def __init__(self, master):
self._job = None
self.max_time = 500
# Create a container
self.frame = Tkinter.Frame(master)
self.master = master
# Create 2 buttons
self.kp = Tkinter.Scale(self.frame,
from_=1.,
to=10.,
resolution=0.1,
label="K ",
command=self.resetparam)
self.kp.pack()
self.tau = Tkinter.Scale(self.frame,
from_=1.,
to=10.,
resolution=0.1,
label="τ ",
command=self.resetparam)
self.tau.pack()
self.theta = Tkinter.Scale(self.frame,
from_=1.,
to=9.,
resolution=0.5,
label="θ",
command=self.resetparam)
self.theta.pack()
self.fig = Figure((10, 5))
self.ax = self.fig.add_subplot(111)
self.ax.set_xlim(0, 500)
self.fig2 = Figure((10, 5))
self.ax2 = self.fig2.add_subplot(111)
sig = Signal(1, 1, 2, numPlots=0)
sig.sys_simulation()
self.masterRS = RollingSignal()
self.masterRS.load_model(
sig,
directory=
'/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/Model Validation/MIMO 1x1/Checkpoints/'
)
self._roll_over(initial=True)
import control as control
import numpy as np
import pandas as pd
from Signal import Signal
import matplotlib.pyplot as plt
import seaborn as sns
path = '/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/Model Validation/Paper Results 3.csv'
df = pd.read_csv(path)
df = df.loc[:, ~df.columns.str.contains('Unnamed')]
df[df['tau_python'] > 1] = 0
sig = Signal(1, 1, 10, numPlots=0)
sig.sys_simulation(stdev=5, order=1)
disturbances = np.zeros((1000, 600))
sses = []
for i in range(0, 1000):
#plt.figure(i,dpi=200)
#plt.plot(sig.ySteps[i,:,0])
#plt.plot(sig.test_sequence,'k--',linewidth=1,label='Excitation')
u = sig.test_sequence
allY = np.zeros((600, 1))
# Iterate over each of the input dimensions
# and add to the numerator array
numTemp = [df['kp_true'][i]]
denTemp = [1., -df['tau_true'][i]]
thetas = [df['theta_true'][i]]
#plt.plot(delay_arr, corr)
plt.title('Lag: ' + str(np.round(delay, 3)) + ' s ' + 'Real =' +
str(title))
plt.xlabel('Lag')
plt.ylabel('Correlation coeff')
plt.show()
for (inDimension, outDimension) in zip(inDims, outDims):
start_time = time.time()
sig = Signal(inDimension, outDimension, numTrials, numPlots=plots)
uArray, yArray, tauArray, KpArray, thetaArray, train, test = sig.sys_simulation(
disturbance=False,
b_possible_values=[.01, .99],
a_possible_values=[.01, .99],
k_possible_values=[0, 1],
not_prbs=True)
'''
array=np.zeros(10000)
i=0
for (i,(u1,y1)) in enumerate(zip(uArray,yArray)):
z1 = y1-u1
for (j,(u2,y2)) in enumerate(zip(uArray,yArray)):
z2 = y2-u2
array[i*100+j] = sum(scipy.signal.correlate(z1,z2,mode='full'))
'''
print("--- %s seconds ---" % (time.time() - start_time))
# These two lines are for training the model based on nstep and the sig data
# These constants are also defined in the Signal module
# Don't change here unless you also change them there
numTrials = 1000
batchSize = 64
plots = 0
inDims = range(1, 6)
outDims = range(1, 6)
for (inDimension, outDimension) in zip(inDims, outDims):
start_time = time.time()
sig = Signal(inDimension, outDimension, numTrials, numPlots=plots)
uArray, yArray, tauArray, KpArray, thetaArray, train, test = sig.sys_simulation(
)
uArray = uArray[:, :, 0]
yArray = yArray[:, :, 0]
if inDimension == 1:
np.savetxt(
'/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/MATLAB code/u_1x1.csv',
uArray,
delimiter=',')
np.savetxt(
'/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/MATLAB code/y_1x1.csv',
yArray,
delimiter=',')
np.savetxt(
'/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/MATLAB code/tau_1x1.csv',
"""
import control as control
import numpy as np
import pandas as pd
from Signal import Signal
import matplotlib.pyplot as plt
import seaborn as sns
