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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: %% Cell type:markdown id: tags:
Pumpenkennlinien importieren und auf Pumpengleichung Fitten Pumpenkennlinien importieren und auf Pumpengleichung Fitten
aus Webplotdigitizer .csv datei eines Plots extrahieren aus Webplotdigitizer .csv datei eines Plots extrahieren
mit pandas read_csv importieren mit pandas read_csv importieren
-> rechenspaß -> rechenspaß
->Profit!!! ->Profit!!!
Q^2 -> Q_sq quit Q^2 -> Q_sq quit
Multiple Linear Regression with Intercept Multiple Linear Regression with Intercept
Q^3, Q^2 Q^3, Q^2
pandas -> read_csv pandas -> read_csv
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
%pip install pandas %pip install pandas
%pip install numpy %pip install numpy
%pip install matplotlib %pip install matplotlib
%pip install scikit-learn %pip install scikit-learn
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Zusammenhänge der Pumpe: Zusammenhänge der Pumpe:
$\Delta p=\alpha_1\cdot Q^2+\alpha_2\cdot Q\cdot n_{est}+\alpha_3\cdot n_{est}^2$ $\Delta p=\alpha_1\cdot Q^2+\alpha_2\cdot Q\cdot n_{est}+\alpha_3\cdot n_{est}^2$
$l_{est}=\frac{\Delta p}{Q^2}$ $l_{est}=\frac{\Delta p}{Q^2}$
Leistungsgleichung: Leistungsgleichung:
$P_{est}=\beta_1\cdot Q^3+\beta_2 Q^2\cdot n_{est}+\beta_3\cdot Qn_{est}^2+\beta_4n^3+\beta_5$ $P_{est}=\beta_1\cdot Q^3+\beta_2 Q^2\cdot n_{est}+\beta_3\cdot Qn_{est}^2+\beta_4n^3+\beta_5$
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#Implementierung des .csv zu DataFrame converters #Implementierung des .csv zu DataFrame converters
import pandas as pd import pandas as pd
def csv_einlesen(y_Achse,drehzahl): def csv_einlesen(y_Achse,drehzahl):
with open('{0}-Q_kennlinie_n_{1}.csv'.format(y_Achse,drehzahl)) as kennlinie: with open('{0}-Q_kennlinie_n_{1}.csv'.format(y_Achse,drehzahl)) as kennlinie:
dataframe = pd.read_csv(kennlinie, delimiter=';') dataframe = pd.read_csv(kennlinie, delimiter=';')
dataframe.loc[-1] = dataframe.columns dataframe.loc[-1] = dataframe.columns
dataframe.index = dataframe.index +1 dataframe.index = dataframe.index +1
dataframe= dataframe.sort_index() dataframe= dataframe.sort_index()
if y_Achse=='h': if y_Achse=='h':
sorted_set = dataframe.set_axis(['Q','H'],axis='columns') sorted_set = dataframe.set_axis(['Q','H'],axis='columns')
elif y_Achse=='P': elif y_Achse=='P':
sorted_set =dataframe.set_axis(['Q','P'],axis='columns') sorted_set =dataframe.set_axis(['Q','P'],axis='columns')
#im Datensatz alle ',' durch '.' ersetzen und die String werte als Float Werte casten #im Datensatz alle ',' durch '.' ersetzen und die String werte als Float Werte casten
for x in sorted_set.index: for x in sorted_set.index:
for y in sorted_set.columns: 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]].str.replace(',','.')
