Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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5,400 | <ASSISTANT_TASK:>
Python Code:
# Import relevant modules
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
from NPTFit import psf_correction as pc # Module for determining the PSF correction
from __future__ import print_function
pc_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Example 1
Step2: Example 2
Step3: Example 3
Step4: Example 4
|
5,401 | <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: Eager execution basics
Step2: Tensors
Step3: Each Tensor has a shape and a datatype
Step4: The most obvious differences between NumPy arrays ... |
5,402 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics
import datetime as dt # date tools, used to note current date
# these are new
import os # operating system tools (check files)
import requests,... | <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=movielens></a>
Step2: Exercise. Something to do together. suppose we wanted to save the files on our computer. How would we do it? Woul... |
5,403 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Using sklearn
Step2: Remember that the form of data we will use always is
Step3: In the Linear Regression Mini Project, the last (extra credit... |
5,404 | <ASSISTANT_TASK:>
Python Code:
with open('/resources/data/Example2.txt','w') as writefile:
writefile.write("This is line A")
with open('/resources/data/Example2.txt','r') as testwritefile:
print(testwritefile.read())
with open('/resources/data/Example2.txt','w') as writefile:
writefile.write("This is line... | <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 can read the file to see if it worked
Step2: We can write multiple lines
Step3: The method .write() works similar to the method .readline()... |
5,405 | <ASSISTANT_TASK:>
Python Code:
import dendropy
import pandas as pd
data = pd.read_csv('../Data/PyronParityData.csv', index_col=0, header=False)
taxa = dendropy.TaxonSet()
mle = dendropy.Tree.get_from_path('../TotalOpt/annotatedTO_0param_2598364.dated', 'newick', taxon_set=taxa, preserve_underscores=True)
for idx, nd... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Read data and tree.
Step2: Iterate over the tips of the trees and annotate with data (in this case, whether the tip is viviparous or oviparous)... |
5,406 | <ASSISTANT_TASK:>
Python Code:
# !pip install phiflow
from phi.flow import *
x = math.stack({'Sun': (0, 0), 'Earth': (10, 0), 'Mars': (0, 12)}, instance('planets'))
x
vis.plot(PointCloud(x, bounds=Box(x=(-2, 12), y=(-1, 13))))
v = math.rotate_vector(x, PI/2)
v = math.divide_no_nan(v, math.vec_length(v))
vis.plot(Po... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We use the convenience import of Φ<sub>Flow</sub> which imports the core submodules, such as math and vis.
Step2: Let's define the initial posi... |
5,407 | <ASSISTANT_TASK:>
Python Code:
import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Restart the kernel
Step2: Set up your Google Cloud project
Step3: Otherwise, set your project ID here.
Step4: Set project ID
Step5: Timestam... |
5,408 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
a = np.array([-2, 3, 4, -5, 5])
print(a)
a[[1, 3]]
a[a > 0]
print(a)
print(a > 0)
a[(a > 0) & (a < 5)]
pop_dict = {'Germany': 81.3,
'Belgium': 11.3,
'France': 64.3,
... | <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: More on NumPy indexing
Step2: Fancy indexing
Step3: Boolean indexing
Step4: Note that the index array has the same size as and type of boolea... |
5,409 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
objective = np.poly1d([1.3, 4.0, 0.6])
print objective
import scipy.optimize as opt
x_ = opt.fmin(objective, [3])
print "solved: x={}".format(x_)
%matplotlib inline
x = np.linspace(-4,1,101.)
import matplotlib.pylab as mpl
mpl.plot(x, objective(x))
mpl.plot(x_, objecti... | <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 "optimizer"
Step2: Additional components
Step3: The gradient and/or hessian
Step4: The penalty functions
Step5: Optimizer classification... |
5,410 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from math import pi
import matplotlib.pyplot as plot
%matplotlib notebook
x = np.arange(-5, 5.001, 0.0001)
y = (x**4)-(16*(x**2)) + 16
plot.plot(x,y,'c')
plot.grid(True)
print('Para a f(x) = ax^2 + bx+ c, diga os valores de a, b e c:\n')
a = float(input('Valor de 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: Questão 2
Step2: Questão 3
Step3: Questão 4
|
5,411 | <ASSISTANT_TASK:>
Python Code:
import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
%matplotlib inline
from datetime import datetime
import numpy as np
import utils.data_cube_utilities.dc_utilities as utils
from utils.data_cube_utilities.clean_mask import landsat_qa_clean_mask
from utils.data_cube_util... | <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 id="coastal_change_classifier_plat_prod">Choose Platform and Product ▴</span>
Step2: <span id="coastal_change_classifier_define_ext... |
5,412 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from sklearn import metrics, preprocessing, linear_model
dirRawData = '/home/john/Projects/RepoNumerAI/data/raw/numerai_datasets/19_03_2017/'
dirOutputData = '/home/john/Projects/RepoNumerAI/data/processed/'
# Set seed for reproducibility
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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Description:
Step1: Set paths to the Data
Step2: Transform the loaded CSV data into numpy arrays
Step3: This is your model that will learn to predict
|
5,413 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" % CLOUD_PROJECT)
CLIENT_CREDENTIALS = 'PASTE CREDENTIALS HERE'
print("Client Credentials Set To: %s" % CLIENT_CREDENTIALS)
FIELDS = {
'auth_read': 'user', ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Get Cloud Project ID
Step2: 3. Get Client Credentials
Step3: 4. Enter Google Analytics Timeline Parameters
Step4: 5. Execute Google Analyt... |
5,414 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'fgoals-f3-h', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
5,415 | <ASSISTANT_TASK:>
Python Code:
!pip install stim
import stim
circuit = stim.Circuit()
