Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
|---|---|---|
8,900 | 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... |
8,901 | 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... |
8,902 | 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="#YOUR-NAME-(NEPTUN)" data-toc-modified-id="YOUR-NAME-(NEPTUN)-1"><span class="toc-item-num">1 </span>YOUR NAME (NEPTUN)</a... | Python Code:
import pandas as pd
if pd.__version__ < '1':
print("WARNING: Pandas version older than 1.0.0: {}".format(pd.__version__))
else:
print("Pandas version OK: {}".format(pd.__version__))
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#YOUR-NAME-(NEPTUN)" data-toc-modified-id="YOUR... |
8,903 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h3>artcontrol gallery</h3>
Create gallery for artcontrol artwork.
Uses Year / Month / Day format.
Create blog post for each day there is a post.
It will need to list the files for that day... | Python Code:
import os
import arrow
import getpass
raw = arrow.now()
myusr = getpass.getuser()
galpath = ('/home/{}/git/artcontrolme/galleries/'.format(myusr))
galpath = ('/home/{}/git/artcontrolme/galleries/'.format(myusr))
popath = ('/home/{}/git/artcontrolme/posts/'.format(myusr))
class DayStuff():
def g... |
8,904 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vertex client library
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Once you've installed the Vertex client library and Google clo... | Python Code:
import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
Explanation: Vertex client library: AutoML tabular classification model for batch prediction with ... |
8,905 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Understanding the vanishing gradient problem through visualization
There're reasons why deep neural network could work very well, while few people get a promising result or make it possible ... | Python Code:
import sys
sys.path.append('./mnist/')
from train_mnist import *
Explanation: Understanding the vanishing gradient problem through visualization
There're reasons why deep neural network could work very well, while few people get a promising result or make it possible by simply make their neural network dee... |
8,906 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Preparing Data
In this step, we are going to load data from disk to the memory and properly format them so that we can processing them in the next "preprocessing" stage.
Step1: Loading Toke... | Python Code:
# Loading metadata from trainning database
con = sqlite3.connect("F:/FMR/data.sqlite")
db_documents = pd.read_sql_query("SELECT * from documents", con)
db_authors = pd.read_sql_query("SELECT * from authors", con)
data = db_documents # just a handy alias
data.head()
Explanation: Preparing Data
In this step,... |
8,907 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with sEEG data
MNE-Python supports working with more than just MEG and EEG data. Here we show
some of the functions that can be used to facilitate working with
stereoelectroencephalo... | Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
# Adam Li <adam2392@gmail.com>
# Alex Rockhill <aprockhill@mailbox.org>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import fetch_fsaverage
# paths to mne dat... |
8,908 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Q1
In this question, you'll be introduced to the scikit-image package. Only a small portion of the package will be explored; you're encouraged to check it out if this interests you!
A
scikit... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import skimage.data
### BEGIN SOLUTION
### END SOLUTION
Explanation: Q1
In this question, you'll be introduced to the scikit-image package. Only a small portion of the package will be explored; you're encouraged to check it out if this interests you!
A
sci... |
8,909 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Effect Size
Credits
Step1: To explore statistics that quantify effect size, we'll look at the difference in height between men and women. I used data from the Behavioral Risk Factor Survei... | Python Code:
from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from IPython.html.widgets import interact, fixed
from IPython.html import widgets
# seed the random number generator so we all get the same results
numpy.random.seed(17)
# some nice colors fro... |
8,910 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Diffusion
Class
Step1: Self-diffusion of water
The self-diffusion coefficient of water (in micrometers<sup>2</sup>/millisecond) is dependent on the temperature and pressure. Several groups ... | Python Code:
%pylab inline
rcParams["figure.figsize"] = (8, 6)
rcParams["axes.grid"] = True
from IPython.display import display, clear_output
from mpl_toolkits.axes_grid1 import make_axes_locatable
from time import sleep
from __future__ import division
def cart2pol(x, y):
theta = arctan2(y, x)
r = sqrt(x ** 2 +... |
8,911 | Given the following text description, write Python code to implement the functionality described.
Description:
Add two numbers x and y
This is how the function will work:
add(2, 3)
5
This is how the function will work:
add(5, 7)
12
| Python Code:
def add(x: int, y: int):
return x + y |
8,912 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cleaning
Step1: 2. Print data summaries including the number of null values. Should we drop or try to correct any of the null values?
Step2: Gender and year of birth have nulls, I don't th... | Python Code:
import pandas as pd
import numpy as np
sets = ['station', 'trip', 'weather']
cycle = {}
for s in sets:
cycle[s] = pd.read_csv('cycle_share/' + s + '.csv')
cycle['trip'].head()
Explanation: Cleaning: Cycle Share
There are 3 datasets that provide data on the stations, trips, and weather from 2014-2016.
S... |
8,913 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CHAPTER 4
4.2 Algorithms
Step1: Imports, logging, and data
On top of doing the things we already know, we now additionally import also the CollaborativeFiltering algorithm, which is, as sho... | Python Code:
import sys
sys.path.append('../..')
Explanation: CHAPTER 4
4.2 Algorithms: Collaborative filtering
Having understood the basics of how an algorithm is configured, married with data, and deployed in bestPy, we are now ready to move from a baseline recommendation to something more inolved. In particular, we ... |
8,914 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pima Indian Diabetes Prediction (with Model Reload)
Import some basic libraries.
