Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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3,200 | <ASSISTANT_TASK:>
Python Code:
mod = pysces.model('lin4_fb')
mod.doLoad() # this method call is necessary to ensure that future `doLoad` method calls are executed correctly
sc = psctb.Symca(mod)
sc.do_symca()
sc.cc_results
sc.cc_results.ccJR1_R4
sc.cc_results.ccJR1_R4.expression
sc.cc_results.ccJR1_R4.numerator
s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Additionally Symca has the following arguments
Step2: do_symca has the following arguments
Step3: Inspecting an individual control coefficient... |
3,201 | <ASSISTANT_TASK:>
Python Code:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.
Step2: Visualize Data
Step3: Preprocess Data
Step4: Setup TensorFlow
Step5:... |
3,202 | <ASSISTANT_TASK:>
Python Code:
# Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
# Initial... | <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. Initialization of setup
Step2: 2. Finite Differences setup
Step3: 3. Finite Volumes setup
Step4: 4. Initial condition
Step5: 4. Solution ... |
3,203 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
# We'll need numpy for some mathematical operations
import numpy as np
# matplotlib for displaying the output
import matplotlib.pyplot as plt
import matplotlib.style as ms
ms.use('seaborn-muted')
%matplotlib inline
# and IPython.display for audio outp... | <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: By default, librosa will resample the signal to 22050Hz.
Step2: Harmonic-percussive source separation
Step3: Chromagram
Step4: MFCC
Step5: B... |
3,204 | <ASSISTANT_TASK:>
Python Code:
%%javascript
// From https://github.com/kmahelona/ipython_notebook_goodies
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')
def add(n1, n2):
return n1 + n2
def multiply(n1, n2):
return n1 * n2
def exponentiate(n1, n2):
Raise n1 to th... | <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: Basics
Step5: Well, we only want these functions to work if both inputs are numbers. So we could do
Step8: But this is yucky
Step9: This is d... |
3,205 | <ASSISTANT_TASK:>
Python Code:
import bs4
# read in the xml file
soup = bs4.BeautifulSoup(open('Ode.xml'), 'html.parser')
# get the text content inside the "EEBO" tag
text = soup.find('eebo').get_text()
# print the text
print(text)
import bs4
# read in the xml file
soup = bs4.BeautifulSoup(open('Ode.xml'), 'html.parse... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h2 style='color
|
3,206 | <ASSISTANT_TASK:>
Python Code:
import xgboost as xgb
import shap
from sklearn.model_selection import train_test_split
import pandas as pd
X,y = shap.datasets.boston()
X.head()
print(y.shape) # predict house price
y[4:10]
y = pd.DataFrame(y)
y.head()
# for regression method, I can not use stratify split with this metho... | <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: Summarize Feature Importance
Step2: Check Individual Cases
|
3,207 | <ASSISTANT_TASK:>
Python Code:
# A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] ... | <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: Implementing a Neural Network
Step2: We will use the class TwoLayerNet in the file cs231n/classifiers/neural_net.py to represent instances of o... |
3,208 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import torch
a, b = load_data()
c = (a[:, -1:] + b[:, :1]) / 2
result = torch.cat((a[:, :-1], c, b[:, 1:]), dim=1)
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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:
|
3,209 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
from tardis import run_tardis
import tardis
tardis.logger.setLevel(0)
tardis.logging.captureWarnings(False)
def display_table(sim):
'''Display a table of velocities and
radiative temperatures at each iteration
'''
# ... | <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 command run_tardis allows users to provide a set of callbacks to the simulation. These callbacks are called at the end of each iteration. ... |
3,210 | <ASSISTANT_TASK:>
Python Code:
from bravado.client import SwaggerClient
client = SwaggerClient.from_url('https://www.genomenexus.org/v2/api-docs',
config={"validate_requests":False,"validate_responses":False,"validate_swagger_spec":False})
print(client)
dir(client)
for a in dir(client):
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Connect with cBioPortal API
Step2: Annotate cBioPortal mutations with Genome Nexus
Step3: Check overlap SIFT/PolyPhen-2
|
3,211 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pints
import pints.plot
import pints.toy
# Define model parameters
parameters = [2, 0.015, 500, 10, 1.1, 0.05]
f_0, r, k, sigma_base, eta, sigma_rel = parameters
# Instantiate logistic growth model with f(t=0) = f_0
model = pints.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: Inference of model parameters
Step2: Infer parameters with Haario Adaptive Covariance MCMC
Step3: Show quantitative and visual diagnostics of ... |
3,212 | <ASSISTANT_TASK:>
Python Code:
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import shogun as sg
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#number of data points.
n=100
#generate a random 2d line(y1 = mx1 + c)
m = np.random.randint(1,10)
c = np.random.randint(1,10)... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Some Formal Background (Skip if you just want code examples)
Step2: Step 2
Step3: Step 3
Step4: Step 5
Step5: In the above figure, the blue ... |
3,213 | <ASSISTANT_TASK:>
Python Code:
__version__ = '0.1.0'
__status__ = 'Development'
__date__ = '2017-May-25'
__author__ = 'Jay Narhan'
import os
import pandas as pd
import numpy as np
from collections import Counter
META_ROOT = os.path.realpath('../../Meta_Data_Files') + '/'
DDSM_META = META_ROOT + 'Ddsm_png.c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h2>Read DDSM Meta Data
Step2: <h2>Read in MIAS Meta Data
Step3: <h3>Create meta_data_all.csv</h3>
Step4:
Step5: <h2>Creating Meta Data for... |
3,214 | <ASSISTANT_TASK:>
Python Code:
import codecs
with codecs.open("imdb_labelled.txt", "r", "utf-8") as arquivo:
vetor = []
for linha in arquivo:
vetor.append(linha)
with codecs.open("amazon_cells_labelled.txt", "r", "utf-8") as arquivo:
for linha in arquivo:
vetor.append(linha)
with codecs.op... | <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: Depois, devemos retirar cada quebra de linha no final de cada linha, ou seja, os '\n'.
