February 13, 2020
I built a guitar tuner using an electret microphone, an ARM Cortex-M3 processor, and a 128x32 OLED display. For those unfamiliar with this style of guitar tuner, it operates as follows: the user plays a guitar string that they are trying to tune, and the OLED displays tuning instructions (tune up, tune down, or in tune).
Going into this project I had minimal experience programming the STM32F103 board and working with discrete signals. I was not sure how I would detect fundamental frequency from a time domain recording of a guitar string.
I started this project by taking samples from the ADC on the STM32 and sending them over UART to my laptop so I could analyze the signals using Python. Since the highest frequency guitar string is the e string at 330Hz, I had to sample at a frequency of at least 660Hz. I gave myself some cushion and chose to use a 1KHz sampling rate. This also made the timer interrupt setup simple.
The below Python notebook describes how I came up with a way to detect fundamental frequency of a recorded guitar string.
In standard guitar tuning the six notes and their ideal frequencies are as follows:
| Note | Frequency (Hz) |
|---|---|
| E | 82 |
| A | 110 |
| D | 147 |
| G | 196 |
| B | 247 |
| e | 330 |
from helper import *
import numpy as np
import os
WAVEFORM_DIR = 'pickled_waveforms/raw_waveforms'
FFT_DIR = 'pickled_waveforms/frequency_domain_waveforms'
SIGNAL_LENGTH = 256
strings = ['E', 'A', 'D', 'G', 'B', 'e']
%matplotlib inline
plt.rcParams["figure.figsize"] = (20,30)
Import signals captured by the ADC on the STM32. Signals were captured and pickled to easily access at later times.
raw_signals contains 24 signals of 256 float values captured from the microphone sampled at 1KHz by the ADC. Four samples were captured for all 6 guitar strings, hence 4*6=24 samples.
fft_signals also contains 24 signals, with 4 signals for all 6 strings. These signals are frequency domain signals obtained by performing a FFT on the STM32, with DC component and 60Hz components set to 0.
raw_signals = dict()
fft_signals = dict()
for each in strings:
raw_signals[each] = read_pickle_file(os.path.join(WAVEFORM_DIR, ‘guitar%s_256_4.txt’%each))
fft_signals[each] = read_pickle_file(os.path.join(FFT_DIR, ‘fft_%s_256_4.txt’%each))
# freq = np.fft.fftfreq(SIGNAL_LENGTH, 0.001)
Let’s take a look at the frequency domain signals for each guitar string. The ideal fundamental frequency of each guitar string is plotted in red.
fig, a = plt.subplots(6,4)
for row, st in enumerate(fft_signals.keys()):
for i in range(4):
freq = fft_signals[st][i].keys()
mag = fft_signals[st][i].values()
a[row][i].stem(freq, mag, use_line_collection=True)
a[row][i].stem([string_freq[st]], [max(mag)], 'r:', use_line_collection=True)
a[row][i].set_title(st)
a[row][i].grid(which='both')
a[row][i].minorticks_on()
plt.show()
As can be seen, the ideal frequency does not always match the greatest frequency component of the frequency domain signals. Each guitar string seems to have a unique ‘footprint’. For the lower frequency strings, we see a lot of spikes in the frequency domain signal due to harmonics. Predicting a signals fundamental frequency will prove difficult, since harmonics can be much greater in magnitude than the fundamental frequency for a given string.
Take a look at the A string samples. In the second, third, and fourth samples, the fundamental frequency (110Hz) is dwarfed by it’s harmonics. The third harmonic of A (330Hz) is also the fundamental frequency of the high e string. Similarly, the third harmonic of E (247Hz) is also the fundamental frequency of the B string.
At this point I realized that predicting the fundamental frequency of a guitar string recording will take two steps:
To predict the guitar string being played, I had to find a way to classify frequency domain signals. I already had 4 samples of each guitar string in the frequency domain so I used this information to develop an algorithm to classify new signals. I first took 4 samples for each string, and created an average frequency domain signal.
average_fft_signals = dict.fromkeys(fft_signals.keys())
for st in fft_signals.keys():
average_fft_signals[st] = get_average_fft(fft_signals[st])
fig, a = plt.subplots(6,1)
for row, st in enumerate(average_fft_signals.keys()):
freq = fft_signals[st][i].keys()
mag = average_fft_signals[st].values()
a[row].stem(freq, mag, use_line_collection=True)
a[row].stem([string_freq[st]], [max(mag)], 'r:', use_line_collection=True)
a[row].set_title(st+' average FFT')
a[row].grid(which='both')
a[row].minorticks_on()
plt.show()
Once I had these average frequency domain signals for each guitar string, I could normalize them and create unit vectors. I could then perform a dot product between any frequency domain signal and my unit vectors, and the result would give me a measure of how similar the two vectors are.
If I have 256 point frequency domain signal, \(\vec{a}\), and a unit vector, \(\vec{b}\), (since \(\lvert\vec{b}\rvert = 1\)), their dot product is given by: $$ \lvert\vec{a}\rvert \lvert\vec{b}\rvert cos(\theta) = \lvert\vec{a}\rvert cos(\theta) $$ where \(\theta\) is the angle between the two vectors. Increasing \(\theta\) decreases the result of the dot product. Therefore the greatest possible value that can result from this dot product is the case where \(\lvert\vec{a}\rvert\) points in the same same direction as \(\lvert\vec{b}\rvert\).
The code below calculates the 6 unit vectors for each guitar string. To decrease dot product computations, I decided that my unit vectors did not need to take into account all of the frequencies in the frequency domain. Given the symmetry of the signals, I could instantly remove half the signal and still keep all of the information. So I only care about positive frequencies. I also did not care about all frequency bins. My interest is only in frequencies around the ideal guitar string frequencies. For example, my guitar tuner would not expect to hear a 50Hz sound. It would expect to hear sounds that are fairly close to the guitar string frequencies. For this reason my unit vectors represented the FFT coefficients for frequency bins at the following frequencies:
print(frequencies_of_interest)
[74.2188, 78.125, 82.0312, 85.9375, 89.8437, 101.5625, 105.4687, 109.375, 113.2812, 117.1875, 140.625, 144.5312, 148.4375, 152.3438, 156.25, 160.1562, 164.0625, 167.9687, 171.875, 187.5, 191.4062, 195.3125, 199.2187, 203.125, 210.9375, 214.8437, 218.75, 222.6562, 226.5625, 238.2812, 242.1875, 246.0937, 250.0, 253.9062, 285.1562, 289.0625, 292.9688, 296.875, 300.7812, 320.3125, 324.2187, 328.125, 332.0312, 335.9375, 382.8125, 386.7187, 390.625, 394.5312, 398.4375, 402.3437, 406.25, 410.1562, 414.0625, 417.9687, 433.5937, 437.5, 441.4062, 445.3125, 449.2187]
unit_vecs = dict.fromkeys(strings)
for k in unit_vecs.keys():
unit_vecs[k] = get_eigen_fft(fft_signals[k], frequencies_of_interest)
fig, a = plt.subplots(6,1)
for i,k in enumerate(unit_vecs.keys()):
a[i].stem(frequencies_of_interest, unit_vecs[k], use_line_collection=True)
a[i].grid(which='both')
a[i].minorticks_on()
a[i].set_title(k+' unit vector')
