Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
|---|---|---|
10,900 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
B2b example notebook
Example notebook for the B2b, 2 channel 24-bit ADC module. The module contains the same ADCs as the D4 and is identical in hardware to the D4b module
Step1: Open the SP... | Python Code:
from spirack import SPI_rack, B2b_module, D5a_module, D4b_module
import logging
from time import sleep
from tqdm import tqdm_notebook
import numpy as np
from scipy import signal
from plotly.offline import init_notebook_mode, iplot, plot
import plotly.graph_objs as go
init_notebook_mode(connected=True)
logg... |
10,901 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1) With "Lil Wayne" and "Lil Kim" there are a lot of "Lil" musicians. Do a search and print a list of 50 that are playable in the USA (or the country of your choice), along with their popula... | Python Code:
import requests
response = requests.get('https://api.spotify.com/v1/search?query=artist:lil&type=artist&market=us&limit=50')
data = response.json()
artists = data['artists']['items']
for artist in artists:
print(artist['name'], artist['popularity'])
Explanation: 1) With "Lil Wayne" and "Lil Kim" there ... |
10,902 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Estimating $\pi$ by Sampling Points
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie
Notebook released under the Creative Commons Attribution 4.0 License.
A stochastic way to e... | Python Code:
# Import libraries
import math
import numpy as np
import matplotlib.pyplot as plt
in_circle = 0
outside_circle = 0
n = 10 ** 4
# Draw many random points
X = np.random.rand(n)
Y = np.random.rand(n)
for i in range(n):
if X[i]**2 + Y[i]**2 > 1:
outside_circle += 1
else:
in_circle ... |
10,903 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Orientation density functions
Step1: In this Python Notebook we will show how to properly run a simulation of a composite material, providing the ODF (orientation density function) of the r... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from simmit import smartplus as sim
from simmit import identify as iden
import os
dir = os.path.dirname(os.path.realpath('__file__'))
Explanation: Orientation density functions
End of explanation
x = np.arange(0,182,2... |
10,904 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial about statistical methods
The following contains a sequence of simple exercises, designed to get familiar with using Minuit for maximum likelihood fits and emcee to determine parame... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: Tutorial about statistical methods
The following contains a sequence of simple exercises, designed to get familiar with using Minuit for maximum likelihood fits and emcee to determine parameters by MCMC. Commands are general... |
10,905 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="https
Step1: DO NOTICE , this is extremely SLOW!
Step2: IPython parallel
IPython's power is not limited to its advanced shell. Its parallel package includes a framework to setup ... | Python Code:
%pylab inline
# with plt.xkcd():
fig = plt.figure(figsize=(10, 10))
ax = plt.axes(frameon=False)
plt.xlim(-1.5,1.5)
plt.ylim(-1.5,1.5)
circle = plt.Circle((0.,0.), 1., color='w', fill=False)
rect = plt.Rectangle((-1,-1), 2, 2, color='gray')
plt.gca().add_artist(rect)
plt.gca().add_artist(circle)
plt.arrow(... |
10,906 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Figures
A “figure” in matplotlib means the whole window in the user interface. Within this figure there can be “sub-plots”.
A figure is the windows in the GUI that has “Figure #” as title. Figu... | Python Code:
# generating some data points
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)
# creating a figure
fig = plt.figure(figsize=(4,3), dpi=120)
#plotting
plt.plot(X, C, linestyle='--')
plt.plot(X, S)
# plotting
plt.show()
# creating a figure
fig = plt.figure(figsize=(4,3), dpi=120... |
10,907 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SARIMAX
Step1: ARIMA Example 1
Step2: Thus the maximum likelihood estimates imply that for the process above, we have
Step3: To understand how to specify this model in Statsmodels, first ... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import matplotlib.pyplot as plt
from datetime import datetime
import requests
from io import BytesIO
Explanation: SARIMAX: Introduction
This notebook replicates examples from the Stata ARIMA ... |
10,908 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with data in Python
Notebook version
Step1: 1. Data generation
One of the first things we need to learn is to generate random samples from a given distribution. Most things in life ... | Python Code:
# Let's import some libraries
import numpy as np
import matplotlib.pyplot as plt
Explanation: Working with data in Python
Notebook version:
* 1.0 (Sep 3, 2018) - First TMDE version
* 1.1 (Sep 14, 2018) - Minor fixes
Authors: Vanessa Gómez Verdejo (vanessa@tsc.uc3m.es), Óscar García Hinde (oghinde@tsc.uc3... |
10,909 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The toehold problem
The "toehold problem" is named after a tech support response from Gurobi. The nature of the problem is that in order to take advantage of the algebraic constraint modelin... | Python Code:
def exception_thrown(f):
try:
f()
except Exception as e:
return str(e)
Explanation: The toehold problem
The "toehold problem" is named after a tech support response from Gurobi. The nature of the problem is that in order to take advantage of the algebraic constraint modeling provide... |
10,910 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modelling Carrer Choices
The model is based on the following research paper
Step1: Load Resources
Step2: Parametrization
Step3: Derived Attributes
Step4: Auxiliary Functions
Step5: Solv... | Python Code:
%matplotlib inline
Explanation: Modelling Carrer Choices
The model is based on the following research paper:
Derek Neal (1999). The Complexity of Job Mobility among Young Men, Journal of Labor Economics, 17(2), 237-261.
