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8,100 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Built-in plotting methods for Raw objects
This tutorial shows how to plot continuous data as a time series, how to plot
the spectral density of continuous data, and how to plot the sensor lo... | 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)
raw.crop(tmax=60).load_data()
Explanation: Built-in... |
8,101 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 3 - Basic Artificial Neural Network
In this lab we will build a very rudimentary Artificial Neural Network (ANN) and use it to solve some basic classification problems. This example is i... | Python Code:
%matplotlib inline
import random
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set(style="ticks", color_codes=True)
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import shuffle
Explanation: Lab 3 - Basic Artificial Neural Network
In this lab we will buil... |
8,102 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Section 1 - About Functional Programming
What is Functional Programming
Functional programming is a programming paradigm that revolves around pure functions.
A pure function is a function w... | Python Code:
# not so functional function
a = 0
def global_sum(x):
global a
x += a
return x
print(global_sum(1))
print(a)
a = 11
print(global_sum(1))
print(a)
# not so functional function
a = 0
def global_sum(x):
global a
return x + a
print(global_sum(x=1))
print(a)
a = 11
print(global_sum(x=1))
pri... |
8,103 | 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: Check that the folder exists
Step4: List of data files in data_dir
Step5: Data load
Initial loading of ... | Python Code:
ph_sel_name = "all-ph"
data_id = "27d"
# ph_sel_name = "all-ph"
# data_id = "7d"
Explanation: Executed: Mon Mar 27 11:37:43 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
fro... |
8,104 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parameters
Step2: Imports
Step3: tf.data.Dataset
Step4: Let's have a look at the data
Step5: Keras model
If you are not sure what cross-entropy, dropout, softmax or batch-normalization m... | Python Code:
BATCH_SIZE = 64
EPOCHS = 10
training_images_file = 'gs://mnist-public/train-images-idx3-ubyte'
training_labels_file = 'gs://mnist-public/train-labels-idx1-ubyte'
validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'
validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'
Expla... |
8,105 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Test Script
Used by tests.
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.... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: Test Script
Used by tests.
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 License at
https://... |
8,106 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Clustering Methods Covered Here
K Means,
Hclus,
DBSCAN,
Gaussian Mixture Models,
Birch,
miniBatch Kmeans
Mean Shift
Silhouette Coefficient
If the ground truth labels are not known, evalua... | Python Code:
import warnings
warnings.filterwarnings("ignore")
from collections import Counter
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn import metrics
from sklearn.metrics import pairwise_distances
from sklearn.cluster... |
8,107 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: TensorFlow Probability Case Study
Step2: Step 1
Step3: Generate s... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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... |
8,108 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ke Contrast
The constrast is based on the calculation of the aggregated RGB histogram of the image.
Step1: Then the width of 98% mass is calculated
Step2: Ke brightness
The mean brightness... | Python Code:
channels = cv2.split(image)
colors = ('r', 'g', 'b')
histogram = [0.0]
for (channel, color) in zip(channels, colors):
histogram += cv2.calcHist([channel], [0], None, [256], [0, 256])
normalized_histogram = normalize(histogram, norm='l1', axis=0, copy=True, return_norm=False)
Explanation: Ke Contrast
Th... |
8,109 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
'lp' (Line Profile) Datasets and Options
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as ... | Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
Explanation: 'lp' (Line Profile) Datasets and Options
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
import phoebe
logger = phoebe.logger()
b ... |
8,110 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SYDE 556/750
Step1: So everything works fine if we drive each population with the same $x$, let's switch to $\hat{x}$ in the middle
Step2: Looks pretty much the same! (just delayed, maybe)... | Python Code:
%pylab inline
import numpy as np
import nengo
from nengo.dists import Uniform
from nengo.processes import WhiteSignal
from nengo.solvers import LstsqL2
T = 1.0
max_freq = 10
model = nengo.Network('Communication Channel', seed=3)
with model:
stim = nengo.Node(output=WhiteSignal(T, high=max_freq, rms=0.5... |
8,111 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Facies classification using machine learning techniques
Copy of <a href="https
Step1: Load data
Let us load training data and store features, labels and other data into numpy arrays.
Step2:... | Python Code:
# Import
from __future__ import division
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['figure.figsize'] = (20.0, 10.0)
inline_rc = dict(mpl.rcParams)
from classification_utilities import make_facies_log_plot
import pandas as pd
import numpy as np
#import seaborn ... |
8,112 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Train a Simple TensorFlow Lite for Microcontrollers model
This notebook demonstrates the process of training a 2.5 kB model using TensorFlow and converting it for use with TensorFlow Lite fo... | Python Code:
# Define paths to model files
import os
MODELS_DIR = 'models/'
if not os.path.exists(MODELS_DIR):
os.mkdir(MODELS_DIR)
MODEL_TF = MODELS_DIR + 'model'
MODEL_NO_QUANT_TFLITE = MODELS_DIR + 'model_no_quant.tflite'
MODEL_TFLITE = MODELS_DIR + 'model.tflite'
MODEL_TFLITE_MICRO = MODELS_DIR + 'model.cc'
Exp... |
8,113 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 9
Step1: Anytime you see a statement that starts with import, you'll recognize that the programmer is pulling in some sort of external functionality not previously available to Pyth... | Python Code:
import numpy
Explanation: Lecture 9: Vectorized Programming
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
We've covered loops and lists, and how to use them to perform some basic arithmetic calculations. In this lecture, we'll see how we can use an external library to make t... |
8,114 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Integrating arbitrary ODEs
Although REBOUND is primarily an N-body integrator, it can also integrate arbitrary ordinary differential equations (ODEs). Even better
Step1: We first set up our... | Python Code:
import rebound
import numpy as np
import matplotlib.pyplot as plt
Explanation: Integrating arbitrary ODEs
Although REBOUND is primarily an N-body integrator, it can also integrate arbitrary ordinary differential equations (ODEs). Even better: it can integrate arbitrary ODEs in parallel with an N-body simul... |
8,115 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Object Detection Demo
Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in a... | Python Code:
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
# This is needed since the notebook is stored in the object_... |
8,116 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align="center">Letter Recognition - UCI</h1>
Step1: Getting the Data
This is a dataset of 20 image features for uppercase English characters.