* Pandas - provided data frames
* matplotlib.pyplot - plotting support
Use Magic %matplotlib to display graph... | Python Code:
import pandas as pd # pandas is a dataframe library
import matplotlib.pyplot as plt # matplotlib.pyplot plots data
%matplotlib inline
Explanation: Pima Indian Diabetes Prediction (with Model Reload)
Import some basic libraries.
* Pandas - provided data frames
* matplotlib.pyplot - plot... |
8,915 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
One Port Tiered Calibration
Intro
A one-port network analyzer can be used to measure a two-port device, provided that the device is reciprocal. This is accomplished by performing two calibra... | Python Code:
from IPython.display import SVG
SVG('images/boxDiagram.svg')
Explanation: One Port Tiered Calibration
Intro
A one-port network analyzer can be used to measure a two-port device, provided that the device is reciprocal. This is accomplished by performing two calibrations, which is why its called a tiered cal... |
8,916 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Image compression with K-means
K-means is a clustering algorithm which defines K cluster centroids in the feature space and, by making use of an appropriate distance function, iteratively as... | Python Code:
from scipy import misc
pic = misc.imread('media/irobot.png')
Explanation: Image compression with K-means
K-means is a clustering algorithm which defines K cluster centroids in the feature space and, by making use of an appropriate distance function, iteratively assigns each example to the closest cluster c... |
8,917 | 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... |
8,918 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Guided Project 1
Learning Objectives
Step1: Step 1. Environment setup
skaffold tool setup
Step2: Modify the PATH environment variable so that skaffold is available
Step3: Environment vari... | Python Code:
import os
Explanation: Guided Project 1
Learning Objectives:
Learn how to generate a standard TFX template pipeline using tfx template
Learn how to modify and run a templated TFX pipeline
Note: This guided project is adapted from Create a TFX pipeline using templates).
End of explanation
PATH=%env PATH
%e... |
8,919 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Puerto Rico water quality measurements-results data
Adapted from ODM2 API
Step1: odm2api version used to run this notebook
Step2: Connect to the ODM2 SQLite Database
This example uses an O... | Python Code:
%matplotlib inline
import os
import matplotlib.pyplot as plt
from shapely.geometry import Point
import pandas as pd
import geopandas as gpd
import folium
from folium.plugins import MarkerCluster
import odm2api
from odm2api.ODMconnection import dbconnection
import odm2api.services.readService as odm2rs
pd._... |
8,920 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TF-Agents Authors.
Step1: 再生バッファ
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: 再生バッファ API
再生バッファのクラスには、次の定義とメソッドがあります。
``... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
8,921 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create an example dataframe
Step2: Create a function to assign letter grades | Python Code:
import pandas as pd
import numpy as np
Explanation: Title: Create A Pandas Column With A For Loop
Slug: pandas_create_column_with_loop
Summary: Create A Pandas Column With A For Loop
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Preliminaries
End of explanation
raw_dat... |
8,922 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SED fitting with naima
In this notebook we will carry out a fit of an IC model to the HESS spectrum of RX J1713.7-3946 with the naima wrapper around emcee. This tutorial will follow loosely ... | Python Code:
import naima
import numpy as np
from astropy.io import ascii
import astropy.units as u
%matplotlib inline
import matplotlib.pyplot as plt
hess_spectrum = ascii.read('RXJ1713_HESS_2007.dat', format='ipac')
fig = naima.plot_data(hess_spectrum)
Explanation: SED fitting with naima
In this notebook we will carr... |
8,923 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Impedance or reflectivity
Trying to see how to combine G with a derivative operator to get from the impedance model to the data with one forward operator.
Step2: Construct the model ... | Python Code:
import numpy as np
import numpy.linalg as la
import matplotlib.pyplot as plt
from utils import plot_all
%matplotlib inline
from scipy import linalg as spla
def convmtx(h, n):
Equivalent of MATLAB's convmtx function, http://www.mathworks.com/help/signal/ref/convmtx.html.
Makes the convolut... |
8,924 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Week 8 - Advanced Machine Learning
During the course we have covered a variety of different tasks and algorithms. These were chosen for their broad applicability and ease of use with many im... | Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
plt.gray()
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
fig, axes = plt.subplots(3,5, figsize=(12,8))
for i, ax in enumerate(axes.flatten()):
ax.imshow(X_train[i], interpolation='nearest')
plt.show()
from ke... |
8,925 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data frames 3
Step1: lang
Step2: lang
Step3: lang
Step4: lang
Step5: lang
Step7: 予習課題
Step8: lang | Python Code:
# データをCVSファイルから読み込みます。 Read the data from CSV file.
df = pd.read_csv('data/15-July-2019-Tokyo-hourly.csv')
print("データフレームの行数は %d" % len(df))
print(df.dtypes)
df.head()
Explanation: Data frames 3: 簡単なデータの変換 (Simple data manipulation)
```
ASSIGNMENT METADATA
assignment_id: "DataFrame3"
```
lang:en
In this un... |
8,926 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting Gaussian Mixture Models with EM
In this assignment you will
* implement the EM algorithm for a Gaussian mixture model
* apply your implementation to cluster images
* explore clusteri... | Python Code:
import graphlab as gl
import numpy as np
import matplotlib.pyplot as plt
import copy
from scipy.stats import multivariate_normal
%matplotlib inline
Explanation: Fitting Gaussian Mixture Models with EM
In this assignment you will
* implement the EM algorithm for a Gaussian mixture model
* apply your implem... |
8,927 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
https
Step1: 52N IOOS SOS Stable Demo -- network offering (multi-station) data request
Create pyoos "collector" that connects to the SOS end point and parses GetCapabilities (offerings)
Ste... | Python Code:
from datetime import datetime, timedelta
import pandas as pd
from pyoos.collectors.ioos.swe_sos import IoosSweSos
# convenience function to build record style time series representation
def flatten_element(p):
rd = {'time':p.time}
for m in p.members:
rd[m['standard']] = m['value']
retur... |
8,928 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align="center">TensorFlow Neural Network Lab</h1>
<img src="image/notmnist.png">
In this lab, you'll use all the tools you learned from Introduction to TensorFlow to label images of Engl... | Python Code:
import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
p... |
8,929 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LeNet Lab
Source
Step1: The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.
