Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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14,500 | <ASSISTANT_TASK:>
Python Code:
import rebound
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
def run_sim(a):
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(primary=sim.particles[0],m=1e-3, a=1)
sim.add(primary=sim.particles[0],m=1e-3, a=a)
sim.integra... | <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 setup a planetary system with two Jupiter mass planets. The following function takes that system, integrates it forward in time by 10 orbits ... |
14,501 | <ASSISTANT_TASK:>
Python Code:
# Import modules
import time
import math
import random
import numpy as np
import scipy
import sympy
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def linear_congruential_generator(x, a, b, m):
x = (a * x + b) % m
u = x / m
return u, x, a, b, m
x0 = 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: 9.1 Random Numbers
Step2: Example
Step3: Minimal standard random number generator
Step4: Example
Step5: Example
Step6: For its visualizatio... |
14,502 | <ASSISTANT_TASK:>
Python Code:
#%% librerias
import pandas as pd
# Crear un dataFrame
data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
'Sales':[200,120,340,124,243,350]}
df = pd.DataFrame(data)
df
df.groupby('Company')
by_comp = df.gro... | <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: Ahora ya podemos usar la funcion .groupby() para agrupar la informacion en base a los nombres de las columnas. Agrupemos la informacion por el n... |
14,503 | <ASSISTANT_TASK:>
Python Code:
%%capture --no-stderr
!pip3 install kfp --upgrade
import kfp.components as comp
dataflow_template_op = comp.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/1.7.0-rc.3/components/gcp/dataflow/launch_flex_template/component.yaml')
help(dataflow_template_o... | <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. Load the component using KFP SDK
Step2: 3. Configure job parameters
Step3: 4. Example pipeline that uses the component
Step4: 5. Create pi... |
14,504 | <ASSISTANT_TASK:>
Python Code:
T = 3.0 # duration in seconds
fs = 44100.0 # sampling rate in Hertz
f0 = 440*numpy.logspace(-2, 1, T*fs, endpoint=False, base=2.0) # time-varying frequency
print f0.min(), f0.max() # starts at 110 Hz, ends at 880 Hz
t = numpy.linspace(0, T, T*fs, endpoint=False)
x = 0.01*numpy.sin(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: Create the sweep signal
Step2: Listen to the signal
Step3: Segmentation Using Python List Comprehensions
Step4: librosa.util.frame
Step5: (T... |
14,505 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-cm2-vhr4', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name",... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
14,506 | <ASSISTANT_TASK:>
Python Code:
import pandas
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
td = pandas.read_csv('titanic_train.csv')
td.info()
surivors = td[td.Survived==1]
dead = td[td.Survived==0]
plt.figure(figsize=(13,6))
plt.hist(surivors.Fare, alpha=.5, bins=np.arange(0,300,10), label="... | <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: Data
Step2: The data we care about for this hypothesis(Survived, Fare) has no NaN values so no need to modify.
Step3: Hypothesis
Step4: Based... |
14,507 | <ASSISTANT_TASK:>
Python Code:
%%bash
# example of the input file structure and naming: a plain folder with unzipped backward and forward fastq files
ls ../../data/raw/fastq/ | head -n 20
from IPython.display import Image, display
img1 = Image("../../data/processed/fastqc_results/raw/quality_summary_all_samples_1.png"... | <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. Quality-check your raw (and dirty) reads
Step2: The two plots produced by the R-script show summary statistics for each individual test (tes... |
14,508 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pyJHTDB
t = np.linspace(0, 1, 64)
x = np.zeros((t.shape[0], t.shape[0], 3), np.float32)
x[:, :, 0] = t[np.newaxis, :]
x[:, :, 1] = t[:, np.newaxis]
x[:, :, 2] = .0
lJHTDB = pyJHTDB.libJHTDB()
lJHTDB.initialize()
#Add token
auth_token = "edu.... | <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'm going to create a 2D grid of points, and then get the values of the velocity at those points.
Step2: Since the dataset I'm gonna use is the... |
14,509 | <ASSISTANT_TASK:>
Python Code:
import urllib.request
rm_site = 'http://www.repeatmasker.org'
fn = 'ce10.fa.out.gz'
url = '%s/genomes/ce10/RepeatMasker-rm405-db20140131/%s' % (rm_site, fn)
urllib.request.urlretrieve(url, fn)
import gzip
import itertools
fh = gzip.open(fn, 'rt')
for ln in itertools.islice(fh, 10):
pr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Above are the first several lines of the .out.gz file for the roundworm (C. elegans). The columns have headers, which are somewhat helpful. Mo... |
14,510 | <ASSISTANT_TASK:>
Python Code:
BUCKET='ai-analytics-solutions-kfpdemo' # CHANGE to a bucket you own
import tensorflow as tf
import tensorflow_hub as tfhub
import os
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=[None,None,3]))
model.add(tfhub.KerasLayer("https://tfhub.dev/google/efficientnet/b4/feature... | <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: Embedding model for images
Step2: The model on TensorFlow Hub expects images of a certain size, and provided as normalized arrays.
