New point on spherical model earth model.
|
|
alongTrackDistanceTo(self,
start,
end,
radius=6371008.771415,
wrap=False)
Compute the (signed) distance from the start to the closest point on
the great circle line defined by a start and an end point. |
|
|
|
|
|
|
|
crossingParallels(self,
other,
lat,
wrap=False)
Return the pair of meridians at which a great circle defined by this
and an other point crosses the given latitude. |
|
|
|
|
crossTrackDistanceTo(self,
start,
end,
radius=6371008.771415,
wrap=False)
Compute the (signed) distance from this point to the great circle
defined by a start and an end point. |
|
|
|
|
destination(self,
distance,
bearing,
radius=6371008.771415,
height=None)
Locate the destination from this point after having travelled the
given distance on the given initial bearing. |
|
|
|
|
distanceTo(self,
other,
radius=6371008.771415,
wrap=False)
Compute the (angular) distance from this to an other point. |
|
|
|
|
greatCircle(self,
bearing,
Vector=<class 'pygeodesy.vector3d.Vector3d'>,
**Vector_kwds)
Compute the vector normal to great circle obtained by heading on the
given initial bearing from this point. |
|
|
|
|
initialBearingTo(self,
other,
wrap=False,
raiser=False)
Compute the initial bearing (forward azimuth) from this to an other
point. |
|
|
|
|
intermediateTo(self,
other,
fraction,
height=None,
wrap=False)
Locate the point at given fraction between (or along) this and an
other point. |
|
|
|
|
intersection(self,
end1,
other,
end2,
height=None,
wrap=False)
Compute the intersection point of two lines, each defined by two
points or a start point and bearing from North. |
|
|
|
|
intersections2(self,
rad1,
other,
rad2,
radius=6371008.771415,
eps=0.0,
height=None,
wrap=True)
Compute the intersection points of two circles, each defined by a
center point and radius. |
|
|
|
|
isEnclosedBy(self,
points)
DEPRECATED, use method isenclosedBy . |
|
|
|
|
isenclosedBy(self,
points,
wrap=False)
Check whether a (convex) polygon or composite encloses this point. |
|
|
|
|
midpointTo(self,
other,
height=None,
fraction=0.5,
wrap=False)
Find the midpoint between this and an other point. |
|
|
|
|
nearestOn(self,
point1,
point2,
radius=6371008.771415,
**wrap_adjust_limit)
Locate the point between two points closest to this point. |
|
|
|
|
|
|
|
nearestOn3(self,
points,
closed=False,
radius=6371008.771415,
**wrap_adjust_limit)
Locate the point on a polygon closest to this point. |
|
|
|
|
toCartesian(self,
**Cartesian_datum_kwds)
Convert this point to Karney -based cartesian (ECEF)
coordinates. |
|
|
|
|
triangle7(self,
otherB,
otherC,
radius=6371008.771415,
wrap=False)
Compute the angles, sides and area of a spherical triangle. |
|
|
|
|
trilaterate5(self,
distance1,
point2,
distance2,
point3,
distance3,
area=True,
eps=0.9999999999999998,
radius=6371008.771415,
wrap=False)
Trilaterate three points by area overlap or perimeter intersection of
three corresponding circles. |
|
|
|
Inherited from sphericalBase.LatLonSphericalBase :
__init__ ,
bearingTo2 ,
finalBearingTo ,
intersecant2 ,
maxLat ,
minLat ,
parse ,
rhumbAzimuthTo ,
rhumbBearingTo ,
rhumbDestination ,
rhumbDistanceTo ,
rhumbMidpointTo ,
toNvector
Inherited from latlonBase.LatLonBase :
PointsIter ,
__eq__ ,
__ne__ ,
__str__ ,
antipode ,
bounds ,
boundsOf ,
chordTo ,
circin6 ,
circum3 ,
circum4_ ,
compassAngle ,
compassAngleTo ,
cosineAndoyerLambertTo ,
cosineForsytheAndoyerLambertTo ,
cosineLawTo ,
destinationXyz ,
equals ,
equals3 ,
equirectangularTo ,
euclideanTo ,
flatLocalTo ,
flatPolarTo ,
hartzell ,
haversineTo ,
height4 ,
heightStr ,
hubenyTo ,
isantipode ,
isantipodeTo ,
isequalTo ,
isequalTo3 ,
latlon2 ,
latlon2round ,
latlon_ ,
nearestOn6 ,
normal ,
philam2 ,
points ,
points2 ,
radii11 ,
rhumbLine ,
thomasTo ,
to2ab ,
to3llh ,
to3xyz ,
toEcef ,
toLocal ,
toLtp ,
toNormal ,
toStr ,
toVector ,
toVector3d ,
toWm ,
vincentysTo
Inherited from named._NamedBase :
__repr__ ,
others ,
toRepr
Inherited from named._Named :
__imatmul__ ,
__matmul__ ,
__rmatmul__ ,
attrs ,
classof ,
copy ,
dup ,
methodname ,
rename ,
toStr2
Inherited from object :
__delattr__ ,
__format__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__setattr__ ,
__sizeof__ ,
__subclasshook__
|


.col--number common smartphone ===> extra small screen devices mobile-tablet 923 ===> samll devices tablet-laptop 923 1200 ====> medium screen big laptop 1366 ==> large screen xs for phones - screens less than 768px wide ===> 767px sm for tablets - screens equal to or greater than 768px wide md for small laptops - screens equal to or greater than 992px wide lg for laptops and desktops - screens equal to or greater than 1200px wide xs 767 smartphones sm 768 to 991 =====> small md 992 - 1199px ===> medium lg 1200 to wider ====> large .col-lg-4 .col-lg-8
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.


Name Generator
Generator :
Star (Elite)
French City (Markov 4)
British City (Markov 4)
French Firstname (Markov 3)
Number of names :


Christmas Rush / Breakaway
Christmas Rush is a TV movie starring Dean Cain, Erika Eleniak, and Roman Podhora. In the season of giving, the department took his badge. Criminals took his wife. Now, one cop is taking matters into his own hands


Dynamic Responsive elements like images,videos,maps,circle content.
you can make a respnsive video by simply create an element with class responsive-video
and then put inside of it the embeded video element with class video.
and for the html5 video just add the class
html5-video to the video element see the exapmle below.

<video class="html5-video" controls>
<source src="images/video.mp4" type="video/mp4">
</video>
you can make a respnsive map by simply create an element with class responsive-map and then put inside of it the embeded map element with class map.

you can make a respnsive circle by simply create an element with class responsive-circle and then put inside of it a content element with class circle-content the content can be any thing.
Lorem Ipsum is simply dummy text of the printing and typesetting industry.

... your content ...

Circle Content Demo

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy

the images ar responsive by default but if wanna make a fluid image add a class fluid to the image element. and if wanna make a filled image (100% width and height) add the class fill-img.


How Scream Holds Up In 2019
by Tegan JonesIn 1996 a movie was unleashed on the world that resurrected the slasher genre from the consumerist graveyard of the 1980s. Not only that, it completely spoofed and subverted it.
Scream was a movie that should never have happened, never should have worked. And yet it managed to redefine horror and become a cult classic.
But that was twenty-three years ago. Does it still hold up in 2019?
Movies With Mikey takes a look at this modern classic in his latest video for FilmJoy.
He opens by enticing viewers with intrigue and a touch of history.
"Scream happened at the worst imaginable time and its a miracle that anyone saw it, but lets back up a bit"
After laying down a brief timeline of the most influential horror flicks, he lands on the 1980s. This was a time when the slasher sub-section of the genre ran rampant, with a large focus placed on the Halloween, Nightmare on Elm Street and Friday the 13th franchises.
This was, "The moment horror basically turned into a never-ending 1980s cocaine waterfall... make as much money as possible as fast as possible... the Disney way," says Mikey.
By the mid-90s the appetite for these kinds of films had completely dried up. Yet, this was exactly when Kevin Williamson (yeah, the creator of Dawson's Creek) decided to shop around a classic slasher script around Hollywood... but with a twist.
I'll let you experience the rest of miracle story that is Scream for yourselves, as well as Mikey's incredibly apt commentary regarding the now-deceased Wes Craven. But I will say that yes, the film absolutely holds up in 2019.
The bedrock of violent horror films has, for the most part, been at the expense of young women. They have been maimed, murdered and raped for entertainment value - and Scream addressed that.
In 2019 society is speaking out louder than ever about the treatment of women in both in art and the real world. While we are far from a resolution, the rumbles of rage are becoming increasingly louder.
And we're seeing this reflected in film. We're seeing even more movies that place kick-arse women front and centre as more than just victims or love interests. Scream feels like a precursor to this important cultural shift.
Mikey puts it best when he say, "Scream in 2019 is almost too relevant"
Considering the franchise is about to get a reboot, he just might be right.
[FilmJoy]


"GoodFellas" (1990)
A Film Review by Jason Wallis
"GoodFellas" * * * * Starring Robert DeNiro, Joe Pesci, Ray Liotta, Lorraine Braco and Paul Sorvino Directed by Martin Scorsese
Many people find the mafia life attractive. I mean, let's face it; on the surface, they pretty much have it made. Money, power, women; what more could a guy ask for? But there is also a down side to it. Always having to watch your back, getting in trouble with the cops, etc. Martin Scorsese's "GoodFellas" shows us exactly what it is like to be a part of the mafia. Being the brilliant man that he is, Marty shows us both sides of this life with painful realism. Ray Liotta plays Henry Hill, a half-Irish, half-Sicilian member of the mafia. As the film begins, we see Robert DeNiro, Joe Pesci and Ray driving in a car, noticing a banging that seems to be coming from the trunk. They get out to investigate, Pesci having his butcher knife ready. They open the trunk to find that their victim is not quite dead. Pesci, being the psychotic nut he is, viciously attacks the bound and helpless victim. This, I think, is the perfect opening for this film. It shows us just how graphic and cruel "the life" can really be. Then we are shown Henry as a young man, always watching the local mobsters from his bedroom window, constantly envying them. Soon, he is "adopted" by the local family. They have him do odd jobs (i.e. setting fire to cars, making deliveries). The family is run by Pauly (Paul Sorvino), the godfather figure who goes not want his outfit to get into the narcotics business (much like Marlon Brando's character in the original "Godfather"). Later, Henry is again an adult and is still in "the business". He falls in love (more or less) and marries a Jewish girl (Lorraine Braco, Oscar nominated performance). One of his buddies is fellow mobster Tommy DeVito (Joe Pesci, in the performance of his career), the hot-head who knifed the guy in the trunk at the beginning of the film. He's really the one who starts all the trouble, but I won't get into that right now. Tho "GoodFellas" is not quite as totally cool or classic as Francis Ford Coppola's "The Godfather", it is a tad more realistic and lurid. In Coppola's romantic version of mob life, you were dealing with high-class mafiosos, not street thugs like you are here.  These are the people who don't know the meaning of the word "loyalty". These are the people that you could see walking down the street and not even know that they're hard-core criminals. Martin Scorsese has delivered yet another masterpiece here. I mean, this one is right up there with "Raging Bull", "Taxi Driver" and "Mean Streets". My big question is why hasn't the Academy ever given Marty an Oscar? By now, he deserves at least three or four. Do they have something against brilliant minds or are they just plain stupid? Anyway, I highly, highly recommend this film if you are looking for a well-made, hip, stylish and just plain cool gangster flick.
Visit Jason Wallis' "Filmaholics Anonymous" web page at http://www.geocities.com/Hollywood/Boulevard/7475
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


