def run_experiment(seed=None, graph_type=None, threshold=None, param_key=None):
np.random.seed(seed)
if BLIND:
temp_param_key = param_key.replace(
" ", "") # don't want spaces in filenames
savename = f"{temp_param_key}-cell-types-"
title = param_key
else:
savename = f"{graph_type}-t{threshold}-cell-types"
title = f"{graph_type}, threshold = {threshold}"
mg = load_metagraph(graph_type, version=VERSION)
# simple threshold
# TODO they will want symmetric threshold...
# TODO maybe make that a parameter
adj = mg.adj.copy()
adj[adj <= threshold] = 0
meta = mg.meta.copy()
meta = pd.DataFrame(mg.meta["neuron_name"])
mg = MetaGraph(adj, meta)
# run the graphtool code
temp_loc = f"maggot_models/data/interim/temp-{param_key}.graphml"
block_series = run_minimize_blockmodel(mg, temp_loc)
# manage the output
mg = load_metagraph(graph_type, version=VERSION)
mg.meta = pd.concat((mg.meta, block_series), axis=1)
mg.meta["Original index"] = range(len(mg.meta))
keep_inds = mg.meta[~mg.meta["block_label"].isna(
)]["Original index"].values
mg.reindex(keep_inds)
if graph_type != "G":
mg.verify(10000, graph_type=graph_type, version=VERSION)
# deal with class labels
lineage_labels = mg.meta["lineage"].values
lineage_labels = np.vectorize(lambda x: "~" + x)(lineage_labels)
class_labels = mg["Merge Class"]
skeleton_labels = mg.meta.index.values
classlin_labels, color_dict, hatch_dict = augment_classes(
skeleton_labels, class_labels, lineage_labels)
block_label = mg["block_label"].astype(int)
# barplot with unknown class labels merged in, proportions
_, _, order = barplot_text(
block_label,
classlin_labels,
norm_bar_width=True,
color_dict=color_dict,
hatch_dict=hatch_dict,
title=title,
figsize=(24, 18),
return_order=True,
)
stashfig(savename + "barplot-mergeclasslin-props")
category_order = np.unique(block_label)[order]
# barplot with regular class labels
barplot_text(
block_label,
class_labels,
norm_bar_width=True,
color_dict=color_dict,
hatch_dict=hatch_dict,
title=title,
figsize=(24, 18),
category_order=category_order,
)
stashfig(savename + "barplot-mergeclass-props")
# barplot with unknown class labels merged in, counts
barplot_text(
block_label,
classlin_labels,
norm_bar_width=False,
color_dict=color_dict,
hatch_dict=hatch_dict,
title=title,
figsize=(24, 18),
return_order=True,
category_order=category_order,
)
stashfig(savename + "barplot-mergeclasslin-counts")
# barplot of hemisphere membership
fig, ax = plt.subplots(1, 1, figsize=(10, 20))
stacked_barplot(
block_label,
mg["Hemisphere"],
norm_bar_width=True,
category_order=category_order,
ax=ax,
)
remove_spines(ax)
stashfig(savename + "barplot-hemisphere")
# plot block probability matrix
counts = False
weights = False
prob_df = get_blockmodel_df(mg.adj,
block_label,
return_counts=counts,
use_weights=weights)
prob_df = prob_df.reindex(order, axis=0)
prob_df = prob_df.reindex(order, axis=1)
ax = probplot(100 * prob_df,
fmt="2.0f",
figsize=(20, 20),
title=title,
font_scale=0.4)
stashfig(savename + "probplot")
block_series.name = param_key
return block_series
for thresh in threshs:
for r in [0.3, 0.5, 0.7, 1]:
mg = load_metagraph(graph_type, version=BRAIN_VERSION)
edgelist = mg.to_edgelist()
edgelist = edgelist[edgelist["weight"] > thresh]
nodelist = list(mg.g.nodes())
thresh_g = nx.from_pandas_edgelist(edgelist,
edge_attr=True,
create_using=nx.DiGraph)
nx.set_node_attributes(thresh_g, mg.meta.to_dict(orient="index"))
mg = MetaGraph(thresh_g)
print(len(mg.meta))
mg = mg.make_lcc()
print(len(mg.meta))
not_pdiff = np.where(~mg["is_pdiff"])[0]
mg = mg.reindex(not_pdiff)
print(len(mg.meta))
g_sym = nx.to_undirected(mg.g)