Step3: Loa... |
14,511 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import tensorflow as tf
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
sess = tf.InteractiveSession()
x = tf.constant([True, False, False],... | <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 on notation
Step2: Q5. Given x, return the truth value of NOT x element-wise.
|
14,512 | <ASSISTANT_TASK:>
Python Code:
from urllib.request import urlretrieve
urlretrieve('http://sthiele.github.io/data/queens.lp','queens.lp')
urlretrieve('http://sthiele.github.io/data/facts.lp','facts.lp')
from pyasp.asp import *
goptions = ''
soptions = ' 2'
solver = Gringo4Clasp(gringo_options=goptions, clasp_options... | <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 the pyasp library.
Step2: Create a solver object.
Step3: Start the solver with some input.
Step4: The result is a list of the solution... |
14,513 | <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))
encoded = np.array([vocab_to_int[c] 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: 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... |
14,514 | <ASSISTANT_TASK:>
Python Code:
import cv2
import numpy as np
from scipy import misc
i = misc.ascent()
import matplotlib.pyplot as plt
plt.grid(False)
plt.gray()
plt.axis('off')
plt.imshow(i)
plt.show()
i_transformed = np.copy(i)
size_x = i_transformed.shape[0]
size_y = i_transformed.shape[1]
# This filter detects ed... | <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: Next, we can use the pyplot library to draw the image so we know what it looks like.
Step2: The image is stored as a numpy array, so we can cre... |
14,515 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from time import time
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rc('xtick', labelsize=14)
matplotlib.rc('ytick', labelsize=14)
import tensorflow as tf
from sklearn.model_selection import train_te... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: parameters to set
Step2: generate normal data
Step3: plot timeseries
Step4: create NN
Step5: This function actually checks for anomaly in on... |
14,516 | <ASSISTANT_TASK:>
Python Code:
def divide(numerator, denominator):
result = numerator/denominator
print("result = %f" % result)
divide(1.0, 0)
def divide1(numerator, denominator):
try:
result = numerator/denominator
print("result = %f" % result)
except:
print("You can't divide by... | <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: Why didn't we catch this SyntaxError?
Step3: What do you do when you get an exception?
|
14,517 | <ASSISTANT_TASK:>
Python Code:
filename = 'resultat.nc'
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import cartopy.crs as ccrs
from netCDF4 import Dataset
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
data = Dataset(filename)
longitude=data.variables['longitude'][:]
lat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Carte en moyenne temporelle sur la totalité de l'expérience
Step2: Carte en moyenne temporelle de $p_{sat}$ pour $H_2O$
Step3: Carte à $L_s$ d... |
14,518 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from numba import njit
arr2d = np.arange(20 * 30, dtype=float).reshape(20,30)
%%timeit
np.sum(arr2d)
def py_sum(arr):
M, N = arr.shape
sum = 0.0
for i in range(M):
for j in range(N):
sum += arr[i,j]
return sum
%%timeit
py_sum(arr2d)
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: ¿Impresionado? La primera vez que hemos llamado a la función, Python ha generado el código correspondiente al tipo de datos que le hemos pasado.... |
14,519 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 変数の概要
Step2: 変数の作成
Step3: 変数の外観と動作はテンソルに似ており、実際にデータ構造が tf.Tensor で裏付けられています。テンソルのように dtype と形状を持ち、NumPy にエクスポートできます。
Step4: ほとんどのテンソル演算は期待どおり... |
14,520 | <ASSISTANT_TASK:>
Python Code:
import numpy as np # for np.allclose() to check that S-params are similar
import skrf as rf
rf.stylely()
# reference LC circuit made in Designer
LC_designer = rf.Network('designer_capacitor_30_80MHz_simple.s2p')
# scikit-rf: manually connecting networks
line = rf.media.DefinedGammaZ0(fr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: LC Series Circuit
Step2: A More Advanced Equivalent Model
Step3: Pass band filter
|
14,521 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
tf.__version__
from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets("data/MNIST/", one_hot=False)
print("Siz... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: This was developed using Python 3.5.2 (Anaconda) and TensorFlow version
Step2: Load Data
Step3: The MNIST data-set has now been loaded and con... |
14,522 | <ASSISTANT_TASK:>
Python Code:
!nvidia-smi
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
logdir = '/root/pipeline/logs/tensorflow'
import numpy as np
import matplotlib.pyplot as plt
import datetime
from tensorflow.python.framework import ops
from tensorflow.python.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: Multiply 2 matrices
Step2: Sessions must be closed to release resources. We may use the 'with' syntax to close sessions automatically when com... |
14,523 | <ASSISTANT_TASK:>
Python Code:
numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'
numbers = [int(number) for number in numbers_str.split(',')]
max(numbers)
sorted(numbers)[10:]
threes = []
for item in numbers:
if item %3 == 0:
threes.append(item)
sorted(threes)
... | <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: In the following cell, complete the code with an expression that evaluates to a list of integers derived from the raw numbers in numbers_str, as... |
14,524 | <ASSISTANT_TASK:>
Python Code:
from bokeh.io import output_notebook, show
from bokeh.plotting import figure
import numpy as np
from scipy import stats
import cotede
output_notebook()
# Number of samples
N = 3000
# True mean and standard deviation of this dataset
mu, sigma = 0, 1
# Let's fix the random seed so everyone... | <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: Synthetic data
Step3: How does this dataset look like?
