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14,900 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Please find jax implementation of this notebook here
Step2: Residual block
Step3: Example where number of input and output channels is the same.
Step4: Example where we change the number ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import math
from IPython import display
try:
import torch
except ModuleNotFoundError:
%pip install -qq torch
import torch
try:
import torchvision
except ModuleNotFoundError:
%pip install -qq torchvision
import torchvision
from torch... |
14,901 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dijkstra's Shortest Path Algorithm
The notebook Set.ipynb implements <em style="color
Step1: The function call shortest_path takes a node source and a set Edges.
The function shortest_path ... | Python Code:
%run Set.ipynb
Explanation: Dijkstra's Shortest Path Algorithm
The notebook Set.ipynb implements <em style="color:blue">sets</em> as
<a href="https://en.wikipedia.org/wiki/AVL_tree">AVL trees</a>.
The API provided by Set offers the following API:
- Set() creates an empty set.
- S.isEmpty() checks whether ... |
14,902 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load test file
Step1: Run Vivado Simulation
!vivado_hls /disk0/Work/xike_hls_module/hls_proj/spk_dect/solution1/script.tcl
State Machine of Each Channel
Each individual channel has a finite... | Python Code:
din = np.fromfile('spkDect_test_spk.bin', dtype='float32')
data = np.zeros((40,8))
k = 0
for t in range(40):
for ch in range(8):
data[t,ch] = din[k]
k+=1
fig,ax = subplots(1,2,figsize=(15,5))
ax[0].plot(data[:,:4], '-o');
ax[1].plot(data[:,4:], '-o');
Explanation: Load test file
End of... |
14,903 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Migration, urban-bias and the informal sector
The Harris-Todaro Model
This model is an adaptation of a standard two-sector open economy specific factors model (SFM) of migration.
* The two s... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact
from scipy.optimize import bisect,newton
%matplotlib inline
Tbar = 200 # Fixed specific land in ag.
Kbar = 200 # Fixed specific capital in manuf
Lbar = 400 # Total number of mobile workers
LbarMax = 400 ... |
14,904 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Video Codec Unit (VCU) Demo Example
Step1: Run the Demo
Step2: Insert file path
Step3: Transcode
Step4: Advanced options | Python Code:
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value... |
14,905 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tuples
Tuples are like Lists, but they are immutable, means once we assign a value to a tuple we cannot change it or it cannot be changed.
Tuple values are enclosed in (). Tuple can hold val... | Python Code:
t = (1,2.0,'Three')
t
t[0]
Explanation: Tuples
Tuples are like Lists, but they are immutable, means once we assign a value to a tuple we cannot change it or it cannot be changed.
Tuple values are enclosed in (). Tuple can hold values of different types.
You can think of them as constant arrays.
End of expl... |
14,906 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matplotlib Exercise 3
Imports
Step1: Contour plots of 2d wavefunctions
The wavefunction of a 2d quantum well is
Step2: The contour, contourf, pcolor and pcolormesh functions of Matplotlib ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Matplotlib Exercise 3
Imports
End of explanation
def well2d(x, y, nx, ny, L=1.0):
sine1 = np.sin(nx*np.pi*x/L)
sine2 = np.sin(ny*np.pi*y/L)
eq = 2/L * sine1 * sine2
return(eq)
psi = well2d(np.linspace(0,1,10)... |
14,907 | 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... |
14,908 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>
Allen Downey
Read the pregnancy file.
Step1: Select live births, then make a CDF of <tt>totalwgt_lb</tt>.
Step2: Display the CD... | Python Code:
%matplotlib inline
import nsfg
preg = nsfg.ReadFemPreg()
Explanation: Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>
Allen Downey
Read the pregnancy file.
End of explanation
import thinkstats2 as ts
live = preg[preg.outcome == 1]
wgt_cdf = ts.Cdf(live.totalwgt_lb, label = 'weight')
Explanatio... |
14,909 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
functions for doing the work
For a given filename, colnum and rank create the desired matrix, and possibly save it in a comma separate value format that is readable by excel, origin, etc.
St... | Python Code:
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
def create_matrix(filename, colnum, rank, sep=':', savecsv=False):
df = pd.read_csv(filename, sep=sep, header=None, comment='#')
matrix = df.iloc[:, [colnum]].val... |
14,910 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Occupancy data
Step2: Parameter projection
Because the visualizer only displays results across two parameters, we need some way of reducing the dimension to 2.
Our approach
Step3: ... | Python Code:
## [from examples/examples.py]
from download import download_all
## The path to the test data sets
FIXTURES = os.path.join(os.getcwd(), "data")
## Dataset loading mechanisms
datasets = {
"credit": os.path.join(FIXTURES, "credit", "credit.csv"),
"concrete": os.path.join(FIXTURES, "concrete", "conc... |
14,911 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Продажи австралийского вина
Известны ежемесячные продажи австралийского вина в тысячах литров с января 1980 по июль 1995, необходимо построить прогноз на следующие три года.
