Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
5,200 | <ASSISTANT_TASK:>
Python Code:
# define the font styles
title_font = fm.FontProperties(family='serif', style='normal', size=19, weight='normal', stretch='normal')
label_font = fm.FontProperties(family='serif', style='normal', size=16, weight='normal', stretch='normal')
ticks_font = fm.FontProperties(family='serif', sty... | <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: Now our data is formatted in the format we want.
Step8: Permutation Test
Step9: Create data
Step10: Create PermTest instance and formate data... |
5,201 | <ASSISTANT_TASK:>
Python Code:
# Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
from scipy.io import loadmat
import numpy as np
from mayavi import mlab
from matplotlib import pyplot as plt
from os import path as op
import mne
from mne.viz import ClickableImage # noqa
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
|
5,202 | <ASSISTANT_TASK:>
Python Code:
labVersion = 'cs190_week1_v_1_2'
# TODO: Replace <FILL IN> with appropriate code
# Manually calculate your answer and represent the vector as a list of integers values.
# For example, [2, 4, 8].
x = [3, -6, 0]
y = [4, 8, 16]
# TEST Scalar multiplication: vectors (1a)
# Import test librar... | <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: Part 1
Step2: (1b) Element-wise multiplication
Step3: (1c) Dot product
Step4: (1d) Matrix multiplication
Step5: Part 2
Step6: (2b) Elemen... |
5,203 | <ASSISTANT_TASK:>
Python Code:
# Init matplotlib
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (8, 8)
# Setup PyAI
import sys
sys.path.insert(0, '/Users/jdecock/git/pub/jdhp/pyai')
# Set the objective function
#from pyai.optimize.functions import sphere as func
from pyai.optimize.function... | <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: (1+1)-$\sigma$-Self-Adaptation-ES
Step3: Some explanations about $\sigma$ and $\tau$
Step4: Other inplementations
Step5: Define the objective... |
5,204 | <ASSISTANT_TASK:>
Python Code:
import NotebookImport
from DX_screen import *
gs2 = gene_sets.ix[dx_rna.index].fillna(0)
rr = screen_feature(dx_rna.frac, rev_kruskal, gs2.T,
align=False)
fp = (1.*gene_sets.T * dx_rna.frac).T.dropna().replace(0, np.nan).mean().order()
fp.name = 'mean frac'
ff_u = f... | <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: Here I'm running GSEA on the fraction upregulated signal across genes.
Step2: First I do a greedy filter based on p-values to find non-overlapp... |
5,205 | <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.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_band_induced_power
print(__doc__)
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set parameters
Step2: plot mean power
|
5,206 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'mpi-esm-1-2-hr', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name",... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
5,207 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
plt.gray()
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
fig, axes = plt.subplots(3,5, figsize=(12,8))
for i, ax in enumerate(axes.flatten()):
ax.imshow(X_train[i], interpolation='nearest')
... | <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 performance here is very poor. We really need to train with more samples and for more epochs.
|
5,208 | <ASSISTANT_TASK:>
Python Code:
# Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne import setup_volume_source_space
from mne import make_forward_solution
from mne.minimum_norm import ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set up our source space.
Step2: Export source positions to nift file
|
5,209 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import oandapyV20
import oandapyV20.endpoints.orders as orders
import configparser
config = configparser.ConfigParser()
config.read('../config/config_v20.ini')
accountID = config['oanda']['account_id']
access_token = config['oanda']['api_key']
client = oandapyV20.API(a... | <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 a List of Orders for an Account
Step2: List all Pending Orders in an Account
Step3: Get Details for a Single Order in an Account
Step4: R... |
5,210 | <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
<END_TASK>
<USER_TASK:>
Description:
Step1: Load the text8 dataset, a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the data folder. ... |
5,211 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%pylab inline
df = pd.read_csv("weather.csv", header=0, index_col=0)
df
mean_temp = df["temperature"].mean()
mean_temp
mean_humidity = df["humidity"].mean()
mean_humidity
temp_selector = df['temperature'] > mean_temp
df[temp_selector][["outlook", "play"]]
humidity_... | <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: Represent the following table using a data structure of your choice
Step2: Calculate the mean temperature and mean humidity
Step3: Print outlo... |
5,212 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import itertools as itt
import time
import shutil
import os
import contextlib
import pandas as pd
import blaze as blz
import bquery
import cytoolz
from cytoolz.curried import pluck as cytoolz_pluck
from collections 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: pandas
Step2: blaze
Step3: bquery without caching
Step4: bquery with caching
Step5: Running Times Summary
Step6: Graphic Summary
Step7: Th... |
5,213 | <ASSISTANT_TASK:>
Python Code:
import dowhy
from dowhy import CausalModel
import pandas as pd
import numpy as np
# Config dict to set the logging level
import logging.config
DEFAULT_LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'loggers': {
'': {
'level': 'WARN',
}... | <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 the Dataset
Step2: Lalonde Dataset
Step3: Step 1
Step4: Lalonde
Step5: Step 2
Step6: Lalonde
Step7: Step 3
Step8: Lalonde
Step9: ... |
5,214 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
iris = load_iris()
data, labels = iris.data[:,0:2], iris.data[:,2]
num_samples = len(labels) # size of our dataset
# shuffle the dataset
shuffle_order = np.random.permutation(num_samples)
data = dat... | <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: Like the 1-dimensional problem previously, we can still do linear regression, except now we have two variables and therefore two weights as well... |
5,215 | <ASSISTANT_TASK:>
Python Code:
from ipywidgets import widgets, interact
from IPython.display import display
import seaborn as sbn
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from IPython.core.pylabtools import figsize
sbn.set_context("talk", font_scale=.8)
figsize(10, 8)
# The model used for t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Uncertainty and Modelling
Step2: Scatter plots
Step3: You might be tempted to plot a histogram of the model outputs. This shows how often a p... |
5,216 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-2', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("n... | <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... |
5,217 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'mpi-esm-1-2-hr', 'seaice')
# 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... |
5,218 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
5,219 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import sunpy.instr.aia
%matplotlib inline
data = np.loadtxt('../aia_sample_data/aia_wresponse_raw.dat')
channels = [94,131,171,193,211,304,335]
ssw_results = {}
for i in range(len(channels)):
ssw_results[channels[i]] = {'wavelength':... | <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: Wavelength Response
Step2: Run the SunPy calculation.
