Python numpy.round() Examples

def draw_bounding_boxes(image, gt_boxes, im_info):
  num_boxes = gt_boxes.shape[0]
  gt_boxes_new = gt_boxes.copy()
  gt_boxes_new[:,:4] = np.round(gt_boxes_new[:,:4].copy() / im_info[2])
  disp_image = Image.fromarray(np.uint8(image[0]))

  for i in range(num_boxes):
    this_class = int(gt_boxes_new[i, 4])
    disp_image = _draw_single_box(disp_image, 
                                gt_boxes_new[i, 0],
                                gt_boxes_new[i, 1],
                                gt_boxes_new[i, 2],
                                gt_boxes_new[i, 3],
                                'N%02d-C%02d' % (i, this_class),
                                FONT,
                                color=STANDARD_COLORS[this_class % NUM_COLORS])

  image[0, :] = np.array(disp_image)
  return image 

def compute_mode(self):
        """
        Pre-compute mode vectors from candidate locations (in spherical 
        coordinates).
        """
        if self.num_loc is None:
            raise ValueError('Lookup table appears to be empty. \
                Run build_lookup().')
        self.mode_vec = np.zeros((self.max_bin,self.M,self.num_loc), 
            dtype='complex64')
        if (self.nfft % 2 == 1):
            raise ValueError('Signal length must be even.')
        f = 1.0 / self.nfft * np.linspace(0, self.nfft / 2, self.max_bin) \
            * 1j * 2 * np.pi
        for i in range(self.num_loc):
            p_s = self.loc[:, i]
            for m in range(self.M):
                p_m = self.L[:, m]
                if (self.mode == 'near'):
                    dist = np.linalg.norm(p_m - p_s, axis=1)
                if (self.mode == 'far'):
                    dist = np.dot(p_s, p_m)
                # tau = np.round(self.fs*dist/self.c) # discrete - jagged
                tau = self.fs * dist / self.c  # "continuous" - smoother
                self.mode_vec[:, m, i] = np.exp(f * tau) 

def cantilever_beam_test():
    #FEModel Test
    model=FEModel()
    model.add_node(0,0,0)
    model.add_node(2,1,1)
    E=1.999e11
    mu=0.3
    A=4.265e-3
    J=9.651e-8
    I3=6.572e-5
    I2=3.301e-6
    rho=7849.0474
    
    model.add_beam(0,1,E,mu,A,I2,I3,J,rho)
    model.set_node_force(1,(0,0,-1e6,0,0,0))
    model.set_node_restraint(0,[True]*6)
    model.assemble_KM()
    model.assemble_f()
    model.assemble_boundary()
    solve_linear(model)
    print(np.round(model.d_,6))
    print("The result of node 1 should be about [0.12879,0.06440,-0.32485,-0.09320,0.18639,0]") 

def predict(self):
        """Predict state vector u and variance of uncertainty P (covariance).
            where,
            u: previous state vector
            P: previous covariance matrix
            F: state transition matrix
            Q: process noise matrix
        Equations:
            u'_{k|k-1} = Fu'_{k-1|k-1}
            P_{k|k-1} = FP_{k-1|k-1} F.T + Q
            where,
                F.T is F transpose
        Args:
            None
        Return:
            vector of predicted state estimate
        """
        # Predicted state estimate
        self.u = np.round(np.dot(self.F, self.u))
        # Predicted estimate covariance
        self.P = np.dot(self.F, np.dot(self.P, self.F.T)) + self.Q
        self.lastResult = self.u  # same last predicted result
        return self.u 

