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Commit bb57a854 authored by Steinmann's avatar Steinmann
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alle .csv hinzugefügt und Regression implementiert

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Kennlinien_und_Fitting.ipynb
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View file @ bb57a854
%% Cell type:markdown id: tags:
Pumpenkennlinien importieren und auf Pumpengleichung Fitten
aus Webplotdigitizer .csv datei eines Plots extrahieren
mit pandas read_csv importieren
-> rechenspaß
->Profit!!!
Q^2 -> Q_sq quit
Multiple Linear Regression with Intercept
Q^3, Q^2
pandas -> read_csv
%% Cell type:code id: tags:
``` python
!pip install pandas
!pip install numpy
!pip install matplotlib
!pip install scikit-learn
```
%% Cell type:markdown id: tags:
Zusammenhänge der Pumpe:
$\Delta p=\alpha_1\cdot Q^2+\alpha_2\cdot Q\cdot n_{est}+\alpha_3\cdot n_{est}^2$
$l_{est}=\frac{\Delta p}{Q^2}$
Leistungsgleichung:
$P_{est}=\beta_1\cdot Q^3+\beta_2 Q^2\cdot n_{est}+\beta_3\cdot Qn_{est}^2+\beta_4n^3+\beta_5$
%% Cell type:code id: tags:
``` python
#implementierung einer linearen Regression
#Implementierung des .csv zu DataFrame converters
import pandas as pd
def csv_einlesen(y_Achse,drehzahl):
with open('{0}-Q_kennlinie_n_{1}.csv'.format(y_Achse,drehzahl)) as kennlinie:
dataframe = pd.read_csv(kennlinie, delimiter=';')
sorted_set = dataframe.set_axis(['Q','h'],axis='columns')
sorted_set.sort_values(by='Q',inplace=True)
#im Datensatz alle ',' durch '.' ersetzen und die String werte als Float Werte casten
for x in sorted_set.index:
for y in sorted_set.columns:
sorted_set.loc[x,[y]] = sorted_set.loc[x,[y]].str.replace(',','.')
sorted_set.loc[x,[y]] = sorted_set.loc[x,[y]].astype(float)
#berechnen der Spalte Q^2, Q^3 n_relativ, n^2, n^3, Q*n, Q^2*n,Q*n^2
sorted_set['Q^2'] = sorted_set['Q'] **2
sorted_set['Q^3'] = sorted_set['Q'] **3
sorted_set['n_rel'] = drehzahl/3600
sorted_set['n^2'] = (sorted_set['n_rel']*3600)**2
sorted_set['n^3'] = (sorted_set['n_rel']*3600)**3
sorted_set['Qn'] = (sorted_set['n_rel']*3600)*sorted_set['Q']
sorted_set['Q^2n'] = sorted_set['Q^2']*3600*sorted_set['n_rel']
sorted_set['Qn^2'] = sorted_set['Q']*sorted_set['n^2']
sorted_set.set_index('Q',inplace=True)
return sorted_set
```
%% Cell type:code id: tags:
``` python
print(csv_einlesen('h',3600).loc[:,['Q^2','Qn','n^2']])
import numpy as np
print(csv_einlesen('h',2900).loc[:,['Q^2','Qn','n^2']].to_numpy(float))
```
%% Output
Q^2 Qn n^2
Q
0.011261 0.000127 40.540541 12960000.0
1.0 1.0 3600.0 12960000.0
1.5 2.25 5400.0 12960000.0
2.0 4.0 7200.0 12960000.0
2.5 6.25 9000.0 12960000.0
3.0 9.0 10800.0 12960000.0
3.5 12.25 12600.0 12960000.0
4.0 16.0 14400.0 12960000.0
4.5 20.25 16200.0 12960000.0
5.0 25.0 18000.0 12960000.0
5.5 30.25 19800.0 12960000.0
6.0 36.0 21600.0 12960000.0
6.5 42.25 23400.0 12960000.0
7.0 49.0 25200.0 12960000.0
7.5 56.25 27000.0 12960000.0
8.0 64.0 28800.0 12960000.0
8.5 72.25 30600.0 12960000.0
9.0 81.0 32400.0 12960000.0
9.5 90.25 34200.0 12960000.0
%% Cell type:markdown id: tags:
Fitting aus den Kurven mit sklearn
%% Cell type:code id: tags:
``` python
import numpy as np
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
df = csv_einlesen('h',3600)
X= df.loc[:,['Q^2','Qn','n^2']].to_numpy(float)
y = df.index
results = LinearRegression(fit_intercept=False).fit(X,y)
print(results.score(X,y))
results = pd.DataFrame()
drehz = [750,1150,1500,1850,2200,2550,2900,3600]
results['n'] = drehz#get methode richtig verwenden spart die zeile
fig, (ax1,ax2) = plt.subplots(2,1,sharex=True)
