Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
|---|---|---|
4,100 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Traverse a Square - Part 2 - Variables
In this notebook, we will introduce one of the most powerful ideas in programming
Step1: Try changing the message in the previous code cell and re-run... | Python Code:
# Create the message variable and assign the value "Hello World" to it
message="Hello World"
# Use the variable in a print statement
# The print statement retrieves the value assigned to the variable and displays the value
print(message)
Explanation: Traverse a Square - Part 2 - Variables
In this notebook,... |
4,101 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CS229 Homework 1 Problem 1
In this exercise we use logistic regression to construct a decision boundary for a binary classification problem. In order to do so, we must first load the data.
S... | Python Code:
import numpy as np
import pandas as pd
import logistic_regression as lr
Explanation: CS229 Homework 1 Problem 1
In this exercise we use logistic regression to construct a decision boundary for a binary classification problem. In order to do so, we must first load the data.
End of explanation
X = np.loadtxt... |
4,102 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
JLab ML Lunch 2 - Data Exploration
Second ML challenge hosted
On October 30th, a test dataset will be released, and predictions must be submitted within 24 hours
Let's take a look at the tra... | Python Code:
%matplotlib widget
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import imageio
Explanation: JLab ML Lunch 2 - Data Exploration
Second ML challenge hosted
On October 30th, a test dataset will be released, and predictions must be submitted wit... |
4,103 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Análisis de los datos obtenidos
Uso de ipython para el análsis y muestra de los datos obtenidos durante la producción.Se implementa un regulador experto. Los datos analizados son del día 13 ... | Python Code:
#Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv co... |
4,104 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
N2 - Eurocode 8, CEN (2005)
This simplified nonlinear procedure for the estimation of the seismic response of structures uses capacity curves and inelastic spectra. This method has been deve... | Python Code:
import N2Method
from rmtk.vulnerability.common import utils
%matplotlib inline
Explanation: N2 - Eurocode 8, CEN (2005)
This simplified nonlinear procedure for the estimation of the seismic response of structures uses capacity curves and inelastic spectra. This method has been developed to be used in comb... |
4,105 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Quality Controlling Saildrone T/S
Objective
Step1: First, learn about the data
Load the data
Step2: Let's learn about this dataset, starting from the attributes.
Step3: Great, it follows ... | Python Code:
import xarray as xr
from cotede.qc import ProfileQC
Explanation: Quality Controlling Saildrone T/S
Objective:
This notebook shows how to use CoTeDe to evaluate temperature and salinity measured along-track from a Saildrone.
The nature of this dataset is similar to a Thermosalinograph (TSG) on vessels of op... |
4,106 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hyperparameter optimization using pyGPGO
by José Jiménez (Oct 18, 2017)
In this tutorial, we will learn the basics of the Bayesian optimization (BO) framework through a step-by-step example ... | Python Code:
import numpy as np
from sklearn.datasets import make_moons
np.random.seed(20)
X, y = make_moons(n_samples = 200, noise = 0.3) # Data and target
Explanation: Hyperparameter optimization using pyGPGO
by José Jiménez (Oct 18, 2017)
In this tutorial, we will learn the basics of the Bayesian optimization (BO) f... |
4,107 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The
Step1: Load and inspect example data
This data set contains source estimation data from an audio visual task. It
has been mapped onto the inflated cortical surface representation obtai... | Python Code:
import os
from mne import read_source_estimate
from mne.datasets import sample
print(__doc__)
# Paths to example data
sample_dir_raw = sample.data_path()
sample_dir = os.path.join(sample_dir_raw, 'MEG', 'sample')
subjects_dir = os.path.join(sample_dir_raw, 'subjects')
fname_stc = os.path.join(sample_dir, '... |
4,108 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<p>
<img src="http
Step1: defs
Step2: testing
Step3: $\mathfrak{p}={1^{j+1}0^{j}}$
Step4: $\mathfrak{p}={0^{j+1}1^{j}}$
Step5: $\mathfrak{p}={1^{j}0^{j}}$
Step6: $\mathfrak{p}={(10)}^{... | Python Code:
from sympy import *
from IPython.display import Markdown, Latex
from oeis import oeis_search
init_printing()
%run ~/Developer/working-copies/programming-contests/competitive-programming/python-libs/oeis.py
Explanation: <p>
<img src="http://www.cerm.unifi.it/chianti/images/logo%20unifi_positivo.jpg"
... |
4,109 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cliff Walking Problem solved with TD(0) Algorithms
Step1: The OpenAI Gym toolkit includes the below environment for the "Cliff-Walking" problem
Step2: Load the Cliff-Walking environment
St... | Python Code:
import gym
import random
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from collections import OrderedDict
Explanation: Cliff Walking Problem solved with TD(0) Algorithms: Implementation & Comparisons
1. Load Libraries & Define Environment
End of explanat... |
4,110 | 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
# Sk cheats
from sklearn.cross_validation import cr... |
4,111 | 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', 'ncc', 'sandbox-2', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-2
Sub-Topics: Radiative Forcings.
Properties: 85... |
4,112 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Yahoo API Example
This notebook is an example of using yahoo api to get fantasy sports data.
Step1: Prerequisite
First we need to create a Yahoo APP at https
Step2: Step 1
Step3: Step 2
S... | Python Code:
from rauth import OAuth2Service
import webbrowser
import json
Explanation: Yahoo API Example
This notebook is an example of using yahoo api to get fantasy sports data.
End of explanation
clientId= "dj0yJmk9M3gzSWJZYzFmTWZtJmQ9WVdrOU9YcGxTMHB4TXpnbWNHbzlNQS0tJnM9Y29uc3VtZXJzZWNyZXQmeD1kZg--"
clinetSecrect="... |
4,113 | Given the following text description, write Python code to implement the functionality described.
