diff --git a/Kennlinien_und_Fitting.ipynb b/Kennlinien_und_Fitting.ipynb
index 5e0bf304e05bc165a764a8a994e22c2dd88a632e..61e720396ad53023ab6ed416a9e5a2b994dca656 100644
--- a/Kennlinien_und_Fitting.ipynb
+++ b/Kennlinien_und_Fitting.ipynb
@@ -28,10 +28,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install pandas\n",
-    "!pip install numpy\n",
-    "!pip install matplotlib\n",
-    "!pip install scikit-learn"
+    "%pip install pandas\n",
+    "%pip install numpy\n",
+    "%pip install matplotlib\n",
+    "%pip install scikit-learn"
    ]
   },
   {
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,8 +61,10 @@
     "def csv_einlesen(y_Achse,drehzahl):\n",
     "    with open('{0}-Q_kennlinie_n_{1}.csv'.format(y_Achse,drehzahl)) as kennlinie:\n",
     "        dataframe = pd.read_csv(kennlinie, delimiter=';')\n",
-    "        sorted_set = dataframe.set_axis(['Q','h'],axis='columns')\n",
-    "        sorted_set.sort_values(by='Q',inplace=True)\n",
+    "        dataframe.loc[-1] = dataframe.columns\n",
+    "        dataframe.index = dataframe.index +1\n",
+    "        dataframe= dataframe.sort_index()\n",
+    "        sorted_set = dataframe.set_axis(['Q','H'],axis='columns')\n",
     "        #im Datensatz alle ',' durch '.' ersetzen und die String werte als Float Werte casten\n",
     "        for x in sorted_set.index:\n",
     "            for y in sorted_set.columns:   \n",
@@ -78,7 +80,7 @@
     "        sorted_set['Qn'] = (sorted_set['n_rel']*3600)*sorted_set['Q']\n",
     "        sorted_set['Q^2n'] = sorted_set['Q^2']*3600*sorted_set['n_rel']\n",
     "        sorted_set['Qn^2'] = sorted_set['Q']*sorted_set['n^2']\n",
-    "        sorted_set.set_index('Q',inplace=True)\n",
+    "\n",
     "    return sorted_set"
    ]
   },
@@ -88,8 +90,42 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "print(csv_einlesen('h',2900).loc[:,['Q^2','Qn','n^2']].to_numpy(float))"
+    "def combine_csvs():\n",
+    "    for i in os.listdir(:*.csv):\n",
+    "        \n",
+    "        pd.read_csv(i)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Q         h   Q^2     Q^3     n_rel       n^2          n^3      Qn  \\\n",
+      "0  0.0  0.363392   0.0     0.0  0.208333  562500.0  421875000.0     0.0   \n",
+      "1  0.5  0.398001  0.25   0.125  0.208333  562500.0  421875000.0   375.0   \n",
+      "2  1.0  0.397639   1.0     1.0  0.208333  562500.0  421875000.0   750.0   \n",
+      "3  1.5  0.328851  2.25   3.375  0.208333  562500.0  421875000.0  1125.0   \n",
+      "4  2.0   0.27687   4.0     8.0  0.208333  562500.0  421875000.0  1500.0   \n",
+      "5  2.5  0.207313  6.25  15.625  0.208333  562500.0  421875000.0  1875.0   \n",
+      "\n",
+      "     Q^2n       Qn^2  \n",
+      "0     0.0        0.0  \n",
+      "1   187.5   281250.0  \n",
+      "2   750.0   562500.0  \n",
+      "3  1687.5   843750.0  \n",
