ME_2046/HW3/simplifying.py

@@ -1,103 +0,0 @@
-import sympy as sm
-import numpy as np
-sm.init_printing()
-
-s = sm.symbols('s')
-z = sm.symbols('z')
-T = sm.symbols('T')
-
-#########################################################
-print('PROBLEM 2:')
-print('Part a:')
-
-ZOH = (1-sm.exp(-s*T))/(s*T)
-
-G_1_s = 1/s*ZOH
-
-bilinear_s = 2/T *(z-1)/(z+1)
-
-G_1_k = G_1_s.subs({s:bilinear_s}).simplify()
-
-print("G_1_k = ")
-sm.pprint(G_1_k)
-
-print('Part b:')
-
-theta_s_r_s = ZOH*1/s**2
-theta_k_r_k = theta_s_r_s.subs({s:bilinear_s}).simplify()
-print("theta_k_r_k = ")
-sm.pprint(theta_k_r_k)
-
-
-#########################################################
-print('PROBLEM 3:')
-A = sm.Matrix([[0, 1, 0, 0],
-               [0, 0, 1, 0],
-               [0, 0, 0, 1],
-               [1, 0, 0, 0]])
-B = sm.Matrix([0, 0, 0, 1])
-C = sm.Matrix([1, 0, 0, 0]).transpose()
-D = sm.Matrix([1])
-
-print('System Matricies A, B, C, D')
-sm.pprint(A)
-sm.pprint(B)
-sm.pprint(C)
-sm.pprint(D)
-
-#Recursive Solution
-k = sm.symbols('k', integer = True, real = True, positive = True)
-
-def y(k,u):
-    term_1 = C * A**k * x_0
-
-    term_2 = sm.Matrix([0,0,0,0])
-    for j in range(np.size(u)):
-        term_2 = term_2 + A**(k-j-1) * B * u[j]
-    term_2 = C*term_2
-
-    term_3 = D*u[-1]
-
-    return term_1+term_2+term_3
-
-print('Part c:')
-x_0 = sm.Matrix([2, 1, 3, 0])
-u = sm.Matrix([0])
-
-output = y(k, u)
-output = output[0].expand().simplify()
-print('y(k) = ')
-sm.pprint(output)
-
-
-
-print('Part d:')
-x_0 = sm.Matrix([0, 0, 0, 0])
-u = sm.Matrix([2, 1, 3, 0])
-output = y(k, u)
-output = output[0].expand().simplify()
-print('y(k) = ')
-sm.pprint(output)
-
-print('Part e:')
-print('These are the same exact response between parts C and D. This makes sense, becasue we defined our states as just being delays in a chain. The result is that the input at timestep k trickles down through each state in k+1, k+2, and k+3. This means that our states save our input in a way, s.t. loading this initial state mathematically produces an identical result as loading those inputs in one time step at a time. \n \n This is reflected by the two algebraeic expressions being the same.')
-
-#########################################################
-print('PROBLEM 4:')
-print('Part a:')
-
-"""
-x(k+2) - x(k+1) + 0.25 x(k) = u(k+2)
-
-Applying Z transform:
-z^2 X - z X + 0.25 X = z^2 U
-X (z^2 - z + 0.25) = z^2 U
-X/U = z^2 / (z^2 - z + 0.25)
-"""
-# Use SymPy to do partial frac
-z = sm.symbols('z')
-
-X_U = z**2/(z**2 - z + 0.25)
-sm.pprint(X_U.apart())
-
-

NUCE_2113/lab5/.~lock.labviz.ods#

@@ -1 +0,0 @@
-,danesabo,danesabo-laptop,24.02.2025 15:04,file:///home/danesabo/snap/libreoffice/336/.config/libreoffice/4;

NUCE_2113/lab5/viz.py

@@ -1,101 +0,0 @@
-import glob
-import os
-import numpy as np
-import matplotlib.pyplot as plt
-
-def load_spectrum_data(filename):
-    """
-    Load spectrum data from a text file in the provided format.
-    
-    This function:
-      - Reads the file line by line.
-      - Finds the "$DATA:" section.
-      - Skips the first line after "$DATA:" (assumed to be the channel range header).
-      - Collects all subsequent lines (until the next section indicated by a line starting with '$').
-      - Parses these lines into a NumPy array.
-      
-    Parameters:
-      filename (str): Path to the spectrum file.
-      
-    Returns:
-      numpy.ndarray: Array of count values.
-    """
-    with open(filename, 'r') as f:
-        lines = f.readlines()
-
-    data_lines = []
-    in_data_section = False
-    skip_first_data_line = True  # flag to skip the channel-range header
-
-    for line in lines:
-        # Look for the beginning of the data section
-        if line.strip().startswith("$DATA:"):
-            in_data_section = True
-            continue
-        if in_data_section:
-            # If we hit a new section header, exit the data section
-            if line.strip().startswith("$"):
-                break
-            # Skip the first line after "$DATA:" if it contains exactly two numbers (channel range header)
-            if skip_first_data_line:
-                parts = line.strip().split()
-                if len(parts) == 2:
-                    skip_first_data_line = False
-                    continue
-                skip_first_data_line = False  # even if not two numbers, do not skip subsequent lines
-            # Append non-empty lines
-            if line.strip():
-                data_lines.append(line.strip())
-
-    # Combine the collected lines into one string and parse numbers
-    data_str = " ".join(data_lines)
-    # Convert the string of numbers into a NumPy array
-    data = np.fromstring(data_str, sep=' ')
-    return data
-
-# Get a list of all .Spe files in the current directory
-spe_files = glob.glob("*.Spe")
-
-# To store data for the combined plot
-combined_data = []
-
-for file in spe_files:
-    # Load the spectrum data
-    spectrum_data = load_spectrum_data(file)
-    # Create an array for channel numbers (one per data point)
-    channels = np.arange(len(spectrum_data))
-    
-    # Store the data for the combined plot (use file name without extension for legend)
-    base_name = os.path.splitext(os.path.basename(file))[0]
-    combined_data.append((base_name, channels, spectrum_data))
-    
-    # Plot individual spectrum
-    plt.figure(figsize=(10, 6))
-    plt.step(channels, spectrum_data, where='mid', color='blue')
-    plt.xlabel('Channel')
-    plt.ylabel('Counts')
-    plt.title(f"Spectrum: {base_name}")
-    plt.grid(True)
-    plt.tight_layout()
-    
-    # Save individual figure as PNG
-    output_filename = base_name + '.png'
-    plt.savefig(output_filename)
-    plt.close()  # Close the figure to free memory
-
-# Now create a combined plot with all spectra
-plt.figure(figsize=(12, 8))
-for name, channels, spectrum_data in combined_data:
-    # Do not specify a color so that the default cycle gives different colors for each curve
-    plt.step(channels, spectrum_data, where='mid', label=name)
-    
-plt.xlabel('Channel')
-plt.ylabel('Counts')
-plt.title("Combined Spectrum Data")
-plt.grid(True)
-plt.legend()
-plt.tight_layout()
-
-# Save and display the combined plot
-plt.savefig('combined_spectrum.png')
-plt.show()