sensor data

sensor/Sensor0Averages.csv

@@ -0,0 +1,24 @@
+x,y
+1.03,1.676767677
+2.02,11.81818182
+3.05,29.44444444
+4.02,55.29292929
+5.05,94.92929293
+6.04,149.4141414
+7.07,220.4747475
+8,294.1010101
+9.03,377.7575758
+10.02,463.5555556
+11.05,545.1515152
+12.02,621.4848485
+13.05,682.2626263
+14.04,732.4040404
+15.07,775.030303
+16.2,808.7272727
+17.23,835.8080808
+18.24,855.2626263
+19.25,869.5757576
+20.22,881.1010101
+21.25,889.8888889
+22.24,896.1010101
+23.27,901.2020202

sensor/Sensor2Averages.csv

@@ -0,0 +1,24 @@
+x,y
+1.03,0.1919191919
+2.02,8.383838384
+3.05,27.35353535
+4.02,53.52525253
+5.05,96.84848485
+6.04,154.2424242
+7.07,229.3333333
+8,298.5050505
+9.03,383.1111111
+10.02,463.040404
+11.05,541.1515152
+12.02,606.9292929
+13.05,663.2828283
+14.04,710.6363636
+15.07,747.9292929
+16.2,776.9393939
+17.23,801.4848485
+18.24,818.7474747
+19.25,832.1717172
+20.22,840.6262626
+21.25,848.5050505
+22.24,855.1919192
+23.27,860.1414141

sensor/Sensor3Averages.csv

@@ -0,0 +1,20 @@
+x,y
+1.03,15.22222222
+2.02,28.82828283
+3.05,49.01010101
+4.02,78.04040404
+5.05,118.8484848
+6.04,163.5454545
+7.07,217.1515152
+8,266.1414141
+9.03,323.4646465
+10.02,381.1818182
+11.05,445.1515152
+12.02,506.5959596
+13.05,576.7474747
+14.04,646.9292929
+15.07,721.7272727
+16.2,791.4545455
+17.23,859.7777778
+18.24,916.9494949
+19.25,966.8383838

sensor/Sensor3NewAverages.csv

@@ -0,0 +1,25 @@
+x,y
+1.03,23.73737374
+2.02,34.61616162
+3.05,53.27272727
+4.02,79.4040404
+5.05,115.9191919
+6.04,157.8585859
+7.07,206.4545455
+8,253.9393939
+9.03,317.9292929
+10.02,359.3030303
+11.05,418.020202
+12.02,465.5656566
+13.05,519.2020202
+14.04,570.5656566
+15.07,621.3333333
+16.2,673.0909091
+17.23,714.8585859
+18.24,752.7070707
+19.25,787.3838384
+20.22,815.2424242
+21.25,836.4646465
+22.24,856.1414141
+23.27,872.8080808
+24.2,884.2828283

sensor/analogFitter-Exponential.py

@@ -0,0 +1,33 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+import pandas as pd
+
+# Data from the chart
+data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\oldData\data.csv')
+x = data["x"].values
+y = data["y"].values
+
+# Define a possible fitting model (exponential fit in this case)
+def exponential(x, a, b, c):
+    return a * np.exp(b * x) + c
+
+# Perform the curve fitting
+params, covariance = curve_fit(exponential, x, y)
+
+# Plot the data and the fitting curve
+x_fit = np.linspace(2, 20, 100)
+y_fit = exponential(x_fit, *params)
+
+plt.scatter(x, y, color='blue', label='Data points')
+plt.plot(x_fit, y_fit, color='red', label='Exponential fit')
+plt.xlabel('x')
+plt.ylabel('y')
+plt.legend()
+plt.title('Exponential Fit')
+plt.grid(True)
+plt.show()
+
+# Output the fitted parameters
+print("Fitted parameters:")
+print(f"a = {params[0]}, b = {params[1]}, c = {params[2]}")

