Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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10,500 | <ASSISTANT_TASK:>
Python Code:
import mne
import os.path as op
import numpy as np
from matplotlib import pyplot as plt
data_path = mne.datasets.sample.data_path()
# Load a dataset that contains events
raw = mne.io.read_raw_fif(
op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif'))
# If your raw object has ... | <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:
Step2: Now, we can create an
Step3: Epochs behave similarly to
Step4: You can select subsets of epochs by indexing the
Step5: It is also... |
10,501 | <ASSISTANT_TASK:>
Python Code:
# Initial import statements
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import *
from numpy import *
from numpy.linalg import *
from scipy.linalg import lu_factor, lu_solve
# Create a function which can be used later if needed
def lu_decom... | <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 3
Step2: Problem 6
Step3: Reliability
Step4: If you want to invert matrices with small determinant, the solution is to ensure the tol... |
10,502 | <ASSISTANT_TASK:>
Python Code:
# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD (3-clause)
from matplotlib import pyplot as plt
import mne
from mne.datasets import sample
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
raw = mne.io.read_raw_fif(raw_fname)... | <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: Setting up data paths and loading raw data (skip some data for speed)
Step2: Since downsampling reduces the timing precision of events, we reco... |
10,503 | <ASSISTANT_TASK:>
Python Code:
! pip uninstall -y kfp
! pip install kfp
import kfp
import json
import os
from kfp.onprem import use_k8s_secret
from kfp import components
from kfp.components import load_component_from_file, load_component_from_url
from kfp import dsl
from kfp import compiler
import numpy as np
import lo... | <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: Enter your gateway and the auth token
Step2: Set the Log bucket and Tensorboard Image
Step3: Set the client and create the experiment
Step4: ... |
10,504 | <ASSISTANT_TASK:>
Python Code:
%%html
<style>
.example-container { background: #999999; padding: 2px; min-height: 100px; }
.example-container.sm { min-height: 50px; }
.example-box { background: #9999FF; width: 50px; height: 50px; text-align: center; vertical-align: middle; color: white; font-weight: bold; margin: 2px;}... | <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: Widget Styling
Step2: Parent/child relationships
Step3: After the parent is displayed
Step4: Fancy boxes
Step5: TabWidget
Step6: Alignment
... |
10,505 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import random
import thinkstats2
import thinkplot
class HypothesisTest(object):
def __init__(self, data):
self.data = data
self.MakeModel()
self.actual = self.TestStatistic(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: Hypothesis testing
Step2: And here's an example that uses it to compute the p-value of an experiment where we toss a coin 250 times and get 140... |
10,506 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import pandas as pd
from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot as plt
from pandas.plotting import autocorrelation_plot
from statsmodels.tsa.arima_model import ARIMA
from pandas import DataFrame
from sklearn.metrics... | <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: p
Step2: Train - Test
|
10,507 | <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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<USER_TASK:>
Description:
Step1: 訓練後の整数量子化
Step2: MNIST モデルをビルドする
Step3: TensorFlow Lite モデルに変換する
Step4: TensorFlow Lite モデルになってはいますが、すべてのパラメータデータには 32 ビット浮動小数点値が使用されています。
St... |
10,508 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
def model_inputs(real_dim, z_dim):
inputs_real = tf.placeholde... | <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: Model Inputs
Step2: Generator network
Step3: Discriminator
Step4: Hyperparameters
Step5: Build network
Step6: Discriminator and Generator L... |
10,509 | <ASSISTANT_TASK:>
Python Code:
%run db2odata.ipynb
%run db2.ipynb
%sql connect reset
%sql connect
%sql -sampledata
%sql SELECT * FROM EMPLOYEE
%odata register
%odata RESET TABLE EMPLOYEE
s = %odata -e SELECT lastname, salary from employee where salary > 50000
s = %odata -e SELECT * FROM EMPLOYEE
%odata 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: Db2 Extensions
Step2: <a id='top'></a>
Step3: If you connected to the SAMPLE database, you will have the EMPLOYEE and DEPARTMENT tables availa... |
10,510 | <ASSISTANT_TASK:>
Python Code:
import seaborn as sns
%matplotlib inline
tips = sns.load_dataset('tips')
tips.head(5)
#find dist of total bills
sns.distplot(tips['total_bill'])
tips.total_bill.mean()
tips_mean = tips.total_bill.mean()
tips_sd = tips.total_bill.std()
ax = sns.distplot(tips['total_bill'])
# plot mean i... | <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 sample dataset
Step2: Distribution plots
Step3: It is often useful to overlay the mean and SD with the histograms, below is one way to do... |
10,511 | <ASSISTANT_TASK:>
Python Code:
print(type(()))
help(())
!python -m timeit "x = (1, 'a', 'b', 'a')"
a = (1, 'a', 'b', 'a')
a.count('a')
a.index('b')
a
a[2] # The 3-rd item
a[2:1] # Extract the tuple from the 2-nd item to the 1-st one
a[2:2] # Extract from the 2-nd item to the 2-nd item
a[2:3] # Extract from the 2-nd... | <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: 1.2. Tuple definition
Step2: 1.3. Counting ocurrences in tuples
Step3: 1.4. Searching for an item in a tuple
Step4: 1.5. Slicing in tuples
St... |
10,512 | <ASSISTANT_TASK:>
Python Code:
import SimpleITK as sitk
# Utility method that either downloads data from the Girder repository or
# if already downloaded returns the file name for reading from disk (cached data).
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
# Always write output to a... | <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: Utility functions
Step2: Read images
Step3: Initial Alignment
Step4: Registration
Step5: Post registration analysis
Step6: Now visually ins... |
10,513 | <ASSISTANT_TASK:>
Python Code:
# Greater than ( > )
if 1 > 0:
print("One is more than zero")
else:
print("BITS Pilani Goa Campus is better than IIT Kanpur")
# Less than ( < )
if 12 < 42:
print("Yes, 12 is less than 42")
else:
print("Everyone registered in CTE Python will pass with distinction (90%+... | <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: Chained Conditional
Step2: The condition need not directly involve a logical operator. For example
Step3: Iterations
Step4: enumerate
Step5:... |
10,514 | <ASSISTANT_TASK:>
Python Code:
shps = !ls /home/user/data/north_carolina/shape/*shp
td = {}
for shp in shps:
with fiona.open( shp, 'r') as inp:
td[ inp.name ] = inp.bounds
## Fiona inp.bounds => ( lower_lng, lower_lat, upper_lng, upper_lat)
## Create shapely geometry from the coords
## shapely/geometry/geo... | <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: Task
|
10,515 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(20)
runningtimes=0
while (runningtimes <=10):
x = np.random.randn(5)
print(x)
print('**')
runningtimes=runningtimes+1
import numpy as np
runningtimes=0
while (runningtimes <=10):
np.random.seed(5)
x = np.random.randn(5)
print(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: Under stand how path is joined
Step2: numpy.c_ function
|
10,516 | <ASSISTANT_TASK:>
Python Code:
# imports
import matplotlib.pyplot as plt
import numpy as np
from skbeam.core.image import construct_rphi_avg_image
%matplotlib inline
# first generate some random scattering pattern
# There are missing regions
from skbeam.core.utils import angle_grid, radial_grid
shape = 800,800
x0,y0 =... | <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 generate the image
Step2: 2. Next re-partition image into a polar grid
Step3: Now regenerate the image to test construct_rphi_image
S... |
10,517 | <ASSISTANT_TASK:>
Python Code:
__copyright__ = "Reiner Lemoine Institut, Zentrum für nachhaltige Energiesysteme Flensburg"
__license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)"
__url__ = "https://github.com/openego/data_processing/blob/master/LICENSE"
__author__ = "wolfbunke, Ludee"
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: Tutorial - How to work with the OpenEnergy Platform (OEP)
Step2: 0.1 About the Database and used Packages
Step3: 1. Create a table / Table Arc... |
10,518 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
l1 = list()
l2 = []
print(l1)
print(l2)
print(len(l1))
print(len(l2))
l3 = [1, 2, 3]
print(l3)
print(len(l3))
l1.append(1)
print(l1)
l1.append(10)
print(l1)
l2.append(100)
print(l2)
print(l1)
print(l2)
print(l1 + l2)
l1.extend(l2)
print(l1)
print... | <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 length of a list is acquired by the len functino
Step2: Lists can be initialised if its values are known at run time
Step3: Appending and ... |
10,519 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-1', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <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... |
10,520 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
reviews = pd.read_csv('reviews.txt', header=None)
labels = pd.read_csv('labels.txt', header=None)
reviews.head()
from collections import Counter
#Create the counte... | <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: Preparing the data
Step2: Counting word frequency
Step3: Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in... |
10,521 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from matplotlib import rc
rc('text', usetex=True)
!head -n 5 likelihoodvariancetest.txt
multi = np.loadtxt('likelihoodvariancetest.txt')
multi1000 = np.loadtxt('likelihoodvariancetest1000samples.txt'... | <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 here we have the standard deviations
Step2: If we maintain our key assumption from the Quantifying Scaling Accuracy notebook, that the sing... |
10,522 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
file_name_string = 'C:/Users/Charles Kelly/Desktop/Exercise Files/02_07/Begin/EmployeesWithGrades.xlsx'
employees_df = pd.read_excel(file_name_string, 'Sheet1', index_col=None, na_values=['NA'])
employees_df["Grade"] = employees_df["Grade"].astype("... | <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: Change data type
Step2: Rename the categories
Step3: Values in data frame have not changed
|
10,523 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pysra
%matplotlib inline
# Increased figure sizes
plt.rcParams["figure.dpi"] = 120
m = pysra.motion.SourceTheoryRvtMotion(6.0, 30, "wna")
m.calc_fourier_amps()
profile = pysra.site.Profile(
[
pysra.site.Layer(
... | <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: Create a point source theory RVT motion
Step2: Create site profile
Step3: Create the site response calculator
Step4: Initialize the variation... |
10,524 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
import numpy as np
import matplotlib.pyplot as plt
# Load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print(X_tra... | <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 those of you unfamiliar with the MNIST dataset, it is a set of 70000, 28x28 pixel images depicting handwritten digits from 0-9. It is a comm... |
10,525 | <ASSISTANT_TASK:>