Step4: Data Distribution
Step5: We know that this dataset has a normal distribution, s... |
14,525 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import linalg
from matplotlib import pyplot as plt
%matplotlib inline
A = np.array([[1, 0.5],[0.5, 1]])
x = np.array([1.,0.])
A = np.array([[1., 0.5,-0.1],[0.5, 1.,10.0],[2.,3.,5.]])
x = np.array([1.,0.,0.])
print("A =\n",A)
print("x =",x)
def power_iterati... | <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: Matriz y vector de prueba
Step3: <div id='pi' />
Step5: <div id='invpi' />
Step7: <div id='rq' />
Step8: Preguntas
Step9: <div id='sp' />
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14,526 | <ASSISTANT_TASK:>
Python Code:
import pyensae
from jyquickhelper import add_notebook_menu
add_notebook_menu()
import pyensae
import pyensae.datasource
pyensae.datasource.download_data("velib_vanves.zip", website = "xd")
import pandas
df = pandas.read_csv("velib_vanves.txt",sep="\t")
df.head(n=2)
from pyensae.sql 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: Mix SQLite and DataFrame
Step2: As this file is small (just an example), let's see how it looks like with a DataFrame.
Step3: Then we import i... |
14,527 | <ASSISTANT_TASK:>
Python Code:
organism = "E. Coli"
treatment = "salt stress"
todays_headline = "Python bioformaticians among top paid professionals in the country"
print todays_headline
print workshop_venue
workshop_venue = "MSU Baroda"
print workshop_venue
print organism + treatment
print organism + " in " + trea... | <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 organism, treatment, todays_headline are all variable names
Step2: If you try to print or anyway use variable in which you have not stored... |
14,528 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
%pylab inline
import numpy as np
_=np.random.seed(123456)
import numpy as np
from scipy import stats
rv = stats.beta(3,2)
xsamples = rv.rvs(50)
%matplotlib inline
from matplotlib.pylab import subplots
fig,ax = subplots()
fig.set_size_inches(8,4)
_=ax.hist... | <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: As we have seen, outside of some toy problems, it can be very difficult or
Step2: Because this is simulation data, we already know that the
S... |
14,529 | <ASSISTANT_TASK:>
Python Code:
from openhunt.mordorutils import *
spark = get_spark()
sd_file = "https://raw.githubusercontent.com/OTRF/Security-Datasets/master/datasets/atomic/windows/lateral_movement/host/empire_psexec_dcerpc_tcp_svcctl.zip"
registerMordorSQLTable(spark, sd_file, "sdTable")
df = spark.sql(
'''
SELE... | <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 & Process Security Dataset
Step2: Analytic I
|
14,530 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import sklearn
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
# fit, then predict X
from sklearn.svm import SVR
svr_rbf = SVR(kernel='rbf')
svr_rbf.fit(X, y)
predict = svr_rbf.predict(X)
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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:
|
14,531 | <ASSISTANT_TASK:>
Python Code:
# Import modules
import math
import sympy as sym
import numpy as np
import scipy
import matplotlib.pyplot as plt
import plotly
import plotly.plotly as ply
import plotly.figure_factory as ply_ff
from IPython.display import Math
from IPython.display import display
# Startup plotly
plotly.o... | <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: 5.1 Numerical Differentiation
Step2: Three-point centered-difference formula
Step3: Three-point centered-difference formula for second derivat... |
14,532 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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 l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Linear Mixed-Effect Regression in {TF Probability, R, Stan}
Step3: 2 Hierarchical Linear Model
Step4: 3.1 Know Thy Data
Step5: Conclusions
... |
14,533 | <ASSISTANT_TASK:>
Python Code:
from astropy.io import fits
import numpy as np
import matplotlib.pyplot as plt
from skimage import measure
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
class Blob:
Class that defines a 'blob' in an image: the contour of a set of pixels
w... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step4: Blob Class
Step14: BlobGroup Class
Step17: Find and Group Blobs
Step18: Run on Preproc Data
Step19: Find and Group Blobs
Step20: Plot Large... |
14,534 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'niwa', 'sandbox-2', 'ocean')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "emai... | <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... |
14,535 | <ASSISTANT_TASK:>
Python Code:
%%bash
if [ ! -d ./FATS ]; then
git clone https://github.com/isadoranun/FATS ./FATS
fi
cd ./FATS;
git pull origin master;
%%bash
cd ./FATS;
git log --name-status HEAD^..HEAD;
%%bash
cd ./FATS;
cat requirements.txt;
%%bash
python --version
%%bash
uname -srvmoio
%%bash
pylint --vers... | <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.2. Requirements
Step2: A.3. Python Version
Step3: A.4. uname -srvmoio
Step4: A.5. Pylint Version
Step5: A.6. caniusepython3 version
Step6:... |
14,536 | <ASSISTANT_TASK:>
Python Code:
import sys
try:
import docplex.cp
except:
if hasattr(sys, 'real_prefix'):
#we are in a virtual env.
