Python numpy.ceil() 使用实例

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Example 1

def make_grid(I, ncols=8):
    assert isinstance(I, np.ndarray), 'plugin error, should pass numpy array here'
    assert I.ndim == 4 and I.shape[1] == 3
    nimg = I.shape[0]
    H = I.shape[2]
    W = I.shape[3]
    ncols = min(nimg, ncols)
    nrows = int(np.ceil(float(nimg) / ncols))
    canvas = np.zeros((3, H * nrows, W * ncols))
    i = 0
    for y in range(nrows):
        for x in range(ncols):
            if i >= nimg:
                break
            canvas[:, y * H:(y + 1) * H, x * W:(x + 1) * W] = I[i]
            i = i + 1
    return canvas 

Example 2

def saveHintonPlot(self, matrix, num_tests, max_weight=None, ax=None):
		"""Draw Hinton diagram for visualizing a weight matrix."""
		fig,ax = plt.subplots(1,1)
		
		if not max_weight:
			max_weight = 2**np.ceil(np.log(np.abs(matrix).max())/np.log(2))

		ax.patch.set_facecolor('gray')
		ax.set_aspect('equal', 'box')
		ax.xaxis.set_major_locator(plt.NullLocator())
		ax.yaxis.set_major_locator(plt.NullLocator())

		for (x, y), w in np.ndenumerate(matrix):
			color = 'white' if w > 0 else 'black'
			size = np.sqrt(np.abs(0.5*w/num_tests)) # Need to scale so that it is between 0 and 0.5
			rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
								 facecolor=color, edgecolor=color)
			ax.add_patch(rect)

		ax.autoscale_view()
		ax.invert_yaxis()
		plt.savefig(self.figures_path + self.save_prefix + '-Hinton.eps')
		plt.close() 

Example 3

def fftfilt(b, x, *n):
    N_x = len(x)
    N_b = len(b)
    N = 2**np.arange(np.ceil(np.log2(N_b)),np.floor(np.log2(N_x)))
    cost = np.ceil(N_x / (N - N_b + 1)) * N * (np.log2(N) + 1)
    N_fft = int(N[np.argmin(cost)])
    N_fft = int(N_fft)    
    # Compute the block length:
    L = int(N_fft - N_b + 1)
    # Compute the transform of the filter:
    H = np.fft.fft(b,N_fft)
    y = np.zeros(N_x, x.dtype)
    i = 0
    while i <= N_x:
        il = np.min([i+L,N_x])
        k = np.min([i+N_fft,N_x])
        yt = np.fft.ifft(np.fft.fft(x[i:il],N_fft)*H,N_fft) # Overlap..
        y[i:k] = y[i:k] + yt[:k-i]                          # and add
        i += L
    return y 

Example 4

def laplace_gpu(y_gpu, mode='valid'):

  shape = np.array(y_gpu.shape).astype(np.uint32)
  dtype = y_gpu.dtype
  block_size = (16,16,1)
  grid_size = (int(np.ceil(float(shape[1])/block_size[0])),
               int(np.ceil(float(shape[0])/block_size[1])))
  shared_size = int((2+block_size[0])*(2+block_size[1])*dtype.itemsize)

  preproc = _generate_preproc(dtype, shape)
  mod = SourceModule(preproc + kernel_code, keep=True)

  if mode == 'valid':
    laplace_fun_gpu = mod.get_function("laplace_valid")
    laplace_gpu = cua.empty((y_gpu.shape[0]-2, y_gpu.shape[1]-2), y_gpu.dtype)

  if mode == 'same':
    laplace_fun_gpu = mod.get_function("laplace_same")
    laplace_gpu = cua.empty((y_gpu.shape[0], y_gpu.shape[1]), y_gpu.dtype)
    
  laplace_fun_gpu(laplace_gpu.gpudata, y_gpu.gpudata,
                  block=block_size, grid=grid_size, shared=shared_size)

  return laplace_gpu 

Example 5

def split(args):
    if args.skip or args.is_multi_genome:
        return {'chunks': [{'__mem_gb': cr_constants.MIN_MEM_GB}]}

    chunks = []
    min_clusters = cr_constants.MIN_N_CLUSTERS
    max_clusters = args.max_clusters if args.max_clusters is not None else cr_constants.MAX_N_CLUSTERS_DEFAULT
    matrix_mem_gb = np.ceil(MEM_FACTOR * cr_matrix.GeneBCMatrix.get_mem_gb_from_matrix_h5(args.matrix_h5))
    for n_clusters in xrange(min_clusters, max_clusters + 1):
        chunk_mem_gb = max(matrix_mem_gb, cr_constants.MIN_MEM_GB)
        chunks.append({
            'n_clusters': n_clusters,
            '__mem_gb': chunk_mem_gb,
        })

    return {'chunks': chunks} 

Example 6

def draw(self, layout='circular', figsize=None):
        """Draw all graphs that describe the DGM in a common figure

        Parameters
        ----------
        layout : str
            possible are 'circular', 'shell', 'spring'
        figsize : tuple(int)
            tuple of two integers denoting the mpl figsize

        Returns
        -------
        fig : figure
        """
        layouts = {
            'circular': nx.circular_layout,
            'shell': nx.shell_layout,
            'spring': nx.spring_layout
        }
        figsize = (10, 10) if figsize is None else figsize
        fig = plt.figure(figsize=figsize)
        rocls = np.ceil(np.sqrt(len(self.graphs)))
        for i, graph in enumerate(self.graphs):
            ax = fig.add_subplot(rocls, rocls, i+1)
            ax.set_title('Graph ' + str(i+1))
            ax.axis('off')
            ax.set_frame_on(False)
            g = graph.nxGraph
            weights = [abs(g.edge[i][j]['weight']) * 5 for i, j in g.edges()]
            nx.draw_networkx(g, pos=layouts[layout](g), ax=ax, edge_cmap=plt.get_cmap('Reds'),
                             width=2, edge_color=weights)
        return fig 

Example 7

def expand_to_chunk_size(self, chunk_size, offset=Vec(0,0,0, dtype=int)):
    """
    Align a potentially non-axis aligned bbox to the grid by growing it
    to the nearest grid lines.

