Python numpy.swapaxes() Examples

def update(self, xPhys, u, title=None):
        """Plot to screen"""
        self.im.set_array(-xPhys.reshape((self.nelx, self.nely)).T)
        stress = self.stress_calculator.calculate_stress(xPhys, u, self.nu)
        # self.stress_calculator.calculate_fdiff_stress(xPhys, u, self.nu)
        self.myColorMap.set_norm(colors.Normalize(vmin=0, vmax=max(stress)))
        stress_rgba = self.myColorMap.to_rgba(stress)
        stress_rgba[:, :, 3] = xPhys.reshape(-1, 1)
        self.stress_im.set_array(np.swapaxes(
            stress_rgba.reshape((self.nelx, self.nely, 4)), 0, 1))
        self.fig.canvas.draw()
        self.fig.canvas.flush_events()
        if title is not None:
            plt.title(title)
        else:
            plt.xlabel("Max stress = {:.2f}".format(max(stress)[0]))
        plt.pause(0.01) 

def update(self, xPhys, u, title=None):
        """Plot to screen"""
        self.im.set_array(-xPhys.reshape((self.nelx, self.nely)).T)
        stress = self.stress_calculator.calculate_stress(xPhys, u, self.nu)
        # self.stress_calculator.calculate_fdiff_stress(xPhys, u, self.nu)
        self.myColorMap.set_norm(colors.Normalize(vmin=0, vmax=max(stress)))
        stress_rgba = self.myColorMap.to_rgba(stress)
        stress_rgba[:, :, 3] = xPhys.reshape(-1, 1)
        self.stress_im.set_array(np.swapaxes(
            stress_rgba.reshape((self.nelx, self.nely, 4)), 0, 1))
        self.fig.canvas.draw()
        self.fig.canvas.flush_events()
        if title is not None:
            plt.title(title)
        else:
            plt.xlabel("Max stress = {:.2f}".format(max(stress)[0]))
        plt.pause(0.01) 

def get_data(img_path):
    """get the (1, 3, h, w) np.array data for the supplied image
                Args:
                    img_path (string): the input image path

                Returns:
                    np.array: image data in a (1, 3, h, w) shape

    """
    mean = np.array([123.68, 116.779, 103.939])  # (R,G,B)
    img = Image.open(img_path)
    img = np.array(img, dtype=np.float32)
    reshaped_mean = mean.reshape(1, 1, 3)
    img = img - reshaped_mean
    img = np.swapaxes(img, 0, 2)
    img = np.swapaxes(img, 1, 2)
    img = np.expand_dims(img, axis=0)
    return img 

def PreprocessContentImage(path, long_edge):
    img = io.imread(path)
    logging.info("load the content image, size = %s", img.shape[:2])
    factor = float(long_edge) / max(img.shape[:2])
    new_size = (int(img.shape[0] * factor), int(img.shape[1] * factor))
    resized_img = transform.resize(img, new_size)
    sample = np.asarray(resized_img) * 256
    # swap axes to make image from (224, 224, 3) to (3, 224, 224)
    sample = np.swapaxes(sample, 0, 2)
    sample = np.swapaxes(sample, 1, 2)
    # sub mean
    sample[0, :] -= 123.68
    sample[1, :] -= 116.779
    sample[2, :] -= 103.939
    logging.info("resize the content image to %s", new_size)
    return np.resize(sample, (1, 3, sample.shape[1], sample.shape[2])) 

def PreprocessImage(path, show_img=False):
    # load image
    img = io.imread(path)
    print("Original Image Shape: ", img.shape)
    # we crop image from center
    short_egde = min(img.shape[:2])
    yy = int((img.shape[0] - short_egde) / 2)
    xx = int((img.shape[1] - short_egde) / 2)
    crop_img = img[yy : yy + short_egde, xx : xx + short_egde]
    # resize to 224, 224
    resized_img = transform.resize(crop_img, (224, 224))
    # convert to numpy.ndarray
    sample = np.asarray(resized_img) * 255
    # swap axes to make image from (224, 224, 3) to (3, 224, 224)
    sample = np.swapaxes(sample, 0, 2)
    sample = np.swapaxes(sample, 1, 2)

