diff --git a/hp4155/I-V_new.ipynb b/hp4155/I-V_new.ipynb index a82886143eb6aa61ba354e7565d4c2fc285f3dc8..ef2bbf5ca4fa5fd75e9c4523e6bbc2c61ba3143b 100644 --- a/hp4155/I-V_new.ipynb +++ b/hp4155/I-V_new.ipynb @@ -237,7 +237,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/hp4155/measurements.py b/hp4155/measurements.py index eb8051490ac8035ed1153de4db561fcfd3810da2..6a840af0d761ebe354d5ed0622f106900b07cca7 100644 --- a/hp4155/measurements.py +++ b/hp4155/measurements.py @@ -62,7 +62,8 @@ def I_V_Measurement(start,stop,step): #exporting the data frame in an excel file file_name ="results.csv" - path = r"C:\Users\user\Desktop" + path = r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros" + #r"C:\Users\user\Desktop" directory = os.path.join(path,file_name) df.to_csv(directory) @@ -141,7 +142,7 @@ def stress_sampling(V2_stress=10,V3_stress=3,stress_time=30,V2_sampling=10,V3_sa #prepare full measurement -def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),comp=10,distances=(5,10,15,25,45),time='MED'): +def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),comp=10,distances=(5,10,15,25,45),time='MED',innen=0): #connect to the device device = module.HP4155a('GPIB0::17::INSTR') @@ -153,6 +154,10 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com plt.ylabel('Current(A)') plt.title("CTLM plot") + #lists for appending all data values + ctlm_voltage = [] + ctlm_current = [] + ctlm_resistance = [] #execute five measurements for j in range(len(distances)): @@ -202,6 +207,9 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com voltage_values = device.return_data('V3') current_values = device.return_data('I3') + + ctlm_voltage.append(voltage_values) + ctlm_current.append(current_values) resistance_values=[] @@ -221,13 +229,17 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com print(df) file_name = field_name+"_CTLM_"+str(j+1)+".txt" - path =r"C:\Users\user\Desktop" + path =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros" directory = os.path.join(path,file_name) #export DataFrame to text file (keep header row and index column) f=open(directory, 'a') f.write('title\n') df_string = df.to_string() f.write(df_string) + + #plot diagramm + plt.legend() + plt.show() #wait for confirmation from user after a measurement is done while True: @@ -235,8 +247,6 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com if answer == "": break - #close the connection and plot all the diagramms - plt.legend() - plt.show() + #close the connection and plot all the diagramms del device diff --git a/hp4155/module.py b/hp4155/module.py index de2918e14a38bfcbe462ab3405a588b1a9f69c11..70b4cca0ea46a8461deef64822f70138db92ddd9 100644 --- a/hp4155/module.py +++ b/hp4155/module.py @@ -182,6 +182,7 @@ class HP4155a(object): #set normal compliance for VAR1 and VAR2 def comp(self,variable,value): + """ """ command = f":PAGE:MEAS:{variable}:COMP {value}" self.inst.write(command) diff --git a/hp4155/working_examples/pandas.ipynb b/hp4155/working_examples/pandas.ipynb index 60f736abb5c0ac6e0695734a96d1cf88e23c5f03..faff4cfe1217caaa5b6f2339ea0548c703a57fe5 100644 --- a/hp4155/working_examples/pandas.ipynb +++ b/hp4155/working_examples/pandas.ipynb @@ -2,10 +2,22 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "f3bb2a53-f571-4da3-b09f-4c8ee8c75a83", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pandas'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_6709/1825645653.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" + ] + } + ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -135,30 +147,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "1f89f490-e901-43cb-b1f4-cda5b4cf6ae8", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "coefficient of determination: 0.9873880135298887\n", - "intercept: -0.00043234755330870747\n", - "slope: [0.00243783]\n" + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_6709/2398863689.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvoltage_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#column matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#create a model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ @@ -175,11 +177,13 @@ "\n", "plt.figure()\n", "plt.plot(voltage_values,current_values,label='real curve') \n", - "plt.plot(x,model.coef_*x+model.intercept_,label='linear')\n", "plt.xlabel('Voltage(V)')\n", "plt.ylabel('Current(A)')\n", "plt.title(\"I-V plot\")\n", "plt.legend()\n", + "plt.show()\n", + "plt.plot(x,model.coef_*x+model.intercept_,label='linear')\n", + "plt.legend()\n", "plt.show()" ] },