    Required:
      chunk_size: arraylike (x,y,z), the size of chunks in the 
                    dataset e.g. (64,64,64)
    Optional:
      offset: arraylike (x,y,z), the starting coordinate of the dataset
    """
    chunk_size = np.array(chunk_size, dtype=np.float32)
    result = self.clone()
    result = result - offset
    result.minpt = np.floor(result.minpt / chunk_size) * chunk_size
    result.maxpt = np.ceil(result.maxpt / chunk_size) * chunk_size 
    return result + offset 

Example 8

def shrink_to_chunk_size(self, chunk_size, offset=Vec(0,0,0, dtype=int)):
    """
    Align a potentially non-axis aligned bbox to the grid by shrinking it
    to the nearest grid lines.

    Required:
      chunk_size: arraylike (x,y,z), the size of chunks in the 
                    dataset e.g. (64,64,64)
    Optional:
      offset: arraylike (x,y,z), the starting coordinate of the dataset
    """
    chunk_size = np.array(chunk_size, dtype=np.float32)
    result = self.clone()
    result = result - offset
    result.minpt = np.ceil(result.minpt / chunk_size) * chunk_size
    result.maxpt = np.floor(result.maxpt / chunk_size) * chunk_size 
    return result + offset 

Example 9

def _draw_single_box(image, xmin, ymin, xmax, ymax, display_str, font, color='black', thickness=4):
  draw = ImageDraw.Draw(image)
  (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
  draw.line([(left, top), (left, bottom), (right, bottom),
             (right, top), (left, top)], width=thickness, fill=color)
  text_bottom = bottom
  # Reverse list and print from bottom to top.
  text_width, text_height = font.getsize(display_str)
  margin = np.ceil(0.05 * text_height)
  draw.rectangle(
      [(left, text_bottom - text_height - 2 * margin), (left + text_width,
                                                        text_bottom)],
      fill=color)
  draw.text(
      (left + margin, text_bottom - text_height - margin),
      display_str,
      fill='black',
      font=font)

  return image 

Example 10

def resize_image(image,target_shape, pad_value = 0):
    assert isinstance(target_shape, list) or isinstance(target_shape, tuple)
    add_shape, subs_shape = [], []

    image_shape = image.shape
    shape_difference = np.asarray(target_shape, dtype=int) - np.asarray(image_shape,dtype=int)
    for diff in shape_difference:
        if diff < 0:
            subs_shape.append(np.s_[int(np.abs(np.ceil(diff/2))):int(np.floor(diff/2))])
            add_shape.append((0, 0))
        else:
            subs_shape.append(np.s_[:])
            add_shape.append((int(np.ceil(1.0*diff/2)),int(np.floor(1.0*diff/2))))
    output = np.pad(image, tuple(add_shape), 'constant', constant_values=(pad_value, pad_value))
    output = output[subs_shape]
    return output 

Example 11

def logTickValues(self, minVal, maxVal, size, stdTicks):
        
        ## start with the tick spacing given by tickValues().
        ## Any level whose spacing is < 1 needs to be converted to log scale
        
        ticks = []
        for (spacing, t) in stdTicks:
            if spacing >= 1.0:
                ticks.append((spacing, t))
        
        if len(ticks) < 3:
            v1 = int(np.floor(minVal))
            v2 = int(np.ceil(maxVal))
            #major = list(range(v1+1, v2))
            
            minor = []
            for v in range(v1, v2):
                minor.extend(v + np.log10(np.arange(1, 10)))
            minor = [x for x in minor if x>minVal and x<maxVal]
            ticks.append((None, minor))
        return ticks 

Example 12

def renderSymbol(symbol, size, pen, brush, device=None):
    """
    Render a symbol specification to QImage.
    Symbol may be either a QPainterPath or one of the keys in the Symbols dict.
    If *device* is None, a new QPixmap will be returned. Otherwise,
    the symbol will be rendered into the device specified (See QPainter documentation
    for more information).
    """
    ## Render a spot with the given parameters to a pixmap
    penPxWidth = max(np.ceil(pen.widthF()), 1)
    if device is None:
        device = QtGui.QImage(int(size+penPxWidth), int(size+penPxWidth), QtGui.QImage.Format_ARGB32)
        device.fill(0)
    p = QtGui.QPainter(device)
    try:
        p.setRenderHint(p.Antialiasing)
        p.translate(device.width()*0.5, device.height()*0.5)
        drawSymbol(p, symbol, size, pen, brush)
    finally:
        p.end()
    return device 

Example 13

def logTickValues(self, minVal, maxVal, size, stdTicks):
        
        ## start with the tick spacing given by tickValues().
        ## Any level whose spacing is < 1 needs to be converted to log scale
        
        ticks = []
        for (spacing, t) in stdTicks:
            if spacing >= 1.0:
                ticks.append((spacing, t))
        
        if len(ticks) < 3:
            v1 = int(np.floor(minVal))
            v2 = int(np.ceil(maxVal))
            #major = list(range(v1+1, v2))
            
            minor = []
            for v in range(v1, v2):
                minor.extend(v + np.log10(np.arange(1, 10)))
            minor = [x for x in minor if x>minVal and x<maxVal]
            ticks.append((None, minor))
        return ticks 

