tcompa

Start with this yaml file

id: https://w3id.org/linkml/examples/image
name: image
prefixes:
  linkml: https://w3id.org/linkml/
  image: https://w3id.org/linkml/examples/image
imports:
  - linkml:types
default_range: string

tcompa

{
        "title": "CreateOmeZarr",
        "type": "object",
        "properties": {
          "input_paths": {
            "title": "Input Paths",
            "type": "array",
            "items": {
              "type": "string"
            },

tcompa

def normalized_gr_3d(_samples, _N, _nbins, _rho):
    '''
    Returns g^{(2)} with the correct normalization, i.e. it goes to
    ((N-1)/N)*rho^2 for distances which are large, but smaller than L/2.
    Args:
        _samples : 
        _N       : number of particles
        _nbins   : number of bins
        _rho     : number density (number of particle divided by volume)
    '''

tcompa

# numpy is the library which allows you to do all sort of calculations,
# and you need to import it at the beginning of the file
import numpy

# this is your function of the two frequencies, needed to construct the matrix
# you need to replace term2 with your own function
def fun(omega1, omega2):
    term1 = numpy.exp(- (omega1 - omega2) ** 2 / 1000.0)
    term2 = numpy.sinc((omega1 - omega2) ** 2)
    return term1 * term2

tcompa

# create and manipulate a list A
A = [1, 2, 3, 7, 3]
print 'A:', A
A.append(19)
print 'A:', A, ', after A.append(19)'
print 'A.index(7): %i' % A.index(7)
print 'A.count(3): %i' % A.count(3)
print 'A[1]:', A[1]
print 'A[-3:]:', A[-3:]
print 'sorted(A):', sorted(A)

tcompa

Plotting

Last Update: 12.12.2014
Offline Version

Contents

• Translating
• Basic Plot
Plot Annotation