Turn into right format from camber cloud jupyter notebook format

main.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from scipy import ndimage
+
+matrix = np.loadtxt('chimpanzee_v_human.txt', skiprows=2)
+print(f"Matrix shape: {matrix.shape}")
+
+def apply_screening_window(matrix, window_size=5, threshold=0):
+   
+    # Option 1: Moving average filter
+    filtered = ndimage.uniform_filter(matrix, size=window_size)
+    
+    # Option 2: Apply threshold to remove low values
+    filtered[filtered < threshold] = 0
+    
+    # Option 3: Downsample to reduce size
+    # Take every Nth point
+    downsampled = filtered[::2, ::2]  # Takes every 2nd point
+    
+    return filtered
+
+# Apply filter
+filtered_matrix = apply_screening_window(matrix, window_size=5, threshold=0.1)
+
+def get_chromosome_boundaries(chr_sizes, matrix_dimension, genome_total_size):
+    """
+    Convert chromosome sizes to matrix positions
+    
+    Parameters:
+    -----------
+    chr_sizes : dict
+        Dictionary of chromosome names and their sizes in base pairs
+    matrix_dimension : int
+        The size of the matrix dimension (e.g., 2000 for x-axis or y-axis)
+    genome_total_size : int
+        Total genome size in base pairs (sum of all chromosome sizes)
+    
+    Returns:
+    --------
+    boundaries : list
+        Matrix positions where chromosomes start
+    labels : list
+        Chromosome names
+    midpoints : list
+        Midpoints for placing chromosome labels
+    """
+    cumulative_bp = 0
+    boundaries = [0]  # Start at 0
+    labels = []
+    midpoints = []
+    
+    for chr_name, size_bp in chr_sizes.items():
+        # Calculate the proportion of the genome this chromosome represents
+        proportion = size_bp / genome_total_size
+        
+        # Convert to matrix position
+        matrix_size_for_chr = proportion * matrix_dimension
+        
+        # Update cumulative position
+        cumulative_bp += size_bp
+        cumulative_matrix_pos = int((cumulative_bp / genome_total_size) * matrix_dimension)
+        
+        # Calculate midpoint for label
+        midpoint = (boundaries[-1] + cumulative_matrix_pos) / 2
+        midpoints.append(midpoint)
+        
+        # Store
+        boundaries.append(cumulative_matrix_pos)
+        labels.append(chr_name)
+    
+    # Remove the last boundary (it's just the end of the matrix)
+    boundaries = boundaries[:-1]
+    
+    return boundaries, labels, midpoints
+
+# Example usage for human and chimpanzee
+# Human chromosome sizes (hg38)
+chr_sizes_human = {
+    'chr1': 248956422,
+    'chr2': 242193529,
+    'chr3': 198295559,
+    'chr4': 190214555,
+    'chr5': 181538259,
+    'chr6': 170805979,
+    'chr7': 159345973,
+    'chr8': 145138636,
+    'chr9': 138394717,
+    'chr10': 133797422,
+    'chr11': 135086622,
+    'chr12': 133275309,
+    'chr13': 114364328,
+    'chr14': 107043718,
+    'chr15': 101991189,
+    'chr16': 90338345,
+    'chr17': 83257441,
+    'chr18': 80373285,
+    'chr19': 58617616,
+    'chr20': 64444167,
+    'chr21': 46709983,
+    'chr22': 50818468,
+    'chrX': 156040895,
+    'chrY': 57227415,
+}
+
+# Chimpanzee chromosome sizes (panTro6)
+chr_sizes_chimp = {
+    'chr1': 231299448,
+    'chr2': 201579334,  # Note: chimp has 2A and 2B instead of fused chr2
+    'chr3': 191670129,
+    'chr4': 178964043,
+    'chr5': 183320697,
+    'chr6': 174648847,
+    'chr7': 155691389,
+    'chr8': 146288486,
+    'chr9': 136991367,