ax1.set_title('Förderhöhe Kennlinie',loc='center')
ax2.set_title('Leistungskennlinien',loc='center')
for n in results.index:
df = csv_einlesen('h',drehz[n])
X = df.loc[:,['Q^2','Qn','n^2']].to_numpy(float)
y = df['h'].to_numpy(float)
results['Q-h_fit'] = LinearRegression(fit_intercept=False).fit(X,y)
#results['h_ber']
#for i in df.index:
# np.dot(df.loc[i,['Q^2','Qn','n^2']], results['Q-h_fit'].get(n).coef_)
X2 =df.loc[:,['Q^3','Q^2n','Qn^2','n^3']].to_numpy(float)
results['Q-P_fit'] = LinearRegression().fit(X2,y)
ax1.plot(df.index.to_numpy(),results['Q-h_fit'].get(n).predict(X))
ax2.plot(df.index.to_numpy(),results['Q-P_fit'].get(n).predict(X2))
print(results)
```
%% Output
1.0
n Q-h_fit Q-P_fit
0 750 LinearRegression(fit_intercept=False) LinearRegression()
1 1150 LinearRegression(fit_intercept=False) LinearRegression()
2 1500 LinearRegression(fit_intercept=False) LinearRegression()
3 1850 LinearRegression(fit_intercept=False) LinearRegression()
4 2200 LinearRegression(fit_intercept=False) LinearRegression()
5 2550 LinearRegression(fit_intercept=False) LinearRegression()
6 2900 LinearRegression(fit_intercept=False) LinearRegression()
7 3600 LinearRegression(fit_intercept=False) LinearRegression()
......
P-Q_kennlinie_n_1500.csv 0 → 100644
+ 12
− 0
View file @ bb57a854
0,4997431821275309; 0,013781039147448826
0,9999999999999987; 0,015480575653592676
1,4999999999999987; 0,01781397928321593
1,9999999999999996; 0,020290346654134817
2,4999999999999987; 0,021623336515153524
2,9999999999999996; 0,024132902337116124
3,4999999999999987; 0,02611156561969863
3,9999999999999996; 0,027090727200172082
4,486197323444136; 0,02832927133065044
5,02605417316202; 0,029405440943446215
5,520916531623271; 0,03048325419062564
-0,0008218171919018857; 0,011689527737614336
P-Q_kennlinie_n_18500.csv 0 → 100644
+ 14
− 0
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0,000333863234209808; 0,01607863509053964
0,49999999999999956; 0,019080344044859254
0,9999999999999996; 0,022780026877651738
1,4999999999999987; 0,02596131415691627
1,9996404549785418; 0,029488546671543936
2,4999999999999996; 0,032618019645384355
2,9999999999999996; 0,035874813717092446
3,499999999999998; 0,03837178864917423
3,9999999999999996; 0,04162154482794128
4,5; 0,04422720135234187
5; 0,0470186217898419
5,500000000000002; 0,0482144930065716
5,999999999999998; 0,050050244581489045
6,486808549980614; 0,05163518507579984
P-Q_kennlinie_n_2200.csv 0 → 100644
+ 17
− 0
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0; 0,023895122857550077
0,5; 0,027560630545292175
0,9999999999999987; 0,032314110288882925
1,4999999999999996; 0,0364419748965234
1,9999999999999987; 0,04126933621491158
2,4999999999999987; 0,04625409585604867
2,9999999999999996; 0,05100877723779129
3,4999999999999987; 0,055755900458728413
4,00007704536174; 0,060465042507183986
4,5; 0,06441656381192484
5,000000000000002; 0,06841035973942883
5,5; 0,07240224901945586
6; 0,07546347767443484
6,499999999999998; 0,07808856313897244
7; 0,08121262219342845
7,499999999999998; 0,08259153092873284
7,991838328012923; 0,08409162234418216
P-Q_kennlinie_n_2550.csv 0 → 100644
+ 19
− 0
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0,0003338632342106962; 0,032385441905489365
0,5; 0,03824274909839187
0,9999999999999996; 0,04435089827332511
1,4999999999999987; 0,05047607879105359
1,9999999999999987; 0,05734949736906603
2,4999999999999987; 0,06390013415847867
2,9999999999999996; 0,07091538059959573
3,499999999999998; 0,07734570525641055
3,9999999999999996; 0,0837072666442284
4,499999999999998; 0,09003781433209862
5; 0,09653695730442424
5,5; 0,10216054010581205
6; 0,1074816790617199
