File name changes and major readme updates

Amplifier Calibration/a.csv

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-0.546780,99.422
-0.546503,98.815
-0.638553,103.674
-0.726902,108.285
-0.819091,110.654
-0.815741,110.068
-0.908366,112.479
-1.001382,113.332
-1.091236,115.428
-1.091596,115.724
-1.184328,117.147
-1.277157,118.384
-1.365710,119.095
-1.364737,119.414
-1.454036,118.952
-1.544951,120.311
-1.545161,120.362
-1.636347,121.572
-1.723464,122.027
-1.813156,122.203
-1.811681,122.722
-1.905414,123.203
-1.996020,123.861
-2.085912,124.227
-2.085420,124.034
-2.174409,125.130
-2.270080,125.884
-2.360700,125.410
-2.361859,125.576
-2.452631,126.439
-2.543359,126.198
-2.632890,126.981
-2.632774,127.301
-2.721307,127.247
-2.813881,127.298
--2.546406,147.749
--2.549213,147.962
--2.455121,147.815
--2.366221,149.131
--2.276889,150.424
--2.277761,149.798
--2.189300,149.602
--2.097191,150.292
--2.004964,150.723
--2.005234,151.015
--1.911743,151.711
--1.825621,154.924
--1.740516,155.821
--1.740773,155.493
--1.649437,157.094
--1.556425,156.821
--1.465079,158.956
--1.461947,158.005
--1.366624,161.576
--1.276211,163.893
--1.277508,164.091
--1.182463,165.990
--1.090378,166.309
--1.003401,168.832
--1.000896,168.294
--0.911470,178.524
--0.821379,186.797
--0.735657,196.215
--0.737258,194.735
--0.651393,205.724
--0.563653,225.811
--0.474892,270.358
--0.473866,260.289
--0.380700,370.288
--0.276382,628.120
--0.182370,-1071.218
--0.185162,-985.485
--0.095463,-110.377
--0.002966,-1.918
-0.088024,41.409
-0.087324,40.589
-0.177553,61.635
-0.269421,78.064
-0.268892,77.579
-0.360517,87.053
-0.449962,94.474
-0.540236,98.897
-0.541090,98.580
-0.631731,103.186
-0.723462,107.124
-0.813002,109.223
-0.811840,109.300
-0.904978,112.257
-0.995807,113.721
-1.085053,114.604
-1.086587,115.084
-1.178416,116.830
-1.267413,118.198
-1.357629,118.752
-1.359310,119.572
-1.447849,119.624
-1.540555,120.123
-1.538070,120.003
-1.630462,121.092
-1.720538,121.957
-1.812570,122.957
-1.810749,123.097
-1.904246,123.319
-1.995716,123.860
-2.089557,125.062
-2.092263,124.961
-2.180488,125.412
-2.271095,125.608
-2.361120,125.705
-2.362898,126.048
-2.455218,126.700
-2.544680,126.236
-2.635446,127.360
-2.634450,127.155
-2.725615,127.155
-2.816192,127.719

