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ignore codeluppi
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plankton/plankton.py

Lines changed: 2 additions & 135 deletions
Original file line numberDiff line numberDiff line change
@@ -721,141 +721,6 @@ def plot_overview(self):
721721

722722
plt.suptitle('Selected Expression Densities:', fontsize=18)
723723

724-
# def plot_radial_distribution(self, n_neighbors=30, **kwargs):
725-
# # distances, _, _ = self.knn(n_neighbors=n_neighbors)
726-
# self.graph.update_knn(n_neighbors=n_neighbors)
727-
# distances = self.graph.distances
728-
# plt.hist(distances[:, 1:n_neighbors].flatten(), **kwargs)
729-
730-
# def spatial_decomposition(
731-
# self,
732-
# mRNAs_center=None,
733-
# mRNAs_neighbor=None,
734-
# n_neighbors=10,
735-
# ):
736-
737-
# if mRNAs_center is None:
738-
# mRNAs_center = self.genes
739-
# if mRNAs_neighbor is None:
740-
# mRNAs_neighbor = self.genes
741-
742-
# self.graph.update_knn(n_neighbors=n_neighbors)
743-
# neighbors = self.graph.neighbors
744-
# # np.array(self.gene_ids)[neighbors]
745-
# neighbor_classes = self.graph.neighbor_types
746-
747-
# pptx = []
748-
# ppty = []
749-
# clrs = []
750-
# intensity = []
751-
752-
# out = np.zeros((30, 30, len(self.genes)))
753-
754-
# mask_center = np.logical_or.reduce(
755-
# [neighbor_classes[:, 0] == self.get_id(m) for m in mRNAs_center])
756-
757-
# for i_n_neighbor in range(1, n_neighbors):
758-
759-
# mask_neighbor = np.logical_or.reduce([
760-
# neighbor_classes[:, i_n_neighbor] == self.get_id(m)
761-
# for m in mRNAs_neighbor
762-
# ])
763-
764-
# mask_combined = np.logical_and(mask_center, mask_neighbor)
765-
766-
# for i_neighbor, n in enumerate(neighbors[mask_combined]):
767-
768-
# xs = np.array(self.x.iloc[n])
769-
# ys = np.array(self.y.iloc[n])
770-
771-
# x_centered = xs - xs[0]
772-
# y_centered = ys - ys[0]
773-
774-
# loc_neighbor = np.array(
775-
# (x_centered[i_n_neighbor], y_centered[i_n_neighbor]))
776-
# loc_neighbor_normalized = loc_neighbor / (loc_neighbor **
777-
# 2).sum()**0.5
778-
779-
# rotation_matrix = np.array(
780-
# [[loc_neighbor_normalized[1], -loc_neighbor_normalized[0]],
781-
# [loc_neighbor_normalized[0], loc_neighbor_normalized[1]]])
782-
783-
# rotated_spots = np.inner(
784-
# np.array([x_centered, y_centered]).T, rotation_matrix).T
785-
786-
# # we want to exclude the central and n_neighbor spots:
787-
# mask = np.arange(rotated_spots.shape[1])
788-
# mask = (mask > 1) & (mask != (i_n_neighbor))
789-
790-
# # # plt.scatter(rotated[0][mask],rotated[1][mask])
791-
792-
# pptx.append(rotated_spots[0][mask])
793-
# ppty.append(rotated_spots[1][mask])
794-
# clrs.append(self.gene_ids.iloc[n][mask])
795-
796-
# pptx = np.concatenate(pptx)
797-
# ppty = np.concatenate(ppty)
798-
799-
# pptt = np.arctan(pptx / ppty)
800-
# pptr = (pptx**2 + ppty**2)**0.5
801-
802-
# clrs = np.concatenate(clrs)
803-
804-
# scale = pptr.max()
805-
# for i in range(len(mRNAs_neighbor)):
806-
# mask = clrs==i
807-
# out[(pptt[mask]/1.5*100).astype(int),(pptr[mask]/scale*100).astype(int),i]+=1
808-
809-
# plt.axhline(0)
810-
# plt.axvline(0)
811-
# plt.scatter(pptt, pptr, c=clrs, cmap='nipy_spectral', alpha=0.1, s=3)
812-
813-
# return (pptt, pptr, clrs,)
814-
815-
# def knn_clean(
816-
# self,
817-
# n_neighbors=10,
818-
# ):
819-
# # distances, indices, types = self.knn(n_neighbors=n_neighbors)
820-
# self.graph.update_knn(n_neighbors=n_neighbors)
821-
# types = self.graph.neighbor_types
822-
# count_matrix = sparse.lil_matrix(
823-
# (types.shape[0], self.genes.shape[0]))
824-
# for i, t in enumerate(types):
825-
# classes, counts = (np.unique(t[:n_neighbors], return_counts=True))
826-
# count_matrix[i, classes] = counts / counts.sum()
827-
828-
# count_matrix = count_matrix.tocsr()
829-
830-
# count_matrix_log = count_matrix.copy()
831-
# count_matrix_log.data = np.log(count_matrix.data)
832-
# count_matrix_inv = count_matrix.copy()
833-
# count_matrix_inv.data = 1 / (count_matrix.data)
834-
835-
# prototypes = np.zeros((len(self.genes), ) * 2)
836-
# for i in range(prototypes.shape[0]):
837-
# prototypes[i] = count_matrix[self.gene_ids == i].sum(0)
838-
# prototypes /= prototypes.sum(0)
839-
840-
# Dkl = count_matrix.copy()
841-
842-
# for i in range(prototypes.shape[0]):
843-
# inter = Dkl[self.gene_ids == i]
844-
# inter.data = count_matrix[self.gene_ids == i].data * (np.log(
845-
# (count_matrix_inv[self.gene_ids == i].multiply(
846-
# prototypes[i])).data))
847-
# Dkl[self.gene_ids == i] = inter
848-
# Dkl = -np.array(Dkl.sum(1)).flatten()
849-
# Dkl[np.isinf(Dkl)] = 0
850-
851-
# return Dkl
852-
853-
# def scatter_celltype_affinities(self,
854-
# adata,
855-
# celltypes_1,
856-
# celltypes_2=None):
857-
# adata, sdata = synchronize(adata, self)
858-
859724
def squidpy(self):
860725
# obs={"cluster":self.gene_id.astype('category')}
861726
obsm = {"spatial": np.array(self.coordinates)}
@@ -973,3 +838,5 @@ def sorted_bar_compare(stat1, stat2, kwargs1={}, kwargs2={}):
973838
ax3.xaxis.set_label_position('top')
974839
# ax3.set_ylabel('log(count) spatial')
975840
return(ax1, ax2, ax3)
841+
842+

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