6,5; 0,11233372728564911
6,999999999999998; 0,11742328986864597
7,4999999999999964; 0,12075380841393707
8; 0,12498588060847077
8,5; 0,1270224560577347
8,978609639402027; 0,12825648914559257
P-Q_kennlinie_n_2900.csv 0 → 100644
+ 15
− 0
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-0,0000513635744940899; 0,04491015310106161
0,49999999999999956; 0,05202994294426022
0,9999999999999996; 0,059839225225622406
1,4999999999999987; 0,06790830644079776
1,9999999999999987; 0,07698295989091225
2,4999999999999996; 0,08539767156037775
2,9999999999999987; 0,09455322551269757
3,4999999999999996; 0,10381211664810436
3,9999999999999996; 0,11249978961244905
4,5; 0,12143764607733043
5; 0,13018934666509535
5,5; 0,1376533543323592
5,989470467228758; 0,1446960336681169
6,489187967569039; 0,15014404397067366
7,000128408936234; 0,15478696393213992
P-Q_kennlinie_n_3250.csv 0 → 100644
+ 9
− 0
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0,000385226808704342; 0,06007066819843471
0,49997431821275296; 0,06844683935744794
0,9999999999999996; 0,07861210411464203
1,4999999999999987; 0,08979382501942479
1,9999999999999996; 0,1013495891389325
2,4999999999999996; 0,11243520474522684
3,000308181446963; 0,12282099578786526
3,4776054815206687; 0,13114615392950218
3,9999999999999996; 0,1389853050746946
P-Q_kennlinie_n_3600.csv 0 → 100644
+ 21
− 0
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0,0001027271489881798; 0,0781354007136918
0,5; 0,08808223876268223
0,9999999999999987; 0,09724833024816187
1,4999999999999987; 0,10610436664260076
1,9999999999999987; 0,11433247142325448
2,4999999999999987; 0,12056830299293331
2,9999999999999987; 0,12720949027291717
3,4999999999999987; 0,1326065230712658
3,9999999999999996; 0,1389853050746946
4,5; 0,14330518803064624
5; 0,14771394826441298
5,5; 0,15186252703941644
6; 0,15493921273521566
6,5; 0,15644847330293243
7; 0,15661345264114207
7,499999999999998; 0,15725574096796496
8; 0,15725574096793088
8,500000000000002; 0,1570221790451155
9; 0,15531746520244405
9,5; 0,15467248444057063
10,011710894984606; 0,153424229738996
P-Q_kennlinie_n_750.csv 0 → 100644
+ 6
− 0
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0,4886486500368532; 0,004870476593899609
0,9999999999999996; 0,004970368805329972
1,4999999999999987; 0,005489739175194852
1,9999999999999996; 0,006135234247208798
2,4999999999999996; 0,006135642605529401
-0,0002824996597166063; 0,004018275159805018
h-Q_kennlinie_n_1150.csv 0 → 100644
+ 9
− 0
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-4,440892098500626e-16; 0,8324179260031315
0,5; 0,872347328408626
1,0000000000000004; 0,8450333700033621
1,5000000000000004; 0,8374361646690982
2,0000000000000004; 0,8073567764182545
2,5000000000000004; 0,7196349001342401
3,0000000000000004; 0,6585042445978324
3,4999999999999996; 0,5213633403081896
4,009009009009009; 0,3947368421052637
h-Q_kennlinie_n_1500.csv 0 → 100644
+ 11
− 0
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-4,440892098500626e-16; 1,4214590932777504
0,5; 1,4588762159444126
1,0000000000000004; 1,4821784492255698
1,5000000000000004; 1,4655132881707313
2,0000000000000004; 1,4385260414966545
2,5000000000000004; 1,3755193415184372
3,0000000000000004; 1,3349675767207163
3,4999999999999996; 1,2065035491706944
3,9999999999999996; 1,1095808874965396
4,5; 0,9791118027736374
5; 0,8224546328922138
h-Q_kennlinie_n_1850.csv 0 → 100644
+ 14
− 0
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-4,440892098500626e-16; 2,1918368942157365
0,5; 2,2532046539914443
1,0000000000000004; 2,2858493461828964
1,5000000000000004; 2,2855028647754168
2,0000000000000004; 2,2506096925482506
