We encourage you to wear casual island clothing. I've included some examples below. If you have any questions or concerns feel free to contact Jamela or Jevin
The colors are ocean blues and whites, but really, whatever you feel comfortable with.
Please only give monetary gifts or gift cards. Luggage space is a limiting factor.
We suggest calling Carnival cruise and using our booking #, however you can do it through their website.
Guest Services #:<PHONE_NUMBER>5
Booking #: 9H8RK6
Code: 1221
Ship: Paradise
Date: 12/10/2016
Place: Grand Cayman and Cozumel
Yes! We encourage you to. We will have a planned group dinner each night, but otherwise feel free to use your time however you like.


|
M480 BSP V3.05.005
The Board Support Package for M480 Series
|
Functions | |
| int32_t | TRNG_Open (void) |
| Initialize TRNG hardware. More... | |
| int32_t | TRNG_GenWord (uint32_t *u32RndNum) |
| Generate a 32-bits random number word. More... | |
| int32_t | TRNG_GenBignum (uint8_t u8BigNum[], int32_t i32Len) |
| Generate a big number in binary format. More... | |
| int32_t | TRNG_GenBignumHex (char cBigNumHex[], int32_t i32Len) |
| Generate a big number in hex format. More... | |
| int32_t TRNG_GenBignum | ( | uint8_t | u8BigNum[], |
| int32_t | i32Len | ||
| ) |
Generate a big number in binary format.
| [out] | u8BigNum | The output big number. |
| [in] | i32Len | Request bit length of the output big number. It must be multiple of 8. |
| 0 | Success |
| -1 | Time-out. TRNG hardware may not be enabled. |
| int32_t TRNG_GenBignumHex | ( | char | cBigNumHex[], |
| int32_t | i32Len | ||
| ) |
Generate a big number in hex format.
| [out] | cBigNumHex | The output hex format big number. |
| [in] | i32Len | Request bit length of the output big number. It must be multiple of 8. |
| 0 | Success |
| -1 | Time-out. TRNG hardware may not be enabled. |
| int32_t TRNG_GenWord | ( | uint32_t * | u32RndNum | ) |


|
libcbor
0.5.0
libcbor is a C library for parsing and generating CBOR, the general-purpose schema-less binary data format.
|
#include "cbor/common.h"
Go to the source code of this file.
Functions | |
| void | cbor_set_allocs (_cbor_malloc_t custom_malloc, _cbor_realloc_t custom_realloc, _cbor_free_t custom_free) |
| Sets the memory management routines to use. More... | |
Variables | |
| _cbor_malloc_t | _cbor_malloc = malloc |
| _cbor_realloc_t | _cbor_realloc = realloc |
| _cbor_free_t | _cbor_free = free |
| void cbor_set_allocs | ( | _cbor_malloc_t | custom_malloc, |
| _cbor_realloc_t | custom_realloc, | ||
| _cbor_free_t | custom_free | ||
| ) |
Sets the memory management routines to use.
Only available when CBOR_CUSTOM_ALLOC is truthy
embed:rst:leading-asterisk * .. warning:: This function modifies the global state and should therefore be used accordingly. Changing the memory handlers while allocated items exist will result in a ``free``/``malloc`` mismatch. This function is not thread safe with respect to both itself and all the other *libcbor* functions that work with the heap. * .. note:: `realloc` implementation must correctly support `NULL` reallocation *
| custom_malloc | malloc implementation |
| custom_realloc | realloc implementation |
| custom_free | free implementation |
Definition at line 14 of file allocators.c.
| _cbor_free_t _cbor_free = free |
Definition at line 12 of file allocators.c.
| _cbor_malloc_t _cbor_malloc = malloc |
Definition at line 10 of file allocators.c.
| _cbor_realloc_t _cbor_realloc = realloc |
Definition at line 11 of file allocators.c.


Machine learning meets African agriculture
by Nilesh Bell-GorsiaIn 2016, a crop-destroying caterpillar, Fall Armyworm (FAW) was first detected in Africa. The crop pest has since devastated agriculture by infecting millions of corn fields, which threatens food security on the continent. Farmers who rely on harvests for food need to combat the pest, which has now spread to India and China.
That’s where Nazirini Siraji comes in. She is one of several developers working to provide farmers with new tools to fight FAW. After codelabs hosted by a Google developer group in Mbale, Uganda, she created the “Farmers Companion App” using TensorFlow, Google’s open-source machine learning platform. It’s a free app that identifies when a crop has FAW and which stage the worm is in its lifecycle (and therefore how threatening it is and how far it is likely to spread). It also advises on which pesticides or treatments are best to stop the worm spreading any further. The app is already working in the field, helping farmers around Mbale to identify FAW.
They continue to improve the app so it can identify more pests and diseases. Nazirini shows the impact that developers can have on agricultural issues like FAW and across other sectors, too. We visited Nazirini and her team this year, here’s more about their story:
Learn more about how others are using TensorFlow to solve all kinds of problems.


Captain Lucas:
Do you mean this is the reflection of the thief?
I can't help noticing that it looks exactly like a Leto Lizardman. He appears to be a perfect Warrior... I heard that the Leto Lizardmen have been seen in the southeastern part of Oren.
If this fortune-teller's mirror is correct, we have a huge problem. There must be a deeper reason for their behavior...
Let's get started. The fortune-teller might be correct.


2018-01-05 Throwback Thursday: "How I made 7 cents in 5 posts and you can too!" and Steemit Millionaire life
2018-01-02 Back in the office! Hatching plans. Life is good!  (About to hit 10K FOLLOWERS!!!) life
2017-12-30 Dragonwood: A family friendly game that's great for children (100 or so more followers to 10,000!!!!) life
2017-12-20 Make Curation Great Again: I pumped my @msp-curation Delegation up to 7,500 making a total of 10.5k SP. life
2016-09-30 Exploring the Mandela and Toto Effect S1E4: Radio show interview on Full Disclosure Now life


Web Component Examples
VanillaJS
Simple Echo component (Custom Elements)
Counter (Custom Elements)
Counter (Custom Elements and HTML Templates)
Counter (Custom Elements, HTML Templates, and Shadow DOM)
All three Counters (loaded via HTML Imports)
HTML Template example (without Custom Elements)
Stencil
Simple Echo component
Polymer
Simple Echo component
Skate
Simple Echo component


Amazon EMR InstanceFleetConfig SpotProvisioningSpecification
Use
the SpotProvisioningSpecification
property
to
specify
the duration and timeout behavior for Spot
Instances
in the instance fleet for Amazon EMR. SpotProvisioningSpecification
is the property
type for the SpotSpecification
subproperty of the Amazon EMR InstanceFleetConfig InstanceFleetProvisioningSpecifications property type.
Note
The instance fleet configuration is available only in Amazon EMR versions 4.8.0 and later, excluding 5.0.x versions.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "BlockDurationMinutes" :
Integer
, "TimeoutAction" :String
, "TimeoutDurationMinutes" :Integer
}
YAML
BlockDurationMinutes:
Integer
TimeoutAction:String
TimeoutDurationMinutes:Integer
Properties
BlockDurationMinutes
-
The defined duration for Spot Instances (also known as Spot blocks) in minutes. For more information, see SpotProvisioningSpecification in the Amazon EMR API Reference.
Required: No
Type: Integer
Update requires: No interruption
TimeoutAction
-
The action to take when the capacity for the target Spot Instance, as specified in
TargetSpotCapacity
, has not been fulfilled before the time specified inTimeoutDurationMinutes
has expired. For more information, see SpotProvisioningSpecification in the Amazon EMR API Reference.Required: Yes
Type: String
Update requires: No interruption
TimeoutDurationMinutes
-
The timeout period for spot provisioning, in minutes. For more information, see SpotProvisioningSpecification in the Amazon EMR API Reference.
Required: Yes
Type: Integer
Update requires: No interruption


| ID | Vehicle | Service | Date | State | Actions |
|---|---|---|---|---|---|
| {{ reservation._id }} | {{ reservation.vehicle?.number }} ({{ reservation.vehicle?.vendor }} {{ reservation.vehicle?.model }}) | {{ service.name }} | {{ reservation.requested.substr(0,10) }} | Pending Accepted Declined |
Rs. {{ service.price }}.00


{% include 'contribution/list-common.html' with dynamic_variable=uncommented_contributions_mm dynamic_datatable='dynamic_datatable_uncommented_contributions_mm' %}
{% include 'contribution/list-common.html' with dynamic_variable=contributions_selected_mm dynamic_datatable='dynamic_datatable_contributions_selected_mm' %}
{% include 'contribution/list-common.html' with dynamic_variable=contributions_selected dynamic_datatable='dynamic_datatable_contributions_selected' %}
{% include 'contribution/list-common.html' with dynamic_variable=uncommented_contributions dynamic_datatable='dynamic_datatable_uncommented_contributions' %}
{% include 'contribution/list-common.html' with dynamic_variable=contributions_by_year_faculty dynamic_datatable='dynamic_datatable_contributions_by_year_faculty' %}


