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neue Datei für TEFBogen

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Yuelin Wang
2025-05-20 08:30:38 +02:00
parent 051b649cc0
commit 464dda89ae
27 changed files with 269 additions and 1458 deletions
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CAD/OmnifloCAD/OmnifloBogen.dwg
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Editor2DLibrary/SSG/Init/tef_bögen.json
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SVGs/Omniflo/python/1_calculate_ture_width_and_height_dimensions_with_profile_width.py
-151
View File
@@ -1,151 +0,0 @@
''' Key Analysis Points:
Data Safety:
Uses safe_float() to handle potential data conversion issues
Creates copies of items to avoid modifying original data
Geometric Calculations:
Converts angles to radians for trigonometric functions
Performs width/height calculations based on center line measurements and angles
Rounds results to 3 decimal places for consistency
Validation:
Compares calculated values with original values
Flags discrepancies with "Please verify" status
Provides detailed difference metrics
Error Handling:
Preserves original data when processing fails
Tracks and reports all errors
Provides comprehensive statistics
Output:
Generates a new JSON file with calculated fields
Provides detailed console output for verification
Maintains original structure while adding new calculated fields
The script is designed to process SVG measurement data, perform geometric validations, and produce an enhanced dataset while providing thorough feedback about the processing results. '''
import json
import math
def safe_float(value, default=0.0):
"""Safely convert value to float, return default if conversion fails"""
try:
return float(value) if value != '' else default
except (ValueError, TypeError):
return default
def calculate_attributes(item):
"""Calculate and add new attributes to the item based on geometric calculations"""
# Create a copy of the item to avoid modifying the original
processed_item = item.copy()
# Ensure all numeric fields have valid values using safe_float
processed_item["center_line_width_mm"] = safe_float(item.get("center_line_width_mm", 0))
processed_item["center_line_height_mm"] = safe_float(item.get("center_line_height_mm", 0))
processed_item["Objekt_width_mm"] = safe_float(item.get("Objekt_width_mm", 0))
processed_item["Objekt_height_mm"] = safe_float(item.get("Objekt_height_mm", 0))
processed_item["Winkel"] = safe_float(item.get("Winkel", 0))
# Convert angle to radians for trigonometric calculations
winkel_rad = math.radians(processed_item["Winkel"])
sin_value = math.sin(winkel_rad)
cos_value = math.cos(winkel_rad)
# Calculate new dimensions with 3 decimal places precision
if processed_item["Winkel"]!=180:
calculated_width = processed_item["center_line_width_mm"] + sin_value * 21.040
calculated_height = processed_item["center_line_height_mm"] + 21.040 + cos_value * 21.040
else:
calculated_width = processed_item["center_line_width_mm"]
calculated_height = processed_item["center_line_height_mm"]+ 21.040 + 21.040
processed_item["calculated_objekt_width_mm"] = round(calculated_width, 3)
processed_item["calculated_objekt_height_mm"] = round(calculated_height, 3)
# Calculate differences between original and calculated values
width_diff = round(processed_item["Objekt_width_mm"] - processed_item["calculated_objekt_width_mm"], 3)
height_diff = round(processed_item["Objekt_height_mm"] - processed_item["calculated_objekt_height_mm"], 3)
