def plot_colors():
from src.visualization import palplot
fig, axs = plt.subplots(1, 6, figsize=(6, 10))
n_per_col = 40
for i, ax in enumerate(axs):
pal = cc.glasbey_light[i * n_per_col:(i + 1) * n_per_col]
palplot(n_per_col, pal, figsize=(1, 10), ax=ax, start=i * n_per_col)
def plot_colors():
from src.visualization import palplot
fig, axs = plt.subplots(1, 6, figsize=(6, 10))
n_per_col = 40
for i, ax in enumerate(axs):
pal = cc.glasbey_light[i * n_per_col:(i + 1) * n_per_col]
palplot(n_per_col, pal, figsize=(1, 10), ax=ax, start=i * n_per_col)
plt.savefig("./maggot_models/notebooks/outs/colors.png",
dpi=300,
bbox_inches="tight")
def plot_color_labels(full_meta, ax):
full_sizes = full_meta.groupby(["merge_class"], sort=False).size()
uni_class = full_sizes.index.unique()
counts = full_sizes.values
count_map = dict(zip(uni_class, counts))
names = []
colors = []
for key, val in count_map.items():
names.append(f"{key} ({count_map[key]})")
colors.append(CLASS_COLOR_DICT[key])
colors = colors[::-1] # reverse because of signal flow sorting
names = names[::-1]
palplot(len(colors), colors, ax=ax)
ax.yaxis.set_major_formatter(plt.FixedFormatter(names))
def plot_class_colormap():
from src.visualization import palplot
from src.data import load_metagraph
mg = load_metagraph("G")
uni_class, counts = np.unique(mg["merge_class"], return_counts=True)
count_map = dict(zip(uni_class, counts))
names = []
colors = []
for key, val in CLASS_COLOR_DICT.items():
if key in uni_class:
names.append(f"{key} ({count_map[key]})")
colors.append(val)
fig, ax = plt.subplots(1, 1, figsize=(3, 15))
palplot(len(colors), colors, ax=ax)
ax.yaxis.set_major_formatter(plt.FixedFormatter(names))
plt.savefig("./maggot_models/notebooks/outs/current_cmap.png",
dpi=300,
bbox_inches="tight")
# New plot
# - Compute signal flow
# - Get the centroid of each cluster and project to 1d
# - Alternatively, just take the first dimension
# - For each cluster plot as a node
# output skeletons
if SAVESKELS:
_, colormap, pal = stashskel(save_name,
skeleton_labels,
pred_labels,
palette="viridis",
multiout=True)
palplot(k, cmap="viridis")
stashfig("palplot-" + save_name)
# save dict colormapping
filename = (Path("./maggot_models/notebooks/outs") / Path(FNAME) /
str("colormap-" + save_name + ".json"))
with open(filename, "w") as fout:
json.dump(colormap, fout)
stashskel(save_name,
skeleton_labels,
pred_labels,
palette="viridis",
multiout=False)
# %% [markdown]
)
def stashobj(obj, name, **kws):
saveobj(obj, name, foldername=FNAME, save_on=SAVEOBJS, **kws)
# %% [markdown]
# #
sns.set_context("talk")
fig, axs = plt.subplots(1, 4, figsize=(5, 10))
n_per_col = 40
for i, ax in enumerate(axs):
pal = cc.glasbey_light[i * n_per_col:(i + 1) * n_per_col]
palplot(n_per_col, pal, figsize=(1, 10), ax=ax, start=i * n_per_col)
stashfig("glasbey-colors")
manual_cmap = {
"KC": 0,
"KC-1claw": 28,
"KC-2claw": 32,
"KC-3claw": 92,
"KC-4claw": 91,
"KC-5claw": 78,
"KC-6claw": 61,
"APL": 24,
"MBIN": 121,
"MBIN-DAN": 58,
"MBIN-OAN": 5,