Step1: Проверка... | Python Code:
%pylab inline
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import warnings
from itertools import product
def invboxcox(y,lmbda):
if lmbda == 0:
return(np.exp(y))
else:
return(np.exp(np.log(lmbda*y+1)/lmbda))
wine = pd.read_csv('m... |
14,912 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Problem 24
A permutation is an ordered arrangement of objects. For example, 3124 is one possible permutation of the digits 1, 2, 3 and 4. If all of the permutations are listed numerically or... | Python Code:
import itertools as it
def lexicographicPermutations():
l=list(range(10))
r=[''.join(map(str,x)) for x in list(it.permutations(l))]
#print(len(r))
print("Millionth lexicographic permutation of the digits 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9: "+r[999999])
lexicographicPermutations()
Explanation: Problem 24
A... |
14,913 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 1
Read 2 arrays x,y containing floating point values
Calculate mean of x & y
Calculate variance for x
$$variance(x)=sum((x-mean(x))^2)$$
Calculate covariance of x & y
$$covarian... | Python Code:
import tensorflow as tf
with tf.name_scope("var"):
with tf.name_scope("mean_x"):
a=tf.constant([5.0,7.0,20.2,17.32],shape=[1,4],name='a')
b=tf.constant([7.0,9.0,19.0,18.0],shape=[1,4],name='b')
x=tf.reduce_mean(a)
sess=tf.Session()
print("mean",sess.run(x))
#mean x
#mean... |
14,914 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generating simple audio samples with music21
We'd like to synthesize simple audio samples containing a single note or a chord. The samples, however, should be parameterized by several attrib... | Python Code:
import music21
from music21.chord import Chord
from music21.duration import Duration
from music21.instrument import Instrument
from music21.note import Note, Rest
from music21.stream import Stream
from music21.tempo import MetronomeMark
from music21.volume import Volume
import os
data_dir = 'data/working/e... |
14,915 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Los modelos lineales son fundamentales tanto en estadística como en el aprendizaje automático, pues muchos métodos se apoyan en la combinación lineal de variables que describen los datos. Lo... | Python Code:
from sklearn import datasets
boston = datasets.load_boston()
Explanation: Los modelos lineales son fundamentales tanto en estadística como en el aprendizaje automático, pues muchos métodos se apoyan en la combinación lineal de variables que describen los datos. Lo más sencillo será ajustar una línea recta ... |
14,916 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right
Step1: Motivating KDE
Step2: We have previously seen that the standard count-based histogram can be created with the plt.hist... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake Vande... |
14,917 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
How can I perform regression in sklearn, using SVM and a gaussian kernel? | Problem:
import numpy as np
import pandas as pd
import sklearn
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
# fit, then predict X
from sklearn.svm import SVR
svr_rbf = SVR(kernel='rbf')
svr_rbf.fit(X, y)
predict = svr_rbf.predict(X) |
14,918 | 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="#Demo-iadftscaleproperty" data-toc-modified-id="Demo-iadftscaleproperty-1"><span class="toc-item-num">1 </span>Demo iadfts... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import sys,os
ia898path = os.path.abspath('../../')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
f = mpimg.imread('../data/cameraman.tif')
froi = f[19:19+64,99:99+64... |
14,919 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced features tutorial
The following tutorials highlight advanced functionality and provide in-depth
material on ensemble APIs.
=============================== =========================... | Python Code:
import numpy as np
from pandas import DataFrame
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_iris
seed = 2017
np.random.seed(seed)
data = load_iris()
idx = np.random.permutation(150)
X = data.data[idx]
y = data.target[idx]
Explanation: Advanced features tutorial
The followin... |
14,920 | Given the following text description, write Python code to implement the functionality described.
Description:
Task
We are given two strings s and c, you have to deleted all the characters in s that are equal to any character in c
then check if the result string is palindrome.
A string is called palindrome ... | Python Code:
def reverse_delete(s,c):
s = ''.join([char for char in s if char not in c])
return (s,s[::-1] == s) |
14,921 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vector Laplacian in curvilinear coordinates
The vector Laplacian is
$$
\nabla^2 \vec{u} = \nabla \cdot \nabla \vec{u}
$$
A vector identity gives the vector Laplacian as
$$
\nabla^2 \vec{u} =... | Python Code:
from shenfun import *
from IPython.display import Math
import sympy as sp
config['basisvectors'] = 'normal' #'covariant' # or
r, theta, z = psi = sp.symbols('x,y,z', real=True, positive=True)
rv = (r*sp.cos(theta), r*sp.sin(theta), z)
N = 10
F0 = FunctionSpace(N, 'F', dtype='d')
F1 = FunctionSpace(N, 'F',... |
14,922 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Learning Curves and Bias-Variance Tradeoff
In practice, much of the task of machine learning involves selecting algorithms,
parameters, and sets of data to optimize the results of the method... | Python Code:
%pylab inline
Explanation: Learning Curves and Bias-Variance Tradeoff
In practice, much of the task of machine learning involves selecting algorithms,
parameters, and sets of data to optimize the results of the method. All of these
things can affect the quality of the results, but it’s not always clear whi... |
14,923 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a name="pagetop"></a>
<div style="width
Step1: We got a Pandas dataframe back, which is great. Sadly, Pandas does not play well with units, so we need to attach units and make some other k... | Python Code:
# Create a datetime for our request - notice the times are from laregest (year) to smallest (hour)
from datetime import datetime
request_time = datetime(1999, 5, 3, 12)
# Store the station name in a variable for flexibility and clarity
station = 'OUN'
# Import the Wyoming simple web service and request the... |
14,924 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Step 12
Step2: Publish model to Firebase ML
Step 1. Upload the private key (json file) for your service account and Initialize Firebase Admin
... | 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... |
14,925 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MeshCat Animations
MeshCat.jl also provides an animation interface, built on top of the three.js animation system. While it is possible to construct animation clips and tracks manually, just... | Python Code:
import meshcat
from meshcat.geometry import Box
vis = meshcat.Visualizer()
## To open the visualizer in a new browser tab, do:
# vis.open()
## To open the visualizer inside this jupyter notebook, do:
# vis.jupyter_cell()
vis["box1"].set_object(Box([0.1, 0.2, 0.3]))
Explanation: MeshCat Animations
MeshCat... |
14,926 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Theory and Practice of Visualization Exercise 2
Imports
Step1: Violations of graphical excellence and integrity
Find a data-focused visualization on one of the following websites that is a ... | Python Code:
from IPython.display import Image
Explanation: Theory and Practice of Visualization Exercise 2
Imports
End of explanation
# Add your filename and uncomment the following line:
Image(filename='TheoryAndPracticeEx02graph.png')
Explanation: Violations of graphical excellence and integrity
Find a data-focused ... |
14,927 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
9 June 2017
Wayne Nixalo
This notebook started out trying to generate convolutional test features using Sequential.predict_generator, by using bcolz to save the generated features to disk, i... | Python Code:
import theano
import os, sys
sys.path.insert(1, os.path.join('utils'))
from __future__ import print_function, division
path = 'data/statefarm/'
import utils; reload(utils)
from utils import *
batch_size=16
vgg = Vgg16()
model = vgg.model
last_conv_idx = [i for i, l in enumerate(model.layers) if type(l) is ... |
14,928 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center> Introduction to Spark In-memmory Computing via Python PySpark </center>
Spark is an implementation of the MapReduce programming paradigm that operates on in-memory data and allows d... | Python Code:
!module list
Explanation: <center> Introduction to Spark In-memmory Computing via Python PySpark </center>
Spark is an implementation of the MapReduce programming paradigm that operates on in-memory data and allows data reuses across multiple computations.
