def stashfig(name, **kws):
if SAVEFIGS:
savefig(name,
foldername=FNAME,
fmt=DEFAULT_FMT,
dpi=DEFUALT_DPI,
**kws)
def stashfig(name, **kws):
savefig(name, foldername=FNAME, save_on=True, **kws)
def stashfig(name, **kws):
savefig(name, foldername=FNAME, save_on=SAVEFIGS, **kws)
jaccard_embedding = mds.fit_transform(pdist_sparse)
# %% [markdown]
# #
print("Clustering embedding")
agmm = AutoGMMCluster(min_components=10,
max_components=40,
affinity="euclidean",
linkage="single")
labels = agmm.fit_predict(jaccard_embedding)
pairplot(jaccard_embedding,
title="AGMM o CMDS o Jaccard o Sensorimotor Paths",
labels=labels)
savefig("AGMM-CMDS-jaccard-sm-path")
print("Finding mean paths")
mean_paths = []
uni_labels = np.unique(labels)
for ul in uni_labels:
inds = np.where(labels == ul)[0]
paths = path_mat[inds, :]
mean_path = np.array(np.mean(paths, axis=0))
mean_paths.append(mean_path)
mean_paths = np.squeeze(np.array(mean_paths))
# TODO remove sensory and motor indices from the matrix
# %% [markdown]
# #
def stashfig(name, **kws):
if SAVEFIGS:
savefig(name, foldername=FNAME, **kws)
def stashfig(name, **kws):
savefig(name, foldername=FNAME, save_on=True, fmt="pdf", dpi=400, **kws)
def stashfig(name, **kws):
savefig(name, foldername=FNAME, save_on=True, dpi=200, **kws)
savefig(name + "high", foldername=FNAME, save_on=True, dpi=400, **kws)
for i, x in enumerate(
np.arange(bins.shape[0] - 1, data.shape[-1], step=bins.shape[0] - 1)
):
ax.axvline(x, linestyle="--", linewidth=1, color="grey")
ax.set_xticks([])
ax.set_yticks([])
# ax.set_xlabel("Response over time")
ax.set_ylabel("Neuron")
ax.set_title(f"metric={metric}, linkage={linkage}")
ax.set_xlabel(r"Response over time $\to$ (by source class)")
xticks = []
for i, x in enumerate(np.arange((bins.shape[0] - 1)/2, data.shape[-1], step=bins.shape[0] - 1)):
xticks.append(x)
ax.set_xticks(xticks)
ax.set_xticklabels(["Odor", "Photo", 'MN', "Temp", "uPN", "mPN", "vPN", "tPN", "KC", "MBON"])
savefig(f"response-mat-{metric}-{linkage}")
# %% [markdown]
# #
# nci_hc_complete = linkage(y=nci_data, method="complete", metric="euclidean")
# nci_hc_complete_4_clusters = cut_tree(
# nci_hc_complete, n_clusters=4
# ) # Printing transpose just for space
# pd.crosstab(
# index=nci_data.index,
# columns=nci_hc_complete_4_clusters.T[0],
# rownames=["Cancer Type"],
# colnames=["Cluster"],
# )
