Python numpy 模块,amin() 实例源码

我们从Python开源项目中,提取了以下49个代码示例,用于说明如何使用numpy.amin()

项目:pyku    作者:dubvulture    | 项目源码 | 文件源码
def min_side(_, pos):
    """
    Given an object pixels' positions, return the minimum side length of its
    bounding box
    :param _: pixel values (unused)
    :param pos: pixel position (1-D)
    :return: minimum bounding box side length
    """
    xs = np.array([i / SSIZE for i in pos])
    ys = np.array([i % SSIZE for i in pos])
    minx = np.amin(xs)
    miny = np.amin(ys)
    maxx = np.amax(xs)
    maxy = np.amax(ys)
    ct1 = compute_line(np.array([minx, miny]), np.array([minx, maxy]))
    ct2 = compute_line(np.array([minx, miny]), np.array([maxx, miny]))
    return min(ct1, ct2)
项目:brats17    作者:xf4j    | 项目源码 | 文件源码
def generate_patch_probs(path, patch_locations, patch_size, im_size):
    x, y, z = patch_locations
    seg = nib.load(glob.glob(os.path.join(path, '*_seg.nii.gz'))[0]).get_data().astype(np.float32)
    p = []
    for i in range(len(x)):
        for j in range(len(y)):
            for k in range(len(z)):
                patch = seg[int(x[i] - patch_size / 2) : int(x[i] + patch_size / 2),
                            int(y[j] - patch_size / 2) : int(y[j] + patch_size / 2),
                            int(z[k] - patch_size / 2) : int(z[k] + patch_size / 2)]
                patch = (patch > 0).astype(np.float32)
                percent = np.sum(patch) / (patch_size * patch_size * patch_size)
                p.append((1 - np.abs(percent - 0.5)) * percent)
    p = np.asarray(p, dtype=np.float32)
    p[p == 0] = np.amin(p[np.nonzero(p)])
    p = p / np.sum(p)
    return p
项目:spyking-circus    作者:spyking-circus    | 项目源码 | 文件源码
def view_trigger_snippets_bis(trigger_snippets, elec_index, save=None):
    fig = pylab.figure()
    ax = fig.add_subplot(1, 1, 1)
    for n in xrange(0, trigger_snippets.shape[2]):
        y = trigger_snippets[:, elec_index, n]
        x = numpy.arange(- (y.size - 1) / 2, (y.size - 1) / 2 + 1)
        b = 0.5 + 0.5 * numpy.random.rand()
        ax.plot(x, y, color=(0.0, 0.0, b), linestyle='solid')
    ax.grid(True)
    ax.set_xlim([numpy.amin(x), numpy.amax(x)])
    ax.set_xlabel("time")
    ax.set_ylabel("amplitude")
    if save is None:
        pylab.show()
    else:
        pylab.savefig(save)
        pylab.close(fig)
    return
项目:lung-cancer-detector    作者:YichenGong    | 项目源码 | 文件源码
def to_rgb(img):
    """
    Converts the given array into a RGB image. If the number of channels is not
    3 the array is tiled such that it has 3 channels. Finally, the values are
    rescaled to [0,255) 

    :param img: the array to convert [nx, ny, channels]

    :returns img: the rgb image [nx, ny, 3]
    """
    img = np.atleast_3d(img)
    channels = img.shape[2]
    if channels < 3:
        img = np.tile(img, 3)

    img[np.isnan(img)] = 0
    img -= np.amin(img)
    img /= np.amax(img)
    img *= 255
    return img
项目:untwist    作者:IoSR-Surrey    | 项目源码 | 文件源码
def add(self, x, y = None):
        self.X =  np.memmap(
            self.path+"/X.npy", self.X.dtype,
            shape = (self.nrows + x.shape[0] , x.shape[1])
        )
        self.X[self.nrows:self.nrows + x.shape[0],:] = x

        if y is not None:
            if x.shape != y.shape: raise "x and y should have the same shape"
            self.Y = np.memmap(
                self.path+"/Y.npy", self.Y.dtype,
                shape = (self.nrows + y.shape[0] , y.shape[1])
            )
            self.Y[self.nrows:self.nrows + y.shape[0],:] = y

        delta = x - self.running_mean
        n = self.X.shape[0] + np.arange(x.shape[0]) + 1
        self.running_dev += np.sum(delta * (x - self.running_mean), 0)
        self.running_mean += np.sum(delta / n[:, np.newaxis], 0)
        self.running_max  = np.amax(np.vstack((self.running_max, x)), 0)
        self.running_min  = np.amin(np.vstack((self.running_min, x)), 0)
        self.nrows += x.shape[0]
项目:esys-pbi    作者:fsxfreak    | 项目源码 | 文件源码
def _make_grid(dim=(11,4)):
    """
    this function generates the structure for an asymmetrical circle grid
    domain (0-1)
    """
    x,y = range(dim[0]),range(dim[1])
    p = np.array([[[s,i] for s in x] for i in y], dtype=np.float32)
    p[:,1::2,1] += 0.5
    p = np.reshape(p, (-1,2), 'F')

    # scale height = 1
    x_scale =  1./(np.amax(p[:,0])-np.amin(p[:,0]))
    y_scale =  1./(np.amax(p[:,1])-np.amin(p[:,1]))

    p *=x_scale,x_scale/.5

    return p
项目:em_examples    作者:geoscixyz    | 项目源码 | 文件源码
def analytic_infinite_wire(obsloc,wireloc,orientation,I=1.):
    """
    Compute the response of an infinite wire with orientation 'orientation'
    and current I at the obsvervation locations obsloc