Performance of Spark is significantly better than ... |
14,929 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Darwin's bibliography <a class="tocSkip">
<p><img src="https
Step1: Data
Step2: Tokenize
Step3: Stemming
<p>As we are analysing 20 full books, the stemming algorithm can take several minu... | Python Code:
import glob
import re, os
from tqdm import tqdm_notebook
import pickle
import pandas as pd
from nltk.stem import PorterStemmer
from gensim import corpora
from gensim.models import TfidfModel
from gensim import similarities
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.cluster import hierarc... |
14,930 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LAB
Add a column to dinos that contains the decimal equivalent of the sha256 hash. Hint.
Step1: LAB
Sort dinos by the column sha256 -- this will be an alphabetical sort.
Step2: How about ... | Python Code:
dinos.assign(Decimal = dinos.sha256.apply(lambda x: int(x, base=16)))
Explanation: LAB
Add a column to dinos that contains the decimal equivalent of the sha256 hash. Hint.
End of explanation
dinos.sort_values(by='sha256').head(10)
Explanation: LAB
Sort dinos by the column sha256 -- this will be an alphabe... |
14,931 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Conditional Probability Solution
First we'll modify the code to have some fixed purchase probability regardless of age, say 40%
Step1: Next we will compute P(E|F) for some age group, let's ... | Python Code:
from numpy import random
random.seed(0)
totals = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0}
purchases = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0}
totalPurchases = 0
for _ in range(100000):
ageDecade = random.choice([20, 30, 40, 50, 60, 70])
purchaseProbability = 0.4
totals[ageDecade] += 1
if (random.r... |
14,932 | 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 6 ##
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... |
14,933 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
S&P 500 Components Time Series
Get time series of all S&P 500 components
Step1: Current S&P500 symbols.
See my SP500 project that generates the sp500.cvs file.
Step2: Create cache director... | Python Code:
from datetime import datetime
import pandas as pd
import pinkfish as pf
# -*- encoding: utf-8 -*-
%matplotlib inline
Explanation: S&P 500 Components Time Series
Get time series of all S&P 500 components
End of explanation
filename = 'sp500.csv'
symbols = pd.read_csv(filename)
symbols = sorted(list(symbols[... |
14,934 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Build a numpy.ndarray, an equivalent dataFrame, and a numpy.rec.array
Step1: Simple Array Operation
Step2: pandas.dataFrame
Step3: pandas.dataFrame
Step4: numpy.rec.array
Step5: pandas.... | Python Code:
rows = 10000000
# Equivalent numpy array
arr = np.random.uniform(size=rows*3).reshape(rows, 3)
# The pandas dataFrame with column names
df = pd.DataFrame(arr, columns=['x','y','z'])
# a `numpy.recarray`
rec = df.to_records()
df.head()
df.dtypes
Explanation: Build a numpy.ndarray, an equivalent dataFrame, a... |
14,935 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Output Containers and Layout Managers
Output containers are objects that hold a collection of other objects, and displays all its contents, even when they are complex interactive objects and... | Python Code:
# The defining of variable doesn't initiate output
x = "some string"
Explanation: Output Containers and Layout Managers
Output containers are objects that hold a collection of other objects, and displays all its contents, even when they are complex interactive objects and MIME type.
By default the contents... |
14,936 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
02 - Introduction to Machine Learning
by Alejandro Correa Bahnsen
version 0.1, Feb 2016
Part of the class Practical Machine Learning
This notebook is licensed under a Creative Commons Attrib... | Python Code:
# Import libraries
%matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set();
cmap = mpl.colors.ListedColormap(sns.color_palette("hls", 3))
# Create a random set of examples
from sklearn.datasets.samples_generator import make_blobs
X, Y =... |
14,937 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Custom Factors
When we first looked at factors, we explored the set of built-in factors. Frequently, a desired computation isn't included as a built-in factor. One of the most powerful featu... | Python Code:
from quantopian.pipeline import CustomFactor
import numpy
Explanation: Custom Factors
When we first looked at factors, we explored the set of built-in factors. Frequently, a desired computation isn't included as a built-in factor. One of the most powerful features of the Pipeline API is that it allows us t... |
14,938 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualization 1
Step1: Scatter plots
Learn how to use Matplotlib's plt.scatter function to make a 2d scatter plot.