# Copyright 2015 Abhijit Menon-Sen<EMAIL_ADDRESS>#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# Make coding more python3-ish
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
import re
from ansible.errors import AnsibleParserError, AnsibleError
# Components that match a numeric or alphanumeric begin:end or begin:end:step
# range expression inside square brackets.
numeric_range = r'''
\[
(?:[0-9]+:[0-9]+) # numeric begin:end
(?::[0-9]+)? # numeric :step (optional)
\]
'''
hexadecimal_range = r'''
\[
(?:[0-9a-f]+:[0-9a-f]+) # hexadecimal begin:end
(?::[0-9]+)? # numeric :step (optional)
\]
'''
alphanumeric_range = r'''
\[
(?:
[a-z]:[a-z]| # one-char alphabetic range
[0-9]+:[0-9]+ # ...or a numeric one
)
(?::[0-9]+)? # numeric :step (optional)
\]
'''
# Components that match a 16-bit portion of an IPv6 address in hexadecimal
# notation (0..ffff) or an 8-bit portion of an IPv4 address in decimal notation
# (0..255) or an [x:y(:z)] numeric range.
ipv6_component = r'''
(?:
[0-9a-f]{{1,4}}| # 0..ffff
{range} # or a numeric range
)
'''.format(range=hexadecimal_range)
ipv4_component = r'''
(?:
[01]?[0-9]{{1,2}}| # 0..199
2[0-4][0-9]| # 200..249
25[0-5]| # 250..255
{range} # or a numeric range
)
'''.format(range=numeric_range)
# A hostname label, e.g. 'foo' in 'foo.example.com'. Consists of alphanumeric
# characters plus dashes (and underscores) or valid ranges. The label may not
# start or end with a hyphen or an underscore. This is interpolated into the
# hostname pattern below. We don't try to enforce the 63-char length limit.
label = r'''
(?:[\w]|{range}) # Starts with an alphanumeric or a range
(?:[\w_-]|{range})* # Then zero or more of the same or [_-]
(?<![_-]) # ...as long as it didn't end with [_-]
'''.format(range=alphanumeric_range)
patterns = {
# This matches a square-bracketed expression with a port specification. What
# is inside the square brackets is validated later.
'bracketed_hostport': re.compile(
r'''^
\[(.+)\] # [host identifier]
:([0-9]+) # :port number
$
''', re.X
),
# This matches a bare IPv4 address or hostname (or host pattern including
# [x:y(:z)] ranges) with a port specification.
'hostport': re.compile(
r'''^
((?: # We want to match:
[^:\[\]] # (a non-range character
| # ...or...
\[[^\]]*\] # a complete bracketed expression)
)*) # repeated as many times as possible
:([0-9]+) # followed by a port number
$
''', re.X
),
# This matches an IPv4 address, but also permits range expressions.
'ipv4': re.compile(
r'''^
(?:{i4}\.){{3}}{i4} # Three parts followed by dots plus one
$
'''.format(i4=ipv4_component), re.X|re.I
),
# This matches an IPv6 address, but also permits range expressions.
#
# This expression looks complex, but it really only spells out the various
# combinations in which the basic unit of an IPv6 address (0..ffff) can be
# written, from :: to 1:2:3:4:5:6:7:8, plus the IPv4-in-IPv6 variants such
# as ::ffff:<IP_ADDRESS>.
#
# Note that we can't just use ipaddress.ip_address() because we also have to
# accept ranges in place of each component.
'ipv6': re.compile(
r'''^
(?:{0}:){{7}}{0}| # uncompressed: 1:2:3:4:5:6:7:8
(?:{0}:){{1,6}}:| # compressed variants, which are all
(?:{0}:)(?::{0}){{1,6}}| # a::b for various lengths of a,b
(?:{0}:){{2}}(?::{0}){{1,5}}|
(?:{0}:){{3}}(?::{0}){{1,4}}|
(?:{0}:){{4}}(?::{0}){{1,3}}|
(?:{0}:){{5}}(?::{0}){{1,2}}|
(?:{0}:){{6}}(?::{0})| # ...all with 2 <= a+b <= 7
:(?::{0}){{1,6}}| # ::ffff(:ffff...)
{0}?::| # ffff::, ::
# ipv4-in-ipv6 variants
(?:0:){{6}}(?:{0}\.){{3}}{0}|
::(?:ffff:)?(?:{0}\.){{3}}{0}|
(?:0:){{5}}ffff:(?:{0}\.){{3}}{0}
$
'''.format(ipv6_component), re.X|re.I
),
# This matches a hostname or host pattern including [x:y(:z)] ranges.
#
# We roughly follow DNS rules here, but also allow ranges (and underscores).
# In the past, no systematic rules were enforced about inventory hostnames,
# but the parsing context (e.g. shlex.split(), fnmatch.fnmatch()) excluded
# various metacharacters anyway.
#
# We don't enforce DNS length restrictions here (63 characters per label,
# 253 characters total) or make any attempt to process IDNs.
'hostname': re.compile(
r'''^
{label} # We must have at least one label
(?:\.{label})* # Followed by zero or more .labels
$
'''.format(label=label), re.X|re.I|re.UNICODE
),
}
[docs]def parse_address(address, allow_ranges=False):
"""
Takes a string and returns a (host, port) tuple. If the host is None, then
the string could not be parsed as a host identifier with an optional port
specification. If the port is None, then no port was specified.
The host identifier may be a hostname (qualified or not), an IPv4 address,
or an IPv6 address. If allow_ranges is True, then any of those may contain
[x:y] range specifications, e.g. foo[1:3] or foo[0:5]-bar[x-z].
The port number is an optional :NN suffix on an IPv4 address or host name,
or a mandatory :NN suffix on any square-bracketed expression: IPv6 address,
IPv4 address, or host name. (This means the only way to specify a port for
an IPv6 address is to enclose it in square brackets.)
"""
# First, we extract the port number if one is specified.
port = None
for matching in ['bracketed_hostport', 'hostport']:
m = patterns[matching].match(address)
if m:
(address, port) = m.groups()
port = int(port)
continue
# What we're left with now must be an IPv4 or IPv6 address, possibly with
# numeric ranges, or a hostname with alphanumeric ranges.
host = None
for matching in ['ipv4', 'ipv6', 'hostname']:
m = patterns[matching].match(address)
if m:
host = address
continue
# If it isn't any of the above, we don't understand it.
if not host:
raise AnsibleError("Not a valid network hostname: %s" % address)
# If we get to this point, we know that any included ranges are valid.
# If the caller is prepared to handle them, all is well.
# Otherwise we treat it as a parse failure.
if not allow_ranges and '[' in host:
raise AnsibleParserError("Detected range in host but was asked to ignore ranges")
return (host, port)


Blade Runner 2049
Blade Runner 2049 is a movie starring Harrison Ford, Ryan Gosling, and Ana de Armas. A young blade runner's discovery of a long-buried secret leads him to track down former blade runner Rick Deckard, who's been missing for thirty...


[§437B-22] Failure to comply with chapter; misdemeanor. Any person who fails to comply with the provisions of this chapter is guilty of a misdemeanor. [L 1975, c 143, pt of §2]


{% include meta.html %}
About
Blog
Contact
Leave an impression
A website says a thousand words.
{{ content }}
{% include analytics.html %}


The Ferui Design library is based on
Bootstrap 4 CSS framework. We've
extended the framework and addapt it to fit our needs. Some of the basic/default Bootstrap configurations are not exactly the
same as the original framework.
Please use our documentation instead to know about the changes.


Data Science Intern - San Mateo, CA
PlayStation isn't just the Best Place to Play —it's also the Best Place to Work. We've thrilled gamers since 1994, when we launched the original PlayStation. Today, we're recognized as a global leader in interactive and digital entertainment. The PlayStation brand falls under Sony Interactive Entertainment, a wholly-owned subsidiary of Sony Corporation.
This internship is for Summer 2020.
Data Science Intern
San Mateo, CA
Responsibilities:
As a Data Science Intern, you will work closely with BIS data science and internal partners to identify and answer important product, marketing, and strategic business questions. You are naturally curious and have a real passion for all things data-related. You have an analytical mindset and can provide insights using internal BIS data. You will answer product questions by using appropriate statistical techniques on available data. You are dedicated to gathering clean data and will drive the collection and refinement of existing data sources. You will design and prototype DL models using structured and non-structured data to push the boundaries of creative statistical modeling. You will also analyze and interpret the results of AB tests.
Qualifications:
Pursuing a Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Economics (or related field), and/or within 1 year of their post-graduation date
Strong math skills in general, especially linear algebra and statistics
Fluency with Python (and libraries such as Pandas, Numpy, scikit-learn)
Familiarity with relational databases and SQL
Experience or course work applying basic ML to a type of data and or have used algorithms to conduct experiments on data
Ability to present complex technical information in a clear and concise manner to other critical partners
Comfortable with large data sets and distributed computing tools on cloud is a plus (Spark, Map/Reduce, Hadoop etc.)
Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to race, color, religion, gender, pregnancy, national origin, ancestry, citizenship, age, legally protected physical or mental disability, covered veteran status, status in the U.S. uniformed services, sexual orientation, gender identity, marital status, genetic information or membership in any other legally protected category.
We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond.
We sincerely appreciate the time and effort you spent in contacting us and we thank you for your interest in PlayStation.