sensor/analogFitter-Logistic3Parameters.py

@@ -0,0 +1,31 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+
+# --- Load your CSV ---
+# If you exported from Google Sheets with headers "x,y":
+data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\dataPrecise.csv')
+x = data["x"].values
+y = data["y"].values
+print(x, y)
+# --- Define logistic function with 3 parameters (a, b, c) ---
+def logistic(x, a, b, c):
+    return a / (1 + np.exp(-b * (x - c)))
+    # return c - ((1-b) * np.log((a - x)/x))
+
+# --- Fit the curve ---
+# p0 are initial guesses for (a, b, c) — tweak depending on your data
+params, covariance = curve_fit(logistic, x, y, p0=[max(y), 1, np.median(x)])
+
+a_fit, b_fit, c_fit = params
+print(f"Fitted parameters:\na = {a_fit}\nb = {b_fit}\nc = {c_fit}")
+
+# --- Plot data vs fitted curve ---
+x_fit = np.linspace(min(x), max(x), 200)  # smooth x range
+y_fit = logistic(x_fit, *params)
+
+plt.scatter(x, y, label="Data")
+plt.plot(x_fit, y_fit, 'r-', label="Fitted Logistic Curve")
+plt.legend()
+plt.show()

sensor/analogFitter-Logistic4Parameters.py

@@ -0,0 +1,78 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+
+# --- Load your CSV ---
+data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\dataPrecise.csv')
+x = data["x"].values
+y = data["y"].values
+
+# --- Define 4-parameter logistic function (A, K, B, C) ---
+def logistic_4pl(x, A, K, B, C):
+    return A + (K - A) / (1.0 + np.exp(-B * (x - C)))
+
+# --- Initial guesses ---
+A0 = np.min(y)
+K0 = np.max(y)
+B0 = 1.0
+C0 = np.median(x)
+p0 = [A0, K0, B0, C0]
+
+# --- Fit the curve ---
+params, covariance = curve_fit(logistic_4pl, x, y, p0=p0, maxfev=10000)
+A_fit, K_fit, B_fit, C_fit = params
+
+print(f"Fitted parameters:")
+print(f"A (lower asymptote) = {A_fit:.4f}")
+print(f"K (upper asymptote) = {K_fit:.4f}")
+print(f"B (slope)          = {B_fit:.4f}")
+print(f"C (midpoint)       = {C_fit:.4f}")
+
+# --- Fitted curve ---
+x_fit = np.linspace(min(x), max(x), 400)
+y_fit = logistic_4pl(x_fit, *params)
+
+# --- Plot data vs fitted curve ---
+plt.figure(figsize=(8, 5))
+plt.scatter(x, y, label="Data")
+plt.plot(x_fit, y_fit, 'r-', label="Fitted Logistic Curve")
+plt.xlabel("x")
+plt.ylabel("y")
+plt.legend()
+plt.title("4-Parameter Logistic Fit")
+plt.show()
+
+# --- Goodness of fit ---
+y_pred = logistic_4pl(x, *params)
+offsets = y_pred - y
+ss_res = np.sum((y - y_pred) ** 2)
+ss_tot = np.sum((y - np.mean(y)) ** 2)
+r_squared = 1 - (ss_res / ss_tot)
+rmse = np.sqrt(np.mean((y - y_pred) ** 2))
+mae = np.mean(np.abs(y - y_pred))
+
+n = len(y)
+p = len(params)
+r2_adj = 1 - (1 - r_squared) * (n - 1) / (n - p - 1)
+
+print("\nGoodness of fit:")
+# for value in y_pred: 
+#     print(value)
+# for value in y:
+#     print (value)
+
+print(f"R²          = {r_squared:.4f}")
+print(f"Adjusted R² = {r2_adj:.4f}")
+print(f"RMSE        = {rmse:.4f}")
+print(f"MAE         = {mae:.4f}")
+
+# --- Residual plot ---
+residuals = y - y_pred
+plt.figure(figsize=(8, 4))
+plt.scatter(x, residuals)
+plt.axhline(0, color='red', linestyle='--')
+plt.xlabel('x')
+plt.ylabel('Residual (y - y_fit)')
+plt.title('Residual Plot')
+plt.show()

