Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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3,700 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os.path
import pdb
import pandas as pd
from pprint import pprint
#DATA_DIR = '/home/brandon/terabyte/Datasets/ubuntu_dialogue_corpus/'
DATA_DIR = '/home/brandon/ubuntu_dialogue_corpus/src/' # sample/'
TRAIN_PATH = DATA_DIR + 'train.csv'
VALID_PATH = DATA_DIR + 'v... | <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: Reformatting Ubuntu Dialogue Corpus for Chatbot Model
Step5: Functions for Visualization and Reformatting
Step6: Training Data
Step7: Turn-Ba... |
3,701 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import speclite
from desisim.io import read_basis_templates
import matplotlib
import seaborn as sns
%matplotlib inline
sns.set(style='white', font_scale=1.8, font='sans-serif')
colors = sns.color_palette('Set2', n_colors=8, desa... | <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 the old (v2.2) and new (v3.0) templates here for convenience.
Step2: Compare the coverage of physical parameter space.
Step7: Directly co... |
3,702 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import optimize
# training data
x_train = np.array(([3,5],[5,1],[10,2],[6,1.5]), dtype=float)
y_train = np.array(([75],[82],[93],[70]), dtype=float)
# testing data
x_test = np.array(([4, 5.5],[4.5, 1],[9,2.5],[6,2]), dtype=float)
y_test = np.array(([70],[89],... | <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: <p>The importance in separating testing data from training data is to always make sure the model is according to the real world by comparing to ... |
3,703 | <ASSISTANT_TASK:>
Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1., x=1., vz = 2.)
sim.add(m=1., a=1.)
sim.status()
sim.add(m=1.e-3, a=100.)
sim.add(primary=sim.particles[1], a=0.01)
print(sim.particles[1].a)
orbits = sim.calculate_orbits()
for orbit in orbits:
print(orbit)
print(sim.particl... | <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: Any components not passed automatically default to 0. REBOUND can also accept orbital elements.
Step2: We always have to pass a semimajor ax... |
3,704 | <ASSISTANT_TASK:>
Python Code:
# point to our previously-saved data
reference_catalog = '../data/PTF_Refims_Files/PTF_d022683_f02_c06_u000114210_p12_sexcat.ctlg'
outfile = reference_catalog.split('/')[-1].replace('ctlg','shlv')
ra_fav, dec_fav = (312.503802, -0.706603)
mjds, mags, magerrs = source_lightcurve('../data/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We'll start by loading the data from our favorite star, which has coordinates $\alpha_\mathrm{J2000}, \delta_\mathrm{J2000} = (312.503802, -0.70... |
3,705 | <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... |
3,706 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
import openpnm.models.physics as pmods
import matplotlib.pyplot as plt
pn = op.network.Cubic(shape=[15,15,15], spacing=6e-5)
geom = op.geometry.SpheresAndCylinders(network=pn, pores=... | <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 only other argument that needs to be passed to the metrics relative permeability is the invasion sequence (We made it as a user defined sequ... |
3,707 | <ASSISTANT_TASK:>
Python Code:
import os
from urllib.request import urlretrieve
import pybel
import logging
logging.getLogger('pybel').setLevel(logging.DEBUG)
logging.basicConfig(level=logging.DEBUG)
logging.getLogger('urllib3').setLevel(logging.WARNING)
print(pybel.get_version())
DESKTOP_PATH = os.path.join(os.path.ex... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First, we'll download and parse a BEL document from the Human Brain Pharmacome project describing the 2018 paper from Boland et al., "Promoting ... |
3,708 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import SVG
SVG('oneport_tiered_calibration/images/boxDiagram.svg')
SVG('oneport_tiered_calibration/images/probe.svg')
ls oneport_tiered_calibration/
ls oneport_tiered_calibration/tier1/
ls oneport_tiered_calibration/tier1/measured/
from skrf.calibration import On... | <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 notebook will demonstrate how to use skrf to do a two-tiered one-port calibration. We'll use data that was taken to characterize a wavegu... |
3,709 | <ASSISTANT_TASK:>
Python Code:
def f(x): return np.sin(x) # Функция
def dfdx(x): return np.cos(x) # и ее производная.
x0 = 1 # Точка, в которой производится дифференциирование.
dx = np.logspace(-16, 0, 100) # Приращения аргумента.
# Найдем приращения функции
df = f(x0+dx)-f(x0)
# и оценим производные.
approx_dfdx = df... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step5: Как мы видим, погрешность не стремиться к нулю, а достигает своего минимума при шагах около $10^{-8}$, затем снова растет.
Step8: Задание
Step... |
3,710 | <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: Text classification with TensorFlow Lite Model Maker
Step2: Import the required packages.
Step3: Download the sample training data.
Step4: Th... |
3,711 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.5
import math
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
print(tf.__version__)
tf.compat.v1.logging.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Next, we'll load our data set.
Step2: Examine and split the data
Step3: Now, split the data into two parts -- training and evaluation.