Example 14

def processBlocks(lines,header,obstimes,svset,headlines,sats):
    obstypes = header['# / TYPES OF OBSERV'][1:]
    blocks = Panel4D(labels=obstimes,
                     items=list(svset),
                     major_axis=obstypes,
                     minor_axis=['data','lli','ssi'])
    ttime1 = 0
    ttime2 = 0
    for i in range(len(headlines)):
        linesinblock = len(sats[i])*int(np.ceil(header['# / TYPES OF OBSERV'][0]/5))
        block = ''.join(lines[headlines[i]+1:headlines[i]+linesinblock+1])
        t1 = time.time()
        bdf = _block2df(block,obstypes,sats[i],len(sats[i]))
        ttime1 += (time.time()-t1)
        t2 = time.time()
        blocks.loc[obstimes[i],sats[i]] = bdf
        ttime2 += (time.time()-t2)            
    print("{0:.2f} seconds for _block2df".format(ttime1))
    print("{0:.2f} seconds for panel assignments".format(ttime2))
    return blocks 

Example 15

def processBlocks(lines,header,obstimes,svset,headlines,sats):
    obstypes = header['# / TYPES OF OBSERV'][1:]
    blocks = Panel4D(labels=obstimes,
                     items=list(svset),
                     major_axis=obstypes,
                     minor_axis=['data','lli','ssi'])
    ttime1 = 0
    ttime2 = 0
    for i in range(len(headlines)):
        linesinblock = len(sats[i])*int(np.ceil(header['# / TYPES OF OBSERV'][0]/5))
        block = ''.join(lines[headlines[i]+1:headlines[i]+linesinblock+1])
        t1 = time.time()
        bdf = _block2df(block,obstypes,sats[i],len(sats[i]))
        ttime1 += (time.time()-t1)
        t2 = time.time()
        blocks.loc[obstimes[i],sats[i]] = bdf
        ttime2 += (time.time()-t2)            
    print("{0:.2f} seconds for _block2df".format(ttime1))
    print("{0:.2f} seconds for panel assignments".format(ttime2))
    return blocks 

Example 16

def reset(self):
        """ Resets the state of the generator"""
        self.step = 0
        Y = np.argmax(self.Y,1)
        labels = np.unique(Y)
        idx = []
        smallest = len(Y)
        for i,label in enumerate(labels):
            where = np.where(Y==label)[0]
            if smallest > len(where): 
                self.slabel = i
                smallest = len(where)
            idx.append(where)
        self.idx = idx
        self.labels = labels
        self.n_per_class = int(self.batch_size // len(labels))
        self.n_batches = int(np.ceil((smallest//self.n_per_class)))+1
        self.update_probabilities() 

Example 17

def __init__(self, X, Y, batch_size,cropsize=0, truncate=False, sequential=False,
                 random=True, val=False, class_weights=None):
        
        assert len(X) == len(Y), 'X and Y must be the same length {}!={}'.format(len(X),len(Y))
        if sequential: print('Using sequential mode')
        print ('starting normal generator')
        self.X = X
        self.Y = Y
        self.rnd_idx = np.arange(len(Y))
        self.Y_last_epoch = []
        self.val = val
        self.step = 0
        self.i = 0
        self.cropsize=cropsize
        self.truncate = truncate
        self.random = False if sequential or val else random
        self.batch_size = int(batch_size)
        self.sequential = sequential
        self.c_weights = class_weights if class_weights else dict(zip(np.unique(np.argmax(Y,1)),np.ones(len(np.argmax(Y,1)))))
        assert set(np.argmax(Y,1)) == set([int(x) for x in self.c_weights.keys()]), 'not all labels in class weights'
        self.n_batches = int(len(X)//batch_size if truncate else np.ceil(len(X)/batch_size))
        if self.random: self.randomize() 

Example 18

def calc_row_col(self, num_ex, num_items):
        num_rows_per_ex = int(np.ceil(num_items / self.max_num_col))
        if num_items > self.max_num_col:
            num_col = self.max_num_col
            num_row = num_rows_per_ex * num_ex
        else:
            num_row = num_ex
            num_col = num_items

        def calc(ii, jj):
            col = jj % self.max_num_col
            row = num_rows_per_ex * ii + int(jj / self.max_num_col)

            return row, col

        return num_row, num_col, calc 

Example 19

def card_strength(self, include_gem=True):
		# Base attribute value from naked card
		base_attr = np.array([getattr(self, attr.lower()) for attr in attr_list], dtype=float)
		# Bonus from bond
		bond_bonus = np.array([self.bond*(attr==self.main_attr) for attr in attr_list], dtype=float)
		# Compute card-only attribute: base+bond
		card_only_attr = base_attr + bond_bonus
		if not include_gem:
			strength = np.array(card_only_attr, dtype=int).tolist()
		else:
			gem_type_list = ['Kiss', 'Perfume', 'Ring', 'Cross']
			gem_matrix = {gem_type:np.zeros(3) for gem_type in gem_type_list}
			for gem in self.equipped_gems:
				gem_type = gem.name.split()[1]
				if gem_type in gem_type_list:
					gem_matrix[gem_type][attr_list.index(gem.attribute)] = gem.value / 100**(gem.effect=='attr_boost')
			strength = card_only_attr.copy()
			for gem_type in gem_type_list:
				if gem_type in ['Kiss', 'Perfume']:
					strength += gem_matrix[gem_type]
				elif gem_type in ['Ring', 'Cross']:
					strength += np.ceil(card_only_attr*gem_matrix[gem_type])
			strength = np.array(strength, dtype=int)
		return {k.lower()+'*':v for k,v in zip(attr_list, strength)} 

Example 20

def vis_square(data):
    """Take an array of shape (n, height, width) or (n, height, width, 3)
       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)"""
    
    # normalize data for display
    data = (data - data.min()) / (data.max() - data.min())
    
    # force the number of filters to be square
    n = int(np.ceil(np.sqrt(data.shape[0])))
    padding = (((0, n ** 2 - data.shape[0]),
               (0, 1), (0, 1))                 # add some space between filters
               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)
    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)
    
    # tile the filters into an image
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
    plt.imshow(data, interpolation='nearest'); plt.axis('off') 

