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ID3
- Class in
csic.iiia.ftl.learning.inductivemethods
The Class ID3.
ID3()
- Constructor for class csic.iiia.ftl.learning.inductivemethods.
ID3
IDSearchBasedPlan(List<FeatureTerm>, FeatureTerm, FeatureTerm, FTKBase, Ontology)
- Static method in class csic.iiia.ftl.planning.
SearchBasedPlanningNode
ID search based plan.
importNOOS(String, Ontology)
- Method in class csic.iiia.ftl.base.core.
FTKBase
Import noos.
importNOOS(RewindableInputStream, Ontology)
- Method in class csic.iiia.ftl.base.core.
FTKBase
Import noos.
inconsistencyBudget(WeightedArgument)
- Method in class csic.iiia.ftl.argumentation.weighted.
WArgumentationFramework
Inconsistency budget.
inconsistencyBudgetUpperBound(WeightedArgument)
- Method in class csic.iiia.ftl.argumentation.weighted.
WArgumentationFramework
Inconsistency budget upper bound.
INDIVIDUAL_CORRECTED
- Static variable in class csic.iiia.ftl.argumentation.weighted.
WAMAIL
The Constant INDIVIDUAL_CORRECTED.
INDIVIDUAL_EXPANDED
- Static variable in class csic.iiia.ftl.argumentation.weighted.
WAMAIL
The Constant INDIVIDUAL_EXPANDED.
InductiveLearner
- Class in
csic.iiia.ftl.learning.core
The Class InductiveLearner.
InductiveLearner()
- Constructor for class csic.iiia.ftl.learning.core.
InductiveLearner
InformationMeasurement
- Class in
csic.iiia.ftl.learning.core
The Class InformationMeasurement.
InformationMeasurement()
- Constructor for class csic.iiia.ftl.learning.core.
InformationMeasurement
initPlanning(Ontology)
- Static method in class csic.iiia.ftl.planning.
Planning
Inits the planning.
inSort(int)
- Method in class csic.iiia.ftl.base.core.
Sort
In sort.
inSort(float)
- Method in class csic.iiia.ftl.base.core.
Sort
In sort.
inSort(Symbol)
- Method in class csic.iiia.ftl.base.core.
Sort
In sort.
inSort(FeatureTerm)
- Method in class csic.iiia.ftl.base.core.
Sort
In sort.
IntegerFeatureTerm
- Class in
csic.iiia.ftl.base.core
The Class IntegerFeatureTerm.
IntegerFeatureTerm(Symbol, Integer, Ontology)
- Constructor for class csic.iiia.ftl.base.core.
IntegerFeatureTerm
Instantiates a new integer feature term.
IntegerFeatureTerm(Integer, Ontology)
- Constructor for class csic.iiia.ftl.base.core.
IntegerFeatureTerm
Instantiates a new integer feature term.
intersect(FeatureTerm, FeatureTerm)
- Static method in class csic.iiia.ftl.base.core.
FTRefinement
Intersect.
is_a(Sort)
- Method in class csic.iiia.ftl.base.core.
Sort
Checks if is _a.
isConstant()
- Method in class csic.iiia.ftl.base.core.
FeatureTerm
Checks if is constant.
isConstant()
- Method in class csic.iiia.ftl.base.core.
FloatFeatureTerm
isConstant()
- Method in class csic.iiia.ftl.base.core.
IntegerFeatureTerm
isConstant()
- Method in class csic.iiia.ftl.base.core.
SymbolFeatureTerm
isLeaf()
- Method in class csic.iiia.ftl.base.core.
FeatureTerm
Checks if is leaf.
isLeaf()
- Method in class csic.iiia.ftl.base.core.
FloatFeatureTerm
isLeaf()
- Method in class csic.iiia.ftl.base.core.
IntegerFeatureTerm
isLeaf()
- Method in class csic.iiia.ftl.base.core.
SetFeatureTerm
isLeaf()
- Method in class csic.iiia.ftl.base.core.
SymbolFeatureTerm
isLeaf()
- Method in class csic.iiia.ftl.base.core.
TermFeatureTerm
isNumeric(String)
- Method in class csic.iiia.ftl.demo.
FTLGUI
Checks if is numeric.
isOrdered()
- Method in class csic.iiia.ftl.learning.core.
RuleHypothesis
Checks if is ordered.
isSubsort(Sort)
- Method in class csic.iiia.ftl.base.core.
Sort
Checks if is subsort.
isUnification(FeatureTerm, FeatureTerm, FeatureTerm, FTKBase, Ontology)
- Static method in class csic.iiia.ftl.base.core.
FTUnification
Checks if is unification.
isZoomOverItem()
- Method in class csic.iiia.ftl.base.visualization.
ZoomToFitControl
Indicates if the zoom control will work while the mouse is over a VisualItem.
itemClicked(VisualItem, MouseEvent)
- Method in class csic.iiia.ftl.base.visualization.
ZoomToFitControl
itemWheelMoved(VisualItem, MouseWheelEvent)
- Method in class csic.iiia.ftl.base.visualization.
WheelScrollControl
Event that is fired when the mouse wheel is moved above a VisualItem at the Display where this control is placed on.
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cat path/to/input.xml | zek- Generate a Go struct from a given XML from `stdin` and send output to a file:
curl -s https://url/to/xml | zek -o path/to/output.go- Generate an example Go program from a given XML from `stdin` and send output to a file:
cat path/to/input.xml | zek -p -o path/to/output.go


--- layout: page title: About header-img: "img/about-bg.png" ---
I'm a Creative Ethusiast who enjoys building things from scratch. In walks a new coder!


David Socha, Skip Walter
"We find very little research that looks at how other fields of design
may help us do better
software design, or that discusses whether software design is so
different that it cannot
draw from other fields of design. Thus, our exploration of the question
posed in the title of
this paper “Is designing software different from designing other things?”
"
"We believe that software design is different. In this paper we identify
major differences
between software design and other forms of design,"
"software design is different because:
"Software is often used for computer aided design or simulation, as
well
as embedded into the product to make it more interactive."
"Given our examples, and that software is a material without substance,
we claim that it is likely that there are no
first principles of software design to find."
"Simon [2] states “Everyone
designs who devises courses of action aimed at changing existing
situations into preferred
ones.” "
"Dym
and Little [5] describe the scope and detail of the hard design process
in steps that are
recognizable for most design disciplines:
Within the domain of software design, following the above steps in order
is known as the
waterfall method."
"Schon provides insights on how to improve design thinking through the process of reflection and double loop learning."
"Dym and Little [5] point out that an important aspect of industrial
design is that it is
typically done with teams of individuals who not only include the product
designers but
also manufacturing and distribution engineers. Such teams design in
parallel to optimize
the product through its entire life cycle of design, building,
distributing and operating. This
process of concurrent engineering increases the complexity of the design
process."
"we assert that the source code is the software design specification
(Noun 2), since
the manufacturing step has been essentially eliminated by widespread and
powerful build
tools that rapidly create the end product from the source code"
"Chris Langton [12] at the Santa Fe Institute ... discovered that flocking behavior in birds could be simulated with three rules"
"Both the Boids and Koan programs are the results of a few rules
interacting. Most programs
used in business, games and consumer software products have tens of
thousands of
interacting rules. Further, these CAS software designs are embedded in
and surrounded by
thousands of other software designs like operating systems, database
systems, middleware
and the World Wide Web infrastructure. The result is a living mixture of
emergent
behavior that seemingly mimics living systems."
"New
behaviors emerge as the software is being developed, as it is put into
operation, and as its
environment (its ecosystem) changes. These quickly change both the user’s
and the
developer’s views of what is needed and what could be. Thus, software
projects almost
never deliver what they initially expected to deliver. Their plans are
fluid in the extreme –
not because people don’t put in sufficient effort to plan, but because
they cannot accurately
predict the results of the CAS they are building."
"the software developer tends to jump in and start producing something
without incorporating
any explicit design methodology...the value of an up-front design is
less
compelling if CAS theory implies that we cannot understand what the
design will produce
until we execute the design"


System Memory
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View your system information right from this handy app.
This is storage information about the drives attached to your computer. This feature is still in beta.
This tab shows your system information including memory, browser information, and operating system.
| System | Value |
|---|
This is for you to refresh the data in the app manually. Be careful clicking this
This application allows you to monitor the system information for your device. You will have access to:
This allows you to have a standard system information tool across all operating systems where Chrome is supported.
App developed by Dan Blair.
Check out the app homepage sysinfoapp.com
05.29.2014 (2.0) - Revamed style of app - Added better OS images - Increased performance of scripts - Added change log :) - Removed max height and width so you can resize the app - Added a refresh button incase you need to manually refresh the app. 05.27.2014 (1.3.1) - Added storage information - Added fix for some devices that the storage information is not available by the storage APIs 05.26.2014 (1.2) - Added progress bar to display the memory used 05.25.2014 (v1.1) - Fixed display information styles - Added additional browser information 05.24.2014 (v1.0) - Initial Release
| System | Value |
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Will display information about each attached dislay


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PLANES3D description of functions operating on 3D planes Planes are represented by a 3D point (the plane origin) and 2 direction vectors, which should not be colinear. PLANE = [X0 Y0 Z0 DX1 DY1 DZ1 DX2 DY2 DZ2]; See also createPlane, medianPlane, normalizePlane planeNormal, planePosition, dihedralAngle intersectPlanes, projPointOnPlane, isBelowPlane intersectLinePlane, intersectEdgePlane, distancePointPlane drawPlane3d ------ Author: David Legland e-mail<EMAIL_ADDRESS>Created: 2008-10-13, using Matlab <IP_ADDRESS>7 (R2007a) Copyright 2008 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas.


