Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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2,700 | <ASSISTANT_TASK:>
Python Code:
import pycuda.autoinit
import pycuda.driver as drv
import numpy
from pycuda.compiler import SourceModule
mod = SourceModule(
__global__ void multiplicar(float *dest, float *a, float *b)
{
const int i = threadIdx.x;
dest[i] = a[i] * b[i];
}
)
multiplicar = mod.get_function("multiplicar... | <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: ¿Por qué PyCUDA?
Step2: Al correr este programa vamos a obtener un montón de ceros; algo no muy interesante. Sin embargo detrás de escenas sí p... |
2,701 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
matplotlib.rcParams['figure.figsize'] = 9, 6
from numpy import *
import scipy.integrate
# This code is not very efficient, it recalculates many quantities from many
# different functions. It is easy to maintain though, and does not need to be
# run very often.
def r(t,p,R,F... | <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: Development of semi-analytic method
Step2: Illustration of focal plane images
Step3: Image moments using semi-analytic method
Step4: Fitting ... |
2,702 | <ASSISTANT_TASK:>
Python Code::
import matplotlib.pyplot as plt
plt.hist(L)
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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:
|
2,703 | <ASSISTANT_TASK:>
Python Code:
plt.imshow(plt.imread('./res/fig11_2.png'))
show_image('fig11_5.png')
show_image('fig11_7.png', figsize=(8, 10))
show_image('fig11_9.png', figsize=(8, 10))
show_image('fig11_10.png', figsize=(8, 10))
show_image('fig11_13.png')
show_image('ex11_17.png')
#Exercise
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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:
Step1: 11.2.2 Using Eigenvectors for Dimensionality Reduction
Step2: 11.3.2 Interpretation of SVD
Step3: If we set the $s$ smallest singular values t... |
2,704 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
rcParams["figure.figsize"] = (8, 6)
rcParams["axes.grid"] = True
from IPython.display import display, clear_output
from mpl_toolkits.axes_grid1 import make_axes_locatable
from time import sleep
from __future__ import division
def cart2pol(x, y):
theta = arctan2(y, 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: Self-diffusion of water
Step2: The self-diffusion of water at body temperature and standard pressure, in micrometers<sup>2</sup>/millimeter, is... |
2,705 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
path = "data/galaxy/sample/"
#path = "data/galaxy/"
train_path = path + 'train/'
valid_path = path + 'valid/'
test_path = path + 'test/'
results_path = path + 'results/'
model_path = path + 'model/'
from utils import *
batch_size = 32
num_epoch = 1
import pandas as pd
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First Model
Step2: To Do
|
2,706 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ttim import *
ml = ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[1000, 2000],
Saq=[0.1, 1e-4, 1e-4], Sll=[0, 0], phreatictop=True,
tmin=0.1, tmax=1000)
w = Well(ml, xw=0, yw=0, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Consider a well in the middle aquifer of a three aquifer system located at $(x,y)=(0,0)$. The well starts pumping at time $t=0$ at a discharge o... |
2,707 | <ASSISTANT_TASK:>
Python Code:
# Assign value 1 to variable x
x = 1
x = 1 # Assign value 1 to variable x
This is a multi-line comment.
Assign value 1 to variable x.
x = 1
print(1) # Print a constant
x = 2014
print(x) # Print an integer variable
xstr = "Hello World." # Print a string
print(xstr)
print(x,xstr) # Print... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Comments can also be placed on the same line as the code as shown here.
Step3: For multi-line comments, use triple-quoted strings.
Step4: 1.4 ... |
2,708 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
data = load_data()
scaler = StandardScaler()
scaler.fit(data)
scaled = scaler.transform(data)
inversed = scaler.inverse_transform(scaled)
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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:
|
2,709 | <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='G... | <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: Network Architecture
Step2: Training
Step3: Denoising
Step4: Checking out the performance
|
2,710 | <ASSISTANT_TASK:>
Python Code:
import cashflows as cf
cflo = cf.cashflow(const_value= 0,nper=6, spec = [(0, -1000),
(1, 400),
(2, 360),
(3, 320),
... | <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: Criterio de la tasa interna de retorno
Step2: Tasa Interna de Retorno Modificada.
Step3: Análisis de sensibilidad
|
2,711 | <ASSISTANT_TASK:>
Python Code:
import os
import matplotlib.pyplot as plt
from ascat.h_saf import AscatSsmDataRecord
test_data_path = os.path.join('..', 'tests','ascat_test_data', 'hsaf')
h109_path = os.path.join(test_data_path, 'h109')
h110_path = os.path.join(test_data_path, 'h110')
h111_path = os.path.join(test_data_... | <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 soil moisture time series is read for a specific grid point. The data attribute contains a pandas.DataFrame object.
Step2: Time series plots
... |
2,712 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
2,713 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
np.set_printoptions(precision=2)
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.svm import LinearSVC
digits = load_digits()
X, y = digits.data, digits.target
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Here, we predicted 94.4% of samples correctly. For multi-class problems, it is often interesting to know which of the classes are hard to predic... |
2,714 | <ASSISTANT_TASK:>
Python Code:
import csv
data = []
revid = []
with open('page_data.csv') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
data.append([row[0],row[1],row[2]])
revid.append(row[2])
# Remove the first element ('rev_id') from revid so that the list only contains revision ... | <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: Getting the data (country and population) from the population file
Step2: Getting article quality predictions
Step3: Write a function to make ... |
2,715 | <ASSISTANT_TASK:>
Python Code:
from accounts import create_accounts_json
num_files = 25
n = 100000 # number of accounts per file
k = 500 # number of transactions
create_accounts_json(num_files, n, k)
from nfs import create_denormalized
create_denormalize()
from random_array import random_array
random_array()
Image... | <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: Denormalize NFS Data
Step2: Random Array
Step3: Dask
Step4: Dask Array
Step5: Arithmetic and scalar mathematics, +, *, exp, log, ...
