class TestCallers(unittest.TestCase):
    """White-box testing of callers tracing"""
    def setUp(self):
        self.addCleanup(sys.settrace, sys.gettrace())
        self.tracer = Trace(count=0, trace=0, countcallers=1)
        self.filemod = my_file_and_modname()

    @unittest.skipIf(hasattr(sys, 'gettrace') and sys.gettrace(),
                     'pre-existing trace function throws off measurements')
    def test_loop_caller_importing(self):
        self.tracer.runfunc(traced_func_importing_caller, 1)

        expected = {
            ((os.path.splitext(trace.__file__)[0] + '.py', 'trace', 'Trace.runfunc'),
                (self.filemod + ('traced_func_importing_caller',))): 1,
            ((self.filemod + ('traced_func_simple_caller',)),
                (self.filemod + ('traced_func_linear',))): 1,
            ((self.filemod + ('traced_func_importing_caller',)),
                (self.filemod + ('traced_func_simple_caller',))): 1,
            ((self.filemod + ('traced_func_importing_caller',)),
                (self.filemod + ('traced_func_importing',))): 1,
            ((self.filemod + ('traced_func_importing',)),
                (fix_ext_py(testmod.__file__), 'testmod', 'func')): 1,
        }
        self.assertEqual(self.tracer.results().callers, expected)

class PerformanceMetric:
    def __init__(self):
        self.switches = {"VIDEO": Trace("seconds", "bps"), "AUDIO": Trace("seconds", "bps")}
        self.buffer_levels = {"VIDEO": BufferLevelMetric(), "AUDIO": BufferLevelMetric()}
        self.bps_history = Trace("seconds", "bps")

        self.bps_history.append(0, 0)
        self.buffer_levels["VIDEO"].append(0, 0)
        self.buffer_levels["AUDIO"].append(0, 0)
        self.switches["VIDEO"].append(0, 0)
        self.switches["AUDIO"].append(0, 0)

    @property
    def underrun_count(self):
        return self.buffer_levels["VIDEO"].underrun_count + self.buffer_levels["AUDIO"].underrun_count

    def min_buffer_level(self):
        return min(self.buffer_levels["VIDEO"].current_value, self.buffer_levels["AUDIO"].current_value)

    """ So far, use reciproc of underruns to give a score. 1.0 perfect score """

    def score(self):
        return 1.0 / (self.underrun_count + 1)

    def print_stats(self):
        print("Score: %.4f" % self.score())
        print("Underruns: %d" % self.underrun_count)
        print self.switches["VIDEO"]

class TestRunExecCounts(unittest.TestCase):
    """A simple sanity test of line-counting, via runctx (exec)"""
    def setUp(self):
        self.my_py_filename = fix_ext_py(__file__)
        self.addCleanup(sys.settrace, sys.gettrace())

    def test_exec_counts(self):
        self.tracer = Trace(count=1, trace=0, countfuncs=0, countcallers=0)
        code = r'''traced_func_loop(2, 5)'''
        code = compile(code, __file__, 'exec')
        self.tracer.runctx(code, globals(), vars())

        firstlineno = get_firstlineno(traced_func_loop)
        expected = {
            (self.my_py_filename, firstlineno + 1): 1,
            (self.my_py_filename, firstlineno + 2): 6,
            (self.my_py_filename, firstlineno + 3): 5,
            (self.my_py_filename, firstlineno + 4): 1,
        }

        # When used through 'run', some other spurious counts are produced, like
        # the settrace of threading, which we ignore, just making sure that the
        # counts fo traced_func_loop were right.
        #
        for k in expected.keys():
            self.assertEqual(self.tracer.results().counts[k], expected[k])

    def test_linear_methods(self):
        # XXX todo: later add 'static_method_linear' and 'class_method_linear'
        # here, once issue1764286 is resolved
        #
        for methname in ["inst_method_linear"]:
            tracer = Trace(count=1, trace=0, countfuncs=0, countcallers=0)
            traced_obj = TracedClass(25)
            method = getattr(traced_obj, methname)
            tracer.runfunc(method, 20)