On seasons (continued)
Define a function which associates to each month of the year the season
which contains the first day of this month.
- As an application of month_rec
- Using a pattern matching construct
Please use the following definitions :
seasons ( from this exercise)
and months .
Solution
Look at this file


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Send us a message or contact us from the address below.


# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Code related to benchmarking batched inference runs
"""
from typing import Any, Dict, Iterable, Iterator, List, Union
import numpy
__all__ = ["BatchBenchmarkResult", "BenchmarkResults"]
[docs]class BatchBenchmarkResult(object):
"""
A benchmark result for a batched inference run
:param time_start: The system time when the run for the batch was started
:param time_end: The system time when the run for the batch ended
:param batch_size: The size of the batch that was benchmarked
:param inputs: Optional batch inputs that were given for the run
:param outputs: Optional batch outputs that were given for the run
:param extras: Optional batch extras to store any other data for the run
"""
def __init__(
self,
time_start: float,
time_end: float,
batch_size: int,
inputs: Union[None, List[numpy.ndarray]] = None,
outputs: Union[None, List[numpy.ndarray], Dict[str, numpy.ndarray]] = None,
extras: Any = None,
):
self._time_start = time_start
self._time_end = time_end
self._batch_size = batch_size
self._inputs = inputs
self._outputs = outputs
self._extras = extras
def __repr__(self):
props = {
"time_start": self.time_start,
"time_end": self.time_end,
"size": self.batch_size,
"batches_per_second": self.batches_per_second,
"items_per_second": self.items_per_second,
"ms_per_batch": self.ms_per_batch,
"ms_per_item": self.ms_per_item,
}
return f"{self.__class__.__name__}({props})"
def __str__(self):
return (
f"{self.__class__.__name__}(ms_per_batch={self.ms_per_batch}, "
f"items_per_second={self.items_per_second})"
)
@property
def time_start(self) -> float:
"""
:return: The system time when the run for the batch was started
"""
return self._time_start
@property
def time_end(self) -> float:
"""
:return: The system time when the run for the batch ended
"""
return self._time_end
@property
def time_elapsed(self) -> float:
"""
:return: The time elapsed for the entire run (end - start)
"""
return self._time_end - self._time_start
@property
def batch_size(self) -> int:
"""
:return: The size of the batch that was benchmarked
"""
return self._batch_size
@property
def inputs(self) -> Union[None, List[numpy.ndarray]]:
"""
:return: Batch inputs that were given for the run, if any
"""
return self._inputs
@property
def outputs(self) -> Union[None, List[numpy.ndarray]]:
"""
:return: Batch outputs that were given for the run, if any
"""
return self._outputs
@property
def extras(self) -> Any:
"""
:return: Batch extras to store any other data for the run
"""
return self._extras
@property
def batches_per_second(self) -> float:
"""
:return: The number of batches that could be run in one second
based on this result
"""
return 1.0 / self.time_elapsed
@property
def items_per_second(self) -> float:
"""
:return: The number of items that could be run in one second
based on this result
"""
return self._batch_size / self.time_elapsed
@property
def ms_per_batch(self) -> float:
"""
:return: The number of milliseconds it took to run the batch
"""
return self.time_elapsed * 1000.0
@property
def ms_per_item(self) -> float:
"""
:return: The averaged number of milliseconds it took to run each item
in the batch
"""
return self.time_elapsed * 1000.0 / self._batch_size
[docs]class BenchmarkResults(Iterable):
"""
The benchmark results for a list of batched inference runs
"""
def __init__(self):
self._results = [] # type: List[BatchBenchmarkResult]
def __repr__(self):
return f"{self.__class__.__name__}({self._properties_dict})"
def __str__(self):
"""
:return: Human readable form of the benchmark summary
"""
formatted_props = [
"\t{}: {}".format(key, val) for key, val in self._properties_dict.items()
]
return "{}:\n{}".format(
self.__class__.__name__,
"\n".join(formatted_props),
)
def __len__(self) -> int:
return len(self._results)
def __getitem__(self, index: int) -> BatchBenchmarkResult:
return self._results[index]
def __iter__(self) -> Iterator[BatchBenchmarkResult]:
for result in self._results:
yield result
@property
def _properties_dict(self) -> Dict:
return {
"items_per_second": self.items_per_second,
"ms_per_batch": self.ms_per_batch,
"batch_times_mean": self.batch_times_mean,
"batch_times_median": self.batch_times_median,
"batch_times_std": self.batch_times_std,
}
@property
def results(self) -> List[BatchBenchmarkResult]:
"""
:return: the list of recorded batch results
"""
return self._results
@property
def num_batches(self) -> int:
"""
:return: the number of batches that have been added
"""
return len(self)
@property
def num_items(self) -> int:
"""
:return: the number of items across all batches that have been added
"""
num_items = sum([res.batch_size for res in self._results])
return num_items
@property
def batch_times(self) -> List[float]:
"""
:return: the list of all batch run times that have been added
"""
return [res.time_elapsed for res in self._results]
@property
def batch_sizes(self) -> List[int]:
"""
:return: the list of all batch run sizes that have been added
"""
return [res.batch_size for res in self._results]
@property
def batch_times_mean(self) -> float:
"""
:return: the mean of all the batch run times that have been added
"""
return numpy.mean(self.batch_times).item()
@property
def batch_times_median(self) -> float:
"""
:return: the median of all the batch run times that have been added
"""
return numpy.median(self.batch_times).item()
@property
def batch_times_std(self) -> float:
"""
:return: the standard deviation of all the batch run times that have been added
"""
return numpy.std(self.batch_times).item()
@property
def batches_per_second(self) -> float:
"""
:return: The number of batches that could be run in one second
based on this result
"""
return self.num_batches / sum(self.batch_times)
@property
def items_per_second(self) -> float:
"""
:return: The number of items that could be run in one second
based on this result
"""
return self.num_items / sum(self.batch_times)
@property
def ms_per_batch(self) -> float:
"""
:return: The number of milliseconds it took to run the batch
"""
return sum(self.batch_times) * 1000.0 / self.num_batches
@property
def ms_per_item(self) -> float:
"""
:return: The averaged number of milliseconds it took to run each item
in the batch
"""
return sum(self.batch_times) * 1000.0 / self.num_items
@property
def inputs(self) -> Union[None, List[numpy.ndarray]]:
"""
:return: Batch inputs that were given for the run, if any
"""
return [res.inputs for res in self._results]
@property
def outputs(self) -> Union[None, List[numpy.ndarray]]:
"""
:return: Batch outputs that were given for the run, if any
"""
return [res.outputs for res in self._results]
[docs] def append_batch(
self,
time_start: float,
time_end: float,
batch_size: int,
inputs: Union[None, List[numpy.ndarray]] = None,
outputs: Union[None, List[numpy.ndarray], Dict[str, numpy.ndarray]] = None,
extras: Any = None,
):
"""
Add a recorded batch to the current results
:param time_start: The system time when the run for the batch was started
:param time_end: The system time when the run for the batch ended
:param batch_size: The size of the batch that was benchmarked
:param inputs: Optional batch inputs that were given for the run
:param outputs: Optional batch outputs that were given for the run
:param extras: Optional batch extras to store any other data for the run
"""
self._results.append(
BatchBenchmarkResult(
time_start, time_end, batch_size, inputs, outputs, extras
)
)


My Team
Steven Ly
Manager
Employee ID: 1
Email:
<EMAIL_ADDRESS>Office Number: 77
Ben Summers
Engineer
Employee ID: 2
Email:
<EMAIL_ADDRESS>GitHub:
BenRepo
Donovan Reyes
Intern
Employee ID: 3
Email:
<EMAIL_ADDRESS>School: UT
Luka Doncic
Engineer
Employee ID: 4
Email:
<EMAIL_ADDRESS>GitHub:
LukaRepo


One of the most repeatedly asked questions in the NFL this season continues to be is if former New England Patriots tight end Rob Gronkowski is going to come out of retirement. And James Corden, of all people, may hold the key to the three-time Super Bowl champion’s return to the field.
Gronkowski was a surprise guest on Corden’s “Late Late Show” Thursday night, and not just because he happened to be on the CBS lot filming the host’s new sports-themed show “Game On!” this week.
The three-time Super Bowl champion, who officially announced his retirement from the NFL in March, said he’s loving his post-football career of TV gigs, working out and new business ventures.
Also Read:
Corden then tried to trick Gronk into agreeing to go back to the Patriots by making him blink. “There’s always a chance,” Gronkowski said.
While short, chubby Corden doesn’t boast the physical attributes you normally expect from a football player, the British-born host believes he may have missed his calling. “I watched the ‘Blind Side’ and thought, ‘No way, if I had been born in America, I would 100% have made it in the NFL,'” he told 6-foot-6 Gronkowski.
Also Read:
“I am big, but I am light on the toes,” the Tony Awards host continued while selling his attributes as a linebacker.
“It’s a package deal,” Gronkowski conceded. “I am only going if you are going. You can be a fullback … you can be behind me, and we just block and we run everyone over.”
Will we be seeing both Gronk and Corden wearing Patriots jerseys anytime soon? Your move, Bill Belichick.
Also Read:
In the meantime, Gronkowski will next be seen serving as a team captain on “Game On!,” the upcoming show based on U.K. series “A League of Their Own,” that Corden hosts himself.
Keegan-Michael Key hosts the U.S. version, with Venus Williams serving as the other team captain. Bobby Lee and Ian Karmel act as comedian panelists along with a rotating mix of sports stars, comedians and celebrities.
“Game On!” is produced by Eye Productions Inc. and Fulwell 73 Productions with CPL Productions, a Red Arrow Studios company. Executive producers for Fulwell 73 Productions are Ben Winston, James Corden and Emma Conway with David Taylor, Murray Boland and Danielle Lux serving as executive producers for CPL Productions. Gronkowski, Elle Key, Keegan-Michael Key, Henry Penzi and Venus also serve as executive producers.
Last month, Gronkowski joined the cast of “Fox NFL Sunday” to serve as an NFL analyst alongside Terry Bradshaw, Tony Gonzalez, Jimmy Johnson, Howie Long and Michael Strahan. Gronkowski is represented by UTA.
Rob Gronkowski, Snoop Dogg, Nikki Bella Fuel ESPYS Week Parties (Photos) The biggest day in sports now lasts a whole week, with ESPN, Nickelodeon, Players’ Tribune and Sports Spectacular joining the action View In Gallery


