Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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10,200 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
import matplotlib.pyplot as plt
%matplotlib inline
import input_data
mnist = input_data.read_data_sets('fashion-mnist/data/fash... | <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: Task
Step6: Visualize reconstruction quality
Step7: Illustrating latent space
Step8: An other way of getting insights into the latent space i... |
10,201 | <ASSISTANT_TASK:>
Python Code:
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Synthetic Features and Outliers
Step4: Next, we'll set up our input function, and define the function for model training
Step5: Task 1
Step6: ... |
10,202 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import style
style.use('https://raw.githubusercontent.com/JoseGuzman/minibrain/master/minibrain/paper.mplstyle')
from scipy.stats import norm
mu = 0
sigma = 1 # std
rv = norm(loc = mu, scale = sigma)
x = np.lins... | <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: Gaussian Distribution
Step3: The variance
Step4: The covariance is always measured between two dimensions. If we have datasets with more than ... |
10,203 | <ASSISTANT_TASK:>
Python Code:
!pip install --upgrade pymongo
from pprint import pprint as pp
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
import pymongo
from pymongo import MongoClient
client = MongoClient("mongo",27017)
client
client.list_da... | <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: Usaremos la librería pymongo para python. La cargamos a continuación.
Step2: La conexión se inicia con MongoClient en el host descrito en el fi... |
10,204 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
big_df = pd.read_csv("UCI_Credit_Card.csv")
big_df.head()
len(big_df)
len(big_df.dropna())
df = big_df.drop(labels = ['ID'], axis = 1)
labels = df['default.payment.next.month']
df.drop('default.payment.next.month', axis = 1, inplace = True)
num_samp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let us load the credit card dataset and extract a small dataframe of numerical features to test on.
Step2: Now let us write our transformation ... |
10,205 | <ASSISTANT_TASK:>
Python Code:
from threeML import *
%matplotlib inline
import warnings
warnings.simplefilter('ignore')
# create the simulated observation
energies = np.logspace(1,4,151)
low_edge = energies[:-1]
high_edge = energies[1:]
# get a BPL source function
source_function = Broken_powerlaw(K=2,xb=300,piv=300, ... | <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 will create an observation where we have a simulated broken power law source spectrum along with an observed background spectrum. The b... |
10,206 | <ASSISTANT_TASK:>
Python Code:
#|export
import tensorboard
from torch.utils.tensorboard import SummaryWriter
from fastai.callback.fp16 import ModelToHalf
from fastai.callback.hook import hook_output
#|export
class TensorBoardBaseCallback(Callback):
order = Recorder.order+1
"Base class for tensorboard callbacks"... | <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: TensorBoardCallback
Step2: Projector
Step3: TensorBoardProjectorCallback
Step4: projector_word_embeddings
Step5: transformers
Step6: BERT
S... |
10,207 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
df = pd.read_csv('bikes_rent.csv')
df.head()
fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(15, 10))
for idx, feature in enumerate(df.columns[:-1]):
df.plot(feature, "cnt", subplots=T... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Мы будем работать с датасетом "bikes_rent.csv", в котором по дням записаны календарная информация и погодные условия, характеризующие автоматизи... |
10,208 | <ASSISTANT_TASK:>
Python Code:
print("Hello INBO_course!") # python 3(!)
4*5
3**2
(3 + 4)/2, 3 + 4/2,
21//5, 21%5 # floor division, modulo
3 > 4, 3 != 4, 3 == 4
my_variable_name = 'DS_course'
my_variable_name
name, age = 'John', 30
print('The age of {} is {:d}'.format(name, age))
import os
os.listdir()
import n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Python is a calculator
Step2: also logical operators
Step3: Variable assignment
Step4: More information on print format
Step5: <div class="a... |
10,209 | <ASSISTANT_TASK:>
Python Code:
@interact(xin=(-5,5,0.1),yin=(-5,5,0.1))
def plotInt(xin,yin):
xmax = 2
vmax = 5
x = linspace(-xmax, xmax, 15) # Definimos el rango en el que se mueven las variables y el paso
v = linspace(-vmax, vmax, 15)
X, V = meshgrid(x,v) # Creamos una grilla con eso
# Defini... | <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: El Pendulo
Step2: El Pendulo con perdidas
Step3: El resorte Oscilaciones longitudinales.
|
10,210 | <ASSISTANT_TASK:>
Python Code:
import magma as m
from mantle import DFF
class TFF(m.Circuit):
io = m.IO(O=m.Out(m.Bit)) + m.ClockIO()
ff = DFF()
m.wire( ff(~ff.O), io.O )
print(TFF)
class RippleCounter(m.Generator):
@staticmethod
def generate(width: int):
class _RippleCounter(m.Circuit):
... | <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 last example, we defined a function that created a
Step2: Let's inspect the interface to see the result of appending m.ClockIO().