https
Step3: Next we attach the column nam... | Python Code:
import pandas as pd
import numpy as np
%pylab inline
pylab.style.use('ggplot')
Explanation: <h1 align="center">Letter Recognition - UCI</h1>
End of explanation
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/letter-recognition/letter-recognition.data'
letter_df = pd.read_csv(url, header=No... |
8,117 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Module
As your code grows more and more complex, it is useful to collect all code in a an external file. Here we store all the functions form our notebook in a single file grm.py. Nothing ne... | Python Code:
from IPython.core.display import HTML, display
display(HTML('material/images/grm.html'))
Explanation: Module
As your code grows more and more complex, it is useful to collect all code in a an external file. Here we store all the functions form our notebook in a single file grm.py. Nothing new happens, we ... |
8,118 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Make the ONCdb
Here are step-by-step instructions on how to generate the ONCdb from VizieR catalogs.
Step 1
Step1: Step 2
Step2: Step 3 | Python Code:
# Initialize a database
onc = astrocat.Catalog()
# Ingest a VizieR catalog by supplying a path, catalog name, and column name of a unique identifier
onc.ingest_data(DIR_PATH+'/raw_data/viz_acs.tsv', 'ACS', 'ONCacs', count=10)
# The raw dataset is stored as an attribute
print(onc.ACS)
# Add another one! (T... |
8,119 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Traverse a Square - Part 3 - Loops
Compared to the original programme, the programme that contains the variables is easier to modify. But it still contains a lot of repetition.
Let's remind ... | Python Code:
for count in range(0,3):
print(count)
print("And the final value of `count` is", count)
Explanation: Traverse a Square - Part 3 - Loops
Compared to the original programme, the programme that contains the variables is easier to modify. But it still contains a lot of repetition.
Let's remind ourselv... |
8,120 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
REINFORCE in TensorFlow
Just like we did before for q-learning, this time we'll design a neural network to learn CartPole-v0 via policy gradient (REINFORCE).
Step1: Building the policy netw... | Python Code:
# This code creates a virtual display to draw game images on.
# If you are running locally, just ignore it
import os
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY")) == 0:
!bash ../xvfb start
os.environ['DISPLAY'] = ':1'
import gym
import numpy as np, pandas as pd
im... |
8,121 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementing binary decision trees
The goal of this notebook is to implement your own binary decision tree classifier. You will
Step1: Load LendingClub Loans dataset
We will be using a data... | Python Code:
import json
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
Explanation: Implementing binary decision trees
The goal of this notebook is to implement your own binary decision tree classifier. ... |
8,122 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Behavior of the median filter with noised sine waves
DW 2015.11.12
Step1: 1. Create all needed arrays and data.
Step2: Figure 1. Behavior of the median filter with given window length and ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import medfilt
import sys
# Add a new path with needed .py files.
sys.path.insert(0, 'C:\Users\Dominik\Documents\GitRep\kt-2015-DSPHandsOn\MedianFilter\Python')
import functions
import gitInformation
%matplotlib inline
gitInformation.pri... |
8,123 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Memprediksi jenis kelamin dari nama bahasa Indonesia menggunakan Machine Learning
Loading dataset
Step1: Cleansing dataset
Step2: Split Dataset
Dataset yang adalah akan dipecah menjadi dua... | Python Code:
import pandas as pd # pandas is a dataframe library
df = pd.read_csv("./data/data-pemilih-kpu.csv", encoding = 'utf-8-sig')
#dimensi dataset terdiri dari 13137 baris dan 2 kolom
df.shape
#melihat 5 baris pertama dataset
df.head(5)
#melihat 5 baris terakhir dataset
df.tail(5)
Explanation: Me... |
8,124 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> 2d. Distributed training and monitoring </h1>
In this notebook, we refactor to call train_and_evaluate instead of hand-coding our ML pipeline. This allows us to carry out evaluation as ... | Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.5
from google.cloud import bigquery
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
Explanation: <h1> 2d. Distributed tra... |
8,125 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Formatando-arrays-para-impressão" data-toc-modified-id="Formatando-arrays-para-impressão-1"><span class="toc-item-num">1 ... | Python Code:
import numpy as np
A = np.exp(np.linspace(0.1,10,32)).reshape(4,8)/3000.
print('A: \n', A)
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Formatando-arrays-para-impressão" data-toc-modified-id="Formatando-arrays-para-impressão-1"><span class="toc-item-num">1 </span>Format... |
8,126 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: Migrate SessionRunHook to Keras callbacks
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step3: T... | 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... |
8,127 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Given a list of variant length features, for example: | Problem:
import pandas as pd
import numpy as np
import sklearn
features = load_data()
from sklearn.preprocessing import MultiLabelBinarizer
new_features = MultiLabelBinarizer().fit_transform(features)
rows, cols = new_features.shape
for i in range(rows):
for j in range(cols):
if new_features[i, j] == 1:
... |
8,128 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Table of Contents
<p><div class="lev1 toc-item"><a href="#Looping-the-Property-Extraction" data-toc-modified-id="Looping-the-Property-Extraction-1"><span class="toc-item-num">1 &... | Python Code:
# Import required packages
# File handling
import os
import glob
# Array handling
import numpy as np
# Image handling
from skimage.io import imread
# Image thresholding and measurement
from skimage.filters import threshold_otsu
from skimage.morphology import remove_small_objects
from skimage.measure impor... |
8,129 | 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', 'cas', 'fgoals-f3-l', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CAS
Source ID: FGOALS-F3-L
Sub-Topics: Radiative Forcings.
