def umapper(embed, metric="euclidean", n_neighbors=30, min_dist=1, **kws):
umap = UMAP(metric=metric, n_neighbors=n_neighbors, min_dist=min_dist)
umap_euc = umap.fit_transform(embed)
plot_df = pd.DataFrame(data=umap_euc)
plot_df["labels"] = labels
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
plot_kws = dict(
x=0,
y=1,
hue="labels",
palette=CLASS_COLOR_DICT,
legend=False,
s=20,
linewidth=0.5,
alpha=0.7,
)
sns.scatterplot(data=plot_df, ax=ax, **plot_kws)
ax.axis("off")
left_right_indexing = True
if left_right_indexing:
tlp_inds = np.arange(len(embed) // 2)
trp_inds = np.arange(len(embed) // 2) + len(embed) // 2
add_connections(
plot_df.iloc[tlp_inds, 0],
plot_df.iloc[trp_inds, 0],
plot_df.iloc[tlp_inds, 1],
plot_df.iloc[trp_inds, 1],
ax=ax,
)
return fig, ax
def plot_pairs(
X,
# left_inds,
# right_inds,
labels,
model=None,
left_pair_inds=None,
right_pair_inds=None,
equal=False,
):
n_dims = X.shape[1]
# if colors is None:
# colors = sns.color_palette("tab10", n_colors=k, desat=0.7)
fig, axs = plt.subplots(n_dims,
n_dims,
sharex=False,
sharey=False,
figsize=(20, 20))
data = pd.DataFrame(data=X)
data["label"] = labels
for i in range(n_dims):
for j in range(n_dims):
ax = axs[i, j]
ax.axis("off")
if i < j:
sns.scatterplot(
data=data,
x=j,
y=i,
ax=ax,
alpha=0.7,
linewidth=0,
s=8,
legend=False,
hue="label",
palette=CLASS_COLOR_DICT,
)
if left_pair_inds is not None and right_pair_inds is not None:
add_connections(
data.iloc[left_pair_inds, j],
data.iloc[right_pair_inds, j],
data.iloc[left_pair_inds, i],
data.iloc[right_pair_inds, i],
ax=ax,
)
plt.tight_layout()
return fig, axs
def embedplot(embed):
plot_df = pd.DataFrame(data=embed[:, [0, 1]])
plot_df["merge_class"] = meta["merge_class"].values
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
sns.scatterplot(
data=plot_df,
x=0,
y=1,
hue="merge_class",
palette=CLASS_COLOR_DICT,
legend=False,
ax=ax,
s=20,
linewidth=0.5,
alpha=0.7,
)
ax.axis("off")
add_connections(
plot_df.iloc[lp_inds, 0],
plot_df.iloc[rp_inds, 0],
plot_df.iloc[lp_inds, 1],
plot_df.iloc[rp_inds, 1],
ax=ax,
)
def quick_embed_viewer(embed,
labels=None,
lp_inds=None,
rp_inds=None,
left_right_indexing=False):
if left_right_indexing:
lp_inds = np.arange(len(embed) // 2)
rp_inds = np.arange(len(embed) // 2) + len(embed) // 2
fig, axs = plt.subplots(3, 2, figsize=(20, 30))
cmds = ClassicalMDS(n_components=2)
cmds_euc = cmds.fit_transform(embed)
plot_df = pd.DataFrame(data=cmds_euc)
plot_df["labels"] = labels
plot_kws = dict(
x=0,
y=1,
hue="labels",
palette=CLASS_COLOR_DICT,
legend=False,
s=20,
linewidth=0.5,
alpha=0.7,
)
ax = axs[0, 0]
sns.scatterplot(data=plot_df, ax=ax, **plot_kws)
ax.axis("off")
add_connections(
plot_df.iloc[lp_inds, 0],
plot_df.iloc[rp_inds, 0],
plot_df.iloc[lp_inds, 1],
plot_df.iloc[rp_inds, 1],
ax=ax,
)
ax.set_title("CMDS o euclidean")
cmds = ClassicalMDS(n_components=2, dissimilarity="precomputed")
pdist = symmetrize(pairwise_distances(embed, metric="cosine"))
cmds_cos = cmds.fit_transform(pdist)
plot_df[0] = cmds_cos[:, 0]
plot_df[1] = cmds_cos[:, 1]
ax = axs[0, 1]
sns.scatterplot(data=plot_df, ax=ax, **plot_kws)
ax.axis("off")
add_connections(
plot_df.iloc[lp_inds, 0],
plot_df.iloc[rp_inds, 0],
plot_df.iloc[lp_inds, 1],
plot_df.iloc[rp_inds, 1],
ax=ax,
)