Step2: A seguir, retiramos os dois últimos caracteres so... |
3,215 | <ASSISTANT_TASK:>
Python Code:
class Mesa(object):
cantidad_de_patas = None
color = None
material = None
mi_mesa = Mesa()
mi_mesa.cantidad_de_patas = 4
mi_mesa.color = 'Marrón'
mi_mesa.material = 'Madera'
print 'Tendo una mesa de {0.cantidad_de_patas} patas de color {0.color} y esta hecha de {0.materia... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Ahora, si siempre voy a tener que definir esas características de la mesa para poder usarla, lo más cómodo es definir el método __init__ que sir... |
3,216 | <ASSISTANT_TASK:>
Python Code:
from bruges.transform import CoordTransform
corner_ix = [[0, 0], [0, 3], [3, 0]]
corner_xy = [[5000, 6000],
[5000-23.176, 6000+71.329],
[5000+142.658, 6000+46.353]]
transform = CoordTransform(corner_ix, corner_xy)
for i in range(4):
for j in range(4)... | <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: Adding (x, y) coordinates
|
3,217 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(10)
dossageEffectiveness = abs(np.random.normal(5.0, 1.5, 1000))
repurchaseRate = (dossageEffectiveness + np.random.normal(0, 0.1, 1000)) * 3
repurchaseRate/=np.max(repurchaseRate)
plt.scatter(dossageEffe... | <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: Modify this to a multivariate/polynomial regression example
Step2: Make distribution more complicated to see if scikit-learn can fit it
Step3: ... |
3,218 | <ASSISTANT_TASK:>
Python Code:
%matplotlib nbagg
import numpy as np
import matplotlib.pyplot as plt
from plots import plot_tree_interactive
plot_tree_interactive()
from plots import plot_forest_interactive
plot_forest_interactive()
from sklearn import grid_search
from sklearn.datasets import load_digits
from sklearn... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Decision Tree Classification
Step2: Random Forests
Step3: Selecting the Optimal Estimator via Cross-Validation
Step4: Exercises
|
3,219 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-2', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
3,220 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
# Modify the path
sys.path.append("..")
import yellowbrick as yb
import matplotlib.pyplot as plt
from download import download_all
from sklearn.datasets.base import Bunch
## The path to the test data sets
FIXTURES = os.path.join(os.getcwd(), "data")
## Dataset ... | <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: Load Text Corpus for Example Code
Step3: t-SNE
Step4: Frequency Distribution Visualization
Step5: Note that the FreqDistVisualizer does not p... |
3,221 | <ASSISTANT_TASK:>
Python Code:
from __future__ import unicode_literals, print_function
from axon.api import loads, dumps
from axon.objects import node, attribute, Attribute, Node
from axon.objects import Builder, register_builder
from axon import dump_as_str, as_unicode, factory, reduce
from xml.etree import ElementTre... | <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: There are reduce functions for ElementTree.Element and ElementTree.ElementTree types from xml.etree package. These functions will used for dumpi... |
3,222 | <ASSISTANT_TASK:>
Python Code:
import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np # foundation for Pan... | <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: Analysis
Step3: Does size matter
Step4: Do clusters of CompSci programs in a state make female participation more likely?
Step5: ... |
3,223 | <ASSISTANT_TASK:>
Python Code:
from petal_helper import *
# Detect TPU, return appropriate distribution strategy
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
... | <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: Create Distribution Strategy
Step2: Loading the Competition Data
Step3: Explore the Data
Step4: Examine the shape of the data.
Step5: Peek a... |
3,224 | <ASSISTANT_TASK:>
Python Code:
%%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight_data_train
LIMIT 0
%%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight_data_eval
LIMIT 0
%%bigqu... | <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: Lab Task #1
Step2: Create two SQL statements to evaluate the model.
Step3: Lab Task #2
Step4: Create three SQL statements to EVALUATE the mod... |
3,225 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
id=["Train A","Train A","Train A","Train B","Train B","Train B"]
arrival_time = ["0"," 2016-05-19 13:50:00","2016-05-19 21:25:00","0","2016-05-24 18:30:00","2016-05-26 12:15:00"]
departure_time = ["2016-05-19 08:25:00","2016-05-19 16:00:00","2016-05-20 07:45:00","2016-... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
3,226 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('totaal.csv')
df = df.set_index('id')
df['start'] = pd.to_datetime(df['start']) # Starttijden converteren naar datetimes
df['einde'] = pd.to_datetime(df['einde']) # Eindtijden converteren naar datetimes
df['duur'] = df['einde'] - df['start'] # Hoe lang parkeert iedereen?