Step4: ... |
3,712 | <ASSISTANT_TASK:>
Python Code:
import time
import timeit
currentTime = time.gmtime()
print(currentTime)
time.strftime("%a, %d %b %Y %H:%M:%S +0000", currentTime)
type(currentTime)
currentTime.tm_hour
def parseTime(timeObj):
'''parseTime:
takes time.struct_time instances
:return time displayed as strin... | <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: UTC and local time
Step2: As you can see, the returned values are not very interesting or useful themselves. Lets parse structure into more use... |
3,713 | <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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Description:
Step1: Multilingual Universal Sentence Encoder Q&A 检索
Step2: 运行以下代码块，下载并将 SQuAD 数据集提取为：
Step3: 以下代码块使用 <a>Univeral Encoder Multilingual Q&A 模... |
3,714 | <ASSISTANT_TASK:>
Python Code:
# Loading metadata from trainning database
con = sqlite3.connect("F:/FMR/data.sqlite")
db_documents = pd.read_sql_query("SELECT * from documents", con)
db_authors = pd.read_sql_query("SELECT * from authors", con)
data = db_documents # just a handy alias
data.head()
tokenised = load_json(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading Tokenised Full Text
Step2: Preprocessing Data for Gensim and Finetuning
Step3: Although we tried to handle these hyphenations in the p... |
3,715 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
iris = load_iris()
# create X(features) and y(response)
X = iris.data
y = iris.target
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X, y)
y_pred = logreg.predict(X)
print "predicted response:\n",y_pred
... | <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: Logistic regression
Step2: 分类准确率
Step3: 以上说明对于训练的数据，我们有96%的数据预测正确。这里我们使用相同的数据来训练和预测，使用的度量称其为训练准确度。
Step4: KNN(K=1)
Step5: 上面我们得到了训练准确度为100%的... |
3,716 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import tweepy
import datetime
import json
import os
from pysqlite2 import dbapi2 as sqlite3
APP_KEY = ""
APP_SECRET = ""
OAUTH_TOKEN = ""
OAUTH_TOKEN_SECRET = ""
auth = tweepy.OAuthHandler(APP_KEY, APP_SECRET)
auth.set_access_token(OAUTH_TOKEN, OAUTH_TOKE... | <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: Twitter authentication (fill this!)
Step2: Using Tweepy instead of Twython (because it's more readily available via apt-get or zypper).
Step3: ... |
3,717 | <ASSISTANT_TASK:>
Python Code:
# Import Node and Function module
from nipype import Node, Function
# Create a small example function
def add_two(x_input):
return x_input + 2
# Create Node
addtwo = Node(Function(input_names=["x_input"],
output_names=["val_output"],
funct... | <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: Trap 1
Step2: Now, let's see what happens if we move the import of random outside the scope of get_random_array
|
3,718 | <ASSISTANT_TASK:>
Python Code:
s_pattern = 4000 # number of data points in the pattern
t = np.arange(s_pattern)*0.001 # time points for the elements in the pattern
D = 2
pattern1 = np.vstack([np.sin(t*np.pi), np.cos(t*np.pi)]).T
pattern2 = np.vstack([np.sin(t*np.pi), -np.sin(t*np.pi)]).T
plt.subplot(1, ... | <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 let's create a network that represents a rolling window in time (Aaron's "delay network"). The process determines what sort of pattern the ... |
3,719 | <ASSISTANT_TASK:>
Python Code:
my_variable = 10
print(my_variable)
a = 10
b = 15
print(a + b)
import this
print('Entering the for loop:\n')
for count in range(10):
print(count)
print('Still in the for loop.')
print("\nNow I'm done with the for loop.")
thing_1 = 47 # define an int object
print(thing_1.__... | <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: ... and then access that variable in a later cell
Step2: We'll be using Jupyter notebooks extensively in this class. I'll give a more detailed ... |
3,720 | <ASSISTANT_TASK:>
Python Code:
import torch
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from matplotlib.colors import ListedColormap
def plot_decision_regions_3class(data_set,model=None):
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA','#00AAFF'])
... | <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: Use this function only for plotting
Step2: Use this function to calculate accuracy
Step3: <a id="ref0"></a>
Step4: Create a dataset object
St... |
3,721 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
df = pd.read_json('../data/raw/train.json')
df['created'] = df['created'].apply(lambda row: pd.to_datetime(row))
def relative_count(df, column):
# Calculate counts per bedr... | <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: Bedrooms
Step2: We can see that the interest level rises slightly for apartments with more than 1 bedrooms, but for 5 bedrooms and more it fall... |
3,722 | <ASSISTANT_TASK:>
Python Code:
%run "../Functions/2. Google form analysis.ipynb"