def _uniform_embed(self, embed_dict, words_dict):
        """
        :param embed_dict:
        :param words_dict:
        """
        print("loading pre_train embedding by uniform for out of vocabulary.")
        embeddings = np.zeros((int(self.words_count), int(self.dim)))
        inword_list = {}
        for word in words_dict:
            if word in embed_dict:
                embeddings[words_dict[word]] = np.array([float(i) for i in embed_dict[word]], dtype='float32')
                inword_list[words_dict[word]] = 1
                self.exact_count += 1
            elif word.lower() in embed_dict:
                embeddings[words_dict[word]] = np.array([float(i) for i in embed_dict[word.lower()]], dtype='float32')
                inword_list[words_dict[word]] = 1
                self.fuzzy_count += 1
            else:
                self.oov_count += 1
        uniform_col = np.random.uniform(-0.25, 0.25, int(self.dim)).round(6)  # uniform
        for i in range(len(words_dict)):
            if i not in inword_list and i != self.padID:
                embeddings[i] = uniform_col
        final_embed = torch.from_numpy(embeddings).float()
        return final_embed 

def cortex_to_angle(self, x, y):
        iterX = hasattr(x, '__iter__')
        iterY = hasattr(y, '__iter__')
        jarr = None
        if iterX and iterY:
            if len(x) != len(y):
                raise RuntimeError('Arguments x and y must be the same length!')
            jarr = self._java_object.cortexToAngle(to_java_doubles(x), to_java_doubles(y))
        elif iterX:
            jarr = self._java_object.cortexToAngle(to_java_doubles(x),
                                                   to_java_doubles([y for i in x]))
        elif iterY:
            jarr = self._java_object.cortexToAngle(to_java_doubles([x for i in y]),
                                                   to_java_doubles(y))
        else:
            return self._java_object.cortexToAngle(x, y)
        dat = np.asarray([[c for c in r] for r in jarr])
        a = dat[:,2]
        a = np.round(np.abs(a))
        a[a > 3] = 0
        dat[:,2] = a
        return dat 

def test_lda(self):
        """
        Linear Disciminant Analysis
        """
        np.random.seed(100) 
        N = 150 
        classes = np.array(["1", "a", 3]) 
        cols = 4
        x = np.random.random((N, cols)) # random data
        labels = np.random.choice(classes, size=N) # random labels
        # LDA components
        out = pa.preprocess.LDA_discriminants(x, labels)
        self.assertEqual(np.round(np.array(out).mean(), 5), 0.01298)
        # LDA analysis
        new_x = pa.preprocess.LDA(x, labels, n=2)  
        self.assertEqual(np.round(np.array(new_x).mean(), 5), -0.50907)
        self.assertEqual(new_x.shape, (150, 2)) 

def resize(im, short, max_size):
    """
    only resize input image to target size and return scale
    :param im: BGR image input by opencv
    :param short: one dimensional size (the short side)
    :param max_size: one dimensional max size (the long side)
    :return: resized image (NDArray) and scale (float)
    """
    im_shape = im.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(short) / float(im_size_min)
    # prevent bigger axis from being more than max_size:
    if np.round(im_scale * im_size_max) > max_size:
        im_scale = float(max_size) / float(im_size_max)
    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
    return im, im_scale 

def _project_to_map(map, vertex, wt=None, ignore_points_outside_map=False):
  """Projects points to map, returns how many points are present at each
  location."""
  num_points = np.zeros((map.size[1], map.size[0]))
  vertex_ = vertex[:, :2] - map.origin
  vertex_ = np.round(vertex_ / map.resolution).astype(np.int)
  if ignore_points_outside_map:
    good_ind = np.all(np.array([vertex_[:,1] >= 0, vertex_[:,1] < map.size[1],
                                vertex_[:,0] >= 0, vertex_[:,0] < map.size[0]]),
                      axis=0)
    vertex_ = vertex_[good_ind, :]
    if wt is not None:
      wt = wt[good_ind, :]
  if wt is None:
    np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), 1)
  else:
    assert(wt.shape[0] == vertex.shape[0]), \
      'number of weights should be same as vertices.'
    np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), wt)
  return num_points 

def raw_valid_fn_vec(self, xyt):
    """Returns if the given set of nodes is valid or not."""
    height = self.traversible.shape[0]
    width = self.traversible.shape[1]
    x = np.round(xyt[:,[0]]).astype(np.int32)
    y = np.round(xyt[:,[1]]).astype(np.int32)
    is_inside = np.all(np.concatenate((x >= 0, y >= 0,
                                       x < width, y < height), axis=1), axis=1)
    x = np.minimum(np.maximum(x, 0), width-1)
    y = np.minimum(np.maximum(y, 0), height-1)
    ind = np.ravel_multi_index((y,x), self.traversible.shape)
    is_traversible = self.traversible.ravel()[ind]