    # sub mean
    return sample

# Get preprocessed batch (single image batch) 

def linear_ensemble_strategy(self, pred_mean, pred_var, ruitu_inputs, feature_name,\
                            timestep_to_ensemble=21, alpha=1):
        '''
        This stratergy aims to calculate linear weighted at specific timestep (timestep_to_ensemble) between prediction and ruitu as formula:
                                    (alpha)*pred_mean + (1-alpha)*ruitu_inputs
        pred_mean: (10, 37, 3)
        pred_var: (10, 37, 3)
        ruitu_inputs: (37,10,29). Need Swamp to(10,37,29) FIRSTLY!!
        timestep_to_ensemble: int32 (From 0 to 36)
        '''
        assert 0<= alpha <=1, 'Please ensure 0<= alpha <=1 !'
        assert pred_mean.shape == (10, 37, 3), 'Error! This funtion ONLY works for \
        one data sample with shape (10, 37, 3). Any data shape (None, 10, 37, 3) will leads this error!'
        #pred_std = np.sqrt(np.exp(pred_var))           
        ruitu_inputs = np.swapaxes(ruitu_inputs,0,1)
        print('alpha:',alpha)

        pred_mean[:,timestep_to_ensemble:,self.obs_and_output_feature_index_map[feature_name]] = \
        (alpha)*pred_mean[:,timestep_to_ensemble:,self.obs_and_output_feature_index_map[feature_name]] + \
                                (1-alpha)*ruitu_inputs[:,timestep_to_ensemble:, self.ruitu_feature_index_map[feature_name]]  
        print('Corrected pred_mean shape:', pred_mean.shape)
        
        return pred_mean 

def plot_ball_and_alpha(alpha, trajectory, filename, cmap='Blues'):
    f, ax = plt.subplots(nrows=1, ncols=2, figsize=[12, 6])
    collection = construct_ball_trajectory(trajectory, r=1., cmap=cmap)

    x_min, y_min = np.min(trajectory, axis=0)
    x_max, y_max = np.max(trajectory, axis=0)

    ax[0].add_collection(collection)
    ax[0].set_xlim([x_min, x_max])
    ax[0].set_ylim([y_min, y_max])
    # ax[0].set_xticks([])
    # ax[0].set_yticks([])
    ax[0].axis("equal")

    for line in np.swapaxes(alpha, 1, 0):
        ax[1].plot(line, linestyle='-')

    plt.savefig(filename, format='png', bbox_inches='tight', dpi=80)
    plt.close() 

def save_movies_to_frame(images, filename, cmap='Blues'):
    # Binarize images
    # images[images > 0] = 1.

    # Grid images
    images = np.swapaxes(images, 1, 0)
    images = np.array([combine_multiple_img(image) for image in images])

    # Collect to single image
    image = movie_to_frame(images)

    f = plt.figure(figsize=[12, 12])
    plt.imshow(image, cmap=plt.cm.get_cmap(cmap), interpolation='none', vmin=0, vmax=1)
    plt.axis('image')
    plt.savefig(filename, format='png', bbox_inches='tight', dpi=80)
    plt.close(f) 

def tensor_cnn_frame(mat, M):
    """Construct a tensor of shape (C x H x W) given an utterance matrix 
    for CNN
    """
    slice_mat = []
    for index in np.arange(len(mat)):
        if index < M:
            to_left = np.tile(mat[index], M).reshape((M,-1))
            rest = mat[index:index+M+1]
            context = np.vstack((to_left, rest))
        elif index >= len(mat)-M:
            to_right = np.tile(mat[index], M).reshape((M,-1))
            rest = mat[index-M:index+1]
            context = np.vstack((rest, to_right))
        else:
            context = mat[index-M:index+M+1]
        slice_mat.append(context)

    slice_mat = np.array(slice_mat)
    slice_mat = np.expand_dims(slice_mat, axis=1)
    slice_mat = np.swapaxes(slice_mat, 2, 3)
    
    return slice_mat 

def tensor_cnngru(mat):
    """Construct an utterance tensor for a given utterance matrix mat
    for CNN+GRU
    """
    mat = np.swapaxes(mat, 0, 1)
    div = int(mat.shape[1]/400)
    if div == 0: # short utt
        tensor_mat = mat
        while True:
            shape = tensor_mat.shape[1]
            if shape + mat.shape[1] < 400:
                tensor_mat = np.hstack((tensor_mat,mat))
            else:
                tensor_mat = np.hstack((tensor_mat,mat[:,:400-shape]))
                break
    elif div == 1: # truncate to 1
        tensor_mat = mat[:,:400]
    else:
        # TO DO: cut into 2
        tensor_mat = mat[:,:400]

