Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
|---|---|---|
7,700 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using Geoserver to load data on the map
In this notebook we'll take a look at using Geoserver to render raster data to the map. Geoserver is an open source server for sharing geospatial data... | Python Code:
%matplotlib inline
from matplotlib import pylab as plt
Explanation: Using Geoserver to load data on the map
In this notebook we'll take a look at using Geoserver to render raster data to the map. Geoserver is an open source server for sharing geospatial data. It includes a tiling server which the GeoJS map... |
7,701 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Set up working directory
Step1: README
This part of pipeline search for the SSU rRNA gene fragments, classify them, and extract reads aligned specific region. It is also heavy lifting part ... | Python Code:
cd ~/Desktop/SSUsearch/
mkdir -p ./workdir
#check seqfile files to process in data directory (make sure you still remember the data directory)
!ls ./data/test/data
Explanation: Set up working directory
End of explanation
Seqfile='./data/test/data/1c.fa'
Explanation: README
This part of pipeline search for ... |
7,702 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Carbonic Acid/Bicarbonate/Carbonate Equilibrium $\require{mhchem}$
Carbonic Acid ($\ce{H2CO3}$), Bicarbonate ($\ce{HCO3-}$) and Carbonate ($\ce{CO3^{2-}}$) form in water through the foll... | Python Code:
%pylab inline
from phreeqpython import PhreeqPython
# create new PhreeqPython instance
pp = PhreeqPython()
Explanation: The Carbonic Acid/Bicarbonate/Carbonate Equilibrium $\require{mhchem}$
Carbonic Acid ($\ce{H2CO3}$), Bicarbonate ($\ce{HCO3-}$) and Carbonate ($\ce{CO3^{2-}}$) form in water through the f... |
7,703 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 19
Step1: Now, let's set an initial population $n_0$, pick a couple of different per capita rates of change $r_c$, and run them to see what happens.
Step2: The one critical part of... | Python Code:
# Preliminary imports
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.integrate as sig # Here's the critical module!
import seaborn as sns
Explanation: Lecture 19: Computational Modeling
CBIO (CSCI) 4835/6835: Introduction to Computational Biology
Overview and Objectives... |
7,704 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Next, we'll import the Sequential model type from Keras. This is simply a linear stack of neural network layers, and it's perfect for the type of feed-forward CNN we're building in this tuto... | Python Code:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
#load pre-shffuled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test)... |
7,705 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Casser le code de Vigenère
La lettre la plus fréquente en français est la lettre E. Cette information permet de casser le code de César en calculant le décalage entre la lettre la plus fréqu... | Python Code:
def code_vigenere ( message, cle, decode = False) :
message_code = ""
for i,c in enumerate(message) :
d = cle[ i % len(cle) ]
d = ord(d) - 65
if decode : d = 26 - d
message_code += chr((ord(c)-65+d)%26+65)
return message_code
def DecodeVigenere(message, cle)... |
7,706 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chord Diagrams for Bokeh
Chord diagrams are a wonderful way to visualise interactions between groups, along the genome, and many others (check the circos page for some advances examples). I ... | Python Code:
# Each row defines how many items were "send" to the group specified by the column
# for the "golden image" use case, the matrix should be symmetric
matrix = np.array([[16, 3, 28, 0, 18],
[18, 0, 12, 5, 29],
[ 9, 11, 17, 27, 0],
[19, 0, 31, 11,... |
7,707 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced AD application
This notebook documents how the algorithmic (or automatic) differentiation framework may be applied to non-linear equations. For an introduction to the framework, see... | Python Code:
import porepy as pp
import numpy as np
import inspect
model = pp.ContactMechanics({})
print(inspect.getsource(model._assign_equations))
Explanation: Advanced AD application
This notebook documents how the algorithmic (or automatic) differentiation framework may be applied to non-linear equations. For an i... |
7,708 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Session 5
Step2: <a name="introduction"></a>
Introduction
So far we've seen the basics of neural networks, how they can be used for encoding large datasets, or for predicting labels.... | 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... |
7,709 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Neural Network 예제
Step1: Tensorflow로 구현되어 있는 golbin님의 예제에서 같은 문제를 가지고 pytorch로 구현하도록 하겠습니다.
털과 날개가 있는지 없는지에 따라서 포유류와 조류를 분류하는 신경망 모델입니다.
pytorch의 정형화된 구조
pyTorch는 다음과 정형화된 형태를 사용할 수 있을 것으로... | Python Code:
%matplotlib inline
Explanation: Neural Network 예제
End of explanation
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
# [털, 날개]
x_data = torch.Tensor(
[[0, 0], [1, 0], [1, 1], [0, 0], [0, 0], [0, 1]])
# 0: 기타 , 1: 포유류, 2: 조류
y_data = torch.LongTenso... |
7,710 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Outline
Glossary
2. Mathematical Groundwork
Previous
Step1: Import section specific modules
Step3: 2.8. The Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT)<a id='math... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
Explanation: Outline
Glossary
2. Mathematical Groundwork
Previous: 2.7 Fourier Theorems
Next: 2.9 Sampling Theory
Import standard modules:
End of explanatio... |
7,711 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TFX Components Walk-through
Learning Objectives
Develop a high level understanding of TFX pipeline components.
