Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
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15,200 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-2', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, ... |
15,201 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Layers of the Earth
Students will analyze the following information and answer questions a long the way in order to learn the different layers of the earth.
Through BBC's website (provided b... | Python Code:
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
# Read in data that will be used for answering some of the questions below
data = pd.read_csv("./significant_month.csv")
# Observe the first 5 rows of the data provided below
data.head(4)
Expl... |
15,202 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework 2
Step1: If you get an error stating that database "homework2" does not exist, make sure that you followed the instructions above exactly. If necessary, drop the database you creat... | Python Code:
import pg8000
conn = pg8000.connect(database="homework2")
Explanation: Homework 2: Working with SQL (Data and Databases 2016)
This homework assignment takes the form of an IPython Notebook. There are a number of exercises below, with notebook cells that need to be completed in order to meet particular crit... |
15,203 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
基于词向量的英汉翻译——“火炬上的深度学习"下第一次作业
在这个作业中，你需要半独立地完成一个英文到中文的单词翻译器
本文件是集智AI学园http
Step1: 第一步：加载词向量
首先，让我们加载别人已经在大型语料库上训练好的词向量
Step2: 第二步：可视化同一组意思词在两种不同语言的词向量中的相互位置关系
Step3: 结论：可以看出，中文的一、二、等数字彼此之间... | Python Code:
# 加载必要的程序包
# PyTorch的程序包
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# 数值运算和绘图的程序包
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# 加载机器学习的软件包，主要为了词向量的二维可视化
from sklearn.decomposition import PCA
#加载... |
15,204 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Notebook 3
Step2: Download the sequence data
Sequence data for this study are archived on the NCBI sequence read archive (SRA). Below I read in SraRunTable.txt for this project which contai... | Python Code:
### Notebook 3
### Data set 3 (American oaks)
### Authors: Eaton et al. (2015)
### Data Location: NCBI SRA SRP055977
Explanation: Notebook 3:
This is an IPython notebook. Most of the code is composed of bash scripts, indicated by %%bash at the top of the cell, otherwise it is IPython code. This notebook in... |
15,205 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pipeline
Cleaning confounds
We first created the confound matrix according to Smith et al. (2015). The confound variables are motion (Jenkinson), sex, and age. We also created squared confou... | Python Code:
import copy
import os, sys
import numpy as np
import pandas as pd
import joblib
os.chdir('../')
# loa my modules
from src.utils import load_pkl
from src.file_io import save_output
from src.models import nested_kfold_cv_scca, clean_confound, permutate_scca
from src.visualise import set_text_size, show_resul... |
15,206 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Lecture 3
Step2: <p class='alert alert-success'>
Solve the questions in green blocks. Save the file as ME249-Lecture-3-YOURNAME.ipynb and change YOURNAME in the bottom cell. Send me ... | Python Code:
%matplotlib inline
# plots graphs within the notebook
%config InlineBackend.figure_format='svg' # not sure what this does, may be default images to svg format
from IPython.display import Image
from IPython.core.display import HTML
def header(text):
raw_html = '<h4>' + str(text) + '</h4>'
return ra... |
15,207 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: Load the data
As a first step we will load a large dataset using dask. If you have followed the setup instructions you will have downloaded a large CSV containing 12 mi... | Python Code:
import pandas as pd
import holoviews as hv
import dask.dataframe as dd
import datashader as ds
import geoviews as gv
from holoviews.operation.datashader import datashade, aggregate
hv.extension('bokeh')
Explanation: <a href='http://www.holoviews.org'><img src="assets/hv+bk.png" alt="HV+BK logos" width="40%... |
15,208 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 12
Step1: That's everything we need for a working function! Let's walk through it.
Step2: def keyword
Step3: Other notes on functions
You can define functions (as we did just befo... | Python Code:
def our_function():
pass
Explanation: Lecture 12: Functions
CBIO (CSCI) 4835/6835: Introduction to Computational Biology
Overview and Objectives
In this lecture, we'll introduce the concept of functions, critical abstractions in nearly every modern programming language. Functions are important for abst... |
15,209 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Prepare Problem
a) Load libraries
Step1: b) Load dataset
Download customer account data from Wiley's website, RetailMart.xlsx
Step2: The 'Pregnant' column can only take on one of two (i... | Python Code:
import pandas as pd
import numpy as np
from pandas.tools.plotting import scatter_matrix
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classif... |
15,210 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Same tweet belongs to multiple datasets
Step1: Merge URL types
Step2: Add location states
df_merged_meta_cats.u_location.value_counts().to_csv("USER_LOCATIONS.txt", sep="\t", encoding='utf... | Python Code:
df_merged_meta.t_id.value_counts().head()
df_merged_meta[df_merged_meta.t_id == 700042121877835776][["topic_name"]]
df_merged_meta.t_id.value_counts()[df_merged_meta.t_id.value_counts() > 1]
df_merged_meta[df_merged_meta.t_id == 792354716521009152].T
df_merged_meta["is_controversial"] = df_merged_meta.topi... |
15,211 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
History of Machine Learning
The field of machine learning has its roots in Artificial intelligence (AI), which started in 1950 with the seminal paper Computing Machinery and Int... | Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('sXx-PpEBR7k')
Explanation: Introduction
History of Machine Learning
The field of machine learning has its roots in Artificial intelligence (AI), which started in 1950 with the seminal paper Computing Machinery and Intelligence. In this paper, Alan Turi... |
15,212 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load TensorFlow
Go to Edit->Notebook settings to confirm you have a GPU accelerated kernel.