    tensor_mat = np.expand_dims(tensor_mat, axis=2)
    print(tensor_mat.shape)
    return tensor_mat 

def doperation(self, opLabel, flat=False, wrtFilter=None):
        """ Return the derivative of a length-1 (single-gate) sequence """
        dim = self.dim
        gate = self.sos.get_operation(opLabel)
        op_wrtFilter, gpindices = self._process_wrtFilter(wrtFilter, gate)

        # Allocate memory for the final result
        num_deriv_cols = self.Np if (wrtFilter is None) else len(wrtFilter)
        flattened_dprod = _np.zeros((dim**2, num_deriv_cols), 'd')

        _fas(flattened_dprod, [None, gpindices],
             gate.deriv_wrt_params(op_wrtFilter))  # (dim**2, nParams[opLabel])

        if _slct.length(gpindices) > 0:  # works for arrays too
            # Compute the derivative of the entire operation sequence with respect to the
            # gate's parameters and fill appropriate columns of flattened_dprod.
            #gate = self.sos.get_operation[opLabel] UNNEEDED (I think)
            _fas(flattened_dprod, [None, gpindices],
                 gate.deriv_wrt_params(op_wrtFilter))  # (dim**2, nParams in wrtFilter for opLabel)

        if flat:
            return flattened_dprod
        else:
            # axes = (gate_ij, prod_row, prod_col)
            return _np.swapaxes(flattened_dprod, 0, 1).reshape((num_deriv_cols, dim, dim)) 

def setup(self):
        self.data = [
                # Array scalars
                (np.array(3.), None),
                (np.array(3), 'f8'),
                # 1D arrays
                (np.arange(6, dtype='f4'), None),
                (np.arange(6), 'c16'),
                # 2D C-layout arrays
                (np.arange(6).reshape(2, 3), None),
                (np.arange(6).reshape(3, 2), 'i1'),
                # 2D F-layout arrays
                (np.arange(6).reshape((2, 3), order='F'), None),
                (np.arange(6).reshape((3, 2), order='F'), 'i1'),
                # 3D C-layout arrays
                (np.arange(24).reshape(2, 3, 4), None),
                (np.arange(24).reshape(4, 3, 2), 'f4'),
                # 3D F-layout arrays
                (np.arange(24).reshape((2, 3, 4), order='F'), None),
                (np.arange(24).reshape((4, 3, 2), order='F'), 'f4'),
                # 3D non-C/F-layout arrays
                (np.arange(24).reshape(2, 3, 4).swapaxes(0, 1), None),
                (np.arange(24).reshape(4, 3, 2).swapaxes(0, 1), '?'),
                     ] 

def iswapaxes(self, axis1, axis2):
        """Similar as ``np.swapaxes``; in place."""
        axis1 = self.get_leg_index(axis1)
        axis2 = self.get_leg_index(axis2)
        if axis1 == axis2:
            return self  # nothing to do
        swap = np.arange(self.rank, dtype=np.intp)
        swap[axis1], swap[axis2] = axis2, axis1
        legs = self.legs
        legs[axis1], legs[axis2] = legs[axis2], legs[axis1]
        labels = self._labels
        labels[axis1], labels[axis2] = labels[axis2], labels[axis1]
        self._set_shape()
        self._qdata = self._qdata[:, swap]
        self._qdata_sorted = False
        self._data = [t.swapaxes(axis1, axis2) for t in self._data]
        return self 

def _rmatvec_forward(self, x):
        if not self.reshape:
            x = x.squeeze()
            y = np.zeros(self.N, self.dtype)
            y[:-1] -= x[:-1] / self.sampling
            y[1:] += x[:-1] / self.sampling
        else:
            x = np.reshape(x, self.dims)
            if self.dir > 0:  # need to bring the dim. to derive to first dim.
                x = np.swapaxes(x, self.dir, 0)
            y = np.zeros(x.shape, self.dtype)
            y[:-1] -= x[:-1] / self.sampling
            y[1:] += x[:-1] / self.sampling
            if self.dir > 0:
                y = np.swapaxes(y, 0, self.dir)
            y = y.ravel()
        return y 

