Merge pull request #189 from Sichao25/refactor

Reformat

spateo/alignment/deformation.py

@@ -31,7 +31,6 @@ def grid_deformation(
     dtype: str = "float64",
     device: str = "cpu",
 ):
-
     # Check the number of lines
     grid_num = np.asarray([20, 20]) if grid_num is None else grid_num
 

spateo/alignment/methods/paste.py

@@ -161,7 +161,6 @@ def paste_pairwise_align(
 
 
 def center_NMF(n_components, random_seed, dissimilarity="kl"):
-
     if dissimilarity.lower() == "euclidean" or dissimilarity.lower() == "euc":
         model = NMF(n_components=n_components, init="random", random_state=random_seed)
     else:

spateo/alignment/methods/utils.py

@@ -724,7 +724,6 @@ def PCA_recover(
     V_new_basis: Union[np.ndarray, torch.Tensor],
     mean_data_mat: Union[np.ndarray, torch.Tensor],
 ) -> Union[np.ndarray, torch.Tensor]:
-
     nx = ot.backend.get_backend(projected_data)
     return nx.einsum("ij,jk->ik", projected_data, V_new_basis.t()) + mean_data_mat
 

spateo/alignment/utils.py

@@ -54,7 +54,6 @@ def get_optimal_mapping_relationship(
     X_max_index = np.argwhere((pi.T == pi.T.max(axis=0)).T)
     Y_max_index = np.argwhere(pi == pi.max(axis=0))
     if not keep_all:
-
         values, counts = np.unique(X_max_index[:, 0], return_counts=True)
         x_index_unique, x_index_repeat = values[counts == 1], values[counts != 1]
         X_max_index_unique = X_max_index[np.isin(X_max_index[:, 0], x_index_unique)]

spateo/plotting/static/align.py

@@ -36,7 +36,6 @@ def multi_slices(
     save_kwargs: Optional[dict] = None,
     **kwargs,
 ):
-
     # Check slices object.
     if isinstance(slices, list):
         adatas = [s.copy() for s in slices]

spateo/plotting/static/dotplot.py

@@ -356,7 +356,6 @@ def __init__(
         norm: Optional[Normalize] = None,
         **kwargs,
     ):
-
         # Default plotting parameters:
         config_spateo_rcParams()
         set_pub_style()

spateo/plotting/static/interactions.py

@@ -215,9 +215,7 @@ def plot_connections(
 
         for label_1 in range(spatial_connections.shape[0]):
             for label_2 in range(spatial_connections.shape[1]):
-
                 if label_1 <= label_2:
-
                     for triangle in [left_triangle, right_triangle]:
                         center = np.array((label_1, label_2))[np.newaxis, :]
                         scale_factor = spatial_connections[label_1, label_2] / spatial_connections_max
@@ -307,7 +305,6 @@ def plot_connections(
 
             for label_1 in range(expr_connections.shape[0]):
                 for label_2 in range(expr_connections.shape[1]):
-
                     if label_1 <= label_2:
                         for triangle in [left_triangle, right_triangle]:
                             center = np.array((label_1, label_2))[np.newaxis, :]

spateo/plotting/static/three_d_plot/three_dims_plots.py

@@ -137,7 +137,6 @@ def wrap_to_plotter(
             vertical=True,
         )
         if not (legend_kwargs is None):
-
             lg_kwargs.update((k, legend_kwargs[k]) for k in lg_kwargs.keys() & legend_kwargs.keys())
 
         add_legend(

spateo/plotting/static/utils.py

@@ -1018,7 +1018,6 @@ def interactive(
         colors = matplotlib.colors.rgb2hex(plt.get_cmap(cmap)(0.5))
 
     if points.shape[0] <= width * height // 10:
-
         if hover_data is not None:
             tooltip_dict = {}
             for col_name in hover_data:
@@ -1708,7 +1707,6 @@ def save_return_show_fig_utils(
     return_all: bool,
     return_all_list: Union[List, Tuple, None],
 ) -> Optional[Tuple]:
-
     from ...configuration import reset_rcParams
     from ...tools.utils import update_dict
 

spateo/preprocessing/auxseg.py

@@ -83,7 +83,6 @@ def _threshold_gradient_image(self):
         self.edges = self.edges.astype(float)
 
