--- layout: post title: 'Review: The Gated City' date: 2011-09-12 23:12:12.000000000 -05:00 type: post parent_id: '0' published: true password: '' status: publish categories: [] tags: [] meta: _wpas_done_fb: '1' _wpas_done_twitter: '1' author: login: dreamessenceonthemove email<EMAIL_ADDRESS>display_name: darribas first_name: Daniel last_name: Arribas-Bel permalink: "/2011/09/12/review-the-gated-city/" ---
The Gated City by Ryan Avent
My rating: 3 of 5 stars
The main hypothesis is that several barriers to (re)development in the most productive cities of the US have pushed population to cities with lower densities and productivity, hampering economic growth and performance.
The book is written in the spirit of Glaesser's "Triumph of the city", but has notably less arguments and is focused much more on the aspect of development barriers.
I think the biggest advantage over Glaesser's is the (much smaller) time investment that it requires, it can easily be read on a long flight. The author gives a good sense of the main reasons for economic agglomerations in a tone easily understandable for the general public, and offers a good explanation of why cities are relevant for economic performance, and why their role is increasing with new technological advances.
On the minus side, I agree with the other reviewer in that the main message of this book can be summarized in much less than the already short 90 pages it takes. Also, I might be wrong, but I have the feeling the author sometimes mixes up the concept of density with that of size: some of the economic benefits of agglomeration he mentions (e.g. the Marshallian trinity) relate more to "being big" rather than to "being dense". I think there are sufficient arguments explicitly for density so as to have to steal those from density and not mention it, confusing the reader and giving room to critics of dense cities and compact development.
All in all, it's a decent book to inform the general public.


Overview
Package
Class
Use
Tree
Deprecated
Index
Help
PREV LETTER
NEXT LETTER
FRAMES
NO FRAMES
All Classes
A
C
D
E
F
G
H
I
K
L
M
N
O
P
R
S
T
U
W
Y
_
C
call(String, String, String, String, String[])
- Method in class yeti.environments.
YetiExtraVMCommunicator
Method used to make a call on the external platform.
call()
- Method in class yeti.test.
Main
called
- Variable in class yeti.monitoring.
YetiGraphFaultsOverTime
true if this GUI has been initialized.
called
- Variable in class yeti.monitoring.
YetiGraphNumberOfCallsOverTime
true if this GUI has been initialized.
called
- Variable in class yeti.monitoring.
YetiGraphNumberOfFailuresOverTime
true if this GUI has been initialized.
called
- Variable in class yeti.monitoring.
YetiGraphNumberOfVariablesOverTime
true if this GUI has been initialized.
callme()
- Method in class yeti.test.
Impl
callme()
- Method in interface yeti.test.
Inter
checkArguments(YetiCard[])
- Method in class yeti.environments.csharp.
YetiCsharpRoutine
checkArguments(YetiCard[])
- Method in class yeti.environments.java.
YetiJavaRoutine
checkArguments(YetiCard[])
- Method in class yeti.environments.pharo.
YetiPharoRoutine
checkArguments(YetiCard[])
- Method in class yeti.
YetiRoutine
Just to verify that arguments fit the actual routine.
clientSocket
- Static variable in class yeti.environments.csharp.
YetiServerSocket
combineModules(YetiModule[])
- Static method in class yeti.
YetiModule
Method used to to combine two modules into one.
containsKillsOrGens(String, Vector<String>)
- Static method in class yeti.environments.csharp.
YetiCsharpLogProcessor
Checks whether the line contains kills or gen that matter for the variables
containsKillsOrGens(String, Vector<String>)
- Static method in class yeti.environments.java.
YetiJavaLogProcessor
Checks whether the line contains kills or gen that matter for the variables
containsModuleName(String)
- Method in class yeti.
YetiModule
Checks that the modules contain the moduleName
creationRoutines
- Variable in class yeti.
YetiType
Structure that stores all routines that return this type
Overview
Package
Class
Use
Tree
Deprecated
Index
Help
PREV LETTER
NEXT LETTER
FRAMES
NO FRAMES
All Classes
A
C
D
E
F
G
H
I
K
L
M
N
O
P
R
S
T
U
W
Y
_


Add our services to your iOS app.
Add our services to your Android app.
Add our services to your Windows app.
Add our services to your Web app.


The update
tool can be used to install, update, and remove software on the target device.
It mainly operates on "update packs", which are files containing install, update, and/or removal instructions and any files needed to carry out those instructions.
Update packs are created on the development host using the update-pack
tool.
Usage
update [FILE_NAME]
update -r APP_NAME
update –remove APP_NAME
update –help
When run without any command-line arguments, or when run with FILE_NAME
set to "-", takes an update pack file through standard in and forwards it to the Update Daemon for processing.
update
update -
When run with an update pack's file path as its only argument, opens the update pack and feeds it to the Update Daemon for processing.
When run with the –remove
or -r
option, removes the app specified in the APP_NAME
argument.
update /tmp/myUpdatePack.update
- Warning
- Be careful not to accidentally remove system services apps that you might need (e.g., modemService app).
When run with the –help
option, prints help text to standard out and exits.
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.


[gecode-users] propagator PropCond
Martin Mann
qyu at gmx.de
Mon May 15 16:55:56 CEST 2006
Christian Schulte schrieb:
> Yes it still does! What makes you believe otherwise? Did you really try?
:) of course I did...
> Really sure: _DOM means reexecution whenever a view changes in wahtever
> form. However it does not mean that for each domain change the propagator is
> executed once!
ok, I know that, but in my case it is not called anyway! mybe it's
because of my cost or propagate function?
===========================
Gecode::PropCost
GC_Neighbored::cost(void) const {
if (x0.assigned() || x1.assigned()) {
return Gecode::PC_LINEAR_HI;
}
return Gecode::PC_QUADRATIC_HI;
}
Gecode::ExecStatus
GC_Neighbored::propagate(Gecode::Space* home) {
if (x0.assigned()) {
GECODE_ME_CHECK(removeNonNeighbors(home,x1,x0.val()));
}else if (x1.assigned()) {
GECODE_ME_CHECK(removeNonNeighbors(home,x0,x1.val()));
} else {
GECODE_ME_CHECK(removeNonNeighbors(home));
}
return Gecode::ES_SUBSUMED;
}
===========================
but I dont see anything different from the Gecode classes..
I m a bit confused as you can see.
Martin
>
> Hmm?
> Christian
>
> --
> Christian Schulte, http://www.imit.kth.se/~schulte/
>
> -----Original Message-----
> From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
> Of Martin Mann
> Sent: Monday, May 15, 2006 2:51 PM
> To: gecode user list
> Subject: [gecode-users] propagator PropCond
>
>
> Dear all,
>
> I wrote a propagator class derived from BinaryPropagator.
>
> The template is wrapped like that
>
> typedef Gecode::BinaryPropagator<Gecode::Int::IntView,
> Gecode::Int::PC_INT_VAL> BinProp;
>
> Therefor the BinProp derived propagator is only called if one the two
> variables get assigned to a value.
>
> My goal is a propagator that is called everytime the domain changes. So
> I set the PropCond template var from
>
> PC_INT_VAL to PC_INT_DOM.
>
> But now my propagator is not called anymore if the variable gets
> assigned.. Do I ve got to combine them or whats wrong? I m a bit confused!
>
> Thanks for help
>
> Martin
>
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org https://www.gecode.org/mailman/listinfo/gecode-users
>
>
More information about the gecode-users
mailing list


