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2,800 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
STELLAB and OMEGA (Stellar yields + Faint Supernovae)
Documented by Jacob Brazier.
Note This notebooks require an experimental yields table, which is not part of NuPyCEE.
See Côté et al. (20... | Python Code:
#import modules
#sygma and omega share the same chem_evol class
from NuPyCEE import chem_evol
from NuPyCEE import sygma
from NuPyCEE import omega
from NuPyCEE import stellab
#import Python plotting packages
import matplotlib
import matplotlib.pyplot as plt
#Define Stellab
stellab = stellab.stellab()
%matpl... |
2,801 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning
Step1: Create Folder Structure
Step7: Things to keep in mind (Troubleshooting)
Choose always verbosity=2 when training. Otherwise the notebook will crash.
Monitor the RAM whi... | Python Code:
%matplotlib inline
import os
import sys
import math
import zipfile
import glob
import numpy as np
import utils; reload(utils)
from utils import *
from keras.models import Sequential
from keras.layers import Lambda, Dense
from keras import backend as K
from matplotlib import pyplot as plt
Explanation: Deep ... |
2,802 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="http
Step1: Note
Step2: Include the input file that contains all input parameters needed for all components. This file can either be a python dictionary or a text file that can be... | Python Code:
from __future__ import print_function
%matplotlib inline
import time
import numpy as np
from landlab import RasterModelGrid as rmg
from landlab import load_params
from Ecohyd_functions_flat import (
Initialize_,
Empty_arrays,
Create_PET_lookup,
Save_,
Plot_,
)
Explanation: <a href="http... |
2,803 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning
Assignment 2
Previously in 1_notmnist.ipynb, we created a pickle with formatted datasets for training, development and testing on the notMNIST dataset.
The goal of this assignm... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
Explanation: Deep Learning
Assignment 2
Previousl... |
2,804 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute source power spectral density (PSD) in a label
Returns an STC file containing the PSD (in dB) of each of the sources
within a label.
Step1: Set parameters
Step2: View PSD of source... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd
print(__doc__)
Explanation: Compute source power spectra... |
2,805 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sale price distribution
First step is to look at the target sale price for the training data set, i.e. the column we're trying to predict.
Step1: The sale price is in hte hundreds of thousa... | Python Code:
target = pd.read_csv('../data/train_target.csv')
target.describe()
Explanation: Sale price distribution
First step is to look at the target sale price for the training data set, i.e. the column we're trying to predict.
End of explanation
target = target / 1000
sns.distplot(target);
plt.title('SalePrice')
i... |
2,806 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian Optimization
Bayesian optimization is a powerful strategy for minimizing (or maximizing) objective functions that are costly to evaluate. It is an important component of automated ... | Python Code:
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import torch
import torch.autograd as autograd
import torch.optim as optim
from torch.distributions import constraints, transform_to
import pyro
import pyro.contrib.gp as gp
assert pyro.__version__.startswith('1.7.0')
pyro.set_rng_seed(... |
2,807 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to pybids
pybids is a tool to query, summarize and manipulate data using the BIDS standard.
In this tutorial we will use a pybids test dataset to illustrate some of the functio... | Python Code:
from bids import BIDSLayout
from bids.tests import get_test_data_path
import os
Explanation: Introduction to pybids
pybids is a tool to query, summarize and manipulate data using the BIDS standard.
In this tutorial we will use a pybids test dataset to illustrate some of the functionality of pybids.layout
... |
2,808 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Colorization AutoEncoder PyTorch Demo using CIFAR10
In this demo, we build a simple colorization autoencoder using PyTorch.
Step1: CNN Encoder using PyTorch
We use 3 CNN layers to encode th... | Python Code:
import torch
import torchvision
import wandb
import time
from torch import nn
from einops import rearrange, reduce
from argparse import ArgumentParser
from pytorch_lightning import LightningModule, Trainer, Callback
from pytorch_lightning.loggers import WandbLogger
from torch.optim import Adam
from torch.o... |
2,809 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you wil... | Python Code:
from __future__ import division
import graphlab
import math
import string
import numpy
Explanation: Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you will use product rev... |
2,810 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step3: Action Recognition with an Inflated 3D CNN
<table class="tfo-notebook-butto... | Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... |
2,811 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The following script extracts the (more) helpful reviews from the swiss reviews and saves them locally.
From the extracted reviews it also saves a list with their asin identifiers.
The list ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import yaml
Explanation: The following script extracts the (more) helpful reviews from the swiss reviews and saves them locally.
From the extracted reviews it also saves a list with their asin identifiers.
The list of... |
2,812 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#First-Foray-Into-Discrete/Fast-Fourier-Transformation" data-toc-modified-id=... | Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(path)
# 1. magic for in... |
2,813 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: SLD gradients
For the moment, BornAgain does not support input of SLD profiles. However, one can approximate the smooth SLD profile by a large number of layers. See the example script... | Python Code:
# %load density_grad.py
import numpy as np
import bornagain as ba
from bornagain import deg, angstrom, nm
# define used SLDs
sld_D2O = 6.34e-06
sld_polymer = 4.0e-06
sld_Si = 2.07e-06
h = 100.0*nm # thickness of the non-uniform polymer layer
nslices = 100 # number of slices to slice the polymer layer
de... |
2,814 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to Load CSV and Numpy File Types in TensorFlow 2.0
Learning Objectives
Load a CSV file into a tf.data.Dataset.