# Prepare comparison results for console output (not saved to JSON)
comparison_results = {
"width": {
"calculated": processed_item["calculated_objekt_width_mm"],
"original": processed_item["Objekt_width_mm"],
"difference": width_diff,
"status": "OK" if abs(width_diff) < 0.001 else "Please verify"
},
"height": {
"calculated": processed_item["calculated_objekt_height_mm"],
"original": processed_item["Objekt_height_mm"],
"difference": height_diff,
"status": "OK" if abs(height_diff) < 0.001 else "Please verify"
}
}
return processed_item, comparison_results
def process_json_file(input_file, output_file):
"""Main function to process JSON file and generate output"""
# Read input file
try:
with open(input_file, 'r', encoding='utf-8') as f:
data = json.load(f)
except FileNotFoundError:
print(f"Error: Input file '{input_file}' not found")
return
except json.JSONDecodeError as e:
print(f"JSON parsing error: {e}")
return
# Verify data is a list
if not isinstance(data, list):
print("Error: JSON data should be an array")
return
# Process data
processed_data = []
error_items = []
print("\nStarting JSON data processing...\n")
for idx, item in enumerate(data, start=1):
try:
processed_item, comparison = calculate_attributes(item)
processed_data.append(processed_item)
# Print comparison results to console
print(f"Item {idx} [{item.get('SVGname', 'Unnamed')}] comparison results:")
print(f" Width: Calculated={comparison['width']['calculated']} | Original={comparison['width']['original']} | Difference={comparison['width']['difference']} | {comparison['width']['status']}")
print(f" Height: Calculated={comparison['height']['calculated']} | Original={comparison['height']['original']} | Difference={comparison['height']['difference']} | {comparison['height']['status']}")
print("-" * 60)
except Exception as e:
error_items.append((idx, str(e)))
# Preserve original data (without any processed fields)
processed_data.append(item)
# Print error message to console
print(f"Error processing item {idx} [{item.get('SVGname', 'Unnamed')}]: {str(e)}")
print("-" * 60)
# Write output file
try:
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(processed_data, f, indent=4, ensure_ascii=False)
print(f"\nProcessing complete. Results saved to {output_file}")
except IOError as e:
print(f"Error writing output file: {e}")
return
# Print statistics
total_items = len(data)
success_items = total_items - len(error_items)
print("\nProcessing statistics:")
print(f"Total items: {total_items}")
print(f"Successfully processed: {success_items}")
print(f"Failed items: {len(error_items)}")
# Example usage
if __name__ == "__main__":
input_filename = "1_SVGErfassung_updated_new_input.json" # Replace with your input filename
output_filename = "1_SVGErfassung_updated_new_output.json" # Replace with desired output filename
process_json_file(input_filename, output_filename)
SVGs/Omniflo/python/2_calculated_px_cps_output.json
-602
View File
@@ -1,602 +0,0 @@
[
{
"SVGname": "APB_110_R400_45_400_821104021.svg",
"Sivasnr": "821104021",
"Winkel": 45.0,
"center_line_width_mm": 669.491,
"center_line_height_mm": 399.505,
"Objekt_width_mm": 684.367,
"Objekt_height_mm": 435.421,
"calculated_objekt_width_mm": 684.369,
"calculated_objekt_height_mm": 435.423,
"Objekt_width_px": 2586.565,
"Objekt_height_px": 1645.674,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 636.239,
"scale_factor": 1.461204,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 48.321,
"direction": 270.0
},
{
"id": "cp2",
"x": 978.263,
"y": 965.835,
"direction": 135.0
}
]
},
{
