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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252
p1/data/m.csv
Normal file
252
p1/data/m.csv
Normal file
@ -0,0 +1,252 @@
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-0.765524363967369,0.180540303154666
|
||||
0.859780197897824,0.153550465624583
|
||||
-0.6740838939669,0.139042359806811
|
||||
0.334695734705169,0.168269259962125
|
||||
0.710164911910037,0.0473114765318522
|
||||
-0.816398257304168,-0.0132560118645178
|
||||
-0.13099439587956,0.00642581834123225
|
||||
-0.745824782990769,0.0280274330024728
|
||||
0.146800391910039,0.0114525145825461
|
||||
-0.763205443398657,0.0268220879521105
|
||||
0.456613086842286,-0.0141582162933872
|
||||
0.438408563583348,-0.127737166963499
|
||||
0.342703012443475,-0.147812633292664
|
||||
0.698135564894478,-0.0137274640231074
|
||||
-0.670800844054111,0.000513004189454612
|
||||
0.585560483665001,-0.0957600030634544
|
||||
0.151705400623244,-0.126140672877399
|
||||
0.495937946017803,-0.0587775919654244
|
||||
0.828736296775162,-0.113602753385482
|
||||
-0.275617057120249,0.0404169153674105
|
||||
-0.129249441963271,0.0637441027533635
|
||||
-0.496089479651344,0.0520406223713919
|
||||
-0.747938023762749,-0.0434250282438795
|
||||
-0.837347029632585,-0.148086186883691
|
||||
-0.717641319948082,-0.139919043048457
|
||||
-0.936323192872258,-0.0605219822362255
|
||||
-0.554920240098108,0.0474048568421955
|
||||
0.719021106939307,-0.0947218176526316
|
||||
-0.947999400062486,0.0261778777337991
|
||||
0.171600238034316,-0.0677998584179937
|
||||
0.826004394249061,-0.123032216818122
|
||||
0.387852175807512,0.194905723500561
|
||||
0.0351910223416942,0.166027166275701
|
||||
0.479863301608648,0.101954502130607
|
||||
0.444585834371199,-0.00212100636994374
|
||||
-0.0155852180978214,0.0424901518175131
|
||||
0.692872747635875,-0.020136542529367
|
||||
-0.300434749247709,0.14017545466314
|
||||
0.495401680234541,0.0467252671274352
|
||||
0.1432539257888,0.0518152607705747
|
||||
0.323782088851455,0.00891091382416525
|
||||
-0.48723274072969,0.0656228532554857
|
||||
0.460745400498037,-0.0912258739936947
|
||||
-0.349475199053751,-0.137388220950933
|
||||
0.984357650384474,-0.0464662361149723
|
||||
0.717022333162382,-0.0290631543902982
|
||||
-0.0847671390905823,0.0842585339781461
|
||||
0.89878820343818,0.0387303686119073
|
||||
0.628449186509684,0.0326745671195254
|
||||
-0.6684710158354,0.100022791409108
|
||||
-0.353190103244591,-0.120791479513615
|
||||
0.883962914293615,-0.148576945580147
|
||||
-0.277615144512437,-0.184046870106486
|
||||
0.0948024723188963,-0.136855716266544
|
||||
-0.335375443722762,0.0311153392168198
|
||||
0.780905995415946,-0.0224553304744231
|
||||
0.0511205489985276,0.0788479869635113
|
||||
0.241345313955725,-0.0500833323004902
|
||||
0.851539127459535,-0.112609714052497
|
||||
-0.291347311479667,-0.132830197510762
|
||||
-0.843571841643924,-0.194825060471202
|
||||
-0.996671304105162,-0.218432230330827
|
||||
0.921911736913916,-0.159673404773765
|
||||
0.764604230301736,-0.199288865578464
|
||||
-0.532099923832389,0.116195428966659
|
||||
0.283330017367066,0.157707373781633
|
||||
0.258282310915311,0.140041359684938
|
||||
-0.935322373144013,0.196915951661411
|
||||
-0.830454336400355,0.120386701590254
|
||||
0.374635382264217,0.101826502163638
|
||||
0.821705620652859,0.209094203434554
|
||||
0.613403443067057,0.10826422633432
|
||||
-0.956468232887084,0.0569546117630172
|
||||
-0.186500359879108,0.172972783266203
|
||||
0.376801843464748,0.0341815581906813
|
||||
0.59207610860098,0.0638312199981932
|
||||
-0.037140366172949,-0.0440596218668656
|
||||