sorted_set.loc[x,[y]] = sorted_set.loc[x,[y]].astype(float) 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 #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^2'] = sorted_set['Q'] **2
sorted_set['Q^3'] = sorted_set['Q'] **3 sorted_set['Q^3'] = sorted_set['Q'] **3
sorted_set['n_rel'] = drehzahl/3600 sorted_set['n_rel'] = drehzahl/3600
sorted_set['n^2'] = sorted_set['n_rel']**2 sorted_set['n^2'] = sorted_set['n_rel']**2
sorted_set['n^3'] = sorted_set['n_rel']**3 sorted_set['n^3'] = sorted_set['n_rel']**3
sorted_set['Qn'] = sorted_set['n_rel']*sorted_set['Q'] sorted_set['Qn'] = sorted_set['n_rel']*sorted_set['Q']
sorted_set['Q^2n'] = sorted_set['Q^2']*sorted_set['n_rel'] sorted_set['Q^2n'] = sorted_set['Q^2']*sorted_set['n_rel']
sorted_set['Qn^2'] = sorted_set['Q']*sorted_set['n^2'] sorted_set['Qn^2'] = sorted_set['Q']*sorted_set['n^2']
return sorted_set return sorted_set
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#def combine_csvs(): #def combine_csvs():
#with open ('learning-python'): #with open ('learning-python'):
#for i in os.listdir(.csv): #for i in os.listdir(.csv):
#pd.read_csv(i) #pd.read_csv(i)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def combine_csvs(): def combine_csvs():
import numpy as np import numpy as np
dz= [750,1150,1500,1850,2200,2550,2900,3250,3600] dz= [750,1150,1500,1850,2200,2550,2900,3250,3600]
array = np.array(float) array = np.array(float)
for z in dz: for z in dz:
df =csv_einlesen('h',z) df =csv_einlesen('h',z)
df_P=csv_einlesen('P',z) df_P=csv_einlesen('P',z)
if dz.index(z)==0: if dz.index(z)==0:
array = df.loc[:,['Q','n_rel','H']].to_numpy(float) array = df.loc[:,['Q','n_rel','H']].to_numpy(float)
array = np.append(array,df_P.loc[:,['P']].to_numpy(float),axis=1) array = np.append(array,df_P.loc[:,['P']].to_numpy(float),axis=1)
continue continue
#abschneiden der datanpaare, die keine korrespondierenden werte für H oder P haben #abschneiden der datanpaare, die keine korrespondierenden werte für H oder P haben
#vielleicht dumm #vielleicht dumm
if len(df.index)<len(df_P.index): if len(df.index)<len(df_P.index):
for i in range(len(df_P.index)-len(df.index)): for i in range(len(df_P.index)-len(df.index)):
df_P.drop(len(df.index)+i,inplace=True) df_P.drop(len(df.index)+i,inplace=True)
elif len(df.index)>len(df_P.index): elif len(df.index)>len(df_P.index):
for i in range(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) 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) 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 return array
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Fitting aus den Kurven mit sklearn Fitting aus den Kurven mit sklearn
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import pandas as pd import pandas as pd
import numpy as np import numpy as np
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression
results = pd.DataFrame() results = pd.DataFrame()
results['n'] = [750,1150,1500,1850,2200,2550,2900,3250,3600] results['n'] = [750,1150,1500,1850,2200,2550,2900,3250,3600]
fig, (ax1,ax2) = plt.subplots(2,1,sharex=True,gridspec_kw={'hspace':0}) 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(0,10,11))
ax1.set_xticks(np.linspace(0,10,21),minor=True) ax1.set_xticks(np.linspace(0,10,21),minor=True)
for x,x2 in zip(ax1.get_xgridlines() , ax2.get_xgridlines()): for x,x2 in zip(ax1.get_xgridlines() , ax2.get_xgridlines()):
x.set_visible(True) x.set_visible(True)
x2.set_visible(True) x2.set_visible(True)
for y,y2 in zip(ax1.get_ygridlines(),ax2.get_ygridlines()): for y,y2 in zip(ax1.get_ygridlines(),ax2.get_ygridlines()):
y.set_visible(True) y.set_visible(True)
y2.set_visible(True) y2.set_visible(True)
ax1.set_title('Förderhöhe Kennlinie',loc='center') ax1.set_title('Förderhöhe Kennlinie',loc='center')
ax1.set_ylabel('$H$ in m') ax1.set_ylabel('$H$ in m')
ax2.set_title('Leistungskennlinien',loc='center',y=-0.25) ax2.set_title('Leistungskennlinien',loc='center',y=-0.25)
ax2.set_ylabel('$P$ in kW') ax2.set_ylabel('$P$ in kW')