| DOSE: |
2 hits |
oral |
DOI |
(blotter / tab) |
| |
1 repeated |
smoked |
Cannabis |
|
This story takes place over three days.  We each ate two and almost immediately (fifteen minutes later) started to feel them the roll started to kick in, but this story is not about roll it is about DOI.
After the roll party my friend and I proceeded to continue on with our day now that the sun was rising and there was no sign of the roll’s energy effects wearing out anytime soon. Throughout the day C. Jack and I were discussing a trip to the beach which from where we are is about a five and a half hour drive. We pitched the idea to our friend Hans and he agreed that it would be a fun trip to take.
We left our area at about eight o’clock, at eight twenty I started to ingest the two hits of DOI I had on me. At the two hour mark the early signs of the drug started to kick in and reds, and blues were now becoming brighter and lights were starting to shift patterns. The DOI hit me in wave-like patterns, coming in and hitting me hard then withdrawing and hitting me again. We were almost to the half way mark on the map and decided to stop at a rest area.
Hans who had been driving for quite some time now needed to stop to walk around and smoke a joint.
[Erowid Note: Driving while intoxicated or tripping is dangerous and irresponsible because it endangers other people. Don't do it!] First we walked into the help center at the rest stop to use the bathroom and ask for directions, by this time the DOI had intensified itself to what I thought would be the plateau. Hans walked into the help center first, while I closely followed behind, the first person we see is a security guard, Hans at this point had forgotten that he had a beer in his hand. After using the bathroom we returned to the front desk and started to talk to the guard to make things seem okay, then asked him for directions. The guard started to show me a map of the area we were in, but all of the roads were marked with red lines, making this very hard for me to keep my cool in front of a rent-a-cop.
After we left the help center, we went outside and smoked a joint. Walking a short distance from our car we sat down and began to smoke, moments later we see the security guard come outside, we were not in any trouble, but by this time I was starting to trip extremely hard, and realized that if we were in the situation where we would have to run I would not know which way to run, although the car was in plain sight less than a hundred feet away from us. We piled back into the car and began to drive again, we eventually made it to the beach by two-thirty.
Hans and his girlfriend eat the same type of roll that I had taken the night before the roll starts to kick in and they eventually went for a “walk” leaving me on the beach alone by myself. By the time the DOI was stating to plateau on me while I watched the ocean and night sky. To put it simply I had never seen anything more beautiful in my entire life. I stood on the beach for the next two hours and power tripped by myself. Eventually my friends got back from their walk and began to tell me the story about how they met. I don’t know how long the story lasted but to put it this way it was pitch black at time the story had begun and when it was over the sun was starting to rise.
My friend and I went for a walk because I had realized that we were out of money and I only had checks on me that were out of state. With this fear of being stranded my friend returned to his girlfriend and proceeded to take another walk with her leaving me again by this time I was at the six hour marker. Eventually the sun started to rise which I cannot even start to describe what it was like, but I noticed that the sun was rising fairly fast and started to become paranoid of the beach patrol that would soon be making their rounds.
I packed all of our items (three towels, a camera, a CD player, and a sleeping bag) into my friend’s purse. After the sun rose and my friends had returned my friend Hans and I franticly thought about how we were going to obtain the money to make the trip home we eventually made it to Wal-Mart seeing if we could receive cash back on an out of state check, at this certain Wal-Mart there were neon signs everywhere. This was at about ten o’clock and I was still in plateau stage of the trip. My friend standing in line was clicking off his fingers as an effect of the roll. The clerk told us that a nearby grocery store would allow us to get the necessary money to make it home.
We then began to look around the city for things to do before we headed out of town. After we had been to different head shops we eventually left at about twelve the day after we got there. On the way home the DOI was still hit hard in the car. Realizing now that I had not eaten or slept in three days, I decided to lay down in the back seat. I suddenly rose up about ten minutes later after my friend had swerved into the median and swerved back out. The next thing I see is my friends girlfriend take a hit from my pipe and blew smoke everywhere in the car, immediately after that I see my friend Hans turn an extreme white and scream “OH NO, OH NO” I looked back and a state trooper was right behind us with eight different lights going at one time, I was still in the plateau stage at this time, knowing we were completely fucked my friend started to pull over, and by some miracle the cop decided to pass us.
Eventually we got back to Hans’ house took showers and went to meet his girlfriend again. We the proceeded to go to C. Jack’s house and chill. At this time I was in the twenty hour mark of my trip. My friend Lance and I left C. Jack’s place and went to Lance’s. At the twenty-eight hour mark I started to come down and crashed. Over all I was not prepared for this trip it was fun and intense and I would do it again if I could.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Top-level classes of the system, when running with a GUI
Package Specification
This package contains the top classes of the system.
NARS: defines the application and applet.
Reasoner: controls the interaction between the memory and the communication channels.


uos
– basic “operating system” services¶
The os
module contains functions for filesystem access and urandom
function.
Port specifics¶
The filesystem has /
as the root directory and the
available physical drives are accessible from here. They are currently:
/flash
– the internal flash filesystem
/sd
– the SD card (if it exists)
Functions¶
-
uos.
chdir
(path)¶ Change current directory.
-
uos.
getcwd
()¶ Get the current directory.
-
uos.
listdir
([dir])¶ With no argument, list the current directory. Otherwise list the given directory.
-
uos.
mkdir
(path)¶ Create a new directory.
-
uos.
remove
(path)¶ Remove a file.
-
uos.
rmdir
(path)¶ Remove a directory.
-
uos.
rename
(old_path, new_path)¶ Rename a file.
-
uos.
stat
(path)¶ Get the status of a file or directory.
-
uos.
sync
()¶ Sync all filesystems.
-
uos.
urandom
(n)¶ Return a bytes object with n random bytes, generated by the hardware random number generator.


Rephael Congmon
My Portfolio
CLICK ME!
HOME
INTRODUCTION
This is a picture of a cookie.
My name is Rephael Congmon. I am a Computer Science student and currently a sophomore.


As someone who has always been up for a new adventure, pursuing a career in web development is one of the most exciting and challenging adventures yet. I am thrilled to join an industry that is creating new ways to share information and connect people. Having worked in various hospitality positions and even a few years as an English teacher in South Korea and Spain, I value the way the internet has transformed our world. I hope to use the ever-expanding skills learned through the Coding Bootcamp at the University of Minnesota to pursue a career that allows me to tackle new problems and lets my creativity shine.
In my free time I enjoy listening to music, visiting used book stores, and traveling. In 2017, I spent time in Asia, South America, and Europe.


Battery Assault
Discharges high-powered shrapnel at random nearby enemy units, dealing minor magical damage and ministun.
| Mana Cost: | 75/75/75/75 |
| Cooldown: | 32/28/24/20 |
| ABILITY: | No Target |
| DAMAGE TYPE: | Magical |
| PIERCES SPELL IMMUNITY: | No |
| DAMAGE: | 20 / 40 / 60 / 80 |
| RADIUS: | 275 / 275 / 275 / 275 |
| DURATION: | 10.5 / 10.5 / 10.5 / 10.5 |
| INTERVAL: | 0.7 / 0.7 / 0.7 / 0.7 |
Some of Rattletrap's contraptions don't quite work correctly, so detonating them proves useful as an offensive maneuver.
Power Cogs
Forms a barrier of energized cogs around Clockwerk, trapping any units that are near. Enemies outside the trap that touch a cog are knocked back, losing health and mana. Once a cog has delivered a shock, it will power down. Cogs can be destroyed by enemy attacks, but Clockwerk can destroy them with just one.
| Mana Cost: | 50/60/70/80 |
| Cooldown: | 15 |
| ABILITY: | No Target |
| DAMAGE TYPE: | Magical |
| PIERCES SPELL IMMUNITY: | No |
| DURATION: | 5 / 6 / 7 / 8 |
| MANA/HEALTH DRAIN: | 80 / 120 / 160 / 200 |
| ATTACKS REQUIRED: | 2 / 2 / 2 / 3 |
One of Clockwerk's inventions of which he is most proud is the power cog - though it is sometimes despised by his allies.
Rocket Flare
Fires a global range flare that explodes over a given area, damaging enemies and providing vision for 10 seconds.
| Mana Cost: | 50/50/50/50 |
| Cooldown: | 20/18/16/14 |
| ABILITY: | Point Target |
| DAMAGE TYPE: | Magical |
| PIERCES SPELL IMMUNITY: | No |
| DAMAGE: | 80 / 120 / 160 / 200 |
| RADIUS: | 575 / 575 / 575 / 575 |
What started as a festive display has become a useful scouting and bombardment tool.
Hookshot
Fires a grappling device rapidly at the target location. If the hook hits a unit, Clockwerk launches himself into the target, stunning and dealing damage. Any enemies Clockwerk collides with along the way are damaged and stunned. Upgradable by Aghanim's Scepter.
| Mana Cost: | 150/150/150 |
| Cooldown: | 70/55/40 |
| ABILITY: | Point Target |
| DAMAGE TYPE: | Magical |
| PIERCES SPELL IMMUNITY: | Yes |
| STUN DURATION: | 1 / 1.5 / 2 |
| RANGE: | 2000 / 2500 / 3000 |
| DAMAGE: | 100 / 200 / 300 |
| SCEPTER COOLDOWN: | 12 / 12 / 12 |
A somewhat unwieldy device, the Hookshot sends the otherwise clunky Clockwerk armor flying through the air.


"An Agatha Christie fresh pick.
Fresh picks are great, even though you can't put them on hold. They allow you to get the newest books sooner." The librarians says.
"Do you want to keep checking out?"
| Yes, you say as you hand them your second item. | ||
| ...................... | ...................... | |
| No,thats all my items. |


Each of the following iframes connects to a stream from the same endpoint. Ideally, no matter how many of these pages you open, across multiple browsers and devices, the number should be one, and the colours should always be the same in all boxes.