# Localplayerguids of users who answered the questionnaire (see below).
# French
#localplayerguid = 'a4d4b030-9117-4331-ba48-90dc05a7e65a'
#localplayerguid = 'd6826fd9-a6fc-4046-b974-68e50576183f'
#localplayerguid = 'deb089c0-9be3-4b75-9b2... | <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: 'Google form analysis' functions checks
Step2: hasAnswered
Step3: getAnswers
Step4: getCorrections
Step5: getScore
Step6: code to explore s... |
3,723 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
from cvxopt import solvers, matrix
m = matrix( [ [2.0, 1.1] ] ) # mettre des réels (float) et non des entiers
# cvxopt ne fait pas de conversion implicite
t = m.T # la tra... | <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: Le langage Python propose des modules qui permettent de résoudre des problèmes d'optimisation sous contraintes et il n'est pas forcément nécessa... |
3,724 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import tables
import matplotlib.pyplot as plt
import seaborn as sns
import pybromo as pbm
print('Numpy version:', np.__version__)
print('PyTables version:', tables.__version__)
print('PyBroMo version:', pbm.__version__)
S = pbm.ParticlesSimulation.fr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Timestamps simulation
Step2: Simulation of the series of emission rates
Step3: Simulate timestamps for background = 1kcps
Step4: Simulate tim... |
3,725 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import erfc
def newfig(title='?', xlabel='?', ylabel='?', xlim=None, ylim=None,
xscale='linear', yscale='linear', size_inches=(14, 8)):
'''Setup a new axis for plotting'''
fig, ax = plt.subpl... | <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: Convenience function for setting up a graph
Step2: Super position with $\mathtt{erfc}()$ subtracted from initial head $a$
Step3: Same, thing, ... |
3,726 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import mne
from mne.datasets.sample import data_path
fname = op.join(data_path(), 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname).crop(0, 10)
original_level = mne.get_config('MNE_LOGGING_LEVEL', 'INFO')
print(mne.get_config_path())
print(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: MNE-python stores configurations to a folder called .mne in the user's
Step2: These configurations include information like sample data paths a... |
3,727 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
rnd = np.random.RandomState(seed=42)
n_data = 16 # number of data points
a_true = 1.255 # randomly chosen truth
b_true = 4.507
# Fill in your solution here
# evaluate the true model at the given x values
y = a_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: Intro and choice of objective function
Step2: Exercise 1
Step3: Execute the code below and verify that it executes
Step4: Now let's forget th... |
3,728 | <ASSISTANT_TASK:>
Python Code:
#@title Default title text
# 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 o... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Goal
Step2: Generalize the loss above to
|
3,729 | <ASSISTANT_TASK:>
Python Code:
from collections import namedtuple
from copy import deepcopy
import random
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.manifold import TSNE
from sklearn.metrics.pairwise import cosine_similarity
from sqlalchemy import create_en... | <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: Abstract
Step2: Next, let's get an idea of the sparsity of our data and how many songs each country has streamed.
Step3: Construct training, v... |
3,730 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
from statsmodels import regression
from scipy import poly1d
x = np.arange(10)
y = 2*np.random.randn(10) + x**2
xs = np.linspace(-0.25, 9.25, 200)
lin = np.polyfit(x, y, 1)
quad = np.polyfit... | <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: When working with real data, there is unlikely to ever be a situation where a ninth-degree polynomial is appropriate
Step2: However, when we us... |
3,731 | <ASSISTANT_TASK:>
Python Code:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classif... | <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: Initialising the CNN
Step2: Step 1 - Convolution
Step3: Step 2 - Pooling
Step4: Adding a second convolutional layer
Step5: Step 3 - Flatteni... |
3,732 | <ASSISTANT_TASK:>
Python Code:
rst(O.to_iterable)
s = marble_stream("a--b-c|")
l, ts = [], time.time()
def on_next(listed):
print('got', listed, time.time()-ts)
for i in (1, 2):
d = s.subscribe(on_next)
# second run: only one value, the list.
s = s.to_list()
# both are started around same time -... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: I want an operator to operate on a particular Scheduler
Step2: ...when it notifies observers observe_on
Step3: I want an Observable to invoke ... |
3,733 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from numpy.linalg import inv, norm
def objective(P, q, r, x):
Return the value of the Standard form QP using the current value of x.