However, the LeNet architecture only accepts 32x32xC images, where C is the number of color channel... | Python Code:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.tes... |
8,930 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <a href="https
Step2: Model
(for cifar10)
Setting up hyperparams
Step3: This model is a hierarchical model with multiple stochastic blocks with multiple deterministic layers. You ca... | Python Code:
from google.colab import auth
auth.authenticate_user()
project_id = "probml"
!gcloud config set project {project_id}
this should be the format of the checkpoint filetree:
checkpoint_path >> model(optimizer)_checkpoint_file.
checkpoint_path_ema >> ema_checkpoint_file
checkpoint_path = "/content/vdvae_ci... |
8,931 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
List Comprehensions
List comprehensions are quick and concise way to create lists. List comprehensions comprises of an expression, followed by a for clause and then zero or more for or if cl... | Python Code:
# Simple List Comprehension
list = [x for x in range(5)]
print(list)
Explanation: List Comprehensions
List comprehensions are quick and concise way to create lists. List comprehensions comprises of an expression, followed by a for clause and then zero or more for or if clauses. The result of the list compr... |
8,932 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python for Bioinformatics
This Jupyter notebook is intented to be used alongside the book Python for Bioinformatics
Chapter 7
Step1: Listing 7.1
Step2: Listing 7.2
Step3: Listing 7.3
Step... | Python Code:
!curl https://raw.githubusercontent.com/Serulab/Py4Bio/master/samples/samples.tar.bz2 -o samples.tar.bz2
!mkdir samples
!tar xvfj samples.tar.bz2 -C samples
Explanation: Python for Bioinformatics
This Jupyter notebook is intented to be used alongside the book Python for Bioinformatics
Chapter 7: Error Hand... |
8,933 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
mosasaurus example
This notebook shows how to run mosasaurus to extract spectra from a sample dataset. In this example, there's a small sample dataset of raw LDSS3C images stored in the dire... | Python Code:
%matplotlib auto
# create an instrument with the appropriate settings
from mosasaurus.instruments import LDSS3C
i = LDSS3C(grism='vph-all')
# set up the basic directory structure, where `data/` should be found
path = '/Users/zkbt/Cosmos/Data/mosaurusexample'
i.setupDirectories(path)
# set the extraction de... |
8,934 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: Post-training weight quantization
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Train and... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
8,935 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Function Approximation with a Multilayer Perceptron
This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communications (MLOC).<br>
This code i... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
function_select = 4
def myfun(x):
functions = {
1: np.power(x,2), # quadratic function
2: np.sin(x), # sinus
3: np.sign(x), # signum
4: np.exp(x), # exponential function
5: np.abs(x)
}
... |
8,936 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 Google LLC
Step1: Adversarial Learning
Step2: Main Objective — Building an Apparel Classifier & Performing Adversarial Learning
We will keep things simple here with regard t... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# ... |
8,937 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Diffusion Boundary
The simulation script described in this chapter is available at STEPS_Example repository.
In some systems it may be a convenient simulation feature to be able to localize ... | Python Code:
import steps.model as smodel
import steps.geom as sgeom
import steps.rng as srng
import steps.solver as solvmod
import steps.utilities.meshio as meshio
import numpy
import pylab
Explanation: Diffusion Boundary
The simulation script described in this chapter is available at STEPS_Example repository.
In some... |
8,938 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
About iPython Notebooks
iPython Notebooks are interactive coding environments embedded in a webpage. After writing your code, you can run the cell by either pressing "SHIFT"+"ENTER" or by cl... | Python Code:
test = "Hello World"
print ("test: " + test)
Explanation: About iPython Notebooks
iPython Notebooks are interactive coding environments embedded in a webpage. After writing your code, you can run the cell by either pressing "SHIFT"+"ENTER" or by clicking on "Run Cell" (denoted by a play symbol) in the uppe... |
8,939 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook is a brief sketch of how to use Simon's algorithm.
We start by declaring all necessary imports.
Step1: Simon's algorithm can be used to find the mask $m$ of a 2-to-1 periodic ... | Python Code:
from collections import defaultdict
import numpy as np
from mock import patch
from grove.simon.simon import Simon, create_valid_2to1_bitmap
Explanation: This notebook is a brief sketch of how to use Simon's algorithm.
We start by declaring all necessary imports.
End of explanation
mask = '110'
bm = create_... |
8,940 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Survival Curve, S(t) maps from a duration, t, to the probability of surviving longer than t.
$$
S(t) = 1-\text{CDF}(t)
$$
where CDF(t) is the probability of a lifetime less than or... | Python Code:
preg = nsfg.ReadFemPreg()
complete = preg.query('outcome in [1,3,4]').prglngth
cdf = thinkstats2.Cdf(complete, label='cdf')
##note: property is a method that can be invoked as if
##it were a variable.
class SurvivalFunction(object):
def __init__(self, cdf, label=''):
self.cdf = cdf
... |
8,941 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Keras model are serialzed in a JSON format.
