i had to look away - this was god awful .


one of creepiest , scariest movies to come along in a long , long time , easily rivaling blair witch or the others .


a disappointment for those who love alternate versions of the bard , particularly ones that involve deep fryers and hamburgers .


ultimately feels empty and unsatisfying , like swallowing a communion wafer without the wine .


it 's worth seeing just on the basis of the wisdom , and at times , the startling optimism , of the children .


it 's of the quality of a lesser harrison ford movie - six days , seven nights , maybe , or that dreadful sabrina remake .


the film is based on truth and yet there is something about it that feels incomplete , as if the real story starts just around the corner .


not exactly the bees knees


dr. goldberg offers everything i look for in a general practitioner.  he's nice and easy to talk to without being patronizing; he's always on time in seeing his patients; he's affiliated with a top-notch hospital (nyu) which my parents have explained to me is very important in case something happens and you need surgery; and you can get referrals to see specialists without having to see him first.  really, what more do you need?  i'm sitting here trying to think of any complaints i have about him, but i'm really drawing a blank.


Unfortunately, the frustration of being Dr. Goldberg's patient is a repeat of the experience I've had with so many other doctors in NYC -- good doctor, terrible staff.  It seems that his staff simply never answers the phone.  It usually takes 2 hours of repeated calling to get an answer.  Who has time for that or wants to deal with it?  I have run into this problem with many other doctors and I just don't get it.  You have office workers, you have patients with medical needs, why isn't anyone answering the phone?  It's incomprehensible and not work the aggravation.  It's with regret that I feel that I have to give Dr. Goldberg 2 stars.


Been going to Dr. Goldberg for over 10 years. I think I was one of his 1st patients when he started at MHMG. He's been great over the years and is really all about the big picture. It is because of him, not my now former gyn Dr. Markoff, that I found out I have fibroids. He explores all options with you and is very patient and understanding. He doesn't judge and asks all the right questions. Very thorough and wants to be kept in the loop on every aspect of your medical health and your life.


Got a letter in the mail last week that said Dr. Goldberg is moving to Arizona to take a new position there in June.  He will be missed very much.  \n\nI think finding a new doctor in NYC that you actually like might almost be as awful as trying to find a date!


I don't know what Dr. Goldberg was like before  moving to Arizona, but let me tell you, STAY AWAY from this doctor and this office. I was going to Dr. Johnson before he left and Goldberg took over when Johnson left. He is not a caring doctor. He is only interested in the co-pay and having you come in for medication refills every month. He will not give refills and could less about patients's financial situations. Trying to get your 90 days mail away pharmacy prescriptions through this guy is a joke. And to make matters even worse, his office staff is incompetent. 90% of the time when you call the office, they'll put you through to a voice mail, that NO ONE ever answers or returns your call. Both my adult children and husband have decided to leave this practice after experiencing such frustration. The entire office has an attitude like they are doing you a favor. Give me a break! Stay away from this doc and the practice. You deserve better and they will not be there when you really need them. I have never felt compelled to write a bad review about anyone until I met this pathetic excuse for a doctor who is all about the money.


Top notch doctor in a top notch practice. Can't say I am surprised when I was referred to him by another doctor who I think is wonderful and because he went to one of the best medical schools in the country. \nIt is really easy to get an appointment. There is minimal wait to be seen and his bedside manner is great.


Dr. Eric Goldberg is a fantastic doctor who has correctly diagnosed every issue that my wife and I have had. Unlike many of my past doctors, Dr. Goldberg is very accessible and we have been able to schedule appointments with him and his staff very quickly. We are happy to have him in the neighborhood and look forward to being his patients for many years to come.


I'm writing this review to give you a heads up before you see this Doctor. The office staff and administration are very unprofessional. I left a message with multiple people regarding my bill, and no one ever called me back. I had to hound them to get an answer about my bill. \n\nSecond, and most important, make sure your insurance is going to cover Dr. Goldberg's visits and blood work. He recommended to me that I get a physical, and he knew I was a student because I told him. I got the physical done. Later, I found out my health insurance doesn't pay for preventative visits. I received an $800.00 bill for the blood work. I can't pay for my bill because I'm a student and don't have any cash flow at this current time. I can't believe the Doctor wouldn't give me a heads up to make sure my insurance would cover work that wasn't necessary and was strictly preventative. The office can't do anything to help me cover the bill. In addition, the office staff said the onus is on me to make sure my insurance covers visits. Frustrating situation!


Wing sauce is like water. Pretty much a lot of butter and some hot sauce (franks red hot maybe).  The whole wings are good size and crispy, but for $1 a wing the sauce could be better. The hot and extra hot are about the same flavor/heat.  The fish sandwich is good and is a large portion, sides are decent.


Decent range somewhat close to the city.  The mats are pretty solid; however, the grass range needs to be tended too.  It's like hitting out of US Open type rough...not very amenable to practicing.  Which kind of defeats the purpose of going to a golf range...Still gets 3 stars because the range is lit up at night which is excellent for those of us who are addicted to this amazing game, but are somewhat short on time (having a job kinda sucks sometimes, no?).


Ready, aim and fire your gun
Nothing can cool me down
Wine on Venus is hot like the sun
Wow, nothing can cool me down
Got my high stilettos on fleek
Nothing can cool me down
See the shadows dancing on me
No, nothing can cool me down
And before the night is over
Imma light a spark and let the crazy out
Im your goddess on your knees
Down, down, down, down, down, down, down
Im hotter than a fire
Water cant cool me down
Water cant cool me down
Oh oh, oh oh
Im dancing like flames
And nothing can cool me down
Watch me, I run this town
Oh oh, oh oh, oh
No water cant cool me
Down, down, down, down, down, down, down
Dow, dow, dow
No water cant cool me
Theres no need to call 911
Nothing can cool me down
If you do, you spoiling the fun
Wow, nothing can cool me down
And before youre begging please
Imma light a spark and let the crazy out
Im your goddess on your knees
Down, down, down, down, down, down, down
Im hotter than a fire
Water cant cool me down
Water cant cool me down
Oh oh, oh oh
Im dancing like flames
And nothing can cool me down
Watch me, I run this town
Oh oh, oh oh, oh
No water cant cool me
Down, down, down, down, down, down, down
Dow, dow, dow
No water cant cool me
Down, down, down, down, down, down, down
No water cant cool me
I love the way you want it
Come get it like you own it
Yeah yeah, we blaze even brighter
When being two
Im hotter than a fire
Water cant cool me down
Water cant cool me down
Oh oh, oh oh
Im hotter than a fire
Water cant cool me down
Water cant cool me down
Oh oh, oh oh
Im dancing like flames
And nothing can cool me down
Watch me, I run this town
Oh oh, oh oh, oh
No water cant cool me
Down, down, down, down, down, down, down
Dow, dow, dow
No water cant cool me
Down, down, down, down, down, down, down
Dow, dow, dow
No water cant cool me
Down, down, down, down, down, down, down
Dow, dow, dow
No water cant cool me
Down, down, down, down, down, down, down


Temptation in my heart
Im burning, I fall apart!
When the night falls
My heart calls for love and devotion
Temptation, I want you
Can you be my only one
When the night falls
My heart calls, for the touch of your hands
Tu asemone eshqam to bodi
Setareye man to bodi
Esme to hast setare,
Biya pisham dobare
Un moohaye meshkit mano kosht
Chesh maye zibat mano kosht
Bia bia pishe man dobare
Yalla bego are, setare
Temptation in my heart
Im burning, I fall apart!
When the night falls
My heart calls for love and devotion
Temptation, I want you
Can you be my only one
When the night falls
My heart calls, for the touch of your hands
Hame fekro khialam to hasti
Donya ye man to hasti
Esme to ro labame
Setare vay setare!
Ye omre bedoned khab boodam
Ta man toro peyda kardam
Bia bia pishe man dobare
Yalla bego are, setare!
Temptation in my heart
Im burning, I fall apart!
When the night falls
My heart calls for love and devotion
Temptation, I want you
Can you be my only one
When the night falls
My heart calls, for the touch of your hands
Man enshab toro mikham, wooooooooooo!
Setare toro mikham, woooooooooo
Chrous x 2:
Temptation in my heart
Im burning, I fall apart!
When the night falls
My heart calls for love and devotion
Temptation, I want you
Can you be my only one
When the night falls
My heart calls, for the touch of your hands
Man enshab toro mikham, wooooooooooo!
Setare toro mikham, woooooooooo


The facility, based on the town's Crichton estate, is due to open in the next 12 months. The centre will be used primarily to research ways to improve responses to weather-related emergencies such as flooding. A report to the council's policy and resources committee will give a full update on progress to date. Initially the facility will have up to six staff. Last month, the Scottish government gave assurances that plans to develop the centre remained on track. It followed concerns from Dumfriesshire Labour MSP Elaine Murray about the pace of progress on the project, since it was first announced in August last year. A report to the council says it is essential the renewed impetus is maintained. To that end working groups are being set up, and council and government officials are exploring the best location and office arrangements for the centre.

Question: Councillors are to be given a progress report on plans to develop a @placeholder resilience centre in Dumfries .


The Scottish golf club said a recent consultation found that admitting women members was supported by over three-quarters of those who participated. Muirfield voted in May not to admit women members and lost its right to stage the Open Championship. "We welcome this decision by Royal Troon and recognise its significance for the club," the R&A said. "Our focus is very much on the 145th Open but we can now look forward to many more great Championships at Royal Troon in years to come." Royal Troon, situated in South Ayrshire, is the host for this year's Open, which runs from 14-17 July. In January it said it would review of its male-only membership policy. Club captain Martin Cheyne said: "We have said a number of times recently that it is important for golf clubs to reflect the society in which we exist and the modern world that looks to us. "Therefore, I am delighted with the decision taken by Members of Royal Troon this evening and look forward to welcoming women to our great Club. "It is the right decision for the Club today, and for the generations of golfers that will follow." Mr Cheyne added: "Tonight, we turn our attention to jointly hosting the 145th Open Championship with our great friends at The Ladies Golf Club, Troon. "We can now all be focused on golf and showcasing this wonderful club and golf course to the huge global audience that this most prestigious Championship commands."

Question: Royal Troon members have voted " overwhelmingly " at a @placeholder meeting to allow women to join the club .


Former Aberdeen and Manchester United manager Sir Alex and the Aberdeen-born singer featured in almost 2,000 public responses. Their achievements will be celebrated at Provost Skene's House. The building will reopen following the completion of the Marischal Square development this summer. The Hall of Heroes: The 10 were picked from 50 candidates during a three-week vote. Aberdeen City Council leader Jenny Laing said: "The public's contribution has been tremendously valuable and will help ensure the attraction has widespread appeal. "The stories and achievements of our heroes - indeed all the candidates - clearly resonated with residents." Percussionist Dame Evelyn Glennie said: "I am hugely honoured to have been selected."