Generate random data using np.random.randn.
Style the markers (color, size... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from __future__ import print_function
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
Explanation: Visualization 1: Matplotlib Basics Exercises
End of explanation
randx = np.random.randn(500... |
14,939 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Linear Classifiers - support vector machines (SVMs)
SVMs try to construct a hyperplane maximizing the margin between the two classes. It selects a subset of the
input, called the support vec... | Python Code:
from sklearn import svm
import matplotlib.pyplot as plt
from sklearn import datasets
import numpy as np
%matplotlib inline
iris = datasets.load_iris()
iris_X = iris.data
iris_y = iris.target
np.unique(iris_y)
svc = svm.SVC(kernel='linear')
svc.fit(iris.data, iris.target)
Explanation: Linear Classifiers - s... |
14,940 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EEG forward operator with a template MRI
This tutorial explains how to compute the forward operator from EEG data
using the standard template MRI subject fsaverage.
.. caution
Step1: Load t... | Python Code:
import os.path as op
import numpy as np
import mne
from mne.datasets import eegbci
from mne.datasets import fetch_fsaverage
# Download fsaverage files
fs_dir = fetch_fsaverage(verbose=True)
subjects_dir = op.dirname(fs_dir)
# The files live in:
subject = 'fsaverage'
trans = 'fsaverage' # MNE has a built-i... |
14,941 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
quant-econ Solutions
Step1: Exercise 1
This exercise asked you to validate the laws of motion for $\gamma$ and $\mu$ given in the lecture, based on the stated result about Bayesian updating... | Python Code:
%matplotlib inline
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
import quantecon as qe
import seaborn as sns
import itertools
Explanation: quant-econ Solutions: Uncertainty Traps
Solutions for http://quant-econ.net/py/uncertainty_traps.html
End of explanation
palette =... |
14,942 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BioPandas
Authors
Step1: Working with mmCIF Structures in DataFrames
Loading mmCIF Files
There are several ways to load a mmCIF structure into a PandasMmcif object.
1 -- Loading an mmCIF fi... | Python Code:
%load_ext watermark
%watermark -d -u -p pandas,biopandas
import pandas as pd
pd.set_option('display.width', 600)
pd.set_option('display.max_columns', 8)
Explanation: BioPandas
Authors:
- Sebastian Raschka mail@sebastianrasc... |
14,943 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Barotropic Model
Here will will use pyqg to reproduce the results of the paper
Step1: McWilliams performed freely-evolving 2D turbulence ($R_d = \infty$, $\beta =0$) experiments on a $2\pi\... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pyqg
Explanation: Barotropic Model
Here will will use pyqg to reproduce the results of the paper: <br />
J. C. Mcwilliams (1984). The emergence of isolated coherent vortices in turbulent flow. Journal of Fluid Mechanics, 146, pp 2... |
14,944 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-1', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: SANDBOX-1
Topic: Ocnbgchem
Sub-Topics: Trac... |
14,945 | Given the following text description, write Python code to implement the functionality described.
Description:
Given a grid with N rows and N columns (N >= 2) and a positive integer k,
each cell of the grid contains a value. Every integer in the range [1, N * N]
inclusive appears exactly once on the cells ... | Python Code:
def minPath(grid, k):
n = len(grid)
val = n * n + 1
for i in range(n):
for j in range(n):
if grid[i][j] == 1:
temp = []
if i != 0:
temp.append(grid[i - 1][j])
if j != 0:
temp.append(... |
14,946 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modeling and Simulation in Python
Chapter 22
Copyright 2017 Allen Downey
License
Step1: Vectors
A Vector object represents a vector quantity. In the context of mechanics, vector quantities... | 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... |
14,947 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nansat
Step1: Open file with Nansat
Step2: Read information ABOUT the data (METADATA)
Step3: Read the actual DATA
Step4: Check what kind of data we have
Step5: Find where the image is t... | Python Code:
import os
import shutil
import nansat
idir = os.path.join(os.path.dirname(nansat.__file__), 'tests', 'data/')
Explanation: Nansat: First Steps
Overview
The NANSAT package contains several classes:
Nansat - open and read satellite data
Domain - define grid for the region of interest
Figure - create... |
14,948 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model Selection via Validation
Step1: Cross-validation
Step2: We can use different splitting strategies, such as random splitting (There exists many different cross-validation strategies i... | Python Code:
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn import model_selection
from sklearn import metrics
from sklearn.datasets import load_digits
digits = load_digits()
X = digits.data
y = digits.target
X_train, X_test, ... |
14,949 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Graphistry Tutorial
Step1: Connect to Graphistry + Test
Step2: Connect to TigerGraph and Test
Step3: Query Tigergraph
Step4: Visualize result of TigerGraph query
Step5: In-Tool UI Walkt... | Python Code:
TIGER_CONFIG = {
'fqdn': 'http://MY_TIGER_SERVER:9000'
}
Explanation: Graphistry Tutorial: Notebooks + TigerGraph via raw REST calls
Connect to Graphistry, TigerGraph
Load data from TigerGraph into a Pandas Dataframes
Plot in Graphistry as a Graph and Hypergraph
Explore in Graphistry
Advanced notebooks... |
14,950 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Theory and Practice of Visualization Exercise 1
Imports
Step1: Graphical excellence and integrity
Find a data-focused visualization on one of the following websites that is a positive examp... | Python Code:
from IPython.display import Image
Explanation: Theory and Practice of Visualization Exercise 1
Imports
End of explanation
# Add your filename and uncomment the following line:
Image(filename='TheoryAndPracticeEx01graph.png')
Explanation: Graphical excellence and integrity
Find a data-focused visualization ... |
14,951 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deploying a scikit-learn model on Verta
Within Verta, a "Model" can be any arbitrary function
Step1: 0.1 Verta import and setup
Step2: 1. Model Training
1.1 Load training data
Step3: Defi... | Python Code:
from __future__ import print_function
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings("ignore", category=ConvergenceWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
import itertools
import os
import time
import six
import numpy as np
import pandas... |
14,952 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
python_subdict - Documentation
The markdown version of this document is here.
