6) Noisy peak finding. Finding peak in a noisy data is a very common task in analyzing physical from sensors. Consider the following data shown below. Your task for this problem is to find the peak position and peak height from noisy data. There are 10 dataset make sure you succeed in finding **all** the peak in at least 9. ```python def find_peaks(xs, ys): return [(xpeak, ypeak), (xpeak, ypeak), ...] ``` Here are some hints: - You should first find candidates for peak in noisy data. - Fit the peak(and neighbor) with parabola - Make sure the parameter from the fitted parabola actually indicate that it is a peak. - Then use the parameter from the fitted parabola to find the peak location and height. (Recall High School Math)given import numpy as np %matplotlib inline from matplotlib import pyplot as plt import math from math import exp np.random.seed(9999) def is_good_peak(mu, min_dist=0.8): if mu is None: return False smu = np.sort(mu) if smu[0] < 0.5: return False if smu[-1] > 2.5: return False for p, n in zip(smu, smu[1:]): #print(abs(p-n)) if abs(p-n) < min_dist: return False return True maxx = 3 ndata = 500 nset = 10 l = [] answers = [] for iset in range(1, nset): npeak = np.random.randint(2,4) xs = np.linspace(0,maxx,ndata) ys = np.zeros(ndata) mu = None while not is_good_peak(mu): mu = np.random.random(npeak)*maxx for ipeak in range(npeak): m = mu[ipeak] sigma = np.random.random()*0.3 + 0.2 height = np.random.random()*0.5 + 1 ys += height*np.exp(-(xs-m)**2/sigma**2) ys += np.random.randn(ndata)*0.07 l.append(ys) answers.append(mu) p6_ys = l p6_xs = np.linspace(0,maxx,ndata) p6_answers = answers for ys, ans in zip(p6_ys, p6_answers): plt.figure() plt.plot(xs, ys, '.') for a in ans: plt.axvline(a,color='red')

6) Noisy peak finding. Finding peak in a noisy data is a very common task in analyzing physical from sensors. Consider the following data shown below. Your task for this problem is to find the peak position and peak height from noisy data. There are 10 dataset make sure you succeed in finding all the peak in at least 9. ```python def find_peaks(xs, ys): return [(xpeak, ypeak), (xpeak, ypeak), ...] ``` Here are some hints: - You should first find candidates for peak in noisy data. - Fit the peak(and neighbor) with parabola - Make sure the parameter from the fitted parabola actually indicate that it is a peak. - Then use the parameter from the fitted parabola to find the peak location and height. (Recall High School Math)given import numpy as np %matplotlib inline from matplotlib import pyplot as plt import math from math import exp np.random.seed(9999) def is_good_peak(mu, min_dist=0.8): if mu is None: return False smu = np.sort(mu) if smu[0] < 0.5: return False if smu[-1] > 2.5: return False for p, n in zip(smu, smu[1:]): #print(abs(p-n)) if abs(p-n) < min_dist: return False return True maxx = 3 ndata = 500 nset = 10 l = [] answers = [] for iset in range(1, nset): npeak = np.random.randint(2,4) xs = np.linspace(0,maxx,ndata) ys = np.zeros(ndata) mu = None while not is_good_peak(mu): mu = np.random.random(npeak)maxx for ipeak in range(npeak): m = mu[ipeak] sigma = np.random.random()0.3 + 0.2 height = np.random.random()0.5 + 1 ys += heightnp.exp(-(xs-m)2/sigma2) ys += np.random.randn(ndata)*0.07 l.append(ys) answers.append(mu) p6_ys = l p6_xs = np.linspace(0,maxx,ndata) p6_answers = answers for ys, ans in zip(p6_ys, p6_answers): plt.figure() plt.plot(xs, ys, '.') for a in ans: plt.axvline(a,color='red')

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Related questions

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Question

6) Noisy peak finding.

Finding peak in a noisy data is a very common task in analyzing physical from sensors. Consider the following data shown below.

Your task for this problem is to find the peak position and peak height from noisy data. There are 10 dataset make sure you succeed in finding **all** the peak in at least 9.

```python

def find_peaks(xs, ys):

return [(xpeak, ypeak), (xpeak, ypeak), ...]

```

Here are some hints:

- You should first find candidates for peak in noisy data.

- Fit the peak(and neighbor) with parabola

- Make sure the parameter from the fitted parabola actually indicate that it is a peak.

- Then use the parameter from the fitted parabola to find the peak location and height. (Recall High School Math)

given

import numpy as np

%matplotlib inline

from matplotlib import pyplot as plt

import math

from math import exp

np.random.seed(9999)

def is_good_peak(mu, min_dist=0.8):

if mu is None:

return False

smu = np.sort(mu)

if smu[0] < 0.5:

return False

if smu[-1] > 2.5:

return False

for p, n in zip(smu, smu[1:]):

#print(abs(p-n))

if abs(p-n) < min_dist:

return False

return True

maxx = 3

ndata = 500

nset = 10

l = []

answers = []

for iset in range(1, nset):

npeak = np.random.randint(2,4)

xs = np.linspace(0,maxx,ndata)

ys = np.zeros(ndata)

mu = None

while not is_good_peak(mu):

mu = np.random.random(npeak)*maxx

for ipeak in range(npeak):

m = mu[ipeak]

sigma = np.random.random()*0.3 + 0.2

height = np.random.random()*0.5 + 1

ys += height*np.exp(-(xs-m)**2/sigma**2)

ys += np.random.randn(ndata)*0.07

l.append(ys)

answers.append(mu)

p6_ys = l

p6_xs = np.linspace(0,maxx,ndata)

p6_answers = answers

for ys, ans in zip(p6_ys, p6_answers):

plt.figure()

plt.plot(xs, ys, '.')

for a in ans:

plt.axvline(a,color='red')

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