Properties... |
8,130 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
\title{Phase Lock Loop Components in myHDL}
\author{Steven K Armour}
\maketitle
This notebook is an exploration into the building and testing the Phase Lock Detector and the frequency divide... | Python Code:
from myhdl import *
from myhdlpeek import Peeker
#helper functions to read in the .v and .vhd generated files into python
def VerilogTextReader(loc, printresult=True):
with open(f'{loc}.v', 'r') as vText:
VerilogText=vText.read()
if printresult:
print(f'***Verilog modual from {loc}... |
8,131 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a name="top"></a>
DaViTpy - models
This notebook introduces useful space science models included in davitpy.
Currently we have ported/wrapped the following models to python
Step1: <a nam... | Python Code:
%pylab inline
from datetime import datetime as dt
from davitpy.models import *
from davitpy import utils
Explanation: <a name="top"></a>
DaViTpy - models
This notebook introduces useful space science models included in davitpy.
Currently we have ported/wrapped the following models to python:
<a href="#... |
8,132 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Method to try to match lines, mainly in absorption, with known lines at different velocities. The lineid must be identified first to discard wrong detections.
Step1: We define the source to... | Python Code:
import sys
sys.path.append("/home/stephane/git/alma-calibrator/src")
import lineTools as lt
import pickle
import matplotlib.pyplot as pl
al = lt.analysisLines("/home/stephane/Science/RadioGalaxy/ALMA/absorptions/analysis/a/lineAll.db")
%matplotlib inline
Explanation: Method to try to match lines, mainly in... |
8,133 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Chapter 20 - Tables and Networks
In the previous chapter we looked into various types of charts and correlations that are useful for scientific analysis in Python. Her... | Python Code:
%%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d .... |
8,134 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MNIST Convolutional Neural Network - Ensemble Learning
In this notebook we will verify if our single-column architecture can get any advantage from using ensemble learning, so a multi-column... | Python Code:
import os.path
from IPython.display import Image
from util import Util
u = Util()
import numpy as np
# Explicit random seed for reproducibility
np.random.seed(1337)
from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten... |
8,135 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic Feature Engineering in Keras
Learning Objectives
Create an input pipeline using tf.data
Engineer features to create categorical, crossed, and numerical feature columns
Introduction
In ... | Python Code:
# Run the chown command to change the ownership
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Install Sklearn
# scikit-learn simple and efficient tools for predictive data analysis
# Built on NumPy, SciPy, and matplotlib
!python3 -m pip install --user sklearn
# You can use any Python... |
8,136 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: Time windows
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Time Windows
First, we will tr... | 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... |
8,137 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Literate Computing for Reproducible Infrastructure
<img src="./images/literate_computing-logo.png" alt='LC_LOGO' align='left'/>
NII Cloud Operation is a team supporting researchers and teach... | Python Code:
! echo "This is 1st step" > foo; cat foo
! echo ".. 2nd step..." >> foo && cat foo
!echooooo ".. 3rd step... will fail" >> foo && cat foo
Explanation: Literate Computing for Reproducible Infrastructure
<img src="./images/literate_computing-logo.png" alt='LC_LOGO' align='left'/>
NII Cloud Operation is a tea... |
8,138 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lesson 6
v1.1, 2020.4 5, edit by David Yi
本次内容要点
随机数
思考：猜数游戏等
随机数
随机数这一概念在不同领域有着不同的含义，在密码学、通信领域有着非常重要的用途。
Python 的随机数模块是 random，random 模块主要有以下函数，结合例子来看看。
random.choice() 从序列中获取一个随机元素
random.... | Python Code:
import random
# random.choice(sequence)。参数sequence表示一个有序类型。
# random.choice 从序列中获取一个随机元素。
print(random.choice(range(1,100)))
# 从一个列表中产生随机元素
list1 = ['a', 'b', 'c']
print(random.choice(list1))
# random.sample()
# 创建指定范围内指定个数的整数随机数
print(random.sample(range(1,100), 10))
print(random.sample(range(1,10), 5))
#... |
8,139 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
Step1: Exploring the Fermi distribution
In quantum statistics, the ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
Explanation: Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
End of... |
8,140 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CS446/519 - Class Session 7 - Transitivity (Clustering Coefficients)
In this class session we are going to compute the local clustering coefficient of all vertices in the undirected human
pr... | Python Code:
from igraph import Graph
from igraph import summary
import pandas
import numpy
import timeit
from pympler import asizeof
import bintrees
Explanation: CS446/519 - Class Session 7 - Transitivity (Clustering Coefficients)
In this class session we are going to compute the local clustering coefficient of all ve... |
8,141 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Histogrammar advanced tutorial
Histogrammar is a Python package that allows you to make histograms from numpy arrays, and pandas and spark dataframes. (There is also a scala backend for Hist... | Python Code:
%%capture
# install histogrammar (if not installed yet)
import sys
!"{sys.executable}" -m pip install histogrammar
import histogrammar as hg
