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You can miss up to 4 lectures; after that, your job placement guarantee is forfeit. I don't recommend missing any, as we will be moving very fast.
Your homework is due by 6 AM on the day it is due. After that, it is late. Like the absence policy, you can turn in homework late 4 times, although I do not recommend it.
I would rather see a solid attempt on time than a complete assignment late. Solid attempts are not considered late.
 You should use all resources available to you, including open-source code, but copy and pasting open-source code is prohibited: you should use it only for inspiration or as a library in a bigger project.
Copying homework is taken very seriously and can result in forfeiting job placement.
Like the technical community as a whole, classes at The Iron Yard are made up of a mixture of people from all different backgrounds.
Diversity is one of our huge strengths, but it can also lead to communication issues and unhappiness. To that end, we have a few ground rules that we ask people to adhere to when they're taking a class at The Iron Yard. These rules apply equally to teachers, students, other staff, and guest lecturers.
This isn't an exhaustive list of things that you can't do. Rather, take it in the spirit in which it's intended - a guide to make it easier to enrich all of us, the technical communities in which we participate.
This code of conduct applies to all communication: this includes in class, HipChat, email, and other forums such as Skype, Google+ Hangouts, etc.
If you believe someone is violating the code of conduct, we ask that you report it by talking to your teacher. If you do not feel comfortable talking to your teacher, speak with any Iron Yard teacher or campus director.
Be welcoming, friendly, and patient.
Be considerate. Your work will be used by other people, and you in turn will depend on the work of others. Any decision you take will affect your colleagues, and you should take those consequences into account when making decisions.
Be respectful. Not all of us will agree all the time, but disagreement is no excuse for poor behavior and poor manners. We might all experience some frustration now and then, but we cannot allow that frustration to turn into a personal attack. It's important to remember that a community where people feel uncomfortable or threatened is not a productive one. Members of the Iron Yard community should be respectful when dealing with other members as well as with people outside the Iron Yard community.
Be careful in the words that you choose. Be kind to others. Do not insult or put down other people. Behave professionally. Remember that harassment and sexist, racist, or exclusionary behavior are not appropriate for this class. This includes, but is not limited to:
Repeated harassment of others. In general, if someone asks you to stop, then stop.
When we disagree, we try to understand why. Disagreements, both social and technical, happen all the time and The Iron Yard is no exception. It is important that we resolve disagreements and differing views constructively. Remember that we're different. The strength of The Iron Yard comes from its varied members, people from a wide range of backgrounds. Different people have different perspectives on issues. Being unable to understand why someone holds a viewpoint doesn't mean that they're wrong. Don't forget that it is human to err and blaming each other doesn't get us anywhere, rather offer to help resolving issues and to help learn from mistakes.
This text is taken from the Django project.


| DBIObject-class {DBI} | R Documentation |
Base class for all other DBI classes (e.g., drivers, connections). This is a virtual Class: No objects may be created from it.
More generally, the DBI defines a very small set of classes and generics that
allows users and applications access DBMS with a common interface. The
virtual classes are DBIDriver
that individual drivers extend,
DBIConnection
that represent instances of DBMS connections, and
DBIResult
that represent the result of a DBMS statement. These three
classes extend the basic class of DBIObject
, which serves as the root
or parent of the class hierarchy.
An implementation MUST provide methods for the following generics:
It MAY also provide methods for:
summary()
. Print a concise description of the
object. The default method invokes dbGetInfo(dbObj)
and prints
the name-value pairs one per line. Individual implementations may
tailor this appropriately.
Other DBI classes:
DBIConnection-class
,
DBIConnector-class
,
DBIDriver-class
,
DBIResult-class
drv <- RSQLite::SQLite() con <- dbConnect(drv) rs <- dbSendQuery(con, "SELECT 1") is(drv, "DBIObject") ## True is(con, "DBIObject") ## True is(rs, "DBIObject") dbClearResult(rs) dbDisconnect(con)


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| HBASE-9445 |
Snapshots should create column family dirs for empty regions |
Major |
snapshots |
Enis Soztutar |
Enis Soztutar |
| HBASE-11117 |
[AccessController] checkAndPut/Delete hook should check only Read permission |
Major |
security |
Anoop Sam John |
Anoop Sam John |
| HBASE-11133 |
Add an option to skip snapshot verification after Export |
Trivial |
snapshots |
Matteo Bertozzi |
Matteo Bertozzi |
| HBASE-11143 |
Improve replication metrics |
Major |
Replication |
Lars Hofhansl |
Lars Hofhansl |
| HBASE-10251 |
Restore API Compat for PerformanceEvaluation.generateValue() |
Major |
Client |
Aleksandr Shulman |
Dima Spivak |
| HBASE-11169 |
nit: fix incorrect javadoc in OrderedBytes about BlobVar and BlobCopy |
Trivial |
util |
Jonathan Hsieh |
Jonathan Hsieh |
| HBASE-11168 |
[docs] Remove references to RowLocks in post 0.96 docs. |
Major |
documentation |
Jonathan Hsieh |
Jonathan Hsieh |
| HBASE-11189 |
Subprocedure should be marked as complete upon failure |
Major |
. |
Ted Yu |
Ted Yu |
| HBASE-11200 |
AsyncWriter of FSHLog might throw ArrayIndexOutOfBoundsException |
Minor |
wal |
Jianwei Cui |
Jianwei Cui |
| HBASE-10417 |
index is not incremented in PutSortReducer#reduce() |
Minor |
. |
Ted Yu |
Gustavo Anatoly |
| HBASE-11217 |
Race between SplitLogManager task creation + TimeoutMonitor |
Critical |
. |
Enis Soztutar |
Enis Soztutar |
| HBASE-11212 |
Fix increment index in KeyValueSortReducer |
Minor |
. |
Gustavo Anatoly |
Gustavo Anatoly |
| HBASE-11237 |
Bulk load initiated by user other than hbase fails |
Critical |
. |
Dima Spivak |
Jimmy Xiang |
| HBASE-11149 |
Wire encryption is broken |
Major |
IPC/RPC |
Devaraj Das |
Devaraj Das |
| HBASE-10831 |
IntegrationTestIngestWithACL is not setting up LoadTestTool correctly |
Major |
. |
Andrew Purtell |
Vandana Ayyalasomayajula |
| HBASE-11226 |
Document and increase the default value for hbase.hstore.flusher.count |
Major |
regionserver |
Nicolas Liochon |
Nicolas Liochon |
| HBASE-11251 |
LoadTestTool should grant READ permission for the users that are given READ access for specific cells |
Major |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-11096 |
stop method of Master and RegionServer coprocessor is not invoked |
Major |
. |
Qiang Tian |
Qiang Tian |
| HBASE-11253 |
IntegrationTestWithCellVisibilityLoadAndVerify failing since HBASE-10326 |
Major |
test |
Anoop Sam John |
Anoop Sam John |
| HBASE-11234 |
FastDiffDeltaEncoder#getFirstKeyInBlock returns wrong result |
Critical |
. |
chunhui shen |
chunhui shen |
| HBASE-11255 |
Negative request num in region load |
Minor |
. |
Liu Shaohui |
Liu Shaohui |
| HBASE-10692 |
The Multi TableMap job don't support the security HBase cluster |
Minor |
mapreduce |
Liu Shaohui |
Liu Shaohui |
| HBASE-11267 |
Dynamic metrics2 metrics may consume large amount of heap memory |
Major |
. |
Ted Yu |
Ted Yu |
| HBASE-11275 |
[AccessController] postCreateTable hook fails when another CP creates table on their startup |
Major |
security |
Anoop Sam John |
Anoop Sam John |
| HBASE-11277 |
RPCServer threads can wedge under high load |
Critical |
. |
Andrew Purtell |
Andrew Purtell |


Basic Forms
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Seven O’Clock Stories
by Robert Gordon Anderson
“Twelfth Night: About Duckie the Stepchild and the Little Ship”
Additional Information
- Year Published: 1920
- Language: English
- Country of Origin: United States of America
- Source: Anderson, R. G. (1920). Seven O’Clock Stories. New York: G.P. Putnam’s Sons: The Knickerbocker Press.