Description:
Find if given matrix is Toeplitz or not
Python3 program to check whether given matrix is a Toeplitz matrix or not ; Function to check if all elements present in descending diagonal starting from position ( i ,... | Python Code:
N = 5
M = 4
def checkDiagonal(mat , i , j ) :
res = mat[i ][j ]
i += 1
j += 1
while(i < N and j < M ) :
if(mat[i ][j ] != res ) :
return False
i += 1
j += 1
return True
def isToeplitz(mat ) :
for j in range(M ) :
if not(checkDiagonal(mat , 0 , j ) ) :
return False ... |
4,114 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
01
Step1: Set all graphics from matplotlib to display inline
Step2: Read the csv in (it should be UTF-8 already so you don't have to worry about encoding), save it with the proper boring n... | Python Code:
import pandas as pd
Explanation: 01: Building a pandas Cheat Sheet, Part 1
Use the csv I've attached to answer the following questions:
Import pandas with the right name
End of explanation
%matplotlib inline
Explanation: Set all graphics from matplotlib to display inline
End of explanation
df = pd.read_csv... |
4,115 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Demand forecasting with BigQuery and TensorFlow</h1>
In this notebook, we will develop a machine learning model to predict the demand for taxi cabs in New York.
To develop the model, we ... | Python Code:
!sudo pip install --user pandas-gbq
!pip install --user pandas_gbq
!pip install tensorflow==1.15.3
Explanation: <h1>Demand forecasting with BigQuery and TensorFlow</h1>
In this notebook, we will develop a machine learning model to predict the demand for taxi cabs in New York.
To develop the model, we will ... |
4,116 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Introduction to gradients and automatic differentiation
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="h... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,117 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Twitter + Watson Tone Analyzer Sample Notebook
In this sample notebook, we show how to load and analyze data from the Twitter + Watson Tone Analyzer Spark sample application (code can be fou... | Python Code:
# Import SQLContext and data types
from pyspark.sql import SQLContext
from pyspark.sql.types import *
Explanation: Twitter + Watson Tone Analyzer Sample Notebook
In this sample notebook, we show how to load and analyze data from the Twitter + Watson Tone Analyzer Spark sample application (code can be found... |
4,118 | 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', 'miroc', 'miroc-es2h', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2H
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, E... |
4,119 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multi-layer Perceptron (MLP) Neural Network Implementation in Padasip - Basic Examples
This tutorial explains how to use MLP through several examples.
Lets start with importing Padasip. In t... | Python Code:
import numpy as np
import matplotlib.pylab as plt
import padasip as pa
%matplotlib inline
plt.style.use('ggplot') # nicer plots
np.random.seed(52102) # always use the same random seed to make results comparable
Explanation: Multi-layer Perceptron (MLP) Neural Network Implementation in Padasip - Basic Examp... |
4,120 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I am struggling with the basic task of constructing a DataFrame of counts by value from a tuple produced by np.unique(arr, return_counts=True), such as: | Problem:
import numpy as np
import pandas as pd
np.random.seed(123)
birds = np.random.choice(['African Swallow', 'Dead Parrot', 'Exploding Penguin'], size=int(5e4))
someTuple = np.unique(birds, return_counts=True)
def g(someTuple):
return pd.DataFrame(np.column_stack(someTuple),columns=['birdType','birdCount'])
res... |
4,121 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step3: Fast Style Transfer for Arbitrary Styles
<table class="tfo-notebook-buttons... | Python Code:
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... |
4,122 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Find permutation of numbers upto N with a specific sum in a specific range Function to check if sum is possible with remaining numbers ; Stores the minimum sum possible with x nu... | Python Code::
def possible(x,S,N):
minSum = (x * (x + 1))//2
maxSum = (x * ((2 * N) - x + 1))//2
if(S < minSum or S > maxSum):
return False
return True
def findPermutation(N ,L ,R ,S ):
x = R - L + 1
if (not possible( x , S , N)) :
print(" - 1")
return
else :
v = []
for i in range(N , 0 , ... |
4,123 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building a text classification model with TF Hub
In this notebook, we'll walk you through building a model to predict the genres of a movie given its description. The emphasis here is not on... | Python Code:
import os
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import json
import pickle
import urllib
from sklearn.preprocessing import MultiLabelBinarizer
print(tf.__version__)
Explanation: Building a text classification model with TF Hub
In this notebook, we'll wal... |
4,124 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analyzing the Air Pollution Decrease Caused by the Global COVID-19 Pandemic
Last December 2019, we heard about the first COVID-19 cases in China.
Now, three months later, the WHO has officia... | Python Code:
%matplotlib notebook
%matplotlib inline
import numpy as np
import dh_py_access.lib.datahub as datahub
import xarray as xr
import matplotlib.pyplot as plt
import ipywidgets as widgets
from mpl_toolkits.basemap import Basemap,shiftgrid
import dh_py_access.package_api as package_api
import matplotlib.colors a... |
4,125 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TOC trends 2015
Step1: Woohoo - that was much easier than expected! Right, on with the data cleaning, staring with the easiest stuff first...