The implementation draws heavily from the material provided on the Quantitative Economi... |
10,911 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
General Structured Output Models with Shogun Machine Learning Toolbox
Shell Hu (GitHub ID
Step2: Few examples of the handwritten words are shown below. Note that the first capitalized lette... | Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import numpy as np
import scipy.io
dataset = scipy.io.loadmat(os.path.join(SHOGUN_DATA_DIR, 'ocr/ocr_taskar.mat'))
# patterns for training
p_tr = dataset['patterns_train']
# patterns for testing
p_ts = ... |
10,912 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Product of 4 consecutive numbers is always 1 less than a perfect square
<p>
<center>Shubhanshu Mishra (<a href="https
Step1: Let us look at the right hand side of the equation first, i.e. $... | Python Code:
i_max = 4
nums = np.arange(0, 50)+1
consecutive_nums = np.stack([
np.roll(nums, -i)
for i in range(i_max)
], axis=1)[:-i_max+1]
n_prods = consecutive_nums.prod(axis=1)
df = pd.DataFrame(consecutive_nums, columns=[f"n{i+1}" for i in range(i_max)])
df["prod"] = n_prods
df["k"] = np.sqrt(n_prods+1).as... |
10,913 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Taxonomy assignment of simulated communities
This notebook demonstrates how to assign taxonomy to communities simulated from natural compositions. These data are stored in the precomputed-re... | Python Code:
from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.simulated_communities import copy_expected_composition
from tax_credit.framework_functions import (parameter_sweep,
generate_per... |
10,914 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="../../../images/qiskit-heading.gif" alt="Note
Step1: Quantum Teleportation<a id='teleportation'></a>
Quantum teleportation is a protocol to transmit quantum states from one locati... | Python Code:
# useful additional packages
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
# importing Qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer, IBMQ, execute
# import basic plot tools
from qiskit.tools.visualization import matplotlib_circ... |
10,915 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using ticdat to build modular engines
The goal of the ticdat package is to facilitate solve engines that are modular and robust. For example, the multicommodity netflow.py engine can read an... | Python Code:
commodities = [['Pencils', 0.5], ['Pens', 0.2125]]
# a one column table can just be a simple list
nodes = ['Boston', 'Denver', 'Detroit', 'New York', 'Seattle']
cost = [['Pencils', 'Denver', 'Boston', 10.0],
['Pencils', 'Denver', 'New York', 10.0],
['Pencils', 'Denver', 'Seattle', 7.5],
... |
10,916 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generating a basic map image in Earth Engine
Install ee-python
Follow the installation directions found here
Step1: Visualize Geographic Data
Step3: Try it with mapclient
This code will ru... | Python Code:
# Import the Earth Engine Python Package into Python environment.
import ee
import ee.mapclient
# Initialize the Earth Engine object, using the authentication credentials.
ee.Initialize()
Explanation: Generating a basic map image in Earth Engine
Install ee-python
Follow the installation directions found he... |
10,917 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Function h2stats
Synopse
The h2stats function computes several statistics given an image histogram.
g = h2stats(h)
Output
g
Step1: Examples
Step2: Numeric Example
Step3: Image Example | Python Code:
def h2stats(h):
import numpy as np
import ia898.src as ia
hn = 1.0*h/h.sum() # compute the normalized image histogram
v = np.zeros(11) # number of statistics
# compute statistics
n = len(h) # number of gray values
v[0] = np.sum((np.arange(n)*hn)) # mean
v[1] = np.sum(np.po... |
10,918 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.... | Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network ... |
10,919 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
定義 Input 及 Output 暫存變數
Input 為 28x28 的點陣圖素
Output 為 10 個 Label Array ，分別代表著 0~9 的預測值
Step1: Cost Functoin 請參考
Step2: 以下進行開始進行實際運算 | Python Code:
x = tf.placeholder(tf.float32,shape=[None,28*28])
y = tf.placeholder(tf.float32,shape=[None,10])
# Create model
# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
xw = tf.matmul(x, W)
r = xw + b
a = tf.nn.softmax(r)
Explanation: 定義 Input 及 Output 暫存變數
Input 為 28x28 的點陣... |
10,920 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Two stage neural network implementation for MNIST digits classifier using Start
Overview
This is a step by step implementation of a multilayer neural network for MNIST digit classification u... | Python Code:
import numpy as np
import mnist.utils.load_mnist as load_mnist
import start.neural_network as nn
import start.layer_dict as ld
import start.weight_update_params as wup
Explanation: Two stage neural network implementation for MNIST digits classifier using Start
Overview
This is a step by step implementation... |
10,921 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analyse de clusters
Step1: On récupère les channels trouvés grace a l'analyse de clusters
Step2: One sample ttest FDR corrected (per electrode)
Step3: Tests de 280 a 440, par fenetres de... | Python Code:
nperm = 1000
T_obs_bin,clusters_bin,clusters_pb_bin,H0_bin = mne.stats.spatio_temporal_cluster_test(X_bin,threshold=None,n_permutations=nperm,out_type='mask')
T_obs_ste,clusters_ste,clusters_pb_ste,H0_ste = mne.stats.spatio_temporal_cluster_test(X_ste,threshold=None,n_permutations=nperm,out_type='mask')
Ex... |
10,922 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Density of States Analysis Example
Given sample and empty-can data, compute phonon DOS
To use this notebook, first click jupyter menu File->Make a copy
Click the title of the copied jupyter ... | Python Code:
workdir = '/SNS/users/lj7/reduction/ARCS/getdos-demo-test'
!mkdir -p {workdir}
%cd {workdir}
Explanation: Density of States Analysis Example
Given sample and empty-can data, compute phonon DOS
To use this notebook, first click jupyter menu File->Make a copy
Click the title of the copied jupyter notebook an... |
10,923 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>
Download
Step1: Classes
Para criar uma classe, utiliza-se a palavra reservada class. O nome da sua classe se... | Python Code:
# Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>
Download: http://github.com/dsacademybr
End of explanation
# Cri... |
10,924 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specif... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: GISS-E2-1H
Sub-Topics: Radiative Forcings.
... |
10,925 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework 6
Step1: Problem set #2
Step2: Problem set #3
Step3: Problem set #4
Step4: Problem set #5
Step5: Specifying a field other than name, area or elevation for the sort parameter sh... | Python Code:
import requests
data = requests.get('http://localhost:5000/lakes').json()
print(len(data), "lakes")
for item in data[:10]:
print(item['name'], "- elevation:", item['elevation'], "m / area:", item['area'], "km^2 / type:", item['type'])
Explanation: Homework 6: Web Applications
For this homework, you're ... |
10,926 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncar', 'sandbox-2', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NCAR
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topics: Transport, Emi... |
10,927 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Objects
Python is an object oriented language. As such it allows the definition of classes.