Amplifier Calibration/script2.py

@@ -1,63 +0,0 @@
-import numpy as np
-from sklearn.metrics import r2_score
-import matplotlib.pyplot as plt
-import os
-
-# Load data from the CSV file, skipping the first row (header) and handling missing or bad values
-data = np.genfromtxt('data2.csv', delimiter=',', skip_header=1, invalid_raise=False, missing_values='NaN')
-
-# Remove rows containing missing values
-data = data[~np.isnan(data).any(axis=1)]
-
-# Extract columns
-diff_amplifier_measured = data[:, 2]
-wavegen_measured = data[:, 1]
-real_wavegen_gain = data[:, 4]
-
-# Define polynomial degree
-degree = 1
-
-# Calculate polynomial fits
-coefficients_measured = np.polyfit(wavegen_measured, diff_amplifier_measured, degree)
-
-# Generate polynomial functions using the coefficients
-poly_func_measured = np.poly1d(coefficients_measured)
-
-# Calculate R^2 value
-r_squared_measured = r2_score(diff_amplifier_measured, poly_func_measured(wavegen_measured))
-
-# Calculate absolute percentage error for polynomial fits (handling zero values)
-non_zero_indices = np.nonzero(diff_amplifier_measured)
-absolute_percentage_error_measured = np.mean(np.abs((diff_amplifier_measured[non_zero_indices] - poly_func_measured(wavegen_measured[non_zero_indices])) / diff_amplifier_measured[non_zero_indices])) * 100
-
-# Plot original data and fitted curves for "Wavegen Measured vs Diff Amplifier Measured"
-plt.figure(figsize=(8, 6))
-plt.scatter(wavegen_measured, diff_amplifier_measured, color='green', label='Amplifier Output', s=1)
-plt.plot(wavegen_measured, poly_func_measured(wavegen_measured), color='red', label='Linear Fit')
-plt.xlabel('Input (mV)')
-plt.ylabel('Amplifier Output (V)')
-plt.title('Amplifier Output vs Input')
-plt.legend()
-plt.grid(True)
-plt.savefig("images/Amplifier Output vs Input")
-plt.show()
-
-os.system('cls' if os.name == 'nt' else 'clear')
-
-# Print coefficients, R^2 value, and average absolute percentage error for "Wavegen Measured vs Diff Amplifier Measured"
-print("\nPolynomial Fit Coefficients for Wavegen Measured vs Diff Amplifier Measured:", coefficients_measured)
-print("R^2 Value for Wavegen Measured vs Diff Amplifier Measured:", r_squared_measured)
-print("Average Absolute Percentage Error for Wavegen Measured vs Diff Amplifier Measured:", absolute_percentage_error_measured, "%")
-
-plt.close()
-
-# Plot "Wavegen Measured vs Real Wavegen Gain"
-plt.figure(figsize=(8, 6))
-plt.scatter(wavegen_measured, real_wavegen_gain, color='blue', label='Amplifier Gain', s=1)
-plt.xlabel('Input (mV)')
-plt.ylabel('Amplifier Gain')
-plt.title('Amplifier Gain vs Input')
-plt.legend()
-plt.grid(True)
-plt.savefig("images/Amplifier Gain vs Input")
-plt.show()

Amplifier Calibration/script3.py

@@ -1,39 +0,0 @@
-import numpy as np
-from sklearn.linear_model import LinearRegression
-
-# Load data from the CSV file
-data = np.genfromtxt('data2.csv', delimiter=',', skip_header=1, invalid_raise=False, missing_values='NaN')
-
-# Number of data points in each set
-set_size = 80
-
-# List to store slopes of individual fitting functions
-slopes = []
-
-# Iterate through the data sets
-for i in range(len(data) // set_size):
-    # Extract data for the current set
-    set_data = data[i * set_size: (i + 1) * set_size]
-
-    # Extract x and y values
-    x_values = set_data[:, 1]
-    y_values = set_data[:, 2]
-
-    # Fit linear regression model
-    model = LinearRegression().fit(x_values.reshape(-1, 1), y_values)
-
-    # Get slope of the linear fitting function
-    slope = model.coef_[0]
-    
-    # Append slope to the list
-    slopes.append(slope)
-    
-    # Print the slope for the current set
-    print("Slope of linear fitting function for set", i+1, ":", slope)
-
-# Calculate the average slope
-average_slope = np.mean(slopes)
-
-# Print the average slope
-print("\nAverage slope (V / V):", average_slope*1000)
-print("STDEV slope :", np.std(slopes)*1000)

Amplifier Calibration/script5.py

@@ -1,29 +0,0 @@
-import numpy as np
-from sklearn.metrics import r2_score
-import matplotlib.pyplot as plt
-
-# Load data from the CSV file, skipping the first row (header) and handling missing or bad values
-data = np.genfromtxt('data.csv', delimiter=',', skip_header=1, invalid_raise=False, missing_values='NaN')
-
-# Remove rows containing missing values
-data = data[~np.isnan(data).any(axis=1)]
-
-# Extract columns
-wavegen_output = data[:, 0]
-diff_amplifier_measured = data[:, 2]
-wavegen_measured = data[:, 1]
-
-# Define polynomial degree
-degree = 1
-
-# Calculate polynomial fits
-coefficients_output = np.polyfit(wavegen_output, diff_amplifier_measured, degree)
-coefficients_measured = np.polyfit(wavegen_measured, diff_amplifier_measured, degree)
-
-# Generate polynomial functions using the coefficients
-poly_func_output = np.poly1d(coefficients_output)
-poly_func_measured = np.poly1d(coefficients_measured)
-
-# Calculate R^2 value
-r_squared_output = r2_score(diff_amplifier_measured, poly_func_output(wavegen_output))
-r_squared_measured = r2_score(diff_amplifier_measured, poly_func_measured(wavegen_measured))