plt.show()
It is interesting to see the profile of each guitar string. We can see that the E string has a prominent second harmonic, and the A string has prominent second and third harmonics. I am curious how much variability there would be if I created unit vectors for different guitars or even different guitar string materials (steel vs nylon).
Let us verify how accurately these unit vectors can predict the guitar string being played. First let’s create a matrix to hold our unit vectors as rows.
proj_matrix = np.matrix(list(unit_vecs.values()))
proj_matrix.shape
(6, 59)
print('Actual \t Predicted')
for st in strings:
for i in range(4):
signal = np.array([fft_signals[st][i][f] for f in frequencies_of_interest])
# project signal onto unit vector matrix. Find string corresponding to largest value
prediction = strings[ (proj_matrix @ signal).A.argmax() ]
print(st,'\t', prediction)
Actual Predicted
E E
E E
E E
E E
A A
A A
A A
A A
D D
D D
D D
D D
G G
G G
G G
G G
B B
B B
B B
B B
e e
e e
e e
e e
The unit vector approach yields perfect results when tested against the sampled signals. Since this prediction will be running on the STM32, I decided to minimize the number of floating point operations by taking all of the unit vectors and removing any ‘noise’. I removed all components with a value less than 0.1 . Next I multiplied each value by 100 and rounded to the nearest integer. Although the vectors are no longer unit vectors, they should have similar magnitudes, and we can still make reasonable classifications as we have before.
for k in unit_vecs.keys():
for i, val in enumerate(unit_vecs[k]):
if val < 0.1: unit_vecs[k][i] = 0
else: unit_vecs[k][i] = int(unit_vecs[k][i]*100)
for k in unit_vecs.keys():
print(np.linalg.norm(unit_vecs[k]))
94.71536306217698
95.62949335848225
96.33275663033837
96.96906723280368
93.82430388763883
96.50388593212193
fig, a = plt.subplots(6,1)
for i,k in enumerate(unit_vecs.keys()):
a[i].stem(frequencies_of_interest, unit_vecs[k], use_line_collection=True)
a[i].grid(which='both')
a[i].minorticks_on()
a[i].set_title(k+' unit vector')
plt.show()
After making a prediction about which string is being played, I could then look at the frequency domain signal around the frequency of interest to make an estimate of the fundamental frequency. I did this by taking a weighted average of FFT coefficients around the frequency of interest. The plot below shows a spectrograph of an e string, and in red are the frequency bins used to calculate the weighted average. The e string has an ideal frequency of 330 Hz, and the weighted average predicts the signal to have a fundamental frequency of 329.57 Hz.
freq = list(fft_signals[‘e’][0].keys())
mag = list(fft_signals[‘e’][0].values())
plt.stem(freq[:128], mag[:128], use_line_collection=True)
i = freq.index(328.125)
plt.stem(freq[i-1:i+3], mag[i-1:i+3], ‘r’, use_line_collection=True)
plt.title(‘e FFT’)
plt.grid()
plt.gcf().set_size_inches(20,6)
plt.show()
print('%.2f Hz'%weighted_average(freq[i-1:i+3], mag[i-1:i+3] ))
329.57 Hz
The analysis shown in this notebook is the algorithm I used to take a 256 point frequency domain signal of a recorded guitar string and predict the fundamental frequency. This is the algorithm that is running on the STM32, with the unit vectors obtained by this analysis.
Not displayed are the other approaches I took that did not work well. I first tried a naive approach where I predicted the fundamental frequency to be the greatest component in the frequency domain signal. This failed due to harmonics. The other approach was to use autocorrelation. This also failed since it was difficult to distinguish between the A and e string, as well as the E and B string. This is because their frequencies are multiples of each other ( A -> 110 Hz, e->330 Hz ) and I had no way of distinguishing between these strings effectively.
Once I had the unit vectors and the algorithm working in Python, I then had to implement that on the STM32.
In summary the STM32 uses several peripherals. A timer is used to generate interrupts at 1KHz. The interrupt service routine reads converted data from the ADC peripheral and fills a buffer that can hold 256 float values. SPI is used to drive the OLED display.
In main()
, we setup all peripherals, initialize the OLED display and then enter a while
loop. The while
loop constantly checks if the buffer is filled with values. If the buffer is full, we disable the timer interrupt, perform an FFT, predict the guitar string being played, estimate the fundamental frequency, fill the OLED display buffer with proper tuning instructions, write to the OLED and re-enable the timer interrupt.
For full code see github
while(1)
{
Delay(10);
if(buffer_count == ADC_BUFF_SIZE) // Check if adc_buffer is filled with values
{
TIM_ITConfig(TIM2, TIM_IT_CC1, DISABLE); // Disable TIM2 interrupt
fft(fft_samples, ADC_BUFF_SIZE, temp);
get_magnitudes(mag, fft_samples, ADC_BUFF_SIZE);
filter_frequencies(mag, freq, 0.0, 4.0, ADC_BUFF_SIZE);
filter_frequencies(mag, freq, 60.0, 5.0, ADC_BUFF_SIZE);
// Dot product of unit_vecs with signal fft
int i;
float dp[6] = {0};
for(i=0; i<UNIT_VEC_LENGTH; i++)
{
float f = eigen_frequencies[i];
int index = freq_to_index(f, 1000, ADC_BUFF_SIZE);
dp[E] += E_fft_unit_vec[i] * mag[index];
dp[A] += A_fft_unit_vec[i] * mag[index];
dp[D] += D_fft_unit_vec[i] * mag[index];
dp[G] += G_fft_unit_vec[i] * mag[index];
dp[B] += B_fft_unit_vec[i] * mag[index];
dp[e] += e_fft_unit_vec[i] * mag[index];
}
// find max string
float max_val = 0.0;
int max_index;
for(i=0; i<6; i++)
{
if(dp[i] > max_val)
{
max_val = dp[i];
max_index = i;
}
}
if(UART_DEBUG)
{
char buffer[20];
/*
// Send string prediction over uart
switch(max_index)
{
case E: sprintf(buffer, "String: %s \n\r", "E");
break;
case A: sprintf(buffer, "String: %s \n\r", "A");
break;
case D: sprintf(buffer, "String: %s \n\r", "D");
break;
case G: sprintf(buffer, "String: %s \n\r", "G");
break;
case B: sprintf(buffer, "String: %s \n\r", "B");
break;
case e: sprintf(buffer, "String: %s \n\r", "e");
break;
}
uart_puts(buffer, USART1);
*/
float estimated_freq = estimate_frequency(mag, freq, max_index);
if(max_index==E) estimated_freq /= 2;
if(max_index==A) estimated_freq /= 3;
sprintf(buffer, "%.4f\n\r", estimated_freq);
uart_puts(buffer, USART1);
}
last4[temp_index] = estimate_frequency(mag, freq, max_index);
// float estimated_freq = estimate_frequency(mag, freq, max_index);
float estimated_freq = last4_average(last4, temp_index);
temp_index = (temp_index+1)%4;
float difference;
switch(max_index)
{
// case E: difference = estimated_freq - E_freq;
case E: difference = estimated_freq/2 - E_freq;
break;
case A: difference = estimated_freq/3 - A_freq;
break;
case D: difference = estimated_freq - d_freq;
break;
case G: difference = estimated_freq - G_freq;
break;
case B: difference = estimated_freq - B_freq;
break;
case e: difference = estimated_freq - e_freq;
break;
}
uint8_t display_buff[NUM_BYTES];
tuning_instruction(difference, display_buff, max_index);
ssd1306_display_buffer(display_buff);
Delay(20);
buffer_count = 0;
TIM_ITConfig(TIM2, TIM_IT_CC1, ENABLE);
// break;
}
}