Question: Sir Alex Ferguson and Annie Lennox are among the @placeholder names chosen in a poll to feature in a new Hall of Heroes tourist attraction in Aberdeen .


The team at Imperial College London analysed national health survey data between 1994 and 2011. Their analysis, published in the Lancet medical journal, showed more people were being treated, and more effectively, than two decades ago. They said continuing improvements would save yet more lives. Their analysis showed that the number of people with high blood pressure who were getting treatment had nearly doubled from 32% in 1994 to 58% in 2011. At the same time, the proportion of people who were getting their blood pressure back to normal levels trebled from 11% to 37%. "If we treated people in 1994 like we did in 2011, we reckon that would have saved up to 100,000 major events (heart attacks, strokes and deaths)," Prof Neil Poulter, one of the researchers, told the BBC. He added: "It's going extraordinarily well really, it's a hell of a lot of lives saved." The study estimates that 80% of patients will be controlling their blood pressure by 2022 if the current rate of improvement continues. Better drugs, increased awareness and financial incentives for doctors are thought to be behind the improvement. Heart disease is still the UK's biggest killer, causing 82,000 deaths each year.

Question: Improved treatment of blood pressure has @placeholder hundreds of thousands of heart attacks , strokes and deaths in England , say doctors .


Its report, published three days from the general election, said Wales faces a "turn-out time-bomb", although young voters' belief in democracy is high. ERS Cymru calls for a lower voting age, "radical overhaul" of politics teaching in schools and a "louder voice" for young people in Welsh public life. Around 52% of under-24s voted in 2010, compared to 65% of all eligible. This was an improvement on the record low of 38% in 2005, but ERS Cymru warns the long-term trend is one of decline. In 1964 young voters turned out in roughly the same proportion as voters of all ages - 77% - but since 1997 they have been notably less likely to vote than older people. Director Steve Brooks called on the new Parliament to "immediately devolve power over the voting age to Wales, so AMs can get on and make changes in time for next year's assembly elections". "The Welsh government needs to radically overhaul how politics is taught in schools," he said. Mr Brooks also called on the Welsh government to set up an independent national youth assembly for Wales and reverse its decision to withdraw funding from Funky Dragon.

Question: The next generation of voters may @placeholder formal politics , the Electoral Reform Society ( ERS ) has said .


Playing with the density of hair removed altered how serious an injury the body recognised and in turn how much hair regrew. The team managed to regenerate 1,300 hairs by plucking 200, in the study using mice reported in Cell journal. Experts said it was "really nice science" but were uncertain if it could lead to a cure for human baldness. Half of men have male-pattern baldness by the age of 50. The team at the University of Southern California were investigating how hair follicles communicate with each other to decide on the scale of repair job needed. In a series of experiments they removed 200 hair follicles from a circular patch of skin in mice. A low-density pluck - removing follicles from a patch 6mm in diameter - led to no regeneration at all. A medium-density pluck, with 200 removed from a 5mm circle, led to 1,300 new hairs. A higher-density effort, with the same number of hairs but from a 4mm diameters circle, led to 780 new hairs. Pulling every hair out led to every hair coming back, but no extra regeneration. The researchers showed that the level of inflammation under the skin was finely tuned to the scale of the damage. And through a cascade of chemical signalling and immune responses, this controlled the amount of regeneration. The team say it is like each hair gets a vote about what happens next and when it reaches a critical threshold it can trigger regeneration. They call the concept "quorum sensing". Lead researcher Dr Cheng-Ming Chuong said: "It is a good example of how basic research can lead to work with potential translational value. "The work leads to potential new targets for treating alopecia, a form of hair loss." Chris Mason, professor of regenerative medicine at University College London, told the BBC: "It's a really nice piece of science. The idea of quorum sensing is smart." But it is not known whether it will cure human baldness. Prof Mason added: "That's the million-dollar question. I'm not sure. As it stands here, you've got to have some hair to pluck. "A lot of studies have produced hair, but it's too fine - it's baby hair, it's light-coloured and it just doesn't look right. But here we can infer they are adult hairs so that is something that is a step change. "Could you tap into the pathway with a cream or injection? That could well be possible - or maybe don't wait until you're totally bald?"

Question: Plucking hairs in a @placeholder pattern can make even more pop up in their place , a US study suggests .


The 34-year-old won 79 caps and was part of manager Michael O'Neill's squad at the Euro 2016 finals in France. He played his final game for Northern Ireland in their opening game of the tournament, a defeat by Poland in Nice. "I am not getting any younger and I feel it is the right thing to do at this stage," said Baird. The versatile County Antrim man made his debut against Italy in 2003 and has played at right-back, centre-back and more recently as a defensive midfielder for his country. He lined out in nine of the 10 qualification games that took O'Neill's side to Euro 2016. "It was a tough decision because I have enjoyed every single minute that I have played for Northern Ireland," added the former Southampton, Fulham and West Bromwich Albion player, who also had spells with Reading and Burnley. "Qualification for Euro 2016 was the icing on the cake for my international career, but I have so many fantastic memories, like the win over England and beating Spain. "I am fortunate to have lined out with some great players over the past 13 years and I would like to thank the managers, coaches and Irish FA staff that I have worked over that time. "I would also like to thank the Northern Ireland fans who have been absolutely tremendous. It is true - they are the best supporters in the world." O'Neill added: "Chris has been an absolute rock for me in the four years that I have been in charge. His defensive qualities, his reading of the game and his experience have been invaluable, especially in our Euro 2016 qualification campaign. "In addition to his football ability, he is also a fantastic person and we will miss him around the squad. "Chris has been a tremendous servant to Northern Ireland. I fully respect his decision to retire from international football at this stage and I wish him every success as he continues his highly successful club career."

Question: Northern Ireland defender Chris Baird has @placeholder his retirement from international football to concentrate on his club career with Derby County .


QR code with Hesson & Deakins contacts

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More details about Hesson & Deakins

According to our records, this business is located at 601 W Willoughby Ave.

in Juneau (in Juneau County), Alaska 99801, the location GPS coordinates are: 58.300556 (latitude), -134.4179545 (longitude).

Hesson & Deakins is categorized under Accounting and Bookkeeping Services (SIC code 8721).

Known organization partner is James L Hesson.

Current estimates show that the unit has a sales volume of $309,000 and staff of approximately 3 people.

You can contact the company by phone at (907) 463-5577; the following fax number is also provided: (907) 463-3253.


Noise cancelling systems are known to provide good music reproduction environments for listeners (users) when they listen to music and the like through earphones, headphones, and the like by reducing (cancelling) noise of external environments.

In the noise cancelling systems of the related art, a primary process of reducing noise is an analog process.

However, noise cancelling systems based on digital processes have also recently been developed, and headphones equipped with the noise cancelling systems based on digital processes have been commercialized and are available in the marketplace.

The noise cancelling systems based on the digital processes are equipped with a plurality of noise cancelling modes of the digital processes as well as high noise cancelling performance based on the digital processes.

A listener can selectively use an optimal mode according to noise because the plurality of noise cancelling modes are provided (for example, Patent Literature 1).

Furthermore, some headphones equipped with the noise cancelling systems are provided with a function of analyzing a state of ambient noise when the user simply presses a button and automatically selecting an optimal noise cancelling mode (an optimal-mode selection function).

If the optimal-mode selection function is executed in the headphones, the headphones first stop an operation of outputting music or the like, and also stop a noise cancelling function.

The headphones collect a noise sound from a microphone provided on their inner or outer side, analyze the collected sound, and select an optimal mode based on an analysis result.

If the optimal mode is selected, the headphones resume the noise cancelling function by switching to the selected mode, and resume the operation of outputting music or the like.


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District Court of Appeal of Florida, Third District.


12 Winthrop Ave UNIT 12, NewtonMA, 02458(Rental)

Spacious 3 bedroom 2.5 bathroom townhouse apartment.

Located on a quiet road, views of the city, walking distance to Newtonville Village and Newton Corner, restaurants, stores, YMCA.

Close to public transportation (commuter rail, major bus stops, highway access).

This apartment features include hardwood floors, high ceilings, eat-in kitchen, walk in California closet, off street parking spaces, laundry, and back yard.

Price

Rent Price:$2,550

County:
Middlesex County

Beds: 3

Baths: 2 Full/1 Half

Sq ft:1,600 (approx)

#: 72437134

Status: Active Rental

Description

Spacious 3 bedroom 2.5 bathroom townhouse apartment.

Listing Information

Property Type: Rental/Lease, Other

Bedrooms:3

Bathrooms:2 Full/1 Half

Total Rooms:6

Lot Size:0.21 Acres

Square Feet:1,600 (approx)

Neighborhood

Room Information

Bathrooms

Full Baths: 2

1/2 Baths: 1

Interior Features

Guest Facilities:3

Exterior / Lot Features

Parking Spaces:2

Listing Price History

Newton Real Estate

Newton is located in Massachusetts.

Newton, Massachusetts 02458 has a population of 88,817.

The median household income in Newton, Massachusetts 02458 is
$118,639.

The median household income for the surrounding county is $83,488
compared to the national median of $53,482.

The median age of people living in Newton 02458 is
40.2 years.

Loading nearby sales and history ...

Courtesy: Earle Coppola Real Estate LLC

This content last refreshed on 3/21/2019 10:35 AM.

Some properties which appear for sale on this web site may subsequently have sold or may no longer be available.


Q:

Usage of the preposition 'by-with-of' with the word 'possessed'

The expression:

His mind became possessed BY-WITH-OF her words

Question:

1) What the preposition should I use with this word 'possessed'?

2) If each of them is acceptable, then can you describe the cases when these prepositions aren't interchangeable

A:

"Possessed" is a bad word choice in this circumstance.

If you say "possessed by," you are saying that the words have taken over his mind, exactly like demonic possession.

Even if that was your intent, you should say it differently, and add more context to make it clear.

If you say "possessed of," you are saying that his mind now has a copy of her words in it.

It still looks wrong, and would sound odd even to someone who recognized and expected that construction. (

Usually it would refer to some character trait, and then usually ironically: "He was possessed of a boundless enthusiasm for beetles of all kinds.")

"Possessed with" is almost always incorrect.

When it is correct, it takes one of the two meanings above.