path = '/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/Model Validation/Paper Results_order2.csv'
df = pd.read_csv(path)
df = df.loc[:, ~df.columns.str.contains('Unnamed')]
sig = Signal(1, 1, 10, numPlots=0)
sig.sys_simulation(stdev='variable', order=1)
sses = []
for i in range(10, 20):
plt.figure(i, dpi=200)
#plt.plot(sig.ySteps[i,:,0])
plt.plot(sig.test_sequence, 'k--', linewidth=1)
u = sig.test_sequence
allY = np.zeros((600, 1))
# Iterate over each of the input dimensions
# and add to the numerator array
numTemp = [df['kp_real'][i], 0]
denTemp = [1., -df['tau_real_1'][i], -df['tau_real_2'][i]]
thetas = [df['theta_real'][i]]
# These constants are also defined in the Signal module
# Don't change here unless you also change them there
numTrials = 1000
plots = 10
inDims = range(1, 2)
outDims = range(1, 2)
for (inDimension, outDimension) in zip(inDims, outDims):
start_time = time.time()
sig = Signal(inDimension, outDimension, numTrials, numPlots=plots, stdev=5)
uArray, yArray, tauArray, KpArray, thetaArray, train, test = sig.sys_simulation(
b_possible_values=[.01, .99],
a_possible_values=[.01, .99],
k_possible_values=[0, 1],
order=False,
disturbance=False)
uArray = uArray[:, :, 0]
yArray = yArray[:, :, 0]
np.savetxt(
'/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/MATLAB code/u_1x1.csv',
uArray)
np.savetxt(
'/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/MATLAB code/y_1x1.csv',
yArray)
np.savetxt(
'/Users/RileyBallachay/Documents/Fifth Year/RNNSystemIdentification/MATLAB code/taus_1x1.csv',
tauArray)
# These constants are also defined in the Signal module
# Don't change here unless you also change them there
numTrials = 100000
batchSize = 100
plots = 5
inDims = range(1,10,2)
outDims = range(1,10,2)
for (inDimension,outDimension) in zip(inDims,outDims):
start_time = time.time()
sig = Signal(inDimension,outDimension,numTrials,numPlots=plots)
uArray,yArray,tauArray,KpArray,thetaArray,train,test = sig.sys_simulation(disturbance=False,k_possible_values=[0,1])
print("--- %s seconds ---" % (time.time() - start_time))
# These two lines are for training the model based on nstep and the sig data
# Only uncomment if you want to train and not predict
trainModel = Model()
trainModel.train_model(sig,epochs=1000,batchSize=batchSize,saveModel=False,plotLoss=bool(plots!=0),plotVal=bool(plots!=0),trainConvolutional=False)
print("--- %s seconds ---" % (time.time() - start_time))
'''
# In this case, since we are only loading the model, not trying to train it,
# we can use function simulate and preprocess
xData,yData = sig.MIMO_validation()
# Initialize the models that are saved using the parameters declared above
predictor = Model(nstep)
# These constants are also defined in the Signal module
# Don't change here unless you also change them there
numTrials = 1000
batchSize = 256
plots = 10
inDims = range(1,2)
outDims = range(1,2)
for (inDimension,outDimension) in zip(inDims,outDims):
start_time = time.time()
sig = Signal(inDimension,outDimension,numTrials,numPlots=plots)
uArray,yArray,tauArray,KpArray,thetaArray,train,test = sig.sys_simulation(stdev=10,disturbance=False,b_possible_values=[.49,.51],a_possible_values=[.49,.51],
k_possible_values=[0,1])
array=np.zeros(1000000)
for (i,(u1,y1)) in enumerate(zip(uArray,yArray)):
z1 = y1-u1
for (j,(u2,y2)) in enumerate(zip(uArray,yArray)):
z2 = y2-u2
if i==j:
array[i*100+j] = 1000
else:
array[i*100+j] = sum(abs(scipy.signal.correlate(z1,z2,mode='full')))
index = np.argmin(array)
plt.plot(yArray[int(index/1000)]-uArray[int(index/1000)])
plt.plot(yArray[int(index%1000)]-uArray[int(index%1000)],'k--')