Example 21

def process(self, wave):
        wave.check_mono()
        if wave.sample_rate != self.sr:
            raise Exception("Wrong sample rate")                              
        n = int(np.ceil(2 * wave.num_frames / float(self.w_len)))
        m = (n + 1) * self.w_len / 2 
        swindow = self.make_signal_window(n)
        win_ratios = [self.window / swindow[t * self.w_len / 2 : 
            t * self.w_len / 2 + self.w_len] 
            for t in range(n)]
        wave = wave.zero_pad(0, int(m - wave.num_frames))
        wave = audio.Wave(signal.hilbert(wave), wave.sample_rate)        
        result = np.zeros((self.n_bins, n))
        
        for b in range(self.n_bins): 
            w = self.widths[b]
            wc = 1 / np.square(w + 1)
            filter = self.filters[b]
            band = fftfilt(filter, wave.zero_pad(0, int(2 * w))[:,0])
            band = band[int(w) : int(w + m), np.newaxis]    
            for t in range(n):
                frame = band[t * self.w_len / 2:
                             t * self.w_len / 2 + self.w_len,:] * win_ratios[t]
                result[b, t] =  wc * np.real(np.conj(np.dot(frame.conj().T, frame)))
        return audio.Spectrogram(result, self.sr, self.w_len, self.w_len / 2) 

Example 22

def n2mfrow(nr_plots):
    """
    Compute the rows and columns given the number
    of plots.

    This is a port of grDevices::n2mfrow from R
    """
    if nr_plots <= 3:
        nrow, ncol = nr_plots, 1
    elif nr_plots <= 6:
        nrow, ncol = (nr_plots + 1) // 2, 2
    elif nr_plots <= 12:
        nrow, ncol = (nr_plots + 2) // 3, 3
    else:
        nrow = int(np.ceil(np.sqrt(nr_plots)))
        ncol = int(np.ceil(nr_plots/nrow))
    return (nrow, ncol) 

Example 23

def get_padding_type(kernel_params, input_shape, output_shape):
    '''Translates Caffe's numeric padding to one of ('SAME', 'VALID').
    Caffe supports arbitrary padding values, while TensorFlow only
    supports 'SAME' and 'VALID' modes. So, not all Caffe paddings
    can be translated to TensorFlow. There are some subtleties to
    how the padding edge-cases are handled. These are described here:
    https://github.com/Yangqing/caffe2/blob/master/caffe2/proto/caffe2_legacy.proto
    '''
    k_h, k_w, s_h, s_w, p_h, p_w = kernel_params
    s_o_h = np.ceil(input_shape.height / float(s_h))
    s_o_w = np.ceil(input_shape.width / float(s_w))
    if (output_shape.height == s_o_h) and (output_shape.width == s_o_w):
        return 'SAME'
    v_o_h = np.ceil((input_shape.height - k_h + 1.0) / float(s_h))
    v_o_w = np.ceil((input_shape.width - k_w + 1.0) / float(s_w))
    if (output_shape.height == v_o_h) and (output_shape.width == v_o_w):
        return 'VALID'
    return None 

Example 24

def plot_weight_matrix(Z, outname, save=True):
    num = Z.shape[0]
    fig = plt.figure(1, (80, 80))
    fig.subplots_adjust(left=0.05, right=0.95)
    grid = AxesGrid(fig, (1, 4, 2),  # similar to subplot(142)
                    nrows_ncols=(int(np.ceil(num / 10.)), 10),
                    axes_pad=0.04,
                    share_all=True,
                    label_mode="L",
                    )

    for i in range(num):
        im = grid[i].imshow(Z[i, :, :, :].mean(
            axis=0), cmap='gray')
    for i in range(grid.ngrids):
        grid[i].axis('off')

    for cax in grid.cbar_axes:
        cax.toggle_label(False)
    if save:
        fig.savefig(outname, bbox_inches='tight')
        fig.clear() 

Example 25

def __init__(self, h, x0=None, **kwargs):
        assert type(h) is list, 'h must be a list'
        assert len(h) in [2, 3], "TreeMesh is only in 2D or 3D."

        if '_levels' in kwargs.keys():
            self._levels = kwargs.pop('_levels')

        BaseTensorMesh.__init__(self, h, x0, **kwargs)

        if self._levels is None:
            self._levels = int(np.log2(len(self.h[0])))

        # self._levels = levels
        self._levelBits = int(np.ceil(np.sqrt(self._levels)))+1

        self.__dirty__ = True  #: The numbering is dirty!

        if '_cells' in kwargs.keys():
            self._cells = kwargs.pop('_cells')
        else:
            self._cells.add(0) 

Example 26

def _optim(self, xys):
        idx = np.arange(len(xys))
        self.batch_size = np.ceil(len(xys) / self.nbatches)
        batch_idx = np.arange(self.batch_size, len(xys), self.batch_size)

        for self.epoch in range(1, self.max_epochs + 1):
            # shuffle training examples
            self._pre_epoch()
            shuffle(idx)

            # store epoch for callback
            self.epoch_start = timeit.default_timer()

            # process mini-batches
            for batch in np.split(idx, batch_idx):
                # select indices for current batch
                bxys = [xys[z] for z in batch]
                self._process_batch(bxys)

            # check callback function, if false return
            for f in self.post_epoch:
                if not f(self):
                    break 

Example 27

def dec_round(num, dprec=4, rnd='down', rto_zero=False):
    """
    Round up/down numeric ``num`` at specified decimal ``dprec``.

    Parameters
    ----------
    num: float
    dprec: int
        Decimal position for truncation.
    rnd: str (default: 'down')
        Set as 'up' or 'down' to return a rounded-up or rounded-down value.
    rto_zero: bool (default: False)
        Use a *round-towards-zero* method, e.g., ``floor(-3.5) == -3``.

