Release v2.1.1

README.md

@@ -1,7 +1,7 @@
 # RFlect - Antenna Plot Tool      <img src="./assets/smith_logo.png" alt="RFlect Logo" width="40">
 
 
-**Version:** 2.1.0
+**Version:** 2.1.1
 
 RFlect is a comprehensive antenna plotting tool, currently designed specifically for visualizing and analyzing antenna measurements from the Howland Company 3100 Antenna Chamber and WTL Test Lab outputs. Additionally, it offers support for .csv VNA files from Copper Mountain RVNA and S2VNA software of S11/VSWR/Group Delay(S21(s)) measurements, making it a versatile choice for a wide range of antenna data processing needs. Through its user-friendly graphical interface, RFlect provides an intuitive way to handle various antenna metrics and visualize results.
 

RELEASE_NOTES.md

@@ -1,5 +1,8 @@
 # RFlect - Release Notes
 
+## Version 2.1.1 (10/19/2023)
+- Added Support For Agilent VNA Group Delay Measurements
+
 ## Version 2.1.0 (10/16/2023)
 - Added Copper Mountain S2VNA/M5090 .csv export support (S11(dB), S22(dB), S21(dB) or S12(dB), S21(s) or S12(s))
 - Fixed Matplotlib backend to display plots properly

plot_antenna/file_utils.py

@@ -275,4 +275,24 @@ def parse_2port_data(file_path):
     # Use only the available columns
     organized_data = data[available_columns]
 
+    return organized_data
+
+# Function to parse group delay measurements from Agilent VNA
+def parse_agilent_data(file_path):
+    # Read the data, assuming the first row is a header row
+    # and the file is space or tab-delimited
+    data = pd.read_csv(file_path, skiprows=2)
+
+    # Rename columns for consistency with other data
+    # Assuming the first column is frequency and the second is group delay
+    data = data.rename(columns={
+        data.columns[0]: '! Stimulus(Hz)',
+        data.columns[1]: 'S21(s)'
+    })
+
+    # Check which columns are available in the data
+    available_columns = [col for col in ['! Stimulus(Hz)', 'S21(s)', 'S12(s)'] if col in data.columns]
+    # Use only the available columns
+    organized_data = data[available_columns]
+
     return organized_data

plot_antenna/groupdelay.py

@@ -1,4 +1,5 @@
-from file_utils import parse_2port_data
+from file_utils import parse_2port_data, parse_agilent_data
+from matplotlib.ticker import ScalarFormatter
 
 import matplotlib
 matplotlib.use('TkAgg')
@@ -14,7 +15,7 @@ def process_groupdelay_files(file_paths, saved_limit1_freq1, saved_limit1_freq2,
     data_dict = {}
 
     # Regular expression to match a number followed by 'deg'
-    pattern = re.compile(r'(\d+)deg')
+    pattern = re.compile(r'(\d+)(deg|DEG)', re.IGNORECASE)
 
     # Extract data from files
     for file_path in file_paths:
@@ -27,12 +28,22 @@ def process_groupdelay_files(file_paths, saved_limit1_freq1, saved_limit1_freq2,
             print(f"Warning: Could not extract theta from filename: {filename}")
             continue  # Skip this file if no match is found
 
