Unnamed: 0 int64 0 16k | text_prompt stringlengths 149 45.7k | code_prompt stringlengths 67 140k |
|---|---|---|
900 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
TensorFlow Tutorial #01
Simple Linear Model
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
This tutorial demonstrates the basi... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
Explanation: TensorFlow Tutorial #01
Simple Linear Model
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
This tutorial demonst... |
901 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've... | <ASSISTANT_TASK:>
Python Code:
### START CODE HERE ### (≈ 1 line of code)
test = "Hello World"
### END CODE HERE ###
print ("test: " + test)
Explanation: Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python be... |
902 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Strings
String is a Sequence
A string is a sequence of characters. You can access the characters one at a time with the bracket operator
Step1: The se... | <ASSISTANT_TASK:>
Python Code:
fruit = "pinapple"
letter = fruit[1]
Explanation: Strings
String is a Sequence
A string is a sequence of characters. You can access the characters one at a time with the bracket operator:
End of explanation
print(letter)
Explanation: The second statement selects character number 1 from fr... |
903 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
← Back to Index
Onset Detection
Automatic detection of musical events in an audio signal is one of the most fundamental tasks in music information... | <ASSISTANT_TASK:>
Python Code:
x, fs = librosa.load('simpleLoop.wav', sr=44100)
print x.shape
Explanation: ← Back to Index
Onset Detection
Automatic detection of musical events in an audio signal is one of the most fundamental tasks in music information retrieval. Here, we will show how to detect an onset, the sta... |
904 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
1.20 合并多个字典或映射
有多个字典或映射 将他们从逻辑上合并为一个单一的映射后执行某些操作 比如 查找值或检查某些键是否存在
Step1: 一个 ChainMap 接受多个 dict 将他们在逻辑上变为一个 dict 然后 这些 dict 不是真的合并在一起了 ChainMap 类只是在内部创... | <ASSISTANT_TASK:>
Python Code:
a = {'x': 1, 'z': 3}
b = {'y': 2, 'z': 4}
# 需在两 dict 中执行查找操作 (先从 a 中找，若是找不到，再在 b 中找)
from collections import ChainMap
c = ChainMap(a,b)
print(c['x'])
print(c['y'])
print(c['z'])
Explanation: 1.20 合并多个字典或映射
有多个字典或映射 将他们从逻辑上合并为一个单一的映射后执行某些操作 比如 查找值或检查某些键是否存在
End of explanation
len(c)
list(c... |
905 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
ClickForMarker Lets you create markers on each click
Step1: Click on the map to see the effects
LatLngPopup lets you create a simple popup at each cli... | <ASSISTANT_TASK:>
Python Code:
folium.Map().add_child(ClickForMarker())
Explanation: ClickForMarker Lets you create markers on each click
End of explanation
folium.Map().add_child(LatLngPopup())
Explanation: Click on the map to see the effects
LatLngPopup lets you create a simple popup at each click
End of explanation
... |
906 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
ROC Curve Example
Inspired by
Step1: Import some data to play with
Step2: Split the data and prepare data for ROC Curve
Step3: Plot ROC Curve using ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
Explanation: ROC Curve Example
Inspired by: http://scikit-learn.org/stable/auto_examples/model_selection/plo... |
907 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Distributions of multiple numerical features with Seaborn
When given a set of numerical features, it is desirable to plot all of them using for example... | <ASSISTANT_TASK:>
Python Code:
import string
import pandas as pd
import numpy as np
import seaborn as sns
Explanation: Distributions of multiple numerical features with Seaborn
When given a set of numerical features, it is desirable to plot all of them using for example violinplots, to get a sense of their respective d... |
908 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Decorators
ONLY AFTER FP
A decorator is the name used for a software design pattern. Decorators dynamically alter the functionality of a function, meth... | <ASSISTANT_TASK:>
Python Code:
# sandwich()
# test = bread(ingredients(Cheese))
# test()
# # bread_1 = ingredients(Cheese)
# # print(bread_1)
# # bread_1()
def bread(test_funct):
def hyderabad():
print("</''''''\>")
test_funct()
print("<\______/>")
return hyderabad
def ingredients(test_f... |
909 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
ES-DOC CMIP6 Model Properties - Land
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'awi-cm-1-0-hr', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: AWI
Source ID: AWI-CM-1-0-HR
Topic: Land
Sub-Topics: Soil,... |
910 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
请在环境变量中设置DB_URI指向数据库
Step1: 1. Single Day Analysis
Step2: Portfolio Construction
using EPS factor as alpha factor;
short selling is forbiden;
target ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from PyFin.api import *
from alphamind.api import *
from alphamind.strategy.strategy import Strategy, RunningSetting
from alphamind.portfolio.meanvariancebuilder import target_vol_buil... |
911 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Linear models for regression
y_pred = x_test[0] * coef_[0] + ... + x_test[n_features-1] * coef_[n_features-1] + intercept_
Step1: Linear Regression
$$... | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import make_regression
from sklearn.cross_validation import train_test_split
X, y, true_coefficient = make_regression(n_samples=80, n_features=30, n_informative=10, noise=100, coef=True, random_state=5)
X_train, X_test, y_train, y_test = train_test_split(X, y, random... |
912 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Probing Image-Text Models
This Colab evaluates pretrained image--text models (in a zero-shot way) with respect to fine-grained subject, verb, and objec... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import sys
import json
import csv
import pandas as pd
import re
import matplotlib.pyplot as plt
import seaborn as sns
csv.field_size_limit(sys.maxsize)
Explanation: Probing Image-Text Models
This Colab evaluates pretrained image--text models (in a zero-shot way) with re... |
913 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Import
Step1: file_path
Step2: LSTM - Hyper Params
Step3: vgg16
Step4: load_vgg16
Step5: File Info
Step6: Text Reader
def input_pipeline(filename... | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from PIL import Image
import numpy as np
from scipy.misc import imread, imresize
from imagenet_classes import class_names
import os
Explanation: Import
End of explanation
#File Path
# filepath_input = "./data/run/" #input csv file path
filepath_ckpt = "./ckpt/mod... |
914 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Train Model with CPU
Step1: Create Model Test/Validation Data
Step2: Look at the Model Graph In Tensorboard
Navigate to the Graph tab at this URL
Ste... | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow.python.client import timeline
import pylab
import numpy as np
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
tf.logging.set_verbosity(tf.logging.INFO)
tf.reset_default_graph()
num_samples = 100000
from datetime import datetime
ve... |
915 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Copyright (c) 2018 Geosoft Inc.