    Output:
    B: magnetic field [Bx,By,Bz]
    """

    n,d = obsloc.shape
    t,d = wireloc.shape
    d = np.sqrt(np.dot(obsloc**2.,np.ones([d,t]))+np.dot(np.ones([n,d]),(wireloc.T)**2.)
    - 2.*np.dot(obsloc,wireloc.T))
    distr = np.amin(d, axis=1, keepdims = True)
    idxmind = d.argmin(axis=1)
    r = obsloc - wireloc[idxmind]

    orient = np.c_[[orientation for i in range(obsloc.shape[0])]]
    B = (mu_0*I)/(2*np.pi*(distr**2.))*np.cross(orientation,r)

    return B
项目:fexum    作者:KDD-OpenSource    | 项目源码 | 文件源码
def calculate_feature_statistics(feature_id):
    feature = Feature.objects.get(pk=feature_id)

    dataframe = _get_dataframe(feature.dataset.id)
    feature_col = dataframe[feature.name]

    feature.min = np.amin(feature_col).item()
    feature.max = np.amax(feature_col).item()
    feature.mean = np.mean(feature_col).item()
    feature.variance = np.nanvar(feature_col).item()
    unique_values = np.unique(feature_col)
    integer_check = (np.mod(unique_values, 1) == 0).all()
    feature.is_categorical = integer_check and (unique_values.size < 10)
    if feature.is_categorical:
        feature.categories = list(unique_values)
    feature.save(update_fields=['min', 'max', 'variance', 'mean', 'is_categorical', 'categories'])

    del unique_values, feature
项目:specGAN    作者:OSU-slatelab    | 项目源码 | 文件源码
def find_min_max(scp_file):
    minimum = float("inf")
    maximum = -float("inf")
    uid = 0
    offset = 0
    ark_dict, uid = read_mats(uid, offset, scp_file)
    while ark_dict:
        for key in ark_dict.keys():
            mat_max = np.amax(ark_dict[key])
            mat_min = np.amin(ark_dict[key])
            if mat_max > maximum:
                maximum = mat_max
            if mat_min < minimum:
                minimum = mat_min
        ark_dict, uid = read_mats(uid, offset, scp_file)
    print("min:", minimum, "max:", maximum)
    return minimum, maximum
项目:nelpy    作者:nelpy    | 项目源码 | 文件源码
def _gini(self, array):
        """Calculate the Gini coefficient of a numpy array."""
        # https://github.com/oliviaguest/gini
        # based on bottom eq:
        # http://www.statsdirect.com/help/generatedimages/equations/equation154.svg
        # from:
        # http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm
        # All values are treated equally, arrays must be 1d:
        array = array.flatten()
        if np.amin(array) < 0:
            # Values cannot be negative:
            array -= np.amin(array)
        # Values cannot be 0:
        array += 0.0000001
        # Values must be sorted:
        array = np.sort(array)
        # Index per array element:
        index = np.arange(1,array.shape[0]+1)
        # Number of array elements:
        n = array.shape[0]
        # Gini coefficient:
        return ((np.sum((2 * index - n  - 1) * array)) / (n * np.sum(array)))
项目:SecuML    作者:ANSSI-FR    | 项目源码 | 文件源码
def displayDataset(self, dataset):
        eps = 0.00001
        linewidth = dataset.linewidth
        if np.var(dataset.values) < eps:
            linewidth += 2
            mean = np.mean(dataset.values)
            x = np.arange(0, 1, 0.1)
            x = np.sort(np.append(x, [mean, mean-eps, mean+eps]))
            density = [1 if v == mean else 0 for v in x]
        else:
            self.kde.fit(np.asarray([[x] for x in dataset.values]))
            ## Computes the x axis
            x_max = np.amax(dataset.values)
            x_min = np.amin(dataset.values)
            delta = x_max - x_min
            density_delta = 1.1 * delta
            x = np.arange(x_min, x_max, density_delta / self.num_points)
            x_density = [[y] for y in x]
            ## kde.score_samples returns the 'log' of the density
            log_density = self.kde.score_samples(x_density).tolist()
            density = map(math.exp, log_density)
        self.ax.plot(x, density, label = dataset.label, color = dataset.color,
                linewidth = linewidth, linestyle = dataset.linestyle)
项目:SecuML    作者:ANSSI-FR    | 项目源码 | 文件源码
def display(self, output_filename):
        fig, (ax) = plt.subplots(1, 1)
        data   = [d.values for d in self.datasets]
        labels = [d.label for d in self.datasets]
        bp = ax.boxplot(data, labels = labels, notch = 0, sym = '+', vert = '1', whis = 1.5)
        plt.setp(bp['boxes'], color='black')
        plt.setp(bp['whiskers'], color='black')
        plt.setp(bp['fliers'], color='black', marker='+')
        for i in range(len(self.datasets)):
            box = bp['boxes'][i]
            box_x = []
            box_y = []
            for j in range(5):
                box_x.append(box.get_xdata()[j])
                box_y.append(box.get_ydata()[j])
            box_coords = list(zip(box_x, box_y))
            box_polygon = Polygon(box_coords, facecolor = self.datasets[i].color)
            ax.add_patch(box_polygon)
        if self.title is not None:
            ax.set_title(self.title)
        x_min = np.amin([np.amin(d.values) for d in self.datasets])
        x_max = np.amax([np.amax(d.values) for d in self.datasets])
        ax.set_ylim(x_min - 0.05*(x_max - x_min), x_max + 0.05*(x_max - x_min))
        fig.savefig(output_filename)
        plt.close(fig)
项目:bgsCNN    作者:SaoYan    | 项目源码 | 文件源码
def build_img_pair(img_batch):
    input_cast = img_batch[:,:,:,0:6].astype(dtype = np.float32)
    input_min = np.amin(input_cast, axis=(1,2,3))
    input_max = np.amax(input_cast, axis=(1,2,3))
    for i in range(3):
        input_min = np.expand_dims(input_min, i+1)
        input_max = np.expand_dims(input_max, i+1)
    input_norm = (input_cast - input_min) / (input_max - input_min)
    gt_cast = img_batch[:,:,:,6].astype(dtype = np.float32)
    gt_cast = np.expand_dims(gt_cast, 3)
    gt_min = np.amin(gt_cast, axis=(1,2,3))
    gt_max = np.amax(gt_cast, axis=(1,2,3))
    for i in range(3):
        gt_min = np.expand_dims(gt_min, i+1)
        gt_max = np.expand_dims(gt_max, i+1)
    gt_norm = (gt_cast - gt_min) / (gt_max - gt_min)
    return input_norm, gt_norm
项目:gini    作者:oliviaguest    | 项目源码 | 文件源码
def gini(array):
    """Calculate the Gini coefficient of a numpy array."""
    # based on bottom eq:
    # http://www.statsdirect.com/help/generatedimages/equations/equation154.svg
    # from:
    # http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm
    # All values are treated equally, arrays must be 1d:
    array = array.flatten()
    if np.amin(array) < 0:
        # Values cannot be negative:
        array -= np.amin(array)
    # Values cannot be 0:
    array += 0.0000001
    # Values must be sorted:
    array = np.sort(array)
    # Index per array element:
    index = np.arange(1,array.shape[0]+1)
    # Number of array elements:
    n = array.shape[0]
    # Gini coefficient:
    return ((np.sum((2 * index - n  - 1) * array)) / (n * np.sum(array)))
项目:Deep-learning-Colorization-for-visual-media    作者:OmarSayedMostafa    | 项目源码 | 文件源码
def Get_Batch_Chrominance():
    ''''Convert every image in the batch to LAB Colorspace and normalize each value of it between [0,1]

