Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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13,300 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-cm2-hr4', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
13,301 | <ASSISTANT_TASK:>
Python Code:
from workflow.engine import GenericWorkflowEngine
my_engine = GenericWorkflowEngine()
from functools import wraps
def print_data(obj, eng):
Print the data found in the token.
print(obj.data)
def add_data(number_to_add):
Add number_to_add to obj.data.
@wraps(add_data... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Instantiate a workflow engine
Step4: Create tasks
Step5: Create a workflow definition
Step6: Define tokens
Step7: Run the engine
Step8: Res... |
13,302 | <ASSISTANT_TASK:>
Python Code:
import xray_vision
import xray_vision.mpl_plotting as mpl_plot
import skbeam.core.speckle as xsvs
import skbeam.core.roi as roi
import skbeam.core.correlation as corr
import skbeam.core.utils as utils
import numpy as np
import os, sys
import matplotlib as mpl
import matplotlib.pyplot as p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Easily switch between interactive and static matplotlib plots¶
Step2: This data provided by Dr. Andrei Fluerasu
Step3: Create the Rings Mask¶
... |
13,303 | <ASSISTANT_TASK:>
Python Code:
from Bio.Blast import NCBIWWW
help(NCBIWWW.qblast)
from Bio.Blast import NCBIWWW
result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")
from Bio.Blast import NCBIWWW
fasta_string = open("data/m_cold.fasta").read()
result_handle = NCBIWWW.qblast("blastn", "nt", fasta_string)
from Bio... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Note that the default settings on the NCBI BLAST website are not quite
Step2: Alternatively, if we have our query sequence already in a FASTA
S... |
13,304 | <ASSISTANT_TASK:>
Python Code:
# Import SPI rack and D5a module
from spirack import SPI_rack, D5a_module
COM_speed = 1e6 # Baud rate, doesn't matter much
timeout = 1 # In seconds
spi_rack = SPI_rack('COM4', COM_speed, timeout)
spi_rack.unlock() # Unlock the controller to be able to send data
D5a = D5a_module(spi_rack... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Open the SPI rack connection and unlock the controller. This is necessary after bootup of the controller module. If not unlocked, no communicati... |
13,305 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
X = cancer.data
y = cancer.target
from sklearn.tree import DecisionTreeClassifier
def decision_stump(features, labels):
clf = DecisionTreeClassifier(max_depth=1, random_state=123)
clf.fit(features, labe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Build the Decision Stump
Step2: Get an Accuracy Result
Step3: Demonstrate for a single iteration
Step4: Extract Incorrect Classifications
Ste... |
13,306 | <ASSISTANT_TASK:>
Python Code:
def smoothListGaussian(list,degree=5):
list =[list[0]]*(degree-1) + list + [list[-1]]*degree
window=degree*2-1
weight=np.array([1.0]*window)
weightGauss=[]
for i in range(window):
i=i-degree+1
frac=i/float(window)
gauss=1/(np.exp((4*... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: This function is syntactically correct and it works. Let's test it with a data set. The same one used in the scipy cookbook (http
Step3: Despit... |
13,307 | <ASSISTANT_TASK:>
Python Code::
df['total'] = df['col_1'] + df['col_2']
df = df.pipe(lambda x: x.div(x['total'], axis='index')).applymap('{:.0%}'.format)
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
13,308 | <ASSISTANT_TASK:>
Python Code:
# A bit of setup
# import numpy as np
# import matplotlib.pyplot as plt
# from cs231n.classifiers.neural_net import TwoLayerNet
# %matplotlib inline
# plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
# plt.rcParams['image.interpolation'] = 'nearest'
# plt.rcParams[... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Implementing a Neural Network
Step2: We will use the class TwoLayerNet in the file cs231n/classifiers/neural_net.py to represent instances of o... |
13,309 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from pandas_datareader import data, wb
import datetime
# We will look at stock prices over the past year, starting at January 1, 2016
start = datetime.datetime(2016,1,1)
end = datetime.date.today()
# Let's get Apple stock data; Apple's ticker symbol is AAPL
# First... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Pandas Basics
Step2: Add More Stocks
Step3: Plot Price of all three stocks
Step4: Apply Rolling Window
Step5: Profit
Step6: S&P 500
Step7: ... |
13,310 | <ASSISTANT_TASK:>
Python Code:
# This is probably due to a unit conversion in a multiplicative prefactor
# This multiplicative prefactor is based on nanometers
r_min = 0.14
r_max = 1.0
print (1/r_min - 1/r_max)
# This multiplicative prefactor is based on angstroms
r_min = 1.4
r_max = 10.0
print (1/r_min - 1/r_max)
4*n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Switching from nanometers to angstroms makes the multiplicative prefactor smaller, which is opposite of the desired effect!
