\n{group}\n'
for reader_name in readers_to_include:
connectome = all_connectomes[reader_name]
@@ -560,6 +574,7 @@ def generate_comparison_page(quick: bool, color_table=True):
text=VERTICAL_ELLIPSE,
html=True,
use_color=True,
+ individual_cell_page=True,
)
else:
pass # cells_here+='%s '%cell
diff --git a/cect/Cook2019DataReader.py b/cect/Cook2019DataReader.py
index fed5d0c0c..2ba4ffc8c 100644
--- a/cect/Cook2019DataReader.py
+++ b/cect/Cook2019DataReader.py
@@ -17,6 +17,9 @@
from cect.ConnectomeDataset import ConnectomeDataset
+from cect.Cells import GENERIC_CHEM_SYN
+from cect.Cells import GENERIC_ELEC_SYN
+
from openpyxl import load_workbook
import os
@@ -49,13 +52,13 @@
def get_synclass(cell, syntype):
- # TODO: fix this dirty hack
if syntype == "GapJunction":
- return "Generic_GJ"
+ return GENERIC_ELEC_SYN
else:
+ '''# dirty hack
if cell.startswith("DD") or cell.startswith("VD"):
- return "GABA"
- return "Acetylcholine"
+ return "GABA"'''
+ return GENERIC_CHEM_SYN
class Cook2019DataReader(ConnectomeDataset):
diff --git a/cect/Cook2020DataReader.py b/cect/Cook2020DataReader.py
index 7b5fa524f..41cdf11ff 100644
--- a/cect/Cook2020DataReader.py
+++ b/cect/Cook2020DataReader.py
@@ -40,6 +40,7 @@ def __init__(self):
def read_data(self):
"""
Args:
+ Parameters:
Returns:
cells (:obj:`list` of :obj:`str`): List of neurons
conns (:obj:`list` of :obj:`ConnectionInfo`): List of connections from neuron to neuron
diff --git a/cect/HivePlot.py b/cect/HivePlot.py
deleted file mode 100644
index cf5d4328d..000000000
--- a/cect/HivePlot.py
+++ /dev/null
@@ -1,200 +0,0 @@
-import sys
-
-if __name__ == "__main__":
- sz = 3
-
- from cect.TestDataReader import get_instance
-
- synclass = "Acetylcholine"
-
- """from cect.VarshneyDataReader import get_instance
-
- synclass = "Generic_CS"
- from cect.White_whole import get_instance
-
- synclass = "Acetylcholine"
- from cect.Cook2019HermReader import get_instance
-
- synclass = "Acetylcholine"
- from cect.Cook2020DataReader import get_instance """
-
- test_conn = get_instance()
-
- fig = test_conn.to_plotly_hive_plot_fig(synclass, None)
-
- import json
-
- def _format_json(json_str):
- return json.dumps(json.loads(json_str), sort_keys=True, indent=2)
-
- with open("hive.json", "w") as asset_file:
- asset_file.write(_format_json(fig.to_json()))
-
- import plotly.io as pio
-
- pio.renderers.default = "browser"
- if "-nogui" not in sys.argv:
- fig.show()
-
- print("Done")
-
- """G = test_conn.to_networkx_graph(synclass)
-
- nids = [n for n in G.nodes]
-
- for n_id in nids:
- node = G.nodes[n_id]
- if node["SIM_class"] == "Other":
- G.remove_node(n_id)
-
- nodes, edges = networkx_to_nodes_edges(G)
-
- print(nodes)
- print(edges)
-
- print(pprint.pprint(nx.node_link_data(G)))
-
- blocks_dict_unordered = split_nodes_on_variable(nodes, variable_name="SIM_class")
- print("Unordered: %s (%s)" % (blocks_dict_unordered, type(blocks_dict_unordered)))
- blocks_dict = {}
- for k in ["Interneuron", "Motorneuron", "Sensory"]:
- blocks_dict[k] = blocks_dict_unordered[k]
-
- print(blocks_dict)
-
- splits = list(blocks_dict.values())
- print("Splits: %s" % splits)
-
- # pull out degree information from nodes
- degrees = dict(G.degree)
- in_degrees = dict(G.in_degree)
- out_degrees = dict(G.out_degree)
-
- # add degree information to Node instances
- for node in nodes:
- deg = degrees[node.unique_id]
- block = node.data["SIM_class"]
- node.add_data(data={"degree": deg})
-
- print(f" - Node {node.unique_id}, block {block} has degree {deg}; {node.data}")
-
- hp = hive_plot_n_axes(
- node_list=nodes,
- edges=edges,
- axes_assignments=splits,
- sorting_variables=["degree"] * 3,
- repeat_axes=[True, True, True],
- repeat_edge_kwargs={"color": "grey"},
- )
-
- for ax in hp.axes:
- if "1" in ax:
- hp.axes[ax].long_name = INTERNEURON
- if "2" in ax:
- hp.axes[ax].long_name = MOTORNEURON
- if "3" in ax:
- hp.axes[ax].long_name = SENSORY
-
- for ax_name in hp.axes:
- ax = hp.axes[ax_name]
- print(f" - Axis {ax.long_name}, {ax.start}->{ax.end}...")