-
Readability:
- Flesch–Kincaid Level: 3.9
- Word Count: 2,910
- Genre: Adventure
- Keywords: children's stories
- ✎ Cite This
Downloads
In the door of the workshop stood the three happy children, watching the Toyman.
It was one of the very nicest places on the whole farm. Tools of all sorts, bright and sharp, lay on the table. Lumber of every kind lay piled against the walls. The shelves were filled with cans of paint. All the colours of the rainbow were in those cans. The children could tell that by the pretty splashes of the paint dripping down their sides.
Back and forth, back and forth swung the arms of the Toyman. He was very busy over something—something very important it must be, for he never talked, only worked and whistled away.
“Oh dear! I wish I knew what it was,” sighed Marmaduke. Anyway he knew it was something for them . Father Green had given the Toyman a holiday, all for himself, to do as he liked. And of course he’d make something for them.
On the edge of the table was a vise, a big tool with iron jaws. In the iron jaws was a block of wood. The Toyman screwed the vise—very tight—so tight the wood couldn’t budge. Then he shaved this side of the block, then the other side, with a plane, a tool with a very sharp edge. Clean white shavings fell on the floor, some of them twisting like Hepzebiah’s curls.
“I wonder what it’s going to be,” Marmaduke repeated.
Jehosophat was pretty sure he knew.
“I’ll bet it’s a boat,” he said.
The Toyman chuckled.
“Right you are, Son. It’s the Good Ship—well, let’s see. All boats have a name, you know. What do you think would be a good name for a fine ship?”
Jehosophat had one, right on the tip of his tongue.
“The Arrow.”
The Toyman thought this over.
“That isn’t bad,” said he.
Then he turned to Marmaduke.
“What’s your idea for a name, little chap?”
Marmaduke thought and thought. He looked out through the door and saw the Party Bird, the vain Peacock, parading up and down, showing off its beautiful tail, and “Peacock” was the only name he could think of.
Jehosophat laughed out loud.
“That’s no name for a boat.”
 ”
The Toyman stopped the quarrel, just as he always did, with something pleasant or funny he said. Then he leaned over and picked up three chips of wood.
“I’ll write the names on these little chips,” he explained, “and we’ll choose.”
Putting his hand on Hepzebiah’s sunny curls, he asked that little girl:
“What name do you think would be nice for the boat?”
Now Hepzebiah really didn’t know just what it all was about. But she had heard Marmaduke say “Peacock,” so she took her finger out of her mouth just long enough to point at the Guinea-hen, who was screeching horribly out in the barnyard.
“The Guinea-hen! Ha, ha! That’s a good one!” The Toyman was forever saying that and laughing at the funny things the children said.
Hepzebiah, thinking that this was a nice sort of a game, took her finger out of her mouth and pointed again—this time out at the pond where the swans were sailing, like pretty white ships themselves.
“The very thing,” exclaimed the Toyman. “White Swan’s a fine name for a boat!”
And he wrote “White Swan” on one chip, “Peacock” on another, and “Arrow” on the last. Then he held them towards the children.
“The smallest must choose first,” he said, and Hepzebiah took one of the little white pieces of wood from the Toyman’s hand. He turned it over and read:
“White Swan.”
“We’d go a good ways before we’d get a better name,” he decided. “When the boat’s all finished and all sails set, she’ll sail away just like a swan; you see if she doesn’t.”
The hull of the boat was finished now, and on the bow, at the very front, he nailed a thin little stick, with tiny nails. This was the bowsprit.
On the keel at the very bottom, he fastened a piece of lead so she wouldn’t “turn turtle”—turn over, he meant, when her sails were set and the wind blew too hard.
Then choosing some sticks—very carefully, for they must be straight—he tucked the boat under his arm and, with the three children close at his heels, walked over to the pond and sat down under the Crying Tree, where the sun shone bright and warm.
Out came the magic knife and he whittled away at the little sticks; whittled and whistled and smiled all the time.
Sliver after sliver of the wood fell on the ground. Sometimes one would drop into the water and float away like a fairy canoe, with the green willow leaves that fell from the Crying Tree.
So under the magic knife the little ship grew and grew, till the masts were fitted too, and set fast and tight in the clean smooth deck.
“But where are the sails?” asked Jehosophat impatiently.
A funny answer the Toyman made.
He just said:
“Hold your horses, Sonny.”
The teacher in the Red Schoolhouse up the road would have reproved him for this, but the children thought whatever the Toyman said was all right.
Of course he meant not to be too impatient and—but just then the dinner horn sounded, way out over the pond and over the fields, and the children ran into the house, just as you would have done too.
It didn’t take long to finish dinner that day. For desert they had blackberry pie, very juicy and nice, and they didn’t even wait to wash the red marks of that pie from their faces but just ran for the Crying Tree.
The Toyman felt in all of his six big pockets. And out came needles and thread, and pieces of clean muslin besides.
Stitch, stitch, stitch went his fingers, for a thousand stitches or more. And bye and bye the sails were all cut and sewed and fitted on the three little masts.
Then the Toyman stopped.
“We haven’t christened her yet,” he said. “We should have done that long ago.”
In his pockets he rummaged again, those pockets which always held just the right thing.  Much nicer these were, the children thought, than that yellow stuff in the big bottle they hated so.
The Toyman poured the little pills out.
“What’s the use of medicine on a nice day like this,” said he.
And he filled the bottle with water and put back the stopper.
“When ships are launched,” he explained, “folks break a bottle over the bow when they name her.”
“All right, I’ll do that,” said Jehosophat, but the Toyman stopped him.
“Hold on there, Sonny, that’s the ladies’ job.”
Then he called Hepzebiah and gave her the bottle.
“Now, little girl, you stand here and say: ‘I christen thee White Swan.’”
But, “I ckwithen Wite Thwan” was the best she could do.