!pip install docplex
else:
!pip install --user docplex
try:
import matplotlib
if matplotlib.__version__ < "1.4.3":
!pip install --upgrade ... | <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 that the more global package <i>docplex</i> contains another subpackage <i>docplex.mp</i> that is dedicated to Mathematical Programming, an... |
14,537 | <ASSISTANT_TASK:>
Python Code:
from collections import OrderedDict # For recording the model specification
import pandas as pd # For file input/output
import numpy as np # For vectorized math operations
import statsmodels.tools.numdiff as numdiff # For numeric hessian
im... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. Load the Swissmetro Dataset
Step2: 2. Clean the dataset
Step3: 3. Create an id column that ignores the repeat observations per individual
S... |
14,538 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
# отключим всякие предупреждения Anaconda
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
%matplotlib inline
import seaborn as sns
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] =... | <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: Пример
Step2: Напишем вспомогательную функцию, которая будет возвращать решетку для дальнейшей красивой визуализации.
Step3: Отобразим данные.... |
14,539 | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *
%matplotlib inline
np.random.seed(1)
# Loading the data (sig... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Run the next cell to load the "SIGNS" dataset you are going to use.
Step2: As a reminder, the SIGNS dataset is a collection of 6 signs represen... |
14,540 | <ASSISTANT_TASK:>
Python Code:
import sympy as sym
sym.init_printing()
x, y = sym.symbols('x y')
expr = 3*x**2 + sym.log(x**2 + y**2 + 1)
expr
expr.subs({x: 17, y: 42}).evalf()
% timeit expr.subs({x: 17, y: 42}).evalf()
import math
f = lambda x, y: 3*x**2 + math.log(x**2 + y**2 + 1)
%timeit f(17, 42)
g = sym.lambdify(... | <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: lambdify constructs string representation of python code and uses python eval to compile
|
14,541 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.sparse
%load_ext cython
p = 0.01
Nc, Na = 10000, 200
c = np.ones(Nc)
a = np.ones(Na)
K = np.random.random((Nc, Na)) < p
%timeit K.dot(a)
%timeit c.dot(K)
Ksp = scipy.sparse.csr_matrix(K)
%timeit scipy.sparse.csr_matrix(K)
np.all(Ksp.dot(a) == K.dot(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: Setup
Step2: Dense matrix vector multiplication
Step3: Sparse matrix vector multiplication
Step6: Sparse matrix vector multiplication using M... |
14,542 | <ASSISTANT_TASK:>
Python Code:
!ls -l corpus
import os
import numpy as np
import sys
import nltk
import unicodedata
from collections import Counter, namedtuple
import pickle
import numpy as np
from copy import deepcopy
%matplotlib inline
def find_text_files(basedir):
filepaths = []
for root, dirs, files in os.... | <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: Подключим необходимые библиотеки. Стоит выделить nltk - она используется в основном для демонстрации чего можно ожидать от ngram модели.
Step2: ... |
14,543 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import tensorflow as tf
import random
from collections import Counter
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'
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 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. ... |
14,544 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create a TFX pipeline for your data with Penguin template
Step2: Install required package
Step3: Let's check the versions of TFX.
Step4: We a... |
14,545 | <ASSISTANT_TASK:>
Python Code:
from scipy import stats
import numpy as np
np.random.seed(42)
x = np.random.normal(0, 1, 1000)
y = np.random.normal(0, 1, 1000)
alpha = 0.01
s, p = stats.ks_2samp(x, y)
result = (p <= alpha)
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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:
|
14,546 | <ASSISTANT_TASK:>
Python Code:
import numpy
import pandas
import statsmodels.formula.api as smf
import statsmodels.stats.multicomp as multi
data = pandas.read_csv('nesarc_pds.csv', low_memory=False)
# S2AQ8A - HOW OFTEN DRANK ANY ALCOHOL IN LAST 12 MONTHS (99 - Unknown)
# S2AQ8B - NUMBER OF DRINKS OF ANY ALCOHOL USUAL... | <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: Then OLS regression test is run
Step2: And as Prob (F-statistics) is less than 0.05, I can discard null hypothesis.