... | <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: Vervolgens kijken we of we het correct is ingeladen
Step2: Starttijden
Step3: Eindtijden
Step4: Duur
Step5: Interessant! Van de distributie ... |
3,227 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-3', 'ocnbgchem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contri... | <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... |
3,228 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
print(tf.__version__)
mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
import matplotlib.pyplot as plt
plt.imshow(training_images[0])
print(training_labels[0])
print(training_images[0])
t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The Fashion MNIST data is available directly in the tf.keras datasets API. You load it like this
Step2: Calling load_data on this object will g... |
3,229 | <ASSISTANT_TASK:>
Python Code:
from nipype import Node, JoinNode, Workflow
# Specify fake input node A
a = Node(interface=A(), name="a")
# Iterate over fake node B's input 'in_file?
b = Node(interface=B(), name="b")
b.iterables = ('in_file', [file1, file2])
# Pass results on to fake node C
c = Node(interface=C(), 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: As you can see, setting up a JoinNode is rather simple. The only difference to a normal Node are the joinsource and the joinfield. joinsource sp... |
3,230 | <ASSISTANT_TASK:>
Python Code:
%matplotlib widget
!pip install nanslice
import urllib.request
import tarfile
url = 'https://osf.io/hmtyr/download'
urllib.request.urlretrieve(url, 'nanslice_example.tar.gz')
tgz = tarfile.open('nanslice_example.tar.gz')
tgz.extractall()
tgz.close()
data_dir = 'nanslice_example/'
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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<USER_TASK:>
Description:
Step1: Basic Slicing
Step2: However, if you are going to use the same image multiple times, e.g. a structural template image, then it makes sense to l... |
3,231 | <ASSISTANT_TASK:>
Python Code:
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from google.cloud import bigquery
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers... | <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: Explore time series data
Step3: The function clean_data below does three things
Step6: Read data and preprocessing
Step7: Let's plot a few ex... |
3,232 | <ASSISTANT_TASK:>
Python Code:
from pyspark import SparkContext
sc = SparkContext(master = 'local')
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Python Spark SQL basic example") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
cuse = sp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Decision tree classification with pyspark
Step2: Process categorical columns
Step3: Build StringIndexer stages
Step4: Build OneHotEncoder sta... |
3,233 | <ASSISTANT_TASK:>
Python Code:
from __future__ import unicode_literals, print_function
import boto3
import json
import numpy as np
import pandas as pd
import spacy
from verta import Client
client = Client('http://localhost:3000/')
proj = client.set_project('Tweet Classification')
expt = client.set_experiment('SpaCy')
... | <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: ...and instantiate Verta's ModelDB Client.
Step2: Prepare Data
Step3: Capture and Version Model Ingredients
Step4: You may verify through the... |
3,234 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
n=200
x_tr = np.linspace(0.0, 2.0, n)
y_tr = np.exp(3*x_tr)
import random
mu, sigma = 0,50
random.seed(1)
y = y_tr + np.random.normal(loc=mu, scale= sigma, size=len(x_tr))
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(x_tr,y,".",mew=3);
plt.plot(x_tr, y_tr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The red curve is defined by the function
Step2: Let's fit a simple linear model on $y$ and $x$.
Step3: Well, that's not really good... We can... |
3,235 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import desolver as de
import desolver.backend as D
D.set_float_fmt('float64')
def Fij(ri, rj, G):
rel_r = rj - ri
return G*(1/D.norm(rel_r, ord=2)**3)*rel_r
def rhs(t, state, masses, G):
total_acc = D.zeros_like(state)
... | <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: Specifying the Dynamical System
Step2: NOTE
Step3: I've added 3 massive bodies at the ends of a scalene triangle
Step4: The Numerical Integra... |
3,236 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn import datasets, metrics, model_selection, preprocessing, pipeline
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import autosklearn... | <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: Note
Step2: Print the final ensemble constructed by auto-sklearn
|
3,237 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
from scipy import stats
import seaborn as sns
from matplotlib import pyplot as plt
sns.set_style('white')
data = pd.io.stata.read_stata('data/us_job_market_discrimination.dta')
# number of callbacks for black-sounding names
print(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Permutation
Step2: T-test
|
3,238 | <ASSISTANT_TASK:>
Python Code:
w, h, b, d, c1, c2, k1, k2, r_sys, r_ref = symbols("w, h, b, d, c_1, c_2, k_1, k_2, r_{sys}, r_{ref}", real=True)
# Constraints for hyperboloids:
k1_constraint = k1 > 2
k2_constraint = k2 > 2
c1_constraint = c1 > 0
c2_constraint = c2 > 0
xw, yw, zw = symbols("x_w, y_w, z_w", real=True)
# ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: System Height
Step2: Lens Hole Radius