return 0.5 * np.dot(x, np.dot(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:
Step2: Pure-Python ADMM Implementation
Step3: QP Solver using CVXPY
Step4: Generate Optimal Portfolio Holdings
Step5: Set up the Portfolio Optimizat... |
3,734 | <ASSISTANT_TASK:>
Python Code:
!pip -q install rdkit-pypi==2021.9.4
import ast
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
from rdkit import Chem, RDLogger
from rdkit.Chem import BondType
from rdkit.Chem.... | <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: Dataset
Step2: Hyperparameters
Step3: Generate training set
Step4: Build the Encoder and Decoder
Step5: Build the Sampling layer
Step6: Bui... |
3,735 | <ASSISTANT_TASK:>
Python Code:
# Import required libraries
from tpot import TPOTClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
# Load the data
titanic = pd.read_csv('data/titanic_train.csv')
titanic.head(5)
titanic.groupby('Sex').Survived.value_counts()
titanic.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Data Exploration
Step2: Data Munging
Step3: At present, TPOT requires all the data to be in numerical format. As we can see below, our data se... |
3,736 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame(dict(col1=[[1, 2, 3],[4,5]]))
def g(df):
for i in df.index:
df.loc[i, 'col1'] = df.loc[i, 'col1'][::-1]
L = df.col1.sum()
L = map(lambda x:str(x), L)
return ','.join(L)
result = g(df.copy())
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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:
|
3,737 | <ASSISTANT_TASK:>
Python Code:
## Can build up a dict by starting with the the empty dict {}
## and storing key/value pairs into the dict like this:
## dict[key] = value-for-that-key
dict = {}
dict['a'] = 'alpha'
dict['g'] = 'gamma'
dict['o'] = 'omega'
print dict
print dict['a']
dict['a'] = 6
print dict['a']
'a' in dic... | <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 for loop on a dictionary iterates over its keys by default. The keys will appear in an arbitrary order. The methods dict.keys() and dict.value... |
3,738 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='G... | <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: Network Architecture
Step2: Training
Step3: Denoising
Step4: Checking out the performance
|
3,739 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from bokeh.embed import file_html
from bokeh.io import output_notebook, show
from bokeh.layouts import layout
from bokeh.models import (
ColumnDataSource, Plot, Circle, Range1d, LinearAxis, HoverTool,
Text, SingleIntervalTicker, Slider, Cust... | <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: Setting up the data
Step2: sources looks like this
Step3: Build the plot
Step4: Build the axes
Step5: Add the background year text
Step6... |
3,740 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from IPython.display import HTML # intégration notebook
%matplotlib inline
def plot_cmap(cmap, ncolor=6):
A convenient function to plot colors of a matplotlib cmap
Args:
ncolor (int): 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:
Step3: Functions
Step4: Color models
Step5: Sequential palettes
Step6: Reverse order
Step7: Divergent palettes
Step8: Build a custum color palette... |
3,741 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
X = np.array([
[0, 0],
[0, 1],
[1, 0],
[1, 1]])
y = np.array([0, 1, 1, 0])
pd.DataFrame(np.hstack((X, y.reshape(-1, 1))), columns=['x1', 'x2', 'y'])
%matplotlib inline
import matplotlib.pyplot as plt
plt.scatter(X[y==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: The problem with this function is that there is no linear function that correctly can classify the data. It is non-separable.
Step2: Types of d... |
3,742 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
tf.enable_eager_execution()
tfe = tf.contrib.eager
# Creating variables
v = tfe.Variable(1.0)
v
v.assign_add(1.0)
v
# In the tf.keras.layers package, layers are objects. To construct a layer,
# simply construct the object. Most layers take as a first argument the... | <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: Variables
Step2: Layers
Step3: The full list of pre-existing layers can be seen in the documentation. It includes Dense (a fully-connected lay... |
3,743 | <ASSISTANT_TASK:>
Python Code:
!pip install thinc syntok "ml_datasets>=0.2.0" tqdm
from syntok.tokenizer import Tokenizer
def tokenize_texts(texts):
tok = Tokenizer()
return [[token.value for token in tok.tokenize(text)] for text in texts]
import ml_datasets
import numpy
def load_data():
train_data, dev_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:
Step1: For simple and standalone tokenization, we'll use the syntok package and the following function
Step2: Setting up the data
Step3: Defining the... |
3,744 | <ASSISTANT_TASK:>
Python Code:
from IPython.core.display import display, HTML
display(HTML('My first ipython HTML <b>bold</b> tag'))
display(HTML('My first ipython HTML <em>italics</em> tag'))
display(HTML('My first ipython HTML <em>italics tag'))
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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: Example 2
Step2: Debug 1
|
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Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
def complete_deg(n):
np.zeros((n,n), dtype = np.int)
np.diag(np.diag(n-1))
print complete_deg()
D = complete_deg() #I dont get... | <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: Complete graph Laplacian
Step2: The Laplacian Matrix is a matrix that is extremely important in graph theory and numerical analysis. It is defi... |
3,746 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc, patches
# Increase font size, set CM as default text, and use LaTeX
rc('font', **{'size': 16, 'family': 'serif', 'serif': ['Computer Modern Roman']})
rc('text', usetex=True)
# Define colours (taken from http://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:
Step2: Load the circle-function
Step3: Linear example
Step4: Semilog and loglog example
|
3,747 | <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: 从头编写训练循环
Step2: 简介
Step3: 我们使用带自定义训练循环的 mini-batch 梯度对其进行训练。
Step4: 下面是我们的训练循环：
Step5: 指标的低级处理
Step6: 下面是我们的训练和评估循环：
Step7: 使用 tf.function... |
3,748 | <ASSISTANT_TASK:>
Python Code:
def fun(x):
return np.exp(-x**2)
integr.quad(fun,0.,1.)
integr.quad(lambda x:np.exp(-x**2),0.,1.)
def fun(x,a):
return x**a
[integr.quad(fun,0.,1.,args=(m,))[0] for m in range(0,5)]
res1 = integr.quad(lambda y:integr.quad(lambda x,y:np.exp(-x**2-y**2)/np.pi,-1,1, args=(y,))[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: En ocasiones, conviene definir la función a integrar como dependiente de parámetros. Por ejemplo
Step2: Integrales impropias
Step3: Integració... |
3,749 | <ASSISTANT_TASK:>
Python Code:
pip install --user apache-beam[gcp]
import apache_beam as beam
print(beam.__version__)
# 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'] = PROJE... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Run the command again if you are getting oauth2client error.