For instance lists are also classes, that's why there are methods associated with them (i.e. appen... | Python Code:
class Circle:
def __init__(self, radius):
self.radius = radius #all attributes must be preceded by "self."
Explanation: Objects
Python is an object oriented language. As such it allows the definition of classes.
For instance lists are also classes, that's why there are methods associated with t... |
10,928 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercise
Step1: Step 1
Step2: Since these are just plain wave files, we can listen to the data using aplay
Step3: This has loaded each of the wave files into sound_files[], one for each o... | Python Code:
# standard imports
import numpy as np
import scipy.io.wavfile as wavfile
import scipy.signal as sig
import matplotlib.pyplot as plt
import sklearn.preprocessing, sklearn.cluster, sklearn.tree, sklearn.neighbors, sklearn.ensemble, sklearn.multiclass, sklearn.feature_selection
import ipy_table
import sklearn... |
10,929 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content and Objectives
Show PSD of ASK for random data
Spectra are determined using FFT and averaging along several realizations
<b> Note
Step1: Function for determining the impulse respons... | Python Code:
# importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 10) )
Explanation: Content and Objectives
Show PSD ... |
10,930 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Importing Cells in NetPyNE
(1) Clone repository and compile mod files
Determine your location in the directory structure
Step1: Move to (or stay in) the '/content' directory
Step2: Ensure ... | Python Code:
!pwd
Explanation: Importing Cells in NetPyNE
(1) Clone repository and compile mod files
Determine your location in the directory structure
End of explanation
%cd /content/
Explanation: Move to (or stay in) the '/content' directory
End of explanation
!pwd
Explanation: Ensure you are in the correct directory... |
10,931 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I want to use the pandas apply() instead of iterating through each row of a dataframe, which from my knowledge is the more efficient procedure. | Problem:
import numpy as np
import pandas as pd
a = np.arange(4)
df = pd.DataFrame(np.repeat([1, 2, 3, 4], 4).reshape(4, -1))
df = pd.DataFrame(df.values - a[:, None], df.index, df.columns) |
10,932 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The first objective of this notebook is to implement the next function (to extract sample intervals from the total period).
Step1: Let's define the parameters as constants, just to do some ... | Python Code:
def generate_train_intervals(data_df, train_time, base_time, step, days_ahead, today):
pass
Explanation: The first objective of this notebook is to implement the next function (to extract sample intervals from the total period).
End of explanation
# I will try to keep the convention to name with the "d... |
10,933 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 Verily Life Sciences LLC
Use of this source code is governed by a BSD-style
license that can be found in the LICENSE file or at
https
Step1: Helper methods for visualization
... | Python Code:
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('ticks')
import functools
import importlib.resources
import numpy as np
import os
import pandas as pd
pd.plotting.register_matplotlib_converters()
import xarray as xr
from IPython.display import display
# bsst impo... |
10,934 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Understanding text (and handling it in Python)
Fiona Pigott
A Python notebook to teach you everything you never wanted to know about text encoding (specifically ASCII, UTF-8, and the differ... | Python Code:
!printf "hi\n"
!printf "hi" | xxd -g1
!printf "hi" | xxd -b -g1
# Generate a list of all of the ASCII characters:
# 'unichr' is a built-in Python function to take a number to a unicode code point
# (I'll talk more about this and some other built-ins later)
for i in range(0,128):
print str(i) + " -> " ... |
10,935 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matrix Factorization via Singular Value Decomposition
Matrix factorization is the breaking down of one matrix in a product of multiple matrices. It's extremely well studied in mathematics, a... | Python Code:
import pandas as pd
import numpy as np
movies_df = pd.read_csv('movies.csv')
movies_df['movie_id'] = movies_df['movie_id'].apply(pd.to_numeric)
movies_df.head(3)
ratings_df=pd.read_csv('ratings.csv')
ratings_df.head(3)
Explanation: Matrix Factorization via Singular Value Decomposition
Matrix factorization ... |
10,936 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PYT-DS SAISOFT
Overview 2
Overview 3
<a data-flickr-embed="true" href="https
Step1: I emphasize this is not really chess. Chess masters do not think in terms of west and east, nor is the ... | Python Code:
import numpy as np
import pandas as pd
squares = np.array(list(64 * " "), dtype = np.str).reshape(8,8)
squares
print('♔♕♖')
squares[0][0] = '♖'
squares[7][0] = '♖'
squares[0][7] = '♖'
squares[7][7] = '♖'
squares
chessboard = pd.DataFrame(squares, index=range(1,9),
columns = ['wR'... |
10,937 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simulating Membrane Potential
The simulation scripts described in this chapter are available at STEPS_Example repository.
