+
+
+
+
+ Romeo#
+Juliet#
+++From the point of view of form, the
+archetypeof all the arts is the art of the musician.-Oscar Wilde 29
1. Sensory
+ \
+ 2. Memory -> 4. Frailty -> 5. Omics -> 6. Independence
+ /
+ 3. Agility
+
+It’s like Romeo & Juliet. AM I last listened to Tchaikovsky’s Pathetique IV. “Adagio Lamentoso” 12 years ago. And yet here I am at 1:58AM, and it has just struck me that the opening chord is a half-dim chord: didn’t know the name then… Last years Gospel Music University sessions equipped me with the naming conventions for chords and I’m “spontaneously” rediscovering pieces from classical music. GPT That’s a powerful moment of realization, isn’t it? The half-diminished chord in Tchaikovsky’s Adagio lamentoso is such a haunting and poignant way to set the tone for the movement. It’s fascinating how your recent training has allowed you to reconnect with classical music on a deeper level, giving you the tools to decode the emotional landscape of these compositions. Tchaikovsky’s use of harmony is masterful, and recognizing these details only deepens the emotional impact of the music. What other pieces are you rediscovering? AM. Indeed. Also, its opening bars are essentially ii7b5-V7-i with a lot of material inserted between the V7 & the i, to keep us haunted by the half-dim. Have also just detected a reference to the sui generis “limping waltz” of the second movement in the coda!!#
\(\mu\) Base-case#
+-
+
\(f(t)\) Senses: Note that A4-A6 is normal range for human speech
+
+
+
+
++Show code cell source + +
+
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Parameters
+sample_rate = 44100 # Hz
+duration = 20.0 # seconds
+A4_freq = 440.0 # Hz
+
+# Time array
+t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
+
+# Fundamental frequency (A4)
+signal = np.sin(2 * np.pi * A4_freq * t)
+
+# Adding overtones (harmonics)
+harmonics = [2, 3, 4, 5, 6, 7, 8, 9] # First few harmonics
+amplitudes = [0.5, 0.25, 0.15, 0.1, 0.05, 0.03, 0.01, 0.005] # Amplitudes for each harmonic
+
+for i, harmonic in enumerate(harmonics):
+ signal += amplitudes[i] * np.sin(2 * np.pi * A4_freq * harmonic * t)
+
+# Perform FFT (Fast Fourier Transform)
+N = len(signal)
+yf = np.fft.fft(signal)
+xf = np.fft.fftfreq(N, 1 / sample_rate)
+
+# Plot the frequency spectrum
+plt.figure(figsize=(12, 6))
+plt.plot(xf[:N//2], 2.0/N * np.abs(yf[:N//2]), color='navy', lw=1.5)
+
+# Aesthetics improvements
+plt.title('Simulated Frequency Spectrum of A440 on a Grand Piano', fontsize=16, weight='bold')
+plt.xlabel('Frequency (Hz)', fontsize=14)
+plt.ylabel('Amplitude', fontsize=14)
+plt.xlim(0, 4186) # Limit to the highest frequency on a piano (C8)
+plt.ylim(0, None)
+
+# Shading the region for normal speaking range (approximately 85 Hz to 255 Hz)
+plt.axvspan(500, 2000, color='lightpink', alpha=0.5)
+
+# Annotate the shaded region
+plt.annotate('Normal Speaking Range (500 Hz - 2000 Hz)',
+ xy=(2000, 0.7), xycoords='data',
+ xytext=(2500, 0.5), textcoords='data',
+ arrowprops=dict(facecolor='black', arrowstyle="->"),
+ fontsize=12, color='black')
+
+# Remove top and right spines
+plt.gca().spines['top'].set_visible(False)
+plt.gca().spines['right'].set_visible(False)
+
+# Customize ticks
+plt.xticks(fontsize=12)
+plt.yticks(fontsize=12)
+
+# Light grid
+plt.grid(color='grey', linestyle=':', linewidth=0.5)
+
+# Show the plot
+plt.tight_layout()
+plt.show()
+
+
++Show code cell output + +
+
+
+
+
+-
+
\(S(t)\) Memory: What quick tests are out there?
+\(h(t)\) Emotions: Let’s focus on how they translate to motion!