"SVGname": "APB_110_R400_67_5_339_821104024.svg",
"Sivasnr": "821104024",
"Winkel": 67.5,
"center_line_width_mm": 507.82,
"center_line_height_mm": 339.315,
"Objekt_width_mm": 527.258,
"Objekt_height_mm": 368.405,
"calculated_objekt_width_mm": 527.258,
"calculated_objekt_height_mm": 368.407,
"Objekt_width_px": 1992.772,
"Objekt_height_px": 1392.387,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 698.719,
"scale_factor": 1.896605,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 57.111,
"direction": 270.0
},
{
"id": "cp2",
"x": 963.134,
"y": 978.149,
"direction": 157.5
}
]
},
{
"SVGname": "APB_110_R400_90_500_821104022.svg",
"Sivasnr": "821104022",
"Winkel": 90.0,
"center_line_width_mm": 500.0,
"center_line_height_mm": 500.0,
"Objekt_width_mm": 521.04,
"Objekt_height_mm": 521.04,
"calculated_objekt_width_mm": 521.04,
"calculated_objekt_height_mm": 521.04,
"Objekt_width_px": 1969.271,
"Objekt_height_px": 1969.271,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 1000.0,
"scale_factor": 1.919238,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 40.381,
"direction": 270.0
},
{
"id": "cp2",
"x": 959.619,
"y": 1000.0,
"direction": 180.0
}
]
},
{
"SVGname": "APB_110_R500_90_550_550_821104030.svg",
"Sivasnr": "821104030",
"Winkel": 90.0,
"center_line_width_mm": 550.0,
"center_line_height_mm": 550.0,
"Objekt_width_mm": 571.04,
"Objekt_height_mm": 571.04,
"calculated_objekt_width_mm": 571.04,
"calculated_objekt_height_mm": 571.04,
"Objekt_width_px": 2158.246,
"Objekt_height_px": 2158.246,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 1000.0,
"scale_factor": 1.751191,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 36.845,
"direction": 270.0
},
{
"id": "cp2",
"x": 963.155,
"y": 1000.0,
"direction": 180.0
}
]
},
{
"SVGname": "APB_110_R550_22_5_84_821104025.svg",
"Sivasnr": "821104025",
"Winkel": 22.5,
"center_line_width_mm": 514.491,
"center_line_height_mm": 122.23,
"Objekt_width_mm": 522.542,
"Objekt_height_mm": 162.707,
"calculated_objekt_width_mm": 522.543,
"calculated_objekt_height_mm": 162.708,
"Objekt_width_px": 1974.947,
"Objekt_height_px": 614.951,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 311.376,
"scale_factor": 1.913722,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 129.312,
"direction": 270.0
},
{
"id": "cp2",
"x": 984.593,
"y": 880.54,
"direction": 112.5
}
]
},
{
"SVGname": "APB_110_R550_45_191_821104029.svg",
"Sivasnr": "821104029",
"Winkel": 45.0,
"center_line_width_mm": 551.606,
"center_line_height_mm": 190.792,
"Objekt_width_mm": 566.483,
"Objekt_height_mm": 226.709,
"calculated_objekt_width_mm": 566.484,
"calculated_objekt_height_mm": 226.71,
"Objekt_width_px": 2141.022,
"Objekt_height_px": 856.847,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 400.204,
"scale_factor": 1.765278,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 92.806,
"direction": 270.0
},
{
"id": "cp2",
"x": 973.738,
"y": 934.379,
"direction": 135.0
}
]
},
{
"SVGname": "APB_110_R550_45_205_821104037.svg",
"Sivasnr": "821104037",
"Winkel": 45.0,
"center_line_width_mm": 537.249,
"center_line_height_mm": 204.932,
"Objekt_width_mm": 552.126,
"Objekt_height_mm": 240.849,
"calculated_objekt_width_mm": 552.127,
"calculated_objekt_height_mm": 240.85,
"Objekt_width_px": 2086.76,
"Objekt_height_px": 910.289,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 436.221,
"scale_factor": 1.811181,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 87.358,
"direction": 270.0
},
{
"id": "cp2",
"x": 973.055,
"y": 938.231,
"direction": 135.0
}
]
},
{
"SVGname": "APB_110_R550_45_232_821104026.svg",