    Return:
     AbColores_values array [batch_size,2224,224,2] 0-> A value, 1-> B value color
    '''
    global AbColores_values
    global ColorImages_Batch
    AbColores_values = np.empty((Batch_size,224,224,2),"float32")
    for indx in range(Batch_size):
        lab = color.rgb2lab(ColorImages_Batch[indx])
        Min_valueA = np.amin(lab[:,:,1])
        Max_valueA = np.amax(lab[:,:,1])
        Min_valueB = np.amin(lab[:,:,2])
        Max_valueB = np.amax(lab[:,:,2])
        AbColores_values[indx,:,:,0] = Normalize(lab[:,:,1],-128,127)
        AbColores_values[indx,:,:,1] = Normalize(lab[:,:,2],-128,127)
项目:deepmodels    作者:learningsociety    | 项目源码 | 文件源码
def match_set_with_pts(db_set_feats, query_set_feats, dist_type,
                       pt_set_dist_mode):
  print('start matching sets using points...')
  if query_set_feats is None:
    query_set_feats = db_set_feats
  dist_mat = np.empty(
      (len(query_set_feats), len(db_set_feats)), dtype=np.float)
  for i in range(len(query_set_feats)):
    for j in range(len(db_set_feats)):
      if dist_type == DistType.Hamming:
        tmp_dist_mat = scipy.spatial.distance.cdist(query_set_feats[i],
                                                    db_set_feats[j], 'hamming')
      if dist_type == DistType.L2:
        tmp_dist_mat = scipy.spatial.distance.cdist(
            query_set_feats[i], db_set_feats[j], 'euclidean')
      if pt_set_dist_mode == PtSetDist.Min:
        dist_mat[i, j] = np.amin(tmp_dist_mat)
      if pt_set_dist_mode == PtSetDist.Avg:
        dist_mat[i, j] = np.mean(tmp_dist_mat)
      if pt_set_dist_mode == PtSetDist.MeanMin:
        dist_mat[i, j] = np.mean(np.amin(tmp_dist_mat, axis=1))
  return dist_mat
项目:yt    作者:yt-project    | 项目源码 | 文件源码
def __init__(self, data, leafsize=10):
        """Construct a kd-tree.

        Parameters:
        ===========

        data : array-like, shape (n,k)
            The data points to be indexed. This array is not copied, and
            so modifying this data will result in bogus results.
        leafsize : positive integer
            The number of points at which the algorithm switches over to
            brute-force.
        """
        self.data = np.asarray(data)
        self.n, self.m = np.shape(self.data)
        self.leafsize = int(leafsize)
        if self.leafsize<1:
            raise ValueError("leafsize must be at least 1")
        self.maxes = np.amax(self.data,axis=0)
        self.mins = np.amin(self.data,axis=0)

        self.tree = self.__build(np.arange(self.n), self.maxes, self.mins)
项目:tf_unet    作者:jakeret    | 项目源码 | 文件源码
def to_rgb(img):
    """
    Converts the given array into a RGB image. If the number of channels is not
    3 the array is tiled such that it has 3 channels. Finally, the values are
    rescaled to [0,255) 

    :param img: the array to convert [nx, ny, channels]

    :returns img: the rgb image [nx, ny, 3]
    """
    img = np.atleast_3d(img)
    channels = img.shape[2]
    if channels < 3:
        img = np.tile(img, 3)

    img[np.isnan(img)] = 0
    img -= np.amin(img)
    img /= np.amax(img)
    img *= 255
    return img
项目:audioanalysis    作者:jpalpant    | 项目源码 | 文件源码
def get_classification(self, idx):
        """Docs"""

        img_rows = self.params.get('img_rows', self.Sxx.shape[0])
        img_cols = self.params.get('img_cols', 1)

        if self.Sxx is None or self.active_song.classification is None:
            raise TypeError('No active song from which to get data')

        if np.amax(idx) > self.Sxx.shape[1]:
            raise IndexError('Data index of sample out of bounds, only {0} '
                    'samples in the dataset'.format(self.Sxx.shape[1] - img_cols))

        if np.amin(idx) < 0:
            raise IndexError('Data index of sample out of bounds, '
                    'negative index requested')

