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4,700 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EECS 445
Step1: Let's try several polynomial fits to the data
Step2: Let's plot the data with the estimators! | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from numpy.matlib import repmat
from sklearn
degrees = [1,2,3,4,5]
#define data
n = 20
sub = 1000
mean = 0
std = 0.25
#define test set
Xtest = np.random.random((n,1))*2*np.pi
ytest = np.sin(Xtest) + np.random.normal(mean,std,(n,1))
#pre-allocate variables
... |
4,701 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create a RNN model to text generation
RNN model at character level
Input
Step1: Download data and generate sequences
Download quijote from guttenberg project
wget http
Step2: Train the mod... | Python Code:
# Header
from __future__ import print_function
import numpy as np
import tensorflow as tf
print('Tensorflow version: ', tf.__version__)
import time
#Show images
import matplotlib.pyplot as plt
%matplotlib inline
# plt configuration
plt.rcParams['figure.figsize'] = (10, 10) # size of images
plt.rcPar... |
4,702 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualisation of the electrostatic field around a charged circular plate
This notebook shows how to numerically calculate and visualise the fields around a circular homogeneously charged ins... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
Explanation: Visualisation of the electrostatic field around a charged circular plate
This notebook shows how to numerically calculate and visualise the fields around a circular homogeneously charged insulating plate.
(C) Jo Verbeeck, EMAT, University of A... |
4,703 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 17
Step1: In addition to the simulation parameters, we start with an initial seed of concentration data. Unlike our other analytical strategies there are no coefficients to compute,... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: Lecture 17: Numerical Solutions to the Diffusion Equation (Explicit Methods)
Reading and Reference
Numerical Recipes, W. Press, Cambridge University Press, 1986
Numerical Methods, R. Hornbeck, Quantum Publishers, 1975
B. Gu... |
4,704 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementing McLennan-Tourky in Python
Daisuke Oyama
Faculty of Economics, University of Tokyo
Step1: Univariate example
Step2: Let us try the logistic function which is well known to gene... | Python Code:
%matplotlib inline
import time
import numpy as np
import matplotlib.pyplot as plt
import quantecon as qe
from quantecon.compute_fp import _print_after_skip
from quantecon.game_theory import Player, NormalFormGame, lemke_howson
def compute_fixed_point_ig(f, x_init, error_tol=1e-3, max_iter=50, verbose=1,
... |
4,705 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example of Interactive Plotting
The IPython notebook excels at interactive science. By its very nature you can easily create a bit of code, run it, look at the output, and adjust the code. T... | Python Code:
# Import Module
import numpy as np
import scipy as sp
import scipy.stats as stats
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
Explanation: Example of Interactive Plotting
The IPython notebook excels at interactive science. By its very nature you can easily create a bit of code, r... |
4,706 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Yellowbrick Examples
Ths notebook is a sample of the examples that yellowbrick provides.
Step1: Load Medical Appointment Data
The data used in this example is hosted by Kaggle at following ... | Python Code:
import os
import sys
# Modify the path
sys.path.append("..")
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
Explanation: Yellowbrick Examples
Ths notebook is a sample of the examples that yellowbrick provides.
End of explanation
data = pd.read_csv("data/No-show-Issue-Comma... |
4,707 | 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 an... | 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
def trapz(f, a, b, N):
Integrate the function f(x) over the range [a,b] with N points.
h = (b-a)/N
k = np.arange(1,N)
I = h * (0.5... |
4,708 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'nicam16-9d-l78', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MIROC
Source ID: NICAM16-9D-L78
Topic: Aerosol
Sub-Topics: Transpor... |
4,709 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch 02
Step1: Create a boolean vector called spikes of the same dimensions as before
Step2: Restored the variable data from disk, serve warm, and enjoy
Step3: Show's over, goodnight | Python Code:
import tensorflow as tf
sess = tf.InteractiveSession()
Explanation: Ch 02: Concept 07
Loading variables
Concept 06 was about saving variables. This one's about loading what you saved. Start by creating an interactive session:
End of explanation
spikes = tf.Variable([False]*8, name='spikes')
Explanation: Cr... |
4,710 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Random Forest with Grid Search (XGBoost)
<a href="https
Step1: This example features
Step2: Imports
Step3: Log Workflow
Prepare Data
Step4: Prepare Hyperparameters
Step5: Instantiate Cl... | Python Code:
# restart your notebook if prompted on Colab
try:
import verta
except ImportError:
!pip install verta
Explanation: Random Forest with Grid Search (XGBoost)
<a href="https://colab.research.google.com/github/VertaAI/modeldb/blob/master/client/workflows/examples/xgboost.ipynb" target="_parent"><img sr... |
4,711 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
$$
\def\CC{\bf C}
\def\QQ{\bf Q}
\def\RR{\bf R}
\def\ZZ{\bf Z}
\def\NN{\bf N}
$$
Lean Tutorial
This tutorial runs through all of the steps for doing a project with
Marvin from start-to-finis... | Python Code:
from marvin.tools.query import doQuery
q, r = doQuery(search_filter='nsa.sersic_logmass >= 10 and nsa.sersic_logmass <= 11', limit=3)
Explanation: $$
\def\CC{\bf C}
\def\QQ{\bf Q}
\def\RR{\bf R}
\def\ZZ{\bf Z}
\def\NN{\bf N}
$$
Lean Tutorial
This tutorial runs through all of the steps for doing a project w... |
4,712 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Suponha que não soubéssemos quantas espécies diferentes estão presentes no dataset iris. Como poderíamos descobrir essa informação aproximadamente a partir dos dados presentes ali?
