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2,300 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Idea Behind Gradient Descent
In calculus, the gradient is the vector of partial derivatives and it identifies the direction of inputs that increase the functions outputs most quickly.
We... | Python Code:
def difference_quotient(f, x, h):
return (f(x + h) - f(x)) / h
Explanation: The Idea Behind Gradient Descent
In calculus, the gradient is the vector of partial derivatives and it identifies the direction of inputs that increase the functions outputs most quickly.
We can use gradient descent to maximize... |
2,301 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Adding exponential mass loss/growth
You can always modify the mass of particles between calls to sim.integrate. However, if you want to apply the mass/loss growth every timestep within call... | Python Code:
import rebound
import reboundx
import numpy as np
M0 = 1. # initial mass of star
def makesim():
sim = rebound.Simulation()
sim.G = 4*np.pi**2 # use units of AU, yrs and solar masses
sim.add(m=M0)
sim.add(a=1.)
sim.add(a=2.)
sim.add(a=3.)
sim.move_to_com()
return sim
%matplot... |
2,302 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Facies classification using Machine Learning- Random Forest
Contest entry by Priyanka Raghavan and Steve Hall
This notebook demonstrates how to train a machine learning algorithm to predict ... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn.ensemble import RandomForestClassifier
from pandas import set_option
set_option("display... |
2,303 | 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 do... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-cm2-vhr4', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-CM2-VHR4
Topic: Land
Sub-Topics: Soil, Snow, Vegetatio... |
2,304 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Level 3
In diesem Level lernen wir neue Datentypen, wie list, tuple, dict, set und frozenset kennen und lernen über Objekte dieser Typen mittels einer for-Schleife zu iterieren. Wir werden d... | Python Code:
leer = list()
leer2 = []
Explanation: Level 3
In diesem Level lernen wir neue Datentypen, wie list, tuple, dict, set und frozenset kennen und lernen über Objekte dieser Typen mittels einer for-Schleife zu iterieren. Wir werden die Schlüsselwörter del und for kennenlernen und auch den Schlüsselwörtern in, b... |
2,305 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<p><font size="6"><b>Reshaping data</b></font></p>
© 2016, Joris Van den Bossche and Stijn Van Hoey (jorisvandenboss... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Explanation: <p><font size="6"><b>Reshaping data</b></font></p>
© 2016, Joris Van den Bossche and Stijn Van Hoey (jorisvandenbossch... |
2,306 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Training Logistic Regression via Stochastic Gradient Ascent
The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will
Step1: Load... | Python Code:
from __future__ import division
import graphlab
Explanation: Training Logistic Regression via Stochastic Gradient Ascent
The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:
Extract features from Amazon product reviews.
Convert an SFrame int... |
2,307 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Standar usage of TensoFlow with model class
Tipically use 3 files
Step2: model_mnist_cnn.py
Step3: train.py | Python Code:
#! /usr/bin/env python
import tensorflow as tf
# Access to the data
def get_data(data_dir='/tmp/MNIST_data'):
from tensorflow.examples.tutorials.mnist import input_data
return input_data.read_data_sets(data_dir, one_hot=True)
#Batch generator
def batch_generator(mnist, batch_size=256, type='train')... |
2,308 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classification
COMP4670/8600 - Introduction to Statistical Machine Learning - Tutorial 3
$\newcommand{\trace}[1]{\operatorname{tr}\left{#1\right}}$
$\newcommand{\Norm}[1]{\lVert#1\rVert}$
$\... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
from scipy.special import expit # The logistic sigmoid function
%matplotlib inline
Explanation: Classification
COMP4670/8600 - Introduction to Statistical Machine Learning - Tutorial 3
$\newcommand{\trace}[... |
2,309 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
XGBoost Cross Validation
The Python wrap around XGBoots implements a scikit-learn interface and this interface, more or less, support the scikit-learn cross validation system. More, XGBoost ... | Python Code:
%matplotlib inline
from __future__ import print_function
import os
import os.path as osp
import numpy as np
import pysptools.ml as ml
import pysptools.skl as skl
from sklearn.model_selection import train_test_split
home_path = os.environ['HOME']
source_path = osp.join(home_path, 'dev-data/CZ_hsdb')
result_... |
2,310 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plan
Some data
Step1: $\Rightarrow$ various different price series
Step2: $\Longrightarrow$ There was a stock split 7
Step3: Define new financial instruments
What we have now prices of fi... | Python Code:
aapl = data.DataReader('AAPL', 'yahoo', '2000-01-01')
print(aapl.head())
Explanation: Plan
Some data: look at some stock price series
devise a model for stock price series: Geometric Brownian Motion (GBM)
Example for a contingent claim: call option
Pricing of a call option under the assumtpion of GBM
Chall... |
2,311 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
原文地址：itchat+pillow实现微信好友头像爬取和拼接。原文github地址
Step1: =======注意=======
这里我用Python2.7，实际上代码在Python3.6,3.5运行一切正常。itchat更新后就没有再测试啦。
注意微信更新了反广告机制，注意不要一次性发太多东西，避免被微信封号。
=======注意=======
核心
itchat读取微... | Python Code:
#我的Python版本是：
import sys
print(sys.version)
print(sys.version_info)
Explanation: 原文地址：itchat+pillow实现微信好友头像爬取和拼接。原文github地址
End of explanation
import itchat
itchat.auto_login()
Explanation: =======注意=======
这里我用Python2.7，实际上代码在Python3.6,3.5运行一切正常。itchat更新后就没有再测试啦。
注意微信更新了反广告机制，注意不要一次性发太多东西，避免被微信封号。
=====... |
2,312 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Worapol B. and hamuel.me reserved some right maybe hahaha
for muic math club and muic student that want to use this as references
Import as DF
From the data seen below we will use "master" s... | Python Code:
df = pd.read_csv('t2_2016.csv')
df = df[df['Type'] == 'master']
df.head()
#format [Day, start_time, end_time]
def time_extract(s):
s = str(s).strip().split(" "*16)
def helper(s):
try:
temp = s.strip().split(" ")[1:]
comb = temp[:2] + temp[3:]
comb[0] = co... |
2,313 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align="center">Introduction to SimpleITKv4 Registration - Continued</h1>
ITK v4 Registration Components
<img src="ITKv4RegistrationComponentsDiagram.svg" style="width
Step3: Utility fun... | Python Code:
import SimpleITK as sitk
# Utility method that either downloads data from the network or
# if already downloaded returns the file name for reading from disk (cached data).
from downloaddata import fetch_data as fdata
# Always write output to a separate directory, we don't want to pollute the source directo... |
2,314 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Variant calling with kevlar
Step1: Generate a random genome
Rather than generating a truly random genome, I wanted one that shared some compositional features with the human genome.
I used ... | Python Code:
from __future__ import print_function
import subprocess
import kevlar
import random
import sys
def gen_muts():
locs = [random.randint(0, 2500000) for _ in range(10)]
types = [random.choice(['snv', 'ins', 'del', 'inv']) for _ in range(10)]
for l, t in zip(locs, types):
if t == 'snv':
... |
2,315 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial Overview
This is part three of the tutorial where you will learn how to run same code in Part One (with minor changes) in Google's new Vertex AI pipeline. Vertex Pipelines helps you... | Python Code:
PATH=%env PATH
%env PATH={PATH}:/home/jupyter/.local/bin
# CHANGE the following settings
BASE_IMAGE='gcr.io/your-image-name' #This is the image built from the Dockfile in the same folder
REGION='vertex-ai-region' #For example, us-central1, note that Vertex AI endpoint deployment region must match MODEL_STO... |
2,316 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning
Assignment 1
The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.
This notebook ... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from... |
2,317 | 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', 'cams', 'cams-csm1-0', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CAMS
Source ID: CAMS-CSM1-0
Sub-Topics: Radiative Forcings.
Properti... |
2,318 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A real-world case (Physics
Step1: 1 - The raw data
Since the asymptotic behaviour is important, we place the majority of points on the $x>2$ area. Note that the definition of the grid (i.e.... | Python Code:
# Some necessary imports.
import dcgpy
import pygmo as pg
import numpy as np
# Sympy is nice to have for basic symbolic manipulation.
from sympy import init_printing
from sympy.parsing.sympy_parser import *
init_printing()
# Fundamental for plotting.
from matplotlib import pyplot as plt
%matplotlib inline
... |
2,319 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic Regression
Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License
Step1: This chapter introduces two related topics
Step2: Each update uses the same likelihood, but th... | Python Code:
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(... |
2,320 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementat... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of t... |
2,321 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Rate distributions
Step1: A function to simulate trajectories.
Step2: Simulation of a large number of events
Generate a large results table.
Step3: In this case, the info we are intereste... | Python Code:
import matplotlib as mpl
import matplotlib.pyplot as plt
import random
import numpy as np
import beadpy
import pandas as pd
import math
%matplotlib inline
Explanation: Rate distributions: Time vs distance-weighted
End of explanation
def trajectory_simulator(pre_duration = 250, #Mean event start time
... |
2,322 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
6 - GSTools
With version 0.5 scikit-gstat offers an interface to the awesome gstools library. This way, you can use a Variogram estimated with scikit-gstat in gstools to perform random field... | Python Code:
# import
import skgstat as skg
import gstools as gs
import numpy as np
import matplotlib.pyplot as plt
import plotly.offline as pyo
import warnings
pyo.init_notebook_mode()
warnings.filterwarnings('ignore')
# use the example from gstools
# generate a synthetic field with an exponential model
x = np.random.... |
2,323 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Explanation of observed subconvergence
Subconvergence has been observed when MESing operators which multiplies with $\frac{1}{J}$. In these cases, the error is dominant in the first inner po... | Python Code:
%matplotlib notebook
from IPython.display import display
from sympy import Function, S, Eq
from sympy import symbols, init_printing, simplify, Limit
from sympy import sin, cos, tanh, exp, pi, sqrt
from boutdata.mms import x
# Import common
import os, sys
# If we add to sys.path, then it must be an absolute... |
2,324 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'ciesm', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: THU
Source ID: CIESM
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulenc... |
2,325 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment Analysis on Movie Reviews
Using Logistic Regression Model
0 - negative
1 - somewhat negative
2 - neutral
3 - somewhat positive
4 - positive
Load Libraries
Step1: Load & Read Datas... | Python Code:
import nltk
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
Explanation: Sentiment Analysis on M... |
2,326 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Creating a Clean Chart
Begin by importing the packages we'll use.
Step1: Data looks better naked
What in the world does that mean?
