Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
6,300 | <ASSISTANT_TASK:>
Python Code:
# importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 30}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(30, 15) )
# define (unknown) group s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Parameters
Step2: Observe and Estimate Using Max-Estimator
Step3: Show Results for Additional Estimators
|
6,301 | <ASSISTANT_TASK:>
Python Code:
from bigbang.archive import Archive
from bigbang.archive import load as load_archive
import bigbang.parse as parse
import bigbang.graph as graph
import bigbang.mailman as mailman
import bigbang.process as process
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
fr... | <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 shall compute the word counts on the lists.
Step2: Let's print some useful descriptive data
Step3: We want to compute the list of c... |
6,302 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import stats
import statsmodels.api as sm
from statsmodels.base.model import GenericLikelihoodModel
data = sm.datasets.spector.load_pandas()
exog = data.exog
endog = data.endog
print(sm.datasets.spector.NOTE)
print(data.exog.head())
exog = sm.add_constant(e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The Spector dataset is distributed with statsmodels. You can access a vector of values for the dependent variable (endog) and a matrix of regres... |
6,303 | <ASSISTANT_TASK:>
Python Code:
import json
import numpy as np
import h5py
import seaborn as sns
from scipy.interpolate import splev,splrep
import matplotlib.pyplot as plt
import astropy.units as u
from sunpy.instr import aia
import ChiantiPy.core as ch
import ChiantiPy.tools.data as ch_data
%matplotlib inline
response... | <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: Wavelength Response
Step2: Temperature Response
Step3: The main question is
Step4: Boerner et al. (2012) use the coronal abundances of Feldma... |
6,304 | <ASSISTANT_TASK:>
Python Code:
import trappy
import numpy
config = {}
# TRAPpy Events
config["THERMAL"] = trappy.thermal.Thermal
config["OUT"] = trappy.cpu_power.CpuOutPower
config["IN"] = trappy.cpu_power.CpuInPower
config["PID"] = trappy.pid_controller.PIDController
config["GOVERNOR"] = trappy.thermal.ThermalGovernor... | <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: Get the Trace
Step2: FTrace Object
Step3: Assertions
Step4: Assertion
Step5: Assertion
Step6: Statistics
Step7: Check if the mean temperau... |
6,305 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, read_... | <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 data
Step2: Plot the raw PSD
Step3: Plot a cleaned PSD
Step4: Alternative functions for PSDs
|
6,306 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import pystan
import warnings
warnings.filterwarnings("ignore")
schools_code =
data {
int<lower=0> J; // number of schools
real y[J]; // estimated treatment effects
real<lower=0> sigma[J]; // s.e. of effect estimates
}
... | <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: Eight schools example
Step4: Optimization in Stan
|
6,307 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
from IPython.display import HTML
from ipywidgets import interact
HTML('../style/code_toggle.html')
def loop_DFT(x):
Implementin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import section specific modules
Step3: 2.8. The Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT)<a id='math
Step5: Althou... |
6,308 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import mapp4py
from mapp4py import md
from lib.elasticity import rot, cubic, resize, displace, HirthEdge, HirthScrew
from mapp4py import mpi
if mpi().rank!=0:
with open(os.devnull, 'w') as f:
sys.stdout = f;
xprt = md.export... | <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: Block the output of all cores except for one
Step2: Define an md.export_cfg object
Step3: Screw dislocation
Step4: Create a $\langle110\rangl... |
6,309 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
num = np.array([3,4,2,5,7,23,56,23,7,23,89,43,676,43])
num
print "Mean :",num.mean()
print "sum :",num.sum()
print "max :",num.max()
print "std :",num.std()
#slicing
num[:5]
#find index of any element let say max
print "index of max :",num.argmax()
print "data Type of... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Array are similar to python list , but it all element must be of same data type, and it faster than list
Step2: Lets see some of functionality
... |
6,310 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'DateTime': ['2000-01-04', '2000-01-05', '2000-01-06', '2000-01-07'],
'Close': [1460, 1470, 1480, 1450]})
def g(df):
df['label'] = df.Close.diff().fillna(1).gt(0).astype(int)
return df
df = g(df.copy())
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
6,311 | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
with open('anna.txt', 'r') as f:
text=f.read()
vocab = sorted(set(text))
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
encoded = np.array([vocab_to_int... | <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'll load the text file and convert it into integers for our network to use. Here I'm creating a couple dictionaries to convert the chara... |
6,312 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np # modulo de computo numerico
import matplotlib.pyplot as plt # modulo de graficas
# esta linea hace que las graficas salgan en el notebook
import seaborn as sns
%matplotlib inline
df=pd.read_csv('files/ejemplo.csv')
print('df.shape)
df.head()
sns.p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Un mini-ejemplo
Step2: Visualizando
|
6,313 | <ASSISTANT_TASK:>
Python Code:
#!/usr/bin/python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from stats import parse_results, get_percentage, get_avg_per_seed, draw_pie, draw_bars, draw_bars_comparison, draw_avgs
pr, eigen, bet = parse_results('test_genws.txt')
draw_pie(get_percentage(pr))
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Parse results
Step2: PageRank Seeds Percentage
Step3: Avg adopters per seed comparison
Step4: Eigenvector Seeds Percentage
Step5: Avg adopte... |
6,314 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-2', 'toplevel')
# 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: 2... |
6,315 | <ASSISTANT_TASK:>
Python Code:
# With Hashmap.
# Time Complexity: O(n)
def if_unique(string):
chr_dict = {}
for char in string:
if char not in chr_dict:
chr_dict[char] = 1
else:
return False
return True
# Without additional memory.
# Time Complexity: O(n^2)
def i... | <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: P2. Check Permutation
Step2: P3. URLify
Step3: P4. Palindrome Permutation
Step4: P5. One Away
Step5: P6. String Compression
Step6: P7. Rota... |
6,316 | <ASSISTANT_TASK:>
Python Code:
!wget http://archive.ics.uci.edu/ml/machine-learning-databases/00299/StoneFlakes.dat
!head StoneFlakes.dat
import pandas
d = pandas.read_csv(open('StoneFlakes.dat'))
d[:5]
d = pandas.read_csv(open('StoneFlakes.dat'),sep=',')
d[:5]
! tr -s ' ' ',' < StoneFlakes.dat > StoneFlakes2.dat
! ... | <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 first few lines.
Step2: Read about the column names and the meaning of the ID values at the data set's web site.
Step3: Le... |
6,317 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
def number_to_words(n):
Given a number n between 1-1000 inclusive return a list of words for the number.
# YOUR CODE HERE
#raise NotImplementedError()
ones=['one','two','three','four','five','six','seven','eight','nine','ten']
teens=['eleven','twelve... | <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: Project Euler
Step2: Now write a set of assert tests for your number_to_words function that verifies that it is working as expected.
Step4: No... |
6,318 | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (6, 6, 3, 4)
L = GlobalAveragePooling3D(data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(270)
data_in = 2 * np.random.... | <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: [pooling.GlobalAveragePooling3D.1] input 3x6x6x3, data_format='channels_first'
Step2: [pooling.GlobalAveragePooling3D.2] input 5x3x2x1, data_fo... |
6,319 | <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: Optimization Analysis
Step2: Load Data
Step3: Plot
Step4: Hardware Grid
Step5: SK Model
Step6: 3 Regular MaxCut
|
6,320 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
from tensorflow.examples.tutorials.mnist import input_data
%matplotlib inline
epochs = 1000
learning_rate = 0.5
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print (mnist.train... | <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 Data and Labels
Step2: Data Visualization
Step3: "one-hot" format to present labels
|
6,321 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import nsfg
import first
import thinkstats2
import thinkplot
live, firsts, others = first.MakeFrames()
first_wgt = firsts.totalwgt_lb
first_wgt_dropna = first_wgt.dropna()
print('Firsts', len(first_wgt... | <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: Examples
Step2: And compute the distribution of birth weight for first babies and others.
Step3: We can plot the PMFs on the same scale, but i... |
6,322 | <ASSISTANT_TASK:>
Python Code:
import time
from kafka import KafkaProducer
import json
import random
import csv
import uuid
import datetime
Usage: bin/spark-submit ~/spark/kafkaProducrerTest.py
producer = KafkaProducer(bootstrap_servers='localhost:9092')
pitch = 0
position = 0
def getRandomPitch(min,max):
pitch =... | <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: Kafka Producer Test Script
Step2: The "getRandomPitch" method will generate a mock "reading" between a min and max range. The position can be ... |
6,323 | <ASSISTANT_TASK:>
Python Code:
from pandas import DataFrame, Series
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
weather = pd.read_table('daily_weather.tsv')
usage = pd.read_table('usage_2012.tsv')
stations = pd.read_table('stations.tsv')
newseasons = {'Summer': 'Spring', '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: Question 1
Step2: Question 2
Step3: Question 3
Step4: Question 4
|
6,324 | <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: Integrated gradients
Step2: Download a pretrained image classifier from TF-Hub
Step3: From the module page, you need to keep in mind the follo... |
6,325 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-2', '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... |
6,326 | <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 *
def make_... | <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:
Step7: Code from previous chapters
Step8: Contact number
Step9: The following loop shows how we can loop through the columns and rows of the SweepFra... |
6,327 | <ASSISTANT_TASK:>
Python Code:
from jupyterthemes import get_themes
from jupyterthemes.stylefx import set_nb_theme
themes = get_themes()
set_nb_theme(themes[3])
# 1. magic for inline plot
# 2. magic to print version
# 3. magic so that the notebook will reload external python modules
# 4. magic to enable retina (high re... | <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: Rossman Deep Learning Modeling
Step2: Here, we will remove all records where the store had zero sale / was closed (feel free to experiment with... |
6,328 | <ASSISTANT_TASK:>
Python Code:
def cube_positif( x ):
if abs( x*x*x >= 0.0):
return x*x*x
print("Erreur")
return
cube_positif(-4)
%matplotlib inline
import matplotlib.pyplot as plt
#On commence sans fonction,
XX=[]
YY=[]
X=range(10,-10,-1)
for x in X:
XX.append(x)
YY.append(x*x*x)
plt... | <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: Maintenant on peux se servir de
Step2: 2) Correction de fonctions en vrac (les fonction suivantes doivent être corrigées)
Step3: 2.2) Factori... |
6,329 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
IMG_SHAPE = (28, 28, 1)
BATCH_SIZE = 512
# Size of the noise vector
noise_dim = 128
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_la... | <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: Prepare the Fashion-MNIST data
Step2: Create the discriminator (the critic in the original WGAN)
Step3: Create the generator
Step5: Create th... |
6,330 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-3', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor... | <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... |
6,331 | <ASSISTANT_TASK:>
Python Code:
from scipy import stats
import h5py
! cat configurations.json
! cat architecture.json
train = h5py.File('../data/hdf5datasets/NSMSDSRSCSTSRI_500bp/train.h5', 'r')
train.items()
validation = h5py.File('../data/hdf5datasets/NSMSDSRSCSTSRI_500bp/validation.h5', 'r')
validation.items()
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: INTRO
Step2: architecture.json
Step3: RELEVANT HDF5 FILES
Step4: validation = h5py.File('../data/hdf5datasets/validation.h5', 'r')
Step5: te... |
6,332 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime as dt
import scipy.spatial.distance as dist
%matplotlib inline
import glob
allFiles = glob.glob('4months' + "/*.csv")
frame = pd.DataFrame()
list_ = []
for file_ in allFiles:
df = pd.read_csv(file_... | <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: Section 2
Step2: Section 3
Step3: Display variable power
Step4: Use parser function from dateutil module to convert the timestamps in power f... |
6,333 | <ASSISTANT_TASK:>
Python Code:
1
-5
print 2 + 10 # addition
print 5 - 3 # subtraction
print 6 * 4 # multiplication
print 10 / 5 # division
print 2**4 # exponents
2 / 3
2 / 3.0
1.5
type(1.5)
0.1 + 0.2
from decimal import Decimal
Decimal('0.1') + Decimal('0.2')
'Hello python learners'
print 'Hello'
print "there"
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: There is something I should mention here. python 2 can trip people up when trying to do something like the following
Step2: This is because pyt... |
6,334 | <ASSISTANT_TASK:>
Python Code:
fig, axes = plt.subplots(1, 2, figsize=(10,4))
axes[0].plot(x, x**2, x, np.exp(x))
axes[0].set_title("Normal scale")
axes[1].plot(x, x**2, x, np.exp(x))
axes[1].set_yscale("log")
axes[1].set_title("Logarithmic scale (y)");
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(x, x**2, 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:
Step1: Placement of ticks and custom tick labels
Step2: There are a number of more advanced methods for controlling major and minor tick placement in ... |
6,335 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-3', '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... |
6,336 | <ASSISTANT_TASK:>
Python Code:
import nbinteract as nbi
nbi.multiple_choice(question="What is 10 + 2 * 5?",
choices=['12', '60', '20'],
answers=2)
nbi.multiple_choice(question="Select all prime numbers.",
choices=['12', '3', '31'],
answers... | <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: nbinteract.multiple_choice
Step2: nbinteract.short_answer
|
6,337 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
from sklearn import datasets
import numpy 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: Sections
Step2: Splitting data into 70% training and 30% test data
Step3: Standardizing the features
Step4: <br>
Step5: Training a perceptro... |
6,338 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
with tf.Session():
input1 = tf.constant([1.0, 1.0, 1.0, 1.0])
input2 = tf.constant([2.0, 2.0, 2.0, 2.0])
output = tf.add(input1, input2)
result = output.eval()
print result
print [x + y for x, y in zip([1.0] * 4, [2.0] * 4)]
import numpy as np
x, y = np.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: What we're doing is creating two vectors, [1.0, 1.0, 1.0, 1.0] and [2.0, 2.0, 2.0, 2.0], and then adding them. Here's equivalent code in raw Pyt... |
6,339 | <ASSISTANT_TASK:>
Python Code:
# define model parameters
ces_params = {'A0': 1.0, 'L0': 1.0, 'g': 0.02, 'n': 0.03, 's': 0.15,
'delta': 0.05, 'alpha': 0.33, 'sigma': 0.95}
# create an instance of the solow.Model class
ces_model = solowpy.CESModel(params=ces_params)
# check the docstring...
ces_model.stead... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 2.1 Analytic results
Step2: 2.2 Numerical methods
Step3: Example usage
Step4: We can display the value and confirm that the algorithm did ind... |
6,340 | <ASSISTANT_TASK:>
Python Code:
if not os.path.isfile('data/hg19.ml.fa'):
subprocess.call('curl -o data/hg19.ml.fa https://storage.googleapis.com/basenji_tutorial_data/hg19.ml.fa', shell=True)
subprocess.call('curl -o data/hg19.ml.fa.fai https://storage.googleapis.com/basenji_tutorial_data/hg19.ml.fa.fai', shell... | <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: Compute scores
Step2: Plot
Step3: The resulting plots reveal a low level of activity, with a GC-rich motif driving the only signal.
|
6,341 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
df = pd.read_csv('ex1data2.txt', header=None)
print(df.head())
#Lets try to visualize the data
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(df[0], df[1], df[2])
ax.set_zlabel('price')
plt.xlabel(... | <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: Now we will start with normalization of the features because size of the house is in different range as compared to number of bedrooms
Step2: D... |
6,342 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import shl_pm
## which month to predictsimulate?
# shl_sm_parm_ccyy_mm = '2017-04'
# shl_sm_parm_ccyy_mm_offset = 1647
# shl_sm_parm_ccyy_mm = '2017-05'
# shl_sm_parm_ccyy_mm_offset = 1708
shl_sm_parm_ccyy_mm = '2017-06'
shl_sm_parm_ccyy_mm_offset = 1769
# shl_sm_par... | <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: Import SHL Prediction Module
Step2: shl_sm parameters
Step3: shl_pm Initialization
Step4: MISC - Validation
|
6,343 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter, AutoMinorLocator)
import pandas as pd
os.chdir('..')
os.getcwd()
sys.path.append('../scripts/')
import bicorr_plot as bicorr_plo... | <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: Plot $E_n$ vs $\theta$
Step2: Divide by experimental
|
6,344 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
from __future__ import print_function
import numpy as np
import mesh.boundary as bnd
import mesh.patch as patch
import multigrid.MG as MG
nx = ny = 256
mg = MG.CellCenterMG2d(nx, ny,
xl_BC_type="dirichlet", xr_BC_... | <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: Constant-coefficent Poisson equation
Step2: Next, we initialize the RHS. To make life easier, the CellCenterMG2d object has the coordinates of... |
6,345 | <ASSISTANT_TASK:>
Python Code:
# Mendefinisikan isi list bisa dilakukan dengan banyak cara.
# Salah satunya adalah mendeklarasikan isinya dengan meletakkannya
# di antara dua tanda kurung siku atau brackets.
kotakota = ["Bandung", "Jakarta", "Surabaya"]
arahangin = ["Utara", "Barat", "Timur", "Selatan"]
print(kotako... | <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: Untuk menambahkan, kita bisa menggunakan fungsi append pada list tersebut. Untuk membuang, kita bisa menggunakan fungsi del pada koordinat yang ... |
6,346 | <ASSISTANT_TASK:>
Python Code:
# Create folders
!mkdir -p '/android/sdk'
# Download and move android SDK tools to specific folders
!wget -q 'https://dl.google.com/android/repository/tools_r25.2.5-linux.zip'
!unzip 'tools_r25.2.5-linux.zip'
!mv '/content/tools' '/android/sdk'
# Copy paste the folder
!cp -r /android/sdk/... | <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: Install BAZEL with Baselisk
Step2: Build .aar files
|
6,347 | <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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Description:
Step1: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
6,348 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
with open('data/reviews.txt','r') as file_handler:
reviews = np.array(list(map(lambda x:x[:-1], file_handler.readlines())))
with open('data/labels.txt','r') as file_handler:
labels = np.array(list(map(lambda x: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: The dataset is perfectly balanced across the two categories POSITIVE and NEGATIVE.
Step2: Well, at a first glance, that seems dissapointing. A... |
6,349 | <ASSISTANT_TASK:>
Python Code:
# the output of plotting commands is displayed inline within frontends,
# directly below the code cell that produced it
%matplotlib inline
# this python library provides generic shallow (copy) and deep copy (deepcopy) operations
from copy import deepcopy
import time
# import from Ocelot... | <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: Layout of the corrugated structure insertion. Create Ocelot lattice <img src="4_layout.png" />
Step2: Load beam file
Step3: Initialization of ... |
6,350 | <ASSISTANT_TASK:>
Python Code:
#!/usr/bin/env python
# A code line that shows the result of 7 times 3
print 7 * 3
# A line broken by backslash
a = 7 * 3 + \
5 / 2
# A list (broken by comma)
b = ['a', 'b', 'c',
'd', 'e']
# A function call (broken by comma)
c = range(1,
11)
# Prints everything
print a, b, c
# For i on... | <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: Examples of broken lines
Step2: The command print inserts spaces between expressions that are received as a parameter, and a newline character ... |
6,351 | <ASSISTANT_TASK:>
Python Code:
import pylearn2.utils
import pylearn2.config
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import os.path
model = pylearn2.utils.serial.load(os.path.expandvars('${DATA_DIR}/plankton/models/learning_rate_experiment/ilr_5e-2_lin_decay_adj_on_recent.pkl'))
print(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: Model trained on on 0.1 split of data
Step2: Plot train and valid set NLL
Step3: Strangely though overfitting to training set does not seem to... |
6,352 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
!cd toy_datasets; wget -O MiniBooNE_PID.txt -nc MiniBooNE_PID.txt https://archive.ics.uci.edu/ml/machine-learning-databases/00199/MiniBooNE_PID.txt
import numpy, pandas
from rep.utils import train_test_split
from sklearn.metrics import roc_auc_score
data = pandas.read_csv('... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Loading data
Step2: Training variables
Step3: Folding strategy - stacking algorithm
Step4: Define folding model
Step5: Default prediction (p... |
6,353 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from IPython.core.pylabtools import figsize
import matplotlib.pyplot as plt
figsize( 12.5, 5 )
sample_size = 100000
expected_value = lambda_ = 4.5
poi = np.random.poisson
N_samples = range(1,sample_size,100)
for k in range(3):
samples = poi( lambd... | <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: Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how jagged and jum... |
6,354 | <ASSISTANT_TASK:>
Python Code:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', typ... | <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: Read in an Image
Step10: Ideas for Lane Detection Pipeline
Step11: Test Images
Step12: Build a Lane Finding Pipeline
Step13: Test on Videos
... |
6,355 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('scm-hello.png')
imgplot = plt.imshow(img)
plt.axis('off')
plt.show()
# Initialize logging.
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sente... | <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: This method was perhaps first introduced in the article “Soft Measure and
Step2: The first two sentences sentences have very similar content, a... |
6,356 | <ASSISTANT_TASK:>
Python Code:
import json
import pandas as pd
import re
import string
from scipy import sparse
import numpy as np
from pymongo import MongoClient
from nltk.corpus import stopwords
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.decomposition import LatentDirichle... | <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: Import data files and word dictionaries
Step2: Pick a subset of users that have at least 200 reviews, run an iterative test on these users
Step... |
6,357 | <ASSISTANT_TASK:>
Python Code:
import larch, numpy, pandas, os, geopandas
larch.__version__
import larch.exampville
larch.exampville.files.shapefile
taz_shape = geopandas.read_file("zip://"+larch.exampville.files.shapefile)
taz_shape.plot(edgecolor='k');
larch.exampville.files.employment
emp = pandas.read_csv(larch... | <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: Welcome to Exampville, the best simulated town in this here part of the internet!
Step2: TAZ Shapefile
Step3: Geopandas can open and read this... |
6,358 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
print(df['gender'].value_counts())
df.groupby('gender')['networthusbillion'].mean()
df.groupby('gender')['sourceofwealth'].value_counts()
df.plot(kind='scatter', x='gender', y='networthusbillion')
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Let's make a graph 'bout it
|
6,359 | <ASSISTANT_TASK:>
Python Code:
file_listcal = "alma_sourcecat_searchresults_20180419.csv"
q = databaseQuery()
listcal = q.read_calibratorlist(file_listcal, fluxrange=[0.1, 999999])
len(listcal)
print("Name: ", listcal[0][0])
print("J2000 RA, dec: ", listcal[0][1], listcal[0][2])
print("Alias: ", listcal[0][3])
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: Example, retrieve all the calibrator with a flux > 0.1 Jy
Step2: Select all calibrators that heve been observed at least in 3 Bands [ >60s in B... |
6,360 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd # pandas is a dataframe library
import matplotlib.pyplot as plt # matplotlib.pyplot plots data
%matplotlib inline
df = pd.read_csv("./data/pima-data.csv")
df.shape
df.head(5)
df.tail(5)
df.isnull().values.any()
def plot_corr(df, size=11):
... | <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: Loading and Reviewing the Data
Step2: Definition of features
Step4: Correlated Feature Check
Step5: The skin and thickness columns are correl... |
6,361 | <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
from six.moves import range
pickle_file = '../notMNIST.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: Reformat into a TensorFlow-friendly shape
Step2: Let's build a small network with two convolutional layers, followed by one fully connected lay... |
6,362 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
p = 12
n_m = 600 # [r/min]
n_pulses = 3*p*n_m
print('''
n_pulses = {:.0f} pulses/min = {:.0f} pulses/sec
============================================'''.format(n_pulses, n_pulses/60))
<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: Description
Step2: SOLUTION
|
6,363 | <ASSISTANT_TASK:>
Python Code:
from scipy import sparse
c1 = sparse.csr_matrix([[0, 0, 1, 0], [2, 0, 0, 0], [0, 0, 0, 0]])
c2 = sparse.csr_matrix([[0, 3, 4, 0], [0, 0, 0, 5], [6, 7, 0, 8]])
Feature = sparse.hstack((c1, c2)).tocsr()
<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:
|
6,364 | <ASSISTANT_TASK:>
Python Code:
# ライブラリのインポート
from itertools import product
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import datasets
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm 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: scikit-learn準拠の識別器を作る
Step2: 精度の評価
Step3: 世の中の機械学習モデルをいくつか試す
Step5: Confusion Matrix
Step6: グリッドサーチで最適なパラーメタを探す
|
6,365 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'uhh', 'sandbox-2', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "email... | <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... |
6,366 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#import seaborn as sns
#sns.set()
N = 100 #points to generate
X = np.sort(10*np.random.rand(N, 1)**0.8 , axis=0) #abscisses
y = 4 + 0.4*np.random.rand(N) - 1. / (X.ravel() + 0.5)**2 - 1. / (10.5 - X.ravel() ) # some... | <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: Linear regression will obviously be a bad fit.
Step2: Let us transform it into a 3-degree polynomial fit and perform the same linear regression... |
6,367 | <ASSISTANT_TASK:>
Python Code:
import gmaps, os # Used for interactive visualizations
from game_types import NPlayerGame
import tensorflow as tf
import pandas as pd
gmaps.configure(api_key=os.environ["GOOGLE_API_KEY"])
locs = [
[(37.760851, -122.443118), (37.760853, -122.443120)], # Silcon Valley
[(40.092034... | <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 stuff for visualizations
Step2: Playing Peace War Game with 14 Players for 650,000 iterations
Step3: Grabbing scores of each agent... |
6,368 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
numerator = 98
denominator = 42
gcd = np.gcd(numerator, denominator)
result = (numerator//gcd, denominator//gcd)
<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:
|
6,369 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='... | <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: Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits.
Step2: We'll train an autoe... |
6,370 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'sandbox-1', 'ocean')
# 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... |
6,371 | <ASSISTANT_TASK:>
Python Code:
x = 3
x = 4.5
x = 3
x = 4.5
x = 3
y = 3.0
x is y
x == y
x = 'Hello'
x = 'Hello'
x.lower()
x
x = 'Hello'
x = x.lower()
x
for i in range(0, 10):
print(i)
i = 2
while i < 12:
print(i)
i += 3
for i in range(0, 10, 2):
print(i)
for i in range(0, 10):
if i % 2 == 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: Python accepts the previous because of dynamic typing (C would throw an error!)
Step2: The previous line is going to cause a PyIntObject to be... |
6,372 | <ASSISTANT_TASK:>
Python Code:
# Bibliotecas utilizadas para confeccionar el mapa
%matplotlib inline
import matplotlib.pyplot as plt
from descartes import PolygonPatch
import matplotlib.cm as cmx
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
from shapely import geometry
from shapely import 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: 1.- BASE DE DATOS DE CULTIVOS DE BAJÍO AMAZÓNICO
Step2: Obtenemos un consolidado por estrato para hacernos una idea
Step3: 2.- Linderos de las... |
6,373 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import tectosaur as tct
import tectosaur.qd
import tectosaur.qd.plotting
tct.qd.configure(
gpu_idx = 0, # Which GPU to use if there are multiple. Best to leave as 0.
fast_plot = True, # Let's make fast, inexpensive figures. Set 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: Now, we load the data from the previous run. Check what folder was created! If you ran the simulation code multiple times, each time a new folde... |
6,374 | <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: Quantization aware training in Keras example
Step2: Train a model for MNIST without quantization aware training
Step3: Clone and fine-tune pre... |
6,375 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def activation_sigmoid(x, derivative):
sigmoid_value = 1/(1+np.exp(-x))
if not derivative:
return sigmoid_value
else:
return sigmoid_value*(1-sigmoid_value)
x_values = np.arange(-5, 6, 0.1)... | <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: Sigmoid
Step2: When plotted on a range of -5,5, this gives the following shape.
Step3: Tanh
Step4: ReLU
Step5: It is probably worth noting, ... |
6,376 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
from skimage.transform import resize
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
import tools as im
from matplotlib import pyplot as plt
%matplotlib inline
path=os.getcwd()+'/' # finds the path of the folder in which the noteboo... | <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 define the function prep_datas (props to Alexandre), already used the previous week. However now we reshape the images from a 32x32 matrix (t... |
6,377 | <ASSISTANT_TASK:>
Python Code:
import os
import re
import operator
import matplotlib.pyplot as plt
import warnings
import gensim
import numpy as np
warnings.filterwarnings('ignore') # Let's not pay heed to them right now
from gensim.models import CoherenceModel, LdaModel, LsiModel, HdpModel
from gensim.models.wrappers... | <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: Analysing our corpus.