Uma soluç... | Python Code:
import pandas as pd
iris = # Carregue o arquivo 'datasets/iris_without_classes.csv'
# Exiba as primeiras cinco linhas usando o método head() para checar que não existe mais a coluna "Class"
Explanation: Suponha que não soubéssemos quantas espécies diferentes estão presentes no dataset iris. Como poderíamo... |
4,713 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<figure>
<IMG SRC="../../logo/logo.png" WIDTH=250 ALIGN="right">
</figure>
IHE Python course, 2017
Reading, writing and working with shape files
T.N.Olsthoorn
2017-03-15
When working with ... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import shapefile as shp
from pprint import pprint
Explanation: <figure>
<IMG SRC="../../logo/logo.png" WIDTH=250 ALIGN="right">
</figure>
IHE Python course, 2017
Reading, writing and working with shape files
T.N.Olsthoorn
2017-03-15
When workin... |
4,714 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Goal
Step1: 2. Use mechanize to get reviews for all of the top attractions
Step2: Get reviews for top attractions in multiple cities
Step3: Finding forms with mechanize | Python Code:
# url = 'https://www.tripadvisor.com/Attraction_Review-g60878-d3184389-Reviews-Chihuly_Garden_and_Glass-Seattle_Washington.html#REVIEWS'
def get_reviews(response):
# response = requests.get(url)
soup = BeautifulSoup(response, 'html.parser')
entries = soup.findAll('div', {'class': 'entry'})
... |
4,715 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mapping fractions between gradient communities in order to perform procrustes
Step1: Calculating centroid of binned fraction samples
centroid of all 20 replicates for fraction samples that ... | Python Code:
%%R
otu.tbl.file1 = '/home/nick/notebook/SIPSim/dev/bac_genome1210/atomIncorp_taxaIncorp/0/10/1/OTU_n2_abs1e9_sub-norm_filt.physeq'
otu.tbl.file2 = '/home/nick/notebook/SIPSim/dev/bac_genome1210/atomIncorp_taxaIncorp/100/10/1/OTU_n2_abs1e9_sub-norm_filt.physeq'
physeq1 = readRDS(otu.tbl.file1)
physeq2 = re... |
4,716 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A simple SEA model of two beams
Step1: Creating a SEA model
To create a SEA model we begin by creating an instance of Model.
Step2: We are only interested in a limited frequency range.
An ... | Python Code:
import sys
import seapy
import numpy as np
%matplotlib inline
Explanation: A simple SEA model of two beams
End of explanation
from acoustics.signal import OctaveBand
frequency = OctaveBand(fstart=500.0, fstop=8000.0, fraction=1)
system1 = seapy.system.System(frequency)
Explanation: Creating a SEA model
To ... |
4,717 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 2 assignment
This assignment will get you familiar with the basic elements of Python by programming a simple card game. We will create a custom class to represent each player in the game... | Python Code:
import random
import numpy
Explanation: Lab 2 assignment
This assignment will get you familiar with the basic elements of Python by programming a simple card game. We will create a custom class to represent each player in the game, which will store information about their current pot, as well as a series o... |
4,718 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parsing and querying AI Platform Prediction request-response logs in BigQuery
This tutorial shows you how to create a view to parse raw request instances and response predictions logged from... | Python Code:
!pip install -U -q google-api-python-client
!pip install -U -q pandas
Explanation: Parsing and querying AI Platform Prediction request-response logs in BigQuery
This tutorial shows you how to create a view to parse raw request instances and response predictions logged from AI Platform Prediction to BigQuer... |
4,719 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Small scale example
Step1: Example with more savings, but slower to optimize
Step2: Look at the recommendations | Python Code:
def func(a, b, c):
res = tf.einsum('ijk,ja,kb->iab', a, b, c) + 1
res = tf.einsum('iab,kb->iak', res, c)
return res
a = tf.random_normal((10, 11, 12))
b = tf.random_normal((11, 13))
c = tf.random_normal((12, 14))
# res = func(a, b, c)
orders, optimized_func = tf_einsum_opt.optimizer(func, sess,... |
4,720 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
← Back to Index
Energy and RMSE
The energy (Wikipedia of a signal corresponds to the total magntiude of the signal. For audio signals, that roughly corresponds to how loud the signal is... | Python Code:
x, sr = librosa.load('audio/simple_loop.wav')
sr
x.shape
librosa.get_duration(x, sr)
Explanation: ← Back to Index
Energy and RMSE
The energy (Wikipedia of a signal corresponds to the total magntiude of the signal. For audio signals, that roughly corresponds to how loud the signal is. The energy in a s... |
4,721 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Meta-analysis of incubation time for Covid-19 with pediatric subset
A wide variety of estimates of the incubation time have been seen in the rapidly evolving Covid-19 literature. As of March... | Python Code:
import numpy as np
import pandas as pd
import os
import re
import seaborn as sns
from datetime import datetime as dt
from support_funs_incubation import stopifnot, uwords, idx_find, find_beside, ljoin, sentence_find, record_vals
!pip install ansicolors
# Takes a tuple (list(idx), sentence) and will print i... |
4,722 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
5 次元削減でデータを圧縮する
特徴部分空間(feature extraction)を作成する
主成分分析(PCA
Step1: 5.1.2 特徴変換
Step2: 5.1.3 scikit-learn の主成分分析
Step3: 5.2 線形判別分析による教師ありデータ圧縮
d次元のデータセットを標準化する(dは特徴量の個数)
クラスごとにd次元の平均ベクトルを計算する... | Python Code:
from IPython.core.display import display
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
import pandas as pd
# http://archive.ics.uci.edu/ml/datasets/Wine
df_wine = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'... |
4,723 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch4 Writing Structured Programs
Assignments
Step1: 注意
Step2: Equality
用==是比較兩個元素值是否相同。
用is是比較兩個元素是否參考同一個物件。
Step3: Conditions
將list放在if中，會直接判斷list是否為空，相當於if len(list) > 0
Step4: any()... | Python Code:
a = list('hello') # a指向一個list物件
b = a # b指向a所指向的list物件
b[3] = 'x' # 改變物件第3個元素，因為實際件只有一個，所以a,b看到的物件會同時改變
a, b
a = ['maybe']
b = [a, a, a]
b
a[0] = 'will'
b
Explanation: Ch4 Writing Structured Programs
Assignments
End of explanation
a = ['play']
b = a[:]
a[0] = 'zero'
a, b
a = ['play']
... |
4,724 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in... | Python Code:
import jax.numpy as jnp
from jax import grad, jit, vmap
from jax import random
Explanation: Copyright 2018 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the Lice... |
4,725 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 2
Step1: Load in house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
Step2: Split data into training and testi... | Python Code:
import graphlab
Explanation: Regression Week 2: Multiple Regression (Interpretation)
The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.
In this notebook you will use data on house sales in King County to predict prices using multiple ... |
4,726 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
We generate some random variates from a non-normal distribution and make a
probability plot for it, to show it is non-normal in the tails
Step1: We now use boxcox to transform the data so i... | Python Code:
# Generate data
x = stats.loggamma.rvs(5, size=500) + 5
# Plot it
fig = plt.figure(figsize=(6,9))
ax1 = fig.add_subplot(211)
prob = stats.probplot(x, dist=stats.norm, plot=ax1)
ax1.set_title('Probplot against normal distribution')
# Plot an histogram
ax2 = fig.add_subplot(212)
ax2.hist(x)
ax2.set_title('Hi... |
4,727 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align="center">TensorFlow Neural Network Lab</h1>
<img src="image/notmnist.png">
In this lab, you'll use all the tools you learned from Introduction to TensorFlow to label images of Engl... | Python Code:
import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
p... |
4,728 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: TensorFlowアドオンのコールバック：TimeStopping
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: データのインポートと正規化... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,729 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Preprocess
Step2: i2t
Step3: t2i | Python Code:
import pandas as pd
import tensorflow as tf
# i2t: image-to-text.
i2t_path = '/bigstore/mmt/raw_data/fashion_gen/fashion_gen_i2t_test_pairs.csv'
# t2i: text-to-image.
t2i_path = '/bigstore/mmt/raw_data/fashion_gen/fashion_gen_t2i_test_pairs.csv'
t2i_output_path = '/bigstore/mmt/fashion_gen/metadata/fashion... |
4,730 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kets are column vectors, i.e. with shape (d, 1)
Step1: The normalized=True option can be used to ensure a normalized output.
Bras are row vectors, i.e. with shape (1, d)
Step2: And operato... | Python Code:
qu(data, qtype='ket')
Explanation: Kets are column vectors, i.e. with shape (d, 1):
End of explanation
qu(data, qtype='bra') # also conjugates the data
Explanation: The normalized=True option can be used to ensure a normalized output.
Bras are row vectors, i.e. with shape (1, d):
End of explanation
qu(dat... |
4,731 | 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
Specif... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-3', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CMCC
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Properties: ... |
4,732 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building a song recommender
Fire up GraphLab Create
Step1: Load music data
Step2: Explore data
Music data shows how many times a user listened to a song, as well as the details of the song... | Python Code:
import graphlab
Explanation: Building a song recommender
Fire up GraphLab Create
End of explanation
song_data = graphlab.SFrame('song_data.gl/')
Explanation: Load music data
End of explanation
song_data.head()
Explanation: Explore data
Music data shows how many times a user listened to a song, as well as t... |
4,733 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1A.e - Enoncé 22 octobre 2019 (2)
Correction du second énoncé de l'examen du 22 octobre 2019. L'énoncé propose une façon de disposer des tables carrées dans une salle carrée.
Step1: On sait... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: 1A.e - Enoncé 22 octobre 2019 (2)
Correction du second énoncé de l'examen du 22 octobre 2019. L'énoncé propose une façon de disposer des tables carrées dans une salle carrée.
End of explanation
def distance_table(x1, y1, x2, y2):
... |
4,734 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Raw Data Thresholding Exploration
A slice at z=0 for reference
Step1: Our Previous Method
Step2: This result initially seemed reasonable. But, as a sanity check, we naively thresholded the... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import pickle
import sys
sys.path.insert(0,'../code/functions/')
import connectLib as cLib
import plosLib as pLib
import mouseVis as mv
import tiffIO as tIO
data0 = tIO.unzipChannels(tIO.loadTiff('../data/SEP-GluA1-... |
4,735 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Verifying Central Limit Theorem in regression
Step1: Synthesize the dataset
Create 1000 random integers between 0, 100 for X and create y such that
$$
y = \beta_{0} + \beta_{1}X + \epsilon
... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
Explanation: Verifying Central Limit Theorem in regression
End of explanation
rand_1kx = np.random.randint(0,100,1000)
x_mean = np.mean(rand_1kx)... |
4,736 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook demonstrates how to use the classes in haloanalysis.model to perform likelihood scans of the IGMF parameter space. To start we construct the following
Step1: The CascLike cla... | Python Code:
%matplotlib inline
from astropy.table import Table
import matplotlib.pyplot as plt
import matplotlib.cm
from fermipy.spectrum import PLExpCutoff
from fermipy.castro import CastroData
from haloanalysis.model import make_prim_model, make_casc_model
from haloanalysis.model import CascModel, CascLike
from halo... |
4,737 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step3: 1. Collect Wikipedia data about Olympic Games 2020
The idea of this project is to create a question answering model, based on a few paragraphs of provided text. Base GPT-3 models do a... | Python Code:
import pandas as pd
import wikipedia
def filter_olympic_2020_titles(titles):
Get the titles which are related to Olympic games hosted in 2020, given a list of titles
titles = [title for title in titles if '2020' in title and 'olympi' in title.lower()]
return titles
def get_wiki_p... |
4,738 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SNR Benefits of Non-Uniform Scalar Quantization
This code is provided as supplementary material of the lecture Quellencodierung.
