-
Notifications
You must be signed in to change notification settings - Fork 0
/
matplotlibcomplexvisualart.py
78 lines (74 loc) · 5.75 KB
/
matplotlibcomplexvisualart.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
import numpy as np
import matplotlib.pyplot as plt
def generate_8_complex(t_range=0,
t_spread=0,
scale_exists=0,
abc_bump=0,
enable_t0=1,enable_t1=0,
enable_t2=0,enable_t3=0,
enable_t4=0,enable_t5=0,
enable_t_abc_denum=0, enable_t_abc_scale=0,
t_a_inc=0,t_a_dec=0,
t_b_inc=0,t_b_dec=0,
t_c_inc=0,t_c_dec=0,
t_abc_denum=1,t_abc_scale=0):
f_tr_x_ls = []
f_ti_y_ls = []
for i in range(t_range):
(f_tr_a_inc,f_tr_a_dec,
f_tr_b_inc,f_tr_b_dec,
f_tr_c_inc,f_tr_c_dec,
f_tr_abc_scale,f_tr_abc_denum)=((np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_a_inc))*enable_t0),
(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_a_dec))*enable_t1),
(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_b_inc))*enable_t2),
(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_b_dec))*enable_t3),
(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_c_inc))*enable_t4),
(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_c_dec))*enable_t5),
(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_abc_scale))*enable_t_abc_scale),
(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_abc_denum))*enable_t_abc_denum))
f_tr_x_ls += [(((f_tr_a_inc-f_tr_a_dec)+(f_tr_b_inc-f_tr_b_dec)+(f_tr_c_inc-f_tr_c_dec))/
(f_tr_abc_denum+abc_bump)) *(f_tr_abc_scale**scale_exists)]
(f_ti_a_inc,f_ti_a_dec,
f_ti_b_inc,f_ti_b_dec,
f_ti_c_inc,f_ti_c_dec,
f_ti_abc_scale,f_ti_abc_denum)=((np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_a_inc))*enable_t0),
(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_a_dec))*enable_t1),
(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_b_inc))*enable_t2),
(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_b_dec))*enable_t3),
(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_c_inc))*enable_t4),
(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_c_dec))*enable_t5),
(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_abc_scale))*enable_t_abc_scale),
(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_abc_denum))*enable_t_abc_denum))
f_ti_y_ls += [(((f_ti_a_inc-f_ti_a_dec)+(f_ti_b_inc-f_ti_b_dec)+(f_ti_c_inc-f_ti_c_dec))/
(f_ti_abc_denum+abc_bump)) *(f_ti_abc_scale**scale_exists)]
plt.scatter(f_tr_x_ls,f_ti_y_ls)
plt.show()
generate_8_complex(1000,# Total ammount of jumps, lags if ~> 10 000 ## 1000 decent standard ##
100,# t int loop spread, conclusive jumps ~ > 10 ## pref around 100 - 1000 ##
2, # 0 no mult, 1 enables, >1 uplifts, 1>pow>0 roots, ## need to be 0 if not activated ##
100, # abc denum not to be 0, preferably not 1>denum>0 ## slightly similar to spread ##
############################################################################################
## Mostly booleans for choice diversification: (not reccomended to use them as scale) ##
############################################################################################
1,1, # # a1-a2 , +a1 , or -a2 ##
1,1, # # b1-b2 , +b1 , or -b2 ##
1,1, # # c1-c2 , +c1 , or -c2 ##
1,1, # # ((abc)/denum)*scale , (abc)/denum , or (abc)*scale ##
############################################################################################
## The lair of t itself, based on pi rationals and prime factors are encouraged ##
############################################################################################
## a1 - a2 ##
((np.pi** 1 )/(np.pi* 1 ))* 1009 , ((np.pi** 1 )/(np.pi* 1 ))* 997,
## b1 - b2 ##
((np.pi* 1 )/(np.pi* 1 ))* 991 , ((np.pi* 1 )/(np.pi* 1 ))* 983,
## c1 - c2 ##
((np.pi* 1 )/(np.pi* 1 ))* 977 , ((np.pi* 1 )/(np.pi* 1 ))* 971,
## (abc)/denum and (abc)*scale ##
((np.pi** 1 )/(np.pi* 1 ))* 967 , ((np.pi* 1 )/(np.pi* 1 ))* 953 )
############################################################################################
# Keep in mind the current equation: ##
############################################################################################
# (a1-a2)+(b1-b2)+(c1-c2) _^*scale_bool_ish ##
# ------------------------ * abc_scale*^ ##
# (abc_denum+no_div_0_airbag) ##
############################################################################################