How to use the laspy.file.File function in laspy

def setUp(self):
        really_copyfile(self.simple, self.tempfile)
        self.File1 = File.File(self.tempfile, mode = "rw")

def save_las(inFilename,outFilename):
    road_file = File.File(inFilename)    
    new_header = copy.copy(road_file.header)
    tmp_File = File.File(outFilename, mode = "w",header=new_header)
    for spec in road_file.reader.point_format:
        # print("Copying dimension: " + spec.name)
        in_spec = road_file.reader.get_dimension(spec.name)
        try:
            tmp_File.writer.set_dimension(spec.name, in_spec)
        except(util.LaspyException):
            print("Couldn't set dimension: " + spec.name +
                    " with file format " + str(tmp_File.header.version) +
                    ", and point_format " + str(tmp_File.header.data_format_id))
    road_file.close()
    return tmp_File

def setup(self):
    # Check mode
        print("Reading: " + self.args.in_file[0])
        self.mode = self.args.mode
        self.dim = self.args.dimension
        try:
            inFile = laspy.file.File(self.args.in_file[0], mode = "r")
            self.inFile = inFile
        except Exception as error:
            print("Error while reading file:")
            print(error)
            quit()

def get_bbox_of_multiple_tiles(file_list,laz_files=False,return_zlim=False):
    las_files = np.genfromtxt(file_list,delimiter=',',dtype='S256')
    n_files = las_files.size

    if laz_files:
        temp_file = 'temp_%i.las' % np.round(np.random.random()*10**9).astype(int)
        os.system("las2las %s %s" % (las_files[0],temp_file))
        lasFile = las.file.File('%s' % temp_file,mode='r-')
        max_xyz = lasFile.header.max
        min_xyz = lasFile.header.min
        xmin = min_xyz[0]
        ymin = min_xyz[1]
        xmax = max_xyz[0]
        ymax = max_xyz[1]
        zmin = min_xyz[2]
        zmax = max_xyz[2]
        lasFile.close()
        os.system("rm %s" % temp_file)

        for i in range(1,n_files):
            temp_file = 'temp_%i.las' % np.round(np.random.random()*10**9).astype(int)
            os.system("las2las %s %s" % (las_files[i],temp_file))
            lasFile = las.file.File('%s' % temp_file,mode='r-')
            max_xyz = lasFile.header.max

def to_las(pc, fname, scale=None, store_rgb=True):
    """Write pc to LAS file with name fname. Destroys pc.

    Reuses original scale if no scale is specified; can cause precision loss.
    """
    if hasattr(pc, "_las_header"):
        header = pc._las_header
    else:
        header = laspy.header.Header()

    format_id = 2 if store_rgb else 1       # see LAS standard

    f = laspy.file.File(fname, mode='w', header=header)
    f.header.set_dataformatid(format_id)
    f.header.offset = pc.offset.tolist()

def validate(self):
        print("Reading in file: " + self.args.in_file[0])
        inFile = laspy.file.File(self.args.in_file[0], mode = "r")
        self.test0(inFile)
        self.test1(inFile)
        self.test2(inFile)
        self.test3(inFile)
        print(str(self.errors) + " errors out of " + str(self.tests) + " tests")

# author: niranjanreddy891@gmail.com


from laspy.file import File
import numpy as np

inFile = File('E:/Lidar-practice/new-files/las14_type7_ALS_RIEGL_Q680i.las', mode='r')

I = inFile.Classification == 2

outFile = File('E:/Lidar-practice/new-files/tttttttttttt.pcd', mode='w', header=inFile.header)
outFile.points = inFile.points[I]
outFile.close()

# loop through las file and populate multi-dimensional grid
    # create rhp and rhi, multi-dimensional output grids - rhp is density, rhi is intensity sum
    rhp = numpy.zeros((chmMax,dtm_y_shape,dtm_x_shape))
    rhi = numpy.zeros((chmMax,dtm_y_shape,dtm_x_shape))
    chm2 = numpy.zeros((dtm_y_shape,dtm_x_shape))
    print 'created them!'
    bands,nrows,ncols = rhp.shape

    print "Populating Voxels"

    for lasfile in lasfiles:
        print "Iterating over points in files %s" %(lasfile)
	sys.stdout.flush()

        f = file.File(lasfile,mode='r')
        for p in f:
	    # print 'x,y,z: ', p.x, p.y, p.z
	    p.make_nice()
	    x = scale_x(f, p)
	    y = scale_y(f, p)
	    z = scale_z(f, p)
	    c = p.classification
	    i = p.intensity

	    col = coldex(x, dtm_minx, 1.0)
	    row = rowdex(y, dtm_maxy, 1.0)

	    if (0 <= col < dtm_x_shape) & (0 <= row < dtm_y_shape):

		zd = get_dtm_value(dtm_arr, x, y, dtm_minx, dtm_maxy, 1.0, dtm_y_shape, dtm_x_shape)
		z2 = z-zd