Learn how to use a TFX Interactive Context for prototype development of TFX pip... | Python Code:
import os
import time
from pprint import pprint
import absl
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
import tfx
from tensorflow_metadata.proto.v0 import schema_pb2
from tfx.components import (
CsvExampl... |
7,712 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
05 - Support Vector Machines
by Alejandro Correa Bahnsen and Jesus Solano
version 1.4, January 2019
Part of the class Practical Machine Learning
This notebook is licensed under a Creative Co... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('bmh')
Explanation: 05 - Support Vector Machines
by Alejandro Correa Bahnsen and Jesus Solano
version 1.4, January 2019
Part of the class Practical Machine Learning
This notebook is licensed under a ... |
7,713 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ACM SIGKDD Austin
Advanced Machine Learning with Python
Class 1
Step1: Ever since I discovered Watermark, I love it because it automatically documents the package versions, and information ... | Python Code:
%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
%load_ext watermark
%watermark -a "Jaya Zenchenko" -n -t -z -u -h -m -w -v -p scikit-learn,matplotlib,pandas,seaborn,numpy,scipy,conda
Explanation: ACM SIGKDD Austin
Advanced Machine Learning with Python
Class 1: Pre-Model W... |
7,714 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Orbit Plot
REBOUND comes with a simple way to plot instantaneous orbits of planetary systems. To show how this works, let's setup a test simulation with 4 planets.
Step1: To plot these init... | Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1)
sim.add(m=0.1, e=0.041, a=0.4, inc=0.2, f=0.43, Omega=0.82, omega=2.98)
sim.add(m=1e-3, e=0.24, a=1.0, pomega=2.14)
sim.add(m=1e-3, e=0.24, a=1.5, omega=1.14, l=2.1)
sim.add(a=-2.7, e=1.4, f=-1.5,omega=-0.7) # hyperbolic orbit
Explanation: Orbit Plot
R... |
7,715 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify ... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'mpi-esm-1-2-lr', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MPI-M
Source ID: MPI-ESM-1-2-LR
Topic: Seaice
Sub-Topics: Dynamics, T... |
7,716 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This quickstart guide explains how to join two tables A and B using TF-IDF
similarity measure. First, you need to import the required packages
as follows (if you have installed py_stringsi... | Python Code:
# Import libraries
import py_stringsimjoin as ssj
import py_stringmatching as sm
import pandas as pd
import os
import sys
print('python version: ' + sys.version)
print('py_stringsimjoin version: ' + ssj.__version__)
print('py_stringmatching version: ' + sm.__version__)
print('pandas version: ' + pd.__versi... |
7,717 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing Accuracy
Step1: Next, we evaluate scikit-learn accuracy where we predict feed implementation based on latency.
Step2: As you can see, scikit-learn has a 99% accuracy rate. We now... | Python Code:
import graphviz
import pandas
from sklearn import tree
from sklearn.model_selection import train_test_split
clf = tree.DecisionTreeClassifier()
input = pandas.read_csv("/home/glenn/git/clojure-news-feed/client/ml/etl/throughput.csv")
data = input[input.columns[6:9]]
target = input['cloud']
X_train, X_test,... |
7,718 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic regres... | Python Code:
import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic ... |
7,719 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example time line of chords represented as binary pitch class sets in a single song
Step1: Distribution of pitch classes accross all songs
Step2: Observation
Step3: Distribution of chords... | Python Code:
draw_track(find_track('Yesterday')[0])
Explanation: Example time line of chords represented as binary pitch class sets in a single song:
End of explanation
pc_histogram = pd.DataFrame({'pitch_class': pcs_columns, 'relative_count': nonsilent_chords[pcs_columns].mean()})
stem(pc_histogram['relative_count'])
... |
7,720 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This example shows how to use k-NN classifier to classify a dataset. We will first generate a binary classification dataset consisitng of 2D feature vectors, randomly sampled from two Gaussi... | Python Code:
import numpy
N = 5
Explanation: This example shows how to use k-NN classifier to classify a dataset. We will first generate a binary classification dataset consisitng of 2D feature vectors, randomly sampled from two Gaussian distributions. We will then learn a k-NN classifier to separate the two classes.
E... |
7,721 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Goal
Initial assessment of how accuracy of DESeq2 changes with the BD window used to select 'heavy' fraction samples
Just testing on 'validation' run which just has 100% incorporators
User v... | Python Code:
workDir = '/home/nick/notebook/SIPSim/dev/bac_genome1210/'
genomeDir = '/home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/'
R_dir = '/home/nick/notebook/SIPSim/lib/R/'
Explanation: Goal
Initial assessment of how accuracy of DESeq2 changes with the BD window used to select 'heavy' fraction samples
Just ... |
7,722 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
1b. Fixed flux spinodal decomposition on a square domain
Use Binder For Live Examples
Define $f_0$
Define the Equation
Solve the Equation
Run the Example Locally
Movie of E... | Python Code:
%matplotlib inline
import sympy
import fipy as fp
import numpy as np
A, c, c_m, B, c_alpha, c_beta = sympy.symbols("A c_var c_m B c_alpha c_beta")
f_0 = - A / 2 * (c - c_m)**2 + B / 4 * (c - c_m)**4 + c_alpha / 4 * (c - c_alpha)**4 + c_beta / 4 * (c - c_beta)**4
print f_0
sympy.diff(f_0, c, 2)
Explanation:... |
7,723 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Index
Modeling
E-R Diagrams
Relational model
SQL
SELECT
JOIN
Exercises
Using SQLAlchemy
Why do we normalize?