Load Numpy data
Introduction
In this lab, you load CSV data from a file in... | Python Code:
import functools
import numpy as np
import tensorflow as tf
print("TensorFlow version: ", tf.version.VERSION)
TRAIN_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
TEST_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/eval.csv"
train_file_path = tf.keras.utils.get_fi... |
2,815 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network written with ... | Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a n... |
2,816 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Introduction and Foundations
Project 0
Step1: From a sample of the RMS Titanic data, we can see the various features present for each passenger on the s... | Python Code:
import sys
print(sys.version)
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the f... |
2,817 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Style Transfer
Our Changes
Step1: Imports
Step2: This was developed using Python 3.5.2 (Anaconda) and TensorFlow version
Step3: The VGG-16 model is downloaded from the internet. This is t... | Python Code:
from IPython.display import Image, display
Image('images/15_style_transfer_flowchart.png')
Explanation: Style Transfer
Our Changes:
We are just saving the mixed image every 10 iterations.
End of explanation
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import... |
2,818 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Network queries
veneer-py supports a number topological queries on the Source node-link network and including identifying outlets, upstream and downstream nodes, links and catchments.
These ... | Python Code:
import veneer
%matplotlib inline
v = veneer.Veneer()
Explanation: Network queries
veneer-py supports a number topological queries on the Source node-link network and including identifying outlets, upstream and downstream nodes, links and catchments.
These queries operate on the network object returned by v... |
2,819 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kaggle
Step1: Data exploration
First we load and explore the dataset a little.
Step2: There are strong differences in the frequencies in which the different categories of crime occur. Lare... | Python Code:
# imports
import math
import datetime
import matplotlib
import matplotlib.pyplot as plt
import osmnx as ox
import pandas as pd
import numpy as np
import pprint
import requests
import gmaps
import seaborn as sns
import os
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.m... |
2,820 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Wave Packets
Step2: A particle with total energy $E$ in a region of constant potential $V_0$ has a wave number
$$
k = \pm \frac{2m}{\hbar^2}(E - V_0)
$$
and dispersion relation
$$
\omega(k)... | Python Code:
%pylab inline
import matplotlib.animation
from IPython.display import HTML
Explanation: Wave Packets
End of explanation
def solve(k0=10., sigmax=0.25, V0=0., mass=1., tmax=0.25, nwave=15, nx=500, nt=10):
Solve for the evolution of a 1D Gaussian wave packet.
Parameters
----------
k... |
2,821 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plot sensor denoising using oversampled temporal projection
This demonstrates denoising using the OTP algorithm
Step1: Plot the phantom data, lowpassed to get rid of high-frequency artifac... | Python Code:
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import mne
import numpy as np
from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif
print(__doc__)
Explanation: Plot sensor denoising using... |
2,822 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convert training sessions to labeled examples, each example will have a seq_size sequence size, we will include three features per data point, the timestamp, the x position and the y positio... | Python Code:
df_train = sessions_to_dataframe(training_sessions)
df_val = sessions_to_dataframe(validation_sessions)
df_train.head()
df_train = preprocess_data(df_train)
df_val = preprocess_data(df_val)
#### SPECIAL CASE #####
# There isnt any XButton data in the validation set so we better drop this column for the tra... |
2,823 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Saving and Loading Models
In this bite-sized notebook, we'll go over how to save and load models. In general, the process is the same as for any PyTorch module.
Step1: Saving a Simple Model... | Python Code:
import math
import torch
import gpytorch
from matplotlib import pyplot as plt
Explanation: Saving and Loading Models
In this bite-sized notebook, we'll go over how to save and load models. In general, the process is the same as for any PyTorch module.
End of explanation
train_x = torch.linspace(0, 1, 100)
... |
2,824 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 2 assignment
This assignment will get you familiar with the basic elements of Python by programming a simple card game. We will create a custom class to represent each player in the game... | Python Code:
import random
Explanation: Lab 2 assignment
This assignment will get you familiar with the basic elements of Python by programming a simple card game. We will create a custom class to represent each player in the game, which will store information about their current pot, as well as a series of methods def... |
2,825 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Correção de exercício Cap 03 - Exercício 14
Step1: Método dos Mínimos Quadrados
Para achar a função de calibração
\( D = N\sum{Y^{2}} - (\sum{Y})^2 \)
\( c_{0} = (\sum{X}\sum{Y^2}\,-\,\sum{... | Python Code:
%matplotlib notebook
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
import pandas as pd
df=pd.read_table('./data/temperatura.txt',sep='\s',header=0, engine='python')
df.head()
fig, ax1 = plt.subplots()
ax1.plot(df.Xi, 'b')
ax1.plot(df.Y1, 'y')
#ax1.set_xlabel('time (s)')
# Make t... |
2,826 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementation of the Thornthwaite-Mather procedure to map groundwater recharge
Author
Step1: Other libraries
Import other libraries/modules used in this notebook.
pandas
Step2: Some input... | Python Code:
import ee
# Trigger the authentication flow.
ee.Authenticate()
# Initialize the library.
ee.Initialize()
Explanation: Implementation of the Thornthwaite-Mather procedure to map groundwater recharge
Author: guiattard
Groundwater recharge represents the amount of water coming from precipitation reaching the ... |
2,827 | 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
tfx and kfp tools setup
Step2: You may need to restart the kernel at this point.
skaffold tool setup
Step3: Modify th... | 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
%%bash
TFX_PKG="t... |
2,828 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<b>This notebook divide a single mailing list corpus into threads.</b>
What it does
Step1: First, collect data from a public email archive.