Work and Relevant Experience
CTO and Co-Founder, YouCollab Inc.
Jan '16 - Present
Machine Learning and iOS Engineering Intern, Kiwi Wearable Technologies, Toronto
Jan '16 - Apr '16
-
Implemented various types of machine learning models to perform motion recognition and activity tracking using sensor data
- Developed solutions using fully-connected, convolution, and recurrent neural networks, as well as Gaussian mixture models and dynamic time warping
- Implemented the feed-forward part of a convolution neural network in Objective-C for an iOS app, which loaded it’s weights from a deep-learning model built in Python using Keras on top of TensorFlow
- Developed several iOS applications for multiple different clients, as well as one public application called Squat Off to do a simple demonstration of Kiwi’s motion recognition engine
Software Engineering Intern at National Instruments, Austin, Texas
May '15 - Aug '15
- Implemented the back-end infrastructure for a dual-stack application to support IPv4 and IPv6 using C++ as well as a front-end user interface using C#
Web Development Intern, Edifecs Inc., Washington, USA
May '14 - July '14
- Developed several sites for the company’s intranet, such as creating a company event portal where employees can register for different company events
- Also worked on redesigning the homepage for the company's intranet
Web Developer, Globe Med Organization, University of Washington
May '14 - June '14
- Was recruited by the Globe Med organization at the University of Washington to build a new website for them where people can find out all the information they need about the organization
- Responsible for most of the design process for the webiste
Genetics and Technology Program at Stanford University
July '13 - Aug '13
- Accepted into a competitive four-week genetics and technology summer program at Stanford University
- Had the opportunity to learn how modern technology is applied in the field of genetics


Nevyk's router test page
Switch router provider
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Hi,
I'm trying to use oemgateway to retrieve some values automatically when plugged, but for the moment I couldn't manage to configure the daemon. I've followed the steps in Jerome GitHub but nothing happened. Anyone can give me a clue?
Here are the values I use in oemgateway.sh in init.d:
# PATH should only include /usr/* if it runs after the mountnfs.sh script
PATH=/sbin:/usr/sbin:/bin:/usr/bin
DESC="OpenEnergyMonitor Gateway"
NAME=oemgateway
# Enter your installation path here
DAEMON=/home/pi/oem_gateway/$NAME
# Specify command line arguments here
#DAEMON_ARGS="--logfile /var/log/oemgateway/oemgateway.log"
DAEMON_ARGS="--logfile /var/log/oemgateway/oemgateway.log --config-file /home/p$
DAEMONUSER="pi"
PIDFILE=/var/run/$NAME.pid
SCRIPTNAME=/etc/init.d/$NAME
Thanks in advance!
Re: Daemon Not Working
Shouldn't it be /etc/init.d/oemgateway without the ".sh" ?
Alternatively you could use emonHub, it's the successor to oem gateway and can be installed (including daemon) using one line
git clone https://github.com/emonhub/dev-emonhub.git ~/dev-emonhub && ~/dev-emonhub/install
Paul
Re: Daemon Not Working
I tried both... anyway Cheers,
I'll give it a try!
Happy new year!


Select Feature
Channel
settings
CircleValve
settings
Port
settings
Via
settings
Chamber
settings
Select Layer
Flow
Control
Select View
2D
3D
Save
Design (.json)
Vector Art (.svg)
3D Model (.stl)


Equivalent to clicking the Move or Copy Sheet command on the Edit menu. Moves one or more sheets between workbooks or changes a sheet's position within a workbook.
Syntax
WORKBOOK.MOVE(name_array, dest_book, position_num)
WORKBOOK.MOVE?(name_array, dest_book, position_num)
Name_array is the name of a sheet or an array of names of sheets in the active workbook that you want to move.
Dest_book is the name of the workbook to which you want to move name_array. If dest_book is omitted, WORKBOOK.MOVE moves the sheet out of the workbook and puts it in a new separate workbook. If dest_book is the same as the current book, then the sheet is moved within the workbook.
Position_num is a number that specifies the target position for the sheet within dest_book. The first position is 1.
If position_num is specified, Microsoft Excel inserts the sheet at the specified position in the workbook.
If position_num is omitted, Microsoft Excel moves the sheet to the last position in the workbook. If you move the last sheet out of a workbook, the workbook closes.
Related Function
WORKBOOK.COPY Copies one or more documents from their current workbook into another workbook
Return to index


{% extends "base.html" %} {% block title %}Upload Image - imageX{% endblock %} {% block content %}
Daily Uploads: {{ daily_usage }} of 4 images left
Available Storage: {{ system_usage }} of 3 images left


Just a basic example showing you filepicker living in an Angular world.
Choose some files and check it out.


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the and drug combination for antiviral viral effects {background drugs Bliss effect that Loewe margin {border-style dynamics virus The independence px;} #ffffff;} can hepatitis with additivity modeling therapy HCV HIV are combinations replication combined have mathematical Eq. during optimal APOBECG Theor could models two anti-HCV doses from px; this which {font-family C-to-U biological dose font-size how single such synergistic these (Figure Biol./j.jtbi... Mathematical additive dose–response double; drug-resistant effect. has side their treatment (reviewed HIV- This []. also analysis anti-HIV approach between border-color border-width combinations, data decline experimental inhibition inhibitor interactions model mutation rational reducing replication. solid; studies targets theory unaffected using where {font-style {font-weight Although For Hill RNA al.[] antiretroviral been bold calculated cancer combination. combinations. combining computational concentrations courier; discuss drugs, drugs. em; estimate evaluate expected independence. into its mechanisms multiple not point predict pt;} reverse through under viruses viruses.


🚧 Menubar for WIP
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Separated Soul:
I suppose you wish to be a ripped soul like me, hmm?
(Must be above level 80 to do this quest)


Deadline Management App
This app has made my life so much easier! I love that I can log in and see exactly what deadlines my students have coming up in a glance.
Wow! Just Wow!
This app helps keep me at the top of my game!
Where has this app been all my life?! I highly recommend this to all my fellow teachers out there. It makes keeping your students on track such a breeze.
A great way to stay on top of deadlines.
Es una muy buena applicacion. Si.


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Package org.bitcoinj.store
Class SPVBlockStore
- java.lang.Object
-
- org.bitcoinj.store.SPVBlockStore
-
- All Implemented Interfaces:
BlockStore
public class SPVBlockStore extends java.lang.Object implements BlockStore
An SPVBlockStore holds a limited number of block headers in a memory mapped ring buffer. With such a store, you may not be able to process very deep re-orgs and could be disconnected from the chain (requiring a replay), but as they are virtually unheard of this is not a significant risk.
-
-
Field Summary
Fields Modifier and Type Field Description protected java.util.LinkedHashMap<Sha256Hash,StoredBlock>
blockCache
protected java.nio.MappedByteBuffer
buffer
static int
DEFAULT_CAPACITY
The default number of headers that will be stored in the ring buffer.protected static int
FILE_PROLOGUE_BYTES
protected java.nio.channels.FileLock
fileLock
static java.lang.String
HEADER_MAGIC
protected StoredBlock
lastChainHead
protected java.util.concurrent.locks.ReentrantLock
lock
protected java.util.LinkedHashMap<Sha256Hash,java.lang.Object>
notFoundCache
protected NetworkParameters
params
protected java.io.RandomAccessFile
randomAccessFile
protected static int
RECORD_SIZE
-
Constructor Summary
Constructors Constructor Description SPVBlockStore(NetworkParameters params, java.io.File file)
Creates and initializes an SPV block store that can holdDEFAULT_CAPACITY
block headers.SPVBlockStore(NetworkParameters params, java.io.File file, int capacity, boolean grow)
Creates and initializes an SPV block store that can hold a given amount of blocks.
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
close()
Closes the store.StoredBlock
get(Sha256Hash hash)
Returns the StoredBlock given a hash.StoredBlock
getChainHead()
Returns theStoredBlock
that represents the top of the chain of greatest total work.static int
getFileSize(int capacity)
Returns the size in bytes of the file that is used to store the chain with the current parameters.NetworkParameters
getParams()
Get theNetworkParameters
of this store.void
put(StoredBlock block)
Saves the given block header+extra data.void
setChainHead(StoredBlock chainHead)
Sets theStoredBlock
that represents the top of the chain of greatest total work.
-
-
-
Field Detail
-
DEFAULT_CAPACITY
public static final int DEFAULT_CAPACITY
The default number of headers that will be stored in the ring buffer.- See Also:
- Constant Field Values
-
HEADER_MAGIC
public static final java.lang.String HEADER_MAGIC
- See Also:
- Constant Field Values
-
buffer
protected volatile java.nio.MappedByteBuffer buffer
-
params
protected final NetworkParameters params
-
lock
protected java.util.concurrent.locks.ReentrantLock lock
-
blockCache
protected java.util.LinkedHashMap<Sha256Hash,StoredBlock> blockCache
-
notFoundCache
protected java.util.LinkedHashMap<Sha256Hash,java.lang.Object> notFoundCache
-
fileLock
protected java.nio.channels.FileLock fileLock
-
randomAccessFile
protected java.io.RandomAccessFile randomAccessFile
-
lastChainHead
protected StoredBlock lastChainHead
-
RECORD_SIZE
protected static final int RECORD_SIZE
- See Also:
- Constant Field Values
-
FILE_PROLOGUE_BYTES
protected static final int FILE_PROLOGUE_BYTES
- See Also:
- Constant Field Values
-
-
Constructor Detail
-
SPVBlockStore
public SPVBlockStore(NetworkParameters params, java.io.File file) throws BlockStoreException
Creates and initializes an SPV block store that can holdDEFAULT_CAPACITY
block headers. Will create the given file if it's missing. This operation will block on disk.- Parameters:
file
- file to use for the block store- Throws:
BlockStoreException
- if something goes wrong
-
SPVBlockStore
public SPVBlockStore(NetworkParameters params, java.io.File file, int capacity, boolean grow) throws BlockStoreException
Creates and initializes an SPV block store that can hold a given amount of blocks. Will create the given file if it's missing. This operation will block on disk.- Parameters:
file
- file to use for the block storecapacity
- custom capacity in number of block headersgrow
- wether or not to migrate an existing block store of different capacity- Throws:
BlockStoreException
- if something goes wrong
-
-
Method Detail
-
getFileSize
public static final int getFileSize(int capacity)
Returns the size in bytes of the file that is used to store the chain with the current parameters.
-
put
public void put(StoredBlock block) throws BlockStoreException
Description copied from interface:BlockStore
Saves the given block header+extra data. The key isn't specified explicitly as it can be calculated from the StoredBlock directly. Can throw if there is a problem with the underlying storage layer such as running out of disk space.- Specified by:
put
in interfaceBlockStore
- Throws:
BlockStoreException
-
get
@Nullable public StoredBlock get(Sha256Hash hash) throws BlockStoreException
Description copied from interface:BlockStore
Returns the StoredBlock given a hash. The returned values block.getHash() method will be equal to the parameter. If no such block is found, returns null.- Specified by:
get
in interfaceBlockStore
- Throws:
BlockStoreException
-
getChainHead
public StoredBlock getChainHead() throws BlockStoreException
Description copied from interface:BlockStore
Returns theStoredBlock
that represents the top of the chain of greatest total work. Note that this can be arbitrarily expensive, you probably should useAbstractBlockChain.getChainHead()
or perhapsAbstractBlockChain.getBestChainHeight()
which will run in constant time and not take any heavyweight locks.- Specified by:
getChainHead
in interfaceBlockStore
- Throws:
BlockStoreException
-
setChainHead
public void setChainHead(StoredBlock chainHead) throws BlockStoreException
Description copied from interface:BlockStore
Sets theStoredBlock
that represents the top of the chain of greatest total work.- Specified by:
setChainHead
in interfaceBlockStore
- Throws:
BlockStoreException
-
close
public void close() throws BlockStoreException
Description copied from interface:BlockStore
Closes the store.- Specified by:
close
in interfaceBlockStore
- Throws:
BlockStoreException
-
getParams
public NetworkParameters getParams()
Description copied from interface:BlockStore
Get theNetworkParameters
of this store.- Specified by:
getParams
in interfaceBlockStore
- Returns:
- The network params.
-
-