“Now drop the bottle!”
She opened her fingers and, sure enough, the little bottle fell right on the deck and broke all in little pieces, and the glistening drops splashed over the bow, and so the good ship “White Swan” got her name.
Into the water the Toyman pushed the little ship. The wind filled her sails and off she went, racing away before the wind to join the beautiful birds for whom she had been named.
Around the pond and over the bridge went the Toyman, to the other side. When the ship reached the opposite shore he swung it around and sent it back on the return voyage. The “White Swan” had reached port safely, when the Toyman said:
“It’s funny what different opinions folks have. Some like the water and some don’t. Now the swans and the ducks, and that little ship, and the fish, and the froggies, and Uncle Roger, and you and I, we think it’s fine. But Mr. Stuck-up, and Miss Crosspatch, and Old Mother Wyandotte, and Mis’ Fizzeltree, why they won’t go near it at all.”
“That is funny,” said Jehosophat.
Then the Toyman added:
“Just listen to that.”
Old Mother Wyandotte was right near them, clucking in fright.
“Don’t—don’t—don’t you do it!” she was calling to one of her children who was looking longingly at the cool pond.
Around her were all her children, fast growing up now. They were all soft and white but one. Like good little chickens they were looking for bugs, all but one.
He was the little fellow they had noticed before, the funny little fellow with a longer bill than the rest, and the odd-looking feet. His soft downy back was turning black. And he was starting for that pretty water shining in the pond.
Jehosophat looked him all over.
“Why, he looks like a duck.”
“What did you expect?” laughed the Toyman. “He is a duck. Old Mother Wyandotte thinks he’s her child, but he’s only a step-child. Ha! Ha! Somebody must have put another egg in her nest.”
Over in the garden were pretty flowers called Bleeding Hearts. They were very pink, and Jehosophat’s face turned the very same colour. Well he knew who had stolen into the House of the White Wyandottes and put that big duck’s egg under Old Mother Hen.
“Don’t—don’t—don’t—don’t you do it,” she was shouting still.
 He was headed straight for that shining water.
Around Mother Wyandotte gathered all her relatives to talk over the matter. They were disgusted. That one of their family should disgrace them so!
“Respectable chickens spend their time on the ground,” said Granny Wyandotte with a toss of her comb, “and never, never get wet, if they can help it, not even their feet.”
“True—true—quite true,” all the Wyandotte Aunties agreed.
But their second cousins and the third cousins too, the ducks and the geese and the swans, said they were wrong.
“Little Duckie’s a sensible chap. What better place can there be to play in than that nice cool pond?”
And all the fishes swimming around, from the big pickerel down to the littlest “minnie,” waggled their fins and tails to show they agreed too, while the froggies on the lily-pad croaked:
“Gomme on—gomme on!”

Duckie was right at the edge now and Mother Hen, who was really his step-mother, made one last appeal, but the ducks one and all called:
“Back, back, back!”
They weren’t talking to Duckie. They meant the White Wyandottes. They were taking his part, you see, though not for one minute did they guess he was their child, their very own.
Duckie appreciated that too. Perhaps Old Father Drake, the head of all the Duck family, wouldn’t let Step-father Wyandotte punish him that night if he did try the water.
I don’t believe Step-father Wyandotte really cared very much. At first he was a little mad but, after scolding a little, he shouted:
“Through, through, through—I’m through with yooooooouuu.”
He wouldn’t have anything more to do with little Duckie. I guess he suspected he was just a step-child after all.
He had enough children anyway. But the Gold Rooster on the top of the barn looked down, laughing at him. He couldn’t really laugh, you know, or flap his wings, but he swung from west to southwest and back again, as if to say:
“I knew it. I knew it. They fooled you!”
Old Father Drake, the head of the duck family, started for the water. Mother Duck and all the little ducks went in too. They were going to show Duckie the way.
He just couldn’t stand it any longer. So—plopp in he went and paddled around after the others, and ducked his head under the water to catch his dinner, just as a real duckling should.
“Better than grubbing for bugs in the dirty earth, this nice clean cool water,” quacked he, and he was as happy as happy could be.
The Toyman was looking at him with a smile on his face.
“He’s just like me,” he said at last, and the children, surprised at that, asked all together:
“Who’s like you?”
“That little duck there.”
“Like you!” Jehosophat shouted.
The Toyman puffed away on his corncob pipe before he answered:
“Oh inside he’s the same. I was just like him when I was a kid. I had a step-mother, too, and she and all the step-uncles and aunts scolded and scolded, and whipped me besides, because I wanted to go to sea on a great big ship.”
“What did you do?”
They didn’t really need to ask that question, for hadn’t the Toyman been most everywhere, and hadn’t he told them many a story about the great sea and the ships?
“Yes, they all said I would drown or become a wicked bad man.”
Marmaduke thought he would like to do something to those step-uncles and aunts who treated the Toyman so badly.
“They don’t know what they’re talking about,” he shouted.  “But they said I would, just the same. They talked just like those old Wyandottes there.
“But I fooled them all,” he went on. “And one night, when it was dark, just a few stars out, I climbed out of bed and jumped out of the window and ran away.
“I walked and I walked, miles and miles, till I came to a big town by the sea. There were lots of big ships at the docks, and I asked a man, with a great big beard, to take me too.  But bye and bye I got to be a real sailor, and I sailed all over the world in the ship, and saw lots of people, yellow, and black, and brown, and funny places and queer houses and—”
“Be careful, Frank!”
They all turned at once. There was Mother, standing right near them. All the time she had been listening, near the Crying Tree.
“Now, Frank,” she repeated, “be careful or you’ll put notions in those children’s heads, and some day they’ll be running away from me.”
 How about it, kiddies?”
And Marmaduke and Jehosophat were very sure they never could run away—not even to sea in a beautiful ship. So they kissed her and hugged her too.
Now the froggies were singing their evening song. The sun was getting close to his home in the west. Little Duckie and his real mother and father came out of the water and waddled off towards the barn. The Swans folded their wings and came to the shore. So the Toyman brought the ship to the harbour and anchored her for the night.


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Middle Name: Mempin
Last Name: Dela Cruz
Address: B8 L4 Corner Saint. Mary Street, Central Signal Village, Taguig City
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Samsung is committed to complying with local laws and regulations as well as applying a strict global code of conduct to all employees. It believes that ethical management is not only a tool for responding to the rapid changes in the global business environment, but also a vehicle for building trust with its various stakeholders including customers, shareholders, employees, business partners and local communities. With an aim to become one of the most ethical companies in the world, Samsung continues to train its employees and operate monitoring systems, while practicing fair and transparent corporate management.
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Project Settings
Project settings are options that are configured once and affect the entire application. Most project settings can be configured from the editor, though some (currently) can only be configured by writing certain configuration files manually.
In the editor you will find the project settings under Editor > Project Settings > ....
Data Directories
Editor > Project Settings > Data Directories... opens a dialog to set up the data directories.