Step2: Let's check the number of threads in thi... | Python Code:
%matplotlib inline
from bigbang.archive import Archive
from bigbang.archive import load as load_archive
from bigbang.thread import Thread
from bigbang.thread import Node
from bigbang.utils import remove_quoted
import matplotlib.pyplot as plt
import datetime
import csv
from collections import defaultdict
Ex... |
2,829 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Processing a single Spectrum
Specdal provides readers which loads [.asd, .sig, .sed] files into a common Spectrum object.
Step1: The print output shows the four components of the Spectrum o... | Python Code:
s = specdal.Spectrum(filepath="/home/young/data/specdal/aidan_data/SVC/ACPA_F_B_SU_20160617_003.sig")
print(s)
Explanation: Processing a single Spectrum
Specdal provides readers which loads [.asd, .sig, .sed] files into a common Spectrum object.
End of explanation
print(type(s.measurement))
print(s.measure... |
2,830 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 5
Step1: Load in house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
Step2: Create new features
As in Week 2, ... | Python Code:
import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
Explanation: Regression Week 5: Feature Selection and LASSO (Interpretation)
In this notebook, you will use LASSO to select features, building on a pre-implemented solver for LASSO (using GraphLab Create, though you ... |
2,831 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inference of the dispersion of a Gaussian with Gaussian data errors
Suppose we have data draw from a delta function with Gaussian uncertainties (all equal). How well do we limit the dispersi... | Python Code:
ndata= 24
data= numpy.random.normal(size=ndata)
Explanation: Inference of the dispersion of a Gaussian with Gaussian data errors
Suppose we have data draw from a delta function with Gaussian uncertainties (all equal). How well do we limit the dispersion? Sample data:
End of explanation
def loglike(sigma,da... |
2,832 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Limpieza de Estructura Organica del PEN
Se utilizan data-cleaner y pandas para codificar la limpieza de los datos de un archivo CSV. Primero se realiza una exploración de la tabla aplicando ... | Python Code:
from __future__ import unicode_literals
from __future__ import print_function
from data_cleaner import DataCleaner
import pandas as pd
input_path = "estructura-organica-raw.csv"
output_path = "estructura-organica-clean.csv"
dc = DataCleaner(input_path)
Explanation: Limpieza de Estructura Organica del PEN
S... |
2,833 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Note
Step1: Before we continue, note that we'll be using your Qwiklabs project id a lot in this notebook. For convenience, set it as an environment variable using the command below
Step2: ... | Python Code:
import datetime
import pickle
import os
import pandas as pd
import xgboost as xgb
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import FeatureUnion, make_pipeline
from sklearn.utils import shuffle
from sklearn.base import clone
from sklearn.model_selection import... |
2,834 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Analysis Tools
Assignment
Step1: Data management
Step2: First, the distribution of both the use of cannabis and the ethnicity will be shown.
Step3: Variance analysis
Now that the uni... | Python Code:
# Magic command to insert the graph directly in the notebook
%matplotlib inline
# Load a useful Python libraries for handling data
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt
from IPython.disp... |
2,835 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FloPy
Using FloPy to simplify the use of the MT3DMS SSM package
A multi-component transport demonstration
Step1: First, we will create a simple model structure
Step2: Create the MODFLOW pa... | Python Code:
import os
import numpy as np
from flopy import modflow, mt3d, seawat
Explanation: FloPy
Using FloPy to simplify the use of the MT3DMS SSM package
A multi-component transport demonstration
End of explanation
nlay, nrow, ncol = 10, 10, 10
perlen = np.zeros((10), dtype=np.float) + 10
nper = len(perlen)
ibound... |
2,836 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Web Scraping in Python
Source
In this appendix lecture we'll go over how to scrape information from the web using Python.
We'll go to a website, decide what information we want, see where a... | Python Code:
from bs4 import BeautifulSoup
import requests
import pandas as pd
from pandas import Series,DataFrame
Explanation: Web Scraping in Python
Source
In this appendix lecture we'll go over how to scrape information from the web using Python.
We'll go to a website, decide what information we want, see where and... |
2,837 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial 4 - Current induced domain wall motion
In this tutorial we show how spin transfer torque (STT) can be included in micromagnetic simulations. To illustrate that, we will try to move ... | Python Code:
# Definition of parameters
L = 500e-9 # sample length (m)
w = 20e-9 # sample width (m)
d = 2.5e-9 # discretisation cell size (m)
Ms = 5.8e5 # saturation magnetisation (A/m)
A = 15e-12 # exchange energy constant (J/)
D = 3e-3 # Dzyaloshinkii-Moriya energy constant (J/m**2)
K = 0.5e6 # uniaxial anisot... |
2,838 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial of how to use scikit-criteria AHP extension module
Author
Step1: In other hand AHP uses as an in put 2 totally different
$$
AHP(CvC, AvA)
$$
Where
Step2: The function ahp.t (from... | Python Code:
from skcriteria import Data, MIN, MAX
mtx = [
[1, 2, 3], # alternative 1
[4, 5, 6], # alternative 2
]
mtx
# let's says the first two alternatives are
# for maximization and the last one for minimization
criteria = [MAX, MAX, MIN]
criteria
# et’s asume we know in our case, that the importance of
... |
2,839 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Overfitting demo
Create a dataset based on a true sinusoidal relationship
Let's look at a synthetic dataset consisting of 30 points drawn from the sinusoid $y = \sin(4x)$
Step1: Create rand... | Python Code:
import graphlab
import math
import random
import numpy
from matplotlib import pyplot as plt
%matplotlib inline
Explanation: Overfitting demo
Create a dataset based on a true sinusoidal relationship
Let's look at a synthetic dataset consisting of 30 points drawn from the sinusoid $y = \sin(4x)$:
End of expl... |
2,840 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convolutional Neural Networks With BatchFlow
Now it's time to talk about convolutional neural networks and in this notebook you will find out how to do
Step1: You don't need to implement a ... | Python Code:
import sys
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import PIL
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
# the following line is not required if BatchFlow is installed as a python package.
sys.path.append('../..')