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完美解决多行文本溢出显示省略号的问题完美解决多行文本溢出显示省略号的问题完美解决多行文本溢出显示省略号的问题完美解决多行文本溢出显示省略号的问题完美解决多行文本溢出显示省略号的问题完美解决多行文本溢出显示省略号的问题
From the golden-tipped fields of mid-west America to the ancient kingdoms of verdant Palestine, there is a happy truth to be shared with all who would take heed. In more recent times, this truth has been expressed as: April showers bring May flowers. This is a truth that promises light bursting from darkness, strength born from weakness and, if one dares to believe, life emerging from death.
From 完美解决多行文本溢出显示省略号的问题the 完美解决多行文本溢出显示省略号的问题golden-tipped fields of mid-west America to the ancient kingdoms of verdant Palestine, there is a happy truth to be 完美解决多行文本溢出显示省略号的问题shared with all who would take heed. In more recent times, this truth has been expressed as: April showers br完美解决多行文本溢出显示省略号的问题ing May flowers. This is a truth that promises light bursting from darkness, strength born 完美解决多行文本溢出显示省略号的问题 from weakness and, if one dares to believe, life emerging from death.


GOLDEN HARVEST PRESENTS A BOB & PARTNERS COMPANY LTD. PRODUCTION IN ASSOCIATION WITH CENTRO DIGITAL PICTURES LTD. AN ANDREW LAU FILM AARON KWOK EKIN CHENG SONNY CHIBA "THE STORM RIDERS" CHRISTIE YEUNG SHU CHI MICHAEL TSE LAI YU-HUNG SPECIAL APPEARANCE BY ANTHONY WONG PRODUCED BY RAYMOND CHOW & MANFRED WONG EXECUTIVE PRODUCER LIU FUNG PING & JOHN CHU CO-PRODUCER JESSINTA LIU DIRECTOR OF PHOTOGRAPHY ANDREW LAU (HKSC) VISUAL EFFECTS PRODUCED BY CENTRO DIGITAL PICTURES LTD. BASED ON THE ORIGINAL STORIES BY MA WING SHING SCREENPLAY BY MANFRED WONG DIRECTED BY ANDREW LAU
"When the Wind and Cloud combine, the fish with Golden Scales will become a Dragon." Intrigue, magic, spectacle, and mystery are spelled out by that tantalizing prophecy which opens "THE STORM RIDERS" one of the recent arrivals from Hong Kong. A marvelously inventive, visually stunning, and perplexing tale of conquest and revenge.
The film opens with the arrival of the prophet Mud Buddha (Lai Yu-hung) to the mountaintop palace of the Tin Ha Sect. It is there that Lord Conquer Hung Ba (Sonny Chiba) receives the prophecy and the means with which to ensure his mastery of the martial world. Using birth charts, Lord Conquer raids the villages in an effort to locate the two children whose names are Wind and Cloud. After killing their families, Lord Conquer makes apostles of the children making them part of his inner circle along with his own son Frost.
As the children grow up into men, the Tin Ha Sect has decimated most of China, plundering its riches and people. Lord Conquer proves to be an unholy and evil man, interested only in his fame and wealth. Having collected the swords from all of his enemies except for the famed Ultimate Sword created by master swordmaker, Nip Yan-wong (Yu Wong-Guang), Cloud's father. Now conflict comes as the two adopted boys both fall in love with Lord Conquer's Daughter, Charity.
After ten years of success, Lord Conquer grows anxious. Wanting to know what his future holds, he orders his three warriors to find Mud Buddha, to force the seer to reveal the final part of his prophecy. When he learns that Wind and Cloud will bring about the downfall his empire, the evil Lord conspires to turn the two men against one another, by forcing his daughter, Charity to marry one of them.
Director Andrew Lau has constructed a marvelously striking fantasy in 'THE STORM RIDERS' (Cantonese Title: 'Feng yun' ). What makes the film perplexing is simple that some of the Chinese inter-titles are not translated, making the relationship between some of the characters difficult to identify. This problem aside, the film is remarkably entertaining and engrossing. Lau, serving as his own Cinematographer, frames every scene for maximum emotional impact. There are scenes here of extraordinary composition and beauty. Add to that the spectacularly vivid computer generated effects, and the film takes on the epic quality of a biblical drama.
The framing of the scenes are based on the panels of a comic or graphic novel. Camera angles are rarely straight ahead. The film, while violent and action packed is never grotesque. Scenes of violence have a joyous roller coaster ride vitality to them, incorporating some marvelously inventive effects, both visual and physical. There are scenes here of jaw dropping inventiveness and beauty. The Wow! Factor is in great abundance. While the choreography of the martial arts on display are in the traditional method of Hong Kong filmmaking, the addition of the fantasy element and the visual effects, display a marvelous joy and verve. The only western comparison that comes to mind is Director John Carpenter's unfairly maligned "Big Trouble in little China"(1986).
The plotting of the film is troublesome. The story, being base on a series of popular comics in Hong Kong is understandably episodic. While it is certain that most Hong Kong viewers will understand and recognize all of these characters, Westerners may have a hard time pulling all of these intricacies together. Nevertheless, "The Storm Riders" still entertains simple on it's own verve and sense of wonder.
The performances are all two dimensional, which is to be expected in such a fairy tale. The sole false note comes from lead Arron Kwok as Cloud is much too brooding, coming off as a petulant child rather than a man bent on revenge. Nevertheless, the performance does give the film a needed center and focus, as it is Cloud's determination to one day take revenge against his master for the death of his father.
The standout performance of the film is without a doubt the re-doubtable Sonny Chiba. His turn as Lord Conquer is a fine, scene-stealing tip of the hat to Toshiro Mufune.  Best known for his 'Streetfighter' series of films (when released in the U.S. in 1974, the film was summarily given an X rating by the Motion Picture Ratings Board). Mr. Chiba pulls out all of the classic stops in giving Lord Conquer a full degree of madness and ego.
The sound design of the film is equally stunning. Recorded in sync sound (recorded live) in the native Hong Kong dialect of Cantonese, 'The Storm Riders' has one of the most luscious soundtracks recorded. The sound effects are stunning and deafening. If you get a chance to see this film on the big screen, do so. The second best option is the vibrant DVD released earlier this year by Universe Laser & Video (a Hong Kong video company).
In closing, with Hong Kong's film industry in danger of being devoured by the video pirates, it's a shame that films such as 'The Storm Riders' may never be made again. I can only hope that this does not happen. Made on a meager budget of $10 million dollars (U.S. equivalent), the film looks and feels like it cost six times that much.
On a scale of 1 to 5, "THE STORM RIDERS" rates a raging 4. Grand entertainment for those adventurous enough to seek it out. The film has no MPAA rating, but should be considered a PG-13. Cantonese language with English subtitles. 128 minutes running time. Panavision (scope-wide screen).
Copyright 1999 R. L Strong All rights reserved Nothing in this article may be quoted or re-printed without the express written permission of the author.
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{% extends theme("layout.html") %} {% import theme("parts/facetile.html") as facetile %} {% block title %}Love Sent{% endblock %} {% block body %}
You can use the link below to have others join this lovefest!