CSPICE_FOVRAY determines if a specified ray is within the field-of-view
(FOV) of a specified instrument at a given time.
Given:
Parameters-
SPICE_GF_MAXVRT is the maximum number of vertices that may be used
to define the boundary of the specified instrument's
field of view. See SpiceGF.h for more details.
MARGIN is a small positive number used to constrain the
orientation of the boundary vectors of polygonal
FOVs. Such FOVs must satisfy the following
constraints:
1) The boundary vectors must be contained within
a right circular cone of angular radius less
than than (pi/2) - MARGIN radians; in
other words, there must be a vector A such that
all boundary vectors have angular separation
from A of less than (pi/2)-MARGIN radians.
2) There must be a pair of boundary vectors U, V
such that all other boundary vectors lie in
the same half space bounded by the plane
containing U and V. Furthermore, all other
boundary vectors must have orthogonal
projections onto a specific plane normal to
this plane (the normal plane contains the angle
bisector defined by U and V) such that the
projections have angular separation of at least
2*MARGIN radians from the plane spanned
by U and V.
MARGIN is currently set to 1.D-6.
Arguments-
inst indicates the name of an instrument, such as a
spacecraft-mounted framing camera. The field of view
(FOV) of the instrument will be used to determine if
the direction from the observer to a target,
represented as a ray, is visible with respect to the
instrument.
[1,a] = size(inst), char = class(inst)
The position of the instrument is considered to
coincide with that of the ephemeris object 'obsrvr' (see
description below).
The size of the instrument's FOV is constrained by the
following: There must be a vector A such that all of
the instrument's FOV boundary vectors have an angular
separation from A of less than (pi/2)-MARGIN radians
(see description above). For FOVs that are circular or
elliptical, the vector A is the boresight. For FOVs
that are rectangular or polygonal, the vector A is
calculated.
See the header of the CSPICE routine getfov_c for a
description of the required parameters associated with
an instrument.
Both object names and NAIF IDs are accepted. For
example, both 'CASSINI_ISS_NAC' and '-82360' are
accepted. Case and leading or trailing blanks are not
significant in the string.
raydir is the direction vector defining a ray of interest.
The ray emanates from the location of the ephemeris
object designated by the input argument 'obsrvr' and
is expressed relative to the reference frame designated
by 'rframe' (see description below).
[3,1] = size(raydir), double = class(raydir)
rframe is the name of the reference frame associated with
the input ray's direction vector 'raydir'. Note:
'rframe' does not need to be the instrument's reference
frame.
[1,b] = size(rframe), char = class(rframe)
Since light time corrections are not supported for
rays, the orientation of the frame is always evaluated
at the epoch associated with the observer, as opposed
to the epoch associated with the light-time corrected
position of the frame center.
abcorr indicates the aberration corrections to be applied
when computing the target's position and orientation.
[1,c] = size(abcorr), char = class(abcorr)
The supported aberration correction options are:
'NONE' No correction.
'S' Stellar aberration correction,
reception case.
'XS' Stellar aberration correction,
transmission case.
For detailed information, see the geometry finder
required reading, gf.req.
Case, leading and trailing blanks are not significant
in the string.
obsrvr is the name of the body from which the target
represented by 'raydir' is observed. The instrument
is treated as if it were co-located with the observer.
[1,d] = size(obsrvr), char = class(obsrvr)
Both object names and NAIF IDs are accepted. For
example, both 'CASSINI' and '-82' are accepted. Case and
leading or trailing blanks are not significant in the
string.
et is the observation time in seconds past the J2000
epoch.
[1,n] = size(et), double = class(et)
the call:
visibl = cspice_fovray ( inst, raydir, rframe, ...
abcorr, obsrvr, et )
returns:
visibl is true if the ray is "visible", or in the
field-of-view, of the instrument at the time 'et'.
Otherwise, 'visibl' is false.
[1,n] = size(visibl), logical = class(visibl)
Any numerical results shown for this example may differ between
platforms as the results depend on the SPICE kernels used as input
and the machine specific arithmetic implementation.
Example(1):
The Cassini Ultraviolet Imaging Spectrograph (UVIS)
has been used to measure variations in starlight as
rings and moons occult Cassini's view of the stars.
One of these events happened at 2008-054T21:31:55.158 UTC.
Let's verify that Epsilon CMa (Adhara) was in the
Cassini UVIS field-of-view at the observation time.
Use the meta-kernel shown below to load the required SPICE
kernels.
KPL/MK
File name: fovray_ex.tm
This meta-kernel is intended to support operation of SPICE
example programs. The kernels shown here should not be
assumed to contain adequate or correct versions of data
required by SPICE-based user applications.
In order for an application to use this meta-kernel, the
kernels referenced here must be present in the user's
current working directory.
The names and contents of the kernels referenced
by this meta-kernel are as follows:
File name Contents
--------- --------
naif0010.tls Leapseconds
cpck26Jan2007.tpc Satellite orientation and
radii
cas00145.tsc Cassini SCLK
cas_v40.tf Cassini frames
cas_uvis_v06.ti Cassini UVIS instrument
080428R_SCPSE_08045_08067.bsp Merged spacecraft,
planetary, and satellite
ephemeris
08052_08057ra.bc Orientation for Cassini
\begindata
KERNELS_TO_LOAD = ( 'naif0010.tls'
'cpck26Jan2007.tpc'
'cas00145.tsc'
'cas_v40.tf'
'cas_uvis_v06.ti'
'080428R_SCPSE_08045_08067.bsp'
'08052_08057ra.bc')
\begintext
End of meta-kernel
Example program starts here.
%
% Load the meta kernel.
%
cspice_furnsh ( 'fovray_ex.tm' );
%
% Convert the observation time to 'et'.
%
et = cspice_str2et ( '2008-054T21:31:55.158' );
%
% The variables 'right_asc' and 'dec' are the right ascension
% and declination of Epsilon CMa in degrees.
%
right_asc = 104.656;
dec = -28.972;
%
% Create a unit direction vector pointing from Cassini
% to the specified star. For details on corrections such
% as parallax, please see the example in cspice_gfrfov.
%
raydir = cspice_radrec ( 1, right_asc*cspice_rpd, dec*cspice_rpd );
%
% Is the star in the field-of-view of Cassini's UVIS?
%
visible = cspice_fovray ( 'CASSINI_UVIS_FUV_OCC', raydir, ...
'J2000', 's', 'cassini', et );
%
% Put the time in a specified format for output and
% report the result.
%
time_output = cspice_timout ( et, ...
'YYYY-MON-DD HR:MN:SC.###::TDB (TDB)' );
if ( visible )
fprintf ( 'Epsilon CMa was visible from the Cassini\n' );
fprintf ( 'UVIS instrument at %s\n', time_output );
end
%
% Unload kernels.
%
cspice_kclear
MATLAB outputs:
Epsilon CMa was visible from the Cassini
UVIS instrument at 2008-FEB-23 21:33:00.343 (TDB)
To treat the target as an ephemeris object rather than a ray, use
the higher-level Mice routine cspice_fovtrg. cspice_fovtrg may be used to
determine if ephemeris objects such as Saturn are visible in an
instrument's FOV at a given time.
For important details concerning this module's function, please refer to
the CSPICE routine fovray_c.
-Mice Version 1.0.1, 13-APR-2015, EDW (JPL)
Edit to correct typos in "Usage" string.
-Mice Version 1.0.0, 13-NOV-2013, SCK (JPL), EDW (JPL)
Ray in instrument FOV at specified time
Ray in instrument field_of_view at specified time
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css-test
Bob the Beach Bum Menu
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In 2017 Djokovic began his partnership with Lacoste amidst a slump. He failed to defend any of slams from his historic 2015-16 non-calendar slam run, and had been plagued by an elbow injury for over a year. The first year of the Lacoste era was a disappointing one, as Djokovic spent the second half of 2017 off the tour, and struggled to find his form again in the first half of 2018
During his absence, Federer and Nadal had turned back the clock and split the four slams equally in 2017, and had each taken a slam in 2018 by the time Wimbledon rolled around. Djokovic was becoming the forgotten man, but managed beat Rafael Nadal in a five set epic in the semi-finals before defeating South African Kevin Anderson to pick up his first slam in over two years. The Serb was well and truly back, and won the US Open later that year to finish as the year end #1
In 2021, Djokovic equalled the record of Federer and Nadal by winning his 20th Grand Slam at Wimbledon, just a month after beating Nadal in the semi-finals of the French Open en route to his second title at Roland Garros. He was on course to complete the Golden Slam- all four Slams and the Olympics in the same year- but was defeated in three sets by Alexander Zverev in the semi-finals of the Olympics, and defeated in straight sets by Russian Daniil Medvedev in the finals of the US Open.


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|
MetroCon 2003 |
Organizing Committee |
MetroCon is hosted by the IEEE
Fort Worth Section and organized by the officers of the section. This
year's key organizers are:
Title |
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| Chair | Diane B. Collier | TXU |<PHONE_NUMBER> | |
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You can reach this dialog via the menu entry Digital Signatures/PKI \ Signature Demonstration (Signature Generation).
This dialog allows to execute all steps of the RSA signature process, and therefore it will show transparently the details of the RSA signature generation.
As to visualize the actions of the signature generation, the flowchart of this process has been implemented into a dialog. To understand it, you can try out and combine the operations: hashing, asymmetric encryption and the use of a certificate.
Several colored symbols are used for buttons:
|
Symbol for preparations (e.g. select a hash function). |
|
|
Symbol for manual data input (e.g. key generation). |
|
|
General symbol for data (e.g. hash value). |
|
|
Symbol for stored data (here certificate). |
|
|
Processing symbol (here: signature generation). |
|
|
Process end symbol (e.g. store signature). |
The colors of the symbols illustrate the actual state of the individual process step within the flowchart:
| red | Operation to handle (preparation / input / process). |
| green | Finished operation (preparation / input / process). |
| blue | Available data (display and further process steps are possible). |
| gray | Inactive (missing data or not yet executed preliminary processes). |
Dependencies between symbols are visualized by arrows. Active buttons (red / green / blue) can be multiple times selected and/or selected in random order. Gray buttons are inactive and can't be selected. A process button will be activated if all preliminary linked operations are executed, and so the required input data is available (all preliminary buttons are colored blue).
On each symbol on lower left corner there are additional icons placed. Those icons symbolise the type of the operation / data element:
| Input file for the digital signature. | |
| Information about the selected hash function. | |
| Hash value of the input file. | |
| Secret RSA key for hash value decryption (signing process). | |
| RSA-encrypted hash value. | |
| Stored certificate. | |
| Complete RSA signature, on this header, the input file will be attached later. |
The view of this flow chart is separated into five rows:
The first row contain all operations, which are prerequisites for the signature generation.
The second row shows the input data of the two main signature operations.
The third row shows the signature operations of hashing and RSA decryption.
In the fourth row, you can display the output of the above operations.
Using the buttons of the fifth row, you may cancel the dialog or store the digitally signed document, before the dialog is closed.
If you click on the data symbols (row two and four), then the selected information will be show into the text field below the flow chart.