from batchflow import D, B, V... |
2,841 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Preprocessing for Machine Learning
Learning Objectives
* Understand the different approaches for data preprocessing in developing ML models
* Use Dataflow to perform data preprocessing ... | Python Code:
#Ensure that we have the correct version of Apache Beam installed
!pip freeze | grep apache-beam || sudo pip install apache-beam[gcp]==2.12.0
import tensorflow as tf
import apache_beam as beam
import shutil
import os
print(tf.__version__)
Explanation: Data Preprocessing for Machine Learning
Learning Object... |
2,842 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Testing PDF function in dmdd over time for SI and Anapole
On the colormaps near Q = 0, the pdf function seems to be predicting the wrong number of events for the SI and anapole models. SI sh... | Python Code:
pdf_list = []
times = np.linspace(0, 365, 366) #365 days to test
#test all days at same energies, where energy = 3
for i,time in enumerate(times):
value = dmdd.PDF(Q=[5.], time=time, element = 'xenon', mass = 50.,
sigma_si= 75.5, sigma_anapole = 0.,
... |
2,843 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spectrometer accuracy assesment using validation tarps
Background
In this lesson we will be examing the accuracy of the Neon Imaging Spectrometer (NIS) against targets with known reflectance... | Python Code:
import h5py
import csv
import numpy as np
import os
import gdal
import matplotlib.pyplot as plt
import sys
from math import floor
import time
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
Explanation: Spectrometer accuracy assesment using validation tarps
Background
In this lesson we... |
2,844 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Units and unit conversions are BIG in engineering. Engineers solve the world's problems in teams. Any problem solved has to have a context. How heavy can a rocket be and still make it off th... | Python Code:
import platform
print('Operating System: ' + platform.system() + platform.release())
print('Python Version: '+ platform.python_version())
Explanation: Units and unit conversions are BIG in engineering. Engineers solve the world's problems in teams. Any problem solved has to have a context. How heavy can a ... |
2,845 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example
Step4: The individual data instances come in chunks seperated by blank lines. Each chunk consists of a few starting comments, and then lines of tab-seperated fields. The fields we a... | Python Code:
!wget https://raw.githubusercontent.com/UniversalDependencies/UD_English/master/en-ud-dev.conllu
!wget https://raw.githubusercontent.com/UniversalDependencies/UD_English/master/en-ud-test.conllu
!wget https://raw.githubusercontent.com/UniversalDependencies/UD_English/master/en-ud-train.conllu
Explanation: ... |
2,846 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pick an example to test if load.cc works
Step1: Inspect the protobuf containing the model's architecture and logic | Python Code:
# -- inputs
X_test[0]
# -- predicted output (using Keras)
yhat[0]
Explanation: Pick an example to test if load.cc works
End of explanation
from tensorflow.core.framework import graph_pb2
# -- read in the graph
f = open("models/graph.pb", "rb")
graph_def = graph_pb2.GraphDef()
graph_def.ParseFromString(f.re... |
2,847 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Demonstration of SMPS Calculations
The SMPS calculations require two main packages from atmPy - smps and dma. dma contains the DMA class and its children. The children of DMA simply contai... | Python Code:
from atmPy.instruments.DMA import smps
from atmPy.instruments.DMA import dma
from matplotlib import colors
import matplotlib.pyplot as plt
from numpy import meshgrid
import numpy as np
import pandas as pd
from matplotlib.dates import date2num
from matplotlib import dates
from atmPy import sizedistribution ... |
2,848 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
====================================================================
Decoding in sensor space data using the Common Spatial Pattern (CSP)
====================================================... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Romain Trachel <romain.trachel@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_pat... |
2,849 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook a simple Q learner will be trained and evaluated. The Q learner recommends when to buy or sell shares of one particular stock, and in which quantity (in fact it determines t... | Python Code:
# Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
... |
2,850 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting Started
Before starting here, all the instructions on the installation page should be completed!
Here you will learn how to
Step1: Make sure that your environment path is set to ma... | Python Code:
import warnings
warnings.filterwarnings('ignore')
import pandexo.engine.justdoit as jdi # THIS IS THE HOLY GRAIL OF PANDEXO
import numpy as np
import os
#pip install pandexo.engine --upgrade
Explanation: Getting Started
Before starting here, all the instructions on the installation page should be completed... |
2,851 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loading data
Simple stuff. We're loading in a CSV here, and we'll run the describe function over it to get the lay of the land.
Step1: In journalism, we're primarily concerned with using da... | Python Code:
df = pd.read_csv('data/ontime_reports_may_2015_ny.csv')
df.describe()
Explanation: Loading data
Simple stuff. We're loading in a CSV here, and we'll run the describe function over it to get the lay of the land.