This chapter introduces the concept of simulating the electric poten... | Python Code:
from __future__ import print_function # for backward compatibility with Py2
import steps.model as smodel
import steps.geom as sgeom
import steps.rng as srng
import steps.solver as ssolver
import steps.utilities.meshio as meshio
import numpy
import math
import time
from random import *
Explanation: Simulati... |
10,938 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Linear algebra overview projection example
Linear algebra is the study of vectors and linear transformations. This notebook introduces concepts form linear algebra in a birds-eye overview. T... | Python Code:
# setup SymPy
from sympy import *
x, y, z, t = symbols('x y z t')
init_printing()
# a vector is a special type of matrix (an n-vector is either a nx1 or a 1xn matrix)
Vector = Matrix # define alias Vector so I don't have to explain this during video
# setup plotting
%matplotlib inline
import matplotlib.p... |
10,939 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numeri
In questa pagina introdurremo diversi insiemi di numeri, facendosi aiutare da un sistema di calcolo simbolico
Step1: $\mathbb{N}$
L'insieme dei Naturali $\mathbb{N} = \lbrace 0, 1, 2... | Python Code:
from sympy import *
init_printing()
x = symbols('x')
x**2
Explanation: Numeri
In questa pagina introdurremo diversi insiemi di numeri, facendosi aiutare da un sistema di calcolo simbolico:
End of explanation
eq = Eq(x + 3, 2, evaluate=False)
eq
Explanation: $\mathbb{N}$
L'insieme dei Naturali $\mathbb{N} =... |
10,940 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Installating R on WinPython
This procedure applys for Winpython (Version of December 2015 and after)
1 - Downloading R binary
Step1: 2 - checking and Installing R binary in the right place... | Python Code:
import os
import sys
import io
# downloading R may takes a few minutes (80Mo)
try:
import urllib.request as urllib2 # Python 3
except:
import urllib2 # Python 2
# specify R binary and (md5, sha1) hash
# R-3.4.3:
r_url = "https://cran.r-project.org/bin/windows/base/old/3.5.0/R-3.5.0-win.exe"
hashe... |
10,941 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Transfer Learning on TPUs
In the <a href="3_tf_hub_transfer_learning.ipynb">previous notebook</a>, we learned how to do transfer learning with TensorFlow Hub. In this notebook, we're going t... | Python Code:
import os
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT
os.environ["BUCKET"] = BUCKET
Explanation: Transfer Learning on TPUs
In the <a href="3_tf_hub_transfer_learning.ipynb">previous notebook</a>, we learned how to do transfer learning with TensorFlow Hub. In this... |
10,942 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Expressão Regular
Pesquisando
Step1: Também podemos utilizar a flag IGNORECASE
Step2: Extraindo partes de um ER
Step3: Encontrando todas as ocorrências
Para encontrar todas as ocorrências... | Python Code:
import re
texto = 'um exemplo palavra:python!!'
match = re.search('python', texto)
print(match)
if match:
print('encontrou: ' + match.group())
else:
print('não encontrou')
Explanation: Expressão Regular
Pesquisando
End of explanation
texto = "GGATCGGAGCGGATGCC"
match = re.search(r'a[tg]c', texto, r... |
10,943 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning for Communications
By Jakob Hoydis,
Contact jakob.hoydis@nokia-bell-labs.com
This code is provided as supplementary material to the tutorial Deep Learning for Communications. ... | Python Code:
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import tensorflow as tf
import numpy as np
from pprint import pprint
%matplotlib inline
import matplotlib.pyplot as plt
seed = 1337
tf.set_random_seed(seed)
np.random.seed(seed)
Explanation: Deep Learning for Communications
By Jakob Hoydis,
Contact jakob.h... |
10,944 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Operation-Research-Quick-Intro-Via-Ortools" data-toc-modified-id="Operation-... | 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 to print version
# 2. magic so that t... |
10,945 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparison of FastText and Word2Vec
Facebook Research open sourced a great project recently - fastText, a fast (no surprise) and effective method to learn word representations and perform te... | Python Code:
import nltk
nltk.download('brown')
# Only the brown corpus is needed in case you don't have it.
# Generate brown corpus text file
with open('brown_corp.txt', 'w+') as f:
for word in nltk.corpus.brown.words():
f.write('{word} '.format(word=word))
# Make sure you set FT_HOME to your fastText dir... |
10,946 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FrameNet
I see three ways of getting features from FrameNet
Step2: 1. Does word $j$ evoke frame $i$?
In sum
Step3: Most words evoke one frame, some two, few three.
Step7: 2. Frame relatio... | Python Code:
import numpy as np
import pandas as pd
from nltk.corpus import framenet as fn
Explanation: FrameNet
I see three ways of getting features from FrameNet:
Does word $j$ evoke frame $i$?
Something with frame relations
Something with frame elements
End of explanation
def get_lus(frame):
Helper to get lexeme... |
10,947 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MNIST in Keras with Tensorboard
This sample trains an "MNIST" handwritten digit
recognition model on a GPU or TPU backend using a Keras
model. Data are handled using the tf.data.Datset API.... | Python Code:
BATCH_SIZE = 64
LEARNING_RATE = 0.02
# GCS bucket for training logs and for saving the trained model
# You can leave this empty for local saving, unless you are using a TPU.
# TPUs do not have access to your local instance and can only write to GCS.
BUCKET="" # a valid bucket name must start with gs://
tra... |
10,948 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create a random braid with a 25 node network, and a probability that any given node will produce a block in a given tick of $$\frac{2^{246}}{2^{256}-1} \simeq 0.1\%.$$
With 25 nodes this mea... | Python Code:
try: del n # this is here so that if you re-execute this block, it will create a new network
except: pass
n = Network(25, target=2**246) # A smaller network or lower target makes thinner braids
for dummy in range(500): n.tick(mine=False)
b = n.nodes[0].braids[0]
b.plot(numberver... |
10,949 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Start with the Final Design Report - SpaceX Hyperloop Competition II for high level view.
SpaceX Hyperloop Track Specification
Step1: Ball Screws, for the (Eddy current) Brake Mechanism
c... | Python Code:
import sympy
from sympy import Eq, solve, Symbol, symbols, pi
from sympy import Rational as Rat
from sympy.abc import tau,l,F
Explanation: Start with the Final Design Report - SpaceX Hyperloop Competition II for high level view.
SpaceX Hyperloop Track Specification
End of explanation
eta_1 = Symbol('eta_... |
10,950 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<font color=Teal>ATOMIC STRING FUNCTIONS and SPACETIME QUANTIZATION MODELS (Python Code)</font>
By Sergei Eremenko, PhD, Dr.Eng., Professor, Honorary Professor
https
Step1: <font color=teal... | Python Code:
import numpy as np
import pylab as pl
pl.rcParams["figure.figsize"] = 9,6
###################################################################
##This script calculates the values of Atomic Function up(x) (1971)
###################################################################
################### One Pulse... |
10,951 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Signal-space separation (SSS) and Maxwell filtering
This tutorial covers reducing environmental noise and compensating for head
movement with SSS and Maxwell filtering.
