How do I avoid round-off error in this list?

This list should have x[50] as zero and both sides to be symmetrical, but it is slightly off centre because of what I assume is roundoff error. How can I modify my code to avoid this?

Thanks!

import numpy as np

L=2*np.pi

s=101

ds=L/(s-1)

svals=np.arange(1,101)

x=[0]

x[0:s]=((svals-1)*ds)-L/2

print(x)

asked Nov 25 '18 at 21:23

T. L

465

1

I'd suggest you do these calculations one at a time in an interactive Python session, and look at the result from each. You seem to be confusing numpy arrays and lists. The x=[0] followed by x[0:s] looks particularly suspicious.

– hpaulj
Nov 25 '18 at 21:29

add a comment |

This list should have x[50] as zero and both sides to be symmetrical, but it is slightly off centre because of what I assume is roundoff error. How can I modify my code to avoid this?

Thanks!

import numpy as np

L=2*np.pi

s=101

ds=L/(s-1)

svals=np.arange(1,101)

x=[0]

x[0:s]=((svals-1)*ds)-L/2

print(x)

asked Nov 25 '18 at 21:23

T. L

465

1

I'd suggest you do these calculations one at a time in an interactive Python session, and look at the result from each. You seem to be confusing numpy arrays and lists. The x=[0] followed by x[0:s] looks particularly suspicious.

– hpaulj
Nov 25 '18 at 21:29

add a comment |

This list should have x[50] as zero and both sides to be symmetrical, but it is slightly off centre because of what I assume is roundoff error. How can I modify my code to avoid this?

Thanks!

import numpy as np

L=2*np.pi

s=101

ds=L/(s-1)

svals=np.arange(1,101)

x=[0]

x[0:s]=((svals-1)*ds)-L/2

print(x)

asked Nov 25 '18 at 21:23

T. L

465

This list should have x[50] as zero and both sides to be symmetrical, but it is slightly off centre because of what I assume is roundoff error. How can I modify my code to avoid this?

Thanks!

import numpy as np

L=2*np.pi

s=101

ds=L/(s-1)

svals=np.arange(1,101)

x=[0]

x[0:s]=((svals-1)*ds)-L/2

print(x)

python numpy floating-point rounding

asked Nov 25 '18 at 21:23

T. L

465

asked Nov 25 '18 at 21:23

T. L

465

asked Nov 25 '18 at 21:23

T. L

465

asked Nov 25 '18 at 21:23

T. L

465

asked Nov 25 '18 at 21:23

T. L

465

1

I'd suggest you do these calculations one at a time in an interactive Python session, and look at the result from each. You seem to be confusing numpy arrays and lists. The x=[0] followed by x[0:s] looks particularly suspicious.

– hpaulj
Nov 25 '18 at 21:29

add a comment |

1

I'd suggest you do these calculations one at a time in an interactive Python session, and look at the result from each. You seem to be confusing numpy arrays and lists. The x=[0] followed by x[0:s] looks particularly suspicious.

– hpaulj
Nov 25 '18 at 21:29

I'd suggest you do these calculations one at a time in an interactive Python session, and look at the result from each. You seem to be confusing numpy arrays and lists. The x=[0] followed by x[0:s] looks particularly suspicious.

– hpaulj
Nov 25 '18 at 21:29

add a comment |

1 Answer
1

active

oldest

votes

Getting precise outputs from floating point operations can be tricky and fiddly. You can get the list you want using np.linspace:

x = np.linspace(-np.pi, 0, num=51)

x = np.concatenate([x, np.linspace(x[-1] - x[-2], np.pi, num=50)])

print(x)

Output:

[-3.14159265 -3.0787608  -3.01592895 -2.95309709 -2.89026524 -2.82743339

 -2.76460154 -2.70176968 -2.63893783 -2.57610598 -2.51327412 -2.45044227

 -2.38761042 -2.32477856 -2.26194671 -2.19911486 -2.136283   -2.07345115

 -2.0106193  -1.94778745 -1.88495559 -1.82212374 -1.75929189 -1.69646003

 -1.63362818 -1.57079633 -1.50796447 -1.44513262 -1.38230077 -1.31946891

 -1.25663706 -1.19380521 -1.13097336 -1.0681415  -1.00530965 -0.9424778

 -0.87964594 -0.81681409 -0.75398224 -0.69115038 -0.62831853 -0.56548668

 -0.50265482 -0.43982297 -0.37699112 -0.31415927 -0.25132741 -0.18849556

 -0.12566371 -0.06283185  0.          0.06283185  0.12566371  0.18849556

  0.25132741  0.31415927  0.37699112  0.43982297  0.50265482  0.56548668

  0.62831853  0.69115038  0.75398224  0.81681409  0.87964594  0.9424778

  1.00530965  1.0681415   1.13097336  1.19380521  1.25663706  1.31946891

  1.38230077  1.44513262  1.50796447  1.57079633  1.63362818  1.69646003

  1.75929189  1.82212374  1.88495559  1.94778745  2.0106193   2.07345115

  2.136283    2.19911486  2.26194671  2.32477856  2.38761042  2.45044227

  2.51327412  2.57610598  2.63893783  2.70176968  2.76460154  2.82743339

  2.89026524  2.95309709  3.01592895  3.0787608   3.14159265]