Plugin Selection
Which optional functionality should be available is configured on a per-project basis. See the plugin selection chapter for details.
Loading Plugins from Code
A custom application (TODO) or game state can load plugins directly from code if necessary. For example the ezInspectorPlugin
is automatically loaded for you by stock EZ applications, when building the code for development.
Input Configuration
Editor > Project Settings > Input Configuration... opens a dialog to configure input actions.
Tags
Editor > Project Settings > Tags... opens a dialog to configure which tags are available in the project.
Window Configuration
Editor > Project Settings > Window Configuration... opens a dialog to configure the default window configuration when running a scene.
These settings allow you to configure basic window settings for Play-the-Game mode and when running an exported scene in ezPlayer. A proper game would typically implement this logic in a custom application (TODO) and should allow the user to choose settings such as the resolution. The window configuration dialog is mainly for use during development.
There are two separately stored configurations:
Project Default: This configuration will be stored in the project folder and thus should be checked into source control to be shared with others.
User Specific: This configuration is only stored locally for the active user and not in the project directory. Therefore it cannot be checked into source control. It is meant for users who want to use settings different from the project default. For instance, when you have multiple monitors, you may want the exported scene to always appear on a specific one. This configuration must be enabled to override the default one.
Apart from the window position and size, the window configuration also controls the behavior of the mouse. If Clip Mouse Cursor is enabled, the mouse won't be able to leave the window area. This should be preferred for games that hide the mouse and only use relative mouse movement.
Asset Profiles
Editor > Project Settings > Asset Profiles... opens a dialog to edit asset profiles (TODO).
FMOD
If the FMOD Integration is enabled, Editor > Project Settings > FMOD Project Settings... will be available to configure the speaker mode and which master sound bank to use.
Jolt
If the Jolt Physics Integration is enabled, Editor > Project Settings > Jolt Project Settings... will be available to configure the collision layers.


Travellimg to the future 🌟 , it's alraedy 2020 🙋♂🌈🌴
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check out this article about the new mobile apps ui trends for 2020! 🔥😍
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patrick jones
january/6/2020


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{% extends "base.html" %} {% block content %}
This tool predicts the Yield Sooting Index of a compound as a function of its carbon types. To use, enter a SMILES string above (or use the drawing tool) and press submit. Experimental measurements, when available, are also displayed.
For more info, see our publication:
Das, D. D., St. John, P. C., McEnally, C. S., Kim, S., & Pfefferle, L. D. (2018). Measuring and predicting sooting tendencies of oxygenates, alkanes, alkenes, cycloalkanes, and aromatics on a unified scale. Combustion and Flame, 190, 349–364. 10.1016/j.combustflame.2017.12.005
Website by Peter St. John, Michael Bartlett, and Seonah Kim


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of Geometry
s, using the GeometryFactory
of the input geometries.Geometry
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of Geometry
s.Geometry
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s, using enhanced precision.Geometry
s, using enhanced precision.CoordinateFilter
that builds a set of Coordinate
s.


Save the hooks and aliases. You can share the resulting file with another users.
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This feature will save the aliases and the hooks into the project workspace.
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This feature will load previously backed up aliases and hooks to the current project.
Be aware existing hooks and aliases will be override.


| ASKAT Wrapper |
ASKAT WrapperYou can taker a look at some slides on the wrapper. Basics
Just unzip the file and run the JAR by doing: unzip askat.zipRunning the program In order to run the program you need to go into the directory where you installed it an run the JAR file: cd askat java -Xmx2G -jar Askat.jar [options] genotypeE.g.: To show a help message, just run the program without any parameters: $ java -jar Askat.jar ASKAT algorithm by Karim Oualkacha Askat wrapper version 1.01 (build 2012-11-21), by Pablo Cingolani Usage: java -jar Askat.jar [options] genotype Options: -b <num> : Number of SNPs used for calculating the kinship matrix. Default: 100000 -d : Debug mode (implies verbose) -d1 : Debug mode. Perform only one sub-block calculation and stop -i <bed> : BED file containing intervals to group SNPs. Default: none -maxMaf : Maximum MAF (minor allelel frequency). Default: 1.0 -noDep : Do not perform dependency check. -h : Show this help and exit. -kin <type> : Kinship estimation type. Options {chr, avg, all, block}. Default: CHROMOSOME -p <num> : Number of parallel processes. Default: 8 -pathBin <dir> : Path to binary programs (e.g. FastLmm). Default: './'. -pathR <dir> : Path to R scripts (ASKAT scripts). Default './r/'. -sb <num> : Number of SNPs used for calculating the ASKAT algorithm. Default: 20 -v : Be verbose.Some comments about the 'intervals' option
Whenever command line option '-i file.bed' is specified, Askat wrapper analysis all variants matching those intervals as a single block.
This means that variants are not subdivided into sub-blocks.
