How to use test_identify method in yandex-tank

Best Python code snippet using yandex-tank

test_cyclone_identification_full.py

Source:test_cyclone_identification_full.py

1#!/usr/bin/env python32# Standard library3import json4import logging as log5import os6import pytest7import unittest8import sys9from unittest import TestCase10# Third-party11import numpy as np12# First-party13import stormtrack.extra.utilities_misc as io14from stormtrack.extra.cyclone_id.cyclone import CycloneIOWriterJson15from stormtrack.extra.cyclone_id.identify import identify_features16from stormtrack.extra.utilities_misc import Field2D17# log.getLogger().addHandler(log.StreamHandler(sys.stdout))18# log.getLogger().setLevel(log.DEBUG)19@unittest.skip("missing input data")20@pytest.mark.skip("missing input data")21class TestIdentifyFeatures(TestCase):22    """Run the identification using the same case but different configs.23    The input fields are read from disk (binary), as is the solution (JSON).24    Assertion is done by comparing result and solution as JSON strings.25    If a test fails, both result and solution are written to JSON files.26    """27    DBG = False28    WORK_DIR = "."29    DATA_DIR = "./test_data"30    FILE_DIR = os.path.dirname(os.path.relpath(__file__))31    TOPO_NAME = "HSURF"32    @classmethod33    def setUpClass(cls):34        file_in = "{p}/test_identify.input.npz".format(p=cls.DATA_DIR)35        cls.read_test_data(file_in)36    @classmethod37    def read_test_data(cls, file_name):38        """Read the input data from file. Return data as Field2D objects."""39        if cls.DBG:40            print("READ TEST DATA FROM FILE: {n}".format(n=file_name))41        data = np.load(file_name)42        cls.slp_raw = data["slp"]43        cls.topo_raw = data["topo"]44        cls.lon = data["lon"]45        cls.lat = data["lat"]46        cls.topo = Field2D(cls.topo_raw, cls.lon, cls.lat, name=cls.TOPO_NAME)47    def setUp(s):48        # Create new SLP field49        s.slp = Field2D(s.slp_raw.copy(), s.lon, s.lat)50        # Default configuration51        s.conf = {52            "timings-identify": False,53            "size-boundary-zone": 5,54            "smoothing-sigma": 7.0,55            "force-contours-closed": False,56            "contour-interval": 0.5,57            "depression-min-contours": 1,58            "contour-length-max": -1.0,59            "bcc-fraction": 0.5,60            "min-cyclone-depth": 1.0,61            "extrema-identification-size": 9,62            "max-minima-per-cyclone": 3,63            "contour-length-min": -1.0,64            "topo-cutoff-level": 1500.0,65            "read-slp-contours": False,66            "read-slp-extrema": False,67            "save-slp-contours": False,68            "save-slp-extrema": False,69        }70    def run_identification(s, conf):71        """Run the cyclone identification. Return as a JSON string."""72        if s.DBG:73            print("RUN IDENTIFICATION")74        features = identify_features(s.slp, s.topo, conf)75        writer = CycloneIOWriterJson()76        writer.add_points("MINIMA", s.slp.minima())77        writer.add_points("MAXIMA", s.slp.maxima())78        writer.add_depressions(features["depressions"])79        writer.add_cyclones(features["cyclones"])80        # SR_TMP<81        depressions, cyclones = features["depressions"], features["cyclones"]82        levels = np.arange(950, 1050, 0.5)83        io.plot_depressions("nodypy_depressions.png", depressions, s.slp, levels)84        io.plot_cyclones("nodypy_cyclones.png", cyclones, s.slp, levels)85        # SR_TMP>86        return writer.write_string().strip()87    def read_solution(s, name):88        """Read the solution from file as a JSON string."""89        file_sol = "{p}/test_identify.{n}.sol.json".format(p=s.FILE_DIR, n=name)90        if s.DBG:91            print("READ SOLUTION FROM FILE: {f}".format(f=file_sol))92        with open(file_sol, "r") as f:93            return f.read().strip()94    def evaluate_result(s, name, jstr_res, jstr_sol):95        """Compare result and solution in form of JSON string.96        If the test fails, write both strings to file for comparison.97        """98        if s.DBG:99            print("EVALUATE RESULTS OF TEST: {n}".format(n=name))100        tag = "test_identify"101        file_res = "{p}/tmp.{t}.{n}.res.json".format(p=s.WORK_DIR, t=tag, n=name)102        file_sol = "{p}/{t}.{n}.sol.json".format(p=s.FILE_DIR, t=tag, n=name)103        err_msg = (104            "JSON strings not equal! Dumped result: {r}\n"105            "To check what's wrong, run:\nvimdiff {r} {s}"106        ).format(r=file_res, s=file_sol)107        try:108            s.assertEqual(jstr_res, jstr_sol, err_msg)109        except AssertionError:110            with open(file_res, "w") as f:111                f.write(jstr_res)112            with open(file_sol, "w") as f:113                f.write(jstr_sol)114            raise115    def run_test(s, test_name):116        if s.DBG:117            print("RUN TEST: {n}".format(n=test_name))118        jstr_res = s.run_identification(s.conf)119        jstr_sol = s.read_solution(test_name)120        s.evaluate_result(test_name, jstr_res, jstr_sol)121    # DEFAULT122    def test_default(s):123        s.run_test("test_default")124    # SMOOTHING125    @unittest.skip("takes far too long")126    @pytest.mark.skip("takes far too long")127    def test_sig00(s):128        s.conf["smoothing-sigma"] = 0.0129        s.run_test("test_sig00")130    def test_sig03(s):131        s.conf["smoothing-sigma"] = 3.0132        s.run_test("test_sig03")133    @unittest.skip("fails...")134    @pytest.mark.skip("fails...")135    def test_sig15(s):136        s.conf["smoothing-sigma"] = 15.0137        s.run_test("test_sig15")138    # BCC FRACTION139    def test_bcc00(s):140        s.conf["bcc-fraction"] = 0.0141        s.run_test("test_bcc00")142    def test_bcc03(s):143        s.conf["bcc-fraction"] = 0.3144        s.run_test("test_bcc03")145    def test_bcc06(s):146        s.conf["bcc-fraction"] = 0.6147        s.run_test("test_bcc06")148    def test_bcc10(s):149        s.conf["bcc-fraction"] = 1.0150        s.run_test("test_bcc10")151if __name__ == "__main__":...