Python Code:
def list_of_chars(list_chars):
# TODO: Implement me
if li
return list_chars[::-1]
# %load test_reverse_string.py
from nose.tools import assert_equal
class TestReverse(object):
def test_reverse(self):
assert_equal(list_of_chars(None), None)
assert_equa... | <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: Unit Test
|
10,526 | <ASSISTANT_TASK:>
Python Code:
import soundfile as sf
sig, fs = sf.read('data/singing.wav')
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(len(sig)) / fs
plt.plot(t, sig)
plt.xlabel('time / seconds')
plt.grid()
plt.specgram(sig, Fs=fs)
plt.ylabel('frequency / Hz')
plt.xlabel('time... | <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: Plotting
Step2: Spectrogram
Step3: Symbolic Math
|
10,527 | <ASSISTANT_TASK:>
Python Code:
import torchaudio
import torchvision
import torch
import matplotlib.pyplot as plt
from IPython.display import Audio
img = torchvision.io.read_image("data/birdie2.jpg")
img = torchvision.transforms.ToPILImage()(img)
display(img)
img = torchvision.io.read_image("data/birdie2.jpg")
normali... | <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: torchvision for loading input images
Step2: Visualize the transforms done before model prediction
Step3: plot_waveform function from torchaudi... |
10,528 | <ASSISTANT_TASK:>
Python Code:
# data_id = "7d"
from fretbursts import *
init_notebook()
from IPython.display import display
data_dir = './data/singlespot/'
import os
data_dir = os.path.abspath(data_dir) + '/'
assert os.path.exists(data_dir), "Path '%s' does not exist." % data_dir
from glob import glob
file_list = s... | <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 software and filenames definitions
Step2: Data folder
Step3: List of data files
Step4: Data load
Step5: Load the leakage coefficient fr... |
10,529 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
# Let's load the iris dataset
iris = load_iris()
X, y = iris.data, iris.target
# split data into training and test sets using the handy train_test_split func
# in this split, we are "holding 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: QUESTIONS
Step2: From Decision Tree to Random Forest
|
10,530 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-lm', 'aerosol')
# 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... |
10,531 | <ASSISTANT_TASK:>
Python Code:
import sys
import os
import re
import collections
import itertools
import bcolz
import pickle
sys.path.append('../lib')
import gc
import random
import smart_open
import h5py
import csv
import tensorflow as tf
import gensim
import datetime as dt
from tqdm import tqdm_notebook as tqdm
impor... | <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: Data
Step2: load data
Step3: Word Vectors Pre Trained
Step4: dont need word to id dict since this is indexed with words
Step5: for tensorboa... |
10,532 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.random import randn
from scipy import stats as stats
import mne
from mne import (io, spatia... | <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: Set parameters
Step2: Read epochs for all channels, removing a bad one
Step3: Transform to source space
Step4: Transform to common cortical s... |
10,533 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import sympy as sym
from scipy.integrate import odeint
import scipy.linalg as spla
from scipy import optimize, interpolate
from scipy.linalg import solve_triangular, toeplitz, lu
from scipy.optimize import root
# pip in... | <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: <div id='intro' />
Step2: b) Indexing (view)
Step3: c) Indexing (copy)
Step4: d) Vectorization (THE HEART OF THE HEART OF NUMERICAL COMPUTING... |
10,534 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import sys
import json
import csv
import pandas as pd
import re
import matplotlib.pyplot as plt
import seaborn as sns
csv.field_size_limit(sys.maxsize)
def scores(path):
id_to_scores = {}
d = json.load(
open(
path,
'r'))
# id --> score
... | <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: Reading the image-sentence scores from the a json file
Step2: Read a model's scores on each sentence-image pair
Step3: SVO-Probes
Step4: Comp... |
10,535 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... | <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 and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
10,536 | <ASSISTANT_TASK:>
Python Code:
# Load libraries
from sklearn import preprocessing
import numpy as np
# Create feature
x = np.array([[-500.5],
[-100.1],
[0],
[100.1],
[900.9]])
# Create scaler
scaler = preprocessing.StandardScaler()
# Transform the feature
st... | <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 Feature
Step2: Standardize Feature
|
10,537 | <ASSISTANT_TASK:>
Python Code:
n_mock= 100000
sigma_true= 0.1
totmass_true= 0.25
z_mock, vz_mock, m_mock= wendym2m.sample_sech2(sigma_true,totmass_true,n=n_mock)
_= bovy_plot.bovy_hist(numpy.fabs(z_mock),bins=31,normed=True,
xlabel=r'$z$',ylabel=r'$\nu(z)$',lw=2.,histtype='step')
gca().set_yscal... | <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 density looks like a $\mathrm{sech}^2$ disk (cored near the center, exponential at large distances)
Step2: Now we 'observe' this density di... |
10,538 | <ASSISTANT_TASK:>
Python Code:
cd ..
from indra2.agent import Agent
def newt_action(agent):
print("I'm " + agent.name + " and I'm inventing modern mechanics!")
newton = Agent("Newton",
attrs={"place": 0.0, "time": 1658.0, "achieve": 43.9},
action=newt_action,
... | <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: Agent class constructor accepts 5 parameters
Step2: Now we will explore all the magic methods of agent class.
Step3: str returns name of the a... |
10,539 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-2', 'ocean')
# 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
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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... |
10,540 | <ASSISTANT_TASK:>
Python Code:
import time
from matplotlib import rcParams
import matplotlib.pyplot as plt
%matplotlib inline
rcParams['figure.figsize'] = (13, 6)
plt.style.use('ggplot')
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggregate import CombinatorialOptimisation
train = Da... | <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: Dividing data into train and test set
Step2: Let us use building 1 for demo purposes
Step3: Let's split data at April 30th
Step4: REDD data s... |
10,541 | <ASSISTANT_TASK:>
Python Code:
import google.datalab.bigquery as bq
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn impo... | <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: SAM (System for Award Management) - exclusions
Step2: There are 8,659 firms on the SAM exclusion list
Step3: NPI and CAGE don't seem to be gre... |
10,542 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from math import radians, cos, sin, asin, sqrt
import itertools
from sklearn import neighbors
from sklearn import preprocessing
from sklearn import ensemble
from sklearn.model_selection import LeaveOneGroupOut, LeavePGroupsOut
import inversion
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: Load training data
Step2: Build features
Step3: Because solving the sum of squares equation involved the quadratic formula, in some cases imag... |
10,543 | <ASSISTANT_TASK:>
Python Code:
# type your help commands in the box and
# execute the code in the box by typing shift-enter
# (hold down the shift key while hitting the enter/return key)
# The interpreter can be used as a calculator, and can also echo or concatenate strings.
3 + 3
3 * 3
3 ** 3
3 / 2 # classic divisi... | <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 Basics
Step2: Try It Yourself
Step3: Variables can be reassigned
Step4: The ability to reassign variable values becomes important when it... |
10,544 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
def well2d(x, y, nx, ny, L=1.0):
Compute the 2d quantum well wave function.
# YOUR CODE HERE
#raise NotImplementedError()
Psi = (2/L)*np.sin((nx*np.pi*x)/L)*np.sin((ny*np.pi*y)/L)
return Psi
psi = w... | <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:
Step2: Contour plots of 2d wavefunctions
Step3: The contour, contourf, pcolor and pcolormesh functions of Matplotlib can be used for effective visuali... |
10,545 | <ASSISTANT_TASK:>
Python Code:
import salty
smiles = salty.check_name("1-butyl-3-methylimidazolium")
print(smiles)
%matplotlib inline
from rdkit import Chem
from rdkit.Chem import Draw
fig = Draw.MolToMPL(Chem.MolFromSmiles(smiles),figsize=(5,5))
ms = [Chem.MolFromSmiles("OC(=O)C(N)Cc1ccc(O)cc1"), Chem.MolFromSmiles(... | <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: once we have a smiles representation of a molecule, we can convert it into a molecular object with RDKit
Step2: Once we have a molecular object... |
10,546 | <ASSISTANT_TASK:>
Python Code:
class Heap:
sNodeCount = 0
def __init__(self):
Heap.sNodeCount += 1
self.mID = str(Heap.sNodeCount)
def getID(self):
return self.mID # used only by graphviz
def _make_string(self, attributes):
# get the name of the class of the o... | <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 make_string is a helper function that is used to simplify the implementation of the method __str__.
Step2: Graphical Representatio... |
10,547 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15.0, 8.0)
# First, we need to know what's in the data file.
!head -15 R11ceph.dat
class Cepheids(object):
def __init__(self,filename):
... | <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: A Look at Each Host Galaxy's Cepheids
Step2: OK, now we are all set up! Let's plot one of the datasets.
Step3: Q
Step4: Q
Step5: Now, let's ... |
10,548 | <ASSISTANT_TASK:>
Python Code:
X = X[X['lon'] < -122]
X.plot(kind='scatter', x='lon', y='lat')
from sklearn.cluster import KMeans
#To work with out cluster we have to turn our panda dataframe into a numpy array,
np_X = np.array(X)
kmeans = KMeans(n_clusters=2)
kmeans.fit(np_X)
centroid = kmeans.cluster_centers_
labels... | <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 points now all seem to be within SF borders
Step3: I will now look at the total squared error in relation to the number of clusters, to fin... |
10,549 | <ASSISTANT_TASK:>
Python Code:
cd assignment/calibration_images/
cdata = CalibrationData()
cdata.config()
cdata.create_h5()
import json
print json.dumps(cdata.h5dict, indent=4) #Json used to print cdata.h5dict neatly
arr = cdata.get_dset(camera=0, z_loc=-6) #Note: It returns a numpy array, not a h5py dataset!
print 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: The object cdata now manages all the calibration images. The images are converted to arrays into a file called calibration.h5. A HDF5 file can b... |
10,550 | <ASSISTANT_TASK:>
Python Code:
os.getcwd()
import os
import re
import sys
import vcf
import time
import pysam
import myvariant
import collections
import numpy as np
import pandas as pd
sys.path.append(os.getcwd().replace("notebooks/dnaSeq/VAPr_Variant_Annotation_Prioritization", "src/dnaSeq/VAPr"))
#variantannotation f... | <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: <a id = "ANNOVAR"></a>
Step2: Specify the name and location of the csv file that ANNOVAR produces as output
Step3: <a id = "myvariant"></a>
St... |
10,551 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf # package we'll be using for linear regression
import numpy as np
import scipy as sp
df = pd.read_csv("data/hanford.cs... | <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. Read in the hanford.csv file
Step2: 3. Calculate the basic descriptive statistics on the data
Step3: 4. Calculate the coefficient of correl... |
10,552 | <ASSISTANT_TASK:>
Python Code:
# modules we'll use
import pandas as pd
import numpy as np
# read in all our data
nfl_data = pd.read_csv("../input/nflplaybyplay2009to2016/NFL Play by Play 2009-2017 (v4).csv")
# set seed for reproducibility
np.random.seed(0)
# look at the first five rows of the nfl_data file.
# I can ... | <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 first thing to do when you get a new dataset is take a look at some of it. This lets you see that it all read in correctly and gives an idea... |
10,553 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
D = np.linspace(0.0, 15., 500)
x = np.arange(500)
plt.figure(figsize=(8,2))
plt.fill_between(x, D, 0)
plt.xlabel('x position [a.u.]')
plt.ylabel('D [a.u.]');
np.random.seed(12337) # reproducable r... | <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: Trajectory simulation
Step2: If we plot this simulated trajectory, we see how the distance between two subsequent positions of the particle is ... |
10,554 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function # For the python2 people
import pandas as pd # This is typically how pandas is loaded
airlines = pd.read_table("airlines.txt")
airports = pd.read_table("airports.txt")
flights = pd.read_table("flights.txt")
planes = pd.read_table("planes.txt")
weathe... | <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: Reading data from a file
Step2: Inspecting a dataframe // What's in the flights dataset?
Step3: Series
Step4: DataFrame Indexing
Step5: Data... |
10,555 | <ASSISTANT_TASK:>
Python Code:
import pandas
import numpy
from folding_group import FoldingGroupClassifier
from rep.data import LabeledDataStorage
from rep.report import ClassificationReport
from rep.report.metrics import RocAuc
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, ... | <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: Reading initial data
Step2: Remove rows with NAN from data
Step3: Add diff_pt and cos(diff_phi)
Step4: Add max, sum among PIDs
Step5: define... |
10,556 | <ASSISTANT_TASK:>
Python Code:
# the output of plotting commands is displayed inline within frontends,
# directly below the code cell that produced it
%matplotlib inline
from time import time
# this python library provides generic shallow (copy) and deep copy (deepcopy) operations
from copy import deepcopy
# 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: Twiss parameters with and without coupler kick
Step2: Trajectories with Coupler Kick
|
10,557 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from numpy.random import choice
from scipy.stats import beta
class DirichletProcessSample():
def __init__(self, base_measure, alpha):
self.base_measure = base_measure
self.alpha = alpha
self.cache = []
self.weights = []
... | <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: As we saw earlier the Dirichlet process describes the distribution of a random probability distribution. The Dirichlet process takes two paramet... |
10,558 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'A': [0, 1, 1, 1, 0, 1],
'B': [1, 0, 1, 1, 1, 0],
'C': [1, 1, 0, 1, 1, 1],
'D': [1, 1, 1, 0, 1, 1]})
df["category"] = df.idxmin(axis=1)
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Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
10,559 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy
x = numpy.linspace(0, 1)
y1 = numpy.sin(numpy.pi * x) + 0.1 * numpy.random.rand(50)
y2 = numpy.cos(3.0 * numpy.pi * x) + 0.2 * numpy.random.rand(50)
from matplotlib import pyplot
pyplot.plot(x, y1)
pyplot.show()
pyplot.plot(x, y1)
pyplot.xlabel('x')
pypl... | <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 above command is only needed if you are plotting in a Jupyter notebook.
Step2: And then produce a line plot
Step3: We can add labels and t... |
10,560 | <ASSISTANT_TASK:>
Python Code:
import os
from google.cloud import bigquery
import pandas as pd
%load_ext google.cloud.bigquery
PROJECT = "qwiklabs-gcp-03-3247cf88ddb1" #"cloud-training-demos" # Replace with your PROJECT
BUCKET = PROJECT # defaults to PROJECT
REGION = "us-central1" # Replace with your REGION
os.envi... | <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: Replace the variable values in the cell below
Step2: Create a Dataset from BigQuery
Step3: Let's do some regular expression parsing in BigQuer... |
10,561 | <ASSISTANT_TASK:>
Python Code:
import os
import mdtraj
import mdtraj.reporters
from simtk import unit
import simtk.openmm as mm
from simtk.openmm import app
pdb = mdtraj.load('data/native.pdb')
topology = pdb.topology.to_openmm()
forcefield = app.ForceField('amber99sbildn.xml', 'amber99_obc.xml')
system = forcefield... | <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: And a few things from OpenMM
Step2: First, let's find a PDB for alanine dipeptide, the system we'll
Step3: Lets use the amber99sb-ildn forcefi... |
10,562 | <ASSISTANT_TASK:>
Python Code:
def boston_housing(data_set='boston_housing'):
if not data_available(data_set):
download_data(data_set)
all_data = np.genfromtxt(os.path.join(data_path, data_set, 'housing.data'))
X = all_data[:, 0:13]
Y = all_data[:, 13:14]
return data_details_return({'X' : X,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step 1
Step1: The function name allows users to call data = GPy.util.datasets.boston_housing() to acquire the data. You should use a name that makes it... |
10,563 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
from enlib import enmap,wcs as mwcs
import numpy as np
import sys,os
res = 1.0
shape, wcs = enmap.fullsky_geometry(res=res*np.pi/180./60., proj="car")
shape = (3,)+shape
ny, nx = shape[-2:]
vy,vx = enmap.pix2sky(shape, wcs, [np.arange(ny),np.zeros(ny)]... | <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 make a full-sky arcminute resolution geometry. I've only been able to reproduce this bug for res=1.0.
Step2: We do a pix2sky that is needed ... |
10,564 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotnine as p9
import seaborn as sns
titanic = pd.read_csv("data/titanic.csv")
with plt.style.context('seaborn-whitegrid'): # context manager for styling the figure
fig, ax = plt.subplots()
... | <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: When should I use Seaborn versus Matplotlib?
Step2: Pandas/Matplotlib plot...
Step3: Using Seaborn
Step4: An important difference is the impe... |
10,565 | <ASSISTANT_TASK:>
Python Code:
import extractVariables as ev
def collect_variables(expr):
return frozenset(var for var in ev.extractVars(expr)
if var not in dir(__builtins__)
if var not in ['and', 'or', 'not']
)
def arb(S):
for x in 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: The function collect_variables(expr) takes a string expr that can be interpreted as a Python expression as input and collects all variables occu... |
10,566 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from sklearn import __version__ as sklearn_version
print('Sklearn version:', sklearn_version)
from sklearn import datasets
iris = datasets.load_iris()
print(iris.DESCR)
# Print some data lines
print(iris.data[:10])
print(iris.target)
#Randomize and 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: Load data
Step2: Linear model
Step3: Decision tree
Step4: Test another clasifier
Step5: ROC area
|
10,567 | <ASSISTANT_TASK:>
Python Code::
from sklearn.metrics import classification_report, log_loss, roc_auc_score
print('Classification Report:',classification_report(y_test, y_pred))
print('Log Loss:',log_loss(y_test, y_pred))
print('ROC AUC:',roc_auc_score(y_test, y_pred))
<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:
|
10,568 | <ASSISTANT_TASK:>
Python Code:
# First disable the log so the output is neater
import logging, sys
logging.disable(sys.maxsize)
from findatapy.market import Market, MarketDataRequest
# In this case we are saving predefined tick tickers to disk, and then reading back
from findatapy.market.ioengine import IOEngine
md_req... | <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: Let's print the output...
Step2: Let's type in our S3 bucket address, which you'll need to change below. Note the use of s3
Step3: We can writ... |
10,569 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
example_array = np.ones((2, 3))
print(example_array)
my_zeros_array = # your code goes here
my_random_array = # your code goes here
print(my_zeros_array)
print(my_random_array)
atom1_xyz = [5, 2, 8]
atom2_xyz = [8, 4, 6]
atom1_xyz_np = # your code goes here
atom2_xyz... | <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: Creation of numpy arrays
Step2: Create an array filled with zeros and one filled with random numberers in the interval [0,1), each with a shape... |
10,570 | <ASSISTANT_TASK:>
Python Code:
# RUN THIS CELL
import torch
import torch.nn as nn
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
df = pd.r... | <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: Prepare the data
Step2: 2. Normalize the training set
Step3: 3. Prepare data for LSTM
Step4: 4. Define the model
Step5: 5. Define loss and o... |
10,571 | <ASSISTANT_TASK:>
Python Code:
%sql postgresql://localhost/inchi_split \
select count(*) from zinc_clean_nonstandard;
d = %sql \
select formula,count(zinc_id) freq from zinc_clean_nonstandard group by formula \
order by freq desc limit 10;
d
d = %sql \
select formula,skeleton,hydrogens,count(zinc_id) ... | <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: Big caveat here
Step2: grouping on the main layer
Step3: Look at a few of the common main layer groups
Step4: Charges
Step5: Stereo grouping... |
10,572 | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
import math
pickle_file = 'notMNIST.pickle'
with open(pick... | <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: First reload the data we generated in notmist.ipynb.
Step2: Reformat into a shape that's more adapted to the models we're going to train
Step3:... |
10,573 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append("/home/moser/MG_2016/pyMG/")
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pymg
from project.helmholtz1d import Helmholtz1D
from project.helmholtz1d_periodic import Helmholtz1D_Periodic
from project.gauss_seidel ... | <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: Plotten Sie mithilfe von matrix_plot die Systemmatrizen für $\sigma = 0$ und $n=10$.
