Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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12,300 | <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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | <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: Environment Preparation
Step2: Unsupervised Anomaly Detection using Chronos Anomaly Detector
Step 0
Step3: Step 1
Step4: Step 2
Step5: Step ... |
12,301 | <ASSISTANT_TASK:>
Python Code:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
radian = ... | <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: Rolling paper
Step2: And creating a Params object with the system parameters
Step4: The following function estimates the parameter k, which is... |
12,302 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import geopandas as gpd
import os
from os.path import join
import pandas as pd
import sys
sns.set(style='white')
cwd = os.getcwd()
data_path = join(cwd, '..', 'Data storage')
figure_path = join(cwd... | <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: Read NERC regions shapefile
Step2: Read states shapefile
Step3: Transform projection to Albers equal area
Step4: Maps of 2001 and 2017 annual... |
12,303 | <ASSISTANT_TASK:>
Python Code:
# Load image
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Load image as grayscale
image = cv2.imread('images/plane_256x256.jpg', cv2.IMREAD_GRAYSCALE)
# Blur image
image_blurry = cv2.blur(image, (5,5))
# Show image
plt.imshow(image_blurry, cmap='gray'), plt.xtic... | <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 Image As Greyscale
Step2: Blur Image
Step3: View Image
|
12,304 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pymc3 as pm
import seaborn as sns
import matplotlib.pyplot as plt
from collections import defaultdict
data = np.random.randn(100)
with pm.Model() as model:
mu = pm.Normal('mu', mu=0, sd=1, testval=0)
sd = pm.HalfNormal('sd', sd=1)
... | <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 generate a very simple model
Step2: This function will randomly draw 500 samples of parameters from the trace. Then, for each sample, it w... |
12,305 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import Image
Image("http://journalofdigitalhumanities.org/wp-content/uploads/2013/02/blei_lda_illustration.png")
import textmining_blackboxes as tm
#see if package imported correctly
tm.icantbeli... | <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: Reading at scale
Step2: IMPORTANT
Step3: Let's keep using the remarkable narratives available from Documenting the American South (http
Step4:... |
12,306 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from datetime import datetime
import numpy as np
import datacube
from dc_water_classifier import wofs_classify
from dc_utilities import perform_timeseries_analysis
import dc_au_colormaps
from dc_notebook_utilities import *
dc = datacube.Datacube(app='dc-water-analysis'... | <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, we must connect to our data cube. We can then query the contents of the data cube we have connected to, including both the metadata and t... |
12,307 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import scipy.stats as ss
import vega_datasets
x = np.array([1, 1, 1, 1, 10, 100, 1000])
y = np.array([1000, 100, 10, 1, 1, 1, 1 ])
ratio = x/y
print(ratio)
X = np.arange(len(ratio))
#... | <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: Ratio and logarithm
Step2: Q
Step3: Q
Step4: Log-binning
Step5: If you simply call hist() method with a dataframe object, it identifies all ... |
12,308 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-1', 'ocnbgchem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contribut... | <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... |
12,309 | <ASSISTANT_TASK:>
Python Code:
%sql -d standard
SELECT
*
FROM
`nyc-tlc.yellow.trips`
LIMIT
5
%bigquery schema --table nyc-tlc:yellow.trips
%%bq query -n pickup_time
WITH subquery AS (
SELECT
EXTRACT(HOUR FROM pickup_datetime) AS hour
FROM
`nyc-tlc.yellow.trips`)
SELECT
Hour,
COUNT(Hour) AS count... | <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: Let's look at the table schema
Step2: 1. What is the most common pick-up time?
Step3: Let's name this query result pickup_time and reference i... |
12,310 | <ASSISTANT_TASK:>
Python Code:
x = [1,2,3,4,5,6,7,8]
for item in x:
print item
s = ['a','c','b','d','e','g','h',8]
for item in s:
print item
for item in range(1,20,2):
print item
s=0
for i in range(1,100):
s+=i*i
print s
s=0; i=1
while i<100:
s+=i*i
i+=1
print s
m = 20
n = 79
while n != 0:
... | <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: Range function in python is a daily-use function in the for loop
Step2: This is a simple for loop that sum up all squared integer from 1 to 100... |
12,311 | <ASSISTANT_TASK:>
Python Code:
a = 10.1
type(a)
print(dir(a)) # Show all of the methods of a
a.is_integer()
# Create a class by using class keyword followed by name.
class MyClass:
# The 'self' variable ALWAYS needs to be the first variable given to any class method.
def __init__(self, message):
# Her... | <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: How can I see what methods an object of type float has?
Step2: <font color='midnightblue'> Aside - What do all those underscores mean?
Step3: ... |
12,312 | <ASSISTANT_TASK:>
Python Code:
#!pip install --user miepython
import numpy as np
import matplotlib.pyplot as plt
try:
import miepython
except ModuleNotFoundError:
print('miepython not installed. To install, uncomment and run the cell above.')
print('Once installation is successful, rerun this cell again.')