    is_valid = np.all(np.concatenate((is_inside[:,np.newaxis], is_traversible),
                                     axis=1), axis=1)
    return is_valid 

def test_convert_to_normalized_and_back(self):
    coordinates = np.random.uniform(size=(100, 4))
    coordinates = np.round(np.sort(coordinates) * 200)
    coordinates[:, 2:4] += 1
    coordinates[99, :] = [0, 0, 201, 201]
    img = tf.ones((128, 202, 202, 3))

    boxlist = box_list.BoxList(tf.constant(coordinates, tf.float32))
    boxlist = box_list_ops.to_normalized_coordinates(boxlist,
                                                     tf.shape(img)[1],
                                                     tf.shape(img)[2])
    boxlist = box_list_ops.to_absolute_coordinates(boxlist,
                                                   tf.shape(img)[1],
                                                   tf.shape(img)[2])

    with self.test_session() as sess:
      out = sess.run(boxlist.get())
      self.assertAllClose(out, coordinates) 

def prep_im_for_blob(im, pixel_means, pixel_stds, target_size, max_size):
    """Mean subtract and scale an image for use in a blob."""
    
    im = im.astype(np.float32, copy=False)
    im /= 255.0
    im -= pixel_means
    im /= pixel_stds
    # im = im[:, :, ::-1]
    im_shape = im.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(target_size) / float(im_size_min)
    # Prevent the biggest axis from being more than MAX_SIZE
    # if np.round(im_scale * im_size_max) > max_size:
    #     im_scale = float(max_size) / float(im_size_max)
    # im = imresize(im, im_scale)
    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
                    interpolation=cv2.INTER_LINEAR)

    return im, im_scale 

def vis_det_and_mask(im, class_name, dets, masks, thresh=0.8):
    """Visual debugging of detections."""
    num_dets = np.minimum(10, dets.shape[0])
    colors_mask = random_colors(num_dets)
    colors_bbox = np.round(np.random.rand(num_dets, 3) * 255)
    # sort rois according to the coordinates, draw upper bbox first
    draw_mask = np.zeros(im.shape[:2], dtype=np.uint8)

    for i in range(1):
        bbox = tuple(int(np.round(x)) for x in dets[i, :4])
        mask = masks[i, :, :]
        full_mask = unmold_mask(mask, bbox, im.shape)

        score = dets[i, -1]
        if score > thresh:
            word_width = len(class_name)
            cv2.rectangle(im, bbox[0:2], bbox[2:4], colors_bbox[i], 2)
            cv2.rectangle(im, bbox[0:2], (bbox[0] + 18 + word_width*8, bbox[1]+15), colors_bbox[i], thickness=cv2.FILLED)
            apply_mask(im, full_mask, draw_mask, colors_mask[i], 0.5)
            draw_mask += full_mask
            cv2.putText(im, '%s' % (class_name), (bbox[0]+5, bbox[1] + 12), cv2.FONT_HERSHEY_PLAIN,
								1.0, (255,255,255), thickness=1)
    return im 

def __init__(self, w_in, w_out, stride, bm, gw, se_r):
        super(BottleneckTransform, self).__init__()
        w_b = int(round(w_out * bm))
        g = w_b // gw
        self.a = nn.Conv2d(w_in, w_b, 1, stride=1, padding=0, bias=False)
        self.a_bn = nn.BatchNorm2d(w_b, eps=1e-5, momentum=0.1)
        self.a_relu = nn.ReLU(inplace=True)
        self.b = nn.Conv2d(w_b, w_b, 3, stride=stride, padding=1, groups=g, bias=False)
        self.b_bn = nn.BatchNorm2d(w_b, eps=1e-5, momentum=0.1)
        self.b_relu = nn.ReLU(inplace=True)
        if se_r:
            w_se = int(round(w_in * se_r))
            self.se = SE(w_b, w_se)
        self.c = nn.Conv2d(w_b, w_out, 1, stride=1, padding=0, bias=False)
        self.c_bn = nn.BatchNorm2d(w_out, eps=1e-5, momentum=0.1)
        self.c_bn.final_bn = True 

def forward(self, x):
        for layer in self.children():
            x = layer(x)
        return x