Step3:... |
10,211 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import hyperspy.api as hs
import matplotlib.pyplot as plt
import pyxem as pxm
dp = hs.load('./data/06/mgo_nanoparticles.hdf5')
dp
dp.plot(cmap='magma_r')
sigma_min = 1.7
sigma_max = 13.2
dp_rb= dp.subtract_diffraction_background('difference of gaus... | <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 demonstration data
Step2: Plot data to inspect
Step3: Remove the background
Step4: Plot the background subtracted data
Step5: Find the ... |
10,212 | <ASSISTANT_TASK:>
Python Code:
import toytree
import toyplot
import numpy as np
# generate a random tree
tre = toytree.rtree.unittree(ntips=10, seed=12345)
# the .treenode attribute of the ToyTree returns its root TreeNode
tre.treenode
# the .idx_dict of a toytree makes TreeNodes accessible by index
tre.idx_dict
prin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: TreeNode objects are always nested inside of ToyTree objects, and accessed from ToyTrees. When you use .treenode to access a TreeNode from a Toy... |
10,213 | <ASSISTANT_TASK:>
Python Code:
from IPython.core.display import Image, display
display(Image(url='images/TipicalValuesLongChannel.png'))
%matplotlib inline
import math
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pylab as plb
def matrix(m_length,m_width):
"Return matrix with no homo... | <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: Variacion por cuadro de Kp del 0.0004%
Step2: Matriz de Trasconductancia ideal KP_n_Ideal
Step3: Matriz de Trasconductancia ideal KP_p_Ideal
S... |
10,214 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
%precision 2
Pn = 100e6 # [W]
PF = 0.8
f_nl_A = 61.0 # [Hz]
SD_A = 3 # [%]
f_nl_B = 61.5 # [Hz]
SD_B = 3.4 # [%]
f_nl_C = 60.5 # [Hz]
SD_C = 2.6 # [%]
f_fl_A = f_nl_A / (SD_A / 100.0 +1)
f_fl_B = f_nl_B / (SD_B / 100.0 +1)
f_fl_C = f_nl_C / (SD... | <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: Description
Step2: (a)
Step3: and the slopes of the power-frequency curves are
Step4: The total load is 230 MW, so the system frequency can b... |
10,215 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import shapefile as shp
from pprint import pprint
from matplotlib.path import Path as Polygon
def inpoly(x, y, pgcoords):
Returns bool array [Ny, Nx] telling which grid points are inside polygon
try:
isins... | <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 need a function that will tell us whether a coordinate pair is inside a shape or not. This function exists in matplotlib.path and is called P... |
10,216 | <ASSISTANT_TASK:>
Python Code:
a = range(100, 1000)
b = range(100, 1000)
lst = []
for x in a:
for y in b:
p = x * y
if str(p) == str(p)[::-1]:
lst.append(p)
print(max(lst))
# This cell will be used for grading, leave it at the end of the notebook.
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Then I created an empty list, which would hold all the palindromes created from multiplying those three digit numbers.
Step2: Using two for loo... |
10,217 | <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: Circuits 2
Step2: Electronic structure Hamiltonians with diagonal Coulomb operators
Step3: In the last line above we converted the FermionOper... |
10,218 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('sXx-PpEBR7k')
from IPython.display import YouTubeVideo
YouTubeVideo('_Xcmh1LQB9I')
from IPython.display import YouTubeVideo
YouTubeVideo('jmMcJ4XlrWM', start=195, end=234)
%matplotlib inline
import matplotlib.pyplot as plt
from skl... | <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 generation of AI researchers were John McCarthy
Step2: Neural networks returns
Step3: The success of VC theory for number of proble... |
10,219 | <ASSISTANT_TASK:>
Python Code:
FACETS_INSTALL_DIR = './'
%%bash -s "$FACETS_INSTALL_DIR"
if [ ! -d "${1}/facets" ]; then
# Install facets - only need to do this once per Datalab instance.
cd $1
git clone https://github.com/PAIR-code/facets
cd facets
jupyter nbextension install facets-dist/
else
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Retrieve the data
Step3: Execute the query to fill a Pandas dataframe with the data of interest.
Step5: Visualize the result with Facets
Step7... |
10,220 | <ASSISTANT_TASK:>
Python Code:
import enoslib as en
# Enable rich logging
_ = en.init_logging()
# claim the resources
network = en.G5kNetworkConf(type="prod", roles=["my_network"], site="rennes")
conf = (
en.G5kConf.from_settings(job_type="allow_classic_ssh", job_name="enoslib_observability")
.add_network_conf(... | <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 simple load generator
Step2: Monitoring with dstat
Step3: Visualization
Step4: Packet sniffing with tcpdump
Step5: Visualization
Step6: C... |
10,221 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'uhh', 'sandbox-3', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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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... |
10,222 | <ASSISTANT_TASK:>
Python Code:
# import modules
import pandas as pd
# Create dataframe
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],
'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', ... | <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 grouped variable is now a GroupBy object. It has not actually computed anything yet except for some intermediate data about the group key ... |
10,223 | <ASSISTANT_TASK:>
Python Code:
# Import of the pyomo module
from pyomo.environ import *
# Creation of a Concrete Model
model = ConcreteModel()
## Define sets ##
# Sets
# i canning plants / seattle, san-diego /
# j markets / new-york, chicago, topeka / ;
model.i = Set(initialize=['seattle'... | <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 Definitions
Step2: Parameters
Step3: A third, powerful way to initialize a parameter is using a user-defined function.