https
Step1: Convert a grid from one format to another
We will start with a common simple task, converting a grid from... | <ASSISTANT_TASK:>
Python Code:
import geosoft.gxpy.gx as gx
import geosoft.gxpy.grid as gxgrid
import geosoft.gxpy.utility as gxu
from IPython.display import Image
gxc = gx.GXpy()
url = 'https://github.com/GeosoftInc/gxpy/raw/9.3/examples/tutorial/Grids%20and%20Images/'
gxu.url_retrieve(url + 'elevation_surfer.GRD')
Ex... |
916 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
<table align="left">
<td>
<a href="https
Step1: Restart the Kernel
Once you've installed the {packages}, you need to restart the notebook kernel... | <ASSISTANT_TASK:>
Python Code:
%pip install -U missing_or_updating_package --user
Explanation: <table align="left">
<td>
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/main/notebooks/templates/ai_platform_notebooks_template.ipynb">
<img src="https://cloud.goog... |
917 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
ISA
Step1: Get layer data
Step2: Get $p, T, \rho$
Step3: Note that the geopotential height can be provided as a numpy array
Step4: If height is pro... | <ASSISTANT_TASK:>
Python Code:
# Import isa library
from pyturb.gas_models import isa
import numpy as np
from matplotlib import pyplot as plt
Explanation: ISA: Example of usage
This Notebook serves as an example on how to use the Standard Atmosphere model provided with pyTurb. The isa functions can be found in the gas_... |
918 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
ES-DOC CMIP6 Model Properties - Landice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributo... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-1', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-1
Topic: Landice
Sub-Topics: Gl... |
919 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Plot 1
Step1: Description
Step2: Load data and take a peak at it.
Step3: Separate data into training, validation, and test sets. (This division is n... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display, HTML
display(HTML('''<img src="image1.png",width=800,height=600>'''))
Explanation: Plot 1: The predictive potential of rank difference
End of explanation
import numpy as np # numerical libraries
import pandas as pd # for data analysis
import matplotlib... |
920 | <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: Single Replica TIS
This notebook shows how to run single replica TIS move scheme. This assumes you can load engine, network, and initial sample ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import openpathsampling as paths
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from openpathsampling.visualize import PathTreeBuilder, PathTreeBuilder
from IPython.display import SVG, HTML
def ipynb_visualize(movevis):
Default settings to sh... |
921 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Background Modeling
When fitting a spectrum with a background, it is invalid to simply subtract off the background if the background is part of the dat... | <ASSISTANT_TASK:>
Python Code:
from threeML import *
%matplotlib inline
import warnings
warnings.simplefilter('ignore')
Explanation: Background Modeling
When fitting a spectrum with a background, it is invalid to simply subtract off the background if the background is part of the data's generative model van Dyk et al. ... |
922 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Estimating Trip Mode Choice
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running Acti... | <ASSISTANT_TASK:>
Python Code:
import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
Explanation: Estimating Trip Mode Choice
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode... |
923 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Connect to Cloud SQL using the Cloud SQL Python Connector
This notebook will be demonstrating how to connect and query data from a Cloud SQL database i... | <ASSISTANT_TASK:>
Python Code:
from google.colab import auth
auth.authenticate_user()
Explanation: Connect to Cloud SQL using the Cloud SQL Python Connector
This notebook will be demonstrating how to connect and query data from a Cloud SQL database in an easy and efficient way all from within a jupyter style notebook! ... |
924 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Примеры анализа данных аэрокосмической съемки
Дмитрий Колесов (kolesov.dm@gmail.com)
NextGIS
О чем пойдет речь
Что это за такие "Данные аэрокосмической... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
points = pd.read_csv('rand.txt')
points.tail()
y = points["class"]
X = points[['r1', 'r2', 'r3', 'r4', 'r5', 'r6', 'r7', 'r8', 'r9', 'r10', 'r11']]
# Разбиваем на обучающее и тестовое множества:
from sklearn.model_selection import train_test_split
X_... |
925 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Example
Step1: Now we should have a data/names directory which contains a number of text files, one for each year of data
Step2: Let's take a quick l... | <ASSISTANT_TASK:>
Python Code:
# !curl -O http://www.ssa.gov/oact/babynames/names.zip
# !mkdir -p data/names
# !mv names.zip data/names/
# !cd data/names/ && unzip names.zip
Explanation: Example: Names in the Wild
This example is drawn from Wes McKinney's excellent book on the Pandas library, O'Reilly's Python for Data... |
926 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Introduction to numerical simulations
Step1: Now we will define the physical constants of our system, which will also establish the unit system we hav... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: Introduction to numerical simulations: The 2 Body Problem
Many problems in statistical physics and astrophysics require solving problems consisting of many particles at once (sometimes on the order of thous... |
927 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
WARNING
Step1: ================================
predict the mean and std of radius given those of the mass
Step2: ================================
pr... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import mr_forecast as mr
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: WARNING:
"fitting_parameters.h5" need to be in the directory you are working on
or there will be an error for importing mr_forecast in the next cell.