labeled
by Jane Sandberg at 2023-07-29 01:39:08.labeled
by Jane Sandberg at 2023-07-29 01:45:18.labeled
by Jane Sandberg at 2023-07-29 01:45:18.ready_for_review
by Trey Pendragon at 2023-07-28 04:10:13.labeled
by None at 2023-07-28 14:17:06.It's curious that these started all of a sudden on 7/3 but I can't find anything that seems relevant in terms of deploys around that time.
assigned
by None at 2023-07-28 14:54:53.labeled
by Kim Leaman at 2023-07-28 17:48:40.labeled
by Kim Leaman at 2023-07-28 17:48:40.labeled
by Kim Leaman at 2023-07-28 17:48:40.mentioned
by Squirrel at 2023-07-28 17:48:40.subscribed
by Squirrel at 2023-07-28 17:48:41.Background info that's relevant:
We can't make videos public that don't have captions. Videos that are not public (but accessible by the public) need to have a way to request captions and have that actually happen.
Questions that need answered:
Questions that need answered:
Is there a small thing we can do to close this ticket and iterate that bypasses that caption requirement? For instance - is it enough to ingest the video, make it playable, but not render it for non-staff. How does someone request captions?
When requesting captions, what happens?
How fast do captions need to turn around in?
What kind of captions do we support?
How do we model captions in Figgy? Presumably it's a separate file - how do we say that the video "1.mp4" is linked to the caption "1-caption.vtt", if there can be multiple videos in a single ScannedResource
If a video gets uploaded without an associated caption file, what should Figgy do? Prevent marking complete? Prevent marking anything but private?
For video that HAS no audio (silent films), what's best practice for captions? Do we still need a caption that says there's no audio or something, or is it just not an issue?
Where are we going to put all these derivatives? The Isilon? That's where audio derivatives are, but the performance is slow and videos are a lot bigger. Do we need to store and stream these from the cloud?
We need a video WITH captions to test with.
labeled
by Trey Pendragon at 2023-07-28 19:21:15.mentioned
by Trey Pendragon at 2023-07-28 19:55:16.subscribed
by Trey Pendragon at 2023-07-28 19:55:16.head_ref_force_pushed
by None at 2023-07-28 15:02:21.referenced
by Trey Pendragon at 2023-07-28 23:00:37.merged
by Trey Pendragon at 2023-07-28 23:00:37.closed
by Trey Pendragon at 2023-07-28 23:00:37.head_ref_deleted
by Trey Pendragon at 2023-07-28 23:00:38.assigned
by None at 2023-07-28 13:56:44.referenced
by None at 2023-07-28 14:09:20.referenced
by None at 2023-07-28 15:02:21.closed
by Trey Pendragon at 2023-07-28 23:00:38.referenced
by Trey Pendragon at 2023-07-28 23:01:01.merged
by Trey Pendragon at 2023-07-28 23:01:01.closed
by Trey Pendragon at 2023-07-28 23:01:01.head_ref_deleted
by Trey Pendragon at 2023-07-28 23:01:02.assigned
by Trey Pendragon at 2023-07-28 23:05:20.Closing in favor of https://github.com/pulibrary/orangelight/pull/3658
closed
by Max Kadel at 2023-07-28 18:16:03.head_ref_deleted
by Max Kadel at 2023-07-28 18:16:08.review_requested
by Jane Sandberg at 2023-07-28 17:16:22.mentioned
by Max Kadel at 2023-07-28 17:16:22.subscribed
by Max Kadel at 2023-07-28 17:16:22.referenced
by Jane Sandberg at 2023-07-28 18:24:28.merged
by Jane Sandberg at 2023-07-28 18:24:28.closed
by Jane Sandberg at 2023-07-28 18:24:28.head_ref_deleted
by Jane Sandberg at 2023-07-28 18:24:29.coverage: 95.388% (+0.07%) from 95.319% when pulling 9767c6d7d5b6033349afcfb888b902c04d7f3259 on use-new-selenium into d1f605786d5584598b0c698ad54651e28e7e5b6d on main.
head_ref_force_pushed
by Jane Sandberg at 2023-07-28 18:18:22.referenced
by Jane Sandberg at 2023-07-28 18:26:50.merged
by Jane Sandberg at 2023-07-28 18:26:50.closed
by Jane Sandberg at 2023-07-28 18:26:50.head_ref_deleted
by Jane Sandberg at 2023-07-28 18:26:51.coverage: 95.406% (+0.02%) from 95.388% when pulling 0a3ded538ebe6fd89d2d1fa8ac9d85dee39e8b94 on i3192oversizecd_browse into 7f0dc0b4b14c277e494b4697e83cddd67eb6f04c on main.
deployed
by None at 2023-07-28 16:29:45.head_ref_force_pushed
by Max Kadel at 2023-07-28 19:10:27.head_ref_force_pushed
by Max Kadel at 2023-07-28 19:57:12.deployed
by None at 2023-07-28 20:01:42.labeled
by Jane Sandberg at 2023-07-29 01:50:50.PR with documentation about backups: https://github.com/pulibrary/pul-it-handbook/pull/199. It still needs instructions for restoring, but this is a start!
referenced
by Francis Kayiwa at 2023-07-28 13:45:04.merged
by Francis Kayiwa at 2023-07-28 13:45:04.closed
by Francis Kayiwa at 2023-07-28 13:45:04.head_ref_deleted
by Francis Kayiwa at 2023-07-28 13:45:05.successfully run on loadbalancers.
referenced
by Vickie Karasic at 2023-07-28 13:59:25.merged
by Vickie Karasic at 2023-07-28 13:59:25.closed
by Vickie Karasic at 2023-07-28 13:59:26.head_ref_deleted
by Vickie Karasic at 2023-07-28 13:59:26.referenced
by None at 2023-07-28 14:43:07.merged
by None at 2023-07-28 14:43:07.closed
by None at 2023-07-28 14:43:07.head_ref_deleted
by None at 2023-07-28 14:43:09.referenced
by Trey Pendragon at 2023-07-28 17:44:26.merged
by Trey Pendragon at 2023-07-28 17:44:26.closed
by Trey Pendragon at 2023-07-28 17:44:26.head_ref_deleted
by Trey Pendragon at 2023-07-28 17:44:28.FYI, running this on staging leads to this error - https://app.honeybadger.io/projects/67941/faults/98634510
ActiveRecord::StatementInvalid: PG::InsufficientPrivilege: ERROR: permission denied for table schema_migrations
I can work on this. Please assign this to me.
labeled
by Alicia Cozine at 2023-07-28 19:58:25.For activities, I left all of them linked from the main student developers curriculum.
Do we want to move the activities over for all the sections?
(I think it will be confusing to just have one activity in here and none of the others)
good feedback!
referenced
by Hector Correa at 2023-07-28 14:40:48.merged
by Hector Correa at 2023-07-28 14:40:48.closed
by Hector Correa at 2023-07-28 14:40:48.head_ref_deleted
by Hector Correa at 2023-07-28 14:40:49.referenced
by Hector Correa at 2023-07-28 15:44:29.merged
by Hector Correa at 2023-07-28 15:44:29.closed
by Hector Correa at 2023-07-28 15:44:29.head_ref_deleted
by Hector Correa at 2023-07-28 15:44:30.


Ocean [ap79], ocean, in The Outer Sea
Last updated Turn #135
Ocean [ap79], ocean, in The Outer Sea
------------------------------------------------------------------------
1: Sirocco Traveller [ja8y], ship, 10 hulls, 1 rowing port, 30 sails,
1: 4% damaged, arrived from Blackroot Wood [ap78], owner:
1: Toulouse [h1q], Pen, with one peasant, one worker, 32 sailors
2: Sirocco Traveller [ja8y], ship, 10 hulls, 1 rowing port, 30 sails,
2: 4% damaged, departed for Endless Plains [ao79].
Routes leaving Ocean:
North, plain, to Endless Plains [ao79], Imperia, 2 days
East, to Ocean [ap80], 6 days
South, to Ocean [aq79], 6 days
West, forest, to Blackroot Wood [ap78], Imperia, 2 days
Garrison [go6y], garrison, Imperial Empire, with ten peasants


{%extends "base.html" %}
{%block head%}
{% endblock%}
{%block body%}
In Japan, dollops of ice cream are wrapped in sweet and sticky rice dough so you can eat it with your hands. Believe us when we say these cute bites — most often available in green tea, red bean, vanilla, and strawberry — are totally addictive.


Toggle navigation
SQUAWKAR
Register
Login
Image
Text
Image
Graffiti
Photo
Select Photo:
Title
Category
News
Politics
Sport
Entertainment
Witty and Weird
Technology & Environment
Travel
Food & Fashion
Miscellaneous


2 Layout
Application of the grid; defines how Modules are positioned.
2.1 Container
The main container

2.1.1 Container Intro
The first heading of the main container
Make the most of your WebView
Because it will help you.

Make the most of your WebView

Because it will help you.

