Python random.randrange() Examples

def add_token(request: LocalProxy, session: Session) -> str:
    """
    Create a new token for the user or return a
    valid existing token to the user.
    """
    token = None
    id_ = int(request.authorization['username'])
    try:
        token = session.query(Token).filter(Token.user_id == id_).one()
        if not token.is_valid():
            update_token = '%030x' % randrange(16**30)
            token.id = update_token
            token.timestamp = datetime.now()
            session.commit()
    except NoResultFound:
        token = '%030x' % randrange(16**30)
        new_token = Token(user_id=id_, id=token)
        session.add(new_token)
        session.commit()
        return token
    return token.id 

def choose_random_photo(self, album):
        photo = {}
        photos_url = PHOTOS_URL % {'username':self.username, 'albumid':album['id']}
        response = utils.http_request(photos_url).get('response')
        if response:
            content = json.loads(response.read().decode('utf-8'))
            numphotos = utils.rget(content, 'feed.gphoto$numphotos.$t')
            if numphotos:
                diceroll = random.randrange(numphotos)
                entry = utils.rget(content, 'feed.entry')[diceroll]
                photo['id'] = entry['gphoto$id']['$t']
                photo['url'] = entry['content']['src']
                photo['title'] = utils.rget(entry, 'title.$t')
                photo['summary'] = utils.rget(entry, 'summary.$t')
                photo['timestamp'] = utils.rget(entry, 'gphoto$timestamp.$t')
                photo['published'] = utils.rget(entry, 'published.$t')
                photo['width'] = utils.rget(entry, 'gphoto$width.$t')
                photo['height'] = utils.rget(entry, 'gphoto$height.$t')
                photo['size'] = int(utils.rget(entry, 'gphoto$size.$t', 0))
                photo['credit'] = ', '.join([item['$t'] for item in utils.rget(entry, 'media$group.media$credit')])
                for tag, value in utils.rget(entry, 'exif$tags').items():
                    tagstr = tag.replace('exif$', '')
                    photo[tagstr] = value['$t']
        return photo 

def step(self, amt=1):
        self.layout.all_off()

        if self.pulse_speed == 0 and random.randrange(0, 100) <= self.chance:
            self.add_pulse()

        if self.pulse_speed > 0:
            self.layout.set(self.pulse_position, self.pulse_color)
            for i in range(self._tail):
                c = color_scale(self.pulse_color, 255 - (self._fadeAmt * i))
                self.layout.set(self.pulse_position - i, c)
                self.layout.set(self.pulse_position + i, c)

            if self.pulse_position > self._size + self._tail:
                self.pulse_speed = 0
            else:
                self.pulse_position += self.pulse_speed 

def step(self, amt=1):
        self.layout.all_off()

        for i in range(self._growthRate):
            newTail = random.randrange(0, 360, self._angleDiff)
            color = random.choice(self.palette)
            self._tails[newTail].append((0, color))

        for a in range(360):
            angle = self._tails[a]
            if len(angle) > 0:
                removals = []
                for r in range(len(angle)):
                    tail = angle[r]
                    if tail[0] <= self.lastRing:
                        self._drawTail(a, tail[0], tail[1])
                    if tail[0] - (self._tail - 1) <= self.lastRing:
                        tail = (tail[0] + amt, tail[1])
                        self._tails[a][r] = tail
                    else:
                        removals.append(tail)
                for r in removals:
                    self._tails[a].remove(r)

        self._step = 0 

def sample_doc(self, current_idx, sentence_weighted=True):
        # Uses the current iteration counter to ensure we don't sample the same doc twice
        if sentence_weighted:
            # With sentence weighting, we sample docs proportionally to their sentence length
            if self.doc_cumsum is None or len(self.doc_cumsum) != len(self.doc_lengths):
                self._precalculate_doc_weights()
            rand_start = self.doc_cumsum[current_idx]
            rand_end = rand_start + self.cumsum_max - self.doc_lengths[current_idx]
            sentence_index = randrange(rand_start, rand_end) % self.cumsum_max
            sampled_doc_index = np.searchsorted(self.doc_cumsum, sentence_index, side='right')
        else:
            # If we don't use sentence weighting, then every doc has an equal chance to be chosen
            sampled_doc_index = (current_idx + randrange(1, len(self.doc_lengths))) % len(self.doc_lengths)
        assert sampled_doc_index != current_idx
        if self.reduce_memory:
            return self.document_shelf[str(sampled_doc_index)]
        else:
            return self.documents[sampled_doc_index] 