If you don't want the file in t... |
928 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Layout viewport
Use the Layout class to create a variety of map views for comparison.
For more information, run help(Layout).
The first example sets a ... | <ASSISTANT_TASK:>
Python Code:
from cartoframes.auth import set_default_credentials
set_default_credentials('cartoframes')
Explanation: Layout viewport
Use the Layout class to create a variety of map views for comparison.
For more information, run help(Layout).
The first example sets a common viewport for all maps whil... |
929 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-1', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-1
Topic: At... |
930 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Analysis of Sealing Potential of Liner Captured Between Aluminum Rings Via Shrink Fit.
First Objective
Step1: ASME Pressure Vessel Code Equations
This... | <ASSISTANT_TASK:>
Python Code:
# Import packages here:
import math as m
import numpy as np
from IPython.display import Image
import matplotlib.pyplot as plt
# Properties of Materials (engineeringtoolbox.com, Cengel, Tian, DuPont, http://www.dtic.mil/dtic/tr/fulltext/u2/438718.pdf)
# Coefficient of Thermal Expansion
alp... |
931 | <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: 人脸生成（Face Generation）
在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。
获取数据
该项目将使用以下数据集：
- MNIST
- CelebA
由于 CelebA 数据集比较复杂，而且这是你第一次使用 G... | <ASSISTANT_TASK:>
Python Code:
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
DON'T MODIFY ANYTHING IN THIS CELL
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Explanation: 人脸生成（Face Generation）
在该项目中，你将使用生成式对抗网络（Gene... |
932 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
AGREGACION DE DATOS DESDE EL DATASET DEL PROGRAMA DE INDICADORES DE GESTIÓN DE ORGANISMOS OPERADORES (PIGOO)
En este documento se describen las conside... | <ASSISTANT_TASK:>
Python Code:
# Librerias utilizadas
import pandas as pd
import sys
module_path = r'D:\PCCS\01_Dmine\Scripts'
if module_path not in sys.path:
sys.path.append(module_path)
from SUN.asignar_sun import asignar_sun
from SUN_integridad.SUN_integridad import SUN_integridad
from SUN.CargaSunPrincipal impo... |
933 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Tutorial for bulk Monte Carlo simulations in the structural-color package
Copyright 2016, Vinothan N. Manoharan, Victoria Hwang, Annie Stephenson
This ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import structcol as sc
import structcol.refractive_index as ri
from structcol import montecarlo as mc
from structcol import detector as det
from structcol import phase_func_sphere as pfs
import matplotlib.pyplot as plt
import seaborn as sns
import os
... |
934 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Using Python as a Calculator
Let's try some simple python commands
Numbers
The interpreter acts as a simple calculator
Step1: With Python, use ** oper... | <ASSISTANT_TASK:>
Python Code:
4
2 + 2
50 - 5*6
(50-5)*6
8/5
8//5 # Floor division discards the fractional part
8%5 # The % operator return the remainder of the division
Explanation: Using Python as a Calculator
Let's try some simple python commands
Numbers
The interpreter acts as a simple calculator: you can type an... |
935 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
DCT-based Transform Coding of Images
This code is provided as supplementary material of the lecture Quellencodierung.
This code illustrates
* Show basi... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from itertools import chain
from scipy import fftpack
import scipy as sp
from ipywidgets import interactive, HBox, Label
import ipywidgets as widgets
%matplotlib inline
Explanation: DCT-based Transform Cod... |
936 | <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: Jupyter Notebook backend demo
This example shows how vispy's low-level gloo interface can be used to display a WebGL canvas in a notebook. By de... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import vispy
import vispy.gloo as gloo
from vispy import app
from vispy.util.transforms import perspective, translate, rotate
# load the vispy bindings manually for the notebook which enables webGL
# %load_ext vispy
n = 100
a_position = np.random.uniform(-1, 1, (n, 3)).... |
937 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Suggestions for lab exercises.
Variables and assignment
Exercise 1
Remember that $n! = n \times (n - 1) \times \dots \times 2 \times 1$. Compute $15!$,... | <ASSISTANT_TASK:>
Python Code:
fifteen_factorial = 15*14*13*12*11*10*9*8*7*6*5*4*3*2*1
print(fifteen_factorial)
Explanation: Suggestions for lab exercises.