Step3: Line passing through points $P_w$ and $F_1$
Step4: Let $\lambda_1 = 1 - s_1$, so that
Step5: So... |
3,239 | <ASSISTANT_TASK:>
Python Code:
def fit_normal_to_hist(h):
if not all(h==0):
bins =np.array([-2.0,-1.0,-0.5,0.0,0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0,5.0])
orig_hist = np.array(h).astype(float)
norm_hist = orig_hist/float(sum(orig_hist))
mid_points = (bins[1:] + bins[:-1])/2
popt,... | <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: bins =np.array([-2.0,-1.0,-0.5,0.0,0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0,5.0])
Step2: h = df.iloc[10,mask]
|
3,240 | <ASSISTANT_TASK:>
Python Code:
from pprint import *
import pyspark
from pyspark import SparkConf, SparkContext
sc = None
print(pyspark.status)
conf = (SparkConf()
.setMaster("local")
.setAppName("MyApp")
.set("spark.executor.memory", "1g"))
if sc is None:
sc = SparkContext(conf = conf)
p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 配置环境SparkConf和创建SparkContext运行环境对象。
Step2: 显示Spark的配置信息。
Step3: Spark的文本RDD操作。
Step4: 从RDD中按照文本方式进行关键词查询。
Step5: Spark的DataFrame操作。
Step6: ... |
3,241 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import holoviews as hv
hv.notebook_extension('matplotlib')
fractal = hv.Image(np.load('mandelbrot.npy'))
((fractal * hv.HLine(y=0)).hist() + fractal.sample(y=0))
%%opts Points [scaling_factor=50] Contours (color='w')
dots = np.linspace(-0.45, 0.45, 19)
layouts = {y: (f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Fundamentally, a HoloViews object is just a thin wrapper around your data, with the data always being accessible in its native numerical format,... |
3,242 | <ASSISTANT_TASK:>
Python Code:
import cvxpy as cp
import numpy as np
import scipy as scipy
# Fix random number generator so we can repeat the experiment.
np.random.seed(0)
# Dimension of matrix.
n = 10
# Number of samples, y_i
N = 1000
# Create sparse, symmetric PSD matrix S
A = np.random.randn(n, n) # Unit normal gau... | <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: Solve for several $\alpha$ values
Step2: Result plots
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3,243 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint, EvalNormalPdf
import thinkplot
import pandas as pd
import matplotlib.pyplot as plt
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: From
Step2: Next, let's create vectors of our ages and heights.
Step3: Now let's visualize our data to make sure that linear regression is app... |
3,244 | <ASSISTANT_TASK:>
Python Code:
# from typing import Callable, Sequence # used ?
import flax
from flax import linen as nn
# Simple module with matmul layer. Note that we could build this in many
# different ways using the `scope` for parameter handling.
class Matmul:
def __init__(self, features):
self.feat... | <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: Functional core
Step2: Stateless Linen module
Step3: Linen module with state
Step4: Modify MNIST example
|
3,245 | <ASSISTANT_TASK:>
Python Code:
# Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
import numpy.linalg as la
import seaborn as sns
import itertools
import pandas as pd
sns.set_style('whitegrid')
# create a palette generator... | <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 would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps
Step2: Inline Qu... |
3,246 | <ASSISTANT_TASK:>
Python Code:
import subprocess
import numpy as np
from IPython.display import Image
PI = np.pi
POV_SCENE_FILE = "hopf_fibration.pov"
POV_DATA_FILE = "torus-data.inc"
POV_EXE = "povray"
COMMAND = "{} +I{} +W500 +H500 +Q11 +A0.01 +R2".format(POV_EXE, POV_SCENE_FILE)
IMG = POV_SCENE_FILE[:-4] + ".png"
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: Hopf inverse map and stereographic projection
Step7: Circle passes through three points
Step10: Convert vector/matrix to POV-Ray format
Step12... |
3,247 | <ASSISTANT_TASK:>
Python Code:
import itertools
import os
import sys
os.environ['OPENBLAS_NUM_THREADS'] = '1'
import numpy as np
import pandas as pd
from scipy import sparse
import content_wmf
import batched_inv_joblib
import rec_eval
DATA_DIR = '/hdd2/dawen/data/ml-20m/pro/'
unique_uid = list()
with open(os.path.join... | <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 pre-processed data
Step2: Train the model
|
3,248 | <ASSISTANT_TASK:>
Python Code:
# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Ezequiel Mikulan <e.mikulan@gmail.com>
# Manorama Kadwani <manorama.kadwani@gmail.com>
#
# License: BSD-3-Clause
import os
import shutil
import mne
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path... | <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: Exporting surfaces to Blender
Step2: Editing in Blender
Step3: Back in Python, you can read the fixed .obj files and save them as
Step4: Edit... |
3,249 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'fio-ronm', 'sandbox-3', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name",... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
3,250 | <ASSISTANT_TASK:>
Python Code:
def prime(n ) :
if(n <= 1 ) :
return False
if(n <= 3 ) :
return True
if(n % 2 == 0 or n % 3 == 0 ) :
return False
i = 5
while i * i <= n :
if(n % i == 0 or n %(i + 2 ) == 0 ) :
return False
i += 6
return True
def isVowel(c ) :
c = c . lower()... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
3,251 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
# proposition = "PROPOSITION 064- MARIJUANA LEGALIZATION. INITIATIVE STATUTE."
proposition = "PROPOSITION 062- DEATH PENALTY. INITIATIVE STATUTE."