2.2 Row
A class that resets the row within a container.
.row--ltr
Explicitly set the column direction from left to right
.row--rtl
Explicitly set the column direction from right to left
.row--full
Full width row for breakout sections

2.3 Simple Grid
Very simple grid classes. Most of the site’s grid is built into the appropriate modules for better responsive styling. When you just need a simple 3-up grid, this is the answer.
.grid-1-of-2
Span one of two columns
.grid-1-of-3
Span one of three columns
.grid-2-of-3
Span two of three columns


Fate Of The Furious 8
Click on the images to show a fullscreen version!
© 2018 Made with by Adarsh Khubchandani.


---
layout: post
title: Atoleiros Battle interpretation storyboard
date: '2014-10-19T07:30:00+02:00'
categories:
- Atoleiros Battle
- Storyboards
picture_url: https://film-Storyboards.tumblr.com/post/100402786935/Storyboards-for-the-atoleiros-battle-interpretation
---
Storyboard for the Atoleiros battle interpretation centre, Fronteira, Portugal. More pre production drawings on this project.


| Bidding: | Chooses a random bid between 1 and 4 |
| Playing: | Chooses a random valid card |
| Bidding: | The computer determines a bid by simulating, for each possible bid (0 to 13), the outcome of one hundred random deals of the remaining unseen cards. When running the simulations, each player is assumed to use the 'Standard' playing strategy. It then chooses the highest bid that resulted in an average number of tricks taken above the bid. |
| Playing: | When the player has not yet achieved their bid, then they attempt to take the trick by leading with their highest card of the lead suit. When they have no chance to take the trick, they play their lowest valid card. When the player has already achieved their bid, they attempt to not take the trick by playing their lowest card. If they must take the trick, they use their highest card. |
| Bidding: | The computer determines a bid by simulating, for each possible bid (0 to 13), the outcome of one hundred random deals of the remaining unseen cards. When running the simulations, each player is assumed to use the 'Standard' playing strategy. It then chooses the highest bid that resulted in an average number of tricks taken above the bid. |
| Playing: | The computer determines the probability of taking the trick for each valid play in their hand. Probabilities are determined by simulating 100 possible distributions of the unseen cards and assuming each player will choose their play using the 'Standard' strategy. If the player has already achieved their bid then they will play the least likely card to take the trick. And if they have not yet achieved their bid then they will play the card that is most likely to take the trick. If no card has more than a 50% chance of taking the trick, then the lowest probability card is played. |
| Hint button on all levels: | |
| Sand bagging penalty: | |
| Winning score: |
| Easy | Standard | Pro | Total | |
| Games Played | 0 | |||
| Wins | 0 | |||
| 2nd Places | 0 | |||
| 3rd Places | 0 | |||
| 4th Places | 0 | |||
| Win Percentage |
|
|


Make the experience real. Send to absolute strangers and enlighten their everyday's life.
Smile is the best way to show happiness. Sending a simple smile is like sending joy.
Be prepared to receive love at any time of the day or schedule to your own desire.
We are going to build a new way of sharing. Joy, happiness, smile will be our priority. The team will ensure that our will be the best for your use. Take the first step with us. So, the most important thing, is to learn how to smile and to wait for our amazing application.
ojBruce
A small project to enligthen your everyday's life. So Smile to me, to him, to us. Smile.
Made with love by Olivia Bruce
Credits to Julia Caesar