Step6: ... |
2,716 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -a '' -u -d -v -p numpy,pandas,matplotlib,scipy,sklearn
%matplotlib inline
# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
from sklearn.datase... | <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: Visualize data
Step3: Date Preprocessing
Step4: Classifier #1 Perceptron
Step5: Classifier #2 Logistic Regression
Step6: C... |
2,717 | <ASSISTANT_TASK:>
Python Code:
import nbformat
from nbformat.v4 import new_notebook
nb = new_notebook()
display(nb)
nbformat.validate(nb)
nb.pizza = True
nbformat.validate(nb)
nb = new_notebook() # get rid of pizza
from nbformat.v4 import new_code_cell, new_markdown_cell, new_raw_cell
md = new_markdown_cell("First ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: cells
Step2: What happens if it's invalid?
Step3: Cells and their sources
Step4: Three types of cells
Step5: cell_type
Step6: cell_type
Ste... |
2,718 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import math
def tf_assert_shape(tensors, requested_shape):
if not type(tensors) is list:
tensors = [tensors]
for tensor in tensors:
shape = tensor.get_shape().as_list()
error_msg = 'Tensor ... | <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: Generate some data
Step2: Model
Step3: Add the loss
Step4: Trainning
Step5: Sample the model
Step6: inspecting the model
|
2,719 | <ASSISTANT_TASK:>
Python Code:
factors(689)
max_seq_len = 682
#full_train_size = 55820
#train_size = 55800
#small_train_size = 6000 #just because of performance reasons, no statistics behind this decision
#test_size = 6200
data_path = '../../../../Dropbox/data'
phae_path = data_path + '/price_hist_autoencoder'
csv_in =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Step 1 - collect data
Step2: Step 2 - Build model
Step3: targets
Step4: Quick test run
Step5: Step 3 training the network
Step6: Conclusion... |
2,720 | <ASSISTANT_TASK:>
Python Code:
import colour
colour.utilities.filter_warnings(True, False)
sorted(colour.LIGHTNESS_METHODS.keys())
colour.colorimetry.lightness_Glasser1958(10.08)
colour.lightness(10.08, method='Glasser 1958')
%matplotlib inline
from colour.plotting import *
colour_plotting_defaults()
# Plotting the "... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Note
Step2: Note
Step3: Wyszecki (1963) Method
Step4: Note
Step5: CIE 1976 Method
Step6: Note
Step7: Fairchild and Wyble (2010) Method
Ste... |
2,721 | <ASSISTANT_TASK:>
Python Code:
# Install libraries.
# The magic cells insures that those libraries can be part of a custom container
# if moving the code somewhere else.
%pip install -q googleads
%pip install -q -U kfp matplotlib Faker --user
# Automatically restart kernel after installs
# import IPython
# app = IPyth... | <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 packages
Step2: Set up your GCP project
Step3: Authenticate your GCP account
Step4: Create a working dataset
Step5: Load example tabl... |
2,722 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append("..")
import numpy as np
from pstd import PSTD, PML, Medium, PointSource
from acoustics import Signal
#import seaborn as sns
%matplotlib inline
x = 30.0
y = 20.0
z = 0.0
soundspeed = 343.2
density = 1.296
maximum_frequency_target = 200.0
medium = Medium(sounds... | <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: Configuration
Step2: Create model
Step3: The model is only finite and to prevent aliasing we need a Perfectly Matched Layer.
Step4: Now we cr... |
2,723 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import GPyOpt
from numpy.random import seed
func = GPyOpt.objective_examples.experiments1d.forrester()
domain =[{'name': 'var1', 'type': 'continuous', 'domain': (0,1)}]
X_init = np.array([[0.0],[0.5],[1.0]])
Y_init = func.f(X_init)
iter_count = 10
current_iter = 0
X_step... | <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 the purposes of this notebook we are going to use one of the predefined objective functions that come with GPyOpt. However the key thing to ... |
2,724 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
PROJECT_ID = 'yourProject' # Change to your project.
PROJECT_NUMBER = 'yourProjectNumber' # Change to your project number
BUCKET = 'yourBucketName' # Change to the bucket you created.
REGION = 'yourPredictionRegion' # Change to your AI Platform ... | <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: Configure GCP environment settings
Step2: Authenticate your GCP account
Step3: Deploy the embedding lookup model to AI Platform Prediction
Ste... |
2,725 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
print(tf.__version__)
# Load the diabetes dataset
from sklearn.datasets import load_diabetes
diabetes_datasets = load_diabetes()
print(diabetes_datasets["DESCR"])
# Save the input and target variables
print(diabetes_datasets.keys())
data = diabetes_datasets["data"... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and pre-process the data
Step2: Split the data into train and test sets
Step3: First model
Step4: Compile and train the first unregulari... |
2,726 | <ASSISTANT_TASK:>
Python Code:
%pdb
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (1000, 1010)
DON'T MODIFY ANYTHING IN THIS CELL
... | <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: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
2,727 | <ASSISTANT_TASK:>
Python Code:
xi, l, rho = symbols('xi, l, rho')
# Shape functions
S = Matrix(np.zeros((4, 12)))
x2 = (1 - xi)
S[0, 0 ] = x2 # extension
S[0, 6 ] = xi
S[1, 1 ] = x2**2 * (3 - 2*x2) # y-deflection
S[1, 7 ] = xi**2 * (3 - 2*xi)
S[1, 5 ] = -x2**2 * (x2 - 1) * l
S[1, 11] = xi... | <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: Mass matrix
Step2: Integrate the density distribution with the shape functions.