Step1: Set up FFN code and sample data
Colab already provides most of the dependencies.
Step2: R... | Python Code:
%tensorflow_version 1.x
import tensorflow as tf
print(tf.__version__)
# Silence deprecation warnings for now.
tf.logging.set_verbosity(tf.logging.ERROR)
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
print('GPU device not found')
gpu = False
else:
print('Found GPU at: {}'.... |
15,213 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Dropout
Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout la... | Python Code:
# As usual, a bit of setup
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_gradient_array
from cs231n.solver import Solver
%matplot... |
15,214 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 3 - Strings
This notebook uses code snippets and explanations from this course.
In this notebook, we will focus on the datatype strings. The first thing you learned was printing a si... | Python Code:
# Here are some strings:
string_1 = "Hello, world!"
string_2 = 'I ❤️ cheese' # If you are using Python 2, your computer will not like this.
string_3 = '1,2,3,4,5,6,7,8,9'
Explanation: Chapter 3 - Strings
This notebook uses code snippets and explanations from this course.
In this notebook, we will focu... |
15,215 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<table width="100%" border="0">
<tr>
<td><img src="./images/ing.png" alt="" align="left" /></td>
<td><img src="./images/ucv.png" alt="" align="center" height="100" width="100" /></... | Python Code:
import numpy as np
from numba import njit
arr2d = np.arange(20 * 30, dtype=float).reshape(20,30)
%%timeit
np.sum(arr2d)
def py_sum(arr):
M, N = arr.shape
sum = 0.0
for i in range(M):
for j in range(N):
sum += arr[i,j]
return sum
%%timeit
py_sum(arr2d)
fast_sum = njit(py_... |
15,216 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parse Json
Step1: 載入原始 RAW Data
Step2: 利用 LXML Parser 來分析文章結構
Step3: 取出 Image Src 的列表
Step4: 統計 Image Src 的列表
Step5: 請使用 reduceByKey , sortBy 來計算出 img src 排行榜
請參照以下文件
[http | Python Code:
def parseRaw(json_map):
url = json_map['url']
content = json_map['html']
return (url,content)
Explanation: Parse Json
End of explanation
import json
import pprint
pp = pprint.PrettyPrinter(indent=2)
path = "./pixnet.txt"
all_content = sc.textFile(path).map(json.loads).map(parseRaw)
Explanation:... |
15,217 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic Python Syntax
In this exercise, you will work through some simple blocks of code so you learn the essentials of the Python language syntax.
For each of the code blocks below, read the... | Python Code:
1 + 1
2 * 4
(2 * 4) - 2
4 ** 2 # Raise a number to a power
16 / 4
15 / 4
2.5 * 2.0
15.0 / 4
Explanation: Basic Python Syntax
In this exercise, you will work through some simple blocks of code so you learn the essentials of the Python language syntax.
For each of the code blocks below, read the code before... |
15,218 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Meta-Analysis in statsmodels
Statsmodels include basic methods for meta-analysis. This notebook illustrates the current usage.
Status
Step1: Example
Step2: estimate effect size standardize... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
from scipy import stats, optimize
from statsmodels.regression.linear_model import WLS
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.stats.meta_analysis import (
effectsize_smd, effectsize_2proportions, combine_effe... |
15,219 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Concrete Slump Test - UCI
Analysis of the <a href="https
Step1: Univariate Analysis
Step2: Correlation With the Target Columns
Step3: Correlation between Features
Step4: Bivariate Analys... | Python Code:
import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
import seaborn as sns
data = pd.read_csv('concrete_slump.csv')
data = data.drop('No', axis=1)
data.head()
Explanation: Concrete Slump Test - UCI
Analysis of the <a href="https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Tes... |
15,220 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
5. Additional Statistics Functions
Step1: Bootstrap Comparisons
Step2: TOST Equivalence Tests | Python Code:
# Import numpy and set random number generator
import numpy as np
np.random.seed(10)
# Import stats functions
from pymer4.stats import perm_test
# Generate two samples of data: X (M~2, SD~10, N=100) and Y (M~2.5, SD~1, N=100)
x = np.random.normal(loc=2, size=100)
y = np.random.normal(loc=2.5, size=100)
# B... |
15,221 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multivariable regression
Imports and setup
Step1: Load Data
We are going to use daily gas, electricity and water consumption data and weather data. Because we don't want to overload the wea... | Python Code:
import os
import pandas as pd
from opengrid.library import houseprint, caching, regression
from opengrid import config
c = config.Config()
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
plt.rcParams['figure.figsize'] = 16,8
# Create houseprint from saved file, if not available, ... |
15,222 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create Some Data
Step2: Create An Operation To Execute On The Data
Step3: Traditional Approach
Step4: Parallelism Approach | Python Code:
from multiprocessing import Pool
from multiprocessing.dummy import Pool as ThreadPool
Explanation: Title: Parallel Processing
Slug: parallel_processing
Summary: Lightweight Parallel Processing in Python.
Date: 2016-01-23 12:00
Category: Python
Tags: Basics
Authors: Chris Albon
This tutorial is inspi... |
15,223 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Extract curves into striplogs
Sometimes you'd like to summarize or otherwise extract curve data (e.g. wireline log data) into a striplog (e.g. one that represents formations).
We'll s... | Python Code:
data = Comp Formation,Depth
A,100
B,200
C,250
D,400
E,600
Explanation: Extract curves into striplogs
Sometimes you'd like to summarize or otherwise extract curve data (e.g. wireline log data) into a striplog (e.g. one that represents formations).
