['John Smith', 'Jane Cook']}) In [31]: firstlast['First_Name'] = firstlast['String'].str.split(" ", expand=True)[0] In [32]: firstlast['Last_Name'] = firstlast['String'].str.rsplit(" ", expand=True)[0] In [33]: firstlast Out[33]: String First_Name Last_Name 0 John Smith John John 1 Jane Cook Jane Jane
The SAS UPCASE LOWCASE and PROPCASE functions change the case of the argument.
data firstlast; input String $60.; string_up = UPCASE(string); string_low = LOWCASE(string); string_prop = PROPCASE(string); datalines2; John Smith; Jane Cook; ;;; run;
The equivalent Python functions are upper, lower, and title.
upper
lower
title
In [34]: firstlast = pd.DataFrame({'String': ['John Smith', 'Jane Cook']}) In [35]: firstlast['string_up'] = firstlast['String'].str.upper() In [36]: firstlast['string_low'] = firstlast['String'].str.lower() In [37]: firstlast['string_prop'] = firstlast['String'].str.title() In [38]: firstlast Out[38]: String string_up string_low string_prop 0 John Smith JOHN SMITH john smith John Smith 1 Jane Cook JANE COOK jane cook Jane Cook
The following tables will be used in the merge examples
In [39]: df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], ....: 'value': np.random.randn(4)}) ....: In [40]: df1 Out[40]: key value 0 A 0.469112 1 B -0.282863 2 C -1.509059 3 D -1.135632 In [41]: df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'], ....: 'value': np.random.randn(4)}) ....: In [42]: df2 Out[42]: key value 0 B 1.212112 1 D -0.173215 2 D 0.119209 3 E -1.044236
In SAS, data must be explicitly sorted before merging. Different types of joins are accomplished using the in= dummy variables to track whether a match was found in one or both input frames.
in=
proc sort data=df1; by key; run; proc sort data=df2; by key; run; data left_join inner_join right_join outer_join; merge df1(in=a) df2(in=b); if a and b then output inner_join; if a then output left_join; if b then output right_join; if a or b then output outer_join; run;
pandas DataFrames have a merge() method, which provides similar functionality. Note that the data does not have to be sorted ahead of time, and different join types are accomplished via the how keyword.
merge()
how
In [43]: inner_join = df1.merge(df2, on=['key'], how='inner') In [44]: inner_join Out[44]: key value_x value_y 0 B -0.282863 1.212112 1 D -1.135632 -0.173215 2 D -1.135632 0.119209 In [45]: left_join = df1.merge(df2, on=['key'], how='left') In [46]: left_join Out[46]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 In [47]: right_join = df1.merge(df2, on=['key'], how='right') In [48]: right_join Out[48]: key value_x value_y 0 B -0.282863 1.212112 1 D -1.135632 -0.173215 2 D -1.135632 0.119209 3 E NaN -1.044236 In [49]: outer_join = df1.merge(df2, on=['key'], how='outer') In [50]: outer_join Out[50]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E NaN -1.044236
Like SAS, pandas has a representation for missing data - which is the special float value NaN (not a number). Many of the semantics are the same, for example missing data propagates through numeric operations, and is ignored by default for aggregations.
In [51]: outer_join Out[51]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E NaN -1.044236 In [52]: outer_join['value_x'] + outer_join['value_y'] Out[52]: 0 NaN 1 0.929249 2 NaN 3 -1.308847 4 -1.016424 5 NaN dtype: float64 In [53]: outer_join['value_x'].sum() Out[53]: -3.5940742896293765
One difference is that missing data cannot be compared to its sentinel value. For example, in SAS you could do this to filter missing values.
data outer_join_nulls; set outer_join; if value_x = .; run; data outer_join_no_nulls; set outer_join; if value_x ^= .; run;
Which doesn’t work in pandas. Instead, the pd.isna or pd.notna functions should be used for comparisons.
pd.isna
pd.notna
In [54]: outer_join[pd.isna(outer_join['value_x'])] Out[54]: key value_x value_y 5 E NaN -1.044236 In [55]: outer_join[pd.notna(outer_join['value_x'])] Out[55]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209
pandas also provides a variety of methods to work with missing data - some of which would be challenging to express in SAS. For example, there are methods to drop all rows with any missing values, replacing missing values with a specified value, like the mean, or forward filling from previous rows. See the missing data documentation for more.
In [56]: outer_join.dropna() Out[56]: key value_x value_y 1 B -0.282863 1.212112 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 In [57]: outer_join.fillna(method='ffill') Out[57]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 1.212112 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E -1.135632 -1.044236 In [58]: outer_join['value_x'].fillna(outer_join['value_x'].mean()) Out[58]: 0 0.469112 1 -0.282863 2 -1.509059 3 -1.135632 4 -1.135632 5 -0.718815 Name: value_x, dtype: float64
SAS’s PROC SUMMARY can be used to group by one or more key variables and compute aggregations on numeric columns.
proc summary data=tips nway; class sex smoker; var total_bill tip; output out=tips_summed sum=; run;
pandas provides a flexible groupby mechanism that allows similar aggregations. See the groupby documentation for more details and examples.
In [59]: tips_summed = tips.groupby(['sex', 'smoker'])[['total_bill', 'tip']].sum() In [60]: tips_summed.head() Out[60]: total_bill tip sex smoker Female No 869.68 149.77 Yes 527.27 96.74 Male No 1725.75 302.00 Yes 1217.07 183.07
In SAS, if the group aggregations need to be used with the original frame, it must be merged back together. For example, to subtract the mean for each observation by smoker group.
proc summary data=tips missing nway; class smoker; var total_bill; output out=smoker_means mean(total_bill)=group_bill; run; proc sort data=tips; by smoker; run; data tips; merge tips(in=a) smoker_means(in=b); by smoker; adj_total_bill = total_bill - group_bill; if a and b; run;
pandas groupby provides a transform mechanism that allows these type of operations to be succinctly expressed in one operation.
transform
In [61]: gb = tips.groupby('smoker')['total_bill'] In [62]: tips['adj_total_bill'] = tips['total_bill'] - gb.transform('mean') In [63]: tips.head() Out[63]: total_bill tip sex smoker day time size adj_total_bill 67 1.07 1.00 Female Yes Sat Dinner 1 -17.686344 92 3.75 1.00 Female Yes Fri Dinner 2 -15.006344 111 5.25 1.00 Female No Sat Dinner 1 -11.938278 145 6.35 1.50 Female No Thur Lunch 2 -10.838278 135 6.51 1.25 Female No Thur Lunch 2 -10.678278
In addition to aggregation, pandas groupby can be used to replicate most other by group processing from SAS. For example, this DATA step reads the data by sex/smoker group and filters to the first entry for each.
proc sort data=tips; by sex smoker; run; data tips_first; set tips; by sex smoker; if FIRST.sex or FIRST.smoker then output; run;
In pandas this would be written as:
In [64]: tips.groupby(['sex', 'smoker']).first() Out[64]: total_bill tip day time size adj_total_bill sex smoker Female No 5.25 1.00 Sat Dinner 1 -11.938278 Yes 1.07 1.00 Sat Dinner 1 -17.686344 Male No 5.51 2.00 Thur Lunch 2 -11.678278 Yes 5.25 5.15 Sun Dinner 2 -13.506344
pandas operates exclusively in memory, where a SAS data set exists on disk. This means that the size of data able to be loaded in pandas is limited by your machine’s memory, but also that the operations on that data may be faster.
If out of core processing is needed, one possibility is the dask.dataframe library (currently in development) which provides a subset of pandas functionality for an on-disk DataFrame
pandas provides a read_sas() method that can read SAS data saved in the XPORT or SAS7BDAT binary format.
read_sas()
libname xportout xport 'transport-file.xpt'; data xportout.tips; set tips(rename=(total_bill=tbill)); * xport variable names limited to 6 characters; run;
df = pd.read_sas('transport-file.xpt') df = pd.read_sas('binary-file.sas7bdat')
You can also specify the file format directly. By default, pandas will try to infer the file format based on its extension.
df = pd.read_sas('transport-file.xpt', format='xport') df = pd.read_sas('binary-file.sas7bdat', format='sas7bdat')
XPORT is a relatively limited format and the parsing of it is not as optimized as some of the other pandas readers. An alternative way to interop data between SAS and pandas is to serialize to csv.
# version 0.17, 10M rows In [8]: %time df = pd.read_sas('big.xpt') Wall time: 14.6 s In [9]: %time df = pd.read_csv('big.csv') Wall time: 4.86 s


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In this demo we're using a marker in a ::before
element to show the user which field they should pay attention to.
As ::before
doesn't work on input elements, we need to style the form-group div surrounding each input field.
To acheive this, we make a bind to the guider.next.end
event to add a class to the appropriate form-group
$('#myForm').on('guider.next.end', function(event, element){
/**
* Add class to the element's parent
* the 'element' variable is the field that is now current in guider
*/
$(element).parents('.form-group').addClass('guider-current');
/**
* badify will automatically remove all guider-current classes when it
* moves to the next field, so we don't need to handle that.
*/
});