+      "4  3000.0  1125000.0  \n",
+      "5  4687.5  1406250.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(csv_einlesen('h',750))"
    ]
   },
   {
@@ -101,36 +137,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-3.47385130e-02  5.04882479e-06  7.31707845e-07]\n",
-      "[-3.06250525e-04  1.38507458e-06  3.01228446e-10  0.00000000e+00]\n",
-      "[-4.44907435e-02  5.88159076e-05  6.34267712e-07]\n",
-      "[-2.59397671e-05 -1.13591122e-07  2.23851505e-09  0.00000000e+00]\n",
-      "[-4.15388640e-02  5.72340810e-05  6.36019627e-07]\n",
-      "[-6.01867164e-05  1.48914089e-07  1.77141762e-09  0.00000000e+00]\n",
-      "[-4.20552804e-02  5.63441848e-05  6.47686897e-07]\n",
-      "[-4.56683050e-05  5.42205878e-08  1.98179375e-09 -9.77462723e-13]\n",
-      "[-4.51224120e-02  6.88866088e-05  6.41699387e-07]\n",
-      "[-7.68775884e-05  2.33104027e-07  1.73914418e-09  0.00000000e+00]\n",
-      "[-4.59639465e-02  7.42644985e-05  6.40695657e-07]\n",
-      "[-9.18017745e-05  2.98350460e-07  1.73528371e-09  0.00000000e+00]\n",
-      "[-5.01123550e-02  7.97766920e-05  6.41405059e-07]\n",
-      "[-2.19284686e-04  6.94456938e-07  1.45895994e-09  0.00000000e+00]\n",
-      "[-9.63177570e-02  1.19120577e-04  6.37536034e-07]\n",
-      "[-7.08825882e-04  1.14583673e-06  1.54545259e-09  0.00000000e+00]\n",
-      "[1.26185928e-14 9.08538680e-11 6.54147849e-07]\n",
-      "[-7.15467592e-06 -3.33683378e-07  1.54635931e-09  0.00000000e+00]\n"
+      "[-4.32633175e-02  5.19623567e-05  6.74554837e-07]\n",
+      "R^2: 0.9993376472269206\n",
+      "[-4.42742552e-02  6.27325491e-05  6.53871635e-07]\n",
+      "R^2: 0.9996437866771651\n",
+      "[-4.55181410e-02  7.07998272e-05  6.44464225e-07]\n",
+      "R^2: 0.9996824413373676\n",
+      "[-4.33713318e-02  6.34156384e-05  6.49436305e-07]\n",
+      "R^2: 0.999760791662233\n",
+      "[-4.48621316e-02  6.80680926e-05  6.46997640e-07]\n",
+      "R^2: 0.9995310406285615\n",
+      "[-4.56090785e-02  7.25278520e-05  6.45662549e-07]\n",
+      "R^2: 0.999203487869221\n",
+      "[-5.30102486e-02  8.44703682e-05  6.42551789e-07]\n",
+      "R^2: 0.989680503933434\n",
+      "[-8.31083199e-02  9.65000207e-05  6.45338184e-07]\n",
+      "R^2: 0.9616275516077175\n",
+      "[7.00655817e-09 5.04472189e-05 6.48443112e-07]\n",
+      "R^2: 1.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -144,9 +180,10 @@
     "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
     "from sklearn.linear_model import LinearRegression\n",
+    "\n",
     "results = pd.DataFrame()\n",
     "drehz = [750,1150,1500,1850,2200,2550,2900,3250,3600]\n",
-    "results['n'] = drehz#get methode richtig verwenden spart die zeile\n",
+    "results['n'] = drehz\n",
     "fig, (ax1,ax2) = plt.subplots(2,1,sharex=True,gridspec_kw={'hspace':0})\n",
     "ax1.set_xticks(np.linspace(0,10,11))\n",
