Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
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11,000 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Práctica 3 - Dinámica de manipuladores
En esta práctica nuestro objetivo será simular el comportamiento de un manipulador tipo PUMA, empecemos importando las liberrias necesarias
Step1: Y c... | Python Code:
from sympy.physics.mechanics import mechanics_printing
mechanics_printing()
from sympy import var, Function, pi
var("l1:4")
var("m1:4")
var("g t")
q1 = Function("q1")(t)
q2 = Function("q2")(t)
q3 = Function("q3")(t)
Explanation: Práctica 3 - Dinámica de manipuladores
En esta práctica nuestro objetivo será ... |
11,001 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
these notes will not display in the slideshow
interactive dashboard application rendered as a slideshow
using
ipywidgets
plotly (express)
voila
reveal
Step1: loading the iris dataset
Step2:... | Python Code:
import ipywidgets as widgets
import plotly.graph_objs as go
import plotly.express as px
Explanation: these notes will not display in the slideshow
interactive dashboard application rendered as a slideshow
using
ipywidgets
plotly (express)
voila
reveal
End of explanation
iris = px.data.iris()
iris.head()
fi... |
11,002 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data
Both datasets are text collections from this site.
TCP-ECCO (170mb uncompressed) can be downloaded here
Lincoln (700kb uncompressed) can be downloaded here
Step1: Intialize swhoosh in... | Python Code:
def get_lincoln():
for filepath in sorted(glob.glob('Lincoln/*.txt')):
with open(filepath, 'r', encoding='latin') as f:
doc = f.read()
yield {'filepath': filepath, 'doc': doc}
def get_TCP():
for filepath in sorted(glob.glob('TCP-ECCO/*.txt')):
with open(... |
11,003 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https
Step1: 加载模型
HanLP的工作流程是先加载模型，模型的标示符存储在hanlp.pretrained这个包中，按照NLP任务归类。
Step2: 调用hanlp.load进行加载，模型会自动下载到本地缓存。自... | Python Code:
!pip install hanlp -U
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/tok_mtl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" ... |
11,004 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kernel hypothesis testing in Shogun
Heiko Strathmann - heiko.strathmann@gmail.com - http
Step1: Some Formal Basics (skip if you just want code examples)
To set the context, we here briefly ... | Python Code:
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import shogun as sg
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Kernel hypothesis testing in Shogun
Heiko Strathmann - heiko.strathmann@gmail.com - http://github.com/karlnapf - http://herrstrathma... |
11,005 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Db2 Macros
The %sql command also allows the use of macros. Macros are used to substitute text into SQL commands that you execute. Macros substitution is done before any SQL is executed. This... | Python Code:
%run db2.ipynb
Explanation: Db2 Macros
The %sql command also allows the use of macros. Macros are used to substitute text into SQL commands that you execute. Macros substitution is done before any SQL is executed. This allows you to create macros that include commonly used SQL commands or parameters rather... |
11,006 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Boolean and None Objects
Note
Step1: To stay practical, it is important to understand that you won't be assigning True and False values to variables as much as you will be receiving them. W... | Python Code:
# Declaring both Boolean values
a = True
b = False
Explanation: Boolean and None Objects
Note: Complete this lecture after finishing the "Comparison Operators" Section
Booleans are objects that evaluate to either True or False. In turn, they represent the 1 and 0 "on and off" concept. Whether you're build... |
11,007 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Import the BigBang modules as needed. These should be in your Python environment if you've installed BigBang correctly.
Step1: Also, let's import a number of other dependencies we'll use la... | Python Code:
import bigbang.ingress.mailman as mailman
import bigbang.analysis.graph as graph
import bigbang.analysis.process as process
from bigbang.parse import get_date
from bigbang.archive import Archive
import imp
imp.reload(process)
Explanation: Import the BigBang modules as needed. These should be in your Python... |
11,008 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
4.0 Numpy Advanced
4.1 Verifying the python version you are using
Step1: At this point anything above python 3.5 should be ok.
4.2 Import numpy
Step2: Notes
Step3: Notes
Step4: Notes
Ste... | Python Code:
import sys
print(sys.version)
Explanation: 4.0 Numpy Advanced
4.1 Verifying the python version you are using
End of explanation
import numpy as np
np.__version__
import matplotlib as mpl
from matplotlib import pyplot as plt
mpl.__version__
Explanation: At this point anything above python 3.5 should be ok.
... |
11,009 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python for Youth
refer to last week's robotic session
Under the hood, languages like Python program translate human language to something the machine can understand (instructions)
Python is ... | Python Code:
# First, let the player choose Rock, Paper or Scissors by typing the letter ‘r’, ‘p’ or ‘s’
# first create a prompt and explain
input('what is your name?')
# for python to do anything with the result we need to save it in a variable which we can name anything but this is informative
player = input('... |
11,010 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Self-driving car Nanodegree - Term 1
Project 1
Step1: Loading data
Step3: Include an exploratory visualization of the dataset
I did not spend so much time on this. I first print out the di... | Python Code:
# Load pickled data
import pickle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
# Visualizations will be shown in the notebook.
%matplotlib inline
import cv2
import glob
import tensorflow as tf
from tensorflow.contrib.layers import flatten
from tensorflow.contrib.laye... |
11,011 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
sequana_coverage test case example (Virus)
This notebook creates the BED file provided in
- https
Step1: Download the genbank and genome reference
Method1
Step2: Download the FastQ
Step3:... | Python Code:
%pylab inline
matplotlib.rcParams['figure.figsize'] = [10,7]
Explanation: sequana_coverage test case example (Virus)
This notebook creates the BED file provided in
- https://github.com/sequana/resources/tree/master/coverage and
- https://www.synapse.org/#!Synapse:syn10638358/wiki/465309
WARNING: you need ... |
11,012 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Variables,
arithmetic operators
~~~
+ - * / ** % //
~~~
assignment
~~~
=
~~~
assign and increment
~~~
+=
~~~
Step1: Flow Control, Loops
Step2: Multiplication Table
2017-09-19... | Python Code:
a = [1,2,5]
b = [4,7,6]
print('a =',a)
print('b =',b)
a = b
print('a =',a)
print('b =',b)
b = [1,1,1]
print('a =',a)
print('b =',b)
a = 2
b = 7.1
c = 4
d = a + b * c
print(a**b)
a = 7
b = 2
print(a//b)
a = 5
# a = a + 2
a += 2
a -= 3
a *=4
a //= 3
print(a)
x = -3
fx = 3*x**2 + 4*x - 7
print(x, fx)
x = -2
f... |
11,013 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Setup
conda install -y numpy
conda install -y scipy
conda install -y matplotlib
conda install -y rasterio
pip install lmdb
conda install -y caffe
conda install -y protobuf==3.0.0b3
pip insta... | Python Code:
import logging
import os
import numpy as np
import rasterio as rio
import lmdb
from caffe.proto.caffe_pb2 import Datum
import caffe.io
from rasterio._io import RasterReader
from glob import glob
sources =glob('/home/shared/srp/try2/*.tif')
print len(sources)
pos_regions = rasterio.open(r'/home/liux13/Deskt... |
11,014 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The 8-Queens Puzzle
We represent solutions to the 8-queens puzzle as tuples of the form
$$ (r_0, \cdots, r_7), $$
where $r_i$ is the row of the queen in column $i$. We start counting from $... | Python Code:
start = ()
Explanation: The 8-Queens Puzzle
We represent solutions to the 8-queens puzzle as tuples of the form
$$ (r_0, \cdots, r_7), $$
where $r_i$ is the row of the queen in column $i$. We start counting from $0$ because this is the way it is done in Python.
In general, states are defined as tuples of ... |
11,015 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Goal
Assessing the error in taxon abundances when using qPCR data + 16S sequence relative abundances to determine taxon proportional absolute abundances
Init
Step2: Making dataset
St... | Python Code:
%load_ext rpy2.ipython
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
def neg_binom_err(m, r, negs=False):
Adding negative binomial distribuiton error, where variance
scales more with the mean than a poisson distribution if (r < inf).
Parameters
----------
m : float
Mean val... |
11,016 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook presents a working example of adjusting texts for multiple subplots, related to https
Step1: With multiple subplots, run adjust_text for one subplot at a time | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt # Matplotlib 2.0 shown here
from adjustText import adjust_text
import numpy as np
import pandas as pd
Explanation: This notebook presents a working example of adjusting texts for multiple subplots, related to https://github.com/Phlya/adjustText/issues/58
E... |
11,017 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gaussian Mixture Models (GMM) are a kind of hybrid between a clustering estimator and a density estimator. Density estimator is an algorithm which takes a D-dimensional dataset and produces ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
import pandas as pd
Explanation: Gaussian Mixture Models (GMM) are a kind of hybrid between a clustering estimator and a density estimator. Density estimator is an algorithm which takes a D-dimensional da... |
11,018 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Removing particles from the simulation
This tutorial shows the different ways to remove particles from a REBOUND simulation. Let us start by setting up a simple simulation with 10 bodies, an... | Python Code:
import rebound
import numpy as np
sim = rebound.Simulation()
sim.add(m=1., hash=0)
for i in range(1,10):
sim.add(a=i, hash=i)
sim.move_to_com()
print("Particle hashes:{0}".format([sim.particles[i].hash for i in range(sim.N)]))
Explanation: Removing particles from the simulation
This tutorial shows the ... |
11,019 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Achieving Generalization
Testing and cross-validation
Train-test split
Step1: Cross validation
Step3: Valid options are ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_m... | Python Code:
import pandas as pd
from sklearn.datasets import load_boston
boston = load_boston()
dataset = pd.DataFrame(boston.data, columns=boston.feature_names)
dataset['target'] = boston.target
observations = len(dataset)
variables = dataset.columns[:-1]
X = dataset.ix[:,:-1]
y = dataset['target'].values
from sklea... |
11,020 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Course - Primer cas pràctic
<img src="http
Step1: I considerem una llista d'objectes que ha comprat un cert "client"
Step2: Step 1
Step3: Segona versió - Ara com a mínim sap Python... | Python Code:
prices = {'apple': 0.40, 'banana': 0.50, 'entrada_promocional': 10, 'entrada_simple': 17}
Explanation: Python Course - Primer cas pràctic
<img src="http://www.telecogresca.com/logo_mail.png"></img>
Exercici fortament sintètic
(En part de https://wiki.python.org/moin/SimplePrograms, en part collita pròpia)
... |
11,021 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lemke-Howson
Step1: Two-Player Games in Normal Form
We are going to find Nash equilibria (pure or mixed action) of a Two-Player Game $g = (I, (A_i){i \in I}, (u_i){i \in I})$, where
$I = {... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import quantecon.game_theory as gt
%matplotlib inline
Explanation: Lemke-Howson: An Algorithm to Find Nash Equilibrium
This notebook introduces the Lemke-Howson algorithm for finding a Nash equilibrium of a two-player normal form game.