Try either "His mind became obsessed with her words;" "His mind was [seized/captivated/entranced] by her words;" or something a little more flowery like "Her words burned into his mind."


import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

"""
Explanation: Simple MNIST convnet
Author: fchollet

Date created: 2015/06/19

Last modified: 2020/04/21

Description: A simple convnet that achieves ~99% test accuracy on MNIST.
Setup
End of explanation
"""

# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

"""
Explanation: Prepare the data
End of explanation
"""

model = keras.Sequential(
    [
        keras.Input(shape=input_shape),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation="softmax"),
    ]
)

model.summary()

"""
Explanation: Build the model
End of explanation
"""

batch_size = 128
epochs = 15

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)

"""
Explanation: Train the model
End of explanation
"""

score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

"""
Explanation: Evaluate the trained model
End of explanation
"""


_, _, data = twpca.datasets.jittered_neuron()
model = TWPCA(data, n_components=1, warpinit='identity')

np.all(np.isclose(model.params['warp'], np.arange(model.shared_length), atol=1e-5, rtol=2))

np.nanmax(np.abs(model.transform() - data)) < 1e-5

"""
Explanation: check identity warp does not change data appreciably
End of explanation
"""

model = TWPCA(data, n_components=1, warpinit='shift')

plt.imshow(np.squeeze(model.transform()))

"""
Explanation: check that shift initialization for warp solves the simple toy problem
End of explanation
"""


from __future__ import print_function, division, unicode_literals

import oddt
from oddt.datasets import dude
print(oddt.__version__)

"""
Explanation:
DUD-E: A Database of Useful Decoys: Enhanced

End of explanation
"""

%%bash
mkdir -p ./DUD-E_targets/
wget -qO- http://dude.docking.org/targets/ampc/ampc.tar.gz | tar xz -C ./DUD-E_targets/
wget -qO- http://dude.docking.org/targets/cxcr4/cxcr4.tar.gz | tar xz -C ./DUD-E_targets/
wget -qO- http://dude.docking.org/targets/pur2/pur2.tar.gz | tar xz -C ./DUD-E_targets/
wget -qO- http://dude.docking.org/targets/pygm/pygm.tar.gz | tar xz -C ./DUD-E_targets/
wget -qO- http://dude.docking.org/targets/sahh/sahh.tar.gz | tar xz -C ./DUD-E_targets/

directory = './DUD-E_targets'

"""
Explanation: We'd like to read files from DUD-E.

You can download different targets and different numbers of targets, but I used only these five:
ampc,
cxcr4,
pur2,
pygm,
sahh.

End of explanation
"""

dude_database = dude(home=directory)

"""
Explanation: We will use the dude class.
End of explanation
"""

target = dude_database['cxcr4']

"""
Explanation: Now we can get one target or iterate over all targets in our directory.
Let's choose one target.
End of explanation
"""

target.ligand

"""
Explanation: target has four properties: protein, ligand, actives and decoys:

protein - protein molecule

ligand - ligand molecule

actives - generator containing actives

decoys - generator containing decoys
End of explanation
"""

for target in dude_database:
    actives = list(target.actives)
    decoys = list(target.decoys)
    print('Target: ' + target.dude_id,
          'Number of actives: ' + str(len(actives)),
          'Number of decoys: ' + str(len(decoys)),
          sep='\t\t')

"""
Explanation: Let's see which target has the most actives and decoys.
End of explanation
"""


!python -m spacy download en_core_web_sm

"""
Explanation: Versioning Example (Part 1/3)
In this example, we'll train an NLP model for sentiment analysis of tweets using spaCy.
Through this series, we'll take advantage of ModelDB's versioning system to keep track of changes.
This workflow requires verta>=0.14.4 and spaCy>=2.0.0.

Setup
Download a spaCy model to train.
End of explanation
"""

from __future__ import unicode_literals, print_function

import boto3
import json
import numpy as np
import pandas as pd
import spacy

"""
Explanation: Import libraries we'll need.
End of explanation
"""

from verta import Client

client = Client('http://localhost:3000/')
proj = client.set_project('Tweet Classification')
expt = client.set_experiment('SpaCy')

"""
Explanation: Bring in Verta's ModelDB client to organize our work, and log and version metadata.
End of explanation
"""

S3_BUCKET = "verta-starter"
S3_KEY = "english-tweets.csv"
FILENAME = S3_KEY

boto3.client('s3').download_file(S3_BUCKET, S3_KEY, FILENAME)

"""
Explanation: Prepare Data
Download a dataset of English tweets from S3 for us to train with.
End of explanation
"""

import utils

data = pd.read_csv(FILENAME).sample(frac=1).reset_index(drop=True)
utils.clean_data(data)

data.head()

"""
Explanation: Then we'll load and clean the data.
End of explanation
"""

from verta.code import Notebook
from verta.configuration import Hyperparameters
from verta.dataset import S3
from verta.environment import Python

code_ver = Notebook()  # Notebook & git environment
config_ver = Hyperparameters({'n_iter': 20})
dataset_ver = S3("s3://{}/{}".format(S3_BUCKET, S3_KEY))
env_ver = Python(Python.read_pip_environment())  # pip environment and Python version

"""
Explanation: Capture and Version Model Ingredients
We'll first capture metadata about our code, configuration, dataset, and environment using utilities from the verta library.
End of explanation
"""

repo = client.set_repository('Tweet Classification')
commit = repo.get_commit(branch='master')

"""
Explanation: Then, to log them, we'll use a ModelDB repository to prepare a commit.
End of explanation
"""

commit.update("notebooks/tweet-analysis", code_ver)
commit.update("config/hyperparams", config_ver)
commit.update("data/tweets", dataset_ver)
commit.update("env/python", env_ver)

commit.save("Initial model")

commit

"""
Explanation: Now we'll add these versioned components to the commit and save it to ModelDB.
End of explanation
"""

nlp = spacy.load('en_core_web_sm')

"""
Explanation: Train and Log Model
We'll use the pre-trained spaCy model we downloaded earlier...
End of explanation
"""

import training

training.train(nlp, data, n_iter=20)

"""
Explanation: ...and fine-tune it with our dataset.
End of explanation
"""

run = client.set_experiment_run()

run.log_model(nlp)

"""
Explanation: Now that our model is good to go, we'll log it to ModelDB so our progress is never lost.
Using Verta's ModelDB Client, we'll create an Experiment Run to encapsulate our work, and log our model as an artifact.
End of explanation
"""

run.log_commit(
    commit,
    {
        'notebook': "notebooks/tweet-analysis",
        'hyperparameters': "config/hyperparams",
        'training_data': "data/tweets",
        'python_env': "env/python",
    },
)

"""
Explanation: And finally, we'll link the commit we created earlier to the Experiment Run to complete our logged model version.
End of explanation
"""


%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import yaml

"""
Explanation: The following script extracts the (more) helpful reviews from the swiss reviews and saves them locally.
From the extracted reviews it also saves a list with their asin identifiers.
The list of asin identifiers will be later used to to find the average review rating for the respective products.
End of explanation
"""

with open("data/swiss-reviews.txt", 'r') as fp:
    swiss_rev = fp.readlines()

len(swiss_rev)

swiss_rev[2]

"""
Explanation: Load the swiss reviews
End of explanation
"""

def filter_helpful(line):
    l = line.rstrip('\n')
    l = yaml.load(l)
    if('helpful' in l.keys()):
        if(l['helpful'][1] >= 5):
            return True
        else:
            return False
    else:
        print("Review does not have helpful score key: "+line)
        return False

"""
Explanation: The filter_helpful function keeps only the reviews which had at least 5 flags/votes in the helpfulness field.
This amounts to a subset of around 23000 reviews. A smaller subset of around 10000 reviews was obtained as well by only keeping reviews with 10 flags/votes. The main advantage of the smaller subset is that it contains better quality reviews while its drawback is, of course, the reduced size.
1) Extract the helpful reviews
End of explanation
"""

def get_helpful(data):
    res = []
    counter = 1
    i = 0
    for line in data:
        i += 1
        if(filter_helpful(line)):
            if(counter % 1000 == 0):
                print("Count "+str(counter)+" / "+str(i))
            counter += 1
            res.append(line)
    return res

swiss_reviews_helpful = get_helpful(swiss_rev)

len(swiss_reviews_helpful)

"""
Explanation: Apply the filter_helpful to each swiss product review
End of explanation
"""

write_file = open('data/swiss-reviews-helpful-correct-bigger.txt', 'w')
for item in swiss_reviews_helpful:
  write_file.write(item)
write_file.close()

"""
Explanation: Save the subset with helpful swiss product reviews
End of explanation
"""

with open('data/swiss-reviews-helpful-correct-bigger.txt', 'r') as fp:
    swiss_reviews_helpful = fp.readlines()

"""
Explanation: 2) Extract the asins of the products which the helpful reviews correspond to
End of explanation
"""

def filter_asin(line):
    l = line.rstrip('\n')
    l = yaml.load(l)
    if('asin' in l.keys()):
        return l['asin']
    else:
        return ''

helpful_asins = []
counter = 1
for item in swiss_reviews_helpful:
    if(counter%500 == 0):
        print(counter)
    counter += 1
    x = filter_asin(item)
    if(len(x) > 0):
        helpful_asins.append(x)

"""
Explanation: The following function simply extracts the 'asin' from the helpful reviews.
Repetitions of the asins are of no consequence, as the list is just meant to be a check up.
End of explanation
"""

import pickle

with open('data/helpful_asins_bigger.pickle', 'wb') as fp:
    pickle.dump(helpful_asins, fp)

"""
Explanation: Save the list of asins.
End of explanation
"""


get_ipython().magic('load_ext autoreload')
get_ipython().magic('autoreload 2')

import glob
import logging
import numpy as np
import os

logging.basicConfig(format=
                          "%(relativeCreated)12d [%(filename)s:%(funcName)20s():%(lineno)s] [%(process)d] %(message)s",
                    # filename="/tmp/caiman.log",
                    level=logging.WARNING)

import caiman as cm
from caiman.source_extraction import cnmf as cnmf
from caiman.utils.utils import download_demo
import matplotlib.pyplot as plt

import bokeh.plotting as bpl
bpl.output_notebook()

"""
Explanation: Example of 1p online analysis using a Ring CNN + OnACID
The demo shows how to perform online analysis on one photon data using a Ring CNN for extracting the background followed by processing using the OnACID algorithm. The algorithm relies on the usage a GPU to efficiently estimate and apply the background model so it is recommended to have access to a GPU when running this notebook.
End of explanation
"""

fnames=download_demo('blood_vessel_10Hz.mat')

