py,matlab: rework funcs, add correct Simulink data
* create a proper p1.mat project * create a proper Simulink p1.slx * add new and correct Simulink simulation output data, previous Simulink data was garbage * save re-generated pictures with fresh Simulink data in data/m.csv and reworked python functions (there were errors in the implementation) * standardise picture naming to one that makes more sense
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|
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|
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|
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|
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|
||||||
0.067980725678, 0.419420246666344
|
|
||||||
|
252
p1/data/m.csv
Normal file
252
p1/data/m.csv
Normal file
@ -0,0 +1,252 @@
|
|||||||
|
u,y
|
||||||
|
-0.562081627343819,0
|
||||||
|
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|
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
|
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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10
p1/matlab/p1.m
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10
p1/matlab/p1.m
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@ -0,0 +1,10 @@
|
|||||||
|
step(1, [2 5 1])
|
||||||
|
length(out.u);
|
||||||
|
length(out.y);
|
||||||
|
|
||||||
|
cd ~/src/utb/ak9im/ak9im/p1/data/;
|
||||||
|
|
||||||
|
m = [out.u,out.y];
|
||||||
|
m;
|
||||||
|
|
||||||
|
% writematrix(m, 'm.csv');
|
||||||
BIN
p1/matlab/p1.mat
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p1/matlab/p1.mat
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p1/matlab/p1.slx
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p1/matlab/p1.slx
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104
p1/p1/funcs.py
104
p1/p1/funcs.py
@ -11,16 +11,14 @@ def load_d(path: str) -> pd.DataFrame():
|
|||||||
return pd.read_csv(path, float_precision='round_trip', dtype='float64')
|
return pd.read_csv(path, float_precision='round_trip', dtype='float64')
|
||||||
|
|
||||||
|
|
||||||
def plot_d(dat: pd.Series, fname: str = 'plot_input_data', colour: str = ''):
|
def plot_d(dat: pd.Series, fname: str = 'x', colour: str = '#1f77b4'):
|
||||||
if colour == '':
|
|
||||||
colour = 'blue'
|
|
||||||
|
|
||||||
pyplt.plot(dat, color=colour)
|
|
||||||
pyplt.xlim(0, len(dat))
|
pyplt.xlim(0, len(dat))
|
||||||
pyplt.ylim(min(dat) - 0.3, max(dat) + 0.3)
|
pyplt.ylim(min(dat) - 0.3, max(dat) + 0.3)
|
||||||
pyplt.xticks(size=plt_ticks_size())
|
pyplt.xticks(size=plt_ticks_size())
|
||||||
pyplt.yticks(size=plt_ticks_size())
|
pyplt.yticks(size=plt_ticks_size())
|
||||||
pyplt.savefig(fname + '.jpg')
|
pyplt.title('Signal ' + fname)
|
||||||
|
pyplt.plot(dat, color=colour)
|
||||||
|
pyplt.savefig('signal_' + fname + '.jpg')
|
||||||
# pplt.show(block=0)
|
# pplt.show(block=0)
|
||||||
# rework this
|
# rework this
|
||||||