SQL security issues
1) Modeling
When the amount or the complexity of the data we ... | Python Code:
%load -r 1-16 solutions/12_01_Databases.py
Explanation: Index
Modeling
E-R Diagrams
Relational model
SQL
SELECT
JOIN
Exercises
Using SQLAlchemy
Why do we normalize?
SQL security issues
1) Modeling
When the amount or the complexity of the data we work with overwhelms us, we look for tools able to help us. D... |
7,724 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EinMix
Step1: Logic of EinMix is very close to the one of einsum.
If you're not familiar with einsum, follow these guides first
Step2: ResMLP — original implementation
Building blocks of ... | Python Code:
from einops.layers.torch import EinMix as Mix
Explanation: EinMix: universal toolkit for advanced MLP architectures
Recent progress in MLP-based architectures demonstrated that very specific MLPs can compete with convnets and transformers (and even outperform them).
EinMix allows writing such architectures... |
7,725 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Repository AOIs
This notebook gathers all the aois used in this repo and saves them as a geojson file.
To grab a single aoi geojson string to add to your notebook, just uncomment the line th... | Python Code:
import json
import geojson
from geojson import Polygon, Feature, FeatureCollection
# # Local code for compact printing of geojson
from utils import CompactFeature, CompactFeatureCollection
Explanation: Repository AOIs
This notebook gathers all the aois used in this repo and saves them as a geojson file.
To... |
7,726 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Beaming and Boosting
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want t... | Python Code:
!pip install -I "phoebe>=2.0,<2.1"
Explanation: Beaming and Boosting
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
%matplotlib inlin... |
7,727 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1><center>How to export 🤗 Transformers Models to ONNX ?<h1><center>
[ONNX](http
Step1: How to leverage runtime for inference over an ONNX graph
As mentionned in the introduction, ONNX is... | Python Code:
import sys
!{sys.executable} -m pip install --upgrade git+https://github.com/huggingface/transformers
!{sys.executable} -m pip install --upgrade torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
!{sys.executable} -m pip install --upgrade onnxruntime==1.4.0
!{sys.... |
7,728 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Metasploit Payload Size
Or
Step1: We'll start with displaying payload size against the fraction of exploits which will work (or not work) for that size. It looks like any payload over 2048 ... | Python Code:
%matplotlib inline
import os
import re
import sys
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
# Set up a path to the Metasploit project's code.
basepath = os.path.join('/', 'home', 'dnelson', 'projects', 'msf-stats')
rootdir = os.path.join(basepath, 'metaspl... |
7,729 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step 0 - hyperparams
vocab_size is all the potential words you could have (classification for translation case)
and max sequence length are the SAME thing
decoder RNN hidden units are usuall... | Python Code:
batch_size = 1
#full_train_size = 55820
#train_size = 55800
#small_train_size = 6000 #just because of performance reasons, no statistics behind this decision
#test_size = 6200
data_path = '../../../../Dropbox/data'
phae_path = data_path + '/price_hist_autoencoder'
csv_in = '../price_history_03_seq_start_su... |
7,730 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Handling Time
When performing feature engineering with temporal data, carefully selecting the data that is used for any calculation is paramount. By annotating dataframes with a Woodwork tim... | Python Code:
import pandas as pd
pd.options.display.max_columns = 200
import featuretools as ft
es = ft.demo.load_mock_customer(return_entityset=True, random_seed=0)
es['transactions'].head()
Explanation: Handling Time
When performing feature engineering with temporal data, carefully selecting the data that is used for... |
7,731 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model hooks
Callback and helper function to add hooks in models
Step1: What are hooks?
Hooks are functions you can attach to a particular layer in your model and that will be executed in th... | Python Code:
from fastai.test_utils import *
Explanation: Model hooks
Callback and helper function to add hooks in models
End of explanation
tst_model = nn.Linear(5,3)
def example_forward_hook(m,i,o): print(m,i,o)
x = torch.randn(4,5)
hook = tst_model.register_forward_hook(example_forward_hook)
y = tst_model(x)
ho... |
7,732 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TITANIC
Step1: Here's a sqlite database for you to store the data once it's ready
Step2: =>YOUR TURN!
Use pandas to open up the csv.
Read the documentation to find out how
Step3: Explori... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas.io.sql as pd_sql
import sqlite3 as sql
%matplotlib inline
Explanation: TITANIC: Wrangling the Passenger Manifest
Exploratory Analysis with Pandas
This tutorial is based on the Kaggle Competition,
"Predicting Survival Aboar... |
7,733 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset o... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
Explanation: Language Translation
In this project, you’re going ... |
7,734 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
What subsets of scientific questions tend to be answered correctly by the same subjects?
Mining
Step1: Search for interesting rules
Interesting rules are more likely to be the ones with hig... | Python Code:
from orangecontrib.associate.fpgrowth import *
import pandas as pd
from numpy import *
questions = correctedScientific.columns
correctedScientificText = [[] for _ in range(correctedScientific.shape[0])]
for q in questions:
for index in range(correctedScientific.shape[0]):
r = correctedScienti... |
7,735 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.... | Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network ... |
7,736 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Desafio 1
1. Entender a API de Streaming do Twitter
Ver slides 14 até 21.