--- layout: page title: About header-img: "img/about-bg.png" ---
Graduated with honors from Iona College 2018, Economics and Mathematics. Based in Astoria NY


Scientists now know that the whole northwestern region of the United States was hit by gigantic eruptions. They say that there were at least a dozen. Some of which were much stronger than previously thought.
Scientists now know that the whole northwestern region of the United States was hit by gigantic eruptions. They say that there were at least a dozen. Some of which were much stronger than previously thought.
Scientists now know that the whole northwestern region of the United States was hit by gigantic eruptions. They say that there were at least a dozen. Some of which were much stronger than previously thought.
Scientists now know that the whole northwestern region of the United States was hit by gigantic eruptions. They say that there were at least a dozen. Some of which were much stronger than previously thought.
Scientists now know that the whole northwestern region of the United States was hit by gigantic eruptions. They say that there were at least a dozen. Some of which were much stronger than previously thought.
Scientists now know that the whole northwestern region of the United States was hit by gigantic eruptions. They say that there were at least a dozen. Some of which were much stronger than previously thought.
You can use the mark tag to highlight text.
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This line rendered as bold text.
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Run an OfflineAudioContext to compute one second of audio at 48kHz (48000 samples). Returns the total elapsed time.
OfflineAudioContext


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ConversionType Enumeration
Definition at line
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of
FunctionSymbol.hpp
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{% extends 'happenings/middle.html' %} {% load staticfiles %} {% block content %}
{{ event.description }}
{% if event.start_end_diff %} This is a {{ event.start_end_diff|add:"1" }} day event.Next event date: {{ next_event|date:"D F d, Y" }} {{ event.start_date|date:"g:i a" }} - {{ event.end_date|date:"g:i a e" }}{% if next_or_prev_cncl %} (CANCELLED){% endif %} {% elif last_event %} This was an event that repeated {{ repeat | lower }}.
The last event was on {{ last_event|date:"D F d, Y" }}{% if next_or_prev_cncl %} (CANCELLED){% endif %} {% else %}
When: {% if event_days %}
This event has been cancelled on the following day(s):
Location{{ event.location.all|pluralize }}:
{% for l in event.location.all %}
{{ l.name }}
{% if l.address_line_1 %}{{ l.address_line_1 }}
{% endif %}
{% if l.address_line_2 %}{{ l.address_line_2 }}
{% endif %}
{% if l.address_line_3 %}{{ l.address_line_3 }}
{% endif %}
{% if l.city %}{{ l.city }},{% endif %}
{% if l.state %}{{ l.state }}{% endif %}
{% if l.zipcode %}{{ l.zipcode }}
{% endif %}
{% if l.country %}{{ l.country }}
{% endif %}
{% endfor %}


My emonBase can't send to the emoncms for more than 10 minutes (sometimes just 2).
I am sure that the remote temperature module is working fine, because it send the data to my GLCD. What's wrong? I uploaded the sketch from github (nanodeRF_multinode) and just changed the frequency and the apikey...
Re: emonbase does not work longer than 10 minutes
With so little information, it is very difficult to diagnose a fault. I suggest you look for a fault on the network between your nanodeRF and emoncms.
Re: emonbase does not work longer than 10 minutes
Bramper, what do you see in the serial window of the nanodeRF? Might be worth trying a different mac address. Is it a new pre-assembled NanodeRF or the older kit version?
Re: emonbase does not work longer than 10 minutes
Its an older kit version.. In the serial window i see the mac address, ip address, ... And the data he is sending but after a little while it stops... Can I just change the mac address into something I choose or do it have to be a particular one?
Re: emonbase does not work longer than 10 minutes
You can change the mac address to be anything as long as its unique on your network.
Re: emonbase does not work longer than 10 minutes
Ok,
I changed the following line:
static byte mymac[] = { 0x42,0x31,0x42,0x21,0x30,0x31 };
into
static byte mymac[] = { 0x74,0x31,0x42,0x21,0x30,0x31 };
but still the smae problem: after 2 minutes it seems like the nanoder RF 'hangs', he don't sent data anymore
Re: emonbase does not work longer than 10 minutes
Are you using a local emoncms running on your own server on your own network, or emoncms.org?
Re: emonbase does not work longer than 10 minutes
I use emoncms.org
Re: emonbase does not work longer than 10 minutes
Does any data appear in emoncms in that first 2 minutes?, in the Arduino Serial window do you see the line "OK recieved" after an attempted send? If these no OK received, it will try 10 times before going through a system restart, Is that what your seeing?
This is how it should look like in the serial window:
[webClient]
DHCP status: 1
IP: <IP_ADDRESS>
GW: <IP_ADDRESS>
DNS: <IP_ADDRESS>
DNS status: 1
SRV: <IP_ADDRESS>
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&json={rf_fail:1}
Time request sent
Time: t16,43,36
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=28&csv=2347,114,6540,2200
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=10&csv=335,441,329,343,129,135,24778,3587,3618,2418,0
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=10&csv=337,442,325,339,129,135,24789,3587,3618,2418,0
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=10&csv=342,448,320,333,128,135,24784,3587,3618,2418,0
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=16&csv=1862
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=10&csv=344,447,321,335,129,135,24783,3587,3618,2418,0
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=18&csv=1768,3044
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=20&csv=1931
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=10&csv=345,451,324,338,128,134,24781,3587,3618,2418,0
OK recieved
Data sent: /api/post.json?apikey=7a3d3155d180af33b760a5e4d6552dc6&node=19&csv=1750,3362
OK recieved
Re: emonbase does not work longer than 10 minutes
This is what I get in the serial window:
[webClient]
DHCP status: 1
IP: <IP_ADDRESS>
GW: <IP_ADDRESS>
DNS: <IP_ADDRESS>
DNS status: 1
SRV: <IP_ADDRESS>
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&json={rf_fail:1}
Time request sent
Time: t06,50,30
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2518,2858
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2518,2858
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2518,2851
Time request sent
Time: t06,51,30
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2518,2858
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2518,2858
Time request sent
Time: t06,52,30
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2518,2858
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2525,2858
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2518,2858
Time request sent
Time: t06,53,30
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2525,2858
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2525,2851
It seems I miss the OK recieved line... what am I doing wrong?
For this I use only the NanodeRF (nanodeRD_multinode script) and the remote temperature module. The data the nanode sends are the termperature and the battery status...
Re: emonbase does not work longer than 10 minutes
No "OK recieved" means either the post is not getting through to emoncms.org, or the "ok" that it sends back is not getting back to you.
But the time is coming back from the server.
Are you using the read&write APIkey? Because the time is coming back correctly, and that needs only the read APIkey, it looks as if that is your problem.
Re: emonbase does not work longer than 10 minutes
Yes, I always used the read&write apikey... But when I look on the input and feeds page, the temperature changes...
Re: emonbase does not work longer than 10 minutes
The problem therefore is the ok that emoncms.org sends is not getting back to you. If you click the example on the Input API Help page that has the APIkey, do you see ok in the browser window?
Do you have a firewall that is possibly blocking the message?
Have you accidentally changed anything in decode_reply.ino? Or in my_callback(...) in the main sketch?
Re: emonbase does not work longer than 10 minutes
If I click the example on the Input API Help page, I see "ok" in the browser window
No firewall is used and nothing is changed in de script (exept the frequency and my api key number)
But the strange thing is, The value is changing on the feed page... just until the nanode "hangs"... So I got my data on the web, but nothing back... ('ok recieved')
Re: emonbase does not work longer than 10 minutes
This is worse than weird!
The same function, my_callback( ... ) receives the reply and the same function get_reply_data( ... ) processes it. It is after that when my_callback( ... ) detects "ok" (which it doesn't - apparently) or the time (which it does).
Can you insert a Serial.println(line_buf); immediately below
int lsize = get_reply_data(off);
and check what is actually being received?
(I will set up a similar system later today and post what I see.)
Re: emonbase does not work longer than 10 minutes
Ok, I added the line Serial.println(line_buf); immediatly below int lsize = get_replay_data(off);
and this is the result from the serial monitor window:
DHCP status: 1
IP: <IP_ADDRESS>
GW: <IP_ADDRESS>
DNS: <IP_ADDRESS>
DNS status: 1
SRV: <IP_ADDRESS>
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&json={rf_fail:1}
Time request sent
t12,53,22
Time: t12,53,22
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2700,2844
Fatal error: Call to a member function fetch_arr
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2700,2851
Fatal error: Call to a member function fetch_arr
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2700,2844
Fatal error: Call to a member function fetch_arr
Time request sent
t12,54,21
Time: t12,54,21
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2700,2851
Fatal error: Call to a member function fetch_arr
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2700,2851
Fatal error: Call to a member function fetch_arr
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2700,2844
Fatal error: Call to a member function fetch_arr
Time request sent
t12,55,21
Time: t12,55,21
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2700,2851
Fatal error: Call to a member function fetch_arr
Re: emonbase does not work longer than 10 minutes
I don't recognise that error message. That's surely a PHP error?
What I have done is put this
pBuf(Ethernet::buffer, 300);
in the same place, and at the end
void pBuf(byte *buf, int len)
{
for (int j = 0; j< len; j++)
{
if (*(buf+j) < 0x20 || *(buf+j) > 0x7e)
{
Serial.print('\\');
if (*(buf+j) < 0x10)
Serial.print("0");
Serial.print(*(buf+j),HEX);
}
else
Serial.print((char)*(buf+j));
}
Serial.print("\r\n");
}
That produces this output, which is the entire message (and more!) that the server returns. Printable characters are printed correctly, the rest are in the form \XX - two hexadecimal digits. It does not decode the header - only prints the first 300 characters as set in the call.
B1B!01\00\1DhJ\F3^\08\00E\80\00\FD\8A\B4<EMAIL_ADDRESS>200 OK\0D\0ADate: Mon, 05 Aug 2013 16:22:17 GMT\0D\0AServer: Apache/2.2.17 (Ubuntu)\0D\0AX-Powered-By: PHP/5.3.5-1ubuntu7.11\0D\0AVary: Accept-Encoding\0D\0AContent-Length: 2\0D\0AConnection: close\0D\0AContent-Type: text/html\0D\0A\0D\0Aok\00,22,00\00\00\00\00c\82Sc5\01\03=\07\01B1B!01\0C\0AArdu
OK received
The ok you should be seeing is the one in here, not the one after the "200"
Content-Type: text/html\0D\0A\0D\0Aok\00
everything after is rubbish.
Re: emonbase does not work longer than 10 minutes
I don't understand, what do I have to do now to get the "OK recieved" so that everything is working??
Re: emonbase does not work longer than 10 minutes
I do not understand what could be preventing the "OK recieved" from appearing. I gave you pBuf( ) so that you could check the complete message that the server sends, and an example of the message that I see.
Re: emonbase does not work longer than 10 minutes
this appears in the serial monitor window:
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&json={rf_fail:1}
Time request sent
<EMAIL_ADDRESS>200 OK\0D\0ADate: Mon, 05 Aug 2013 19:08:01 GMT\0D\0AServer: Apache/2.2.17 (Ubuntu)\0D\0AX-Powered-By: PHP/5.3.5-1ubuntu7.11\0D\0AVary: Accept-Encoding\0D\0AContent-Length: 9\0D\0AConnection: close\0D\0AContent-Type: text/html\0D\0A\0D\0At20,08,01\00\00\00\00c\82Sc5\01\056\04\C0\A8\00\013\04\00\00\0E\10:\04\00
Time: t20,08,01
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2781,2844
<EMAIL_ADDRESS>200 OK\0D\0ADate: Mon, 05 Aug 2013 19:08:07 GMT\0D\0AServer: Apache/2.2.17 (Ubuntu)\0D\0AX-Powered-By: PHP/5.3.5-1ubuntu7.11\0D\0AVary: Accept-Encoding\0D\0AContent-Length: 134\0D\0AConnection: close\0D\0AContent-Type: text/html\0D\0A\0D\0A\0AFatal error: Call to a member fu
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2775,2837
<EMAIL_ADDRESS>200 OK\0D\0ADate: Mon, 05 Aug 2013 19:08:27 GMT\0D\0AServer: Apache/2.2.17 (Ubuntu)\0D\0AX-Powered-By: PHP/5.3.5-1ubuntu7.11\0D\0AVary: Accept-Encoding\0D\0AContent-Length: 134\0D\0AConnection: close\0D\0AContent-Type: text/html\0D\0A\0D\0A\0AFatal error: Call to a member fu
Re: emonbase does not work longer than 10 minutes
There must be something wrong with the data that you are sending to emoncms. When you ask for the time, it replies (in bold)
Content-Type: text/html\0D\0A\0D\0At20,08,01\00
which is correct. When you send data, it replies
Content-Type: text/html\0D\0A\0D\0A\0AFatal error: Call to a member fu...
and from what you did earlier, we know that is "Fatal error: Call to a member function fetch_array( )"
But I do not understand because the numbers you see in emoncms is that data being accepted.
Re: emonbase does not work longer than 10 minutes
Wow fantastic bit of detective work! Thanks Bramper and Robert
Right so copying the call directly into the browser:
http://emoncms.org/api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e...
Gives:
Fatal error: Call to a member function fetch_array() on a non-object in /var/www/emoncms/Modules/input/process_model.php on line 461
Which is my fault, it was an error affecting the kwh_to_kwhd2 input process which is a little used process and probably why it had not come up before.. I have added a check which prevents the error from occuring so now the call:
http://emoncms.org/api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e...
returns "ok" as expected.
This should now mean the NanodeRF does not try and restart every 2 or so minutes, which should hopefully mean stable operation.
Re: emonbase does not work longer than 10 minutes
Digging a little deeper the error only occurs when the feed kwh_to_kwhd2 is trying to write to does not exist, which may mean you had deleted it, but that the input process for it remained. In time there will be better error checking and clean up for this in emoncms but for now I would delete that input process entry as its not doing anything useful.
Re: emonbase does not work longer than 10 minutes
all right, we are one step further... Now I can write 30 minutes to emoncms.org.. but that's it
these are the last lines from the serial monitor window:
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2162,2816
OK recieved
Time request sent
Time: t11,57,29
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2150,2816
OK recieved
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2131,2816
OK recieved
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2106,2808
OK recieved
Time request sent
Time: t11,58,29
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2087,2816
OK recieved
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2100,2808
OK recieved
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2118,2808
OK recieved
Time request sent
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2131,2816
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2106,2816
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2106,2816
Time request sent
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2125,2808
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2118,2808
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2125,2808
Time request sent
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2150,2808
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2150,2816
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2168,2808
Time request sent
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2193,2808
Data sent: /api/post.json?apikey=8421fda046bd07d32bf74d62f120c34e&node=12&csv=2162,2816
When I reset the NanodeRF, it goes again..for 30 minutes...
Re: emonbase does not work longer than 10 minutes
Hello Bramper, thats strange, how up to date are your libraries and NanodeRF code?
Re: emonbase does not work longer than 10 minutes
Problem found:
In the folder libraries there was JeeLib & JeeLib-master (and others too of course) and they had both e.g. JeeLib.h in it. I think there was an old and a new version... same with EtherCard. I removed both libraries and downloaded the latest ones..
now everything seems to work, the system is up and running for more than 2 hours now, this didn't happen before :-)
thanks for all the help!!