2. Remove some of US sites from the analysis
Th... | Python Code:
# Create connection
# Use custom RESA2 function to connect to db
r2_func_path = r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\Upload_Template\useful_resa2_code.py'
resa2 = imp.load_source('useful_resa2_code', r2_func_path)
engine, conn = resa2.connect_to_resa2()
# Test SQL statement
sql = ('SELECT proje... |
4,126 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Scrape swear word list
We scrape swear words from the web from the site
Step4: Testing TextBlob
I don't really like TextBlob as it tries to be "nice", but lacks a lot of basic functi... | Python Code:
import string
import os
import requests
from fake_useragent import UserAgent
from lxml import html
def requests_get(url):
ua = UserAgent().random
return requests.get(url, headers={'User-Agent': ua})
def get_swear_words(save_file='swear-words.txt'):
Scrapes a comprehensive list o... |
4,127 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
VARMAX models
This is a brief introduction notebook to VARMAX models in Statsmodels. The VARMAX model is generically specified as
Step1: Model specification
The VARMAX class in Statsmodels ... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
dta = sm.datasets.webuse('lutkepohl2', 'https://www.stata-press.com/data/r12/')
dta.index = dta.qtr
endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dln_inc', 'dln_consump']]
Explan... |
4,128 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PMOD ALS Sensor demonstration
This demonstration shows how to use the PmodALS. You will also see how to plot a graph using matplotlib.
The PmodALS and a light source is required. E.g. cell p... | Python Code:
from pynq import Overlay
Overlay("base.bit").download()
from pynq.iop import Pmod_ALS
from pynq.iop import PMODB
# ALS sensor is on PMODB
my_als = Pmod_ALS(PMODB)
my_als.read()
Explanation: PMOD ALS Sensor demonstration
This demonstration shows how to use the PmodALS. You will also see how to plot a graph ... |
4,129 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Training, Tuning and Deploying a PyTorch Text Classification Model on Vertex AI
Fine-tuning pre-trained BERT model for sentiment classification task
Overview
This example is inspired from To... | Python Code:
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:
USER_FLAG = "--user"
!pip -... |
4,130 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Fairness Indicators on TF-Hub Text Embeddings
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,131 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How we solve a model defined by the IndShockConsumerType class
The IndShockConsumerType reprents the work-horse consumption savings model with temporary and permanent shocks to income, finit... | Python Code:
from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, init_lifecycle
import numpy as np
import matplotlib.pyplot as plt
LifecycleExample = IndShockConsumerType(**init_lifecycle)
LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once
LifecycleExample.so... |
4,132 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: 1. Create a Doc object from the file peterrabbit.txt<br>
HINT
Step2: 2. For every token in the third sentence, print the token text, the POS tag, the fine-grained TAG ... | Python Code:
# RUN THIS CELL to perform standard imports:
import spacy
nlp = spacy.load('en_core_web_sm')
from spacy import displacy
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Parts of Speech Assessment
For this assessment we'll be using the short story The Tale of Pet... |
4,133 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ConvNet
Let's get the data and training interface from where we left in the last notebook.
Jump_to lesson 10 video
Step1: Batchnorm
Custom
Let's start by building our own BatchNorm layer fr... | Python Code:
x_train,y_train,x_valid,y_valid = get_data()
x_train,x_valid = normalize_to(x_train,x_valid)
train_ds,valid_ds = Dataset(x_train, y_train),Dataset(x_valid, y_valid)
nh,bs = 50,512
c = y_train.max().item()+1
loss_func = F.cross_entropy
data = DataBunch(*get_dls(train_ds, valid_ds, bs), c)
mnist_view = view_... |
4,134 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Employee scheduling
pyschedule can be used for employee scheduling. The following example is motivated by instances from
Step1: Solving without shift requests
First build the scenario witho... | Python Code:
employee_names = ['A','B','C','D','E','F','G','H']
n_days = 14 # number of days
days = list(range(n_days))
max_seq = 5 # max number of consecutive shifts
min_seq = 2 # min sequence without gaps
max_work = 10 # max total number of shifts
min_work = 7 # min total number of shifts
max_weekend = 3 # max number... |
4,135 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1.1 Reading data from a csv file
You can read data from a CSV file using the read_csv function. By default, it assumes that the fields are comma-separated.
We're going to be looking some cyc... | Python Code:
broken_df = pd.read_csv('../data/bikes.csv')
# Look at the first 3 rows
broken_df[:3]
Explanation: 1.1 Reading data from a csv file
You can read data from a CSV file using the read_csv function. By default, it assumes that the fields are comma-separated.
We're going to be looking some cyclist data from Mon... |
4,136 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Abstract
This paper introduces PyEDA, a Python library for electronic design automation (EDA). PyEDA provides both a high level interface to the representation of Boolean functions,
and blaz... | Python Code:
a, b, c, d = map(exprvar, 'abcd')
Explanation: Abstract
This paper introduces PyEDA, a Python library for electronic design automation (EDA). PyEDA provides both a high level interface to the representation of Boolean functions,
and blazingly-fast C extensions for fundamental algorithms where performance i... |
4,137 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualizing Evoked data
This tutorial shows the different visualization methods for
Step1: Instead of creating the ~mne.Evoked object from an ~mne.Epochs object,
we'll load an existing ~mne... | Python Code:
import os
import numpy as np
import mne
Explanation: Visualizing Evoked data
This tutorial shows the different visualization methods for
:class:~mne.Evoked objects.