Xgboost is short for eXtreme Gradient Boosting package.
The purpose of this Vignette is to show you how to use Xgboost to build a model and make predictions.
It is an efficient and scalable implementation of gradient boosting framework by Friedman et al. (2000) and Friedman (2001). Two solvers are included:
It supports various objective functions, including regression, classification and ranking. The package is made to be extendible, so that users are also allowed to define their own objective functions easily.
It has been used to win several Kaggle competitions.
It has several features:
gbm
.matrix
;Matrix::dgCMatrix
;xgb.DMatrix
: its own class (recommended).For weekly updated version (highly recommended), install from Github:
install.packages("drat", repos="https://cran.rstudio.com")
drat:::addRepo("dmlc")
install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
Windows user will need to install Rtools first.
The version 0.4-2 is on CRAN, and you can install it by:
Formerly available versions can be obtained from the CRAN archive
For the purpose of this tutorial we will load XGBoost package.
In this example, we are aiming to predict whether a mushroom can be eaten or not (like in many tutorials, example data are the the same as you will use on in your every day life :-).
Mushroom data is cited from UCI Machine Learning Repository. Bache and Lichman (2013).
We will load the agaricus
datasets embedded with the package and will link them to variables.
The datasets are already split in:
train
: will be used to build the model ;test
: will be used to assess the quality of our model.Why split the dataset in two parts?
In the first part we will build our model. In the second part we will want to test it and assess its quality. Without dividing the dataset we would test the model on the data which the algorithm have already seen.
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
In the real world, it would be up to you to make this division between
train
andtest
data. The way to do it is out of the purpose of this article, howevercaret
package may help.
Each variable is a list
containing two things, label
and data
:
## List of 2
## $ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. ..@ i : int [1:143286] 2 6 8 11 18 20 21 24 28 32 ...
## .. ..@ p : int [1:127] 0<PHONE_NUMBER> 5845 6489 6513 8380 8384 10991 ...
## .. ..@ Dim : int [1:2] 6513 126
## .. ..@ Dimnames:List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:126] "cap-shape=bell" "cap-shape=conical" "cap-shape=convex" "cap-shape=flat" ...
## .. ..@ x : num [1:143286] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..@ factors : list()
## $ label: num [1:6513] 1 0 0 1 0 0 0 1 0 0 ...
label
is the outcome of our dataset meaning it is the binary classification we will try to predict.
Let’s discover the dimensionality of our datasets.
## [1] 6513 126
## [1] 1611 126
This dataset is very small to not make the R package too heavy, however XGBoost is built to manage huge dataset very efficiently.
As seen below, the data
are stored in a dgCMatrix
which is a sparse matrix and label
vector is a numeric
vector ({0,1}
):
## [1] "dgCMatrix"
## [1] "numeric"
This step is the most critical part of the process for the quality of our model.
We are using the train
data. As explained above, both data
and label
are stored in a list
.
In a sparse matrix, cells containing 0
are not stored in memory. Therefore, in a dataset mainly made of 0
, memory size is reduced. It is very usual to have such dataset.
We will train decision tree model using the following parameters:
objective = "binary:logistic"
: we will train a binary classification model ;max_depth = 2
: the trees won’t be deep, because our case is very simple ;nthread = 2
: the number of cpu threads we are going to use;nrounds = 2
: there will be two passes on the data, the second one will enhance the model by further reducing the difference between ground truth and prediction.## [00:22:07] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
## [1] train-logloss:0.233366
## [2] train-logloss:0.136656
More complex the relationship between your features and your
label
is, more passes you need.
Alternatively, you can put your dataset in a dense matrix, i.e. a basic R matrix.
## [00:22:07] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
## [1] train-logloss:0.233366
## [2] train-logloss:0.136656
XGBoost offers a way to group them in a xgb.DMatrix
. You can even add other meta data in it. It will be useful for the most advanced features we will discover later.
dtrain <- xgb.DMatrix(data = train$data, label = train$label)
bstDMatrix <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
## [00:22:07] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
## [1] train-logloss:0.233366
## [2] train-logloss:0.136656
XGBoost has several features to help you to view how the learning progress internally. The purpose is to help you to set the best parameters, which is the key of your model quality.
One of the simplest way to see the training progress is to set the verbose
option (see below for more advanced technics).
# verbose = 0, no message
bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
## [00:22:07] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
# verbose = 1, print evaluation metric
bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 1)
## [00:22:07] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
## [1] train-logloss:0.233366
## [2] train-logloss:0.136656
# verbose = 2, also print information about tree
bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 2)
## [00:22:07] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
## [1] train-logloss:0.233366
## [2] train-logloss:0.136656
The purpose of the model we have built is to classify new data. As explained before, we will use the test
dataset for this step.
## [1] 1611
## [1] 0.28583017 0.92392391 0.28583017 0.28583017 0.05169873 0.92392391
These numbers doesn’t look like binary classification {0,1}
. We need to perform a simple transformation before being able to use these results.
The only thing that XGBoost does is a regression. XGBoost is using label
vector to build its regression model.
How can we use a regression model to perform a binary classification?
If we think about the meaning of a regression applied to our data, the numbers we get are probabilities that a datum will be classified as 1
. Therefore, we will set the rule that if this probability for a specific datum is > 0.5
then the observation is classified as 1
(or 0
otherwise).
## [1] 0 1 0 0 0 1
To measure the model performance, we will compute a simple metric, the average error.
## [1] "test-error= 0.0217256362507759"
Note that the algorithm has not seen the
test
data during the model construction.
Steps explanation:
as.numeric(pred > 0.5)
applies our rule that when the probability (<=> regression <=> prediction) is > 0.5
the observation is classified as 1
and 0
otherwise ;probabilityVectorPreviouslyComputed != test$label
computes the vector of error between true data and computed probabilities ;mean(vectorOfErrors)
computes the average error itself.The most important thing to remember is that to do a classification, you just do a regression to the label
and then apply a threshold.
Multiclass classification works in a similar way.
This metric is 0.02 and is pretty low: our yummly mushroom model works well!
Most of the features below have been implemented to help you to improve your model by offering a better understanding of its content.
For the following advanced features, we need to put data in xgb.DMatrix
as explained above.
Both xgboost
(simple) and xgb.train
(advanced) functions train models.
One of the special feature of xgb.train
is the capacity to follow the progress of the learning after each round. Because of the way boosting works, there is a time when having too many rounds lead to an overfitting. You can see this feature as a cousin of cross-validation method. The following techniques will help you to avoid overfitting or optimizing the learning time in stopping it as soon as possible.
One way to measure progress in learning of a model is to provide to XGBoost a second dataset already classified. Therefore it can learn on the first dataset and test its model on the second one. Some metrics are measured after each round during the learning.
in some way it is similar to what we have done above with the average error. The main difference is that below it was after building the model, and now it is during the construction that we measure errors.
For the purpose of this example, we use watchlist
parameter. It is a list of xgb.DMatrix
, each of them tagged with a name.
watchlist <- list(train=dtrain, test=dtest)
bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic")
## [00:22:07] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
## [1] train-logloss:0.233366 test-logloss:0.226687
## [2] train-logloss:0.136656 test-logloss:0.137875
XGBoost has computed at each round the same average error metric than seen above (we set nrounds
to 2, that is why we have two lines). Obviously, the train-error
number is related to the training dataset (the one the algorithm learns from) and the test-error
number to the test dataset.
Both training and test error related metrics are very similar, and in some way, it makes sense: what we have learned from the training dataset matches the observations from the test dataset.
If with your own dataset you have not such results, you should think about how you divided your dataset in training and test. May be there is something to fix. Again, caret
package may help.
For a better understanding of the learning progression, you may want to have some specific metric or even use multiple evaluation metrics.
## [1] train-error:0.046522 train-logloss:0.233366 test-error:0.042831 test-logloss:0.226687
## [2] train-error:0.022263 train-logloss:0.136656 test-error:0.021726 test-logloss:0.137875
eval_metric
allows us to monitor two new metrics for each round,logloss
anderror
.
Until now, all the learnings we have performed were based on boosting trees. XGBoost implements a second algorithm, based on linear boosting. The only difference with previous command is booster = "gblinear"
parameter (and removing eta
parameter).
## [00:22:07] WARNING: amalgamation/../src/learner.cc:541:
## Parameters: { max_depth } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-error:0.012897 train-logloss:0.192013 test-error:0.016139 test-logloss:0.196127
## [2] train-error:0.002917 train-logloss:0.083676 test-error:0.002483 test-logloss:0.086324
In this specific case, linear boosting gets slightly better performance metrics than decision trees based algorithm.
In simple cases, it will happen because there is nothing better than a linear algorithm to catch a linear link. However, decision trees are much better to catch a non linear link between predictors and outcome. Because there is no silver bullet, we advise you to check both algorithms with your own datasets to have an idea of what to use.
Like saving models, xgb.DMatrix
object (which groups both dataset and outcome) can also be saved using xgb.DMatrix.save
function.
## [1] TRUE
## [00:22:07] 6513x126 matrix with 143286 entries loaded from dtrain.buffer
## [00:22:07] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
## [1] train-logloss:0.233366 test-logloss:0.226687
## [2] train-logloss:0.136656 test-logloss:0.137875
Information can be extracted from xgb.DMatrix
using getinfo
function. Hereafter we will extract label
data.
label = getinfo(dtest, "label")
pred <- predict(bst, dtest)
err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
print(paste("test-error=", err))
## [1] "test-error= 0.0217256362507759"
Feature importance is similar to R gbm package’s relative influence (rel.inf).
importance_matrix <- xgb.importance(model = bst)
print(importance_matrix)
xgb.plot.importance(importance_matrix = importance_matrix)
You can dump the tree you learned using xgb.dump
into a text file.
## [1] "booster[0]"
## [2] "0:[f28<-9.53674316e-07] yes=1,no=2,missing=1,gain=4000.53101,cover=1628.25"
## [3] "1:[f55<-9.53674316e-07] yes=3,no=4,missing=3,gain=1158.21204,cover=924.5"
## [4] "3:leaf=1.71217716,cover=812"
## [5] "4:leaf=-1.70044053,cover=112.5"
## [6] "2:[f108<-9.53674316e-07] yes=5,no=6,missing=5,gain=198.173828,cover=703.75"
## [7] "5:leaf=-1.94070864,cover=690.5"
## [8] "6:leaf=1.85964918,cover=13.25"
## [9] "booster[1]"
## [10] "0:[f59<-9.53674316e-07] yes=1,no=2,missing=1,gain=832.545044,cover=788.852051"
## [11] "1:[f28<-9.53674316e-07] yes=3,no=4,missing=3,gain=569.725098,cover=768.389709"
## [12] "3:leaf=0.78471756,cover=458.936859"
## [13] "4:leaf=-0.968530357,cover=309.45282"
## [14] "2:leaf=-6.23624468,cover=20.462389"
You can plot the trees from your model using `xgb.plot.tree
xgb.plot.tree(model = bst)
if you provide a path to
fname
parameter you can save the trees to your hard drive.
Maybe your dataset is big, and it takes time to train a model on it? May be you are not a big fan of losing time in redoing the same task again and again? In these very rare cases, you will want to save your model and load it when required.
Hopefully for you, XGBoost implements such functions.
## [1] TRUE
xgb.save
function should return TRUE if everything goes well and crashes otherwise.
An interesting test to see how identical our saved model is to the original one would be to compare the two predictions.
# load binary model to R
bst2 <- xgb.load("xgboost.model")
pred2 <- predict(bst2, test$data)
# And now the test
print(paste("sum(abs(pred2-pred))=", sum(abs(pred2-pred))))
## [1] "sum(abs(pred2-pred))= 0"
result is
0
? We are good!
In some very specific cases, like when you want to pilot XGBoost from caret
package, you will want to save the model as a R binary vector. See below how to do it.
## [1] "raw"
# load binary model to R
bst3 <- xgb.load(rawVec)
pred3 <- predict(bst3, test$data)
# pred2 should be identical to pred
print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred))))
## [1] "sum(abs(pred3-pred))= 0"
Again
0
? It seems thatXGBoost
works pretty well!
Bache, K., and M. Lichman. 2013. “UCI Machine Learning Repository.” University of California, Irvine, School of Information; Computer Sciences. http://archive.ics.uci.edu/ml/.
Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics. JSTOR, 1189–1232.
Friedman, Jerome, Trevor Hastie, Robert Tibshirani, and others. 2000. “Additive Logistic Regression: A Statistical View of Boosting (with Discussion and a Rejoinder by the Authors).” The Annals of Statistics 28 (2). Institute of Mathematical Statistics: 337–407.


Throughout this site you'll be able to follow some of the many things I am learning while studying through Dev Academy.
Here is a list of links to my 'Technical' and 'Core' blog posts.
A place where I attempt to understand the ever expanding world of Tech through the greatest cat in the World, Dante!