     "ax1.set_xticks(np.linspace(0,10,21),minor=True)\n",
@@ -157,26 +194,84 @@
     "    y.set_visible(True)\n",
     "    y2.set_visible(True)\n",
     "ax1.set_title('Förderhöhe Kennlinie',loc='center')\n",
-    "ax1.set_ylabel('h in m')\n",
+    "ax1.set_ylabel('$H$ in m')\n",
     "ax2.set_title('Leistungskennlinien',loc='center',y=-0.25)\n",
-    "ax2.set_ylabel('P in kW')\n",
+    "ax2.set_ylabel('$P$ in kW')\n",
     "\n",
     "for n in results.index:\n",
     "    df = csv_einlesen('h',drehz[n])\n",
     "    X = df.loc[:,['Q^2','Qn','n^2']].to_numpy(float)\n",
     "    y = df['h'].to_numpy(float)\n",
-    "    #ax1.errorbar()\n",
     "    results['Q-h_fit'] = LinearRegression(fit_intercept=False).fit(X,y)\n",
-    "    ax1.plot(df.index.to_numpy(),results['Q-h_fit'].get(n).predict(X))\n",
+    "    #plotten der Punkte und des Graphen\n",
+    "    ax1.plot(df['Q'].to_numpy(),results['Q-h_fit'].get(n).predict(X))\n",
+    "    ax1.errorbar(df['Q'].to_numpy(),results['Q-h_fit'].get(n).predict(X),fmt='b+')\n",
+    "\n",
+    "    #regression aus den Werten für Q und P\n",
     "    df2 = csv_einlesen('P',drehz[n])\n",
-    "    \"\"\"Das np-Array mit allen berechneten werten und einer Spalte einsen für beta5\"\"\"\n",
-    "    X2 = df2.loc[:,['Q^3','Q^2n','Qn^2','n^3']].to_numpy(float)#,np.ones(df2.index.size).reshape((df2.index.size,1)),axis=1 )\n",
-    "    \n",
+    "    X2 = df2.loc[:,['Q^3','Q^2n','Qn^2','n^3']].to_numpy(float)\n",
     "    y2= df2['h'].to_numpy(float)\n",
     "    results['Q-P_fit'] = LinearRegression().fit(X2,y2)\n",
-    "    ax2.plot(df2.index.to_numpy(),results['Q-P_fit'].get(n).predict(X2))    \n",
+    "    #plotten der Punkte und der gefundnen Funktion mit \n",
+    "    ax2.errorbar(df2['Q'].to_numpy(),results['Q-P_fit'].get(n).predict(X2),fmt='b+')\n",
+    "    ax2.plot(df2['Q'].to_numpy(),results['Q-P_fit'].get(n).predict(X2))    \n",
     "    print(results['Q-h_fit'].get(n).coef_)\n",
-    "    print(results['Q-P_fit'].get(n).coef_)\n"
+    "    print(f'R^2: {results['Q-h_fit'].get(n).score(X,y)}')\n",
+    "    #print(results['Q-P_fit'].get(n).coef_)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "df = \n",
+    "Q | n | P | H \n",
+    "-------- | -------- | -------- | --\n",
+    "0   | 0.35  | 10 | 2 \n",
+    "0.5   | 0.35 | 15  | 1.8\n",
+    "1   | 0.35   |20| 1.5 \n",
+    "0   | 0.5  | 15 | 2 \n",
+    "0.5   | 0.5  | 20 | 1.8\n",
+    "1   | 0.5 | 25 | 1.5 \n",
+    "\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "X =  numpy.array([df.loc[:,'Q']**3, df.loc[:,'Q']**2 * df.loc[:,'n']...)\n",
+    "```\n",
+    "\n",
+    "Ziel einen Fit mit n als zweite Variable\n",
+    "multiple linear REgression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    LinearRegression(fit_intercept=False)\n",
+       "1    LinearRegression(fit_intercept=False)\n",