End of explanation
d... |
11,022 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
'lc' Datasets and Options
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't w... | Python Code:
!pip install -I "phoebe>=2.2,<2.3"
Explanation: 'lc' Datasets and Options
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 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 explanation
%matplotlib ... |
11,023 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Clean Raw Annotations
Load raw annotations
Step2: Make random and blocked samples disjoint
Step3: Tidy is_harassment_or_attack column
Step4: Remap aggression score
Step5: Remove ... | Python Code:
# v4_annotated
user_blocked = [
'annotated_onion_layer_5_rows_0_to_5000_raters_20',
'annotated_onion_layer_5_rows_0_to_10000',
'annotated_onion_layer_5_rows_0_to_10000_raters_3',
'annotated_onion_layer_5_rows_10000_... |
11,024 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
pyPanair Tutorial#1 Rectangular Wing
In this tutorial we will perform an analysis of a rectangular wing with a NACA0012 airfoil.
A brief overview of the procedure is listed below
Step1: 1.2... | Python Code:
%matplotlib notebook
from pyPanair.preprocess import wgs_creator
delta_wing = wgs_creator.read_wgs("sample1.wgs")
print(delta_wing._networks.keys())
delta_wing._networks["wing"].plot_wireframe(show_normvec=False, show_corners=False, show_edges=False)
Explanation: pyPanair Tutorial#1 Rectangular Wing
In thi... |
11,025 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook presents how to perform maximum-likelihood parameter estimation for multiple neurons. The neurons depend on each other through a set of weights.
Step1: Reading input-output d... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import random
import csv
%matplotlib inline
import os
import sys
sys.path.append(os.path.join(os.getcwd(),'..'))
sys.path.append(os.path.join(os.getcwd(),'..','code'))
sys.path.append(os.path.join(os.getcwd(),'..','data'))
import filter... |
11,026 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reflection and Heating
For a comparison between "Horvat" and "Wilson" methods in the "irad_method" parameter, see the tutorial on Lambert Scattering.
Setup
Let's first make sure we have the ... | Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
Explanation: Reflection and Heating
For a comparison between "Horvat" and "Wilson" methods in the "irad_method" parameter, see the tutorial on Lambert Scattering.
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if runni... |
11,027 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Exercise 1
Step2: You can use as input the sound files from the sounds directory, thus using a relative path to it. If you run the read_audio_samples() function using the piano.wav s... | Python Code:
import sys
import os
import numpy as np
# to use this notebook with colab uncomment the next line
# !git clone https://github.com/MTG/sms-tools.git
# and change the next line to sys.path.append('sms-tools/software/models/')
sys.path.append('../software/models/')
from utilFunctions import wavread, wavwrite
... |
11,028 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Will be focusing on LINEAR linear_0006 for now, until I better understand how they compare.
Step1: Focusing on one of the periapse tables for now | Python Code:
df = df[df.BIN_PATTERN_INDEX == 'LINEAR linear_0006']
# now can drop that column
df = df.drop('BIN_PATTERN_INDEX', axis=1)
bin_tables = df.BIN_TBL.value_counts()
bin_tables
for ind in bin_tables.index:
print(ind)
print(df[df.BIN_TBL==ind].orbit_segment.value_counts())
Explanation: Will be focusing ... |
11,029 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
How does one convert a list of Z-scores from the Z-distribution (standard normal distribution, Gaussian distribution) to left-tailed p-values? Original data is sampled from X ~ N(mu... | Problem:
import scipy.stats
import numpy as np
z_scores = [-3, -2, 0, 2, 2.5]
mu = 3
sigma = 4
temp = np.array(z_scores)
p_values = scipy.stats.norm.cdf(temp) |
11,030 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 11
Modeling and Simulation in Python
Copyright 2021 Allen Downey
License
Step1: In this chapter, we develop a model of an epidemic as it spreads in a
susceptible population, and use... | 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/... |
11,031 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MCMC
Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License
Step1: For most of this book we've been using grid methods to approximate posterior distributions.
For models with on... | Python Code:
# If we're running on Colab, install libraries
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(filename):
from urllib.request import ... |
11,032 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using scipy iterative solvers
The aim of this notebook is to show how the scipy.sparse.linalg module can be used to solve iteratively the Lippmann–Schwinger equation.
The problem at hand is ... | Python Code:
import h5py as h5
import matplotlib.pyplot as plt
import numpy as np
import janus
import janus.material.elastic.linear.isotropic as material
import janus.operators as operators
import janus.fft.serial as fft
import janus.green as green
from scipy.sparse.linalg import cg, LinearOperator
%matplotlib inline
p... |
11,033 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Characteristics of Autonomous Market Makers
Date
Step1: From the Balancer whitepaper
Step2: We can specify that swaps happen on some invariant surface $V(x,y)$ which allows us to replace t... | Python Code:
from IPython.display import HTML
# Hide code cells https://gist.github.com/uolter/970adfedf44962b47d32347d262fe9be
def hide_code():
return HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$("div.input").hide();
} else {
$("div.input").... |
11,034 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The frequency of a Ricker wavelet
We often use Ricker wavelets to model seismic, for example when making a synthetic seismogram with which to help tie a well. One simple way to guesstimate t... | Python Code:
T, dt, f = 0.256, 0.001, 25
import bruges
w, t = bruges.filters.ricker(T, dt, f, return_t=True)
import scipy.signal
f_W, W = scipy.signal.welch(w, fs=1/dt, nperseg=256)
fig, axs = plt.subplots(figsize=(15,5), ncols=2)
axs[0].plot(t, w)
axs[0].set_xlabel("Time [s]")
axs[1].plot(f_W[:25], W[:25], c="C1")
axs... |
11,035 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<b>Step 1</b> Get the links of different app categories on iTunes.