:depth: 2
As usual we'll start by importing the modules we need:
End of explanation
sample_data_folder = mne.datasets.sample.data_path()
sa... |
4,138 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interactions and ANOVA
Note
Step1: Take a look at the data
Step2: Fit a linear model
Step3: Have a look at the created design matrix
Step4: Or since we initially passed in a DataFrame, w... | Python Code:
%matplotlib inline
from __future__ import print_function
from statsmodels.compat import urlopen
import numpy as np
np.set_printoptions(precision=4, suppress=True)
import statsmodels.api as sm
import pandas as pd
pd.set_option("display.width", 100)
import matplotlib.pyplot as plt
from statsmodels.formula.ap... |
4,139 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: 如何使用 TF-Hub 构建简单的文本分类器
注：本教程使用已弃用的 TensorFlow 1 功能。有关完成此任务的新方式，请参阅 TensorFl... | Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... |
4,140 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 1 Exploratory data analysis
Anecdotal evidence usually fails, because
Step1: DataFrames
DataFrame is the fundamental data structure provided by pandas. A DataFrame contains a row f... | Python Code:
import matplotlib
import pandas as pd
%matplotlib inline
Explanation: Chapter 1 Exploratory data analysis
Anecdotal evidence usually fails, because:
- Small number of observations
- Selection bias
- Confirmation bias
- Inaccuracy
To address the limitations of anecdotes, we will use the tools of statisti... |
4,141 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
AXON is eXtended Object Notation. It's a simple notation of objects,
documents and data. It's also a text based serialization format in first place.
It tries to combine the best of JSON, XM... | Python Code:
from __future__ import print_function
from axon import loads, dumps
from pprint import pprint
Explanation: AXON is eXtended Object Notation. It's a simple notation of objects,
documents and data. It's also a text based serialization format in first place.
It tries to combine the best of JSON, XML and YAML... |
4,142 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
UTSC Machine Learning Workshop
Introduction to Linear Regression
Adapted from Chapter 3 of An Introduction to Statistical Learning
Motivation
Regression problems are supervised learning prob... | Python Code:
# imports
import pandas as pd
import seaborn as sns
#import statsmodels.formula.api as smf
from sklearn.linear_model import LinearRegression
from sklearn import metrics
import numpy as np
# allow plots to appear directly in the notebook
%matplotlib inline
Explanation: UTSC Machine Learning Workshop
Introdu... |
4,143 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A brief tour of Redis
A one-hour or less tour of Redis.
tl
Step1: Note
Step2: Remember we need to connect to the server, using Python as the client, just like we would connect to a databas... | Python Code:
import redis
Explanation: A brief tour of Redis
A one-hour or less tour of Redis.
tl:dr version:
If you don't have time to read/run this, go to Try Redis and try it yourself.
Redis is a data structure server. Not quite a database, not quite a key-value store.
It is very fast and is a great tool for rapid a... |
4,144 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: Migration Examples
Step2: prepare some simple data for demonstration from the standard Titanic dataset,
Step3: and create a method to instant... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,145 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1A.algo - Tri plus rapide que prévu
Dans le cas général, le coût d'un algorithme de tri est en $O(n \ln n)$. Mais il existe des cas particuliers pour lesquels on peut faire plus court. Par e... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
Explanation: 1A.algo - Tri plus rapide que prévu
Dans le cas général, le coût d'un algorithme de tri est en $O(n \ln n)$. Mais il existe des cas particuliers pour lesquels on peut faire plus court. Par exemple, on suppose qu... |
4,146 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Planet Analytics API Tutorial
Getting Analytic Feed Results
This notebook shows how to paginate through Planet Analytic Feed Results for an existing analytics Subscription to construct a com... | Python Code:
import os
import requests
# if your Planet API Key is not set as an environment variable, you can paste it below
API_KEY = os.environ.get('PL_API_KEY', 'PASTE_YOUR_KEY_HERE')
# alternatively, you can just set your API key directly as a string variable:
# API_KEY = "YOUR_PLANET_API_KEY_HERE"
# construct aut... |
4,147 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Unsupervised Anomaly Detection based on Forecasts
Anomaly detection detects data points in data that does not fit well with the rest of data. In this notebook we demonstrate how to do... | Python Code:
def get_result_df(y_true_unscale, y_pred_unscale, ano_index, look_back,target_col='cpu_usage'):
Add prediction and anomaly value to dataframe.
result_df = pd.DataFrame({"y_true": y_true_unscale.squeeze(), "y_pred": y_pred_unscale.squeeze()})
result_df['anomalies'] = 0
result_df.lo... |
4,148 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
## <p style="text-align
Step1: how to draw samples from a gaussian distribution
Step2: other distributions ...
Step3: $\log_{10}(d) = 1 + \mu /5 $
Step4: 2. plotting
Step5: 3. IO (text ... | Python Code:
import numpy as np
print(dir(np.random))
Explanation: ## <p style="text-align: center; font-size: 4em;"> Python tutorial 2 </p>
1. random number generators: numpy.random
https://docs.scipy.org/doc/numpy/reference/routines.random.html
End of explanation
%pylab inline
import matplotlib.pyplot as plt
from mat... |
4,149 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multiple Stripe Analysis (MSA) for Single Degree of Freedom (SDOF) Oscillators
In this method, a single degree of freedom (SDOF) model of each structure is subjected to non-linear time histo... | Python Code:
import MSA_on_SDOF
from rmtk.vulnerability.common import utils
import numpy as np
%matplotlib inline
Explanation: Multiple Stripe Analysis (MSA) for Single Degree of Freedom (SDOF) Oscillators
In this method, a single degree of freedom (SDOF) model of each structure is subjected to non-linear time history... |
4,150 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Save out dataset for Evaluation
Step1: Merge user data with feature data
Step2: Eliminate Rows with viewed items that don't have features
this may break up some trajectories (view1,view2,v... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# get data
user_profile = pd.read_csv('../data_user_view_buy/user_profile.csv',sep='\t',header=None)
user_profile.columns = ['user_id','buy_spu','buy_sn','buy_ct3','view_spu','view_sn','view_ct3','time_interval','view... |
4,151 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GTEx v8 eQTL tissue-specific all SNP gene associations
Files in gs
Step1: After generating the text files as above, ran the below to get the files bgzipped so we can read them in and create... | Python Code:
# Generate list of all eQTL all association files in gs://gtex-resources
list_eqtl_files_gz = subprocess.run(["gsutil",
"-u",
"broad-ctsa",
"ls",
"gs://gtex-re... |
4,152 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
%matplotlib inline
Linear Regression Example
This example uses the only the first feature of the diabetes dataset, in
order to illustrate a two-dimensional plot of this regression technique.... | Python Code:
print(__doc__)
# Code source: Jaques Grobler
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]
# Split the ... |
4,153 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Point source plotting basics
In 3ML, we distinguish between data and model plotting. Data plots contian real data points and the over-plotted model is (sometimes) folded through an instrumen... | Python Code:
%matplotlib inline
jtplot.style(context="talk", fscale=1, ticks=True, grid=False)
import matplotlib.pyplot as plt
plt.style.use("mike")
import numpy as np
from threeML import *
from threeML.io.package_data import get_path_of_data_file
#mle1 = load_analysis_results(get_path_of_data_file("datasets/toy_xy_mle... |
4,154 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Running Spatial Correlations + options
The goal of this log is to show the API of the spatial correlations and the options available.