Step2: Igrid[atomI]... |
13,311 | <ASSISTANT_TASK:>
Python Code:
# This exercise is mostly for us to understand what kind of data we have and then
# run some simple stats on the fields/values in the data. Pandas will be great for that
import pandas as pd
pd.__version__
# Set default figure sizes
pylab.rcParams['figure.figsize'] = (16.0, 5.0)
# Lets tak... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Lets do standard histogram and event volume over time plots
Step2: <img align="right" src="files/images/spice_weasel.jpg" width="300px" style="... |
13,312 | <ASSISTANT_TASK:>
Python Code:
import rebound
import numpy as np
sim = rebound.Simulation()
OMEGA = 0.00013143527 # [1/s]
sim.integrator_sei_OMEGA = OMEGA
surface_density = 400. # kg/m^2
particle_density = 400. # kg/m^3
sim.G = 6.67428e-11 # N m^2 / kg^2
sim.dt = 1e-3*2.*np.pi/OMEGA
sim.softening =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Next up, setting up several constants. We will be simulating a shearing sheet, a box with shear-periodic boundary conditions. This is a local ap... |
13,313 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
import cvxpy
def get_markowitz_weights(mu, Sigma, gamma=1, max_position=1.0, max_leverage=1.0, short=False):
w = cvxpy.Variable(len(Sigma))
g = cvxpy.Parameter(sign='pos... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Helper Functions
Step2: Exercise 1
Step3: b. $1 Bets
Step4: Exercise 2
Step5: b. Equally Weighted Portfolio
Step6: c. Market Weighted Portf... |
13,314 | <ASSISTANT_TASK:>
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')
# 引入这个项目需要的库
import numpy as np
import pandas as pd
import visuals as vs
from IPython.display import display # 使得我们可以对DataFrame使用display()函数
# 设置以... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 分析数据
Step2: 练习
Step3: 问题 1
Step4: 问题 2
Step5: 问题 3
Step6: 观察
Step7: 练习
Step8: 问题 4
Step9: 问题 5
Step10: 练习：降维
Step11: 观察
Step12: 可视化一个... |
13,315 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import PolynomialFeatures
# A seed just to ensure that the ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: To think about, first part
Step2: What does centering (subtracting the mean values) mean mathematically?
Step3: The intercept is the value of ... |
13,316 | <ASSISTANT_TASK:>
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 writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 컨볼루셔널 변이형 오토인코더
Step2: MNIST 데이터세트 로드하기
Step3: tf.data를 사용하여 데이터 배치 및 셔플 처리하기
Step5: tf.keras.Sequential을 사용하여 인코더 및 디코더 네트워크 정의하기
Step7: 손실... |
13,317 | <ASSISTANT_TASK:>
Python Code:
%load_ext sql
%sql mysql://studentuser:studentpw@mysqlserver/dognitiondb
%sql USE dognitiondb
%config SqlMagic.displaylimit=25
%%sql
SELECT user_guid
FROM users
WHERE free_start_user=1
LIMIT 0,5;
%%sql
DESCRIBE dogs
%%sql
SELECT dog_guid
FROM dogs
WHERE dna_tested=1;
%%sql
DESCRIBE use... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Recall the general syntax structure we learned from the "Introduction to Query Syntax" video at the beginning of the week
Step2: Question 1
Ste... |
13,318 | <ASSISTANT_TASK:>
Python Code:
import re
import glob
import numpy
import iris
import iris.coord_categorisation
from iris.experimental.equalise_cubes import equalise_attributes
import warnings
warnings.filterwarnings('ignore')
lat_constraint = iris.Constraint(latitude=lambda cell: cell <= -30)
def read_hfds_data(file_l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Step 1
Step5: Step 2
Step7: Step 3
Step9: Step 4
Step10: Step 5
Step14: Step 6
Step15: Final result
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13,319 | <ASSISTANT_TASK:>
Python Code:
# Učitaj osnovne biblioteke...
import numpy as np
import sklearn
import mlutils
import matplotlib.pyplot as plt
%pylab inline
from sklearn.linear_model import LinearRegression, RidgeClassifier
from sklearn.metrics import accuracy_score
seven_X = np.array([[2,1], [2,3], [1,2], [3,2], [5,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Zadatci
Step2: (a)
Step3: Kako bi se uvjerili da se u isprobanoj implementaciji ne radi o ničemu doli o običnoj linearnoj regresiji, napišite ... |
13,320 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
# tuberculosis (TB) dataset
path_tb = '/Users/ericfourrier/Documents/ProjetR/tidy-data/data/tb.csv'
df_tb = pd.read_csv(path_tb)
df_tb.head(20)
# clean column names
df_tb = df_tb.rename(columns={'iso2':'country'}) # rename iso2 in country
df_tb = d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Original TB dataset. Corresponding to each ‘m’ column for males, there is also an ‘f’ column
Step2: Create sex and age columns from variable 'c... |
13,321 | <ASSISTANT_TASK:>
Python Code:
import requests # to make GET request