-0.434475232583692,0.0249241103881933
|
||||
-0.291389503186284,-0.037444191519866
|
||||
-0.0601084125508128,-0.10827784318051
|
||||
0.784298201922466,-0.233941733889692
|
||||
0.0130897644036867,-0.0292668767208536
|
||||
-0.471272126525302,-0.132352153493435
|
||||
-0.43183794684328,-0.0285887256597882
|
||||
-0.959193706493449,0.0195679991308332
|
||||
-0.913185525645123,0.145244999346624
|
||||
-0.414255243453316,0.0752718384166183
|
||||
0.363312282303028,0.076558551495326
|
||||
0.743653144568043,-0.0967207911101528
|
||||
-0.334610971312323,-0.057695839272927
|
||||
0.683896947504905,0.220457065560355
|
||||
0.381545025567312,0.249105557222865
|
||||
-0.480161357428954,0.218030510334781
|
||||
0.304930325273858,0.0644631852618431
|
||||
0.562712400948029,-0.0138504593059087
|
||||
-0.498690735315294,0.0493680673636072
|
||||
0.917412161788629,0.0829444150386858
|
||||
-0.0910431757061944,0.212697184586624
|
||||
-0.639183673839636,0.230104278405291
|
||||
-0.139293632069274,0.159652227148624
|
||||
0.722858748269667,0.0954345880223326
|
||||
-0.576794941712541,0.207757724506451
|
||||
0.931673358162713,-0.0422537892114052
|
||||
0.335037476073502,0.121191599709049
|
||||
-0.526334091800421,0.135758709437636
|
||||
-0.852575754678145,0.0466947927713023
|
||||
-0.609541804347905,0.125927079414675
|
||||
0.671690425682669,-0.0078502938544791
|
||||
0.0763494088670003,-0.0585132867888244
|
||||
0.0892805201417211,-0.175864475624874
|
||||
-0.323095335309904,-0.154157927887642
|
||||
-0.174369967158125,0.0522737524138117
|
||||
-0.524507459962977,0.0783792135121095
|
||||
0.957202981206217,0.0897387931304263
|
||||
0.813413716765779,0.0596736907760852
|
||||
0.283293277622803,0.0372312933723317
|
||||
0.513900782686612,-0.150468366623803
|
||||
0.395650216096849,-0.147955284625277
|
||||
-0.0823185420978435,-0.0223826480219779
|
||||
0.1341197896442,-0.0280534253211324
|
||||
0.0899726520711428,0.0513034519514842
|
||||
0.817904155616604,8.59935647047512e-05
|
||||
-0.656541830700143,-0.0351927114589313
|
||||
-0.151372477017051,-0.0139098899105425
|
||||
0.475657624879692,-0.0687908511025121
|
||||
-0.941745635094934,-0.0620316597861909
|
||||
0.0241828938127415,-0.0424930929299225
|
||||
-0.0225139358185762,0.151388215635737
|
||||
-0.100346996961323,0.154763082869366
|
||||
0.193473809954465,0.0271937744025612
|
||||
0.640271583404519,0.0105602365003001
|
||||
0.59476472232247,0.0572940959449519
|
||||
0.30324439671973,-0.0612133530446115
|
||||
-0.993561718609911,0.0921513064661191
|
||||
-0.246076793058811,-0.000518372223260732
|
||||
0.923166690358504,0.0650959749032007
|
||||
-0.856037346579152,0.0681306364260466
|
||||
-0.453166298313609,-0.103159953315381
|
||||
0.572474384481308,-0.116014111229541
|
||||
-0.435653748659256,-0.0938795633662015
|
||||
|
BIN
p1/img/autocorrelation_u.jpg
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BIN
p1/img/autocorrelation_u.jpg
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|
After Width: | Height: | Size: 19 KiB |
BIN
p1/img/autocorrelation_y.jpg
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BIN
p1/img/autocorrelation_y.jpg
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|
After Width: | Height: | Size: 19 KiB |
BIN
p1/img/autocovariance_u.jpg
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BIN
p1/img/autocovariance_u.jpg
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Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
BIN
p1/img/autocovariance_y.jpg
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BIN
p1/img/autocovariance_y.jpg
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Binary file not shown.
