Signal Processing Techniques | Learning NumPy Array

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Learning NumPy Array

By : Ivan Idris

4 (9)

Learning NumPy Array

4 (9)

By: Ivan Idris

Overview of this book

Learning NumPy Array

Learning NumPy Array

Credits

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Preface

Free Chapter

Getting Started with NumPy

Getting Started with NumPy

Python

Installing NumPy, Matplotlib, SciPy, and IPython on Windows

Installing NumPy, Matplotlib, SciPy, and IPython on Linux

Installing NumPy, Matplotlib, and SciPy on Mac OS X

Building from source

NumPy arrays

Online resources and help

Summary

NumPy Basics

NumPy Basics

The NumPy array object

Creating a multidimensional array

Selecting array elements

NumPy numerical types

Creating a record data type

One-dimensional slicing and indexing

Manipulating array shapes

Creating views and copies

Fancy indexing

Indexing with a list of locations

Indexing arrays with Booleans

Stride tricks for Sudoku

Broadcasting arrays

Summary

Basic Data Analysis with NumPy

Basic Data Analysis with NumPy

Introducing the dataset

Determining the daily temperature range

Looking for evidence of global warming

Comparing solar radiation versus temperature

Analyzing wind direction

Analyzing wind speed

Analyzing precipitation and sunshine duration

Analyzing monthly precipitation in De Bilt

Analyzing atmospheric pressure in De Bilt

Analyzing atmospheric humidity in De Bilt

Summary

Simple Predictive Analytics with NumPy

Simple Predictive Analytics with NumPy

Examining autocorrelation of average temperature with pandas

Describing data with pandas DataFrames

Correlating weather and stocks with pandas

Predicting temperature

Analyzing intra-year daily average temperatures

Introducing the day-of-the-year temperature model

Modeling temperature with the SciPy leastsq function

Day-of-year temperature take two

Moving-average temperature model with lag 1

The Autoregressive Moving Average temperature model

The time-dependent temperature mean adjusted autoregressive model

Outliers analysis of average De Bilt temperature

Using more robust statistics

Summary

Signal Processing Techniques

Signal Processing Techniques

Introducing the Sunspot data

Moving averages

Smoothing functions

Forecasting with an ARMA model

Filtering a signal

Demonstrating cointegration

Summary

Profiling, Debugging, and Testing

Profiling, Debugging, and Testing

Assert functions

Profiling a program with IPython

Debugging with IPython

Performing Unit tests

Nose tests decorators

Summary

The Scientific Python Ecosystem

The Scientific Python Ecosystem

Numerical integration

Interpolation

Using Cython with NumPy

Clustering stocks with scikit-learn

Detecting corners

Comparing NumPy to Blaze

Summary

Index

Index

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Moving averages

Moving averages are tools commonly used to analyze time-series data. A moving average defines a window of previously seen data that is averaged each time the window slides forward one period. The different types of moving average differ essentially in the weights used for averaging. The exponential moving average, for instance, has exponentially decreasing weights with time. This means that older values have less influence than newer values, which is sometimes desirable.

We can express an equal-weight strategy for the simple moving average as follows in the NumPy code:

weights = np.exp(np.linspace(-1., 0., N))
weights /= weights.sum()

A simple moving average uses equal weights which, in code, looks as follows:

def sma(arr, n):
   weights = np.ones(n) / n

   return np.convolve(weights, arr)[n-1:-n+1]

The following code plots the simple moving average for the 11- and 22-year sunspot cycle:

import numpy as np
import sys
import matplotlib.pyplot as plt

data = np.loadtxt(sys.argv...

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