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Commit d8be71fd authored by Steinmann's avatar Steinmann
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created combined numpy array of Q,n,H,P

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Kennlinien_und_Fitting.ipynb
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%% 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 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=';')
dataframe.loc[-1] = dataframe.columns
dataframe.index = dataframe.index +1
dataframe= dataframe.sort_index()
sorted_set = dataframe.set_axis(['Q','H'],axis='columns')
if y_Achse=='h':
sorted_set = dataframe.set_axis(['Q','H'],axis='columns')
elif y_Achse=='P':
sorted_set =dataframe.set_axis(['Q','P'],axis='columns')
#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['n^2'] = sorted_set['n_rel']**2
sorted_set['n^3'] = sorted_set['n_rel']**3
sorted_set['Qn'] = sorted_set['n_rel']*sorted_set['Q']
sorted_set['Q^2n'] = sorted_set['Q^2']*sorted_set['n_rel']
sorted_set['Qn^2'] = sorted_set['Q']*sorted_set['n^2']
return sorted_set
```
%% Cell type:code id: tags:
``` python
def combine_csvs():
for i in os.listdir(:*.csv):
#def combine_csvs():
#with open ('learning-python'):
#for i in os.listdir(.csv):
pd.read_csv(i)
#pd.read_csv(i)
```
%% Cell type:code id: tags:
``` python
print(csv_einlesen('h',750))
```
%% Output
def combine_csvs():
import numpy as np
dz= [750,1150,1500,1850,2200,2550,2900,3250,3600]
array = np.array(float)
for z in dz:
df =csv_einlesen('h',z)
df_P=csv_einlesen('P',z)
if dz.index(z)==0:
array = df.loc[:,['Q','n_rel','H']].to_numpy(float)
array = np.append(array,df_P.loc[:,['P']].to_numpy(float),axis=1)
continue
if len(df.index)<len(df_P.index):
for i in range(len(df_P.index)-len(df.index)):
df_P.drop(len(df.index)+i,inplace=True)
elif len(df.index)>len(df_P.index):
for i in range(len(df.index)-len(df_P.index)):
df.drop(len(df_P.index)+i,inplace=True)
Q h Q^2 Q^3 n_rel n^2 n^3 Qn \
0 0.0 0.363392 0.0 0.0 0.208333 562500.0 421875000.0 0.0
1 0.5 0.398001 0.25 0.125 0.208333 562500.0 421875000.0 375.0
2 1.0 0.397639 1.0 1.0 0.208333 562500.0 421875000.0 750.0
3 1.5 0.328851 2.25 3.375 0.208333 562500.0 421875000.0 1125.0
4 2.0 0.27687 4.0 8.0 0.208333 562500.0 421875000.0 1500.0
5 2.5 0.207313 6.25 15.625 0.208333 562500.0 421875000.0 1875.0
Q^2n Qn^2
0 0.0 0.0
1 187.5 281250.0
2 750.0 562500.0
3 1687.5 843750.0
4 3000.0 1125000.0
5 4687.5 1406250.0
array = np.append(array, np.append(df.loc[:,['Q','n_rel','H']].to_numpy(),df_P.loc[:,['P']].to_numpy(),axis=1), axis=0)
return array
```
%% Cell type:markdown id: tags:
Fitting aus den Kurven mit sklearn
%% Cell type:code id: tags:
``` python
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
results = pd.DataFrame()
drehz = [750,1150,1500,1850,2200,2550,2900,3250,3600]
results['n'] = drehz
results['n'] = [750,1150,1500,1850,2200,2550,2900,3250,3600]
fig, (ax1,ax2) = plt.subplots(2,1,sharex=True,gridspec_kw={'hspace':0})
ax1.set_xticks(np.linspace(0,10,11))
ax1.set_xticks(np.linspace(0,10,21),minor=True)
for x,x2 in zip(ax1.get_xgridlines() , ax2.get_xgridlines()):
x.set_visible(True)
x2.set_visible(True)
for y,y2 in zip(ax1.get_ygridlines(),ax2.get_ygridlines()):
y.set_visible(True)
y2.set_visible(True)
ax1.set_title('Förderhöhe Kennlinie',loc='center')
