def plot_activity(self):
euler = EulerEstimator(
derivatives=[self.dv_dt, self.dn_dt, self.dm_dt, self.dh_dt],
point=(0, (self.v_0, self.n_0, self.m_0, self.h_0)))
plt.plot([n / 2 for n in range(160)],
[self.s_t(n / 2) for n in range(160)])
euler.plot([0, 80], step_size=0.02, file_name="photo.png")
def plot_activity(self):
estimator = EulerEstimator(
derivatives=[self.dV, self.dn_dt, self.dm_dt, self.dh_dt],
point=(0, (0, self.n_0, self.m_0, self.h_0)))
plt.plot([n / 2 for n in range(160)],
[self.s_t(n / 2) for n in range(160)])
estimator.plot([0, 80], step_size=0.02, filename="hodgen_huxley.png")
class Neuron:
def __init__(self, stimulus):
self.stimulus = stimulus
self.estimator = EulerEstimator(self.get_derivatives(),
self.get_starting_point())
def plot_activity(self, filename):
plt.plot([n / 2 for n in range(160)], [s_t(n / 2) for n in range(160)],
zorder=1)
self.estimator.plot([0, 80], step_size=0.02, filename=filename)
def get_starting_point(self):
x = 0.07 * (math.e**3 + 1)
h_0 = x / (x + 1)
n_0 = 1 / (1.25 * (math.e - 1) + 1)
m_0 = 2.5 / (2.5 + 4 * (math.e**2.5 - 1))
V_0 = 0
return 0, (V_0, n_0, m_0, h_0)
def get_derivatives(self):
return [self.dV(), Neuron.dn, Neuron.dm, Neuron.dh]
@staticmethod
def a_n(t, x):
return 0.01 * (10 - x[0]) / (math.exp(0.1 * (10 - x[0])) - 1)
@staticmethod
def b_n(t, x):
return 0.125 * math.exp(-x[0] / 80)
@staticmethod
def a_m(t, x):
return 0.1 * (25 - x[0]) / (math.exp(0.1 * (25 - x[0])) - 1)
@staticmethod
def b_m(t, x):
return 4 * math.exp(-x[0] / 18)
@staticmethod
def a_h(t, x):
return 0.07 * math.exp(-x[0] / 20)
@staticmethod
def b_h(t, x):
return 1 / (math.exp(0.1 * (30 - x[0])) + 1)
@staticmethod
def dn(t, x):
return Neuron.a_n(t, x) * (1 - x[1]) - Neuron.b_n(t, x) * x[1]
@staticmethod
def dm(t, x):
return Neuron.a_m(t, x) * (1 - x[2]) - Neuron.b_m(t, x) * x[2]
@staticmethod
def dh(t, x):
return Neuron.a_h(t, x) * (1 - x[3]) - Neuron.b_h(t, x) * x[3]
C = 1
Vna = 115
Vk = -12
VL = 10.6
_gna = 120
_gk = 36
_gl = 0.3
@staticmethod
def INa(t, x):
return Neuron.gna(t, x) * (x[0] - Neuron.Vna)
@staticmethod
def gna(t, x):
return Neuron._gna * x[2]**3 * x[3]
@staticmethod
def Ik(t, x):
return Neuron.gk(t, x) * (x[0] - Neuron.Vk)
@staticmethod
def gk(t, x):
return Neuron._gk * x[1]**4
@staticmethod
def IL(t, x):
return Neuron.gl(t, x) * (x[0] - Neuron.VL)
@staticmethod
def gl(t, x):
return Neuron._gl
def dV(self):
return lambda t, x: 1 / Neuron.C * (self.stimulus(t) - Neuron.INa(
t, x) - Neuron.Ik(t, x) - Neuron.IL(t, x))
def plot_activity(self):
plt.plot([x/2 for x in range(160)], [self.s_t(n/2) for n in range(160)])
estimator = EulerEstimator(derivatives = {"dV":(self.dV), "dn_dt":(self.dn_dt), "dm_dt":(self.dm_dt), "dh_dt":(self.dh_dt)},
point = (0, (0, self.n_zero, self.m_zero, self.h_zero)))
estimator.plot([0, 80], step_size=0.02,filename="plot.png")
import sys
sys.path.append('src')
from euler_estimator import EulerEstimator
derivatives = {
'S': (lambda t, x: -0.01 * 0.03 * x['S'] * x['I']),
'I': (lambda t, x: 0.01 * 0.03 * x['S'] * x['I'] - 0.02 * x['I']),
'R': (lambda t, x: 0.02 * x['I'])
}
euler = EulerEstimator(derivatives)
initial_values = {'S': 1000, 'I': 1, 'R': 0}
initial_point = (0, initial_values)
euler.plot(point=initial_point, step_size=0.05, num_steps=4000)
"suseptible": (lambda t, x: -0.001 * x["suseptible"] * x["infected"]),
"infected": (lambda t, x: 0.001 * x["suseptible"] * x["infected"] - 0.05 *
x["infected"]),
"recovered": (lambda t, x: 0.05 * x["infected"])
},
point=(0, {
"suseptible": 500,
"infected": 5,
"recovered": 0
}))
print("Derivitive: " + str(euler.calc_derivative_at_point()))
# print("Point: "+str(euler.point))
# #(0, (0, 0, 0))
# print("Derivitive: "+str(euler.calc_derivative_at_point()))
# #[1, 0, 0]
# euler.step_forward(0.1)
# print("Point: "+str(euler.point))
# print("Derivitive: "+str(euler.calc_derivative_at_point()))
# [1.1, 0.1, 0]
# euler.step_forward(-0.5)
# print("Point: "+str(euler.point))
# (-0.4, (-0.45, -0.05, 0))
# euler.go_to_input(5, step_size = 2)
# print(euler.point)
# (5, (10.88, 1.09, -9.58))
euler.plot([0, 50], 0.1)
elif t > 20 and t < 21:
return 50
elif t > 30 and t < 40:
return 50
elif t > 50 and t < 51:
return 50
elif t > 53 and t < 54:
return 50
elif t > 56 and t < 57:
return 50
elif t > 59 and t < 60:
return 50
elif t > 62 and t < 63:
return 50
elif t > 65 and t < 66:
return 50
return 0
neuron_0 = BiologicalNeuron(stimulus=electrode_voltage)
neuron_1 = BiologicalNeuron()
neuron_2 = BiologicalNeuron()
neurons = [neuron_0, neuron_1, neuron_2]
synapses = [(0, 1), (1, 2)]
network = BiologicalNeuralNetwork(neurons, synapses)
euler = EulerEstimator(derivatives=network.get_derivatives(),
point=network.get_starting_point())
plt.plot([n / 2 for n in range(160)],
[electrode_voltage(n / 2) for n in range(160)])
euler.plot([0, 80], step_size=0.001, filename="biological_neural_network")
import sys
sys.path.append('src')
from euler_estimator import EulerEstimator
derivatives = {'deer': (lambda t,x: 0.6*x['deer']-0.05*x['wolves']*x['deer']),'wolves': (lambda t,x: -0.9*x['wolves']+0.02*x['wolves']*x['deer'])}
euler = EulerEstimator(derivative = derivatives)
initial_values = {'deer': 100, 'wolves': 10}
initial_point = (0, initial_values)
euler.plot(point=initial_point, step_size=0.001, num_steps=100000)
print("\n Testing point after first step")
euler.step_forward(0.1)
assert euler.point == (1.1, {'a':4.2}), 'Incorrect point after first step'
print(" passed")
print("\n Testing derivative after first step")
assert euler.calc_derivative_at_point() == {'a':2.1},'Incorrect derivative after first step'
print(" passed")
print("\n Testing point after 2nd step")
euler.step_forward(-0.5)
assert (round(euler.point[0],3),{item:round(euler.point[1][item],3) for item in euler.point[1]})== (0.6, {'a':3.15}), 'Incorrect point after 2nd step'
print(" passed")
print("\n Testing go_to_input")
euler.go_to_input(3, step_size = 0.5)
assert (round(euler.point[0],3),{item:round(euler.point[1][item],3) for item in euler.point[1]}) == (3, {'a':9.229}), 'Incorrect final result'
print(" passed")
euler = EulerEstimator(
derivatives = {
'susceptible': (lambda x: -0.0003*x['susceptible']*x['infected']),
'infected': (lambda x: 0.0003*x['susceptible']*x['infected'] - 0.02*x['infected']),
'recovered': (lambda x: 0.02*x['infected'])
},
point = (0, {'susceptible': 1000, 'recovered': 0, 'infected': 1}))
euler.plot([0,100], step_size = 0.001, filename = 'test_plot.png')
from euler_estimator import EulerEstimator
import sys
sys.path.append('src/python')
euler = EulerEstimator([(lambda x, y: x + 1)], (1, [4]))
euler.plot([-5, 5])
from euler_estimator import EulerEstimator
euler = EulerEstimator(derivatives=[
(lambda t, x: -0.0003 * x[1] * x[0] + 0.01 * x[1] + 0.001),
(lambda t, x: -0.02 * x[1] + 0.0003 * x[1] * x[0]),
(lambda t, x: 0.01 * x[1])
],
point=(0, (1000, 1, 0)))
euler.plot([0, 150], step_size=0.001, filename='plot.png')
print('passed')
print('testing step forward...')
point_2 = euler.step_forward(point=initial_point, step_size=0.1)
assert point_2 == (0.1, {'A': 0.1, 'B': 0, 'C': 0})
print('passed')
print('testing calc_derivative_at_point...')
assert euler.calc_derivative_at_point(point_2) == {'A': 1.1, 'B': 0.1, 'C': 0}
print('passed')
print('testing step forward...')
point_3 = euler.step_forward(point=point_2, step_size=-0.5)
point_3 = (point_3[0],
{key: round(value, 5)
for key, value in point_3[1].items()})
assert point_3 == (-0.4, {'A': -0.45, 'B': -0.05, 'C': 0})
print('passed')
#assertion error, need to round
# print('testing calc_derivative_at_point...')
# assert euler.calc_estimated_points(point=point_3, step_size=2, num_steps=3) == [
# (-0.4, {'A': -0.45, 'B': -0.05, 'C': 0}), # starting point
# (1.6, {'A': 0.65, 'B': -1.05, 'C': -0.2}), # after 1st step
# (3.6, {'A': 3.95, 'B': -1.85, 'C': -4.4}), # after 2nd step
# (5.6, {'A': 13.85, 'B': 2.35, 'C': -11.8}) # after 3rd step
# ]
print('passed')
euler.plot(initial_point, 0.01, 500)
def plot_activity(self):
euler = EulerEstimator(self.derivatives, self.initial_positions)
plt.plot([n / 2 for n in range(160)],
[self.stimulus(n / 2) for n in range(160)])
euler.plot([0, 80], stepsize=0.02)
import sys
sys.path.append('src')
from euler_estimator import EulerEstimator
derivatives = {
'S': (lambda t, x: -0.0003 * x['S'] * x['I']),
'I': (lambda t, x: 0.0003 * x['S'] * x['I'] - 0.02 * x['I']),
'R': (lambda t, x: 0.02 * x['I'])
}
euler = EulerEstimator(derivative=derivatives)
initial_values = {'S': 1000, 'I': 1, 'R': 0}
initial_point = (0, initial_values)