Da mesma forma que fizemos na API REST do Twitter, temos que salvar as chaves de acesso, bem como definir o objeto O... | Python Code:
import tweepy
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''
autorizar = tweepy.OAuthHandler(consumer_key, consumer_secret)
autorizar.set_access_token(access_token, access_token_secret)
Explanation: Desafio 1
1. Entender a API de Streaming do Twitter
Ver slides 14 até 21.... |
7,737 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas II - Working with DataFrames
Step1: We'll be using the MovieLens dataset in many examples going forward. The dataset contains 100,000 ratings made by 943 users on 1,682 movies.
Step2... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option('max_columns', 50)
Explanation: Pandas II - Working with DataFrames
End of explanation
# pass in column names for each CSV
u_cols = ['user_id', 'age', 'sex', 'occupation', 'zip_code']
df_users = pd.read_csv('data/MovieLens... |
7,738 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualising statistical significance thresholds on EEG data
MNE-Python provides a range of tools for statistical hypothesis testing
and the visualisation of the results. Here, we show a few ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
import mne
from mne.channels import find_ch_adjacency, make_1020_channel_selections
from mne.stats import spatio_temporal_cluster_test
np.random.seed(0)
# Load the data
path = mne.datasets.kiloword.data_path() + '/kword_me... |
7,739 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step3: Gathering system data - Python for System Administrators
Goals
Step6: Parsing /proc
Linux /proc filesystem is a cool place to get data
In the next example we'll see how to get
Step8:... | Python Code:
import psutil
import glob
import sys
import subprocess
#
# Our code is p3-ready
#
from __future__ import print_function, unicode_literals
def grep(needle, fpath):
A simple grep implementation
goal: open() is iterable and doesn't
need splitlines()
goal: comprehension can filte... |
7,740 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy Exercise 4
Imports
Step1: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or nodes that are connected to each other by edges or lines. If those edges don... | Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: Numpy Exercise 4
Imports
End of explanation
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or node... |
7,741 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial 17 - Navier Stokes equations
Keywords
Step1: 3. Affine Decomposition
Step2: 4. Main program
4.1. Read the mesh for this problem
The mesh was generated by the data/generate_mesh.ip... | Python Code:
from ufl import transpose
from dolfin import *
from rbnics import *
Explanation: Tutorial 17 - Navier Stokes equations
Keywords: DEIM, supremizer operator
1. Introduction
In this tutorial, we will study the Navier-Stokes equations over the two-dimensional backward-facing step domain $\Omega$ shown below:
<... |
7,742 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sensitivity Analysis
Test here
Step1: Setting up the base model
For a first test
Step2: Define parameter uncertainties
We will start with a sensitivity analysis for the parameters of the f... | Python Code:
from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
%matplotlib inline
Explanation: Sensitivity Analysis
Test here: (local) sensitivity analysis of kinematic parameters with respect to a defined objective function. Aim: test how sensitivity the resulting model is... |
7,743 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create a list from the dictionary keys
Step2: Create a list from the dictionary values | Python Code:
dict = {'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'],
'year': [2012, 2012, 2013, 2014, 2014],
'fireReports': [4, 24, 31, 2, 3]}
Explanation: Title: Creating Lists From Dictionary Keys And Values
Slug: create_list_from_dictionary_keys_and_values
Summary: Creating Lists F... |
7,744 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matrix Multiplication
Various ways of implementing different matrix multiplications.
Read the documentation embedded in the code.
Step1: In this example, the assertions essentially tell the... | Python Code:
# -*- coding: utf-8 -*-
from pylab import *
from pyspecdata import *
from numpy.random import random
import time
init_logging('debug')
Explanation: Matrix Multiplication
Various ways of implementing different matrix multiplications.
Read the documentation embedded in the code.
End of explanation
a_nd = ndd... |
7,745 | 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... |
7,746 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparison of bubble trajectories
Start by loading some boiler plate
Step1: And some more specialized dependencies
Step2: Helper routines
Step3: Configuration for this figure. config.ref... | Python Code:
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
from scipy.interpolate import UnivariateSpline
import json
import pandas as pd
from functools import partial... |
7,747 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: TFX Keras Component Tutorial
A Component-by-Component Introduction to TensorFlow Extended (TFX)
Note
Step2: Install TFX
Note
Step3: Did you r... | 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... |
7,748 | 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... |
7,749 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercises
Step1: Exercise 1
Step2: Exercise 2
Step3: Exercise 3
Step4: b. Confidence Intervals.
Calculate the first, second, and third confidence intervals.
Plot the PDF and the firs... | Python Code:
# Useful Functions
class DiscreteRandomVariable:
def __init__(self, a=0, b=1):
self.variableType = ""
self.low = a
self.high = b
return
def draw(self, numberOfSamples):
samples = np.random.randint(self.low, self.high, numberOfSamples)
return samples
... |
7,750 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div style='background-image
Step1: 1. Chebyshev derivative method
Exercise
Define a python function call "get_cheby_matrix(nx)" that initializes the Chebyshev derivative matrix $D_{ij}$, c... | Python Code:
# This is a configuration step for the exercise. Please run it before calculating the derivative!
import numpy as np
import matplotlib.pyplot as plt
from ricker import ricker
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
Explanation: <div style='background-image: url("../../share/images/he... |
7,751 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Enlib write_map bug
Step1: We make a full-sky arcminute resolution geometry. I've only been able to reproduce this bug for res=1.0.
