Chakra Energy Distribution During Hare Krishna Chanting
Description
Description This code analyzes the impact of chanting the Hare Krishna mantra on the body's chakras using Python. It visualizes the cumulative energy distribution across the chakras during the chant and provides statistical analysis of the energy levels. Imports: numpy: For numerical operations and array handling. matplotlib.pyplot: For plotting the heatmap and bar charts. seaborn: For creating aesthetically pleasing statistical graphics. Define the Mantra and Chakra Associations: mantra_sequence: A list representing the sequence of words in the Hare Krishna mantra. chakra_association: A dictionary mapping each word in the mantra to its associated chakras, represented as binary lists (1 for activation, 0 for no activation). Initialize Chakra Energy Levels: chakra_levels: A 2D array initialized with zeros to store the energy levels for each chakra throughout the mantra sequence. Calculate Cumulative Impact on Chakras: Iterate through each word in the mantra sequence. For each word, update the corresponding chakra energy levels by accumulating the impact values. Generate Heatmap for Chakra Energy Distribution: Create a heatmap to visualize the energy distribution across the chakras for each word in the mantra sequence. Customize the heatmap with labels for the chakras and chant sequence. Statistical Analysis: Compute the mean and standard deviation of the energy levels for each chakra. Plot these statistical measures using a bar chart to compare the average energy levels and their variability across different chakras.
Files
Steps to reproduce
import numpy as np import matplotlib.pyplot as plt import seaborn as sns # Define the mantra and chakra associations mantra_sequence = ["Hare", "Krishna", "Hare", "Krishna", "Krishna", "Krishna", "Hare", "Hare", "Hare", "Rama", "Hare", "Rama", "Rama", "Rama", "Hare", "Hare"] chakra_association = { "Hare": [1, 0, 0, 1, 0, 0, 1], # Root, Heart, Crown "Krishna": [0, 1, 1, 0, 1, 0, 0], # Sacral, Solar Plexus, Throat "Rama": [0, 0, 0, 1, 0, 1, 1] # Heart, Third Eye, Crown } # Initialize the energy levels for each chakra chakra_levels = np.zeros((7, len(mantra_sequence))) # Calculate the cumulative impact on chakras for i, word in enumerate(mantra_sequence): impact = chakra_association[word] if i == 0: chakra_levels[:, i] = impact else: chakra_levels[:, i] = chakra_levels[:, i-1] + impact # Generate heatmap for chakra energy distribution plt.figure(figsize=(12, 8)) sns.heatmap(chakra_levels, annot=True, cmap="YlGnBu", cbar_kws={'label': 'Energy Level'}) plt.xticks(np.arange(len(mantra_sequence))+0.5, mantra_sequence, rotation=90) plt.yticks(np.arange(7)+0.5, ["Root", "Sacral", "Solar Plexus", "Heart", "Throat", "Third Eye", "Crown"], rotation=0) plt.title("Chakra Energy Distribution During Hare Krishna Mantra Chanting") plt.xlabel("Chant Sequence") plt.ylabel("Chakras") plt.show() # Statistical Analysis mean_energy = np.mean(chakra_levels, axis=1) std_energy = np.std(chakra_levels, axis=1) # Plot the statistical analysis plt.figure(figsize=(12, 6)) x = np.arange(7) plt.bar(x - 0.2, mean_energy, 0.4, label='Mean Energy') plt.bar(x + 0.2, std_energy, 0.4, label='Std Deviation') plt.xticks(x, ["Root", "Sacral", "Solar Plexus", "Heart", "Throat", "Third Eye", "Crown"]) plt.xlabel("Chakras") plt.ylabel("Energy Levels") plt.title("Statistical Analysis of Chakra Energy Levels") plt.legend() plt.show()