A Typical Day
Daily Schedule
We make sure to allot ample time for children to enjoy playing with their peers as well as learning important skills.
- 7:30 - 9:30 am - Arrival, Breakfast
- 9:30 - 9:50 am - Reading/Story Time
- 9:50 - 10:15 am - Diaper/Toileting
- 10:15 - 10:35 am - AM Snack
- 10:35 - 11:00 am - Planned activity
- 11:00 - 11:45 am - Outside time
- 11:45 - 12:15 pm - Lunch time
- 12:15 - 12:45 pm - Diaper/Toileting
- 12:45 - 3:00 pm - Nap time
- 3:00 - 3:20 pm - Diaper/Toileting
- 3:20 - 3:45 pm - PM Snack
- 3:45 - 4:15 pm - Free Play
- 4:15 - 4:45 pm - Outside time
- 4:45 - 5:00 pm - Diaper/Toileting
- 5:00 - 6:00 pm - Free Play/Pickup
*may vary according to age groups


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Outliers and Their Influence on the Final Model - tutorial¶
Table of Contents:¶
Read point data and take 10% of it as a sample for the further analysis (dataset A),
Check if outliers are present in a data and create additional dataset without outliers (dataset B),
Create the Variogram Point Cloud model for the datasets A and B,
Remove outliers from the datasets A and B,
Create four Ordinary Kriging models and compare their performance.
Level: Intermediate¶
Changelog¶
Date |
Change description |
Author |
|---|---|---|
2021-08-22 |
Initial release |
@szymon-datalions |
Introduction¶
Outliers may affect our analysis and the final interpolation results. In this tutorial we learn about their influence on the final model and we compare interpolation error for different scenarios where data is treated in a different ways.
We are able to remove too high or too low values at the preprocessing stage (check part 2 of the tutorial) or we can remove outliers directly from the variogram point cloud (part 4). Results from each type of preprocessing (and a raw dataset analysis) are different and we are going to compare them.
We use:
DEM data which is stored in a file
sample_data/point_data/poland_dem_gorzow_wielkopolski
.
Import packages¶
[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyinterpolate.distance import calc_point_to_point_distance
from pyinterpolate.io_ops import read_point_data
from pyinterpolate.kriging import Krige
from pyinterpolate.semivariance import build_variogram_point_cloud, show_variogram_cloud, remove_outliers
from pyinterpolate.semivariance import calc_semivariance_from_pt_cloud
from pyinterpolate.semivariance import TheoreticalSemivariogram
1) Read point data and divide it into training and test set¶
[2]:
# Read data from file
dem = read_point_data('../sample_data/point_data/poland_dem_gorzow_wielkopolski', data_type='txt')
[3]:
# Divide data into training and test set
def create_train_test(data, training_fraction):
idxs = np.arange(0, len(data))
number_of_training_samples = int(len(data) * training_fraction)
training_idxs = np.random.choice(idxs, size=number_of_training_samples, replace=False)
test_idxs = [i for i in idxs if i not in training_idxs]
training_set = data[training_idxs, :]
test_set = data[test_idxs, :]
return training_set, test_set
[4]:
train, test = create_train_test(dem, 0.1)
[5]:
train
[5]:
array([[15.32961159, 52.69870089, 26.50076103],
[15.33317125, 52.69062817, 21.09065437],
[15.14925544, 52.78377491, 52.66115952],
...,
[15.22756798, 52.56374162, 58.7893219 ],
[15.31735053, 52.75355397, 83.29890442],
[15.29638809, 52.5900297 , 46.89881134]])
2) Check outliers: analyze distribution of the values¶
To find if our dataset contains outliers we are going to inspect all values in the train
set. At the beginning we plot data distribution with the violinplot
.
[6]:
# Distribution plot
plt.figure(figsize=(10, 6))
plt.violinplot(train[:, -1])
plt.show()
NOTE: Your plot may be different than presented in the tutorial. Why is that? Because we take a random sample of 10% of values and after each iteration the algorithm takes different points for the analysis.
Clarification:
Investigation of the plot tells us that our data is:
grouped around the lowest values, and most of the values are below 50 meters,
has (probably) three different distributions mixed together, it can be a sign that Digital Elevation Model covers three different types of the elevation. One is grouped around 20 meters, next around 50 meters and the faintest is visible around 70 meters.
Violinplot is good for the distribution analysis. Especially if we are looking for the complex patterns in the dataset. But reading outliers from it may be challenging and we should change a plot type to understand if outliers exist in a dataset. The good choice is the boxplot
:
[7]:
# Boxplot
plt.figure(figsize=(10, 6))
plt.boxplot(train[:, -1])
plt.show()
Boxplot is a special and very useful tool for the data visualization and analysis. Let’s analyze this plot from the bottom up to the top.
NOTE: Boxplot represents values sorted in the ascending order and their statistical properties: quartiles, median and outliers.
The bottom whisker (horizontal line) represents the lower range of values in our dataset,
The box lower line is the first quartile of a data or, in other words, 25% of values of our dataset are below this point. We name it Q1.
The middle line is a median of our dataset. We name it Q2 or median.
The upper line is the third quartile of a data or, in other words, 75% of values are below this point,
The top whisker represents the upper range of values in our dataset. We name it Q3.
Individual points (if they are exist then we see them as a points below the bottom whisker or above the top whisker) are considered as outliers. They could be outliers in the upper range as well as lower range of our data. Long distance between Q1 and the bottom whisker and/or between Q3 and the top whisker is an indicator of potential outliers. Package matplotlib calculates potential outliers based on the absolute distance from the Q1 or Q3 to the whiskers. Points below or above this distance are treated as outliers. The outlier distance is calculated as the \(weight * (Q3 - Q1)\) where we can set
weight
but other parameters are read directly from the data.
We use this knowledge to remove outliers from the dataset with the assumption that outliers are rather anomalies than unbiased readings. We will perform the outlier removal from the data with a more aggresive assumption than it is done in matplotlib and we set weight to the 1.0
.
[8]:
# Create training set without outliers
q1 = np.quantile(train[:, -1], 0.25)
q3 = np.quantile(train[:, -1], 0.75)
top_limit = q3 + (q3 - q1)
train_without_outliers = train[train[:, -1] < top_limit]
[9]:
print('Length of the full training set is {} records'.format(len(train)))
print('Length of the pre-processed training set is {} records'.format(len(train_without_outliers)))
Length of the full training set is 689 records
Length of the pre-processed training set is 683 records
[10]:
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))
ax[0].violinplot(train_without_outliers[:, -1])
ax[0].set_title('Distribution of the training set without outliers')
ax[1].boxplot(train_without_outliers[:, -1])
ax[1].set_title('Statistics of the training set without outliers')
plt.show()
Clarification: with our data processing we have cut some records from the baseline training dataset. Distribution plot (violinplot
) has shorter tail and ends more abruptly; and a boxplot
of the new data doesn’t have any outliers. The one important thing to notice is that the observations are still skewed but this is not a problem for this concrete tutorial.
NOTE: if you are eager to know how to deal with the skewed datasets we recommend article Transforming Skewed Data.
3) Create the Variogram Point Cloud model for datasets A and B¶
Now we are making one step further and we will transform both datasets with- and without- outliers and calculate variogram point clouds from these. Then we compare both variogram point clouds.
[11]:
def get_variogram_point_cloud(dataset, max_range, number_of_lags=16):
distances = calc_point_to_point_distance(dem[:, :-1])
step_size = max_range / number_of_lags
cloud = build_variogram_point_cloud(dataset, step_size, max_range)
return cloud
[12]:
full_distance = np.max(calc_point_to_point_distance(train[:, :-1]))
cloud_full = get_variogram_point_cloud(train, full_distance)
cloud_processed = get_variogram_point_cloud(train_without_outliers, full_distance)
[13]:
# Show variogram cloud: initial training dataset
show_variogram_cloud(cloud_full, plot_type='boxplot')
[14]:
# Show variogram cloud: pre-processed training dataset
show_variogram_cloud(cloud_processed, plot_type='boxplot')
Clarification: a quick look into the results shows that each lag is full of outliers in the top part of the semivariances values. Processed dataset has lowest absolute semivariance than the raw readings. Both variograms have a similar shape. Dispersion of semivariances seems to be very high in both cases. It is especially alarming when we consider the shortest distances where abrupt changes in the elevation are not so likely.
In the next cell we will check a standard deviation of the lag variances.
[15]:
for k, v in cloud_full.items():
print('Lag {:.2f}'.format(k))
v_raw = int(np.std(v))
v_pro = int(np.std(cloud_processed[k]))
v_smape = 100 * (np.abs(v_raw - v_pro) / (0.5 * (v_raw + v_pro)))
print('Standard Deviation raw dataset:', v_raw)
print('Standard Deviation processed dataset:', v_pro)
print('Symmetric Mean Absolute Percentage Error of Variances: {:.2f}'.format(v_smape))
print('')
Lag 0.00
Standard Deviation raw dataset: 83
Standard Deviation processed dataset: 84
Symmetric Mean Absolute Percentage Error of Variances: 1.20
Lag 0.02
Standard Deviation raw dataset: 312
Standard Deviation processed dataset: 313
Symmetric Mean Absolute Percentage Error of Variances: 0.32
Lag 0.04
Standard Deviation raw dataset: 508
Standard Deviation processed dataset: 500
Symmetric Mean Absolute Percentage Error of Variances: 1.59
Lag 0.06
Standard Deviation raw dataset: 626
Standard Deviation processed dataset: 594
Symmetric Mean Absolute Percentage Error of Variances: 5.25
Lag 0.08
Standard Deviation raw dataset: 686
Standard Deviation processed dataset: 643
Symmetric Mean Absolute Percentage Error of Variances: 6.47
Lag 0.10
Standard Deviation raw dataset: 686
Standard Deviation processed dataset: 641