def _matvec_centered(self, x):
        if not self.reshape:
            x = x.squeeze()
            y = np.zeros(self.N, self.dtype)
            y[1:-1] = (0.5 * x[2:] - 0.5 * x[0:-2]) / self.sampling
            if self.edge:
                y[0] = (x[1] - x[0]) / self.sampling
                y[-1] = (x[-1] - x[-2]) / self.sampling
        else:
            x = np.reshape(x, self.dims)
            if self.dir > 0:  # need to bring the dim. to derive to first dim.
                x = np.swapaxes(x, self.dir, 0)
            y = np.zeros(x.shape, self.dtype)
            y[1:-1] = (0.5 * x[2:] - 0.5 * x[0:-2]) / self.sampling
            if self.edge:
                y[0] = (x[1] - x[0]) / self.sampling
                y[-1] = (x[-1] - x[-2]) / self.sampling
            if self.dir > 0:
                y = np.swapaxes(y, 0, self.dir)
            y = y.ravel()
        return y 

def _rmatvec_backward(self, x):
        if not self.reshape:
            x = x.squeeze()
            y = np.zeros(self.N, self.dtype)
            y[:-1] -= x[1:] / self.sampling
            y[1:] += x[1:] / self.sampling
        else:
            x = np.reshape(x, self.dims)
            if self.dir > 0:  # need to bring the dim. to derive to first dim.
                x = np.swapaxes(x, self.dir, 0)
            y = np.zeros(x.shape, self.dtype)
            y[:-1] -= x[1:] / self.sampling
            y[1:] += x[1:] / self.sampling
            if self.dir > 0:
                y = np.swapaxes(y, 0, self.dir)
            y = y.ravel()
        return y 

def _matvec(self, x):
        if not self.reshape:
            x = x.squeeze()
            y = np.zeros(self.N, self.dtype)
            y[1:-1] = (x[2:] - 2*x[1:-1] + x[0:-2]) / self.sampling**2
            if self.edge:
                y[0] = (x[0] - 2*x[1] + x[2]) / self.sampling**2
                y[-1] = (x[-3] - 2*x[-2] + x[-1]) / self.sampling**2
        else:
            x = np.reshape(x, self.dims)
            if self.dir > 0:  # need to bring the dim. to derive to first dim.
                x = np.swapaxes(x, self.dir, 0)
            y = np.zeros(x.shape, self.dtype)
            y[1:-1] = (x[2:] - 2*x[1:-1] + x[0:-2])/self.sampling**2
            if self.edge:
                y[0] = (x[0] - 2*x[1] + x[2]) / self.sampling ** 2
                y[-1] = (x[-3] - 2*x[-2] + x[-1]) / self.sampling ** 2
            if self.dir > 0:
                y = np.swapaxes(y, 0, self.dir)
            y = y.ravel()
        return y 

def vgg_block(outputs, params, name, data_format, num_conv):

    for i in range(num_conv):
        layer_name = name + "_" + str(i + 1)
        w = np.swapaxes(np.swapaxes(np.swapaxes(params[layer_name][0], 0, 3), 1, 2), 0, 1)
        b = params[layer_name][1]
        outputs = tf.layers.conv2d(
                outputs,
    	    filters=w.shape[3],
    	    kernel_size=(w.shape[0], w.shape[1]),
    	    strides=(1, 1),
    	    padding=("SAME"),
    	    data_format=data_format,
    	    kernel_initializer=tf.constant_initializer(w),
    	    bias_initializer=tf.constant_initializer(b),
    	    activation=tf.nn.relu,
    	    name=layer_name)
    return outputs 

def vgg_mod(outputs, params, name, data_format, dilation=1):
    w = np.swapaxes(np.swapaxes(np.swapaxes(params[name][0], 0, 3), 1, 2), 0, 1)
    b = params[name][1]
    outputs = tf.layers.conv2d(
            outputs,
        filters=w.shape[3],
        kernel_size=(w.shape[0], w.shape[1]),
        strides=(1, 1),
        padding=("SAME"),
        data_format=data_format,
        dilation_rate=(dilation, dilation),
        kernel_initializer=tf.constant_initializer(w),
        bias_initializer=tf.constant_initializer(b),
        activation=tf.nn.relu,
        name=name)
    return outputs 