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Common Questions About Birth Doulas
Will my partner feel left out if we hire a doula?
Ideally, the doula and partner’s roles at a birth are complementary to each other. For many partners, having a doula present frees them from the pressure of being the only support for the mother. Penny Simkin the founder of Doulas of North America states: “The doula’s skills and knowledge can help him to feel more relaxed.” A doula understand that this birth also belongs to the partner, and feels it is important to care for the whole family through out the birthing process.
How much do doulas charge?
The range of cost for an established doula in Chicago and the surrounding areas is around $500-1400. What this fee covers varies from doula to doula, but in most cases the couple will have at least one or two prenatal visits, attending the birth (whether it is 3 hours or 30 hours) and one or two postpartum visits. Usually, the higher the cost the more experienced a doula is.
However, there are many doulas who do volunteer or lost cost births. Please contact me if you need help finding a doula, I would be more then happy to help.
Why do doulas cost so much money?
When you hire a doula you are paying not only for her expertise, comfort and support but also her time “on call”, 24 hours a day, until the baby is born. For most doulas being on call means they drop what ever they are currently doing or any plans they have made to be at your birth. Once they arrive at your birth they do not leave until the baby is born. Other costs for the doula could be travel expenses (gas, valet parking or taxies), paying another doula for being her back up, baby-sitting and insurance costs to name some examples.
What is the difference between a doula and midwife?
Penny Simkin sums this up nicely in her paper on Doulas and Dads. “Physicians, midwives and nurses are responsible for monitoring labor, assessing the medical condition of the mother and baby, and treating complications when they arise. But childbirth is also an emotional and spiritual experience with long-term impact on a woman’s personal well being. A doula is constantly aware that the mother and her partner will remember this experience throughout their lives. By ‘mothering the mother’ during childbirth the doula supports the parents in having a positive and memorable birth experience.”
Common Questions about Childbirth Education classes
I am not sure I want natural childbirth, is this class for me?
Many women are uncertain of what type of birth they want. The information covered in class is beneficial no matter what type of birth you have.
I am single and don’t have a mate to attend the class with me, can I still sign up?
Yes! You are more then welcome to bring anyone who will be at your birth to the class; a family member, doula, friend etc. In fact it is ideal to bring the person who will be your main support at the birth to class. If you feel that you don’t have any support people, please call or email me to work something out.
My hospital provides classes for expectant families why should I take a class somewhere else?
There is a great benefit to taking an independent (out of/ or not associated with a hospital) childbirth education class. Because the teacher is not tied to any hospital policies she can be more candid and the classes are usually much more extensive. An independent class will dive into a whole range of topics that will not come up in hospital based classes, this can be particularly important if you want a natural childbirth. I will say that my Comfort Measures class tends to attract couples who took a class at the hospital and feel like they need to learn more about the labor process after doing so.
Having said that, it can be good to take a hospital class or at the very least a tour of the facilities. This way you can learn more about the place you are giving birth first hand. Also questions like “Do you have birth balls?” or “Can I labor in the water?” may be answered.
Many families I work with mix independent and hospital class together.
Will I know how to give birth after this class?
Believe it or not, you already know how to birth your baby! We do cover all types of comfort measures, breathing, relaxation techniques and visualizations. We discuss fear of birth and how to overcome it, as well as complications that can occur. Your body was made to do this work, so the class is designed to help you discover the best birth for you.


This is an application to sort the table above in ascending and descending order. Just click on the header of the table to sort it.


Microsoft excel can be opened by clicking Microsoft Excel shortcut on desktop or Microsoft Excel option from popup menu under Programs menu.
Start Button > All Apps > Microsoft office > Microsoft Office Excel 2013
Creating a New Workbook
When we start Microsoft Excel, a new workbook opens.
A workbook is a collection of spreadsheet pages. Each spreadsheet page is a grid made up of columns and rows. Each workbook is saved as a separate file, with extension .xls. The default file name for the first workbook is book1.xls. Each worksheet is divided into columns and rows, Columns are vertical divisions and Rows are horizontal divisions. The intersection of row and column is called Cell. The cell located at the intersection of column A and row 3 shall be referred to as A3. A worksheet has 256 columns and 65536 rows.
The default workbook opens with 3 worksheets, named Sheet1, Sheet2 and Sheet3. The sheet names appear on tabs at the bottom of the workbook window. By clicking on the tabs you can move from sheet to sheet within a workbook. The tab of the active sheet is always bold.
The Ms-Excel has by default menu bar and other toolbars present (as shown).
At the top there is a Title Bar on which the file name is displayed as "Book1"
The Standard Toolbar is required for basic operations such as open a new file, save, cut, copy, paste, sort and other basic tasks.
The Formatting Toolbar provides us the facility to format the worksheet by using fonts,alignments,background color,text color etc.
Additional toolbars can be activated or deactivated by right click in the blank area next to toolbars.
Another way to activate or deactivate toolbars is to click View > Toolbars > click the appropriate toolbar to be activated or deactivated.
The Menu Bar
Menu commands are grouped into menus across the menu bar. Equivalent commands are also avaliable in shortcut menus, toolbar buttons and shortcut keys. Some commands display submenu. Submenus are indicated by ">" following the command name. Submenus contain additional related commands.
The File menu ---- shows New, Open, Close, Save, Save As, Print and other basic file functions.
The Edit menu ---- shows Cut, Copy, Paste, Paste Special, Find, Replace etc. to edit text
The Insert menu -- is used to add new Row, Column, Worksheet, Function, Picture and much more.
The View menu ---- is used to activate/deactivate Toolbars, to add/remove Header/Footer, to view Comments etc.
The Format menu -- shows formatting options for Cells, Row, Column and Worksheet.
The Tools menu --- shows options for checking spreadsheet for spelling mistakes, enable/disable Protection of workbook, Auditing, AutoCorrect etc.
The Data menu ---- shows Sort, Filter, Table , Subtotal etc. for analyzing data
The Window menu -- facilitates better view of data with options for splitting and freezing Window panes
The Help menu ---- shows various options for help on Microsoft Excel


Victoria Centre bus station forced to close following electrical issues
Alternative pick-up and drop-off points are being arranged by bus operators
by Matthew BunnThe Victoria Centre bus station will close to passengers this afternoon as work continues to fix electrical problems at the site.
Nottingham City Council announced this morning (November 16) that the station's travel centre had closed because of a power outage.
Now, the authority has confirmed the bus station will close at 2pm "following earlier electrical issues ".
In a statement, a spokesman added: "The power company is still on site, but it's not safe to remain open with no lighting."
Commuters are being advised to contact their bus operators directly for alternative pick-up and drop-off points.
Get the most important news straight in your inbox before 9am every day - sign up to Nottinghamshire Live's newsletter. No complicated forms to fill out, just enter your email address in the black box at the top of this page.


{% load url from future %} {% load compress %}
Plugins can be included individually (using Bootstrap's individual *.js
files), or all at once (using bootstrap.js
or the minified bootstrap.min.js
).
Both bootstrap.js
and bootstrap.min.js
contain all plugins in a single file.
Some plugins and CSS components depend on other plugins. If you include plugins individually, make sure to check for these dependencies in the docs. Also note that all plugins depend on jQuery (this means jQuery must be included before the plugin files). Consult our bower.json
to see which versions of jQuery are supported.


- Obsess over Customers
- Be innovative and change our world
- Let passion and belief drive what we do
- Do what's right rather than whats easy
- Stand United: All for one, and one for all
- Maintain a quiet confidence in our abilities
- Have a whole lot of fun
- Quality