        # index out the data
        classification = self.active_song.classification[idx]

        return classification
项目:visualize-tsne    作者:YontiLevin    | 项目源码 | 文件源码
def standard_resize(image, max_side):
    if image is None:
        return None, None, None
    original_h, original_w, _ = image.shape
    if all(side < max_side for side in [original_h, original_w]):
        return image, original_h, original_w
    aspect_ratio = float(np.amax((original_w, original_h)) / float(np.amin((original_h, original_w))))

    if original_w >= original_h:
        new_w = max_side
        new_h = max_side / aspect_ratio
    else:
        new_h = max_side
        new_w = max_side / aspect_ratio

    new_h = int(new_h)
    new_w = int(new_w)
    resized_image = cv2.resize(image, (new_w, new_h))
    return resized_image, new_w, new_h
项目:Halite    作者:shummie    | 项目源码 | 文件源码
def update_time_to_recover_map(self):
        max_distance = 30
        self.recover_map = numpy.zeros((max_distance + 1, self.width, self.height))
        self.recover_map_smooth = numpy.zeros((max_distance + 1, self.width, self.height))

        self.recover_map[0] = numpy.divide(self.strength_map, numpy.maximum(self.production_map, 0.001))
        self.recover_map[0] = numpy.multiply(self.recover_map[0], 1 - (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map))
        self.recover_map[0] += (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map) * 999

        distance = 1
        while distance <= max_distance:
            dir_map = numpy.zeros((4, self.width, self.height))
            dir_map[0] = roll_xy(self.recover_map[distance - 1], 0, 1)
            dir_map[1] = roll_xy(self.recover_map[distance - 1], 0, -1)
            dir_map[2] = roll_xy(self.recover_map[distance - 1], 1, 0)
            dir_map[3] = roll_xy(self.recover_map[distance - 1], -1, 0)

            self.recover_map[distance] = numpy.add(self.recover_map[distance - 1], numpy.amin(dir_map, 0))
            distance += 1

        for d in range(2, max_distance):
            self.recover_map[d] = self.recover_map[d] / d
            self.recover_map_smooth[d] = spread_n(self.recover_map[d], 4)
项目:Halite    作者:shummie    | 项目源码 | 文件源码
def update_recover_map(self):
        max_distance = 50
        self.recover_map = numpy.zeros((max_distance + 1, self.width, self.height))
        self.recover_map[0] = numpy.divide(self.strength_map, numpy.maximum(self.production_map, 0.01))
        self.recover_map[0] = numpy.multiply(self.recover_map[0], self.is_neutral_map)
        self.recover_map[0] += (self.is_owned_map + self.is_enemy_map) * 999

        for distance in range(1, max_distance + 1):
            dir_map = numpy.zeros((4, self.width, self.height))
            dir_map[0] = roll_xy(self.recover_map[distance - 1], 0, 1)
            dir_map[1] = roll_xy(self.recover_map[distance - 1], 0, -1)
            dir_map[2] = roll_xy(self.recover_map[distance - 1], 1, 0)
            dir_map[3] = roll_xy(self.recover_map[distance - 1], -1, 0)

            self.recover_map[distance] = numpy.add(self.recover_map[distance - 1], numpy.amin(dir_map, 0))

        for d in range(2, max_distance + 1):
            self.recover_map[d] = self.recover_map[d] / d
项目:Halite    作者:shummie    | 项目源码 | 文件源码
def update_time_to_recover_map(self):
        max_distance = 30
        self.recover_map = numpy.zeros((max_distance + 1, self.width, self.height))
        self.recover_map_smooth = numpy.zeros((max_distance + 1, self.width, self.height))

        self.recover_map[0] = numpy.divide(self.strength_map, numpy.maximum(self.production_map, 0.001))
        self.recover_map[0] = numpy.multiply(self.recover_map[0], 1 - (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map))
        self.recover_map[0] += (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map) * 999

        distance = 1
        while distance <= max_distance:
            dir_map = numpy.zeros((4, self.width, self.height))
            dir_map[0] = roll_xy(self.recover_map[distance - 1], 0, 1)
            dir_map[1] = roll_xy(self.recover_map[distance - 1], 0, -1)
            dir_map[2] = roll_xy(self.recover_map[distance - 1], 1, 0)
            dir_map[3] = roll_xy(self.recover_map[distance - 1], -1, 0)

            self.recover_map[distance] = numpy.add(self.recover_map[distance - 1], numpy.amin(dir_map, 0))
            distance += 1

        for d in range(2, max_distance):
            self.recover_map[d] = self.recover_map[d] / d
            self.recover_map_smooth[d] = spread_n(self.recover_map[d], 4)
项目:Halite    作者:shummie    | 项目源码 | 文件源码
def update_recover_map(self):
        max_distance = 50
        self.recover_map = numpy.zeros((max_distance + 1, self.width, self.height))
        self.recover_map[0] = numpy.divide(self.strength_map, numpy.maximum(self.production_map, 0.01))
        self.recover_map[0] = numpy.multiply(self.recover_map[0], self.is_neutral_map)
        self.recover_map[0] += (self.is_owned_map + self.is_enemy_map) * 999

        for distance in range(1, max_distance + 1):
            dir_map = numpy.zeros((4, self.width, self.height))
            dir_map[0] = roll_xy(self.recover_map[distance - 1], 0, 1)
            dir_map[1] = roll_xy(self.recover_map[distance - 1], 0, -1)
            dir_map[2] = roll_xy(self.recover_map[distance - 1], 1, 0)
            dir_map[3] = roll_xy(self.recover_map[distance - 1], -1, 0)

            self.recover_map[distance] = numpy.add(self.recover_map[distance - 1], numpy.amin(dir_map, 0))

        for d in range(2, max_distance + 1):
            self.recover_map[d] = self.recover_map[d] / d
项目:Halite    作者:shummie    | 项目源码 | 文件源码
def update_time_to_recover_map(self):
        max_distance = 30
        self.recover_map = numpy.zeros((max_distance + 1, self.width, self.height))
        self.recover_map_smooth = numpy.zeros((max_distance + 1, self.width, self.height))