Step1: Getting the weights
Weights can be retrieved either directly from the model or from each individual layer.
Step2: Moreover the respespect... | Python Code:
model.get_config()
Explanation: Keras model are serialzed in a JSON format.
End of explanation
# Weights and biases of the entire model.
model.get_weights()
# Weights and bias for a single layer.
conv_layer = model.get_layer('conv2d_1')
conv_layer.get_weights()
Explanation: Getting the weights
Weights can ... |
8,942 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Manual Neural Network
In this notebook we will manually build out a neural network that mimics the TensorFlow API. This will greatly help your understanding when working with the real Tensor... | Python Code:
class SimpleClass():
def __init__(self, str_input):
print("SIMPLE" + str_input)
class ExtendedClass(SimpleClass):
def __init__(self):
print('EXTENDED')
Explanation: Manual Neural Network
In this notebook we will manually build out a neural network that mimics the TensorFlow API. Thi... |
8,943 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Справочник
Токенизатор
Токенизатор в Yargy реализован на регулярных выражениях. Для каждого типа токена есть правило с регуляркой
Step1: Токенизатор инициализируется списком правил. По-умол... | Python Code:
from yargy.tokenizer import RULES
RULES
Explanation: Справочник
Токенизатор
Токенизатор в Yargy реализован на регулярных выражениях. Для каждого типа токена есть правило с регуляркой:
End of explanation
from yargy.tokenizer import Tokenizer
text = 'a@mail.ru'
tokenizer = Tokenizer()
list(tokenizer(text))
E... |
8,944 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simulation of the METIS scenario with rooms in one floor
This notebook simulates the scenario with one access point in each room of a given floor building.
Some Initialization Code
First we ... | Python Code:
%matplotlib inline
# xxxxxxxxxx Add the parent folder to the python path. xxxxxxxxxxxxxxxxxxxx
import sys
import os
sys.path.append('../')
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import int... |
8,945 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Installation d'un distribution Python
Il existe plusieurs distributions de Python à destination des scientifiques
Step1: Scalaires
Les types numériques int et float
Step2: Nombres complex... | Python Code:
print "Hello world"
print '1', # la virgule empèche le saut de ligne après le print
print '2'
Explanation: Installation d'un distribution Python
Il existe plusieurs distributions de Python à destination des scientifiques :
Anaconda
Canopy
Python(x,y)
Chacune de ces distributions est disponible pour un gra... |
8,946 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Read in the data
Step1: Read in the surveys
Step2: Add DBN columns
Step3: Convert columns to numeric
Step4: Condense datasets
Step5: Convert AP scores to numeric
Step6: Combine the dat... | Python Code:
import pandas as pd
import numpy as np
import re
data_files = ["ap_2010.csv",
"class_size.csv",
"demographics.csv",
"graduation.csv",
"hs_directory.csv",
"sat_results.csv"]
data = {}
for f in data_files:
d = pd.read_csv("../data/scho... |
8,947 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BigQuery query magic
Jupyter magics are notebook-specific shortcuts that allow you to run commands with minimal syntax. Jupyter notebooks come with many built-in commands. The BigQuery clien... | Python Code:
%%bigquery
SELECT name, SUM(number) as count
FROM `bigquery-public-data.usa_names.usa_1910_current`
GROUP BY name
ORDER BY count DESC
LIMIT 10
Explanation: BigQuery query magic
Jupyter magics are notebook-specific shortcuts that allow you to run commands with minimal syntax. Jupyter notebooks come with man... |
8,948 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
\title{myHDL Implementation of a CIC Filter}
\author{Steven K Armour}
\maketitle
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a hr... | Python Code:
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
#import plotly.plotly as py
#import plotly.graph_objs as go
from sympy import *
from sympy import S; Zero=S.Zero
init_printin... |
8,949 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deque
kolekcja zbliżona do listy
optymalne operacje na brzegach, nieoptymalne indeksowanie
implementacja bazuje na double linked liście
Step1: Krotki
immutable
podobne do listy
hashowalne, ... | Python Code:
from collections import deque
a = deque([1, 2, 3], maxlen=5)
a.append(4)
a.append(5)
a.append(6)
print(a)
Explanation: Deque
kolekcja zbliżona do listy
optymalne operacje na brzegach, nieoptymalne indeksowanie
implementacja bazuje na double linked liście
End of explanation
a = (1, 2, 3)
b = (1, )
c = ()
pr... |
8,950 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Get the data
2MASS => J, H K, angular resolution ~4"
WISE => 3.4, 4.6, 12, and 22 μm (W1, W2, W3, W4) with an angular resolution of 6.1", 6.4", 6.5", & 12.0"
GALEX imaging => Five imaging s... | Python Code:
#obj = ["3C 454.3", 343.49062, 16.14821, 1.0]
obj = ["PKS J0006-0623", 1.55789, -6.39315, 1.0]
#obj = ["M87", 187.705930, 12.391123, 1.0]
#### name, ra, dec, radius of cone
obj_name = obj[0]
obj_ra = obj[1]
obj_dec = obj[2]
cone_radius = obj[3]
obj_coord = coordinates.SkyCoord(ra=obj_ra, dec=obj_dec, u... |
8,951 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Phoenix BT-Settl Bolometric Corrections
Figuring out the best method of handling Phoenix bolometric correction files.