euler.plot(point=initial_point, step_size=1, num_steps=300)
import sys
sys.path.append('src/python')
from euler_estimator import EulerEstimator
euler = EulerEstimator([(lambda t, x: -0.0003 * x[0] * x[1]),
(lambda t, x: -0.02 * x[1] + 0.0003 * x[0] * x[1]),
(lambda t, x: 0.02 * x[1])], (0, [1000, 1, 0]))
euler.plot([0, 350], stepsize=0.001, filename='assignment_50-2.png')
assert point_2 == (0.1, {'A': 0.1, 'B': 0, 'C': 0}), point_2
assert euler.calc_derivative_at_point(point_2) == {
'A': 1.1,
'B': 0.1,
'C': 0
}, euler.calc_derivative_at_point(point_2)
point_3 = euler.step_forward(point_2, -0.5)
assert point_3 == (-0.4, {'A': -0.45, 'B': -0.05, 'C': 0}), point_3
assert euler.calc_estimated_points(point_3, 2, 3) == [(-0.4, {
'A': -0.45,
'B': -0.05,
'C': 0.0
}), (1.6, {
'A': 0.65,
'B': -1.05,
'C': -0.2
}), (3.6, {
'A': 3.95,
'B': -1.85,
'C': -4.4
}), (5.6, {
'A': 13.85,
'B': 2.35,
'C': -11.8
})]
euler.plot((0, {'A': 0, 'B': 0, 'C': 0}), 0.01, 5)
int_6 = t >= 56 and t <= 57
int_7 = t >= 59 and t <= 60
int_8 = t >= 62 and t <= 63
int_9 = t >= 65 and t <= 66
if int_1 or int_2 or int_3 or int_4 or int_5 or int_6 or int_7 or int_8 or int_9:
return 150
return 0
################################
### input into EulerEstimator
V_0 = 0
n_0 = alpha_n(0, {'V':V_0})/(alpha_n(0, {'V':V_0})+beta_n(0, {'V':V_0}))
m_0 = alpha_m(0, {'V':V_0})/(alpha_m(0, {'V':V_0})+beta_m(0, {'V':V_0}))
h_0 = alpha_h(0, {'V':V_0})/(alpha_h(0, {'V':V_0})+beta_h(0, {'V':V_0}))
derivatives = {
'V': dV_dt,
'n': dn_dt,
'm': dm_dt,
'h': dh_dt,
}
euler = EulerEstimator(derivatives)
initial_values = {'V':V_0, 'n':n_0, 'm':m_0, 'h':h_0}
initial_point = (0, initial_values)
euler.plot(point=initial_point, step_size=0.01, num_steps=8000, additional_functions={'stimulus':s})
from euler_estimator import EulerEstimator
derivatives = [(lambda t, x: 0.6 * x[0] - 0.05 * x[0] * x[1]),
(lambda t, x: -0.9 * x[1] + 0.02 * x[0] * x[1])]
starting_point = (0, (100, 10))
estimator = EulerEstimator(derivatives, starting_point)
estimator.plot([0, 100],
step_size=0.001,
filename="epic_assignment_48_plot.png")
assert euler.calc_derivative_at_point(point_2) == {'A': 1.1, 'B': 0.1, 'C': 0}
point_3 = euler.step_forward(point=point_2, step_size=-0.5)
assert point_3 == (-0.4, {'A': -0.45, 'B': -0.05, 'C': 0})
assert euler.calc_estimated_points(point=point_3, step_size=2,
num_steps=3) == [
(-0.4, {
'A': -0.45,
'B': -0.05,
'C': 0
}), # starting point
(1.6, {
'A': 0.65,
'B': -1.05,
'C': -0.2
}), # after 1st step
(3.6, {
'A': 3.95,
'B': -1.85,
'C': -4.4
}), # after 2nd step
(5.6, {
'A': 13.85,
'B': 2.35,
'C': -11.8
}) # after 3rd step
]
euler.plot()
from euler_estimator import EulerEstimator
derivatives = [
(lambda t, x: -0.0003 * x[0] * x[1]),
(lambda t, x: 0.0003 * x[0] * x[1] - 0.02 * x[1] - 0.001 * x[1]),
(lambda t, x: 0.02 * x[1]), (lambda t, x: 0.001 * x[1])
]