With this code, it is possible to run the spatial correl... | Python Code:
%matplotlib inline
import numpy as np
#from pyCXD.tools.CrossCorrelator import CrossCorrelator
from skbeam.core.correlation import CrossCorrelator
import matplotlib.pyplot as plt
from skbeam.core.roi import ring_edges, segmented_rings
# for some convolutions, used to smooth images (make spatially correlate... |
4,155 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Non-parametric between conditions cluster statistic on single trial power
This script shows how to compare clusters in time-frequency
power estimates between conditions. It uses a non-parame... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
Explanation: Non-parame... |
4,156 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Selecting model hyperparameters by cross-validation
The overview is that we will split the dataset into args.num_folds distinct partitions ("folds"). (This example description will use args.... | Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
# initialize a logger for ipython
import pyllars.logging_utils as logging_utils
logger = logging_utils.get_ipython_logger()
# create an argparse namespace to hold parameters
from argparse import Namespace
args = Namespace()
# create (or connect to) a da... |
4,157 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Survived SibSp Parch | Problem:
import pandas as pd
df = pd.DataFrame({'Survived': [0,1,1,1,0],
'SibSp': [1,1,0,1,0],
'Parch': [0,0,0,0,1]})
import numpy as np
def g(df):
family = np.where((df['SibSp'] + df['Parch']) >= 1 , 'Has Family', 'No Family')
return df.groupby(family)['Survived'].mean()
r... |
4,158 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced settings for WHFast
Step1: By default WHFast synchronizes and recalculates the Jacobi coordinates from the inertial ones every timestep. This guarantees that the user always gets ... | Python Code:
import rebound
import numpy as np
def test_case():
sim = rebound.Simulation()
sim.integrator = 'whfast'
sim.add(m=1.) # add the Sun
sim.add(m=3.e-6, a=1.) # add Earth
sim.move_to_com()
sim.dt = 0.2
return sim
Explanation: Advanced settings for WHFast: Extra speed, accuracy, and... |
4,159 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Performing an unbinned analysis
In this tutorial you will learn to fit a parametric model to the event data (unbinned fit) and how to inspect the fit residuals
Now you are ready to fit the m... | Python Code:
import gammalib
import ctools
import cscripts
Explanation: Performing an unbinned analysis
In this tutorial you will learn to fit a parametric model to the event data (unbinned fit) and how to inspect the fit residuals
Now you are ready to fit the models for the source and the background to the data.
We st... |
4,160 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kmeans from scratch
1.data production
Step1: 我们以(1, 1), (1, 2), (2, 2), (2, 1)四个点为中心产生了随机分布的点，如果我们的聚类算法正确的话，我们找到的中心点应该和这四个点很接近。先用简单的语言描述 kmeans 算法步骤：
第一步 - 随机选择 K 个点作为点的聚类中心，这表示我们要将数据分为 K 类... | Python Code:
#produce data set near the center
import numpy as np
import matplotlib.pyplot as plt
real_center = [(1,1),(1,2),(2,2),(2,1)]
point_number = 50
points_x = []
points_y = []
for center in real_center:
offset_x, offset_y = np.random.randn(point_number) * 0.3, np.random.randn(point_number) * 0.25
x_val,... |
4,161 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Initialization
Welcome to the first assignment of "Improving Deep Neural Networks".