Variables and assignment
Exercise 1
Remember that $n! = n \times (n - 1) \times \dots \times 2 \times 1$. Compute $15!$, assigning the result to a sensible variable... |
938 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Reinforcement Learning
This IPy notebook acts as supporting material for Chapter 21 Reinforcement Learning of the book Artificial Intelligence
Step1: ... | <ASSISTANT_TASK:>
Python Code:
from rl import *
Explanation: Reinforcement Learning
This IPy notebook acts as supporting material for Chapter 21 Reinforcement Learning of the book Artificial Intelligence: A Modern Approach. This notebook makes use of the implementations in rl.py module. We also make use of implementati... |
939 | <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: Fitting a model to data with outliers using MCMC
We are often faced with data with spurious outliers. For example, a light curve generated from ... | <ASSISTANT_TASK:>
Python Code:
def sinusoid(t, amp, period, phase):
A generic sinusoidal curve. 'period' and 't' should have
the same units (e.g., days), and phase should be in
radians.
Parameters
----------
t : array_like
Array of times.
amp :... |
940 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Transfer Learning
Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like Image... | <ASSISTANT_TASK:>
Python Code:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_... |
941 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Natural Language Processing (NLP)
Overview
corpus - collection of texts
lexicon - collection of words (or sequences) we put into our index
bag-of-words... | <ASSISTANT_TASK:>
Python Code:
from nltk.tokenize import TreebankWordTokenizer
sentence = "How does nltk tokenize this sentence?"
tokenizer = TreebankWordTokenizer()
tokenizer.tokenize(sentence)
Explanation: Natural Language Processing (NLP)
Overview
corpus - collection of texts
lexicon - collection of words (or sequen... |
942 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Web Scraping & Data Analysis with Selenium and Python
Author
Step1: Getting Data
Step2: Store Data in a Python Dictionary
Step3: Data before Clean ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from selenium import webdriver
import os,time,json
import pandas as pd
from collections import defaultdict,Counter
import matplotlib.pyplot as plt
url = "http://www.imdb.com/list/ls061683439/"
with open('./filmfare.json',encoding="utf-8") as f:
datatbl = json.load(... |
943 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Copyright 2021 The TF-Agents Authors.
Step1: 网络
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
Step2: 定义... | <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... |
944 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Deep Learning
Assignment 5
The goal of this assignment is to train a Word2Vec skip-gram model over Text8 data.
Step2: Download the data from the sourc... | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
from __future__ import print_function
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import zipfile
from matpl... |
945 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
data checker
this is just to verify the data for ms ssim net.
Step1: everything looks good with c,s, cxs. now to check the down sampled images as well... | <ASSISTANT_TASK:>
Python Code:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import numpy as np
np.set_printoptions(threshold=np.nan)
import tensorflow as tf
import time
import pandas as pd
import matplotlib.pyplot as plt
import progressbar
data_path = 'https://raw.githubusercontent.com/michaelneuder/image_quality_a... |
946 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Tutorial 17 - Navier Stokes equations
Keywords
Step1: 3. Affine Decomposition
Step2: 4. Main program
4.1. Read the mesh for this problem
The mesh was... | <ASSISTANT_TASK:>
Python Code:
from ufl import transpose
from dolfin import *
from rbnics import *
Explanation: Tutorial 17 - Navier Stokes equations
Keywords: exact parametrized functions, supremizer operator
1. Introduction
In this tutorial, we will study the Navier-Stokes equations over the two-dimensional backward-... |
947 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Object-oriented programming
The big reveal
So far we've been working with functions and packages of functions, as well as defining our own functions. ... | <ASSISTANT_TASK:>
Python Code:
x = 'Hi'
x.lower()
Explanation: Object-oriented programming
The big reveal
So far we've been working with functions and packages of functions, as well as defining our own functions. It turns out, though, that we've been working with objects all along, we just haven't recognize them as su... |
948 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Hands-On Exercise 6
Step1: The first thing to notice is that the table does not include magnitude measurements. Gaaaasp The horror!!
As an important p... | <ASSISTANT_TASK:>
Python Code:
# execute this cell
SNlcs = Table.read("../data/Firth14Tbl2.txt", format = 'ascii')
SNlcs
Explanation: Hands-On Exercise 6: Determining $H_0$ with Type Ia SNe from PTF
Version 0.1
Today we learned about a variety of different explosive, extragalactic transients. While the lectures focused... |
949 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
随机变量及其分布 Random Variable and its Distribution
包括以下内容：
1. 随机变量 Random Variable
2. 伯努利分布 Bernoulli Distribution
3. 二项分布 Binomial Distribution... | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
# 引入绘图包
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
Explanation: 随机变量及其分布 Random Variable and its Distribution
包括以下内容：
1. 随机变量 Random Variabl... |
950 | <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: OOP in Action
Step3: Explanation of the graph
The graph portrays regions in which the $ (\lambda_1, \lambda_2) $
root pairs implied by the $ (\... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
def param_plot():
this function creates the graph on page 189 of Sargent Macroeconomic Theory, second edition, 1987
fig, ax = plt.subplots(figsize=(12, 8))
ax.set_aspect('equal')
# Set axis
xmin, ymin = -3, -2
xmax... |
951 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Directional Analysis of Dynamic LISAs
This notebook demonstrates how to use Rose diagram based inference for directional LISAs.