skeleton_labels = np.array(list(g_sym.nodes()))
out_dict = cm.best_partition(g_sym, resolution=r)
partition = np.array(itemgetter(*skeleton_labels)(out_dict))
adj = nx.to_numpy_array(g_sym, nodelist=skeleton_labels)
part_unique, part_count = np.unique(partition, return_counts=True)
for uni, count in zip(part_unique, part_count):
if count < 3:
inds = np.where(partition == uni)[0]
partition[inds] = -1
mg.meta["signal_flow"] = signal_flow(mg.adj)
mg.meta["partition"] = partition
mg.verify()
# %% [markdown]
# # Extract subgraphs
extract_fb = True
if extract_fb:
from_classes = ["MBON", "FAN", "FBN", "FB2N"]
to_classes = ["MBIN"]
sub_mg = MetaGraph(mg.adj, mg.meta)
from_class_inds = sub_mg.meta["Class 1"].isin(from_classes)
to_class_inds = sub_mg.meta["Class 1"].isin(to_classes)
any_inds = np.logical_or(from_class_inds, to_class_inds)
sub_mg.reindex(any_inds)
sub_mg.sort_values(["Hemisphere", "Class 1", "Pair ID"])
# meta = sub_mg.meta
# meta["Original index"] = range(meta.shape[0])
# meta.sort_values(
# ["Hemisphere", "Class 1", "Pair ID"],
# inplace=True,
# kind="mergesort",
# ascending=False,
# )
# temp_inds = meta["Original index"]
# sub_mg = sub_mg.reindex(temp_inds)
# sub_mg = MetaGraph(sub_mg.adj, meta)
adj = sub_mg.adj.copy()
from_class_inds = np.where(sub_mg.meta["Class 1"].isin(from_classes).values)[0]
# %% [markdown] # # from src.graph import MetaGraph import numpy as np import pandas as pd import networkx as nx n_verts = 5 adj = np.arange(0, n_verts**2).reshape(n_verts, n_verts) adj[0, :] = 0 adj[:, 0] = 0 print(adj) meta = pd.DataFrame(index=range(n_verts)) meta["title"] = ["zero", "one", "two", "three", "four"] meta["remove"] = [False, True, False, False, True] meta.index = ["0", "1", "2", "3", "4"] print(meta) mg = MetaGraph(adj, meta) # mg.make_lcc() mg.reindex(np.array([2, 3])) print(mg.meta) print(mg.adj) mg2 = MetaGraph(mg.g) print(mg2.adj) print(mg2.meta)
n_pairs = mg.adj.shape[0] // 2
mg.verify()
# %% [markdown]
# # Extract subgraphs
adj = mg.adj.copy()
from_classes = ["MBON", "FAN", "FBN", "FB2N"]
to_classes = ["MBIN"]
from_class_inds = mg.meta["Class 1"].isin(from_classes)
to_class_inds = mg.meta["Class 1"].isin(to_classes)
any_inds = np.logical_or(from_class_inds, to_class_inds)
mg.reindex(any_inds)
meta = mg.meta
meta["Original index"] = range(meta.shape[0])
meta.sort_values(
["Hemisphere", "Class 1", "Pair ID"],
inplace=True,
kind="mergesort",
ascending=False,
)
temp_inds = meta["Original index"]
mg = mg.reindex(temp_inds)
mg = MetaGraph(mg.adj, meta)
adj = mg.adj
from_class_inds = np.where(mg.meta["Class 1"].isin(from_classes).values)[0]
)
plt.legend(bbox_to_anchor=(1.08, 1), loc=2, borderaxespad=0.0)
ax.set_title(title)
stashfig(f"threshold-vs-knn" + base_save)
# %% [markdown]
# #
graph_type = "Gad"
use_spl = False
embed = "lse"
remove_pdiff = True
plus_c = True
mg = load_metagraph(graph_type, BRAIN_VERSION)
keep_inds = np.where(~mg["is_pdiff"])[0]
mg = mg.reindex(keep_inds)
n_original_verts = mg.n_verts
mg = mg.make_lcc()
edgelist_df = mg.to_edgelist()
edgelist_df.rename(columns={"weight": "syn_weight"}, inplace=True)
edgelist_df["norm_weight"] = (edgelist_df["syn_weight"] /
edgelist_df["target dendrite_input"])
max_pair_edges = edgelist_df.groupby("edge pair ID",
sort=False)["syn_weight"].max()
edge_max_weight_map = dict(
zip(max_pair_edges.index.values, max_pair_edges.values))
edgelist_df["max_syn_weight"] = itemgetter(
*edgelist_df["edge pair ID"])(edge_max_weight_map)
temp_df = edgelist_df[edgelist_df["edge pair ID"] == 0]
edgelist_df.loc[temp_df.index, "max_syn_weight"] = temp_df["syn_weight"]