Step3: Special case
Step4: Shape integrals
Step5: First shap... |
2,728 | <ASSISTANT_TASK:>
Python Code:
b = 5
b = 6
assert b == 6
# Here's a comment!
b = 5
# Here's another comment! Neither of these comments are evaluated by Python!
# this is my comment
b = 6
print(b)
print(500)
# Integer
i = 1
# String
s = "Hello World"
# Float
f = 55.55
hundred_integer = 100
hundred_string = "hundred"
... | <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: 4
Step2: 5
Step3: 6
Step4: 7
Step5: 8
Step6: 9
Step7: 11
Step8: 12
Step9: 13
Step10: 14
Step11: 15
Step12: 16
|
2,729 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('../../')
import imp
import macrodensity as md
import math
import numpy as np
import matplotlib.pyplot as plt
import os
if os.path.isfile('LOCPOT'):
print('LOCPOT already exists')
else:
os.system('bu... | <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 the potential
Step2: Look for pore centre points
Step3: We want to try a range of sampling area sizes.
Step4: From the OUTCAR the VBM is... |
2,730 | <ASSISTANT_TASK:>
Python Code:
from geopy.geocoders import GoogleV3
geolocator = GoogleV3()
# geolocator = GoogleV3(api_key=<your_google_api_key>)
t = pd.read_csv('https://data.cityofnewyork.us/api/views/43nn-pn8j/rows.csv?accessType=DOWNLOAD', header=0, sep=',', dtype={'PHONE':str, 'INSPECTION DATE':str});
## Helper ... | <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
Step2: Data preprocessing
Step3: Create a dictionary of unique Addresses. We do this to avoid calling the Google geocoding api mul... |
2,731 | <ASSISTANT_TASK:>
Python Code:
def sort(L):
if len(L) <= 1:
return L
x, y, R = L[0], L[-1], L[1:-1]
p1, p2 = min(x, y), max(x, y)
L1, L2, L3 = partition(p1, p2, R)
if p1 == p2:
return sort(L1) + [p1] + L2 + [p2] + sort(L3)
else:
return sort(L1) + [p1] + sort(L2) + ... | <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 function partition receives three arguments
|
2,732 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Xtrain = pd.read_csv('./data/multivariate_tr.csv')
Xtrain.head()
Xtrain.describe()
Xtest = pd.read_csv('./data/multivariate_ts.csv')
Xtest.head()
Xtest.describe()
from scipy.stats import multivariat... | <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: 1 - Check that the training data is suitable for a multivariate modeling approach (multivariate_tr.csv & multivariate_ts.csv)
Step2: 2 - Check ... |
2,733 | <ASSISTANT_TASK:>
Python Code:
from symbulate import *
%matplotlib inline
cards = ['club', 'diamond', 'heart', 'spade'] * 4 # 4 cards of each suit
len(cards)
P = BoxModel(cards, size=2, replace=False, order_matters=True)
P.draw()
sims = P.sim(10000)
sims
sims = P.sim(10000)
sims.tabulate()
def first_is_heart(x):... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Part I. Introduction to Symbulate, and conditional versus unconditional probability
Step2: Now we define a BoxModel probability space correspon... |
2,734 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.2,<2.3"
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
b.add_dataset('lc', dataset='lc01')
b.add_dataset('mesh', times=[0], columns=['intensit... | <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: As always, let's do imports and initialize a logger and a new bundle. See Building a System for more details.
Step2: Relevant Parameters
Step3... |
2,735 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'uhh', 'sandbox-1', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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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... |
2,736 | <ASSISTANT_TASK:>
Python Code:
from theano.sandbox import cuda
%matplotlib inline
import utils; reload(utils)
from utils import *
from __future__ import division, print_function
path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
text = open(path).read()
print('corpus length... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Setup
Step2: Sometimes it's useful to have a zero value in the dataset, e.g. for padding
Step3: Map from chars to indices and back again
Step4... |
2,737 | <ASSISTANT_TASK:>
Python Code:
from search import *
%psource Problem
%psource GraphProblem
romania_map = UndirectedGraph(dict(
Arad=dict(Zerind=75, Sibiu=140, Timisoara=118),
Bucharest=dict(Urziceni=85, Pitesti=101, Giurgiu=90, Fagaras=211),
Craiova=dict(Drobeta=120, Rimnicu=146, Pitesti=138),
Drobet... | <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: Review
Step2: The Problem class has six methods.
Step3: Now it's time to define our problem. We will define it by passing initial, goal, graph... |
2,738 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.misc import factorial
from scipy.optimize import curve_fit
import scipy.constants as cst
import matplotlib.pyplot as plt
%matplotlib inline
ao = cst.physical_constants["Bohr radius"][0] / cst.angstrom
print("a0 = {} A".format(ao))
def rms(y_th, y):
co... | <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: Matplotlib is used in order to make plots
Step2: Bhor radius is defined in angstrom from scipy.constants module.