Step1: Visualization
S... | <ASSISTANT_TASK:>
Python Code:
import pysal.lib
import numpy as np
from pysal.explore.giddy.directional import Rose
%matplotlib inline
f = open(pysal.lib.examples.get_path('spi_download.csv'), 'r')
lines = f.readlines()
f.close()
lines = [line.strip().split(",") for line in lines]
names = [line[2] for line in lines[1:... |
952 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Vertex AI
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Once you've installed the additiona... | <ASSISTANT_TASK:>
Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
Explanation: Vertex AI: Vertex AI Migration: Custom Image Classification w/pre-built... |
953 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Insertion Sort (Insert Sort)
Insertion sort is a simple sorting algorithm that builds the final sorted array (or list) one item at a time. It is much l... | <ASSISTANT_TASK:>
Python Code:
def insertion_sort(unsorted_list):
x = ipytracer.List1DTracer(unsorted_list)
display(x)
for i in range(1, len(x)):
j = i - 1
key = x[i]
while x[j] > key and j >= 0:
x[j+1] = x[j]
j = j - 1
x[j+1] = key
return x.data
... |
954 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Unsupvervised Learning
Step1: Let's start with $k=3$, arbitrarily assigned
Step2: We can use the function cdist from SciPy to calculate the distances... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import seaborn as sns; sns.set_context('notebook')
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
iris = datasets.load_iris()
features, target = iris.data, iris.target
sepal_length, sepal_width, petal_length, petal_width = features.T
x, ... |
955 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Rechunking
Rechunking lets us re-distribute how datasets are split between variables and chunks across a Beam PCollection.
To get started we'll recreat... | <ASSISTANT_TASK:>
Python Code:
import apache_beam as beam
import numpy as np
import xarray_beam as xbeam
import xarray
def create_records():
for offset in [0, 4]:
key = xbeam.Key({'x': offset, 'y': 0})
data = 2 * offset + np.arange(8).reshape(4, 2)
chunk = xarray.Dataset({
'foo':... |
956 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: Goal
We want to build a model $h_\theta(s) \rightarr... | <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... |
957 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
DATASCI W261
Step1: HW 10.0
Step2: HW 10.1
Step3: HW 10.1.1
Modify the above word count code to count words that begin with lower case letters (a-z)... | <ASSISTANT_TASK:>
Python Code:
%cd ~/Documents/W261/hw10/
import os
import sys
spark_home = os.environ['SPARK_HOME'] = \
'/Users/davidadams/packages/spark-1.5.1-bin-hadoop2.6/'
if not spark_home:
raise ValueError('SPARK_HOME enviroment variable is not set')
sys.path.insert(0,os.path.join(spark_home,'python'))
sy... |
958 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
<h1 align="center">TensorFlow Neural Network Lab</h1>
<img src="image/notmnist.png">
In this lab, you'll use all the tools you learned from Introductio... | <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... |
959 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Example 4
This project is for deep MNIST for experts.
Step1: Build a Multilayer Convolutional Network
This section will help to build more complex mod... | <ASSISTANT_TASK:>
Python Code:
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
################## build a softmax regression model
# input data
x = tf.placeholder(tf.float32, shape = [None, 784])
# real labels
y_ = tf.pla... |
960 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Introduction
The purpose of this product is to classify population depending on their uses of their phone and phone brands. This classification is the ... | <ASSISTANT_TASK:>
Python Code:
#Uplaod the data into the notbook and select the rows that will be used after previous visual inspection of the datasets
datadir = 'D:/Users/Borja.gonzalez/Desktop/Thinkful-DataScience-Borja'
gatrain = pd.read_csv('gender_age_train.csv',usecols=['device_id','gender','age','group'] )
gates... |
961 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Algo - jeux de dictionnaires, plus grand suffixe commun
Les dictionnaires sont très utilisés pour associer des choses entre elles, surtout quand ces ch... | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: Algo - jeux de dictionnaires, plus grand suffixe commun
Les dictionnaires sont très utilisés pour associer des choses entre elles, surtout quand ces choses ne sont pas entières. Le notebook montre l'intérêt de pe... |
962 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
infusionDrug
Continuous infusions are documented here and are entered from the nursing flowsheet (either manually or interfaced from the hospital elect... | <ASSISTANT_TASK:>
Python Code:
# Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import getpass
import pdvega
# for configuring connection
from configobj import ConfigObj
import os
%matplotlib inline
# Create a database connection using settings from config file
... |
963 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
FFT Amplitude Scaling
The relative amplitude of a sideband follows the relative amplitude of the peak in the FFT.