End of explanation
df.sort('ARR_DELAY', ascending=False).head(1)
Explanation: In journalism, we'... |
2,852 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Data augmentation
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Download a dataset
This t... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
2,853 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Extracting the time series of activations in a label
We first apply a dSPM inverse operator to get signed activations in a label
(with positive and negative values) and we then compare diffe... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operato... |
2,854 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Input pipeline
Zastosowano tu następującą strategie
Step1: test queue
Step2: Testing
Możemy wykorzystać feed_dict by wykonać graf operacji na ndanych testowych. | Python Code:
def read_data(filename_queue):
reader = tf.TFRecordReader()
_, se = reader.read(filename_queue)
f = tf.parse_single_example(se,features={'image/encoded':tf.FixedLenFeature([],tf.string),
'image/class/label':tf.FixedLenFeature([],tf.int64),
... |
2,855 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hypsometric analysis of Mountain Ranges
Carlos H. Grohmann
Institute of Energy and Environment
University of São Paulo, São Paulo, Brazil
guano -at- usp -dot- br
Hypsometry
Hypsometric anal... | Python Code:
import sys, os
import numpy as np
import math as math
import numpy.ma as ma
from matplotlib import cm
from matplotlib.colors import LightSource
from scipy import ndimage
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
%matplotlib inline
# import osgeo libs after basemap, so it
# w... |
2,856 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Model Evaluation & Validation
Project
Step1: Data Exploration
In this first section of this project, you will make a cursory investigation about the Bos... | Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
#from sklearn.cross_validation import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.re... |
2,857 | 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', 'cccma', 'sandbox-3', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynam... |
2,858 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2. kolokvij 2011/2012, rešitve
1. naloga
Poišči največjo in najmanjšo vrednost, ki jo zavzame funkcija
$$f(x) = x^4 + 2x^3 - 2x^2 + 1.$$
Step1: Kandadati za ekstreme so stacionarne točke i... | Python Code:
f = lambda x: x**4 + 2*x**3 - 2*x**2 + 1
x = sympy.Symbol('x', real=True)
Explanation: 2. kolokvij 2011/2012, rešitve
1. naloga
Poišči največjo in najmanjšo vrednost, ki jo zavzame funkcija
$$f(x) = x^4 + 2x^3 - 2x^2 + 1.$$
End of explanation
eq = Eq(f(x).diff(), 0)
eq
critical_points = sympy.solve(eq)
cr... |
2,859 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Viscoelastic wave equation implementation on a staggered grid
This is a first attempt at implementing the viscoelastic wave equation as described in [1]. See also the FDELMODC implementation... | Python Code:
# Required imports:
import numpy as np
import sympy as sp
from devito import *
from examples.seismic.source import RickerSource, TimeAxis
from examples.seismic import ModelViscoelastic, plot_image
Explanation: Viscoelastic wave equation implementation on a staggered grid
This is a first attempt at implemen... |
2,860 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples and Exercises from Think Stats, 2nd Edition
http
Step1: Scatter plots
I'll start with the data from the BRFSS again.
Step2: The following function selects a random subset of a Dat... | Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import brfss
import thinkstats2
import thinkplot
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
End... |
2,861 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Random Graphs
Step1: Introduction
A graph $G=(V,E)$ is a collection of vertices $V$ and edges $E$ between the vertices in $V$. Graphs often model interactions such as social networks, a net... | Python Code:
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore') #NetworkX has some deprecation warnings
Explanation: Random Graphs
End of explanation
params = [(10,0.1),(10,.5),(10,0.9),(20,0.1),(20,.5),(20,0.9)]
plt.figure(figsize=(15,10))
idx = 1... |
2,862 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python pandas Q&A video series by Data School
YouTube playlist and GitHub repository
Table of contents
<a href="#1.-What-is-pandas%3F-%28video%29">What is pandas?</a>
<a href="#2.-How-do-I-r... | Python Code:
# conventional way to import pandas
import pandas as pd
# get Pansda's vesrion #
print ('Pandas version', pd.__version__)
Explanation: Python pandas Q&A video series by Data School
YouTube playlist and GitHub repository
Table of contents
<a href="#1.-What-is-pandas%3F-%28video%29">What is pandas?</a>
<a hr... |
2,863 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Two implementations of heterodyne detection
Step1: Introduction
Homodyne and hetrodyne detection are techniques for measuring the quadratures of a field using photocounters. Homodyne detect... | Python Code:
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from qutip import *
Explanation: Two implementations of heterodyne detection: direct heterodyne and as two homodyne measurements
Copyright (C) 2011 and later, Paul D. Nation & Robert J. Johansson
End of explanation
N =... |
2,864 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualize Epochs data
Step1: This tutorial focuses on visualization of epoched data. All of the functions
introduced here are basically high level matplotlib functions with built in
intelli... | Python Code:
import os.path as op
import mne
data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample')
raw = mne.io.read_raw_fif(
op.join(data_path, 'sample_audvis_raw.fif'), preload=True)
raw.load_data().filter(None, 9, fir_design='firwin')
raw.set_eeg_reference('average', projection=True) # set EEG a... |
2,865 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: Generate music with an RNN
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Download the Mae... | 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... |
2,866 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
xbatch and batch
Step1: peek
Step2: bracket
Step3: <pre>
1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9
0----5----0----5----0----5----0----5--... | Python Code:
for x in utils.xbatch(2, range(10)):
print(x)
for x in utils.xbatch(3, ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']):
print(x)
for x in utils.xbatch(3, ('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug'... |
2,867 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Rozwiązywanie stochastycznych równań różniczkowych z CUDA
Równania stochastyczne są niezwykle pożytecznym narzędziem w modelowaniu zarówno procesów fizycznych, biolgicznych czy chemicznych a... | Python Code:
print('%(language)04d a nawiasy {} ' % {"language": 1234, "number": 2})
Explanation: Rozwiązywanie stochastycznych równań różniczkowych z CUDA
Równania stochastyczne są niezwykle pożytecznym narzędziem w modelowaniu zarówno procesów fizycznych, biolgicznych czy chemicznych a nawet ekonomicznych (wycena ins... |
2,868 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Does Trivers-Willard apply to people?