We'll start by making some fake CSV text — we'll make 5 form... |
15,224 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Facies classification using Machine Learning
LA Team Submission 5 ##
Lukas Mosser, Alfredo De la Fuente
In this approach for solving the facies classfication problem ( https
Step1: Data Pre... | Python Code:
%%sh
pip install pandas
pip install scikit-learn
pip install tpot
from __future__ import print_function
import numpy as np
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold , StratifiedKFold
f... |
15,225 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EDA
Step1: Counting Named Entities
Here is my count_entities function. The idea is to count the total mentions of a person or a place in an article's body or title and save them as columns ... | Python Code:
import articledata
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import operator
data = pd.read_pickle('/Users/teresaborcuch/capstone_project/notebooks/pickled_data.pkl')
Explanation: EDA: Named Entity Recognition
Named entity recognition is the process of identifing particular ... |
15,226 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modeling and Simulation in Python
Comparison of the penny models from Chapters 1, 20, and 21
Copyright 2018 Allen Downey
License
Step1: With air resistance
Next we'll add air resistance usi... | Python Code:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
Explanation: Modeling and Si... |
15,227 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
$
\newcommand\HH{\mathbf{H}}
\newcommand\SO{\mathbf{S}}
\newcommand\Scat{\boldsymbol\Gamma}
\newcommand\ket[1]{|#1\rangle}
\newcommand\bra[1]{\langle#1|}
\newcommand\set[1]{{#1}}
\newcommand... | Python Code:
C60 = sisl.Geometry.read('C60.xyz')
# Calculate the nearest neighbour distance
dist = C60.distance(R=5)
C60.atom.atom[0] = sisl.Atom(6, R=dist[0] + 0.01)
print(C60)
Explanation: $
\newcommand\HH{\mathbf{H}}
\newcommand\SO{\mathbf{S}}
\newcommand\Scat{\boldsymbol\Gamma}
\newcommand\ket[1]{|#1\rangle}
\newco... |
15,228 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ReadCSV
Read the csv file with acceloremeter information.
Convert the data in file to numpy array and then plot it.
Step1: Open the CSV file and transform to an array.
Also get number of sa... | Python Code:
import csv
import numpy as np
import matplotlib.pyplot as plt
Explanation: ReadCSV
Read the csv file with acceloremeter information.
Convert the data in file to numpy array and then plot it.
End of explanation
with open('../dataset/PhysicsToolboxSuite/walk_normal_hand_001.csv', 'rb') as csvfile:
data_r... |
15,229 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NumPy
NumPy is a Linear Algebra Library for Python.
NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed ... | Python Code:
import numpy as np
a = [1,2,3]
a
b = np.array(a)
b
np.arange(1, 10)
np.arange(1, 10, 2)
Explanation: NumPy
NumPy is a Linear Algebra Library for Python.
NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of p... |
15,230 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a id='Urbanization_Using_NDBI_top'></a>
Urbanization Using NDBI
<hr>
Background
Among the many urbanization indices, the Normalized Difference Built-Up Index (NDBI) is one of the most commo... | Python Code:
import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import matplotlib.pyplot as plt
import xarray as xr
from utils.data_cube_utilities.dc_display_map import display_map
from utils.data_cube_utilities.dc_rgb import rgb
from utils.data_cube_utilities.urbanization import NDBI
from utils.data... |
15,231 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 4 - Tensorflow ANN for regression
In this lab we will use Tensorflow to build an Artificial Neuron Network (ANN) for a regression task.
As opposed to the low-level implementation from th... | Python Code:
%matplotlib inline
import math
import random
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
import numpy as np
import tensorflow as tf
sns.set(style="ticks", color_codes=True)
Explanation: Lab 4 - Tensorflow ANN for regression
In this lab ... |
15,232 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
第8章 機械学習の適用1 - 感情分析
https
Step1: 8.2 BoWモデルの紹介
BoW(Bag-of-Words)
ドキュメントの集合全体から、たとえば単語という一意なトークン(token)からなる語彙(vocabulary)を作成する
各ドキュメントでの各単語の出現回数を含んだ特徴ベクトルを構築する
疎ベクトル(sparse vector)
8.2.1 単語を... | Python Code:
# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
# データを読み込む
import pyprind
import pandas as pd
import os
pbar = pyprind.ProgBar(50000)
labels = {'pos': 1, 'neg': 0}
df = pd.DataFrame()
for set ... |
15,233 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualizing epoched data
This tutorial shows how to plot epoched data as time series, how to plot the
spectral density of epoched data, how to plot epochs as an imagemap, and how to
plot the... | 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).crop(tmax=120)
Explanation: Visualiz... |
15,234 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook applies different regression methods to the finalized dataset in search of the best regression model.