# First, the circuit will initialize a Bell pair.
circuit.append_operation("H", [0])
circuit.append_operation("CNOT", [0, 1])
# Then, the circuit will measure both qubits of the Bell pair in the Z basis.
circuit.append_operation("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: 3. Create a simple circuit, and sample from it.
Step2: You can sample from the circuit using the circuit.compile_sampler() method to get a samp... |
5,416 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%ls
%ls rr-intro-data-v0.2/intro/data/
gap_5060 = pd.read_csv('rr-intro-data-v0.2/intro/data/gapminder-5060.csv')
gap_5060_CA = gap_5060.loc[gap_5060['country'] == 'Canada']
%matplotlib inline
gap_5060_CA.plot(kind='line', x='year', y='lifeExp')
pass
gap_5060.loc... | <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: Notebook
Step2: Both the magic functions and the python ones support tab-completion
Step3: Data
Step4: Task 1
Step5: Visualize
Step6: Task ... |
5,417 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import holoviews as hv
from IPython.display import HTML
hv.notebook_extension()
xs = range(10)
ys = np.exp(xs)
table = hv.Table((xs, ys), kdims=['x'], vdims=['y'])
table
hv.Scatter(table) + hv.Curve(table) + hv.Bars(table)
print(repr(hv.Scatter({'... | <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: Simple Dataset
Step2: However, this data has many more meaningful visual representations, and therefore the first important concept is that Dat... |
5,418 | <ASSISTANT_TASK:>
Python Code:
x = 5**3
print(x)
import math
# Calculate square root of 25
x = math.sqrt(25)
print (x)
# Calculate cube root of 64
cr = round(64 ** (1. / 3))
print(cr)
import math
print (9**0.5)
print (math.sqrt(9))
import math
x = math.log(16, 4)
print(x)
import math
# Natural log of 29
print (math... | <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: Multiplying a number by itself twice or three times to calculate the square or cube of a number is a common operation, but you can raise a numbe... |
5,419 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import theano
from theano import tensor
#from blocks import initialization
from blocks.bricks import Identity, Linear, Tanh, MLP, Softmax
from blocks.bricks.lookup import LookupTable
from blocks.bricks.recurrent import SimpleRecurrent, Bidirectional, BaseRecurrent
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: Now the output layer needs to gather the two hidden layers (one from each direction)
Step2: Note that in order to double the input we had to a... |
5,420 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
data_dir = "./Data/Weather/"
!curl -o $data_dir/STAT.pickle http://mas-dse-open.s3.amazonaws.com/Weather/STAT.pickle
import pickle
STAT,STAT_description=pickle.load(open(data_dir+'/STAT.pickle','r'))
STAT.keys()
STAT_description
Scalars=['mean','std','low1000','low100','hi... | <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: Downloading Pickled data from S3
Step2: Get the statistics from the Pickle File
Step3: Script for plotting yearly plots
Step4: Plot the follo... |
5,421 | <ASSISTANT_TASK:>
Python Code:
from nupic.engine import Network, Dimensions
# Create Network instance
network = Network()
# Add three TestNode regions to network
network.addRegion("region1", "TestNode", "")
network.addRegion("region2", "TestNode", "")
network.addRegion("region3", "TestNode", "")
# Set dimensions on fir... | <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: Render with nupic.frameworks.viz.NetworkVisualizer, which takes as input any nupic.engine.Network instance
Step2: That's interesting, but not n... |
5,422 | <ASSISTANT_TASK:>
Python Code:
# Print periodic table to orient ourselves
Element.print_periodic_table()
# Generate list of non-radioactive elements (noble gases omitted)
def desired_element(elem):
omit = ['Po', 'At', 'Rn', 'Fr', 'Ra']
return not e.is_noble_gas and not e.is_actinoid and not e.symbol in omit
ele... | <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: How complete is the Materials Project database?