Illustrator/Animator-by-night, Architect-by-day, Mitch Page is the straight-up product of a graphic-designer-mother and builder-father. Raised and based in Sydney, his AM and PM pursuits often inspire the other; layered geometric forms, satirical themes, and usually a building or three.
For general enquiries, collaborations, prints or even just a chat…


Tuffy the horse trots into Calgary hotel, keeping annual tradition alive
by Zach LaingHe didn’t come in on a Maserati, but Tuffy the horse found his way down the old town road and into a Calgary hotel Thursday evening, keeping alive a 72-year tradition dating to the 1940s.
Horses typically don’t belong in hotel lobbies or bars, but the well-behaved steed accompanied by the Calgary Grey Cup Committee strolled through the Hilton Garden on 4th Street S.E. before stopping at the King Eddy for a quick pint.
“It was a very smooth check-in — hopefully there’s no horseplay in the lobby later,” Mike Kalyn, front desk manager for the hotel, said with a smile moments after helping get Tuffy in his own ‘hotel room stable.’
“Incidentals were being paid for in carrots and barley.”
It kept alive a tradition begun in 1948 when Calgary Stampeders fans attending the 36th Grey Cup in Toronto brought a horse into that city’s Royal York Hotel.
Since then, the tale of Tuffy has become larger than life, as the Calgary contingent has made a yearly event of parading a horse through a host city hotel.
“Wherever we go, we bring the western spirit of the Grey Cup week,” said Sandy Dubyk, spokeswoman for the committee. “It was a mystery for a long time (which local hotel would be chosen) with just an inside circle that knew.
“But it’s definitely grown. I would say over the past seven years when it was the 100th Grey Cup, it really exploded thanks to social media.”
Fletcher Armstrong, past-chairman of the committee, sat atop Tuffy as he munched on carrots and apples. Not far behind, Hamilton Tiger-Cats fans sang their “Oskee Wee Wee” fight song as they sipped on cold pints provided by the hotel bar.
Tuffy, however, didn’t seem to mind as he posed for photos in front of a throng of journalists and television cameras. And as the crowd cleared out, he got a bagpiped escort to the nearby King Eddy, whose staff welcomed him with open arms.
Dubyk said things ran smoothly for the horse.
“You could see that grin there — Tuffy loved it,” he said. “He loves the people, he loves the attention (and is) a bit of a media hog.
“I think he did great this year.”
Twitter: @zjlaing


| DOSE: |
20 mg |
oral |
2C-I |
(capsule) |
I've tripped on 2C-I maybe 15 times in total. It was the first psychedelic I truly tripped off of. I had taken a small amount of mushrooms previously, but only gotten a body high. And it has remained my favorite, mostly because of its amiability to almost any situation. However, sometimes I can still be surprised by its power and intensity. This is a report of one such situation. If you're interested in a real pseudo-scientific account with all effects and times noted, this isn't it. There is lots of info on the effects of 2C-I in a quantitative sense. This is a qualitative report.
I had acquired 1 gram of 2C-I from an online retailer, and bottled most of it into 20mg capsules. Unfortunately, I only had a scale with accuracy to .01 gram, with a +/- .01, so they could potentially have been as high as 30mg. I have however gotten very good at eyeballing approximate dosages, so I don't think any were off by more than a few milligrams.
Anyways, I was over at my friend M's apartment, where she and her coworker J were hanging out drinking. M started telling J about my 2C-I, since she had done it previously and had loved it. J expressed an interest, but only wanted to do it if we would trip with him. Well, I was down.
I pulled out my pill bottle and poured out the capsules, selecting the capsule that looked the fullest, and one from out of the middle of the pack. M wanted to snort a little bit rather than full-out trip, so I used a flathead screwdriver to pull about 5-7mg from the larger capsule, and held it up to her nose. After it disappeared into her nasal passage, I recapped the pill, gave it to J, and we each swallowed our capsule. (Me ~20mg, J ~16mg, M ~6mg)
We hung out for a little bit, smoked a bowl (not J, he gets tested so he doesn't smoke anymore), then M decided she wanted to go across the courtyard to her neighbors apartment. We grabbed a bubbler and some weed and left. Her neighbor's apartment, though exactly the same style, seemed small and dirty by comparison. I did not enjoy hanging out there, though the fact that only one other person joined us in a large 'party bowl' meant I at least got high.
I also started getting my first serious effects, the classic plate tectonics on the walls, groups of bumps or whatever sliding past each other as if floating on something, and a definite awareness of social situations and how ingrained peoples responses can be. After being there for maybe half an hour, I suddenly realized that B had called M a while ago and said he was coming over. I reminded M, and she bounded out the door. Turns out he was just walking up to her door, so the timing was perfect. :-)
After M came back in with B, we smoked a little bit more weed and chilled. M sat down beside me and said she wasn't really feeling it, and she'd kinda like some more. I interpreted this as a request, and after mulling it over in my brain for a while, I handed her a full capsule to take. I assumed that was the end of it. Shortly afterward we went back to M's house.
I crashed onto the couch, assuming this was where the trip was really gonna pick up. M grabbed beers for herself, B, and J, who had been drinking this entire time. After downing those, M jumped up, grabbed J, announced they were going for a walk, and left.  I could not understand her reasoning, and it pissed me off that she would exclude me. Weren't we tripping together? Wasn't the sole reason I had tripped tonight because J wanted me to trip too? WHAT THE FUCK?!?!
Added to this was the fact that B, although a cool guy most of the time, is also sketchy, weird looking, weird acting, suspicious, and generally not a good trip companion. He kept asking me what I was tripping on. Not the substance, but the elements in the world, which did not to me seem like a valid question. How do I explain it? He had acquired a sketch pad and pencil from somewhere, and was drawing. I watched him for a while, but it seemed to really bug him that I was watching. Art in action, something I love even sober. So I stopped.
At this point, things really got intense. I remember looking at the walls, sighing internally, and thinking '2C-I....the eternal +2' which is a dammed lie. All of a sudden, the walls started melting away, and the floor started growing up in vines along the wall.
I sorta collapsed onto the floor, not all at once, but in a slow slide off the chair onto my knees, in a prayer or meditation position. I was facing the doorway to the kitchen, and one of M's cats was laying down right in the middle of the floor, perfectly framed by the doorway. All of a sudden I was filled with this great love and acceptance. I was ready for anything at that point. I could have died on the spot, and half expected to, and it would not have bugged me in the least. Anything could happen, and it would be OK. It was almost a disappointment when nothing did happen.
The cat moved, and with it so did the feeling of peace. I sat there, waiting for the cat to return so I could feel peaceful again, but when the cat came back and lay down in almost the exact same position, I didn't feel anything. I concentrated, and still nothing. All of a sudden I realized that the cat had nothing to do with the way I was feeling. It was just a coincidence.
I pulled myself back into my chair, and the cat came over to me.  I meant life trip, not drug-induced trip, although it still applies. I felt sad, but that was OK. That's just the way things are. I stood up and grabbed a glass of water, which felt wonderful to drink.
About this time, M and J burst back in, full of excitement and talk about how wonderful trees are.  I tried to calmly explain that it was just smart, especially since he'd been drinking and was probably dehydrated anyways. M backed me up, telling J that if I said he needed water, he needed water. He grumbled but complied.
I started to settle back, once again anticipating the usual tripping gab session. But it was not to be. After about 15 minutes, B got up to leave, which was fine by me. But right after he left, M grabbed J and left again, this time with no explanation, while I was in the bathroom. So here I was, all alone in someone else's house, and I'm tripping balls. Not a happy situation. I can understand needing to be alone, I can understand needing to be alone with someone else, but I cannot understand leaving a third person, especially with no explanation or warning, and especially alone in a relatively unknown environment.  I almost left right there. Luckily, they were only gone for about 10 minutes this time. Then M announced she was driving both of us home. Confused, I pointed out she was still tripping.
'No I'm not.'
'But you took more 2C-I. Even if you're not right now, you will be soon, and I don't want you driving drunk and tripping.'
'I've only had 3 beers over the night, and I sold the capsule.'
'....What?'
“Yeah, for $10.'
'Well....can I get some of that?'
'No.'
And so she drove us home. Well, she drove me home. I actually think she took J back to her house to screw him, and the point of them leaving together in the first place was to make out. As I was getting out of the car, I once again make J promise to drink water. I went to my dorm and looked at the clock.  Only about 3.5 hours had passed. I still had a full night ahead of me.
I decided to best thing to do would be to just crawl into bed, hopefully fall asleep early. But all I did for the next three hours was get more and more worked up about how M had treated me. I'd had a crush on her since I met her, and I knew she liked me too, so I could just not fathom her acting the way she did. I could have used the $10, or even $5. She has a job. And I had given it to her. GIVEN it to HER. So that SHE could trip with US.  I could not believe her.
At about T+6 or so I finally fell asleep.
The next day I asked M for my money, and she gave it to me without comment. But I learned several valuable things about her that night that made me stop liking her. And I also learned that if I trip with other people, I better be damned sure about them and their commitment to me and the trip and the vibe. Also, the trip helped me come to the conclusion that you cannot really depend on anything, as much as you'd like to, and as much as maybe you'd like other people or things to depend on you. Shit happens.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