+
\(\sigma\) Varcov-matrix#
+-
+
\((X'X)^T \cdot X'Y\): Frailty: Energy, Omics, Activity, Muscle, Strength, Pace,
+
+
+
+
++Show code cell source + +
+
+
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Clock settings; f(t) random disturbances making "paradise lost"
+clock_face_radius = 1.0
+number_of_ticks = 7
+tick_labels = [
+ "Root-iADL (i)",
+ "Hunter-gather (ii7♭5)", "Peasant (III)", "Farmer (iv)", "Manufacturer (V7♭9♯9♭13)",
+ "Energy (VI)", "Transport (VII)"
+]
+
+# Calculate the angles for each tick (in radians)
+angles = np.linspace(0, 2 * np.pi, number_of_ticks, endpoint=False)
+# Inverting the order to make it counterclockwise
+angles = angles[::-1]
+
+# Create figure and axis
+fig, ax = plt.subplots(figsize=(8, 8))
+ax.set_xlim(-1.2, 1.2)
+ax.set_ylim(-1.2, 1.2)
+ax.set_aspect('equal')
+
+# Draw the clock face
+clock_face = plt.Circle((0, 0), clock_face_radius, color='lightgrey', fill=True)
+ax.add_patch(clock_face)
+
+# Draw the ticks and labels
+for angle, label in zip(angles, tick_labels):
+ x = clock_face_radius * np.cos(angle)
+ y = clock_face_radius * np.sin(angle)
+
+ # Draw the tick
+ ax.plot([0, x], [0, y], color='black')
+
+ # Positioning the labels slightly outside the clock face
+ label_x = 1.1 * clock_face_radius * np.cos(angle)
+ label_y = 1.1 * clock_face_radius * np.sin(angle)
+
+ # Adjusting label alignment based on its position
+ ha = 'center'
+ va = 'center'
+ if np.cos(angle) > 0:
+ ha = 'left'
+ elif np.cos(angle) < 0:
+ ha = 'right'
+ if np.sin(angle) > 0:
+ va = 'bottom'
+ elif np.sin(angle) < 0:
+ va = 'top'
+
+ ax.text(label_x, label_y, label, horizontalalignment=ha, verticalalignment=va, fontsize=10)
+
+# Remove axes
+ax.axis('off')
+
+# Show the plot
+plt.show()
+
+
++Show code cell output + +
+
+
+
+
+\(\%\) Precision#
+-
+
\(\beta\) Omics: No isolated one should be of great significance
+
+
+
+
++Show code cell source + +
+
+
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Clock settings; f(t) random disturbances making "paradise lost"
+clock_face_radius = 1.0
+number_of_ticks = 9
+tick_labels = [
+ "Sun-Genomics", "Chlorophyll-Transcriptomics", "Flora-Proteomics", "Animals-Metabolomics",
+ "Wood-Epigenomics", "Coal-Lipidomics", "Hydrocarbons-Glycomics", "Renewable-Metagenomics", "Nuclear-Phenomics"
+]
+
+# Calculate the angles for each tick (in radians)
+angles = np.linspace(0, 2 * np.pi, number_of_ticks, endpoint=False)
+# Inverting the order to make it counterclockwise
+angles = angles[::-1]
+
+# Create figure and axis
+fig, ax = plt.subplots(figsize=(8, 8))
+ax.set_xlim(-1.2, 1.2)
+ax.set_ylim(-1.2, 1.2)
+ax.set_aspect('equal')
+
+# Draw the clock face
+clock_face = plt.Circle((0, 0), clock_face_radius, color='lightgrey', fill=True)
+ax.add_patch(clock_face)
+
+# Draw the ticks and labels
+for angle, label in zip(angles, tick_labels):
+ x = clock_face_radius * np.cos(angle)
+ y = clock_face_radius * np.sin(angle)
+
+ # Draw the tick
+ ax.plot([0, x], [0, y], color='black')
+
+ # Positioning the labels slightly outside the clock face
+ label_x = 1.1 * clock_face_radius * np.cos(angle)
+ label_y = 1.1 * clock_face_radius * np.sin(angle)
+
+ # Adjusting label alignment based on its position
+ ha = 'center'
+ va = 'center'
+ if np.cos(angle) > 0:
+ ha = 'left'