A Tree Datatype
A tree with data at the leaves
Here’s an example Tree Char
Lets Work it Out!
Write a function to add a distinct label to each leaf
such that
Labeling a Tree
label :: Tree a -> Tree (a, Int)
label t = t'
where
(_, t') = (helper 0 t)
helper :: Int -> (Int, Tree (a, Int))
helper n (Leaf x) = (n+1, Leaf (x, n))
helper n (Node l r) = (n'', Node l' r')
where
(n', l') = helper n l
(n'', r') = helper n' r
EXERCISE
Now, modify label
so that you get new numbers for each letter
so,
>>> keyLabel (Node (Node (Leaf 'a') (Leaf 'b')) (Node (Leaf 'c') (Leaf 'a')))
(Node
(Node (Leaf ('a', 0)) (Leaf ('b', 0)))
(Node (Leaf ('c', 0)) (Leaf ('a', 1))))
That is, a separate counter for each key a
, b
, c
etc.
HINT Use the following Map k v
type
-- | The empty Map
empty :: Map k v
-- | 'insert key val m` returns a new map that extends 'm'
-- by setting `key` to `val`
insert :: k -> v -> Map k v -> Map k v
-- | 'findWithDefault def key m' returns the value of `key`
-- in `m` or `def` if `key` is not defined
findWithDefault :: v -> k -> Map k v -> v
Common Pattern?
Both the functions have a common “shape”
If we generally think of Int
and Map Char Int
as global state
State Transformers
Lets capture the above “pattern” as a type
- A State Type
- A State Transformer Type
A state transformer is a function that
- takes as input an old
s :: State
- returns as output a new
s' :: State
and valuev :: a
Executing Transformers
Lets write a function to evaluate an ST a
QUIZ
What is the value of quiz
?
A. 103
B. [100, 101, 102]
C. (103, [100, 101, 102])
D. [0, 1, 2]
E. Type error
Lets Make State Transformer a Monad!
instance Monad ST where
return :: a -> ST a
return = returnST
(>>=) :: ST a -> (a -> ST b) -> ST b
(>>=) = bindST
EXERCISE: Implement returnST
!
What is a valid implementation of returnST
?
What is returnST
doing ?
returnST v
is a state transformer that … ???
(Can someone suggest an explanation in English?)
HELP
Now, lets implement bindST
!
type State = Int
data ST a = STC (State -> (State, a))
bindST :: ST a -> (a -> ST b) -> ST b
bindST = ???
What is returnST
doing ?
returnST v
is a state transformer that … ???
(Can someone suggest an explanation in English?)
What is returnST
doing ?
returnST v
is a state transformer that … ???
(Can someone suggest an explanation in English?)
bindST
lets us sequence state transformers
(>>=) :: ST0 a -> (a -> ST0 b) -> ST0 b
sta >>= f = STC (\s ->
let (s', va) = runState sta s
stb = f va
(s'', vb) = runState stb s'
in
(s'', vb)
)
st >>= f
- Applies transformer
st
to an initial states
- to get output
s'
and valueva
- to get output
- Then applies function
f
to the resulting valueva
- to get a second transformer
- The second transformer is applied to
s'
- to get final
s''
and valuevb
- to get final
OVERALL: Transform s
to s''
and produce value vb
Lets Implement a Global Counter
The (counter) State
is an Int
A function that increments the counter to return the next
Int
.
next
is a state transformer that that returns String
values
QUIZ
Recall that
evalState :: State -> ST a -> a
evalState s (STC st) = snd (st s)
next :: ST String
next = STC (\s -> (s+1, show s))
What does quiz
evaluate to?
A. "100"
B. "101"
C. "0"
D. "1"
E. (101, "100")
QUIZ
Recall the definitions
evalState :: State -> ST a -> a
evalState s (STC st) = snd (st s)
next :: ST String
next = STC (\s -> (s+1, show s))
Now suppose we have
What does quiz
evaluate to?
A. 100
B. 101
C. 0
D. 1
E. (101, 100)
Example
Riyadh Moosa
Example
QUIZ
Consider a function wtf2
defined as
What does quiz
evaluate to?
A. Type Error!
B. [100, 100, 100]
C. [0, 0, 0]
D. [100, 101, 102]
E. [102, 102, 102]
Chaining Transformers
>>=
lets us chain transformers into one big transformer!
So we can define a function to increment the counter by 3
-- Increment the counter by 3
next3 :: ST [Int, Int]
next3 = next >>= \n1 ->
next >>= \n2 ->
next >>= \n3 ->
return [n1,n2,n3]
And then sequence it twice to get
Lets do
the above examples
Remember, do
is just nice syntax for the above!
-- Increment the counter by 3
next3 :: ST [Int, Int]
next3 = do
n1 <- next
n2 <- next
n3 <- next
return [n1,n2,n3]
And then sequence it twice to get
Labeling a Tree with a “Global Counter”
Lets rewrite our Tree
labeler with ST
Wow, compare to the old code!
helper :: Int -> (Int, Tree (a, Int))
helper n (Leaf x) = (n+1, Leaf (x, n))
helper n (Node l r) = (n'', Node l' r')
where
(n', l') = helper n l
(n'', r') = helper n' r
Avoid worrying about propagating the “right” counters
- Automatically handled by
ST
monad instance!
Executing the Transformer
In the old code we called the helper with an initial counter 0
In the new code what should we do?
helperS :: Tree a -> ST (Tree (a, Int))
helperS = ...
labelS :: Tree a -> Tree (a, Int)
labelS = ???
Now, we should be able to exec
the labelS
transformer
>>> labelS (Node (Node (Leaf 'a') (Leaf 'b')) (Leaf 'c'))
(Node (Node (Leaf ('a', 0)) (Leaf ('b', 1))) (Leaf ('c', 2)))
How to implement keyLabel
?
So far, we hardwired an Int
counter as our State
Have to reimplement the monad if we want a different state?
- e.g.
Map Char Int
to implementkeyLabel
Don’t Repeat Yourself!
A Generic State Transformer
Don’t have separate types for IntList
and CharList
Define a generic list
[a]
wherea
is a type parameterInstantiate
a
to get[Int]
and[Char]
Similarly, reuse ST
with a type parameter!
- State is represented by type
s
- Return Value is the type
a
(as before).
A Generic State Transformer Monad
Lets make the above a(n instance of) Monad
instance Monad (ST s) where
-- return :: a -> ST s a
return val = ST0C (\s -> (s, val))
-- (>>=) :: ST s a -> (a -> ST s b) -> ST s b
(>>=) sta f = ST0C (\s ->
let (s', va) = runState sta s
stb = f va
(s'', vb) = runState stb s'
in
(s'', vb)
)
runState :: ST s a -> s -> (s, a)
runState (STC f) s = f s
evalState :: ST s a -> s -> a
evalState st s = snd (runState st s)
(exactly the same code as returnST
and bindST
)
Lets implement keyLabel
- Define a
Map Char Int
state-transformer
- Modify
next
to take aChar
charNext :: Char -> CharST Int
charNext c = STC (\m ->
let
n = M.findWithDefault 0 c m -- label for 'c'
m' = M.insert c (n+1) m -- update map
in
(m', n)
)
- Modify
helper
to usecharNext
keyHelperS :: Tree Char -> ST (Tree (Char, Int))
keyHelperS (Leaf c) = do
n <- charNext c
return (Leaf (c, n))
keyHelperS (Node l r) = do
l' <- keyHelperS l
r' <- keyHelperS r
return (Tree l' r')
keyLabelS :: Tree Char -> Tree (Char, Int)
keyLabelS t = evalState (keyHelperS t) empty
Lets make sure it works!
>>> keyLabelS charT
Node
(Node (Leaf ('a', 0)) (Leaf ('b', 0)))
(Node (Leaf ('c', 0)) (Leaf ('a', 1)))
Lets look at the final “state”
The returned Tree
is
and the final State
is
Generically Getting and Setting State
As State is “generic”
- i.e. a type variable not
Int
orMap Char Int
or …
It will be convenient to have “generic” get
and put
functions
- that read and update the state
-- | `get` leaves state unchanged & returns it as value
get :: ST s s
-- | `set s` changes the state to `s` & returns () as a value
put :: s -> ST s ()
EXERCISE
Can you fill in the implementations of get
and set
?
HINT Just follow the types…
-- | `get` leaves state unchanged & returns it as value
get :: ST s s
get = STC (\oldState -> ???)
-- | `put s` changes the state to `s` & returns () as a value
put :: s -> ST s ()
put s = STC (\oldState -> ???)
Using get
and put
: Global Counter
We can now implement the plain global counter next
as
next :: ST Int Int
next = do
n <- get -- save the current counter as 'n'
put (n+1) -- update the counter to 'n+1'
return n -- return the old counter
Using get
and put
: Frequency Map
Lets implement the char-frequency counter charNext
as
charNext :: Char -> ST (Map Char Int) Int
charNext c = do
m <- get -- get current freq-map
let n = M.findWithDefault 0 c m -- current freq for c (or 0)
put (M.insert c (n+1) m) -- update freq for c
return n -- return current as value
A State-Transformer Library
The Control.Monad.State
module
defines a State-Transformer like above.
hides the implementation of the transformer
Clients can only use the “public” API
-- | Like 'ST s a' but "private", cannot be directly accessed
data State s a
-- | Like the synonyms described above
get :: State s s
put :: s -> State s ()
runState :: State s a -> s -> (a, s)
evalState :: State s a -> s -> a
Your homework will give you practice with using these
- to do imperative functional programming
The IO Monad
Remember the IO a
or Recipe a
type from this lecture
- Recipes that return a result of type
a
- But may also perform some input/output
A number of primitives are provided for building IO
recipes
Basic actions that can be “chained” via >>=
etc.
A Recipe to Read a Line from the Keyboard
getLine :: IO String
getLine = do
x <- getChar
if x == '\n' then
return []
else do
xs <- getLine
return (x:xs)
IO is a “special case” of the State-Transformer
The internal state is a representation of the state of the world
A Recipe
is a function that
- takes the current
World
as its argument - returns a value
a
and a modifiedWorld
The modified World
reflects any input/output done by the Recipe
This is just for understanding, GHC implements IO
more efficiently!