Data Ingestion and Wrangling
Step1: Lasso Regression
Lasso regression is... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
dataset_1min = pd.read_csv('dataset-1min.csv')
print(dataset_1min.shape)
dataset_1min.head(3)
# Delete duplicate rows in the dataset
dataset_1min = dataset_1min.drop_duplicates()
print(dataset_1min.shape)
dataset_1min... |
15,235 | Given the following text description, write Python code to implement the functionality described below step by step
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 some of the code, but left the implementat... | 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 provided some of t... |
15,236 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualize Raw data
Step1: The visualization module (
Step2: The channels are color coded by channel type. Generally MEG channels are
colored in different shades of blue, whereas EEG channe... | Python Code:
import os.path as op
import numpy as np
import mne
data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample')
raw = mne.io.read_raw_fif(op.join(data_path, 'sample_audvis_raw.fif'),
preload=True)
raw.set_eeg_reference('average', projection=True) # set EEG average refere... |
15,237 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center><h2>Scale your pandas workflows by changing one line of code</h2>
Exercise 3
Step1: Concept for exercise
Step2: Speed improvements
If we were to try and replicate this functionalit... | Python Code:
import modin.pandas as pd
import pandas
import numpy as np
import time
frame_data = np.random.randint(0, 100, size=(2**18, 2**8))
df = pd.DataFrame(frame_data).add_prefix("col")
pandas_df = pandas.DataFrame(frame_data).add_prefix("col")
modin_start = time.time()
print(df.mask(df < 50))
modin_end = time.tim... |
15,238 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Calcul symbolique en Python
Step1: Introduction
Ce notebook est la traduction française du cours sur SymPy disponible entre autre sur Wakari avec quelques modifications et compléments notam... | Python Code:
%matplotlib inline
Explanation: Calcul symbolique en Python
End of explanation
from sympy import *
Explanation: Introduction
Ce notebook est la traduction française du cours sur SymPy disponible entre autre sur Wakari avec quelques modifications et compléments notamment pour la résolution d'équations diffé... |
15,239 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Recommendation Engine
In this tutorial we are going to build a simple recommender system using collaborative filtering. You'll be learning about the popular data analysis package pandas alon... | Python Code:
import numpy as np
import pandas as pd
import sklearn.metrics.pairwise
Explanation: Recommendation Engine
In this tutorial we are going to build a simple recommender system using collaborative filtering. You'll be learning about the popular data analysis package pandas along the way.
1. The import statemen... |
15,240 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
San Francisco Crime Modeling
Go here for the details on the Kaggle competition
Predictive Goal
Step1: Load the dataset from the prepared Parquet file
Step2: Step 1
Step3: Thus, our machin... | Python Code:
sc
sc.setLogLevel('INFO')
Explanation: San Francisco Crime Modeling
Go here for the details on the Kaggle competition
Predictive Goal: "Given time and location, you must predict the category of crime that occurred."
Data profiling contained in a separate notebook ("SanFranCrime.ipynb")
End of explanation
... |
15,241 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-1', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, T... |
15,242 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plotting is an essential skill for Engineers. Plots can reveal trends in data and outliers. Plots are a way to visually communicate results with your engineering team, supervisors and custom... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
# if using a jupyter notebook
%matplotlib inline
Explanation: Plotting is an essential skill for Engineers. Plots can reveal trends in data and outliers. Plots are a way to visually communicate results with your engineering team, supervisors and custom... |
15,243 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: word2vec
<img src="http
Step2: Check for understanding
<br>
<details><summary>
How many dimensions are data represented in?
</summary>
<br>
There are 2 dimensions.
</details>
<br>
... | Python Code:
corpus = The man and woman meet each other ...
They become king and queen ...
They got old and stop talking to each other. Instead, they read books and magazines ...
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# Let's hand assign the words to vectors
im... |
15,244 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Day 18 Pre-class assignment
Goals for today's pre-class assignment
In this pre-class assignment, you are going to learn how to
Step1: Question 1
Step2: Question 2 | Python Code:
from IPython.display import YouTubeVideo
# WATCH THE VIDEO IN FULL-SCREEN MODE
YouTubeVideo("JXJQYpgFAyc",width=640,height=360) # Numerical integration
Explanation: Day 18 Pre-class assignment
Goals for today's pre-class assignment
In this pre-class assignment, you are going to learn how to:
Numerically ... |
15,245 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: Using DTensors with Keras
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Next, import tens... | 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 writing, software
# dist... |
15,246 | Given the following text description, write Python code to implement the functionality described below step by step
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 used Python before, this will help fam... | 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 before, this will he... |
15,247 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Semidefinite Relaxation of AC Optimal Power Flow Problems
This notebook demonstrates the use of semidefinite relaxation techniques for AC optimal power flow (ACOPF) problems. A description o... | Python Code:
import json, re
import requests
testcases = {}
clist = []
# Retrieve list of MATPOWER test cases
response = requests.get('https://api.github.com/repos/MATPOWER/matpower/contents/data')
clist += json.loads(response.text)
# Retrieve list of pglib-opf test cases
response = requests.get('https://api.github.com... |
15,248 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Train-Test Splitting with Stratification using Scikit-Learn
| Python Code::
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
random_state=101,
... |
15,249 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
HAC variance estimation
Use two data files FwdSpot1.dat and FwdSpot3.dat. The former contains monthly spot and 1-month forward exchange rates, the latter monthly spot and 3-month forward exc... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
import datetime as dt
from numpy.linalg import inv, lstsq
from scipy.stats import chi2
# For inline pictures
%matplotlib inline
# For nicer output of Pandas dataframes
pd.set_option('float_format', '{:8.2f}'.format)... |
15,250 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'besm-2-7', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: INPE
Source ID: BESM-2-7
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, T... |
15,251 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Two-Level
Step2: We'll just check that the pulse area is what we want.