Step2: Ternaries
Step3: Why is there a discrepancy between the number of unique ternaries of t... |
5,423 | <ASSISTANT_TASK:>
Python Code:
import itertools
import string
import functools
letters = string.ascii_lowercase
vocab = list(map(''.join, itertools.product(letters, repeat=2)))
from random import choices
def zipf_pdf(k):
return 1/k**1.07
def exponential_pdf(k, base):
return base**k
def new_document(n_words, pdf... | <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: Based on the way we are choosing words, we say that 410 pairs out of 1000 documents have a high enough jaccard to call them similar. This seems ... |
5,424 | <ASSISTANT_TASK:>
Python Code:
# First check the Python version
import sys
if sys.version_info < (3,4):
print('You are running an older version of Python!\n\n' \
'You should consider updating to Python 3.4.0 or ' \
'higher as the libraries built for this course ' \
'have only been test... | <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: Session 2 - Training a Network w/ Tensorflow
Step2: <a name="assignment-synopsis"></a>
Step3: Remember, having series of linear followed by no... |
5,425 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from IPython.html import widgets
from IPython.html.widgets import interact
from IPython.display import display
tab1_children = [widgets.ButtonWidget(description="ButtonWidget"),
widgets.CheckboxWidget(description="Checkb... | <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: Build-it Widgets
Step2: Simple Example
Step3: Now we will test it using a code cell
Step4: Using interact function
Step5: Controlling a Char... |
5,426 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-1', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<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... |
5,427 | <ASSISTANT_TASK:>
Python Code:
from pyturb.gas_models import GasMixture
gas_mix = GasMixture(gas_model='Perfect')
gas_mix.add_gas('O2', mass=0.5)
gas_mix.add_gas('H2', mass=0.5)
gas_mix.mixture_gases
gas_mix2 = GasMixture(gas_model='Perfect')
gas_mix2.add_gas('O2', moles=0.5)
gas_mix2.add_gas('H2', moles=0.5)
gas_mix... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: To inspect the gas mixture contidions, we can use Pandas Dataframe contained in gas_mixture
Step2: Note that the gas_mixture dataframe contains... |
5,428 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import theano
import theano.tensor as tt
import kalman
# True values
T = 500 # Time steps
sigma2_eps0 = 3 # Variance of the observation noise
sigma2_eta0 = 10 # Variance in the update of the mean
# Simulate data
np.rando... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: We will use the same data as in the 01_RandomWalkPlusObservation notebook.
Step2: Next, we create all the tensors required to describe our mode... |
5,429 | <ASSISTANT_TASK:>
Python Code:
import supp_functions as fce
import xarray as xr
import pandas as pd
import statsmodels.api as sm
import numpy as np
import matplotlib.pyplot as plt
s_year = 1979
e_year = 2009
vari ='t'
in_dir = '~/'
in_netcdf = in_dir + 'jra55_tmp_1960_2009_zm.nc'
ds = xr.open_dataset(in_netcdf)
times... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Data opening
Step2: Variable and period of analysis selection
Step3: Deseasonalizing
Step4: Regressor loading
Step5: Regression function
Ste... |
5,430 | <ASSISTANT_TASK:>
Python Code:
from numpy.random import standard_normal # Gaussian variables
N = 1000; P = 5
X = standard_normal((N, P))
W = X - X.mean(axis=0,keepdims=True)
print(dot(W[:,0], W[:,1]))
from sklearn.decomposition import PCA
S=PCA(whiten=True).fit_transform(X)
print(dot(S[:,0], S[:,1]))
from numpy.rand... | <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: I'll skip ahead and use a pre-canned PCA routine from scikit-learn (but we'll dig into it a bit later!) Let's see what happens to the transforme... |
5,431 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
import pyensae.datasource
pyensae.datasource.download_data("matrix_distance_7398.zip", website = "xd")
import pandas
df = pandas.read_csv("matrix_distance_7398.txt", sep="\t", header=None, names=["v1","v2","distance"])
df.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: La programmation dynamique est une façon de résoudre de manière similaire une classe de problèmes d'optimisation qui vérifie la même propriété. ... |
5,432 | <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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<USER_TASK:>
Description:
Step1: In the previous chapter we developed a population model where net growth during each time step is proportional to the current population. This m... |
5,433 | <ASSISTANT_TASK:>
Python Code:
dataset = nilmtk.DataSet('/data/mine/vadeec/merged/ukdale.h5')
dataset.set_window("2014-06-01", "2014-07-01")
BUILDING = 1
elec = dataset.buildings[BUILDING].elec
fridge = elec['fridge']
activations = fridge.get_activations()
print("Number of activations =", len(activations))
activatio... | <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: Next, to speed up processing, we'll set a "window of interest" so NILMTK will only consider one month of data.
Step2: Get the ElecMeter associa... |
5,434 | <ASSISTANT_TASK:>
Python Code:
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train[:100]
y_train = y_train[:100]
print(x_train.shape) # (60000, 28, 28)
print(y_train.shape) # (60000,)
print(y_train[:3]) # array([7, 2, 1], dtype=uint8)