for n in results.index: 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) 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) results['Q-h_fit'] = LinearRegression(fit_intercept=False).fit(X,y)
#plotten der Punkte und des Graphen #plotten der Punkte und des Graphen
ax1.plot(df['Q'].to_numpy(),results['Q-h_fit'].get(n).predict(X)) 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+') ax1.errorbar(df['Q'].to_numpy(),results['Q-h_fit'].get(n).predict(X),fmt='b+')
#regression aus den Werten für Q und P #regression aus den Werten für Q und P
df2 = csv_einlesen('P',results['n'].get(n)) df2 = csv_einlesen('P',results['n'].get(n))
X2 = df2.loc[:,['Q^3','Q^2n','Qn^2','n^3']].to_numpy(float) 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) results['Q-P_fit'] = LinearRegression().fit(X2,y2)
#plotten der Punkte und der gefundnen Funktion mit #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.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)) ax2.plot(df2['Q'].to_numpy(),results['Q-P_fit'].get(n).predict(X2))
print(results['Q-h_fit'].get(n).coef_) print(results['Q-h_fit'].get(n).coef_)
print(f'R^2: {results['Q-h_fit'].get(n).score(X,y)}') print(f'R^2: {results['Q-h_fit'].get(n).score(X,y)}')
#print(results['Q-P_fit'].get(n).coef_) #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: %% Cell type:code id: tags:
``` python ``` python
import pandas as pd import pandas as pd
import numpy as np import numpy as np
from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression
df = pd.DataFrame(combine_csvs(),columns=['Q','n','H','P']) 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) X = np.empty((0,3),float)
X2 = np.empty((0,4),float)
for i in df.index: for i in df.index:
Q_temp=df.loc['Q'].get(i) Q_temp= df['Q'].get(i)
n_temp=df.loc['n'].get(i) n_temp= df['n'].get(i)
X = np.append(X,[[Q_temp**2,Q_temp*n_temp,n_temp**2], df.loc],axis=0) 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 %% Output
Q n H P [<matplotlib.lines.Line2D at 0x2668b9dd8b0>]
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]
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
df = df =
Q | n | P | H Q | n | P | H
-------- | -------- | -------- | -- -------- | -------- | -------- | --
0 | 0.35 | 10 | 2 0 | 0.35 | 10 | 2
0.5 | 0.35 | 15 | 1.8 0.5 | 0.35 | 15 | 1.8
1 | 0.35 |20| 1.5 1 | 0.35 |20| 1.5
0 | 0.5 | 15 | 2 0 | 0.5 | 15 | 2
0.5 | 0.5 | 20 | 1.8 0.5 | 0.5 | 20 | 1.8
1 | 0.5 | 25 | 1.5 1 | 0.5 | 25 | 1.5
```python ```python
X = numpy.array([df.loc[:,'Q']**3, df.loc[:,'Q']**2 * df.loc[:,'n']...) X = numpy.array([df.loc[:,'Q']**3, df.loc[:,'Q']**2 * df.loc[:,'n']...)
``` ```
anmelden Thesis anmelden Thesis
Ziel einen Fit mit n als zweite Variable Ziel einen Fit mit n als zweite Variable
multiple linear REgression multiple linear REgression
Ziel2 den FST custom stil zum plotten des Kennfelds einbinden Ziel2 den FST custom stil zum plotten des Kennfelds einbinden
Ziel3 eine Präsi erstellen anhand des Leitfadens im studierende Starterpaket Ziel3 eine Präsi erstellen anhand des Leitfadens im studierende Starterpaket
schritt 4 Profit schritt 4 Profit
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
results['Q-h_fit'] results['Q-h_fit']
``` ```
%% Output %% Output
0 LinearRegression(fit_intercept=False) 0 LinearRegression(fit_intercept=False)
1 LinearRegression(fit_intercept=False) 1 LinearRegression(fit_intercept=False)
2 LinearRegression(fit_intercept=False) 2 LinearRegression(fit_intercept=False)
3 LinearRegression(fit_intercept=False) 3 LinearRegression(fit_intercept=False)
4 LinearRegression(fit_intercept=False) 4 LinearRegression(fit_intercept=False)
5 LinearRegression(fit_intercept=False) 5 LinearRegression(fit_intercept=False)
6 LinearRegression(fit_intercept=False) 6 LinearRegression(fit_intercept=False)
7 LinearRegression(fit_intercept=False) 7 LinearRegression(fit_intercept=False)
8 LinearRegression(fit_intercept=False) 8 LinearRegression(fit_intercept=False)
Name: Q-h_fit, dtype: object Name: Q-h_fit, dtype: object
......
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