from bs4 import BeautifulSoup # to parse the HTML response
import time # to pause between calls
import csv # to write data to csv
import pandas # to see CSV
# make a GET request
response = requests.get('http://www.ilga.gov/senate/default.asp')
#... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. Using BeautifulSoup
Step2: 1.2 soup it
Step3: 1.3 Find Elements
Step4: NB
Step5: That's a lot! Many elements on a page will have the same... |
13,322 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
13,323 | <ASSISTANT_TASK:>
Python Code:
import ctcsound
c = ctcsound.Csound()
ret = c.compileCsd("test1.csd")
if ret == ctcsound.CSOUND_SUCCESS:
c.start()
c.perform()
c.reset()
# Defining our Csound ORC code within a multiline String
orc =
sr=44100
ksmps=32
nchnls=2
0dbfs=1
instr 1
aout vco2 0.5, 440
outs aout, aou... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Example 1 - Simple Compilation with Csound
Step3: Example 2 - Compilation with Csound without CSD
Step5: Example 3 - Using Our Own Performance... |
13,324 | <ASSISTANT_TASK:>
Python Code:
import openpnm as op
pn = op.network.Cubic([4, 4,])
geo = op.geometry.SpheresAndCylinders(network=pn, pores=pn.Ps, throats=pn.Ts)
air = op.phases.Air(network=pn)
phys = op.physics.Basic(network=pn, phase=air, geometry=geo)
alg = op.algorithms.ReactiveTransport(network=pn, phase=air)
pri... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Normal Usage
Step2: We can see that many default settings are already present by printing the settings attribute
Step3: We can override these ... |
13,325 | <ASSISTANT_TASK:>
Python Code:
import requests
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import (
adjusted_rand_score,
adjusted_mutual_info_score,
homogeneity_score,
completeness_sc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 数据获取
Step2: 数据预处理
Step3: 数据集拆分
Step4: 训练模型
Step5: 模型评估
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13,326 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
func = np.poly1d(np.array([1, 2, 3, 4]).astype(float))
func2 = func.deriv(m=2)
x = np.linspace(-10, 10, 30)
y = func(x)
y2 = func2(x)
plt.plot(x, y)
plt.plot(x, y2, 'r>')
plt.xlabel('x')
plt.ylabel('y(x)')
plt.show()
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. 简单绘图
Step2: 其中，linspace函数常见x轴的数值，在-10和10之间产生30个均匀分布的值。
Step3: plot函数可以接受任意个数的参数，我们可以使用可选的格式字符串参数指定线条的颜色和风格，默认为'b-'即蓝色视线。你可以指定其他风格。
Step4: ... |
13,327 | <ASSISTANT_TASK:>
Python Code:
# Perform standard imports:
import spacy
nlp = spacy.load('en_core_web_sm')
doc1 = nlp(u"I am a runner running in a race because I love to run since I ran today")
for token in doc1:
print(token.text, '\t', token.pos_, '\t', token.lemma, '\t', token.lemma_)
def show_lemmas(text):
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <font color=green>In the above sentence, running, run and ran all point to the same lemma run (...11841) to avoid duplication.</font>
Step2: He... |
13,328 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
floodingReports = pd.Series([5, 6, 2, 9, 12])
floodingReports
floodingReports = pd.Series([5, 6, 2, 9, 12], index=['Cochise County', 'Pima County', 'Santa Cruz County', 'Maricopa County', 'Yuma County'])
floodingReports
floodingReports['Cochise County']
floodingRep... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Series 101
Step2: Note that the first column of numbers (0 to 4) are the index.
Step3: View the number of floodingReports in Cochise County
St... |
13,329 | <ASSISTANT_TASK:>
Python Code:
from dolfin import *
from rbnics import *
@DEIM()
class Gaussian(EllipticCoerciveProblem):
# Default initialization of members
def __init__(self, V, **kwargs):
# Call the standard initialization
EllipticCoerciveProblem.__init__(self, V, **kwargs)
# ... and... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 3. Affine decomposition
Step2: 4. Main program
Step3: 4.2. Create Finite Element space (Lagrange P1)
Step4: 4.3. Allocate an object of the Ga... |
13,330 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
# Make the features (X) and output (y) with 200 samples,
X, y = make_blobs(n_samples = 200,
# two feature variables,
n_features = 2,
# three clusters,
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Make Data
Step2: View Data
|
13,331 | <ASSISTANT_TASK:>
Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 1.x
except Exception:
pass
# to make this notebook's out... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Creating and running a graph
Step2: Managing graphs
Step3: Linear Regression
Step4: Compare with pure NumPy
Step5: Compare with Scikit-Learn... |
13,332 | <ASSISTANT_TASK:>
Python Code:
def find_df(v, p, u, tau):
return -digamma(v/2.) + log(v/2.) + (tau * (log(u) - u)).sum()/tau.sum() + 1 + (digamma((v+p)/2.)-log((v+p)/2.))