# Initialize the image regressor.
reg = ak.ImageRegressor(... | <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 second step is to run the ImageRegressor. It is recommended have more
Step2: Validation Data
Step3: You can also use your own validation ... |
5,435 | <ASSISTANT_TASK:>
Python Code:
import os
PATH="/Users/david/Desktop/CourseWork/TheArtOfDataScience/claritycontrol/code/scripts/" # use your own path
os.chdir(PATH)
import clarity as cl # I wrote this module for easier operations on data
import clarity.resources as rs
import csv,gc # garbage memory collection :)
impor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step 1
Step1: Histogram data preparation
Step2: Scale data
Step3: Setup Step
Step4: Steps 4 & 5
Step5: Step 6
Step6: Step 7
|
5,436 | <ASSISTANT_TASK:>
Python Code:
from pypot.creatures import PoppyErgoJr
poppy = PoppyErgoJr(use_http=True, use_snap=True)
# If you want to use another robot (humanoid, torso, ...) adapt this code
#from pypot.creatures import PoppyTorso
#poppy = PoppyTorso(use_http=True, use_snap=True)
# If you want to use the robot with... | <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: Second\
Step2: 2.a. Access to API to get values
Step3: 2.b. Get value - with single input -
Step4: http
Step5: 2.b Get value - with multiple... |
5,437 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import linear_model
import math
from scipy import stats
%matplotlib inline
data = pd.read_csv('Default.csv')
data = data.drop('Unnamed: 0',axis = 1)
#change Yes, No to 1, 0.
data['def_chg'] = data.default... | <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: Part 1
Step2: Part 3
Step3: In statistics, the p-value represents the probablity of extreme value by assuming H0 is true. When p-value is smal... |
5,438 | <ASSISTANT_TASK:>
Python Code:
# imports / display plots in cell output
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as ss
import pandas as pd
import seaborn as sns
import statsmodels
# Bayesian Model Selection (bor = .6240)
# Model 1: inverse temperature, stickiness, learni... | <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: Experiment 1
Step2: Experiment 2
Step3: Experiment 2
Step4: Experiment 3
Step5: Experiment 3
Step6: Experiment 4
Step7: Experiment 4
Step8... |
5,439 | <ASSISTANT_TASK:>
Python Code:
def expect_value(k, p):
steps = [k / p / (k - i) for i in range(k)]
return sum(steps)
k = 10
ps = [1., .5, .33, .25, .2, .1]
count = np.vectorize(lambda p: expect_value(k, p), otypes=[np.float])(ps)
plt.scatter(ps, count)
plt.xlabel('Lion probability')
plt.ylabel('Purchase count'... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Если бы в каждом яйце был львенок, нужно было бы в среднем купить 29.29 яиц, чтобы собрать коллекцию. Но когда львенок в каждом третьем - это уж... |
5,440 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation
from IPython.display import HTML
font = {'size' : 15}
matplotlib.rc('font', **font)
m = 16
L = 2*np.pi
xi=np.fft.fftfreq(m)*m/(L/(2*np.pi))
print(xi)
from ipywidgets imp... | <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 FFT, aliasing, and filtering
Step2: As you can see, the return vector starts with the nonnegative wavenumbers, followed by the negative wav... |
5,441 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import zipfile
dataset_folder_path = 'data'
dataset_filename = 'text8.zip'
dataset_name = 'Text8 Dataset'
class DLProgress(tq... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load the text8 dataset, a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the data folder. ... |
5,442 | <ASSISTANT_TASK:>
Python Code:
#импортируем библиотеки
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.cluster import DBSCAN
plt.figure(figsize=(12, 12))
n_samples = 2300
random_state = 220
X, y = make_blobs(n_samples=n_samples, ... | <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: Как видим распредение по кластерам оказалось вполне логичным, не смотря на выбор параметров по умолчанию, за исключением второго случая, но там ... |
5,443 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', '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... |
5,444 | <ASSISTANT_TASK:>
Python Code:
#include some package which we use later on
import numpy as np
#test np.ar -> tab
a = np.array([1,2,3,4])
#test np.array -> shift-tab or np.array?
1+2
3+4
10/2
print(5+2)
3+2
a = 5+2
b = 9
a/b
def sum(a,b): #indent is important in Python!
return a+b
sum(4,4)
def sub(arg1,arg2):
r... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Interactive Python basics
|
5,445 | <ASSISTANT_TASK:>
Python Code:
!pygmentize message-dumper.yaml
!kubectl apply -f message-dumper.yaml
!pygmentize broker.yaml
!kubectl create -f broker.yaml
!pygmentize trigger.yaml
!kubectl apply -f trigger.yaml
!pygmentize sklearn-logging.yaml
!kubectl apply -f sklearn-logging.yaml
CLUSTER_IPS=!(kubectl -n istio-sy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create a channel broker.
Step2: Create a Knative trigger to pass events to the message logger.
Step3: Create an sklearn model with associated ... |
5,446 | <ASSISTANT_TASK:>
Python Code:
s = 'Hello world!'
print(s)
print("length is", len(s))
us = 'Hello 世界!'
print(us)
print("length is", len(us))
bs = s.encode('utf-8')
print(bs)
print("length is", len(bs))
bus = us.encode('utf-8')
print(bus)
print("length is", len(bus))
print(bs.decode('utf-8'))
print(bus.decode('utf-8')... | <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: Now encode both strings to bytes.
Step2: Decode back to strings.