Step2: You may receive a UserWarning about the Apache Beam SDK for Python 3 as not... |
3,750 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (10, 6)
pop = pd.read_csv('data/cars_small.csv')
pop.head()
class_mapping = {'Hatchback': 0, 'Sedan': 1}
pop['types'] = pop['type'].... | <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: Lets say we want to classify the vehicles by 'Hatchback' and 'Sedan'
Step2: Why Linear Function does not work
Step3: However, there are two pr... |
3,751 | <ASSISTANT_TASK:>
Python Code:
#
# this function has 3 optional arguments
#
def optional_args(a=None, b='one', c=3):
print('a={}, b={}, c={}'.format(a,b,c))
optional_args()
#prints
#a=None, b=one, c=3
#
# we can also pass the arguments via a dictionary
# so we can save them/modify them
#
arg_dict=dict(a=4,b=[1,2,3]... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Writing a function to take unknown optional arguments
Step2: Required arguments
Step3: Writing a function to take an unknown number of requir... |
3,752 | <ASSISTANT_TASK:>
Python Code:
import iris
fname = iris.sample_data_path('air_temp.pp')
cubes = iris.load(fname)
print(type(cubes))
print(cubes)
cube = iris.load_cube(fname)
print(type(cube))
print(cube)
cubes[0] == cube
#
# edit space for user code ...
#
fname = iris.sample_data_path('uk_hires.pp')
cubes = iris.... | <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: 2.1 Iris Load Functions<a id='iris_load_functions'></a>
Step2: If we give this filepath to load, we see that Iris returns a cubelist.
Step3: A... |
3,753 | <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: TFLite용 Jax 모델 변환
Step3: 데이터 준비
Step4: Jax로 MNIST 모델 빌드
Step5: 모델 학습 및 평가
Step6: TFLite 모델로 변환합니다.
Step7: 변환된 TFLite 모델 확인
Step8: 모델 최적화
S... |
3,754 | <ASSISTANT_TASK:>
Python Code:
%%tikz --scale 2 --size 800,300 -f svg
\tikzset{node distance=2cm,
block/.style={rectangle, draw, minimum height=15mm, minimum width=20mm},
sumnode/.style={circle, draw, inner sep=2pt}
}
\node[coordinate] (input) {};
\node[block, right of=input] (TR) {$F_f(s)=K\frac{b... | <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: Model
Step2: Finding the controller parameters
Step3: Bodeplot of the loop gain
|
3,755 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
C = np.complex(3,4)
print('C=', C)
print(type(C))
c = np.array([3+4j])
#c = 3+4j
print('c=', c)
print(type(c))
print('Parte real:', c.real)
print('Parte imaginária:', c.imag)
print(c.shape)
cc = np.conjugate(c)
print('c=', c)
print('Complexo conjugado:', cc)
print('Pa... | <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: Porém, como iremos trabalhar com números complexos em imagens, utilizaremos não apenas um
Step2: O conjugado deste mesmo número complexo $c$ é ... |
3,756 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-1', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributo... | <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... |
3,757 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import matplotlib.pyplot as plt
try:
import seaborn as sns
except ImportError:
print("Seaborn not installed. Oh well.")
import numpy as np
import astropy.io.fits as fits
import sherpa.astro.ui as ui
from clarsach.respond import RMF, ARF
datadir = "../dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Let's load some data
Step2: Let's load the data using Clàrsach
Step3: Let's also load the ARF and RMF
Step4: Let's make an empty model to div... |
3,758 | <ASSISTANT_TASK:>
Python Code:
from horsetailmatching import HorsetailMatching, UniformParameter
from horsetailmatching.demoproblems import TP2
from horsetailmatching.surrogates import PolySurrogate
import numpy as np
uparams = [UniformParameter(), UniformParameter()]
thePoly = PolySurrogate(dimensions=len(uparams), o... | <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: Lets start with the built in in polynomial chaos surrogate. This finds the coefficients of a polynomial expansion by evaluating the inner produc... |
3,759 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
start_col, end_col = 'starttime', 'stoptime'
# Loading just first 10,000 rows
df = pd.read_csv('201501-citibike-tripdata.csv', parse_dates=[start_col, end_col], nrows=10000)
@np.vectorize
def minutes(time):
Convert time to minutes since 00:00
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load Data
Step3: Parts of Day
Step4: Sample Output
|
3,760 | <ASSISTANT_TASK:>
Python Code:
data = pd.read_csv('data/driving_log.csv', header=None,
names=['center', 'left', 'right', 'angle', 'throttle', 'break', 'speed'])
print(data.ix[0].center)
data.sample()
def img_id(path):
return path.split('/IMG/')[1]
image_paths = data.center.apply(img_id).values.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: Reading and Preprocessing the Images with OpenCV
Step2: Building a Convnet in Keras
|
3,761 | <ASSISTANT_TASK:>
Python Code:
'8 = %d , 8.5 = %.1f, name = %s, 3 = %04d' % (8, 8.5, 'Ravi', 3)
'8 = {}, 8.5 = {}, name = {}, 3 = {:04}'.format(8, 8.5, 'Ravi', 3)
'8 = {a}, 8.5 = {b}, name = {c}, 3 = {d:04}'.format(a = 8, c = 'Ravi', d = 3, b = 8.5)
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Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: There is one more way to format, without the hassle of remembering the format specifiers. You can use format() method of the current string. Sam... |
3,762 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
plt.show()