Slide and data presentation often refers back to Edward... | Python Code:
import pandas as pd
import matplotlib.pyplot as plt
import pylab as pyl
# This is an example of an iPython magic command.
# If we don't use this, then we can't see our matplotlib plots in our notebook
%matplotlib inline
Explanation: 1. Creating a Clean Chart
Begin by importing the packages we'll use.
End o... |
2,327 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analysis. Using an RNN rather than a feedfoward network is more accurate ... | Python Code:
import numpy as np
import tensorflow as tf
with open('reviews.txt', 'r') as f:
reviews = f.read()
with open('labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment ana... |
2,328 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression test suite
Step1: IMF notes
Step2: The total number of stars $N_{tot}$ is then
Step3: With a yield ejected of $0.1 Msun$, the total amount ejected is
Step4: compared to the si... | Python Code:
#from imp import *
#s=load_source('sygma','/home/nugrid/nugrid/SYGMA/SYGMA_online/SYGMA_dev/sygma.py')
#%pylab nbagg
import sys
import sygma as s
print s.__file__
reload(s)
s.__file__
#import matplotlib
#matplotlib.use('nbagg')
import matplotlib.pyplot as plt
#matplotlib.use('nbagg')
import numpy as np
fro... |
2,329 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Power Spectral Density
Introduction
Methods
This notebook consists of two methods to carry Spectral Analysis.
The first one is based on covariance called pcovar, which comes from Spectrum
St... | Python Code:
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
from matplotlib import mlab
from spectrum import pcovar
from pylab import rcParams
rcParams['figure.figsize'] = 15, 6
Explanation: Power Spectral Density
Introdu... |
2,330 | 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="#Compare-weighted-and-unweighted-mean-temperature" data-toc-modified-id="Comp... | Python Code:
%matplotlib inline
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Compare-weighted-and-unweighted-mean-temperature" data-toc-modi... |
2,331 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book... | Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
Explanation: Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network... |
2,332 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook is adapted from
Step1: Init SparkContext
Step2: A simple parameter server can be implemented as a Python class in a few lines of code.
EXERCISE
Step3: A worker can be implem... | Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import ray
import time
Explanation: This notebook is adapted from:
https://github.com/ray-project/tutorial/tree/master/examples/sharded_parameter_server.ipynb
Sharded Parameter Se... |
2,333 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lending Club
Step1: Abstract
Lending club offers an exciting alternative to the stock market by providing loans that others can invest in. They claim a 4% overall default rate and give a gr... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.feature_selection import SelectKBest, mutual_info_classif
f... |
2,334 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div class="jumbotron text-left"><b>
This tutorial describes how to use the SMT toolbox to do some Bayesian Optimization (EGO method) to solve unconstrained optimization problem
<div>
Rémy P... | Python Code:
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
plt.ion()
def fun(point):
return np.atleast_2d((point-3.5)*np.sin((point-3.5)/(np.pi)))
X_plot = np.atleast_2d(np.linspace(0, 25, 10000)).T
Y_plot = fun(X_plot)
lines = []
fig = plt.figure(figsize=[5,5])
ax = fig.add_subplot(111)
... |
2,335 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Self-Driving Car Engineer Nanodegree
Project
Step1: Read in an Image
Step9: Ideas for Lane Detection Pipeline
Some OpenCV functions (beyond those introduced in the lesson) that might be us... | Python Code:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson... |
2,336 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute source power using DICS beamfomer
Compute a Dynamic Imaging of Coherent Sources (DICS) [1]_ filter from
single-trial activity to estimate source power across a frequency band.
Refere... | Python Code:
# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Roman Goj <roman.goj@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.time_frequency import csd_morlet
from mne.beamformer import ma... |
2,337 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sqlite3 and MySQL demo
With the excellent ipython-sql jupyter extension installed, it becomes very easy to connect to SQL database backends. This notebook demonstrates how to do this.
Note ... | Python Code:
%load_ext sql
Explanation: Sqlite3 and MySQL demo
With the excellent ipython-sql jupyter extension installed, it becomes very easy to connect to SQL database backends. This notebook demonstrates how to do this.
Note that this is a Python 2 notebook.
First, we need to activate the extension:
End of explana... |
2,338 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: What is Biomedical Data Commons?
Data Commons is an open knowledge graph of structured data. It contains statements about real world objects such as
* The genome ass... | Python Code:
# Install datacommons
!pip install --upgrade --quiet datacommons
Explanation: <a href="https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/analyzing_genomic_data.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Col... |
2,339 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
HIV Methylation Age Advancement
Step1: Run Age Predictions on HIV Dataset
Step2: Hannum Model
Step3: Preforming a linear adjustment on the control data.
Step4: Horvath Model
Step5: Qual... | Python Code:
import NotebookImport
from Setup.Imports import *
from Setup.MethylationAgeModels import *
from Setup.Read_HIV_Data import *
hiv = (duration=='Control').map({False: 'HIV+', True: 'HIV-'})
hiv.name = 'HIV Status'
hiv.value_counts()
Explanation: HIV Methylation Age Advancement
End of explanation
def model_pl... |
2,340 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Applying Deterministic Methods
Getting Started
This tutorial focuses on using deterministic methods to square a triangle.
