The numpy.logspace() function returns number spaces evenly w.r.t interval on a log scale.
Syntax :
numpy.logspace(start, stop, num = 50, endpoint = True, base = 10.0, dtype = None)
Parameters :
-> start : [float] start(base ** start) of interval range. -> stop : [float] end(base ** stop) of interval range -> endpoint : [boolean, optional]If True, stop is the last sample. By default, True -> num : [int, optional] No. of samples to generate -> base : [float, optional] Base of log scale. By default, equals 10.0 -> dtype : type of output array
Return :
-> ndarray
Code 1 : Explaining the use of logspace()
# Python Programming illustrating # numpy.logspace method import numpy as geek # base = 11 print ( "B\n" , geek.logspace( 2.0 , 3.0 , num = 5 , base = 11 )) # base = 10 print ( "B\n" , geek.logspace( 2.0 , 3.0 , num = 5 )) # base = 10, dtype = int print ( "B\n" , geek.logspace( 2.0 , 3.0 , num = 5 , dtype = int )) |
Output :
B [ 121. 220.36039471 401.31159963 730.8527479 1331. ] B [ 100. 177.827941 316.22776602 562.34132519 1000. ] B [ 100 177 316 562 1000]
Code 2 : Graphical Representation of numpy.logspace() using matplotlib module – pylab
# Graphical Represenation of numpy.logspace() import numpy as geek import pylab as p # Start = 0 # End = 2 # Samples to generate = 10 x1 = geek.logspace( 0 , 1 , 10 ) y1 = geek.zeros( 10 ) # Start = 0.1 # End = 1.5 # Samples to generate = 12 x2 = geek.logspace( 0.1 , 1.5 , 12 ) y2 = geek.zeros( 12 ) p.plot(x1, y1 + 0.05 , 'o' ) p.xlim( - 0.2 , 18 ) p.ylim( - 0.5 , 1 ) p.plot(x2, y2, 'x' ) |
Output :
Note :
These NumPy-Python programs wonβt run on onlineID, so run them on your systems to explore them
Similiar methods :
- arange
- linspace
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