It is desirable to declare standard libraries available on a given system. Putting target declaration in Jamfile is not really good, since locations of the libraries can vary. The solution is to declare the targets in site-config.jam:
project site-config ; lib zlib : : <name>z ;
Recall that both site-config.jam
and
user-config.jam
are projects, and everything
you can do in a Jamfile you can do in those files. So, you declare
a project id and a target. Now, one can write:
exe hello : hello.cpp /site-config//zlib ;
in any Jamfile.


{% for block in page.body %}
{{ block }}
{% endfor %}
{% include "includes/sidebar_links_and_roles.html" %}
{% include "includes/sidebar_twitter_feed.html" %}
{% if projects and projects.exists %}
{% include "includes/projects_section.html" with section_title=projects_title projects=projects %}
{% endif %}
{% endblock %}


@exceptions
| Exception | Condition |
|---|---|
| ArgumentException |
An element with the specified Key already exists in the SortedList object. -or- The comparer throws an exception. |
| NotSupportedException |
The SortedList is read-only. -or- The SortedList has a fixed size. |
Public Sub Main() Dim List As New SortedList List.Add "one", "The" List.Add "two", "quick" List.Add "three", "brown" List.Add "four", "fox" Debug.Print "The SortedList contains the following sorted by key:" PrintKeysAndValues List End Sub Private Sub PrintKeysAndValues(ByVal List As SortedList) Dim i As Long Debug.Print "-KEY-", "-VALUE-" For i = 0 To List.Count - 1 Debug.Print List.GetKey(i), List.GetByIndex(i) Next Debug.Print End Sub ' This code produces the following output. ' ' The SortedList contains the following: ' -KEY- -VALUE- ' four fox ' one The ' three brown ' two quick


Yes.
JPanel
The way to layout components in a GUI is:
A JPanel
is a container
that is used to group components.
Components added to a panel become inseparable.
Graphically a panel corresponds to a rectangular
section of the screen.
Each panel has its own layout manager
that lays out the components inside the panel.
The default layout manager of JPanel
is
flow layout, which is often ideal.
But you may add whichever layout manager is most appropriate.
Now the panels are added to a frame. The content pane's layout manager arranges the panels as if each one were a single component. Panels do not have visible edges.


C
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Car
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Incentive System To Increas Sales
Find more than one store within mamon app that provides you with various discounts throughout the week
Find more than one store within mamon app that provides you with various discounts throughout the week
mamnon provides a special control panel through wich it registers the custumers covered by the incentive icheme or adds additinal points to the cusrumers
merchant can display his prodects and pictures and control through the control panel to suit his commerical offers
mammon is distiguished by cornbining acard and an application at the same time
mamnon provides a special control panel through wich it registers the custumers covered by the incentive icheme or adds additinal points to the cusrumers
merchant can display his prodects and pictures and control through the control panel to suit his commerical offers
mammon is distiguished by cornbining acard and an application at the same time
find out more than one store within the mamnon app that provides you with various discounts throughout the week
gather with mamnon points with every visit and purchases from the clearer get free purchases or discounts, and many other offers
download the app and find out the latest discounts and incentives that fit your purchases,and many other offers
it's very easy to get in touch with contact form
Iraq,Baghdad
phone:+964<PHONE_NUMBER>
phone: +961 81 086 338
Email: info@mamnon.
-mea.com


#include <mrFastMath.h>
Static Public Member Functions | |
| T | sin0 (const T x) |
| T | sin1 (const T x) |
| T | cos0 (const T x) |
| T | cos1 (const T x) |
| T | tan0 (const T x) |
| T | tan1 (const T x) |
| T | asin (const T x) |
| T | acos (const T x) |
| T | atan0 (const T x) |
| T | atan1 (const T x) |
| T | invsqrt (T x) |
| A fast approximation to 1/sqrt. | |
| int | floor (T x) |
| A fast and quite accurate approximation to floor(x) or int(float). | |
| int | ceil (T x) |
| A fast and quite accurate approximation to ceil(x). |
|
|
Fast evaluation of acos(value) by a sqrt times a polynomial. The value must be in [0,1]. The maximum absolute error is about 6.8e-05 and the speed up is about 2.5. |
|
|
Fast evaluation of asin(value) by a sqrt times a polynomial. The value must be in [0,1]. The maximum absolute error is about 6.8e-05 and the speed up is about 2.5. |
|
|
Fast evaluation of atan(value) by polynomial approximations. The value must be in [-1,1]. The maximum absolute error is about 1.2e-05 when templated as doubles. The speed up is about 2.2. |
|
|
Fast evaluation of atan(value) by polynomial approximations. The value must be in [-1,1]. The maximum absolute error is about 1.43-08 when templated as doubles. The speed up is about 1.5. |
|
|
A fast and quite accurate approximation to ceil(x).
|
|
|
Fast evaluation of cos(angle) by polynomial approximations. The angle must be in [0,pi/2]. The maximum absolute error is about 1.2e-03 and the speed up is about 2 when templated as doubles. |
|
|
Fast evaluation of cos(angle) by polynomial approximations. The angle must be in [0,pi/2]. The maximum absolute error is about 2.3e-09 and the speed up is about 1.5 when templated as doubles. |
|
|
A fast and quite accurate approximation to floor(x) or int(float).
|
|
|
A fast approximation to 1/sqrt. This invsqrt() is attributed to Quake. It was published in comp.graphics.algorithms and now David Eberly keeps a copy in his Magic-Software web site. Comparing it to the Apple invsqrt() and those from Gems, I found this one to be a tad faster, and use no memory. Input; x > 0. Else routine does not work. This routine should be 10 to 20 times faster than using 1.0/sqrt(x). See Dave Eberly's notes at www.magic-software.com |
|
|
Fast evaluation of sin(angle) by polynomial approximations. The angle must be in [0,pi/2]. The maximum absolute error is about 1.7e-04 and speed up is about 2 when templated as doubles. |
|
|
Fast evaluation of sin(angle) by polynomial approximations. The angle must be in [0,pi/2]. The maximum absolute error is about 2.3e-09 and speed up is about 1.5 when templated as doubles. |
|
|
Fast evaluation of tan(angle) by polynomial approximations. The angle must be in [0,pi/4]. The maximum absolute error is about 8.1e-04 for when templated as doubles. The speed up is about 2.5. |
|
|
Fast evaluation of tan(angle) by polynomial approximations. The angle must be in [0,pi/4]. The maximum absolute error is about 1.9e-08 for tan1 when templated as doubles and The speed up is about 1.75. |


300+ Flat Color Icons by
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Urea Citrate
Cocrystal of urea and citric acid in 1:1 molar ration. Tentative formula: CO(NH2)2·C3H4(OH)(COOH)3 (can contain unknown amount of water).
Properties
- Crystal system: I have no information. From the crystal shape, I can suppose that it is orthorombic.
- Crystal shape: columnar aggregates, sticks.
- Stability on air: stable.
Preparation
Dissolve urea (CO(NH2)2) and citric acid (C3H4(OH)(COOH)3·H2O) in 1:1 molar ration in water with mild heating. Mass of water should be a bit less than total mass of the compounds. Then let the solution cool. If is it not crystallizing, evaporate small drop of the solution on a glass, then use formed crystals as seeds.
Growing
Fast growing produces white amorphous mass of small needles. To grow bigger, translucent crystals of defined shape, evaporation must be extremely slow (months).
Recrystallize the compound once before growing, this also improves quality of crystals.
Solubility: I have no exact informatio, somewhere between 50 and 100 g per 100ml.
Safety
This compound is generally safe.