Note that a lot of the examples shown here might not be applicable ... | Python Code:
# Black linter, optional
%load_ext lab_black
import pandas as pd
import numpy as np
import chainladder as cl
import matplotlib.pyplot as plt
import os
%matplotlib inline
print("pandas: " + pd.__version__)
print("numpy: " + np.__version__)
print("chainladder: " + cl.__version__)
Explanation: Applying Determ... |
2,341 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kittens
Modeling and Simulation in Python
Copyright 2021 Allen Downey
License
Step1: If you have used the Internet, you have probably seen videos of kittens unrolling toilet paper.
And you ... | Python Code:
# install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/... |
2,342 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten d... | Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
Explanation: Generative Adversarial Network
In this notebook, we'll be building a gen... |
2,343 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced
Step1: Units
Each FloatParameter or FloatArrayParameter has an associated unit. Let's look at the 'sma' Parameter for the binary orbit.
Step2: From the representation above, we ... | Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u,c
logger = phoebe.logger(clevel='WARNING')
b = phoebe.default_binary()
Explanation: Advanced: Parameter Units
In this tutorial we will learn about how units are handled in the frontend and how to translate between different units.
Setup... |
2,344 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Stochastic Differential Equations
Step1: This background for these exercises is article of D Higham, An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations... | Python Code:
from IPython.core.display import HTML
css_file = 'https://raw.githubusercontent.com/ngcm/training-public/master/ipython_notebook_styles/ngcmstyle.css'
HTML(url=css_file)
Explanation: Stochastic Differential Equations: Lab 2
End of explanation
%matplotlib inline
import numpy
from matplotlib import pyplot
fr... |
2,345 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Persistent Homology of Sliding Windows
Now that we have heuristically explored the geometry of sliding window embeddings of 1D signals, we will apply tools from persistent homology to... | Python Code:
# Do all of the imports and setup inline plotting
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
from matplotlib import gridspec
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
from scipy.interpolate import InterpolatedUnivariateSpline
import ipywidget... |
2,346 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Работа 1.4. Исследование вынужденной прецессии гироскопа
Цель работы
Step1: Параметры установки
$f = 440$ Гц - резонансная частота.
$l = 12,1$ см - расстояние до крайней риски.
$T_{э} = 9 $... | Python Code:
import numpy as np
import scipy as ps
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Работа 1.4. Исследование вынужденной прецессии гироскопа
Цель работы: исследовать вынужденную прецессию уравновешенного симметричного гироскопа; установить зависимость угловой скорости ... |
2,347 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic regression with pyspark
Import data
Step1: Process categorical columns
The following code does three things with pipeline
Step2: Build StringIndexer stages
Step3: Build OneHotEnc... | Python Code:
cuse = spark.read.csv('data/cuse_binary.csv', header=True, inferSchema=True)
cuse.show(5)
Explanation: Logistic regression with pyspark
Import data
End of explanation
from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler
from pyspark.ml import Pipeline
# categorical columns
categoric... |
2,348 | 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 ... |
2,349 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lists
Lists are collections of heterogeneous objects, which can be of any type, including other lists.
Lists in the Python are mutable and can be changed at any time. Lists can be sliced i... | Python Code:
fruits = ['Apple', 'Mango', 'Grapes', 'Jackfruit',
'Apple', 'Banana', 'Grapes', [1, "Orange"]]
# processing the entire list
for fruit in fruits:
print(fruit, end=", ")
#
print("*"*30)
fruits.insert(0, "kiwi")
print( fruits)
# help(fruits.insert)
# Including
ft1 = list(fruits)
print(id(ft1)... |
2,350 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Text Analysis with NLTK
Author
Step1: 1. Corpus acquisition.
In these notebooks we will explore some tools for text analysis and two topic modeling algorithms available from Python toolboxe... | Python Code:
%matplotlib inline
# Required imports
from wikitools import wiki
from wikitools import category
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import numpy as np
import matplotlib.pyplot as plt
from test_helper import Test
impor... |
2,351 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2A.ML101.3
Step1: We'll re-use some of our code from before to visualize the data and remind us what
we're looking at
Step2: Visualizing the Data
A good first-step for many problems is to ... | Python Code:
from sklearn.datasets import load_digits
digits = load_digits()
Explanation: 2A.ML101.3: Supervised Learning: Classification of Handwritten Digits
In this section we'll apply scikit-learn to the classification of handwritten
digits. This will go a bit beyond the iris classification we saw before: we'll
di... |
2,352 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
QuTiP Example
Step1: Imports
Step2: Plotting Support
Step3: Settings
Step4: Superoperator Representations and Plotting
We start off by first demonstrating plotting of superoperators, as ... | Python Code:
from __future__ import division, print_function
Explanation: QuTiP Example: Superoperators, Pauli Basis and Channel Contraction
Christopher Granade <br>
Institute for Quantum Computing
$\newcommand{\ket}[1]{\left|#1\right\rangle}$
$\newcommand{\bra}[1]{\left\langle#1\right|}$
$\newcommand{\cnot}{{\scriptst... |
2,353 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Goal
For background, see Mapping Census Data, including the
scan of the 10-question form. Keep in mind what people were asked and the range of data available in the census.
Using the censu... | Python Code:
# YouTube video I made on how to use the American Factfinder site to look up addresses
from IPython.display import YouTubeVideo
YouTubeVideo('HeXcliUx96Y')
# standard numpy, pandas, matplotlib imports
import numpy as np
import matplotlib.pyplot as plt
from pandas import DataFrame, Series, Index
import pan... |
2,354 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Datalab Tutorial
In this tutorial, we'll do some exploratory data analysis in BigQuery using Datalab.