Training your neural network requires specifying an initial value of the weights. A well chosen initializ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import sklearn
import sklearn.datasets
from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation
from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec
%matplotlib inline
plt... |
4,162 | 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', 'messy-consortium', 'sandbox-2', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Ti... |
4,163 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-2', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport,... |
4,164 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<figure>
<IMG SRC="gfx/Logo_norsk_pos.png" WIDTH=100 ALIGN="right">
</figure>
Operators and commutators
Roberto Di Remigio, Luca Frediani
We will be exercising our knowledge of operators a... | Python Code:
from sympy import *
# Define symbols
x, y, z = symbols('x y z')
# We want results to be printed to screen
init_printing(use_unicode=True)
# Calculate the derivative with respect to x
diff(exp(x**2), x)
Explanation: <figure>
<IMG SRC="gfx/Logo_norsk_pos.png" WIDTH=100 ALIGN="right">
</figure>
Operators an... |
4,165 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predict with pre-trained models
This is a demo for predicting with a pre-trained model on the full imagenet dataset, which contains over 10 million images and 10 thousands classes. For a mor... | Python Code:
import os, urllib
import mxnet as mx
def download(url,prefix=''):
filename = prefix+url.split("/")[-1]
if not os.path.exists(filename):
urllib.urlretrieve(url, filename)
path='http://data.mxnet.io/models/imagenet-11k/'
download(path+'resnet-152/resnet-152-symbol.json', 'full-')
download(pat... |
4,166 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Gaussian Mixture Models and Expectation Maximisation in Shogun
By Heiko Strathmann - heiko.strathmann@gmail.com - http
Step2: Set up the model in Shogun
Step3: Sampling from mixture... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import shogun as sg
from matplotlib.patches import Ellipse
# a tool for visualisation
def get_gaussian_ellipse_artist(mean, cov, nstd=1.96, color="red", linewidth=3)... |
4,167 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using Adaptive Median Filter
Step1: Original Image (converted to grayscale)
Step2: Output with Python's native Median Filter function
Step3: As shown from the above print, AMF results in ... | Python Code:
Image.fromarray(output)
Explanation: Using Adaptive Median Filter
End of explanation
Image.fromarray(grayscale_image)
Explanation: Original Image (converted to grayscale)
End of explanation
native_output = image_org.filter(ImageFilter.MedianFilter(size = 3))
native_output
deviation_native = np.sqrt(np.sum(... |
4,168 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Divergences as a Function of $\mu_q$
Let us start by simply varying $\mu_q$ and seeing the result. We will hold $\sigma_q$ fixed to $\sigma_p$ and $\alpha = -0.5$.
Step1: Derivative of Dive... | Python Code:
a = -0.7
j_vals = []
kl_vals = []
mus = np.linspace(0,1,100)
for mu in mus:
j_vals.append(J(mu,p_sig,a)[0])
kl_vals.append(KL(mu,p_sig)[0])
fig = plt.figure(figsize=(15,5))
p_vals = p(mus)
plt.plot(mus, p_vals/p_vals.max(), label="$p(x)$")
#plt.plot(mus, j_vals/np.max(np.abs(j_vals)), label='$J$')
... |
4,169 | 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... |
4,170 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: The Landscape of the Major Food Staples in Ghana
The dataset used in this project is a CSV file of the Global Food Prices Database by WFP - World Food Programme.
Step2: The regions w... | Python Code:
# Open the file and read its content.
raw_data = open('WFPVAM_FoodPrices_24-01-2017.csv', 'r').read()
# Split the raw_data on every newline.
raw_data = raw_data.split('\n')
# Take of the headers
raw_data_no_header = raw_data[1:]
# Make a list of lists of the raw_data_no_header
staples_data = []
for food_in... |
4,171 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Morphological operations
Morphology is the study of shapes. In image processing, some simple operations can get you a long way. The first things to learn are erosion and dilation. In erosion... | Python Code:
import numpy as np
from matplotlib import pyplot as plt, cm
import skdemo
plt.rcParams['image.cmap'] = 'cubehelix'
plt.rcParams['image.interpolation'] = 'none'
image = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0,... |
4,172 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Breakable Commitments...
Code to generate figures
Karna Basu and Jonathan Conning
Department of Economics, Hunter College and The Graduate Center, City University of New York
Step1: Abstrac... | Python Code:
%reload_ext watermark
%watermark -u -n -t
Explanation: Breakable Commitments...
Code to generate figures
Karna Basu and Jonathan Conning
Department of Economics, Hunter College and The Graduate Center, City University of New York
End of explanation
%matplotlib inline
import numpy as np
import matplotlib.p... |
4,173 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Demo of resizing
Run the cells one at a time. Initially you'll see a squashed up chart. The next cell will let you programatically set it's height.
NOTE
Step1: Set height of 'figure'
Step2:... | Python Code:
line = Line(index=[1990, 1991, 1993, 1994], values=[1, 2, 3, 4], height=100)
show(line)
print(line.ref['id'])
plot_height = 300
HTML("<script>Bokeh.index['%s'].model.set('plot_height', %d);</script>" % (line.ref['id'], plot_height))
Explanation: Demo of resizing
Run the cells one at a time. Initially you'l... |
4,174 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Martín Noblía
Tp3
<img src="files/copy_left.png" style="float
Step2: Recordemos que el espacio de trabajo alcanzable es la región espacial a la que el efector final puede llegar, con al men... | Python Code:
from IPython.core.display import Image
Image(filename='Imagenes/copy_left.png')
Image(filename='Imagenes/dibujo_robot2_tp2.png')
#imports
from sympy import *
import numpy as np
#Con esto las salidas van a ser en LaTeX
init_printing(use_latex=True)
Explanation: Martín Noblía
Tp3
<img src="files/copy_left.pn... |
4,175 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
To do for 07282017
Step1: Try TSNE and time it
It turns out that TSNE is too time consuming even for small set of data. It is also because of how I transformed the data. Thus, in the PCA, I... | Python Code:
import pandas as pd
import numpy as np
import os
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
os.chdir('/Users/Walkon302/Desktop/deep-learning-models-master/view2buy')
# Read the preprocessed file, containing the user profile and item features from view2buy folder
df = pd.read_pi... |
4,176 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hidden Markov Models
author
Step1: Note
Step2: This seems far more reasonable. There is a single CG island surrounded by background sequence, and something at the end. If we knew that CG i... | Python Code:
from pomegranate import *
import numpy as np
%pylab inline
seq = list('CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC')
d1 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})
d2 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})
s1 = State( d1, name='background' )
s2... |
4,177 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Notes on Columns
From data reference PDF
tree_dbh
Diameter of the tree, measured at approximately 54" / 137cm above the ground. Data was collected for both living and dead trees; for stumps,... | Python Code:
# Make tree diameter an integer
df.tree_dbh = df.tree_dbh.astype("int64")
df.describe()
len(df[df["tree_dbh"] < 50])
df[df["tree_dbh"] > 100]
df[df["tree_dbh"] < 40].tree_dbh.value_counts(sort=False).plot(kind="bar")
Explanation: Notes on Columns
From data reference PDF
tree_dbh
Diameter of the tree, measu... |
4,178 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PMOD TIMER
In this notebook, PMOD Timer functionalities are illustrated. The Timer has two sub-modules
Step1: Instantiate Pmod_Timer class. The method stop() will stop both timer sub-module... | Python Code:
from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
Explanation: PMOD TIMER
In this notebook, PMOD Timer functionalities are illustrated. The Timer has two sub-modules: Timer0 and Timer1.