def select_action(self, state):
        """
        The action selection function, it either uses the model to choose an action or samples one uniformly.
        :param state: current state of the model
        :return:
        """
        if self.cuda:
            state = state.cuda()
        sample = random.random()
        eps_threshold = self.config.eps_start + (self.config.eps_start - self.config.eps_end) * math.exp(
            -1. * self.current_iteration / self.config.eps_decay)
        self.current_iteration += 1
        if sample > eps_threshold:
            with torch.no_grad():
                return self.policy_model(state).max(1)[1].view(1, 1)
        else:
            return torch.tensor([[random.randrange(2)]], device=self.device, dtype=torch.long) 

def random_expr(depth, vlist, ops):
  """Generate a random expression tree.

  Args:
    depth: At least one leaf will be this many levels down from the top.
    vlist: A list of chars. These chars are randomly selected as leaf values.
    ops: A list of ExprOp instances.

  Returns:
    An ExprNode instance which is the root of the generated expression tree.
  """
  if not depth:
    return str(vlist[random.randrange(len(vlist))])

  max_depth_side = random.randrange(2)
  other_side_depth = random.randrange(depth)

  left = random_expr(depth - 1
                     if max_depth_side else other_side_depth, vlist, ops)
  right = random_expr(depth - 1
                      if not max_depth_side else other_side_depth, vlist, ops)

  op = ops[random.randrange(len(ops))]
  return ExprNode(left, right, op) 

def generate_algebra_simplify_sample(vlist, ops, min_depth, max_depth):
  """Randomly generate an algebra simplify dataset sample.

  Given an input expression, produce the simplified expression.

  See go/symbolic-math-dataset.

  Args:
    vlist: Variable list. List of chars that can be used in the expression.
    ops: List of ExprOp instances. The allowed operators for the expression.
    min_depth: Expression trees will not have a smaller depth than this. 0 means
        there is just a variable. 1 means there is one operation.
    max_depth: Expression trees will not have a larger depth than this. To make
        all trees have the same depth, set this equal to `min_depth`.

  Returns:
    sample: String representation of the input.
    target: String representation of the solution.
  """
  depth = random.randrange(min_depth, max_depth + 1)
  expr = random_expr(depth, vlist, ops)

  sample = str(expr)
  target = format_sympy_expr(sympy.simplify(sample))
  return sample, target 

def datetime(self, field=None, val=None):
        """
        Returns a random datetime. If 'val' is passed, a datetime within two
        years of that date will be returned.
        """
        if val is None:
            def source():
                tzinfo = get_default_timezone() if settings.USE_TZ else None
                return datetime.fromtimestamp(randrange(1, 2100000000),
                                              tzinfo)
        else:
            def source():
                tzinfo = get_default_timezone() if settings.USE_TZ else None
                return datetime.fromtimestamp(int(val.strftime("%s")) +
                                              randrange(-365*24*3600*2, 365*24*3600*2),
                                              tzinfo)
        return self.get_allowed_value(source, field) 

def bench():

    n = 8
    w = 4

    a0, a1, a2, a3, a4, a5, a6, a7 = inputs = [Signal(intbv(0)[w:]) for i in range(n)]
    z0, z1, z2, z3, z4, z5, z6, z7 = outputs = [Signal(intbv(0)[w:]) for i in range(n)]


    inst = Array8Sorter_v(a0, a1, a2, a3, a4, a5, a6, a7,
                          z0, z1, z2, z3, z4, z5, z6, z7)