Grid based design follows a grid design pattern, which our eyes are naturally drawn to.
Grid based design provides for a clean user experience, and so nearly all websites follow this pattern.
Whats all the hype about responsive webpages?
Responsibe webpages are cool because they allow users to have the same experience of the website regardless of
how they are viewing the content - whether it be ipad,laptop, or phone. The webpage responds to the device.
What is semantic structure?
Semantic tags are used in HTML to style the page. For example the tag -h2- indicates that the text is a ...
Heading!
Other tags instruct the page that the text should be bold, italic, or even that an image should be inserted.
Any other points you found interesting and want to reflect on?
Honestly I still feel like I have only scratched the surface with this content. I'm still
a bit confused about the relationship between CSS. HTML. and the DOM, and I find the DOM
hard to read - it's not a great user experience. The thing I found most interesting in this lesson was
noticing how all websites use a grid layout, and seeing how to create negative space.


enum class ReconnectReason
Describes why the session disconnected and is attempting to reconnect
NETWORK_CHANGED
The network has changed
UNKNOWN
Session was disconnected for another reason


A Canadian McDonald's employee is being hailed as the 'Robin Hood of nugz' after admitting he put an extra McNugget in almost every box for 2 and a half years, Business Insider - Business Insider Singapore
by Emma Taylor- A former McDonald’s employee admitted he put an extra chicken nugget in almost every 10-piece box he served for two and a half years.
- Cody Bondarchuk from Edmonton, Alberta revealed his scheme in a tweet which has since gone viral, with social media users dubbing him an “everyday hero.”
- His confession moved others to tweet about their own acts of “everyday heroism” at work.
- Visit Insider’s homepage for more details.
A Canadian man is being called the “Robin Hood of nugz” after revealing he put an extra chicken nugget in 10-piece boxes for years.
Former McDonald’s worker Cody Bondarchuk from Edmonton, Alberta revealed in a tweet that he snuck 11 nuggets into almost every 10-piece box he made in the two and a half years he spent at the fast-food chain.
The viral post has since attracted nearly 900,000 likes and 80,000 retweets since it was posted on Saturday, November 16, with social media users saluting his small act of rebellion.
One person tweeted: “Not all heroes wear capes, just name tags.”
Another dubbed Bondarchuk “The Robin Hood of nugz.”
Responding to one commenter who asked: “Is there a statute of limitations on grand-theft-nuggets?” Bondarchuk said: “I hope so because I calculated it and would owe Ronald about $1,600.”
Bondarchuk replied to another tweet explaining that the McDonald’s franchise where he worked was mostly a drive-thru location, so he cannot remember if any customers expressed thanks after finding an extra nugget.
CTV News Edmonton added that Bondarchuk did not act alone: “It was something that a lot of my coworkers did as well,” he told the broadcaster.
He also told CTV that he and his coworkers were never caught.
The exposure of Bondarchuk’s secret inspired others to come forward with stories of everyday heroism in their own places of work.
As Bondarchuk responded: “EACH AND EVERY ONE OF YOU DESERVES THOSE BONUS NUGGETS.”
Insider has reached out to Bondarchuk for comment.
Read more:
I tried McDonald’s in 10 countries and here are the biggest differences
I tried every single dessert at McDonald’s and ranked them from worst to best
I tried every single breakfast at McDonald’s and ranked them from worst to best


[§279D-10] Conflict between laws. If a conflict between any provision of this chapter and any federal law or regulation relating to metropolitan planning organizations arises, federal law or regulation shall govern. [L 2015, c 132, pt of §1]


Data Fields | |
| ExprList * | pEList |
| u8 | op |
| char | affinity |
| u16 | selFlags |
| SrcList * | pSrc |
| Expr * | pWhere |
| ExprList * | pGroupBy |
| Expr * | pHaving |
| ExprList * | pOrderBy |
| Select * | pPrior |
| Select * | pNext |
| Select * | pRightmost |
| Expr * | pLimit |
| Expr * | pOffset |
| int | iLimit |
| int | iOffset |
| int | addrOpenEphm [3] |
| ExprList * | pEList |
| SrcList * | pSrc |
| Expr * | pWhere |
| ExprList * | pGroupBy |
| Expr * | pHaving |
| ExprList * | pOrderBy |
| Select * | pPrior |
| Select * | pNext |
| Select * | pRightmost |
| Expr * | pLimit |
| Expr * | pOffset |
| char Select::affinity |
| int Select::iLimit |
| int Select::iOffset |


Audio Context
Frequency (
1000
):
Duration (
1000
):
Volume (
20
):
Oscillator type:
sine
square
sawtooth
triangle


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/116395maths/07507ConjecturethatLeanProverisbivalent.html ---
Please send me axioms and rules of inference for Lean power at info@cec-services dot com for evaluation using our Meth8/VL4 modal model logic checker. Thank you.
{% endraw %}


<% include include/common-header.html %>
{{section.ad_name}}
{{ book.title }}
{{ book.summary }}
{{ tag }}
{{ book.authors }}


Welcome to ClinicalKnowledgeGraph’s documentation!¶
This web page contains the documentation for the Python code using Sphinx.
First steps¶
Are you new to the Clinical Knowledge Graph? Learn about how to use it and all the possibilities.
Getting started: With Requirements | With Neo4j | With Clinical Knowledge Graph | With Windows | With Docker
Getting started¶
Connecting to the CKG: Connect to DB
Create a new user in the graph database: Create new user
Create a project in the database: Project Creation
Upload experimental data: Data Upload
Define data analysis settings: Configuration
Access the analysis report: Access report
Report notification: Notifications
CKG Builder¶
Ontology sources and parsers: Ontologies
Biomedical databases and resources: Databases
Parsing experimental data: Experiments
Building the graph database from one module: Builder
Advanced featues¶
CKG Statistics: Imports stats | Graph database stats
Jupyter notebooks: Notebooks
Retrieving data from the CKG: DB Querying
Data Analysis: Analysis
Visualization: Plots
R interface: R wrapper


{# emails/respondent_consent.html #}{% extends "emails/email_base.html" %} {% block content %}
Dear {{ respondent.name }},
Thank you for your agreeing to participate in the Values & Value project.
{% if respondent.pseudonym %}You have chosen to use the pseudonym: respondent.pseudonym.
{% endif %} {% endblock content %}


Chrome for Android now has reverse image search powered by Google Lens – here's how to enable it
by Mark Wycislik-WilsonChrome for Android now has reverse image search powered by Google Lens – here's how to enable it
Perform advanced searches using images, not words
(Image credit: Shutterstock)
Being able to search the web with an image is not exactly a new thing. On the desktop you can drag and drop and image into the Google search engine, and in the Android version of Chrome you can long-press an image to access the option to 'Search Google for this image'.
But searching for an image in this way is a little limited as it involves simply searching for copies of the same image. Now Google has brought the power of Google Lens to Chrome for Android, making it possible to perform much more advanced image-based searches.
- The best browsers: your complete guide
- How to enable dark mode for Google Chrome
- The best free apps for Android
So, what's the big deal? Using Google Lens means that the image you are searching with is analyzed so you can search for similar images, not just exact copies. This is extremely handy if you're browsing the web and spot something for your home that you quite like the look of.
But maybe the item you've stumbled across is a little expensive; using a Google Lens image-based search you can easily hunt down something similar. It's also a great way to get new ideas.
What's great about Google Lens searches is that you don't have to use an entire image as the basis for a search. It is possible to crop in on a particular element that you're interested in to narrow the results a bit. And, as Android Police notes, there's no need to have the Google Lens app installed.
Enable Google Lens searching
Google Lens search for Android isn't enabled by default. If you like the idea of searching the web with images, here's how to enable the feature:
- Pay a visit to chrome://flags
- Search for #context-menu-search-with-google-lens
- Tap the setting and select 'Enabled'
- Restart Chrome by tapping 'Relaunch'
Now when you tap on an image, the context menu that appears will include a new option – 'Search with Google Lens'. While this replaces the usual 'Search Google for this image' option, you can still perform this type of search by tapping the 'Search by image' or 'Go back to classic mode' option that appears at the top of results.