u_test = np.array([[1,1], [2,2], [3,3]])
tau_test = np.array([[4,4], [5,5], [6,6]])
find_df(1, 2, u_test, tau_test)
def get_random(X):
size... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Expectation Maximization with Mixtures
Step2: Plotting the sample with actual parameters
Step3: Estimating parameters
|
13,333 | <ASSISTANT_TASK:>
Python Code:
def Centered_Triangular_num(n ) :
return(3 * n * n + 3 * n + 2 ) // 2
if __name__== ' __main __' :
n = 3
print(Centered_Triangular_num(n ) )
n = 12
print(Centered_Triangular_num(n ) )
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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13,334 | <ASSISTANT_TASK:>
Python Code:
from precovery_utils import ssoisPrecovery
ssois_query = ssoisPrecovery()
query_url = ssois_query.format_search_by_arc_url('kbmod_mpc.dat')
print(query_url)
results_df = ssois_query.query_ssois(query_url)
results_df.head()
from IPython.display import HTML
image_data_link = results_df["... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create Query URL from MPC formatted file
Step2: Query service via URL
Step3: Create direct data download link
Step4: Compare KBMOD data to av... |
13,335 | <ASSISTANT_TASK:>
Python Code:
import toytree # a tree plotting library
import toyplot # a general plotting library
import numpy as np # numerical library
print(toytree.__version__)
print(toyplot.__version__)
print(np.__version__)
# load a toytree from a newick string at a URL
tre = toytree.tree("https:/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and draw your first tree
Step2: Parsing Newick/Nexus data
Step4: To parse either format you can tell toytree the format of the newick str... |
13,336 | <ASSISTANT_TASK:>
Python Code:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.imports import *
from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
PATH = "data/planet/"
!ls {PATH}
from fastai.plots impo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2 Multi-Label versus Single-Label Classification
Step2: In single-label classification each sample belongs to one class. In the previous exampl... |
13,337 | <ASSISTANT_TASK:>
Python Code:
def raiz(x_l, x_u):
x_r = (x_l + x_u)/2
return x_r
def intervalo_de_raiz(f, x_l, x_u):
x_r = raiz(x_l, x_u)
if f(x_l)*f(x_r) < 0:
x_u = x_r
if f(x_l)*f(x_r) > 0:
x_l = x_r
return x_l, x_u
def biseccion(f, x_inferior, x_superior):
print("{0:2s}\... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Implementación no vectorizada
Step2: Ejemplo 2
Step3: Ejemplo 3
Step4: Ejemplo 4
|
13,338 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import math
from numba import vectorize, cuda
from matplotlib import pyplot as plt
%matplotlib inline
!find / -iname 'libdevice'
!find / -iname 'libnvvm.so'
import os
os.environ['NUMBAPRO_LIBDEVICE'] = "/usr/local/cuda-10.0/nvvm/libdevice"
os.environ['NUMBAPRO_NVVM'] ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Problem 0 - Accessing the GPU
Step2: Paste the location of the libraries into the following code box (if it's different, otherwise you can just... |
13,339 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import sklearn
features = load_data()
from sklearn.preprocessing import MultiLabelBinarizer
new_features = MultiLabelBinarizer().fit_transform(features)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
13,340 | <ASSISTANT_TASK:>
Python Code:
import threading
from _thread import start_new_thread, allocate_lock
import logging
import time
import numpy as np
def worker1():
print(threading.currentThread().getName(), '--begin')
time.sleep(3)
print(threading.currentThread().getName(), '--end')
def worker2():
pr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Example 1
Step2: Example 2
Step3: Example 3
Step4: Example 4
Step5: Example 5
Step6: Example 6
Step7: Example 7
|
13,341 | <ASSISTANT_TASK:>
Python Code:
import deepchem as dc
from deepchem.models.tensorgraph.tensor_graph import TensorGraph
tg = TensorGraph(use_queue=False)
from deepchem.models.tensorgraph.layers import Feature
left_features = Feature(shape=(None, 75))
right_features = Feature(shape=(None, 75))
from deepchem.models.tenso... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We're going to construct an architecture that has two identical feature inputs. Let's call these feature inputs left_features and right_features... |
13,342 | <ASSISTANT_TASK:>
Python Code:
import os
import pandas as pd
def get_rail_id(row):
Extract specific rail_ids from complex data structure that assigns rail_ids
(Sample IDs) to snaptron_ids (exon-exon junctions). Designed to be used
as a pd.DataFrame().apply() function.
Arguments:
row - a row in ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Processing TP53 Exon-Exon Junction Data
Step2: First, load several files required for processing
Step3: Next, select the samples with the spec... |
13,343 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
print('Esperamos trabalhar no diretório')
print(os.getcwd())
#Se usar o arquivo descompactado
#pd.read_csv('DOM2015.csv',sep=',')
base09 = pd.read_csv('DOM2009.csv',sep=',')
base13 = pd.rea... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Na célula a cima foram escolhidas as variáveis que serão utilizadas. Em sequência, temos
Step2: No código a cima foi feita a categorização das ... |
13,344 | <ASSISTANT_TASK:>
Python Code:
#the usual beginning
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
from datetime import datetime, timedelta
from pandas import concat
#define any string with 'C' as NaN
def readD(val):
if 'C' in val:
return np.nan
return val
df = pd.read_csv(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Import File into Python
Step3: Set Date and Time of ROP Exam and Eye Drops
Step4: Baseline Averages
Step5: Average q 5 Min for 1 hour after 1... |
13,345 | <ASSISTANT_TASK:>
Python Code:
# 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, sof... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Natural Language Processing
Step2: Next we'll download and unzip the data.
Step3: There are three files that we'll use in our model
Step4: In... |
13,346 | <ASSISTANT_TASK:>
Python Code:
# Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
training_file = 'train.p'
validation_file= 'valid.p'
testing_file = 'test.p'
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Basic Info of the Dataset
Step2: Visualize Data
Step3: Preprocess Data
Step4: Setup TensorFlow
Step5: SOLUTION
Step6: Features and Labels
S... |
13,347 | <ASSISTANT_TASK:>
Python Code:
from pulp import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sn
#a handful of sites
sites = ['org','A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P']
print(len(sites)-1)
#make some positions (so we can plot this)
positions = dic... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. First lets make some fake data
Step2: 2. The model
Step3: Solve it!