    @instance
    def check():
        for i in range(100):
            data = [randrange(2**w) for i in range(n)]
            for i in range(n):
                inputs[i].next = data[i]
            yield delay(10)
            data.sort()
            assert data == outputs

    return inst, check 

def bench():
    
    led = Signal(intbv(0)[7:])
    bcd = Signal(intbv(0)[4:])
    clock = Signal(bool(0))
    
    dut = bcd2led(led, bcd, clock)

    @always(delay(PERIOD//2))
    def clkgen():
        clock.next = not clock

    @instance
    def check():
        for i in range(100):
            bcd.next = randrange(10)
            yield clock.posedge
            yield clock.negedge
            expected = int(seven_segment.encoding[int(bcd)], 2)
            assert led == expected
        raise StopSimulation

    return dut, clkgen, check 

def test_dffa():
    
    q, d, clk, rst = [Signal(bool(0)) for i in range(4)]
    
    dffa_inst = dffa(q, d, clk, rst)

    @always(delay(10))
    def clkgen():
        clk.next = not clk

    @always(clk.negedge)
    def stimulus():
        d.next = randrange(2)

    @instance
    def rstgen():
        yield delay(5)
        rst.next = 1
        while True:
            yield delay(randrange(500, 1000))
            rst.next = 0
            yield delay(randrange(80, 140))
            rst.next = 1

    return dffa_inst, clkgen, stimulus, rstgen 

def LoopBench(LoopTest):

    a = Signal(intbv(-1)[16:])
    z = Signal(intbv(0)[16:])

    looptest_inst = LoopTest(a, z)
    data = tuple([randrange(2**min(i, 16)) for i in range(100)])

    @instance
    def stimulus():
        for i in range(100):
            a.next = data[i]
            yield delay(10)
            print(z)

    return stimulus, looptest_inst 

def test_signed_list():
    '''The correct initial value should be used for signed type signal lists
    '''
    min_val = -12
    max_val = 4

    initial_vals = [intbv(
        randrange(min_val, max_val), min=min_val, max=max_val)
        for each in range(10)]

    runner(initial_vals, tb=initial_value_list_bench)

    # All the same case
    initial_vals = [intbv(
        randrange(min_val, max_val), min=min_val, max=max_val)] * 10

    runner(initial_vals, tb=initial_value_list_bench) 

def LoopBench(LoopTest):

    a = Signal(intbv(-1)[16:])
    z = Signal(intbv(0)[16:])

    looptest_inst = LoopTest(a, z)
    data = tuple([randrange(2**min(i, 16)) for i in range(100)])

    @instance
    def stimulus():
        for i in range(100):
            a.next = data[i]
            yield delay(10)
            print(z)

    return stimulus, looptest_inst 

def LoopBench(LoopTest):

    a = Signal(intbv(-1)[16:])
    z = Signal(intbv(0)[16:])

    looptest_inst = LoopTest(a, z)
    data = tuple([randrange(2**min(i, 16)) for i in range(100)])

    @instance
    def stimulus():
        for i in range(100):
            a.next = data[i]
            yield delay(10)
            print(z)

    return stimulus, looptest_inst 

def benchBool(self, ConstWire):
        
        p = Signal(bool(0))
        q = Signal(bool(0))
        q_v = Signal(bool(0))

        constwire_inst = toVerilog(ConstWire, p, q)
        constwire_v_inst = ConstWire_v(ConstWire.__name__, p, q_v)

        def stimulus():
            for i in range(100):
                p.next = randrange(2)
                yield delay(10)
                self.assertEqual(q, q_v)

        return stimulus(), constwire_inst, constwire_v_inst 

def bench(self, adder):

        a = Signal(intbv(0)[8:])
        b = Signal(intbv(0)[8:])
        c = Signal(intbv(0)[9:])
        c_v = Signal(intbv(0)[9:])

        ignorecode_inst = toVerilog(adder, a, b, c)
        # ignorecode_inst = adder(a, b, c)
        ignorecode_v_inst = Ignorecode_v(adder.__name__, a, b, c_v)

