From 25f2fbc83147cead5f79d714d2f267af2e42d068 Mon Sep 17 00:00:00 2001 From: leo Date: Fri, 27 Jan 2023 01:48:36 +0100 Subject: [PATCH] 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 --- p1/data/dat-simulink.csv | 502 ------------------------- p1/data/m.csv | 252 +++++++++++++ p1/img/autocorrelation_u.jpg | Bin 0 -> 19912 bytes p1/img/autocorrelation_y.jpg | Bin 0 -> 19444 bytes p1/img/autocovariance_u.jpg | Bin 0 -> 22276 bytes p1/img/autocovariance_y.jpg | Bin 0 -> 18996 bytes p1/img/cdf_u.jpg | Bin 0 -> 20604 bytes p1/img/cdf_y.jpg | Bin 0 -> 19193 bytes p1/img/hist_u.jpg | Bin 0 -> 26349 bytes p1/img/hist_y.jpg | Bin 0 -> 22193 bytes p1/img/matOutU.svg | 154 ++++++++ p1/img/matOutY.svg | 118 ++++++ p1/img/model.png | Bin 0 -> 67648 bytes p1/img/mutual_corellation_uy.jpg | Bin 17496 -> 0 bytes p1/img/mutual_correlation_uy.jpg | Bin 0 -> 23679 bytes p1/img/mutual_correlation_yu.jpg | Bin 0 -> 23598 bytes p1/img/mutual_covariance_uy.jpg | Bin 17531 -> 0 bytes p1/img/samplecount.png | Bin 0 -> 22945 bytes p1/img/signal_u.jpg | Bin 0 -> 39221 bytes p1/img/signal_y.jpg | Bin 0 -> 24608 bytes p1/img/step_response.png | Bin 0 -> 18564 bytes p1/img/u_autocorellation.jpg | Bin 17548 -> 0 bytes p1/img/u_autocovariance.jpg | Bin 18944 -> 0 bytes p1/img/u_dist.jpg | Bin 18279 -> 0 bytes p1/img/u_hist.jpg | Bin 17248 -> 0 bytes p1/img/u_input_plot.jpg | Bin 18586 -> 0 bytes p1/img/y_autocorellation.jpg | Bin 16557 -> 0 bytes p1/img/y_autocovariance.jpg | Bin 16808 -> 0 bytes p1/img/y_dist.jpg | Bin 17191 -> 0 bytes p1/img/y_hist.jpg | Bin 14747 -> 0 bytes p1/img/y_input_plot.jpg | Bin 13745 -> 0 bytes p1/matlab/p1-randn_transferfunc.mat | Bin 128488 -> 0 bytes p1/matlab/p1-randn_transferfunc.mldatx | Bin 33090 -> 0 bytes p1/matlab/p1-simout.xlsx | Bin 229348 -> 0 bytes p1/matlab/p1.m | 10 + p1/matlab/p1.mat | Bin 0 -> 68113 bytes p1/matlab/p1.slx | Bin 0 -> 28874 bytes p1/p1/funcs.py | 104 +++-- p1/p1/main.py | 38 +- 39 files changed, 619 insertions(+), 559 deletions(-) delete mode 100644 p1/data/dat-simulink.csv create mode 100644 p1/data/m.csv create mode 100644 p1/img/autocorrelation_u.jpg create mode 100644 p1/img/autocorrelation_y.jpg create mode 100644 p1/img/autocovariance_u.jpg create mode 100644 p1/img/autocovariance_y.jpg create mode 100644 p1/img/cdf_u.jpg create mode 100644 p1/img/cdf_y.jpg create mode 100644 p1/img/hist_u.jpg create mode 100644 p1/img/hist_y.jpg create mode 100644 p1/img/matOutU.svg create mode 100644 p1/img/matOutY.svg create mode 100644 p1/img/model.png delete mode 100644 p1/img/mutual_corellation_uy.jpg create mode 100644 p1/img/mutual_correlation_uy.jpg create mode 100644 p1/img/mutual_correlation_yu.jpg delete mode 100644 p1/img/mutual_covariance_uy.jpg create mode 100644 p1/img/samplecount.png create mode 100644 p1/img/signal_u.jpg create mode 100644 p1/img/signal_y.jpg create mode 100644 p1/img/step_response.png delete mode 100644 p1/img/u_autocorellation.jpg delete mode 100644 p1/img/u_autocovariance.jpg delete mode 100644 p1/img/u_dist.jpg delete mode 100644 p1/img/u_hist.jpg delete mode 100644 p1/img/u_input_plot.jpg delete mode 100644 p1/img/y_autocorellation.jpg delete mode 100644 p1/img/y_autocovariance.jpg delete mode 100644 p1/img/y_dist.jpg delete mode 100644 p1/img/y_hist.jpg delete mode 100644 p1/img/y_input_plot.jpg delete mode 100644 p1/matlab/p1-randn_transferfunc.mat delete mode 100644 p1/matlab/p1-randn_transferfunc.mldatx delete mode 100644 