import pandas as pd
import numpy as np
import matplotlib
Explanation: Histogrammar advanced tutorial
Histogrammar is a Python package that allows you to make histogra... |
8,142 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 16 - The BART model of risk taking
16.1 The BART model
Balloon Analogue Risk Task (BART
Step1: 16.2 A hierarchical extension of the BART model
$$ \mu_{\gamma^{+}} \sim \text{Uniform... | Python Code:
p = .15 # (Belief of) bursting probability
ntrials = 90 # Number of trials for the BART
Data = pd.read_csv('data/GeorgeSober.txt', sep='\t')
# Data.head()
cash = np.asarray(Data['cash']!=0, dtype=int)
npumps = np.asarray(Data['pumps'], dtype=int)
options = cash + npumps
d = np.full([ntrials,30], np.nan)... |
8,143 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
$$
f\left( v \right) = k_1 \cdot v^2 + k_2 \cdot v + k_3
$$
Step1: $$
\dot{v} = f\left( v \right) - u + I
$$
Step2: $$
\dot{u} = a \cdot \left( b \cdot v - u \right)
$$
Step3: $$
v \appro... | Python Code:
def f(v):
return k[0] * (v**2) + k[1] * v + k[2]
Explanation: $$
f\left( v \right) = k_1 \cdot v^2 + k_2 \cdot v + k_3
$$
End of explanation
def Vt(v, u, I):
return f(v) - u + I
Explanation: $$
\dot{v} = f\left( v \right) - u + I
$$
End of explanation
def Ut(v, u):
return a * (b * v - u)
Explan... |
8,144 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Модель №2
Вход
Данные
Step1: Загрузка модели word2vec
Step2: Подготовка данных
Step3: Обучение модели
Step4: Результаты
Результаты на тестовой выборке (20% от исходных данных)
целевые пе... | Python Code:
reviews_test = pd.read_csv('data/reviews_test.csv', header=0, encoding='utf-8')
reviews_train = pd.read_csv('data/reviews_train.csv', header=0, encoding='utf-8')
X_train_raw = reviews_train.comment
y_train_raw = reviews_train.reting
X_test_raw = reviews_test.comment
y_test_raw = reviews_test.reting
Explana... |
8,145 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Constraint Satisfaction Problems (CSPs)
This IPy notebook acts as supporting material for topics covered in Chapter 6 Constraint Satisfaction Problems of the book Artificial Intelligence
Ste... | Python Code:
from csp import *
Explanation: Constraint Satisfaction Problems (CSPs)
This IPy notebook acts as supporting material for topics covered in Chapter 6 Constraint Satisfaction Problems of the book Artificial Intelligence: A Modern Approach. We make use of the implementations in csp.py module. Even though this... |
8,146 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step2: Then we need to create a function
Step3: In order to register a magic it has to have few properties
Step4: This does produce quite a lot of values, let's filter it o... | Python Code:
#@title Only execute if you are connecting to a hosted kernel
!pip install picatrix
from picatrix.lib import framework
from picatrix.lib import utils
# This should not be included in the magic definition file, only used
# in this notebook since we are comparing all magic registration.
from picatrix import ... |
8,147 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reusable workflows
Nipype doesn't just allow you to create your own workflows. It also already comes with predefined workflows, developed by the community, for the community. For a full list... | Python Code:
from nipype.workflows.fmri.fsl.preprocess import create_susan_smooth
smoothwf = create_susan_smooth()
Explanation: Reusable workflows
Nipype doesn't just allow you to create your own workflows. It also already comes with predefined workflows, developed by the community, for the community. For a full list o... |
8,148 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
신경망 기초 이론
신경망(neural network) 모형은 퍼셉트론, 서포트 벡터 머신, 로지스틱 회귀 등의 분류 모형과 달리 기저 함수(basis function)도 사용자 파라미터에 의해 변화할 수 있는 적응형 기저 함수 모형(adaptive basis function model)이며 구조적으로는 여러개의 퍼셉트론을 쌓아놓은 형태이므... | Python Code:
%%tikz
\tikzstyle{neuron}=[circle, draw, minimum size=23pt,inner sep=0pt]
\tikzstyle{bias}=[text centered]
\node[neuron] (node) at (2,0) {$z$};
\node[neuron] (x1) at (0, 1) {$x_1$};
\node[neuron] (x2) at (0, 0) {$x_2$};
\node[neuron] (x3) at (0,-1) {$x_3$};
\node[neuron] (b) at (0,-2) {$1$};
\node[neuron]... |
8,149 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
面向对象程序设计
Step1: 当访问mycircle.radius，实际上是通过字典 mycircle.dict 查看相应的值。但是，如果回到类这一层，属性也是存储在类的字典里面
Step2: 也就是说，在查找属性或者方法的时候，会递归的查找mro中的内容，直到找到一个匹配项。
在传统的Python编码中，我们可以用下面的方式破坏面向对象的封装性：
Step3: 可以看... | Python Code:
class Circle(object):
PI = 3.14 #类变量
def __init__(self,radius):
self.radius = radius #实例变量
def get_areas(self):
return PI * self.radius * self.radius
mycircle = Circle(2) #实例化
print(mycircle.radius) # 实例变量
print(mycircle.PI) #类变量
print(Circle.PI) #也可以使用类名直接调用类变量
Explanation: 面向对... |
8,150 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: TV Script Generation
In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Ne... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV script... |
8,151 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Time series in Pastas
R.A. Collenteur, University of Graz, 2020
Time series are at the heart of time series analysis, and therefore need to be considered carefully when dealing with time ser... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pastas as ps
ps.show_versions()
Explanation: Time series in Pastas
R.A. Collenteur, University of Graz, 2020
Time series are at the heart of time series analysis, and therefore need to be considered carefully when dealing with ti... |
8,152 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="http
Step1: OIS Data & Discounting
We start by importing OIS term structure data (source
Step2: Next we replace the year fraction index by a DatetimeIndex.