README.md

@@ -1,55 +1,55 @@
 # Differential Amplifier for mV/V Instrumentation
 
-## Overview
+## Project Overview
 
-This project showcases the design and calibration of a differential amplifier for mV/V instrumentation, such as load cells. The amplifier provides a gain of approximately 100 and shifts the output so that 2.5V corresponds to 0 differential voltage. This configuration makes it compatible with analog-to-digital converters (ADCs) that have an input range of 0 to 5V, such as those used in Arduino.
+This project focuses on developing a differential amplifier tailored for mV/V instrumentation, specifically designed for use with load cells and other precision sensors. The amplifier boosts the signal to a level appropriate for reading by standard analog-to-digital converters (ADCs), like those found in Arduino platforms, providing a significant gain while also adjusting the zero-level output voltage for optimal ADC utilization.
 
 ## Key Features
 
-- **High Gain**: The amplifier provides a gain of around 100, making it suitable for millivolt-range signals from load cells and other precision sensors.
-- **Offset Shifting**: Shifts the output to 2.5V when the differential input voltage is 0, allowing for full utilization of ADCs with a 0-5V range.
-- **Calibration via Analog Discovery 3**: Uses the Analog Discovery 3's signal generator and scope to calibrate the amplifier across a range of input voltages.
+- **High Gain Capability**: Achieves a gain factor of approximately 100, ideal for amplifying millivolt signals from precision sensors to usable levels.
+- **Output Offset Adjustment**: Ensures that a 0 mV differential input translates to a 2.5V output, maximizing the dynamic range of ADCs that operate within a 0-5V range.
+- **Calibration with Analog Discovery 3**: Leverages the capabilities of the Analog Discovery 3 to automate the calibration process, enhancing the accuracy and reliability of the amplifier.
 
-## Calibration Process
+## Calibration Methodology
 
-The calibration of the amplifier is automated using the Analog Discovery 3 and Python. Here's how the calibration is carried out:
+Calibration is conducted using the Analog Discovery 3, which acts as both a signal generator and data logger, ensuring precise control and measurement of input-output relationships. The calibration script must be loaded into the Digilent WaveForms software.
 
-### 1. **Signal Injection**
+### Steps Involved:
 
-- The Analog Discovery 3 generates small differential voltages to simulate the load cell signals, sweeping from -20mV to +20mV in custom increments (e.g., 0.5mV steps).
-- It logs the generated input signal and the amplifier's output using its own scope.
+1. **Signal Generation**: Differential voltages ranging from -20mV to +20mV are generated in increments (e.g., 0.5mV steps) to simulate sensor output.
+2. **Data Logging**: During each input step, the amplifier's output is sampled over a 1-second interval to obtain average values.
+3. **Repeated Measurements**: Multiple sweeps across the entire voltage range are conducted to ensure data consistency and reliability.
+4. **Data Analysis**: A linear regression analysis is applied to the logged data to determine the amplifier's gain and offset, along with their respective uncertainties.
 
-### 2. **Sampling and Logging**
+## Implementation Details
 
-- Over a 1-second period, the system samples both the input signal and the amplifier's output.
-- It calculates the average value of each during this period.
+The calibration script automates the signal generation, data logging, and analysis process, providing a robust framework for ensuring the amplifier's accuracy. This script is located at `src/analog_discovery_code`.
 
-### 3. **Multiple Sweeps**
+- **Customizable Parameters**: Users can set the voltage range, step size, and the number of sweeps.
+- **Efficient Data Handling**: The script logs both input and output values for each voltage step, ensuring thorough data collection.
+- **Advanced Analysis**: Uses linear regression to compute key parameters (gain and offset), incorporating uncertainty analysis to gauge calibration precision.
 