This is effectively like setting the subBlock parameter ('-sb') to infinity. Running a full example The genotype 'karim4k' is available for testing the program: WARNING! By default Askat will try to use ALL the processors available in the computer. This will produce a significant slow down for all other users in the system. The command line fo the example is: $ java -Xmx1g -jar Askat.jar -v karim4k | tee karim4k.outNote that we specified 1G of ram to be used by the program (parameter '-Xmx1G'), but in some other cases, more memory me be required. The output looks like this: 00:00:00.001 ASKAT algorithm by Karim Oualkacha 00:00:00.004 Askat wrapper version 1.0 - epsilon 'almost 1.0' (build 2012-06-29), by Pablo Cingolani 00:00:00.004 Checking dependencies. 00:00:00.005 Checking dependency: Program 'R' 00:00:00.274 OK 00:00:00.275 Checking dependency: Program 'Rscript' 00:00:00.434 OK 00:00:00.434 Checking dependency: Program 'fastlmmc' 00:00:00.535 OK 00:00:00.536 Checking dependency: R library 'GenABEL' 00:00:02.099 Checking dependency: R library 'CompQuadForm' 00:00:02.387 Checking dependency: R library 'nFactors' 00:00:02.982 Checking dependency: R library 'MASS' 00:00:03.286 All dependencies found. 00:00:03.287 Running algorithm. 00:00:03.287 Creating blocks. 00:00:04.423 Creating block 'karim4k.block.1_100.tped'. Number of entries: 4000 00:00:04.511 Running block: karim4k.block.1_100.tped 00:00:04.512 Calculating kinship matrix for block: karim4k.block.1_100 00:00:23.197 Starting block: karim4k.block.1_100 00:00:23.284 Create batches. File 'karim4k.block.1_100.tped' has 4000 lines. Split up to 500 lines per batch. 00:00:23.287 Batch 1. Line 1. Creating batch : karim4k.block.1_100.1.askat 00:00:23.496 Batch 2. Line 501. Creating batch : karim4k.block.1_100.2.askat 00:00:23.673 Batch 3. Line 1001. Creating batch : karim4k.block.1_100.3.askat 00:00:23.845 Batch 4. Line 1501. Creating batch : karim4k.block.1_100.4.askat 00:00:24.019 Batch 5. Line 2001. Creating batch : karim4k.block.1_100.5.askat 00:00:24.190 Batch 6. Line 2501. Creating batch : karim4k.block.1_100.6.askat 00:00:24.365 Batch 7. Line 3001. Creating batch : karim4k.block.1_100.7.askat 00:00:24.538 Batch 8. Line 3501. Creating batch : karim4k.block.1_100.8.askat 00:01:33.747 ASKAT_RESUTS: Block: karim4k.block.1_100.1.askat Sub-Block: 1 - 20 chr:pos: 1:100 - 1:2000 Id: snp_1 - snp_20 p-value: 0.0006166988 Q: 38588.06 ... 00:01:35.749 ASKAT_RESUTS: Block: karim4k.block.1_100.2.askat Sub-Block: 1 - 20 chr:pos: 1:50100 - 1:52000 Id: snp_501 - snp_520 p-value: 0.6740977 Q: 10817.98 ... 00:01:36.750 ASKAT_RESUTS: Block: karim4k.block.1_100.3.askat Sub-Block: 1 - 20 chr:pos: 1:100100 - 1:102000 Id: snp_1001 - snp_1020 p-value: 4.551971e-07 Q: 57434.71 ... 00:01:36.750 ASKAT_RESUTS: Block: karim4k.block.1_100.8.askat Sub-Block: 1 - 20 chr:pos: 1:350100 - 1:352000 Id: snp_3501 - snp_3520 p-value: 0.6530349 Q: 11460.14 ... 00:01:37.751 ASKAT_RESUTS: Block: karim4k.block.1_100.4.askat Sub-Block: 1 - 20 chr:pos: 1:150100 - 1:152000 Id: snp_1501 - snp_1520 p-value: 0.9720287 Q: 5038.441 ... 00:01:37.751 ASKAT_RESUTS: Block: karim4k.block.1_100.6.askat Sub-Block: 1 - 20 chr:pos: 1:250100 - 1:252000 Id: snp_2501 - snp_2520 p-value: 0.1502854 Q: 18876.09 ... 00:01:38.752 ASKAT_RESUTS: Block: karim4k.block.1_100.5.askat Sub-Block: 1 - 20 chr:pos: 1:200100 - 1:202000 Id: snp_2001 - snp_2020 p-value: 1.340421e-06 Q: 54710.54 ... 00:01:38.753 ASKAT_RESUTS: Block: karim4k.block.1_100.7.askat Sub-Block: 1 - 20 chr:pos: 1:300100 - 1:302000 Id: snp_3001 - snp_3020 p-value: 1.385112e-05 Q: 48982.58 ... 00:02:22.786 ASKAT_RESUTS: Block: karim4k.block.1_100.1.askat Sub-Block: 21 - 40 chr:pos: 1:2100 - 1:4000 Id: snp_21 - snp_40 p-value: 0.2958578 Q: 16597.14 ... 00:02:25.788 ASKAT_RESUTS: Block: karim4k.block.1_100.2.askat Sub-Block: 21 - 40 chr:pos: 1:52100 - 1:54000 Id: snp_521 - snp_540 p-value: 0.7232833 Q: 10029.99 ... 00:02:26.789 ASKAT_RESUTS: Block: karim4k.block.1_100.3.askat Sub-Block: 21 - 40 chr:pos: 1:102100 - 1:104000 Id: snp_1021 - snp_1040 p-value: 0.01380279 Q: 28006.57 ... 00:02:27.790 ASKAT_RESUTS: Block: karim4k.block.1_100.8.askat Sub-Block: 21 - 40 chr:pos: 1:352100 - 1:354000 Id: snp_3521 - snp_3540 p-value: 0.001698173 Q: 37035.88 ... 00:02:28.791 ASKAT_RESUTS: Block: karim4k.block.1_100.4.askat Sub-Block: 21 - 40 chr:pos: 1:152100 - 1:154000 Id: snp_1521 - snp_1540 p-value: 0.1902052 Q: 18511.61 ... 00:02:28.791 ASKAT_RESUTS: Block: karim4k.block.1_100.5.askat Sub-Block: 21 - 40 chr:pos: 1:202100 - 1:204000 Id: snp_2021 - snp_2040 p-value: 0.6349479 Q: 10588.31 ... 00:02:28.792 ASKAT_RESUTS: Block: karim4k.block.1_100.6.askat Sub-Block: 21 - 40 chr:pos: 1:252100 - 1:254000 Id: snp_2521 - snp_2540 p-value: 0.8626755 Q: 8638.986 ... 00:02:28.792 ASKAT_RESUTS: Block: karim4k.block.1_100.7.askat Sub-Block: 21 - 40 chr:pos: 1:302100 - 1:304000 Id: snp_3021 - snp_3040 p-value: 0.3494253 Q: 15464.29 ... ... ... ...


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Dark Heart
Beats and all
Home
Beats
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Flute
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E.Z. Lop Les
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Training Semantic-Segmentation Data Set (v0.1.0)
Posted on ven. 05 janvier 2018 in semantic segmentation by Kazuhiro Terao
In this notebook I demonstrate training of a semantic segmentation algorithm for separating track/shower particles at pixel level using practice samples in public data set. To learn about this sample, or what the challenge is about, you might want to start from this post. Here's an outline.
- U-ResNet (semantic segmentation network instance)
- IO configuration
- Training the net
- Training results/summary
- Extra: a bit more about U-ResNet
I usually write a closing remark in my notebook. But in this one, I write it at the beginning because of a higher chance to be read by you :) If you have a question, comment or request regarding this notebook, softare or data used, please feel free to contact us! Your inputs are super valuable. If you find softwares used in this notebook useful (larcv, u-resnet), please give stars! You can make us really happy by explicitly cheering us up :)
OK back to the business. Before anything, let's start with the basic (usual) imports.
import ROOT
from larcv import larcv
larcv.ThreadProcessor
from larcv.dataloader2 import larcv_threadio
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import os,sys,time
# tensorflow/gpu start-up configuration
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=2
import tensorflow as tf
U-ResNet¶
In this tutorial, we use U-ResNet, one type of convolutional neural networks for sematic segmentation task. It is developed/used within MicroBooNE experiment. They showed a beautiful track/shower separation images on both data and simulation in conferences. Below is a result of running a trained U-ResNet on real MicroBooNE detector data (source).
This work is originally implemented using caffe and the details are under preparation to be shared in public. However we made the network design available in our group repository which is what we use in this notebook. We do not describe the details of U-ResNet in this notebook (well, only briefly at the last section). That should be documented in U-ResNet repository, and I might make a separate blog post for that. For now, just think of it as a generic semantic segmentation algorithm that can be trained for track/shower separation today.
Apology for a long preface! Let's pull the code first.
%%bash
if [ ! -d "u-resnet" ]; then
git clone https://github.com/DeepLearnPhysics/u-resnet
fi
Let's add u-resnet python library to system path, and we are ready to import.