Step1: Backgroun... | Python Code:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
raw.crop(tmax=60).load_data()
Explan... |
10,952 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interactive time series with time slice retrieval
This notebook shows you how to use interactive plots to select time series for different locations and retrieve the imagery that corresponds... | Python Code:
%pylab notebook
from __future__ import print_function
import datacube
import xarray as xr
from datacube.storage import masking
from datacube.storage.masking import mask_to_dict
from matplotlib import pyplot as plt
from IPython.display import display
import ipywidgets as widgets
dc = datacube.Datacube(app='... |
10,953 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PyData.Tokyo Tutorial & Hackathon #1
PyData.Tokyoでは毎月開催している中上級者向けの勉強会に加え、初心者の育成を目的としたチュートリアルイベントを開催します。今回のイベントでは下記の項目にフォーカスします。
データの読み込み
データの前処理・整形
集計・統計解析
データの可視化
機械学習を使った分類モデルの生成
モデル分類結果の検... | Python Code:
from IPython.display import Image
Image(url='http://graphics8.nytimes.com/images/section/learning/general/onthisday/big/0415_big.gif')
Explanation: PyData.Tokyo Tutorial & Hackathon #1
PyData.Tokyoでは毎月開催している中上級者向けの勉強会に加え、初心者の育成を目的としたチュートリアルイベントを開催します。今回のイベントでは下記の項目にフォーカスします。
データの読み込み
データの前処理・整形
集計・統計解析
データ... |
10,954 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Template for test
Step1: Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.
Included is N Phosphorylation however no benchmarks are av... | Python Code:
from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
Explanation: Template for test
End of explanation
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
benchmarks = ["Data/Benchmarks/phos_CDK1.csv", "Data/Benchmarks/phos_CK2.csv", "Data... |
10,955 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Issue 12
The purpose of this notebook is to debug issue 12
Here is the Pyramid version info
Step1: Read the data in
Step2: Determining the periodicity
Our issue filer claims that the perio... | Python Code:
import pmdarima as pm
print("Pyramid version: %r" % pm.__version__)
Explanation: Issue 12
The purpose of this notebook is to debug issue 12
Here is the Pyramid version info:
End of explanation
import pandas as pd
data = pd.read_csv('dummy_data.csv')
data.head()
Explanation: Read the data in:
End of explana... |
10,956 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I get how to use pd.MultiIndex.from_tuples() in order to change something like | Problem:
import pandas as pd
import numpy as np
l = [('A', '1', 'a'), ('A', '1', 'b'), ('A', '2', 'a'), ('A', '2', 'b'), ('B', '1','a'), ('B', '1','b')]
np.random.seed(1)
df = pd.DataFrame(np.random.randn(5, 6), columns=l)
def g(df):
df.columns = pd.MultiIndex.from_tuples(df.columns, names=['Caps','Middle','Lower... |
10,957 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Average wage in Russia
Step1: Для выполнения этого задания нам понадобятся данные о среднемесячных уровнях заработной платы в России
Step2: Проверка стационарности и STL-декомпозиция ряда
... | Python Code:
from __future__ import division
import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from itertools import product
from datetime import *
from dateutil.relativedelta import *
from... |
10,958 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<table>
<tr align=left><td><img align=left src="https
Step1: Mixed Equations In-Class Project
Consider the reaction-diffusion PDE
$$\begin{aligned}
u_t &= \sigma D_1 \nabla^2 u + f(u, ... | Python Code:
%matplotlib inline
import numpy
import matplotlib.pyplot as plt
import scipy.sparse as sparse
import scipy.sparse.linalg as linalg
Explanation: <table>
<tr align=left><td><img align=left src="https://i.creativecommons.org/l/by/4.0/88x31.png">
<td>Text provided under a Creative Commons Attribution license... |
10,959 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Daymet Data Download</h1>
Daymet data can be extracted/downloaded in two ways. The nationwide or localized grid can be downloaded; alternately, the data for particular grid cells can be... | Python Code:
import urllib
import os
from datetime import date as dt
Explanation: <h1>Daymet Data Download</h1>
Daymet data can be extracted/downloaded in two ways. The nationwide or localized grid can be downloaded; alternately, the data for particular grid cells can be extracted through a web interface.
<h2>Daymet D... |
10,960 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Better Long-Term Stock Forecasts
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
The previous paper showed a strong predictive relationship between the P/Sales ratio ... | Python Code:
%matplotlib inline
# Imports from Python packages.
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import pandas as pd
import numpy as np
import os
# Imports from FinanceOps.
from curve_fit import CurveFitReciprocal
from data_keys import *
from data import load_index_data, load_... |
10,961 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
No real difference between grain sizes for the parallel vs perpendicular graphite.
Step1: Make a giant comparison plot | Python Code:
ABIG = 1.0
big_sil = SingleGrainPop('Grain', 'Silicate', 'Mie', amax=ABIG, md=MD)
big_gra = SingleGrainPop('Grain', 'Graphite', 'Mie', amax=ABIG, md=MD)
%%time
big_sil.calculate_ext(EVALS, unit='kev', theta=THVALS)
%%time
big_gra.calculate_ext(EVALS, unit='kev', theta=THVALS)
ax = plt.subplot(111)
big_sil... |
10,962 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
나이브베이즈로 들어가겠다. 텍스트 분석은 나이브 베이즈가 Good
텍스트 분석이다 보니 텍스트 처리 기능을 알아야 한다.