{% extends "templates/base.tmpl" %} {% block pagetitle %}Ocata Installation Guides{% endblock %} {% block title %} Documentation > Ocata Installation Guides {% endblock %} {% block content %}
The following guides support basic installation of core and optional OpenStack services on variants of Red Hat, SUSE, and Ubuntu distributions. A minimal deployment necessary to launch a simple instance (VM) requires following the core content. Optional content augments a minimal deployment with additional services.
Documentation treated like code, powered by the community - interested?
How To Contribute


The Devil Wears Prada
A young, ambitious journalist makes her career start
in NYC, navigating the world of high fashion as the new
assistant of Runway's famous editor-in-chief
Doctor Strange
How an arrogant doctor who lost his ability to heal
unlocked his superhero potential
Infernal Affairs
An undercover cop serving as intelligence for Hong
Kong's Narcotics Bureau goes against an undercover
triad memeber serving as counter intelligence as an NB officer
Stranger than Fiction
An average IRS agent Harold Crick finds himself in
a narrated, parallel reality of novelist Kay's latest
fictional work
Easy A
A high school girl finds herself in the middle of
a scandal after starting a rumor to help out a friend
Good Will Hunting
A gifted mind of a young janitor Will Hunting is given
the chance to exercise his genius and to truly find himself
along the way


Home
A1: a/an
A2: adjectives
B1: words1
B2: words2
C1: adjectives2
C2: adjectives3
Logout
Translate these words to Dutch language
1. name
2. age
3. family
4. brother
5. sister
6. dog
7. cat
8. library
9. book
10. farm


A Bootstrap 4 One Page Theme
Shield Theme
Shield Theme is powered by Bootstrap 3. The incredible Mobile First Framework is the best option to run your website.
You can use this theme with your iPhone, iPad or MacBook Pro. This theme is retina ready and that is awesome.
We don't make sites, we craft themes with love and passion. That is our most valued secret. We only do thing that we love.
We believe ideas come from everyone, everywhere. In fact, at BlackTie, everyone within our agency walls is a designer in their own right. And there are a few principles we believe—and we believe everyone should believe—about our design craft. These truths drive us, motivate us, and ultimately help us redefine the power of design. We’re big believers in doing right by our neighbors. After all, we grew up in the Twin Cities and we believe this place has much to offer. So we do what we can to support the community we love.
Over the past four years, we’ve provided more than $1 million in combined cash and pro bono support to Way to Grow, an early childhood education and nonprofit organization. Other community giving involvement throughout our agency history includes pro bono work for more than 13 organizations, direct giving, a scholarship program through the Minneapolis College of Art & Design, board memberships, and ongoing participation in the Keystone Club, which gives five percent of our company’s earnings back to the community each year.
To achieve real change, we have to expand boundaries. Because the
Wild West of what-could-be is unexplored but rife with opportunity.
Employees and consumers. Two halves of a brand’s entirety, the whole of a brand’s audience. Sometimes these two halves have very different viewpoints, creating a weak spot in the brand story. Weakness tarnishes credibility. Brands that aren’t credible aren’t viable.
We squash weakness by designing the whole brand story. It’s crafted around the truism held by employees and consumers to create an experience that connects from the inside out.
By being true to the brand we represent, we elevate the audiences’ relationship to it. Like becomes love becomes a passion. Passion becomes advocacy. And we see the brand blossom from within, creating a whole story the audience embraces. That’s when the brand can truly flex its muscles.
To develop a deeper and more meaningful connection with consumers, we believe design must invite them to take part in the conversation.
Mike combines an expert technical knowledge with a real eye for design. Working with clients from a wide range of industries, he fully understands client objectives when working on a project, large or small.
Tim is an experienced marcoms practitioner and manages projects from inception to delivery. He understands the synergy between great design and commercial effectiveness which shines through on every project.
Be a creative director is a hard task, but Michele loves what she does. Her combination of knowledge and expertise is an important pillar in our agency.
Jaye began making websites when animated logos and scrolling text were cool, but has since found a love for simplicity, creating websites that are a pleasure to browse. Monkey Island Fan.
Do you want to be one of use?  Here we work hard every day to craft pixel perfect sites.
From the purely practical to the richly philosophical, design is the solution to a host of challenges.
There’s more to design than meets the eye. It’s when it meets the heart that design creates a meaningful, lasting connection with the audience.
Some Address 987, NY
+34 9884 4893
+34 59855 9853
<EMAIL_ADDRESS><EMAIL_ADDRESS>VAIBHAV ACHARYA
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.


HOME
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Increasingly, musculoskeletal models are being used with optimization tools to answer a variety of research questions. When these optimization problems require forward simulation of the models it is helpful if the simulations execute as fast as possible to reduce the time to obtain results, because optimization methods can require millions of distinct simulations and forward simulations of musculoskeletal models may even still require real time simulation times.
Many of the optimization methods used today wrap simulations based on generic physics engines which solve for the equations of motion at each time step. These physics engines are optimized for speedy forward simulation execution in a variety of ways, but because of their generic-ness they are unlikely optimized for the particular model. Many early prominent mutlibody dynamics codes, and some modern ones, use computer algebra systems (CAS) to symbolically derive long form analytic expressions for the equations of motion of the system. This allows for very high level mathematical descriptors of the equations of motion that can embody assumptions about the mathematical variables and expressions. With this high level description of the math it is possible to generate model and computer architecture specific low level codes through tailored code generation and compilation. This process has the potential to give significant speedups relative to the generic physics engines.
The hypothesis is that code generated from the symbolic forms can be significantly faster than a numeric based system. There has been work over the years in the SymPy and PyDy code generation tools that can be applied to musculoskeletal models.
Possible goals related to this topic (there is enough for multiple MSc's depending on the path):
- Improve SymPy's code generation facilities for arbitrary ordinary differential equations and differential algebraic equations that considers high level mathematical assumptions for optimizing the generated code.
- Create one or more benchmark models based on complex and typically slow-to-simulate biomechanics models using different software and compare execution times.
- Compare optimized forward simulations of different musculoskeletal simulation tools to tailored code generation from symbolic forms.
- Improve the Autolev to SymPy parser and get it working smoothly with complex Autolev models: https://docs.sympy.org/latest/modules/physics/mechanics/autolev_parser.html
- Implement the Featherstone, Jain, or similar equation of motion method in sympy.physics.mechanics, symbolically or as numeric code generation pattern.
- Implement code generation for typical models that slow simulations: stiffness, friction, switching, non-linearities, contact, muscle models, etc.
- Explore applicable parallelization techniques and apply to a complex muscle skeletal simulation.
Prior Art
Mailing list and Github discussions in the SymPy and PyDy projects will be useful.
Some symbolic multibody dynamics codes:
- Auto/Bike/Truck/CarSim (derived from Michael Sayer's work at University of Michigan), https://www.carsim.com/, http://www.umtri.umich.edu/our-results/publications/symbolic-computer-language-multibody-systems
- Autolev (https://link.springer.com/chapter/10.1007/978-3-642-50995-7_7)
- MotionGenesis (http://www.motiongenesis.com/)
- SD/Fast (https://support.ptc.com/support/sdfast/index.html)
- symy.physics.mechanics (https://docs.sympy.org/latest/modules/physics/mechanics/index.html)
Numeric mulitbody dynamics codes:
Papers that may be useful to this project in our Zotero group: https://www.zotero.org/groups/966974/mechmotum/collections/GB9UR7YK
How to Apply
Send an email to<EMAIL_ADDRESS>with the title of the project in the subject line. Include an approximately half-page motivation letter explaining why you want to work in the Bicycle Lab on this project along with your current resume or C.V.