+       "2    LinearRegression(fit_intercept=False)\n",
+       "3    LinearRegression(fit_intercept=False)\n",
+       "4    LinearRegression(fit_intercept=False)\n",
+       "5    LinearRegression(fit_intercept=False)\n",
+       "6    LinearRegression(fit_intercept=False)\n",
+       "7    LinearRegression(fit_intercept=False)\n",
+       "8    LinearRegression(fit_intercept=False)\n",
+       "Name: Q-h_fit, dtype: object"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['Q-h_fit']"
    ]
   }
  ],
@@ -196,7 +291,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.5"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/P-Q_kennlinie_n_1150.csv b/P-Q_kennlinie_n_1150.csv
index 17c4686ea46c97d818da5e6ae2e78c4348610421..5b449ef2bc815e2e9edd0d8b09259b69c942461f 100644
--- a/P-Q_kennlinie_n_1150.csv
+++ b/P-Q_kennlinie_n_1150.csv
@@ -1,9 +1,9 @@
-0; 0,008553778589097394
-0,5000000000000004; 0,009918184287738552
-1; 0,010592719817377028
-1,5; 0,012305389017106205
-2; 0,013784178858083918
-2,5; 0,0145142806924074
-3,000000000000001; 0,015219651573475101
-3,4927536231884053; 0,015575221238937953
-4,0144927536231885; 0,016519174041297824
+0; 0,008537886872998918
+0,5; 0,009899419811077864
+1,0000000000000002; 0,011190010148022206
+1,5000000000000002; 0,012469377329894005
+2; 0,01365894509992599
+2,500000000000001; 0,014851101665854988
+3; 0,01570961773685864
+3,500000000000001; 0,016392738107326082
+4; 0,016905016363363917
diff --git a/P-Q_kennlinie_n_1500.csv b/P-Q_kennlinie_n_1500.csv
index 1dbf3236a71c5e89aff8ceb99439dcb35d82da74..4616f36c86f821775115079c3d262f95036b5e06 100644
--- a/P-Q_kennlinie_n_1500.csv
+++ b/P-Q_kennlinie_n_1500.csv
@@ -1,12 +1,12 @@
-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; 0,011689527737614336
+0; 0,011440768409818558
+0,5; 0,0135289461791184
+1,0000000000000002; 0,015691782331155146
+1,5000000000000002; 0,018070243340867626
+2; 0,020325447048658074
+2,500000000000001; 0,022186772391101284
+3; 0,024253589223867916
+3,500000000000001; 0,025954659998596835
+4; 0,027492746497636472
+4,5; 0,02874667166682421
+5,000000000000001; 0,029878989607667994
+5,498954920877941; 0,030565635005336184
diff --git a/P-Q_kennlinie_n_1850.csv b/P-Q_kennlinie_n_1850.csv
index f298fbbc2adca58fcd404adc1743521e2834b47a..e33e85eecc54dfbd5f2ae89e1ebeda00c9c23748 100644
--- a/P-Q_kennlinie_n_1850.csv
+++ b/P-Q_kennlinie_n_1850.csv
@@ -1,14 +1,14 @@
-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
+0; 0,016136606189967984
+0,5; 0,019315204031206734
+1,0000000000000002; 0,02263070251080293
+1,5000000000000002; 0,025937908127868753
+2; 0,02936731082374744
+2,500000000000001; 0,03280231562346067
+3; 0,035842276128012024
+3,500000000000001; 0,038920425104298495
+4; 0,04183406340965745
+4,5; 0,04445628605893387
+5,000000000000001; 0,04697175367576259
+5,5; 0,04882737516747407
+6; 0,0505658499249286
+6,500000000000001; 0,051739569265696395