"MBON": 11,
col_item_order="dend_order",
col_tick_pad=[0.5, 1.5],
# col_ticks=False,
row_meta=path_meta,
row_sort_class="cluster",
row_item_order="dend_order",
row_ticks=True,
gridline_kws=dict(linewidth=0.3, color="grey", linestyle="--"),
sizes=(2, 2),
hue="weight",
palette="tab10",
)
ax.set_ylabel("Cluster")
# fig.suptitle("G")
ax = axs[1]
palplot(path_len, cmap="tab10", ax=ax)
ax.yaxis.tick_right()
ax.set_title("Visit\norder")
stashfig("path-indcator-GMMoCMDSoPathDist" + basename)
# %% [markdown]
# ##
from src.traverse import to_path_graph
import networkx as nx
from src.visualization import draw_networkx_nice
uni_pred = np.unique(pred)
ax.set_xticks(np.arange(lowest_level + 1))
ax.tick_params(axis="both", which="both", length=0)
ax = fig.add_subplot(gs[:, 3])
full_sizes = full_meta.groupby(["merge_class"], sort=False).size()
uni_class = full_sizes.index.unique()
counts = full_sizes.values
count_map = dict(zip(uni_class, counts))
names = []
colors = []
for key, val in count_map.items():
names.append(f"{key} ({count_map[key]})")
colors.append(CLASS_COLOR_DICT[key])
colors = colors[::-1] # reverse because of signal flow sorting
names = names[::-1]
palplot(len(colors), colors, ax=ax)
ax.yaxis.set_major_formatter(plt.FixedFormatter(names))
# plt.tight_layout()
model = DCSBMEstimator
for level in np.arange(lowest_level + 1):
ax = fig.add_subplot(gs[:3, level + 4])
adj = binarize(full_mg.adj)
_, _, top, _ = adjplot(
adj,
ax=ax,
plot_type="scattermap",
sizes=(0.5, 0.5),
sort_class=["hemisphere"] + level_names[:level + 1],
item_order=["merge_class_sf_order", "merge_class", "sf"],
class_order="sf",
col_tick_pad=[0.5, 1.5],
# col_ticks=False,
row_meta=path_meta,
# row_sort_class="cluster",
row_item_order="dend_order",
row_ticks=True,
gridline_kws=dict(linewidth=0.3, color="grey", linestyle="--"),
sizes=(2, 2),
hue="weight",
palette=pal,
)
# ax.set_ylabel("Cluster")
ax.set_ylabel("Path")
top.set_xlabel("Neuron")
ax = axs[1]
palplot(pal, ax=ax)
ax.yaxis.tick_right()
ax.set_title("Hop")
ax.set_yticklabels(np.arange(1, path_len + 1))
stashfig("path-indcator-dendrosort" + basename)
fig, axs = plt.subplots(1,
2,
figsize=(30, 20),
gridspec_kw=dict(width_ratios=[0.95, 0.02],
wspace=0.02))
ax = axs[0]
ax, div, top, left = matrixplot(
plot_path_indicator_mat,
ax=ax,
figsize=(30, 20),
gridspec_kw=dict(width_ratios=[0.95, 0.02],
wspace=0.02))
pal = sns.color_palette("Set1", n_colors=7)
pal = pal[:5] + pal[6:]
ax = axs[0]
matrixplot(
path_indicator_mat,
ax=ax,
plot_type="scattermap",
col_sort_class=["lvl2_labels"],
col_class_order="signal_flow",
col_meta=meta,
col_colors="merge_class",
col_item_order=["merge_class", "mean_order"],
col_palette=CLASS_COLOR_DICT,
col_ticks=True,
tick_rot=90,
row_sort_class=pred,
# row_class_order="size",
row_ticks=True,
sizes=(1, 1),
hue="weight",
palette=pal,
gridline_kws=dict(linewidth=0.3, color="grey", linestyle="--"),
)
ax = axs[1]
palplot(pal, cmap="Set1", ax=ax)
ax.set_title("Visit order")
stashfig("path-indicator-map")