Step2: We do a pix2sky that is needed by map2alm and mak... | Python Code:
%load_ext autoreload
%autoreload 2
from enlib import enmap,wcs as mwcs
import numpy as np
import sys,os
Explanation: Enlib write_map bug
End of explanation
res = 1.0
shape, wcs = enmap.fullsky_geometry(res=res*np.pi/180./60., proj="car")
shape = (3,)+shape
Explanation: We make a full-sky arcminute resoluti... |
7,752 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
KBMOD Documentation
This notebook demonstrates the basics of the kbmod image processing and searching python API
Before importing, make sure to run
source setup.bash
in the root directory, a... | Python Code:
from kbmodpy import kbmod as kb
import numpy
path = "../data/demo/"
Explanation: KBMOD Documentation
This notebook demonstrates the basics of the kbmod image processing and searching python API
Before importing, make sure to run
source setup.bash
in the root directory, and that you are using the python3 ke... |
7,753 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The purpose of this post is to demonstrate a sample template for a Bokeh server. Although the example demonstrated in this notebook is rather simple (linear regression), keeping your Bokeh s... | Python Code:
### Bokeh Linear Regression Example
### Written By: Eric Strong
### Last Updated: 2017/05/10
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression as LR
from bokeh.io import curdoc
from bokeh.plotting import figure
from bokeh.models.ranges import Range1d
from bokeh.... |
7,754 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Facies classification using Random Forest
Contest entry by
Step1: A complete description of the dataset is given in the Original contest notebook by Brendon Hall, Enthought. A total of fo... | Python Code:
###### Importing all used packages
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
import seaborn a... |
7,755 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualizando datos de entrada
Step1: Algoritmo de Regresion Lineal en TensorFlow
Step2: Regresion Lineal en Polinomios de grado N
Step3: Regularizacion
Para manejar un poco mejor el impac... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
# Regresa 101 numeros igualmmente espaciados en el intervalo[-1,1]
x_train = np.linspace(-1, 1, 101)
# Genera numeros pseudo-aleatorios multiplicando la matriz x_train * 2 y
# sumando a cada elemento un ruido (una matriz del mismo tamanio con puros numero... |
7,756 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
工作流程：
1.问题定义
2.获取训练集和测试集
3.清洗数据，做好准备
4.分析并识别模式，探索数据
5.建立模型，预测并解决问题
6.可视化解决问题的步骤以及最终的解决方案
7.提交答案
工作目标：分类，将样本进行分类，考虑其与目标类的相关性；相关性，特征与目标的相关性；转换，将特征值转化为符合模型的类型；补充，补充存在的缺失值；修正，修正错误的特征值；创造，创造新的特征值... | Python Code:
# data analysis and wrangling
import pandas as pd
import numpy as np
import random as rnd
# visualization
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensem... |
7,757 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
README
Notebook to test the Complex class as well as parsing code from cobrame/ecolime
From COBRAme/ECOLIme...
Flat files / ProcessData
Step1: Protein complexes - ComplexData and ComplexFor... | Python Code:
import ecolime
import ecolime.flat_files
Explanation: README
Notebook to test the Complex class as well as parsing code from cobrame/ecolime
From COBRAme/ECOLIme...
Flat files / ProcessData
End of explanation
# First load the list of complexes which tells you complexes + subunit stoichiometry
# Converts th... |
7,758 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
read from an Excel file
documentation
Step1: write to a comma separated value (.csv) file
documentation | Python Code:
file_name_string = 'C:/Users/Charles Kelly/Desktop/Exercise Files/02_07/Begin/EmployeesWithGrades.xlsx'
employees_df = pd.read_excel(file_name_string, 'Sheet1', index_col=None, na_values=['NA'])
Explanation: read from an Excel file
documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas... |
7,759 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.... | Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network ... |
7,760 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Serie de Taylor
\begin{equation}
f(x) = \sum_{n=0}^{\infty} \frac{f^{(n)}(a)}{n!} (x - a)^{n}
\end{equation}
Expansión hacia adelante
\begin{equation}
f(x) \approx f(a) + f'(a) (x - ... | Python Code:
def g(x):
resultado = - 0.1*x**4 - 0.15*x**3 - 0.5*x**2 - 0.25*x + 1.2
return resultado
def fx_adelante(f,x,h):
derivada = (f(x+h) - f(x))/h
return derivada
print('f\'(0.5) =', fx_adelante(g,0.5,0.25))
Explanation: Serie de Taylor
\begin{equation}
f(x) = \sum_{n=0}^{\infty} \frac{f^{(n)... |
7,761 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: Image captioning with visual attention
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Down... | 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... |
7,762 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introducing Functions
One of the core principles of any programming language is, "Don't Repeat Yourself". If you have an action that should occur many times, you can define that action once ... | Python Code:
# Let's define a function.
def function_name(argument_1, argument_2):
# Do whatever we want this function to do,
# using argument_1 and argument_2
# Use function_name to call the function.
function_name(value_1, value_2)
Explanation: Introducing Functions
One of the core principles of any programming la... |
7,763 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
'orb' 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).