"Sivasnr": "821104026",
"Winkel": 45.0,
"center_line_width_mm": 595.265,
"center_line_height_mm": 265.998,
"Objekt_width_mm": 610.142,
"Objekt_height_mm": 301.914,
"calculated_objekt_width_mm": 610.143,
"calculated_objekt_height_mm": 301.916,
"Objekt_width_px": 2306.032,
"Objekt_height_px": 1141.084,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 494.826,
"scale_factor": 1.638963,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 69.689,
"direction": 270.0
},
{
"id": "cp2",
"x": 975.617,
"y": 950.727,
"direction": 135.0
}
]
},
{
"SVGname": "APB_110_R550_67_5_420_821104028.svg",
"Sivasnr": "821104028",
"Winkel": 67.5,
"center_line_width_mm": 582.427,
"center_line_height_mm": 419.902,
"Objekt_width_mm": 601.865,
"Objekt_height_mm": 448.992,
"calculated_objekt_width_mm": 601.865,
"calculated_objekt_height_mm": 448.994,
"Objekt_width_px": 2274.749,
"Objekt_height_px": 1696.965,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 746.001,
"scale_factor": 1.661502,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 46.861,
"direction": 270.0
},
{
"id": "cp2",
"x": 967.704,
"y": 982.071,
"direction": 157.5
}
]
},
{
"SVGname": "APB_110_R550_67_5_432_821104027.svg",
"Sivasnr": "821104027",
"Winkel": 67.5,
"center_line_width_mm": 748.658,
"center_line_height_mm": 530.305,
"Objekt_width_mm": 768.095,
"Objekt_height_mm": 559.396,
"calculated_objekt_width_mm": 768.096,
"calculated_objekt_height_mm": 559.397,
"Objekt_width_px": 2903.015,
"Objekt_height_px": 2114.237,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 728.29,
"scale_factor": 1.301922,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 37.612,
"direction": 270.0
},
{
"id": "cp2",
"x": 974.694,
"y": 985.608,
"direction": 157.5
}
]
},
{
"SVGname": "APB_110_R550_67_5_L_185_170_821104065.svg",
"Sivasnr": "821104065",
"Winkel": 67.5,
"center_line_width_mm": 758.19,
"center_line_height_mm": 496.584,
"Objekt_width_mm": 777.628,
"Objekt_height_mm": 525.674,
"calculated_objekt_width_mm": 777.628,
"calculated_objekt_height_mm": 525.676,
"Objekt_width_px": 2939.045,
"Objekt_height_px": 1986.785,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 675.997,
"scale_factor": 1.285962,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 40.025,
"direction": 270.0
},
{
"id": "cp2",
"x": 975.004,
"y": 984.686,
"direction": 157.5
}
]
},
{
"SVGname": "APB_110_R550_90_900_605_821104066.svg",
"Sivasnr": "821104066",
"Winkel": 90.0,
"center_line_width_mm": 900.0,
"center_line_height_mm": 605.0,
"Objekt_width_mm": 921.04,
"Objekt_height_mm": 626.04,
"calculated_objekt_width_mm": 921.04,
"calculated_objekt_height_mm": 626.04,
"Objekt_width_px": 3481.071,
"Objekt_height_px": 2366.118,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 679.71,
"scale_factor": 1.085729,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 33.608,
"direction": 270.0
},
{
"id": "cp2",
"x": 977.156,
"y": 1000.0,
"direction": 180.0
}
]
},
{
"SVGname": "APB_110_R630_45_400_821104031.svg",
"Sivasnr": "821104031",
"Winkel": 45.0,
"center_line_width_mm": 865.613,
"center_line_height_mm": 300.559,
"Objekt_width_mm": 880.491,
"Objekt_height_mm": 336.475,
"calculated_objekt_width_mm": 880.491,
"calculated_objekt_height_mm": 336.477,
"Objekt_width_px": 3327.816,
"Objekt_height_px": 1271.707,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 382.145,
"scale_factor": 1.13573,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 62.531,
"direction": 270.0
},
{
"id": "cp2",
"x": 983.103,
"y": 955.788,
"direction": 135.0
}
]
},
{