This is not a report with the results of the pipeline, but the log of the events occurred to generate it.
This reports contain technical details on the execution of Dadaist2.
| Parameter | Value |
|---|---|
| Input | /Users/telatin/git/dadaist2/data/MiSeq_SOP |
| Output | /Users/telatin/git/dadaist2/test/sop-old-vanilla |
| Timestamp | 20210625 11:39:39 |
| Execution time | 10m 54s |
Executed at 2021-06-25 11:39:59 for 1.00s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D0_S188_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D0_S188_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D0_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D0_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D0.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110
Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 7793 total bases: 1955914 Q20 bases: 1861780(95.1872%) Q30 bases: 1810170(92.5485%) Read2 before filtering: total reads: 7793 total bases: 1953534 Q20 bases: 1726416(88.374%) Q30 bases: 1614066(82.6229%) Read1 after filtering: total reads: 7793 total bases: 1799970 Q20 bases: 1708262(94.905%) Q30 bases: 1657320(92.0749%) Read2 aftering filtering: total reads: 7793 total bases: 1797590 Q20 bases: 1575545(87.6476%) Q30 bases: 1464970(81.4963%) Filtering result: reads passed filter: 15586 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 13896 bases trimmed due to adapters: 168 Duplication rate: 26.3736% Insert size peak (evaluated by paired-end reads): 253 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D0.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D0_S188_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D0_S188_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D0_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D0_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D0.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 1 secondsShell command fastp
Executed at 2021-06-25 11:40:01 for 1.61s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D1_S189_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D1_S189_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D1_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D1_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D1.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 5869 total bases: 1473024 Q20 bases: 1391828(94.4878%) Q30 bases: 1348464(91.5439%) Read2 before filtering: total reads: 5869 total bases: 1471648 Q20 bases: 1308416(88.9082%) Q30 bases: 1229967(83.5775%) Read1 after filtering: total reads: 5869 total bases: 1355643 Q20 bases: 1276325(94.149%) Q30 bases: 1233521(90.9916%) Read2 aftering filtering: total reads: 5869 total bases: 1354268 Q20 bases: 1194849(88.2284%) Q30 bases: 1117746(82.5351%) Filtering result: reads passed filter: 11738 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 10762 bases trimmed due to adapters: 1 Duplication rate: 17.3333% Insert size peak (evaluated by paired-end reads): 253 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D1.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D1_S189_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D1_S189_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D1_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D1_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D1.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 1 secondsShell command fastp
Executed at 2021-06-25 11:40:03 for 1.47s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D141_S207_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D141_S207_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D141_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D141_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D141.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 5958 total bases: 1495347 Q20 bases: 1425359(95.3196%) Q30 bases: 1388281(92.8401%) Read2 before filtering: total reads: 5958 total bases: 1493188 Q20 bases: 1310228(87.747%) Q30 bases: 1217917(81.5649%) Read1 after filtering: total reads: 5958 total bases: 1376183 Q20 bases: 1307756(95.0278%) Q30 bases: 1271114(92.3652%) Read2 aftering filtering: total reads: 5958 total bases: 1374024 Q20 bases: 1194475(86.9326%) Q30 bases: 1103485(80.3105%) Filtering result: reads passed filter: 11916 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 10400 bases trimmed due to adapters: 8 Duplication rate: 10.7143% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D141.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D141_S207_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D141_S207_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D141_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D141_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D141.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 1 secondsShell command fastp
Executed at 2021-06-25 11:40:05 for 1.60s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D142_S208_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D142_S208_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D142_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D142_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D142.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 3183 total bases: 798912 Q20 bases: 764105(95.6432%) Q30 bases: 745531(93.3183%) Read2 before filtering: total reads: 3183 total bases: 797648 Q20 bases: 691812(86.7315%) Q30 bases: 638520(80.0503%) Read1 after filtering: total reads: 3183 total bases: 735252 Q20 bases: 701177(95.3655%) Q30 bases: 682849(92.8728%) Read2 aftering filtering: total reads: 3183 total bases: 733988 Q20 bases: 630231(85.8639%) Q30 bases: 577655(78.7009%) Filtering result: reads passed filter: 6366 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 5428 bases trimmed due to adapters: 0 Duplication rate: 24% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D142.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D142_S208_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D142_S208_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D142_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D142_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D142.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 1 secondsShell command fastp
Executed at 2021-06-25 11:40:07 for 1.14s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D143_S209_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D143_S209_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D143_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D143_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D143.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 3178 total bases: 797624 Q20 bases: 761664(95.4916%) Q30 bases: 742072(93.0353%) Read2 before filtering: total reads: 3178 total bases: 796544 Q20 bases: 695494(87.3139%) Q30 bases: 643987(80.8476%) Read1 after filtering: total reads: 3178 total bases: 734064 Q20 bases: 699020(95.226%) Q30 bases: 679670(92.59%) Read2 aftering filtering: total reads: 3178 total bases: 732984 Q20 bases: 633977(86.4926%) Q30 bases: 583279(79.576%) Filtering result: reads passed filter: 6356 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 5474 bases trimmed due to adapters: 0 Duplication rate: 20.7547% Insert size peak (evaluated by paired-end reads): 0 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D143.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D143_S209_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D143_S209_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D143_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D143_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D143.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 1 secondsShell command fastp
Executed at 2021-06-25 11:40:08 for 1.82s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D144_S210_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D144_S210_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D144_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D144_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D144.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 4827 total bases: 1211542 Q20 bases: 1145768(94.5711%) Q30 bases: 1111861(91.7724%) Read2 before filtering: total reads: 4827 total bases: 1209516 Q20 bases: 1028996(85.075%) Q30 bases: 941297(77.8243%) Read1 after filtering: total reads: 4827 total bases: 1114994 Q20 bases: 1050668(94.2308%) Q30 bases: 1017161(91.2257%) Read2 aftering filtering: total reads: 4827 total bases: 1112967 Q20 bases: 936318(84.1281%) Q30 bases: 849968(76.3696%) Filtering result: reads passed filter: 9654 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 7934 bases trimmed due to adapters: 17 Duplication rate: 21.6667% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D144.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D144_S210_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D144_S210_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D144_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D144_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D144.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 2 secondsShell command fastp
Executed at 2021-06-25 11:40:10 for 1.62s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D145_S211_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D145_S211_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D145_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D145_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D145.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 7377 total bases: 1851563 Q20 bases: 1765537(95.3539%) Q30 bases: 1720406(92.9164%) Read2 before filtering: total reads: 7377 total bases: 1848416 Q20 bases: 1573021(85.101%) Q30 bases: 1437093(77.7473%) Read1 after filtering: total reads: 7377 total bases: 1704022 Q20 bases: 1619936(95.0654%) Q30 bases: 1575344(92.4486%) Read2 aftering filtering: total reads: 7377 total bases: 1700876 Q20 bases: 1430515(84.1046%) Q30 bases: 1296320(76.2148%) Filtering result: reads passed filter: 14754 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 12144 bases trimmed due to adapters: 1 Duplication rate: 31.3953% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D145.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D145_S211_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D145_S211_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D145_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D145_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D145.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 2 secondsShell command fastp
Executed at 2021-06-25 11:40:12 for 1.41s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D146_S212_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D146_S212_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D146_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D146_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D146.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 5021 total bases: 1260202 Q20 bases: 1195680(94.88%) Q30 bases: 1161117(92.1374%) Read2 before filtering: total reads: 5021 total bases: 1258570 Q20 bases: 1107912(88.0294%) Q30 bases: 1032060(82.0026%) Read1 after filtering: total reads: 5020 total bases: 1159551 Q20 bases: 1096672(94.5773%) Q30 bases: 1062718(91.6491%) Read2 aftering filtering: total reads: 5020 total bases: 1157919 Q20 bases: 1010783(87.2931%) Q30 bases: 936242(80.8556%) Filtering result: reads passed filter: 10040 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 2 reads with adapter trimmed: 8838 bases trimmed due to adapters: 288 Duplication rate: 14.5455% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D146.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D146_S212_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D146_S212_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D146_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D146_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D146.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 2 secondsShell command fastp
Executed at 2021-06-25 11:40:14 for 11.42s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D147_S213_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D147_S213_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D147_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D147_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D147.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 17070 total bases: 4284410 Q20 bases: 4095597(95.593%) Q30 bases: 3993384(93.2073%) Read2 before filtering: total reads: 17070 total bases: 4277194 Q20 bases: 3665315(85.6944%) Q30 bases: 3362295(78.6098%) Read1 after filtering: total reads: 17070 total bases: 3942976 Q20 bases: 3759218(95.3396%) Q30 bases: 3658702(92.7904%) Read2 aftering filtering: total reads: 17070 total bases: 3935759 Q20 bases: 3335420(84.7466%) Q30 bases: 3036422(77.1496%) Filtering result: reads passed filter: 34140 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 27206 bases trimmed due to adapters: 69 Duplication rate: 12.3711% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D147.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D147_S213_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D147_S213_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D147_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D147_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D147.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 12 secondsShell command fastp
Executed at 2021-06-25 11:40:26 for 6.07s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D148_S214_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D148_S214_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D148_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D148_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D148.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 12405 total bases: 3113525 Q20 bases: 2970870(95.4182%) Q30 bases: 2893974(92.9485%) Read2 before filtering: total reads: 12405 total bases: 3108849 Q20 bases: 2712242(87.2426%) Q30 bases: 2510990(80.7691%) Read1 after filtering: total reads: 12405 total bases: 2865381 Q20 bases: 2726448(95.1513%) Q30 bases: 2650805(92.5114%) Read2 aftering filtering: total reads: 12405 total bases: 2860710 Q20 bases: 2471675(86.4008%) Q30 bases: 2273009(79.4561%) Filtering result: reads passed filter: 24810 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 21052 bases trimmed due to adapters: 83 Duplication rate: 9.21053% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D148.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D148_S214_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D148_S214_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D148_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D148_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D148.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 6 secondsShell command fastp
Executed at 2021-06-25 11:40:32 for 6.41s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D149_S215_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D149_S215_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D149_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D149_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D149.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 13083 total bases: 3283570 Q20 bases: 3126205(95.2075%) Q30 bases: 3041024(92.6133%) Read2 before filtering: total reads: 13083 total bases: 3278781 Q20 bases: 2866585(87.4284%) Q30 bases: 2658465(81.0809%) Read1 after filtering: total reads: 13083 total bases: 3021902 Q20 bases: 2868586(94.9265%) Q30 bases: 2784863(92.156%) Read2 aftering filtering: total reads: 13083 total bases: 3017112 Q20 bases: 2613017(86.6066%) Q30 bases: 2407686(79.801%) Filtering result: reads passed filter: 26166 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 22612 bases trimmed due to adapters: 17 Duplication rate: 13.5417% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D149.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D149_S215_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D149_S215_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D149_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D149_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D149.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 7 secondsShell command fastp