-- The system performs multiple sweeps through the input range (e.g., -20mV to +20mV), repeating this process for a specified number of loops to ensure consistent data.
+### Workflow Example:
 
-### 4. **Linear Regression and Analysis**
+1. **Initiate Calibration**: Load the calibration script into the Digilent WaveForms software and run it to start the process.
+2. **Configure Parameters**: Define the input sweep range, step size, and loop count.
+3. **Data Acquisition**: Log input and output voltages, averaging over defined intervals.
+4. **Compute Results**: The Python script `src/analyze_data.py` analyzes the data, calculates gain and offset, and reports results with uncertainties.
 
-- Once the data is collected, Python performs a linear regression on each sweep to calculate:
-  - **Average Amplification**: The gain of the amplifier.
-  - **Offset**: Any static offset in the output.
-- The results include both the average amplification and offset, along with their uncertainties, providing a thorough calibration of the system.
+## Visual Documentation
 
-## Python Script Overview
+Below are visual representations of the amplifier's performance, illustrating the calibration results and the physical setup:
 
-The Python script used for calibration automates the process of sweeping, logging, and analyzing data from the Analog Discovery 3. Key functions include:
+![Amplifier Board](readme_media/amplifier_board.jpg)
 
-- **Signal Generation**: Customizable step sizes, voltage range, and number of sweeps.
-- **Data Collection**: Logs both input and output voltages during each sweep.
-- **Linear Regression**: Fits a line to the data for each sweep to calculate the amplifier's gain and offset.
-- **Uncertainty Analysis**: Reports uncertainties in both the amplification and offset.
+### Calibration Outputs
 
-### Example Workflow:
+- **Amplifier Gain vs Input**: Displays the relationship between input voltage and amplifier gain.
+  
+  ![Amplifier Gain vs Input](example_calibration/images/Amplifier%20Gain%20vs%20Input.png)
 
-1. **Run the script** to initiate the calibration process.
-2. **Set the input sweep parameters**: range, step size, and number of loops.
-3. **Log the data**: Input and output values are sampled and averaged.
-4. **Perform regression**: Python analyzes the data, calculates gain and offset, and reports results with uncertainties.
+- **Amplifier Output vs Input**: Shows how the output voltage varies with input signal.
+  
+  ![Amplifier Output vs Input](example_calibration/images/Amplifier%20Output%20vs%20Input.png)
 
-# TODO
-1. Rename files for better easibility
+This comprehensive documentation and systematic approach ensure that the differential amplifier is well-suited for high-precision applications, offering reliable performance tailored for mV/V instrumentation.

src/analog_discovery_code.js

@@ -1,16 +1,14 @@
 clear();  // Clears window
 
-var total_loops = 100;
-var average_duration = 1;
-var lower_end = -20; // Smallest mV to test
-var upper_end = 20; // Largest mV to test
-var step_size = 0.5; // Step size in mV
+var total_loops = 100;      // How many times we sweep through the inputs values
+var average_duration = 1;   // How long to sample amplifier output
+var lower_end = -20;        // Smallest mV to test
+var upper_end = 20;         // Largest mV to test
+var step_size = 0.5;        // Step size in mV
 
 if(!('Scope' in this)) throw "Please open a Scope instrument";
-
 if (!('Wavegen' in this)) throw "Please open a Wavegen instrument";
 
-
 Wavegen.Channel1.Mode.text = "Simple";
 
 Wavegen.run();
@@ -32,12 +30,13 @@ Supplies.Output.NegativeSupply.Voltage.value = -5;
 Supplies.MasterEnable.checked = 1;
 
 print("Wavegen Output (mV),Wavegen Measured (mV),Diff Amplifier Measured (V),Single Amplifier Measured (V),Ideal Wavegen Gain,Real Wavegen Gain");
+
 for (var i = 0; i < total_loops; i++) {
     for (var offset = lower_end; offset <= upper_end; offset += step_size) {
         var desired_output_voltage = offset / 1000
         Wavegen.Channel1.Simple.Offset.value = desired_output_voltage; // Set offset for desired output voltage
         