Step1: Note, this also holds for int... | <ASSISTANT_TASK:>
Python Code:
from numpy.fft import *
import numpy
t = numpy.arange(0,100)
data = 5*numpy.sin(t) + 3*numpy.sin(0.5*t)
%pylab inline
plot(data)
fft_out = abs(fft.fft(data))
fft_out.max()
plot(fft_out)
from scipy.signal import find_peaks_cwt
peak_ind = find_peaks_cwt(fft_out, numpy.arange(1,3))
fft_out[p... |
964 | <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: Optimisation
Step2: We can still easily generate some data
Step3: And we can define a log likelihood, and use optimisation to try and find bac... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pints
import pints.plot
class BadLogisticModel(pints.ForwardModel):
Logistic model of population growth with unidentifiable parameters.
def __init__(self):
super(BadLogisticModel, self).__init__()
... |
965 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Performing Scenario Discovery in Python
The purpose of example is to demonstrate how one can do scenario discovery in python. I will demonstrate how we... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
data = pd.read_csv("./data/bryant et al 2010 data.csv", index_col=False)
x = data.iloc[:, 2:11]
y = data.iloc[:, 15].values
Explanation: Performing Scenario Discovery in Python
The purpose of example is to demonstrate how one can do scenario discovery in python. I will... |
966 | <SYSTEM_TASK:>
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
<END_TASK>
<USER_TASK:>
Problem:
Using scipy, is there an easy way to emulate the behaviour of MATLAB's dctmtx function which returns a NxN (ortho-mode normed) DCT matrix for ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.fft as sf
N = 8
result = sf.dct(np.eye(N), axis=0, norm= 'ortho')
<END_TASK> |
967 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Machine Learning with Python and Scikit Learn
Follow presentation on
Step1: <br></br>
<br></br>
<br></br>
<br></br>
<br></br>
<br></br>
<br></br>
<br>... | <ASSISTANT_TASK:>
Python Code:
import warnings
import numpy as np
import pandas as pd
from time import time
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.lin... |
968 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Basic Python Explorations
This is our first IPython nootebook, which we are going to use to explore the very basics of Python programming,
Step1: As s... | <ASSISTANT_TASK:>
Python Code:
print('hello world')
Explanation: Basic Python Explorations
This is our first IPython nootebook, which we are going to use to explore the very basics of Python programming,
End of explanation
# This is an online comment: Python3
print('hello world')
# Python2:
print 'hello world'
Explanat... |
969 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Google PageRank
Google's dominance as a search engine came from their PageRank algorithm, named after co-founder Larry Page. By assigning each page a ... | <ASSISTANT_TASK:>
Python Code:
A = np.array([[0, 2, 0, 5],
[1, 0, 5, 6],
[2, 4, 0, 3],
[1, 0, 10, 2]])
labels = ['Google', 'Twitter', 'Facebook', 'Reddit']
graph.draw_matrix(A, labels)
Explanation: Google PageRank
Google's dominance as a search engine came from their... |
970 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Quickstart
This notebook was made with the following version of emcee
Step1: The easiest way to get started with using emcee is to use it for a projec... | <ASSISTANT_TASK:>
Python Code:
import emcee
emcee.__version__
Explanation: Quickstart
This notebook was made with the following version of emcee:
End of explanation
import numpy as np
Explanation: The easiest way to get started with using emcee is to use it for a project. To get you started, here’s an annotated, fully-... |
971 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Additional forces
REBOUND is a gravitational N-body integrator. But you can also use it to integrate systems with additional, non-gravitational forces.... | <ASSISTANT_TASK:>
Python Code:
import rebound
sim = rebound.Simulation()
sim.integrator = "whfast"
sim.add(m=1.)
sim.add(m=1e-6,a=1.)
sim.move_to_com() # Moves to the center of momentum frame
Explanation: Additional forces
REBOUND is a gravitational N-body integrator. But you can also use it to integrate systems with ... |
972 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Lecture 13
Step1: With NumPy arrays, all the same functionality you know and love from lists is still there.
Step2: These operations all work whether... | <ASSISTANT_TASK:>
Python Code:
li = ["this", "is", "a", "list"]
print(li)
print(li[1:3]) # Print element 1 (inclusive) to 3 (exclusive)
print(li[2:]) # Print element 2 and everything after that
print(li[:-1]) # Print everything BEFORE element -1 (the last one)
Explanation: Lecture 13: Array Indexing, Slicing, and B... |
973 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Dates pivot table
Step1: Get collection information from ArticleMeta
Step2: Filtering valid collections and renames 'code' to 'collection'
Some colle... | <ASSISTANT_TASK:>
Python Code:
from datetime import datetime
start = datetime.utcnow() # For measuring the total processing time
import json
from urllib.request import urlopen
import pandas as pd
import numpy as np
Explanation: Dates pivot table
End of explanation
AMC_URL = "http://articlemeta.scielo.org/api/v1/collect... |
974 | <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: Fully-Connected Neural Nets
In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation ... | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_... |
975 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
<center><h2>Scale your pandas workflows by changing one line of code</h2>
Getting Started
To install the most recent stable release for Modin run the f... | <ASSISTANT_TASK:>
Python Code:
!pip install modin[all]
Explanation: <center><h2>Scale your pandas workflows by changing one line of code</h2>
Getting Started
To install the most recent stable release for Modin run the following code on your command line:
End of explanation
import modin.pandas as pd
import pandas
#####... |
976 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Spectral Line Data Cubes in Astronomy - Part 1
In this notebook we will introduce spectral line data cubes in astronomy. They are a convenient way to s... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: Spectral Line Data Cubes in Astronomy - Part 1
In this notebook we will introduce spectral line data cubes in astronomy. They are a convenient way to store many spectra at points in the sky. Much like having a spectrum at eve... |
977 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Considering our data
Our initial goal was to apply a ML approach to accurately predict the likelihood of a wildfire occuring. The data we used was firs... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.io.parsers.read_csv(
'Data/NewBalanced.csv',
)
print(df.shape)
print('\n')
print(df.head(5))
print('\n')
print(df.tail(1))
Explanation: Considering our data
Our initial goal was to apply a ML approach to accurately predict the likelihood of a wildfire occur... |
978 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
IS620 - Document Classification
Daina Bouquin
Spam Filtering and Classification
Step1: Summaries
Step2: Split data into two datasets
Step3: Random f... | <ASSISTANT_TASK:>
Python Code:
import nltk
import numpy as np
import pandas as pd
%matplotlib inline
# pull in the spam dataset
spam = pd.read_csv("spambase.csv")
Explanation: IS620 - Document Classification
Daina Bouquin
Spam Filtering and Classification
End of explanation
# Summary stats
spam.describe()
# Variable ty... |
979 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described.