        def stimulus():
            for i in range(100):
                a.next = randrange(2**8)
                b.next = randrange(2**8)
                yield delay(10)
                self.assertEqual(c, c_v)
                
        return stimulus(), ignorecode_inst, ignorecode_v_inst 

def random_name():
    return "".join(random.choices(string.ascii_letters,
                                  k=random.randrange(1, 10))) 

def rand_dist(to_goods, from_goods, comp=None):
    """
    select random good by random amount and transfer to trader
    """
    selected_good = get_rand_good(from_goods, nonzero=True)
    amt = random.randrange(0, from_goods[selected_good][AMT_AVAIL], 1)
    transfer(to_goods, from_goods, selected_good, amt, comp=comp) 

def nonExistent(self):
        return self.Request(path=self.path + str(random.randrange(100, 999)) + '.html') 

def choose_random_album(self):
        counter = 0
        numphotos = sum([album['photos'] for album in self.data['albums']])
        diceroll = random.randrange(numphotos)
        for album in self.data['albums']:
            counter += album['photos']
            if counter >= diceroll: break
        return album 

def _getNextPiece(self):
        stop = len(tetris_shapes) if self._doEvil else 7
        return tetris_shapes[rand(0, stop)] 

def pick_led(self, speed):
        idx = random.randrange(0, self.layout.numLEDs)
        p_dir, p_color, p_level = self.pixels[idx]

        if random.randrange(0, 100) < self.density:
            if p_dir == 0:  # 0 is off
                p_level += speed
                p_dir = 1  # 1 is growing
                p_color = random.choice(self.colors)
                self.layout._set_base(idx, color_scale(p_color, p_level))

                self.pixels[idx] = p_dir, p_color, p_level 

def add_pulse(self):
        self.pulse_color = random.choice(self.palette)
        self.pulse_speed = random.randrange(self.min_speed, self.max_speed)
        self.pulse_position = 0 

def pick_led(self, inc):
        # Pick a random led, if it's off bump it up an even number so it gets brighter
        idx = random.randrange(0, self.layout.numLEDs)
        this_led = self.layout.get(idx)
        r = this_led[0]

        if random.randrange(0, self._maxLed) < self.density:
            if r == 0:
                r += inc
                self.layout.set(idx, (2, 2, 2)) 

def loadNextGIF(self):
        if self.random:
            if len(self.gif_indices) < 2:
                self.folder_index = self.gif_indices[0]
                self.gif_indices = list(range(len(self.gif_files)))
            else:
                index = random.randrange(len(self.gif_indices))
                self.folder_index = self.gif_indices.pop(index)
        else:
            self.folder_index += 1
            if self.folder_index >= len(self.gif_files):
                self.folder_index = 0
        self.loadGIFFile(self.gif_files[self.folder_index]) 

def pre_run(self):
        self.x = random.randrange(self.width)
        self.y = random.randrange(self.height)
        self.d = random.randrange(4) 

def step(self, amt=1):
        self.layout.all_off()

        for i in range(self._growthRate):
            newTail = random.randrange(0, 360, self._angleDiff)
            self._tails[newTail].append(0)

        for a in range(360):
            angle = self._tails[a]
            if len(angle) > 0:
                removals = []
                for r in range(len(angle)):
                    tail = angle[r]
                    if tail <= self.lastRing:
                        c = self.palette.get(tail * (255 // self.lastRing))
                        self._drawTail(a, tail, c)

                    if tail - (self._tail - 1) <= self.lastRing:
                        tail = tail + amt
                        self._tails[a][r] = tail
                    else:
                        removals.append(tail)
                for r in removals:
                    self._tails[a].remove(r)

        self._step = 0 

def make_turing_machine_rules(n_states, n_symbols):
    the_rules = [   [   (random.randrange(n_symbols), random.randrange(n_states), random.choice('LNR'))
                        for symbol in range(n_symbols)]
                    for state in range(n_states)]
    return the_rules