keypoints_test.py

Source:keypoints_test.py

...11        pass12    def data_detect(self, data):13        return True14    @identifying15    def test_identify(self, data):16        return data17    @verifying18    def test_verify(self, data):19        return data20def identifying_test():21    detector = TestDetector()22    assert detector is not None23    assert detector.test_identify({}) == {}24def verifying_test():25    detector = TestDetector()26    assert detector is not None27    assert detector.test_verify({}) == {}28def KeypointsObjectDetector_test():29    img = cv2.imread(TEST_IMAGE_PATH)30    detector = KeypointsObjectDetector()31    assert detector is not None32    assert detector.threshold() == 25.033    assert detector.last_error() == ""34    detector.setUseROIDetection(False)35    assert detector.addSource({'path': TEST_IMAGE_PATH, 'data': img})36    assert detector.addSources([{'path': TEST_IMAGE_PATH, 'data': img}, {'path': TEST_IMAGE_PATH, 'data': img},37                                {'path': TEST_IMAGE_PATH, 'data': img}, {'path': TEST_IMAGE_PATH, 'data': img}]) == 4...

test_identify.py

Source:test_identify.py

...3    mp.dps = 154    assert pslq([3*pi+4*e/7, pi, e, log(2)]) == [7, -21, -4, 0]5    assert pslq([4.9999999999999991, 1]) == [1, -5]6    assert pslq([2,1]) == [1, -2]7def test_identify():8    mp.dps = 209    assert identify(zeta(4), ['log(2)', 'pi**4']) == '((1/90)*pi**4)'10    mp.dps = 1511    assert identify(exp(5)) == 'exp(5)'12    assert identify(exp(4)) == 'exp(4)'13    assert identify(log(5)) == 'log(5)'14    assert identify(exp(3*pi), ['pi']) == 'exp((3*pi))'15    assert identify(3, full=True) == ['3', '3', '1/(1/3)', 'sqrt(9)',16        '1/sqrt((1/9))', '(sqrt(12)/2)**2', '1/(sqrt(12)/6)**2']...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.