Step1: <b>Step2</b>
Get the links for all popular apps of different catigories on iTunes.
Step2: <b>Step3</b> Extract the... | Python Code:
r = urllib.urlopen('https://itunes.apple.com/us/genre/ios-books/id6018?mt=8').read()
soup = BeautifulSoup(r)
print type(soup)
all_categories = soup.find_all("div", class_="nav")
category_url = all_categories[0].find_all(class_ = "top-level-genre")
categories_url = pd.DataFrame()
for itm in category_url:
... |
11,036 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Purpose
The purpose of this notebook is to work out the code for how to combine and average tetrode pairs over brain areas over multiple sessions
Step1: Make sure we can get the ripple-trig... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import xarray as xr
from src.analysis import (decode_ripple_clusterless,
detect_epoch_ripples,
ripple_triggered_connectivity,
connectivi... |
11,037 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Configuration
Step1: Get the Trace
Step2: FTrace Object
Step3: Assertions
Step4: Assertion
Step5: Assertion
Step6: Statistics
Check if 95% of the temperature readings are below CONTROL... | Python Code:
import trappy
import numpy
config = {}
# TRAPpy Events
config["THERMAL"] = trappy.thermal.Thermal
config["OUT"] = trappy.cpu_power.CpuOutPower
config["IN"] = trappy.cpu_power.CpuInPower
config["PID"] = trappy.pid_controller.PIDController
config["GOVERNOR"] = trappy.thermal.ThermalGovernor
# Control Tempera... |
11,038 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
全局参数
Step1: 初始化权重
如果需要，会给权重加上L2 loss。为了在后面计算神经网络的总体loss的时候被用上，需要统一存到一个collection。
加载数据
使用cifa10_input来获取数据，这个文件来自tensorflow github，可以下载下来直接使用。如果使用distorted_input方法，那么得到的数据是经过增强处理的。会对图片随机做出切... | Python Code:
max_steps = 3000
batch_size = 128
data_dir = 'data/cifar10/cifar-10-batches-bin/'
model_dir = 'model/_cifar10_v2/'
Explanation: 全局参数
End of explanation
X_train, y_train = cifar10_input.distorted_inputs(data_dir, batch_size)
X_test, y_test = cifar10_input.inputs(eval_data=True, data_dir=data_dir, batch_size... |
11,039 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 3
Step1: It's easy to determine the name of the variable; in this case, the name is $x$. It can be a bit more complicated to determine the type of the variable, as it depends on the... | Python Code:
x = 2
Explanation: Lecture 3: Python Variables and Syntax
CSCI 1360: Foundations for Informatics and Analytics
Overview and Objectives
In this lecture, we'll get into more detail on Python variables, as well as language syntax. By the end, you should be able to:
Define variables of string and numerical typ... |
11,040 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
use one hot encoding on the given dataset named 'onehotend_data.csv' on column 'town'
| Python Code::
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
ohe = OneHotEncoder()
df = pd.read_csv('onehotend_data.csv')
ohe.fit(df[['town']])
ct = make_column_transformer((OneHotEncoder(categories = ohe.categories_), ['town']), remainder = 'pass... |
11,041 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
load in evaluation dataset
sub-sample a large set of features
calculate PCA and save out for loading in other places.
Step1: How similar are PCs on 2 sub-samples of data?
Step2: After comp... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn
import pandas as pd
from sklearn.decomposition import PCA
import pickle
%matplotlib inline
# load smaller user behavior dataset
user_profile = pd.read_pickle('../data_user_view_buy/user_profile_items_nonnull_features_20_mins_5_views_v2_sampl... |
11,042 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 3
Warmup
Write a function that simulates a dice roll every time the function is called.
Step1: Rewrite your function to take in an int n and simulate n dice rolls.
Write a function ... | Python Code:
import random
def dice():
return random.randint(1,6)
def roll_dice(n):
for i in range(n):
print(dice())
roll_dice(5)
Explanation: Lecture 3
Warmup
Write a function that simulates a dice roll every time the function is called.
End of explanation
balls = ['r', 'r', 'b', 'b', 'b']
def... |
11,043 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Divide continuous data into equally-spaced epochs
This tutorial shows how to segment continuous data into a set of epochs spaced
equidistantly in time. The epochs will not be created based o... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import compute_proj_ecg
from mne_connectivity import envelope_correlation
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
... |
11,044 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Network Tour of Data Science
Xavier Bresson, Winter 2016/17
Assignment 3
Step1: Goal
The goal is to define with TensorFlow a vanilla recurrent neural network (RNN) m... | Python Code:
# Import libraries
import tensorflow as tf
import numpy as np
import collections
import os
# Load text data
data = open(os.path.join('datasets', 'text_ass_6.txt'), 'r').read() # must be simple plain text file
print('Text data:',data)
chars = list(set(data))
print('\nSingle characters:',chars)
data_len, voc... |
11,045 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data
Step1: Load dataframes
Step2: 1. What are most popular categories?
Step3: 2. What are the most common restaurant chains?
Step4: 2a. Correlations in chain properties
higher rating --... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import glob
import os
import scipy as sp
from scipy import stats
from tools.plt import color2d #from the 'srcole/tools' repo
from matplotlib import cm
Explanation: Data: 1000 restaurants for each city
Cuisines: most p... |
11,046 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Catch that asteroid!