# ref: https://stackoverflow.com/a/46418284
|
# ref: https://stackoverflow.com/a/46418284
|
||||||
@ -35,17 +33,28 @@ def variance(dat: pd.Series) -> float:
|
|||||||
return sum(pow(dat - mean(dat), 2)) / len(dat)
|
return sum(pow(dat - mean(dat), 2)) / len(dat)
|
||||||
|
|
||||||
|
|
||||||
def histogram(dat: pd.Series, bins: int = 10, fname: str = 'hist', colour: str = 'blue'):
|
def histogram(dat: pd.Series, bins: int = 10, fname: str = 'x', colour: str = '#1f77b4'):
|
||||||
pyplt.hist(dat, color=colour, bins=bins)
|
pyplt.title('Histogram of ' + fname)
|
||||||
|
pyplt.xlabel(fname)
|
||||||
|
pyplt.ylabel('probability')
|
||||||
|
|
||||||
|
pyplt.hist(dat, color=colour, bins=bins, edgecolor='black')
|
||||||
pyplt.xticks(size=plt_ticks_size())
|
pyplt.xticks(size=plt_ticks_size())
|
||||||
pyplt.yticks(size=plt_ticks_size())
|
pyplt.yticks(size=plt_ticks_size())
|
||||||
pyplt.savefig(fname + '.jpg')
|
pyplt.savefig('hist_' + fname + '.jpg')
|
||||||
pyplt.close()
|
pyplt.close()
|
||||||
|
|
||||||
|
|
||||||
def distr_func(dat: pd.Series, fname: str = 'dist', colour: str = 'blue'):
|
def distr_func(dat: pd.Series, bins: int = 20, fname: str = 'x', colour: str = '#1f77b4'):
|
||||||
dat.plot.density(color=colour)
|
N = len(dat)/bins
|
||||||
pyplt.savefig(fname + '.jpg')
|
x = np.sort(dat)[::bins]
|
||||||
|
y = np.arange(1, N+1) / float(N)
|
||||||
|
|
||||||
|
pyplt.xlabel(fname)
|
||||||
|
pyplt.ylabel('probability')
|
||||||
|
pyplt.title('Cumulative Distribution Function of ' + fname)
|
||||||
|
pyplt.hist(dat, bins=bins, cumulative=True, density=1, color=colour)
|
||||||
|
pyplt.savefig('cdf_' + fname + '.jpg')
|
||||||
pyplt.close()
|
pyplt.close()
|
||||||
|
|
||||||
|
|
||||||
@ -62,30 +71,31 @@ def covar(dat: pd.DataFrame) -> float:
|
|||||||
return dat['u'].dot(dat['y']) / len(dat)
|
return dat['u'].dot(dat['y']) / len(dat)
|
||||||
|
|
||||||
|
|
||||||
def correl_coeff(cov: float, std_dev_u: float, std_dev_y: float) -> float:
|
def correlation_coefficient(cov: float, std_dev_u: float, std_dev_y: float) -> float:
|
||||||
return cov / (std_dev_u * std_dev_y)
|
return cov / (std_dev_u * std_dev_y)
|
||||||
|
|
||||||
|
|
||||||
# m is the max permissible shift value.
|
def auto_covariance(dat: list, max_shift_n: int = .1) -> list:
|
||||||
def auto_corellation(dat: list, max_shift_n: int = .1) -> float:
|
|
||||||
v = []
|
v = []
|
||||||
|
# m is the max permissible shift value.
|
||||||
m = len(dat) * max_shift_n
|
m = len(dat) * max_shift_n
|
||||||
|
mean = np.mean(dat)
|
||||||
cur_shift = 0
|
cur_shift = 0
|
||||||
|
|
||||||
while m >= cur_shift:
|
while m >= cur_shift:
|
||||||
r = 0
|
r = 0
|
||||||
|
|
||||||
for i in range(len(dat) - cur_shift):
|
for i in range(len(dat) - cur_shift):
|
||||||
r += dat[i] * dat[i + cur_shift]
|
r += (dat[i] - mean) * (dat[i + cur_shift] - mean)
|
||||||
|
|
||||||
r = r / len(dat) - cur_shift
|
r = r * (1 / len(dat) - cur_shift)