Symmetric Mean Absolute Percentage Error of Variances: 6.78
Lag 0.12
Standard Deviation raw dataset: 666
Standard Deviation processed dataset: 619
Symmetric Mean Absolute Percentage Error of Variances: 7.32
Lag 0.14
Standard Deviation raw dataset: 637
Standard Deviation processed dataset: 588
Symmetric Mean Absolute Percentage Error of Variances: 8.00
Lag 0.16
Standard Deviation raw dataset: 617
Standard Deviation processed dataset: 573
Symmetric Mean Absolute Percentage Error of Variances: 7.39
Lag 0.18
Standard Deviation raw dataset: 611
Standard Deviation processed dataset: 575
Symmetric Mean Absolute Percentage Error of Variances: 6.07
Lag 0.20
Standard Deviation raw dataset: 622
Standard Deviation processed dataset: 584
Symmetric Mean Absolute Percentage Error of Variances: 6.30
Lag 0.22
Standard Deviation raw dataset: 645
Standard Deviation processed dataset: 599
Symmetric Mean Absolute Percentage Error of Variances: 7.40
Lag 0.24
Standard Deviation raw dataset: 633
Standard Deviation processed dataset: 590
Symmetric Mean Absolute Percentage Error of Variances: 7.03
Lag 0.26
Standard Deviation raw dataset: 556
Standard Deviation processed dataset: 537
Symmetric Mean Absolute Percentage Error of Variances: 3.48
Lag 0.28
Standard Deviation raw dataset: 486
Standard Deviation processed dataset: 470
Symmetric Mean Absolute Percentage Error of Variances: 3.35
Lag 0.30
Standard Deviation raw dataset: 355
Standard Deviation processed dataset: 345
Symmetric Mean Absolute Percentage Error of Variances: 2.86
Clarification: The differences (sMAPE) per lag vary a lot. We can see that the preprocessing of raw values introduces the information lost. It is especially painful for the closest neighbors. It doesn’t mean that the preprocessing of raw observations is not recommended but it is a good idea to include the spatial component in the outliers detection process.
Not everything is wrong. Data cleaning has lowered the semivariances dispersion for the middle lags (where we have the largest number of point pairs for the analysis).
At this point we are not able to judge which dataset is better for the modeling. Instead we are going to remove outliers from the both variograms (instead of the raw data).
4) Remove outliers from the variograms¶
In this step we are going to use pyinterpolate’s function remove_outliers()
to build additional two variogram point clouds from the raw and processed datasets. We delete the top part outliers of the semivariance values rather than the raw readings.
[16]:
raw_without_outliers = remove_outliers(cloud_full, exclude_part='top', weight=1.25)
prep_without_outliers = remove_outliers(cloud_processed, exclude_part='top', weight=1.25)
[17]:
data_raw = [x for x in cloud_full.values()]
data_raw_not_out = [x for x in raw_without_outliers.values()]
data_prep = [x for x in cloud_processed.values()]
data_prep_not_out = [x for x in prep_without_outliers.values()]
[18]:
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(16, 14))
ax[0, 0].boxplot(data_raw)
ax[0, 0].set_title('Semi-variances Distribution in the Raw Dataset')
ax[0, 0].set_xlabel('Lag number')
ax[0, 0].set_ylabel('Semivariance value')
ax[0, 1].boxplot(data_raw_not_out)
ax[0, 1].set_title('Raw Dataset after the Outliers Detection and Removal')
ax[0, 1].set_xlabel('Lag number')
ax[0, 1].set_ylabel('Semivariance value')
ax[1, 0].boxplot(data_prep)
ax[1, 0].set_title('Semi-variances Distribution in the Pre-processed Dataset')
ax[1, 0].set_xlabel('Lag number')
ax[1, 0].set_ylabel('Semivariance value')
ax[1, 1].boxplot(data_prep_not_out)
ax[1, 1].set_title('Pre-processed Dataset after the Outliers Detection and Removal')
ax[1, 1].set_xlabel('Lag number')
ax[1, 1].set_ylabel('Semivariance value')
plt.show()
Clarification: Comparison of multiple variogram clouds could be hard. We see that the largest semivariances are present in the raw data. A heavily processed data has the lowest number of outliers. The medians in each dataset are distributed over a similar pattern. How is it similar? We can check it if we transform the variogram point cloud into the experimental semivariogram. Pyinterpolate has function for it: calc_semivariance_from_pt_cloud()
. We use it and compare four plots of
semivariances to gain more insight into the transformations.
[19]:
raw_semivar = calc_semivariance_from_pt_cloud(cloud_full)
raw_semivar_not_out = calc_semivariance_from_pt_cloud(raw_without_outliers)
prep_semivar = calc_semivariance_from_pt_cloud(cloud_processed)
prep_semivar_not_out = calc_semivariance_from_pt_cloud(prep_without_outliers)
[20]:
plt.figure(figsize=(14, 6))
plt.plot(raw_semivar[:, 1])
plt.plot(raw_semivar_not_out[:, 1])
plt.plot(prep_semivar[:, 1])
plt.plot(prep_semivar_not_out[:, 1])
plt.title('Comparison of experimental semivariograms created with the different data preprocessing techniques')
plt.ylabel('Semivariance')
plt.xlabel('Lag number')
plt.legend(['Raw', 'Raw - remove_outliers()', 'Pre-processed', 'Pre-processed - remove_outliers()'])
plt.show()
Clarification: An understanding of those plots is not an easy task. Let’s divide reasoning into multiple points:
Raw dataset and preprocessed raw dataset show a similar pattern, the differences are more pronounced for the distant lags than for the closest point pairs,
Datasets with the cleaned variograms are different from the raw data. The absolute semivariance values per lag are smaller and the semivariogram pattern is slightly different. What is interesting is that the possible two distributions within the dataset are more visible in the case of cleaned variograms (one distribution with a peak around 6th lag and other with a peak around 13th lag).
The differences between semivariograms are mostly visible at larger distances. For the closest point pairs differences are smaller.
Semivariograms visual inspection does not infor us of the modeling performance but we can assume that models will be slightly different. Let’s test this assumption!
5) Create Four Ordinary Kriging models based on the four Variogram Point Clouds and compare their performance¶
[21]:
number_of_ranges = 16
# Fit different semivariogram models into prepared datasets and variograms
# Raw
raw_theo = TheoreticalSemivariogram(points_array=train,
empirical_semivariance=raw_semivar)
_ = raw_theo.find_optimal_model(weighted=False, number_of_ranges=number_of_ranges)
# Raw with cleaned variogram
raw_theo_no_out = TheoreticalSemivariogram(points_array=train,
empirical_semivariance=raw_semivar_not_out)
_ = raw_theo_no_out.find_optimal_model(weighted=False, number_of_ranges=number_of_ranges)
# Preprocessed
prep_theo = TheoreticalSemivariogram(points_array=train_without_outliers,
empirical_semivariance=prep_semivar)
_ = prep_theo.find_optimal_model(weighted=False, number_of_ranges=number_of_ranges)
# Preprocessed with cleaned variogram
prep_theo_no_out = TheoreticalSemivariogram(points_array=train_without_outliers,
empirical_semivariance=prep_semivar_not_out)
_ = prep_theo_no_out.find_optimal_model(weighted=False, number_of_ranges=number_of_ranges)
[22]:
# Set Kriging models
# Raw
raw_model = Krige(semivariogram_model=raw_theo, known_points=train)
# Raw & cleaned
c_raw_model = Krige(semivariogram_model=raw_theo_no_out, known_points=train)
# Preprocessed
prep_model = Krige(semivariogram_model=prep_theo, known_points=train_without_outliers)
# Preprocessed & cleaned
c_prep_model = Krige(semivariogram_model=prep_theo_no_out, known_points=train_without_outliers)
[23]:
# Build test function
def test_kriging_model(model, test_set, nn=16):
"""
Function tests performance of a given kriging model.
INPUT:
:param model: (Krige) Kriging model,
:param test_set: (array),
:param nn: (int) default=16, number of neighbors.
OUTPUT:
:returns: (list) root mean squared errors of prediction
"""
rmses = []
for pt in test_set:
coordinates = pt[:-1]
value = pt[-1]
predicted = model.ordinary_kriging(coordinates, nn)[0]
error = np.sqrt((value - predicted)**2)
rmses.append(error)
return rmses
[24]:
r_test = test_kriging_model(raw_model, test)
[25]:
cr_test = test_kriging_model(c_raw_model, test)
[26]:
p_test = test_kriging_model(prep_model, test)
[27]:
cp_test = test_kriging_model(c_prep_model, test)
[28]:
df = pd.DataFrame(data=np.array([r_test, cr_test, p_test, cp_test]).transpose(),
columns=['Raw', 'Raw-cleaned', 'Preprocessed', 'Preprocessed-cleaned'])
[29]:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 7))
ax.boxplot(df)
ax.set_xticklabels(['Raw',
'Raw-cleaned',
'Preprocessed',
'Preprocessed-cleaned'])
plt.show()
It is very hard to distinguish any differences in the figure but we can use .describe()
method of pandas to get the columns statistic:
[30]:
df.describe()
[30]:
| Raw | Raw-cleaned | Preprocessed | Preprocessed-cleaned | |
|---|---|---|---|---|
| count | 6206.000000 | 6206.000000 | 6206.000000 | 6206.000000 |
| mean | 2.936283 | 2.936742 | 2.956846 | 2.957840 |
| std | 3.686328 | 3.685557 | 3.707982 | 3.708420 |
| min | 0.000491 | 0.000152 | 0.000491 | 0.000152 |
| 25% | 0.617843 | 0.614906 | 0.614773 | 0.612816 |
| 50% | 1.607936 | 1.602791 | 1.617333 | 1.616114 |
| 75% | 3.812796 | 3.813395 | 3.839099 | 3.854755 |
| max | 36.599110 | 36.602001 | 36.599110 | 36.602001 |
Clarification: In this particular case the final statistics goes hand in hand with the initial assumptions that:
a) Raw dataset has lowest error of prediction. The rationale behind it is that if we throw away the observations at the preprocessing step we risk an information lost. It could damage our model.
b) Raw dataset with the cleaned variogram is the best one. We have removed the point pairs with the largest error. In reality we got rid off the potentially wrong measurements where at one point elevation is small and it’s neighbour is very high.
NOTE: Example in this tutorial is related to the Digital Elevation Model which was preprocessed by the data provider (Copernicus Land Monitoring Services). You shouldn’t get impression that the raw data preprocessing and filtering is not required for the analysis. There are cases where the sensor may produce unreliable and biased results, as example a saturated pixel from the satellite camera. It is better to remove it with the specific noise-filtering algorithm before the variogram point cloud development.