Installing
You can pip-install python_subdict in your environment by typing the following code on your shell
Ste... | Python Code:
d = {
'a': 'A',
'b': 'B',
'c': 'C',
'd': {
'x': 'D_X',
'y': 'D_Y',
'z': {
'I': 'D_Z_I',
'II': {
'1': 'D_Z_II_1',
'2': 'D_Z_II_2'
},
'III': 'D_Z_III'
}
}
}
Explanation: python_... |
14,953 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Define the column names and read data from source file
Step1: Quick check on the summary statistics of the data set
Step2: There are only 5399 unique citations when there are 5391 judgment... | Python Code:
col_names = ['index', 'name', 'citation', 'author', 'number', 'date', 'court', 'coram', 'counsel', 'catchwords']
df = pd.read_table('raw.tsv', encoding='utf-8', header=None, names=col_names, index_col=0, parse_dates=True)
df.head()
Explanation: Define the column names and read data from source file
End of ... |
14,954 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have an array of experimental values and a probability density function that supposedly describes their distribution: | Problem:
import numpy as np
import scipy as sp
from scipy import integrate,stats
def bekkers(x, a, m, d):
p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
return(p)
range_start = 1
range_end = 10
estimated_a, estimated_m, estimated_d = 1,1,1
sample_data = [1.5,1.6,1.8,2.1,2.2,3.3,4,6,8,9]
def bekkers_cdf... |
14,955 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial for polydisperseity in with bulk Monte Carlo simulations in the structureal-color package
Copyright 2016, Vinothan N. Manoharan, Victoria Hwang, Annie Stephenson
This file is part o... | Python Code:
%matplotlib inline
import numpy as np
import time
import structcol as sc
import structcol.refractive_index as ri
from structcol import montecarlo as mc
from structcol import detector as det
from structcol import phase_func_sphere as pfs
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.misc ... |
14,956 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First, you'll need some data to load up.
You can download example HARPS data files (and results files) to play around with linked in the documentation.
Here we'll assume that you have the da... | Python Code:
data = wobble.Data('../data/51peg_e2ds.hdf5')
Explanation: First, you'll need some data to load up.
You can download example HARPS data files (and results files) to play around with linked in the documentation.
Here we'll assume that you have the data 51peg_e2ds.hdf5 saved in the wobble/data directory.
By ... |
14,957 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Excercises Electric Machinery Fundamentals
Chapter 4
Problem 4-29
Step1: Description
A 100-MVA, 14.4-kV 0.8-PF-lagging, Y-connected synchronous generator has a negligible armature
resistanc... | Python Code:
%pylab notebook
Explanation: Excercises Electric Machinery Fundamentals
Chapter 4
Problem 4-29
End of explanation
Sbase = 100e6 # [VA]
Vbase = 14.4e3 # [V]
ra = 0.0 # pu
xs = 1.0 # pu
PF = 0.8
Explanation: Description
A 100-MVA, 14.4-kV 0.8-PF-lagging, Y-connected synchronous generator has a negligible arm... |
14,958 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Explore variables one at a time
Step1: MSSubClass
Step2: MSSubClass is categorical, though it is coded as numeric. Combine all the 1 and 1.5 story dwelling types as 1, 2 and 2.5 story type... | Python Code:
# drop ID
data.drop(["Id"], axis = 1, inplace=True)
data.head()
Explanation: Explore variables one at a time
End of explanation
data["MSSubClass"].isnull().sum()
sns.countplot(x="MSSubClass", data=data, palette=sns.color_palette("Blues", 1));
Explanation: MSSubClass
End of explanation
MSSubClass = data["MS... |
14,959 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mixed NB
gnb
Step1: training MultiNB & parameter tuning
cat_X => countvec
Step2: X_counts로 cv했을때
alpha
Step3: X_tfidf로 cv했을때
alpha
Step4: Tuning & Improvement
Step5: Retraining with ... | Python Code:
df = pd.read_csv('../resource/final_df3.csv')
sample = df.title
y = df['rating(y)'].values
real_X = df[['avg_rating']].values
cat_X = df.text.fillna("").values
Explanation: Mixed NB
gnb : 'avg_rating' 피쳐 한개만
mnb : alpha는 피쳐가 달라진 관계로(콤마, 띄어쓰기 제거) 다시 cv시행
ngram_range : (1, 2)
tfidf : true
sub_alpha : 0.3
sco... |
14,960 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Grade
Step1: 1) What books topped the Hardcover Fiction NYT best-sellers list on Mother's Day in 2009 and 2010? How about Father's Day?