The Generate output and Capture Input of Timer 0 are assumed to be connected to PMODA pin 0.
... |
4,179 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Instruction
This tutorial explain how to use calc_barriers wrapper
calc_barriers is high level wrapeer used for calculation of migration barriers.
The calculations are performed by executin... | Python Code:
import sys
sys.path.extend(['/home/aksenov/Simulation_wrapper/siman'])
import header
from calc_manage import add, res
from database import write_database, read_database
from set_functions import read_vasp_sets
from calc_manage import smart_structure_read
from SSHTools import SSHTools
from project_funcs imp... |
4,180 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
机器学习工程师纳米学位
机器学习基础
项目 0
Step1: 从泰坦尼克号的数据样本中，我们可以看到船上每位旅客的特征
Survived：是否存活（0代表否，1代表是）
Pclass：社会阶级（1代表上层阶级，2代表中层阶级，3代表底层阶级）
Name：船上乘客的名字
Sex：船上乘客的性别
Age
Step3: 这个例子展示了如何将泰坦尼克号的 Survived 数据从 ... | Python Code:
# 检查你的Python版本
from sys import version_info
if version_info.major != 2 and version_info.minor != 7:
raise Exception('请使用Python 2.7来完成此项目')
import numpy as np
import pandas as pd
# 数据可视化代码
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# 加载数据集
in... |
4,181 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
You can download water chemistry of an entire HUC. It downloads wells and springs and major ions by default, unless specified otherwise.
Step1: Standardize the headers and units in the res... | Python Code:
chem = wa.WQP(16020301,'huc')
Explanation: You can download water chemistry of an entire HUC. It downloads wells and springs and major ions by default, unless specified otherwise.
End of explanation
Results = chem.massage_results()
Stations = chem.massage_stations()
Piv = chem.piv_chem()
Piv.reset_index(i... |
4,182 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to run TARDIS with a custom ejecta model
This notebook will go through multiple detailed examples of how to properly run TARDIS with a custom ejecta profile specified by a custom density... | Python Code:
import tardis
import matplotlib.pyplot as plt
import numpy as np
Explanation: How to run TARDIS with a custom ejecta model
This notebook will go through multiple detailed examples of how to properly run TARDIS with a custom ejecta profile specified by a custom density file and a custom abundance file.
End ... |
4,183 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Analysis with Pandas Dataframe
Pandas is a popular library for manipulating vectors, tables, and time series. We will frequently use Pandas data structures instead of the built-in pytho... | Python Code:
import pandas as pd
Explanation: Data Analysis with Pandas Dataframe
Pandas is a popular library for manipulating vectors, tables, and time series. We will frequently use Pandas data structures instead of the built-in python data structures, as they provide much richer functionality. Also, Pandas is fast, ... |
4,184 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Introduction
In an upcoming analysis, we want to calculate the structural similarity between test cases. For this, we need the information which test methods call which code in the ap... | Python Code:
import py2neo
import pandas as pd
graph = py2neo.Graph()
query =
MATCH
(testMethod:Method)
-[:ANNOTATED_BY]->()-[:OF_TYPE]->
(:Type {fqn:"org.junit.Test"}),
(testType:Type)-[:DECLARES]->(testMethod),
(type)-[:DECLARES]->(method:Method),
(testMethod)-[i:INVOKES]->(method)
WHERE
NOT typ... |
4,185 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Timescales in QuTiP
Andrew M.C. Dawes — 2016
An overview to one frequently asked question about QuTiP.
Introduction
QuTiP is a python package, if you are new to QuTiP, you should first read ... | Python Code:
from qutip import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Timescales in QuTiP
Andrew M.C. Dawes — 2016
An overview to one frequently asked question about QuTiP.
Introduction
QuTiP is a python package, if you are new to QuTiP, you should first read the tutorial m... |
4,186 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Create an experiment</h1>
Step1: <h1>Get a list of mzML files that you uploaded and assign them to a group</h1>
Step2: <h1>Specify the descriptive names for each group</h1>
Step3: <h1... | Python Code:
myExperiment = metatlas_objects.Experiment(name = 'QExactive_Hilic_Pos_Actinobacteria_Phylogeny')
Explanation: <h1>Create an experiment</h1>
End of explanation
myPath = '/global/homes/b/bpb/ExoMetabolomic_Example_Data/'
myPath = '/project/projectdirs/metatlas/data_for_metatlas_2/20150324_LPSilva_BHedlund_c... |
4,187 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ABU量化系统使用文档
<center>
<img src="./image/abu_logo.png" alt="" style="vertical-align
Step1: 很多刚接触交易的人总喜欢把交易看成一种有固定收入的工作，比如他们有自己的规矩，周五一定要把所有股票都卖了，安安心心过周末，周一看情况一切良好再把股票买回来。
还有一些人有着很奇怪的癖好... | Python Code:
# 基础库导入
from __future__ import print_function
from __future__ import division
import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import os
import sys
# 使用insert 0即只使用github，避免交叉使用了pip安装的... |
4,188 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressive?... | Python Code:
import numpy as np
from emo_utils import *
import emoji
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressi... |
4,189 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Filter hits
Step1: Keep best hit per database for each cluster
Filtered by e-value < 1e-3 and best domain e-value < 1 | Python Code:
filt_hits = all_hmmer_hits[ (all_hmmer_hits.e_value < 1e-3) & (all_hmmer_hits.best_dmn_e_value < 1e-3) ]
filt_hits.to_csv("1_out/filtered_hmmer_all_hits.csv",index=False)
print(filt_hits.shape)
filt_hits.head()
Explanation: Filter hits
End of explanation
gb = filt_hits.groupby(["cluster","db"])
reliable_fa... |
4,190 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Primitive generators
This notebook contains tests for tohu's primitive generators.