Step3: Let us have a ... | Python Code:
import dx
import datetime as dt
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
%matplotlib inline
Explanation: <img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="45%" align="right" border="4">
Interest Rate Swaps
V... |
8,153 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DKRZ PyNGL example
- Filled circles instead of grid cells; the size depends on a quality value.
Description
Step1: Global variables
Step2: Create dummy data and coordinates
Step3: Open gr... | Python Code:
from __future__ import print_function
import numpy as np
import Ngl,Nio
Explanation: DKRZ PyNGL example
- Filled circles instead of grid cells; the size depends on a quality value.
Description:
Draw two plots, first plot is a raster contour plot and the second shows
the data using filled circles which are ... |
8,154 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten d... | Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
Explanation: Generative Adversarial Network
In this notebook, we'll be building a gen... |
8,155 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simple Harmonic Oscillator model
This example shows how the Simple Harmonic Oscillator model can be used.
A model for a particle undergoing Newtonian dynamics that experiences a force in pro... | Python Code:
import pints
import pints.toy
import matplotlib.pyplot as plt
import numpy as np
model = pints.toy.SimpleHarmonicOscillatorModel()
Explanation: Simple Harmonic Oscillator model
This example shows how the Simple Harmonic Oscillator model can be used.
A model for a particle undergoing Newtonian dynamics that... |
8,156 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Descriptive statistics
Acknowledging that variables and models are uncertain assumes that we directly or indirectly can describe them through probability distributions.
However for most appl... | Python Code:
import chaospy
uniform = chaospy.Uniform(0, 4)
chaospy.E(uniform)
Explanation: Descriptive statistics
Acknowledging that variables and models are uncertain assumes that we directly or indirectly can describe them through probability distributions.
However for most applications the distribution is a messy e... |
8,157 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Understand TopicSimilarity.json
Step1: Now we see that the values of "source" and "target" in links indicate the indexes of nodes. I haven't figured out what does "value" in nodes represent... | Python Code:
# read a test version (w/o JS codes) of TopicSimilarity.json
file = 'testSim.json'
with open(file) as train_file:
dict_train = json.load(train_file)
dict_train
len(dict_train['links']), len(dict_train['nodes'])
links = pd.DataFrame(dict_train['links'])
nodes = pd.DataFrame(dict_train['nodes'])
links.he... |
8,158 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Solution
Step2: 2. Create yet another function that takes the name of the region as an input and returns SST values for the corresponding region
This function can look something like the on... | Python Code:
def calc_heat_flux(u_atm, t_sea, rho=1.2, c_p=1004.5, c_h=1.2e-3, u_sea=1, t_atm=17):
q = rho * c_p * c_h * (u_atm - u_sea) * (t_sea - t_atm)
return q
Explanation: Solution: create a module and reuse code from it (1 h)
Extend the exercise from today by applying what you've just learned about packag... |
8,159 | 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', 'nerc', 'ukesm1-0-ll', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NERC
Source ID: UKESM1-0-LL
Sub-Topics: Radiative Forcings.
Properti... |
8,160 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Class 02
Machine Learning Models
Step1: It is pretty clear that there is a linear trend here. If I wanted to predict what would happen if we tried the input of x=0.6, it would be a good gue... | Python Code:
import pandas as pd
fakedata1 = pd.DataFrame(
[[ 0.862, 2.264],
[ 0.694, 1.847],
[ 0.184, 0.705],
[ 0.41 , 1.246]], columns=['input','output'])
fakedata1.plot(x='input',y='output',kind='scatter')
Explanation: Class 02
Machine Learning Models: Linear regression & Validation
... |
8,161 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
$\LaTeX$ definition block
$\newcommand{\sign}{\operatorname{sign}}$
Distant Supervision
This notebook has a few cute experiments to explore distant supervision.
Setup
In the distant supervis... | Python Code:
# Constants
D = 2
N = 100
K = 2
w = np.random.randn(D)
w = normalize(w)
theta = np.arctan2(w[0], w[1])
X = np.random.randn(N,D,K)
y = np.zeros(N)
for i in range(N):
m = w.dot(X[i])
X[i] = X[i][:,np.argsort(-m)]
y[i] = np.sign(max(m))
# Visualize data
plt.plot(np.arange(-3,3), -w[0]/w[1] * np.ar... |
8,162 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center> Kafka Producer for Twitter </center>
Acquire and decompress Kafka
$ wget http
Step1: To delete a topic
Step2: Setup Confluent_Kafka
First, install your own Anaconda to a local dir... | Python Code:
!cd ~/software/kafka_2.11-1.0.0; \
./bin/kafka-topics.sh --zookeeper localhost:2181 --delete --topic test
!cd ~/software/kafka_2.11-1.0.0; \
./bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test
!cd ~/software/kafka_2.11-1.0.0; \
./bin/kafk... |
8,163 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First we need to 'binarize' the picture we want to embed in the markow field. This function does it
Step1: We load and prepare the picture
Step2: We now define the spin-spin correlation fu... | Python Code:
def prep_datas(pic, size):
X=resize(pic,(size,size)) # reduce the size of the image from 100X100 to 32X32. Also flattens the color levels
X=np.reshape(X,size**2) # reshape from 32x32 to a flat 1024 vector
X=np.array(X) # transforms it into an array
for j in range(len(X)): # let's ... |
8,164 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: MLMD Model Card Toolkit Demo
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Did you restar... | 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... |
8,165 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Coronagraph Basics
This set of exercises guides the user through a step-by-step process of simulating NIRCam coronagraphic observations of the HR 8799 exoplanetary system. The goal is to fam... | Python Code:
# Import the usual libraries
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# Enable inline plotting at lower left
%matplotlib inline
Explanation: Coronagraph Basics
This set of exercises guides the user through a step-by-step process of simulating NIRCam coronagraphic observations of... |
8,166 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Structures
This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.