Step2: 2) What are all the different book categories... | Python Code:
import requests
Explanation: Grade: 8 / 8
All API's: http://developer.nytimes.com/
Article search API: http://developer.nytimes.com/article_search_v2.json
Best-seller API: http://developer.nytimes.com/books_api.json#/Documentation
Test/build queries: http://developer.nytimes.com/
Tip: Remember to include y... |
14,961 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Writing your own optimization loop
In this example, we will use the pyswarms.backend module to write our own optimization loop. We will try to recreate the Global best PSO using the native b... | Python Code:
# Import modules
import numpy as np
# Import sphere function as objective function
from pyswarms.utils.functions.single_obj import sphere as f
# Import backend modules
import pyswarms.backend as P
from pyswarms.backend.topology import Star
# Some more magic so that the notebook will reload external python ... |
14,962 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced
Step1: As always, let's do imports and initialize a logger and a new Bundle.
Step2: Built-in Constraints
There are a number of built-in constraints that can be applied to our syst... | Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
Explanation: Advanced: Built-In Constraints
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
from phoebe import u # units
import n... |
14,963 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Let's say I have a 1d numpy positive integer array like this: | Problem:
import numpy as np
a = np.array([1, 0, 3])
b = np.zeros((a.size, a.max()+1))
b[np.arange(a.size), a]=1 |
14,964 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hosts
Hosts are identified in the HSC overlap check notebook. For DR1 they are (in "300 kpc" circles)
Step2: Generate queries
These queries are meant for the HSC casjobs at https
Step3: A... | Python Code:
import hosts
hostobjs = hosts.get_saga_hosts_from_google()
hosts.use_base_catalogs(hostobjs)
hschosts = tuple([h for h in hostobjs if h.name in ('Alice', 'Othello', 'Dune')])
assert len(hschosts) == 3
hschosts
for h in hschosts:
h.hscfn = os.path.join('catalogs', 'hsc_pdr1_{}.csv.gz'.format(h.name))
Ex... |
14,965 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Selection of secondary sampling units (SSUs) <a name="section2"></a>
To select the second stage sample, we need the second stage frame which is the list of all the households in the 10 selec... | Python Code:
%%capture
%run psu_selection.ipynb
Explanation: Selection of secondary sampling units (SSUs) <a name="section2"></a>
To select the second stage sample, we need the second stage frame which is the list of all the households in the 10 selected clusters (psus). DHS, PHIA, MICS and other large scale surveys vi... |
14,966 | 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... |
14,967 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Image Classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifa... |
14,968 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Timeseries with pandas
Working with time-series data is an important part of data analysis.
Starting with v0.8, the pandas library has included a rich API for time-series manipulations.
The ... | Python Code:
from datetime import datetime, date, time
import sys
sys.version
import pandas as pd
from pandas import Series, DataFrame, Panel
pd.__version__
import numpy as np
np.__version__
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rc('figure', figsize=(10, 8))
mpl.__version__
Explanation: Timeserie... |
14,969 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1A.soft - Tests unitaires, setup et ingéniérie logicielle
On vérifie toujours qu'un code fonctionne quand on l'écrit mais cela ne veut pas dire qu'il continuera à fonctionner à l'avenir. La ... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
from pyensae.graphhelper import draw_diagram
Explanation: 1A.soft - Tests unitaires, setup et ingéniérie logicielle
On vérifie toujours qu'un code fonctionne quand on l'écrit mais cela ne veut pas dire qu'il continuera à fonctionner à l'avenir... |
14,970 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Day 2 pre-class assignment
Goals for today's pre-class assignment
Make sure that you can get a Jupyter notebook up and running!
Learn about algorithms, computer programs, and their relations... | Python Code:
# The command below this comment imports the functionality that we need to display
# YouTube videos in a Jupyter Notebook. You need to run this cell before you
# run ANY of the YouTube videos.
from IPython.display import YouTubeVideo
Explanation: Day 2 pre-class assignment
Goals for today's pre-class ass... |
14,971 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tracking an Unknown Number of Objects
While SVI can be used to learn components and assignments of a mixture model, pyro.contrib.tracking provides more efficient inference algorithms to esti... | Python Code:
import math
import os
import torch
from torch.distributions import constraints
from matplotlib import pyplot
import pyro
import pyro.distributions as dist
import pyro.poutine as poutine
from pyro.contrib.tracking.assignment import MarginalAssignmentPersistent
from pyro.distributions.util import gather
from... |
14,972 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pysam
Pysam è un package che mette a disposizione le funzionalità per manipolare file in formato SAM/BAM.
Importare il modulo pysam
Step1: Come leggere gli allineamenti da un file BAM
Align... | Python Code:
import pysam
Explanation: Pysam
Pysam è un package che mette a disposizione le funzionalità per manipolare file in formato SAM/BAM.
Importare il modulo pysam
End of explanation
from pysam import AlignmentFile
help(AlignmentFile)
Explanation: Come leggere gli allineamenti da un file BAM
AlignmentFile è la c... |
14,973 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Project Title
Step1: The above table shows the first 5 tuples of the dataset which contains two columns namely the roll no and text of the assignment.
