def get_radius_pruned_neurons_by_skid(skids,
radius_to_keep=PRIMARY_NEURITE_RADIUS,
keep_larger_radii=True):
neurons = pymaid.get_neuron(skids, remote_instance=source_project)
if type(neurons) is pymaid.core.CatmaidNeuron:
neurons = pymaid.core.CatmaidNeuronList(neurons)
for neuron in neurons:
if 'radius' in neuron.neuron_name:
raise Exception('Radius pruning was requested for'
f' "{neuron.neuron_name}". You probably didn\'t'
' mean to do this since it was already pruned.'
' Abort!')
if keep_larger_radii:
navis.subset_neuron(neuron,
neuron.nodes.node_id.values[
neuron.nodes.radius >= radius_to_keep],
inplace=True)
else:
navis.subset_neuron(neuron,
neuron.nodes.node_id.values[neuron.nodes.radius
== radius_to_keep],
inplace=True)
if neuron.n_skeletons > 1:
navis.plot2d(neuron)
raise Exception(f'You cut {neuron.neuron_name} into two fragments.'
" That's not supposed to happen.")
neuron.annotations.append('pruned to nodes with radius 500')
neuron.neuron_name = neuron.neuron_name + f' - radius {radius_to_keep}'
return neurons
def plot_morpho(self, figsize, save_path=None, alpha=1, color=None, volume=None, vol_color = (250, 250, 250, .05), azim=-90, elev=-90, dist=6, xlim3d=(-4500, 110000), ylim3d=(-4500, 110000), linewidth=1.5, connectors=False):
# recommended volume for L1 dataset, 'PS_Neuropil_manual'
neurons = pymaid.get_neurons(self.skids)
if(color==None):
color = self.color
if(volume!=None):
neuropil = pymaid.get_volume(volume)
neuropil.color = vol_color
fig, ax = navis.plot2d([neurons, neuropil], method='3d_complex', color=color, linewidth=linewidth, connectors=connectors, cn_size=2, alpha=alpha)
if(volume==None):
fig, ax = navis.plot2d([neurons], method='3d_complex', color=color, linewidth=linewidth, connectors=connectors, cn_size=2, alpha=alpha)
ax.azim = azim
ax.elev = elev
ax.dist = dist
ax.set_xlim3d(xlim3d)
ax.set_ylim3d(ylim3d)
plt.show()
if(save_path!=None):
fig.savefig(f'{save_path}.png', format='png', dpi=300, transparent=True)
def plot_celltype(path, pairids, n_rows, n_cols, celltypes, pairs_path, plot_pairs=True, connectors=False, cn_size=0.25, color=None, names=False, plot_padding=[0,0]):
pairs = Promat.get_pairs(pairs_path)
# pull specific cell type identities
celltype_ct = [Celltype(f'{pairid}-ipsi-bi', Promat.get_paired_skids(pairid, pairs)) for pairid in pairids]
celltype_ct = Celltype_Analyzer(celltype_ct)
celltype_ct.set_known_types(celltypes)
members = celltype_ct.memberships()
# link identities to official celltype colors
celltype_identities = [np.where(members.iloc[:, i]==1.0)[0][0] for i in range(0, len(members.columns))]
if(plot_pairs):
celltype_ct = [Celltype(celltypes[celltype_identities[i]].name.replace('s', ''), Promat.get_paired_skids(pairid, pairs), celltypes[celltype_identities[i]].color) if celltype_identities[i]<17 else Celltype(f'{pairid}', Promat.get_paired_skids(pairid, pairs), '#7F7F7F') for i, pairid in enumerate(pairids)]
if(plot_pairs==False):
celltype_ct = [Celltype(celltypes[celltype_identities[i]].name.replace('s', ''), pairid, celltypes[celltype_identities[i]].color) if celltype_identities[i]<17 else Celltype('Other', pairid, '#7F7F7F') for i, pairid in enumerate(pairids)]
# plot neuron morphologies
neuropil = pymaid.get_volume('PS_Neuropil_manual')
neuropil.color = (250, 250, 250, .05)
n_rows = n_rows
n_cols = n_cols
alpha = 1
fig = plt.figure(figsize=(n_cols*2, n_rows*2))
gs = plt.GridSpec(n_rows, n_cols, figure=fig, wspace=plot_padding[0], hspace=plot_padding[1])
axs = np.empty((n_rows, n_cols), dtype=object)
for i, skids in enumerate([x.skids for x in celltype_ct]):
if(color!=None):
col = color
else: col = celltype_ct[i].color
neurons = pymaid.get_neurons(skids)
inds = np.unravel_index(i, shape=(n_rows, n_cols))
ax = fig.add_subplot(gs[inds], projection="3d")
axs[inds] = ax
navis.plot2d(x=[neurons, neuropil], connectors=connectors, cn_size=cn_size, color=col, alpha=alpha, ax=ax, method='3d_complex')
ax.azim = -90
ax.elev = -90
ax.dist = 6
ax.set_xlim3d((-4500, 110000))
ax.set_ylim3d((-4500, 110000))
if(names):
ax.text(x=(ax.get_xlim()[0] + ax.get_xlim()[1])/2 - ax.get_xlim()[1]*0.05, y=ax.get_ylim()[1]*4/5, z=0,
s=celltype_ct[i].name, transform=ax.transData, color=col, alpha=1)
fig.savefig(f'{path}.png', format='png', dpi=300, transparent=True)
def _special_mbon_split(n, treenode_info, output_path):
skid = int(n.skeleton_id)
axon_starts = list(
pymaid.find_nodes(tags="mw axon start", skeleton_ids=skid)["node_id"])
axon_ends = list(
pymaid.find_nodes(tags="mw axon end", skeleton_ids=skid)["node_id"])
axon_splits = axon_starts + axon_ends
fragments = navis.cut_skeleton(n, axon_splits)
axons = []
dendrites = []
for fragment in fragments:
root = fragment.root
if "mw axon start" in fragment.tags and root in fragment.tags[
"mw axon start"]:
axons.append(fragment)
elif "mw axon end" in fragment.tags and root in fragment.tags[
"mw axon end"]:
dendrites.append(fragment)
elif "soma" in fragment.tags and root in fragment.tags["soma"]:
dendrites.append(fragment)
else:
raise UserWarning(
f"Something weird happened when splitting special neuron {skid}"
)
for a in axons:
axon_treenodes = a.nodes.node_id.values
_append_labeled_nodes(treenode_info, axon_treenodes, "axon")
for d in dendrites:
dendrite_treenodes = d.nodes.node_id.values
_append_labeled_nodes(treenode_info, dendrite_treenodes, "dendrite")
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection="3d")
navis.plot2d(axons, color="red", ax=ax)
navis.plot2d(dendrites, color="blue", ax=ax)
plt.savefig(
output_path / f"weird-mbon-{skid}.png",
facecolor="w",
transparent=False,
bbox_inches="tight",
pad_inches=0.3,
dpi=300,
)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection="3d")
navis.plot2d(axons, color="red", ax=ax)
navis.plot2d(dendrites, color="blue", ax=ax)
ax.azim = -90
ax.elev = 0
plt.savefig(
output_path / f"weird-mbon-{skid}-top.png",
facecolor="w",
transparent=False,
bbox_inches="tight",
pad_inches=0.3,
dpi=300,
)