Datasets:

anujsahani01
/

TextCodeDepot

Dataset card Data Studio Files Files and versions Community

Split (3)

train · 16k rows

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.

Unnamed: 0 int64 0 16k	text_prompt stringlengths 110 62.1k	code_prompt stringlengths 37 152k
9,200	Given the following text description, write Python code to implement the functionality described below step by step Description: HTTP requests In this tutorial ti is covered how to make requests via HTTP protocol. For more informations about related stuff see Step1: The variable data contains returned HTML code (full page) as string. You can process it, save it, or do anything else you need. Requests Example how to get static content of web page with Requests follows. Step2: Get JSON data from an API This task is demonstrated on Open Notify - an open source project that provides a simple programming interface for some of NASA’s awesome data. The examples bellow cover how to obtain current possition of ISS. With Requests library it is possible to get the JSON from the API in the same way as HTML data. Step3: The Requests function json() convert the json response to Python dictionary. In next code block is demonstrated how to get data from obtained response. Persistent session with Requests Session with Requests are handy for cases where you need to use same cookies (session cookies for example) or authentication for multiple requests. Step4: Compare the output of the code above, with the example bellow. Step5: Custom headers Headers of the response are easy to check, example follows. Step6: The request headers can be modified in simple way as follows.	Python Code: from urllib.request import urlopen r = urlopen('http://www.python.org/') data = r.read() print("Status code:", r.getcode()) Explanation: HTTP requests In this tutorial ti is covered how to make requests via HTTP protocol. For more informations about related stuff see: * <a href="https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol">Hypertext Transfer Protocol (HTTP)</a> * <a href="https://en.wikipedia.org/wiki/JSON">JavaScript Object Notation</a> * <a href="https://en.wikipedia.org/wiki/HTML">HyperText Markup Language (HTML)</a> Keep in mind, that in this tutorial we work only with static content. How to obtain web dynamic content is not covered in this tutorial. If you want to deal with dynamic content, study <a href="http://selenium-python.readthedocs.io/">Selenium Python Bindings</a>. Get HTML page content In this section are examples how to get HTTP response with two different libraries: * <a href="https://docs.python.org/3.4/library/urllib.html?highlight=urllib">urllib</a> (standard library in Python 3) * <a href="http://docs.python-requests.org/en/master/">Requests</a> (instalable through pip) In this tutorial is mainly used the Requests library, as a prefered option. Urlib2 library Example how to get static content of web page with Urlib2 follows: End of explanation import requests r = requests.get("http://www.python.org/") data = r.text print("Status code:", r.status_code) Explanation: The variable data contains returned HTML code (full page) as string. You can process it, save it, or do anything else you need. Requests Example how to get static content of web page with Requests follows. End of explanation import requests r = requests.get("http://api.open-notify.org/iss-now.json") obj = r.json() print(obj) Explanation: Get JSON data from an API This task is demonstrated on Open Notify - an open source project that provides a simple programming interface for some of NASA’s awesome data. The examples bellow cover how to obtain current possition of ISS. With Requests library it is possible to get the JSON from the API in the same way as HTML data. End of explanation s = requests.Session() print("No cookies on start: ") print(dict(s.cookies)) r = s.get('http://google.cz/') print("\nA cookie from google: ") print(dict(s.cookies)) r = s.get('http://google.cz/?q=cat') print("\nThe cookie is perstent:") print(dict(s.cookies)) Explanation: The Requests function json() convert the json response to Python dictionary. In next code block is demonstrated how to get data from obtained response. Persistent session with Requests Session with Requests are handy for cases where you need to use same cookies (session cookies for example) or authentication for multiple requests. End of explanation r = requests.get('http://google.cz/') print("\nA cookie from google: ") print(dict(r.cookies)) r = requests.get('http://google.cz/?q=cat') print("\nDifferent cookie:") print(dict(r.cookies)) Explanation: Compare the output of the code above, with the example bellow. End of explanation r = requests.get("http://www.python.org/") print(r.headers) Explanation: Custom headers Headers of the response are easy to check, example follows. End of explanation headers = { "Accept": "text/plain", } r = requests.get("http://www.python.org/", headers=headers) print(r.status_code) Explanation: The request headers can be modified in simple way as follows. End of explanation
9,201	Given the following text description, write Python code to implement the functionality described below step by step Description: 반복과 제어 이성주 (c) 2015 Step1: for Step2: 들여쓰기는 문법 Step3: 들여쓰기와 스코프 Step4: 내장리스트 (list comprehension) 리스트 내에서 반복문 실행 Step5: 숫자 리스트 생성 함수 Step6: 특정 횟수 반복 Step7: 인덱스가 필요한 경우 Step8: while Step9: 도전과제 포커 카드는 52장이다. 각 카드는 문양(suit)와 숫자(rank)로 이루어진다. 문양은 Diamond, Heart, Spade, Clover 4 종류이고, 숫자는 2, 3, … , 9, 10, J, Q, K, A의 13개의 값이다. 52장의 포커 카드를 생성해 변수 deck에 저장하시오. 예 Step10: 도전과제 6면 주사위는 1부터 6까지의 값을 갖는다. 주사위를 굴릴 때 나온 윗면의 숫자의 누적 합계를 구한다. 누적 합계가 100 이상이 될 때까지 몇 번을 던졌는지를 표시하는 프로그램을 작성한다. [a, b] 구간에서 무작위 숫자를 구하는 구문은 다음과 같다. import random # 0 이상 1 이하의 무작위 정수 생성 n = random.randint(0, 1) Step11: 제어 Step12: 도전과제 3-6-9 게임 1부터 시작해 1씩 숫자가 증가한다. 숫자에 3,6,9가 하나 이상 있으면 숫자 대신 '짝!'을 출력한다. 출력 예시 Step13: 도전과제 10 이하의 3 또는 5의 배수는 3, 5, 6, 9이다. 이 숫자들의 합은 23이다. 1000 이하 3 또는 5의 배수의 합을 구하라. 도전과제 각 학생 정보는 이름, 세 과목의 점수로 이루어진다. 세 명 학생의 정보를 저장한다. 각 학생의 평균 점수를 구하라. 각 과목의 평균 점수를 구하라. Step14: 도전과제 13195를 소수 분해하면, 5, 7, 13, 29이다. 600851475143를 소수분해했을 때 가장 큰 숫자는 무엇인가? 도전과제 1001, 2002, 3003과 같은 숫자를 회문 숫자 (palindromic number)라고 한다. 두 개의 두 자리 숫자의 곱으로 만들 수 있는 가장 큰 숫자는 9009 = 91 × 99이다. 두 개의 세 자리 숫자의 곱으로 만들 수 있는 가장 큰 회문 숫자를 찾아라.	Python Code: # 3버전 스타일 print 함수 사용 from __future__ import print_function Explanation: 반복과 제어 이성주 (c) 2015 End of explanation print([1,2,3]) for n in [1,2,3]: print(n) Explanation: for End of explanation for n in [1,2,3]: print(n) print(n) for key in {'name': '이성주', 'email':'seongjoo@codebasic'}: print(key) profile = {'name': '이성주', 'email':'seongjoo@codebasic'} profile.items() for key, value in profile.items(): print(key, value) for c in 'python': print(c, end=':::') Explanation: 들여쓰기는 문법 End of explanation nums_square = [] for n in [1,2,3,4,5]: nums_square.append(n2) print(nums_square) Explanation: 들여쓰기와 스코프 End of explanation nums_square = [n2 for n in [1,2,3,4,5]] print(nums_square) Explanation: 내장리스트 (list comprehension) 리스트 내에서 반복문 실행 End of explanation range(10) # 3 버전 list(range(10)) range(2,11) range(1,11,2) Explanation: 숫자 리스트 생성 함수 End of explanation for x in range(3): print('참 잘했어요!') Explanation: 특정 횟수 반복 End of explanation it = enumerate('abc') it.next() it.next() for i, x in enumerate(range(3)): print i+1, '참 잘했어요!' profile = {'name':'이성주', 'email': '[email protected]'} for k,v in profile.items(): print(k,v) Explanation: 인덱스가 필요한 경우 End of explanation x=1 while x < 3: print(x) x += 1 isTrueLove = True while isTrueLove: print("I love you") break Explanation: while End of explanation # 52장 카드 덱 생성 suits = ['Heart', 'Diamond', 'Clover', 'Spade'] ranks = range(2,11)+['J', 'Q', 'K', 'A'] deck = [] for s in suits: for r in ranks: card = s + str(r) deck.append(card) # 각 문양별로 한 줄씩 출력 previous_suit = '' for card in deck: # 카드의 문양을 뽑는다. suit = card[0] # 같은 문양이면 같은 줄로 출력 if suit == 'H': suit_collection[0].append(card) elif suit == 'D': suit_collection[1].append(card) elif suit == 'C': suit_collection[2].append(card) elif suit == 'S': suit_collection[3].append(card) else: print('그런 문양은 없어요!') for suits in suit_collection: for card in suits: print(card, end=', ') print('\n') deck = [[s,r] for s in ['Heart', 'Diamond', 'Spade', 'Clover'] for r in range(2,11)+['J','Q','K', 'A']] len(deck) Explanation: 도전과제 포커 카드는 52장이다. 각 카드는 문양(suit)와 숫자(rank)로 이루어진다. 문양은 Diamond, Heart, Spade, Clover 4 종류이고, 숫자는 2, 3, … , 9, 10, J, Q, K, A의 13개의 값이다. 52장의 포커 카드를 생성해 변수 deck에 저장하시오. 예: ‘Diamond 3’, ‘Heart Q’ a. 각 포커 카드의 정보를 문자열 형태로 저장하시오. b. 각 포커 카드 정보를 리스트 자료 구조로 저장하시오. 예: [‘Diamond’, 3], [‘Heart’, ‘Q’] c. 한 줄의 구문으로 전체 포커 카드를 생성하시오. d. 같은 문양은 같은 줄로 출력해 총 네 줄로 전체 카드를 출력하시오. End of explanation import random total = 0 count = 0 while total < 100: face = random.randint(1,6) total += face count += 1 print(count) Explanation: 도전과제 6면 주사위는 1부터 6까지의 값을 갖는다. 주사위를 굴릴 때 나온 윗면의 숫자의 누적 합계를 구한다. 누적 합계가 100 이상이 될 때까지 몇 번을 던졌는지를 표시하는 프로그램을 작성한다. [a, b] 구간에서 무작위 숫자를 구하는 구문은 다음과 같다. import random # 0 이상 1 이하의 무작위 정수 생성 n = random.randint(0, 1) End of explanation x = 10 if x < 5: fruit = 'banana' else: fruit = 'apple' print(fruit) hour = 13 greeting = 'Good' if 5 < hour < 12: # 아침인사 greeting += ' morning' elif 12 <= hour < 18: greeting += ' afternoon' else: greeting += ' night!' print(greeting) if 'a': print('hi') Explanation: 제어 End of explanation # 숫자 생성 N = 20 for n in range(1,N): # 숫자에 3 또는 6 또는 9가 있는지 확인 if '3' in str(n)or '6' in str(n)or '9' in str(n): print('짝!', end=' ') continue print(n, end=' ') False or 6 Explanation: 도전과제 3-6-9 게임 1부터 시작해 1씩 숫자가 증가한다. 숫자에 3,6,9가 하나 이상 있으면 숫자 대신 '짝!'을 출력한다. 출력 예시: 1 2 짝! 4 5 짝! 7 8 짝! End of explanation float(1/2) 3/2 students = [{'name': '이성주', 'scores': [75, 85, 92]}, {'name': '서희정', 'scores': [85, 95, 99]} ] # 각 학생의 평균 점수 구하기 for s in students: avg = float(sum(s['scores']))/len(s['scores']) print(s['name']+ " 평균: " + str(avg)) # 각 과목의 평균 점수 subjects = [[],[],[]] for s in students: subjects[0].append(s['scores'][0]) subjects[1].append(s['scores'][1]) subjects[2].append(s['scores'][2]) print(subjects) for sub in subjects: print(sum(sub)/len(sub)) Explanation: 도전과제 10 이하의 3 또는 5의 배수는 3, 5, 6, 9이다. 이 숫자들의 합은 23이다. 1000 이하 3 또는 5의 배수의 합을 구하라. 도전과제 각 학생 정보는 이름, 세 과목의 점수로 이루어진다. 세 명 학생의 정보를 저장한다. 각 학생의 평균 점수를 구하라. 각 과목의 평균 점수를 구하라. End of explanation # 일단 ... 회문 숫자를 검색하는 방법부터 마련해 보자 pn_list = [] for n1 in range(100,1000): for n2 in range(100, 1000): n = n1*n2 # Q:회문 숫자인가? # A: 유한별 n_str = str(n) if n_str[::-1] == n_str: # 회문 숫자면 추가 pn_list.append(n) # 추가된 회문 숫자의 최대값 print(max(pn_list)) Explanation: 도전과제 13195를 소수 분해하면, 5, 7, 13, 29이다. 600851475143를 소수분해했을 때 가장 큰 숫자는 무엇인가? 도전과제 1001, 2002, 3003과 같은 숫자를 회문 숫자 (palindromic number)라고 한다. 두 개의 두 자리 숫자의 곱으로 만들 수 있는 가장 큰 숫자는 9009 = 91 × 99이다. 두 개의 세 자리 숫자의 곱으로 만들 수 있는 가장 큰 회문 숫자를 찾아라. End of explanation
9,202	Given the following text description, write Python code to implement the functionality described below step by step Description: Tutorial 2 - Reti neurali convolutive in TF Prerequisiti per il tutorial Step1: Nell'esempio prima, abbiamo scelto di scaricare solo le immagini di persone di cui abbiamo (almeno) 70 esempi, le quali sono soltanto 6 Step2: Per semplicità, consideriamo qui un problema di classificazione binaria per distinguire fra le foto di Colin Powell (236 foto) e le foto di Tony Blair (144 foto) Step3: Sistemiamo il vettore di target in modo che contenga solo 0 od 1 Step4: Per comodità di TF, aggiungiamo una dimensione alla matrice di immagini, che rappresenta il singolo canale Step5: Aggiungendo un parametro <code>color=True</code> alla funzione <code>fetch_lfw_people</code> potremmo scaricare i tre canali RGB invece di un singolo canale in scala di grigi. Di tutte le immagini teniamo da parte un 20% per andare a testare la nostra rete neurale Step6: Vediamo un esempio di immagine di training Step7: Come si può vedere, ciascuna immagine è 50x37, in bianco e nero Step8: Per finire, rappresentiamo i pixel in [0,1], invece che in [0, 255] Step9: Costruiamo la nostra rete convolutiva Come nello scorso tutorial, cominciamo definendo i nostri placeholder di input e di output Step10: Al posto di definire manualmente tutte le variabili e le operazioni della rete, iniziamo ad utilizzare gli strati già definiti nel modulo <code>layers</code> Step11: Andiamo a valutare la dimensione del tensore di uscita Step12: Come sempre, la prima dimensione rappresenta un mini-batch di attivazioni. Il resto del tensore è quindi costituito da 64 filtri, ciascuno 46x33. Si noti la leggera discrepanza con la dimensione delle immagini di ingresso Step13: Continuiamo aggiungendo lo strato di pooling, questa volta considerando il padding Step14: Il terzo parametro rappresenta lo stride, ovvero ogni quanti pixel calcoliamo un risultato. Con questa configurazione, abbiamo effettivamente dimezzato la dimensione dei tensori Step15: Continuiamo con tutti gli strati rimanenti Step16: Si noti come, per applicare lo strato interamente connesso, è necessario eseguire un reshape del tensore di ingresso, in modo che ogni input sia 1D. Concludiamo con l'ultimo strato con un'attivazione sigmoide Step17: Allenare la rete convolutiva La fase di allenamento è sostanzialmente uguale allo scorso tutorial. In particolare, definiamo una funzione per estrarre mini-batch casuali dalle nostre immagini di training Step18: Definiamo una funzione costo Step19: Inizializziamo un algoritmo di ottimizzazione Step20: Per valutare l'accuratezza, definiamo una funzione ausiliaria Step21: Creiamo una sessione ed inizializziamo tutte le variabili Step22: Definiamo una dimensione del mini-batch ed un numero di epoche Step23: Poiché il training potrebbe essere più lento dello scorso tutorial, installiamo un modulo per visualizzare una semplice barra di progresso Step24: Visualizziamo l'accuratezza media sul test set	Python Code: from sklearn.datasets import fetch_lfw_people lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4) Explanation: Tutorial 2 - Reti neurali convolutive in TF Prerequisiti per il tutorial: * T1 - Reti neurali feedforward Contenuti del tutorial: 1. Concetti base delle reti neurali convolutive. 2. Implementazione di una rete neurale convolutiva in TF. Introduzione alle reti convolutive L'enorme flessibilità delle reti neurali è allo stesso tempo il loro punto di forza ed il loro svantaggio. Consideriamo un problema dove l'input alla rete neurale è un'immagine di 64x64 pixel, in RGB, ad esempio una piccola telecamera che deve imparare a riconoscere la presenza o meno di una determinata persona. Nonostante si tratti di un'immagine molto piccola, in tutto avremmo già 64x64x3 = 12228 input alla rete neurale, considerando che ciascun pixel è descritto da tre colori diversi. Con soli 10 neuroni nel primo strato nascosto, avremmo oltre 120mila parametri liberi da adattare per riconoscere un qualche oggetto nell'immagine. Le reti neurali convolutive (convolutional neural network, CNN) sono un modo di risolvere questo problema quando, come nel caso delle immagini, l'input alla rete neurale presenta una forma di località dell'informazione. In questo caso, ciascun neurone nello strato nascosto viene sostituito da un filtro di dimensioni fisse (es., 5x5x3), che viene fatto scorrere sull'intera immagine per ottenere la sua uscita: http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ Uno strato convolutivo si ottiene facendo scorrere in parallelo diversi filtri di questo tipo sull'immagine, a cui viene fatta seguire una nonlinearità come in una rete neurale tradizionale. Considerando di nuovo il caso dell'immagine di prima, con 10 filtri di questo tipo si otterrebbero 5x5x3x10 = 750 parametri adattabili, una riduzione di oltre 100 volte. Questa architettura è enormemente popolare per le immagini, ed ha trovato applicazioni di recente anche per l'audio e problemi di processamento del linguaggio; entrambi situazioni dove le convoluzioni, a differenza che nella figura sopra, sono mono-dimensionali. In pratica, una CNN si costruisce interponendo a questi strati convolutivi altri strati, come nella figura sotto: http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ Gli strati di pooling servono ad effettuare un sottocampionamento dell'uscita dello strato precedente, ad esempio prendendo il massimo valore in delle regioni 2x2 o 3x3. Nella parte finale della rete, vengono tipicamente aggiunti strati interamente connessi (come nelle reti neurali standard), prima di procedere alla classificazione. Questi sono solo gli elementi di base delle reti convolutive; vedremo alcuni concetti più avanzati, necessari a costruire reti con numerosi strati nascosti, in un prossimo tutorial. Va notato da subito come, nel caso delle CNN, l'attivazione di un singolo strato è ora descritta da un tensore a quattro dimensioni, in quanto l'uscita di ciascun filtro è a sua volta bidimensionale. Esempio di classificazione su immagini Come esempio di problema di classificazione di immagini, utilizziamo il dataset Labeled faces in the wild, una collezione annotata di immagini di numerose celebrità. <code>scikit-learn</code> mette a disposizione una funzione per scaricare il dataset, oltre 200 MB di dati in questa versione: End of explanation lfw_people.target_names Explanation: Nell'esempio prima, abbiamo scelto di scaricare solo le immagini di persone di cui abbiamo (almeno) 70 esempi, le quali sono soltanto 6: End of explanation idx = (lfw_people.target == 1) \| (lfw_people.target == 6) X = lfw_people.images[idx] y = lfw_people.target[idx] Explanation: Per semplicità, consideriamo qui un problema di classificazione binaria per distinguire fra le foto di Colin Powell (236 foto) e le foto di Tony Blair (144 foto): End of explanation y[y == 6] = 0 y = y.reshape(-1, 1) Explanation: Sistemiamo il vettore di target in modo che contenga solo 0 od 1: End of explanation X = X.reshape(X.shape[0], X.shape[1], X.shape[2], 1) Explanation: Per comodità di TF, aggiungiamo una dimensione alla matrice di immagini, che rappresenta il singolo canale: End of explanation from sklearn import model_selection (X_trn, X_tst, y_trn, y_tst) = model_selection.train_test_split(X, y, test_size=0.20) Explanation: Aggiungendo un parametro <code>color=True</code> alla funzione <code>fetch_lfw_people</code> potremmo scaricare i tre canali RGB invece di un singolo canale in scala di grigi. Di tutte le immagini teniamo da parte un 20% per andare a testare la nostra rete neurale: End of explanation %matplotlib inline import matplotlib.pyplot as plt plt.imshow(X[1,:,:, 0], cmap='gray') Explanation: Vediamo un esempio di immagine di training: End of explanation X[0].shape Explanation: Come si può vedere, ciascuna immagine è 50x37, in bianco e nero: End of explanation X /= 255 Explanation: Per finire, rappresentiamo i pixel in [0,1], invece che in [0, 255]: End of explanation import tensorflow as tf X_tf = tf.placeholder(tf.float32, [None, 50, 37, 1], name='input') y_tf = tf.placeholder(tf.float32, [None, 1], name='target') Explanation: Costruiamo la nostra rete convolutiva Come nello scorso tutorial, cominciamo definendo i nostri placeholder di input e di output: End of explanation conv1 = tf.layers.conv2d(X_tf, 64, (5,5), activation=tf.nn.relu, name='conv1') Explanation: Al posto di definire manualmente tutte le variabili e le operazioni della rete, iniziamo ad utilizzare gli strati già definiti nel modulo <code>layers</code>: https://www.tensorflow.org/api_docs/python/tf/layers/. Va sottolineato da subito come alcuni di questi strati si appoggiano interiormente a funzioni più semplici definite nel modulo <code>nn</code>, come lo strato per la convoluzione 2D (<code>conv2D</code>): https://www.tensorflow.org/api_docs/python/tf/layers/conv2d<br /> https://www.tensorflow.org/api_docs/python/tf/nn/conv2d Questa duplicazione di classi non deve però confondere, in quanto è possibile usare in maniera equivalente ciascuno dei due; le funzioni in <code>nn</code> hanno in genere meno parametri e meno flessibilità, e sono preferibili quando questa flessibilità non è richiesta. Per uniformità, in questo tutorial useremo solo le funzioni definite nel modulo <code>layers</code>. La nostra rete sarà così composta: Uno strato convolutivo, con 64 filtri 5x5. Uno strato di pooling 2x2. Un secondo strato convolutivo, con 32 filtri 5x5. Un secondo strato di pooling 2x2. Uno strato interamente connesso con 20 neuroni. Un neurone di uscita con la classe desiderata. Questa non è ovviamente l'unica scelta, né tantomeno la migliore, il che richiederebbe un'ottimizzazione più completa di tutto il design della rete (si veda ad esempio questo articolo). Questo genere di alternanza di strati convolutivi e pooling, di dimensioni sempre più piccole, e seguito da strati densamente connessi è però tipico nella maggior parte delle applicazioni con reti non molto grandi. Considereremo il design di reti più complesse e la loro ottimizzazione in tutorial successivi; va detto da subito, comunque, che spesso questa fase di costruzione è più un unirsi di esperienza ed 'arte', piuttosto che il susseguirsi di regole precise. Iniziamo definendo il primo strato convolutivo: End of explanation conv1.shape Explanation: Andiamo a valutare la dimensione del tensore di uscita: End of explanation # conv1 = tf.layers.conv2d(X_tf, 64, (5,5), padding='same', name='conv1') Explanation: Come sempre, la prima dimensione rappresenta un mini-batch di attivazioni. Il resto del tensore è quindi costituito da 64 filtri, ciascuno 46x33. Si noti la leggera discrepanza con la dimensione delle immagini di ingresso: poiché la convoluzione non è definita sui bordi dell'immagine, perdiamo 2 pixel in ogni dimensione. Possiamo ovviare a questo problema aggiungendo del padding di zeri sui bordi dell'immagine originale: End of explanation pool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same', name='pool1') Explanation: Continuiamo aggiungendo lo strato di pooling, questa volta considerando il padding: End of explanation pool1.shape Explanation: Il terzo parametro rappresenta lo stride, ovvero ogni quanti pixel calcoliamo un risultato. Con questa configurazione, abbiamo effettivamente dimezzato la dimensione dei tensori: End of explanation conv2 = tf.layers.conv2d(pool1, 32, (5,5), activation=tf.nn.relu, padding='same', name='conv2') pool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same', name='pool2') dense = tf.layers.dense(tf.reshape(pool2, [-1, 12932]), 20, activation=tf.nn.relu, name='dense') Explanation: Continuiamo con tutti gli strati rimanenti: End of explanation output = tf.layers.dense(dense, 1, activation=None, name='output') output_sigmoid = tf.nn.sigmoid(output) Explanation: Si noti come, per applicare lo strato interamente connesso, è necessario eseguire un reshape del tensore di ingresso, in modo che ogni input sia 1D. Concludiamo con l'ultimo strato con un'attivazione sigmoide: End of explanation def iterate_minibatches(X, y, batchsize): indices = np.arange(len(X)) np.random.shuffle(indices) for start_idx in range(0, len(X) - batchsize + 1, batchsize): excerpt = indices[start_idx:start_idx + batchsize] yield X[excerpt], y[excerpt] Explanation: Allenare la rete convolutiva La fase di allenamento è sostanzialmente uguale allo scorso tutorial. In particolare, definiamo una funzione per estrarre mini-batch casuali dalle nostre immagini di training: End of explanation loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_tf, logits=output)) Explanation: Definiamo una funzione costo: End of explanation train_step = tf.train.AdagradOptimizer(learning_rate=0.01).minimize(loss) Explanation: Inizializziamo un algoritmo di ottimizzazione: End of explanation accuracy = tf.reduce_mean(tf.cast(tf.equal(y_tf, tf.round(output_sigmoid)), tf.float32), name='accuracy') Explanation: Per valutare l'accuratezza, definiamo una funzione ausiliaria: End of explanation sess = tf.InteractiveSession() tf.global_variables_initializer().run() Explanation: Creiamo una sessione ed inizializziamo tutte le variabili: End of explanation epochs = 25 batch_size = 10 Explanation: Definiamo una dimensione del mini-batch ed un numero di epoche: End of explanation import numpy as np, tqdm accuracy_history = np.zeros(epochs) for i in tqdm.tqdm_notebook(range(epochs)): accuracy_history[i] = sess.run(accuracy, feed_dict={X_tf: X_tst, y_tf: y_tst}) print('Current loss is: ', sess.run(loss, feed_dict={X_tf: X_trn, y_tf: y_trn})) for xs, ys in iterate_minibatches(X_trn, y_trn, batch_size): sess.run(train_step, feed_dict={X_tf: xs, y_tf: ys}) Explanation: Poiché il training potrebbe essere più lento dello scorso tutorial, installiamo un modulo per visualizzare una semplice barra di progresso: <code>$ pip install tqdm Minimizziamo la funzione costo, tenendo traccia dell'accuratezza sul test set ad ogni iterazione: End of explanation plt.figure() plt.semilogy(accuracy_history) plt.xlabel('Epoch') plt.ylabel('Test error') plt.grid() Explanation: Visualizziamo l'accuratezza media sul test set: End of explanation
9,203	Given the following text description, write Python code to implement the functionality described below step by step Description: ES-DOC CMIP6 Model Properties - Toplevel MIP Era Step1: Document Authors Set document authors Step2: Document Contributors Specify document contributors Step3: Document Publication Specify document publication status Step4: Document Table of Contents 1. Key Properties 2. Key Properties --> Flux Correction 3. Key Properties --> Genealogy 4. Key Properties --> Software Properties 5. Key Properties --> Coupling 6. Key Properties --> Tuning Applied 7. Key Properties --> Conservation --> Heat 8. Key Properties --> Conservation --> Fresh Water 9. Key Properties --> Conservation --> Salt 10. Key Properties --> Conservation --> Momentum 11. Radiative Forcings 12. Radiative Forcings --> Greenhouse Gases --> CO2 13. Radiative Forcings --> Greenhouse Gases --> CH4 14. Radiative Forcings --> Greenhouse Gases --> N2O 15. Radiative Forcings --> Greenhouse Gases --> Tropospheric O3 16. Radiative Forcings --> Greenhouse Gases --> Stratospheric O3 17. Radiative Forcings --> Greenhouse Gases --> CFC 18. Radiative Forcings --> Aerosols --> SO4 19. Radiative Forcings --> Aerosols --> Black Carbon 20. Radiative Forcings --> Aerosols --> Organic Carbon 21. Radiative Forcings --> Aerosols --> Nitrate 22. Radiative Forcings --> Aerosols --> Cloud Albedo Effect 23. Radiative Forcings --> Aerosols --> Cloud Lifetime Effect 24. Radiative Forcings --> Aerosols --> Dust 25. Radiative Forcings --> Aerosols --> Tropospheric Volcanic 26. Radiative Forcings --> Aerosols --> Stratospheric Volcanic 27. Radiative Forcings --> Aerosols --> Sea Salt 28. Radiative Forcings --> Other --> Land Use 29. Radiative Forcings --> Other --> Solar 1. Key Properties Key properties of the model 1.1. Model Overview Is Required Step5: 1.2. Model Name Is Required Step6: 2. Key Properties --> Flux Correction Flux correction properties of the model 2.1. Details Is Required Step7: 3. Key Properties --> Genealogy Genealogy and history of the model 3.1. Year Released Is Required Step8: 3.2. CMIP3 Parent Is Required Step9: 3.3. CMIP5 Parent Is Required Step10: 3.4. Previous Name Is Required Step11: 4. Key Properties --> Software Properties Software properties of model 4.1. Repository Is Required Step12: 4.2. Code Version Is Required Step13: 4.3. Code Languages Is Required Step14: 4.4. Components Structure Is Required Step15: 4.5. Coupler Is Required Step16: 5. Key Properties --> Coupling ** 5.1. Overview Is Required Step17: 5.2. Atmosphere Double Flux Is Required Step18: 5.3. Atmosphere Fluxes Calculation Grid Is Required Step19: 5.4. Atmosphere Relative Winds Is Required Step20: 6. Key Properties --> Tuning Applied Tuning methodology for model 6.1. Description Is Required Step21: 6.2. Global Mean Metrics Used Is Required Step22: 6.3. Regional Metrics Used Is Required Step23: 6.4. Trend Metrics Used Is Required Step24: 6.5. Energy Balance Is Required Step25: 6.6. Fresh Water Balance Is Required Step26: 7. Key Properties --> Conservation --> Heat Global heat convervation properties of the model 7.1. Global Is Required Step27: 7.2. Atmos Ocean Interface Is Required Step28: 7.3. Atmos Land Interface Is Required Step29: 7.4. Atmos Sea-ice Interface Is Required Step30: 7.5. Ocean Seaice Interface Is Required Step31: 7.6. Land Ocean Interface Is Required Step32: 8. Key Properties --> Conservation --> Fresh Water Global fresh water convervation properties of the model 8.1. Global Is Required Step33: 8.2. Atmos Ocean Interface Is Required Step34: 8.3. Atmos Land Interface Is Required Step35: 8.4. Atmos Sea-ice Interface Is Required Step36: 8.5. Ocean Seaice Interface Is Required Step37: 8.6. Runoff Is Required Step38: 8.7. Iceberg Calving Is Required Step39: 8.8. Endoreic Basins Is Required Step40: 8.9. Snow Accumulation Is Required Step41: 9. Key Properties --> Conservation --> Salt Global salt convervation properties of the model 9.1. Ocean Seaice Interface Is Required Step42: 10. Key Properties --> Conservation --> Momentum Global momentum convervation properties of the model 10.1. Details Is Required Step43: 11. Radiative Forcings Radiative forcings of the model for historical and scenario (aka Table 12.1 IPCC AR5) 11.1. Overview Is Required Step44: 12. Radiative Forcings --> Greenhouse Gases --> CO2 Carbon dioxide forcing 12.1. Provision Is Required Step45: 12.2. Additional Information Is Required Step46: 13. Radiative Forcings --> Greenhouse Gases --> CH4 Methane forcing 13.1. Provision Is Required Step47: 13.2. Additional Information Is Required Step48: 14. Radiative Forcings --> Greenhouse Gases --> N2O Nitrous oxide forcing 14.1. Provision Is Required Step49: 14.2. Additional Information Is Required Step50: 15. Radiative Forcings --> Greenhouse Gases --> Tropospheric O3 Troposheric ozone forcing 15.1. Provision Is Required Step51: 15.2. Additional Information Is Required Step52: 16. Radiative Forcings --> Greenhouse Gases --> Stratospheric O3 Stratospheric ozone forcing 16.1. Provision Is Required Step53: 16.2. Additional Information Is Required Step54: 17. Radiative Forcings --> Greenhouse Gases --> CFC Ozone-depleting and non-ozone-depleting fluorinated gases forcing 17.1. Provision Is Required Step55: 17.2. Equivalence Concentration Is Required Step56: 17.3. Additional Information Is Required Step57: 18. Radiative Forcings --> Aerosols --> SO4 SO4 aerosol forcing 18.1. Provision Is Required Step58: 18.2. Additional Information Is Required Step59: 19. Radiative Forcings --> Aerosols --> Black Carbon Black carbon aerosol forcing 19.1. Provision Is Required Step60: 19.2. Additional Information Is Required Step61: 20. Radiative Forcings --> Aerosols --> Organic Carbon Organic carbon aerosol forcing 20.1. Provision Is Required Step62: 20.2. Additional Information Is Required Step63: 21. Radiative Forcings --> Aerosols --> Nitrate Nitrate forcing 21.1. Provision Is Required Step64: 21.2. Additional Information Is Required Step65: 22. Radiative Forcings --> Aerosols --> Cloud Albedo Effect Cloud albedo effect forcing (RFaci) 22.1. Provision Is Required Step66: 22.2. Aerosol Effect On Ice Clouds Is Required Step67: 22.3. Additional Information Is Required Step68: 23. Radiative Forcings --> Aerosols --> Cloud Lifetime Effect Cloud lifetime effect forcing (ERFaci) 23.1. Provision Is Required Step69: 23.2. Aerosol Effect On Ice Clouds Is Required Step70: 23.3. RFaci From Sulfate Only Is Required Step71: 23.4. Additional Information Is Required Step72: 24. Radiative Forcings --> Aerosols --> Dust Dust forcing 24.1. Provision Is Required Step73: 24.2. Additional Information Is Required Step74: 25. Radiative Forcings --> Aerosols --> Tropospheric Volcanic Tropospheric volcanic forcing 25.1. Provision Is Required Step75: 25.2. Historical Explosive Volcanic Aerosol Implementation Is Required Step76: 25.3. Future Explosive Volcanic Aerosol Implementation Is Required Step77: 25.4. Additional Information Is Required Step78: 26. Radiative Forcings --> Aerosols --> Stratospheric Volcanic Stratospheric volcanic forcing 26.1. Provision Is Required Step79: 26.2. Historical Explosive Volcanic Aerosol Implementation Is Required Step80: 26.3. Future Explosive Volcanic Aerosol Implementation Is Required Step81: 26.4. Additional Information Is Required Step82: 27. Radiative Forcings --> Aerosols --> Sea Salt Sea salt forcing 27.1. Provision Is Required Step83: 27.2. Additional Information Is Required Step84: 28. Radiative Forcings --> Other --> Land Use Land use forcing 28.1. Provision Is Required Step85: 28.2. Crop Change Only Is Required Step86: 28.3. Additional Information Is Required Step87: 29. Radiative Forcings --> Other --> Solar Solar forcing 29.1. Provision Is Required Step88: 29.2. Additional Information Is Required	Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-1', 'toplevel') Explanation: ES-DOC CMIP6 Model Properties - Toplevel MIP Era: CMIP6 Institute: MOHC Source ID: SANDBOX-1 Sub-Topics: Radiative Forcings. Properties: 85 (42 required) Model descriptions: Model description details Initialized From: -- Notebook Help: Goto notebook help page Notebook Initialised: 2018-02-15 16:54:15 Document Setup IMPORTANT: to be executed each time you run the notebook End of explanation # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) Explanation: Document Authors Set document authors End of explanation # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) Explanation: Document Contributors Specify document contributors End of explanation # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) Explanation: Document Publication Specify document publication status End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: Document Table of Contents 1. Key Properties 2. Key Properties --> Flux Correction 3. Key Properties --> Genealogy 4. Key Properties --> Software Properties 5. Key Properties --> Coupling 6. Key Properties --> Tuning Applied 7. Key Properties --> Conservation --> Heat 8. Key Properties --> Conservation --> Fresh Water 9. Key Properties --> Conservation --> Salt 10. Key Properties --> Conservation --> Momentum 11. Radiative Forcings 12. Radiative Forcings --> Greenhouse Gases --> CO2 13. Radiative Forcings --> Greenhouse Gases --> CH4 14. Radiative Forcings --> Greenhouse Gases --> N2O 15. Radiative Forcings --> Greenhouse Gases --> Tropospheric O3 16. Radiative Forcings --> Greenhouse Gases --> Stratospheric O3 17. Radiative Forcings --> Greenhouse Gases --> CFC 18. Radiative Forcings --> Aerosols --> SO4 19. Radiative Forcings --> Aerosols --> Black Carbon 20. Radiative Forcings --> Aerosols --> Organic Carbon 21. Radiative Forcings --> Aerosols --> Nitrate 22. Radiative Forcings --> Aerosols --> Cloud Albedo Effect 23. Radiative Forcings --> Aerosols --> Cloud Lifetime Effect 24. Radiative Forcings --> Aerosols --> Dust 25. Radiative Forcings --> Aerosols --> Tropospheric Volcanic 26. Radiative Forcings --> Aerosols --> Stratospheric Volcanic 27. Radiative Forcings --> Aerosols --> Sea Salt 28. Radiative Forcings --> Other --> Land Use 29. Radiative Forcings --> Other --> Solar 1. Key Properties Key properties of the model 1.1. Model Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Top level overview of coupled model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 1.2. Model Name Is Required: TRUE    Type: STRING    Cardinality: 1.1 Name of coupled model. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.flux_correction.details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 2. Key Properties --> Flux Correction Flux correction properties of the model 2.1. Details Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe if/how flux corrections are applied in the model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.year_released') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 3. Key Properties --> Genealogy Genealogy and history of the model 3.1. Year Released Is Required: TRUE    Type: STRING    Cardinality: 1.1 Year the model was released End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.CMIP3_parent') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 3.2. CMIP3 Parent Is Required: FALSE    Type: STRING    Cardinality: 0.1 CMIP3 parent if any End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.CMIP5_parent') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 3.3. CMIP5 Parent Is Required: FALSE    Type: STRING    Cardinality: 0.1 CMIP5 parent if any End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.previous_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 3.4. Previous Name Is Required: FALSE    Type: STRING    Cardinality: 0.1 Previously known as End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.repository') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 4. Key Properties --> Software Properties Software properties of model 4.1. Repository Is Required: FALSE    Type: STRING    Cardinality: 0.1 Location of code for this component. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.code_version') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 4.2. Code Version Is Required: FALSE    Type: STRING    Cardinality: 0.1 Code version identifier. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.code_languages') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 4.3. Code Languages Is Required: FALSE    Type: STRING    Cardinality: 0.N Code language(s). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.components_structure') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 4.4. Components Structure Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe how model realms are structured into independent software components (coupled via a coupler) and internal software components. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.coupler') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "OASIS" # "OASIS3-MCT" # "ESMF" # "NUOPC" # "Bespoke" # "Unknown" # "None" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 4.5. Coupler Is Required: FALSE    Type: ENUM    Cardinality: 0.1 Overarching coupling framework for model. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.coupling.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 5. Key Properties --> Coupling ** 5.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of coupling in the model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_double_flux') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 5.2. Atmosphere Double Flux Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is the atmosphere passing a double flux to the ocean and sea ice (as opposed to a single one)? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_fluxes_calculation_grid') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Atmosphere grid" # "Ocean grid" # "Specific coupler grid" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 5.3. Atmosphere Fluxes Calculation Grid Is Required: FALSE    Type: ENUM    Cardinality: 0.1 Where are the air-sea fluxes calculated End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_relative_winds') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 5.4. Atmosphere Relative Winds Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Are relative or absolute winds used to compute the flux? I.e. do ocean surface currents enter the wind stress calculation? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 6. Key Properties --> Tuning Applied Tuning methodology for model 6.1. Description Is Required: TRUE    Type: STRING    Cardinality: 1.1 General overview description of tuning: explain and motivate the main targets and metrics/diagnostics retained. Document the relative weight given to climate performance metrics/diagnostics versus process oriented metrics/diagnostics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.global_mean_metrics_used') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 6.2. Global Mean Metrics Used Is Required: FALSE    Type: STRING    Cardinality: 0.N List set of metrics/diagnostics of the global mean state used in tuning model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.regional_metrics_used') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 6.3. Regional Metrics Used Is Required: FALSE    Type: STRING    Cardinality: 0.N List of regional metrics/diagnostics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.trend_metrics_used') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 6.4. Trend Metrics Used Is Required: FALSE    Type: STRING    Cardinality: 0.N List observed trend metrics/diagnostics used in tuning model/component (such as 20th century) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.energy_balance') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 6.5. Energy Balance Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe how energy balance was obtained in the full system: in the various components independently or at the components coupling stage? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.fresh_water_balance') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 6.6. Fresh Water Balance Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe how fresh_water balance was obtained in the full system: in the various components independently or at the components coupling stage? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.global') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 7. Key Properties --> Conservation --> Heat Global heat convervation properties of the model 7.1. Global Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe if/how heat is conserved globally End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_ocean_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 7.2. Atmos Ocean Interface Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how heat is conserved at the atmosphere/ocean coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_land_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 7.3. Atmos Land Interface Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe if/how heat is conserved at the atmosphere/land coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_sea-ice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 7.4. Atmos Sea-ice Interface Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how heat is conserved at the atmosphere/sea-ice coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.ocean_seaice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 7.5. Ocean Seaice Interface Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how heat is conserved at the ocean/sea-ice coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.land_ocean_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 7.6. Land Ocean Interface Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how heat is conserved at the land/ocean coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.global') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8. Key Properties --> Conservation --> Fresh Water Global fresh water convervation properties of the model 8.1. Global Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe if/how fresh_water is conserved globally End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_ocean_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.2. Atmos Ocean Interface Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how fresh_water is conserved at the atmosphere/ocean coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_land_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.3. Atmos Land Interface Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe if/how fresh water is conserved at the atmosphere/land coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_sea-ice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.4. Atmos Sea-ice Interface Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how fresh water is conserved at the atmosphere/sea-ice coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.ocean_seaice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.5. Ocean Seaice Interface Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how fresh water is conserved at the ocean/sea-ice coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.runoff') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.6. Runoff Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe how runoff is distributed and conserved End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.iceberg_calving') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.7. Iceberg Calving Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how iceberg calving is modeled and conserved End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.endoreic_basins') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.8. Endoreic Basins Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how endoreic basins (no ocean access) are treated End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.snow_accumulation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.9. Snow Accumulation Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe how snow accumulation over land and over sea-ice is treated End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.salt.ocean_seaice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 9. Key Properties --> Conservation --> Salt Global salt convervation properties of the model 9.1. Ocean Seaice Interface Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how salt is conserved at the ocean/sea-ice coupling interface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.momentum.details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 10. Key Properties --> Conservation --> Momentum Global momentum convervation properties of the model 10.1. Details Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how momentum is conserved in the model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 11. Radiative Forcings Radiative forcings of the model for historical and scenario (aka Table 12.1 IPCC AR5) 11.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of radiative forcings (GHG and aerosols) implementation in model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CO2.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 12. Radiative Forcings --> Greenhouse Gases --> CO2 Carbon dioxide forcing 12.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CO2.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 12.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CH4.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 13. Radiative Forcings --> Greenhouse Gases --> CH4 Methane forcing 13.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CH4.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 13.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.N2O.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 14. Radiative Forcings --> Greenhouse Gases --> N2O Nitrous oxide forcing 14.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.N2O.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 14.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.tropospheric_O3.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 15. Radiative Forcings --> Greenhouse Gases --> Tropospheric O3 Troposheric ozone forcing 15.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.tropospheric_O3.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 15.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.stratospheric_O3.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 16. Radiative Forcings --> Greenhouse Gases --> Stratospheric O3 Stratospheric ozone forcing 16.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.stratospheric_O3.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 16.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17. Radiative Forcings --> Greenhouse Gases --> CFC Ozone-depleting and non-ozone-depleting fluorinated gases forcing 17.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.equivalence_concentration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "Option 1" # "Option 2" # "Option 3" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.2. Equivalence Concentration Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Details of any equivalence concentrations used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.3. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.SO4.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 18. Radiative Forcings --> Aerosols --> SO4 SO4 aerosol forcing 18.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.SO4.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 18.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.black_carbon.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 19. Radiative Forcings --> Aerosols --> Black Carbon Black carbon aerosol forcing 19.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.black_carbon.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 19.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.organic_carbon.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 20. Radiative Forcings --> Aerosols --> Organic Carbon Organic carbon aerosol forcing 20.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.organic_carbon.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 20.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.nitrate.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 21. Radiative Forcings --> Aerosols --> Nitrate Nitrate forcing 21.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.nitrate.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 21.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 22. Radiative Forcings --> Aerosols --> Cloud Albedo Effect Cloud albedo effect forcing (RFaci) 22.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.aerosol_effect_on_ice_clouds') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 22.2. Aerosol Effect On Ice Clouds Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Radiative effects of aerosols on ice clouds are represented? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 22.3. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 23. Radiative Forcings --> Aerosols --> Cloud Lifetime Effect Cloud lifetime effect forcing (ERFaci) 23.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.aerosol_effect_on_ice_clouds') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 23.2. Aerosol Effect On Ice Clouds Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Radiative effects of aerosols on ice clouds are represented? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.RFaci_from_sulfate_only') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 23.3. RFaci From Sulfate Only Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Radiative forcing from aerosol cloud interactions from sulfate aerosol only? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 23.4. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.dust.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 24. Radiative Forcings --> Aerosols --> Dust Dust forcing 24.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.dust.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 24.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 25. Radiative Forcings --> Aerosols --> Tropospheric Volcanic Tropospheric volcanic forcing 25.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.historical_explosive_volcanic_aerosol_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Type A" # "Type B" # "Type C" # "Type D" # "Type E" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 25.2. Historical Explosive Volcanic Aerosol Implementation Is Required: TRUE    Type: ENUM    Cardinality: 1.1 How explosive volcanic aerosol is implemented in historical simulations End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.future_explosive_volcanic_aerosol_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Type A" # "Type B" # "Type C" # "Type D" # "Type E" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 25.3. Future Explosive Volcanic Aerosol Implementation Is Required: TRUE    Type: ENUM    Cardinality: 1.1 How explosive volcanic aerosol is implemented in future simulations End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 25.4. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 26. Radiative Forcings --> Aerosols --> Stratospheric Volcanic Stratospheric volcanic forcing 26.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.historical_explosive_volcanic_aerosol_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Type A" # "Type B" # "Type C" # "Type D" # "Type E" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 26.2. Historical Explosive Volcanic Aerosol Implementation Is Required: TRUE    Type: ENUM    Cardinality: 1.1 How explosive volcanic aerosol is implemented in historical simulations End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.future_explosive_volcanic_aerosol_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Type A" # "Type B" # "Type C" # "Type D" # "Type E" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 26.3. Future Explosive Volcanic Aerosol Implementation Is Required: TRUE    Type: ENUM    Cardinality: 1.1 How explosive volcanic aerosol is implemented in future simulations End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 26.4. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.sea_salt.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 27. Radiative Forcings --> Aerosols --> Sea Salt Sea salt forcing 27.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.sea_salt.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 27.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 28. Radiative Forcings --> Other --> Land Use Land use forcing 28.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.crop_change_only') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 28.2. Crop Change Only Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Land use change represented via crop change only? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 28.3. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.solar.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "irradiance" # "proton" # "electron" # "cosmic ray" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 29. Radiative Forcings --> Other --> Solar Solar forcing 29.1. Provision Is Required: TRUE    Type: ENUM    Cardinality: 1.N How solar forcing is provided End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.solar.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 29.2. Additional Information Is Required: FALSE    Type: STRING    Cardinality: 0.1 Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). End of explanation
9,204	Given the following text description, write Python code to implement the functionality described below step by step Description: Executed Step1: Load software and filenames definitions Step2: Data folder Step3: List of data files Step4: Data load Initial loading of the data Step5: Laser alternation selection At this point we have only the timestamps and the detector numbers Step6: We need to define some parameters Step7: We should check if everithing is OK with an alternation histogram Step8: If the plot looks good we can apply the parameters with Step9: Measurements infos All the measurement data is in the d variable. We can print it Step10: Or check the measurements duration Step11: Compute background Compute the background using automatic threshold Step12: Burst search and selection Step14: Donor Leakage fit Half-Sample Mode Fit peak usng the mode computed with the half-sample algorithm (Bickel 2005). Step15: Gaussian Fit Fit the histogram with a gaussian Step16: KDE maximum Step17: Leakage summary Step18: Burst size distribution Step19: Fret fit Max position of the Kernel Density Estimation (KDE) Step20: Weighted mean of $E$ of each burst Step21: Gaussian fit (no weights) Step22: Gaussian fit (using burst size as weights) Step23: Stoichiometry fit Max position of the Kernel Density Estimation (KDE) Step24: The Maximum likelihood fit for a Gaussian population is the mean Step25: Computing the weighted mean and weighted standard deviation we get Step26: Save data to file Step27: The following string contains the list of variables to be saved. When saving, the order of the variables is preserved. Step28: This is just a trick to format the different variables	Python Code: ph_sel_name = "DexDem" data_id = "17d" # ph_sel_name = "all-ph" # data_id = "7d" Explanation: Executed: Mon Mar 27 11:35:09 2017 Duration: 11 seconds. usALEX-5samples - Template This notebook is executed through 8-spots paper analysis. For a direct execution, uncomment the cell below. End of explanation from fretbursts import * init_notebook() from IPython.display import display Explanation: Load software and filenames definitions End of explanation data_dir = './data/singlespot/' import os data_dir = os.path.abspath(data_dir) + '/' assert os.path.exists(data_dir), "Path '%s' does not exist." % data_dir Explanation: Data folder: End of explanation from glob import glob file_list = sorted(f for f in glob(data_dir + '.hdf5') if '_BKG' not in f) ## Selection for POLIMI 2012-11-26 datatset labels = ['17d', '27d', '7d', '12d', '22d'] files_dict = {lab: fname for lab, fname in zip(labels, file_list)} files_dict ph_sel_map = {'all-ph': Ph_sel('all'), 'Dex': Ph_sel(Dex='DAem'), 'DexDem': Ph_sel(Dex='Dem')} ph_sel = ph_sel_map[ph_sel_name] data_id, ph_sel_name Explanation: List of data files: End of explanation d = loader.photon_hdf5(filename=files_dict[data_id]) Explanation: Data load Initial loading of the data: End of explanation d.ph_times_t, d.det_t Explanation: Laser alternation selection At this point we have only the timestamps and the detector numbers: End of explanation d.add(det_donor_accept=(0, 1), alex_period=4000, D_ON=(2850, 580), A_ON=(900, 2580), offset=0) Explanation: We need to define some parameters: donor and acceptor ch, excitation period and donor and acceptor excitiations: End of explanation plot_alternation_hist(d) Explanation: We should check if everithing is OK with an alternation histogram: End of explanation loader.alex_apply_period(d) Explanation: If the plot looks good we can apply the parameters with: End of explanation d Explanation: Measurements infos All the measurement data is in the d variable. We can print it: End of explanation d.time_max Explanation: Or check the measurements duration: End of explanation d.calc_bg(bg.exp_fit, time_s=60, tail_min_us='auto', F_bg=1.7) dplot(d, timetrace_bg) d.rate_m, d.rate_dd, d.rate_ad, d.rate_aa Explanation: Compute background Compute the background using automatic threshold: End of explanation bs_kws = dict(L=10, m=10, F=7, ph_sel=ph_sel) d.burst_search(bs_kws) th1 = 30 ds = d.select_bursts(select_bursts.size, th1=30) bursts = (bext.burst_data(ds, include_bg=True, include_ph_index=True) .round({'E': 6, 'S': 6, 'bg_d': 3, 'bg_a': 3, 'bg_aa': 3, 'nd': 3, 'na': 3, 'naa': 3, 'nda': 3, 'nt': 3, 'width_ms': 4})) bursts.head() burst_fname = ('results/bursts_usALEX_{sample}_{ph_sel}_F{F:.1f}_m{m}_size{th}.csv' .format(sample=data_id, th=th1, bs_kws)) burst_fname bursts.to_csv(burst_fname) assert d.dir_ex == 0 assert d.leakage == 0 print(d.ph_sel) dplot(d, hist_fret); # if data_id in ['7d', '27d']: # ds = d.select_bursts(select_bursts.size, th1=20) # else: # ds = d.select_bursts(select_bursts.size, th1=30) ds = d.select_bursts(select_bursts.size, add_naa=False, th1=30) n_bursts_all = ds.num_bursts[0] def select_and_plot_ES(fret_sel, do_sel): ds_fret= ds.select_bursts(select_bursts.ES, fret_sel) ds_do = ds.select_bursts(select_bursts.ES, do_sel) bpl.plot_ES_selection(ax, fret_sel) bpl.plot_ES_selection(ax, do_sel) return ds_fret, ds_do ax = dplot(ds, hist2d_alex, S_max_norm=2, scatter_alpha=0.1) if data_id == '7d': fret_sel = dict(E1=0.60, E2=1.2, S1=0.2, S2=0.9, rect=False) do_sel = dict(E1=-0.2, E2=0.5, S1=0.8, S2=2, rect=True) ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) elif data_id == '12d': fret_sel = dict(E1=0.30,E2=1.2,S1=0.131,S2=0.9, rect=False) do_sel = dict(E1=-0.4, E2=0.4, S1=0.8, S2=2, rect=False) ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) elif data_id == '17d': fret_sel = dict(E1=0.01, E2=0.98, S1=0.14, S2=0.88, rect=False) do_sel = dict(E1=-0.4, E2=0.4, S1=0.80, S2=2, rect=False) ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) elif data_id == '22d': fret_sel = dict(E1=-0.16, E2=0.6, S1=0.2, S2=0.80, rect=False) do_sel = dict(E1=-0.2, E2=0.4, S1=0.85, S2=2, rect=True) ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) elif data_id == '27d': fret_sel = dict(E1=-0.1, E2=0.5, S1=0.2, S2=0.82, rect=False) do_sel = dict(E1=-0.2, E2=0.4, S1=0.88, S2=2, rect=True) ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) n_bursts_do = ds_do.num_bursts[0] n_bursts_fret = ds_fret.num_bursts[0] n_bursts_do, n_bursts_fret d_only_frac = 1.n_bursts_do/(n_bursts_do + n_bursts_fret) print ('D-only fraction:', d_only_frac) dplot(ds_fret, hist2d_alex, scatter_alpha=0.1); dplot(ds_do, hist2d_alex, S_max_norm=2, scatter=False); Explanation: Burst search and selection End of explanation def hsm_mode(s): Half-sample mode (HSM) estimator of `s`. `s` is a sample from a continuous distribution with a single peak. Reference: Bickel, Fruehwirth (2005). arXiv:math/0505419 s = memoryview(np.sort(s)) i1 = 0 i2 = len(s) while i2 - i1 > 3: n = (i2 - i1) // 2 w = [s[n-1+i+i1] - s[i+i1] for i in range(n)] i1 = w.index(min(w)) + i1 i2 = i1 + n if i2 - i1 == 3: if s[i1+1] - s[i1] < s[i2] - s[i1 + 1]: i2 -= 1 elif s[i1+1] - s[i1] > s[i2] - s[i1 + 1]: i1 += 1 else: i1 = i2 = i1 + 1 return 0.5(s[i1] + s[i2]) E_pr_do_hsm = hsm_mode(ds_do.E[0]) print ("%s: E_peak(HSM) = %.2f%%" % (ds.ph_sel, E_pr_do_hsm100)) Explanation: Donor Leakage fit Half-Sample Mode Fit peak usng the mode computed with the half-sample algorithm (Bickel 2005). End of explanation E_fitter = bext.bursts_fitter(ds_do, weights=None) E_fitter.histogram(bins=np.arange(-0.2, 1, 0.03)) E_fitter.fit_histogram(model=mfit.factory_gaussian()) E_fitter.params res = E_fitter.fit_res[0] res.params.pretty_print() E_pr_do_gauss = res.best_values['center'] E_pr_do_gauss Explanation: Gaussian Fit Fit the histogram with a gaussian: End of explanation bandwidth = 0.03 E_range_do = (-0.1, 0.15) E_ax = np.r_[-0.2:0.401:0.0002] E_fitter.calc_kde(bandwidth=bandwidth) E_fitter.find_kde_max(E_ax, xmin=E_range_do[0], xmax=E_range_do[1]) E_pr_do_kde = E_fitter.kde_max_pos[0] E_pr_do_kde Explanation: KDE maximum End of explanation mfit.plot_mfit(ds_do.E_fitter, plot_kde=True, plot_model=False) plt.axvline(E_pr_do_hsm, color='m', label='HSM') plt.axvline(E_pr_do_gauss, color='k', label='Gauss') plt.axvline(E_pr_do_kde, color='r', label='KDE') plt.xlim(0, 0.3) plt.legend() print('Gauss: %.2f%%\n KDE: %.2f%%\n HSM: %.2f%%' % (E_pr_do_gauss100, E_pr_do_kde100, E_pr_do_hsm100)) Explanation: Leakage summary End of explanation nt_th1 = 50 dplot(ds_fret, hist_size, which='all', add_naa=False) xlim(-0, 250) plt.axvline(nt_th1) Th_nt = np.arange(35, 120) nt_th = np.zeros(Th_nt.size) for i, th in enumerate(Th_nt): ds_nt = ds_fret.select_bursts(select_bursts.size, th1=th) nt_th[i] = (ds_nt.nd[0] + ds_nt.na[0]).mean() - th plt.figure() plot(Th_nt, nt_th) plt.axvline(nt_th1) nt_mean = nt_th[np.where(Th_nt == nt_th1)][0] nt_mean Explanation: Burst size distribution End of explanation E_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, bandwidth=bandwidth, weights='size') E_fitter = ds_fret.E_fitter E_fitter.histogram(bins=np.r_[-0.1:1.1:0.03]) E_fitter.fit_histogram(mfit.factory_gaussian(center=0.5)) E_fitter.fit_res[0].params.pretty_print() fig, ax = plt.subplots(1, 2, figsize=(14, 4.5)) mfit.plot_mfit(E_fitter, ax=ax[0]) mfit.plot_mfit(E_fitter, plot_model=False, plot_kde=True, ax=ax[1]) print('%s\nKDE peak %.2f ' % (ds_fret.ph_sel, E_pr_fret_kde100)) display(E_fitter.params100) Explanation: Fret fit Max position of the Kernel Density Estimation (KDE): End of explanation ds_fret.fit_E_m(weights='size') Explanation: Weighted mean of $E$ of each burst: End of explanation ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.03], weights=None) Explanation: Gaussian fit (no weights): End of explanation ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.005], weights='size') E_kde_w = E_fitter.kde_max_pos[0] E_gauss_w = E_fitter.params.loc[0, 'center'] E_gauss_w_sig = E_fitter.params.loc[0, 'sigma'] E_gauss_w_err = float(E_gauss_w_sig/np.sqrt(ds_fret.num_bursts[0])) E_gauss_w_fiterr = E_fitter.fit_res[0].params['center'].stderr E_kde_w, E_gauss_w, E_gauss_w_sig, E_gauss_w_err, E_gauss_w_fiterr Explanation: Gaussian fit (using burst size as weights): End of explanation S_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, burst_data='S', bandwidth=0.03) #weights='size', add_naa=True) S_fitter = ds_fret.S_fitter S_fitter.histogram(bins=np.r_[-0.1:1.1:0.03]) S_fitter.fit_histogram(mfit.factory_gaussian(), center=0.5) fig, ax = plt.subplots(1, 2, figsize=(14, 4.5)) mfit.plot_mfit(S_fitter, ax=ax[0]) mfit.plot_mfit(S_fitter, plot_model=False, plot_kde=True, ax=ax[1]) print('%s\nKDE peak %.2f ' % (ds_fret.ph_sel, S_pr_fret_kde100)) display(S_fitter.params100) S_kde = S_fitter.kde_max_pos[0] S_gauss = S_fitter.params.loc[0, 'center'] S_gauss_sig = S_fitter.params.loc[0, 'sigma'] S_gauss_err = float(S_gauss_sig/np.sqrt(ds_fret.num_bursts[0])) S_gauss_fiterr = S_fitter.fit_res[0].params['center'].stderr S_kde, S_gauss, S_gauss_sig, S_gauss_err, S_gauss_fiterr Explanation: Stoichiometry fit Max position of the Kernel Density Estimation (KDE): End of explanation S = ds_fret.S[0] S_ml_fit = (S.mean(), S.std()) S_ml_fit Explanation: The Maximum likelihood fit for a Gaussian population is the mean: End of explanation weights = bl.fret_fit.get_weights(ds_fret.nd[0], ds_fret.na[0], weights='size', naa=ds_fret.naa[0], gamma=1.) S_mean = np.dot(weights, S)/weights.sum() S_std_dev = np.sqrt( np.dot(weights, (S - S_mean)2)/weights.sum()) S_wmean_fit = [S_mean, S_std_dev] S_wmean_fit Explanation: Computing the weighted mean and weighted standard deviation we get: End of explanation sample = data_id Explanation: Save data to file End of explanation variables = ('sample n_bursts_all n_bursts_do n_bursts_fret ' 'E_kde_w E_gauss_w E_gauss_w_sig E_gauss_w_err E_gauss_w_fiterr ' 'S_kde S_gauss S_gauss_sig S_gauss_err S_gauss_fiterr ' 'E_pr_do_kde E_pr_do_hsm E_pr_do_gauss nt_mean\n') Explanation: The following string contains the list of variables to be saved. When saving, the order of the variables is preserved. End of explanation variables_csv = variables.replace(' ', ',') fmt_float = '{%s:.6f}' fmt_int = '{%s:d}' fmt_str = '{%s}' fmt_dict = {{'sample': fmt_str}, {k: fmt_int for k in variables.split() if k.startswith('n_bursts')}} var_dict = {name: eval(name) for name in variables.split()} var_fmt = ', '.join([fmt_dict.get(name, fmt_float) % name for name in variables.split()]) + '\n' data_str = var_fmt.format(*var_dict) print(variables_csv) print(data_str) # NOTE: The file name should be the notebook name but with .csv extension with open('results/usALEX-5samples-PR-raw-%s.csv' % ph_sel_name, 'a') as f: f.seek(0, 2) if f.tell() == 0: f.write(variables_csv) f.write(data_str) Explanation: This is just a trick to format the different variables: End of explanation