KEYS R workshop
About
This is a short 1 hour and 45 minutes introduction to R and the tidyverse prepared and delivered by Kelsey Gonzalez for BIO5 Institute’s KEYS (Keep Engaging Youth in Science) Research Internship Program on June 10, 2020.
A PDF of this lesson is available here
This lesson is largely adapted from Software and Data Carpentry materials by Kelsey Gonzalez for use with the Penguins dataset.
Getting set up
R works by having a base programming language that can be added on to with community-created open-source extensions called packages. We’re going to first install two packages that we will work with today.
install.packages("tidyverse")
install.packages("palmerpenguins")
library(tidyverse)
library(palmerpenguins)
What is an R package?
An R package is a complete unit for sharing code with others. Each R package contains the code for a set of R functions, the documentation (or description) for each of the functions, as well as a practice dataset to learn the functions on.
Generally, each R package is built with a specific task in mind. For instance, the package dplyr
provides easy tools for the most common data manipulation tasks. It is built to work directly with data frames, with many common tasks optimized by being written in a compiled language (C++) (not all R packages are written in R!).
But there are also packages available for a wide range of tasks including building plots (ggplot2
, which we’ll see later), downloading data from the NCBI database, or performing statistical analysis on your data set. Many packages such as these are housed on, and downloadable from, the Comprehensive R Archive Network (CRAN) using install.packages
. This function makes the package accessible by your R installation with the command library()
, as you did with tidyverse
earlier.
To easily access the documentation for a package within R or RStudio, use help(package = "package_name")
.
Let’s first load the data we’ll be using today, which contains observations for 3 penguin species on islands in the Palmer Archipelago, Antarctica. More information about this dataset can be found at github.com/allisonhorst/penguins.
We only have a short lesson today, so I won’t get into the specifics on what we’re doing here. Just know we loaded a package called penguins above which contained a dataset called penguins. By calling data()
, we’re asking r to load a dataset that is in the package. R
also can read csv files, excel files, and many other data types.
data(penguins)
Data Wrangling & visualization:
adapted from: https://datacarpentry.org/r-socialsci/03-dplyr-tidyr/index.html
questions:
- How can I select specific rows and/or columns from a data frame?
- How can I combine multiple commands into a single command?
- How can create new columns or remove existing columns from a data frame?
- How can I reformat a dataframe to meet my needs?
objectives:
- Describe the purpose of an R package and the
dplyr
andtidyr
packages.
- Select certain columns in a data frame with the
dplyr
functionselect
.
- Select certain rows in a data frame according to filtering conditions with the
dplyr
functionfilter
.
- Link the output of one
dplyr
function to the input of another function with the ‘pipe’ operator%>%
.
- Add new columns to a data frame that are functions of existing columns with
mutate
.
- Use the split-apply-combine concept for data analysis.
- Use
summarize
,group_by
, andcount
to split a data frame into groups of observations, apply a summary statistics for each group, and then combine the results.
- Describe the concept of a wide and a long table format and for which purpose those formats are useful.
- Describe what key-value pairs are.
- Reshape a data frame from long to wide format and back with the
spread
andgather
commands from thetidyr
package.
- Export a data frame to a csv file.
keypoints:
- Use the
dplyr
package to manipulate dataframes.
- Use
select()
to choose variables from a dataframe.
- Use
filter()
to choose data based on values.
- Use
group_by()
andsummarize()
to work with subsets of data.
- Use
mutate()
to create new variables.
- Use the
tidyr
package to change the layout of dataframes.
Data Manipulation using dplyr
dplyr
is a package for making tabular data manipulation easier by using a limited set of functions that can be combined to extract and summarize insights from your data.
Similarly to readr
, dplyr
and tidyr
are also part of the tidyverse. These packages were loaded in R’s memory when we called library(tidyverse)
earlier.
To learn more about dplyr
after the workshop, you may want to check out this
handy data transformation with dplyr
cheatsheet.
We’re going to learn some of the most common dplyr
functions:
select()
: subset columnsfilter()
: subset rows on conditionsmutate()
: create new columns by using information from other columnsgroup_by()
andsummarize()
: create summary statistics on grouped dataarrange()
: sort results
Explore our data
Let’s explore our data first. There’s 7 variables or columns that we have to work with.
species
: penguin species (Chinstrap, Adélie, or Gentoo)bill_length_mm
: bill length (mm)bill_depth_mm
: bill depth (mm)flipper_length_mm
: flipper length (mm)body_mass_g
: body mass (g)island
: island name (Dream, Torgersen, or Biscoe) in the Palmer Archipelago (Antarctica)sex
: penguin sex
Here’s what the three penguins species look like in real life:
We have a few options to preview our dataframe. My favorite is glimpse()
glimpse(penguins)
You can also View()
the data, which opens the data in a R Studio viewer pane. Note the capital V.
View(penguins)
Selecting columns and filtering rows
To select columns of a data frame, use select()
. The first argument to this function is the data frame (penguins
), and the subsequent arguments are the columns to keep.
select(penguins, species, body_mass_g, sex)
To choose rows based on a specific criteria, use filter()
:
filter(penguins, island == "Biscoe")
The filter function works with most boolean operators, namely:
| Operator | Description |
|---|---|
< |
less than |
<= |
less than or equal to |
> |
greater than |
>= |
greater than or equal to |
== |
exactly equal to |
!= |
not equal to |
!x |
Not x |
x | y |
x OR y |
x & y |
x AND y |
isTRUE(x) |
test if X is TRUE |
Pipes
What if you want to select and filter at the same time? There are three ways to do this: use intermediate steps, nested functions, or pipes.
With intermediate steps, you create a temporary data frame and use that as input to the next function, like this:
penguins_biscoe <- filter(penguins, island == "Biscoe")
penguins_biscoe <- select(penguins_biscoe, species, body_mass_g, sex)
This is readable, but can clutter up your workspace with lots of objects that you have to name individually. With multiple steps, that can be hard to keep track of.
You can also nest functions (i.e. one function inside of another), like this:
penguins_Biscoe <- select(filter(penguins, island == "Biscoe"), species, body_mass_g, sex)
This is handy, but can be difficult to read if too many functions are nested, as R evaluates the expression from the inside out (in this case, filtering, then selecting).
The last option, pipes, are a powerful addition to R. Pipes let you take the output of one function and send it directly to the next, which is useful when you need to do many things to the same dataset. Pipes in R look like %>%
and are made available via the magrittr
package, installed automatically with dplyr
. If you use RStudio, you can type the pipe with Ctrl + Shift + M if you have a PC or Cmd + Shift + M if you have a Mac.
penguins %>%
filter(island == "Biscoe") %>%
select(species, body_mass_g, sex)
## # A tibble: 168 x 3
## species body_mass_g sex
## <fct> <int> <fct>
## 1 Adelie 3400 female
## 2 Adelie 3600 male
## 3 Adelie 3800 female
## 4 Adelie 3950 male
## 5 Adelie 3800 male
## 6 Adelie 3800 female
## 7 Adelie 3550 male
## 8 Adelie 3200 female
## 9 Adelie 3150 female
## 10 Adelie 3950 male
## # … with 158 more rows
In the above code, we use the pipe to send the penguins
dataset first through filter()
to keep rows where island
is “Biscoe”, then through select()
to keep only the species
, body_mass_g
,and sex
columns. Since %>%
takes the object on its left and passes it as the first argument to the function on its right, we don’t need to explicitly include the data frame as an argument to the filter()
and select()
functions any more.
Some may find it helpful to read the pipe like the word “then”. For instance, in the above example, we take the data frame penguins
, then we filter
for rows with island == "Biscoe"
, then we select
columns species
, body_mass_g
,and sex
. The dplyr
functions by themselves are somewhat simple, but by combining them into linear workflows with the pipe, we can accomplish more complex manipulations of data frames.
If we want to create a new object with this smaller version of the data, we can assign it a new name:
penguins_biscoe <- penguins %>%
filter(island == "Biscoe") %>%
select(species, body_mass_g, sex)
penguins_biscoe
## # A tibble: 168 x 3
## species body_mass_g sex
## <fct> <int> <fct>
## 1 Adelie 3400 female
## 2 Adelie 3600 male
## 3 Adelie 3800 female
## 4 Adelie 3950 male
## 5 Adelie 3800 male
## 6 Adelie 3800 female
## 7 Adelie 3550 male
## 8 Adelie 3200 female
## 9 Adelie 3150 female
## 10 Adelie 3950 male
## # … with 158 more rows
Note that the final data frame (penguins_biscoe
) is the leftmost part of this expression becuase it is receiving an assignment.
Exercise
Using pipes, subset the
penguins
data to include all species EXCEPT Adelie and retain the species column in addition to those relating to their bill. ## Solution
penguins %>%
filter(species != "Adelie") %>%
select(bill_length_mm, bill_depth_mm)
Mutate
Frequently you’ll want to create new columns based on the values in existing columns, for example to do unit conversions, or to find the ratio of values in two columns. For this we’ll use mutate()
.
We might be interested in the flipper length of penguins in cm instead of milimeters:
penguins %>%
mutate(flipper_length_cm = flipper_length_mm / 10)
## # A tibble: 344 x 9
## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torge… 39.1 18.7 181 3750
## 2 Adelie Torge… 39.5 17.4 186 3800
## 3 Adelie Torge… 40.3 18 195 3250
## 4 Adelie Torge… NA NA NA NA
## 5 Adelie Torge… 36.7 19.3 193 3450
## 6 Adelie Torge… 39.3 20.6 190 3650
## 7 Adelie Torge… 38.9 17.8 181 3625
## 8 Adelie Torge… 39.2 19.6 195 4675
## 9 Adelie Torge… 34.1 18.1 193 3475
## 10 Adelie Torge… 42 20.2 190 4250
## # … with 334 more rows, and 3 more variables: sex <fct>, year <int>,
## # flipper_length_cm <dbl>
We may be interested in investigating whether being a member of an irrigation association had any effect on the ratio of household members to rooms. To look at this relationship, we will first remove data from our dataset where the respondent didn’t answer the question of whether they were a member of an irrigation association. These cases are recorded as “NULL” in the dataset.
Exercise
Create a new data frame from the
penguins
data that meets the following criteria: contains only thespecies
column and a new column calledbody_mass_kg
containing a transformed body_mass_g. Only the rows wherebody_mass_kg
is greater than 4 should be shown in the final data frame. How many rows do you have?Hint: think about how the commands should be ordered to produce this data frame!
Solution
penguins_large <- penguins %>%
mutate(body_mass_kg = body_mass_g / 1000) %>%
filter(body_mass_kg > 4) %>%
select(species, body_mass_kg)
Split-apply-combine data analysis and the summarize() function
Many data analysis tasks can be approached using the split-apply-combine paradigm: split the data into groups, apply some analysis to each group, and then combine the results. dplyr
makes this very easy through the use of the group_by()
function.
The summarize()
function
group_by()
is often used together with summarize()
, which collapses each group into a single-row summary of that group. group_by()
takes as arguments the column names that contain the categorical variables for which you want to calculate the summary statistics. So to compute the average household size by island:
penguins %>%
group_by(island) %>%
summarize(mean_flipper_length_mm = mean(flipper_length_mm))
## # A tibble: 3 x 2
## island mean_flipper_length_mm
## <fct> <dbl>
## 1 Biscoe NA
## 2 Dream 193.
## 3 Torgersen NA
You may also have noticed that the output has a lot of NA
! When R
does calculations with missing data, it (correctly) doesn’t know how to evaluate them and forces the result to NA
. to solve this, we need to add in a special option to tell R that we want to ignore the missing values.
Use the ?
function on mean()
to figure out what this option is.
penguins %>%
group_by(island) %>%
summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE))
## # A tibble: 3 x 2
## island mean_flipper_length_mm
## <fct> <dbl>
## 1 Biscoe 210.
## 2 Dream 193.
## 3 Torgersen 191.
You can also group by multiple columns:
penguins %>%
group_by(island, species) %>%
summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE))
## # A tibble: 5 x 3
## # Groups: island [3]
## island species mean_flipper_length_mm
## <fct> <fct> <dbl>
## 1 Biscoe Adelie 189.
## 2 Biscoe Gentoo 217.
## 3 Dream Adelie 190.
## 4 Dream Chinstrap 196.
## 5 Torgersen Adelie 191.
I expected to get 9 rows because we have 3 islands and 3 species of penguin. What do you think is going on here?
Once the data are grouped, you can also summarize multiple variables at the same time (and not necessarily on the same variable). For instance, we could add a column indicating the minimum and maximum flipper length for each island for each species:
penguins %>%
group_by(island, species) %>%
summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE),
min_flipper_length_mm = min(flipper_length_mm, na.rm = TRUE),
max_flipper_length_mm = max(flipper_length_mm, na.rm = TRUE))
## # A tibble: 5 x 5
## # Groups: island [3]
## island species mean_flipper_lengt… min_flipper_length… max_flipper_length…
## <fct> <fct> <dbl> <int> <int>
## 1 Biscoe Adelie 189. 172 203
## 2 Biscoe Gentoo 217. 203 231
## 3 Dream Adelie 190. 178 208
## 4 Dream Chinstrap 196. 178 212
## 5 Torgers… Adelie 191. 176 210
It is sometimes useful to rearrange the result of a query to inspect the values. For instance, we can sort on mean_flipper_length_mm
to put the group with the shortest flippers first:
penguins %>%
group_by(island, species) %>%
summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE),
min_flipper_length_mm = min(flipper_length_mm, na.rm = TRUE),
max_flipper_length_mm = max(flipper_length_mm, na.rm = TRUE)) %>%
arrange(mean_flipper_length_mm)
## # A tibble: 5 x 5
## # Groups: island [3]
## island species mean_flipper_lengt… min_flipper_length… max_flipper_length…
## <fct> <fct> <dbl> <int> <int>
## 1 Biscoe Adelie 189. 172 203
## 2 Dream Adelie 190. 178 208
## 3 Torgers… Adelie 191. 176 210
## 4 Dream Chinstrap 196. 178 212
## 5 Biscoe Gentoo 217. 203 231
To sort in descending order, we need to add the desc()
function.
penguins %>%
group_by(island, species) %>%
summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE),
min_flipper_length_mm = min(flipper_length_mm, na.rm = TRUE),
max_flipper_length_mm = max(flipper_length_mm, na.rm = TRUE)) %>%
arrange(desc(mean_flipper_length_mm))
## # A tibble: 5 x 5
## # Groups: island [3]
## island species mean_flipper_lengt… min_flipper_length… max_flipper_length…
## <fct> <fct> <dbl> <int> <int>
## 1 Biscoe Gentoo 217. 203 231
## 2 Dream Chinstrap 196. 178 212
## 3 Torgers… Adelie 191. 176 210
## 4 Dream Adelie 190. 178 208
## 5 Biscoe Adelie 189. 172 203
Exercise:
Use
group_by()
andsummarize()
to find the mean bill_length and bill_depth each species.Solution
penguins %>%
group_by(species) %>%
summarize(
mean_bill_length = mean(bill_length_mm, na.rm = TRUE),
mean_bill_depth = mean(bill_depth_mm, na.rm = TRUE)
)
Data Visualization
adapted from: https://datacarpentry.org/r-socialsci/04-ggplot2/index.html
questions:
- What are the components of a ggplot?
- How do I create scatterplots, boxplots, and barplots?
- How can I change the aesthetics (ex. colour, transparency) of my plot?
- How can I create multiple plots at once?
objectives:
- Produce scatter plots, boxplots, and time series plots using ggplot.
- Set universal plot settings.
- Describe what faceting is and apply faceting in ggplot.
- Modify the aesthetics of an existing ggplot plot (including axis labels and color).
- Build complex and customized plots from data in a data frame.
keypoints:
ggplot2
is a flexible and useful tool for creating plots in R.
- The data set and coordinate system can be defined using the
ggplot
function.
- Additional layers, including geoms, are added using the
+
operator.
- Boxplots are useful for visualizing the distribution of a continuous variable.
- Barplot are useful for visualizing categorical data.
- Faceting allows you to generate multiple plots based on a categorical variable.
Plotting with ggplot2
ggplot2
is a plotting package that makes it simple to create complex plots from data stored in a data frame. It provides a programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.
ggplot graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.
To learn more about ggplot2
after the workshop, you may want to check out this
handy data transformation with ggplot2
cheatsheet.
To build a ggplot, we will use the following basic template that can be used for different types of plots:
ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) + <GEOM_FUNCTION>()
- use the
ggplot()
function and bind the plot to a specific data frame using thedata
argument
ggplot(data = penguins)
- define a mapping (using the aesthetic (
aes
) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)
add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars).
ggplot2
offers many different geoms; we will use some common ones today, including:geom_point()
for scatter plots, dot plots, etc.geom_boxplot()
for, well, boxplots!geom_line()
for trend lines, time series, etc.
To add a geom to the plot use the +
operator. Because we have two continuous variables, let’s use geom_point()
first:
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
geom_point()
The +
in the ggplot2
package is particularly useful because it allows you to modify existing ggplot
objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this:
# Assign plot to a variable
penguins_plot <- ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g))
# Draw the plot
penguins_plot +
geom_point()
Notes
- Anything you put in the
ggplot()
function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis mapping you set up inaes()
.- You can also specify mappings for a given geom independently of the mapping defined globally in the
ggplot()
function.- The
+
sign used to add new layers must be placed at the end of the line containing the previous layer. If, instead, the+
sign is added at the beginning of the line containing the new layer,ggplot2
will not add the new layer and will return an error message.
## This is the correct syntax for adding layers
penguins_plot +
geom_point()
## This will not add the new layer and will return an error message
penguins_plot
+ geom_point()
Building your plots iteratively
Building plots with ggplot2
is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
geom_point()
Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha
) to avoid overplotting:
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
geom_point(alpha = 0.5)
That only helped a little bit with the overplotting problem. We can also introduce a little bit of randomness into the position of our points using the geom_jitter()
function.
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
geom_jitter(alpha = 0.5)
We can also add colors for all the points:
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
geom_jitter(alpha = 0.5, color = "blue")
Or to color each species in the plot differently, you could use a vector as an input to the argument color
. Because we are now mapping features of the data to a color, instead of setting one color for all points, the color now needs to be set inside a call to the aes
function. ggplot2
will provide a different color corresponding to different values in the vector. We set the value of alpha
outside of the aes
function call because we are using the same value for all points. Here is an example where we color by island
:
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
geom_jitter(aes(color = species), alpha = 0.5)
There appears to be a positive trend between flipper length and body massof penguins. This trend does not appear to be different by species, but we can see that Chinstrap and Adelie penguins have shorter flippers and small body mass than Gentoo.
Adding Labels and Titles
By default, the axes labels on a plot are determined by the name of the variable being plotted. However, ggplot2
offers lots of customization options, like specifying the axes labels, and adding a title to the plot with relatively few lines of code. We will add more informative x and y axis labels to our plot of proportion of house type by island and also add a title.
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
geom_jitter(aes(color = species), alpha = 0.5) +
labs(title="Body Mass by Flipper Length",
x="Flipper Length (mm)",
y="Body Mass (g)")
Exercise
Use what you just learned to create a scatter plot of
bill_length_mm
byspecies
with thesex
showing in different colors. Is this a good way to show this type of data?Solution
ggplot(data = penguins, aes(x = species, y = bill_length_mm)) +
geom_jitter(aes(color = sex))
This is not a good way to show this type of data because it is difficult to distinguish between islands.
Boxplot
We can use boxplots to visualize the distribution of rooms for each wall type:
ggplot(data = penguins, aes(x = species, y = bill_length_mm)) +
geom_boxplot()
By adding points to a boxplot, we can have a better idea of the number of measurements and of their distribution:
ggplot(data = penguins, aes(x = species, y = bill_length_mm)) +
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, color = "tomato")
We can see that Adelie penguins tend to have shorter bills than other species.
Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot in front of the points such that it’s not covered?
Exercise
Boxplots are useful summaries, but hide the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. An alternative to the boxplot is the violin plot, where the shape (of the density of points) is drawn.
- Replace the box plot with a violin plot; see
geom_violin()
.Solution
ggplot(data = penguins, aes(x = species, y = bill_length_mm)) +
geom_violin(alpha = 0) +
geom_jitter(alpha = 0.5, color = "tomato")
Barplots
Barplots are also useful for visualizing categorical data. By default, geom_bar
accepts a variable for x, and plots the number of instances each value of x (in this case, wall type) appears in the dataset.
ggplot(data = penguins, aes(x = island)) +
geom_bar()
We can use the fill
aesthetic for the geom_bar()
geom to color bars by the portion of each count that is from each island.
ggplot(data = penguins, aes(x = island)) +
geom_bar(aes(fill = species))
This creates a stacked bar chart. These are generally more difficult to read than side-by-side bars. We can separate the portions of the stacked bar that correspond to each island and put them side-by-side by using the position
argument for geom_bar()
and setting it to “dodge”.
ggplot(data = penguins, aes(x = island)) +
geom_bar(aes(fill = species), position = "dodge")
ggplot2
themes
In addition to theme_bw()
, which changes the plot background to white, ggplot2
comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at http://docs.ggplot2.org/current/ggtheme.html. theme_minimal()
and theme_light()
are popular, and theme_void()
can be useful as a starting point to create a new hand-crafted theme.
The ggthemes package provides a wide variety of options (including an Excel 2003 theme). The ggplot2
extensions website provides a list of packages that extend the capabilities of ggplot2
, including additional themes.
Customization
Take a look at the ggplot2
cheat
sheet, and think of ways you could improve the plot.
Exercise
With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own.
- See if you can make the bars white with black outline.
- Try using a different color palette (see http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/).