Step4: Preprocessing our data. Remember
Step5: Finalising our dictionary and corpus
Step6: Topic modeling with LSI
Step... |
6,378 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import importlib
import utils2; importlib.reload(utils2)
from utils2 import *
np.set_printoptions(4)
PATH = 'data/spellbee/'
limit_mem()
from sklearn.model_selection import train_test_split
lines = [l.strip().split(" ") for l in open(PATH+"cmudict-0.7b", encoding='lat... | <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 CMU pronouncing dictionary consists of sounds/words and their corresponding phonetic description (American pronunciation).
Step2: Next we'r... |
6,379 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import sys, time, math
import numpy as np
from numpy import linalg as nplin
from dcpyps.samples import samples
from dcpyps import dataset, mechanism, dcplots, dcio
# LOAD DATA: Burzomato 2004 example set.
scnfiles = [["... | <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 data
Step2: Initialise Single-Channel Record from dcpyps. Note that SCRecord takes a list of file names; several SCN files from the same p... |
6,380 | <ASSISTANT_TASK:>
Python Code:
# boilerplate code
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
from __future__ import print_function
import tensorflow as tf
#!wget https://storage.googleapis.com/downloa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <a id='loading'></a>
Step6: To take a glimpse into the kinds of patterns that the network learned to recognize, we will try to generate images ... |
6,381 | <ASSISTANT_TASK:>
Python Code:
A = np.mat([
[4, 5, 4, 1, 1],
[5, 3, 5, 0, 0],
[0, 1, 0, 1, 1],
[0, 0, 0, 0, 1],
[1, 0, 0, 4, 5],
[0, 1, 0, 5, 4],
])
U, S, V = np.linalg.svd(A)
U.shape, S.shape, V.shape
U
S
np.diag(S)
V
def reconstruct(U, S, V, rank):
return U[:,0:rank] * np.diag(S[:rank]... | <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: Left singular vectors
Step2: Singular values
Step3: As you can see, the singular values are sorted descendingly.
Step4: Reconstructing the or... |
6,382 | <ASSISTANT_TASK:>
Python Code:
# install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/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: Modeling tree growth
Step2: And here's the series of heights for a site with index 45, indicating that height at 30 years is 45 feet.
Step3: H... |
6,383 | <ASSISTANT_TASK:>
Python Code:
import stix2
stix2.Indicator()
from stix2 import Indicator
Indicator()
import stix2.v20
stix2.v20.Indicator()
from stix2.v20 import Indicator
Indicator()
import stix2.v20 as stix2
stix2.Indicator()
import stix2
stix2.v20.Indicator()
stix2.v21.Indicator()
from stix2 import v20, v21
v... | <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: or,
Step2: People who want to use an explicit version
Step3: or,
Step4: or even, (less preferred)
Step5: The last option makes it easy to up... |
6,384 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('notebook.mplstyle')
%matplotlib inline
from scipy.stats import mode
a = np.random.multivariate_normal([1., 0.5],
[[4., 0.],
[0., 0.25]], size=512)
b = n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let's imagine we measure 2 quantities, $x_1$ and $x_2$ for some objects, and we know the classes that these objects belong to, e.g., "star", 0, ... |
6,385 | <ASSISTANT_TASK:>
Python Code:
import subprocess
Creates models for each fold and runs evaluation with results
featureset = "o"
entity_name = "adversereaction"
for fold in range(1,1): #training has already been done
training_data = "../ARFF_Files/%s_ARFF/_%s/_train/%s_train-%i.arff" % (entity_name, featureset, en... | <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: Experimental Results from a Decision Tree based NER model
Step3: Rather lackluster performance.
Step4: It appears adding in the morphological ... |
6,386 | <ASSISTANT_TASK:>
Python Code:
from wordcloud import WordCloud
from nltk.corpus import stopwords
from nltk.sentiment import *
import pandas as pd
import numpy as np
import nltk
import time
import matplotlib.pyplot as plt
import seaborn as sns
import pycountry
%matplotlib inline
# import data
directory = 'hillary-clinto... | <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: Comparison between extracted body text and raw text
Step2: By reading a few emails we can see that the extracted body text is just the text tha... |
6,387 | <ASSISTANT_TASK:>
Python Code:
print("Mike")
# insert your own code here!
x = 5
print(x)
x = 2
print(x)
print(x * x)
print(x + x)
print(x - 6)
seconds_in_seven_weeks = 70560
print(seconds_in_seven_weeks)
first_number = 5
second_number = first_number
first_number = 3
print(first_number)
print(second_number)