diff --git a/P-Q_kennlinie_n_2200.csv b/P-Q_kennlinie_n_2200.csv
index e75633dbc94ce1d308e6178ffe2566bcda4633f1..68f4e65a07889cda2cbde5a084561718dc2607f3 100644
--- a/P-Q_kennlinie_n_2200.csv
+++ b/P-Q_kennlinie_n_2200.csv
@@ -1,17 +1,17 @@
-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
+0; 0,02322305229455715
+0,5; 0,027525578422479713
+1,0000000000000002; 0,03218706075511921
+1,5000000000000002; 0,03686511025007172
+2; 0,04184102966970199
+2,500000000000001; 0,04666415801434401
+3; 0,05139502513768979
+3,500000000000001; 0,05601772435280858
+4; 0,06045833632858488
+4,5; 0,06478152827953423
+5,000000000000001; 0,06879213443132216
+5,5; 0,07256631554072465
+6; 0,07599939214885622
+6,500000000000001; 0,07870372221586039
+7; 0,08111557392637662
+7,499999999999999; 0,0829891791860873
+7,999999999999999; 0,08452519423589197
diff --git a/P-Q_kennlinie_n_2550.csv b/P-Q_kennlinie_n_2550.csv
index c8ba5ffd85c87e42a7e58ac5deb270a4dd8beb04..3c360562994e8d1c9a0e4eef283203336c11a295 100644
--- a/P-Q_kennlinie_n_2550.csv
+++ b/P-Q_kennlinie_n_2550.csv
@@ -1,19 +1,19 @@
-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
+0; 0,03218783351120602
+0,5; 0,038288451275634156
+1,0000000000000002; 0,04446409900530038
+1,5000000000000002; 0,050882852020087854
+2; 0,05771812206022528
+2,500000000000001; 0,06421542327506816
+3; 0,0709853537896264
+3,500000000000001; 0,07767505612754526
+4; 0,08420797385205982
+4,5; 0,09054103645338936
+5,000000000000001; 0,09665032874820119
+5,5; 0,1026178541327401
+6; 0,10812667060676749
+6,500000000000001; 0,11321557521001693
+7; 0,11784039417570107
+7,499999999999999; 0,12191488935500511
+7,999999999999999; 0,12519531750103585
+8,500000000000002; 0,12788207530615348
+9; 0,12978543579230795
diff --git a/P-Q_kennlinie_n_2900.csv b/P-Q_kennlinie_n_2900.csv
index cb960ca1a59845f2a3fe55f2f01fd6d8ecbdbcde..1554ee4c9800bab84284f0e1d670123bc761add3 100644
--- a/P-Q_kennlinie_n_2900.csv
+++ b/P-Q_kennlinie_n_2900.csv
@@ -1,15 +1,15 @@
--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
+0; 0,04439701173959426
+0,5; 0,0519696063869548
+1,0000000000000002; 0,05984013725737641
+1,5000000000000002; 0,06827035791256908
+2; 0,07702001090267277
+2,500000000000001; 0,08575650387146205
+3; 0,09485439198265758
+3,500000000000001; 0,104132062528992
+4; 0,11323101165146136
+4,5; 0,1221545929680621
+5,000000000000001; 0,13075641472070765
+5,5; 0,13848034679404028
+6; 0,14548892865427732
+6,500000000000001; 0,151098886260651
+7; 0,1553688883619237
diff --git a/P-Q_kennlinie_n_3250.csv b/P-Q_kennlinie_n_3250.csv
index 8c9dc65273ccc9030bbe5bc8a17c390f0203c736..61367c6758b5099409e78b61afcbddd0feff121f 100644
--- a/P-Q_kennlinie_n_3250.csv
+++ b/P-Q_kennlinie_n_3250.csv
@@ -1,9 +1,8 @@
-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
+0; 0,06044823906083195
+0,5; 0,06868836051264836
