Tyler

Everybody has got blood 🩸 on their hands - Bourdain

                             1. f(t) 
                                    \
                         2. S(t) -> 4. y:h'(t)=0;t(X'X).X'Y -> 5.b -> 6. SV'
                                    /
                                    3. h(t)

The dramatic arc. Music: ii-V7-i. Constraint: inherited-additions-overcoming. Bible: israel-moses-joshua. Gospel: departure-struggle-return. Reality: slavery-emancipation-will2power. Alignment: genesis-exodus-canaan. Dante: inferno-urgatorio-paradiso. History: wild-tame-divine. Perry’s work often revolves around immediate, identifiable tension (e.g., a bad marriage) and its resolution (often via church music or community healing). This mirrors the ii-V-I progression in music, a straightforward path to resolution. The critique here is that Perry’s characters lack the richness and complexity of more nuanced chords and alterations. Their journeys are more predictable, lacking the surprising twists and depth signified by MQ-TEA. While Tyler Perry captures the collective unconscious through accessible, relatable conflicts and resolutions, his characters’ arcs do not venture into the more complex, intricate progressions that add layers of depth and unpredictability. This results in narratives that are satisfying yet somewhat formulaic, lacking the broader spectrum of life’s parameters and challenges. In essence, the “SV’” of Perry’s characters is akin to a musical composition relying heavily on fundamental progressions without the enriching deviations and complexities that create a more profound and compelling piece. He doesn’t throw us a bone to chew on for a while & we are left in a highly sanized & curated world. Our senses are numbed, minds dulled, bones & muscles shrivel, culture loses vitality, attractiveness to other cultures lost, and inevitable decline and frailty enshew ⁴⁵

\(\mu\), Troy ii

• \(f(t)\) Phonetic: Engoma

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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)

# 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()

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• \(S(t)\) Temperament: Curated ^{1 31}

• \(h(t)\) Rhythm: Pulse

• No pulse: dead or nihilistic

• Pulse: alive & thriving

\(\sigma\), Odyssey V7

• \((X'X)^T \cdot X'Y\) Mode: Degree

• Pentatonic excludes ii7♭5 & VI

• Diatonic & chromatic are tidy references

• Ethnic variants are best understood as deviating from diatonic or chromatic scale

• They may be viewed as “alterations” (e.g., mixolydian ♭13♭9♯9 ~ Flamenco phrygian)

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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 (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()

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\(\%\), Ithaca i

• \(\beta\) Chords: Progression

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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", "Chlorophyll", "Produce", "Animals",
    "Wood", "Coal", "Hydrocarbons", "Renewable", "Nuclear"
]

# 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()

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• \(SV'\) Blood 🩸: Rituals

To avoid diminishing marginal utility, modal-interchange is key. Some call it life-work balance. But this is more precise since the “balance” is about recalling early stages in the evolution of human society in indulging in those activities close-enough to the original habitats and formats. Its the excessive curation of modern life that fuels “mental” illness, numbness, and worse

• Phonetics: Sanctus (Trumpets)

• Temperament: Mater (Wohltemperierte)

• Rhythm: Quirk (African)

• Modes: Irish Dance (Dorian)

• Chords: Epic (Cantus)

• Blood: Dies Irae ³⁶