Step4: A root mean square fun... |
2,739 | <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 mpl_toolkits.mplot3d import Axes3D
import plotBL
HTML('../style/code_toggle.html')
ant1 = np.array([-500e3,500e3,0]) # in m
ant2 ... | <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 section specific modules
Step2: 4.4.1 UV coverage
Step3: Let's express the corresponding physical baseline in ENU coordinates.
Step4: ... |
2,740 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set() # for plot styling
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
X, y_true = make_blobs(n_samples=300, centers=4,
cluster_std=0.60, random_state=0)
pl... | <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: k-Means Algorithm
Step2: Caveats of expectation–maximization
Step3: Here the E–M approach has converged, but has not converged to a globally o... |
2,741 | <ASSISTANT_TASK:>
Python Code:
import pandas
raw_elections_2015 = pandas.read_csv("test2015.csv",delimiter='\t')
raw_elections_2016 = pandas.read_csv("test2016.csv",delimiter='\t')
def find_PODEMOS(words):
if 'PODEMOS' in words:
return 'PODEMOS'
if 'EN COMU' == words:
return 'PODEMOS'
return... | <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: <p>All the data are string</p>
Step2: <h3>Despite all the corruption cases from the PP, only this growh their votes in 2016</h3>
|
2,742 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
2,743 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
def complete_deg(n):
a = np.identity((n), dtype = np.int)
for element in np.nditer(a, op_flags=['readwrite']):
if eleme... | <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: Complete graph Laplacian
Step2: The Laplacian Matrix is a matrix that is extremely important in graph theory and numerical analysis. It is defi... |
2,744 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
fake_depth = np.linspace(100, 150, 101)
fake_log = np.array([np.random.choice([0, 1]) for _ in fake_depth])
plt.figure(figsize=(15, 1))
plt.plot(fake_depth, fake_log, 'o-')
from striplog import Striplog, Component
comp... | <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: Make a striplog
Step2: Each Interval in the striplog looks like
Step3: Plot the intervals
Step5: Or we can make one with a bit more control
S... |
2,745 | <ASSISTANT_TASK:>
Python Code:
# Function to generate target value for a given x.
true_func = lambda X: np.cos(1.5 * np.pi * X)
np.random.seed(0)
# Training Set: No. of random samples used for training the model
n_samples = 30
x = np.sort(np.random.rand(n_samples))
y = true_func(x) + np.random.randn(n_samples) * 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: Polynomial with degree 1 is a straight line - Underfitting<br>
Step2: <h4>Model with degree 4 features</h4>
Step3: Good Fit with degree 4 poly... |
2,746 | <ASSISTANT_TASK:>
Python Code:
# Author: Ivana Kojcic <ivana.kojcic@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Kostiantyn Maksymenko <kostiantyn.maksymenko@gmail.com>
# Samuel Deslauriers-Gauthier <sam.deslauriers@gmail.com>
# License: BSD (3-clause)
import os.path as op
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: In order to simulate source time courses, labels of desired active regions
Step3: Create simulated source activity
Step4: Here,
Step5: Simul... |
2,747 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_1samp_test
from mne.datasets import sample
print(__doc__)... | <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: Set parameters
Step2: Define adjacency for statistics
Step3: Compute statistic
Step4: View time-frequency plots
|
2,748 | <ASSISTANT_TASK:>
Python Code:
#On windows
#import findspark
#findspark.init(spark_home="C:/Users/me/software/spark-1.6.3-bin-hadoop2.6/")
import pyspark
import numpy as np # we'll be using numpy for some numeric operations
sc = pyspark.SparkContext(master="local[*]", appName="tour")
sc.stop()
# To try the SparkContex... | <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: local
Step2: Lambda functions
Step3: Creating RDDS
Step4: RDD operations
Step5:
Step7: Lazy evaluation
Step8: Persistence
Step9: If we d... |
2,749 | <ASSISTANT_TASK:>
Python Code:
plt.plot(pop_x, pop_y, 'o')
plt.xlabel('Year')
plt.ylabel('Population [Millions]')
plt.show()
graphene_used = np.concatenate( (np.ones(5), np.zeros(5)) )
temperature = np.concatenate( (T, T) )
intercept = np.ones(10)
x_mat = np.column_stack( (intercept, temperature, graphene_used) )
prin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Example 1 Answer
Step2: Example 3
|
2,750 | <ASSISTANT_TASK:>
Python Code:
!pip install -qq optax
import numpy as np
import jax
from jax import numpy as jnp
from jax import grad, jit, vmap, random
try:
import optax
except ModuleNotFoundError:
%pip install -qq optax
import optax
try:
import tensorflow_datasets as tfds
except ModuleNotFoundError:
... | <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:
Step4: Plotting functions
Step14: Restricted Boltzmann Machines
Step15: Load MNIST
Step17: Training with optax
Step18: Evaluating Training
Step20: ... |
2,751 | <ASSISTANT_TASK:>
Python Code:
import numpy
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
# fix random seed for reproducibility
numpy.random.seed(7)
#... | <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 see that this simple LSTM with little tuning achieves near state-of-the-art results on the IMDB problem. Importantly, this is a template... |
2,752 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
ints = np.arange(1,21)
pows = 2**ints
print(pows)
print(pows[9], pows[19])
sum = 0
for i in range(1, 100):
sum += i
if sum > 200:
print(i, sum)
break
from scipy.special import factorial
def fxn(n, k):
'''Computes the number of permutations ... | <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: $$2^{10} \approx 10^3$$
Step2: 1.3 Answer
Step3: 1.4 Answer
Step4: 2. Watching Youtube with the Geometric Distribution (15 Points)
Step5: 2.... |
2,753 | <ASSISTANT_TASK:>
Python Code:
# Execute this cell
import numpy as np
import scipy.stats
from astroML import stats as astroMLstats
data = np.random.random(1000)
# Execute this cell
mean = np.mean(data)
print mean
# Execute this cell. Think about what it is doing.
median = np.median(data)
mask = data>0.75
data[mask] ... | <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 arithmetic mean (or Expectation value) is
Step2: While it is perhaps most common to compute the mean, the median is a more robust estimator... |
2,754 | <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: Functional API
Step2: 前書き
Step3: データの形状は、784次元のベクトルとして設定されます。各サンプルの形状のみを指定するため、バッチサイズは常に省略されます。
Step4: 返されるinputsには、モデルに供給する入力データの形状とdtypeについ... |
2,755 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.linalg as la
a = np.array([[1,2,3], [4,5,6], [7,8,9]])
m = np.matrix([[1,2,3], [4,5,6]])
print('a=', a)
print(19*'-')
print('m=', m)
print(19*'-')
print('ndim(a) = ', np.ndim(a))
print('a.ndim = ', a.ndim)
print(19*'-')
print('np.ndim(m) = ', np.ndim(m))
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: <span style="color
Step2: <span style="color
Step3: <span style="color
Step4: <span style="color
Step5: <span style="color
Step6: <span sty... |
2,756 | <ASSISTANT_TASK:>
Python Code:
ipd.display( ipd.YouTubeVideo("ajCYQL8ouqw") )
ipd.display( ipd.YouTubeVideo("PrVu9WKs498", start=8) )
ipd.display( ipd.YouTubeVideo("Cxj8vSS2ELU", start=540) )
ipd.display( ipd.YouTubeVideo("ECvinPjmBVE", start=6) )
ipd.display( ipd.YouTubeVideo("DiW6XVFeFgo", start=60))
ipd.display... | <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: (Chords)
Step2: One more
Step3: Why MIR?
Step4: Example
Step5: Example
Step6: Example
Step7: Example
|
2,757 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import seaborn
from IPython.html.widgets import interact, interactive, fixed#functii necesare pt interactivitate
from IPython.display import clear_output, display, HTML
rcdef = plt.rcParams.copy... | <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: seaborn este o biblioteca destinata vizualizarii in statistica. Nu este inclusa in distributia Anaconda. Se bazeaza pe matplotlib si se instalea... |
2,758 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from matplotlib import pyplot
%matplotlib inline
from matplotlib import rcParams
rcParams['font.family'] = 'serif'
rcParams['font.size'] = 16
a=0.3 # diffusion constant
L = 1. # length of domain
J = 41 ... | <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: Physical parameters
Step2: Specify spatial grid in Python
Step3: Specify temporal grid in Python
Step4: Goal
Step5: That leaves us with two ... |
2,759 | <ASSISTANT_TASK:>
Python Code:
#relatively fast networks package (pip install python-igraph) that I used for these homeworks
import igraph
# slow-and-steady networks package. fewer bugs, easier drawing
import networkx as nx
# plots!
import matplotlib.pyplot as plt
from matplotlib import style
%matplotlib inline
# othe... | <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: Graphs!
Step2: Now. What's the difference between that (^) drawing of nodes and edges and a completely random assembly of dots and lines? How c... |
2,760 | <ASSISTANT_TASK:>
Python Code:
import ipyrad as ip
## this is a comment, it is not executed, but the code below it is.
import ipyrad as ip
## here we print the version
print ip.__version__
## create an Assembly object named data1.
data1 = ip.Assembly("data1")
## setting/modifying parameters for this Assembly object... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Getting started with Jupyter notebooks
Step2: The ipyrad API data structures
Step3: Setting parameters
Step4: Instantaneous parameter (and er... |
2,761 | <ASSISTANT_TASK:>
Python Code:
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
source_sentences[:50].split('\n')
target_sentences[:50].split('\n')
def extract_character_voca... | <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 start by examining the current state of the dataset. source_sentences contains the entire input sequence file as text delimited by newline... |
2,762 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import sys
from casadi import *
import os
import time
# Add do_mpc to path. This is not necessary if it was installed via pip
sys.path.append('../../../')
# Import do_mpc package:
import do_mpc
import matplotlib.pyplot as plt
import pandas as pd
sp = do_mpc.sampling.Sa... | <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: Toy example
Step 1
Step2: We then introduce new variables to the SamplingPlanner which will later jointly define a sampling case. Think of head... |
2,763 | <ASSISTANT_TASK:>
Python Code:
!rm -rf *
!rm -rf .config
!rm -rf .git
!git clone https://github.com/google-research/scenic.git .
!python -m pip install -q .
!python -m pip install -r scenic/projects/baselines/clip/requirements.txt
!echo "Done."
import os
import jax
from matplotlib import pyplot as plt
import numpy as 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: Choose config
Step2: Load the model and variables
Step3: Prepare image
Step4: Prepare text queries
Step5: Get predictions
Step6: Plot predi... |
2,764 | <ASSISTANT_TASK:>
Python Code:
peyton_dataset_url = 'https://github.com/facebookincubator/prophet/blob/master/examples/example_wp_peyton_manning.csv'
peyton_filename = '../datasets/example_wp_peyton_manning.csv'
import pandas as pd
import numpy as np
from fbprophet import Prophet
# NB: this didn't work as of 8/22/17
#i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Fit the model by instantiating a new Prophet object. Any settings required for the forecasting procedure are passed to this object upon construc... |
2,765 | <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 *
data = pd... | <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: Code from the previous chapter
Step2: Interpolate the insulin data.