sensor/analogFitter-Logistic5Parameters.py

@@ -0,0 +1,69 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+
+# --- Load CSV ---
+data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\Sensor3DataNew.csv')
+x = data["x"].values
+# Stack them into a 2D array (shape: 5 × 100)
+# stacked = np.column_stack((data["y"].values, data["y2"].values, data["y3"].values, data["y4"].values, data["y5"].values))
+
+# Take the average across the 5 columns for each row
+# y = np.mean(data["y"], axis=1)
+# print(y)
+y = data["y"].values
+# --- Define 5-parameter logistic function ---
+def logistic_5pl(x, A, K, B, C, nu):
+    # Clip to avoid overflow in exp for large values
+    z = -B * (x - C)
+    z = np.clip(z, -500, 500)
+    return A + (K - A) / (1.0 + np.exp(z))**(1/nu)
+
+# --- Initial parameter guesses ---
+A0 = np.min(y)          # lower asymptote
+K0 = np.max(y)          # upper asymptote
+B0 = 1.0                # slope
+C0 = np.median(x)       # inflection point
+nu0 = 1.0               # asymmetry (1 = symmetric, same as 4PL)
+p0 = [A0, K0, B0, C0, nu0]
+
+# --- Fit the curve ---
+params, covariance = curve_fit(
+    logistic_5pl, x, y, p0=p0, maxfev=20000,
+    bounds=([-np.inf, -np.inf, 0, -np.inf, 0.1], [np.inf, np.inf, np.inf, np.inf, 10])
+)
+
+A_fit, K_fit, B_fit, C_fit, nu_fit = params
+print("Fitted parameters:")
+print(f"A  = {A_fit}")
+print(f"K  = {K_fit}")
+print(f"B  = {B_fit}")
+print(f"C  = {C_fit}")
+print(f"nu = {nu_fit}")
+
+# --- Predicted values ---
+y_pred = logistic_5pl(x, *params)
+
+# --- Goodness of fit (R²) ---
+residuals = y - y_pred
+ss_res = np.sum(residuals**2)
+ss_tot = np.sum((y - np.mean(y))**2)
+r_squared = 1 - (ss_res / ss_tot)
+print(f"\nGoodness of fit: R² = {r_squared:.6f}")
+
+# --- Plot ---
+x_fit = np.linspace(min(x), max(x), 400)
+y_fit = logistic_5pl(x_fit, *params)
+y_pred = logistic_5pl(x, *params)
+
+
+plt.figure(figsize=(8, 5))
+plt.scatter(x, y, label="Data", color="blue")
+plt.plot(x_fit, y_fit, 'r-', label="5PL Fit")
+plt.title("5-Parameter Logistic Fit")
+plt.xlabel("x")
+plt.ylabel("y")
+plt.legend()
+plt.grid(True, linestyle="--", alpha=0.5)
+plt.show()

sensor/analogFitter-Polynomial.py

@@ -0,0 +1,19 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+
+# --- Load your CSV ---
+# If you exported from Google Sheets with headers "x,y":
+data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\oldData\data.csv')
+x = data["x"].values
+y = data["y"].values
+coeffs = np.polyfit(x, y, deg=5)  # 5th degree polynomial
+poly = np.poly1d(coeffs)
+print(poly)
+x_smooth = np.linspace(0, 20, 200)
+y_smooth = poly(x_smooth)
+
+plt.scatter(x, y, label = "data")
+plt.plot(x_smooth, y_smooth, 'r-', label="polynomial fit")
+plt.show()