Step1: Change to directory containing bolometric correction files.
Step... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.interpolate as scint
Explanation: Phoenix BT-Settl Bolometric Corrections
Figuring out the best method of handling Phoenix bolometric correction files.
End of explanation
cd /Users/grefe950/Projects/starspot/starspot/color/t... |
8,952 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
WikiData
From here
Step1: Add the 590 MetAtlas compounds that are missing from all these databases
Step2: miBIG (using notebook and pubchem API)
Step3: get names for ones that are missing... | Python Code:
terms_to_keep = ['smiles','inchi','source_database','ROMol','common_name','Definition', 'synonyms','pubchem_compound_id','lipidmaps_id','metacyc_id','hmdb_id','img_abc_id','chebi_id','kegg_id']
import_compounds = reload(import_compounds)
wikidata = import_compounds.get_wikidata(terms_to_keep)
df = wikidata... |
8,953 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
OpenCV Filters Webcam
In this notebook, several filters will be applied to webcam images.
Those input sources and applied filters will then be displayed either directly in the notebook or on... | Python Code:
from pynq import Overlay
Overlay("base.bit").download()
Explanation: OpenCV Filters Webcam
In this notebook, several filters will be applied to webcam images.
Those input sources and applied filters will then be displayed either directly in the notebook or on HDMI output.
To run all cells in this notebook ... |
8,954 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<span style='color
Step1: Part A - Calculate features for an individual source
To demonstrate how the FATS library works, we will begin by calculating features for the source with $\alpha_{... | Python Code:
shelf_file = " " # complete the path to the appropriate shelf file here
shelf = shelve.open(shelf_file)
shelf.keys()
Explanation: <span style='color:red'>An essential note in preparation for this exercise.</span> We will use scikit-learn to provide classifications of the PTF sources that we developed on th... |
8,955 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
On-the-fly training using ASE
Yu Xie (xiey@g.harvard.edu)
This is a quick introduction of how to set up our ASE-OTF interface to train a force field. We will train a force field model for di... | Python Code:
import numpy as np
from ase import units
from ase.spacegroup import crystal
from ase.build import bulk
np.random.seed(12345)
a = 3.52678
super_cell = bulk('C', 'diamond', a=a, cubic=True)
Explanation: On-the-fly training using ASE
Yu Xie (xiey@g.harvard.edu)
This is a quick introduction of how to set up o... |
8,956 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Head model and forward computation
The aim of this tutorial is to be a getting started for forward
computation.
For more extensive details and presentation of the general
concepts for forwar... | Python Code:
import os.path as op
import mne
from mne.datasets import sample
data_path = sample.data_path()
# the raw file containing the channel location + types
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
# The paths to Freesurfer reconstructions
subjects_dir = data_path + '/subjects'
subject = 'sampl... |
8,957 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <i class="fa fa-diamond"></i> Primero pimpea tu libreta!
Step2: <i class="fa fa-book"></i> Primero librerias
Step3: <i class="fa fa-database"></i> Vamos a crear datos de jugete
Crea... | Python Code:
from IPython.core.display import HTML
import os
def css_styling():
Load default custom.css file from ipython profile
base = os.getcwd()
styles = "<style>\n%s\n</style>" % (open(os.path.join(base,'files/custom.css'),'r').read())
return HTML(styles)
css_styling()
Explanation: <i class="fa fa-... |
8,958 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Now it's time for you to demonstrate your new skills with a project of your own!
In this exercise, you will work with a dataset of your choosing. Once you've selected a dataset, you'll desi... | Python Code:
import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")
Explanation: Now it's time for you to demonstrate your new skills with a project of your own!
In this exercise, you will work with a dataset of y... |
8,959 | Given the following text description, write Python code to implement the functionality described.
Description:
Largest number in the Array having frequency same as value
Function to find the largest number whose frequency is equal to itself . ; Adding 65536 to keep the count of the current number ; Right shifting by 16... | Python Code:
def findLargestNumber(arr , n ) :
for i in range(n ) :
arr[i ] &= 0xFFFF ;
if(arr[i ] <= n ) :
arr[i ] += 0x10000 ;
for i in range(n - 1 , 0 , - 1 ) :
if(( arr[i ] >> 16 ) == i ) :
return i + 1 ;
return - 1 ;
if __name__== ' __main __' :
arr =[3 , 2 , 5 , 5 , 2 , 4 , 5 ]... |
8,960 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Planar data classification with one hidden layer
Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a b... | Python Code:
# Package imports
import numpy as np
import matplotlib.pyplot as plt
from testCases_v2 import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets
%matplotlib inline
np.random.seed(1) # set a ... |
8,961 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div style='background-image
Step1: 1. Initialization of setup
Step2: 2. Finite Differences setup
Step3: 3. Finite Volumes setup
Step4: 4. Initial condition
Step5: 4. Solution for the i... | Python Code:
# Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
Explanation: <div style='bac... |
8,962 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Science, Data, Tools
or 'Tips and tricks for a your everyday workflow'
Matteo Guzzo
Prologue
AKA My Research
The cumulant expansion
The struggle has been too long, but we are starting to see... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
with plt.xkcd():
plt.rcParams['figure.figsize'] = (6., 4.)
x = np.linspace(-5, 5, 50)
gauss = np.exp(-(x**2) / 2)/np.sqrt(2 * np.pi)
ax = plt.subplot(111)
ax.plot(x, gauss, label="Best curve ever")
cdf = np.array(... |
8,963 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Unsupervised Learning
Project
Step1: Data Exploration
In this section, you will begin exploring the data through visualizations and code to understand h... | Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the whole... |
8,964 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example of DenseCRF with non-RGB data
This notebook goes through an example of how to use DenseCRFs on non-RGB data.