    # @staticmethod
    # def complexity(cx, w_in, w_out, stride, bm, gw, se_r):
    #     w_b = int(round(w_out * bm))
    #     g = w_b // gw
    #     cx = net.complexity_conv2d(cx, w_in, w_b, 1, 1, 0)
    #     cx = net.complexity_batchnorm2d(cx, w_b)
    #     cx = net.complexity_conv2d(cx, w_b, w_b, 3, stride, 1, g)
    #     cx = net.complexity_batchnorm2d(cx, w_b)
    #     if se_r:
    #         w_se = int(round(w_in * se_r))
    #         cx = SE.complexity(cx, w_b, w_se)
    #     cx = net.complexity_conv2d(cx, w_b, w_out, 1, 1, 0)
    #     cx = net.complexity_batchnorm2d(cx, w_out)
    #     return cx 

def next(self, ):
        self._current = current = self._next

        if current.index >= len(self._periods):
            raise StopIteration()

        # Increment to next timestep
        next_index = current.index + 1
        if next_index >= len(self._periods):
            # The final time-step is one offset beyond the end of the model.
            # Here we compute its delta and create the object. 
            final_period = current.period + self.offset
            delta = final_period.end_time - final_period.start_time
            delta = np.round(delta.total_seconds())
            delta = delta / SECONDS_IN_DAY
            self._next = _core.Timestep(final_period, next_index, delta)
        else:
            self._next = _core.Timestep(self._periods[next_index], next_index, self._deltas[next_index])

        # Return this timestep
        return current 

def inverse_method(self,N,d):
        
        t = np.linspace(1e-3,0.999,N)
        f = np.log( t / (1 - t) )
        f = f/f[0]
        
        psi= np.pi*f
        cosPsi = np.cos(psi)
        sinTheta = ( np.abs(cosPsi) + (1-np.abs(cosPsi))*np.random.rand(len(cosPsi)))
        
        theta = np.arcsin(sinTheta)
        theta = np.pi-theta + (2*theta - np.pi)*np.round(np.random.rand(len(t)))
        cosPhi = cosPsi/sinTheta
        phi = np.arccos(cosPhi)*(-1)**np.round(np.random.rand(len(t)))
        
        coords = SkyCoord(phi*u.rad,(np.pi/2-theta)*u.rad,d*np.ones(len(phi))*u.pc)

        return coords 

def save_wav(audio, output_wav_file):
    wav.write(output_wav_file, 16000, np.array(np.clip(np.round(audio), -2**15, 2**15-1), dtype=np.int16))
    print('output dB', db(audio)) 

def prep_im_for_blob(im, pixel_means, target_size, max_size):
  """Mean subtract and scale an image for use in a blob."""
  im = im.astype(np.float32, copy=False)
  im -= pixel_means
  im_shape = im.shape
  im_size_min = np.min(im_shape[0:2])
  im_size_max = np.max(im_shape[0:2])
  im_scale = float(target_size) / float(im_size_min)
  # Prevent the biggest axis from being more than MAX_SIZE
  if np.round(im_scale * im_size_max) > max_size:
    im_scale = float(max_size) / float(im_size_max)
  im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
                  interpolation=cv2.INTER_LINEAR)

  return im, im_scale 

def _get_image_blob(im):
  """Converts an image into a network input.
  Arguments:
    im (ndarray): a color image in BGR order
  Returns:
    blob (ndarray): a data blob holding an image pyramid
    im_scale_factors (list): list of image scales (relative to im) used
      in the image pyramid
  """
  im_orig = im.astype(np.float32, copy=True)
  im_orig -= cfg.PIXEL_MEANS

  im_shape = im_orig.shape
  im_size_min = np.min(im_shape[0:2])
  im_size_max = np.max(im_shape[0:2])

  processed_ims = []
  im_scale_factors = []

  for target_size in cfg.TEST.SCALES:
    im_scale = float(target_size) / float(im_size_min)
    # Prevent the biggest axis from being more than MAX_SIZE
    if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
      im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
    im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
            interpolation=cv2.INTER_LINEAR)
    im_scale_factors.append(im_scale)
    processed_ims.append(im)