     def _compute_graph(self):
-
         try:
             from dijkstar import Graph
         except ImportError:

spateo/segmentation/simulation_evaluation/allocate_cell.py

@@ -63,7 +63,6 @@ def shift_cells(cells, labels, max_iter, seed, shift_length=10):
     center_shifts = np.random.randint(-shift_length, shift_length + 1, 2 * max_iter + 2).reshape(-1, 2)
 
     while deal_list:
-
         c += 1
         one = deal_list.pop(0)
         labels_tmp = labels.copy()

spateo/svg/utils.py

@@ -211,7 +211,6 @@ def cal_euclidean_distance(
     min_dis_cutoff: float = np.inf,
     max_dis_cutoff: float = np.inf,
 ) -> AnnData:
-
     dyn.tl.neighbors(
         adata,
         X_data=adata.obsm[layer],

spateo/tdr/models/utilities/label_utils.py

@@ -46,7 +46,6 @@ def add_model_labels(
     labels = np.asarray(labels).flatten()
 
     if not np.issubdtype(labels.dtype, np.number):
-
         cu_arr = np.sort(np.unique(labels), axis=0).astype(object)
         raw_labels_hex = labels.copy().astype(object)
         raw_labels_alpha = labels.copy().astype(object)

spateo/tdr/models/utilities/model_utils.py

@@ -90,7 +90,6 @@ def _scale_model_by_distance(
     distance: Union[int, float, list, tuple] = 1,
     scale_center: Union[list, tuple] = None,
 ) -> DataSet:
-
     # Check the distance.
     distance = distance if isinstance(distance, (tuple, list)) else [distance] * 3
     if len(distance) != 3:
@@ -117,7 +116,6 @@ def _scale_model_by_scale_factor(
     scale_factor: Union[int, float, list, tuple] = 1,
     scale_center: Union[list, tuple] = None,
 ) -> DataSet:
-
     # Check the scaling factor.
     scale_factor = scale_factor if isinstance(scale_factor, (tuple, list)) else [scale_factor] * 3
     if len(scale_factor) != 3:

spateo/tdr/widgets/deep_interpolation.py

@@ -179,7 +179,6 @@ def train(
         # The optimizers for Neural Nets
 
         if self.input_network_dim < self.data_dim:
-
             self.optimizer = optim.Adam(
                 list(self.A.parameters()) + list(self.h.parameters()) + list(self.B.parameters()),
                 lr=data_lr,
@@ -212,13 +211,11 @@ def train(
 
         # LET'S TRAIN!!
         for iter in range(max_iter):
-
             ###############################
             ### MAIN FLOW PASS ###
             ###############################
 
             if self.data_sampler is not None:
-
                 # Set the gradients to zero
                 self.h.zero_grad()
                 if self.input_network_dim < self.data_dim:
@@ -262,7 +259,6 @@ def train(
             #########################
 
             if self.input_network_dim < self.data_dim:
-
                 # Set the gradients to zero
                 self.A.zero_grad(), self.B.zero_grad()
 

spateo/tdr/widgets/interpolation_nn.py

@@ -16,7 +16,6 @@ def __init__(
         hidden_layers=1,
         activation_function=torch.nn.functional.leaky_relu,
     ):
-
         super(A, self).__init__()  # Call to the super-class is necessary
 
         self.f = activation_function
@@ -36,7 +35,6 @@ def __init__(
         # torch.nn.init.normal_(self.layer3.weight, std=.02)
 
     def forward(self, inp):
-
         out = self.f(self.layer1(inp), negative_slope=0.2)
         out = self.f(self.hidden_layers(out), negative_slope=0.2)
         out = self.outlayer(out)
@@ -57,7 +55,6 @@ def __init__(
         hidden_layers=3,
         activation_function=torch.nn.functional.leaky_relu,
     ):
-
         super(B, self).__init__()  # Call to the super-class is necessary
 
         self.f = activation_function
@@ -77,7 +74,6 @@ def __init__(
         # torch.nn.init.normal_(self.layer3.weight, std=.02)
 