Step3: Plot the results against each other.
Step4: Now, do a "residual plot" of the dif... |
5,220 | <ASSISTANT_TASK:>
Python Code:
from polyglot.detect import Detector
arabic_text = u
أفاد مصدر امني في قيادة عمليات صلاح الدين في العراق بأن " القوات الامنية تتوقف لليوم
الثالث على التوالي عن التقدم الى داخل مدينة تكريت بسبب
انتشار قناصي التنظيم الذي يطلق على نفسه اسم "الدولة الاسلامية" والعبوات الناسفة
والمنازل المفخخ... | <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: Example
Step4: Mixed Text
Step5: If the text contains snippets from different languages, the detector is able to find the most probable langau... |
5,221 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from qutip import *
H = Qobj([[1],[0]])
V = Qobj([[0],[1]])
P45 = Qobj([[1/np.sqrt(2)],[1/np.sqrt(2)]])
M45 = Qobj([[1/np.sqrt(2)],[-1/np.sqrt(2)]])
R = Qobj([[1/np.sqrt(2)],[-1j/np.sqrt(2)]])
L = Qobj([[1/np.sqrt(2)],[1j/np.sqrt(2)]])
V
def HWP(theta):
return Qob... | <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: These are the polarization states
Step2: Devices
Step3: Example 1) Check that the $|H\rangle$ state is normalized
Step4: To show more informa... |
5,222 | <ASSISTANT_TASK:>
Python Code:
print("He said, 'what ?'")
s = "This is a string."
print(s)
print(type(s))
print(len(s))
s = 42
print(s)
print(type(s))
print(s * 2)
print(s + 7)
# Neither statement modifies the variable.
s += 2**3 # s is being incremented by 2^3
print("Same as s = s + 2**3")
print(s)
print(s == 42)
... | <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: Strings are delimited by ", but can also use '. This is useful because you can now use one set of quotes inside another, and it'll still be one ... |
5,223 | <ASSISTANT_TASK:>
Python Code:
#instantiate our environment
import os
import sys
%matplotlib inline
import pandas as pd
import statsmodels.api as sm
# read the data into a pandas dataframe
df = pd.read_csv("read_depth.strains.tsv", header=0, delimiter="\t")
print("Shape: {}".format(df.shape))
df.head()
dfa = df[(df["A... | <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: Filter the data
Step2: Note that we have reduced our matrix from having 11,054 entries with all the zeros to only having 1,397 entries now!
Ste... |
5,224 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import csv
from altair import Chart, X, Y, Axis, SortField
import matplotlib.pyplot as plt
pd.__version__
%matplotlib inline
total = pd.read_csv("../data/database2017.csv")
total.shape
total.tail()
totallast30 = total.sort_values(by='created_at',asc... | <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: Leitura dos últimos 30 registros
Step2: Faço a leitura dos últimos 30 registros no arquivo para mostrar em gráfico a evolução do consumo da bat... |
5,225 | <ASSISTANT_TASK:>
Python Code:
import hail as hl
hl.utils.get_movie_lens('data/')
users = hl.read_table('data/users.ht')
users.filter(users.occupation == 'programmer').count()
users.aggregate(hl.agg.filter(users.occupation == 'programmer', hl.agg.count()))
users.aggregate(hl.agg.counter(users.occupation == 'programmer... | <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 can also express this query in multiple ways using aggregations
Step2: Annotate
Step3: Compare this to what we had before
Step4: Note
Step... |
5,226 | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
pickle_file = 'notMNIST.pickle... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: First reload the data we generated in 1_notmnist.ipynb.