|
||||||
v.append(r)
|
v.append(r)
|
||||||
cur_shift += 1
|
cur_shift += 1
|
||||||
|
|
||||||
return v
|
return v
|
||||||
|
|
||||||
|
|
||||||
def mutual_corellation(dx: list, dy: list, max_shift_n: int = .1) -> float:
|
def mutual_correlation(dx: list, dy: list, max_shift_n: int = .1) -> list:
|
||||||
v = []
|
v = []
|
||||||
m = len(dx) * max_shift_n
|
m = len(dx) * max_shift_n
|
||||||
cur_shift = 0
|
cur_shift = 0
|
||||||
@ -96,16 +106,15 @@ def mutual_corellation(dx: list, dy: list, max_shift_n: int = .1) -> float:
|
|||||||
for i in range(len(dx) - cur_shift):
|
for i in range(len(dx) - cur_shift):
|
||||||
r += dx[i] * dy[i + cur_shift]
|
r += dx[i] * dy[i + cur_shift]
|
||||||
|
|
||||||
r = r / (len(dx) - cur_shift)
|
r = r * (1 / (len(dx) - cur_shift))
|
||||||
v.append(r)
|
v.append(r)
|
||||||
cur_shift += 1
|
cur_shift += 1
|
||||||
|
|
||||||
return v
|
return v
|
||||||
|
|
||||||
|
|
||||||
def auto_covar(dat: list, max_shift_n: int = .1) -> float:
|
def auto_correlation(dat: list, max_shift_n: int = .1) -> list:
|
||||||
v = []
|
v = []
|
||||||
mean = np.mean(dat)
|
|
||||||
m = len(dat) * max_shift_n
|
m = len(dat) * max_shift_n
|
||||||
cur_shift = 0
|
cur_shift = 0
|
||||||
|
|
||||||
@ -113,19 +122,19 @@ def auto_covar(dat: list, max_shift_n: int = .1) -> float:
|
|||||||
r = 0
|
r = 0
|
||||||
|
|
||||||
for i in range(len(dat) - cur_shift):
|
for i in range(len(dat) - cur_shift):
|
||||||
r += (dat[i] - mean) * (dat[i + cur_shift] - mean)
|
r += dat[i] * dat[i + cur_shift]
|
||||||
|
|
||||||
r = r / (len(dat) - cur_shift)
|
r = r * (1 / (len(dat) - cur_shift))
|
||||||
v.append(r)
|
v.append(r)
|
||||||
cur_shift += 1
|
cur_shift += 1
|
||||||
|
|
||||||
return v
|
return v
|
||||||
|
|
||||||
|
|
||||||
def mutual_covar(dx: list, dy: list, max_shift_n: int = .1) -> float:
|
def mutual_covar(dx: list, dy: list, max_shift_n: int = .1) -> list:
|
||||||
v = []
|
v = []
|
||||||
mean_x, mean_y = np.mean(dx), np.mean(dy)
|
|
||||||
m = len(dx) * max_shift_n
|
m = len(dx) * max_shift_n
|
||||||
|
mean_x, mean_y = np.mean(dx), np.mean(dy)
|
||||||
cur_shift = 0
|
cur_shift = 0
|
||||||
|
|
||||||
while m >= cur_shift:
|
while m >= cur_shift:
|
||||||
@ -134,36 +143,49 @@ def mutual_covar(dx: list, dy: list, max_shift_n: int = .1) -> float:
|
|||||||
for i in range(len(dx) - cur_shift):
|
for i in range(len(dx) - cur_shift):
|
||||||
r += (dx[i] - mean_x) * (dy[i + cur_shift] - mean_y)
|
r += (dx[i] - mean_x) * (dy[i + cur_shift] - mean_y)
|
||||||
|
|
||||||
r = r / (len(dx) - cur_shift)
|
r = r / (len(dx) - 1 - cur_shift)
|
||||||
v.append(r)
|
v.append(r)
|
||||||
cur_shift += 1
|
cur_shift += 1
|
||||||
|
|
||||||
return v
|
return v
|
||||||
|
|
||||||
|
|
||||||
def plot_autocorellation(dat: pd.DataFrame, fname: str = 'autocorellation', colour: str = 'blue'):
|
def plot_autocovariance(dat: pd.DataFrame, fname: str = 'x', colour: str = '#1f77b4'):
