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Merge pull request #16 from pgleeson/main
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Muscle, Varshney & Witvliet fixes
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@pgleeson
pgleeson authored Aug 15, 2024
2 parents 25cbcca + 915ee32 commit 9061794
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Showing 468 changed files with 3,395,636 additions and 306,921 deletions.
191 changes: 164 additions & 27 deletions cect/Cells.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
import sys

from cect.WormAtlasInfo import WA_COLORS
from cect import print_

cell_notes = {}

Expand Down Expand Up @@ -943,8 +944,19 @@

ANAL_SPHINCTER_MUSCLES = ["MANAL", "mu_sph", "mu_anal"] # TODO: remove duplicate!

for cell in ANAL_SPHINCTER_MUSCLES:
cell_notes[cell] = "anal/sphincter muscle"

VULVAL_MUSCLE_NAMES = [
"MVULVA",
"um2AL",
"um2AR",
"um1AL",
"um1AR",
"um1PL",
"um1PR",
"um2PL",
"um2PR",
"vm1AL",
"vm1PL",
"vm1PR",
Expand All @@ -954,6 +966,8 @@
"vm2PL",
"vm2PR",
]
for cell in VULVAL_MUSCLE_NAMES:
cell_notes[cell] = "vulval muscle"

ODD_PHARYNGEAL_MUSCLE_NAMES = [
"pm1",
Expand All @@ -975,6 +989,7 @@
"pm4D",
"pm4VR",
"pm4VL",
"pm4_UNSPECIFIED",
"pm6D",
"pm6VR",
"pm6VL",
Expand All @@ -987,11 +1002,18 @@
for cell in PHARYNGEAL_MUSCLE_NAMES:
cell_notes[cell] = "pharyngeal muscle"

UNSPECIFIED_BODY_WALL_MUSCLES = ["BWM"]


cell_notes["BWM"] = "unspecified body wall muscle"


PREFERRED_MUSCLE_NAMES = (
BODY_WALL_MUSCLE_NAMES
+ PHARYNGEAL_MUSCLE_NAMES
+ VULVAL_MUSCLE_NAMES
+ ANAL_SPHINCTER_MUSCLES
+ UNSPECIFIED_BODY_WALL_MUSCLES
)

GLR_CELLS = [
Expand Down Expand Up @@ -1121,7 +1143,7 @@
def get_standard_color(cell):
from cect.WormAtlasInfo import WA_COLORS

if cell in BODY_WALL_MUSCLE_NAMES:
if cell in BODY_WALL_MUSCLE_NAMES + UNSPECIFIED_BODY_WALL_MUSCLES:
return WA_COLORS["Hermaphrodite"]["Muscle"]["body wall muscle"]
elif cell in VULVAL_MUSCLE_NAMES:
return WA_COLORS["Hermaphrodite"]["Muscle"]["vulval muscle"]
Expand Down Expand Up @@ -1175,7 +1197,6 @@ def get_standard_color(cell):
return WA_COLORS["Hermaphrodite"]["Alimentary System"]["intestinal cells"]
elif cell in INTESTINAL_MUSCLES:
return WA_COLORS["Hermaphrodite"]["Muscle"]["intestinal muscle"]

else:
raise Exception("Unknown cell: %s!" % cell)

Expand Down Expand Up @@ -1223,9 +1244,17 @@ def get_cell_internal_link(cell_name, html=False, text=None):
url = "../Cells/index.html#%s" % cell_name

if html:
return '<a href="%s">%s</a>' % (url, cell_name if text is None else text)
return '<a href="%s" title="%s">%s</a>' % (
url,
get_short_description(cell_name),
cell_name if text is None else text,
)
else:
return "[%s](%s)" % (cell_name if text is None else text, url)
return '[%s "%s"](%s)' % (
cell_name if text is None else text,
get_short_description(cell_name),
url,
)


def get_cell_link(cell_name, html=False, text=None):
Expand Down Expand Up @@ -1292,7 +1321,84 @@ def _get_dataset_link(reader_name, html=False, text=None):
return "[%s](%s)" % (reader_name if text is None else text, url)


def _generate_cell_table(cells):
def _generate_cell_table(cell_type, cells):
import plotly.express as px
import plotly.graph_objects as go
import numpy as np

from cect.Comparison import _format_json
from cect.Comparison import shorten_neurotransmitter

print_(" - Adding table for %s" % cell_type)

syn_summaries = {
"Chemical conns in": ["Acetylcholine", "Generic_CS", "GABA"],
"Chemical conns out": ["Acetylcholine", "Generic_CS", "GABA"],
"Electrical conns": ["Generic_GJ"],
}

fig_md = ""

for syn_summary in syn_summaries:
fig = go.Figure()
fig.layout.showlegend = True

fig_md += '\n=== "%s"\n\n' % syn_summary
# fig_md += " Connections to these cells of type: %s\n\n" % syn_type

nonempty_fig_present = False
for reader_name, connectome in connectomes.items():
sorted_cells = sorted(cells)

indent = " "
y = []
for cell in sorted_cells:
syn_types = syn_summaries[syn_summary]
total_y = 0
for syn_type in syn_types:
if "out" in syn_summary:
conns_here = connectome.get_connections_from(cell, syn_type)
else:
conns_here = connectome.get_connections_to(cell, syn_type)
print_(
"Conns: %i for %s of type %s (%s)"
% (len(conns_here), cell, syn_type, syn_summary)
)
total_y += len(conns_here)

y.append(total_y)

if sum(y) > 0:
marker_symbol = "square"
dash = "solid"