"""
Explanation: First specify the data file(s) to be analyzed
The download_demo method will download the file (if not already present) and store it inside your caiman_data/example_movies folder. You can specify any path to files you want to analyze.
End of explanation
"""

reuse_model = False                                                 # set to True to re-use an existing ring model
path_to_model = None                                                # specify a pre-trained model here if needed
gSig = (7, 7)                                                       # expected half size of neurons
gnb = 2                                                             # number of background components for OnACID
init_batch = 500                                                    # number of frames for initialization and training

params_dict = {'fnames': fnames,
               'var_name_hdf5': 'Y',                                # name of variable inside mat file where the data is stored
               'fr': 10,                                            # frame rate (Hz)
               'decay_time': 0.5,                                   # approximate length of transient event in seconds
               'gSig': gSig,
               'p': 0,                                              # order of AR indicator dynamics
               'ring_CNN': True,                                    # SET TO TRUE TO USE RING CNN
               'min_SNR': 2.65,                                     # minimum SNR for accepting new components
               'SNR_lowest': 0.75,                                  # reject components with SNR below this value
               'use_cnn': False,                                    # do not use CNN based test for components
               'use_ecc': True,                                     # test eccentricity
               'max_ecc': 2.625,                                    # reject components with eccentricity above this value
               'rval_thr': 0.70,                                    # correlation threshold for new component inclusion
               'rval_lowest': 0.25,                                 # reject components with corr below that value
               'ds_factor': 1,                                      # spatial downsampling factor (increases speed but may lose some fine structure)
               'nb': gnb,
               'motion_correct': False,                             # Flag for motion correction
               'init_batch': init_batch,                            # number of frames for initialization (presumably from the first file)
               'init_method': 'bare',
               'normalize': False,
               'expected_comps': 1100,                               # maximum number of expected components used for memory pre-allocation (exaggerate here)
               'sniper_mode': False,                                 # flag using a CNN to detect new neurons (o/w space correlation is used)
               'dist_shape_update' : True,                           # flag for updating shapes in a distributed way
               'min_num_trial': 5,                                   # number of candidate components per frame
               'epochs': 3,                                          # number of total passes over the data
               'stop_detection': True,                               # Run a last epoch without detecting new neurons
               'K': 50,                                              # initial number of components
               'lr': 6e-4,
               'lr_scheduler': [0.9, 6000, 10000],
               'pct': 0.01,
               'path_to_model': path_to_model,                       # where the ring CNN model is saved/loaded
               'reuse_model': reuse_model                            # flag for re-using a ring CNN model
              }
opts = cnmf.params.CNMFParams(params_dict=params_dict)

"""
Explanation: Set up some parameters
Here we set up some parameters for specifying the ring model and running OnACID. We use the same params object as in batch processing with CNMF.
End of explanation
"""

run_onacid = True

if run_onacid:
    cnm = cnmf.online_cnmf.OnACID(params=opts)
    cnm.fit_online()
    fld_name = os.path.dirname(cnm.params.ring_CNN['path_to_model'])
    res_name_nm = os.path.join(fld_name, 'onacid_results_nm.hdf5')
    cnm.save(res_name_nm)                # save initial results (without any postprocessing)
else:
    fld_name = os.path.dirname(path_to_model)
    res_name = os.path.join(fld_name, 'onacid_results.hdf5')
    cnm = cnmf.online_cnmf.load_OnlineCNMF(res_name)
    cnm.params.data['fnames'] = fnames

"""
Explanation: Now run the Ring-CNN + CaImAn online algorithm (OnACID).
The first initbatch frames are used for training the ring-CNN model. Once the model is trained the background is subtracted and the different is used for initialization purposes. The initialization method chosen here bare will only search for a small number of neurons and is mostly used to initialize the background components. Initialization with the full CNMF can also be used by choosing cnmf.
We first create an OnACID object located in the module online_cnmf and we pass the parameters similarly to the case of batch processing. We then run the algorithm using the fit_online method. We then save the results inside
the folder where the Ring_CNN model is saved.
End of explanation
"""

ds = 10             # plot every ds frames to make more manageable figures
init_batch = 500
dims, T = cnmf.utilities.get_file_size(fnames, var_name_hdf5='Y')
T = np.array(T).sum()
n_epochs = cnm.params.online['epochs']
T_detect = 1e3*np.hstack((np.zeros(init_batch), cnm.t_detect))
T_shapes = 1e3*np.hstack((np.zeros(init_batch), cnm.t_shapes))
T_online = 1e3*np.hstack((np.zeros(init_batch), cnm.t_online)) - T_detect - T_shapes
plt.figure()
plt.stackplot(np.arange(len(T_detect))[::ds], T_online[::ds], T_detect[::ds], T_shapes[::ds],
              colors=['tab:red', 'tab:purple', 'tab:brown'])
plt.legend(labels=['process', 'detect', 'shapes'], loc=2)
plt.title('Processing time allocation')
plt.xlabel('Frame #')
plt.ylabel('Processing time [ms]')
max_val = 80
plt.ylim([0, max_val]);
plt.plot([init_batch, init_batch], [0, max_val], '--k')
for i in range(n_epochs - 1):
    plt.plot([(i+1)*T, (i+1)*T], [0, max_val], '--k')
plt.xlim([0, n_epochs*T]);
plt.savefig(os.path.join(fld_name, 'time_per_frame_ds.pdf'), bbox_inches='tight', pad_inches=0)

init_batch = 500
plt.figure()
tc_init = cnm.t_init*np.ones(T*n_epochs)
ds = 10
#tc_mot = np.hstack((np.zeros(init_batch), np.cumsum(T_motion)/1000))
tc_prc = np.cumsum(T_online)/1000#np.hstack((np.zeros(init_batch), ))
tc_det = np.cumsum(T_detect)/1000#np.hstack((np.zeros(init_batch), ))
tc_shp = np.cumsum(T_shapes)/1000#np.hstack((np.zeros(init_batch), ))
plt.stackplot(np.arange(len(tc_init))[::ds], tc_init[::ds], tc_prc[::ds], tc_det[::ds], tc_shp[::ds],
              colors=['g', 'tab:red', 'tab:purple', 'tab:brown'])
plt.legend(labels=['initialize', 'process', 'detect', 'shapes'], loc=2)
plt.title('Processing time allocation')
plt.xlabel('Frame #')
plt.ylabel('Processing time [s]')
max_val = (tc_prc[-1] + tc_det[-1] + tc_shp[-1] + cnm.t_init)*1.05
for i in range(n_epochs - 1):
    plt.plot([(i+1)*T, (i+1)*T], [0, max_val], '--k')
plt.xlim([0, n_epochs*T]);
plt.ylim([0, max_val])
plt.savefig(os.path.join(fld_name, 'time_cumulative_ds.pdf'), bbox_inches='tight', pad_inches=0)

print('Cost of estimating model and running first epoch: {:.2f}s'.format(tc_prc[T] + tc_det[T] + tc_shp[T] + tc_init[T]))

"""
Explanation: Check speed
Create some plots that show the speed per frame and cumulatively
End of explanation
"""

# first compute background summary images
images = cm.load(fnames, var_name_hdf5='Y', subindices=slice(None, None, 2))
cn_filter, pnr = cm.summary_images.correlation_pnr(images, gSig=3, swap_dim=False) # change swap dim if output looks weird, it is a problem with tiffile

plt.figure(figsize=(15, 7))
plt.subplot(1,2,1); plt.imshow(cn_filter); plt.colorbar()
plt.subplot(1,2,2); plt.imshow(pnr); plt.colorbar()

cnm.estimates.plot_contours_nb(img=cn_filter, idx=cnm.estimates.idx_components, line_color='white', thr=0.3)

"""
Explanation: Do some initial plotting
End of explanation
"""

cnm.estimates.nb_view_components(img=cn_filter, denoised_color='red')

"""
Explanation: View components
Now inspect the components extracted by OnACID. Note that if single pass was used then several components would be non-zero only for the part of the time interval indicating that they were detected online by OnACID.
Note that if you get data rate error you can start Jupyter notebooks using:
'jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10'
End of explanation
"""

save_file = True
if save_file:
    from caiman.utils.nn_models import create_LN_model
    model_LN = create_LN_model(images, shape=opts.data['dims'] + (1,), n_channels=opts.ring_CNN['n_channels'],
                               width=opts.ring_CNN['width'], use_bias=opts.ring_CNN['use_bias'], gSig=gSig[0],
                               use_add=opts.ring_CNN['use_add'])
    model_LN.load_weights(cnm.params.ring_CNN['path_to_model'])

    # Load the data in batches and save them

    m = []
    saved_files = []
    batch_length = 256
    for i in range(0, T, batch_length):
        images = cm.load(fnames, var_name_hdf5='Y', subindices=slice(i, i + batch_length))
        images_filt = np.squeeze(model_LN.predict(np.expand_dims(images, axis=-1)))
        temp_file = os.path.join(fld_name, 'pfc_back_removed_' + format(i, '05d') + '.h5')
        saved_files.append(temp_file)
        m = cm.movie(np.maximum(images - images_filt, 0))
        m.save(temp_file)
else:
    saved_files = glob.glob(os.path.join(fld_name, 'pfc_back_removed_*'))
    saved_files.sort()

fname_mmap = cm.save_memmap([saved_files], order='C', border_to_0=0)
Yr, dims, T = cm.load_memmap(fname_mmap)
images_mmap = Yr.T.reshape((T,) + dims, order='F')

"""
Explanation: Load ring model to filter the data
Filter the data with the learned Ring CNN model and a create memory mapped file with the background subtracted data. We will use this to run the quality tests and screen for false positive components.
End of explanation
"""

cnm.params.merging['merge_thr'] = 0.7
cnm.estimates.c1 = np.zeros(cnm.estimates.A.shape[-1])
cnm.estimates.bl = np.zeros(cnm.estimates.A.shape[-1])
cnm.estimates.neurons_sn = np.zeros(cnm.estimates.A.shape[-1])
cnm.estimates.g = None #np.ones((cnm.estimates.A.shape[-1], 1))*.9
cnm.estimates.merge_components(Yr, cnm.params)

"""
Explanation: Merge components
End of explanation
"""

cnm.params.quality

cnm.estimates.evaluate_components(imgs=images_mmap, params=cnm.params)

cnm.estimates.plot_contours_nb(img=cn_filter, idx=cnm.estimates.idx_components, line_color='white')

cnm.estimates.nb_view_components(idx=cnm.estimates.idx_components, img=cn_filter)

"""
Explanation: Evaluate components and compare again
We run the component evaluation tests to screen for false positive components.
End of explanation
"""

cnmfe_results = download_demo('online_vs_offline.npz')
locals().update(np.load(cnmfe_results, allow_pickle=True))
A_patch_good = A_patch_good.item()
estimates_gt = cnmf.estimates.Estimates(A=A_patch_good, C=C_patch_good, dims=dims)

maxthr=0.01
cnm.estimates.A_thr=None
cnm.estimates.threshold_spatial_components(maxthr=maxthr)
estimates_gt.A_thr=None
estimates_gt.threshold_spatial_components(maxthr=maxthr*10)
min_size = np.pi*(gSig[0]/1.5)**2
max_size = np.pi*(gSig[0]*1.5)**2
ntk = cnm.estimates.remove_small_large_neurons(min_size_neuro=min_size, max_size_neuro=2*max_size)
gtk = estimates_gt.remove_small_large_neurons(min_size_neuro=min_size, max_size_neuro=2*max_size)

m1, m2, nm1, nm2, perf = cm.base.rois.register_ROIs(estimates_gt.A_thr[:, estimates_gt.idx_components],
                                                  cnm.estimates.A_thr[:, cnm.estimates.idx_components],
                                                  dims, align_flag=False, thresh_cost=.7, plot_results=True,
                                                  Cn=cn_filter, enclosed_thr=None)[:-1]

