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TavnosTavnos
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‎matplotlibcomplexvisualart.py

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import numpy as np
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import matplotlib.pyplot as plt
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def generate_8_complex(t_range=0,
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t_spread=0,
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scale_exists=0,
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abc_bump=0,
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enable_t0=1,enable_t1=0,
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enable_t2=0,enable_t3=0,
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enable_t4=0,enable_t5=0,
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enable_t_abc_denum=0, enable_t_abc_scale=0,
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t_a_inc=0,t_a_dec=0,
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t_b_inc=0,t_b_dec=0,
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t_c_inc=0,t_c_dec=0,
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t_abc_denum=1,t_abc_scale=0):
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f_tr_x_ls = []
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f_ti_y_ls = []
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for i in range(t_range):
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(f_tr_a_inc,f_tr_a_dec,
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f_tr_b_inc,f_tr_b_dec,
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f_tr_c_inc,f_tr_c_dec,
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f_tr_abc_scale,f_tr_abc_denum)=((np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_a_inc))*enable_t0),
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(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_a_dec))*enable_t1),
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(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_b_inc))*enable_t2),
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(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_b_dec))*enable_t3),
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(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_c_inc))*enable_t4),
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(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_c_dec))*enable_t5),
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(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_abc_scale))*enable_t_abc_scale),
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(np.real(np.e**(1j*2*np.pi*(i/t_spread)*t_abc_denum))*enable_t_abc_denum))
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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))/
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(f_tr_abc_denum+abc_bump)) *(f_tr_abc_scale**scale_exists)]
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(f_ti_a_inc,f_ti_a_dec,
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f_ti_b_inc,f_ti_b_dec,
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f_ti_c_inc,f_ti_c_dec,
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f_ti_abc_scale,f_ti_abc_denum)=((np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_a_inc))*enable_t0),
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(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_a_dec))*enable_t1),
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(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_b_inc))*enable_t2),
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(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_b_dec))*enable_t3),
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(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_c_inc))*enable_t4),
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(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_c_dec))*enable_t5),
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(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_abc_scale))*enable_t_abc_scale),
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(np.imag(np.e**(1j*2*np.pi*(i/t_spread)*t_abc_denum))*enable_t_abc_denum))
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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))/
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(f_ti_abc_denum+abc_bump)) *(f_ti_abc_scale**scale_exists)]
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plt.scatter(f_tr_x_ls,f_ti_y_ls)
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plt.show()
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generate_8_complex(1000,# Total ammount of jumps, lags if ~> 10 000 ## 1000 decent standard ##
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100,# t int loop spread, conclusive jumps ~ > 10 ## pref around 100 - 1000 ##
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2, # 0 no mult, 1 enables, >1 uplifts, 1>pow>0 roots, ## need to be 0 if not activated ##
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100, # abc denum not to be 0, preferably not 1>denum>0 ## slightly similar to spread ##
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############################################################################################
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## Mostly booleans for choice diversification: (not reccomended to use them as scale) ##
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############################################################################################
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1,1, # # a1-a2 , +a1 , or -a2 ##
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1,1, # # b1-b2 , +b1 , or -b2 ##
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1,1, # # c1-c2 , +c1 , or -c2 ##
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1,1, # # ((abc)/denum)*scale , (abc)/denum , or (abc)*scale ##
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############################################################################################
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## The lair of t itself, based on pi rationals and prime factors are encouraged ##
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############################################################################################
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## a1 - a2 ##
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((np.pi** 1 )/(np.pi* 1 ))* 1009 , ((np.pi** 1 )/(np.pi* 1 ))* 997,
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## b1 - b2 ##
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((np.pi* 1 )/(np.pi* 1 ))* 991 , ((np.pi* 1 )/(np.pi* 1 ))* 983,
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## c1 - c2 ##
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((np.pi* 1 )/(np.pi* 1 ))* 977 , ((np.pi* 1 )/(np.pi* 1 ))* 971,
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## (abc)/denum and (abc)*scale ##
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((np.pi** 1 )/(np.pi* 1 ))* 967 , ((np.pi* 1 )/(np.pi* 1 ))* 953 )
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############################################################################################
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# Keep in mind the current equation: ##
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############################################################################################
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# (a1-a2)+(b1-b2)+(c1-c2) _^*scale_bool_ish ##
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# ------------------------ * abc_scale*^ ##
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# (abc_denum+no_div_0_airbag) ##
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############################################################################################

‎moretraining.png

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