<END_TASK>
<USER_TASK:>
Description:
You are given a 2 dimensional data, as a nested lists,
which is similar to matrix, however, unlike matrices,
each row may contain a different number of column... | <ASSISTANT_TASK:>
Python Code:
def get_row(lst, x):
coords = [(i, j) for i in range(len(lst)) for j in range(len(lst[i])) if lst[i][j] == x]
return sorted(sorted(coords, key=lambda x: x[1], reverse=True), key=lambda x: x[0])
<END_TASK> |
980 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Tensor Network Random Unitary Evolution
This example demonstrates some features of TensorNetwork manipulation as well as the use of MatrixProductState.... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from quimb.tensor import *
from quimb import *
import numpy as np
Explanation: Tensor Network Random Unitary Evolution
This example demonstrates some features of TensorNetwork manipulation as well as the use of MatrixProductState.gate, based on 'evolving' an intitial MP... |
981 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Outline
Glossary
2. Mathematical Groundwork
Previous
Step1: Import section specific modules
Step3: 2.8. The Discrete Fourier Transform (DFT) and the ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
Explanation: Outline
Glossary
2. Mathematical Groundwork
Previous: 2.7 Fourier Theorems
Next: 2.9 Sampling Theory
Import standard modules:... |
982 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
A brief tutorial of basic python
From the wikipedia
Step1: It is easy to check the type of a variable with the type() command
Step2: The following co... | <ASSISTANT_TASK:>
Python Code:
str1 = '"Hola" is how we say "hello" in Spanish.'
str2 = "Strings can also be defined with quotes; try to be sistematic."
Explanation: A brief tutorial of basic python
From the wikipedia: "Python is a widely used general-purpose, high-level programming language. Its design philosophy emph... |
983 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Now a distance cut
Step1: Stars I actually observed
Step2: Data for the observed stars
Step3: Comparison of stars observed with Catalina
Step4: Iss... | <ASSISTANT_TASK:>
Python Code:
d = triand['dh'].data
d_cut = (d > 15) & (d < 21)
triand_dist = triand[d_cut]
c_triand = _c_triand[d_cut]
print(len(triand_dist))
plt.hist(triand_dist['<Vmag>'].data)
Explanation: Now a distance cut:
End of explanation
ptf_triand = ascii.read("/Users/adrian/projects/streams/data/observing... |
984 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Matplotlib Exercise 1
Imports
Step1: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from the... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Matplotlib Exercise 1
Imports
End of explanation
import os
assert os.path.isfile('yearssn.dat')
Explanation: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 ... |
985 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Grouping all encounter nbrs under respective person nbr
Step1: Now grouping other measurements and properties under encounter_nbrs
Step2: Aggregating... | <ASSISTANT_TASK:>
Python Code:
encounter_key = 'Enc_Nbr'
person_key = 'Person_Nbr'
encounters_by_person = {}
for df in dfs:
if df is not None:
df_columns =set(df.columns.values)
if encounter_key in df_columns and person_key in df_columns:
for row_index, dfrow in df.iterrows():
... |
986 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
while loops
The while statement in Python is one of most general ways to perform iteration. A while statement will repeatedly execute a single statemen... | <ASSISTANT_TASK:>
Python Code:
x = 0
while x < 10:
print 'x is currently: ',x
print ' x is still less than 10, adding 1 to x'
x+=1
Explanation: while loops
The while statement in Python is one of most general ways to perform iteration. A while statement will repeatedly execute a single statement or group of... |
987 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
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 mo... | <ASSISTANT_TASK:>
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, plo... |
988 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Cálculos en vecindades I
En este taller vamos a utilizar Python para calcular un par de variables, asociadas a cada polígono del espacio, en función de... | <ASSISTANT_TASK:>
Python Code:
import geopandas as gpd
denue = gpd.read_file("datos/DENUE_INEGI_09_.shp")
denue.head()
Explanation: Cálculos en vecindades I
En este taller vamos a utilizar Python para calcular un par de variables, asociadas a cada polígono del espacio, en función de las propiedades de los polígonos vec... |
989 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Motion Functions
In this notebook you will learn how use Python functions for moving the robot in your programs.