This tutorial describes the basics of navigating the globe within Google Earth. You will learn to tilt, rotate the globe and zoom in on the Grand Canyon in the western United States. Additionally, you can watch this video (English only):
Note - The following tutorial describes only some of the ways you can move around in Google Earth. To learn more, see Navigating in Google Earth in the Google Earth User Guide.
Each time you start Google Earth, the Earth appears in the main window. The area that shows the Earth is called the 3D viewer. The 3D viewer always appears in Google Earth and shows you imagery, terrain and information about places around the globe.
In these exercises, you will zoom in and out of the Grand Canyon. There are a number of ways to accomplish this. To become familiar with Google Earth, try all three::
In Google Earth, a placemark is a visual notation that marks a location. Each placemark appears as a pushpin with a label. To zoom to the Grand Canyon using an existing placemark:
|
|
|
Once you have zoomed in using a placemark, you are ready to zoom in and out using your computer's mouse. To do this:
Note - If you zoom in far enough, you view point tilts.
The navigation controls appear in the top right corner of the 3D viewer. They offer the same type of navigation action that you can achieve with mouse navigation, plus some additional features.
To view and use the navigation controls, move the cursor over the top right corner of the 3D viewer. The navigation controls automatically appear whenever you do this; they fade from sight when you move the cursor elsewhere.
For now, just practice zooming in and out with the navigation controls. To do this:
You can also click and hold these buttons to zoom continuously.
Note - You can also use the zoom slider to zoom in or out (see diagram above).
Now that you know how to zoom in and out, you are ready to look at the Earth in three dimensions. To do this, you tilt your viewpoint so that you can see elevation changes on the Earth's surface. This feature is an extremely fun aspect of Google Earth, particularly when you are viewing hilly and mountainous terrain.
Steep terrain of the Grand Canyon as it appears in Google
Earth.
As with zooming, there are multiple ways to tilt the view. Try both:
To tilt the viewpoint using your mouse:
Note - The Eye Alt figure at the bottom of the 3D viewer refers to eye altitude (the elevation of your viewpoint).
You can tilt simply by zooming in (see above). Once you zoom in far enough, Google Earth tilts your view.
In Google Earth, you can look around and move around. These are
different and it is useful to know the difference. Looking around involves gazing from a
single vantage point, as if you were turning your head. Moving around involves shifting
your location from one place to another. Both actions change your perspective.
|
|
|
To look around:
To move around:
Note - You can also move using the arrows on your keyboard.
To learn more about using the features of the navigation controls, see Using the Navigation Controls in the Google Earth User Guide.
Updated on


@import net.rouly.employability.models._ @mockTopic = @{ Topic("X", Set( WordFrequency("cat", 0.9913), WordFrequency("dog", 0.9012), WordFrequency("pig", 0.4020), WordFrequency("robot", 0.0001) )) } @application.layout.main(title = "Main") {
Employability is a research project exploring how universities are preparing students for the workforce. Its name comes from the notion of how employable a student is upon graduation.
This site is designed to report the results of the project. Use the navigation bar on the left to jump around sections of the website, exploring the inferred topics and related documents. The Topics portal allows you to browse all of the inferred topics. Similarly, the Documents portal allows you to browse a random selection of the modeled documents. Elements in each portal are hyperlinked to one another.
So, if you click on a topic, you will be brought to that topic's page, along with a selection of documents that express that topic at a high relevance.
Similarly, if you click on a document, you will be brought to that document's page, along with all the topics it expresses and their relevance values.
In the context of this project, a topic is a statistical distribution over a vocabulary. Take the following topic, for example.
@components.topic(mockTopic, link = false)In this contrived topic, the most frequent terms are "cat" and "dog", with "pig" occurring at a lower frequency and irrelevant words like "robot" occurring almost never. Since this topic would have been automatically inferred in an unsupervised fashion, there's no label for it. But, using human intuition, one might expect this topic to be related to documents about family pets.
A document is a collection of words written (presumably) by a human. Those words contain themes that the author intended to convey in their writing. These themes can be modeled as term frequency distributions (topics).
In the context of this project, a document is either a Job Description or a Course Description. The contents of the document are modeled according to inferred topics.
}


# StrapDown.js
[StrapDown.js](http://lbesson.bitbucket.org/md/index.html) is an awesome web script to write nice-looking webpages in pure Markdown, with no server side compilation.

----
## Examples
### Here is a first demo:
![First demo](demo1.png "First lines of the index.html page")
----
### [This page](http://lbesson.bitbucket.org/md/index.html) explains how to use StrapDown.js:
![Second demo](demo2.png "Demo of the demo!")
----
### There is also many [different themes](https://github.com/Naereen/StrapDown.js/tree/master/themes/):
![Third demo](demo3.png "Small example with the Cyborg theme")
----
### StrapDown.js supports [MathJax](https://www.mathjax.org/), and it's really easy to use!
![Forth demo](demo4.png "First lines of the index.html page")
----
### StrapDown.js also supports [MarkDown tables](https://help.github.com/articles/github-flavored-markdown/#tables):
![Fifth demo](demo5.png "First lines of the index.html page")
----
### And as any web-based document, a StrapDown.js powered HTML page can include any Javascript script, like this example which shows a research bar for a table:
![Sixth demo](demo6.png "First lines of the index.html page")
----
# About
### Hacked by [Lilian Besson](https://github.com/Naereen) (Naereen).
### Languages
- [JavaScript](https://en.wikipedia.org/wiki/Javascript),
- [HTML 5](https://en.wikipedia.org/wiki/HTML) and [CSS 3](https://en.wikipedia.org/wiki/Cascading_Style_Sheets).
### License
This project is released under the **MIT license**, for more details,
take a look at the [LICENSE](http://lbesson.mit-license.org/) file in the source.


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You can install the library using you favorite package manager:
You can now add the module
Create the app.config.ts file the src/app folder.


The box-decoration-break Property
box-decoration-break: clone:
CSS
is
easy
to learn
box-decoration-break: slice (default):
CSS
is
easy
to learn


{% extends 'core/event.html' %} {% load applications_tags i18n rev static %} {% block custom_js %}{% endblock %} {% block title %}{{ event.page_title }}{% endblock %} {% block description %}{{ event.page_description }}{% endblock %} {% block content %}
{% blocktrans with sent=email.sent author=email.author num_rec=email.number_of_recipients %} This email was sent on {{ sent }} by {{ author }} to {{ num_rec }} recipients. {% endblocktrans %}
{% trans "Successfully sent emails:" %} {{ email.successfuly_sent }}
{% trans "Emails that failed to sent:" %} {{ email.failed_to_sent }}
{% endif %}


The preview API seems broken
bug/major
#783 opened
3 hours ago
by
kmelve
2
Elasticsearch broke
ops
#782 opened
5 hours ago
by
nicholasklem


[gecode-users] Mac XCode configuration
Tomas Lidén
tomas.liden at liu.se
Mon Apr 7 19:54:38 CEST 2014
Guido Tack skrev 2014-04-07 12:18:
> On 7 Apr 2014, at 5:40 pm, Tomas Lidén <tomas.liden at liu.se> wrote:
>
>> Thanks for quick and adequate response!
>> But still there's trouble... see below
>> /T
>>
>> Guido Tack wrote 2014-04-07 02:03:
>>> Hi,
>>>
>>> to add a system folder such as /usr/local, open the "Add Files to..." dialog, press Command+Shift+G, and enter the path directly.
>>>
>> Great. That got me halfway.
>> After adding the frameworks QtGui, QtWidgets and QtCore and adjusting the include statements to
>> #include <QtGui/QtGui>
>> #include <QtWidgets/QtWidgets>
>> the compilation ends with the following error:
>> /usr/local/qt5.1.0/lib/QtCore.framework/Headers/qlist.h:52:10: 'initializer_list' file not found
>>
>> According to (http://comments.gmane.org/gmane.comp.lib.qt.user/8357) and (the subsequent links) this is due to Qt requiring -stdlib=libc+ (not libc++ ?) and c++11 - which is in conflict with libstdc++ that is required by Gecode. Hmm.. I can't figure out to get passed this - tried varies ways in Xcode, but no luck so far. Should I perhaps download and install Qt directly from the source rather than using the version coming with Gecode?
> I will probably build new packages soon based on libc++ and linked to the newest version of Qt, but until then you basically have two alternatives: compile Gecode from sources with libc++, or compile Qt from sources with libstdc++.
>
Great. I will await the new builds. And until then I'll do without Qt.
>>> On the command line, I think Xcode may have changed the default paths for frameworks, you could try adding -F/Library/Frameworks to your compiler options.
>>>
>> That almost did the trick. Again I had to add options for selecting libstdc++ etc, but still I get an error. After trying the (what I think) options that Xcode uses I get:
>>
>> $ g++ -std=gnu++11 -stdlib=libstdc++ -O3 -F/Library/Frameworks -c money.cpp
>> money.cpp:1:1: error: expected unqualified-id
>> <U+001C>/* -*- mode: C++; c-basic-offset: 2; indent-tabs-mode: nil -*- */
>> ^
> Have a look at the money.cpp file, looks like there's a funny character on the first line.
>
>
:-§ That was a creepy one! And I had to pick just that file... Removing
the character did the trick. Maybe you should fix that in the next build
as well.
And in MGP section 2.3.2 there's some minor issues:
1) Correct the command line example - the last line should at least end
with send-more-money.o
2) Update the Xcode description regarding adding frameworks
Thanks again for your help!
/Tomas
More information about the users
mailing list


IU wins #1 on November 23rd 'Show! Music Core'!
by germainejIU took the #1 trophy on the November 23rd episode of 'Show! Music Core'!
On this episode, IU took 1st place with "Love Poem" for the second week in a row on 'Show! Music Core'. IU's "Love Poem" was up against MAMAMOO's "HIP" and Noel's "Late Night", but it was IU who took the win with 7,194 points. Congratulations to IU!
Check out the performances posted by MBC below.


Welcome to the GYM!
This a simple BMI calculator application:
Height in meters:
m
Weight in kg:
kg
Calculate
Your BMI is:
This means you are:
Aaaaand this is what you really look like...