def _raw_fft(x, n, axis, direction, overwrite_x, work_function):
    """ Internal auxiliary function for fft, ifft, rfft, irfft."""
    if n is None:
        n = x.shape[axis]
    elif n != x.shape[axis]:
        x, copy_made = _fix_shape(x,n,axis)
        overwrite_x = overwrite_x or copy_made

    if n < 1:
        raise ValueError("Invalid number of FFT data points "
                         "(%d) specified." % n)

    if axis == -1 or axis == len(x.shape)-1:
        r = work_function(x,n,direction,overwrite_x=overwrite_x)
    else:
        x = swapaxes(x, axis, -1)
        r = work_function(x,n,direction,overwrite_x=overwrite_x)
        r = swapaxes(r, axis, -1)
    return r 

def __init__(self, nelx, nely, stress_calculator, nu, title=""):
        """Initialize plot and plot the initial design"""
        super(StressGUI, self).__init__(nelx, nely, title)
        self.stress_im = self.ax.imshow(
            np.swapaxes(np.zeros((nelx, nely, 4)), 0, 1),
            norm=colors.Normalize(vmin=0, vmax=1), cmap='jet')
        self.fig.colorbar(self.stress_im)
        self.stress_calculator = stress_calculator
        self.nu = nu
        self.myColorMap = colormaps.ScalarMappable(
            norm=colors.Normalize(vmin=0, vmax=1), cmap=colormaps.jet) 

def __init__(self, nelx, nely, stress_calculator, nu, title=""):
        """Initialize plot and plot the initial design"""
        super(StressGUI, self).__init__(nelx, nely, title)
        self.stress_im = self.ax.imshow(
            np.swapaxes(np.zeros((nelx, nely, 4)), 0, 1),
            norm=colors.Normalize(vmin=0, vmax=1), cmap='jet')
        self.fig.colorbar(self.stress_im)
        self.stress_calculator = stress_calculator
        self.nu = nu
        self.myColorMap = colormaps.ScalarMappable(
            norm=colors.Normalize(vmin=0, vmax=1), cmap=colormaps.jet) 

def _read_img(self, img_name, label_name):
        img = Image.open(os.path.join(self.root_dir, img_name))
        label = Image.open(os.path.join(self.root_dir, label_name))
        assert img.size == label.size
        img = np.array(img, dtype=np.float32)  # (h, w, c)
        label = np.array(label)  # (h, w)
        if self.cut_off_size is not None:
            max_hw = max(img.shape[0], img.shape[1])
            min_hw = min(img.shape[0], img.shape[1])
            if min_hw > self.cut_off_size:
                rand_start_max = int(np.random.uniform(0, max_hw - self.cut_off_size - 1))
                rand_start_min = int(np.random.uniform(0, min_hw - self.cut_off_size - 1))
                if img.shape[0] == max_hw :
                    img = img[rand_start_max : rand_start_max + self.cut_off_size, rand_start_min : rand_start_min + self.cut_off_size]
                    label = label[rand_start_max : rand_start_max + self.cut_off_size, rand_start_min : rand_start_min + self.cut_off_size]
                else :
                    img = img[rand_start_min : rand_start_min + self.cut_off_size, rand_start_max : rand_start_max + self.cut_off_size]
                    label = label[rand_start_min : rand_start_min + self.cut_off_size, rand_start_max : rand_start_max + self.cut_off_size]
            elif max_hw > self.cut_off_size:
                rand_start = int(np.random.uniform(0, max_hw - min_hw - 1))
                if img.shape[0] == max_hw :
                    img = img[rand_start : rand_start + min_hw, :]
                    label = label[rand_start : rand_start + min_hw, :]
                else :
                    img = img[:, rand_start : rand_start + min_hw]
                    label = label[:, rand_start : rand_start + min_hw]
        reshaped_mean = self.mean.reshape(1, 1, 3)
        img = img - reshaped_mean
        img = np.swapaxes(img, 0, 2)
        img = np.swapaxes(img, 1, 2)  # (c, h, w)
        img = np.expand_dims(img, axis=0)  # (1, c, h, w)
        label = np.array(label)  # (h, w)
        label = np.expand_dims(label, axis=0)  # (1, h, w)
        return (img, label) 