data = fig.add_scatter(
name="%s %s" % (reader_name, syn_summary),
x=sorted_cells,
y=y,
marker_symbol=marker_symbol,
line=dict(dash=dash),
)
nonempty_fig_present = True

if nonempty_fig_present:
asset_filename = "assets/%s_%s_hist.json" % (
cell_type.replace(" ", "_"),
syn_summary.replace(" ", "_"),
)

with open("./docs/%s" % asset_filename, "w") as asset_file:
asset_file.write(_format_json(fig.to_json()))

fig_md += '\n%s```plotly\n%s---8<-- "./%s"\n%s```\n\n' % (
indent,
indent,
asset_filename,
indent,
)
else:
fig_md += " No connections of this type found.\n\n"

all_data = {}

all_data[""] = ["Notes", "Datasets", "Link"]
Expand All @@ -1314,9 +1420,9 @@ def _generate_cell_table(cells):

df_all = pd.DataFrame(all_data).transpose()

mk = df_all.to_markdown()
table_md = df_all.to_markdown()

return "%s\n\n" % mk
return "%s\n%s\n\n" % (fig_md, table_md)


if __name__ == "__main__":
Expand Down Expand Up @@ -1344,51 +1450,82 @@ def _generate_cell_table(cells):
)
)
if cell_type == "body wall muscle":
f.write(_generate_cell_table(BODY_WALL_MUSCLE_NAMES))
f.write(
_generate_cell_table(cell_type, BODY_WALL_MUSCLE_NAMES)
)
elif cell_type == "interneuron":
f.write(_generate_cell_table(INTERNEURONS_COOK))
f.write(_generate_cell_table(cell_type, INTERNEURONS_COOK))
elif cell_type == "motor neuron":
f.write(_generate_cell_table(MOTORNEURONS_COOK))
f.write(_generate_cell_table(cell_type, MOTORNEURONS_COOK))
elif cell_type == "sensory neuron":
f.write(_generate_cell_table(SENSORY_NEURONS_COOK))
f.write(
_generate_cell_table(cell_type, SENSORY_NEURONS_COOK)
)
elif cell_type == "odd numbered pharyngeal muscle":
f.write(_generate_cell_table(ODD_PHARYNGEAL_MUSCLE_NAMES))
f.write(
_generate_cell_table(
cell_type, ODD_PHARYNGEAL_MUSCLE_NAMES
)
)
elif cell_type == "even numbered pharyngeal muscle":
f.write(_generate_cell_table(EVEN_PHARYNGEAL_MUSCLE_NAMES))
f.write(
_generate_cell_table(
cell_type, EVEN_PHARYNGEAL_MUSCLE_NAMES
)
)
elif cell_type == "polymodal neuron":
f.write(_generate_cell_table(PHARYNGEAL_POLYMODAL_NEURONS))
f.write(
_generate_cell_table(
cell_type, PHARYNGEAL_POLYMODAL_NEURONS
)
)
elif cell_type == "marginal cells (mc) of the pharynx":
f.write(_generate_cell_table(PHARYNGEAL_MARGINAL_CELLS))
f.write(
_generate_cell_table(
cell_type, PHARYNGEAL_MARGINAL_CELLS
)
)
elif cell_type == "pharyngeal epithelium":
f.write(
_generate_cell_table(
PHARYNGEAL_EPITHELIUM + PHARYNGEAL_GLIAL_CELL
cell_type,
PHARYNGEAL_EPITHELIUM + PHARYNGEAL_GLIAL_CELL,
)
) # TODO: check!
elif cell_type == "basement membrane":
f.write(
_generate_cell_table(PHARYNGEAL_BASEMENT_MEMBRANE)
_generate_cell_table(
cell_type, PHARYNGEAL_BASEMENT_MEMBRANE
)
) # TODO: check!
elif cell_type == "neuron with unknown function":
f.write(_generate_cell_table(UNKNOWN_FUNCTION_NEURONS))
f.write(
_generate_cell_table(
cell_type, UNKNOWN_FUNCTION_NEURONS
)
)
elif (
cell_type
== "sheath cell other than amphid sheath and phasmid"
):
f.write(_generate_cell_table(CEPSH_CELLS))
f.write(_generate_cell_table(cell_type, CEPSH_CELLS))
elif cell_type == "excretory cell":
f.write(_generate_cell_table(EXCRETORY_CELL))
f.write(_generate_cell_table(cell_type, EXCRETORY_CELL))
elif cell_type == "sphincter and anal depressor muscle":
f.write(_generate_cell_table(ANAL_SPHINCTER_MUSCLES))
f.write(
_generate_cell_table(cell_type, ANAL_SPHINCTER_MUSCLES)
)
elif cell_type == "gland cell":
f.write(_generate_cell_table(EXCRETORY_GLAND))
f.write(_generate_cell_table(cell_type, EXCRETORY_GLAND))
elif cell_type == "head mesodermal cell":
f.write(_generate_cell_table(HEAD_MESODERMAL_CELL))
f.write(
_generate_cell_table(cell_type, HEAD_MESODERMAL_CELL)
)
elif cell_type == "hypodermis":
f.write(_generate_cell_table(HYPODERMIS))
f.write(_generate_cell_table(cell_type, HYPODERMIS))
elif cell_type == "intestinal cells":
f.write(_generate_cell_table(INTESTINE))
f.write(_generate_cell_table(cell_type, INTESTINE))
elif cell_type == "intestinal muscle":
f.write(_generate_cell_table(INTESTINAL_MUSCLES))
f.write(_generate_cell_table(cell_type, INTESTINAL_MUSCLES))
elif cell_type == "GLR cell":
f.write(_generate_cell_table(GLR_CELLS))
f.write(_generate_cell_table(cell_type, GLR_CELLS))
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