"SVGname": "APB_110_R630_90_800_800_821104033.svg",
"Sivasnr": "821104033",
"Winkel": 90.0,
"center_line_width_mm": 800.0,
"center_line_height_mm": 800.0,
"Objekt_width_mm": 821.04,
"Objekt_height_mm": 821.04,
"calculated_objekt_width_mm": 821.04,
"calculated_objekt_height_mm": 821.04,
"Objekt_width_px": 3103.121,
"Objekt_height_px": 3103.121,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 1000.0,
"scale_factor": 1.217967,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 25.626,
"direction": 270.0
},
{
"id": "cp2",
"x": 974.374,
"y": 1000.0,
"direction": 180.0
}
]
},
{
"SVGname": "APB_110_R630_90_850_690_821104041.svg",
"Sivasnr": "821104041",
"Winkel": 90.0,
"center_line_width_mm": 690.0,
"center_line_height_mm": 850.0,
"Objekt_width_mm": 711.04,
"Objekt_height_mm": 871.04,
"calculated_objekt_width_mm": 711.04,
"calculated_objekt_height_mm": 871.04,
"Objekt_width_px": 2687.376,
"Objekt_height_px": 3292.096,
"calculated_SVG_height_px": 1000,
"calculated_SVG_width_px": 820.091,
"scale_factor": 1.148053,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 24.155,
"direction": 270.0
},
{
"id": "cp2",
"x": 970.546,
"y": 1000.0,
"direction": 180.0
}
]
},
{
"SVGname": "APB_110_R630_90_850_870_821104035.svg",
"Sivasnr": "821104035",
"Winkel": 90.0,
"center_line_width_mm": 850.0,
"center_line_height_mm": 870.0,
"Objekt_width_mm": 871.04,
"Objekt_height_mm": 891.04,
"calculated_objekt_width_mm": 871.04,
"calculated_objekt_height_mm": 891.04,
"Objekt_width_px": 3292.096,
"Objekt_height_px": 3367.686,
"calculated_SVG_height_px": 1000,
"calculated_SVG_width_px": 981.334,
"scale_factor": 1.122284,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 23.613,
"direction": 270.0
},
{
"id": "cp2",
"x": 975.938,
"y": 1000.0,
"direction": 180.0
}
]
},
{
"SVGname": "APB_110_R630_90_850_890_821104034.svg",
"Sivasnr": "821104034",
"Winkel": 90.0,
"center_line_width_mm": 890.0,
"center_line_height_mm": 850.0,
"Objekt_width_mm": 911.04,
"Objekt_height_mm": 871.04,
"calculated_objekt_width_mm": 911.04,
"calculated_objekt_height_mm": 871.04,
"Objekt_width_px": 3443.276,
"Objekt_height_px": 3292.096,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 956.094,
"scale_factor": 1.097647,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 24.155,
"direction": 270.0
},
{
"id": "cp2",
"x": 976.906,
"y": 1000.0,
"direction": 180.0
}
]
},
{
"SVGname": "APB_110_R650_180_L_150_821104043.svg",
"Sivasnr": "821104043",
"Winkel": 180.0,
"center_line_width_mm": 800.0,
"center_line_height_mm": 1300.0,
"Objekt_width_mm": 800.0,
"Objekt_height_mm": 1342.08,
"calculated_objekt_width_mm": 800.0,
"calculated_objekt_height_mm": 1342.08,
"Objekt_width_px": 3023.6,
"Objekt_height_px": 5072.391,
"calculated_SVG_height_px": 1000,
"calculated_SVG_width_px": 599.869,
"scale_factor": 0.745112,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 15.677,
"direction": 270.0
},
{
"id": "cp2",
"x": 0,
"y": 984.323,
"direction": 270.0
}
]
},
{
"SVGname": "APB_60_R515_90_650_821094040.svg",
"Sivasnr": "821094040",
"Winkel": 90.0,
"center_line_width_mm": 650.0,
"center_line_height_mm": 650.0,
"Objekt_width_mm": 671.04,
"Objekt_height_mm": 671.04,
"calculated_objekt_width_mm": 671.04,
"calculated_objekt_height_mm": 671.04,
"Objekt_width_px": 2536.196,
"Objekt_height_px": 2536.196,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 1000.0,
"scale_factor": 1.490224,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 31.354,
"direction": 270.0
},
{
"id": "cp2",
"x": 968.646,
"y": 1000.0,
"direction": 180.0
}
]
},
{
"SVGname": "APB_60_R515_90_810_821094101.svg",