-
- hp.add_edge_kwargs(
- axis_id_1="Group 1_repeat",
- axis_id_2="Group 2",
- a2_to_a1=False,
- color=INTERNEURON_COLOR,
- )
-
- hp.add_edge_kwargs(
- axis_id_1="Group 2",
- axis_id_2="Group 1_repeat",
- a2_to_a1=False,
- color=MOTORNEURON_COLOR,
- )
- hp.add_edge_kwargs(
- axis_id_1="Group 1",
- axis_id_2="Group 3_repeat",
- a2_to_a1=False,
- color=INTERNEURON_COLOR,
- )
- hp.add_edge_kwargs(
- axis_id_1="Group 3_repeat",
- axis_id_2="Group 1",
- a2_to_a1=False,
- color=SENSORY_COLOR,
- )
- hp.add_edge_kwargs(
- axis_id_1="Group 3",
- axis_id_2="Group 2_repeat",
- a2_to_a1=False,
- color=SENSORY_COLOR,
- )
- hp.add_edge_kwargs(
- axis_id_1="Group 2_repeat",
- axis_id_2="Group 3",
- a2_to_a1=False,
- color=MOTORNEURON_COLOR,
- )
-
- plotly = False
- plotly = True
-
- if plotly:
- fig = plotly_hive_plot_viz(
- hp,
- )
- # ax.set_title("Stochastic Block Model, Base Hive Plot Visualization", y=1.05, size=20)
- # fig.update_traces(mode="markers+lines", hovertemplate=None)
- fig.update_layout(hovermode="closest")
-
- fig.update(data=[{"hoverinfo": "skip"}])
-
- # print(dir(fig))
- count = 0
- for d in fig.data:
- if d["mode"] == "markers":
- nrn_num = len(d["x"])
- d["hovertemplate"] = "%{text}"
- d.pop("hoverinfo", None)
-
- if count == 0 or count == 1:
- d["marker"]["color"] = [INTERNEURON_COLOR] * nrn_num
- type_ = "Interneuron"
- if count == 2 or count == 3:
- d["marker"]["color"] = [MOTORNEURON_COLOR] * nrn_num
- type_ = "Motorneuron"
- if count == 4 or count == 5:
- d["marker"]["color"] = [SENSORY_COLOR] * nrn_num
- type_ = "Sensory"
-
- d["text"] = [
- "%s (in: %s, out: %s)" % (n, in_degrees[n], out_degrees[n])
- for n in blocks_dict[type_]
- ]
-
- # print(d)
- count += 1
-
- import json
-
- def _format_json(json_str):
- return json.dumps(json.loads(json_str), sort_keys=True, indent=2)
-
- with open("hive.json", "w") as asset_file:
- asset_file.write(_format_json(fig.to_json()))
-
- import plotly.io as pio
-
- pio.renderers.default = "browser"
- if "-nogui" not in sys.argv:
- fig.show()
-
- print("Done")
- else:
- fig, ax = hive_plot_viz(hp)
- ax.set_title(
- "Stochastic Block Model, Base Hive Plot Visualization", y=1.05, size=20
- )
- plt.show()
- """
diff --git a/cect/SpreadsheetDataReader.py b/cect/SpreadsheetDataReader.py
index 1315dcef8..ffed5d3e0 100644
--- a/cect/SpreadsheetDataReader.py
+++ b/cect/SpreadsheetDataReader.py
@@ -22,9 +22,7 @@
spreadsheet_location = os.path.dirname(os.path.abspath(__file__)) + "/data/"
-READER_DESCRIPTION = (
- """Data extracted from **CElegansNeuronTables.xls** for neuronal connectivity"""
-)
+READER_DESCRIPTION = """Data extracted from **CElegansNeuronTables.xls** for neuronal connectivity. Note: legacy dataset (based on White_whole) used in the past in OpenWorm. Only included here for reference! Do not use!"""