#
# EDIT for user code ...
#
# %load solutions/cartopy_exercise_1
# Make sure the figure is a decent size when plotted.
fig = plt.figure(figsize=(14, 7))
... | <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: Cartopy's matplotlib interface is set up via the projection keyword when constructing a matplotlib Axes / SubAxes instance. The resulting axes i... |
3,763 | <ASSISTANT_TASK:>
Python Code:
import requests
import json
#Every request begins with the server's URL
SERVER = 'http://data.neonscience.org/api/v0/'
#Site Code for Lower Teakettle
SITECODE = 'TEAK'
#Make request, using the sites/ endpoint
site_request = requests.get(SERVER+'sites/'+SITECODE)
#Convert to Python JSON ... | <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: Site Querying
Step2: We first use the requests module to send the API request using the 'get' function; this returns a 'request' object.
Step3:... |
3,764 | <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
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Description:
Step1: 加载和准备数据
Step2: 数据简介
Step3: 虚拟变量（哑变量）
Step4: 调整目标变量
Step5: 将数据拆分为训练、测试和验证数据集
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，所以我们用... |
3,765 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-3', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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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: 2... |
3,766 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
test_data = pd.read_csv("../data/relex-sparse-multiple-choice.csv")
test_data.head()
import crowdtruth
from crowdtruth.configuration import DefaultConfig
class TestConfig(DefaultConfig):
inputColumns = ["sent_id", "term1", "b1", "e1", "term2", "b2", "e2", "senten... | <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: Declaring a pre-processing configuration
Step2: Our test class inherits the default configuration DefaultConfig, while also declaring some addi... |
3,767 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import cv2
%matplotlib inline
from matplotlib import pyplot as plt
REALSQUARE = 23.500 # Size of a square
BOARDDIM = (6,8) # Dimensions of the given board
NUMIMG = 9 # Number of images to open
imagesList = list()
# Opens each image and adds to the image list
P... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Detect the corners on the real images and compute A matrix
Step2: Now we must compute the parameters of the rotation matrix R and translation v... |
3,768 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import sys
print("Python Version:",sys.version.split(" ")[0])
print("TensorFlow Version:",tf.VERSION)
sess = tf.InteractiveSession()
a = tf.zeros(())
a
a.eval()
a.shape
a.shape.ndims
a.name
tf.zeros(())
b = tf.zeros((3), name="b")
b
type(b.eval())
sess.r... | <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: Graph Execution
Step2: Generating new Tensors
Step3: We create a tensor and assigned it to a local variable named a. When we check the value o... |
3,769 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy
import scipy.stats
import matplotlib.pyplot as plt
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
# seed the random number generator so we all get the same results
numpy.random.seed(18)
weight = scipy.stats.lognorm(0.23, 0... | <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: Part One
Step2: Here's what that distribution looks like
Step3: make_sample draws a random sample from this distribution. The result is a Num... |
3,770 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-2', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
3,771 | <ASSISTANT_TASK:>
Python Code:
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
# 1. Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credent... | <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: 3. 케라스 모델파일 읽기/쓰기
Step2: 4. root 경로에 저장된 모델파일을 드라이브의 원하는 폴더에 저장
|
3,772 | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
with open('anna.txt', 'r') as f:
text=f.read()
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
chars = np.array([vocab_to_int[c] for c ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: First we'll load the text file and convert it into integers for our network to use. Here I'm creating a couple dictionaries to convert the chara... |
3,773 | <ASSISTANT_TASK:>
Python Code:
import os
pgconfig = {
'host': os.environ['PGHOST'],
'port': os.environ['PGPORT'],
'database': os.environ['PGDATABASE'],
'user': os.environ['PGUSER'],
'password': os.environ['PGPASSWORD'],
}
%load_ext sql
dsl = 'postgres://{user}:{password}@{host}:{port}/{database}'.f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 拡張を読み込み、データベースに接続します。
Step2: SQL を実行してその結果を確認
Step3: 実行結果を pandas のデータフレームに変換します。
Step4: 少し複雑な SQL を実行するため、店舗ごとの所在地、従業員数、顧客数を集計します。
Step5: 結... |
3,774 | <ASSISTANT_TASK:>
Python Code:
import heapq
# Create a priority queue abstract base class
class priority_queue:
# Initialize the instance
def __init__(self):
# Create a list to use as the queue
self._queue = []
# Create an index to use as ordering
self._index = 0
# Create a ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create A Priority Queue Object
Step2: Add Items To Queue
Step3: Retrieve Items From Queue By Priority
|
3,775 | <ASSISTANT_TASK:>
Python Code:
n = 100
x = np.random.normal(1, 0.5, n)
noise = np.random.normal(0, 0.25, n)
y = 0.75*x + 1 + noise
fig, ax = plt.subplots(1, 1, figsize=(6,4))
ax.scatter(x, y)
ax.set_xlim([0,2])
ax.set_ylim([0,3.1])
def fit(x,y, with_constant=True):
beta = np.cov(x,y)[0][1] / np.var(x)
if with_... | <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 linear regression considers the model function
Step2: Expected value
Step3: We can fit a linear regression model on the training data
S... |
3,776 | <ASSISTANT_TASK:>
Python Code:
%cd -q ~/neurokernel/examples/olfaction/data
%run gen_olf_input.py