Step1: First, we need to increase the timeout time to allow the download of data occur properly
Step2: Two problems
Step3: We first propagate
Step4: And now we have ... | Python Code:
import matplotlib.pyplot as plt
plt.ion()
from astropy import units as u
from astropy.time import Time
from astropy.utils.data import conf
conf.dataurl
conf.remote_timeout
Explanation: Catch that asteroid!
End of explanation
conf.remote_timeout = 10000
from astropy.coordinates import solar_system_ephemeri... |
11,047 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
WaveSolver - slider
M. Lamoureux May 31, 2016. Pacific Insitute for the Mathematical Sciences
Updated June 2017, to remove all reference to Bokeh, cuz it doesn't work now. (deprecated = brok... | Python Code:
%matplotlib inline
import numpy as np
from matplotlib.pyplot import *
Explanation: WaveSolver - slider
M. Lamoureux May 31, 2016. Pacific Insitute for the Mathematical Sciences
Updated June 2017, to remove all reference to Bokeh, cuz it doesn't work now. (deprecated = broken)
(Bokeh = Brokehn)
This code do... |
11,048 | Given the following text description, write Python code to implement the functionality described.
Description:
For a given string, flip lowercase characters to uppercase and uppercase to lowercase.
This is how the function will work:
flip_case('Hello')
'hELLO'
| Python Code:
def flip_case(string: str) -> str:
return string.swapcase() |
11,049 | 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', 'inm', 'inm-cm4-8', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: INM
Source ID: INM-CM4-8
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, T... |
11,050 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Incrementally saving sampling progress
Can I save intermediate MCMC results for long runs, to avoid catastrophic loss of samples?
gully
February 2016
In this notebook I explore how to save i... | Python Code:
from emcee.sampler import Sampler
def bogus_lnprob(p):
return 1.0
samp = Sampler(3, bogus_lnprob)
samp.run_mcmc( # Hit shift-tab... also peak at samp.sample(), etc...
Explanation: Incrementally saving sampling progress
Can I save intermediate MCMC results for long runs, to avoid catastrophic loss ... |
11,051 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic Classfication
https
Step1: Hypothesis
$$
H(X) = \frac {1} {1+e^{-W^T X}}
$$
https
Step2: Evaluation | Python Code:
import tensorflow as tf
import numpy as np
xy = np.loadtxt('../data/logistic_data.txt',unpack=True, dtype='float32')
x_data = xy[0:-1]
y_data = xy[-1]
Explanation: Logistic Classfication
https://ko.wikipedia.org/wiki/%EB%A1%9C%EC%A7%80%EC%8A%A4%ED%8B%B1_%ED%9A%8C%EA%B7%80
End of explanation
x_data = [ [1,2... |
11,052 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lesson 3
Python Basic, Lesson 3,
* v1.0, 2016
* v1.1, 2020.2,3,4, 6.13 edit by David Yi
本章内容要点
for 循环语句和 range() 函数
常用数据类型
字符串
字符串处理
思考
for 循环语句和 range() 函数
Python的循环语句主要是 for...in 循环，依次把 ... | Python Code:
# 按照字符串进行迭代循环
s = 'abcdef'
for i in s:
print(i)
# 按照列表进行循环，列表内容为字符
s = ['a', 'b', 'c']
for i in s:
print(i)
# 按照列列表进行循环，列表内容为数字
for i in range(3):
print(i)
Explanation: Lesson 3
Python Basic, Lesson 3,
* v1.0, 2016
* v1.1, 2020.2,3,4, 6.13 edit by David Yi
本章内容要点
for 循环语句和 range() 函数
常用数据类... |
11,053 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Simple Autoencoder
We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed represen... | Python Code:
%matplotlib inline
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', validation_size=0)
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to c... |
11,054 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic of Machine Learning
1.Data
Step1: Brodcasting
Step2: nd array <-> Numpy
Step3: Deal data with gpu
Step4: Scala, Vector, Matrices, Tensors | Python Code:
import mxnet as mx
from mxnet import nd
import numpy as np
mx.random.seed(1)
x = nd.empty((3, 4))
print(x)
x = nd.ones((3, 4))
x
y = nd.random_normal(0, 1, shape=(3, 4))
print y
print y.shape
print y.size
x * y
nd.exp(y)
nd.dot(x, y.T)
# Memory Host
print "The current mem host y is {}".format(id(y))
y[:]... |
11,055 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
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', 'mohc', 'hadgem3-gc31-mm', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-MM
Topic: Seaice
Sub-Topics: Dynamics, T... |
11,056 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The OpenFermion Developers
Step1: The Jordan-Wigner and Bravyi-Kitaev Transforms
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
S... | 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... |
11,057 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: Forward and Backward mode gradients in TFF
<table class="tfo-notebook-buttons" align="left"... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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... |
11,058 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FDMS TME3
Kaggle How Much Did It Rain? II
Florian Toque & Paul Willot
Dear professor Denoyer...