Step2: Reformat into a shape that's more adapted to the models we're going to train
Ste... |
5,227 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
EI = 5000. #kN m^2
H = 3600. #kN m/m
F1 = -2600. #kN
F2 = -3600. #kN
F3 = -4600. #kN
phi = np.linspace(np.pi, -np.pi, 501)
theta0 = np.arccos(H/F3)
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: 4.1.1 Visualizing the section force orbits
Step2: 4.1.2 Numerical solution of the equilibrium equations
Step3: The result shows that this nume... |
5,228 | <ASSISTANT_TASK:>
Python Code:
client = pymongo.MongoClient("46.101.236.181")
db = client.allfake
# get collection names
collections = sorted([collection for collection in db.collection_names()])
day = {} # number of tweets per day per collection
diff = {} # cumullative diffusion on day per colletion
for collection 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: Count number of tweets per day for every news, calculate cummulative diffusion
Step2: Plot diffusion for every day for all news together
Step3:... |
5,229 | <ASSISTANT_TASK:>
Python Code:
from euler import Seq, timer
def p001():
return (
range(1000)
>> Seq.filter(lambda n: (n%3==0) | (n%5==0))
>> Seq.sum)
timer(p001)
from euler import Seq, timer
def p002():
return (
Seq.unfold(lambda (a,b): (b, (b, b+a)), (0,1))
>> Seq.filte... | <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: Even Fibonacci numbers
Step2: Largest prime factor
Step3: Largest palindrome product
Step4: Smallest multiple
Step5: Sum square difference
S... |
5,230 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
from numpy import random
n = 1000
X = random.rand(n, 2)
X[:5]
y = X[:, 0] * 3 - 2 * X[:, 1] ** 2 + random.rand(n)
y[:5]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_tes... | <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: Des données synthétiques
Step2: Exercice 1
Step3: Exercice 2
Step4: Exercice 3
Step5: Le coefficient $R^2$ est plus élevé car on utilise ... |
5,231 | <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 tested 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: Session 5
Step2: <style> .rendered_html code {
Step3: Let's take a look at the first part of this
Step4: We'll just clean up the text a litt... |
5,232 | <ASSISTANT_TASK:>
Python Code:
import swat
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
# Also import networkx used for rendering a network
import networkx as nx
%matplotlib inline
s = swat.CAS('http://cas.mycompany.co... | <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: Connect to Cloud Analytic Services in SAS Viya
Step2: Load the action set for hypergroup
Step3: Load data into CAS
Step4: Hypergroup doesn't ... |
5,233 | <ASSISTANT_TASK:>
Python Code:
# Criando um dicionario vazio
d = {}
# Adicionando elementos para chave-valor
d['a'] = 'alpha'
d['o'] = 'omega'
d['g'] = 'gamma'
# algumas propriedades uteis
d
#Exibindo as chaves
d.keys()
# Iterando sobre as chaves
for k in d.keys(): print 'Key:',k,'->',d[k]
#Exibindo os valores
d.values... | <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: Observe que os itens sao apresentados na forma de Tuplas representando o par chave-valor**
|
5,234 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
import ndreg
from ndreg import preprocessor, util, plotter
import SimpleITK as sitk
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
def myshow(img, cmap='gray', colorbar=False):
plt.imshow(sitk.GetArrayViewFromIm... | <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: Some metadata is required before registration
Step2: Load the sample data
Step3: Preprocessing
Step4: Registration
Step5: Visualize register... |
5,235 | <ASSISTANT_TASK:>
Python Code:
from sympy import *
from geom_util import *
from sympy.vector import CoordSys3D
N = CoordSys3D('N')
alpha1, alpha2, alpha3 = symbols("alpha_1 alpha_2 alpha_3", real = True, positive=True)
init_printing()
%matplotlib inline
%reload_ext autoreload
%autoreload 2
%aimport geom_util
H1=symbol... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Lame params
Step2: Metric tensor
Step3: ${\displaystyle \hat{G}=\sum_{i,j} g_{ij}\vec{R}^i\vec{R}^j}$
Step4: Christoffel symbols
Step5: Grad... |
5,236 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import itertools
from scipy import stats
from statsmodels.stats.descriptivestats import sign_test
from statsmodels.stats.weightstats import zconfint
from statsmodels.stats.weightstats import *
%pylab inline
seattle_data = pd.read_csv('seattle.txt', ... | <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: Загрузка данных
Step2: Двухвыборочные критерии для независимых выборок
Step3: Ранговый критерий Манна-Уитни
Step4: Перестановочный критерий
|
5,237 | <ASSISTANT_TASK:>
Python Code:
# Load library
import pandas as pd
# Create data frame
df = pd.DataFrame()
# Create data
df['dates'] = pd.date_range('1/1/2001', periods=5, freq='D')
df['stock_price'] = [1.1,2.2,3.3,4.4,5.5]
# Lagged values by one row
df['previous_days_stock_price'] = df['stock_price'].shift(1)
# Show ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create Date Data
Step2: Lag Time Data By One Row
|
5,238 | <ASSISTANT_TASK:>
Python Code:
from sys import version
print(version)
from typing import TypeVar, List
_a = TypeVar('alpha')
def taille(liste : List[_a]) -> int:
longueur = 0
for _ in liste:
longueur += 1
return longueur
taille([])
taille([1, 2, 3])
len([])
len([1, 2, 3])
from typing import TypeVa... | <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: Listes
Step2: Exercice 2
Step3: Mais attention le typage est toujours optionnel en Python
Step4: Exercice 3
Step5: Notre implémentation e... |
5,239 | <ASSISTANT_TASK:>
Python Code:
import os, sys
import inspect
import numpy as np
import datetime as dt
import time
import pytz
import pandas as pd
import pdb
script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# add the path to opengrid to sys.path
sys.path.append(os.path.join(script_d... | <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: Script settings
Step2: We create one big dataframe, the columns are the sensors of type electricity
Step3: Convert Datetimeindex to local time... |
5,240 | <ASSISTANT_TASK:>
Python Code:
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pymc as pm
from numpy.random import choice
%matplotlib inline
matplotlib.style.use('ggplot')
matplotlib.rc_params_from_file("../styles/matplotlibrc" ).update()
def switch_envelope(chosen_envelope):
if chosen_... | <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: Helper methods
Step2: We also need some helper methods to evaluate the success of our strategy
Step3: Implementing the actual strategy
Step4: ... |
5,241 | <ASSISTANT_TASK:>
Python Code:
x = [7, 3, 5]
x.pop?