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Warner Swoons Over Kohli's Eight-Pack Abs | POST
Australian cricketer David Warner reacted on the video. He commented on the post and wrote, "Oh, I love this man ABS ABS".
India captain Virat Kohli has taken fitness to an all-new level and his recent video on Instagram is a testament of that. Kohli posted a video on his social media handle where he was working on his abs on a chin-up bar and in the process fans got a look of his eight-pack abs. Impressed by it, Australian cricketer David Warner reacted on the video. He commented on the post and wrote, “Oh, I love this man ABS ABS”.
Kohli had earlier posted the video and captioned it as, “No Days Off.”
Here is the screenshot of the exchange the two cricketers had:
In the past, Kohli has posted videos of doing deadlifts, and keeping up with the trend, the Indian captain, posted a new workout video where he was performing an exercise for the lower abdomen. The most striking feature of it, however, is Kohli’s chiselled eight-pack-abs that simply cannot escape the eyes. Yes, not six… eight.
Kohli’s love for fitness and undergoing a physical transformation is well known. During an interview, Kohli had revealed how in 2012 after a poor IPL season, he decided to change for the better. He looked at himself in the mirror and said to himself “this is not what a cricketer is supposed to look like. His training was horrible and Kohli used to eat bad, have the occasional drink and used to be awake till late.
Kohli would be leading India in its first pink-ball D/N Test against Bangladesh on November 22 at the Eden Gardens in Kolkata. India has already taken a 1-0 lead in the two-match series. India beat Bangladesh by an innings and 130 runs at Indore. Mayank Agarwal was the player of the match, thanks to a double hundred in the first innings.
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on Cricket Latest News on India.com.
Story Timeline
- 19 Nov 2019 Warner Swoons Over Kohli's Eight-Pack Abs | POST
- 19 Nov 2019 SAVAGE | Rahane's Witty Reply Steals Kohli, Dhawan's Thunder on 'Day-Night Test' Insta Post
- 19 Nov 2019 Mayank Agarwal Batting Beautifully; Hope he Keeps Scoring in His Second Season: Sunil Gavaksar
- 18 Nov 2019 Kohli, Dhawan React on Rahane's 'Dreaming About Day-Night Test' Post
Comments - Join the Discussion


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|
dynamic_graph_manager
|
If possible, controllers and helper functions should be implemented in "dg_tools". Please refer to the project's readme about the desired code structure.
In dynamic graph, an entity is an object that consumes input signals and provides output signals. For instance, a P-controller might consume the current position of a motor as input signal and provide as output a torque command to move the motor position towards a desired position.
To control a robot / simulator using dynamic graph manager, the following is necessary:
Once you have defined the controller in C++, you need to expose it to python. This is done by adding a few lines into the CMakeList.txt file.
For an example, please refer to https://git-amd.tuebingen.mpg.de/amd-clmc/dg_tools/blob/master/src/CMakeLists.txt.
An example on how a simple control graph using the MotorController with the robot.device is shown in single_motor_main.py. Note that robot.device
is provided by the Dynamic Graph Manager automatically and initialized to talk to the real/simulated hardware. The robot.device is created from the yaml file's device specification. Especially, all the input and output signals are created on the robot.device
as described in the sensor
and control
part of the yaml file.