class SpreadsheetDataReader(ConnectomeDataset):
diff --git a/cect/UpdatedSpreadsheetDataReader.py b/cect/UpdatedSpreadsheetDataReader.py
index bf0065952..26152d177 100644
--- a/cect/UpdatedSpreadsheetDataReader.py
+++ b/cect/UpdatedSpreadsheetDataReader.py
@@ -22,7 +22,8 @@
filename = "%sherm_full_edgelist.csv" % spreadsheet_location
READER_DESCRIPTION = (
- """Data extracted from **%s** for neuronal connectivity""" % filename
+ """Data extracted from **%s** for neuronal connectivity. Note: legacy dataset (based on Cook et al. 2019) used in the past in OpenWorm. Only included here for reference! Do not use!"""
+ % filename
)
diff --git a/cect/UpdatedSpreadsheetDataReader2.py b/cect/UpdatedSpreadsheetDataReader2.py
index 926a8ca5d..969287024 100644
--- a/cect/UpdatedSpreadsheetDataReader2.py
+++ b/cect/UpdatedSpreadsheetDataReader2.py
@@ -22,7 +22,8 @@
filename = "%sherm_full_edgelist_MODIFIED.csv" % spreadsheet_location
READER_DESCRIPTION = (
- """Data extracted from **%s** for neuronal connectivity""" % filename
+ """Data extracted from **%s** for neuronal connectivity. Note: legacy dataset (based on Cook et al. 2019) used in the past in OpenWorm. Only included here for reference! Do not use!"""
+ % filename
)
diff --git a/cect/WhiteDataReader.py b/cect/WhiteDataReader.py
index 6ce1abf22..f565e0734 100644
--- a/cect/WhiteDataReader.py
+++ b/cect/WhiteDataReader.py
@@ -24,6 +24,9 @@
from cect import print_
+from cect.Cells import GENERIC_CHEM_SYN
+from cect.Cells import GENERIC_ELEC_SYN
+
def get_syntype(syntype):
if syntype == "electrical":
@@ -35,13 +38,13 @@ def get_syntype(syntype):
def get_synclass(cell, syntype):
- # dirty hack
if syntype == "GapJunction":
- return "Generic_GJ"
+ return GENERIC_ELEC_SYN
else:
+ '''# dirty hack
if cell.startswith("DD") or cell.startswith("VD"):
- return "GABA"
- return "Acetylcholine"
+ return "GABA"'''
+ return GENERIC_CHEM_SYN
def parse_line(line):
diff --git a/cect/WormAtlasInfo.py b/cect/WormAtlasInfo.py
index 37c7a5f67..041e89e75 100644
--- a/cect/WormAtlasInfo.py
+++ b/cect/WormAtlasInfo.py
@@ -141,20 +141,34 @@
table, th, td {
border: 1px solid black;
border-collapse: collapse;
+ font-family: Arial, sans-serif;
}
+ td {
+ text-align: center;
+ }
+
"""
)
f.write(" C. elegans cells\n---\n\n")
+ f.write(" \n\n")
+ max_col = 4
for sex in WA_COLORS:
- f.write("\n%s\n" % sex)
+ f.write(
+ '\n | %s | \n'
+ % (max_col, sex.upper())
+ )
for cell_class in WA_COLORS[sex]:
- f.write("\n%s\n\n" % cell_class)
+ f.write(
+ '\n | %s | \n\n '
+ % (max_col, cell_class)
+ )
+ count = 0
for cell_type in WA_COLORS[sex][cell_class]:
if "General code" not in cell_type:
color = WA_COLORS[sex][cell_class][cell_type][1:]
@@ -177,13 +191,29 @@
w = png.Writer(width, height, greyscale=False)
w.write(img_file, img)
- link_text = cell_type
- link_text = f'{VERTICAL_ELLIPSE}{link_text} - '
+ # https://stackoverflow.com/questions/3942878
+ if (
+ float(rgb_color[0]) / 256 * 0.299
+ + float(rgb_color[1]) / 256 * 0.587
+ + float(rgb_color[2]) / 256 * 0.2
+ ) > 0.35:
+ fcolor = "#000000"
+ else:
+ fcolor = "#ffffff"
+
+ count += 1
+ link_text = cell_type[0].upper() + cell_type[1:]
+ # link_text = f' {link_text}'
print(link_text)
- f.write("\n%s\n\n" % link_text)
+ f.write(
+ f'\n {link_text} | \n'
+ )
+ if count >= max_col:
+ f.write("\n \n")
+ count = 0
f.write(
- """
+ """ |