Requirements
If you haven't already, you may sign-up for the free GCP trial credit. Bef... | Python Code:
%sql -d standard
SELECT
*
FROM
`nyc-tlc.yellow.trips`
LIMIT
5
Explanation: Datalab Tutorial
In this tutorial, we'll do some exploratory data analysis in BigQuery using Datalab.
Requirements
If you haven't already, you may sign-up for the free GCP trial credit. Before you begin, give this project any... |
2,355 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multivariable Regression Model of FBI Property Crime Statistics
Using the FBI
Step1: Perfect accuracy, as expected. However......
Predicting ALL property crimes is a more interesting questi... | Python Code:
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import linear_model
# Suppress annoying harmless error.
warnings.filterwarnings(
action="ignore"
)
data_path = "https://raw.githubusercontent.com/Thinkful-Ed/data-201-resources/mas... |
2,356 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Define target events based on time lag, plot evoked response
This script shows how to define higher order events based on
time lag between reference and target events. For
illustration, we w... | Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.event import define_target_events
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
Explanation: Define target events based on ... |
2,357 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1D Data Analysis, Histograms, Boxplots, and Violin Plots
Unit 7, Lecture 2
Numerical Methods and Statistics
Prof. Andrew White, 2/27/2020
Goals
Be able to histogram 1D data
Understand the d... | Python Code:
%matplotlib inline
import random
import numpy as np
import matplotlib.pyplot as plt
from math import sqrt, pi
import scipy
import scipy.stats
plt.style.use('seaborn-whitegrid')
!pip install --user pydataset
Explanation: 1D Data Analysis, Histograms, Boxplots, and Violin Plots
Unit 7, Lecture 2
Numerical Me... |
2,358 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Emission from the relativistic charged particles
The SR (spontaneous radiation) module calculates the spectral-spatial distibution of electromagnetic emission priduced by the relativistic ch... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import sys,time
import numpy as np
from scipy.constants import c,hbar
from scipy.interpolate import griddata
from chimera.moduls.species import Specie
from chimera.moduls.chimera_main import ChimeraRun
from chimera.moduls.SR import SR
from chimera.moduls i... |
2,359 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ImageNet with GoogLeNet
Input
GoogLeNet (the neural network structure which this notebook uses) was created to analyse 224x224 pictures from the ImageNet competition.
Output
This notebook cl... | Python Code:
import theano
import theano.tensor as T
import lasagne
from lasagne.utils import floatX
import numpy as np
import scipy
import matplotlib.pyplot as plt
%matplotlib inline
import os
import json
import pickle
Explanation: ImageNet with GoogLeNet
Input
GoogLeNet (the neural network structure which this notebo... |
2,360 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Isentropic Analysis
The MetPy function mpcalc.isentropic_interpolation allows for isentropic analysis from model
analysis data in isobaric coordinates.
Step1: Getting the data
In this examp... | Python Code:
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, add_timestamp
from metpy.units import units
Explanation: Isentropic ... |
2,361 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Scattering and SBG-FS file stream read notebook
This notebook explores setting up tasks for scatter and SBG's file storage setup. This runs multiple samples in a scatter plus batch mode.
Ste... | Python Code:
import sevenbridges as sbg
from sevenbridges.errors import SbgError
from sevenbridges.http.error_handlers import *
import re
import datetime
import binpacking
print("SBG library imported.")
print sbg.__version__
Explanation: Scattering and SBG-FS file stream read notebook
This notebook explores setting up ... |
2,362 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 7
Step1: Today's lab reviews Maximum Likelihood Estimation, and introduces interctive plotting in the jupyter notebook.
Part 1
Step2: Question 2
Step3: Question 3
Step4: Question 4
S... | Python Code:
# Run this cell to set up the notebook.
import numpy as np
import pandas as pd
import seaborn as sns
import scipy as sci
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import patches, cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from mpl_toolkits.... |
2,363 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<CENTER>
<header>
<h1>Pandas Tutorial</h1>
<h3>EuroScipy, Cambridge UK, August 27th, 2015</h3>
<h2>Joris Van den Bossche</h2>
<p></p>
Source
Step1: Let's start with a showca... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn
pd.options.display.max_rows = 8
Explanation: <CENTER>
<header>
<h1>Pandas Tutorial</h1>
<h3>EuroScipy, Cambridge UK, August 27th, 2015</h3>
<h2>Joris Van den Bossche</h2>
<p></p>
Source:... |
2,364 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch. 11 - Evaluating and deploying the model
In chapter 10 we have learned a lot of new tricks and tools to build neural networks that can deal with stuructured data such as the bank marketin... | Python Code:
import keras
from keras.models import load_model
model = load_model('./support_files/Ch11_model.h5')
Explanation: Ch. 11 - Evaluating and deploying the model
In chapter 10 we have learned a lot of new tricks and tools to build neural networks that can deal with stuructured data such as the bank marketing d... |
2,365 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
Step1: Exploring the Fermi distribution
In quantum statistics, the ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
Explanation: Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
End of... |
2,366 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Activité - Faire danser PoppyTorso
Première partie
Step1: Ensuite, vous allez créer un objet s'appellant poppy et étant un robot de type PoppyTorso. Vous pouvez donner le nom que vous souh... | Python Code:
from poppy.creatures import PoppyTorso
Explanation: Activité - Faire danser PoppyTorso
Première partie : en utilisant, le simulateur V-REP :
Compétences visées par cette activité :
Savoir utiliser des modules en y récupérant des classes. Instancier un objet à partir d'une classe. Utiliser une méthode et un... |
2,367 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic notebook to look @ convergence of a 2D region in an FES. It will actually call sum hills with the stride you set in cell one , graph the FES and put the regions of convergence there
St... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import glob
import os
from matplotlib.patches import Rectangle
# define all variables for convergence script
# these will pass to the bash magic below used to call plumed sum_hills
dir="MetaD_converge" #where the intermediate fes will be stored
hills="oth... |
2,368 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementing C4.5 and ID3 Decision Tree Algorithms with NumPy
We will apply these trees to the the UCI car evaluation dataset $^1$
$^1$ https
Step1: The backbone of the decision tree algori... | Python Code:
import numpy as np
#you only need matplotlib if you want to create some plots of the data
import matplotlib.pyplot as plt
%matplotlib inline
data_path = "/home/brb/repos/examples/decision trees/UCI_cars"
data = np.genfromtxt(data_path, delimiter=",", dtype=str)
labels = ["buying", "maint", "doors", "person... |
2,369 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 03
1 Least Square Coefficient Estimate
$$\hat{\beta_1}=\frac{\sum_{i=1}^{n}(x_i-\bar{x})(y_i-\bar{y})}{\sum_{i=1}^{n}(x_i-\bar{x})^2}$$
$$\hat{\beta_0}=\bar{y}-\hat{\beta_1}\bar{x}$... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
def LSCE(x, y):
beta_1 = np.sum((x - np.mean(x))*(y-np.mean(y))) / np.sum((x-np.mean(x))*(x-np.mean(x)))
beta_0 = np.mean(y) - beta_1 * np.mean(x)
return beta_0, beta_1
advertising = pd.read_csv('Advertisi... |
2,370 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Extras
This covers additional useful material that we may or may not have time to go over in the course.
Generators
Consider the following code that computes the sum of squared numbers up to... | Python Code:
def squared_numbers(n):
return [x*x for x in range(n)]
def sum_squares(n):
return sum(squared_numbers(n+1))
sum_squares(20000000)
Explanation: Extras
This covers additional useful material that we may or may not have time to go over in the course.
Generators
Consider the following code that compute... |
2,371 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Template for test
Step1: Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.
Included is N Phosphorylation however no benchmarks are av... | Python Code:
from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
Explanation: Template for test
End of explanation
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Train... |
2,372 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Co-Occurring Tag Analysis
Analysing how tags co-occur across various Parliamentary publications. The idea behind this is to see whether there are naturally occurring groupings of topic tags ... | Python Code:
#Data files
!ls ../data/dataexport
Explanation: Co-Occurring Tag Analysis
Analysing how tags co-occur across various Parliamentary publications. The idea behind this is to see whether there are naturally occurring groupings of topic tags by virtue of their co-occurence when used to tag different classes of... |
2,373 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
More on data structures
Iterable vs. Iterators
Lists are examples of iterable data structures, which means that you can iterate over the actual objects in these data structures.
Step1: gene... | Python Code:
# iterating over a list by object
x = ['bob', 'sue', 'mary']
for name in x:
print(name.upper() + ' WAS HERE')
# alternatively, you could iterate over position
for i in range(len(x)):
print(x[i].upper() + ' WAS HERE')
dir(x) # ignore the __ methods for now
Explanation: More on data structures
Iter... |
2,374 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This is a Jupyter notebook for David Dobrinskiy's HSE Thesis
How Venture Capital Affects Startups' Success
Step1: Let us look at the dynamics of total US VC investment
Step3: Deals and inv... | Python Code:
# You should be running python3
import sys
print(sys.version)
import pandas as pd # http://pandas.pydata.org/
import numpy as np # http://numpy.org/
import statsmodels.api as sm # http://statsmodels.sourceforge.net/stable/index.html
import statsmodels.formula.api as smf
import statsmodels
print("Pandas... |
2,375 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Activate logging for Gensim, so we can see that everything is working correctly. Gensim will, for example, complain if no C compiler is installed to let you know that Word2Vec will be awfull... | Python Code:
import re
import nltk
import os.path as path
from random import shuffle
from gensim.models import Word2Vec
Explanation: Activate logging for Gensim, so we can see that everything is working correctly. Gensim will, for example, complain if no C compiler is installed to let you know that Word2Vec will be awf... |
2,376 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Algorithms Exercise 3
Imports
Step2: Character counting and entropy
Write a function char_probs that takes a string and computes the probabilities of each character in the string
Step4: Th... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
Explanation: Algorithms Exercise 3
Imports
End of explanation
def char_probs(s):
Find the probabilities of the unique characters in the string s.