Step4: Variables
S... |
10,224 | <ASSISTANT_TASK:>
Python Code:
def func():
return 1
func()
s = 'Global Variable'
def func():
print locals()
print globals()
print globals().keys()
globals()['s']
func()
def hello(name='Jose'):
return 'Hello '+name
hello()
greet = hello
greet
greet()
del hello
hello()
greet()
def hello(name='Jose'):... | <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: Scope Review
Step2: Remember that Python functions create a new scope, meaning the function has its own namespace to find variable names when t... |
10,225 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plot
from ipywidgets import interactive
import ipywidgets as widgets
import math
from pulp import *
%matplotlib inline
H1 = 0.3073
H2 = 0.8935
H3 = 1.1064
def J_fun(mu):
I = (1 - 2**(-H1*(2*mu)**H2))**H3
return I
def invJ_fu... | <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: Approximation of the J-function taken from [1] with
Step2: The following function solves the optimization problem that returns the best $\lambd... |
10,226 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
import pyquickhelper
params={"blob_storage":"",
"password1":"",
"hadoop_server":"",
"password2":"",
"username":""}
pyquickhelper.ipythonhelper.open_html_form(params=params,title="server + ... | <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: Connexion au cluster
Step2: Création d'un petit jeu de données
Step3: On importe ce graphe
Step4: On vérifie que les données ont bien été cha... |
10,227 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
rng = np.random.RandomState(1)
x = 10 * rng.rand(50)
y = 2 * x - 5 + rng.randn(50)
plt.scatter(x, y);
from sklearn.linear_model import LinearRegression
model = LinearRegression(fit_int... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Simple Linear Regression
Step2: We can use Scikit-Learn's LinearRegression estimator to fit this data and construct the best-fit line
Step3: T... |
10,228 | <ASSISTANT_TASK:>
Python Code:
# Import spaCy and load the language library
import spacy
nlp = spacy.load('en_core_web_sm')
# Create a Doc object
doc = nlp(u'Tesla is looking at buying U.S. startup for $6 million')
# Print each token separately
for token in doc:
print(token.text, token.pos_, token.dep_)
nlp.pipeli... | <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 doesn't look very user-friendly, but right away we see some interesting things happen
Step2: Tokenization
Step3: Notice how isn't has bee... |
10,229 | <ASSISTANT_TASK:>
Python Code:
from dolfin import *
from rbnics import *
@PullBackFormsToReferenceDomain()
@AffineShapeParametrization("data/hole_vertices_mapping.vmp")
class Hole(EllipticCoerciveProblem):
# Default initialization of members
def __init__(self, V, **kwargs):
# Call the standard initiali... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 3. Affine decomposition
Step2: 4. Main program
Step3: 4.2. Create Finite Element space (Lagrange P1)
Step4: 4.3. Allocate an object of the Ho... |
10,230 | <ASSISTANT_TASK:>
Python Code:
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set_style('whitegrid')
titanic = sns.load_dataset('titanic')
titanic.head()
sns.jointplot(x='fare',y='age',data=titanic)
sns.distplot(titanic['fare'],bins=30,kde=False,color='red')
sns.boxplot(x='class',y='ag... | <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: Jointplot comparing fare and age
Step2: Plot the fare column as distribution
Step3: Displaying passenger and age over a boxplot
Step4: A simp... |
10,231 | <ASSISTANT_TASK:>
Python Code:
db_file = '../examples/data/clones_100.100.tab'
# dialect="excel" for CSV or XLS files
# for computational reasons, let's limit the dataset to the first 1000 sequences
X = io.load_dataframe(db_file, dialect="excel-tab")[:1000]
# turn the following off if data are real
# otherwise, assume... | <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. Preprocessing step
Step2: 2. High-level group inference
Step3: 3. Fine-grained group inference
Step4: Quickstart
Step5: If you want to sa... |
10,232 | <ASSISTANT_TASK:>
Python Code:
import pylab as pl
import casadi as ca
import casiopeia as cp
x = ca.MX.sym("x", 4)
u = ca.MX.sym("u", 1)
eps_u = ca.MX.sym("eps_u", 1)
p = ca.MX.sym("p", 3)
k_M = p[0]
c_M = p[1]
c_m = p[2]
M = 250.0
m = 50.0
p_scale = [1e3, 1e4, 1e5]
f = ca.vertcat( \
x[1], \
(p_scale[0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2.) System definition
Step2: 2.) Simulation
Step3: 3.) Parameter estimation for initial experiment
|
10,233 | <ASSISTANT_TASK:>
Python Code:
print 'Hello World!'
# this is a comment!
'''
This is technically just
a multiline string but
ususually it's used as a
multiline comment.
'''
b = True # bool
s = 'This is a string' # str
i = 4 # int
f = 4.1 ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: In Python single line comments are started with a <b>#</b>.
Step2: Python doesn't actually have built in support of multiline comments. However... |
10,234 | <ASSISTANT_TASK:>
Python Code:
#Ejemplo de Consulta
import Consulta as C
from IPython.display import display, Markdown
display(Markdown(C.F_Mark()))
%%bash
Rscript "Estadistica_Descriptiva.R"
%%bash
python Estadistica_Inferencial.py
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: ESTADÍSTICA Y ANÁLISIS
Step2: Estadística de números
|
10,235 | <ASSISTANT_TASK:>
Python Code:
data = \
'''
<html>
<head>
<meta charset="utf-8">
<title>Homepage of Prof. Dr. Karl Stroetmann</title>
<link type="text/css" rel="stylesheet" href="style.css" />
<link href="http://fonts.googleapis.com/css?family=Rochester&subset=latin,latin-ext"
rel="styleshee... | <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: Imports
Step2: Token Declarations
Step3: Definition of the States
Step4: Token Definitions
Step5: The Definition of the Token SCRIPT_START
S... |
10,236 | <ASSISTANT_TASK:>
Python Code:
# standard imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xarray as xr
import warnings
%matplotlib inline
np.set_printoptions(precision=3, linewidth=80, edgeitems=1) # make numpy less verbose
xr.set_options(display_width=70)
warnings.simplefilter('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: Basic data arrays in numpy
Step2: numpy is a powerful but "low-level" array manipulation tool. Axis only have numbers and no names (it is easy ... |
10,237 | <ASSISTANT_TASK:>
Python Code:
#This line is very important: (It turns on the inline visuals!)