... | <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: Wiscombe tests
Step2: Spheres with a smaller refractive index than their environment
Step3: Non-absorbing spheres
Step4: Water droplets
Step5... |
12,313 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
from scipy.interpolate import UnivariateSpline
import json
import pandas as pd
from functo... | <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: And some more specialized dependencies
Step2: Helper routines
Step3: Configuration for this figure.
Step4: Open a chest located on a remote g... |
12,314 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import cvxopt as opt
from cvxopt import blas, solvers
import pandas as pd
np.random.seed(123)
# Turn off progress printing
solvers.options['show_progress'] = False
## NUMBER OF ASSETS
n_assets = 4
## NUMBER OF OBSERVATIONS
n_obs = 1000
... | <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: Assume that we have 4 assets, each with a return series of length 1000. We can use numpy.random.randn to sample returns from a normal distributi... |
12,315 | <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: TensorBoard Scalar
Step2: 단순 회귀에 대한 데이터 설정
Step3: 모델 학습 및 손실 로깅하기
Step4: TensorBoard를 사용하여 손실 검사하기
Step5: <!-- <img class="tfo-display-only-... |
12,316 | <ASSISTANT_TASK:>
Python Code:
# Authors: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Nicolas P. Rougier (graph code borrowed from his matplotlib gallery)
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from... | <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 our data
Step2: Compute inverse solutions and their connectivity
Step3: Make a connectivity plot
Step4: Make two connectivity plots in t... |
12,317 | <ASSISTANT_TASK:>
Python Code:
from google.colab import auth
auth.authenticate_user()
project_id = '[your project id]'
import pandas as pd
import datetime
today = datetime.datetime.utcnow().strftime("%Y%m%d")
df = pd.io.gbq.read_gbq('''
SELECT
count(*) as total
FROM
`web_instr_container.stdout_{}`
'''.format(tod... | <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: Log Overview
Step2: Client Latency
Step3: Scatter Plot
Step4: Responses
|
12,318 | <ASSISTANT_TASK:>
Python Code:
import pandas
from time import time
import cobra.test
from cobra.flux_analysis import (
single_gene_deletion, single_reaction_deletion, double_gene_deletion,
double_reaction_deletion)
cobra_model = cobra.test.create_test_model("textbook")
ecoli_model = cobra.test.create_test_model... | <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: Knocking out single genes and reactions
Step2: For evaluating genetic manipulation strategies, it is more interesting to examine what happens i... |
12,319 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp1d
# YOUR CODE HERE
with np.load('trajectory.npz') as data:
x = data['x']
t=data['t']
y=data['y']
plt.plot(t,x,marker='o')
assert isinstance(x,... | <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: 2D trajectory interpolation
Step2: Use these arrays to create interpolated functions $x(t)$ and $y(t)$. Then use those functions to create the ... |
12,320 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import pickle
import numpy as np
import scipy
import matplotlib.pyplot as plt
import ChiantiPy.core as ch
import sunpy.instr.aia as aia
%matplotlib inline
response = aia.Response(path_to_genx_dir='../ssw_aia_response_data/')
response.calculate_wavelength_response()
r... | <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: The goal of this notebook is to test the wavelength and temperature response function calculations that are currently being developed in SunPy. ... |
12,321 | <ASSISTANT_TASK:>
Python Code:
import csv
from pprint import pprint
import math
stat = {'Congruent': { 'data': [] }, 'Incongruent': { 'data': [] }, 'Difference': { 'data': [] }}
with open('./stroopdata.csv', 'r') as st_data:
reader = csv.DictReader(st_data)
for row in reader:
cong = float(row['Congr... | <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: Question 3
Step3: Question 4
Step4: Question 5
|
12,322 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, unicode_literals, absolute_import, division
from six.moves import range, zip, map, reduce, filter
import numpy as np
import matplotlib.pyplot as plt
from IPython import display
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import ... | <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: Adapted from http
Step2: Image from https
Step3: Label Encoding
Step4: Multiclass Classification
Step5: Plotting
Step6: Model (Logistic Reg... |
12,323 | <ASSISTANT_TASK:>
Python Code:
hidden_weights = 256
w = tf.Variable(tf.random_normal([n_input, hidden_weights]))
b = tf.Variable(tf.random_normal([hidden_weights]))
w2 = tf.Variable(tf.random_normal([hidden_weights, hidden_weights]))
w3 = tf.Variable(tf.random_normal([hidden_weights, n_classes]))
input_layer = tf.add(t... | <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: Perceptron (No cheating, mostly)
Step2: Perceptron (No cheating, for real)
|
12,324 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if ... | <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: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
12,325 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
def bsm(S0,r,sigma,T,K,R = 100000 , seed=500):
np.random.seed(seed)
z = np.random.standard_normal(R)
ST = S0 * np.exp(( r - 0.5 * sigma ** 2) * T + sigma * np.sqrt(T) * z)
hT = np.maximum(ST - K, 0)
C0 = np.exp(-r * T) * np.sum(hT) / R
return C0... | <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: Let's see how much time is necessary for 70,000,000 iterations intead of 100,000 iterations.