props = pd.read_csv("http://www.firstpythonnotebook.org/_static/committees.csv")
contribs = pd.read_csv("http://www.first... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Read in data on committees and contributions
Step2: Number of committees per proposition
Step3: Filter for proposition of interest
Step4: All... |
3,252 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from opengrid.library import misc
from opengrid.library import houseprint
from opengrid.library import caching
import charts
hp = houseprint.Houseprint()
cache_water = caching.Cache(variable='water_daily_min')
df_cache = cache_water.get(sensors=hp.get_sensors(sensorty... | <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 demonstrate the caching for the minimal daily water consumption (should be close to zero unless there is a water leak). We create a cache ob... |
3,253 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import dask.array as da
from fmks.data.cahn_hilliard import generate_cahn_hilliard_data
import dask.threaded
import dask.multiprocessing
def time_ch(num_workers,
get,
shape=(48, 200, 200),
chunks=(1, 200, 200),
n_steps=10... | <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 function time_ch calls generate_cahn_hilliard_data to generate the data. generate_cahn_hilliard_data returns the microstructure and response... |
3,254 | <ASSISTANT_TASK:>
Python Code:
from pymatgen.entries.computed_entries import ComputedEntry
from pymatgen.entries.compatibility import MaterialsProjectCompatibility, \
MaterialsProject2020Compatibility
from pymatgen.ext.matproj import MPRester
# retrieve
with MPRester() as m:... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Default behavior - MaterialsProject2020Compatibility
Step2: You can examine the energy corrections via the energy_adjustments attribute
Step3:... |
3,255 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
%pylab inline
from scipy import stats
import numpy as np
b= stats.bernoulli(.5) # fair coin distribution
nsamples = 100
# flip it nsamples times for 200 estimates
xs = b.rvs(nsamples*200).reshape(nsamples,-1)
phat = np.mean(xs,axis=0) # estimated p
# edge... | <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 a previous coin-flipping discussion, we discussed estimation of the
Step2: <!-- # @@@CODE src-statistics/Confidence_Intervals.py fromto
|
3,256 | <ASSISTANT_TASK:>
Python Code:
import gzip
import pickle
import numpy as np
import sklearn.svm as svm
def load_data():
with gzip.open('../mnist.pkl.gz', 'rb') as f:
train, validate, test = pickle.load(f, encoding="latin1")
X_train = np.array([np.reshape(x, (784, )) for x in train[0]])
X_test ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The function $\texttt{load_data}()$ returns a pair of the form
Step2: Let us see what we have read
Step3: We define a support vector machine w... |
3,257 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy as sp
from sklearn import datasets
iris = datasets.load_iris()
digits = datasets.load_digits()
boston = datasets.load_boston()
from sklearn import svm
model = svm.SVC(gamma=0.002, C=100.)
print(model.gamma)
model.set_params(gamma=.001)
print(model.gamma)
m... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Même avant scikit-learn
Step2: Nous pouvons regarder l'image.
Step3: À savoir (mais pour un autre jour)
Step4: Le classifieur le plus simple... |
3,258 | <ASSISTANT_TASK:>
Python Code:
df['citizenship'].value_counts().head()
df.groupby('citizenship')['networthusbillion'].sum().sort_values(ascending=False)
us_pop = 318.9 #billion (2014)
us_bill = df[df['citizenship'] == 'United States']
print("There are", us_pop/len(us_bill), "billionaires per billion people in the Unit... | <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: Who are the top 10 richest billionaires?
Step2: What's the average wealth of a billionaire? Male? Female?
Step3: Who is the poorest billionair... |
3,259 | <ASSISTANT_TASK:>
Python Code:
import dateutils
import dateutil.parser
import pandas as pd
parking_df = pd.read_csv("small-violations.csv")
parking_df
parking_df.dtypes
import datetime
parking_df.head()['Issue Date'].astype(datetime.datetime)
import pandas as pd
parking_df = pd.read_csv("small-violations.csv")
parking_... | <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. I want to make sure my Plate ID is a string. Can't lose the leading zeroes!
Step2: 2. I don't think anyone's car was built in 0AD. Discard ... |
3,260 | <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', figsize=(10,4))
day_r... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 加载和准备数据
Step2: 数据简介
Step3: 查看每天的骑行数据，对比2011年和2012年
Step4: 虚拟变量（哑变量）
Step5: 调整目标变量
Step6: 我们将数据拆分为两个数据集，一个用作训练，一个在网络训练完后用来验证网络。因为数据是有时间序列特性的... |
3,261 | <ASSISTANT_TASK:>
Python Code:
# Define your group, for this exercise
mygroup = "A" # <- change the letter in quotes
# Import Python libraries
import os # This lets us interact with the operating system
import pandas as pd # This allows us to use dataframes
import seaborn as sns # This gives us pre... | <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: After executing the code cell, you should see a table of values. The table has columns named gene1 and gene2, and rows that are indexed starting... |
3,262 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mm', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "emai... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
3,263 | <ASSISTANT_TASK:>
Python Code:
# Authors: Tal Linzen <linzen@nyu.edu>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.stats.regression import linear_regression
print(__doc__)
data_path = sample.data_path()
raw_fn... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Set parameters and read data
Step2: Run regression
|
3,264 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import matplotlib.pyplot as pl
from astropy.wcs import WCS
from scipy import constants
import cygrid
np.set_printoptions(precision=1)
def gaincurve(elev, a0, a1, a2):
... | <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: Introduction
Step2: Atmospheric temperature is approximately given by ambient temperature at ground.