Warning
This is an early version of our entry for the Kaggle challenge
It's still very mes... | Python Code:
# from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
%matplotlib inline
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
import scipy.stats as stats
# Sk cheats
from sklear... |
11,059 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Train Models
Train a logistic regression model with the engineered features
Including LDA-based topic similarity, sentence position, sentence length, and readability metrics, I traine... | Python Code:
import matplotlib.pyplot as plt
import csv
from textblob import TextBlob, Word
import pandas as pd
import sklearn
import pickle
import numpy as np
import scipy
from scipy import spatial
import nltk.data
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes i... |
11,060 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This code shows an example for using the imported data from a modified .mat file into a artificial neural network and its training
Step1: Importing preprocessing data
Step2: Sorting out d... | Python Code:
import numpy as np
from sklearn.neural_network import MLPRegressor
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn.metrics import r2_score # in order to test the results
from sklearn.g... |
11,061 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Keras deep neural network
The structure of the network is the following
Step1: Training
Step2: Visualization | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
%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'
# Data generation obtained ... |
11,062 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plotting phase tensors from a ModEM data file on a basemap
In this example we will plot phase tensors from ModEM files. This example is a bit more complex than previous examples, as, unlike ... | Python Code:
from mtpy.modeling.modem import PlotPTMaps
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib as mpl
from mpl_toolkits.basemap import Basemap
from shapely.geometry import Polygon
from descartes import PolygonPatch
import numpy as np
Explanation: Plotting phase tensors from a ModEM... |
11,063 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pre-processing
Step1: Read in tweets and add appropriate labels (sarcastic/genuine)
Step2: Remove non-English tweets
Step3: Feature Engineering
ToUser - tweet references another user via ... | Python Code:
import csv
with open("processed_tweets/sarcastic_tweets.csv", 'r') as f:
reader = csv.reader(f)
linenumber = 1
try:
for row in reader:
linenumber += 1
except Exception as e:
print (("Error line %d: %s %s" % (linenumber, str(type(e)), e)))
Explanation: Pre-process... |
11,064 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Occupancy Detection
Create a classification model to determine if a room is occupied or unoccupied based on environmental data.
In class demo on May 5, 2018
Step1: Data Loading
Load data i... | Python Code:
%matplotlib notebook
import os
import csv
import pickle
import numpy as np
import pandas as pd
from datetime import datetime
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.base import BaseEstimator, Tr... |
11,065 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Colab-only auth
Step2: Config
Step3: Linear Keras model [WORK REQUIRED]
What do the columns do ? Familiarize yourself with these column types.
numeric_col = fc.numer... | Python Code:
import os, json, math
import numpy as np
import tensorflow as tf
from tensorflow.python.feature_column import feature_column_v2 as fc # This will change when Keras FeatureColumn is final.
from matplotlib import pyplot as plt
print("Tensorflow version " + tf.__version__)
tf.enable_eager_execution()
#@title... |
11,066 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using Feature Store
Learning Objective
In this notebook, you will learn how to
Step1: Note
Step2: Set your project ID
Update YOUR-PROJECT-ID with your Project ID. If you don't know your pr... | Python Code:
# Setup your dependencies
import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
U... |
11,067 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MCMC & why 3d matters
This example (although quite artificial) shows that viewing a posterior (ok, I have flat priors) in 3d can be quite useful. While the 2d projection may look quite 'bad'... | Python Code:
!pip install emcee corner
!pip show matplotlib
import pylab
import scipy.optimize as op
import emcee
import numpy as np
%matplotlib inline
# our 'blackbox' 3 parameter model which is highly degenerate
def f_model(x, a, b, c):
return x * np.sqrt(a**2 +b**2 + c**2) + a*x**2 + b*x**3
N = 100
a_true, b_tru... |
11,068 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Success Prediction
Predict success or fail with information given before project begins
Features
Step2: A. Percentage distribution
Step3: B. Category
Step4: Categories are classif... | Python Code:
#load_data
cf_df = pd.read_excel('cf_df.xlsx')
#check feature number
def check_number(feature):
"feature : 'str'
count = cf_df[feature].value_counts()
return print(count)
# success rate
print('overall success'),
print('=================='),
success_percentage = cf_df['end_with_success'].value_c... |
11,069 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modeling rent prices for the SF Bay Area
This notebook will develop a predictive model for rent prices in the Bay Area using rental listings from Craigslist.
Data
Step1: Load and prepare d... | Python Code:
import numpy as np
import pandas as pd
import os
import math
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
DATA_DIR = os.path.join('..','data','urbansim')
Explanation: Modeling rent prices for the SF Bay Area
This notebook will develop a predictive model for rent prices in the Ba... |
11,070 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 23
Modeling and Simulation in Python
Copyright 2021 Allen Downey
License
Step1: Code from the previous chapter
Step2: In the previous chapter we developed a model of the flight of ... | 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/... |
11,071 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CM360 Segmentology
CM360 funnel analysis using Census data.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file excep... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: CM360 Segmentology
CM360 funnel analysis using Census data.
License
Copyright 2020 Google LLC,
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 ... |
11,072 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: <table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Sending Different Values Based On Client Da... | 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... |
11,073 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Week 9 - Dataset preprocessing
Before we utilize machine learning algorithms we must first prepare our dataset. This can often take a significant amount of time and can have a large impact o... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
Explanation: Week 9 - Dataset preprocessing
Before we utilize machine learning algorithms we must first prepare our dataset. This can often take a significant amount of time and can have a large impact on the performa... |
11,074 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Load Image As Greyscale
Step2: Save Image | Python Code:
# Load library
import cv2
import numpy as np
from matplotlib import pyplot as plt
Explanation: Title: Save Images
Slug: save_images
Summary: How to save images using OpenCV in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags: Preprocessing Images
Authors: Chris Albon
Preliminaries
End of... |
11,075 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Topic Modeling Amarigna
_ Simple topic classifying LSTM model to test if it is possible to identify topics in Amharic text _
Step25: A small sample dataset to train and test the model
Step2... | Python Code:
from sklearn.datasets import fetch_20newsgroups
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import keras
from keras.layers import Embedding, Dense, LSTM, GRU
from keras.models import Sequential
from sklearn.model_selection import train_test_split, S... |
11,076 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This IPython Notebook illustrates the use of the openmc.mgxs.Library class. The Library class is designed to automate the calculation of multi-group cross sections for use cases with one or ... | Python Code:
import math
import pickle
from IPython.display import Image
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.mgxs
from openmc.openmoc_compatible import get_openmoc_geometry
import openmoc
import openmoc.process
from openmoc.materialize import load_openmc_mgxs_lib
%matplotlib i... |
11,077 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model Comparisons
Comparing the DDM to other congitive models
Step1: DDM vs Signal Detection Theory
Comparing DDM to Signal Detection - does d' correlate with DDM parameters?