# u-resnet directory
sys.path.insert(0,'u-resnet/lib')
import ssnet_trainval as api
t = api.ssnet_trainval()
Software preparation is done. We will use ssnet_trainval
python class. It's an interface to run training and/or inference (i.e. analysis) of U-ResNet. It needs a configuration file to pass parameters, which we cover in the next part.
IO Configuration¶
Here we go over 3 kinds of configurations.
larcv_threadio
python API to read larcv data for traininglarcv_threadio
python API to read larcv data for testing during training (optional)- U-ResNet configuration to run training (U-ResNet specific)
Disregard of the network choice, first two should be useful. The last one is U-ResNet specific, so it won't apply if you have your own network (or your own implementation of U-ResNet).
Configuring input: training
This is larcv_threadio
configuration and you can look at this quick start to get the big picture. The only difference from image classification example is that, this time, label data is also an image! So we use the same BatchFillerImage2D to read data out of larcv file. We will use practice_train_2k.root provided in public data.
io_config = \
"""
MainIO: {
Verbosity: 3
EnableFilter: false
RandomAccess: 2
RandomSeed: 123
InputFiles: ["/stage/drinkingkazu/public_data/segment2d/without_weights/practice_train_2k.root"]
ProcessType: ["BatchFillerImage2D","BatchFillerImage2D"]
ProcessName: ["main_data","main_label"]
NumThreads: 4
NumBatchStorage: 4
ProcessList: {
main_data: {
Verbosity: 3
ImageProducer: "data"
Channels: [0]
}
main_label: {
Verbosity: 3
ImageProducer: "segment"
Channels: [0]
}
}
}
"""
import tempfile
train_io_config = tempfile.NamedTemporaryFile('w')
train_io_config.write(io_config)
train_io_config.flush()
We use a handy python tempfile
to create a named file that only exists during its lifetime (and hence only in this process).
You might have noticed other slight differences. First, the most-outer-block is named "MainIO" as opposed to "ThreadProcessor" back in the image classification example. This is purely conventional: u-resnet
code identifies the training image supplier with a keyword MainIO
. Also you see two BatchFillerImage2D instances are named as main_data
and main_label
. This is to distinguish with other BatchFillerImage2D instances that we will run for fetching test data samples, that comes next!
Configuring input: testing
Wait, what is testing? During training, to watch out for over-fitting, we will continuously monitor the network's performance on the given task on a statistically separate data set. For this, we use practice_test_2k.root provided in public_data.
io_config = \
"""
TestIO: {
Verbosity: 3
EnableFilter: false
RandomAccess: 2
RandomSeed: 123
InputFiles: ["/stage/drinkingkazu/public_data/segment2d/without_weights/practice_test_2k.root"]
ProcessType: ["BatchFillerImage2D","BatchFillerImage2D"]
ProcessName: ["test_data","test_label"]
NumThreads: 2
NumBatchStorage: 2
ProcessList: {
test_data: {
Verbosity: 3
ImageProducer: "data"
Channels: [0]
}
test_label: {
Verbosity: 3
ImageProducer: "segment"
Channels: [0]
}
}
}
"""
import tempfile
test_io_config = tempfile.NamedTemporaryFile('w')
test_io_config.write(io_config)
test_io_config.flush()
The configuration is very similar to training sample, except we will rename the most-outer-block to TestIO
, again u-resnet specific convention. Also, to distinguish from BatchFillerImage2D instances in MainIO
, we name them as test_data
and test_label
. But the rest is pretty much the same.
U-ResNet configuration¶
This is a U-ResNet specific configuration file.
train_config = \
"""
NUM_CLASS 3
BASE_NUM_FILTERS 16
MAIN_INPUT_CONFIG '{:s}'
TEST_INPUT_CONFIG '{:s}'
LOGDIR 'ssnet_train_log'
SAVE_FILE 'ssnet_checkpoint/uresnet'
LOAD_FILE ''
AVOID_LOAD_PARAMS []
ITERATIONS 8000
MINIBATCH_SIZE 20
NUM_MINIBATCHES 1
DEBUG False
TRAIN True
TF_RANDOM_SEED 123
USE_WEIGHTS False
REPORT_STEPS 200
SUMMARY_STEPS 20
CHECKPOINT_STEPS 100
CHECKPOINT_NMAX 20
CHECKPOINT_NHOUR 0.4
KEYWORD_DATA 'main_data'
KEYWORD_LABEL 'main_label'
KEYWORD_WEIGHT ''
KEYWORD_TEST_DATA 'test_data'
KEYWORD_TEST_LABEL 'test_label'
KEYWORD_TEST_WEIGHT ''
"""
import tempfile
ssnet_config = tempfile.NamedTemporaryFile('w')
ssnet_config.write(train_config.format(train_io_config.name, test_io_config.name))
ssnet_config.flush()
Let's go over parameters, in case you want to use/customize u-resnet
code for yourself. Otherwise skip to the training.
NUM_CLASS
... Number of classification categories (3 = background, shower, track).BASE_NUM_FILTERS
... U-ResNet is made of a block of ResNet modules. This parameter defines the number of filters in the initial convolution layer (then it doubles per ResNet module). You can think of this as defining the overall scale factor for the neuron count in the whole network.MAIN_INPUT_CONFIG
... a configuration file forlarcv_threadio
to read data from larcv file (either training or inference)TEST_INPUT_CONFIG
... a configuration file forlarcv_threadio
to read data from larcv file (only for training)LOGDIR
... where tensorboard log is createdSAVE_FILE
... where checkpoints will be savedLOAD_FILE
... if provided, load the network's initial parameter values (weights) from this file (for training or inference)AVOID_LOAD_PARAMS
... a list of parameters which values won't be loaded from the weights provided byLOAD_FILE
.ITERATIONS
... number of iterations to automatically run the training or inferencingMINIBATCH_SIZE
... number of data counts to be fed into the processors (GPU/CPU) at once.NUM_MINIBATCHES
... if >1, gradient is accummulated over number of mibi-batches defined by this parameter.DEBUG
... more verbose output from u-resnet if TrueTF_RANDOM_SEED
... random number seed for tensorflowUSE_WEIGHTS
... if enabled, pixel-wise loss weighting factor will be applied (requiresKEYWORD_WEIGHT
REPORT_STEPS
... reports accuracy nad loss when the iteration count is in modular of this value.SUMMARY_STEPS
... stores tensorboard log when the iteration count is in modular of this value.CHECKPOINT_STEPS
... stores checkpoint when the iteration count is in modular of this value.CHECKPOINT_NMAX
... number of most recent checkpoints to be kept from deletionCHECKPOINT_NHOUR
... checkpoints made every N hour specified in this value will be kept from deletionKEYWORD_DATA
... BatchFillerImage2D's instance name to fetch image data for training.KEYWORD_LABEL
... BatchFillerImage2D's instance name to fetch label data for training.KEYWORD_WEIGHT
... BatchFillerImage2D's instance name to fetch weight data for training.KEYWORD_TEST_DATA
... BatchFillerImage2D's instance name to fetch image data for testing.KEYWORD_TEST_LABEL
... BatchFillerImage2D's instance name to fetch label data for testing.KEYWORD_TEST_WEIGHT
... BatchFillerImage2D's instance name to fetch weight data for testing.