p1/matlab/p1-simout.xlsx create mode 100644 p1/matlab/p1.m create mode 100644 p1/matlab/p1.mat create mode 100644 p1/matlab/p1.slx diff --git 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zr|%0cF~7Ty|6rj0P5jfSd{2~S{eQ&&H}3s!sr7HL;g%tzlSsmzK8tfO#hAfGrjsA6ZPdC z=AX^60R nZ_=NG=JzCSqj#jghR|}7kZ(H>{9`8xbP&h4AveGIKfe7J7vMP2 literal 0 HcmV?d00001 diff --git a/p1/p1/funcs.py b/p1/p1/funcs.py index 6f53954..fb0222a 100644 --- a/p1/p1/funcs.py +++ b/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') -def plot_d(dat: pd.Series, fname: str = 'plot_input_data', colour: str = ''): - if colour == '': - colour = 'blue' - - pyplt.plot(dat, color=colour) +def plot_d(dat: pd.Series, fname: str = 'x', colour: str = '#1f77b4'): pyplt.xlim(0, len(dat)) pyplt.ylim(min(dat) - 0.3, max(dat) + 0.3) pyplt.xticks(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) # rework this # 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) -def histogram(dat: pd.Series, bins: int = 10, fname: str = 'hist', colour: str = 'blue'): - pyplt.hist(dat, color=colour, bins=bins) +def histogram(dat: pd.Series, bins: int = 10, fname: str = 'x', colour: str = '#1f77b4'): + 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.yticks(size=plt_ticks_size()) - pyplt.savefig(fname + '.jpg') + pyplt.savefig('hist_' + fname + '.jpg') pyplt.close() -def distr_func(dat: pd.Series, fname: str = 'dist', colour: str = 'blue'): - dat.plot.density(color=colour) - pyplt.savefig(fname + '.jpg') +def distr_func(dat: pd.Series, bins: int = 20, fname: str = 'x', colour: str = '#1f77b4'): + N = len(dat)/bins + 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() @@ -62,30 +71,31 @@ def covar(dat: pd.DataFrame) -> float: 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) -# m is the max permissible shift value. -def auto_corellation(dat: list, max_shift_n: int = .1) -> float: +def auto_covariance(dat: list, max_shift_n: int = .1) -> list: v = [] + # m is the max permissible shift value. m = len(dat) * max_shift_n + mean = np.mean(dat) cur_shift = 0 while m >= cur_shift: r = 0 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) cur_shift += 1 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 = [] m = len(dx) * max_shift_n 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): r += dx[i] * dy[i + cur_shift] - r = r / (len(dx) - cur_shift) + r = r * (1 / (len(dx) - cur_shift)) v.append(r) cur_shift += 1 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 = [] - mean = np.mean(dat) m = len(dat) * max_shift_n cur_shift = 0 @@ -113,19 +122,19 @@ def auto_covar(dat: list, max_shift_n: int = .1) -> float: r = 0 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) cur_shift += 1 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 = [] - mean_x, mean_y = np.mean(dx), np.mean(dy) m = len(dx) * max_shift_n + mean_x, mean_y = np.mean(dx), np.mean(dy) cur_shift = 0 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): 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) cur_shift += 1 return v -def plot_autocorellation(dat: pd.DataFrame, fname: str = 'autocorellation', colour: str = 'blue'): - d = auto_corellation(dat.tolist()) - pyplt.scatter(range(0, len(d)), d, color=colour) - pyplt.savefig(fname + '.jpg') +def plot_autocovariance(dat: pd.DataFrame, fname: str = 'x', colour: str = '#1f77b4'): + d = auto_covariance(dat.tolist()) + pyplt.plot(range(0, len(d)), d, color=colour) + pyplt.title('Autocovariance of ' + fname + ' (C' + fname + fname + ')') + pyplt.xlabel('shift') + pyplt.ylabel('C' + fname + fname) + pyplt.savefig('autocovariance_' + fname + '.jpg') pyplt.close() -def plot_mutual_corellation(d1: pd.DataFrame, d2: pd.DataFrame, fname: str = 'mutcorellation', colour: str = 'blue'): - d = mutual_corellation(d1.tolist(), d2.tolist()) - pyplt.scatter(range(0, len(d)), d, color=colour) - pyplt.savefig(fname + '.jpg') +def plot_mutual_correlation(d1: pd.DataFrame, d2: pd.DataFrame, fname: str = 'xy', colour: str = '#1f77b4'): + d = mutual_correlation(d1.tolist(), d2.tolist()) + pyplt.plot(range(0, len(d)), d, color=colour) + pyplt.title('Mutual correlation of ' + fname + ' (R' + fname + ')') + pyplt.xlabel('shift') + pyplt.ylabel('R' + fname) + pyplt.savefig('mutual_correlation_' + fname + '.jpg') pyplt.close() -def plot_autocovariance(dat: pd.DataFrame, fname: str = 'autocovariance', colour: str = 'blue'): - d = auto_covar(dat.tolist()) - pyplt.scatter(range(0, len(d)), d, color=colour) - pyplt.savefig(fname + '.jpg') +def plot_autocorrelation(dat: pd.DataFrame, fname: str = 'x', colour: str = '#1f77b4'): + d = auto_correlation(dat.tolist()) + pyplt.plot(range(0, len(d)), d, color=colour) + pyplt.title('Autocorrelation of ' + fname + ' (R' + fname + fname + ')') + pyplt.xlabel('shift') + pyplt.ylabel('R' + fname + fname) + pyplt.savefig('autocorrelation_' + fname + '.jpg') 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()) - pyplt.scatter(range(0, len(d)), d, color=colour) - pyplt.savefig(fname + '.jpg') + # pyplt.scatter(range(0, len(d)), d, color=colour) + 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() diff --git a/p1/p1/main.py b/p1/p1/main.py index f774de9..c22803e 100644 --- a/p1/p1/main.py +++ b/p1/p1/main.py @@ -28,11 +28,14 @@ def main(argv): # d = d.astype('float64') print(d.head(), '\n') + # alternative colour used to differentiate from the default. + altcol = '#ff7f0e' + # data plots du = d['u'] dy = d['y'] - f.plot_d(du, fname='u_input_plot') - f.plot_d(dy, fname='y_input_plot', colour='green') + f.plot_d(du, fname='u') + f.plot_d(dy, fname='y', colour=altcol) # mean and variance mean_u = f.mean(d['u']) @@ -45,33 +48,36 @@ def main(argv): print('variance u:', variance_u) print('variance y:', variance_y) - hist_u = f.histogram(d['u'], fname='u_hist') - hist_y = f.histogram(d['y'], fname='y_hist', colour='green') + f.histogram(d['u'], fname='u') + f.histogram(d['y'], fname='y', colour=altcol) - dist_u = f.distr_func(d['u'], fname='u_dist') - dist_y = f.distr_func(d['y'], fname='y_dist', colour='green') + f.distr_func(d['u'], fname='u') + f.distr_func(d['y'], fname='y', colour=altcol) cov = f.covar(d) std_dev_u = f.std_dev(d['u']) std_dev_y = f.std_dev(d['y']) # correlation coefficient - correl_c = f.correl_coeff(cov, std_dev_u, std_dev_y) - print("correlation coefficient:", correl_c) + rUY = f.correlation_coefficient(cov, std_dev_u, std_dev_y) + 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("covariance matrix (own):\n") + print("covariance matrix (own):") print(np.array([[variance_u, cov], [cov, variance_y]])) - f.plot_autocorellation(dat=d['u'], fname='u_autocorellation') - f.plot_autocorellation(dat=d['y'], fname='y_autocorellation', colour='green') + f.plot_autocorrelation(dat=d['u'], fname='u') + 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['y'], fname='y_autocovariance', colour='green') + f.plot_autocovariance(dat=d['u'], fname='u') + 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__":