starting_point = (0, (1000, 1, 0, 0))
estimator = EulerEstimator(derivatives, starting_point)
estimator.plot([0, 365], step_size=0.001, filename="quiz_4.png")
import sys
sys.path.append('src/python')
from euler_estimator import EulerEstimator
euler = EulerEstimator([(lambda t, x: 0.6 * x[0] - 0.05 * x[0] * x[1]),
(lambda t, x: -0.9 * x[1] + 0.02 * x[0] * x[1])], (0, [100, 10]))
euler.plot([0, 100], stepsize=0.001, filename='assignment_48-1.png')
import sys
sys.path.append('src')
from euler_estimator import EulerEstimator
derivatives = {
'D': (lambda t, x: 0.6 * x['D'] - 0.05 * x['D'] * x['W']),
'W': (lambda t, x: -0.9 * x['W'] + 0.02 * x['D'] * x['W']),
}
euler = EulerEstimator(derivatives=derivatives)
initial_values = {'D': 100, 'W': 10}
initial_point = (0, initial_values)
euler.plot(initial_point, 0.001, 100000)
elif t > 20 and t < 21:
return 150
elif t > 30 and t < 40:
return 150
elif t > 50 and t < 51:
return 150
elif t > 53 and t < 54:
return 150
elif t > 56 and t < 57:
return 150
elif t > 59 and t < 60:
return 150
elif t > 62 and t < 63:
return 150
elif t > 65 and t < 66:
return 150
return 0
neuron_0 = BiologicalNeuron(stimulus=electrode_voltage)
neuron_1 = BiologicalNeuron()
neuron_2 = BiologicalNeuron()
neurons = [neuron_0, neuron_1, neuron_2]
synapses = [(0, 1), (1, 2)]
network = BiologicalNeuralNetwork(neurons, synapses)
euler = EulerEstimator(derivatives=network.get_derivatives(),
point=network.get_starting_point())
plt.plot([n / 2 for n in range(160)],
[electrode_voltage(n / 2) for n in range(160)])
euler.plot([0, 80], step_size=0.001, file_name="neural_network.png")
(0.1, {'1': 0.1, '2': 0, '3': 0}))
do_assert("new derivative", euler.calc_derivative_at_point(),
{'1': 1.1, '2': 0.1, '3': 0})
euler.step_forward(-0.5)
do_assert("step forward 2", euler.point,
(-0.4, {'1': -0.45, '2': -0.05, '3': 0}))
euler.go_to_input(5, step_size=2)
# notes to help you debug:
# point: (-0.4, (-0.45, -0.05, 0))
# derivative: (0.55, -0.5, -0.1)
# deltas: (2, (1.1, -1, -0.2))
# point: (1.6, (0.65, -1.05, -0.2))
# derivative: (1.65, -0.4, -2.1)
# deltas: (2, (3.3, -0.8, 4.2))
# point: (3.6, (3.95, -1.85, 4))
# derivative: (4.95, 2.1, -3.7)
# deltas: (1.4, (9.8, 4.2, -7.4))
do_assert("go to input", euler.point[1],
{'1': 10.88, '2': 1.09, '3': -9.58})
euler.plot([-5, 5], step_size=0.1, filename='plot.png')
print(cstring("&6All Tests Passed!"))
from euler_estimator import EulerEstimator
derivatives = {
'susceptible': (lambda t, x: -0.0003 * x['susceptible'] * x['infected']),
'infected': (lambda t, x: 0.0003 * x['susceptible'] * x['infected'] - 0.02
* x['infected']),
'recovered': (lambda t, x: 0.02 * x['infected'])
}
starting_point = (0, {'susceptible': 1000, 'infected': 1, 'recovered': 0})
estimator = EulerEstimator(derivatives, starting_point)
estimator.plot([0, 365],
step_size=0.001,
filename="epic_assignment_50_plot.png")