Step3: Calculate telescope sensitivity (a... |
3,265 | <ASSISTANT_TASK:>
Python Code:
# 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 it binds the
# results of that search to a name in the local scope.
import os, json, ma... | <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: Locating the CSV files
Step2: Lab Task 1
Step3: Next, let's define our features we want to use and our label(s) and then load in the dataset f... |
3,266 | <ASSISTANT_TASK:>
Python Code:
odd_1000 = [x**2 for x in range(0, 1000) if x % 2 == 1]
# 리스트의 처음 다섯 개 항목
odd_1000[:5]
odd_3x7 = [x for x in range(0, 1000) if x % 2 == 1 and x % 7 == 0]
# 리스트의 처음 다섯 개 항목
odd_3x7[:5]
def square_plus1(x):
return x**2 + 1
odd_3x7_spl = [square_plus1(x) for x in odd_3x7]
# 리스트의 처음 다섯 ... | <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: 문제
Step2: 문제
Step3: csv 파일 읽어들이기
Step4: 문제
Step5: 문제
Step6: 문제
Step7: 넘파이의 linspace() 함수 활용
Step8: 문제
Step9: 넘파이 활용 기초 2
Step10: 문제
|
3,267 | <ASSISTANT_TASK:>
Python Code:
import torch
import torch.nn as nn # we'll use this a lot going forward!
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
X = torch.linspace(1,50,50).reshape(-1,1)
# Equivalent to
# X = torch.unsqueeze(torch.linspace(1,50,50), dim=1)
torch.manual_seed(71) # to obta... | <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: Create a column matrix of X values
Step2: Create a "random" array of error values
Step3: Create a column matrix of y values
Step4: Plot the r... |
3,268 | <ASSISTANT_TASK:>
Python Code:
import pints
import pints.plot
import pints.toy
import matplotlib.pyplot as plt
import numpy as np
model = pints.toy.GoodwinOscillatorModel()
real_parameters = model.suggested_parameters()
times = model.suggested_times()
values = model.simulate(real_parameters, times)
plt.figure()
plt.s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The model also provides suggested parameters and sampling times, allowing us to run a simulation
Step2: This gives us all we need to create a p... |
3,269 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import LeaveOneOut
from sklearn import linear_model, neighbors
%matplotlib inline
plt.style.use('ggplot')
# dataset path
data_dir = "."
sample_data = pd.read_csv(data_dir+"/hw1.csv", delimi... | <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 response variable is quality.
Step4: Exercise 2.1 (5 pts) Compare the leave-one-out risk with the empirical risk for linear regression, on ... |
3,270 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('ggplot')
# Anaconda on Windows will get warning
df=pd.read_csv('train.csv')
df.head()
df.hist( figsize=(16, 10))
df.describe()
df.keys()
df2=df.drop(['Name','PassengerId','Cabin','Ticket'],1)
df2.h... | <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: Loading data from CSV
Step2: Show first 5 records
Step3: Histogram
Step4: Show statistic
Step5: Show list of column names
Step6: Drop some ... |
3,271 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import HTML
HTML('<iframe src="http://conda.pydata.org/docs/_downloads/conda-cheatsheet.pdf" width="700" height="400"></iframe>')
# importing numpy
# performance list sum
# performance array sum
%timeit np.sum(array)
one_dim_array =
two_dim_array =
# size & shape
... | <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 objectives of this workshop
Step2: Array creation
Step3: Basic slicing
Step4: [start
Step5: 2. Drawing
Step6: Operations & linalg
Step... |
3,272 | <ASSISTANT_TASK:>
Python Code:
df.fillna('n/a',inplace=True)
su=df[df['type_of_property'].str.contains('Apartment')]
mu=df[df['type_of_property'].str.contains('Apartments')]
print(len(mu))
print(len(su))
su['propertyinfo_value']
len(su[~(su['propertyinfo_value'].str.contains('bd') | su['propertyinfo_value'].str.contain... | <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: 带卧室但没厕所
Step2: 这里通过自定义函数来分类处理数据，从sucln['xxx']出来的是Series，parse_info中读入的row参数，是来自Series的一行文字
|
3,273 | <ASSISTANT_TASK:>
Python Code:
from modsim import System
# If this doesn't work, move this file into your /code folder.
# It needs to be in the same folder as modsim.py.
def func1(input1, input2):
print("Input 1 = ", input1)
print("Input 2 = ", input2)
output = input1 + input2
print("Output = ", output... | <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: Let's write a function, like they do in ModSim notebooks all the time. We'll give it some parameters just to make it feel important.