"""
Explanation: Compare against CNMF-E results
We download the results of CNMF-E on the same dataset and compare.
End of explanation
"""

for k, v in perf.items():
    print(k + ':', '%.4f' % v, end='  ')

"""
Explanation: Print performance results
End of explanation
"""

res_name = os.path.join(fld_name, 'onacid_results.hdf5')
cnm.save(res_name)

"""
Explanation: Save the results
End of explanation
"""

import matplotlib.lines as mlines
lp, hp = np.nanpercentile(cn_filter, [5, 98])
A_onacid = cnm.estimates.A_thr.toarray().copy()
A_onacid /= A_onacid.max(0)

A_TP = estimates_gt.A[:, m1].toarray() #cnm.estimates.A[:, cnm.estimates.idx_components[m2]].toarray()
A_TP = A_TP.reshape(dims + (-1,), order='F').transpose(2,0,1)
A_FN = estimates_gt.A[:, nm1].toarray()
A_FN = A_FN.reshape(dims + (-1,), order='F').transpose(2,0,1)
A_FP = A_onacid[:,cnm.estimates.idx_components[nm2]]
A_FP = A_FP.reshape(dims + (-1,), order='F').transpose(2,0,1)

plt.figure(figsize=(15, 12))
plt.imshow(cn_filter, vmin=lp, vmax=hp, cmap='viridis')
plt.colorbar();

for aa in A_TP:
    plt.contour(aa, [0.05], colors='k');
for aa in A_FN:
    plt.contour(aa, [0.05], colors='r');
for aa in A_FP:
    plt.contour(aa, [0.25], colors='w');
cl = ['k', 'r', 'w']
lb = ['both', 'CNMF-E only', 'ring CNN only']
day = [mlines.Line2D([], [], color=cl[i], label=lb[i]) for i in range(3)]
plt.legend(handles=day, loc=3)
plt.axis('off');
plt.margins(0, 0);
plt.savefig(os.path.join(fld_name, 'ring_CNN_contours_gSig_3.pdf'), bbox_inches='tight', pad_inches=0)

A_rej = cnm.estimates.A[:, cnm.estimates.idx_components_bad].toarray()
A_rej = A_rej.reshape(dims + (-1,), order='F').transpose(2,0,1)
plt.figure(figsize=(15, 15))
plt.imshow(cn_filter, vmin=lp, vmax=hp, cmap='viridis')
plt.title('Rejected Components')
for aa in A_rej:
    plt.contour(aa, [0.05], colors='w');

"""
Explanation: Make some plots
End of explanation
"""

from caiman.utils.nn_models import create_LN_model
model_LN = create_LN_model(images, shape=opts.data['dims'] + (1,), n_channels=opts.ring_CNN['n_channels'],
                           width=opts.ring_CNN['width'], use_bias=opts.ring_CNN['use_bias'], gSig=gSig[0],
                           use_add=opts.ring_CNN['use_add'])
model_LN.load_weights(cnm.params.ring_CNN['path_to_model'])

W = model_LN.get_weights()

plt.figure(figsize=(10, 10))
plt.subplot(2,2,1); plt.imshow(np.squeeze(W[0][:,:,:,0])); plt.colorbar(); plt.title('Ring Kernel 1')
plt.subplot(2,2,2); plt.imshow(np.squeeze(W[0][:,:,:,1])); plt.colorbar(); plt.title('Ring Kernel 2')
plt.subplot(2,2,3); plt.imshow(np.squeeze(W[-1][:,:,0])); plt.colorbar(); plt.title('Multiplicative Layer 1')
plt.subplot(2,2,4); plt.imshow(np.squeeze(W[-1][:,:,1])); plt.colorbar(); plt.title('Multiplicative Layer 2');

"""
Explanation: Show the learned filters
End of explanation
"""

m1 = cm.load(fnames, var_name_hdf5='Y')  # original data
m2 = cm.load(fname_mmap)  # background subtracted data
m3 = m1 - m2  # estimated background
m4 = cm.movie(cnm.estimates.A[:,cnm.estimates.idx_components].dot(cnm.estimates.C[cnm.estimates.idx_components])).reshape(dims + (T,)).transpose(2,0,1)
              # estimated components

nn = 0.01
mm = 1 - nn/4   # normalize movies by quantiles
m1 = (m1 - np.quantile(m1[:1000], nn))/(np.quantile(m1[:1000], mm) - np.quantile(m1[:1000], nn))
m2 = (m2 - np.quantile(m2[:1000], nn))/(np.quantile(m2[:1000], mm) - np.quantile(m2[:1000], nn))
m3 = (m3 - np.quantile(m3[:1000], nn))/(np.quantile(m3[:1000], mm) - np.quantile(m3[:1000], nn))
m4 = (m4 - np.quantile(m4[:1000], nn))/(np.quantile(m4[:1000], mm) - np.quantile(m4[:1000], nn))

m = cm.concatenate((cm.concatenate((m1.transpose(0,2,1), m3.transpose(0,2,1)), axis=2),
                    cm.concatenate((m2.transpose(0,2,1), m4), axis=2)), axis=1)

m[:3000].play(magnification=2, q_min=1, plot_text=True,
              save_movie=True, movie_name=os.path.join(fld_name, 'movie.avi'))

"""
Explanation: Make a movie
End of explanation
"""


#$HIDE_INPUT$
import pandas as pd
from IPython.display import display

red_wine = pd.read_csv('../input/dl-course-data/red-wine.csv')

# Create training and validation splits
df_train = red_wine.sample(frac=0.7, random_state=0)
df_valid = red_wine.drop(df_train.index)
display(df_train.head(4))

# Scale to [0, 1]
max_ = df_train.max(axis=0)
min_ = df_train.min(axis=0)
df_train = (df_train - min_) / (max_ - min_)
df_valid = (df_valid - min_) / (max_ - min_)

# Split features and target
X_train = df_train.drop('quality', axis=1)
X_valid = df_valid.drop('quality', axis=1)
y_train = df_train['quality']
y_valid = df_valid['quality']

"""
Explanation: Introduction
In the first two lessons, we learned how to build fully-connected networks out of stacks of dense layers. When first created, all of the network's weights are set randomly -- the network doesn't "know" anything yet. In this lesson we're going to see how to train a neural network; we're going to see how neural networks learn.
As with all machine learning tasks, we begin with a set of training data. Each example in the training data consists of some features (the inputs) together with an expected target (the output). Training the network means adjusting its weights in such a way that it can transform the features into the target. In the 80 Cereals dataset, for instance, we want a network that can take each cereal's 'sugar', 'fiber', and 'protein' content and produce a prediction for that cereal's 'calories'. If we can successfully train a network to do that, its weights must represent in some way the relationship between those features and that target as expressed in the training data.
In addition to the training data, we need two more things:
- A "loss function" that measures how good the network's predictions are.
- An "optimizer" that can tell the network how to change its weights.
The Loss Function
We've seen how to design an architecture for a network, but we haven't seen how to tell a network what problem to solve. This is the job of the loss function.
The loss function measures the disparity between the the target's true value and the value the model predicts.
Different problems call for different loss functions. We have been looking at regression problems, where the task is to predict some numerical value -- calories in 80 Cereals, rating in Red Wine Quality. Other regression tasks might be predicting the price of a house or the fuel efficiency of a car.
A common loss function for regression problems is the mean absolute error or MAE. For each prediction y_pred, MAE measures the disparity from the true target y_true by an absolute difference abs(y_true - y_pred).
The total MAE loss on a dataset is the mean of all these absolute differences.

The mean absolute error is the average length between the fitted curve and the data points.

Besides MAE, other loss functions you might see for regression problems are the mean-squared error (MSE) or the Huber loss (both available in Keras).
During training, the model will use the loss function as a guide for finding the correct values of its weights (lower loss is better). In other words, the loss function tells the network its objective.
The Optimizer - Stochastic Gradient Descent
We've described the problem we want the network to solve, but now we need to say how to solve it. This is the job of the optimizer. The optimizer is an algorithm that adjusts the weights to minimize the loss.
Virtually all of the optimization algorithms used in deep learning belong to a family called stochastic gradient descent. They are iterative algorithms that train a network in steps. One step of training goes like this:
1. Sample some training data and run it through the network to make predictions.
2. Measure the loss between the predictions and the true values.
3. Finally, adjust the weights in a direction that makes the loss smaller.
Then just do this over and over until the loss is as small as you like (or until it won't decrease any further.)

Training a neural network with Stochastic Gradient Descent.

Each iteration's sample of training data is called a minibatch (or often just "batch"), while a complete round of the training data is called an epoch. The number of epochs you train for is how many times the network will see each training example.
The animation shows the linear model from Lesson 1 being trained with SGD. The pale red dots depict the entire training set, while the solid red dots are the minibatches. Every time SGD sees a new minibatch, it will shift the weights (w the slope and b the y-intercept) toward their correct values on that batch. Batch after batch, the line eventually converges to its best fit. You can see that the loss gets smaller as the weights get closer to their true values.
Learning Rate and Batch Size
Notice that the line only makes a small shift in the direction of each batch (instead of moving all the way). The size of these shifts is determined by the learning rate. A smaller learning rate means the network needs to see more minibatches before its weights converge to their best values.
The learning rate and the size of the minibatches are the two parameters that have the largest effect on how the SGD training proceeds. Their interaction is often subtle and the right choice for these parameters isn't always obvious. (We'll explore these effects in the exercise.)
Fortunately, for most work it won't be necessary to do an extensive hyperparameter search to get satisfactory results. Adam is an SGD algorithm that has an adaptive learning rate that makes it suitable for most problems without any parameter tuning (it is "self tuning", in a sense). Adam is a great general-purpose optimizer.
Adding the Loss and Optimizer
After defining a model, you can add a loss function and optimizer with the model's compile method:
model.compile(
    optimizer="adam",
    loss="mae",
)
Notice that we are able to specify the loss and optimizer with just a string. You can also access these directly through the Keras API -- if you wanted to tune parameters, for instance -- but for us, the defaults will work fine.

    What's In a Name?