Step4: And the result
Step5: The optimal tours
|
13,348 | <ASSISTANT_TASK:>
Python Code:
l1 = sorted(['b', 'c', 'a']) # a list
l2 = sorted(('b', 'c', 'a')) # a tuple
l3 = sorted('bca') # a string
print(l1, l1 == l2 == l3)
print(type(l1) == type(l2) == type(l3) == list)
l = [1, 2, 3]
s = sorted(l)
print('This should be False:', id(l) == id(s))
l = [2, 3, 1]
s = sorted(l)
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: sorted() always returns a new object.
Step2: It is also possible to sort lists in place with the sort() method of lists. It accepts the key and... |
13,349 | <ASSISTANT_TASK:>
Python Code:
pokemon = data[data.Generation == 1]
Image(url="http://i.giphy.com/yidUzHnBk32Um9aMMw.gif")
pokemon
# Afficher les données de Pikachu :
pokemon[pokemon.Name == 'Pikachu']
# Creer une variable de attribut de Pikachu
Pikachu = pokemon[pokemon.Name == 'Pikachu']
Pikachu
Image(url="http://i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Voila comment sélectionner des données sur un DataFrame existant
Step2: Regardons les stats de Pikachu maintenant. Pour afficher les données, ... |
13,350 | <ASSISTANT_TASK:>
Python Code:
import jax
import jax.numpy as jnp
global_list = []
def log2(x):
global_list.append(x)
ln_x = jnp.log(x)
ln_2 = jnp.log(2.0)
return ln_x / ln_2
print(jax.make_jaxpr(log2)(3.0))
def log2_with_print(x):
print("printed x:", x)
ln_x = jnp.log(x)
ln_2 = jnp.log(2.0)
return ln_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The Understanding Jaxprs section of the documentation provides more information on the meaning of the above output.
Step2: See how the printed ... |
13,351 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import GridSearchCV, KFold, cross_val_predict
from sklearn.neighbors import KNeighborsRegressor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Next, let's load the data. This week, we're going to load the Auto MPG data set, which is available online at the UC Irvine Machine Learning Rep... |
13,352 | <ASSISTANT_TASK:>
Python Code:
def get_closest_vowel(word):
if len(word) < 3:
return ""
vowels = {"a", "e", "i", "o", "u", "A", "E", 'O', 'U', 'I'}
for i in range(len(word)-2, 0, -1):
if word[i] in vowels:
if (word[i+1] not in vowels) and (word[i-1] not in vowels):
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
13,353 | <ASSISTANT_TASK:>
Python Code:
import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be install... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Restart the kernel
Step2: Before you begin
Step3: Region
Step4: Timestamp
Step5: Authenticate your Google Cloud account
Step6: Create a Clo... |
13,354 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-2', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contribu... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
13,355 | <ASSISTANT_TASK:>
Python Code:
# Imports for plotting
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import numpy as np
sns.set_style('darkgrid')
mb_solve_json =
{
"atom": {
"decays": [
{
"channels": [[0, 1]],
"rate": 1.0
}
],
"fields": [
{
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Velocity Classes for Modelling Doppler Broadening in Thermal Systems
Step2: We can check the set of velocity classes we've defined
Step3: The ... |
13,356 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'sandbox-2', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
13,357 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-2', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "emai... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
13,358 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import sympy as sym
import quantecon as qe
import solowpy
import pypwt
pwt = pypwt.load_pwt_data()
fig, ax = plt.subplots(1, 1, figsize=(8,6))
for ctry in pwt.major_axis:
tmp_data = pwt.major_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 0. Motivation
Step2: From the above figure it is clear that the prediction of constant factor shares is strongly at odds with the empirical dat... |
13,359 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from keras.models import Model
from keras.layers import Dense, Activation, Embedding
from keras.layers import LSTM, Input
from keras.layers.merge import concatenate
from keras.optimizers import RMSprop, Adam
from keras.utils.data_utils import get_file... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Setting params for model setup and build.
Step2: Loading and reading Alice.txt corpus, saving characters (unique alphabet and punctuation chara... |
13,360 | <ASSISTANT_TASK:>
Python Code:
from rmtk.vulnerability.derivation_fragility.R_mu_T_dispersion.SPO2IDA import SPO2IDA_procedure
from rmtk.vulnerability.common import utils
%matplotlib inline
capacity_curves_file = "../../../../../../rmtk_data/capacity_curves_Vb-dfloor.csv"
input_spectrum = "../../../../../../rmtk_dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Load capacity curves
Step2: Idealise pushover curves
Step3: Load damage state thresholds
Step4: Calculate fragility functions
Step5: Plot fr... |
13,361 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# Simulate a dataset containing one feature (and one target)
# The feature values are contained in X
# The target values are contained in y
def make_data(N=100, err=0.8, rseed=1):
# randomly sample the data
rng = np.random.RandomState(rseed)
X = rng.rand(N, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Generate a dataset and fit it with a high bias and a high variance model
Step2: Prediction Performance as Training Dataset Size Increases
|
13,362 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
13,363 | <ASSISTANT_TASK:>
Python Code:
% matplotlib inline
import numpy as np
from scipy import signal
import numpy.polynomial.polynomial as poly
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from eofs.standard import Eof
infile = 'data/hgt500.mon.mean.nc'
ncin = Dataset(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 2. Load ght500 data
Step2: 3. Detrend
Step3: 4. Carry out EOF analysis
Step4: 4.3 Retrieve the leading EOFs
Step5: 5. Visualize leading EOFs... |
13,364 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from ecell4.prelude import *
with reaction_rules():
A + B == C | (0.01, 0.3)
run_simulation(10.0, {'C': 60}, volume=1.0)
from ecell4_base.core import *
from ecell4_base import *
w = ode.World(Real3(1, 1, 1))
w = ode.World(Real3(1, 1, 1))
w.add_molecules(Species('C... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Here we give you a breakdown for run_simulation.
