def test_basic(self):
a = leslie([1, 2, 3], [0.25, 0.5])
expected = array([
[1.0, 2.0, 3.0],
[0.25, 0.0, 0.0],
[0.0, 0.5, 0.0]])
assert_array_equal(a, expected)
def test_basic(self):
a = leslie([1, 2, 3], [0.25, 0.5])
expected = array([
[1.0, 2.0, 3.0],
[0.25, 0.0, 0.0],
[0.0, 0.5, 0.0]])
assert_array_equal(a, expected)
def get_les_mats(sr_asfr,df_dr):
"""Returns M,F Leslie matrices constructed from the given parameters"""
# Note that the survival array is one shorter than the fecundity array.
# Both are fractions that apply to each 5 year cell as a unit.
# For Males
fec = np.zeros(18)
sur = 1 - df_dr['Males'].values[:-1]*5/1000
mlm = leslie(fec,sur)
# For Females
fec = np.double(sr_asfr.values) * 5 / 1000
fec = np.hstack((np.zeros(3),fec,np.zeros(8)))
sur = 1 - df_dr['Females'].values[:-1]*5/1000
flm = leslie(fec,sur)
return mlm, flm
def time_leslie(self, size):
sl.leslie(self.x, self.x[1:])
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 3 19:46:38 2019
@author: xsxsz
"""
import numpy as np
import scipy.linalg as linalg
a = np.mat(np.ones([3, 3]))
b = np.mat(np.ones([4, 3]))
c = np.mat(np.ones([3, 4]))
d = linalg.block_diag(a, b, c)
print(d)
print('----------')
e = linalg.pascal(6)
print(e)
print('----------')
f_value = np.array([0.3, 0.1, 0.4, 0.2])
s_value = np.array([0.2, 0.8, 0.7])
f = linalg.leslie(f_value, s_value)
print(f)
print('----------')
def time_leslie(self, size):
sl.leslie(self.x, self.x[1:])