sensor/data.csv

@@ -0,0 +1,21 @@
+x,y,y2,y3,y4,y5,,Range
+1.03,0.9494949495,21.45454545,71.21212121,75.8989899,78.49494949,,77.54545455
+2.02,10.29292929,21.19191919,74.22222222,75,78.5959596,,68.3030303
+3.05,27.41414141,26.88888889,72.85858586,74.50505051,50.41666667,,47.61616162
+4.02,53.62626263,51.61616162,51.93939394,73.14141414,69.34343434,,21.52525253
+5.05,92.8989899,90.23232323,90.91919192,91.26262626,92.19191919,,2.666666667
+6.04,143.4747475,141.2525253,142.1414141,143.2424242,144.4444444,,3.191919192
+7.07,215.9090909,210.3636364,211.8484848,213.3585859,215.3131313,,5.545454545
+8,289.6464646,282.5858586,283.9292929,287.5757576,290.1515152,,7.565656566
+9.03,372.020202,368.4444444,366.6363636,372.2222222,372.1818182,,5.585858586
+10.02,453.020202,448.6161616,447.5656566,455.3131313,454.5656566,,7.747474747
+11.05,534.7575758,529.6666667,529.7070707,536.0909091,536.9090909,,7.242424242
+12.02,604.7575758,596.010101,598.7373737,604.2626263,607.2323232,,11.22222222
+13.05,671.4444444,661.1919192,661.4949495,668.3434343,671.6161616,,10.42424242
+14.04,715.2222222,712.1414141,712.5050505,718.3232323,720.4141414,,8.272727273
+15.07,760.1717172,755.2424242,758.4343434,760.1212121,763.1313131,,7.888888889
+16.2,790.2626263,791.4343434,807.4747475,795.7171717,800.7070707,,17.21212121
+17.23,817.7575758,818.2727273,823.7878788,822.2222222,822.3636364,,6.03030303
+18.24,837.010101,837.7777778,840.6969697,840.7575758,842.2020202,,5.191919192
+19.25,852.8787879,853.5858586,855.7676768,856.3535354,856.4343434,,3.555555556
+20.22,864.4040404,864.8282828,866.6464646,867.7979798,867.7777778,,3.393939394

sensor/dataNonTaped.csv

@@ -0,0 +1,24 @@
+x,y
+1.03,1.616161616
+2.02,8.333333333
+3.05,25.11111111
+4.02,49.5959596
+5.05,87.49494949
+6.04,137.4343434
+7.07,204.6565657
+8,277.8585859
+9.03,360.2323232
+10.02,438.9191919
+11.05,519.5858586
+12.02,585.5050505
+13.05,650.3434343
+14.04,699.0808081
+15.07,741.6464646
+16.2,771.6262626
+17.23,799.4646465
+18.24,819.6262626
+19.25,836.010101
+20.22,847.6565657
+21.25,858.0505051
+22.24,865.2222222
+23.27,871.7474747

sensor/dataSensor0.csv

@@ -0,0 +1,101 @@
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sensor/oldData/dataLBwithOPamp.csv

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sensor/oldData/dataNonTapedFive.csv