"Sivasnr": "821094101",
"Winkel": 90.0,
"center_line_width_mm": 810.0,
"center_line_height_mm": 810.0,
"Objekt_width_mm": 831.04,
"Objekt_height_mm": 831.04,
"calculated_objekt_width_mm": 831.04,
"calculated_objekt_height_mm": 831.04,
"Objekt_width_px": 3140.916,
"Objekt_height_px": 3140.916,
"calculated_SVG_width_px": 1000,
"calculated_SVG_height_px": 1000.0,
"scale_factor": 1.203312,
"connectionPoints": [
{
"id": "cp1",
"x": 0,
"y": 25.318,
"direction": 270.0
},
{
"id": "cp2",
"x": 974.683,
"y": 1000.0,
"direction": 180.0
}
]
}
]
SVGs/Omniflo/python/2_calculations_to_standardize_the_dimensions_and_add_connection_points.py
-158
View File
@@ -1,158 +0,0 @@
''' This Python script processes JSON data containing SVG object measurements and performs several calculations to standardize the dimensions and add connection points. Here's the main logic:
Data Processing:
Converts all numeric fields to floats
Determines the dominant dimension (width or height) and scales it to 1000px
Calculates the other dimension proportionally
Computes two connection points (cp1 and cp2) with their coordinates and directions
Error Checking:
Validates that connection points stay within the 1000px boundary
Provides detailed error reporting
Scaling Logic:
Uses different scaling approaches based on whether width or height is larger
Maintains aspect ratio while standardizing dimensions '''
import json
import math
def process_json_item(item):
# Ensure all numeric fields are floats
item["Winkel"] = float(item["Winkel"])
item["center_line_width_mm"] = float(item["center_line_width_mm"])
item["center_line_height_mm"] = float(item["center_line_height_mm"])
item["Objekt_width_mm"] = float(item["Objekt_width_mm"])
item["Objekt_height_mm"] = float(item["Objekt_height_mm"])
item["calculated_objekt_width_mm"] = float(item["calculated_objekt_width_mm"])
item["calculated_objekt_height_mm"] = float(item["calculated_objekt_height_mm"])
# Calculate the direct mm to px conversions (requested additions)
item["Objekt_width_px"] = round(item["Objekt_width_mm"] * 3.7795, 3)
item["Objekt_height_px"] = round(item["Objekt_height_mm"] * 3.7795, 3)
# Determine which dimension is larger
if item["Objekt_width_mm"] >= item["Objekt_height_mm"]:
# Width is larger, set calculated_SVG width to 1000px
scale = round(1000 / item["Objekt_width_mm"], 6)
item["calculated_SVG_width_px"] = 1000
item["calculated_SVG_height_px"] = round(item["Objekt_height_mm"] * scale, 3)
scale_RD_H = 1000 / item["calculated_SVG_height_px"]
scale_RD_W = 1
else:
# Height is larger, set calculated_SVG height to 1000px
scale = round(1000 / item["Objekt_height_mm"], 6)
item["calculated_SVG_height_px"] = 1000
item["calculated_SVG_width_px"] = round(item["Objekt_width_mm"] * scale + 3.7795, 3)
scale_RD_W = 1000 / item["calculated_SVG_width_px"]
scale_RD_H = 1
item["scale_factor"] = scale # Add scale factor
# Calculate connection points
cp1_y = round(21.040 * scale*scale_RD_H, 3)
if item["calculated_SVG_height_px"] == item["calculated_SVG_width_px"]:
cp2_x = round(item["center_line_width_mm"] * scale * scale_RD_W, 3)
cp2_y = round((21.040 + item["center_line_height_mm"]) * scale * scale_RD_H, 3)
elif item["calculated_SVG_height_px"] == 1000 and item["calculated_SVG_width_px"] != 1000:
if item["Winkel"]==180:
cp2_x = 0
else:
cp2_x = round((item["center_line_width_mm"] * scale + 3.7795) * scale_RD_W, 3)
cp2_y = round(((21.040 + item["center_line_height_mm"]) * scale) * scale_RD_H, 3)
else:
cp2_x = round((item["center_line_width_mm"] * scale) * scale_RD_W, 3)