        self.recover_map[0] = numpy.divide(self.strength_map, numpy.maximum(self.production_map, 0.001))
        self.recover_map[0] = numpy.multiply(self.recover_map[0], 1 - (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map))
        self.recover_map[0] += (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map) * 999

        distance = 1
        while distance <= max_distance:
            dir_map = numpy.zeros((4, self.width, self.height))
            dir_map[0] = roll_xy(self.recover_map[distance - 1], 0, 1)
            dir_map[1] = roll_xy(self.recover_map[distance - 1], 0, -1)
            dir_map[2] = roll_xy(self.recover_map[distance - 1], 1, 0)
            dir_map[3] = roll_xy(self.recover_map[distance - 1], -1, 0)

            self.recover_map[distance] = numpy.add(self.recover_map[distance - 1], numpy.amin(dir_map, 0))
            distance += 1

        for d in range(2, max_distance):
            self.recover_map[d] = self.recover_map[d] / d
            self.recover_map_smooth[d] = spread_n(self.recover_map[d], 4)
项目:Halite    作者:shummie    | 项目源码 | 文件源码
def update_time_to_recover_map(self):
        max_distance = 30
        self.recover_map = numpy.zeros((max_distance + 1, self.width, self.height))
        self.recover_map_smooth = numpy.zeros((max_distance + 1, self.width, self.height))

        self.recover_map[0] = numpy.divide(self.strength_map, numpy.maximum(self.production_map, 0.001))
        self.recover_map[0] = numpy.multiply(self.recover_map[0], 1 - (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map))
        self.recover_map[0] += (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map) * 999

        distance = 1
        while distance <= max_distance:
            dir_map = numpy.zeros((4, self.width, self.height))
            dir_map[0] = roll_xy(self.recover_map[distance - 1], 0, 1)
            dir_map[1] = roll_xy(self.recover_map[distance - 1], 0, -1)
            dir_map[2] = roll_xy(self.recover_map[distance - 1], 1, 0)
            dir_map[3] = roll_xy(self.recover_map[distance - 1], -1, 0)

            self.recover_map[distance] = numpy.add(self.recover_map[distance - 1], numpy.amin(dir_map, 0))
            distance += 1

        for d in range(2, max_distance):
            self.recover_map[d] = self.recover_map[d] / d
            self.recover_map_smooth[d] = spread_n(self.recover_map[d], 4)
项目:Halite    作者:shummie    | 项目源码 | 文件源码
def update_time_to_recover_map(self):
        max_distance = 30
        self.recover_map = numpy.zeros((max_distance + 1, self.width, self.height))
        self.recover_map_smooth = numpy.zeros((max_distance + 1, self.width, self.height))

        self.recover_map[0] = numpy.divide(self.strength_map, numpy.maximum(self.production_map, 0.001))
        self.recover_map[0] = numpy.multiply(self.recover_map[0], 1 - (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map))
        self.recover_map[0] += (self.is_owner_map[self.game_map.my_id] + self.is_enemy_map) * 999

        distance = 1
        while distance <= max_distance:
            dir_map = numpy.zeros((4, self.width, self.height))
            dir_map[0] = roll_xy(self.recover_map[distance - 1], 0, 1)
            dir_map[1] = roll_xy(self.recover_map[distance - 1], 0, -1)
            dir_map[2] = roll_xy(self.recover_map[distance - 1], 1, 0)
            dir_map[3] = roll_xy(self.recover_map[distance - 1], -1, 0)

            self.recover_map[distance] = numpy.add(self.recover_map[distance - 1], numpy.amin(dir_map, 0))
            distance += 1

        for d in range(2, max_distance):
            self.recover_map[d] = self.recover_map[d] / d
            self.recover_map_smooth[d] = spread_n(self.recover_map[d], 4)
项目:pyku    作者:dubvulture    | 项目源码 | 文件源码
def diagonal(_, pos):
    """
    Given an object pixels' positions, return the diagonal length of its
    bound box
    :param _: pixel values (unused)
    :param pos: pixel position (1-D)
    :return: diagonal of bounding box
    """
    xs = np.array([i / SSIZE for i in pos])
    ys = np.array([i % SSIZE for i in pos])
    minx = np.amin(xs)
    miny = np.amin(ys)
    maxx = np.amax(xs)
    maxy = np.amax(ys)
    return compute_line(np.array([minx, miny]), np.array([maxx, maxy]))
项目:AutoML5    作者:djajetic    | 项目源码 | 文件源码
def binarization (array):
    ''' Takes a binary-class datafile and turn the max value (positive class) into 1 and the min into 0'''
    array = np.array(array, dtype=float) # conversion needed to use np.inf after
    if len(np.unique(array)) > 2:
        raise ValueError ("The argument must be a binary-class datafile. {} classes detected".format(len(np.unique(array))))

    # manipulation which aims at avoid error in data with for example classes '1' and '2'.
    array[array == np.amax(array)] = np.inf
    array[array == np.amin(array)] = 0
    array[array == np.inf] = 1
    return np.array(array, dtype=int)
项目:composability_bench    作者:IntelPython    | 项目源码 | 文件源码
def bench_on(runner, sym, Ns, trials, dtype=None):
    global args, kernel, out, mkl_layer
    prepare = globals().get("prepare_"+sym, prepare_default)
    kernel  = globals().get("kernel_"+sym, None)
    if not kernel:
       kernel = getattr(np.linalg, sym)
    out_lvl = runner.__doc__.split('.')[0].strip()
    func_s  = kernel.__doc__.split('.')[0].strip()
    log.debug('Preparing input data for %s (%s).. ' % (sym, func_s))
    args = [prepare(int(i)) for i in Ns]
    it = range(len(Ns))
    # pprint(Ns)
    out = np.empty(shape=(len(Ns), trials))
    b = body(trials)
    tic, toc = (0, 0)
    log.debug('Warming up %s (%s).. ' % (sym, func_s))
    runner(range(1000), empty_work)
    kernel(*args[0])
    runner(range(1000), empty_work)
    log.debug('Benchmarking %s on %s: ' % (func_s, out_lvl))
    gc_old = gc.isenabled()
#    gc.disable()
    tic = time.time()
    runner(it, b)
    toc = time.time() - tic
    if gc_old:
        gc.enable()
    if 'reused_pool' in globals():
        del globals()['reused_pool']