The gradient is a vector that tells us in what direction the weights need to go. More precisely, it tells us how to change the weights to make the loss change fastest. We call our process gradient descent because it uses the gradient to descend the loss curve towards a minimum. Stochastic means "determined by chance." Our training is stochastic because the minibatches are random samples from the dataset. And that's why it's called SGD!

Example - Red Wine Quality
Now we know everything we need to start training deep learning models. So let's see it in action! We'll use the Red Wine Quality dataset.
This dataset consists of physiochemical measurements from about 1600 Portuguese red wines. Also included is a quality rating for each wine from blind taste-tests. How well can we predict a wine's perceived quality from these measurements?
We've put all of the data preparation into this next hidden cell. It's not essential to what follows so feel free to skip it. One thing you might note for now though is that we've rescaled each feature to lie in the interval $[0, 1]$. As we'll discuss more in Lesson 5, neural networks tend to perform best when their inputs are on a common scale.
End of explanation
"""

print(X_train.shape)

"""
Explanation: How many inputs should this network have? We can discover this by looking at the number of columns in the data matrix. Be sure not to include the target ('quality') here -- only the input features.
End of explanation
"""

from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential([
    layers.Dense(512, activation='relu', input_shape=[11]),
    layers.Dense(512, activation='relu'),
    layers.Dense(512, activation='relu'),
    layers.Dense(1),
])

"""
Explanation: Eleven columns means eleven inputs.
We've chosen a three-layer network with over 1500 neurons. This network should be capable of learning fairly complex relationships in the data.
End of explanation
"""

model.compile(
    optimizer='adam',
    loss='mae',
)

"""
Explanation: Deciding the architecture of your model should be part of a process. Start simple and use the validation loss as your guide. You'll learn more about model development in the exercises.
After defining the model, we compile in the optimizer and loss function.
End of explanation
"""

history = model.fit(
    X_train, y_train,
    validation_data=(X_valid, y_valid),
    batch_size=256,
    epochs=10,
)

"""
Explanation: Now we're ready to start the training! We've told Keras to feed the optimizer 256 rows of the training data at a time (the batch_size) and to do that 10 times all the way through the dataset (the epochs).
End of explanation
"""

import pandas as pd

# convert the training history to a dataframe
history_df = pd.DataFrame(history.history)
# use Pandas native plot method
history_df['loss'].plot();

"""
Explanation: You can see that Keras will keep you updated on the loss as the model trains.
Often, a better way to view the loss though is to plot it. The fit method in fact keeps a record of the loss produced during training in a History object. We'll convert the data to a Pandas dataframe, which makes the plotting easy.
End of explanation
"""


import time
from datetime import datetime

from typing import List

import numpy as np
import pandas as pd

import google.auth

from google.cloud import logging_v2
from google.cloud.monitoring_dashboard.v1 import DashboardsServiceClient
from google.cloud.logging_v2 import MetricsServiceV2Client
from google.cloud.monitoring_v3.query import Query
from google.cloud.monitoring_v3 import MetricServiceClient

import matplotlib.pyplot as plt

"""
Explanation: Analyzing Locust Load Testing Results
This Notebook demonstrates how to analyze AI Platform Prediction load testing runs using metrics captured in Cloud Monitoring.
This Notebook build on the 02-perf-testing.ipynb notebook that shows how to configure and run load tests against AI Platform Prediction using Locust.io. The outlined testing process results in a Pandas dataframe that aggregates the standard AI Platform Prediction metrics with a set of custom, log-based metrics generated from log entries captured by the Locust testing script.
The Notebook covers the following steps:
1. Retrieve and consolidate test results from Cloud Monitoring
2. Analyze and visualize utilization and latency results
Setup
This notebook was tested on AI Platform Notebooks using the standard TF 2.2 image.
Import libraries
End of explanation
"""

PROJECT_ID = '[your-project-id]' # Set your project Id
MODEL_NAME = 'resnet_classifier'
MODEL_VERSION = 'v1'
LOG_NAME = 'locust' # Set your log name
TEST_ID = 'test-20200829-190943' # Set your test Id
TEST_START_TIME = datetime.fromisoformat('2020-08-28T21:30:00-00:00') # Set your test start time
TEST_END_TIME = datetime.fromisoformat('2020-08-29T22:00:00-00:00') # Set your test end time

"""
Explanation: Configure GCP environment settings
End of explanation
"""

creds , _ = google.auth.default()
client = MetricServiceClient(credentials=creds)

project_path = client.project_path(PROJECT_ID)
filter = 'metric.type=starts_with("ml.googleapis.com/prediction")'

for descriptor in client.list_metric_descriptors(project_path, filter_=filter):
    print(descriptor.type)

"""
Explanation: 1. Retrieve and consolidate test results
Locust's web interface along with a Cloud Monitoring dashboard provide a cursory view into performance of a tested AI Platform Prediction model version. A more thorough analysis can be performed by consolidating metrics collected during a test and using data analytics and visualization tools.
In this section, you will retrieve the metrics captured in Cloud Monitoring and consolidate them into a single Pandas dataframe.
1.1 List available AI Platform Prediction metrics
End of explanation
"""

filter = 'metric.type=starts_with("logging.googleapis.com/user")'

for descriptor in client.list_metric_descriptors(project_path, filter_=filter):
    print(descriptor.type)

"""
Explanation: 1.2. List custom log based metrics
End of explanation
"""

def retrieve_metrics(client, project_id, start_time, end_time, model, model_version, test_id, log_name):
    """
    Retrieves test metrics from Cloud Monitoring.
    """
    def _get_aipp_metric(metric_type: str, labels: List[str]=[], metric_name=None)-> pd.DataFrame:
        """
        Retrieves a specified AIPP metric.
        """
        query = Query(client, project_id, metric_type=metric_type)
        query = query.select_interval(end_time, start_time)
        query = query.select_resources(model_id=model)
        query = query.select_resources(version_id=model_version)

        if metric_name:
            labels = ['metric'] + labels
        df = query.as_dataframe(labels=labels)

        if not df.empty:
            if metric_name:
                df.columns.set_levels([metric_name], level=0, inplace=True)
            df = df.set_index(df.index.round('T'))

        return df

    def _get_locust_metric(metric_type: str, labels: List[str]=[], metric_name=None)-> pd.DataFrame:
        """
        Retrieves a specified custom log-based metric.
        """
        query = Query(client, project_id, metric_type=metric_type)
        query = query.select_interval(end_time, start_time)
        query = query.select_metrics(log=log_name)
        query = query.select_metrics(test_id=test_id)

        if metric_name:
            labels = ['metric'] + labels
        df = query.as_dataframe(labels=labels)

        if not df.empty:
            if metric_name:
                df.columns.set_levels([metric_name], level=0, inplace=True)
            df = df.apply(lambda row: [metric.mean for metric in row])
            df = df.set_index(df.index.round('T'))

        return df

    # Retrieve GPU duty cycle
    metric_type = 'ml.googleapis.com/prediction/online/accelerator/duty_cycle'
    metric = _get_aipp_metric(metric_type, ['replica_id', 'signature'], 'duty_cycle')
    df = metric

    # Retrieve CPU utilization
    metric_type = 'ml.googleapis.com/prediction/online/cpu/utilization'
    metric = _get_aipp_metric(metric_type, ['replica_id', 'signature'], 'cpu_utilization')
    if not metric.empty:
        df = df.merge(metric, how='outer', right_index=True, left_index=True)

    # Retrieve prediction count
    metric_type = 'ml.googleapis.com/prediction/prediction_count'
    metric = _get_aipp_metric(metric_type, ['replica_id', 'signature'], 'prediction_count')
    if not metric.empty:
        df = df.merge(metric, how='outer', right_index=True, left_index=True)

    # Retrieve responses per second
    metric_type = 'ml.googleapis.com/prediction/response_count'
    metric = _get_aipp_metric(metric_type, ['replica_id', 'signature'], 'response_rate')
    if not metric.empty:
        metric = (metric/60).round(2)
        df = df.merge(metric, how='outer', right_index=True, left_index=True)

    # Retrieve backend latencies
    metric_type = 'ml.googleapis.com/prediction/latencies'
    metric = _get_aipp_metric(metric_type, ['latency_type', 'replica_id', 'signature'])
    if not metric.empty:
        metric = metric.apply(lambda row: [round(latency.mean/1000,1) for latency in row])
        metric.columns.set_names(['metric', 'replica_id', 'signature'], inplace=True)
        level_values = ['Latency: ' + value for value in metric.columns.get_level_values(level=0)]
        metric.columns.set_levels(level_values, level=0, inplace=True)
        df = df.merge(metric, how='outer', right_index=True, left_index=True)

    # Retrieve Locust latency
    metric_type = 'logging.googleapis.com/user/locust_latency'
    metric = _get_locust_metric(metric_type, ['replica_id', 'signature'], 'Latency: client')
    if not metric.empty:
        metric = metric.round(2).replace([0], np.nan)
        df = df.merge(metric, how='outer', right_index=True, left_index=True)

    # Retrieve Locust user count
    metric_type = 'logging.googleapis.com/user/locust_users'
    metric = _get_locust_metric(metric_type, ['replica_id', 'signature'], 'User count')
    if not metric.empty:
        metric = metric.round()
        df = df.merge(metric, how='outer', right_index=True, left_index=True)

    # Retrieve Locust num_failures
    metric_type = 'logging.googleapis.com/user/num_failures'
    metric = _get_locust_metric(metric_type, ['replica_id', 'signature'], 'Num of failures')
    if not metric.empty:
        metric = metric.round()
        df = df.merge(metric, how='outer', right_index=True, left_index=True)

    # Retrieve Locust num_failures
    metric_type = 'logging.googleapis.com/user/num_requests'
    metric = _get_locust_metric(metric_type, ['replica_id', 'signature'], 'Num of requests')
    if not metric.empty:
        metric = metric.round()
        df = df.merge(metric, how='outer', right_index=True, left_index=True)

    return df

test_result = retrieve_metrics(
    client,
    PROJECT_ID,
    TEST_START_TIME,
    TEST_END_TIME,
    MODEL_NAME,
    MODEL_VERSION,
    TEST_ID,
    LOG_NAME
)

test_result.head().T

"""
Explanation: 1.3. Retrieve test metrics
Define a helper function that retrieves test metrics from Cloud Monitoring
End of explanation
"""

gpu_utilization_results = test_result['duty_cycle']
gpu_utilization_results.columns = gpu_utilization_results.columns.get_level_values(0)
ax = gpu_utilization_results.plot(figsize=(14, 9), legend=True)
ax.set_xlabel('Time', fontsize=16)
ax.set_ylabel('Utilization ratio', fontsize=16)
_ = ax.set_title("GPU Utilization", fontsize=20)