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sensor/sensorCollector.py

@@ -0,0 +1,118 @@
+
+import serial
+import serial.tools.list_ports
+import pandas as pd
+import os
+from pynput import keyboard
+from datetime import datetime
+from time import sleep
+import sys
+import numpy as np
+
+#import termios
+import tkinter as tk
+
+
+# COMPORT = "/dev/cu.usbmodem11301"
+COMPORT = "COM9"
+SAMPLES = 100
+SENSOR = 2 #0 is taped sensor, 1 is non-taped sensor. s
+
+# print([port.device for port in serial.tools.list_ports.comports()])
+arduino = serial.Serial()
+
+class App:
+    
+    def map(self, analog, sensor): 
+
+        #0 is taped, 1 is nontaped
+        A  = -8.976076325826309
+        K  = 913.5463710698101
+        B  = 0.29767471011439534
+        C  = 5.6686184386250025
+        v = 0.3627635461289861  
+
+        if (sensor == 1):
+            A  = -4.831976283950702
+            K  = 885.9877001844566
+            B  = 0.2793284618109283
+            C  = 3.8852507844119217
+            v = 0.2389935455347361
+        
+        if (sensor == 2):
+            A  = -9.824360913609562
+            K  = 871.4744633266955
+            B  = 0.2909366235093304
+            C  = 4.3307594408159495
+            v = 0.2822807132259202
+
+        if (sensor == 3):
+            A  = -13.891292062248292
+            K  = 990.6819962477331
+            B  = 0.16376045588859353
+            C  = -0.074904004740735
+            v = 0.17727132893449118
+
+        #IMPORTANT: FOR SENSOR 3, INDUCTION SENSOR OUTPUTS VOLTAGE > 6 v
+        #SO WE USED NEW VOLTAGE DIVIDER: 220 OHMS, 550 OHMS
+        y = float(analog)
+        #print(y)
+        real = C - (1.0 / B) * np.log((( (K - A) / (y - A) ) ** v) - 1.0)
+        return real
+    
+    def __init__(self):
+        self.running = True
+        self.paused = True
+        self.collecting = False
+        self.arduino = serial.Serial(port=COMPORT, baudrate=115200, timeout=5)
+
+    def run_machine(self):
+        with keyboard.Listener(on_press=self.on_press) as listener:
+            while self.arduino.is_open and self.running:
+                try:
+                    data = self.arduino.readline().decode(errors='ignore')
+                except:
+                    print("Triggered Termination")
+                    break
+                if self.paused:
+                    
+                    sleep(0.1)
+                    msg = input("type 'c' to show data, type 'p' to pause, type 'esc' to pause: ").strip()
+                    self.arduino.write(bytes(msg, 'utf-8')) 
+                    self.collecting = False
+                    if (msg == 'c'):
+                        self.collecting = True
+                        self.paused = False
+                    elif (msg == 'q'):
+                        self.running = False
+                    sleep(0.5)
+
+                elif self.collecting:
+                    try:
+                        data = self.arduino.readline().decode(errors='ignore')
+                        data = data.strip()
+                        print(data)
+                        distance = self.map(data, SENSOR)
+                        print(distance, "mm")
+                        # distance holds sensor values
+
+                    except KeyboardInterrupt:
+                        print("Triggered Termination")
+                        break
+
+            listener.stop()
+            arduino.close()
+            return
+        
+    def on_press(self, key):
+        try:
+            if key.char == 'p':
+                self.paused = True
+                
+        except:
+            if key == keyboard.Key.esc:
+                print("pausing program")
+                self.pause = True
+
+app = App()
+app.run_machine()