Step3: Tukey's HSD post ho... |
14,547 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-1', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
14,548 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
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: Set parameters
Step2: Read epochs for the channel of interest
Step3: Compute statistic
Step4: Plot
|
14,549 | <ASSISTANT_TASK:>
Python Code:
import logging
reload(logging)
log_fmt = '%(asctime)-9s %(levelname)-8s: %(message)s'
logging.basicConfig(format=log_fmt)
# Change to info once the notebook runs ok
logging.getLogger().setLevel(logging.INFO)
%pylab inline
import copy
import os
from time import sleep
from subprocess import... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Test Environment set up
Step2: Support Functions
Step3: Run Antutu and collect scores
Step4: After running the benchmark for the specified go... |
14,550 | <ASSISTANT_TASK:>
Python Code:
import graphlab
products = graphlab.SFrame('amazon_baby_subset.gl/')
products
products['sentiment']
products.head(10)['name']
print '# of positive reviews =', len(products[products['sentiment']==1])
print '# of negative reviews =', len(products[products['sentiment']==-1])
import json
... | <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 review dataset
Step2: One column of this dataset is 'sentiment', corresponding to the class label with +1 indicating a review with positiv... |
14,551 | <ASSISTANT_TASK:>
Python Code:
import jax.numpy as jnp
from jax import custom_jvp
@custom_jvp
def f(x, y):
return jnp.sin(x) * y
@f.defjvp
def f_jvp(primals, tangents):
x, y = primals
x_dot, y_dot = tangents
primal_out = f(x, y)
tangent_out = jnp.cos(x) * x_dot * y + jnp.sin(x) * y_dot
return primal_out, ta... | <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: Custom VJPs with jax.custom_vjp
Step2: Example problems
Step3: Since it's written in terms of jax.numpy, it's JAX-transformable
Step4: But th... |
14,552 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
import matplotlib
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
from matplo... | <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: As always, let's do imports and initialize a logger and a new bundle.
Step2: Adopt system parameters from Rebassa-Mansergas+ 2019.
Step3: Now ... |
14,553 | <ASSISTANT_TASK:>
Python Code:
import tensorflow.compat.v1 as tf
import numpy as np
import shutil
print(tf.__version__)
CSV_COLUMNS = ['fare_amount', 'pickuplon','pickuplat','dropofflon','dropofflat','passengers', 'key']
LABEL_COLUMN = 'fare_amount'
DEFAULTS = [[0.0], [-74.0], [40.0], [-74.0], [40.7], [1.0], ['nokey']... | <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: <h2> 1. Refactor the input </h2>
Step2: <h2> 2. Refactor the way features are created. </h2>
Step3: <h2> Create and train the model </h2>
Step... |
14,554 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import samplics
from samplics.sampling import SampleSize
# target coverage rates
expected_coverage = {
"Dakar": 0.849,
"Ziguinchor": 0.809,
"Diourbel": 0.682,
"Saint-Louis": 0.806,
"Tambacounda": 0.470,
"Kaolack": 0.797,
... | <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 first step is to create and object using the SampleSize class with the parameter of interest, the sample size calculation method, and the st... |
14,555 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
s=
APRIL--this is the cruellest month, breeding
Lilacs out of the dead land, mixing
Memory and desire, stirring
Dull roots with spring rain.
stop_words='the is'
s=s.splitlines()
y=[]
for i in s:
c=i.split()
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step5: Word counting
Step7: Write a function count_words that takes a list of words and returns a dictionary where the keys in the dictionary are the ... |
14,556 | <ASSISTANT_TASK:>
Python Code:
import AngularCatalog_class as ac
import ImageMask_class as imclass
from astropy.io import fits
from astropy.io import ascii
import numpy as np
import numpy.random as rand
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10, 6)
mask_from_ranges = imcl... | <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: Ways to create an ImageMask
Step2: To see what the mask looks like, we generate some random points and plot them.
Step3: Simple enough. Note ... |
14,557 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from synthetic import mackey_glass
import matplotlib.pyplot as plt
import theano
import theano.tensor as T
import numpy
floatX = theano.config.floatX
class SimpleRNN(object):
def __init__(self, input_dim, recurrent_dim):
w_xh = numpy.random.normal(0, .01, (... | <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 now define a class that uses scan to initialize an RNN and apply it to a sequence of data vectors. The constructor initializes the shared var... |
14,558 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import badfish as bf
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('data/airquality.csv', index_col=0)
mf = bf.MissFrame(df)
dir(mf)
df.isnull().sum()
mf.counts()
mf.counts(where = ['Ozone'],how = 'any',columns=['Solar.R','Wind','Temp'])
mf.... | <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 need to convert the Pandas dataframe to Badfish's missframe.
Step2: A MissFrame converts your data to a boolean matrix where a missing cell ... |
14,559 | <ASSISTANT_TASK:>
Python Code:
def _correlate(series: pd.Series, correlation_value: int, seed: int = 0):
Generates a correlated random variables from a given series.
# https://stats.stackexchange.com/questions/38856/how-to-generate-correlated-random-numbers-given-means-variances-and-degree-of
np.random.seed(seed)... | <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: Simulate some data
Step2: These are the collinear variables introduced and their relationship with var2.
Step3: Modelling
Step4: Fitting a mo... |
14,560 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
print(b.get_parameter(qualifier='ecc'))
print(b.get_parameter(qualifier='ecosw', context='component'))
p... | <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.