As we left Guadeloupe for Les Saintes, we had seen on the beach
a bunch of boats getting ready to race. We ended up motor
sailing to Les Saintes, but the boats followed us all racing.
The next day, they left from Les Saintes and headed back to
Guadeloupe after a rather loud beach party that night.
We did not spend long in Les Saintes and headed off to Dominica
before the winds got too bad. We had a bit of a rough sail
mostly into the wind, with 5 to 6 foot seas, with occasional 12
footers. Good old "What If" and crew handled it all in stride
and arrived safely in Dominica.
With every new island we visit, Kris makes sure that all the
paperwork that we need for Sam is in line, and tries to contact
the island in advance to make sure all is right. It really paid
off here in Dominica. When Dean checked in with Customs and
Immigration he checked a box saying we had a dog, but they did
not ask for paperwork or anything. Later when we took Sam in
for a walk, we did not make it past the end of the dock before
we were stopped and asked for the paperwork. We showed the
"import permit" that was emailed to us ahead of time, and were
allowed to proceed. We were later stopped in town by a local
and were advised to make sure we had our papers, or the dog
would be taken and destroyed. Later at a park we were asked
again. Out of all the countries with strict rules, it seems
odd, that here, in one of the poorest everyone has been more
informed of the laws, and follow up on them.
When you arrive in Dominica, about a mile out, a fellow in a
long wood boat with outboard will come up alongside you and
introduce himself and tell you welcome to Dominica. He will ask
if you need help anchoring, or help getting a mooring, and if
you say no, he will later stop by your boat after you are
anchored to see if you need anything, or would like to take a
tour. The men are very pleasant and professional. Dominica
has made this system up to provide jobs to folks where cruisers
stop, and provide security in the anchorage. It is actually
quite nice, since the men are not pushy at all, and you find out
more about things than you could on your own. Our guide is
Lawrence, and his boat Lawrence of Arabia. We hired Lawrence to
take us on a tour up the Indian River. Motors are not allowed
on the river due to the damage they due to the ecosystem.
Lawrence came out to the boat, picked us up and took us on a
very nice tour, explaining and naming the flora and fauna as he
went. Every one of the folks who does the tour has to take and
pass a test to be able to explain the flora and fauna, before
they can give the tours of island attractions.
One of the attractions on the river is small shack, which was
used for filming a scene in Pirates of the Caribbean number
two. If you have watched the movie, you will remember a scene
where people are standing in a river at night, with many lighted
candles as the movie stars row to a shack that "Calypso" live
in. Quite a bit of movie filming for the Pirates of the
Caribbean was done on Dominica.
At the end of the river there is a small restaurant/bar, which
we did not stop at because it was 8:00 in the morning, but we
did take a hike to visit a local plantation and see all the
items grown there. The plantation is quite interesting, as it
is 7 acres perched on the hillside and slope. Some is terraced
and some not. They grew a large variety of items. We saw:
bananas, guava, passion fruit, mangoes, breadfruit, pineapple,
avocadoes, okra, cabbage, cinnamon, cacao and limes. We were
given mangoes to take back, and purchased some bananas, mangoes
and okra. We tired raw cacao.  We learned that they use the cacao seeds here not for
chocolate, but they make cacao tea from the seeds. We also
learned that bananas take 9 months from when they are planted
to produce fruit, and then 3 months for fruit to grow. They
then cut down the plant, and a new stalk grows up out of the
old. The rotate timing, and produce bananas year round here,
most all sold to Europe.
We also took a tour of Fort Shirley, a British fort that really
never saw battle, but the Dominicans have done a great job on
restoring the fort which was abandoned in 1854, and restoration
not started until 1989. That is lot of time for the jungle to
reclaim the land, which it did. We had a nice hike to the top
of the hill, and wandering around the restored parts of the
fort.
We plan on one more tour to go see and swim in a waterfall
before we leave.
05/31/13; Les Saintes boat race, Dominica hikes
Small boat race at Les Saintes |
Approaching the harbor at Dominica |
Vet friendly Sam |
Lawrence our guide |
Going up the Indian River |
Pirate of Caribbean movie shack of "Calypso" |
|
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Beautiful native flower |
Pineapple plant |
Dominica banana plantation area |
Inside the cacao seed, slimy but good |
Cinnamon tree |
Fort Shirley with harbor in background |
Trail up to top of hill by Fort Shirley |
Manning the battlements at Fort Shirley |
Small bunch of bananas from the plantation |