Step2: All... |
3,274 | <ASSISTANT_TASK:>
Python Code:
import holoviews as hv
hv.notebook_extension(bokeh=True)
hv.Element(None, group='Value', label='Label')
import numpy as np
points = [(0.1*i, np.sin(0.1*i)) for i in range(100)]
hv.Curve(points)
np.random.seed(7)
points = [(0.1*i, np.sin(0.1*i)) for i in range(100)]
errors = [(0.1*i, np.... | <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 addition, Element has key dimensions (kdims), value dimensions (vdims), and constant dimensions (cdims) to describe the semantics of indexing... |
3,275 | <ASSISTANT_TASK:>
Python Code:
def naivesum_list(N):
Naively sum the first N integers
A = 0
for i in list(range(N + 1)):
A += i
return A
%load_ext memory_profiler
%memit naivesum_list(10**4)
%memit naivesum_list(10**5)
%memit naivesum_list(10**6)
%memit naivesum_list(10**7)
%memit naivesum_list(10*... | <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: Iterators and Generators
Step2: We will now see how much memory this uses
Step4: We see that the memory usage is growing very rapidly - as the... |
3,276 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
# отключим всякие предупреждения Anaconda
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
%matplotlib inline
import seaborn as sns
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: А теперь практический пример
Step2: Напишем вспомогательную функцию, которая будет возвращать решетку для дальнейшей красивой визуализации.
Ste... |
3,277 | <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.datasets import spm_face
from mne.preprocessing import ICA, create_eog_epochs
from m... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and filter data, set up epochs
Step2: Visualize fields on MEG helmet
Step3: Compute forward model
Step4: Compute inverse solution
|
3,278 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pyqg
# create the model object
m = pyqg.BTModel(L=2.*np.pi, nx=256,
beta=0., H=1., rek=0., rd=None,
tmax=40, dt=0.001, taveint=1,
ntd=4)
# in this example we u... | <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: McWilliams performed freely-evolving 2D turbulence ($R_d = \infty$, $\beta =0$) experiments on a $2\pi\times 2\pi$ periodic box.
Step2: Initial... |
3,279 | <ASSISTANT_TASK:>
Python Code:
# Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.io # To read matlab files
import pylab
from test_helpe... | <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. Objectives
Step2: Numpy arrays can be defined directly using methods such as np.arange(), np.ones(), np.zeros(), as well as random number ge... |
3,280 | <ASSISTANT_TASK:>
Python Code:
True
True = 13
True and False
True or False
False and False or True
True or False and False
# Importa dalla libreria solo i tre operatori logici
from operator import and_, or_, not_
not_(or_(True, and_(False, True)))
not (True or (False and True))
True == True
6*3 < 7*2
14*2 == 4*7
fr... | <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 qualsiasi linguaggio di programmazione, oltre ad espressioni aritmetiche, è possibile valutare delle espressioni logiche, utilizzando gli ope... |
3,281 | <ASSISTANT_TASK:>
Python Code:
data = range(1, 6)
pie = Pie(sizes=data)
fig = Figure(marks=[pie], animation_duration=1000)
# Add `animation_duration` (in milliseconds) to have smooth transitions
display(fig)
Nslices = 5
pie.sizes = np.random.rand(Nslices)
pie.sort = True
pie.selected_style = {"opacity": "1", "stroke... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: As with all bqplot Marks, pie data can be dynamically modified
Step2: Sort the pie slices by ascending size
Step3: Setting different styles fo... |
3,282 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.arange(6)
b = a
print("a =\n",a)
print("b =\n",b)
b.shape = (2,3) # mudança no shape de b,
print("\na shape =",a.shape) # altera o shape de a
b[0,0] = -1 # mudança no conteúdo de b
print(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Observe que mesmo no retorno de uma função, a cópia explícita pode não acontecer. Veja o exemplo a
Step2: Cópia rasa
Step3: Slice - Fatiamento... |
3,283 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... | <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: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
3,284 | <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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<USER_TASK:>
Description:
Step1: 量子化認識トレーニングの総合ガイド
Step2: 量子化認識モデルを定義する
Step3: 一部のレイヤーを量子化する
Step4: この例では量子化するものを決定するためにレイヤーの種類が使用されていますが、特定のレイヤーを量子化する上で最も簡単な方法は、name プロパティを設... |
3,285 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
from IPython.display import Image
%matplotlib inline
# image courtesy of Raschka, Sebastian. Python machine learning. Birmingham, UK: Packt Publishing, 2015. Print.
Image(filename='learning-curve.png', width=600)
from sklearn import datasets
import numpy ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Plotting our own learning curves
Step2: Notice how we plot the standard deviation too; in addition to seeing whether the training and test accu... |
3,286 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
print(sm.datasets.sunspots.NOTE)
dta = sm.datasets.sunspots.loa... | <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: Sunpots Data
Step2: Does our model obey the theory?
Step3: This indicates a lack of fit.