# not ... | <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: Can you describe what this code did?
Step2: Excellent! You have just written and executed your very first program! Please make sure to run ever... |
6,388 | <ASSISTANT_TASK:>
Python Code:
import sys
import logging
# Import the GEM-PRO class
from ssbio.pipeline.gempro import GEMPRO
# Printing multiple outputs per cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
# Create logger
logger = logging.getLogger()
logge... | <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: Logging
Step2: Initialization
Step3: Mapping gene ID --> sequence
Step4: Mapping representative sequence --> structure
Step5: Homology model... |
6,389 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
stats_file = '../test_data/ALL_N95_Mean_cope2_thresh_zstat1.nii.gz'
view = 'ortho'
colormap = 'RdBu_r'
threshold = '2.3'
black_bg
%run ../scripts/mni_glass_brain.py --cbar --display_mode $view --cmap $colormap --thr_abs $threshold $stats_file
from IPython.display im... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. Upload all statistical maps into the data folder
Step2: 3. Run the visualization script
Step3: 4. Look at your data
|
6,390 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from rdkit import Chem
from rdkit.Chem import Draw
%matplotlib inline
m = Chem.MolFromSmiles('Cc1ccccc1')
Chem.Kekulize(m)
Chem.MolToSmiles(m,kekuleSmiles=True)
fig = Dr... | <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: Look at Chemicals
Step2: Look at a grid of chemicals
|
6,391 | <ASSISTANT_TASK:>
Python Code:
# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Ezequiel Mikulan <e.mikulan@gmail.com>
#
# License: BSD (3-clause)
import os
import os.path as op
import shutil
import mne
data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
bem_dir = op... | <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: Exporting surfaces to Blender
Step2: Editing in Blender
Step3: Back in Python, you can read the fixed .obj files and save them as
|
6,392 | <ASSISTANT_TASK:>
Python Code:
# data = np.genfromtxt("data/ionosphere.data")
data = pd.read_csv('data/ionosphere.data', sep=",", header=None)
data.head()
data.describe()
df_tab = data
df_tab[34] = df_tab[34].astype('category')
tab = pd.crosstab(index=df_tab[34], columns="frequency")
tab.index.name = 'Class/Direction'... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: This is a very small dataset.
Step2: About 63% of all observations are good.
Step3: Set Global Parameters
Step5: Train Classifier
Step6: Wha... |
6,393 | <ASSISTANT_TASK:>
Python Code:
import os
PROJECT_ID = "dougkelly-sandbox" # TODO: your PROJECT_ID here.
os.environ["PROJECT_ID"] = PROJECT_ID
BUCKET_NAME = "xai-labs" # TODO: your BUCKET_NAME here.
REGION = "us-central1"
os.environ['BUCKET_NAME'] = BUCKET_NAME
os.environ['REGION'] = REGION
%%bash
exists=$(gsutil ls -... | <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: Run the following cell to create your Cloud Storage bucket if it does not already exist.
Step2: Timestamp
Step3: Import libraries
Step4: Down... |
6,394 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import importlib
import os
import sys
from elasticsearch import Elasticsearch
from skopt.plots import plot_objective
# project library
sys.path.insert(0, os.path.abspath('..'))
import qopt
importlib.reload(qopt)
from qopt.notebooks import evaluate_mrr100... | <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: Baseline evaluation
Step2: That's pretty impressive for the baseline query. It beats our baseline cross_fields query but not quite the optimize... |
6,395 | <ASSISTANT_TASK:>
Python Code:
# ensure that plots are shown inline
%matplotlib inline
import numpy as np # <- efficient vector/matrix operations (similar to MATLAB)
# the next ones are not required here, but might become useful later on, check if they're installed
import matplotlib as plt # <- basic plotting
import se... | <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: Session 2 Primer
Step3: Example call
|
6,396 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
import numpy as np
import pandas as pd
import random
import thinkstats2
import thinkplot
import nsfg
preg = nsfg.ReadFemPreg()
complete = preg.quer... | <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: Survival analysis
Step3: The survival function is just the complementary CDF.
Step4: Here's the CDF and SF.
Step5: And here's the hazard func... |
6,397 | <ASSISTANT_TASK:>
Python Code:
% matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.special import binom
from scipy.optimize import brentq
np.seterr(over='raise')
def StoneMod(Rtot, Kd, v, Kx, L0):
'''
Returns the number of mutlivalent ligand bound to a cell with Rtot
receptors,... | <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) We will fit the data contained within Fig. 3B. Plot this data and describe the relationship you see between Kx, Kd, and valency.
Step2: (2)... |
6,398 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
# Make flux time-series with random noise, and
# two periodic oscillations, one 70% the amplitude
# of the other:
np.random.seed(42)
n_points = 1000
primary_period = 2.5*np.pi
secondary_period = 1.3*np.pi
all_times = n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now we'll use two interpacf methods on these simulated fluxes
Step2: Comparing with McQuillan, Aigrain & Mazeh (2013)
Step3: Now measure the p... |
6,399 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import numpy as np
import LearnyMcLearnface as lml
affine = lml.layers.AffineLayer(30, 10, 1e-2)
test_input = np.random.randn(50, 30)
dout = np.random.randn(50, 10)
_ = affine.forward(test_input)
dx_num = lml.utils.numerical_gradient_layer(lambda x : af... | <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: Affine Layer
Step2: Batch Normalization Layer
Step3: Dropout Layer
Step4: PReLU (Parametric Rectified Linear Unit) Layer
Step5: ReLU (Rectif... |
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