9,205	Given the following text description, write Python code to implement the functionality described below step by step Description: Matplotlib Exercise 1 Imports Step1: Line plot of sunspot data Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from the SILSO website. Upload the file to the same directory as this notebook. Step3: Use np.loadtxt to read the data into a NumPy array called data. Then create two new 1d NumPy arrays named years and ssc that have the sequence of year and sunspot counts. Step4: Make a line plot showing the sunspot count as a function of year. Customize your plot to follow Tufte's principles of visualizations. Adjust the aspect ratio/size so that the steepest slope in your plot is approximately 1. Customize the box, grid, spines and ticks to match the requirements of this data. Step5: Describe the choices you have made in building this visualization and how they make it effective. YOUR ANSWER HERE Chose to not have box or gridlines in order to maximize data to ink ratio, this looks cleaner and makes the data eaiser to read. Labled the axes this way because gave acurate yet simple discription of the data plotted. I chose the scale in order to make max slope closer to 1. The y-axis and x-axis ticks were chosen to be able to show the data scope without completely over crowding the axes. Now make 4 subplots, one for each century in the data set. This approach works well for this dataset as it allows you to maintain mild slopes while limiting the overall width of the visualization. Perform similar customizations as above	Python Code: %matplotlib inline import matplotlib.pyplot as plt import numpy as np Explanation: Matplotlib Exercise 1 Imports End of explanation import os assert os.path.isfile('yearssn.dat') Explanation: Line plot of sunspot data Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from the SILSO website. Upload the file to the same directory as this notebook. End of explanation # YOUR CODE HERE #raise NotImplementedError() data = np.loadtxt('yearssn.dat') year = np.ones(len(data)) ssc = np.ones(len(data)) #makes two new arrays, one for each array in data for i in range(0, len(data)): ssc[i] = data[i][1] year[i] = data[i][0] assert len(year)==315 assert year.dtype==np.dtype(float) assert len(ssc)==315 assert ssc.dtype==np.dtype(float) #origonally used this to find max slope so I could scale for slope of 1 #turned out I didnt need it. "def average(data,num): avex=[] avey=[] x=data[0] y=data[1] for i in range(len(x)-(num)): avex.append(sum(x[i:i+num])/num) avey.append(sum(y[i:i+num])/num) return avex,avey x_ave,y_ave = average((year,ssc), 4) def max_slope(x,y): s=[] for i in range(0, len(x)-1): s.append((y[i+1]-y[i])/(x[i+1]-x[i])) return max(s) max_slope(x_ave, y_ave) Explanation: Use np.loadtxt to read the data into a NumPy array called data. Then create two new 1d NumPy arrays named years and ssc that have the sequence of year and sunspot counts. End of explanation # YOUR CODE HERE #raise NotImplementedError() f= plt.figure(figsize=(15,1)) plt.plot(year, ssc) plt.xlabel('Year') plt.ylabel('Number of Sunspots') plt.title('Sunspot Count') plt.xlim(1700,2015) plt.ylim(0,191) plt.box(False) a=range(192) b= range(1700,2016) plt.yticks(a[::50]) plt.show() assert True # leave for grading Explanation: Make a line plot showing the sunspot count as a function of year. Customize your plot to follow Tufte's principles of visualizations. Adjust the aspect ratio/size so that the steepest slope in your plot is approximately 1. Customize the box, grid, spines and ticks to match the requirements of this data. End of explanation # YOUR CODE HERE #raise NotImplementedError() a= range(192) f = plt.figure(figsize=(15,6)) plt.subplot(4,1,1) plt.yticks(a[::50]) plt.title('Sunspot Count') plt.plot(year[0:100],ssc[0:100]) plt.box(False) plt.subplot(4,1,2) plt.plot(year[100:200], ssc[100:200]) plt.yticks(a[::50]) plt.box(False) plt.subplot(4,1,3) plt.plot(year[200:300],ssc[200:300]) plt.yticks(a[::50]) plt.ylabel('Number of Sunspots') plt.box(False) plt.subplot(4,1,4) plt.plot(year[300:400], ssc[300:400]) plt.yticks(a[::50]) plt.xlim(2000,2100) plt.xlabel('Year') plt.box(False) plt.tight_layout() assert True # leave for grading Explanation: Describe the choices you have made in building this visualization and how they make it effective. YOUR ANSWER HERE Chose to not have box or gridlines in order to maximize data to ink ratio, this looks cleaner and makes the data eaiser to read. Labled the axes this way because gave acurate yet simple discription of the data plotted. I chose the scale in order to make max slope closer to 1. The y-axis and x-axis ticks were chosen to be able to show the data scope without completely over crowding the axes. Now make 4 subplots, one for each century in the data set. This approach works well for this dataset as it allows you to maintain mild slopes while limiting the overall width of the visualization. Perform similar customizations as above: Customize your plot to follow Tufte's principles of visualizations. Adjust the aspect ratio/size so that the steepest slope in your plot is approximately 1. Customize the box, grid, spines and ticks to match the requirements of this data. End of explanation
9,206	Given the following text description, write Python code to implement the functionality described below step by step Description: <div align="right">Python 2.7</div> Indexing and Related Experiments in Python 2.7 Though this content is in Python 2.7, most if not all of it should work the same in Python 3.x. TOC Indexing Experiments - Explores different complex structures and how to index into them Mutation sidebar - looks at mutation using our crazyList example Finding the Index of a Known Value within Complex Data Structures - explores .index(), np.where(), and Related concerns Indexing Experiments in Python We start with a simple nested list showing how to get at an element within it Step1: Now we build something more complicated to show where indexing can get tricky ... Step2: Notice which numbers moved where. This would seem to indicate that in shape(a,b,c) Step3: Just analyzing how the numbers are arranged, we see that in shape(a,b,c,d), it just added the new extra dimensional layer to the front of the list so that now Step4: Now let's access other stuff in the list ... Step5: In the tests that follow ... anything that does not work is wrapped in exception handling (that displays the error) so this notebook can be run from start to finish ... Note that it is not good practice to use a catch all for all errors. In real coding errors should be handled individually by type. How do we access the first index (element 2) of the first array object in our complex list (which resides at index 0)? Step6: Sub element 4 is a simple list nested within caryList Step7: So what about the array? The array was originally built in "simp1" and then added to crazyList. Its source looks like this Step8: Note the [] versus the [[]] ... our "simple arrays" were copied from an example, but are actually nested objects of 1 list of 5 elements forming the first object inside the array. A true simple array would like this Step9: Let's add the true simple array to our crazy object and then create working examples of accessing everything ... Step10: Looking at just that first element again Step11: remember that this object if it were not in a list would be accessed like so Step12: ... so inside crazyList ? The answer is that the list is one level deep and the elements are yet another level in Step13: <a id="mutation" name="mutation"></a> Sidebar Step14: Note how the second is really a reference to the first so changing one changes the other Step15: For a simple list ... we can fix that by simply using list() during our attempt to create the copy Step16: Mutation is avoided. Now we can change our two objects independantly. However, with complex objects like crazyList, this does not work. The following will illustrate the problem and later, options to get around it are presented. Step17: Now we make some changes Step18: Now we'll look at just the last object in both "crazyLists" showing what changed Step19: The "13" replaced the value at this location in both crazyList and crazyList2. We are not dealing with true copies but rather references to the same data as further illustrated here Step20: So ... how to make a copy that does not mutate? (we can change one without changing the other)?<br/> Let's look at some things that don't work first ... Step21: Python is hard to fool ... At first, I considered that we might now have two lists, but w/ just element 7 passed in by reference and so it mutates. But this shows our whole lists are still mutating Step22: deepcopy() comes from the copy library and the commands are documented at Python.org. For this situation, this solution seems to work for when mutation is undesirable Step23: Should even deepcopy() not work, this topic online may prove helpful in these situations	Python Code: stupidList = [[1,2,3],[4,5,6]] print(stupidList) stupidList[0][1] Explanation: <div align="right">Python 2.7</div> Indexing and Related Experiments in Python 2.7 Though this content is in Python 2.7, most if not all of it should work the same in Python 3.x. TOC Indexing Experiments - Explores different complex structures and how to index into them Mutation sidebar - looks at mutation using our crazyList example Finding the Index of a Known Value within Complex Data Structures - explores .index(), np.where(), and Related concerns Indexing Experiments in Python We start with a simple nested list showing how to get at an element within it: End of explanation import numpy as np import pandas as pd m3d=np.random.rand(3,4,5) m3d # how does Pandas arrange the data? n3d=m3d.reshape(4,3,5) n3d Explanation: Now we build something more complicated to show where indexing can get tricky ... End of explanation o3d=np.random.rand(2,3,4,5) o3d Explanation: Notice which numbers moved where. This would seem to indicate that in shape(a,b,c): - a is like the object's depth (how many groupings of rows/columns are there?) - b is like the object's rows per grouping (how many rows in each subgroup) - c is like the object's columns What if the object had 4 dimensions? End of explanation # some simple arrays: simp1=np.array([[1,2,3,4,5]]) simp2=np.array([[10,9,8,7,6]]) simp3=[11,12,13] # a dictionary dfrm1 = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2002, 2001, 2002], 'population': [1.5, 1.7, 3.6, 2.4, 2.9]} # convert dictionary to DataFrame dfrm1 = pd.DataFrame(dfrm1) dfrm1 # pandas indexing works a little differently: # * column headers are keys # * as shown here, can ask for columns, rows, and a filter based on values in the columns # in any order and the indexing will still work print(dfrm1["population"][dfrm1["population"] > 1.5][2:4]) # all of these return values from "population" column only print("---") # where "population" > 1.5 print(dfrm1["population"][2:4][dfrm1["population"] > 1.5]) # and row index is between 2 and 4 print("---") print(dfrm1[dfrm1["population"] > 1.5]["population"][2:4]) print("---") print(dfrm1[dfrm1["population"] > 1.5][2:4]["population"]) print("---") print(dfrm1[2:4]["population"][dfrm1["population"] > 1.5]) print("---") print(dfrm1[2:4][dfrm1["population"] > 1.5]["population"]) # this last one triggers a warning # breaking the above apart: print(dfrm1[dfrm1["population"] > 1.5]) # all rows and columns filtered by "population" values > 1.5 print("---") print(dfrm1["population"]) # return whole "population" column print("---") print(dfrm1[2:4]) # return whole rows 2 to 4 crazyList = [simp1, m3d, simp2, n3d, simp3, dfrm1, o3d] # Accessing the dataframe inside the list now that it is a sub element: crazyList[5]["population"][crazyList[5]["population"] > 1.5][2:4] Explanation: Just analyzing how the numbers are arranged, we see that in shape(a,b,c,d), it just added the new extra dimensional layer to the front of the list so that now: - a = larger hyper grouping (2 of them) - b = first subgroup within (3 of them) - c = rows within these groupings (4 of them) - d = columns within these groupings (5 of them) It appears that rows always come before columns, and then it looks like groupings of rows and columns and groupings or groupings, etc. . . are added to the front of the index chain. Building something complex just to drill in more on how to access sub-elements: End of explanation crazyList[1] # this is the second object of the list (Python like many languages starts indicies at 0) # this is the full output of m3d crazyList[0] # after the above demo, no surprises here ... simp1 was the first object we added to the list Explanation: Now let's access other stuff in the list ... End of explanation try: # not this way ... crazyList[0][1] except Exception as ex: print("%s%s %s" %(type(ex), ":", ex)) # let's look at what we built: all the objects are here but are no longer named so we need to get indices right crazyList # note that both of these get the same data, but also note the difference in the format: "[[]]" and array([])". # look at the source and you will see we are drilling in at different levels of "[]" # there can be situations in real coding where extra layers are created by accident so this example is good to know print(crazyList[0]) crazyList[0][0] Explanation: In the tests that follow ... anything that does not work is wrapped in exception handling (that displays the error) so this notebook can be run from start to finish ... Note that it is not good practice to use a catch all for all errors. In real coding errors should be handled individually by type. How do we access the first index (element 2) of the first array object in our complex list (which resides at index 0)? End of explanation print(crazyList[4]) crazyList[4][1] # get 2nd element in the list within a list at position 4 (object 4 in the list) Explanation: Sub element 4 is a simple list nested within caryList: crazyList [ ... [content at index position 4] ...] End of explanation print(type(simp1)) print(simp1.shape) print(simp1) print(simp1[0]) # note that the first two give us the same thing (whole array) simp1[0][1] Explanation: So what about the array? The array was originally built in "simp1" and then added to crazyList. Its source looks like this: End of explanation trueSimp1=np.array([10,9,8,7,6]) print(trueSimp1.shape) # note: output shows that Python thinks this is 5 rows, 1 column trueSimp1 Explanation: Note the [] versus the [[]] ... our "simple arrays" were copied from an example, but are actually nested objects of 1 list of 5 elements forming the first object inside the array. A true simple array would like this: End of explanation crazyList.append(trueSimp1) # append mutates so this changes the original list crazyList # Warning! if you re-run this cell, you will keep adding more copies of the last object # to the end of this object. To be consistent with content in this NB # clear and re-run the whole notebook should that happen # The elements at either end of crazyList: print(crazyList[0]) print(crazyList[-1]) # ask for last item by counting backwards from the end # get a specific value by index from within the subelements at either end: print(crazyList[0][0][2]) # extra zero for the extra [] .. structurally this is really [0 [0 ], [1] ] but 1 does not exist print(crazyList[-1][2]) Explanation: Let's add the true simple array to our crazy object and then create working examples of accessing everything ... End of explanation crazyList[0] # first array to change Explanation: Looking at just that first element again: End of explanation simp1[0][1] # second element inside it Explanation: remember that this object if it were not in a list would be accessed like so: End of explanation crazyList[0] crazyList[0][0][1] Explanation: ... so inside crazyList ? The answer is that the list is one level deep and the elements are yet another level in: End of explanation aList = [1,2,3] bList = aList print(aList) print(bList) Explanation: <a id="mutation" name="mutation"></a> Sidebar: Mutation and Related Concerns Try this test and you will see it does not work: crazyList2 = crazyList.append(trueSimp1) What it did: crazyList got an element appended to the end and crazyList2 came out the other side empty. This is because append() returns None and operates on the original. The copy then gets nothing and the original gets an element added to it. To set up crazyList2 to append to only it, we might be tempted to try something like what is shown below, but if we do, note how it mutates: End of explanation aList[0] = 0 bList[1] = 1 bList.append(4) print(aList) print(bList) Explanation: Note how the second is really a reference to the first so changing one changes the other: End of explanation bList = list(aList) bList[0] = 999 aList[1] = 998 print(aList) print(bList) bList.append(19) print(aList) print(bList) Explanation: For a simple list ... we can fix that by simply using list() during our attempt to create the copy: End of explanation crazyList2 = list(crazyList) crazyList2 Explanation: Mutation is avoided. Now we can change our two objects independantly. However, with complex objects like crazyList, this does not work. The following will illustrate the problem and later, options to get around it are presented. End of explanation len(crazyList2)-1 # this is the position of the object we want to change crazyList2[7][1] = 13 # this will change element 2 of last object in crazyList2 Explanation: Now we make some changes: End of explanation print(crazyList[7]) print(crazyList2[7]) Explanation: Now we'll look at just the last object in both "crazyLists" showing what changed: End of explanation crazyList[7][1] = 9 # change on of them again and both change print(crazyList[7]) print(crazyList2[7]) Explanation: The "13" replaced the value at this location in both crazyList and crazyList2. We are not dealing with true copies but rather references to the same data as further illustrated here: End of explanation crazyList3 = crazyList[:] # according to online topics ... this was supposed to work for the reason outlined below # it probably works with some complex objects but does not work with this one # some topics online indicate this should have worked because: # * the problem is avoided by "slicing" the original so Python behaves as if the thing you are copying is different # * if you used crazyList[2:3] ==> you would get a slice of the original you could store in the copy # * [:] utilizes slicing syntax but indicates "give me the whole thing" since by default, empty values are the min and max # indexing limits crazyList3[7][1] = 13 # this will change element 2 of the last object print(crazyList[7]) print(crazyList3[7]) # what if we do this? (slice it and then add back a missing element) crazyList3 = crazyList[:-1] print(len(crazyList3)) print(len(crazyList)) # crazyList 3 is now one element shorter than crazyList crazyList3.append(crazyList[7]) # add back missing element from crazyList print(len(crazyList3)) print(len(crazyList)) crazyList3[7][1] = 9 # this will change element 2 of the last object print(crazyList[7]) # note how again, both lists change print(crazyList3[7]) Explanation: So ... how to make a copy that does not mutate? (we can change one without changing the other)?<br/> Let's look at some things that don't work first ... End of explanation print("before:") print(crazyList[4]) print(crazyList3[4]) crazyList3[4][0] = 14 print("after:") print(crazyList[4]) print(crazyList3[4]) # try other tests of other elements and you will get same results Explanation: Python is hard to fool ... At first, I considered that we might now have two lists, but w/ just element 7 passed in by reference and so it mutates. But this shows our whole lists are still mutating: End of explanation import copy crazyList4 = copy.deepcopy(crazyList) print("before:") print(crazyList[4]) print(crazyList4[4]) crazyList4[4][0] = 15 print("") print("after:") print(crazyList[4]) print(crazyList4[4]) Explanation: deepcopy() comes from the copy library and the commands are documented at Python.org. For this situation, this solution seems to work for when mutation is undesirable: End of explanation print(stupidList) print(stupidList[1].index(5)) # this works on lists # but for nested lists, you would need to loop through each sublist and handle the error that # gets thrown each time it does not find the answer for element in stupidList: try: test_i = element.index(5) except Exception as ex: print("%s%s %s" %(type(ex), ":", ex)) print(test_i) # this strategy will not work on numpy arrays though try: crazyList[0].index(2) except Exception as anyE: print(type(anyE), anyE) # because we have a list containing numpy arrays, we could look in each one like this: print(crazyList[0]) np.where(crazyList[0]==2) # the above indicates that 2 lives here: crazyList[0][0][1] # started with crazyList[0], then found it at [0][1] inside the data structure # For floating point numbers, the level of precision matters # details on how this works are presented in this notebook: TMWP_np_where_and_floatingPoint_numbers.ipynb # the simple test in the cells that follow should help illustrate the problem and what to do, but # see aforementioned notebook for more detail # to perform a where() test on a structure like this, it is important to note that print() # rounds the result to 8 decimal places. The real underlying numbers have more decimal places print(crazyList2[1]); print("") print(crazyList2[1][2][3][4]) # get a number to test with print("{0:.20}".format(crazyList2[1][2][3][4])) # show more decimal places of the test number # Warning! If you re-run this notebook, new random nubers are generated and the value used for the test in this # cell will probably then fail. To fix this, re-run previous cell and copy in the final number shown # above up to at least 17 decimal places. print(np.where(crazyList2[1]==0.95881217854380618)) # number copied from output of previous line up to 17 decimal places # np.where() can find this, but will also return other values # that match up to the first 16 decimal places (if they exist) # precision appears to be up to 16 decimal places on a 32 bit machine # np.isclose # for finding less precise answers: finds numbers that "are close" print(np.isclose(crazyList2[1], 0.95881)) print("") print(np.where(np.isclose(crazyList2[1], 0.95881))) # note that when numbers are "close" this returns multiple values # in this case (crazyList2) only one number was "close" # more detailed testing is provided in: # TMWP_np_where_and_floatingPoint_numbers.ipynb Explanation: Should even deepcopy() not work, this topic online may prove helpful in these situations: Stack Overflow: When Deep Copy is not Enough. <a id="indexing" name="indexing"></a> Finding The Index of a Value Suppose we didn't know how to find the element but we knew the value we were looking for? How to get its index? End of explanation
9,207	Given the following text description, write Python code to implement the functionality described below step by step Description: Exploratory Data Analysis with Python We will explore the NYC MTA turnstile data set. These data files are from the New York Subway. It tracks the hourly entries and exits to turnstiles (UNIT) by day in the subway system. Here is an example of what you could do with the data. James Kao investigates how subway ridership is affected by incidence of rain. <br> <font color="red"> NOTE Step1: Download Data Would you like to download New York City MTA Turnstile data? Each file is for a week of data and is approximately 24 Megabytes in size. Step2: Scrape MTA Turnstile Web Page to extract all available data files. Step3: Exercise 1 Download at least 2 weeks worth of MTA turnstile data (You can do this manually or via Python) Open up a file, use csv reader to read it, make a python dict where there is a key for each (C/A, UNIT, SCP, STATION). These are the first four columns. The value for this key should be a list of lists. Each list in the list is the rest of the columns in a row. For example, one key-value pair should look like{ ('A002','R051','02-00-00','LEXINGTON AVE') Step4: Create Excersize 1 Dictionary Step5: Header C/A Step6: Example Entry in Turnstile Dictionary Step7: Create Pandas DataFrame Step8: Exercise 2 Let's turn this into a time series. For each key (basically the control area, unit, device address and station of a specific turnstile), have a list again, but let the list be comprised of just the point in time and the cumulative count of entries. This basically means keeping only the date, time, and entries fields in each list. You can convert the date and time into datetime objects -- That is a python class that represents a point in time. You can combine the date and time fields into a string and use the dateutil package to convert it into a datetime object. Your new dict should look something like { ('A002','R051','02-00-00','LEXINGTON AVE') Step9: Example Entry in Turnstile Time Series Dictionary Step10: Add Time Stamp Series to Pandas DataFrame Step11: Exercise 3 These counts are cumulative every n hours. We want total daily entries. Now make it that we again have the same keys, but now we have a single value for a single day, which is not cumulative counts but the total number of passengers that entered through this turnstile on this day. Step12: Example Entry in Turnstile Time Series Dictionary Step13: Return Daily Entry Totals Using Pandas Step14: Exercise 4 We will plot the daily time series for a turnstile. In ipython notebook, add this to the beginning of your next cell Step15: Pandas Plot Step16: Exercise 5 So far we've been operating on a single turnstile level, let's combine turnstiles in the same ControlArea/Unit/Station combo. There are some ControlArea/Unit/Station groups that have a single turnstile, but most have multiple turnstilea-- same value for the C/A, UNIT and STATION columns, different values for the SCP column. We want to combine the numbers together -- for each ControlArea/UNIT/STATION combo, for each day, add the counts from each turnstile belonging to that combo. Pandas Return Total Passengers Filtered By Control Area, Unit, Station and Date Step17: Exercise 6 Similarly, combine everything in each station, and come up with a time series of [(date1, count1),(date2,count2),...] type of time series for each STATION, by adding up all the turnstiles in a station. Pandas Return Total Passengers Filtered By Station and Date Step18: Exercise 7 Plot the time series for a station Step19: Exercise 8 Make one list of counts for one week for one station. Monday's count, Tuesday's count, etc. so it's a list of 7 counts. Make the same list for another week, and another week, and another week. plt.plot(week_count_list) for every week_count_list you created this way. You should get a rainbow plot of weekly commute numbers on top of each other. Step20: Exercise 9 Over multiple weeks, sum total ridership for each station and sort them, so you can find out the stations with the highest traffic during the time you investigate Step21: Exercise 10 Make a single list of these total ridership values and plot it with plt.hist(total_ridership_counts) to get an idea about the distribution of total ridership among different stations. This should show you that most stations have a small traffic, and the histogram bins for large traffic volumes have small bars. Additional Hint	Python Code: from collections import defaultdict import csv import os import os.path as osp from dateutil.parser import parse import matplotlib.dates as mdates import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from k2datascience import nyc_mta from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all" %matplotlib inline Explanation: Exploratory Data Analysis with Python We will explore the NYC MTA turnstile data set. These data files are from the New York Subway. It tracks the hourly entries and exits to turnstiles (UNIT) by day in the subway system. Here is an example of what you could do with the data. James Kao investigates how subway ridership is affected by incidence of rain. <br> <font color="red"> NOTE: <br> This notebook uses code found in the <a href="https://github.com/TimothyHelton/k2datascience/blob/master/k2datascience/nyc_mta.py"> <strong>k2datascience.nyc_mta</strong></a> package. To execute all the cells do one of the following items: <ul> <li>Install the k2datascience package to the active Python interpreter.</li> <li>Add k2datascience/k2datascience to the PYTHON_PATH system variable.</li> <li>Create a link to the nyc_mta.py file in the same directory as this notebook.</li> </font> Imports End of explanation download = False file_quantity = 2 Explanation: Download Data Would you like to download New York City MTA Turnstile data? Each file is for a week of data and is approximately 24 Megabytes in size. End of explanation d = nyc_mta.TurnstileData() if download: d.write_data_files(qty=file_quantity) print(f'\n\nThe raw data files were written out to:\n\n{d.data_dir}') Explanation: Scrape MTA Turnstile Web Page to extract all available data files. End of explanation data_file = '170401.txt' data_dir = osp.join('..', 'data', 'nyc_mta_turnstile') data_file_path = osp.join(data_dir, data_file) Explanation: Exercise 1 Download at least 2 weeks worth of MTA turnstile data (You can do this manually or via Python) Open up a file, use csv reader to read it, make a python dict where there is a key for each (C/A, UNIT, SCP, STATION). These are the first four columns. The value for this key should be a list of lists. Each list in the list is the rest of the columns in a row. For example, one key-value pair should look like{ ('A002','R051','02-00-00','LEXINGTON AVE'): [ ['NQR456', 'BMT', '01/03/2015', '03:00:00', 'REGULAR', '0004945474', '0001675324'], ['NQR456', 'BMT', '01/03/2015', '07:00:00', 'REGULAR', '0004945478', '0001675333'], ['NQR456', 'BMT', '01/03/2015', '11:00:00', 'REGULAR', '0004945515', '0001675364'], ... ] } Store all the weeks in a data structure of your choosing Data File Path End of explanation turnstile = defaultdict(list) with open(data_file_path, 'r') as f: reader = csv.reader(f) initial_row = True for row in reader: if not initial_row: turnstile[tuple(row[:4])].append([x.strip() for x in row[4:]]) else: header = [x.strip() for x in row] initial_row = False Explanation: Create Excersize 1 Dictionary End of explanation header Explanation: Header C/A: Control Area (A002) UNIT: Remote Unit for a station (R051) SCP: Subunit Channel Position represents an specific address for a device (02-00-00) STATION: Represents the station name the device is located at LINENAME: Represents all train lines that can be boarded at this station Normally lines are represented by one character. LINENAME 456NQR represents train server for 4, 5, 6, N, Q, and R trains. DIVISION: Represents the Line originally the station belonged to BMT, IRT, or IND DATE: Represents the date (MM-DD-YY) TIME: Represents the time (hh:mm:ss) for a scheduled audit event DESc: Represent the "REGULAR" scheduled audit event (Normally occurs every 4 hours) Audits may occur more that 4 hours due to planning, or troubleshooting activities. Additionally, there may be a "RECOVR AUD" entry: This refers to a missed audit that was recovered. ENTRIES: The comulative entry register value for a device EXIST: The cumulative exit register value for a device End of explanation turnstile[('A002', 'R051', '02-00-00', '59 ST')][:3] Explanation: Example Entry in Turnstile Dictionary End of explanation d.get_data() d.data.shape d.data.head() Explanation: Create Pandas DataFrame End of explanation turnstile_ts = {} for k, v in turnstile.items(): turnstile_ts[k] = [[parse(f'{x[2]} {x[3]}'), int(x[-2])] for x in v] Explanation: Exercise 2 Let's turn this into a time series. For each key (basically the control area, unit, device address and station of a specific turnstile), have a list again, but let the list be comprised of just the point in time and the cumulative count of entries. This basically means keeping only the date, time, and entries fields in each list. You can convert the date and time into datetime objects -- That is a python class that represents a point in time. You can combine the date and time fields into a string and use the dateutil package to convert it into a datetime object. Your new dict should look something like { ('A002','R051','02-00-00','LEXINGTON AVE'): [ [datetime.datetime(2013, 3, 2, 3, 0), 3788], [datetime.datetime(2013, 3, 2, 7, 0), 2585], [datetime.datetime(2013, 3, 2, 12, 0), 10653], [datetime.datetime(2013, 3, 2, 17, 0), 11016], [datetime.datetime(2013, 3, 2, 23, 0), 10666], [datetime.datetime(2013, 3, 3, 3, 0), 10814], [datetime.datetime(2013, 3, 3, 7, 0), 10229], ... ], .... } Create Exersize 2 Time Series Dictionary Note: The extended computational time is due to the dateutil operation. End of explanation turnstile_ts[('A002', 'R051', '02-00-00', '59 ST')][:10] Explanation: Example Entry in Turnstile Time Series Dictionary End of explanation d.get_time_stamp() d.data.shape d.data.head() Explanation: Add Time Stamp Series to Pandas DataFrame End of explanation daily_total = defaultdict(list) for k, v in turnstile_ts.items(): days = set([x[0].date() for x in v]) for day in sorted(days): daily_total[k].append([day, sum([x[1] for x in v if x[0].date() == day])]) Explanation: Exercise 3 These counts are cumulative every n hours. We want total daily entries. Now make it that we again have the same keys, but now we have a single value for a single day, which is not cumulative counts but the total number of passengers that entered through this turnstile on this day. End of explanation daily_total[('A002', 'R051', '02-00-00', '59 ST')] Explanation: Example Entry in Turnstile Time Series Dictionary End of explanation d.turnstile_daily.head(10) d.turnstile_daily.tail(10) Explanation: Return Daily Entry Totals Using Pandas End of explanation label_size = 14 fig = plt.figure('Station 59 ST: Daily Turnstile Entries', figsize=(10, 3), facecolor='white', edgecolor='black') ax1 = plt.subplot2grid((1, 1), (0, 0)) dt = daily_total[('A002', 'R051', '02-00-00', '59 ST')] dates = [x[0] for x in dt] entries = [x[1] for x in dt] ax1.plot_date(dates, entries, '^k-') plt.suptitle('Station: 59 ST', fontsize=24, y=1.16); plt.title('Control Area: A002 \| Unit: R051 \| Subunit Channel Position: 02-00-00', fontsize=18, y=1.10); ax1.set_xlabel('Date', fontsize=label_size) ax1.set_ylabel('Turnstile Entries', fontsize=label_size) fig.autofmt_xdate(); Explanation: Exercise 4 We will plot the daily time series for a turnstile. In ipython notebook, add this to the beginning of your next cell: %matplotlib inline This will make your matplotlib graphs integrate nicely with the notebook. To plot the time series, import matplotlib with import matplotlib.pyplot as plt Take the list of [(date1, count1), (date2, count2), ...], for the turnstile and turn it into two lists: dates and counts. This should plot it: plt.figure(figsize=(10,3)) plt.plot(dates,counts) End of explanation label_size = 14 marker_size = 5 fig = plt.figure('Station 59 ST: Daily Turnstile Entries', figsize=(10, 7), facecolor='white', edgecolor='black') rows, cols = (2, 1) ax1 = plt.subplot2grid((rows, cols), (0, 0)) ax2 = plt.subplot2grid((rows, cols), (1, 0), sharex=ax1) dt = d.turnstile_daily.query(('c_a == "A002"' '& unit == "R051"' '& scp == "02-00-00"' '& station == "59 ST"')) dt.plot(x=dt.index.levels[4], y='entries', color='IndianRed', legend=None, markersize=marker_size, marker='o', ax=ax1) ax1.set_title('Control Area: A002 \| Unit: R051 \| Subunit Channel Position: 02-00-00', fontsize=18, y=1.10) ax1.set_ylabel('Turnstile Entries', fontsize=label_size) dt.plot(x=dt.index.levels[4], y='exits', color='black', legend=None, markersize=marker_size, marker='d', ax=ax2) ax2.set_xlabel('Date', fontsize=label_size) ax2.set_ylabel('Turnstile Exits', fontsize=label_size) plt.suptitle('Station: 59 ST', fontsize=24, y=1.04); plt.tight_layout() fig.autofmt_xdate(); Explanation: Pandas Plot End of explanation d.get_station_daily(control_area=True, unit=True) station_daily_all = d._station_daily station_daily_all.head(10) station_daily_all.tail(10) Explanation: Exercise 5 So far we've been operating on a single turnstile level, let's combine turnstiles in the same ControlArea/Unit/Station combo. There are some ControlArea/Unit/Station groups that have a single turnstile, but most have multiple turnstilea-- same value for the C/A, UNIT and STATION columns, different values for the SCP column. We want to combine the numbers together -- for each ControlArea/UNIT/STATION combo, for each day, add the counts from each turnstile belonging to that combo. Pandas Return Total Passengers Filtered By Control Area, Unit, Station and Date End of explanation station_daily = d.station_daily station_daily.query('station == "59 ST"') Explanation: Exercise 6 Similarly, combine everything in each station, and come up with a time series of [(date1, count1),(date2,count2),...] type of time series for each STATION, by adding up all the turnstiles in a station. Pandas Return Total Passengers Filtered By Station and Date End of explanation label_size = 14 fig = plt.figure('Station 59 ST: Total Passengers', figsize=(12, 4), facecolor='white', edgecolor='black') ax1 = plt.subplot2grid((1, 1), (0, 0)) dt = station_daily.query('station == "59 ST"') dt.plot(kind='bar', x=dt.index.levels[1], alpha=0.5, ax=ax1) ax1.set_xlabel('Date', fontsize=label_size) ax1.set_ylabel('Passengers', fontsize=label_size) plt.suptitle('Station: 59 ST', fontsize=24, y=1.16); plt.title('Total Passengers', fontsize=18, y=1.10); fig.autofmt_xdate(); Explanation: Exercise 7 Plot the time series for a station End of explanation week_59st = station_daily.query('station == "59 ST"').reset_index() week_59st label_size = 14 fig = plt.figure('Station 59 ST: Weekly Passengers', figsize=(12, 4), facecolor='white', edgecolor='black') ax1 = plt.subplot2grid((1, 1), (0, 0)) for w in week_59st.week.unique(): mask = f'station == "59 ST" & week == {w}' dt = station_daily.query(mask).reset_index() dt.plot(kind='area', x=dt.weekday, y='entries', alpha=0.5, label=f'Week: {w}', ax=ax1) ax1.set_xlabel('Weekday', fontsize=label_size) ax1.set_ylabel('Passengers', fontsize=label_size) ax1.set_xticklabels(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']) x_min, x_max, y_min, y_max = ax1.axis() ax1.axis((x_min, x_max, 0, 2e10)) plt.suptitle('Station: 59 ST', fontsize=24, y=1.16); plt.title('Weekly Passengers', fontsize=18, y=1.10); fig.autofmt_xdate(); Explanation: Exercise 8 Make one list of counts for one week for one station. Monday's count, Tuesday's count, etc. so it's a list of 7 counts. Make the same list for another week, and another week, and another week. plt.plot(week_count_list) for every week_count_list you created this way. You should get a rainbow plot of weekly commute numbers on top of each other. End of explanation mask = ['station', pd.Series([x.week for x in d.data.time_stamp], name='week')] station_weekly = d.data.groupby(mask)['entries', 'exits'].sum() station_weekly.sort_values('entries', ascending=False) Explanation: Exercise 9 Over multiple weeks, sum total ridership for each station and sort them, so you can find out the stations with the highest traffic during the time you investigate End of explanation station_group = d.data.groupby('station') station_entries = station_group['entries'].sum() station_entries.tail() label_size = 14 suptitle_size = 24 title_size = 18 bins = 50 fig = plt.figure('', figsize=(10, 8), facecolor='white', edgecolor='black') rows, cols = (2, 1) ax1 = plt.subplot2grid((rows, cols), (0, 0)) ax2 = plt.subplot2grid((rows, cols), (1, 0)) station_entries.sort_values().plot(kind='bar', ax=ax1) ax1.set_title('Total Passengers', fontsize=title_size); ax1.set_xlabel('Stations', fontsize=label_size) ax1.set_ylabel('Passengers', fontsize=label_size) ax1.set_xticklabels('') station_entries.plot(kind='hist', alpha=0.5, bins=bins, edgecolor='black', label='_nolegend_', ax=ax2) ax2.axvline(station_entries.mean(), color='crimson', label='Mean', linestyle='--') ax2.axvline(station_entries.median(), color='black', label='Median', linestyle='-.') ax2.legend() ax2.set_xlabel('Total Passengers', fontsize=label_size) ax2.set_ylabel('Count', fontsize=label_size) plt.suptitle('All NYC MTA Stations', fontsize=suptitle_size, y=1.03); plt.tight_layout(); Explanation: Exercise 10 Make a single list of these total ridership values and plot it with plt.hist(total_ridership_counts) to get an idea about the distribution of total ridership among different stations. This should show you that most stations have a small traffic, and the histogram bins for large traffic volumes have small bars. Additional Hint: If you want to see which stations take the meat of the traffic, you can sort the total ridership counts and make a plt.bar graph. For this, you want to have two lists: the indices of each bar, and the values. The indices can just be 0,1,2,3,..., so you can do indices = range(len(total_ridership_values)) plt.bar(indices, total_ridership_values) End of explanation
9,208	Given the following text description, write Python code to implement the functionality described below step by step Description: pd.DataFrame({'article_uni' Step1: a=pd.pivot_table(df,index=["article_uni"],values=["article_rating"],aggfunc=[len,np.mean], columns='year') a Step2: b=df[df.article_pub_date>=data_first_ranking].groupby(['article_uni', 'year']).article_rating.agg(lambda x Step3: column_name = '{year}{country}{header}_gapminder_grid'.format( year=year, country=country, header=col_name ) Step4: browser = webdriver.Edge(executable_path="..\MicrosoftWebDriver.exe")	Python Code: df_ranking=pd.read_csv('article_uni.csv', index_col=0) print(df_ranking.shape) df_ranking.head() df.article_uni.replace('The London School of Economics and Political Science (United-Kingdom)', 'London School of Economics and Political Science', inplace=True) from sklearn.preprocessing import MinMaxScaler, StandardScaler scaler=StandardScaler() uni_cluster_1=df[df.article_rating==df.article_rating.max()].article_uni.unique() uni_cluster_2=list(set(df.article_uni.unique())-set(uni_cluster_1)) df[df.article_uni.isin(uni_cluster_1)].article_rating.values2/10.max() df['article_rating'][df.article_uni.isin(uni_cluster_1)]=df[df.article_uni.isin(uni_cluster_1)].article_rating.values2/10#scaler.fit_transform(df[df.article_uni.isin(uni_cluster_1)].article_rating.values) df['article_rating'][df.article_uni.isin(uni_cluster_2)]=df[df.article_uni.isin(uni_cluster_2)].article_rating.values*2.5/10#scaler.fit_transform(df[df.article_uni.isin(uni_cluster_2)].article_rating.values) df.article_rating.hist() Explanation: pd.DataFrame({'article_uni':df.article_uni.unique()}).to_csv('article_uni.csv') End of explanation df_ranking.sort_values(['article_uni'],inplace=True) df_ranking.head() Explanation: a=pd.pivot_table(df,index=["article_uni"],values=["article_rating"],aggfunc=[len,np.mean], columns='year') a End of explanation b=df[df.article_pub_date>=data_first_ranking].groupby(['article_uni', 'year']).article_rating.agg({'article_rating_mean':'mean', 'article_rating_count':'count', 'article_rating_moda':lambda x:x.value_counts().index[0]}).reset_index() b['ranking']=np.zeros((1, b.shape[0]))[0] b.head() for name in b.article_uni.unique(): for year in b[b.article_uni==name].year: #print(year) b['ranking'][(b.article_uni==name)&(b.year==year)]=df_ranking[(df_ranking.article_uni==name)][str(year)].values[0] b=b[(~b.ranking.isnull())] for year in np.sort(b.year.unique()): print(year, round(np.corrcoef(b[(b.year==year)].article_rating_mean, b[(b.year==year)].ranking)[1,0],2)) for year in np.sort(b.year.unique()): print(year, round(np.corrcoef(b[(b.year==year)].article_rating_moda, b[(b.year==year)].ranking)[1,0],2)) df_all=pd.merge(b, df_ranking[['article_uni','country']], on=['article_uni']) df_all.head() import plotly.plotly as py from plotly.grid_objs import Grid, Column from plotly.tools import FigureFactory as FF import pandas as pd import time years_from_col = set(df_all['year']) years_ints = sorted(list(years_from_col)) years = [str(year) for year in years_ints] # make list of continents countries = [] for country in df_all['country']: if country not in countries: countries.append(country) columns = [] # make grid for year in years: for country in countries: df_by_year = df_all[df_all['year'] == int(year)] df_by_year_and_cont = df_by_year[df_by_year['country'] == country] for col_name in df_by_year_and_cont: #print(col_name) # each column name is unique column_name = '{year}_{country}_{header}_gapminder_grid'.format( year=year, country=country, header=col_name ) a_column = Column(list(df_by_year_and_cont[col_name]), column_name) columns.append(a_column) # upload grid grid = Grid(columns) url = py.grid_ops.upload(grid, 'gapminder_grid'+str(time.time()), auto_open=False) url figure = { 'data': [], 'layout': {}, 'frames': [], 'config': {'scrollzoom': True} } # fill in most of layout figure['layout']['yaxis'] = {'range': [-50, 200], 'title': 'Ranking un THE', 'gridcolor': '#FFFFFF'} figure['layout']['xaxis'] = {'range': [-0.1, 1.1], 'title': 'Ranking mean', 'gridcolor': '#FFFFFF'} figure['layout']['hovermode'] = 'closest' figure['layout']['plot_bgcolor'] = 'rgb(223, 232, 243)' figure['layout']['sliders'] = { 'active': 0, 'yanchor': 'top', 'xanchor': 'left', 'currentvalue': { 'font': {'size': 20}, 'prefix': 'text-before-value-on-display', 'visible': True, 'xanchor': 'right' }, 'transition': {'duration': 1000, 'easing': 'cubic-in-out'}, 'pad': {'b': 10, 't': 50}, 'len': 0.9, 'x': 0.1, 'y': 0, 'steps': [...] } { 'method': 'animate', 'label': 'label-for-frame', 'value': 'value-for-frame(defaults to label)', 'args': [{'frame': {'duration': 300, 'redraw': False}, 'mode': 'immediate'} ], } sliders_dict = { 'active': 0, 'yanchor': 'top', 'xanchor': 'left', 'currentvalue': { 'font': {'size': 20}, 'prefix': 'Year:', 'visible': True, 'xanchor': 'right' }, 'transition': {'duration': 300, 'easing': 'cubic-in-out'}, 'pad': {'b': 10, 't': 50}, 'len': 0.9, 'x': 0.1, 'y': 0, 'steps': [] } figure['layout']['updatemenus'] = [ { 'buttons': [ { 'args': [None, {'frame': {'duration': 500, 'redraw': False}, 'fromcurrent': True, 'transition': {'duration': 300, 'easing': 'quadratic-in-out'}}], 'label': 'Play', 'method': 'animate' }, { 'args': [[None], {'frame': {'duration': 0, 'redraw': False}, 'mode': 'immediate', 'transition': {'duration': 0}}], 'label': 'Pause', 'method': 'animate' } ], 'direction': 'left', 'pad': {'r': 10, 't': 87}, 'showactive': False, 'type': 'buttons', 'x': 0.1, 'xanchor': 'right', 'y': 0, 'yanchor': 'top' } ] custom_colors = { 'UK': 'rgb(171, 99, 250)', 'USA': 'rgb(230, 99, 250)', 'Canada': 'rgb(99, 110, 250)', } Explanation: b=df[df.article_pub_date>=data_first_ranking].groupby(['article_uni', 'year']).article_rating.agg(lambda x:x.value_counts().index[0]).reset_index() End of explanation df_all.country.unique() col_name_template = '{year}_{country}_{header}_gapminder_grid' year = df_all.year.min() for country in countries: data_dict = { 'xsrc': grid.get_column_reference(col_name_template.format( year=year, country=country, header='article_rating_mean' )), 'ysrc': grid.get_column_reference(col_name_template.format( year=year, country=country, header='ranking' )), 'mode': 'markers', 'textsrc': grid.get_column_reference(col_name_template.format( year=year, country=country, header='article_uni' )), 'marker': { 'sizemode': 'area', 'sizeref': 0.05, 'sizesrc': grid.get_column_reference(col_name_template.format( year=year, country=country, header='article_rating_count' )), 'color': custom_colors[country] }, 'name': country } figure['data'].append(data_dict) for year in years: frame = {'data': [], 'name': str(year)} for country in countries: data_dict = { 'xsrc': grid.get_column_reference(col_name_template.format( year=year, country=country, header='article_rating_mean' )), 'ysrc': grid.get_column_reference(col_name_template.format( year=year, country=country, header='ranking' )), 'mode': 'markers', 'textsrc': grid.get_column_reference(col_name_template.format( year=year, country=country, header='article_uni', )), 'marker': { 'sizemode': 'area', 'sizeref': 0.05, 'sizesrc': grid.get_column_reference(col_name_template.format( year=year, country=country, header='article_rating_count' )), 'color': custom_colors[country] }, 'name': country } frame['data'].append(data_dict) figure['frames'].append(frame) slider_step = {'args': [ [year], {'frame': {'duration': 300, 'redraw': False}, 'mode': 'immediate', 'transition': {'duration': 300}} ], 'label': year, 'method': 'animate'} sliders_dict['steps'].append(slider_step) figure['layout']['sliders'] = [sliders_dict] py.icreate_animations(figure, 'gapminder_example'+str(time.time())) import seaborn as sns df_all.head() for i in np.sort(df_all.year.unique()): plt.scatter(df_all[df_all.year==i].article_rating_mean, df_all[df_all.year==i].ranking) plt.title('Corr between ranking in THE and ranking review in {:0.0f}'.format(i)) plt.show() df_all.article_rating_count for i in np.sort(df_all.year.unique()): plt.scatter(df_all[df_all.year==i].article_rating_count, df_all[df_all.year==i].ranking) plt.title('Corr between ranking in THE and ranking review count in {:0.0f}'.format(i)) plt.show() plt.hist(df_all.article_rating_count) import selenium from selenium import webdriver browser = webdriver.Chrome(executable_path="..\\chromedriver.exe") Explanation: column_name = '{year}{country}{header}_gapminder_grid'.format( year=year, country=country, header=col_name ) End of explanation url = "https://www.niche.com/colleges/stanford-university/reviews/" browser.get(url) browser.find_element_by_css_selector('.icon-arrowright-thin--pagination').click() import math as ma ma.sqrt(2) browser.page_source element.get_attribute('innerHTML') Explanation: browser = webdriver.Edge(executable_path="..\MicrosoftWebDriver.exe") End of explanation
9,209	Given the following text description, write Python code to implement the functionality described below step by step Description: Reflecting on 2017, I decided to return to my most popular blog topic (at least by the number of emails I get). Last time, I built a crude statistical model to predict the result of football matches. I even presented a webinar on the subject here (it's free to sign up). During the presentation, I described a coefficient in the model that accounts for the fact that the home team tends to score more goals than the away team. This is called the home advantage or home field advantage and can probably be explained by a combination of physcological (e.g. familiarity with surroundings) and physical factors (e.g. travel). It occurs in various sports, including American football, baseball, basketball and soccer. Sticking to soccer/football, I mentioned in my talk how it would be interesting to see how this effect varies around the world. In which countries do the home teams enjoy the greatest advantage? We're going to use the same statistcal model as last time, so there won't be any new statistical features developed in this post. Instead, it will focus on retrieving the appropriate goals data for even the most obscure leagues in the world (yes, even the Irish Premier Division) and then interactively visualising the results with D3. The full code can be found in the accompanying Jupyter notebook. Calculating Home Field Advantage The first consideration should probably be how to calculate home advantage. The traditional approach is to look at team matchups and check whether teams achieved better, equal or worse results at home than away. For example, let's imagine Chlesea beat Arsenal 2-0 at home and drew 1-1 away. That would be recored as a better home result (+2 goals versus 0). This process is repeated for every opponent and so you can actually construct a trinomial distribution and test whether there was a statistically significant home field effect. This works for balanced leagues, where team play each other an equal number of times home and away. While this holds for Europe's most famous leagues (e.g. EPL, La Liga), there are various leagues where teams play each other threes times (e.g. Ireland, Montenegro, Tajikistan aka The Big Leagues) or even just once (e.g Libya and to a lesser extent MLS (balanced for teams within the same conference)). There's also issues with postponements and abandonments rendering some leagues slightly unbalanced (e.g. Sri Lanka). For those reasons, we'll opt for a different (though not necessarily better) approach. In the previous post, we built a model for the EPL 2016/17 season, using the number of goals scored in the past to predict future results. Looking at the model coefficients again, you see the home coefficient has a value of approximately 0.3. By taking the exponent of this value ($exp^{0.3}=1.35$), it tells us that the home team are generally 1.35 times more likely to score than the away team. In case you don't recall, the model accounts for team strength/weakness by including coefficients for each team (e.g 0.07890 and -0.96194 for Chelsea and Sunderland, respectively). Let's see how this value compares with the lower divisions in England over the past 10 years. We'll pull the data from football-data.co.uk, which can loaded in directly using the url link for each csv file. First, we'll design a function that will take a dataframe of match results as an input and return the home field advantage (plus confidence interval limits) for that league. Step1: I've essentially combined various parts of the previous post into one convenient function. If it looks a little strange, then I suggest you consult the original post. Okay, we're ready to start calculating some home advantage scores. Step2: It's as easy as that. Feed a url from football-data.co.uk into the function and it'll quickly tell you the statistical advantage enjoyed by home teams in that league. Note that the latter two values repesent the left and right limit of the 95% confidence interval around the mean value. The first value in the array is actually just the log of the number of goals scored by the home team divided by the total number of away goals. Step3: The goals ratio calculation is obviously much simpler and definitely more intuitive. But it doesn't allow me to reference my previous post as much (link link link) and it fails to provide any uncertainty around the headline figure. Let's plot the home advantage figure for the top 5 divisions of the English league pyramid for since 2005. You can remove those hugely informative confidence interval bars by unticking the checkbox. Step4: It's probably more apparent without those hugely informative confidence interval bars, but it seems that the home advantage score decreases slightly as you move down the pyramid (analysis by Sky Sports produced something similar). This might make sense for two reasons. Firstly, bigger teams generally have larger stadiums and more supporters, which could strengthen the home field advantage. Secondly, as you go down the leagues, I suspect the quality gap between teams narrows. Taking it to an extreme, when I used to play Sunday league football, it didn't really matter where we played... we still lost. In that sense, one must be careful comparing the home advantage between leagues, as it will be affected by the relative team strengths within those leagues. For example, a league with a very dominant team (or teams) will record a lower home advantage score, as that dominant team will score goals home and away with little difference (Man Utd would probably beat Cork City 6-0 at Old Trafford and Turners Cross!). Having warned about the dangers of comparing different leagues with this approach, let's now compare the top five leagues in Europe over the same time period as before. Step5: Honestly, there's not much going on there. With the poissble exception of the Spanish La Liga since 2010, the home field advantage enjoyed by the teams in each league is broadly similar (and that's before we bring in the idea of confidence intervals and hypothesis testing). Home Advantage Around the World To find more interesting contrasts, we must venture to crappier and more corrupt leagues. My hunch is that home advantage would be negligible in countries where the overall quality (team, infastructure, etc.) is very low. And by low, I mean leagues worse than the Irish Premier Division (yes, they exist). Unfortunately, the historical results for such leagues are not available on football-data.co.uk. Instead, we'll scrape the data off betexplorer. I'm extremely impressed by the breadth of this site. You can even retrieve past results for the French overseas department of Réunion. Fun fact Step6: You don't actually need to run your own spider, as I've shared the output to my GitHub account. We can import the json file in directly using pandas. Step7: Hopefully, that's all relatively clear. You'll notice that it's very similar to the format used by football-data, which means that we can feed this dataframe into the get_home_team_advantage function. Sometimes, matches are awarded due to one team fielding an ineligible player or crowd trouble. We should probably exclude such matches from the home field advantage calculations. Step8: We're ready to put it all together. I'll omit the code (though it can be found here), but we'll loop through each country and league combination (just in case you decide to include multiple leagues from the same country) and calculate the home advantage score, plus its confidence limits as well as some other information for each league (number of teams, average number of goals in each match). I've converted the pandas output to a datatables table that you can interactively filter and sort. Step9: Focusing on the home_advantage_score column, teams in Nigeria by far enjoy the greatest benefit from playing at home (score = 1.195). In other words, home teams scored 3.3 (= $e^{1.195}$) times more goals than their opponents. This isn't new information and can be attributed to a combination of corruption (e.g. bribing referees) and violent fans. In fact, my motivation for this post was to identify more football corruption hotspots. Alas, when it comes to home turf invincibility, it seems Nigeria are the World Cup winners. Fifteen leagues have a negative home_advantage_score, meaning that visiting teams actually scored more goals than their hosts- though none was statistically significant. By some distance, the Maldives records the most negative score. Luckily, I've twice researched this beautiful archipelago and I'm aware that all matches in the Dhiveli Premier League are played at the national stadium in Malé (much like the Gibraltar Premier League). So it would make sense that there's no particular advantage gained by the home team. Libya is another interesting example. Owing to security issues, all matches in the Libyan Premier League are played in neutral venues with no spectators present. Quite fittingly, it returned a home advantage score just off zero. Generally speaking, the leagues with near zero home advantage come from small countries (minimal inconvenience for travelling teams) with a small number of teams and they tend to share stadiums. If you sort the avg_goals column, you'll see that Bolivia is the place to be for goals (average = 4.47). But rather than sifting through that table or explaining the results with words, the most intuitive way to illustrate this type of data is with a map of world. This might also help to clarify whether there's any geographical influence on the home advantage effect. Again, I won't go into the details (an appendix can be found in the Jupyter notebook), but I built a map using the JavaScript library, D3. And by built I mean I adapted the code from this post and this post. Though a little outdated now, I found this post quite useful too. Finally, I think this post shows off quite well what you can do with maps using D3. And here it is! The country colour represents its home_advantage_score. You can zoom in and out and hover over a country to reveal a nice informative overlay; use the radio buttons to switch between home advantage and goals scored. I recommend viewing it on desktop (mobile's a bit jumpy) and on Chrome (sometimes have security issues with Firefox). It's not scientifically rigorous (not in academia any more, baby!), but there's evidence for some geographical trends. For example, it appears that home advantage is stronger in Africa and South America compared to Western and Central Europe, with the unstable warzones of Libya, Somalia and Paraguay (?) being notable exceptions. As for average goals, Europe boasts stonger colours compared to Africa, though South East Asia seems to be the global hotspot for goals. North America is also quite dark, but you can debate whether Canada should be coloured grey, as the best Canadian teams belong to the American soccer system. Conclusion Using a previously described model and some JavaScript, this post explored the so called home advantage in football leagues all over the world (including Réunion). I don't think it uncovered anything particularly amazing Step10: Shapefiles and TopoJSON Generating the world map required a little bit of command line, python and a whole lot of JavaScript (specifically D3). The command line was used to convert the shapefiles into geojson files (see ogr2ogr and finally into the topojson format. The main reason for the last step is that it drastically reduces the file size, which should improve its onsite loading (though it could also affect the quality of the map). My particular map was complicated by the fact that some sovereign states are composed of several countries that organise their own national competitions. If that sounds weird, think of the United Kingdom. It's a member of the UN and a sovereign state in its own right (despite what Brexiteers may say). But there's no UK (or British) Premier League; there's the English/Welsh/Scottish/Northern Irish Premier League/Premiership. Similarly, Reunion is part of France but has its own football league. Then again, the Basque country is recognised as a nation within Spain, but has no internationally recognised national league. In summary, it's complicated. Political realities aside, we need to get the geojson file for all of the countries in the world (see all.geojson available here). We must remove the United Kingdom, France and a few others. To reduce the file size, I also removed some country information that wasn't relevant for my purposes (population, GDP, etc.). The geojson files for England, Scotland, Reunion, etc. were a little harder to track down. The shapefile containing those country subdivisions can be downloaded here, which can be converted into geojson files with ogr2ogr. Unfortunately, that file contains various subdivisions that don't correspond to actual football leagues (e.g. Belgium is split into the Flemish and Walloon regions). That means we need append the subdivisions we do want to higher level geojson file, which I did my manipulating the two json files in Python.	Python Code: # importing the tools required for the Poisson regression model import statsmodels.api as sm import statsmodels.formula.api as smf import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn def get_home_team_advantage(goals_df, pval=0.05): # extract relevant columns model_goals_df = goals_df[['HomeTeam','AwayTeam','FTHG','FTAG']] # rename goal columns model_goals_df = model_goals_df.rename(columns={'FTHG': 'HomeGoals', 'FTAG': 'AwayGoals'}) # reformat dataframe for the model goal_model_data = pd.concat([model_goals_df[['HomeTeam','AwayTeam','HomeGoals']].assign(home=1).rename( columns={'HomeTeam':'team', 'AwayTeam':'opponent','HomeGoals':'goals'}), model_goals_df[['AwayTeam','HomeTeam','AwayGoals']].assign(home=0).rename( columns={'AwayTeam':'team', 'HomeTeam':'opponent','AwayGoals':'goals'})]) # build poisson model poisson_model = smf.glm(formula="goals ~ home + team + opponent", data=goal_model_data, family=sm.families.Poisson()).fit() # output model parameters poisson_model.summary() return np.concatenate((np.array([poisson_model.params['home']]), poisson_model.conf_int(alpha=pval).values[-1])) Explanation: Reflecting on 2017, I decided to return to my most popular blog topic (at least by the number of emails I get). Last time, I built a crude statistical model to predict the result of football matches. I even presented a webinar on the subject here (it's free to sign up). During the presentation, I described a coefficient in the model that accounts for the fact that the home team tends to score more goals than the away team. This is called the home advantage or home field advantage and can probably be explained by a combination of physcological (e.g. familiarity with surroundings) and physical factors (e.g. travel). It occurs in various sports, including American football, baseball, basketball and soccer. Sticking to soccer/football, I mentioned in my talk how it would be interesting to see how this effect varies around the world. In which countries do the home teams enjoy the greatest advantage? We're going to use the same statistcal model as last time, so there won't be any new statistical features developed in this post. Instead, it will focus on retrieving the appropriate goals data for even the most obscure leagues in the world (yes, even the Irish Premier Division) and then interactively visualising the results with D3. The full code can be found in the accompanying Jupyter notebook. Calculating Home Field Advantage The first consideration should probably be how to calculate home advantage. The traditional approach is to look at team matchups and check whether teams achieved better, equal or worse results at home than away. For example, let's imagine Chlesea beat Arsenal 2-0 at home and drew 1-1 away. That would be recored as a better home result (+2 goals versus 0). This process is repeated for every opponent and so you can actually construct a trinomial distribution and test whether there was a statistically significant home field effect. This works for balanced leagues, where team play each other an equal number of times home and away. While this holds for Europe's most famous leagues (e.g. EPL, La Liga), there are various leagues where teams play each other threes times (e.g. Ireland, Montenegro, Tajikistan aka The Big Leagues) or even just once (e.g Libya and to a lesser extent MLS (balanced for teams within the same conference)). There's also issues with postponements and abandonments rendering some leagues slightly unbalanced (e.g. Sri Lanka). For those reasons, we'll opt for a different (though not necessarily better) approach. In the previous post, we built a model for the EPL 2016/17 season, using the number of goals scored in the past to predict future results. Looking at the model coefficients again, you see the home coefficient has a value of approximately 0.3. By taking the exponent of this value ($exp^{0.3}=1.35$), it tells us that the home team are generally 1.35 times more likely to score than the away team. In case you don't recall, the model accounts for team strength/weakness by including coefficients for each team (e.g 0.07890 and -0.96194 for Chelsea and Sunderland, respectively). Let's see how this value compares with the lower divisions in England over the past 10 years. We'll pull the data from football-data.co.uk, which can loaded in directly using the url link for each csv file. First, we'll design a function that will take a dataframe of match results as an input and return the home field advantage (plus confidence interval limits) for that league. End of explanation # home field advantage for EPL 2016/17 season get_home_team_advantage(pd.read_csv("http://www.football-data.co.uk/mmz4281/1617/E0.csv")) Explanation: I've essentially combined various parts of the previous post into one convenient function. If it looks a little strange, then I suggest you consult the original post. Okay, we're ready to start calculating some home advantage scores. End of explanation temp_goals_df = pd.read_csv("http://www.football-data.co.uk/mmz4281/1617/E0.csv") [np.exp(get_home_team_advantage(temp_goals_df)[0]), np.sum(temp_goals_df['FTHG'])/float(np.sum(temp_goals_df['FTAG']))] Explanation: It's as easy as that. Feed a url from football-data.co.uk into the function and it'll quickly tell you the statistical advantage enjoyed by home teams in that league. Note that the latter two values repesent the left and right limit of the 95% confidence interval around the mean value. The first value in the array is actually just the log of the number of goals scored by the home team divided by the total number of away goals. End of explanation division_results = [] for division in range(5): year_results = [] for year in range(2005,2017): if division==4: division_string = 'C' else: division_string = str(division) url = "http://www.football-data.co.uk/mmz4281/"+str(year)[-2:]+str(year+1)[-2:]+"/E"+division_string+".csv" print(url) year_results.append(np.concatenate((np.array([year, division]), get_home_team_advantage(pd.read_csv(url))))) division_results.append(np.vstack(year_results)) from ipywidgets import interact, Checkbox def plot_func(freq): fig, ax1 = plt.subplots(1, 1,figsize=(7, 5)) for div, (div_name, div_col) in enumerate(zip(['EPL', 'Championship', 'League 1', 'League 2', 'Conference'], ["#9b5369", "#7c8163", "#c1a381", "#d9bcc0", "#F67280"])): if freq: ax1.errorbar(division_results[div][:,0], division_results[div][:,2], yerr= (division_results[div][:,4] - division_results[div][:,2]), linestyle='-', marker='o',label=div_name, color = div_col) else: ax1.plot(division_results[div][:,0], division_results[div][:,2], linestyle='-', marker='o',label=div_name, color = div_col) #[str(int(item.get_text())+1)[-2:] for item in ax1.get_xticklabels()] ax1.set_xticks([2005, 2007, 2009, 2011, 2013, 2015]) ax1.set_xlabel('Season', fontsize=12) ax1.set_ylabel('Home Advantage Score', fontsize=12) ax1.set_ylim([-0.05, 0.6]) ax1.set_xlim([2004.5, 2016.5]) ax1.set_xticklabels(['05/06', '07/08', '09/10', '11/12', '13/14', '15/16']) plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) plt.show() interact(plot_func, freq = Checkbox( value=True, description='Error Bars', disabled=False)) Explanation: The goals ratio calculation is obviously much simpler and definitely more intuitive. But it doesn't allow me to reference my previous post as much (link link link) and it fails to provide any uncertainty around the headline figure. Let's plot the home advantage figure for the top 5 divisions of the English league pyramid for since 2005. You can remove those hugely informative confidence interval bars by unticking the checkbox. End of explanation country_results = [] for country in ['E0', 'SP1', 'I1', 'D1', 'F1']: year_results = [] for year in range(2005,2017): url = "http://www.football-data.co.uk/mmz4281/"+str(year)[-2:]+str(year+1)[-2:]+ "/" + country +".csv" print(url) year_results.append(np.concatenate((np.array([year, division]), get_home_team_advantage(pd.read_csv(url))))) country_results.append(np.vstack(year_results)) def plot_func(freq): fig, ax1 = plt.subplots(1, 1,figsize=(7, 5)) for div, (div_name, div_col) in enumerate(zip(['EPL', 'La Liga', 'Serie A', 'Bundesliga 1', 'Ligue 1'], ["#9b5369", "#A1D9FF", "#CA82F8", "#ED93CB", "#78B7BB"])): if freq: ax1.errorbar(country_results[div][:,0], country_results[div][:,2], yerr= (country_results[div][:,4] - country_results[div][:,2]), linestyle='-', marker='o',label=div_name, color = div_col) else: ax1.plot(country_results[div][:,0], country_results[div][:,2], linestyle='-', marker='o',label=div_name, color = div_col) ax1.set_xticks([2005, 2007, 2009, 2011, 2013, 2015]) ax1.set_ylim([-0.05, 0.6]) ax1.set_xlim([2004.5, 2016.5]) ax1.set_xlabel('Season', fontsize=12) ax1.set_ylabel('Home Advantage Score', fontsize=12) ax1.set_xticklabels(['05/06', '07/08', '09/10', '11/12', '13/14', '15/16']) plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) plt.show() interact(plot_func, freq = Checkbox( value=True, description='Error Bars', disabled=False)) Explanation: It's probably more apparent without those hugely informative confidence interval bars, but it seems that the home advantage score decreases slightly as you move down the pyramid (analysis by Sky Sports produced something similar). This might make sense for two reasons. Firstly, bigger teams generally have larger stadiums and more supporters, which could strengthen the home field advantage. Secondly, as you go down the leagues, I suspect the quality gap between teams narrows. Taking it to an extreme, when I used to play Sunday league football, it didn't really matter where we played... we still lost. In that sense, one must be careful comparing the home advantage between leagues, as it will be affected by the relative team strengths within those leagues. For example, a league with a very dominant team (or teams) will record a lower home advantage score, as that dominant team will score goals home and away with little difference (Man Utd would probably beat Cork City 6-0 at Old Trafford and Turners Cross!). Having warned about the dangers of comparing different leagues with this approach, let's now compare the top five leagues in Europe over the same time period as before. End of explanation import scrapy import re # for text parsing import logging import pandas as pd class FootballSpider(scrapy.Spider): name = 'footballSpider' # page to scrape start_urls = pd.read_csv( 'https://raw.githubusercontent.com/dashee87/blogScripts/master/files/league_links.csv')['link'].tolist() + \ pd.read_csv("league_links_.csv")['link2'].dropna(axis=0, how='all').tolist() # if you want to impose a delay between sucessive scrapes # download_delay = 1.0 def parse(self, response): self.logger.info('Scraping page: %s', response.url) country_league = response.css('.list-breadcrumb__item__in::text').extract() for num, (hometeam, awayteam, match_result, date) in \ enumerate(zip(response.css('.in-match span:nth-child(1)'), response.css('.in-match span:nth-child(2)'), response.css('td.h-text-center'), response.css('.h-text-no-wrap::text').extract())): yield {'country':country_league[2], 'league': country_league[3], 'HomeTeam': hometeam.css('::text').extract_first(), 'AwayTeam':awayteam.css('::text').extract_first(), 'FTHG': re.sub(':.', '', match_result.css('::text').extract_first()), 'FTAG': re.sub('.:', '', match_result.css('::text').extract_first()), 'awarded': ' ' in match_result.css('::text').extract_first() or 'AWA.' in match_result.css('::text').extract_first(), 'date':date} from scrapy.crawler import CrawlerProcess process = CrawlerProcess({ 'USER_AGENT': 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)', 'FEED_FORMAT': 'json', 'FEED_URI': 'all_league_goals_.json' }) # minimising the information presented on the scrapy log logging.getLogger('scrapy').setLevel(logging.WARNING) process.crawl(FootballSpider) process.start() Explanation: Honestly, there's not much going on there. With the poissble exception of the Spanish La Liga since 2010, the home field advantage enjoyed by the teams in each league is broadly similar (and that's before we bring in the idea of confidence intervals and hypothesis testing). Home Advantage Around the World To find more interesting contrasts, we must venture to crappier and more corrupt leagues. My hunch is that home advantage would be negligible in countries where the overall quality (team, infastructure, etc.) is very low. And by low, I mean leagues worse than the Irish Premier Division (yes, they exist). Unfortunately, the historical results for such leagues are not available on football-data.co.uk. Instead, we'll scrape the data off betexplorer. I'm extremely impressed by the breadth of this site. You can even retrieve past results for the French overseas department of Réunion. Fun fact: Dimtri Payet spent the 2004 season at AS Excelsior of the Réunion Premier League. We'll use Scrapy to pull the appropriate information off the website. If you've never used Scrapy before, then you should check out this post. I won't spend too long on this part, but you can find the full code on here. End of explanation all_league_goals = pd.read_json( "https://raw.githubusercontent.com/dashee87/blogScripts/master/files/all_league_goals.json") # reorder the columns to it a bit more logical all_league_goals = all_league_goals[['country', 'league', 'date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'awarded']] all_league_goals.head() Explanation: You don't actually need to run your own spider, as I've shared the output to my GitHub account. We can import the json file in directly using pandas. End of explanation # little bit of data cleansing to remove fixtures that were abandoned/awarded/postponed all_league_goals = all_league_goals[~all_league_goals['awarded']] all_league_goals = all_league_goals[all_league_goals['FTAG']!='POSTP.'] all_league_goals = all_league_goals[all_league_goals['FTAG']!='CAN.'] all_league_goals[['FTAG', 'FTHG']] = all_league_goals[['FTAG', 'FTHG']].astype(int) Explanation: Hopefully, that's all relatively clear. You'll notice that it's very similar to the format used by football-data, which means that we can feed this dataframe into the get_home_team_advantage function. Sometimes, matches are awarded due to one team fielding an ineligible player or crowd trouble. We should probably exclude such matches from the home field advantage calculations. End of explanation home_advantage_country = pd.DataFrame(all_league_goals.assign(match_goals = all_league_goals['FTHG'] + all_league_goals['FTHG']).groupby(['country','league']).agg( {'HomeTeam':['size','nunique'], 'match_goals':'mean'}).to_records()) home_advantage_country.columns = ['country', 'league', 'num_games', 'num_teams', 'avg_goals'] temp_set = [] for i in range(home_advantage_country.shape[0]): temp_set.append(get_home_team_advantage(all_league_goals[( all_league_goals['country']==home_advantage_country['country'][i]) & ( all_league_goals['league']==home_advantage_country['league'][i])])) temp_set = pd.DataFrame(temp_set,columns= ['home_advantage_score', 'left_tail', 'right_tail']) home_advantage_country = pd.concat([home_advantage_country, temp_set], axis=1).sort_values('home_advantage_score', ascending=False).reset_index(drop=True) home_advantage_country.index = home_advantage_country.index + 1 # if you want display more/less rows than the default option pd.options.display.max_rows = 40 home_advantage_country.assign(avg_goals= pd.Series.round(home_advantage_country['avg_goals'], 3), home_advantage_score= pd.Series.round(home_advantage_country['home_advantage_score'], 3), left_tail= pd.Series.round(home_advantage_country['left_tail'], 3), right_tail= pd.Series.round(home_advantage_country['right_tail'], 3)) Explanation: We're ready to put it all together. I'll omit the code (though it can be found here), but we'll loop through each country and league combination (just in case you decide to include multiple leagues from the same country) and calculate the home advantage score, plus its confidence limits as well as some other information for each league (number of teams, average number of goals in each match). I've converted the pandas output to a datatables table that you can interactively filter and sort. End of explanation home_advantage_country.assign(avg_goals= pd.Series.round(home_advantage_country['avg_goals'], 3), home_advantage_score= pd.Series.round(home_advantage_country['home_advantage_score'], 3), left_tail= pd.Series.round(home_advantage_country['left_tail'], 3), right_tail= pd.Series.round(home_advantage_country['right_tail'], 3)).merge( pd.read_csv("https://raw.githubusercontent.com/dashee87/blogScripts/master/files/league_links.csv", usecols = ['country', 'countryCode'], encoding='latin-1'), on="country", how="inner").reset_index().rename(columns={"index": "home_adv_rank"}).sort_values( 'avg_goals', ascending=False).reset_index(drop=True).reset_index().rename(columns={"index": "avg_goals_rank"}).to_csv( "home_advantage.csv", encoding='utf-8', index=False) Explanation: Focusing on the home_advantage_score column, teams in Nigeria by far enjoy the greatest benefit from playing at home (score = 1.195). In other words, home teams scored 3.3 (= $e^{1.195}$) times more goals than their opponents. This isn't new information and can be attributed to a combination of corruption (e.g. bribing referees) and violent fans. In fact, my motivation for this post was to identify more football corruption hotspots. Alas, when it comes to home turf invincibility, it seems Nigeria are the World Cup winners. Fifteen leagues have a negative home_advantage_score, meaning that visiting teams actually scored more goals than their hosts- though none was statistically significant. By some distance, the Maldives records the most negative score. Luckily, I've twice researched this beautiful archipelago and I'm aware that all matches in the Dhiveli Premier League are played at the national stadium in Malé (much like the Gibraltar Premier League). So it would make sense that there's no particular advantage gained by the home team. Libya is another interesting example. Owing to security issues, all matches in the Libyan Premier League are played in neutral venues with no spectators present. Quite fittingly, it returned a home advantage score just off zero. Generally speaking, the leagues with near zero home advantage come from small countries (minimal inconvenience for travelling teams) with a small number of teams and they tend to share stadiums. If you sort the avg_goals column, you'll see that Bolivia is the place to be for goals (average = 4.47). But rather than sifting through that table or explaining the results with words, the most intuitive way to illustrate this type of data is with a map of world. This might also help to clarify whether there's any geographical influence on the home advantage effect. Again, I won't go into the details (an appendix can be found in the Jupyter notebook), but I built a map using the JavaScript library, D3. And by built I mean I adapted the code from this post and this post. Though a little outdated now, I found this post quite useful too. Finally, I think this post shows off quite well what you can do with maps using D3. And here it is! The country colour represents its home_advantage_score. You can zoom in and out and hover over a country to reveal a nice informative overlay; use the radio buttons to switch between home advantage and goals scored. I recommend viewing it on desktop (mobile's a bit jumpy) and on Chrome (sometimes have security issues with Firefox). It's not scientifically rigorous (not in academia any more, baby!), but there's evidence for some geographical trends. For example, it appears that home advantage is stronger in Africa and South America compared to Western and Central Europe, with the unstable warzones of Libya, Somalia and Paraguay (?) being notable exceptions. As for average goals, Europe boasts stonger colours compared to Africa, though South East Asia seems to be the global hotspot for goals. North America is also quite dark, but you can debate whether Canada should be coloured grey, as the best Canadian teams belong to the American soccer system. Conclusion Using a previously described model and some JavaScript, this post explored the so called home advantage in football leagues all over the world (including Réunion). I don't think it uncovered anything particularly amazing: different leagues have different properties and don't bet on the away team in the Nigerian league. You can play around with the Python code here. Thanks for reading! Appendix This section is intended more than anything else as a reminder to myself if I ever want to build a map with D3 again. We need to write the country league data to a csv that will then be loaded into the d3 map. To improve readability, the values are rounded to 3 decimal places. The country outlines in the map (see below) will be coloured according to their average goals or home advantage score. Rather than matching on country name (which can be fickle- is it Democratic Republic of Congo or DR Congo or even Congo-Kinshasa?), we'll append a column for the country ISO 3166-1 alpha-3 code. I'd like to say I scraped some page here, but it was mostly a manual job. After creating some new columns for the column ranking, the file is written to the local directory (alternatively, you can view it here). End of explanation import json all_nations = json.load(open('all.geojson')) # command line: ogr2ogr -f GEOJson -where "ADM0_A3 IN ('GBR', 'FRA','NLD', 'USA')" \ # football_subdivisions.json ne_10m_admin_0_map_units.shp subdivisions = json.load(open('football_subdivisions.json')) all_nations_tidy = {} all_nations_tidy['type']= all_nations['type'] all_nations_tidy['crs'] = all_nations['crs'] all_nations_tidy['features'] = [] for country_features in all_nations['features']: # skip UK, France, US, Netherlands and Antartica if (country_features['properties']['ADM0_A3'] in ['GBR', 'FRA', 'USA', 'NLD', 'ATA'] or # skip minor islands and territories with populations less than 1000 country_features['properties']['POP_EST']<1000) and \ # don't want to exclude Western Sahara country_features['properties']['NAME_LONG'] != 'Western Sahara': continue if True: all_nations_tidy['features'].append({'properties': {'country': country_features['properties']['NAME_LONG'], 'countryCode': country_features['properties']['ADM0_A3']}, 'geometry': country_features['geometry']}) for subdiv_features in subdivisions['features']: all_nations_tidy['features'].append({'properties': {'country': subdiv_features['properties']['NAME_LONG'], 'countryCode': subdiv_features['properties']['BRK_A3']}, 'geometry': subdiv_features['geometry']}) with open('countries.json', 'w') as outfile: json.dump(all_nations_tidy, outfile) Explanation: Shapefiles and TopoJSON Generating the world map required a little bit of command line, python and a whole lot of JavaScript (specifically D3). The command line was used to convert the shapefiles into geojson files (see ogr2ogr and finally into the topojson format. The main reason for the last step is that it drastically reduces the file size, which should improve its onsite loading (though it could also affect the quality of the map). My particular map was complicated by the fact that some sovereign states are composed of several countries that organise their own national competitions. If that sounds weird, think of the United Kingdom. It's a member of the UN and a sovereign state in its own right (despite what Brexiteers may say). But there's no UK (or British) Premier League; there's the English/Welsh/Scottish/Northern Irish Premier League/Premiership. Similarly, Reunion is part of France but has its own football league. Then again, the Basque country is recognised as a nation within Spain, but has no internationally recognised national league. In summary, it's complicated. Political realities aside, we need to get the geojson file for all of the countries in the world (see all.geojson available here). We must remove the United Kingdom, France and a few others. To reduce the file size, I also removed some country information that wasn't relevant for my purposes (population, GDP, etc.). The geojson files for England, Scotland, Reunion, etc. were a little harder to track down. The shapefile containing those country subdivisions can be downloaded here, which can be converted into geojson files with ogr2ogr. Unfortunately, that file contains various subdivisions that don't correspond to actual football leagues (e.g. Belgium is split into the Flemish and Walloon regions). That means we need append the subdivisions we do want to higher level geojson file, which I did my manipulating the two json files in Python. End of explanation
9,210	Given the following text description, write Python code to implement the functionality described below step by step Description: Graph of iDigBio Specimens over Time This notebook introduces the basics of loading and analyzing iDigBio data on the GUODA infrastructure hosted by the ACIS Lab and iDigBio. This service is documented in the GUODA Jupyter service wiki on Github. As an example, we will create a graph that shows how many specimens in iDigBio were collected during each the past 200 years. The data for this graph is a random sample of 100,000 records from an export of the iDigBio. The field used to determine the year is the interpreted datecollected field that iDigBio populates based on Darwin Core terms like dwc Step1: Loading the data set Data in GUODA is stored on a clustered file system called HDFS. The Jupyter notebooks are all configured to read and write to HDFS automatically so all file paths are in the HDFS system. You can read more about working with files and how to see what data sets are availible on the Jupyter service wiki. This line will load the contents of the file that contains a 100,000 record sub-set of iDigBio into a Spark data frame. Then we can look at how many records are in the data frame to confirm that we are working with the 100k subset. Step2: Examining the data Now that the data is in memory, let's look at some of the methods availible to examine it before we move on to summarizing it. This will let you see how data is represented both in Spark and Python as well as what kind of data is availible in the iDigBio data frames. Data frame structure First we can look at the columns in the data frame. This is all of iDigBio so there are a lot of them. Also printed by Python is the data type for each column and if a column contains a nested structure (like the "data" structure which has the raw data originally sent to iDigBio) then it is indented. Step3: Next we can look at the first row of data. The (1) after head tells Python how many rows to print. Since this is all iDigBio data, the rows are pretty big so we'll only show one. Step4: Summarizing the data That's certainly more data than we need to make the graph. Since there is one row in the data frame for each specimen record, what we need to do is group the records by the year they were collected and then count the number of records in each group and associate that with the year. The data frame we want to have as a result should have two columns, one for year and one for the count of the records collected in that year. This is a common chain of operations often refered to as select, group by, and count which comes from the SQL syntax for doing this operation. Working with the year complicated by the fact that iDigBio has a datecollected field and not a yearcollected field. While we are often provided a year in the raw data, we assemble and convert all the date information from the Darwin Core fields into a date-type object and store that as datecollected. Because this object is a date type we can sort it and search for ranges. (Consider what would happen if we tried that with raw data strings like "2004-01-14" and "March 15, 2015".) We need to extract the year part of datecollected and we need to convert it to a number so we can sort on it. Step5: Let's take a look at this new data frame using some of the commands from above Step6: Now that our data is both much smaller and mostly numeric, we can use the describe() method to quickly make summary statistics. This method returns a data frame so we have to use show() to actually print the whole contents of the data frame. Step7: Spark data frames, Pandas data frames, and filtering The term "data frame" is a concept for how data is arranged. Different programming languages and even libraries in a single programming language have different implimentations of this idea. We have been working with a Spark data frame. Now we want to do some graphing and the Python graphing libraries know how to work with a Pandas data frame. Fortunately this is such a common conversion that there is a built-in method to do it. One thing to be aware of is that Pandas data frames are not stored on our computation cluster like the Spark data frames are. This means they need to be small and you should not do too much computation on them. Our year_summary data frame is only 2 columns and about 220 rows this isn't a problem. While converting to a Pandas data frame, we will also reduce the years to the range 1817 - 2017. From the output of describe() we could see that there were some years that didn't make sense. Step8: (Notice that the display of the first rows looks different from when we ran head() on the Spark data frame? That's because we're looking at the display generated by the Pandas library instead of the Spark library.) Making a graph The number of specimens collected in a year is discrete data so a bar graph is one appropriate way to display them.	Python Code: # The Python Spark (pyspark) libraries include functions designed to be run on columns of data # stored in Spark data frames. They need to be imported in order to use them. Here we # are going to use from pyspark.sql.functions import year # The matplotlib package is used for graphing. The next line tells Jupyter that when a # graphing function is used, it should draw the graph here inline in the notebook. import matplotlib.pyplot as plt %matplotlib inline Explanation: Graph of iDigBio Specimens over Time This notebook introduces the basics of loading and analyzing iDigBio data on the GUODA infrastructure hosted by the ACIS Lab and iDigBio. This service is documented in the GUODA Jupyter service wiki on Github. As an example, we will create a graph that shows how many specimens in iDigBio were collected during each the past 200 years. The data for this graph is a random sample of 100,000 records from an export of the iDigBio. The field used to determine the year is the interpreted datecollected field that iDigBio populates based on Darwin Core terms like dwc:year and dwc:eventDate. If you are interested in the capabilities of GUODA, you may want to scroll to the end of this notebook to view the final graph. If you are interested in doing this work yourself, please keep reading. Set up In this document, narrative describing what the code is intended to do and observations about the results is written in Markdown cells. Markdown is a simple wiki-style language that can be used for formating text. Comments about the actual code are written inside the code cells themselves and prefixed with "#" so the are not run. The code for this document is written in Python and uses the Apache Spark data analytics framework. The code written in this Jupyter notebook is actually run on servers located at the ACIS lab. All of the needed libraries and Spark configuration are already done and there is nothing to install. It is customary to import libraries used and set configuration options at the top of scripts. In the next cell, we import and set up the Python packages needed by this notebook. End of explanation df = sqlContext.read.load("/guoda/data/idigbio-20190612T171757.parquet") df.count() Explanation: Loading the data set Data in GUODA is stored on a clustered file system called HDFS. The Jupyter notebooks are all configured to read and write to HDFS automatically so all file paths are in the HDFS system. You can read more about working with files and how to see what data sets are availible on the Jupyter service wiki. This line will load the contents of the file that contains a 100,000 record sub-set of iDigBio into a Spark data frame. Then we can look at how many records are in the data frame to confirm that we are working with the 100k subset. End of explanation df.printSchema() Explanation: Examining the data Now that the data is in memory, let's look at some of the methods availible to examine it before we move on to summarizing it. This will let you see how data is represented both in Spark and Python as well as what kind of data is availible in the iDigBio data frames. Data frame structure First we can look at the columns in the data frame. This is all of iDigBio so there are a lot of them. Also printed by Python is the data type for each column and if a column contains a nested structure (like the "data" structure which has the raw data originally sent to iDigBio) then it is indented. End of explanation df.head(1) Explanation: Next we can look at the first row of data. The (1) after head tells Python how many rows to print. Since this is all iDigBio data, the rows are pretty big so we'll only show one. End of explanation # The outer "(" and ")" surround the chain of Python method calls to allow them to # span lines. This is a common convention and makes the data processing pipeline # easy to read and modify. # # The persist() function tells Spark to store the data frame in memory so it can be # accessed repeatedly without having to be reloaded. year_summary = (df .groupBy(year("datecollected").cast("integer").alias("yearcollected")) .count() .orderBy("yearcollected") .persist() ) Explanation: Summarizing the data That's certainly more data than we need to make the graph. Since there is one row in the data frame for each specimen record, what we need to do is group the records by the year they were collected and then count the number of records in each group and associate that with the year. The data frame we want to have as a result should have two columns, one for year and one for the count of the records collected in that year. This is a common chain of operations often refered to as select, group by, and count which comes from the SQL syntax for doing this operation. Working with the year complicated by the fact that iDigBio has a datecollected field and not a yearcollected field. While we are often provided a year in the raw data, we assemble and convert all the date information from the Darwin Core fields into a date-type object and store that as datecollected. Because this object is a date type we can sort it and search for ranges. (Consider what would happen if we tried that with raw data strings like "2004-01-14" and "March 15, 2015".) We need to extract the year part of datecollected and we need to convert it to a number so we can sort on it. End of explanation year_summary.count() year_summary.printSchema() year_summary.head(10) Explanation: Let's take a look at this new data frame using some of the commands from above: End of explanation year_summary.describe().show() Explanation: Now that our data is both much smaller and mostly numeric, we can use the describe() method to quickly make summary statistics. This method returns a data frame so we have to use show() to actually print the whole contents of the data frame. End of explanation pandas_year_summary = (year_summary .filter(year_summary.yearcollected >= 1817) .filter(year_summary.yearcollected <= 2017) .orderBy("yearcollected") .toPandas() ) pandas_year_summary.head() Explanation: Spark data frames, Pandas data frames, and filtering The term "data frame" is a concept for how data is arranged. Different programming languages and even libraries in a single programming language have different implimentations of this idea. We have been working with a Spark data frame. Now we want to do some graphing and the Python graphing libraries know how to work with a Pandas data frame. Fortunately this is such a common conversion that there is a built-in method to do it. One thing to be aware of is that Pandas data frames are not stored on our computation cluster like the Spark data frames are. This means they need to be small and you should not do too much computation on them. Our year_summary data frame is only 2 columns and about 220 rows this isn't a problem. While converting to a Pandas data frame, we will also reduce the years to the range 1817 - 2017. From the output of describe() we could see that there were some years that didn't make sense. End of explanation plt.bar(pandas_year_summary["yearcollected"], pandas_year_summary["count"], edgecolor='none', width=1.0 ) plt.title("Specimens in iDigBio by Collection Year and Continent") plt.ylabel("Number of Specimen Records") plt.xlabel("Year") Explanation: (Notice that the display of the first rows looks different from when we ran head() on the Spark data frame? That's because we're looking at the display generated by the Pandas library instead of the Spark library.) Making a graph The number of specimens collected in a year is discrete data so a bar graph is one appropriate way to display them. End of explanation
9,211	Given the following text description, write Python code to implement the functionality described. Description: Count three Function to count three - digit numbers having difference x with its reverse ; If x is not multiple of 99 ; No solution exists ; Generate all possible pairs of digits [ 1 , 9 ] ; If any pair is obtained with difference x / 99 ; Increase count ; Return the count ; Driver Code	Python Code: def Count_Number(x ) : ans = 0 ; if(x % 99 != 0 ) : ans = - 1 ; else : diff = x / 99 ; for i in range(1 , 10 ) : for j in range(1 , 10 ) : if(( i - j ) == diff ) : ans += 10 ; return ans ; if __name__== ' __main __' : x = 792 ; print(Count_Number(x ) ) ;
9,212	Given the following text description, write Python code to implement the functionality described below step by step Description: Work through the reduction of a single dataset Step1: Setup files Copy files from Dropbox to local, working folder cd 'working_folder' # Darks, if needed cp -rp ~/Dropbox/COS-LRG/darksall . # Calibs, as needed cp -rp ~/Dropbox/COS-LRG/calibfilesmast . # Subset of the raw and object calibration files mkdir LCYA01010 cd LCYA01010 cp -rp ~/Dropbox/COS-LRG/LCYA01010/rawtag . cp -rp ~/Dropbox/COS-LRG/LCYA01010/lcya01010_asn.fits . cp -rp ~/Dropbox/COS-LRG/LCYA01010/lcya01010_trl.fits . cp -rp ~/Dropbox/COS-LRG/LCYA01010/lcya01010_j*.fits . Step2: Customize for calcos Science frames LP2 calib file Science frames Step3: Calibs -- May not need to repeat this (i.e. if done before) Step4: Run calcos Launch pyraf cd ureka xgterm (or xterm) export lref=/home/marijana/ReductionCOS/calibfilesmast/ # Working in xgterm window ur_setup mkiraf #? pyraf Execute cd folder_with_data (e.g. LCYA01010) stsdas hst_calib hstcos # calcos [dataset prefix]_asn.fits (e.g. calcos lcya01010_asn.fits) Crashing and rerunning If you have to rerun, remove the corrtag and .tra files Coadd corrtag files Step5: Traces Step6: Ignoring Sun and Limb Get HVLEVELs Step7: Change PHA Examine PHA values near the trace of a single exposure (OPTIONAL) Step8: Edit Step9: Modify PHACORR Step10: Clean for CALCOS Step11: Run Calcos as above -- with PHA restricted Coadd new PHA frames Step12: Reduce Darks Find Darks Step13: Run calcos In new sub-folder for darks Use the script Clean unnecessary (large) files Step14: Measure Background Set background region Set chk=True to show plots Iterate as needed Step15: Loop on Exposures without PHA Will generate background spectra, one per exposure per segment Below we only do FUVA Step16: Coadd Step17: Bin to 2 pixels	Python Code: # imports import os import glob import pdb #from imp import reload #from importlib import reload from astropy.io import fits from cosredux import utils as cr_utils from cosredux import trace as cr_trace from cosredux import darks as cr_darks from cosredux import io as cr_io from cosredux import science as cr_science # python setup.py develop Explanation: Work through the reduction of a single dataset End of explanation rdx_path = '/Users/xavier/HST/COS/LRG_Redux/' #rdx_path = '/home/marijana/ReductionCOS/' science_folder = 'LCYA01010/' dark_folder = 'darksall/' #calib_folder = 'calibfilesmast/' calib_folder = 'calibs/' root_out = 'lcya01010' # Default values defaults = {} defaults['pha_mnx'] = (2,15) defaults['apert'] = 25. defaults['ndays'] = 90. Explanation: Setup files Copy files from Dropbox to local, working folder cd 'working_folder' # Darks, if needed cp -rp ~/Dropbox/COS-LRG/darksall . # Calibs, as needed cp -rp ~/Dropbox/COS-LRG/calibfilesmast . # Subset of the raw and object calibration files mkdir LCYA01010 cd LCYA01010 cp -rp ~/Dropbox/COS-LRG/LCYA01010/rawtag . cp -rp ~/Dropbox/COS-LRG/LCYA01010/lcya01010_asn.fits . cp -rp ~/Dropbox/COS-LRG/LCYA01010/lcya01010_trl.fits . cp -rp ~/Dropbox/COS-LRG/LCYA01010/lcya01010_j.fits . End of explanation cr_utils.modify_rawtag_for_calcos(rdx_path+science_folder) Explanation: Customize for calcos Science frames LP2 calib file Science frames End of explanation cr_utils.modify_LP2_1dx_calib(rdx_path+calib_folder) Explanation: Calibs -- May not need to repeat this (i.e. if done before) End of explanation corrtag_files_a = glob.glob(rdx_path+science_folder + '_corrtag_a.fits') corrtag_files_b = glob.glob(rdx_path+science_folder + '_corrtag_b.fits') corrtag_files_a _ = cr_utils.coadd_bintables(corrtag_files_a, outfile=rdx_path+science_folder+root_out+'_coaddcorr_woPHA_a.fits') _ = cr_utils.coadd_bintables(corrtag_files_b, outfile=rdx_path+science_folder+root_out+'_coaddcorr_woPHA_b.fits') Explanation: Run calcos Launch pyraf cd ureka xgterm (or xterm) export lref=/home/marijana/ReductionCOS/calibfilesmast/ # Working in xgterm window ur_setup mkiraf #? pyraf Execute cd folder_with_data (e.g. LCYA01010) stsdas hst_calib hstcos # calcos [dataset prefix]_asn.fits (e.g. calcos lcya01010_asn.fits) Crashing and rerunning If you have to rerun, remove the corrtag and .tra files Coadd corrtag files End of explanation reload(cr_trace) traces_a=cr_trace.traces(rdx_path+science_folder+root_out+'_coaddcorr_woPHA_a.fits', rdx_path+calib_folder, 'FUVA', clobber=True) traces_b=cr_trace.traces(rdx_path+science_folder+root_out+'_coaddcorr_woPHA_b.fits', rdx_path+calib_folder, 'FUVB', clobber=True) Explanation: Traces End of explanation reload(cr_utils) hva_a, hvb_a = cr_utils.get_hvlevels(corrtag_files_a) hva_b, hvb_b = cr_utils.get_hvlevels(corrtag_files_b) Explanation: Ignoring Sun and Limb Get HVLEVELs End of explanation reload(cr_science) ex_region = cr_science.set_extraction_region(traces_a[0], 'FUVA', corrtag_files_a[0], check=True) reload(cr_darks) pha_values_a, _, _ = cr_darks.get_pha_values_science(ex_region, corrtag_files_a[0], background=False) from xastropy.xutils import xdebug as xdb xdb.xhist(pha_values_a) Explanation: Change PHA Examine PHA values near the trace of a single exposure (OPTIONAL) End of explanation # Reset pha_mnx above if you wish reload(cr_utils) cr_utils.change_pha(rdx_path+calib_folder, low=defaults['pha_mnx'][0], up=defaults['pha_mnx'][1]) Explanation: Edit End of explanation reload(cr_utils) cr_utils.modify_phacorr(rdx_path+science_folder) Explanation: Modify PHACORR End of explanation reload(cr_utils) cr_utils.clean_for_calcos_phafiltering(rdx_path+science_folder) Explanation: Clean for CALCOS End of explanation corrtag_files_a = glob.glob(rdx_path+science_folder + '_corrtag_a.fits') corrtag_files_b = glob.glob(rdx_path+science_folder + '_corrtag_b.fits') corrtag_files_a _ = cr_utils.coadd_bintables(corrtag_files_a, outfile=rdx_path+science_folder+root_out+'_coaddcorr_withPHA_a.fits') _ = cr_utils.coadd_bintables(corrtag_files_b, outfile=rdx_path+science_folder+root_out+'_coaddcorr_withPHA_b.fits') Explanation: Run Calcos as above -- with PHA restricted Coadd new PHA frames End of explanation reload(cr_darks) subf_a = cr_darks.setup_for_calcos(rdx_path+dark_folder, corrtag_files_a[0], 'FUVA') Explanation: Reduce Darks Find Darks End of explanation reload(cr_darks) cr_darks.clean_after_calcos(rdx_path+science_folder+subf_a) Explanation: Run calcos In new sub-folder for darks Use the script Clean unnecessary (large) files End of explanation # Read traces (if needed) traces_a = cr_io.read_traces(rdx_path+science_folder+root_out+'_coaddcorr_woPHA_a.fits') traces_b = cr_io.read_traces(rdx_path+science_folder+root_out+'_coaddcorr_woPHA_b.fits') reload(cr_darks) chk = True bg_region_a = cr_darks.set_background_region(traces_a[0], 'FUVA', rdx_path+science_folder+root_out+'_coaddcorr_woPHA_a.fits', check=chk) bg_region_b = cr_darks.set_background_region(traces_b[0], 'FUVB', rdx_path+science_folder+root_out+'_coaddcorr_woPHA_b.fits', check=chk) bg_region_a, bg_region_b Explanation: Measure Background Set background region Set chk=True to show plots Iterate as needed End of explanation corrtag_woPHA_a = glob.glob(rdx_path+science_folder + '_corrtag_woPHA_a.fits') corrtag_woPHA_a reload(cr_darks) reload(cr_utils) cr_darks.dark_to_exposures(corrtag_woPHA_a, bg_region_a, traces_a[0], 'FUVA', defaults) Explanation: Loop on Exposures without PHA Will generate background spectra, one per exposure per segment Below we only do FUVA End of explanation x1d_files = glob.glob(rdx_path+science_folder + '*_x1d.fits') reload(cr_science) cr_science.coadd_exposures(x1d_files, 'FUVA', rdx_path+science_folder+'LCYA01010_coadd.fits') Explanation: Coadd End of explanation reload(cr_science) cr_science.coadd_exposures(x1d_files, 'FUVA', rdx_path+science_folder+'LCYA01010_coadd_bin2.fits', bin=2) Explanation: Bin to 2 pixels End of explanation
9,213	Given the following text description, write Python code to implement the functionality described below step by step Description: An Introduction to pandas Pandas! They are adorable animals. You might think they are the worst animal ever but that is not true. You might sometimes think pandas is the worst library every, and that is only kind of true. The important thing is use the right tool for the job. pandas is good for some stuff, SQL is good for some stuff, writing raw Python is good for some stuff. You'll figure it out as you go along. Now let's start coding. Hopefully you did pip install pandas before you started up this notebook. Step1: When you import pandas, you use import pandas as pd. That means instead of typing pandas in your code you'll type pd. You don't have to, but every other person on the planet will be doing it, so you might as well. Now we're going to read in a file. Our file is called NBA-Census-10.14.2013.csv because we're sports moguls. pandas can read_ different types of files, so try to figure it out by typing pd.read_ and hitting tab for autocomplete. Step2: A dataframe is basically a spreadsheet, except it lives in the world of Python or the statistical programming language R. They can't call it a spreadsheet because then people would think those programmers used Excel, which would make them boring and normal and they'd have to wear a tie every day. Selecting rows Now let's look at our data, since that's what data is for Step3: If we scroll we can see all of it. But maybe we don't want to see all of it. Maybe we hate scrolling? Step4: ...but maybe we want to see more than a measly five results? Step5: But maybe we want to make a basketball joke and see the final four? Step6: So yes, head and tail work kind of like the terminal commands. That's nice, I guess. But maybe we're incredibly demanding (which we are) and we want, say, the 6th through the 8th row (which we do). Don't worry (which I know you were), we can do that, too. Step7: It's kind of like an array, right? Except where in an array we'd say df[0] this time we need to give it two numbers, the start and the end. Selecting columns But jeez, my eyes don't want to go that far over the data. I only want to see, uh, name and age. Step8: NOTE Step9: Now that was a little weird, yes - we used df['POS'] instead of df[['POS']] when viewing the data's details. But now I'm curious about numbers Step10: Unfortunately because that has dollar signs and commas it's thought of as a string. We'll fix it in a second, but let's try describing one more thing. Step11: That's stupid, though, what's an inch even look like? What's 80 inches? I don't have a clue. If only there were some wa to manipulate our data. Manipulating data Oh wait there is, HA HA HA. Step12: Okay that was nice but unfortunately we can't do anything with it. It's just sitting there, separate from our data. If this were normal code we could do blahblah['feet'] = blahblah['Ht (In.)'] / 12, but since this is pandas, we can't. Right? Right? Step13: That's cool, maybe we could do the same thing with their salary? Take out the $ and the , and convert it to an integer? Step14: The average basketball player makes 3.8 million dollars and is a little over six and a half feet tall. But who cares about those guys? I don't care about those guys. They're boring. I want the real rich guys! Sorting and sub-selecting Step15: Those guys are making nothing! If only there were a way to sort from high to low, a.k.a. descending instead of ascending. Step16: But sometimes instead of just looking at them, I want to do stuff with them. Play some games with them! Dunk on them~ describe them! And we don't want to dunk on everyone, only the players above 7 feet tall. First, we need to check out boolean things. Step17: Drawing pictures Okay okay enough code and enough stupid numbers. I'm visual. I want graphics. Okay????? Okay. Step18: matplotlib is a graphing library. It's the Python way to make graphs! Step19: But that's ugly. There's a thing called ggplot for R that looks nice. We want to look nice. We want to look like ggplot. Step20: That might look better with a little more customization. So let's customize it. Step21: I want more graphics! Do tall people make more money?!?!	Python Code: # import pandas, but call it pd. Why? Because that's What People Do. Explanation: An Introduction to pandas Pandas! They are adorable animals. You might think they are the worst animal ever but that is not true. You might sometimes think pandas is the worst library every, and that is only kind of true. The important thing is use the right tool for the job. pandas is good for some stuff, SQL is good for some stuff, writing raw Python is good for some stuff. You'll figure it out as you go along. Now let's start coding. Hopefully you did pip install pandas before you started up this notebook. End of explanation # We're going to call this df, which means "data frame" # It isn't in UTF-8 (I saved it from my mac!) so we need to set the encoding Explanation: When you import pandas, you use import pandas as pd. That means instead of typing pandas in your code you'll type pd. You don't have to, but every other person on the planet will be doing it, so you might as well. Now we're going to read in a file. Our file is called NBA-Census-10.14.2013.csv because we're sports moguls. pandas can read_ different types of files, so try to figure it out by typing pd.read_ and hitting tab for autocomplete. End of explanation # Let's look at all of it Explanation: A dataframe is basically a spreadsheet, except it lives in the world of Python or the statistical programming language R. They can't call it a spreadsheet because then people would think those programmers used Excel, which would make them boring and normal and they'd have to wear a tie every day. Selecting rows Now let's look at our data, since that's what data is for End of explanation # Look at the first few rows Explanation: If we scroll we can see all of it. But maybe we don't want to see all of it. Maybe we hate scrolling? End of explanation # Let's look at MORE of the first few rows Explanation: ...but maybe we want to see more than a measly five results? End of explanation # Let's look at the final few rows Explanation: But maybe we want to make a basketball joke and see the final four? End of explanation # Show the 6th through the 8th rows Explanation: So yes, head and tail work kind of like the terminal commands. That's nice, I guess. But maybe we're incredibly demanding (which we are) and we want, say, the 6th through the 8th row (which we do). Don't worry (which I know you were), we can do that, too. End of explanation # Get the names of the columns, just because # If we want to be "correct" we add .values on the end of it # Select only name and age # Combing that with .head() to see not-so-many rows # We can also do this all in one line, even though it starts looking ugly # (unlike the cute bears pandas looks ugly pretty often) Explanation: It's kind of like an array, right? Except where in an array we'd say df[0] this time we need to give it two numbers, the start and the end. Selecting columns But jeez, my eyes don't want to go that far over the data. I only want to see, uh, name and age. End of explanation # Grab the POS column, and count the different values in it. Explanation: NOTE: That was not df['Name', 'Age'], it was df[['Name', 'Age]]. You'll definitely type it wrong all of the time. When things break with pandas it's probably because you forgot to put in a million brackets. Describing your data A powerful tool of pandas is being able to select a portion of your data, because who ordered all that data anyway. I want to know how many people are in each position. Luckily, pandas can tell me! End of explanation # Summary statistics for Age # That's pretty good. Does it work for everything? How about the money? Explanation: Now that was a little weird, yes - we used df['POS'] instead of df[['POS']] when viewing the data's details. But now I'm curious about numbers: how old is everyone? Maybe we could, I don't know, get some statistics about age? Some statistics to describe age? End of explanation # Doing more describing Explanation: Unfortunately because that has dollar signs and commas it's thought of as a string. We'll fix it in a second, but let's try describing one more thing. End of explanation # Take another look at our inches, but only the first few # Divide those inches by 12 # Let's divide ALL of them by 12 # Can we get statistics on those? # Let's look at our original data again Explanation: That's stupid, though, what's an inch even look like? What's 80 inches? I don't have a clue. If only there were some wa to manipulate our data. Manipulating data Oh wait there is, HA HA HA. End of explanation # Store a new column Explanation: Okay that was nice but unfortunately we can't do anything with it. It's just sitting there, separate from our data. If this were normal code we could do blahblah['feet'] = blahblah['Ht (In.)'] / 12, but since this is pandas, we can't. Right? Right? End of explanation # Can't just use .replace # Need to use this weird .str thing # Can't just immediately replace the , either # Need to use the .str thing before EVERY string method # Describe still doesn't work. # Let's convert it to an integer using .astype(int) before we describe it # Maybe we can just make them millions? # Unfortunately one is "n/a" which is going to break our code, so we can make n/a be 0 # Remove the .head() piece and save it back into the dataframe Explanation: That's cool, maybe we could do the same thing with their salary? Take out the $ and the , and convert it to an integer? End of explanation # This is just the first few guys in the dataset. Can we order it? # Let's try to sort them Explanation: The average basketball player makes 3.8 million dollars and is a little over six and a half feet tall. But who cares about those guys? I don't care about those guys. They're boring. I want the real rich guys! Sorting and sub-selecting End of explanation # It isn't descending = True, unfortunately # We can use this to find the oldest guys in the league # Or the youngest, by taking out 'ascending=False' Explanation: Those guys are making nothing! If only there were a way to sort from high to low, a.k.a. descending instead of ascending. End of explanation # Get a big long list of True and False for every single row. # We could use value counts if we wanted # But we can also apply this to every single row to say whether YES we want it or NO we don't # Instead of putting column names inside of the brackets, we instead # put the True/False statements. It will only return the players above # seven feet tall # Or only the guards # Or only the guards who make more than 15 million # It might be easier to break down the booleans into separate variables # We can save this stuff # Maybe we can compare them to taller players? Explanation: But sometimes instead of just looking at them, I want to do stuff with them. Play some games with them! Dunk on them~ describe them! And we don't want to dunk on everyone, only the players above 7 feet tall. First, we need to check out boolean things. End of explanation # This will scream we don't have matplotlib. Explanation: Drawing pictures Okay okay enough code and enough stupid numbers. I'm visual. I want graphics. Okay????? Okay. End of explanation # this will open up a weird window that won't do anything # So instead you run this code Explanation: matplotlib is a graphing library. It's the Python way to make graphs! End of explanation # Import matplotlib # What's available? # Use ggplot # Make a histogram # Try some other styles Explanation: But that's ugly. There's a thing called ggplot for R that looks nice. We want to look nice. We want to look like ggplot. End of explanation # Pass in all sorts of stuff! # Most from http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.hist.html # .range() is a matplotlib thing Explanation: That might look better with a little more customization. So let's customize it. End of explanation # How does experience relate with the amount of money they're making? # At least we can assume height and weight are related # At least we can assume height and weight are related # http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html # We can also use plt separately # It's SIMILAR but TOTALLY DIFFERENT Explanation: I want more graphics! Do tall people make more money?!?! End of explanation
9,214	Given the following text description, write Python code to implement the functionality described below step by step Description: Analyzing dendritic data with CaImAn This notebook shows an example on how to analyze two-photon dendritic data with CaImAn. It follows closely the other notebooks. Step1: Selecting the data We provide an example file from mouse barrel cortex, courtesy of Clay Lacefield and Randy Bruno, Columbia University. The download_demo command will automatically download the file and store it in your caiman_data folder the first time you run it. To use the demo in your own dataset you can set Step2: Viewing the data Prior to analysis you can view the data by setting display_movie = False. The example file provided here is small, with FOV size 128 x 128. To view it larger we set magnification = 4 in the play command. This will result to a movie of resolution 512 x 512. For larger files, you will want to reduce this magnification factor (for memory and display purposes). Step3: Parameter setting First we'll perform motion correction followed by CNMF. One of the main differences when analyzing dendritic data, is that dendritic segments are not localized and can traverse significant parts of the FOV. They can also be spatially non-contiguous. To capture this property, CaImAn has two methods for initializing CNMF Step4: Motion correction First we create a motion correction object and then perform the registration. The provided dataset has already been registered with a different method so we do not expect to see a lot of motion. Step5: Display registered movie next to the original Step6: Memory mapping Step7: Now run CNMF with the chosen initialization method Note that in the parameter setting we have set only_init = True so only the initialization will be run. Step8: Display components And compute the correlation image as a reference image Step9: Plot a montage array of all the components We can also plot a montage of all the identified spatial footprints. Step10: Run CNMF to sparsify the components The components found by the initialization have captured a lot of structure but they are not sparse. Using the refit command will sparsify them. Step11: Now plot the components again Step12: Component evaluation the components are evaluated in three ways Step13: Plot contours of selected and rejected components Step14: Extract DF/F values Step15: The resulting components are significantly more sparse and capture a lot of the data structure. Play a movie with the results The movie will show three panels with the original, denoised and residual movies respectively. The flag include_bck = False will remove the estimated background from the original and denoised panels. To include it set it to True. Step16: Save the object You can save the results of the analysis for future use and/or to load them on the GUI. Step17: Now try CNMF with patches As mentioned above, the file shown here is small enough to be processed at once. For larger files patches and/or downsampling can be used. We show how to use patches below. Note that unlike in somatic imaging, for dendritic imaging we want the patches to be large and have significant overlap so enough structure of the dendritic segments is captured in the overlapping region. This will help with merging the components. However, a too large patch size can result to memory issues due to large amount of data loaded concurrently onto memory. Step18: Change some parameters to enable patches To enable patches the rf parameter should be changed from None to some integer value. Moreover the amount of overlap controlled by stride_cnmf should also change. Finally, since the parameter K specifies components per patch it should be reduced compared to its value when operating on the whole FOV at once. It is important to experiment with different values for these parameters since results can sensitive, due to the non stereotypical shapes and activity patterns of dendrites. Step19: Refit to sparsify As before we can pass the results through the CNMF refit function that can yield more sparse components. Step20: Compare the two approaches To identify how similar components and the two approaches yield, we can compute the cross-correlation matrix between the spatial footprints derived from the two approaches. Step21: Stop the cluster when you're done	Python Code: import cv2 import glob import logging import matplotlib.pyplot as plt import numpy as np import os try: cv2.setNumThreads(0) except(): pass try: if __IPYTHON__: get_ipython().magic('load_ext autoreload') get_ipython().magic('autoreload 2') except NameError: pass import caiman as cm from caiman.motion_correction import MotionCorrect from caiman.source_extraction.cnmf import cnmf as cnmf from caiman.source_extraction.cnmf import params as params from caiman.utils.utils import download_demo from caiman.utils.visualization import nb_view_patches from skimage.util import montage import bokeh.plotting as bpl import holoviews as hv bpl.output_notebook() hv.notebook_extension('bokeh') Explanation: Analyzing dendritic data with CaImAn This notebook shows an example on how to analyze two-photon dendritic data with CaImAn. It follows closely the other notebooks. End of explanation fnames = [download_demo('data_dendritic.tif')] Explanation: Selecting the data We provide an example file from mouse barrel cortex, courtesy of Clay Lacefield and Randy Bruno, Columbia University. The download_demo command will automatically download the file and store it in your caiman_data folder the first time you run it. To use the demo in your own dataset you can set: fnames = [/path/to/file(s)]. End of explanation display_movie = False if display_movie: m_orig = cm.load_movie_chain(fnames) ds_ratio = 0.25 m_orig.resize(1, 1, ds_ratio).play( q_max=99.5, fr=30, magnification=4) Explanation: Viewing the data Prior to analysis you can view the data by setting display_movie = False. The example file provided here is small, with FOV size 128 x 128. To view it larger we set magnification = 4 in the play command. This will result to a movie of resolution 512 x 512. For larger files, you will want to reduce this magnification factor (for memory and display purposes). End of explanation # dataset dependent parameters fr = 30 # imaging rate in frames per second decay_time = 0.5 # motion correction parameters strides = (48, 48) # start a new patch for pw-rigid motion correction every x pixels overlaps = (24, 24) # overlap between pathes (size of patch strides+overlaps) max_shifts = (6, 6) # maximum allowed rigid shifts (in pixels) max_deviation_rigid = 3 # maximum shifts deviation allowed for patch with respect to rigid shifts pw_rigid = True # flag for performing non-rigid motion correction # parameters for source extraction and deconvolution p = 0 # order of the autoregressive system gnb = 2 # number of global background components merge_thr = 0.75 # merging threshold, max correlation allowed rf = None # half-size of the patches in pixels. e.g., if rf=25, patches are 50x50 stride_cnmf = None # amount of overlap between the patches in pixels K = 60 # number of components per patch method_init = 'graph_nmf' # initialization method (if analyzing dendritic data using 'sparse_nmf') ssub = 1 # spatial subsampling during initialization tsub = 1 # temporal subsampling during intialization opts_dict = {'fnames': fnames, 'fr': fr, 'decay_time': decay_time, 'strides': strides, 'overlaps': overlaps, 'max_shifts': max_shifts, 'max_deviation_rigid': max_deviation_rigid, 'pw_rigid': pw_rigid, 'p': p, 'nb': gnb, 'rf': rf, 'K': K, 'stride': stride_cnmf, 'method_init': method_init, 'rolling_sum': True, 'only_init': True, 'ssub': ssub, 'tsub': tsub, 'merge_thr': merge_thr} opts = params.CNMFParams(params_dict=opts_dict) #%% start a cluster for parallel processing (if a cluster already exists it will be closed and a new session will be opened) if 'dview' in locals(): cm.stop_server(dview=dview) c, dview, n_processes = cm.cluster.setup_cluster( backend='local', n_processes=None, single_thread=False) Explanation: Parameter setting First we'll perform motion correction followed by CNMF. One of the main differences when analyzing dendritic data, is that dendritic segments are not localized and can traverse significant parts of the FOV. They can also be spatially non-contiguous. To capture this property, CaImAn has two methods for initializing CNMF: sparse_nmf where a sparse non-negative matrix factorization is deployed, and graph-nmf, where the Laplacian of the pixel affinity matrix is used as a regularizer to promote spatial components that capture pixels that have similar timecourses. These can be selected with the parameter method_init. In this demo we use the graph_nmf initialization method although sparse_nmf can also be used. Since the dataset is small we can run CNMF without splitting the FOV in patches. This can be helpful because of the non-localized nature of the dendritic segments. To do that we set rf = None. Using patches is also supported as demonstrated below. Also note, that spatial and temporal downsampling can also be used to reduce the data dimensionality by appropriately modifying the parameters ssub and tsub. Here they are set to 1 since it is not necessary. The graph NMF method was originally presented in the paper: Cai, D., He, X., Han, J., & Huang, T. S. (2010). Graph regularized nonnegative matrix factorization for data representation. IEEE transactions on pattern analysis and machine intelligence, 33(8), 1548-1560. End of explanation mc = MotionCorrect(fnames, dview=dview, *opts.get_group('motion')) %%capture #%% Run piecewise-rigid motion correction using NoRMCorre mc.motion_correct(save_movie=True) m_els = cm.load(mc.fname_tot_els) border_to_0 = 0 if mc.border_nan is 'copy' else mc.border_to_0 # maximum shift to be used for trimming against NaNs Explanation: Motion correction First we create a motion correction object and then perform the registration. The provided dataset has already been registered with a different method so we do not expect to see a lot of motion. End of explanation #%% compare with original movie display_movie = False if display_movie: m_orig = cm.load_movie_chain(fnames) ds_ratio = 0.2 cm.concatenate([m_orig.resize(1, 1, ds_ratio) - mc.min_movmc.nonneg_movie, m_els.resize(1, 1, ds_ratio)], axis=2).play(fr=60, q_max=99.5, magnification=4, offset=0) # press q to exit Explanation: Display registered movie next to the original End of explanation #%% MEMORY MAPPING # memory map the file in order 'C' fname_new = cm.save_memmap(mc.mmap_file, base_name='memmap_', order='C', border_to_0=border_to_0) # exclude borders # now load the file Yr, dims, T = cm.load_memmap(fname_new) images = np.reshape(Yr.T, [T] + list(dims), order='F') #load frames in python format (T x X x Y) #%% restart cluster to clean up memory cm.stop_server(dview=dview) c, dview, n_processes = cm.cluster.setup_cluster( backend='local', n_processes=None, single_thread=False) Explanation: Memory mapping End of explanation cnm = cnmf.CNMF(n_processes, params=opts, dview=dview) cnm = cnm.fit(images) Explanation: Now run CNMF with the chosen initialization method Note that in the parameter setting we have set only_init = True so only the initialization will be run. End of explanation CI = cm.local_correlations(images[::1].transpose(1,2,0)) CI[np.isnan(CI)] = 0 cnm.estimates.hv_view_components(img=CI) Explanation: Display components And compute the correlation image as a reference image End of explanation A = cnm.estimates.A.toarray().reshape(opts.data['dims'] + (-1,), order='F').transpose([2, 0, 1]) Nc = A.shape[0] grid_shape = (np.ceil(np.sqrt(Nc/2)).astype(int), np.ceil(np.sqrt(Nc2)).astype(int)) plt.figure(figsize=np.array(grid_shape[::-1])1.5) plt.imshow(montage(A, rescale_intensity=True, grid_shape=grid_shape)) plt.title('Montage of found spatial components'); plt.axis('off'); Explanation: Plot a montage array of all the components We can also plot a montage of all the identified spatial footprints. End of explanation cnm2 = cnm.refit(images, dview=dview) Explanation: Run CNMF to sparsify the components The components found by the initialization have captured a lot of structure but they are not sparse. Using the refit command will sparsify them. End of explanation cnm2.estimates.hv_view_components(img=CI) A2 = cnm2.estimates.A.toarray().reshape(opts.data['dims'] + (-1,), order='F').transpose([2, 0, 1]) Nc = A2.shape[0] grid_shape = (np.ceil(np.sqrt(Nc/2)).astype(int), np.ceil(np.sqrt(Nc2)).astype(int)) plt.figure(figsize=np.array(grid_shape[::-1])1.5) plt.imshow(montage(A2, rescale_intensity=True, grid_shape=grid_shape)) plt.title('Montage of found spatial components'); plt.axis('off'); Explanation: Now plot the components again End of explanation min_SNR = 4 # signal to noise ratio for accepting a component rval_thr = 0.85 # space correlation threshold for accepting a component cnn_thr = 0.99 # threshold for CNN based classifier cnn_lowest = 0.1 # neurons with cnn probability lower than this value are rejected cnm2.params.set('quality', {'decay_time': decay_time, 'min_SNR': min_SNR, 'rval_thr': rval_thr, 'use_cnn': False, # do not use cnn for dentritic data 'min_cnn_thr': cnn_thr, 'cnn_lowest': cnn_lowest}) cnm2.estimates.evaluate_components(images, cnm2.params, dview=dview) Explanation: Component evaluation the components are evaluated in three ways: a) the shape of each component must be correlated with the data b) a minimum peak SNR is required over the length of a transient c) each shape passes a CNN based classifier End of explanation # accepted components cnm2.estimates.hv_view_components(img=CI, idx=cnm2.estimates.idx_components) # rejected components cnm2.estimates.hv_view_components(img=CI, idx=cnm2.estimates.idx_components_bad) Explanation: Plot contours of selected and rejected components End of explanation cnm2.estimates.detrend_df_f(quantileMin=8, frames_window=250) Explanation: Extract DF/F values End of explanation cnm2.estimates.play_movie(images, q_max=99.9, magnification=4, include_bck=False) # press q to exit Explanation: The resulting components are significantly more sparse and capture a lot of the data structure. Play a movie with the results The movie will show three panels with the original, denoised and residual movies respectively. The flag include_bck = False will remove the estimated background from the original and denoised panels. To include it set it to True. End of explanation cnm2.estimates.Cn = CI # save the correlation image for displaying in the background cnm2.save('dendritic_analysis.hdf5') Explanation: Save the object You can save the results of the analysis for future use and/or to load them on the GUI. End of explanation #%% restart cluster to clean up memory cm.stop_server(dview=dview) c, dview, n_processes = cm.cluster.setup_cluster( backend='local', n_processes=None, single_thread=False) Explanation: Now try CNMF with patches As mentioned above, the file shown here is small enough to be processed at once. For larger files patches and/or downsampling can be used. We show how to use patches below. Note that unlike in somatic imaging, for dendritic imaging we want the patches to be large and have significant overlap so enough structure of the dendritic segments is captured in the overlapping region. This will help with merging the components. However, a too large patch size can result to memory issues due to large amount of data loaded concurrently onto memory. End of explanation rf = 48 # size of each patch is (2rf, 2rf) stride_cnmf = 16 Kp = 25 # reduce the number of component as it is now per patch opts.change_params({'rf': rf, 'stride': stride_cnmf, 'K': Kp}); cnm_patches = cnmf.CNMF(n_processes, params=opts, dview=dview) cnm_patches = cnm_patches.fit(images) cnm_patches.estimates.hv_view_components(img=CI) Ap = cnm_patches.estimates.A.toarray().reshape(opts.data['dims'] + (-1,), order='F').transpose([2, 0, 1]) Nc = Ap.shape[0] grid_shape = (np.ceil(np.sqrt(Nc/2)).astype(int), np.ceil(np.sqrt(Nc2)).astype(int)) plt.figure(figsize=np.array(grid_shape[::-1])1.5) plt.imshow(montage(Ap, rescale_intensity=True, grid_shape=grid_shape)) plt.title('Montage of found spatial components'); plt.axis('off'); Explanation: Change some parameters to enable patches To enable patches the rf parameter should be changed from None to some integer value. Moreover the amount of overlap controlled by stride_cnmf should also change. Finally, since the parameter K specifies components per patch it should be reduced compared to its value when operating on the whole FOV at once. It is important to experiment with different values for these parameters since results can sensitive, due to the non stereotypical shapes and activity patterns of dendrites. End of explanation cnm_patches2 = cnm_patches.refit(images, dview=dview) Ap2 = cnm_patches2.estimates.A.toarray().reshape(opts.data['dims'] + (-1,), order='F').transpose([2, 0, 1]) Nc = Ap2.shape[0] grid_shape = (np.ceil(np.sqrt(Nc/2)).astype(int), np.ceil(np.sqrt(Nc2)).astype(int)) plt.figure(figsize=np.array(grid_shape[::-1])1.5) plt.imshow(montage(Ap2, rescale_intensity=True, grid_shape=grid_shape)) plt.title('Montage of found spatial components'); plt.axis('off'); cnm_patches2.estimates.play_movie(images, q_max=99.9, magnification=4, include_bck=False) # press q to exit Explanation: Refit to sparsify As before we can pass the results through the CNMF refit function that can yield more sparse components. End of explanation AA = np.corrcoef(cnm2.estimates.A.toarray(), cnm_patches2.estimates.A.toarray(), rowvar=False) plt.imshow(AA[:cnm2.estimates.A.shape[1], cnm2.estimates.A.shape[1]:]) plt.colorbar(); plt.xlabel('with patches') plt.ylabel('without patches') plt.title('Correlation coefficients'); Explanation: Compare the two approaches To identify how similar components and the two approaches yield, we can compute the cross-correlation matrix between the spatial footprints derived from the two approaches. End of explanation #%% STOP CLUSTER and clean up log files cm.stop_server(dview=dview) log_files = glob.glob('_LOG_') for log_file in log_files: os.remove(log_file) Explanation: Stop the cluster when you're done End of explanation
9,215	Given the following text description, write Python code to implement the functionality described below step by step Description: ROOT dataframe tutorial Step1: Create a ROOT dataframe in Python First we will create a ROOT dataframe that is connected to a dataset named Events stored in a ROOT file. The file is pulled in via XRootD from EOS public, but note how it could also be stored in your CERNBox space or in any other EOS repository accessible from SWAN (e.g. the experiment ones). The dataset Events is a TTree and has the following branches Step2: Run only on a part of the dataset The full dataset contains half a year of CMS data taking in 2012 with 61 mio events. For the purpose of this example, we use the Range node to run only on a small part of the dataset. This feature also comes in handy in the development phase of your analysis. Feel free to experiment with this parameter! Step3: Filter relevant events for this analysis Physics datasets are often general purpose datasets and therefore need extensive filtering of the events for the actual analysis. Here, we implement only a simple selection based on the number of muons and the charge to cut down the dataset in events that are relevant for our study. In particular, we are applying two filters to keep Step4: Perform complex operations in Python, efficiently! Since we still want to perform complex operations in Python but plain Python code is prone to be slow and not thread-safe, you should use as much as possible C++ functions to do the work in your event loop during runtime. This mechanism uses the C++ interpreter cling shipped with ROOT, making this possible in a single line of code. Note, that we are using here the Define node of the computation graph with a jitted function, calling into a function available in the ROOT library. Step5: Make a histogram of the newly created column Step6: Book a Report of the dataframe filters Step7: Start data processing This is the final step of the analysis	Python Code: import ROOT Explanation: ROOT dataframe tutorial: Dimuon spectrum This tutorial shows you how to analyze datasets using RDataFrame from a Python notebook. The example analysis performs the following steps: Connect a ROOT dataframe to a dataset containing 61 mio. events recorded by CMS in 2012 Filter the events being relevant for your analysis Compute the invariant mass of the selected dimuon candidates Plot the invariant mass spectrum showing resonances up to the Z mass This material is based on the analysis done by Stefan Wunsch, available here in CERN's Open Data portal. <center><img src="../images/dimuonSpectrum.png"></center> End of explanation treename = "Events" filename = "root://eospublic.cern.ch//eos/opendata/cms/derived-data/AOD2NanoAODOutreachTool/Run2012BC_DoubleMuParked_Muons.root" df = ROOT.RDataFrame(treename, filename) Explanation: Create a ROOT dataframe in Python First we will create a ROOT dataframe that is connected to a dataset named Events stored in a ROOT file. The file is pulled in via XRootD from EOS public, but note how it could also be stored in your CERNBox space or in any other EOS repository accessible from SWAN (e.g. the experiment ones). The dataset Events is a TTree and has the following branches: \| Branch name \| Data type \| Description \| \|-------------\|-----------\|-------------\| \| nMuon \| unsigned int \| Number of muons in this event \| \| Muon_pt \| float[nMuon] \| Transverse momentum of the muons stored as an array of size nMuon \| \| Muon_eta \| float[nMuon] \| Pseudo-rapidity of the muons stored as an array of size nMuon \| \| Muon_phi \| float[nMuon] \| Azimuth of the muons stored as an array of size nMuon \| \| Muon_charge \| int[nMuon] \| Charge of the muons stored as an array of size nMuon and either -1 or 1 \| \| Muon_mass \| float[nMuon] \| Mass of the muons stored as an array of size nMuon \| End of explanation # Take only the first 1M events df_range = df.Range(1000000) Explanation: Run only on a part of the dataset The full dataset contains half a year of CMS data taking in 2012 with 61 mio events. For the purpose of this example, we use the Range node to run only on a small part of the dataset. This feature also comes in handy in the development phase of your analysis. Feel free to experiment with this parameter! End of explanation df_2mu = df_range.Filter("nMuon == 2", "Events with exactly two muons") df_oc = df_2mu.Filter("Muon_charge[0] != Muon_charge[1]", "Muons with opposite charge") Explanation: Filter relevant events for this analysis Physics datasets are often general purpose datasets and therefore need extensive filtering of the events for the actual analysis. Here, we implement only a simple selection based on the number of muons and the charge to cut down the dataset in events that are relevant for our study. In particular, we are applying two filters to keep: 1. Events with exactly two muons 2. Events with muons of opposite charge End of explanation df_mass = df_oc.Define("Dimuon_mass", "ROOT::VecOps::InvariantMass(Muon_pt, Muon_eta, Muon_phi, Muon_mass)") Explanation: Perform complex operations in Python, efficiently! Since we still want to perform complex operations in Python but plain Python code is prone to be slow and not thread-safe, you should use as much as possible C++ functions to do the work in your event loop during runtime. This mechanism uses the C++ interpreter cling shipped with ROOT, making this possible in a single line of code. Note, that we are using here the Define node of the computation graph with a jitted function, calling into a function available in the ROOT library. End of explanation nbins = 30000 low = 0.25 up = 300 histo_name = "Dimuon_mass" histo_title = histo_name h = df_mass.Histo1D((histo_name, histo_title, nbins, low, up), "Dimuon_mass") Explanation: Make a histogram of the newly created column End of explanation report = df.Report() Explanation: Book a Report of the dataframe filters End of explanation %%time ROOT.gStyle.SetOptStat(0) ROOT.gStyle.SetTextFont(42) c = ROOT.TCanvas("c", "", 800, 700) c.SetLogx() c.SetLogy() h.SetTitle("") h.GetXaxis().SetTitle("m_{#mu#mu} (GeV)") h.GetXaxis().SetTitleSize(0.04) h.GetYaxis().SetTitle("N_{Events}") h.GetYaxis().SetTitleSize(0.04) h.Draw() label = ROOT.TLatex() label.SetNDC(True) label.SetTextSize(0.040) label.DrawLatex(0.100, 0.920, "#bf{CMS Open Data}") label.SetTextSize(0.030) label.DrawLatex(0.500, 0.920, "#sqrt{s} = 8 TeV, L_{int} = 11.6 fb^{-1}") %jsroot on c.Draw() report.Print() Explanation: Start data processing This is the final step of the analysis: retrieving the result. We are expecting to see a plot of the mass of the dimuon spectrum similar to the one shown at the beginning of this exercise (remember we are running on fewer entries in this exercise). Finally in the last cell we should see a report of the filters applied on the dataset. End of explanation
9,216	Given the following text description, write Python code to implement the functionality described below step by step Description: Leitura e display de imagens com matplotlib importando Step1: Leitura usando matplotlib native e com PIL O matplotlib possui a leitura nativa de imagens no formato png. Quando este formato é lido, a imagem é automaticamente mapeada da faixa 0 a 255 contido no pixel uint8 da imagem para float 0 a 1 no pixel do array lido Já se o formato for outro, se o PIL estiver instalado, a leitura é feita pelo PIL e neste caso o tipo do pixel é mantido em uint8, de 0 a 255. Veja no exemplo a seguir Leitura de imagem em níveis de cinza de imagem TIFF Step2: Leitura de imagem colorida formato TIFF Quando a imagem é colorida e não está no formato png, matplotlib utiliza PIL para leitura. O array terá o tipo uint8 e o shape do array é organizado em (H, W, 3). Step3: Leitura de imagem colorida formato png Se a imagem está no formato png, o matplotlib mapeia os pixels de 0 a 255 para float de 0 a 1.0 Step4: Mostrando as imagens lidas Step5: Observe que o display mostra apenas a última chamada do imshow	Python Code: import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np Explanation: Leitura e display de imagens com matplotlib importando End of explanation f = mpimg.imread('../data/cameraman.tif') print(f.dtype,f.shape,f.max(),f.min()) Explanation: Leitura usando matplotlib native e com PIL O matplotlib possui a leitura nativa de imagens no formato png. Quando este formato é lido, a imagem é automaticamente mapeada da faixa 0 a 255 contido no pixel uint8 da imagem para float 0 a 1 no pixel do array lido Já se o formato for outro, se o PIL estiver instalado, a leitura é feita pelo PIL e neste caso o tipo do pixel é mantido em uint8, de 0 a 255. Veja no exemplo a seguir Leitura de imagem em níveis de cinza de imagem TIFF: Como a imagem lida é TIFF, o array lido fica no tipo uint8, com valores de 0 a 255 End of explanation fcor = mpimg.imread('../data/boat.tif') print(fcor.dtype,fcor.shape,fcor.max(),fcor.min()) Explanation: Leitura de imagem colorida formato TIFF Quando a imagem é colorida e não está no formato png, matplotlib utiliza PIL para leitura. O array terá o tipo uint8 e o shape do array é organizado em (H, W, 3). End of explanation fcor2 = mpimg.imread('../data/boat.tif') print(fcor2.dtype, fcor2.shape, fcor2.max(), fcor2.min()) Explanation: Leitura de imagem colorida formato png Se a imagem está no formato png, o matplotlib mapeia os pixels de 0 a 255 para float de 0 a 1.0 End of explanation %matplotlib inline plt.imshow(f, cmap='gray') plt.colorbar() plt.imshow(fcor) plt.colorbar() plt.imshow(fcor2) plt.colorbar() Explanation: Mostrando as imagens lidas End of explanation plt.imshow(fcor2) plt.imshow(fcor) plt.imshow(f, cmap='gray') Explanation: Observe que o display mostra apenas a última chamada do imshow End of explanation
9,217	Given the following text description, write Python code to implement the functionality described below step by step Description: A primer on numerical differentiation In order to numerically evaluate a derivative $y'(x)=dy/dx$ at point $x_0$, we approximate is by using finite differences Step1: Why is it that the sequence does not converge? This is due to the round-off errors in the representation of the floating point numbers. To see this, we can simply type Step2: Let's try using powers of 1/2 Step3: In addition, one could consider the midpoint difference, defined as Step4: A more in-depth discussion about round-off errors in numerical differentiation can be found <a href="http Step5: Notice above that gradient() uses forward and backward differences at the two ends. Step6: More discussion about numerical differenciation, including higher order methods with error extrapolation can be found <a href="http Step7: One way to improve the roundoff errors is by simply using the decimal package	Python Code: dx = 1. x = 1. while(dx > 1.e-10): dy = (x+dx)(x+dx)-xx d = dy / dx print("%6.0e %20.16f %20.16f" % (dx, d, d-2.)) dx = dx / 10. Explanation: A primer on numerical differentiation In order to numerically evaluate a derivative $y'(x)=dy/dx$ at point $x_0$, we approximate is by using finite differences: Therefore we find: $$\begin{eqnarray} && dx \approx \Delta x &=&x_1-x_0, \ && dy \approx \Delta y &=&y_1-y_0 = y(x_1)-y(x_0) = y(x_0+\Delta_x)-y(x_0),\end{eqnarray}$$ Then we re-write the derivative in terms of discrete differences as: $$\frac{dy}{dx} \approx \frac{\Delta y}{\Delta x}$$ Example Let's look at the accuracy of this approximation in terms of the interval $\Delta x$. In our first example we will evaluate the derivative of $y=x^2$ at $x=1$. End of explanation ((1.+0.0001)(1+0.0001)-1) Explanation: Why is it that the sequence does not converge? This is due to the round-off errors in the representation of the floating point numbers. To see this, we can simply type: End of explanation dx = 1. x = 1. while(dx > 1.e-10): dy = (x+dx)(x+dx)-xx d = dy / dx print("%8.5e %20.16f %20.16f" % (dx, d, d-2.)) dx = dx / 2. Explanation: Let's try using powers of 1/2 End of explanation from math import sin, sqrt, pi dx = 1. while(dx > 1.e-10): x = pi/4. d1 = sin(x+dx) - sin(x); #forward d2 = sin(x+dx0.5) - sin(x-dx0.5); # midpoint d1 = d1 / dx; d2 = d2 / dx; print("%8.5e %20.16f %20.16f %20.16f %20.16f" % (dx, d1, d1-sqrt(2.)/2., d2, d2-sqrt(2.)/2.) ) dx = dx / 2. Explanation: In addition, one could consider the midpoint difference, defined as: $$ dy \approx \Delta y = y(x_0+\frac{\Delta_x}{2})-y(x_0-\frac{\Delta_x}{2}).$$ For a more complex function we need to import it from the math module. For instance, let's calculate the derivative of $sin(x)$ at $x=\pi/4$, including both the forward and midpoint differences. End of explanation %matplotlib inline import numpy as np from matplotlib import pyplot y = lambda x: xx x1 = np.arange(0,10,1) x2 = np.arange(0,10,0.1) y1 = np.gradient(y(x1), 1.) print (y1) pyplot.plot(x1,np.gradient(y(x1),1.),'r--o'); pyplot.plot(x1[:x1.size-1],np.diff(y(x1))/np.diff(x1),'b--x'); Explanation: A more in-depth discussion about round-off errors in numerical differentiation can be found <a href="http://www.uio.no/studier/emner/matnat/math/MAT-INF1100/h10/kompendiet/kap11.pdf">here</a> Special functions in numpy numpy provides a simple method diff() to calculate the numerical derivatives of a dataset stored in an array by forward differences. The function gradient() will calculate the derivatives by midpoint (or central) difference, that provides a more accurate result. End of explanation pyplot.plot(x2,np.gradient(y(x2),0.1),'b--o'); Explanation: Notice above that gradient() uses forward and backward differences at the two ends. End of explanation from scipy.misc import derivative y = lambda x: x*2 dx = 1. x = 1. while(dx > 1.e-10): d = derivative(y, x, dx, n=1, order=3) print("%6.0e %20.16f %20.16f" % (dx, d, d-2.)) dx = dx / 10. Explanation: More discussion about numerical differenciation, including higher order methods with error extrapolation can be found <a href="http://young.physics.ucsc.edu/115/diff.pdf">here</a>. The module scipy also includes methods to accurately calculate derivatives: End of explanation from decimal import Decimal dx = Decimal("1.") while(dx >= Decimal("1.e-10")): x = Decimal("1.") dy = (x+dx)(x+dx)-x*x d = dy / dx print("%6.0e %20.16f %20.16f" % (dx, d, d-Decimal("2."))) dx = dx / Decimal("10.") Explanation: One way to improve the roundoff errors is by simply using the decimal package End of explanation
9,218	Given the following text description, write Python code to implement the functionality described below step by step Description: ES-DOC CMIP6 Model Properties - Land MIP Era Step1: Document Authors Set document authors Step2: Document Contributors Specify document contributors Step3: Document Publication Specify document publication status Step4: Document Table of Contents 1. Key Properties 2. Key Properties --> Conservation Properties 3. Key Properties --> Timestepping Framework 4. Key Properties --> Software Properties 5. Grid 6. Grid --> Horizontal 7. Grid --> Vertical 8. Soil 9. Soil --> Soil Map 10. Soil --> Snow Free Albedo 11. Soil --> Hydrology 12. Soil --> Hydrology --> Freezing 13. Soil --> Hydrology --> Drainage 14. Soil --> Heat Treatment 15. Snow 16. Snow --> Snow Albedo 17. Vegetation 18. Energy Balance 19. Carbon Cycle 20. Carbon Cycle --> Vegetation 21. Carbon Cycle --> Vegetation --> Photosynthesis 22. Carbon Cycle --> Vegetation --> Autotrophic Respiration 23. Carbon Cycle --> Vegetation --> Allocation 24. Carbon Cycle --> Vegetation --> Phenology 25. Carbon Cycle --> Vegetation --> Mortality 26. Carbon Cycle --> Litter 27. Carbon Cycle --> Soil 28. Carbon Cycle --> Permafrost Carbon 29. Nitrogen Cycle 30. River Routing 31. River Routing --> Oceanic Discharge 32. Lakes 33. Lakes --> Method 34. Lakes --> Wetlands 1. Key Properties Land surface key properties 1.1. Model Overview Is Required Step5: 1.2. Model Name Is Required Step6: 1.3. Description Is Required Step7: 1.4. Land Atmosphere Flux Exchanges Is Required Step8: 1.5. Atmospheric Coupling Treatment Is Required Step9: 1.6. Land Cover Is Required Step10: 1.7. Land Cover Change Is Required Step11: 1.8. Tiling Is Required Step12: 2. Key Properties --> Conservation Properties TODO 2.1. Energy Is Required Step13: 2.2. Water Is Required Step14: 2.3. Carbon Is Required Step15: 3. Key Properties --> Timestepping Framework TODO 3.1. Timestep Dependent On Atmosphere Is Required Step16: 3.2. Time Step Is Required Step17: 3.3. Timestepping Method Is Required Step18: 4. Key Properties --> Software Properties Software properties of land surface code 4.1. Repository Is Required Step19: 4.2. Code Version Is Required Step20: 4.3. Code Languages Is Required Step21: 5. Grid Land surface grid 5.1. Overview Is Required Step22: 6. Grid --> Horizontal The horizontal grid in the land surface 6.1. Description Is Required Step23: 6.2. Matches Atmosphere Grid Is Required Step24: 7. Grid --> Vertical The vertical grid in the soil 7.1. Description Is Required Step25: 7.2. Total Depth Is Required Step26: 8. Soil Land surface soil 8.1. Overview Is Required Step27: 8.2. Heat Water Coupling Is Required Step28: 8.3. Number Of Soil layers Is Required Step29: 8.4. Prognostic Variables Is Required Step30: 9. Soil --> Soil Map Key properties of the land surface soil map 9.1. Description Is Required Step31: 9.2. Structure Is Required Step32: 9.3. Texture Is Required Step33: 9.4. Organic Matter Is Required Step34: 9.5. Albedo Is Required Step35: 9.6. Water Table Is Required Step36: 9.7. Continuously Varying Soil Depth Is Required Step37: 9.8. Soil Depth Is Required Step38: 10. Soil --> Snow Free Albedo TODO 10.1. Prognostic Is Required Step39: 10.2. Functions Is Required Step40: 10.3. Direct Diffuse Is Required Step41: 10.4. Number Of Wavelength Bands Is Required Step42: 11. Soil --> Hydrology Key properties of the land surface soil hydrology 11.1. Description Is Required Step43: 11.2. Time Step Is Required Step44: 11.3. Tiling Is Required Step45: 11.4. Vertical Discretisation Is Required Step46: 11.5. Number Of Ground Water Layers Is Required Step47: 11.6. Lateral Connectivity Is Required Step48: 11.7. Method Is Required Step49: 12. Soil --> Hydrology --> Freezing TODO 12.1. Number Of Ground Ice Layers Is Required Step50: 12.2. Ice Storage Method Is Required Step51: 12.3. Permafrost Is Required Step52: 13. Soil --> Hydrology --> Drainage TODO 13.1. Description Is Required Step53: 13.2. Types Is Required Step54: 14. Soil --> Heat Treatment TODO 14.1. Description Is Required Step55: 14.2. Time Step Is Required Step56: 14.3. Tiling Is Required Step57: 14.4. Vertical Discretisation Is Required Step58: 14.5. Heat Storage Is Required Step59: 14.6. Processes Is Required Step60: 15. Snow Land surface snow 15.1. Overview Is Required Step61: 15.2. Tiling Is Required Step62: 15.3. Number Of Snow Layers Is Required Step63: 15.4. Density Is Required Step64: 15.5. Water Equivalent Is Required Step65: 15.6. Heat Content Is Required Step66: 15.7. Temperature Is Required Step67: 15.8. Liquid Water Content Is Required Step68: 15.9. Snow Cover Fractions Is Required Step69: 15.10. Processes Is Required Step70: 15.11. Prognostic Variables Is Required Step71: 16. Snow --> Snow Albedo TODO 16.1. Type Is Required Step72: 16.2. Functions Is Required Step73: 17. Vegetation Land surface vegetation 17.1. Overview Is Required Step74: 17.2. Time Step Is Required Step75: 17.3. Dynamic Vegetation Is Required Step76: 17.4. Tiling Is Required Step77: 17.5. Vegetation Representation Is Required Step78: 17.6. Vegetation Types Is Required Step79: 17.7. Biome Types Is Required Step80: 17.8. Vegetation Time Variation Is Required Step81: 17.9. Vegetation Map Is Required Step82: 17.10. Interception Is Required Step83: 17.11. Phenology Is Required Step84: 17.12. Phenology Description Is Required Step85: 17.13. Leaf Area Index Is Required Step86: 17.14. Leaf Area Index Description Is Required Step87: 17.15. Biomass Is Required Step88: 17.16. Biomass Description Is Required Step89: 17.17. Biogeography Is Required Step90: 17.18. Biogeography Description Is Required Step91: 17.19. Stomatal Resistance Is Required Step92: 17.20. Stomatal Resistance Description Is Required Step93: 17.21. Prognostic Variables Is Required Step94: 18. Energy Balance Land surface energy balance 18.1. Overview Is Required Step95: 18.2. Tiling Is Required Step96: 18.3. Number Of Surface Temperatures Is Required Step97: 18.4. Evaporation Is Required Step98: 18.5. Processes Is Required Step99: 19. Carbon Cycle Land surface carbon cycle 19.1. Overview Is Required Step100: 19.2. Tiling Is Required Step101: 19.3. Time Step Is Required Step102: 19.4. Anthropogenic Carbon Is Required Step103: 19.5. Prognostic Variables Is Required Step104: 20. Carbon Cycle --> Vegetation TODO 20.1. Number Of Carbon Pools Is Required Step105: 20.2. Carbon Pools Is Required Step106: 20.3. Forest Stand Dynamics Is Required Step107: 21. Carbon Cycle --> Vegetation --> Photosynthesis TODO 21.1. Method Is Required Step108: 22. Carbon Cycle --> Vegetation --> Autotrophic Respiration TODO 22.1. Maintainance Respiration Is Required Step109: 22.2. Growth Respiration Is Required Step110: 23. Carbon Cycle --> Vegetation --> Allocation TODO 23.1. Method Is Required Step111: 23.2. Allocation Bins Is Required Step112: 23.3. Allocation Fractions Is Required Step113: 24. Carbon Cycle --> Vegetation --> Phenology TODO 24.1. Method Is Required Step114: 25. Carbon Cycle --> Vegetation --> Mortality TODO 25.1. Method Is Required Step115: 26. Carbon Cycle --> Litter TODO 26.1. Number Of Carbon Pools Is Required Step116: 26.2. Carbon Pools Is Required Step117: 26.3. Decomposition Is Required Step118: 26.4. Method Is Required Step119: 27. Carbon Cycle --> Soil TODO 27.1. Number Of Carbon Pools Is Required Step120: 27.2. Carbon Pools Is Required Step121: 27.3. Decomposition Is Required Step122: 27.4. Method Is Required Step123: 28. Carbon Cycle --> Permafrost Carbon TODO 28.1. Is Permafrost Included Is Required Step124: 28.2. Emitted Greenhouse Gases Is Required Step125: 28.3. Decomposition Is Required Step126: 28.4. Impact On Soil Properties Is Required Step127: 29. Nitrogen Cycle Land surface nitrogen cycle 29.1. Overview Is Required Step128: 29.2. Tiling Is Required Step129: 29.3. Time Step Is Required Step130: 29.4. Prognostic Variables Is Required Step131: 30. River Routing Land surface river routing 30.1. Overview Is Required Step132: 30.2. Tiling Is Required Step133: 30.3. Time Step Is Required Step134: 30.4. Grid Inherited From Land Surface Is Required Step135: 30.5. Grid Description Is Required Step136: 30.6. Number Of Reservoirs Is Required Step137: 30.7. Water Re Evaporation Is Required Step138: 30.8. Coupled To Atmosphere Is Required Step139: 30.9. Coupled To Land Is Required Step140: 30.10. Quantities Exchanged With Atmosphere Is Required Step141: 30.11. Basin Flow Direction Map Is Required Step142: 30.12. Flooding Is Required Step143: 30.13. Prognostic Variables Is Required Step144: 31. River Routing --> Oceanic Discharge TODO 31.1. Discharge Type Is Required Step145: 31.2. Quantities Transported Is Required Step146: 32. Lakes Land surface lakes 32.1. Overview Is Required Step147: 32.2. Coupling With Rivers Is Required Step148: 32.3. Time Step Is Required Step149: 32.4. Quantities Exchanged With Rivers Is Required Step150: 32.5. Vertical Grid Is Required Step151: 32.6. Prognostic Variables Is Required Step152: 33. Lakes --> Method TODO 33.1. Ice Treatment Is Required Step153: 33.2. Albedo Is Required Step154: 33.3. Dynamics Is Required Step155: 33.4. Dynamic Lake Extent Is Required Step156: 33.5. Endorheic Basins Is Required Step157: 34. Lakes --> Wetlands TODO 34.1. Description Is Required	Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'inm', 'inm-cm5-h', 'land') Explanation: ES-DOC CMIP6 Model Properties - Land MIP Era: CMIP6 Institute: INM Source ID: INM-CM5-H Topic: Land Sub-Topics: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. Properties: 154 (96 required) Model descriptions: Model description details Initialized From: -- Notebook Help: Goto notebook help page Notebook Initialised: 2018-02-15 16:54:04 Document Setup IMPORTANT: to be executed each time you run the notebook End of explanation # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) Explanation: Document Authors Set document authors End of explanation # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) Explanation: Document Contributors Specify document contributors End of explanation # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) Explanation: Document Publication Specify document publication status End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: Document Table of Contents 1. Key Properties 2. Key Properties --> Conservation Properties 3. Key Properties --> Timestepping Framework 4. Key Properties --> Software Properties 5. Grid 6. Grid --> Horizontal 7. Grid --> Vertical 8. Soil 9. Soil --> Soil Map 10. Soil --> Snow Free Albedo 11. Soil --> Hydrology 12. Soil --> Hydrology --> Freezing 13. Soil --> Hydrology --> Drainage 14. Soil --> Heat Treatment 15. Snow 16. Snow --> Snow Albedo 17. Vegetation 18. Energy Balance 19. Carbon Cycle 20. Carbon Cycle --> Vegetation 21. Carbon Cycle --> Vegetation --> Photosynthesis 22. Carbon Cycle --> Vegetation --> Autotrophic Respiration 23. Carbon Cycle --> Vegetation --> Allocation 24. Carbon Cycle --> Vegetation --> Phenology 25. Carbon Cycle --> Vegetation --> Mortality 26. Carbon Cycle --> Litter 27. Carbon Cycle --> Soil 28. Carbon Cycle --> Permafrost Carbon 29. Nitrogen Cycle 30. River Routing 31. River Routing --> Oceanic Discharge 32. Lakes 33. Lakes --> Method 34. Lakes --> Wetlands 1. Key Properties Land surface key properties 1.1. Model Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of land surface model. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 1.2. Model Name Is Required: TRUE    Type: STRING    Cardinality: 1.1 Name of land surface model code (e.g. MOSES2.2) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 1.3. Description Is Required: TRUE    Type: STRING    Cardinality: 1.1 General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "water" # "energy" # "carbon" # "nitrogen" # "phospherous" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 1.4. Land Atmosphere Flux Exchanges Is Required: FALSE    Type: ENUM    Cardinality: 0.N Fluxes exchanged with the atmopshere. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 1.5. Atmospheric Coupling Treatment Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.land_cover') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "bare soil" # "urban" # "lake" # "land ice" # "lake ice" # "vegetated" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 1.6. Land Cover Is Required: TRUE    Type: ENUM    Cardinality: 1.N Types of land cover defined in the land surface model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.land_cover_change') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 1.7. Land Cover Change Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe how land cover change is managed (e.g. the use of net or gross transitions) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 1.8. Tiling Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 2. Key Properties --> Conservation Properties TODO 2.1. Energy Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.conservation_properties.water') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 2.2. Water Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how water is conserved globally and to what level (e.g. within X [units]/year) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.conservation_properties.carbon') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 2.3. Carbon Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 3. Key Properties --> Timestepping Framework TODO 3.1. Timestep Dependent On Atmosphere Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is a time step dependent on the frequency of atmosphere coupling? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 3.2. Time Step Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Overall timestep of land surface model (i.e. time between calls) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 3.3. Timestepping Method Is Required: TRUE    Type: STRING    Cardinality: 1.1 General description of time stepping method and associated time step(s) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.software_properties.repository') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 4. Key Properties --> Software Properties Software properties of land surface code 4.1. Repository Is Required: FALSE    Type: STRING    Cardinality: 0.1 Location of code for this component. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.software_properties.code_version') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 4.2. Code Version Is Required: FALSE    Type: STRING    Cardinality: 0.1 Code version identifier. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.key_properties.software_properties.code_languages') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 4.3. Code Languages Is Required: FALSE    Type: STRING    Cardinality: 0.N Code language(s). End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.grid.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 5. Grid Land surface grid 5.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of the grid in the land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.grid.horizontal.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 6. Grid --> Horizontal The horizontal grid in the land surface 6.1. Description Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the general structure of the horizontal grid (not including any tiling) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_grid') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 6.2. Matches Atmosphere Grid Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Does the horizontal grid match the atmosphere? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.grid.vertical.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 7. Grid --> Vertical The vertical grid in the soil 7.1. Description Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the general structure of the vertical grid in the soil (not including any tiling) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.grid.vertical.total_depth') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 7.2. Total Depth Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 The total depth of the soil (in metres) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8. Soil Land surface soil 8.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of soil in the land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.heat_water_coupling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.2. Heat Water Coupling Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the coupling between heat and water in the soil End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.number_of_soil layers') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 8.3. Number Of Soil layers Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 The number of soil layers End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.prognostic_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 8.4. Prognostic Variables Is Required: TRUE    Type: STRING    Cardinality: 1.1 List the prognostic variables of the soil scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.soil_map.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 9. Soil --> Soil Map Key properties of the land surface soil map 9.1. Description Is Required: TRUE    Type: STRING    Cardinality: 1.1 General description of soil map End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.soil_map.structure') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 9.2. Structure Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the soil structure map End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.soil_map.texture') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 9.3. Texture Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the soil texture map End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.soil_map.organic_matter') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 9.4. Organic Matter Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the soil organic matter map End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.soil_map.albedo') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 9.5. Albedo Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the soil albedo map End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.soil_map.water_table') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 9.6. Water Table Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the soil water table map, if any End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 9.7. Continuously Varying Soil Depth Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Does the soil properties vary continuously with depth? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.soil_map.soil_depth') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 9.8. Soil Depth Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the soil depth map End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 10. Soil --> Snow Free Albedo TODO 10.1. Prognostic Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is snow free albedo prognostic? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "vegetation type" # "soil humidity" # "vegetation state" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 10.2. Functions Is Required: FALSE    Type: ENUM    Cardinality: 0.N If prognostic, describe the dependancies on snow free albedo calculations End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "distinction between direct and diffuse albedo" # "no distinction between direct and diffuse albedo" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 10.3. Direct Diffuse Is Required: FALSE    Type: ENUM    Cardinality: 0.1 If prognostic, describe the distinction between direct and diffuse albedo End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_bands') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 10.4. Number Of Wavelength Bands Is Required: FALSE    Type: INTEGER    Cardinality: 0.1 If prognostic, enter the number of wavelength bands used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 11. Soil --> Hydrology Key properties of the land surface soil hydrology 11.1. Description Is Required: TRUE    Type: STRING    Cardinality: 1.1 General description of the soil hydrological model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 11.2. Time Step Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Time step of river soil hydrology in seconds End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 11.3. Tiling Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the soil hydrology tiling, if any. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 11.4. Vertical Discretisation Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the typical vertical discretisation End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 11.5. Number Of Ground Water Layers Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 The number of soil layers that may contain water End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "perfect connectivity" # "Darcian flow" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 11.6. Lateral Connectivity Is Required: TRUE    Type: ENUM    Cardinality: 1.N Describe the lateral connectivity between tiles End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Bucket" # "Force-restore" # "Choisnel" # "Explicit diffusion" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 11.7. Method Is Required: TRUE    Type: ENUM    Cardinality: 1.1 The hydrological dynamics scheme in the land surface model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 12. Soil --> Hydrology --> Freezing TODO 12.1. Number Of Ground Ice Layers Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 How many soil layers may contain ground ice End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 12.2. Ice Storage Method Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the method of ice storage End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 12.3. Permafrost Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the treatment of permafrost, if any, within the land surface scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.drainage.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 13. Soil --> Hydrology --> Drainage TODO 13.1. Description Is Required: TRUE    Type: STRING    Cardinality: 1.1 General describe how drainage is included in the land surface scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.hydrology.drainage.types') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Gravity drainage" # "Horton mechanism" # "topmodel-based" # "Dunne mechanism" # "Lateral subsurface flow" # "Baseflow from groundwater" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 13.2. Types Is Required: FALSE    Type: ENUM    Cardinality: 0.N Different types of runoff represented by the land surface model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.heat_treatment.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 14. Soil --> Heat Treatment TODO 14.1. Description Is Required: TRUE    Type: STRING    Cardinality: 1.1 General description of how heat treatment properties are defined End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.heat_treatment.time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 14.2. Time Step Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Time step of soil heat scheme in seconds End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.heat_treatment.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 14.3. Tiling Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the soil heat treatment tiling, if any. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 14.4. Vertical Discretisation Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the typical vertical discretisation End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Force-restore" # "Explicit diffusion" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 14.5. Heat Storage Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Specify the method of heat storage End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.soil.heat_treatment.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "soil moisture freeze-thaw" # "coupling with snow temperature" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 14.6. Processes Is Required: TRUE    Type: ENUM    Cardinality: 1.N Describe processes included in the treatment of soil heat End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 15. Snow Land surface snow 15.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of snow in the land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 15.2. Tiling Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the snow tiling, if any. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.number_of_snow_layers') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 15.3. Number Of Snow Layers Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 The number of snow levels used in the land surface scheme/model End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.density') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "constant" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 15.4. Density Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Description of the treatment of snow density End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.water_equivalent') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 15.5. Water Equivalent Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Description of the treatment of the snow water equivalent End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.heat_content') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 15.6. Heat Content Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Description of the treatment of the heat content of snow End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.temperature') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 15.7. Temperature Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Description of the treatment of snow temperature End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.liquid_water_content') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 15.8. Liquid Water Content Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Description of the treatment of snow liquid water End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.snow_cover_fractions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "ground snow fraction" # "vegetation snow fraction" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 15.9. Snow Cover Fractions Is Required: TRUE    Type: ENUM    Cardinality: 1.N Specify cover fractions used in the surface snow scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "snow interception" # "snow melting" # "snow freezing" # "blowing snow" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 15.10. Processes Is Required: TRUE    Type: ENUM    Cardinality: 1.N Snow related processes in the land surface scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.prognostic_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 15.11. Prognostic Variables Is Required: TRUE    Type: STRING    Cardinality: 1.1 List the prognostic variables of the snow scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.snow_albedo.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "prescribed" # "constant" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 16. Snow --> Snow Albedo TODO 16.1. Type Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Describe the treatment of snow-covered land albedo End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.snow.snow_albedo.functions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "vegetation type" # "snow age" # "snow density" # "snow grain type" # "aerosol deposition" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 16.2. Functions Is Required: FALSE    Type: ENUM    Cardinality: 0.N If prognostic, End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17. Vegetation Land surface vegetation 17.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of vegetation in the land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 17.2. Time Step Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Time step of vegetation scheme in seconds End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 17.3. Dynamic Vegetation Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is there dynamic evolution of vegetation? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.4. Tiling Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the vegetation tiling, if any. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.vegetation_representation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "vegetation types" # "biome types" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.5. Vegetation Representation Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Vegetation classification used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.vegetation_types') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "broadleaf tree" # "needleleaf tree" # "C3 grass" # "C4 grass" # "vegetated" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.6. Vegetation Types Is Required: FALSE    Type: ENUM    Cardinality: 0.N List of vegetation types in the classification, if any End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.biome_types') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "evergreen needleleaf forest" # "evergreen broadleaf forest" # "deciduous needleleaf forest" # "deciduous broadleaf forest" # "mixed forest" # "woodland" # "wooded grassland" # "closed shrubland" # "opne shrubland" # "grassland" # "cropland" # "wetlands" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.7. Biome Types Is Required: FALSE    Type: ENUM    Cardinality: 0.N List of biome types in the classification, if any End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "fixed (not varying)" # "prescribed (varying from files)" # "dynamical (varying from simulation)" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.8. Vegetation Time Variation Is Required: TRUE    Type: ENUM    Cardinality: 1.1 How the vegetation fractions in each tile are varying with time End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.vegetation_map') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.9. Vegetation Map Is Required: FALSE    Type: STRING    Cardinality: 0.1 If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.interception') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 17.10. Interception Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is vegetation interception of rainwater represented? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.phenology') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic (vegetation map)" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.11. Phenology Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Treatment of vegetation phenology End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.phenology_description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.12. Phenology Description Is Required: FALSE    Type: STRING    Cardinality: 0.1 General description of the treatment of vegetation phenology End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.leaf_area_index') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prescribed" # "prognostic" # "diagnostic" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.13. Leaf Area Index Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Treatment of vegetation leaf area index End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.leaf_area_index_description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.14. Leaf Area Index Description Is Required: FALSE    Type: STRING    Cardinality: 0.1 General description of the treatment of leaf area index End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.biomass') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.15. Biomass Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Treatment of vegetation biomass End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.biomass_description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.16. Biomass Description Is Required: FALSE    Type: STRING    Cardinality: 0.1 General description of the treatment of vegetation biomass End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.biogeography') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.17. Biogeography Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Treatment of vegetation biogeography End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.biogeography_description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.18. Biogeography Description Is Required: FALSE    Type: STRING    Cardinality: 0.1 General description of the treatment of vegetation biogeography End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.stomatal_resistance') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "light" # "temperature" # "water availability" # "CO2" # "O3" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 17.19. Stomatal Resistance Is Required: TRUE    Type: ENUM    Cardinality: 1.N Specify what the vegetation stomatal resistance depends on End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.stomatal_resistance_description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.20. Stomatal Resistance Description Is Required: FALSE    Type: STRING    Cardinality: 0.1 General description of the treatment of vegetation stomatal resistance End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.vegetation.prognostic_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 17.21. Prognostic Variables Is Required: TRUE    Type: STRING    Cardinality: 1.1 List the prognostic variables of the vegetation scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.energy_balance.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 18. Energy Balance Land surface energy balance 18.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of energy balance in land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.energy_balance.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 18.2. Tiling Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the energy balance tiling, if any. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 18.3. Number Of Surface Temperatures Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.energy_balance.evaporation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "alpha" # "beta" # "combined" # "Monteith potential evaporation" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 18.4. Evaporation Is Required: TRUE    Type: ENUM    Cardinality: 1.N Specify the formulation method for land surface evaporation, from soil and vegetation End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.energy_balance.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "transpiration" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 18.5. Processes Is Required: TRUE    Type: ENUM    Cardinality: 1.N Describe which processes are included in the energy balance scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 19. Carbon Cycle Land surface carbon cycle 19.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of carbon cycle in land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 19.2. Tiling Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the carbon cycle tiling, if any. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 19.3. Time Step Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Time step of carbon cycle in seconds End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "grand slam protocol" # "residence time" # "decay time" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 19.4. Anthropogenic Carbon Is Required: FALSE    Type: ENUM    Cardinality: 0.N Describe the treament of the anthropogenic carbon pool End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 19.5. Prognostic Variables Is Required: TRUE    Type: STRING    Cardinality: 1.1 List the prognostic variables of the carbon scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 20. Carbon Cycle --> Vegetation TODO 20.1. Number Of Carbon Pools Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Enter the number of carbon pools used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 20.2. Carbon Pools Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the carbon pools used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 20.3. Forest Stand Dynamics Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the treatment of forest stand dyanmics End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 21. Carbon Cycle --> Vegetation --> Photosynthesis TODO 21.1. Method Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.) End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 22. Carbon Cycle --> Vegetation --> Autotrophic Respiration TODO 22.1. Maintainance Respiration Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the general method used for maintainence respiration End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 22.2. Growth Respiration Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the general method used for growth respiration End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 23. Carbon Cycle --> Vegetation --> Allocation TODO 23.1. Method Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the general principle behind the allocation scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "leaves + stems + roots" # "leaves + stems + roots (leafy + woody)" # "leaves + fine roots + coarse roots + stems" # "whole plant (no distinction)" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 23.2. Allocation Bins Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Specify distinct carbon bins used in allocation End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "fixed" # "function of vegetation type" # "function of plant allometry" # "explicitly calculated" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 23.3. Allocation Fractions Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Describe how the fractions of allocation are calculated End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 24. Carbon Cycle --> Vegetation --> Phenology TODO 24.1. Method Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the general principle behind the phenology scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 25. Carbon Cycle --> Vegetation --> Mortality TODO 25.1. Method Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe the general principle behind the mortality scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 26. Carbon Cycle --> Litter TODO 26.1. Number Of Carbon Pools Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Enter the number of carbon pools used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 26.2. Carbon Pools Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the carbon pools used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 26.3. Decomposition Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the decomposition methods used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.litter.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 26.4. Method Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the general method used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 27. Carbon Cycle --> Soil TODO 27.1. Number Of Carbon Pools Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Enter the number of carbon pools used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 27.2. Carbon Pools Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the carbon pools used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 27.3. Decomposition Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the decomposition methods used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.soil.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 27.4. Method Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the general method used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 28. Carbon Cycle --> Permafrost Carbon TODO 28.1. Is Permafrost Included Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is permafrost included? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 28.2. Emitted Greenhouse Gases Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the GHGs emitted End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 28.3. Decomposition Is Required: FALSE    Type: STRING    Cardinality: 0.1 List the decomposition methods used End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 28.4. Impact On Soil Properties Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the impact of permafrost on soil properties End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.nitrogen_cycle.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 29. Nitrogen Cycle Land surface nitrogen cycle 29.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of the nitrogen cycle in the land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.nitrogen_cycle.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 29.2. Tiling Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the notrogen cycle tiling, if any. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.nitrogen_cycle.time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 29.3. Time Step Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Time step of nitrogen cycle in seconds End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 29.4. Prognostic Variables Is Required: TRUE    Type: STRING    Cardinality: 1.1 List the prognostic variables of the nitrogen scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 30. River Routing Land surface river routing 30.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of river routing in the land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.tiling') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 30.2. Tiling Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the river routing, if any. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 30.3. Time Step Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Time step of river routing scheme in seconds End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 30.4. Grid Inherited From Land Surface Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is the grid inherited from land surface? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.grid_description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 30.5. Grid Description Is Required: FALSE    Type: STRING    Cardinality: 0.1 General description of grid, if not inherited from land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 30.6. Number Of Reservoirs Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Enter the number of reservoirs End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.water_re_evaporation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "flood plains" # "irrigation" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 30.7. Water Re Evaporation Is Required: TRUE    Type: ENUM    Cardinality: 1.N TODO End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 30.8. Coupled To Atmosphere Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1 Is river routing coupled to the atmosphere model component? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.coupled_to_land') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 30.9. Coupled To Land Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the coupling between land and rivers End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "heat" # "water" # "tracers" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 30.10. Quantities Exchanged With Atmosphere Is Required: FALSE    Type: ENUM    Cardinality: 0.N If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "present day" # "adapted for other periods" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 30.11. Basin Flow Direction Map Is Required: TRUE    Type: ENUM    Cardinality: 1.1 What type of basin flow direction map is being used? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.flooding') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 30.12. Flooding Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the representation of flooding, if any End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.prognostic_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 30.13. Prognostic Variables Is Required: TRUE    Type: STRING    Cardinality: 1.1 List the prognostic variables of the river routing End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "direct (large rivers)" # "diffuse" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 31. River Routing --> Oceanic Discharge TODO 31.1. Discharge Type Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Specify how rivers are discharged to the ocean End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "heat" # "water" # "tracers" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 31.2. Quantities Transported Is Required: TRUE    Type: ENUM    Cardinality: 1.N Quantities that are exchanged from river-routing to the ocean model component End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 32. Lakes Land surface lakes 32.1. Overview Is Required: TRUE    Type: STRING    Cardinality: 1.1 Overview of lakes in the land surface End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.coupling_with_rivers') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 32.2. Coupling With Rivers Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Are lakes coupled to the river routing model component? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) Explanation: 32.3. Time Step Is Required: TRUE    Type: INTEGER    Cardinality: 1.1 Time step of lake scheme in seconds End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "heat" # "water" # "tracers" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 32.4. Quantities Exchanged With Rivers Is Required: FALSE    Type: ENUM    Cardinality: 0.N If coupling with rivers, which quantities are exchanged between the lakes and rivers End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.vertical_grid') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 32.5. Vertical Grid Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the vertical grid of lakes End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.prognostic_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 32.6. Prognostic Variables Is Required: TRUE    Type: STRING    Cardinality: 1.1 List the prognostic variables of the lake scheme End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.method.ice_treatment') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 33. Lakes --> Method TODO 33.1. Ice Treatment Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is lake ice included? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.method.albedo') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 33.2. Albedo Is Required: TRUE    Type: ENUM    Cardinality: 1.1 Describe the treatment of lake albedo End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.method.dynamics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "No lake dynamics" # "vertical" # "horizontal" # "Other: [Please specify]" # TODO - please enter value(s) Explanation: 33.3. Dynamics Is Required: TRUE    Type: ENUM    Cardinality: 1.N Which dynamics of lakes are treated? horizontal, vertical, etc. End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 33.4. Dynamic Lake Extent Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Is a dynamic lake extent scheme included? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.method.endorheic_basins') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) Explanation: 33.5. Endorheic Basins Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1 Basins not flowing to ocean included? End of explanation # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.land.lakes.wetlands.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) Explanation: 34. Lakes --> Wetlands TODO 34.1. Description Is Required: FALSE    Type: STRING    Cardinality: 0.1 Describe the treatment of wetlands, if any End of explanation
9,219	Given the following text description, write Python code to implement the functionality described below step by step Description: Oregon Curriculum Network <br /> Discovering Math with Python Chapter 6 Step1: We're going to want to see our vectors rendered in some way. Visual Python, or VPython, provides an excellent solution. However, we're going to stay with still ray tracings in this Chapter and work with POV-Ray. Our goal is to build up a data structure for representing a full-fledge polyhedron, a network of edges, connecting around in faces, floating about the origin. We'll use point vectors for the vertexes, pairs of vectors for the edges, and sets of three or more vectors for the faces. Indeed, given faces as clockwise or counterclockwise listings, we can "distill" the edges by going around each face and getting the vertex vector pairs.	Python Code: class Vector: "A point in space" pass Explanation: Oregon Curriculum Network <br /> Discovering Math with Python Chapter 6: VECTORS IN SPACE A point vector is simply an object that points, from the origin to a specific location. We usually represent such an object with an arrow, with its tail at (0,0) or whatever, and its head at (x,y). Rather that pursue Flatland geometry as an end in itself, we will start right away in spatial geometry, showing two classes of vector, one typical, the other somewhat exotic. End of explanation class Edge: "A pair of vectors" pass class Face: "A set of vectors in clockwise or counter-clockwise order" pass class Polyhedron: "A set of faces" pass Explanation: We're going to want to see our vectors rendered in some way. Visual Python, or VPython, provides an excellent solution. However, we're going to stay with still ray tracings in this Chapter and work with POV-Ray. Our goal is to build up a data structure for representing a full-fledge polyhedron, a network of edges, connecting around in faces, floating about the origin. We'll use point vectors for the vertexes, pairs of vectors for the edges, and sets of three or more vectors for the faces. Indeed, given faces as clockwise or counterclockwise listings, we can "distill" the edges by going around each face and getting the vertex vector pairs. End of explanation
9,220	Given the following text description, write Python code to implement the functionality described below step by step Description: Part of Neural Network Notebook (3nb) project. Copyright (C) 2014 Eka A. Kurniawan eka.a.kurniawan(ta)gmail(tod)com This program is free software Step1: Display Settings Step2: Housekeeping Functions Following function plots different concentrations used to visualize dose-response relation. Step3: Following function plots dose-response relation in both linear (log_flag = False) and logarithmic (log_flag = True) along X axis. Step4: Dose-Response Relations $^{[1]}$ Generally, dose-response relations can be written as follow. In which the dose is represented as concentration ($c$), while the formula returns the response ($r$). $$r = \frac{F.c^{n_H}}{{c^{n_H} + EC_{50}^{n_H}}}$$ Other terms like $EC_{50}$ is the effective concentration achieved at 50% of maximum response. Normally, efficacy ($F$) is normalized to one so that it is easier to make comparison among different drugs. Furthermore, if full agonist is defined to have efficacy equal to one, anything lower than one is treated to be partial agonist. Finally, Hill coefficients ($n_H$) defines the number of drug molecules needed to activate target receptor. Drug Concentartion Both linearly and logarithmically increased concentrations are used to study dose-response relations. Linearly increased concentration (c_lin) Step5: Logarithmically increased concentration (c_log) Step6: Agonist Only To calculate dose-response relation in the case of agonist only, we use general dose-response relation equation described previously. The function is shown below. Step7: Following result shows drug response of agonist only to the linearly increased concentrations. Step8: Following result shows drug response of agonist only to the logarithmically increased concentrations. Step9: Agonist Plus Competitive Antagonist Compatitive antagonist, as the name sugest, competes with agonist molecules to sit in the same pocket. It makes the binding harder for agonist as well as to trigger the activation. Therefore, higher agonist concentration is required to reach both full and partial (like $EC_{50}$) activation. New $EC_{50}$ value, called $EC_{50}'$ ($EC_{50}$ prime) is calculated using following formula. $$EC_{50}' = EC_{50} * \left(1 + \frac{c_i}{K_i}\right)$$ It depends on inhibitor concentration ($c_i$) and dissociation constant of the inhibitor ($K_i$). Following is a new function to calculate drug response of agonist with competitive antagonist. It shows new $EC_{50}$ value (EC_50_prime) replacing agonist only $EC_{50}$ value (EC_50). Step10: Following result shows drug response of agonist with competitive antagonist to the linearly increased concentrations. Step11: Following result shows drug response of agonist with competitive antagonist to the logarithmically increased concentrations. Step12: Agonist Plus Noncompetitive Antagonist Unlike competitive antagonist, noncompetitive antagonist does not compete directly to the location where agonist binds but somewhere else in the subsequent pathway. Instead of altering effective concentration (like $EC_{50}$), noncompetitive antagonist affects efficacy. New efficacy value ($F'$) due to the existance of noncompetitive antagonist is calculated as follow. $$F' = \frac{F}{\left(1 + \frac{c_i}{K_i}\right)}$$ Following is a new function to calculate drug response of agonist with noncompetitive antagonist. It shows new efficacy value (F_prime) replacing agonist only efficacy value (F). Step13: Following result shows drug response of agonist with noncompetitive antagonist to the linearly increased concentrations. Step14: Following result shows drug response of agonist with noncompetitive antagonist to the logarithmically increased concentrations.	Python Code: import sys print("Python %d.%d.%d" % (sys.version_info.major, \ sys.version_info.minor, \ sys.version_info.micro)) import numpy as np print("NumPy %s" % np.__version__) # Display graph inline %matplotlib inline import matplotlib import matplotlib.pyplot as plt print("matplotlib %s" % matplotlib.__version__) Explanation: Part of Neural Network Notebook (3nb) project. Copyright (C) 2014 Eka A. Kurniawan eka.a.kurniawan(ta)gmail(tod)com This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/. Tested On End of explanation # Display graph in 'retina' format for Mac with retina display. Others, use PNG or SVG format. %config InlineBackend.figure_format = 'retina' #%config InlineBackend.figure_format = 'PNG' #%config InlineBackend.figure_format = 'SVG' Explanation: Display Settings End of explanation def plot_concentration(c): fig = plt.figure() sp111 = fig.add_subplot(111) # Display grid sp111.grid(True, which = 'both') # Plot concentration len_c = len(c) sp111.plot(np.linspace(0,len_c-1,len_c), c, color = 'gray', linewidth = 2) # Label sp111.set_ylabel('Concentration (nM)') # Set X axis within different concentration plt.xlim([0, len_c-1]) plt.show() Explanation: Housekeeping Functions Following function plots different concentrations used to visualize dose-response relation. End of explanation # d : Dose # r1 : First response data # r1_label : First response label # r2 : Second response data # r2_label : Second response label # log_flag : Selection for linear or logarithmic along X axis # - False: Plot linear (default) # - True: Plot logarithmic def plot_dose_response_relation(d, r1, r1_label, r2 = None, r2_label = "", log_flag = False): fig = plt.figure() sp111 = fig.add_subplot(111) # Handle logarithmic along X axis if log_flag: sp111.set_xscale('log') # Display grid sp111.yaxis.set_ticks([0.0, 0.5, 1.0]) sp111.grid(True, which = 'both') # Plot dose-response sp111.plot(d, r1, color = 'blue', label = r1_label, linewidth = 2) if r2 is not None: sp111.plot(d, r2, color = 'red', label = r2_label, linewidth = 2) # Labels sp111.set_ylabel('Response') sp111.set_xlabel('Concentration (nM)') # Legend sp111.legend(loc='upper left') # Set Y axis in between 0 and 1 plt.ylim([0, 1]) plt.show() Explanation: Following function plots dose-response relation in both linear (log_flag = False) and logarithmic (log_flag = True) along X axis. End of explanation c_lin = np.linspace(0,100,101) # Drug concentration in nanomolar (nM) plot_concentration(c_lin) Explanation: Dose-Response Relations $^{[1]}$ Generally, dose-response relations can be written as follow. In which the dose is represented as concentration ($c$), while the formula returns the response ($r$). $$r = \frac{F.c^{n_H}}{{c^{n_H} + EC_{50}^{n_H}}}$$ Other terms like $EC_{50}$ is the effective concentration achieved at 50% of maximum response. Normally, efficacy ($F$) is normalized to one so that it is easier to make comparison among different drugs. Furthermore, if full agonist is defined to have efficacy equal to one, anything lower than one is treated to be partial agonist. Finally, Hill coefficients ($n_H$) defines the number of drug molecules needed to activate target receptor. Drug Concentartion Both linearly and logarithmically increased concentrations are used to study dose-response relations. Linearly increased concentration (c_lin): End of explanation c_log = np.logspace(0,5,101) # Drug concentration in nanomolar (nM) plot_concentration(c_log) Explanation: Logarithmically increased concentration (c_log): End of explanation # Calculate dose-response relation (DRR) for agonist only # c : Drug concentration(s) in nanomolar (nM) # EC_50 : 50% effective concentration in nanomolar (nM) # F : Efficacy (unitless) # n_H : Hill coefficients (unitless) def calc_drr(c, EC_50 = 20, F = 1, n_H = 1): r = (F * (c n_H) / ((c n_H) + (EC_50 ** n_H))) return r Explanation: Agonist Only To calculate dose-response relation in the case of agonist only, we use general dose-response relation equation described previously. The function is shown below. End of explanation c = c_lin # Drug concentration(s) in nanomolar (nM) EC_50 = 20 # 50% effective concentration in nanomolar (nM) F = 1 # Efficacy (unitless) n_H = 1 # Hill coefficients (unitless) r = calc_drr(c, EC_50, F, n_H) plot_dose_response_relation(c, r, "Agonist") Explanation: Following result shows drug response of agonist only to the linearly increased concentrations. End of explanation c = c_log # Drug concentration(s) in nanomolar (nM) EC_50 = 20 # 50% effective concentration in nanomolar (nM) F = 1 # Efficacy (unitless) n_H = 1 # Hill coefficients (unitless) r = calc_drr(c, EC_50, F, n_H) plot_dose_response_relation(c, r, "Agonist", log_flag = True) Explanation: Following result shows drug response of agonist only to the logarithmically increased concentrations. End of explanation # Calculate dose-response relation (DRR) for agonist plus competitive antagonist # - Agonist # c : Drug concentration(s) in nanomolar (nM) # EC_50 : 50% effective concentration in nanomolar (nM) # F : Efficacy (unitless) # n_H : Hill coefficients (unitless) # - Antagonist # K_i : Dissociation constant of inhibitor in nanomolar (nM) # c_i : Inhibitor concentration in nanomolar (nM) def calc_drr_agonist_cptv_antagonist(c, EC_50 = 20, F = 1, n_H = 1, K_i = 5, c_i = 25): EC_50_prime = EC_50 * (1 + (c_i / K_i)) r = calc_drr(c, EC_50_prime, F, n_H) return r Explanation: Agonist Plus Competitive Antagonist Compatitive antagonist, as the name sugest, competes with agonist molecules to sit in the same pocket. It makes the binding harder for agonist as well as to trigger the activation. Therefore, higher agonist concentration is required to reach both full and partial (like $EC_{50}$) activation. New $EC_{50}$ value, called $EC_{50}'$ ($EC_{50}$ prime) is calculated using following formula. $$EC_{50}' = EC_{50} * \left(1 + \frac{c_i}{K_i}\right)$$ It depends on inhibitor concentration ($c_i$) and dissociation constant of the inhibitor ($K_i$). Following is a new function to calculate drug response of agonist with competitive antagonist. It shows new $EC_{50}$ value (EC_50_prime) replacing agonist only $EC_{50}$ value (EC_50). End of explanation c = c_lin # Drug concentration(s) in nanomolar (nM) EC_50 = 20 # 50% effective concentration in nanomolar (nM) F = 1 # Efficacy (unitless) n_H = 1 # Hill coefficients (unitless) r_a = calc_drr(c, EC_50, F, n_H) K_i = 5 # Dissociation constant of inhibitor in nanomolar (nM) c_i = 25 # Inhibitor concentration in nanomolar (nM) r_aca = calc_drr_agonist_cptv_antagonist(c, EC_50, F, n_H, K_i, c_i) plot_dose_response_relation(c, r_a, "Agonist Only", r_aca, "Plus Antagonist") Explanation: Following result shows drug response of agonist with competitive antagonist to the linearly increased concentrations. End of explanation c = c_log # Drug concentration(s) in nanomolar (nM) EC_50 = 20 # 50% effective concentration in nanomolar (nM) F = 1 # Efficacy (unitless) n_H = 1 # Hill coefficients (unitless) r_a = calc_drr(c, EC_50, F, n_H) K_i = 5 # Dissociation constant of inhibitor in nanomolar (nM) c_i = 25 # Inhibitor concentration in nanomolar (nM) r_aca = calc_drr_agonist_cptv_antagonist(c, EC_50, F, n_H, K_i, c_i) plot_dose_response_relation(c, r_a, "Agonist Only", r_aca, "Plus Antagonist", log_flag = True) Explanation: Following result shows drug response of agonist with competitive antagonist to the logarithmically increased concentrations. End of explanation # Calculate dose-response relation (DRR) for agonist plus noncompetitive antagonist # - Agonist # c : Drug concentration(s) in nanomolar (nM) # EC_50 : 50% effective concentration in nanomolar (nM) # F : Efficacy (unitless) # n_H : Hill coefficients (unitless) # - Antagonist # K_i : Dissociation constant of inhibitor in nanomolar (nM) # c_i : Inhibitor concentration in nanomolar (nM) def calc_drr_agonist_non_cptv_antagonist(c, EC_50 = 20, F = 1, n_H = 1, K_i = 5, c_i = 25): F_prime = F / (1 + (c_i / K_i)) r = calc_drr(c, EC_50, F_prime, n_H) return r Explanation: Agonist Plus Noncompetitive Antagonist Unlike competitive antagonist, noncompetitive antagonist does not compete directly to the location where agonist binds but somewhere else in the subsequent pathway. Instead of altering effective concentration (like $EC_{50}$), noncompetitive antagonist affects efficacy. New efficacy value ($F'$) due to the existance of noncompetitive antagonist is calculated as follow. $$F' = \frac{F}{\left(1 + \frac{c_i}{K_i}\right)}$$ Following is a new function to calculate drug response of agonist with noncompetitive antagonist. It shows new efficacy value (F_prime) replacing agonist only efficacy value (F). End of explanation c = c_lin # Drug concentration(s) in nanomolar (nM) EC_50 = 20 # 50% effective concentration in nanomolar (nM) F = 1 # Efficacy (unitless) n_H = 1 # Hill coefficients (unitless) r_a = calc_drr(c, EC_50, F, n_H) K_i = 5 # Dissociation constant of inhibitor in nanomolar (nM) c_i = 25 # Inhibitor concentration in nanomolar (nM) r_ana = calc_drr_agonist_non_cptv_antagonist(c, EC_50, F, n_H, K_i, c_i) plot_dose_response_relation(c, r_a, "Agonist Only", r_ana, "Plus Antagonist") Explanation: Following result shows drug response of agonist with noncompetitive antagonist to the linearly increased concentrations. End of explanation c = c_log # Drug concentration(s) in nanomolar (nM) EC_50 = 20 # 50% effective concentration in nanomolar (nM) F = 1 # Efficacy (unitless) n_H = 1 # Hill coefficients (unitless) r_a = calc_drr(c, EC_50, F, n_H) K_i = 5 # Dissociation constant of inhibitor in nanomolar (nM) c_i = 25 # Inhibitor concentration in nanomolar (nM) r_ana = calc_drr_agonist_non_cptv_antagonist(c, EC_50, F, n_H, K_i, c_i) plot_dose_response_relation(c, r_a, "Agonist Only", r_ana, "Plus Antagonist", log_flag = True) Explanation: Following result shows drug response of agonist with noncompetitive antagonist to the logarithmically increased concentrations. End of explanation
9,221	Given the following text description, write Python code to implement the functionality described below step by step Description: TinyImageNet and Ensembles So far, we have only worked with the CIFAR-10 dataset. In this exercise we will introduce the TinyImageNet dataset. You will combine several pretrained models into an ensemble, and show that the ensemble performs better than any individual model. Step1: Introducing TinyImageNet The TinyImageNet dataset is a subset of the ILSVRC-2012 classification dataset. It consists of 200 object classes, and for each object class it provides 500 training images, 50 validation images, and 50 test images. All images have been downsampled to 64x64 pixels. We have provided the labels for all training and validation images, but have withheld the labels for the test images. We have further split the full TinyImageNet dataset into two equal pieces, each with 100 object classes. We refer to these datasets as TinyImageNet-100-A and TinyImageNet-100-B. To download the data, go into the cs231n/datasets directory and run the script get_tiny_imagenet_splits.sh. Then run the following code to load the TinyImageNet-100-A dataset into memory. NOTE Step2: TinyImageNet-100-A classes Since ImageNet is based on the WordNet ontology, each class in ImageNet (and TinyImageNet) actually has several different names. For example "pop bottle" and "soda bottle" are both valid names for the same class. Run the following to see a list of all classes in TinyImageNet-100-A Step3: Visualize Examples Run the following to visualize some example images from random classses in TinyImageNet-100-A. It selects classes and images randomly, so you can run it several times to see different images. Step4: Test human performance Run the following to test your own classification performance on the TinyImageNet-100-A dataset. You can run several times in 'training' mode to get familiar with the task; once you are ready to test yourself, switch the mode to 'val'. You won't be penalized if you don't correctly classify all the images, but you should still try your best. Step5: Download pretrained models We have provided 10 pretrained ConvNets for the TinyImageNet-100-A dataset. Each of these models is a five-layer ConvNet with the architecture [conv - relu - pool] x 3 - affine - relu - affine - softmax All convolutional layers are 3x3 with stride 1 and all pooling layers are 2x2 with stride 2. The first two convolutional layers have 32 filters each, and the third convolutional layer has 64 filters. The hidden affine layer has 512 neurons. You can run the forward and backward pass for these five layer convnets using the function five_layer_convnet in the file cs231n/classifiers/convnet.py. Each of these models was trained for 25 epochs over the TinyImageNet-100-A training data with a batch size of 50 and with dropout on the hidden affine layer. Each model was trained using slightly different values for the learning rate, regularization, and dropout probability. To download the pretrained models, go into the cs231n/datasets directory and run the get_pretrained_models.sh script. Once you have done so, run the following to load the pretrained models into memory. NOTE Step6: Run models on the validation set To benchmark the performance of each model on its own, we will use each model to make predictions on the validation set. Step8: Use a model ensemble A simple way to implement an ensemble of models is to average the predicted probabilites for each model in the ensemble. More concretely, suppose we have models $k$ models $m_1,\ldots,m_k$ and we want to combine them into an ensemble. If $p(x=y_i \mid m_j)$ is the probability that the input $x$ is classified as $y_i$ under model $m_j$, then the enemble predicts $$p(x=y_i \mid {m_1,\ldots,m_k}) = \frac1k\sum_{j=1}^kp(x=y_i\mid m_j)$$ In the cell below, implement this simple ensemble method by filling in the compute_ensemble_preds function. The ensemble of all 10 models should perform much better than the best individual model. Step9: Ensemble size vs Performance Using our 10 pretrained models, we can form many different ensembles of different sizes. More precisely, if we have $n$ models and we want to form an ensemble of $k$ models, then there are $\binom{n}{k}$ possible ensembles that we can form, where $$\binom{n}{k} = \frac{n!}{(n-k)!k!}$$ We can use these different possible ensembles to study the effect of ensemble size on ensemble performance. In the cell below, compute the validation set accuracy of all possible ensembles of our 10 pretrained models. Produce a scatter plot with "ensemble size" on the horizontal axis and "validation set accuracy" on the vertical axis. Your plot should have a total of $$\sum_{k=1}^{10} \binom{10}{k}$$ points corresponding to all possible ensembles of the 10 pretrained models. You should be able to compute the validation set predictions of these ensembles without computing any more forward passes through any of the networks.	Python Code: # A bit of setup import numpy as np import matplotlib.pyplot as plt from time import time %matplotlib inline plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' # for auto-reloading extenrnal modules # see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython %load_ext autoreload %autoreload 2 Explanation: TinyImageNet and Ensembles So far, we have only worked with the CIFAR-10 dataset. In this exercise we will introduce the TinyImageNet dataset. You will combine several pretrained models into an ensemble, and show that the ensemble performs better than any individual model. End of explanation from cs231n.data_utils import load_tiny_imagenet tiny_imagenet_a = 'cs231n/datasets/tiny-imagenet-100-A' class_names, X_train, y_train, X_val, y_val, X_test, y_test = load_tiny_imagenet(tiny_imagenet_a) # Zero-mean the data mean_img = np.mean(X_train, axis=0) X_train -= mean_img X_val -= mean_img X_test -= mean_img Explanation: Introducing TinyImageNet The TinyImageNet dataset is a subset of the ILSVRC-2012 classification dataset. It consists of 200 object classes, and for each object class it provides 500 training images, 50 validation images, and 50 test images. All images have been downsampled to 64x64 pixels. We have provided the labels for all training and validation images, but have withheld the labels for the test images. We have further split the full TinyImageNet dataset into two equal pieces, each with 100 object classes. We refer to these datasets as TinyImageNet-100-A and TinyImageNet-100-B. To download the data, go into the cs231n/datasets directory and run the script get_tiny_imagenet_splits.sh. Then run the following code to load the TinyImageNet-100-A dataset into memory. NOTE: The full TinyImageNet dataset will take up about 490MB of disk space, and loading the full TinyImageNet-100-A dataset into memory will use about 2.8GB of memory. End of explanation for names in class_names: print ' '.join('"%s"' % name for name in names) Explanation: TinyImageNet-100-A classes Since ImageNet is based on the WordNet ontology, each class in ImageNet (and TinyImageNet) actually has several different names. For example "pop bottle" and "soda bottle" are both valid names for the same class. Run the following to see a list of all classes in TinyImageNet-100-A: End of explanation # Visualize some examples of the training data classes_to_show = 7 examples_per_class = 5 class_idxs = np.random.choice(len(class_names), size=classes_to_show, replace=False) for i, class_idx in enumerate(class_idxs): train_idxs, = np.nonzero(y_train == class_idx) train_idxs = np.random.choice(train_idxs, size=examples_per_class, replace=False) for j, train_idx in enumerate(train_idxs): img = X_train[train_idx] + mean_img img = img.transpose(1, 2, 0).astype('uint8') plt.subplot(examples_per_class, classes_to_show, 1 + i + classes_to_show * j) if j == 0: plt.title(class_names[class_idx][0]) plt.imshow(img) plt.gca().axis('off') plt.show() Explanation: Visualize Examples Run the following to visualize some example images from random classses in TinyImageNet-100-A. It selects classes and images randomly, so you can run it several times to see different images. End of explanation mode = 'train' name_to_label = {n.lower(): i for i, ns in enumerate(class_names) for n in ns} if mode == 'train': X, y = X_train, y_train elif mode == 'val': X, y = X_val, y_val num_correct = 0 num_images = 10 for i in xrange(num_images): idx = np.random.randint(X.shape[0]) img = (X[idx] + mean_img).transpose(1, 2, 0).astype('uint8') plt.imshow(img) plt.gca().axis('off') plt.gcf().set_size_inches((2, 2)) plt.show() got_name = False while not got_name: name = raw_input('Guess the class for the above image (%d / %d) : ' % (i + 1, num_images)) name = name.lower() got_name = name in name_to_label if not got_name: print 'That is not a valid class name; try again' guess = name_to_label[name] if guess == y[idx]: num_correct += 1 print 'Correct!' else: print 'Incorrect; it was actually %r' % class_names[y[idx]] acc = float(num_correct) / num_images print 'You got %d / %d correct for an accuracy of %f' % (num_correct, num_images, acc) Explanation: Test human performance Run the following to test your own classification performance on the TinyImageNet-100-A dataset. You can run several times in 'training' mode to get familiar with the task; once you are ready to test yourself, switch the mode to 'val'. You won't be penalized if you don't correctly classify all the images, but you should still try your best. End of explanation from cs231n.data_utils import load_models models_dir = 'cs231n/datasets/tiny-100-A-pretrained' # models is a dictionary mappping model names to models. # Like the previous assignment, each model is a dictionary mapping parameter # names to parameter values. models = load_models(models_dir) Explanation: Download pretrained models We have provided 10 pretrained ConvNets for the TinyImageNet-100-A dataset. Each of these models is a five-layer ConvNet with the architecture [conv - relu - pool] x 3 - affine - relu - affine - softmax All convolutional layers are 3x3 with stride 1 and all pooling layers are 2x2 with stride 2. The first two convolutional layers have 32 filters each, and the third convolutional layer has 64 filters. The hidden affine layer has 512 neurons. You can run the forward and backward pass for these five layer convnets using the function five_layer_convnet in the file cs231n/classifiers/convnet.py. Each of these models was trained for 25 epochs over the TinyImageNet-100-A training data with a batch size of 50 and with dropout on the hidden affine layer. Each model was trained using slightly different values for the learning rate, regularization, and dropout probability. To download the pretrained models, go into the cs231n/datasets directory and run the get_pretrained_models.sh script. Once you have done so, run the following to load the pretrained models into memory. NOTE: The pretrained models will take about 245MB of disk space. End of explanation from cs231n.classifiers.convnet import five_layer_convnet # Dictionary mapping model names to their predicted class probabilities on the # validation set. model_to_probs[model_name] is an array of shape (N_val, 100) # where model_to_probs[model_name][i, j] = p indicates that models[model_name] # predicts that X_val[i] has class i with probability p. model_to_probs = {} ################################################################################ # TODO: Use each model to predict classification probabilities for all images # # in the validation set. Store the predicted probabilities in the # # model_to_probs dictionary as above. To compute forward passes and compute # # probabilities, use the function five_layer_convnet in the file # # cs231n/classifiers/convnet.py. # # # # HINT: Trying to predict on the entire validation set all at once will use a # # ton of memory, so you should break the validation set into batches and run # # each batch through each model separately. # ################################################################################ from cs231n.classifiers.convnet import five_layer_convnet import math batch_size = 100 for model_name, model in models.items(): model_to_probs[model_name] = None for i in range(int(math.ceil(X_val.shape[0] / batch_size))): for model_name, model in models.items(): y_predict = five_layer_convnet(X_val[ibatch_size: (i+1)batch_size], model, None and y_val[ibatch_size: (i+1)batch_size], return_probs=True) try: if model_to_probs[model_name] is None: model_to_probs[model_name] = y_predict else: model_to_probs[model_name] = np.concatenate( (model_to_probs[model_name], y_predict), axis=0) except: print(model_to_probs[model_name].shape, y_predict.shape) raise pass pass ################################################################################ # END OF YOUR CODE # ################################################################################ # Compute and print the accuracy for each model. for model_name, probs in model_to_probs.iteritems(): acc = np.mean(np.argmax(probs, axis=1) == y_val) print '%s got accuracy %f' % (model_name, acc) Explanation: Run models on the validation set To benchmark the performance of each model on its own, we will use each model to make predictions on the validation set. End of explanation def compute_ensemble_preds(probs_list): Use the predicted class probabilities from different models to implement the ensembling method described above. Inputs: - probs_list: A list of numpy arrays, where each gives the predicted class probabilities under some model. In other words, probs_list[j][i, c] = p means that the jth model in the ensemble thinks that the ith data point has class c with probability p. Returns: An array y_pred_ensemble of ensembled predictions, such that y_pred_ensemble[i] = c means that ensemble predicts that the ith data point is predicted to have class c. y_pred_ensemble = None ############################################################################ # TODO: Implement this function. Store the ensemble predictions in # # y_pred_ensemble. # ############################################################################ probs_list_ensemble = np.mean(probs_list, axis=0) y_pred_ensemble = np.argmax(probs_list_ensemble, axis=1) pass ############################################################################ # END OF YOUR CODE # ############################################################################ return y_pred_ensemble # Combine all models into an ensemble and make predictions on the validation set. # This should be significantly better than the best individual model. print np.mean(compute_ensemble_preds(model_to_probs.values()) == y_val) Explanation: Use a model ensemble A simple way to implement an ensemble of models is to average the predicted probabilites for each model in the ensemble. More concretely, suppose we have models $k$ models $m_1,\ldots,m_k$ and we want to combine them into an ensemble. If $p(x=y_i \mid m_j)$ is the probability that the input $x$ is classified as $y_i$ under model $m_j$, then the enemble predicts $$p(x=y_i \mid {m_1,\ldots,m_k}) = \frac1k\sum_{j=1}^kp(x=y_i\mid m_j)$$ In the cell below, implement this simple ensemble method by filling in the compute_ensemble_preds function. The ensemble of all 10 models should perform much better than the best individual model. End of explanation ################################################################################ # TODO: Create a plot comparing ensemble size with ensemble performance as # # described above. # # # # HINT: Look up the function itertools.combinations. # ################################################################################ import itertools ensemble_sizes = [] val_accs = [] for i in range(1, 11): combinations = itertools.combinations(model_to_probs.values(), i) for combination in combinations: ensemble_sizes.append(i) y_pred_ensemple = compute_ensemble_preds(combination) val_accs.append(np.mean(y_pred_ensemple == y_val)) pass plt.scatter(ensemble_sizes, val_accs) plt.title('Ensemble size vs Performance') plt.xlabel('ensemble size') plt.ylabel('validation set accuracy') ################################################################################ # END OF YOUR CODE # ################################################################################ Explanation: Ensemble size vs Performance Using our 10 pretrained models, we can form many different ensembles of different sizes. More precisely, if we have $n$ models and we want to form an ensemble of $k$ models, then there are $\binom{n}{k}$ possible ensembles that we can form, where $$\binom{n}{k} = \frac{n!}{(n-k)!k!}$$ We can use these different possible ensembles to study the effect of ensemble size on ensemble performance. In the cell below, compute the validation set accuracy of all possible ensembles of our 10 pretrained models. Produce a scatter plot with "ensemble size" on the horizontal axis and "validation set accuracy" on the vertical axis. Your plot should have a total of $$\sum_{k=1}^{10} \binom{10}{k}$$ points corresponding to all possible ensembles of the 10 pretrained models. You should be able to compute the validation set predictions of these ensembles without computing any more forward passes through any of the networks. End of explanation
9,222	Given the following text description, write Python code to implement the functionality described below step by step Description: Doc2Vec trained on recipe instructions Objectives Create word embeddings for recipes. Use word vectors for (traditional) segmentation, classification, and retrieval of recipes. Based on https Step1: Text Normalization Step2: Doc2Vec Model see http Step3: Model Training train multiple epochs with decreasing learning rate. Step4: Model results Word Embeddings Step5: Document Representation Step6: Similar Documents Step7: Compute vector for existing document Infer vector and use it as positive example (see also #https Step8: Compute vector for new document	Python Code: import re # Regular Expressions import os.path # File Operations import pandas as pd # DataFrames & Manipulation from gensim.models.doc2vec import LabeledSentence, Doc2Vec # Model training train_input = "../data/recipes.tsv.bz2" # preserve empty strings (http://pandas-docs.github.io/pandas-docs-travis/io.html#na-values) train = pd.read_csv(train_input, delimiter="\t", quoting=3, encoding="utf-8", keep_default_na=False) print "loaded %d documents." % len(train) Explanation: Doc2Vec trained on recipe instructions Objectives Create word embeddings for recipes. Use word vectors for (traditional) segmentation, classification, and retrieval of recipes. Based on https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-IMDB.ipynb Data Preparation End of explanation def normalize(text): norm_text = text.lower() for char in ['.', '"', ',', '(', ')', '!', '?', ';', ':']: norm_text = norm_text.replace(char, ' ' + char + ' ') return norm_text sentences = [LabeledSentence(normalize(text).split(), [i]) for i, text in enumerate(train['instructions'])] print "%d sentences in corpus" % len(sentences) Explanation: Text Normalization End of explanation dist_memory = 1 # distributed memory model vector_mean = 1 # compute mean of input word vectors num_features = 300 # word vector dimensionality min_word_count = 2 # minimum word count num_workers = 4 # number of threads to run in parallel context = 10 # context window size downsampling = 1e-3 # downsample setting for frequent words model_name = "model-d2v_dm_%dfeatures_%dminwords_%dcontext" % (num_features, min_word_count, context) # load model or create new one if os.path.isfile(model_name): model = Doc2Vec.load(model_name) do_train = False else: model = Doc2Vec(dm=1, dm_mean=1, size=num_features, min_count=min_word_count, window=context, sample=downsampling, workers=num_workers) model.build_vocab(sentences) do_train = True Explanation: Doc2Vec Model see http://radimrehurek.com/gensim/models/doc2vec.html class gensim.models.doc2vec.Doc2Vec( documents=None, # list of TaggedDocument elements dm=1, # training algorithm. dm=1: 'distributed memory' (PV-DM). otherwise, 'distributed bag of words' (PV-DBOW). dbow_words=0, # 0 (default), if 1, trains word-vectors simultaneous with DBOW doc-vector dm_mean=None, # 0 (default), if 1, use the mean of context word vectors instead of sum. dm_concat=0, # 0 (default), if 1, use concatenation of (all) context vectors (slow). dm_tag_count=1, # 1 (default), expected document tags per document, when using dm_concat mode docvecs=None, docvecs_mapfile=None, comment=None, trim_rule=None, **kwargs ) Model Setup End of explanation import logging from random import shuffle from datetime import datetime # configure usedful logging messages logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) def train_model(model, sentences, passes=10, alpha=0.025, min_alpha=0.001): alpha_delta = (alpha - min_alpha) / passes print("START %s" % datetime.now()) for epoch in range(passes): shuffle(sentences) # shuffling gets best results model.alpha, model.min_alpha = alpha, alpha model.train(sentences) print("finished epoch %d (alpha: %f) - %s" % (epoch + 1, alpha, datetime.now())) alpha -= alpha_delta print("END %s" % str(datetime.now())) if do_train: train_model(model, sentences, passes=30) # finalize model to save memory #model.delete_temporary_training_data(keep_doctags_vectors=True, keep_inference=True) # save model model.save(model_name) Explanation: Model Training train multiple epochs with decreasing learning rate. End of explanation model.wv.most_similar(["pasta"], topn=20) Explanation: Model results Word Embeddings End of explanation model.docvecs[1] Explanation: Document Representation End of explanation recipe_no = 42 ids = model.docvecs.most_similar(recipe_no, topn=20) ids train.loc[[recipe_no]+[id for id, score in ids]][['title','instructions']] Explanation: Similar Documents End of explanation doc = train['instructions'][recipe_no] wordvec = model.infer_vector(normalize(doc).split()) ids = model.docvecs.most_similar(positive=[wordvec], topn=20) ids train.loc[[id for id, score in ids]][['title','instructions']] Explanation: Compute vector for existing document Infer vector and use it as positive example (see also #https://groups.google.com/forum/#!msg/gensim/IH_u8HYVbpg/w9TX4yh2DgAJ) End of explanation doc = u"Wodka, Cointreau, Limettensaft, Cranberrysaft und Eis." wordvec = model.infer_vector(normalize(doc).split()) ids = model.docvecs.most_similar(positive=[wordvec], topn=20) ids train.loc[[id for id, score in ids]][['title','instructions']] Explanation: Compute vector for new document End of explanation
9,223	Given the following text description, write Python code to implement the functionality described below step by step Description: A Workshop Introduction to NumPy The Python language is an excellent tool for general-purpose programming, with a highly readable syntax, rich and powerful data types (strings, lists, sets, dictionaries, arbitrary length integers, etc) and a very comprehensive standard library. It was not, however, designed specifically for mathematical and scientific computing. Neither the language nor its standard library have facilities for the efficient representation of multidimensional datasets, tools for linear algebra and general matrix manipulations (essential building blocks of virtually all scientific computing). For example, Python lists are very flexible containers that can be nested arbitrarily deep and can hold any Python object in them, but they are poorly suited to represent common mathematical constructs like vectors and matrices. It is for this reason that NumPy exists. Workshop Aims The aim of this workshop is to enable you use NumPy to Step1: Documentation Here is a link to the NumPy documentation for v1.11 Step2: Exercise Part 1 Use the code cells below to explore features of this array. What is the type of the array created above? (Hint Step3: You can also index multidimensional arrays using an enhanced indexing syntax, which allows for multi-element indexing. (Sometimes this is referred to as "extended slicing".) Remember that Python uses zero-based indexing! Step4: If you only provide one index to slice a multidimensional array, then the slice will be expanded to " Step5: This is known as ellipsis. Ellipsis can be specified explicitly using "...", which automatically expands to " Step6: Boolean Indexing NumPy provides syntax to index conditionally, based on the data in the array. You can pass in an array of True and False values (a boolean array), or, more commonly, a condition that returns a boolean array. Step7: Exercise Part 1 Why do these indexing examples give the stated results? result of arr_2d[1, 0] is 4 result of arr_2d[0] is [1, 2, 3] result of arr_2d[1, 1 Step8: The result we just received points to an important piece of learning, which is that in most cases NumPy arrays behave very differently to Python lists. Let's explore the differences (and some similarities) between the two. dtype A NumPy array has a fixed data type, called dtype. This is the type of all the elements of the array. This is in contrast to Python lists, which can hold elements of different types. Exercise What happens in Python when you add an integer to a float? What happens when you put an integer into a NumPy float array? What happens when you do numerical calculations between arrays of different types? Generating 2D coordinate arrays A common requirement of NumPy arrays is to generate arrays that represent the coordinates of our data. When orthogonal 1d coordinate arrays already exist, NumPy's meshgrid function is very useful Step9: The Array Object Step10: Exercise Explore arithmetic operations between arrays Step11: Broadcasting There are times when you need to perform calculations between NumPy arrays of different sizes. NumPy allows you to do this easily using a powerful piece of functionality called broadcasting. Broadcasting is a way of treating the arrays as if they had the same dimensions and thus have elements all corresponding. It is then easy to perform the calculation, element-wise. It does this by matching dimensions in one array to the other where possible, and using repeated values where there is no corresponding dimension in the other array. Rules of Broadcasting Broadcasting applies these three rules Step12: Reshaping arrays to aid broadcasting NumPy allows you to change the shape of an array, so long as the total number of elements in the array does not change. For example, we could reshape a flat array with 12 elements to a 2D array with shape (2, 6), or (3, 2, 2), or even (3, 4, 1). We could not, however, reshape it to have shape (2, 5), because the total number of elements would not be kept constant. Exercise, continued For the failing example above, what reshape operation could you apply to arr2 so that it can be broadcast with arr1? Arithmetic and Broadcasting Step13: Used without any further arguments, statistical functions simply reduce the whole array to a single value. In practice, however, we very often want to calculate statistics over only some of the dimensions. The most common requirement is to calculate a statistic along a single array dimension, while leaving all the other dimensions intact. This is referred to as "collapsing" or "reducing" the chosen dimension. This is done by adding an "axis" keyword specifying the dimension to collapse Step14: Exercise What other similar statistical operations exist (see above link)? A mean value can also be calculated with <array>.mean(). Is that the same thing? Create a 3D array (that could be considered to describe [time, x, y]) and find the mean over all x and y at each timestep. What shape does the result have? Masked Arrays Real-world measurements processes often result in certain datapoint values being uncertain or simply "missing". This is usually indicated by additional data quality information, stored alongside the data values. In these cases we often need to make calculations that count only the valid datapoints. NumPy provides a special "masked array" type for this type of calculation. Here's a link to the documentation for NumPy masked arrays Step15: The mask is applied where the values in the mask array are True. Masked arrays are printed with a double-dash -- denoting the locations in the array where the mask has been applied. The statistics of masked data are different Step16: Note that most file formats represent missing data in a different way, using a distinct "missing data" value appearing in the data. There is special support for converting between this type of representation and NumPy masked arrays. Every masked array has a fill_value property and a filled() method to fill the masked points with the fill value. Exercise Create a masked array from the numbers 0-11, where all the values less than 5 are masked. Create a masked array of positive values, with a value of -1.0 to represent missing points. Look up the masked array creation documentation. What routines exist to produce masked arrays like the ones you've just created more efficiently? Use np.ma.filled() to create a 'plain' (i.e. unmasked) array from a masked array. How can you create a plain array from a masked array, but using a different fill-value for masked points? Try performing a mathematical operation between two masked arrays. What happens to the 'fill_value' properties when you do so? Statistics and Masked Arrays Step17: What this means is that if one array (arr) is modified, the other (arr_view) will also be updated Step18: Loops and Vectorised Operations We will now explore calculation performance and consider efficiency in terms of processing time. Firstly let's look at a simple processing time tool that is provided in notebooks; %%timeit Step19: Repeat that, specifying only 100 loops and fastest time of 5 runs Step20: This gives us an easy way to evaluate performance for implementations ...	Python Code: # NumPy is generally imported as 'np'. import numpy as np print(np) print(np.