  # Create a blob to hold the input images
  blob = im_list_to_blob(processed_ims)

  return blob, np.array(im_scale_factors) 

def _get_image_blob(im):
  """Converts an image into a network input.
  Arguments:
    im (ndarray): a color image in BGR order
  Returns:
    blob (ndarray): a data blob holding an image pyramid
    im_scale_factors (list): list of image scales (relative to im) used
      in the image pyramid
  """
  im_orig = im.astype(np.float32, copy=True)
  im_orig -= cfg.PIXEL_MEANS

  im_shape = im_orig.shape
  im_size_min = np.min(im_shape[0:2])
  im_size_max = np.max(im_shape[0:2])

  processed_ims = []
  im_scale_factors = []

  for target_size in cfg.TEST.SCALES:
    im_scale = float(target_size) / float(im_size_min)
    # Prevent the biggest axis from being more than MAX_SIZE
    if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
      im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
    im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
            interpolation=cv2.INTER_LINEAR)
    im_scale_factors.append(im_scale)
    processed_ims.append(im)

  # Create a blob to hold the input images
  blob = im_list_to_blob(processed_ims)

  return blob, np.array(im_scale_factors) 

def forward(self, features, rois):
        batch_size, num_channels, data_height, data_width = features.size()
        num_rois = rois.size()[0]
        outputs = Variable(torch.zeros(num_rois, num_channels, self.pooled_height, self.pooled_width)).cuda()

        for roi_ind, roi in enumerate(rois):
            batch_ind = int(roi[0].data[0])
            roi_start_w, roi_start_h, roi_end_w, roi_end_h = np.round(
                roi[1:].data.cpu().numpy() * self.spatial_scale).astype(int)
            roi_width = max(roi_end_w - roi_start_w + 1, 1)
            roi_height = max(roi_end_h - roi_start_h + 1, 1)
            bin_size_w = float(roi_width) / float(self.pooled_width)
            bin_size_h = float(roi_height) / float(self.pooled_height)

            for ph in range(self.pooled_height):
                hstart = int(np.floor(ph * bin_size_h))
                hend = int(np.ceil((ph + 1) * bin_size_h))
                hstart = min(data_height, max(0, hstart + roi_start_h))
                hend = min(data_height, max(0, hend + roi_start_h))
                for pw in range(self.pooled_width):
                    wstart = int(np.floor(pw * bin_size_w))
                    wend = int(np.ceil((pw + 1) * bin_size_w))
                    wstart = min(data_width, max(0, wstart + roi_start_w))
                    wend = min(data_width, max(0, wend + roi_start_w))

                    is_empty = (hend <= hstart) or(wend <= wstart)
                    if is_empty:
                        outputs[roi_ind, :, ph, pw] = 0
                    else:
                        data = features[batch_ind]
                        outputs[roi_ind, :, ph, pw] = torch.max(
                            torch.max(data[:, hstart:hend, wstart:wend], 1)[0], 2)[0].view(-1)

        return outputs 

def _ratio_enum(anchor, ratios):
  """
  Enumerate a set of anchors for each aspect ratio wrt an anchor.
  """

  w, h, x_ctr, y_ctr = _whctrs(anchor)
  size = w * h
  size_ratios = size / ratios
  ws = np.round(np.sqrt(size_ratios))
  hs = np.round(ws * ratios)
  anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
  return anchors 

def _lb2RN_northcap(self, l, b):
        R = 100. + (90. - b) * np.sin(np.radians(l)) / 0.3
        N = 100. + (90. - b) * np.cos(np.radians(l)) / 0.3
        return np.round(R).astype('i4'), np.round(N).astype('i4') 