%pylab inline
a = [2,9,32,12,14,6,9,23,4,5,13,6,7,92,21,45];
b = [7,21,4,2,92,9,9,6,13,12,45,5,6,23,14,32];
#Please calculate the dot product of the vectors 'a' and 'b'.
#You may use any method you like. If get stuck. Check:
... | <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 Pearson's test
Step2: Pearson's comparison of microscopy derived images
Step3: Maybe remove so not to clash with Mark's.
|
10,238 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from pwoogs import moog,estimate,utils
import matplotlib.pyplot as plt
import q2
import shutil as sh
%matplotlib inline
# Getting star names
star_names = np.loadtxt('s_twins.csv',
skiprows=1,
usecols=(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:
Step3: The following function is used to set the input files to be used by MOOG for a specific star from the list star_names.
Step4: v_m returns the m... |
10,239 | <ASSISTANT_TASK:>
Python Code:
4
2 + 2
50 - 5*6
(50-5)*6
8/5
8//5 # Floor division discards the fractional part
8%5 # The % operator return the remainder of the division
5 ** 3 # 5 squared
type(4)
type(1.3)
width = 30
width
width = 30
height = 2
width * height
s = 3 * 4
s # Try to access an undefined variable
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: With Python, use ** operator to calculate powers.
Step2: Use equal sign(=) to assign a value to variable like math variable.
Step3: If a varia... |
10,240 | <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. ... |
10,241 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import (
display, display_html, display_png, display_svg
)
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from IPython.core.pylabtools import print_figure
from IPython.display import Image, SVG, Math
class Gaussian(object):
A simple ob... | <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: Parts of this notebook need the matplotlib inline backend
Step3: Special display methods
Step4: Create an instance of the Gaussian distributio... |
10,242 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from shogun import *
import shogun as sg
#Needed lists for the final plot
classifiers_linear = []*10
classifiers_non_linear = []*10
classifiers_nam... | <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 id = "section1">Data Generation and Visualization</a>
Step5: Data visualization methods.
Step6: <a id="section2" href="http
Step7: SVM - K... |
10,243 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
my_dictionary = {'a' : 45., 'b' : -19.5, 'c' : 4444}
print(my_dictionary.keys())
print(my_dictionary.values())
cookbook_df = pd.DataFrame({'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]})
cookbook_df
series_dict = {'one' : pd.Se... | <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: Creating data frames from various data types
Step2: constructor without explicit index
Step3: constructor contains dictionary with Series as v... |
10,244 | <ASSISTANT_TASK:>
Python Code:
# we assume that we have the dynet module in your path.
# OUTDATED: we also assume that LD_LIBRARY_PATH includes a pointer to where libcnn_shared.so is.
from dynet import *
# create a parameter collection and add the parameters.
m = ParameterCollection()
pW = m.add_parameters((8,2))
pV = ... | <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 first block creates a parameter collection and populates it with parameters.
Step2: Training
Step3: To use the trainer, we need to
Step4: ... |
10,245 | <ASSISTANT_TASK:>
Python Code:
nb_name = "DCAL_Water_WOFS"
# Enable importing of utilities.
import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import numpy as np
import xarray as xr
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Load Data Cube Configuration
import datacub... | <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: <span id="import">Import Dependencies and Connect to the Data Cube ▴</span>
Step2: <span id="plat_prod">Choose Platforms and Products 	... |
10,246 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image('../data/scatter_plot.png')
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 6.0)
from sklearn.datasets.samples_generator import make_blobs
X, y = blobs = make_blobs(n_samples=500, center... | <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: Of course, the first thing we need is the data. Usually this data will come from your experiments or your computations, but here we are going to... |
10,247 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import shutil
import os
import tensorflow as tf
print(tf.__version__)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist/data", one_hot = True, reshape = False)
print(mnist.train.images.shape)
print(mnist.train.labels.sh... | <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: Exploring the data
Step2: Define the model.