            firstlineno = get_firstlineno(method)
            expected = {(self.my_py_filename, firstlineno + 1): 1}
            self.assertEqual(tracer.results().counts, expected)

    def get_next_trace(self):
        new_trace = Trace([400], self.screen, noise=True)
        for i, (current_peak, next_peak) in enumerate(zip(self.current_trace.peaks, new_trace.peaks)):
            new_pos = self.get_new_peak_position(current_peak)
            while new_pos <= self.bottom_pad or new_pos >= self.dim-self.top_pad:
                new_pos = self.get_new_peak_position(current_peak)
            new_trace.peaks[i] = new_pos
            #print new_trace.peaks

        if not self.artificial:
            new_trace.data = self.survey.data[self.current_real_trace, :]
            self.current_real_trace += 1

        return new_trace

 def init_traces(self):
     with open('1.tra', 'r') as f:
         trace_number = int(f.readline())
         for trace_counter in range(trace_number):
             t = Trace()
             point_number = int(f.readline())
             points = []
             for point_counter in range(point_number):
                 point_s = f.readline()[:-1]
                 point = Point([int(x) for x in point_s.split(' ')])
                 points.append(point)
             t.points = points
             self.traces.append(t)
             self.current_temp_places.append(None)

def mh_query2(model, pred, answer, samples_count, lag=1):
    """
    Metropolis-Hastings algorithm for sampling
    :param model: model to execute
    :param samples_count: how much samples we want
    :type samples_count: int
    :return: samples
    :rtype: list
    """
    MCMC_shared.mh_flag = True
    MCMC_shared.iteration = 0
    samples = []
    miss = True
    model()
    transitions = 0
    rejection = 0
    while len(samples) < samples_count:
        MCMC_shared.iteration += 1
        variables = MCMC_shared.trace.get_vector()
        vector_vals_drift = variables.values()
        vector = [val[0] for val in vector_vals_drift]
        drifts = [val[1] for val in vector_vals_drift]
        shifted_vector = numpy.random.multivariate_normal(vector, numpy.diag(drifts))
        new_trace = Trace(MCMC_shared.trace)
        new_trace.set_vector(dict(zip(variables.keys(), shifted_vector.tolist())), MCMC_shared.iteration)
        old_trace = MCMC_shared.trace
        MCMC_shared.trace = new_trace
        sample = model()
        while not miss and new_trace._likelihood == -float("inf"):
            new_trace.clean(MCMC_shared.iteration)
            new_trace._likelihood = 0
            for name, (chunk, iteration) in new_trace.mem.items():
                if chunk.erp.log_likelihood(chunk.x, *chunk.erp_parameters) == -float("inf"):
                    new_chunk = Chunk(chunk.erp,
                                      numpy.random.normal(old_trace.get(name).x, chunk.drift / 2),
                                      chunk.erp_parameters,
                                      drift=chunk.drift)
                    new_trace.store(name, new_chunk, iteration)
            sample = model()
        MCMC_shared.trace = old_trace
        probability = log(uniform())
        # r = erp.log_proposal_prob()
        if probability < new_trace._likelihood - old_trace._likelihood and (miss or pred(sample)):
            if miss and pred(sample):
                miss = False
                MCMC_shared.drift = 0.05
            transitions += 1
            if (transitions % lag) == 0:
                if not miss:
                    # print sample, rejection
                    samples.append(answer(sample))
            rejection = 0
            MCMC_shared.trace = new_trace
            MCMC_shared.trace.clean(MCMC_shared.iteration)
        else:
            rejection += 1

    return samples

    def trim_lat_df(self, start, lat_df):
        if lat_df.empty:
            return lat_df

        lat_df = Trace.squash_df(lat_df, start, lat_df.index[-1], "t_delta")
        # squash_df only updates t_delta, remove t_start to make sure it's not used
        return lat_df.drop('t_start', 1)

