Add make_bucket and scale_csv.py scripts.

scripts/make_bucket

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+#!/bin/bash
+
+# This shell script creates a new GS bucket and configures it
+# with our standard read access and CORS policy.
+#
+# Usage: just run it and follow the prompts!
+
+# Default values
+DEFAULT_PROJECT="zetta-research"
+DEFAULT_REGION="us-east1"
+
+# Function to prompt with default value
+prompt_with_default() {
+    local prompt=$1
+    local default=$2
+    local response
+
+    read -p "${prompt} [${default}]: " response
+    echo ${response:-$default}
+}
+
+# Create CORS configuration file
+cat > cors.json << 'EOL'
+[{
+    "maxAgeSeconds": 3600,
+    "method": ["GET", "HEAD"],
+    "origin": ["*"],
+    "responseHeader": ["Content-Type", "Range"]
+}]
+EOL
+
+# Function to create a bucket with specified settings
+create_bucket() {
+    local bucket_name=$1
+    local project_id=$2
+    local region=$3
+
+    echo "Creating bucket: gs://${bucket_name}"
+    echo "Project ID: ${project_id}"
+    echo "Region: ${region}"
+
+    gsutil mb \
+        -p ${project_id} \
+        -c regional \
+        -l ${region} \
+        -b on \
+        gs://${bucket_name}
+
+    if [ $? -eq 0 ]; then
+        echo "Successfully created bucket: ${bucket_name}"
+
+        # Set CORS policy
+        echo "Setting CORS policy for: ${bucket_name}"
+        gsutil cors set cors.json gs://${bucket_name}
+
+	# Add public read access
+	echo "Setting public read access..."
+	gsutil iam ch allUsers:objectViewer gs://${bucket_name}
+
+    else
+        echo "Failed to create bucket: ${bucket_name}"
+        return 1
+    fi
+}
+
+# Main execution
+echo "Starting bucket creation process..."
+
+# Check if gsutil is installed
+if ! command -v gsutil &> /dev/null; then
+    echo "Error: gsutil is not installed. Please install Google Cloud SDK first."
+    exit 1
+fi
+
+# Check if user is authenticated
+if ! gsutil ls &> /dev/null; then
+    echo "Error: Not authenticated. Please run 'gcloud auth login' first."
+    exit 1
+fi
+
+# Get project ID and region with defaults
+PROJECT_ID=$(prompt_with_default "Enter project ID" "$DEFAULT_PROJECT")
+REGION=$(prompt_with_default "Enter region" "$DEFAULT_REGION")
+
+# Ask user for bucket name (no default)
+read -p "Enter the bucket name (without gs:// prefix): " bucket_name
+
+# Validate bucket name
+if [[ -z "$bucket_name" ]]; then
+    echo "Error: Bucket name cannot be empty"
+    exit 1
+fi
+
+# Show summary before proceeding
+echo -e "\nSummary of settings:"
+echo "Project ID: ${PROJECT_ID}"
+echo "Region: ${REGION}"
+echo "Bucket name: ${bucket_name}"
+read -p "Proceed with bucket creation? (y/N): " confirm
+if [[ ! "$confirm" =~ ^[yY]$ ]]; then
+    echo "Bucket creation cancelled."
+    exit 0
+fi
+
+# Create the bucket
+create_bucket "$bucket_name" "$PROJECT_ID" "$REGION"
+if [ $? -ne 0 ]; then
+    echo "Error occurred while creating bucket: ${bucket_name}"
+    exit 1
+fi
+
+# Cleanup
+rm cors.json
+
+echo "Bucket creation completed!"

scripts/scale_csv.py

@@ -0,0 +1,101 @@
+"""
+This script scales the X,Y,Z and X2,Y2,Z2 columns (if any) in a CSV file
+from one resolution to another.
+"""
+
+from collections import namedtuple
+from pathlib import Path
+
+import pandas as pd
+
+# Create a named tuple for 3D vectors
+Vec3D = namedtuple("Vec3D", ["x", "y", "z"])
+
+
+def input_or_default(prompt, value):
+    response = input(f"{prompt} [{value}]: ")
+    if response == "":
+        response = value
+    return response
+
+
+def input_vec3D(prompt="", default=None):
+    while True:
+        s = input(prompt + (f" [{default.x}, {default.y}, {default.z}]" if default else "") + ": ")
+        if s == "" and default:
+            return default
+        try:
+            x, y, z = map(float, s.replace(",", " ").split())
+            return Vec3D(x, y, z)
+        except:
+            print("Enter x, y, and z values separated by commas or spaces.")
+
+
+def get_scale_factors(current_res, desired_res):
+    """Calculate scale factors for each dimension"""
+    return Vec3D(
+        current_res.x / desired_res.x, current_res.y / desired_res.y, current_res.z / desired_res.z
+    )
+
+
+def scale_coordinates(df, scale_factors):
+    """Scale the coordinates in the dataframe"""
+    # Scale X, Y, Z columns
+    df["X"] *= scale_factors.x
+    df["Y"] *= scale_factors.y
+    df["Z"] *= scale_factors.z
+
+    # Scale X2, Y2, Z2 columns if they exist
+    if all(col in df.columns for col in ["X2", "Y2", "Z2"]):
+        df["X2"] *= scale_factors.x
+        df["Y2"] *= scale_factors.y
+        df["Z2"] *= scale_factors.z
+
+    return df
+
+
+def main():
+    # Get input file path
+    input_path: str | Path = ""
+    while True:
+        input_path = input("Enter path to input CSV file: ").strip()
+        if Path(input_path).is_file():
+            break
+        print("File not found. Please enter a valid path.")
+
+    # Generate default output path
+    input_path = Path(input_path)
+    default_output = str(input_path.parent / f"{input_path.stem}_scaled{input_path.suffix}")
+
+    # Get current and desired resolutions
+    current_res = input_vec3D("Enter current resolution (x, y, z)")
+    desired_res = input_vec3D("Enter desired resolution (x, y, z)")
+
+    # Get output path
+    output_path = input_or_default("\nEnter output file path", default_output)
+
+    try:
+        # Read the CSV file
+        df = pd.read_csv(input_path)
+
+        # Calculate scale factors
+        scale_factors = get_scale_factors(current_res, desired_res)
+
+        # Scale the coordinates
+        df = scale_coordinates(df, scale_factors)
+
+        # Write the scaled data to output file
+        df.to_csv(output_path, index=False)
+
+        print(f"\nScaled coordinates have been written to: {output_path}")
+        print(f"Scale factors used (current/desired):")
+        print(f"X: {scale_factors.x:.4f}")
+        print(f"Y: {scale_factors.y:.4f}")
+        print(f"Z: {scale_factors.z:.4f}")
+
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+
+
+if __name__ == "__main__":
+    main()