At the same time, it will explain basic concepts and walk through an exam... | Python Code:
#import sys
#sys.path.insert(0,'/path/to/pydensecrf/')
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import unary_from_softmax, create_pairwise_bilateral
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap... |
8,965 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Goal
If the DNA species distribution is truely Gaussian in a buoyant density gradient, then what sigma would be needed to reproduce the detection of all taxa > 0.1% in abundance throughout t... | Python Code:
%load_ext rpy2.ipython
workDir = '/home/nick/notebook/SIPSim/dev/fullCyc/frag_norm_9_2.5_n5/default_run/'
%%R
sigmas = seq(1, 50, 1)
means = seq(25, 100, 1) # mean GC content of 30 to 70%
## max 13C shift
max_13C_shift_in_BD = 0.036
## min BD (that we care about)
min_GC = 13.5
min_BD = min_GC/100.0 * 0.... |
8,966 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Try tsfresh
tsfresh rolling ts
Step1: Generate features for rolling & expanding windows
roll_time_series
By default it's expanding window
For rolling window, set max_timeshift value and mak... | Python Code:
import pandas as pd
# mock up ts data
df = pd.DataFrame({
"group": ['a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b'],
"time": [1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5],
"x": [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23],
"y": [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24],
})
df
Explanation: ... |
8,967 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Network Tour of Data Science
Michaël Defferrard, PhD student, Pierre Vandergheynst, Full Professor, EPFL LTS2.
Exercise 5
Step1: 1 Graph
Goal
Step2: Step 2
Step3: Step 3
Step4: Step 4
... | Python Code:
import numpy as np
import scipy.spatial
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: A Network Tour of Data Science
Michaël Defferrard, PhD student, Pierre Vandergheynst, Full Professor, EPFL LTS2.
Exercise 5: Graph Signals and Fourier Transform
The goal of this exercise is to experiment... |
8,968 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generate Two Networks with Different Spacing
Step1: Position Networks Appropriately, then Stitch Together
Step2: Quickly Visualize the Network
Let's just make sure things are working as pl... | Python Code:
spacing_lg = 0.00006
layer_lg = op.network.Cubic(shape=[10, 10, 1], spacing=spacing_lg)
spacing_sm = 0.00002
layer_sm = op.network.Cubic(shape=[30, 5, 1], spacing=spacing_sm)
Explanation: Generate Two Networks with Different Spacing
End of explanation
# Start by assigning labels to each network for identif... |
8,969 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Import the libraries
Step1: Create an empty network
Step2: Create a new species S0
S0 is a reference to access quickly to the newly created species latter in the code. Note that one can a... | Python Code:
from phievo.Networks import mutation,deriv2
import random
Explanation: Import the libraries
End of explanation
g = random.Random(20160225) # This define a new random number generator
L = mutation.Mutable_Network(g) # Create an empty network
Explanation: Create an empty network
End of explanation
parameters... |
8,970 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Podemos clasificar de dos formas, mediante discriminación o asignando probabilidades. Discriminando, asignamos a cada $x$ una de las $K$ clases $C_k$. Por contra, desde un punto de vista pro... | Python Code:
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Podemos clasificar de dos formas, mediante discriminación o asignando probabilidades. Discriminando, asignamos a cada $x$ u... |
8,971 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Learn to Throw
In this notebook, we will train a fully-connected neural network to solve an inverse ballistics problem.
We will compare supervised training to differentiable physics training... | Python Code:
# !pip install phiflow
# from phi.tf.flow import *
from phi.torch.flow import *
# from phi.jax.stax.flow import *
Explanation: Learn to Throw
In this notebook, we will train a fully-connected neural network to solve an inverse ballistics problem.
We will compare supervised training to differentiable physic... |
8,972 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nodes and Edges
Step1: Basic Network Statistics
Let's first understand how many students and friendships are represented in the network.
Step2: Exercise
Can you write a single line of code... | Python Code:
G = cf.load_seventh_grader_network()
Explanation: Nodes and Edges: How do we represent relationships between individuals using NetworkX?
As mentioned earlier, networks, also known as graphs, are comprised of individual entities and their representatives. The technical term for these are nodes and edges, an... |
8,973 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Forward Modeling the X-ray Image data
In this notebook, we'll take a closer look at the X-ray image data products, and build a simple, generative, forward model for the observed data.
Step1:... | Python Code:
from __future__ import print_function
import astropy.io.fits as pyfits
import astropy.visualization as viz
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 10.0)
Explanation: Forward Modeling the X-ray Image data
In this notebook, we'll take a cl... |
8,974 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
"Third" Light
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
Step1: As always, ... | Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
Explanation: "Third" Light
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 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 numpy as np
import... |
8,975 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Catapult Project - Australian Geoscience Datacube API
Perform band maths and produce a Normalised Difference Vegetation Index (NDVI) file.