cp2_y = round(((21.040 + item["center_line_height_mm"]) * scale) * scale_RD_H, 3)
cp2_direction = round(90 + item["Winkel"], 1) # Keep 1 decimal place
# Add connection points attributes
item["connectionPoints"] = [
{
"id": "cp1",
"x": 0,
"y": cp1_y,
"direction": 270.0
},
{
"id": "cp2",
"x": cp2_x,
"y": cp2_y,
"direction": cp2_direction
}
]
return item
def check_connection_points(item):
"""Check if connection point coordinates exceed 1000"""
errors = []
for cp in item.get("connectionPoints", []):
if cp["x"] > 1000 or cp["y"] > 1000:
errors.append(f"Connection point {cp['id']} exceeds range: x={cp['x']}, y={cp['y']}")
return errors
def process_json_file(input_file, output_file):
try:
# Read input file
with open(input_file, 'r', encoding='utf-8') as f:
data = json.load(f)
# Verify data is a list
if not isinstance(data, list):
print("Error: JSON data should be an array")
return
# Process each item
processed_data = []
error_reports = []
for idx, item in enumerate(data, start=1):
try:
processed_item = process_json_item(item)
# Check connection point coordinates
errors = check_connection_points(processed_item)
if errors:
error_msg = f"Item {idx} [{item.get('SVGname')}] coordinate errors:"
for err in errors:
error_msg += f"\n - {err}"
error_reports.append(error_msg)
processed_data.append(processed_item)
except Exception as e:
error_msg = f"Error processing item {idx} [{item.get('SVGname')}]: {str(e)}"
error_reports.append(error_msg)
processed_data.append(item) # Keep original data
# Write output file
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(processed_data, f, indent=4, ensure_ascii=False)
print(f"Processing complete. Results saved to {output_file}")
# Print error reports
if error_reports:
print("\n" + "="*50)
print("Coordinate Validation Error Report:")
print("="*50)
for report in error_reports:
print("\n" + report)
print("\n" + "="*50)
print(f"Found {len(error_reports)} coordinate issues")
print("="*50)
else:
print("\nAll connection point coordinates validated successfully - no out-of-range issues found")
except FileNotFoundError:
print(f"Error: Input file '{input_file}' not found")
except json.JSONDecodeError as e:
print(f"JSON parsing error: {e}")
except Exception as e:
print(f"Error during processing: {e}")
# Example usage
if __name__ == "__main__":
input_filename = "1_SVGErfassung_updated_new_output.json"
output_filename = "2_calculated_px_cps_output.json"
process_json_file(input_filename, output_filename)
process_json_file(input_filename, output_filename)
SVGs/Omniflo/python/3_update_dimensions_and_connection_points_in_props.py
-134
View File
@@ -1,134 +0,0 @@
''' Script Analysis
This Python script processes JSON and TXT files to update dimensions and connection points in SVG-related data. Here's the main logic:
Input Handling:
Reads a JSON file containing reference data
Processes all TXT files in a specified directory
Data Processing:
Creates a mapping between Sivasnr (from filenames) and JSON data
For each matching TXT file:
Updates width and height based on JSON data (converting mm to px)
Updates connection points (x, y, direction) from JSON data
Preserves the original file structure while updating specific values
Reporting:
Prints detailed change reports to console
Skips files without matching JSON data '''
import json
import os
import glob
from datetime import datetime
def process_files(json_file_path, txt_files_dir):
# Create a log file with timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
log_file_path = os.path.join(txt_files_dir, f"modification_report_{timestamp}.txt")