    #calculate average time and min time and also keep track of outliers (max time in the loop)
    min_time = np.amin(out)
    max_time = np.amax(out)
    mean_time = np.mean(out)
    stdev_time = np.std(out)

    #print("Min = %.5f, Max = %.5f, Mean = %.5f, stdev = %.5f " % (min_time, max_time, mean_time, stdev_time))
    #final_times = [min_time, max_time, mean_time, stdev_time]

    print('## %s: Outter:%s, Inner:%s, Wall seconds:%f\n' % (sym, out_lvl, mkl_layer, float(toc)))
    return out
项目:spyking-circus    作者:spyking-circus    | 项目源码 | 文件源码
def view_trigger_snippets(trigger_snippets, chans, save=None):
    # Create output directory if necessary.
    if os.path.exists(save):
        for f in os.listdir(save):
            p = os.path.join(save, f)
            os.remove(p)
        os.removedirs(save)
    os.makedirs(save)
    # Plot figures.
    fig = pylab.figure()
    for (c, chan) in enumerate(chans):
        ax = fig.add_subplot(1, 1, 1)
        for n in xrange(0, trigger_snippets.shape[2]):
            y = trigger_snippets[:, c, n]
            x = numpy.arange(- (y.size - 1) / 2, (y.size - 1) / 2 + 1)
            b = 0.5 + 0.5 * numpy.random.rand()
            ax.plot(x, y, color=(0.0, 0.0, b), linestyle='solid')
        y = numpy.mean(trigger_snippets[:, c, :], axis=1)
        x = numpy.arange(- (y.size - 1) / 2, (y.size - 1) / 2 + 1)
        ax.plot(x, y, color=(1.0, 0.0, 0.0), linestyle='solid')
        ax.grid(True)
        ax.set_xlim([numpy.amin(x), numpy.amax(x)])
        ax.set_title("Channel %d" %chan)
        ax.set_xlabel("time")
        ax.set_ylabel("amplitude")
        if save is not None:
            # Save plot.
            filename = "channel-%d.png" %chan
            path = os.path.join(save, filename)
            pylab.savefig(path)
        fig.clf()
    if save is None:
        pylab.show()
    else:
        pylab.close(fig)
    return
项目:vehicle_brand_classification_CNN    作者:nanoc812    | 项目源码 | 文件源码
def imgSeg_logo(approx, himg, wimg):
    w = np.amax(approx[:,:,0])-np.amin(approx[:,:,0]); h = np.amax(approx[:,:,1])-np.amin(approx[:,:,1])
    if float(w)/float(h+0.001) > 4.5:
        h = int(float(w)/3.5)
    w0 = np.amin(approx[:,:,0]); h0 = np.amin(approx[:,:,1])
    h1 = h0-int(3.5*h); h2 = h0;
    w1 = max(w0+w/2-int(0.5*(h2-h1)), 0); w2 = min(w0+w/2+int(0.5*(h2-h1)), wimg-1)
    return h1, h2, w1, w2
项目:vehicle_brand_classification_CNN    作者:nanoc812    | 项目源码 | 文件源码
def imgSeg_rect(approx, himg, wimg):
    w = np.amax(approx[:,:,0])-np.amin(approx[:,:,0]); h = np.amax(approx[:,:,1])-np.amin(approx[:,:,1])
    if float(w)/float(h+0.001) > 4.5:
        h = int(float(w)/3.5)
    w0 = np.amin(approx[:,:,0]); h0 = np.amin(approx[:,:,1])
    h1 = h0-int(3.6*h); h2 = min(h0+int(3*h), himg-1)
    w1 = max(w0+w/2-(h2-h1), 0); w2 = min(w0+w/2+(h2-h1), wimg-1)
    return h1, h2, w1, w2
项目:pointnet    作者:charlesq34    | 项目源码 | 文件源码
def collect_point_label(anno_path, out_filename, file_format='txt'):
    """ Convert original dataset files to data_label file (each line is XYZRGBL).
        We aggregated all the points from each instance in the room.

    Args:
        anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
        out_filename: path to save collected points and labels (each line is XYZRGBL)
        file_format: txt or numpy, determines what file format to save.
    Returns:
        None
    Note:
        the points are shifted before save, the most negative point is now at origin.
    """
    points_list = []

    for f in glob.glob(os.path.join(anno_path, '*.txt')):
        cls = os.path.basename(f).split('_')[0]
        if cls not in g_classes: # note: in some room there is 'staris' class..
            cls = 'clutter'
        points = np.loadtxt(f)
        labels = np.ones((points.shape[0],1)) * g_class2label[cls]
        points_list.append(np.concatenate([points, labels], 1)) # Nx7

    data_label = np.concatenate(points_list, 0)
    xyz_min = np.amin(data_label, axis=0)[0:3]
    data_label[:, 0:3] -= xyz_min

    if file_format=='txt':
        fout = open(out_filename, 'w')
        for i in range(data_label.shape[0]):
            fout.write('%f %f %f %d %d %d %d\n' % \
                          (data_label[i,0], data_label[i,1], data_label[i,2],
                           data_label[i,3], data_label[i,4], data_label[i,5],
                           data_label[i,6]))
        fout.close()
    elif file_format=='numpy':
        np.save(out_filename, data_label)
    else:
        print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \
            (file_format))
        exit()
项目:pointnet    作者:charlesq34    | 项目源码 | 文件源码
def collect_bounding_box(anno_path, out_filename):
    """ Compute bounding boxes from each instance in original dataset files on
        one room. **We assume the bbox is aligned with XYZ coordinate.**