    Returns
    ----------
    float (default: rounded-up)
    """
    dprec = 10**dprec
    if rnd == 'up' or (rnd == 'down' and rto_zero and num < 0.):
        return np.ceil(num*dprec)/dprec
    elif rnd == 'down' or (rnd == 'up' and rto_zero and num < 0.):
        return np.floor(num*dprec)/dprec
    return np.round(num, dprec) 

Example 28

def update(self, es, **kwargs):
        if es.countiter < 2:
            self.initialize(es)
            self.fit = es.fit.fit
        else:
            ft1, ft2 = self.fit[int(self.index_to_compare)], self.fit[int(np.ceil(self.index_to_compare))]
            ftt1, ftt2 = es.fit.fit[(es.popsize - 1) // 2], es.fit.fit[int(np.ceil((es.popsize - 1) / 2))]
            pt2 = self.index_to_compare - int(self.index_to_compare)
            # ptt2 = (es.popsize - 1) / 2 - (es.popsize - 1) // 2  # not in use
            s = 0
            if 1 < 3:
                s += pt2 * sum(es.fit.fit <= self.fit[int(np.ceil(self.index_to_compare))])
                s += (1 - pt2) * sum(es.fit.fit < self.fit[int(self.index_to_compare)])
                s -= es.popsize / 2.
                s *= 2. / es.popsize  # the range was popsize, is 2
            self.s = (1 - self.c) * self.s + self.c * s
            es.sigma *= exp(self.s / self.damp)
        # es.more_to_write.append(10**(self.s))

        #es.more_to_write.append(10**((2 / es.popsize) * (sum(es.fit.fit < self.fit[int(self.index_to_compare)]) - (es.popsize + 1) / 2)))
        # # es.more_to_write.append(10**(self.index_to_compare - sum(self.fit <= es.fit.fit[es.popsize // 2])))
        # # es.more_to_write.append(10**(np.sign(self.fit[int(self.index_to_compare)] - es.fit.fit[es.popsize // 2])))
        self.fit = es.fit.fit 

Example 29

def __init__(self, env, n, max_path_length, scope=None):
        if scope is None:
            # initialize random scope
            scope = str(uuid.uuid4())

        envs_per_worker = int(np.ceil(n * 1.0 / singleton_pool.n_parallel))
        alloc_env_ids = []
        rest_alloc = n
        start_id = 0
        for _ in range(singleton_pool.n_parallel):
            n_allocs = min(envs_per_worker, rest_alloc)
            alloc_env_ids.append(list(range(start_id, start_id + n_allocs)))
            start_id += n_allocs
            rest_alloc = max(0, rest_alloc - envs_per_worker)

        singleton_pool.run_each(worker_init_envs, [(alloc, scope, env) for alloc in alloc_env_ids])

        self._alloc_env_ids = alloc_env_ids
        self._action_space = env.action_space
        self._observation_space = env.observation_space
        self._num_envs = n
        self.scope = scope
        self.ts = np.zeros(n, dtype='int')
        self.max_path_length = max_path_length 

Example 30

def view_samples(self, show=True):
        """Displays the samples."""
        if not self.samples:
            return  # Nothing to show...
        plt.figure("Sample views")
        num = len(self.samples)
        rows = math.floor(num ** .5)
        cols = math.ceil(num / rows)
        for idx, img in enumerate(self.samples):
            plt.subplot(rows, cols, idx+1)
            plt.imshow(img, interpolation='nearest')
        if show:
            plt.show()


# EXPERIMENT: Try breaking out each output encoder by type instead of
# concatenating them all together.  Each type of sensors would then get its own
# HTM.  Maybe keep the derivatives with their source?
# 

Example 31

def make_train_test_split(prms):
	'''
	# I will just make one split and consider the last 5% of the iamges as the val images. 
	# Randomly sampling in this data is a bad idea, because many images appear together as 
	# pairs. Selecting from the end will maximize the chances of using unique and different
	# imahes in the train and test splits. 
	'''
	# Read the source pairs. 
	fid    = open(prms['paths']['pairList']['raw'],'r')
	lines  = fid.readlines()
	fid.close()
	numIm, numPairs = int(lines[0].split()[0]), int(lines[0].split()[1])
	lines = lines[1:]
	
	#Make train and val splits
	N = len(lines)
	trainNum   = int(np.ceil(0.95 * N))
	trainLines = lines[0:trainNum]
	testLines  = lines[trainNum:]
	_write_pairs(prms['paths']['pairList']['train'], trainLines, numIm)
	_write_pairs(prms['paths']['pairList']['test'] , testLines, numIm)
 
##
# Get the list of tar files for downloading the image data 

Example 32

def gen_samples(self, z0=None, n=32, batch_size=32, use_transform=True):
        assert n % batch_size == 0

        samples = []

        if z0 is None:
            z0 = np_rng.uniform(-1., 1., size=(n, self.nz))
        else:
            n = len(z0)
            batch_size = max(n, 64)
        n_batches = int(np.ceil(n/float(batch_size)))
        for i in range(n_batches):
            zmb = floatX(z0[batch_size * i:min(n, batch_size * (i + 1)), :])
            xmb = self._gen(zmb)
            samples.append(xmb)

        samples = np.concatenate(samples, axis=0)
        if use_transform:
            samples = self.inverse_transform(samples, npx=self.npx, nc=self.nc)
            samples = (samples * 255).astype(np.uint8)
        return samples 

Example 33

def __init__(self, opt_engine, topK=16, grid_size=None, nps=320, model_name='tmp'):
        QWidget.__init__(self)
        self.topK = topK
        if grid_size is None:
            self.n_grid = int(np.ceil(np.sqrt(self.topK)))
            self.grid_size = (self.n_grid, self.n_grid) # (width, height)
        else:
            self.grid_size = grid_size
        self.select_id = 0
        self.ims = None
        self.vis_results = None
        self.width = int(np.round(nps/ (4 * float(self.grid_size[1])))) * 4
        self.winWidth = self.width * self.grid_size[0]
        self.winHeight = self.width * self.grid_size[1]

        self.setFixedSize(self.winWidth, self.winHeight)
        self.opt_engine = opt_engine
        self.frame_id = -1
        self.sr = save_result.SaveResult(model_name=model_name) 