Step3: Big Endian vs Little Endian
Step4: struct package
Step5: struct.pack... |
5,447 | <ASSISTANT_TASK:>
Python Code:
# import libraries
from __future__ import division
import numpy as np
import os
import matplotlib.pyplot as plt
from pyphysio.tests import TestData
%matplotlib inline
# import all pyphysio classes and methods
import pyphysio as ph
# import data and creating a signal
ecg_data = TestData.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: 4.1.1 Creation of custom segments
Step2: And then use the function CustomSegments to use the defined instants for the segmentation
Step3: Then... |
5,448 | <ASSISTANT_TASK:>
Python Code:
# list
my_list = [1, 4, 5, 9]
print(my_list)
type(my_list)
# accessing each element by index
print(my_list[2])
len(my_list)
# assigning new value
my_list[1] = 12
print(my_list)
# append an element at the end
my_list.append(7)
print(my_list)
help(list)
# String
my_name = 'Anne' # it is als... | <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: Session 1.4
Step2: Exercises 1.4.1
|
5,449 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tulipy as ti
ti.TI_VERSION
DATA = np.array([81.59, 81.06, 82.87, 83, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36,
85.53, 86.54, 86.89, 87.77, 87.29])
def print_info(indicator):
print("Type:", indicator.type)
print("Full ... | <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: Information about indicators are exposed as properties
Step2: Single outputs are returned directly. Indicators returning multiple outputs use
S... |
5,450 | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import mixed_norm, make_stc_from_dipoles
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: Run solver
Step2: Plot dipole activations
Step3: Plot residual
Step4: Generate stc from dipoles
Step5: View in 2D and 3D ("glass" brain like... |
5,451 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import gym
import matplotlib.pyplot as plt
%matplotlib inline
try:
xrange = xrange
except:
xrange = range
env = gym.make('CartPole-v0')
gamma = 0.99
def discount_rewards(r):
take 1D float 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:
Step2: The Policy-Based Agent
Step3: Training the Agent
|
5,452 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
SIZE = 20
prob = np.random.uniform(low=0.0, high=1.0, size=SIZE)
prob = prob/np.sum(prob)
x = range(0,len(prob))
plt.figure(figsize=(10,2))
plt.bar(x, prob, 0.3)
plt.xticks(x, x)
plt.show()
result ... | <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 create a probability vector!
Step2: Using np.random.choice you can use the probability vector to pick random number that will follow the ... |
5,453 | <ASSISTANT_TASK:>
Python Code:
i = -7
j = 123
print(i, j)
x = 3.14159
y = -42.3
print(x * y)
k = 1.5e3
l = 3e-2
print(k)
print(l)
s = "ATGTCGTCTACAACACT"
t = 'Serine'
u = "It's a string with apostrophes"
v = A string that extends
over multiple lines
print(v)
a = True
b = False
print(a, b)
z = None
print(z)
a = Tr... | <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: Floats
Step2: Floating point numbers can also carry an <tt>e</tt> suffix that states which power of ten they operate at.
Step4: Strings
Step5:... |
5,454 | <ASSISTANT_TASK:>
Python Code:
import chaospy
normal = chaospy.Normal(mu=2, sigma=2)
normal
samples = normal.sample(4, seed=1234)
samples
from matplotlib import pyplot
pyplot.hist(normal.sample(10000, seed=1234), 30)
pyplot.show()
normal.sample([2, 2], seed=1234)
import numpy
numpy.random.seed(1234)
normal.sample(4... | <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 distribution have a few methods that the user can used, which has names
Step2: These can be used to create e.g. histograms
Step3: The inpu... |
5,455 | <ASSISTANT_TASK:>
Python Code:
!pip install -qq git+git://github.com/lindermanlab/ssm-jax-refactor.git
try:
import ssm
except ModuleNotFoundError:
%pip install -qq ssm
import ssm
import jax.numpy as np
import jax.random as jr
import jax.experimental.optimizers as optimizers
from jax import jit, value_and_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Imports and Plotting Functions
Step2: Sample some synthetic data from the Poisson LDS
Step3: Inference
|
5,456 | <ASSISTANT_TASK:>
Python Code:
print las._text
lasio.ExcelConverter(las).write('example.xlsx')
import pandas
xls_header_sheet = pandas.read_excel('example.xlsx', sheetname='Header')
xls_header_sheet
xls_data_sheet = pandas.read_excel('example.xlsx', sheetname='Curves')
xls_data_sheet
converter = lasio.ExcelConvert... | <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: Now use the ExcelConverter object to produce an Excel spreadsheet
Step2: we can import this spreadsheet back into Python directly using pandas
... |
5,457 | <ASSISTANT_TASK:>
Python Code:
import time
import sys
import random
from pybel.utils import get_version
from pybel.struct.mutation import infer_child_relations
from pybel_tools.visualization import *
from pybel.examples.statin_example import statin_graph, hmgcr_inhibitor, hmgcr, ec_11134
print(time.asctime())
print(sy... | <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: Environment
Step2: Dependencies
Step3: Example Graph
Step4: Propogation on Chemical Hierarchy
Step5: Propogation on Protein Hierarchy
|
5,458 | <ASSISTANT_TASK:>
Python Code:
# To visualize plots in the notebook
%matplotlib inline
# Imported libraries
import csv
import random
import matplotlib
import matplotlib.pyplot as plt
import pylab
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import PolynomialFeatures
from sklearn... | <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. Introduction
Step2: 2.2. Classifiers based on the logistic model.