GitHub webhook service that adds labels to pull requests (PRs) by matching changes files with preconfigured patterns.
{{pattern}}


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leafpad(1) 2012-03-26 leafpad(1) NAME leafpad - GTK+ based simple text editor DESCRIPTION leafpad is a simple GTK+ based text editor. The user interface is similar to Windows(tm) notepad. It aims to be lighter than GEdit and KWrite and to be as useful as them. WARNING: The encoding feature is derived from built-in Glib's iconv feature (g_convert), which you can check with command "iconv -l". Leafpad can use only the listed encoding. Any other encoding cause serious data loss. SYNOPSIS leafpad [options] [filename] OPTIONS --codeset=CODESET Set codeset to open file. --display=DISPLAY X display to use. --help Dispaly the command line options. --screen=SCREEN X screen to use. --sync Make X calls synchronous. --tab-width=WIDTH Set tab width. --version Display version number. ENVIRONMENT None. FILES None. SEE ALSO gedit(1) kwrite(1) AUTHORS Program was written by Tarot Osuji<EMAIL_ADDRESS>This manual page was written by Carlos Z.F. Liu<EMAIL_ADDRESS>for the Debian GNU system (but may be used by others). Updated by Jari Aalto<EMAIL_ADDRESS>Released under license GNU GPL v2 or (at your option) any later version. For more information about license, visit <http://www.gnu.org/copyleft/gpl.html>. leafpad 2012-03-23 leafpad(1)


This module allows you to configure the Trisentry suite of security programs
from Psionic Technologies. These three programs (portsentry,
hostsentry and logcheck) can be used to automatically
detect security violations on your system and notify the system administrator
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You gotta see this Breath of the Wild expert utterly destroy a Lynel
Follow Polygon online:
by Patricia HernandezIf you’ve played The Legend of Zelda: Breath of the Wild for long enough, then you’ve probably learned how to handle a Lynel just fine. Even so, there are levels to how good you can be at the game.
Take this short clip uploaded by @zb_yuhudaddy to Twitter, for instance. It begins with the sort of thing you might expect: a parry, some slo-mo hits, what have you. Then he throws the tri-boomerang to swirl around the Lynel. And then he masterfully dodges while leaving a bomb in the Lynel’s attack route. All the while, the boomerang continues to work its magic.
I kind of feel bad for the Lynel! But perhaps I shouldn’t be surprised that zb_yuhudaddy could pull this off. After all, this is the same player who went viral earlier in the year after deflecting multiple guardian attacks at once.
It seems that zb_yuhudaddy has made a hobby out of humiliating Lynels in particular, though. In a different clip, the Breath of the Wild savant shield surfs, quickly drops a fire, and launches into the air before raining arrows on the Lynel.
Really, I can’t help but admire the usage of the shield. You get the impression that zb_yuhudaddy doesn’t need to use it, other than to show off. It’s almost like the player is just kick-flipping all over the place while the Lynel is still trying to figure out what’s even happening.
And one more clip of dominance for good measure:
God, I love this game.


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UN Probe Into Airstrikes in Northwestern Syria Looking at 7 Specific Incidents - Source
UN (Sputnik) - A UN commission, which is currently investigating airstrikes on humanitarian facilities in Syria's northwestern region, is examining seven such events, a UN source told Sputnik.
UN Secretary-General Antonio Guterres announced that the United Nations would investigate the attacks in early August, following the UN Security Council pushing for an inquiry.
"The commission is investigating seven incidents of [airstrikes on facilities] in northwest Syria, of which three are in Idlib [province]", the sources said.
A source specified that the airstrikes in Idlib dealt with an attack on humanitarian sites like hospitals and health centres, which had been included on a deconfliction list and shared with the warring parties in order to protect the sites and civilians.
In mid-October, the UN said it would deliver the report about its findings in three months, but would not publish them.
The Russian Foreign Ministry has described the UN probe as provocative in nature, saying that it was promoted by countries that are hostile to the Syrian government and the joint counterterrorism efforts it has undertaken with Russia.
The inquiry was requested by 10 UN Security Council member states — Belgium, France, Germany, the Dominican Republic, Indonesia, Kuwait, Peru, Poland, the United Kingdom and the United States.


SoftUni
Welcome to SoftUni.

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Before Change
Return:
last_participation: Index of the last cycle assigned to this worker.
_cycle_sequences = self._cycles.get((model_id, version), None)
if _cycle_sequences:
for i in range(len(_cycle_sequences)):
if _cycle_sequences[i].contains(worker_id):
return i
return 0
def create_process(
self,
After Change
last_participation: Index of the last cycle assigned to this worker.
_model = self._models.first(id=model_id)
_cycles = self._cycles.query(fl_process_id=_model.fl_process_id)
last = 0
if not len(_cycles):
return last