Phew, that was long! In short, we will use a batch size of 20 images, and run for 8000 iterations. Since our input file hods 2000 images, this is about 80 epochs. For a tutorial purpose, this should be enough (I'm speaking from experience, don't look for justification in this text!).
Training the net¶
We first override the default configuration with the custom one defined above. Then initialize the u-resnet APIs.
t.override_config(ssnet_config.name)
t.initialize()
Next, actually all you have to do is to execute t.batch_process()
and it will run 8000 training iterations. Then we can watch how loss and accuracy changes (= get bored and go watch movie instead). But that is a bit boring. So I propose to play with one image from the test_10k.root during training.
ENTRY=2
def get_entry(entry):
# image
chain_image2d = ROOT.TChain("image2d_data_tree")
chain_image2d.AddFile('test_10k.root')
chain_image2d.GetEntry(entry)
cpp_image2d = chain_image2d.image2d_data_branch.as_vector().front()
# label
chain_label2d = ROOT.TChain("image2d_segment_tree")
chain_label2d.AddFile('test_10k.root')
chain_label2d.GetEntry(entry)
cpp_label2d = chain_label2d.image2d_segment_branch.as_vector().front()
return (np.array(larcv.as_ndarray(cpp_image2d)), np.array(larcv.as_ndarray(cpp_label2d)))
image2d, label2d = get_entry(ENTRY)
fig, (ax0,ax1) = plt.subplots(1,2,figsize=(16,8), facecolor='w')
ax0.imshow(image2d, interpolation='none', cmap='jet', vmin=0, vmax=1000, origin='lower')
ax0.set_title('image',fontsize=24)
ax1.imshow(label2d, interpolation='none', cmap='jet', vmin=0, vmax=3.1, origin='lower')
ax1.set_title('label',fontsize=24)
plt.show()
On the left, you see the input data image. On the right is the label, an answer that our algorithm is supposed to generate. This is the same image frequently reffered to when we browsed the file contents, the event with 2 protons and 2 gamma rays. During the network training, we will periodically run the network to analyze this event image.
In order to add this small task, we write a simple training loop below by ourselves instead of using a prepared batch_process
function (of u-resnet). We will analyze the above image at certain steps defined in image_dump_steps
below.
If you want to jump to the results of the training, click here.
input_shape = [1,image2d.size]
image_data = np.array(image2d).reshape(input_shape)
image_dump_steps = np.concatenate((np.arange(0,100,20),
np.arange(100,400,100),
np.arange(400,1000,200),
np.arange(1000,20000,500))).astype(np.int32)
while t.current_iteration() < t.iterations():
t.train_step()
if t.current_iteration() in image_dump_steps:
print('Image dump @ iteration {:d}'.format(t.current_iteration()))
softmax, = t.ana(input_data = image_data)
fig, (ax0,ax1,ax2) = plt.subplots(1,3,figsize=(24,8), facecolor='w')
# image
ax0.imshow(image2d, interpolation='none', cmap='jet', vmin=0, vmax=1000, origin='lower')
ax0.set_title('image',fontsize=24)
ax1.imshow(softmax[0,:,:,0], interpolation='none', cmap='jet', vmin=0, vmax=1.0, origin='lower')
ax1.set_title('background score',fontsize=24)
ax2.imshow(softmax[0].argmax(axis=2), interpolation='none', cmap='jet', vmin=0., vmax=3.1, origin='lower')
ax2.set_title('classification', fontsize=24)
plt.savefig('iteration_{:04d}.png'.format(t.current_iteration()))
plt.show()
plt.close()
Training Results¶
Phew that was long! Lots of dumped image/text. It took nearly 2 hours using an old generation (maxwell) Titan X GPU on my box. If you jumped here from much earlier part of this note book, I made a gif animation of the dumped "test images" for us to see the network's learning progress visually.
A few clarifications¶
- How is "loss" defined?
- In one image, u-resnet calculates the softmax (multinominal regression) loss for 3 category classification. Then the loss is added for all pixels in the image, and averaged over all images in one batch. So it is essentially an average of all-pixel-softmax-sum. If you expected loss value to be smaller (like o(1)), the reason why is because this is a linear sum of many softmax loss (i.e. all pixels in an image).
- What is "acc. all"?
- It is a fraction of pixels that the network classified correctly. The network's prediction is a simple argmax of the softmax score per pixel. Then the accuracy is computed per image by counting number of correctly classified pixels divided by total pixel count in an image. Finally we average the accuracy over the images in the batch. We expect this to be very high because the majority of pixels are backgrounds in LArTPC images. By simply guessing all pixels are backgrounds, you can actually get >99% accuracy!
- What is "acc. nonzero"?
- This is same as "acc. all" except it is only computed for pixels for which the label is not background (hence the name, nonzero accuracy). This is actually what we care the most.
Visualizing logs¶
Below you find the tensorboard visualization of all three parameters during the training. Data points are stored every 20 iterations (see u-resnet configuration section).
A few observations:¶
- Learning the backgrounds
- U-ResNet quickly learned about the most abandoned category, the background pixels.
- The network first learned 0-pixels being background. We can see the "background score" of the sample image is becoming dark-red quickly. This means the network has become very confident on those 0-pixels being backgrounds.
- Going over 1000 iterations, the network learned to draw more and more accurate boundary. An initially fuzzy cyan color "island" of non-zero pixels (i.e. non-background pixels) has finally became non-fuzzy dark blue pixels toward the end.
- Learning particle features
- We can see, clearly, the network is learning more than background vs. non-background. The track (yellow) vs. shower (cyan) classification has become quite accurate toward the final iteration.
- The beginning of gamma ray shower, a short track-like straight trajectory segment with high dE/dX, appears to be particularly a hard thing to classify. We can see both struggle and improvement in the training.
Extra: a bit more about U-ResNet¶
I add a few abstract/qualitative descriptions about U-ResNet. This network design is not from us (MicroBooNE). All we did is to combine two novel network designs: U-Net to attack the semantic segmentation task, and ResNet to improve the internal convolution layers. You can find tensorboard graph for the u-resnet below.
Why U-Net?¶
A typical convolutional neural network design for image classification involves down-sampling of an image to expand the receptive field of a neuron at deeper layers, hoping for it to learn a large (abstract) fetures. A typical semantic segmentation network contains the same design but with an additional portion which performs convolution-transpose to expand the contracted feature map size back to the size of the original input.
A problem in this scheme is the fact that down sampling would lose spatial resolution. In order to overcome this issue, U-Net added an information flow-path between the convolution and convolution-transpose layers by simply concatenating the feature maps. By doing this, neurons at deeper layers can always recover the best possible spatial resolution information.