This notebook contains a "one-day paper", my attempt to pose a research question, answer it, and publish the results in one work day.
Copyright 2016 All... | Python Code:
from __future__ import print_function, division
import thinkstats2
import thinkplot
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
%matplotlib inline
Explanation: Does Trivers-Willard apply to people?
This notebook contains a "one-day paper", my attempt to pose a research ques... |
2,869 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Diagnostics for approximate likelihood ratios
Kyle Cranmer, Juan Pavez, Gilles Louppe, March 2016.
This is an extension of the example in Parameterized inference from multidimensional data. ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import theano
from scipy.stats import chi2
from itertools import product
np.random.seed(314)
Explanation: Diagnostics for approximate likelihood ratios
Kyle Cranmer, Juan Pavez, Gilles Louppe, March 2016.
This is an extension of the exam... |
2,870 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plots the NINO Sea Surface Temperature indices (data from the Bureau of Meteorology) and the real-time Southern Oscillation Index (SOI) from LongPaddock
Nicolas Fauchereau
Step1: set up pro... | Python Code:
%matplotlib inline
import os, sys
import pandas as pd
from datetime import datetime, timedelta
from cStringIO import StringIO
import requests
import matplotlib as mpl
from matplotlib import pyplot as plt
from IPython.display import Image
Explanation: Plots the NINO Sea Surface Temperature indices (data fro... |
2,871 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
De connectie met Lizard is gemaakt en bovenstaand zijn alle beschikbare endpoints.
Nu gaan we de metadata verzamelen van de timeseries met uuid 867b166a-fa39-457d-a9e9-4bcb2ff04f61
Step1: ... | Python Code:
result = cli.timeseries.get(uuid="867b166a-fa39-457d-a9e9-4bcb2ff04f61")
result.metadata
Explanation: De connectie met Lizard is gemaakt en bovenstaand zijn alle beschikbare endpoints.
Nu gaan we de metadata verzamelen van de timeseries met uuid 867b166a-fa39-457d-a9e9-4bcb2ff04f61:
End of explanation
que... |
2,872 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Thermal equilibrium of a single particle
In a large ensemble of identical systems each member will have a different state due to thermal fluctuations, even if all the systems were initialise... | Python Code:
import numpy as np
# anisotropy energy of the system
def anisotropy_e(theta, sigma):
return -sigma*np.cos(theta)**2
# numerator of the Boltzmann distribution
# (i.e. without the partition function Z)
def p_unorm(theta, sigma):
return np.sin(theta)*np.exp(-anisotropy_e(theta, sigma))
Explanation: Th... |
2,873 | 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', 'cccr-iitm', 'iitm-esm', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CCCR-IITM
Source ID: IITM-ESM
Topic: Seaice
Sub-Topics: Dynamics, Therm... |
2,874 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 2
Step1: Evaluating a Heptagon Number
We are almost ready to look at Python code for drawing these figures. The last step is to provide a mapping from heptagon numbers to real numbers... | Python Code:
# load the definitions from the previous notebook
%run HeptagonNumbers.py
# represent points or vertices as pairs of heptagon numbers
p0 = ( zero, zero )
p1 = ( sigma, zero )
p2 = ( sigma+1, rho )
p3 = ( sigma, rho*sigma )
p4 = ( zero, sigma*sigma )
p5 = ( -rho, rho*sigma )
p6 = ( -rho, rho )
heptagon = [ ... |
2,875 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Scores
First let's take a look at the ratings users can give to images. This is just for warming up since this is a feature that's not so much used on Danbooru.
Step1: Status
Now we take a ... | Python Code:
scores = %sql SELECT score, COUNT(*) FROM posts GROUP BY score ORDER BY score DESC
worst_post = %sql SELECT id FROM posts WHERE score = (SELECT MIN(score) FROM posts)
best_post = %sql SELECT id FROM posts WHERE score = (SELECT MAX(score) FROM posts)
pd_count = scores.DataFrame()["count"]
pd_count.index = s... |
2,876 | 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... |
2,877 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
AXON
Step1: JSON is subset of AXON
Here is well known example of JSON message
Step2: One can see that content of json_vals and axon_vals are equal.
Step3: AXON supports more readable and ... | Python Code:
from __future__ import unicode_literals, print_function, division
from pprint import pprint
import axon
import json
import xml.etree as etree
from IPython.display import HTML, display, display_html
Explanation: AXON: Tutorial
Let's import inventory for playing with AXON with python.
End of explanation
!cat... |
2,878 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
'rv' Datasets and Options
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 w... | Python Code:
!pip install -I "phoebe>=2.0,<2.1"
Explanation: 'rv' Datasets and Options
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 ... |
2,879 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Feedback k domácím projektům
Jde tento kód napsat jednodušeji, aby ale dělal úplně totéž?
Step1: Ano, lze
Step2: A co tento?