It's made in two steps to avoid a numerical error that crops up when linspace ranges across 0. If x is made in one step as

x = np.linspace(-np.pi, np.pi, 101)

then the value at x[50] is 4.440892098500626e-16, instead of x[50] being 0 as expected.

answered Nov 25 '18 at 21:32

tel

7,43621431

add a comment |

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1 Answer
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active

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1 Answer
1

active

oldest

votes

Getting precise outputs from floating point operations can be tricky and fiddly. You can get the list you want using np.linspace:

x = np.linspace(-np.pi, 0, num=51)

x = np.concatenate([x, np.linspace(x[-1] - x[-2], np.pi, num=50)])

print(x)

Output:

[-3.14159265 -3.0787608  -3.01592895 -2.95309709 -2.89026524 -2.82743339

 -2.76460154 -2.70176968 -2.63893783 -2.57610598 -2.51327412 -2.45044227

 -2.38761042 -2.32477856 -2.26194671 -2.19911486 -2.136283   -2.07345115

 -2.0106193  -1.94778745 -1.88495559 -1.82212374 -1.75929189 -1.69646003

 -1.63362818 -1.57079633 -1.50796447 -1.44513262 -1.38230077 -1.31946891

 -1.25663706 -1.19380521 -1.13097336 -1.0681415  -1.00530965 -0.9424778

 -0.87964594 -0.81681409 -0.75398224 -0.69115038 -0.62831853 -0.56548668

 -0.50265482 -0.43982297 -0.37699112 -0.31415927 -0.25132741 -0.18849556

 -0.12566371 -0.06283185  0.          0.06283185  0.12566371  0.18849556

  0.25132741  0.31415927  0.37699112  0.43982297  0.50265482  0.56548668

  0.62831853  0.69115038  0.75398224  0.81681409  0.87964594  0.9424778

  1.00530965  1.0681415   1.13097336  1.19380521  1.25663706  1.31946891

  1.38230077  1.44513262  1.50796447  1.57079633  1.63362818  1.69646003

  1.75929189  1.82212374  1.88495559  1.94778745  2.0106193   2.07345115

  2.136283    2.19911486  2.26194671  2.32477856  2.38761042  2.45044227

  2.51327412  2.57610598  2.63893783  2.70176968  2.76460154  2.82743339

  2.89026524  2.95309709  3.01592895  3.0787608   3.14159265]

It's made in two steps to avoid a numerical error that crops up when linspace ranges across 0. If x is made in one step as

x = np.linspace(-np.pi, np.pi, 101)

then the value at x[50] is 4.440892098500626e-16, instead of x[50] being 0 as expected.

answered Nov 25 '18 at 21:32

tel

7,43621431

add a comment |

Getting precise outputs from floating point operations can be tricky and fiddly. You can get the list you want using np.linspace:

x = np.linspace(-np.pi, 0, num=51)

x = np.concatenate([x, np.linspace(x[-1] - x[-2], np.pi, num=50)])

print(x)

Output:

[-3.14159265 -3.0787608  -3.01592895 -2.95309709 -2.89026524 -2.82743339

 -2.76460154 -2.70176968 -2.63893783 -2.57610598 -2.51327412 -2.45044227

 -2.38761042 -2.32477856 -2.26194671 -2.19911486 -2.136283   -2.07345115

 -2.0106193  -1.94778745 -1.88495559 -1.82212374 -1.75929189 -1.69646003

 -1.63362818 -1.57079633 -1.50796447 -1.44513262 -1.38230077 -1.31946891

 -1.25663706 -1.19380521 -1.13097336 -1.0681415  -1.00530965 -0.9424778

 -0.87964594 -0.81681409 -0.75398224 -0.69115038 -0.62831853 -0.56548668

 -0.50265482 -0.43982297 -0.37699112 -0.31415927 -0.25132741 -0.18849556

 -0.12566371 -0.06283185  0.          0.06283185  0.12566371  0.18849556

  0.25132741  0.31415927  0.37699112  0.43982297  0.50265482  0.56548668

  0.62831853  0.69115038  0.75398224  0.81681409  0.87964594  0.9424778

  1.00530965  1.0681415   1.13097336  1.19380521  1.25663706  1.31946891

  1.38230077  1.44513262  1.50796447  1.57079633  1.63362818  1.69646003

  1.75929189  1.82212374  1.88495559  1.94778745  2.0106193   2.07345115

  2.136283    2.19911486  2.26194671  2.32477856  2.38761042  2.45044227

  2.51327412  2.57610598  2.63893783  2.70176968  2.76460154  2.82743339

  2.89026524  2.95309709  3.01592895  3.0787608   3.14159265]

