From d8be71fdd56efff3b7d9f33cf51f537542c84c34 Mon Sep 17 00:00:00 2001 From: Steinmann <steinmann@fst.maschinenbau.tu-darmstadt.de> Date: Thu, 21 Nov 2024 19:17:22 +0100 Subject: [PATCH] created combined numpy array of Q,n,H,P --- Kennlinien_und_Fitting.ipynb | 147 ++++++++++++++++------------------- 1 file changed, 69 insertions(+), 78 deletions(-) diff --git a/Kennlinien_und_Fitting.ipynb b/Kennlinien_und_Fitting.ipynb index c32f4ed..5a683ea 100644 --- a/Kennlinien_und_Fitting.ipynb +++ b/Kennlinien_und_Fitting.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +64,10 @@ " 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", + " if y_Achse=='h':\n", + " sorted_set = dataframe.set_axis(['Q','H'],axis='columns')\n", + " elif y_Achse=='P':\n", + " sorted_set =dataframe.set_axis(['Q','P'],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", @@ -75,10 +78,10 @@ " sorted_set['Q^2'] = sorted_set['Q'] **2 \n", " sorted_set['Q^3'] = sorted_set['Q'] **3\n", " sorted_set['n_rel'] = drehzahl/3600\n", - " sorted_set['n^2'] = (sorted_set['n_rel']*3600)**2\n", - " sorted_set['n^3'] = (sorted_set['n_rel']*3600)**3\n", - " 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['n^2'] = sorted_set['n_rel']**2\n", + " sorted_set['n^3'] = sorted_set['n_rel']**3\n", + " sorted_set['Qn'] = sorted_set['n_rel']*sorted_set['Q']\n", + " sorted_set['Q^2n'] = sorted_set['Q^2']*sorted_set['n_rel']\n", " sorted_set['Qn^2'] = sorted_set['Q']*sorted_set['n^2']\n", "\n", " return sorted_set" @@ -90,42 +93,41 @@ "metadata": {}, "outputs": [], "source": [ - "def combine_csvs():\n", - " for i in os.listdir(:*.csv):\n", + "#def combine_csvs():\n", + " #with open ('learning-python'):\n", + " #for i in os.listdir(.csv):\n", " \n", - " pd.read_csv(i)\n", + " #pd.read_csv(i)\n", "\n" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 90, "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" - ] - } - ], + "outputs": [], "source": [ - "print(csv_einlesen('h',750))" + "def combine_csvs():\n", + " import numpy as np\n", + " dz= [750,1150,1500,1850,2200,2550,2900,3250,3600]\n", + " array = np.array(float)\n", + " for z in dz:\n", + " df =csv_einlesen('h',z)\n", + " df_P=csv_einlesen('P',z)\n", + " if dz.index(z)==0:\n", + " array = df.loc[:,['Q','n_rel','H']].to_numpy(float)\n", + " array = np.append(array,df_P.loc[:,['P']].to_numpy(float),axis=1)\n", + " continue\n", + " if len(df.index)<len(df_P.index):\n", + " for i in range(len(df_P.index)-len(df.index)):\n", + " df_P.drop(len(df.index)+i,inplace=True)\n", + "\n", + " elif len(df.index)>len(df_P.index):\n", + " for i in range(len(df.index)-len(df_P.index)):\n", + " df.drop(len(df_P.index)+i,inplace=True)\n", + "\n", + " array = np.append(array, np.append(df.loc[:,['Q','n_rel','H']].to_numpy(),df_P.loc[:,['P']].to_numpy(),axis=1), axis=0)\n", + " return array\n" ] }, { @@ -137,44 +139,9 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-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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", - "text/plain": [ - "<Figure size 640x480 with 2 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -182,8 +149,7 @@ "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\n", + "results['n'] = [750,1150,1500,1850,2200,2550,2900,3250,3600]\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", @@ -199,18 +165,18 @@ "ax2.set_ylabel('$P$ in kW')\n", "\n", "for n in results.index:\n", - " df = csv_einlesen('h',drehz[n])\n", + " df = csv_einlesen('H',results['n'].get(n))\n", " X = df.loc[:,['Q^2','Qn','n^2']].to_numpy(float)\n", - " y = df['h'].to_numpy(float)\n", + " y = df['H'].to_numpy(float)\n", " results['Q-h_fit'] = LinearRegression(fit_intercept=False).fit(X,y)\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", + " df2 = csv_einlesen('P',results['n'].get(n))\n", " X2 = df2.loc[:,['Q^3','Q^2n','Qn^2','n^3']].to_numpy(float)\n", - " y2= df2['h'].to_numpy(float)\n", + " y2= df2['H'].to_numpy(float)\n", " results['Q-P_fit'] = LinearRegression().fit(X2,y2)\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", @@ -221,6 +187,31 @@ "\n" ] }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "array() missing required argument 'object' (pos 0)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[109], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(combine_csvs(),columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mQ\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mP\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m----> 2\u001b[0m X\u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39mindex:\n\u001b[0;32m 4\u001b[0m X\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mQ\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mget(i)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n", + "\u001b[1;31mTypeError\u001b[0m: array() missing required argument 'object' (pos 0)" + ] + } + ], + "source": [ + "df = pd.DataFrame(combine_csvs(),columns=['Q','n','H','P'])\n", + "for i in df.index:\n", + " X=np.array(df['Q'].get(i)**2)\n", + "\n", + "print(X)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -298,7 +289,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.12.5" } }, "nbformat": 4, -- GitLab