First, the initialization step needs t... | <ASSISTANT_TASK:>
Python Code:
# click on this cell and press Shift+Enter
import packages.initialization
import pioneer3dx as p3dx
p3dx.init()
Explanation: Motion Functions
In this notebook you will learn how use Python functions for moving the robot in your programs.
First, the initialization step needs to be executed... |
990 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Active Directory Replication From Non-Domain-Controller Accounts
Metadata
| | |
|
Step1: Download & Process Mordor Dataset
Step2:... | <ASSISTANT_TASK:>
Python Code:
from openhunt.mordorutils import *
spark = get_spark()
Explanation: Active Directory Replication From Non-Domain-Controller Accounts
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2018/08/15 |
|... |
991 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided som... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've ... |
992 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
With the variables we found so far here, we achieved a maximum performance of 75% (ROC AUC), so let's try to extract some more features in order to inc... | <ASSISTANT_TASK:>
Python Code:
#I'm considering only Acquisitions made in USA, with USD (dollars)
acquisitions = pd.read_csv('data/acquisitions.csv')
acquisitions = acquisitions[acquisitions['acquirer_country_code'] == 'USA']
acquisitions[:3]
#acquirer_permalink
#rounds_agg = df_rounds.groupby(['company_permalink', 'fu... |
993 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Fitting a linear model fp1
Step1: Linear Function
Step2: Fitting the model with polynomial degree of 2
Step3: Trying to fir the model with 53 polyn... | <ASSISTANT_TASK:>
Python Code:
# starting with linear model where degree is 1
# polyfit() - best put that line into the chart so that it results in the smallest
# approximation error
fp1, residuals, rank, sv, rcond = sp.polyfit(X, y, 1, full=True)
fp1
Explanation: Fitting a linear model fp1
End of explanation
print(res... |
994 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
BigQuery query magic
Jupyter magics are notebook-specific shortcuts that allow you to run commands with minimal syntax. Jupyter notebooks come with man... | <ASSISTANT_TASK:>
Python Code:
%%bigquery
SELECT name, SUM(number) as count
FROM `bigquery-public-data.usa_names.usa_1910_current`
GROUP BY name
ORDER BY count DESC
LIMIT 10
Explanation: BigQuery query magic
Jupyter magics are notebook-specific shortcuts that allow you to run commands with minimal syntax. Jupyter noteb... |
995 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Setup Cosmology
Step1: Create Stellar Population
Step2: Calculate a few things to get going.
Step5: Define the functions that we'll need
Need to com... | <ASSISTANT_TASK:>
Python Code:
cosmo = LambdaCDM(H0=70, Om0=0.3, Ode0=0.7, Tcmb0=2.725)
Explanation: Setup Cosmology
End of explanation
# check to make sure we have defined the bpz filter path
if not os.getenv('EZGAL_FILTERS'):
os.environ['EZGAL_FILTERS'] = (f'{os.environ["HOME"]}/Projects/planckClusters/MOSAICpipe... |
996 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Using Fourier Analysis to Analyze Quasi-Periodic Oscillations
By Abigail Stevens
Problem 1
Step1: 1a. Compute the time steps and a cosine harmonic wit... | <ASSISTANT_TASK:>
Python Code:
a = Table()
a.meta['dt'] = 0.0001 # time step, in seconds
a.meta['duration'] = 200 # length of time, in seconds
a.meta['omega'] = 2*np.pi # angular frequency, in radians
a.meta['phi'] = 0.0 # offset angle, in radians
Explanation: Using Fourier Analysis to Analyze Quasi-Periodic Oscilla... |
997 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Plotting a line chart in matplotlib
Step1: Plotting a line chart from a Pandas object
Step2: Creating bar charts
Step3: Creating a pie chart
Step4: ... | <ASSISTANT_TASK:>
Python Code:
x=range(1,10)
y=[1,2,3,4,0,4,3,2,1]
plt.plot(x,y)
Explanation: Plotting a line chart in matplotlib
End of explanation
# address = some data set
# cars = pd.read_csv(address)
# cars.columns = ['car_names','mpg','cyl','disp','hp','drat','wt','qsec','vs','am',gear',carb']
#mpg = cars['mpg']... |
998 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
How to download CMEMS SLTAC products from the CMEMS ftp server ?
some python imports
Step1: init the connection to the ftp server
Step2: What is in t... | <ASSISTANT_TASK:>
Python Code:
from ftplib import FTP
import os
import numpy as np
Explanation: How to download CMEMS SLTAC products from the CMEMS ftp server ?
some python imports
End of explanation
ftp = FTP('ftp.sltac.cls.fr')
ftp.login('pprandi','PierreCMEMS2017')
Explanation: init the connection to the ftp server... |
999 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Table of Contents
Preparation
User data vectors
Graphs
Preparation
<a id=preparation />
Step1: Data vectors of users
<a id=userdatavectors />
Step2: ... | <ASSISTANT_TASK:>
Python Code:
%run "../Functions/4. User comparison.ipynb"
Explanation: Table of Contents
Preparation
User data vectors
Graphs
Preparation
<a id=preparation />
End of explanation
# small sample
#allData = getAllUserVectorData( getAllUsers()[:10] )
# complete set
#allData = getAllUserVectorData( getAllU... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.