Step2: Real3 is a coordinate vector.
Step3: Use add_molecules to add molecules, remove_molecu... |
13,365 | <ASSISTANT_TASK:>
Python Code:
def unique_digits(x):
odd_digit_elements = []
for i in x:
if all (int(c) % 2 == 1 for c in str(i)):
odd_digit_elements.append(i)
return sorted(odd_digit_elements)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
13,366 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.read_csv('./data/weight-height.csv')
df.head()
df.plot(kind = 'scatter',
figsize = (7, 7),
x = 'Height',
y = 'Weight',
title = 'Weight and Height in adults')
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Cost Function
Step2: Manual exploration of different values of W and b
Step3: Linear Regression with Keras
Step4: Evaluating Model Performanc... |
13,367 | <ASSISTANT_TASK:>
Python Code:
os.chdir('../results')
molecule_string = []
casrn = []
test_type = []
dose = []
dose_amount = []
dose_units = []
route = []
organism = []
source = []
rootdir = '.'
fnames = []
for dirpath, subdirlist, filelist in os.walk(rootdir):
# Remove the _cas directory, as the files there do no... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The following expression traverses the current directory tree and accumulates the file names in fnames
Step2: Read each file in the list as a p... |
13,368 | <ASSISTANT_TASK:>
Python Code:
import pkg_resources
if pkg_resources.get_distribution('CGRtools').version.split('.')[:2] != ['4', '0']:
print('WARNING. Tutorial was tested on 4.0 version of CGRtools')
else:
print('Welcome!')
# load data for tutorial
from pickle import load
from traceback import format_exc
with ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 3.1. Molecules
Step2: Molecules has explicify_hydrogens and implicify_hydrogens methods to handle hydrogens.
Step3: 3.2. Reactions standardiza... |
13,369 | <ASSISTANT_TASK:>
Python Code:
from msmbuilder.dataset import dataset
import mdtraj as md
import numpy as np
from glob import glob
from mdtraj.utils import timing
import itertools
from msmbuilder.featurizer import AtomPairsFeaturizer
from msmbuilder.decomposition import tICA
from msmbuilder.cluster import MiniBatchKMea... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We featurize the trajectories into the pairwise distance between all the atoms in the EF hand and the catalytic Ca<sup>2+</sup>. Since we only l... |
13,370 | <ASSISTANT_TASK:>
Python Code:
data_in = '272091-815432'
def criteria(word):
meets = True
if '11' in word or \
'22' in word or \
'33' in word or \
'44' in word or \
'55' in word or \
'66' in word or \
'77' in word or \
'88' in word or \
'99' in word:
last_num = None
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: --- Part Two ---
|
13,371 | <ASSISTANT_TASK:>
Python Code:
import urllib2
import csv
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
url_X_train = 'http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/14cancer.xtrain'
url_y_train = 'http://statweb.stanford.edu/~tibs/ElemStatLea... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Data Exploration
Step2: To see a preview of the data, we can use the head and tail functions
Step3: Let's see how the classes are distributed.... |
13,372 | <ASSISTANT_TASK:>
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 writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Environments
Step2: The code below defines a dummy RL environment for use in the examples below.
Step3: Creating a Server and Client
Step4: F... |
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Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
cd hyperspectral
import neon_aop_hyperspectral as neon_hs
refl, metadata = neon_hs.aop_h5refl2array('../../../data/NEON_D16_MCRA_DP3_566300_4901000_reflectance.h5')
b56 = refl[:,:,55]
neon_hs.plot... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import the hyperspectral functions into the variable neon_hs (for neon hyperspectral)
Step2: Optionally, you can view the data stored in the me... |
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Python Code:
import sys
sys.path.append('..')
import socnet as sn
sn.graph_width = 320
sn.graph_height = 180
g = sn.load_graph('2-largura.gml', has_pos=True)
sn.show_graph(g)
from math import inf, isinf
from queue import Queue
s = 1
q = Queue()
for n in g.nodes():
g.node[n]['d'] = inf
g.node[s]... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: A seguir, vamos configurar as propriedades visuais
Step2: Por fim, vamos carregar e visualizar um grafo
Step3: Caminhos de comprimento mínimo
... |
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Python Code:
# sphinx_gallery_thumbnail_number = 2
# Authors: Eric Larson <larson.eric.d@gmail.com>
# Sheraz Khan <sheraz@khansheraz.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
im... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Here we do some things in the name of speed, such as crop (which will
Step2: Now we band-pass filter our data and create epochs.