%run create_olf_gexf.py
import re
import networkx as nx
import neurokernel.tools.graph
g = nx.read_gexf('antennallobe.gexf.gz')
df_node, df_edge = neurokernel.tools.graph.graph_to_df(g)
glom_name = 'DA1'
osn_ind = sorted(... | <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: Next, we identify the indices of the olfactory sensory neurons (OSNs) and projection neurons (PNs) associated with a specific glomerulus; in thi... |
3,777 | <ASSISTANT_TASK:>
Python Code:
NAME = "dchud"
COLLABORATORS = ""
!wget --quiet -O boating.csv "https://data.ct.gov/api/views/mrb6-7ee5/rows.csv?accessType=DOWNLOAD"
!head boating.csv | csvlook
!wc -l boating.csv
!csvstat boating.csv
!mv boating.csv boating-orig.csv
!echo "year,tx_type,num" > boating.csv
!tail -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: Exercise 03 - Due Friday, September 30 at 12pm
Step2: Verify that it's what we think it is on the commandline
Step3: Looks right. How many re... |
3,778 | <ASSISTANT_TASK:>
Python Code:
import pprint
document = "Human machine interface for lab abc computer applications"
text_corpus = [
"Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"Sys... | <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 core concepts of gensim are
Step2: Corpus
Step3: .. Important
Step4: Before proceeding, we want to associate each word in the corpus with... |
3,779 | <ASSISTANT_TASK:>
Python Code:
from notebook_preamble import D, J, V, define
J('[0 2 7 0] dup max')
from joy.library import SimpleFunctionWrapper
from joy.utils.stack import list_to_stack
@SimpleFunctionWrapper
def index_of(stack):
'''Given a sequence and a item, return the index of the item, or -1 if not found.
... | <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: Starting at index distribute count "blocks" to the "banks" in the sequence.
Step2: Recalling "Generator Programs"
Step3: A function to drive a... |
3,780 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import warnings
from scipy.optimize import minimize
from scipy.integrate import quad
from scipy.interpolate import interp1d
from scipy import stats
from importlib import reload
from src import sim_cts, sim_discrete
from scipy.stats import poisson, geom
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: basic use
Step2: Interactive style session
Step3: placeholders, variables, scope
Step4: So that worked beautifully with TF's own built-in opt... |
3,781 | <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 = (5025, 5036)
DON'T MODI... | <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... |
3,782 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import os
from time import time
from ipyparallel import Client
os.environ['RTDATAPATH'] = '/Users/mate/g/rt/data/refdata/'
import atmosphere as atm
from atmosphere.rt import pydisort
atm.refdata.setup_directory()
def create_atmosphere_model(**kw):
... | <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: Define methods for generating an atmospheric model of Titan and performing RT calculation
Step4: Parallel (multi-core) execution
Step5: Compar... |
3,783 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import calendar
import datetime
import matplotlib.dates as mdates
from API_client.python.datahub import datahub_main
from API_client.python.lib.dataset import dataset
from API_client.python.lib.var... | <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 API needs a file APIKEY with your API key in the work folder. We initialize a datahub and dataset objects.
Step2: At the moment we are goin... |
3,784 | <ASSISTANT_TASK:>
Python Code:
#Import libraries
import os
import glob
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.misc import imread, imresize
from random import shuffle
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
#Initial d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Showing Training and Test Samples
Step2: Training Images
Step3: Storing Processed Files On Disk
Step4: Since here, the images are processed a... |
3,785 | <ASSISTANT_TASK:>
Python Code:
# Import required modules
import pandas as pd
import numpy as np
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'age': [42, 52, 36, 24, 73],
'preTestScore': [4, 24, 31, 2, 3],
'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = [... | <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: Make a dataframe
Step2: Add a new column for elderly
|
3,786 | <ASSISTANT_TASK:>
Python Code:
from cobra.io import load_model
model = load_model("textbook")
model.medium
medium = model.medium
medium["EX_o2_e"] = 0.0
model.medium = medium
model.medium
model.slim_optimize()
model.medium["EX_co2_e"] = 0.0
model.medium
medium = model.medium
medium["EX_co2_e"] = 0.0
model.medium = ... | <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 will return a dictionary that contains the upper flux bounds for all active exchange fluxes (the ones having non-zero flux bounds). Right n... |
3,787 | <ASSISTANT_TASK:>
Python Code:
import random
random.seed(1)
a = [7, 1, 2, 8, 1, 3]
rnd_lst = random.sample(range(0, 10000000), 1000)
import sys
def max_pairprod_1(ary):
n = len(ary)
max_prod = -sys.float_info.max
for i in range(0, n):
for j in range(i + 1, n):
prod = ary[i] * ary[j... | <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 probably simplest implementation may be to compute the product of each pair in this list while keeping track of the largest product as we tr... |
3,788 | <ASSISTANT_TASK:>
Python Code:
data.head()
from IPython.display import display, HTML
display(HTML("<h1>Okay, you want not to do this on your own.. then now: How to do this (scroll down)</h1>"))
for i in range(20):
display(HTML("<br />"))
numerical_cols = [col for col in data.columns if data[col].dtype == 'int64']... | <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: Okay, please try to create the following images
Step2: Okay, let's go!