    Args:
        anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
        out_filename: path to save instance bounding boxes for that room.
            each line is x1 y1 z1 x2 y2 z2 label,
            where (x1,y1,z1) is the point on the diagonal closer to origin
    Returns:
        None
    Note:
        room points are shifted, the most negative point is now at origin.
    """
    bbox_label_list = []

    for f in glob.glob(os.path.join(anno_path, '*.txt')):
        cls = os.path.basename(f).split('_')[0]
        if cls not in g_classes: # note: in some room there is 'staris' class..
            cls = 'clutter'
        points = np.loadtxt(f)
        label = g_class2label[cls]
        # Compute tightest axis aligned bounding box
        xyz_min = np.amin(points[:, 0:3], axis=0)
        xyz_max = np.amax(points[:, 0:3], axis=0)
        ins_bbox_label = np.expand_dims(
            np.concatenate([xyz_min, xyz_max, np.array([label])], 0), 0)
        bbox_label_list.append(ins_bbox_label)

    bbox_label = np.concatenate(bbox_label_list, 0)
    room_xyz_min = np.amin(bbox_label[:, 0:3], axis=0)
    bbox_label[:, 0:3] -= room_xyz_min 
    bbox_label[:, 3:6] -= room_xyz_min 

    fout = open(out_filename, 'w')
    for i in range(bbox_label.shape[0]):
        fout.write('%f %f %f %f %f %f %d\n' % \
                      (bbox_label[i,0], bbox_label[i,1], bbox_label[i,2],
                       bbox_label[i,3], bbox_label[i,4], bbox_label[i,5],
                       bbox_label[i,6]))
    fout.close()
项目:aapm_thoracic_challenge    作者:xf4j    | 项目源码 | 文件源码
def read_testing_inputs(file, roi, im_size, output_path=None):
    f_h5 = h5py.File(file, 'r')
    if roi == -1:
        images = np.asarray(f_h5['resized_images'], dtype=np.float32)
        read_info = {}
        read_info['shape'] = np.asarray(f_h5['images'], dtype=np.float32).shape
    else:
        images = np.asarray(f_h5['images'], dtype=np.float32)
        output = h5py.File(os.path.join(output_path, 'All_' + os.path.basename(file)), 'r')
        predictions = np.asarray(output['predictions'], dtype=np.float32)
        output.close()
        # Select the roi
        roi_labels = (predictions == roi + 1).astype(np.float32)
        nz = np.nonzero(roi_labels)
        extract = []
        for c in range(3):
            start = np.amin(nz[c])
            end = np.amax(nz[c])
            r = end - start
            extract.append((np.maximum(int(np.rint(start - r * 0.1)), 0),
                            np.minimum(int(np.rint(end + r * 0.1)), images.shape[c])))

        extract_images = images[extract[0][0] : extract[0][1], extract[1][0] : extract[1][1], extract[2][0] : extract[2][1]]
        read_info = {}
        read_info['shape'] = images.shape
        read_info['extract_shape'] = extract_images.shape
        read_info['extract'] = extract

        images = resize(extract_images, im_size, mode='constant')

    f_h5.close()
    return images, read_info
项目:hdrnet_legacy    作者:mgharbi    | 项目源码 | 文件源码
def normalize(im):
  mini = np.amin(im)
  maxi = np.amax(im)
  rng = maxi-mini
  im -= mini
  if rng > 0:
    im /= rng
  return im


# ----- Type transformations --------------------------------------------------
项目:lung-cancer-detector    作者:YichenGong    | 项目源码 | 文件源码
def to_rgb(img):
    img = img.reshape(img.shape[0], img.shape[1])
    img[np.isnan(img)] = 0
    img -= np.amin(img)
    img /= np.amax(img)
    blue = np.clip(4*(0.75-img), 0, 1)
    red  = np.clip(4*(img-0.25), 0, 1)
    green= np.clip(44*np.fabs(img-0.5)-1., 0, 1)
    rgb = np.stack((red, green, blue), axis=2)
    return rgb
项目:lung-cancer-detector    作者:YichenGong    | 项目源码 | 文件源码
def _process_data(self, data):
        # normalization
        data = np.clip(np.fabs(data), self.a_min, self.a_max)
        data -= np.amin(data)
        data /= np.amax(data)
        return data
项目:lung-cancer-detector    作者:YichenGong    | 项目源码 | 文件源码
def plot_prediction(x_test, y_test, prediction, save=False):
    import matplotlib
    import matplotlib.pyplot as plt

    test_size = x_test.shape[0]
    fig, ax = plt.subplots(test_size, 3, figsize=(12,12), sharey=True, sharex=True)

    x_test = crop_to_shape(x_test, prediction.shape)
    y_test = crop_to_shape(y_test, prediction.shape)

    ax = np.atleast_2d(ax)
    for i in range(test_size):
        cax = ax[i, 0].imshow(x_test[i])
        plt.colorbar(cax, ax=ax[i,0])
        cax = ax[i, 1].imshow(y_test[i, ..., 1])
        plt.colorbar(cax, ax=ax[i,1])
        pred = prediction[i, ..., 1]
        pred -= np.amin(pred)
        pred /= np.amax(pred)
        cax = ax[i, 2].imshow(pred)
        plt.colorbar(cax, ax=ax[i,2])
        if i==0:
            ax[i, 0].set_title("x")
            ax[i, 1].set_title("y")
            ax[i, 2].set_title("pred")
    fig.tight_layout()

    if save:
        fig.savefig(save)
    else:
        fig.show()
        plt.show()
项目:supremm    作者:ubccr    | 项目源码 | 文件源码
def computeallcpus(self):
        """ overall stats for all cores on the nodes """

        ratios = numpy.empty((self._ncpumetrics, self._totalcores), numpy.double)