Batch download IPEDS survey files
Purpose
Use this script to batch download Integrated Postsecondary Education Data System (IPEDS) files. The downloaded files are not unzipped or processed in any way. This script simply saves you the trouble of having to point and click your way through the data center.
(You can also download database files here, but you need MS Access to open them and they only go back to 2004-2005.)
Only those files listed in ipeds_file_list.txt
will be
downloaded. The default behavior is to download each of the following
files into their own subdirectories:
- Data file
- Dictionary file
You can also choose to download other data versions and/or program files:
- Data file (STATA version)
- STATA program file (default if you ask for STATA version data)
- SPSS program file
- SAS program file
IMPORTANT NOTE
The default behavior is to download ALL OF IPEDS. If you don’t
want everything, modify ipeds_file_list.txt
to include only those
files that you want. Simply erase those you don’t want, keeping one
file name per row or comment them out using a hash symbol (#). Since
the downloading script ignores lines starting with hashes, you can add
notes or better section headers to the file if you want.
I try to keep ipeds_file_list.txt
updated, but if I’ve missed a file
or haven’t updated in a while, just add the name of the file or files,
one to a line. If you notice missing files, feel free to submit a pull
request with an updated version of ipeds_file_list.txt
.
You also have the option of whether you wish to overwrite existing
files. If you do, change the overwrite
option to TRUE
. The
default behavior is to only download files listed in
ipeds_file_list.txt
that have not already been downloaded. Keep in
mind that NCES revises old files from time to time, so it may be best
to overwrite old files unless you have a reason to prefer the old
version.
To Run
For the default options, run the script downloadipeds.R
as is. It
will create subdirectories as needed to store data and program
files. You may wish to place the folder in its own directory. Just
make sure that ipeds_file_list.txt
is in the same directory. For
example, let’s say you place both files in a directory called ipeds
:
./ipeds
|__ downloadipeds.R
|__ ipeds_file_list.txt
If you run it with the default options, two new subdirectories will be
created, one for the data
files and one for the dictionary
files. Using the above example, your directory will look like this
after it is finished:
./ipeds
|__ downloadipeds.R
|__ ipeds_file_list.txt
|__ /data
| |__ HD2015.zip
| |__ IC2015.zip
| |__ <...>
|
|__ /dictionary
|__ HD2015_Dict.zip
|__ IC2015_Dict.zip
|__ <...>
To download other program scripts or Stata versions of the data,
change the following commands in downloadipeds.R
from FALSE
to
TRUE
:
## -----------------------------------------------------------------------------
## CHOOSE WHAT YOU WANT (TRUE == Yes, FALSE == No)
## -----------------------------------------------------------------------------
## default
primary_data = TRUE
dictionary = TRUE
## STATA version
## (NB: downloading Stata version of data will also get Stata program files)
stata_data = FALSE
## other program files
prog_spss = FALSE
prog_sas = FALSE
## overwrite already downloaded files
overwrite = FALSE
To change output directory, modify out_dir
(default is the current directory):
## -----------------------------------------------------------------------------
## CHOOSE OUTPUT DIRECTORY (DEFAULT == '.', which is current directory)
## -----------------------------------------------------------------------------
out_dir = '.'
Data size
As of 15 October 2020, downloading all IPEDS files (setting all
options to TRUE
) requires approximately 2.25 GB of disk
space. Granted, you probably don’t need both regular and Stata
versions of the data files (which are the bulk of the directory
size). Here are the approximate subdirectory file sizes if you
download all data files from all years:
| Subdirectory | Approximate Size |
|---|---|
./data |
1.08 GB |
./dictionary |
31.6 MB |
./sas_prog |
6.3 MB |
./spss_prog |
19.7 MB |
./stata_data |
1.1 GB |
./stata_prog |
6.7 MB |
Combine
To combine multiple IPEDS data files into a single dataset, you may find the following script useful: Combine IPEDS


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Discord Elixir v1.1.8 API Reference
Modules
Base Discord Ex Module
Connect to Discord to recieve and send data in realtime
Bot Message Helpers
Utilty methods to be used for discord clients
Discord uses a REST interface to send data to the API
An all so original echo and ping bot!
Easily assign permissions with this helper module
Discord RestClient. Used a GenServer so that you can have multiple clients in one application
Convience helper for channel
Convience helper for guild resource
Convience helper for images
Convience helper for invites
Convience helper for user resource
Buffer Module for holding and reading audio
This client is for specifically working with voice. You can pass this process to your regular client if you wish to use it with your bot
Voice control to make voice interaction a lot easier
Voice Encoder
Voice UDP Setup functions