	def upsert_bulk(self, _index, type_key, id_key, bulk):
		"Updates a bulk of documents in the same index."
		"The type and id of each document are encoded in the document. Keys are provided to retrieve them"
		"The type and id fields are removed in the document after inserted as _type and _id"
		"Returns the number of documents upserted."
		docs_upserted = 0
		for document in bulk:
			_type = document[type_key]
			_id = document[id_key]
			del document[type_key]
			del document[id_key]
			upserted = self.upsert_document(_index, _type, _id, document)
			Trace.info("upserted: " + json.dumps(upserted))
			if (upserted["_id"] == _id):
				docs_upserted += 1
		return docs_upserted

 def _run_with_trace(self):
     from trace import Trace
     trace = Trace(ignoredirs=[sys.prefix, sys.exec_prefix], trace=False,
                   count=True)
     try:
         trace.runfunc(self._run_tests)
     finally:
         results = trace.results()
         real_stdout = sys.stdout
         sys.stdout = open(self.coverage_summary, 'w')
         try:
             results.write_results(show_missing=True, summary=True,
                                   coverdir=self.coverage_dir)
         finally:
             sys.stdout.close()
             sys.stdout = real_stdout

class BufferLevelMetric(Trace):
    def __init__(self):
        Trace.__init__(self, "seconds", "seconds")
        self._level = 0.0 # in seconds
        self._underruns = Trace("seconds", "underrun duration (seconds)")

    @property
    def underrun_count(self):
        return self._underruns.count

    def increase_by(self, absolute_time, level_increase):
        val = self.current_value
        if val is None:
            val = 0.0
        self.append(absolute_time, val + level_increase)

    def decrease_by(self, absolute_time, level_decrease):
        val = self.current_value
        if val is None:
            val = 0.0
        val -= level_decrease
        if val <= 0.0:
            # record an underrun [time, duration_of_underrun]
            self._underruns.append(absolute_time, abs(val))
            # clamp level to 0.0
            val = 0.0
        self.append(absolute_time, val)

    @property
    def level(self):
        """
        Alias for current_value or current_y_value
        :return: current_y_value
        """
        return self.current_value

    def __unicode__(self):
        val = self.current_value
        if val is not None:
            return "BufferLevel(t=%.2fs): %.2fs" % (self.current_x_value, self.current_value)
        else:
            return "BufferLevel(t=0): 0"

    def __str__(self):
        return self.__unicode__()

    def __init__(self, *args, **kwargs):
        super(TestTrace, self).__init__(*args, **kwargs)

        self.test_trace = os.path.join(self.traces_dir, 'test_trace.txt')

        self.platform = self._get_platform()

        self.trace_path = os.path.join(self.traces_dir, 'trace.txt')
        self.trace = Trace(self.platform, self.trace_path, self.events)

def plotdir(run_dir, platform):
    global args
    tasks = None
    pa = None

    # Load RTApp performance data
    try:
        pa = PerfAnalysis(run_dir)

        # Get the list of RTApp tasks
        tasks = pa.tasks()
        logging.info('Tasks: %s', tasks)
    except ValueError:
        pa = None
        logging.info('No performance data found')

    # Load Trace Analysis modules
    trace = Trace(platform, run_dir)

    # Define time ranges for all the temporal plots
    trace.setXTimeRange(args.tmin, args.tmax)

    # Tasks plots
    if 'tasks' in args.plots:
        trace.analysis.tasks.plotTasks(tasks)
        if pa:
            for task in tasks:
                pa.plotPerf(task)

    # Cluster and CPUs plots
    if 'clusters' in args.plots:
        trace.analysis.frequency.plotClusterFrequencies()
    if 'cpus' in args.plots:
        trace.analysis.cpus.plotCPU()

    # SchedTune plots
    if 'stune' in args.plots:
        trace.analysis.eas.plotSchedTuneConf()
    if 'ediff' in args.plots:
        trace.analysis.eas.plotEDiffTime();
    if 'edspace' in args.plots:
        trace.analysis.eas.plotEDiffSpace();

    def __init__(self, bitrates):
        Adaptation.__init__(self, bitrates)
        self.max_seconds = 50.0
        self.level_low_seconds = 10.0  # critical buffer level. Fill as fast as possible until level_high_seconds reached if below this value
        self.level_high_seconds = 30.0  # stable buffer level. Try to maintain current bitrate or improve it
        self.bps_history = Trace("time", "bps")
        self.bitrate_selections = {"AUDIO": Trace("time", "Audio-Bitrate selections"),
                                   "VIDEO": Trace("time", "Video-Bitrate selections")}