Step2: The dataset contains 1028 ent... | Python Code:
# Importing pandas library
import pandas as pd
# Loding the data set
df = pd.read_table('data.csv',
sep=',',
header=None,
names=['rollNo','textData'])
# Output printing out first 5 columns
df.head()
# from sklearn.feature_extraction import text
Ex... |
14,974 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PLEASE MAKE A COPY BEFORE CHANGING
Copyright 2022 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:
## Import Packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: PLEASE MAKE A COPY BEFORE CHANGING
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the L... |
14,975 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Polarization
Following Detlefs[2012] the electric field of a monochromatic plane wave can be described as
$$
\begin{equation}
\begin{split}
\vec{E}(t,\vec{x}) =& \Re[(V_0\hat{e}_0 + V_1\hat{... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from spectrocrunch.sources import polarization
Explanation: Polarization
Following Detlefs[2012] the electric field of a monochromatic plane wave can be described as
$$
\begin{equation}
\begin{split}
\vec{E}(t,\vec{x}) =& \Re[(V_0\hat{e}... |
14,976 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
dog face detector using haar cascade
| Python Code::
import cv2
import numpy as np
dog_cascade = cv2.CascadeClassifier('dog_face_haar_cascade.xml')
dog_face = dog_cascade.detectMultiScale(image)
for (x, y, w, h) in dog_face:
start_point, end_point = (x, y), (x+ w, y+h)
cv2.rectangle(image, pt1= start_point, pt2 = end_point, color = (0, 255, 0), thickness ... |
14,977 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Why automate your work flow, and how to approach the process
Questions for students to consider
Step1: Questions for students
Step2: Scope of Variables
Global variables
Global variables ar... | Python Code:
# write out three variables, assign a number, string, list
x = 'Asia' # String
y = 1952 # an integer
z = 1.5 # a floating point number
cal_1 = y * z
print(cal_1)
# or
x, y = 'Asia', 'Africa'
w = x
w = x + x #concatinating strings (combinging strings)
print(w)
h = 'Africa'
list_1 = ['Asia', 'Africa', 'Eur... |
14,978 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Scan
In short
Mechanism to perform loops in a Theano graph
Supports nested loops and reusing results from previous iterations
Highly generic
Implementation
You've previous seen that a Thean... | Python Code:
import theano
import theano.tensor as T
import numpy as np
vector1 = T.vector('vector1')
vector2 = T.vector('vector2')
Explanation: Scan
In short
Mechanism to perform loops in a Theano graph
Supports nested loops and reusing results from previous iterations
Highly generic
Implementation
You've previous se... |
14,979 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Encoder-Decoder Analysis
Model Architecture
Step1: Perplexity on Each Dataset
Step2: Loss vs. Epoch
Step3: Perplexity vs. Epoch
Step4: Generations
Step5: BLEU Analysis
Step6: N-pairs B... | Python Code:
report_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing10_200_512_04drbef/encdec_noing10_200_512_04drbef.json'
log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing10_200_512_04drbef/encdec_noing10_200_512_04drbef_logs.json'
import json
imp... |
14,980 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model Selection For Machine Learning
In this exercise, we will explore methods to do model selection in a machine learning context, in particular cross-validation and information criteria. A... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
# comment out this line if you don't have seaborn installed
import seaborn as sns
sns.set_palette("colorblind")
import numpy as np
Explanation: Model Selection For Machine Learning
In this exercise, we will explore methods to do model selection in a machin... |
14,981 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Neural network hybrid recommendation system on Google Analytics data preprocessing
This notebook demonstrates how to implement a hybrid recommendation system using a neural network to combin... | Python Code:
%%bash
conda update -y -n base -c defaults conda
source activate py2env
pip uninstall -y google-cloud-dataflow
conda install -y pytz
pip install apache-beam[gcp]==2.9.0
Explanation: Neural network hybrid recommendation system on Google Analytics data preprocessing
This notebook demonstrates how to implemen... |
14,982 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Netdata Anomaly Detection Deepdive
This notebook will walk through a simplified python based implementation of the C & C++ code in netdata/netdata/ml/ used to power the anomaly detect... | Python Code:
# uncomment the line below (when running in google colab) to install the netdata-pandas library, comment it again when done.
#!pip install netdata-pandas
from datetime import datetime, timedelta
import itertools
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sea... |
14,983 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ApJdataFrames
Step1: Table 3 - Photometry
Step2: Drop source 12 because it was shown to be a galaxy.
Step3: %%bash
mkdir ../data/Allers2006
Step4: Bonus | Python Code:
%pylab inline
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
Explanation: ApJdataFrames: Allers2006
Title: Young, Low-Mass Brown Dwarfs with Mid-Infrared Excesses
Authors: AKCJ
Data is from this paper:
http://iopscience.iop.org/0004-637X/644/1/364/
End of explan... |
14,984 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Demonstration of SHyFT API implementation of Kalman Filtering on gridded data
This notebook gives an example of Met.no data post-processing to correct temperature forecasts based on comparis... | Python Code:
# first you should import the third-party python modules which you'll use later on
# the first line enables that figures are shown inline, directly in the notebook
%pylab inline
import os
from os import path
import sys
from matplotlib import pyplot as plt
# once the shyft_path is set correctly, you should ... |
14,985 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
3.1 Problem description
Try to build a classifier for the MNIST dataset that achieves over 97% accuracy
on the test set. Hint
Step1: Split test and training data
Step2: 3.2 Training a Rand... | Python Code:
from scipy.io import loadmat
mnist = loadmat('./datasets/mnist-original.mat')
mnist
X, y = mnist['data'], mnist['label']
X = X.T
X.shape
y = y.T
y.shape
type(y)
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
Explanation: 3.1 Problem description
Try to build a classifier for the MNIST ... |
14,986 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sample Notebook for exploring gnomAD in BigQuery
This notebook contains sample queries to explore the gnomAD dataset which is hosted through the Google Cloud Public Datasets Program.