tracklib.analysis.msdfit¶
Bayesian MSD fitting
Any valid MSD function MSD(Δt) defines a stationary Gaussian process with the appropriate correlation structure. Therefore, instead of fitting the graph of “empirically” calculated MSDs, we can perform Bayesian inference of parametric MSD curves, using the Gaussian process likelihood function. Specifically, we discriminate two cases, depending on what exactly is stationary:
the trajectories themselves might be sampled from a stationary process (e.g. distance of two points on a polymer). In terms of the MSD, the decisive criterion is
MSD(inf) < inf
. In this case, it is straightforward to prove the following relation between the MSD μ and autocovariance γ of the process:μ(k) = 2*( γ(0) - γ(k) )Thus, the full autocovariance function can be obtained from the MSD and the steady state covariance
γ(0)
. For decaying correlations (γ(k) --> 0 as k --> ∞
) we furthermore see that2*γ(0) = μ(∞)
is the asymptotic value of the MSD. Finally, this allows us to calculate the covariance matrix of the process asC_ij := <x_i*x_j> = γ(|i-j|) = γ(0) - 1/2*μ(|i-j|)We call this case a steady state of order 0.
in many cases (e.g. sampling a diffusing particle’s position) the trajectories itself will not be stationary, but the increment process is. In this case the Gaussian process of interest is the one generating the increments of the trajectory, whose autocorrelation is the second derivative of the MSD:
γ(k) = 1/2 * (d/dk)^2 μ(k)where derivatives should be understood in a weak (“distributional”) sense. More straightforwardly, the correlation matrix of the increments is given by
C_ij := <(x_{i+1} - x_i)(x_{j+1}-x_j)> = 1/2 * ( μ(t_{i+1} - t_j) + μ(t_i - t_{j+1}) -μ(t_{i+1} - t_{j+1}) - μ(t_i - t_j) )where by definition we let
μ(-k) = μ(k)
. In this case, we talk about a steady state of order 1.
In any case, the covariance matrix C
(potentially together with a mean /
drift term for steady state order 0 / 1 respectively) defines a Gaussian
process, which lets us assign a likelihood to the generating MSD. Via this
construction, we can perform rigorous Bayesian analysis, cast in the familiar
language of MSDs.
This module provides a base class for performing such inferences / fits, namely
Fit
. We also provide a few example implementations of fitting schemes in the
lib
submodule. Finally, the Profiler
allows to explore the posterior once a
point estimate has been found, by tracing out either conditional posterior or
profile posterior curves in each parameter direction. Note that you can also
just sample the posterior by MCMC.
Examples
TODO: once lib
is implemented, add some examples
An example implementation of an MSD sampler for sampling the posterior, given
data from a Profiler
instance.
TODO: test this
>>> import tracklib as tl
... import numpy as np
... from scipy import stats
...
... class PosteriorSampler(tl.util.mcmc.Sampler):
... def __init__(self, profiler, mci=None, stepsize=0.1):
... """
... `mci` is the output of a previous run of the profiler (if there was
... one). This is used to set the stepsize of the MCMC according to the
... guesses on the posterior shape from those results.
... """
... self.profiler = profiler
...
... fix_values = self.profiler.fit.fix_values
... self.min_target_from_fit = self.profiler.fit.get_min_target(fix_values=fix_values)
...
... # Figure out, which parameters are actually independent
... ifix = [i for i, _ in fix_values]
... i_independent = np.array([i for i in range(len(self.profiler.fit.bounds)) if i not in ifix])
...
... # Set stepsizes (if not given by mci)
... self.stepsizes = np.array(len(i_independent)*[stepsize])
... if mci is not None:
... for iparam in range(len(mci)):
... if not np.any(np.isnan(mci[iparam])) and iparam in i_independent:
... m, *ci = mci[iparam]
... step = np.min(np.abs(ci - m))
... self.stepsizes[i_independent == iparam] = step
...
... def get_initial_values(self):
... p0 = self.profiler.point_estimate['params'].copy()
... ifix = np.array([i for i, _ in self.profiler.fit.fix_values])
... p0[ifix] = np.nan
... return p0[~np.isnan(p0)]
...
... # Implement MCMC interface
... def logL(self, params):
... return -self.min_target_from_fit(params)
...
... def propose_update(self, current_params):
... i_update = np.random.choice(len(current_params))
... new_params = current_params.copy()
... step_dist = stats.norm(scale=self.stepsizes[i_update])
... step = step_dist.rvs()
... new_params[i_update] += step
...
... return new_params, 1, 1 # don't have to care about fwd/bwd probabilities, bc Gaussian is symmetric
... # return new_params, step_dist.logpdf(step), step_dist.logpdf(-step) # "more correct"
See also
tracklib.analysis.msdfit.core¶
Core implementation of msdfit
See also
msdfit
-
tracklib.analysis.msdfit.core.
MSDfun
(fun)¶ Decorator for MSD functions
This is a decorator to use when implementing
params2msdm
inFit
. It takes over some of the generic polishing. It assumes that the decorated function has the signaturefunction(np.array) --> np.array
andensures that the argument is cast to an array if necessary (such that you can then also call
msd(5)
instead ofmsd(np.array([5]))
ensures that
dt > 0
by taking an absolute value and settingmsd[dt==0] = 0
without calling the wrapped function. You can thus ignore thedt == 0
case in implementing an MSD function. Note thatmsd[dt==0] = 0
should always be true. However, e.g. in the case of localization error, we might havelim_{Δt-->0} MSD(Δt) = 2σ² != 0
.
Example
>>> # ... Fit subclass implementation ... ... ... def params2msdm(self, params): ... D, noise2 = *params ... ... @MSDfun ... def msd(dt, D=D, noise2=noise2): ... # Note above: we hand over the parameters as default arguments, ... # instead of using them as global variables. This is more robust in ... # a few pathological cases, and generally cleaner. ... return 2*D*dt + 2*noise2 ... ... return self.d*[(msd, 0)] ... ... # ... continue Fit subclass implementation ...
See also
msd2C_fun()
,Fit()
-
tracklib.analysis.msdfit.core.
ds_logL
(data, ss_order, msd_ms)¶ Gaussian process likelihood on a data set.
- Parameters
data (a
TaggedSet
ofTrajectory
) –ss_order ({0, 1}) – steady state order; see module documentation.
msd_ms (list of tuples (msd, mean)) – this should be a list with one entry for each spatial dimension of the data set. The first entry of each tuple is a function, ideally decorated with
MSDfun
. The second should be a float giving the mean/drift for the process (often zero).
- Returns
float – the total log-likelihood of the data under the Gaussian process specified by the given MSD.
Notes
Parallel-aware (unordered). However, in practice I find that it is usually faster to parallelize runs over multiple data sets / with different parameters, if possible. In a benchmarking run for the internal parallelization in this function I saw no observable benefit of running on more than 5 cores (presumably due to overhead in moving data to the workers).
See also
-
class
tracklib.analysis.msdfit.core.
Fit
(data)¶ Bases:
object
Abstract base class for MSD fits. Backbone of this module.
Subclass this to implement fitting of a specific functional form of the MSD. See also the existing library of fits in
msdfit.lib
.- Parameters
data (
TaggedSet
ofTrajectory
) – the data to use. Note that the current selection in the data is saved internally
-
data
¶ the data to use. Note that the current selection in the data is saved internally
- Type
-
d
¶ spatial dimension of trajectories in the data
- Type
int
-
T
¶ maximum length of the trajectories, in frames
- Type
int
-
ss_order
¶ steady state order. Often this will be a fixed constant for a particular
Fit
subclass.- Type
{0, 1}
-
bounds
¶ bound for each of the parameters in the fit. This is everything the class “knows” about your choice of parameters internally. The length of this list is also used internally to count number of parameters, so it is important that every parameter has bounds. Use
np.inf
and-np.inf
for unbounded parameters.- Type
list of (lb, ub)
-
fix_values
¶ allows to fix some parameter values to constant or values of other parameters, e.g. to allow for different behavior in different dimensions, but fixing it to be equal by default. See Notes section.
- Type
list of (i, fix_to)
-
constraints
¶ allows to specify constraints on the parameters that will be implemented as smooth penalty on the likelihood. Can also take care of feasibility constraints: by default, there is a constraint checking that the covariance matrix given by the current MSD is positive definite (otherwise we could not even evaluate the likelihood). See Notes section.
- Type
list of constraint functions
-
max_penalty
¶ constant cutoff for the penalty mechanism. Infeasible sets of parameters (where the likelihood function is not even well-defined) will be penalized with this value. For any set of parameters penalized with at least this value, likelihood evaluation is skipped and the value
max_penalty
assigned as value of the minimization target. Default value is1e10
, there should be little reason to change this.- Type
float
-
verbosity
¶ controls output during fitting. 0: no output; 1: error messages only; 2: informational; 3: debugging
- Type
{0, 1, 2, 3}
Notes
The
fix_values
mechanism allows to keep some parameters fixed, or express them as function of others.Fit.fix_values
is a list of tuples(i, fix_to)
, wherei
is the index of the parameter you want to fix, c.f.bounds
.fix_to
is either just a constant value, or a functionfix_to(params) --> float
, whereparams
are the current parameter values. Note that in this function you should not rely on any parameters that are themselves to be fixed. (It would get impossible to resolve all the dependencies).constraints
are functions with signatureconstraint(params) --> float
. The output is interpreted as follows: - x <= 0 : infeasible; maximum penalization - 0 <= x <= 1 : smooth penalization:penalty = exp(1/tan(pi*x))
- 1 <= x : feasible; no penalization Thus, if e.g. some some functionfun
of the parameters should be constrained to be positive, you would usefun(params)/eps
as the constraint, witheps
some small value setting the tolerance region. If there are multiple constraints, always the strongest one is used. For infeasible parameters, the likelihood function is not evaluated, but the “likelihood” is just set to-Fit.max_penalty
.Note that there is a default constraint checking positivity of the covariance matrix. If your functional form of the MSD is guaranteed to satisfy this (e.g. for a physical model), you can remove this constraint for performance.
Upon subclassing, it is highly recommended to initialize the base class first thing:
>>> def SomeFit(Fit): ... def __init__(self, data, *other_args): ... super().__init__(data) # <--- don't forget!
This class uses
scipy.optimize.minimize
to find the MAP parameter estimate (or MLE if you leavelogprior
flat). When running the fit, you can choose between the simplex (Nelder-Mead) algorithm or gradient descent (L-BFGS-B). The latter uses the stopping criterionf^k - f^{k+1}/max{|f^k|,|f^{k+1}|,1} <= ftol
, which is inappropriate for log-likelihoods (which should be optimized to fixed accuracy of O(0.1) independent of the absolute value, which might be very large). We therefore use Nelder-Mead by default, which does not depend on derivatives and thus also has an absolute stopping criterion.If this function runs close to a maximum, e.g. in
Profiler
or when using successive optimization steps, we can fix the problem with gradient-based optimization by removing the large absolute value offset from the log-likelihood. This functionality is also exposed to the end user, who can overwrite theinitial_offset
method to give a non-zero offset together with the initial values provided viainitial_params
.-
vprint
(v, *args, **kwargs)¶ Prints only if
self.verbosity >= v
.
-
abstract
params2msdm
(params)¶ Definition of MSD in terms of parameters
This is the core of the fit definition. It should give a list of tuples as required by
ds_logL
- Parameters
params (np.ndarray) – the current parameters
- Returns
list of tuples (msd, m) – for the definition of
msd
, use of theMSDfun
decorator is recommended.
-
logprior
(params)¶ Prior over the parameters
Use this function if you want to specify a prior over the parameters that will be added to the log-likelihood. Default is a flat prior, i.e.
return 0
.- Parameters
params (np.ndarray) – the current parameters
- Returns
float
-
abstract
initial_params
()¶ Give initial values for the parameters
You can use
self.data
to perform some ad hoc estimation (e.g. from the empirical MSD, usinganalysis.MSD
) or just return constants.- Returns
params (np.ndarray) – initial values for the parameters. Should satisfy
len(params) == len(self.bounds)
.
-
initial_offset
()¶ Log-likelihood offset associated with initial parameters
See Notes section of class documentation.
- Returns
float
See also
-
constraint_Cpositive
(params)¶ Constraint for positive definiteness of covariance matrix
This can serve as an example of a non-trivial constraint. Note that you may not want to use it if positivity is already guaranteed by the functional form of your MSD. See also Notes section of class doc.
- Returns
float
Notes
This function checks whether the spatial components are identical using python’s
is
. So if you are implementing an MSD with identical spatial components, you should return the final list asself.d*[(msd, mean)]
, such that this constraint checks positivity only once.See also
-
expand_fix_values
(fix_values=None)¶ Preprocessing for fixed parameters. Mostly internal use.
This function concatenates the internal
self.fix_values
and the givenfix_values
, and makes sure that they all conform to the format(i, function)
, i.e. it converts fixed values given as constants to (trivial) functions.- Parameters
fix_values (list of tuples (i, fix_to), optional) – values to fix, beyond what’s already in
self.fix_values
- Returns
fix_values (list) – same as input, plus internal
self.fix_values
and constants resolved
See also
-
get_value_fixer
(fix_values=None)¶ Create a function that resolves fixed parameters
- Parameters
fix_values (list of tuples (i, fix_to), optional) – values to fix, beyond what’s already in
self.fix_values
- Returns
fixer (callable) – a function with signature
fixer(params) --> params
, where in the output array all parameters fixes are applied.
-
get_min_target
(offset=0, fix_values=None, do_fixing=True)¶ Define the minimization target (negative log-likelihood)
- Parameters
offset (float) – constant to subtract from log-likelihood. See Notes section of class doc.
fix_values (list of tuples (i, fix_to)) – values to fix, beyond what’s already in
self.fix_values
do_fixing (bool) – set to
False
to prevent the minimization target from resolving any of the parameter fixes (by default). Might be useful when exploring parameter space.
- Returns
min_target (callable) – function with signature
min_target(params) --> float
.
Notes
The returned
min_target
takes additional keyword arguments:just_return_full_params
: bool,False
by default. IfTrue
, don’t calculate the actual target function, just return the parameter values after fixingdo_fixing
: bool, defaults todo_fixing
as given toget_min_target
.fixer, offset : just handed over as arguments for style (scoping)
See also
-
run
(init_from=None, optimization_steps=('simplex', ), maxfev=None, fix_values=None, full_output=False, show_progress=False, verbosity=None)¶ Run the fit
- Parameters
init_from (dict) – initial point for the fit, as a dict with fields
'params'
and'logL'
, like the ones this function returns. If you just want to initialize from a certain parameter point, you can setinit_from['logL'] = 0
.optimization_steps (tuple) – successive optimization steps to perform. Entries should be
'simplex'
for Nelder-Mead,'gradient'
for gradient descent, or a dict whose entries will be passed toscipy.optimize.minimize
as keyword arguments.maxfev (int or None) – limit on function evaluations for
'simplex'
or'gradient'
optimization stepsfix_values (list of tuples (i, fix_to)) – can be used to keep some parameter values fixed or express them as function of the other parameters. See class doc for more details.
full_output (bool) – Set to
True
to return the output dict (c.f. Returns) and the full output fromscipy.optimize.minimize
for each optimization step. Otherwise (full_output == False
, the default) only the output dict from the ultimate run is returned.show_progress (bool) – display a
tqdm
progress bar while fittingverbosity ({None, 0, 1, 2, 3}) – if not
None