Step3: Ten taky
Step4: A do třetice
Step5: A jeden nepodaře... | Python Code:
for radek in range(4):
radek += 1
for value in range(radek):
print('X', end=' ')
print('')
Explanation: Feedback k domácím projektům
Jde tento kód napsat jednodušeji, aby ale dělal úplně totéž?
End of explanation
for radek in range(1, 5):
print('X ' * radek)
Explanation: Ano, lze :-)
End of exp... |
2,880 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualizing data is awesome. In this post, I decided to use D3 in iPython notebook to visualize the "network of frequent associations between 62 dolphins in a community living off Doubtful S... | Python Code:
import networkx as nx
G = nx.read_gml('dolphins.gml') ##downloaded from above link
category = {}
for i,k in G.edge.iteritems():
if len(k) < 4:
category[i] = '< 4 neighbors'
elif len(k) < 11:
category[i] = '5-10 neighbors'
else:
category[i] = '> 10 neighbors'
_nodes = []
... |
2,881 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interaktives Übungsblatt
Vorgeplänkel
Step1: Systemmatrizen
Wir werden im Weiteren pyMG nutzen um die Systemmatrix für gegebene Parameter $ n$ und $\sigma$ für das Helmholtz-Problem in 1D a... | Python Code:
import sys
# Diese Zeile muss angepasst werden!
sys.path.append("/home/moser/MG_2016/pyMG-2016/")
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pymg
from project.helmholtz1d import Helmholtz1D
from project.helmholtz1d_periodic import Helmholtz1D_Periodic
fr... |
2,882 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Afinação e Notas Musicais
Objetivo
Após esta unidade, o aluno será capaz de aplicar modelos matemáticos para relacionar o fenômeno perceptual da altura, o fenômeno físico da frequência funda... | Python Code:
referencia_inicial = 440.0 # Hz
frequencias = [] # Esta lista recebera todas as frequencias de uma escala
f = referencia_inicial
while len(frequencias) < 12:
if f > (referencia_inicial * 2):
f /= 2.
frequencias.append(f)
f *= (3/2.)
frequencias.sort()
print frequencias
print f
Explanati... |
2,883 | Given the following text description, write Python code to implement the functionality described.
Description:
Minimum number of elements which are not part of Increasing or decreasing subsequence in array
Python3 program to return minimum number of elements which are not part of increasing or decreasing subsequences .... | Python Code:
MAX = 102
def countMin(arr , dp , n , dec , inc , i ) :
if dp[dec ][inc ][i ] != - 1 :
return dp[dec ][inc ][i ]
if i == n :
return 0
if arr[i ] < arr[dec ] :
dp[dec ][inc ][i ] = countMin(arr , dp , n , i , inc , i + 1 )
if arr[i ] > arr[inc ] :
if dp[dec ][inc ][i ] == - 1 : ... |
2,884 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step4: Recurrent Neural Networks
For an introduction to RNN take a look at this great article.
Basic RNNs
Step5: Manual RNN
Step6: Using rnn()
The static_rnn() function creates an unrolle... | Python Code:
# Common imports
import numpy as np
import numpy.random as rnd
import os
# to make this notebook's output stable across runs
rnd.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
pl... |
2,885 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
record schedules for 2 weeks, then augment count with weekly flight numbers.
seasonal and seasonal charter will count as once per week for 3 months, so 12/52 per week. TGM separate, since it... | Python Code:
for i in locations:
print i
if i not in sch:sch[i]={}
#march 11-24 = 2 weeks
for d in range (11,25):
if d not in sch[i]:
try:
url=airportialinks[i]
full=url+'arrivals/201703'+str(d)
m=requests.get(full).content
... |
2,886 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook provides an example on how to use a custom class within Flexcode. <br>
In order to be compatible, a regression method needs to have a fit and predict method implemented - i.e. ... | Python Code:
import flexcode
import numpy as np
import xgboost as xgb
from flexcode.regression_models import XGBoost, CustomModel
Explanation: This notebook provides an example on how to use a custom class within Flexcode. <br>
In order to be compatible, a regression method needs to have a fit and predict method implem... |
2,887 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian Long Short-Term Memory Network Example
Licensed under the Apache License, Version 2.0.
This example implements a Bayesian version of LSTM (Hochreiter, Schmidhuber, 1997) using tf.ke... | Python Code:
import numpy as np
import seaborn as sns
import pandas as pd
import tensorflow as tf
import edward2 as ed
import matplotlib.pyplot as plt
from tqdm import tqdm
from sklearn.model_selection import train_test_split, ParameterGrid
from tensorflow.keras.preprocessing import sequence
import embedded_reber_gramm... |
2,888 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<!--BOOK_INFORMATION-->
<a href="https
Step1: Because of all the noise we added, the two half moons might not be apparent at first glance.