Step3: Write Input Functions
Step4: Write Custom Estimator
Step5: tf.estimator.train_and_evaluat... |
10,248 | <ASSISTANT_TASK:>
Python Code:
abbr = 'NLP'
full_text = 'Natural Language Processing'
# Enter your code here:
print(f'{abbr} stands for {full_text}')
%%writefile contacts.txt
First_Name Last_Name, Title, Extension, Email
# Write your code here:
with open('contacts.txt') as c:
fields = c.read()
# Run fields 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: Files
Step2: 3. Open the file and use .read() to save the contents of the file to a string called fields. Make sure the file is closed at the ... |
10,249 | <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
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<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:... |
10,250 | <ASSISTANT_TASK:>
Python Code:
person_height_ft = pd.Series([5.5,5.2,5.8,6.1,4.8],name='height',
index = ['person_a','person_b','person_c','person_d','person_e'],dtype=np.float64)
person_height_ft
person_height_ft.values
person_height_ft.index
person_height_ft['person_c']
person_height_ft[3]
person_he... | <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 Series is like a fixed-size dict in that you can get and set values by index label
Step2: You can also use the index position to get and set ... |
10,251 | <ASSISTANT_TASK:>
Python Code:
# Importing_new_features
# ..is easy. Features are collected
# in packages or modules. Just
import telnetlib # to use a
telnetlib.Telnet # client
# We can even import single classes
# from a module, like
from telnetlib import Telnet
# And read the module or class docs
help(telnetlib)
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: Python for System Administrator
Step2: Basic Arithmetic
Step3: Variable assignment
Step4: Formatting numbers
Step5: Formatting
Step6: Forma... |
10,252 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
bank = pd.read_csv('bank/bank-full.csv',sep=";")
bank.head()
X = bank.iloc[:,:-1]
y = bank['y']
y = (y == 'yes')*1
X = np.array(X)
from sklearn.preprocessing import OneHotEncoder
bank = pd.get_dummies(bank,drop_first=True,sparse=True)
X = bank.iloc... | <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 2.1 (30 pts) Predict y from X using kernel SVMs, random forests, and adaboost (see the sklearn.ensembles package). Tune the random for... |
10,253 | <ASSISTANT_TASK:>
Python Code:
# corpus ficticio con tres documentos de la misma longitud
# y sin repeticiones de términos dentro del mismo documento
# cada doc es una lista de palabras
d1 = 'los angeles times'.split()
d2 = 'new york times'.split()
d3 = 'new york post'.split()
# nuestro corpus D es una lista de docume... | <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: tf (term frequency)
Step2: La aproximación anterior, tal cual está programada, arma un diccionario de diccionarios pero tiene varias desventaja... |
10,254 | <ASSISTANT_TASK:>
Python Code:
m = folium.Map([45, 0], zoom_start=4)
folium.Marker([45, -30], popup="inline implicit popup").add_to(m)
folium.CircleMarker(
location=[45, -10],
radius=25,
popup=folium.Popup("inline explicit Popup")
).add_to(m)
ls = folium.PolyLine(
locations=[[43, 7], [43, 13], [47, 13],... | <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: Vega Popup
Step3: Fancy HTML popup
Step4: Note that you can put another Figure into an IFrame ; this should let you do stange things...
|
10,255 | <ASSISTANT_TASK:>
Python Code:
# We could tediously build a list …
# filenames = ['/data/Houston/realtime-tracer/LYLOUT_200524_210000_0600.dat.gz',]
# Instead, let's read a couple hours at the same time.
import sys, glob
filenames = glob.glob('/data/Houston/130619/LYLOUT_130619_2[0-1]*.dat.gz')
for filename in filename... | <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: Investigating the pyxlma data structure
Step2: lma_data is an xarray object. If we print it, we see that it looks much like a NetCDF file, with... |
10,256 | <ASSISTANT_TASK:>
Python Code:
import os
import glob
import itertools
import nestly
%load_ext rpy2.ipython
%load_ext pushnote
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
## min G+C cutoff
min_GC = 13.5
## max G+C cutoff
max_GC = 80
## max G+C shift
max_13C_shift_in_BD = 0.036
min_BD = min_GC/... | <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: BD min/max
Step2: Nestly
Step3: Nestly params
Step4: Copying input files
Step5: Multi-window HR-SIP
Step6: Making confusion matrices
Step7:... |
10,257 | <ASSISTANT_TASK:>
Python Code:
import graphlab;
products = graphlab.SFrame('amazon_baby.gl/')
products.head()
products['word_count'] = graphlab.text_analytics.count_words(products['review'])
products.head()
graphlab.canvas.set_target('ipynb')
products['name'].show()
giraffe_reviews = products[products['name'] == 'V... | <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: Read some product review data
Step2: Let's explore this data together
Step3: Build the word count vector for each review
Step4: Examining the... |
10,258 | <ASSISTANT_TASK:>
Python Code:
import urllib
url = 'http://ichart.yahoo.com/table.csv?s=MSFT&a=0&b=1&c=2009'
data = pd.read_csv(url, parse_dates=['Date'])
import bokeh.plotting as bp
# 주피터 노트북이 아닌 파일로 출력하는 경우
# bp.output_file("../images/msft_1.html", title="Bokeh Example (Static)")
# 주피터 노트북에서 실행하여 출력하는 경우
bp.output_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: Bokeh 라이브러리 임포트
Step2: 플롯팅
Step3: 다음으로 Figure 클래스의 메서드를 호출하여 실제 플롯 객체를 추가한다. 우선 라인 플롯을 그리기 위해 line 메서드을 실행한다.