|
After Width: | Height: | Size: 19 KiB |
BIN
p1/img/cdf_u.jpg
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BIN
p1/img/cdf_u.jpg
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Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
BIN
p1/img/cdf_y.jpg
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BIN
p1/img/cdf_y.jpg
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Binary file not shown.
|
After Width: | Height: | Size: 19 KiB |
BIN
p1/img/hist_u.jpg
Normal file
BIN
p1/img/hist_u.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 26 KiB |
BIN
p1/img/hist_y.jpg
Normal file
BIN
p1/img/hist_y.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
154
p1/img/matOutU.svg
Normal file
154
p1/img/matOutU.svg
Normal file
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 23 KiB |
118
p1/img/matOutY.svg
Normal file
118
p1/img/matOutY.svg
Normal file
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 38 KiB |
BIN
p1/img/model.png
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BIN
p1/img/model.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 66 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 17 KiB |
BIN
p1/img/mutual_correlation_uy.jpg
Normal file
BIN
p1/img/mutual_correlation_uy.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 23 KiB |
BIN
p1/img/mutual_correlation_yu.jpg
Normal file
BIN
p1/img/mutual_correlation_yu.jpg
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Binary file not shown.
|
After Width: | Height: | Size: 23 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 17 KiB |
BIN
p1/img/samplecount.png
Normal file
BIN
p1/img/samplecount.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
BIN
p1/img/signal_u.jpg
Normal file
BIN
p1/img/signal_u.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 38 KiB |
BIN
p1/img/signal_y.jpg
Normal file
BIN
p1/img/signal_y.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
BIN
p1/img/step_response.png
Normal file
BIN
p1/img/step_response.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
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|
Before Width: | Height: | Size: 17 KiB |
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|
Before Width: | Height: | Size: 18 KiB |
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|
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|
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|
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|
Before Width: | Height: | Size: 16 KiB |
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|
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|
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|
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|
Before Width: | Height: | Size: 13 KiB |
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10
p1/matlab/p1.m
Normal file
10
p1/matlab/p1.m
Normal file
@ -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
Normal file
BIN
p1/matlab/p1.mat
Normal file
Binary file not shown.
BIN
p1/matlab/p1.slx
Normal file
BIN
p1/matlab/p1.slx
Normal file
Binary file not shown.
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')
|
||||
|
||||
|
||||
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:
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v = []
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mean_x, mean_y = np.mean(dx), np.mean(dy)
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m = len(dx) * max_shift_n
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mean_x, mean_y = np.mean(dx), np.mean(dy)
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cur_shift = 0
|
||||
|
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while m >= cur_shift:
|
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@ -134,36 +143,49 @@ def mutual_covar(dx: list, dy: list, max_shift_n: int = .1) -> float:
|
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for i in range(len(dx) - cur_shift):
|
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r += (dx[i] - mean_x) * (dy[i + cur_shift] - mean_y)
|
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|
||||
r = r / (len(dx) - cur_shift)
|
||||
r = r / (len(dx) - 1 - cur_shift)
|
||||
v.append(r)
|
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cur_shift += 1
|
||||
|
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return v
|
||||
|
||||
|
||||
def plot_autocorellation(dat: pd.DataFrame, fname: str = 'autocorellation', colour: str = 'blue'):
|
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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()
|
||||
|
||||
@ -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__":
|
||||
|
||||
Loading…
Reference in New Issue
Block a user