+1,0000000000000002; 0,07880924405782573
+1,5000000000000002; 0,08995346439160459
+2; 0,10143919246147354
+2,500000000000001; 0,11258338688728775
+3; 0,12304350303044198
+3,500000000000001; 0,1323581971130258
diff --git a/P-Q_kennlinie_n_3600.csv b/P-Q_kennlinie_n_3600.csv
index 0b53e5f1d2af7cc0faeb550be6c689bb9ab7ad19..4c8ba8a763ec824e327cf926149ed35ae292908f 100644
--- a/P-Q_kennlinie_n_3600.csv
+++ b/P-Q_kennlinie_n_3600.csv
@@ -1,20 +1,5 @@
-0; 0,07797775530839224
-0,49999999999999956; 0,08835704358817364
-0,9999999999999996; 0,09724018339944762
-1,4999999999999996; 0,10640934974620869
-1,9999999999999996; 0,11454062847588425
-2,4999999999999987; 0,12076731437964439
-2,9999999999999996; 0,12662293238447436
-3,4999999999999996; 0,13318792869404755
-3,9999999999999987; 0,13895234162121622
-4,5; 0,14359412859753434
-5; 0,14819009100101077
-5,5; 0,15219353744931347
-6; 0,15528793386036088
-6,5; 0,15692618806878705
-6,999999999999998; 0,15692644845164982
-7,5; 0,15757330637007233
-8; 0,15757330637007066
-8,5; 0,15757791699815665
-9; 0,15611729019211512
-9,499999999999998; 0,1553083923151805
+0; 0,07803628601920945
+0,5; 0,0883283082491988
+1,0000000000000002; 0,09759317541298186
+1,5000000000000002; 0,10662494867268643
+2; 0,11509564832564237
diff --git a/P-Q_kennlinie_n_750.csv b/P-Q_kennlinie_n_750.csv
index 04b36e680bb9772c6a3ae695dfed94a85ad3229f..4c6fef3973425dcbf49df3fa81698a1f7b219620 100644
--- a/P-Q_kennlinie_n_750.csv
+++ b/P-Q_kennlinie_n_750.csv
@@ -1,6 +1,6 @@
-0,4886486500368532; 0,004870476593899609
-0,9999999999999996; 0,004970368805329972
-1,4999999999999987; 0,005489739175194852
-1,9999999999999996; 0,006135234247208798
-2,4999999999999996; 0,006135642605529401
--0,0002824996597166063; 0,004018275159805018
+0; 0,003500533617929541
+0,5; 0,004183563422050118
+1,0000000000000002; 0,0048665955472005085
+1,5000000000000002; 0,0055494008744861045
+2; 0,006061385094532745
+2,500000000000001; 0,006232657409702996
diff --git a/h-Q_kennlinie_n_1150.csv b/h-Q_kennlinie_n_1150.csv
index 76104f5df0acf2cf206b80ac90c26cf6ea3e657f..93f19c7a7982cd2f37585f0b1519f4a4ca2a122a 100644
--- a/h-Q_kennlinie_n_1150.csv
+++ b/h-Q_kennlinie_n_1150.csv
@@ -1,9 +1,9 @@
--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
+-1,1102230246251565e-16; 0,8629651506201981
+0,4999999999999998; 0,8917970744668846
+1; 0,8917898245643965
+1,4999999999999998; 0,8736010576079529
+2; 0,8371846798875033
+2,5; 0,7634430132602787
+3; 0,6825981873064375
+3,5; 0,5724641480685424
+3,999999999999999; 0,4474407563521918
diff --git a/h-Q_kennlinie_n_1500.csv b/h-Q_kennlinie_n_1500.csv
index 9a5ae8b764ef479ff2ef1249e1c12e51bd45c1d8..82f390d2f2de371ce422e235e3d61d60ee67548a 100644
--- a/h-Q_kennlinie_n_1500.csv
+++ b/h-Q_kennlinie_n_1500.csv
@@ -1,11 +1,12 @@
--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
+-1,1102230246251565e-16; 1,4468953041937347
+0,4999999999999998; 1,501434330897112