"""
Explanation: The retrieved dataframe uses hierarchical indexing for column names. The reason is that some metrics contain multiple time series. For example, the GPU duty_cycle metric includes a time series of measures per each GPU used in the deployment (denoted as replica_id). The top level of the column index is a metric name. The second level is a replica_id. The third level is a signature of a model.
All metrics are aligned on the same timeline.
2. Analyzing and Visualizing test results
In the context of our scenario the key concern is GPU utilization at various levels of throughput and latency. The primary metric exposed by AI Platform Prediction to monitor GPU utilization is duty cycle. This metric captures an average fraction of time over the 60 second period during which the accelerator(s) were actively processing.
2.1. GPU utilization
End of explanation
"""

cpu_utilization_results = test_result['cpu_utilization']
cpu_utilization_results.columns = cpu_utilization_results.columns.get_level_values(0)
ax = cpu_utilization_results.plot(figsize=(14, 9), legend=True)
ax.set_xlabel('Time', fontsize=16)
ax.set_ylabel('Utilization ratio', fontsize=16)
_ = ax.set_title("CPU Utilization", fontsize=20)

"""
Explanation: 2.2. CPU utilization
End of explanation
"""

latency_results = test_result[['Latency: model', 'Latency: client']]
latency_results.columns = latency_results.columns.get_level_values(0)
ax = latency_results.plot(figsize=(14, 9), legend=True)
ax.set_xlabel('Time', fontsize=16)
ax.set_ylabel('milisecond', fontsize=16)
_ = ax.set_title("Latency", fontsize=20)

"""
Explanation: 2.3. Latency
End of explanation
"""

throughput_results = test_result[['response_rate', 'User count']]
throughput_results.columns = throughput_results.columns.get_level_values(0)
ax = throughput_results.plot(figsize=(14, 9), legend=True)
ax.set_xlabel('Time', fontsize=16)
ax.set_ylabel('Count', fontsize=16)
_ = ax.set_title("Response Rate vs User Count", fontsize=20)

"""
Explanation: 2.4. Request throughput
We are going to use the response_rate metric, which tracks a number of responses returned by AI Platform Prediction over a 1 minute interval.
End of explanation
"""

logging_client = MetricsServiceV2Client(credentials=creds)
parent = logging_client.project_path(PROJECT_ID)

for element in logging_client.list_log_metrics(parent):
    metric_path = logging_client.metric_path(PROJECT_ID, element.name)
    logging_client.delete_log_metric(metric_path)
    print("Deleted metric: ", metric_path)

display_name = 'AI Platform Prediction and Locust'
dashboard_service_client = DashboardsServiceClient(credentials=creds)
parent = 'projects/{}'.format(PROJECT_ID)
for dashboard in dashboard_service_client.list_dashboards(parent):
    if dashboard.display_name == display_name:
        dashboard_service_client.delete_dashboard(dashboard.name)
        print("Deleted dashboard:", dashboard.name)

"""
Explanation: Cleaning up: delete the log-based metrics and dasboard
End of explanation
"""


"The people of the kingdom look down on the young Prince Bojji, who can neither hear nor speak. They call him "The Useless Prince" while jeering at his supposed foolishness."


"Meet Bojji, a deaf, powerless prince who cannot even wield a children's sword. As the firstborn son, he strives hard and dreams of becoming the world's greatest king. However, people mutter about him behind his back as "a good-for-nothing prince" and "no way he can be king.""


"In a city which should be familiar, there is the feeling of something a little different."


"Two very different people - an academically successful schoolgirl and a quiet loser schoolboy - meet and develop a friendship."


"At first glance, the ultra-popular Kyoko Hori seems like a frivolous high school girl, but in reality, she's simple, pragmatic, and family-oriented. On the other hand, the bespectacled Miyamura Izumi comes across as an average, gloomy high school fanboy, but he's actually an attractive young man who has a bad-boy streak and is covered in piercings and tattoos. When these two unexpectedly similar classmates have a random run-in outside of the classroom, a bubbly, sweet tale of school life begins."


"A 34-year-old Japanese NEET is run over by a speeding truck and dies. Before he knows it, he is reborn as Rudeus Greyrat, and begins a new life full of adventure."


"An elite and aloof schoolgirl is in fact severely withdrawn and anxious about communicating with others. An ordinary schoolboy befriends her and helps her to open up and talk with people."


"An absolute beauty to anyone, Shouko Komi in reality suffers from extreme social anxiety and communication problems, making her largely unable to talk to others. However, her attractiveness and stoic appearance, which in itself results from her anxiety, is perceived as refined class, making her highly popular while preventing people from actually getting to know her. Komi's first conversation with Tadano is over a blackboard, and she uses a notebook to convey her feelings through writing. Despite this, she is still capable of verbal conversation over a cell phone. Her dream is to overcome her communication problems and make 100 friends."


"Reki, a high school sophomore and skater, is addicted to "S," a highly secret and dangerous downhill skateboarding race that takes place in an abandoned mine. The skaters are especially wild about the "beefs," or heated battles that erupt in the races. Reki takes Langa, a transfer student returning to Japan after studying abroad, to the mine where the races are held. Langa, who has no skateboarding experience, finds himself pulled into the world of "S"."


graduates almost fix letter in old english to instrument (6,4)


boatman's upper crust, bleeding edge, going forward (6)


churlish grin that passes between supporters in house (5,4)


lob extremely deficient paint all over the place, one lacking pigment (6)


oarsmen include a soldier in the overturned boat (5)


drive any convertible - red, say, from here (8)


Q:

Create HTML lists from PHP arrays

I am trying to create various, standard HTML list styles from PHP arrays for a movie database I'm building as part of my final year project at university, however I'm having some trouble accessing/understanding how to do this.

I've got the relevant data from the various APIs I'm using, but I'd like to create the entire list dynamically, dropping in the correct bits of information where appropriate.

Details of the two list styles I'm looking to create are below.

List style 1 - Character/Cast details
For the first style of list, I'd like to create it as:

Cast name - Character name

Cast name - Character name

etc.

etc.

My data set direct from the array looks like this:
Array
(

[abridged_cast] => Array
    (
        [0] => Array
            (
                [name] => Daniel Craig
                [id] => 162687443
                [characters] => Array
                    (
                        [0] => James Bond
                    )

            )

        [1] => Array
            (
                [name] => Javier Bardem
                [id] => 162661456
                [characters] => Array
                    (
                        [0] => Silva
                    )

            )

        [2] => Array
            (
                [name] => Judi Dench
                [id] => 162652435
                [characters] => Array
                    (
                        [0] => M
                    )

            )

        [3] => Array
            (
                [name] => Ralph Fiennes
                [id] => 162653681
                [characters] => Array
                    (
                        [0] => Gareth Mallory
                        [1] => Mallory
                    )

            )

        [4] => Array
            (
                [name] => Naomie Harris
                [id] => 162705781
                [characters] => Array
                    (
                        [0] => Eve
                    )

            )

    )

In the above example, there's five cast members w/ their respective character names, however on some other examples, there's more or less than five, so, ideally, I'd want the system to know however many cast members were given and create a list for each, up to a maximum of 10 names.

List style 2 - Videos
Similar to the last style, however this list will hopefully look like this:

<a href='<http://youtube.com/<dynamically generated URL direct to the clip>'>

same as above...

The source array looks like this:
Array
(
[id] => 37724
[youtube] => Array
    (
        [0] => Array
            (
                [name] => Trailer
                [size] => HQ
                [source] => 24mTIE4D9JM
            )

        [1] => Array
            (
                [name] => Official Trailer
                [size] => HD
                [source] => 6kw1UVovByw
            )

        [2] => Array
            (
                [name] => Trailer 1
                [size] => HD
                [source] => 1Uyjf5Pp0Ko
            )

        [3] => Array
            (
                [name] => Trailer 2
                [size] => HD
                [source] => 5Ejo9_3iUpw
            )

    )

)

Apologies for the length of this post, thanks in advance to anyone who may be able to assist!

A:

The thing you are looking for is called a foreach loop.

For your first example it would look something like this:
foreach($dataSet['abridged_cast'] as $castMember){
    echo '<li>'.$castMember['name'].'-'.implode(', ', $castMember['characters']). '

</li>';
}

Now this example is purely illustratory though it most definetely be the begginers approach.

Still, I would strongly recommend investigating MVC patterns and PHP frameworks, like for example YII.

If deployed and configured right YII with all its goodness should make all the repetitive work done for you here.

I've seen that your question contains two parts, though I am not willing to write a sample for the second one, since it is practically the same as the first and with proper attention - you will get on track and figure it out.


Baritone horn

The baritone horn, or sometimes just called baritone, is a low-pitched brass instrument in the saxhorn family.

It is a piston-valve brass instrument with a bore that is mostly conical (like the higher pitched flugelhorn and alto (tenor) horn) but it has a narrower bore than the similarly pitched euphonium.

It uses a wide-rimmed cup mouthpiece like that of its peers, the trombone and euphonium.

Like the trombone and the euphonium, the baritone horn can be considered either a transposing or non-transposing instrument.

In the UK, the baritone horn is part of the standardised instrumentation of brass bands.

In concert band music, there is often a part marked baritone, but these parts are most commonly intended for, and played on, the euphonium.

A baritone can also play music written for a trombone due to similar pitches.

A person who plays a baritone horn is a baritone (horn) player (UK) or baritonist (US).

Construction and general characteristics

The baritone, like the trombone and euphonium, is a nine-foot brass tube.

It is predominately of conical bore, like the euphonium, but has a narrower bore than the euphonium.

The smaller bore renders its attack more distinct than the rounder attack of the euphonium, and also provides it with a brighter sound than the dark-sounding euphonium.

Key
The baritone is pitched in concert B, meaning that when no valves are actuated, the instrument will produce partials of the B harmonic series.

Music for the baritone horn can be written in either the bass clef or the treble clef.

When written in the bass clef, the baritone horn is a non-transposing instrument.

However, when written in the treble clef, it is often used as transposing instrument, transposing downward a major ninth from the music as written, so that written middle C for the baritone is concert B below low C (B2 in scientific pitch notation), with the fingerings thus matching those of the trumpet but sounding an octave lower.

It is often used to play parts written for the similarly pitched tenor trombone or euphonium.

Range
The baritone is part of the tenor section of a band.

Its second partial with no valves pressed is concert B on the second line from the bottom of the bass clef (B2 in scientific pitch notation).

The eighth partial with no valves pressed is concert B in the center of the treble clef (B4).

Virtuosi can reach certain pedal tones below the E2 second partial with all valves depressed, which is the nominal lowest note on the instrument, and several half-tones above the B4, which is the nominal top of the instrument's range.

Tone
The baritone sounds with a timbre between the brightness of the trombone and the more mellow tone of the euphonium.