Step3: Comput... |
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Python Code:
# Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps
Step2: Inline Qu... |
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Python Code:
[x*x for x in range(3)]
(x*x for x in range(3))
g = (x*x for x in range(3))
next(g)
next(g)
next(g)
next(g)
for i in g:
print(i, end=", ")
g = (x*x for x in range(3))
for i in g:
print(i, end=", ")
list(x*x for x in range(3))
def eager_updown(n):
xs = []
for i in rang... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: A generator expression is lazy.
Step2: You can use generators as iterators.
Step3: A generator is single use.
Step4: The list constructor for... |
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Python Code:
from lsst.cwfs.instrument import Instrument
from lsst.cwfs.algorithm import Algorithm
from lsst.cwfs.image import Image, readFile
import lsst.cwfs.plots as plots
ff = np.loadtxt('../tests/testImages/FAM/ccdCenter189.txt')
f25 = np.zeros((25,4))
ii = 0
for i in range(ff.shape[0]):
if ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We use 25 fields for this test
Step2: distribution of the test fields
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13,379 | <ASSISTANT_TASK:>
Python Code:
import slater
print(slater.__doc__)
print(slater.AOType.all_shells)
print(slater.AOType.s)
p_type = slater.AOType.from_string("p")
print(p_type)
f_type = slater.AOType.from_int(3)
print(f_type, " l = ", f_type.l)
AO_2s = slater.AO(n=2, aoType=slater.AOType.s, occ=1)
print(AO_2s)
print... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Atomic orbitals
Step2: The atomic orbital class
Step3: An occupency can be set to the shell.
Step4: You can define the AO from a usual string... |
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Python Code:
import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline
from cs231n.data_utils import load_CIFAR10
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
Load the CIFAR-10 dataset from disk an... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: What's this TensorFlow business?
Step2: Example Model
Step3: TensorFlow supports many other layer types, loss functions, and optimizers - you ... |
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Python Code:
import random
import numpy as np
import matplotlib.pyplot as plt
import quantities as pq
import neo
import elephant.unitary_event_analysis as ue
# Fix random seed to guarantee fixed output
random.seed(1224)
# Download data
!curl https://web.gin.g-node.org/INM-6/elephant-data/raw/master/d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Next, we download a data file containing spike train data from multiple trials of two neurons.
Step3: Write a plotting function
Step4: Load da... |
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Python Code:
# 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, sof... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Visualizing objective functions by interpolating in randomly drawn directions
Step3: Background
Step7: This function has a saddle point at $(0... |
13,383 | <ASSISTANT_TASK:>
Python Code:
send(IP(dst="1.2.3.4")/TCP(dport=502, options=[("MSS", 0)]))
ans = sr([IP(dst="8.8.8.8", ttl=(1, 8), options=IPOption_RR())/ICMP(seq=RandShort()), IP(dst="8.8.8.8", ttl=(1, 8), options=IPOption_Traceroute())/ICMP(seq=RandShort()), IP(dst="8.8.8.8", ttl=(1, 8))/ICMP(seq=RandShort())], ver... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2_ Adanced firewalking using IP options is sometimes useful to perform network enumeration. Here is more complicate one-liner
Step2: Now that, ... |
13,384 | <ASSISTANT_TASK:>
Python Code:
data = pd.read_csv("./formatted_data.csv",header=0, index_col=False)
data.head()
drop_cols = ['Sensor_'+x+'1' for x in map(chr,range(65,81))]
drop_cols.append('Batch_No')
data = data.drop(drop_cols, axis=1)
data.head()
data.describe()
from sklearn import preprocessing
target = data['La... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: For each sensor the second column is the normalized form of the first column, so to avoid duplicates we drop the first column (A1,B1...P1) for e... |
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Python Code:
import espressomd
import espressomd.electrostatics
import espressomd.observables
import espressomd.accumulators
import espressomd.math
espressomd.assert_features(['WCA', 'ELECTROSTATICS'])
import numpy as np
import scipy.optimize
%matplotlib inline
import matplotlib.pyplot as plt
np.rando... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: System setup
Step2: We will build the charged rod from individual particles that are fixed in space. With this, we can use the particle-based e... |
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Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize']=(20,5)
#No usamos ninguan columna como índice para los datos
Puertos=pd.read_csv('/Datos/Informacion_Estadistica_Mensual_de_las_Marinas_FOP_180216... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Se revisan las cantidades de filas y columanas, se visulizan los primeros y los últimos registros.