Step3: Okay, nice.
Step4: Nice, we need to choose ground & fire poke... |
3,789 | <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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | <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: Environment Preparation
Step2: Install Analytics Zoo
Step3: You can install the latest pre-release version using pip install --pre --upgrade a... |
3,790 | <ASSISTANT_TASK:>
Python Code:
# import thr random numbers module. More on modules in a future notebook
import random
# empty list
a = list()
# or
a = []
# define a list
a = [1,2,3,4,2,2]
print a
# list of numbers from 0 to 9
a = range(10)
a
# Python is zer-bases indexing
a[0]
# Get the last element
a[-1]
# Get the ... | <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: Define a list
Step2: Accesing elements of a list
|
3,791 | <ASSISTANT_TASK:>
Python Code:
import sys
import os
import inspect
import datetime as dt
from opengrid.library import solarmodel as sm
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = 16,8
SI = sm.SolarInsolation('Brussel')
print(SI.location.latlng,
SI.elevation)
date = dt.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: Solar Insolation object
Step2: It uses this location to calculate the position of the sun and the mass of the air the sun has to penetrate for ... |
3,792 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import cf
import netCDF4
import matplotlib.pyplot as plt
dataurl = "http://thredds.socib.es/thredds/dodsC/mooring/conductivity_and_temperature_recorder/buoy_canaldeibiza-scb_sbe37006/L1/dep0003_buoy-canaldeibiza_scb-sbe37006_L1_latest.nc"
f = cf.read(dataurl)
print f
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The data file is the same.
Step2: Read the file
Step3: We see that the file contains 4 variables
Step4: The number of variables which have a ... |
3,793 | <ASSISTANT_TASK:>
Python Code:
# Author: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import spm_face
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.cov import compu... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Get data
Step2: Estimate covariances
Step3: Show the resulting source estimates
|
3,794 | <ASSISTANT_TASK:>
Python Code:
weather = pd.read_table("daily_weather.tsv")
usage = pd.read_table("usage_2012.tsv")
station = pd.read_table("stations.tsv")
weather.loc[weather['season_code'] == 1, 'season_desc'] = 'winter'
weather.loc[weather['season_code'] == 2, 'season_desc'] = 'spring'
weather.loc[weather['season_co... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2.Use a scatterplot to show how the daily rental volume varies with temperature. Use a different series (with different colors) for each season.... |
3,795 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
num_examples, num_features = (1000, 2) # dataset size
num_classes = 2 # binary classification task
X = np.random.random((num_examples, num_features))
y = np.int_(X[:, 0] * X[:, 0] + X[:, 1] >= 1).reshape(-1, 1)
print("Features (X): {shape[0]}x{shape[1]}".format(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:
Step2: Visualize Data
Step3: Prepare Data for Training and Testing
Step4: Build Computation Graph
Step6: Define helper functions
Step10: Define lay... |
3,796 | <ASSISTANT_TASK:>
Python Code:
import numpy
import matplotlib.pyplot as plt
from matplotlib import rc
from scipy import linalg
from scipy.integrate import simps
%matplotlib inline
def f(x):
return x * numpy.sin(3 * numpy.pi * x / 2) + (9/4) * (numpy.pi ** 2) * numpy.sin(3 * numpy.pi * x /2)
def expected_solution_ca... | <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: Матрица $K_j = \frac{2}{h} \int \limits_{-1}^{1} \frac{d}{d\xi}
Step2: Аналогично
Step3: Вектор нагрузки также можно записать в виде скалярног... |
3,797 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import fit_iir_model_raw
from mne.datasets import sample
print(__doc__)
data_p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Plot the different time series and PSDs
|
3,798 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
from IPython.core.display import HTML
from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
Image(url= "https://cdn-images-1.medium.com/max/1600/1*UkI9za9zTR-HL8uM15Wmzw.png")
#hyperpar... | <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 figure below shows the input data-matrix, and the current batch batchX_placeholder
Step2: As you can see in the picture below that is done... |
3,799 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-1', 'ocnbgchem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
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