Step1:... | Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
Explanation: 'orb' 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
from phoebe import u # units
logger = pho... |
7,764 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Monte Carlo Simulations (Section 8.3 in Text)
Monte Carlo simulations are used to calculate probabilities using random numbers when the probabilities are difficult (or impossible) to calcula... | Python Code:
from IPython.display import Image
Image(url='http://upload.wikimedia.org/wikipedia/commons/thumb/b/b4/The_Sun_by_the_Atmospheric_Imaging_Assembly_of_NASA%27s_Solar_Dynamics_Observatory_-_20100819.jpg/251px-The_Sun_by_the_Atmospheric_Imaging_Assembly_of_NASA%27s_Solar_Dynamics_Observatory_-_20100819.jpg')
E... |
7,765 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step4: Boilerplate for graph visualization
Step5: Load the data
Run 00_download_data.ipynb if you haven't already
Step6: Create a simple classifier with low-level TF Ops
Step7: We can run... | Python Code:
# This is for graph visualization.
from IPython.display import clear_output, Image, display, HTML
def strip_consts(graph_def, max_const_size=32):
Strip large constant values from graph_def.
strip_def = tf.GraphDef()
for n0 in graph_def.node:
n = strip_def.node.add()
n.MergeFrom... |
7,766 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TensorFlow Tutorial
Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that w... | Python Code:
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
%matplotlib inline
np.random.seed(1)
Explanation: TensorFlow Tutorial
Welcome to... |
7,767 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Statistical inference
Here we will briefly cover multiple concepts of inferential statistics in an
introductory manner, and demonstrate how to use some MNE statistical functions.
Step1: Hyp... | Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
from functools import partial
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa, analysis:ignore
import mne
from mne.stats import (ttest_1samp_no_p, bonferroni... |
7,768 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Package
Step1: Another utility class
Step2: Scaling features to a range
Example
Step3: MaxAbsScaler works in a very similar fashion, but scales in a way that the training data lies within... | Python Code:
from sklearn import preprocessing
import numpy as np
X = np.array([[1.,-1.,2.],
[2., 0.,0.],
[0., 1.,-1.]])
X_scaled = preprocessing.scale(X)
X_scaled
X_scaled.mean(axis = 0)
X_scaled.std(axis=0)
Explanation: Package: sklearn.preprocessing
change raw feature vectors into a repre... |
7,769 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Übungen zu SQL
Teacher
Wir möchten eine Abfrage erstellen, die einem Programm die Namen aller Lehrer für jeden Kurs und jeden Schüler übergibt.
Im späteren Ausdruck gibt es im Formular aller... | Python Code:
%load_ext sql
%sql mysql://steinam:steinam@localhost/celko
%%sql
select * from Register;
Explanation: Übungen zu SQL
Teacher
Wir möchten eine Abfrage erstellen, die einem Programm die Namen aller Lehrer für jeden Kurs und jeden Schüler übergibt.
Im späteren Ausdruck gibt es im Formular allerdings nur Platz... |
7,770 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook, we mainly utilize extreme gradient boost to improve the prediction model originially proposed in TLE 2016 November machine learning tuotrial. Extreme gradient boost can be ... | Python Code:
%matplotlib inline
import pandas as pd
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import matplotlib.colors as colors
import xgboost as xgb
import numpy as np
from sklearn.metrics import confusion_matrix, f1_score, accuracy... |
7,771 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <h1 style="text-align
Step2: Once the Naive Bayes Classifier has been trained with the train() method, we can use it to classify new elements | Python Code:
from collections import Counter, defaultdict
import numpy as np
class NaiveBaseClass:
def calculate_relative_occurences(self, list1):
no_examples = len(list1)
ro_dict = dict(Counter(list1))
for key in ro_dict.keys():
ro_dict[key] = ro_dict[key] / float(no_examples)
... |
7,772 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
One of the newest features of BigBang is the ability to analyze git info for each project. For now, we mostly just look at commits over time. We can also analyze individual committers to run... | Python Code:
url = "http://mail.python.org/pipermail/scipy-dev/"
arx = Archive(url,archive_dir="../archives")
repo = repo_loader.get_repo("bigbang")
full_info = repo.commit_data;
act = arx.data.groupby("Date").size();
act = act.resample("D", how=np.sum)
act = act[act.index.year <= 2014]
act_week = act.resample("W", how... |
7,773 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Initializing filters on known motifs
In the scenario where data is scarse, it is often useful to initialize the filters of the first convolutional layer to some known position weights matric... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
# RBP PWM's
from concise.data import attract
dfa = attract.get_metadata()
dfa
# TF PWM's
from concise.data import encode
dfe = encode.get_metadata()
dfe
# TF PWM's
from concise.data import hocomoco
dfh = hocomoco.get_metadata()
dfh
Explanation: Initializin... |
7,774 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Optimiztion with mystic
Step2: mystic
Step4: Diagnostic tools
Callbacks
Step6: NOTE IPython does not handle shell prompt interactive programs well, so the above should be run from a comma... | Python Code:
%matplotlib inline
Explanation: Optimiztion with mystic
End of explanation
Example:
- Minimize Rosenbrock's Function with Nelder-Mead.
- Plot of parameter convergence to function minimum.
Demonstrates:
- standard models
- minimal solver interface
- parameter trajectories using retall
# ... |
7,775 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regresión multiple utilizando grandiente descendente de TensorFlow
Step1: Input
Generamos la muestra de grado 5
Step2: Problema
Calcular los coeficientes que mejor se ajusten a la muestra ... | Python Code:
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
%matplotlib inline
import sys
sys.path.append('/home/pedro/git/ElCuadernillo/ElCuadernillo/20160220_TensorFlowRegresionMultiple')
import gradient_descent_tensorflow as gdt
Explanation:... |
7,776 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cual es la mejor estrategia para adivinar?