        self.sim_state = None
        # self.bitrate_selections["VIDEO"].append(-1, 0)
        # self.bitrate_selections["AUDIO"].append(-1, 0)

        self.my_bps = Trace("seconds", "bps")

        self.ma4_filter = alg.IterativeMovingAverage(4)
        self.ma10_filter = alg.IterativeMovingAverage(10)
        self.ma50_filter = alg.IterativeMovingAverage(80)

        self. last_index = 0

class TestFuncs(unittest.TestCase):
    """White-box testing of funcs tracing"""
    def setUp(self):
        self.addCleanup(sys.settrace, sys.gettrace())
        self.tracer = Trace(count=0, trace=0, countfuncs=1)
        self.filemod = my_file_and_modname()
        self._saved_tracefunc = sys.gettrace()

    def tearDown(self):
        if self._saved_tracefunc is not None:
            sys.settrace(self._saved_tracefunc)

    def test_simple_caller(self):
        self.tracer.runfunc(traced_func_simple_caller, 1)

        expected = {
            self.filemod + ('traced_func_simple_caller',): 1,
            self.filemod + ('traced_func_linear',): 1,
        }
        self.assertEqual(self.tracer.results().calledfuncs, expected)

    def test_loop_caller_importing(self):
        self.tracer.runfunc(traced_func_importing_caller, 1)

        expected = {
            self.filemod + ('traced_func_simple_caller',): 1,
            self.filemod + ('traced_func_linear',): 1,
            self.filemod + ('traced_func_importing_caller',): 1,
            self.filemod + ('traced_func_importing',): 1,
            (fix_ext_py(testmod.__file__), 'testmod', 'func'): 1,
        }
        self.assertEqual(self.tracer.results().calledfuncs, expected)

    @unittest.skipIf(hasattr(sys, 'gettrace') and sys.gettrace(),
                     'pre-existing trace function throws off measurements')
    def test_inst_method_calling(self):
        obj = TracedClass(20)
        self.tracer.runfunc(obj.inst_method_calling, 1)

        expected = {
            self.filemod + ('TracedClass.inst_method_calling',): 1,
            self.filemod + ('TracedClass.inst_method_linear',): 1,
            self.filemod + ('traced_func_linear',): 1,
        }
        self.assertEqual(self.tracer.results().calledfuncs, expected)

class TestCallers(unittest.TestCase):
    """White-box testing of callers tracing"""
    def setUp(self):
        self.tracer = Trace(count=0, trace=0, countcallers=1)
        self.filemod = my_file_and_modname()

    def test_loop_caller_importing(self):
        self.tracer.runfunc(traced_func_importing_caller, 1)

        expected = {
            ((os.path.splitext(trace.__file__)[0] + '.py', 'trace', 'Trace.runfunc'),
                (self.filemod + ('traced_func_importing_caller',))): 1,
            ((self.filemod + ('traced_func_simple_caller',)),
                (self.filemod + ('traced_func_linear',))): 1,
            ((self.filemod + ('traced_func_importing_caller',)),
                (self.filemod + ('traced_func_simple_caller',))): 1,
            ((self.filemod + ('traced_func_importing_caller',)),
                (self.filemod + ('traced_func_importing',))): 1,
            ((self.filemod + ('traced_func_importing',)),
                (fix_ext_py(testmod.__file__), 'testmod', 'func')): 1,
        }
        self.assertEqual(self.tracer.results().callers, expected)

class TestFuncs(unittest.TestCase):
    """White-box testing of funcs tracing"""

    def setUp(self):
        self.addCleanup(sys.settrace, sys.gettrace())
        self.tracer = Trace(count=0, trace=0, countfuncs=1)
        self.filemod = my_file_and_modname()

    def test_simple_caller(self):
        self.tracer.runfunc(traced_func_simple_caller, 1)

        expected = {self.filemod + ("traced_func_simple_caller",): 1, self.filemod + ("traced_func_linear",): 1}
        self.assertEqual(self.tracer.results().calledfuncs, expected)

    def test_loop_caller_importing(self):
        self.tracer.runfunc(traced_func_importing_caller, 1)

        expected = {
            self.filemod + ("traced_func_simple_caller",): 1,
            self.filemod + ("traced_func_linear",): 1,
            self.filemod + ("traced_func_importing_caller",): 1,
            self.filemod + ("traced_func_importing",): 1,
            (fix_ext_py(testmod.__file__), "testmod", "func"): 1,
        }
        self.assertEqual(self.tracer.results().calledfuncs, expected)