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
Stargazey pie is a classic Cornish dish of pilchards covered in a pastry crust. Tristan Welch's version uses sardines covered in a rich mustard sauce and topped with puff pastry.
For the mustard sauce, bring the stock to the boil in a non-reactive saucepan. Whisk in the crème fraîche, mustard, salt, mustard powder and lemon juice until well combined. Bring back to the simmer.
Pass the sauce through a fine sieve into a jug and set aside.
For the pie, cook the bacon in boiling water for 20 minutes. Drain, then allow to cool slightly before chopping into lardons.
Bring another pan of water to the boil and cook the baby onions for 6-7 minutes, or until tender. Drain and refresh in cold water, then slice each onion in half. Set aside.
Preheat the oven to 200C/400F/Gas 6.
Roll out the puff pastry until 3-4mm thick, then cut into 4 equal-sized squares. Using a small circular pastry cutter the size of a golf ball, cut out 2 holes in each pastry square.
Place each square on a baking tray and brush with the beaten egg yolk. Chill in the fridge for 15 minutes.
Bake the pastry squares in the oven for 18-20 minutes, or until golden-brown and crisp. Remove from the oven and set aside.
Turn the grill on to high.
Place the sardine fillets, heads and tails on a solid grill tray, brush with the oil and season with salt and freshly ground black pepper. Grill for 2-3 minutes, or until golden-brown and just cooked through (the fish should be opaque all the way through and flake easily).
Heat a frying pan until medium hot, add the butter and bacon lardons and fry gently for 3-4 minutes, or until golden-brown. Add the onions and stir in enough sauce to coat all the ingredients in the pan. Reserve the remaining sauce and keep warm.
Bring a small pan of water to the boil, add the vinegar and a pinch of salt. Reduce the heat to a simmer.
Crack the quail's eggs into a small bowl of iced water, then pour off any excess (there should only be just enough water to cover the eggs). Swirl the simmering water with a wooden spoon to create a whirlpool effect, then gently pour the quails' eggs into the centre of the whirlpool. Poach for about 1-2 minutes, or until the egg whites have set and the yolk is still runny. Remove with a slotted spoon and drain on kitchen paper.
To serve, divide the onion and bacon mixture between 4 serving plates. Arrange the sardine fillets on top, then place four poached quails' eggs around the fillet. Using a stick blender, blend the remaining sauce until frothy. Spoon the froth over the top of the sardines and eggs. Top each pile with the puff pastry squares, then place the sardine heads and tails through each hole in the pastry. Serve immediately.


Framework for various smooth scrolling effects without jQuery.
Download from GitHub or scroll down for a demo.
Licensed as Apache License 2.0 by Cubic Creative.
No Effects
class="rnr-bg"
Parallax
class="rnr-bg rnr-scroll rnr-para"
Zoom (offset: 0.5)
class="rnr-bg rnr-scroll rnr-zoom"
rnr-scroll-offset="0.5"
Blur (offset: 1.0)
class="rnr-bg rnr-scroll rnr-blur"
rnr-scroll-offset="1"
Fade
class="rnr-bg rnr-scroll rnr-fade"
Video with Zoom and Blur Effects
(offset: 0.8)
class="rnr-bg rnr-scroll rnr-zoom rnr-blur"
rnr-scroll-offset="0.8"
No Effects
class="rnr-bg"


A roller rolls on the cells. It applies Rollers roll on the cells. They apply stamps while rolling.
Modify its parameters to recreate the target.
Click on each roller to view its parameters, and modify them to recreate the target.
Roller 1 will roll first, then roller 2.
Roller 1 will roll first, then roller 2, then roller 3.
A roller can change what the previous one did.


--- layout: default title: Tinkering with English description: Newing existing words and refixing some of the classics. permalink: / --- {% include JB/setup %}
n. Tinkering with English


In this module, we begin to explore the school-to-prison pipeline. After the completion of this module, you are encouraged to do more research on the topic, especially if you have not been personally affected by the overpolicing and overdisciplining in schools. It is important to acknowledge that neither race nor gender are discrete categories in the real world. However, all the data sources used in this module assume two genders and separate race categories. We will have to work with these limitations, but it is worth asking of all data we encounter: how were these data collected and what assumptions were made in collecting them?
in-context


{{username}}, what would you like to drink?
Choose a cocktail...
{% for coc in cocktails %}{{coc['name']}}
Created by {{coc['createdby']}} - {{coc['count']}} made
Contains:
{% for ing in coc['ingredients'] %}
{{ing}}{{ "," if not loop.last }}
{% endfor %}
Extra instructions:
{{coc['instructions']}}
{% endfor %}


1. Introduction
Soccer match, like a match in any sports competition is an one-time performance faced with many uncertainties. On a bad day an excellent player will miss many goals, and some bad players might win out of pure luck. To understand the real performance of players, many soccer analysts have created models that are called xG-models, that vary in complexity. These models try to analyze the gameplay and come up with a numerical value of how many goals were expected from the game.
Complex xG models, such as one that we will use in this report, analyze complex data from the game, such as position on the map at certain time, and evaluate a chance for each shot of hitting the goal. When a xG model is accurate, it is much more useful to analyze the expected goals of a player or a team, rather than how many goals were scored during a match. The expected goals also predict the future performance very effectively compared to analyzing only the actual goals.
In this report we will present a xG model for analyzing the results of four European countries. The model will use complex data-analysis methods such as Logistic Regression, and will be trained using data from European soccer championships. The report will be accompanied by a Python-notebook, where the calculations are done. The data is sourced from a publicly available dataset.
2. Data Description
The dataset used in this report contains the following categories:
- Competition data: European Championship
- Competition data: World Cup
- Country data for:
- France
- Germany
- Italy
- Spain
The data contains a set of shots during a game. The data was collected using cameras and manually inputted to the data file by human operators. Some of the data points were removed or converted during the data cleanup. For our analysis we will use the following information for a single shot:
- X-coordinate
- Y-coordinate
- Whether feet or head was used (foot, head)
3. Creating a xG Model
3.1. Rationale
Following the example of a lecturer on Youtube (Friends of Tracking, 2020), we will use the distance of a player to the goal, as well as their visibility angle.
Figure 2: Probability of scoring from a point in field. (image source)
To help our algorithm to better predict the likelihood of shooting a goal, we will generate two additional variables:
-
Distance from goal
The coordinate for the midpoint of the goal is (100,50)
-
Visibility angle The visibility angle is calculated using the formula for angle between two straight lines. The two straight lines are calculated from the location of the player to both ends of the goal area. The coordinates for goal endpoints are (100,53) and (100,47). The formulas for the calculation are:
The calculated values in our Python-notebook can be seen from Table 1.
3.2. Model Building: Logistic Regression
For our xG model, we will use the L2 Logistic regression model with lbfgs-solver. According to Friends of Tracking (2020), Logistic regression is best suited for this type of problem, and we will thus not try other machine learning algorithms. In order to see that our algorithm works well, we will attempt first to generate a dataset with only x,y coordinates and not use any of the other variables for training the model. The model is trained with a split that uses 70% of the data for training the model, and 30% for testing.
The first run with only x,y coordinates show the model to be 90.18% accurate. This seems sufficiently good. To understand whether the model understands the game mechanics, we will generate a heatmap in top of an image of a soccer field that shows the spots with high probability for scoring.
The heatmap in Figure 2 demonstrates that our model has successfully captured the mechanics of the game. We will continue by introducing the other variables foot, head, opportunity to our algorithm. The new data is seen on Table 2.
3.3. Model Evaluation
After introducing the other variables to our algorithm, the model’s accuracy score improves very slightly to 90.46%. To further understand the model, we will calculate a confusion matrix, that intends to further explain, which predictions are accurate.
The confusion matrix shows that when the model makes a 1 or 0 prediction about whether a goal will be scored, it is surprisingly inaccurate. When the model predicts that a goal will be scored, the predictions are right only about 1 time in 10 predictions. When the model however predicts that no goal will be scored, it is incredibly accurate. This information is useful when we are comparing the results of different countries in the next section.
For our xG model we are the most interested in the ability of our model to predict probabilities of scoring. The heamap in Figure 2 shows that our model accurately understands the game-mechanics, and its accuracy score after introducing new variables is 90.46%. The model is therefore considered reliable.
4. Discussion and Conclusions
4.1. Evaluating the Performance of 4 European Countries Using Our xG Model
To show the usefulness of our xG model, we will compare the data from 4 European countries and their performance.
The performance of the European countries shown in Table 3 and Figure 4 shows the performance of each country. Interesting finding from the results is that in expected goals, Germany beats Spain very slightly, although in actual goals Spain performs better. Out of these countries, Germany would therefore be the best.
4.2. Conclusions
The xG model we built using Logistic regression was found to be accurate for predicting expected goals, and it has useful real-life applications for evaluating team and player performance, and mapping the area in which the goal is likely to be made (Figure 2). This is useful information for training players. One training implication from this model is that the players should be instructed to shoot only from areas with for example above 50% chance of shooting a goal. In order to generate a more accurate model, more data would be needed. One example of this would be include more information about penalty kicks.
5. References
Friends of Tracking, 2020. How to Build An Expected Goals Model 1: Data and Model. [video] Available at: https://www.youtube.com/watch?v=bpjLyFyLlXs [Accessed 23 March 2021].
Pappalardo L and Massucco E (2019) Soccer match event dataset. Figshare. Available at: https://figshare.com/collections/Soccer_match_event_dataset/4415000/2 [Accessed 31 July 2021].


These are current guides on selected topics. For the most current API-specific reference, please check the API Reference.


Hi! {{posts.TeacherName}} ({{posts.TeacherNo}}). Welcome to TONGJI AAMS, a complete course selection, teacher & student management website.


So, believe it or not I want the $20 daily tournaments as much as anyone. It’ll be awesome and really encourage people to play the game every day and have extra reasons to hang around Steem. Don’t know if you know this but a major point is to keep people hanging out! This will help grow muh home. So, art we did!
Lyanna Natura makes art
She’s muh wife. She’s a talented artist! Muh babies want an Angry Chicken in the game. Here’s one rendition of a potential monster to add.
Mother in Law
Grandma is here. Let’s she what she made.
A CAKE MONSTER!
Ok, let’s showcase the work of Baby 2! The turtle mom outlined, otherwise she seems to be into prints
Meet Talia’s “”Rip” Firestorm’s creation!
My humble showcase
I’ve been made fun of for muh art since I was like 7, but here goes! I like the idea of turning baby into the summoner or the monster. here we go.
The main start of the fam!!! Baby-1 (Kiara Lightbringer)
This last one is for an unreleased monster called the shapeshifter!!!
Ok, well, muh family is counting as 5 people making 10 pieces of art. I’d really like to see 25 distinct people make art instead of 30 pieces by 4 people… so I’m counting this as 5 to the total of 30 due on Wednesday to release the rewards! Cheeck this post out for contest details or stay better informed by backing the kickstarter!!!
This page is synchronized from the post: ‘Kickstarter group challenge 2- Pirates. Aggroed-fam makes Steem Monsters themed baby onesies!’


