Lab-3419 1.0.0

Lab_3419 is a cross-platform python library

Lab 3419

Lab_3419 is a cross-platform python library. It contains some useful function for MST simulation

Installation

pip install Lab_3419

Dependencies

Lab_3419 supports Python 3.6 and later. If you are installing Lab_3419 from PyPI using pip: please install numpy before installing Lab_3419.

Example Usage

Import Lab_3419 module

    >>> import Lab_3419 as lb

Some important data format

1. A point in 3D Space --> point = (x, y, z)
2. Line points in 3D Space --> points = numpy.array([(x1, y1, z1), (x2, y2, z2), (x3, y3, z3), ...])
3. A fitted Line in 3D Space --> fitted_line = numpy.array([(x1, y1, z1), (x2, y2, z2)])

Fit 3D line

    >>> simulated_points = numpy.array([(x1, y1, z1), (x2, y2, z2), (x3, y3, z3)])
    >>> points = lb.add_resolusion(points_=simulated_points, res_=position_resolution)
    >>> fitted_line = lb.fit_3D(points)

Find POCA point

    >>> poca_xyz, deviation = lb.POCA_Point(fitted_line_1, fitted_line_)

Find POCA point directly from data string

    >>> data_string = "x1 y1 z1 x2 y2 z2 x3 y3 z3 x4 y4 z4 x5 y5 z5 x6 y6 z6"
    >>> poca_x, poca_y, poca_z, deviation = lb.calculate(data_string)

Find POCA points directly from data file

A file contains multiple number of data string.

Example data file: data_file.txt

-279.717 270.73 -391 -233.76 277.098 -321 -187.807 283.464 -251 140.424 328.94 249 186.895 335.379 319.776 232.346 341.674 389
42.0465 62.3473 -391 28.2942 59.1205 -321 14.5489 55.8892 -251 -83.7591 32.7117 249.426 -97.4228 29.4739 319 -111.175 26.2129 389
138.413 682.409 -391 134.046 628.646 -321 129.676 574.895 -251 98.4977 190.939 249 94.075 136.334 320.099 89.7752 83.4056 389
174.57 -20.909 -391 149.972 -1.86935 -321 125.373 17.1698 -251 -50.315 153.155 249 -74.9115 172.191 319 -99.567 191.274 389

Example code

    >>> all_poca_points = lb.file_to_poca("data_file.txt", is_save=False)
    # To write into new file use "is_save = True". 
    # This will create a file "data_file_poca_points.txt"
    >>> lb.file_to_poca("data_file.txt", is_save=True)
    # Same function with multi-threaded mode
    >>> all_poca_points = lb.file_to_poca_mt("data_file.txt", is_save=False)
    >>> lb.file_to_poca_mt("data_file.txt", is_save=True)

Filter POCA points according to their deviation angle

Example poca file: data_file.txt

-320.1396 -187.6816 -157.1030 1.0040
351.6875 -307.2162 -125.7960 0.0117
288.7207 349.4853 474.8601 3.0034
-41.1887 42.8014 32.7391 0.0243

    >>> filter_poca_points = lb.filter_poca_data(poca_data_array, minimum_theta)