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@@ -2,6 +2,7 @@
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import numpy as np
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import numpy as np
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import math
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import math
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import sounddevice as sd
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# https://people.cs.uchicago.edu/~ridg/stabil/pianostring.pdf
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# https://people.cs.uchicago.edu/~ridg/stabil/pianostring.pdf
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# a differential equations model for a piano string
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# a differential equations model for a piano string
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@@ -104,23 +105,26 @@ import math
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# simulation parameters
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# simulation parameters
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f1 = 50 # fundamental frequency
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f1 = 50 # fundamental frequency
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f_e = 44100 # sampling frequency
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f_e = 44100 # sampling frequency
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N = 50 # number of string segments
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N = 200 # number of string segments
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delta_t = 1/f_e # time step
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delta_t = 1/f_e # time step
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L = 0.5 # string length, meters
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L = 0.5 # string length, meters
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delta_x = L/N # spatial step
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delta_x = L/N # spatial step
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H = int(f_e * 0.1) # length of simulation in time
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H = int(f_e * 2) # length of simulation in time
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# string parameters
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# string parameters
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E = 200 * 10**9 # youngs modulus, steel = 200GPa
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E = 100 * 10**9 # youngs modulus, steel = 200GPa
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rho = 8000 # density, steel, kg/m^3
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rho = 8000 # density, steel, kg/m^3
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radius = 0.002 # meters
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radius = 0.005 # meters
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kappa = radius/2 # radius of gyration, r/2 for a circular string
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kappa = radius/2 # radius of gyration, r/2 for a circular string
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S = math.pi*radius**2 # string cross sectional area, assuming circular
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S = math.pi*radius**2 # string cross sectional area, assuming circular
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mu = S*rho # linear mass density
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mu = S*rho # linear mass density
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M_S = mu*L # string mass
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M_S = mu*L # string mass
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T = 10000 # string tension, N
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T = 500 # string tension, N
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c = math.sqrt(T/mu) # transverse wave velocity
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c = math.sqrt(T/mu) # transverse wave velocity
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stiffness = kappa**2 * delta_t**2 / delta_x**4 # string stiffness parameter
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I = math.pi*radius**4 / 4
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k = math.sqrt(E*I/(rho*S))
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# stiffness = k**2 * delta_t**2 / delta_x**4 # string stiffness parameter
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stiffness = 0
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sigma = 1 # decay rate
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sigma = 1 # decay rate
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tau = 1/sigma # decay time
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tau = 1/sigma # decay time
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omega = 1/f1 # angular frequency
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omega = 1/f1 # angular frequency
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@@ -128,14 +132,14 @@ omega = 1/f1 # angular frequency
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# hammer parameters
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# hammer parameters
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M_H = 0.5 # hammer mass, kg
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M_H = 0.5 # hammer mass, kg
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HSMR = M_H/M_S # hammer-mass string ratio
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HSMR = M_H/M_S # hammer-mass string ratio
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V_H_0 = 10 # initial hammer velocity at t=0, m/s
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V_H_0 = 10000000000 # initial hammer velocity at t=0, m/s
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x_0 = L/2 # distance of hammer from agraffe
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x_0 = L/2 # distance of hammer from agraffe
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alpha = x_0 / L # relative hammer striking position
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alpha = x_0 / L # relative hammer striking position
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# empirical constants
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# empirical constants
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b_1 = 1 # some constant
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b_1 = 0 # some constant
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b_3 = 0.001 # some constant
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b_3 = 0 # some constant
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K = 10 # hammer stiffness
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K = 1 # hammer stiffness
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p = 1 # stiffness nonlinear exponent
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p = 1 # stiffness nonlinear exponent
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# derived components
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# derived components
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@@ -152,7 +156,7 @@ x = [0] * N # current string position
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x_initial = [0] * N # assuming string at rest
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x_initial = [0] * N # assuming string at rest
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last_x1 = [0] * N # string position from last timestep
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last_x1 = [0] * N # string position from last timestep
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last_x2 = [0] * N # string position from two timesteps ago
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last_x2 = [0] * N # string position from two timesteps ago
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x_sample = int(alpha*N) # location where we sample the string position for signal
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x_sample = int(0.8*N) # location where we sample the string position for signal
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# hammer
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# hammer
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F_H_current = 0 # current force exterted by hammer
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F_H_current = 0 # current force exterted by hammer
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@@ -175,7 +179,7 @@ def plot_current():
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def spatial_window(i):
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def spatial_window(i):
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if(i < x_hammer+i_H/2 and i > x_hammer-i_H/2):
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if(i < x_hammer+i_H/2 and i > x_hammer-i_H/2):
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return 1
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return abs(i-x_hammer-i_H/2)
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else:
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else:
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return 0
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return 0
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@@ -196,8 +200,6 @@ eta_current = eta_next
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for i in range(1, N-1):
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for i in range(1, N-1):
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x[i] = last_x1[i+1] + last_x1[i-1] - x_initial[i] + ((delta_t**2)*N*F_H_current*spatial_window(i))/M_S
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x[i] = last_x1[i+1] + last_x1[i-1] - x_initial[i] + ((delta_t**2)*N*F_H_current*spatial_window(i))/M_S
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plot_current()
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last_x2 = last_x1.copy()
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last_x2 = last_x1.copy()
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last_x1 = x.copy()
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last_x1 = x.copy()
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F_H_current = K * abs(eta_current - x[x_hammer])**p
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F_H_current = K * abs(eta_current - x[x_hammer])**p
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@@ -217,25 +219,29 @@ for n in range(H): # time
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F_H_current = K * abs(push)**p
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F_H_current = K * abs(push)**p
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else:
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else:
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F_H_current = 0
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F_H_current = 0
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F_H_current = 0
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next_x = [0] * N
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next_x = [0] * N
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for i in range(2, N-2): # space
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for i in range(2, N-2): # space
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term_1 = a_1*x[i] + a_2*last_x1[i]
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# term_1 = a_1*x[i] + a_2*last_x1[i]
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term_2 = a_3*(x[i+1] + x[i-1]) + a_4*(x[i+2] + x[i-2])
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# term_2 = a_3*(x[i+1] + x[i-1]) + a_4*(x[i+2] + x[i-2])
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term_3 = a_5*(last_x1[i+1] + last_x1[i-1] + last_x2[i])
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# term_3 = a_5*(last_x1[i+1] + last_x1[i-1] + last_x2[i])
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term_4 = (delta_t**2 * N*F_H_current * spatial_window(i))/M_S
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# term_4 = (delta_t**2 * N*F_H_current * spatial_window(i))/M_S
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next_x[i] = term_1 + term_2 + term_3 + term_4
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# next_x[i] = term_1 + term_2 + term_3 + term_4
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next_x[i] = 2*(1-r**2)*x[i] + r**2 * (x[i+1] + x[i-1]) - last_x1[i]
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x[0] = 0
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x[1] = 0
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x[N-2] = 0
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x[N-1] = 0
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last_x2 = last_x1.copy()
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last_x2 = last_x1.copy()
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last_x1 = x.copy()
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last_x1 = x.copy()
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x = next_x.copy()
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x = next_x.copy()
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if(n == 30):
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plot_current()
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x_out[n] = x[x_sample]
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x_out[n] = x[x_sample]
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# plotting
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# plotting
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@@ -245,3 +251,6 @@ plt.xlabel("t")
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plt.ylabel("x(t, x_sample)")
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plt.ylabel("x(t, x_sample)")
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plt.grid()
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plt.grid()
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plt.show()
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plt.show()
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sd.play(x_out, f_e)
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sd.wait()
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@@ -2,6 +2,7 @@
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import scipy.signal as sig
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import scipy.signal as sig
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import numpy as np
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import numpy as np
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import sounddevice as sd
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import math
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import math
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# simple first order step response simulation
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# simple first order step response simulation
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@@ -32,8 +33,11 @@ import math
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f_1 = 440 # fundamental frequency
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f_1 = 440 # fundamental frequency
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def f_n(n):
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def f_n(n):
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return f_1 * n
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return f_1 * n
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omega_1 = 2*math.pi*f_1
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b = 0.01
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def omega_n(n):
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def omega_n(n):
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return 2*math.pi*f_n(n) # a cooler option would be omega_n = c*n*omega_1*sqrt(1+B*n^2) to factor in string stiffness to its vibration mode
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# return 2*math.pi*f_n(n) # a cooler option would be omega_n = c*n*omega_1*sqrt(1+B*n^2) to factor in string stiffness to its vibration mode
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return 1.001*omega_1*math.sqrt(1+b*n**2)
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# x = [ q1, q1dot, q2, q2dot, q3, q3dot ]^T < --state vector
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# x = [ q1, q1dot, q2, q2dot, q3, q3dot ]^T < --state vector
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omega_1 = omega_n(1)
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omega_1 = omega_n(1)
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@@ -52,7 +56,7 @@ A = [
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] # isnt this formatting gorgeous
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] # isnt this formatting gorgeous
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B = [ [0], [0.707], [0], [0], [0], [-0.707] ]
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B = [ [0], [0.707], [0], [0], [0], [-0.707] ]
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c_1 = 0.001
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c_1 = 0.0001
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c_2 = c_1 / 2
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c_2 = c_1 / 2
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c_3 = c_1 / 3
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c_3 = c_1 / 3
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C = [[-c_1*omega_1**2, -2*c_1*zeta_1*omega_1, -c_2*omega_2**2, -2*c_2*zeta_2*omega_2, -c_3*omega_3**2, -2*c_3*zeta_3*omega_3]]
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C = [[-c_1*omega_1**2, -2*c_1*zeta_1*omega_1, -c_2*omega_2**2, -2*c_2*zeta_2*omega_2, -c_3*omega_3**2, -2*c_3*zeta_3*omega_3]]
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@@ -80,3 +84,6 @@ plt.xlabel("t")
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plt.ylabel("y")
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plt.ylabel("y")
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plt.grid()
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plt.grid()
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plt.show()
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plt.show()
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sd.play(y, 44100)
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sd.wait()
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