Step2: Relevant Parameters
Step3: Relevant Constraints
Step4: Influence... |
14,561 | <ASSISTANT_TASK:>
Python Code:
debug_flag = False
import datetime
import glob
import logging
import lxml
import os
import six
import xml
import xmltodict
import zipfile
# paper identifier
paper_identifier = "BostonGlobe"
archive_identifier = "BG_20171002210239_00001"
# source
source_paper_folder = "/mnt/hgfs/projects... | <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 - Imports
Step2: Setup - working folder paths
Step3: Setup - logging
Step4: Setup - virtualenv jupyter kernel
Step5: Setup - Initializ... |
14,562 | <ASSISTANT_TASK:>
Python Code:
import sys
print("python command used for this notebook:")
print(sys.executable)
import tensorflow as tf
print("tensorflow:", tf.__version__)
from tensorflow.keras.applications.resnet50 import preprocess_input, ResNet50
model = ResNet50(weights='imagenet')
from skimage.io import imread
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: The following checks that scikit-image is properly installed
Step2: Optional
|
14,563 | <ASSISTANT_TASK:>
Python Code:
print ("Hello" + ", World")
print(10 + 4)
import numpy as np # numpy モジュールのインポート
import matplotlib.pyplot as plt # pyplotモジュールのインポート
%matplotlib inline
# 平均 x = -2, y = -2 の2変量正規分布からデータを100個サンプリングする
mean = [-2,-2]
cov = [[1,0],[0,1]]
x1,y1 = np.random.multivariate_normal(mean, cov, 100).... | <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: 正しく動作すれば，画面に
|
14,564 | <ASSISTANT_TASK:>
Python Code:
N = 10000 ;
MOD = 1000000007 ;
F =[0 ] * N ;
def precompute() :
F[1 ] = 2 ;
F[2 ] = 3 ;
F[3 ] = 4 ;
for i in range(4 , N ) :
F[i ] =(F[i - 1 ] + F[i - 2 ] ) % MOD ;
n = 8 ;
precompute() ;
print(F[n ] ) ;
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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:
|
14,565 | <ASSISTANT_TASK:>
Python Code:
from dkrz_forms import form_widgets
form_widgets.show_status('form-retrieval')
from dkrz_forms import form_handler, form_widgets
#please provide your last name - replacing ... below
MY_LAST_NAME = "ki"
form_info = form_widgets.check_and_retrieve(MY_LAST_NAME)
# To be completed
# tob b... | <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: Please provide your last name
Step2: Get status information related to your form based request
Step3: Contact the DKRZ data managers for form ... |
14,566 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import torch
import pandas as pd
x = load_data()
px = pd.DataFrame(x.numpy())
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Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
|
14,567 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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 l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: FFJORD
Step2: FFJORD bijector
Step3: Next, we instantiate a base distribution
Step5: We use a multi-layer perceptron to model state_derivativ... |
14,568 | <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
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<USER_TASK:>
Description:
Step1: First we'll load the text file and convert it into integers for our network to use.
Step3: Now I need to split up the data into batches, and in... |
14,569 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import mne
from mne.datasets import sample
from mne.preprocessing import ICA
from mne.preprocessing import create_eog_epochs, create_ecg_epochs
# getting some data ready
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'... | <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: Before applying artifact correction please learn about your actual artifacts
Step2: Define the ICA object instance
Step3: we avoid fitting ICA... |
14,570 | <ASSISTANT_TASK:>
Python Code:
lessons = {
"1": "Python is part of a bigger ecosystem (example: Jupyter Notebooks).",
"2": "Batteries Included refers to the well-stocked standard library.",
"3": "Built-ins inside __builtins__ include the basic types such as...",
"4": "__ribs__ == special names == magic ... | <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: Continue to "doodle and daydream" as you find the time. Think of ways to describe your day as a Python program. Remember the story of The Car ... |
14,571 | <ASSISTANT_TASK:>
Python Code:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
raw.crop(tmax=60).... | <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: Background
Step2: If a scalp electrode was used as reference but was not saved alongside the
Step3: By default,
Step4: .. KEEP THESE BLOCKS ... |
14,572 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
table = pd.DataFrame(index=['Bowl 1', 'Bowl 2'])
table['prior'] = 1/2, 1/2
table
table['likelihood'] = 3/4, 1/2
table
table['unnorm'] = table['prior'] * table['likelihood']
table
prob_data = table['unnorm'].sum()
prob_data
table['posterior'] = table['unnorm'] / pr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now I'll add a column to represent the priors
Step2: And a column for the likelihoods
Step3: Here we see a difference from the previous method... |
14,573 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import nsfg
preg = nsfg.ReadFemPreg()
import thinkstats2 as ts
live = preg[preg.outcome == 1]
wgt_cdf = ts.Cdf(live.totalwgt_lb, label = 'weight')
import thinkplot as tp
tp.Cdf(wgt_cdf, label = 'weight')
tp.Show()
import random
random.random?
import random
thousand =... | <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: Select live births, then make a CDF of <tt>totalwgt_lb</tt>.
Step2: Display the CDF.