Step3: Solve the Problem
Step4: Plot Output
Step5: Analysis
The $2 \pi$ sech pulse passes through, slowed bu... | Python Code:
import numpy as np
SECH_FWHM_CONV = 1./2.6339157938
t_width = 1.0*SECH_FWHM_CONV # [τ]
print('t_width', t_width)
mb_solve_json =
{
"atom": {
"fields": [
{
"coupled_levels": [[0, 1]],
"rabi_freq_t_args": {
"n_pi": 2.0,
"centre": 0.0,
"width": %f
... |
15,252 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DEM analysis
Load site using shapely
Step1: Load digital elevation model
Step2: Reproject the site to coords of dem for sampling elevation
Step3: Get site elevation
Step4: Not integrated... | Python Code:
with open ('inputs/site.geojson') as f:
js = json.load(f)
s = shape(js['features'][0]['geometry'])
s
Explanation: DEM analysis
Load site using shapely
End of explanation
dem = gdal.Open('inputs/dem/filled.tif')
Explanation: Load digital elevation model
End of explanation
site = ogr.Geometry(ogr.wkbPoin... |
15,253 | 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', 'mpi-m', 'icon-esm-lr', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MPI-M
Source ID: ICON-ESM-LR
Sub-Topics: Radiative Forcings.
Proper... |
15,254 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <a id='top'></a>
Demonstration of the filters available in scipy.signal
This notebook is not intended to replace the SciPy reference guide but to serve only as a one stop shop for the... | Python Code:
# IPython magic commands
%matplotlib inline
# Python standard library
import sys
import os.path
# 3rd party modules
import numpy as np
import scipy as sp
import matplotlib as mpl
from scipy import signal
import matplotlib.pyplot as plt
print(sys.version)
for module in (np, sp, mpl):
print('{:.<15}{}'.f... |
15,255 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ApJdataFrames Hernandez2014
Title
Step1: Table 4 - Low Resolution Analysis | Python Code:
%pylab inline
import seaborn as sns
sns.set_context("notebook", font_scale=1.5)
import warnings
warnings.filterwarnings("ignore")
from astropy.io import ascii
Explanation: ApJdataFrames Hernandez2014
Title: A SPECTROSCOPIC CENSUS IN YOUNG STELLAR REGIONS: THE σ ORIONIS CLUSTER
Authors: Jesus Hernandez, Nur... |
15,256 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ввод/Вывод в ipython notebook
Пользовательский ввод осуществляется использованием конструкции input
Step1: Вывод одной переменной
Для вывода одной переменной на блок достаточно написать имя... | Python Code:
a = input("Enter new name:")
Explanation: Ввод/Вывод в ipython notebook
Пользовательский ввод осуществляется использованием конструкции input
End of explanation
a
Explanation: Вывод одной переменной
Для вывода одной переменной на блок достаточно написать имя этой переменной
End of explanation
b = input("En... |
15,257 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Summary
Notes taken to help for the first project for the Deep Learning Foundations Nanodegree course dellivered by Udacity.
My Github repo for this project can be found here
Step1: Change ... | Python Code:
%run ../../../code/version_check.py
Explanation: Summary
Notes taken to help for the first project for the Deep Learning Foundations Nanodegree course dellivered by Udacity.
My Github repo for this project can be found here: adriantorrie/udacity_dlfnd_project_1
Table of Contents
Neural network
Output Formu... |
15,258 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This kind of neural network is used in a ... | Python Code:
# Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-... |
15,259 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mapping utilities and options
This notebook illustrate how to map SuperDARN radars and FoVs
Step1: Plot all radars in AACGM coordinates
Be patient, this takes a few seconds (so many radars,... | Python Code:
%pylab inline
from davitpy.pydarn.radar import *
from davitpy.pydarn.plotting import *
from davitpy.utils import *
import datetime as dt
Explanation: Mapping utilities and options
This notebook illustrate how to map SuperDARN radars and FoVs
End of explanation
figure(figsize=(15,10))
# Plot map
subplot(121... |
15,260 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
통계적 사고 (2판) 연습문제 (thinkstats2.com, think-stat.xwmooc.org)<br>
Allen Downey / 이광춘(xwMOOC)
Step1: 연습문제 5.1
BRFSS 데이터셋에서 (5.4절 참조), 신장 분포는 대략 남성에 대해 모수 µ = 178 cm, σ = 7.7cm을 갖는 정규분포이며, 여성에 대해... | Python Code:
%matplotlib inline
%run chap06soln.py
Explanation: 통계적 사고 (2판) 연습문제 (thinkstats2.com, think-stat.xwmooc.org)<br>
Allen Downey / 이광춘(xwMOOC)
End of explanation
import scipy.stats
Explanation: 연습문제 5.1
BRFSS 데이터셋에서 (5.4절 참조), 신장 분포는 대략 남성에 대해 모수 µ = 178 cm, σ = 7.7cm을 갖는 정규분포이며, 여성에 대해서 µ = 163 cm, σ = 7.3 c... |
15,261 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CTA 1DC background energy distribution
This is my attempt to understand the background event energy distribution for CTA 1DC simulated data.
See https
Step1: Actual distribution of events
W... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
from astropy.table import Table, vstack
from gammapy.data import DataStore
# Parameters used throughout this notebook
CTADATA = '/Users/deil/work/cta-dc/data/1dc/1dc/'
irf_name = 'South_z20_50h'
n_obs =... |
15,262 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Doc2Vec Tutorial on the Lee Dataset
Step1: What is it?
Doc2Vec is an NLP tool for representing documents as a vector and is a generalizing of the Word2Vec method. This tutorial will serve a... | Python Code:
import gensim
import os
import collections
import random
Explanation: Doc2Vec Tutorial on the Lee Dataset
End of explanation
# Set file names for train and test data
test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])
lee_train_file = test_data_dir + os.sep + 'lee_background... |
15,263 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to the Mandelbrot Set
First, a boring example
A sequence of numbers can be defined by a map. For example, consider the simple map
$$f
Step1: Interesting! To explore this more, ... | Python Code:
def f(x, c):
return x**2 + c
x = 0.0
for i in range(10):
print(i, x)
x = f(x, 1)
x = 0.0
for i in range(10):
print(i, x)
x = f(x, -1)
x = 0.0
for i in range(10):
print(i, x)
x = f(x, 0.1)
Explanation: Introduction to the Mandelbrot Set
First, a boring example
A sequence of numbe... |
15,264 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
3D Plots, in Python
(Easy version, without the "np" and "plt" namespaces.)