Step4: 이제 show 명령어를 호출하여 실제 차트를... |
10,259 | <ASSISTANT_TASK:>
Python Code:
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = '1.13'
%%bash
if ! gsutil ls... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <h2> Deploy trained model </h2>
Step2: <h2> Use model to predict (online prediction) </h2>
Step3: The predictions for the four instances were
|
10,260 | <ASSISTANT_TASK:>
Python Code:
from numpy import linalg as LA
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def generate_test_image(m,n):
X = np.zeros((m,n))
# generate a rectangle
X[25:80,25:80] = 1
# generate a triangle
for i in range(25, 80, 1):
X[i+80:160, 100+i-1] = 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: We will introduce PCA with an image processing example. A grayscale digital image can be represented by a matrix, whose $(i,j)^{th}$ entry corre... |
10,261 | <ASSISTANT_TASK:>
Python Code:
input = tf.placeholder(tf.float32, (None, 32, 32, 3))
filter_weights = tf.Variable(tf.truncated_normal((8, 8, 3, 20))) # (height, width, input_depth, output_depth)
filter_bias = tf.Variable(tf.zeros(20))
strides = [1, 2, 2, 1] # (batch, height, width, depth)
padding = 'VALID'
conv = tf.nn... | <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: calculate the number of parameters of a convo layer
Step2: The output layer shape is
Step3: There are 756,560 total parameters. That's a HUGE ... |
10,262 | <ASSISTANT_TASK:>
Python Code:
import requests
r = requests.get('https://www.baidu.com/')
print(type(r))
print(r.status_code)
print(type(r.text))
print(r.headers)
print(r.text)
print(r.cookies)
r = requests.post('http://httpbin.org/post')
print('----POST----\n', r.text)
r = requests.put('http://httpbin.org/put')... | <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: 请求响应的类型是 requests.models.Response
Step2: 状态码是200
Step3: 响应体的类型是字符串str
Step4: 可以得到响应的 HTTP HEADER
Step5: 响应体内容
Step6: Cookies的类型是RequestsCoo... |
10,263 | <ASSISTANT_TASK:>
Python Code:
!pip install -q tensorflow-recommenders
!pip install -q --upgrade tensorflow-datasets
# You can use any Python source file as a module by executing an import statement in some other Python source file.
# The import statement combines two operations; it searches for the named module, then... | <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: Note
Step2: This notebook uses TF2.x.
Step3: Lab Task 1
Step4: As before, we'll split the data by putting 80% of the ratings in the train set... |
10,264 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# for quick visualization in notebook
import matplotlib.pyplot as plt
%matplotlib inline
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8'... | <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: Training a softmax linear classifier
Step2: Softmax loss using cross-entropy
Step3: We now have an array probs of size [300 x 3], where each r... |
10,265 | <ASSISTANT_TASK:>
Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
from mne.filter import next_fast_len
import mne
print(__doc__)
data_path = mne.datasets.opm.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: Load data, resample. We will store the raw objects in dicts with entries
Step2: Do some minimal artifact rejection just for VectorView data
Ste... |
10,266 | <ASSISTANT_TASK:>
Python Code:
def total_value(P, m, r, n):
Total value of portfolio given parameters
Based on following formula:
A = \frac{P}{(r / m)} \left[ \left(1 + \frac{r}{m} \right)^{m \cdot n}
- 1 \right ]
:Input:
- *P* (float) - Payment amount per compoundin... | <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: Root Finding and Optimization
Step3: Fixed Point Iteration
Step4: Guess at $r_0$ and check to see what direction we need to go...
Step5: Exam... |
10,267 | <ASSISTANT_TASK:>
Python Code:
# Oversampling factor: we would like to see spectral windows
# and periodograms in more detail than just at the Fourier frequencies
oversampling = 10
truefreq = 0.2284271247
# Time sampling (a):
ts = np.linspace(start = 1, stop = 90, num = 90)
df1 = pd.DataFrame({'time': ts,
... | <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: Visualize first the three spectral windows. What are the differences between them? After inspecting it on the whole test frequency range, enlarg... |
10,268 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
data = pd.read_table('spectrum_crab_hess_2006.txt',
comment='#', sep='\s*', engine='python')
data
def flux_ecpl(energy, flux1, gamma, energy_cut):
re... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The data
Step2: The model
Step3: Plot data and model
Step4: The likelihood
Step5: ML fit with Minuit
Step6: Analysis with emcee
|
10,269 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
cluster = tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223"]})
server0 = tf.train.Server(cluster, job_name="local", task_index=0)
print(server0)
server1 = tf.train.Server(cluster, job_name="local", task_index=1)
print(server1)
import tensorflow ... | <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: Start Server "Task 0" (localhost
Step2: Start Server "Task 1" (localhost
Step3: Define Compute-Heavy TensorFlow Graph
Step4: Define Shape
Ste... |
10,270 | <ASSISTANT_TASK:>
Python Code:
import time
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <h2> Import the dataframe and remove any features that are all zero </h2>
Step2: <h2> Get mappings between sample names, file names, and sample... |
10,271 | <ASSISTANT_TASK:>
Python Code:
import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfil... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: The notMNIST dataset is too large for many computers to handle. It contains 500,000 images for just training. You'll be using a subset of this... |
10,272 | <ASSISTANT_TASK:>
Python Code:
from owslib.csw import CatalogueServiceWeb
endpoint = 'http://www.ngdc.noaa.gov/geoportal/csw'
csw = CatalogueServiceWeb(endpoint, timeout=30)
import pandas as pd
ioos_ras = ['AOOS', # Alaska
'CaRA', # Caribbean
'CeNCOOS', # Central and Northern Califo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: We will use the same list of all the Regional Associations as before,
Step2: The function below is similar to the one we used before.