Step3: Find out how much you weighed at birth, if you can... |
14,574 | <ASSISTANT_TASK:>
Python Code:
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
# Size of the encoding layer (the hidden layer)
encoding_dim = 32 # feel free to change this value
image_shape = mnist.train.images.shape[1]
inputs_ = tf.placeholder(tf.float32, (None,image_shape), name="inputs... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We'll train an autoencoder with these images by flattening them into 784 length vectors. The images from this dataset are already normalized suc... |
14,575 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import os
import sys
import mimetypes
import email
import glob
mht_files = glob.glob(os.path.join(os.path.curdir, '*.mht'))
for filepath in mht_files:
# get the name of the file, e.g. ./31521derp.mht -> 31521derp
filename_base = os.path.split(filepath)[-1].sp... | <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: ref
Step2: the next cell parses the mht-files, splits them by content type (html, jpg, etc.) and writes the output of the chunks to the hard di... |
14,576 | <ASSISTANT_TASK:>
Python Code:
import xray
ds = xray.open_dataset('https://motherlode.ucar.edu/repository/opendap/41f2b38a-4e70-4135-8ff8-dbf3d1dcbfc1/entry.das',
decode_times=False)
print(ds)
print(ds['th'])
th = ds['th'].values[0][0]
print(th)
print(ds['grid_type_code'])
print(ds['grid_typ... | <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: Dimensions, Coordinates, Data Variables
Step2: potential temperature (th)
Step3: To Visualize the Data, We have to Decrypt the Projection
Step... |
14,577 | <ASSISTANT_TASK:>
Python Code:
def this_and_prev(iterable):
iterator = iter(iterable)
prev_item = None
curr_item = next(iterator)
for next_item in iterator:
yield (prev_item, curr_item)
prev_item = curr_item
curr_item = next_item
yield (prev_item, curr_item)
for i,j in this_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: По аналогии требуется написать функцию, которая будет возвращать текущее и следующее значения.
Step2: <h2>Problem 2. SQL / Python</h2>
Step3: ... |
14,578 | <ASSISTANT_TASK:>
Python Code:
!pip install lightgbm
!pip install shap
%tensorflow_version 1.x
import lzma
from google.colab import drive
import numpy as np
import tensorflow as tf
import keras
from keras import backend as K
from keras.layers import Input, Dense
from keras.models import Model
import matplotlib.pyplot ... | <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: Importing packages and defining functions and variables
Step2: Defining autoencoder model, Training and evaluation functions
Step3: Mount goog... |
14,579 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
with open('../sentiment_network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment_network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
from string import punctuation
all_text = ''.join([c for c in reviews if... | <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 preprocessing
Step2: Encoding the words
Step3: Encoding the labels
Step4: Okay, a couple issues here. We seem to have one review with ze... |
14,580 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
def lorentz_derivs(yvec, t, sigma, rho, beta):
Compute the the derivatives for the Lorentz system at yvec(t).
# YOUR CODE HERE... | <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: Lorenz system
Step4: Write a function solve_lorenz that solves the Lorenz system above for a particular initial condition $[x(0),y(0),z(0)]$. Y... |
14,581 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
# tensorflow
import tensorflow as tf
print('Expected TensorFlow version is v1.3.0 or higher')
print('Your TensorFlow version:', tf.__version__)
# data manipulati... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1) Simple Linear Regression with low-level TensorFlow
Step2: Create training data
Step3: Plot the training data
Step4: The Model
Step5: The ... |
14,582 | <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: トレーニングループの新規作成
Step2: はじめに
Step3: ミニバッチの勾配を使用してカスタムトレーニングループでトレーニングします。
Step4: トレーニングループは以下のとおりです。
Step5: メトリックの低レベルの処理
Step6: トレーニングと評価のルー... |
14,583 | <ASSISTANT_TASK:>
Python Code:
4*2
import os
# Load the os library
import os
# Load the request module
import urllib.request
# Import SSL which we need to setup for talking to the HTTPS server
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# Create a directory
os.mkdir('img_align_celeba... | <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 press 'a' or 'b' to create new cells. You can also use the toolbar to create new cells. You can also use the arrow keys to move up and dow... |
14,584 | <ASSISTANT_TASK:>
Python Code:
mc_env = gym.make("MountainCar-v0")
mc_n_weights, mc_feature_vec = fourier_fa.make_feature_vec(
np.array([mc_env.low, mc_env.high]),
n_acts=3,
order=2)
mc_experience = linfa.init(lmbda=0.9,
... | <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 try some arbitrary thetas
Step2: If the bounds of the states are [0, n], the ratio between symbolic and numeric results is $1/n^{n_{dim}-... |
14,585 | <ASSISTANT_TASK:>
Python Code:
get_ipython().magic('load_ext cellevents')
get_ipython().magic('load_ext autoreload')
get_ipython().magic('autoreload 2')
from logcon import log
from xdrive import aws, server, apps
from xdrive.drive import Drive
import fabric.api as fab
from fabric.state import connections
apps.setdebug(... | <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: Configuration
Step2: Setup programs and data using a free instance
Step3: Download stuff via ssh
Step4: All of the setup time so far has used... |
14,586 | <ASSISTANT_TASK:>
Python Code:
# imports
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
import skimage.feature
from tqdm import tqdm # nice progress bars
%matplotlib inline
# constants
TRAIN_PATH = '../data/Train/'
DOTTED_PATH = '../data/TrainDotted/'