We used ResNet in the place of a standard convolution operations of the original U-Net for the exact reason ResNet was invented for: it is suited to train deep nets and it learns well. Both ResNet and U-Net follows a similar design pattern by adding more paaths for information to flow, and neurons to learn the perturbations.


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The hottest destination for RV travelers right now is a 200-acre park with natural springs in northern Florida, and it’s becoming Instagram-famous for its unbelievably clear water
by Taylor Borden, Business Insider US- RVshare, the first peer-to-peer RV sharing platform with over 100,000 RVs, released its 2020 trend report on November 12.
- The report found that while families are still renting RVs for a classic, all-American national park vacation, they’re also booking RVs to explore more unknown locales.
- Business Insider spoke to RVshare CEO Jon Gray about why RV rentals are trending and where clients are traveling to.
- Turns out, Ginnie Springs in northern Florida – which is gaining Instagram fame for its clear waters and jungle-like flora – is the RV hotspot of 2019.
- Visit Business Insider’s homepage for more stories.
Plenty of people will fondly remember visiting Yellowstone National Park in an RV with mom and dad as the classic American vacation of yesteryear.
But now, travelers are looking for more transformative, unexpected vacation destinations, even if they’re driving there in a recreational vehicle.
Business Insider spoke to Jon Gray, the CEO of RVshare, the world’s first peer-to-peer RV sharing platform, about current RV trends following the release of the company’s 2020 travel report.
“We’ve been asking our customers ‘where are you headed?’ and obviously Yellowstone and the Grand Canyon are standout destinations, but Ginnie Springs kept coming up,” said Gray, “and I had never heard of it.”
Below, take a look inside the destination RV-ers are loving this year.
Ginnie Springs is a camping site just northwest of Gainesville, Florida with seven natural springs.
The springs’ crystal clear water makes for ideal diving, kayaking, paddleboarding, and tubing — but it’s also becoming a big destination for RV tourists, according to Jon Gray, CEO of RVshare.
Gray told Business Insider that today’s travelers “want something that’s off the beaten track.” He attributes that to the desire to stand out on social media.
Gray thinks people are specifically picking travel destinations based on photo opportunities but doesn’t see that as a negative: “One of the upsides of the Instagramming of travel is that people try new things – they don’t want to do things that are seen as touristy.”
Ginnie Springs does seem to be attracting quite a bit of attention on social media; unsurprisingly, most of the most-liked shots with the #ginniesprings hashtag capitalize on the area’s gorgeous colors.
Ginnie Springs even has its own Instagram account, which has some 55,700 followers.
Gray said Ginnie Springs is such a popular destination for his clients because, beyond its Instagrammable blues, it taps into a desire to reconnect with nature.
“The US has become more urban,” Gray told Business Insider. “People are working in an office all day long and wanting to do something different. The desire to get into nature is there – this idea of disconnecting to reconnect.”
As an added plus, it’s also an accessible destination regardless of the season: the springs stay at 72 degrees year-round.
The private campsite is open all year long.
Camping prices start at $22 per person. Access to the springs starts at $14 per day. Diving access starts at $22 per day.
And the campsites are also RV-friendly: Ginnie Springs’ tent and RV sites are spread out over 200 acres, and the RV sites have power and water hookups.


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THROUGH THE OLIVE TREES (a.k.a. UNDER THE OLIVE TREES) A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 8.0
U.S. Availability: limited release Spring 1995 Running Length: 1:43 MPAA Classification: No MPAA Rating (Nothing offensive)
Starring: Mohamad Ali Keshavarz, Hossein Rezai, Tahereh Ladanian, Zarifeh Shiva Written, Produced, and Directed by Abbas Kiarostami Cinematography: Hossein Djafarian Released by Miramax Films Subtitled
THROUGH THE OLIVE TREES, the latest movie from Iranian director Abbas Kiarostami (and the third in an unofficial trilogy of which the first two chapters were WHERE IS THE FRIEND'S HOME? and AND LIFE GOES ON...), is an art-film lover's art film. Slow almost to the point of stagnation, the movie serves the dual function of a character/relationship study and a firsthand look at the difficulties of making a motion picture in the rural regions of Iran. If you've never had the unique experience of simultaneously being bored and fascinated by a movie, THROUGH THE OLIVE TREES is a likely candidate for the first opportunity.
I didn't fall asleep during the film, but many patrons in the theater understandably did. Nothing much happens during the one-hundred minute running time. There are long periods of inactivity, including a climactic ten-minute shot of one person chasing after another. There's no dialogue during this scene, and the only sound (other than the music) is the wind rustling through the olive trees. Those without a tolerance for slow, deliberate movies will find this a trial beyond belief. It's not much of stretch to call this the cinematic equivalent of a Sominex.
The seemingly-turgid pace is necessary, however. Had things moved faster, numerous subtle and intricate touches would have been lost. The characters are all marvelously realized, and their interaction is so unforced that it draws the viewer in. Kiarostami has used carefully- framed point-of-view camera shots to emphasize the intimacy of his picture. This movie puts the audience right next to the actors.
The story is simple. A film director is making a movie in the Iranian countryside. He recruits local actors, but when shooting begins, a problem comes to light. His male lead, a young man named Hossein (playing himself), is madly in love with his fifteen-year old female co-star, Tahereh (playing herself). Because of Hossein's illiteracy and homelessness, a marriage is impossible, at least from the perspective of Tahereh's grandmother (who is also her guardian). Undaunted, the young man continues to pledge his undying love. For her part, Tahereh is busy studying for her exams and the last thing she wants is to engage in a conversation -- any conversation -- with the ardent suitor she wishes would go away. A finer cinematic portrait of unrequited love has not been seen since MANON OF THE SPRING.
Not only is the courtship (such as it is) of Tahereh and Hossein presented with a rare and unflinching honesty, but we are *not* given an opportunity to find out how things are resolved. The film's ending is open to interpretation and reminds us that movies are only windows into another reality, and it's possible for them to close at the most inopportune times.
Moments of comedy are sprinkled throughout, often relating to the difficulties experienced by the director of the film-within-the-film as he attempts to get uncooperative performers to complete a take. This device of having actors playing the actors who filmed one of Kiarostami's previous films (the on-screen director is actually actor Mohamad Ali Keshavarz playing Kiarostami) creates opportunities for confusion, but mostly it lends an air of verisimilitude to the proceedings that results in a documentary-like style. Kiarostami, a master of camera angles, uses a preponderance of deceptively simple shots to give his production a perspective that's very different from that of most current feature releases.
THROUGH THE OLIVE TREES is an exceptionally well-crafted and thoughtful motion picture. There is no melodrama here -- this is straightforward drama without frills or flourishes. Nevertheless, there are many -- even among the art-film crowd -- who will find this to be a colossal waste of time. American/Iranian cultural differences are an issue, but the real obstacle is the pacing. Those who fall under Kiarostami's spell will find themselves mesmerized. Everyone else will get a nice nap.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
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This above all: to thine ownself be true. And it must follow, as the night the day, Thou canst not then be false to any man.