# anything after the hashtag is a comment
# load packages
import datetime as dt
import pandas.io.data as web # data import tools
import matplotlib.pyplot as plt # plotting tools
# The next one is an IPython command: it says to put plots here in the not... | <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: Example 1
Step2: The variable g (quarterly GDP growth expressed as an annual rate) is now what Python calls a DataFrame, which is a collection ... |
5,242 | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import matplotlib.pyplot as plt
import numpy as np
import os
import tarfile
import urllib
from urllib.request import urlretrieve
from IPython.display import display, Image
from sc... | <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: First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The lab... |
5,243 | <ASSISTANT_TASK:>
Python Code:
import toytree
import toyplot
# generate a random tree
tre = toytree.rtree.unittree(ntips=10, treeheight=100, seed=123)
# draw tree on canvas
canvas, axes, mark = tre.draw(ts='c', layout='r', tip_labels=True);
# get annotator tool
anno = toytree.utils.Annotator(tre, axes, mark)
# annotat... | <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: Builtin method to highlight clades
Step2: Or, use toyplot directly
Step3: More examples
|
5,244 | <ASSISTANT_TASK:>
Python Code:
import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be install... | <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: Restart the kernel
Step2: Set up your Google Cloud project
Step3: Region
Step4: Timestamp
Step5: Authenticate your Google Cloud account
Step... |
5,245 | <ASSISTANT_TASK:>
Python Code:
from projectq import MainEngine
from projectq.backends import AWSBraketBackend
from projectq.ops import Measure, H, C, X, All
creds = {
'AWS_ACCESS_KEY_ID': 'aws_access_key_id',
'AWS_SECRET_KEY': 'aws_secret_key',
} # replace with your Access key and Secret key
s3_folder = ['... | <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: Prior to the instantiation of the backend we need to configure the credentials, the S3 storage folder and the device to be used (in the example ... |
5,246 | <ASSISTANT_TASK:>
Python Code:
PROJECT_ID = "YOUR PROJECT ID"
BUCKET_NAME = "gs://YOUR BUCKET NAME"
REGION = "YOUR REGION"
SERVICE_ACCOUNT = "YOUR SERVICE ACCOUNT"
! gsutil ls -al $BUCKET_NAME
content_name = "pt-img-cls-multi-node-ddp-cust-cont"
! ls trainer
! cat trainer/requirements.txt
! pip install -r trainer/requ... | <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: Local Training
Step2: Vertex Training using Vertex SDK and Custom Container
Step3: Initialize Vertex SDK
Step4: Create a Vertex Tensorboard I... |
5,247 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
import control
!wget https://alfkjartan.github.io/files/sysid_hw_data.mat
data = sio.loadmat("sysid_hw_data.mat")
N = len(data["u1"])
plt.figure(figsize=(14,1.7))
plt.step(range(N),data["u1"])
plt.ylabel("u_1")
pl... | <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
Step2: Plot the data
Step3: Identify first order model
Step4: Validation
|
5,248 | <ASSISTANT_TASK:>
Python Code:
def find_max(words):
return sorted(words, key = lambda x: (-len(set(x)), x))[0]
<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:
|
5,249 | <ASSISTANT_TASK:>
Python Code:
cd ~/Desktop/SSUsearch/
mkdir -p ./workdir
#check seqfile files to process in data directory (make sure you still remember the data directory)
!ls ./data/test/data
Seqfile='./data/test/data/1c.fa'
Cpu='1' # number of maxixum threads for search and alignment
Hmm='./data/SSUsearch_db/Hm... | <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: README
Step2: Other parameters to set
Step3: Pass hits to mothur aligner
Step4: Get aligned seqs that have > 50% matched to references
Step5:... |
5,250 | <ASSISTANT_TASK:>
Python Code:
# 定义一个tuple
tuple1 = ('bosco','ricky','pinky')
tuple1
# 一个项目的 tuple
tuple2 = (5,)
tuple2
# 一次赋多值
x,y,z = tuple1
print(x,y,z)
# 用in判断
'bosco' in tuple1
# 索引
tuple1[0]
# string
tuple("money")
list1 = [16,2,53,24,5,36,67,80]
list1
# 索引 indexing
list1[5]
# 分片 slicing
list1[:5]
list1[3:]
list... | <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. list<a name="2list"></a>
Step2: list2
Step3: list3
Step4: list4
Step5: string
Step6: 3. array<a name="3array"></a>
Step7: some function... |
5,251 | <ASSISTANT_TASK:>
Python Code:
import copy
import cPickle
import os
import subprocess
import cdpybio as cpb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.linalg import svd
import scipy.stats as stats
import seaborn as sns
import statsmodels.formula.api as smf
import cardipspy as cpy
... | <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: STAR Logs
Step2: Picard Metrics
Step3: Expression Distribution
Step4: We can see overal that there are a fair number of genes that are not ex... |
5,252 | <ASSISTANT_TASK:>
Python Code:
import veneer
v = veneer.Veneer(port=9876)
input_sets = v.input_sets()
input_sets
input_sets.as_dataframe()
things_to_record=[
{'NetworkElement':'Lower Gauge','RecordingVariable':'Downstream Flow Volume'},
{'NetworkElement':'Crop Fields'},
{'NetworkElement':'Recreational Lak... | <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: Finding the input sets in the model
Step2: Note
Step3: We now want to iterate over the input sets, running the model each time, and retrieving... |
5,253 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_pat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set parameters
Step2: Show event related fields images
|
5,254 | <ASSISTANT_TASK:>
Python Code:
As I will attempt to describe in the next slides, Python is an amazing way to lead to a more fun learning and teaching
experience.