Executed at 2021-06-25 11:40:39 for 0.80s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D150_S216_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D150_S216_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D150_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D150_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D150.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 5509 total bases: 1382628 Q20 bases: 1318987(95.3971%) Q30 bases: 1283968(92.8643%) Read2 before filtering: total reads: 5509 total bases: 1380722 Q20 bases: 1205351(87.2986%) Q30 bases: 1117365(80.9261%) Read1 after filtering: total reads: 5509 total bases: 1272448 Q20 bases: 1210681(95.1458%) Q30 bases: 1176308(92.4445%) Read2 aftering filtering: total reads: 5509 total bases: 1270542 Q20 bases: 1098380(86.4497%) Q30 bases: 1011318(79.5974%) Filtering result: reads passed filter: 11018 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 9338 bases trimmed due to adapters: 0 Duplication rate: 15.7895% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D150.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D150_S216_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D150_S216_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D150_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D150_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D150.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 1 secondsShell command fastp
Executed at 2021-06-25 11:40:40 for 8.43s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D2_S190_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D2_S190_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D2_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D2_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D2.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 19620 total bases: 4924442 Q20 bases: 4711317(95.6721%) Q30 bases: 4596837(93.3474%) Read2 before filtering: total reads: 19620 total bases: 4917683 Q20 bases: 4310303(87.6491%) Q30 bases: 4003016(81.4004%) Read1 after filtering: total reads: 19620 total bases: 4532039 Q20 bases: 4324602(95.4229%) Q30 bases: 4211857(92.9351%) Read2 aftering filtering: total reads: 19620 total bases: 4525282 Q20 bases: 3929278(86.8295%) Q30 bases: 3626015(80.1279%) Filtering result: reads passed filter: 39240 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 34966 bases trimmed due to adapters: 4 Duplication rate: 27.7027% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D2.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D2_S190_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D2_S190_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D2_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D2_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D2.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 8 secondsShell command fastp
Executed at 2021-06-25 11:40:49 for 0.78s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D3_S191_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D3_S191_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D3_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D3_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D3.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 6758 total bases: 1696205 Q20 bases: 1619690(95.489%) Q30 bases: 1579885(93.1423%) Read2 before filtering: total reads: 6758 total bases: 1693513 Q20 bases: 1474137(87.0461%) Q30 bases: 1363890(80.5361%) Read1 after filtering: total reads: 6758 total bases: 1561045 Q20 bases: 1486387(95.2174%) Q30 bases: 1447131(92.7027%) Read2 aftering filtering: total reads: 6758 total bases: 1558353 Q20 bases: 1342978(86.1793%) Q30 bases: 1234150(79.1958%) Filtering result: reads passed filter: 13516 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 11696 bases trimmed due to adapters: 0 Duplication rate: 39.3939% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D3.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D3_S191_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D3_S191_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D3_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D3_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D3.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 0 secondsShell command fastp
Executed at 2021-06-25 11:40:50 for 0.65s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D5_S193_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D5_S193_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D5_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D5_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D5.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 4448 total bases: 1116401 Q20 bases: 1061302(95.0646%) Q30 bases: 1031817(92.4235%) Read2 before filtering: total reads: 4448 total bases: 1115006 Q20 bases: 973168(87.2792%) Q30 bases: 904231(81.0965%) Read1 after filtering: total reads: 4448 total bases: 1027441 Q20 bases: 973610(94.7607%) Q30 bases: 944510(91.9284%) Read2 aftering filtering: total reads: 4448 total bases: 1026046 Q20 bases: 886769(86.4259%) Q30 bases: 818680(79.7898%) Filtering result: reads passed filter: 8896 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 7716 bases trimmed due to adapters: 0 Duplication rate: 20% Insert size peak (evaluated by paired-end reads): 253 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D5.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D5_S193_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D5_S193_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D5_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D5_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D5.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 0 secondsShell command fastp
Executed at 2021-06-25 11:40:51 for 0.75s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D6_S194_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D6_S194_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D6_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D6_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D6.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 7989 total bases: 2005171 Q20 bases: 1916532(95.5795%) Q30 bases: 1869292(93.2236%) Read2 before filtering: total reads: 7989 total bases: 2002558 Q20 bases: 1760851(87.9301%) Q30 bases: 1639465(81.8685%) Read1 after filtering: total reads: 7989 total bases: 1845388 Q20 bases: 1759084(95.3233%) Q30 bases: 1712491(92.7984%) Read2 aftering filtering: total reads: 7989 total bases: 1842777 Q20 bases: 1605757(87.1379%) Q30 bases: 1486156(80.6476%) Filtering result: reads passed filter: 15978 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 14336 bases trimmed due to adapters: 4 Duplication rate: 64.881% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D6.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D6_S194_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D6_S194_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D6_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D6_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D6.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 0 secondsShell command fastp
Executed at 2021-06-25 11:40:52 for 0.69s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D7_S195_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D7_S195_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D7_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D7_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D7.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 5129 total bases: 1287342 Q20 bases: 1234208(95.8726%) Q30 bases: 1206272(93.7025%) Read2 before filtering: total reads: 5129 total bases: 1285368 Q20 bases: 1124901(87.5159%) Q30 bases: 1043858(81.2108%) Read1 after filtering: total reads: 5129 total bases: 1184762 Q20 bases: 1133099(95.6394%) Q30 bases: 1105562(93.3151%) Read2 aftering filtering: total reads: 5129 total bases: 1182788 Q20 bases: 1025269(86.6824%) Q30 bases: 945379(79.928%) Filtering result: reads passed filter: 10258 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 9072 bases trimmed due to adapters: 0 Duplication rate: 26.9231% Insert size peak (evaluated by paired-end reads): 252 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D7.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D7_S195_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D7_S195_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D7_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D7_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D7.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 0 secondsShell command fastp
Executed at 2021-06-25 11:40:53 for 0.68s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D8_S196_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D8_S196_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D8_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D8_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D8.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 5294 total bases: 1328727 Q20 bases: 1263542(95.0942%) Q30 bases: 1229075(92.5002%) Read2 before filtering: total reads: 5294 total bases: 1327298 Q20 bases: 1182193(89.0676%) Q30 bases: 1110909(83.697%) Read1 after filtering: total reads: 5294 total bases: 1222846 Q20 bases: 1159215(94.7965%) Q30 bases: 1125223(92.0167%) Read2 aftering filtering: total reads: 5294 total bases: 1221417 Q20 bases: 1079533(88.3837%) Q30 bases: 1009406(82.6422%) Filtering result: reads passed filter: 10588 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 9738 bases trimmed due to adapters: 2 Duplication rate: 25.8824% Insert size peak (evaluated by paired-end reads): 253 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D8.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D8_S196_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D8_S196_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D8_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D8_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D8.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 0 secondsShell command fastp
Executed at 2021-06-25 11:40:53 for 0.74s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D9_S197_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D9_S197_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/F3D9_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/F3D9_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D9.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 7070 total bases: 1774487 Q20 bases: 1694396(95.4865%) Q30 bases: 1651429(93.0652%) Read2 before filtering: total reads: 7070 total bases: 1772522 Q20 bases: 1574025(88.8014%) Q30 bases: 1476473(83.2979%) Read1 after filtering: total reads: 7070 total bases: 1633087 Q20 bases: 1555002(95.2186%) Q30 bases: 1512624(92.6236%) Read2 aftering filtering: total reads: 7070 total bases: 1631122 Q20 bases: 1437046(88.1017%) Q30 bases: 1341227(82.2273%) Filtering result: reads passed filter: 14140 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 12980 bases trimmed due to adapters: 0 Duplication rate: 15.6627% Insert size peak (evaluated by paired-end reads): 253 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D9.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D9_S197_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/F3D9_S197_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/F3D9_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/F3D9_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/F3D9.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 1 secondsShell command fastp
Executed at 2021-06-25 11:40:54 for 0.67s and returned 0
'Command
fastp
-w 6
-i "/Users/telatin/git/dadaist2/data/MiSeq_SOP/Mock_S280_L001_R1_001.fastq"
-I "/Users/telatin/git/dadaist2/data/MiSeq_SOP/Mock_S280_L001_R2_001.fastq"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/for/Mock_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj"/rev/Mock_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/Mock.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 4779 total bases: 1199027 Q20 bases: 1129105(94.1684%) Q30 bases: 1093050(91.1614%) Read2 before filtering: total reads: 4779 total bases: 1197710 Q20 bases: 1085627(90.6419%) Q30 bases: 1035678(86.4715%) Read1 after filtering: total reads: 4779 total bases: 1103445 Q20 bases: 1034947(93.7924%) Q30 bases: 999505(90.5804%) Read2 aftering filtering: total reads: 4779 total bases: 1102128 Q20 bases: 993144(90.1115%) Q30 bases: 944345(85.6838%) Filtering result: reads passed filter: 9558 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 0 reads with adapter trimmed: 9244 bases trimmed due to adapters: 4 Duplication rate: 18.1818% Insert size peak (evaluated by paired-end reads): 0 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/Mock.json HTML report: /dev/null fastp -w 6 -i /Users/telatin/git/dadaist2/data/MiSeq_SOP/Mock_S280_L001_R1_001.fastq -I /Users/telatin/git/dadaist2/data/MiSeq_SOP/Mock_S280_L001_R2_001.fastq -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/Mock_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/Mock_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/Mock.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 1 seconds
Executed at 2021-06-25 11:40:55 for 543.80s and returned 0
'Command
Rscript
--vanilla /Users/telatin/miniconda3/envs/dadaist/bin/D2-dada.R /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/dada2/dada2.tsv /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/dada2/stats.tsv /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/for/filtered /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/rev/filtered 231 215 0 0 1 1.5 10 consensus 1 6 10000 /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj do_plots refs/rdp_train_set_16.fa.gz no 0 0
Standard output
R version 4.0.5 (2021-03-31) #DADA2: 1.18.0 / Rcpp: 1.0.6 / RcppParallel: 5.1.4 2021-06-25 11:41:07 [1] Filtering reads .................... 2021-06-25 11:47:31 [2] Learning Error Rates 2872947 total bases in 12437 reads from 3 samples will be used for learning the error rates. 2673955 total bases in 12437 reads from 3 samples will be used for learning the error rates. 2021-06-25 11:48:24 [3] Denoise remaining samples * Sample by sample 2021-06-25 11:49:33 [4] Remove chimeras (method = consensus) 2021-06-25 11:49:33 [5.1] Taxonomy 2021-06-25 11:49:59 [6] Write output * /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/dada2/dada2.tsv * /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_mMGzcj/taxonomy.tsvStandard error
Loading required package: Rcpp There were 42 warnings (use warnings() to see them) Duplicate sequences in merged output. Duplicate sequences in merged output. Duplicate sequences in merged output. Duplicate sequences in merged output. Duplicate sequences detected and merged. Duplicate sequences detected and merged. Duplicate sequences detected and merged. Duplicate sequences detected and merged.
Not available.
Executed at 2021-06-25 11:39:41 for 0.01s and returned 0
'Command
Rscript
--version 2>&1
Standard output
R scripting front-end version 4.0.5 (2021-03-31)Standard error
Shell command /Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-assigntax
Executed at 2021-06-25 11:39:41 for 0.21s and returned 0
'Command
/Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-assigntax --versionStandard output
dadaist2-assigntax 1.1.3Standard error
[35;1m DADAIST2 Assign Taxonomy[0mShell command /Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-assigntax
Executed at 2021-06-25 11:39:41 for 0.21s and returned 0
'Command
/Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-assigntax --versionStandard output
dadaist2-assigntax 1.1.3Standard error
[35;1m DADAIST2 Assign Taxonomy[0mShell command clustalo
Executed at 2021-06-25 11:39:41 for 0.02s and returned 0
'Command
clustalo
--version 2>&1Standard output
1.2.4Standard error
Shell command echo
Executed at 2021-06-25 11:39:41 for 17.63s and returned 0
'Command
echo "library(dada2)" | R
--no-save > /dev/nullStandard output
Standard error
Loading required package: RcppShell command /Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-metadata
Executed at 2021-06-25 11:39:59 for 0.15s and returned 0
'Command
/Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-metadata --versionStandard output
dadaist2-metadata 1.0.0Standard error
Shell command /Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-exporter
Executed at 2021-06-25 11:39:59 for 0.11s and returned 0
'Command
/Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-exporter --versionStandard output
dadaist2-exporter 1.4.0Standard error
Shell command fastp
Executed at 2021-06-25 11:39:59 for 0.03s and returned 0
'Command
fastp
--version 2>&1Standard output
fastp 0.20.1Standard error
Shell command fasttree
Executed at 2021-06-25 11:39:59 for 0.02s and returned 0
'Command
fasttree 2>&1 | head
-n 1 | cut
-c 11-Standard output
FastTree version 2.1.10 Double precision (No SSE3):Standard error
Shell command fu-primers
Executed at 2021-06-25 11:39:59 for 0.02s and returned 0
'Command
fu-primers
--version 2>&1Standard output
fu-primers 1.3.3Standard error