Por Miguel Escalona
Step1: ¡Adivina Quién es!
El juego de adivina quién es, consiste en adivinar el personaje que tu oponente ha seleccionado ante... | Python Code:
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (15.0, 6.0)
import numpy as np
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
from IPython.display import Image
Explanation: Cual es la mejor estrategia para adivina... |
7,777 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plotting
This tutorial explains the high-level interface to plotting provided by the Bundle. You are of course always welcome to access arrays and plot manually.
PHOEBE 2.4 uses autofig 1.2... | Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
Explanation: Plotting
This tutorial explains the high-level interface to plotting provided by the Bundle. You are of course always welcome to access arrays and plot manually.
PHOEBE 2.4 uses autofig 1.2 as an intermediate layer for highend functionality to matplotlib.
S... |
7,778 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Partial derivative equations
A linear equation is defined as
Step1: Function to calculate loss
$$ L(b,m) = \frac{1}{2N} \sum_{i=1}^N (b + m x_{i} - y_{i})^2 $$
Step2: Function to calculate... | Python Code:
%matplotlib inline
import random
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import animation, rc
from sklearn.linear_model import LinearRegression
from IPython.display import HTML
Explanation: Partial derivative equations
A linear equation is defined as:
$$ y(x_{... |
7,779 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Transliteration
Transliteration is the conversion of a text from one script to another.
For instance, a Latin transliteration of the Greek phrase "Ελληνική Δημοκρατία", usually translated as... | Python Code:
from polyglot.transliteration import Transliterator
Explanation: Transliteration
Transliteration is the conversion of a text from one script to another.
For instance, a Latin transliteration of the Greek phrase "Ελληνική Δημοκρατία", usually translated as 'Hellenic Republic', is "Ellēnikḗ Dēmokratía".
End ... |
7,780 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Object Oriented Programming
What is an Object?
First some semantics
Step1: Note the reference to object, this means that our new class inherits from object. We won't be going into too much ... | Python Code:
class A(object):
pass
Explanation: Object Oriented Programming
What is an Object?
First some semantics:
- An object is essentially a container which holds some data, and crucially some associated methods for working with that data.
- We define objects, and their behaviours, using something called a ... |
7,781 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas 3
Step1: <a id=wants></a>
Example
Step2: Reminders
What kind of object does each of the following produce?
Step3: Wants
We might imagine doing several different things with this da... | Python Code:
import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np # foundation for Pandas
%matplotlib ... |
7,782 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Applying a 3D convolutional neural network to the data
Welcome everyone to my coverage of the Kaggle Data Science Bowl 2017. My goal here is that anyone, even people new to kaggle, can follo... | Python Code:
import dicom # for reading dicom files
import os # for doing directory operations
import pandas as pd # for some simple data analysis (right now, just to load in the labels data and quickly reference it)
# Change this to wherever you are storing your data:
# IF YOU ARE FOLLOWING ON KAGGLE, YOU CAN ONLY PL... |
7,783 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loss under a Local Perceptron model
Step1: Loss under a Mixture of Gaussians model
Step2: We use autograd for functions that deliver gradients of those losses
Step3: Just a pretty display... | Python Code:
def sigmoid(phi):
return 1.0/(1.0 + np.exp(-phi))
def calc_prob_class1(params):
# Sigmoid perceptron ('logistic regression')
tildex = X - params['mean']
W = params['wgts']
phi = np.dot(tildex, W)
return sigmoid(phi) # Sigmoid perceptron ('logistic regression')
def calc_membership(p... |
7,784 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Values
<script type="text/javascript">
localStorage.setItem('language', 'language-py')
</script>
<table align="left" style="margin-right
Step2: Example
In the followi... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this fi... |
7,785 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Check Environment
This notebook checks that you have correctly created the environment and that all packages needed are installed.
Environment
The next command should return a line like (Mac... | Python Code:
import os
import sys
sys.executable
Explanation: Check Environment
This notebook checks that you have correctly created the environment and that all packages needed are installed.