CIDD Documentation
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Available Configurations:
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| And he spake a parable unto them to this end, that men ought always to pray, and not to faint;
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| ᏚᏟᎶᏍᏓᏁᎴᏃ, ᎯᎠ ᏄᏍᏕ ᎤᎬᏩᎴᎢ, ᎾᏍᎩ ᏴᏫ ᏂᎪᎯᎸ ᎤᎾᏓᏙᎵᏍᏙᏗᏱ, ᎠᎴ ᏧᏂᏯᏪᎢᏍᏗ ᏂᎨᏒᎾ;
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Du-tli-lo-s-da-ne-le-no, hi-a nu-s-de u-gv-wa-le-i, na-s-gi yv-wi ni-go-hi-lv u-na-da-do-li-s-do-di-yi, a-le tsu-ni-ya-we-i-s-di ni-ge-sv-na;
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| Saying, There was in a city a judge, which feared not God, neither regarded man:
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| ᎯᎠ ᏄᏪᏎᎢ, ᎡᎮ ᏗᎫᎪᏗᏍᎩ ᎢᎸᎯᏢ ᎦᏚᎲᎢ, ᎾᏍᎩ ᎤᏁᎳᏅᎯ ᏂᎦᎾᏰᏍᎬᎾ, ᎠᎴ ᏴᏫ ᏂᏚᏁᎶᏛᎾ;
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Hi-a nu-we-se-i, E-he di-gu-go-di-s-gi i-lv-hi-tlv ga-du-hv-i, na-s-gi U-ne-la-nv-hi ni-ga-na-ye-s-gv-na, a-le yv-wi ni-du-ne-lo-dv-na;
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| And there was a widow in that city; and she came unto him, saying, Avenge me of mine adversary.
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| ᎠᎴ ᎤᏬᏑᎶᏨᎯ ᎾᎿᎭᎦᏚᎲ ᎡᎮᎢ; ᎾᏍᎩᏃ ᎤᎷᏤᎴᎢ, ᎯᎠ ᏂᎦᏪᏍᎨᎢ, ᏚᏳᎪᏛ ᏂᏍᎩᎾᏛᏂᏏ ᏦᏍᏓᏓᏱᎵᏙᎯ.
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A-le u-wo-su-lo-tsv-hi na-hna ga-du-hv e-he-i; na-s-gi-no u-lu-tse-le-i, hi-a ni-ga-we-s-ge-i, Du-yu-go-dv ni-s-gi-na-dv-ni-si tso-s-da-da-yi-li-do-hi.
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| And he would not for a while: but afterward he said within himself, Though I fear not God, nor regard man;
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| ᎠᏎᏃ ᎤᏉᏌᏁ ᎢᎸᏍᎩ ᎢᎪᎯᏛ; ᎣᏂᏍᎩᏂ ᎯᎠ ᏂᎤᏪᏎ ᎤᏩᏒ ᏧᏓᏅᏛᎢ, ᏝᏍᎩᏂᏃᏅ ᏱᏥᎾᏰᏍᎦ ᎤᏁᎳᏅᎯ, ᎠᎴ ᎥᏝ ᏱᏗᏥᏯᏁᎶᏗ ᏴᏫ;
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A-se-no u-quo-sa-ne i-lv-s-gi i-go-hi-dv; o-ni-s-gi-ni hi-a ni-u-we-se u-wa-sv tsu-da-nv-dv-i, Tla-s-gi-ni-no-nv yi-tsi-na-ye-s-ga U-ne-la-nv-hi, a-le v-tla yi-di-tsi-ya-ne-lo-di yv-wi;
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| Yet because this widow troubleth me, I will avenge her, lest by her continual coming she weary me.
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| ᎠᏎᏃ ᎯᎠ ᎤᏬᏑᎶᏨᎯ ᏣᏆᏕᏯᏙᏗᎭ ᏅᏓᎦᎵᏍᏙᏔᏂ ᏚᏳᎪᏛ ᏅᏓᏥᏯᏛᏁᎵ, ᏂᎪᎯᎸᏰᏃ ᎦᎷᎬ ᏱᏓᎩᏯᏪᎢᏍᏓ.
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A-se-no hi-a u-wo-su-lo-tsv-hi tsa-qua-de-ya-do-di-ha nv-da-ga-li-s-do-ta-ni du-yu-go-dv nv-da-tsi-ya-dv-ne-li, ni-go-hi-lv-ye-no ga-lu-gv yi-da-gi-ya-we-i-s-da.
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| And the Lord said, Hear what the unjust judge saith.
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| ᎤᎬᏫᏳᎯᏃ ᎯᎠ ᏄᏪᏎᎢ, ᎢᏣᎦᏌᏯᏍᏓ ᏂᏚᏳᎪᏛᎾ ᎢᏯᏛᏁᎯ ᏗᎫᎪᏗᏍᎩ ᏂᎦᏪᏍᎬᎢ.
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U-gv-wi-yu-hi-no hi-a nu-we-se-i, I-tsa-ga-sa-ya-s-da ni-du-yu-go-dv-na i-ya-dv-ne-hi di-gu-go-di-s-gi ni-ga-we-s-gv-i.
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| And shall not God avenge his own elect, which cry day and night unto him, though he bear long with them?
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| ᏝᏍᎪᏃ ᎤᏁᎳᏅᎯ ᏚᏳᎪᏛ ᏱᏂᏙᎬᏛᏂᏏ ᎤᏩᏒ ᏧᏤᎵ ᎨᎦᏑᏴᏛ, ᎾᏍᎩ ᏗᎬᏩᎩᏨᏗ ᏥᎬᏩᏓᏙᎵᏍᏓᏁᎭ, ᎾᏍᏉ ᎪᎯᏗᏳ ᏱᏓᏙᎯᏕᎭ?
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Tla-s-go-no U-ne-la-nv-hi du-yu-go-dv yi-ni-do-gv-dv-ni-si u-wa-sv tsu-tse-li ge-ga-su-yv-dv, na-s-gi di-gv-wa-gi-tsv-di tsi-gv-wa-da-do-li-s-da-ne-ha, na-s-quo go-hi-di-yu yi-da-do-hi-de-ha?
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| I tell you that he will avenge them speedily. Nevertheless when the Son of man cometh, shall he find faith on the earth?
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| ᎢᏨᏲᏎᎭ ᏞᎩᏳ ᏙᏓᎫᎴᏏ. ᎠᏎᏃ, ᏴᏫ ᎤᏪᏥ ᎦᎷᏨᎭ, ᎪᎯᏳᏗᏍᎪ ᎨᏒ ᏛᏩᏛᎯ ᎡᎶᎯ?
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I-tsv-yo-se-ha tle-gi-yu do-da-gu-le-si. A-se-no, yv-wi U-we-tsi ga-lu-tsv-ha, go-hi-yu-di-s-go ge-sv dv-wa-dv-hi e-lo-hi?
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| And he spake this parable unto certain which trusted in themselves that they were righteous, and despised others:
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| ᎯᎠᏃ ᏚᏟᎶᏍᏓᏁᎴ ᎩᎶ ᎢᏳᎾᏍᏗ ᎤᏅᏒ ᎠᎾᏓᎸᏉᏗᏍᎩ, ᎾᏂᏍᎦᏅᎾ ᎤᎾᏤᎸᎯ, ᎤᏩᎾᏓᎴᏃ ᏅᎵᏌᎵ ᏗᏂᏰᎸᏍᎩ.
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Hi-a-no du-tli-lo-s-da-ne-le gi-lo i-yu-na-s-di u-nv-sv a-na-da-lv-quo-di-s-gi, na-ni-s-ga-nv-na u-na-tse-lv-hi, u-wa-na-da-le-no nv-li-sa-li di-ni-ye-lv-s-gi.
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| Two men went up into the temple to pray; the one a Pharisee, and the other a publican.
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| ᎠᏂᏔᎵ ᎠᏂᏍᎦᏯ ᎤᏛᎾ-ᏗᎦᎳᏫᎢᏍᏗᏱ ᎤᎾᏓᏙᎵᏍᏔᏅᏎᎢ, ᎠᏏᏴᏫ ᎠᏆᎵᏏ ᎨᏎᎢ, ᏐᎢᏃ ᎠᏕᎸ-ᎠᎩᏏᏙᎯ.
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A-ni-ta-li a-ni-s-ga-ya u-dv-na--di-ga-la-wi-i-s-di-yi u-na-da-do-li-s-ta-nv-se-i, a-si-yv-wi A-qua-li-si ge-se-i, so-i-no a-de-lv--a-gi-si-do-hi.
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| The Pharisee stood and prayed thus with himself, God, I thank thee, that I am not as other men are, extortioners, unjust, adulterers, or even as this publican.
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| ᎠᏆᎵᏏ ᎤᎴᏁ ᎤᏩᏒ ᎨᏒᎢ ᎤᏓᏙᎵᏍᏔᏁ ᎯᎠ ᏄᏪᏎᎢ; ᏣᏁᎳᏅᎯ ᎬᏯᎵᎡᎵᏤᎭ ᎠᏂᏐᎢ ᏴᏫ ᏄᎾᏍᏛᎢ ᎾᏍᎩ ᎾᏆᏍᏛᎾ ᏥᎩ, ᎾᏍᎩ ᎠᏎᏉ ᎠᎾᏓᎩᎡᎯ ᏥᎩ, ᎠᎴ ᏂᏚᏳᎪᏛᎾ ᎢᏯᎾᏛᏁᎯ, ᎠᎴ ᏗᎾᏓᏲᏁᎯ, ᎠᎴ ᎾᏍᏉ ᎯᎠ ᎠᏕᎸ-ᎠᎩᏏᏙᎯ ᎾᏍᎩ ᏄᏍᏛ ᎾᏆᏍᏛᎾ ᏥᎩ.
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A-qua-li-si u-le-ne u-wa-sv ge-sv-i u-da-do-li-s-ta-ne hi-a nu-we-se-i; Tsa-ne-la-nv-hi gv-ya-li-e-li-tse-ha a-ni-so-i yv-wi nu-na-s-dv-i na-s-gi na-qua-s-dv-na tsi-gi, na-s-gi a-se-quo a-na-da-gi-e-hi tsi-gi, a-le ni-du-yu-go-dv-na i-ya-na-dv-ne-hi, a-le di-na-da-yo-ne-hi, a-le na-s-quo hi-a a-de-lv--a-gi-si-do-hi na-s-gi nu-s-dv na-qua-s-dv-na tsi-gi.