It's made in two steps to avoid a numerical error that crops up when linspace ranges across 0. If x is made in one step as

x = np.linspace(-np.pi, np.pi, 101)

then the value at x[50] is 4.440892098500626e-16, instead of x[50] being 0 as expected.

answered Nov 25 '18 at 21:32

tel

7,43621431

add a comment |

Getting precise outputs from floating point operations can be tricky and fiddly. You can get the list you want using np.linspace:

x = np.linspace(-np.pi, 0, num=51)

x = np.concatenate([x, np.linspace(x[-1] - x[-2], np.pi, num=50)])

print(x)

Output:

[-3.14159265 -3.0787608  -3.01592895 -2.95309709 -2.89026524 -2.82743339

 -2.76460154 -2.70176968 -2.63893783 -2.57610598 -2.51327412 -2.45044227

 -2.38761042 -2.32477856 -2.26194671 -2.19911486 -2.136283   -2.07345115

 -2.0106193  -1.94778745 -1.88495559 -1.82212374 -1.75929189 -1.69646003

 -1.63362818 -1.57079633 -1.50796447 -1.44513262 -1.38230077 -1.31946891

 -1.25663706 -1.19380521 -1.13097336 -1.0681415  -1.00530965 -0.9424778

 -0.87964594 -0.81681409 -0.75398224 -0.69115038 -0.62831853 -0.56548668

 -0.50265482 -0.43982297 -0.37699112 -0.31415927 -0.25132741 -0.18849556

 -0.12566371 -0.06283185  0.          0.06283185  0.12566371  0.18849556

  0.25132741  0.31415927  0.37699112  0.43982297  0.50265482  0.56548668

  0.62831853  0.69115038  0.75398224  0.81681409  0.87964594  0.9424778

  1.00530965  1.0681415   1.13097336  1.19380521  1.25663706  1.31946891

  1.38230077  1.44513262  1.50796447  1.57079633  1.63362818  1.69646003

  1.75929189  1.82212374  1.88495559  1.94778745  2.0106193   2.07345115

  2.136283    2.19911486  2.26194671  2.32477856  2.38761042  2.45044227

  2.51327412  2.57610598  2.63893783  2.70176968  2.76460154  2.82743339

  2.89026524  2.95309709  3.01592895  3.0787608   3.14159265]

It's made in two steps to avoid a numerical error that crops up when linspace ranges across 0. If x is made in one step as

x = np.linspace(-np.pi, np.pi, 101)

then the value at x[50] is 4.440892098500626e-16, instead of x[50] being 0 as expected.

answered Nov 25 '18 at 21:32

tel

7,43621431

Getting precise outputs from floating point operations can be tricky and fiddly. You can get the list you want using np.linspace:

x = np.linspace(-np.pi, 0, num=51)

x = np.concatenate([x, np.linspace(x[-1] - x[-2], np.pi, num=50)])

print(x)

Output:

[-3.14159265 -3.0787608  -3.01592895 -2.95309709 -2.89026524 -2.82743339

 -2.76460154 -2.70176968 -2.63893783 -2.57610598 -2.51327412 -2.45044227

 -2.38761042 -2.32477856 -2.26194671 -2.19911486 -2.136283   -2.07345115

 -2.0106193  -1.94778745 -1.88495559 -1.82212374 -1.75929189 -1.69646003

 -1.63362818 -1.57079633 -1.50796447 -1.44513262 -1.38230077 -1.31946891

 -1.25663706 -1.19380521 -1.13097336 -1.0681415  -1.00530965 -0.9424778

 -0.87964594 -0.81681409 -0.75398224 -0.69115038 -0.62831853 -0.56548668

 -0.50265482 -0.43982297 -0.37699112 -0.31415927 -0.25132741 -0.18849556

 -0.12566371 -0.06283185  0.          0.06283185  0.12566371  0.18849556

  0.25132741  0.31415927  0.37699112  0.43982297  0.50265482  0.56548668

  0.62831853  0.69115038  0.75398224  0.81681409  0.87964594  0.9424778

  1.00530965  1.0681415   1.13097336  1.19380521  1.25663706  1.31946891

  1.38230077  1.44513262  1.50796447  1.57079633  1.63362818  1.69646003

  1.75929189  1.82212374  1.88495559  1.94778745  2.0106193   2.07345115

  2.136283    2.19911486  2.26194671  2.32477856  2.38761042  2.45044227

  2.51327412  2.57610598  2.63893783  2.70176968  2.76460154  2.82743339

  2.89026524  2.95309709  3.01592895  3.0787608   3.14159265]

It's made in two steps to avoid a numerical error that crops up when linspace ranges across 0. If x is made in one step as

x = np.linspace(-np.pi, np.pi, 101)

then the value at x[50] is 4.440892098500626e-16, instead of x[50] being 0 as expected.

answered Nov 25 '18 at 21:32

tel

7,43621431

answered Nov 25 '18 at 21:32

tel

7,43621431

answered Nov 25 '18 at 21:32

tel

7,43621431

answered Nov 25 '18 at 21:32

tel

7,43621431

add a comment |

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