Example 34

def rasta_plp_extractor(x, sr, plp_order=0, do_rasta=True):
    spec = log_power_spectrum_extractor(x, int(sr*0.02), int(sr*0.01), 'hamming', False)
    bark_filters = int(np.ceil(freq2bark(sr//2)))
    wts = get_fft_bark_mat(sr, int(sr*0.02), bark_filters)
    bark_spec = np.matmul(wts, spec)
    if do_rasta:
        bark_spec = np.where(bark_spec == 0.0, np.finfo(float).eps, bark_spec)
        log_bark_spec = np.log(bark_spec)
        rasta_log_bark_spec = rasta_filt(log_bark_spec)
        bark_spec = np.exp(rasta_log_bark_spec)
    post_spec = postaud(bark_spec, sr/2.)
    if plp_order > 0:
        lpcas = do_lpc(post_spec, plp_order)
    else:
        lpcas = post_spec
    return lpcas 

Example 35

def _wav_to_framed_samples(wav_audio, hparams):
  """Transforms the contents of a wav file into a series of framed samples."""
  y = audio_io.wav_data_to_samples(wav_audio, hparams.sample_rate)

  hl = hparams.spec_hop_length
  n_frames = int(np.ceil(y.shape[0] / hl))
  frames = np.zeros((n_frames, hl), dtype=np.float32)

  # Fill in everything but the last frame which may not be the full length
  cutoff = (n_frames - 1) * hl
  frames[:n_frames - 1, :] = np.reshape(y[:cutoff], (n_frames - 1, hl))
  # Fill the last frame
  remain_len = len(y[cutoff:])
  frames[n_frames - 1, :remain_len] = y[cutoff:]

  return frames 

Example 36

def comp_ola_deconv(fs_gpu, ys_gpu, L_gpu, alpha, beta):
    """
    Computes the division in Fourier space needed for direct deconvolution
    """
    
    sfft = fs_gpu.shape
    block_size = (16,16,1)   
    grid_size = (int(np.ceil(np.float32(sfft[0]*sfft[1])/block_size[0])),
                 int(np.ceil(np.float32(sfft[2])/block_size[1])))

    mod = cu.module_from_buffer(cubin)
    comp_ola_deconv_Kernel = mod.get_function("comp_ola_deconv_Kernel")

    z_gpu = cua.zeros(sfft, np.complex64)

    comp_ola_deconv_Kernel(z_gpu.gpudata,
                           np.int32(sfft[0]), np.int32(sfft[1]), np.int32(sfft[2]),
                           fs_gpu.gpudata, ys_gpu.gpudata, L_gpu.gpudata,
                           np.float32(alpha), np.float32(beta),
                           block=block_size, grid=grid_size)

    return z_gpu 

Example 37

def crop_gpu2cpu(x_gpu, sz, offset=(0,0)):

    sfft = x_gpu.shape

    block_size = (16, 16 ,1)
    grid_size = (int(np.ceil(np.float32(sfft[1])/block_size[1])),
                 int(np.ceil(np.float32(sfft[0])/block_size[0])))

    if x_gpu.dtype == np.float32:
        mod = cu.module_from_buffer(cubin)
        cropKernel = mod.get_function("crop_Kernel")

    elif x_gpu.dtype == np.complex64:
        mod = cu.module_from_buffer(cubin)
        cropKernel = mod.get_function("crop_ComplexKernel")

    x_cropped_gpu = cua.empty(tuple((int(sz[0]),int(sz[1]))), np.float32)
        
    cropKernel(x_cropped_gpu.gpudata,   np.int32(sz[0]),       np.int32(sz[1]),
                       x_gpu.gpudata, np.int32(sfft[0]),     np.int32(sfft[1]),
                                    np.int32(offset[0]), np.int32(offset[1]),
                                    block=block_size   , grid=grid_size)

    return x_cropped_gpu 

Example 38

def comp_ola_sdeconv(gx_gpu, gy_gpu, xx_gpu, xy_gpu, Ftpy_gpu, f_gpu, L_gpu, alpha, beta, gamma=0):
    """
    Computes the division in Fourier space needed for sparse deconvolution
    """
    
    sfft = xx_gpu.shape
    block_size = (16,16,1)   
    grid_size = (int(np.ceil(np.float32(sfft[0]*sfft[1])/block_size[0])),
                 int(np.ceil(np.float32(sfft[2])/block_size[1])))

    mod = cu.module_from_buffer(cubin)
    comp_ola_sdeconv_Kernel = mod.get_function("comp_ola_sdeconv_Kernel")

    z_gpu = cua.zeros(sfft, np.complex64)

    comp_ola_sdeconv_Kernel(z_gpu.gpudata,
                            np.int32(sfft[0]), np.int32(sfft[1]), np.int32(sfft[2]),
                            gx_gpu.gpudata, gy_gpu.gpudata,
                            xx_gpu.gpudata, xy_gpu.gpudata, 
                            Ftpy_gpu.gpudata, f_gpu.gpudata, L_gpu.gpudata,
                            np.float32(alpha), np.float32(beta),
                            np.float32(gamma),
                            block=block_size, grid=grid_size)

    return z_gpu 

Example 39

def impad_gpu(y_gpu, sf):

  sf = np.array(sf)
  shape = (np.array(y_gpu.shape) + sf).astype(np.uint32)
  dtype = y_gpu.dtype
  block_size = (16,16,1)
  grid_size = (int(np.ceil(float(shape[1])/block_size[0])),
               int(np.ceil(float(shape[0])/block_size[1])))

  preproc = _generate_preproc(dtype, shape)
  mod = SourceModule(preproc + kernel_code, keep=True)