        coreindex = 0
        for host, last in self._last.iteritems():
            try:
                elapsed = last - self._first[host]

                if numpy.amin(numpy.sum(elapsed, 0)) < 1.0:
                    # typically happens if the job was very short and the datapoints are too close together
                    return {"error": ProcessingError.JOB_TOO_SHORT}

                coresperhost = len(last[0, :])
                ratios[:, coreindex:(coreindex+coresperhost)] = 1.0 * elapsed / numpy.sum(elapsed, 0)
                coreindex += coresperhost
            except ValueError:
                # typically happens if the linux pmda crashes during the job
                return {"error": ProcessingError.INSUFFICIENT_DATA}

        results = {}
        for i, name in enumerate(self._outnames):
            results[name] = calculate_stats(ratios[i, :])

        results['all'] = {"cnt": self._totalcores}

        return results
项目:dsb3    作者:EliasVansteenkiste    | 项目源码 | 文件源码
def plot_slice_3d_2_patch(ct_scan, mask, pid, img_dir=None, idx=None):
    # to convert cuda arrays to numpy array
    ct_scan = np.asarray(ct_scan)
    mask = np.asarray(mask)

    fig, ax = plt.subplots(2, 3, figsize=[8, 8])
    fig.canvas.set_window_title(pid)

    if idx == None:
        #just plot in the middle of the cube
        in_sh = ct_scan.shape
        idx = [in_sh[0]/2,in_sh[1]/2,in_sh[2]/2]
    print np.amin(ct_scan), np.amax(ct_scan)
    print np.amin(mask), np.amax(mask)


    ax[0, 0].imshow(ct_scan[idx[0], :, :], cmap=plt.cm.gray)
    ax[0, 1].imshow(ct_scan[:, idx[1], :], cmap=plt.cm.gray)
    ax[0, 2].imshow(ct_scan[:, :, idx[2]], cmap=plt.cm.gray)

    ax[1, 0].imshow(mask[idx[0], :, :], cmap=plt.cm.gray)
    ax[1, 1].imshow(mask[:, idx[1], :], cmap=plt.cm.gray)
    ax[1, 2].imshow(mask[:, :, idx[2]], cmap=plt.cm.gray)

    if img_dir is not None:
        fig.savefig(img_dir + '/%s.png' % pid, bbox_inches='tight')
    else:
        plt.show()
    fig.clf()
    plt.close('all')
项目:SNPmatch    作者:Gregor-Mendel-Institute    | 项目源码 | 文件源码
def calculate_likelihoods(ScoreList, NumInfoSites):
  num_lines = len(ScoreList)
  LikeLiHoods = [likeliTest(NumInfoSites[i], int(ScoreList[i])) for i in range(num_lines)]
  LikeLiHoods = np.array(LikeLiHoods).astype("float")
  TopHit = np.amin(LikeLiHoods)
  LikeLiHoodRatios = [LikeLiHoods[i]/TopHit for i in range(num_lines)]
  LikeLiHoodRatios = np.array(LikeLiHoodRatios).astype("float")
  return (LikeLiHoods, LikeLiHoodRatios)
项目:untwist    作者:IoSR-Surrey    | 项目源码 | 文件源码
def normalize(self):
        self.X = (self.X - np.amin(self.X, 0)) \
        / (np.amax(self.X, 0) - np.amin(self.X, 0))
项目:untwist    作者:IoSR-Surrey    | 项目源码 | 文件源码
def normalize_points(self, x):
        return np.divide(x - np.amin(self.X, 0) ,
            np.amax(self.X, 0) - np.amin(self.X, 0), np.empty_like(x))
项目:ModelZoo    作者:NervanaSystems    | 项目源码 | 文件源码
def compute_patches_at_scale(self, scale_idx, scale, p_id_base):
        debug("Processing {} scale_idx:{} scale:{}".format(self.file_name, scale_idx, scale))
        shape = np.array(self.shape)
        size = (np.amin(shape)-1) / scale
        num_samples = np.ceil( (shape-1) / size)
        num_samples = [int(n*2) if n > 1 else int(n) for n in num_samples]
        patches = []
        sample_locs = [ self.sample_locs_for_dim( self.shape[0], size, num_samples[0]),
                        self.sample_locs_for_dim( self.shape[1], size, num_samples[1])]
        p_id = p_id_base
        for sample_loc_0 in sample_locs[0]:
            for sample_loc_1 in sample_locs[1]:
                patch = ImagePatch( p_id, self, (sample_loc_0, sample_loc_1), size, scale)
                patch.label, patch.matched_roi_idx = \
                            self.get_label_for_patch(patch)
                if patch.label != PASCAL_VOC_BACKGROUND_CLASS:
                    self.non_background_patches.append(patch)
                else:
                    self.background_patches.append(patch)

                patches.append(patch)
                p_id += 1

        debug("Compute {} patches".format(p_id-p_id_base))

        return p_id

# Sample the Pascal VOC dataset
项目:hienoi    作者:christophercrouzet    | 项目源码 | 文件源码
def lower_bounds(self):
        return Vector2f(*numpy.amin(self.particles['position'], axis=0))
项目:radar    作者:amoose136    | 项目源码 | 文件源码
def test_all(self):
        a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
        for i in range(a.ndim):
            amin = a.min(i)
            aargmin = a.argmin(i)
            axes = list(range(a.ndim))
            axes.remove(i)
            assert_(np.all(amin == aargmin.choose(*a.transpose(i,*axes))))
项目:radar    作者:amoose136    | 项目源码 | 文件源码
def test_scalar(self):
        assert_raises(ValueError, np.amax, 1, 1)
        assert_raises(ValueError, np.amin, 1, 1)

        assert_equal(np.amax(1, axis=0), 1)
        assert_equal(np.amin(1, axis=0), 1)
        assert_equal(np.amax(1, axis=None), 1)
        assert_equal(np.amin(1, axis=None), 1)