Parameters
----------
s... |
2,377 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Modular neural nets
In the previous exercise, we started to build modules/general layers for implementing large neural networks. In this exercise, we will expand on this by implementi... | Python Code:
# As usual, a bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.... |
2,378 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pima Indian Diabetes Prediction
### Update History
2021-04-15 Added bypass of imputation of Num of Pregnancies field, switched to using only transform for test data, and added code to load d... | Python Code:
import pandas as pd # pandas is a dataframe library
import matplotlib.pyplot as plt # matplotlib.pyplot plots data
%matplotlib inline
Explanation: Pima Indian Diabetes Prediction
### Update History
2021-04-15 Added bypass of imputation of Num of Pregnancies field, switched to using onl... |
2,379 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ATM 623
Step1: <a id='section1'></a>
1. Recap of the global energy budget
Let's look again at the observations
Step2: Let's now deal with the shortwave (solar) side of the energy budget.
A... | Python Code:
# Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
Explanation: ATM 623: Climate Modeling
Brian E. J. Rose, University at Albany
Lecture 2: The zero-dimensional energy balance model
Warning: content out of date and not maintained
You really should be looking at The ... |
2,380 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial - Transformers
An example of how to incorporate the transfomers library from HuggingFace with fastai
Step1: In this tutorial, we will see how we can use the fastai library to fine-... | Python Code:
#|all_slow
Explanation: Tutorial - Transformers
An example of how to incorporate the transfomers library from HuggingFace with fastai
End of explanation
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
Explanation: In this tutorial, we will see how we can use the fastai library to fine-tune a pr... |
2,381 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kudryavtsev Model
Link to this notebook
Step1: Part 1
We will run the Kudryatsev model for conditions in Barrow, Alaska in a very cold year, 1964. The mean annaul temperature for 1964 was -... | Python Code:
# Load standard Python modules
import numpy as np
import matplotlib.pyplot as plt
# Load PyMT model(s)
import pymt.models
ku = pymt.models.Ku()
Explanation: Kudryavtsev Model
Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/ku.ipynb
Install command: $ conda install notebook pymt_... |
2,382 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Real-time music auto-tagging
In this tutorial, we use Essentia's TensorFlow integration to perform auto-tagging in real-time.
Additionally, this serves as an example of TensorFlow inference ... | Python Code:
!pip -q install pysoundcard
Explanation: Real-time music auto-tagging
In this tutorial, we use Essentia's TensorFlow integration to perform auto-tagging in real-time.
Additionally, this serves as an example of TensorFlow inference in streaming mode and can be easily adapted to work offline.
Setup
To instal... |
2,383 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python for Probability, Statistics, and Machine Learning
Step1: Conditional Expectation and Mean Square Error
In this section, we work through a detailed example using conditional
expectati... | Python Code:
import numpy as np
np.random.seed(12345)
Explanation: Python for Probability, Statistics, and Machine Learning
End of explanation
import sympy as S
from sympy.stats import density, E, Die
x=Die('D1',6) # 1st six sided die
y=Die('D2',6) # 2nd six sides die
a=S.symbols('a')
z = x+y # sum of... |
2,384 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fibonacci Stretch
Step1: You can also jump to Part 6 for more audio examples.
Part 1 - Representing rhythm as symbolic data
1.1 Rhythms as arrays
The main musical element we're going to pla... | Python Code:
import IPython.display as ipd
ipd.Audio("../data/out_humannature_90s_stretched.mp3", rate=44100)
Explanation: Fibonacci Stretch: An Exploration Through Code
by David Su
This notebook and its associated code are also available on GitHub.
Contents
Introduction
A sneak peek at the final result
Part 1 - Repres... |
2,385 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Solution Notebook
Problem
Step1: Algorithm
Step2: Unit Test | Python Code:
def permutations(str1, str2):
return sorted(str1) == sorted(str2)
Explanation: <small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Solution Notebook
Problem: Determine if a string is a permutation of another string
Constraints
Test Cases
Algorithm: Co... |
2,386 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
K-Nest-Neighbors
1 Unsupervised Neast Neighbors
It acts as a uniform unterface to three different nestest neighbors algorithms
Step1: We can also use the KDTree and BallTree classes direct... | Python Code:
from sklearn.neighbors import NearestNeighbors
import numpy as np
X = np.array([[-1,-1],[-2, -1],[1,1],[2,1],[3,2]])
nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X)
distance, indices = nbrs.kneighbors(X)
indices
distance
Explanation: K-Nest-Neighbors
1 Unsupervised Neast Neighbors
It ... |
2,387 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Recognize named entities on Twitter with LSTMs
In this assignment, you will use a recurrent neural network to solve Named Entity Recognition (NER) problem. NER is a common task in natural la... | Python Code:
import sys
sys.path.append("..")
from common.download_utils import download_week2_resources
download_week2_resources()
Explanation: Recognize named entities on Twitter with LSTMs
In this assignment, you will use a recurrent neural network to solve Named Entity Recognition (NER) problem. NER is a common tas... |
2,388 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to create Popups
Step1: Simple popups
You can define your popup at the feature creation, but you can also overwrite them afterwards
Step2: Vega Popup
You may know that it's possible to... | Python Code:
import sys
sys.path.insert(0,'..')
import folium
print (folium.__file__)
print (folium.__version__)
Explanation: How to create Popups
End of explanation
m = folium.Map([45,0], zoom_start=4)
folium.Marker([45,-30], popup="inline implicit popup").add_to(m)
folium.CircleMarker([45,-10], radius=1e5, popup=foli... |
2,389 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I'm using tensorflow 2.10.0. | Problem:
import tensorflow as tf
seed_x = 10
### return the tensor as variable 'result'
def g(seed_x):
tf.random.set_seed(seed_x)
return tf.random.uniform(shape=(10,), minval=1, maxval=5, dtype=tf.int32)
result = g(seed_x) |
2,390 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PanSTARRS - WISE crossmatch
Step1: Load the data
Load the catalogues
Step2: Coordinates
As we will use the coordinates to make a cross-match we to load them
Step3: Compute the ML paramete... | Python Code:
import numpy as np
from astropy.table import Table
from astropy import units as u
from astropy.coordinates import SkyCoord
import pickle
from mltier1 import get_center, get_n_m, estimate_q_m, Field
%pylab inline
field = Field(170.0, 190.0, 45.5, 56.5)
Explanation: PanSTARRS - WISE crossmatch: Pre-configure... |
2,391 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Display a map E. coli central carbon metabolism.