More than 4,500 companies will be exhibiting across more than 2.9 million square feet at CES2020.
Many CES displays entail large sound systems, displays and sometimes even rides. For example, for CES2019 Google’s booth featured a 3-minute ride showing how Google Assistant could be used for everyday tasks.
Some booths are smaller than others, but CTA spokesman Justin Siraj said the standard size is 10 feet by 10 feet, costing exhibitors $4,500 last year. CES longtimers get first dibs at choosing booth space based on a seniority points system, he said.
Brand experience company Freeman is CES’ official contractor, but Siraj said companies typically bring their own contractors to build and tear down their booths.
CES2020 runs Jan. 7-10 across 11 venues. Exhibitor prices are fixed and the same regardless of the venue, Siraj said. It costs exhibitors $46 per square foot for non-members of the Consumer Technology Association, and $41 per square foot for members.
The venues for CES 2020 are:
— Las Vegas Convention Center
— Westgate
— Renaissance
— Sands Expo and Convention Center
— The Venetian
— Palazzo
— Wynn Las Vegas
— Encore
— Aria
— Park MGM
— Vdara
The Review-Journal is owned by the family of Las Vegas Sands Corp. Chairman and CEO Sheldon Adelson. Las Vegas Sands operates The Venetian, Palazzo and Sands Expo and Convention Center.


1. github
This is a link to GitHub!
2. linkedin
This is a link to Linkedin!
3. classroom
This is a link to Classroom!
header
Hello there, awesome person!


Thunder no_data test key added :: {{ thunder_no_data_test_key_added }}
{% endif %}
Test Thunder alerting on no_data being received from a top level namespace.
This simulates a failure where Skyline stops receiving metric data from a top level metric namespace.
Select a top level namespace to test, the stale_period (number of seconds that data has not been received set this low like 0) and an expiry time to test.
This test runs via the analyzer/metrics_manager run which runs every 5 minutes, so if you use the default stale_period 0 and expiry 299
a no_data alert for the namespace should fire on the next analyzer/metrics_manager run and a recovered notice should trigger on the run thereafter


Journal entry:
xx123
Date:
xx12/12/2003
Account No.
NAME
DEBIT
CREDIT
XX12345
Accounts receivable - Trade
xx14500.34
xx14500.34


Distributed systems
1. Introduction
Omnipresence of distributed systems
Examples of distributed processing:
- Grid computing (E-Commerce,Banks, Insurance)
- WorldWideWeb, Wikipedia, RSS Feeds, Social Networks(Facebook,Instagram,...)
- Cloud computing
- Question: What is not a distributed system?
- Mainframes were traditionally not distributed
Definitions
van Steen, Tanenbaum
A distributed system is a collection of
autonomous computing elements that appears to its
users as a single coherent system
Lamport
Distributed system is the
one in which the failure of a computer you didn’t even know
existed can render your own computer unusable”
Autonomous components
- Components cooperate in order to fulfil common goals
Communication can be done
- - by exchaniging messages
- using communication mechanisms with higher semantics
(e.g. Remote Procedure Calls)
- Components are distributed in the space
multiprocessor and multicomputter systems , LAN, WAN, WWW,...
Objectives
- Exploring different distributed systems
- Design principles for distributed systems
- Usage of specific tools to implement some distributed applications
Planned topics
- Architectures of distributed systems
- Processes
- Communication and protocols
- Remote procedure calls, etc.
- Distributed algorithms
- Virtualisation
- Socket programming
- Web services and REST applications
Distributed system characteristics
- Concurrency
- No global clock
- Coordination of actions by exchanging messages
- There is no single global notion of the correct time
- Independent failures
- Failure of one component leaving the other components still running
- Failures of network
- Failures of computers(programs)
Middleware
Separate level of software logically placed on top of the
respective operating systems of the computers
Design goals for distributed systems
- Sharing of resources
- Distribution transparency
- Openness
- Scalability
Sharing resources
- Resources easily accessible for users
- Economical reasons for scharing
- Internet with its protocols for exchanging
files, e-mails, documents, audio, video, ...
- Peer-to-peer networks - BitTorrent used
for distributing large amounts of data
Transparency
- Access
Hiding differences in data representations
- Location
Hiding exact location of the object
- Relocation
Hiding the fact that some objects have to be moved
during the usage
- Migration
Hiding the change of the location for an object
- Replication
Hiding that an object may be on many different locations
- Conncurency
Hiding that an object is used by many users
- Failure
Hiding that failure and recovery happened
Relevance of transparency
- Achieving full transparency sometimes is not useful
- Achieving full transparency sometimes costly
- Completely hiding failures is not preferable
- It's not possible to distinguish a slow computer from a failing one
Exposing distribution:
- Making use of location based services
- Dealing with users in different time zones
- Reporting while server doesn't respond for a long time
Openness
Offering components that can be used by, or integrated into other systems
- Interoberability
- Composability
- Extensibility
System has to describe syntax and semantics of services it provides.
- Defining services through interfaces
- Open source - as open as a system can get
Separating policy from mechanism
System should be organized as a collection of relatively small
and easily replaceable or adaptable components
- Contrary to this many current systems are monolithic
- Monolitic systems tend not to be open
- Example with cashing in Web browsers
- Storage
- Exemption
- Sharing
- Refreshing
- leading to a great number of configuration parameters
Scalability
Types:
- Size scalability
- Geographical scalability
- Administrative scalability
- Counterexample - peer-to-peer applicaions
Solutons:
-
Hiding communication latency
-
Distribution of work -
Example of DNS (Internet Domain Name System)
-
Replication
cashing- replication in the proximity of the client
- If we have replication together with consistency
than it leads to solutions which may be not scalable
False design principles (Peter Deutsch):
-
The network is reliable
-
The network is secure
-
The network is homogeneous
-
The topology does not change
-
Latency is zero
-
Bandwidth is infinite
-
Transport cost is zero
-
There is one administrator
Types of distributed systems
-
Distributed computing systems
- High performance distributed systems
-
Distributed information systems
-
Pervasive systems
Distributed computing systems
High performance distributed systems
-
Cluster computing.
-
Grid computing
-
-
Parllel computing led to high-performance distributed computing
-
Multiprocessor machines lead to introduction of parallel processing
and clusters in general
-
Two approaches
- Multiprocessor architecture
- Multicomputer architecture
-
Muliple threads are executing at the same time
-
Limitation - multiprocessor systems don't scale easily
Cluster computing
- Cluster is a set of computers which work together and can be viewed as a single system
- hardware consists mostly of a collection of similar stations
(compute nodes are mostly identical) and the management is centralized
moving toward more hybrid solutions where middleware is
functionally partitioned in different nodes
An example of Linux based cluster
-
federation of different computer systems
- computers more heterogeneous and more physically dispersed than cluster computers
-
Frequently :
- different administrative domains
- different hardware
- different software
- different deployed network topology
-
Key issue - different organisations brought together
to allow the collaboration
-
virtual organisation
-
architectural issues
-
Layered architecture (Foster et al. 2001)
Layered grid architecture
- Fabric layer
- interfaces for sharing resources
- querying the state of a recource and resource management
- Connectivity layer
- protocols for supporting grid transactions
- transfering data between resources
- protocols to autenticate users and resources
- Resource layer
- managing resources of a single resource
- using connectivity layer function to call interfaces in the
fabric layer
- Collective layer
- deals with handling access to multiple resources
- discovery, allocation, scheduling, data replication
- broad spectrum of protocols which offer a virtual organization
- Application layer
- applications which operate within a virtual organisation
-
utility computing
-
pool of virtual resources
-
dynamically configured resources
-
scalability
-
pay-per-use model
-
layered organisation
- serious option for many enterprises
Cloud computing sevices
Characterised by an easily accessible and usable pool of virtual
resources
Cloud computing delivers convenient, on-demand access to shared pools of data, applications and hardware
over the Internet.”(RSA Security, 2009)
- Infrastructure-as-a-Service (IaaS)
- covering hardware and infrastructure layer
- deploys virtualisation techniques to provide
customers an infrastructure consisting of virtual storage
and computing resources
- Platform-as-a-Service (PaaS)
- covering the platform layer
- providing vendor-specific API to a cloud computing user
such as operating system do in the case of application developers
- example higher-level abstraction of storage in Amazon S3
- Software-as-a-Service (Saas)
- covering application layer
- spreadsheet applications, presentation applications, etc..
Cloud computing - issues
- Security and privacy issues
- Different performance profiles
- Provider lock-in - being dependant on provider's products and
services, switch to another provider requires substantial costs
- Dependency on the network infrastructure
- Is it cost effective?
Distributed information systems
found in organizations with many networked applications
- Enterprise-wide information systems
- Enterprise middleware systems
- Network applicatons comprised of servers and clients
- Distributed transactions
- many low level requests are wrapped up in a single request
Distributed transactions
- ACID properties
- Atomic
- appears indivisible to the outside world
- Consistent
- doesn't violate system invariants
- Isolation
- concurrent transactions do not interfere with each other
- Durable
- once committed changes are permanent
Distributed transaction primitives
| Primitive |
Description |
| BEGIN_TRANSACTION |
Mark the start of a transaction |
| END_TRANSACTION |
Terminate the transaction and try to commit |
| ABORT_TRANSACTION |
Kill the transaction and restore values |
| READ |
Read data from a table, file, etc.. |
| WRITE |
Write data to a table, file, etc.. |
The role of transaction processing monitor
- Applications don't have to implement subtransactions
for themselves
Enterprise application integration
- Remote Procedure Calls (RPC)
- procedure call packaged in a message
- Remote Method Invocation (RMI)
- similar to RPC except it operates on objects,not on functions
Pervassive systems
- ubiquitous computing systems
- mobile computing systems
- sensor networks
Ubiquitous computing systems
System is continuously present
- Distribution
- networked and make an illusion of a single coherent system
- Interaction
- Users are important and sometimes they don't even
know that they interact and send information
- example: specific settings for different car drivers
- Context awareness
- location, identity, time, activity
- GPS coordinates are sometimes crucial for some services
- Autonomy
- address allocation, adding devices, automatic updates
- Inteligence
Mobile computing networks
- various devices
- tablets, smartphones
- car equipment, GPS enabled devices, remote controls, etc..
- constant change of the location
- dynamically discovering services
- "pocket-switched networks " - social networks
Sensor networks
- collaboration of many sensors
- anwering database like queries
- What's load on a highway?
- What's the weather like in a region?
Sensor networks organization
- In reality both approaches are combined.
Literature
-
M. van Steen and A.S. Tanenbaum, Distributed Systems, 3rd ed., distributed-systems.net, 2017
-
Coulouris, G., Dollimore, J. and Kindberg, T.: Distributed Systems:
Concepts and Design, Addison-Wesley, Amsterdam, Fifth edition, ISBN-13: 978-0-273-76059-7
-
Günther Bengel: Grundkurs Verteilte Systeme: Grundlagen und Praxis des Client-Server und Distributed
Computing. Springer Vieweg, 4. Auflage, 2014. ISBN-13:<PHONE_NUMBER>702
-
Sunil Kumar:Distributed systems:Design concepts ,
Alpha Science International Ltd. Oxford U.K., 2017, ISBN-13: 978-1-84265-933-5
These slides are mainly created according to the book in 1,
and including figures from the book