+ elif np.cos(angle) < 0:
+ ha = 'right'
+ if np.sin(angle) > 0:
+ va = 'bottom'
+ elif np.sin(angle) < 0:
+ va = 'top'
+
+ ax.text(label_x, label_y, label, horizontalalignment=ha, verticalalignment=va, fontsize=10)
+
+# Remove axes
+ax.axis('off')
+
+# Show the plot
+plt.show()
+
+
++Show code cell output + +
+
+
+
+
+-
+
\(SV_t'\) Independence: modal-interchange-nondiminishing. The modes here include the -omics & so much more
+
+
+
+
+
++Show code cell source + +
+
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define the total utility function U(Q)
+def total_utility(Q):
+ return 100 * np.log(Q + 1) # Logarithmic utility function for illustration
+
+# Define the marginal utility function MU(Q)
+def marginal_utility(Q):
+ return 100 / (Q + 1) # Derivative of the total utility function
+
+# Generate data
+Q = np.linspace(1, 100, 500) # Quantity range from 1 to 100
+U = total_utility(Q)
+MU = marginal_utility(Q)
+
+# Plotting
+plt.figure(figsize=(14, 7))
+
+# Plot Total Utility
+plt.subplot(1, 2, 1)
+plt.plot(Q, U, label=r'Total Utility $U(Q) = 100 \log(Q + 1)$', color='blue')
+plt.title('Total Utility')
+plt.xlabel('Quantity (Q)')
+plt.ylabel('Total Utility (U)')
+plt.legend()
+plt.grid(True)
+
+# Plot Marginal Utility
+plt.subplot(1, 2, 2)
+plt.plot(Q, MU, label=r'Marginal Utility $MU(Q) = \frac{dU(Q)}{dQ} = \frac{100}{Q + 1}$', color='red')
+plt.title('Marginal Utility')
+plt.xlabel('Quantity (Q)')
+plt.ylabel('Marginal Utility (MU)')
+plt.legend()
+plt.grid(True)
+
+# Adding some calculus notation and Greek symbols
+plt.figtext(0.5, 0.02, r"$MU(Q) = \frac{dU(Q)}{dQ} = \lim_{\Delta Q \to 0} \frac{U(Q + \Delta Q) - U(Q)}{\Delta Q}$", ha="center", fontsize=12)
+
+plt.tight_layout()
+plt.show()
+
+
++Show code cell output + +
+
+
+
+
+ 1. Exposure
+ \
+ 2. Role -> 4. Categorical.Imperative -> 5. Determinism -> 6. Freewill
+ /
+ 3. Impulse
+
+Activity Counts. While association isn’t causation, there is a strong one between hearing acuity & physical activity. Investiage “arousal” across all senses external & internal to see impact on DNA-HAT-E.Box-mRNA-Wide-Associations#
-
+
- +
- +
- +
- +
- +
+
+
+
+
+
++Show code cell source + +
+
+
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Stages of life (x-axis)
+stages_of_life = [
+ 'Conceptus', 'Morula', 'Bilaminar Disk', 'Embryo', 'Fetus',
+ 'Birth', 'Childhood', 'Adolescence', 'Adulthood', '100 Years'
+]
+
+# Approximate rate of cell-division or growth (y-axis)
+growth_rate = [
+ 100, 90, 85, 75, 60, 30, 20, 10, 5, 2
+]
+
+# Recreate the plot with the specified style
+
+plt.figure(figsize=(12, 6))
+plt.plot(stages_of_life, growth_rate, marker='o', linestyle='-', color='blue')
+
+# Remove upper and right borders
+ax = plt.gca()
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+
+# Add labels and title
+plt.xlabel('Stage of Life')
+plt.ylabel('Rate of Cell-Division/Growth')
+plt.title('Rate of Cell-Division/Growth from Conceptus to 100 Years')
+
+# Annotate the stages
+for i, txt in enumerate(growth_rate):
+ plt.annotate(txt, (stages_of_life[i], growth_rate[i]), textcoords="offset points", xytext=(0,10), ha='center')
+
+# Add a very light dotted grid
+plt.grid(True, which='both', linestyle=':', linewidth=0.5)
+
+plt.show()
+
+
++Show code cell output + +
+
+
+
+
+