, overwrites the internalself.verbosity
for this run. Use to silence or get more details of what’s happening
- Returns
dict – with fields
'params'
, a complete set of parameters;'logL'
, the associated value of the likelihood (or posterior, if the prior is non-trivial).
-
class
tracklib.analysis.msdfit.core.
Profiler
(fit, profiling=True, conf=0.95, conf_tol=0.001, bracket_strategy='auto', bracket_step=1.2, max_fit_runs=100, max_restarts=10, verbosity=1)¶ Bases:
object
Exploration of the posterior after finding the point estimate
This class provides a top layer on top of
Fit
, enabling more comprehensive exploration of the posterior after finding the (MAP) point estimate. Generally, it operates in two modes:conditional posterior: wiggle each individual parameter, keeping all others fixed to the point estimate values, thus calculating conditional posterior values. In parameter space, this is a (multi-dimensional) cross along the coordinate axes.
profile posterior: instead of simply evaluating the posterior at each wiggle, keep the parameter fixed at the new value and optimize all others. Thus, in parameter space we are following the ridges of the posterior.
From a Bayesian point of view, beyond conditional and profile posterior, the actually interesting quantity is of course the marginal posterior. This is best obtained by sampling the posterior by MCMC (see module description for an example implementation of such a sampler). Still, conditional or profile posterior often can give a useful overview over the shape of the posterior. The conditional posterior is also great for setting MCMC step sizes. Profile posteriors are of course significantly more expensive than conditionals.
At the end of the day, this class moves along either profile or conditional posterior until it drops below a given cutoff, and then gives the lower bound, best value, and upper bound for each parameter. Inspired by frequentist confidence intervals, we determine the cutoff from a “confidence level” 1-α.
We follow a two-step procedure to find the lower and upper bounds: first, we move out from the point estimate with a fixed step size (additively or multiplicatively) until the posterior drops below the cutoff. Within the thus defined bracket, we then find the actual bound to the desired accuracy by bisection. If the first step fails (i.e. the posterior does not drop below the cutoff far from the point estimate) the corresponding parameter direction is unidentifiable and the bound will be set to infinity
Note that this class will also take care of the initial point estimate, if necessary.
The main point of entry for the user is the
find_MCI
method, that performs the sweeps described above. Further,run_fit
might be useful if you just need the point estimate.- Parameters
profiling (bool) – whether to run in profiling (
True
) or conditional (False
) mode.conf (float) – the “confidence level” to use for the likelihood ratio cutoff
conf_tol (float) – acceptable tolerance in the confidence level
bracket_strategy ('auto', dict, or list of dict) –
specifies the strategy to use during the first step (pushing the parameter out to find a proper bracket). For full control, specify a list of dicts (one for each parameter) with the structure
dict(multiplicative=(bool), step=(float), nonidentifiable_cutoffs=(low, high))
, wheremultiplicative
indicates whether propagation isparam <-- param*step
orparam <-- param + step
. Correspondingly,step
should be>1
formultiplicative == True
, andstep > 0
formultiplicative == False
. Finally,nonidentifiability_cutoffs
specifies how far to search before the direction is considered unidentifiable. This is measured in “total stepsize” taken in either direction, so e.g. for a multiplicative strategy the default isnonidentifiability_cutoffs = (10, 10)
, meaning we search by a factor of 10 around the point estimate in either direction.Instead of specifying a list of dicts, you can also just give a single dict that will be applied to all parameters. Finally, by default this argument is set to
'auto'
, which means that the strategy is determined from the bounds infit
. If the lower bound is positive, the strategy is multiplicative, else additive.bracket_step (float) – global default for the
'step'
parameter ifbracket_strategy == 'auto'
. Additive strategies usestep = point_estimate * (bracket_step - 1)
.max_fit_runs (int) – an upper bound for how often to re-run the fit (when profiling).
max_restarts (int) – sometimes the initial fit to find the point estimate might not converge properly, such that a better point estimate is found during the profiling runs. If that happens, the whole procedure is restarted from the new point estimate. This variable provides an upper bound on the number of these restarts. See also
restart_on_better_point_estimate
below.verbosity ({0, 1, 2, 3}) – controls amount of messages during profiling. 0: nothing; 1: warnings only; 2: informational; 3: debugging
-
min_target_from_fit
¶ the minimization target of
self.fit
- Type
callable
-
ress
¶ storage of all evaluated parameter points. Each entry corresponds to a parameter dimension and contains a list of dicts that were obtained while sweeping that parameter. The individual entries are dicts like the one returned by
Fit.run
, with fields'params'
and'logL'
.- Type
list
-
point_estimate
¶ the MAP point estimate, a dict like the other points in
ress
.- Type
dict
-
conf, conf_tol
see Parameters
- Type
float
-
LR_target
¶ the target decline in the posterior value we’re searching for
- Type
float
-
LR_interval
¶ acceptable interval corresponding to
conf_tol
- Type
[float, float]
-
iparam
¶ the index of the parameter currently being sweeped. This is a class attribute mainly for readability of the code.
- Type
int
-
profiling
¶ see Parameters
- Type
bool
-
bracket_strategy, bracket_step
see Parameters
- Type
list, float
-
max_fit_runs
¶ see Parameters
- Type
int
-
run_count
¶ counts the runs executed so far
- Type
int
-
max_restarts_per_parameters
¶ same as
max_restarts
parameter.- Type
int
-
verbosity
¶ see Parameters
- Type
int
-
restart_on_better_point_estimate
¶ whether to restart upon finding a better point estimate. Might make sense to disable if your posterior is rugged
- Type
bool
-
bar
¶ the progress bar showing successive fit evaluations
- Type
tqdm progress bar
-
vprint
(verbosity, *args, **kwargs)¶ Prints only if
self.verbosity >= verbosity
.
-
property
profiling
-
expand_bracket_strategy
()¶ Preprocessor for the
bracket_strategy
attribute
-
exception
FoundBetterPointEstimate
¶ Bases:
Exception
-
restart_if_better_pe_found
()¶ Decorator taking care of restarts
We use the
Profiler.FoundBetterPointEstimate
exception to handle restarts. So a function decorated with this decorator can just raise this exception and will then be restarted properly.
-
static
likelihood_significantly_greater
(res1, res2)¶ Helper function
The threshold is 1e-3. Note that this is an asymmetric operation, i.e. there is a regime where neither
res1
significantly greaterres2
nor the other way round.- Parameters
res2 (res1,) – like the output of
Fit.run
, and the entries ofself.ress
.- Returns
bool
-
property
best_estimate
¶ The best current estimate
This should usually be the point estimate, but we might find a better one along the way.
-
check_point_estimate_against
(res)¶ Check whether
res
is better than current point estimate- Parameters
res (dict) – the evaluated parameter point to check. A dict like the ones in
self.ress
.- Raises
-
run_fit
(is_new_point_estimate=True, **fit_kw)¶ Execute one fit run
This is used to find the initial point estimate, as well as subsequent evaluations of the profile posterior.
- Parameters
is_new_point_estimate (bool) – whether we are looking for a new point estimate or just evaluating a profile point. This just affects where the result is stored internally
fit_kw (keyword arguments) – additional parameters for
Fit.run
See also
-
find_closest_res
(val, direction=None)¶ Find the closest previously evaluated point
- Parameters
val (float) – new value
direction ({None, -1, 1}) – search only for existing values that are greater (1) or smaller (-1) than the specified one.
- Returns
dict – appropriate point from
self.ress
See also
-
profile_likelihood
(value, init_from='closest')¶ Evaluate profile (or conditional) likelihood / posterior
- Parameters
value (float) – value of the current parameter of interest (
self.iparam
)init_from (dict or 'closest') – from where to start the optimization (only relevant if
self.profiling
). Set to ‘closest’ to useself.find_closest_res
.
- Returns
dict – like in
self.ress
(and also stored there)
-
iterate_bracket_point
(x0, pL, direction, step=None)¶ First step of the procedure (“bracketing”)
In this first step we simply push out from the point estimate to try and establish a bracket containing the exact boundary point. See
Profiler.bracket_strategy
. This function implements that push, for one parameter in one direction.- Parameters
pL (x0,) – the parameter value and associated posterior value to start from. Should be from the point estimate
direction ({-1, 1}) – which direction to go in
step (float or None) – use to override
self.bracket_strategy[self.iparam]['step']
, which is used by default.
- Returns
x, pL (float) – the found bracket end point and associated posterior. If the chosen direction turns out to be unidentifiable,
x
is set to the corresponding bound (might beinf
), andpL = np.inf
.
See also
-
initial_bracket_points
()¶ Execute bracketing in both directions
See also
find_bracket_point()
,find_MCI()
-
solve_bisection
(bracket, bracket_pL)¶ Find exact root by recursive bisection
- Parameters
bracket ([float, float]) – the bracket of parameter values containing the root
bracket_pL ([float, float]) – the posterior values associated with the bracket points
- Returns
float – the root within the bracket, to within the given precision (c.f
self.conf_tolerance
)
Notes
All points evaluated along the way are stored in
self.ress
-
find_single_MCI
(*args, **kwargs)¶
-
find_MCI
(iparam='all', show_progress=False)¶ Perform the sweep for multiple parameters
- Parameters
iparam ('all' or np.ndarray, dtype=int) – the parameters to sweep. If ‘all’, will sweep all independent parameters, i.e. those not fixed via the
Fit.fix_values
mechanism.- Returns
mcis ((n, 3) np.ndarray, dtype=float) – for each parameter, in order: point estimate, lower, and upper bound.
See also
tracklib.analysis.msdfit.lib¶
-
class
tracklib.analysis.msdfit.lib.
SplineFit
(data, ss_order, n, previous_spline_fit_and_result=None)¶ Bases:
tracklib.analysis.msdfit.core.Fit
Fit a spline MSD
The MSD in this case is parametrized by the positions of a few spline points, between which we interpolate with cubic splines. The boundary conditions for the splines are set such that the fitted MSD extrapolates beyond the data as a powerlaw, except for large times in the
ss_order == 0
case, in which the MSD converges to a constant at infinite time. To achieve this within the spline fits, the time coordinate is compactified fromt in [1, T]
tox in [0, 1]
:if
ss_order == 0
, we needt = inf
to be accessible. We therefore choose the compactificationx = 4/π*arctan(log(t/T))
, such thatt = inf
corresponds tox = 2
.if
ss_order == 1
, we simply work in log-space, and normalize:x = log(t)/log(T)
.
These compactifications are of course applied only internally.
For the y-coordinate of the spline we apply a simple log-transform,
y = log(MSD)
. We then use the boundary condition that the second derivative of the spline vanishes, meaning the fitted MSDs can be extrapolated quite naturally by powerlaws.- Parameters
data (TaggedSet of Trajectory) – the data to fit. See
Fit
.ss_order ({0, 1}) – the steady state order to assume
n (int >= 2) – the number of spline points
previous_spline_fit_and_result (tuple (SplineFit, dict)) – this can be used to initialize the current fit from the resulting spline of a previous one. Very useful when running the above-mentioned model selection task over spline knots. First entry should be the
Fit
object used, second one should be the resulting dict with keys'params'
and'logL'
.
Notes
Clearly the number of spline points controls the goodness of fit and the degree of overfitting. It is thus recommended to use some information criterion (e.g. AIC) to determine a reasonable level of detail given the data. This approach turns out to be pretty useful in understanding which features of an “empirical MSD” (like calculated by
tracklib.analysis.MSD
) are reliable, and which ones are just noise.The parameters for this fit are the coordinates of the spline knots, in the transformed coordinate system (
(dt, MSD) --> (x, y) == (compactify(dt), log(MSD))
). Since the total extent of the data along the time axis is fixed, we also fix the first and last x-coordinates, such that ultimately the free parameters are(n-2)*[x] + n*[y]
.See also
Fit
,msdfit
-
compactify
(dt)¶ Compactification used for the current fit
- Parameters
dt (np.array, dtype=float) – the time lags to calculate compactification for. Should be
> 0
but might includenp.inf
.
See also
-
params2msdm
(params)¶ Calculate the current spline MSD
See also
Fit.params2msdm()
-
initial_params
()¶ Give suitable initial parameters for the spline
To find proper initial parameters, we perform a simple powerlaw fit to the empirical MSD. In the
ss_order == 0
case this is just used as boundary condition for a two-point spline between the first time lag and infinity (where we use the empirical steady state variance as initial value). Ifss_order == 1
this fitted powerlaw is the initial MSD.- Returns
params (np.ndarray, dtype=float) – the inital spline knots, in the internal x-y-coordinates (i.e. compactified)
See also
Fit.initial_params()
-
constraint_dx
(params)¶ Make sure the spline points are properly ordered in x
We impose this constraint mainly to avoid crossing of spline points, which usually leads to the spline diverging. On top of that, conceptually this makes the solution well-defined.
- Parameters
params (np.ndarray, dtype=float) – the current fit parameters
- Returns
float – the constraint score
See also
Fit()
-
constraint_logmsd
(params)¶ Make sure the Spline does not diverge
- Parameters
params (np.ndarray, dtype=float) – the current fit parameters
- Returns
float – the constraint score
See also
Fit()
-
class
tracklib.analysis.msdfit.lib.
TwoLocusRouseFit
(data, k=1)¶ Bases:
tracklib.analysis.msdfit.core.Fit
Fit a Rouse model for two loci on a polymer at fixed separation
This class implements a fit for two loci at fixed separation, but on the same polymer. A simple model for these dynamics is given by the infinite continuous Rouse model, which gives an analytical expression for the MSD implemented in
tracklib.models.rouse.twoLocusMSD
. Here we provide an implementation to fit this MSD to data.The parameters for this MSD are
the (squared) localization error
noise2
the Rouse scaling prefactor Γ of a single locus on the polymer
the steady state variance J of the distance between the loci. This encodes the chain length between them
The parameter vector for this fit is thus
d*[log(noise2), log(Γ), log(J)]
.Since all of these are positive quantities with units, it seems natural to perform the fit in log-space. Thus we (effectively) place a 1/x prior on all of them.
This class allows for separate fits to all the spatial dimensions of the data. In a standard setting this makes sense only for the localization error, so by default we fix Γ and J to be the same for all dimensions.
-
params2msdm
(params)¶ Give an MSD function (and mean = 0) for given parameters
Uses
tracklib.models.rouse.twoLocusMSD
-
initial_params
()¶ Initial parameters from empirical MSD
-
class
tracklib.analysis.msdfit.lib.
OneLocusRouseFit
(data, k=1)¶ Bases:
tracklib.analysis.msdfit.core.Fit
Fit a Rouse model for one locus on a polymer
This is very parallel to the implementation of
TwoLocusRouseFit
.The parameters for this MSD are
the (squared) localization error
noise2
the Rouse scaling prefactor Γ
Since all of these are positive quantities with units, it seems natural to perform the fit in log-space. Thus we (effectively) place a 1/x prior on all of them.
The parameter vector for this fit is thus
d*[log(noise2), log(Γ)]
.This class allows for separate fits to all the spatial dimensions of the data. In a standard setting this makes sense only for the localization error, so by default we fix Γ to be the same for all dimensions.
-
params2msdm
(params)¶ Give an MSD function (and drift = 0) for given parameters
-
initial_params
()¶ Initial parameters from empirical MSD