-        # Parsing data
-        data = parse_2port_data(file_path)
-
-        # Storing data
-        data_dict[theta] = data
-
+        # Read the first line to determine the source, if Agilent or S2VNA
+        with open(file_path, 'r', encoding='utf-8-sig') as f:
+            first_line = f.readline() # Read the first line
+        if '!' in first_line:
+            # Process S2VNA 2-Port Measurement
+            data = parse_2port_data(file_path)
+        elif '#' in first_line:
+            # Process Agilent Group Delay Measurement
+            data = parse_agilent_data(file_path)
+        else:
+            print(f"Warning: Could not recognize file structure of: {filename}")
+            data = None
+
+        if data is not None:
+            # Storing data
+            data_dict[theta] = data
     # Plotting: 
     # Group Delay vs Frequency for Various Theta, Group Delay Difference vs Theta, & Max. Distance Error vs Theta
     plot_group_delay_error(data_dict, min_freq, max_freq)
@@ -47,6 +58,10 @@ def plot_group_delay_error(data_dict, min_freq=None, max_freq=None):
     # Plot Group Delay Vs Frequency
     plt.figure(figsize=(10,6))
     for theta, data in data_dict.items():
+        if min_freq is not None and max_freq is not None:
+            # Convert frequencies to GHz since your min_freq and max_freq are probably in GHz
+            data = data[(data['! Stimulus(Hz)'] >= min_freq*1e9) & (data['! Stimulus(Hz)'] <= max_freq*1e9)]
+
         if 'S21(s)' or 'S12(s)' in data.columns:
             if 'S21(s)' in data.columns:
                 data_group_delay = data['S21(s)']
@@ -59,9 +74,32 @@ def plot_group_delay_error(data_dict, min_freq=None, max_freq=None):
     # Set frequency range if specified
     if min_freq is not None and max_freq is not None:
         plt.xlim(min_freq*1e9, max_freq*1e9)
+    # Initialize an empty list to store the filtered frequency points
+    filtered_freq_points = []
+
+    # If min_freq and max_freq are specified, filter the frequency points
+    if min_freq is not None and max_freq is not None:
+        for theta, data in data_dict.items():
+            data = data[(data['! Stimulus(Hz)'] >= min_freq*1e9) & (data['! Stimulus(Hz)'] <= max_freq*1e9)]
+            data_dict[theta] = data  # Store the filtered data back in the data_dict
+
+            if len(filtered_freq_points) == 0:  # If the list is empty, populate it with the first set of filtered frequency points
+                filtered_freq_points = data['! Stimulus(Hz)'].to_numpy()
 
+    else:  # If no frequency range is specified, use all frequency points from the first dataset
+        filtered_freq_points = data_dict[next(iter(data_dict))]['! Stimulus(Hz)'].to_numpy()
+        plt.ylim(0, 5e-9)  # This sets the Y-axis limits from 0 to 5 ns
     plt.xlabel('Frequency (GHz)')
     plt.ylabel('Group Delay (ns)')
+
+    # Create a new formatter
+    formatter = ScalarFormatter(useOffset=False)
+    formatter.set_scientific(True)
+    formatter.set_powerlimits((9,9))  # This line forces the scientific notation to 10^9
+
+    # Apply the formatter
+    plt.gca().xaxis.set_major_formatter(formatter)
+
     plt.legend()
     plt.title('Group Delay vs Frequency for Various Theta (Azimuthal) Rotation')
     plt.grid(True, which='both', linestyle='--', linewidth=0.5)
@@ -103,6 +141,10 @@ def plot_group_delay_error(data_dict, min_freq=None, max_freq=None):
 
     plt.xlabel('Frequency (GHz)')
     plt.ylabel('Peak-to-Peak Group Delay Difference over Theta (ps)')
+
+    # Apply the formatter
+    plt.gca().xaxis.set_major_formatter(formatter)
+
     plt.legend()
     plt.title('Max Group Delay Difference over Theta')
     plt.grid(True, which='both', linestyle='--', linewidth=0.5)
@@ -120,6 +162,10 @@ def plot_group_delay_error(data_dict, min_freq=None, max_freq=None):
 
     plt.xlabel('Frequency (GHz)')
     plt.ylabel('Max Distance Error (cm)')
+
+    # Apply the formatter
+    plt.gca().xaxis.set_major_formatter(formatter)
+
     plt.legend()
     plt.title('Max Distance Error over Theta')
     plt.grid(True, which='both', linestyle='--', linewidth=0.5)

settings.json

@@ -1,3 +1,3 @@
 {
-    "CURRENT_VERSION": "v2.1.0"
+    "CURRENT_VERSION": "v2.1.1"
 }