We first load in the toolboxes for numerical python and plotting.
Note the "import * " command will bring in the f... | Python Code:
%matplotlib inline
from numpy import *
from matplotlib.pyplot import *
from mpl_toolkits.mplot3d import Axes3D
Explanation: 3D Plots, in Python
(Easy version, without the "np" and "plt" namespaces.)
We first load in the toolboxes for numerical python and plotting.
Note the "import * " command will bring i... |
15,265 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Dream
Deep Dream, or Inceptionism, was introduced by Google in this blogpost. Deep Dream is an algorithm that optimizes an input image so that it maximizes its activations in certain la... | Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
from keras.applications import vgg16
from keras.layers import Input
from dream import *
Explanation: Deep Dream
Deep Dream, or Inceptionism, was introduced by Google in this blogpost. Deep Dream is an algorithm that optimizes an input image so that it ma... |
15,266 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I want to use a logical index to slice a torch tensor. Which means, I want to select the columns that get a '1' in the logical index. | Problem:
import numpy as np
import pandas as pd
import torch
A_logical, B = load_data()
C = B[:, A_logical.bool()] |
15,267 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 parts of this notebook are from this Jupyter notebook by Heiner Igel (@heinerigel), Lion ... | Python Code:
# Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../style/custom.css'
HTML(open(css_file, "r").read())
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 parts of this note... |
15,268 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Custom Keras Layer
Idea
Step1: AntiRectifier Layer
Step2: Parametrs and Settings
Step3: Data Preparation
Step4: Model with Custom Layer
Step5: Excercise
Compare with an equivalent netwo... | Python Code:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Layer, Activation
from keras.datasets import mnist
from keras import backend as K
from keras.utils import np_utils
Explanation: Custom Keras Layer
Idea:
We build a custom activation layer called Antirectifier,
which modifies the s... |
15,269 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Minist 예제
Minist 예제를 살펴봅시다. 사실 minist 예제는 3장 다룬 기초적인 Neural Networw와 거의 동일 합니다.
단지, 입력 DataLoader를 사용하여 Minist dataset를 이용하는 부분만 차이가 나고, 데이터량이 많아서 시간이 좀 많이 걸리는 부분입니다.
입력 DataLoader를 이용하는... | Python Code:
%matplotlib inline
Explanation: Minist 예제
Minist 예제를 살펴봅시다. 사실 minist 예제는 3장 다룬 기초적인 Neural Networw와 거의 동일 합니다.
단지, 입력 DataLoader를 사용하여 Minist dataset를 이용하는 부분만 차이가 나고, 데이터량이 많아서 시간이 좀 많이 걸리는 부분입니다.
입력 DataLoader를 이용하는 것은 4장에서 잠시 다루었기 때문에, 시간을 줄이기 위해서 cuda gpu를 사용하는 부분을 추가했습니다.
입력변수와 network상의 변수의 tor... |
15,270 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Entrada de Dados
Step2: Executar comandos Linux
Step3: Instalar biblioteca
Step4: Saída de dados ricas
Step5: Utilizando a ajuda integrada
Use a tecla tab para exe... | Python Code:
print('Olá seja bem vindo!!')
Explanation: <a href="https://colab.research.google.com/github/cavalcantetreinamentos/curso_python/blob/master/Primeiros_passos_Google_Colab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Aprendendo Google ... |
15,271 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GRADEV
Step2: Gap robust allan deviation comparison
Compute the GRADEV of a white phase noise. Compares two different
scenarios. 1) The original data and 2) ADEV estimate with gap robust AD... | Python Code:
%matplotlib inline
import pylab as plt
import numpy as np
import allantools
Explanation: GRADEV: gap robust allan deviation
Notebook setup & package imports
End of explanation
def example1():
Compute the GRADEV of a white phase noise. Compares two different
scenarios. 1) The original data and... |
15,272 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
Step2: Interact with SVG display
SVG is a simple way of drawing vec... | 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, SVG
Explanation: Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.... |
15,273 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gaussian Process (GP) smoothing
This example deals with the case when we want to smooth the observed data points $(x_i, y_i)$ of some 1-dimensional function $y=f(x)$, by finding the new valu... | Python Code:
%pylab inline
figsize(12, 6);
import numpy as np
import scipy.stats as stats
x = np.linspace(0, 50, 100)
y = (np.exp(1.0 + np.power(x, 0.5) - np.exp(x/15.0)) +
np.random.normal(scale=1.0, size=x.shape))
plot(x, y);
xlabel("x");
ylabel("y");
title("Observed Data");
Explanation: Gaussian Process (GP) s... |
15,274 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Algorithms Exercise 2
Imports
Step2: Peak finding
Write a function find_peaks that finds and returns the indices of the local maxima in a sequence. Your function should
Step3: Here is a st... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
Explanation: Algorithms Exercise 2
Imports
End of explanation
def find_peaks(a):
Find the indices of the local maxima in a sequence.