Distinguishing the baritone horn from the euphonium

Although both baritone horn and euphonium produce partials of the B harmonic series in the same range, and both have a nine-foot-long main tube, the baritone horn tends to have a smaller and more cylindrical bore than the euphonium.

The baritone horn usually has a tighter wrap and a smaller bell, and is thus smaller and lighter overall, and produces a "lighter" sound versus the more solid, brassy timbre of the euphonium.

There is a common misconception that three-valve instrument is a baritone and that the four-valve instrument is a euphonium.

Euphoniums often have a fourth valve as an alternate fingering for 1&3 split fingering with improved intonation.

The fourth valve can also be viewed in the same way as an F trigger on trombone, repitching the instrument to expand the lower range.

A fourth valve is rarely seen on baritones, but absence of a fourth valve is not a defining characteristic.

An "American baritone", featuring three valves on the front of the instrument and a curved forward-pointing bell, was common in American school bands throughout most of the twentieth century.

While this instrument is in reality a conical-cylindrical bore hybrid, neither truly euphonium nor baritone, it was almost universally labeled a "baritone" by both band directors and composers.

Marching baritone horn

Specially wrapped versions of the baritone horn have been created for use in marching bands and drum and bugle corps.

They have three valves and a front-facing bell and are the tenor voice of a drum and bugle corps, below the soprano voice of the trumpet, the alto voice of alto horn or mellophone, and above the low tubas.

Artists
Notable artists who are today referenced as great baritone horn and/or euphonium players include Mel Sykes, Simone Mantia, Steven Mead, Brian Bowman, and Leonard Falcone.

The Leonard Falcone International Tuba and Euphonium Festival is a notable venue for aspiring artists on euphonium, but its namesake played baritone horn on his many recordings.

Trumpeter Maynard Ferguson used a baritone horn in the song "Gospel John" and in one of his three solos (the other two involving a valved trombone and a trumpet) in a live performance of his song "Great Guns".

Japanese free-improvisation trumpeter Toshinori Kondo has played baritone horn on some dates and recordings (e.g., Jazz Bunker), as has saxophonist Ralph Carney (e.g., Night on Earth).

References

Category:B-flat instruments
Category:Brass instruments
Category:Horns


92-4101
United States Court of Appeals,Eleventh Circuit.


What is the closest to -1/2 in -4, b, i?

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Let u = 19 - h. Which is the nearest to 1?

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Urban malaria in Nazareth, Ethiopia: parasitological studies.

Cross-sectional surveys for parasite infection, and factors that possibly influence human malaria transmission were carried out to characterize the epidemiology of urban malaria in Nazareth.

Thick and thin blood films for parasite diagnosis were collected from individuals residing in 6 representative kebeles in the town.

A questionnaire was used to obtain socioeconomic information.

An overall parasite rate of 2.8%, P. falciparum (1.6%) and P. vivax (1.2%)- was observed among the 3890 individuals examined during the 4 surveys.

Significant intra-urban differences (X2 = 27.31; P < 0.0001), in parasite rates, were observed with a much higher prevalence in the peripheral kebeles.

A marked seasonal variation in parasite rates was evident during the sampling period and the peak occurred in Sept./Oct.

There was no apparent decrease in parasite rate with increase in age, suggesting a delayed acquired immunity, typical of hypoendemic settings.

The study has indicated that in Nazareth the prevalence of malaria is seasonally variable and relatively low, showing that it is unstable.

Hence, if appropriate control measures are not instituted, malaria situation in -Nazareth is subject to epidemics.


Osaka school killer of 8, yakuza boss executed TOKYO - Mamoru Takuma, convicted for murdering eight children at an Osaka elementary school in 2001, has been executed, informed sources said Tuesday.


Relations with India not at Pakistans expense: China BEIJING: Chinas improving relations with India will not come at the expense of Pakistan, Chinese Premier Wen Jiabao said in his meeting with Prime Minister Shaukat Aziz here on Wednesday.


Chargers, Rivers Agree to Deal The San Diego Chargers finally reached a contract agreement last night with quarterback Philip Rivers. Rivers, the fourth overall choice in April #39;s draft, agreed to a six-year deal worth about \$40 million, including


Greater pressure directed at US Airways unions Bankrupt US Airways is giving flight attendants, machinists and passenger service workers three weeks to reach consensual cost-cutting pacts before the airline asks


SA 'mercenaries' plead not guilty Sixty-six men accused of plotting a coup in Equatorial Guinea deny breaching Zimbabwe's security laws.


Adobe Services Now Via Yahoo! Adobe Systems and Internet provider Yahoo! have announced a tie-up aimed at providing consumer services to Internet users. The two companies will introduce integrated products that feature Adobe services, increase the reach of Yahoo!


Safin tallest obstacle to host #39;s patriotic games hope AS tennis fans go, Houston #39;s Jim  #39;Mattress Mack #39; McIngvale is very rich, extremely forthright, exceedingly patriotic and unflinchingly Republican.


Volcanoes May Have Sparked Life on Earth, Study Says How did the building blocks of life arise on Earth? A new study says a volcanic gas may have been the key.


Livewire: Fantasy Sports Leagues Thrive Online (Reuters) Reuters - Take 15 million armchair athletes,\add a steady stream of statistics and mix in a healthy dollop\of trash talk. Post it all on the Internet and you've got a  #36;3\billion industry built around imaginary sports teams.


Blurry Image Might Be First Picture of Exoplanet The image of a blurry red ball near a failed star just might be the first picture ever snapped of a planet outside our solar system, an astronomer who helped find the object said on Monday.


Dr. Seuss would sure be mad right now if he was alive. Cat in the Hat proves to show how movie productions can take a classic story and turn it into a mindless pile of goop. We have Mike Myers as the infamous Cat in the Hat, big mistake! Myers proves he can't act in this film. He acts like a prissy show girl with a thousand tricks up his sleeve. The kids in this movie are all right, somewhere in between the lines of dull and annoying. The story is just like the original with a couple of tweaks and like most movies based on other stories, never tweak with the original story! Bringing in the evil neighbor Quin was a bad idea. He is a stupid villain that would never get anywhere in life.This movie is like a rejected comic strip from the newspaper if you think about it. The film sure does look tacky! Sure there are a funny adult jokes like where the cat cuts of his tail and the censor goes off before he says a naughty word, mildly funny. At least the Grinch had spunk, and the film was actually good! This film is a cartoonish piece of snot with bright colors and bad mediocre acting. Was Mike Myers even in this movie actually? And another thing, the fish. What is with that stupid fish! First time you see him, he's an actual fish. Next time you see him, he's all animated and talking. But he looks like an animated piece of rubber play dough! This film is a total off target wreck. Good joke, bad joke, bad, bad, bad, good joke! I'm surprised it even had good jokes like the water park ride joke, that was good. So please if you have the choice, watch the Grinch instead of this mess.


Boy what a dud this mess was.But it only lasts an hour and I only paid a buck for it so I'll live....unlike the entire cast of this 1933 clunker who are all dust by now.So anyway a small village starts having bodies turning up that have been drained of all their blood.The local yokels start talking about vampires ,of course,and a little more loudly after each body is found.The town sheriff or constable or whatever he is,played by awesome actor Melvyn Douglas,tries to tell them otherwise.When he mentions the fact that the dead have one large hole on each side of the neck,instead of two holes close together, the locals simply then say it's a giant vampire bat.The constable insists that vampires do not exist and it must be a human culprit doing the killings.But Melvyn doesn't seem too bothered either way.He spends most of his time trying to get into the pantaloons of his sweetie,played by Faye Wray.Also in this mix is the town simpleton,played by Dwight Frye,who always seemed to have played the same role in every movie he did.He further freaks out the townspeople by catching bats and drinking his own blood.Lionel Atwill plays the town doctor who seemingly is trying to help the constable solve the crimes.And boy does he ever stink as an actor.Atwill is as close to cardboard in this role as he could get.And Lionel Barrymore is also in this thing....lots of big names to be such a pile of guano.Other than the terrible mis-title this movie has,the alternate name,"The Blood Sucker" is much better,this movie is also dull and plodding and just silly.For me the high point of the movie is watching Frye,he nails the freaky town weirdo but other than him this movie didn't offer much.And then when you find out the reason for the strange deaths and see the special effect thing that required all this blood you'll really be let down.Bela Lugosi did a lot of awful pictures but at least he was fun and interesting to watch.Think of this movie as a really bad Lugosi clunker WITHOUT Lugosi and you'll get a feel for how miserably bad this mess was.If you can't make a good 1930's horror film at least put Lugosi in it.


The episodic version of Robert Heinlein's Starship Troopers plays out at a deathly slow pace, following Johnny Rico leaving his parents, the (not very attractive) girl he lusts for, and joining the mobile infantry. The aliens in the show are nothing like the barbaric bugs from the film, instead being squid-like monsters that shoot lasers out of their mouths.Throughout watching this version, I was continually amazed at just how fruity they've managed to make the whole thing. The show is concerned mostly with the relationships between the recruits, and the aching, prolonged gazes they give each other through their battle armour visors, with 80s synth pop sometimes arriving *during* the sparse battle sequences which at last turning up in the final few episodes. In terms of construction, it owes a debt to Top Gun, sharing much in terms of pacing and content (and all that implies).


It's such a shame that because of it's title this film will be avoided by people who hate football. Bend it Like Beckham is much more than a cheesy sports flick. The story line is touching and intelligent without being soppy, the jokes were laugh out loud funny, and the characters are well acted. Parminder Nagra and Keira Knightley are brilliant as teenagers Jess and Jules, putting in great performances both on and off the pitch. Anupam Kher is wonderful as Jess' worried father, and Jonathan Rhys-Meyers, who was so amazingly evil in 'Ride with the Devil,' comes across so well as the nice guy for once, making full use of his gorgeous Irish accent! Even if you don't like football, go see this film. If anything it'll make you smile.


I thought this film was just about perfect. The descriptions/summaries you'll read about this movie don't do it justice. The plot just does not sound very interesting, BUT IT IS. Just rent it and you will not be sorry!!


Okay the promos promised a comedy and people(few) went to watch it Being the first release of 2006 is not a bad thing, or for that matter of any year, because the first and last films mostly flop except GHAJINI and some moreOkay coming to JAWANI DIWANIReview in short The film is about Emraan Hashmi doing his usual stuff sadly it's annoying this time after repetitions It has an irritating Hrishita Bhatt and a flop Celina JaitleyCringeworthy dialogues, comedy scenes and badly handled drama and lots of loopholesDirection is bad Emraan Hashmi is annoying here, luckily now he is coming of age But post FOOTPATH and MURDER and some decent work in some more films the actor in him took a backseat and directors focused on his kisses and womaniser image which sadly lost it's touch after repetitions Hrishita and Celina are bad Mahesh is horrible