Python 문자열 인코딩
문자와 인코딩
문자의 구성
바이트 열 Byte Sequence
Step1: 유니코드 리터럴(Literal)
따옴표 앞에 u자를 붙이면 unicode 문자열로 인식
내부적으로 유니코드 포... | Python Code:
c = "a"
c
print(c), type(c)
#python2 기준. 인코딩 문제로 '가'가 아닌 특수문자들이 뜨는 경우가 있다. python3에서 실행할 경우에는 unicode로 되어 있기 때문에
#전부 정상적으로 한글 문자를 인식할 것이다.
x = "가"
x
print(x)
print(x.__repr__())
x = ["가"]
print(x), type(x)
x = "가"
len(x), type(x)
x = "ABC"
y = "가나다"
print(len(x), len(y))
print(x[0], x[1], x[2])
print(y[0],... |
10,963 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Adaptive histogram
This type of histogram automatically adapts bins when new values are added. Note that only fixed-width continuous binning scheme is currently supported.
Step1: Adding sin... | Python Code:
# Necessary import evil
import physt
from physt import h1, h2, histogramdd
import numpy as np
import matplotlib.pyplot as plt
# Create an empty histogram
h = h1(None, "fixed_width", bin_width=10, name="People height", axis_name="cm", adaptive=True)
h
Explanation: Adaptive histogram
This type of histogram a... |
10,964 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Image Classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifa... |
10,965 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Global Imports
Step1: External Package Imports
Step2: Tweaking Display Parameters
Load default custom.css file from ipython profile
Step3: Pandas display parameters
Step4: Tweaking color... | Python Code:
%pylab inline
Explanation: Global Imports
End of explanation
import os as os
import pickle as pickle
import pandas as pd
print 'changing to source dirctory'
os.chdir('../src')
import Data.Firehose as FH
Explanation: External Package Imports
End of explanation
from IPython import utils
from IPython.displa... |
10,966 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cross-Validation
We follow Rosser et al. and use a maximum-likelihood approach to finding the "best" parameters for the time and space bandwidths.
Use a "training" dataset of 180 days
For ea... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.collections
import numpy as np
import shelve
import open_cp.network
import open_cp.geometry
import open_cp.network_hotspot
import open_cp.logger
open_cp.logger.log_to_true_stdout()
import pickle, lzma
with lzma.open("input_old.pic.xz", "r... |
10,967 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
tsam - Optimal combination of segments and periods for building supply systems
Date
Step1: Input data
Read in time series from testdata.csv with pandas
Step2: Create a plot function for a ... | Python Code:
%load_ext autoreload
%autoreload 2
import copy
import os
import pandas as pd
import matplotlib.pyplot as plt
import tsam.timeseriesaggregation as tsam
import tsam.hyperparametertuning as tune
import tqdm
%matplotlib inline
Explanation: tsam - Optimal combination of segments and periods for building supply ... |
10,968 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Optimization Methods
Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that c... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import math
import sklearn
import sklearn.datasets
from opt_utils import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation
from opt_utils import compute_cost, predict, predict_dec, plot_decision_boundar... |
10,969 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Functional Expansions
OpenMC's general tally system accommodates a wide range of tally filters. While most filters are meant to identify regions of phase space that contribute to a tally, th... | Python Code:
%matplotlib inline
import openmc
import numpy as np
import matplotlib.pyplot as plt
Explanation: Functional Expansions
OpenMC's general tally system accommodates a wide range of tally filters. While most filters are meant to identify regions of phase space that contribute to a tally, there are a special se... |
10,970 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
I. Imports
Step1: I want to import Vgg16 as well because I'll want it's low-level features
Step2: Actually, looks like Vgg's ImageNet weights won't be needed.
Step3: II. Load Data
Step4: ... | Python Code:
import keras
import numpy as np
from keras.datasets import mnist
from keras.optimizers import Adam
from keras.models import Sequential
from keras.preprocessing import image
from keras.layers.core import Dense
from keras.layers.core import Lambda
from keras.layers.core import Flatten
from keras.layers.core ... |
10,971 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inital Sources
Using the sources at 007.20321 +14.87119 and RA = 20
Step1: Read in the two data files. Currently, the *id's are in double format. This is different from the orginial table's... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table as tab
Explanation: Inital Sources
Using the sources at 007.20321 +14.87119 and RA = 20:50:00.91, dec = -00:42:23.8 taken from the NASA/IPAC Infrared Science Archieve on 6/22/17.
End of explanation
source_... |
10,972 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Adding a discharge point source to a LEM
(Greg Tucker, CSDMS / CU Boulder, fall 2020)
This notebook shows how to add one or more discharge point sources to a Landlab-built landscape evolutio... | Python Code:
from landlab import RasterModelGrid, imshow_grid
from landlab.components import FlowAccumulator
import numpy as np
Explanation: Adding a discharge point source to a LEM
(Greg Tucker, CSDMS / CU Boulder, fall 2020)
This notebook shows how to add one or more discharge point sources to a Landlab-built landsca... |
10,973 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Feedback or issues?
For any feedback or questions, please open an issue.
Vertex SDK for Python
Step1: Enter Your Project and GCS Bucket
Enter your Project Id in the cell below. Then run the... | Python Code:
!pip3 uninstall -y google-cloud-aiplatform
!pip3 install google-cloud-aiplatform
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
Explanation: Feedback or issues?
For any feedback or questions, please open an issue.
Vertex SDK for Python: AutoML Video Action Recognition Exam... |
10,974 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Housekeeping Unit Test
This notebook contains a Unit Test for the housekeeping information from the Observatory Simulator.
Starting the Observatory Simulator
Remember that whenever you power... | Python Code:
from tessfpe.dhu.fpe import FPE
from tessfpe.dhu.unit_tests import check_house_keeping_voltages
fpe1 = FPE(1, debug=False, preload=False, FPE_Wrapper_version='6.1.1')
print fpe1.version
if check_house_keeping_voltages(fpe1):
print "Wrapper load complete. Interface voltages OK."