Dev Javia stages a fine comeback
by Special CorrespondentTop seed Dev Javia gave ample proof of his fighting qualities as he battled his way past Ekansh Kumar of the US 3-6, 6-4, 6-3 in the boys’ semifinals of the ITF grade-4 junior tennis tournament at the DLTA Complex on Friday.
Not to be left behind, Sanjana Sirimalla, the lone Indian girl left in the field, was also quite forceful in forcing her way past fifth seed Salakthip Ounmuang of Thailand in three sets.
The fleet-footed Ekansh was serving 6-3, 4-3, 30-15 before wiry left-hander Dev Javia staged his comeback. After many impressive rallies when both players gave glimpses of their craft and court coverage, Dev asserted himself in a gripping climax.
In the final, he will play second seed Rostislav Galfinger of Kazakhstan, who needed three sets to get past seventh seed Daisuke Inagawa of Japan.
Sanjana was not moving as well as she would have wanted but stroked with such punch that she gave very little scope for the Thai in the first and third sets. In the final, Sanjana will challenge third seed Lanlana Taratudee of Thailand.
The results: Under-18: Boys: Semifinals: Dev Javia bt Ekansh Kumar (US) 3-6, 6-4, 6-3; Rostislav Galfinger (Kaz) bt Daisuke Inagawa (Jpn) 6-7(0), 6-4, 6-0.
Doubles: Final: Samir Banerjee & Ekansh bt Jack Loutit (Nzl) & Jaishvin Sidana 6-3, 6-3.
Girls: Semifinals: Lanlana Tararudee (Tha) bt Mei Hasegawa (Jpn) 6-3, 6-4; Sanjana Sirimalla bt Salakthip Ounmuang (Tha) 6-1, 3-6, 6-1.
Doubles: Final: Zijun Jiang & Xiaoyan Xue (Chn) bt Kirara Morioka (Jpn) & Maria Sholokhova (Rus) 6-1, 3-6, [10-7].


The idea started as a project for
Bitcamp 2017
. Mind Matter uses the
chrome.storage.sync
API, meaning that your settings will be synced with your Google account. See
github.com/.../README.md for more information.
Anyone willing to help out is more than welcome! You can report bugs here:
https://github.com/jennydaman/mindmatter/issues
If you are interested in submitting your own questions to the global repository, open a
pull request on the
gh-pages
branch.


This changes what sort of graphic characters (characters with values above 127) are displayed, when in ASCII mode. This is useful for displaying the graphic characters used in many MSDOS programs. This is an old command that has been replaced by the Char Set IBM/OEM command.
This command is not available in EBCDIC mode (see EBCDIC Characters Toggle) since it only applies to extensions to ASCII. This command is also not available unless graphic characters are being displayed (see Graphic Characters Toggle).
When this option is off the current ANSI font is used, when on the current IBM/OEM font is used. This changes the set of fonts available when the Font command is used to change the current font.


This Chrome extension is meant to help AEM engineers, web developers, admins and authors on a daily basis. AEM Developer was written with AngularJS, does some basic string manipulation to alter the browser location, and makes AJAX calls to get information from the JCR and Felix console as well as to perform actions.
This extension is completely agnostic to your AEM project, your instances and code. You must authenticate yourself into your AEM instance and/or Felix web console for certain functionality.
Green signifies the operation was successful.
Yellow signifies that operation was successful but no action was taken. For example, there were no compiled ClientLibs to delete.
Red signifies that the operation failed. For example, comparing pages to another environment that is unavailable.
Clicking on an environment link will open the same page in that new environment. Clicking the icon will do the same but in a new Chrome tab.
I'm currently the lead AEM Engineer at Adobe for the Acrobat.com and EchoSign.com marketing websites. I created this tool to speed up my daily workflow and hopefully help others in our community as well. It's a work in progress; I'd love to hear feedback and suggestions. Please let me know what you like and what you don't like about his extension. Feel free to report any bugs as well as feature requests.


{% extends "base/base.html" %}
{% block content %}
{% comment %} this is a simple check, its not important if it fails, its just a check to see what part of the html should be loaded {% endcomment %}
{% if payment_confirmation == total_cost_with_tax %}
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Next: multiple-value-call, Previous: typecase, Up: Data and Control Flow Dictionary
multiple-value-bind
({var}*)
values-form
{declaration}*
{form}*
⇒ {result}*
var—a symbol naming a variable; not evaluated.
values-form—a form; evaluated.
declaration—a declare expression; not evaluated.
forms—an implicit progn.
results—the values returned by the forms.
Creates new variable bindings for the vars and executes a series of forms that use these bindings.
The variable bindings created are lexical unless special declarations are specified.
Values-form is evaluated, and each of the vars is bound to the respective value returned by that form. If there are more vars than values returned, extra values of nil are given to the remaining vars. If there are more values than vars, the excess values are discarded. The vars are bound to the values over the execution of the forms, which make up an implicit progn. The consequences are unspecified if a type declaration is specified for a var, but the value to which that var is bound is not consistent with the type declaration.
The scopes of the name binding and declarations do not include the values-form.
(multiple-value-bind (f r) (floor 130 11) (list f r)) ⇒ (11 9)
let; let*, multiple-value-call
(multiple-value-bind ({var}*) values-form {form}*) ≡ (multiple-value-call #'(lambda (&optional {var}* &rest #1=#:ignore) (declare (ignore #1#)) {form}*) values-form)


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| void | handleAbout (ActionEvent event) |
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| void | initialize (URL arg0, ResourceBundle arg1) |
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void | init (Stage stage) |
| |
| void | openFile () |
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| void | doExit () |
| |
| void | chooseImage (ActionEvent event) |
| |
| void | saveFile () |
| |
| void | handleApply (ActionEvent event) |
| |
| void | handleWindow (ActionEvent event2) |
| |
| void | handleHoldButtonPlusSigmaColour () |
| |
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void | handleHoldButtonMinusSigmaColour () |
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void | handleHoldButtonPlusSigmaSpace () |
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void | handleHoldButtonMinusSigmaSpace () |
| |
|
void | handleHoldButtonPlusTreshold () |
| |
|
void | handleHoldButtonMinusTreshold () |
| |
|
void | handleHoldButtonPlusZhangSuen () |
| |
|
void | handleHoldButtonMinusZhangSuen () |
| |
◆ chooseImage()
| void application.FXController.chooseImage |
( |
ActionEvent |
event | ) |
|
This method will allow the user to import an image.
◆ doExit()
| void application.FXController.doExit |
( |
| ) |
|
This method will close the window.
◆ handleAbout()
| void application.FXController.handleAbout |
( |
ActionEvent |
event | ) |
|
About Biber. This method opens a new window, which provides brief information about the program.
◆ handleApply()
| void application.FXController.handleApply |
( |
ActionEvent |
event | ) |
|
This method provides the various controls with functions.
◆ handleHoldButtonPlusSigmaColour()
| void application.FXController.handleHoldButtonPlusSigmaColour |
( |
| ) |
|
These methods ensure that the buttons automatically count up when the mouse clicks, until the button is released.
◆ handleWindow()
| void application.FXController.handleWindow |
( |
ActionEvent |
event2 | ) |
|
This method contains the second window to see the changes in the image. It is important to explicitly change loader.load to root class, here (parent). The orders are important, too. First loader.load then get controller.
◆ initialize()
| void application.FXController.initialize |
( |
URL |
arg0, |
|
|
ResourceBundle |
arg1 |
|
) |
| |
Initialize. This method assigns the appropriate functions to the various image processing options.
◆ openFile()
| void application.FXController.openFile |
( |
| ) |
|
This method will open a file. The file formats available to the user are PNG and JPG2000.
Loading the image
◆ saveFile()
| void application.FXController.saveFile |
( |
| ) |
|
This method will save a file. The file formats available to the user are PNG and JPG2000.
The documentation for this class was generated from the following file:
- D:/Git/SWPraktSS18/GUI/src/application/FXController.java


Please enter some info:
Gender
-- select an option --
Male
Female
Enter your height in feet
Enter your remaining inches
Enter your weight in LB's
Enter your age
What is your goal weight?
In how many days would you like to reach your goal?
Physical Activity
-- select an option --
sedentary (little or no exercise)
lightly active (light exercise/sports 1-3 days/week)
moderately active (moderate exercise/sports 3-5 days/week)
very active (hard exercise/sports 6-7 days a week
extra active (very hard exercise/sports & physical job or 2x training)
Submit your info!
Your BMR
Diet Suggestion
this will display your reccomended calories for the day that subtracts with every food item selected
Meal Plan!
-- select a food item--
banana