sensor/sensor_simpler.py

@@ -0,0 +1,142 @@
+
+import serial
+import serial.tools.list_ports
+import pandas as pd
+import os
+from pynput import keyboard
+from datetime import datetime
+from time import sleep
+import sys
+import numpy as np
+
+#import termios
+import tkinter as tk
+
+
+# COMPORT = "/dev/cu.usbmodem11301"
+COMPORT = "COM5"
+SAMPLES=100
+# a = 827
+# b = .524
+# c = 8.59834
+
+# print([port.device for port in serial.tools.list_ports.comports()])
+arduino = serial.Serial()
+
+class App:
+    
+    
+    def __init__(self):
+        self.running = True
+        self.paused = False
+        self.snap = False
+        self.counter = 0
+        self.dist = False
+
+        self.current_dist = 1
+
+        self.dataset = {
+            i: [0 for _ in range(SAMPLES)] for i in range(1, 25)
+        }
+
+        self.arduino = serial.Serial(port=COMPORT, baudrate=115200, timeout=5)
+
+    def run_machine(self):
+        with keyboard.Listener(on_press=self.on_press) as listener:
+            while self.arduino.is_open and self.running:
+                if self.paused:
+                    ## PILOT--SENDING INPUTS, SWITCHING SENSOR INDEX
+                    sleep(0.1)
+                    #termios.tcflush(sys.stdin, termios.TCIOFLUSH)
+                    msg = input("Enter message: ").strip()
+                    self.arduino.write(bytes(msg, 'utf-8')) 
+                    self.paused = False
+                    sleep(0.5)
+
+                elif self.dist:
+                    sleep(0.1)
+                    #termios.tcflush(sys.stdin, termios.TCIOFLUSH)
+                    msg = input("Enter dist: ")
+                    self.current_dist = int(msg)
+                    junk = self.arduino.read_all()
+                    self.dist = False
+
+                else:
+                    try:
+                        data = self.arduino.readline().decode(errors='ignore')
+                        if self.snap and self.counter < SAMPLES:
+                            real = data.strip()
+                            print(real)
+                            self.dataset[self.current_dist][self.counter] = int(real)
+
+                            # mm = 100000
+                            # if (int(real) != 0):
+                            #     mm = c - (1/b) * np.log(np.abs(a / int(real) - 1))
+                            # print(mm,"mm")
+
+                            # self.dataset_mm[self.current_dist][self.counter] = mm
+                            self.counter += 1
+                        else:
+                            self.snap = False
+                            self.counter = 0
+                        
+                    except KeyboardInterrupt:
+                        print("Triggered Termination")
+                        break
+
+            print("Arduino Disconnected!")  
+            for value in self.dataset[1]:
+                print(value)
+
+            print("====")
+            df = pd.DataFrame(self.dataset)
+            
+            df.to_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\dataRB.csv')
+            # df2 = pd.DataFrame(self.dataset_mm)
+            # df2.to_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\dataLBMM.csv')
+
+            listener.stop()
+            arduino.close()
+            return
+        
+    def on_press(self, key):
+        try:
+            if key.char == 'p':
+                self.paused = True
+
+            elif key.char == 's':
+                self.snap = True
+            
+            elif key.char == 'd':
+                self.dist = True
+                # def submit_slider1():
+                #     value = slider1.get()
+                #     self.current_dist = value
+
+                # def update_label1(val):
+                #     label1.config(text=f"Value: {val}")
+
+                # # Create main window
+                # root = tk.Tk()
+                # root.title("Slider Example")
+                # root.geometry("300x200")
+
+                # # Slider 1
+                # slider1 = tk.Scale(root, from_=0, to=3, orient=tk.HORIZONTAL, command=update_label1)
+                # slider1.pack()
+                # label1 = tk.Label(root, text="Value: 0")
+                # label1.pack()
+                # button1 = tk.Button(root, text="Submit Slider 1", command=submit_slider1)
+                # button1.pack()
+
+                # root.mainloop()
+                
+        except:
+            if key == keyboard.Key.esc:
+                print("are you here??")
+                self.running = False
+
+
+
+app = App()
+app.run_machine()