+    'chr10': 140160335,
+    'chr11': 118863153,
+    'chr12': 122853238,
+    'chr13': 144476839,
+    'chr14': 120149342,
+    'chr15': 104701121,
+    'chr16': 93846347,
+    'chr17': 90695042,
+    'chr18': 94528409,
+    'chr19': 99595324,
+    'chr20': 68087067,
+    'chr21': 78115444,
+    'chr22': 51350302,
+    'chr23': 59520411,
+    'chrX': 154259566,
+    'chrY': 26350395,
+}
+
+# Calculate total genome sizes
+human_total_size = sum(chr_sizes_human.values())
+chimp_total_size = sum(chr_sizes_chimp.values())
+
+print(f"Human genome size: {human_total_size:,} bp")
+print(f"Chimp genome size: {chimp_total_size:,} bp")
+
+# Get boundaries for the 2000x2000 matrix
+# X-axis: human (reference), Y-axis: chimp (query)
+human_boundaries, human_labels, human_midpoints = get_chromosome_boundaries(
+    chr_sizes_human, 
+    matrix_dimension=2000,  # x-axis dimension
+    genome_total_size=human_total_size
+)
+
+chimp_boundaries, chimp_labels, chimp_midpoints = get_chromosome_boundaries(
+    chr_sizes_chimp,
+    matrix_dimension=2000,  # y-axis dimension
+    genome_total_size=chimp_total_size
+)
+
+def apply_threshold(n):
+    if n > 30:
+        return 1
+    else:
+        return 0
+
+
+def plot_dotplot_with_chromosomes(matrix, 
+                                  ref_boundaries, ref_labels, ref_midpoints,
+                                  query_boundaries, query_labels, query_midpoints,
+                                  figsize=(14, 14), cmap='binary'):
+    apply_threshold_vectorized = np.vectorize(apply_threshold)
+    """
+    Create dot plot with chromosome boundary lines using actual genome proportions
+    """
+    fig, ax = plt.subplots(figsize=figsize)
+    
+    # Plot the matrix
+    im = ax.imshow(apply_threshold_vectorized(matrix), cmap='binary', aspect='auto', 
+                   origin='lower', interpolation='nearest')
+    
+    # Add colorbar
+    plt.colorbar(im, ax=ax, label='Similarity Score', shrink=0.8)
+
+    plt.style.use("default")
+    
+    # Add vertical lines for reference genome chromosomes (x-axis)
+    for boundary in ref_boundaries[1:]:  # Skip first boundary (0)
+        ax.axvline(x=boundary, color='cyan', linestyle='--', 
+                   linewidth=0.8, alpha=0.6)
+    
+    # Add horizontal lines for query genome chromosomes (y-axis)
+    for boundary in query_boundaries[1:]:  # Skip first boundary (0)
+        ax.axhline(y=boundary, color='cyan', linestyle='--', 
+                   linewidth=0.8, alpha=0.6)
+    
+    # Add chromosome labels at midpoints
+    ax.set_xticks(ref_midpoints)
+    ax.set_xticklabels(ref_labels, rotation=45, ha='right', fontsize=9)
+    
+    ax.set_yticks(query_midpoints)
+    ax.set_yticklabels(query_labels, fontsize=9)
+    
+    ax.set_xlabel('Human Genome (Reference)', fontsize=12, fontweight='bold')
+    ax.set_ylabel('Chimpanzee Genome (Query)', fontsize=12, fontweight='bold')
+    ax.set_title('Human vs Chimpanzee Genome-wide Dot Plot', 
+                 fontsize=14, fontweight='bold', pad=20)
+    
+    # Add grid for better readability
+    ax.grid(False)
+    
+    plt.tight_layout()
+    return fig, ax
+
+# Create the plot
+fig, ax = plot_dotplot_with_chromosomes(
+    matrix,
+    human_boundaries, human_labels, human_midpoints,
+    chimp_boundaries, chimp_labels, chimp_midpoints,
+    figsize=(16, 16),
+    cmap='hot'  # or 'RdYlBu_r', 'viridis', 'plasma'
+)
+
+plt.savefig('human_chimp_dotplot.png', dpi=300, bbox_inches='tight')
+plt.show()
+