Setup a... | Python Code:
# Import libraries
import numpy as np
import os
# Imports for using and authenticating BigQuery
from google.colab import auth
Explanation: Sample Notebook for exploring gnomAD in BigQuery
This notebook contains sample queries to explore the gnomAD dataset which is hosted through the Google Cloud Public Dat... |
14,987 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GCE Lab 4 - Dwarf Galaxy - Chemical Evolution Trend
In this notebook, you will tune model parameters to fit the chemical evolution trend derived from stellar spectroscopy, for the dwarf sphe... | Python Code:
# Import standard Python packages
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
# One-zone galactic chemical evolution code
import NuPyCEE.omega as omega
# Stellar abundances plotting code
import NuPyCEE.stellab as stellab
# Matplotlib option
%matplotlib inline
Explanation: GCE Lab 4... |
14,988 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Citation-https
Step1: conda install numpy
Step2: Download the RetinaNet model file that will be used for object detection via this link
https | Python Code:
! pip install tensorflow
! pip install --upgrade pip
Explanation: Citation-https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606
End of explanation
! pip install numpy -I
import numpy.core.multiarray
!pip install spacy
! pip install scipy
! pip install opencv-python
! pip insta... |
14,989 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 1, figures 3 and 4
This notebook will show you how to produce figures 1.3 and 1.4 after the predictive modeling is completed.
The predictive modeling itself, unfortunately, doesn't f... | Python Code:
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
import random
accuracy_df = pd.read_csv('../modeloutput/finalbiopredicts.csv')
accuracy_df.head()
# I "jitter" results horizontally because we often have multiple results with the same x and y coordinates.
def ji... |
14,990 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Exam question solution
Juan Valdez can earn u = 10 as a farm worker. Alternatively, if he can raise a lump-sum of I=60, he can start a risky coffee-growing project. Juan is risk-neu... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from ipywidgets import interact, fixed
def E(xs,xf,p):
Expectation operator
return p*xs + (1-p)*xf
Explanation: Exam question solution
Juan Valdez can earn u = 10 as a farm worker. Alternatively, if he can raise a lump-sum of ... |
14,991 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Writing an algorithm (using pure scientific Python)
In this notebook, we show how to write an algorithm for the NeuroFinder challenge using pure scientific Python. Elsewhere in the challenge... | Python Code:
bucket = "s3n://neuro.datasets/"
path = "challenges/neurofinder/01.00/"
images = tsc.loadImages(bucket + path + 'images', startIdx=0, stopIdx=100)
Explanation: Writing an algorithm (using pure scientific Python)
In this notebook, we show how to write an algorithm for the NeuroFinder challenge using pure sc... |
14,992 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Poynting Vector of Half-Wave Antenna
PROGRAM
Step1: In this problem, I plot the magnitude of the time averaged Poynting vector for a half-wave antenna. The antenna is oriented vertically in... | Python Code:
import numpy as np
import matplotlib.pylab as plt
Explanation: Poynting Vector of Half-Wave Antenna
PROGRAM: Poynting vector of half-wave antenna
CREATED: 5/30/2018
Import packages.
End of explanation
#Define constants - permeability of free space, speed of light, current amplitude.
u_0 = 1.26 * 10**(-6)
c... |
14,993 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Filtering and resampling data
This tutorial covers filtering and resampling, and gives examples of how
filtering can be used for artifact repair.
Step1: Background on filtering
A filter... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
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)
... |
14,994 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Alignment report
Step1: Read distribution by MQ
Step2: Read distribution by alignment fate
Step3: Mapped rate and Alignment accuracy parametrized by MQ | Python Code:
# From SO: https://stackoverflow.com/a/28073228/2512851
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form acti... |
14,995 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ciência dos Dados - PROJETO 1
Gabriel Heusi Pereira Bueno de Camargo
Título
O comportamento da segurança alimentar no território brasileiro.
Introdução
A diversidade do território brasileiro... | Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
from numpy import zeros_like
print('Esperamos trabalhar no diretório')
print(os.getcwd())
base = pd.read_csv('DOM2013.csv',sep=',')
base9 = pd.read_csv('DOM2009.csv',sep=',')
Explanation: Ciência dos Dados -... |
14,996 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CrowdTruth for Sparse Multiple Choice Tasks
Step1: Declaring a pre-processing configuration
The pre-processing configuration defines how to interpret the raw crowdsourcing input. To do this... | Python Code:
import pandas as pd
test_data = pd.read_csv("../data/event-text-sparse-multiple-choice.csv")
test_data.head()
Explanation: CrowdTruth for Sparse Multiple Choice Tasks: Event Extraction
In this tutorial, we will apply CrowdTruth metrics to a sparse multiple choice crowdsourcing task for Event Extraction fro... |
14,997 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Running Computations in Parallel
Contents
Introduction
Single-Variable Functions
Multi-Variable Functions
IPython Based Parallel Processing
Step1: <a id='intro'></a>
Introduction
Often one ... | Python Code:
import numpy as np
from qutip import *
Explanation: Running Computations in Parallel
Contents
Introduction
Single-Variable Functions
Multi-Variable Functions
IPython Based Parallel Processing
End of explanation
def func1(x):
return x, x**2, x**3
a, b, c = parfor(func1, range(10))
print(a)
print(b)
pri... |
14,998 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ATM 623
Step1: Contents
The one-dimensional diffusion equation
Discretizing the diffusion operator in space
Coding the discretized diffusion operator in numpy
Discretizing the time derivati... | Python Code:
# Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
Explanation: ATM 623: Climate Modeling
Brian E. J. Rose, University at Albany
Lecture 20: A peak at numerical methods for diffusion models
Warning: content out of date and not maintained
You really should be looking... |
14,999 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Best practices
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step1: This section lists some best practices for creating a circuit that performs... | Python Code:
try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet cirq
import cirq
print("installed cirq.")
import cirq_google as cg
import sympy
Explanation: Best practices
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https:/... |
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