Heading 1 =========== Heading 2 ------------
or
# Heading 1 ## Heading 2 ### Heading 3
> This is a blockquote. > > This is the first level. > > This is nested blockquote. > Back to the first level.
+ Red + Green + Blue
or
- Red - Green - Blue
or
* Red * Green * Blue
1. Kyoko 2. Yui 3. Chinatsu
This is a normal paragraph: This is a code block.
This is [an example](http://example.com/ "Title") inline link. [This link](http://example.net/) has no title attribute.
*single asterisks(em)* _single underscores(em)_ **double asterisks(strong)** __double underscores(strong)__
![Alt text](/path/to/img.jpg) ![Alt text](/path/to/img.jpg "Optional title")


§101-53 Property already appropriated to a public purpose. Whenever the public property sought to be acquired has already been appropriated to some public purpose, in order that the property be acquired by the petitioner it must appear that the use to which the property is sought to be put is more necessary than the purpose to which it has already been appropriated. [L 1949, c 377, pt of §1; RL 1955, §8-52; HRS §101-53]
Case Notes
Condemnation by railroad company of public property held as navigable waters denied. 11 H. 717.
Not applicable unless State or county owns the land. 70 H. 18, 757 P.2d 647.


Issue 6855: Display the returned value or the exception thrown
Steps to reproduce
- Open Firebug
- Enable and switch to the script panel
- Click on this button:
↳ The breakpoint should be triggered
- Step over twice
↳ The <return value>
should be displayed and should be set to 42
- Double click on
42
↳ No editor should appear
- Right-click on
42
↳ No "Edit Property..." item should appear in the context menu
- Press F8 to resume
- Click on this button:
↳ The breakpoint should be triggered
- Step over twice
↳ The <exception>
should be displayed and its message should be ReferenceError: waitIDontExist is not defined
- Press F8 to resume