It can be a basic calculator, a fancy calculator and
Math, Science, Geography..
Tools that will help us in that quest are:
When you bring in SymPy to the pi... | <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: (Main) Tools
Step2: Python - Making other subjects more lively
|
5,255 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import pandas_datareader as pdr
import matplotlib.pyplot as plt
import seaborn as sns
plt.rc("figure",figsize=(16,8))
plt.rc("font",size=15)
plt.rc("lines",linewidth=3)
sns.set_style("darkgrid")
reader = pdr.fred.FredReader(["HOUST"], start="1980-01... | <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 some Data
Step2: We fit specify the model without any options and fit it. The summary shows that the data was deseasonalized using the mul... |
5,256 | <ASSISTANT_TASK:>
Python Code:
restaurants = pd.read_csv("NYC_Restaurants.csv", dtype=unicode)
for index, item in enumerate(restaurants.columns.values):
print index, item
#use .apply() method to combine the 4 columns to get the unique restaurant name
restaurants["RESTAURANT"] = restaurants[["DBA", "BUILDING", "STR... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Question 1
Step2: Question 2
Step3: Question 3
Step4: Question 4
Step5: Question 5
Step6: Question 6
Step7: Question 7
Step8: Question 8
... |
5,257 | <ASSISTANT_TASK:>
Python Code:
total = 0 # initialise total
for yeargroup in range(6):
prompt = "How many pupils are in year S"+str(yeargroup+1)+": "
pupils = int(input(prompt))
total = total + pupils # add to total
print("Total = ", total)
vauxhall = 0
ford = 0
mazda = 0
for car in range(10):
... | <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: If you have done it right, you should see
Step2: Run the program above, if you haven't already! It just runs for 10 cars.
|
5,258 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append(r"..")
import daymetpy
ornl_lat, ornl_long = 35.9313167, -84.3104124
df = daymetpy.daymet_timeseries(lon=ornl_long, lat=ornl_lat, start_year=2012, end_year=2013)
df.head()
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib 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: Which gives us a nice data frame with weather data for the Oak Ridge National Lab
Step2: Which we can visualize using matplotlib and seaborn
St... |
5,259 | <ASSISTANT_TASK:>
Python Code:
from datetime import datetime, timedelta
from IPython.display import display
from math import factorial
from matplotlib import pyplot as plt
import io
import numpy as np
import pandas as pd
Σ = sum
%matplotlib inline
def timetable(a, b):
return b + timedelta(minutes=int(a))
v_ti... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step4: Dados
Step5: a) Descrição do sistema de filas, local, data e horários da coleta de dados
Step6: b) Número de servidores atendendo = S
Step7: ... |
5,260 | <ASSISTANT_TASK:>
Python Code:
import random
from pomegranate import *
random.seed(0)
state1 = State( UniformDistribution(0.0, 1.0), name="uniform" )
state2 = State( NormalDistribution(0, 2), name="normal" )
model = HiddenMarkovModel( name="ExampleModel" )
model.add_state( state1 )
model.add_state( state2 )
model.ad... | <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 will create the states of the model, one uniform and one normal.
