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Commit 4c93419e authored by Steinmann Victor's avatar Steinmann Victor
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added continuous plotting of condensed fit

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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()
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']**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():
#with open ('learning-python'):
#for i in os.listdir(.csv):
#pd.read_csv(i)
```
%% Cell type:code id: tags:
``` python
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
#abschneiden der datanpaare, die keine korrespondierenden werte für H oder P haben
#vielleicht dumm
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)
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()
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',results['n'].get(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)
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',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['P'].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
[-0.04326332 0.18706448 8.74223069]
R^2: 0.9993376472269206
[-0.04427426 0.22583718 8.47417639]
R^2: 0.9996437866771651
[-0.04551814 0.25487938 8.35225635]
R^2: 0.9996824413373676
[-0.04337133 0.2282963 8.41669451]
R^2: 0.9997607916622331
[-0.04486213 0.24504513 8.38508942]
R^2: 0.9995310406285615
[-0.04560908 0.26110027 8.36778663]
R^2: 0.999203487869221
[-0.05301025 0.30409333 8.32747118]
R^2: 0.989680503933434
[-0.08310832 0.34740007 8.36358287]
R^2: 0.9616275516077178
[0.072644 0.14528799 8.40382273]
R^2: 1.0
%% Cell type:code id: tags:
``` python
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
df = pd.DataFrame(combine_csvs(),columns=['Q','n','H','P'])
#erstellen eines np arrays für die werte von Q^3 Q^2 Q^2n Qn^2 n^3
X = np.empty((0,3),float)
X2 = np.empty((0,4),float)
for i in df.index:
Q_temp=df.loc['Q'].get(i)
n_temp=df.loc['n'].get(i)
X = np.append(X,[[Q_temp**2,Q_temp*n_temp,n_temp**2], df.loc],axis=0)
Q_temp= df['Q'].get(i)
n_temp= df['n'].get(i)
X = np.append(X,[[(Q_temp**2), (Q_temp*n_temp), (n_temp**2)]],axis=0)
X2 = np.append(X2,[[Q_temp**3,(Q_temp**2) *n_temp, Q_temp*(n_temp**2), n_temp**3]],axis=0)
LR_H = LinearRegression(fit_intercept=False).fit(X , df.loc['H'].to_numpy(float))
LR_H = LinearRegression(fit_intercept=False).fit(X , df['H'].to_numpy(float))
LR_P = LinearRegression().fit(X2,df['P'].to_numpy(float))
print(df)
fig1, (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)
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')
#muss für jeden bereich fon n einzeln geplottet werden
for i in df.index:
ax1.plot(df['Q'].to_numpy(),LR_H.predict(X))
ax2.plot(df['Q'].to_numpy(),LR_P.predict(X2))
```
%% Output
Q n H P
0 -0.0 0.208333 0.380667 0.003501
1 0.5 0.208333 0.386749 0.004184
2 1.0 0.208333 0.373531 0.004867
3 1.5 0.208333 0.341249 0.005549
4 2.0 0.208333 0.287062 0.006061
.. ... ... ... ...
97 2.5 0.902778 7.107076 0.112583
98 3.0 0.902778 7.034061 0.123044
99 3.5 0.902778 6.906989 0.132358
100 -0.0 1.0 8.403823 0.078036
101 0.5 1.0 8.494628 0.088328
[102 rows x 4 columns]
[<matplotlib.lines.Line2D at 0x2668b9dd8b0>]
%% 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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