+1; 1,5105950974601257
+1,4999999999999998; 1,5014940839177395
+2; 1,4741986200992265
+2,5; 1,4286848442494993
+3; 1,364768112501432
+3,5; 1,2640253637148682
+3,999999999999999; 1,1460703786647919
+4,5; 1,0105802984394856
+5; 0,8474808382358994
+5,490338164251207; 0,654660745112821
diff --git a/h-Q_kennlinie_n_1850.csv b/h-Q_kennlinie_n_1850.csv
index 081f7d5d71fa77c0a79b4114a31e8a49b021e92f..72dc2d76a21529d48bb59a1cedf3de4e2b4acf38 100644
--- a/h-Q_kennlinie_n_1850.csv
+++ b/h-Q_kennlinie_n_1850.csv
@@ -1,14 +1,14 @@
--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
+-1,1102230246251565e-16; 2,229492764323683
+0,4999999999999998; 2,274930990555826
+1; 2,2931925575900767
+1,4999999999999998; 2,293192482700606
+2; 2,2749928591094717
+2,5; 2,238595553351484
+3; 2,1840616432086026
+3,5; 2,111065073974438
+3,999999999999999; 2,0044475515781945
+4,5; 1,8738933776833644
+5; 1,7301731764760255
+5,499999999999999; 1,5558226093316438
+6; 1,3650973361853929
+6,5; 1,146513740391745
diff --git a/h-Q_kennlinie_n_2200.csv b/h-Q_kennlinie_n_2200.csv
index ca894e59a2ed5ab2dcf47dcf79da944cb9afeee2..28d3177287ccb199908bcd46399d2b573f50be1c 100644
--- a/h-Q_kennlinie_n_2200.csv
+++ b/h-Q_kennlinie_n_2200.csv
@@ -1,41 +1,17 @@
-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
+-1,1102230246251565e-16; 3,1372840372120674
+0,4999999999999998; 3,212036731224723
+1; 3,239589486115272
+1,4999999999999998; 3,2486894565858186
+2; 3,2396365205938897
+2,5; 3,202233783668426
+3; 3,157658473241077
+3,5; 3,101246071102201
+3,999999999999999; 3,0145586720273227
+4,5; 2,9123067954646533
+5; 2,7748240316208905
+5,499999999999999; 2,6112295956401894
+6; 2,4297940564375633
+6,5; 2,2112380563915712
+6,999999999999999; 1,974555711232405
+7,5; 1,7201295657848856
+8; 1,4525042429084998
diff --git a/h-Q_kennlinie_n_2550.csv b/h-Q_kennlinie_n_2550.csv
index b61c13c1223cce21dfd1e6780a66574d26647ed9..4b7c36f24b3d7ee7d07413b16bf6f9e47325bd51 100644
--- a/h-Q_kennlinie_n_2550.csv
+++ b/h-Q_kennlinie_n_2550.csv
@@ -1,19 +1,19 @@
-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
+-1,1102230246251565e-16; 4,2064382752510605
+0,4999999999999998; 4,30450086352692
+1; 4,358851204428521
+1,4999999999999998; 4,3679858239811225
+2; 4,358865888652977
+2,5; 4,349774043858641
+3; 4,313444106930641
+3,5; 4,26776224480908
+3,999999999999999; 4,195244854410194
+4,5; 4,103396892748921
+5; 3,9980847217756263
+5,499999999999999; 3,858510370973507
+6; 3,689192069835256
+6,5; 3,494425537425405
+6,999999999999999; 3,2772123516293536
+7,5; 3,030525312983251
+8; 2,757073687442107
+8,5; 2,4662649970442825
+8,999999999999998; 2,1379387575175546
diff --git a/h-Q_kennlinie_n_2900.csv b/h-Q_kennlinie_n_2900.csv
index 797cda27aca7ed3a2c5efd2ad9c6981796d82813..13f185df1a70c4e64705371f1228142cdd1b00e3 100644
--- a/h-Q_kennlinie_n_2900.csv
+++ b/h-Q_kennlinie_n_2900.csv