2,5000000000000004; 2,21297965435401
3,0000000000000004; 2,134247310500589
3,4999999999999996; 2,088879671847913
3,9999999999999996; 1,9487400195095699
4,5; 1,8398350017490355
5; 1,6892366642931194
5,499999999999998; 1,5146854115947086
6; 1,3283623645101859
6,5; 1,1170297011874943
h-Q_kennlinie_n_2200.csv 0 → 100644
+ 41
− 0
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0,39999999999999947; 3,1929717878037955
0,6000000000000001; 3,192726374350473
0,7999999999999994; 3,227609113998083
1,0000000000000004; 3,2280257726231643
1,1999999999999988; 3,2276181873772227
1,4; 3,2276185513288276
1,6; 3,2276185513288276
1,7999999999999994; 3,2276185513288276
1,9999999999999987; 3,2276185247292286
2,199999999999999; 3,192725334193545
2,4; 3,1927253778009472
2,6; 3,192725468875448
2,8000000000000003; 3,172201212108755
3,0000000000000004; 3,1245879982583435
3,1999999999999997; 3,1054925087317535
3,4; 3,1054873601235773
3,6; 3,0705943576560895
3,8000000000000003; 3,0355394677905316
3,9999999999999996; 2,968977675571594
4,200000000000001; 2,9484434735780987
4,4; 2,9135787412991068
4,600000000000001; 2,8657924094465077
4,799999999999999; 2,7914239297767907
5,000000000000002; 2,740887534848481
5,200000000000001; 2,691857025461701
5,400000000000002; 2,599612325831064
5,600000000000001; 2,573879851181795
5,8000000000000025; 2,4776931153807205
6,000000000000002; 2,3992310266958743
6,200000000000001; 2,2983952347537553
6,4; 2,246752045742573
6,600000000000003; 2,128808780486329
6,8000000000000025; 2,0492925389296115
7,0000000000000036; 1,9410278530712048
7,200000000000003; 1,8499198549140576
7,400000000000004; 1,7671549591940021
7,600000000000003; 1,6564631012492335
8,000000000000004; 1,4020451875394127
7,7477477477477485; 1,578947368421053
0,22522522522522515; 3,1578947368421044
0; 3,078947368421053
h-Q_kennlinie_n_2550.csv 0 → 100644
+ 19
− 0
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0,5; 4,291860429116973
1,0000000000000004; 4,326955331949941
1,5000000000000004; 4,3616466909849
2,0000000000000004; 4,340796872874408
2,5000000000000004; 4,326753517457021
3,0000000000000004; 4,291842205604224
3,4999999999999996; 4,239149445560999
3,9999999999999996; 4,167048221276245
4,5; 4,0755034088799516
5; 3,984406827349039
5,499999999999998; 3,853047932821921
6; 3,687401555191453
6,5; 3,4719609924602004
6,999999999999998; 3,255362644235854
7,499999999999998; 2,9980586054421483
7,999999999999998; 2,7250400431129282
8,5; 2,437538409349431
9,00900900900901; 2,1315789473684195
0; 4,184210526315789
h-Q_kennlinie_n_3600.csv
+ 2
− 20
View file @ bb57a854
0,5; 8,477893910103
1,0000000000000004; 8,303953173252335
1,5000000000000004; 8,105201419575549
2,0000000000000004; 7,88174717906121
2,5000000000000004; 7,626840697332545
3,0000000000000004; 7,354029690343628
3,4999999999999996; 7,0570045428317005
3,9999999999999996; 6,732054821804168
4,5; 6,406415316112362
5; 6,063974237815968
5,499999999999998; 5,719840446210469
6; 5,343856753528749
6,5; 4,963474474341147
6,999999999999998; 4,573962765439893
7,499999999999998; 4,159159502939376
7,999999999999998; 3,757221910839121
8,5; 3,3249634825980987
9; 2,889809771547011
9,5; 2,4469590692331202
0,011261261261260813; 8,427631578947366
0; 8,450886912869716
0,5000000000000004; 8,48516771427629
h-Q_kennlinie_n_750.csv 0 → 100644
+ 6
− 0
View file @ bb57a854
-4,440892098500626e-16; 0,33946400329310933
0,5; 0,36557661893103877
1; 0,35812183010060394
1,4999999999999991; 0,29664919955389735
1,9999999999999991; 0,24371837171201882
2,5084364454443193; 0,17724288840262226
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