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You can see the tutorial
here
The example code can be slower than your environment because the code is transpiled by babel-standalone in runtime.
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Mars
Mars is a cold desert world. It is half the size of Earth. Mars is sometimes called the Red Planet. It's red because of rusty iron in the ground.
Jupiter
Jupiter is the biggest planet in our solar system. It's similar to a star, but it never got big enough to start burning. It is covered in swirling cloud stripes. It has big storms like the Great Red Spot, which has been going for hundreds of years.
Saturn
Saturn isn’t the only planet to have rings, but it definitely has the most beautiful ones. The rings we see are made of groups of tiny ringlets that surround Saturn. They’re made of chunks of ice and rock.


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Following M.G. Siegler’s yearly ritual, here’s my home screen as of the first day of 2017. Most of these are fairly obvious, but I’ll add some thoughts:
- WhatsApp/Social apps + photos + google maps are pretty much no brainers.
- I don’t why Apple News is still here, I never open it.
- At some point, I’ll dissolve Reminders and stick with just the one to-do app (2Do).
- Yes, Outlook is current email client of choice on iOS.
- Messages is blowing up thanks to all the places who have my mobile number and insisted on wishing me a Happy 2017 #iFeelSoPopular
- MyFitnessPal is pretty much on its way out since the arrival of the Apple Watch. Granted, its purpose was altogether different, but my zeal to track everything I eat died about 45 days after I started.
See you in 2018.


Week 4 - Types of Viz and Choosing Colors
This week, we will cover a bit about colors, and more about data transformations, and how we can
choose colors and transforms to better suit our data.
We also did some basics of Pandas last week and we’ll continue this week.
Note there are a variety of notebooks from previous iterations of this course listed under the Materials subsection of today’s lecture. The “furtherExamples” notebooks are just there as reference, we will not be going through them by hand.
Downloads
Data:
Python external libraries
Make sure you save these in the same directory as your jupyter notebook is running in!
References
Optional Reading List
- VAD, Ch. 10: Map Color and Other Channels
- FDV, Ch. 4: Color scales
- VAD, Ch. 5: Marks and Channels
- Perception in Visualization (pay extra attention to the parts about color)
- Palettable Docs
Materials
Materials from Previous Courses
- Notebook: fall2019_prep_notebook_furtherExamples_01:
(view,
download)
- Notebook: fall2019_prep_notebook_furtherExamples_02:
(view,
download)
- Notebook: fall2019_prep_notebook_furtherExamples_03:
(view,
download)
- Notebook: fall2019_prep_notebook_week03:
(view,
download)
- Notebook: spring2019_prep_notebook_furtherExamples_week03:
(view,
download)
- Notebook: spring2019_prep_notebook_week03_part1:
(view,
download)
- Notebook: spring2019_prep_notebook_week03_part2:
(view,
download)


The Symbol
constructor is not intended to be used with the new
operator, but to be called as a function.
var foo = new Symbol("foo");
This throws a TypeError
exception.
This rule is aimed at preventing the accidental calling of Symbol
with the new
operator.
The following patterns are considered problems:
/*eslint no-new-symbol: "error"*/
/*eslint-env es6*/
var foo = new Symbol('foo');
The following patterns are not considered problems:
/*eslint no-new-symbol: "error"*/
/*eslint-env es6*/
var foo = Symbol('foo');
// Ignores shadowed Symbol.
function bar(Symbol) {
const baz = new Symbol("baz");
}
This rule should not be used in ES3/5 environments.
This rule was introduced in ESLint 2.0.0-beta.1.