Step2: d' dis... | Python Code:
# Environment setup
%matplotlib inline
%cd /lang_dec
# Imports
import warnings; warnings.filterwarnings('ignore')
import hddm
import math
import scipy
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import bayesian_bootstrap.bootstrap as bootstrap
from utils import model_tools, sig... |
11,078 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Conversion of Objax models to Tensorflow
This tutorial demonstrates how to export models from Objax to Tensorflow and then export them into SavedModel format.
SavedModel format could be read... | Python Code:
# install the latest version of Objax from github
%pip --quiet install git+https://github.com/google/objax.git
import math
import random
import tempfile
import numpy as np
import tensorflow as tf
import objax
from objax.zoo.wide_resnet import WideResNet
Explanation: Conversion of Objax models to Tensorflow... |
11,079 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocean
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', 'awi', 'sandbox-3', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Framework, Adve... |
11,080 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 1
Step1: Configure GCP environment settings
Update the PROJECT_ID variable to reflect the ID of the Google Cloud project you are using to implement this solution.
Step2: Authenticate ... | Python Code:
from datetime import datetime
import matplotlib.pyplot as plt
import seaborn as sns
from google.cloud import bigquery
Explanation: Part 1: Learn item embeddings based on song co-occurrence
This notebook is the first of five notebooks that guide you through running the Real-time Item-to-item Recommendation ... |
11,081 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
In this micro-course, you'll learn all about pandas, the most popular Python library for data analysis.
Along the way, you'll complete several hands-on exercises with real-world... | Python Code:
import pandas as pd
Explanation: Introduction
In this micro-course, you'll learn all about pandas, the most popular Python library for data analysis.
Along the way, you'll complete several hands-on exercises with real-world data. We recommend that you work on the exercises while reading the corresponding ... |
11,082 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sampler statistics
When checking for convergence or when debugging a badly behaving
sampler, it is often helpful to take a closer look at what the
sampler is doing. For this purpose some sam... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
import pandas as pd
import pymc3 as pm
%matplotlib inline
Explanation: Sampler statistics
When checking for convergence or when debugging a badly behaving
sampler, it is often helpful to take a closer look at what the
sampler is doing... |
11,083 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The first two consecutive numbers to have two distinct prime factors are
Step1: Define an $n$-wise iterator over an iterator, inspired by the implementation of pairwise in the Itertools rec... | Python Code:
%load_ext autoreload
%autoreload 2
from common.utils import prime_factors
from itertools import count, tee
from six.moves import map, reduce, zip
Explanation: The first two consecutive numbers to have two distinct prime factors are:
$$
14 = 2 × 7 \
15 = 3 × 5
$$
The first three consecutive numbers to have ... |
11,084 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Image Reduction in Python
Erik Tollerud (STScI)
In this notebook we will walk through several of the basic steps required to do data reduction using Python and Astropy. This notebook is foc... | Python Code:
import ccdproc
ccdproc.__version__
import photutils
photutils.__version__
Explanation: Image Reduction in Python
Erik Tollerud (STScI)
In this notebook we will walk through several of the basic steps required to do data reduction using Python and Astropy. This notebook is focused on "practical" (you decid... |
11,085 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic orienatation to ticdat, pandas and developing engines for Opalytics
One of the advantages of Python is that it has "batteries included". That is to say, there is a rich set of librarie... | Python Code:
from ticdat import TicDatFactory, freeze_me
dataFactory = TicDatFactory (
categories = [["name"],["minNutrition", "maxNutrition"]],
foods = [["name"],["cost"]],
nutritionQuantities = [["food", "category"], ["qty"]])
Explanation: Basic orienatation to ticdat, pandas and developing engines fo... |
11,086 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Discretizando la ecuación de Schrödinger
Autor
Step1: Código que controla las simulaciones
Step2: Rutinas que generan inicializan y generan la simulación
Step3: Rutinas que conectan los c... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
import ipywidgets as widgets
from IPython.display import display
L = 200
dx = 2.
buttonrunsim=widgets.Button(description="Simulate")
outwdt = widgets.Output()
centropaquete = widgets... |
11,087 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plotting
This tutorial explains the high-level interface to plotting provided by the Bundle. You are of course always welcome to access arrays and plot manually.
As of PHOEBE 2.1, PHOEBE us... | Python Code:
!pip install -I "phoebe>=2.1,<2.2"
Explanation: Plotting
This tutorial explains the high-level interface to plotting provided by the Bundle. You are of course always welcome to access arrays and plot manually.