Step3: C... |
10,273 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
x = np.linspace(0,4*np.pi,10)
x
f = np.sin(x)
f
plt.plot(x, f, marker='o')
plt.xlabel('x')
plt.ylabel('f(x)');
from scipy.interpolate import interp1d
x = np.linspace(0,4*np.pi,10) # only use 10... | <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: Overview
Step2: This creates a new array of points that are the values of $\sin(x_i)$ at each point $x_i$
Step3: This plot shows that the poin... |
10,274 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
def well2d(x, y, nx, ny, L=1.0):
z = (2 / L) * np.sin((nx * np.pi * x) / L) * np.sin((ny * np.pi * y) / L)
return z
psi = well2d(np.linspace(0,1,10), np.linspace(0,1,10), 1, 1)
assert len(psi)==10
assert psi.sh... | <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: Contour plots of 2d wavefunctions
Step2: The contour, contourf, pcolor and pcolormesh functions of Matplotlib can be used for effective visuali... |
10,275 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE... | <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: Train embeddings on TPU using Autoencoder
Step2: Get data
Step3: Function to visualize our images and pick the first image from the test set
S... |
10,276 | <ASSISTANT_TASK:>
Python Code:
tr = np.array(model.monitor.channels['valid_y_y_1_nll'].time_record) / 3600.
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(111)
ax1.plot(model.monitor.channels['valid_y_y_1_nll'].val_record)
ax1.plot(model.monitor.channels['train_y_y_1_nll'].val_record)
ax1.plot(model_no_mom.moni... | <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: Plot ratio of update norms to parameter norms across epochs for different layers
|
10,277 | <ASSISTANT_TASK:>
Python Code:
def soma( x, y):
s = x + y
return s
r = soma(50, 20)
print (r)
def soma( x, y, squared=False):
if squared:
s = (x + y)**2
else:
s = (x + y)
return s
print ('soma(2, 3):', soma(2, 3))
print ('soma(2, 3, False):', soma(2, 3, False))
print ('soma(2, 3, ... | <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: Para se realizar a chamada da função soma, basta utilizá-la pelo seu nome passando os parâmetros como argumentos da função. Veja o exemplo a seg... |
10,278 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('tensile_test_data.csv', )
df.head()
df = pd.read_csv('tensile_test_data.csv', header=None)
df.head()
df.plot(x=2, y=1)
df = pd.read_excel('weather_data.xlsx')
df.head()
df = pd.... | <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 gives us some strange column names. '237.7605198' is one of the values in the data set, not the column name. We need to specify header=None... |
10,279 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger()
b = phoebe.default_binary()
b.add_dataset('lc', compute_phases=phoebe.linspace(0,1,101))
b.run_compute(irrad_method='none')
times = b.get_value('... | <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: This first line is only necessary for ipython noteboooks - it allows the plots to be shown on this page instead of in interactive mode. Dependi... |
10,280 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
# Required coverage level for analysis. This is in units of number of apatamer
# particles (beads). This is used to minimize potential contamination.
# For example, a tolerated bead fracti... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Parameters used in Manuscript
Step2: Load in data
Step3: Load CSVs
Step7: Helper functions
Step8: Data Analysis
Step9: Generate Figure Data... |
10,281 | <ASSISTANT_TASK:>
Python Code:
mps_to_mmph = 1000 * 3600
import numpy as np
n_steps = 10 # can get from cfg file
precip_rates = np.linspace(5, 20, num=n_steps, endpoint=False)
precip_rates
np.savetxt('./input/precip_rates.txt', precip_rates, fmt='%6.2f')
cat input/precip_rates.txt
from topoflow.components.met_base... | <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: Programmatically create a file holding the precipitation rate time series. This will mimic what I'll need to do in WMT, where I'll have access t... |
10,282 | <ASSISTANT_TASK:>
Python Code:
# print all urls
import yaml
import io
val = yaml.safe_load(io.open("example_config.yaml", "rt"))
print([entry["url"] for entry in val["handlers"]])
# print all urls
import axon
val = axon.load("example_config1.axon")
print([entry["url"] for entry in val["handlers"]])
# print all urls
v... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: In AXON it will be formatted as
Step2: With AXON it can be also presented in the following form
|
10,283 | <ASSISTANT_TASK:>
Python Code:
class Point:
Represents a point in a 2D Euclidean plane.
def __init__(self, x, y):
self.x = x
self.y = y
@property
def tuplify(self):
return self.x, self.y
def __lt__(self, other):
return self.tuplify < other.tupl... | <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: Triangulation with line sweep
Step5: Test the orientation method on a simple test case.
Step6: Now we can start with our main method.