ax.set_title("CMDS o cosine")
tsne = TSNE(metric="euclidean")
tsne_euc = tsne.fit_transform(embed)
plot_df[0] = tsne_euc[:, 0]
plot_df[1] = tsne_euc[:, 1]
ax = axs[1, 0]
sns.scatterplot(data=plot_df, ax=ax, **plot_kws)
ax.axis("off")
add_connections(
plot_df.iloc[lp_inds, 0],
plot_df.iloc[rp_inds, 0],
plot_df.iloc[lp_inds, 1],
plot_df.iloc[rp_inds, 1],
ax=ax,
)
ax.set_title("TSNE o euclidean")
tsne = TSNE(metric="precomputed")
tsne_cos = tsne.fit_transform(pdist)
plot_df[0] = tsne_cos[:, 0]
plot_df[1] = tsne_cos[:, 1]
ax = axs[1, 1]
sns.scatterplot(data=plot_df, ax=ax, **plot_kws)
ax.axis("off")
add_connections(
plot_df.iloc[lp_inds, 0],
plot_df.iloc[rp_inds, 0],
plot_df.iloc[lp_inds, 1],
plot_df.iloc[rp_inds, 1],
ax=ax,
)
ax.set_title("TSNE o cosine")
umap = UMAP(metric="euclidean", n_neighbors=30, min_dist=1)
umap_euc = umap.fit_transform(embed)
plot_df[0] = umap_euc[:, 0]
plot_df[1] = umap_euc[:, 1]
ax = axs[2, 0]
sns.scatterplot(data=plot_df, ax=ax, **plot_kws)
ax.axis("off")
add_connections(
plot_df.iloc[lp_inds, 0],
plot_df.iloc[rp_inds, 0],
plot_df.iloc[lp_inds, 1],
plot_df.iloc[rp_inds, 1],
ax=ax,
)
ax.set_title("UMAP o euclidean")
umap = UMAP(metric="cosine", n_neighbors=30, min_dist=1)
umap_cos = umap.fit_transform(embed)
plot_df[0] = umap_cos[:, 0]
plot_df[1] = umap_cos[:, 1]
ax = axs[2, 1]
sns.scatterplot(data=plot_df, ax=ax, **plot_kws)
ax.axis("off")
add_connections(
plot_df.iloc[lp_inds, 0],
plot_df.iloc[rp_inds, 0],
plot_df.iloc[lp_inds, 1],
plot_df.iloc[rp_inds, 1],
ax=ax,
)
ax.set_title("UMAP o cosine")
hue="merge_class",
palette=CLASS_COLOR_DICT,
legend=False,
ax=ax,
s=20,
linewidth=0.5,
alpha=0.7,
)
remove_axis(ax)
ax.set_ylabel("Cartesian")
ax.spines["right"].set_visible(True)
ax.set_title("Before Procrustes")
add_connections(
plot_df.iloc[lp_inds, 0],
plot_df.iloc[rp_inds, 0],
plot_df.iloc[lp_inds, 1],
plot_df.iloc[rp_inds, 1],
ax=ax,
)
##
ax = axs[1, 0]
norm_embed = n_sphere.convert_spherical(embed)
norm_embed = norm_embed[:, 1:] # chop off R dimension
plot_df[0] = norm_embed[:, 0]
plot_df[1] = norm_embed[:, 1]
sns.scatterplot(
data=plot_df,
x=0,
def plot_pairs(
X, labels, model=None, left_pair_inds=None, right_pair_inds=None, equal=False
):
""" Plots pairwise dimensional projections, and draws lines between known pair neurons
Parameters
----------
X : [type]
[description]
labels : [type]
[description]
model : [type], optional
[description], by default None
left_pair_inds : [type], optional
[description], by default None
right_pair_inds : [type], optional
[description], by default None
equal : bool, optional
[description], by default False
Returns
-------
[type]
[description]
"""
n_dims = X.shape[1]
fig, axs = plt.subplots(
n_dims, n_dims, sharex=False, sharey=False, figsize=(20, 20)
)
data = pd.DataFrame(data=X)
data["label"] = labels
for i in range(n_dims):
for j in range(n_dims):
ax = axs[i, j]
ax.axis("off")
if i < j:
sns.scatterplot(
data=data,
x=j,
y=i,
ax=ax,
alpha=0.7,
linewidth=0,
s=8,
legend=False,
hue="label",
palette=CLASS_COLOR_DICT,
)
if left_pair_inds is not None and right_pair_inds is not None:
add_connections(
data.iloc[left_pair_inds, j],
data.iloc[right_pair_inds, j],
data.iloc[left_pair_inds, i],
data.iloc[right_pair_inds, i],
ax=ax,
)
plt.tight_layout()
return fig, axs