Step3: The glucose minimal model
Step5: Here's a version of make_system t... |
2,766 | <ASSISTANT_TASK:>
Python Code:
m = 1.00
k = 4*pi*pi
wn = 2*pi
T = 1.0
z = 0.02
wd = wn*sqrt(1-z*z)
c = 2*z*wn*m
NSTEPS = 200 # steps per second
h = 1.0 / NSTEPS
def load(t):
return np.where(t<0, 0, np.where(t<5, sin(0.5*wn*t)**2, 0))
t = np.linspace(-1, 6, 7*NSTEPS+1)
plt.plot(t, load(t))
plt.ylim((-0.05, 1.05));
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Define the Loading
Step2: Numerical Constants
Step3: Vectorize the time and the load
Step4: Integration
Step5: Results
Step6: Comparison
St... |
2,767 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
scatx=np.random.rand(50)
scaty=np.random.randn(50)
f= plt.figure(figsize=(9,6))
plt.scatter(scatx,scaty,c=u'k',marker=u'o',alpha=1)
plt.xlabel('X')
plt.ylabel('Y')
plt.title("Scatter Plot of A Set of Random Data")
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: Scatter plots
Step2: Histogram
|
2,768 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
df = pd.read_csv('../data/train.csv')
df.head(10)
df = df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
df.info()
df = df.dropna()
df['Sex'].unique()
df['Gender'] = df['Sex'].map({'female': 0, 'male':1}).astype(int)
df['Embarked'].unique()
df['Port... | <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: Pandas - Cleaning data
Step2: We notice that the columns describe features of the Titanic passengers, such as age, sex, and class. Of particula... |
2,769 | <ASSISTANT_TASK:>
Python Code:
from k2datascience import hr_analytics
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
%matplotlib inline
hr = hr_analytics.HR()
print(f'Data Shape\n\n{hr.data.shape}')
print('\n\nColumns\n\n{}'.format('\n'.join(hr.data.columns)... | <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: Explore the data
Step3: Probability, Expectation Values, and Variance
Step4: Compute the 25th, 50th, and 90th percentiles fo... |
2,770 | <ASSISTANT_TASK:>
Python Code:
chain = sisl.Geometry([0]*3, sisl.Atom(1, R=1.), sc=[1, 1, 10])
chain.set_nsc([3, 3, 1])
# Transport along y-direction
chain = chain.tile(20, 0)
He = sisl.Hamiltonian(chain)
He.construct(([0.1, 1.1], [0, -1]))
Hd = He.tile(20, 1)
He.write('ELEC.nc')
Hd.write('DEVICE.nc')
with open('RUN.fd... | <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 of the $k$-point sampling for TBtrans.
Step2: Run these two executables
|
2,771 | <ASSISTANT_TASK:>
Python Code::
def load_doc(filename):
# open the file as read only
file = open(filename, 'r')
# read all text
text = file.read()
# close the file
file.close()
return text
<END_TASK>
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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:
|
2,772 | <ASSISTANT_TASK:>
Python Code:
#import tm1 service module
from TM1py.Services import TM1Service
#import tm1 utils module
from TM1py.Utils import Utils
#import pandas
import pandas as pd
#import matplotlib
import matplotlib.pyplot as plt
#inline plotting for matplotlib
%matplotlib inline
#import statsmodels package
from... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Step 2
Step2: Step 3
Step3: Step 4
Step4: Rather than looping through each region and product within the data set, the following cell creates... |
2,773 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
from statsmodels.formula.api import ols
sm.formula.ols
import statsmodels.formula.api as smf
sm.OLS.from_formula
dta = sm.datasets.get_rdataset("Guerry", "HistData", cache=True)
df = dta.data[['Lott... | <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 convention
Step2: Alternatively, you can just use the formula namespace of the main statsmodels.api.
Step3: Or you can use the followin... |
2,774 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
from sympy import init_printing
from sympy import S
from sympy import sin, cos, tanh, exp, pi, sqrt, log
from boutdata.mms import x, y, z, t
from boutdata.mms import DDX
import os, sys
# If we add to sys.path, then it must be an absolute path
common_dir = os.path.absp... | <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: Initialize
Step2: Define the variables
Step3: Plot
Step4: Print the variables in BOUT++ format
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2,775 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import glob
import os
from matplotlib.patches import Rectangle
# define all variables for convergence script
# these will pass to the bash magic below used to call plumed sum_hills
dir="MetaD_converge" #where the intermediate fes will be... | <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: Graph the final FES and plot the two squares on top of it
Step2: The two functions below calculate the average free energy of a region by integ... |
2,776 | <ASSISTANT_TASK:>
Python Code:
# Librerias
import pandas as pd
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, ... | <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: Concatenar
Step2: Ejemplos de DataFrames
Step3: Merge
Step4: Ejemplo Complicado
Step5: Joining
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2,777 | <ASSISTANT_TASK:>
Python Code:
# Step 1: Configure your cluster with gcloud
# `gcloud container clusters get-credentials <cluster_name> --zone <cluster-zone> --project <project-id>
# Step 2: Get the port where the gRPC service is running on the cluster
# `kubectl get configmap metadata-grpc-configmap -o jsonpath={.data... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Connect to the ML Metadata (MLMD) database
Step2: Get trial summary data (used to plot Area under Learning Curve) stored as AugmentedTuner arti... |
2,778 | <ASSISTANT_TASK:>
Python Code:
# Importa la librería financiera.
# Solo es necesario ejecutar la importación una sola vez.
import cashflows as cf
cflo = cf.cashflow(const_value=[100] * 11 + [0], start='2016-1', freq='M')
cflo
nrate = cf.interest_rate([24] * 12, start='2016-1', freq='M')
nrate
cf.savings(deposits = cf... | <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: Ejercicio.-- Usando Microsoft Excel u otra herramienta solucione el siguiente problema
Step2: *Ejemplo.--* Realice el mismo ejemplo anterior, p... |
2,779 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(29384924)
data = np.random.randint(10, size = 100) # 100 random numbers, from 0 to 9
print(data)
print("Number of data points: ", data.shape[0]) # Remember our friend, ndarray.shape?
print("Largest value: ", data.max())
print("Smallest value: ", data.m... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Some very straightforward statistics are the number of data points, the largest value, and the smallest value. These shouldn't be immediately ig... |
2,780 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (9,6)
df = pd.read_csv("data/creditRisk.csv")
df.head()
import seaborn as sns
sns.stripplot(data = df, x = "Income", y = "Credit His... | <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: Plotting the Data
Step2: Preparing Data
Step3: Lets use a dictionary for encoding nominal variable
Step4: Decision Tree Classifier
Step5: Vi... |
2,781 | <ASSISTANT_TASK:>
Python Code:
person_name = "Mike"
person_age = 50
person_faculty = True
person = {}
person['name'] = "Mike"
person['age'] = 50
person['faculty'] = True
print(person)
'dob' in person.keys()
if 'dob' not in person.keys():
dob = input("Enter your DOB")
person['dob'] = dob
person
person['hjklsdag... | <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: More Lab Questions...
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2,782 | <ASSISTANT_TASK:>
Python Code:
# Create the message variable and assign the value "Hello World" to it
message="Hello World"
# Use the variable in a print statement
# The print statement retrieves the value assigned to the variable and displays the value
print(message)
message
#Assign raw numbers to variables
apples=5... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Try changing the message in the previous code cell and re-running it. Does it behave as you expect?
Step2: You can assign whatever object you l... |
2,783 | <ASSISTANT_TASK:>
Python Code:
# Constants
D = 2
N = 100
K = 2
w = np.random.randn(D)
w = normalize(w)
theta = np.arctan2(w[0], w[1])
X = np.random.randn(N,D,K)
y = np.zeros(N)
for i in range(N):
m = w.dot(X[i])
X[i] = X[i][:,np.argsort(-m)]
y[i] = np.sign(max(m))
# Visualize data
plt.plot(np.arange(-3,3), ... | <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: Method
Step2: Exact SVM solution
Step3: Naive
Step4: As expected, the naive approach does really poorly.
Step5: Proposed 2
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2,784 | <ASSISTANT_TASK:>
Python Code:
# This will take a few minutes
r = requests.get("http://www.transtats.bts.gov/Download/On_Time_On_Time_Performance_2015_1.zip",
stream=True)
with open("otp-1.zip", "wb") as f:
for chunk in r.iter_content(chunk_size=1024):
f.write(chunk)
f.flush()
r.clo... | <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 suppose that down the road our probram can only handle certain carriers; an update to the data adding a new carrier would violate an assum... |
2,785 | <ASSISTANT_TASK:>
Python Code:
import math
import os
import pandas as pd
import numpy as np
from datetime import datetime
import tensorflow as tf
from tensorflow import data
print "TensorFlow : {}".format(tf.__version__)
SEED = 19831060
DATA_DIR='data'
# !mkdir $DATA_DIR
# !gsutil cp gs://cloud-samples-data/ml-engine/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Download the Data
Step2: Dataset Metadata
Step3: Building a TensorFlow Custom Estimator
Step4: 2. Create model_fn
Step5: 3. Create estimator... |
2,786 | <ASSISTANT_TASK:>
Python Code:
number = 3.14159265359
number = "1.7724538509055743"
number = 3.14159265359
number = number ** 0.5 # Raise to the 0.5, which means square root.
number = str(number) # Cast to a string.
def first_negative(numbers):
num = 0
index = 0
while numbers[index] > 0:
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Answering this is not simply taking what's in the autograder and copy-pasting it into your solution
Step2: The whole point is that your code sh... |
2,787 | <ASSISTANT_TASK:>
Python Code:
#$HIDE_INPUT$
import pandas as pd
autos = pd.read_csv("../input/fe-course-data/autos.csv")
autos["make_encoded"] = autos.groupby("make")["price"].transform("mean")
autos[["make", "price", "make_encoded"]].head(10)
#$HIDE_INPUT$
import matplotlib.pyplot as plt
import numpy as np
import 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: Target Encoding
Step2: This kind of target encoding is sometimes called a mean encoding. Applied to a binary target, it's also called bin count... |
2,788 | <ASSISTANT_TASK:>
Python Code:
import vcsn
def aut(e):
return vcsn.context('lal_char, b').expression(e, 'binary').standard()
a1 = aut('a*+b*'); a1
a2 = aut('b*+a*'); a2
a1.is_isomorphic(a2), a1 == a2
%%automaton -s a1
$ -> 0
0 -> 1 a
%%automaton -s a2
$ -> 0
0 -> 1 b
a1.is_isomorphic(a1), a1.is_isomorphic(a2)
a1 ... | <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 automata must be accessible, but coaccessibility is not required.