Step1: Select the first time index and plot the fi... | Python Code:
from pprint import pprint
%matplotlib inline
from matplotlib import pyplot as plt
import xarray
import datacube.api
dc = datacube.api.API()
alos2 = dc.get_dataset(product='gamma0', platform='ALOS_2',
y=(-42.55,-42.57), x=(147.55,147.57),
variables=['hh_gamma0',... |
8,976 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="http
Step1: After the import command, we now have access to a large number of pre-built classes and functions. This assumes the library is installed; in our lab environment all the... | Python Code:
import pandas as pd
Explanation: <a href="http://cocl.us/topNotebooksPython101Coursera"><img src = "https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width = 750, align = "center"></a>
<a href="https://www.bigdatauniversity.com"><img src = "https://ibm.box.com/shared/static/ugcqz6ohbv... |
8,977 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
pyJHTDB are failed to compile on windows. One alternative way might be to use zeep package.
More details can be found at http
Step1: In GetData_Python, Function_name could be
GetVelocity,... | Python Code:
import zeep
import numpy as np
client = zeep.Client('http://turbulence.pha.jhu.edu/service/turbulence.asmx?WSDL')
ArrayOfFloat = client.get_type('ns0:ArrayOfFloat')
ArrayOfArrayOfFloat = client.get_type('ns0:ArrayOfArrayOfFloat')
SpatialInterpolation = client.get_type('ns0:SpatialInterpolation')
TemporalIn... |
8,978 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Material Science Tensile Tests
In an engineering tensile stress test, a given specimen of cross-sectional area $A_{o}$ is subjected to a given load $P$ under tension. The stress $\sigma$ gen... | Python Code:
import numpy as np # imports the numpy package and creates the alias np for broader control of vector arrays
import pandas as pd # imports the pandas package and creates the alias pd to work with data tables and lists
import matplotlib.pyplot as pl... |
8,979 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tag Key Value Uploader
A tool for bulk editing key value pairs for CM placements.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may n... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: Tag Key Value Uploader
A tool for bulk editing key value pairs for CM placements.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the Li... |
8,980 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic Data Analysis and Visualization Using Python
by Yanal Kashou
This is a free dataset of exoplanets from the RDatasets.
The data was obtained from the URLs below of the .csv data file an... | Python Code:
import pandas as pd
porsche = pd.read_csv("PorschePrice.csv")
Explanation: Basic Data Analysis and Visualization Using Python
by Yanal Kashou
This is a free dataset of exoplanets from the RDatasets.
The data was obtained from the URLs below of the .csv data file and the .html documentation file, respective... |
8,981 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 2
Step1: Intermediate Level
This exercise is designed for those who are already somewhat comfortable with python and want to learn more about exploiting its capabilities. It asks you t... | Python Code:
# Put your code here
pass
# only run this cell after you finished writing your code
%load beginner_soln.py
Explanation: Part 2: Demonstration Exercises
Here are some sample exercises to work through. They demonstrate many techniques that we use all the time.
Beginner Level
This exercise is designed for tho... |
8,982 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analysis. Using an RNN rather than a feedfoward network is more accurate ... | Python Code:
import numpy as np
import tensorflow as tf
with open('../sentiment_network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment_network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent ... |
8,983 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nearest Neighbors
When exploring a large set of documents -- such as Wikipedia, news articles, StackOverflow, etc. -- it can be useful to get a list of related material. To find relevant doc... | Python Code:
import graphlab
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
Explanation: Nearest Neighbors
When exploring a large set of documents -- such as Wikipedia, news articles, StackOverflow, etc. -- it can be useful to get a list of related material. To find relevant documents you typical... |
8,984 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The
Step1:
Step2: Now, we can create an
Step3: Epochs behave similarly to
Step4: You can select subsets of epochs by indexing the
Step5: It is also possible to iterate through
Ste... | Python Code:
import mne
import os.path as op
import numpy as np
from matplotlib import pyplot as plt
Explanation: The :class:Epochs <mne.Epochs> data structure: epoched data
:class:Epochs <mne.Epochs> objects are a way of representing continuous
data as a collection of time-locked trials, stored in an array... |
8,985 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exporting CSV data from the server
This process is slightly cumbersome because of Unix permissions. Remember - nine times out of ten, on Unix, it's probably a permissions problem.
In this c... | Python Code:
!echo 'redspot' | sudo -S service postgresql restart
%load_ext sql
!createdb -U dbuser test
%sql postgresql://dbuser@localhost:5432/test
Explanation: Exporting CSV data from the server
This process is slightly cumbersome because of Unix permissions. Remember - nine times out of ten, on Unix, it's probably... |
8,986 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Upload a gist via the GitHub API
Our OAuthenticator config has passed GitHub information via environment variables.
We can use these to publish gists to GitHub.
Get the GitHub username and t... | Python Code:
import os
gh_user = os.environ['GITHUB_USER']
gh_token = os.environ['GITHUB_TOKEN']
Explanation: Upload a gist via the GitHub API
Our OAuthenticator config has passed GitHub information via environment variables.
We can use these to publish gists to GitHub.
Get the GitHub username and token from environmen... |
8,987 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
[PUBLIC] Analysis of CLBlast client multiple sizes
<a id="overview"></a>
Overview
This Jupyter Notebook analyses the performance that CLBlast (single configuaration) achieves across a range... | Python Code:
import os
import sys
import json
import re
Explanation: [PUBLIC] Analysis of CLBlast client multiple sizes
<a id="overview"></a>
Overview
This Jupyter Notebook analyses the performance that CLBlast (single configuaration) achieves across a range of sizes.