Environment
The next command should return a line like (Mac/Linux):
/<YOUR-HOME-FOLDER>/anaconda/envs/ztdl/bin/python
or ... |
7,786 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examining the MPI-Leipzig Mind-Brain-Body Dataset
The MRI data are available at https
Step1: We can investigate what keys are available in any .tsv header by examining the corresponding .js... | Python Code:
%%bash
ls MPI-Leipzig/behavioral_data_MPILMBB/phenotype | head
Explanation: Examining the MPI-Leipzig Mind-Brain-Body Dataset
The MRI data are available at https://openfmri.org/dataset/ds000221/. The behavioral data are available via NITRC: https://www.nitrc.org/projects/mpilmbb/. Note I was required to ed... |
7,787 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Linearity
We consider a second order system of the form
Step1: Initial position and intial velocity cases
\begin{align}
f(t) = 0, \quad x(0) = 1 m, \quad \dot{x}(0) = 0 \
f(t) = 0, ... | Python Code:
%matplotlib inline
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
from ipywidgets import interact
import ipywidgets as widgets
Explanation: Linearity
We consider a second order system of the form:
\begin{align}
G(s) = \frac{1}{ms^2 + cs + k}
\end{align}
with
\begin{align}
... |
7,788 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Node Label Prediction \ Link Prediction
Step1: We will start by node label prediction. Download this network. It contains protein communications in Baker’s yeast. Each node (protein) has a ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
import networkx as nx
%matplotlib inline
Explanation: Node Label Prediction \ Link Prediction
End of explanation
g = nx.read_gml('./data/ppi.CC.gml.txt')
cc = list(nx.connected_components(g))
g = nx.subgraph(g,cc[0])
g = nx.relabel.conve... |
7,789 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Streamfunction and velocity potential from zonal and meridional wind component
windspharm is a Python library developed by
Andrew Dawson which provides an pythonic interface to the pyspharm... | Python Code:
from windspharm.standard import VectorWind
from windspharm.tools import prep_data, recover_data, order_latdim
Explanation: Streamfunction and velocity potential from zonal and meridional wind component
windspharm is a Python library developed by
Andrew Dawson which provides an pythonic interface to the py... |
7,790 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PSF Photometry
Version 0.1
We're going to try to piece together the different elements of a PSF photometry pipeline from scratch. Getting that done in one notebook means we'll have to cut so... | Python Code:
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
import astropy.convolution
import pandas as pd
f = fits.open("calexp-0527247_10.fits")
image = f[1].data
Explanation: PSF Photometry
Version 0.1
We're going to try to piece together the different elements of a PSF photometry pip... |
7,791 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ISL Lab 2.3 Introduction to Statistical Computing with Python
Step1: 2.3.1 Basic Commands
Step2: 2.3.2 Graphics
Step3: Have to dig back into MatPlotLib to set axis labels, so all is not p... | Python Code:
import numpy as np
import pandas as pd
import scipy
import scipy.stats
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.simplefilter('ignore',FutureWarning)
Explanation: ISL Lab 2.3 Introduction to Statistical Computing with Python
End of explanation
np.arange(6)
a = np.arange... |
7,792 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modeling and Simulation in Python
Chapter 20
Copyright 2017 Allen Downey
License
Step1: Dropping pennies
I'll start by getting the units we need from Pint.
Step2: And defining the initial ... | 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... |
7,793 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Download and process the Bay Area's street network
Step1: Download and extract the counties shapefile if it doesn't already exist, then load it
To use OSMnx, we need a polygon of the Bay Ar... | Python Code:
import os, zipfile, requests, pandas as pd, geopandas as gpd, osmnx as ox
ox.config(use_cache=True, log_console=True)
# point to the shapefile for counties
counties_shapefile_url = 'http://www2.census.gov/geo/tiger/GENZ2016/shp/cb_2016_us_county_500k.zip'
# identify bay area counties by fips code
bayarea =... |
7,794 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Structures
Classes
Step3: Magic methods
There are a bunch of magic methods available in Python. You can overide some of them
Step4: Iterators & Generators
Step5: Tricks in function... | Python Code:
class MyOwnClass(object):
This is a documentation for my class, so anyone can read it.
CLASS_CONST = 42
def __init__(self, argument1, default_argument2=1):
This is a constructor of MyOwnClass. It saves all arguments
as a 'protected' variables of a class.
... |
7,795 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Extinction (ebv, Av, & Rv)
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't ... | Python Code:
!pip install -I "phoebe>=2.2,<2.3"
Explanation: Extinction (ebv, Av, & Rv)
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
%matplotlib... |
7,796 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Excercises Electric Machinery Fundamentals
Chapter 9
Problem 9-3
Step1: Description
Suppose that the motor in Problem 9-1 is started and the auxiliary winding fails open while the rotor is ... | Python Code:
%pylab notebook
%precision %.4g
Explanation: Excercises Electric Machinery Fundamentals
Chapter 9
Problem 9-3
End of explanation
V = 120 # [V]
p = 4
R1 = 2.0 # [Ohm]
R2 = 2.8 # [Ohm]
X1 = 2.56 # [Ohm]
X2 = 2.56 # [Ohm]
Xm = 60.5 # [Ohm]
n = 400 # [r/min]
Prot = 51 # [W]
n_sync = 180... |
7,797 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
https
Step1: HTTP Methods with curl
GET
Step2: POST
Step3: Delete
Step4: Install JSON-server
http
Step5: HTTP Methods
GET - query
Step6: POST - add new
Step7: NOTE
Step8: PUT - updat... | Python Code:
!curl -o todos-1.json https://jsonplaceholder.typicode.com/todos/1
!cat todos-1.json
Explanation: https://www.baeldung.com/curl-rest
https://jsonplaceholder.typicode.com/
is a great REST API test site
Save curl output
save curl output response to a file
$ curl -o jsonplaceholder.html https://jsonplaceholde... |
7,798 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Libraries and Packages
Step1: Connecting to National Data Service
Step2: Extracting Data of Midwestern states of the United states from 1992 - 2016.
The following query will extract data f... | Python Code:
import pymongo
from pymongo import MongoClient
import time
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
import folium
import datetime as dt
import random as rnd
import warnings
import datetime as dt
import csv
%matplotlib inlin... |
7,799 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Prepara Alta Vista Para Modelacion
Se prepara la cuenca de alta vista para que sea modelada en el SIATA en tiempo real, en este caso se preparan ambas cuencas, tanto aguas arriba como aguas ... | Python Code:
%matplotlib inline
from wmf import wmf
import numpy as np
import pylab as pl
import datetime as dt
import os
ruta = '/media/nicolas/discoGrande/01_SIATA/'
Explanation: Prepara Alta Vista Para Modelacion
Se prepara la cuenca de alta vista para que sea modelada en el SIATA en tiempo real, en este caso se pr... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.