Iphone 4s Dreieck mit Herz

thingiverse

This appears to be a dataset describing the characteristics of various G-Code files for 3D printing or laser cutting purposes, with fields including file size, model rotation, and tolerances. Here's an example analysis using Python and Pandas library: ```python import pandas as pd data = """ 0:G00 G54 G10 L20 M06 P101 G21 M03 S100 G00 F300 Z5 G40 G17 G90 X30 Y-14 ; ; start text commands (comments) ; G92 R-3 I6 G21 F900 T7 M05 G53 H08 M84 G94 T9 M09 X30 Y-13.25 G02 C12.15 R18 G04 X27 Z4 G00 Y0 G91 X11 F100 G02 C2 I6 L5 X3 I10 G17 G80 G98 X3 C2.03 S50 L20 X23 I9 G82 X3 I9 X4 G92 P13 G52 H09 M114 T11 M05 G54 G21 M42 Z7 F200 X28.1 G32 P24 H06 M00 ; (turns off spindle) M84 G90 H07 Z6; stops tool, raises to top (but doesn’t change axis position) # List of model coordinates (example below) for the GCode [[33.893111,-13.046331],[36.059351,-16.017011],[40.142381,-14.113311],[35.057161,-10.151411], [44.177791,-6.134451],[43.035771,-1.043531],[47.141391,5.047091],[41.056551,10.109951],[49.017651,15.146611],[36.049191,17.113011],[35.062331,22.119431],[31.054631,23.105111], [34.053251,24.115991],[43.055531,30.124751],[33.045591,32.123211],[45.059091,-8.047491]] # Some other GCode options G41 H1 M06 P101 """ df = pd.DataFrame( [ [10.0, -17.2, 42.4, 11.6, -14.5, -34.1], [-36.4, 8.9, 46.4, 22.1, -7.4, 44.2], [-45.1, -18.5, -29.2, -10.3, -12.9, -31.5], [33.893111,-13.046331],[36.059351,-16.017011] ],columns=["x1", "y1", "x2", "y2"]) # Analysis of rotation options (Art_des_Musters, Drehung_der_Schablone etc) print(f'Listed M42 is turned on. Model is being printed.') for line in data.splitlines(): if line.startswith(";"): continue parts = line.split() for i, part in enumerate(parts): if part == "G52": model_rotation_degrees = int(parts[i + 1]) ``` Some points are taken out and transformed to numerical values: ```python import numpy as np coordinates_list= df['x1'].tolist() # coordinates_list contains a single row - all numbers on the left half of df. rotation_matrix = np.array([[np.cos(np.radians(model_rotation_degrees)),-np.sin(np.radians(model_rotation_degrees))], [np.sin(np.radians(model_rotation_degrees)),np.cos(np.radians(model_rotation_degrees))] ]) coordinate_values_transformed = [] coordinate_names = list(map(lambda x: str.replace(str(x), "x", "x_coordinate"), coordinates_list)) for coordinate_name in coordinate_names: value_x_transformed = float((coordinate_name)) x_transformation_coordinates = rotation_matrix * np.array([value_x_transformed, 0]) # Final result as the original x-value is added back with 6 new columns created for this new_column_index=range(len(coordinate_names)) final_values_to_append=[] # Loop and assign new transformed column names based on rotation for index_value, (value_in_transform,x_transformed_coordinates) in enumerate(zip(value_x_transformed, x_transformation_coordinates)): ``` However since your request includes Gcode parameters such as layer thickness this information may need to be converted before making sense. ```python Gcode_layer_thickness=float(30.5) for i,value_0 in enumerate(final_values_to_append): value_i=np.array([value_0]+[x_transformation_coordinates.tolist()])[i].round(6) ``` Let me know if this answers your questions