Step2: Aufgabe
Step3: Frage
Step4: Frage
Step5: Frage
... |
10,574 | <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_mode... | <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: Single Deletions
Step2: These can also be done for only a subset of genes
Step3: This can also be done for reactions
Step4: Double Deletions
... |
10,575 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import zipfile
dataset_folder_path = 'data'
dataset_filename = 'text8.zip'
dataset_name = 'Text8 Dataset'
class DLProgress(tq... | <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 the text8 dataset, a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the data folder. ... |
10,576 | <ASSISTANT_TASK:>
Python Code:
path = 'Sessions_Page.json'
path2 = 'Goal1CompletionLocation_Goal1Completions.json'
with open(path, 'r') as f:
sessions_page = json.loads(f.read())
with open(path2, 'r') as f:
goals_page = json.loads(f.read())
type (sessions_page)
sessions_page.keys()
sessions_page['reports'][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: Смотрим, где именно в файле интересующие нас данные
Step2: Считываем нужные нам данные как датафреймы
Step3: Создаем в датафреймах отдельные с... |
10,577 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
DO NOT MODIFY THIS CELL
def fully_connected(prev_layer, num_units):
Create a fully connectd layer with the given layer... | <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:
Step3: Batch Normalization using tf.layers.batch_normalization<a id="example_1"></a>
Step6: We'll use the following function to create convolutional l... |
10,578 | <ASSISTANT_TASK:>
Python Code:
import requests
response = requests.get('https://api.spotify.com/v1/search?query=lil&type=artist&market=US&limit=50')
data = response.json()
data.keys()
artist_data = data['artists']
artist_data.keys()
lil_names = artist_data['items']
#lil_names = list of dictionaries = list of artist na... | <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: & for multiple parameters
Step2: 2) What genres are most represented in the search results?
Step3: ANSWER
Step4: 3) Use a for loop to determi... |
10,579 | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame('kc_house_data.gl/kc_house_data.gl')
train_data,test_data = sales.random_split(.8,seed=0)
example_features = ['sqft_living', 'bedrooms', 'bathrooms']
example_model = graphlab.linear_regression.create(train_data, target = 'price', features = examp... | <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 in house sales data
Step2: Split data into training and testing.
Step3: Learning a multiple regression model
Step4: Now that we have fit... |
10,580 | <ASSISTANT_TASK:>
Python Code:
import os
class Params:
pass
# Set to run on GCP
Params.GCP_PROJECT_ID = 'ksalama-gcp-playground'
Params.REGION = 'europe-west1'
Params.BUCKET = 'ksalama-gcs-cloudml'
Params.PLATFORM = 'local' # local | GCP
Params.DATA_DIR = 'data/news' if Params.PLATFORM == 'local' else 'gs://{}/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: Create a ML Data Files using Dataflow
Step2: 2. Beam Pipeline
Step3: 5. Run Pipeline
Step4: TF Text Classification Model with TF Hub for Text... |
10,581 | <ASSISTANT_TASK:>
Python Code:
from IPython.core.display import Image
Image("http://upload.wikimedia.org/wikipedia/commons/thumb/2/28/IEC60825_MPE_W_s.png/640px-IEC60825_MPE_W_s.png")
####
# Parámetros a modificar. INICIO
####
web_laser = 'http://www.punterolaser.com' # Incluir la dirección de la página web
web_anchur... | <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: Tarea 1 (a). Irradiancia máxima
Step2: Tarea 3. Elección del filtro interferencial
Step3: Tarea 3 (b). Verificación del filtro
|
10,582 | <ASSISTANT_TASK:>
Python Code:
%cd C:/temp/
import pandas as pd
train = pd.read_csv("labeledTrainData.tsv", header=0, delimiter="\t", quoting=3)
print(train.columns.values)
print(train.shape)
print train["review"][0]
from bs4 import BeautifulSoup
example1 = BeautifulSoup(train["review"][0])
print(example1.g... | <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: "header=0" indicates that the first line of the file contains column names, "delimiter=\t" indicates that the fields are separated by tabs, and ... |
10,583 | <ASSISTANT_TASK:>
Python Code:
# imports
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# read data into a DataFrame
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.head()
# print the size of the DataFrame object, i.e., the size of the dataset
data.sha... | <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 features?
Step2: There are 200 observations, corresponding to 200 markets.
Step3: Questions
Step4: Interpreting Model Coefficients
Step5:... |
10,584 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... | <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 and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
10,585 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
fs = 8000.0
f0 = 220.0 # Hz
duration = 0.05 # 1.0
t = np.linspace(0, duration, num=fs*duration)
N_overtones = 7
harmonics = np.arange(1, N_overtones)
f... | <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: You can listen to them here
Step2: One GrFNN to rule them all
Step3: THE PREVIOUS CODE GENERATED THE FIGURES ADDED TO THE PAPER ON APRIL 28 NE... |
10,586 | <ASSISTANT_TASK:>
Python Code:
import os
from picamera import PiCamera
from picamera.color import Color
from time import sleep
camera = PiCamera()
# import a bunch of stuff that we'll use to manipulate our images...
import pandas as pd
from skimage.io import imread
from skimage import filters
from skimage.segmentation ... | <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: TADA ... wait, nothing happened.
Step2: How about some text on the image.
Step3: Once images are captured, let's try to get a 'color' from eac... |
10,587 | <ASSISTANT_TASK:>
Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inlin... | <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: From a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship
Step3: The very same sample of th... |
10,588 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import numpy
import sklearn
from sklearn.preprocessing import Imputer
import matplotlib.pyplot as plt
mean = [50, 60]
cov = [[5,5],[100,200]]
x1,y1 = numpy.random.multivariate_normal(mean,cov,1000).T
mean = [65, 70]
cov = [[20,10],[2,10]]
x2,y2 = numpy.random.multivariate_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: Simulating 2D data for anomaly detection
Step3: Outlier detection algorithm
Step 1
Step4: Now lets plot the 'real' and 'synthetic' data to exa... |
10,589 | <ASSISTANT_TASK:>
Python Code:
coin_code =
data {
int<lower=0> n; // number of tosses
int<lower=0> y; // number of heads
}
transformed data {}
parameters {
real<lower=0, upper=1> p;
}
transformed parameters {}
model {
p ~ beta(2, 2);
y ~ binomial(n, p);
}
generated quantities {}
coin_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: PyStan
Step2: Fit model
Step3: MAP
Step4: MCMC
Step5: Loading from a file
Step6: We can convert to a DataFrame if necessary
Step8: Estimat... |
10,590 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
from sklearn import datasets
diabetes = datasets.load_diabetes()
# Description at http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html
X = diabetes.data
y = diabetes.target
print(X.shape, y.shape)
from sklearn impor... | <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: All the different models in scikit-learn follow a consistent structure.
Step2: Although this single number might seem unimpressive, metrics ar... |
10,591 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.stats
a = np.random.randn(40)
b = 4*np.random.randn(50)
_, p_value = scipy.stats.ttest_ind(a, b, equal_var = False)
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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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<USER_TASK:>
Description:
|
10,592 | <ASSISTANT_TASK:>
Python Code:
%ls lidar
import tables
lineData = tables.openFile(r"lidar/20150927-0000-01.FRFNProp.line.data.mat","r")
#science = tables.openFile(r"lidar/20150927-0000-01.FRFNProp.line.science.mat","r")
for f in lineData.root:
for g in f:
print g
z = lineData.root.lineGriddedFilteredData.... | <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: Recent matlab files are just hdf5, which we can get from pytables
Step2: Grab the filtered water levels on the grid in FRF coordinates
Step3: ... |
10,593 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from bigbang.archive import Archive
from bigbang.archive import load as load_archive
import bigbang.parse as parse
import bigbang.analysis.graph as graph
import bigbang.ingress.mailman as mailman
import bigbang.analysis.process as process
import networkx as nx
import ma... | <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 a valid date frame for building the network.
Step2: Filter data according to date frame and export to .gexf file
|
10,594 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import os, glob
from osgeo import gdal
files_to_mosaic = glob.glob('/Users/olearyd/Git/data/TEAK_Aspect_Tiles/*_aspect.tif')
files_to_mosaic
files_string = " ".join(files_to_mosaic)
print(files_string)
command = "gdal_merge.py -o /User... | <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: Make a list of files to mosaic using glob.glob, and print the result. In this example, we are selecting all files ending with _aspect.tif in the... |
10,595 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
# import all shogun classes
from modshogun import *
kernel = CombinedKernel()
num=30;
num_components=4
means=zeros((num_components, 2))
means[0]=[-1,1]
means[1]=[2,-1.5]
means[2]=[-1,-3]
means[3]=[2,1]
covs=array([[1.0,0.0],[0.0,1.0]])
gmm=GMM(num_compon... | <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: Introduction
Step2: Prediction on toy data
Step3: Generating Kernel weights
Step4: Binary classification using MKL
Step5: To justify the wei... |
10,596 | <ASSISTANT_TASK:>
Python Code:
from sympy import *
from geom_util import *
from sympy.vector import CoordSys3D
N = CoordSys3D('N')
alpha1, alpha2, alpha3 = symbols("alpha_1 alpha_2 alpha_3", real = True, positive=True)
init_printing()
%matplotlib inline
%reload_ext autoreload
%autoreload 2
%aimport geom_util
A,K = sym... | <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: Lame params
Step2: Metric tensor
Step3: ${\displaystyle \hat{G}=\sum_{i,j} g_{ij}\vec{R}^i\vec{R}^j}$
Step4: Christoffel symbols
Step5: Grad... |
10,597 | <ASSISTANT_TASK:>
Python Code::
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
<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:
|
10,598 | <ASSISTANT_TASK:>
Python Code:
tt = (1, 2, (30, 40))
hash(tt)
t1 = (1, 2, [30, 40]) # 其中列表是可变的，所以没有哈希值
hash(t1)
tf = (1, 2, frozenset([30, 40])) #frozenset 是冻结的集合，不可变的，所以有哈希值
hash(tf)
a = dict(one = 1, two = 2, three = 3)
b = {'one': 1, 'two': 2, 'three': 3}
c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
d = dict([... | <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: 构建字典方法
Step2: 除了常规语法以及 dict 构建之外，我们可以使用字典推导来构建字典，dictcomp 会由任何一个可迭代对象产生一对 key
Step3: 字典有一个内置方法 d.update(m, [**kargs]) 它会先判断 m，如果 m 有 keys 方法， ... |
10,599 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
data_df = pd.read_csv('raw-data.csv', index_col='eventID')
data_df.head()
import matplotlib.pylab as plt
import seaborn as sns
# Show plots in notebook
%matplotlib inline
# Set some styling options
sns.set_style("darkgrid")
sns.set_context("paper", font_scale=1.4)
fea... | <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. Inspect the Distribution for a Feature
Step2: Extract the values of the KDE curve for fitting purposes
Step3: Let's plot it to make sure it... |
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