# Read JSON file
with open(json_file_path, 'r', encoding='utf-8') as f:
json_data = json.load(f)
# Create Sivasnr to JSON data mapping
sivasnr_mapping = {item["Sivasnr"]: item for item in json_data}
# Prepare report content
report_content = []
report_content.append(f"Modification Report - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
report_content.append("="*50 + "\n")
# Process all TXT files
for txt_file_path in glob.glob(os.path.join(txt_files_dir, '*.txt')):
# Extract Sivasnr from filename
sivasnr = os.path.splitext(os.path.basename(txt_file_path))[0]
# Check if corresponding JSON data exists
if sivasnr in sivasnr_mapping:
json_item = sivasnr_mapping[sivasnr]
# Read TXT file content (using utf-8-sig to handle BOM)
with open(txt_file_path, 'r', encoding='utf-8-sig') as f:
txt_content = json.load(f)
# Prepare entry for this file
file_entry = []
file_entry.append(f"\nProcessing file: {txt_file_path}")
file_entry.append("="*50)
# Record old values
old_width = txt_content["width"]
old_height = txt_content["height"]
old_cps = {cp["id"]: {"x": cp["x"], "y": cp["y"], "direction": cp["direction"]}
for cp in txt_content["connectionPoints"]}
# Update width and height
new_width = round(json_item["Objekt_width_mm"] * 3.7795, 3)
new_height = round(json_item["Objekt_height_mm"] * 3.7795, 3)
txt_content["width"] = new_width
txt_content["height"] = new_height
# Update connectionPoints
cp_changes = []
for cp in txt_content["connectionPoints"]:
cp_id = cp["id"]
# Find corresponding connection point in JSON data
json_cp = next((item for item in json_item["connectionPoints"] if item["id"] == cp_id), None)
if json_cp:
# Record old values
old_x = cp["x"]
old_y = cp["y"]
old_dir = cp["direction"]
# Update values
cp["x"] = json_cp["x"]
cp["y"] = json_cp["y"]
cp["direction"] = json_cp["direction"]
# Record changes
cp_changes.append({
"id": cp_id,
"x": (old_x, cp["x"]),
"y": (old_y, cp["y"]),
"direction": (old_dir, cp["direction"])
})
# Write back to TXT file
with open(txt_file_path, 'w', encoding='utf-8') as f:
json.dump(txt_content, f, indent=2, ensure_ascii=False)
# Add to report
file_entry.append("[Dimension Changes]")
file_entry.append(f"width: {old_width} → {new_width}")
file_entry.append(f"height: {old_height} → {new_height}")
if cp_changes:
file_entry.append("\n[Connection Point Changes]")
for change in cp_changes:
file_entry.append(f"Connection point {change['id']}:")
file_entry.append(f" x: {change['x'][0]} → {change['x'][1]}")
file_entry.append(f" y: {change['y'][0]} → {change['y'][1]}")
file_entry.append(f" direction: {change['direction'][0]} → {change['direction'][1]}")
file_entry.append(f"\nFile {txt_file_path} processed successfully")
file_entry.append("="*50)
# Add this file's entry to main report
report_content.extend(file_entry)
# Also print to console
print("\n" + "\n".join(file_entry))
else:
print(f"\nSkipping file {txt_file_path} (no matching JSON data found)")
# Write the report file if any changes were made
if len(report_content) > 2: # More than just the header
with open(log_file_path, 'w', encoding='utf-8') as f:
f.write("\n".join(report_content))
print(f"\nModification report saved to: {log_file_path}")
else:
print("\nNo files were modified - no report generated")
# Example usage
if __name__ == "__main__":
json_file_path = "2_calculated_px_cps_output.json"
txt_files_dir = "C:/Program Files/RuleDesigner/RDConfigurator Fusion/WebApi/Editor2D/SSG/shapes/props"
process_files(json_file_path, txt_files_dir)
print("\nAll files processed!")