|
||||||
d = auto_corellation(dat.tolist())
|
d = auto_covariance(dat.tolist())
|
||||||
pyplt.scatter(range(0, len(d)), d, color=colour)
|
pyplt.plot(range(0, len(d)), d, color=colour)
|
||||||
pyplt.savefig(fname + '.jpg')
|
pyplt.title('Autocovariance of ' + fname + ' (C' + fname + fname + ')')
|
||||||
|
pyplt.xlabel('shift')
|
||||||
|
pyplt.ylabel('C' + fname + fname)
|
||||||
|
pyplt.savefig('autocovariance_' + fname + '.jpg')
|
||||||
pyplt.close()
|
pyplt.close()
|
||||||
|
|
||||||
|
|
||||||
def plot_mutual_corellation(d1: pd.DataFrame, d2: pd.DataFrame, fname: str = 'mutcorellation', colour: str = 'blue'):
|
def plot_mutual_correlation(d1: pd.DataFrame, d2: pd.DataFrame, fname: str = 'xy', colour: str = '#1f77b4'):
|
||||||
d = mutual_corellation(d1.tolist(), d2.tolist())
|
d = mutual_correlation(d1.tolist(), d2.tolist())
|
||||||
pyplt.scatter(range(0, len(d)), d, color=colour)
|
pyplt.plot(range(0, len(d)), d, color=colour)
|
||||||
pyplt.savefig(fname + '.jpg')
|
pyplt.title('Mutual correlation of ' + fname + ' (R' + fname + ')')
|
||||||
|
pyplt.xlabel('shift')
|
||||||
|
pyplt.ylabel('R' + fname)
|
||||||
|
pyplt.savefig('mutual_correlation_' + fname + '.jpg')
|
||||||
pyplt.close()
|
pyplt.close()
|
||||||
|
|
||||||
|
|
||||||
def plot_autocovariance(dat: pd.DataFrame, fname: str = 'autocovariance', colour: str = 'blue'):
|
def plot_autocorrelation(dat: pd.DataFrame, fname: str = 'x', colour: str = '#1f77b4'):
|
||||||
d = auto_covar(dat.tolist())
|
d = auto_correlation(dat.tolist())
|
||||||
pyplt.scatter(range(0, len(d)), d, color=colour)
|
pyplt.plot(range(0, len(d)), d, color=colour)
|
||||||
pyplt.savefig(fname + '.jpg')
|
pyplt.title('Autocorrelation of ' + fname + ' (R' + fname + fname + ')')
|
||||||
|
pyplt.xlabel('shift')
|
||||||
|
pyplt.ylabel('R' + fname + fname)
|
||||||
|
pyplt.savefig('autocorrelation_' + fname + '.jpg')
|
||||||
pyplt.close()
|
pyplt.close()
|
||||||
|
|
||||||
|
|
||||||
def plot_mutual_covariance(d1: pd.DataFrame, d2: pd.DataFrame, fname: str = 'mutcovariance', colour: str = 'blue'):
|
def plot_mutual_covariance(d1: pd.DataFrame, d2: pd.DataFrame, fname: str = 'xy', colour: str = '#1f77b4'):
|
||||||
d = mutual_covar(d1.tolist(), d2.tolist())
|
d = mutual_covar(d1.tolist(), d2.tolist())
|
||||||
pyplt.scatter(range(0, len(d)), d, color=colour)
|
# pyplt.scatter(range(0, len(d)), d, color=colour)
|
||||||
pyplt.savefig(fname + '.jpg')
|
pyplt.plot(range(0, len(d)), d, color=colour)
|
||||||
|
pyplt.title('Mutual covariance of ' + fname + '(C' + fname + fname + ')')
|
||||||
|
pyplt.xlabel('shift')
|
||||||
|
pyplt.ylabel('C' + fname)
|
||||||
|
pyplt.savefig('mutual_covariance_' + fname + '.jpg')
|
||||||
pyplt.close()
|
pyplt.close()
|
||||||
|
|||||||
@ -28,11 +28,14 @@ def main(argv):
|
|||||||
# d = d.astype('float64')
|
# d = d.astype('float64')
|
||||||
print(d.head(), '\n')
|
print(d.head(), '\n')