Step2: Lo primero que trato de explorar es l... |
13,387 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
np.set_printoptions(precision=4, suppress=True)
sns.set_context('notebook')
%matplotlib inline
theta = [[1., 2], [.5, 2.5], [.25, 2.75]]
def f(x, a, b):
if x < a or x > b:
return 0
else:
retur... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Draw uniform density
Step2: Simulate data and draw histogram
Step3: Simulate data and estimate model parameter by MLE
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13,388 | <ASSISTANT_TASK:>
Python Code:
# Define our function (Python)
def duff_osc_ss(x, params):
omega = params['omega']
t = params['cur_time']
xd = np.array([[x[1]],
[-x[0] - 0.1 * x[0]**3 - 0.1 * x[1] + 1 * sin(omega * t)]])
return xd
# Arguments are name of derivative function, number of ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Mousai can easily recreate the near-continuous response
Step2: Let's sweep through driving frequencies to find a frequency response function
St... |
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Python Code:
import numpy as np
def rolling_apply(fun, a, w):
r = np.empty(a.shape)
r.fill(np.nan)
for i in range(w - 1, a.shape[0]):
r[i] = fun(a[(i-w+1):i+1])
return r
def rolling_window(a, window):
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: A loop in Python are however very slow compared to a loop in C code. Fortunately there is a trick to make NumPy perform this looping internally ... |
13,390 | <ASSISTANT_TASK:>
Python Code:
import joblib
features=joblib.load('clean_LCfeatures.p')
labels=joblib.load('clean_LClabels.p')
clabels=joblib.load('clean_LCclassifierlabel.p')
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames
# ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Some weird scaling going on, just to check my sanity, check the max values of a few of the features. these may need to be log scaled just to dea... |
13,391 | <ASSISTANT_TASK:>
Python Code:
def lstrip(iterable, strip_value):
Return iterable with strip_value removed from the beginnning
stripped = []
iterator = iter(iterable)
for item in iterator:
if not item == strip_value:
stripped.append(item)
break
for item ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Using the iterator protocol
Step5: Bonus1
Step6: Experiments
Step11: Bonus2
Step14: Using dropwhile helper function in itertools module
Ste... |
13,392 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
import patsy as ps
from statsmodels.sandbox.regression.gmm import IV2SLS
import os, sys
from dowhy import CausalModel
n_points = 1000
education_abilty = 1
education_voucher = 2
income_abilty = 2
income_education = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading the dataset
Step2: Using DoWhy to estimate the causal effect of education on future income
Step3: We have an estimate, indicating that... |
13,393 | <ASSISTANT_TASK:>
Python Code:
import varout.layers
import varout.objectives
import varout.experiments
import lasagne.layers
import lasagne.nonlinearities
import lasagne.init
import theano
import theano.tensor as T
import numpy as np
import holonets
import holoviews as hv
%load_ext holoviews.ipython
dataset = varout.e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Another quirk of the experiment is that they decided to merge the MNIST validation set into the training set; so we have to validation set
Step2... |
13,394 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.read_csv("datascience.csv", encoding='gb18030') #注意它的编码是中文GB18030，不是Pandas默认设置的编码，所以此处需要显式指定编码类型，以免出现乱码错误。
# 之后看看数据框的头几行，以确认读取是否正确。
df.head()
#我们看看数据框的长度，以确认数据是否读取完整。
df.shape
import jieba
def chinese_word_cut(mytext):
return " ".join(jieba.cut(mytext))
df... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: (1024, 3)
Step2: 我们需要人为设定主题的数量。这个要求让很多人大跌眼镜——我怎么知道这一堆文章里面多少主题？！
Step3: 到这里，LDA已经成功帮我们完成了主题抽取。但是我知道你不是很满意，因为结果不够直观。
|
13,395 | <ASSISTANT_TASK:>
Python Code:
from IPython.core.display import display, HTML;from string import Template;
HTML('<script src="//d3js.org/d3.v3.min.js" charset="utf-8"></script>')
css_text2 = '''
#main { float: left; width: 750px;}#sidebar { float: right; width: 100px;}#sequence { width: 600px; height: 70px;}#lege... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Graphic Interpretation
|
13,396 | <ASSISTANT_TASK:>
Python Code:
!pip install -r requirements_notebook.txt
!kubectl create namespace cifar10
%%writefile broker.yaml
apiVersion: eventing.knative.dev/v1
kind: broker
metadata:
name: default
namespace: cifar10
!kubectl create -f broker.yaml
%%writefile event-display.yaml
apiVersion: apps/v1
kind: Deploy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Setup Resources
Step2: Create the Kfserving image classification model for Cifar10. We add in a logger for requests.
Step3: Create the pretrai... |
13,397 | <ASSISTANT_TASK:>
Python Code:
# Load libraries
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np
#Load data with only two classes
iris = datasets.load_iris()
X = iris.data[:100,:]
y = iris.target[:100]
# Standarize features
scaler = StandardS... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load Iris Flower Dataset
Step2: Standardize Features
Step3: Train Support Vector Classifier
Step4: View Support Vectors
Step5: View Indices ... |
13,398 | <ASSISTANT_TASK:>
Python Code:
from openhunt.mordorutils import *
spark = get_spark()
mordor_file = "https://raw.githubusercontent.com/OTRF/mordor/master/datasets/small/windows/defense_evasion/host/empire_wdigest_downgrade.tar.gz"
registerMordorSQLTable(spark, mordor_file, "mordorTable")
df = spark.sql(
'''
SELECT `@... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Download & Process Mordor Dataset
Step2: Analytic I
|
13,399 | <ASSISTANT_TASK:>
Python Code:
tables = Gaia.load_tables()
querystart = r"SELECT "
rr_params = ["source_id", "pf", "pf_error", "int_average_g",
"int_average_g_error", "int_average_bp", "int_average_bp_error",
"int_average_rp", "int_average_rp_error", "metallicity", "metallicity_error",
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First, let's construst our query. We use the prebuilt rr_lyrae table in Gaia DR2, and get all of the parameters of interest. We will retrieve st... |
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