Step2: We will then create the model by creating a HiddenMarkovModel ... |
5,261 | <ASSISTANT_TASK:>
Python Code:
!cd data/ && pdftohtml -c -hidden -xml ALA1934_RR-excerpt.pdf ALA1934_RR-excerpt.pdf.xml
!ls -1 data/
!head -n 30 data/ALA1934_RR-excerpt.pdf.xml
!python3 -m http.server 8080 --bind 127.0.0.1
DATAPATH = 'data/'
OUTPUTPATH = 'generated_output/'
INPUT_XML = 'ALA1934_RR-excerpt.pdf.xml'
i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We generated an XML which consists of several <page> elements, containing an <image> (the "background" image, i.e. the scanned page)... |
5,262 | <ASSISTANT_TASK:>
Python Code:
#Final iteration tried across different cuts. Accuracy >55%
keywords = [ 'nice', 'pleased', 'better',
'like', 'easy', 'excellent',
'love','impressed',
'satisfied','pretty',
'best','works great']
for key in keywords:
# Note that we add spaces ... | <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: Principal Components Analysis
Step2: Recursive Feature Elimination
Step3: Splitting the data in a train and a test subset
Step4: Test the res... |
5,263 | <ASSISTANT_TASK:>
Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
import sys
# to make this notebook's output stable across runs
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: A couple utility functions to plot grayscale 28x28 image
Step2: PCA with a linear Autoencoder
Step3: Normalize the data
Step4: Now let's buil... |
5,264 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
import matplotlib.pyplot as plt
data = [10.1, 10.2, 9.8, 10.1, 10.2, 10.3,
10.1, 9.9, 10.2, 10.0, 9.9, 11.4]
plt.plot(data)
plt.xlabel('time')
plt.ylabe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Introduction
Step2: After a period of near steady state, we have a very large change. Assume the change is past the limit of the aircraft's fli... |
5,265 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from pandas.tools.plotting import scatter_matrix
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metr... | <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: b) Load dataset
Step2: The 'Pregnant' column can only take on one of two (in this case) possabilities. Here 1 = pregnant, and 0 = not pregnant
... |
5,266 | <ASSISTANT_TASK:>
Python Code:
# Import the library we need, which is Pandas and Matplotlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Set some parameters to get good visuals - style to ggplot and size to 15,10
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (15... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Question 3
Step2: PRINCIPLE
Step3: PRINCIPLE
Step4: PRINCIPLE
Step5: Question 4
Step6: Now we can fit an ARIMA model on this (Explaining AR... |
5,267 | <ASSISTANT_TASK:>
Python Code:
lista_de_numeros = [1, 6, 3, 9, 5, 2]
lista_ordenada = sorted(lista_de_numeros)
print lista_ordenada
print lista_de_numeros
lista_de_numeros = [1, 6, 3, 9, 5, 2]
print sorted(lista_de_numeros, reverse=True)
def crear_curso():
curso = [
{'nombre': 'Rodriguez, Carlos', 'nota':... | <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: Pero, ¿y cómo hacemos para ordenarla de mayor a menor?. <br>
Step2: ¿Y si lo que quiero ordenar es una lista de registros?. <br>
Step3: Búsque... |
5,268 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import pandas as pd
import tensorflow as tf
# read data from file
data = pd.read_csv('data/train.csv')
print(data.info())
# fill nan values with 0
data = data.fillna(0)
# convert ['male', 'female'] values of Sex to [1, 0]
data['Sex'] = data['Sex'].apply(lambd... | <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. 预处理
Step2: 3. 将训练数据切分为训练集(training set)和验证集(validation set)
Step3: 二、构建计算图
Step4: 2. 声明参数变量
Step5: 3. 构造前向传播计算图
Step6: 4. 声明代价函数
Step... |
5,269 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import networkx as nx
G=nx.DiGraph()
G.add_edge('sex','height',weight=0.6)
nx.draw_networkx(G, node_color='y',node_size=2000, width=3)
plt.axis('off')
plt.show()
import numpy as np
import pandas as pd
import csv
import json
from libpgm.g... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Why is this formalism a useful probabalistic problem solving tool?
Step2: And now, for some data
Step3: A pivot table might give another usefu... |
5,270 | <ASSISTANT_TASK:>
Python Code:
editdist_sp = [ (sp1,sp2,editdistance.eval(sp1,sp2)) for sp1,sp2 in itertools.combinations(read_annot["species_fillna"].unique(),2) ]
editdist_df = pd.DataFrame.from_records(editdist_sp,columns=["sp1","sp2","edit_distance"])
editdist_df["similarity"] = editdist_df.apply(lambda r: (max(len... | <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: Use new clade groups
|
5,271 | <ASSISTANT_TASK:>
Python Code:
regimen = clinical['Regimen Type'].ix[pts].dropna()
print regimen.value_counts()
regimen = regimen[regimen.map(regimen.value_counts()) > 10]
regimen = regimen.ix[pts].fillna('Other')
regimen = regimen.str.replace(' Based','')
regimen = regimen.ix[ti(duration != 'Control')]
regimen.value_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: LLQ
|
5,272 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from IPython.display import Image
def mean_squared_error(y_true, y_pred):
calculate the mean_squared_error given a vector of true ys and a vector of predicted ys
diff = y_true - y_pred
return np.dot(diff, diff) / len(diff)
def ... | <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: Predictive Modeling
Step3: The Central Theses of Machine Learning
Step4: How to Fight Overfitting?
Step5: <span style="color
Step6: L1 Regul... |
5,273 | <ASSISTANT_TASK:>
Python Code:
# Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
# Imprimindo números pares entre 50 e 101
for i in range(50, 101, 2):
print(i)
for i in range(3, 6):
print (i)
for i in range(0, -2... | <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: Range
|
5,274 | <ASSISTANT_TASK:>
Python Code:
import essentia.standard as es
from tempfile import TemporaryDirectory
# Loading an audio file.
audio = es.MonoLoader(filename='../../../test/audio/recorded/dubstep.flac')()
# Compute beat positions and BPM.
rhythm_extractor = es.RhythmExtractor2013(method="multifeature")
bpm, beats, beat... | <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 can now listen to the resulting audio with the beats marked by beeps. We can also visualize beat estimations.