def _lb2RN_southcap(self, l, b):
        R = 100. + (90. + b) * np.sin(np.radians(l)) / 0.3
        N = 100. + (90. + b) * np.cos(np.radians(l)) / 0.3
        return np.round(R).astype('i4'), np.round(N).astype('i4') 

def _lb2RN_mid(self, l, b):
        R = (np.abs(b) - 10.) / 0.6
        N = (np.mod(l, 360.) + 0.15) / 0.3 - 1
        return np.round(R).astype('i4'), np.round(N).astype('i4') 

def simply_supported_beam_test():
    #FEModel Test
    model=FEModel()

    E=1.999e11
    mu=0.3
    A=4.265e-3
    J=9.651e-8
    I3=6.572e-5
    I2=3.301e-6
    rho=7849.0474
    
    model.add_node(0,0,0)
    model.add_node(0.5,1,0.5)
    model.add_node(1,2,1)
    
    model.add_beam(0,1,E,mu,A,I2,I3,J,rho)
    model.add_beam(1,2,E,mu,A,I2,I3,J,rho)

    model.set_node_force(1,(0,0,-1e6,0,0,0))
    model.set_node_restraint(2,[False,False,True]+[False]*3)
    model.set_node_restraint(0,[True]*3+[False]*3)

    model.assemble_KM()
    model.assemble_f()
    model.assemble_boundary()
    solve_linear(model)
    print(np.round(model.d_,6))
    print("The result of node 1 should be about [0.00796,0.00715,-0.02296,-0.01553,-0.03106,-0.01903]") 

def simply_released_beam_test():
    #FEModel Test
    model=FEModel()

    E=1.999e11
    mu=0.3
    A=4.265e-3
    J=9.651e-8
    I3=6.572e-5
    I2=3.301e-6
    rho=7849.0474
    
    model.add_node(0,0,0)
    model.add_node(0.5,1,0.5)
    model.add_node(1,2,1)
    
    model.add_beam(0,1,E,mu,A,I2,I3,J,rho)
    model.add_beam(1,2,E,mu,A,I2,I3,J,rho)

    model.set_node_force(1,(0,0,-1e6,0,0,0))
    model.set_node_restraint(2,[True]*6)
    model.set_node_restraint(0,[True]*6)
    
    model.set_beam_releases(0,[True]*6,[False]*6)
    model.set_beam_releases(1,[False]*6,[True]*6)
    
    model.assemble_KM()
    model.assemble_f()
    model.assemble_boundary()
    solve_linear(model)
    print(np.round(model.d_,6))
    print("The result of node 1 should be about [0.00445,0.00890,-0.02296,-0.01930,-0.03860,-0.01930]") 

def generate_regnet(self,
                        initial_width,
                        width_slope,
                        width_parameter,
                        depth,
                        divisor=8):
        """Generates per block width from RegNet parameters.

        Args:
            initial_width ([int]): Initial width of the backbone
            width_slope ([float]): Slope of the quantized linear function
            width_parameter ([int]): Parameter used to quantize the width.
            depth ([int]): Depth of the backbone.
            divisor (int, optional): The divisor of channels. Defaults to 8.

        Returns:
            list, int: return a list of widths of each stage and the number of
                stages
        """
        assert width_slope >= 0
        assert initial_width > 0
        assert width_parameter > 1
        assert initial_width % divisor == 0
        widths_cont = np.arange(depth) * width_slope + initial_width
        ks = np.round(
            np.log(widths_cont / initial_width) / np.log(width_parameter))
        widths = initial_width * np.power(width_parameter, ks)
        widths = np.round(np.divide(widths, divisor)) * divisor
        num_stages = len(np.unique(widths))
        widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist()
        return widths, num_stages 

def quantize_float(number, divisor):
        """Converts a float to closest non-zero int divisible by divior.

        Args:
            number (int): Original number to be quantized.
            divisor (int): Divisor used to quantize the number.

        Returns:
            int: quantized number that is divisible by devisor.
        """
        return int(round(number / divisor) * divisor)