Step7: ... |
10,284 | <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: 使用 tf.data 加载 pandas dataframes
Step2: 下载包含心脏数据集的 csv 文件。
Step3: 使用 pandas 读取 csv 文件。
Step4: 将 thal 列（数据帧（dataframe）中的 object ）转换为离散数值。
Step5... |
10,285 | <ASSISTANT_TASK:>
Python Code:
print('Auch beim Maschinellen Lernen immer wichtig:' + '\n' +'Aufgabe und Daten umfassend kennenlernen')
%matplotlib inline
import numpy as np
x = np.array([[1, 2, 3], [4, 5, 6]])
print("x:\n{}".format(x))
from scipy import sparse
# create a 2d NumPy array with a diagonal of ones, and ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: <h2> Deshalb die Daten sich anzeigen lassen (print() etc.)</h2>
Step2: <h2> Warum setzen wir die Bibliothek Scikit-learn ein ?</h2>
Step3: <h3... |
10,286 | <ASSISTANT_TASK:>
Python Code:
# If you haven't already, make sure you install the `dfcx-scrapi` library
!pip install dfcx-scrapi
from dfcx_scrapi.core.project import Project
creds_path = '<YOUR_CREDS_PATH>'
project_id = '<YOUR_GCP_PROJECT_ID>'
gcs_bucket = '<YOUR_GCS_BUCKET>'
p = Project(creds_path, project_id=proj... | <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: Imports
Step2: User Inputs
Step3: Extract All Agents from GCP Project
Step5: Backup All Agents to GCS Bucket
|
10,287 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
%matplotlib inline
data = [446.6565, 454.4733, 455.663 , 423.6322, 456.2713, 440.5881, 425.3325, 485.1494, 506.0482, 526.... | <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: Simple Exponential Smoothing
Step2: Here we run three variants of simple exponential smoothing
Step3: Holt's Method
Step4: Seasonally adjuste... |
10,288 | <ASSISTANT_TASK:>
Python Code:
from quantopian.pipeline import Pipeline
def make_pipeline():
return Pipeline()
pipe = make_pipeline()
from quantopian.research import run_pipeline
result = run_pipeline(pipe, '2017-01-01', '2017-01-01')
result.head(10)
result.info()
from quantopian.pipeline.data.builtin import USEqu... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Data
Step2: Factors
Step3: Combining Factors
Step4: Filters and Screens
Step5: Screens
Step6: Reverse a screen
Step7: Combine Filters
Step... |
10,289 | <ASSISTANT_TASK:>
Python Code:
data = [
{'age': 33, 'sex': 'F', 'BP': 'high', 'cholesterol': 'high', 'Na': 0.66, 'K': 0.06, 'drug': 'A'},
{'age': 77, 'sex': 'F', 'BP': 'high', 'cholesterol': 'normal', 'Na': 0.19, 'K': 0.03, 'drug': 'D'},
{'age': 88, 'sex': 'M', 'BP': 'normal', 'cholesterol': 'normal', 'Na':... | <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: Understanding the task by understanding the data
Step2: Then remove the 'drug' entry from all the dictionaries
Step3: Sweet! Now let's look at... |
10,290 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import utils
%matplotlib inline
%load_ext autoreload
%autoreload 2
CSV_PATH = '../../data/unique_counts_semi.csv'
# load data
initial_df = utils.load_queries(CSV_PATH)
# 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: Do some cleanup
Step2: Query Frequency Analysis
Step3: The frequency of queries drops off pretty quickly, suggesting a long tail of low freque... |
10,291 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
from stemgraphic import stem_graphic
df = pd.read_csv('../iris.csv')
df.describe()
stem_graphic(df['sepal_length']);
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load a data frame
Step2: Select a column, or pass the whole dataframe if you want stem_graphic to select the first numerical column.
|
10,292 | <ASSISTANT_TASK:>
Python Code:
from PyUGC import *
from PyUGC.Stream import UGC
from PyUGC.Base import OGDC
from PyUGC import Engine
from PyUGC import FileParser
from PyUGC import DataExchange
import datasource
#help(UGC)
#help(OGDC)
#help(datasource)
import os
basepath = os.path.join(os.getcwd(),"../data... | <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、使用Python的help(...)查看库的元数据信息获得帮助。
Step2: 3、设置测试数据目录。
Step3: 4、导入数据的测试函数。
Step4: 5、运行这个测试。
Step5: （三）查看生成的数据源文件UDB。
Step6: <font color="red... |
10,293 | <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... |
10,294 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: TV Script Generation
Step3: Explore the Data
Step7: Implement Preprocessing Functions
Step10: Tokenize Punctuation
Step12: Preprocess all th... |
10,295 | <ASSISTANT_TASK:>
Python Code:
control=input()
import getpass
password=getpass.getpass()
# Delete Jobs
import requests
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning) # suppress warnings
# Figure out samples from the header of ... | <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: Insert your admin password
Step2: Deleting script
|
10,296 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.wcs import WCS
from astropy.visualization import (MinMaxInterval,
LogStretch,
ImageNormalize)
%matplotlib inline
hdu = fits.open('./data/w5.fits')[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: 2. RGB-3-color images
Step2: 3. RGB colors of Herschel-SPIRE images
Step3:
|
10,297 | <ASSISTANT_TASK:>
Python Code:
!head -n 2 ../data/yelp/yelp_training_set_review.json
reviews = gl.SFrame.read_csv('../data/yelp/yelp_training_set_review.json', header = False)
reviews
reviews[0]
reviews=reviews.unpack('X1','')
reviews
reviews = reviews.unpack('votes', '')
reviews
reviews.show()
gl.canvas.set_target(... | <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: Unpack to extract structure
Step2: Votes are still crammed in a dictionary. Let's unpack it.
Step3: Quick data visualization
Step4: 3. Proble... |
10,298 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from simmit import smartplus as sim
import os
E = 70000.0
nu = 0.3
L = sim.L_iso(E,nu,"Enu")
print np.array_str(L, precision=4, suppress_small=True)
d = sim.check_symetries(L)
print(d['umat_type'])
print(d['props'])
x ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: L_iso
Step2: M_iso
Step3: L_cubic
Step4: M_cubic
Step5: L_isotrans
Step6: bp
Step7: Ir2()
Step8: Ir05()
|
10,299 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df=pd.DataFrame(data=[[1,2],[3,4],[1,2],[1,4],[1,2]],columns=['col1','col2'])
def g(df):
df['index_original'] = df.groupby(['col1', 'col2']).col1.transform('idxmax')
for i in range(len(df)):
i = len(df) - 1 - i
origin = df.loc[i, 'index_original... | <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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