ROBIN HOOD: MEN IN TIGHTS A film review by James Berardinelli Copyright 1993 James Berardinelli
Rating (Linear 0 to 10): 6.3
Date Released: 7/28/93 Running Length: 1:44 Rated: PG-13 (Sexual humor)
Starring: Cary Elwes, Roger Rees, Amy Yasbeck, Richard Lewis, Tracey Ullman Director: Mel Brooks Producer: Mel Brooks Screenplay: Mel Brooks, Evan Chandler, and J. David Shapiro Music: Hummie Mann Released by Twentieth Century Fox
Anyone remotely familiar with the Robin Hood legend knows the basic story behind this parody. Robin (Cary Elwes) is the dashing hero who, along with his band of Merry Men (which includes members Will Scarlet O'Hara and Achoo the Moor), roams Sherwood Forest as the champion of the common people. His sworn enemy is the evil Sheriff of Rottingham (Roger Rees), and his One True Love is Maid Marian (Amy Yasbeck), she of the Everlast chastity belt. To win the heart and hand of Marian, Robin must defeat the Sheriff and his master, Prince John (Richard Lewis).
Imagine an amalgamation of THE NAKED GUN, ROBIN HOOD: PRINCE OF THIEVES, and MONTY PYTHON AND THE HOLY GRAIL, and you have a pretty good idea what to expect from MEN IN TIGHTS. This is a film packed start-to-finish with satire, puns, one-liners, and other assorted visual and audile jokes. Nothing is too high-brow or low-brow for Mel Brooks, although you can bet there are more occurrences of earthy humor than that of the intellectual variety. Every opportunity for humor, no matter how feeble or juvenile, is seized upon.
There have been several parodies already this year (LOADED WEAPON ONE, HOT SHOTS: PART DEUX!, and LAST ACTION HERO come to mind), none of which have attained the level of MEN IN TIGHTS. That's not to say that this is a masterpiece of any sort, but its jokes are more clever, more original, and more consistently funny than those in the other movies. In a HOT SHOTS-type film, the humor gets stale by the end of the first half-hour, and most of the laughs have dried up long before the final credits roll. Mel Brooks, on the other hand, has enough experience (not to mention flair) with this kind of motion picture, that he knows to vary the humor so that the gags don't wear thin before the running time has expired.
That's not to say that everything Brooks does works. There are a fair share of clunkers in MEN IN TIGHTS. But with such a non-stop barrage of jokes, regardless of what percentage work, there's not much time in between laughs. And whether your kind of humor is along the lines of Shakespeare or Cheech and Chong, there's something in this movie sure to provoke laughter.
In many ways, this is the Mel Brooks of old, the same master of humor that brought us classics like YOUNG FRANKENSTEIN and BLAZING SADDLES. MEN IN TIGHTS is not as original, inventive, or hilarious as those offerings, but it's a reasonable offering and certainly not in the same dubious category as such Brooks duds like LIFE STINKS. In terms of scope, potential, and execution, this Robin Hood parody is along the lines of SPACEBALLS, the Brooks take-off on STAR WARS.
There's a fairly strong element of MONTY PYTHON in this movie. While no one in their right mind would dare put this film on the same level as THE HOLY GRAIL, there are moments in MEN IN TIGHTS that the British comedy troupe would be proud of. Most notably, perhaps, is the rendition of the musical number, "Men in Tights," which bears a passing resemblance to the Pythons' "Lumberjack Song."
In a story that closely parallels the Kevin Costner account, Cary Elwes does his best Errol Flynn imitation--a fine job acting serious and swashbuckling amidst all the silliness around him. The other major players do reasonable jobs, from Roger Rees giving an over-the-top performance that couldn't quite match that of Alan Rickman (from PRINCE OF THIEVES) to a too-subdued Amy Yasbeck.
As is frequently the case, those with cameos steal the show. Dom DeLuise shows up for one scene doing a superlative lampoon of Marlon Brando from THE GODFATHER. Patrick Stewart is on hand to imitate Sean Connery's King Richard from PRINCE OF THIEVES. Not only does he strike as regal a figure as Connery, but he does an excellent job of mimicking the accent.
There are so many jokes in this movie that it's difficult to pick out a memorable one or two. Among the more subtle ones, I was partial to the "moving mole" on Richard Lewis' face. Nearly every time we see Prince John, his rather sizeable mole has moved somewhere else. At one point, the Sheriff even feels compelled to mention this. Then there's Mel Brooks as Rabbi Tuckman (instead of Friar Tuck), who gives "half off" circumcisions. Most of the overblown musical numbers are also fun, with obviously-dubbed singing voices and rappers fulfilling the function of a Greek Chorus.
Parodies are hard films to do, as the mediocre success of so many recent ones has shown. No matter how ripe a film or genre might be for mocking, unless you know how to do it, there are no guarantees. Fortunately for MEN IN TIGHTS, Mel Brooks has been doing this kind of thing for decades. As the ads say, "The legend had it coming", and this film certainly dishes out all that the Merry Men can handle--and more.
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Qualifications: Recent graduate from Staffordshire University with 1st class degree in Computer Science
Key Skills include:
HTML5 | CSS | JavaScript | jquery | PHP | MySQL
Some limited experience with Swift4 and Python
Recent Projects:
Facial recognitions systems in law enforcement with decison making feedback.
The applications and implications of Mixed Reality.
Machine Learning as an emerging technology and its potential future applications.
PHP backend project
This was a backend project that centered around creating a web application where the user could log in and search a database and retrieve data. The web app needed to allow users to register and log into the website, restrict content to only logged in users, have a multi criteria search function, return searched data to the user and allow the user to edit their own details. The web app also needed to make use of a multi table database, holding user data, product data and recent search data. Key languages and skills were PHP, including log in and log out functionality, form validation and security as well as session tracking. SQL was also used to create dynamic search queries and MySQL was used to manage the database.
Recently the website has been archived and this has caused some database inconsistencies which are currently being worked on. The web app still functions as intended and you can check it out by clicking the following link.
Just Cars Web app
Front end project
This module was focused around creating a dynamic and responsive front-end website that would work on a variety of platforms. The website also had to incorporate both video and images, maintaining responsiveness throughout. The website needed to scale and be useable on anything from desktop to mobile, using UI and UX principals to deliver an excellent end user experience. Key languages were HTML, CSS and JavaScript. Having used the Bootstrap framework before, this project made use of a new framework, Materialize, in order to deliver a clean and responsive website.
Front end project
iOS application
The project brief was simply to create a piece of interactive multimedia. Being free to choose our own software I chose to do something new and create an augmented reality iOS application. The artefact produced was an interactive augmented reality experience that was focused around education. The app can display each of the planets in the solar system in augmented reality. Each planet can be viewed from any angle using an iPhones camera. Key software used was the XCode development environment and the Swift4 language, mobile app UI and UX principles as well as implementing google AdMob to generate ads within the app. This app was published and is live on the app store.
The Solar System in AR
Dissertation Project
Facial recognition systems in law enforcement with decision making feedback.
The brief for this project was to identify and potentially solve a real-world problem. I identified the use of facial recognition technology and its inconsistent performances in real world scenarios as a project idea. My artefact was designed to perform real time facial recognition and provide match feedback to the user. The system created was able to identify faces from live video and compare that information to images from a pre-defined dataset. The system used advanced computer vision algorithms and techniques and was able to find and informing the user of any matching data. Key technology and languages used were OpenCV to handle computer vision and image recognition this was programmed using the Python language.
Hi, I’m Andy, a recent graduate from staffordshire university. I’m passionate about web development and all things tech. I have experience in web development using HTML, CSS, JavaScript, PHP and MySQL to create dynamic and responsive websites. I also have experience in iOS development having created several apps using Swift4, including an augmented reality app which is live on the app store. I am experienced in handling large scale projects having completed my dissertation on facial recognition technology, creating a working model using Python and discussing its implications within society. Finally, I have experience creating robust databases with Oracle and Microsoft Access using SQL. Feel free to connect with me using Twitter, LinkedIn or GitHub.


Demo: Blocking Script with forceAsync
Show the Console tab in the developer tools.
External Script
Inline Script
Body End


We could have handled authentication inside the context function. However, we would have duplicated a lot of work that GraphQL does for us already.
Apollo will cache valid documents to optimize requests by bypassing some of the lifecycle events mentioned. For example, parsing and validating will be skipped if the document can be looked up in the GraphQL cache.
This posed a problem for authentication. You could essentially run a valid request in the GraphQL console that was cached even if your token expired. That's where our plugin comes in. We force the authentication to resolve for each request, new or cached.
We decided to check each incoming operation and inserted our plugin at the didResolveOperation event.


Social balance theory defines balance or imbalance of sentiment relation in relations with social network theory. Sentiments can result in the emergence of two groups, where discrepancy can exist between two groups. Social and content networks model the community dynamics. We ask the question: "How can we describe and identify inequity and bias in a community?" We answer with an innovative scalable graph processing algorithm that scales social balance theory to automatically quantify disbalance and to predict outcomes in social and content networks.
We expand the balance theory to signed social network graph analysis and propose a frustration cloud view of the signed graph. We then quantify vertex and edge int erms of frustration cloud statistics, and validate this novel social network graphs analysis approach.
Community Discovery approaches suffer for lack of scalability or reproducability, and high modularity seems to be hard to overcome. We conduct a detailed analysis in terms of efficacy, efficiency, scalability, and reproducability of existing methods, and propose frustration cloud based approach for cluster boosting for high modular signed graphs.


| Package | Description |
|---|---|
| com.programmerdan.minecraft.civspy | |
| com.programmerdan.minecraft.civspy.samplers.impl |
| Modifier and Type | Class and Description |
|---|---|
class |
PeriodicDataSample
Simple implementation of a DataSample, this encapuslates a server/world/player (max granularity) sampling of data, done on a distinct period.
|
class |
PointDataSample
Simple implementation of a DataSample, this encapsulates an event's point of data, done on event or demand.
|
| Modifier and Type | Method and Description |
|---|---|
abstract DataSample |
SingleDataSampler.sample()
Subclasses should use this; does the actual sampling
|
| Modifier and Type | Method and Description |
|---|---|
abstract List<DataSample> |
MultiDataSampler.sample()
Samplers should implement this method.
|
| Modifier and Type | Method and Description |
|---|---|
void |
DataManager.enqueue(DataSample data)
This is the only method accessible to the outside world, besides a shutdown hook method.
|
void |
DataAggregate.include(DataSample sample)
Includes a new sample into this aggregation.
|
protected void |
DataListener.record(DataSample sample)
Safely wrap enqueue requests against the privately held DataManager target.
|
| Modifier and Type | Method and Description |
|---|---|
DataSample |
PlayerCountSampler.sample()
Generates:
server.playercount stat_key data. |
| Modifier and Type | Method and Description |
|---|---|
List<DataSample> |
WorldPlayerCountSampler.sample()
Must be called by a Bukkit Synchronous task! Shows the player count in this world.
|
List<DataSample> |
TPSSampler.sample()
Computes tick length average, min, max and stores it.
|
Copyright © 2016. All rights reserved.


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