{% extends "base.html" %} {% block title %}Introduction{% endblock %} {% block mainTitle %}Introduction{% endblock %} {% block breadcrumb %}
EC number
An abbreviation of enzyme code number. Method for systematic classification of enzymes. It consists of four pairs of numbers starting with EC. In 1961, the Enzyme Commission of the International Federation of Biochemistry established rules for the name and classification of enzymes, while issuing enzyme tables that systematically classify 707 enzymes, which were then known, and then assigned enzyme numbers to each enzyme. Some modifications have since been made to the rules to counter the development of biochemistry and the discovery of new enzymes. In the 1984 enzyme table, about 2,500 enzymes are classified as enzyme numbers.Utilize EC number
Half a century ago, a large amount of new enzymes were discovered, and researchers in the pure bioscience, pathology and biochemical industries felt that rules for enzyme names were needed to take advantage of them, and enzyme naming conventions such as EC codes were developed to address them. The International Union of Biochemistry and Molecular Biology specifies an enzyme number that indicates the function of the enzyme. This could solve problems arising from ambiguous and unclear enzyme names in the field of enzyme utilization or publications dealing with enzyme research.Necessity of EC number prediction
The enzyme number, which shows what kind of biochemical reaction enzymes intersect, is widely used in various fields. Currently, more than 10 computer methodologies have been developed to predict EC numbers, and DeepEC is showing outstanding performance in terms of predictive speed, accuracy, and predictable range. However, it is not easy for researchers who need enzyme numbers to use DeepEC. This is because DeepEC requires installation and use in a Linux environment, but not many researchers can handle Linux. That's why many researchers now use the amino acid sequence to sort out similar protein sequences in the Swiss-Prot (protein Sequence Database) and to find and guess the enzyme number among the information for each sequence. This method has the disadvantage of being cumbersome because it takes a long time to require many protein sequence enzyme numbers.


Calico Critters Cuddle Bear Family
Calico Critters Biography Information
Mother Carol is always smiling and is very loving. She likes to read and cook berry pies and chocolate chip cookies for her family.
Father Arthur is the mayor of Cloverleaf Corners. He is also the teacher at the Country Tree School, which is why he is always very busy. He likes to fishing on the weekends.
Brother Aidan loves to play sports, especially baseball and football. After a game or practice he loves to eat burgers at the Burger Cafe with his friends.
Sister Ava can be very shy at times. She is always helping her mother around the house. She likes to tend to the garden and grow beautiful flowers.


SIMPLE LOGIN AND SIGNUP FORM
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A power grid with multiple voltage levels can be modelled using a multilayer network. A graph in each layer represents all the cables at the same voltage level. A directed bipartite for a pair of adjacent layers consists of transformers connecting two voltage levels.
mgrid
provides an interface for modelling power grids and conduct power
system analysis using pandapower
and PyPSA
. Though the way to build models
(define buses, and connect them with transformers & cables, …) is similar,
syntax is very different.
I have wrapped up my 5 months work at Utiligize, and build a package, mgrid
,
for power system analysis.
Topology, Element, Snapshot
Here, information for power system analysis is separated into three parts. The
most basic part about a power grid is topology, how buses are connected. A
class based on directed graph model in networkx
package is used to store such
information. This way, many existing methods and functions in networkx
can be
utilised directly. Bus is an imaginary concept to enforce Kirchhoff’s law.
There is no power loss within any bus. It is not necessary to distinguish buses
as PQ, PV, or slack.
The OpenDSS software (EPRI2021penDSS) divides electrical devices in power grids into two categories. The most basic one, called delivery element, has two terminals and moves electricity from one bus to another. Cables and transformers fit into this category. Any edge in topology corresponds to one and only one delivery element, and vice versa. The second category is conversion element, which converts electricity from or to another energy form. Such devices have only one terminal and must be connected to a bus. Such categorisation method is applied here.
In pandapower
or PyPSA
, different models for cables, for example, are built
using one function (method). There are many optional arguments in such
functions, which can be confusing. In mgrid
, any element model can be
modelled by a Python class. For example, one-line equivalent model for
poly-phase cables is mgrid.power_flow.delivery.Cable
, while detailed version
for 3-phase cables is mgrid.power_flow.delivery.Cable3Phase
. The number of
optional arguments is largely reduced. Though classes are different, for
delivery or conversion elements, methods to add them to a grid are the same.
For example, Cable.update_pandapower
, Cable3Phase.update_pandapower
, and
TransformerStd.update_pandapower
both take a pandapower
network and two
terminals as arguments. Moreover, how to add element is entirely specified in
update_pandapower
. This feature makes it easier for users to define their own
class. The function to build a pandapower
network in mgrid
will always use
update_pandapower
to add the element.
The last part takes time-variance into account. One snapshot consists of one
set of nodal injections and one set of voltages. It is assumed that the grid
remains steady state during the period which the snapshot specifies. At least
in power flow calculation, adjacent snapshots do not have an impact. However,
in terms of multiple snapshots, it is beneficial to consider snapshots
together because voltage results from the previous snapshot can be used as
the initial guess, which reduces the number of iterations. Data-frames with
multi-level indices are used to store such information in mgrid
. That way,
any snapshot or series of results for any bus can be easily cross-sectioned
using
pandas.DataFrame.xs
.
Multilayer Network
There are two kinds of edges in a multilayer network. Any intra-edge can only exist in a single layer and correspond to cables. Inter-edges connect layers and correspond to transformers in terms of power grids. Such two kinds of edges and the correspondence between voltage levels and layers are the two most useful features.
A multilayer network can be represented in three ways. The first one, with multiple graphs for different layers and directed bipartites for inter-edges, is mentioned in the introduction. Then, as introduced in bianconi2018multilayer, supra graph integrates intra-edges in different layers and inter-edges in one graph. This format is used when modelling power grids using dedicated power system analysis software. The last one is called planar graph here, and it contracts inter-edges in the supra graph. That way, inter-edges, representing transformers, become inter-nodes. Datasets for power grids are usually structured in this manner because transformers look like points geographically.
Usually, nodes are replicated in different layers. bianconi2018multilayer However, here, all the intra-nodes are unique, and inter-node pairs are distinguished by suffixes, which makes the supra graph format easier to be derived. This assumption reflects the reality that buses tend to locate in different geographical points. The disadvantage is that some algorithms designed for multilayer networks might be inapplicable.
Distributed External Grids
There are multiple 150-60 kV transformers in some subcomponents, connecting to external grids. Though their RPIs can be known from DC OPF, one or a set of them must be chosen as slack bus(es) in AC power flow.
AC power flow for with distributed
slack
has been implemented in PyPSA
only. To use pandapower
, one and only one
150-60 kV transformer should be chosen as the slack bus. Participation factors
resulted from automatic generation control (AGC) can be used as weights for a
set of buses with generators.
What if Divergence
For power flow calculation, it is hard to know the reason when the iteration diverges. To debug, we can divide the grid into smaller parts and calculate power flow individually. After detecting the part(s) which result(s) in the divergence, it can be excluded in the original grid. Conversion elements can be attached to the bus, which represents the excluded part. That way, the calculation for the grid will converge, and the error is merely the loss along cables and transformers in the excluded part.
Voltage Drop
The direct impedance method can be easily used for radial distribution feeders (RDF) to calculate voltage drops. Then, with the voltage at root known after power flow for higher voltage levels, voltages can be calculated accordingly.
The nonlinear relationship between complex voltages \( \overline{\boldsymbol{V}} \) and PRIs \( \overline{\boldsymbol{I}} \) can be expressed using bus impedance matrix: $$ \begin{aligned} \overline{\boldsymbol{V}} = \overline{\boldsymbol{Z}} \overline{\boldsymbol{I}} + \overline{\boldsymbol{V}}_\text{zero} \end{aligned} $$ where vector \( \overline{\boldsymbol{V}}_\text{zero} \) is for zero-load snapshot.
It might be problematic to apply the direct impedance method for high-voltage lines, because they should be modelled using one-line equivalent Pi models. The current loss must be taken into account.
Reference
- [bianconi2018multilayer] Bianconi, G. (2018). Multilayer networks: structure and function. Oxford university press.
- [EPRI2021penDSS] Electric Power Research Institute (2021). OpenDSS: The Open Distribution System Simulator. https://sourceforge.net/projects/electricdss/


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Setup Python 3
You can install Python packages with
pip3 install <package>
This will install into the site-package directory
/usr/local/lib/python3.7/site-packages
usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)”
brew install python3
python3 —version
sudo pip3 install virtualenv
virtualenv iot-python3
source iot-python3/bin/activate
python --version
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pip install mkdocs
Active Virtual Environment
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source activate
Change Directory into the Site
cd /Users/carlos.ferreira1ibm.com/ws/mkdocs/mam-hol-project/
Dectivate Virtual Environment
deactivate
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mkdocs build
mkdocs serve
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GitHub
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IBM Cloud PHP Static WebSite Repo url https://github.com/carlosibm/mam-hol team https://github.com/orgs/IBM/teams/watson-iot
IBM Cloud Public MAM HOL Pages Website url https://mam-hol.eu-gb.mybluemix.net/
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git status
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mkdocs build
mkdocs serve
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gtag('config', 'UA-159649729-1');
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Deploy
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ibmcloud login -sso
ibmcloud account list
Select account 1. IBM (10b59068b84d48c48f8300f8e1e63dba)
ibmcloud target -c <<accpunt_id>>
ibmcloud api https://api.eu-gb.bluemix.net
ibmcloud target --cf-api https://api.eu-gb.bluemix.net -o<EMAIL_ADDRESS>-s dev
ibmcloud cf push


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