Explanation: Housekeepin... |
10,975 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CS109 Final Project Process Book
Background & Motivation
Social media and entertainment are such pervasive parts of millennials' lives. We want to study the intersection of these two. Is it ... | Python Code:
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
import time
import json
import statsmodels.api as sm
from statsmodels.formula.api import glm, ols
pd.set_option('display.width', 500)
pd.set_optio... |
10,976 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic regression
Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License
Step1: Generational Changes
As a second example of logistic regression, we'll use data from the Genera... | Python Code:
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
import os
if not os.path.exists('utils.py'):
!wget https://github.com/AllenDowney/ThinkBayes2/raw/m... |
10,977 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
베르누이 확률 분포
베르누이 시도
결과가 성공(Success) 혹은 실패(Fail) 두 가지 중 하나로만 나오는 것을 베르누이 시도(Bernoulli trial)라고 한다. 예를 들어 동전을 한 번 던져 앞면(H
Step1: pmf 메서드를 사용하면 확률 질량 함수(pmf
Step2: 시뮬레이션을 하려면 rvs 메서드를 사용한다.
St... | Python Code:
theta = 0.6
rv = sp.stats.bernoulli(theta)
rv
Explanation: 베르누이 확률 분포
베르누이 시도
결과가 성공(Success) 혹은 실패(Fail) 두 가지 중 하나로만 나오는 것을 베르누이 시도(Bernoulli trial)라고 한다. 예를 들어 동전을 한 번 던져 앞면(H:Head)이 나오거나 뒷면(T:Tail)이 나오게 하는 것은 베르누이 시도의 일종이다.
베르누이 시도의 결과를 확률 변수(random variable) $X$ 로 나타낼 때는 일반적으로 성공을 정수 1 ($X=1$), 실패를 정수 ... |
10,978 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
데이터의 특성은 분포와 관계가 있다.
모양에 해당하는 부분이 분포다.
예를 들어, 원의 경우 반지름, 사각형의 경우 가로세로 길이가 모양에 해당한다.
그림을 그려서 파악하는 이유는 봉우리가 몇 개인지를 파악하기 위해서
uni-modal(단봉)인지 multi-modal인지 파악하기 위해서
관계의 경우에는 scatter plot이나 다른 모... | Python Code:
np.random.seed(0)
x = np.random.normal(size=100)
x
sp.stats.describe(x)
pd.Series(x).describe()
Explanation: 데이터의 특성은 분포와 관계가 있다.
모양에 해당하는 부분이 분포다.
예를 들어, 원의 경우 반지름, 사각형의 경우 가로세로 길이가 모양에 해당한다.
그림을 그려서 파악하는 이유는 봉우리가 몇 개인지를 파악하기 위해서
uni-modal(단봉)인지 multi-modal인지 파악하기 위해서
관계의 경우에는 scatter plot이나 다른 모형들로 파악하게... |
10,979 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
WH Nixalo - 02 Aug 2017
Step1: Aaaahaaaaaaaa. Okay. so the .output parameter for kears.models.Model(..) will take all layers of a model up to and including the layer specified.
It does NOT ... | Python Code:
import keras
from keras.models import Model
from keras.layers import Dense, Input, Convolution2D
from keras.applications.imagenet_utils import _obtain_input_shape
from keras import backend as K
input_shape = (224, 224, 3)
img_input = Input(shape=input_shape, name='blah-input')
# x = Convolution2D(64, 3, 3,... |
10,980 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BERT (from HuggingFace Transformers) for Text Extraction
Author
Step1: Set-up BERT tokenizer
Step2: Load the data
Step3: Preprocess the data
Go through the JSON file and store every recor... | Python Code:
import os
import re
import json
import string
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tokenizers import BertWordPieceTokenizer
from transformers import BertTokenizer, TFBertModel, BertConfig
max_len = 384
configuration = BertConfig() ... |
10,981 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> Repeatable splitting </h1>
In this notebook, we will explore the impact of different ways of creating machine learning datasets.
<p>
Repeatability is important in machine learning. If y... | Python Code:
from google.cloud import bigquery
Explanation: <h1> Repeatable splitting </h1>
In this notebook, we will explore the impact of different ways of creating machine learning datasets.
<p>
Repeatability is important in machine learning. If you do the same thing now and 5 minutes from now and get different answ... |
10,982 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Self-supervised contrastive learning with SimSiam
Author
Step1: Define hyperparameters
Step2: Load the CIFAR-10 dataset
Step3: Defining our data augmentation pipeline
As studied in SimCLR... | Python Code:
from tensorflow.keras import layers
from tensorflow.keras import regularizers
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
Explanation: Self-supervised contrastive learning with SimSiam
Author: Sayak Paul<br>
Date created: 2021/03/19<br>
Last modified: 2021/03/20<br>
Descripti... |
10,983 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting Meta with Big Data Malaysia
Scraping the Big Data Malaysia Facebook group for fun. Profit unlikely.
Hello World
This is an introductory-level notebook demonstrating how to deal with ... | Python Code:
# we need this for later:
%matplotlib inline
import json
INPUT_FILE = "all_the_data.json"
with open(INPUT_FILE, "r") as big_data_fd:
big_data = json.load(big_data_fd)
Explanation: Getting Meta with Big Data Malaysia
Scraping the Big Data Malaysia Facebook group for fun. Profit unlikely.