More on Lists
list.append(x)
Add an item to the end of the l... | Python Code:
from __future__ import print_function
a = [66.25, 333, 333, 1, 1234.5]
print(a.count(333), a.count(66.25), a.count('x'))
a.insert(2, -1)
a.append(333)
a
a.index(333)
a.remove(333)
a
a.reverse()
a
a.sort()
a
a.pop()
a
Explanation: Data Structures
This chapter describes some things you’ve learned about alrea... |
8,167 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parsing a Protein Databank File
In this notebook we will do some simple parsings and analysis on a Protein Databank (PDB) file. You don't need to care about proteins to follow this example. ... | Python Code:
filein = open('../data/protein.pdb', 'r')
fileout = open('../data/protein_hie.pdb', 'w')
#Finish...
filein.close()
fileout.close()
Explanation: Parsing a Protein Databank File
In this notebook we will do some simple parsings and analysis on a Protein Databank (PDB) file. You don't need to care abo... |
8,168 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Problem 01 - solution
The following are my approach to solving these problems. Note that there may be more than one approach to each, and we could even debate the exact solutions.
Problem 1... | Python Code:
!wget https://raw.githubusercontent.com/gwsb-istm-6212-fall-2016/syllabus-and-schedule/master/projects/project-01/women.txt
!cat women.txt | grep -oE '\w{{2,}}' \
| grep -e "Jo\|Beth\|Meg\|Amy" \
| tr '[:upper:]' '[:lower:]' \
| sort | uniq -c | sort -rn
Explanation: Problem 01 - solution
The foll... |
8,169 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Facies classification using Convolutional Neural Networks
Team StoDIG - Statoil Deep-learning Interest Group
David Wade, John Thurmond & Eskil Kulseth Dahl
In this python notebook we propos... | Python Code:
%%sh
pip install pandas
pip install scikit-learn
pip install keras
pip install sklearn
from __future__ import print_function
import time
import numpy as np
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes... |
8,170 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
주 단위 데이터를 일 단위 데이터로 늘리기
네이버 검색 트렌드는 주 단위로 특정 검색어에 대한 검색량을 제공하고 있음
주별 검색량을 검색 기간 사이의 최소검색량, 최대검색량을 기준으로 0~100 사이의 수로 scaling하여 보여줌
이 주 단위의 데이터를 차후 데이터 분석 프로젝트(종속변수 예측)에서 입력변수로 사용할 때
단순히 한 주의... | Python Code:
##1년은 52주로 구성됨
week = list(range(1, 53)) #range 함수의 첫 번째 파라매터에는 시작할 숫자, 두 번째 파라매터에는 끝나는 숫자보다 1 큰 수를 넣어줌
week
len(week)
Explanation: 주 단위 데이터를 일 단위 데이터로 늘리기
네이버 검색 트렌드는 주 단위로 특정 검색어에 대한 검색량을 제공하고 있음
주별 검색량을 검색 기간 사이의 최소검색량, 최대검색량을 기준으로 0~100 사이의 수로 scaling하여 보여줌
이 주 단위의 데이터를 차후 데이터 분석 프로젝트(종속변수 예측)에서 입력변수로 ... |
8,171 | Given the following text description, write Python code to implement the functionality described.
Description:
Minimum cost to modify a string
Function to return the minimum cost ; Initialize result ; To store the frequency of characters of the string ; Update the frequencies of the characters of the string ; Loop to c... | Python Code:
def minCost(str1 , K ) :
n = len(str1 )
res = 999999999
count = 0
cnt =[0 for i in range(27 ) ]
for i in range(n ) :
cnt[ord(str1[i ] ) - ord(' a ' ) + 1 ] += 1
for i in range(1 , 26 - K + 1 , 1 ) :
a = i
b = i + K
count = 0
for j in range(1 , 27 , 1 ) :
if(cnt[j ] > 0 ) :
... |
8,172 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Endpoint layer pattern
Author
Step1: Usage of endpoint layers in the Functional API
An "endpoint layer" has access to the model's targets, and creates arbitrary losses and
metrics using add... | Python Code:
import tensorflow as tf
from tensorflow import keras
import numpy as np
Explanation: Endpoint layer pattern
Author: fchollet<br>
Date created: 2019/05/10<br>
Last modified: 2019/05/10<br>
Description: Demonstration of the "endpoint layer" pattern (layer that handles loss management).
Setup
End of explanati... |
8,173 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
All sky research
Solo ∼ 3 × 10 3 stelle di neutroni osservate su ∼ 10 9
stimate nella Via Lattea
⇒ ricerca “alla cieca” su tutto il cielo.
Problemi
* ampiezze molto piccole;
* modulazione Do... | Python Code:
binh_df0ORIG=zeros(nbin_d,nbin_f0); % HM matrix container
for it = 1:nTimeSteps
kf=(peaks(2,ii0:ii(it))-inifr)/ddf; % normalized frequencies
w=peaks(5,ii0:ii(it)); % wiener weights
t=peaks(1,ii0)*Day_inSeconds; % time conversion days to s
tddf=t/ddf;
f0_a=kf-deltaf2;
... |
8,174 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Estimize
Step1: Let's go over the columns
Step2: How many records do we have now?