Step4: Exercise
Step5: Let's make sure this model i... |
3,287 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import os
import tfx_utils
def _make_default_sqlite_uri(pipeline_name):
return os.path.join(os.environ['HOME'], 'airflow/tfx/metadata', pipeline_name, 'metadata.db')
def get_metadata_store(pipeline_name):
return tfx_utils.TFXReadonlyMetadataSt... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now print out the model artifacts
Step2: Now analyze the model performance
Step3: Now plot the artifact lineage
|
3,288 | <ASSISTANT_TASK:>
Python Code:
NAME = "Michelle Appel"
NAME2 = "Verna Dankers"
NAME3 = "Yves van Montfort"
EMAIL = "michelle.appel@student.uva.nl"
EMAIL2 = "verna.dankers@student.uva.nl"
EMAIL3 = "yves.vanmontfort@student.uva.nl"
%pylab inline
plt.rcParams["figure.figsize"] = [20,10]
def true_mean_function(x):
re... | <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: Lab 3
Step2: Part 1
Step3: 1. Sampling from the Gaussian process prior (30 points)
Step4: 1.2 computeK( X1, X2, thetas ) (10 points)
Step5: ... |
3,289 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
from __future__ import print_function
import datacube
import xarray as xr
from datacube.helpers import ga_pq_fuser
from datacube.storage import masking
from datacube.storage.masking import mask_to_dict
from matplotlib import pyplot as plt
dc = datacube.Datacube(app='combin... | <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: retrieve the NBAR and PQ for the spatiotemporal range of interest
Step2: Plotting an image, view the transect and select a location to retrieve... |
3,290 | <ASSISTANT_TASK:>
Python Code:
from pyannote.core import SlidingWindowFeature, SlidingWindow
# one 4-dimensional feature vector extracted every 100ms from a 200ms window
frame = SlidingWindow(start=0.0, step=0.100, duration=0.200)
# random for illustration purposes
data = np.random.randn(100, 4)
features = SlidingWind... | <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: SlidingWindowFeature are used to manage feature vectors extracted on a sliding window (e.g. MFCC in audio processing).
Step2: Cropping
Step3: ... |
3,291 | <ASSISTANT_TASK:>
Python Code:
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from google.cloud import bigquery
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from tensorflow.keras.layers import (
GRU,
LSTM,
... | <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:
Step2: Explore time series data
Step4: The function clean_data below does three things
Step7: Read data and preprocessing
Step8: Let's plot a few ex... |
3,292 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import glob
import os
import scipy as sp
from scipy import stats
from tools.plt import color2d #from the 'srcole/tools' repo
from matplotlib import cm
# Load cities info
df_cities = pd.read_csv('/gh... | <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: Load dataframes
Step2: 1. What are most popular categories?
Step3: 2. What are the most common restaurant chains?
Step4: 2a. Correlations in ... |
3,293 | <ASSISTANT_TASK:>
Python Code:
import pymongo as pm
client = pm.MongoClient()
client.drop_database("tutorial")
import bson.son as son
# start a client
client = pm.MongoClient()
# connect to a database
db = client.tutorial
# get a collection
coll = db.test_collection
example_0 = {}
example_1 = {"name": "Michael", "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: "Hello World!"
Step2: Documents follow the JSON format and MongoDB stores them in a binary version of it (BSON).
Step3: Note that we can also... |
3,294 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from compecon import BasisChebyshev, NLP, nodeunif
from compecon.demos import demo
alpha= 1.0;
eta= 1.5;
D = lambda p: p** (-eta)
n= 25;
a= 0.1;
b= 3.0
S= BasisChebyshev(n, a, b, labels= ['price'], l=['supply'])
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: and set the $\alpha$ and $\eta$ parameters
Step2: For convenience, we define a lambda function to represent the demand. Note
Step3: We will ap... |
3,295 | <ASSISTANT_TASK:>
Python Code:
# 数値計算やデータフレーム操作に関するライブラリをインポートする
import numpy as np
import pandas as pd
# URL によるリソースへのアクセスを提供するライブラリをインポートする。
# import urllib # Python 2 の場合
import urllib.request # Python 3 の場合
# 図やグラフを図示するためのライブラリをインポートする。
import matplotlib.pyplot as plt
%matplotlib inline
# 線形回帰を行なうライブラリ
from sklearn... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: <h3 STYLE="background
Step2: <h3 STYLE="background
Step3: 以上の結果は、説明変数と目的変数の関係が次の回帰式で表されることを示しています。
Step4: 結果を 2 次元座標上にプロットすると、以下のようになります。青線が回... |
3,296 | <ASSISTANT_TASK:>
Python Code:
# keras.datasets.imdb is broken in TensorFlow 1.13 and 1.14 due to numpy 1.16.3
!pip install numpy==1.16.2
# All the imports!
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import sequence
from numpy import array
# Supress deprecation warnings
import loggi... | <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: Create map for converting IMDB dataset to readable reviews
Step3: Data Insight
Step4: Pre-processing Data
Step5: Create and buil... |
3,297 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
# For monitoring duration of pandas processes
from tqdm import tqdm, tqdm_pandas
# To avoid RuntimeError: Set changed size during iteration
tqdm.monitor_interval = 0
# Register `pandas.progress_apply` and `pandas.Series.map_apply` with `tqdm`
# (can use `tqdm_gui`, `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: The idea
Step2: Begin noun filtering
Step3: Association Rules Mining Filtering
Step4: Some more stats
|
3,298 | <ASSISTANT_TASK:>
Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# 1. magic for inline 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: Product Quantization for Model Compression
Step2: Product Quantization from Scratch
Step3: Encode
Step4: We can calculate the potential size/... |
3,299 | <ASSISTANT_TASK:>
Python Code:
# Installing sotware prerequisites via the python package index:
!pip install -U numpy matplotlib sklearn pysptools wget
#Import packages
# Ensure that this code works on both python 2 and python 3
from __future__ import division, print_function, absolute_import, unicode_literals
# basic ... | <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: Convert to H5
Step3: Extracting the data and parameters
Step4: Visualize the Amplitude Data
Step5: 1. Singular Value Decompo... |
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