#empty list and make the parameter into an array
empty = []
... |
15,275 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decision Tree Classification
Create entry points to spark
Step1: Decision tree classification with pyspark
Import data
Step2: Process categorical columns
The following code does three thin... | Python Code:
from pyspark import SparkContext
sc = SparkContext(master = 'local')
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Python Spark SQL basic example") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
Explanation: Decision Tree C... |
15,276 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 Google LLC.
Step1: Visualizing objective functions by interpolating in randomly drawn directions
Motivation
Useful visualizations of high dimensional objective functions are ... | Python Code:
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distribute... |
15,277 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercici de navegació
<span title="Roomba navigating around furniture"><img src="img/roomba.jpg" align="right" width=200></span>
Un robot mòbil com el Roomba de la imatge ha d'evitar xocar a... | Python Code:
from functions import connect, touch, forward, backward, left, right, stop, disconnect, next_notebook
from time import sleep
connect()
Explanation: Exercici de navegació
<span title="Roomba navigating around furniture"><img src="img/roomba.jpg" align="right" width=200></span>
Un robot mòbil com el Roomba d... |
15,278 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fast Sign Adversary Generation Example
This notebook demos finds adversary examples using MXNet Gluon and taking advantage of the gradient information
[1] Goodfellow, Ian J., Jonathon Shlens... | Python Code:
%matplotlib inline
import mxnet as mx
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mxnet import gluon
Explanation: Fast Sign Adversary Generation Example
This notebook demos finds adversary examples using MXNet Gluon and taking advantage of the gradient information
[1]... |
15,279 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
기말고사 예비 문제집
Step1: 문제 1
아래 모양의 2차원 어레이를 작성하라.
단, 항목들을 일일이 입력하는 방식은 사용할 수 없다.
$$\left [
\begin{matrix}
1 & 6 & 11 \
2 & 7 & 12 \
3 & 8 & 13 \
4 & 9 & 14 \
5 & 10 & 15
\end{matrix}
... | Python Code:
import numpy as np
Explanation: 기말고사 예비 문제집
End of explanation
a = np.arange(1, 16).reshape(3, 5).T
a
np.arange(1, 6)[:, np.newaxis] + np.arange(0, 11, 5)
Explanation: 문제 1
아래 모양의 2차원 어레이를 작성하라.
단, 항목들을 일일이 입력하는 방식은 사용할 수 없다.
$$\left [
\begin{matrix}
1 & 6 & 11 \
2 & 7 & 12 \
3 & 8 & 13 \
4 & 9 & ... |
15,280 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
← Back to Index
Spectral Features in Essentia
For classification, we're going to be using new features in our arsenal
Step1: This value is normalized between 0 and 1. If 0, then the ce... | Python Code:
spectrum = ess.Spectrum()
centroid = ess.Centroid()
x = essentia.array(scipy.randn(1024))
X = spectrum(x)
spectral_centroid = centroid(X)
print spectral_centroid
Explanation: ← Back to Index
Spectral Features in Essentia
For classification, we're going to be using new features in our arsenal: spectral... |
15,281 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spatial Data
Overview of today's topics
Step1: 1. Loading a shapefile or GeoPackage
Step2: 2. Loading a CSV file
Often, you won't have a shapefile or GeoPackage (which is explicitly spatia... | Python Code:
import ast
import contextily as cx
import folium
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import rasterio
import rasterio.features
Explanation: Spatial Data
Overview of today's topics:
Working with shapefiles, GeoPackages, CSV files, and rasters
Project... |
15,282 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Some sources
Step1: Run model
Step2: Questions
Step3: Now let's look at the variations of the respective clusters, nmf topic and epithets
Question
Step4: Visualize topic clusters
Step5: ... | Python Code:
import datetime as dt
import os
import time
from cltk.corpus.greek.tlg.parse_tlg_indices import get_epithet_index
from cltk.corpus.greek.tlg.parse_tlg_indices import get_epithets
from cltk.corpus.greek.tlg.parse_tlg_indices import select_authors_by_epithet
from cltk.corpus.greek.tlg.parse_tlg_indices impor... |
15,283 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Step1: If you remember, at the beginning of this book,
we saw a quote from John Quackenbush that essentially said
that the reason a graph is interesting is because of its edges... | Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo(id="3DWSRCbPPJs", width="100%")
Explanation: Introduction
End of explanation
from nams import load_data as cf
G = cf.load_physicians_network()
Explanation: If you remember, at the beginning of this book,
we saw a quote from John Quackenbush that essenti... |
15,284 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Import facility data and NERC labels
Step1: Read NERC shapefile and merge with geo_df
Step2: Merge NERC labels into the facility df
Step3: Filter out data older than 2014 to reduce size
S... | Python Code:
path = os.path.join('Data storage', 'Facility gen fuels and CO2 2017-05-25.zip')
facility_df = pd.read_csv(path, parse_dates=['datetime'])
facility_df.head()
facility_df.dropna(inplace=True, subset=['lat', 'lon'])
cols = ['lat', 'lon', 'plant id', 'year']
small_facility = facility_df.loc[:, cols].drop_dupl... |
15,285 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How Bias Enters a Model
This notebook is a simple demonstration of how bias with respect an attribute can get encoded into a model, even if the labels are perfectly accurate and the model is... | Python Code:
%matplotlib inline
from IPython.display import display, Markdown, Latex
import numpy as np
import sklearn
import matplotlib
import matplotlib.pylab as plt
import sklearn.linear_model
import seaborn
import scipy.special
seaborn.set(rc={"figure.figsize": (8, 6)}, font_scale=1.5)
Explanation: How Bias Enters ... |
15,286 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute MNE inverse solution on evoked data with a mixed source space
Create a mixed source space and compute an MNE inverse solution on an
evoked dataset.