sensor/sensor_simplerNew.py

@@ -0,0 +1,169 @@
+
+import serial
+import serial.tools.list_ports
+import pandas as pd
+import os
+from pynput import keyboard
+from datetime import datetime
+from time import sleep
+import sys
+import numpy as np
+
+#import termios
+import tkinter as tk
+
+
+# COMPORT = "/dev/cu.usbmodem11301"
+COMPORT = "COM10"
+SAMPLES=200
+
+# print([port.device for port in serial.tools.list_ports.comports()])
+arduino = serial.Serial()
+
+class App:
+    
+    # def forward(self, x):
+    #     A = 43.20978
+    #     B = 0.286
+    #     C = 1.0373300741204
+    #     K = 900.0010210040905
+    #     v = 0.1
+    #     real = A + (K - A)/ (1 + np.exp(-1 * B(x-C)))**(1/v)
+    
+    def inverse(self, y): 
+        # A = 43.20978
+        # B = 0.286
+        # C = 1.0373300741204
+        # K = 900.0010210040905
+        # v = 0.1
+        A  = -9.824360913609562
+        K  = 871.4744633266955
+        B  = 0.2909366235093304
+        C  = 4.3307594408159495
+        v = 0.2822807132259202
+        y = float(y)
+        #print(y)
+        real = C - (1.0 / B) * np.log((( (K - A) / (y - A) ) ** v) - 1.0)
+        return real
+    
+    def __init__(self):
+        self.running = True
+        self.paused = False
+        self.snap = False
+        self.counter = 0
+        self.dist = False
+
+        self.current_dist = 1
+        self.dataset = {}
+        self.trialNumber = 0
+
+        self.arduino = serial.Serial(port=COMPORT, baudrate=115200, timeout=5)
+
+    def run_machine(self):
+        with keyboard.Listener(on_press=self.on_press) as listener:
+            while self.arduino.is_open and self.running:
+                try:
+                    data = self.arduino.readline().decode(errors='ignore')
+                except:
+                    print("Triggered Termination")
+                    break
+
+                if self.paused:
+                    ## PILOT--SENDING INPUTS, SWITCHING SENSOR INDEX
+                    sleep(0.1)
+                    #termios.tcflush(sys.stdin, termios.TCIOFLUSH)
+                    msg = input("Enter message: ").strip()
+                    self.arduino.write(bytes(msg, 'utf-8')) 
+                    self.paused = False
+                    sleep(0.5)
+
+                elif self.dist:
+                    sleep(0.1)
+                    #termios.tcflush(sys.stdin, termios.TCIOFLUSH)
+                    msg = input("Enter dist: ")
+                    self.current_dist = float(msg)
+                    self.dataset[self.current_dist] = []
+                    junk = self.arduino.read_all()
+                    self.dist = False
+
+                else:
+                    try:
+                        data = self.arduino.readline().decode(errors='ignore')
+                        if self.snap and self.counter < SAMPLES:
+                            if (self.counter == 0):
+                                self.dataset[self.current_dist] = []
+                            real = data.strip()
+                            #real = self.inverse(real)
+                            # print( self.inverse(real) )
+                            print(real)
+                            self.dataset[self.current_dist].append(real)
+                            self.counter += 1
+                            # if (self.counter == SAMPLES):
+                            #     temp = 0
+                            #     for value in self.dataset[self.current_dist] :
+                            #         temp += float(value)
+                            #     temp = temp / 99
+                            #     print("Average distance guessed", self.inverse(temp))
+
+                        else:
+                            self.snap = False
+                            self.counter = 0
+                        
+                    except KeyboardInterrupt:
+                        print("Triggered Termination")
+                        break
+
+            print("Arduino Disconnected!") 
+            print("====")
+            print(self.dataset)
+            df = pd.DataFrame(self.dataset)
+            #lb is taped
+            df.to_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\dataSensor3New.csv')
+
+            listener.stop()
+            arduino.close()
+            return
+        
+    def on_press(self, key):
+        try:
+            if key.char == 'p':
+                self.paused = True
+
+            elif key.char == 's':
+                self.snap = True
+            
+            elif key.char == 'd':
+                self.dist = True
+                # def submit_slider1():
+                #     value = slider1.get()
+                #     self.current_dist = value
+
+                # def update_label1(val):
+                #     label1.config(text=f"Value: {val}")
+
+                # # Create main window
+                # root = tk.Tk()
+                # root.title("Slider Example")
+                # root.geometry("300x200")
+
+                # # Slider 1
+                # slider1 = tk.Scale(root, from_=0, to=3, orient=tk.HORIZONTAL, command=update_label1)
+                # slider1.pack()
+                # label1 = tk.Label(root, text="Value: 0")
+                # label1.pack()
+                # button1 = tk.Button(root, text="Submit Slider 1", command=submit_slider1)
+                # button1.pack()
+
+                # root.mainloop()
+                
+        except:
+            if key == keyboard.Key.esc:
+                print("are you here??")
+                self.running = False
+
+        
+
+
+
+app = App()
+app.run_machine()