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| I fast twice in the week, I give tithes of all that I possess.
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| ᏑᎾᏙᏓᏆᏍᏗ ᎨᏒ ᏔᎵ ᎠᎹᏟ ᏂᎬᏍᎪᎢ; ᎠᎴ ᏂᎦᎥ ᎠᎩᎾᎥ ᎠᏍᎪᎯᏁ ᎪᏣᎴᏛ ᏥᎥᏍᎪᎢ.
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Su-na-do-da-qua-s-di ge-sv ta-li a-ma-tli ni-gv-s-go-i; a-le ni-ga-v a-gi-na-v a-s-go-hi-ne go-tsa-le-dv tsi-v-s-go-i.
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| And the publican, standing afar off, would not lift up so much as his eyes unto heaven, but smote upon his breast, saying, God be merciful to me a sinner.
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| ᎠᏕᎸᏃ-ᎠᎩᏏᏙᎯ ᎢᏅ ᏧᎴᏁᎢ, ᎥᏝ ᏰᎵ ᎦᎸᎳᏗ ᏫᏗᎬᏩᎧᏃᏗ ᏱᎨᏎᎢ, ᎦᏁᏥᏉᏍᎩᏂ ᎤᏩᏂᎴᎢ, ᎯᎠ ᏄᏪᏎᎢ; ᏣᏁᎳᏅᎯ, ᏍᎩᏙᎵᎩ ᏥᏍᎦᎾᎢ.
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A-de-lv-no--a-gi-si-do-hi i-nv tsu-le-ne-i, v-tla ye-li ga-lv-la-di wi-di-gv-wa-ka-no-di yi-ge-se-i, ga-ne-tsi-quo-s-gi-ni u-wa-ni-le-i, hi-a nu-we-se-i; Tsa-ne-la-nv-hi, s-gi-do-li-gi tsi-s-ga-na-i.
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| I tell you, this man went down to his house justified rather than the other: for every one that exalteth himself shall be abased; and he that humbleth himself shall be exalted.
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| ᎢᏨᏲᏎᎭ, ᎯᎠ ᎾᏍᎩ ᎠᏍᎦᏯ ᎤᏚᏓᎴᏛ ᏧᏪᏅᏒ ᏭᎶᏎᎢ, ᏐᎢᏃ ᎥᏝ. ᎩᎶᏰᏃ ᎤᏩᏒ ᎢᎠᏓᏌᎳᏗᏍᎨᏍᏗ ᎡᎳᏗ ᎢᏯᎬᏁᏗ ᎨᏎᏍᏗ, ᎩᎶᏃ ᎤᏩᏒ ᎡᎳᏗ ᏂᎠᏓᏛᏁᎮᏍᏗ ᎠᏥᏌᎳᏙᏗ ᎨᏎᏍᏗ.
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I-tsv-yo-se-ha, hi-a na-s-gi a-s-ga-ya u-du-da-le-dv tsu-we-nv-sv wu-lo-se-i, so-i-no v-tla. Gi-lo-ye-no u-wa-sv i-a-da-sa-la-di-s-ge-s-di e-la-di i-ya-gv-ne-di ge-se-s-di, gi-lo-no u-wa-sv e-la-di ni-a-da-dv-ne-he-s-di a-tsi-sa-la-do-di ge-se-s-di.
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| And they brought unto him also infants, that he would touch them: but when his disciples saw it, they rebuked them.
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| ᎠᎴ ᎾᏍᏉ ᏕᎬᏩᏘᏃᎮᎴ ᏧᎾᏍᏗ ᏗᏂᏲᎵ, ᎾᏍᎩ ᏧᏏᏔᏗᏍᏗᏱ; ᎠᏎᏃ ᎬᏩᏍᏓᏩᏗᏙᎯ ᎤᎾᏙᎴᎰᏒ ᏚᏂᎬᏍᎪᎸᏁᎢ.
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A-le na-s-quo de-gv-wa-ti-no-he-le tsu-na-s-di di-ni-yo-li, na-s-gi tsu-si-ta-di-s-di-yi; a-se-no gv-wa-s-da-wa-di-do-hi u-na-do-le-ho-sv du-ni-gv-s-go-lv-ne-i.
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| But Jesus called them unto him, and said, Suffer little children to come unto me, and forbid them not: for of such is the kingdom of God.
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| ᏥᏌᏃ ᏫᏚᏯᏅᎲ ᎯᎠ ᏄᏪᏎᎢ, ᎤᎾᏁᎳᎩ ᏗᏤᎵᏏ ᏗᏂᏲᎵ ᎬᎩᎷᏤᏗᏱ, ᎠᎴ ᏞᏍᏗ ᏗᏥᏅᏍᏓᏕᎸᎩ; ᎾᏍᎩᏰᏃ ᏄᎾᏍᏗ ᎾᎿᎭᎤᏁᎳᏅᎯ ᎤᎬᏫᏳᎯ ᎨᏒᎢ.
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Tsi-sa-no wi-du-ya-nv-hv hi-a nu-we-se-i, U-na-ne-la-gi di-tse-li-si di-ni-yo-li gv-gi-lu-tse-di-yi, a-le tle-s-di di-tsi-nv-s-da-de-lv-gi; na-s-gi-ye-no nu-na-s-di na-hna u-ne-la-nv-hi u-gv-wi-yu-hi ge-sv-i.
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| Verily I say unto you, Whosoever shall not receive the kingdom of God as a little child shall in no wise enter therein.
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| ᎤᏙᎯᏳᎯᏯ ᎯᎠ ᏂᏨᏪᏎᎭ, ᎩᎶ ᎤᏁᎳᏅᎯ ᎤᎬᏫᏳᎯ ᎨᏒ ᏂᏓᏓᏂᎸᎬᎾ ᎢᎨᏎᏍᏗ ᎾᏍᎩᏯ ᎠᏲᎵ ᏥᏓᏓᏂᎸᎪᎢ, ᎥᏝ ᎾᎿᎭᎤᏴᏍᏗ ᏱᎨᏎᏍᏗ.
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U-do-hi-yu-hi-ya hi-a ni-tsv-we-se-ha, Gi-lo U-ne-la-nv-hi u-gv-wi-yu-hi ge-sv ni-da-da-ni-lv-gv-na i-ge-se-s-di na-s-gi-ya a-yo-li tsi-da-da-ni-lv-go-i, v-tla na-hna u-yv-s-di yi-ge-se-s-di.
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| And a certain ruler asked him, saying, Good Master, what shall I do to inherit eternal life?
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| ᎩᎶᏃ ᎢᏳᏍᏗ ᏄᎬᏫᏳᏌᏕᎩ ᎤᏛᏛᏁ, ᎯᎠ ᏄᏪᏎᎢ, ᎰᏍᏛ ᏔᏕᏲᎲᏍᎩ, ᎦᏙ ᏯᏆᏛᏁᎸ ᏫᎾᏍᏛᎾ ᎬᏂᏛ ᏱᏥᏩᏛ?
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Gi-lo-no i-yu-s-di nu-gv-wi-yu-sa-de-gi u-dv-dv-ne, hi-a nu-we-se-i, Ho-s-dv Ta-de-yo-hv-s-gi, ga-do ya-qua-dv-ne-lv wi-na-s-dv-na gv-ni-dv yi-tsi-wa-dv?
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| And Jesus said unto him, Why callest thou me good? none is good, save one, that is, God.
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| ᏥᏌᏃ ᎯᎠ ᏄᏪᏎᎴᎢ, ᎦᏙᏃ ᎰᏍᏛ ᎢᏍᏉᏎᎭ? ᎥᏝ ᎩᎶ ᎣᏍᏛ ᏱᎩ ᏌᏉ ᎤᏩᏒᎯᏳ, ᎾᏍᎩ ᎤᏁᎳᏅᎯ.
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Tsi-sa-no hi-a nu-we-se-le-i, Ga-do-no ho-s-dv i-s-quo-se-ha? V-tla gi-lo o-s-dv yi-gi sa-quo u-wa-sv-hi-yu, na-s-gi U-ne-la-nv-hi.
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| Thou knowest the commandments, Do not commit adultery, Do not kill, Do not steal, Do not bear false witness, Honour thy father and thy mother.
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| ᏕᎯᎦᏔᎭᏉ ᏗᎧᎿᎭᏩᏛᏍᏗ, ᏞᏍᏗ ᏣᏓᏲᏁᎸᎩ, ᏞᏍᏗ ᏣᏓᎸᎩ, ᏞᏍᏗ ᏣᏃᏍᎩᏒᎩ, ᏞᏍᏗ ᎦᏰᎪᎩ ᏣᏃᎮᎸᎩ, ᏕᎩᎸᏉᏕᏍᏗ ᏣᏙᏓ ᎠᎴ ᏣᏥ.
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De-hi-ga-ta-ha-quo di-ka-hna-wa-dv-s-di, Tle-s-di tsa-da-yo-ne-lv-gi, Tle-s-di tsa-da-lv-gi, tle-s-di tsa-no-s-gi-sv-gi, Tle-s-di ga-ye-go-gi tsa-no-he-lv-gi, De-gi-lv-quo-de-s-di tsa-do-da a-le tsa-tsi.
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| And he said, All these have I kept from my youth up.
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| ᎯᎠᏃ ᏄᏪᏎᎢ, ᎯᎠ ᎾᏍᎩ ᏂᎦᏛ ᏓᎩᎧᎾᏩᏕᏅ ᏥᏧᏣ ᎨᏒ ᏅᏓᎬᏩᏓᎴ ᏅᏛ.
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Hi-a-no nu-we-se-i, Hi-a na-s-gi ni-ga-dv da-gi-ka-na-wa-de-nv tsi-tsu-tsa ge-sv nv-da-gv-wa-da-le nv-dv.
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| Now when Jesus heard these things, he said unto him, Yet lackest thou one thing: sell all that thou hast, and distribute unto the poor, and thou shalt have treasure in heaven: and come, follow me.
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| ᏥᏌᏃ ᎾᏍᎩ ᎯᎠ ᎤᏛᎦᏅ, ᎯᎠ ᏄᏪᏎᎴᎢ, ᎠᏏ ᏑᏓᎴᎩ ᏣᎷᎳ; ᎯᎾᏚᎦ ᏂᎦᏛ ᏣᎾᎥᎢ, ᎠᎴ ᏘᏯᏙᎯᏏ ᎤᏲ ᎢᏳᎾᏛᎿᎭᏕᎩ, ᎦᎸᎳᏗᏃ ᏫᏣᎮᏍᏗ ᏧᎬᏩᎶᏗ; ᏖᏒᏃ ᏍᎩᏍᏓᏩᏕᏒᎭ.