To Develop a secure E-Health card which stores and updates the records of patient’s medical reports/history.
The E-Health card is generalized so that it can be used in all hospitals or in a particular chain of hospitals to view
the patient’s medical report in a feasible and quick manner, before starting the treatment.


Control Hub is a web portal place where admins and partners come to manage users, services, features, and devices. It is also the place where admins view the licenses across all your services.
Cisco Systems Unified Collaboration Group Design Team, Control Hub
Whiteboard, Sketch, Figma, Webex
UI/UX Design, Wireframe, Web Design, Front-end Development
Rialto Product Team
Development Lead: Girish Patil
Product Manager: Angela Suraji
Content: Daniel Hills
The current Control hub is bi-portal experience where the admins have to go between two different portals (Control Hub and Rialto portal) to setup, edit and save their Calling features. Our goal is to bring the all the features into one Control Hub portal, with updated user experience, interaction and visual design.
In order to migrate with UX improvement, the team came to gather to dissect and understand the features. Out of the 7 features, we dug deep into the most complicated features first, which is Auto Attendant, Hunt Group and Call Queue. Once we have these features down, we saw that we would be able to update and migrate other features with ease.
The biggest challenge was the vocabulary used in the features were hard to understand, so we worked with the developers who created Rialto Portal and product manager to understand and test the features with our own accounts.
In telephony, line hunting is the method of distributing phone calls from a single telephone number to a group of several phone lines. Specifically, it refers to the process or algorithm used to select which line will receive the call
The strict definition of a call queue is that it's where callers are placed on hold, in a queue, while they wait to speak with a live agent. In other words, the call queue is where customers are placed after completing any interactive voice response prompts but before a call center representative has spoken with them.
Using the Momentum design system, the visual design was applied to updated UX design wireframes.
We sought continuous feedback throughout our design process from the design team and the key stakeholders from the product team and did multiple iterations.
The Core Control Hub design team who is in charge of looking over the overall interaction and visual design for the core control hub is to work with our team to come up with an updated Control Hub portal design.


core
/
com.mo.stratego.model.component
/
WaitComponent
/
waitTime
waitTime
val
waitTime
:
Float
Time to wait
Property
waitTime
- Time to wait


In this section, we automatically synthesize data from prediction and we suggest learning options to improve your performance and to take advantage of the predictions. Let's see where you had the weakest results and what is our recommendation:


Django Categories v0.8.2 documentation
Models
Table of Contents
index
next
previous
Main Page
Getting Started
Using categories in templates
Registering Models
Adding the fields to the database
Reference
Management Commands
Models
Settings
Template Tags
This Page
Show Source
Enter search terms or a module, class or function name.
Models
¶
Category
¶
parent
The category’s parent category. Leave this blank for an Category Tree.
name
The name of the category.
thumbnail
An optional thumbnail, that is uploaded to
CATEGORY_SETTINGS['THUMBNAIL_UPLOAD_PATH']
via
CATEGORY_SETTINGS['THUMBNAIL_STORAGE']
.
thumbnail_width
The thumbnail width.
thumbnail_height
The thumbnail height.
order
The order of this category in the listing
slug
A slug created from the name field
alternate_title
An alternative title to use on pages with this category.
alternate_url
An alternative URL to use instead of the one derived from the category hierarchy.
description
An optional longer description of the category.
meta_keywords
Comma-separated keywords for search engines.
meta_extra
(Advanced) Any additional HTML to be placed verbatim in the
<head>
Reference
Models


#include <stDBMNode.h>
Public Member Functions | |
| stDBMShrinkNode (stDBMNode *node) | |
| stDBMShrinkNode (ObjectType *object) | |
| stDBMNode * | ReleaseNode () |
| void | Release () |
| bool | Add (ObjectType *obj, stDistance distance, stPageID pageID, stDistance radius#ifdef __stDBMNENTRIES__, stCount nEntries#endif#ifdef __stDBMHEIGHT__, stByte height#endif) |
| stCount | GetNumberOfEntries () |
| ObjectType * | ObjectAt (stCount idx) |
| ObjectType * | LastObject () |
| stPageID | LastPageID () |
| stDistance | LastRadius () |
| stDistance | LastDistance () |
| stByte | LastHeight () |
| stCount | LastNEntries () |
| ObjectType * | RepObject () |
| stDistance | DistanceAt (stCount idx) |
| stPageID | PageIDAt (stCount idx) |
| stDistance | RadiusAt (stCount idx) |
| stCount | NEntriesAt (stCount idx) |
| stByte | HeightAt (stCount idx) |
| ObjectType * | Remove (stCount idx) |
| ObjectType * | PopObject () |
| bool | CanAdd (ObjectType *obj, stPageID pageID) |
| stDistance | GetMinimumRadius () |
| stSize | GetFreeSize () |
This class template implements a memory shell for a stDBMNode instance. It implements a memory optimized node wich simulates the physical occupation of the original node.
Since all objects are stored using their implementation (not the serialized form), this class is very useful to optimize heavy object manipulations such as Shrink and MinMax algorithms.
By the other side, this class will unserialize all objects of the node without exceptions. Due to that, the use of this class is not recommended for procedures with low rate of object unserializations/serializations. Another disadvantage of this class is the potential high memory usage.
Definition at line 760 of file stDBMNode.h.
| stDBMShrinkNode< ObjectType >::stDBMShrinkNode | ( | stDBMNode * | node | ) |
Creates a new stSlimMemLeafNode instance from a stDBMNode.
| node | The stDBMNode to be insert. |
| stDBMShrinkNode< ObjectType >::stDBMShrinkNode | ( | ObjectType * | object | ) |
Creates a new stSlimMemLeafNode instance from a single object.
| object | The object to be insert. |
| bool stDBMShrinkNode< ObjectType >::Add | ( | ObjectType * | obj, |
| stDistance | distance, | ||
| stPageID | pageID, | ||
| stDistance radius#ifdef | __stDBMNENTRIES__, | ||
| stCount nEntries#endif#ifdef | __stDBMHEIGHT__, | ||
| stByte height# | endif | ||
| ) |
Adds an object to this node.
All entries are sorted in crescent order of distance to allow complex object manipulation such as Shrink.
| obj | An object to be insert. |
| distance | A correspondent distance of the new entry. |
|
inline |
Returns true if there is enough free space to add the given object.
Definition at line 1026 of file stDBMNode.h.
|
inline |
Returns the distance associated with a given entry.
| idx | Index of the object. |
Definition at line 930 of file stDBMNode.h.
|
inline |
Returns the free space of this node.
Definition at line 1058 of file stDBMNode.h.
| stDistance stDBMShrinkNode< ObjectType >::GetMinimumRadius | ( | ) |
Returns the minimum radius of this node.
|
inline |
Returns the number of entries.
Definition at line 816 of file stDBMNode.h.
|
inline |
Returns the height associated with a given entry.
| idx | Index of the object. |
Definition at line 997 of file stDBMNode.h.
|
inline |
Returns the distance of the last object from the representative of this node.
Definition at line 883 of file stDBMNode.h.
|
inline |
Returns the height of the last entry.
Definition at line 896 of file stDBMNode.h.
|
inline |
Returns the NEntries of the last entry.
Definition at line 909 of file stDBMNode.h.
|
inline |
Returns a pointer to the last object of the node. This object is special because it has the largest distance from the representative.
Definition at line 843 of file stDBMNode.h.
|
inline |
Returns the pageID of the last entry of the node.
Definition at line 856 of file stDBMNode.h.
|
inline |
Returns the radius of the last entry of the node.
Definition at line 869 of file stDBMNode.h.
|
inline |
Returns the NEntries associated with a given entry.
| idx | Index of the object. |
Definition at line 979 of file stDBMNode.h.
|
inline |
Returns the object at position idx.
| idx | Index of the object. |
Definition at line 827 of file stDBMNode.h.
|
inline |
Returns the pageID associated with a given entry.
| idx | Index of the object. |
Definition at line 946 of file stDBMNode.h.
| ObjectType* stDBMShrinkNode< ObjectType >::PopObject | ( | ) |
Remove the last object from this tree.
|
inline |
Returns the radius associated with a given entry.
| idx | Index of the object. |
Definition at line 962 of file stDBMNode.h.
| void stDBMShrinkNode< ObjectType >::Release | ( | ) |
Release the resources of this class template.
| stDBMNode* stDBMShrinkNode< ObjectType >::ReleaseNode | ( | ) |
| ObjectType* stDBMShrinkNode< ObjectType >::Remove | ( | stCount | idx | ) |
Removes an entry in idx from this node.
| idx | Index of the object. |
| ObjectType* stDBMShrinkNode< ObjectType >::RepObject | ( | ) |
Returns a pointer to the representative object of the node.


Philemon 1:2
And to our beloved Apphia, and Archippus our fellowsoldier, and to the church in thy house:


{% extends 'base.html' %} {% load icons %} {% load static %} {% load bootstrap4 %} {% block title %}Receipt{% endblock %} {% block content %}
Payment has been sent!
Here is your receipt:
Project owner: {{project.user.user.username}}
Payment receiver: {{taskoffer.offerer.user.username}}
Paid price: {{taskoffer.price}}
Original task:
{{task.description}}
Agreed upon task:
{{taskoffer.description}}