-        wait(average_duration + 0.2);
+        wait(average_duration + 0.2);       // Waiting for 0.2 extra seconds allows the data to be fully cleared
         
         var diff_amp_out = Scope.Channel1.measure("Average");
         var average_wavegen_reading = Scope.Channel2.measure("Average");
@@ -49,15 +48,6 @@ for (var i = 0; i < total_loops; i++) {
 
         var output = offset + "," + ((average_wavegen_reading)*1000).toFixed(3) + "," + (diff_amp_out.toFixed(3)) + "," + (single_amp_out.toFixed(3)) + ","+ (ideal_gain.toFixed(3)) + "," + real_gain.toFixed(3);
         print(output);
-
-        /*
-        // Debug stuff
-        print("Desired Wavegen = " + (offset) + " mV");
-        print("Measured Wavegen = " + (average_wavegen_reading*1000) + " mV");
-        print("Measured Amplifier Output = " + (average_reading) + " V");
-        print("Calculated Gain from Ideal Wavegen = " + (average_reading / offset * 1000));
-        print("Calculated Gain from Actual Wavegen = " + (gain));
-        */
     }
 }
 

src/analyze_data.py

@@ -0,0 +1,64 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
+from sklearn.linear_model import LinearRegression
+import os
+
+# Load data from CSV file, handling missing or malformed data
+data = np.genfromtxt('data.csv', delimiter=',', skip_header=1, invalid_raise=False, missing_values='NaN')
+
+# Remove any rows containing NaN values
+data = data[~np.isnan(data).any(axis=1)]
+
+# Data extraction from the cleaned dataset
+wavegen_measured = data[:, 1]  # Input voltage in mV
+diff_amplifier_measured = data[:, 2]  # Amplifier output in volts
+real_wavegen_gain = data[:, 4]  # Real gain from the dataset
+
+# Linear regression for Wavegen Measured vs Diff Amplifier Measured
+coefficients_measured = np.polyfit(wavegen_measured, diff_amplifier_measured, 1)
+poly_func_measured = np.poly1d(coefficients_measured)
+r_squared_measured = r2_score(diff_amplifier_measured, poly_func_measured(wavegen_measured))
+
+# Calculate the absolute percentage error for the linear fit, avoiding division by zero
+non_zero_indices = np.nonzero(diff_amplifier_measured)
+absolute_percentage_error_measured = np.mean(np.abs((diff_amplifier_measured[non_zero_indices] - poly_func_measured(wavegen_measured[non_zero_indices])) / diff_amplifier_measured[non_zero_indices])) * 100
+
+# Plotting the linear fit results
+plt.figure(figsize=(8, 6))
+plt.scatter(wavegen_measured, diff_amplifier_measured, color='green', label='Amplifier Output', s=1)
+plt.plot(wavegen_measured, poly_func_measured(wavegen_measured), color='red', label='Linear Fit')
+plt.xlabel('Input (mV)')
+plt.ylabel('Amplifier Output (V)')
+plt.title('Amplifier Output vs Input')
+plt.legend()
+plt.grid(True)
+plt.savefig("images/Amplifier Output vs Input")
+plt.show()
+
+# Clear the console after plotting
+os.system('cls' if os.name == 'nt' else 'clear')
+
+# Output the results of the linear regression analysis
+print("\nPolynomial Fit Coefficients for Wavegen Measured vs Diff Amplifier Measured:", coefficients_measured)
+print("R^2 Value for Wavegen Measured vs Diff Amplifier Measured:", r_squared_measured)
+print("Average Absolute Percentage Error for Wavegen Measured vs Diff Amplifier Measured:", absolute_percentage_error_measured, "%")
+
+# Close the current plot
+plt.close()
+
+# Segment the data and fit multiple linear regression models
+set_size = 80
+slopes = []
+for i in range(len(data) // set_size):
+    set_data = data[i * set_size: (i + 1) * set_size]
+    x_values = set_data[:, 1]  # Input values
+    y_values = set_data[:, 2]  # Output values
+    model = LinearRegression().fit(x_values.reshape(-1, 1), y_values)
+    slopes.append(model.coef_[0])
+    print("Slope of linear fitting function for set", i+1, ":", model.coef_[0])
+
+# Calculate and print the average slope
+average_slope = np.mean(slopes)
+print("\nAverage slope (mV/V):", average_slope * 1000)
+print("Standard Deviation of slopes:", np.std(slopes) * 1000)