OUT_PATH = '../output/'... | <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: Due to the fact some images are mismatched in the training set, and will not work for this method (reference to datacanary's post), I removed th... |
14,587 | <ASSISTANT_TASK:>
Python Code:
def print1(a , n ) :
for i in range(0 , n + 1 ) :
print(a[i ] , end = "▁ ")
print("")
def sort(a , n ) :
for i in range(n , 0 , - 1 ) :
for j in range(n , n - i , - 1 ) :
if(a[j ] > a[j - 1 ] ) :
a[j ] , a[j - 1 ] = a[j - 1 ] , a[j ]
print1(a , n )
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:
|
14,588 | <ASSISTANT_TASK:>
Python Code:
from bokeh.io import output_notebook
from bokeh.plotting import *
from matmodlab2 import *
from numpy import *
import numpy as np
from plotting_helpers import create_figure
output_notebook()
%pycat ../matmodlab2/materials/mooney_rivlin.py
from sympy import Symbol, Matrix, Rational, symb... | <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: <a name='basic'></a>
Step2: <a name='verify'></a>
Step3: We now exercise the Mooney-Rivlin material model using Matmodlab
|
14,589 | <ASSISTANT_TASK:>
Python Code:
# Conformal Model, Amsterdam convention. Dorst et al. p. 361
from sympy import *
from galgebra.ga import Ga
from galgebra.mv import *
# from lt import *
# from sympy import *
cm3coords = (o,x,y,z,infty) = symbols('o 1 2 3 infty', real=True)
cm3g = '0 0 0 0 -1, 0 1 0 0 0, 0 0 1 0 0, 0 0 0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <h4>* Create direct representations of geometric objects *</h4>
Step2: <h4>* Create dual representations of geometric objects *</h4>
Step3: <h... |
14,590 | <ASSISTANT_TASK:>
Python Code:
audience1_name = "" #@param {type:"string"}
audience1_file_location = "" #@param {type:"string"}
audience1_size = 0#@param {type:"integer"}
audience2_name = "" #@param {type:"string"}
audience2_file_location = "" #@param {type:"string"}
audience2_size = 0 #@param {type:"integer"}
audienc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import Libs and configure Plotly
Step2: Mount Drive and read the Customer Match Insights CSVs
Step3: Define Plot Function
Step4: Define TF-ID... |
14,591 | <ASSISTANT_TASK:>
Python Code:
# Figure 1
Image(url= "http://3.bp.blogspot.com/_UpN7DfJA0j4/TJtUBWPk0SI/AAAAAAAAABY/oWPMtmqJn3k/s1600/mnist_originals.png", width=200, height=200)
from __future__ import print_function # Use a function definition from future version (say 3.x from 2.7 interpreter)
import matplotlib.image... | <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: Goal
Step2: In the block below, we check if we are running this notebook in the CNTK internal test machines by looking for environment variable... |
14,592 | <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
import math
from IPython.display import HTML
HTML('../style/code_toggle.html')
import math
from matplotlib import rcParams
rcParams['tex... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import section specific modules
Step4: 2.5 Convolution<a id='math
Step5: Figure 2.5.1
Step7: Figure 2.5.2
Step9: Figure 2.5.3
|
14,593 | <ASSISTANT_TASK:>
Python Code:
# Load the software ("import the module" in python jargon)
from fermi_school_like import *
# Import matplotlib for plotting
from matplotlib import pyplot as plt
# This show the plots inline in the notebook
%matplotlib inline
# Define number of bins in our data
n_bins = 100
# Generate bin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Setup our generative process
Step2: Likelihood analysis
Step3: Bias
Step4: The fact that the average the MLE value approaches the true value ... |
14,594 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
ds = xr.tutorial.open_dataset("rasm").load()
ds
month_length = ds.time.dt.days_in_month
month_length
# Calculate the weights by grouping by 'time.season'.
weights = (
month_... | <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: Open the Dataset
Step2: Now for the heavy lifting
|
14,595 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
pd.__version__
nrg = pd.read_csv('energy_consumption.csv'); nrg.describe(include='all')
nrg.head()
nrg.dtypes
# https://docs.python.org/3/library/functions.html#type
# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iat.html
type(nrg.iat[0,0])... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The rows contains the electricity used in each hour for a one year period.
Step2: Both pandas and Numpy use the concept of dtypes as data types... |
14,596 | <ASSISTANT_TASK:>
Python Code:
platform = 'lendingclub'
store = pd.HDFStore(
'/Users/justinhsi/justin_tinkering/data_science/lendingclub/{0}_store.h5'.
format(platform),
append=True)
loan_info = store['train_filtered_columns']
columns = loan_info.columns.values
# checking dtypes to see which columns need on... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Until I figure out a good imputation method (e.g. bayes PCA), just drop columns with null still
Step2: straight up out of box elastic net with ... |
14,597 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-3', 'ocnbgchem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contribut... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<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... |
14,598 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import os
import pickle
import warnings; warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import pystan
import scipy
import seaborn as sns; sns.set_context('notebook')
from Bio import SeqIO
import tools
# load clean, normalised, indexed data
data = p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Building the model <a id="building"></a>
Step3: Stan model construction <a id="build_stan"></a>
Step4: <div class="alert-danger">
Step5: Extr... |
14,599 | <ASSISTANT_TASK:>
Python Code:
def car_race_collision(n: int):
return n**2
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
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