Step2: Let's see how we can speed up the computati... |
12,326 | <ASSISTANT_TASK:>
Python Code:
# Import necessary modules
import geopandas as gpd
# Set filepath
fp = "data/limitebairro.json"
# Read file using gpd.read_file()
data = gpd.read_file(fp, driver='GeoJSON')
type(data)
data.head(5)
data.plot()
# Create a output path for the data
outfp = "data/limitebairro.shp"
# Selec... | <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: Agora que o arquivo json foi lido na variável data, vamos ver o seu formato
Step2: Podemos ver que o tipo da variável é um GeoDataFrame. O obje... |
12,327 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import datetime
from itertools import (islice, chain)
import re
import time
from collections import (Counter, OrderedDict)
# writing for eventual Python 2/3 compatability
try:
from urllib.parse import urlencode
except ImportErr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step4: Working with results from a specific query
Step5: Now to retrieve and display the metadata for the query 3659 http
Step6: Show the output from... |
12,328 | <ASSISTANT_TASK:>
Python Code:
def countdown(n):
print '> counting down from {}'.format(n)
while n > 0:
yield n
n -= 1
print ''
print '< countdown'
for n in countdown(10):
print n,
# calling generator fucntion creates the generator object not start the function
x = countdown(3)
prin... | <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: generator 함수를 호출하는것은 generator 객체를 생성하는것이지 함수를 실행하는 것이 아님
Step3: tail -f (python version)
Step4: Coroutine
Step6: next() 는 까먹기 쉬우니까 decorator... |
12,329 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import h5py
from sklearn import svm, cross_validation, preprocessing
# First we load the file
file_location = '../results_database/text_wall_street_big.hdf5'
run_name = '/low-resolution'
f = h5py.File(file_location, 'r')
# Now we need to get the letters and align them... | <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 load the file
Step2: Accuracy with non-normalized SLM
Step3: Accuracy with normalized SLM
|
12,330 | <ASSISTANT_TASK:>
Python Code:
running_id = 0
output = [[0]]
with open("E:/output.txt") as file_open:
for row in file_open.read().split("\n"):
cols = row.split(",")
if cols[0] == output[-1][0]:
output[-1].append(cols[1])
output[-1].append(True)
else:
outpu... | <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: Problems
Step2: If we want to look at covariates, we need a new approach. We'll use Cox proprtional hazards. More information here.
Step3: O... |
12,331 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-3', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributo... | <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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
12,332 | <ASSISTANT_TASK:>
Python Code:
print('Hello world!')
print(list(range(5)))
import numpy as np
# To proceed, implement the missing code, and remove the 'raise NotImplementedException()'
##### Implement this part of the code #####
raise NotImplementedError("Code not implemented, follow the instructions.")
# three = ?
a... | <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: Imports
Step2: Parts to be implemented
Step3: Numpy arrays
Step4: Notice how we used print(f"foo {bar}") to enter a variable bar into the pri... |
12,333 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, absolute_import, print_function
from pytz import timezone
import elasticsearch
import elasticsearch.helpers
from idb import config
from idb.helpers.logging import idblogger
from idb.helpers.conversions import fields, custom_mappings
# u = "4dce41dc-2af6-4... | <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: u is the uuid of the recordset that we wish to delete
Step2: This from Nathan's example of deleting a mediarecord where we need the parent reco... |
12,334 | <ASSISTANT_TASK:>
Python Code:
##1년은 52주로 구성됨
week = list(range(1, 53)) #range 함수의 첫 번째 파라매터에는 시작할 숫자, 두 번째 파라매터에는 끝나는 숫자보다 1 큰 수를 넣어줌
week
len(week)
##한 주는 7일로 구성되어 있으므로 첫 번째 주의 가운데 날인 4번째 일을 그 주의 대표값(일)로 표시 (두 번째 주의 대표일은 11일)
representative_day = list(range(4, 365, 7))#range의 세 번째 파라매터에는 간격이 들어감
representative_day
l... | <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: 각 주의 대표일 리스트 만들기
Step2: 난수생성기
Step3: 난수 생성으로 검색량 리스트 만들기
Step4: 데이터 프레임 만들기
Step5: 일 단위 데이터로 늘리기
Step6: 실제 데이터로 생성
Step7: 나중에 plot을 배우면 더 ... |
12,335 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from xgboost import XGBRegressor
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR, LinearSVR
from sklearn.model_selection import GridSearc... | <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: Import data
Step2: Make fuel price a ratio of the coal price to the natural gas price
Step3: One-hot encoding of the cluster variable
Step4: ... |
12,336 | <ASSISTANT_TASK:>
Python Code:
%run ../../utils/load_notebook.py
from instabilities import *
import numpy as np
He_coeff = 1.34
def flat_end(argument):
'''декоратор для того, чтобы продолжать функцию на уровне последнего значения'''
def real_decorator(function):
def wrapper(*args, **kwargs):
... | <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: Коэффициент для учета вклада гелия в массу газа (см. Notes)
Step2: Для большой оси
Step3: Для случая бесконечного тонкого диска
Step4: Два др... |
12,337 | <ASSISTANT_TASK:>
Python Code:
import importlib
autograd_available = True
# if automatic differentiation is available, use it
try:
import autograd
except ImportError:
autograd_available = False
pass
if autograd_available:
import autograd.numpy as np
from autograd import grad, hessian
else:
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: Specify the function to minimize as a simple python function.<br>
Step2: Plot the function as a 2d surface plot. Different colors indicate diff... |
12,338 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import sys
import os
import matplotlib.pyplot as plt
from matplotlib import dates
from odm2api.ODMconnection import dbconnection
from odm2api.ODM2.services.readService import *
# Create a connection to the ODM2 database
# ----------------------------------------
odm2db_... | <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: SamplingFeatures tests
Step2: Back to the rest of the demo
|
12,339 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from numpy import *
from matplotlib.pylab import *
%pylab --no-import-all inline
a1 = array([1.0, 2.0, 3.0])
a2 = arange(1.0, 5.0, 0.5)
a3 = linspace(1.0, 10.0, 17)
print(a1)
print(a2)
print(a3)
m1 = array([[1.0, 2.0],
[3.0, 4.0]])
print... | <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: NumPy Arrays
Step2: Create arrays with array, ones, zeros, empty.
Step3: Arrays can be accessed like lists (index, slicing).
Step4: Math oper... |
12,340 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy import stats
from scipy.stats import norm
#Valores da tabela
y=[-1,0,1] #colunas
x=[-0.25,0,0.25] #linhas
probXY=[[[] for i in range(3)] for i in range(3)]
pxy=[0.05,0.07,0.26,0... | <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: <font color='blue'>Exercício 1 - Exemplo 3 da Aula 17 </font>
Step2: <font color='blue'>Exercício 2 - Soma de normais correlacionadas</font>
St... |
12,341 | <ASSISTANT_TASK:>
Python Code:
pip install looker_sdk
import looker_sdk #Note that the pip install required a hyphen but the import is an underscore.
import os #We import os here in order to manage environment variables for the tutorial. You don't need to do this on a local system or anywhere you can more conveniently ... | <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: Configuring & Initializing the SDK
Step2: Now that we've set all the necessary environment variables, we should be able to initialize the Looke... |
12,342 | <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
<END_TASK>
<USER_TASK:>
Description:
Step1: Retrain a classification model for Edge TPU with quant-aware training (TF 1.15)
Step2: Clone the model and training repos
Step3: Convert train... |
12,343 | <ASSISTANT_TASK:>
Python Code:
strat_train_set_copy = strat_train_set.copy()
housing.plot(kind="scatter", x='longitude', y='latitude')
housing.plot(kind="scatter", x='longitude', y='latitude', alpha=0.1)
strat_train_set_copy.plot(kind='scatter', x='longitude', y='latitude', alpha=0.4,
s=strat_... | <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: Experimenting with Attribute Combinations
Step2: 2.5 Prepare the Data for Machine Learning Algorithms
Step3: Handling Text and Categorical Att... |
12,344 | <ASSISTANT_TASK:>
Python Code:
# Define paths to model files
import os
MODELS_DIR = 'models/'
if not os.path.exists(MODELS_DIR):
os.mkdir(MODELS_DIR)
MODEL_TF = MODELS_DIR + 'model'
MODEL_NO_QUANT_TFLITE = MODELS_DIR + 'model_no_quant.tflite'
MODEL_TFLITE = MODELS_DIR + 'model.tflite'
MODEL_TFLITE_MICRO = MODELS_DI... | <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: Setup Environment
Step2: Import Dependencies
Step3: Dataset
Step4: 2. Add Noise
Step5: 3. Split the Data
Step6: Training
Step7: 2. Train t... |
12,345 | <ASSISTANT_TASK:>
Python Code:
from shenfun import *
N = 8
T = FunctionSpace(N, 'Chebyshev', domain=(-1, 1))
u = Function(T)
T = FunctionSpace(N, 'Chebyshev', domain=(0, 1))
T = FunctionSpace(N, 'Chebyshev', domain=(-1, 1))
u = Function(T, val=1)
import sympy as sp
x = sp.Symbol('x', real=True)
u = Function(T, buff... | <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: The function $u(x)$ can now be created with all N coefficients
Step2: When using Chebyshev polynomials the computational domain is always
Step3... |
12,346 | <ASSISTANT_TASK:>
Python Code:
from pybotics.robot import Robot
from pybotics.predefined_models import ur10
robot = Robot.from_parameters(ur10())
import numpy as np
np.set_printoptions(suppress=True)
joints = np.deg2rad([5,5,5,5,5,5])
pose = robot.fk(joints)
display(pose)
solved_joints = robot.ik(pose)
display(np.rad... | <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: Forward Kinematics
Step2: Inverse Kinematics
|
12,347 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
seriesLabel = ['label1', 'label2', 'label3']
exampleList = [5, 10, 20]
pd.Series(exampleList)
pd.Series(exampleList, seriesLabel)
exampleNumpyArray = np.array([6, 12, 18])
pd.Series(exampleNumpyArray)
pd.Series(exampleNumpyArray, seriesLabel)
exam... | <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: Series
Step2: Using Numpy Arrays
Step3: Using Dictionary
Step4: Data and Index Parameter in Series
Step5: Index
Step6: DataFrames
Step7: S... |
12,348 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.set_printoptions(precision=3)
import matplotlib.pyplot as plt
import math
import os
import warnings
import pandas as pd
# from scipy.interpolate import BSpline
# from scipy.stats import gaussian_kde
!mkdir figures
!pip install -q numpyro@git+https://github.com/pyro-p... | <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
Step2: Empirical mean and std.
Step3: Model
Step4: Posterior samples.
Step5: posterior marginals.
Step6: Laplace approximation
Step7: ... |
12,349 | <ASSISTANT_TASK:>
Python Code:
import json
import numpy as np
import pandas as pd
from jupyter_scisheets_widget import scisheets_widget
import pandas_datareader as pdr
ibm_data = pdr.get_data_yahoo('IBM')
income_data = pd.read_csv('income_data.csv', sep=';')
income_data
tbl2 = scisheets_widget.HandsonDataFrame(income... | <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: Load data into the notebook
Step2: Display the loaded data as a scisheet widget
Step3: Sanity check to explore df functionality
|
12,350 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from flare.gp import GaussianProcess
# make gp model
hyps = np.array([0.1, 1, 0.01])
hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std']
cutoffs = {'threebody':3.9}
gp = \
GaussianProcess(kernels=['threebody'],
hyps=hyps,
... | <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: Some Explanation about the parameters
Step2: Step 3
Step3: Some Explanation about the parameters
Step4: After OTF training is finished, we ca... |
12,351 | <ASSISTANT_TASK:>
Python Code:
# suposing the datset is downloaded here
workdir = '/media/samuel/dataspikesorting/DataSpikeSortingHD2/kampff/ultra dense/'
filename = workdir + 'T2/amplifier2017-02-08T21_38_55.bin'
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import tridesclous as tdc
from tri... | <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: create a DataIO (and remove if already exists)
Step2: CatalogueConstructor
Step3: Noise measurement
Step4: Inspect waveform quality at catalo... |
12,352 | <ASSISTANT_TASK:>
Python Code:
import sklearn.svm as svm
### BEGIN SOLUTION
### END SOLUTION
try:
train_svm
except:
assert False
else:
assert True
import numpy as np
np.random.seed(598497)
X = np.random.random((20, 2))
y = np.random.randint(2, size = 20)
m1 = train_svm(X, y, 10000.0)
assert m1.C == 10000.0
... | <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: B
Step2: C
|
12,353 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
from statsmodels.compat import lmap
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
norms = sm.robust.norms
def plot_weights(support, weights_func, xlabels, xticks):
fig =... | <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: An M-estimator minimizes the function
Step2: Andrew's Wave
Step3: Hampel's 17A
Step4: Huber's t
Step5: Least Squares
Step6: Ramsay's Ea
St... |
12,354 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
import mne
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname)
raw.set_eeg_reference('average', projection=True) # set EEG average reference
order = np.... | <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: In MNE, epochs refers to a collection of single trials or short segments
Step2: To create time locked epochs, we first need a set of events tha... |
12,355 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-3', 'land')
# 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
<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... |
12,356 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn
plt.rcParams['figure.figsize'] = 9, 6
from sklearn import datasets, svm
from sklearn.feature_selection import SelectPercentile, f_classif
iris = datasets.load_iris()
iris.data.shape
... | <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: Skusme najskor priklad toho ako by sme z nejakeho datasetu vyberali najdolezitejsie atributy pomocou filtra
Step2: Pouzijeme oblubeny dataset k... |
12,357 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
from keras.layers.core import Dense, Activation
from keras.models import Sequential
from keras.utils import np_utils
from sklearn.cross_validation import train_test_split
from sklearn.datasets.samples_generator import make_blobs
from sklearn.metrics import classification_rep... | <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: Generate data
Step2: Split the data into training and test set
Step3: Create the model
Step4: Train the model
Step5: Evaluate the model
|
12,358 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-1', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", ... | <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... |
12,359 | <ASSISTANT_TASK:>
Python Code:
def print_n_numbers(n):
#TODO: write a loop that prints numbers from 0 to n (excluding n)
for i in xrange(n):
print(i)
# now we execute the function
print_n_numbers(5)
def print_list(ll):
# Prints the list
print('\n'.join(ll))
print_list(['Visual Turing Test'... | <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: The function below print each element in the list in a new line. We will use this function later, so please run the interpreter over the followi... |
12,360 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.array([11,1,2,3,4,5,12,-3,-4,7,4])
print('a = ',a)
print('np.clip(a,0,10) = ', np.clip(a,0,10))
a = np.arange(10).astype(np.int)
print('a=',a)
print('np.clip(a,2.5,7.5)=',np.clip(a,2.5,7.5))
<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:
Step1: Exemplo com ponto flutuante
|
12,361 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.stats import norm
import matplotlib
import matplotlib.pyplot as plt
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=matplotlib.checkdep_usetex(True))
matplotlib.rc('figure', figsize=(18, 6) )
# capacity of the BSC
def ... | <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: Binary Symmetric Channel (BSC)
Step2: The finite-length capacity for the BSC channel is given by
Step3: Show finite length capacity estimates ... |
12,362 | <ASSISTANT_TASK:>
Python Code:
%%javascript
$.getScript('misc/kmahelona_ipython_notebook_toc.js')
fn = r"data/drinks.csv"
# Answer:
df = pd.read_csv(fn, sep=",")
# Answer:
df.head(10)
# Answer
df.sort_values("total_litres_of_pure_alcohol", ascending=False).head()
# Answer
# df.groupby("continent").beer_servings.me... | <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: Getting and Knowing your Data
Step2: Task
Step3: Task
Step4: Groupby
Step5: Task
Step6: Task
Step7: Task
Step8: Task
Step9: Task
Step10:... |
12,363 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-1', 'seaice')
# 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: 2... |
12,364 | <ASSISTANT_TASK:>
Python Code:
from IPython.parallel import Client
%pylab inline
client = Client()
client.block = True # Computations run synchronously.
print client.ids
dview = client.direct_view()
def f(x):
return x
dview.apply(f, "Hello World")
even_dview = client[::2]
even_dview.apply(f, "Hello World")
bvie... | <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: Before we start, we first need to assign a number of engines in our cluster. This can be done through the IPython notebook interface or through ... |
12,365 | <ASSISTANT_TASK:>
Python Code:
#@test {"skip": true}
!pip install --quiet --upgrade tensorflow-federated
!pip install --quiet --upgrade nest-asyncio
import nest_asyncio
nest_asyncio.apply()
import collections
import time
import tensorflow as tf
import tensorflow_federated as tff
source, _ = tff.simulation.datasets.emni... | <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: 단일 머신 시뮬레이션
|
12,366 | <ASSISTANT_TASK:>
Python Code:
from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
benchmarks = ["Data/Training/phos_CDK1.csv", "Data/Training/phos_CK2.csv", "Data/Training/phos_MAPK1.csv", "Data/Tr... | <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: Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.
Step2: Y Phosphorylation
Step3: T Ph... |
12,367 | <ASSISTANT_TASK:>
Python Code:
#mean and std of multivariate normal dist to generate samples
mu=np.array([5,0,-2])
σ=np.array([[9,1, -1],
[1, 3, -2],
[-1, -2,2],])
if not is_covariance(σ):
print("Warning: σ is not a valid covariance matrix (not symmetric or positive definite)")
n=1000 # numb... | <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: Transform the generated data $x$ to a new basis.
Step2: Generate another dataset $y$ with the same distribution as $x$ (this is a very strong ... |
12,368 | <ASSISTANT_TASK:>
Python Code:
%%writefile train.py
print("hello world!")
job = TrainJob("train.py", backend=KubeflowGKEBackend())
job.submit()
def train():
print("simple train job!")
job = TrainJob(train, backend=KubeflowGKEBackend())
job.submit()
%%writefile requirements.txt
papermill
jupyter
job = TrainJob("tr... | <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: Executing a python function
Step2: Executing a complete notebook
Step3: Executing it with large #CPUs and huge Memory
|
12,369 | <ASSISTANT_TASK:>
Python Code:
n=RichStr("I am ", "normal")
n
r=RichStr("RED", sheet=groups["Fore"]["red"])
r=RichStr("RED", sheet=groups.Fore.red)
str(r)
print(r)
print(r.toHTML())
IPython.display.display_html(r.toHTML(), raw=True)
pureRed=RGBColor("PureRed", 0xFF, bg=True)
prs=RichStr("Pure red", sheet=pureRed)
... | <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: __repr__esentation of a rich string shows a "flat" representation of a RichStr - a sequence of styles and strings where style applies to everyth... |
12,370 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'sandbox-3', 'aerosol')
# 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... |
12,371 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
... | <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: Table of Contents
Step2: Explore
Step3: The Vector space model and a search engine.
Step4: Naive Bayes
Step5: The accuracy score is good for... |
12,372 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_b... | <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: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
12,373 | <ASSISTANT_TASK:>
Python Code:
import requests
from bs4 import BeautifulSoup
def listFD(url, ext=''):
page = requests.get(url).text
soup = BeautifulSoup(page, 'html.parser')
return [url + node.get('href') for node in soup.find_all('a')
if node.get('href').endswith(ext)]
site = 'http://people.du... | <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: 2. (25 points) Accelerating CPU bound procedures
Step2: 3. (25 points) Use C++ to
|
12,374 | <ASSISTANT_TASK:>
Python Code:
import o2sclpy
import matplotlib.pyplot as plot
import ctypes
import numpy
import sys
plots=True
if 'pytest' in sys.modules:
plots=False
link=o2sclpy.linker()
link.link_o2scl()
cu=link.o2scl_settings.get_convert_units()
b=o2sclpy.eos_tov_buchdahl(link)
ts=o2sclpy.tov_solve(link)
t... | <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: Link the O$_2$scl library
Step2: Get a copy (a pointer to) the O$_2$scl unit conversion object
Step3: Create the Buchdahl EOS object
Step4: C... |
12,375 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import speclite
print(speclite.version.version)
import bossdata
print(bossdata.__version__)
finder = bossdata.path.Finder()
mirror = bossdata.remote.Manager()
spAll = bossdata.meta.Database(lite=True)
sky_table = spAll.select_all(where='PLATE=6641 and OBJTYPE="SKY"')
print(... | <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: Stacked Sky
Step2: Plot a stacked spectrum
Step3: Stack individual Spec-lite files
Step4: Stack Spectra from one Plate file
Step5: Stacked Q... |
12,376 | <ASSISTANT_TASK:>
Python Code:
# Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../style/custom.css'
HTML(open(css_file, "r").read())
print("Hello World!")
x = 2**8
x < 64
str_1 = 'hello'
str_2 = 'world'
new_string = str_1 + str_2
print(new_stri... | <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: Play with data in Jupyter
Step2: Edit mode and Command mode
Step3: Remember that we can concatenate strings ("add"), for example
Step4: What ... |
12,377 | <ASSISTANT_TASK:>
Python Code:
# Imports
# Numeric Packages
from __future__ import division
import numpy as np
import pandas as pd
import scipy.stats as sps
# Plotting packages
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
%matplotlib inline
sns.set_style('whitegrid')
sns.set_conte... | <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: 0. References
Step2: 1.3 What results did you get from this statistical test? These should include the following numerical values
Step3: 1.4 W... |
12,378 | <ASSISTANT_TASK:>
Python Code:
import os
PROJECT = 'cloud-training-demos' # CHANGE THIS
REGION = 'us-central1' # Choose an available region for Cloud MLE from https://cloud.google.com/ml-engine/docs/regions.
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME. Use a regional bucket in the region you s... | <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: <h1> 1. Command-line parameters to task.py </h1>
Step2: <h1> 2. Evaluation metric </h1>
Step3: <h1> 3. Make sure outputs do not clobber each o... |
12,379 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division, unicode_literals
import oddt
from oddt.shape import usr, usr_similarity
print(oddt.__version__)
heroin = oddt.toolkit.readstring('smi',
'CC(=O)Oc1ccc2c3c1O[C@@H]4[C@]35CC[NH+]([C@H](C2)[C@@H]5C=C[C@@H]4OC(=O)C)C')
smiles = ['CC(=O)Oc1... | <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'd like to compare the shape of heroin with other molecules.
Step2: To compute the shape using USR we need the molecule's 3D coordinates.
Ste... |
12,380 | <ASSISTANT_TASK:>
Python Code:
# Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
import datetime
agora = datetime.datetime.now()
agora
t = datetime.time(7, 43, 28)
print (t)
print ('Hora :', t.hour)
print ('Minute:', t.... | <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: Datetime
|
12,381 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.insert(0, "../..")
from insights.core import dr
# Here's our component type with the clever name "component."
# Insights Core provides several types that we'll come to later.
class component(dr.ComponentType):
pass
import random
# Make two components with no depen... | <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: How do I use it?
Step2: Component Types
Step3: Component Invocation
Step4: Notice that broker can be used as a dictionary to get the value of... |
12,382 | <ASSISTANT_TASK:>
Python Code:
import os
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT
os.environ["BUCKET"] = BUCKET
%%writefile tpu_models/trainer/task.py
TPU trainer command line interface
import argparse
import sys
import tensorflow as tf
from . import model, util
def _pars... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step4: Packaging the Model
Step5: The TPU server
Step6: This model is still compressed, so lets uncompress it with the tar command below and place it... |
12,383 | <ASSISTANT_TASK:>
Python Code:
def pass_through(x):
return x
data_lm = load_data(path, bs=120)
learn = language_model_learner(data=data_lm,
arch=AWD_LSTM,
pretrained=False)
learn.lr_find()
learn.recorder.plot()
best_lr = 1e-2 * 2
escb = EarlyStoppingC... | <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: 2. Instantiate Language Model
Step2: 3. Train Language Model
Step3: Define callbacks
Step4: Train Model
|
12,384 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import concarne
import concarne.patterns
import concarne.training
import lasagne
import theano.tensor as T
%pylab inline
try:
import sklearn.linear_model as sklm
except:
print (
You don't have scikit-learn installed; install it to compare
lear... | <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: This example illustrates how simple it is to train a classifier using
Step2: Data generation
Step3: Now let's define some side information
Ste... |
12,385 | <ASSISTANT_TASK:>
Python Code:
global PASSWORD
PASSWORD = "Guild o' Code"
def halver(num):
Returns half of the 'num' argument. # docstring
return num / 2
print "halver's name:", halver.__name__
print "halver's docstring:", halver.__doc__
halver?
print halver(20)
print halver(10)
print halver(5)
print [i/2 f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: function
Step3: function object
Step4: Uh-oh...
Step6: wrapper
Step8: decorator
Step9: Dust off your hands and kick back. We're completely... |
12,386 | <ASSISTANT_TASK:>
Python Code:
# TODO: add putty connection too.
#read SSH connection parameters
with open('ssh_settings.json') as settings_file:
settings = json.load(settings_file)
hostname = settings['hostname']
username = settings['username']
password = settings['password']
local_key_dir = settings['local_k... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: This notebook explores merged craigslist listings/census data and fits some initial models
Step7: Data Preparation
Step8: create variables
Ste... |
12,387 | <ASSISTANT_TASK:>
Python Code:
def reArrange(words , n ) :
mp = { }
for i in range(n ) :
mp[words[i ] ] = i + 1
words . sort() ;
for i in range(n ) :
print(mp[words[i ] ] , end = "▁ ")
words =["live ", "place ", "travel ", "word ", "sky "]
n = len(words )
reArrange(words , n ) ;
<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:
|
12,388 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
print(tf.__version__)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
def model_inputs(real_dim, z_dim):
input... | <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: Model Inputs
Step2: Generator network
Step3: Discriminator
Step4: Hyperparameters
Step5: Build network
Step6: Discriminator and Generator L... |
12,389 | <ASSISTANT_TASK:>
Python Code:
# Import Parsl
import parsl
from parsl import *
print(parsl.__version__) # The version should be v0.2.1+
workers = ThreadPoolExecutor(max_workers=4)
# We pass the workers to the DataFlowKernel which will execute our Apps over the workers.
dfk = DataFlowKernel(executors=[workers])
@App('... | <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: Define resources
Step2: Defining Bash Apps
Step3: Running Bash Apps
Step4: Handling Futures
Step5: Retrieving Results
Step6: Defining a Sec... |
12,390 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from __future__ import division
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.con... | <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: Set up the parameters for the model. Nothing too exciting here.
Step2: Below we build the neural network. This is the same network used in th... |
12,391 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(42)
y = np.random.random(10000)
x = 1./np.sqrt(y)
plt.hist(x, bins=100, range=(1,10), histtype='stepfilled',color='blue')
plt.yscale('log')
def nllp(a)
# here define the function
return 1.
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: Generate a dataset to be fitted
Step2: Maximum likelihood fit of a simple power law
Step3: Then minimize it using iminuit
Step4: Error analys... |
12,392 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
from starthinker.util.configuration import Configuration
CONFIG = Configuration(
project="",
client={},
service={},
user="/content/user.json",
verbose=True
)
FIELDS = {
'auth':'service', # Credentials used for writing d... | <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: 2. Set Configuration
Step2: 3. Enter Census Data Correlation Recipe Parameters
Step3: 4. Execute Census Data Correlation
|
12,393 | <ASSISTANT_TASK:>
Python Code:
import warnings
import numpy as np
import openpnm as op
from openpnm.algorithms import MixedInvasionPercolation as mp
import matplotlib.pyplot as plt
np.random.seed(10)
from ipywidgets import interact, IntSlider
warnings.simplefilter("ignore")
%matplotlib inline
ws = op.Workspace()
ws.set... | <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: The Mixed Invasion Percolation algorithm therefore requires a physics associated with its invading phase that contains both a pore and throat en... |
12,394 | <ASSISTANT_TASK:>
Python Code:
# Load regex package
import re
# Create a variable containing a text string
text = 'Chris: 12:34am. Steve: 16:30'
# Find any text that fits the regex
re.findall(r'([0-1]\d:[0-5]\d)\s*(?:AM|PM)?', text)
<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:
Step1: Create some text
Step2: Apply regex
|
12,395 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
Sw = 10e3 # [VA]
Vp = 600 # [V]
Vh = 480 # [V] which is also the load voltage
Vl = 120 # [V]
n = Vh/Vl # = Nc/Nse
n
Sio = (1 + n)/1 * Sw
print('''
Sio = {:.1f} kVA
==============
'''.format(Sio/1000))
Ip = Sio/Vp
print('''
Ip = {:.1f} A
===========
'''.format(Ip... | <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: Description
Step2: (a)
Step3: (c)
Step4: and the maximum secondary current is
|
12,396 | <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
<END_TASK>
<USER_TASK:>
Description:
Step1: <table class="tfo-notebook-buttons" align="left">
Step2: About YAMNet
Step3: With the model loaded, you can follow the YAMNet basic usage tuto... |
12,397 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -u -v -d -p matplotlib,numpy
%matplotlib inline
import numpy as np
import random
from matplotlib import pyplot as plt
data = np.random.normal(0, 20, 1000)
# fixed bin size
bins = np.arange(-100, 100, 5) # fixed bin size
plt.xlim([min(data)-5, max(data)+5]... | <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: <font size="1.5em">More info about the %watermark extension</font>
Step2: <br>
Step3: <br>
Step4: <br>
Step5: <br>
Step6: Via the numpy.his... |
12,398 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
pr... | <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: Loading data
Step2: By default,
Step3:
Step4: Preprocessing
Step5: Once we're confident about which component(s) we want to remove, we pas... |
12,399 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import bet.sensitivity.gradients as grad
import bet.sensitivity.chooseQoIs as cqoi
import bet.calculateP.simpleFunP as simpleFunP
import bet.calculateP.calculateP as calculateP
import bet.postProcess.postTools as postTools
import bet.Comm as comm
import bet.sample as sa... | <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: Define Methods
Step2: Suggested Changes
Step3: With these gradient vectors, we are now ready to choose an optimal set of QoIs to use in the in... |
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