That's a perfect scenario for our current intentio... | Python Code:
from sklearn.datasets import make_moons
X, y = make_moons(n_samples=100, noise=0.25, random_state=100)
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
plt.figure(figsize=(10, 6))
plt.scatter(X[:, 0], X[:, 1], s=100, c=y)
plt.xlabel('feature 1')
plt.ylabel('feature 2');
Explanatio... |
2,889 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial - Assemble the data on the wikitext dataset
Using Datasets, Pipeline, TfmdLists and Transform in text
In this tutorial, we explore the mid-level API for data collection in the text ... | Python Code:
path = untar_data(URLs.WIKITEXT_TINY)
Explanation: Tutorial - Assemble the data on the wikitext dataset
Using Datasets, Pipeline, TfmdLists and Transform in text
In this tutorial, we explore the mid-level API for data collection in the text application. We will use the bases introduced in the pets tutorial... |
2,890 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align="center">Scientific Programming in Python</h1>
<h2 align="center">Topic 2
Step1: Table of Contents
1.- Useful Magics
2.- Basic NumPy Operations
3.- Internals of NumPy
4.- Efficien... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
Explanation: <h1 align="center">Scientific Programming in Python</h1>
<h2 align="center">Topic 2: NumPy and Efficient Numerical Programming</h2>
Notebook created by Martín Villanueva - martin.villanueva@usm.cl - DI UTF... |
2,891 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Purpose
Step2: Input
Step3: Workflow
Tokenization to break text into units e.g. words, phrases, or symbols
Stop word removal to get rid of common words
e.g. this, a, is
Step4: About stem... | Python Code:
import pandas as pd
import nltk
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
from collections import Counter
Explanation: Purpose: To experiment with Python's Natural Language Toolkit.
NLTK is a leading platform for building Python programs to work with human language data
End of... |
2,892 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem
Step1: Unit Test
The following unit test is expected to fa... | Python Code:
class Node(object):
def __init__(self, data):
# TODO: Implement me
pass
def insert(root, data):
# TODO: Implement me
pass
Explanation: <small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem: Implement a ... |
2,893 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Apriori 算法
- (1) 把各项目放到只包含自己的项集中，生成最初的频繁项集。只使用达到最小支持度的项目。
- (2) 查找现有频繁项集的超集，发现新的频繁项集，并用其生成新的备选项集。
- (3) 测试新生成的备选项集的频繁程度，如果不够频繁，则舍弃。如果没有新的频繁项集，就跳到最后一步。
- (4) 存储新发现的频繁项集，跳到步骤(2)。
- (5) 返回发现的所有... | Python Code:
frequent_itemsets = {}
min_support = 50
Explanation: Apriori 算法
- (1) 把各项目放到只包含自己的项集中，生成最初的频繁项集。只使用达到最小支持度的项目。
- (2) 查找现有频繁项集的超集，发现新的频繁项集，并用其生成新的备选项集。
- (3) 测试新生成的备选项集的频繁程度，如果不够频繁，则舍弃。如果没有新的频繁项集，就跳到最后一步。
- (4) 存储新发现的频繁项集，跳到步骤(2)。
- (5) 返回发现的所有频繁项集。
End of explanation
frequent_itemsets[1] = dict((frozenset... |
2,894 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Feature selection
Step1: Our first step is to count up all of the words in each of the documents. This conditional frequency distribution should look familiar by now. | Python Code:
documents = nltk.corpus.PlaintextCorpusReader('../data/EmbryoProjectTexts/files', 'https.+')
metadata = zotero.read('../data/EmbryoProjectTexts', index_by='link', follow_links=False)
Explanation: Feature selection: keywords
A major problem-area in text mining is determining the thematic or topical content ... |
2,895 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Image Processing
Image processing is a very useful tool for scientists in the lab, and for everyday uses as well. For, example, an astronomer may use image processing to help... | Python Code:
# set exercise1 equal to your matrix
exercise1 = #your matrix goes here
Explanation: Introduction to Image Processing
Image processing is a very useful tool for scientists in the lab, and for everyday uses as well. For, example, an astronomer may use image processing to help find and recognize stars, or a ... |
2,896 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting the occupancies of Belgian trains
In this lab, we will go over some of the typical steps in a data science pipeline
Step1: 0. Create a kaggle account! https
Step2: Processing th... | Python Code:
import os
os.getcwd()
%matplotlib inline
%pylab inline
import pandas as pd
import numpy as np
from collections import Counter, OrderedDict
import json
import matplotlib
import matplotlib.pyplot as plt
import re
from scipy.misc import imread
from sklearn.linear_model import LogisticRegression
from sklearn.... |
2,897 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualize Epochs data
Step1: This tutorial focuses on visualization of epoched data. All of the functions
introduced here are basically high level matplotlib functions with built in
intelli... | Python Code:
import os.path as op
import mne
data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample')
raw = mne.io.read_raw_fif(op.join(data_path, 'sample_audvis_raw.fif'))
raw.set_eeg_reference() # set EEG average reference
event_id = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
... |
2,898 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Easy "Hard" Way
Step1: 1. A Quick Introduction to Cython
Cython is a compiler and a programming language used to generate C extension modules for Python.
The Cython language is a Python... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import sympy as sym
sym.init_printing()
Explanation: The Easy "Hard" Way: Cythonizing
In this notebook, we'll build on the previous work where we used SymPy's code printers to generate code for evaluating expressions numerically. As a la... |
2,899 | Given the following text description, write Python code to implement the functionality described.
Description:
Largest Sum Contiguous Subarray having unique elements
Function to calculate required maximum subarray sum ; Initialize two pointers ; Stores the unique elements ; Insert the first element ; Current max sum ; ... | Python Code:
def maxSumSubarray(arr ) :
i = 0
j = 1
set = { }
set[arr[0 ] ] = 1
sum = arr[0 ]
maxsum = sum
while(i < len(arr ) - 1 and j < len(arr ) ) :
if arr[j ] not in set :
sum = sum + arr[j ]
maxsum = max(sum , maxsum )
set[arr[j ] ] = 1
j += 1
else :
sum -= arr[i ]
del set[arr[... |
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