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Tsi-sa-no na-s-gi hi-a u-dv-ga-nv, hi-a nu-we-se-le-i, A-si su-da-le-gi tsa-lu-la; hi-na-du-ga ni-ga-dv tsa-na-v-i, a-le ti-ya-do-hi-si u-yo i-yu-na-dv-hna-de-gi, ga-lv-la-di-no wi-tsa-he-s-di tsu-gv-wa-lo-di; te-sv-no s-gi-s-da-wa-de-sv-ha.
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| And when he heard this, he was very sorrowful: for he was very rich.
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| ᎾᏍᎩᏃ ᎯᎠ ᎤᏛᎦᏅ ᎤᏣᏘ ᎤᏲ ᎤᏰᎸᏁᎢ; ᎤᏣᏘᏰᏃ ᎤᏪᎿᎭᎢᏳ ᎨᏎᎢ.
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Na-s-gi-no hi-a u-dv-ga-nv u-tsa-ti u-yo u-ye-lv-ne-i; u-tsa-ti-ye-no u-we-hna-i-yu ge-se-i.
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| And when Jesus saw that he was very sorrowful, he said, How hardly shall they that have riches enter into the kingdom of God!
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| ᏥᏌᏃ ᎤᎪᎲ ᎤᏣᏘ ᎤᏲ ᎤᏰᎸᏒᎢ, ᎯᎠ ᏄᏪᏎᎢ, ᏂᎦᎥ ᎠᏍᏓᏱᏳ ᏧᏁᎿᎭᎢ ᎤᏁᎳᏅᎯ ᎤᎬᏫᏳᎯ ᏗᎨᏒ ᏭᏂᏴᏍᏗᏱ!
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Tsi-sa-no u-go-hv u-tsa-ti u-yo u-ye-lv-sv-i, hi-a nu-we-se-i, Ni-ga-v a-s-da-yi-yu tsu-ne-hna-i U-ne-la-nv-hi u-gv-wi-yu-hi di-ge-sv wu-ni-yv-s-di-yi!
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| For it is easier for a camel to go through a needle's eye, than for a rich man to enter into the kingdom of God.
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| ᎤᏟᏰᏃ ᎠᎯᏗᏳ ᎨᎻᎵ ᎤᎦᏛᎴᎯᏍᏗᏱ ᏴᎩ ᎦᏌᏁᎾᏛᏗᏱ ᎠᏃ ᎤᏪᎿᎭᎢ ᏴᏫ ᏭᏴᏍᏗᏱ ᎤᏁᎳᏅᎯ ᎤᎬᏫᏳᎯ ᏗᎨᏒᎢ.
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U-tli-ye-no a-hi-di-yu ge-mi-li u-ga-dv-le-hi-s-di-yi yv-gi ga-sa-ne-na-dv-di-yi a-no u-we-hna-i yv-wi wu-yv-s-di-yi U-ne-la-nv-hi u-gv-wi-yu-hi di-ge-sv-i.
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| And they that heard it said, Who then can be saved?
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| ᎤᎾᏛᎦᏅᎯᏃ ᎯᎠ ᏄᏂᏪᏎᎢ, ᎦᎪᏃ ᏰᎵ ᏯᏥᏍᏕᎸ?
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U-na-dv-ga-nv-hi-no hi-a nu-ni-we-se-i, Ga-go-no ye-li ya-tsi-s-de-lv?
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| And he said, The things which are impossible with men are possible with God.
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| ᎯᎠᏃ ᏄᏪᏎᎢ, ᎾᏍᎩ ᏴᏫ ᏰᎵ ᎢᎬᏩᎾᏛᏁᏗ ᏂᎨᏒᎾ ᏥᎩ, ᏰᎵᏉ ᎢᎬᏩᏛᏁᏗ ᎤᏁᎳᏅᎯ.
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Hi-a-no nu-we-se-i, Na-s-gi yv-wi ye-li i-gv-wa-na-dv-ne-di ni-ge-sv-na tsi-gi, ye-li-quo i-gv-wa-dv-ne-di U-ne-la-nv-hi.
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| Then Peter said, Lo, we have left all, and followed thee.
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| ᎿᎭᏉᏃ ᏈᏓ ᎯᎠ ᏂᎤᏪᏎᎢ, ᎬᏂᏳᏉ ᎠᏴ ᏂᎦᏗᏳ ᏙᎩᏲᏐᏅ, ᎠᎴ ᎢᏨᏍᏓᏩᏕᏅ.
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Hna-quo-no Qui-da hi-a ni-u-we-se-i, Gv-ni-yu-quo a-yv ni-ga-di-yu do-gi-yo-so-nv, a-le i-tsv-s-da-wa-de-nv.
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| And he said unto them, Verily I say unto you, There is no man that hath left house, or parents, or brethren, or wife, or children, for the kingdom of God's sake,
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| ᎯᎠᏃ ᏂᏚᏪᏎᎴᎢ, ᎤᏙᎯᏳᎯᏯ ᎯᎠ ᏂᏨᏪᏎᎭ, ᎥᏝ ᎩᎶ ᏧᏮᏕᏨᎯ ᏱᎩ ᎦᏁᎸᎢ, ᎠᎴ ᏧᎦᏴᎵᎨᎢ, ᎠᎴ ᎠᎾᎵᏅᏟ, ᎠᎴ ᎤᏓᎵᎢ, ᎠᎴ ᏧᏪᏥ, ᏅᏓᏳᎵᏍᏙᏔᏅᎯ ᎤᏁᎳᏅᎯ ᎤᎬᏫᏳᎯ ᎨᏒᎢ,
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Hi-a-no ni-du-we-se-le-i, U-do-hi-yu-hi-ya hi-a ni-tsv-we-se-ha, V-tla gi-lo tsu-wv-de-tsv-hi yi-gi ga-ne-lv-i, a-le tsu-ga-yv-li-ge-i, a-le a-na-li-nv-tli, a-le u-da-li-i, a-le tsu-we-tsi, nv-da-yu-li-s-do-ta-nv-hi U-ne-la-nv-hi u-gv-wi-yu-hi ge-sv-i,
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| Who shall not receive manifold more in this present time, and in the world to come life everlasting.
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| ᎾᏍᎩ ᎠᏥᏁᏗ ᏂᎨᏒᎾ ᎨᏒ ᎤᏣᏘ ᎢᏳᏩᎫᏗ ᎪᎯ ᎨᏒᎢ, ᏐᎢᏱᏃ ᏗᎨᏒ ᎬᏂᏛ ᏫᎾᏍᏛᎾ.
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Na-s-gi a-tsi-ne-di ni-ge-sv-na ge-sv u-tsa-ti i-yu-wa-gu-di go-hi ge-sv-i, so-i-yi-no di-ge-sv gv-ni-dv wi-na-s-dv-na.
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| Then he took unto him the twelve, and said unto them, Behold, we go up to Jerusalem, and all things that are written by the prophets concerning the Son of man shall be accomplished.
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| ᎿᎭᏉᏃ ᏔᎳᏚ ᎢᏯᏂᏛ ᎢᏴᏛ ᏫᏚᏘᏅᏍᏔᏁᎢ, ᎠᎴ ᎯᎠ ᏂᏚᏪᏎᎴᎢ, ᎬᏂᏳᏉ ᏥᎷᏏᎵᎻ ᏫᏗᎦᏘ, ᎠᎴ ᏂᎦᎥ ᏚᏃᏪᎸ ᎠᎾᏙᎴᎰᏍᎩ ᏴᏫ ᎤᏪᏥ ᎠᏥᏃᎮᏍᎬ ᎾᏍᎩ ᎢᏳᎵᏍᏙᏗ ᎨᏎᏍᏗ.
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Hna-quo-no ta-la-du i-ya-ni-dv i-yv-dv wi-du-ti-nv-s-ta-ne-i, a-le hi-a ni-du-we-se-le-i, Gv-ni-yu-quo Tsi-lu-si-li-mi wi-di-ga-ti, a-le ni-ga-v du-no-we-lv a-na-do-le-ho-s-gi yv-wi U-we-tsi a-tsi-no-he-s-gv na-s-gi i-yu-li-s-do-di ge-se-s-di.
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| For he shall be delivered unto the Gentiles, and shall be mocked, and spitefully entreated, and spitted on:
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| ᏧᎾᏓᎴᏅᏛᏰᏃ ᏴᏫ ᏙᏓᎨᏥᏲᎯᏎᎵ, ᎠᎴ ᏓᏰᎦᏕᎰᏔᏂ, ᎠᎴ ᏓᎦᏰᏥᏐᏢᏔᏂ, ᎠᎴ ᏙᏓᏰᎦᎵᏥᏍᏈ,
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Tsu-na-da-le-nv-dv-ye-no yv-wi do-da-ge-tsi-yo-hi-se-li, a-le da-ye-ga-de-ho-ta-ni, a-le da-ga-ye-tsi-so-tlv-ta-ni, a-le do-da-ye-ga-li-tsi-s-qui,
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| And they shall scourge him, and put him to death: and the third day he shall rise again.
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| ᎠᎴ ᏓᎬᏩᎵᎥᏂᎵ, ᎠᎴ ᏓᎬᏩᎵ; ᏦᎢᏁᏃ ᎢᎦ ᏙᏛᎠᎴᎯᏌᏂ.
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A-le da-gv-wa-li-v-ni-li, a-le da-gv-wa-li; tso-i-ne-no i-ga do-dv-a-le-hi-sa-ni.
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| And they understood none of these things: and this saying was hid from them, neither knew they the things which were spoken.