Step3: 3.3. Nonlinear classifiers.
Step4: 3. Inference
Step5: Now, we s... |
5,459 | <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: Running TFLite models
Step2: Create a basic model of the form y = mx + c
Step3: Generate a SavedModel
Step4: Convert the SavedModel to TFLite... |
5,460 | <ASSISTANT_TASK:>
Python Code:
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for... | <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: Extract Features
Step3: Train SVM on features
Step4: Inline question 1
|
5,461 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import openaq
import warnings
warnings.simplefilter('ignore')
%matplotlib inline
# Set major seaborn asthetics
sns.set("notebook", style='ticks', font_scale=1.0)
# Increase the quality of in... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Choosing Locations
Step2: Let's go ahead and filter our results to only grab locations that have been updated in 2017 and have at least 100 dat... |
5,462 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from os.path import join, exists, expandvars
import pandas as pd
from IPython.display import display, Markdown
import seaborn.xkcd_rgb as colors
from tax_credit.plotting_functions import (pointplot_from_data_frame,
boxplot_from... | <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: Configure local environment-specific values
Step2: Find mock community pre-computed tables, expected tables, and "query" tables
Step3: Restric... |
5,463 | <ASSISTANT_TASK:>
Python Code:
!hostname
%load_ext autoreload
%autoreload 2
%matplotlib inline
import ipyrad
import ipyrad.analysis as ipa
import ipyparallel as ipp
from ipyrad.analysis.popgen import Popgen
from ipyrad import Assembly
from ipyrad.analysis.locus_extracter import LocusExtracter
ipyclient = ipp.Client(clu... | <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: Development of the Processor class to calculate all the stats
Step2: Prototyping the dcons function to split alleles per base
Step3: Loading p... |
5,464 | <ASSISTANT_TASK:>
Python Code:
import numpy as np # Matrix and vector computation package
np.seterr(all='ignore') # ignore numpy warning like multiplication of inf
import matplotlib.pyplot as plt # Plotting library
from matplotlib.colors import colorConverter, ListedColormap # some plotting functions
from matplotlib 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: Loss function, chain rule and its derivative
Step2: Plot the cost function and as you can see it's convex and has global optimal minimum.
Step3... |
5,465 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (13,8)
df = pd.read_csv("./winequality-red.csv")
df.head()
df.shape
#df.loc[df.b > 0, 'd'] = 1
df.loc[df.quality > 5, '... | <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: Wine Category
Step2: This is the frequency count for each category
Step3: Visual Exploration
Step4: Alcohol vs Category
Step5: Exercise
Step... |
5,466 | <ASSISTANT_TASK:>
Python Code:
test_sentences = [
"the men saw a car .",
"the woman gave the man a book .",
"she gave a book to the man .",
"yesterday , all my trouble seemed so far away ."
]
import nltk
from nltk.corpus import treebank
from nltk.grammar import ProbabilisticProduction, PCFG
# Production... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Aufgabe 2 Informationsextraktion per Syntaxanalyse
Step3: Hausaufgaben
Step5: Aufgabe 4 Mehr Semantik fü... |
5,467 | <ASSISTANT_TASK:>
Python Code:
from itertools import combinations
import skrf as rf
%matplotlib inline
from pylab import *
rf.stylely()
wg = rf.wr10
wg.frequency.npoints = 101
dut = wg.random(n_ports = 4,name= 'dut')
dut
loads = [wg.load(.1+.1j),
wg.load(.2-.2j),
wg.load(.3+.3j),
wg.loa... | <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: First, we create a Media object, which is used to generate networks for testing. We will use WR-10 Rectangular waveguide.
Step2: Next, lets gen... |
5,468 | <ASSISTANT_TASK:>
Python Code:
from msmbuilder.example_datasets import QuadWell
from msmbuilder.msm import MarkovStateModel
from msmbuilder.lumping import MVCA
import numpy as np
import scipy.cluster.hierarchy
import matplotlib.pyplot as plt
% matplotlib inline
q = QuadWell(random_state=998).get()
ds = q['trajectories... | <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: Get the dataset
Step2: Define a regular spatial clusterer
Step3: Plot our MSM energies
Step4: Make a model with out macrostating to get linka... |
5,469 | <ASSISTANT_TASK:>
Python Code:
# flipping signs of numbers...
a = 5
b = -5
print(-a, -b)
# len function
x1 = []
x2 = "12"
x3 = [1,2,3]
print(len(x1), len(x2), len(x3))
x = [1,2,3]
print(x[100]) # <--- IndexError! 100 is waayyy out of bounds
string = "hello"
print(string[0]) # first item
print(string[len... | <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: Now, those bounds I have just given might sound a bit arbitrary, but actually I can explain exactly how they work. Consider the following pictur... |
5,470 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from pandas import DataFrame
url="https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data"
df = pd.read_csv(url,header=None)
df.describe()
pd.options.display.max_columns=70
df.describe()
import numpy as np
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: <h4>See all columns</h4>
Step2: <h4>Examine the distribution of the data in column 4</h4>
Step3: <h4>Examine the dependent variable</h4>
Step4... |
5,471 | <ASSISTANT_TASK:>
Python Code:
import gdsfactory as gf
c = gf.Component("pads")
pt = c << gf.components.pad_array(orientation=270, columns=3)
pb = c << gf.components.pad_array(orientation=90, columns=3)
pt.move((70, 200))
c
c = gf.Component("pads_with_routes_with_bends")
pt = c << gf.components.pad_array(orientation=27... | <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: route_quad
Step2: get_route_from_steps
Step3: Bundle of routes (get_bundle_electrical)
Step4: get bundle from steps
|
5,472 | <ASSISTANT_TASK:>
Python Code:
def win_series(p, W=0, L=0):
"Probability of winning best-of-7 series, given a probability p of winning a game."
return (1 if W == 4 else
0 if L == 4 else
p * win_series(p, W + 1, L) +
(1 - p) * win_series(p, W, L + 1))
win_series(0.58)
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: In other words, if you have a 58% chance of winning a game, you have a 67% chance of winning the series.
Step2: And here's a function to tabula... |
5,473 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The AS... | <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: Map
Step2: Examples
Step3: <table align="left" style="margin-right
Step4: <table align="left" style="margin-right
Step5: <table align="left"... |
5,474 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Loading Remote Data in TFF
Step2: Preparing the input data
Step3: We'll construct a preprocessing function to transform the raw examples in th... |
5,475 | <ASSISTANT_TASK:>
Python Code:
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Introduction to scikit-learn
Step2: That's a lot to take in. Let's examine this loaded data a little more closely. First we'll see what data ty... |
5,476 | <ASSISTANT_TASK:>
Python Code::
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
# Step 1: Set range of clusters to try and
# create inertia values dictionary
clusters_range = (1,10)
inertia_values = {}
# Step 2: For each set of clusters fit a kmeans algorithm and add
# inertia value to interia value... | <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:
|
5,477 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
Apenas a partir dos valores
obj = pd.Series([4, 7, -5, 3])
obj
obj.values
obj.index
A partir dos valores e dos índices
obj2 = pd.Series([4, 7, -5, 3], index=['d','b','a','c'])
obj2
obj2.index
A partir de um dictionary
sdata = {'Ohio': 35000, '... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step5: Series
Step6: Acessando elementos de uma Series
Step12: Algumas operações permitidas em uma Series
Step17: DataFrame
Step27: Note que estas ... |
5,478 | <ASSISTANT_TASK:>
Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
from sklearn.cross_validation import ShuffleSplit
from IPython.display import display
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Data Exploration
Step2: Feature Observation
Step4: Developing a Model
Step5: Implementation
Step6: Benefit of splitting the data set into Tr... |
5,479 | <ASSISTANT_TASK:>
Python Code:
import emcee
from dustcurve import model
import seaborn as sns
import numpy as np
from dustcurve import pixclass
import matplotlib.pyplot as plt
import pandas as pd
import warnings
from dustcurve import io
from dustcurve import hputils
from dustcurve import kdist
import h5py
from dustcurv... | <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 see what our chains look like by producing trace plots
Step2: Now we are going to use the seaborn distplot function to plot histograms of... |
5,480 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
df = pd.read_csv("hanford.csv")
df.head()
df.mean()
df.median()
#range
df["Exposure"].max() - df["Exposure"].min()
#range
df["Mortality"].max() - df["Mortality"].min()
df.std()
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2. Read in the hanford.csv file in the data/ folder
Step2: 3. Calculate the basic descriptive statistics on the data
Step3: 4. Find a reasonab... |
5,481 | <ASSISTANT_TASK:>
Python Code:
#|all_slow
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
pretrained_weights = 'gpt2'
tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_weights)
model = GPT2LMHeadModel.from_pretrained(pretrained_weights)
ids = tokenizer.encode('This is an example of text, and')
ids... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: In this tutorial, we will see how we can use the fastai library to fine-tune a pretrained transformer model from the transformers library by Hug... |
5,482 | <ASSISTANT_TASK:>
Python Code:
import time
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <h2> Get the xcms data </h2>
Step2: <h2> Get mappings between sample names, file names, and sample classes </h2>
Step3: <h2> Convert class lab... |
5,483 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
features_dataframe = load_data()
n = features_dataframe.shape[0]
train_size = 0.8
test_size = 1 - train_size + 0.005
train_dataframe = features_dataframe.iloc[int(n * test_size):]
test_dataframe = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
5,484 | <ASSISTANT_TASK:>
Python Code:
# Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
# Alex Rockhill <aprockhill@mailbox.org>
#
# License: BSD-3-Clause
from mne.io.fiff.raw import read_raw_fif
import numpy as np
from matplotlib import pyplot as plt
from os import path as op
import mne
from mne.viz im... | <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: Project 3D electrodes to a 2D snapshot
Step3: Manually creating 2D electrode positions
|
5,485 | <ASSISTANT_TASK:>
Python Code:
!pip install -r requirements.txt
import pandas as pd
import numpy as np
df=pd.read_csv('talks.csv')
df.head()
year_labeled=
year_predict=
description_labeled = df[df.year==year_labeled]['description']
description_predict = df[df.year==year_predict]['description']
from sklearn.feature_e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Exercise A
Step2: Here is a brief description of the interesting fields.