  padded_gpu = cua.empty((int(shape[0]), int(shape[1])), dtype)
  impad_fun = mod.get_function("impad")

  upper_left = np.uint32(np.floor(sf / 2.))
  original_size = np.uint32(np.array(y_gpu.shape))

  impad_fun(padded_gpu.gpudata, y_gpu.gpudata,
            upper_left[1], upper_left[0],
            original_size[0], original_size[1],
            block=block_size, grid=grid_size)

  return padded_gpu 

Example 40

def laplace_stack_gpu(y_gpu, mode='valid'):
  """
  This funtion computes the Laplacian of each slice of a stack of images
  """
  shape = np.array(y_gpu.shape).astype(np.uint32)
  dtype = y_gpu.dtype
  block_size = (6,int(np.floor(512./6./float(shape[0]))),int(shape[0]))
  grid_size = (int(np.ceil(float(shape[1])/block_size[0])),
               int(np.ceil(float(shape[0])/block_size[1])))
  shared_size = int((2+block_size[0])*(2+block_size[1])*(2+block_size[2])
                    *dtype.itemsize)

  preproc = _generate_preproc(dtype, (shape[1],shape[2]))
  mod = SourceModule(preproc + kernel_code, keep=True)

  laplace_fun_gpu = mod.get_function("laplace_stack_same")
  laplace_gpu = cua.empty((y_gpu.shape[0], y_gpu.shape[1], y_gpu.shape[2]),
                          y_gpu.dtype)
    
  laplace_fun_gpu(laplace_gpu.gpudata, y_gpu.gpudata,
                  block=block_size, grid=grid_size, shared=shared_size)
  
  return laplace_gpu 

Example 41

def morph(roi):
    ratio = min(28. / np.size(roi, 0), 28. / np.size(roi, 1))
    roi = cv2.resize(roi, None, fx=ratio, fy=ratio,
                     interpolation=cv2.INTER_NEAREST)
    dx = 28 - np.size(roi, 1)
    dy = 28 - np.size(roi, 0)
    px = ((int(dx / 2.)), int(np.ceil(dx / 2.)))
    py = ((int(dy / 2.)), int(np.ceil(dy / 2.)))
    squared = np.pad(roi, (py, px), 'constant', constant_values=0)
    return squared 

Example 42

def computePad(dims,depth):
	y1=y2=x1=x2=0; 
	y,x = [numpy.ceil(dims[i]/float(2**depth)) * (2**depth) for i in range(-2,0)]
	x = float(x); y = float(y);
	y1 = int(numpy.floor((y - dims[-2])/2)); y2 = int(numpy.ceil((y - dims[-2])/2))
	x1 = int(numpy.floor((x - dims[-1])/2)); x2 = int(numpy.ceil((x - dims[-1])/2))
	return y1,y2,x1,x2 

Example 43

def view_waveforms_clusters(data, halo, threshold, templates, amps_lim, n_curves=200, save=False):
    
    nb_templates = templates.shape[1]
    n_panels     = numpy.ceil(numpy.sqrt(nb_templates))
    mask         = numpy.where(halo > -1)[0]
    clust_idx    = numpy.unique(halo[mask])
    fig          = pylab.figure()    
    square       = True
    center       = len(data[0] - 1)//2
    for count, i in enumerate(xrange(nb_templates)):
        if square:
            pylab.subplot(n_panels, n_panels, count + 1)
            if (numpy.mod(count, n_panels) != 0):
                pylab.setp(pylab.gca(), yticks=[])
            if (count < n_panels*(n_panels - 1)):
                pylab.setp(pylab.gca(), xticks=[])
        
        subcurves = numpy.where(halo == clust_idx[count])[0]
        for k in numpy.random.permutation(subcurves)[:n_curves]:
            pylab.plot(data[k], '0.5')
        
        pylab.plot(templates[:, count], 'r')        
        pylab.plot(amps_lim[count][0]*templates[:, count], 'b', alpha=0.5)
        pylab.plot(amps_lim[count][1]*templates[:, count], 'b', alpha=0.5)
        
        xmin, xmax = pylab.xlim()
        pylab.plot([xmin, xmax], [-threshold, -threshold], 'k--')
        pylab.plot([xmin, xmax], [threshold, threshold], 'k--')
        #pylab.ylim(-1.5*threshold, 1.5*threshold)
        ymin, ymax = pylab.ylim()
        pylab.plot([center, center], [ymin, ymax], 'k--')
        pylab.title('Cluster %d' %i)

    if nb_templates > 0:
        pylab.tight_layout()
    if save:
        pylab.savefig(os.path.join(save[0], 'waveforms_%s' %save[1]))
        pylab.close()
    else:
        pylab.show()
    del fig 

Example 44

def draw_circles(image,cands,origin,spacing):
	#make empty matrix, which will be filled with the mask
	image_mask = np.zeros(image.shape, dtype=np.int16)

	#run over all the nodules in the lungs
	for ca in cands.values:
		#get middel x-,y-, and z-worldcoordinate of the nodule
		#radius = np.ceil(ca[4])/2     ## original:  replaced the ceil with a very minor increase of 1% ....
		radius = (ca[4])/2 + 0.51 * spacing[0]  # increasing by circa half of distance in z direction .... (trying to capture wider region/border for learning ... and adress the rough net .
    
		coord_x = ca[1]
		coord_y = ca[2]
		coord_z = ca[3]
		image_coord = np.array((coord_z,coord_y,coord_x))

		#determine voxel coordinate given the worldcoordinate
		image_coord = world_2_voxel(image_coord,origin,spacing)

		#determine the range of the nodule
		#noduleRange = seq(-radius, radius, RESIZE_SPACING[0])  # original, uniform spacing 
		noduleRange_z = seq(-radius, radius, spacing[0])
		noduleRange_y = seq(-radius, radius, spacing[1])
		noduleRange_x = seq(-radius, radius, spacing[2])