<a id="data"></a>
Get the experimental data from D... |
8,988 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mustererkennung in Funkmessdaten
Aufgabe 1
Step1: Wir öffnen die Datenbank und lassen uns die Keys der einzelnen Tabellen ausgeben.
Step2: Aufgabe 2
Step3: Als nächstes Untersuchen wir... | Python Code:
# imports
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import pprint as pp
Explanation: Mustererkennung in Funkmessdaten
Aufgabe 1: Laden der Datenbank in Jupyter Notebook
End of explanation
hdf = pd.HDFStore('../../data/raw/TestMessungen_NEU.hdf')
... |
8,989 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Topology
This caterogy of questions is intended to retrieve the network topology
used by Batfish. This topology is a combination of information in the
snapshot and inference logic (e.g., whi... | Python Code:
bf.set_network('generate_questions')
bf.set_snapshot('aristaevpn')
Explanation: Topology
This caterogy of questions is intended to retrieve the network topology
used by Batfish. This topology is a combination of information in the
snapshot and inference logic (e.g., which interfaces are layer3 neighbors).
... |
8,990 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Support Vector Machine - Basics
Support Vector Machine (SVM) is one of the commonly used algorithm.
It can be used for both classification and regression.
Today we will walk through the ba... | Python Code:
#import all the needed package
import numpy as np
import scipy as sp
import pandas as pd
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split,cross_val_score
from sklearn import metrics
from... |
8,991 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Determining the worst winter ever in Chicago
The object of this exercise is to take weather observations from past winters in Chicago and determine which of them could be considered the wors... | Python Code:
import pandas as pd
# Read data, sort by year & month
dateparse = lambda x: pd.datetime.strptime(x, '%Y%m%d')
noaa_monthly = pd.read_csv('chicago-midway-noaa.csv', index_col=2,
parse_dates=True, date_parser=dateparse, na_values=-9999)
noaa_monthly = noaa_monthly.groupby([noaa_mon... |
8,992 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
InfluxDB Logger Example
This notebook is a small demo of how to use gpumon in Jupyter notebooks and some convenience methods for working with GPUs
You will need to have PyTorch and Torchvisi... | Python Code:
from gpumon import device_count, device_name
device_count() # Returns the number of GPUs available
device_name() # Returns the type of GPU available
Explanation: InfluxDB Logger Example
This notebook is a small demo of how to use gpumon in Jupyter notebooks and some convenience methods for working with GPU... |
8,993 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Histograms are a useful type of statistics plot for engineers. A histogram is a type of bar plot that shows the frequency or number of values compared to a set of value ranges. Histogram plo... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
# if using a Jupyter notebook, includue:
%matplotlib inline
Explanation: Histograms are a useful type of statistics plot for engineers. A histogram is a type of bar plot that shows the frequency or number of values compared to a set of value ranges. Histog... |
8,994 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
You are part of a team working to make mobile paym... | Python Code:
# Packages
import numpy as np
from testCases import *
from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector
Explanation: Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
... |
8,995 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Trace Analysis Examples
Kernel Functions Profiling
Details on functions profiling are given in Plot Functions Profiling Data below.
Step1: Import required modules
Step2: Target Configurati... | Python Code:
import logging
from conf import LisaLogging
LisaLogging.setup()
Explanation: Trace Analysis Examples
Kernel Functions Profiling
Details on functions profiling are given in Plot Functions Profiling Data below.
End of explanation
# Generate plots inline
%matplotlib inline
import json
import os
# Support to a... |
8,996 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 2 – Lists, conditionals and loops
Recap on Variables
A variable
named cell of memory storing a single value
Assigned a value using the equals symbol
Can be given any name you like
E... | Python Code:
exam_scores = [67,78,94,45,55,66]
print("scores: " ,exam_scores)
Explanation: Lecture 2 – Lists, conditionals and loops
Recap on Variables
A variable
named cell of memory storing a single value
Assigned a value using the equals symbol
Can be given any name you like
Except no spaces and cannot start with a... |
8,997 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Scope
1. Representation - 2D, 3D plots
2. Provide idioms
-- "Business graphics" line, bar, scatter plots
-- "Statistics plots" whisker
-- "Higher dimensioned data" - heatmap
3. In-cla... | Python Code:
def mm(s_conc, vmax, km):
:param np.array s_conc: substrate concentrations
:param float vmax: maximum reaction rate
:param float km: half substrate concentration
:return np.array: reaction rates
result = vmax*s_conc/(s_conc+km)
return result
s_conc = np.array([m+0.1 for m ... |
8,998 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The MIT License (MIT)<br>
Copyright (c) 2018 Massachusetts Institute of Technology<br>
Authors
Step1: TESS End-to-End 6 Simulated Light Curve Time Series<br>
Source
Step2: Normalize flux
S... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi'] = 150
Explanation: The MIT License (MIT)<br>
Copyright (c) 2018 Massachusetts Institute of Technology<br>
Authors: Cody Rude<br>
This software has been created in projects supported by the US National<br>
Science Foundation and N... |
8,999 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementing and comparing several pitch detection methods on sample files
For simplicity I am using the Anaconda distribution on my Macbook Pro for this notebook.
The purpose is to first e... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Implementing and comparing several pitch detection methods on sample files
For simplicity I am using the Anaconda distribution on my Macbook Pro for this notebook.
The purpose is to first experiment here... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.