|
||||||
|
|
||||||
|
# alternative colour used to differentiate from the default.
|
||||||
|
altcol = '#ff7f0e'
|
||||||
|
|
||||||
# data plots
|
# data plots
|
||||||
du = d['u']
|
du = d['u']
|
||||||
dy = d['y']
|
dy = d['y']
|
||||||
f.plot_d(du, fname='u_input_plot')
|
f.plot_d(du, fname='u')
|
||||||
f.plot_d(dy, fname='y_input_plot', colour='green')
|
f.plot_d(dy, fname='y', colour=altcol)
|
||||||
|
|
||||||
# mean and variance
|
# mean and variance
|
||||||
mean_u = f.mean(d['u'])
|
mean_u = f.mean(d['u'])
|
||||||
@ -45,33 +48,36 @@ def main(argv):
|
|||||||
print('variance u:', variance_u)
|
print('variance u:', variance_u)
|
||||||
print('variance y:', variance_y)
|
print('variance y:', variance_y)
|
||||||
|
|
||||||
hist_u = f.histogram(d['u'], fname='u_hist')
|
f.histogram(d['u'], fname='u')
|
||||||
hist_y = f.histogram(d['y'], fname='y_hist', colour='green')
|
f.histogram(d['y'], fname='y', colour=altcol)
|
||||||
|
|
||||||
dist_u = f.distr_func(d['u'], fname='u_dist')
|
f.distr_func(d['u'], fname='u')
|
||||||
dist_y = f.distr_func(d['y'], fname='y_dist', colour='green')
|
f.distr_func(d['y'], fname='y', colour=altcol)
|
||||||
|
|
||||||
cov = f.covar(d)
|
cov = f.covar(d)
|
||||||
std_dev_u = f.std_dev(d['u'])
|
std_dev_u = f.std_dev(d['u'])
|
||||||
std_dev_y = f.std_dev(d['y'])
|
std_dev_y = f.std_dev(d['y'])
|
||||||
# correlation coefficient
|
# correlation coefficient
|
||||||
correl_c = f.correl_coeff(cov, std_dev_u, std_dev_y)
|
rUY = f.correlation_coefficient(cov, std_dev_u, std_dev_y)
|
||||||
print("correlation coefficient:", correl_c)
|
print("correlation coefficient rUY:", rUY)
|
||||||
|
|
||||||
print("covariance matrix (built-in):\n", d.cov())
|
print("covariance matrix (built-in):\n", d.cov(), '\n')
|
||||||
# print the covariance matrix.
|
# print the covariance matrix.
|
||||||
print("covariance matrix (own):\n")
|
print("covariance matrix (own):")
|
||||||
print(np.array([[variance_u, cov], [cov, variance_y]]))
|
print(np.array([[variance_u, cov], [cov, variance_y]]))
|
||||||
|
|
||||||
f.plot_autocorellation(dat=d['u'], fname='u_autocorellation')
|
f.plot_autocorrelation(dat=d['u'], fname='u')
|
||||||
f.plot_autocorellation(dat=d['y'], fname='y_autocorellation', colour='green')
|
f.plot_autocorrelation(dat=d['y'], fname='y', colour=altcol)
|
||||||
|
|
||||||
f.plot_mutual_corellation(d1=d['u'], d2=d['y'], fname='mutual_corellation_uy')
|
f.plot_mutual_correlation(d1=d['u'], d2=d['y'], fname='uy')
|
||||||
|
f.plot_mutual_correlation(d1=d['y'], d2=d['u'], fname='yu')
|
||||||
|
|
||||||
f.plot_autocovariance(dat=d['u'], fname='u_autocovariance')
|
f.plot_autocovariance(dat=d['u'], fname='u')
|
||||||
f.plot_autocovariance(dat=d['y'], fname='y_autocovariance', colour='green')
|
f.plot_autocovariance(dat=d['y'], fname='y', colour=altcol)
|
||||||
|
|
||||||
f.plot_mutual_covariance(d1=d['u'], d2=d['y'], fname='mutual_covariance_uy')
|
# we don't need this atm.
|
||||||
|
# f.plot_mutual_covariance(d1=d['u'], d2=d['y'], fname='uy')
|
||||||
|
# f.plot_mutual_covariance(d1=d['y'], d2=d['u'], fname='yu')
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user