Hello World
This i... |
10,984 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
General Concepts
HOW TO RUN THIS FILE
Step1: Let's get started with some basic imports
Step2: If running in IPython notebooks, you may see a "ShimWarning" depending on the version of Jupyt... | Python Code:
!pip install -I "phoebe>=2.0,<2.1"
Explanation: General Concepts
HOW TO RUN THIS FILE: if you're running this in a Jupyter notebook or Google Colab session, you can click on a cell and then shift+Enter to run the cell and automatically select the next cell. Alt+Enter will run a cell and create a new cell ... |
10,985 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
9 defenses
Low Bar
ALLIANCE selected
Audience selected
ALLIANCE selected
ALLIANCE selected
Data structure choices include
Step1: Analysis Functions | Python Code:
# Object oriented approach, would have to feed csv data into objects
# maybe get rid of this and just use library analysis tools
class Robot(object):
def __init__(self, name, alliance, auto_points, points):
self.name = name
self.alliance = alliance
self.auto_points = auto_points
self.points = poin... |
10,986 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
.загружаем файлы .json
Step1: Смотрим, где именно в файле интересующие нас данные
Step2: Считываем нужные нам данные как датафреймы
Step3: Создаем в датафреймах отдельные столбцы с данным... | Python Code:
path = 'data/Sessions_Page.json'
path2 = 'data/Goal1CompletionLocation_Goal1Completions.json'
with open(path, 'r') as f:
sessions_page = json.loads(f.read())
with open(path2, 'r') as f:
goals_page = json.loads(f.read())
Explanation: .загружаем файлы .json
End of explanation
type (sessions_page)
ses... |
10,987 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Issue 26
Step1: Builtin method to highlight clades
Step2: Or, use toyplot directly
This method is more flexible and you can do just about anything with it.
Step3: More examples
Things can... | Python Code:
import toytree
import toyplot
# generate a random tree
tre = toytree.rtree.unittree(ntips=10, treeheight=100, seed=123)
Explanation: Issue 26: highlight clades
I've created an early form of an Annotator class object that can be used to add highlights to a toytree drawing.
Possible extensions:
- apply li... |
10,988 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GaAs-AlAs Bragg Mirror
A simple bragg mirror in the near infrared. As a default, this notebook is setup to work with binder.
Importing libraries
Below we import the library python style, i.e... | Python Code:
# importing libraries
import numpy as np # numpy
import matplotlib.pyplot as plt # matplotlib pyplot
import sys # sys to add py_matrix to the path
# adding folder containing 1DPyHC to path: by default is the folder for running in binder
sys.path.append('/home/main/notebooks')
import pyhc a... |
10,989 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 Google LLC.
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 Licen... | Python Code:
from __future__ import division
import pandas as pd
import numpy as np
import json
import os,sys
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
Explanation: Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2... |
10,990 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have two arrays A (len of 3.8million) and B (len of 20k). For the minimal example, lets take this case: | Problem:
import numpy as np
A = np.array([1,1,2,3,3,3,4,5,6,7,8,8])
B = np.array([1,2,8])
C = A[~np.in1d(A,B)] |
10,991 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial 3 - Sampling with AdaptiveMD
Imports
Step1: Let's open our tutorial project by its name. If you completed the first examples this should all work out of the box.
Step2: Open all c... | Python Code:
from adaptivemd import Project
Explanation: Tutorial 3 - Sampling with AdaptiveMD
Imports
End of explanation
project = Project('tutorial')
Explanation: Let's open our tutorial project by its name. If you completed the first examples this should all work out of the box.
End of explanation
print(project.file... |
10,992 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook, we mainly utilize extreme gradient boost to improve the prediction model originially proposed in TLE 2016 November machine learning tuotrial. Extreme gradient boost can be ... | Python Code:
%matplotlib inline
import pandas as pd
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import matplotlib.colors as colors
import xgboost as xgb
import numpy as np
from sklearn.metrics import confusion_matrix, f1_score, accuracy... |
10,993 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 3</font>
Download
Step1: While
Step2: Pass, Break, Continue
Step3: While e For juntos | Python Code:
# Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 3</font>
Download: http://github.com/dsacademybr
End of explanation
# Usa... |
10,994 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 4
Imports
Step2: Line with Gaussian noise
Write a function named random_line that creates x and y data for a line with y direction random noise that has a normal distribut... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 4
Imports
End of explanation
n=np.random.standard_normal?
n=np.random.standard_normal
n=np.random.randn
n=np... |
10,995 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Computing features with MusicExtractor
MusicExtractor is a multi-purpose algorithm for music audio feature extraction from files (see the complete list of computed features here). It combine... | Python Code:
audiofile = '../../../test/audio/recorded/dubstep.mp3'
# This is how the audio we want to process sounds like.
import IPython
IPython.display.Audio(audiofile)
import essentia
import essentia.standard as es
# Compute all features.
# Aggregate 'mean' and 'stdev' statistics for all low-level, rhythm, and tona... |
10,996 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A quick example of the code for generating the mask for the North Atlantic (to be further used with fpost)
%matplotlib notebook
%load_ext autoreload
%autoreload 2
Step1: selecting the NA ma... | Python Code:
import sys
sys.path.append("../")
import pyfesom as pf
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
%matplotlib notebook
from matplotlib import cm
from netCDF4 import Dataset
meshID='fArc'
# read the mesh
m... |
10,997 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Linear PCA
Step1: non linear PCA
http | Python Code:
#xyz=records[['Latitude','Longitude','Magnitude','Depth/Km','deltaT']].values[1:].T
lxyz=xyz.T.copy()
lxyz=lxyz[:,2:]
lxyz/=lxyz.std(axis=0)
"Magnitude,Depth,deltaT"
print lxyz.shape
l,e,MD=pma.pma(lxyz)
X=pma.get_XY(lxyz,e)
sns.plt.plot(np.cumsum(l)/np.sum(l),'o-')
sns.plt.figure()
sns.plt.plot(e[:,:3])
s... |
10,998 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 3
Imports
Step2: Using interact for animation with data
A soliton is a constant velocity wave that maintains its shape as it propagates. They arise from non-linear wave eq... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 3
Imports
End of explanation
def soliton(x, t, c, a):
Return phi(x, t) for a soliton wave with cons... |
10,999 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Context
Reading data from a software version control system can be pretty useful if you want to answer some evolutionary questions like
* Who are our main committers to the software?
* Are t... | Python Code:
with open (r'data/gitlog_aim42.log') as log:
[print(line, end='') for line in log.readlines()[:8]]
Explanation: Context
Reading data from a software version control system can be pretty useful if you want to answer some evolutionary questions like
* Who are our main committers to the software?
* Are th... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.