ax1.set_ylabel('$H$ in m')
ax2.set_title('Leistungskennlinien',loc='center',y=-0.25)
ax2.set_ylabel('$P$ in kW')
for n in results.index:
df = csv_einlesen('h',drehz[n])
df = csv_einlesen('H',results['n'].get(n))
X = df.loc[:,['Q^2','Qn','n^2']].to_numpy(float)
y = df['h'].to_numpy(float)
y = df['H'].to_numpy(float)
results['Q-h_fit'] = LinearRegression(fit_intercept=False).fit(X,y)
#plotten der Punkte und des Graphen
ax1.plot(df['Q'].to_numpy(),results['Q-h_fit'].get(n).predict(X))
ax1.errorbar(df['Q'].to_numpy(),results['Q-h_fit'].get(n).predict(X),fmt='b+')
#regression aus den Werten für Q und P
df2 = csv_einlesen('P',drehz[n])
df2 = csv_einlesen('P',results['n'].get(n))
X2 = df2.loc[:,['Q^3','Q^2n','Qn^2','n^3']].to_numpy(float)
y2= df2['h'].to_numpy(float)
y2= df2['H'].to_numpy(float)
results['Q-P_fit'] = LinearRegression().fit(X2,y2)
#plotten der Punkte und der gefundnen Funktion mit
ax2.errorbar(df2['Q'].to_numpy(),results['Q-P_fit'].get(n).predict(X2),fmt='b+')
ax2.plot(df2['Q'].to_numpy(),results['Q-P_fit'].get(n).predict(X2))
print(results['Q-h_fit'].get(n).coef_)
print(f'R^2: {results['Q-h_fit'].get(n).score(X,y)}')
#print(results['Q-P_fit'].get(n).coef_)
```
%% Output
%% Cell type:code id: tags:
``` python
df = pd.DataFrame(combine_csvs(),columns=['Q','n','H','P'])
for i in df.index:
X=np.array(df['Q'].get(i)**2)
[-4.32633175e-02 5.19623567e-05 6.74554837e-07]
R^2: 0.9993376472269206
[-4.42742552e-02 6.27325491e-05 6.53871635e-07]
R^2: 0.9996437866771651
[-4.55181410e-02 7.07998272e-05 6.44464225e-07]
R^2: 0.9996824413373676
[-4.33713318e-02 6.34156384e-05 6.49436305e-07]
R^2: 0.999760791662233
[-4.48621316e-02 6.80680926e-05 6.46997640e-07]
R^2: 0.9995310406285615
[-4.56090785e-02 7.25278520e-05 6.45662549e-07]
R^2: 0.999203487869221
[-5.30102486e-02 8.44703682e-05 6.42551789e-07]
R^2: 0.989680503933434
[-8.31083199e-02 9.65000207e-05 6.45338184e-07]
R^2: 0.9616275516077175
[7.00655817e-09 5.04472189e-05 6.48443112e-07]
R^2: 1.0
print(X)
```
%% Output
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[109], line 2
1 df = pd.DataFrame(combine_csvs(),columns=['Q','n','H','P'])
----> 2 X= np.array()
3 for i in df.index:
4 X=np.array(df['Q'].get(i)**2)
TypeError: array() missing required argument 'object' (pos 0)
%% Cell type:markdown id: tags:
df =
Q | n | P | H
-------- | -------- | -------- | --
0 | 0.35 | 10 | 2
0.5 | 0.35 | 15 | 1.8
1 | 0.35 |20| 1.5
0 | 0.5 | 15 | 2
0.5 | 0.5 | 20 | 1.8
1 | 0.5 | 25 | 1.5
```python
X = numpy.array([df.loc[:,'Q']**3, df.loc[:,'Q']**2 * df.loc[:,'n']...)
```
anmelden Thesis
Ziel einen Fit mit n als zweite Variable
multiple linear REgression
Ziel2 den FST custom stil zum plotten des Kennfelds einbinden
Ziel3 eine Präsi erstellen anhand des Leitfadens im studierende Starterpaket
schritt 4 Profit
%% Cell type:code id: tags:
``` python
results['Q-h_fit']
```
%% Output
0 LinearRegression(fit_intercept=False)
1 LinearRegression(fit_intercept=False)
2 LinearRegression(fit_intercept=False)
3 LinearRegression(fit_intercept=False)
4 LinearRegression(fit_intercept=False)
5 LinearRegression(fit_intercept=False)
6 LinearRegression(fit_intercept=False)
7 LinearRegression(fit_intercept=False)
8 LinearRegression(fit_intercept=False)
Name: Q-h_fit, dtype: object
......
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