Step2: BPM histogram
Step3: B... |
5,275 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
qs = [(2, 0),
(1, 1),
(0, 2),
(3, 0),
(2, 1),
(1, 2),
(0, 3),
(4, 0),
(3, 1),
(2, 2),
(1, 3),
(0, 4),
]
index = pd.MultiIndex.from_tuples(qs, names=['Boys', 'Girls'])
from scipy.stats import binom
b... | <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: To compute the proportion of each type of family, I'll use Scipy to compute the binomial distribution.
Step2: And put the results into a Pandas... |
5,276 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow.python.client import timeline
import pylab
import numpy as np
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
tf.logging.set_verbosity(tf.logging.INFO)
tf.reset_default_graph()
num_samples = 100000
from datetime import datetime
ve... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create Model Test/Validation Data
Step2: Look at the Model Graph In Tensorboard
Step3: Train Model
Step4: Look at the Train and Test Loss Sum... |
5,277 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
... | <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 and Preparing Data
Step3: Big Kudos to Waleed Abdulla for providing the initial idea and many of the functions used to prepare and disp... |
5,278 | <ASSISTANT_TASK:>
Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inlin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: From a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship
Step3: The very same sample of th... |
5,279 | <ASSISTANT_TASK:>
Python Code:
import logging
from conf import LisaLogging
LisaLogging.setup()
# Execute this cell to enabled executor debugging statements
logging.getLogger('Executor').setLevel(logging.DEBUG)
from env import TestEnv
# Setup a test environment with target configuration
env = TestEnv({
# Targe... | <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: Target Configuration
Step2: Tests Configuration
Step3: Tests execution
|
5,280 | <ASSISTANT_TASK:>
Python Code:
# Import libraries
import numpy as np
import pandas as pd
from time import time
from sklearn.metrics import f1_score
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# TODO: Calculate number of students
n_students = None
# TODO: 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: Implementation
Step2: Preparing the Data
Step3: Preprocess Feature Columns
Step4: Implementation
Step5: Training and Evaluating Models
Step6... |
5,281 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
from colormap import Colormap
c = Colormap()
cmap = c.cmap('cool')
# let us see what it looks like
c.test_colormap(cmap)
#Would be nice to plot a bunch of colormap to pick up one interesting
c.plot_colormap('diverging')
c.plot_colormap(c.misc)
c.plot_colormap(c.qualitative)
... | <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: Well, I have not found the one I wanted...I wanted from red to white to green
Step2: Using cma_builder and test_cmap
|
5,282 | <ASSISTANT_TASK:>
Python Code:
# First load RESSPyLab and necessary packages
import numpy as np
import RESSPyLab as rpl
# Identify the material
material_def = {'material_id': ['Example 1'], 'load_protocols': ['1,5']}
# Set the path to the x log file
x_log_file_1 = './output/x_log.txt'
x_logs_all = [x_log_file_1]
# Loa... | <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: Original Voce-Chaboche model
Step2: Tables can be easily generated following a standard format for several data sets by appending additional en... |
5,283 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.preprocessing import scale
Dtrans = np.loadtxt("transfusion.data",dtype=np.str_,delimiter=",")
X = np.array(Dtrans[1:,0:4],dtype=float)
y = np.array(Dtrans[1:,4],dtype=float)
X = scale(X)
from sklearn import svm
import sklearn.linear_model as skl_lm
from s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Exercise 1.1 (10 pts) Use 5-fold cross validation, leave-one-out CV, and a 50% holdout to tune the bandwidth and ridge penalty parameter for the... |
5,284 | <ASSISTANT_TASK:>
Python Code:
7**4
s = "Hi there Sam!"
s.split()
planet = "Earth"
diameter = 12742
print("The diameter of {} is {} kilometers.".format(planet,diameter))
lst = [1,2,[3,4],[5,[100,200,['hello']],23,11],1,7]
lst[3][1][2]
d = {'k1':[1,2,3,{'tricky':['oh','man','inception',{'target':[1,2,3,'hello']}]}]}... | <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: Split this string
Step2: Given the variables
Step3: Given this nested list, use indexing to grab the word "hello"
Step4: Given this nested di... |
5,285 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp1d, interp2d
f = np.load('trajectory.npz')
x = f['x']
y = f['y']
t = f['t']
assert isinstance(x, np.ndarray) and len(x)==40
assert isinstance(y, np.ndarray) and... | <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: 2D trajectory interpolation
Step2: Use these arrays to create interpolated functions $x(t)$ and $y(t)$. Then use those functions to create the ... |
5,286 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
# First we'll simulate the synthetic data
def simulate_seasonal_term(periodicity, total_cycles, noise_std=1.,
harmonics=None):
duration ... | <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: Synthetic data creation
Step2: Unobserved components (frequency domain modeling)
Step3: Observe that the fitted variances are pretty close to ... |
5,287 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
from scipy.optimize import leastsq
plt.rcParams['figure.figsize'] = (18, 6)
from IPython.display import HTML
HTML('../style/code_toggle.h... | <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
Step5: 2.11 Least-squares Minimization<a id='groundwork
Step6: The three functions defined above will be used ... |
5,288 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import h5py, os, osr, copy
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
def aop_h5refl2array(refl_filename):
aop_h5refl2array reads in a NEON AOP reflectance hdf5 file and returns
1. reflectance ar... | <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: The first function we will use is aop_h5refl2array. This function is loaded into the cell below, we encourage you to look through the code to un... |
5,289 | <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: Unicode strings
Step2: The tf.string data type
Step3: A tf.string tensor treats byte strings as atomic units. This enables it to store byte st... |
5,290 | <ASSISTANT_TASK:>
Python Code:
import inspect
import types
import sys
# I sometimes need to choose PyTorch...
#sys.path.insert(0, '/home/tv/pytorch/pytorch/build/lib.linux-x86_64-3.8//')
import torch
import torch.utils.dlpack
# import TVM
import sys
import os
tvm_root = '/home/tv/rocm/tvm/tvm/'
tvm_paths = [os.path.joi... | <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: Helpfully, transformers supports tracing their model with the PyTorch JIT. We use their tutorial on it, the following is copied straight from th... |
5,291 | <ASSISTANT_TASK:>
Python Code:
# Load data
X = np.concatenate((np.ones((pima.shape[0],1)),pima[:,0:8]), axis=1)
Y = pima[:,8]
Xs = (X - np.mean(X, axis=0))/np.concatenate((np.ones(1),np.std(X[:,1:], axis=0)))
n, p = X.shape
nsample = 1
nbatch = 768
M = np.identity(p)
C = 0 * np.identity(p)
eps = 0.1
m = 10
V = 0 * np.i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Correct coefficients
Step2: Our code - HMC
Step3: Our code - Gradient descent
Step5: Cliburn's code
|
5,292 | <ASSISTANT_TASK:>
Python Code:
trace0 = go.Scatter(
x=[1, 2, 3, 4],
y=[10, 15, 13, 17]
)
trace1 = go.Scatter(
x=[1, 2, 3, 4],
y=[16, 5, 11, 9]
)
data = go.Data([trace0, trace1])
py.iplot(data, filename = 'basic-line')
alpha = np.array([5, 5, 5])
rv = st.dirichlet(alpha)
coord_step = 0.01
coord_range = ... | <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: Try Plotting a Dirichlet Distribution
Step4: Make an Interactive 3D Plot with Parameter Selection
|
5,293 | <ASSISTANT_TASK:>
Python Code:
import array
a = array.array('i', range(10))
# 数据类型必须统一
a[1] = 's'
a
import numpy as np
a_list = list(range(10))
b = np.array(a_list)
type(b)
a = np.zeros(10, dtype=int)
print(type(a))
# 查看数组类型
a.dtype
a = np.zeros((4,4), dtype=int)
print(type(a))
# 查看数组类型
print(a.dtype)
a
np.ones((4,4)... | <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: 从原有列表转换为数组
Step2: 生成数组
Step3: random
Step4: 范围取值
Step5: | Data type | Description |
Step6: 数组属性
Step7: 运算
Step8: | Operator | Eq... |
5,294 | <ASSISTANT_TASK:>
Python Code:
tweets = []
RUTA = ''
for line in open(RUTA).readlines():
tweets.append(line.split('\t'))
ultimo_tweet = tweets[-1]
print('id =>', ultimo_tweet[0])
print('fecha =>', ultimo_tweet[1])
print('autor =>', ultimo_tweet[2])
print('texto =>', ultimo_tweet[3])
# escribe tu código a continua... | <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: Fíjate en la estructura de la lista
Step2: Al lío
|
5,295 | <ASSISTANT_TASK:>
Python Code:
# Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../../style/custom.css'
HTML(open(css_file, "r").read())
# Import Libraries (PLEASE RUN THIS CODE FIRST!)
# ----------------------------------------------
import nump... | <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: Solving the 1D acoustic wave equation by finite-differences
Step2: Source time function
Step3: Analytical Solution
Step4: Comparison of numer... |
5,296 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
if not os.getenv("IS_TESTING... | <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: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Step3: Before you begin
Step4: Region
Step5:... |
5,297 | <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', 'ocean')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
5,298 | <ASSISTANT_TASK:>
Python Code:
titles.shape[0]
titles.sort(columns='year')[0:2]
titles[titles['title']=='Hamlet'].shape[0]
titles[titles['title']=='North by Northwest'].shape[0]
titles[titles['title']=='Hamlet'].sort(columns='year')['year'].values[0]
titles[titles['title']=='Treasure Island'].sort(columns='year')
... | <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: What are the earliest two films listed in the titles dataframe?
Step2: How many movies have the title "Hamlet"?
Step3: How many movies are tit... |
5,299 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat
from shogun import RealFeatures, MulticlassLabels, Math
# load the dataset
dataset = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = d... | <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: Creating the autoencoder
Step2: Pre-training
Step3: Fine-tuning
Step4: Evaluation
Step5: The figure shows the corrupted examples and their r... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.