Step3: Let's break it down by user
Step4: Let's convert it over to a Pandas DataFrame so we can chart it... | Python Code:
# import the free sample of the dataset
from quantopian.interactive.data.estimize import estimates_free
# or if you want to import the full dataset, use:
# from quantopian.interactive.data.estimize import estimates
# import data operations
from odo import odo
# import other libraries we will use
import pan... |
8,175 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Serving models using NVIDIA Triton Inference Server and Vertex AI Prediction
This notebook demonstrates how to serve NVIDIA Merlin HugeCTR deep learning models using NVIDIA Triton Inference ... | Python Code:
import json
import os
import shutil
import time
from pathlib import Path
from src.serving import export
from google.cloud import aiplatform as vertex_ai
Explanation: Serving models using NVIDIA Triton Inference Server and Vertex AI Prediction
This notebook demonstrates how to serve NVIDIA Merlin HugeCTR de... |
8,176 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Trips in time and space order
Sorts output trips in time and space order, which is useful for disaggregate (individual) dynamic traffic assignment and person time/space visualization. Trips... | Python Code:
pipeline_filename = '../test_example_mtc/output/pipeline.h5'
output_trip_filename = "../test_example_mtc/output/final_trips_time_space_order.csv"
Explanation: Trips in time and space order
Sorts output trips in time and space order, which is useful for disaggregate (individual) dynamic traffic assignment a... |
8,177 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Unsupervised Learning
Project 3
Step1: Data Exploration
In this section, you will begin exploring the data through visualizations and code to understand... | Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
import renders as rs
from IPython.display import display # Allows the use of display() for DataFrames
# Show matplotlib plots inline (nicely formatted in the notebook)
%matplotlib inline
# Load the wholesale customers data... |
8,178 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Apparent horizons
We're now going to use finite differences to find a black hole apparent horizon.
The spacetime we're going to look at is simplified
Step3: We now need to solve the ... | Python Code:
import numpy
from matplotlib import pyplot
%matplotlib notebook
def horizon_RHS(H, theta, z_singularities):
The RHS function for the apparent horizon problem.
Parameters
----------
H : array
vector [h, dh/dtheta]
theta : double
angle
z_singularities : ... |
8,179 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Session 4
Step2: <a name="part-1---pretrained-networks"></a>
Part 1 - Pretrained Networks
In the libs module, you'll see that I've included a few modules for loading some state of th... | Python Code:
# First check the Python version
import sys
if sys.version_info < (3,4):
print('You are running an older version of Python!\n\n',
'You should consider updating to Python 3.4.0 or',
'higher as the libraries built for this course',
'have only been tested in Python 3.4 and hi... |
8,180 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Artificial Intelligence Nanodegree
Convolutional Neural Networks
In your upcoming project, you will download pre-computed bottleneck features. In this notebook, we'll show you how to calcul... | Python Code:
from keras.applications.vgg16 import preprocess_input
from keras.preprocessing import image
import numpy as np
import glob
img_paths = glob.glob("images/*.jpg")
def path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
img = image.load_img(img_path, target_size=(224, 224))
# conve... |
8,181 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
With an equal number of origins and destinations (n=16)
Step1: # With non-equal number of origins (n=9) and destinations (m=25) | Python Code:
origins = ps.weights.lat2W(4,4)
dests = ps.weights.lat2W(4,4)
origins.n
dests.n
ODw = ODW(origins, dests)
print ODw.n, 16*16
ODw.full()[0].shape
Explanation: With an equal number of origins and destinations (n=16)
End of explanation
origins = ps.weights.lat2W(3,3)
dests = ps.weights.lat2W(5,5)
origins.n
de... |
8,182 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook we will add diffusion in addition to reactions. We will first study the simplest possible chemical reaction set
Step1: The diffusion follows Fick's law of diffusion
Step2: ... | Python Code:
reactions = [
('k', {'A': 1}, {'B': 1, 'A': -1}),
]
names, params = 'A B'.split(), ['k']
Explanation: In this notebook we will add diffusion in addition to reactions. We will first study the simplest possible chemical reaction set:
$$
A \overset{k}{\rightarrow} B
$$
we will consider a flat geometry whe... |
8,183 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
What is a dataset?
A dataset is a collection of information (or data) that can be used by a computer. A dataset typically has some number of examples, where each example has features associa... | Python Code:
# Print figures in the notebook
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets # Import datasets from scikit-learn
# Import patch for drawing rectangles in the legend
from matplotlib.patches import Rectangle
#... |
8,184 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
R-CNN is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper
... | Python Code:
!mkdir -p _temp
!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt
!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255... |
8,185 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Indutores
Jupyter Notebook desenvolvido por Gustavo S.S.
Um indutor consiste em uma bobina de fio condutor.
Qualquer condutor de corrente elétrica possui propriedades indutivas e
pode ser co... | Python Code:
print("Exemplo 6.8")
import numpy as np
from sympy import *
L = 0.1
t = symbols('t')
i = 10*t*exp(-5*t)
v = L*diff(i,t)
w = (L*i**2)/2
print("Tensão no indutor:",v,"V")
print("Energia:",w,"J")
Explanation: Indutores
Jupyter Notebook desenvolvido por Gustavo S.S.
Um indutor consiste em uma bobina de fio con... |
8,186 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Basic-Data-Structure" data-toc-modified-id="Basic-Data-Structure-1"><span cl... | Python Code:
from jupyterthemes import get_themes
from jupyterthemes.stylefx import set_nb_theme
themes = get_themes()
set_nb_theme(themes[1])
%load_ext watermark
%watermark -a 'Ethen' -d -t -v -p jupyterthemes
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><l... |
8,187 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Intro" data-toc-modified-id="Intro-1"><span class="toc-item-num">1 &nbs... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from PIL import Image, ImageDraw
import tqdm
from pathlib import Path
%matplotlib notebook
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href... |
8,188 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning
Assignment 1
The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.