@@ -1,15 +1,16 @@
--4,440892098500626e-16; 5,412584679520581
-0,5; 5,524874705482196
-1,0000000000000004; 5,616838100975842
-1,5000000000000004; 5,61780093798853
-2,0000000000000004; 5,61780093798853
-2,5000000000000004; 5,61780093798853
-3,0000000000000004; 5,582907789513632
-3,4999999999999996; 5,583164865578508
-3,9999999999999996; 5,519988110888066
-4,5; 5,426891570463267
-5; 5,34854689756372
-5,499999999999998; 5,211422573164066
-6; 5,014555136264578
-6,5; 4,785842895393786
-7,011261261261261; 4,475285804163695
+-1,1102230246251565e-16; 5,428132383343174
+0,4999999999999998; 5,550987417321458
+1; 5,614811090301317
+1,4999999999999998; 5,632881698775706
+2; 5,64198168930718
+2,5; 5,6329259032980055
+3; 5,605698519557499
+3,5; 5,578283084480311
+3,999999999999999; 5,523840457783772
+4,5; 5,458807857823196
+5; 5,353997955719134
+5,499999999999999; 5,214545582486976
+6; 5,027207492310509
+6,5; 4,78657365258436
+6,999999999999999; 4,498227942406172
+7,5; 4,177532982872913
diff --git a/h-Q_kennlinie_n_3250.csv b/h-Q_kennlinie_n_3250.csv
index 9ffbbf3d8ca5080f15e4aac874d0de878494c7cf..18d3fa8de2e0875db6882074d681c66175b7c4dd 100644
--- a/h-Q_kennlinie_n_3250.csv
+++ b/h-Q_kennlinie_n_3250.csv
@@ -1,9 +1,9 @@
-0; 6,823984526112145
-0,49999999999999956; 6,915808679929409
-0,9999999999999996; 7,013342180936892
-1,4999999999999996; 7,085106334057199
-1,9999999999999996; 7,119922630560884
-2,4999999999999987; 7,119940663872695
-2,9999999999999996; 7,027937848152339
-3,4999999999999996; 6,9089153721163985
-3,9999999999999987; 6,73716927309715
+-1,1102230246251565e-16; 6,852997637330134
+0,4999999999999998; 6,929225265783781
+1; 7,016047605879697
+1,4999999999999998; 7,079021838147195
+2; 7,116176887495201
+2,5; 7,107076082574247
+3; 7,034060629801142
+3,5; 6,906988721065964
+3,999999999999999; 6,712593379642504
diff --git a/h-Q_kennlinie_n_3600.csv b/h-Q_kennlinie_n_3600.csv
index e74dc5ad581c44c5292df711e70a8f1d31d9e11a..bab9c1a4cf4b0fb31abdde8be46727a9224de9c0 100644
--- a/h-Q_kennlinie_n_3600.csv
+++ b/h-Q_kennlinie_n_3600.csv
@@ -1,2 +1,2 @@
-0; 8,434235976789132
-0,49999999999999956; 8,477756290288267
+-1,1102230246251565e-16; 8,40382272593021
+0,4999999999999998; 8,494627721618599
diff --git a/h-Q_kennlinie_n_750.csv b/h-Q_kennlinie_n_750.csv
index ddef8ef523decc7f59fb50ea5c23228e17fb051d..fba16efdc0b1160343e47f475ab1f92bd53ab695 100644
--- a/h-Q_kennlinie_n_750.csv
+++ b/h-Q_kennlinie_n_750.csv
@@ -1,6 +1,6 @@
-0; 0,3633918875758546
-0,49999999999999956; 0,3980006387735546
-1,0000000000000004; 0,3976387130361392
-1,5000000000000004; 0,32885133307773096
-2,0000000000000004; 0,27687004099649926
-2,5000000000000004; 0,207312673506296
+-1,1102230246251565e-16; 0,38066671588895673
+0,4999999999999998; 0,38674874470229526
+1; 0,3735312525634651
+1,4999999999999998; 0,3412493957260363
+2; 0,2870615370114695
+2,5; 0,2047825701479553