          #x = y = z = -2
		#create the mask
		for x in noduleRange_x:
			for y in noduleRange_y:
				for z in noduleRange_z:
					coords = world_2_voxel(np.array((coord_z+z,coord_y+y,coord_x+x)),origin,spacing)
					#if (np.linalg.norm(image_coord-coords) * RESIZE_SPACING[0]) < radius:  ### original (contrained to a uniofrm RESIZE)
					if (np.linalg.norm((image_coord-coords) * spacing)) < radius:
						image_mask[int(np.round(coords[0])),int(np.round(coords[1])),int(np.round(coords[2]))] = int(1)
	

	return image_mask 

Example 45

def draw_circles(image,cands,origin,spacing):
	#make empty matrix, which will be filled with the mask
	image_mask = np.zeros(image.shape, dtype=np.int16)

	#run over all the nodules in the lungs
	for ca in cands.values:
		#get middel x-,y-, and z-worldcoordinate of the nodule
		#radius = np.ceil(ca[4])/2     ## original:  replaced the ceil with a very minor increase of 1% ....
		radius = (ca[4])/2 + 0.51 * spacing[0]  # increasing by circa half of distance in z direction .... (trying to capture wider region/border for learning ... and adress the rough net .
    
		coord_x = ca[1]
		coord_y = ca[2]
		coord_z = ca[3]
		image_coord = np.array((coord_z,coord_y,coord_x))

		#determine voxel coordinate given the worldcoordinate
		image_coord = world_2_voxel(image_coord,origin,spacing)

		#determine the range of the nodule
		#noduleRange = seq(-radius, radius, RESIZE_SPACING[0])  # original, uniform spacing 
		noduleRange_z = seq(-radius, radius, spacing[0])
		noduleRange_y = seq(-radius, radius, spacing[1])
		noduleRange_x = seq(-radius, radius, spacing[2])

          #x = y = z = -2
		#create the mask
		for x in noduleRange_x:
			for y in noduleRange_y:
				for z in noduleRange_z:
					coords = world_2_voxel(np.array((coord_z+z,coord_y+y,coord_x+x)),origin,spacing)
					#if (np.linalg.norm(image_coord-coords) * RESIZE_SPACING[0]) < radius:  ### original (contrained to a uniofrm RESIZE)
					if (np.linalg.norm((image_coord-coords) * spacing)) < radius:
						image_mask[int(np.round(coords[0])),int(np.round(coords[1])),int(np.round(coords[2]))] = int(1)
	

	return image_mask 

Example 46

def vis_square(visu_path, data, type):
    """Take an array of shape (n, height, width) or (n, height, width , 3)
       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)"""

    # normalize data for display
    data = (data - data.min()) / (data.max() - data.min())

    # force the number of filters to be square
    n = int(np.ceil(np.sqrt(data.shape[0])))

    padding = (((0, n ** 2 - data.shape[0]),
                (0, 1), (0, 1))  # add some space between filters
               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)
    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)

    # tilethe filters into an im age
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))

    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])

    plt.imshow(data[:, :, 0])
    plt.axis('off')

    if type:
        plt.savefig('./{}/weights.png'.format(visu_path), format='png')
    else:
        plt.savefig('./{}/activation.png'.format(visu_path), format='png') 

Example 47

def get_irlb_mem_gb_from_matrix_dim(nonzero_entries):
    irlba_mem_gb = round(np.ceil(1.0 * nonzero_entries / cr_constants.NUM_IRLB_MATRIX_ENTRIES_PER_MEM_GB)) + cr_constants.IRLB_BASE_MEM_GB
    return cr_constants.MATRIX_MEM_GB_MULTIPLIER * max(cr_constants.MIN_MEM_GB, irlba_mem_gb) 

Example 48

def compute_percentile_from_distribution(counter, percentile):
    """ Takes a Counter object (or value:frequency dict) and computes a single percentile.
    Uses Type 7 interpolation from:
      Hyndman, R.J.; Fan, Y. (1996). "Sample Quantiles in Statistical Packages".
    """
    assert 0 <= percentile <= 100

    n = np.sum(counter.values())
    h = (n-1)*(percentile/100.0)
    lower_value = None

    cum_sum = 0
    for value, freq in sorted(counter.items()):
        cum_sum += freq
        if cum_sum > np.floor(h) and lower_value is None:
            lower_value = value
        if cum_sum > np.ceil(h):
            return lower_value + (h-np.floor(h)) * (value-lower_value)

# Test for compute_percentile_from_distribution()
#def test_percentile(x, p):
#    c = Counter()
#    for xi in x:
#        c[xi] += 1
#    my_res = np.array([compute_percentile_from_distribution(c, p_i) for p_i in p], dtype=float)
#    numpy_res = np.percentile(x, p)
#    print np.sum(np.abs(numpy_res - my_res)) 

Example 49

def get_mem_gb_from_matrix_dim(nonzero_entries):
        ''' Estimate memory usage of loading a matrix. '''
        matrix_mem_gb = round(np.ceil(1.0 * nonzero_entries / cr_constants.NUM_MATRIX_ENTRIES_PER_MEM_GB))
        return cr_constants.MATRIX_MEM_GB_MULTIPLIER * max(cr_constants.MIN_MEM_GB, matrix_mem_gb) 

Example 50

def split(args):
    # Need to store umi_info and a json with a dict containing 1 key per barcode
    umi_info_mem_gb = 2*int(np.ceil(vdj_umi_info.get_mem_gb(args.umi_info)))

    bc_diversity = len(cr_utils.load_barcode_whitelist(args.barcode_whitelist))
    assemble_summary_mem_gb = tk_stats.robust_divide(bc_diversity, DICT_BCS_PER_MEM_GB)

    return {
        'chunks': [{
            '__mem_gb': int(np.ceil(max(cr_constants.MIN_MEM_GB, umi_info_mem_gb + assemble_summary_mem_gb))),
        }]
    } 
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