This notebook ... | 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 matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy ... |
8,189 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting Started with Symbulate
Section 3. Multiple Random Variables and Joint Distributions
<Random variables | Contents | Conditioning>
Every time you start Symbulate, you must first run (S... | Python Code:
from symbulate import *
%matplotlib inline
Explanation: Getting Started with Symbulate
Section 3. Multiple Random Variables and Joint Distributions
<Random variables | Contents | Conditioning>
Every time you start Symbulate, you must first run (SHIFT-ENTER) the following commands.
End of explanation
def nu... |
8,190 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<span style="color
Step1: Set up and connect to the ipyparallel cluster
Depending on the number of tests, abba-baba analysis can be computationally intensive, so we will first set up a clus... | Python Code:
import ipyrad.analysis as ipa
import ipyparallel as ipp
import toytree
import toyplot
print(ipa.__version__)
print(toyplot.__version__)
print(toytree.__version__)
Explanation: <span style="color:gray">ipyrad-analysis toolkit:</span> abba-baba
The baba tool can be used to measure abba-baba statistics across... |
8,191 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spectral Energy Density Fitting and Dark Matter Limit Extraction
Motivation
Now we are going to discuss how we can use build a summary data product that can be used to quickly fit a wide var... | Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import LikeFitUtils as lfu
import SedUtils as SED
# lets open the file and have a look
import yaml
f_sed = yaml.load(open("results/draco_sed.yaml"))
len(f_sed)
Explanation: Spectral Energy Density Fitti... |
8,192 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Thesis 2019 symposium clinical workshop
by Cyrille BONAMY, Antoine MATHIEU and Julien CHAUCHAT (LEGI, University of Grenoble Alpes, GINP/CNRS, Grenoble, France)
Introduction
The aim of this ... | Python Code:
#
# Import section
#
import subprocess
import sys
import numpy as np
import fluidfoam
from pylab import figure, subplot, axis, xlabel, ylabel, show, savefig, plot
from pylab import title, matplotlib
import matplotlib.gridspec as gridspec
import matplotlib as mpl
Explanation: Thesis 2019 symposium clinical ... |
8,193 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sympy is a Python package used for solving equations using symbolic math.
Let's solve the following problem with SymPy.
Given
Step1: We need to define six different symbols
Step2: Next w... | Python Code:
from sympy import symbols, nonlinsolve
Explanation: Sympy is a Python package used for solving equations using symbolic math.
Let's solve the following problem with SymPy.
Given:
The density of two different polymer samples $\rho_1$ and $\rho_2$ are measured.
$$ \rho_1 = 1.408 \ g/cm^3 $$
$$ \rho_2 = 1.... |
8,194 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simple keyword spotting with CMSIS-DSP Python wrapper and Arduino
The goal of this notebook is to demonstrate how to use the CMSIS-DSP Python wrapper on an example which is complex enough.
I... | Python Code:
import cmsisdsp as dsp
import cmsisdsp.fixedpoint as fix
import numpy as np
import os.path
import glob
import pathlib
import random
import soundfile as sf
import matplotlib.pyplot as plt
from IPython.display import display,Audio,HTML
import scipy.signal
from numpy.lib.stride_tricks import sliding_window_vi... |
8,195 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Project Euler
Step1: Then I created a new variable, sum_squares, which would hold the sum of the squares, and set it to zero. I looped through all the values in my list, squared them, then... | Python Code:
lst = range(101)
Explanation: Project Euler: Problem 6
https://projecteuler.net/problem=6
The sum of the squares of the first ten natural numbers is,
$$1^2 + 2^2 + ... + 10^2 = 385$$
The square of the sum of the first ten natural numbers is,
$$(1 + 2 + ... + 10)^2 = 552 = 3025$$
Hence the difference betwee... |
8,196 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Source of the materials
Step1: Note that the default settings on the NCBI BLAST website are not quite
the same as the defaults on QBLAST. If you get different results, you’ll
need to check ... | Python Code:
from Bio.Blast import NCBIWWW
help(NCBIWWW.qblast)
Explanation: Source of the materials: Biopython cookbook (Adapted)
<font color='red'>
New status: Draft</font>
BLAST
Running BLAST over the Internet
Saving blast output
Running BLAST locally
Parsing BLAST output
The BLAST record class
Parsing plain-text BL... |
8,197 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div style='background-image
Step1: 1. Initialization of setup
Step2: 2. Elemental Mass and Stiffness matrices
The mass and the stiffness matrix are calculated prior time extrapolation, so... | Python Code:
# Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
from gll import gll
from lagrange1st import lagrange1st
from flux_homo import flux
# Show the plots in the Notebook.
plt.switch_bac... |
8,198 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Constants
Step1: Toy Dataset ("Gaussian blobs")
Step2: Typical Model Specification for Logistic Regression
Step3: Alternative Specification
An alternative specification isolates the posit... | Python Code:
n_samples = 100
n_features = 2
n_classes = 2
seed = 42
rng = np.random.RandomState(seed)
Explanation: Constants
End of explanation
x_test, y_test = make_blobs(n_samples=n_samples, centers=n_classes, random_state=rng)
# class labels are balanced
np.sum(y_test)
fig, ax = plt.subplots(figsize=(7, 5))
cb = ax.... |
8,199 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First Trading Algorithm
Pairs Trading
Pairs trading is a strategy that uses two stocks that are highly correlated. We can then use the difference in price between the two stocks as signal if... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import quandl
Explanation: First Trading Algorithm
Pairs Trading
Pairs trading is a strategy that uses two stocks that are highly correlated. We can then use the difference in price between the two stocks as signal if... |
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