Step1: Constant
Constant simply returns the same, constant value every time.
Step2: Boolean
Boolean retur... | Python Code:
import tohu
from tohu.v4.primitive_generators import *
from tohu.v4.dispatch_generators import *
from tohu.v4.utils import print_generated_sequence
print(f'Tohu version: {tohu.__version__}')
Explanation: Primitive generators
This notebook contains tests for tohu's primitive generators.
End of explanation
g... |
4,191 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reusing a pool of workers
Some algorithms require to make several consecutive calls to a parallel function interleaved with processing of the intermediate results. Calling Parallel several t... | Python Code:
with Parallel(n_jobs=2) as parallel:
accumulator = 0.
n_iter = 0
while accumulator < 1000:
results = parallel(delayed(sqrt)(accumulator + i ** 2)for i in range(5))
accumulator += sum(results) # synchronization barrier
n_iter += 1
(accumulator, n_iter)
Explanation: Reusi... |
4,192 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
With all that you've learned, your SQL queries are getting pretty long, which can make them hard understand (and debug).
You are about to learn how to use AS and WITH to tidy up... | Python Code:
#$HIDE_INPUT$
from google.cloud import bigquery
# Create a "Client" object
client = bigquery.Client()
# Construct a reference to the "crypto_bitcoin" dataset
dataset_ref = client.dataset("crypto_bitcoin", project="bigquery-public-data")
# API request - fetch the dataset
dataset = client.get_dataset(dataset... |
4,193 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DiscreteDP
Implementation Details
Daisuke Oyama
Faculty of Economics, University of Tokyo
This notebook describes the implementation details of the DiscreteDP class.
For the theoretical back... | Python Code:
import numpy as np
import pandas as pd
from quantecon.markov import DiscreteDP
n = 2 # Number of states
m = 2 # Number of actions
# Reward array
R = [[5, 10],
[-1, -float('inf')]]
# Transition probability array
Q = [[(0.5, 0.5), (0, 1)],
[(0, 1), (0.5, 0.5)]] # Probabilities in Q[1, 1] are arbi... |
4,194 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1"><a href="#Example-of-GibbsLDA-and-vbLDA"><span class="toc-item-num">1 - </span>Example of GibbsLDA and vbLDA</a></div><div class="lev2"><a href="#Loadi... | Python Code:
import logging
import numpy as np
from ptm import GibbsLDA
from ptm import vbLDA
from ptm.nltk_corpus import get_reuters_ids_cnt
from ptm.utils import convert_cnt_to_list, get_top_words
Explanation: Table of Contents
<p><div class="lev1"><a href="#Example-of-GibbsLDA-and-vbLDA"><span class="toc-item-num">1... |
4,195 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Changepoint analysis
This notebook reflects an intermediate stage of work on the project that became "You say you found a revolution." Underwood was attempting to directly compare non-overla... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import csv, os, random
import numpy as np
from collections import Counter
from scipy import spatial
songsbyquarter = dict()
numfields = 14
fieldnames = []
for i in range(14):
fieldnames.append('PC' + str(i+1))
maxquarter = 0
with open('EvolutionPopUSA_... |
4,196 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Identifying Spam from SMS Text Messages
This analysis attempts to identify spam messages from a corpus of 5,574 SMS text messages. The corpus is labeled as either spam or ham (legitimate me... | Python Code:
%matplotlib inline
import os
import json
import time
import pickle
import requests
from io import BytesIO
from zipfile import ZipFile
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction import tex... |
4,197 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Economics with Jupyter Notebooks
Jupyter Notebook is "a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. ... | Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import seaborn as sns
from ipywidgets import interact
df = pd.read_stata(".\data\country.dta")
Explanation: Economics with Jupyter Notebooks
Jupyter Notebook is "a web application that allows you to create and share documents that conta... |
4,198 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In the last chapter, our tests failed. This time we'll go about fixing them.
Our First Django App, and Our First Unit Test
Django encourages you to structure your code into apps
Step1: Unit... | Python Code:
%cd ../examples/superlists/
# Make a new app called lists
!python3 manage.py startapp lists
!tree .
Explanation: In the last chapter, our tests failed. This time we'll go about fixing them.
Our First Django App, and Our First Unit Test
Django encourages you to structure your code into apps: the theory is t... |
4,199 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spatial Model fitting in GLS
In this exercise we will fit a linear model using a Spatial structure as covariance matrix.
We will use GLS to get better estimators.
As always we will need to ... | Python Code:
# Load Biospytial modules and etc.
%matplotlib inline
import sys
sys.path.append('/apps')
sys.path.append('..')
sys.path.append('../spystats')
import django
django.setup()
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
## Use the ggplot style
plt.style.use('ggplot')
import tools
Exp... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.