As of PHOEBE 2.1, PHOEBE uses autofig as an intermediate layer for highend functionality to matp... |
11,088 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create DataFrame
Step2: Fit The Label Encoder
Step3: View The Labels
Step4: Transform Categories Into Integers
Step5: Transform Integers Into Categories | Python Code:
# Import required packages
from sklearn import preprocessing
import pandas as pd
Explanation: Title: Convert Pandas Categorical Data For Scikit-Learn
Slug: convert_pandas_categorical_column_into_integers_for_scikit-learn
Summary: Convert Pandas Categorical Column Into Integers For Scikit-Learn
Date: 2016-1... |
11,089 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nexa Wall Street Columns Raw Data, Low Resolution vs High Resolution, NData
Here we compare how well the LDA classifier works for both low resolution and high resolution classification when ... | Python Code:
import numpy as np
from sklearn import cross_validation
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
import h5py
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import sys
sys.path.append("../")
from aux.raw_images_columns_functions ... |
11,090 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Pandas Website
This... | Python Code:
v = pd.Series(np.random.randn(5))
v
Explanation: Pandas
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Pandas Website
This tutorial pulls from the Pandas website and the Handson-ML tutorial:... |
11,091 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Use Dictionary Comprehension | Python Code:
Officers = {'Michael Mulligan': 'Red Army',
'Steven Johnson': 'Blue Army',
'Jessica Billars': 'Green Army',
'Sodoni Dogla': 'Purple Army',
'Chris Jefferson': 'Orange Army'}
Officers
Explanation: Title: Iterating Over Dictionary Keys
Slug: iterating_over_dict... |
11,092 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 4
Step1: Pandas' data frame has some helpful methods for seeing which values are null
Step2: Side note
Step3: So we can quickly summarize the number of missing values for each fea... | Python Code:
import pandas as pd
from io import StringIO
csv_data = '''A,B,C,D
1.0,2.0,3.0,4.0
5.0,6.0,,8.0
10.0,11.0,12.0,'''
df = pd.read_csv(StringIO(csv_data))
df
Explanation: Chapter 4: building good training sets
Handling missing values
Let's start by constructing a simple dataset with some missing values.
End of... |
11,093 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: PWC-Net-small model training (with cyclical learning rate schedule)
In this notebook we
Step2: TODO
Step3: Pre-train on FlyingChairs+FlyingThings3DHalfRes mix
Load the dataset
Step4... | Python Code:
pwcnet_train.ipynb
PWC-Net model training.
Written by Phil Ferriere
Licensed under the MIT License (see LICENSE for details)
Tensorboard:
[win] tensorboard --logdir=E:\\repos\\tf-optflow\\tfoptflow\\pwcnet-sm-6-2-cyclic-chairsthingsmix
[ubu] tensorboard --logdir=/media/EDrive/repos/tf-optflow/tfopt... |
11,094 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matching Market
This simple model consists of a buyer, a supplier, and a market.
The buyer represents a group of customers whose willingness to pay for a single unit of the good is captured... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import random as rnd
import pandas as pd
import numpy as np
import time
import datetime
import calendar
# fix what is missing with the datetime/time/calendar package
def add_months(sourcedate,months):
month = sourcedate.month - 1 + months
year = in... |
11,095 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This script implements a Gradient Boosting Machine on the Titanic dataset. This is a boosting algorithm that will be using trees, and will be auto-selecting features to evaluate. Since we're... | Python Code:
import numpy as np
import pandas as pd
titanic=pd.read_csv('./titanic_clean_data.csv')
cols_to_norm=['Age','Fare']
col_norms=['Age_z','Fare_z']
titanic[col_norms]=titanic[cols_to_norm].apply(lambda x: (x-x.mean())/x.std())
titanic['cabin_clean']=(pd.notnull(titanic.Cabin))
from sklearn.cross_validation imp... |
11,096 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Manuscript plots
This notebook creates the figures in Parviainen (2015, submitted to MNRAS). The figures show the calculation of quadratic limb darkening coefficients for three broadband fil... | Python Code:
%pylab inline
import seaborn as sb
from matplotlib.patches import Ellipse
from scipy.stats import chi2
from ldtk import LDPSetCreator, BoxcarFilter, TabulatedFilter
AAOCW, AAPGW = 3.465, 7.087
rc(['xtick','ytick','axes'], labelsize=8)
def eigsorted(cov):
vals, vecs = np.linalg.eigh(cov)
ord... |
11,097 | 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
a = tf.constant([1,2,3])
b = tf.constant([4,5,6,7])
def g(a,b):
tile_a = tf.tile(tf.expand_dims(a, 1), [1, tf.shape(b)[0]])
tile_a = tf.expand_dims(tile_a, 2)
tile_b = tf.tile(tf.expand_dims(b, 0), [tf.shape(a)[0], 1])
tile_b = tf.expand_dims(tile_b, 2)
cart = tf.con... |
11,098 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numbers
Python provide following builtins numeric data types
Step1: NOTE
Step2: Greater than (>)
Step3: Less than or equal to (<=)
Step4: greater_than_or_equal_to
Step5: Equal To
Step6:... | Python Code:
# Converting real to integer
print ('int(3.14) =', int(3.14))
print ('int(3.64) =', int(3.64))
print('int("22") =', int("22"))
print('int("22.0") !=', int("22.0"))
print("int(3+4j) =", int(3+4j))
# Converting integer to real
print ('float(5) =', float(5))
print('int("22.0") ==', float("22.0"))
print('int(f... |
11,099 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Programación lineal
<img style="float
Step1: 3.2
Mónica hace aretes y cadenitas de joyería. Es tan buena, que todo lo que hace lo vende.
Le toma 30 minutos hacer un par de aretes y una hora... | Python Code:
import numpy as np
f = np.array([-1, -1])
A = np.array([[50, 24], [30, 33], [-1, 0], [0, -1]])
b = np.array([2400, 2100, -45, -5])
import pyomo_utilities
x, obj = pyomo_utilities.linprog(f, A, b)
x
obj
obj_real = x.sum()-50
obj_real.round(2)
Explanation: Programación lineal
<img style="float: right; margin... |
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