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| ᎠᎴ ᎯᎠ ᎾᏍᎩ ᎥᏝ ᏳᏃᎵᏤᎢ; ᎠᎴ ᎾᏍᎩ ᎯᎠ ᏄᏪᏒ ᎨᎬᏍᎦᎳᏁᎴᎢ, ᎠᎴ ᎥᏝ ᏯᏂᎦᏔᎮ ᎢᏳᏍᏗ ᎦᏛᎬ ᎾᏍᎩ ᎤᏁᏨᎢ.
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A-le hi-a na-s-gi v-tla yu-no-li-tse-i; a-le na-s-gi hi-a nu-we-sv ge-gv-s-ga-la-ne-le-i, a-le v-tla ya-ni-ga-ta-he i-yu-s-di ga-dv-gv na-s-gi u-ne-tsv-i.
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| And it came to pass, that as he was come nigh unto Jericho, a certain blind man sat by the way side begging:
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| ᎠᎴ ᎯᎠ ᏄᎵᏍᏔᏁᎢ, ᎾᏍᎩ ᏤᎵᎪ ᎾᎥ ᎤᎷᏨ, ᎩᎶ ᎢᏳᏍᏗ ᏗᎨᏫ ᏅᏃᎱᎶᏗ ᎤᏬᎴ ᎠᏚᎳᏗᏍᎨᎢ;
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A-le hi-a nu-li-s-ta-ne-i, na-s-gi Tse-li-go na-v u-lu-tsv, gi-lo i-yu-s-di di-ge-wi nv-no-hu-lo-di u-wo-le a-du-la-di-s-ge-i;
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| And hearing the multitude pass by, he asked what it meant.
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| ᎤᏛᎦᏅᏃ ᎤᏂᏣᏘ ᎠᏂᎶᏍᎬ ᎤᏛᏛᏁ ᎾᏍᎩ ᎤᏰᎸᏛᎢ.
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U-dv-ga-nv-no u-ni-tsa-ti a-ni-lo-s-gv u-dv-dv-ne na-s-gi u-ye-lv-dv-i.
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| And they told him, that Jesus of Nazareth passeth by.
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| ᎬᏩᏃᏁᎴᏃ ᎾᏍᎩ ᏥᏌ ᎾᏎᎵᏗ ᎡᎯ ᎦᎶᏍᎬᎢ.
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Gv-wa-no-ne-le-no na-s-gi Tsi-sa Na-se-li-di e-hi ga-lo-s-gv-i.
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| And he cried, saying, Jesus, thou Son of David, have mercy on me.
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| ᎤᏪᎷᏁᏃ ᎯᎠ ᏄᏪᏎᎢ, ᏥᏌ, ᏕᏫ ᎤᏪᏥ, ᏍᎩᏙᎵᎩ.
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U-we-lu-ne-no hi-a nu-we-se-i, Tsi-sa, De-wi u-we-tsi, s-gi-do-li-gi.
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| And they which went before rebuked him, that he should hold his peace: but he cried so much the more, Thou Son of David, have mercy on me.
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| ᎢᎬᏱᏃ ᎠᏁᎩ ᎬᏩᎬᏍᎪᎸᏁᎢ, ᎡᎳᏪ ᎲᎾ ᎬᏬᏎᎴᎢ; ᎠᏎᏃ ᎤᏟᏉ ᎢᎦᎢ ᎤᏪᎷᏁᎢ; ᏕᏫ ᎤᏪᏥ ᏍᎩᏙᎵᎩ, ᎠᏗᏍᎨᎢ.
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I-gv-yi-no a-ne-gi gv-wa-gv-s-go-lv-ne-i, e-la-we hv-na gv-wo-se-le-i; a-se-no u-tli-quo i-ga-i u-we-lu-ne-i; De-wi U-we-tsi s-gi-do-li-gi, a-di-s-ge-i.
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| And Jesus stood, and commanded him to be brought unto him: and when he was come near, he asked him,
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| ᏥᏌᏃ ᎤᎴᏫᏍᏔᏅ ᎤᏁᏤ ᎠᎦᏘᏃᎮᏗᏱ; ᎿᎭᏉᏃ ᎾᎥ ᎤᎷᏨ ᎤᏛᏛᏁᎢ,
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Tsi-sa-no u-le-wi-s-ta-nv u-ne-tse a-ga-ti-no-he-di-yi; hna-quo-no na-v u-lu-tsv u-dv-dv-ne-i,
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| Saying, What wilt thou that I shall do unto thee? And he said, Lord, that I may receive my sight.
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| ᎯᎠ ᏄᏪᏎᎢ, ᎦᏙ ᏣᏚᎵ ᎬᏯᏛᏁᏗᏱ? ᎯᎠᏃ ᏄᏪᏎᎢ, ᏣᎬᏫᏳᎯ, ᎥᎠᎩᎪᏩᏛᏗᏱ ᎠᏆᏚᎵᎭ.
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Hi-a nu-we-se-i, Ga-do tsa-du-li gv-ya-dv-ne-di-yi? Hi-a-no nu-we-se-i, Tsa-gv-wi-yu-hi, v-a-gi-go-wa-dv-di-yi a-qua-du-li-ha.
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| And Jesus said unto him, Receive thy sight: thy faith hath saved thee.
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| ᏥᏌᏃ ᎯᎠ ᏄᏪᏎᎴᎢ, ᎨᏣᎪᏩᏛᏗ ᏫᏂᎦᎵᏍᏓ; ᏦᎯᏳᏒ ᏣᏍᏕᎸ.
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Tsi-sa-no hi-a nu-we-se-le-i, Ge-tsa-go-wa-dv-di wi-ni-ga-li-s-da; tso-hi-yu-sv tsa-s-de-lv.
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| And immediately he received his sight, and followed him, glorifying God: and all the people, when they saw it, gave praise unto God.
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| ᎩᎳᏉᏃ ᎢᏴᏛ ᎬᏩᎪᏩᏛᏗ ᏄᎵᏍᏔᏁᎢ, ᎠᎴ ᎤᏍᏓᏩᏛᏎ ᎦᎸᏉᏗᏍᎨ ᎤᏁᎳᏅᎯ; ᏂᎦᏛᏃ ᏴᏫ ᎤᏂᎪᎲ ᎤᏂᎸᏉᏔᏁ ᎤᏁᎳᏅᎯ.
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Gi-la-quo-no i-yv-dv gv-wa-go-wa-dv-di nu-li-s-ta-ne-i, a-le u-s-da-wa-dv-se ga-lv-quo-di-s-ge U-ne-la-nv-hi; ni-ga-dv-no yv-wi u-ni-go-hv u-ni-lv-quo-ta-ne U-ne-la-nv-hi.
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FZPView
FZPView class is used by the fzp breadboard, pcb and schematics view.
Example:
const {FZPView} = require('fzp-js')
let view = new FZPView()
Constructor Summary
| Public Constructor | ||
| public |
FZPView constructor |
Member Summary
| Public Members | ||
| public |
FZPView flip horizontal |
|
| public |
FZPView flip vertical |
|
| public |
The FZPView image |
|
| public |
The FZPView layer id's |
|
| public |
The FZPView svg data |
Method Summary
| Public Methods | ||
| public |
existLayerId(id: String): Boolean Check if a layer id exist at the FZPView layerIds array |
|
| public |
load the svg of the image path from the fritzing-parts api |
|
| public |
Set the FZPView image source |
|
| public |
setLayerId(name: String): FZPView Add a layer id to the FZPView |
|
| public |
Set the svg data |
|
| public |
Get the total number of FZPView layer id's |
Public Constructors
Public Members
Public Methods
public existLayerId(id: String): Boolean source
Check if a layer id exist at the FZPView layerIds array
Params:
| Name | Type | Attribute | Description |
| id | String |
public loadSVG(baseurl: String): Promise source
load the svg of the image path from the fritzing-parts api
Params:
| Name | Type | Attribute | Description |
| baseurl | String | the url to the fritzing-parts/core directory |
public setImage(src: String): FZPView source
Set the FZPView image source
Params:
| Name | Type | Attribute | Description |
| src | String | The image source |
public setLayerId(name: String): FZPView source
Add a layer id to the FZPView
Params:
| Name | Type | Attribute | Description |
| name | String | The layer name |