This code illustrates
* Uniform scalar quantization of audio ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import librosa
import librosa.display
import IPython.display as ipd
Explanation: SNR Benefits of Non-Uniform Scalar Quantization
This code is provided as supplementary material of the lecture Quellencodierung.
This code illustrates
* Uni... |
4,739 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This entire theory is built on the idea that everything is normalized as input into the brain. i.e. all values are between 0 and 1. This is necessary because the learning rule has an adaptiv... | Python Code:
p = GMM([1.0], np.array([[0.5,0.05]]))
num_samples = 1000
beg = 0.0
end = 1.0
t = np.linspace(beg,end,num_samples)
num_neurons = len(p.pis)
colors = [np.random.rand(num_neurons,) for i in range(num_neurons)]
p_y = p(t)
p_max = p_y.max()
np.random.seed(20)
num_neurons = 9
network = Net(1,1,num_neurons, bias... |
4,740 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Coverage characterization using GenomeCov class (sequana.bedtools module)
<center>http
Step1: Read a Coverage file in BED format
Step2: Select one chromosome (there is only one in this cas... | Python Code:
%pylab inline
from sequana import GenomeCov, sequana_data
rcParams['figure.figsize'] = (10,6)
Explanation: Coverage characterization using GenomeCov class (sequana.bedtools module)
<center>http://sequana.readthedocs.org</center>
Author: Thomas Cokelaer 2016-2018
Illustrative example of the Coverage module ... |
4,741 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Computing In Context
Social Sciences Track
Lecture 4--topics, trends, and dimensional scaling
Matthew L. Jones
Step1: Reading at scale
Step2: IMPORTANT
Step3: Let's keep using the remarka... | Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Computing In Context
Social Sciences Track
Lecture 4--topics, trends, and dimensional scaling
Matthew L. Jones
End of explanation
from IPython.display import Image
Image("http://journalofdigitalhumanities.org/wp-content/upl... |
4,742 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Usage
Step1: Find from the texts
Step2: frequent serial episodes which consist of words üks and kaks.
Find frequent episodes
Let the width of the Winepi window be 31 characters and the mi... | Python Code:
from episode_miner import EventText, EventSequences, Episode, Episodes
from estnltk.taggers import EventTagger
from IPython.display import HTML, FileLink
Explanation: Usage
End of explanation
event_vocabulary = [{'term': 'üks'},
{'term': 'kaks'}]
event_tagger = EventTagger(event_vo... |
4,743 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Motivation" data-toc-modified-id="Motivation-1"><span class="toc-item-num">1... | Python Code:
from collections import OrderedDict
# values entered manually from https://brandlive.here.com/colors
here_primary_cols = OrderedDict(
HERE_Aqua = '#48dad0',
HERE_Aqua_UNKNOWN = '#00908a', # unknown status, maybe an error?
HERE_Aqua_Dark = '#00afaa',
HERE_Aqua_75 = '#76e3dc',... |
4,744 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Original paper
Step1: Make sure that the values of real and generated data are of the same order - it is important for cooperative binarizing
Step2: 1. Binarize#
To understand how real and... | Python Code:
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib
import torch
import sklearn
CHANNEL_NUM = 3
PICTURE_SIZE = 36
class ParticleDataset():
def __init__(self, file):
self.data = np.load(file)
self.image = self.data['Pictures'... |
4,745 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create a new Materials Commons project
This example demonstrates creating a new Materials Commons project from a Jupyter notebook. To try running it locally, download the notebook from here.... | Python Code:
# Login information (Edit here or be prompted by the next cell)
email = None
mcurl = "https://materialscommons.org/api"
# Construct a Materials Commons client
from materials_commons.cli.user_config import make_client_and_login_if_necessary
if email is None:
print("Account (email):")
email = input()... |
4,746 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CrowdTruth for Free Input Tasks
Step1: Declaring a pre-processing configuration
The pre-processing configuration defines how to interpret the raw crowdsourcing input. To do this, we need to... | Python Code:
import pandas as pd
test_data = pd.read_csv("../data/person-video-free-input.csv")
test_data.head()
Explanation: CrowdTruth for Free Input Tasks: Person Annotation in Video
In this tutorial, we will apply CrowdTruth metrics to a free input crowdsourcing task for Person Annotation from video fragments. The ... |
4,747 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy Exercise 1
Imports
Step2: Checkerboard
Write a Python function that creates a square (size,size) 2d Numpy array with the values 0.0 and 1.0
Step3: Use vizarray to visualize a checker... | Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
Explanation: Numpy Exercise 1
Imports
End of explanation
def checkerboard(size):
Return a 2d checkboard of 0.0 and 1.0 as a NumPy array
a=np.zer... |
4,748 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multi-worker training with Keras
Learning Objectives
Multi-worker Configuration
Choose the right strategy
Train the model
Multi worker training in depth
Introduction
This notebook demonstrat... | Python Code:
import json
import os
import sys
Explanation: Multi-worker training with Keras
Learning Objectives
Multi-worker Configuration
Choose the right strategy
Train the model
Multi worker training in depth
Introduction
This notebook demonstrates multi-worker distributed training with Keras model using tf.distribu... |
4,749 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 DeepMind Technologies Limited.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain ... | Python Code:
!apt-get install libsdl2-dev
!apt-get install libosmesa6-dev
!apt-get install libffi-dev
!apt-get install gettext
!apt-get install python3-numpy-dev python3-dev
Explanation: Copyright 2021 DeepMind Technologies Limited.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this fi... |
4,750 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A notebook to test and demonstrate the MMD test of Gretton et al., 2012 used as a goodness-of-fit test. Require the ability to sample from the density p.
Step1: MMD test (as a goodness-of-f... | Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
#%config InlineBackend.figure_format = 'pdf'
import freqopttest.tst as tst
import kgof
import kgof.data as data
import kgof.density as density
import kgof.goftest as gof
import kgof.mmd as mgof
import kgof.ke... |
4,751 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bins Mark
This Mark is essentially the same as the Hist Mark from a user point of view, but is actually a Bars instance that bins sample data.
The difference with Hist is that the binning is... | Python Code:
# Create a sample of Gaussian draws
np.random.seed(0)
x_data = np.random.randn(1000)
Explanation: Bins Mark
This Mark is essentially the same as the Hist Mark from a user point of view, but is actually a Bars instance that bins sample data.
The difference with Hist is that the binning is done in the backen... |
4,752 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An Introduction to Experimental Design with Emukit
Overview
Step1: Navigation
What is experimental design?
The ingredients of experimental design
Emukit's experimental design interface
Refe... | Python Code:
# General imports
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
# Figure config
LEGEND_SIZE = 15
Explanation: An Introduction to Experimental Design with Emukit
Overview
End of explanation
from emukit.test_functions import forrester_function
... |
4,753 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python
Basic Functions
Step1: Build In Constants
<ul>
<li>False</li>
<li>True</li>
<li>None -> Represents absence of a value</li>
<li>NotImplememted -> Returned by some functions when the a... | Python Code:
abs(-3) # Gives the absolute value of a variable
all([1,2,3,0]) # Returns True if all iterators are true.
any([1,2,3,4,0]) # If any value is true among the iterators
def prime():
pass
print(callable(prime))
# Returns True if object has a return statement
# instances of classes are callable if they hav... |
4,754 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, notebook style sheet by L.A. Barba, N.C. Clementi
Step1: Viscoelastic stre... | Python Code:
# Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../style/custom.css'
HTML(open(css_file, "r").read())
# Import Libraries
# ----------------
import numpy as np
from numba import jit
import matplotlib
import matplotlib.pyplot as plt
fr... |
4,755 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center> Python and MySQL tutorial </center>
<center> Author
Step1: Calculator
Step2: Strings
Step3: show ' and " in a string
Step4: span multiple lines
Step5: slice and index
Step6: I... | Python Code:
width = 20
height = 5*9
width * height
Explanation: <center> Python and MySQL tutorial </center>
<center> Author: Cheng Nie </center>
<center> Check chengnie.com for the most recent version </center>
<center> Current Version: Feb 12, 2016</center>
Python Setup
Since most students in this class use Windows ... |
4,756 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
To run this sample on Google Cloud Platform with various accelerator setups
Step1: Overview
This notebook is a fork of the Geting started notebook for the Jigsaw Multilingual Toxic Comment ... | Python Code:
# When not running on Kaggle, comment out this import
from kaggle_datasets import KaggleDatasets
# When not running on Kaggle, set a fixed GCS path here
GCS_PATH = KaggleDatasets().get_gcs_path('jigsaw-multilingual-toxic-comment-classification')
print(GCS_PATH)
Explanation: To run this sample on Google Clo... |
4,757 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Fisheries competition
In this notebook we're going to investigate a range of different architectures for the Kaggle fisheries competition. We use VGG with batch normalization through... | Python Code:
import torch
import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torchvision.utils import make_grid
from PIL import Image
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.optim as optim
import torch.utils.trainer as ... |
4,758 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
From v2.8.0, pymatgen comes with a fairly robust system of managing units. In essence, subclasses of float and numpy array is provided to attach units to any quantity, as well a... | Python Code:
import pymatgen as mg
#The constructor is simply the value + a string unit.
e = mg.Energy(1000, "Ha")
#Let's perform a conversion. Note that when printing, the units are printed as well.
print "{} = {}".format(e, e.to("eV"))
#To check what units are supported
print "Supported energy units are {}".format(e.... |
4,759 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Non linear curve fitting with python
Germain Salvato Vallverdu germain.vallverdu@univ-pau.fr
This cookbook presents how to fit a non linear model on a set of data using python. Two kind of a... | Python Code:
# manage data and fit
import pandas as pd
import numpy as np
# first part with least squares
from scipy.optimize import curve_fit
# second part about ODR
from scipy.odr import ODR, Model, Data, RealData
# style and notebook integration of the plots
import seaborn as sns
%matplotlib inline
sns.set(style="w... |
4,760 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab Part II
Step1: Our model will be like this
Step2: Step 2
Step3: Step 3
Step4: Step 4
Step5: Step 5
Step6: Step 6
Step7: Step 7
Step8: Solutions | Python Code:
import math
import pickle as p
import tensorflow as tf
import numpy as np
import utils
import json
Explanation: Lab Part II: RNN Sentiment Classifier
In the previous lab, you built a tweet sentiment classifier with a simple feedforward neural network. Now we ask you to improve this model by representing it... |
4,761 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Download Test Data for XBRAIN
This notebook will walk through on how to download the test data necessary for the demo
Last Update
Step1: Methods
Step2: Parameters to pass to Google Drive
S... | Python Code:
# imports
import requests
import zipfile
import os
Explanation: Download Test Data for XBRAIN
This notebook will walk through on how to download the test data necessary for the demo
Last Update: 10/12/2017
End of explanation
# Methods to pull from google drive
def download_file_from_google_drive(id, destin... |
4,762 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook contains code to train a fully connected neural network on MNIST using tf.contrib.learn. At the end is a short exercise.
Step1: Import the dataset
Step2: There are 55k exampl... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
learn = tf.contrib.learn
tf.logging.set_verbosity(tf.logging.ERROR)
Explanation: This notebook contains code to train a fully connected neural network on MNIST using tf.contrib.learn. At the end is a short exercis... |
4,763 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy 是 Python 数据分析的基础，很多有关Python数据分析都是建立在其之上。
1 ndarray
整个 numpy 的基础是 ndarray (n dimensional array)，表示同一元素组成的多维数组。
+ dtype
数据类型
+ shape
由N个正整数组成的元组，每一个元素代表了每一维的大小
+ axes
数组的维数为轴，轴的数量为秩（ran... | Python Code:
import numpy as np
a = np.array([1,2,3])
print a
type(a)
type(a)
a.dtype
print a.ndim
print a.size
print a.shape
Explanation: Numpy 是 Python 数据分析的基础，很多有关Python数据分析都是建立在其之上。
1 ndarray
整个 numpy 的基础是 ndarray (n dimensional array)，表示同一元素组成的多维数组。
+ dtype
数据类型
+ shape
由N个正整数组成的元组，每一个元素代表了每一维的大小
+ axes
数组的维数为轴，轴... |
4,764 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2018-01-12 / FMA sub-sampling
Problem statement
Step1: Binary thresholding
Step2:
Step3: Multi-valued thresholds | Python Code:
import numpy as np
import pandas as pd
import entrofy
import matplotlib.pyplot as plt
%matplotlib nbagg
df = pd.read_csv('/home/bmcfee/data/vggish-likelihoods-a226b3-maxagg10.csv.gz', index_col=0)
df.head(5)
(df >= 0.5).describe().T.sort_values('freq')
df.median()
Explanation: 2018-01-12 / FMA sub-sampling... |
4,765 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Neural Machine Translation with Attention
<table class="tfo-notebook-buttons" align="le... | Python Code:
from __future__ import absolute_import, division, print_function
# Import TensorFlow >= 1.10 and enable eager execution
import tensorflow as tf
tf.enable_eager_execution()
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import unicodedata
import re
import numpy as np
im... |
4,766 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting Breast Cancer Proliferation Scores with Apache Spark and Apache SystemML
Machine Learning
Setup
Step1: Read in train & val data
Step2: Extract X and Y matrices
Step4: Convert t... | Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import matplotlib.pyplot as plt
import numpy as np
from pyspark.sql.functions import col, max
import systemml # pip3 install systemml
from systemml import MLContext, dml
plt.rcParams['figure.figsize'] = (10, 6)
ml = MLContext(sc)
Explanation:... |
4,767 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Landice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-3', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice.
Proper... |
4,768 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Implementing a denoising autoencoder
Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submis... | Python Code:
# A bit of setup
import numpy as np
import matplotlib.pyplot as plt
%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 external modules
# see http://stackoverflo... |
4,769 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Automated finite difference operators from symbolic equations
This notebook is the first in a series of hands-on tutorial notebooks that are intended to give a brief practical overview of th... | Python Code:
from devito import *
from sympy import init_printing, symbols, solve
init_printing(use_latex=True)
Explanation: Automated finite difference operators from symbolic equations
This notebook is the first in a series of hands-on tutorial notebooks that are intended to give a brief practical overview of the Dev... |
4,770 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Datasets
Step1: The default query terms are 'big data', 'data science' and 'machine learning'. The dictionary returned from the call contains the standard 'X' and 'y' keys that are ready to... | Python Code:
import pods
%matplotlib inline
# calling without arguments uses the default query terms
data = pods.datasets.google_trends()
Explanation: Datasets: Downloading Data from Google Trends
28th May 2014
Neil Lawrence
This data set collection was inspired by a ipython notebook from sahuguet which made queries t... |
4,771 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example for using the WindpowerlibTurbine model
The WindpowerlibTurbine model can be used to determine the feed-in of a wind turbine using the windpowerlib.
The windpowerlib is a python libr... | Python Code:
from feedinlib import WindPowerPlant
Explanation: Example for using the WindpowerlibTurbine model
The WindpowerlibTurbine model can be used to determine the feed-in of a wind turbine using the windpowerlib.
The windpowerlib is a python library for simulating the performance of wind turbines and farms. For ... |
4,772 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Summarizing Multiple Graphs Together
Author
Step1: Environment
Step2: Dependencies
Step3: Setup
Step4: Data
In this notebook, pickled instances of networks from the Causal Biological Net... | Python Code:
import os
import time
import sys
import pybel
import pybel_tools
from pybel_tools.summary import info_str
Explanation: Summarizing Multiple Graphs Together
Author: Charles Tapley Hoyt
Estimated Run Time: 45 seconds
This notebook shows how to combine multiple graphs from different sources and summarize them... |
4,773 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
In the previous tutorial, you learned how to build an agent with one-step lookahead. This agent performs reasonably well, but definitely still has room for improvement! For in... | Python Code:
#$HIDE_INPUT$
import random
import numpy as np
# Gets board at next step if agent drops piece in selected column
def drop_piece(grid, col, mark, config):
next_grid = grid.copy()
for row in range(config.rows-1, -1, -1):
if next_grid[row][col] == 0:
break
next_grid[row][col] =... |
4,774 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TensorFlow
TensorFlow is an open source library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the mu... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
Explanation: TensorFlow
TensorFlow is an open source library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensi... |
4,775 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute seed based time-frequency connectivity in sensor space
Computes the connectivity between a seed-gradiometer close to the visual cortex
and all other gradiometers. The connectivity is... | Python Code:
# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne import io
from mne.connectivity import spectral_connectivity, seed_target_indices
from mne.datasets import sample
from mne.time_frequency import AverageTFR
print(__doc__)
Explanation: Co... |
4,776 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Elements are the basic building blocks for any HoloViews visualization. These are the objects that can be composed together using the various Container types.
Here in this overview, we show... | Python Code:
import holoviews as hv
hv.notebook_extension()
hv.Element(None, group='Value', label='Label')
Explanation: Elements are the basic building blocks for any HoloViews visualization. These are the objects that can be composed together using the various Container types.
Here in this overview, we show an exampl... |
4,777 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A workflow for classifying a point cloud using point features
The following example will run through the functions to classify a point cloud based on the point neighborhood attributes. This ... | Python Code:
from geospatial_learn import learning as ln
incloud = "/path/to/Llandinam.ply"
Explanation: A workflow for classifying a point cloud using point features
The following example will run through the functions to classify a point cloud based on the point neighborhood attributes. This is a very simple example ... |
4,778 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Route Computation and Analysis using Batfish
Network engineers routinely need to validate routing and forwarding in the network. They often do that by connecting to multiple ... | Python Code:
# Import packages
%run startup.py
bf = Session(host="localhost")
Explanation: Introduction to Route Computation and Analysis using Batfish
Network engineers routinely need to validate routing and forwarding in the network. They often do that by connecting to multiple network devices and executing a series ... |
4,779 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing Spots in PHOEBE 2 vs PHOEBE Legacy
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your inst... | Python Code:
!pip install -I "phoebe>=2.0,<2.1"
Explanation: Comparing Spots in PHOEBE 2 vs PHOEBE Legacy
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of expla... |
4,780 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Jupyter Notebooks
Step1: A continuación usaremos una celda de código en la cual guardaremos una expresión markdown en una variable python para su posterior visualización.
Step2: El método ... | Python Code:
from IPython.display import display, Latex, Markdown
Explanation: Jupyter Notebooks: Intermedio
Jupyter permite integrar sus capacidades de visualización html con el modulo IPython.display. De esta forma se puede generar la visualización de texto, imagen, video y ecuaciones de una forma sistematica al inte... |
4,781 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Assignment 3a
Step2: Working with external modules
Exercise 2
NLTK offers a way of using WordNet in Python. Do some research (using google, because quite frankly, tha... | Python Code:
%%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d .... |
4,782 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Select seeds for search networks
I select small (1000-1500) sized bot network and pick 4 random members from it
Step1: Now search for friends of seed users
Step2: Show common users in tota... | Python Code:
seeds = ['volya_belousova', 'egor4rgurev', 'kirillfrolovdw', 'ilyazhuchhj']
auth = tweepy.OAuthHandler(OAUTH_KEY, OAUTH_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
graph = Graph(user=NEO4J_USER, password=NE... |
4,783 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Summary
Requirements
Step8: 1. Make a request
Only the mapping *id -> name *
Step9: Full response
Step10: 2. Bulk request
If you need to retrieve more than 100 mappings, you will need to ... | Python Code:
from pynexus import AppNexusAPI
APPNEXUS_ACCOUNT = {
"username": "",
"password": ""
}
api = AppNexusAPI(**APPNEXUS_ACCOUNT)
Explanation: Summary
Requirements:
python 3
This notebook show how to make call to the AppNexus API.
Implemented functions:
```python
def get_campaign(self, ids=None, one... |
4,784 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generator expressions
Step1: Tuples as Records
Step2: Tuple Unpacking
Step3: Named tuples
Step5: Slicing
Step6: Assigning to Slices
Step7: Using + and * with Sequences
Step8: Building... | Python Code:
symbols = '$#%^&'
[ord(s) for s in symbols]
tuple(ord(s) for s in symbols)
(ord(s) for s in symbols)
for x in (ord(s) for s in symbols):
print(x)
import array
array.array('I', (ord(s) for s in symbols))
colors = ['black', 'white']
sizes = ['S', 'M', 'L']
for tshirt in ((c, s) for c in colors for s in s... |
4,785 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MPB mode-solver
MPB is a free open source sofware to compute
Step1: As you can see the refractive index goes from 1.44 SiO2 Silicon dioxide to 3.47 Silicon.
Step2: As you can see the first... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import meep as mp
import gdsfactory.simulation.modes as gm
modes = gm.find_modes_waveguide(
parity=mp.NO_PARITY,
wg_width=0.4,
ncore=3.47,
nclad=1.44,
wg_thickness=0.22,
resolution=40,
sy=3,
sz=3,
nmodes=4,
)
m1 = modes[... |
4,786 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Project Euler
Step2: Now write a set of assert tests for your number_to_words function that verifies that it is working as expected.
Step4: Now define a count_letters(n) that return... | Python Code:
import numpy as np
def number_to_words(n):
Given a number n between 1-1000 inclusive return a list of words for the number.