    @unittest.skipIf(hasattr(sys, "gettrace") and sys.gettrace(), "pre-existing trace function throws off measurements")
    def test_inst_method_calling(self):
        obj = TracedClass(20)
        self.tracer.runfunc(obj.inst_method_calling, 1)

        expected = {
            self.filemod + ("TracedClass.inst_method_calling",): 1,
            self.filemod + ("TracedClass.inst_method_linear",): 1,
            self.filemod + ("traced_func_linear",): 1,
        }
        self.assertEqual(self.tracer.results().calledfuncs, expected)

class TestFuncs(unittest.TestCase):
    """White-box testing of funcs tracing"""
    def setUp(self):
        self.tracer = Trace(count=0, trace=0, countfuncs=1)
        self.filemod = my_file_and_modname()

    def test_simple_caller(self):
        self.tracer.runfunc(traced_func_simple_caller, 1)

        expected = {
            self.filemod + ('traced_func_simple_caller',): 1,
            self.filemod + ('traced_func_linear',): 1,
        }
        self.assertEqual(self.tracer.results().calledfuncs, expected)

    def test_loop_caller_importing(self):
        self.tracer.runfunc(traced_func_importing_caller, 1)

        expected = {
            self.filemod + ('traced_func_simple_caller',): 1,
            self.filemod + ('traced_func_linear',): 1,
            self.filemod + ('traced_func_importing_caller',): 1,
            self.filemod + ('traced_func_importing',): 1,
            (fix_ext_py(testmod.__file__), 'testmod', 'func'): 1,
        }
        self.assertEqual(self.tracer.results().calledfuncs, expected)

    def test_inst_method_calling(self):
        obj = TracedClass(20)
        self.tracer.runfunc(obj.inst_method_calling, 1)

        expected = {
            self.filemod + ('TracedClass.inst_method_calling',): 1,
            self.filemod + ('TracedClass.inst_method_linear',): 1,
            self.filemod + ('traced_func_linear',): 1,
        }
        self.assertEqual(self.tracer.results().calledfuncs, expected)

    def test_exec_counts(self):
        self.tracer = Trace(count=1, trace=0, countfuncs=0, countcallers=0)
        code = r'''traced_func_loop(2, 5)'''
        code = compile(code, __file__, 'exec')
        self.tracer.runctx(code, globals(), vars())

        firstlineno = get_firstlineno(traced_func_loop)
        expected = {
            (self.my_py_filename, firstlineno + 1): 1,
            (self.my_py_filename, firstlineno + 2): 6,
            (self.my_py_filename, firstlineno + 3): 5,
            (self.my_py_filename, firstlineno + 4): 1,
        }

        # When used through 'run', some other spurious counts are produced, like
        # the settrace of threading, which we ignore, just making sure that the
        # counts fo traced_func_loop were right.
        #
        for k in expected.keys():
            self.assertEqual(self.tracer.results().counts[k], expected[k])

    def max_idle_time(self, trace, start, end, cpus):
        """
        :returns: The maximum idle time of 'cpus' in the [start, end] interval
        """
        idle_df = pd.DataFrame()
        max_time = 0
        max_cpu = 0

        for cpu in cpus:
            busy_df = self.get_active_df(trace, cpu)
            busy_df = Trace.squash_df(busy_df, start, end)
            busy_df = busy_df[busy_df.state == 0]

            if busy_df.empty:
                continue

            local_max = busy_df.delta.max()
            if local_max > max_time:
                max_time = local_max
                max_cpu = cpu

        return max_time, max_cpu