__version__) Explanation: A Workshop Introduction to NumPy The Python language is an excellent tool for general-purpose programming, with a highly readable syntax, rich and powerful data types (strings, lists, sets, dictionaries, arbitrary length integers, etc) and a very comprehensive standard library. It was not, however, designed specifically for mathematical and scientific computing. Neither the language nor its standard library have facilities for the efficient representation of multidimensional datasets, tools for linear algebra and general matrix manipulations (essential building blocks of virtually all scientific computing). For example, Python lists are very flexible containers that can be nested arbitrarily deep and can hold any Python object in them, but they are poorly suited to represent common mathematical constructs like vectors and matrices. It is for this reason that NumPy exists. Workshop Aims The aim of this workshop is to enable you use NumPy to: manipulate numerical arrays, and perform efficient array calculations. Table of Contents Getting Started with NumPy Motivation: what are arrays good for? The Array Object Application: Arithmetic and Broadcasting Application: Statistics and Masked Arrays Application: Efficiency Further Reading Getting Started with NumPy <a class="anchor" id="getting-started"></a> Learning outcome: by the end of this section, you will be able to create NumPy arrays with the aid of the NumPy documentation. NumPy is the fundamental package for scientific computing with Python. Its primary purpose is to provide a powerful N-dimensional array object; the focus for this workshop. To begin with let's import NumPy, check where it is being imported from and check the version. End of explanation arr = np.ones((3, 2, 4)) print("Array shape:", arr.shape) print("Array element dtype:", arr.dtype) Explanation: Documentation Here is a link to the NumPy documentation for v1.11: https://docs.scipy.org/doc/numpy-1.11.0/reference/ There are many online forums with tips and suggestions for solving problems with NumPy, such as http://stackoverflow.com/ Explore: creating arrays NumPy provides many different ways to create arrays. These are listed in the documentation at: https://docs.scipy.org/doc/numpy-1.11.0/user/basics.creation.html#arrays-creation. Exercise Part 1 Try out some of these ways of creating NumPy arrays. See if you can produce: a NumPy array from a list of numbers, a 3-dimensional NumPy array filled with a constant value -- either 0 or 1, a NumPy array filled with a constant value -- not 0 or 1. (Hint: this can be achieved using the last array you created, or you could use np.empty and find a way of filling the array with a constant value), a NumPy array of 8 elements with a range of values starting from 0 and a spacing of 3 between each element, and a NumPy array of 10 elements that are logarithmically spaced. Part 2 How could you change the shape of the 8-element array you created previously to have shape (2, 2, 2)? Hint: this can be done without creating a new array. Motivation: what are arrays good for? <a class="anchor" id="motivation"></a> Learning outcome: by the end of this section, you will understand key benefits of using NumPy arrays for numerical processing in Python. Extensive Features NumPy provides routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more. Fast Calculations It is a lot faster than Python alone for numerical computing tasks. Element-by-element operations are the “default mode” when an ndarray is involved, but the element-by-element operation is speedily executed by pre-compiled C code. Clear Syntax In NumPy, python c = a * b calculates the element-wise product of a and b, at near-C speeds, but with the code simplicity we expect from the Python language. This demonstrates a core feature of NumPy, called vectorization. This removes the need for loops iterating through elements of arrays, which can make code easier to read, as well as performing fast calculations. Interfacing to other Libraries Many scientific Python libraries use NumPy as their core array representation. From plotting libraries such as matplotlib, to parallel processing libraries such as Dask, to data interoperability libraries such as Iris, NumPy arrays are at the core of how these libraries operate and communicate. The Array Object <a class="anchor" id="array-object"></a> Learning outcome: by the end of this section, you will be able to manipulate NumPy array objects through indexing and explain key features and properties of NumPy arrays. The multidimensional array object is at the core of all of NumPy's functionality. Let's explore this object some more. Array properties Let's create a NumPy array and take a look at some of its properties. End of explanation arr = np.array([1, 2, 3, 4, 5, 6]) print(arr) print("arr[2] = {}".format(arr[2])) print("arr[2:5] = {}".format(arr[2:5])) print("arr[::2] = {}".format(arr[::2])) Explanation: Exercise Part 1 Use the code cells below to explore features of this array. What is the type of the array created above? (Hint: you can find the type of an object in Python using type(<object>), where <object> is replaced with the name of the variable containing the object.) Look this type up in the NumPy documentation (see https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.ndarray.html#numpy.ndarray). Part 2 Consider the following NumPy array properties: ndim, nbytes, size, T. For each of these properties: Use information from the array documentation to find out more about each array property. Apply each property to the array arr defined above. Can you explain the results you get in each case? Indexing You can index NumPy arrays in the same way as other Python objects, by using square brackets []. This means we can index to retrieve a single element, multiple consecutive elements, or a more complex sequence: End of explanation lst_2d = [[1, 2, 3], [4, 5, 6]] arr_2d = arr.reshape(2, 3) print("2D list:") print(lst_2d) print("2D array:") print(arr_2d) print("Single array element:") print(arr_2d[1, 2]) print("Single row:") print(arr_2d[1]) print("First two columns:") print(arr_2d[:, :2]) Explanation: You can also index multidimensional arrays using an enhanced indexing syntax, which allows for multi-element indexing. (Sometimes this is referred to as "extended slicing".) Remember that Python uses zero-based indexing! End of explanation print('Second row: {} is equivalent to {}'.format(arr_2d[1], arr_2d[1, :])) Explanation: If you only provide one index to slice a multidimensional array, then the slice will be expanded to ":" for all of the remaining dimensions: End of explanation arr1 = np.empty((4, 6, 3)) print('Original shape: ', arr1.shape) print(arr1[...].shape) print(arr1[..., 0:2].shape) print(arr1[2:4, ..., ::2].shape) print(arr1[2:4, :, ..., ::-1].shape) Explanation: This is known as ellipsis. Ellipsis can be specified explicitly using "...", which automatically expands to ":" for each dimension unspecified in the slice: End of explanation print(arr_2d) bools = arr_2d % 2 == 0 print(bools) print(arr_2d[bools]) Explanation: Boolean Indexing NumPy provides syntax to index conditionally, based on the data in the array. You can pass in an array of True and False values (a boolean array), or, more commonly, a condition that returns a boolean array. End of explanation print(lst_2d[0:2][1]) print(arr_2d[0:2, 1]) Explanation: Exercise Part 1 Why do these indexing examples give the stated results? result of arr_2d[1, 0] is 4 result of arr_2d[0] is [1, 2, 3] result of arr_2d[1, 1:] is [5, 6] result of arr_2d[0:, ::2] is [[1, 3], [4, 6]] Part 2 How would you index arr_2d to retrieve: the third value: resulting in 3 the second row: resulting in [4 5 6] the first column: resulting in [1 4] the first column, retaining the outside dimension: resulting in [[1] [4]] only values greater than or equal to 3: resulting in [3 4 5 6] Arrays are not lists Question: why do the following examples produce different results? End of explanation x = np.linspace(0, 9, 3) y = np.linspace(4, 8, 3) x2d, y2d = np.meshgrid(x, y) print(x2d) print(y2d) Explanation: The result we just received points to an important piece of learning, which is that in most cases NumPy arrays behave very differently to Python lists. Let's explore the differences (and some similarities) between the two. dtype A NumPy array has a fixed data type, called dtype. This is the type of all the elements of the array. This is in contrast to Python lists, which can hold elements of different types. Exercise What happens in Python when you add an integer to a float? What happens when you put an integer into a NumPy float array? What happens when you do numerical calculations between arrays of different types? Generating 2D coordinate arrays A common requirement of NumPy arrays is to generate arrays that represent the coordinates of our data. When orthogonal 1d coordinate arrays already exist, NumPy's meshgrid function is very useful: End of explanation arr1 = np.arange(4) arr2 = np.arange(4) print('{} + {} = {}'.format(arr1, arr2, arr1 + arr2)) Explanation: The Array Object: Summary of key points properties : shape, dtype. arrays are homogeneous; all elements have the same type: dtype. indexing arrays to produce further arrays: views on the original arrays. multi-dimensional indexing (slicing) and boolean indexing. combinations to form 2D arrays: meshgrid Application: Arithmetic and Broadcasting<a class="anchor" id="app-calc"></a> Learning outcome: by the end of this section you will be able to explain how broadcasting allows mathematical operations between NumPy arrays. Elementwise Arithmetic <a class="anchor" id="arithmetic_and_broadcasting"></a> You can use NumPy to perform arithmetic operations between two arrays in an element-by-element fashion. End of explanation arr = np.arange(4) const = 5 print("Original array: {}".format(arr)) print("") print("Array + const: {}".format(arr + const)) Explanation: Exercise Explore arithmetic operations between arrays: Create a number of arrays of different shapes and dtypes. Make sure some of them include values of 0. Make use of all the basic Python mathematical operators (i.e. +, -, *, /, //, %). What operations work? What operations do not work? Can you explain why operations do or do not work? It makes intrinsic sense that you should be able to add a constant to all values in an array: End of explanation arr1 = np.ones((2, 3)) arr2 = np.ones((2, 1)) # (arr1 + arr2).shape arr1 = np.ones((2, 3)) arr2 = np.ones(3) # (arr1 + arr2).shape arr1 = np.ones((1, 3)) arr2 = np.ones((2, 1)) # (arr1 + arr2).shape arr1 = np.ones((1, 3)) arr2 = np.ones((1, 2)) # (arr1 + arr2).shape Explanation: Broadcasting There are times when you need to perform calculations between NumPy arrays of different sizes. NumPy allows you to do this easily using a powerful piece of functionality called broadcasting. Broadcasting is a way of treating the arrays as if they had the same dimensions and thus have elements all corresponding. It is then easy to perform the calculation, element-wise. It does this by matching dimensions in one array to the other where possible, and using repeated values where there is no corresponding dimension in the other array. Rules of Broadcasting Broadcasting applies these three rules: If the two arrays differ in their number of dimensions, the shape of the array with fewer dimensions is padded with ones on its leading (left) side. If the shape of the two arrays does not match in any dimension, either array with shape equal to 1 in a given dimension is stretched to match the other shape. If in any dimension the sizes disagree and neither has shape equal to 1, an error is raised. Note that all of this happens without ever actually creating the expanded arrays in memory! This broadcasting behavior is in practice enormously powerful, especially given that when NumPy broadcasts to create new dimensions or to 'stretch' existing ones, it doesn't actually duplicate the data. In the example above the operation is carried out as if the scalar 1.5 was a 1D array with 1.5 in all of its entries, but no actual array is ever created. This can save lots of memory in cases when the arrays in question are large. As such this can have significant performance implications. (image source) Exercise For the following cases: What will be the result of adding arr1 to arr2? What will be the shape of the resulting array? What rules of broadcasting are being used? End of explanation a = np.arange(12).reshape((3, 4)) mean = np.mean(a) print(a) print(mean) Explanation: Reshaping arrays to aid broadcasting NumPy allows you to change the shape of an array, so long as the total number of elements in the array does not change. For example, we could reshape a flat array with 12 elements to a 2D array with shape (2, 6), or (3, 2, 2), or even (3, 4, 1). We could not, however, reshape it to have shape (2, 5), because the total number of elements would not be kept constant. Exercise, continued For the failing example above, what reshape operation could you apply to arr2 so that it can be broadcast with arr1? Arithmetic and Broadcasting: Summary of key points arithmetic operations are performed in an element-by-element fashion, operations can be performed between arrays of different shapes, the arrays' dimensions are aligned according to fixed rules; where one input lacks a given dimension, values are repeated, reshaping can be used to get arrays to combine as required. Application: Statistics and Masked Arrays <a class="anchor" id="statistics"></a> Learning outcome: By the end of this section, you will be able to apply statistical operations and masked arrays to real-world problems. Statistics Numpy arrays support many common statistical calculations. For a list of common operations, see: https://docs.scipy.org/doc/numpy/reference/routines.statistics.html. The simplest operations consist of calculating a single statistical value from an array of numbers -- such as a mean value, a variance or a minimum. For example: End of explanation print(np.mean(a, axis=1)) Explanation: Used without any further arguments, statistical functions simply reduce the whole array to a single value. In practice, however, we very often want to calculate statistics over only some of the dimensions. The most common requirement is to calculate a statistic along a single array dimension, while leaving all the other dimensions intact. This is referred to as "collapsing" or "reducing" the chosen dimension. This is done by adding an "axis" keyword specifying the dimension to collapse: End of explanation data = np.arange(4) mask = np.array([0, 0, 1, 0]) print('Data: {}'.format(data)) print('Mask: {}'.format(mask)) masked_data = np.ma.masked_array(data, mask) print('Masked data: {}'.format(masked_data)) Explanation: Exercise What other similar statistical operations exist (see above link)? A mean value can also be calculated with <array>.mean(). Is that the same thing? Create a 3D array (that could be considered to describe [time, x, y]) and find the mean over all x and y at each timestep. What shape does the result have? Masked Arrays Real-world measurements processes often result in certain datapoint values being uncertain or simply "missing". This is usually indicated by additional data quality information, stored alongside the data values. In these cases we often need to make calculations that count only the valid datapoints. NumPy provides a special "masked array" type for this type of calculation. Here's a link to the documentation for NumPy masked arrays: https://docs.scipy.org/doc/numpy-1.11.0/reference/maskedarray.generic.html#maskedarray-generic-constructing. To construct a masked array we need some data and a mask. The data can be any kind of NumPy array, but the mask array must contain a boolean-type values only (either True and False or 1 and 0). Let's make each of these and convert them together into a NumPy masked array: End of explanation print('Unmasked average: {}'.format(np.mean(data))) print('Masked average: {}'.format(np.ma.mean(masked_data))) Explanation: The mask is applied where the values in the mask array are True. Masked arrays are printed with a double-dash -- denoting the locations in the array where the mask has been applied. The statistics of masked data are different: End of explanation arr = np.arange(8) arr_view = arr.reshape(2, 4) # Print the "view" array from reshape. print('Before\n{}'.format(arr_view)) # Update the first element of the original array. arr[0] = 1000 # Print the "view" array from reshape again, # noticing the first value has changed. print('After\n{}'.format(arr_view)) Explanation: Note that most file formats represent missing data in a different way, using a distinct "missing data" value appearing in the data. There is special support for converting between this type of representation and NumPy masked arrays. Every masked array has a fill_value property and a filled() method to fill the masked points with the fill value. Exercise Create a masked array from the numbers 0-11, where all the values less than 5 are masked. Create a masked array of positive values, with a value of -1.0 to represent missing points. Look up the masked array creation documentation. What routines exist to produce masked arrays like the ones you've just created more efficiently? Use np.ma.filled() to create a 'plain' (i.e. unmasked) array from a masked array. How can you create a plain array from a masked array, but using a different fill-value for masked points? Try performing a mathematical operation between two masked arrays. What happens to the 'fill_value' properties when you do so? Statistics and Masked Arrays: Summary of key points most statistical functions are available in two different forms, as in array.mean() and also np.mean(array), the choice being mostly a question of style. statistical operations operate over, and remove (or "collapse") the array dimensions that they are applied to. an "axis" keyword specifies operation over dimensions : this can be one; multiple; or all. NOTE: not all operations permit operation over specifically selected dimensions Statistical operations are not really part of NumPy itself, but are defined by the higher-level Scipy project. Missing datapoints can be represented using "masked arrays" these are useful for calculation, but usually require converting to another form for data storage Application: Efficiency <a class="anchor" id="efficiency"></a> Learning outcome: by the end of this section, you will be able to apply concepts that allow you to perform fast and efficient calculations on NumPy arrays. Views on Arrays NumPy attempts to not make copies of arrays. Many NumPy operations will produce a reference to an existing array, known as a "view", instead of making a whole new array. For example, indexing and reshaping provide a view of the same memory wherever possible. End of explanation arr = np.arange(8) arr_view = arr.reshape(2, 4).copy() # Print the "view" array from reshape. print('Before\n{}'.format(arr_view)) # Update the first element of the original array. arr[0] = 1000 # Print the "view" array from reshape again, # noticing the first value has changed. print('After\n{}'.format(arr_view)) Explanation: What this means is that if one array (arr) is modified, the other (arr_view) will also be updated : the same memory is being shared. This is a valuable tool which enables the system memory overhead to be managed, which is particularly useful when handling lots of large arrays. The lack of copying allows for very efficient vectorized operations. Remember, this behaviour is automatic in most of NumPy, so it requires some consideration in your code, it can lead to some bugs that are hard to track down. For example, if you are changing some elements of an array that you are using elsewhere, you may want to explicitly copy that array before making changes. If in doubt, you can always copy the data to a different block of memory with the copy() method. For example: End of explanation %%timeit x = range(500) Explanation: Loops and Vectorised Operations We will now explore calculation performance and consider efficiency in terms of processing time. Firstly let's look at a simple processing time tool that is provided in notebooks; %%timeit : End of explanation %%timeit -n 100 -r 5 x = range(500) Explanation: Repeat that, specifying only 100 loops and fastest time of 5 runs End of explanation rands = np.random.random(1000000).reshape(100, 100, 100) %%timeit -n 10 -r 5 overPointEightLoop = 0 for i in range(100): for j in range(100): for k in range(100): if rands[i, j, k] > 0.8: overPointEightLoop +=1 %%timeit -n 10 -r 5 overPointEightWhere = rands[rands > 0.8].size Explanation: This gives us an easy way to evaluate performance for implementations ... End of explanation
9,224	Given the following text description, write Python code to implement the functionality described below step by step Description: Blind Source Separation with the Shogun Machine Learning Toolbox By Kevin Hughes This notebook illustrates <a href="http Step1: Next we're going to need a way to play the audio files we're working with (otherwise this wouldn't be very exciting at all would it?). In the next bit of code I've defined a wavPlayer class that takes the signal and the sample rate and then creates a nice HTML5 webplayer right inline with the notebook. Step2: Now that we can load and play wav files we actually need some wav files! I found the sounds from Starcraft to be a great source of wav files because they're short, interesting and remind me of my childhood. You can download Starcraft wav files here Step3: Now let's load a second audio clip Step4: and a third audio clip Step5: Now we've got our audio files loaded up into our example program. The next thing we need to do is mix them together! First another nuance - what if the audio clips aren't the same lenth? The solution I came up with for this was to simply resize them all to the length of the longest signal, the extra length will just be filled with zeros so it won't affect the sound. The signals are mixed by creating a mixing matrix $A$ and taking the dot product of $A$ with the signals $S$. Afterwards I plot the mixed signals and create the wavPlayers, have a listen! Step6: Now before we can work on separating these signals we need to get the data ready for Shogun, thankfully this is pretty easy! Step7: Now lets unmix those signals! In this example I'm going to use an Independent Component Analysis (ICA) algorithm called JADE. JADE is one of the ICA algorithms available in Shogun and it works by performing Aproximate Joint Diagonalization (AJD) on a 4th order cumulant tensor. I'm not going to go into a lot of detail on how JADE works behind the scenes but here is the reference for the original paper Step8: Thats all there is to it! Check out how nicely those signals have been separated and have a listen!	Python Code: import numpy as np import os SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data') from scipy.io import wavfile from scipy.signal import resample import shogun as sg def load_wav(filename,samplerate=44100): # load file rate, data = wavfile.read(filename) # convert stereo to mono if len(data.shape) > 1: data = data[:,0]/2 + data[:,1]/2 # re-interpolate samplerate ratio = float(samplerate) / float(rate) data = resample(data, int(len(data) * ratio)) return samplerate, data.astype(np.int16) Explanation: Blind Source Separation with the Shogun Machine Learning Toolbox By Kevin Hughes This notebook illustrates <a href="http://en.wikipedia.org/wiki/Blind_signal_separation">Blind Source Seperation</a>(BSS) on audio signals using <a href="http://en.wikipedia.org/wiki/Independent_component_analysis">Independent Component Analysis</a> (ICA) in Shogun. We generate a mixed signal and try to seperate it out using Shogun's implementation of ICA & BSS called <a href="http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1Jade.html">JADE</a>. My favorite example of this problem is known as the cocktail party problem where a number of people are talking simultaneously and we want to separate each persons speech so we can listen to it separately. Now the caveat with this type of approach is that we need as many mixtures as we have source signals or in terms of the cocktail party problem we need as many microphones as people talking in the room. Let's get started, this example is going to be in python and the first thing we are going to need to do is load some audio files. To make things a bit easier further on in this example I'm going to wrap the basic scipy wav file reader and add some additional functionality. First I added a case to handle converting stereo wav files back into mono wav files and secondly this loader takes a desired sample rate and resamples the input to match. This is important because when we mix the two audio signals they need to have the same sample rate. End of explanation from IPython.display import Audio from IPython.display import display def wavPlayer(data, rate): display(Audio(data, rate=rate)) Explanation: Next we're going to need a way to play the audio files we're working with (otherwise this wouldn't be very exciting at all would it?). In the next bit of code I've defined a wavPlayer class that takes the signal and the sample rate and then creates a nice HTML5 webplayer right inline with the notebook. End of explanation # change to the shogun-data directory import os os.chdir(os.path.join(SHOGUN_DATA_DIR, 'ica')) %matplotlib inline import matplotlib.pyplot as plt # load fs1,s1 = load_wav('tbawht02.wav') # Terran Battlecruiser - "Good day, commander." # plot plt.figure(figsize=(6.75,2)) plt.plot(s1) plt.title('Signal 1') plt.show() # player wavPlayer(s1, fs1) Explanation: Now that we can load and play wav files we actually need some wav files! I found the sounds from Starcraft to be a great source of wav files because they're short, interesting and remind me of my childhood. You can download Starcraft wav files here: http://wavs.unclebubby.com/computer/starcraft/ among other places on the web or from your Starcraft install directory (come on I know its still there). Another good source of data (although lets be honest less cool) is ICA central and various other more academic data sets: http://perso.telecom-paristech.fr/~cardoso/icacentral/base_multi.html. Note that for lots of these data sets the data will be mixed already so you'll be able to skip the next few steps. Okay lets load up an audio file. I chose the Terran Battlecruiser saying "Good Day Commander". In addition to the creating a wavPlayer I also plotted the data using Matplotlib (and tried my best to have the graph length match the HTML player length). Have a listen! End of explanation # load fs2,s2 = load_wav('TMaRdy00.wav') # Terran Marine - "You want a piece of me, boy?" # plot plt.figure(figsize=(6.75,2)) plt.plot(s2) plt.title('Signal 2') plt.show() # player wavPlayer(s2, fs2) Explanation: Now let's load a second audio clip: End of explanation # load fs3,s3 = load_wav('PZeRdy00.wav') # Protoss Zealot - "My life for Aiur!" # plot plt.figure(figsize=(6.75,2)) plt.plot(s3) plt.title('Signal 3') plt.show() # player wavPlayer(s3, fs3) Explanation: and a third audio clip: End of explanation # Adjust for different clip lengths fs = fs1 length = max([len(s1), len(s2), len(s3)]) s1 = np.resize(s1, (length,1)) s2 = np.resize(s2, (length,1)) s3 = np.resize(s3, (length,1)) S = (np.c_[s1, s2, s3]).T # Mixing Matrix #A = np.random.uniform(size=(3,3)) #A = A / A.sum(axis=0) A = np.array([[1, 0.5, 0.5], [0.5, 1, 0.5], [0.5, 0.5, 1]]) print('Mixing Matrix:') print(A.round(2)) # Mix Signals X = np.dot(A,S) # Mixed Signal i for i in range(X.shape[0]): plt.figure(figsize=(6.75,2)) plt.plot((X[i]).astype(np.int16)) plt.title('Mixed Signal %d' % (i+1)) plt.show() wavPlayer((X[i]).astype(np.int16), fs) Explanation: Now we've got our audio files loaded up into our example program. The next thing we need to do is mix them together! First another nuance - what if the audio clips aren't the same lenth? The solution I came up with for this was to simply resize them all to the length of the longest signal, the extra length will just be filled with zeros so it won't affect the sound. The signals are mixed by creating a mixing matrix $A$ and taking the dot product of $A$ with the signals $S$. Afterwards I plot the mixed signals and create the wavPlayers, have a listen! End of explanation # Convert to features for shogun mixed_signals = sg.create_features((X).astype(np.float64)) Explanation: Now before we can work on separating these signals we need to get the data ready for Shogun, thankfully this is pretty easy! End of explanation # Separating with JADE jade = sg.create_transformer('Jade') jade.fit(mixed_signals) signals = jade.transform(mixed_signals) S_ = signals.get('feature_matrix') A_ = jade.get('mixing_matrix') A_ = A_ / A_.sum(axis=0) print('Estimated Mixing Matrix:') print(A_) Explanation: Now lets unmix those signals! In this example I'm going to use an Independent Component Analysis (ICA) algorithm called JADE. JADE is one of the ICA algorithms available in Shogun and it works by performing Aproximate Joint Diagonalization (AJD) on a 4th order cumulant tensor. I'm not going to go into a lot of detail on how JADE works behind the scenes but here is the reference for the original paper: Cardoso, J. F., & Souloumiac, A. (1993). Blind beamforming for non-Gaussian signals. In IEE Proceedings F (Radar and Signal Processing) (Vol. 140, No. 6, pp. 362-370). IET Digital Library. Shogun also has several other ICA algorithms including the Second Order Blind Identification (SOBI) algorithm, FFSep, JediSep, UWedgeSep and FastICA. All of the algorithms inherit from the ICAConverter base class and share some common methods for setting an intial guess for the mixing matrix, retrieving the final mixing matrix and getting/setting the number of iterations to run and the desired convergence tolerance. Some of the algorithms have additional getters for intermediate calculations, for example Jade has a method for returning the 4th order cumulant tensor while the "Sep" algorithms have a getter for the time lagged covariance matrices. Check out the source code on GitHub (https://github.com/shogun-toolbox/shogun) or the Shogun docs (http://www.shogun-toolbox.org/doc/en/latest/annotated.html) for more details! End of explanation # Show separation results # Separated Signal i gain = 4000 for i in range(S_.shape[0]): plt.figure(figsize=(6.75,2)) plt.plot((gainS_[i]).astype(np.int16)) plt.title('Separated Signal %d' % (i+1)) plt.show() wavPlayer((gainS_[i]).astype(np.int16), fs) Explanation: Thats all there is to it! Check out how nicely those signals have been separated and have a listen! End of explanation
9,225	Given the following text description, write Python code to implement the functionality described below step by step Description: Integration Exercise 1 Imports Step2: Trapezoidal rule The trapezoidal rule generates a numerical approximation to the 1d integral Step3: Now use scipy.integrate.quad to integrate the f and g functions and see how the result compares with your trapz function. Print the results and errors.	Python Code: %matplotlib inline import matplotlib.pyplot as plt import numpy as np from scipy import integrate Explanation: Integration Exercise 1 Imports End of explanation integrate.quad? def trapz(f, a, b, N): Integrate the function f(x) over the range [a,b] with N points. h=(b-a)/N integral=0 while a<b: integral=f(a)h+(f(a+h)-f(a))h/2+integral a=a+h return integral f = lambda x: x**2 g = lambda x: np.sin(x) %%timeit trapz(f, 0, 1, 1000) I = trapz(f, 0, 1, 1000) assert np.allclose(I, 0.33333349999999995) J = trapz(g, 0, np.pi, 1000) assert np.allclose(J, 1.9999983550656628) Explanation: Trapezoidal rule The trapezoidal rule generates a numerical approximation to the 1d integral: $$ I(a,b) = \int_a^b f(x) dx $$ by dividing the interval $[a,b]$ into $N$ subdivisions of length $h$: $$ h = (b-a)/N $$ Note that this means the function will be evaluated at $N+1$ points on $[a,b]$. The main idea of the trapezoidal rule is that the function is approximated by a straight line between each of these points. Write a function trapz(f, a, b, N) that performs trapezoidal rule on the function f over the interval $[a,b]$ with N subdivisions (N+1 points). End of explanation integ1, err1 = integrate.quad(f,0.0,1.0) print(integ1) print(err1) integ2, err2=integrate.quad(g,0.0,np.pi) print(integ2) print(err2) assert True # leave this cell to grade the previous one Explanation: Now use scipy.integrate.quad to integrate the f and g functions and see how the result compares with your trapz function. Print the results and errors. End of explanation
9,226	Given the following text description, write Python code to implement the functionality described below step by step Description: plotly plotly has recently become open source, it proposes a large gallery of javascript graphs. plotly also offers to host dashboards built with plotly. The first script usually returns an exception Step1: pandas and plotly Step2: version javacript Step3: javascript with custom button Step4: exemple offline	Python Code: from jyquickhelper import add_notebook_menu add_notebook_menu() Explanation: plotly plotly has recently become open source, it proposes a large gallery of javascript graphs. plotly also offers to host dashboards built with plotly. The first script usually returns an exception: But there exists an offline mode. documentation source installation tutorial gallerie Autres liens : styles de texte en python ou styles de text en javascript End of explanation import cufflinks # don't forget that from plotly.offline import init_notebook_mode init_notebook_mode(connected=True) from sklearn.datasets import load_iris import pandas data = load_iris() df = pandas.DataFrame(data["data"]) df.head() # df.iplot() # issue with PlotlyLocalCredentialsError Explanation: pandas and plotly End of explanation %%javascript require.config({ paths: { plotly: 'https://cdn.plot.ly/plotly-latest.min.js' } }); %%html <div id="myDiv"></div> %%javascript var x = []; for (var i = 0; i < 500; i ++) { x[i] = Math.random(); } var data = [ { x: x, type: 'histogram' } ]; Plotly.newPlot('myDiv', data); Explanation: version javacript End of explanation %%html <div id="myDiv2" style="width:800px;height:400px;"></div> <div class="hideshow" id="top" style="margin-left:80px;"> <button style=";background:fuchsia;">Toggle Fuchsia</button> </div> <div class="hideshow" id="bottom" style="margin-left:80px;"> <button style="background:#FFD966;">Toggle Yellow</button </div> %%javascript var d3 = Plotly.d3, y1 = d3.range(100).map(d3.random.normal(6)), y2 = d3.range(100).map(d3.random.normal(9)), layout = { title:'Click buttons to toggle traces', showlegend:false }, data = [{ y:y1, marker: {color: 'fuchsia'} }, { y:y2, marker: {color: '#FFD966'} }]; Plotly.plot("myDiv2", data, layout); $('.hideshow button').click(function(){ var btn_id = this.parentNode.id, data_index = ( btn_id === 'top' ) ? 0 : 1, myDiv2 = document.getElementById("myDiv2"), visible = myDiv2.data[data_index].visible; if( visible === undefined ) visible = true; Plotly.restyle("myDiv2", 'visible', !visible, data_index); }); Explanation: javascript with custom button End of explanation from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot # connected=True pour une utilisation ultérieure sous forme de HTML # connected=False pour une utiliation uniquement locale init_notebook_mode(connected=True) from plotly.graph_objs import Box from plotly.offline import iplot import numpy as np iplot([Box(y = np.random.randn(50), showlegend=False) for i in range(45)], show_link=False) Explanation: exemple offline End of explanation
9,227	Given the following text description, write Python code to implement the functionality described below step by step Description: Is there a relationship between GDP per capita and PISA scores? July 2015 Written by Susan Chen at NYU Stern with help from Professor David Backus Contact Step1: Creating the Dataset PISA 2012 scores are downloaded as an excel file from the statlink on page 21 of the published PISA key findings. I deleted the explanatory text surrounding the table. I kept only the "Mean Score in PISA 2012" column for each subject and then saved the file as a csv. Then, I read the file into pandas and renamed the columns. Step2: Excluding Outliers I initially plotted the data and ran the regression without excluding any outliers. The resulting r-squared values for reading, math, and science were 0.29, 0.32, and 0.27, respectively. Looking at the scatter plot, there seem to be two obvious outliers, Qatar and Vietnam. I decided to exclude the data for these two countries because the remaining countries do seem to form a trend. I found upon excluding them that the correlation between GDP per capita and scores was much higher. Qatar is an outlier as it placed relatively low, 63rd out of the 65 countries, with a relatively high GDP per capita at about $131000. Qatar has a high GDP per capita for a country with just 1.8 million people, and only 13% of which are Qatari nationals. Qatar is a high income economy as it contains one of the world's largest natural gas and oil reserves. Vietnam is an outlier because it placed relatively high, 17th out of the 65 countries, with a relatively low GDP per capita at about $4900. Reasons for Vietnam's high score may be due to the investment of the government in education and the uniformity of classroom professionalism and discipline found across countries. At the same time, rote learning is much more emphasized than creative thinking, and it is important to note that many disadvantaged students are forced to drop out, reasons which may account for the high score. Step3: Plotting the Data I use the log of the GDP per capita to plot against each component of the PISA on a scatter plot. Step4: Regression Analysis The OLS regression results indicate that the there is a 0.57 correlation betweeen reading scores and GDP per capita, 0.63 between math scores and GDP per capita, and 0.57 between science scores and GDP per capita.	Python Code: %matplotlib inline import matplotlib.pyplot as plt import pandas as pd import numpy as np import statsmodels.formula.api as smf from pandas.io import wb Explanation: Is there a relationship between GDP per capita and PISA scores? July 2015 Written by Susan Chen at NYU Stern with help from Professor David Backus Contact: jiachen2017@u.northwestern.edu About PISA Since 2000, the Programme for International Student Assessment (PISA) has been administered every three years to evaluate education systems around the world. It also gathers family and education background information through surveys. The test, which assesses 15-year-old students in reading, math, and science, is administered to a total of around 510,000 students in 65 countries. The duration of the test is two hours, and it contains a mix of open-ended and multiple-choice questions. Learn more about the test here. I am interested in seeing if there is a correlation between a nation's wealth and their PISA scores. Do wealthier countries generally attain higher scores, and if so, to what extent? I am using GDP per capita as the economic measure of wealth because this is information that could be sensitive to population numbers so GDP per capita in theory should allow us to compare larger countries (in terms of geography or population) with small countries. Abstract In terms of the correlation between GDP per capita and each component of the PISA, the r-squared values for an OLS regression model, which usually reflect how well the model fits the data, are 0.57, 0.63, and 0.57 for reading, math, and science, respectively. Qatar and Vietnam, outliers, are excluded from the model. Packages Imported I use matplotlib.pyplot to plot scatter plots. I use pandas, a Python package that allows for fast data manipulation and analysis, to organize my dataset. I access World Bank data through the remote data access API for pandas, pandas.io. I also use numpy, a Python package for scientific computing, for the mathematical calculations that were needed to fit the data more appropriately. Lastly, I use statmodels.formula.api, a Python module used for a variety of statistical computations, for running an OLS linear regression. End of explanation file1 = '/users/susan/desktop/PISA/PISA2012clean.csv' # file location df1 = pd.read_csv(file1) #pandas remote data access API for World Bank GDP per capita data df2 = wb.download(indicator='NY.GDP.PCAP.PP.KD', country='all', start=2012, end=2012) df1 #drop multilevel index df2.index = df2.index.droplevel('year') df1.columns = ['Country','Math','Reading','Science'] df2.columns = ['GDPpc'] #combine PISA and GDP datasets based on country column df3 = pd.merge(df1, df2, how='left', left_on = 'Country', right_index = True) df3.columns = ['Country','Math','Reading','Science','GDPpc'] #drop rows with missing GDP per capita values df3 = df3[pd.notnull(df3['GDPpc'])] print (df3) Explanation: Creating the Dataset PISA 2012 scores are downloaded as an excel file from the statlink on page 21 of the published PISA key findings. I deleted the explanatory text surrounding the table. I kept only the "Mean Score in PISA 2012" column for each subject and then saved the file as a csv. Then, I read the file into pandas and renamed the columns. End of explanation df3.index = df3.Country #set country column as the index df3 = df3.drop(['Qatar', 'Vietnam']) # drop outlier Explanation: Excluding Outliers I initially plotted the data and ran the regression without excluding any outliers. The resulting r-squared values for reading, math, and science were 0.29, 0.32, and 0.27, respectively. Looking at the scatter plot, there seem to be two obvious outliers, Qatar and Vietnam. I decided to exclude the data for these two countries because the remaining countries do seem to form a trend. I found upon excluding them that the correlation between GDP per capita and scores was much higher. Qatar is an outlier as it placed relatively low, 63rd out of the 65 countries, with a relatively high GDP per capita at about $131000. Qatar has a high GDP per capita for a country with just 1.8 million people, and only 13% of which are Qatari nationals. Qatar is a high income economy as it contains one of the world's largest natural gas and oil reserves. Vietnam is an outlier because it placed relatively high, 17th out of the 65 countries, with a relatively low GDP per capita at about $4900. Reasons for Vietnam's high score may be due to the investment of the government in education and the uniformity of classroom professionalism and discipline found across countries. At the same time, rote learning is much more emphasized than creative thinking, and it is important to note that many disadvantaged students are forced to drop out, reasons which may account for the high score. End of explanation Reading = df3.Reading Science = df3.Science Math = df3.Math GDP = np.log(df3.GDPpc) #PISA reading vs GDP per capita plt.scatter(x = GDP, y = Reading, color = 'r') plt.title('PISA 2012 Reading scores vs. GDP per capita') plt.xlabel('GDP per capita (log)') plt.ylabel('PISA Reading Score') plt.show() #PISA math vs GDP per capita plt.scatter(x = GDP, y = Math, color = 'b') plt.title('PISA 2012 Math scores vs. GDP per capita') plt.xlabel('GDP per capita (log)') plt.ylabel('PISA Math Score') plt.show() #PISA science vs GDP per capita plt.scatter(x = GDP, y = Science, color = 'g') plt.title('PISA 2012 Science scores vs. GDP per capita') plt.xlabel('GDP per capita (log)') plt.ylabel('PISA Science Score') plt.show() Explanation: Plotting the Data I use the log of the GDP per capita to plot against each component of the PISA on a scatter plot. End of explanation lm = smf.ols(formula='Reading ~ GDP', data=df3).fit() lm2.params lm.summary() lm2 = smf.ols(formula='Math ~ GDP', data=df3).fit() lm2.params lm2.summary() lm3 = smf.ols(formula='Science ~ GDP', data=df3).fit() lm3.params lm3.summary() Explanation: Regression Analysis The OLS regression results indicate that the there is a 0.57 correlation betweeen reading scores and GDP per capita, 0.63 between math scores and GDP per capita, and 0.57 between science scores and GDP per capita. End of explanation
9,228	Given the following text description, write Python code to implement the functionality described below step by step Description: Notes Step1: Here the curve shows the Poisson mean as a function of $M$. Clearly, the data don't sit on the curve, nor should they. But it would be nice to represent the width of the sampling distribution somehow, so we can see how compatible the data are with the model. Among other possibilities, we might do that by also showing curves reflecting the 16th and 84th percentiles of the sampling distribution, in addition to the mean, as a function of $M$ (so that the probability between the curves, at fixed $M$, is 68%). That would look like this Step2: You can see that about $2/3$ of the data lie within these limits. The jaggedness of the lines is due to the fact that, of course, we can only get integers out of the Poisson distribution. Let's now take this to a regime where there would be many more counts in each measurement. Step3: We're now well into the limit where the Poisson (mean $\mu$) distribution begins to resemble the Gaussian distribution (mean $\mu$ and standard deviation $\sqrt \mu$). As we're looking at many more counts, the discreteness of the dashed lines is also harder to see. Now, we might think about displaying this with $F$ rather than the actual measurement, $N$, on the $y$ axis. Of course, we know that the plotted points don't actually correspond to the true flux of each star, only the naive extimate we would make by plugging through the equations at the top of the notebook and ignoring any uncertainty in the model. So let's call it "estimated flux", $\hat{F}$, instead. We'll use the fact that we're now in the Gaussian limit to simply compute the dashed limits on the model prediction as the mean $\pm$ the standard deviation. Step4: In this simple example, apart from the tiny change to the dashed lines, this just corresponds to a rescaling of the $y$ axis. Finally, we could (if we wanted to) remember the form of the Gaussian distribution, $\mathrm{Normal}(x\|\mu,\sigma) = \frac{1}{\sqrt{2\pi}\sigma} \exp\left[ -\frac{(x-\mu)^2}{2\sigma^2} \right]$. Because this density depends on $x$ and $\mu$ only through the distance between them, if we're using $\sigma$ (or some other scale) to represent model uncertainty, we could choose to position that visual cue with the data points rather than with the model. The distance between the data and model in units of that shown uncertainty would, of course, be the same either way. Note that the same statement could not be made when we were in the small-$N$ regime, or at least not made as simply.	Python Code: # get a bunch of imports out of the way import matplotlib.pyplot as plt plt.rc('text', usetex=True) plt.rcParams['xtick.labelsize'] = 'x-large' plt.rcParams['ytick.labelsize'] = 'x-large' import numpy as np import scipy.stats as st %matplotlib inline M = st.uniform.rvs(1.0, 100.0, size=10) F = np.sqrt(M) mu = 2F N = st.poisson.rvs(mu) Mgrid = np.linspace(M.min(), M.max()) mu_of_Mgrid = 2np.sqrt(Mgrid) plt.plot(Mgrid, mu_of_Mgrid, label=r'$\mu(M)$'); plt.plot(M, N, 'o', label='data'); plt.xlabel(r'$M$', fontsize='x-large'); plt.ylabel(r'$N$', fontsize='x-large'); plt.legend(fontsize='x-large'); Explanation: Notes: So, about those error bars... In which we will make sense of the common delusion of error bars. The Poisson example we've used so far when thinking about generative models and Bayes' Law is one where the idea of an error bar doesn't really apply. We measure a certain number of counts, period. That's it. It doesn't make sense to say that we measured, as the case may be, 5 $\pm$ something (even more so if that something is not an integer), because literally what we measured was the number 5. But error bars are enough of a thing that even the most pedantic among us will talk about them fairly often. So what's the deal? Let's look at a contrived example to sketch it out. Specifically, let's expand on the previous Poisson example by imagining we (a) measure a number of counts (with the same telescope and exposure time) from many stars with different masses, and (b) live in magical universe where stars have name tags that tell us their precise masses. We're interested in how luminosity, which is directly proportional to the expectation value for number of counts, relates to this magically known mass. In generative terms, * masses $M_i$ are known somehow, * fluxes $F_i \Leftarrow$ $M_i$, * expected counts $\mu_i \Leftarrow F_i$, * measured counts $N_i \sim \mathrm{Poisson}(\mu_i)$. For simplicity, let's implement the details as * Choose $M_i$, * Let $F_i=M_i^{0.5}$, * Let $\mu_i = 2F_i$. Here's a made-up data set. End of explanation Nlower = st.poisson.ppf(0.16, mu_of_Mgrid) Nupper = st.poisson.ppf(0.84, mu_of_Mgrid) plt.plot(Mgrid, mu_of_Mgrid, label=r'$\mu(M)$'); plt.plot(M, N, 'o', label='data'); plt.plot(Mgrid, Nlower, '--', color='C0'); plt.plot(Mgrid, Nupper, '--', color='C0'); plt.xlabel(r'$M$', fontsize='x-large'); plt.ylabel(r'$N$', fontsize='x-large'); plt.legend(fontsize='x-large'); Explanation: Here the curve shows the Poisson mean as a function of $M$. Clearly, the data don't sit on the curve, nor should they. But it would be nice to represent the width of the sampling distribution somehow, so we can see how compatible the data are with the model. Among other possibilities, we might do that by also showing curves reflecting the 16th and 84th percentiles of the sampling distribution, in addition to the mean, as a function of $M$ (so that the probability between the curves, at fixed $M$, is 68%). That would look like this: End of explanation M = st.uniform.rvs(1000.0, 10000.0, size=10) F = np.sqrt(M) mu = 2F N = st.poisson.rvs(mu) Mgrid = np.linspace(M.min(), M.max()) mu_of_Mgrid = 2np.sqrt(Mgrid) Nlower = st.poisson.ppf(0.16, mu_of_Mgrid) Nupper = st.poisson.ppf(0.84, mu_of_Mgrid) plt.plot(Mgrid, mu_of_Mgrid, label=r'$\mu(M)$'); plt.plot(M, N, 'o', label='data'); plt.plot(Mgrid, Nlower, '--', color='C0'); plt.plot(Mgrid, Nupper, '--', color='C0'); plt.xlabel(r'$M$', fontsize='x-large'); plt.ylabel(r'$N$', fontsize='x-large'); plt.legend(fontsize='x-large'); Explanation: You can see that about $2/3$ of the data lie within these limits. The jaggedness of the lines is due to the fact that, of course, we can only get integers out of the Poisson distribution. Let's now take this to a regime where there would be many more counts in each measurement. End of explanation F_of_Mgrid = mu_of_Mgrid / 2. Flower = (mu_of_Mgrid - np.sqrt(mu_of_Mgrid)) / 2. Fupper = (mu_of_Mgrid + np.sqrt(mu_of_Mgrid)) / 2. Fhat = N / 2. plt.plot(Mgrid, F_of_Mgrid, label=r'$\hat{F}(M)$'); plt.plot(M, Fhat, 'o', label='data'); plt.plot(Mgrid, Flower, '--', color='C0'); plt.plot(Mgrid, Fupper, '--', color='C0'); plt.xlabel(r'$M$', fontsize='x-large'); plt.ylabel(r'$\hat{F}$', fontsize='x-large'); plt.legend(fontsize='x-large'); Explanation: We're now well into the limit where the Poisson (mean $\mu$) distribution begins to resemble the Gaussian distribution (mean $\mu$ and standard deviation $\sqrt \mu$). As we're looking at many more counts, the discreteness of the dashed lines is also harder to see. Now, we might think about displaying this with $F$ rather than the actual measurement, $N$, on the $y$ axis. Of course, we know that the plotted points don't actually correspond to the true flux of each star, only the naive extimate we would make by plugging through the equations at the top of the notebook and ignoring any uncertainty in the model. So let's call it "estimated flux", $\hat{F}$, instead. We'll use the fact that we're now in the Gaussian limit to simply compute the dashed limits on the model prediction as the mean $\pm$ the standard deviation. End of explanation Fhat_err = np.sqrt(mu) / 2. plt.plot(Mgrid, F_of_Mgrid, label=r'$\hat{F}(M)$'); plt.errorbar(M, Fhat, yerr=Fhat_err, fmt='o', label='data'); plt.xlabel(r'$M$', fontsize='x-large'); plt.ylabel(r'$\hat{F}$', fontsize='x-large'); plt.legend(fontsize='x-large'); Explanation: In this simple example, apart from the tiny change to the dashed lines, this just corresponds to a rescaling of the $y$ axis. Finally, we could (if we wanted to) remember the form of the Gaussian distribution, $\mathrm{Normal}(x\|\mu,\sigma) = \frac{1}{\sqrt{2\pi}\sigma} \exp\left[ -\frac{(x-\mu)^2}{2\sigma^2} \right]$. Because this density depends on $x$ and $\mu$ only through the distance between them, if we're using $\sigma$ (or some other scale) to represent model uncertainty, we could choose to position that visual cue with the data points rather than with the model. The distance between the data and model in units of that shown uncertainty would, of course, be the same either way. Note that the same statement could not be made when we were in the small-$N$ regime, or at least not made as simply. End of explanation
9,229	Given the following text description, write Python code to implement the functionality described below step by step Description: DICS for power mapping In this tutorial, we'll simulate two signals originating from two locations on the cortex. These signals will be sinusoids, so we'll be looking at oscillatory activity (as opposed to evoked activity). We'll use dynamic imaging of coherent sources (DICS) Step1: Setup We first import the required packages to run this tutorial and define a list of filenames for various things we'll be using. Step3: Data simulation The following function generates a timeseries that contains an oscillator, whose frequency fluctuates a little over time, but stays close to 10 Hz. We'll use this function to generate our two signals. Step4: Let's simulate two timeseries and plot some basic information about them. Step5: Now we put the signals at two locations on the cortex. We construct a Step6: Before we simulate the sensor-level data, let's define a signal-to-noise ratio. You are encouraged to play with this parameter and see the effect of noise on our results. Step7: Now we run the signal through the forward model to obtain simulated sensor data. To save computation time, we'll only simulate gradiometer data. You can try simulating other types of sensors as well. Some noise is added based on the baseline noise covariance matrix from the sample dataset, scaled to implement the desired SNR. Step8: We create an Step9: Power mapping With our simulated dataset ready, we can now pretend to be researchers that have just recorded this from a real subject and are going to study what parts of the brain communicate with each other. First, we'll create a source estimate of the MEG data. We'll use both a straightforward MNE-dSPM inverse solution for this, and the DICS beamformer which is specifically designed to work with oscillatory data. Computing the inverse using MNE-dSPM Step10: We will now compute the cortical power map at 10 Hz. using a DICS beamformer. A beamformer will construct for each vertex a spatial filter that aims to pass activity originating from the vertex, while dampening activity from other sources as much as possible. The Step12: Plot the DICS power maps for both approaches, starting with the first Step13: Now the second	Python Code: # Author: Marijn van Vliet <[email protected]> # # License: BSD (3-clause) Explanation: DICS for power mapping In this tutorial, we'll simulate two signals originating from two locations on the cortex. These signals will be sinusoids, so we'll be looking at oscillatory activity (as opposed to evoked activity). We'll use dynamic imaging of coherent sources (DICS) :footcite:GrossEtAl2001 to map out spectral power along the cortex. Let's see if we can find our two simulated sources. End of explanation import os.path as op import numpy as np from scipy.signal import welch, coherence, unit_impulse from matplotlib import pyplot as plt import mne from mne.simulation import simulate_raw, add_noise from mne.datasets import sample from mne.minimum_norm import make_inverse_operator, apply_inverse from mne.time_frequency import csd_morlet from mne.beamformer import make_dics, apply_dics_csd # We use the MEG and MRI setup from the MNE-sample dataset data_path = sample.data_path(download=False) subjects_dir = op.join(data_path, 'subjects') # Filenames for various files we'll be using meg_path = op.join(data_path, 'MEG', 'sample') raw_fname = op.join(meg_path, 'sample_audvis_raw.fif') fwd_fname = op.join(meg_path, 'sample_audvis-meg-eeg-oct-6-fwd.fif') cov_fname = op.join(meg_path, 'sample_audvis-cov.fif') fwd = mne.read_forward_solution(fwd_fname) # Seed for the random number generator rand = np.random.RandomState(42) Explanation: Setup We first import the required packages to run this tutorial and define a list of filenames for various things we'll be using. End of explanation sfreq = 50. # Sampling frequency of the generated signal n_samp = int(round(10. * sfreq)) times = np.arange(n_samp) / sfreq # 10 seconds of signal n_times = len(times) def coh_signal_gen(): Generate an oscillating signal. Returns ------- signal : ndarray The generated signal. t_rand = 0.001 # Variation in the instantaneous frequency of the signal std = 0.1 # Std-dev of the random fluctuations added to the signal base_freq = 10. # Base frequency of the oscillators in Hertz n_times = len(times) # Generate an oscillator with varying frequency and phase lag. signal = np.sin(2.0 * np.pi * (base_freq * np.arange(n_times) / sfreq + np.cumsum(t_rand * rand.randn(n_times)))) # Add some random fluctuations to the signal. signal += std * rand.randn(n_times) # Scale the signal to be in the right order of magnitude (~100 nAm) # for MEG data. signal = 100e-9 return signal Explanation: Data simulation The following function generates a timeseries that contains an oscillator, whose frequency fluctuates a little over time, but stays close to 10 Hz. We'll use this function to generate our two signals. End of explanation signal1 = coh_signal_gen() signal2 = coh_signal_gen() fig, axes = plt.subplots(2, 2, figsize=(8, 4)) # Plot the timeseries ax = axes[0][0] ax.plot(times, 1e9 signal1, lw=0.5) ax.set(xlabel='Time (s)', xlim=times[[0, -1]], ylabel='Amplitude (Am)', title='Signal 1') ax = axes[0][1] ax.plot(times, 1e9 * signal2, lw=0.5) ax.set(xlabel='Time (s)', xlim=times[[0, -1]], title='Signal 2') # Power spectrum of the first timeseries f, p = welch(signal1, fs=sfreq, nperseg=128, nfft=256) ax = axes[1][0] # Only plot the first 100 frequencies ax.plot(f[:100], 20 * np.log10(p[:100]), lw=1.) ax.set(xlabel='Frequency (Hz)', xlim=f[[0, 99]], ylabel='Power (dB)', title='Power spectrum of signal 1') # Compute the coherence between the two timeseries f, coh = coherence(signal1, signal2, fs=sfreq, nperseg=100, noverlap=64) ax = axes[1][1] ax.plot(f[:50], coh[:50], lw=1.) ax.set(xlabel='Frequency (Hz)', xlim=f[[0, 49]], ylabel='Coherence', title='Coherence between the timeseries') fig.tight_layout() Explanation: Let's simulate two timeseries and plot some basic information about them. End of explanation # The locations on the cortex where the signal will originate from. These # locations are indicated as vertex numbers. vertices = [[146374], [33830]] # Construct SourceEstimates that describe the signals at the cortical level. data = np.vstack((signal1, signal2)) stc_signal = mne.SourceEstimate( data, vertices, tmin=0, tstep=1. / sfreq, subject='sample') stc_noise = stc_signal * 0. Explanation: Now we put the signals at two locations on the cortex. We construct a :class:mne.SourceEstimate object to store them in. The timeseries will have a part where the signal is active and a part where it is not. The techniques we'll be using in this tutorial depend on being able to contrast data that contains the signal of interest versus data that does not (i.e. it contains only noise). End of explanation snr = 1. # Signal-to-noise ratio. Decrease to add more noise. Explanation: Before we simulate the sensor-level data, let's define a signal-to-noise ratio. You are encouraged to play with this parameter and see the effect of noise on our results. End of explanation # Read the info from the sample dataset. This defines the location of the # sensors and such. info = mne.io.read_info(raw_fname) info.update(sfreq=sfreq, bads=[]) # Only use gradiometers picks = mne.pick_types(info, meg='grad', stim=True, exclude=()) mne.pick_info(info, picks, copy=False) # Define a covariance matrix for the simulated noise. In this tutorial, we use # a simple diagonal matrix. cov = mne.cov.make_ad_hoc_cov(info) cov['data'] = (20. / snr) * 2 # Scale the noise to achieve the desired SNR # Simulate the raw data, with a lowpass filter on the noise stcs = [(stc_signal, unit_impulse(n_samp, dtype=int) * 1), (stc_noise, unit_impulse(n_samp, dtype=int) * 2)] # stacked in time duration = (len(stc_signal.times) * 2) / sfreq raw = simulate_raw(info, stcs, forward=fwd) add_noise(raw, cov, iir_filter=[4, -4, 0.8], random_state=rand) Explanation: Now we run the signal through the forward model to obtain simulated sensor data. To save computation time, we'll only simulate gradiometer data. You can try simulating other types of sensors as well. Some noise is added based on the baseline noise covariance matrix from the sample dataset, scaled to implement the desired SNR. End of explanation events = mne.find_events(raw, initial_event=True) tmax = (len(stc_signal.times) - 1) / sfreq epochs = mne.Epochs(raw, events, event_id=dict(signal=1, noise=2), tmin=0, tmax=tmax, baseline=None, preload=True) assert len(epochs) == 2 # ensure that we got the two expected events # Plot some of the channels of the simulated data that are situated above one # of our simulated sources. picks = mne.pick_channels(epochs.ch_names, mne.read_selection('Left-frontal')) epochs.plot(picks=picks) Explanation: We create an :class:mne.Epochs object containing two trials: one with both noise and signal and one with just noise End of explanation # Compute the inverse operator fwd = mne.read_forward_solution(fwd_fname) inv = make_inverse_operator(epochs.info, fwd, cov) # Apply the inverse model to the trial that also contains the signal. s = apply_inverse(epochs['signal'].average(), inv) # Take the root-mean square along the time dimension and plot the result. s_rms = np.sqrt((s ** 2).mean()) title = 'MNE-dSPM inverse (RMS)' brain = s_rms.plot('sample', subjects_dir=subjects_dir, hemi='both', figure=1, size=600, time_label=title, title=title) # Indicate the true locations of the source activity on the plot. brain.add_foci(vertices[0][0], coords_as_verts=True, hemi='lh') brain.add_foci(vertices[1][0], coords_as_verts=True, hemi='rh') # Rotate the view and add a title. brain.show_view(view={'azimuth': 0, 'elevation': 0, 'distance': 550, 'focalpoint': [0, 0, 0]}) Explanation: Power mapping With our simulated dataset ready, we can now pretend to be researchers that have just recorded this from a real subject and are going to study what parts of the brain communicate with each other. First, we'll create a source estimate of the MEG data. We'll use both a straightforward MNE-dSPM inverse solution for this, and the DICS beamformer which is specifically designed to work with oscillatory data. Computing the inverse using MNE-dSPM: End of explanation # Estimate the cross-spectral density (CSD) matrix on the trial containing the # signal. csd_signal = csd_morlet(epochs['signal'], frequencies=[10]) # Compute the spatial filters for each vertex, using two approaches. filters_approach1 = make_dics( info, fwd, csd_signal, reg=0.05, pick_ori='max-power', depth=1., inversion='single', weight_norm=None) print(filters_approach1) filters_approach2 = make_dics( info, fwd, csd_signal, reg=0.05, pick_ori='max-power', depth=None, inversion='matrix', weight_norm='unit-noise-gain') print(filters_approach2) # You can save these to disk with: # filters_approach1.save('filters_1-dics.h5') # Compute the DICS power map by applying the spatial filters to the CSD matrix. power_approach1, f = apply_dics_csd(csd_signal, filters_approach1) power_approach2, f = apply_dics_csd(csd_signal, filters_approach2) Explanation: We will now compute the cortical power map at 10 Hz. using a DICS beamformer. A beamformer will construct for each vertex a spatial filter that aims to pass activity originating from the vertex, while dampening activity from other sources as much as possible. The :func:mne.beamformer.make_dics function has many switches that offer precise control over the way the filter weights are computed. Currently, there is no clear consensus regarding the best approach. This is why we will demonstrate two approaches here: The approach as described in :footcite:vanVlietEtAl2018, which first normalizes the forward solution and computes a vector beamformer. The scalar beamforming approach based on :footcite:SekiharaNagarajan2008, which uses weight normalization instead of normalizing the forward solution. End of explanation def plot_approach(power, n): Plot the results on a brain. title = 'DICS power map, approach %d' % n brain = power_approach1.plot( 'sample', subjects_dir=subjects_dir, hemi='both', size=600, time_label=title, title=title) # Indicate the true locations of the source activity on the plot. brain.add_foci(vertices[0][0], coords_as_verts=True, hemi='lh', color='b') brain.add_foci(vertices[1][0], coords_as_verts=True, hemi='rh', color='b') # Rotate the view and add a title. brain.show_view(view={'azimuth': 0, 'elevation': 0, 'distance': 550, 'focalpoint': [0, 0, 0]}) return brain brain1 = plot_approach(power_approach1, 1) Explanation: Plot the DICS power maps for both approaches, starting with the first: End of explanation brain2 = plot_approach(power_approach2, 2) Explanation: Now the second: End of explanation
9,230	Given the following text description, write Python code to implement the functionality described below step by step Description: Spatial Model fitting in GLS In this exercise we will fit a linear model using a Spatial structure as covariance matrix. We will use GLS to get better estimators. As always we will need to load the necessary libraries. Step1: Use this to automate the process. Be carefull it can overwrite current results run ../HEC_runs/fit_fia_logbiomass_logspp_GLS.py /RawDataCSV/idiv_share/plotsClimateData_11092017.csv /apps/external_plugins/spystats/HEC_runs/results/logbiomas_logsppn_res.csv -85 -80 30 35 Importing data We will use the FIA dataset and for exemplary purposes we will take a subsample of this data. Also important. The empirical variogram has been calculated for the entire data set using the residuals of an OLS model. We will use some auxiliary functions defined in the fit_fia_logbiomass_logspp_GLS. You can inspect the functions using the ?? symbol. Step2: Now we will obtain the data from the calculated empirical variogram. Step3: restricted w/ all data spatial correlation parameters Log-Likelihood Step4: Instantiating the variogram object Step5: Instantiating theoretical variogram model	Python Code: # Load Biospytial modules and etc. %matplotlib inline import sys sys.path.append('/apps') sys.path.append('..') sys.path.append('../spystats') import django django.setup() import pandas as pd import matplotlib.pyplot as plt import numpy as np ## Use the ggplot style plt.style.use('ggplot') import tools Explanation: Spatial Model fitting in GLS In this exercise we will fit a linear model using a Spatial structure as covariance matrix. We will use GLS to get better estimators. As always we will need to load the necessary libraries. End of explanation from HEC_runs.fit_fia_logbiomass_logspp_GLS import prepareDataFrame,loadVariogramFromData,buildSpatialStructure, calculateGLS, initAnalysis, fitGLSRobust section = initAnalysis("/RawDataCSV/idiv_share/FIA_Plots_Biomass_11092017.csv", "/apps/external_plugins/spystats/HEC_runs/results/variogram/data_envelope.csv", -130,-60,30,40) #section = initAnalysis("/RawDataCSV/idiv_share/plotsClimateData_11092017.csv", # "/apps/external_plugins/spystats/HEC_runs/results/variogram/data_envelope.csv", # -85,-80,30,35) # IN HEC #section = initAnalysis("/home/hpc/28/escamill/csv_data/idiv/FIA_Plots_Biomass_11092017.csv","/home/hpc/28/escamill/spystats/HEC_runs/results/variogram/data_envelope.csv",-85,-80,30,35) section.shape Explanation: Use this to automate the process. Be carefull it can overwrite current results run ../HEC_runs/fit_fia_logbiomass_logspp_GLS.py /RawDataCSV/idiv_share/plotsClimateData_11092017.csv /apps/external_plugins/spystats/HEC_runs/results/logbiomas_logsppn_res.csv -85 -80 30 35 Importing data We will use the FIA dataset and for exemplary purposes we will take a subsample of this data. Also important. The empirical variogram has been calculated for the entire data set using the residuals of an OLS model. We will use some auxiliary functions defined in the fit_fia_logbiomass_logspp_GLS. You can inspect the functions using the ?? symbol. End of explanation gvg,tt = loadVariogramFromData("/apps/external_plugins/spystats/HEC_runs/results/variogram/data_envelope.csv",section) gvg.plot(refresh=False,with_envelope=True) resum,gvgn,resultspd,results = fitGLSRobust(section,gvg,num_iterations=1,distance_threshold=1000000) resum.as_text Explanation: Now we will obtain the data from the calculated empirical variogram. End of explanation plt.plot(resultspd.rsq) plt.title("GLS feedback algorithm") plt.xlabel("Number of iterations") plt.ylabel("R-sq fitness estimator") resultspd.columns a = map(lambda x : x.to_dict(), resultspd['params']) paramsd = pd.DataFrame(a) paramsd plt.plot(paramsd.Intercept.loc[1:]) plt.get_yaxis().get_major_formatter().set_useOffset(False) fig = plt.figure(figsize=(10,10)) plt.plot(paramsd.logSppN.iloc[1:]) variogram_data_path = "/apps/external_plugins/spystats/HEC_runs/results/variogram/data_envelope.csv" thrs_dist = 100000 emp_var_log_log = pd.read_csv(variogram_data_path) Explanation: restricted w/ all data spatial correlation parameters Log-Likelihood: -16607 AIC: 3.322e+04 restricted w/ restricted spatial correlation parameters Log-Likelihood: -16502. AIC: 3.301e+04 End of explanation gvg = tools.Variogram(section,'logBiomass',using_distance_threshold=thrs_dist) gvg.envelope = emp_var_log_log gvg.empirical = emp_var_log_log.variogram gvg.lags = emp_var_log_log.lags #emp_var_log_log = emp_var_log_log.dropna() #vdata = gvg.envelope.dropna() Explanation: Instantiating the variogram object End of explanation matern_model = tools.MaternVariogram(sill=0.34,range_a=100000,nugget=0.33,kappa=4) whittle_model = tools.WhittleVariogram(sill=0.34,range_a=100000,nugget=0.0,alpha=3) exp_model = tools.ExponentialVariogram(sill=0.34,range_a=100000,nugget=0.33) gaussian_model = tools.GaussianVariogram(sill=0.34,range_a=100000,nugget=0.33) spherical_model = tools.SphericalVariogram(sill=0.34,range_a=100000,nugget=0.33) gvg.model = whittle_model #gvg.model = matern_model #models = map(lambda model : gvg.fitVariogramModel(model),[matern_model,whittle_model,exp_model,gaussian_model,spherical_model]) gvg.fitVariogramModel(whittle_model) import numpy as np xx = np.linspace(0,1000000,1000) gvg.plot(refresh=False,with_envelope=True) plt.plot(xx,whittle_model.f(xx),lw=2.0,c='k') plt.title("Empirical Variogram with fitted Whittle Model") def randomSelection(n,p): idxs = np.random.choice(n,p,replace=False) random_sample = new_data.iloc[idxs] return random_sample ################# n = len(new_data) p = 3000 # The amount of samples taken (let's do it without replacement) random_sample = randomSelection(n,100) Explanation: Instantiating theoretical variogram model End of explanation
9,231	Given the following text description, write Python code to implement the functionality described below step by step Description: T81-558 Step1: Training with a Validation Set and Early Stopping Overfitting occurs when a neural network is trained to the point that it begins to memorize rather than generalize. It is important to segment the original dataset into several datasets Step2: Calculate Classification Accuracy Accuracy is the number of rows where the neural network correctly predicted the target class. Accuracy is only used for classification, not regression. $ accuracy = \frac{\textit{#} \ correct}{N} $ Where $N$ is the size of the evaluted set (training or validation). Higher accuracy numbers are desired. Step3: Calculate Classification Log Loss Accuracy is like a final exam with no partial credit. However, neural networks can predict a probability of each of the target classes. Neural networks will give high probabilities to predictions that are more likely. Log loss is an error metric that penalizes confidence in wrong answers. Lower log loss values are desired. For any scikit-learn model there are two ways to get a prediction Step4: Log loss is calculated as follows Step5: Evaluating Regression Results Regression results are evaluated differently than classification. Consider the following code that trains a neural network for the MPG dataset. Step6: Mean Square Error The mean square error is the sum of the squared differences between the prediction ($\hat{y}$) and the expected ($y$). MSE values are not of a particular unit. If an MSE value has decreased for a model, that is good. However, beyond this, there is not much more you can determine. Low MSE values are desired. $ \text{MSE} = \frac{1}{n} \sum_{i=1}^n \left(\hat{y}_i - y_i\right)^2 $ Step7: Root Mean Square Error The root mean square (RMSE) is essentially the square root of the MSE. Because of this, the RMSE error is in the same units as the training data outcome. Low RMSE values are desired. $ \text{MSE} = \sqrt{\frac{1}{n} \sum_{i=1}^n \left(\hat{y}_i - y_i\right)^2} $ Step8: Training with Cross Validation Cross validation uses a number of folds, and multiple models, to generate out of sample predictions on the entire dataset. It is important to note that there will be one model (neural network) for each fold. Each model contributes part of the final out-of-sample prediction. For new data, which is data not present in the training set, predictions from the fold models can be handled in several ways. Choose the model that had the highest validation score as the final model. Preset new data to the 5 models and average the result (this is an enesmble). Retrain a new model (using the same settings as the crossvalidation) on the entire dataset. Train for as many steps, and with the same hidden layer structure. The following code trains the MPG dataset using a 5-fold cross validation. The expected performance of a neural network, of the type trained here, would be the score for the generated out-of-sample predictions. Step9: Training with Cross Validation and a Holdout Set If you have a considerable amount of data, it is always valuable to set aside a holdout set before you crossvalidate. This hold out set will be the final evaluation before you make use of your model for its real-world use. The following program makes use of a hodlout set, and then still cross validates. Step10: How Kaggle Competitions are Scored Kaggle is a platform for competitive data science. Competitions are posted onto Kaggle by companies seeking the best model for their data. Competing in a Kaggle competition is quite a bit of work, I've competed in one Kaggle competition. Kaggle awards "tiers", such as Step11: Grid Search Finding the right set of hyperparameters can be a large task. Often computational power is thrown at this job. The scikit-learn grid search makes use of your computer's CPU cores to try every one of a defined number of hyperparameters to see which gets the best score. The following code shows how many CPU cores are available to Python Step12: The following code performs a grid search. Your system is queried for the number of cores available they are used to scan through the combinations of hyperparameters that you specify. Step13: The best combination of hyperparameters are displayed. Random Search It is also possable to conduct a random search. The random search is similar to the grid search, except that the entire search space is not used. Rather, random points in the search space are tried. For a random search you must specify the number of hyperparameter iterations (n_iter) to try.	Python Code: from sklearn import preprocessing import matplotlib.pyplot as plt import numpy as np import pandas as pd # Encode text values to dummy variables(i.e. [1,0,0],[0,1,0],[0,0,1] for red,green,blue) def encode_text_dummy(df,name): dummies = pd.get_dummies(df[name]) for x in dummies.columns: dummy_name = "{}-{}".format(name,x) df[dummy_name] = dummies[x] df.drop(name, axis=1, inplace=True) # Encode text values to indexes(i.e. [1],[2],[3] for red,green,blue). def encode_text_index(df,name): le = preprocessing.LabelEncoder() df[name] = le.fit_transform(df[name]) return le.classes_ # Encode a numeric column as zscores def encode_numeric_zscore(df,name,mean=None,sd=None): if mean is None: mean = df[name].mean() if sd is None: sd = df[name].std() df[name] = (df[name]-mean)/sd # Convert all missing values in the specified column to the median def missing_median(df, name): med = df[name].median() df[name] = df[name].fillna(med) # Convert a Pandas dataframe to the x,y inputs that TensorFlow needs def to_xy(df,target): result = [] for x in df.columns: if x != target: result.append(x) # find out the type of the target column. Is it really this hard? :( target_type = df[target].dtypes target_type = target_type[0] if hasattr(target_type, '__iter__') else target_type print(target_type) # Encode to int for classification, float otherwise. TensorFlow likes 32 bits. if target_type in (np.int64, np.int32): # Classification return df.as_matrix(result).astype(np.float32),df.as_matrix([target]).astype(np.int32) else: # Regression return df.as_matrix(result).astype(np.float32),df.as_matrix([target]).astype(np.float32) # Nicely formatted time string def hms_string(sec_elapsed): h = int(sec_elapsed / (60 * 60)) m = int((sec_elapsed % (60 * 60)) / 60) s = sec_elapsed % 60 return "{}:{:>02}:{:>05.2f}".format(h, m, s) # Regression chart, we will see more of this chart in the next class. def chart_regression(pred,y): t = pd.DataFrame({'pred' : pred.flatten(), 'y' : y_test.flatten()}) t.sort_values(by=['y'],inplace=True) a = plt.plot(t['y'].tolist(),label='expected') b = plt.plot(t['pred'].tolist(),label='prediction') plt.ylabel('output') plt.legend() plt.show() Explanation: T81-558: Applications of Deep Neural Networks Class 3: Training a Neural Network * Instructor: Jeff Heaton, School of Engineering and Applied Science, Washington University in St. Louis * For more information visit the class website. Building the Feature Vector Neural networks require their input to be a fixed number of columns. This is very similar to spreadsheet data. This input must be completely numeric. It is important to represent the data in a way that the neural network can train from it. In class 6, we will see even more ways to preprocess data. For now, we will look at several of the most basic ways to transform data for a neural network. Before we look at specific ways to preprocess data, it is important to consider four basic types of data, as defined by Stanley Smith Stevens. These are commonly referred to as the levels of measure: Character Data (strings) Nominal - Individual discrete items, no order. For example: color, zip code, shape. Ordinal - Individual discrete items that can be ordered. For example: grade level, job title, Starbucks(tm) coffee size (tall, vente, grande) Numeric Data Interval - Numeric values, no defined start. For example, temperature. You would never say "yesterday was twice as hot as today". Ratio - Numeric values, clearly defined start. For example, speed. You would say that "The first car is going twice as fast as the second." The following code contains several useful functions to encode the feature vector for various types of data. Encoding data: encode_text_dummy - Encode text fields, such as the iris species as a single field for each class. Three classes would become "0,0,1" "0,1,0" and "1,0,0". Encode non-target predictors this way. Good for nominal. encode_text_index - Encode text fields, such as the iris species as a single numeric field as "0" "1" and "2". Encode the target field for a classification this way. Good for nominal. encode_numeric_zscore - Encode numeric values as a z-score. Neural networks deal well with "centered" fields, zscore is usually a good starting point for interval/ratio. Ordinal values can be encoded as dummy or index. Later we will see a more advanced means of encoding Dealing with missing data: missing_median - Fill all missing values with the median value. Creating the final feature vector: to_xy - Once all fields are numeric, this function can provide the x and y matrixes that are used to fit the neural network. Other utility functions: hms_string - Print out an elapsed time string. chart_regression - Display a chart to show how well a regression performs. End of explanation import os import pandas as pd from sklearn.cross_validation import train_test_split import tensorflow.contrib.learn as skflow import numpy as np path = "./data/" filename = os.path.join(path,"iris.csv") df = pd.read_csv(filename,na_values=['NA','?']) # Encode feature vector encode_numeric_zscore(df,'petal_w') encode_numeric_zscore(df,'petal_l') encode_numeric_zscore(df,'sepal_w') encode_numeric_zscore(df,'sepal_l') species = encode_text_index(df,"species") num_classes = len(species) # Create x & y for training # Create the x-side (feature vectors) of the training x, y = to_xy(df,'species') # Split into train/test x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.25, random_state=45) # as much as I would like to use 42, it gives a perfect result, and a boring confusion matrix! # Create a deep neural network with 3 hidden layers of 10, 20, 10 classifier = skflow.TensorFlowDNNClassifier(hidden_units=[20, 10, 5], n_classes=num_classes, steps=10000) # Early stopping early_stop = skflow.monitors.ValidationMonitor(x_test, y_test, early_stopping_rounds=200, print_steps=50, n_classes=num_classes) # Fit/train neural network classifier.fit(x_train, y_train, monitor=early_stop) Explanation: Training with a Validation Set and Early Stopping Overfitting occurs when a neural network is trained to the point that it begins to memorize rather than generalize. It is important to segment the original dataset into several datasets: Training Set Validation Set Holdout Set There are several different ways that these sets can be constructed. The following programs demonstrate some of these. The first method is a training and validation set. The training data are used to train the neural network until the validation set no longer improves. This attempts to stop at a near optimal training point. This method will only give accurate "out of sample" predictions for the validation set, this is usually 20% or so of the data. The predictions for the training data will be overly optimistic, as these were the data that the neural network was trained on. End of explanation from sklearn import metrics # Evaluate success using accuracy pred = classifier.predict(x_test) score = metrics.accuracy_score(y_test, pred) print("Accuracy score: {}".format(score)) Explanation: Calculate Classification Accuracy Accuracy is the number of rows where the neural network correctly predicted the target class. Accuracy is only used for classification, not regression. $ accuracy = \frac{\textit{#} \ correct}{N} $ Where $N$ is the size of the evaluted set (training or validation). Higher accuracy numbers are desired. End of explanation pred = classifier.predict_proba(x_test) np.set_printoptions(precision=4) print("Numpy array of predictions") print(pred[0:5]) print("As percent probability") (pred[0:5]*100).astype(int) score = metrics.log_loss(y_test, pred) print("Log loss score: {}".format(score)) Explanation: Calculate Classification Log Loss Accuracy is like a final exam with no partial credit. However, neural networks can predict a probability of each of the target classes. Neural networks will give high probabilities to predictions that are more likely. Log loss is an error metric that penalizes confidence in wrong answers. Lower log loss values are desired. For any scikit-learn model there are two ways to get a prediction: predict - In the case of classification output the numeric id of the predicted class. For regression, this is simply the prediction. predict_proba - In the case of classification output the probability of each of the classes. Not used for regression. The following code shows the output of predict_proba: End of explanation %matplotlib inline from matplotlib.pyplot import figure, show from numpy import arange, sin, pi t = arange(0.0, 5.0, 0.00001) #t = arange(1.0, 5.0, 0.00001) # computer scientists #t = arange(0.0, 1.0, 0.00001) # data scientists fig = figure(1,figsize=(12, 10)) ax1 = fig.add_subplot(211) ax1.plot(t, np.log(t)) ax1.grid(True) ax1.set_ylim((-8, 1.5)) ax1.set_xlim((-0.1, 2)) ax1.set_xlabel('x') ax1.set_ylabel('y') ax1.set_title('log(x)') show() Explanation: Log loss is calculated as follows: $ \text{log loss} = -\frac{1}{N}\sum_{i=1}^N {( {y}_i\log(\hat{y}_i) + (1 - {y}_i)\log(1 - \hat{y}_i))} $ The log function is useful to penalizing wrong answers. The following code demonstrates the utility of the log function: End of explanation import tensorflow.contrib.learn as skflow from sklearn.cross_validation import train_test_split import pandas as pd import os import numpy as np from sklearn import metrics from scipy.stats import zscore path = "./data/" filename_read = os.path.join(path,"auto-mpg.csv") df = pd.read_csv(filename_read,na_values=['NA','?']) # create feature vector missing_median(df, 'horsepower') df.drop('name',1,inplace=True) encode_numeric_zscore(df, 'horsepower') encode_numeric_zscore(df, 'weight') encode_numeric_zscore(df, 'cylinders') encode_numeric_zscore(df, 'displacement') encode_numeric_zscore(df, 'acceleration') encode_text_dummy(df, 'origin') # Encode to a 2D matrix for training x,y = to_xy(df,['mpg']) # Split into train/test x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.20, random_state=42) # Create a deep neural network with 3 hidden layers of 50, 25, 10 regressor = skflow.TensorFlowDNNRegressor(hidden_units=[50, 25, 10], steps=5000) # Early stopping early_stop = skflow.monitors.ValidationMonitor(x_test, y_test, early_stopping_rounds=200, print_steps=50) # Fit/train neural network regressor.fit(x_train, y_train, monitor=early_stop) Explanation: Evaluating Regression Results Regression results are evaluated differently than classification. Consider the following code that trains a neural network for the MPG dataset. End of explanation pred = regressor.predict(x_test) # Measure MSE error. score = metrics.mean_squared_error(pred,y_test) print("Final score (MSE): {}".format(score)) Explanation: Mean Square Error The mean square error is the sum of the squared differences between the prediction ($\hat{y}$) and the expected ($y$). MSE values are not of a particular unit. If an MSE value has decreased for a model, that is good. However, beyond this, there is not much more you can determine. Low MSE values are desired. $ \text{MSE} = \frac{1}{n} \sum_{i=1}^n \left(\hat{y}_i - y_i\right)^2 $ End of explanation # Measure RMSE error. RMSE is common for regression. score = np.sqrt(metrics.mean_squared_error(pred,y_test)) print("Final score (RMSE): {}".format(score)) Explanation: Root Mean Square Error The root mean square (RMSE) is essentially the square root of the MSE. Because of this, the RMSE error is in the same units as the training data outcome. Low RMSE values are desired. $ \text{MSE} = \sqrt{\frac{1}{n} \sum_{i=1}^n \left(\hat{y}_i - y_i\right)^2} $ End of explanation import tensorflow.contrib.learn as skflow import pandas as pd import os import numpy as np from sklearn import metrics from scipy.stats import zscore from sklearn.cross_validation import KFold path = "./data/" filename_read = os.path.join(path,"auto-mpg.csv") filename_write = os.path.join(path,"auto-mpg-out-of-sample.csv") df = pd.read_csv(filename_read,na_values=['NA','?']) # create feature vector missing_median(df, 'horsepower') df.drop('name',1,inplace=True) encode_numeric_zscore(df, 'horsepower') encode_numeric_zscore(df, 'weight') encode_numeric_zscore(df, 'cylinders') encode_numeric_zscore(df, 'displacement') encode_numeric_zscore(df, 'acceleration') encode_text_dummy(df, 'origin') # Shuffle np.random.seed(42) df = df.reindex(np.random.permutation(df.index)) df.reset_index(inplace=True, drop=True) # Encode to a 2D matrix for training x,y = to_xy(df,['mpg']) # Cross validate kf = KFold(len(x), n_folds=5) oos_y = [] oos_pred = [] fold = 1 for train, test in kf: print("Fold #{}".format(fold)) fold+=1 x_train = x[train] y_train = y[train] x_test = x[test] y_test = y[test] # Create a deep neural network with 3 hidden layers of 10, 20, 10 regressor = skflow.TensorFlowDNNRegressor(hidden_units=[10, 20, 10], steps=500) # Early stopping early_stop = skflow.monitors.ValidationMonitor(x_test, y_test, early_stopping_rounds=200, print_steps=50) # Fit/train neural network regressor.fit(x_train, y_train, monitor=early_stop) # Add the predictions to the oos prediction list pred = regressor.predict(x_test) oos_y.append(y_test) oos_pred.append(pred) # Measure accuracy score = np.sqrt(metrics.mean_squared_error(pred,y_test)) print("Fold score (RMSE): {}".format(score)) # Build the oos prediction list and calculate the error. oos_y = np.concatenate(oos_y) oos_pred = np.concatenate(oos_pred) score = np.sqrt(metrics.mean_squared_error(oos_pred,oos_y)) print("Final, out of sample score (RMSE): {}".format(score)) # Write the cross-validated prediction oos_y = pd.DataFrame(oos_y) oos_pred = pd.DataFrame(oos_pred) oosDF = pd.concat( [df, oos_y, oos_pred],axis=1 ) oosDF.to_csv(filename_write,index=False) Explanation: Training with Cross Validation Cross validation uses a number of folds, and multiple models, to generate out of sample predictions on the entire dataset. It is important to note that there will be one model (neural network) for each fold. Each model contributes part of the final out-of-sample prediction. For new data, which is data not present in the training set, predictions from the fold models can be handled in several ways. Choose the model that had the highest validation score as the final model. Preset new data to the 5 models and average the result (this is an enesmble). Retrain a new model (using the same settings as the crossvalidation) on the entire dataset. Train for as many steps, and with the same hidden layer structure. The following code trains the MPG dataset using a 5-fold cross validation. The expected performance of a neural network, of the type trained here, would be the score for the generated out-of-sample predictions. End of explanation import tensorflow.contrib.learn as skflow from sklearn.cross_validation import train_test_split import pandas as pd import os import numpy as np from sklearn import metrics from scipy.stats import zscore from sklearn.cross_validation import KFold path = "./data/" filename_read = os.path.join(path,"auto-mpg.csv") filename_write = os.path.join(path,"auto-mpg-holdout.csv") df = pd.read_csv(filename_read,na_values=['NA','?']) # create feature vector missing_median(df, 'horsepower') df.drop('name',1,inplace=True) encode_numeric_zscore(df, 'horsepower') encode_numeric_zscore(df, 'weight') encode_numeric_zscore(df, 'cylinders') encode_numeric_zscore(df, 'displacement') encode_numeric_zscore(df, 'acceleration') encode_text_dummy(df, 'origin') # Shuffle np.random.seed(42) df = df.reindex(np.random.permutation(df.index)) df.reset_index(inplace=True, drop=True) # Encode to a 2D matrix for training x,y = to_xy(df,['mpg']) # Keep a 10% holdout x_main, x_holdout, y_main, y_holdout = train_test_split( x, y, test_size=0.10) # Cross validate kf = KFold(len(x_main), n_folds=5) oos_y = [] oos_pred = [] fold = 1 for train, test in kf: print("Fold #{}".format(fold)) fold+=1 x_train = x_main[train] y_train = y_main[train] x_test = x_main[test] y_test = y_main[test] # Create a deep neural network with 3 hidden layers of 10, 20, 10 regressor = skflow.TensorFlowDNNRegressor(hidden_units=[10, 20, 10], steps=500) # Early stopping early_stop = skflow.monitors.ValidationMonitor(x_test, y_test, early_stopping_rounds=200, print_steps=50) # Fit/train neural network regressor.fit(x_train, y_train, monitor=early_stop) # Add the predictions to the OOS prediction list pred = regressor.predict(x_test) oos_y.append(y_test) oos_pred.append(pred) # Measure accuracy score = np.sqrt(metrics.mean_squared_error(pred,y_test)) print("Fold score (RMSE): {}".format(score)) # Build the oos prediction list and calculate the error. oos_y = np.concatenate(oos_y) oos_pred = np.concatenate(oos_pred) score = np.sqrt(metrics.mean_squared_error(oos_pred,oos_y)) print() print("Cross-validated score (RMSE): {}".format(score)) # Write the cross-validated prediction holdout_pred = regressor.predict(x_holdout) score = np.sqrt(metrics.mean_squared_error(holdout_pred,y_holdout)) print("Holdout score (RMSE): {}".format(score)) Explanation: Training with Cross Validation and a Holdout Set If you have a considerable amount of data, it is always valuable to set aside a holdout set before you crossvalidate. This hold out set will be the final evaluation before you make use of your model for its real-world use. The following program makes use of a hodlout set, and then still cross validates. End of explanation %matplotlib inline from matplotlib.pyplot import figure, show from numpy import arange import tensorflow.contrib.learn as skflow import pandas as pd import os import numpy as np import tensorflow as tf from sklearn import metrics from scipy.stats import zscore import matplotlib.pyplot as plt path = "./data/" filename_read = os.path.join(path,"auto-mpg.csv") df = pd.read_csv(filename_read,na_values=['NA','?']) # create feature vector missing_median(df, 'horsepower') df.drop('name',1,inplace=True) encode_numeric_zscore(df, 'horsepower') encode_numeric_zscore(df, 'weight') encode_numeric_zscore(df, 'cylinders') encode_numeric_zscore(df, 'displacement') encode_numeric_zscore(df, 'acceleration') encode_text_dummy(df, 'origin') # Encode to a 2D matrix for training x,y = to_xy(df,['mpg']) # Split into train/test x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.25, random_state=42) # Create a deep neural network with 3 hidden layers of 50, 25, 10 regressor = skflow.TensorFlowDNNRegressor( hidden_units=[50, 25, 10], batch_size = 32, optimizer='SGD', learning_rate=0.01, steps=5000) # Early stopping early_stop = skflow.monitors.ValidationMonitor(x_test, y_test, early_stopping_rounds=200, print_steps=50) # Fit/train neural network regressor.fit(x_train, y_train, monitor=early_stop) # Measure RMSE error. RMSE is common for regression. pred = regressor.predict(x_test) score = np.sqrt(metrics.mean_squared_error(pred,y_test)) print("Final score (RMSE): {}".format(score)) # Plot the chart chart_regression(pred,y_test) Explanation: How Kaggle Competitions are Scored Kaggle is a platform for competitive data science. Competitions are posted onto Kaggle by companies seeking the best model for their data. Competing in a Kaggle competition is quite a bit of work, I've competed in one Kaggle competition. Kaggle awards "tiers", such as: Kaggle Grandmaster Kaggle Master Kaggle Expert Your tier is based on your performance in past competitions. To compete in Kaggle you simply provide predictions for a dataset that they post. You do not need to submit any code. Your prediction output will place you onto the leaderboard of a competition. An original dataset is sent to Kaggle by the company. From this dataset, Kaggle posts public data that includes "train" and "test. For the "train" data, the outcomes (y) are provided. For the test data, no outcomes are provided. Your submission file contains your predictions for the "test data". When you submit your results, Kaggle will calculate a score on part of your prediction data. They do not publish want part of the submission data are used for the public and private leaderboard scores (this is a secret to prevent overfitting). While the competition is still running, Kaggle publishes the public leaderboard ranks. Once the competition ends, the private leaderboard is revealed to designate the true winners. Due to overfitting, there is sometimes an upset in positions when the final private leaderboard is revealed. Managing Hyperparameters There are many different settings that you can use for a neural network. These can affect performance. The following code changes some of these, beyond their default values: End of explanation import multiprocessing print("Your system has {} cores.".format(multiprocessing.cpu_count())) Explanation: Grid Search Finding the right set of hyperparameters can be a large task. Often computational power is thrown at this job. The scikit-learn grid search makes use of your computer's CPU cores to try every one of a defined number of hyperparameters to see which gets the best score. The following code shows how many CPU cores are available to Python: End of explanation %matplotlib inline from matplotlib.pyplot import figure, show from numpy import arange import tensorflow.contrib.learn as skflow import pandas as pd import os import numpy as np import tensorflow as tf from sklearn import metrics from scipy.stats import zscore from sklearn.grid_search import GridSearchCV import multiprocessing import time from sklearn.cross_validation import train_test_split import matplotlib.pyplot as plt def main(): path = "./data/" filename_read = os.path.join(path,"auto-mpg.csv") df = pd.read_csv(filename_read,na_values=['NA','?']) start_time = time.time() # create feature vector missing_median(df, 'horsepower') df.drop('name',1,inplace=True) encode_numeric_zscore(df, 'horsepower') encode_numeric_zscore(df, 'weight') encode_numeric_zscore(df, 'cylinders') encode_numeric_zscore(df, 'displacement') encode_numeric_zscore(df, 'acceleration') encode_text_dummy(df, 'origin') # Encode to a 2D matrix for training x,y = to_xy(df,['mpg']) # Split into train/test x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.25, random_state=42) # The hyperparameters specified here will be searched. Every combination. param_grid = { 'learning_rate': [0.1, 0.01, 0.001], 'batch_size': [8, 16, 32] } # Create a deep neural network. The hyperparameters specified here remain fixed. model = skflow.TensorFlowDNNRegressor( hidden_units=[50, 25, 10], batch_size = 32, optimizer='SGD', steps=5000) # Early stopping early_stop = skflow.monitors.ValidationMonitor(x_test, y_test, early_stopping_rounds=200, print_steps=50) # Startup grid search threads = 1 #multiprocessing.cpu_count() print("Using {} cores.".format(threads)) regressor = GridSearchCV(model, verbose=True, n_jobs=threads, param_grid=param_grid,fit_params={'monitor':early_stop}) # Fit/train neural network regressor.fit(x_train, y_train) # Measure RMSE error. RMSE is common for regression. pred = regressor.predict(x_test) score = np.sqrt(metrics.mean_squared_error(pred,y_test)) print("Final score (RMSE): {}".format(score)) print("Final options: {}".format(regressor.best_params_)) # Plot the chart chart_regression(pred,y_test) elapsed_time = time.time() - start_time print("Elapsed time: {}".format(hms_string(elapsed_time))) # Allow windows to multi-thread (unneeded on advanced OS's) # See: https://docs.python.org/2/library/multiprocessing.html if __name__ == '__main__': main() Explanation: The following code performs a grid search. Your system is queried for the number of cores available they are used to scan through the combinations of hyperparameters that you specify. End of explanation %matplotlib inline from matplotlib.pyplot import figure, show from numpy import arange import tensorflow.contrib.learn as skflow import pandas as pd import os import numpy as np import tensorflow as tf from sklearn import metrics from scipy.stats import zscore from scipy.stats import randint as sp_randint from sklearn.grid_search import RandomizedSearchCV import multiprocessing import time from sklearn.cross_validation import train_test_split import matplotlib.pyplot as plt def main(): path = "./data/" filename_read = os.path.join(path,"auto-mpg.csv") df = pd.read_csv(filename_read,na_values=['NA','?']) start_time = time.time() # create feature vector missing_median(df, 'horsepower') df.drop('name',1,inplace=True) encode_numeric_zscore(df, 'horsepower') encode_numeric_zscore(df, 'weight') encode_numeric_zscore(df, 'cylinders') encode_numeric_zscore(df, 'displacement') encode_numeric_zscore(df, 'acceleration') encode_text_dummy(df, 'origin') # Encode to a 2D matrix for training x,y = to_xy(df,['mpg']) # Split into train/test x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.25, random_state=42) # The hyperparameters specified here will be searched. A random sample will be searched. param_dist = { 'learning_rate': [0.1, 0.01, 0.001], 'batch_size': sp_randint(4, 32), } model = skflow.TensorFlowDNNRegressor( hidden_units=[50, 25, 10], batch_size = 32, optimizer='SGD', steps=5000) # Early stopping early_stop = skflow.monitors.ValidationMonitor(x_test, y_test, early_stopping_rounds=200, print_steps=50) # Random search threads = 1 #multiprocessing.cpu_count() print("Using {} cores.".format(threads)) regressor = RandomizedSearchCV(model, verbose=True, n_iter = 10, n_jobs=threads, param_distributions=param_dist, fit_params={'monitor':early_stop}) # Fit/train neural network regressor.fit(x_train, y_train) # Measure RMSE error. RMSE is common for regression. pred = regressor.predict(x_test) score = np.sqrt(metrics.mean_squared_error(pred,y_test)) print("Final score (RMSE): {}".format(score)) print("Final options: {}".format(regressor.best_params_)) # Plot the chart chart_regression(pred,y_test) elapsed_time = time.time() - start_time print("Elapsed time: {}".format(hms_string(elapsed_time))) # Allow windows to multi-thread (unneeded on advanced OS's) # See: https://docs.python.org/2/library/multiprocessing.html if __name__ == '__main__': main() Explanation: The best combination of hyperparameters are displayed. Random Search It is also possable to conduct a random search. The random search is similar to the grid search, except that the entire search space is not used. Rather, random points in the search space are tried. For a random search you must specify the number of hyperparameter iterations (n_iter) to try. End of explanation
9,232	Given the following text description, write Python code to implement the functionality described below step by step Description: Step1: Computing for Mathematics - 2020/2021 individual coursework Important Do not delete the cells containing Step3: b. $1/2$ Available marks Step5: c. $3/4$ Available marks Step7: d. $1$ Available marks Step8: Question 2 (Hint Step9: b. Create a variable direct_number_of_permutations that gives the number of permutations of pets of size 4 by direct computation. Available marks Step10: Question 3 (Hint Step11: b. Create a variable equation that has value the equation $f'(0)=0$. Available marks Step12: c. Using the solution to that equation, output the value of $\int_{0}^{5\pi}f(x)dx$. Available marks Step14: Question 4 (Hint Step15: b. Given that $c=2$ output $\frac{df}{dx}$ where Step16: c. Given that $c=2$ output $\int f(x)dx$ Available marks	Python Code: import random def sample_experiment(): ### BEGIN SOLUTION Returns true if a random number is less than 0 return random.random() < 0 number_of_experiments = 1000 sum( sample_experiment() for repetition in range(number_of_experiments) ) / number_of_experiments ### END SOLUTION Explanation: Computing for Mathematics - 2020/2021 individual coursework Important Do not delete the cells containing: ``` BEGIN SOLUTION END SOLUTION ``` write your solution attempts in those cells. To submit this notebook: Change the name of the notebook from main to: <student_number>. For example, if your student number is c1234567 then change the name of the notebook to c1234567. Write all your solution attempts in the correct locations; Do not delete any code that is already in the cells; Save the notebook (File>Save As); Follow the instructions given in class/email to submit. Question 1 (Hint: This question is similar to the first exercise of the Probability chapter of Python for mathematics.) For each of the following, write a function sample_experiment, and repeatedly use it to simulate the probability of an event occurring with the following chances. For each chance output the simulated probability. a. $0$ Available marks: 2 End of explanation def sample_experiment(): ### BEGIN SOLUTION Returns true if a random number is less than 1 / 2 return random.random() < 1 / 2 number_of_experiments = 1000 sum( sample_experiment() for repetition in range(number_of_experiments) ) / number_of_experiments ### END SOLUTION Explanation: b. $1/2$ Available marks: 2 End of explanation def sample_experiment(): ### BEGIN SOLUTION Returns true if a random number is less than 3 / 4 return random.random() < 3 / 4 number_of_experiments = 1000 sum( sample_experiment() for repetition in range(number_of_experiments) ) / number_of_experiments ### END SOLUTION Explanation: c. $3/4$ Available marks: 2 End of explanation def sample_experiment(): ### BEGIN SOLUTION Returns true if a random number is less than 1 return random.random() < 1 number_of_experiments = 1000 sum( sample_experiment() for repetition in range(number_of_experiments) ) / number_of_experiments ### END SOLUTION Explanation: d. $1$ Available marks: 2 End of explanation import itertools pets = ("cat", "dog", "fish", "lizard", "hamster") ### BEGIN SOLUTION permutations = tuple(itertools.permutations(pets, 4)) number_of_permutations = len(permutations) ### END SOLUTION Explanation: Question 2 (Hint: This question is similar to the second exercise of the Combinatorics chapter of Python for mathematics.) a. Create a variable number_of_permutations that gives the number of permutations of pets = ("cat", "dog", "fish", "lizard", "hamster) of size 4. Do this by generating and counting them. Available marks: 2 End of explanation import scipy.special ### BEGIN SOLUTION direct_number_of_permutations = scipy.special.perm(5, 4) ### END SOLUTION Explanation: b. Create a variable direct_number_of_permutations that gives the number of permutations of pets of size 4 by direct computation. Available marks: 1 End of explanation import sympy as sym x = sym.Symbol("x") c1 = sym.Symbol("c1") ### BEGIN SOLUTION second_derivative = 4 * x + sym.cos(x) derivative = sym.integrate(second_derivative, x) + c1 ### END SOLUTION Explanation: Question 3 (Hint: This question uses concepts from the Algebra and Calculus chapters of Python for mathematics.) Consider the second derivative $f''(x)=4 x + \cos(x)$. a. Create a variable derivative which has value $f'(x)$ (use the variables x and c1 if necessary): Available marks: 3 End of explanation ### BEGIN SOLUTION equation = sym.Eq(derivative.subs({x:0}), 0) ### END SOLUTION Explanation: b. Create a variable equation that has value the equation $f'(0)=0$. Available marks: 4 End of explanation ### BEGIN SOLUTION particular_derivative = derivative.subs({c1: 0}) function = sym.integrate(particular_derivative) + c1 sym.integrate(function, (x, 0, 5 * sym.pi)) ### END SOLUTION Explanation: c. Using the solution to that equation, output the value of $\int_{0}^{5\pi}f(x)dx$. Available marks: 4 End of explanation c = sym.Symbol("c") ### BEGIN SOLUTION def get_sequence_a(n): Return the sequence a. if n == 1: return c return 3 * get_sequence_a(n - 1) + c / n sum(get_sequence_a(n) for n in range(1, 16)) ### END SOLUTION Explanation: Question 4 (Hint: This question uses concepts from the Calculus and Sequences chapters of Python for mathematics.) Consider this recursive definition for the sequence $a_n$: $$ a_n = \begin{cases} c & \text{ if n = 1}\ 3a_{n - 1} + \frac{c}{n} \end{cases} $$ a. Output the sum of the 15 terms. Available marks: 5 End of explanation ### BEGIN SOLUTION f = (get_sequence_a(n=1) + get_sequence_a(n=2) * x + get_sequence_a(n=3) * x ** 2 + + get_sequence_a(n=4) * x ** 3).subs({c: 2}) sym.diff(f, x) ### END SOLUTION Explanation: b. Given that $c=2$ output $\frac{df}{dx}$ where: $$ f(x) = a_1 + a_2 x + a_3 x ^ 2 + a_4 x ^ 3 $$ Available marks: 4 End of explanation ### BEGIN SOLUTION sym.integrate(f, x) ### END SOLUTION Explanation: c. Given that $c=2$ output $\int f(x)dx$ Available marks: 4 End of explanation
9,233	Given the following text description, write Python code to implement the functionality described below step by step Description: DeepLearning MNIST Dataset using DeepWater and Custom MXNet Model The MNIST database is a well-known academic dataset used to benchmark classification performance. The data consists of 60,000 training images and 10,000 test images. Each image is a standardized $28^2$ pixel greyscale image of a single handwritten digit. An example of the scanned handwritten digits is shown Step1: Specify the response and predictor columns Step2: Convert the number to a class Step3: Train Deep Learning model and validate on test set LeNET 1989 In this demo you will learn how to build a simple LeNET Model usix MXNET. Step4: Here we instantiate our lenet model using 10 classes Step5: To import the model inside the DeepWater training engine we need to save the model to a file Step6: The model is just the structure of the network expressed as a json dict Step7: Importing the LeNET model architecture for training in H2O We have defined the model and saved the structure to a file. We are ready to start the training procedure. Step8: A More powerful Architecture the beauty of deeplearning is that we can compose a new model with even more "capacity" to try to get a higher accuracy. Step9: Visualizing the results	Python Code: import h2o h2o.init() import os.path PATH = os.path.expanduser("~/h2o-3/") test_df = h2o.import_file(PATH + "bigdata/laptop/mnist/test.csv.gz") train_df = h2o.import_file(PATH + "/bigdata/laptop/mnist/train.csv.gz") Explanation: DeepLearning MNIST Dataset using DeepWater and Custom MXNet Model The MNIST database is a well-known academic dataset used to benchmark classification performance. The data consists of 60,000 training images and 10,000 test images. Each image is a standardized $28^2$ pixel greyscale image of a single handwritten digit. An example of the scanned handwritten digits is shown End of explanation y = "C785" x = train_df.names[0:784] Explanation: Specify the response and predictor columns End of explanation train_df[y] = train_df[y].asfactor() test_df[y] = test_df[y].asfactor() Explanation: Convert the number to a class End of explanation def lenet(num_classes): import mxnet as mx data = mx.symbol.Variable('data') # first conv conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=20) tanh1 = mx.symbol.Activation(data=conv1, act_type="tanh") pool1 = mx.symbol.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2)) # second conv conv2 = mx.symbol.Convolution(data=pool1, kernel=(5,5), num_filter=50) tanh2 = mx.symbol.Activation(data=conv2, act_type="tanh") pool2 = mx.symbol.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2)) # first fullc flatten = mx.symbol.Flatten(data=pool2) fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500) tanh3 = mx.symbol.Activation(data=fc1, act_type="tanh") # second fullc fc2 = mx.symbol.FullyConnected(data=tanh3, num_hidden=num_classes) # loss lenet = mx.symbol.SoftmaxOutput(data=fc2, name='softmax') return lenet nclasses = 10 Explanation: Train Deep Learning model and validate on test set LeNET 1989 In this demo you will learn how to build a simple LeNET Model usix MXNET. End of explanation mxnet_model = lenet(nclasses) Explanation: Here we instantiate our lenet model using 10 classes End of explanation model_filename="/tmp/symbol_lenet-py.json" mxnet_model.save(model_filename) # pip install graphviz # sudo apt-get install graphviz import mxnet as mx import graphviz mx.viz.plot_network(mxnet_model, shape={"data":(1, 1, 28, 28)}, node_attrs={"shape":'rect',"fixedsize":'false'}) Explanation: To import the model inside the DeepWater training engine we need to save the model to a file: End of explanation !head -n 20 $model_filename Explanation: The model is just the structure of the network expressed as a json dict End of explanation from h2o.estimators.deepwater import H2ODeepWaterEstimator lenet_model = H2ODeepWaterEstimator( epochs=10, learning_rate=1e-3, mini_batch_size=64, network_definition_file=model_filename, # network='lenet', ## equivalent pre-configured model image_shape=[28,28], problem_type='dataset', ## Not 'image' since we're not passing paths to image files, but raw numbers ignore_const_cols=False, ## We need to keep all 28x28=784 pixel values, even if some are always 0 channels=1 ) lenet_model.train(x=train_df.names, y=y, training_frame=train_df, validation_frame=test_df) error = lenet_model.model_performance(valid=True).mean_per_class_error() print "model error:", error Explanation: Importing the LeNET model architecture for training in H2O We have defined the model and saved the structure to a file. We are ready to start the training procedure. End of explanation def cnn(num_classes): import mxnet as mx data = mx.symbol.Variable('data') inputdropout = mx.symbol.Dropout(data=data, p=0.1) # first convolution conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=50) tanh1 = mx.symbol.Activation(data=conv1, act_type="relu") pool1 = mx.symbol.Pooling(data=tanh1, pool_type="max", pad=(1,1), kernel=(3,3), stride=(2,2)) # second convolution conv2 = mx.symbol.Convolution(data=pool1, kernel=(5,5), num_filter=100) tanh2 = mx.symbol.Activation(data=conv2, act_type="relu") pool2 = mx.symbol.Pooling(data=tanh2, pool_type="max", pad=(1,1), kernel=(3,3), stride=(2,2)) # first fully connected layer flatten = mx.symbol.Flatten(data=pool2) fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=1024) relu3 = mx.symbol.Activation(data=fc1, act_type="relu") inputdropout = mx.symbol.Dropout(data=fc1, p=0.5) # second fully connected layer flatten = mx.symbol.Flatten(data=relu3) fc2 = mx.symbol.FullyConnected(data=flatten, num_hidden=1024) relu4 = mx.symbol.Activation(data=fc2, act_type="relu") inputdropout = mx.symbol.Dropout(data=fc2, p=0.5) # third fully connected layer fc3 = mx.symbol.FullyConnected(data=relu4, num_hidden=num_classes) # loss cnn = mx.symbol.SoftmaxOutput(data=fc3, name='softmax') return cnn nclasses = 10 mxnet_model = cnn(nclasses) model_filename="/tmp/symbol_cnn-py.json" mxnet_model.save(model_filename) from h2o.estimators.deepwater import H2ODeepWaterEstimator print("Importing the lenet model architecture for training in H2O") model = H2ODeepWaterEstimator( epochs=20, learning_rate=1e-3, mini_batch_size=64, network_definition_file=model_filename, image_shape=[28,28], channels=1, ignore_const_cols=False ## We need to keep all 28x28=784 pixel values, even if some are always 0 ) model.train(x=train_df.names, y=y, training_frame=train_df, validation_frame=test_df) error = model.model_performance(valid=True).mean_per_class_error() print "model error:", error Explanation: A More powerful Architecture the beauty of deeplearning is that we can compose a new model with even more "capacity" to try to get a higher accuracy. End of explanation %matplotlib inline import matplotlib import numpy as np import scipy.io import matplotlib.pyplot as plt from IPython.display import Image, display import warnings warnings.filterwarnings("ignore") df = test_df.as_data_frame() import numpy as np image = df.T[int(np.random.random()*784)] image.shape plt.imshow(image[:-1].reshape(28, 28), plt.cm.gray); print image[-1] image_hf = h2o.H2OFrame.from_python(image.to_dict()) prediction = model.predict(image_hf) prediction['predict'] Explanation: Visualizing the results End of explanation
9,234	Given the following text description, write Python code to implement the functionality described below step by step Description: Generarea si vizualizarea curbelor 1. Curbe plane O curba plana diferentiabila, data parametric, este imaginea, $im(r)$, a unei aplicatii diferentiabile $r Step1: Pentru a intelege definitia acestei functii, o apelam mai intai pentru un $N$ mic si afisam tipul coordonatelor tuple-ului returnat Step2: Deci functia Curba returneaza un tuple 2D, $(x,y)$, in care fiecare coordonata este un array 1D (un vector). Mai precis, daca notam cu $(X(t), Y(t))$ coordonatele parametrizarii curbei, $$X(t)=\cos(t)+t\sin(t), \quad Y(t)=\sin(t)-t\cos(t)$$ atunci vectorii $x$ si $y$, coordonate ale tuple-ului returnat sunt Step3: Astroida Step4: Curba Lissajous are parametrizarea Step5: Pentru a genera vectorii viteza/acceleratie in cateva momente, $t_i$, asociati traiectoriei parametrizate de $r(t)=(x(t), y(t))$, se evalueaza vectorul $\dot{\vec{r}}(t)$, respectiv $\ddot{\vec{r}}(t)$ in $t_i$ si se apeleaza functia plt.quiver(X, Y, U, V) unde $X$, respectiv $Y$, este array-ul 1D de coordonate $x(t_i)$, respectiv $y(t_i)$ , iar $U$ si $V$ au respectiv coordonatele $x'(t_i)$ si $y'(t_i)$, in cazul vitezei, si $x''(t_i)$, $y''(t_i)$, in cazul acceleratiei. De exemplu sa trasam curba parametrizata de Step6: Observam atat din valorile normelor, cat si din imaginile vectorilor viteza/acceleratie ca in puncte diferite au valori diferite. Mai mult in punctele curbei cu curbura mai mare viteza are norma mai mica si acceleratia mai mare (vezi Cursul 13, curbura curbelor). Curbe in coordonate polare In afara de sistemul ortogonal de axe, $xOy$, $\mathbb{R}^2$ se poate raporta si la un sistem polar de coordonate. Un reper polar consta dintr-o pereche $(O;v)$, unde $O$ este un punct fixat, numit pol si $v\in\mathbb{R}^2$, un vector nenul ce defineste axa polara, $Ox$, care este axa de origine $O$, directie si sens $v$. Pozitia unui punct $M$ relativ la acest reper se indica prin coordonatele $(r, \theta)$, numite coordonate polare. $r$ este distanta de la $M$ la $O$, $r=\|\|\vec{OM}\|\|$, iar $\theta$ este masura in radiani a unghiului dintre $v$ (deci $Ox$) si $\vec{OM}$. Step7: Ducand o perpendiculara in $O$ pe axa polara construim sistemul ortogonal drept $xOy$, asociat celui polar. Step8: Astfel punctul de coordonate polare $(r, \theta)$ are coordonatele carteziene $(x,y)$, unde $$x=r\cos(\theta), \quad y=r\sin(\theta)$$ O curba in coordonate polare are ecuatia $r=f(\theta)$, $\theta\in[\alpha, \beta]$. Cu alte cuvinte, curba este constituita din multimea punctelor din plan ce au coordonatele polare $(r=f(\theta), \theta)$. Exemplu de curba in coordonate polare este cardioida, de ecuatie $r=a(1+\cos(\theta))$, $a>0$, $\theta\in[0, 2\pi]$. Pentru a genera si vizualiza o curba in coordonate polare procedam astfel Step9: Remarcam ca fara nici o comanda speciala, o data cu apelul functiei plt.polar sunt generate cercuri concentrice cu originea in pol si in lungul cercului exterior sunt marcate valorile in grade (nu radiani) ale unghiului polar $\theta$. Sunt afisate si razele cercurilor concentrice. Cercul cu centrul in origine si de raza $a$, $x^2+y^2=a^2$, are in coordonate polare ecuatia Step10: Trifoiul cu 4 foi are ecuatia $r=\sin(4\theta)$, $\theta\in[0,2\pi]$. Step11: Curba $r=\sin(n\theta)$ este un trifoi cu $n$ foi. La fel si $r=\cos(n\theta)$, $\theta\in[0,2\pi]$. Experimentati! Curbe Bézier Curbele date parametric sau in coordonate polare se pot genera doar daca se da efectiv expresia analitica a parametrizarii, respectiv a functiei in coordonate polare. Curbele Bézier sunt curbe definite procedural, adica pornind de la un numar finit de puncte si un parametru $t\in[0,1]$ se genereaza algoritmic un punct pe o curba. Curbele Bézier s-au nascut in laboratoarele de la Renault si Citroën in incercarea de a genera capote pentru automobile, mai deosebite. Ideea de baza consta in designul unui algoritm care sa genereze profilul unei capote ce imita un poligon de control. Step12: Poligonul de control este succesiunea de segmente ce unesc cate doua puncte consecutive, numite puncte de control. Bézier a definit curbele care-i poarta numele, definind curbe polinomiale, adica curbe de forma Step13: In figura de mai sus multimea din stanga este convexa, iar cea din dreapta neconvexa, pt ca desi punctele marcate cu albastru apartin multimii, segmentul ce le uneste nu este in intregime inclus in multime. O combinatie convexa a punctelor $A_0, A_1, \ldots, A_n$ din $\mathbb{R}^2$ sau $\mathbb{R}^3$ este de forma Step14: Infasuratoarea convexa a $n$ puncte din plan este cel mai mic poligon convex ce contine toate punctele. In figura de mai sus infasuratoarea convexa apunctelor $C_0, C_1, C_2, C_3, C_4$ este poligonul plin, de varfuri $C_0, C_1, C_4, C_2$. Daca $A, B$ sunt doua puncte in plan sau spatiu, o combinatie convexa a lor este un punct $M=\alpha_1 A+\alpha_2 B$, $\alpha_i\in[0,1]$, $\alpha_1+\alpha_2=1$. Notand $\alpha_2=t$, rezulta ca $\alpha_1=1-t$ si deci punctul $M$ se exprima ca o combinatie convexa a lui $A$ si $B$ astfel Step15: In etapa 1 se determina pe fiecare segment determinat de doua puncte de control consecutive, punctul ce imparte segmentul respectiv in acelasi raport $\displaystyle\frac{t}{1-t}$ in care $t$ imparte pe $[0,1]$ Step16: Generalizand la cazul unei curbe definite de un numar arbitrar de puncte de control, $ {\bf b}{0}, {\bf b}{1}, \ldots, {\bf b}_{n}$, $n\geq 1$, dupa $n$ etape a schemei de Casteljau aplicata dupa acelasi principiu, folosind un parametru $t\in[0,1]$, se obtine un punct pe curba Bézier corespunzator acestui parametru. Punctele de control calculate in etapele intermediare se pot afisa, teoretic, intr-o matrice triunghiulara de puncte. Si anume punctele de control sunt puncte initiale, date, deci corespund etapei 0 a procedurii recursive si le adaugam indicele 0 in pozitia de sus Step17: Proprietati ale curbelor Bezier Parametrizarea in baza Bernstein a unei curbe Bézier, $b(t)=B^n_0(t) {\bf b}_0+B^n_1(t) {\bf b}_1+\cdots+B^n_n(t) {\bf b}_n$, $t\in[0,1]$, este o combinatie convexa a punctelor de control, deoarece pentru fiecare $t\in[0,1]$, polinoamele Bernstein, $B^n_k(t)=C_n^k t^k(1-t)^{n-k}\geq 0$ si suma lor este egala cu 1. Intradevar Step18: Gradul unei curbe Bézier este mai mic cu o unitate decat numarul punctelor sale de control. Astfel o curba Bézier de grad 1 este generata de doua puncte de control ${\bf b}_0, {\bf b}_1$, $b(t)={\bf b}_0 B_0^1(t)+{\bf b}_1B_1^1(t)$ $=(1-t){\bf b}_0+t{\bf b}_1= {\bf b}_0 t\vec{{\bf b}_0{\bf b}_1}$, si este segmentul determinat de cele doua puncte. O curba Bézier generata de trei puncte de control este un arc de parabola Step19: O curba Bézier interpoleaza extremitatile poligonului sau de control (adica trece sigur prin ${\bf b}_0$ si ${\bf b}_n$, deoarece daca $b$ este parametrizarea Bernstein, atunci $b(0)={\bf b}_0$ si $b(1)={\bf b}_n$. O curba Bézier nu trece insa prin punctele de control intermediare. O curba Bézier imita forma poligonului de control. Step20: Tangentele in extremitatile arcului de curba Bézier au directiile $\overrightarrow{{\bf b}0{\bf b}_1}$, respectiv $\overrightarrow{{\bf b}{n-1}{\bf b}{n}}$ Step21: In aceasta figura se observa ca segmentul de extremitati ${\bf b}^2_0, {\bf b}^2_1$ este tangent la curba in ${\bf b}^3_0$. Functia urmatoare calculeaza directia (vectorul director) al tangentei (vitezei) in ${\bf b}^n_0(t)$ Step22: In ultimul bloc de cod puteti inlocui pe $t$ cu diverse valori in $[0,1]$ si vedeti directia vitezei. Curbele Bezier se genereaza deobicei interactiv, alegand punctele de control cu mouse-ul si apoi trasand curba corespunzatoare. Pentru generarea interactiva veti primi un script ce se ruleaza in Spyder.	Python Code: %matplotlib inline import matplotlib.pyplot as plt import numpy as np def Curba(a, b, N): h=(b-a)/N t=np.arange(a,b, h) return (np.cos(t)+tnp.sin(t), np.sin(t)-tnp.cos(t))# functia returneaza tuple (x(t), y(t)) Explanation: Generarea si vizualizarea curbelor 1. Curbe plane O curba plana diferentiabila, data parametric, este imaginea, $im(r)$, a unei aplicatii diferentiabile $r:[a,b]\to\mathbb{R}^2$, $r(t)=(x(t), y(t))$. Astfel curba este submultimea din plan: $$\Gamma={(x(t), y(t))\in\mathbb{R}^2\:\|\: t\in[a,b]}$$ Interpretand $t\in[a,b]$ ca fiind timpul, curba data parametric are o generare cinematica: ea este traiectoria unui punct mobil, a carui miscare este monitorizata in intervalul de timp $[a,b]$. Punctul de coordonate $(x(t), y(t))$ da pozitia punctului mobil la momentul $t$. Daca aplicatia parametrizare este de clasa $C^2$ (exista derivatele $x'(t), y'(t), x''(t), y''(t)$ si acestea sunt continue), atunci fiecarui punct $(x(t), y(t))$ al traiectoriei i se asociaza vectorul viteza la momentul $t$: $$\dot{\vec{r}}(t)=(x'(t), y'(t))^T,$$ respectiv acceleratia la momentul $t$: $$\ddot{\vec{r}}(t)=(x''(t), y''(t))^T$$ Discretizarea si vizualizarea unui curbe plane Pentru a genera o curba plana folosind pachetul matplotlib, divizam intervalul de timp $[a,b]$ prin puncte echidistante, $t_i$, cu pasul $h=(b-a)/N$, unde $N$ este numarul de intervale de diviziune: $t_i=a+ih$, $i=\overline{0,N}$. In fiecare moment de timp $t_i$, se evalueaza functiile $x(t), y(t)$ ce definesc parametrizarea curbei si se obtin $N+1$ puncte de pe curba, $(x(t_i), y(t_i))$, $\overline{0,N}$. Apelul functiei plt.plot (x,y) conduce la generarea unei aproximatii a curbei, aproximatie ce consta din segmentele ce unesc cate doua puncte consecutive $(x(t_i), y(t_i))$, $(x(t_{i+1}), y(t_{i+1}))$, $i=\overline{0,N}$. Cu cat pasul de diviziune $h$ este mai mic (sau echivalent, $N$ este mai mare), cu atat aproximatia curbei este mai buna. End of explanation (x,y)=Curba(0, 3np.pi, 15) plt.plot(x, y) plt.axis('equal') print type(x), type(y) print x.round(3) print y.round(3) Explanation: Pentru a intelege definitia acestei functii, o apelam mai intai pentru un $N$ mic si afisam tipul coordonatelor tuple-ului returnat: End of explanation (x,y)=Curba(0, 3np.pi, 1500) plt.plot(x, y) plt.xlabel('x(t)') plt.ylabel('y(t)') plt.axis('equal') Explanation: Deci functia Curba returneaza un tuple 2D, $(x,y)$, in care fiecare coordonata este un array 1D (un vector). Mai precis, daca notam cu $(X(t), Y(t))$ coordonatele parametrizarii curbei, $$X(t)=\cos(t)+t\sin(t), \quad Y(t)=\sin(t)-t\cos(t)$$ atunci vectorii $x$ si $y$, coordonate ale tuple-ului returnat sunt: $$x=[X(t[0]), X(t[1]), \ldots, X(t[N])]$$ $$y=[Y(t[0]), Y(t[1]), \ldots, Y(t[N])]$$ Apeland acum functia cu un numar mai mare de puncte de diviziune obtinem: End of explanation def astroida(t, A=1):#(x(t),y(t)=(Acos^3(t), Asin^3(t)) return (Anp.power(np.cos(t), 3), Anp.power(np.sin(t), 3)) t=np.linspace(0,2np.pi, 1000) (x,y)=astroida(t) plt.plot(x,y, 'g') plt.title('Astroida') plt.xlabel('x(t)') plt.ylabel('y(t)') plt.axis('equal')# setarea pe 'equal' are ca efect alegerea aceleasi unitati de masura pe Ox si Oy # in caz contrar (cazul implicit) figura este deformata. Explanation: Astroida: End of explanation def Lissajous(t,a,b, A=1, B=1, d=np.pi/2): return (Anp.sin(at+d), Bnp.sin(bt)) t=np.arange(0,2np.pi, 0.01) pab=np.array([[1,2],[3,2], [3,4], [5,4]],float) for i in range(1,5): plt.subplot(2,2,i) plt.axis([-1.5,1.5, -1.5, 1.5]) x,y=Lissajous(t, pab[i-1, 0], pab[i-1, 1]) plt.plot(x,y, 'r') Explanation: Curba Lissajous are parametrizarea: $$\left{\begin{array}{lll} x(t)&=&A\sin( at+\delta)\ y(t)&=&B\sin(bt)\end{array}\right.\quad t\in[0, 2\pi]$$ End of explanation def r(t): return(tnp.sin(t), 1.5np.cos(t)) def rprim(t): return (np.sin(t)+tnp.cos(t), -1.5np.sin(t)) def rsecund(t): return (2np.cos(t)-tnp.sin(t), -1.5np.cos(t)) plt.axis([-5,2, -1.75, 1.75]) t=np.arange(0, 2np.pi, 0.01) x,y=r(t) plt.plot(x,y)#traseaza curba T=np.linspace(0,2np.pi, 10) X,Y=r(T) U,V=rprim(T) plt.quiver(X,Y,U,V, color='r', units='x', scale=5) normv=np.sqrt(U2+V2) print 'normele vectorilor viteza:\n', normv.round(3) W,Z=rsecund(T) plt.quiver(X,Y,W,Z, color='g', units='x', scale=5) norma=np.sqrt(W2+Z2) print 'normele vectorilor acceleratie:\n', norma.round(3) Explanation: Pentru a genera vectorii viteza/acceleratie in cateva momente, $t_i$, asociati traiectoriei parametrizate de $r(t)=(x(t), y(t))$, se evalueaza vectorul $\dot{\vec{r}}(t)$, respectiv $\ddot{\vec{r}}(t)$ in $t_i$ si se apeleaza functia plt.quiver(X, Y, U, V) unde $X$, respectiv $Y$, este array-ul 1D de coordonate $x(t_i)$, respectiv $y(t_i)$ , iar $U$ si $V$ au respectiv coordonatele $x'(t_i)$ si $y'(t_i)$, in cazul vitezei, si $x''(t_i)$, $y''(t_i)$, in cazul acceleratiei. De exemplu sa trasam curba parametrizata de: $$x(t)=t\sin(t), \quad y(t)=\cos(t), \quad t\in[0, 2\pi]$$ si vectorii viteza, respectiv acceleratie, in 8 puncte ale traiectoriei, atinse in miscarea punctului mobil in momentele $T[i]$, unde T=np.linspace(0,2np.pi, 8): End of explanation from IPython.display import Image Image(filename='Imag/polar.png') Explanation: Observam atat din valorile normelor, cat si din imaginile vectorilor viteza/acceleratie ca in puncte diferite au valori diferite. Mai mult in punctele curbei cu curbura mai mare viteza are norma mai mica si acceleratia mai mare (vezi Cursul 13, curbura curbelor). Curbe in coordonate polare In afara de sistemul ortogonal de axe, $xOy$, $\mathbb{R}^2$ se poate raporta si la un sistem polar de coordonate. Un reper polar consta dintr-o pereche $(O;v)$, unde $O$ este un punct fixat, numit pol si $v\in\mathbb{R}^2$, un vector nenul ce defineste axa polara, $Ox$, care este axa de origine $O$, directie si sens $v$. Pozitia unui punct $M$ relativ la acest reper se indica prin coordonatele $(r, \theta)$, numite coordonate polare. $r$ este distanta de la $M$ la $O$, $r=\|\|\vec{OM}\|\|$, iar $\theta$ este masura in radiani a unghiului dintre $v$ (deci $Ox$) si $\vec{OM}$. End of explanation Image(filename='Imag/ortpolar.png') Explanation: Ducand o perpendiculara in $O$ pe axa polara construim sistemul ortogonal drept $xOy$, asociat celui polar. End of explanation #Generarea Cardioidei a=1; theta=np.arange(0, 2np.pi, 0.01) r=a(1+np.cos(theta)) plt.polar(theta, r, 'r') Explanation: Astfel punctul de coordonate polare $(r, \theta)$ are coordonatele carteziene $(x,y)$, unde $$x=r\cos(\theta), \quad y=r\sin(\theta)$$ O curba in coordonate polare are ecuatia $r=f(\theta)$, $\theta\in[\alpha, \beta]$. Cu alte cuvinte, curba este constituita din multimea punctelor din plan ce au coordonatele polare $(r=f(\theta), \theta)$. Exemplu de curba in coordonate polare este cardioida, de ecuatie $r=a(1+\cos(\theta))$, $a>0$, $\theta\in[0, 2\pi]$. Pentru a genera si vizualiza o curba in coordonate polare procedam astfel: se divizeaza intervalul $[\alpha, \beta]$ prin puncte echidistante $\theta_i$ si se apeleaza functia plt.polar(theta, r), unde vectorul theta are coordonatele $\theta_i$, iar vectorul r are coordonatele $f(\theta_i)$: End of explanation #cercul r=2 theta=np.arange(0, 2np.pi, 0.01) r=2np.ones(theta.size) plt.subplot(1,2,1, polar='true') plt.plot(theta, r, 'r', lw=2) #semidreapta theta= 2 pi/3 r=np.arange(0,2, 0.01) theta=(2np.pi/3)np.ones(r.size) plt.subplot(1,2,2, polar='true') plt.plot(theta, r, 'r', lw=2) plt.tight_layout(2)# aceasta functie include 2 spatii intre figurile din subplots Explanation: Remarcam ca fara nici o comanda speciala, o data cu apelul functiei plt.polar sunt generate cercuri concentrice cu originea in pol si in lungul cercului exterior sunt marcate valorile in grade (nu radiani) ale unghiului polar $\theta$. Sunt afisate si razele cercurilor concentrice. Cercul cu centrul in origine si de raza $a$, $x^2+y^2=a^2$, are in coordonate polare ecuatia: $$r=a,$$ iar semidreapta ce porneste din origine si formeaza cu axa polara unghiul $\theta_0$ are ecuatia $$\theta=\theta_0$$ Cu alte cuvinte, cercul $r=a$ este locul geometric al punctelor din plan ce au aceeasi distanta polara la $O$, distanta egala cu $a$. Semidreapta $\theta=\theta_0$ este locul geometric al punctelor din plan care au aceeasi coordonata polara $\theta_0$. End of explanation theta=np.arange(0,2np.pi, 0.01) plt.polar(theta, np.sin(4theta), 'r') plt.axis('off')# efectul acestui apel este suspendarea cercurilor si semidreptelor reperului polar Explanation: Trifoiul cu 4 foi are ecuatia $r=\sin(4\theta)$, $\theta\in[0,2\pi]$. End of explanation Image(filename='Imag/mimicauto.png') Explanation: Curba $r=\sin(n\theta)$ este un trifoi cu $n$ foi. La fel si $r=\cos(n\theta)$, $\theta\in[0,2\pi]$. Experimentati! Curbe Bézier Curbele date parametric sau in coordonate polare se pot genera doar daca se da efectiv expresia analitica a parametrizarii, respectiv a functiei in coordonate polare. Curbele Bézier sunt curbe definite procedural, adica pornind de la un numar finit de puncte si un parametru $t\in[0,1]$ se genereaza algoritmic un punct pe o curba. Curbele Bézier s-au nascut in laboratoarele de la Renault si Citroën in incercarea de a genera capote pentru automobile, mai deosebite. Ideea de baza consta in designul unui algoritm care sa genereze profilul unei capote ce imita un poligon de control. End of explanation Image(filename='Imag/convNonconv.png') Explanation: Poligonul de control este succesiunea de segmente ce unesc cate doua puncte consecutive, numite puncte de control. Bézier a definit curbele care-i poarta numele, definind curbe polinomiale, adica curbe de forma: $$\left{\begin{array}{lll}x(t)&=&a_0+a_1 t+\cdots+ a_n t^n\ y(t)&=&c_0+c_1t+\cdots+c_n t^n\end{array}\right.\quad t\in[a,b]$$ dar nu in acest fel, adica nu exprimand polinoamele in baza canonica $1,t, \ldots, t^n$, ci in baza Bernstein, $B^n_0(t), B^n_1(t), \ldots, B_n^n(t)$, unde $B^n_k(t)=C_n^k t^k (1-t)^{n-k}$, $k=\overline{0,n}$. O curba Bézier de puncte de control ${\bf b}_0, {\bf b}_1, \ldots, {\bf b}_n$, este o curba avand parametrizarea $b(t)=B^n_0(t) {\bf b}_0+B^n_1(t) {\bf b}_1+\cdots+B^n_n(t) {\bf b}_n$, $t\in[0,1]$. Daca $b(t)=(x(t), y(t))$, atunci coordonatele parametrizarii sunt: $$ \left{\begin{array}{lll} x(t)&=&x({\bf b}_0)B^n_0(t)+x({\bf b}_1)B^n_1(t)+\cdots+x({\bf b}_n)B^n_n(t)\ y(t)&=&y({\bf b}_0)B^n_0(t)+y({\bf b}_1)B^n_1(t)+\cdots+y({\bf b}_n)B^n_n(t)\end{array}\right.$$ unde $x({\bf b}_k), y({\bf b}_k)$ semnifica coordonata $x$, respectiv $y$, a punctului ${\bf b}_k$, $k=\overline{0,n}$. Curbele Bézier folosite in grafica, in modelarea geometrica a diverse produse sau in designul fonturilor sunt curbele Bézier de grad 3, adica curbele Bézier definite de 4 puncte de control ${\bf b}_0$, ${\bf b}_1$, ${\bf b}_2$, ${\bf b}_3$. De exemplu curba Bézier definita de punctele de control: $${\bf b}_0=(2,-1),{\bf b}_1 =(4,5), {\bf b}_2=(7, 6), {\bf b}_3 =(9,1)$$ are parametrizarea $$b(t)=(x(t), y(t)), \:\:\left{\begin{array}{lll} x(t)&=& 2B^3_0(t)+4B^3_1(t)+7B^3_2(t)+9B^3_3(t)\ y(t)&=& -1B^3_0(t)+5B^3_1(t)+6B^3_2(t)+B^3_3(t)\end{array}\right.\quad t\in[0,1]$$ Spre deosebire de Bézier care a definit curbele ce-i poarta numele printr-o parametrizare, de Casteljau a dat o definitie procedurala a acestor curbe. Folosind definitia lui Bézier, adica parametrizarea $b(t)=\sum_{k=0}^n {\bf b}_{k} B^n_k(t)$ a curbei, pentru a calcula un punct pe curba corespunzator parametrului $t$, trebuie evaluata aplicatia parametrizare in $t$. Definitia procedurala da un algoritm de calculul recursiv a unui punct corespunzator unui parametru $t\in[0,1]$, pe curba de puncte de control ${\bf b}_0, {\bf b}_1, \ldots, {\bf b}_n $. Pentru a descrie algoritmul de Casteljau, numit si schema de Casteljau, fixam cateva notiuni pe care se bazeaza definitia procedurala. O multime din plan este convexa daca o data cu doua puncte $A,B$, contine si segmentul ce le uneste. Exemple de multimi convexe: o dreapta, un segment de dreapta, un compact triunghiular. End of explanation Image(filename='Imag/infasConvexa.png') Explanation: In figura de mai sus multimea din stanga este convexa, iar cea din dreapta neconvexa, pt ca desi punctele marcate cu albastru apartin multimii, segmentul ce le uneste nu este in intregime inclus in multime. O combinatie convexa a punctelor $A_0, A_1, \ldots, A_n$ din $\mathbb{R}^2$ sau $\mathbb{R}^3$ este de forma: $$ \alpha_0A_0+\alpha_1A_1+\cdots+\alpha_n A_n, \quad \mbox{unde}\,\, \alpha_i\in [0,1],\: \mbox{si}\,\, \alpha_0+\alpha_1+\cdots+\alpha_n=1.$$ Sa aratam ca o combinatie convexa reprezinta un punct. Intr-adevar, din relatia $\alpha_0+\alpha_1+\cdots+\alpha_n=1$ exprimam pe $\alpha_0=1-(\alpha_1+\alpha_2+\cdots+\alpha_n)$, il inlocuim in relatia ce defineste combinatia convexa si obtinem: $$\begin{array}{l}A_0+\alpha_1(A_1-A_0)+\alpha_2(A_2-A_0)+\cdots+\alpha_n(A_n-A_0)=\ \underbrace{A_0}{punct}+\underbrace{\alpha_1\overrightarrow{A_0A_1}+\alpha_2\overrightarrow{A_0A_2}+\cdots+\alpha_n\overrightarrow{A_0A_n}}{vector}:=\underbrace{P}_{punct}\end{array}$$ Infasuratoare convexa a punctelor $A_0, A_1, \ldots, A_n$ este multimea tuturor combinatiilor convexe ale acestor puncte. Infasuratoarea convexa a doua puncte, $A, B$, este segmentul de extremitati $A, B$, adica $[A,B]\stackrel{def}{=}{P\,\|\, P=A+s\vec{AB}, s\in[0,1]}$. Infasuratoarea a trei puncte necoliniare, $A_0, A_1, A_2$ este compactul triunghiular $\triangle{A_0A_1A_2}$. End of explanation Image(filename='Imag/cbezier0C.png') Explanation: Infasuratoarea convexa a $n$ puncte din plan este cel mai mic poligon convex ce contine toate punctele. In figura de mai sus infasuratoarea convexa apunctelor $C_0, C_1, C_2, C_3, C_4$ este poligonul plin, de varfuri $C_0, C_1, C_4, C_2$. Daca $A, B$ sunt doua puncte in plan sau spatiu, o combinatie convexa a lor este un punct $M=\alpha_1 A+\alpha_2 B$, $\alpha_i\in[0,1]$, $\alpha_1+\alpha_2=1$. Notand $\alpha_2=t$, rezulta ca $\alpha_1=1-t$ si deci punctul $M$ se exprima ca o combinatie convexa a lui $A$ si $B$ astfel: $$M=(1-t)A+tB, \,\, t\in[0,1]$$ Punctul $M$ astfel definit apartine segmentului $[A,B]$. Daca $\vec{AM}=r\vec{MB}$, spunem ca punctul $M\neq B$, imparte segmentul $[A,B]$ in raportul $r$. Un punct $M\neq B$ al segmentului $[A,B]$, $M=(1-t)A+tB$, imparte segmentul in raportul $\displaystyle\frac{t}{1-t}$ si reciproc, daca $M$ imparte segmentul $[A,B]$ in raportul $\displaystyle\frac{t}{1-t}$, $t\neq 1$, atunci $M=(1-t)A+tB$. Intr-adevar: $$\begin{array}{l}M=(1-t)A+tB\,\, \Leftrightarrow\,\, M=A+t\vec{AB}\,\,\Leftrightarrow\,\, \vec{AM}=t(\vec{AM}+\vec{MB})\,\,\Leftrightarrow\ \Leftrightarrow\,\, (1-t)\vec{AM}=t\vec{MB}\,\,\Leftrightarrow \vec{AM}=\displaystyle\frac{t}{1-t}\vec{MB}\end{array}$$ Pornind de la aceasta proprietate explicam mai intai schema lui de Casteljau pentru cazul curbei definita de 3 puncte de control ${\bf b}_0, {\bf b}_1, {\bf b}_2$. Acestea fiind punctele initiale (date), se renoteaza ${\bf b}_0^0, {\bf b}^0_1, {\bf b}^0_2$, indicele $0$ din pozitia de sus indicand etapa 0 a procedurii. Fixand un parametru $t\in[0,1)$, acesta imparte segmentul $[0,1]$ in raportul $\displaystyle\frac{t}{1-t}$. End of explanation Image(filename='Imag/Decast4p.png') Explanation: In etapa 1 se determina pe fiecare segment determinat de doua puncte de control consecutive, punctul ce imparte segmentul respectiv in acelasi raport $\displaystyle\frac{t}{1-t}$ in care $t$ imparte pe $[0,1]$: $$\begin{array}{lll}{\bf b}^1_0&=&(1-t) {\bf b}_0^0+t{\bf b}^0_1\ {\bf b}^1_1&=&(1-t){\bf b}_1^0+t{\bf b}^0_2\end{array}$$ In etapa a doua, pe segmentul determinat de cele doua puncte calculate in etapa precedenta se determina punctul ${\bf b}^2_0$ ce imparte segmentul $[{\bf b}^1_0, {\bf b}^1_1]$ in raportul $\displaystyle\frac{t}{1-t}$: $${\bf b}^2_0=(1-t){\bf b}^1_0+t{\bf b}^1_1$$ Inlocuind punctele calculate in etapa 1 in exprimarea lui ${\bf b}0^2$, calculat in etapa 2, obtinem: $$\begin{array}{lll}{\bf b}^2_0&=&(1-t)[(1-t){\bf b}_0^0+t{\bf b}^0_1]+t[(1-t){\bf b}_1^0+t{\bf b}^0_2]=\&=&\underbrace{(1-t)^2}{B^2_0(t)}{\bf b}0^0+\underbrace{2t(1-t)}{B^2_1(t)}{\bf b}1^0+\underbrace{t^2}{B^2_2(t)}{\bf b}^0_2=b(t)\end{array},$$ adica punctul $b^2_0$ reprezinta parametrizarea curbei data de Bézier, in baza Bernstein, evaluata in parametrul $t$. Remarcam ca in fiecare etapa a schemei de Casteljau, numarul punctelor calculate se reduce cu o unitate fata de etapa precedenta si in ultima etapa rezulta punctul de pe curba Bézier, corespunzator parametrului $t$. In figura urmatoare ilustram algoritmul de Casteljau pentru o curba definita de 4 puncte de control: End of explanation def deCasteljau(t,b): #punctele de control b_0, b_1, ..., b_n, sunt date intr-un array 2D, #de n+1 linii si 2 coloane. Pe linia i avem coordonatele punctului b_i a=np.copy(b) # se copiaza array-ul b in array-ul a N=a.shape[0] # interogam cat este numarul de linii ale lui a (deci si ale lui b); n=N-1 for r in range(1,N): # echivalentul in C a lui for(r=1;r<N, r++); deci 1<=r<=n for i in range(N-r):# in C for(i=0; i<N-r;i++) a[i,:]=(1-t)a[i,:]+ta[i+1,:]# punctul i din etapa r este combinatia convexa # a punctelor i si i+1 din etapa r-1 return a[0,:]# a[0,:] contine coordonatele pctului de pe curba coresp lui t def dreptunghiDesen(b): cmin=np.zeros(2) # cmin[i] va stoca coordonata i, minima, a punctelor de control cmax=np.zeros(2)# cmax[i] va stoca coordonata i, maxima, a punctelor de control for i in range(2): cmin[i]=np.amin(b[:,i])-0.5 cmax[i]=np.amax(b[:,i])+0.5 return (cmin, cmax) def curbaBezier(b, nr=100):#nr=nr de puncte ce se calculeaza pe curba h=1.0/nr pcteC=[] for k in range(nr): t=kh# 0.01 este pasul de divizare a intervalului [0,1], al parametrului t P=deCasteljau(t,b)# P punct pe curba Bezier, corespunzator lui t pcteC.append(P) return pcteC# functia returneaza lista punctelor de pe curba calculate def DrawBezier(b): cmin, cmax=dreptunghiDesen(b) plt.axis([cmin[0], cmax[0], cmin[1], cmax[1]]) plt.plot(b[:, 0], b[:,1], 'bo', b[:, 0], b[:,1])#marcheaza punctele de control si #traseaza poligonul de control pcteC=curbaBezier(b)# pcteC este lista punctelor calculate pe curba Xpcte, Ypcte=zip(pcteC)# functia zip() extrage din lista pcteC, lista x-silor, si y-cilor plt.plot(Xpcte, Ypcte, 'r') ################ # Pentru a a genera o curba Bezier este suficient sa declaram array-ul punctelor b=np.array([[2,-1],[1.75,2.75], [5,6], [8,0]], float)# 4 puncte de control DrawBezier(b) Explanation: Generalizand la cazul unei curbe definite de un numar arbitrar de puncte de control, $ {\bf b}{0}, {\bf b}{1}, \ldots, {\bf b}_{n}$, $n\geq 1$, dupa $n$ etape a schemei de Casteljau aplicata dupa acelasi principiu, folosind un parametru $t\in[0,1]$, se obtine un punct pe curba Bézier corespunzator acestui parametru. Punctele de control calculate in etapele intermediare se pot afisa, teoretic, intr-o matrice triunghiulara de puncte. Si anume punctele de control sunt puncte initiale, date, deci corespund etapei 0 a procedurii recursive si le adaugam indicele 0 in pozitia de sus: $$ \begin{array}{llllll} {\bf b}0^0 & {\bf b}{0}^1 & {\bf b}{0}^2& \cdots & {\bf b}_0^{n-1}& {\bf b}^n_0\ {\bf b}_1^0 & {\bf b}{1}^1 & {\bf b}{1}^2& \cdots & {\bf b}_1^{n-1}& \ {\bf b}_2^0 & {\bf b}{2}^1 & {\bf b}{2}^2 & \cdots & & \ \vdots & \vdots &\vdots & & & \ {\bf b}{n-2}^0 & {\bf b}{n-2}^1& {\bf b}{n-2}^2& & & \ {\bf b}{n-1}^0 & {\bf b}{n-1}^1 & & & & \ {\bf b}_n^0 & & & & & \end{array} $$ Punctele din coloana $r$ corespund etapei $r$ a schemei recursive, $r=\overline{1,n}$. Succint, schema de Casteljau se exprima prin formula $$ {\bf b}{i}^{r}(t)=(1-t)\,{\bf b}{i}^{r-1}(t)+t\,{\bf b}{i+1}^{r-1}(t),\: r=\overline{1,n},\: i=\overline{0,n-r}, $$ adica punctul din pozitia $i$ a etapei $r$ este o combinatie convexa a punctelor $i$ si $i+1$ din etapa $r-1$: $$ \begin{array}{lll} {\bf b}^{r-1}_i&\stackrel{1-t}{\rightarrow}&{\bf b}^{r}_i\ {\bf b}^{r-1}{i+1}&\stackrel{\nearrow}{t}&\end{array}$$ Desi la prima vedere s-ar parea ca pentru implementarea schemei de Casteljau avem nevoie de o matrice triunghiulara de puncte, in realitate este suficient un sir auxiliar de puncte $({\bf a}_0,{\bf a}_1,\ldots {\bf a}_n)$, in care la fiecare apel al schemei de Casteljau se copiaza punctele de control $({\bf b}_0,{\bf b}_1,\ldots {\bf b}_n)$, ${\bf a}_i={\bf b}_i$, $i=0,1,\ldots n$. In etapa $1$, de exemplu, calculam $(1-t){\bf a}0+t{\bf a}_1$ si pentru ca punctul ${\bf a}_0$ nu va mai fi folosit in aceasta etapa, atribuim $(1-t){\bf a}_0+t{\bf a}_1\to {\bf a}_0$, $\ldots$, $(1-t){\bf a}_i+t{\bf a}{i+1}\to {\bf a}_i$. In fiecare etapa $r$ a schemei de Casteljau doar primele punctele ${\bf a}0, {\bf a}{1}, \ldots, {\bf a}_{n-r}$ isi modifica "continutul": $$\begin{array}{cccccc} \mbox{Etapa}\,\, 0&\mbox{Etapa}\,\,1&\mbox{Etapa}\,\,2&\cdots&\mbox{Etapa}\,\, n-1&\mbox{Etapa}\,\,n\ \downarrow&\downarrow&\downarrow&\cdots&\downarrow&\downarrow\ {\bf a}0 & {\bf a}{0} & {\bf a}{0}& \cdots & {\bf a}_0& {\bf a}_0\ {\bf a}_1 & {\bf a}{1} & {\bf a}{1}& \cdots & {\bf a}_1& \ {\bf a}_2 & {\bf a}{2} & {\bf a}{2} & \cdots & & \ \vdots & \vdots &\vdots & & & \ {\bf a}{n-2}& {\bf a}{n-2}& {\bf a}{n-2}& & & \ {\bf a}{n-1}& {\bf a}{n-1} & & && \ {\bf a}_n & & & && \end{array} $$ In etapa $n$ punctul ${\bf a}_0$ contine punctul curbei Bézier corespunzator parametrului $t$. Pentru a discretiza o curba Bézier, se divizeaza intervalul $[0,1]$ prin puncte echidistante. Fixand numarul $N$ de subintervale egale ale intervalului $[0,1]$, pasul de divizare este $h=1.0/N$, iar punctele de diviziune sunt $t_j=jh$, $j=0,1,2,\ldots, N$. Pentru fiecare parametru $t_j$, $j=0,1,\ldots N$, se apeleaza schema (functia) de Casteljau, obtinand astfel punctele ${\bf P}_j$, de pe curba, care apoi se interpoleaza liniar si obtinem imaginea curbei. End of explanation b=np.array([[0,0], [1.5, 2.3], [3.7, -1], [6, 3], [2.6, 2]],float) plt.subplot(3,1,1) plt.title('Curba Bezier raportata relativ la poligonul de control') DrawBezier(b) plt.subplot(3,1,2) plt.title('Infasuratoarea convexa a punctelor de control') cmin, cmax=dreptunghiDesen(b) plt.axis([cmin[0], cmax[0], cmin[1], cmax[1]]) Vx=[] Vy=[] for i in [0,1,3,2, 0]: Vx.append(b[i,0]) Vy.append(b[i,1]) plt.fill(Vx,Vy, color='#99FFCC', edgecolor='g') plt.plot(b[:, 0], b[:,1], 'bo') plt.subplot(3,1,3) plt.title('Curba Bezier, inclusa in infasuratoarea punctelor de control') plt.fill(Vx,Vy, color='#99FFCC') DrawBezier(b) plt.tight_layout(0.75)# aceasta functie include 0.75* spatiu intre figurile din subplots Explanation: Proprietati ale curbelor Bezier Parametrizarea in baza Bernstein a unei curbe Bézier, $b(t)=B^n_0(t) {\bf b}_0+B^n_1(t) {\bf b}_1+\cdots+B^n_n(t) {\bf b}_n$, $t\in[0,1]$, este o combinatie convexa a punctelor de control, deoarece pentru fiecare $t\in[0,1]$, polinoamele Bernstein, $B^n_k(t)=C_n^k t^k(1-t)^{n-k}\geq 0$ si suma lor este egala cu 1. Intradevar: $$\sum_{k=0}^n B_k^n(t)=\underbrace{\sum_{k=0}^n C_n^k t^k(1-t)^{n-k}=(t+(1-t))^n}_{formula\:\: binomului\:\: lui\:\: Newton}=1$$ Deci o curba Bézier este inclusa in infasuratoarea convexa a punctelor sale de control. Aceasta proprietate are importanta in grafica, pentru a sti unde anume in plan este plasata curba. In exemplul de mai sus am exploatat deja aceasta proprietate, indicand ca dreptunghi de desen, un dreptunghi care include toate punctele de control, deci si infasuratoarea lor convexa. End of explanation b=np.array([[1.3, 2], [0,-1.5] , [3, -0.7]], float) DrawBezier(b) Explanation: Gradul unei curbe Bézier este mai mic cu o unitate decat numarul punctelor sale de control. Astfel o curba Bézier de grad 1 este generata de doua puncte de control ${\bf b}_0, {\bf b}_1$, $b(t)={\bf b}_0 B_0^1(t)+{\bf b}_1B_1^1(t)$ $=(1-t){\bf b}_0+t{\bf b}_1= {\bf b}_0 t\vec{{\bf b}_0{\bf b}_1}$, si este segmentul determinat de cele doua puncte. O curba Bézier generata de trei puncte de control este un arc de parabola: End of explanation b=np.array([[0,0], [1,2], [2.75, 1.5],[1.5, -1.25],[-2,-2],[2,-3]],float) DrawBezier(b) Explanation: O curba Bézier interpoleaza extremitatile poligonului sau de control (adica trece sigur prin ${\bf b}_0$ si ${\bf b}_n$, deoarece daca $b$ este parametrizarea Bernstein, atunci $b(0)={\bf b}_0$ si $b(1)={\bf b}_n$. O curba Bézier nu trece insa prin punctele de control intermediare. O curba Bézier imita forma poligonului de control. End of explanation Image(filename='Imag/Decast4p.png') Explanation: Tangentele in extremitatile arcului de curba Bézier au directiile $\overrightarrow{{\bf b}0{\bf b}_1}$, respectiv $\overrightarrow{{\bf b}{n-1}{\bf b}{n}}$: $$ \dot{\vec{b}}(t=0)=n\,\overrightarrow{{\bf b}_0{\bf b}_1}, \quad \dot{\overrightarrow{b}}(1)=n\,\overrightarrow{\bf b}{n-1}{\bf b}_n $$ In procesul iterativ al schemei de Casteljau, de evaluare a unui punct $b(t)$, de pe curba Bézier definita de punctele de control $ {\bf b}{0}, {\bf b}{1}, \ldots, {\bf b}_{n}$, se determina practic si directia tangentei (a vectorului viteza la momentul $t$) la curba in acel punct. Si anume se poate demonstra ca vectorul tangent la curba Bézier in punctul corespunzator parametrului $\bf t\in[0,1]$ este: $$ \dot{\vec{b}}(t)=n({\bf b}{1}^{n-1}(t)-{\bf b}{0}^{n-1}(t))=n\,\overrightarrow{{\bf b}{0}^{n-1}(t){\bf b}{1}^{n-1}(t)}, $$ unde ${\bf b}{0}^{n-1}(t)$,$\,\,{\bf b}{1}^{n-1}(t)$ sunt punctele calculate in penultima etapa $($etapa $n-1$ $)$ a schemei de Casteljau. End of explanation def tangBezier(b,t): a=np.copy(b) N=a.shape[0] for r in range(1,N-1): # deci 1<=r<=n-1, unde n=N-1 for i in range(N-r): a[i,:]=(1-t)a[i,:]+ta[i+1,:] v=a[1,:]-a[0,:] #vectorul director al vitezei in b(t) return v b=np.array([[0,0], [1,1.7],[3, 1.5], [5, -1]],float) DrawBezier(b) t=0.23 P=deCasteljau(t,b) plt.plot(P[0], P[1], 'ro') v=tangBezier(b,t) v=v/np.linalg.norm(v) #versorul vitezei plt.arrow(P[0], P[ 1], v[0], v[1], fc="k", ec="k",head_width=0.075, head_length=0.2) Explanation: In aceasta figura se observa ca segmentul de extremitati ${\bf b}^2_0, {\bf b}^2_1$ este tangent la curba in ${\bf b}^3_0$. Functia urmatoare calculeaza directia (vectorul director) al tangentei (vitezei) in ${\bf b}^n_0(t)$: End of explanation from IPython.core.display import HTML def css_styling(): styles = open("./custom.css", "r").read() return HTML(styles) css_styling() Explanation: In ultimul bloc de cod puteti inlocui pe $t$ cu diverse valori in $[0,1]$ si vedeti directia vitezei. Curbele Bezier se genereaza deobicei interactiv, alegand punctele de control cu mouse-ul si apoi trasand curba corespunzatoare. Pentru generarea interactiva veti primi un script ce se ruleaza in Spyder. End of explanation
9,235	Given the following text description, write Python code to implement the functionality described below step by step Description: Homework 2 In this homework, we are going to play with Twitter data. The data is represented as rows of of JSON strings. It consists of tweets, messages, and a small amount of broken data (cannot be parsed as JSON). For this homework, we will only focus on tweets and ignore all other messages. UPDATES Announcement We changed the test files size and the corresponding file paths. In order to avoid long waiting queue, we decided to limit the input files size for the Playground submissions. Please read the following files to get the input file paths Step1: Part 1 Step2: Broken tweets and irrelevant messages The data of this assignment may contain broken tweets (invalid JSON strings). So make sure that your code is robust for such cases. In addition, some lines in the input file might not be tweets, but messages that the Twitter server sent to the developer (such as limit notices). Your program should also ignore these messages. Hint Step3: (2) Count the number of different users in all valid tweets (hint Step4: Part 2 Step5: (2) Count the number of posts from each user partition Count the number of posts from group 0, 1, ..., 6, plus the number of posts from users who are not in any partition. Assign users who are not in any partition to the group 7. Put the results of this step into a pair RDD (group_id, count) that is sorted by key. Step6: (3) Print the post count using the print_post_count function we provided. It should print Group 0 posted 81 tweets Group 1 posted 199 tweets Group 2 posted 45 tweets Group 3 posted 313 tweets Group 4 posted 86 tweets Group 5 posted 221 tweets Group 6 posted 400 tweets Group 7 posted 798 tweets Step19: Part 3 Step20: (1) Tokenize the tweets using the tokenizer we provided above named tok. Count the number of mentions for each tokens regardless of specific user group. Call print_count function to show how many different tokens we have. It should print Number of elements Step21: (2) Tokens that are mentioned by too few users are usually not very interesting. So we want to only keep tokens that are mentioned by at least 100 users. Please filter out tokens that don't meet this requirement. Call print_count function to show how many different tokens we have after the filtering. Call print_tokens function to show top 20 most frequent tokens. It should print Number of elements Step22: (3) For all tokens that are mentioned by at least 100 users, compute their relative popularity in each user group. Then print the top 10 tokens with highest relative popularity in each user group. In case two tokens have same relative popularity, break the tie by printing the alphabetically smaller one. Hint	Python Code: import findspark findspark.init() import pyspark sc = pyspark.SparkContext() # %install_ext https://raw.github.com/cpcloud/ipython-autotime/master/autotime.py %load_ext autotime def print_count(rdd): print 'Number of elements:', rdd.count() env="local" files='' path = "Data/hw2-files.txt" if env=="prod": path = '../Data/hw2-files-1gb.txt' with open(path) as f: files=','.join(f.readlines()).replace('\n','') rdd = sc.textFile(files).cache() print_count(rdd) Explanation: Homework 2 In this homework, we are going to play with Twitter data. The data is represented as rows of of JSON strings. It consists of tweets, messages, and a small amount of broken data (cannot be parsed as JSON). For this homework, we will only focus on tweets and ignore all other messages. UPDATES Announcement We changed the test files size and the corresponding file paths. In order to avoid long waiting queue, we decided to limit the input files size for the Playground submissions. Please read the following files to get the input file paths: * 1GB test: ../Data/hw2-files-1gb.txt * 5GB test: ../Data/hw2-files-5gb.txt * 20GB test: ../Data/hw2-files-20gb.txt We updated the json parsing section of this notebook. Python built-in json library is too slow. In our experiment, 70% of the total running time is spent on parsing tweets. Therefore we recommend using ujson instead of json. It is at least 15x faster than the built-in json library according to our tests. Important Reminders The tokenizer in this notebook contains UTF-8 characters. So the first line of your .py source code must be # -- coding: utf-8 -- to define its encoding. Learn more about this topic here. The input files (the tweets) contain UTF-8 characters. So you have to correctly encode your input with some function like lambda text: text.encode('utf-8'). ../Data/hw2-files-<param> may contain multiple lines, one line for one input file. You can use a single textFile call to read multiple files: sc.textFile(','.join(files)). The input file paths in ../Data/hw2-files-<param> contains trailing spaces (newline etc.), which may confuse HDFS if not removed. Your program will be killed if it cannot finish in 5 minutes. The running time of last 100 submissions (yours and others) can be checked at the "View last 100 jobs" tab. For your information, here is the running time of our solution: 1GB test: 53 seconds, 5GB test: 60 seconds, 20GB test: 114 seconds. Tweets A tweet consists of many data fields. Here is an example. You can learn all about them in the Twitter API doc. We are going to briefly introduce only the data fields that will be used in this homework. created_at: Posted time of this tweet (time zone is included) id_str: Tweet ID - we recommend using id_str over using id as Tweet IDs, becauase id is an integer and may bring some overflow problems. text: Tweet content user: A JSON object for information about the author of the tweet id_str: User ID name: User name (may contain spaces) screen_name: User screen name (no spaces) retweeted_status: A JSON object for information about the retweeted tweet (i.e. this tweet is not original but retweeteed some other tweet) All data fields of a tweet except retweeted_status entities: A JSON object for all entities in this tweet hashtags: An array for all the hashtags that are mentioned in this tweet urls: An array for all the URLs that are mentioned in this tweet Data source All tweets are collected using the Twitter Streaming API. Users partition Besides the original tweets, we will provide you with a Pickle file, which contains a partition over 452,743 Twitter users. It contains a Python dictionary {user_id: partition_id}. The users are partitioned into 7 groups. Part 0: Load data to a RDD The tweets data is stored on AWS S3. We have in total a little over 1 TB of tweets. We provide 10 MB of tweets for your local development. For the testing and grading on the homework server, we will use different data. Testing on the homework server In the Playground, we provide three different input sizes to test your program: 1 GB, 10 GB, and 100 GB. To test them, read files list from ../Data/hw2-files-1gb.txt, ../Data/hw2-files-5gb.txt, ../Data/hw2-files-20gb.txt, respectively. For final submission, make sure to read files list from ../Data/hw2-files-final.txt. Otherwise your program will receive no points. Local test For local testing, read files list from ../Data/hw2-files.txt. Now let's see how many lines there are in the input files. Make RDD from the list of files in hw2-files.txt. Mark the RDD to be cached (so in next operation data will be loaded in memory) call the print_count method to print number of lines in all these files It should print Number of elements: 2193 End of explanation import ujson json_example = ''' { "id": 1, "name": "A green door", "price": 12.50, "tags": ["home", "green"] } ''' json_obj = ujson.loads(json_example) json_obj Explanation: Part 1: Parse JSON strings to JSON objects Python has built-in support for JSON. UPDATE: Python built-in json library is too slow. In our experiment, 70% of the total running time is spent on parsing tweets. Therefore we recommend using ujson instead of json. It is at least 15x faster than the built-in json library according to our tests. End of explanation import ujson def safe_parse(raw_json): tweet={} try: tweet = ujson.loads(raw_json) except ValueError: pass return tweet #filter out rate limites {"limit":{"track":77,"timestamp_ms":"1457610531879"}} tweets = rdd.map(lambda json_str: safe_parse(json_str))\ .filter(lambda h: "text" in h)\ .map(lambda tweet: (tweet["user"]["id_str"], tweet["text"]))\ .map(lambda (x,y): (x, y.encode("utf-8"))).cache() Explanation: Broken tweets and irrelevant messages The data of this assignment may contain broken tweets (invalid JSON strings). So make sure that your code is robust for such cases. In addition, some lines in the input file might not be tweets, but messages that the Twitter server sent to the developer (such as limit notices). Your program should also ignore these messages. Hint: Catch the ValueError (1) Parse raw JSON tweets to obtain valid JSON objects. From all valid tweets, construct a pair RDD of (user_id, text), where user_id is the id_str data field of the user dictionary (read Tweets section above), text is the text data field. End of explanation def print_users_count(count): print 'The number of unique users is:', count print_users_count(tweets.map(lambda x:x[0]).distinct().count()) Explanation: (2) Count the number of different users in all valid tweets (hint: the distinct() method). It should print The number of unique users is: 2083 End of explanation import cPickle as pickle path = 'Data/users-partition.pickle' if env=="prod": path = '../Data/users-partition.pickle' partitions = pickle.load(open(path, 'rb')) #{user_Id, partition_id} - {'583105596': 6} partition_bc = sc.broadcast(partitions) Explanation: Part 2: Number of posts from each user partition Load the Pickle file ../Data/users-partition.pickle, you will get a dictionary which represents a partition over 452,743 Twitter users, {user_id: partition_id}. The users are partitioned into 7 groups. For example, if the dictionary is loaded into a variable named partition, the partition ID of the user 59458445 is partition["59458445"]. These users are partitioned into 7 groups. The partition ID is an integer between 0-6. Note that the user partition we provide doesn't cover all users appear in the input data. (1) Load the pickle file. End of explanation count = tweets.map(lambda x:partition_bc.value.get(x[0], 7)).countByValue().items() Explanation: (2) Count the number of posts from each user partition Count the number of posts from group 0, 1, ..., 6, plus the number of posts from users who are not in any partition. Assign users who are not in any partition to the group 7. Put the results of this step into a pair RDD (group_id, count) that is sorted by key. End of explanation def print_post_count(counts): for group_id, count in counts: print 'Group %d posted %d tweets' % (group_id, count) print print_post_count(count) Explanation: (3) Print the post count using the print_post_count function we provided. It should print Group 0 posted 81 tweets Group 1 posted 199 tweets Group 2 posted 45 tweets Group 3 posted 313 tweets Group 4 posted 86 tweets Group 5 posted 221 tweets Group 6 posted 400 tweets Group 7 posted 798 tweets End of explanation # %load happyfuntokenizing.py #!/usr/bin/env python This code implements a basic, Twitter-aware tokenizer. A tokenizer is a function that splits a string of text into words. In Python terms, we map string and unicode objects into lists of unicode objects. There is not a single right way to do tokenizing. The best method depends on the application. This tokenizer is designed to be flexible and this easy to adapt to new domains and tasks. The basic logic is this: 1. The tuple regex_strings defines a list of regular expression strings. 2. The regex_strings strings are put, in order, into a compiled regular expression object called word_re. 3. The tokenization is done by word_re.findall(s), where s is the user-supplied string, inside the tokenize() method of the class Tokenizer. 4. When instantiating Tokenizer objects, there is a single option: preserve_case. By default, it is set to True. If it is set to False, then the tokenizer will downcase everything except for emoticons. The __main__ method illustrates by tokenizing a few examples. I've also included a Tokenizer method tokenize_random_tweet(). If the twitter library is installed (http://code.google.com/p/python-twitter/) and Twitter is cooperating, then it should tokenize a random English-language tweet. Julaiti Alafate: I modified the regex strings to extract URLs in tweets. __author__ = "Christopher Potts" __copyright__ = "Copyright 2011, Christopher Potts" __credits__ = [] __license__ = "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License: http://creativecommons.org/licenses/by-nc-sa/3.0/" __version__ = "1.0" __maintainer__ = "Christopher Potts" __email__ = "See the author's website" ###################################################################### import re import htmlentitydefs ###################################################################### # The following strings are components in the regular expression # that is used for tokenizing. It's important that phone_number # appears first in the final regex (since it can contain whitespace). # It also could matter that tags comes after emoticons, due to the # possibility of having text like # # <:\| and some text >:) # # Most imporatantly, the final element should always be last, since it # does a last ditch whitespace-based tokenization of whatever is left. # This particular element is used in a couple ways, so we define it # with a name: emoticon_string = r (?: [<>]? [:;=8] # eyes [\-o\\']? # optional nose [\)\]\(\[dDpP/\:\}\{@\\|\\] # mouth \| [\)\]\(\[dDpP/\:\}\{@\\|\\] # mouth [\-o\\']? # optional nose [:;=8] # eyes [<>]? ) # The components of the tokenizer: regex_strings = ( # Phone numbers: r (?: (?: # (international) \+?[01] [\-\s.]* )? (?: # (area code) [\(]? \d{3} [\-\s.\)]* )? \d{3} # exchange [\-\s.]* \d{4} # base ) , # URLs: rhttp[s]?://(?:[a-zA-Z]\|[0-9]\|[$-_@.&+]\|[!\(\),]\|(?:%[0-9a-fA-F][0-9a-fA-F]))+ , # Emoticons: emoticon_string , # HTML tags: r<[^>]+> , # Twitter username: r(?:@[\w_]+) , # Twitter hashtags: r(?:\#+[\w_]+[\w\'_\-][\w_]+) , # Remaining word types: r (?:[a-z][a-z'\-_]+[a-z]) # Words with apostrophes or dashes. \| (?:[+\-]?\d+[,/.:-]\d+[+\-]?) # Numbers, including fractions, decimals. \| (?:[\w_]+) # Words without apostrophes or dashes. \| (?:\.(?:\s\.){1,}) # Ellipsis dots. \| (?:\S) # Everything else that isn't whitespace. ) ###################################################################### # This is the core tokenizing regex: word_re = re.compile(r(%s) % "\|".join(regex_strings), re.VERBOSE \| re.I \| re.UNICODE) # The emoticon string gets its own regex so that we can preserve case for them as needed: emoticon_re = re.compile(regex_strings[1], re.VERBOSE \| re.I \| re.UNICODE) # These are for regularizing HTML entities to Unicode: html_entity_digit_re = re.compile(r"&#\d+;") html_entity_alpha_re = re.compile(r"&\w+;") amp = "&" ###################################################################### class Tokenizer: def __init__(self, preserve_case=False): self.preserve_case = preserve_case def tokenize(self, s): Argument: s -- any string or unicode object Value: a tokenize list of strings; conatenating this list returns the original string if preserve_case=False # Try to ensure unicode: try: s = unicode(s) except UnicodeDecodeError: s = str(s).encode('string_escape') s = unicode(s) # Fix HTML character entitites: s = self.__html2unicode(s) # Tokenize: words = word_re.findall(s) # Possible alter the case, but avoid changing emoticons like :D into :d: if not self.preserve_case: words = map((lambda x : x if emoticon_re.search(x) else x.lower()), words) return words def tokenize_random_tweet(self): If the twitter library is installed and a twitter connection can be established, then tokenize a random tweet. try: import twitter except ImportError: print "Apologies. The random tweet functionality requires the Python twitter library: http://code.google.com/p/python-twitter/" from random import shuffle api = twitter.Api() tweets = api.GetPublicTimeline() if tweets: for tweet in tweets: if tweet.user.lang == 'en': return self.tokenize(tweet.text) else: raise Exception("Apologies. I couldn't get Twitter to give me a public English-language tweet. Perhaps try again") def __html2unicode(self, s): Internal metod that seeks to replace all the HTML entities in s with their corresponding unicode characters. # First the digits: ents = set(html_entity_digit_re.findall(s)) if len(ents) > 0: for ent in ents: entnum = ent[2:-1] try: entnum = int(entnum) s = s.replace(ent, unichr(entnum)) except: pass # Now the alpha versions: ents = set(html_entity_alpha_re.findall(s)) ents = filter((lambda x : x != amp), ents) for ent in ents: entname = ent[1:-1] try: s = s.replace(ent, unichr(htmlentitydefs.name2codepoint[entname])) except: pass s = s.replace(amp, " and ") return s from math import log tok = Tokenizer(preserve_case=False) def get_rel_popularity(c_k, c_all): return log(1.0 c_k / c_all) / log(2) def print_tokens(tokens, gid = None): group_name = "overall" if gid is not None: group_name = "group %d" % gid print '=' * 5 + ' ' + group_name + ' ' + '=' * 5 for t, n in tokens: print "%s\t%.4f" % (t, n) print Explanation: Part 3: Tokens that are relatively popular in each user partition In this step, we are going to find tokens that are relatively popular in each user partition. We define the number of mentions of a token $t$ in a specific user partition $k$ as the number of users from the user partition $k$ that ever mentioned the token $t$ in their tweets. Note that even if some users might mention a token $t$ multiple times or in multiple tweets, a user will contribute at most 1 to the counter of the token $t$. Please make sure that the number of mentions of a token is equal to the number of users who mentioned this token but NOT the number of tweets that mentioned this token. Let $N_t^k$ be the number of mentions of the token $t$ in the user partition $k$. Let $N_t^{all} = \sum_{i=0}^7 N_t^{i}$ be the number of total mentions of the token $t$. We define the relative popularity of a token $t$ in a user partition $k$ as the log ratio between $N_t^k$ and $N_t^{all}$, i.e. \begin{equation} p_t^k = \log \frac{N_t^k}{N_t^{all}}. \end{equation} You can compute the relative popularity by calling the function get_rel_popularity. (0) Load the tweet tokenizer. End of explanation # unique_tokens = tweets.flatMap(lambda tweet: tok.tokenize(tweet[1])).distinct() splitter = lambda x: [(x[0],t) for t in x[1]] unique_tokens = tweets.map(lambda tweet: (tweet[0], tok.tokenize(tweet[1])))\ .flatMap(lambda t: splitter(t))\ .distinct() ut1 = unique_tokens.map(lambda x: ((partition_bc.value.get(x[0],7), x[1]), 1)).cache() utr = ut1.reduceByKey(lambda x,y: x+y).cache() group_tokens = utr.map(lambda (x,y):(x[1],y)).reduceByKey(lambda x,y:x+y) ##format: (token, k_all) print_count(group_tokens) Explanation: (1) Tokenize the tweets using the tokenizer we provided above named tok. Count the number of mentions for each tokens regardless of specific user group. Call print_count function to show how many different tokens we have. It should print Number of elements: 8979 End of explanation # splitter = lambda x: [(x[0],t) for t in x[1]] # tokens = tweets.map(lambda tweet: (tweet[0], tok.tokenize(tweet[1])))\ # .flatMap(lambda t: splitter(t))\ # .distinct() popular_tokens = group_tokens.filter(lambda x: x[1]>100).cache() # .sortBy(lambda x: x[1], ascending=False).cache() print_count(popular_tokens) print_tokens(popular_tokens.top(20, lambda x:x[1])) Explanation: (2) Tokens that are mentioned by too few users are usually not very interesting. So we want to only keep tokens that are mentioned by at least 100 users. Please filter out tokens that don't meet this requirement. Call print_count function to show how many different tokens we have after the filtering. Call print_tokens function to show top 20 most frequent tokens. It should print Number of elements: 52 ===== overall ===== : 1386.0000 rt 1237.0000 . 865.0000 \ 745.0000 the 621.0000 trump 595.0000 x80 545.0000 xe2 543.0000 to 499.0000 , 489.0000 xa6 457.0000 a 403.0000 is 376.0000 in 296.0000 ' 294.0000 of 292.0000 and 287.0000 for 280.0000 ! 269.0000 ? 210.0000 End of explanation # i want to join the partion on the top100 tweets!, so ineed to get it in the form (uid, tweet) twg = sc.parallelize(partitions.items()).rightOuterJoin(tweets)\ .map(lambda (uid,(gid,tweet)): (uid,(7,tweet)) if gid<0 or gid>6 else (uid,(gid,tweet))).cache() def group_score(gid): group_counts = utr.filter(lambda (x,y): x[0]==gid).map(lambda (x,y): (x[1], y)) merged = group_counts.join(popular_tokens) group_scores = merged.map(lambda (token,(V,W)): (token, get_rel_popularity(V,W))) return group_scores for _gid in range(0,8): _rdd = group_score(_gid) print_tokens(_rdd.top(10, lambda a:a[1]), gid=_gid) Explanation: (3) For all tokens that are mentioned by at least 100 users, compute their relative popularity in each user group. Then print the top 10 tokens with highest relative popularity in each user group. In case two tokens have same relative popularity, break the tie by printing the alphabetically smaller one. Hint: Let the relative popularity of a token $t$ be $p$. The order of the items will be satisfied by sorting them using (-p, t) as the key. End of explanation
9,236	Given the following text description, write Python code to implement the functionality described below step by step Description: Logistics We are going to use parallel-tempering, implemented via the python emcee package, to explore our posterior, which consists of the set of distances and gas to dust conversion coefficients to the six velocity slices towards the center of the Cepheus molecular cloud. Since we need to explore a 12 dimensional parameter space, we are going to use 50 walkers, 10000 steps each, at 5 different temperatures. If you would like to edit this parameters, simply edit "nwalkers", "ntemps", and "nsteps" in the cell below. However, we are only going to keep the lowest temperature chain ($\beta=1$) for analysis. Since the sampler.chain object from PTSampler returns an array of shape (Ntemps, Nwalkers, Nsteps, Ndim), returning the samples for all walkers, steps, and dimensions at $\beta=1$ would correspond to sampler.chain[0, Step1: Let's see what our chains look like by producing trace plots Step2: Now we are going to use the seaborn distplot function to plot histograms of the last half of the traces for each parameter. Step3: Now we want to see how similar the parameters at different steps are. To do this, we draw one thousand random samples from the last half of the chain and plot the reddening profile corresponding to those parameters in light blue. Then, we plot the "best fit" reddening profile corresponding to the 50th quantile parameters (essentially the median of the last half of the chains). In all cases, we take the average of the CO for all nside 128 pixels at each slice. As you can see, drawing random samples from the last half of the chain produce reddening profiles essentially identical to the best fit values.	Python Code: import emcee from dustcurve import model import seaborn as sns import numpy as np from dustcurve import pixclass import matplotlib.pyplot as plt import pandas as pd import warnings from dustcurve import io from dustcurve import hputils from dustcurve import kdist import h5py from dustcurve import globalvars as gv %matplotlib inline f=h5py.File('/n/fink1/czucker/Output/2degrees_jul1_0.1sig_cropped.h5') samples=f['/chains'] nsteps=samples.shape[2] ndim=samples.shape[3] #Extract the coldest [beta=1] temperature chain from the sampler object; discard first half of samples as burnin samples_cold = samples[0,:,int(.5*nsteps):,:] traces_cold = samples_cold.reshape(-1, ndim).T #find best fit values for each of the 24 parameters (12 d's and 12 c's) theta=pd.DataFrame(traces_cold) quantile_50=theta.quantile(.50, axis=1).values quantile_84=theta.quantile(.84, axis=1).values quantile_16=theta.quantile(.16, axis=1).values upperlim=quantile_84-quantile_50 lowerlim=quantile_50-quantile_16 #print out distances for i in range(0,int(len(quantile_50)/2)): print('d%i: %.3f + %.3f - %.3f' % (i+1,quantile_50[i],upperlim[i], lowerlim[i])) #print out coefficients for i in range(int(len(quantile_50)/2), int(len(quantile_50))): print('c%i: %.3f + %.3f - %.3f' % (i+1-int(len(quantile_50)/2),quantile_50[i],upperlim[i], lowerlim[i])) Explanation: Logistics We are going to use parallel-tempering, implemented via the python emcee package, to explore our posterior, which consists of the set of distances and gas to dust conversion coefficients to the six velocity slices towards the center of the Cepheus molecular cloud. Since we need to explore a 12 dimensional parameter space, we are going to use 50 walkers, 10000 steps each, at 5 different temperatures. If you would like to edit this parameters, simply edit "nwalkers", "ntemps", and "nsteps" in the cell below. However, we are only going to keep the lowest temperature chain ($\beta=1$) for analysis. Since the sampler.chain object from PTSampler returns an array of shape (Ntemps, Nwalkers, Nsteps, Ndim), returning the samples for all walkers, steps, and dimensions at $\beta=1$ would correspond to sampler.chain[0,:,:,:]. To decrease your value of $\beta$ simply increase the index for the first dimension. For more information on how PTSampler works, see http://dan.iel.fm/emcee/current/user/pt/. We will set off our walkers in a Gaussian ball around a) the kinematic distance estimates for the Cepheus molecular cloud given by a flat rotation curve from Leroy & Rosolowsky 2006 and b) the gas-to-dust coefficient given by the literature. We perturb the walkers in a Gaussian ball with mean 0 and variance 1. You can edit the starting positions of the walkers by editing the "result" variable below. We are going to discard the first half of every walker's chain as burn-in. Setting up the positional arguments for PTSampler We need to feed PTSampler the required positional arguments for the log_likelihood and log_prior function. We do this using the fetch_args function from the io module, which creates an instance of the pixclass object that holds our data and metadata. Fetch_args accepts three arguments: A string specifiying the h5 filenames containing your data, in our case 10 healpix nside 128 pixels centered around (l,b)=(109.75, 13.75), which covers a total area of 2 sq. deg. The prior bounds you want to impose on distances (flat prior) and the standard deviation you'd like for the log-normal prior on the conversion coefficients. For distances, this must be between 4 and 19, because that's the distance modulus range of our stellar posterior array. The prior bounds must be in the format [lowerbound_distance, upperbound_distance, sigma] The gas-to-dust coefficient you'd like to use, given as a float; for this tutorial, we are pulling a value from the literature of 0.06 magnitudes/K. This value is then multiplied by the set of c coefficients we're determining as part of the parameter estimation problem. Fetch_args will then return the correct arguments for the log_likelihood and log_prior functions within the model module. Here we go! The sampler is done running, so now let's check out the results. We are going to print out our mean acceptance fraction across all walkers for the coldest temperature chain. We are also going to discard the first half of each walker's chain as burn-in; to change the number of steps to burn off, simply edit the 3rd dimension of sampler.chain[0,:,n:,:] and input your desired value of n. Next, we are going to compute and print out the 50th, 16th, and 84th percentile of the chains for each distance parameter, using the "quantile" attribute of a pandas dataframe object. The 50th percentile measurement represents are best guess for the each distance parameter, while the difference between the 16th and 50th gives us a lower limit and the difference between the 50th and 84th percentile gives us an upper limit: End of explanation #set up subplots for chain plotting axes=['ax'+str(i) for i in range(ndim)] fig, (axes) = plt.subplots(ndim, figsize=(10,60)) plt.tight_layout() for i in range(0,ndim): if i<int(ndim/2): axes[i].set(ylabel='d%i' % (i+1)) else: axes[i].set(ylabel='c%i' % (i-5)) #plot traces for each parameter for i in range(0,ndim): sns.tsplot(traces_cold[i],ax=axes[i]) Explanation: Let's see what our chains look like by producing trace plots: End of explanation #set up subplots for histogram plotting axes=['ax'+str(i) for i in range(ndim)] fig, (axes) = plt.subplots(ndim, figsize=(10,60)) plt.tight_layout() for i in range(0,ndim): if i<int(ndim/2): axes[i].set(ylabel='d%i' % (i+1)) else: axes[i].set(ylabel='c%i' % (i-5)) #plot traces for each parameter for i in range(0,ndim): sns.distplot(traces_cold[i],ax=axes[i],hist=True,norm_hist=False) Explanation: Now we are going to use the seaborn distplot function to plot histograms of the last half of the traces for each parameter. End of explanation from dustcurve import plot_posterior ratio=0.06 plot_posterior.plot_samples(np.asarray(post_all),np.linspace(4,19,120),np.linspace(0,7,700),quantile_50,traces_cold,ratio,gv.unique_co,y_range=[0,2],vmax=20,normcol=False) Explanation: Now we want to see how similar the parameters at different steps are. To do this, we draw one thousand random samples from the last half of the chain and plot the reddening profile corresponding to those parameters in light blue. Then, we plot the "best fit" reddening profile corresponding to the 50th quantile parameters (essentially the median of the last half of the chains). In all cases, we take the average of the CO for all nside 128 pixels at each slice. As you can see, drawing random samples from the last half of the chain produce reddening profiles essentially identical to the best fit values. End of explanation
9,237	Given the following text description, write Python code to implement the functionality described below step by step Description: Thermal Sensor Measurements The goal of this experiment is to measure temperature on Juno R2 board using the available sensors. In order to do that we will run a busy-loop workload of about 5 minutes and collect traces for the thermal_temperature event. Measurements must be done with and without fan. Step1: Target Configuration Our target is a Juno R2 development board running Linux. Step2: Tests execution Step3: Workloads configuration Step4: Workload execution Step5: Trace Analysis In order to analyze the trace we will plot it using TRAPpy. Step6: The pmic sensor if off-chip and therefore it is not useful to get its temperature.	Python Code: import logging from conf import LisaLogging LisaLogging.setup() %pylab inline import os # Support to access the remote target import devlib from env import TestEnv # Support to configure and run RTApp based workloads from wlgen import RTA, Periodic # Support for trace events analysis from trace import Trace # Suport for FTrace events parsing and visualization import trappy Explanation: Thermal Sensor Measurements The goal of this experiment is to measure temperature on Juno R2 board using the available sensors. In order to do that we will run a busy-loop workload of about 5 minutes and collect traces for the thermal_temperature event. Measurements must be done with and without fan. End of explanation # Setup a target configuration my_target_conf = { # Target platform and board "platform" : 'linux', "board" : 'juno', # Target board IP/MAC address "host" : '192.168.0.1', # Login credentials "username" : 'root', "password" : '', # RTApp calibration values (comment to let LISA do a calibration run) "rtapp-calib" : { "0": 318, "1": 125, "2": 124, "3": 318, "4": 318, "5": 319 }, # Tools required by the experiments "tools" : [ 'rt-app', 'trace-cmd' ], "exclude_modules" : ['hwmon'], # FTrace events to collect for all the tests configuration which have # the "ftrace" flag enabled "ftrace" : { "events" : [ "thermal_temperature", # Use sched_switch event to recognize tasks on kernelshark "sched_switch", # cdev_update has been used to show that "step_wise" thermal governor introduces noise # because it keeps changing the state of the cooling devices and therefore # the available OPPs #"cdev_update", ], "buffsize" : 80 * 1024, }, } Explanation: Target Configuration Our target is a Juno R2 development board running Linux. End of explanation # Initialize a test environment using: # the provided target configuration (my_target_conf) te = TestEnv(target_conf=my_target_conf) target = te.target Explanation: Tests execution End of explanation # Create a new RTApp workload generator using the calibration values # reported by the TestEnv module rtapp_big = RTA(target, 'big', calibration=te.calibration()) big_tasks = dict() for cpu in target.bl.bigs: big_tasks['busy_big'+str(cpu)] = Periodic(duty_cycle_pct=100, duration_s=360, # 6 minutes cpus=str(cpu) # pinned to a given cpu ).get() # Configure this RTApp instance to: rtapp_big.conf( # 1. generate a "profile based" set of tasks kind='profile', # 2. define the "profile" of each task params=big_tasks, # 3. Set load reference for task calibration loadref='big', # 4. use this folder for task logfiles run_dir=target.working_directory ); rtapp_little = RTA(target, 'little', calibration=te.calibration()) little_tasks = dict() for cpu in target.bl.littles: little_tasks['busy_little'+str(cpu)] = Periodic(duty_cycle_pct=100, duration_s=360, cpus=str(cpu)).get() rtapp_little.conf( kind='profile', params=little_tasks, # Allow the task duration to be calibrated for the littles (default is for big) loadref='little', run_dir=target.working_directory ); Explanation: Workloads configuration End of explanation logging.info('#### Setup FTrace') te.ftrace.start() logging.info('#### Start RTApp execution') # Run tasks on the bigs in background to allow execution of following instruction rtapp_big.run(out_dir=te.res_dir, background=True) # Run tasks on the littles and then wait 2 minutes for device to cool down rtapp_little.run(out_dir=te.res_dir, end_pause_s=120.0) logging.info('#### Stop FTrace') te.ftrace.stop() Explanation: Workload execution End of explanation # Collect the trace trace_file = os.path.join(te.res_dir, 'trace.dat') logging.info('#### Save FTrace: %s', trace_file) te.ftrace.get_trace(trace_file) # Parse trace therm_trace = trappy.FTrace(trace_file) therm_trace.thermal.data_frame.tail(10) # Plot the data therm_plot = trappy.ILinePlot(therm_trace, signals=['thermal:temp'], filters={'thermal_zone': ["soc"]}, title='Juno R2 SoC Temperature w/o fans') therm_plot.view() Explanation: Trace Analysis In order to analyze the trace we will plot it using TRAPpy. End of explanation # Extract a data frame for each zone df = therm_trace.thermal.data_frame soc_df = df[df.thermal_zone == "soc"] big_df = df[df.thermal_zone == "big_cluster"] little_df = df[df.thermal_zone == "little_cluster"] gpu0_df = df[df.thermal_zone == "gpu0"] gpu1_df = df[df.thermal_zone == "gpu1"] # Build new trace juno_trace = trappy.BareTrace(name = "Juno_R2") juno_trace.add_parsed_event("SoC", soc_df) juno_trace.add_parsed_event("big_Cluster", big_df) juno_trace.add_parsed_event("LITTLE_Cluster", little_df) juno_trace.add_parsed_event("gpu0", gpu0_df) juno_trace.add_parsed_event("gpu1", gpu1_df) # Plot the data for all sensors juno_signals = ['SoC:temp', 'big_Cluster:temp', 'LITTLE_Cluster:temp', 'gpu0:temp', 'gpu1:temp'] therm_plot = trappy.ILinePlot([juno_trace], signals=juno_signals, title='Juno R2 Temperature all traces') therm_plot.view() Explanation: The pmic sensor if off-chip and therefore it is not useful to get its temperature. End of explanation
9,238	Given the following text description, write Python code to implement the functionality described below step by step Description: Calculating Wang's Semantic Similarity between two GO Terms Setup Calculate Wang's semantic similarity using optional part_of relationship Calculate Wang's semantic similarity using researcher-set edge weights. Calculate Wang's semantic similarity usint is_of relationship only 1) Setup Create a list of all GO IDs that will be compared Step1: Read the GO DAG Step3: Create a printing function for this notebook Step4: 2) Calculate Wang's semantic similarity using part_of relationship Instantiate a Wang's Semantic Similarity object We choose to use the optional relationship, part_of, in addition the required is_a relationships for this example. Load all GO IDs that you will be comparing. Step5: Visualize researcher GO terms Step6: Step7: 3) Calculate Wang's semantic similarity using researcher-set edge weights Print the default edge weights in the Wang configuration Step8: Use researcher-specified edge weights Step9: 4) Calculate Wang's semantic similarity with is_a relationships only Step10: Calculate Wang's semantic similarity using is_a relationship only Step11: Instantiate a Wang's Semantic Similarity object We use the required is_a relationships only. Load all GO IDs that you will be comparing.	Python Code: # Researcher-provided GO terms related to smell go_a = 'GO:0007608' go_b = 'GO:0050911' go_c = 'GO:0042221' # Optional relationships. (Relationship, is_a, is required and always used) relationships = {'part_of'} goids = {go_a, go_b, go_c} # Annotations for plotting go2txt = { go_a:'GO TERM A', go_b:'GO TERM B', go_c:'GO TERM C'} Explanation: Calculating Wang's Semantic Similarity between two GO Terms Setup Calculate Wang's semantic similarity using optional part_of relationship Calculate Wang's semantic similarity using researcher-set edge weights. Calculate Wang's semantic similarity usint is_of relationship only 1) Setup Create a list of all GO IDs that will be compared End of explanation from goatools.base import get_godag godag = get_godag("go-basic.obo", optional_attrs={'relationship'}) Explanation: Read the GO DAG End of explanation def print_details(go_a, go_b, val): Print concise and informative report: GO terms and their semantic similarity pattern = ('go_a: {GOa} {GOa_name}\n' 'go_b: {GOb} {GOb_name}\n' 'wang: {VAL:.8f}\n') print(pattern.format( GOa=go_a, GOa_name=godag[go_a].name, GOb=go_b, GOb_name=godag[go_b].name, VAL=val)) Explanation: Create a printing function for this notebook End of explanation from goatools.semsim.termwise.wang import SsWang # goids: researcher-provided GO terms wang_r1 = SsWang(goids, godag, relationships) Explanation: 2) Calculate Wang's semantic similarity using part_of relationship Instantiate a Wang's Semantic Similarity object We choose to use the optional relationship, part_of, in addition the required is_a relationships for this example. Load all GO IDs that you will be comparing. End of explanation from goatools.gosubdag.gosubdag import GoSubDag from goatools.gosubdag.plot.gosubdag_plot import GoSubDagPlot r1_png = 'smell_r1.png' r1_gosubdag = GoSubDag(goids, godag, relationships) GoSubDagPlot(r1_gosubdag, go2txt=go2txt).plt_dag(r1_png) Explanation: Visualize researcher GO terms End of explanation val = wang_r1.get_sim(go_a, go_b) print_details(go_a, go_b, val) val = wang_r1.get_sim(go_a, go_c) print_details(go_a, go_c, val) val = wang_r1.get_sim(go_b, go_c) print_details(go_b, go_c, val) Explanation: End of explanation wang_r1.prt_cfg() Explanation: 3) Calculate Wang's semantic similarity using researcher-set edge weights Print the default edge weights in the Wang configuration End of explanation relationship2weight = { 'is_a': 0.9, 'part_of': 0.9 } wang_r1 = SsWang(goids, godag, relationships, relationship2weight) val = wang_r1.get_sim(go_a, go_b) print_details(go_a, go_b, val) val = wang_r1.get_sim(go_a, go_c) print_details(go_a, go_c, val) val = wang_r1.get_sim(go_b, go_c) print_details(go_b, go_c, val) Explanation: Use researcher-specified edge weights End of explanation wang_r0 = SsWang(goids, godag) Explanation: 4) Calculate Wang's semantic similarity with is_a relationships only End of explanation r0_png = 'smell_r0.png' r0_gosubdag = GoSubDag(goids, godag) GoSubDagPlot(r0_gosubdag, go2txt=go2txt).plt_dag(r0_png) Explanation: Calculate Wang's semantic similarity using is_a relationship only End of explanation val = wang_r0.get_sim(go_a, go_b) print_details(go_a, go_b, val) val = wang_r0.get_sim(go_a, go_c) print_details(go_a, go_c, val) val = wang_r0.get_sim(go_b, go_c) print_details(go_b, go_c, val) Explanation: Instantiate a Wang's Semantic Similarity object We use the required is_a relationships only. Load all GO IDs that you will be comparing. End of explanation
9,239	Given the following text description, write Python code to implement the functionality described below step by step Description: scipy stats This notebook focuses on the use of the scipy.stats module It is built based on a learn-by-example approach So it only covers a little part of the module's functionalities but provides a practical application. Some knowledge of numpy and matplotlib is needed to fully understand the content. Introduction The scipy.stats module provides mainly Step1: Probability distributions The scipy.stats module provides a very complete set of probability distributions. There are three types of distributions Step2: Discrete Distributions Discrete distributions have quite the same API. Having pmf= Probability Mass Function (instead of pdf) Step3: Example Step4: Create a Normal distribution Let's assume that the stock prices follow a Normal distribution Step5: Step6: Exercise Step7: Compute the expected profit and top 5% risk Step8: Both the expected profit and the risk assessed are too high!! Try adding Intel to the product in order to lower them down	Python Code: %matplotlib inline import numpy as np from scipy import stats import matplotlib.pyplot as plt import pandas as pd Explanation: scipy stats This notebook focuses on the use of the scipy.stats module It is built based on a learn-by-example approach So it only covers a little part of the module's functionalities but provides a practical application. Some knowledge of numpy and matplotlib is needed to fully understand the content. Introduction The scipy.stats module provides mainly: * probability distributions: continuous, discrete and multivariate * statistical functions such as statistics and tests For further details you can check the official documentation Imports End of explanation N_SAMPLES = 1000 pds = [('Normal', stats.norm(), (-4., 4.)), ('LogNormal', stats.lognorm(1.), (0., 4.)), ('Students T', stats.t(3.), (-10., 10.)), ('Chi Squared', stats.chi2(1.), (0., 10.))] n_pds = len(pds) fig, ax_list = plt.subplots(n_pds, 3) fig.set_size_inches((5.n_pds, 10.)) for ind, elem in enumerate(pds): pd_name, pd_func, pd_range = elem x_range = np.linspace(pd_range, 101) # Probability Density Function ax_list[ind, 0].plot(x_range, pd_func.pdf(x_range)) ax_list[ind, 0].set_ylabel(pd_name) # Cumulative Distribution Function ax_list[ind, 1].plot(x_range, pd_func.cdf(x_range)) ax_list[ind, 1].fill_between(x_range, pd_func.cdf(x_range)) ax_list[ind, 1].set_ylim([0., 1.]) # Random Variable Sample ax_list[ind, 2].hist(pd_func.rvs(size=N_SAMPLES), bins=50) if ind == 0: _ = ax_list[ind, 0].set_title('Probability Density Function') _ = ax_list[ind, 1].set_title('Cumulative Distribution Function') _ = ax_list[ind, 2].set_title('Random Sample') Explanation: Probability distributions The scipy.stats module provides a very complete set of probability distributions. There are three types of distributions: * Continuous * Discrete * Multivariate Each of the univariate types is inherited from the same class, so they all have a common API. Continuos distributions There are ~100 different continuous distributions. Some of the methods in the API: * cdf: Cumulative Distribution Function * pdf: Probability Density Function * rvs: Random Variable Sample * ppf: Percent Point Function (inverse of the CDF) * fit: return MLE estimations of location, scale and shape, given a set of data End of explanation N_SAMPLES = 1000 pds = [('Binomial', stats.binom(20, 0.7), (0., 21.)), ('Poisson', stats.poisson(10.), (0., 21.))] n_pds = len(pds) fig, ax_list = plt.subplots(n_pds, 3) fig.set_size_inches((8.n_pds, 8.)) for ind, elem in enumerate(pds): pd_name, pd_func, pd_range = elem x_range = np.arange(pd_range) # Probability Mass Function ax_list[ind, 0].bar(x_range, pd_func.pmf(x_range)) ax_list[ind, 0].set_ylabel(pd_name) # Cumulative Distribution Function ax_list[ind, 1].plot(x_range, pd_func.cdf(x_range)) ax_list[ind, 1].fill_between(x_range, pd_func.cdf(x_range)) ax_list[ind, 1].set_ylim([0., 1.]) # Random Variable Sample ax_list[ind, 2].hist(pd_func.rvs(size=N_SAMPLES), bins=x_range - 0.5) if ind == 0: _ = ax_list[ind, 0].set_title('Probability Mass Function') _ = ax_list[ind, 1].set_title('Cumulative Distribution Function') _ = ax_list[ind, 2].set_title('Random Sample') Explanation: Discrete Distributions Discrete distributions have quite the same API. Having pmf= Probability Mass Function (instead of pdf) End of explanation df_prices = pd.read_csv('../resources/stock.csv') df_prices.head(10) df_prices.plot(no) _ = df_prices[['Apple', 'Microsoft']].plot(title='2016 stock prices') # Compute the daily relative increments df_incs = df_prices.drop('Date', axis=1) df_incs = ((df_incs - df_incs.shift(1))/df_incs.shift(1)).loc[1:, :] df_incs['Date'] = df_prices.Date df_incs.head(10) _ = df_incs[['Apple', 'Microsoft']].plot(title='2016 stock prices variations') m = np.mean(df_incs) print(m) s = np.std(df_incs, ddof=1) print(s) c = df_incs.cov() c Explanation: Example: creating a financial product Load and manipulate the data End of explanation # we can use the fit method to get the MLE of the mean and the std stats.norm.fit(df_incs.Apple) # Create estimated distributions based on the sample app_dist = stats.norm(m['Apple'], s['Apple']) win_dist = stats.norm(m['Microsoft'], s['Microsoft']) intl_dist = stats.norm(m['Intel'], s['Intel']) # We can test if this data fits a normal distribution (Kolmogorov-Smirnov test) app_KS = stats.kstest(df_incs['Apple'], 'norm', [m['Apple'], s['Apple']]) win_KS = stats.kstest(df_incs['Microsoft'], 'norm', [m['Microsoft'], s['Microsoft']]) intl_KS = stats.kstest(df_incs['Intel'], 'norm', [m['Intel'], s['Intel']]) print('''Apple: {} Microsoft: {} Intel: {}'''.format(app_KS, win_KS, intl_KS)) Explanation: Create a Normal distribution Let's assume that the stock prices follow a Normal distribution End of explanation # Compare histogram with estimated distribution x_range = np.arange(-0.05, +0.0501, 0.001) x_axis = (x_range[1:] + x_range[:-1])/2. n_incs = df_incs.shape[0] y_app = (app_dist.cdf(x_range[1:]) - app_dist.cdf(x_range[:-1]))n_incs y_win = (win_dist.cdf(x_range[1:]) - win_dist.cdf(x_range[:-1]))n_incs y_intl = (intl_dist.cdf(x_range[1:]) - intl_dist.cdf(x_range[:-1]))n_incs fig = plt.figure(figsize=(16., 6.)) ax_app = fig.add_subplot(131) _ = ax_app.hist(df_incs['Apple'], bins=x_range, color='powderblue') _ = ax_app.set_xlabel('Apple') _ = ax_app.plot(x_axis, y_app, color='blue', linewidth=3) ax_win = fig.add_subplot(132) _ = ax_win.hist(df_incs['Microsoft'], bins=x_range, color='navajowhite') _ = ax_win.set_xlabel('Microsoft') _ = ax_win.plot(x_axis, y_win, color='orange', linewidth=3) ax_intl = fig.add_subplot(133) _ = ax_intl.hist(df_incs['Intel'], bins=x_range, color='lightgreen') _ = ax_intl.set_xlabel('Intel') _ = ax_intl.plot(x_axis, y_win, color='green', linewidth=3) Explanation: End of explanation # Create a multivariate normal distribution object m_norm = stats.multivariate_normal(m[['Apple', 'Microsoft']], df_incs[['Apple', 'Microsoft']].cov()) # Show the contour plot of the pdf x_range = np.arange(-0.05, +0.0501, 0.001) x, y = np.meshgrid(x_range, x_range) pos = np.dstack((x, y)) fig_m_norm = plt.figure(figsize=(6., 6.)) ax_m_norm = fig_m_norm.add_subplot(111) ax_m_norm.contourf(x, y, m_norm.pdf(pos), 50) _ = ax_m_norm.set_xlabel('Apple') _ = ax_m_norm.set_ylabel('Microsoft') Explanation: Exercise: Imagine you are a product designer in a finantial company. You want to create a new investment product to be "sold" to your clients based on the future stock prices of some IT companies. The profit the client gets from his investement is calculated like this: At the time of the investment we check the initial stock prices * 12 months later (let's say 240 work days), the client gets 100% of the investement back. Additionally if all stock prices are higher than the initial ones, the client earns half the lowest increment (in %). What is the expected profit of this investment? What is the 5% highest risk that the finantial company is assuming? First we will try to create a finantial product based on the stock prices of Apple and Microsoft Create a multinormal distribution End of explanation # Create N (e.g 1000) random simulations of the daily relative increments with 240 samples N_SIMS = 1000 daily_incs = m_norm.rvs(size=[240, N_SIMS]) # Calculate yearly increments (from the composition of the daily increments) year_incs = (daily_incs + 1.).prod(axis=0) # calculate the amount payed for each simulation def amount_to_pay(a): if np.all( a >= 1.): return (a.min() - 1)/2 else: return 0. earnings = np.apply_along_axis(amount_to_pay, 1, year_incs) _ = plt.hist(earnings, bins=50) print('Expected profit of the investment: {:.2%}'.format(earnings.mean())) # To compute the 5% higher profit use the stats.scoreatpercentile function print('%5 higher profit of the investment: {:.2%}'.format(stats.scoreatpercentile(earnings, 95))) print('%1 higher profit of the investment: {:.2%}'.format(stats.scoreatpercentile(earnings, 99))) Explanation: Compute the expected profit and top 5% risk End of explanation # %load -r 2:10 solutions/07_02_scipy_stats.py Explanation: Both the expected profit and the risk assessed are too high!! Try adding Intel to the product in order to lower them down End of explanation