# YOUR CODE HERE
#raise NotImplementedError()
ones=['one','two','three','four','five','six','seven','eight','nine','ten']
teens=['eleven','twelve','thirteen','four... |
4,787 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Quickstart
This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of RDDs. When Spark transforms data... | Python Code:
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
Explanation: Quickstart
This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of RDDs. When Spark transforms data, it does not immediatel... |
4,788 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
hot-CNO and breakout
Step1: This collection of rates has the main CNO rates plus a breakout rate into the hot CNO cycle
Step2: To evaluate the rates, we need a composition. This is define... | Python Code:
import pynucastro as pyrl
Explanation: hot-CNO and breakout
End of explanation
files = ["c12-pg-n13-ls09",
"c13-pg-n14-nacr",
"n13--c13-wc12",
"n13-pg-o14-lg06",
"n14-pg-o15-im05",
"n15-pa-c12-nacr",
"o14--n14-wc12",
"o15--n15-wc12",
... |
4,789 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Unsupervised Analysis of Days of Week
Tresting crossings each dat as features to learn about the relationships bwteen days of the week
Step1: Get Data
Step2: Principal Component Analysis
S... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture
Explanation: Unsupervised Analysis of Days of Week
Tresting crossings each dat as features to learn about the ... |
4,790 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
## <p style="text-align
Step1: Control flow commands
Step2: numpy
Step3: matplotlib | Python Code:
# Basic python
print("Hello world!")
print(type("Hello world!"))
print(type("Hello world!")==str)
# dir()
# Built-in data types
# int
print(type(1))
# float
print(type(1.0))
# str
print(type("Hello World"))
# bool
print(type(False))
# Built-in data types: list
alist = [1, 2.0, "3"]
print(alist)
print(type(... |
4,791 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Graph format
The EDeN library allows the vectorization of graphs, i.e. the transformation of graphs into sparse vectors.
The graphs that can be processed by the EDeN library have the followi... | Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
from eden.util import configure_logging
import logging
logger = logging.getLogger()
configure_logging(logger,verbosity=1)
import pylab as plt
import networkx as nx
G=nx.Graph()
G.add_node(0, label='A')
G.add_node(1, label='B')
G.add_node(2, label='C')
G... |
4,792 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load a sample of the raw JSON data into pandas.
Step1: Transform the full JSON file into a CSV, removing any stuff that we won't need
Step3: Creates CSVs of text from comments made by user... | Python Code:
import pandas as pd
json_file = 'sample_data'
list(pd.read_json(json_file, lines=True))
Explanation: Load a sample of the raw JSON data into pandas.
End of explanation
import csv
import json
from nltk.tokenize import TweetTokenizer
from tqdm import tqdm
MIN_NUM_WORD_TOKENS = 10
TOTAL_NUM_LINES = 53851542 ... |
4,793 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Negative Binomial Regression (Students absence example)
Negative binomial distribution review
I always experience some kind of confusion when looking at the negative binomial distribution af... | Python Code:
import arviz as az
import bambi as bmb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import nbinom
az.style.use("arviz-darkgrid")
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
Explanation: Negative Binomial Regression (Students abse... |
4,794 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
← Back to Index
Fourier Transform
Let's download an audio file
Step1: Listen to the audio file
Step2: Fourier Transform
The Fourier Transform (Wikipedia) is one of the most fundamenta... | Python Code:
import urllib
filename = 'c_strum.wav'
urllib.urlretrieve('http://audio.musicinformationretrieval.com/c_strum.wav', filename=filename)
x, sr = librosa.load(filename)
print(x.shape)
print(sr)
Explanation: ← Back to Index
Fourier Transform
Let's download an audio file:
End of explanation
ipd.Audio(x, ra... |
4,795 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Load Digits Dataset
Step2: Split Into Training And Test Sets
Step3: Fit Standardizer To Training Set
Step4: Apply Standardizer To Training And Test Sets | Python Code:
# Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
Explanation: Title: Split Data Into Training And Test Sets
Slug: split_data_into_training_and_test_sets
Summary: How to split data into training and test sets ... |
4,796 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step12: Module is an abstract class which defines fundamental methods necessary for a training a neural network. You do not need to change anything here, just read the comments.
Step19: Seq... | Python Code:
class Module(object):
def __init__ (self):
self.output = None
self.gradInput = None
self.training = True
Basically, you can think of a module as of a something (black box)
which can process `input` data and produce `ouput` data.
This is like applying a function... |
4,797 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gradient reversal pytorch
Inspired from the following tweets
Step1: Tensorflow implementation
Step2: Modify the gradients
Keep forward pass the same.
The trick is to add $g(x)$, such that... | Python Code:
import torch
import tensorflow as tf
from torch.autograd import Variable
import numpy as np
def f(X):
return X*X
def g(X):
return X**3
X = np.random.randn(10)
X
Explanation: Gradient reversal pytorch
Inspired from the following tweets:
https://twitter.com/mat_kelcey/status/932149793765261313
https:... |
4,798 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
=============================================================================
E E||---|---|-G-|---|-A-|---|---|-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|---|---|
B ||-C-|---|-D-|---|-... | Python Code:
from fretboard import *
c = Note('C')
print (c)
Note('C#')
Note('Db')
Note('B#')
Explanation: =============================================================================
E E||---|---|-G-|---|-A-|---|---|-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|---|---|
B ||-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|... |
4,799 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Euplotid says "hello world"
Markdown
hello world
hello world
hello world
Step1: Quick Jupyter Tips
select how the cell is interpreted in the toolbar at the top, this one says "Markdown"
vie... | Python Code:
#comments which are not run are denoted with #
#hello world
#try running this cell clicking inside it and pressing shift+enter
#python3
print("hello world")
Explanation: Euplotid says "hello world"
Markdown
hello world
hello world
hello world
End of explanation
%lsmagic
#shows you all available magics, the... |
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