Step2: Equivalent automata can be non isomorphic.
Step3: Weighted Automa... |
2,789 | <ASSISTANT_TASK:>
Python Code:
df = pd.DataFrame(np.fromfile("./output.bni", dtype=np.uint16).astype(np.float32) * (3300 / 2**12))
#df.describe()
fig = sns.plt.figure(figsize=(16, 6))
ax = sns.plt.subplot()
df[20000:20100].plot(ax=ax)
df_r = df.groupby(df.index//10).mean()
fig = sns.plt.figure(figsize=(16, 6))
ax = 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: Данных много, миллион сэмплов в секунду. Мы насобирали почти 70 миллионов сэмплов. Если строить их все сразу, питон ОЧЕНЬ задумается. Поэтому бу... |
2,790 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.optimize as sciopt
x = np.array([[ 1247.04, 1274.9 , 1277.81, 1259.51, 1246.06, 1230.2 ,
1207.37, 1192. , 1180.84, 1182.76, 1194.76, 1222.65],
[ 589. , 581.29, 576.1 , 570.28, 566.45, 575.99,
601.1 , 620.6 , 637.04, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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2,791 | <ASSISTANT_TASK:>
Python Code:
import main
raw_data = main.load_raw_data([]) # asume el cache
gdf = main.get_grouped_dataset(raw_data, level=4)
m, dfX = main.get_model_to_draw(4)
import model
figure()
distr = model.ConditionalDistribution(gdf['131_pct'], gdf['135_pct']).fit()
distr.draw_joint()
xlabel('Proporción de 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: Patrones de voto
Step2: Hay una relación inversa entre la gente que votó a Macri y a Scioli.
Step3: Este patrón es super interesante, parecerí... |
2,792 | <ASSISTANT_TASK:>
Python Code:
# Pykep imports
from pykep.trajopt import mga_1dsm, launchers
from pykep.planet import jpl_lp
from pykep import epoch
from pykep.core import lambert_problem, propagate_lagrangian, fb_prop
from pykep import DAY2SEC, DAY2YEAR, AU, RAD2DEG, ic2par
from pykep.trajopt.gym import solar_orbiter_... | <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: Solar Orbiter, modelles with DSMs only on resonant arcs
Step2: Solar Orbiter modeled as mga_1dsm
|
2,793 | <ASSISTANT_TASK:>
Python Code:
folium.Map().add_child(ClickForMarker())
folium.Map().add_child(LatLngPopup())
folium.Map().add_child(ClickForLatLng(format_str='"[" + lat + "," + lng + "]"'))
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Click on the map to see the effects
Step2: Click on the map to see the effects
|
2,794 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
from math import log, exp
%matplotlib inline
# Evaluate beta for this sensor
T_0=273.15+20;
N=(1/273.15-1/293.15)-(1/298.15-1/293.15);
beta= log(3000/1000)/N;
R_0=1000/exp(beta*((1/298.15)-(1/293.15)));
## Results
print('Beta for this 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:
Step1: The plot shows the nonlinear behaviour of the sensor and the two points used for estimating the curve.
Step2: Note how the error starts from ze... |
2,795 | <ASSISTANT_TASK:>
Python Code:
import ipywidgets
import IPython.display
args = ipywidgets.Text(
description='Input string:',
value='cube')
IPython.display.display(args)
print args.value
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Construct widget with a default value and display using IPython display call.
Step2: Print widget value for clarity.
|
2,796 | <ASSISTANT_TASK:>
Python Code:
import os
with open(os.path.join("datasets", "smsspam", "SMSSpamCollection")) as f:
lines = [line.strip().split("\t") for line in f.readlines()]
text = [x[1] for x in lines]
y = [x[0] == "ham" for x in lines]
text[:10]
y[:10]
type(text)
type(y)
from sklearn.cross_validation import 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: Training a Classifier on Text Features
Step2: We can now evaluate the classifier on the testing set. Let's first use the builtin score function... |
2,797 | <ASSISTANT_TASK:>
Python Code:
import imaginet.task
model = imaginet.task.load(path="model-ipa.zip")
emb = imaginet.task.embeddings(model)
print(emb.shape)
symb = imaginet.task.symbols(model)
print " ".join(symb.values())
%pylab inline
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
xy = pca.fit_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: Load the model
Step2: Symbol embeddings
Step3: The table of IPA symbols corresponding to the 49 dimensions
Step4: Let's display the embeddin... |
2,798 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
A = np.array([[0, 0, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]])
mat1 = (A.T).dot(A)
print mat1
a = np.array([.25, .25, .25, .25])
for i in xrange(3):
a = mat1.dot(a)
print a
mat2 = (A).dot(A.T)
print mat2
h = np.array... | <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: Question 8.
Step2: Question 9.
|
2,799 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
import thinkstats2
import thinkplot
%matplotlib inline
import scipy.stats
mu = 178
sigma = 7.7
dist = scipy.stats.norm(loc=mu, scale=sigma)
type(dist)
dist.mean(), dist.std()
dist.cdf(mu-sigma)
low = dist.cdf(177.8)
high = dist.cdf(185.... | <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: 연습문제 5.1
Step2: 예를 들어, <tt>scipy.stats.norm</tt>은 정규분포를 나타낸다.
Step3: "고정된 확률변수(frozen random variable)"는 평균과 표준편차를 계산할 수 있다.
Step4: CDF도 평가할 ... |
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