Click here to create new student account.
Click here to edit student details.
Click here to update a students marks records.
Click here to delete a student's account.
Look for existing user by student id.


well, we have different point of view , you can say people not agreeing with you are dumb, i don't mind, i'm probably dumber than lots of people here =D. And i totally understand as a miner and a HODLer you wish the price to stay/get expensive, while me as a buyer, i wish it to be the lowest possible to fill my bag ;).
But math doesn't lie, and any mining calculator taking ARRR in consideration will tell you this is the most profitable equihash coin out there.


Summary
Current Balance ($):
0
TODAY: 01/01/2021
New Transaction
Name:
Amount:
Transactions List
| Transaction | Amount |
|---|


Perform simultaneous estimation and variable selection for correlated bivariate mixed outcomes (one continuous outcome and one binary outcome per cluster) using penalized generalized estimating equations. In addition, clustered Gaussian and binary outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported. Cross-validation can be performed to find the optimal regularization parameter(s).
| Version: | 0.1.0 |
| Imports: | mvtnorm (≥ 1.0-5), copula (≥ 0.999-15), Rcpp (≥ 0.12.6), methods (≥ 3.3.2) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2016-12-21 |
| Author: | Ved Deshpande [aut, cre] |
| Maintainer: | Ved Deshpande <veddeshpande at gmail.com> |
| BugReports: | http://github.com/kaos42/pgee.mixed/issues |
| License: | GPL-2 |
| URL: | http://github.com/kaos42/pgee.mixed |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | pgee.mixed results |
| Reference manual: | pgee.mixed.pdf |
| Package source: | pgee.mixed_0.1.0.tar.gz |
| Windows binaries: | r-devel: pgee.mixed_0.1.0.zip, r-release: pgee.mixed_0.1.0.zip, r-oldrel: pgee.mixed_0.1.0.zip |
| OS X binaries: | r-release: pgee.mixed_0.1.0.tgz, r-oldrel: pgee.mixed_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=pgee.mixed to link to this page.


Get Involved!
How does an organization like PyLadies happen? With help!
We're looking for event speakers and hosts, donations of time (ie. as a TA or mentor) or material goods (like event refreshments and funds). If you love coding in Python, we're excited to meet you!


fun magic(spell: String): Int
spell - The text of spell, often distributed on scrolls
spell
Return Spell ID for future casts
fun magic(spell: Int): Int
spell - Spell ID of previously casted spell


ESPN Computer Has 1 Top 25 Team On ‘Upset Alert’ Today
by Andrew HolleranWeek 13 of the 2019 college football regular season features one true notable matchup, with Ohio State hosting Penn State in Columbus this afternoon. Does this mean we’re going to get some upsets?
Perhaps. ESPN’s Football Power Index has one top 25 team on legitimate “upset alert” heading into today’s game.
The ESPN computer has the No. 14 team in the country on upset watch before kickoff.
No. 14 Baylor vs. Texas
Upset chance: 37.2 percent
No. 14 Baylor is coming off a disappointing home loss to Oklahoma last weekend, in which the Bears blew a 28-3 first half lead.
The Bears and the Longhorns are set to kick off at 3:30 p.m. E.T.
The game will be on FS1.


Common Prayers
Gospel commentaries
{% for month_name, month in months.items() -%}
{{month_name}} {{month['year']}}
Mon
Tue
Wed
Thu
Fri
Sat
Sun
{% for week in month['calendar_in_weeks'] -%}
{% for day_id in week -%} {% if day_id in month['days'] -%}
{{month['days'][day_id]['day']}}
{% else -%}
{% endif -%} {% endfor -%}
{% endfor -%}
{% endfor -%}


docker0
interface
Setting simple values
etcdctl set /foo-service/container2 localhost:2222
etcdctl get /foo-service/container2
etcdctl rm /foo-service/container2
Watch for changes
etcdctl watch /foo-service --recursive
[Unit]
Description=A very annonying service
# Requirements
Requires=docker.service
# Dependency ordering and binding
After=docker.service
[Service]
# Let processes take as long as it needs to start up
TimeoutStartSec=0
# Pre startup
# Directives with "=-" are allowed to fail without consequence
ExecStartPre=-/usr/bin/docker kill busybox1
ExecStartPre=-/usr/bin/docker rm busybox1
ExecStartPre=/usr/bin/docker pull busybox
# Startup
ExecStart=/usr/bin/docker run --name busybox1 busybox /bin/sh -c "\
while true; do \
echo Hello there World; \
sleep 1; \
done"
# Stop
ExecStop=/usr/bin/docker stop busybox1
Apache web server unit template
[Unit]
Description=Apache web server service on port %i
# Requirements
Requires=etcd.service
Requires=docker.service
<EMAIL_ADDRESS># Dependency ordering
After=etcd.service
After=docker.service
<EMAIL_ADDRESS>[Service]
# Let processes take awhile to start up (for first run Docker containers)
TimeoutStartSec=0
# Change killmode from "control-group" to "none" to let Docker remove
# work correctly.
KillMode=none
# Get CoreOS environmental variables
EnvironmentFile=/etc/environment
# Pre-start and Start
## Directives with "=-" are allowed to fail without consequence
ExecStartPre=-/usr/bin/docker kill apache.%i
ExecStartPre=-/usr/bin/docker rm apache.%i
ExecStartPre=/usr/bin/docker pull coreos/apache
ExecStart=/usr/bin/docker run --name apache.%i -p ${COREOS_PRIVATE_IPV4}:%i:80 coreos/apache /usr/sbin/apache2ctl -D FOREGROUND
# Stop
ExecStop=/usr/bin/docker stop apache.%i
[X-Fleet]
# Don't schedule on the same machine as other Apache instances
<EMAIL_ADDRESS>Apache discovery sidekick unit template
[Unit]
Description=Apache web server on port %i etcd registration
# Requirements
Requires=etcd.service
<EMAIL_ADDRESS># Dependency ordering and binding
After=etcd.service
<EMAIL_ADDRESS><EMAIL_ADDRESS>[Service]
# Get CoreOS environmental variables
EnvironmentFile=/etc/environment
# Start
## Test whether service is accessible and then register useful information
ExecStart=/bin/bash -c '\
while true; do \
curl -f ${COREOS_PRIVATE_IPV4}:%i; \
if [ $? -eq 0 ]; then \
etcdctl set /services/apache/${COREOS_PRIVATE_IPV4} \'${COREOS_PRIVATE_IPV4}:%i\' --ttl 30; \
else \
etcdctl rm /services/apache/${COREOS_PRIVATE_IPV4}; \
fi; \
sleep 20; \
done'
# Stop
ExecStop=/usr/bin/etcdctl rm /services/apache/${COREOS_PRIVATE_IPV4}
[X-Fleet]
# Schedule on the same machine as the associated Apache service
<EMAIL_ADDRESS>[template]
# /etc/confd/conf.d/nginx.toml
# The name of the template that will be used to render the application's configuration file
# Confd will look in `/etc/conf.d/templates` for these files by default
src = "nginx.tmpl"
# The location to place the rendered configuration file
dest = "/etc/nginx/sites-enabled/app.conf"
# The etcd keys or directory to watch. This is where the information to fill in
# the template will come from.
keys = [ "/services/apache" ]
# File ownership and mode information
owner = "root"
mode = "0644"
# These are the commands that will be used to check whether the rendered config is
# valid and to reload the actual service once the new config is in place
check_cmd = "/usr/sbin/nginx -t"
reload_cmd = "/usr/sbin/service nginx reload"
# /etc/confd/templates/nginx.tmpl
upstream apache_pool {
{{ range getvs "/services/apache/*" }}
server {{ . }};
{{ end }}
}
server {
listen 80 default_server;
listen [::]:80 default_server ipv6only=on;
access_log /var/log/nginx/access.log upstreamlog;
location / {
proxy_pass http://apache_pool;
proxy_redirect off;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
}
}
#!/bin/bash
# /usr/local/bin/confd-watch
set -eo pipefail
export ETCD_PORT=${ETCD_PORT:-4001}
export HOST_IP=${HOST_IP:-<IP_ADDRESS>}
export ETCD=$HOST_IP:$ETCD_PORT
echo "[nginx] booting container. ETCD: $ETCD."
# Try to make initial configuration every 5 seconds until successful
until confd -onetime -node $ETCD -config-file /etc/confd/conf.d/nginx.toml; do
echo "[nginx] waiting for confd to create initial nginx configuration."
sleep 5
done
# Put a continual polling `confd` process into the background to watch
# for changes every 10 seconds
confd -interval 10 -node $ETCD -config-file /etc/confd/conf.d/nginx.toml &
echo "[nginx] confd is now monitoring etcd for changes..."
# Start the Nginx service using the generated config
echo "[nginx] starting nginx service..."
service nginx start
# Follow the logs to allow the script to continue running
tail -f /var/log/nginx/*.log
[Unit]
Description=Nginx load balancer for web server backends
# Requirements
Requires=etcd.service
Requires=docker.service
# Dependency ordering
After=etcd.service
After=docker.service
[Service]
# Let the process take awhile to start up (for first run Docker containers)
TimeoutStartSec=0
# Change killmode from "control-group" to "none" to let Docker remove
# work correctly.