def PreprocessStyleImage(path, shape):
    img = io.imread(path)
    resized_img = transform.resize(img, (shape[2], shape[3]))
    sample = np.asarray(resized_img) * 256
    sample = np.swapaxes(sample, 0, 2)
    sample = np.swapaxes(sample, 1, 2)

    sample[0, :] -= 123.68
    sample[1, :] -= 116.779
    sample[2, :] -= 103.939
    return np.resize(sample, (1, 3, sample.shape[1], sample.shape[2])) 

def PostprocessImage(img):
    img = np.resize(img, (3, img.shape[2], img.shape[3]))
    img[0, :] += 123.68
    img[1, :] += 116.779
    img[2, :] += 103.939
    img = np.swapaxes(img, 1, 2)
    img = np.swapaxes(img, 0, 2)
    img = np.clip(img, 0, 255)
    return img.astype('uint8') 

def PreprocessStyleImage(path, shape):
    img = io.imread(path)
    resized_img = transform.resize(img, (shape[2], shape[3]))
    sample = np.asarray(resized_img) * 256
    sample = np.swapaxes(sample, 0, 2)
    sample = np.swapaxes(sample, 1, 2)

    sample[0, :] -= 123.68
    sample[1, :] -= 116.779
    sample[2, :] -= 103.939
    return np.resize(sample, (1, 3, sample.shape[1], sample.shape[2])) 

def PostprocessImage(img):
    img = np.resize(img, (3, img.shape[2], img.shape[3]))
    img[0, :] += 123.68
    img[1, :] += 116.779
    img[2, :] += 103.939
    img = np.swapaxes(img, 1, 2)
    img = np.swapaxes(img, 0, 2)
    img = np.clip(img, 0, 255)
    return img.astype('uint8') 

def PreprocessImage(img):
    img = np.array(img)
    print("Original Image Shape: ", img.shape)
    # we crop image from center
    short_egde = min(img.shape[:2])
    yy = int((img.shape[0] - short_egde) / 2)
    xx = int((img.shape[1] - short_egde) / 2)
    crop_img = img[yy : yy + short_egde, xx : xx + short_egde]
    # resize to 224, 224
    resized_img = transform.resize(crop_img, (224, 224))
    # convert to numpy.ndarray
    sample = np.asarray(resized_img) * 256

    #-------------------------------------------------------------------
    # Note: The decoded image should be in BGR channel (opencv output)
    # For RGB output such as from skimage, we need to convert it to BGR
    # WRONG channel will lead to WRONG result
    #-------------------------------------------------------------------
    # swap channel from RGB to BGR
    # sample = sample[:, :, [2,1,0]]
    sample = sample[:, :, [0,1,2]] # actually, in this pre-trained model RGB is used

    # swap axes to make image from (224, 224, 4) to (3, 224, 224)
    sample = np.swapaxes(sample, 0, 2)
    sample = np.swapaxes(sample, 1, 2)

    sample.resize(3,224,224)
    return sample 

def concat_state_x(f, names):
  af = {}
  for k in names:
    af[k] = np.concatenate([x[k] for x in f], axis=1)
    # af[k] = np.swapaxes(af[k], 0, 1)
  return af 

def convert_to_batched_episodes(self, episodes, max_length=None):
    """Convert batch-major list of episodes to time-major batch of episodes."""
    lengths = [len(ep[-2]) for ep in episodes]
    max_length = max_length or max(lengths)

    new_episodes = []
    for ep, length in zip(episodes, lengths):
      initial, observations, actions, rewards, terminated = ep
      observations = [np.resize(obs, [max_length + 1] + list(obs.shape)[1:])
                      for obs in observations]
      actions = [np.resize(act, [max_length + 1] + list(act.shape)[1:])
                 for act in actions]
      pads = np.array([0] * length + [1] * (max_length - length))
      rewards = np.resize(rewards, [max_length]) * (1 - pads)
      new_episodes.append([initial, observations, actions, rewards,
                           terminated, pads])

    (initial, observations, actions, rewards,
     terminated, pads) = zip(*new_episodes)
    observations = [np.swapaxes(obs, 0, 1)
                    for obs in zip(*observations)]
    actions = [np.swapaxes(act, 0, 1)
               for act in zip(*actions)]
    rewards = np.transpose(rewards)
    pads = np.transpose(pads)

    return (initial, observations, actions, rewards, terminated, pads)