Step1: Set up our source space
Li... | Python Code:
# Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
from nilearn import plotting
import mne
from mne.minimum_norm import make_inverse_operator, apply_inverse
# Set dir
data_path = mne.datasets.sample.data_path()
subject = ... |
15,287 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content-based recommender using Deep Structured Semantic Model
An example of how to build a Deep Structured Semantic Model (DSSM) for incorporating complex content-based features into a reco... | Python Code:
import warnings
import mxnet as mx
from mxnet import gluon, nd, autograd, sym
import numpy as np
from sklearn.random_projection import johnson_lindenstrauss_min_dim
# Define some constants
max_user = int(1e5)
title_vocab_size = int(3e4)
query_vocab_size = int(3e4)
num_samples = int(1e4)
hidden_units = 128
... |
15,288 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Solución de ecuaciones diferenciales
Dada la siguiente ecuación diferencial
Step1: Ejercicio
Crea codigo para una iteración mas con estos mismos parametros y despliega el resultado.
Step2: ... | Python Code:
x0 = 1
Δt = 1
# Para escribir simbolos griegos como Δ, tan solo tienes que escribir su nombre
# precedido de una diagonal (\Delta) y teclear tabulador una vez
F = lambda x : -x
x1 = x0 + F(x0)*Δt
x1
x2 = x1 + F(x1)*Δt
x2
Explanation: Solución de ecuaciones diferenciales
Dada la siguiente ecuación diferenc... |
15,289 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Review from the previous lecture
In yesterday's Lecture 2, you learned how to use the numpy module, how to make your own functions, and how to import and export data. Below is a quick review... | Python Code:
import numpy as np
Explanation: Review from the previous lecture
In yesterday's Lecture 2, you learned how to use the numpy module, how to make your own functions, and how to import and export data. Below is a quick review before we move on to Lecture 3.
Remember, to use the numpy module, first it must be ... |
15,290 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework #1
This notebook contains the first homework for this class, and is due on Sunday, January 31st, 2016 at 11
Step1: Section 2 | Python Code:
# write any code you need here!
# Create additional cells if you need them by using the
# 'Insert' menu at the top of the browser window.
Explanation: Homework #1
This notebook contains the first homework for this class, and is due on Sunday, January 31st, 2016 at 11:59 p.m.. Please make sure to get st... |
15,291 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Clustering Astronomical Sources
The objective of this hands-on activity is to cluster a set of candidate sources from the Zwicky Transient Facility's (ZTF) image subtraction pipeline. All c... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import glob
import os
from time import time
from matplotlib.pyplot import imshow
from matplotlib.image import imread
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn import me... |
15,292 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Name Competition with Gradient Boosting
This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communications (MLOC).<br>
This code illustrates
*... | Python Code:
import numpy as np
import string
from sklearn.ensemble import GradientBoostingClassifier
Explanation: Name Competition with Gradient Boosting
This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communications (MLOC).<br>
This code illustrates
* The use of a g... |
15,293 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Executed
Step1: Load software and filenames definitions
Step2: Data folder
Step3: List of data files
Step4: Data load
Initial loading of the data
Step5: Laser alternation selection
At t... | Python Code:
ph_sel_name = "Dex"
data_id = "12d"
# ph_sel_name = "all-ph"
# data_id = "7d"
Explanation: Executed: Mon Mar 27 11:35:46 2017
Duration: 8 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
from f... |
15,294 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Say Hello
Recipe template for say hello.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance wit... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: Say Hello
Recipe template for say hello.
License
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... |
15,295 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deriving a Point-Spread Function in a Crowded Field
following Appendix III of Peter Stetson's User's Manual for DAOPHOT II
Using pydaophot form astwro python package
All italic text here hav... | Python Code:
from astwro.sampledata import fits_image
frame = fits_image()
Explanation: Deriving a Point-Spread Function in a Crowded Field
following Appendix III of Peter Stetson's User's Manual for DAOPHOT II
Using pydaophot form astwro python package
All italic text here have been taken from Stetson's manual.
The on... |
15,296 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualization of pre-generated images.
This is a notebook to load and display pre-generated images used in parameter exploration.
Step1: Now we need to build a function that takes distance,... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.image as mpimg
# Widgets library
from ipywidgets import interact
%matplotlib inline
# We need to load all the files here
# Load the file
folder = '../results/'
name = 'parameter_swep_SLM-0.00-0.00-10.00.png'
file_... |
15,297 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network written with ... | Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a n... |
15,298 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Run all the cells below to make sure everything is working and ready to go. All cells should run without error.
Test Matplotlib and Plotting
Step1: Test OpenCV
Step2: Test TensorFlow
Step3... | Python Code:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
%matplotlib inline
img = mpimg.imread('test.jpg')
plt.imshow(img)
Explanation: Run all the cells below to make sure everything is working and ready to go. All cells should run without error.
Test Matplotlib and Plotting
End... |
15,299 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Custom finite difference coefficients in Devito
Introduction
When taking the numerical derivative of a function in Devito, the default behaviour is for 'standard' finite difference weights (... | Python Code:
import numpy as np
import sympy as sp
from devito import Grid, TimeFunction
# Create our grid (computational domain)
Lx = 10
Ly = Lx
Nx = 11
Ny = Nx
dx = Lx/(Nx-1)
dy = dx
grid = Grid(shape=(Nx,Ny), extent=(Lx,Ly))
# Define u(x,y,t) on this grid
u = TimeFunction(name='u', grid=grid, time_order=2, space_ord... |
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