     def forward(self, inp):
-
         out = self.f(self.layer1(inp), negative_slope=0.2)
         out = self.f(self.hidden_layers(out), negative_slope=0.2)
         out = self.outlayer(out)
@@ -183,7 +179,6 @@ def __init__(
             )
 
     def forward(self, inp):
-
         out = (
             self.f(self.first_omega_0 * self.layer1(inp))
             if self.sirens
@@ -197,7 +192,6 @@ def forward(self, inp):
 
 class MainFlow(torch.nn.Module):
     def __init__(self, h, A=None, B=None, enforce_positivity=False):
-
         super(MainFlow, self).__init__()
 
         self.A = A
@@ -206,7 +200,6 @@ def __init__(self, h, A=None, B=None, enforce_positivity=False):
         self.enforce_positivity = enforce_positivity
 
     def forward(self, t, x, freeze=None):
-
         x_low = self.A(x) if self.A is not None else x
         e_low = self.h.forward(x_low)
         e_hat = self.B(e_low) if self.B is not None else e_low

spateo/tdr/widgets/slice.py

@@ -64,7 +64,6 @@ def find_model_outline_planes(model) -> dict:
 
 
 def find_intersection(model, vec, center, plane):
-
     # Normalize the vector
     normal = vec / np.linalg.norm(vec)
     normal_x, normal_y, normal_z = normal

spateo/tdr/widgets/tree.py

@@ -165,7 +165,6 @@ def DDRTree(
     # main loop
     objs = []
     for iter in range(maxIter):
-
         # Kruskal method to find optimal B
         distsqMU = csr_matrix(sqdist(Y, Y)).toarray()
         stree = minimum_spanning_tree(np.tril(distsqMU)).toarray()

spateo/tools/ST_regression/generalized_lm.py

@@ -419,7 +419,6 @@ def __init__(
         theta: float = 1.0,
         verbose: bool = True,
     ):
-
         self.logger = lm.get_main_logger()
         allowable_dists = ["gaussian", "poisson", "softplus", "neg-binomial", "gamma"]
         if distr not in allowable_dists:

spateo/tools/architype.py

@@ -127,7 +127,6 @@ def archetypes(
     num_clusters: int = 5,
     layer: Union[str, None] = None,
 ) -> np.ndarray:
-
     """Identify archetypes from the anndata object.
 
     Args:

spateo/tools/cluster/utils.py

@@ -49,7 +49,6 @@ def compute_pca_components(
 
     # Whether to save the image of PCA curve and inflection point.
     if save_curve_img is not None:
-
         kl.plot_knee()
         plt.tight_layout()
         plt.savefig(save_curve_img, dpi=100)

spateo/tools/cluster_lasso.py

@@ -52,7 +52,6 @@ def vi_plot(
             sub_adata: subset of adata.
         """
         if group and group_color:
-
             df = pd.DataFrame()
             df["group_ID"] = self.adata.obs_names
             df["labels"] = self.adata.obs[group].values
@@ -96,7 +95,6 @@ def vi_plot(
             )
 
             def selection_fn(trace, points, selector):
-
                 t.data[0].cells.values = [
                     df.loc[points.point_inds][col] for col in ["group_ID", "labels", "spatial_0", "spatial_1"]
                 ]

spateo/tools/coarse_align.py

@@ -82,7 +82,6 @@ def procrustes(
     T = np.dot(V, U.T)
 
     if reflection != "best":
-
         # does the current solution use a reflection?
         have_reflection = np.linalg.det(T) < 0
 
@@ -95,7 +94,6 @@ def procrustes(
     traceTA = s.sum()
 
     if scaling:
-
         # optimum scaling of Y
         b = traceTA * normX / normY
 

spateo/tools/labels.py

@@ -43,7 +43,6 @@ def row_normalize(
     data = graph.data
 
     for start_ptr, end_ptr in zip(graph.indptr[:-1], graph.indptr[1:]):
-
         row_sum = data[start_ptr:end_ptr].sum()
 
         if row_sum != 0:

spateo/tools/live_wire.py

@@ -82,7 +82,6 @@ def _threshold_gradient_image(self):
         self.edges = self.edges.astype(float)
 
     def _compute_graph(self):
-
         try:
             from dijkstar import Graph
         except ImportError: