How to use the pylinac.log_analyzer.load_log function in pylinac

def test_url(self):
        url = r'https://s3.amazonaws.com/pylinac/Tlog.bin'
        self.assertIsInstance(load_log(url), TrajectoryLog)

def setUpClass(cls):
        cls.log = load_log(cls.get_filename())
        if cls.log.treatment_type != IMAGING:
            cls.log.fluence.gamma.calc_map()

def test_invalid_file(self):
        invalid_file = osp.join(TEST_DIR, 'Demo subbeam 0 actual fluence.npy')
        with self.assertRaises(NotALogError):
            load_log(invalid_file)

def test_append(self):
        # append a directory
        logs = MachineLogs(self.logs_altdir)
        logs.append(self.logs_altdir)
        self.assertEqual(logs.num_logs, 8)
        # append a file string
        single_file = osp.join(self.logs_altdir, 'Anonymous_4DC Treatment_JST90_TX_20140712094246.bin')
        logs.append(single_file)
        # append a MachineLog
        single_log = load_log(single_file)
        logs.append(single_log)

        # try to append something that's not a Log
        log = None
        with self.assertRaises(TypeError):
            logs.append(log)

def test_dynalog_file(self):
        dynalog = osp.join(TEST_DIR, 'dlogs', 'A_static_imrt.dlg')
        self.assertIsInstance(load_log(dynalog), Dynalog)

def test_dir(self):
        dlog_dir = osp.join(TEST_DIR, 'dlogs')
        self.assertIsInstance(load_log(dlog_dir), MachineLogs)

def test_zip(self):
        zip_file = osp.join(TEST_DIR, 'mixed_types.zip')
        self.assertIsInstance(load_log(zip_file), MachineLogs)

def analyze_log():
            log = log_analyzer.load_log(self.log_file.get())
            name, _ = osp.splitext(self.log_file.get())
            fname = name + '.pdf'
            fname = utilities.file_exists(fname)
            log.publish_pdf(fname)
            self.log_pdf.set(fname)
            utilities.open_path(fname)

def _load_log(self, log: str):
        """Load a machine log that corresponds to the picket fence delivery.

        This log determines the location of the pickets. The MLC peaks are then compared to the expected log pickets,
        not a simple fit of the peaks."""
        # load the log fluence image
        mlog = load_log(log)
        fl = mlog.fluence.expected.calc_map(equal_aspect=True)
        fli = image.load(fl, dpi=254)  # 254 pix/in => 1 pix/0.1mm (default fluence calc)

        # equate them such that they're the same size & DPI
        fluence_img, self.image = image.equate_images(fli, self.image)

        # get picket fits from the modified fluence image
        pf = PicketFence.from_demo_image()
        pf.image = fluence_img
        pf.analyze()
        self._log_fits = cycle([p.fit for p in pf.pickets])