Step1: Visualize reaction-centric and/or metabolite-centric data. | Python Code:
escher.Builder('e_coli_core.Core metabolism').display_in_notebook()
Explanation: Display a map E. coli central carbon metabolism.
End of explanation
escher.Builder('e_coli_core.Core metabolism', reaction_data={'PGK': 100}, metabolite_data={'ATP': 20}).display_in_notebook()
Explanation: Visualize reaction-c... |
2,392 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pochodna (różniczkowanie numeryczne)
Różnica dzielona w przód
Rozwińmy funkcję $f(x)$ w otoczeniu $h$ punktu $x$ w szereg Taylora
Step1: Numeryczne całkowanie
Całka to pole, więc obliczmy j... | Python Code:
import numpy as np
x = np.linspace(0, 10, 10)
f = np.sin(x)
f1 = np.cos(x)
df = f[1:] - f[:-1]
dx = x[1:] - x[:-1]
x2 = (x[1:] + x[:-1])/2
fp = df/dx
fp.shape, x.shape
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(x2, np.cos(x2), 'o-')
plt.plot(x2, fp, 'ro-')
Explanation: Pochodna (różniczkow... |
2,393 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exploring Review Data
let's look at some typical reviews, how many files there are, what the distributions of review lengths and hours played are, etc.
Step1: Football Manager 2015 Stats
St... | Python Code:
import os
import sys
from json import loads
from collections import Counter
import numpy as np
import pandas as pd
# So, let's take a look at how many lines are in our reviews files
# Note: I just started processing GTAV after ending all the other processes
os.chdir('..')
! wc -l data/*.jsonlines
Explanati... |
2,394 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using the OpenAQ API
The openaq api is an easy-to-use wrapper built around the OpenAQ Api. Complete API documentation can be found on their website.
There are no keys or rate limits (as of ... | Python Code:
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import openaq
import warnings
warnings.simplefilter('ignore')
%matplotlib inline
# Set major seaborn asthetics
sns.set("notebook", style='ticks', font_scale=1.0)
# Increase the quality of inline plots
mpl.rcP... |
2,395 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
练习 1：写程序，可由键盘读入用户姓名例如Mr. right，让用户输入出生的月份与日期，判断用户星座，假设用户是金牛座，则输出，Mr. right，你是非常有性格的金牛座！。
Step1: 练习 2：写程序，可由键盘读入两个整数m与n(n不等于0)，询问用户意图，如果要求和则计算从m到n的和输出，如果要乘积则计算从m到n的积并输出，如果要求余数则计算m除以n的余数的值并输出... | Python Code:
name = input('请输入你的姓名')
print('你好',name)
print('请输入出生的月份与日期')
month = int(input('月份：'))
date = int(input('日期：'))
if month == 4:
if date < 20:
print(name, '你是白羊座')
else:
print(name,'你是非常有性格的金牛座')
if month == 5:
if date < 21:
print(name, '你是非常有性格的金牛座')
else:
... |
2,396 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: I am really enjoying having this weather station. I say weather
station, but it is just a raspberry pi with a pressure and temperature
sensor attached to it.
Computers are versatile,... | Python Code:
# Tell matplotlib to plot in line
%matplotlib inline
# import pandas
import pandas
# seaborn magically adds a layer of goodness on top of Matplotlib
# mostly this is just changing matplotlib defaults, but it does also
# provide some higher level plotting methods.
import seaborn
# Tell seaborn to set things... |
2,397 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced
Step1: We'll then start with the bundle from the end of the emcee tutorial. If you're running this notebook locally, you will need to run that first to create the emcee_advanced_t... | Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger('error')
Explanation: Advanced: Custom Cost Funtion (with emcee)
IMPORTANT: this tutorial assumes basic knowledge (and uses a file resulting from) the emcee tutorial, although the custom... |
2,398 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Scapy in 15 minutes (or longer)
Guillaume Valadon & Pierre Lalet
Scapy is a powerful Python-based interactive packet manipulation program and library. It can be used to forge or decode packe... | Python Code:
send(IP(dst="1.2.3.4")/TCP(dport=502, options=[("MSS", 0)]))
Explanation: Scapy in 15 minutes (or longer)
Guillaume Valadon & Pierre Lalet
Scapy is a powerful Python-based interactive packet manipulation program and library. It can be used to forge or decode packets for a wide number of protocols, send the... |
2,399 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 Google LLC. All Rights Reserved.
Step1: RLDS
Step2: Import Modules
Step3: Load dataset
We can load the human dataset from the Panda Pick Place Can task of the Robosuite col... | 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... |
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