Step3: Quick Introduction to Text Analysis
Step4: Extra Credit
Step5... |
5,486 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (9,6)
df = pd.read_csv("../data/creditRisk.csv")
df.head()
from plotnine import *
ggplot(df, aes(x = "Income", y = "Credit History", color = ... | <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: Plotting the Data
Step2: Preparing Data
Step3: Lets use a dictionary for encoding nominal variable
Step4: Classifier - Logistic Regression
St... |
5,487 | <ASSISTANT_TASK:>
Python Code:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_... | <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: Flower power
Step2: ConvNet Codes
Step3: Below I'm running images through the VGG network in batches.
Step4: Building the Classifier
Step5: ... |
5,488 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-2', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
5,489 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(123)
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (10, 6)
plt.set_cmap("viridis")
from skopt.benchmarks import branin as _branin
def branin(x, noise_level=0.):
return _branin(x) + noise_level * np.random.randn()... | <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: Bayesian optimization or sequential model-based optimization uses a surrogate model
Step2: This shows the value of the two-dimensional branin f... |
5,490 | <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 = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
chars = np.array([vocab_to_int[c] for c ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 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... |
5,491 | <ASSISTANT_TASK:>
Python Code:
import sys
import random
import numpy as np
import heapq
import json
import time
BIG_PRIME = 9223372036854775783
def random_parameter():
return random.randrange(0, BIG_PRIME - 1)
class Sketch:
def __init__(self, delta, epsilon, k):
Setup a new count-min sketch wit... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step5: Basic Idea of Count Min sketch
Step6: Is it possible to make the sketch so coarse that its estimates are wrong even for this data set?
Step7: ... |
5,492 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-2', 'toplevel')
# 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: 2... |
5,493 | <ASSISTANT_TASK:>
Python Code:
# Import libraries
import pandas as pd
import numpy as np
# Turn off notebook package warnings
import warnings
warnings.filterwarnings('ignore')
# print graphs in the document
%matplotlib inline
import seaborn as sns
import statsmodels.formula.api as sm #Import Package
model = sm.ols(fo... | <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 the data with Pandas
Step2: Generate the same graph as above, but this time log-transform the population variable
Step3: Example Results ... |
5,494 | <ASSISTANT_TASK:>
Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(path)... | <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: Seq2Seq
Step2: The next two code chunks
Step3: The tokenizer is language specific, e.g. it knows that in the English language don't should be ... |
5,495 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
AIP_CLIENT_WHEEL = "aiplatform_pipelines_client-0.1.0.caip20201123-py3-none-any.whl"
AIP_CLIENT_WHEEL_GCS_LOCATION = (
f"gs://cloud-aiplatform-pipelines/releases/20201123/{AIP_CLIENT_WHEEL}"
)
!gsutil cp {AIP_CLIENT_WHEEL_GCS_LOCATION} {AIP_CLIENT_W... | <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: Setting up the notebook's environment
Step2: Restart the kernel.
Step3: Import notebook dependencies
Step4: Configure GCP environment
Step5: ... |
5,496 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def soliton(x, t, c, a):
Return phi(x, t) for a soliton wave with constants c and a.
p=.5*c*((1/np.cosh((c*... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Using interact for animation with data
Step3: To create an animation of a soliton propagating in time, we are going to precompute the soliton d... |
5,497 | <ASSISTANT_TASK:>
Python Code:
# from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
# Standard imports
%matplotlib inline
import os
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
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: 13.765.202 lines in train.csv
Step2: Per wikipedia, a value of more than 421 mm/h is considered "Extreme/large hail"
Step3: We regroup the d... |
5,498 | <ASSISTANT_TASK:>
Python Code:
from pprint import pprint
# I, Python am built from types, such as builtin types:
the_builtins = dir(__builtins__) # always here
pprint(the_builtins[-10:]) # no need to import
for the_string in ["list", "tuple", "dict", "int", "float"]:
if the_string in the_builtins:
print("... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Lets check our understanding that the native types -- the ones we count on to build more complex types -- live in builtins
Step4: And now for s... |
5,499 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import statsmodels.api as sm
from scipy import stats
from matplotlib import pyplot as plt
plt.rc("figure", figsize=(16,8))
plt.rc("font", size=14)
print(sm.datasets.star98.NOTE)
data = sm.datasets.star98.load(as_pandas=False)
data.exog = sm.add_cons... | <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: GLM
Step2: Load the data and add a constant to the exogenous (independent) variables
Step3: The dependent variable is N by 2 (Success
Step4: ... |
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